diff --git a/.github/workflows/static-gh-pages.yml b/.github/workflows/static-gh-pages.yml index 5eab8d1c..a56268dc 100644 --- a/.github/workflows/static-gh-pages.yml +++ b/.github/workflows/static-gh-pages.yml @@ -4,6 +4,11 @@ on: push: branches: - main + pull_request: + branches: + - main + - dev + jobs: docs_to_gh-pages: runs-on: ubuntu-latest diff --git a/cfgs/vision_model/default.yaml b/cfgs/vision_model/default.yaml index 6b117e65..e3c5a8b1 100644 --- a/cfgs/vision_model/default.yaml +++ b/cfgs/vision_model/default.yaml @@ -46,4 +46,5 @@ yolox_darknet53: conf_thres: 0.001 nms_thres: 0.65 weights: "weights/yolox/darknet53/yolox_darknet.pth" - splits: "l13" #"l37" \ No newline at end of file + splits: "l13" #"l37" + squeeze_at_split: False \ No newline at end of file diff --git a/compressai_vision/run/vcm_app_cli/load_eval.py b/compressai_vision/model_wrappers/split_squeezes/squeeze_base.py similarity index 76% rename from compressai_vision/run/vcm_app_cli/load_eval.py rename to compressai_vision/model_wrappers/split_squeezes/squeeze_base.py index 69286bfa..b1fc74f1 100644 --- a/compressai_vision/run/vcm_app_cli/load_eval.py +++ b/compressai_vision/model_wrappers/split_squeezes/squeeze_base.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc +# Copyright (c) 2025, InterDigital Communications, Inc # All rights reserved. # Redistribution and use in source and binary forms, with or without @@ -27,20 +27,25 @@ # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -"""cli load_eval functionality -""" +import torch.nn as nn -def main(p): - print("importing fiftyone") - import fiftyone as fo - print("fiftyone imported") - # dataset = fo.load_dataset(p.dataset_name) - print("removing dataset %s from fiftyone" % (p.dataset_name)) - if not p.y: - input("press enter to continue.. ") - try: - fo.delete_dataset(p.dataset_name) - except ValueError as e: - print("could not deregister because of", e) +class squeeze_base(nn.Module): + def __init__(self, *args, **kwargs): + super().__init__() + + self.squeeze_ftensor = None + self.expand_ftensor = None + + @property + def address(self): + return "PROVIDE URL" + + def squeeze_(self, x): + # You may implement your own + return self.squeeze_ftensor(x) + + def expand_(self, x): + # You may implement your own + return self.expand_ftensor(x) diff --git a/compressai_vision/model_wrappers/split_squeezes/squeeze_yolox.py b/compressai_vision/model_wrappers/split_squeezes/squeeze_yolox.py new file mode 100644 index 00000000..abffbce6 --- /dev/null +++ b/compressai_vision/model_wrappers/split_squeezes/squeeze_yolox.py @@ -0,0 +1,85 @@ +# Copyright (c) 2025, InterDigital Communications, Inc +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted (subject to the limitations in the disclaimer +# below) provided that the following conditions are met: + +# * Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# * Neither the name of InterDigital Communications, Inc nor the names of its +# contributors may be used to endorse or promote products derived from this +# software without specific prior written permission. + +# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY +# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND +# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT +# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A +# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; +# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, +# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR +# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF +# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +import torch.nn as nn + +from .squeeze_base import squeeze_base + + +# for YOLOX-Darknet53 +class three_convs_at_l13(squeeze_base): + def __init__(self, C0, C1, C2, C3): + super().__init__(C0, C1, C2, C3) + + self.fw_block = nn.Sequential( + nn.Conv2d( + in_channels=C0, out_channels=C1, kernel_size=3, padding=1, stride=1 + ), + nn.PReLU(), + nn.Conv2d( + in_channels=C1, out_channels=C2, kernel_size=3, padding=1, stride=2 + ), + nn.PReLU(), + nn.Conv2d( + in_channels=C2, out_channels=C3, kernel_size=1, padding=0, stride=1 + ), + nn.SiLU(inplace=True), + ) + + self.bw_block = nn.Sequential( + nn.Conv2d( + in_channels=C3, out_channels=C2, kernel_size=3, padding=1, stride=1 + ), + nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False), + nn.PReLU(), + nn.Conv2d( + in_channels=C2, out_channels=C1, kernel_size=3, padding=1, stride=1 + ), + nn.PReLU(), + nn.Conv2d( + in_channels=C1, out_channels=C0, kernel_size=1, padding=0, stride=1 + ), + nn.LeakyReLU(negative_slope=0.1, inplace=True), + ) + + @property + def address(self): + return "https://dspub.blob.core.windows.net/compressai-vision/split_squeezes/yolox_darknet53/three_convs_squeeze_at_l13_of_yolox_darknet53-f78179c1.pth" + + def squeeze_(self, x): + return self.fw_block(x) + + def expand_(self, x): + return self.bw_block(x) + + def forward(self, x): + y = self.fw_block(x) + est_x = self.bw_block(y) + return est_x diff --git a/compressai_vision/model_wrappers/yolox.py b/compressai_vision/model_wrappers/yolox.py index 9d116be6..d39ea319 100644 --- a/compressai_vision/model_wrappers/yolox.py +++ b/compressai_vision/model_wrappers/yolox.py @@ -28,7 +28,6 @@ # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -import configparser from enum import Enum from pathlib import Path from typing import Dict, List @@ -40,6 +39,7 @@ from compressai_vision.registry import register_vision_model from .base_wrapper import BaseWrapper +from .split_squeezes import squeeze_yolox __all__ = [ "yolox_darknet53", @@ -76,7 +76,7 @@ def __init__(self, device: str, **kwargs): self.conf_thres = kwargs["conf_thres"] self.nms_thres = kwargs["nms_thres"] - self.supported_split_points = Split_Points + self.squeeze_at_split_enabled = False exp = get_exp(exp_file=None, exp_name="yolov3") @@ -85,9 +85,10 @@ def __init__(self, device: str, **kwargs): assert "splits" in kwargs, "Split layer ids must be provided" self.split_id = str(kwargs["splits"]).lower() - if self.split_id == str(self.supported_split_points.Layer13_Single): + + if self.split_id == str(Split_Points.Layer13_Single): self.split_layer_list = ["l13"] - elif self.split_id == str(self.supported_split_points.Layer37_Single): + elif self.split_id == str(Split_Points.Layer37_Single): self.split_layer_list = ["l37"] else: raise NotImplementedError @@ -100,8 +101,12 @@ def __init__(self, device: str, **kwargs): torch.load(self.model_info["weights"], map_location="cpu")["model"], strict=False, ) + self.model.to(device).eval() + if bool(kwargs["squeeze_at_split"]): + self.enable_squeeze_at_split(self.split_id) + self.yolo_fpn = self.model.backbone self.backbone = self.yolo_fpn.backbone self.head = self.model.head @@ -112,11 +117,38 @@ def __init__(self, device: str, **kwargs): @property def SPLIT_L13(self): - return str(self.supported_split_points.Layer13_Single) + return str(Split_Points.Layer13_Single) @property def SPLIT_L37(self): - return str(self.supported_split_points.Layer37_Single) + return str(Split_Points.Layer37_Single) + + def enable_squeeze_at_split(self, split_id): + from torch.hub import load_state_dict_from_url + + LIST_OF_SQUEEZE_SUPPORT_SPLITS = [str(Split_Points.Layer13_Single)] + + if split_id in LIST_OF_SQUEEZE_SUPPORT_SPLITS: + self.squeeze_at_split_enabled = True + self.squeeze_model = squeeze_yolox.three_convs_at_l13( + C0=256, C1=256, C2=128, C3=128 + ) + + state_dict = load_state_dict_from_url( + self.squeeze_model.address, + progress=True, + check_hash=True, + map_location=self.device, + ) + + self.squeeze_model.load_state_dict(state_dict) + self.squeeze_model.to(self.device).eval() + + else: + self.logger.warning( + f"Squeeze is not available at {split_id}. Currently only available at {LIST_OF_SQUEEZE_SUPPORT_SPLITS}" + ) + self.squeeze_at_split_enabled = False def input_to_features(self, x, device: str) -> Dict: """Computes deep features at the intermediate layer(s) all the way from the input""" @@ -126,9 +158,9 @@ def input_to_features(self, x, device: str) -> Dict: input_size = tuple(img.shape[2:]) if self.split_id == self.SPLIT_L13: - output = self._input_to_feature_at_l13(img) + output = self._input_to_feature_at_l13(img, device) elif self.split_id == self.SPLIT_L37: - output = self._input_to_feature_at_l37(img) + output = self._input_to_feature_at_l37(img, device) else: self.logger.error(f"Not supported split point {self.split_id}") raise NotImplementedError @@ -143,11 +175,11 @@ def features_to_output(self, x: Dict, device: str): if self.split_id == self.SPLIT_L13: return self._feature_at_l13_to_output( - x["data"], x["org_input_size"], x["input_size"] + x["data"], x["org_input_size"], x["input_size"], device ) elif self.split_id == self.SPLIT_L37: return self._feature_at_l37_to_output( - x["data"], x["org_input_size"], x["input_size"] + x["data"], x["org_input_size"], x["input_size"], device ) else: self.logger.error(f"Not supported split points {self.split_id}") @@ -155,17 +187,24 @@ def features_to_output(self, x: Dict, device: str): raise NotImplementedError @torch.no_grad() - def _input_to_feature_at_l13(self, x): + def _input_to_feature_at_l13(self, x, device): """Computes and return feature at layer 13 with leaky relu all the way from the input""" y = self.backbone.stem(x) y = self.backbone.dark2(y) - self.features_at_splits[self.SPLIT_L13] = self.backbone.dark3[0](y) + y = self.backbone.dark3[0](y) + if not self.squeeze_at_split_enabled: + self.features_at_splits[self.SPLIT_L13] = y + return {"data": self.features_at_splits} + + # Further squeeze + smodel = self.squeeze_model.to(device) + self.features_at_splits[self.SPLIT_L13] = smodel.squeeze_(y) return {"data": self.features_at_splits} @torch.no_grad() - def _input_to_feature_at_l37(self, x): + def _input_to_feature_at_l37(self, x, device): """Computes and return feature at layer 37 with 11th residual layer output all the way from the input""" y = self.backbone.stem(x) @@ -177,7 +216,7 @@ def _input_to_feature_at_l37(self, x): @torch.no_grad() def _feature_at_l13_to_output( - self, x: Dict, org_img_size: Dict, input_img_size: List + self, x: Dict, org_img_size: Dict, input_img_size: List, device ): """ performs downstream task using the features from layer 13 @@ -191,8 +230,13 @@ def _feature_at_l13_to_output( """ - y = x[self.SPLIT_L13] + + # Recovery session to expand dimension to original + if self.squeeze_at_split_enabled: + smodel = self.squeeze_model.to(device) + y = smodel.expand_(y) + for proc_module in self.backbone.dark3[1:]: y = proc_module(y) @@ -220,7 +264,7 @@ def _feature_at_l13_to_output( @torch.no_grad() def _feature_at_l37_to_output( - self, x: Dict, org_img_size: Dict, input_img_size: List + self, x: Dict, org_img_size: Dict, input_img_size: List, device ): """ performs downstream task using the features from layer 37 diff --git a/compressai_vision/pipelines/base.py b/compressai_vision/pipelines/base.py index 5809ac87..24305805 100755 --- a/compressai_vision/pipelines/base.py +++ b/compressai_vision/pipelines/base.py @@ -207,7 +207,9 @@ def _prep_features_to_dump(features, n_bits, datacatalog_name): if n_bits == -1: data_features = features["data"] elif n_bits >= 8: - assert n_bits == 8, "currently it only supports dumping features in 8 bits" + assert ( + n_bits == 8 or n_bits == 16 + ), "currently it only supports dumping features in 8 bits or 16 bits" assert datacatalog_name in list( MIN_MAX_DATASET.keys() ), f"{datacatalog_name} does not exist in the pre-computed minimum and maximum tables" @@ -218,7 +220,21 @@ def _prep_features_to_dump(features, n_bits, datacatalog_name): data.min() >= minv and data.max() <= maxv ), f"{data.min()} should be greater than {minv} and {data.max()} should be less than {maxv}" out, _ = min_max_normalization(data, minv, maxv, bitdepth=n_bits) - data_features[key] = out.to(torch.uint8) + + if n_bits <= 8: + data_features[key] = out.to(torch.uint8) + elif n_bits <= 16: + data_features[key] = { + "lsb": torch.bitwise_and( + out.to(torch.int32), torch.tensor(0xFF) + ).to(torch.uint8), + "msb": torch.bitwise_and( + torch.bitwise_right_shift(out.to(torch.int32), 8), + torch.tensor(0xFF), + ).to(torch.uint8), + } + else: + raise NotImplementedError else: raise NotImplementedError @@ -230,15 +246,30 @@ def _post_process_loaded_features(features, n_bits, datacatalog_name): if n_bits == -1: assert "data" in features elif n_bits >= 8: - assert n_bits == 8, "currently it only supports dumping features in 8 bits" + assert ( + n_bits == 8 or n_bits == 16 + ), "currently it only supports dumping features in 8 bits or 16 bits" assert datacatalog_name in list( MIN_MAX_DATASET.keys() ), f"{datacatalog_name} does not exist in the pre-computed minimum and maximum tables" minv, maxv = MIN_MAX_DATASET[datacatalog_name] data_features = {} for key, data in features["data"].items(): - out = min_max_inv_normalization(data, minv, maxv, bitdepth=n_bits) - data_features[key] = out.to(torch.float32) + + if n_bits <= 8: + out = min_max_inv_normalization(data, minv, maxv, bitdepth=n_bits) + data_features[key] = out.to(torch.float32) + elif n_bits <= 16: + lsb_part = data["lsb"].to(torch.int32) + msb_part = torch.bitwise_left_shift(data["msb"].to(torch.int32), 8) + recovery = (msb_part + lsb_part).to(torch.float32) + + out = min_max_inv_normalization( + recovery, minv, maxv, bitdepth=n_bits + ) + data_features[key] = out.to(torch.float32) + else: + raise NotImplementedError features["data"] = data_features else: diff --git a/compressai_vision/run/vcm_app_cli/README.md b/compressai_vision/run/vcm_app_cli/README.md deleted file mode 100644 index 549aa189..00000000 --- a/compressai_vision/run/vcm_app_cli/README.md +++ /dev/null @@ -1,13 +0,0 @@ - -You can access these files in your code without knowing the exact dir location with: - -this: -``` -from compressai_vision.pipelines.fo_vcm.tools import getDataFile - -filename=getDataFile("README.md") -``` -would return the absolute path of _this_ file. - -[../../MANIFEST.in](../../MANIFEST.in) takes care that all files are included into the python package. - diff --git a/compressai_vision/run/vcm_app_cli/__init__.py b/compressai_vision/run/vcm_app_cli/__init__.py deleted file mode 100644 index f449dc7a..00000000 --- a/compressai_vision/run/vcm_app_cli/__init__.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -""" -# importing this takes quite a while! -# ..not anymore since import fiftyone is inside the function -# print("cli: import") -from compressai_vision.cli.clean import main as clean -from compressai_vision.cli.convert_mpeg_to_oiv6 import main as convert_mpeg_to_oiv6 -from compressai_vision.cli.deregister import main as deregister -from compressai_vision.cli.detectron2_eval import main as detectron2_eval -from compressai_vision.cli.download import main as download -from compressai_vision.cli.dummy import main as dummy -from compressai_vision.cli.list import main as list -from compressai_vision.cli.load_eval import main as load_eval -from compressai_vision.cli.register import main as register -from compressai_vision.cli.vtm import main as vtm - -# print("cli: import end") -""" -from . import ( - app, - auto, - clean, - convert_mpeg_to_oiv6, - copy, - deregister, - detectron2_eval, - download, - dummy, - import_custom, - info, - killmongo, - list_, - load_eval, - make_thumbnails, - metrics_eval, - plotter, - register, - show, - vtm, -) - -__all__ = [ - "clean", - "convert_mpeg_to_oiv6", - "deregister", - "detectron2_eval", - "download", - "dummy", - "list_", - "load_eval", - "register", - "vtm", - "auto", - "info", - "killmongo", - "plotter", - "show", - "metrics_eval", - "import_custom", - "make_thumbnails", - "app", - "copy", -] diff --git a/compressai_vision/run/vcm_app_cli/app.py b/compressai_vision/run/vcm_app_cli/app.py deleted file mode 100644 index 32aa52d5..00000000 --- a/compressai_vision/run/vcm_app_cli/app.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""Launch fiftyone app -""" -# import os -import time - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "app", - parents=parents, - help="launch the celebrated fiftyone app for dataset visualization", - ) - req_group = subparser.add_argument_group("required arguments") - req_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default=None, - help="name of the dataset", - ) - opt_group = subparser.add_argument_group("option arguments") - opt_group.add_argument( - "--address", - action="store", - type=str, - required=False, - default=None, - help="address to bind the webapp to", - ) - opt_group.add_argument( - "--port", - action="store", - type=int, - required=False, - default=None, - help="port to bind the webapp to", - ) - - -def main(p): - print("importing fiftyone") - import fiftyone as fo - - print("fiftyone imported") - print() - - if p.dataset_name not in fo.list_datasets(): - print("No such dataset", p.dataset_name) - return 2 - dataset = fo.load_dataset(p.dataset_name) - - if p.address is None: - p.address = fo.config.default_app_address - if p.port is None: - p.port = fo.config.default_app_port - print("Launching app at address %s port %i" % (p.address, p.port)) - print("press CTRL-C to terminate") - print() - # print("Here is your link:") - # print() - # print("https://%s:%i" % (p.address, p.port)) - # print() - fo.launch_app(dataset=dataset, address=p.address, port=p.port) - while True: - try: - time.sleep(1) - except KeyboardInterrupt: - break - fo.close_app() - print("Have a nice day!") diff --git a/compressai_vision/run/vcm_app_cli/auto.py b/compressai_vision/run/vcm_app_cli/auto.py deleted file mode 100644 index 2bf03d86..00000000 --- a/compressai_vision/run/vcm_app_cli/auto.py +++ /dev/null @@ -1,284 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli : Command-line interface tools for compressai-vision -""" -import os -import sys - -from compressai_vision.pipelines.fo_vcm.tools import getDataFile - -# define legit filenames.. there are inconsistencies here -fname_list = [ - "detection_validation_input_5k.lst", - "detection_validation_5k_bbox.csv", # inconsistent name - "detection_validation_labels_5k.csv", - "segmentation_validation_input_5k.lst", - "segmentation_validation_bbox_5k.csv", - "segmentation_validation_labels_5k.csv", - "segmentation_validation_masks_5k.csv", -] - -help_st = """ -compressai-mpeg_vcm-auto-import\n -\n -parameters: - - --datadir=/path/to/datasets directory where all datasets are downloaded by default (optional) - -Automatic downloading of images and importing files provided by MPEG/VCM test conditions into fiftyone - -Before running this command, put the following files into the same directory -(please note 'detection_validation_5k_bbox.csv' name inconsistency): - -""" -for fname in fname_list: - help_st += fname + "\n" -help_st += "\n" - - -class Namespace: - pass - - -def get_(key, path=None): - """So that we use only legit names""" - correct_name = fname_list[fname_list.index(key)] - if path: - correct_name = os.path.join(path, correct_name) - return correct_name - - -def get_inp(inp_, txt=""): - """Ask user for input, provide default input""" - input_ = input("Give " + txt + " [" + inp_ + "]: ") - if len(input_) < 1: - # user just pressed enter - input_ = inp_ # use the default path - return input_ - - -def get_dir(dir_, txt="", make=True, check=False): - """Ask the user for a path. Give also a default path""" - dir_input = input("Give " + txt + " [" + dir_ + "]: ") - if len(dir_input) < 1: - # user just pressed enter - dir_input = dir_ # use the default path - dir_input = os.path.expanduser(dir_input) - if make: - os.makedirs(dir_input, exist_ok=True) - elif check: - basepath = os.path.sep.join( - dir_input.split(os.path.sep)[0:-1] - ) # remove last bit of the path - assert os.path.isdir(basepath), "path " + basepath + " does not exit" - return dir_input - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "mpeg-vcm-auto-import", - parents=parents, - help="auto-imports mpeg-vcm working group files, downloads necessary images from the internet, imports them to fiftyone, etc.", - description=help_st, - ) - # NOTE: help is something show without using this command - # descriptions is shown when the command is used - subparser.add_argument( - "--datadir", - action="store", - type=str, - required=False, - default=None, - help="directory where all datasets are downloaded by default (optional)", - ) - subparser.add_argument( - "--mock", action="store_true", default=False, help="debugging switch: don't use" - ) - subparser.add_argument( - "--use-vcm", - action="store_true", - default=True, - help="Use mpeg-vcm files bundled with compressai-vision", - ) - # subparser = subparsers.add_parser( - # "manual", parents=parents, help="shows complete manual" - # ) - - -def main(p_): # noqa: C901 - from compressai_vision.cli import convert_mpeg_to_oiv6, download, dummy, register - - dirname = p_.datadir - - for fname in fname_list: - if p_.use_vcm: - fname_ = getDataFile(os.path.join("mpeg_vcm_data", fname)) - else: - fname_ = fname - if not os.path.exists(fname_): - print("\nFATAL: missing file", fname_) - print(help_st) - sys.exit(2) - - if p_.use_vcm: - load_dir = getDataFile("mpeg_vcm_data") - else: - load_dir = None - - p = Namespace() - p.mock = p_.mock - # p.y = False - p.y = p_.y - p.dataset_name = "open-images-v6" - - if dirname is None: - dir_ = os.path.join( - "~", "fiftyone", p.dataset_name - ) # ~/fiftyone/open-images-v6 - else: - dir_ = os.path.join(dirname, p.dataset_name) - - if not p_.y: - dir_ = get_dir(dir_, "path to download (MPEG/VCM subset of) OpenImageV6 ") - - source_dir = os.path.join( - dir_, "validation" - ) # "~/fiftyone/open-images-v6/validation" - - print("\n**DOWNLOADING**\n") - p.lists = ( - get_("detection_validation_input_5k.lst", load_dir) - + "," - + get_("segmentation_validation_input_5k.lst", load_dir) - ) - p.split = "validation" - p.dir = dir_ - download.main(p) - - print("\n**CONVERTING MPEG/VCM DETECTION DATA TO OPENIMAGEV6 FORMAT**\n") - p = Namespace() - # p.y = False - p.y = p_.y - - if dirname is None: - mpeg_vcm_dir = os.path.join( - "~", "fiftyone", "oiv6-mpeg-detection-v1" - ) # ~/fiftyone/oiv6-mpeg-detection-v1 - else: - mpeg_vcm_dir = os.path.join(dirname, "oiv6-mpeg-detection-v1") - - if not p_.y: - mpeg_vcm_dir = get_dir( - mpeg_vcm_dir, "imported detection dataset path", make=False, check=False - ) - - p.lists = get_("detection_validation_input_5k.lst", load_dir) - # p.dir = "~/fiftyone/open-images-v6/validation" - p.dir = source_dir - # p.target_dir = "~/fiftyone/oiv6-mpeg-detection-v1" - p.target_dir = mpeg_vcm_dir - p.label = get_("detection_validation_labels_5k.csv", load_dir) - p.bbox = get_("detection_validation_5k_bbox.csv", load_dir) - p.mask = None - convert_mpeg_to_oiv6.main(p) - - print("\n**CONVERTING MPEG/VCM SEGMENTATION DATA TO OPENIMAGEV6 FORMAT**\n") - p = Namespace() - # p.y = False - p.y = p_.y - - if dirname is None: - mpeg_vcm_dir_seg = os.path.join( - "~", "fiftyone", "oiv6-mpeg-segmentation-v1" - ) # ~/fiftyone/mpeg_vcm-segmentation - else: - mpeg_vcm_dir_seg = os.path.join(dirname, "oiv6-mpeg-segmentation-v1") - if not p_.y: - mpeg_vcm_dir_seg = get_dir( - mpeg_vcm_dir_seg, - "imported segmentation dataset path", - make=False, - check=False, - ) - - p.lists = get_("segmentation_validation_input_5k.lst", load_dir) - # p.dir = "~/fiftyone/open-images-v6/validation" - p.dir = source_dir - # p.target_dir = "~/fiftyone/mpeg_vcm-segmentation" - p.target_dir = mpeg_vcm_dir_seg - p.label = get_("segmentation_validation_labels_5k.csv", load_dir) - p.bbox = get_("segmentation_validation_bbox_5k.csv", load_dir) - p.mask = get_("segmentation_validation_masks_5k.csv", load_dir) - convert_mpeg_to_oiv6.main(p) - - print("\n**REGISTERING MPEG/VCM DETECTION DATA INTO FIFTYONE**\n") - p = Namespace() - # p.y = False - p.y = p_.y - dataset_name = "oiv6-mpeg-detection-v1" - if p_.y is False: - dataset_name = get_inp(dataset_name, "name for detection dataset") - p.dataset_name = dataset_name - p.lists = get_("detection_validation_input_5k.lst", load_dir) - p.dir = mpeg_vcm_dir - p.type = "OpenImagesV6Dataset" - if p_.mock: - print("WARNING: mock/debug mode: skipping") - else: - register.main(p) - - print("\n**CREATING DUMMY/MOCK DETECTION DATA FOR YOUR CONVENIENCE SIR**\n") - p = Namespace() - # p.y = False - p.y = p_.y - p.dataset_name = dataset_name - dummy.main(p) - - print("\n**REGISTERING MPEG/VCM SEGMENTATION DATA INTO FIFTYONE**\n") - p = Namespace() - # p.y = False - p.y = p_.y - dataset_name = "oiv6-mpeg-segmentation-v1" - if p_.y is False: - dataset_name = get_inp(dataset_name, "name for segmentation dataset") - p.dataset_name = dataset_name - p.lists = get_("segmentation_validation_input_5k.lst", load_dir) - p.dir = mpeg_vcm_dir_seg - p.type = "OpenImagesV6Dataset" - if p_.mock: - print("WARNING: mock/debug mode: skipping") - else: - register.main(p) - print("\nPlease continue with the compressai-vision command line tool\n") - print("\nGOODBYE\n") - - -if __name__ == "__main__": - main() diff --git a/compressai_vision/run/vcm_app_cli/base_.py b/compressai_vision/run/vcm_app_cli/base_.py deleted file mode 100644 index 805da10f..00000000 --- a/compressai_vision/run/vcm_app_cli/base_.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""Use this stub for adding new cli commands -""" -import os - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "YOUR_COMMAND", parents=parents, help="what is it about" - ) - some_group = subparser.add_argument_group("required arguments for example") - some_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default=None, - help="name of the dataset", - ) - some_group.add_argument( - "--some-dir", - action="store", - type=str, - required=True, - default=None, - help="path to somewhere", - ) - - -def main(p): - """Access arguments from namespace p, say: p.dataset_name""" - # fiftyone - if not p.y: - input("press enter to continue.. ") - print() - - p.some_dir = os.path.expanduser( - p.some_dir - ) # correct path in the case user uses POSIX "~" - - print("importing fiftyone") - import fiftyone as fo - - print("fiftyone imported") - print() - print("datasets currently registered into fiftyone") - print("name, length, first sample path") - for name in fo.list_datasets(): - dataset = fo.load_dataset(name) - n = len(dataset) - if n > 0: - sample = dataset.first() - p = os.path.sep.join(sample["filepath"].split(os.path.sep)[:-1]) - else: - p = "?" - print("%s, %i, %s" % (name, len(dataset), p)) diff --git a/compressai_vision/run/vcm_app_cli/clean.py b/compressai_vision/run/vcm_app_cli/clean.py deleted file mode 100644 index 5be6dbad..00000000 --- a/compressai_vision/run/vcm_app_cli/clean.py +++ /dev/null @@ -1,56 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli list functionality -""" -import os - - -def add_subparser(subparsers, parents): - subparsers.add_parser("clean", parents=parents) - - -def main(p): - print("importing fiftyone") - import fiftyone as fo - - print("fiftyone imported") - print() - try: - username = os.environ["USER"] - except KeyError: - username = "nouser" - print("removing tmp datasets for username", username) - print("WARNING: be sure not to remove datasets currently used by a process") - if not p.y: - input("press enter to continue.. ") - for name in fo.list_datasets(): - if "detectron-run-" + username in name: - print("deleting dataset", name) - fo.delete_dataset(name) diff --git a/compressai_vision/run/vcm_app_cli/convert_mpeg_to_oiv6.py b/compressai_vision/run/vcm_app_cli/convert_mpeg_to_oiv6.py deleted file mode 100644 index 7602c9e7..00000000 --- a/compressai_vision/run/vcm_app_cli/convert_mpeg_to_oiv6.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli convert_mpeg_to_oiv6 functionality -""" -import os - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "convert-mpeg-to-oiv6", - parents=parents, - help="convert the files specified in MPEG/VCM CTC into proper OpenImageV6 format & directory structure", - ) - subparser.add_argument( - "--lists", - action="store", - type=str, - required=False, - default=None, - help="comma-separated list of list files", - ) - subparser.add_argument( - "--dir", - action="store", - type=str, - required=False, - default=None, - help="target/source directory, depends on command", - ) - subparser.add_argument( - "--target_dir", - action="store", - type=str, - required=False, - default=None, - help="target directory for convert_mpeg_to_oiv6", - ) - subparser.add_argument( - "--label", - action="store", - type=str, - required=False, - default=None, - help="mpeg_vcm-formatted image-level labels. Example: detection_validation_labels_5k.csv", - ) - subparser.add_argument( - "--bbox", - action="store", - type=str, - required=False, - default=None, - help="mpeg_vcm-formatted bbox data. Example: detection_validation_5k_bbox.csv", - ) - subparser.add_argument( - "--mask", - action="store", - type=str, - required=False, - default=None, - help="mpeg_vcm-formatted segmask data. Example: segmentation_validation_masks_5k.csv", - ) - - -def main(p): - # compressai_vision - from compressai_vision.conversion import MPEGVCMToOpenImageV6 # imageIdFileList - from compressai_vision.pipelines.fo_vcm.tools import pathExists - - assert p.target_dir is not None, "please give target_dir" - assert ( - p.dir is not None - ), "please specify OpenImageV6 source directory for images (and masks)" - if pathExists(p.target_dir): - print( - "FATAL: target directory %s exists already. Please remove it first" - % (p.target_dir) - ) - return - assert pathExists(p.dir), "directory " + p.dir + " does not exist" - assert ( - p.label is not None - ), "please provide image level labels --label ('detection_validation_labels_5k.csv')" - image_dir = os.path.join(p.dir, "data") - mask_dir = os.path.join(p.dir, "labels", "masks") - assert pathExists(image_dir), "directory " + image_dir + " does not exist" - if p.bbox is not None: - assert pathExists(p.bbox), "file " + p.bbox + " does not exist" - p.bbox = os.path.expanduser(p.bbox) - if p.mask is not None: - assert pathExists(p.mask), "file " + p.mask + " does not exist" - assert pathExists(mask_dir), "directory " + mask_dir + " does not exist" - p.mask = os.path.expanduser(p.mask) - - print() - assert p.lists is not None, "a list file (.lst) required --lists" - fnames = p.lists.split(",") - assert len(fnames) == 1, "please specify exactly one list file for mpeg_vcm convert" - fname = fnames[0] - print("Using list file : ", fname) - print("Images (and masks) from : ", p.dir) - print(" --> from: ", image_dir) - print(" --> from:", mask_dir) - print("Image-level labels from : ", p.label) - print("Detections (bboxes)from : ", p.bbox) - print("Segmasks from : ", p.mask) - print("Final OIV6 format in : ", p.target_dir) - if not p.y: - input("press enter to continue.. ") - MPEGVCMToOpenImageV6( - validation_csv_file=os.path.expanduser(p.label), - list_file=os.path.expanduser(fname), - bbox_csv_file=p.bbox, - segmentation_csv_file=p.mask, - output_directory=os.path.expanduser(p.target_dir), - data_dir=os.path.expanduser(image_dir), - mask_dir=os.path.expanduser(mask_dir), - ) - print("mpeg_vcm convert ready, please check", p.target_dir) diff --git a/compressai_vision/run/vcm_app_cli/copy.py b/compressai_vision/run/vcm_app_cli/copy.py deleted file mode 100644 index c6317e8e..00000000 --- a/compressai_vision/run/vcm_app_cli/copy.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""Create a copy of a dataset for an individual user -""" -import os - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "copy", parents=parents, help="create a copy of the dataset" - ) - req_group = subparser.add_argument_group("required arguments") - req_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default=None, - help="name of the dataset or a comma-separated list of dataset names", - ) - opt_group = subparser.add_argument_group("optional arguments") - opt_group.add_argument( - "--username", - action="store", - type=str, - required=False, - default=None, - help="user name to prepend the dataset names with. default: posix username from env variable USER", - ) - - -def main(p): - if p.username is None: - p.username = os.environ["USER"] - - print("importing fiftyone") - import fiftyone as fo - - print("fiftyone imported") - - for dataset_name in p.dataset_name.split(","): - new_name = p.username + "-" + dataset_name - print("cloning", dataset_name, "into", new_name) - try: - dataset = fo.load_dataset(dataset_name) - except ValueError: - print("WARNING: dataset", dataset_name, "not found - will skip") - continue - # delete new_name if exists already - try: - fo.delete_dataset(new_name) - except ValueError: - pass - else: - print("NOTE: dataset", new_name, "was already there - removed it") - new_dataset = dataset.clone(new_name) - new_dataset.persistent = True diff --git a/compressai_vision/run/vcm_app_cli/deregister.py b/compressai_vision/run/vcm_app_cli/deregister.py deleted file mode 100644 index 5ad39a94..00000000 --- a/compressai_vision/run/vcm_app_cli/deregister.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -"""cli deregister functionality -""" - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "deregister", parents=parents, help="de-register image set from fiftyone" - ) - required_group = subparser.add_argument_group("required arguments") - required_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default=None, - help="name of the dataset", - ) - - -def main(p): # noqa: C901 - print("importing fiftyone") - import fiftyone as fo - - print("fiftyone imported") - - # dataset = fo.load_dataset(p.dataset_name) - print("removing dataset(s) %s from fiftyone" % (p.dataset_name)) - if not p.y: - input("press enter to continue.. ") - - for name in p.dataset_name.split(","): - try: - fo.delete_dataset(name) - except ValueError as e: - print("could not deregister", name, ":", e) diff --git a/compressai_vision/run/vcm_app_cli/detectron2_eval.py b/compressai_vision/run/vcm_app_cli/detectron2_eval.py deleted file mode 100644 index dcc4fbe7..00000000 --- a/compressai_vision/run/vcm_app_cli/detectron2_eval.py +++ /dev/null @@ -1,639 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli detectron2_eval functionality -""" -import copy -import datetime -import json -import os - -from .tools import ( - checkDataset, - checkForField, - checkSlice, - checkVideoDataset, - checkZoo, - getQPars, - loadEncoderDecoderFromPath, - makeEvalPars, - setupDetectron2, - setupVTM, -) - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "detectron2-eval", - parents=parents, - help="evaluate model with detectron2 using OpenImageV6", - ) - required_group = subparser.add_argument_group("required arguments") - compressai_group = subparser.add_argument_group("compressai-zoo arguments") - vtm_group = subparser.add_argument_group("vtm arguments") - optional_group = subparser.add_argument_group("optional arguments") - required_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default=None, - help="name of the dataset", - ) - subparser.add_argument( - "--gt-field", - action="store", - type=str, - required=False, - default="detections", - help="name of the ground truth field in the dataset. Default: detections", - ) - required_group.add_argument( - "--model", - action="store", - type=str, - required=True, - default=None, - nargs="+", - help="name of Detectron2 config model. It can also be possible to list multiple models.", - ) - optional_group.add_argument( - "--output", - action="store", - type=str, - required=False, - default="compressai-vision.json", - help="outputfile. Default: compressai-vision.json", - ) - """TODO: not only oiv6 protocol, but coco etc. - subparser.add_argument( - "--proto", - action="store", - type=str, - required=False, - default=None, - help="evaluation protocol", - ) - """ - compressai_group.add_argument( - "--compressai-model-name", - action="store", - type=str, - required=False, - default=None, - help="name of an existing model in compressai-zoo. Example: 'cheng2020-attn' ", - ) - compressai_group.add_argument( - "--compression-model-path", - action="store", - type=str, - required=False, - default=None, - help="a path to a directory containing model.py for custom development model", - ) - compressai_group.add_argument( - "--half", - action="store_true", - required=False, - default=False, - help="convert model to half floating point (fp16)", - ) - vtm_group.add_argument( - "--vtm", - action="store_true", - default=False, - help="To enable vtm codec. default: False", - ) - vtm_group.add_argument( - "--vtm_dir", - action="store", - type=str, - required=False, - default=None, - help="path to directory with executables EncoderAppStatic & DecoderAppStatic", - ) - vtm_group.add_argument( - "--vtm_cfg", - action="store", - type=str, - required=False, - default=None, - help="vtm config file. Example: 'encoder_intra_vtm.cfg' ", - ) - vtm_group.add_argument( - "--vtm_cache", - action="store", - type=str, - required=False, - default=None, - help="directory to cache vtm bitstreams", - ) - optional_group.add_argument( - "--qpars", - action="store", - type=str, - required=False, - default=None, - help="quality parameters for compressai model or vtm. For compressai-zoo model, it should be integer 1-8. For VTM, it should be integer from 0-51.", - ) - optional_group.add_argument( - "--scale", - action="store", - type=int, - required=False, - default=100, - help="image scaling as per VCM working group docs. Default: 100", - ) - - optional_group.add_argument( - "--ffmpeg", - action="store", - type=str, - required=False, - default="ffmpeg", - help="path of ffmpeg executable. Default: ffmpeg", - ) - optional_group.add_argument( - "--slice", - action="store", - type=str, - required=False, - default=None, - help="use a dataset slice instead of the complete dataset. Example: 0:2 for the first two images. Instead of python slicing string, this can also be a list of sample filepaths in the dataset", - ) - # subparser.add_argument("--debug", action="store_true", default=False) # not here - optional_group.add_argument( - "--progressbar", - action="store_true", - default=False, - help="show fancy progressbar. Default: False", - ) - optional_group.add_argument( - "--progress", - action="store", - type=int, - required=False, - default=1, - help="Print progress this often", - ) - optional_group.add_argument( - "--eval-method", - action="store", - type=str, - required=False, - default="open-images", - help="Evaluation method/protocol: open-images or coco. Default: open-images", - ) - optional_group.add_argument( - "--keep", - action="store_true", - default=False, - help="Keep tmp databased saved or not. Default: False", - ) - return subparser - - -def main(p): # noqa: C901 - # check that only one is defined - defined_codec = "" - for codec in [p.compressai_model_name, p.vtm, p.compression_model_path]: - if codec: - if defined_codec: # second match! - raise AssertionError( - "please define only one of the following: compressai_model_name, vtm or compression_model_path" - ) - defined_codec = codec - - assert p.dataset_name is not None, "please provide dataset name" - assert p.model is not None, "provide Detectron2 model name" - - # fiftyone - print("importing fiftyone") - import fiftyone as fo - - # dataset.clone needs this - from compressai_vision import patch # noqa: F401 - - print("fiftyone imported") - - # compressai_vision - from compressai_vision.evaluation.fo import ( # annex predictions from - annexPredictions, - annexVideoPredictions, - ) - from compressai_vision.evaluation.pipeline import ( - CompressAIEncoderDecoder, - VTMEncoderDecoder, - ) - from compressai_vision.pipelines.fo_vcm.constant import vf_per_scale - - # from compressai_vision.pipelines.fo_vcm.tools import getDataFile - - try: - dataset = fo.load_dataset(p.dataset_name) - except ValueError: - print("FATAL: no such registered dataset", p.dataset_name) - return - - dataset, fr, to = checkSlice(p, dataset) - - compression = True - # print(">", p.compressai_model_name, p.vtm, p.compression_model_path, p.qpars) - if ( - (p.compressai_model_name is None) - and (p.vtm is False) - and (p.compression_model_path is None) - ): - compression = False - # no (de)compression, just eval - assert ( - p.qpars is None - ), "you have provided quality pars but not a (de)compress or vtm model" - qpars = None # this indicates no qpars/pure eval run downstream - - else: - # check quality parameter list - assert p.qpars is not None, "need to provide integer quality parameters" - qpars = getQPars(p) - - if p.compressai_model_name is not None: # compression from compressai zoo - compression_model = checkZoo(p) - if compression_model is None: - print("Can't find compression model") - return 2 - - elif p.compression_model_path is not None: - encoder_decoder_func = loadEncoderDecoderFromPath(p.compression_model_path) - - elif p.vtm: # setup VTM - vtm_encoder_app, vtm_decoder_app, vtm_cfg = setupVTM(p) - - # *** CHOOSE COMPRESSION SCHEME OK *** - - if p.scale is not None: - assert p.scale in vf_per_scale.keys(), "invalid scale value" - - import torch - - device = "cuda" if torch.cuda.is_available() else "cpu" - if p.no_cuda: - device = "cpu" - - model_names = p.model - predictors, models_meta, pred_fields = setupDetectron2(model_names, device) - - # instead, create a unique identifier for the field - # in this run: this way parallel runs dont overwrite - # each other's field - # as the database is the same for each running instance/process - # ui=uuid.uuid1().hex # 'e84c73f029ee11ed9d19297752f91acd' - # predictor_field = "detectron-"+ui - # predictor_field = "detectron-{0:%Y-%m-%d-%H-%M-%S-%f}".format( - # datetime.datetime.now() - # ) - # even better idea: create a temporarily cloned database - try: - username = os.environ["USER"] - except KeyError: - username = "nouser" - tmp_name0 = p.dataset_name + "-{0:%Y-%m-%d-%H-%M-%S-%f}".format( - datetime.datetime.now() - ) - - tmp_name = "detectron-run-{username}-{tmp_name0}".format( - username=username, tmp_name0=tmp_name0 - ) - - eval_method = p.eval_method - eval_methods = ["open-images", "coco"] - # must be checked at this stage so that the whole run doesn't crash in the end - # just because user has fat-fingered the evaluation method - assert eval_method in eval_methods, "ERROR: allowed eval methods:" + str( - eval_methods - ) - - if dataset.media_type == "image": - detection_fields = checkDataset(dataset, fo.core.labels.Detections) - annex_function = annexPredictions - elif dataset.media_type == "video": - detection_fields = checkVideoDataset(dataset, fo.core.labels.Detections) - annex_function = annexVideoPredictions - else: - print("unknown media type", dataset.media_type) - return - - print() - print("Using dataset :", p.dataset_name) - - if p.gt_field not in detection_fields: - print("FATAL: you have requested field '%s' for ground truths" % (p.gt_field)) - print(" : but it doesn't appear in the dataset samples") - print(" : please use compressai-vision show to peek at the fields") - print() - return 2 - - print("Dataset media type :", dataset.media_type) - print("Dataset tmp clone :", tmp_name) - print("Keep tmp dataset? :", p.keep) - print("Image scaling :", p.scale) - if p.slice is not None: # can't use slicing - print("WARNING: Using slice :", str(fr) + ":" + str(to)) - print("Number of samples :", len(dataset)) - print("Torch device :", device) - - assert len(model_names) == len(models_meta) - for e, model_info in enumerate(zip(model_names, models_meta)): - name, meta = model_info - print(f"=== Vision Model #{e} ====") - print("Detectron2 model :", name) - print("Model was trained with :", meta[0]) - print("Eval. results will be saved to datafield") - print(" :", pred_fields[e]) - print("Evaluation protocol :", eval_method) - - if dataset.media_type == "image": - classes = dataset.distinct("%s.detections.label" % (p.gt_field)) - elif dataset.media_type == "video": - classes = dataset.distinct("frames.%s.detections.label" % (p.gt_field)) - classes.sort() - detectron_classes = copy.deepcopy(meta[1].thing_classes) - detectron_classes.sort() - print("Peek model classes :") - print(detectron_classes[0:5], "...") - print("Peek dataset classes :") - print(classes[0:5], "...") - - if p.compressai_model_name is not None: - print("Using compressai model :", p.compressai_model_name) - elif p.compression_model_path is not None: - print("Using custom model.py from") - print(" :", p.compression_model_path) - elif p.vtm: - print("Using VTM ") - if p.vtm_cache: - # assert(os.path.isdir(p.vtm_cache)), "no such directory "+p.vtm_cache - # ..created by the VTMEncoderDecoder class - print("WARNING: VTM USES CACHE IN", p.vtm_cache) - else: - print("** Evaluation without Encoding/Decoding **") - """ - if p.compression_model_checkpoint: - print("WARN: using checkpoint files") - """ - if qpars is not None: - print("Quality parameters :", qpars) - print("Ground truth data field name") - print(" :", p.gt_field) - if len(detection_fields) > 1: - print("--> WARNING: you have more than one detection field in your dataset:") - print(",".join(detection_fields)) - print("be sure to choose the correct one (i.e. for detection or segmentation)") - - # dataset_ = fo.load_dataset(p.dataset_name) # done up there! - - if not checkForField(dataset, p.gt_field): - return - - # print("(if aborted, start again with --resume=%s)" % predictor_field) - print("Progressbar :", p.progressbar) - if p.progressbar and p.progress > 0: - print("WARNING: progressbar enabled --> disabling normal progress print") - p.progress = 0 - print("Print progress :", p.progress) - print("Output file :", p.output) - - if not p.y: - input("press enter to continue.. ") - - # save metadata about the run into the json file - metadata = { - "dataset": p.dataset_name, - "gt_field": p.gt_field, - "tmp-dataset": tmp_name, - "slice": p.slice, - "model": model_names, - "codec": defined_codec, - "qpars": qpars, - } - with open(p.output, "w") as f: - f.write(json.dumps(metadata, indent=2)) - - # please see ../monkey.py for problems I encountered when cloning datasets - # simultaneously with various multiprocesses/batch jobs - print("cloning dataset", p.dataset_name, "to", tmp_name) - dataset = dataset.clone(tmp_name) - dataset.persistent = True - # fo.core.odm.database.sync_database() # this would've helped? not sure.. - - """ - # parameters for dataset.evaluate_detections - # https://voxel51.com/docs/fiftyone/user_guide/evaluation.html#evaluating-videos - eval_args = {"gt_field": p.gt_field, "method": eval_method} - if eval_method == "open-images": - if dataset.get_field("positive_labels"): - eval_args["pos_label_field"] = "positive_labels" - if dataset.get_field("negative_labels"): - eval_args["neg_label_field"] = "negative_labels" - eval_args["expand_pred_hierarchy"] = False - eval_args["expand_gt_hierarchy"] = False - else: - eval_args["compute_mAP"] = True - """ - pred_fields_, eval_args = makeEvalPars( - dataset=dataset, - gt_field=p.gt_field, - predictor_fields=pred_fields, - eval_method=eval_method, - ) - # print(predictor_field_) - # print(eval_args) - - # bpp, mAP values, mAP breakdown per class - def per_class(results_obj): - """take fiftyone/openimagev6 results object & spit - out mAP breakdown as per class - """ - d = {} - for class_ in classes: - d[class_] = results_obj.mAP([class_]) - return d - - xs = [] - ys = [] - maps = [] - - if compression: - for quality in qpars: - enc_dec = None # default: no encoding/decoding - if ( - p.compressai_model_name or p.compression_model_path - ): # compressai model, either from the zoo or from a directory: - if p.compressai_model_name is not None: - # e.g. "bmshj2018-factorized" - print("\nQUALITY PARAMETER: ", quality) - net = ( - compression_model(quality=quality, pretrained=True) - .eval() - .to(device) - ) - if p.half: - net = net.half() - enc_dec = CompressAIEncoderDecoder( - net, - device=device, - scale=p.scale, - ffmpeg=p.ffmpeg, - dump=p.dump, - half=p.half, - ) - else: # or a custom model from a file: - enc_dec = encoder_decoder_func( - quality=quality, - device=device, - scale=p.scale, - ffmpeg=p.ffmpeg, - dump=p.dump, - half=p.half, - ) - elif p.vtm: - enc_dec = VTMEncoderDecoder( - encoderApp=vtm_encoder_app, - decoderApp=vtm_decoder_app, - ffmpeg=p.ffmpeg, - vtm_cfg=vtm_cfg, - qp=quality, - cache=p.vtm_cache, - scale=p.scale, - warn=True, - ) - else: - raise BaseException("program logic error") - enc_dec.computeMetrics(False) - - # append pred_fields_ with the quality point tag - # i.e. detectron-eval_v0 ==> detectron-eval_v0-qp-1 - # this way, if we use the --keep flag, we can - # see detection results for different qpoints - # in the fo app - pred_fields_qp = [] - for pred_field_ in pred_fields_: - pred_fields_qp.append(pred_field_ + "-qp-" + str(quality)) - - bpp = annex_function( - predictors=predictors, - fo_dataset=dataset, - encoder_decoder=enc_dec, - gt_field=p.gt_field, - predictor_fields=pred_fields_qp, - use_pb=p.progressbar, - use_print=p.progress, - ) - - if bpp is None or bpp < 0: - print() - print("Sorry, mAP calculation aborted") - # TODO: implement: - # print("If you want to resume, start again with:") - # print("--continue", predictor_field) - print() - return - - if not p.progressbar: - fo.config.show_progress_bars = False - - bpps = [] - accs = [] - accs_detail = [] - for _, pred_field_ in enumerate(pred_fields_qp): - # print("evaluating dataset", dataset.name) - res = dataset.evaluate_detections(pred_field_, **eval_args) - bpps.append(bpp) - accs.append(res.mAP()) - accs_detail.append(per_class(res)) - - xs.append(bpps) - ys.append(accs) - maps.append(accs_detail) - - else: # a pure evaluation without encoding/decoding - bpp = annex_function( - predictors=predictors, - fo_dataset=dataset, - gt_field=p.gt_field, - predictor_fields=pred_fields_, - use_pb=p.progressbar, - use_print=p.progress, - ) - - bpps = [] - accs = [] - accs_detail = [] - for _, pred_field_ in enumerate(pred_fields_): - # print("evaluating dataset", dataset.name) - res = dataset.evaluate_detections( - pred_field_, - **eval_args, - # gt_field=p.gt_field, - # method="open-images", - # pos_label_field="positive_labels", - # neg_label_field="negative_labels", - # expand_pred_hierarchy=False, - # expand_gt_hierarchy=False, - ) - bpps.append(bpp) - accs.append(res.mAP()) - accs_detail.append(per_class(res)) - - xs.append(bpps) - ys.append(accs) - maps.append(accs_detail) - - # print(">>", metadata) - metadata["bpp"] = xs - metadata["map"] = ys - metadata["map_per_class"] = maps - with open(p.output, "w") as f: - f.write(json.dumps(metadata, indent=2)) - - """maybe not? - print("\nResult output:") - print(json.dumps(metadata, indent=2)) - """ - if not p.keep: - # remove the tmp database - print("deleting tmp database", tmp_name) - fo.delete_dataset(tmp_name) - else: - print("keeping tmp database", tmp_name) - - print("\nDone!\n") - """load with: - with open(p.output,"r") as f: - res=json.load(f) - """ diff --git a/compressai_vision/run/vcm_app_cli/download.py b/compressai_vision/run/vcm_app_cli/download.py deleted file mode 100644 index 6d9d8dee..00000000 --- a/compressai_vision/run/vcm_app_cli/download.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli download functionality -""" -import os - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "download", - parents=parents, - help="download an image set and register it to fiftyone.", - ) - required_group = subparser.add_argument_group("required arguments") - subparser.add_argument( - "--mock", action="store_true", default=False, help="mock tests" - ) - required_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default="open-images-v6", - help="name of the dataset", - ) - subparser.add_argument( - "--lists", - action="store", - type=str, - required=False, - default=None, - help="comma-separated list of list files. Example: detection_validation_input_5k.lst, segmentation_validation_input_5k.lst", - ) - subparser.add_argument( - "--split", - action="store", - type=str, - required=False, - default=None, - help="database sub-name. Example: 'train' or 'validation'", - ) - subparser.add_argument( - "--dir", - action="store", - type=str, - required=False, - default=None, - help="directory where the dataset (images, annotations, etc.) is downloaded. default uses fiftyone default, i.e. ~/fiftyone/", - ) - subparser.add_argument( - "--label-types", - action="store", - type=str, - required=False, - default=None, - nargs="+", - help="a label type or list of label types to download. Supported label types are listed at 'https://docs.voxel51.com/user_guide/dataset_zoo/datasets.html#dataset-zoo-coco-2017'. i.e. coco-2017 supports ('detections', 'segmentations'). By default, only detections are loaded", - ) - - -def main(p): - # fiftyone - print("importing fiftyone") - from fiftyone import zoo as foz # different fiftyone than the patched one.. eh - - print("fiftyone imported") - - # compressai_vision - from compressai_vision.conversion import imageIdFileList - from compressai_vision.pipelines.fo_vcm.tools import pathExists - - if p.dataset_name is None: - p.dataset_name = "open-images-v6" - print() - if p.lists is None: - print( - "WARNING: downloading ALL images. You might want to use the --lists option to download only certain images" - ) - n_images = "?" - image_ids = None - else: - fnames = p.lists.split(",") - for fname in fnames: - assert pathExists(fname) - image_ids = imageIdFileList(*fnames) - if p.mock: - image_ids = image_ids[0:2] - print("WARNING! MOCK TEST OF ONLY TWO SAMPLES!") - n_images = str(len(image_ids)) - print("Using list files: ", p.lists) - print("Number of images: ", n_images) - print("Database name : ", p.dataset_name) - if p.label_types is not None: - print("Loaded labels : ", p.label_types) - print("Subname/split : ", p.split) - print("Target dir : ", p.dir) - - if not p.y: - input("press enter to continue.. ") - print() - - kwargs = {} - if p.split is not None: - kwargs["split"] = p.split - if image_ids is not None: - kwargs["image_ids"] = image_ids - if p.dir is not None: - p.dir = os.path.expanduser(p.dir) - kwargs["dataset_dir"] = p.dir - if p.label_types is not None: - kwargs["label_types"] = p.label_types - - # print(">>>", p.dir) - dataset = foz.load_zoo_dataset(p.dataset_name, **kwargs) - dataset.persistent = True diff --git a/compressai_vision/run/vcm_app_cli/dummy.py b/compressai_vision/run/vcm_app_cli/dummy.py deleted file mode 100644 index 0ed59fdf..00000000 --- a/compressai_vision/run/vcm_app_cli/dummy.py +++ /dev/null @@ -1,72 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli create dummy db functionality -""" - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "dummy", - parents=parents, - help="create & register a dummy database with just the first sample", - ) - required_group = subparser.add_argument_group("required arguments") - required_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default=None, - help="name of the dataset", - ) - - -def main(p): - # fiftyone - print("importing fiftyone") - import fiftyone as fo - - print("fiftyone imported") - - try: - dataset = fo.load_dataset(p.dataset_name) - except ValueError: - print("dataset", p.dataset_name, "does not exist!") - return - dummyname = p.dataset_name + "-dummy" - print("creating dataset", dummyname) - try: - fo.delete_dataset(dummyname) - except ValueError: - pass - dummy_dataset = fo.Dataset(dummyname) - for sample in dataset[0:1]: - dummy_dataset.add_sample(sample) - dummy_dataset.persistent = True diff --git a/compressai_vision/run/vcm_app_cli/import_custom.py b/compressai_vision/run/vcm_app_cli/import_custom.py deleted file mode 100644 index 59224c40..00000000 --- a/compressai_vision/run/vcm_app_cli/import_custom.py +++ /dev/null @@ -1,393 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""Use this stub for adding new cli commands -""" -import os -from pathlib import Path - -from .tools import makeVideoThumbnails - -possible_types = [ - "oiv6-mpeg-v1", # as provided by nokia - "tvd-object-tracking-v1", # TVD - "tvd-image-v1", # TVD - "sfu-hw-objects-v1", # SFU-HW - "flir-mpeg-v1", - "flir-image-rgb-v1", # FLIR -] - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "import-custom", - parents=parents, - help="import some popular custom datasets into fiftyone", - ) - req_group = subparser.add_argument_group("required arguments") - req_group.add_argument( - "--dataset-type", - action="store", - type=str, - required=True, - default=None, - help="dataset type, possible values: " + ",".join(possible_types), - ) - req_group.add_argument( - "--dir", - action="store", - type=str, - required=False, - default=None, - help="root directory of the dataset", - ) - mpeg_group = subparser.add_argument_group("arguments for oiv6-mpeg") - mpeg_group.add_argument( - "--datadir", - action="store", - type=str, - required=False, - default=None, - help="directory where all datasets are downloaded by default (optional)", - ) - mpeg_group.add_argument( - "--mock", action="store_true", default=False, help="debugging switch: don't use" - ) - mpeg_group.add_argument( - "--use-vcm", - action="store_true", - default=True, - help="Use mpeg-vcm files bundled with compressai-vision", - ) - - -def main(p): # noqa: C901 - assert ( - p.dataset_type in possible_types - ), "dataset-type needs to be one of these:" + str(possible_types) - print("importing fiftyone") - import fiftyone as fo - - # from compressai_vision import patch # required by tvd-image-v1 import - # from fiftyone import ViewField as F - - print("fiftyone imported") - try: - dataset = fo.load_dataset(p.dataset_type) - assert dataset is not None # dummy - except ValueError: - pass - else: - print( - "WARNING: dataset %s already exists: will delete and rewrite" - % (p.dataset_type) - ) - - # oiv-mpeg-v1 doesn't need to --dir (p.dir) since it downloads the file itself - if p.dataset_type == "oiv6-mpeg-v1": - # this is the most "ancient" part in this library - # (all started by trying to import oiv-mpeg-v1) - from .auto import main - - # see func add_subparser up there - # main is using the parameters in mpeg_group - main(p) - return - - # rest of the importers require the user to download the files themselves - assert p.dir is not None, "please provide root directory with the --dir argument" - p.dir = os.path.expanduser(p.dir) # correct path in the case user uses POSIX "~" - assert os.path.isdir(p.dir), "can find directory " + p.dir - - print() - print("Importing custom dataset into fiftyone") - print() - print("Dataset type : ", p.dataset_type) - print("Dataset root directory : ", p.dir) - print() - - if p.dataset_type == "sfu-hw-objects-v1": - if not p.y: - input("press enter to continue.. ") - print() - from compressai_vision.conversion.sfu_hw_objects_v1 import ( - register, - video_convert, - ) - - video_convert(p.dir) - register(p.dir) # dataset persistent - """NOTE: not required for this dataset - print() - print("Will create thumbnails for fiftyone app visualization") - print("for your convenience, Sir") - if not p.y: - input("press enter to continue.. (or CTRL-C to abort)") - print() - dataset=fo.load_dataset("sfu-hw-objects-v1") - makeVideoThumbnails(dataset, force=True) - """ - - elif p.dataset_type == "tvd-object-tracking-v1": - if not p.y: - input("press enter to continue.. ") - print() - from compressai_vision.conversion.tvd_object_tracking_v1 import register - - res = register(p.dir) # dataset persistent - if res is not None: - return 2 - print() - print("Will create thumbnails for fiftyone app visualization") - print("for your convenience, Sir") - if not p.y: - input("press enter to continue.. (or CTRL-C to abort)") - print() - dataset = fo.load_dataset("tvd-object-tracking-v1") - makeVideoThumbnails(dataset, force=True) - - elif p.dataset_type == "tvd-image-v1": - print( - """ - After extracting tencent zipfiles: - - TVD_Instance_Segmentation_Annotations.zip - TVD_Object_Detection_Dataset_and_Annotations.zip - - You should have this directory structure: - /path/to/ - TVD_Object_Detection_Dataset_And_Annotations/ - tvd_detection_validation_bbox.csv - tvd_detection_validation_labels.csv - tvd_label_hierarchy.json - tvd_object_detection_dataset/ (IMAGES) - tvd_segmentation_validation_bbox.csv - tvd_segmentation_validation_labels.csv - tvd_segmentation_validation_masks.csv - tvd_validation_masks/ (SEGMASKS) - """ - ) - print("you have defined /path/to = ", p.dir) - print() - print( - """ - OpenImageV6 formatted files and directory structures will be in - - /path/to/TVD_images_detection_v1 - /path/to/TVD_images_segmentation_v1 - """ - ) - if not p.y: - input("press enter to continue.. ") - print() - - mainpath = Path(p.dir) - # bbox - bbox_path = mainpath / "TVD_Object_Detection_Dataset_And_Annotations" - bbox_validation_csv_file = bbox_path / "tvd_detection_validation_labels.csv" - bbox_csv_file = bbox_path / "tvd_detection_validation_bbox.csv" - img_dir = bbox_path / "tvd_object_detection_dataset" - bbox_target_dir = mainpath / "TVD_images_detection_v1" - # seg - seg_validation_csv_file = mainpath / "tvd_segmentation_validation_labels.csv" - seg_csv_file = mainpath / "tvd_segmentation_validation_bbox.csv" - seg_mask_csv_file = mainpath / "tvd_segmentation_validation_masks.csv" - seg_data_dir = mainpath / "tvd_validation_masks" - seg_target_dir = mainpath / "TVD_images_segmentation_v1" - # - from compressai_vision.conversion.mpeg_vcm import MPEGVCMToOpenImageV6 - - # detections - MPEGVCMToOpenImageV6( - validation_csv_file=str(bbox_validation_csv_file), - bbox_csv_file=str(bbox_csv_file), - output_directory=str(bbox_target_dir), - data_dir=str(img_dir), - link=True, - # link=False, - verbose=True, - ) - - # segmentations - MPEGVCMToOpenImageV6( - validation_csv_file=str(seg_validation_csv_file), - bbox_csv_file=str(seg_csv_file), - segmentation_csv_file=str(seg_mask_csv_file), - output_directory=str(seg_target_dir), - mask_dir=str(seg_data_dir), - data_dir=str(img_dir), - # mask_dir: str = None, - link=True, - # link=False, - verbose=True, - append_mask_dir="0", - # since the dir structure provided is erroneous - # create the labels/masks directory ourselves - # and link from labels/masks/0 --> provided segmask dir - ) - - name = "tvd-image-detection-v1" - print("\nRegistering", name) - try: - fo.delete_dataset(name) - except ValueError: - pass - else: - print("WARNING: deleted pre-existing", name) - dataset = fo.Dataset.from_dir( - name=name, - dataset_dir=str(bbox_target_dir), - dataset_type=fo.types.dataset_types.OpenImagesV6Dataset, - # label_types=("detections", "classifications", "relationships", "segmentations"), - label_types=("detections", "classifications"), - load_hierarchy=False, - ) - dataset.persistent = True - - name = "tvd-image-segmentation-v1" - print("\nRegistering", name) - try: - fo.delete_dataset(name) - except ValueError: - pass - else: - print("WARNING: deleted pre-existing", name) - dataset = fo.Dataset.from_dir( - name=name, - dataset_dir=str(seg_target_dir), - dataset_type=fo.types.dataset_types.OpenImagesV6Dataset, - # label_types=("detections", "classifications", "relationships", "segmentations"), - label_types=("detections", "segmentations", "classifications"), - load_hierarchy=False, - ) - dataset.persistent = True - - elif p.dataset_type == "flir-mpeg-v1": - name = "flir-mpeg-detection-v1" - print( - """ - After extraing mpeg-vcm provided zipfile, you should have this directory/file structure: - - /path/to/ - | ├── anchor_results - │ ├── FLIR_anchor_vtm12_bitdepth10.xlsx - │ └── VCM-reporting-template-FLIR_vtm12_d10.xlsm - ├── dataset - │ ├── coco_format_json_annotation - │ │ ├── FLIR_val_thermal_coco_format_jpg.json - │ │ ├── FLIR_val_thermal_coco_format_png.json - │ │ └── Two files differ only in image file format whithin the file, and the rest are the same..txt - │ ├── fine_tuned_model - │ │ └── model_final.pth - │ └── thermal_images [300 entries exceeds filelimit, not opening dir] - ├── mAP_coco.py - └── Readme.txt - - You provided /path/to = %s - """ - % (p.dir) - ) - if not p.y: - input("press enter to continue.. ") - print() - print("\nRegistering", name) - try: - fo.delete_dataset(name) - except ValueError: - pass - else: - print("WARNING: deleted pre-existing", name) - # https://voxel51.com/docs/fiftyone/user_guide/dataset_creation/datasets.html#basic-recipe - dataset = fo.Dataset.from_dir( - name=name, - dataset_type=fo.types.COCODetectionDataset, - data_path=os.path.join(p.dir, "dataset", "thermal_images"), - labels_path=os.path.join( - p.dir, - "dataset", - "coco_format_json_annotation", - "FLIR_val_thermal_coco_format_jpg.json", - ), - ) - dataset.persistent = True - - elif p.dataset_type == "flir-image-rgb-v1": - name = p.dataset_type - print( - """ - After extracting - - FLIR_ADAS_v2.zip - - You should have the following (COCO-formatted) directory/file structure: - - images_rgb_train/ - coco.json - data/ [IMAGES] - images_rgb_val/ - coco.json - data/ [IMAGES] - images_thermal_train/ - coco.json - data/ [IMAGES] - images_thermal_val/ - ... - video_rgb_test/ - ... - video_thermal_test/ - ... - rgb_to_thermal_vid_map.json - - Will import - %s/images_rgb_train - into dataset flir-image-rgb-v1 - """ - % (p.dir) - ) - if not p.y: - input("press enter to continue.. ") - print() - print("\nRegistering", name) - try: - fo.delete_dataset(name) - except ValueError: - pass - else: - print("WARNING: deleted pre-existing", name) - dataset_dir = os.path.join(p.dir, "images_rgb_train") - dataset = fo.Dataset.from_dir( - name=name, - dataset_type=fo.types.COCODetectionDataset, - data_path=os.path.join(dataset_dir), # , "data"), - labels_path=os.path.join(dataset_dir, "coco.json"), - # image_ids = [] # TODO - ) - dataset.persistent = True - - print("HAVE A NICE DAY!") diff --git a/compressai_vision/run/vcm_app_cli/info.py b/compressai_vision/run/vcm_app_cli/info.py deleted file mode 100644 index c8ae87f8..00000000 --- a/compressai_vision/run/vcm_app_cli/info.py +++ /dev/null @@ -1,140 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli.py : Command-line interface tools for compressai-vision -""" -import os -import sys - - -def add_subparser(subparsers, parents): - _ = subparsers.add_parser( - "info", parents=parents, help="shows info about your system" - ) - - -# compressai_vision -def main(p): # noqa: C901 - print("\n*** YOUR VIRTUALENV ***") - print("--> running from :", sys.executable) - - try: - import torch - except ModuleNotFoundError: - print("\nPYTORCH NOT INSTALLED\n") - sys.exit(2) - try: - import detectron2 - except ModuleNotFoundError: - print("\nDETECTRON2 NOT INSTALLED\n") - sys.exit(2) - - try: - import compressai - except ModuleNotFoundError: - print("\nCOMPRESSAI NOT INSTALLED") - sys.exit(2) - - print("\n*** TORCH, CUDA, DETECTRON2, COMPRESSAI ***") - print("torch version :", torch.__version__) - print("cuda version :", torch.version.cuda) - print("detectron2 version :", detectron2.__version__) - print("--> running from :", detectron2.__file__) - print("compressai version :", compressai.__version__) - print("--> running from :", compressai.__file__) - - print("\n*** FIFTYONE ***") - from importlib.metadata import files, version - - util = [p for p in files("fiftyone") if "__init__.py" in str(p)][0] - fo_path = str(util.locate()) - fo_version = version("fiftyone") - print("fiftyone version :", fo_version) - print("--> running from :", fo_path) - - print("\n*** COMPRESSAI-VISION ***") - print("version :", version("compressai-vision")) - print("running from :", __file__) - - print("\n*** CHECKING GPU AVAILABILITY ***") - device = "cuda" if torch.cuda.is_available() else "cpu" - print("device :", device) - - print("\n*** TESTING FFMPEG ***") - c = os.system("ffmpeg -version") - if c > 0: - print("\nRUNNING FFMPEG FAILED\n") - # - print() - try: - adr = os.environ["FIFTYONE_DATABASE_URI"] - except KeyError: - print("NOTICE: Using mongodb managed by fiftyone") - print("Be sure not to have extra mongod server(s) running on your system") - else: - print("NOTICE: You have external mongodb server configured with", adr) - - try: - db_name = os.environ["FIFTYONE_DATABASE_NAME"] - except KeyError: - print( - """ - WARNING: You should set the environment variable FIFTYONE_DATABASE_NAME - in your virtual environment. Different virtual environments (with different - fiftyone versions) should NOT write to the SAME database in the same mongodb server. - """ - ) - else: - print("Fiftyone database name in mongodb:", db_name) - - # fiftyone - print("importing fiftyone..") - import fiftyone as fo - - print("..imported") - - print("\n*** DATABASE ***") - print("info about your connection:") - - print(fo.core.odm.database.get_db_conn()) - print() - - print("\n*** DATASETS ***") - print("datasets currently registered into fiftyone") - print("name, length, first sample path") - for name in fo.list_datasets(): - dataset = fo.load_dataset(name) - n = len(dataset) - if n > 0: - sample = dataset.first() - p = os.path.sep.join(sample["filepath"].split(os.path.sep)[:-1]) - else: - p = "?" - print("%s, %i, %s" % (name, len(dataset), p)) - print() diff --git a/compressai_vision/run/vcm_app_cli/killmongo.py b/compressai_vision/run/vcm_app_cli/killmongo.py deleted file mode 100644 index c01190f1..00000000 --- a/compressai_vision/run/vcm_app_cli/killmongo.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""Kill / clear mongodb - -If there's a runaway mongodb process, etc. or mongodb was terminated unclean. - -:: - - ~/.fiftyone/var/lib/mongo/mongod.lock # kill mongodb, remove this file - - ~/.fiftyone # kill mongodb & remove the mongodb database .. this can happen if fiftyone & mongo versions are not compatible - - fiftyone.core.service.ServiceListenTimeout: fiftyone.core.service.DatabaseService failed to bind to port - - https://github.com/voxel51/fiftyone/issues/1988 # related github issues - https://github.com/voxel51/fiftyone/issues/1334 - -""" - -import glob -import os -import shutil - -# https://voxel51.com/docs/fiftyone/user_guide/config.html - - -def killer(): - print("trying to kill local mongo processes") - os.system("killall -9 mongod") - print( - "killed what could. If you got 'Operation not permitted', you have mongod running as a systemd daemon (use systemctl to shut down)" - ) - - -def stopMongo(): - killer() - for fname in glob.glob( - os.path.expanduser(os.path.join("~", ".fiftyone/var/lib/mongo/*lock*")) - ): - print("removing", fname, "PRESS ENTER TO CONTINUE") - input() - os.remove(fname) - - -def clearMongo(): - try: - adr = os.environ["FIFTYONE_DATABASE_URI"] - except KeyError: - killer() - dirname = os.path.expanduser(os.path.join("~", ".fiftyone")) - print("WARNING: removing local directory", dirname, "PRESS ENTER TO CONTINUE") - input() - shutil.rmtree(dirname) - else: - print("WARNING: You have external mongodb server configured with", adr) - print("Wiping out fiftyone data from there. PRESS ENTER TO CONTINUE") - input() - import mongoengine - - conn = mongoengine.connect(host=adr) - conn.drop_database("fiftyone") - conn.close() - print("Done! !") - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "mongo", parents=parents, help="mongod management" - ) - subsubparsers = subparser.add_subparsers( - help="select subcommand (stop or clear)", dest="subcommand" - ) - subsubparsers.add_parser( - "stop", description="stop local mongodb server and clean lockfiles" - ) - subsubparsers.add_parser("clear", description="remove the local mongodb database") - - -def main(p): - if p.subcommand == "stop": - stopMongo() - elif p.subcommand == "clear": - clearMongo() - else: - print("use -h to see options") diff --git a/compressai_vision/run/vcm_app_cli/list_.py b/compressai_vision/run/vcm_app_cli/list_.py deleted file mode 100644 index c7361a2a..00000000 --- a/compressai_vision/run/vcm_app_cli/list_.py +++ /dev/null @@ -1,72 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli list functionality -""" -import os - - -def add_subparser(subparsers, parents): - _ = subparsers.add_parser( - "list", parents=parents, help="list all datasets registered to fiftyone" - ) - - -def main(p): - # fiftyone - print("importing fiftyone") - import fiftyone as fo - - print("fiftyone imported") - print() - infost = "" - try: - adr = os.environ["FIFTYONE_DATABASE_URI"] - except KeyError: - infost += "mongodb managed by fiftyone, " - else: - infost += "external mongodb server at " + adr + ", " - try: - db_name = os.environ["FIFTYONE_DATABASE_NAME"] - except KeyError: - infost += "default database name 'fiftyone'" - else: - infost += "database name '" + db_name + "'" - print(infost) - print("datasets currently registered into fiftyone") - print("name, length, first sample path") - for name in fo.list_datasets(): - dataset = fo.load_dataset(name) - n = len(dataset) - if n > 0: - sample = dataset.first() - p = os.path.sep.join(sample["filepath"].split(os.path.sep)[:-1]) - else: - p = "?" - print("%s, %i, %s" % (name, len(dataset), p)) diff --git a/compressai_vision/run/vcm_app_cli/main.py b/compressai_vision/run/vcm_app_cli/main.py deleted file mode 100644 index d28ec71c..00000000 --- a/compressai_vision/run/vcm_app_cli/main.py +++ /dev/null @@ -1,182 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli.py : Command-line interface tools for compressai-vision -""" -import argparse -import logging -import sys - -from compressai_vision.pipelines.fo_vcm.tools import getDataFile, quickLog -from compressai_vision.run.vcm_app_cli import ( - app, - auto, - clean, - convert_mpeg_to_oiv6, - copy, - deregister, - detectron2_eval, - download, - dummy, - import_custom, - info, - killmongo, - list_, - load_eval, - make_thumbnails, - metrics_eval, - plotter, - register, - show, - vtm, -) - -COMMANDS = { # noqa: F405 - "clean": clean.main, - "convert-mpeg-to-oiv6": convert_mpeg_to_oiv6.main, - "deregister": deregister.main, - "detectron2-eval": detectron2_eval.main, - "download": download.main, - "dummy": dummy.main, - "list": list_.main, - "load_eval": load_eval.main, - "register": register.main, - "vtm": vtm.main, - "mpeg-vcm-auto-import": auto.main, - "info": info.main, - "mongo": killmongo.main, - "plot": plotter.main, - "show": show.main, - "manual": None, - "metrics-eval": metrics_eval.main, - "import-custom": import_custom.main, - "make-thumbnails": make_thumbnails.main, - "app": app.main, - "copy": copy.main, -} - -coms = "" -for key in COMMANDS: - coms += key + "," - - -def setup_parser(): - common_parser = argparse.ArgumentParser( - add_help=False, formatter_class=argparse.RawTextHelpFormatter - ) - common_parser.add_argument( - "--y", action="store_true", default=False, help="non-interactive run" - ) - common_parser.add_argument( - "--debug", action="store_true", default=False, help="debug verbosity" - ) - common_parser.add_argument( - "--dump", - action="store_true", - default=False, - help="dump intermediate images whenever possible", - ) - common_parser.add_argument( - "--no-cuda", - action="store_true", - default=False, - help="never use cuda, just cpu (when applicable)", - ) - - parser = argparse.ArgumentParser( - description="Includes several subcommands. For full manual, type compressai-vision manual", - add_help=True, - ) - subparsers = parser.add_subparsers(help="select command", dest="command") - - # MANUAL SUBCOMMAND: - _ = subparsers.add_parser("manual") - - clean.add_subparser(subparsers, parents=[common_parser]) - convert_mpeg_to_oiv6.add_subparser(subparsers, parents=[common_parser]) - deregister.add_subparser(subparsers, parents=[common_parser]) - detectron2_eval.add_subparser(subparsers, parents=[common_parser]) - download.add_subparser(subparsers, parents=[common_parser]) - dummy.add_subparser(subparsers, parents=[common_parser]) - list_.add_subparser(subparsers, parents=[common_parser]) - show.add_subparser(subparsers, parents=[common_parser]) - register.add_subparser(subparsers, parents=[common_parser]) - vtm.add_subparser(subparsers, parents=[common_parser]) - copy.add_subparser(subparsers, parents=[common_parser]) - # AUTO IMPORT: - auto.add_subparser(subparsers, parents=[common_parser]) - # INFO: - info.add_subparser(subparsers, parents=[common_parser]) - # MONGO killings & cleanups: - killmongo.add_subparser(subparsers, parents=[common_parser]) - # PLOTTING: - plotter.add_subparser(subparsers, parents=[common_parser]) - # PNSR, MSSIM: - metrics_eval.add_subparser(subparsers, parents=[common_parser]) - # VIDEO: - import_custom.add_subparser(subparsers, parents=[common_parser]) - make_thumbnails.add_subparser(subparsers, parents=[common_parser]) - # APP: - app.add_subparser(subparsers, parents=[common_parser]) - return parser - - -def main(): - parser = setup_parser() - args, unparsed = parser.parse_known_args() - # print(">",args) - # return - for weird in unparsed: - print("invalid argument", weird) - raise SystemExit(2) - - # assert args.command in COMMANDS.keys(), "unknown command" - if args.command not in COMMANDS.keys(): - print("invalid command", args.command) - print("subcommands: " + coms) - sys.exit(2) - - if args.command == "manual": - with open(getDataFile("manual.txt"), "r") as f: - print(f.read()) - return - - if args.debug: - loglev = logging.DEBUG - else: - loglev = logging.INFO - quickLog("CompressAIEncoderDecoder", loglev) - quickLog("VTMEncoderDecoder", loglev) - - func = COMMANDS[args.command] - func(args) # pass cli args to the function in question - - -if __name__ == "__main__": - main() diff --git a/compressai_vision/run/vcm_app_cli/make_thumbnails.py b/compressai_vision/run/vcm_app_cli/make_thumbnails.py deleted file mode 100644 index 11b85393..00000000 --- a/compressai_vision/run/vcm_app_cli/make_thumbnails.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""Use this stub for adding new cli commands -""" -# import os - -from .tools import makeVideoThumbnails - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "make-thumbnails", - parents=parents, - help="Create 'side-data' videos that work with the fiftyone webapp", - ) - some_group = subparser.add_argument_group("required arguments") - some_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default=None, - help="name of the dataset", - ) - some_group.add_argument( - "--force", - action="store", - type=str, - required=False, - default=False, - help="encode files even if they already existed", - ) - - -def main(p): - """https://voxel51.com/docs/fiftyone/user_guide/app.html#multiple-media-fields - - https://voxel51.com/docs/fiftyone/api/fiftyone.utils.video.html#fiftyone.utils.video.reencode_videos - """ - # fiftyone - # if not p.y: - # input("press enter to continue.. ") - # print() - # p.some_dir = os.path.expanduser(p.some_dir) # correct path in the case user uses POSIX "~" - print("importing fiftyone") - import fiftyone as fo - - print("fiftyone imported") - print() - try: - dataset = fo.load_dataset(p.dataset_name) - except ValueError: - print("Sorry, could not find dataset", p.dataset_name) - assert dataset.media_type == "video", "this command works only for video datasets" - print("Will encode webapp-compatible versions of the videos") - print("This WILL take a while!") - if not p.y: - input("press enter to continue.. ") - makeVideoThumbnails(dataset, force=p.force) diff --git a/compressai_vision/run/vcm_app_cli/manual.txt b/compressai_vision/run/vcm_app_cli/manual.txt deleted file mode 100644 index b9eed4c1..00000000 --- a/compressai_vision/run/vcm_app_cli/manual.txt +++ /dev/null @@ -1,363 +0,0 @@ -commands & parameters: - - --y non-interactive - --debug debug verbosity (for some cases) - - - ******** BASIC COMMANDS **************************************************************************** - - manual shows this manual - - info shows info about your system - - mongo mongod management - - stop kills all local mongod servers - clean like stop, but additionally, removes all - fiftyone data from the mongod servers - - download download an image set and register it to fiftyone. - - --dataset-name name of the dataset. Default: "open-images-v6". - --lists list files that define the subset of images to download. - --split typically "train" or "validation". Default: None - (final dataset name is then for example "open-images-v6-validation" - --dir directory where the dataset (images, annotations, etc.) is downloaded - Default: $HOME/fiftyone/dataset-name - - example: - - compressai-vision download \\ - --lists=detection_validation_input_5k.lst,segmentation_validation_input_5k.lst \\ - --dataset-name=open-image-v6 --split=validation - - list list all datasets registered to fiftyone - - show show info about the dataset - - --dataset-name dataset registered name - - - register register image set to fiftyone from local dir - - --dataset-name dataset registered name - --lists lst files that define the subset of images to register - --dir source directory - --type fiftyone.types name. Default: OpenImagesV6Dataset - typical values: - - FiftyOneDataset - OpenImagesV6Dataset - ImageDirectory - - try "dir(fiftyone.types.dataset_types)" in python - see all of them - - deregister de-register image set from fiftyone - - --dataset-name name of the dataset, for example "open-image-v6-validation" - can also be a comma-separated list of dataset names - - copy creates a copy of the dataset to a different username. - You should always use this command when multiple users are using - the same mongodb server. Typically one user imports a dataset and - after this, individual users then take their own copy of it in order - to avoid conflicts / simultaneous reads & writes to the same dataset. - - --dataset-name name of the dataset. Can be a comma-separated list of dataset names - - --username optional. The default is your default posix username - The new name of dataset will be ``username-dataset-name`` - - dummy create & register a dummy dataset with just the first sample - - --dataset-name name of the original dataset - name of the new dataset will be appended with "-dummy" - - app start the awesome fiftyone webapp for dataset visualization - - --address (optional) interface address - --port (optional) port - --dataset-name name of the dataset - - clean remove temporary datasets. - detectron2-eval creates temporary clones of the dataset per each run. - These dataset are automatically cleaned up after detectron2_eval run. - If your run crashed, the temporary dataset might be left in the database - (you can see them with the list command where they appear with the name - "detectron-run-*".) - - - ******** EVALUATION ************************************************************************ - - detectron2-eval evaluate model with detectron2 using OpenImageV6 - evaluation protocol optionally with no (de)compression or - with compressai or vtm. - - --dataset-name name of the fiftyone registered dataset - --model name of the detectron2 model from the zoo, for example: - COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - It can also be a comma-separated list of the models for - multi-task scenario, for example: - COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml, \ - COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml - - Instead if a zoo model, you can also define a python .py file, - from where the detectron2.Predictor is loaded. The .py file - should have method "getCfgPredictor" that returns cfg, Predictor - For an example, please see examples/detectron2/flir.py in the main repo - - --eval-method Evaluation method/protocol for mAP calculations: - open-images or coco. Default: open-images. - --gt-field Name of the ground truth field in the dataset. - Default: detections - --output outputfile, default: compressai-vision.json - --keep when you run detectron2-eval, the original dataset is copied to a - tmp dataset where both the ground-truths and detection results are saved. - Normally the tmp dataset is removed after the evaluation is finished, but you - might want to keep & visualize it in order to see how well the gts and dets - compare to each other. Using this flag keeps the tmp database after evaluation. - - compression models: - - you can choose a compression model from compressai zoo, use vtm - or a model you're currently developing. If no model is chosen, then no (de)compression is - done before passing the image to detectron - - --compressai-model-name CompressAI model from the zoo, for example: bmshj2018_factorized - can be a model name from the CompressAI model zoo (optional) - --compression-model-path load a custom model. Defines a path to directory with a custom development model. - The directory should contain a properly formatted "model.py" file (optional) - --compression-model-checkpoint - a torch checkpoint file for loading the model state (.pth.tar file) - - vtm usage: - - --vtm use vtm (optional) - --vtm_dir specify path to directory "VVCSoftware_VTM/bin", i.e. to the - directory where there are executables "EncoderAppStatic" and - "DecoderAppStatic". If not specified, tries to use the - environmental variable VTM_DIR - --vtm_cfg path to vtm config file. If not specified uses an internal - default file. - --vtm_cache specify a path to directory where the bitstreams are cached. - The program saves bitstream to disk and tries to recycle the - bitstreams saved earlier. - Default: no caching. - NOTE: the path is automatically appended with - "scale/quality-parameter/" (see --scale) - - quality parameters etc: - - --qpars a quality parameters to be used with either compressai or vtm - --scale ffmpeg scaling applied to the image as defined per VCM working group. - values can be: 100 (original size), 75, 50, 25. - 0 = no scaling applied - --ffmpeg specify ffmpeg command. If not specified, uses "ffmpeg". - --slice instead of using the complete dataset, just use a slice of the - dataset: recommended use-case: parallelizing the VTM bitstream production. - Normal python slicing indexes are used, i.e. 0:2 - Can also be a comma-separated list of filepaths in the datasets, i.e.: - /path/to/image1.png,/path/to/image2.png - output: - - --debug enable debug verbosity - --progressbar show a fancy progressbar. Default: false. Nice for interactive runs. - --progress show progress every n:th step, i.e. --progress=10 would print out - progress every tenth step. 0: don't print anything. Default: 1. - Nice for batch jobs. - - example 1 (calculate mAP): - - compressai-vision detectron2_eval - --y --dataset-name=mpeg_vcm-detection \\ - --model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - example 2 (calculate mAP=mAP(bpp) with a model from CompressAI zoo): - - compressai-vision detectron2_eval --y --dataset-name=mpeg-vcm-detection \\ - --model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml \\ - --compressai=bmshj2018_factorized --qpars=1,2,3,4,5,6,7,8 - - example 3 (calculate mAP=mAP(bpp) with a custom development model): - - compressai-vision detectron2_eval --y --dataset-name=mpeg-vcm-detection \\ - --model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml \\ - --modelpath=/path/to/directory --qpars=1 - - metrics-eval evaluate image quality of reconstructed images - using PNSR and SSIM - - --dataset-name name of the dataset - - compressai-zoo arguments: - --compressai-model-name name of an existing model in compressai-zoo. Example: 'cheng2020-attn' - --compression-model-path path to a directory containing model.py for custom development model - - vtm arguments: - --vtm To enable vtm codec. default: False - --vtm_dir path to directory with executables EncoderAppStatic & DecoderAppStatic - --vtm_cfg vtm config file. Example: 'encoder_intra_vtm.cfg' - --vtm_cache directory to cache vtm bitstreams - - optional arguments: - --Output outputfile name - Default: compressai-vision.json - --qpars quality parameters for compressai model or vtm. For compressai-zoo model, it should be integer - 1-8. For VTM, it should be integer from 0-51. - Example: 1,2,3,4,5,6,7,8 - --scale image scaling as per VCM working group docs. - Default: 100 - --ffmpeg path of ffmpeg executable. - Default: ffmpeg - --slice use a dataset slice instead of the complete dataset. - Example: 0:2 for the first two images - Can also be a comma-separated list of filepaths in the datasets, i.e.: - /path/to/image1.png,/path/to/image2.png - --progressbar show fancy progressbar. - Default: False - --progress Print progress this often - - - ******** VTM *************************************************************************** - - vtm generate bitstream with the vtm video encoder - this is done also by the command detectron2_eval, but you - can do the bitstream generation step separately. - The following options are same as in "detectron2_eval" command: - - --dataset-name name of the fiftyone registered dataset - --output outputfile, default: compressai-vision.json - - --vtm_dir specify path to directory "VVCSoftware_VTM/bin", i.e. to the - directory where there are executables "EncoderAppStatic" and - "DecoderAppStatic". If not specified, tries to use the - environmental variable VTM_DIR - --vtm_cfg path to vtm config file. If not specified uses an internal - default file. - --vtm_cache specify a path to directory where the bitstreams are cached. - The program saves bitstream to disk and tries to recycle the - bitstreams saved earlier. - Default: no caching. - NOTE: the path is automatically appended with - "scale/quality-parameter/" (see --scale) - --qpars a quality parameters to be used with either compressai or vtm - --scale ffmpeg scaling applied to the image as defined per VCM working group. - values can be: 100 (original size), 75, 50, 25. - 0 = no scaling applied - --ffmpeg specify ffmpeg command. If not specified, uses "ffmpeg". - --slice instead of using the complete dataset, just use a slice of the - dataset: good for parallelizing the VTM bitstream production - Normal python slicing indexes are used, i.e. 0:2 - Can also be a comma-separated list of filepaths in the datasets, i.e.: - /path/to/image1.png,/path/to/image2.png - --debug enable debug verbosity - --progressbar show a fancy progressbar. Default: false. Nice for interactive runs. - --progress show progress every n:th step, i.e. --progress=10 would print out - progress every tenth step. 0: don't print anything. Default: 1. - Nice for batch jobs. - - --tags pick certain images from the dataset/slice - for example: --tags=0001eeaf4aed83f9,000a1249af2bc5f0 - the tags correspond to _open image ids_ - --keep keep all intermediate files (for debugging) - --check simply reports which bitstream files are missing from the cache - if you enable this, no bitstream verification is done - - example 1: - - compressai-vision vtm - --y --dataset-name=mpeg_vcm-detection --qpars=38,47 --vtm_cache=/path/to/dir - - example 2 (calculate bitstream for first 100 samples in the dataset): - - compressai-vision vtm - --y --dataset-name=mpeg_vcm-detection --qpars=22 --vtm_cache=/path/to/dir --slice=0:100 - - - ****** MPEG-VCM DATASET IMPORTS ******************************************************************************* - - import-custom imports some custom datasets (both image and video datasets) into fiftyone - - --dataset-type particular dataset in question. Possible values are: - - oiv6-mpeg-v1 - sfu-hw-objects-v1 # NOTE: video dataset - tvd-object-tracking-v1 # NOTE: video dataset - tvd-image-v1 # TODO: resulting OpenImageV6 doesn't work with fiftyone - flir-mpeg-v1 - flir-image-rgb-v1 - - oiv6-mpeg-v1 downloads automagically the OpenImageV6 data. For other - dataset types you need to download the files yourself. Refer to documentation - for more details - - --lists file listing for using only a subset of the main dataset - # TODO: special value: "default" -> fetches mpeg-vcm list from data/ - --dir root dir of the dataset (where you placed the files) - --datadir works with oiv6-mpeg-v1 to indicate where the OpenImageV6 subset is donwloaded - (default is ~/fiftyone) - - - ***** VIDEO SPECIFIC ******************************************************************************************** - - make-thumbnails add "thumbnail" videos that are compatible with browser-based applications to the dataset. - this way you will have "side-data" video for visualization in the fiftyone app - while still performing training & evaluation with the original data - (that might not visualize correctly in the webapp) - NOTE: import-custom command performs this step automagically if necessary - - --dataset-name name of the dataset - --force force encoding even if the "thumbnail" videos already existed - - ***** PLOTTING *************************************************************************************************** - - plot plot the json result of detectron2-eval and metrics-eval as mAP-bpp curves - - --dirs list of directories with json files, produced by - detectron2-eval subcommand - - Each directory corresponds to an evaluation of a certain model - done with detectron2-eval: each directory contains a list of json files, - produced by the subcommand detectron2-eval. - - Within one directory, you typically have json files, - produced in a parallel run for - each quality point, for example: 1.json, 2.json, .. - - Or you can have a json files with several quality point results - in each file, say: 1_2_3.json, 4_5.json, .. - - The program knows how to combine these files. - - --symbols list of matplotlib symbols for each plot, - for example: o--k,-g,*:r (optional) - --names list of names to be included into the plot, - for example: vtm,mymodel,mymodel2 (optional) - --eval mAP value without (de)compress and maplotlib symbol, - for example: 0.792,--c - --csv instead of plot, dump json results in csv format - - instructions: - - The compressai-vision detectron2-eval command has produced you json output files - to a certain directory (say, into "model1_results/") - - In a single json file you can have multiple (bpp, mAP) results (for - each quality parameter) - - You can also have several json files, each containing just one or more - (bpp, mAP) results (say, if you have parallelized compressai-vision run - over quality parameters) - - This script handles both situations automatically, you just need to - provide the directory name(s) - - Suppose you want to plot two (bpp, mAP) curves from two models - (results are in "model1_results" and "model2_results"), do this: - - compressai-vision plot --dirs=model1_results,model2_results \\ - --symbols=o--r,x-b --dataset-names=model1,model2 \\ - --eval=0.792,--c - diff --git a/compressai_vision/run/vcm_app_cli/metrics_eval.py b/compressai_vision/run/vcm_app_cli/metrics_eval.py deleted file mode 100644 index be960144..00000000 --- a/compressai_vision/run/vcm_app_cli/metrics_eval.py +++ /dev/null @@ -1,427 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli metrics-eval functionality -""" -# import copy -# import datetime -import json -import math - -from .tools import checkSlice, getQPars, loadEncoderDecoderFromPath, setupVTM - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "metrics-eval", - parents=parents, - help="evaluate model with psnr and ms-ssim", - ) - required_group = subparser.add_argument_group("required arguments") - compressai_group = subparser.add_argument_group("compressai-zoo arguments") - vtm_group = subparser.add_argument_group("vtm arguments") - optional_group = subparser.add_argument_group("optional arguments") - required_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default=None, - help="name of the dataset", - ) - optional_group.add_argument( - "--output", - action="store", - type=str, - required=False, - default="compressai-vision.json", - help="outputfile. Default: compressai-vision.json", - ) - compressai_group.add_argument( - "--compressai-model-name", - action="store", - type=str, - required=False, - default=None, - help="name of an existing model in compressai-zoo. Example: 'cheng2020-attn' ", - ) - compressai_group.add_argument( - "--compression-model-path", - action="store", - type=str, - required=False, - default=None, - help="a path to a directory containing model.py for custom development model", - ) - vtm_group.add_argument( - "--vtm", - action="store_true", - default=False, - help="To enable vtm codec. default: False", - ) - vtm_group.add_argument( - "--vtm_dir", - action="store", - type=str, - required=False, - default=None, - help="path to directory with executables EncoderAppStatic & DecoderAppStatic", - ) - vtm_group.add_argument( - "--vtm_cfg", - action="store", - type=str, - required=False, - default=None, - help="vtm config file. Example: 'encoder_intra_vtm.cfg' ", - ) - vtm_group.add_argument( - "--vtm_cache", - action="store", - type=str, - required=False, - default=None, - help="directory to cache vtm bitstreams", - ) - optional_group.add_argument( - "--qpars", - action="store", - type=str, - required=False, - default=None, - help="quality parameters for compressai model or vtm. For compressai-zoo model, it should be integer 1-8. For VTM, it should be integer from 0-51.", - ) - optional_group.add_argument( - "--scale", - action="store", - type=int, - required=False, - default=100, - help="image scaling as per VCM working group docs. Default: 100", - ) - optional_group.add_argument( - "--ffmpeg", - action="store", - type=str, - required=False, - default="ffmpeg", - help="path of ffmpeg executable. Default: ffmpeg", - ) - optional_group.add_argument( - "--slice", - action="store", - type=str, - required=False, - default=None, - help="use a dataset slice instead of the complete dataset. Example: 0:2 for the first two images", - ) - # subparser.add_argument("--debug", action="store_true", default=False) # not here - optional_group.add_argument( - "--progressbar", - action="store_true", - default=False, - help="show fancy progressbar. Default: False", - ) - optional_group.add_argument( - "--progress", - action="store", - type=int, - required=False, - default=1, - help="Print progress this often", - ) - return subparser - - -def main(p): # noqa: C901 - # check that only one is defined - defined_codec = "" - for codec in [p.compressai_model_name, p.vtm, p.compression_model_path]: - if codec: - if defined_codec: # second match! - raise AssertionError( - "please define only one of the following: compressai_model_name, vtm or compression_model_path" - ) - defined_codec = codec - - assert p.dataset_name is not None, "please provide dataset name" - # fiftyone - print("importing fiftyone") - import fiftyone as fo - - # dataset.clone needs this - # from compressai_vision import patch # noqa: F401 - print("fiftyone imported") - from compressai_vision.evaluation.pipeline import ( - CompressAIEncoderDecoder, - VTMEncoderDecoder, - ) - from compressai_vision.pipelines.fo_vcm.constant import vf_per_scale - - try: - dataset = fo.load_dataset(p.dataset_name) - except ValueError: - print("FATAL: no such registered dataset", p.dataset_name) - return - - dataset, fr, to = checkSlice(p, dataset) - - # print(">", p.compressai_model_name, p.vtm, p.compression_model_path, p.qpars) - if ( - (p.compressai_model_name is None) - and (p.vtm is False) - and (p.compression_model_path is None) - ): - print("please provide compressai_model_name, compression_model_path or use vtm") - return - - # check quality parameter list - assert p.qpars is not None, "need to provide integer quality parameters" - qpars = getQPars(p) - - if p.compressai_model_name is not None: # compression from compressai zoo - from compressai import zoo - - compression_model = getattr(zoo, p.compressai_model_name) - - elif p.compression_model_path is not None: - encoder_decoder_func = loadEncoderDecoderFromPath(p.compression_model_path) - - elif p.vtm: # setup VTM - vtm_encoder_app, vtm_decoder_app, vtm_cfg = setupVTM(p) - - # *** CHOOSE COMPRESSION SCHEME OK *** - - if p.scale is not None: - assert p.scale in vf_per_scale.keys(), "invalid scale value" - - """ - try: - username = os.environ["USER"] - except KeyError: - username = "nouser" - tmp_name0 = p.dataset_name + "-{0:%Y-%m-%d-%H-%M-%S-%f}".format( - datetime.datetime.now() - ) - - tmp_name = "detectron-run-{username}-{tmp_name0}".format( - username=username, tmp_name0=tmp_name0 - ) - """ - import torch - - device = "cuda" if torch.cuda.is_available() else "cpu" - if p.no_cuda: - device = "cpu" - print() - print("Using dataset :", p.dataset_name) - # print("Dataset tmp clone :", tmp_name) - print("Image scaling :", p.scale) - if p.slice is not None: # can't use slicing - print("WARNING: Using slice :", str(fr) + ":" + str(to)) - print("Number of samples :", len(dataset)) - print("Torch device :", device) - if p.compressai_model_name is not None: - print("Using compressai model :", p.compressai_model_name) - elif p.compression_model_path is not None: - print("Using custom model.py from") - print(" :", p.compression_model_path) - elif p.vtm: - print("Using VTM ") - if p.vtm_cache: - # assert(os.path.isdir(p.vtm_cache)), "no such directory "+p.vtm_cache - # ..created by the VTMEncoderDecoder class - print("WARNING: VTM USES CACHE IN", p.vtm_cache) - print("Quality parameters :", qpars) - print("Progressbar :", p.progressbar) - if p.progressbar and p.progress > 0: - print("WARNING: progressbar enabled --> disabling normal progress print") - p.progress = 0 - print("Print progress :", p.progress) - print("Output file :", p.output) - if p.dump: - print("WARNING - dump enabled : will dump intermediate images") - - if not p.y: - input("press enter to continue.. ") - - # save metadata about the run into the json file - metadata = { - "dataset": p.dataset_name, - # "tmp datasetname": tmp_name, - "slice": p.slice, - "codec": defined_codec, - "qpars": qpars, - } - with open(p.output, "w") as f: - f.write(json.dumps(metadata, indent=2)) - - """ - # please see ../monkey.py for problems I encountered when cloning datasets - # simultaneously with various multiprocesses/batch jobs - print("cloning dataset", p.dataset_name, "to", tmp_name) - dataset = dataset.clone(tmp_name) - dataset.persistent = True - # fo.core.odm.database.sync_database() # this would've helped? not sure.. - """ - psnr_lis = [] - mssim_lis = [] - bpp_lis = [] - - import traceback - - import cv2 - - # use open image ids if avail - if dataset.get_field("open_images_id"): - id_field_name = "open_images_id" - else: - id_field_name = "id" - - for quality in qpars: - if ( - p.compressai_model_name or p.compression_model_path - ): # compressai model, either from the zoo or from a directory: - if p.compressai_model_name is not None: - # e.g. "bmshj2018-factorized" - print("\nQUALITY PARAMETER: ", quality) - net = ( - compression_model(quality=quality, pretrained=True) - .eval() - .to(device) - ) - enc_dec = CompressAIEncoderDecoder( - net, device=device, scale=p.scale, ffmpeg=p.ffmpeg, dump=p.dump - ) - else: # or a custom model from a file: - enc_dec = encoder_decoder_func( - quality=quality, - device=device, - scale=p.scale, - ffmpeg=p.ffmpeg, - dump=p.dump, - ) - # net = compression_model(quality=quality).eval().to(device) - # make sure we load just trained models and pre-trained/ updated entropy parameters - - elif p.vtm: - raise (BaseException("metrics calc for VTM not yet implemented")) - enc_dec = VTMEncoderDecoder( - encoderApp=vtm_encoder_app, - decoderApp=vtm_decoder_app, - ffmpeg=p.ffmpeg, - vtm_cfg=vtm_cfg, - qp=quality, - cache=p.vtm_cache, - scale=p.scale, - warn=True, - ) - else: - raise BaseException("program logic error") - - npix_sum = 0 - nbits_sum = 0 - psnr_sum = 0 - mssim_sum = 0 - - from fiftyone import ProgressBar - - if p.progressbar: - pb = ProgressBar(dataset) - - cc = 0 - for sample in dataset: - path = sample.filepath - im = cv2.imread(path) - tag = sample[id_field_name] - if im is None: - print("FATAL: could not read the image file '" + path + "'") - return -1 - try: - nbits, im_ = enc_dec.BGR( - im, tag=tag - ) # include a tag for cases where EncoderDecoder uses caching - except Exception as e: - print("EncoderDecoder failed with '" + str(e) + "'") - print("Traceback:") - traceback.print_exc() - return -1 - if nbits < 0: - # there's something wrong with the encoder/decoder process - # say, corrupt data from the VTMEncode bitstream etc. - print("EncoderDecoder returned error: will try using it once again") - nbits, im_ = enc_dec.BGR(im, tag=tag) - if nbits < 0: - print("EncoderDecoder returned error - again! Will abort calculation") - return -1 - - npix_sum += im_.shape[0] * im_.shape[1] - nbits_sum += nbits - psnr, mssim = enc_dec.getMetrics() - # print(">cc", cc) - # print(">tag", tag) - # print(">psnr, mssim", psnr, mssim, type(psnr)) - if math.isnan(psnr) or math.isnan(mssim): - print( - "getMetrics returned nan - you images are probably corrupt. Will exit now." - ) - return - - psnr_sum += psnr - mssim_sum += mssim - if p.progressbar: - pb.update() - elif p.progress > 0 and ((cc % p.progress) == 0): - print("sample: ", cc, "/", len(dataset) - 1) - cc += 1 - - bpp = nbits_sum / npix_sum - psnr = psnr_sum / len(dataset) - mssim = mssim_sum / len(dataset) - bpp_lis.append(bpp) - psnr_lis.append(psnr) - mssim_lis.append(mssim) - - # print(">>", metadata) - metadata["bpp"] = bpp_lis - metadata["psnr"] = psnr_lis - metadata["mssim"] = mssim_lis - with open(p.output, "w") as f: - f.write(json.dumps(metadata, indent=2)) - - """ - # remove the tmp database - print("deleting tmp database", tmp_name) - fo.delete_dataset(tmp_name) - """ - print("\nDone!\n") - """load with: - with open(p.output,"r") as f: - res=json.load(f) - """ diff --git a/compressai_vision/run/vcm_app_cli/plotter.py b/compressai_vision/run/vcm_app_cli/plotter.py deleted file mode 100644 index 15b0d9ba..00000000 --- a/compressai_vision/run/vcm_app_cli/plotter.py +++ /dev/null @@ -1,316 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli.py : Command-line interface tools for compressai-vision -""" -import csv -import glob -import json -import os -import sys - -import matplotlib.pyplot as plt -import numpy as np - -from compressai_vision.pipelines.fo_vcm.tools import getDataFile - -colors = ["b", "g", "r", "c", "m", "y", "k", "w"] - - -def getBaseline(scale): - path = getDataFile(os.path.join("results", "vtm-scale-" + str(scale) + ".csv")) - if not os.path.exists(path): - print("Sorry, can't find file", path) - sys.exit(2) - print(path, ":") - xs = [] - ys = [] - with open(path) as csvfile: - reader = csv.reader(csvfile, delimiter=" ") - for cols in reader: - if "#" in cols[0]: - print(" ".join(cols[1:])) - continue # this is a comment line - bpp, map_ = float(cols[0]), float(cols[1]) - # print(bpp, map_) - xs.append(bpp) - ys.append(map_) - a = np.array([xs, ys]).transpose() # (2,6) --> (6,2) - return a - - -def jsonFilesToArray(dir_, y_name="map"): - """Reads all json files in a directory. - The files should have key "qpars" and "bpp". - Returns numpy array. - - :param dir_: path to json files - :param y_name: str: "map", "psnr" or "mssim" - - """ - xs = [] - ys = [] - for path in glob.glob(os.path.join(dir_, "*.json")): - print("reading", path) - with open(path, "r") as f: - res = json.load(f) - # print(res) - # res has two lists: res["bpp"] & res["map"]: bpp values and corresponding map values - # assume there is at least res[”bpp"] - try: - xs += res["bpp"] - except KeyError: - print("WARNING: skipping file", path) - continue - """generalize from map to map, psnr & mssim - if "map" in res: - ys += res["map"] - """ - if y_name in res: - ys += res[y_name] - # print(xs, ys) - if len(ys) < 1: - a = np.array(xs).transpose() - a.sort(0) - return a - - a = np.array([xs, ys]).transpose() # (2,6) --> (6,2) - - print("shape:", a.shape) - if len(a.shape) == 3: - if a.shape[0] <= 1: - print("WARNING: will squeeze the extra dimension at 0") - a = a.squeeze(0) - else: - print("FATAL: don't know what to do, please contact Hyomin!") - sys.exit(2) - elif len(a.shape) == 2: - pass - - # a.sort(0) # nopes! sorts separately a[:,0] & a[:,1] - inds = np.argsort(a[:, 0]) # indices that sort according to a[:,0] - print(a.shape) - print(inds) - a = a[inds] - # print(">>") - # print(a) - return a - - -def tx(ax, st, i, j, color): - ax.text( - i, - j, - st, - horizontalalignment="center", - verticalalignment="center", - color=color, - transform=ax.transAxes, - ) - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "plot", - parents=parents, - help="plot y=y(bpp) curve, where y can be mAP, pnsr or ssim", - ) - required_group = subparser.add_argument_group("required arguments") - subparser.add_argument( - "--csv", - action="store_true", - default=False, - help="output result as nicely formated csv table", - ) - subparser.add_argument( - "--target", - action="store", - default="map", - required=False, - help="y-value type: map, psnr or mssim", - ) - required_group.add_argument( - "--dirs", - action="store", - type=str, - required=False, - help="list of directories, each folder contains evaluation result (json files) of certain model done with detectron2-eval or with metrics-eval", - ) - subparser.add_argument( - "--symbols", - action="store", - type=str, - required=False, - help="list of pyplot symbols/colors, e.g: o--k,-b, etc.", - ) - subparser.add_argument( - "--names", action="store", type=str, required=False, help="list of plot names" - ) - subparser.add_argument( - "--eval", - action="store", - type=str, - required=False, - default=None, - help="mAP value without (de)compression and pyplot symbol,for example: 0.792,--c ", - ) - - -def main(p): # noqa: C901 - parsed = p - # for csv and plot needs directory names - assert parsed.dirs is not None, "needs list of directory names" - assert parsed.target in [ - "map", - "psnr", - "mssim", - ], "target must be map, psnr or mssim" - dirs = parsed.dirs.split(",") - arrays = [] - for dir_ in dirs: - dir_ = os.path.expanduser(os.path.join(dir_)) - assert os.path.isdir(dir_), "nonexistent dir " + dir_ - arrays.append(jsonFilesToArray(dir_, y_name=p.target)) - - if parsed.csv: - for dir_, a in zip(dirs, arrays): - print("\n" + dir_ + ":\n") - for bpp, y in a: - print(bpp, y) - return - - if parsed.command != "plot": - print("unknow command", parsed.command) - print("commands are: manual, plot") - sys.exit(2) - - # assert(parsed.colors is not None), "needs list of pyplot color codes" - # assert parsed.symbols is not None, "needs list of pyplot symbol codes" - # assert parsed.names is not None, "needs list of names for plots" - # let's define some default dummy values instead - - if parsed.symbols is None: - print("NOTE: you didn't provide a symbol list, will create one instead") - symbols = [] - else: - symbols = parsed.symbols.split(",") - if parsed.names is None: - print("NOTE: you didn't provide a plot names, will create one instead") - names = [] - else: - names = parsed.names.split(",") - - symbols_aux = ["o--k", "-g", "*:r"] - for i, dir_ in enumerate(dirs): - if parsed.symbols is None: - # cyclic: - symbols.append(symbols_aux[i % len(symbols_aux)]) - if parsed.names is None: - # names.append("plot"+str(i)) - names.append(dir_.split(os.pathsep)[-1]) - - assert ( - len(dirs) == len(symbols) == len(names) - ), "dirs, symbols and names must have the same length" - - if parsed.eval: - eval_lis = parsed.eval.split(",") - if len(eval_lis) < 2: - print("NOTE: you didn't provide symbol for eval baseline, will make up one") - eval_lis.append("--c") - eval_val, eval_symbol = eval_lis - eval_val = float(eval_val) - - """removed: user has to give this explicitly - if parsed.show_baseline: - try: - names.index("VTM") - except ValueError: - pass - else: - print("please don't use reserved name VTM") - sys.exit(2) - a = getBaseline(parsed.show_baseline) - arrays.append(a) - symbols.append("k--*") - names.append("VTM") - """ - - plt.figure(figsize=(6, 6)) - - cc = 0 - for a, symbol, name in zip(arrays, symbols, names): - # print(a.shape, len(a.shape)) - if len(a.shape) < 2: - print(p.target + " value missing, will skip", name) - continue - plt.plot(a[:, 0], a[:, 1], symbol) - ax = plt.gca() - color_ = None - for color in colors: # ["b", "g", ..] - if color in symbol: # i.e. if "b" in symbol - color_ = color - break - if not color: - print("can't resolve color code: please use:", colors) - sys.exit(2) - # print(">>", color) - tx(ax, name, 0.5, 0.50 + cc * 0.05, color_) - cc += 1 - - minx = plt.axis()[0] - maxx = plt.axis()[1] - - if parsed.eval: - plt.plot((minx, maxx), (eval_val, eval_val), eval_symbol) - else: - print("NOTE: you didn't provide evaluation baseline so will not plot it") - - plt.xlabel("bpp") - plt.ylabel(p.target) - print("--> producing out.png to current path") - plt.savefig(os.path.join("out.png")) - print("Done!") - """from the notebook: - plt.plot(vtm[:,0], vtm[:,1], '*-b', markersize=12) - plt.plot(coai[:,0], coai[:,1], '.-r') - plt.plot(mpeg_vcm[:,0], mpeg_vcm[:,1], 'o--k') - minx=plt.axis()[0] - maxx=plt.axis()[1] - plt.plot((minx, maxx), (eval_[:,1], eval_[:,1]), '--g') - ax = plt.gca() - tx(ax, "OUR VTM", 0.5, 0.50, "b") - tx(ax, "COMPRESSAI", 0.5, 0.55, "r") - tx(ax, "EVAL", 0.5, 0.60, "g") - tx(ax, "mpeg_vcm VTM", 0.5, 0.65, "k") - plt.xlabel("bpp") - plt.ylabel("mAP") - plt.title("Detection, scale=100%") - plt.savefig(os.path.join("out.png")) - """ diff --git a/compressai_vision/run/vcm_app_cli/plotter.txt b/compressai_vision/run/vcm_app_cli/plotter.txt deleted file mode 100644 index e208e534..00000000 --- a/compressai_vision/run/vcm_app_cli/plotter.txt +++ /dev/null @@ -1,49 +0,0 @@ -compressai-vision plot [options] - - --dirs list of directories with json files, produced by - detectron2-eval subcommand - - Each directory corresponds to an evaluation of a certain model - done with detectron2-eval: each directory contains a list of json files, - produced by the subcommand detectron2-eval. - - Within one directory, you typically have json files, - produced in a parallel run for - each quality point, for example: 1.json, 2.json, .. - - Or you can have a json files with several quality point results - in each file, say: 1_2_3.json, 4_5.json, .. - - The program knows how to combine these files. - - --symbols list of matplotlib symbols for each plot, - for example: o--k,-g,*:r (optional) - --names list of names to be included into the plot, - for example: vtm,mymodel,mymodel2 (optional) - --eval mAP value without (de)compress and maplotlib symbol, - for example: 0.792,--c - --csv instead of plot, dump json results in csv format - -instructions: - - The compressai-vision detectron2-eval command has produced you json output files - to a certain directory (say, into "model1_results/") - - In a single json file you can have multiple (bpp, mAP) results (for - each quality parameter) - - You can also have several json files, each containing just one or more - (bpp, mAP) results (say, if you have parallelized compressai-vision run - over quality parameters) - - This script handles both situations automatically, you just need to - provide the directory name(s) - - Suppose you want to plot two (bpp, mAP) curves from two models - (results are in "model1_results" and "model2_results"), do this: - - compressai-vision plot --dirs=model1_results,model2_results \\ - --symbols=o--r,x-b --dataset-names=model1,model2 \\ - --eval=0.792,--c - - diff --git a/compressai_vision/run/vcm_app_cli/register.py b/compressai_vision/run/vcm_app_cli/register.py deleted file mode 100644 index 6929767e..00000000 --- a/compressai_vision/run/vcm_app_cli/register.py +++ /dev/null @@ -1,182 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli register functionality -""" -import os - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "register", - parents=parents, - help="register image set to fiftyone from local dir", - ) - required_group = subparser.add_argument_group("required arguments") - required_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default=None, - help="name of the dataset", - ) - subparser.add_argument( - "--lists", - action="store", - type=str, - required=False, - default=None, - help="comma-separated list of list files", - ) - subparser.add_argument( - "--dir", - action="store", - type=str, - required=False, - default=None, - help="target/source directory, depends on command", - ) - subparser.add_argument( - "--type", - action="store", - type=str, - required=False, - default="OpenImagesV6Dataset", - help="image set type to be imported", - ) - - -def main(p): # noqa: C901 - # fiftyone - print("importing fiftyone") - import fiftyone as fo - - print("fiftyone imported") - - # compressai_vision - from compressai_vision.conversion import imageIdFileList - from compressai_vision.pipelines.fo_vcm.tools import pathExists - - assert p.dataset_name is not None, "provide name for your dataset" - assert p.dir is not None, "please provide path to dataset" - - try: - dataset = fo.load_dataset(p.dataset_name) - except ValueError: - pass - else: - print("dataset %s already exists - will deregister it first" % (p.dataset_name)) - if not p.y: - input("press enter to continue.. ") - fo.delete_dataset(p.dataset_name) - - # if p.type != "OpenImagesV6Dataset": - # print("WARNING: not tested for other than OpenImagesV6Dataset - might now work") - - # dataset types are in: - # fo.types.dataset_types.* - # quickstart dataset is of type FiftyOneDataset - try: - dataset_type = getattr(fo.types.dataset_types, p.type) - except AttributeError: - print("WARNING: could not find dataset type from fo.types.dataset_types.*") - print("dataset types:") - for type_ in dir(fo.types.dataset_types): - if type_[0] != "_": - print(type_) - raise - - dataset_dir = os.path.expanduser(p.dir) - assert pathExists(dataset_dir) - print() - if p.lists is None: - print( - "WARNING: using/registering with ALL images. You might want to use the --lists option insted" - ) - n_images = "?" - image_ids = None - else: - fnames = p.lists.split(",") - for fname in fnames: - assert pathExists(fname), "file " + fname + " does not exist" - image_ids = imageIdFileList(*fnames) - n_images = str(len(image_ids)) - - # this was originally written only for OpenImageV6.. - # common args for all dataset types: - kwargs = { - "dataset_dir": dataset_dir, - "dataset_type": dataset_type, - "name": p.dataset_name, - } - if image_ids is not None: - kwargs["image_ids"] = image_ids - - if dataset_type == fo.types.dataset_types.OpenImagesV6Dataset: - label_types = [ - "classifications" - ] # at least image-level classifications required..! - # let's check what data user has imported - if pathExists(os.path.join(p.dir, "labels", "segmentations.csv")): - # segmentations are there allright - label_types.append("segmentations") - print("OpenImagesV6: found segmentations") - if pathExists(os.path.join(p.dir, "labels", "detections.csv")): - # segmentations are there allright - label_types.append("detections") - print("OpenImagesV6: found detections") - # .. in fact, could just list with all .csv files in that dir - kwargs["label_types"] = label_types - print("OpenImagesV6: skipping hierarchies") - kwargs["load_hierarchy"] = False - - print("From directory : ", p.dir) - print("Using list file : ", p.lists) - print("Number of images: ", n_images) - print("Registering name: ", p.dataset_name) - if not p.y: - input("press enter to continue.. ") - print("working..") - try: - dataset = fo.Dataset.from_dir(**kwargs) - except Exception as e: - print("FATAL: registering failed") - print(" : if you need more control on registering please") - print(" : use fiftyone from python interpreter") - print("Exception:") - print(e) - return 2 - print("register: SUCCESS") - dataset.persistent = True # don't forget! - print() - print("** Let's peek at the first sample - check that it looks ok:**") - print() - print(dataset.first()) - print() diff --git a/compressai_vision/run/vcm_app_cli/show.py b/compressai_vision/run/vcm_app_cli/show.py deleted file mode 100644 index 9ebee394..00000000 --- a/compressai_vision/run/vcm_app_cli/show.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli list functionality -""" -import os - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser("show", parents=parents, help="show dataset info") - subparser.add_argument( - "--dataset-name", - action="store", - type=str, - required=True, - default=None, - help="name of the dataset", - ) - - -def main(p): - # fiftyone - import numpy as np - from PIL import Image - - print("importing fiftyone") - import fiftyone as fo - - print("fiftyone imported") - print() - dataset = fo.load_dataset(p.dataset_name) - print("dataset info:") - print(dataset) - print() - sample = dataset.first() - path = sample["filepath"] - if not os.path.exists(path): - print("WARNING: could not find file", path) - return 2 - if dataset.media_type == "image": - print("test-loading first image from", path) - img = Image.open(path) - print("loaded image with dimensions", np.array(img).shape, "ok") - elif dataset.media_type == "video": - import cv2 - - print("test-loading first frame from", path) - vid = cv2.VideoCapture(path) - ok, img = vid.read() - if ok: - print("loaded image with dimensions", np.array(img).shape, "ok") - else: - print("WARNING: could not read a frame from the video file") - vid.release() diff --git a/compressai_vision/run/vcm_app_cli/tools.py b/compressai_vision/run/vcm_app_cli/tools.py deleted file mode 100644 index fb369adc..00000000 --- a/compressai_vision/run/vcm_app_cli/tools.py +++ /dev/null @@ -1,361 +0,0 @@ -import os -from pathlib import Path - - -def getQPars(p): - try: - qpars = [int(i) for i in p.qpars.split(",")] - except Exception as e: - print("problems with your quality parameter list") - raise e - return qpars - - -def loadEncoderDecoderFromPath(compression_model_path): - # compression from a custcom compression model - model_file = Path(compression_model_path) / "model.py" - if model_file.is_file(): - import importlib.util - - try: - spec = importlib.util.spec_from_file_location("module", model_file) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - except Exception as e: - print( - "loading model from directory", - compression_model_path, - "failed with", - e, - ) - raise - else: - assert hasattr( - module, "getEncoderDecoder" - ), "your module is missing getEncoderDecoder function" - encoder_decoder_func = ( - module.getEncoderDecoder - ) # a function that returns EncoderDecoder instance - print("loaded custom model.py") - else: - raise FileNotFoundError(f"No model.py in {compression_model_path}") - return encoder_decoder_func - - -def setupVTM(p): - if p.vtm_dir is None: - try: - vtm_dir = os.environ["VTM_DIR"] - except KeyError as e: - print("please define --vtm_dir or set environmental variable VTM_DIR") - raise e - else: - vtm_dir = p.vtm_dir - - vtm_dir = os.path.expanduser(vtm_dir) - - if p.vtm_cfg is None: - # vtm_cfg = getDataFile("encoder_intra_vtm_1.cfg") - # print("WARNING: using VTM default config file", vtm_cfg) - raise BaseException("VTM config is not defined") - else: - vtm_cfg = p.vtm_cfg - vtm_cfg = os.path.expanduser(vtm_cfg) # some more systematic way of doing these.. - print("Reading vtm config from: " + vtm_cfg) - assert os.path.isfile(vtm_cfg), "vtm config file not found" - # try both filenames.. - vtm_encoder_app = os.path.join(vtm_dir, "EncoderAppStatic") - if not os.path.isfile(vtm_encoder_app): - vtm_encoder_app = os.path.join(vtm_dir, "EncoderAppStaticd") - if not os.path.isfile(vtm_encoder_app): - raise AssertionError("FATAL: can't find EncoderAppStatic(d) in " + vtm_dir) - # try both filenames.. - vtm_decoder_app = os.path.join(vtm_dir, "DecoderAppStatic") - if not os.path.isfile(vtm_decoder_app): - vtm_decoder_app = os.path.join(vtm_dir, "DecoderAppStaticd") - if not os.path.isfile(vtm_decoder_app): - raise AssertionError("FATAL: can't find DecoderAppStatic(d) in " + vtm_dir) - - return vtm_encoder_app, vtm_decoder_app, vtm_cfg - - -def checkSlice(p, dataset): - fr = None - to = None - if p.slice is not None: - print( - "WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS?" - ) - # say, 0:100 - nums = p.slice.split(":") # python slicing? - """ - if len(nums) < 2: - print("invalid slicing: use normal python slicing, say, 0:100") - return - """ - filepaths = p.slice.split(",") # list of filepaths? - if len(nums) >= 2: - # looks like 0:N slice - try: - fr = int(nums[0]) - to = int(nums[1]) - except ValueError: - print("invalid slicing: use normal python slicing, say, 0:100") - raise - assert to > fr, "invalid slicing: use normal python slicing, say, 0:100" - dataset = dataset[fr:to] - elif len(filepaths) >= 1: - from fiftyone import ViewField as F - - query_list = [] - # looks like list of filenames - for filepath in filepaths: - p = Path(filepath).expanduser().absolute() - if not p.is_file(): - print("FATAL: file", str(p), "does not exist") - raise AttributeError("file not found or --slice misinterpretation") - query_list.append(F("filepath") == str(p)) - dataset = dataset[F.any(query_list)] - else: - print("could not interprete --slice") - raise AttributeError("could not interprete --slice") - return dataset, fr, to - - -def setupDetectron2(model_names: list, device): - # Parsing a list of detectron2 models names and return instantiated model with meta information - # *** Detectron imports *** - # Some basic setup: - # Setup detectron2 logger - # import detectron2 - import logging - - from detectron2.utils.logger import setup_logger - - logger = setup_logger() - logger.setLevel(logging.WARNING) - - # import some common detectron2 utilities - from detectron2 import model_zoo - from detectron2.config import get_cfg - from detectron2.data import MetadataCatalog # , DatasetCatalog - - # from detectron2.data.datasets import register_coco_instances - from detectron2.engine import DefaultPredictor - - models = [] - models_meta = [] - pred_fields = [] - for e, name in enumerate(model_names): - if ".py" in name: - # *** LOAD cfg and predictor from an external .py file *** - pyfile = name - print("Trying to load custom Detectron2 model from local file", pyfile) - assert os.path.exists(pyfile), "Can't find " + str(pyfile) - import importlib.util - - try: - spec = importlib.util.spec_from_file_location( - "detectron2_module", pyfile - ) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - except Exception as e: - if hasattr(e, "message"): - print(str(e)) - print("Importing custom detectron model from", pyfile, "failed") - raise - assert hasattr(module, "getCfgPredictor"), ( - "file " + pyfile + " is missing function getCfgPredictor" - ) - print("Loading custom Detectron2 predictor from", pyfile) - """ - cfg, predictor = module.getCfgPredictor( - get_cfg, - model_zoo, - register_coco_instances, - DefaultPredictor, - MetadataCatalog - ) - """ - cfg, predictor = module.getCfgPredictor() - model_dataset = cfg.DATASETS.TRAIN[0] - model_meta = MetadataCatalog.get(model_dataset) - else: - # *** LOAD cfg and predictor from the Detectron2 zoo *** - # cfg encapsulates the model architecture & weights, also threshold parameter, metadata, etc. - cfg = get_cfg() - cfg.MODEL.DEVICE = device - # load config from a file: - cfg.merge_from_file(model_zoo.get_config_file(name)) - # DO NOT TOUCH THRESHOLD WHEN DOING EVALUATION: - # too big a threshold will cut the smallest values - # & affect the precision(recall) curves & evaluation results - # the default value is 0.05 - # value of 0.01 saturates the results (they don't change at lower values) - # cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 - # get weights - cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(name) - # print("expected input colorspace:", cfg.INPUT.FORMAT) - # print("loaded datasets:", cfg.DATASETS) - model_dataset = cfg.DATASETS.TRAIN[0] - # print("model was trained with", model_dataset) - model_meta = MetadataCatalog.get(model_dataset) - print(f"instantiating Detectron2 predictor {e} : {name}") - predictor = DefaultPredictor(cfg) - - models.append(predictor) - models_meta.append((model_dataset, model_meta)) - pred_fields.append(f"detectron-predictions_v{e}") - - return models, models_meta, pred_fields - - -def checkDataset(dataset, doctype): - """Look for fields of certain type in the dataset, say, of type - fiftyone.core.labels.Detections. - - Return a list of matching field names - """ - keys = [] - for key in dataset.get_field_schema(): - df = dataset.get_field(key) - if hasattr(df, "document_type") and df.document_type == doctype: - # print(df) - # df.document_type == fiftyone.core.labels.Detections - keys.append(key) - return keys - - -def checkVideoDataset(dataset, doctype): - """Look for fields of certain type in the dataset, say, of type - fiftyone.core.labels.Detections. - - Return a list of matching field names - """ - keys = [] - for key in dataset.get_frame_field_schema(): - df = dataset.get_field(key) - if hasattr(df, "document_type") and df.document_type == doctype: - # print(df) - # df.document_type == fiftyone.core.labels.Detections - keys.append(key) - return keys - - -def checkZoo(p): - from compressai.zoo import models - - try: - compression_model = models[p.compressai_model_name] - except KeyError: - print(f"Supported model names are {models.keys()}") - return - return compression_model - - -def checkForField(dataset, name): - if dataset.media_type == "image": - if dataset.get_field(name) is None: - print("FATAL: your dataset does not have requested field '" + name + "'") - print("Dataset info:") - print(dataset) - return False - elif dataset.media_type == "video": - if name in dataset.get_frame_field_schema(): - pass - else: - print( - "FATAL: your video dataset's frames do not not have requested field '" - + name - + "'" - ) - print("Dataset info:") - print(dataset) - return False - else: - print("FATAL: unknow media type", dataset.media_type) - return False - return True - - -def makeEvalPars(dataset=None, gt_field=None, predictor_fields=None, eval_method=None): - """Make parameters for Dataset.evaluate_detections method - - Refs: - - - https://voxel51.com/docs/fiftyone/api/fiftyone.core.collections.html#fiftyone.core.collections.SampleCollection.evaluate_detections - - https://voxel51.com/docs/fiftyone/user_guide/evaluation.html#evaluating-videos - - For images & with open image protocol: - - :: - - dataset.evaluate_detections( - "predictions", - gt_field=p.gt_field, - method="open-images", - pos_label_field="positive_labels", - neg_label_field="negative_labels", - expand_pred_hierarchy=False, - expand_gt_hierarchy=False - ) - - - For videos: note the extra "frames.": - - :: - - dataset.evaluate_detections( - "frames.predictions", - gt_field="frames.detections", - eval_key="eval" - ) - - returns args: str, kwargs: dict - """ - if dataset.media_type == "image": - pred_fields_ = predictor_fields - eval_args = {"gt_field": gt_field, "method": eval_method} - if eval_method == "open-images": - if dataset.get_field("positive_labels"): - eval_args["pos_label_field"] = "positive_labels" - if dataset.get_field("negative_labels"): - eval_args["neg_label_field"] = "negative_labels" - eval_args["expand_pred_hierarchy"] = False - eval_args["expand_gt_hierarchy"] = False - else: - eval_args["compute_mAP"] = True - - elif dataset.media_type == "video": - pred_fields_ = ["frames." + field for field in predictor_fields] - eval_args = {"gt_field": "frames." + gt_field, "method": eval_method} - if eval_method == "open-images": - if "positive_labels" in dataset.get_frame_field_schema(): - eval_args["pos_label_field"] = "positive_labels" - if "negative_label" in dataset.get_frame_field_schema(): - eval_args["neg_label_field"] = "negative_labels" - eval_args["expand_pred_hierarchy"] = False - eval_args["expand_gt_hierarchy"] = False - else: - eval_args["compute_mAP"] = True - - return pred_fields_, eval_args - - -def makeVideoThumbnails(dataset, force=False): - """ - :param dataset: video dataset - """ - import fiftyone.utils.video as fouv - - for sample in dataset.iter_samples(progress=True): - sample_dir = os.path.dirname(sample.filepath) - output_path = os.path.join(sample_dir, "web_" + sample.filename) - if (not force) and os.path.isfile(output_path): - print("WARNING: file", output_path, "already exists - will skip") - continue - print("\nRe-encoding", sample.filepath, "to", output_path) - fouv.reencode_video(sample.filepath, output_path) - sample["web_filepath"] = output_path - sample.save() diff --git a/compressai_vision/run/vcm_app_cli/vtm.py b/compressai_vision/run/vcm_app_cli/vtm.py deleted file mode 100644 index 6d612c0d..00000000 --- a/compressai_vision/run/vcm_app_cli/vtm.py +++ /dev/null @@ -1,328 +0,0 @@ -# Copyright (c) 2022-2024 InterDigital Communications, Inc -# All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted (subject to the limitations in the disclaimer -# below) provided that the following conditions are met: - -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# * Neither the name of InterDigital Communications, Inc nor the names of its -# contributors may be used to endorse or promote products derived from this -# software without specific prior written permission. - -# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY -# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND -# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT -# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR -# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, -# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; -# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -"""cli detectron2_eval functionality -""" -import json - -from .tools import checkSlice, getQPars, setupVTM - -# import os - - -def add_subparser(subparsers, parents): - subparser = subparsers.add_parser( - "vtm", parents=parents, help="generate bitstream with the vtm video encoder" - ) - required_group = subparser.add_argument_group("required arguments") - required_group.add_argument( - "--dataset-name", - action="store", - type=str, - required=False, - default=None, - help="name of the dataset", - ) - subparser.add_argument( - "--output", - action="store", - type=str, - required=False, - default="compressai-vision.json", - help="outputfile, default: compressai-vision.json", - ) - - subparser.add_argument( - "--vtm_dir", - action="store", - type=str, - required=False, - default=None, - help="path to directory with executables EncoderAppStatic & DecoderAppStatic", - ) - required_group.add_argument( - "--vtm_cfg", - action="store", - type=str, - required=False, - default=None, - help="vtm config file", - ) - required_group.add_argument( - "--vtm_cache", - action="store", - type=str, - required=True, - default=None, - help="directory to cache vtm bitstreams", - ) - required_group.add_argument( - "--qpars", - action="store", - type=str, - required=False, - default=None, - help="quality parameters for compressai model or vtm", - ) - subparser.add_argument( - "--scale", - action="store", - type=int, - required=False, - default=100, - help="image scaling as per VCM working group docs", - ) - subparser.add_argument( - "--ffmpeg", - action="store", - type=str, - required=False, - default="ffmpeg", - help="ffmpeg command", - ) - subparser.add_argument( - "--slice", - action="store", - type=str, - required=False, - default=None, - help="use a dataset slice instead of the complete dataset. Example: 0:2 for the first two images. Instead of python slicing string, this can also be a list of sample filepaths in the dataset", - ) - subparser.add_argument( - "--progressbar", - action="store_true", - default=False, - help="show fancy progressbar", - ) - subparser.add_argument( - "--progress", - action="store", - type=int, - required=False, - default=1, - help="Print progress this often", - ) - subparser.add_argument( - "--tags", - action="store", - type=str, - required=False, - default=None, - help="vtm: a list of ids to pick from the dataset/slice", - ) - subparser.add_argument( - "--keep", - action="store_true", - default=False, - help="vtm: keep all intermediate files (for debugging)", - ) - # subparser.add_argument("--dump", action="store_true", default=False) # now in main - subparser.add_argument( - "--check", - action="store_true", - default=False, - help="vtm: report if bitstream files are missing", - ) - - -def main(p): # noqa: C901 - import cv2 - - print("importing fiftyone") - # fiftyone - import fiftyone as fo - - ProgressBar = fo.ProgressBar - print("fiftyone imported") - - # compressai_vision - # from compressai_vision.evaluation.fo import ( # annex predictions from - # annexPredictions, - # ) - from compressai_vision.evaluation.pipeline import VTMEncoderDecoder - from compressai_vision.pipelines.fo_vcm.constant import vf_per_scale - - # from compressai_vision.pipelines.fo_vcm.tools import getDataFile - - assert p.dataset_name is not None, "please provide dataset name" - try: - dataset = fo.load_dataset(p.dataset_name) - except ValueError: - print("FATAL: no such registered database", p.dataset_name) - return - assert p.vtm_cache is not None, "need to provide a cache directory" - assert p.qpars is not None, "need to provide quality parameters for vtm" - qpars = getQPars(p) - - vtm_encoder_app, vtm_decoder_app, vtm_cfg = setupVTM(p) - dataset, fr, to = checkSlice(p, dataset) - - # if p.d_list is not None: - # print("WARNING: using only certain images from the dataset/slice") - - # use open image ids if avail - if dataset.get_field("open_images_id"): - id_field_name = "open_images_id" - else: - id_field_name = "id" - - if p.tags is not None: - lis = p.tags.split(",") # 0001eeaf4aed83f9,000a1249af2bc5f0 - from fiftyone import ViewField as F - - dataset = dataset.match(F("id_field_name").contains_str(lis)) - - if p.scale is not None: - assert p.scale in vf_per_scale.keys(), "invalid scale value" - - print() - print("VTM bitstream generation") - if p.vtm_cache: - print("WARNING: VTM USES CACHE IN", p.vtm_cache) - print("Target dir :", p.vtm_cache) - print("Quality points/subdirs :", qpars) - print("Using dataset :", p.dataset_name) - print("Image Scaling :", p.scale) - if p.slice is not None: - print("Using slice :", str(fr) + ":" + str(to)) - if p.tags is not None: - print("WARNING: Picking samples, based on", id_field_name, "field") - print("Number of samples :", len(dataset)) - print("Progressbar :", p.progressbar) - print("Output file :", p.output) - if p.progressbar and p.progress > 0: - print("WARNING: progressbar enabled --> disabling normal progress print") - p.progress = 0 - print("Print progress :", p.progress) - if p.keep: - print("WARNING: keep enabled --> will not remove intermediate files") - if p.check: - print( - "WARNING: checkmode enabled --> will only check if bitstream files exist or not" - "WARNING: doesn't calculate bbp values either" - ) - if not p.y: - input("press enter to continue.. ") - - # save metadata about the run into the json file - metadata = { - "dataset": p.dataset_name, - "just-check": p.check, - "slice": p.slice, - "vtm_cache": p.vtm_cache, - "qpars": qpars, - } - with open(p.output, "w") as f: - f.write(json.dumps(metadata, indent=2)) - - xs = [] - for i in qpars: - print("\nQUALITY PARAMETER", i) - enc_dec = VTMEncoderDecoder( - encoderApp=vtm_encoder_app, - decoderApp=vtm_decoder_app, - ffmpeg=p.ffmpeg, - vtm_cfg=vtm_cfg, - qp=i, - cache=p.vtm_cache, - scale=p.scale, - dump=p.dump, - skip=p.check, # if there's a bitstream file then just exit at call to BGR - keep=p.keep, - warn=True, - ) - # with ProgressBar(dataset) as pb: # captures stdout - if p.progressbar: - pb = ProgressBar(dataset) - - cc = 0 - """ - if p.checkmode: # just report which bitstreams exist in the cache - print() - print("reporting images missing bitstream at '%s'" % enc_dec.getCacheDir()) - print("n / id / open_images_id (use this!) / path") - check_c=0 - """ - npix_sum = 0 - nbits_sum = 0 - for sample in dataset: - cc += 1 - # sample.filepath - path = sample.filepath - im0 = cv2.imread(path) - # tag = path.split(os.path.sep)[-1].split(".")[0] # i.e.: /path/to/some.jpg --> some.jpg --> some - tag = sample[id_field_name] - # print(tag) - nbits, im = enc_dec.BGR(im0, tag=tag) - if nbits < 0: - if p.check: - print( - "WARNING: Bitstream missing for image id={id}, tag={tag}, path={path}".format( - id=sample.id, tag=tag, path=path - ) - ) - continue - # enc_dec.BGR tried to use the existing bitstream file but failed to decode it - print( - "ERROR: Corrupt data for image id={id}, tag={tag}, path={path}".format( - id=sample.id, tag=tag, path=path - ) - ) - # .. the bitstream has been removed - print("ERROR: Trying to regenerate") - # let's try to generate it again - nbits, im = enc_dec.BGR(im0, tag=tag) - if nbits < 0: - print( - "ERROR: DEFINITELY Corrupt data for image id={id}, tag={tag}, path={path} --> CHECK MANUALLY!".format( - id=sample.id, tag=tag, path=path - ) - ) - if not p.check: - # NOTE: use transformed image im - npix_sum += im.shape[0] * im.shape[1] - nbits_sum += nbits - if p.progress > 0 and ((cc % p.progress) == 0): - print("sample: ", cc, "/", len(dataset), "tag:", tag) - if p.progressbar: - pb.update() - - if not p.check: - if (nbits_sum < 1) or (npix_sum < 1): - print("ERROR: nbits_sum", nbits_sum, "npix_sum", npix_sum) - xs.append(None) - else: - xs.append(nbits_sum / npix_sum) - - # print(">>", metadata) - metadata["bpp"] = xs - with open(p.output, "w") as f: - json.dump(metadata, f) - - print("\nDone!\n") diff --git a/docs/source/compressai_vision/pipelines/fo_vcm/conversion.rst b/docs/source/compressai_vision/pipelines/fo_vcm/conversion.rst deleted file mode 100644 index 66ae1245..00000000 --- a/docs/source/compressai_vision/pipelines/fo_vcm/conversion.rst +++ /dev/null @@ -1,34 +0,0 @@ -compressai_vision.pipelines.fo_vcm.conversion -====================================================== - -.. automodule:: compressai_vision.pipelines.fo_vcm.conversion - :members: - :undoc-members: - -detectron2 -~~~~~~~~~~ - -.. automodule:: compressai_vision.pipelines.fo_vcm.conversion.detectron2 - :members: - :undoc-members: - -.. mpeg_vcm -.. ~~~~~~~~ - -.. .. automodule:: compressai_vision.pipelines.fo_vcm.conversion.mpeg_vcm -.. :members: -.. :undoc-members: - -sfu_hw_objects_v1 -~~~~~~~~~~~~~~~~~ - -.. automodule:: compressai_vision.pipelines.fo_vcm.conversion.sfu_hw_objects_v1 - :members: - :undoc-members: - -tvd_object_tracking_v1 -~~~~~~~~~~~~~~~~~~~~~~ - -.. automodule:: compressai_vision.pipelines.fo_vcm.conversion.tvd_object_tracking_v1 - :members: - :undoc-members: \ No newline at end of file diff --git a/docs/source/compressai_vision/pipelines/fo_vcm/faq.rst b/docs/source/compressai_vision/pipelines/fo_vcm/faq.rst deleted file mode 100644 index a00067b6..00000000 --- a/docs/source/compressai_vision/pipelines/fo_vcm/faq.rst +++ /dev/null @@ -1,49 +0,0 @@ - -FAQ -=== - -1. Error: "failed to bind" --------------------------- - -You can get this error message if fiftyone has a problem in connecting -to the mongodb server: - -.. code-block:: text - - fiftyone.core.service.ServiceListenTimeout: fiftyone.core.service.DatabaseService failed to bind to port - -If you haven't defined an external mongodb server, each time the python code imports -fiftyone for the first time, an "internal" mongodb server instance is started - on some occasions that internal mongodb -server might have exited in a "dirty" manner. - -No need to panic. We provide a command-line tool for cleaning things up. Just type: - -.. code-block:: bash - - compressai-vision mongo stop - -2. Error: "you must have fiftyone>x.x.x installed" --------------------------------------------------- - -If you get an error of this kind: - -.. code-block:: text - - You must have fiftyone>=0.17.2 installed in order to migrate from v0.17.2 to v0.16.6, but you are currently running fiftyone==0.16.6. - -First of all, make sure that you are **not running a mongodb server on your linux box**, as fiftyone starts its own (internal/bundled) mongodb server instance! - -The above error occurs typically, when you have created a database with a certain version of fiftyone (say, v0.16.6), but then you have (or someone else has) -touched it with a different version of fiftyone (for example 0.17.2). - -A nice fix to this one is to set a unique database name according to the fiftyone version you are using. - -As described `here `_, the database name is set with the ``FIFTYONE_DATABASE_NAME`` environmental -variable, so set in your virtualenv (in ``bin/activate``) ``export FIFTYONE_DATABASE_NAME=fiftyone-0.16.0`` (or whatever your version number might be). - -To wipe out the (incompatible) database, you can always do this: - -.. code-block:: bash - - compressai-vision mongo clear - diff --git a/docs/source/compressai_vision/pipelines/fo_vcm/fo.rst b/docs/source/compressai_vision/pipelines/fo_vcm/fo.rst deleted file mode 100644 index 7c12f00b..00000000 --- a/docs/source/compressai_vision/pipelines/fo_vcm/fo.rst +++ /dev/null @@ -1,12 +0,0 @@ -compressai_vision.compressai_vision.pipelines.fo_vcm.fo -================================================================ - -.. automodule:: compressai_vision.pipelines.fo_vcm.fo - -fiftyone -~~~~~~~~ - -.. automodule:: compressai_vision.pipelines.fo_vcm.fo.predict - :members: - :undoc-members: - diff --git a/docs/source/compressai_vision/pipelines/fo_vcm/index.rst b/docs/source/compressai_vision/pipelines/fo_vcm/index.rst deleted file mode 100644 index 15417052..00000000 --- a/docs/source/compressai_vision/pipelines/fo_vcm/index.rst +++ /dev/null @@ -1,22 +0,0 @@ - -compressai_vision.pipelines.fo_vcm -=========================================== - -.. currentmodule:: compressai_vision.pipelines.fo_vcm.evaluation - -Tools for evaluating pipelines: - -:: - - Video stream / images --> Encoding --> - calculate bitrate --> Decoding --> Detectron2 predictor - -.. toctree:: - :maxdepth: 2 - - pipeline - fo - conversion - faq - - diff --git a/docs/source/compressai_vision/pipelines/fo_vcm/pipeline.rst b/docs/source/compressai_vision/pipelines/fo_vcm/pipeline.rst deleted file mode 100644 index fe3fee97..00000000 --- a/docs/source/compressai_vision/pipelines/fo_vcm/pipeline.rst +++ /dev/null @@ -1,25 +0,0 @@ -compressai_vision.pipelines.fo_vcm.pipeline -==================================================== - -.. automodule:: compressai_vision.pipelines.fo_vcm.pipeline - -base -~~~~~~~~~~ - -.. automodule:: compressai_vision.pipelines.fo_vcm.pipeline.base - :members: - :undoc-members: - -compressai -~~~~~~~~~~ - -.. automodule:: compressai_vision.pipelines.fo_vcm.pipeline.compressai - :members: - :undoc-members: - -vtm -~~~~~~~~~~ - -.. automodule:: compressai_vision.pipelines.fo_vcm.pipeline.vtm - :members: - :undoc-members: diff --git a/docs/source/compressai_vision/pipelines/remote_inference.rst b/docs/source/compressai_vision/pipelines/remote_inference.rst index 0433178d..9b3d5188 100644 --- a/docs/source/compressai_vision/pipelines/remote_inference.rst +++ b/docs/source/compressai_vision/pipelines/remote_inference.rst @@ -1,5 +1,5 @@ compressai_vision.pipelines.remote_inference -=========================================== +============================================ .. automodule:: compressai_vision.pipelines.remote_inference :members: diff --git a/docs/source/index.rst b/docs/source/index.rst index e6bc381c..133e6ae7 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -27,7 +27,6 @@ To get started, please go to through the installation steps. :hidden: installation - walkthrough cli_usage docker diff --git a/docs/source/tutorials/README.md b/docs/source/tutorials/README.md deleted file mode 100644 index b3b5dba9..00000000 --- a/docs/source/tutorials/README.md +++ /dev/null @@ -1,47 +0,0 @@ - -## Tutorials - -Tutorials are the notebook (``*_nb.ipynb``) files in this directory. - -Please note that you can see the tags that control cell visibility, etc. in python notebook web interface with: -``` -view -> cell toolbar -> tags -``` -Please use these two tags: - -``remove_cell`` and ``bash``. Read also [compile.bash](compile.bash) for more documentation/observations. - -Two scripts are provided: - -[compile.bash](compile.bash) converts ipynb files into rst that can be included into the docs. - -Remember that after running this, you still need to do ``make html`` in the upper-level directory. - -However, converting notebooks to rst should *not* be made part of the automatic doc build process - -[run.bash](run.bash) runs all the notebooks. Carefull with this! - -Both scripts accept as a parameter a single notebook name or various names. Names should be -without the ``_nb.ipynb`` termination, i.e. just ``cli_tutorial_1``, ``detectron2``, etc. - -If no argument is provided, all notebooks are done. - -### Files - -``` -cli_tutorial_1_nb.ipynb -cli_tutorial_2_nb.ipynb -cli_tutorial_3_nb.ipynb -cli_tutorial_4_nb.ipynb -cli_tutorial_5_nb.ipynb -cli_tutorial_6_nb.ipynb -cli_tutorial_7_nb.ipynb - -fiftyone_nb.ipynb - -1: download_nb.ipynb -# 2: convert_nb.ipynb # DEPRECATED -3: detectron2_nb.ipynb -4: evaluate_nb.ipynb -5: encdec_nb.ipynb -``` diff --git a/docs/source/tutorials/aux1.ipynb b/docs/source/tutorials/aux1.ipynb deleted file mode 100644 index 7d33cbb3..00000000 --- a/docs/source/tutorials/aux1.ipynb +++ /dev/null @@ -1,604 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a1db3a38", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_50467/1348678174.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "df4e964f", - "metadata": {}, - "outputs": [], - "source": [ - "# fiftyone\n", - "import fiftyone as fo\n", - "import fiftyone.zoo as foz" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4a97e6da", - "metadata": {}, - "outputs": [], - "source": [ - "ds=fo.load_dataset(\"quickstart\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b6898c18", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Name: quickstart\n", - "Media type: image\n", - "Num samples: 200\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " uniqueness: fiftyone.core.fields.FloatField\n", - " predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e8bef3ad", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ",\n", - " ,\n", - " ,\n", - " ]),\n", - " }>,\n", - " 'uniqueness': 0.8175834390151201,\n", - " 'predictions': ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]),\n", - " }>,\n", - "}>" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds.first()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "76687164", - "metadata": {}, - "outputs": [], - "source": [ - "ds2=fo.load_dataset(\"mpeg-vcm-detection\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8ec1275c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Name: mpeg-vcm-detection\n", - "Media type: image\n", - "Num samples: 5000\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " open_images_id: fiftyone.core.fields.StringField" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds2" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7cc8eed8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n" - ] - } - ], - "source": [ - "f=ds2.get_field(\"detections\")\n", - "print(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "7837b2c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "odict_keys(['id', 'filepath', 'tags', 'metadata', 'positive_labels', 'negative_labels', 'detections', 'open_images_id'])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds2.get_field_schema().keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "1e882bc8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Name: mpeg-vcm-detection\n", - "Media type: image\n", - "Num samples: 5000\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " open_images_id: fiftyone.core.fields.StringField\n" - ] - } - ], - "source": [ - "print(ds2)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "28264352", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ",\n", - " ]),\n", - " 'logits': None,\n", - " }>,\n", - " 'negative_labels': ,\n", - " 'detections': ,\n", - " ]),\n", - " }>,\n", - " 'open_images_id': '0001eeaf4aed83f9',\n", - "}>" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds2.first()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e76878d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/base.ipynb b/docs/source/tutorials/base.ipynb deleted file mode 100644 index 25eea35f..00000000 --- a/docs/source/tutorials/base.ipynb +++ /dev/null @@ -1,77 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "b37649d7", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_12606/1348678174.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3cb2fb36", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/cli_tutorial_1.rst b/docs/source/tutorials/cli_tutorial_1.rst deleted file mode 100644 index e3fe18d1..00000000 --- a/docs/source/tutorials/cli_tutorial_1.rst +++ /dev/null @@ -1,7 +0,0 @@ - -1. Datasets and Evaluation --------------------------- - -.. _cli_tutorial_1: - -.. include:: cli_tutorial_1_nb.rst diff --git a/docs/source/tutorials/cli_tutorial_1_nb.ipynb b/docs/source/tutorials/cli_tutorial_1_nb.ipynb deleted file mode 100644 index 24480fa9..00000000 --- a/docs/source/tutorials/cli_tutorial_1_nb.ipynb +++ /dev/null @@ -1,753 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8ce26285", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "# Tutorial, chapter 1\n", - "\n", - "- Sw stack check with \"info\"\n", - "- downloading, listing\n", - "- detectron2-eval for baseline & for qpoints using a demo slice" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4cb4c91d", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-10T19:28:31.423650Z", - "iopub.status.busy": "2022-10-10T19:28:31.422701Z", - "iopub.status.idle": "2022-10-10T19:28:31.515600Z", - "shell.execute_reply": "2022-10-10T19:28:31.514875Z" - }, - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_52221/1348678174.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2578782b", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-10T19:28:31.518632Z", - "iopub.status.busy": "2022-10-10T19:28:31.518278Z", - "iopub.status.idle": "2022-10-10T19:28:49.742555Z", - "shell.execute_reply": "2022-10-10T19:28:49.741484Z" - }, - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "removing tmp datasets for username sampsa\n", - "WARNING: be sure not to remove datasets currently used by a process\n", - "importing fiftyone\n", - "fiftyone imported\n", - "removing dataset(s) quickstart,quickstart-2-dummy from fiftyone\n", - "could not deregister quickstart : Dataset 'quickstart' not found\n", - "could not deregister quickstart-2-dummy : Dataset 'quickstart-2-dummy' not found\n" - ] - } - ], - "source": [ - "!compressai-vision clean --y\n", - "!compressai-vision deregister --dataset-name=quickstart,quickstart-2-dummy --y" - ] - }, - { - "cell_type": "markdown", - "id": "5644b827", - "metadata": {}, - "source": [ - "In this tutorial chapter you will learn:\n", - "\n", - "- Checking the installed software stack with ``compressai-vision info``\n", - "- Downloading datasets with ``compressai-vision download``\n", - "- Evaluating datasets with ``compressai-vision detectron2-eval`` for creating mAP(bpp) curves\n", - "- Visualize dataset annotations" - ] - }, - { - "cell_type": "markdown", - "id": "12f7ec4a", - "metadata": {}, - "source": [ - "The command line interface (cli) has all the functionality for evaluating your deep-learning compression algorithm against standardized benchmarks.\n", - "\n", - "The cli is accessed with the ``compressai-vision`` command that has several subcommands for handling datasets, evaluating your models with them and for generating plots. In detail:\n", - "\n", - "- ``compressai-vision -h`` gives you a short description of all commands\n", - "- ``compressai-vision manual`` shows you a more thorough description\n", - "- ``compressai-vision subcommand -h`` gives a detailed description of a certain subcommand\n", - "\n", - "The very first subcommand you should try is ``info``. It gives you information about the installed software stack, library versions and registered datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "dd69b871", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-10T19:28:49.747259Z", - "iopub.status.busy": "2022-10-10T19:28:49.746856Z", - "iopub.status.idle": "2022-10-10T19:28:56.517421Z", - "shell.execute_reply": "2022-10-10T19:28:56.516038Z" - }, - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "*** YOUR VIRTUALENV ***\n", - "--> running from : /home/sampsa/silo/interdigital/venv_all/bin/python\n", - "\n", - "*** TORCH, CUDA, DETECTRON2, COMPRESSAI ***\n", - "torch version : 1.9.1+cu102\n", - "cuda version : 10.2\n", - "detectron2 version : 0.6\n", - "--> running from : /home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/detectron2/__init__.py\n", - "compressai version : 1.2.0.dev0\n", - "--> running from : /home/sampsa/silo/interdigital/CompressAI/compressai/__init__.py\n", - "\n", - "*** COMPRESSAI-VISION ***\n", - "version : 0.0.0\n", - "running from : /home/sampsa/silo/interdigital/CompressAI-Vision/compressai_vision/cli/info.py\n", - "\n", - "*** CHECKING GPU AVAILABILITY ***\n", - "device : cpu\n", - "\n", - "*** TESTING FFMPEG ***\n", - "ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers\n", - "built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\n", - "configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\n", - "libavutil 56. 31.100 / 56. 31.100\n", - "libavcodec 58. 54.100 / 58. 54.100\n", - "libavformat 58. 29.100 / 58. 29.100\n", - "libavdevice 58. 8.100 / 58. 8.100\n", - "libavfilter 7. 57.100 / 7. 57.100\n", - "libavresample 4. 0. 0 / 4. 0. 0\n", - "libswscale 5. 5.100 / 5. 5.100\n", - "libswresample 3. 5.100 / 3. 5.100\n", - "libpostproc 55. 5.100 / 55. 5.100\n", - "\n", - "NOTICE: Using mongodb managed by fiftyone\n", - "Be sure not to have extra mongod server(s) running on your system\n", - "importing fiftyone..\n", - "..imported\n", - "fiftyone version: 0.16.6\n", - "\n", - "*** DATABASE ***\n", - "info about your connection:\n", - "Database(MongoClient(host=['localhost:42889'], document_class=dict, tz_aware=False, connect=True, appname='fiftyone'), 'fiftyone')\n", - "\n", - "\n", - "*** DATASETS ***\n", - "datasets currently registered into fiftyone\n", - "name, length, first sample path\n", - "flir-image-rgb-v1, 10318, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/flir/images_rgb_train/data\n", - "oiv6-mpeg-detection-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data\n", - "oiv6-mpeg-detection-v1-dummy, 1, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data\n", - "oiv6-mpeg-segmentation-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-segmentation-v1/data\n", - "open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data\n", - "quickstart-video, 10, /home/sampsa/fiftyone/quickstart-video/data\n", - "sfu-hw-objects-v1, 2, /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill\n", - "tvd-image-detection-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_detection_v1/data\n", - "tvd-image-segmentation-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_segmentation_v1/data\n", - "tvd-object-tracking-v1, 3, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_object_tracking_dataset_and_annotations\n", - "\n" - ] - } - ], - "source": [ - "!compressai-vision info" - ] - }, - { - "cell_type": "markdown", - "id": "6e600757", - "metadata": {}, - "source": [ - "Another basic command is ``list`` that just shows you the registered datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d87abd13", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-10T19:28:56.524873Z", - "iopub.status.busy": "2022-10-10T19:28:56.523711Z", - "iopub.status.idle": "2022-10-10T19:29:04.077618Z", - "shell.execute_reply": "2022-10-10T19:29:04.076194Z" - }, - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "datasets currently registered into fiftyone\n", - "name, length, first sample path\n", - "flir-image-rgb-v1, 10318, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/flir/images_rgb_train/data\n", - "oiv6-mpeg-detection-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data\n", - "oiv6-mpeg-detection-v1-dummy, 1, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data\n", - "oiv6-mpeg-segmentation-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-segmentation-v1/data\n", - "open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data\n", - "quickstart-video, 10, /home/sampsa/fiftyone/quickstart-video/data\n", - "sfu-hw-objects-v1, 2, /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill\n", - "tvd-image-detection-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_detection_v1/data\n", - "tvd-image-segmentation-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_segmentation_v1/data\n", - "tvd-object-tracking-v1, 3, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_object_tracking_dataset_and_annotations\n" - ] - } - ], - "source": [ - "!compressai-vision list" - ] - }, - { - "cell_type": "markdown", - "id": "b5f23dc4", - "metadata": {}, - "source": [ - "Datasets can be registered to and deregistered from fiftyone using the ``register`` and ``deregister`` subcommands, and downloaded and registered directly from [fiftyone dataset zoo](https://voxel51.com/docs/fiftyone/user_guide/dataset_zoo/datasets.html#dataset-zoo-quickstart) with the ``download`` command. Let's use ``download`` to get the \"quickstart\" dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "fff8adeb", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-10T19:29:04.083942Z", - "iopub.status.busy": "2022-10-10T19:29:04.083463Z", - "iopub.status.idle": "2022-10-10T19:29:14.885918Z", - "shell.execute_reply": "2022-10-10T19:29:14.884312Z" - }, - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "WARNING: downloading ALL images. You might want to use the --lists option to download only certain images\n", - "Using list files: None\n", - "Number of images: ?\n", - "Database name : quickstart\n", - "Subname/split : None\n", - "Target dir : None\n", - "\n", - "Dataset already downloaded\n", - "Loading 'quickstart'\n", - " 100% |███████| 200/200 [2.6s elapsed, 0s remaining, 72.6 samples/s] \n", - "Dataset 'quickstart' created\n" - ] - } - ], - "source": [ - "!compressai-vision download --dataset-name=quickstart --y" - ] - }, - { - "cell_type": "markdown", - "id": "91f3c53a", - "metadata": {}, - "source": [ - "Nice, we have ourselves a dataset to play with. A note: the ``--y`` switch makes the command to run in non-interactive mode. Let's take a closer look at the fields that the samples have in this datafield with ``show``:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6cd2b8f5", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-10T19:29:14.893418Z", - "iopub.status.busy": "2022-10-10T19:29:14.892888Z", - "iopub.status.idle": "2022-10-10T19:29:21.268977Z", - "shell.execute_reply": "2022-10-10T19:29:21.267384Z" - }, - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "dataset info:\n", - "Name: quickstart\n", - "Media type: image\n", - "Num samples: 200\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " uniqueness: fiftyone.core.fields.FloatField\n", - " predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - "\n", - "test-loading first image from /home/sampsa/fiftyone/quickstart/data/000880.jpg\n", - "loaded image with dimensions (480, 640, 3) ok\n" - ] - } - ], - "source": [ - "!compressai-vision show --dataset-name=quickstart --y" - ] - }, - { - "cell_type": "markdown", - "id": "c8a3e468", - "metadata": {}, - "source": [ - "Some fields of interests in each sample: ``filepath`` fields have the path to the downloaded images, while ``ground_truth`` fields have the ground-truth bounding boxes (\"quickstart\" dataset is a demo subset of COCO).\n", - "\n", - "Next we'll crunch all the images in the dataset through a Detectron2 predictor and evaluate the results using the COCO evaluation protocol: as a result, we'll get a mAP accuracy for the Detectron2 model. Note that we have to indicate the ground truth field with ``--gt-field=ground_truth``. Option ``--slice=0:2`` takes only the first two samples from the dataset for this demo run. For production runs you should remove it." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9922d0fb", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-10T19:29:21.275591Z", - "iopub.status.busy": "2022-10-10T19:29:21.274817Z", - "iopub.status.idle": "2022-10-10T19:29:42.906986Z", - "shell.execute_reply": "2022-10-10T19:29:42.905724Z" - }, - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\r\n", - "fiftyone imported\r\n", - "WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS?\r\n", - "\r\n", - "Using dataset : quickstart\r\n", - "Dataset tmp clone : detectron-run-sampsa-quickstart-2022-10-10-22-29-27-260938\r\n", - "Image scaling : 100\r\n", - "WARNING: Using slice : 0:2\r\n", - "Number of samples : 2\r\n", - "Torch device : cpu\r\n", - "Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\r\n", - "Model was trained with : coco_2017_train\r\n", - "** Evaluation without Encoding/Decoding **\r\n", - "Ground truth data field name\r\n", - " : ground_truth\r\n", - "Eval. results will be saved to datafield\r\n", - " : detectron-predictions\r\n", - "Evaluation protocol : coco\r\n", - "Progressbar : True\r\n", - "WARNING: progressbar enabled --> disabling normal progress print\r\n", - "Print progress : 0\r\n", - "Output file : detectron2_test.json\r\n", - "Peek model classes :\r\n", - "['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ...\r\n", - "Peek dataset classes :\r\n", - "['bird', 'horse', 'person'] ...\r\n", - "cloning dataset quickstart to detectron-run-sampsa-quickstart-2022-10-10-22-29-27-260938\r\n", - "instantiating Detectron2 predictor\r\n", - "/home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\r\n", - "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.)\r\n", - " return torch.floor_divide(self, other)\r\n", - " 100% |███████████████████████████████████████████████████████████████████| 2/2 error: number of pixels sum < 1\r\n", - "Evaluating detections...\r\n", - " 100% |███████████| 2/2 [9.5ms elapsed, 0s remaining, 211.5 samples/s] \r\n", - "Performing IoU sweep...\r\n", - " 100% |███████████| 2/2 [12.2ms elapsed, 0s remaining, 163.9 samples/s] \r\n", - "deleting tmp database detectron-run-sampsa-quickstart-2022-10-10-22-29-27-260938\r\n", - "\r\n", - "HAVE A NICE DAY!\r\n", - "\r\n" - ] - } - ], - "source": [ - "!compressai-vision detectron2-eval --y --dataset-name=quickstart \\\n", - "--slice=0:2 \\\n", - "--gt-field=ground_truth \\\n", - "--eval-method=coco \\\n", - "--progressbar \\\n", - "--output=detectron2_test.json \\\n", - "--model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml" - ] - }, - { - "cell_type": "markdown", - "id": "dcaea264", - "metadata": {}, - "source": [ - "Let's see what we got:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b8bd8a59", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-10T19:29:42.912614Z", - "iopub.status.busy": "2022-10-10T19:29:42.911793Z", - "iopub.status.idle": "2022-10-10T19:29:43.039444Z", - "shell.execute_reply": "2022-10-10T19:29:43.038505Z" - }, - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\r\n", - " \"dataset\": \"quickstart\",\r\n", - " \"gt_field\": \"ground_truth\",\r\n", - " \"tmp datasetname\": \"detectron-run-sampsa-quickstart-2022-10-10-22-29-27-260938\",\r\n", - " \"slice\": \"0:2\",\r\n", - " \"model\": \"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\",\r\n", - " \"codec\": \"\",\r\n", - " \"qpars\": null,\r\n", - " \"bpp\": [\r\n", - " -1\r\n", - " ],\r\n", - " \"map\": [\r\n", - " 0.5676567656765678\r\n", - " ],\r\n", - " \"map_per_class\": [\r\n", - " {\r\n", - " \"bird\": 0.30297029702970296,\r\n", - " \"horse\": 0.5,\r\n", - " \"person\": 0.9\r\n", - " }\r\n", - " ]\r\n", - "}" - ] - } - ], - "source": [ - "!cat detectron2_test.json" - ] - }, - { - "cell_type": "markdown", - "id": "34dba7f0", - "metadata": {}, - "source": [ - "Now we use again a Detectron2 predictor on our dataset. However, before passing the images to Detectron2 model, they are first compressed and decompressed by using a pre-trained compressai model with a quality parameter 1 (``--qpars=1``).\n", - "\n", - "We could evaluate for several quality parameters in serial by defining a list, i.e: ``--qpars=1,2,3`` and in parallel by launching the command separately for each particular value (say, for calculations in a queue/grid system).\n", - "\n", - "A scaling can be applied on the images, as defined by the mpeg-vcm specifications (``--scale=100``). Again, remember to remove ``--slice=0:2`` for an actual run. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "49dfee98", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-10T19:29:43.043628Z", - "iopub.status.busy": "2022-10-10T19:29:43.043340Z", - "iopub.status.idle": "2022-10-10T19:30:08.408843Z", - "shell.execute_reply": "2022-10-10T19:30:08.407417Z" - }, - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\r\n", - "fiftyone imported\r\n", - "WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS?\r\n", - "\r\n", - "Using dataset : quickstart\r\n", - "Dataset tmp clone : detectron-run-sampsa-quickstart-2022-10-10-22-29-49-246836\r\n", - "Image scaling : 100\r\n", - "WARNING: Using slice : 0:2\r\n", - "Number of samples : 2\r\n", - "Torch device : cpu\r\n", - "Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\r\n", - "Model was trained with : coco_2017_train\r\n", - "Using compressai model : bmshj2018_factorized\r\n", - "Quality parameters : [1]\r\n", - "Ground truth data field name\r\n", - " : ground_truth\r\n", - "Eval. results will be saved to datafield\r\n", - " : detectron-predictions\r\n", - "Evaluation protocol : coco\r\n", - "Progressbar : True\r\n", - "WARNING: progressbar enabled --> disabling normal progress print\r\n", - "Print progress : 0\r\n", - "Output file : compressai_detectron2_test.json\r\n", - "Peek model classes :\r\n", - "['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ...\r\n", - "Peek dataset classes :\r\n", - "['bird', 'horse', 'person'] ...\r\n", - "cloning dataset quickstart to detectron-run-sampsa-quickstart-2022-10-10-22-29-49-246836\r\n", - "instantiating Detectron2 predictor\r\n", - "\r\n", - "QUALITY PARAMETER: 1\r\n", - "/home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\r\n", - "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.)\r\n", - " return torch.floor_divide(self, other)\r\n", - " 100% |███████████████████████████████████████████████████████████████████| 2/2 Evaluating detections...\r\n", - " 100% |███████████| 2/2 [21.9ms elapsed, 0s remaining, 91.5 samples/s] \r\n", - "Performing IoU sweep...\r\n", - " 100% |███████████| 2/2 [30.0ms elapsed, 0s remaining, 66.8 samples/s] \r\n", - "deleting tmp database detectron-run-sampsa-quickstart-2022-10-10-22-29-49-246836\r\n", - "\r\n", - "HAVE A NICE DAY!\r\n", - "\r\n" - ] - } - ], - "source": [ - "!compressai-vision detectron2-eval --y --dataset-name=quickstart \\\n", - "--slice=0:2 \\\n", - "--gt-field=ground_truth \\\n", - "--eval-method=coco \\\n", - "--scale=100 \\\n", - "--progressbar \\\n", - "--qpars=1 \\\n", - "--compressai-model-name=bmshj2018_factorized \\\n", - "--output=compressai_detectron2_test.json \\\n", - "--model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml" - ] - }, - { - "cell_type": "markdown", - "id": "d5eb9a9f", - "metadata": {}, - "source": [ - "Let's see what we got:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "98d778e3", - "metadata": { - "execution": { - "iopub.execute_input": "2022-10-10T19:30:08.414270Z", - "iopub.status.busy": "2022-10-10T19:30:08.413870Z", - "iopub.status.idle": "2022-10-10T19:30:08.542017Z", - "shell.execute_reply": "2022-10-10T19:30:08.541273Z" - }, - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\r\n", - " \"dataset\": \"quickstart\",\r\n", - " \"gt_field\": \"ground_truth\",\r\n", - " \"tmp datasetname\": \"detectron-run-sampsa-quickstart-2022-10-10-22-29-49-246836\",\r\n", - " \"slice\": \"0:2\",\r\n", - " \"model\": \"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\",\r\n", - " \"codec\": \"bmshj2018_factorized\",\r\n", - " \"qpars\": [\r\n", - " 1\r\n", - " ],\r\n", - " \"bpp\": [\r\n", - " 0.18178251121076233\r\n", - " ],\r\n", - " \"map\": [\r\n", - " 0.44477447744774484\r\n", - " ],\r\n", - " \"map_per_class\": [\r\n", - " {\r\n", - " \"bird\": 0.100990099009901,\r\n", - " \"horse\": 0.3333333333333334,\r\n", - " \"person\": 0.9\r\n", - " }\r\n", - " ]\r\n", - "}" - ] - } - ], - "source": [ - "!cat compressai_detectron2_test.json" - ] - }, - { - "cell_type": "markdown", - "id": "0e882ba7", - "metadata": {}, - "source": [ - "Which is a single point on the mAP(bpp) curve. Next you need to produce some more points and then use ``plot`` subcommand. Please refer to the following chapters in this tutorial." - ] - }, - { - "cell_type": "markdown", - "id": "60970a6b", - "metadata": {}, - "source": [ - "Fiftyone comes with a webapp for visualizing your dataset and its annotations (ground truths and predictions). You can launch it from command line with:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d5c13d9c", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "Launching app at address localhost port 5151\n", - "press CTRL-C to terminate\n", - "\n", - "App launched. Point your web browser to http://localhost:5151\n", - "^C\n", - "Have a nice day!\n" - ] - } - ], - "source": [ - "!compressai-vision app --dataset-name=quickstart" - ] - }, - { - "cell_type": "markdown", - "id": "b7f221d5", - "metadata": {}, - "source": [ - "We could also visualize how well the Detectron2 results (calculated above) fit in with the ground truths. In order to do this we should visualize the temporary dataset ``detectron-run-sampsa-quickstart-2022-10-10-22-29-49-246836`` (see above). By default, temporary databases are removed after the evaluation is finished in the ``detectron2-eval`` command. You can preseve them by using the ``--keep`` flag for the ``detectron2-eval`` command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ed74aca", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3.8.10 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "vscode": { - "interpreter": { - "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/cli_tutorial_1_nb.rst b/docs/source/tutorials/cli_tutorial_1_nb.rst deleted file mode 100644 index 4aaaf995..00000000 --- a/docs/source/tutorials/cli_tutorial_1_nb.rst +++ /dev/null @@ -1,426 +0,0 @@ -In this tutorial chapter you will learn: - -- Checking the installed software stack with ``compressai-vision info`` -- Downloading datasets with ``compressai-vision download`` -- Evaluating datasets with ``compressai-vision detectron2-eval`` for - creating mAP(bpp) curves -- Visualize dataset annotations - -The command line interface (cli) has all the functionality for -evaluating your deep-learning compression algorithm against standardized -benchmarks. - -The cli is accessed with the ``compressai-vision`` command that has -several subcommands for handling datasets, evaluating your models with -them and for generating plots. In detail: - -- ``compressai-vision -h`` gives you a short description of all - commands -- ``compressai-vision manual`` shows you a more thorough description -- ``compressai-vision subcommand -h`` gives a detailed description of a - certain subcommand - -The very first subcommand you should try is ``info``. It gives you -information about the installed software stack, library versions and -registered datasets: - -.. code:: bash - - compressai-vision info - - -.. code-block:: text - - - *** YOUR VIRTUALENV *** - --> running from : /home/sampsa/silo/interdigital/venv_all/bin/python - - *** TORCH, CUDA, DETECTRON2, COMPRESSAI *** - torch version : 1.9.1+cu102 - cuda version : 10.2 - detectron2 version : 0.6 - --> running from : /home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/detectron2/__init__.py - compressai version : 1.2.0.dev0 - --> running from : /home/sampsa/silo/interdigital/CompressAI/compressai/__init__.py - - *** COMPRESSAI-VISION *** - version : 0.0.0 - running from : /home/sampsa/silo/interdigital/CompressAI-Vision/compressai_vision/cli/info.py - - *** CHECKING GPU AVAILABILITY *** - device : cpu - - *** TESTING FFMPEG *** - ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers - built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1) - configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared - libavutil 56. 31.100 / 56. 31.100 - libavcodec 58. 54.100 / 58. 54.100 - libavformat 58. 29.100 / 58. 29.100 - libavdevice 58. 8.100 / 58. 8.100 - libavfilter 7. 57.100 / 7. 57.100 - libavresample 4. 0. 0 / 4. 0. 0 - libswscale 5. 5.100 / 5. 5.100 - libswresample 3. 5.100 / 3. 5.100 - libpostproc 55. 5.100 / 55. 5.100 - - NOTICE: Using mongodb managed by fiftyone - Be sure not to have extra mongod server(s) running on your system - importing fiftyone.. - ..imported - fiftyone version: 0.16.6 - - *** DATABASE *** - info about your connection: - Database(MongoClient(host=['localhost:42889'], document_class=dict, tz_aware=False, connect=True, appname='fiftyone'), 'fiftyone') - - - *** DATASETS *** - datasets currently registered into fiftyone - name, length, first sample path - flir-image-rgb-v1, 10318, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/flir/images_rgb_train/data - oiv6-mpeg-detection-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data - oiv6-mpeg-detection-v1-dummy, 1, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data - oiv6-mpeg-segmentation-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-segmentation-v1/data - open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data - quickstart-video, 10, /home/sampsa/fiftyone/quickstart-video/data - sfu-hw-objects-v1, 2, /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill - tvd-image-detection-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_detection_v1/data - tvd-image-segmentation-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_segmentation_v1/data - tvd-object-tracking-v1, 3, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_object_tracking_dataset_and_annotations - - - -Another basic command is ``list`` that just shows you the registered -datasets: - -.. code:: bash - - compressai-vision list - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - datasets currently registered into fiftyone - name, length, first sample path - flir-image-rgb-v1, 10318, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/flir/images_rgb_train/data - oiv6-mpeg-detection-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data - oiv6-mpeg-detection-v1-dummy, 1, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data - oiv6-mpeg-segmentation-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-segmentation-v1/data - open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data - quickstart-video, 10, /home/sampsa/fiftyone/quickstart-video/data - sfu-hw-objects-v1, 2, /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill - tvd-image-detection-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_detection_v1/data - tvd-image-segmentation-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_segmentation_v1/data - tvd-object-tracking-v1, 3, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_object_tracking_dataset_and_annotations - - -Datasets can be registered to and deregistered from fiftyone using the -``register`` and ``deregister`` subcommands, and downloaded and -registered directly from `fiftyone dataset -zoo `__ -with the ``download`` command. Let’s use ``download`` to get the -“quickstart” dataset: - -.. code:: bash - - compressai-vision download --dataset-name=quickstart --y - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - WARNING: downloading ALL images. You might want to use the --lists option to download only certain images - Using list files: None - Number of images: ? - Database name : quickstart - Subname/split : None - Target dir : None - - Dataset already downloaded - Loading 'quickstart' - 100% |███████| 200/200 [2.6s elapsed, 0s remaining, 72.6 samples/s] - Dataset 'quickstart' created - - -Nice, we have ourselves a dataset to play with. A note: the ``--y`` -switch makes the command to run in non-interactive mode. Let’s take a -closer look at the fields that the samples have in this datafield with -``show``: - -.. code:: bash - - compressai-vision show --dataset-name=quickstart --y - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - dataset info: - Name: quickstart - Media type: image - Num samples: 200 - Persistent: True - Tags: [] - Sample fields: - id: fiftyone.core.fields.ObjectIdField - filepath: fiftyone.core.fields.StringField - tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) - metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata) - ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - uniqueness: fiftyone.core.fields.FloatField - predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - - test-loading first image from /home/sampsa/fiftyone/quickstart/data/000880.jpg - loaded image with dimensions (480, 640, 3) ok - - -Some fields of interests in each sample: ``filepath`` fields have the -path to the downloaded images, while ``ground_truth`` fields have the -ground-truth bounding boxes (“quickstart” dataset is a demo subset of -COCO). - -Next we’ll crunch all the images in the dataset through a Detectron2 -predictor and evaluate the results using the COCO evaluation protocol: -as a result, we’ll get a mAP accuracy for the Detectron2 model. Note -that we have to indicate the ground truth field with -``--gt-field=ground_truth``. Option ``--slice=0:2`` takes only the first -two samples from the dataset for this demo run. For production runs you -should remove it. - -.. code:: bash - - compressai-vision detectron2-eval --y --dataset-name=quickstart \ - --slice=0:2 \ - --gt-field=ground_truth \ - --eval-method=coco \ - --progressbar \ - --output=detectron2_test.json \ - --model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - -.. code-block:: text - - importing fiftyone - fiftyone imported - WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS? - - Using dataset : quickstart - Dataset tmp clone : detectron-run-sampsa-quickstart-2022-10-10-22-29-27-260938 - Image scaling : 100 - WARNING: Using slice : 0:2 - Number of samples : 2 - Torch device : cpu - Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - Model was trained with : coco_2017_train - ** Evaluation without Encoding/Decoding ** - Ground truth data field name - : ground_truth - Eval. results will be saved to datafield - : detectron-predictions - Evaluation protocol : coco - Progressbar : True - WARNING: progressbar enabled --> disabling normal progress print - Print progress : 0 - Output file : detectron2_test.json - Peek model classes : - ['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ... - Peek dataset classes : - ['bird', 'horse', 'person'] ... - cloning dataset quickstart to detectron-run-sampsa-quickstart-2022-10-10-22-29-27-260938 - instantiating Detectron2 predictor - /home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. - To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.) - return torch.floor_divide(self, other) - 100% |███████████████████████████████████████████████████████████████████| 2/2 error: number of pixels sum < 1 - Evaluating detections... - 100% |███████████| 2/2 [9.5ms elapsed, 0s remaining, 211.5 samples/s] - Performing IoU sweep... - 100% |███████████| 2/2 [12.2ms elapsed, 0s remaining, 163.9 samples/s] - deleting tmp database detectron-run-sampsa-quickstart-2022-10-10-22-29-27-260938 - - HAVE A NICE DAY! - - - -Let’s see what we got: - -.. code:: bash - - cat detectron2_test.json - - -.. code-block:: text - - { - "dataset": "quickstart", - "gt_field": "ground_truth", - "tmp datasetname": "detectron-run-sampsa-quickstart-2022-10-10-22-29-27-260938", - "slice": "0:2", - "model": "COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml", - "codec": "", - "qpars": null, - "bpp": [ - -1 - ], - "map": [ - 0.5676567656765678 - ], - "map_per_class": [ - { - "bird": 0.30297029702970296, - "horse": 0.5, - "person": 0.9 - } - ] - } - -Now we use again a Detectron2 predictor on our dataset. However, before -passing the images to Detectron2 model, they are first compressed and -decompressed by using a pre-trained compressai model with a quality -parameter 1 (``--qpars=1``). - -We could evaluate for several quality parameters in serial by defining a -list, i.e: ``--qpars=1,2,3`` and in parallel by launching the command -separately for each particular value (say, for calculations in a -queue/grid system). - -A scaling can be applied on the images, as defined by the mpeg-vcm -specifications (``--scale=100``). Again, remember to remove -``--slice=0:2`` for an actual run. - -.. code:: bash - - compressai-vision detectron2-eval --y --dataset-name=quickstart \ - --slice=0:2 \ - --gt-field=ground_truth \ - --eval-method=coco \ - --scale=100 \ - --progressbar \ - --qpars=1 \ - --compressai-model-name=bmshj2018_factorized \ - --output=compressai_detectron2_test.json \ - --model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - -.. code-block:: text - - importing fiftyone - fiftyone imported - WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS? - - Using dataset : quickstart - Dataset tmp clone : detectron-run-sampsa-quickstart-2022-10-10-22-29-49-246836 - Image scaling : 100 - WARNING: Using slice : 0:2 - Number of samples : 2 - Torch device : cpu - Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - Model was trained with : coco_2017_train - Using compressai model : bmshj2018_factorized - Quality parameters : [1] - Ground truth data field name - : ground_truth - Eval. results will be saved to datafield - : detectron-predictions - Evaluation protocol : coco - Progressbar : True - WARNING: progressbar enabled --> disabling normal progress print - Print progress : 0 - Output file : compressai_detectron2_test.json - Peek model classes : - ['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ... - Peek dataset classes : - ['bird', 'horse', 'person'] ... - cloning dataset quickstart to detectron-run-sampsa-quickstart-2022-10-10-22-29-49-246836 - instantiating Detectron2 predictor - - QUALITY PARAMETER: 1 - /home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. - To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.) - return torch.floor_divide(self, other) - 100% |███████████████████████████████████████████████████████████████████| 2/2 Evaluating detections... - 100% |███████████| 2/2 [21.9ms elapsed, 0s remaining, 91.5 samples/s] - Performing IoU sweep... - 100% |███████████| 2/2 [30.0ms elapsed, 0s remaining, 66.8 samples/s] - deleting tmp database detectron-run-sampsa-quickstart-2022-10-10-22-29-49-246836 - - HAVE A NICE DAY! - - - -Let’s see what we got: - -.. code:: bash - - cat compressai_detectron2_test.json - - -.. code-block:: text - - { - "dataset": "quickstart", - "gt_field": "ground_truth", - "tmp datasetname": "detectron-run-sampsa-quickstart-2022-10-10-22-29-49-246836", - "slice": "0:2", - "model": "COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml", - "codec": "bmshj2018_factorized", - "qpars": [ - 1 - ], - "bpp": [ - 0.18178251121076233 - ], - "map": [ - 0.44477447744774484 - ], - "map_per_class": [ - { - "bird": 0.100990099009901, - "horse": 0.3333333333333334, - "person": 0.9 - } - ] - } - -Which is a single point on the mAP(bpp) curve. Next you need to produce -some more points and then use ``plot`` subcommand. Please refer to the -following chapters in this tutorial. - -Fiftyone comes with a webapp for visualizing your dataset and its -annotations (ground truths and predictions). You can launch it from -command line with: - -.. code:: bash - - compressai-vision app --dataset-name=quickstart - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - Launching app at address localhost port 5151 - press CTRL-C to terminate - - App launched. Point your web browser to http://localhost:5151 - ^C - Have a nice day! - - -We could also visualize how well the Detectron2 results (calculated -above) fit in with the ground truths. In order to do this we should -visualize the temporary dataset -``detectron-run-sampsa-quickstart-2022-10-10-22-29-49-246836`` (see -above). By default, temporary databases are removed after the evaluation -is finished in the ``detectron2-eval`` command. You can preseve them by -using the ``--keep`` flag for the ``detectron2-eval`` command. - diff --git a/docs/source/tutorials/cli_tutorial_2.rst b/docs/source/tutorials/cli_tutorial_2.rst deleted file mode 100644 index e2b5ee5f..00000000 --- a/docs/source/tutorials/cli_tutorial_2.rst +++ /dev/null @@ -1,7 +0,0 @@ - -2. Registering Datasets ------------------------ - -.. _cli_tutorial_2: - -.. include:: cli_tutorial_2_nb.rst diff --git a/docs/source/tutorials/cli_tutorial_2_nb.ipynb b/docs/source/tutorials/cli_tutorial_2_nb.ipynb deleted file mode 100644 index a5e5e931..00000000 --- a/docs/source/tutorials/cli_tutorial_2_nb.ipynb +++ /dev/null @@ -1,803 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a9b7f123", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "# Tutorial, chapter 2\n", - "\n", - "- de/registering datasets into fiftyone" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b470ee74", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_46434/1348678174.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d40acd5e", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "removing tmp datasets for username sampsa\n", - "WARNING: be sure not to remove datasets currently used by a process\n", - "importing fiftyone\n", - "fiftyone imported\n", - "removing dataset quickstart-2-dummy from fiftyone\n", - "could not deregister because of Dataset 'quickstart-2-dummy' not found\n", - "importing fiftyone\n", - "fiftyone imported\n", - "removing dataset quickstart-2 from fiftyone\n", - "could not deregister because of Dataset 'quickstart-2' not found\n" - ] - } - ], - "source": [ - "!compressai-vision clean --y\n", - "!compressai-vision deregister --dataset-name=quickstart-2,quickstart-2-dummy --y" - ] - }, - { - "cell_type": "markdown", - "id": "57276ad6", - "metadata": {}, - "source": [ - "In this chapter you will learn:\n", - "\n", - "- registering and deregistering datasets into fiftyone" - ] - }, - { - "cell_type": "markdown", - "id": "206875f2", - "metadata": {}, - "source": [ - "In the previous chapter we downloaded & registered the dataset \"quickstart\" from the fiftyone model zoo:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "dca1edb7", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "datasets currently registered into fiftyone\n", - "name, length, first sample path\n", - "mpeg-vcm-detection, 5000, /home/sampsa/fiftyone/mpeg-vcm-detection/data\n", - "mpeg-vcm-detection-dummy, 1, /home/sampsa/fiftyone/mpeg-vcm-detection/data\n", - "mpeg-vcm-segmentation, 5000, /home/sampsa/fiftyone/mpeg-vcm-segmentation/data\n", - "open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data\n", - "quickstart, 200, /home/sampsa/fiftyone/quickstart/data\n" - ] - } - ], - "source": [ - "!compressai-vision list" - ] - }, - { - "cell_type": "markdown", - "id": "3c8b1ed0", - "metadata": {}, - "source": [ - "All the metadata, ground truth bboxes, etc. reside now in the fiftyone/mongodb database at dataset \"quickstart\". That data was read into the database originally from directory ``~/fiftyone/quickstart``. Let's see what's in there:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a2dd7c36", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data info.json metadata.json\tsamples.json\r\n" - ] - } - ], - "source": [ - "!ls ~/fiftyone/quickstart" - ] - }, - { - "cell_type": "markdown", - "id": "e63a71e5", - "metadata": {}, - "source": [ - "Exactly. Note directory ``data``. That is where the sample images are (they are *not* in the database, but just on the disk as image files). Let's take a look at that:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f39112ce", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "000002.jpg 000889.jpg\t001851.jpg 002598.jpg\t003754.jpg 004416.jpg\r\n", - "000008.jpg 000890.jpg\t001867.jpg 002640.jpg\t003769.jpg 004431.jpg\r\n", - "000020.jpg 000939.jpg\t001888.jpg 002645.jpg\t003805.jpg 004510.jpg\r\n", - "000031.jpg 000957.jpg\t001934.jpg 002660.jpg\t003870.jpg 004514.jpg\r\n", - "000035.jpg 000998.jpg\t001949.jpg 002671.jpg\t003871.jpg 004517.jpg\r\n", - "000058.jpg 001047.jpg\t001951.jpg 002748.jpg\t003880.jpg 004525.jpg\r\n", - "000083.jpg 001057.jpg\t001983.jpg 002799.jpg\t003888.jpg 004534.jpg\r\n", - "000089.jpg 001073.jpg\t002015.jpg 002823.jpg\t003911.jpg 004535.jpg\r\n", - "000145.jpg 001078.jpg\t002022.jpg 002869.jpg\t003964.jpg 004546.jpg\r\n", - "000164.jpg 001118.jpg\t002063.jpg 002905.jpg\t003969.jpg 004548.jpg\r\n", - "000191.jpg 001133.jpg\t002070.jpg 002906.jpg\t003978.jpg 004557.jpg\r\n", - "000192.jpg 001147.jpg\t002086.jpg 002939.jpg\t004039.jpg 004585.jpg\r\n", - "000400.jpg 001154.jpg\t002121.jpg 002953.jpg\t004066.jpg 004590.jpg\r\n", - "000436.jpg 001191.jpg\t002129.jpg 003084.jpg\t004082.jpg 004610.jpg\r\n", - "000452.jpg 001227.jpg\t002143.jpg 003087.jpg\t004095.jpg 004627.jpg\r\n", - "000496.jpg 001289.jpg\t002184.jpg 003107.jpg\t004096.jpg 004651.jpg\r\n", - "000510.jpg 001312.jpg\t002186.jpg 003132.jpg\t004126.jpg 004656.jpg\r\n", - "000557.jpg 001348.jpg\t002233.jpg 003148.jpg\t004131.jpg 004702.jpg\r\n", - "000575.jpg 001394.jpg\t002284.jpg 003154.jpg\t004170.jpg 004713.jpg\r\n", - "000591.jpg 001429.jpg\t002334.jpg 003254.jpg\t004172.jpg 004743.jpg\r\n", - "000594.jpg 001430.jpg\t002353.jpg 003316.jpg\t004180.jpg 004755.jpg\r\n", - "000600.jpg 001586.jpg\t002431.jpg 003344.jpg\t004222.jpg 004775.jpg\r\n", - "000641.jpg 001587.jpg\t002439.jpg 003391.jpg\t004253.jpg 004781.jpg\r\n", - "000643.jpg 001599.jpg\t002450.jpg 003420.jpg\t004255.jpg 004831.jpg\r\n", - "000648.jpg 001614.jpg\t002462.jpg 003486.jpg\t004263.jpg 004852.jpg\r\n", - "000665.jpg 001624.jpg\t002468.jpg 003502.jpg\t004284.jpg 004939.jpg\r\n", - "000696.jpg 001631.jpg\t002489.jpg 003541.jpg\t004292.jpg 004941.jpg\r\n", - "000772.jpg 001634.jpg\t002497.jpg 003614.jpg\t004304.jpg 004965.jpg\r\n", - "000773.jpg 001645.jpg\t002514.jpg 003659.jpg\t004315.jpg 004978.jpg\r\n", - "000781.jpg 001685.jpg\t002538.jpg 003662.jpg\t004316.jpg 004981.jpg\r\n", - "000793.jpg 001698.jpg\t002553.jpg 003665.jpg\t004329.jpg\r\n", - "000807.jpg 001741.jpg\t002586.jpg 003667.jpg\t004330.jpg\r\n", - "000868.jpg 001744.jpg\t002592.jpg 003713.jpg\t004341.jpg\r\n", - "000880.jpg 001763.jpg\t002597.jpg 003715.jpg\t004371.jpg\r\n" - ] - } - ], - "source": [ - "!ls ~/fiftyone/quickstart/data" - ] - }, - { - "cell_type": "markdown", - "id": "df910da7", - "metadata": {}, - "source": [ - "The fiftyone dataset \"quickstart\" has only the paths to these files.\n", - "\n", - "Next suppose you have a dataset already on your disk (say, on the ImageDir format, COCO format, whatever) and you wish to register it into fiftyone.\n", - "\n", - "In order to demo that, let's create a copy of ``~/fiftyone/quickstart``:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "414d173e", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [], - "source": [ - "!cp -r ~/fiftyone/quickstart /tmp/my_data_set" - ] - }, - { - "cell_type": "markdown", - "id": "961d17dc", - "metadata": {}, - "source": [ - "Let's imagine ``/tmp/my_data_set`` is that custom dataset of yours you had already lying around.\n", - "\n", - "We register it to fiftyone with:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "fd7a97b1", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "WARNING: using/registering with ALL images. You should use the --lists option\n", - "From directory : /tmp/my_data_set\n", - "Using list file : None\n", - "Number of images: ?\n", - "Registering name: my_dataset\n", - "\n", - "Ignoring unsupported parameter 'label_types' for importer type \n", - "Ignoring unsupported parameter 'load_hierarchy' for importer type \n", - " 100% |███████| 200/200 [3.0s elapsed, 0s remaining, 65.3 samples/s] \n", - "\n", - "** Let's peek at the first sample - check that it looks ok:**\n", - "\n", - ",\n", - " ,\n", - " ,\n", - " ]),\n", - " }>,\n", - " 'uniqueness': 0.8175834390151201,\n", - " 'predictions': ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]),\n", - " }>,\n", - "}>\n", - "\n" - ] - } - ], - "source": [ - "!compressai-vision register --y \\\n", - "--dataset-name=my_dataset \\\n", - "--dir=/tmp/my_data_set \\\n", - "--type=FiftyOneDataset" - ] - }, - { - "cell_type": "markdown", - "id": "50dffb22", - "metadata": {}, - "source": [ - "here ``--type`` depends on the directory/file structure your data directory has. Typical values are ``FiftyOneDataset, OpenImagesV6Dataset, ImageDirectory``. Please take a look in [here](https://voxel51.com/docs/fiftyone/api/fiftyone.types.dataset_types.html) for more information.\n", - "\n", - "Let's check that the dataset got registered correctly:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8ccd56bd", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "datasets currently registered into fiftyone\n", - "name, length, first sample path\n", - "mpeg-vcm-detection, 5000, /home/sampsa/fiftyone/mpeg-vcm-detection/data\n", - "mpeg-vcm-detection-dummy, 1, /home/sampsa/fiftyone/mpeg-vcm-detection/data\n", - "mpeg-vcm-segmentation, 5000, /home/sampsa/fiftyone/mpeg-vcm-segmentation/data\n", - "my_dataset, 200, /tmp/my_data_set/data\n", - "open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data\n", - "quickstart, 200, /home/sampsa/fiftyone/quickstart/data\n" - ] - } - ], - "source": [ - "!compressai-vision list" - ] - }, - { - "cell_type": "markdown", - "id": "3ad8e6d7", - "metadata": {}, - "source": [ - "A more detailed look into the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fc4be243", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "dataset info:\n", - "Name: my_dataset\n", - "Media type: image\n", - "Num samples: 200\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " uniqueness: fiftyone.core.fields.FloatField\n", - " predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - "\n", - "test-loading first image from /tmp/my_data_set/data/000880.jpg\n", - "loaded image with dimensions (480, 640, 3) ok\n" - ] - } - ], - "source": [ - "!compressai-vision show --dataset-name=my_dataset" - ] - }, - { - "cell_type": "markdown", - "id": "d9c15102", - "metadata": {}, - "source": [ - "Let's deregister the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5d30093e", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "removing dataset my_dataset from fiftyone\n" - ] - } - ], - "source": [ - "!compressai-vision deregister --y --dataset-name=my_dataset" - ] - }, - { - "cell_type": "markdown", - "id": "0e73d869", - "metadata": {}, - "source": [ - "Check it got removed:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6649a447", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "datasets currently registered into fiftyone\n", - "name, length, first sample path\n", - "mpeg-vcm-detection, 5000, /home/sampsa/fiftyone/mpeg-vcm-detection/data\n", - "mpeg-vcm-detection-dummy, 1, /home/sampsa/fiftyone/mpeg-vcm-detection/data\n", - "mpeg-vcm-segmentation, 5000, /home/sampsa/fiftyone/mpeg-vcm-segmentation/data\n", - "open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data\n", - "quickstart, 200, /home/sampsa/fiftyone/quickstart/data\n" - ] - } - ], - "source": [ - "!compressai-vision list" - ] - }, - { - "cell_type": "markdown", - "id": "3b98332a", - "metadata": {}, - "source": [ - "Let's remove the image data as well:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "51ccdf85", - "metadata": {}, - "outputs": [], - "source": [ - "!rm -rf /tmp/my_data_set" - ] - }, - { - "cell_type": "markdown", - "id": "c6867b80", - "metadata": {}, - "source": [ - "A final note/observation before moving to the next tutorial.\n", - "\n", - "If you work with an external, shared mongodb and several people are working on the same datasets, after registering/exporting, each user might want to use the ``compressai-vision copy`` command to create a personal copy of the dataset in order to avoid conflicts (for a more pro multiuser environment you might want to contact voxel51 for their premium version of fiftyone)." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/cli_tutorial_2_nb.rst b/docs/source/tutorials/cli_tutorial_2_nb.rst deleted file mode 100644 index 13dbf582..00000000 --- a/docs/source/tutorials/cli_tutorial_2_nb.rst +++ /dev/null @@ -1,519 +0,0 @@ -In this chapter you will learn: - -- registering and deregistering datasets into fiftyone - -In the previous chapter we downloaded & registered the dataset -“quickstart” from the fiftyone model zoo: - -.. code:: bash - - compressai-vision list - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - datasets currently registered into fiftyone - name, length, first sample path - mpeg-vcm-detection, 5000, /home/sampsa/fiftyone/mpeg-vcm-detection/data - mpeg-vcm-detection-dummy, 1, /home/sampsa/fiftyone/mpeg-vcm-detection/data - mpeg-vcm-segmentation, 5000, /home/sampsa/fiftyone/mpeg-vcm-segmentation/data - open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data - quickstart, 200, /home/sampsa/fiftyone/quickstart/data - - -All the metadata, ground truth bboxes, etc. reside now in the -fiftyone/mongodb database at dataset “quickstart”. That data was read -into the database originally from directory ``~/fiftyone/quickstart``. -Let’s see what’s in there: - -.. code:: bash - - ls ~/fiftyone/quickstart - - -.. code-block:: text - - data info.json metadata.json samples.json - - -Exactly. Note directory ``data``. That is where the sample images are -(they are *not* in the database, but just on the disk as image files). -Let’s take a look at that: - -.. code:: bash - - ls ~/fiftyone/quickstart/data - - -.. code-block:: text - - 000002.jpg 000889.jpg 001851.jpg 002598.jpg 003754.jpg 004416.jpg - 000008.jpg 000890.jpg 001867.jpg 002640.jpg 003769.jpg 004431.jpg - 000020.jpg 000939.jpg 001888.jpg 002645.jpg 003805.jpg 004510.jpg - 000031.jpg 000957.jpg 001934.jpg 002660.jpg 003870.jpg 004514.jpg - 000035.jpg 000998.jpg 001949.jpg 002671.jpg 003871.jpg 004517.jpg - 000058.jpg 001047.jpg 001951.jpg 002748.jpg 003880.jpg 004525.jpg - 000083.jpg 001057.jpg 001983.jpg 002799.jpg 003888.jpg 004534.jpg - 000089.jpg 001073.jpg 002015.jpg 002823.jpg 003911.jpg 004535.jpg - 000145.jpg 001078.jpg 002022.jpg 002869.jpg 003964.jpg 004546.jpg - 000164.jpg 001118.jpg 002063.jpg 002905.jpg 003969.jpg 004548.jpg - 000191.jpg 001133.jpg 002070.jpg 002906.jpg 003978.jpg 004557.jpg - 000192.jpg 001147.jpg 002086.jpg 002939.jpg 004039.jpg 004585.jpg - 000400.jpg 001154.jpg 002121.jpg 002953.jpg 004066.jpg 004590.jpg - 000436.jpg 001191.jpg 002129.jpg 003084.jpg 004082.jpg 004610.jpg - 000452.jpg 001227.jpg 002143.jpg 003087.jpg 004095.jpg 004627.jpg - 000496.jpg 001289.jpg 002184.jpg 003107.jpg 004096.jpg 004651.jpg - 000510.jpg 001312.jpg 002186.jpg 003132.jpg 004126.jpg 004656.jpg - 000557.jpg 001348.jpg 002233.jpg 003148.jpg 004131.jpg 004702.jpg - 000575.jpg 001394.jpg 002284.jpg 003154.jpg 004170.jpg 004713.jpg - 000591.jpg 001429.jpg 002334.jpg 003254.jpg 004172.jpg 004743.jpg - 000594.jpg 001430.jpg 002353.jpg 003316.jpg 004180.jpg 004755.jpg - 000600.jpg 001586.jpg 002431.jpg 003344.jpg 004222.jpg 004775.jpg - 000641.jpg 001587.jpg 002439.jpg 003391.jpg 004253.jpg 004781.jpg - 000643.jpg 001599.jpg 002450.jpg 003420.jpg 004255.jpg 004831.jpg - 000648.jpg 001614.jpg 002462.jpg 003486.jpg 004263.jpg 004852.jpg - 000665.jpg 001624.jpg 002468.jpg 003502.jpg 004284.jpg 004939.jpg - 000696.jpg 001631.jpg 002489.jpg 003541.jpg 004292.jpg 004941.jpg - 000772.jpg 001634.jpg 002497.jpg 003614.jpg 004304.jpg 004965.jpg - 000773.jpg 001645.jpg 002514.jpg 003659.jpg 004315.jpg 004978.jpg - 000781.jpg 001685.jpg 002538.jpg 003662.jpg 004316.jpg 004981.jpg - 000793.jpg 001698.jpg 002553.jpg 003665.jpg 004329.jpg - 000807.jpg 001741.jpg 002586.jpg 003667.jpg 004330.jpg - 000868.jpg 001744.jpg 002592.jpg 003713.jpg 004341.jpg - 000880.jpg 001763.jpg 002597.jpg 003715.jpg 004371.jpg - - -The fiftyone dataset “quickstart” has only the paths to these files. - -Next suppose you have a dataset already on your disk (say, on the -ImageDir format, COCO format, whatever) and you wish to register it into -fiftyone. - -In order to demo that, let’s create a copy of ``~/fiftyone/quickstart``: - -.. code:: bash - - cp -r ~/fiftyone/quickstart /tmp/my_data_set - -Let’s imagine ``/tmp/my_data_set`` is that custom dataset of yours you -had already lying around. - -We register it to fiftyone with: - -.. code:: bash - - compressai-vision register --y \ - --dataset-name=my_dataset \ - --dir=/tmp/my_data_set \ - --type=FiftyOneDataset - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - WARNING: using/registering with ALL images. You should use the --lists option - From directory : /tmp/my_data_set - Using list file : None - Number of images: ? - Registering name: my_dataset - - Ignoring unsupported parameter 'label_types' for importer type - Ignoring unsupported parameter 'load_hierarchy' for importer type - 100% |███████| 200/200 [3.0s elapsed, 0s remaining, 65.3 samples/s] - - ** Let's peek at the first sample - check that it looks ok:** - - , - , - , - ]), - }>, - 'uniqueness': 0.8175834390151201, - 'predictions': , - , - , - , - , - , - , - , - , - , - , - , - , - , - ]), - }>, - }> - - - -here ``--type`` depends on the directory/file structure your data -directory has. Typical values are -``FiftyOneDataset, OpenImagesV6Dataset, ImageDirectory``. Please take a -look in -`here `__ -for more information. - -Let’s check that the dataset got registered correctly: - -.. code:: bash - - compressai-vision list - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - datasets currently registered into fiftyone - name, length, first sample path - mpeg-vcm-detection, 5000, /home/sampsa/fiftyone/mpeg-vcm-detection/data - mpeg-vcm-detection-dummy, 1, /home/sampsa/fiftyone/mpeg-vcm-detection/data - mpeg-vcm-segmentation, 5000, /home/sampsa/fiftyone/mpeg-vcm-segmentation/data - my_dataset, 200, /tmp/my_data_set/data - open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data - quickstart, 200, /home/sampsa/fiftyone/quickstart/data - - -A more detailed look into the dataset: - -.. code:: bash - - compressai-vision show --dataset-name=my_dataset - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - dataset info: - Name: my_dataset - Media type: image - Num samples: 200 - Persistent: True - Tags: [] - Sample fields: - id: fiftyone.core.fields.ObjectIdField - filepath: fiftyone.core.fields.StringField - tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) - metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata) - ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - uniqueness: fiftyone.core.fields.FloatField - predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - - test-loading first image from /tmp/my_data_set/data/000880.jpg - loaded image with dimensions (480, 640, 3) ok - - -Let’s deregister the dataset: - -.. code:: bash - - compressai-vision deregister --y --dataset-name=my_dataset - - -.. code-block:: text - - importing fiftyone - fiftyone imported - removing dataset my_dataset from fiftyone - - -Check it got removed: - -.. code:: bash - - compressai-vision list - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - datasets currently registered into fiftyone - name, length, first sample path - mpeg-vcm-detection, 5000, /home/sampsa/fiftyone/mpeg-vcm-detection/data - mpeg-vcm-detection-dummy, 1, /home/sampsa/fiftyone/mpeg-vcm-detection/data - mpeg-vcm-segmentation, 5000, /home/sampsa/fiftyone/mpeg-vcm-segmentation/data - open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data - quickstart, 200, /home/sampsa/fiftyone/quickstart/data - - -Let’s remove the image data as well: - -.. code:: ipython3 - - rm -rf /tmp/my_data_set - -A final note/observation before moving to the next tutorial. - -If you work with an external, shared mongodb and several people are -working on the same datasets, after registering/exporting, each user -might want to use the ``compressai-vision copy`` command to create a -personal copy of the dataset in order to avoid conflicts (for a more pro -multiuser environment you might want to contact voxel51 for their -premium version of fiftyone). diff --git a/docs/source/tutorials/cli_tutorial_3.rst b/docs/source/tutorials/cli_tutorial_3.rst deleted file mode 100644 index 48fdd3f3..00000000 --- a/docs/source/tutorials/cli_tutorial_3.rst +++ /dev/null @@ -1,7 +0,0 @@ - -3. MPEG-VCM Evaluation ----------------------- - -.. _cli_tutorial_3: - -.. include:: cli_tutorial_3_nb.rst diff --git a/docs/source/tutorials/cli_tutorial_3_nb.ipynb b/docs/source/tutorials/cli_tutorial_3_nb.ipynb deleted file mode 100644 index c439d5b9..00000000 --- a/docs/source/tutorials/cli_tutorial_3_nb.ipynb +++ /dev/null @@ -1,377 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d307e898", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "# Tutorial, chapter 3\n", - "\n", - "\n", - "- mpeg-vcm-auto-import\n", - "- run evaluations for the mpeg-vcm dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "bb280563", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_53288/1348678174.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "markdown", - "id": "b706b51d", - "metadata": {}, - "source": [ - "In this chapter you will learn:\n", - "\n", - "- to import the mpeg-vcm working-group custom datasets\n", - "- running evaluation on dataset" - ] - }, - { - "cell_type": "markdown", - "id": "726bdb7f", - "metadata": {}, - "source": [ - "The mpeg-vcm working group defines several custom datasets for evaluating the performance of your deep-learning de/compression algorithm. For more details, please see the Datasets section of the documentation.\n", - "\n", - "The tricky part is importing all that data into fiftyone. Once we have done that, we can use the CLI tools to evaluate the de/compression model with the mpeg-vcm defined pipeline, i.e.:\n", - "```\n", - "mpeg-vcm custom dataset --> compression and decompression --> Detectron2 predictor --> mAP\n", - "```\n", - "All the datasets can be download and/or registered into fiftyone with the ``compressai-vision import-custom`` command.\n", - "\n", - "For example, after running ``compressai-vision import-custom oiv6-mpeg-v1`` you will have the following datasets:\n", - "\n", - "- ``oiv6-mpeg-detection-v1``\n", - "- ``oiv6-mpeg-segmentation-v1``" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1b4f013b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "datasets currently registered into fiftyone\n", - "name, length, first sample path\n", - "flir-image-rgb-v1, 10318, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/flir/images_rgb_train/data\n", - "oiv6-mpeg-detection-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data\n", - "oiv6-mpeg-detection-v1-dummy, 1, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data\n", - "oiv6-mpeg-segmentation-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-segmentation-v1/data\n", - "open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data\n", - "quickstart, 200, /home/sampsa/fiftyone/quickstart/data\n", - "quickstart-video, 10, /home/sampsa/fiftyone/quickstart-video/data\n", - "sfu-hw-objects-v1, 2, /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill\n", - "tvd-image-detection-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_detection_v1/data\n", - "tvd-image-segmentation-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_segmentation_v1/data\n", - "tvd-object-tracking-v1, 3, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_object_tracking_dataset_and_annotations\n" - ] - } - ], - "source": [ - "!compressai-vision list" - ] - }, - { - "cell_type": "markdown", - "id": "5f1da9a8", - "metadata": {}, - "source": [ - "Now we can continue by evaluating the datasets agains a compressai model, like we did in chapter 1. Before that, let's take a closer look at the dataset ``oiv6-mpeg-detection-v1``:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "30f428b2", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "dataset info:\n", - "Name: oiv6-mpeg-detection-v1\n", - "Media type: image\n", - "Num samples: 5000\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " open_images_id: fiftyone.core.fields.StringField\n", - "\n", - "test-loading first image from /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data/0001eeaf4aed83f9.jpg\n", - "loaded image with dimensions (447, 1024, 3) ok\n" - ] - } - ], - "source": [ - "!compressai-vision show --dataset-name=oiv6-mpeg-detection-v1" - ] - }, - { - "cell_type": "markdown", - "id": "b3ccbe42", - "metadata": {}, - "source": [ - "Detection data ground truths (bounding boxes) in each sample are in the field ``detections``, so we need to use ``--gt-field=detections``. Evaluation method for mAP is the OpenImagesV6 protocol, so we use ``--eval-method=open-images``. For a quick test run we just run the evaluation with the two first images of the dataset with ``--slice=0:2`` (for an actual production run, remove it). \n", - "\n", - "To get an mAP reference value (without any sort of de/compression), we run crunch images through a Detectron2 predictor and compare to the ground truths in field ``detections``:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2975495a", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS?\n", - "instantiating Detectron2 predictor 0 : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\n", - "\n", - "Using dataset : oiv6-mpeg-detection-v1\n", - "Dataset media type : image\n", - "Dataset tmp clone : detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-21-51-787050\n", - "Keep tmp dataset? : False\n", - "Image scaling : 100\n", - "WARNING: Using slice : 0:2\n", - "Number of samples : 2\n", - "Torch device : cpu\n", - "=== Vision Model #0 ====\n", - "Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\n", - "Model was trained with : coco_2017_train\n", - "Eval. results will be saved to datafield\n", - " : detectron-predictions_v0\n", - "Evaluation protocol : open-images\n", - "Peek model classes :\n", - "['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ...\n", - "Peek dataset classes :\n", - "['airplane', 'person'] ...\n", - "** Evaluation without Encoding/Decoding **\n", - "Ground truth data field name\n", - " : detections\n", - "Progressbar : True\n", - "WARNING: progressbar enabled --> disabling normal progress print\n", - "Print progress : 0\n", - "Output file : detectron2_mpeg_vcm.json\n", - "cloning dataset oiv6-mpeg-detection-v1 to detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-21-51-787050\n", - "/home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n", - "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.)\n", - " return torch.floor_divide(self, other)\n", - " 100% |███████████████████████████████████████████████████████████████████| 2/2 Evaluating detections...\n", - " 100% |███████████| 2/2 [24.9ms elapsed, 0s remaining, 80.3 samples/s] \n", - "deleting tmp database detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-21-51-787050\n", - "\n", - "Done!\n", - "\n" - ] - } - ], - "source": [ - "!compressai-vision detectron2-eval --y --dataset-name=oiv6-mpeg-detection-v1 \\\n", - "--slice=0:2 \\\n", - "--gt-field=detections \\\n", - "--eval-method=open-images \\\n", - "--progressbar \\\n", - "--output=detectron2_mpeg_vcm.json \\\n", - "--model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml" - ] - }, - { - "cell_type": "markdown", - "id": "615f9b05", - "metadata": {}, - "source": [ - "Next we create two points on the mAP(bbp) curve for the compressai pre-trained ``bmshj2018_factorized`` model:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8ee9d531", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS?\n", - "instantiating Detectron2 predictor 0 : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\n", - "\n", - "Using dataset : oiv6-mpeg-detection-v1\n", - "Dataset media type : image\n", - "Dataset tmp clone : detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-28-02-372323\n", - "Keep tmp dataset? : False\n", - "Image scaling : 100\n", - "WARNING: Using slice : 0:2\n", - "Number of samples : 2\n", - "Torch device : cpu\n", - "=== Vision Model #0 ====\n", - "Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\n", - "Model was trained with : coco_2017_train\n", - "Eval. results will be saved to datafield\n", - " : detectron-predictions_v0\n", - "Evaluation protocol : open-images\n", - "Peek model classes :\n", - "['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ...\n", - "Peek dataset classes :\n", - "['airplane', 'person'] ...\n", - "Using compressai model : bmshj2018-factorized\n", - "Quality parameters : [1, 2]\n", - "Ground truth data field name\n", - " : detections\n", - "Progressbar : True\n", - "WARNING: progressbar enabled --> disabling normal progress print\n", - "Print progress : 0\n", - "Output file : detectron2_mpeg_vcm_qpars.json\n", - "cloning dataset oiv6-mpeg-detection-v1 to detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-28-02-372323\n", - "\n", - "QUALITY PARAMETER: 1\n", - "/home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n", - "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.)\n", - " return torch.floor_divide(self, other)\n", - " 100% |███████████████████████████████████████████████████████████████████| 2/2 Evaluating detections...\n", - " 100% |███████████| 2/2 [15.2ms elapsed, 0s remaining, 131.9 samples/s] \n", - "\n", - "QUALITY PARAMETER: 2\n", - " 100% |███████████████████████████████████████████████████████████████████| 2/2 Evaluating detections...\n", - " 100% |███████████| 2/2 [21.9ms elapsed, 0s remaining, 91.4 samples/s] \n", - "deleting tmp database detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-28-02-372323\n", - "\n", - "Done!\n", - "\n" - ] - } - ], - "source": [ - "!compressai-vision detectron2-eval --y --dataset-name=oiv6-mpeg-detection-v1 \\\n", - "--slice=0:2 \\\n", - "--gt-field=detections \\\n", - "--eval-method=open-images \\\n", - "--progressbar \\\n", - "--qpars=1,2 \\\n", - "--compressai-model-name=bmshj2018-factorized \\\n", - "--output=detectron2_mpeg_vcm_qpars.json \\\n", - "--model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml" - ] - }, - { - "cell_type": "markdown", - "id": "eb8fb95e", - "metadata": {}, - "source": [ - "Again, for an actual production run, you would remove the ``--slice`` argument. You can run all quality points (bpp values) in a single run, say by defining ``--qpars=1,2,3,4,5,6,7,8``, or if you want to parallelize, send the same command to your queue system several times, each time with a different quality parameter values, i.e. ``--qpars=1``, ``--qpars=2``, etc.\n", - "\n", - "Again, and as explained in tutorial 1 you can visualize your dataset with ``compressai-vision app`` command and compare ground-truths and detections if you use ``--keep`` flag with the ``detectron2-eval`` command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fabd163a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/cli_tutorial_3_nb.rst b/docs/source/tutorials/cli_tutorial_3_nb.rst deleted file mode 100644 index a8760f5b..00000000 --- a/docs/source/tutorials/cli_tutorial_3_nb.rst +++ /dev/null @@ -1,232 +0,0 @@ -In this chapter you will learn: - -- to import the mpeg-vcm working-group custom datasets -- running evaluation on dataset - -The mpeg-vcm working group defines several custom datasets for -evaluating the performance of your deep-learning de/compression -algorithm. For more details, please see the Datasets section of the -documentation. - -The tricky part is importing all that data into fiftyone. Once we have -done that, we can use the CLI tools to evaluate the de/compression model -with the mpeg-vcm defined pipeline, i.e.: - -:: - - mpeg-vcm custom dataset --> compression and decompression --> Detectron2 predictor --> mAP - -All the datasets can be download and/or registered into fiftyone with -the ``compressai-vision import-custom`` command. - -For example, after running -``compressai-vision import-custom oiv6-mpeg-v1`` you will have the -following datasets: - -- ``oiv6-mpeg-detection-v1`` -- ``oiv6-mpeg-segmentation-v1`` - -.. code:: ipython3 - - compressai-vision list - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - datasets currently registered into fiftyone - name, length, first sample path - flir-image-rgb-v1, 10318, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/flir/images_rgb_train/data - oiv6-mpeg-detection-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data - oiv6-mpeg-detection-v1-dummy, 1, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data - oiv6-mpeg-segmentation-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-segmentation-v1/data - open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data - quickstart, 200, /home/sampsa/fiftyone/quickstart/data - quickstart-video, 10, /home/sampsa/fiftyone/quickstart-video/data - sfu-hw-objects-v1, 2, /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill - tvd-image-detection-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_detection_v1/data - tvd-image-segmentation-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_segmentation_v1/data - tvd-object-tracking-v1, 3, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_object_tracking_dataset_and_annotations - - -Now we can continue by evaluating the datasets agains a compressai -model, like we did in chapter 1. Before that, let’s take a closer look -at the dataset ``oiv6-mpeg-detection-v1``: - -.. code:: bash - - compressai-vision show --dataset-name=oiv6-mpeg-detection-v1 - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - dataset info: - Name: oiv6-mpeg-detection-v1 - Media type: image - Num samples: 5000 - Persistent: True - Tags: [] - Sample fields: - id: fiftyone.core.fields.ObjectIdField - filepath: fiftyone.core.fields.StringField - tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) - metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata) - positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications) - negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications) - detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - open_images_id: fiftyone.core.fields.StringField - - test-loading first image from /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data/0001eeaf4aed83f9.jpg - loaded image with dimensions (447, 1024, 3) ok - - -Detection data ground truths (bounding boxes) in each sample are in the -field ``detections``, so we need to use ``--gt-field=detections``. -Evaluation method for mAP is the OpenImagesV6 protocol, so we use -``--eval-method=open-images``. For a quick test run we just run the -evaluation with the two first images of the dataset with ``--slice=0:2`` -(for an actual production run, remove it). - -To get an mAP reference value (without any sort of de/compression), we -run crunch images through a Detectron2 predictor and compare to the -ground truths in field ``detections``: - -.. code:: bash - - compressai-vision detectron2-eval --y --dataset-name=oiv6-mpeg-detection-v1 \ - --slice=0:2 \ - --gt-field=detections \ - --eval-method=open-images \ - --progressbar \ - --output=detectron2_mpeg_vcm.json \ - --model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - -.. code-block:: text - - importing fiftyone - fiftyone imported - WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS? - instantiating Detectron2 predictor 0 : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - Using dataset : oiv6-mpeg-detection-v1 - Dataset media type : image - Dataset tmp clone : detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-21-51-787050 - Keep tmp dataset? : False - Image scaling : 100 - WARNING: Using slice : 0:2 - Number of samples : 2 - Torch device : cpu - === Vision Model #0 ==== - Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - Model was trained with : coco_2017_train - Eval. results will be saved to datafield - : detectron-predictions_v0 - Evaluation protocol : open-images - Peek model classes : - ['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ... - Peek dataset classes : - ['airplane', 'person'] ... - ** Evaluation without Encoding/Decoding ** - Ground truth data field name - : detections - Progressbar : True - WARNING: progressbar enabled --> disabling normal progress print - Print progress : 0 - Output file : detectron2_mpeg_vcm.json - cloning dataset oiv6-mpeg-detection-v1 to detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-21-51-787050 - /home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. - To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.) - return torch.floor_divide(self, other) - 100% |███████████████████████████████████████████████████████████████████| 2/2 Evaluating detections... - 100% |███████████| 2/2 [24.9ms elapsed, 0s remaining, 80.3 samples/s] - deleting tmp database detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-21-51-787050 - - Done! - - - -Next we create two points on the mAP(bbp) curve for the compressai -pre-trained ``bmshj2018_factorized`` model: - -.. code:: bash - - compressai-vision detectron2-eval --y --dataset-name=oiv6-mpeg-detection-v1 \ - --slice=0:2 \ - --gt-field=detections \ - --eval-method=open-images \ - --progressbar \ - --qpars=1,2 \ - --compressai-model-name=bmshj2018-factorized \ - --output=detectron2_mpeg_vcm_qpars.json \ - --model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - -.. code-block:: text - - importing fiftyone - fiftyone imported - WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS? - instantiating Detectron2 predictor 0 : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - Using dataset : oiv6-mpeg-detection-v1 - Dataset media type : image - Dataset tmp clone : detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-28-02-372323 - Keep tmp dataset? : False - Image scaling : 100 - WARNING: Using slice : 0:2 - Number of samples : 2 - Torch device : cpu - === Vision Model #0 ==== - Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - Model was trained with : coco_2017_train - Eval. results will be saved to datafield - : detectron-predictions_v0 - Evaluation protocol : open-images - Peek model classes : - ['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ... - Peek dataset classes : - ['airplane', 'person'] ... - Using compressai model : bmshj2018-factorized - Quality parameters : [1, 2] - Ground truth data field name - : detections - Progressbar : True - WARNING: progressbar enabled --> disabling normal progress print - Print progress : 0 - Output file : detectron2_mpeg_vcm_qpars.json - cloning dataset oiv6-mpeg-detection-v1 to detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-28-02-372323 - - QUALITY PARAMETER: 1 - /home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. - To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.) - return torch.floor_divide(self, other) - 100% |███████████████████████████████████████████████████████████████████| 2/2 Evaluating detections... - 100% |███████████| 2/2 [15.2ms elapsed, 0s remaining, 131.9 samples/s] - - QUALITY PARAMETER: 2 - 100% |███████████████████████████████████████████████████████████████████| 2/2 Evaluating detections... - 100% |███████████| 2/2 [21.9ms elapsed, 0s remaining, 91.4 samples/s] - deleting tmp database detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-28-02-372323 - - Done! - - - -Again, for an actual production run, you would remove the ``--slice`` -argument. You can run all quality points (bpp values) in a single run, -say by defining ``--qpars=1,2,3,4,5,6,7,8``, or if you want to -parallelize, send the same command to your queue system several times, -each time with a different quality parameter values, -i.e. \ ``--qpars=1``, ``--qpars=2``, etc. - -Again, and as explained in tutorial 1 you can visualize your dataset -with ``compressai-vision app`` command and compare ground-truths and -detections if you use ``--keep`` flag with the ``detectron2-eval`` -command. - diff --git a/docs/source/tutorials/cli_tutorial_4.rst b/docs/source/tutorials/cli_tutorial_4.rst deleted file mode 100644 index cbc90239..00000000 --- a/docs/source/tutorials/cli_tutorial_4.rst +++ /dev/null @@ -1,7 +0,0 @@ - -4. Evaluate Custom Model ------------------------- - -.. _cli_tutorial_4: - -.. include:: cli_tutorial_4_nb.rst diff --git a/docs/source/tutorials/cli_tutorial_4_nb.ipynb b/docs/source/tutorials/cli_tutorial_4_nb.ipynb deleted file mode 100644 index 481423a5..00000000 --- a/docs/source/tutorials/cli_tutorial_4_nb.ipynb +++ /dev/null @@ -1,234 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "28098583", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "# Tutorial, chapter 4\n", - "\n", - "- run mpeg-vcm evaluation for your development model" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "bb280563", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_40153/1348678174.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "markdown", - "id": "6b6ac77a", - "metadata": {}, - "source": [ - "In this chapter you will learn:\n", - "\n", - "- to evaluate a custom model you're currently developing agains the mpeg-vcm tests" - ] - }, - { - "cell_type": "markdown", - "id": "74427a33", - "metadata": {}, - "source": [ - "As in the previous chapters, let's first check we have the dataset ``oiv6-mpeg-detection-v1`` available:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1b4f013b", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "datasets currently registered into fiftyone\n", - "name, length, first sample path\n", - "detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-22-40-319395, 2, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data\n", - "detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-24-14-478278, 2, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data\n", - "flir-image-rgb-v1, 10318, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/flir/images_rgb_train/data\n", - "oiv6-mpeg-detection-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data\n", - "oiv6-mpeg-detection-v1-dummy, 1, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data\n", - "oiv6-mpeg-segmentation-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-segmentation-v1/data\n", - "open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data\n", - "quickstart, 200, /home/sampsa/fiftyone/quickstart/data\n", - "quickstart-video, 10, /home/sampsa/fiftyone/quickstart-video/data\n", - "sfu-hw-objects-v1, 2, /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill\n", - "tvd-image-detection-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_detection_v1/data\n", - "tvd-image-segmentation-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_segmentation_v1/data\n", - "tvd-object-tracking-v1, 3, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_object_tracking_dataset_and_annotations\n" - ] - } - ], - "source": [ - "!compressai-vision list" - ] - }, - { - "cell_type": "markdown", - "id": "fb9bdb41", - "metadata": {}, - "source": [ - "In order for your custom model to work with compressai-vision, it needs to be in a separate folder. The entry-point must be called ``model.py``. We provide an example for this: please take a look at the [examples/models/bmshj2018-factorized/](https://github.com/InterDigitalInc/CompressAI-Vision/tree/main/examples/models/bmshj2018-factorized) folder, where you have the following files: \n", - "```\n", - "├── bmshj2018-factorized-prior-1-446d5c7f.pth.tar\n", - "├── bmshj2018-factorized-prior-2-87279a02.pth.tar\n", - "└── model.py\n", - "```\n", - "The ``.pth.tar`` files are the checkpoints of your model, while ``model.py`` contains the pytorch/compressai custom code of your model.\n", - "\n", - "The requirement for ``model.py`` is simple. You need to define this function:\n", - "```\n", - "getEncoderDecoder(quality=None, **kwargs)\n", - "```\n", - "which returns a subclass of an ``EncoderDecoder`` instance. ``EncoderDecoder`` objects know how to do just that: encode and decode an image, and returning the final decoded image with bits-per-pixel value.\n", - "\n", - "``quality`` should be an integer parameter (it will be used by the ``--qpars`` command-line flag). The quality parameter is mapped to a certain model checkpoint file in ``model.py``:\n", - "```\n", - "qpoint_per_file = {\n", - " 1 : \"bmshj2018-factorized-prior-1-446d5c7f.pth.tar\",\n", - " 2 : \"bmshj2018-factorized-prior-2-87279a02.pth.tar\"\n", - "}\n", - "```\n", - "i.e. if you define ``--qpars=1``, the model will use ``bmshj2018-factorized-prior-1-446d5c7f.pth.tar`` from the directory.\n", - "\n", - "As you can learn from the code, ``EncoderDecoder`` object uses an underlying (compressai-based) model to perform the actual encoding/decoding.\n", - "\n", - "The requirement for the model class (``class FactorizedPrior(CompressionModel)`` in the example model.py) are minimal: your model class should have two methods, called ``compress`` and ``decompress``. Method ``compress`` takes in an RGB image tensor and returns bitstream, while ``decompress`` takes in bitstream and returns a recovered image.\n", - "\n", - "The exact signatures are:\n", - "```\n", - "def compress(self, x): -> dict\n", - " # where x is a torch RGB image tensor (batch, 3, H, W) \n", - " ...\n", - " return {\"strings\": STRINGS, \"shape\": SHAPE}\n", - " # STRINGS: a list where STRINGS[0][0] is a bytes object (the encoded bitstream) and SHAPE is some shape information used by your model\n", - " \n", - " \n", - "def decompress(self, STRINGS, SHAPE): -> dict\n", - " # where STRINGS and SHAPE are the objects returned by compress\n", - " ...\n", - " return {\"x_hat\": x_hat}\n", - " # where x_hat is a torch RGB image tensor (batch, 3, H, W)\n", - "```\n", - "This signature/interface is used by the compressai library models. When you have these same methods in *your* custom model, you can use the ``CompressAIEncoderDecoder`` straight out of the box with it.\n", - "\n", - "You can also implement your own ``EncoderDecoder`` class (say, for comparing results to classical codecs like jpeg, etc.). For this, please refer to the example jpeg ``EncoderDecoder`` class in the library tutorial." - ] - }, - { - "cell_type": "markdown", - "id": "916f80b0", - "metadata": { - "tags": [ - "bash" - ] - }, - "source": [ - "So, take a copy of the ``examples/models/bmshj2018-factorized/`` folder into your disk and run:\n", - "```\n", - "compressai-vision detectron2-eval --y \\\n", - "--dataset-name oiv6-mpeg-detection-v1 \\\n", - "--slice=0:2 \\\n", - "--scale=100 \\\n", - "--gt-field=detections \\\n", - "--eval-method=open-images \\\n", - "--progressbar \\\n", - "--compression-model-path /path/to/examples/models/bmshj2018-factorized/ \\\n", - "--qpars=1,2 \\\n", - "--output=detectron2_bmshj2018-factorized.json \\\n", - "--model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\n", - "```\n", - "This will evaluate your custom model with Detectron2." - ] - }, - { - "cell_type": "markdown", - "id": "e6f530b8", - "metadata": {}, - "source": [ - "Again, for an actual production run, you would remove the ``--slice`` argument." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f5b0a52", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/cli_tutorial_4_nb.rst b/docs/source/tutorials/cli_tutorial_4_nb.rst deleted file mode 100644 index 4ee65c2d..00000000 --- a/docs/source/tutorials/cli_tutorial_4_nb.rst +++ /dev/null @@ -1,135 +0,0 @@ -In this chapter you will learn: - -- to evaluate a custom model you’re currently developing agains the - mpeg-vcm tests - -As in the previous chapters, let’s first check we have the dataset -``oiv6-mpeg-detection-v1`` available: - -.. code:: bash - - compressai-vision list - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - datasets currently registered into fiftyone - name, length, first sample path - detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-22-40-319395, 2, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data - detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-24-14-478278, 2, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data - flir-image-rgb-v1, 10318, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/flir/images_rgb_train/data - oiv6-mpeg-detection-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data - oiv6-mpeg-detection-v1-dummy, 1, /home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data - oiv6-mpeg-segmentation-v1, 5000, /home/sampsa/fiftyone/oiv6-mpeg-segmentation-v1/data - open-images-v6-validation, 8189, /home/sampsa/fiftyone/open-images-v6/validation/data - quickstart, 200, /home/sampsa/fiftyone/quickstart/data - quickstart-video, 10, /home/sampsa/fiftyone/quickstart-video/data - sfu-hw-objects-v1, 2, /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill - tvd-image-detection-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_detection_v1/data - tvd-image-segmentation-v1, 167, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_images_segmentation_v1/data - tvd-object-tracking-v1, 3, /media/sampsa/4d0dff98-8e61-4a0b-a97e-ceb6bc7ccb4b/datasets/tvd/TVD_object_tracking_dataset_and_annotations - - -In order for your custom model to work with compressai-vision, it needs -to be in a separate folder. The entry-point must be called ``model.py``. -We provide an example for this: please take a look at the -`examples/models/bmshj2018-factorized/ `__ -folder, where you have the following files: - -:: - - ├── bmshj2018-factorized-prior-1-446d5c7f.pth.tar - ├── bmshj2018-factorized-prior-2-87279a02.pth.tar - └── model.py - -The ``.pth.tar`` files are the checkpoints of your model, while -``model.py`` contains the pytorch/compressai custom code of your model. - -The requirement for ``model.py`` is simple. You need to define this -function: - -:: - - getEncoderDecoder(quality=None, **kwargs) - -which returns a subclass of an ``EncoderDecoder`` instance. -``EncoderDecoder`` objects know how to do just that: encode and decode -an image, and returning the final decoded image with bits-per-pixel -value. - -``quality`` should be an integer parameter (it will be used by the -``--qpars`` command-line flag). The quality parameter is mapped to a -certain model checkpoint file in ``model.py``: - -:: - - qpoint_per_file = { - 1 : "bmshj2018-factorized-prior-1-446d5c7f.pth.tar", - 2 : "bmshj2018-factorized-prior-2-87279a02.pth.tar" - } - -i.e. if you define ``--qpars=1``, the model will use -``bmshj2018-factorized-prior-1-446d5c7f.pth.tar`` from the directory. - -As you can learn from the code, ``EncoderDecoder`` object uses an -underlying (compressai-based) model to perform the actual -encoding/decoding. - -The requirement for the model class -(``class FactorizedPrior(CompressionModel)`` in the example model.py) -are minimal: your model class should have two methods, called -``compress`` and ``decompress``. Method ``compress`` takes in an RGB -image tensor and returns bitstream, while ``decompress`` takes in -bitstream and returns a recovered image. - -The exact signatures are: - -:: - - def compress(self, x): -> dict - # where x is a torch RGB image tensor (batch, 3, H, W) - ... - return {"strings": STRINGS, "shape": SHAPE} - # STRINGS: a list where STRINGS[0][0] is a bytes object (the encoded bitstream) and SHAPE is some shape information used by your model - - - def decompress(self, STRINGS, SHAPE): -> dict - # where STRINGS and SHAPE are the objects returned by compress - ... - return {"x_hat": x_hat} - # where x_hat is a torch RGB image tensor (batch, 3, H, W) - -This signature/interface is used by the compressai library models. When -you have these same methods in *your* custom model, you can use the -``CompressAIEncoderDecoder`` straight out of the box with it. - -You can also implement your own ``EncoderDecoder`` class (say, for -comparing results to classical codecs like jpeg, etc.). For this, please -refer to the example jpeg ``EncoderDecoder`` class in the library -tutorial. - -So, take a copy of the ``examples/models/bmshj2018-factorized/`` folder -into your disk and run: - -:: - - compressai-vision detectron2-eval --y \ - --dataset-name oiv6-mpeg-detection-v1 \ - --slice=0:2 \ - --scale=100 \ - --gt-field=detections \ - --eval-method=open-images \ - --progressbar \ - --compression-model-path /path/to/examples/models/bmshj2018-factorized/ \ - --qpars=1,2 \ - --output=detectron2_bmshj2018-factorized.json \ - --model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - -This will evaluate your custom model with Detectron2. - -Again, for an actual production run, you would remove the ``--slice`` -argument. - diff --git a/docs/source/tutorials/cli_tutorial_5.rst b/docs/source/tutorials/cli_tutorial_5.rst deleted file mode 100644 index f3664567..00000000 --- a/docs/source/tutorials/cli_tutorial_5.rst +++ /dev/null @@ -1,7 +0,0 @@ - -5. Plotting ------------ - -.. _cli_tutorial_5: - -.. include:: cli_tutorial_5_nb.rst diff --git a/docs/source/tutorials/cli_tutorial_5_nb.ipynb b/docs/source/tutorials/cli_tutorial_5_nb.ipynb deleted file mode 100644 index e03157e2..00000000 --- a/docs/source/tutorials/cli_tutorial_5_nb.ipynb +++ /dev/null @@ -1,309 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "af70aa98", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "# Tutorial, chapter 5\n", - "\n", - "- Plot results against vtm baseline" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "bb280563", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_53522/416472496.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "# import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))\n", - "from PIL import Image\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "2a4d61f6", - "metadata": {}, - "source": [ - "In this chapter you will learn:\n", - "\n", - "- to create mAP=mAP(bpp) plots from compressai-vision result files" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6cb5e87c", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "path_to_examples=\"/home/sampsa/silo/interdigital/CompressAI-Vision/examples\"" - ] - }, - { - "cell_type": "markdown", - "id": "585ca8f1", - "metadata": {}, - "source": [ - "Some example results, produced with the ``detectron2-eval`` command for compressai zoo's \"bmshj2018-factorized\" model have been archived into [examples/models/bmshj2018-factorized/](https://github.com/InterDigitalInc/CompressAI-Vision/tree/main/examples/data/interdigital/bmshj2018_factorized), where we have:\n", - "```\n", - "1.json\n", - "2.json\n", - "3.json\n", - "4.json\n", - "5.json\n", - "6.json\n", - "7.json\n", - "8.json\n", - "```\n", - "These are results from a parallel run, where ``compressai-vision detectron2-eval`` was run in parallel for each quality parameter.\n", - "\n", - "Now we can use ``compressai-vision plot`` to plot the results:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "4cdcf5ad", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/2.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/1.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/8.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/7.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/4.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/6.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/5.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/3.json\n", - "--> producing out.png to current path\n", - "have a nice day!\n" - ] - } - ], - "source": [ - "!compressai-vision plot --dirs={path_to_examples}/data/interdigital/bmshj2018_factorized \\\n", - "--symbols=x--r --names=bmshj2018_factorized --eval=0.792,-k" - ] - }, - { - "cell_type": "markdown", - "id": "fc800821", - "metadata": {}, - "source": [ - "Let's see how that looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "6f9d71a6", - "metadata": { - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "im=Image.open(\"out.png\")\n", - "fig = plt.figure()\n", - "fig.set_size_inches((10,10))\n", - "plt.imshow(im)\n", - "ax=plt.gca()\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "id": "61f29be2", - "metadata": {}, - "source": [ - "We can add several plots to the same image. Let's add VTM baseline accuracy from ``examples/data/interdigital/vtm_scale_100``:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e165bad3", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/2.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/1.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/8.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/7.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/4.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/6.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/5.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/3.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_37.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_32.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_47.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_27.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_42.json\n", - "reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_22.json\n", - "--> producing out.png to current path\n", - "have a nice day!\n" - ] - } - ], - "source": [ - "!compressai-vision plot --dirs={path_to_examples}/data/interdigital/bmshj2018_factorized,{path_to_examples}/data/interdigital/vtm_scale_100 \\\n", - "--symbols=x--r,*--k --names=bmshj2018_factorized,vtm --eval=0.792,-b" - ] - }, - { - "cell_type": "markdown", - "id": "ae1cfa65", - "metadata": {}, - "source": [ - "That looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "6d1f3056", - "metadata": { - "tags": [ - "remove_input" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "im=Image.open(\"out.png\")\n", - "fig = plt.figure()\n", - "fig.set_size_inches((10,10))\n", - "plt.imshow(im)\n", - "ax=plt.gca()\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "id": "b4622e7f", - "metadata": {}, - "source": [ - "The ``plot`` command can also be used to produce csv files from the results json files. Please refer to ``compressai-vision plot -h``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "70c3be52", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "vscode": { - "interpreter": { - "hash": "da081151ac47d88d60d8fcf40e771de95b7e82adeca033efb183c8fc7bdd53e4" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/cli_tutorial_5_nb.rst b/docs/source/tutorials/cli_tutorial_5_nb.rst deleted file mode 100644 index 9f1da9f3..00000000 --- a/docs/source/tutorials/cli_tutorial_5_nb.rst +++ /dev/null @@ -1,92 +0,0 @@ -In this chapter you will learn: - -- to create mAP=mAP(bpp) plots from compressai-vision result files - -Some example results, produced with the ``detectron2-eval`` command for -compressai zoo’s “bmshj2018-factorized” model have been archived into -`examples/models/bmshj2018-factorized/ `__, -where we have: - -:: - - 1.json - 2.json - 3.json - 4.json - 5.json - 6.json - 7.json - 8.json - -These are results from a parallel run, where -``compressai-vision detectron2-eval`` was run in parallel for each -quality parameter. - -Now we can use ``compressai-vision plot`` to plot the results: - -.. code:: bash - - compressai-vision plot --dirs={path_to_examples}/data/interdigital/bmshj2018_factorized \ - --symbols=x--r --names=bmshj2018_factorized --eval=0.792,-k - - -.. code-block:: text - - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/2.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/1.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/8.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/7.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/4.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/6.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/5.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/3.json - --> producing out.png to current path - have a nice day! - - -Let’s see how that looks like: - - - -.. image:: cli_tutorial_5_nb_files/cli_tutorial_5_nb_4_0.png - - -We can add several plots to the same image. Let’s add VTM baseline -accuracy from ``examples/data/interdigital/vtm_scale_100``: - -.. code:: bash - - compressai-vision plot --dirs={path_to_examples}/data/interdigital/bmshj2018_factorized,{path_to_examples}/data/interdigital/vtm_scale_100 \ - --symbols=x--r,*--k --names=bmshj2018_factorized,vtm --eval=0.792,-b - - -.. code-block:: text - - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/2.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/1.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/8.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/7.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/4.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/6.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/5.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/bmshj2018_factorized/3.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_37.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_32.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_47.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_27.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_42.json - reading /home/sampsa/silo/interdigital/CompressAI-Vision/examples/data/interdigital/vtm_scale_100/vtm_22.json - --> producing out.png to current path - have a nice day! - - -That looks like: - - - -.. image:: cli_tutorial_5_nb_files/cli_tutorial_5_nb_8_0.png - - -The ``plot`` command can also be used to produce csv files from the -results json files. Please refer to ``compressai-vision plot -h``. - diff --git a/docs/source/tutorials/cli_tutorial_5_nb_files/cli_tutorial_5_nb_4_0.png b/docs/source/tutorials/cli_tutorial_5_nb_files/cli_tutorial_5_nb_4_0.png deleted file mode 100644 index 33ff6fb3..00000000 Binary files a/docs/source/tutorials/cli_tutorial_5_nb_files/cli_tutorial_5_nb_4_0.png and /dev/null differ diff --git a/docs/source/tutorials/cli_tutorial_5_nb_files/cli_tutorial_5_nb_8_0.png b/docs/source/tutorials/cli_tutorial_5_nb_files/cli_tutorial_5_nb_8_0.png deleted file mode 100644 index 8410b5ba..00000000 Binary files a/docs/source/tutorials/cli_tutorial_5_nb_files/cli_tutorial_5_nb_8_0.png and /dev/null differ diff --git a/docs/source/tutorials/cli_tutorial_6.rst b/docs/source/tutorials/cli_tutorial_6.rst deleted file mode 100644 index 6148e433..00000000 --- a/docs/source/tutorials/cli_tutorial_6.rst +++ /dev/null @@ -1,7 +0,0 @@ - -6. VTM benchmark generation ---------------------------- - -.. _cli_tutorial_6: - -.. include:: cli_tutorial_6_nb.rst diff --git a/docs/source/tutorials/cli_tutorial_6_nb.ipynb b/docs/source/tutorials/cli_tutorial_6_nb.ipynb deleted file mode 100644 index 5086bdff..00000000 --- a/docs/source/tutorials/cli_tutorial_6_nb.ipynb +++ /dev/null @@ -1,421 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "af70aa98", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "# Tutorial, chapter 6\n", - "\n", - "- Generate vtm baseline results" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "bb280563", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_65403/416472496.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "# import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))\n", - "from PIL import Image\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "2a4d61f6", - "metadata": {}, - "source": [ - "In this chapter you will learn:\n", - "\n", - "- to generate and cache VTM-encoded bitstream\n", - "- to create VTM baseline results" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6cb5e87c", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "path_to_examples=\"/home/sampsa/silo/interdigital/CompressAI-Vision/examples\"\n", - "path_to_vtm_software=\"/home/sampsa/silo/interdigital/VVCSoftware_VTM\"" - ] - }, - { - "cell_type": "markdown", - "id": "585ca8f1", - "metadata": {}, - "source": [ - "The subcommand ``vtm`` encodes images from your dataset with the VTM program. VTM features state-of-the-art classical video encoding techniques and it is used as a benchmark against your deep-learning encoder's efficiency. You need to download and compile the VTM software yourself according to the instructions in the main documentation.\n", - "\n", - "Why do we need a separate subcommand ``vtm`` for VTM encoding / bitstream generation, instead of just using ``detectron2-eval`` on the fly? i.e. for doing:\n", - "```\n", - "Image --> VTM encoding --> VTM decoding --> Detectron2\n", - "```\n", - "That is because encoding performed by VTM is *very* CPU intensive task, so it is something you don't really want to repeat (encoding 5000 sample images, depending on your qpars value, can take several days..!), so we use the ``vtm`` subcommand to manage, encode and cache the VTM produced bitstreams on disk.\n", - "\n", - "Let's generate some encoded bitstreams." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "738e52ff", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "!rm -rf /tmp/bitstreams" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "17084c17", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "Reading vtm config from: /home/sampsa/silo/interdigital/VVCSoftware_VTM/cfg/encoder_intra_vtm.cfg\n", - "WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS?\n", - "\n", - "VTM bitstream generation\n", - "WARNING: VTM USES CACHE IN /tmp/bitstreams\n", - "Target dir : /tmp/bitstreams\n", - "Quality points/subdirs : [47]\n", - "Using dataset : oiv6-mpeg-detection-v1\n", - "Image Scaling : 100\n", - "Using slice : 0:2\n", - "Number of samples : 2\n", - "Progressbar : False\n", - "Output file : vtm_out.json\n", - "Print progress : 1\n", - "\n", - "QUALITY PARAMETER 47\n", - "VTMEncoderDecoder - INFO - creating /tmp/bitstreams\n", - "VTMEncoderDecoder - WARNING - creating bitstream /tmp/bitstreams/100/47/bin_0001eeaf4aed83f9 with VTMEncode from scratch\n", - "sample: 1 / 2 tag: 0001eeaf4aed83f9\n", - "VTMEncoderDecoder - WARNING - creating bitstream /tmp/bitstreams/100/47/bin_000a1249af2bc5f0 with VTMEncode from scratch\n", - "sample: 2 / 2 tag: 000a1249af2bc5f0\n", - "\n", - "Done!\n", - "\n" - ] - } - ], - "source": [ - "!compressai-vision vtm --y --dataset-name=oiv6-mpeg-detection-v1 \\\n", - "--slice=0:2 \\\n", - "--scale=100 \\\n", - "--progress=1 \\\n", - "--qpars=47 \\\n", - "--vtm_cache=/tmp/bitstreams \\\n", - "--vtm_dir={path_to_vtm_software}/bin \\\n", - "--vtm_cfg={path_to_vtm_software}/cfg/encoder_intra_vtm.cfg \\\n", - "--output=vtm_out.json" - ] - }, - { - "cell_type": "markdown", - "id": "b4622e7f", - "metadata": {}, - "source": [ - "As you can see, bitstreams we're generated and cached into ``/tmp/bitstreams/SCALE/QP``. Let's see what happens if we run the exact same command again: " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "70c3be52", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "Reading vtm config from: /home/sampsa/silo/interdigital/VVCSoftware_VTM/cfg/encoder_intra_vtm.cfg\n", - "WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS?\n", - "\n", - "VTM bitstream generation\n", - "WARNING: VTM USES CACHE IN /tmp/bitstreams\n", - "Target dir : /tmp/bitstreams\n", - "Quality points/subdirs : [47]\n", - "Using dataset : oiv6-mpeg-detection-v1\n", - "Image Scaling : 100\n", - "Using slice : 0:2\n", - "Number of samples : 2\n", - "Progressbar : False\n", - "Output file : vtm_out.json\n", - "Print progress : 1\n", - "\n", - "QUALITY PARAMETER 47\n", - "VTMEncoderDecoder - WARNING - folder /tmp/bitstreams/100/47 exists already\n", - "sample: 1 / 2 tag: 0001eeaf4aed83f9\n", - "sample: 2 / 2 tag: 000a1249af2bc5f0\n", - "\n", - "Done!\n", - "\n" - ] - } - ], - "source": [ - "!compressai-vision vtm --y --dataset-name=oiv6-mpeg-detection-v1 \\\n", - "--slice=0:2 \\\n", - "--scale=100 \\\n", - "--progress=1 \\\n", - "--qpars=47 \\\n", - "--vtm_cache=/tmp/bitstreams \\\n", - "--vtm_dir={path_to_vtm_software}/bin \\\n", - "--vtm_cfg={path_to_vtm_software}/cfg/encoder_intra_vtm.cfg \\\n", - "--output=vtm_out.json" - ] - }, - { - "cell_type": "markdown", - "id": "cd7125f0", - "metadata": {}, - "source": [ - "Instead of generating the bitstreams, the program found them cached on the disk and just verified them.\n", - "\n", - "Let's fool around and corrupt one of the bitstreams:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e1fde11a", - "metadata": {}, - "outputs": [], - "source": [ - "!echo \" \" > /tmp/bitstreams/100/47/bin_000a1249af2bc5f0" - ] - }, - { - "cell_type": "markdown", - "id": "8453415e", - "metadata": {}, - "source": [ - "And run the command again:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "acf85f8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "Reading vtm config from: /home/sampsa/silo/interdigital/VVCSoftware_VTM/cfg/encoder_intra_vtm.cfg\n", - "WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS?\n", - "\n", - "VTM bitstream generation\n", - "WARNING: VTM USES CACHE IN /tmp/bitstreams\n", - "Target dir : /tmp/bitstreams\n", - "Quality points/subdirs : [47]\n", - "Using dataset : oiv6-mpeg-detection-v1\n", - "Image Scaling : 100\n", - "Using slice : 0:2\n", - "Number of samples : 2\n", - "Progressbar : False\n", - "Output file : vtm_out.json\n", - "Print progress : 1\n", - "\n", - "QUALITY PARAMETER 47\n", - "VTMEncoderDecoder - WARNING - folder /tmp/bitstreams/100/47 exists already\n", - "sample: 1 / 2 tag: 0001eeaf4aed83f9\n", - "VTMEncoderDecoder - CRITICAL - VTM encode failed with Warning: Attempt to decode an empty NAL unit\n", - "\n", - "VTMEncoderDecoder - CRITICAL - VTMDecode failed: will skip image 000a1249af2bc5f0 & remove the bitstream file\n", - "ERROR: Corrupt data for image id=6374fc19f8beb066665b85be, tag=000a1249af2bc5f0, path=/home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data/000a1249af2bc5f0.jpg\n", - "ERROR: Trying to regenerate\n", - "VTMEncoderDecoder - WARNING - creating bitstream /tmp/bitstreams/100/47/bin_000a1249af2bc5f0 with VTMEncode from scratch\n", - "sample: 2 / 2 tag: 000a1249af2bc5f0\n", - "\n", - "Done!\n", - "\n" - ] - } - ], - "source": [ - "!compressai-vision vtm --y --dataset-name=oiv6-mpeg-detection-v1 \\\n", - "--slice=0:2 \\\n", - "--scale=100 \\\n", - "--progress=1 \\\n", - "--qpars=47 \\\n", - "--vtm_cache=/tmp/bitstreams \\\n", - "--vtm_dir={path_to_vtm_software}/bin \\\n", - "--vtm_cfg={path_to_vtm_software}/cfg/encoder_intra_vtm.cfg \\\n", - "--output=vtm_out.json" - ] - }, - { - "cell_type": "markdown", - "id": "22c55cbf", - "metadata": {}, - "source": [ - "You can run the ``vtm`` command parallelized over *both* quality parameters *and* dataset slices in order to speed things up. In the case of crashes / data corruption, you can just send the same scripts into your queue system over and over again if necessary.\n", - "\n", - "Finally, you can run ``detectron2-eval`` for the VTM case like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "042fbf8c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS?\n", - "Reading vtm config from: /home/sampsa/silo/interdigital/VVCSoftware_VTM/cfg/encoder_intra_vtm.cfg\n", - "instantiating Detectron2 predictor 0 : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\n", - "\n", - "Using dataset : oiv6-mpeg-detection-v1\n", - "Dataset media type : image\n", - "Dataset tmp clone : detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-47-58-858646\n", - "Keep tmp dataset? : False\n", - "Image scaling : 100\n", - "WARNING: Using slice : 0:2\n", - "Number of samples : 2\n", - "Torch device : cpu\n", - "=== Vision Model #0 ====\n", - "Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\n", - "Model was trained with : coco_2017_train\n", - "Eval. results will be saved to datafield\n", - " : detectron-predictions_v0\n", - "Evaluation protocol : open-images\n", - "Peek model classes :\n", - "['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ...\n", - "Peek dataset classes :\n", - "['airplane', 'person'] ...\n", - "Using VTM \n", - "WARNING: VTM USES CACHE IN /tmp/bitstreams\n", - "Quality parameters : [47]\n", - "Ground truth data field name\n", - " : detections\n", - "Progressbar : False\n", - "Print progress : 1\n", - "Output file : detectron2_vtm.json\n", - "cloning dataset oiv6-mpeg-detection-v1 to detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-47-58-858646\n", - "VTMEncoderDecoder - WARNING - folder /tmp/bitstreams/100/47 exists already\n", - "/home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n", - "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.)\n", - " return torch.floor_divide(self, other)\n", - "sample: 1 / 2\n", - "sample: 2 / 2\n", - "Evaluating detections...\n", - "deleting tmp database detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-47-58-858646\n", - "\n", - "Done!\n", - "\n" - ] - } - ], - "source": [ - "!compressai-vision detectron2-eval --y --dataset-name=oiv6-mpeg-detection-v1 \\\n", - "--slice=0:2 \\\n", - "--scale=100 \\\n", - "--progress=1 \\\n", - "--qpars=47 \\\n", - "--vtm \\\n", - "--vtm_cache=/tmp/bitstreams \\\n", - "--vtm_dir={path_to_vtm_software}/bin \\\n", - "--vtm_cfg={path_to_vtm_software}/cfg/encoder_intra_vtm.cfg \\\n", - "--output=detectron2_vtm.json \\\n", - "--model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b5f3d8d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/cli_tutorial_6_nb.rst b/docs/source/tutorials/cli_tutorial_6_nb.rst deleted file mode 100644 index a802c061..00000000 --- a/docs/source/tutorials/cli_tutorial_6_nb.rst +++ /dev/null @@ -1,243 +0,0 @@ -In this chapter you will learn: - -- to generate and cache VTM-encoded bitstream -- to create VTM baseline results - -The subcommand ``vtm`` encodes images from your dataset with the VTM -program. VTM features state-of-the-art classical video encoding -techniques and it is used as a benchmark against your deep-learning -encoder’s efficiency. You need to download and compile the VTM software -yourself according to the instructions in the main documentation. - -Why do we need a separate subcommand ``vtm`` for VTM encoding / -bitstream generation, instead of just using ``detectron2-eval`` on the -fly? i.e. for doing: - -:: - - Image --> VTM encoding --> VTM decoding --> Detectron2 - -That is because encoding performed by VTM is *very* CPU intensive task, -so it is something you don’t really want to repeat (encoding 5000 sample -images, depending on your qpars value, can take several days..!), so we -use the ``vtm`` subcommand to manage, encode and cache the VTM produced -bitstreams on disk. - -Let’s generate some encoded bitstreams. - -.. code:: ipython3 - - compressai-vision vtm --y --dataset-name=oiv6-mpeg-detection-v1 \ - --slice=0:2 \ - --scale=100 \ - --progress=1 \ - --qpars=47 \ - --vtm_cache=/tmp/bitstreams \ - --vtm_dir={path_to_vtm_software}/bin \ - --vtm_cfg={path_to_vtm_software}/cfg/encoder_intra_vtm.cfg \ - --output=vtm_out.json - - -.. code-block:: text - - importing fiftyone - fiftyone imported - Reading vtm config from: /home/sampsa/silo/interdigital/VVCSoftware_VTM/cfg/encoder_intra_vtm.cfg - WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS? - - VTM bitstream generation - WARNING: VTM USES CACHE IN /tmp/bitstreams - Target dir : /tmp/bitstreams - Quality points/subdirs : [47] - Using dataset : oiv6-mpeg-detection-v1 - Image Scaling : 100 - Using slice : 0:2 - Number of samples : 2 - Progressbar : False - Output file : vtm_out.json - Print progress : 1 - - QUALITY PARAMETER 47 - VTMEncoderDecoder - INFO - creating /tmp/bitstreams - VTMEncoderDecoder - WARNING - creating bitstream /tmp/bitstreams/100/47/bin_0001eeaf4aed83f9 with VTMEncode from scratch - sample: 1 / 2 tag: 0001eeaf4aed83f9 - VTMEncoderDecoder - WARNING - creating bitstream /tmp/bitstreams/100/47/bin_000a1249af2bc5f0 with VTMEncode from scratch - sample: 2 / 2 tag: 000a1249af2bc5f0 - - Done! - - - -As you can see, bitstreams we’re generated and cached into -``/tmp/bitstreams/SCALE/QP``. Let’s see what happens if we run the exact -same command again: - -.. code:: ipython3 - - compressai-vision vtm --y --dataset-name=oiv6-mpeg-detection-v1 \ - --slice=0:2 \ - --scale=100 \ - --progress=1 \ - --qpars=47 \ - --vtm_cache=/tmp/bitstreams \ - --vtm_dir={path_to_vtm_software}/bin \ - --vtm_cfg={path_to_vtm_software}/cfg/encoder_intra_vtm.cfg \ - --output=vtm_out.json - - -.. code-block:: text - - importing fiftyone - fiftyone imported - Reading vtm config from: /home/sampsa/silo/interdigital/VVCSoftware_VTM/cfg/encoder_intra_vtm.cfg - WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS? - - VTM bitstream generation - WARNING: VTM USES CACHE IN /tmp/bitstreams - Target dir : /tmp/bitstreams - Quality points/subdirs : [47] - Using dataset : oiv6-mpeg-detection-v1 - Image Scaling : 100 - Using slice : 0:2 - Number of samples : 2 - Progressbar : False - Output file : vtm_out.json - Print progress : 1 - - QUALITY PARAMETER 47 - VTMEncoderDecoder - WARNING - folder /tmp/bitstreams/100/47 exists already - sample: 1 / 2 tag: 0001eeaf4aed83f9 - sample: 2 / 2 tag: 000a1249af2bc5f0 - - Done! - - - -Instead of generating the bitstreams, the program found them cached on -the disk and just verified them. - -Let’s fool around and corrupt one of the bitstreams: - -.. code:: ipython3 - - echo " " > /tmp/bitstreams/100/47/bin_000a1249af2bc5f0 - -And run the command again: - -.. code:: ipython3 - - compressai-vision vtm --y --dataset-name=oiv6-mpeg-detection-v1 \ - --slice=0:2 \ - --scale=100 \ - --progress=1 \ - --qpars=47 \ - --vtm_cache=/tmp/bitstreams \ - --vtm_dir={path_to_vtm_software}/bin \ - --vtm_cfg={path_to_vtm_software}/cfg/encoder_intra_vtm.cfg \ - --output=vtm_out.json - - -.. code-block:: text - - importing fiftyone - fiftyone imported - Reading vtm config from: /home/sampsa/silo/interdigital/VVCSoftware_VTM/cfg/encoder_intra_vtm.cfg - WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS? - - VTM bitstream generation - WARNING: VTM USES CACHE IN /tmp/bitstreams - Target dir : /tmp/bitstreams - Quality points/subdirs : [47] - Using dataset : oiv6-mpeg-detection-v1 - Image Scaling : 100 - Using slice : 0:2 - Number of samples : 2 - Progressbar : False - Output file : vtm_out.json - Print progress : 1 - - QUALITY PARAMETER 47 - VTMEncoderDecoder - WARNING - folder /tmp/bitstreams/100/47 exists already - sample: 1 / 2 tag: 0001eeaf4aed83f9 - VTMEncoderDecoder - CRITICAL - VTM encode failed with Warning: Attempt to decode an empty NAL unit - - VTMEncoderDecoder - CRITICAL - VTMDecode failed: will skip image 000a1249af2bc5f0 & remove the bitstream file - ERROR: Corrupt data for image id=6374fc19f8beb066665b85be, tag=000a1249af2bc5f0, path=/home/sampsa/fiftyone/oiv6-mpeg-detection-v1/data/000a1249af2bc5f0.jpg - ERROR: Trying to regenerate - VTMEncoderDecoder - WARNING - creating bitstream /tmp/bitstreams/100/47/bin_000a1249af2bc5f0 with VTMEncode from scratch - sample: 2 / 2 tag: 000a1249af2bc5f0 - - Done! - - - -You can run the ``vtm`` command parallelized over *both* quality -parameters *and* dataset slices in order to speed things up. In the case -of crashes / data corruption, you can just send the same scripts into -your queue system over and over again if necessary. - -Finally, you can run ``detectron2-eval`` for the VTM case like this: - -.. code:: ipython3 - - compressai-vision detectron2-eval --y --dataset-name=oiv6-mpeg-detection-v1 \ - --slice=0:2 \ - --scale=100 \ - --progress=1 \ - --qpars=47 \ - --vtm \ - --vtm_cache=/tmp/bitstreams \ - --vtm_dir={path_to_vtm_software}/bin \ - --vtm_cfg={path_to_vtm_software}/cfg/encoder_intra_vtm.cfg \ - --output=detectron2_vtm.json \ - --model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - -.. code-block:: text - - importing fiftyone - fiftyone imported - WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS? - Reading vtm config from: /home/sampsa/silo/interdigital/VVCSoftware_VTM/cfg/encoder_intra_vtm.cfg - instantiating Detectron2 predictor 0 : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - Using dataset : oiv6-mpeg-detection-v1 - Dataset media type : image - Dataset tmp clone : detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-47-58-858646 - Keep tmp dataset? : False - Image scaling : 100 - WARNING: Using slice : 0:2 - Number of samples : 2 - Torch device : cpu - === Vision Model #0 ==== - Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - Model was trained with : coco_2017_train - Eval. results will be saved to datafield - : detectron-predictions_v0 - Evaluation protocol : open-images - Peek model classes : - ['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ... - Peek dataset classes : - ['airplane', 'person'] ... - Using VTM - WARNING: VTM USES CACHE IN /tmp/bitstreams - Quality parameters : [47] - Ground truth data field name - : detections - Progressbar : False - Print progress : 1 - Output file : detectron2_vtm.json - cloning dataset oiv6-mpeg-detection-v1 to detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-47-58-858646 - VTMEncoderDecoder - WARNING - folder /tmp/bitstreams/100/47 exists already - /home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. - To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.) - return torch.floor_divide(self, other) - sample: 1 / 2 - sample: 2 / 2 - Evaluating detections... - deleting tmp database detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-47-58-858646 - - Done! - - - diff --git a/docs/source/tutorials/cli_tutorial_7.rst b/docs/source/tutorials/cli_tutorial_7.rst deleted file mode 100644 index bec4eed0..00000000 --- a/docs/source/tutorials/cli_tutorial_7.rst +++ /dev/null @@ -1,7 +0,0 @@ - -7. Importing and Using Video ----------------------------- - -.. _cli_tutorial_7: - -.. include:: cli_tutorial_7_nb.rst diff --git a/docs/source/tutorials/cli_tutorial_7_nb.ipynb b/docs/source/tutorials/cli_tutorial_7_nb.ipynb deleted file mode 100644 index 5358a9ad..00000000 --- a/docs/source/tutorials/cli_tutorial_7_nb.ipynb +++ /dev/null @@ -1,909 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "77d59ce9", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "# Tutorial, chapter 7\n", - "\n", - "In this tutorial you will learn how to\n", - "\n", - "- Convert and import the ``sfu-hw-objects-v1`` custom video dataset\n", - "- Visualize frames from the video dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b37649d7", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_18457/1348678174.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "markdown", - "id": "ef558334", - "metadata": {}, - "source": [ - "In this tutorial you will learn how to:\n", - "\n", - "- Download and register video datasets\n", - "- Convert and import the ``sfu-hw-objects-v1`` raw custom video data format\n", - "- Play around with video datasets, visualize frames and detection results\n", - "- Evaluate a video dataset\n", - "\n", - "In chapter 2 of this tutorial you learned how to download and register datasets to fiftyone with the ``compressai-vision register`` command.\n", - "\n", - "Exactly the same command works for video datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "21b7ce45", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "\n", - "WARNING: downloading ALL images. You might want to use the --lists option to download only certain images\n", - "Using list files: None\n", - "Number of images: ?\n", - "Database name : quickstart-video\n", - "Subname/split : None\n", - "Target dir : None\n", - "\n", - "Dataset already downloaded\n", - "Loading existing dataset 'quickstart-video'. To reload from disk, either delete the existing dataset or provide a custom `dataset_name` to use\n" - ] - } - ], - "source": [ - "!compressai-vision download --dataset-name=quickstart-video --y" - ] - }, - { - "cell_type": "markdown", - "id": "32f51728", - "metadata": {}, - "source": [ - "If you have your video dataset arranged in one of the standard [video data formats supported by fiftyone](https://voxel51.com/docs/fiftyone/api/fiftyone.types.dataset_types.html), you're good to go.\n", - "\n", - "Manipulating and visualizing video datasets from python works a bit different to image datasets. For this, please see the end of this tutorial.\n", - "\n", - "Next we will import a raw custom dataset, namely the [sfu-hw-objects-v1](http://dx.doi.org/10.17632/hwm673bv4m.1) into fiftyone.\n", - "\n", - "This format consists raw YUV video files and annotations. Let's see how the folder structure is roughly organized. We'll be using in this tutorial a \"mock\" version of the dataset with only two video classes:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6755a591", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "path_to_sfu_hw_objects_v1=\"/home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b5483eb3", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "!find {path_to_sfu_hw_objects_v1} -name \"*.mkv\" | xargs -I + rm +\n", - "!find {path_to_sfu_hw_objects_v1} -name \"*.webm\" | xargs -I + rm +\n", - "!find {path_to_sfu_hw_objects_v1} -name \"*.mp4\" | xargs -I + rm +" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9539b452", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1\r\n", - "├── ClassC\r\n", - "│   ├── Annotations\r\n", - "│   │   └── BasketballDrill [502 entries exceeds filelimit, not opening dir]\r\n", - "│   └── BasketballDrill_832x480_50Hz_8bit_P420.yuv\r\n", - "└── ClassX\r\n", - " ├── Annotations\r\n", - " │   └── BasketballDrill\r\n", - " │   ├── BasketballDrill_832x480_50_seq_001.txt\r\n", - " │   ├── BasketballDrill_832x480_50_seq_002.txt\r\n", - " │   ├── BasketballDrill_832x480_50_seq_003.txt\r\n", - " │   ├── BasketballDrill_832x480_50_seq_004.txt\r\n", - " │   └── BasketballDrill_832x480_object.list\r\n", - " └── BasketballDrill_832x480_50Hz_8bit_P420.yuv -> /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/BasketballDrill_832x480_50Hz_8bit_P420.yuv\r\n", - "\r\n", - "6 directories, 7 files\r\n" - ] - } - ], - "source": [ - "!tree {path_to_sfu_hw_objects_v1} --filelimit=10 | cat" - ] - }, - { - "cell_type": "markdown", - "id": "79b03303", - "metadata": {}, - "source": [ - "Importing mpeg-vcom custom datasets (for more info see Dataset section of the documentation) can be done with ``import-custom`` command. For ``sfu-hw-objects-v1`` it also converts on-the-fly the raw YUV images into proper video format:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "227144a3", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "WARNING: dataset sfu-hw-objects-v1 already exists: will delete and rewrite\n", - "\n", - "Importing a custom video format into fiftyone\n", - "\n", - "Dataset type : sfu-hw-objects-v1\n", - "Dataset root directory : /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1\n", - "\n", - "finding .yuv files from /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1\n", - "ffmpeg -y -f rawvideo -pixel_format yuv420p -video_size 832x480 -i /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/BasketballDrill_832x480_50Hz_8bit_P420.yuv -an -c:v h264 -q 0 /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill/video.mp4\n", - "ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers\n", - " built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\n", - " configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\n", - " libavutil 56. 31.100 / 56. 31.100\n", - " libavcodec 58. 54.100 / 58. 54.100\n", - " libavformat 58. 29.100 / 58. 29.100\n", - " libavdevice 58. 8.100 / 58. 8.100\n", - " libavfilter 7. 57.100 / 7. 57.100\n", - " libavresample 4. 0. 0 / 4. 0. 0\n", - " libswscale 5. 5.100 / 5. 5.100\n", - " libswresample 3. 5.100 / 3. 5.100\n", - " libpostproc 55. 5.100 / 55. 5.100\n", - "\u001b[0;35m[rawvideo @ 0x561a0d3c17c0] \u001b[0m\u001b[0;33mEstimating duration from bitrate, this may be inaccurate\n", - "\u001b[0mInput #0, rawvideo, from '/home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/BasketballDrill_832x480_50Hz_8bit_P420.yuv':\n", - " Duration: 00:00:20.04, start: 0.000000, bitrate: 119808 kb/s\n", - " Stream #0:0: Video: rawvideo (I420 / 0x30323449), yuv420p, 832x480, 119808 kb/s, 25 tbr, 25 tbn, 25 tbc\n", - "Stream mapping:\n", - " Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))\n", - "Press [q] to stop, [?] for help\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0musing cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mprofile High, level 3.0\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0m264 - core 155 r2917 0a84d98 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", - "Output #0, mp4, to '/home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill/video.mp4':\n", - " Metadata:\n", - " encoder : Lavf58.29.100\n", - " Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 832x480, q=-1--1, 25 fps, 12800 tbn, 25 tbc\n", - " Metadata:\n", - " encoder : Lavc58.54.100 libx264\n", - " Side data:\n", - " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1\n", - "frame= 501 fps=143 q=-1.0 Lsize= 3979kB time=00:00:19.92 bitrate=1636.2kbits/s speed=5.67x \n", - "video:3972kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.169325%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mframe I:3 Avg QP:22.61 size: 56539\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mframe P:126 Avg QP:24.67 size: 17479\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mframe B:372 Avg QP:28.66 size: 4556\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mconsecutive B-frames: 1.0% 0.0% 0.0% 99.0%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mmb I I16..4: 13.3% 37.2% 49.4%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mmb P I16..4: 0.1% 11.2% 6.3% P16..4: 42.9% 16.1% 11.6% 0.0% 0.0% skip:11.7%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mmb B I16..4: 0.0% 0.7% 0.4% B16..8: 35.6% 9.2% 3.6% direct: 3.0% skip:47.6% L0:43.7% L1:43.7% BI:12.7%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0m8x8 transform intra:60.9% inter:67.7%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mcoded y,uvDC,uvAC intra: 87.9% 88.2% 66.7% inter: 22.5% 18.1% 4.7%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mi16 v,h,dc,p: 57% 13% 8% 22%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mi8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 11% 8% 9% 7% 19% 17% 10% 9% 9%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mi4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 14% 9% 13% 7% 19% 15% 8% 7% 6%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mi8c dc,h,v,p: 48% 17% 22% 13%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mWeighted P-Frames: Y:0.0% UV:0.0%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mref P L0: 44.5% 27.1% 14.9% 13.6%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mref B L0: 85.6% 10.0% 4.4%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mref B L1: 94.4% 5.6%\n", - "\u001b[1;36m[libx264 @ 0x561a0d3cf300] \u001b[0mkb/s:1623.41\n", - "ffmpeg -y -f rawvideo -pixel_format yuv420p -video_size 832x480 -i /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/BasketballDrill_832x480_50Hz_8bit_P420.yuv -an -c:v h264 -q 0 /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/Annotations/BasketballDrill/video.mp4\n", - "ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers\n", - " built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\n", - " configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\n", - " libavutil 56. 31.100 / 56. 31.100\n", - " libavcodec 58. 54.100 / 58. 54.100\n", - " libavformat 58. 29.100 / 58. 29.100\n", - " libavdevice 58. 8.100 / 58. 8.100\n", - " libavfilter 7. 57.100 / 7. 57.100\n", - " libavresample 4. 0. 0 / 4. 0. 0\n", - " libswscale 5. 5.100 / 5. 5.100\n", - " libswresample 3. 5.100 / 3. 5.100\n", - " libpostproc 55. 5.100 / 55. 5.100\n", - "\u001b[0;35m[rawvideo @ 0x559c0f4467c0] \u001b[0m\u001b[0;33mEstimating duration from bitrate, this may be inaccurate\n", - "\u001b[0mInput #0, rawvideo, from '/home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/BasketballDrill_832x480_50Hz_8bit_P420.yuv':\n", - " Duration: 00:00:20.04, start: 0.000000, bitrate: 119808 kb/s\n", - " Stream #0:0: Video: rawvideo (I420 / 0x30323449), yuv420p, 832x480, 119808 kb/s, 25 tbr, 25 tbn, 25 tbc\n", - "Stream mapping:\n", - " Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))\n", - "Press [q] to stop, [?] for help\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0musing cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mprofile High, level 3.0\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0m264 - core 155 r2917 0a84d98 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", - "Output #0, mp4, to '/home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/Annotations/BasketballDrill/video.mp4':\n", - " Metadata:\n", - " encoder : Lavf58.29.100\n", - " Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 832x480, q=-1--1, 25 fps, 12800 tbn, 25 tbc\n", - " Metadata:\n", - " encoder : Lavc58.54.100 libx264\n", - " Side data:\n", - " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "frame= 501 fps=131 q=-1.0 Lsize= 3979kB time=00:00:19.92 bitrate=1636.2kbits/s speed= 5.2x \n", - "video:3972kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.169325%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mframe I:3 Avg QP:22.61 size: 56539\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mframe P:126 Avg QP:24.67 size: 17479\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mframe B:372 Avg QP:28.66 size: 4556\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mconsecutive B-frames: 1.0% 0.0% 0.0% 99.0%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mmb I I16..4: 13.3% 37.2% 49.4%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mmb P I16..4: 0.1% 11.2% 6.3% P16..4: 42.9% 16.1% 11.6% 0.0% 0.0% skip:11.7%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mmb B I16..4: 0.0% 0.7% 0.4% B16..8: 35.6% 9.2% 3.6% direct: 3.0% skip:47.6% L0:43.7% L1:43.7% BI:12.7%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0m8x8 transform intra:60.9% inter:67.7%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mcoded y,uvDC,uvAC intra: 87.9% 88.2% 66.7% inter: 22.5% 18.1% 4.7%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mi16 v,h,dc,p: 57% 13% 8% 22%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mi8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 11% 8% 9% 7% 19% 17% 10% 9% 9%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mi4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 14% 9% 13% 7% 19% 15% 8% 7% 6%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mi8c dc,h,v,p: 48% 17% 22% 13%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mWeighted P-Frames: Y:0.0% UV:0.0%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mref P L0: 44.5% 27.1% 14.9% 13.6%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mref B L0: 85.6% 10.0% 4.4%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mref B L1: 94.4% 5.6%\n", - "\u001b[1;36m[libx264 @ 0x559c0f454300] \u001b[0mkb/s:1623.41\n", - "video conversion done\n", - "searching for /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/Class*\n", - "Dataset sfu-hw-objects-v1 exists. Will remove it first\n", - "Dataset sfu-hw-objects-v1 created\n", - "\n", - "In class directory /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC\n", - "searching for /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/*\n", - "--> registering video /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill/video.mp4\n", - "--> registered new video sample: ClassC BasketballDrill with 500 frames\n", - "\n", - "In class directory /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX\n", - "searching for /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/Annotations/*\n", - "--> registering video /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/Annotations/BasketballDrill/video.mp4\n", - "--> registered new video sample: ClassX BasketballDrill with 4 frames\n", - "\n", - "Dataset saved\n" - ] - } - ], - "source": [ - "!compressai-vision import-custom --dataset-type=sfu-hw-objects-v1 --dir={path_to_sfu_hw_objects_v1} --y" - ] - }, - { - "cell_type": "markdown", - "id": "77fe21fa", - "metadata": {}, - "source": [ - "In order to demonstrate how video datasets are used, let's continue in python notebook:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0b9ac82b", - "metadata": {}, - "outputs": [], - "source": [ - "import cv2\n", - "import matplotlib.pyplot as plt\n", - "import fiftyone as fo\n", - "from fiftyone import ViewField as F\n", - "from math import floor" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "436068de", - "metadata": {}, - "outputs": [], - "source": [ - "dataset=fo.load_dataset(\"sfu-hw-objects-v1\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c4dfd18f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Name: sfu-hw-objects-v1\n", - "Media type: video\n", - "Num samples: 2\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.VideoMetadata)\n", - " media_type: fiftyone.core.fields.StringField\n", - " class_tag: fiftyone.core.fields.StringField\n", - " name_tag: fiftyone.core.fields.StringField\n", - " custom_id: fiftyone.core.fields.StringField\n", - "Frame fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " frame_number: fiftyone.core.fields.FrameNumberField\n", - " detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset" - ] - }, - { - "cell_type": "markdown", - "id": "63430a82", - "metadata": {}, - "source": [ - "In contrast to image datasets where each sample was an image, now a sample corresponds to a video:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "65c98e4a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ",\n", - "}>" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.first()" - ] - }, - { - "cell_type": "markdown", - "id": "73d35b41", - "metadata": {}, - "source": [ - "There is a reference to the video file and a ``Frames`` object, encapsulating ground truths etc. data for each and every frame. For ``sfu-hw-objects-v1`` in particular, ``class_tag`` corresponds to the class directories (ClassA, ClassB, etc.), while ``name_tag`` to the video descriptive names (BasketballDrill, Traffic, PeopleOnStreeet, etc.). Let's pick a certain video sample:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7956d368", - "metadata": {}, - "outputs": [], - "source": [ - "sample = dataset[ (F(\"name_tag\") == \"BasketballDrill\") & (F(\"class_tag\") == \"ClassC\") ].first()" - ] - }, - { - "cell_type": "markdown", - "id": "5ac06cce", - "metadata": {}, - "source": [ - "Take a look at the first frame ground truth detections (note that frame indices start from 1):" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "536feb2c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ",\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]),\n", - " }>,\n", - "}>" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample.frames[1]" - ] - }, - { - "cell_type": "raw", - "id": "64fc4d6d", - "metadata": {}, - "source": [ - "Start reading the video file with OpenCV:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "9381b713", - "metadata": {}, - "outputs": [], - "source": [ - "vid=cv2.VideoCapture(sample.filepath)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "37f2ec03", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "number of frames: 501\n" - ] - } - ], - "source": [ - "print(\"number of frames:\",int(vid.get(cv2.CAP_PROP_FRAME_COUNT)))" - ] - }, - { - "cell_type": "markdown", - "id": "2e99744f", - "metadata": {}, - "source": [ - "Let's define a small helper function:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "1859d999", - "metadata": {}, - "outputs": [], - "source": [ - "def draw_detections(sample: fo.Sample, vid: cv2.VideoCapture, nframe: int):\n", - " nmax=int(vid.get(cv2.CAP_PROP_FRAME_COUNT))\n", - " if nframe > nmax:\n", - " raise AssertionError(\"max frame is \" + str(nmax))\n", - " ok = vid.set(cv2.CAP_PROP_POS_FRAMES, nframe-1)\n", - " if not ok:\n", - " raise AssertionError(\"seek failed\")\n", - " ok, arr = vid.read() # BGR image in arr\n", - " if not ok:\n", - " raise AssertionError(\"no image\")\n", - " for detection in sample.frames[nframe].detections.detections:\n", - " x0, y0, w, h = detection.bounding_box # rel coords\n", - " x1, y1, x2, y2 = floor(x0*arr.shape[1]), floor(y0*arr.shape[0]), floor((x0+w)*arr.shape[1]), floor((y0+h)*arr.shape[0])\n", - " arr=cv2.rectangle(arr, (x1, y1), (x2, y2), (255, 0, 0), 5)\n", - " return arr" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "7a254716", - "metadata": {}, - "outputs": [], - "source": [ - "img=draw_detections(sample, vid, 200)\n", - "img_ = img[:,:,::-1] # BGR -> RGB" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "67413cca", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(img_)\n", - "vid.release()" - ] - }, - { - "cell_type": "markdown", - "id": "f29ca2bb", - "metadata": {}, - "source": [ - "For now, let's get back to terminal command line.\n", - "\n", - "Everything that you learned for image datasets, applies for video datasets as well: ``compressai-vision import-custom`` can be used to import mpeg-vcm datasets. ``compressai-vision app`` can be used to visualize video datasets interactively. For visualizing videos in the fiftyone app a small tip: when you play video and then stop it, the bboxes might seem to be off. However, when you click the timeline (i.e. seek) to a certain point, they match the video again (seems to be a small bug in the fiftyone video visualization app).\n", - "\n", - "When using the fiftyone app, there is a small catch though. Web-browsers are picky on the type of video they can play. For some video datasets, in order to view them in the app, you need to create separate \"side-data\" videos for visualization. These you can generate these automagically with the ``compressai-vision make-thumbnails`` command. Note that ``compressai-vision import-custom`` generates you these thumbnails on-the-go when you import new video sets. Switching between the main video and \"side-data\" video is demoed in [this animation](https://voxel51.com/docs/fiftyone/_images/app-multiple-media-fields.gif)" - ] - }, - { - "cell_type": "markdown", - "id": "e342d2db", - "metadata": {}, - "source": [ - "In chapters 3 and 4 you learned how to evaluate models (in serial and parallel) with the ``compressai-vision detectron2-eval`` command.\n", - "\n", - "The same command can be used to evaluate video datasets as well. Here the parameter ``--slice`` refers to videos, not individual image (as usual, for a production run, you would remove the ``--slice`` parameter):" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "27c9aa0e", - "metadata": { - "tags": [ - "bash" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "importing fiftyone\n", - "fiftyone imported\n", - "WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS?\n", - "\n", - "Using dataset : sfu-hw-objects-v1\n", - "Dataset media type : video\n", - "Dataset tmp clone : detectron-run-sampsa-sfu-hw-objects-v1-2022-11-10-15-37-24-746313\n", - "Image scaling : 100\n", - "WARNING: Using slice : 1:2\n", - "Number of samples : 1\n", - "Torch device : cpu\n", - "Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\n", - "Model was trained with : coco_2017_train\n", - "** Evaluation without Encoding/Decoding **\n", - "Ground truth data field name\n", - " : detections\n", - "Eval. results will be saved to datafield\n", - " : detectron-predictions\n", - "Evaluation protocol : open-images\n", - "Progressbar : True\n", - "WARNING: progressbar enabled --> disabling normal progress print\n", - "Print progress : 0\n", - "Output file : detectron2_test.json\n", - "Peek model classes :\n", - "['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ...\n", - "Peek dataset classes :\n", - "['chair', 'person', 'sports ball'] ...\n", - "cloning dataset sfu-hw-objects-v1 to detectron-run-sampsa-sfu-hw-objects-v1-2022-11-10-15-37-24-746313\n", - "instantiating Detectron2 predictor\n", - "USING VIDEO /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/Annotations/BasketballDrill/video.mp4\n", - "seeking to 2\n", - "/home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n", - "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.)\n", - " return torch.floor_divide(self, other)\n", - " 100% |███████████████████████████████████████████████████████████████████| 4/4 Evaluating detections...\n", - " 100% |███████████| 1/1 [71.8ms elapsed, 0s remaining, 13.9 samples/s] \n", - "deleting tmp database detectron-run-sampsa-sfu-hw-objects-v1-2022-11-10-15-37-24-746313\n", - "\n", - "Done!\n", - "\n" - ] - } - ], - "source": [ - "!compressai-vision detectron2-eval --y --dataset-name=sfu-hw-objects-v1 \\\n", - "--slice=1:2 \\\n", - "--scale=100 \\\n", - "--progressbar \\\n", - "--output=detectron2_test.json \\\n", - "--model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml" - ] - }, - { - "cell_type": "markdown", - "id": "ba0a58d3", - "metadata": {}, - "source": [ - "Take a look at the results:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7acc4383", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\r\n", - " \"dataset\": \"sfu-hw-objects-v1\",\r\n", - " \"gt_field\": \"detections\",\r\n", - " \"tmp datasetname\": \"detectron-run-sampsa-sfu-hw-objects-v1-2022-11-10-15-37-24-746313\",\r\n", - " \"slice\": \"1:2\",\r\n", - " \"model\": \"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\",\r\n", - " \"codec\": \"\",\r\n", - " \"qpars\": null,\r\n", - " \"bpp\": [\r\n", - " null\r\n", - " ],\r\n", - " \"map\": [\r\n", - " 0.5370370370370371\r\n", - " ],\r\n", - " \"map_per_class\": [\r\n", - " {\r\n", - " \"chair\": 0.1111111111111111,\r\n", - " \"person\": 1.0,\r\n", - " \"sports ball\": 0.5\r\n", - " }\r\n", - " ]\r\n", - "}" - ] - } - ], - "source": [ - "!cat detectron2_test.json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "688beeeb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/cli_tutorial_7_nb.rst b/docs/source/tutorials/cli_tutorial_7_nb.rst deleted file mode 100644 index a7bb2eec..00000000 --- a/docs/source/tutorials/cli_tutorial_7_nb.rst +++ /dev/null @@ -1,610 +0,0 @@ -In this tutorial you will learn how to: - -- Download and register video datasets -- Convert and import the ``sfu-hw-objects-v1`` raw custom video data - format -- Play around with video datasets, visualize frames and detection - results -- Evaluate a video dataset - -In chapter 2 of this tutorial you learned how to download and register -datasets to fiftyone with the ``compressai-vision register`` command. - -Exactly the same command works for video datasets: - -.. code:: bash - - compressai-vision download --dataset-name=quickstart-video --y - - -.. code-block:: text - - importing fiftyone - fiftyone imported - - WARNING: downloading ALL images. You might want to use the --lists option to download only certain images - Using list files: None - Number of images: ? - Database name : quickstart-video - Subname/split : None - Target dir : None - - Dataset already downloaded - Loading existing dataset 'quickstart-video'. To reload from disk, either delete the existing dataset or provide a custom `dataset_name` to use - - -If you have your video dataset arranged in one of the standard `video -data formats supported by -fiftyone `__, -you’re good to go. - -Manipulating and visualizing video datasets from python works a bit -different to image datasets. For this, please see the end of this -tutorial. - -Next we will import a raw custom dataset, namely the -`sfu-hw-objects-v1 `__ into -fiftyone. - -This format consists raw YUV video files and annotations. Let’s see how -the folder structure is roughly organized. We’ll be using in this -tutorial a “mock” version of the dataset with only two video classes: - -.. code:: bash - - tree {path_to_sfu_hw_objects_v1} --filelimit=10 | cat - - -.. code-block:: text - - /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1 - ├── ClassC - │   ├── Annotations - │   │   └── BasketballDrill [502 entries exceeds filelimit, not opening dir] - │   └── BasketballDrill_832x480_50Hz_8bit_P420.yuv - └── ClassX - ├── Annotations - │   └── BasketballDrill - │   ├── BasketballDrill_832x480_50_seq_001.txt - │   ├── BasketballDrill_832x480_50_seq_002.txt - │   ├── BasketballDrill_832x480_50_seq_003.txt - │   ├── BasketballDrill_832x480_50_seq_004.txt - │   └── BasketballDrill_832x480_object.list - └── BasketballDrill_832x480_50Hz_8bit_P420.yuv -> /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/BasketballDrill_832x480_50Hz_8bit_P420.yuv - - 6 directories, 7 files - - -Importing mpeg-vcom custom datasets (for more info see Dataset section -of the documentation) can be done with ``import-custom`` command. For -``sfu-hw-objects-v1`` it also converts on-the-fly the raw YUV images -into proper video format: - -.. code:: bash - - compressai-vision import-custom --dataset-type=sfu-hw-objects-v1 --dir={path_to_sfu_hw_objects_v1} --y - - -.. code-block:: text - - importing fiftyone - fiftyone imported - WARNING: dataset sfu-hw-objects-v1 already exists: will delete and rewrite - - Importing a custom video format into fiftyone - - Dataset type : sfu-hw-objects-v1 - Dataset root directory : /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1 - - finding .yuv files from /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1 - ffmpeg -y -f rawvideo -pixel_format yuv420p -video_size 832x480 -i /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/BasketballDrill_832x480_50Hz_8bit_P420.yuv -an -c:v h264 -q 0 /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill/video.mp4 - ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers - built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1) - 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libavutil 56. 31.100 / 56. 31.100 - libavcodec 58. 54.100 / 58. 54.100 - libavformat 58. 29.100 / 58. 29.100 - libavdevice 58. 8.100 / 58. 8.100 - libavfilter 7. 57.100 / 7. 57.100 - libavresample 4. 0. 0 / 4. 0. 0 - libswscale 5. 5.100 / 5. 5.100 - libswresample 3. 5.100 / 3. 5.100 - libpostproc 55. 5.100 / 55. 5.100 - [rawvideo @ 0x561a0d3c17c0] Estimating duration from bitrate, this may be inaccurate - Input #0, rawvideo, from '/home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/BasketballDrill_832x480_50Hz_8bit_P420.yuv': - Duration: 00:00:20.04, start: 0.000000, bitrate: 119808 kb/s - Stream #0:0: Video: rawvideo (I420 / 0x30323449), yuv420p, 832x480, 119808 kb/s, 25 tbr, 25 tbn, 25 tbc - Stream mapping: - Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264)) - Press [q] to stop, [?] for help - [libx264 @ 0x561a0d3cf300] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 - [libx264 @ 0x561a0d3cf300] profile High, level 3.0 - [libx264 @ 0x561a0d3cf300] 264 - core 155 r2917 0a84d98 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00 - Output #0, mp4, to '/home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill/video.mp4': - Metadata: - encoder : Lavf58.29.100 - Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 832x480, q=-1--1, 25 fps, 12800 tbn, 25 tbc - Metadata: - encoder : Lavc58.54.100 libx264 - Side data: - cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1 - frame= 501 fps=143 q=-1.0 Lsize= 3979kB time=00:00:19.92 bitrate=1636.2kbits/s speed=5.67x - video:3972kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.169325% - [libx264 @ 0x561a0d3cf300] frame I:3 Avg QP:22.61 size: 56539 - [libx264 @ 0x561a0d3cf300] frame P:126 Avg QP:24.67 size: 17479 - [libx264 @ 0x561a0d3cf300] frame B:372 Avg QP:28.66 size: 4556 - [libx264 @ 0x561a0d3cf300] consecutive B-frames: 1.0% 0.0% 0.0% 99.0% - [libx264 @ 0x561a0d3cf300] mb I I16..4: 13.3% 37.2% 49.4% - [libx264 @ 0x561a0d3cf300] mb P I16..4: 0.1% 11.2% 6.3% P16..4: 42.9% 16.1% 11.6% 0.0% 0.0% skip:11.7% - [libx264 @ 0x561a0d3cf300] mb B I16..4: 0.0% 0.7% 0.4% B16..8: 35.6% 9.2% 3.6% direct: 3.0% skip:47.6% L0:43.7% L1:43.7% BI:12.7% - [libx264 @ 0x561a0d3cf300] 8x8 transform intra:60.9% inter:67.7% - [libx264 @ 0x561a0d3cf300] coded y,uvDC,uvAC intra: 87.9% 88.2% 66.7% inter: 22.5% 18.1% 4.7% - [libx264 @ 0x561a0d3cf300] i16 v,h,dc,p: 57% 13% 8% 22% - [libx264 @ 0x561a0d3cf300] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 11% 8% 9% 7% 19% 17% 10% 9% 9% - [libx264 @ 0x561a0d3cf300] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 14% 9% 13% 7% 19% 15% 8% 7% 6% - [libx264 @ 0x561a0d3cf300] i8c dc,h,v,p: 48% 17% 22% 13% - [libx264 @ 0x561a0d3cf300] Weighted P-Frames: Y:0.0% UV:0.0% - [libx264 @ 0x561a0d3cf300] ref P L0: 44.5% 27.1% 14.9% 13.6% - [libx264 @ 0x561a0d3cf300] ref B L0: 85.6% 10.0% 4.4% - [libx264 @ 0x561a0d3cf300] ref B L1: 94.4% 5.6% - [libx264 @ 0x561a0d3cf300] kb/s:1623.41 - ffmpeg -y -f rawvideo -pixel_format yuv420p -video_size 832x480 -i /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/BasketballDrill_832x480_50Hz_8bit_P420.yuv -an -c:v h264 -q 0 /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/Annotations/BasketballDrill/video.mp4 - ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers - built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1) - configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared - libavutil 56. 31.100 / 56. 31.100 - libavcodec 58. 54.100 / 58. 54.100 - libavformat 58. 29.100 / 58. 29.100 - libavdevice 58. 8.100 / 58. 8.100 - libavfilter 7. 57.100 / 7. 57.100 - libavresample 4. 0. 0 / 4. 0. 0 - libswscale 5. 5.100 / 5. 5.100 - libswresample 3. 5.100 / 3. 5.100 - libpostproc 55. 5.100 / 55. 5.100 - [rawvideo @ 0x559c0f4467c0] Estimating duration from bitrate, this may be inaccurate - Input #0, rawvideo, from '/home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/BasketballDrill_832x480_50Hz_8bit_P420.yuv': - Duration: 00:00:20.04, start: 0.000000, bitrate: 119808 kb/s - Stream #0:0: Video: rawvideo (I420 / 0x30323449), yuv420p, 832x480, 119808 kb/s, 25 tbr, 25 tbn, 25 tbc - Stream mapping: - Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264)) - Press [q] to stop, [?] for help - [libx264 @ 0x559c0f454300] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 - [libx264 @ 0x559c0f454300] profile High, level 3.0 - [libx264 @ 0x559c0f454300] 264 - core 155 r2917 0a84d98 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00 - Output #0, mp4, to '/home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/Annotations/BasketballDrill/video.mp4': - Metadata: - encoder : Lavf58.29.100 - Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 832x480, q=-1--1, 25 fps, 12800 tbn, 25 tbc - Metadata: - encoder : Lavc58.54.100 libx264 - Side data: - cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1 - frame= 501 fps=131 q=-1.0 Lsize= 3979kB time=00:00:19.92 bitrate=1636.2kbits/s speed= 5.2x - video:3972kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.169325% - [libx264 @ 0x559c0f454300] frame I:3 Avg QP:22.61 size: 56539 - [libx264 @ 0x559c0f454300] frame P:126 Avg QP:24.67 size: 17479 - [libx264 @ 0x559c0f454300] frame B:372 Avg QP:28.66 size: 4556 - [libx264 @ 0x559c0f454300] consecutive B-frames: 1.0% 0.0% 0.0% 99.0% - [libx264 @ 0x559c0f454300] mb I I16..4: 13.3% 37.2% 49.4% - [libx264 @ 0x559c0f454300] mb P I16..4: 0.1% 11.2% 6.3% P16..4: 42.9% 16.1% 11.6% 0.0% 0.0% skip:11.7% - [libx264 @ 0x559c0f454300] mb B I16..4: 0.0% 0.7% 0.4% B16..8: 35.6% 9.2% 3.6% direct: 3.0% skip:47.6% L0:43.7% L1:43.7% BI:12.7% - [libx264 @ 0x559c0f454300] 8x8 transform intra:60.9% inter:67.7% - [libx264 @ 0x559c0f454300] coded y,uvDC,uvAC intra: 87.9% 88.2% 66.7% inter: 22.5% 18.1% 4.7% - [libx264 @ 0x559c0f454300] i16 v,h,dc,p: 57% 13% 8% 22% - [libx264 @ 0x559c0f454300] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 11% 8% 9% 7% 19% 17% 10% 9% 9% - [libx264 @ 0x559c0f454300] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 14% 9% 13% 7% 19% 15% 8% 7% 6% - [libx264 @ 0x559c0f454300] i8c dc,h,v,p: 48% 17% 22% 13% - [libx264 @ 0x559c0f454300] Weighted P-Frames: Y:0.0% UV:0.0% - [libx264 @ 0x559c0f454300] ref P L0: 44.5% 27.1% 14.9% 13.6% - [libx264 @ 0x559c0f454300] ref B L0: 85.6% 10.0% 4.4% - [libx264 @ 0x559c0f454300] ref B L1: 94.4% 5.6% - [libx264 @ 0x559c0f454300] kb/s:1623.41 - video conversion done - searching for /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/Class* - Dataset sfu-hw-objects-v1 exists. Will remove it first - Dataset sfu-hw-objects-v1 created - - In class directory /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC - searching for /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/* - --> registering video /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassC/Annotations/BasketballDrill/video.mp4 - --> registered new video sample: ClassC BasketballDrill with 500 frames - - In class directory /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX - searching for /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/Annotations/* - --> registering video /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/Annotations/BasketballDrill/video.mp4 - --> registered new video sample: ClassX BasketballDrill with 4 frames - - Dataset saved - - -In order to demonstrate how video datasets are used, let’s continue in -python notebook: - -.. code:: ipython3 - - import cv2 - import matplotlib.pyplot as plt - import fiftyone as fo - from fiftyone import ViewField as F - from math import floor - -.. code:: ipython3 - - dataset=fo.load_dataset("sfu-hw-objects-v1") - -.. code:: ipython3 - - dataset - - - - -.. parsed-literal:: - - Name: sfu-hw-objects-v1 - Media type: video - Num samples: 2 - Persistent: True - Tags: [] - Sample fields: - id: fiftyone.core.fields.ObjectIdField - filepath: fiftyone.core.fields.StringField - tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) - metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.VideoMetadata) - media_type: fiftyone.core.fields.StringField - class_tag: fiftyone.core.fields.StringField - name_tag: fiftyone.core.fields.StringField - custom_id: fiftyone.core.fields.StringField - Frame fields: - id: fiftyone.core.fields.ObjectIdField - frame_number: fiftyone.core.fields.FrameNumberField - detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - - - -In contrast to image datasets where each sample was an image, now a -sample corresponds to a video: - -.. code:: ipython3 - - dataset.first() - - - - -.. parsed-literal:: - - , - }> - - - -There is a reference to the video file and a ``Frames`` object, -encapsulating ground truths etc. data for each and every frame. For -``sfu-hw-objects-v1`` in particular, ``class_tag`` corresponds to the -class directories (ClassA, ClassB, etc.), while ``name_tag`` to the -video descriptive names (BasketballDrill, Traffic, PeopleOnStreeet, -etc.). Let’s pick a certain video sample: - -.. code:: ipython3 - - sample = dataset[ (F("name_tag") == "BasketballDrill") & (F("class_tag") == "ClassC") ].first() - -Take a look at the first frame ground truth detections (note that frame -indices start from 1): - -.. code:: ipython3 - - sample.frames[1] - - - - -.. parsed-literal:: - - , - , - , - , - , - , - , - , - , - ]), - }>, - }> - - - -Start reading the video file with OpenCV: - -.. code:: ipython3 - - vid=cv2.VideoCapture(sample.filepath) - -.. code:: ipython3 - - print("number of frames:",int(vid.get(cv2.CAP_PROP_FRAME_COUNT))) - - -.. code-block:: text - - number of frames: 501 - - -Let’s define a small helper function: - -.. code:: ipython3 - - def draw_detections(sample: fo.Sample, vid: cv2.VideoCapture, nframe: int): - nmax=int(vid.get(cv2.CAP_PROP_FRAME_COUNT)) - if nframe > nmax: - raise AssertionError("max frame is " + str(nmax)) - ok = vid.set(cv2.CAP_PROP_POS_FRAMES, nframe-1) - if not ok: - raise AssertionError("seek failed") - ok, arr = vid.read() # BGR image in arr - if not ok: - raise AssertionError("no image") - for detection in sample.frames[nframe].detections.detections: - x0, y0, w, h = detection.bounding_box # rel coords - x1, y1, x2, y2 = floor(x0*arr.shape[1]), floor(y0*arr.shape[0]), floor((x0+w)*arr.shape[1]), floor((y0+h)*arr.shape[0]) - arr=cv2.rectangle(arr, (x1, y1), (x2, y2), (255, 0, 0), 5) - return arr - -.. code:: ipython3 - - img=draw_detections(sample, vid, 200) - img_ = img[:,:,::-1] # BGR -> RGB - -.. code:: ipython3 - - plt.imshow(img_) - vid.release() - - - -.. image:: cli_tutorial_7_nb_files/cli_tutorial_7_nb_22_0.png - - -For now, let’s get back to terminal command line. - -Everything that you learned for image datasets, applies for video -datasets as well: ``compressai-vision import-custom`` can be used to -import mpeg-vcm datasets. ``compressai-vision app`` can be used to -visualize video datasets interactively. For visualizing videos in the -fiftyone app a small tip: when you play video and then stop it, the -bboxes might seem to be off. However, when you click the timeline -(i.e. seek) to a certain point, they match the video again (seems to be -a small bug in the fiftyone video visualization app). - -When using the fiftyone app, there is a small catch though. Web-browsers -are picky on the type of video they can play. For some video datasets, -in order to view them in the app, you need to create separate -“side-data” videos for visualization. These you can generate these -automagically with the ``compressai-vision make-thumbnails`` command. -Note that ``compressai-vision import-custom`` generates you these -thumbnails on-the-go when you import new video sets. Switching between -the main video and “side-data” video is demoed in `this -animation `__ - -In chapters 3 and 4 you learned how to evaluate models (in serial and -parallel) with the ``compressai-vision detectron2-eval`` command. - -The same command can be used to evaluate video datasets as well. Here -the parameter ``--slice`` refers to videos, not individual image (as -usual, for a production run, you would remove the ``--slice`` -parameter): - -.. code:: bash - - compressai-vision detectron2-eval --y --dataset-name=sfu-hw-objects-v1 \ - --slice=1:2 \ - --scale=100 \ - --progressbar \ - --output=detectron2_test.json \ - --model=COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - - -.. code-block:: text - - importing fiftyone - fiftyone imported - WARNING: using a dataset slice instead of full dataset: SURE YOU WANT THIS? - - Using dataset : sfu-hw-objects-v1 - Dataset media type : video - Dataset tmp clone : detectron-run-sampsa-sfu-hw-objects-v1-2022-11-10-15-37-24-746313 - Image scaling : 100 - WARNING: Using slice : 1:2 - Number of samples : 1 - Torch device : cpu - Detectron2 model : COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml - Model was trained with : coco_2017_train - ** Evaluation without Encoding/Decoding ** - Ground truth data field name - : detections - Eval. results will be saved to datafield - : detectron-predictions - Evaluation protocol : open-images - Progressbar : True - WARNING: progressbar enabled --> disabling normal progress print - Print progress : 0 - Output file : detectron2_test.json - Peek model classes : - ['airplane', 'apple', 'backpack', 'banana', 'baseball bat'] ... - Peek dataset classes : - ['chair', 'person', 'sports ball'] ... - cloning dataset sfu-hw-objects-v1 to detectron-run-sampsa-sfu-hw-objects-v1-2022-11-10-15-37-24-746313 - instantiating Detectron2 predictor - USING VIDEO /home/sampsa/silo/interdigital/mock/SFU-HW-Objects-v1/ClassX/Annotations/BasketballDrill/video.mp4 - seeking to 2 - /home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. - To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.) - return torch.floor_divide(self, other) - 100% |███████████████████████████████████████████████████████████████████| 4/4 Evaluating detections... - 100% |███████████| 1/1 [71.8ms elapsed, 0s remaining, 13.9 samples/s] - deleting tmp database detectron-run-sampsa-sfu-hw-objects-v1-2022-11-10-15-37-24-746313 - - Done! - - - -Take a look at the results: - -.. code:: ipython3 - - cat detectron2_test.json - - -.. code-block:: text - - { - "dataset": "sfu-hw-objects-v1", - "gt_field": "detections", - "tmp datasetname": "detectron-run-sampsa-sfu-hw-objects-v1-2022-11-10-15-37-24-746313", - "slice": "1:2", - "model": "COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml", - "codec": "", - "qpars": null, - "bpp": [ - null - ], - "map": [ - 0.5370370370370371 - ], - "map_per_class": [ - { - "chair": 0.1111111111111111, - "person": 1.0, - "sports ball": 0.5 - } - ] - } - diff --git a/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_18_1.png b/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_18_1.png deleted file mode 100644 index cd2c2ee9..00000000 Binary files a/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_18_1.png and /dev/null differ diff --git a/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_19_1.png b/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_19_1.png deleted file mode 100644 index cd2c2ee9..00000000 Binary files a/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_19_1.png and /dev/null differ diff --git a/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_22_0.png b/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_22_0.png deleted file mode 100644 index 9292656a..00000000 Binary files a/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_22_0.png and /dev/null differ diff --git a/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_22_1.png b/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_22_1.png deleted file mode 100644 index cd2c2ee9..00000000 Binary files a/docs/source/tutorials/cli_tutorial_7_nb_files/cli_tutorial_7_nb_22_1.png and /dev/null differ diff --git a/docs/source/tutorials/compile.bash b/docs/source/tutorials/compile.bash deleted file mode 100755 index e9f8297e..00000000 --- a/docs/source/tutorials/compile.bash +++ /dev/null @@ -1,54 +0,0 @@ -#!/bin/bash -## https://nbconvert.readthedocs.io/en/latest/removing_cells.html - -if [ $# -lt 1 ]; then - dirnames="fiftyone download detectron2 evaluate encdec cli_tutorial_1 cli_tutorial_2 cli_tutorial_3 cli_tutorial_4 cli_tutorial_5 cli_tutorial_6 cli_tutorial_7" -else - dirnames=$@ -fi - -echo $dirnames -# exit 2 - -for dirname in $dirnames -do - #cd $dirname - # jupyter nbconvert --to rst $dirname"_nb.ipynb" \ - jupyter nbconvert --to rst --template tuto_rst $dirname"_nb.ipynb" \ - --TagRemovePreprocessor.enabled=True \ - --TagRemovePreprocessor.remove_cell_tags="['remove_cell']" \ - --TagRemovePreprocessor.remove_input_tags="['remove_input']" \ - --TemplateExporter.extra_template_basedirs=./templates - #cd .. -done -# --HighlightMagicsPreprocessor.enabled=True - -# substitute ! in front of CLI commands with a space -for fname in cli_*_nb.rst -do - echo $fname - sed -i -r "s/ \!/ /g" $fname -done - -# we'd like to have these code blocks in the converted .rst files: -# for bash cells: -# .. code:: bash -# for bash output: -# .. code-block:: text -# -# but we get ".. parsed-literal::" and "code:: ipython3" instead -# -# no idea which part of the pipeline writes that "..parsed-literal::" into the rst file -# -# docs: "Under the hood, nbconvert uses pygments to highlight code" -# nbconvert: notebook (using pygments?) -> rst -# ..nopes can't be. It must be nbconvert without pygments that generates rst -# look here: -# https://nbconvert.readthedocs.io/en/latest/customizing.html#where-are-nbconvert-templates-installed -# -> conversion from ipynb to rst is defined in a template file -# look into: ~/.local/share/jupyter/nbconvert/templates/rst/ -# -# ok, now there's -# templates/tuto_rst/ -# that fixes the problem -# using tag "bash" with input cells formats their input as bash diff --git a/docs/source/tutorials/convert_nb.ipynb b/docs/source/tutorials/convert_nb.ipynb deleted file mode 100644 index 27ecb4f1..00000000 --- a/docs/source/tutorials/convert_nb.ipynb +++ /dev/null @@ -1,450 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bfe873da", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "## 02. Convert\n", - "\n", - "- Create MPEG-VCM working group dataset from OpenImageV6\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "bc06938b", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_69416/1348678174.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "markdown", - "id": "6c620b82", - "metadata": {}, - "source": [ - "In this chapter, we create an evaluation dataset as defined by the MPEG-VCM working group " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4d171492", - "metadata": {}, - "outputs": [], - "source": [ - "# common libs\n", - "import math, os, io, json, cv2, random, logging\n", - "import numpy as np\n", - "# images\n", - "from PIL import Image\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ea9562af", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "your home path is /home/sampsa\n", - "fiftyone dowloads data by default to /home/sampsa/fiftyone\n" - ] - } - ], - "source": [ - "homie=os.path.expanduser(\"~\")\n", - "print(\"your home path is\", homie)\n", - "fodir=os.path.join(homie,'fiftyone')\n", - "print(\"fiftyone dowloads data by default to\", fodir)\n", - "try:\n", - " os.mkdir(fodir)\n", - "except FileExistsError:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f386b4c6", - "metadata": {}, - "outputs": [], - "source": [ - "# fiftyone\n", - "import fiftyone as fo\n", - "import fiftyone.zoo as foz" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f8765e0e", - "metadata": {}, - "outputs": [], - "source": [ - "# CompressAI-Vision\n", - "from compressai_vision.conversion import MPEGVCMToOpenImageV6, imageIdFileList" - ] - }, - { - "cell_type": "markdown", - "id": "38f1313b", - "metadata": {}, - "source": [ - "We expect that you have downloaded correct images and segmentation masks into open-images-v6 folder (as instructed in the previous chapter)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f23dae75", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "contents of /home/sampsa/fiftyone/open-images-v6 :\n", - "/home/sampsa/fiftyone/open-images-v6\r\n", - "├── info.json\r\n", - "└── validation\r\n", - " ├── data [8189 entries exceeds filelimit, not opening dir]\r\n", - " ├── labels\r\n", - " │   ├── classifications.csv\r\n", - " │   ├── detections.csv\r\n", - " │   ├── masks [16 entries exceeds filelimit, not opening dir]\r\n", - " │   ├── relationships.csv\r\n", - " │   └── segmentations.csv\r\n", - " └── metadata\r\n", - " ├── attributes.csv\r\n", - " ├── classes.csv\r\n", - " ├── hierarchy.json\r\n", - " ├── image_ids.csv\r\n", - " └── segmentation_classes.csv\r\n", - "\r\n", - "5 directories, 10 files\r\n" - ] - } - ], - "source": [ - "dir_=os.path.join(fodir,\"open-images-v6\")\n", - "print(\"contents of\", dir_,\":\")\n", - "!tree --filelimit=10 $dir_ | cat" - ] - }, - { - "cell_type": "markdown", - "id": "12b85060", - "metadata": {}, - "source": [ - "So the downloaded images reside in ``~/fiftyone/open-images-v6/data`` and segmentation masks in ``~/fiftyone/open-images-v6/labels/masks``.\n", - "\n", - "We are not going to use the default OpenImageV6 annotations: MPEG/VCM working group provides us with custom-format annotation files we need to convert into OpenImageV6 format. For the detector ground truths, these are:\n", - "```\n", - "detection_validation_5k_bbox.csv = detection bbox annotations\n", - "detection_validation_labels_5k.csv = image-level annotations\n", - "detection_validation_input_5k.lst = list of images used\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6517be21", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "path_to_mpeg_vcm_files=\"/home/sampsa/silo/interdigital/CompressAI-Vision/compressai_vision/data/mpeg_vcm_data\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3e030466", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: define path_to_mpeg_vcm_files\n", - "path_to_images=os.path.join(fodir,\"open-images-v6/validation/data\")\n", - "\n", - "list_file=os.path.join(path_to_mpeg_vcm_files, \"detection_validation_input_5k.lst\")\n", - "bbox_csv_file=os.path.join(path_to_mpeg_vcm_files, \"detection_validation_5k_bbox.csv\")\n", - "validation_csv_file=os.path.join(path_to_mpeg_vcm_files, \"detection_validation_labels_5k.csv\")\n", - "\n", - "assert(os.path.exists(bbox_csv_file)), \"can't find bbox file\"\n", - "assert(os.path.exists(validation_csv_file)), \"can't find labels file\"\n", - "assert(os.path.exists(path_to_images)), \"can't find image directory\"" - ] - }, - { - "cell_type": "markdown", - "id": "0cb9ff3b", - "metadata": {}, - "source": [ - "Now we convert mpeg vmc proprietary format annotation into proper OpenImageV6 format dataset and place it into ``~/fiftyone/mpeg_vcm-detection``\n", - "\n", - "First, remove any previously imported stuff:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "20e92ea4", - "metadata": {}, - "outputs": [], - "source": [ - "!rm -rf ~/fiftyone/mpeg-vcm-*" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5927c6ca", - "metadata": {}, - "outputs": [], - "source": [ - "MPEGVCMToOpenImageV6(\n", - " validation_csv_file=validation_csv_file,\n", - " list_file=list_file,\n", - " bbox_csv_file=bbox_csv_file,\n", - " output_directory=os.path.join(fodir,\"mpeg-vcm-detection\"),\n", - " data_dir=path_to_images\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d0c8d20f", - "metadata": {}, - "source": [ - "let's see what we got:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "583da2d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/sampsa/fiftyone/mpeg-vcm-detection\r\n", - "├── data -> /home/sampsa/fiftyone/open-images-v6/validation/data\r\n", - "├── labels\r\n", - "│   ├── classifications.csv\r\n", - "│   └── detections.csv\r\n", - "└── metadata\r\n", - " ├── attributes.csv\r\n", - " ├── classes.csv\r\n", - " └── image_ids.csv\r\n", - "\r\n", - "3 directories, 5 files\r\n" - ] - } - ], - "source": [ - "!tree --filelimit=10 ~/fiftyone/mpeg-vcm-detection | cat" - ] - }, - { - "cell_type": "markdown", - "id": "ee37b420", - "metadata": {}, - "source": [ - "We have a new OpenImageV6 formatted data/directory structure with new annotations, but it uses images from the official OpenImageV6 dataset (note that link from ``data -> ~/fiftyone/open-images-v6/validation/data``)\n", - "\n", - "The only thing we're left to do, is to register this OpenImageV6 formatted dataset into fiftyone:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "8bdbe1e5", - "metadata": {}, - "outputs": [], - "source": [ - "# remove the dataset in the case it was already registered in fiftyone\n", - "try:\n", - " fo.delete_dataset(\"mpeg-vcm-detection\")\n", - "except ValueError as e:\n", - " print(\"could not delete because of\", e)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "7ab83b26", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 100% |███████████████| 5000/5000 [16.8s elapsed, 0s remaining, 290.4 samples/s] \n" - ] - } - ], - "source": [ - "dataset_type = fo.types.OpenImagesV6Dataset\n", - "dataset_dir = os.path.join(fodir,\"mpeg-vcm-detection\")\n", - "dataset = fo.Dataset.from_dir(\n", - " dataset_dir=dataset_dir,\n", - " dataset_type=dataset_type,\n", - " label_types=(\"detections\",\"classifications\"),\n", - " load_hierarchy=False,\n", - " name=\"mpeg-vcm-detection\",\n", - " image_ids=imageIdFileList(list_file)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "fe93aa6c", - "metadata": {}, - "outputs": [], - "source": [ - "dataset.persistent=True # without this, your dabatase will disappear!" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "1503c32e", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "fo.list_datasets()\n", - "fo.delete_datasets(\"mpeg_vcm-detection\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "f24393da", - "metadata": {}, - "outputs": [], - "source": [ - "## now, in the future, just do\n", - "dataset = fo.load_dataset(\"mpeg-vcm-detection\")" - ] - }, - { - "cell_type": "markdown", - "id": "e394ff3b", - "metadata": {}, - "source": [ - "Finaly, let's also create a dummy dataset for debugging and testing with only one sample:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "2d118f20", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dummy dataset ok\n" - ] - } - ], - "source": [ - "try:\n", - " fo.delete_dataset(\"mpeg-vcm-detection-dummy\")\n", - "except ValueError:\n", - " print(\"no dummmy dataset yet..\")\n", - "dummy_dataset=fo.Dataset(\"mpeg-vcm-detection-dummy\")\n", - "for sample in dataset[0:1]:\n", - " dummy_dataset.add_sample(sample)\n", - "dummy_dataset.persistent=True\n", - "print(\"dummy dataset ok\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7320826e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/convert_nb.rst b/docs/source/tutorials/convert_nb.rst deleted file mode 100644 index 3e3491b1..00000000 --- a/docs/source/tutorials/convert_nb.rst +++ /dev/null @@ -1,208 +0,0 @@ -In this chapter, we create an evaluation dataset as defined by the -MPEG-VCM working group - -.. code:: ipython3 - - # common libs - import math, os, io, json, cv2, random, logging - import numpy as np - # images - from PIL import Image - import matplotlib.pyplot as plt - -.. code:: ipython3 - - homie=os.path.expanduser("~") - print("your home path is", homie) - fodir=os.path.join(homie,'fiftyone') - print("fiftyone dowloads data by default to", fodir) - try: - os.mkdir(fodir) - except FileExistsError: - pass - - -.. code-block:: text - - your home path is /home/sampsa - fiftyone dowloads data by default to /home/sampsa/fiftyone - - -.. code:: ipython3 - - # fiftyone - import fiftyone as fo - import fiftyone.zoo as foz - -.. code:: ipython3 - - # CompressAI-Vision - from compressai_vision.conversion import MPEGVCMToOpenImageV6, imageIdFileList - -We expect that you have downloaded correct images and segmentation masks -into open-images-v6 folder (as instructed in the previous chapter) - -.. code:: ipython3 - - dir_=os.path.join(fodir,"open-images-v6") - print("contents of", dir_,":") - !tree --filelimit=10 $dir_ | cat - - -.. code-block:: text - - contents of /home/sampsa/fiftyone/open-images-v6 : - /home/sampsa/fiftyone/open-images-v6 - ├── info.json - └── validation - ├── data [8189 entries exceeds filelimit, not opening dir] - ├── labels - │   ├── classifications.csv - │   ├── detections.csv - │   ├── masks [16 entries exceeds filelimit, not opening dir] - │   ├── relationships.csv - │   └── segmentations.csv - └── metadata - ├── attributes.csv - ├── classes.csv - ├── hierarchy.json - ├── image_ids.csv - └── segmentation_classes.csv - - 5 directories, 10 files - - -So the downloaded images reside in ``~/fiftyone/open-images-v6/data`` -and segmentation masks in ``~/fiftyone/open-images-v6/labels/masks``. - -We are not going to use the default OpenImageV6 annotations: MPEG/VCM -working group provides us with custom-format annotation files we need to -convert into OpenImageV6 format. For the detector ground truths, these -are: - -:: - - detection_validation_5k_bbox.csv = detection bbox annotations - detection_validation_labels_5k.csv = image-level annotations - detection_validation_input_5k.lst = list of images used - -.. code:: ipython3 - - # TODO: define path_to_mpeg_vcm_files - path_to_images=os.path.join(fodir,"open-images-v6/validation/data") - - list_file=os.path.join(path_to_mpeg_vcm_files, "detection_validation_input_5k.lst") - bbox_csv_file=os.path.join(path_to_mpeg_vcm_files, "detection_validation_5k_bbox.csv") - validation_csv_file=os.path.join(path_to_mpeg_vcm_files, "detection_validation_labels_5k.csv") - - assert(os.path.exists(bbox_csv_file)), "can't find bbox file" - assert(os.path.exists(validation_csv_file)), "can't find labels file" - assert(os.path.exists(path_to_images)), "can't find image directory" - -Now we convert mpeg vmc proprietary format annotation into proper -OpenImageV6 format dataset and place it into -``~/fiftyone/mpeg_vcm-detection`` - -First, remove any previously imported stuff: - -.. code:: ipython3 - - !rm -rf ~/fiftyone/mpeg-vcm-* - -.. code:: ipython3 - - MPEGVCMToOpenImageV6( - validation_csv_file=validation_csv_file, - list_file=list_file, - bbox_csv_file=bbox_csv_file, - output_directory=os.path.join(fodir,"mpeg-vcm-detection"), - data_dir=path_to_images - ) - -let’s see what we got: - -.. code:: ipython3 - - !tree --filelimit=10 ~/fiftyone/mpeg-vcm-detection | cat - - -.. code-block:: text - - /home/sampsa/fiftyone/mpeg-vcm-detection - ├── data -> /home/sampsa/fiftyone/open-images-v6/validation/data - ├── labels - │   ├── classifications.csv - │   └── detections.csv - └── metadata - ├── attributes.csv - ├── classes.csv - └── image_ids.csv - - 3 directories, 5 files - - -We have a new OpenImageV6 formatted data/directory structure with new -annotations, but it uses images from the official OpenImageV6 dataset -(note that link from -``data -> ~/fiftyone/open-images-v6/validation/data``) - -The only thing we’re left to do, is to register this OpenImageV6 -formatted dataset into fiftyone: - -.. code:: ipython3 - - # remove the dataset in the case it was already registered in fiftyone - try: - fo.delete_dataset("mpeg-vcm-detection") - except ValueError as e: - print("could not delete because of", e) - -.. code:: ipython3 - - dataset_type = fo.types.OpenImagesV6Dataset - dataset_dir = os.path.join(fodir,"mpeg-vcm-detection") - dataset = fo.Dataset.from_dir( - dataset_dir=dataset_dir, - dataset_type=dataset_type, - label_types=("detections","classifications"), - load_hierarchy=False, - name="mpeg-vcm-detection", - image_ids=imageIdFileList(list_file) - ) - - -.. code-block:: text - - 100% |███████████████| 5000/5000 [16.8s elapsed, 0s remaining, 290.4 samples/s] - - -.. code:: ipython3 - - dataset.persistent=True # without this, your dabatase will disappear! - -.. code:: ipython3 - - ## now, in the future, just do - dataset = fo.load_dataset("mpeg-vcm-detection") - -Finaly, let’s also create a dummy dataset for debugging and testing with -only one sample: - -.. code:: ipython3 - - try: - fo.delete_dataset("mpeg-vcm-detection-dummy") - except ValueError: - print("no dummmy dataset yet..") - dummy_dataset=fo.Dataset("mpeg-vcm-detection-dummy") - for sample in dataset[0:1]: - dummy_dataset.add_sample(sample) - dummy_dataset.persistent=True - print("dummy dataset ok") - - -.. code-block:: text - - dummy dataset ok - - diff --git a/docs/source/tutorials/detectron2.rst b/docs/source/tutorials/detectron2.rst deleted file mode 100644 index c29ae66f..00000000 --- a/docs/source/tutorials/detectron2.rst +++ /dev/null @@ -1,11 +0,0 @@ -2. Run Detectron2 ------------------ - -:download:`[download tutorial as notebook]` - -Here we run Detectron2 predictor to annex detector/segmentation results into fiftyone database. After that we evaluate the original images. We also show -how to go between Detectron2 and fiftyone datasets/structures. - -.. include:: detectron2_nb.rst - -For more information, see `fiftyone docs on evaluation `_. diff --git a/docs/source/tutorials/detectron2_nb.ipynb b/docs/source/tutorials/detectron2_nb.ipynb deleted file mode 100644 index f63378e7..00000000 --- a/docs/source/tutorials/detectron2_nb.ipynb +++ /dev/null @@ -1,1615 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cb58acae", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "## 2. Detectron2\n", - "\n", - "- Going between detectron2 & fiftyone\n", - "- Annexing detectron2 results to fiftyone\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "bc06938b", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_69566/3813857106.py:5: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "markdown", - "id": "3dd6d39f", - "metadata": {}, - "source": [ - "In this chapter we look into fiftyone/detectron2 interface, how to add detectron2 results into a fiftyone dataset and how to evaluate detectron2 results with fiftyone. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4d171492", - "metadata": {}, - "outputs": [], - "source": [ - "# common libs\n", - "import math, os, io, json, cv2, random, logging, datetime\n", - "import numpy as np\n", - "# torch\n", - "import torch\n", - "from torchvision import transforms\n", - "# images\n", - "from PIL import Image\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ce06206d", - "metadata": {}, - "outputs": [], - "source": [ - "# define a helper function \n", - "def cv2_imshow(img):\n", - " img2 = img[:,:,::-1]\n", - " plt.figure(figsize=(12, 9))\n", - " plt.axis('off')\n", - " plt.imshow(img2)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1db77483", - "metadata": {}, - "outputs": [], - "source": [ - "## *** Detectron imports ***\n", - "import detectron2\n", - "from detectron2.utils.logger import setup_logger\n", - "setup_logger()\n", - "\n", - "# import some common detectron2 utilities\n", - "from detectron2 import model_zoo\n", - "from detectron2.engine import DefaultPredictor\n", - "from detectron2.config import get_cfg\n", - "from detectron2.utils.visualizer import Visualizer\n", - "from detectron2.data import MetadataCatalog, DatasetCatalog" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e246d463", - "metadata": {}, - "outputs": [], - "source": [ - "# CompressAI-Vision\n", - "from compressai_vision.conversion import FO2DetectronDataset # convert fiftyone dataset to Detectron2 dataset\n", - "from compressai_vision.conversion import detectron251 # convert Detectron2 results to fiftyone format\n", - "from compressai_vision.evaluation.fo import annexPredictions # crunch a complete fiftyone dataset through Detectron2 predictor and add the predictions to the fiftyone dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f386b4c6", - "metadata": {}, - "outputs": [], - "source": [ - "# fiftyone\n", - "import fiftyone as fo\n", - "import fiftyone.zoo as foz" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d503052c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cpu\n" - ] - } - ], - "source": [ - "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", - "print(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f2648552", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch: 1.9.1+cu102 / cuda: 10.2 / detectron2: 0.6\n" - ] - } - ], - "source": [ - "print(\"torch:\", torch.__version__, \"/ cuda:\", torch.version.cuda, \"/ detectron2:\", detectron2.__version__)" - ] - }, - { - "cell_type": "markdown", - "id": "8d55e552", - "metadata": {}, - "source": [ - "Let's pick up correct Detectron2 model" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a9593bcc", - "metadata": {}, - "outputs": [], - "source": [ - "## MODEL A\n", - "model_name=\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\"\n", - "## look here:\n", - "## https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md#faster-r-cnn\n", - "\n", - "## MODEL B\n", - "# model_name=\"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\"" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "42a20652", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "expected input colorspace: BGR\n", - "loaded datasets: PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000\n", - "PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000\n", - "PROPOSAL_FILES_TEST: ()\n", - "PROPOSAL_FILES_TRAIN: ()\n", - "TEST: ('coco_2017_val',)\n", - "TRAIN: ('coco_2017_train',)\n", - "model was trained with coco_2017_train\n" - ] - } - ], - "source": [ - "# cfg encapsulates the model architecture & weights, also threshold parameter, metadata, etc.\n", - "cfg = get_cfg()\n", - "cfg.MODEL.DEVICE=device\n", - "# load config from a file:\n", - "cfg.merge_from_file(model_zoo.get_config_file(model_name))\n", - "# DO NOT TOUCH THRESHOLD WHEN DOING EVALUATION:\n", - "# too big a threshold will cut the smallest values & affect the precision(recall) curves & evaluation results\n", - "# the default value is 0.05\n", - "# value of 0.01 saturates the results (they don't change at lower values)\n", - "# cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5\n", - "# get weights\n", - "cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(model_name)\n", - "print(\"expected input colorspace:\", cfg.INPUT.FORMAT)\n", - "print(\"loaded datasets:\", cfg.DATASETS)\n", - "model_dataset=cfg.DATASETS.TRAIN[0]\n", - "print(\"model was trained with\", model_dataset)\n", - "model_meta=MetadataCatalog.get(model_dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "1ab5cd0a", - "metadata": {}, - "outputs": [], - "source": [ - "predictor = DefaultPredictor(cfg)" - ] - }, - { - "cell_type": "markdown", - "id": "ebf24534", - "metadata": {}, - "source": [ - "Get handle to a dataset. We will be using the ``oiv6-mpeg-v1`` dataset. Please go through the CLI Tutorials in order to produce this dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "cf1def5b", - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fo.load_dataset(\"oiv6-mpeg-detection-v1\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b988f86b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Name: oiv6-mpeg-detection-v1\n", - "Media type: image\n", - "Num samples: 5000\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " open_images_id: fiftyone.core.fields.StringField" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset" - ] - }, - { - "cell_type": "markdown", - "id": "74ce4f17", - "metadata": {}, - "source": [ - "We can go from fiftyone dataset to Detectron2 dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "43418d14", - "metadata": {}, - "outputs": [], - "source": [ - "detectron_dataset=FO2DetectronDataset(fo_dataset=dataset, model_catids=model_meta.thing_classes)" - ] - }, - { - "cell_type": "markdown", - "id": "848bdf24", - "metadata": {}, - "source": [ - "Pick a sample:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "1371472f", - "metadata": {}, - "outputs": [], - "source": [ - "d=detectron_dataset[3]" - ] - }, - { - "cell_type": "markdown", - "id": "6a9ee2f4", - "metadata": {}, - "source": [ - "We can visualize that sample also with Detectron2 library tools (although we'd prefer fiftyone with ``fo.launch_app(dataset)``):" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "fe0f0050", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# visualize with Detectron2 tools only\n", - "img = cv2.imread(d[\"file_name\"])\n", - "visualizer = Visualizer(img[:, :, ::-1], metadata=model_meta, scale=0.5)\n", - "out = visualizer.draw_dataset_dict(d)\n", - "cv2_imshow(out.get_image()[:, :, ::-1])" - ] - }, - { - "cell_type": "markdown", - "id": "930af36a", - "metadata": {}, - "source": [ - "Let's try the Detectron2 predictor:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "15ff5a32", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n", - "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.)\n", - " return torch.floor_divide(self, other)\n" - ] - } - ], - "source": [ - "res=predictor(img)" - ] - }, - { - "cell_type": "markdown", - "id": "77d2dbf8", - "metadata": {}, - "source": [ - "We can convert from Detectron2 format to fiftyone detection objects:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "99d98794", - "metadata": {}, - "outputs": [], - "source": [ - "dets=detectron251(res, model_catids=model_meta.thing_classes) # process involves going from class indexes (ints) to class labels (strings)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "53e4578a", - "metadata": { - "tags": [ - "scroll-output" - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - ",\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]),\n", - "}>" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dets" - ] - }, - { - "cell_type": "markdown", - "id": "e55008f4", - "metadata": {}, - "source": [ - "Let's run each image in a fiftyone dataset through the predictor. Results from the predictor will be annexed to the same fiftyone dataset. We use the dummy single-sample dataset ``oiv6-mpeg-detection-v1-dummy`` created in the CLI tutorials with the ``compressai-vision import-custom`` command" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "e30d0386", - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fo.load_dataset(\"oiv6-mpeg-detection-v1-dummy\")" - ] - }, - { - "cell_type": "markdown", - "id": "57117b6b", - "metadata": {}, - "source": [ - "Detectron prediction results are saved during the run into the fiftyone (mongodb) database. Let's define a unique name for the sample field where the detectron results will be saved:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "5f9009d8", - "metadata": {}, - "outputs": [], - "source": [ - "predictor_field='detectron-predictions'" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "199d9b0a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sample: 1 / 1\n" - ] - } - ], - "source": [ - "annexPredictions(predictors=[predictor], fo_dataset=dataset, predictor_fields=[predictor_field])" - ] - }, - { - "cell_type": "markdown", - "id": "4d290fd1", - "metadata": {}, - "source": [ - "After that one, the dataset looks slightly different. Take note that an extra field ``detectron-predictions`` has appeared into the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "9f4e86f6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Name: oiv6-mpeg-detection-v1-dummy\n", - "Media type: image\n", - "Num samples: 1\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " open_images_id: fiftyone.core.fields.StringField\n", - " detectron-predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n" - ] - } - ], - "source": [ - "print(dataset)" - ] - }, - { - "cell_type": "markdown", - "id": "1a8aa67f", - "metadata": {}, - "source": [ - "Let's peek at the first sample:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "bba9fa3a", - "metadata": {}, - "outputs": [], - "source": [ - "sample=dataset.first()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "a13478f8", - "metadata": { - "tags": [ - "scroll-output" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ",\n", - " ]),\n", - " 'logits': None,\n", - " }>,\n", - " 'negative_labels': ,\n", - " 'detections': ,\n", - " ]),\n", - " }>,\n", - " 'open_images_id': '0001eeaf4aed83f9',\n", - " 'detectron-predictions': ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]),\n", - " }>,\n", - "}>\n" - ] - } - ], - "source": [ - "print(sample)" - ] - }, - { - "cell_type": "markdown", - "id": "655772f8", - "metadata": {}, - "source": [ - "Each sample in the dataset contains \"detections\" (ground truths) and \"detectron-predictions\" (predicted values). Now we can run the OpenImageV6 evaluation protocol on the dataset which uses the ground truth and the predictor results:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "31f64ee9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Evaluating detections...\n", - " 100% |█████████████████████| 1/1 [25.5ms elapsed, 0s remaining, 39.2 samples/s] \n" - ] - } - ], - "source": [ - "results = dataset.evaluate_detections(\n", - " predictor_field,\n", - " gt_field=\"detections\",\n", - " method=\"open-images\",\n", - " pos_label_field=\"positive_labels\",\n", - " neg_label_field=\"negative_labels\",\n", - " expand_pred_hierarchy=False,\n", - " expand_gt_hierarchy=False\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6dd04a30", - "metadata": {}, - "source": [ - "After the evaluation we can should remove the detectron results from the database:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "59f0b6d4", - "metadata": {}, - "outputs": [], - "source": [ - "dataset.delete_sample_fields(predictor_field)" - ] - }, - { - "cell_type": "markdown", - "id": "0e01cbe0", - "metadata": {}, - "source": [ - "OpenImageV6 evaluation protocol mAP:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "70385f6e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results.mAP()" - ] - }, - { - "cell_type": "markdown", - "id": "08f382c8", - "metadata": {}, - "source": [ - "Per class mAP:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "a18fb749", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "airplane 1.0\n" - ] - } - ], - "source": [ - "classes = dataset.distinct(\n", - " \"detections.detections.label\"\n", - ")\n", - "for class_ in classes:\n", - " print(class_, results.mAP([class_]))" - ] - }, - { - "cell_type": "markdown", - "id": "25f94465", - "metadata": {}, - "source": [ - "In practice (and what the CLI program does) it is a better idea to create a copy of the complete dataset into a temporary dataset for appending detection results (especially if you are sharing datasets in your grid/cluster) and after getting the mAP results to remove the temporary dataset. On how to do this, please refer to the fiftyone documentation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "315a96eb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/detectron2_nb.rst b/docs/source/tutorials/detectron2_nb.rst deleted file mode 100644 index de80644a..00000000 --- a/docs/source/tutorials/detectron2_nb.rst +++ /dev/null @@ -1,1233 +0,0 @@ -In this chapter we look into fiftyone/detectron2 interface, how to add -detectron2 results into a fiftyone dataset and how to evaluate -detectron2 results with fiftyone. - -.. code:: ipython3 - - # common libs - import math, os, io, json, cv2, random, logging, datetime - import numpy as np - # torch - import torch - from torchvision import transforms - # images - from PIL import Image - import matplotlib.pyplot as plt - -.. code:: ipython3 - - # define a helper function - def cv2_imshow(img): - img2 = img[:,:,::-1] - plt.figure(figsize=(12, 9)) - plt.axis('off') - plt.imshow(img2) - plt.show() - -.. code:: ipython3 - - ## *** Detectron imports *** - import detectron2 - from detectron2.utils.logger import setup_logger - setup_logger() - - # import some common detectron2 utilities - from detectron2 import model_zoo - from detectron2.engine import DefaultPredictor - from detectron2.config import get_cfg - from detectron2.utils.visualizer import Visualizer - from detectron2.data import MetadataCatalog, DatasetCatalog - -.. code:: ipython3 - - # CompressAI-Vision - from compressai_vision.conversion import FO2DetectronDataset # convert fiftyone dataset to Detectron2 dataset - from compressai_vision.conversion import detectron251 # convert Detectron2 results to fiftyone format - from compressai_vision.evaluation.fo import annexPredictions # crunch a complete fiftyone dataset through Detectron2 predictor and add the predictions to the fiftyone dataset - -.. code:: ipython3 - - # fiftyone - import fiftyone as fo - import fiftyone.zoo as foz - -.. code:: ipython3 - - device = 'cuda' if torch.cuda.is_available() else 'cpu' - print(device) - - -.. code-block:: text - - cpu - - -.. code:: ipython3 - - print("torch:", torch.__version__, "/ cuda:", torch.version.cuda, "/ detectron2:", detectron2.__version__) - - -.. code-block:: text - - torch: 1.9.1+cu102 / cuda: 10.2 / detectron2: 0.6 - - -Let’s pick up correct Detectron2 model - -.. code:: ipython3 - - ## MODEL A - model_name="COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml" - ## look here: - ## https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md#faster-r-cnn - - ## MODEL B - # model_name="COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml" - -.. code:: ipython3 - - # cfg encapsulates the model architecture & weights, also threshold parameter, metadata, etc. - cfg = get_cfg() - cfg.MODEL.DEVICE=device - # load config from a file: - cfg.merge_from_file(model_zoo.get_config_file(model_name)) - # DO NOT TOUCH THRESHOLD WHEN DOING EVALUATION: - # too big a threshold will cut the smallest values & affect the precision(recall) curves & evaluation results - # the default value is 0.05 - # value of 0.01 saturates the results (they don't change at lower values) - # cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 - # get weights - cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(model_name) - print("expected input colorspace:", cfg.INPUT.FORMAT) - print("loaded datasets:", cfg.DATASETS) - model_dataset=cfg.DATASETS.TRAIN[0] - print("model was trained with", model_dataset) - model_meta=MetadataCatalog.get(model_dataset) - - -.. code-block:: text - - expected input colorspace: BGR - loaded datasets: PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 - PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 - PROPOSAL_FILES_TEST: () - PROPOSAL_FILES_TRAIN: () - TEST: ('coco_2017_val',) - TRAIN: ('coco_2017_train',) - model was trained with coco_2017_train - - -.. code:: ipython3 - - predictor = DefaultPredictor(cfg) - -Get handle to a dataset. We will be using the ``oiv6-mpeg-v1`` dataset. -Please go through the CLI Tutorials in order to produce this dataset. - -.. code:: ipython3 - - dataset = fo.load_dataset("oiv6-mpeg-detection-v1") - -.. code:: ipython3 - - dataset - - - - -.. parsed-literal:: - - Name: oiv6-mpeg-detection-v1 - Media type: image - Num samples: 5000 - Persistent: True - Tags: [] - Sample fields: - id: fiftyone.core.fields.ObjectIdField - filepath: fiftyone.core.fields.StringField - tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) - metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata) - positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications) - negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications) - detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - open_images_id: fiftyone.core.fields.StringField - - - -We can go from fiftyone dataset to Detectron2 dataset: - -.. code:: ipython3 - - detectron_dataset=FO2DetectronDataset(fo_dataset=dataset, model_catids=model_meta.thing_classes) - -Pick a sample: - -.. code:: ipython3 - - d=detectron_dataset[3] - -We can visualize that sample also with Detectron2 library tools -(although we’d prefer fiftyone with ``fo.launch_app(dataset)``): - -.. code:: ipython3 - - # visualize with Detectron2 tools only - img = cv2.imread(d["file_name"]) - visualizer = Visualizer(img[:, :, ::-1], metadata=model_meta, scale=0.5) - out = visualizer.draw_dataset_dict(d) - cv2_imshow(out.get_image()[:, :, ::-1]) - - - -.. image:: detectron2_nb_files/detectron2_nb_20_0.png - - -Let’s try the Detectron2 predictor: - -.. code:: ipython3 - - res=predictor(img) - - -.. code-block:: text - - /home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. - To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.) - return torch.floor_divide(self, other) - - -We can convert from Detectron2 format to fiftyone detection objects: - -.. code:: ipython3 - - dets=detectron251(res, model_catids=model_meta.thing_classes) # process involves going from class indexes (ints) to class labels (strings) - -.. code:: ipython3 - - dets - - - - -.. parsed-literal:: - - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - ]), - }> - - - -Let’s run each image in a fiftyone dataset through the predictor. -Results from the predictor will be annexed to the same fiftyone dataset. -We use the dummy single-sample dataset ``oiv6-mpeg-detection-v1-dummy`` -created in the CLI tutorials with the -``compressai-vision import-custom`` command - -.. code:: ipython3 - - dataset = fo.load_dataset("oiv6-mpeg-detection-v1-dummy") - -Detectron prediction results are saved during the run into the fiftyone -(mongodb) database. Let’s define a unique name for the sample field -where the detectron results will be saved: - -.. code:: ipython3 - - predictor_field='detectron-predictions' - -.. code:: ipython3 - - annexPredictions(predictors=[predictor], fo_dataset=dataset, predictor_fields=[predictor_field]) - - -.. code-block:: text - - sample: 1 / 1 - - -After that one, the dataset looks slightly different. Take note that an -extra field ``detectron-predictions`` has appeared into the dataset: - -.. code:: ipython3 - - print(dataset) - - -.. code-block:: text - - Name: oiv6-mpeg-detection-v1-dummy - Media type: image - Num samples: 1 - Persistent: True - Tags: [] - Sample fields: - id: fiftyone.core.fields.ObjectIdField - filepath: fiftyone.core.fields.StringField - tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) - metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata) - positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications) - negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications) - detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - open_images_id: fiftyone.core.fields.StringField - detectron-predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - - -Let’s peek at the first sample: - -.. code:: ipython3 - - sample=dataset.first() - -.. code:: ipython3 - - print(sample) - - -.. code-block:: text - - , - ]), - 'logits': None, - }>, - 'negative_labels': , - 'detections': , - ]), - }>, - 'open_images_id': '0001eeaf4aed83f9', - 'detectron-predictions': , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - , - ]), - }>, - }> - - -Each sample in the dataset contains “detections” (ground truths) and -“detectron-predictions” (predicted values). Now we can run the -OpenImageV6 evaluation protocol on the dataset which uses the ground -truth and the predictor results: - -.. code:: ipython3 - - results = dataset.evaluate_detections( - predictor_field, - gt_field="detections", - method="open-images", - pos_label_field="positive_labels", - neg_label_field="negative_labels", - expand_pred_hierarchy=False, - expand_gt_hierarchy=False - ) - - -.. code-block:: text - - Evaluating detections... - 100% |█████████████████████| 1/1 [25.5ms elapsed, 0s remaining, 39.2 samples/s] - - -After the evaluation we can should remove the detectron results from the -database: - -.. code:: ipython3 - - dataset.delete_sample_fields(predictor_field) - -OpenImageV6 evaluation protocol mAP: - -.. code:: ipython3 - - results.mAP() - - - - -.. parsed-literal:: - - 1.0 - - - -Per class mAP: - -.. code:: ipython3 - - classes = dataset.distinct( - "detections.detections.label" - ) - for class_ in classes: - print(class_, results.mAP([class_])) - - -.. code-block:: text - - airplane 1.0 - - -In practice (and what the CLI program does) it is a better idea to -create a copy of the complete dataset into a temporary dataset for -appending detection results (especially if you are sharing datasets in -your grid/cluster) and after getting the mAP results to remove the -temporary dataset. On how to do this, please refer to the fiftyone -documentation. - diff --git a/docs/source/tutorials/detectron2_nb_files/detectron2_nb_18_0.png b/docs/source/tutorials/detectron2_nb_files/detectron2_nb_18_0.png deleted file mode 100644 index 6d825590..00000000 Binary files a/docs/source/tutorials/detectron2_nb_files/detectron2_nb_18_0.png and /dev/null differ diff --git a/docs/source/tutorials/detectron2_nb_files/detectron2_nb_20_0.png b/docs/source/tutorials/detectron2_nb_files/detectron2_nb_20_0.png deleted file mode 100644 index 9fdff6dd..00000000 Binary files a/docs/source/tutorials/detectron2_nb_files/detectron2_nb_20_0.png and /dev/null differ diff --git a/docs/source/tutorials/detectron2_nb_files/detectron2_nb_21_0.png b/docs/source/tutorials/detectron2_nb_files/detectron2_nb_21_0.png deleted file mode 100644 index 9fdff6dd..00000000 Binary files a/docs/source/tutorials/detectron2_nb_files/detectron2_nb_21_0.png and /dev/null differ diff --git a/docs/source/tutorials/dog_512.png b/docs/source/tutorials/dog_512.png deleted file mode 100644 index 9fd3ce8d..00000000 Binary files a/docs/source/tutorials/dog_512.png and /dev/null differ diff --git a/docs/source/tutorials/download.rst b/docs/source/tutorials/download.rst deleted file mode 100644 index f097849a..00000000 --- a/docs/source/tutorials/download.rst +++ /dev/null @@ -1,10 +0,0 @@ - -1. Download Images ------------------- - -:download:`[download tutorial as notebook]` - -Download necessary images from the OpenImageV6 image set. - -.. include:: download_nb.rst - diff --git a/docs/source/tutorials/download_nb.ipynb b/docs/source/tutorials/download_nb.ipynb deleted file mode 100644 index e5fa5707..00000000 --- a/docs/source/tutorials/download_nb.ipynb +++ /dev/null @@ -1,513 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "282d38cf", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "## 01. Download\n", - "\n", - "- Download, inspect & visualize a subset of OpenImageV6 dataset\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "bc06938b", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_69165/1348678174.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "markdown", - "id": "984f65af", - "metadata": {}, - "source": [ - "In this chapter we use fiftyone to download, inspect and visualize a subset of OpenImageV6 images" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4d171492", - "metadata": {}, - "outputs": [], - "source": [ - "# common libs\n", - "import math, os, io, json, cv2, random, logging\n", - "import numpy as np\n", - "# images\n", - "from PIL import Image\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f386b4c6", - "metadata": {}, - "outputs": [], - "source": [ - "# fiftyone\n", - "import fiftyone as fo\n", - "import fiftyone.zoo as foz" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "dc9d610a", - "metadata": {}, - "outputs": [], - "source": [ - "# CompressAI-Vision\n", - "from compressai_vision.conversion import imageIdFileList" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "cb0e19a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "your home path is /home/sampsa\n", - "fiftyone dowloads data by default to /home/sampsa/fiftyone\n" - ] - } - ], - "source": [ - "homie=os.path.expanduser(\"~\")\n", - "print(\"your home path is\", homie)\n", - "fodir=os.path.join(homie,'fiftyone')\n", - "print(\"fiftyone dowloads data by default to\", fodir)\n", - "try:\n", - " os.mkdir(fodir)\n", - "except FileExistsError:\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "id": "72f304c3", - "metadata": {}, - "source": [ - "List all datasets (already) registered to fiftyone" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f8765e0e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-22-40-319395',\n", - " 'detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-24-14-478278',\n", - " 'flir-image-rgb-v1',\n", - " 'oiv6-mpeg-detection-v1',\n", - " 'oiv6-mpeg-detection-v1-dummy',\n", - " 'oiv6-mpeg-segmentation-v1',\n", - " 'open-images-v6-validation',\n", - " 'quickstart',\n", - " 'quickstart-video',\n", - " 'sfu-hw-objects-v1',\n", - " 'tvd-image-detection-v1',\n", - " 'tvd-image-segmentation-v1',\n", - " 'tvd-object-tracking-v1']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fo.list_datasets()" - ] - }, - { - "cell_type": "markdown", - "id": "9abc17bb", - "metadata": {}, - "source": [ - "We use files listing image ids in order to download a subset of OpenImageV6.\n", - "\n", - "Let's use two files: ``detection_validation_input_5k.lst`` and ``segmentation_validation_input_5k.lst``" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "d5dea558", - "metadata": { - "tags": [ - "remove_tag" - ] - }, - "outputs": [], - "source": [ - "path_to_list_file=\"/home/sampsa/silo/interdigital/CompressAI-Vision/compressai_vision/data/mpeg_vcm_data\"" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "ca66ed00", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bef50424c62d12c5.jpg\r\n", - "c540d9c96b6a79a2.jpg\r\n", - "a1b20ed591193c06.jpg\r\n", - "945d6f685752e31b.jpg\r\n", - "d18700eda95548c8.jpg\r\n", - "e2c7ea356ccf3729.jpg\r\n", - "44cee71a77765756.jpg\r\n", - "a63d569332c49ee5.jpg\r\n", - "16774edaeacc5aed.jpg\r\n", - "2e96665b867c4d0f.jpg\r\n" - ] - } - ], - "source": [ - "!head -n10 {path_to_list_file}/detection_validation_input_5k.lst" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6b2866d7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8189\n" - ] - } - ], - "source": [ - "det_lst=os.path.join(path_to_mpeg_vcm_files,\"detection_validation_input_5k.lst\")\n", - "seg_lst=os.path.join(path_to_mpeg_vcm_files, \"segmentation_validation_input_5k.lst\")\n", - "assert(os.path.exists(det_lst)), \"missing file \"+det_lst\n", - "assert(os.path.exists(seg_lst)), \"missing file \"+seg_lst\n", - "lis=imageIdFileList(det_lst, seg_lst)\n", - "print(len(lis))" - ] - }, - { - "cell_type": "markdown", - "id": "c9f4c3f8", - "metadata": {}, - "source": [ - "Tell fiftyone to load the correct subset of OpenImageV6 dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "dea5e9ff", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading split 'validation' to '/home/sampsa/fiftyone/open-images-v6/validation' if necessary\n", - "Necessary images already downloaded\n", - "Existing download of split 'validation' is sufficient\n", - "Loading existing dataset 'open-images-v6-validation'. To reload from disk, either delete the existing dataset or provide a custom `dataset_name` to use\n" - ] - } - ], - "source": [ - "# https://voxel51.com/docs/fiftyone/user_guide/dataset_zoo/datasets.html#dataset-zoo-open-images-v6\n", - "dataset = foz.load_zoo_dataset(\n", - " \"open-images-v6\",\n", - " split=\"validation\",\n", - " # label_types=(\"detections\", \"classifications\", \"relationships\", \"segmentations\") # this is the default\n", - " image_ids=lis\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "a38e5a34", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Name: open-images-v6-validation\n", - "Media type: image\n", - "Num samples: 8189\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " open_images_id: fiftyone.core.fields.StringField\n", - " relationships: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " segmentations: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# take a look at the dataset\n", - "dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "3822fdfb", - "metadata": {}, - "outputs": [], - "source": [ - "# make dataset persistent .. next time you import fiftyone it's still available (loaded into the mongodb that's running in the background)\n", - "dataset.persistent=True" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d826993a", - "metadata": {}, - "outputs": [], - "source": [ - "# next time you need it, load it with:\n", - "dataset = fo.load_dataset(\"open-images-v6-validation\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "32f893d2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ",\n", - " ]),\n", - " 'logits': None,\n", - " }>,\n", - " 'negative_labels': ,\n", - " 'detections': ,\n", - " ]),\n", - " }>,\n", - " 'open_images_id': '0001eeaf4aed83f9',\n", - " 'relationships': None,\n", - " 'segmentations': None,\n", - "}>" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# peek at first sample\n", - "dataset.first()" - ] - }, - { - "cell_type": "markdown", - "id": "c18fa7c7", - "metadata": {}, - "source": [ - "Let's take a look where fiftyone downloaded the files" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a0cd90dd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "contents of /home/sampsa/fiftyone/open-images-v6 :\n", - "/home/sampsa/fiftyone/open-images-v6\r\n", - "├── info.json\r\n", - "└── validation\r\n", - " ├── data [8189 entries exceeds filelimit, not opening dir]\r\n", - " ├── labels\r\n", - " │   ├── classifications.csv\r\n", - " │   ├── detections.csv\r\n", - " │   ├── masks [16 entries exceeds filelimit, not opening dir]\r\n", - " │   ├── relationships.csv\r\n", - " │   └── segmentations.csv\r\n", - " └── metadata\r\n", - " ├── attributes.csv\r\n", - " ├── classes.csv\r\n", - " ├── hierarchy.json\r\n", - " ├── image_ids.csv\r\n", - " └── segmentation_classes.csv\r\n", - "\r\n", - "5 directories, 10 files\r\n" - ] - } - ], - "source": [ - "dir_=os.path.join(fodir,\"open-images-v6\")\n", - "print(\"contents of\", dir_,\":\")\n", - "!tree --filelimit=10 $dir_ | cat" - ] - }, - { - "cell_type": "markdown", - "id": "e40a30e9", - "metadata": {}, - "source": [ - "if you'd like to remove it, do this:\n", - "```\n", - "fo.delete_dataset(\"open-images-v6-validation\")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "c0dac1b1", - "metadata": {}, - "source": [ - "visualize the dataset with\n", - "```\n", - "session = fo.launch_app(dataset)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9451ae70", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "vscode": { - "interpreter": { - "hash": "3037665f174b3a6fb0f50efe07aa50417522d3f7584d9a5dd4e8c45d17b52a0c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/download_nb.rst b/docs/source/tutorials/download_nb.rst deleted file mode 100644 index 244e9427..00000000 --- a/docs/source/tutorials/download_nb.rst +++ /dev/null @@ -1,276 +0,0 @@ -In this chapter we use fiftyone to download, inspect and visualize a -subset of OpenImageV6 images - -.. code:: ipython3 - - # common libs - import math, os, io, json, cv2, random, logging - import numpy as np - # images - from PIL import Image - import matplotlib.pyplot as plt - -.. code:: ipython3 - - # fiftyone - import fiftyone as fo - import fiftyone.zoo as foz - -.. code:: ipython3 - - # CompressAI-Vision - from compressai_vision.conversion import imageIdFileList - -.. code:: ipython3 - - homie=os.path.expanduser("~") - print("your home path is", homie) - fodir=os.path.join(homie,'fiftyone') - print("fiftyone dowloads data by default to", fodir) - try: - os.mkdir(fodir) - except FileExistsError: - pass - - -.. code-block:: text - - your home path is /home/sampsa - fiftyone dowloads data by default to /home/sampsa/fiftyone - - -List all datasets (already) registered to fiftyone - -.. code:: ipython3 - - fo.list_datasets() - - - - -.. parsed-literal:: - - ['detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-22-40-319395', - 'detectron-run-sampsa-oiv6-mpeg-detection-v1-2022-11-16-17-24-14-478278', - 'flir-image-rgb-v1', - 'oiv6-mpeg-detection-v1', - 'oiv6-mpeg-detection-v1-dummy', - 'oiv6-mpeg-segmentation-v1', - 'open-images-v6-validation', - 'quickstart', - 'quickstart-video', - 'sfu-hw-objects-v1', - 'tvd-image-detection-v1', - 'tvd-image-segmentation-v1', - 'tvd-object-tracking-v1'] - - - -We use files listing image ids in order to download a subset of -OpenImageV6. - -Let’s use two files: ``detection_validation_input_5k.lst`` and -``segmentation_validation_input_5k.lst`` - -.. code:: ipython3 - - path_to_list_file="/home/sampsa/silo/interdigital/CompressAI-Vision/compressai_vision/data/mpeg_vcm_data" - -.. code:: ipython3 - - !head -n10 {path_to_list_file}/detection_validation_input_5k.lst - - -.. code-block:: text - - bef50424c62d12c5.jpg - c540d9c96b6a79a2.jpg - a1b20ed591193c06.jpg - 945d6f685752e31b.jpg - d18700eda95548c8.jpg - e2c7ea356ccf3729.jpg - 44cee71a77765756.jpg - a63d569332c49ee5.jpg - 16774edaeacc5aed.jpg - 2e96665b867c4d0f.jpg - - -.. code:: ipython3 - - det_lst=os.path.join(path_to_mpeg_vcm_files,"detection_validation_input_5k.lst") - seg_lst=os.path.join(path_to_mpeg_vcm_files, "segmentation_validation_input_5k.lst") - assert(os.path.exists(det_lst)), "missing file "+det_lst - assert(os.path.exists(seg_lst)), "missing file "+seg_lst - lis=imageIdFileList(det_lst, seg_lst) - print(len(lis)) - - -.. code-block:: text - - 8189 - - -Tell fiftyone to load the correct subset of OpenImageV6 dataset: - -.. code:: ipython3 - - # https://voxel51.com/docs/fiftyone/user_guide/dataset_zoo/datasets.html#dataset-zoo-open-images-v6 - dataset = foz.load_zoo_dataset( - "open-images-v6", - split="validation", - # label_types=("detections", "classifications", "relationships", "segmentations") # this is the default - image_ids=lis - ) - - -.. code-block:: text - - Downloading split 'validation' to '/home/sampsa/fiftyone/open-images-v6/validation' if necessary - Necessary images already downloaded - Existing download of split 'validation' is sufficient - Loading existing dataset 'open-images-v6-validation'. To reload from disk, either delete the existing dataset or provide a custom `dataset_name` to use - - -.. code:: ipython3 - - # take a look at the dataset - dataset - - - - -.. parsed-literal:: - - Name: open-images-v6-validation - Media type: image - Num samples: 8189 - Persistent: True - Tags: [] - Sample fields: - id: fiftyone.core.fields.ObjectIdField - filepath: fiftyone.core.fields.StringField - tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) - metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata) - positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications) - negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications) - detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - open_images_id: fiftyone.core.fields.StringField - relationships: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - segmentations: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - - - -.. code:: ipython3 - - # make dataset persistent .. next time you import fiftyone it's still available (loaded into the mongodb that's running in the background) - dataset.persistent=True - -.. code:: ipython3 - - # next time you need it, load it with: - dataset = fo.load_dataset("open-images-v6-validation") - -.. code:: ipython3 - - # peek at first sample - dataset.first() - - - - -.. parsed-literal:: - - , - ]), - 'logits': None, - }>, - 'negative_labels': , - 'detections': , - ]), - }>, - 'open_images_id': '0001eeaf4aed83f9', - 'relationships': None, - 'segmentations': None, - }> - - - -Let’s take a look where fiftyone downloaded the files - -.. code:: ipython3 - - dir_=os.path.join(fodir,"open-images-v6") - print("contents of", dir_,":") - !tree --filelimit=10 $dir_ | cat - - -.. code-block:: text - - contents of /home/sampsa/fiftyone/open-images-v6 : - /home/sampsa/fiftyone/open-images-v6 - ├── info.json - └── validation - ├── data [8189 entries exceeds filelimit, not opening dir] - ├── labels - │   ├── classifications.csv - │   ├── detections.csv - │   ├── masks [16 entries exceeds filelimit, not opening dir] - │   ├── relationships.csv - │   └── segmentations.csv - └── metadata - ├── attributes.csv - ├── classes.csv - ├── hierarchy.json - ├── image_ids.csv - └── segmentation_classes.csv - - 5 directories, 10 files - - -if you’d like to remove it, do this: - -:: - - fo.delete_dataset("open-images-v6-validation") - -visualize the dataset with - -:: - - session = fo.launch_app(dataset) - diff --git a/docs/source/tutorials/encdec.rst b/docs/source/tutorials/encdec.rst deleted file mode 100644 index e1f8a73c..00000000 --- a/docs/source/tutorials/encdec.rst +++ /dev/null @@ -1,9 +0,0 @@ -.. _encdec: - -4. Creating an EncoderDecoder class ------------------------------------ - -In this tutorial we create a custom EncoderDecoder class to use with ``CompressAI-Vision`` :ref:`evaluation pipelines `. - -.. include:: encdec_nb.rst - diff --git a/docs/source/tutorials/encdec_nb.ipynb b/docs/source/tutorials/encdec_nb.ipynb deleted file mode 100644 index 234e5cdd..00000000 --- a/docs/source/tutorials/encdec_nb.ipynb +++ /dev/null @@ -1,273 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "effc7f25", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "## 5. Write a custom EncodingDecoding class" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c65e873a", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_71761/3813857106.py:5: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "markdown", - "id": "efbad679", - "metadata": {}, - "source": [ - "In this chapter we show how to create your very own EncoderDecoder class.\n", - "\n", - "In order to evaluate your model with our framework, you need a model that works with the ``CompressAIEncoderDecoder`` class. This was discussed in the CLI tutorial. If you need more flexibility, you can write your own ``EncoderDecoder`` class and this is quite simple.\n", - "\n", - "Here we demo a simple ``EncoderDecoder`` class that encodes & decodes using jpeg." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4f30032f", - "metadata": {}, - "outputs": [], - "source": [ - "import logging, io, cv2\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from PIL import Image\n", - "from compressai_vision.evaluation.pipeline import EncoderDecoder" - ] - }, - { - "cell_type": "markdown", - "id": "35d2a54e", - "metadata": {}, - "source": [ - "In the constructor, instantiate a logger and save the provided quality parameter." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4d3786f8", - "metadata": {}, - "outputs": [], - "source": [ - "class JpegEncoderDecoder(EncoderDecoder):\n", - " \n", - " def __init__(self, qp=10):\n", - " self.logger = logging.getLogger(self.__class__.__name__)\n", - " self.qp=qp\n", - " self.reset() # not used in this class" - ] - }, - { - "cell_type": "markdown", - "id": "32c1fc41", - "metadata": {}, - "source": [ - "Define how the image is encoded + decoded and how the bitrate is calculated. We are using BGR since it is the default input format for Detectron2 predictors." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c7649ee2", - "metadata": {}, - "outputs": [], - "source": [ - " def BGR(self, bgr_image, tag=None):\n", - " # bgr_image: numpy BGR24 image: (y,x,3)\n", - " # tag could be used to identify images if we want to cache them\n", - " # BGR -> RGB (as PIL works with RGB)\n", - " rgb_image = bgr_image[:,:,::-1]\n", - " pil_img=Image.fromarray(rgb_image).convert(\"RGB\")\n", - " tmp = io.BytesIO()\n", - " # encode image\n", - " pil_img.save(tmp, format=\"jpeg\", quality=self.qp)\n", - " tmp.seek(0)\n", - " # calculate bits-per-pixel\n", - " filesize = tmp.getbuffer().nbytes\n", - " bpp = filesize * float(8) / (pil_img.size[0] * pil_img.size[1])\n", - " # decode image back\n", - " pil_img2 = Image.open(tmp).convert(\"RGB\")\n", - " # back to BGR\n", - " rgb_image=np.array(pil_img2)\n", - " bgr_image=rgb_image[:,:,::-1]\n", - " # transformed image, bits-per-pixel ready\n", - " return bgr_image, bpp" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5e1429f4", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "# monkey-patch the class (this way it was nicer to write the tutorial)\n", - "# this cell will be hidden with the \"remove_cell\" tag\n", - "JpegEncoderDecoder.BGR=BGR" - ] - }, - { - "cell_type": "markdown", - "id": "68439532", - "metadata": {}, - "source": [ - "So, we have a compact class that defines, in a single method, all necessary transformations and calculates the bitrate. \n", - "\n", - "Once you define an ``EncoderDecoder`` class like this, you can use it with all the rest of the infrastructure provided by ``CompressAI-Vision`` library.\n", - "\n", - "Next, let's see ``JpegEncoderDecoder`` in action." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "fbef6ce7", - "metadata": {}, - "outputs": [], - "source": [ - "bgr_image=cv2.imread(\"dog_512.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1f373226", - "metadata": {}, - "outputs": [], - "source": [ - "encdec=JpegEncoderDecoder(qp=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3cb28617", - "metadata": {}, - "outputs": [], - "source": [ - "transformed_bgr_image, bpp = encdec.BGR(bgr_image)" - ] - }, - { - "cell_type": "markdown", - "id": "37613b44", - "metadata": {}, - "source": [ - "Print bits-per-pixel, compare original and transformed image" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "451fbab5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BPP= 0.16878255208333334\n" - ] - }, - { - "data": { - "image/png": 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6SGlglqvkhaCz2uDmm9d55527tB2DeD4hziSbGw28qqC/+22KXGHmUwxnidAgMwwcpeg/GDIMC/LS342X4Ev+4WAYBrquM5/P6ff7jEYjfN8/N59SisFg8FKixkXL/20xGAy+tAMvpcR1XdbX1ymVSvR6vXPzvMz3+XXg/oP7Z32DoijQNI2iKM59pzzPL1x+NBqh6zpSSoQQ5zrup6RpeuFxehmRBiCO43PL67rO9evXz83b7/dxXfdMXAjDkCiKXmo7fx9RStHv9wnD8GwwPk1T6vX6L1xAqVQqeJ6HZVkopc5EjuVy+Qvdzt8Wd+7cIc9zdnd36fV69Ho91tbW6HQ6Z4Ly3t4e0+mUVquFlBJN01hdXUUIQb/fJwiCM5FqPB5zeHh47l6ytbXF3bt3n5tmGMY5gRSgKAree+89HMfh7bff5uDggMFgQL1eZ3t7m/X1dVZWVlBKnQmXSimSJCEIAo6Pj8/az4lQM0MIha7//UrKOT5WOE5KvZ5SLpfZ2Njg7t27xHHMZDLhk08+YXd3l4ODEffvx9RqsLKis7JSo1ar8tZbb50T908HQn4e/kah5sUL46Lf/zqCRpz9hOcfMl8UaoQQaEIDIcilAjSEMiikZBrPsesVZGwSTHOSUNFUJskkobHmkYUxy2xKQYAUBUmeYLgGXgGT3pxaq0Gr3WQUBORhRsPrYFfKPOvdx3QEq40Vgo7C1HWcCiyiBY93BywOfYwHGrqVs7U+YTGYkJopi9mYsj5BLMocPhngDyK6qyt0rlQoeQ4Vr0RhpiyyEKPikEUpT/tHHIZT7rx9h8PRAmEaPHt2wGI5ZU8qgoOITmeFIPAJowC3ZeHqHrVKE5npbG1cIUyWhPMFioLbr28hShmZt+DKax7DmSIaC4Qw0ExBveMRx1MarRLZIianQGQWw36IYTk4bhs/DRkvpmAK0uwhu/OnNMwVrqyu4tVANEMe776PU3+Hj+9/wts3OpQrLv0oZLg/oly8ir9UKAeigSQZF3SrFoEZ0N5c4eHup2xsX0E3WsTBkvFwxDLu0dGrpDIhDTNyTfD40z7N+jplqyBcDAhGI3RZQiUlWp0yr//GbSw7olVpcGQf0bpWw+7EFJGGKarYQhHnEpnqbK5u86RR58HDe6TlnCjNqNZKaCWBnwwRukYWpTjSI+7H+LOAeDZneZBx7fYtGvUqqAI5U7iehj+IefD+DrfevoGruaysbKPrJk6akqU+09mAxacjFtOUr9xtUegjFoWDq7f4+JN7jOcDkAXL+ZLtxhppkhMmAunqiDwjXyqk0vl45326t6dc27xJyalQr6ww06fMlofUvS5aDkJIiqhE3b4G8ackgSA1JP5kjm4cI4SGVCmGAYYjQWgIrSDPEtIio2qXSWVCHPmYlSqaZWBLC8OpEE5DDCHQLcj1nGUWkxqCWEryLMMzLDAMiqQAXaAKRZ5nWMIiThMKPm+zgExTRJ6hWyY5ikIT6KaBZSiEbmBqOkITJDKhUAaqkDiOiWXrBH6IISTj+YBcZZgAmiIpCrJ5wEa3C6gTsUumPH62h1lq8XDvALOA1za2WVm3WW/9NqbuYKsqjtpGs0vobsx09hTXrrHdWeNK+RXKYgU/GRLExwQ+tKxN1t0OsmqR60tqay7V4DUUC7Y2FL3DfVa8q+z3DlksUryKi6YJkiwhyQoM3QJNEKcJQRSTpCloGqZtoekatm2jAYUskEoRRiFZlhJPY2q1CoYm0FDoOpiuie0YmKZJLjPSLMVxa4Rhir+YE/gzijxnrdvFNASz6QLPLrOYzcizBWGQEixjoiykXLG4+somz56OcPM6nmeRpT5ff/V1NN1ktgiJFz7PBo/xiwTdkrzx9h3WtroYnkTYFmmu8eTeIR89O2JtTfHm3dco4ozPPv2YME65dqOGa9codR2qpTqRHhPoGrFwUYWDXLa42m7xJBjyyptvsVG5w/ff/XPWtzZ4/e07DI8GpPmCazfXufPK20ih4Qc5qWkhDJ2lmdPcbrO8P+dG41WmvRkPPvmM/YMBosgRuoHtWdSqJZI0xfM8NrfWcGyTZztP0A2dvMhB19F1k2S2IIgiSGIcafPZ/QdoQL3aQClBo9HEMHWGgyFKgmlYBIsEQ1hgSCQpgpORw0JKDENDihw0DYyCiuNhGoIw8hEaWKZGnmdM5yGJlOiaQaXkMpvOaWFjmRa6npGlBZnKaa3UiIaCRZEhC508zRlrkqtfuUO9qKIlJn/xF3/FIo4om9AyS0jdIrUUlZLD9kad7fUGzZU6TauKVuuimxZK86jYDqlWkBU5FiajecFkNqOolnAswehgSm7a6OrXZ0T4kksATNPEMAyCIGB3d5dHjw6IY8Hdu6vn5h0MBhRF8RPX+WViwS8apRTD4fCciJBlGbquU6lUaDQaVKtVNE3j+Pj4ufmEELRarV+rSI0v48GDB0RRRKVSodlsUqlUyLKM4XD43HxfJqiMRmOUkiil0DSNLMvORpu/yEXrBGg2m+eifC4iiqJzx9kwDK5du3Zu3uFwyObmJpubm+R5zsHBAfP5/Cdu4+8zg8GAIAiAk+szyzIcx/mFCzXlchnHcTAM40y803X974xQMxwOUUqR5zme57GyskK5XKbValGtVpnP59y/fx/fD3jttVcpl8vU63VarRau6zIcDlksFvT7/bPolVKpdO44X3S/03X9wijINE0ZjaZUqyfiQ6/XYzqdsb29zVtvvcX29jZ5nrO/v8+77757JpDdvHkTpRTj8Zjlcvl5lM+E8Tin0TB4Icjn7zy+D8slXLt2EvGyubnJ6uoqvV6PR48esbe3R5qmNJt1SqUF3a7NK69c4dVX77K+vs6NGzfORVGmafpSz6a/iZeKqPliapMQJ6PDXxRmLgo5FQKE0NA0DU0ITt8DpQJd0yikIs8K0mWKKXKq26vkJclRf4Yny2w0Nng02GNndMyt7W30WCNbJCymx5hVnVzkZGlBSdh4tkvj+g06lSbtjRX0xZSnjw/5zg++w/atO3zwoyeUbcF2t4tj2QRFQLVeJgpSpkcz7nReZXI8YTQ/IPEVh4c9uhubGFGH7//Vp+zdm0EsWG138CyXV167Q7AYsLW1xfuPfoTUXJQt0TTzpMMUZXz7xx8QFwXNeovFZEi9UibMEqSmM/UXJEnM+vpVBoMenm0hZEzgh6x2OxRSMk+WSJERyIQkmdJ0JP1wxvFCEqsGuS7QXRNpZVQdHZka+OMl5qggXRZ4no2MHVbrK4ymexhaCVlI0Cz8KGezVqPRrBHmPrXSJtP9hMefHjDt+3ww+TGvvnGNw90jKmmbjtykCDU+++xdys4Kr6+/Sq0Fu5Nd/uq7H/C4N6TXT3j9td/EMTKubrSIkiUPHr6HqZUIFiH1egPXMBkdTtno1lAtxYPP9piPF7zx+u9RmD5GKyGI52jGktf/q+tgKwI9ZDYu6LbrxDIiC2IMYWHk0Gms8+HBu6QPHmE1XESsMD2N+WKGaZRIJwI5lQSTOYZmstJso5Mz7h3TXK8SaEuCmYVWaLRLFYRpsVzEWNeqtB2H7voKwfEzjIVGEAbEJNgVm0XSp/9sl2gKSTzCKrcYjSSartCFx2gRk4YFEgebnMlwzD9655+SpzN+9GnCzr1dkkHGje5daLpMix57B0/42s0/5H4w5vBwn8a1a+iWSbt1C+3ZZ0TZDLtUYTz1WfghhmlQyJzMyDFyxWK8wHQcijwBI8G1dAIEwXLBla1t5iMfLbdYTqZU7DKGa6IcA3IbZYYUCWSJxNIURZogdDBNA6ELZC6RUpKlKZbjUOQFiZ9QJCmuZ5JlClNYSKUDAksXhMsJSrcwnRJCM2jVmvRHfebJFMcUuLrCNDRyqYgzRbVSJkoUszSlXmtRduskxZwsL0iTnFxoJHFOfzCn6hhM4hEttYmuuRRFjp7VaDlvowmL+HpGerRgMHhGOD6i7a1RadZQ5hilpXTbV2i6HXRRpuy2WLCP4QZ4tRZxskJnteBbv3MDz9jk2c4+jx47eOWcvf0+uYzRdA0LSZ4VIASa0BFKoKFhoGEbBtrnKZ5pGhKnMVmWY1omlqah6xpS5ujmSRiqZTnkWYHjGCTZycuqbVgI28CoKNbW2ty7/wxTs2nWK2QhGBoYwiQMfZqtFq2Wwe7BE44XEzIR0ljv8Frnm6hizGrZpEgznEqF3l6P3eGAiT/DEiZu3SZNJYUU6KbF48Mhf/G9T9ntL3DNKv3+Dg/cmK3OJq+/8QpxNqfZ0uhe66KUQV2t8p9+9F/4ePcer22+jlsqcecb17Af77JVu4rb2OSD735Ef77HRAZ8/XffphZVWNlo4rYqbN5Yo+HU8RyPP/3OX/H99/9fzMYDaq0a//U3/gmvXnmbf/Pv/m+MD4+xhUQ3dBzbxTJtDGGCIZBZxnHvEK/kUqqUKGTBYukjKPCDkCCMSNIEy7YwPl8+y3LG0wUiFwTLgDAM0DSDUtljMY+IowRd1xEiw9QBKcmSgiIvUFIhsxRNM9AtqFYd0jwkS09EOClzLMfm2uoaz57uozKJrnR0zWI0WJCLCkoEOLqN47mYusXm1bvsfvpDNE0iY0kQZWS6ojBjfvjt/8xONOJf/OG3uPLuu+w+mvIoWPD1f/7bXG21qTWa6I0GJbeGIS1kHKFUjDI1lkIxV4r5YsFw9xH7/V0Gx2NWXn+bzu01Hj/cR1oaevL3K4T5kr/7GIaBpmlEUUSvN+Lx4xQhTF4YRAZgOp2+VBi+pmm/tBSKi1IDoig6E6BO00eyLGN3d/fcfjabzb8TQs3e3j6TyZj19XUcx6FarVIUxbn0FyEElmWd+07zeUqWBc+lcui6fk6oyfP8wlS6F1PRvow0Tc+EhlO+7FqYTqesr6/TaXdIkoTHjx+zXC5xXfeltvX3DaUUk8mEOI6xLOvsuK2vr//Ct+W5Ho1mA6UUcRyT5/nfmRQbpRTf//73cV2XVqvF5uYmtVqN5XJJtVplc3MTKSWDwYhnz3xarWO63S7NZvNMuP3444/PUpJOP+vr6+ciMy667jVNo9VqnZsexzGzWYZhJLTbbXTdZLmMKZfL3L55m82VTQazAT/4wXv8u3/3n7l1a5WvfOVtvvrVr7K+ts7/+H/5Hz/flxnjcc54DKUSf++EGoA4hjQt8DyPTruDZVkcHx+zs7PDfD7n1q1bdLtdfH/K7du3+MY3vsE7X3+HjdUNCopzkZAHhweMJz97CjD8BKHmy/xovuhZc+pB88WompMX3M+FHD73qFEnyxXqc2VJAQWooGD9+gqu57KQOUcPhlRFgGxL8iwmDhL+43/8T3z9N9/Cqjms1itYjRKTPMKf+xRZSpwWVCtdhvMhkZHiFxlux0FYBn/27b9iMU9pXV2l3VmjnOU8PnjG06d9Uj/hWneNr915k80/uMX/+d/+H7n/0QGDwwHTRkpnZYXZfoSt6bhND9PTUZpkupjgOSa9wYBUKvb2jljvbLIM5tgll1a3y9Pjp+RJytHOMUIVxPOYas3DKzmoQoGpc9Qb41Zr6E6FWEK12+TZ8Q7zaR9dN3AcDz8UuCJmsLNAFk3MahthuuiWwe7BHqYrUU6CPwzR8xIPHva4snWDhZiwGGlomoNrtdlYuc3B0WOE0lhZvUISZewejNgZHRNNNTp2hyd/eUj3SpWd3YeMno5YLiKsesHhwQ6v/8ZX8B66bK1fQznwFx/+Bc+Gx7z34VOckmA8GFPVD1lva4TZhJWtVeaJIopyNB1m/oTumsvS1TCNGXt7B6SRIMsTOmtNEstisPyIVsskswqybIZ/HHAwiKhlWyhNIVyLCmXeuPYmVgxfef232d0/pFA5NmWi4ZzpbImsaeRZjiGrjJ8eU9Nt/CRlOPDRvBi7axPrEsPQUXWJpnQwJbpdEOUTDDelXLepNFwW1Ra9jweYuo0pNKIgI/B9/IWNJk2q9RqlhsciHoMoMISHpgRSU0hhMJotmGoZR9M+n3z2l6QyRvdNRMnkbucr+AT4+Yi2s4Wu+5Q9nSvdm5RrkNnHCC+lWqkQL+YkWQwFoINCkRQZORpFnGEpEy3XsXCQWYzApGKXiZKQ5XREGhcY0qFIJG7VxbFLJBQUQqcs2pi6ZBD3SJ0c1zCxbAtVFCgKxEliE47pItAQssAoNGy7jNAkWpbjGjaWaZMVMBkPQaYI00C3BRiCLA2wNIFjm7hWwXpzDT2FmUgRyuLG1Wss/Yj86BlIjd7RgM2bHXrHA2iWKZRBuMjolFeolDUyoTGeztlsZSg9BikRGCgtx60WhHs9xsEu80IxzK5gqRE5OsoZsEh8DCwaxh10T+IVFlqRoixFjqJqvE6tbCKKAh2bOBpy640K7YdV+v0lz/aOWIY5uZQITccQBho6qlDYmo6hFEoWhHGI0EGqnHanzXS+RKq/FrtzHUp2mTjKsS0L2zFRKHRNYGomnbU2cbwEPadaKqEJiyKTtJp10iRA5TpJWpDnEXlRYDkGw9GC494u3/qtDqEaYNkphauoV8vMZj65VaAcHTO3aZQqvP3OmzRbbTxrg8L3+MGHf4qy67RXygzmPYJ0yng25Otf/R1GwwP8zGeltUI/7OMZDfb2PuKDj77P8pOA0j+qULpzg2l4jLstkGHMztMnfPe9P8UuaSx8nwf3HrPsj+mPxshBn3KjgZE9pmS1+c73fsxw5xiV5dhFmcdPj/i3/8ufMPMPURqUXBtHNyiiBJVpJDJHKkmhcpJMsvRnCE0gNMFy4SMQWNZJexRK0ahXkUiWizkygzTJEdIg9KMT8c2yCIMTUU3TACFxXA1Ng6xQIE6ESJkp8kInjeITEXM+w7QhTzNMw2KZBDheSpYrDE2SiZwwDjBsQZomDHsRnU4NzXKxlMbBo31G0icrwEpy8kTi6A6j6YK8IpjmR9z6zess8l1e++YmZhzgVu5ASWOcJ3y2u0tN09mWFWxNUUZh5RHLOGQ0Cbn/bI/x8RHxYkymC8pWHblM+PSDp+weLYmUg5I/34jPJX93sGwLpRKm0ylhGLK/v8/a2tq5F/w4js8JCEIINjY2XiqCoV6vI4Tgo48+YrlccuPGDa5fv87e3t65eX/nt3+HBw8f8Ed/9Oc8fhzheXD16gPKZZPlcklRpGxvG5TLJba2tjBN82ykdz6fI4Q4N5K5WCzOpau4rkuz2Tz3nTqdzrl98n3/XBSFpmkvLQysrq6ee4eezWbEcUwYhjx79ozj42OklOe2I6Xkj//4+7TbNnfu3OTOnTtsbW2ddRa+yHg8pt/vfy76n3SiHcc5J3TA305E0ebmBrVaFcdxiOP4LArg8ePHZ/tk2zaWZV2YBvDmm68gZY5tm2d+L5qmnUshOBVxPv74YwaDwVnqx3Q6fan0J6XUuZFty7IYDocURYFt22fiWVEUlEolBoMBUkparRa+75/5vFiWRafTIY5j+v3+c+s8FaReHMAej6ekaUy1WqXValGr1TBN81yq12k01ou8bJrRabTLi7Tb7TNflIcPH7KyssLGxgYffPABw+GQb3zjGyil+Pjjj1ldXT23zo2NDTRNQwjBbDYjDENeeeWVc+dJ13WyLGNnZ4cwDFlfX78wHQdOoqFePHeGaZAkCY7jnAkU29vbdDodfN9nOp0ymUwIguDn8ls67bNubW2xurrKbDZjZ2eHcrl8zvslz/OXbjtHR0dEUUQURUwmE2zb5rXXXmNjY4M0TcmyjO3tbcrlANM0z/rQeZ6f+cAkSXImulQqFdbX16nX689tZ2t9i7X1NT746AP29/cxTfMsou1FPvzwQ1otD9PU+Q//4T8wHI7Y3FwjjmO+853vYFs2SZZwfNxjY2MV0zTp9Xp873vfw/O8M4Gu212h0VCkKXiexgW3mJfm+HhA8sIAkWlqGMZPfrYopTg8zHkxUKXV0imXf/Z0rFIJXBeSJOKDDz5gsVjQ6XQ4PDxE13X+4A/+gJs3b3JwcMDt27colUocHh7y58GfY2om3Y3zvmT9QZ/ZbPbctNMBg9NnTLttE8dfnlr5UqlPX3zYvOg586I/zRfngQvCHTUNISSGrgOK1E+xMEjnEY1Kmdu1Vcg06pUKtpGz9cp1BusNDNNgMlmCKFHRbVZrXerllCL3mfb2GQ17LOYBjI+YJRFetUKztoosCjL/xG9gOvWx6gayiLAKhWN7tCpNslLIbtTHV4rDD56iZS7hcE5vdw7oWCWo1Gw67RpeySbPY5orGzzZe0whCnJlMRwv0BDUSjaHg138xRyVWUx7c4LlkkaniuPZWHlBgcQyHcpWlWt3rpNXFNNJgO8HWJpNu3KdOEvxg5gsTln6S+q2wmq7OI0GQpWp1HWkNmLYH6NCxfJwiOG3qVWbeCUN4WqgJP39CWmU8tbNGwiVMotHzIYpG/UOKrYRfpNtr0Q1Nbl+/RWO/E+Z7j/BT0PKVoWVq112+o9xdmssipgHRw8ZP/CZzkOivODa2jr1joNdicniIcugwnTsUzRMzHKZw/6ASrlOnEV0Og2u3ypz/73HTMZLXrv2TR7s7PPtH/4Xuq+3qNrg9nNyWyNIBeagycf/4Ud861u3sLcMojCjW92gJAyC9Jj94zE3797h0d4DjMwhmC9IdY1wnjMez3nzlZvsTY/RjJxSycPQNWgKcjfDM20MzaLWEAilM59GpDInjxX9xSG+JuhHIbP5jHG2QCmXqlbCNTWCqeLa5nVazQqzZEohYwxNQ0kDU5ckyRLdhXq9StIPqJRrjJczuu3XmM+XzLMxgVogOhHNkoWcOZTbKVFxwDI75MaVdxjP++z05+R6xtaVDqOPjtD1giRN0IWHoRlYrsciBKQgSzJ0KZCY9BcDyuUKtudhC5PUj5GFThiFWKZF2S1TK7dYEDOezfjNu/+YMIj4iB8Q2TMcyyTPM4QmiJMYTRjYroUsFEIpSCSdVoeSV2Wnv4ehG6hEUnM84ixjpiy2rm6ydeUKD54+orNdQ6UhdyubSCG599ln3Lmyxi1rjQcHfd5XPeaLlJJjs9lo0h9HRCpl3J9h5oIkLpDK4c72q2gip9HWUFpBsFgQFGPq5ohCB6kZGIbBdNgDy8duCLLUYGg8YLZ4jD+FJM7JlhXeef1temIHxQRZZCAMbMCxqriFh2FXidMpzS2HV2vXsU2Hr7+zSsmy+bO/+BHf+cGnRGmI67rEaYoqAqq1Mp6jI2WBRFDIDK9SolIpU3YqZFIRBBFS5sRRhIaDnaeUa3WqpSoyC9FdG1nAaDhlcDzCtDSUSGm3amQ5dLttyiWHankdmdrMl1WkljBfLrBNC5nYVGot3nv3YxIRoutwfXuDf/XP/xU/+Oi7HI73CWWO5hoE1hJZCjieLriycYNpLyHICmprHVgssFOLjtNi6+p13nvvO0wmcxZyzCgd8M9+8ybzJ1Pe/eA9/NCnbJWZHCz5o8f/kUfP7lP2PF67dpd3P/mMtIhwbI+VlXVmvZDhfh8hPJb+jP/47/+/ZH5G4EvyzOR/94f/im//2Z8yXkz5zrf/BLNUZmttm0rVYxlM8cMlhS1BFNSqFVzPYekvKFU8siJHooijkHqtRhQkBH5AHKU0WlVqNZcsT4ljSaoUs/ECy3DxSiW8ko1hG8zncxzXwDA1TEsDIbENkzBKCcIUEERxSJIa6EJxMuKgEErDtUsUmUYWKYosJg9ShALdMHGrBkLoZGlBnqUoqcj9BM0E07IZPB1jC50sz3A9k96jfb4dHdEoaQx3F2hWnzs33qKplbl9RXL4ySPmYcbHz3o8y3zsbpOSW+P6ndf43a+8SVNmEBYExyOK+QxT5sSqwBQWvZ2nmONjDhKPRWQToyG0l/N5uOTvNgLQtZMX4SRJSJIE4Kzz+EUuMgr9aSI8vuhreLqeLzP8rVRPfCmUSgjDEXkOR0dTHMf4fF1Qr+tUKgblcvlsudPvoGnauXWfGmZ+kYvECyEEruteEOkxPzs+p7xsZ00I8dx+nnIaBl8UBYvFgsViceHyJ4LCjCSpMJ1OOTg4IEkSoig6JzacvvSfDgBEUUSe54RheO69/G/D96ZarZ51TqSUhGFIlmV4nnd2DWRZ9qViSqdzXvjK85w4jp+bpmnaWcf2NB3GNE2SJPmZBahTse+Lx8kwTt4jkiRhMBw8N++pMKDrOp7nYRjGS6dEJUlKFEW4rothGHieh23b577nabrPi+fKsiw8z/uJ2/my9JfRaMTGxgZRFLG/v39mJHwqYH1pn42T6/mNN94418Z0XT+3/7quY1kWcRwTBAFKKVzXvXC9L4o8p+R5ftYuXdelXquzvbnNZHZiWN3r9ZjNZnzyySc/t/hYLpfPhNpTke3F4/zTtJ0gCM726VSwMQwD13UJw5A8z6nVKpimfubDBX99v4rjmKIozq6x03158V409+cETwMePXrEfD7n2rVrtFqtC43NDw8P8byTe9/Ozg5w0p7u37/P/fv3n/ueq6vls+/xxQg00zQxTZMLbms/E7ZtIcTPfj86ea48P02pn88zxzRPnjVS5oxGI+Ak5TIMQ0zT5Nq1a2xsbHB8fHz2vDw9x8CJv+QLz5jTtLEvousn5348HpOmKY6jsVx++XX8U6U+XeRBcxpV8+L8pwZJL07X4OTdFlBCkKQ5ruXSWilxeHxIs1FCxyRTEZWyjlFJqWxoREHEYjkmHMxoBBGvO2tE0wjdMShiQalaJc1h4+oqO0e7lGoVJoMxnUYZKwMpY3rDY5zI4u3X7qDJgsU8YzHPycMYmaS8vf0m4njObBFSmBpKFtjKRtcL1jpNXn3rFqkssESO0qBcr5NGEVIqCikxDQspFAt/Rpoq9FwHpeGWPSrtKsrRcMsV0jzBMEzKepk0TcjiAiUgKRLSIidKBUiFEB6aWUCe0fRa6JkgXUKn2kVPJ1RKNkm5YLQ3QcUeBg6u7SFVjD+JmB7MUYGGKXSmsyGd1TWS4yWWsOk0KsTTJU1WaOkm66tNaqstJvf2aJZq3Hz1NQoZ49Z0Pvywhxf0ufP6G+zMHlCp2vhBwfr6Glfv1pBuSiKXvP/BZxzOU6RmsLMTEcsx8+mcr7zdZRYMmWiCIAqQhktltYGoScptD0OmSCPBjVOqPZ3IqqMcm8X4pMJUb3xMYy4xrAplwyWcBTw53MWu16gbK8T3PkZIH3SD9koXHAVyTq1aodaokEdLckMipEEW6QivQOgZGTma7iJFhlbK0FOBVyqx9H2iFNJiiTQVSaoRDAIGyxmd5hqN6iqv3L3Ks4NHLOP5SRrG5xd2moakSUy91UToiiJLKJVMlulT3njlLSZhg3waosycj/a/w8pqg/HoiPFkjmlXcEsOeTEkCId89r0jNFtje+sahgn1SpPjwzESHQ2LRqnOakfjcGeXIF8yn/vkuUaeQe5wks4gJUKBKiST4wlrpQ6lssP61gZd12bwbg+nnNLuNvhgPwVykjAmyzNKpRKGZqIb5kmbFaAZAo2TtKvZbI4hDGqlCnqcg1Qocm6+vsI7v/UKuiXwhcnW1TqWVUYvMqb9FCM3mYRjzCtdTF+xpq0jDBtL5FjCYir2KVydcLHgzrVNmk2TVrtOq76BXcrQnAnD4ZJlEJPrA0L2MY0NYjFD6T6qGtGwKmR6Rsn08DzB4f6IunEdxzLpxyZ54aKYEGaHhL7E8WpojkJl4Oqg64okEhwN51gV6DTXsBSYSvBb7/xThsc5+6Mdas06o/GYIFyQyxShuTiWTZSklEoe1UqZLE/p9XvIQqAbBigN09BRmmAezPnaO1+jiCSHOweYtsvR4BB/sUTTDNI0Rzck12+ts/B9btzexnVsTErI0MLQBIn0qVabTKcBYxY06mWi/gLNNpAyZbQMiQqDJNNQuo5p5GhKkouUMJzQbKwSRQFxAq/euUZvOiZZBlTqLoOjEU8ePqDIYmyzhoHL7oM5f6m+i7aEWTwlk/Ctr/7X5JFiZ+c+rlkjjws++PgxRSIpcsHCX2DoFW69eovlcEQYKY4HY6SZ4WkWeZLSWWtjdxxu3n6D1mKGEgt6ywNee+s2K80mf/GXf4quK6QJSqUIq6C7tcr8wYzpYnZiWG2ZlL0yq+0O9+8/YpossByTZruOUzKxlcb6xgrj/gzbMAn8mPZKm5W1Fk+ePWNto4NlaiRphGacjAxqSpBHEalMaTaqxFGGFBLXMdE1HcuwUDJH1zWiKEGkAr2wyFOJ7VhUyjUMLyfNE2SSIzMDgU48S8g0SX21TVxRBPMEw9YwLYtJb8Dvvf0O1dGYR5/2yBwXw71KoVdInRTLWVAq11laAVnZQhMaXl2nWYXJbJfBbEwR2IzmMYoy9XKNPJYMl31MT1HSJPNJSlboJxFD4u+XKeAlPz+WZVGtVp+b9mWp7hcRBAHL5fJslFfTtAsjBeBkFFopxZ07N7Bt8/POzs+f2/93FV3X+Y3f+I0zkevw8JD9/X3u3r17Lvqn1WqxsbHBcrlkPp8zn8+/1JD3l0Wr1WJtbe1sxPin3afTDu2LSCkpl8tnaVKj0ejnukaUUoxGo5cSIF9Mm4KT83RRlNDLRvn8bWBZFnfu3Dn3nS6quLVYLHAch5WVFebzOUqpc5EbcNKpv3Xr1rlO6Onx+yIvVif6RWAaJqu1VVq11ln1qdlsxv37938pvlNfFEx+EhcJhz9vytx8Pj93/Z0arPd6PRqNBo1Gg7W1NT766KNzy1/URgaDEbPZ8yKj6zpsbFwc/fSLptGon9uvn8a0u9US54SaL9H9fuEYhnFhFObLRJr+TNv7aWY+FWu+mPr0xelf5FT1Pv37KbrQEEJRSEDXifOUZRIhZxGTYEkuBZWSSc0zaDeamB1J7kSIMOV6u0y2gGlvzO6Th5TsClohqVVq1FbbtK6tYziKSjrDKZexnQrdukblqxUOh8fM/IDlMKCnT1jttKmWHAx9SV4U/OZbX+cbb38TTy/zox/9BcPxEZrpUMicaqVMrd7Aj0IqjRJaIplMJxRI8lhDi3LSKEEJyehgju6amNJkPJhh6xZr2+ssixlChzxVKE1DWDrlRp1QRiRjHz8uyLIIyxZgmKhIoJkFSgiQDtMkpW17GFYZgclnH39G800Px9bpGE2e+UOW0keYJsI3qGkbVPQGRUvSn49QUcjoMGOwt8Q0UmZCI58mlEo6vmHwyf6AwXt7DEdTtMKj3z/iaLTHw3sl4iOJ1zG4e+s1tIHBMDjALFXoXu9g1gDTZHo8RliCOE9JopQ19zp5qIgHQ97/kw9ZWa8znE3QHI14KbFqK8iS4PbXuxw8m5KoBHsuMQ80ZNsmwkBYFe68dpesMaWfjblWa2IIOOodkJkZk3CfstZidWWVx08e8to7b+O1dcxSwtd/5zau0cIwV3nyZIESKTIS4JtgSqySxLRddEyiPMQtlVCmhb8ImI/nmMKgUm0R5nPM3EEsUqQsyGObtZuvoVmK48WAOMxwrIiKUzoxG01DkjjgcC8mlwm24ZBniqe7n3F0tI8pdOrVKobh8ODpQ+7vn3TgqlaF7VqdSqNJyTCRxzqDwwi7apAXO+S5pL/vI/IKtYaDUnBl8yqVssK2FWmaMe37zI99Fn6MaWvEyZIsSri6foPUzwiJqZWqSFHweO8Rne0rdJorfO+T76GMglAt0ZUgDRIKlVOYDnlaoOs2eZajGTqFUiAkM39GxarhKotgusTUBYEMqHRKXH2twiLfJ5inFDLh2ZNnbN/2yI4FxbSMygxmy5yPB33effwAz7iFaSRE4SGxX8K0TQKZUS/XWW22WMRjjg52sRyLaR4y3jtiNo5wDJtF1sfV6lT0BpE6xo+OSImZLn1mixR31eb+wyPUrMX1K79B1exSaRdkvkluxyxnU/Z35ly/+SY4kBsJ02yBkY8ol9qs5VdJ5RSl5UQqICkS6ise73ztGgd//JjZfEpBflKyO0/JVY6SCiUUjmmxnM9JsxTT8BCagW07pGmCKiQKHctSrG80ee+7nzAZL9H0mCRMMTQdwzJQmsL1LMpli3Z3DYyUSr3OcmCTL8o0Gx0Wvkla6Hztazc5PNohDnxMd4N5GOIHM167c5dc5iig6bWZJxN0W7C5cYWaXWOt3aXe9qCIePZJn72DQ8bDCVmWkgkNjQLPM9la2ebpwVPMzOXTD56hpERXklwYhHZBpeQhTQvXcDBtg1q5ztOH94njlDTPGI7H3Hv4MWub64TzgMOxhtQc8kSCFExnY/7TX/4xba+L1rTIQ6hqdUQJfvzhD8myCKkylMiot2qUKh4Hx3uEaUhRKJI0wYgNEj1kPpth2Q4b6+vkIqPWLKMbkqIQSDJWug02N9cZj2YIQyfM5igtYX1rmyBYIoOEIIpI0hxTM1C6QLc1tq51SeIl08mSSsXD0i0c2yMKI8IkQsqMdFIQhwlJoRCWJEl01m7UKNccbDQWoyn9nk+21NFtk8UkJJM5umui6RmxyNGHM6bzgO0bN9h6cszOQPLwMMHrVphnis71Gygrp7uokeeCiu3SqTWJj5/y7HCMrjTUfB2ZVdBNl9FkwmAWMUsz3nrjdbbKNf5q7wGgMKQkEz9HDPMlfy+xLIuNjY1z05fL5UuNLi+XSyaTCSsrK1iWxXQ65ejoiGq1eq4T+ezZM2q1Gl/96le5du0az5494+nTp0yn01/Y9/m7xhc7e6cRQ+12+1yJ6kqpguu4HB4f8ujRIxaLxbkoh182a2tr3L17l/39fR4/fvxT79NisTgXQXDa9zgtVbxYLDg6OjoTAX8WTkWwlxFqLtp/0zQvbCOLxeJXJtRcFAl3Ov1FZrMZjuPQarXOjkOn0zmXUnQadXM+nWt8Lmqt0+mcE3h/EahTQ//PI9J+mSJuuVz+0vStFzlNY/oiP694NR6Pz61TSsl0OqXX6xEEAfv7+3iex+Hh4bnlL0qF0zRxrmrTz9qOfhY6nc45YeO0FPtPQghBt3vebD2Kol+Kn5FhGD/Xs/Gn3t7f9EdN0z9v9KBpn0fTCIH6/B8ClDppQF+MtFFKnVNez8QcAUqeXCR5nlNoGp989IzVOx5G1UJJRWEWKM/GbFoIJ6ZRrTKfh4ye9Gm5q9hbqyTzCKPiUaQZWILjRQ8tN7FzC2XqJz4BhoPm6iTSZ+kvSMKCItdIIo3lrAAVUW9ZDIYJC3/BrRuv8tu/+0+pN0r8z//z/wlNaZiujVJwsHvMPKjRjgpWunU822V28Iz5KKbjtpFJjNJzttbXORwO0GJBOAy4+voa7UaVaLBEzwRSFmR5gB1LKl0Hw9VxQw2jCCkZgpE/RpXLCKUj0wW6AYVlMJcZS1OSBmPyQnI83md5v0yuJNFxyng8o8gdskhydeUGv/P1rxPEIx7sfMZiqWFqBk8f75CHOX4aEo5SHOGAnOB6Bk6lRGFoLJYhrgRf15gNI1bdGl95/S7v/PYbaGbM1e1ryF4CsX8yOl9ukMhddFexeeUqs8ETul6H1zbv8ODwM5JawHR/TmYVFHZCtdklXPq0WuuoTLJY6lSdW8yyHcwcKtU6//nDH5J2KuS6zdrNNkndRdcE4XzJ3vQB61dW+XR/j2m+oKRGdFY65LuSxXxGfavN9TdWKFddJDFXsjbjZJf5MsVwdNzQpNqpkDoTTMuCAizdg9zB1MtEi4T+ZEqr1SGfxMiSwqRMoWYUaKy0V7m6ucEiO2A+m5PFKfMsI6s3TiJLEFBozCdLNKEhTdh5eIwwYckc2xY0tTbSVpi6zWKeEAQR/WiGkCalZYznlGl2O/DRfcpOmWAZkKaKra3257neJQ4PDgmjCct4SoRPYZm0r9VptOsUScHR8RFG6qEXJQqVoiuDkl2l0ahTrTTYny7Ze/99hJvjVnQ0G6pm5eQho6WkSUKa5liWA0qh5QrD0AjiBAowZIapG2SJDkIRywTQqXmCNI+IxxGj/QDPajCZL/n4gx63rdcpWWXCZI5du85ffXyfReTzv/lnr/Pjv/orFpMlV7de5dHgHiqJaV5bpdAzJtMBSRQTJDFZkZNKyGTEtasOH3z8Cdsrklc2S0zDQwajQyrtEns7R4yOUmQiyeMSr61/hSvdtymxRqeWk8gRo0TH8xzaq1AqayiVIPQCVbj44Zg8DVlt3EGqDov8AZPZMcPpLp2aS33DwtIkyyhD2Dmmo+EaVWSRESY5eRIhUAhTxzQ9yl6ZOCrQ0XBtGz+IsXWb61evMjyaMB3MoRDkeYapmSjDYutKl3k0p9laAyGpuA2mwRHTRcH1jX+BcBs87P8XDNdGFxaGBanoU25ZtBsrBAudncFDrt1eZRGM6LTbtFWFneOIykqV9moH2zAgTxkN99nbG/LxZ4+QWYGhNCzbJVEGhXZSMrPfH6JSQeLnhHFKmiWYmobpafx//su/Z2OlC4ZESYWuTIajIaPpHD+KqTcaaMImjiLmiyWWrtPtrODnEYUn0dDRdANDV2RihlAWsYyJ45QP3vsAWwnKlTIq1TCwKFUrCA2UBm7FIy9ytFij7DiUKxYZkrXOFZJpzG5/l5m/wC07xElI2XOxSja6JWmt1ZhMFgTLBZWqQ39yjK5pZCpDyhTXtfHKFZxqiTiIyPIM03VotBWGrlGtVlld6bLzZAdlWti2Sa1U4vDJlHwMFDrxPOP42YLWWhVRAnQNTYckTLCVTaTnFFJS5AWFkkjDZLXcQExC/t/f+WNEFDLwYw4fPOOtZpXmxslLsL8cY9Y9rrTbXFldp0hjhv1P2WhIdNllMq8TSYOjwYiDcY9IBWiWSauxQjFPSWYpmtAo1EkZ+Ev+ISBOPueqfGnnppnmeU8RpRS+77/Uy2gYhiwWC27fvn1W7WQ0Gl3YiTs+PqZWq3Hr1i3SNEXTNPr9/j9YoeaknO4H1OtVrl69ytWrV1lbW+PKlSvnOn1lu0zNq6HrOsPh8Es9Sn6Z1Ct17t66i67rjEYjNE37qSNqXjz3Ukosy+L69eusra2RJAnj8RjP834uoWY8Hr/U9XxRet2XRdSc+rT8KkjTlE8//fTc9BeFLzjpWNq2Tbvd5t69ewghuHnz5oVCzYMHD851jE9T0b6I67q/cKEmzVL2+nvnUp9+WcfY87yXLrN8aoD8Rb4sxetlWSwW585Jt9tle3ubfn/A/v6Qb3/7O+zt7V1ouP3FwIlTVldX2Nx8XmwoiuLC6+Rvg0ajcc4L6Kep7NVsNs/7wfT7vzSh5qLIs3ORgKe3lXPPW/HCR/v855ds72/endMFv3ATE+Lzdf/134TgZBT5C1WgvpgmdYqmnZTllkKhaaAVEikNsthBZBauKdAFeJ5JJCQPB/voKsYu2wjNpl7u0imv4VZa9A57LP0hmjAIwhi9phHGPrG00TSDMAzQXQPPtEiDlGqpRLicoXSwSjZOuYxp6eQiRBkZWRFTqdRo1Vf4l/+rf8VH9z/hx+//EJFlRL4kWIYozSLLh+we7vPK1btEvZzlZMmrX7mDqkGY+TTbdcaLmO98/13efOstXnnnK8xnB9x2rrCx3iJZRphJnezIp3ywoNQ0SfIclStaN64wvjchmPtYGLRbVZSeYJd1TM+hN+8RRoJl44DmhsPgYIoSHrnm0GpcRS1tzDxCT3Pev/c9dvefsbt3hGmVmfuKIJSYpo2Ljo2FYzjUyiWEoRhGU8ySQ3WlgX84QfoFNiateolv/aM3qXQcChmBqVGvN5ntTbGkQZHNCBYj5js5Je8K690cVzkkfky7tYKt6VjBkHA+o7yicWNtnautMpnMGAdzZssyt9ZvYs4KkumUQy0nblR4vD+g3LGoO4qcCLXQGc8lWidj79EOQzVAWCaaNmeYZtRWyzx5cI+bX/kt7JIkS2PmyYydgwOOR30QLq7psnWrhmEVLHINUWTokU2cSGI/xjYcrAVMxykNwyEOCgpfogxJmufUGw3qTY/9o494tHuPNPRRuYEsNCaTOY5h0qzUOdpfIDKPJJborkm5aqJrFoXICcMpgV1Q123SNKbudSmWJtP5hN0HUwx7hh9HtFtr6Erj8OkR6zc6hPEYu9pk7/AhK80tNCvjycE9MhmgXIkSNpomqDo1apU6YqZQOmjoTA4mLHZD1hqbKJEjdbh26wbBs/eYREuaVhelUiQFmZ5hlQ2EDpGfUHIcgsBHpYo0yonyHN0wsTyTlBjhuZScBqkqmC0mZDLj0w/7OFhcad9iMhoQhAX9o4Bv/PZVPn34Psoz+eTpExZxgKFyvvuj/8xyuuDVV1/jzdffRn0wRetHDCcHHE0CCplim2VWG2VszUC4JqPggMPeSejnYSPk0aN9HN0izmI2VItnj0fYrNCpX2Vr4xUaXpfYOiLPCwpDR2lDNKbkSUhzwyVVBwRBQRFkrNc3SPIqwskZZT/ANFbI8FFKI1eSe08esVXf4NrWFfojn1F8hFuqg7KJ4jlKOsRBwvpqh/lyhqFXqJbq1Ms2vu9j2RquY5FnBdvdaxBpyEBRhBlClzSrdTrNm5QbLqY1oN3YZjwYsFBgN0tkeUJ3pUUsYfpggV5JWd9sc++Tj7DrOpojqdg2Fc/Dad9lOvcpaTq1lst4NmJlo8w8WJIVTQrN5tG9XRbLhKc7x6RLnUrJolqy6a6uMejP6S3GFJlFoSJK5RrDqY+u6VS9MkoT+HEAmWQn9KlX6rSbDZI8JIwChK2z3uqgY558NJsiy+nNh0RZQr1ZQ3MM8qTAREcYUCm5pFnBchmTpD7rG9ewNYFUGdEsQdN0wiggy3N03UAYAt3Q6TSrNKsVChGTowjzOVIoWis1/DDCDyIs2yTLCqZhwEarwvbaFvN3P8WSDpquSKICS7fRLQ0KQblaQ6qUQknaKzUs06CQBZZpo1DM/QXzYH5SPjScU6vXqDSq1JpNnn00ZNILKZYFUz8hCzI6myWcssH6lQZeNmc+W6JJD8c2WfgLdMMjT+EP/9v/AVMfUqkZ5BOf1+0yng6O0FkWGTEh0oT29S1S2+BoeMRgNOR6t8a1Spn5bAXTW2U/6DEcHJGKgGrb4dW7d2jXG4wOjgmmCShBrmto8te/uswlvwCUBkUD1PMdh1HPYGNlhRs3bhCGAUmS0qqXiKKUEwf7zxdX6nNz35crhW1ZFkmSEIbhmVfFRZx29k4NZw0M9p4dsLPzvJlxmmbnfF2EEJRKpXMdxlMPji9iWdaFZpsXdRBODXBf5MXtX3QslFIEQXAusuFlxQqlIMtsQKdcLrO6usr29jaWZZ3rBC7zJVEQEWcxruuytrZ2oT8OnAgTlUqFWq32XBWlRqOB53mfV9jqMZ1OLxxsXS6X547zRaknk+mEB589YDgfkmUnadRKKSqVyrl+ga7r5HlOFEWEYXjmbfLieoUQOI5DlmX4vk9RFDiOc2buHIbh2fJfrFR7imma564/pRTT6fSlhBrHcfA8D9d1z34ahnFhBMCpb89f71NCqeTRbNbOyk9fFHnxN3HqPfTi/tu2TavVOvPLOTWsfhHbtun1emRZhhCCIAiI4xjbtomiCNu2EUJceI0qpS70PrmojPqpb818PieOY3q9Hpqmcfv27XPLh2H4UveSoiiYzWYEQUAURWfXwtra2rk25vv+ue9gWdaFbcLzPMrlMrPZjOFwSBAE3Lhx45xfV71ePycKSCkvTIf7MpFquVyyWCxYLpcsl0uiKDrzlsrz/Ew0Oy3G8+K2Xjz3eZ5TKpWo1aqEIUynIVLuXOi3ddH+VyqVc6JvEAQXGshfuXKFoijOruckSV46DfbLrvPlcnlOFDpt0y9u/8V9h4tFwlNfrC9y6vvzMjgO6LqgVHLP0t0ajQabm5ukaXp2H/M870sHEU6fO3meMxpN8OdT5hOXUqlMrXbS/uM44tGjxwSBjj+voFKJob58H1/KTPjkx6mZkgKhITgRbMTnipAQzwszL3ra/DXq85voSYNWCpTUKWseW+tl9KrA0CXC8bj/6BGNSg1LL0GhY2iK8SJA+SmL5RiVBSgpkAUYlAgDH7tQCN0gWPhU9CphHGBpOqWaxapWYTL0mfrHGK6BJTwO+nsYSvDgyUdsdG6w8BPWrm3xL//7/4Eff/Rj9EKiNI1cnORiVqSLQrKb9fj93/1dZBrzrPcE23Zpddp8/NGnHO5PuPnqHa5/5RUm5JSade62q9SrGsu+T3WiU1hl1P6cbK5hdDQWes7HT+8jHBs3ASET/CDELufcvrrCYDwnC3JalQ45MxDQ9jpEvmAWWzTNDs3VOl5JghHz4cMPmI/nuO4KTmMF0zFZb26RZz7+5ICKZZKGEctEIguNHEmlplGpd4iTjPnRGNe0kDImThcsAg1LL5MbCXlRsN29xfH8U+LkkHzhIg4qqKbOm2/dZriY0rs35NnTATfXr/DOV1/l0dP3OVYP2J99zLdu/Dfo5YLvPn5I1StYv17gjFYZZQYfH7xLfXMDY7CgtV5mNJ1Sb7hEi5iqVWKmliyNFEOz0DAQCAoVs77eRC4yegdHCG+BDJss/BF2VsWQFo1Km1zTKAzIVYTKLZySTTYvSJYpaaCY53PSKMV1PGQOea6IwwTNUlSMMkahcdDbZRnNiTMfqQo2164S+glRGDLozdALlzjSoTAol1xazSZpPscy4K3f+A3e++hHDA6nDPYTLEtw/eYm3eYVBo+mCKWjZRIj8ZgGEWmUkcuUJF7gRyPuP45JM7hy1aZUdpndH6BUQbRc4JRdEDloGbPFgjSP0QxFHEYEfoql2TiORq7FLNWU0X6P/mwX3TipLiYLG9OyGMyPEHqGblgoKYmXEa7pEmQpwtAwNYVpiZMXSQrW16psrd7CsGyOB3vMF0eMJwnd5gqYJfaOx/g5CM3kL9/7HlEWYbgnL1adhst81mcWLqm0yoQiZOf4M5TMKFcNpuMMNIGmbJTS0EWJLM2ZLPpM/BmmrZPFNsdHPuPjHE/zUFrBMl1S9dZpVq8ynwesr0fsDT5loPqsrxYoMyfJj5jHn2E6FkpIkmVClIzptDpoKiJeSpTok2YPsYwVSt4GceIjlIGnbTM5MKhXHYq8hFwqmp0VBsMpCAUiwfGqrK5coVmpkScmFa9Mtdnk6OgIWRRsdK/hxwsSP2NvZ8DBkyNajSpOw+PWzZtcWfsNcjTkkUbb3UCzOoTJHq4LRV7wbO99TFxMV6EbBcdHn7LZ3aI/HjCNlzitmFapzKZ9heF8SSEKJuERmZmgK0WjUiGc53z06ceE8xjTdrl69Q5mkmNZGevrLWy7SuhLWMxYBAHD4Yjf+Z3fIVGSdrvNbDZl//AAyzIwHQOEQirwowhdQJxnVJs1QKEJAyE1LMsgiRMyGVFtVjBdC2wTw9GQWYZt65TKLuHxmCJLaLca3Lh6HdfSebL7EESBbZu4nsdiucT3A3IlKVVKuGWXiJSSWwY/RuUSXJ0SHrbtkOcFIEjiCMMskUudp3v7FKLAdEw0oSiZZW6ubjM4PEKVVhCmw3jZB/MkikvPbZIwpexVSPKEQhUIXRKESyp1F8sx0YSJsgzWb61RqGOmxz5apogmIWMjY/1Gm/Z2lTduXeHHf/4R42lCqhyUNEgSibIK/qf/6d/QuunwL/673+E3N26ymEzIm3WWesw0CYlUjB/ELJZLDg4HFOkSs93gytu/y2oa4VYrDKOUwf0PyFVIvW2ydWuNzkoNx/YI45wwzVBYSHUynnPJPwQMyNs8N/gG7D9d8PqdNd5+9V9y1DtiOp3RaWscHX4ftL8eoTw1X32ZtI7TEtPD4fDMm+bLyvm2222EEAyHQ1zX5ZW7r3Dtww/49nf+8rn5sixjMpmcW9513XMv7d1u90sFiy9SFAX7+/vnpne73QtHwi/a/kXMZrOf2T9D1zXefvs6lUrlLEXg1NzzxWN/mg5yspzOrVu3yLLswhSI0wpGt2/fPut0AVy9epVut8twOOTw8JDDw8MLO5ye572UD8PB0QEHRwdnvzcaDdrtNq3W+XQFx3GIoojBYMBoNGI0Gp0JEF/EsixWV1fPPG+As4pCcRyTJAm+7zMej8+Mh1/c94uEGt/3f+L3gZNjZ9s2juPQ7XZZWVlBKcW3v/3tc/O2Wi0cxzkrCT4eT7h9+w5Xr24/N99Pc31cJCZmWUaz2eTGjRtn6WCTyYQrV66cO85KKZ4+fQqctM3TSjRxHHN0dHQWMXJRRMapye6LpGl6ThR5sbO/WCzo9Xrcvn37QsPulxUvTw26y+Xy2X3k6dOn54SeXq93Tkw9rZz04vpOvW729vbOqjVtbm6ytbV14Xd98fder3duvo2NjXPXnu/7TCYTJpMJ4/GYyWRyVtnq9Pic7tONGzfO3ctM0zzXHqbTKdPpFM/zuHHjb/bPaTQaF56/F6NP8jy/sNrz3bt3zyq6HR8fEwQBuq6/dCTbRULoRX5lruty8+bN56adGqS/yEVt5yIvoVNB8mXwfZBSYZoZrjs/i4w7vR5838f3faIoujBq7VQ4Xl9f/zwdrcfxYcRyWkJfbdFtXWWlscbxcZ97H7z7+VIloMTfVM/tJ6Q+aUgpnzMOPgmo+dyb5vP/nYo2p8LMiQDz1/41p6KNlPKk43UqAGkCgSDLFGW9RrPRYeLMWV+pYbslZosMw7AI4pjJaIaeg1ASP4xAhZRLBnmRIiUk0xipK+azGabloElJOJ6ytXaN414f0zLxyg5lx6JQgslszDJZgqmYTRZ8Nv2Elfo2Ua4jHYXTaNFY6ZIfD0iihBiJZTsUkaJeq2BX4H7/M1zhEUtBo9EgyCI2r26iaTarVzapVCDTJUG05HA6od7dxLEDusImtgyE5rIvfAZaQT8NCE2FZlnoloHKc1wHDF1ycHDMdBwQz0uYqiDTQoRhslbaIhUK6adcXbvG1noVYSc8ePwRmmFTajawvDp2tYFhuXQ7GyTLMdM8J43m4CgS6yRaxDZNSq6B5pnUrrfxh3NUZpKnJ4amhQqJVcif/9mfMJum/P4//t9CbNDfz2mV1rh+10aWp/hygJ8sWRYmm/W3MPwa5dVVrlwNWQ56RHFIgY9nV7Fthe6N2Rt8xmrtNn57QFSeYot1mg0bu+RwNPDZrK0yWR4R1EIKQxCQUrZ0okUOloFnm5TcKttbTZ7cv8d4HnO9eQt/lFCYGdudGywHPpmCMTGdLZeKZyM1gbES4JQE+TgjHubEBQj9JNqhUIpSqUxBjGHoJ+dfFfhxiOWYuLqBqevcuHqDQX/Cw3sP+eRoF9upIkROre5x89ZVHj16gD+f48+WXN9+hWePfoDj2AjT5nDvmLpTx5QmDa/O8dFTGqs3idMU1xUss5D+8THCs4mDEDQLzyxzdfMGJV3y4Wd/giETsqDAKWk0mm0ifUm50E5U78ggjzRqTgWnpBOKkDgISdCpNCqkQchoMCTLCwxPIlVOlktsAXkiEfB5gW4d1/GwtYhURURxRsUrU6rWuPPqVYQ5YxpmjA9mpLkk1wsCNWWRpdilDtutNokeYWkuWRFTKzn0j46xHRdhF2R6xDRWrGg36LZvsDhaULdaRFkCWgFK0D8KqJRtHMPBNctomkmQQyFB122CTAMBxXGEZ1WwTAeweHS/TzyZ81tv/WNcrYVf3GM67vHu/Y9wyhav3X2dIvfIZYCulVgsn5LmOsFij0TOiOOEV263Me2YquxSN+o8OfgAWyvRaXVpt1fJpGScLijRpVBHYLsEs4i67XHnldcI4oAgXXBre51wkVLWKtgljY9+9DGj3TkNr02rUWLjlZt01tfQ3AxRSNAV17fvcqVa5jv3d0mSKd32dbJsyNiPyNWUPJqztbFC7Pt4VIhyh0Z5A5FaJH5GskjBVsz9EKNkMh1NSJc5/d4Az66xub158gJUqqIpnyTN2BsdIdWYR70ezw6OEIYGOnzy4B5pkjB9MibNEqTgc5HEI05i8lySFQXK0JgsZiAEndUummYCGlgGipQbb95mdaXD06d7TP0FQeRTLrlcWb/OSqWBLR0aXoNS1aPkuQxHR8QywSnZCCFJs4Rqo4ZmGgRxglcuUUiFP19i6y4CgzSOsVyXVqVNGicsgwW5yolFjIVNMA6JgiVCCUqWizAkGvDO19/gqFah7rbwQ8mffxSQokATLGYhItVJVE5aZEQyxPEMHMem5DqYmkse66SRRGmKV96+hgw1/vJPf4xemERzyWcf7VPdeJ3KtSa33r7K4nt7zCcFuQKDAktm2PmS5DDmf/m//jEHX32L/vgpYr2Ebiiub24gUsFnH39GxamR+kuUqzMcT8n+8sf85sYGNcvl8d4zhssFEonvzynyFrvP9hmwZGe3RyrVWdCspl1G1PzDQAAXdLSVfvLBAGV8/vsvT76zLAulFPP5nL29PSaTCX7o02o9X0r7opHVf0hc5N1yWo77kkt+XTlNMXtRqDmtevPzrPdFKpXKOVHiIpFCSkmv1yOOY5RSZ1FQLwoiv0wuqkIGF3vM/G1gmua5aKKL0qZ+Gk7LjL8Mq6ur50rDL5fLCw2S/zZIU4hjsKyMUinCMObcv3+fwWDw3HxfFiV0GkUWhiG+7/PhB58ym5mYok4QJBwe9JnPgs+jcV7+WfYTI2qEeLEE9+n/4Ys5VV8Uc04/z6ltAjQhkGdRNgqpFBonaVOar7Nduc17H///CI9DXv3KqzTWuhw9GXK4O0PmGYKUarXCcX+IaeTEiU5axGiZzspal3qjxmgwJYlTKraHJx2ysMCxPAzbxLRNksgnSyLKdROlm1h5jqU3EEuTIBkzCxOmH4/oXtviN3/7t3jw/R8ik5Q4zQjjEIoMLbPY2tpGqy0ZH46oeA2iPKZS81ivr1JQ0F2rYpcknukxVTrDqM8nTxXe4YTt6hUSNwcDxmHEoaHIDB3ylDyLUZqLbQqu3GkSpzPGx0uatRXwPGI/xa1YqBiePeth4tBtvcLa1W0e9d5lHkyZjkdsb26wdzykVm1x7cpNDoYHfPrgPW52u7RLVWZ5gt0yUXWToyc7qMTEkg2SLKfcsljf7jJ6NqVcrp5EV+ga77/7Y+59sMNikbHR/oD2+irBJGO9LejcECzIKQoP049AGXzl5ptst7ZZqhnLwyOq+hUcMyQrLRElWG812ZscM+5n1FSbZ08+IIoyHjz+iCjJqdgdbr5+lSQIyMnxjZOyvIVSmFaZ3NFAFujCQOYK3dEQpsl8HqNq0Cmv8+GTj6g0DdKZSXW1TeuahdcO0HKD6SIhdjQiCXMZYlnuiUF0mrBWrTJdLPBDn7iY0XDrmLqBQFFyXIQOugkyFxwfDIj9jGiZUSo1ccsOnqcTBHMePrhHlmdkacF7P3xEmAaUSzUqtQp5kRAsQ3I/pdmpILTiJK3I1ZCaJIkShJTo2CcNSFlIqQiXASvtBp5t8OywAsuEKC6IwxRdc+h0DaazIUZhMu37iFCn1e3glk1yK8OwBaZpIXNBoDQG4z6u52CkCkyJY9ewNRu75YKSFGQIS2HooIRLkmYU5Eymc5TSMMyYg/5D5uGMVAosrUK0LBioJSYltjfa3Lm1zWG8RxFq7D7uo/IIgTgpr6kCikKhCQ/HE+S5TrdxjSLfpaYpcmWwiAJMrcpsMqG1XqZtGoymC4Qy0YUGykDXHQQGeRwwDyPqZR+o0BvOqXlgljPC4ojxssdwtCCJHOZBTrW8pGp6HPZm6JRQ6VMOdiHOhuQqIQym5LHBtSstOp1bBHNJLOdkkWBttUGapnh6ibvX3sExW3z69I/QzQqrtS5Nz4FMYhgGluEhDI16o8x8sGAy7lPRq9SutFlpNuisNSmttTBMjyTPMCyBYdjEmSTRemTWkHbdxmtKNOly1D9CMWO1U8HSbZy6xcpKh8lSYtseK+02uzt7CGXgz2Ncu8XB8T73Pz3CKsrYeoVpvMRyTWbBhOlkgdIEge/jL32yTKJbDqZhEOUx1UoFZSryOCVKA2zbBVlgWDpZkZEXObZbAk2QpAlpnlGt1zAclywvTqJaNNBLFrmmEIaJQDCfzhCmxLQ9hKOhuxpXbmyeCCPpEjPXUTMJlsCzy+RpigSE0PBKJUzHBaEI/AiZKsLAP/FSExqvXXmFrfo1RsMej/x7LPMUpSQbKysMj/u0Si0MdJSUYBSkKuZR/4DaVg2zVKX34WMiFWLYJobhkMxzypUquirAMNGNEpajYZo6SZRj6Doi0fB0k2W4JCEATaPW8chjEHpOxS2x8+NdxjsjSo7H+s0NbjUrFEmIGC1Yzmfc+Oo6wnDZ253jD0Zops3wYEKyDIgGEZYpGM+WbL/1Co8Ojigig0alzPJej4ORzswMOOjtIXSYj31UWvDD735InkJJaxL1MjAMlBIIJSiKv/3KGZdc8mWcdo6m0ymDwYA8z5nOpucMPJVSv3L/lV8lk8nkXLRAvV6/FGou+bXmVBR5scPf6/V+LqHmNHXoi9Tr9XORHhelDhVFwe7u7ln01WnU16+yLSmlOD4+Pjf9ojSfvw0syzp3z/1pKv1dhK7rL23E3Gg0zkVB/jKqep3y1a+2qVQqrKw0WV/v0m632d/f5969e8/Nd1HEHpxcU6dRNycRkD5CNNE07cxcP8uyn9oH6CXKc//1jgEo5OfTTsxv/toN/Lxo88VoGjiNpuFE31ECTegnZoZ5QRZKmo0tZkvB/Xc/JY0crJUSw+mSNC8wMoWGQRYUJyVfkwLbsimERawyUk2QzJdUKw1yKyOZRUhh489iojRGT2OMzCSWGYtojuO5VMs6mlKUvTIr17ZplqvM9vd48OxDjif3sG2T1VcbiGVEubDRdZNREpDMoVVtsMRnMhhhbiiqbptQ5gTTkNSAwXxI0zRxkoS253G0VMx3hrxzZYuZoXNs5sgwIdQ04iQizFKEAegafrigulZFSp08FWxe22QyiBASHEtj9+EQQ1XRqYEZ46YT5uGC3njEiflPjf3jY2KZUTVyhqNddp7eY7qYsVEzqZZMVurbLGSMKgpW7RUO530+vr+HVAJL6Vxtb1HVa7QrFewyzMMZP/rBh1hmFc/O+PCj97irvYahCqLBlGOzIFARqmIiZIahQsaHD7GTkP3wgMPJYxq1OqubHbRGwtPxQyaLOXlaULe6OHqD29e+Qu/BQwazXW6+dpPaioNlS/Z3x6hMUeg5pgoRmkOmMlyvhGNY2LlLkuaE6QK3prEMIybLHhVVJ1jGCM2j2bjK5hur1F/Juf/wfbyiRMUssYwDkjhF13SKPMcync9TKxzCPCLPFZZtEAr/pJKMcPDsEromkKqg7LaJ05SV1Sq9Z4c4toZtS4TQmY5Tdp/usbLWoNGsEoQFpXITzdCIs4A0i8gTSXuljJQpg8WSVNdJijmlikvsF2RRgePZYCgSYmq1KvtHexQqxbQNdHQMYWBpBoZtE/kxtVKTdvkq+wfHzPYGtGlhmBrCVXg1F92AJMqQucJ2y2xeLZMWIYVIEbqF0HJ0oVGYObnMkbpCaCZZkVOullFZhp6nzGc+w8EBO09tHj3dYzDJ0ITBtfUNdp4doaKcG5vXKVuSTF8ymh+R9iGZJngtg61ul3k0YFnEJLlPSsE8HOJpXcrlFZKjHVbXaxhGg5ZM0LOMw/2QLDLJpMJQVUxAIbAMF9esEAUZeSqQMmF4HKJaOkWecjhZcv/oe0hSdp7tY1JFp4lMEh7fn5KFfdI8ZTKcUq8lTKcF/nJC7IOgYDp8APlt6o3H6JZg4PdY9zpMZxOmoxGacHj7jX9CUZhUHIdXrv4eKloQhUtyK0ZqGnEekoQpqS84eNynrOtsX1tHFzZbmzdIVYZrOtS9Fst5Qbmsc2NbY+/oIVExYm2jipHBs0f71JpVklBimgVIMC2XOC2o1kwqukTlBtNRzkFvjl3yyBJFGir6+yHjkaJdr2JqiuF4QpgtcRyTKEhYhjGyUFiaQbns4nkuSZ7hOi5uzUXqBaWqgyLH80pkeY5hmcRJimFZKAGzxQLL0EmzjG63SxwrbMvBdk6q6DmOTbve5Hivx+HuAe3VFldurlOqOrhuhU57jXQec+/hp7z2zdcRvkZ/qiMigeNUif0AXdNBCLIsxzAECEW0CNEU6MIgKyQSjUJoqEzRrNSp2C6Firhx6y4Vy+Nq802ysCBYxBiWxb0n95gXAX/x/R9he2AKjSJSuFULQ3OpWDXK9TJogjCanAjGjoVhG6AgzQo0KdFUQRaHJHFEGCoWoU9trYKmGZQcaNQd+rtjdu8PUMLAK2mUmi7bX9vmG//766zUclZKm/jTGumkzOOPHvHDB5/hpymG1JC5TlyklCotPn3wKXlcoNslrt16na91v0IcRxwP+nTaLdQowTIccqmIopw8zZinS2RoIjE/N/gXCPGrqVByySVw0kH4YhWT8XhMpVKh2+0+N1+SJPT7/V/RXv7qmU6n51IGdF0/Nwp9ySW/ThRFcWG7PT4+/rmEms3NzXOpeNVq9Vxn/yT97PmULikle3t7lMtlut0u9XqdZrP5KxdqBoPBuUihlZWVn7vE98twml74Ij9vRM3q6upLrWOx+P+z92dPliTpdSf4U7Xd7F67q+9brLlWJbKq0ACIpbuFhDSFlHmYeejHeZ0/qGX+h56RpkhPjwwJkDJDEMRSAKsqUZlZucYevrvf/dpupqrz4OmeEeGRqMjKrCII5BGJxc2vrbrY/Y5+3zkL7t2799y236SD3fb2KqurF382NzcZDocvLY2t6/pa2ZsQgnfffZe7d+9y9+7dC7ImKTl6Ikimklky5+jo6EpcfXt7+5Wv65cQNRed5TL1SQjxpYjxpV4NX7o9XX7mRfbtYr9nG+lyvwsXKS0FZ4s5H3/yPlurLdxig4effUbnPGZxnhHIFp4MWOZLgp6FIzwcP6TXXWGWjahVwv7xCXWh2Oxt0G23qYqas2xEtzugrEp01eA1IaVQaANVnZFlCqMrPKlZZCdUVYmSGY5bkSZnpDm4Hoi8YThYIeoOWHUMk9MFg5Uu+fSYwVqE62nScoayHOLuGr3NkOVyzOH0nNXNOxjP52xcUowLPqmOWXYcrH4XYykqy0IXNbYl0RiKPEeVgnoqmQLteJVylOHVLeoc8iRF2l3sIGZndZdlesLDg59zeHDC1sYaVVay3dviXlESh5pPPvoQ3/dRRUlse4zPj1mGLrNFCcKlyitMUhNGHSptcKRDHLg0VU1a5vzi5JjBWodUzfn+O2+zmKWcnU+Z5UtOzo7wA5uz+YjjSUqa59jDgMwf4YuQuNtQcsj5/AGlnrHIU55+mLCS1Cj3i3IbyydqSSqVE4Yd4tYanh/SHbRpeSBtg6rAd2I8p0I1CV7go5Sm7Vn0+xGL44xsWVLqBcZSeK7FbHEMNmgDO3uv8e6bPyJ+M+WsOsDoPqPRDJWPMTZ4zUWPjIddTMvnbLwgKRLiXovp+RGWB8aDJgdRw9l4iuNb2KGkH9R0ohgfm0Eck9YlWVrg+wGttkfYclnZbJGXCXbg4zo2eVlRVhUg6cRDjITRbErTaKpK4OcFru2gGoVlJKH0UQiMW3P3zg4/+cv3ePLoEXfvvMvu5g4ffjzGD0KEU5BnC3TZJ7YHHH/yMW4V0um28NqG1prEdH2SukFK2Fzd4sn+EbXIUbJCUSGRWCi00mhl0BiUActx6a8H3HhtBcfb5PhozoPP98mWc46eHpGMK6qkYnd3wO07Aw7OTzFC0B562HbGZx/vI2wbvcgITMCNtd/izs4O48UjPnzyc7StaIRBBJrA05wdnVFbLk4QMWit4UaK0eEZd+/ucngyIZlnCMeDJgQBlhvhuhHoilqV1LVDkWoKTxG1I5qy4uNP7jFfzFAKNvohZSnRBoq6QtUKy+qyWArSIsESPq24g2dphKmYzRaMjjV/85P/wN237lI0NkWlkK6D68dIGtLsMULErA83+O23fofDg78gCHsYp+bjzz/GVA2zyZiTkxH99pD1Xg/LcWi3ulSmojAlrrKYLc5pRWsk6QLLTdje67F/0JCOFRYl0rI4W07wTISy2lh2QNkY5klNu9cwnc7Y7N8kXzh4zgrruyE7tPjFzz8j9iMGqwGrqxGmqmiVMRLBfJpQ1RVNbfjRj75POp8gMDSNoe3FVGjyJkfnDa6QtOI2Bonn2tj2hX21rDVpcqF63w5D2q2YfrdHZMfkVck8S3FsG8+S2ArevHWXKAgpnAoZWRSqYK93C60EVaGxhcvP33uf2I+xfInre5RNg+W4mEZdWUtKA57j0m+1if0uZW5YdVvMkgW1MTyZPwZVcvudO8wWIzqDDkVZ0olXELnNzRsRs3zJeTnDKwOW+QQ/EvSGF8Jvbc9DFB75qGJvbZW0KXj4OCHwPCpR0WiN74Xc2blFJ26jy4J8lnD85IzTwzG+5WIcibENQc+HQFA5mrDtY2HIyzmTcUr23pLpIuCNHwwY9iVOVbF/7xcsDs6xlMBUChxDVizRKKgt0mSCZTxcN+Tdt/6QHbvH/fKU1W6AmjVMp2f4gU8jL0QiLcvCdUOWSqGNRgiJhUB95/r0TxqWZV2tBl5mrGj9m7O/vSyzr+uaNE2Zz+eEYXRN7NKyrGuB2cuEY+EiOHyV1div0tt51f2/Ck3TXNv/chW2rusrd1Tbtq9pODyboa6UoqqqKw2WS32WS/R6vZcKjX7VtV8K95ZlidYaIcSVzk1d19R1jdYCz/MoyxJjDLZ9Mc+/asB2KXVwaensed6VCO/lPSmlrjIdLrUxLleqv67IblEUVFV1pVcipbzWT15FW+cSLyv3sG37KrPh8h7gQhfjWdvoy/7UNA1lWaO1wff9l67AX0pFPIvLioQX+6Xv+y91nZJSPtdXv055oOu62LZ9tf+zWkeXBIrv+/iej9IXjkCWZeE4zlVfeRUhZq31tX4LF0H4y3RSXtz2MkHZL5MAnofjOPi+j2VZ2LZ9sahjuBZY13V91WcsyyIIAlqt1pW474vnetX+81Xz0cs+d30ukygFL05JL3vEXyfT5VXHrW3btNvtq/7QNM3VGFVKXY1R27ZfOhd/1TFd170ak5d99mXXdHZ2xtOnT69d+zcpRyvLEvWSLzgXbtbgONbVdV3qG02nU2azGf1+nyzLWF1dfW7fNM24d+/Rc/dkWYJbt25z+/Ztdnd3ybKMPKsolhOSaU3TNFdOiF+XDPx7R/TlRPGsMLCUEgwo9NUkL+WXHeZlD1/IL/RsnuttF59TWqOl4fFszNniGC/M6K1Ioo0+g05A0g2QmY9nelhOxNH8iJtbNzgYH5GmJULYNPXFynCZVUz3F9y+scuNG1vMpwsakRO1fNJFgVYS2zQEQYgfOhRFzmKRUUhFJgss65zuYIXNtQHJeEHU8VjZHOI0kmqueHJwim5J+tseblwxsGJwNOODOdOzKcPdTZRQ+KFFNwrJFhUn+ZSDpzPa3TX6bWjFFpNsSnI2whgfN/QpLY3lOGjVILWFo2zKkUBLj7PxjE7LIU0yskWN9Ft0+x0qnbJ/8hmOX2F1JPYyJHQ3SOZPiFpdaqDbaiNsiXYt2mHM3soKlS557/7HZLVNNxrimTbra6+TzQ5oOYrAczBNQ16UGOMxWdT86f/3r/jdP3qXJEs42D8lCAZUZcqnHz/g5hsbtHqQ5prFYolVpmRySmwr7i8/xwtszpdnBK2Q/aMx2m3TXVpkQiFc0CbhaPoxv/XabQ6Pj7n35DM2N1bprXUZ+iFOu8b+rRb5tEdnJWV/nOGrFjYVvUGH8WxEnlSU84pG5dhugKUDijJj4Y7p9T3ufn+T7bdbHKdPKZOSt9++y2e/+DtOH+Q044LOoIN0LNAVSZ1T0zBbThGZoigybBcCv4XjGFzbQjcCZIOwLB4//pjsJMFTLpZjkamGsN9ifaOP5Whsz6GoMxZliTIWurz4AuzYgiCIuLl9i48+ep+8vhBivSi9gqKsKXSGshsQEU1msbV5B0e1aNs7fPSTA1biLW7v3eDjzz5guLLKZHFAlaU8/fwxH/ynz8hPNIN4QHc4wIoclvWMwHJxPZuiLKhUShB55HVFmlZcJCkIVG5wLRsLkGgaFHuvrbB1M8AJFYdPJxwfjYg8id0E2Mple9hmtSXYWumRpGO6qxbbNzbZ3owoMwcTRIwOnpLrko0bq8R9wJ7SbtkM4k1kFrO61ud8lDPmPUKvx87KkFZgePudmySLCU5j+LP/9Ge888MfEI0Cnj45x7c7CNtCGENVVniexJce4/EUKT2aAgpV0e22WS5yZBPhuIAucXBpjKEdBFSmQCmJsKEuJI2wqIqClhcwG0/AuFRpRF0GnBzkaO1SqwovatHpdlhMT3jw8GNCd4293R1qPaMRBzSyy2SSkS5mPH34mEWRXdhMOzWZNcMPWlihy2g+YlGek1V9tCnwvBMW8wTL1bx59x1Cv42uS3phm0U2wRIpTd3Qad/EjQxC+li2ZP9gQdGE1F7Dje9vMv1gwaf3PuGf/eCPcF0oi4StzRUUJWHgUCYRp0dTtJEYY+NZLkJr2nGA0pqqgrRqmCULpCuwhEMQRzi2i7AsjNEgDJaxGY3PWc4WSCRow83dG6wMVrixsstH9z4ntUu0UHS7Mbd39siSjM5Kiw8++DH9psfv/PYP6PbaZGlDq9Nh9LMJUzXh5u2AIk0xjUIi8cKAPEkRWEjTcHvvBmhF0y5p+0PW+re4M9jl//m//680VUMmlhih2F7fwQ8blGPT6sXYtLm5fYOiXDAejdh4rc+GiVksOxirZLDZpzPoEUctImeAXsL5yYiBZZjXS7zYQ3hQNQ1xu4t0HLIyIeoFDLe7bN7c5LP37nP06THCspjMp5ylY9r9mEYKwtDDtg03br1NfzNmtRvTD4b87c9/yuf2E1rhjN2tdSI/5Od/+QG+D4NOQNmUHJ6egdLYgYPshbzzg9/htc4e//k//AmjdRdX26SjKakpEI7ANA2tKCQOYxanOWmTYkkQF62F/nusIb/DP36srq5yeHjIv/k3/4atrS1WVobMZtcdTX5dyLIM13XZ3Ny8Ev2czWYvDe5edSXyUmzzV8XLxC6/Dg4ODq4FfG+99RYAjx49QinF66+/zs2bN69lFWit+fTTT0nTlKOjI8bjMa7r8vTp02vity+zQ/4qMeHL6zo4+FLo1/f9K4vq/f197t9/wHIp+Vf/6od8+OGHnJ2dsbe3x1tvvcX+/v4rCUlf6g0dHh4SRRF/+Id/yO/+7u9yY+8G88Wc4+NjTk5OmE6nPHny5Cowj+OYOI6/su1fdp4/+7M/u/acB4PBNcevr4PV1dVrwWGn07lymCrLksPDQ4Ig4F/+y3/JdDq9so2ez+dIKdnf3+fnP39Ip+PzR3/0O3Q6nWvZAb7vs7Ky8tw2pRSffPLJNZHcH/3oR9eyHS7P+2y5zNbW1isH5q+//jo7OztMJhOOjo6u7N07nQ4ff/wxWmv++T//5/yPv/8/cjo55W//y98yHA555513+OSTT3jvvfdeKePBGPNSEe7Nzc1r+jH379+/5jwUBAF7e3vPbfuqe7y07h4MBmxsbNBr95hP5ty/f/+5z1mWxb/+1//6ueN8lVD4s23/y/CsuPclXkYo+b7/0rlsd/e6kPHp6em159xqtej3+9c++01gWRa+73N6esqTJ094/Pgx0+mUd99994oo6/V6r/ws4OLeP/zwQ7rdLuvr6xdt0uu9lGhzHOdbzw588OAxjx49T9JpbTg/r+j3JTdvrrC3t8fa2hplWV7NJZd9dj6fX3uPFEWJlIJer311Txd9pMvR0dEVKWiM9QWB2zAYDK60ei7F018Vr+T69Oz/BRZCXvzv+dKmZ12iXhhA5ottz+ndXPx1kXltKFXDYlGANpikoVAJC50SBTGDXotQ9VAqQjrwNNlHWjYKzfrGCq4laXktkmWBbCw6nR7GBqcjEWhabQ9hQbE0dPwI7Jo0S1HaAh1S1xVuKBCOJCuXDLsD7myvo3TOslgwznJcP6a33WNwZw3dzDk8eIQuah4+eMp8bGitBjQNZHlCr9MlXo9YHtfs75/QuC6BBG3nnDYFm+srLA8nGFljUVPJGtXU2NJBYuPJiG7cYZHMmC9HTBcCaSxc26dSiqCS6Kqi1Q5IqgrHChCOi4gCQivg5/d+QqFTjmcLTACVKVnqhlmTcnB0SFoalLFwPZeVXofvvfE9Hn5oODn4HFcKXCtAIfECm5XNDZ4+/IxPP73P+eQYh5B2K6IuBcLyyJaKWXWCtiKs0ML1HFp2h2IBB8UEt5ScjKds+yGB36YoG8aHGYulIGq7dLohOl0yPX6Pjz77FBXXrAQD/MhlvbXDv/uP/47X3nyXH7zzRzx69Of4pk1XdelGgrSoGS8LRGkuSuGsNqJw6QUrFKTkWYqya7Q14/H8ffImo9MNGS0OWCxGuCZEaBtTWLSDNi2/jSNylsmYqsqQlsGzLEwjsJWLFg3S1XTjiLLIsaTEsSy61gpb8QZFWTJOUzqbfdp9i1ovqbUCy0EbD2k7mEqAVvS6Laq65PH+5yzTBe04Js+XtHsutiMYZ2Nqr8Fruzhdh363z+tvv4bvB9hqBVNnzKZjXMfBsgxJltLUhnSRUZ1l5KOKgBa9The75bFxY48xn1OqjLRMqKhQxYy6qlGNIksabBfW1rpYbkO9LGl7MXlRooSFhcaLLBSK+axCGhujCjwnRleCzZUeUTdkdjZipko8W2FZmsOTA9KFomW1CbWD7oVM61OO3r/P6qCNbVuErXU2ojX2NvbYf3rG09GHvNaxuBGsMpo+QlsTet0+Bw+O+MFvvcv6bpvbr29j1D0ePj7GEgJlSkBd6F1JgW039Dt9QjvGNgJHlRjpEEY+RZOBqrDdGv+L+SnudkgWJZVqqJqGqsmAmiZvaBqPdmuA1hZxMKRKwdE2WbFknjV4w038yKMbDllpb7FIFzw4ep+//uu/JPI6qMolW5ZYTcRGf43Bxgqnk1OOF/s0nkVvtY/Ia0LbA9GwXIzp9mKqWhJ6G5hyiOAcvxtRA1lSYqRAWoLj4ymr2ys4nkQKi/msIOxv8eD0mI8+u8cv3rvPb33/Dmk6Y7o8xXIshBY4tsALfLpdh3yeoI1D2Gox7EcIpWm0YZHnnI5GlAqkZRFKn9Bv4Tg+juPSGIVj24Chqhv8wMdzXGgMeZphDQa40uajTz9mvJxTOoKyznEywVk65uT0iEk2p73SJgw9dF1zPHpKp73BT3/6HidPTwlXQ7QSFHlF6F1kljX6QsvJQjJY3WDYGxA4NlprtnZv0+QWD588QFkNy8kYpSu0qzk8PaEz6GL5Lr7f5fbqW8TK49P791iWp4iwYjyaMEkSwsCF0wl1pmEjIPcTAjcisw2N0qzublPonEoUxGELoQVZNseyBcKzqLUGR7N5d5PtWzdpC4t7H/2CShrCfpd8pDl4vI+/2mfwxhq9FYtQSuqqYHdrk3GeIh2Hpmg43j+nMIrt1VVWOwEf3fuUtucjpaaz1qF98xb/7Pf+gMef3Of+08/IixipLNJsimgqpOMgpUboi/JhU1kIZbCkBHPxrv5N1L9/h/828Cor5L8OvGpw+U3S8H9T+GXP8GXOKi/+PBgMrrJc0jRlsVh8ozKRr8KlI00YhjiOQ7fbYbk8IUkSfvCDH/B//B9/wocfPuSP//iPv5GmiDGG+WJ+5dp0aSX9bFD7q7Rtu92+Rh69TLvk6+JlbfLiNqXUc/dzeU9VVX3Rhh5h6F/ZX7+I5XL5UqHSdrt9rY9UVXWNvLq0qv77rvvvwyeffMLDhw/xPI9Wq8XOzs6VTkoYhvi+j23bJGWC4e/vs78qXjyO53nXSDbHcb7W/LBcLjk/P+dnP/sZeV4Agm63+9znnq0U+bZgjOHzzz+/1nZxHL88ieEbznnf9lxY1zWj0Yg0TRFC0G63r2W/fN1zPpsd+CySJLlGsF7afn+bGA57/MEf/Oi5bU3T8MknnxOGPt1u9yqL72VO1a/6PhRCsFgsnv8uZSzKsr76/a+KX5Ijd3lggxASMCilkfK6wLAxX5Y/fSkmbD1z41/aeBujMRikACktpKqQwuHBe0/4Z//D66x+/4ccPn3CgyefoTpQOwlWXtAYB781oBW06Q87BG0P2xL0oh692OZUjvClz8rqGrVTkqYFqqyRQuAHPqpIcdsRi8USjSEpK5TwaUdtXDtge3uX0/ERcdu5IDrOjnGkYTybYfk1YafHKD3h8f1HtEufVa9FMyvw/ZiNzU3COELpBtVoiionr1NKURIFERvtDk+nU8bJgtE4J/b72LGhUWdYrkXcXcPCo5yWzJ/OsV2XRTGnFhWRCWiFIW7LJ00gnRdYNrh2yOx4Qtf10LJkoc94fPYRs9GIoNWlcQyZTpmPpkjajJOMpqxxnB5+5XFr+yY3v7eKa9f4URthuSRZwq29XWbLKUk9o7MaUD+sOTkeIew2s2RKVXyMZft4botetMajyUOCgUNvsMXW5jZuZ8pnH3/C+LSknte4QRvH9TEqwTaasvRwqy2Sx3MIBNvDAJGN2NvucbxoEzs+rtWws7rJ0Sc1gZnz+98LWHZWefCg5I3XV1lfDfnPj3+KthywKqK4TTNvqKsGEUF3GFKPUkxj8dMf/5zN720Q9G2ODx4zGZ0w229IH5VoJfFamsiHN377Fk51wqIjyWvo9GMq5VEpUMqi1RtQywY7gLRcoDObMql5e/d1NvsrJPmSttWjpKKoU5CwTHNKbSNdm8jyqNIUaduAYrmYgKhpdyJUoQgdD9fWFM2cTOc47RDjuoiWhWiVHM8+pd3usVQ5rdBGWRZ+aFFUOcfnT5Aa5icL0gOFp0JW+kP2bt1gzpQHJ/cx8RJFSVqWhEFIIxRJMmWRNBhlUxQ1ZaS4vd3H35RsDnqcHo2Zzg2WaZFMJVmlmYwqTCVxpUtdlWBbyNrD8z1aXpvqOCPRNYf1lJu3dyjNElkLQtdlnjWcns9ZjlPOzyf4sSDwT1npDgmdEteJkELSCT1e396hOpjyaP9TfnTrf+LOjd+jtvbRzhykQumaqixwZYPlalRTUKsapQWu65AuM9qdNr60SM6WBL6HEzjUfoCkRts5lnCpK0O/v0JTjDBNiagLLAGmdjCNR+iGdMIBmpx2GJMmOd1WF09Ami0YTc5Y6azQ97cwjSEvS/703/97ltOUQdvD0w5tt8f6cEhvuElno4spf8FsOaEWiqennxEQE7V6oApsW5MX57TjkJZn0+34JLXN/vQM1xXUOkFXkjgMWAki4u4KRbXAtW1UOWd2dsZHDz8GVXPn5k1ee+0mk+UheTOjHftMZmO8MECrC6ew23e3GI9n9Pp9uqHNeDxlkWaUTU0rjmg7AYvZEle4zEdLmlLTX+ljWRIp5UXJlG7o9mJ828U0BqEvNFsePtrH8T0qS5OrikbXTJMFyecfkZcZ0rPBKBbjKfviIa0oYu6XLKYLfv+3/5Dz5Iym0QS+f5FFY9vYno3RsL25w/b6BrPxGYsyYZkteTQ+QNXw2QefIRt4s/M2XbnN0/MnOLWDVBJVa2TboaJhWi45XD7BihXStbAKnxALKs30NKXnrmFVF5kyKSmu52LqitHZKaP5GYVTsbu7TTfq0o17gKGuGyzHwY0chO1jtVu8ub3J5t0uBYog7tFKWvzpv/13nKuSpEixJgIZeyTzBRU2gd0CpTi7f0QyzYg6bRqjeLh/QHdljTj0qLMEL5REQcjp2WPK6ZjCLymTM7K0xugK348omgojaqLAZr2/xvjhE7S+cLG6eHcLxHdEzXf4Dv9gIKXkxo0bLJfLqwyByWRyVc7xbWI8HpPn+ZUo8cbGBkmSUBQFN2/epK5d9vcX9MIeUvzq84RS6irj5DKjZjKZ4Lru1ypLehH/NfV5mqbh+Pj4uYya2WyG7/sIIdjbu8iOmM1mWJZFu91+bv88z6/pX0gpuXv37jXy/NKe/dvE/fv3WSwWbG5ucuvWLba3t1FKcX5+Tq/Xu7KoPzk9uWZ7/OtCu91+ZfHZlyHPc8bjMUdHRzx48JDDwyV7e7f4n//nf/EtXuVX4/j4+Frp1t7e3rW2/4eIPM+vspmEEKysrFzL+Pq2MBqNXmq7/W2j3+9f0zpTStHtdp8jZr4NEvz8/Px50tjYZJnPhQX3r45fklHz/ERxWZt56fz0JdN08YXvkp18PrNGfjnhfGHvbcQXydaCL0QMBY2EZFGRzHLsOKG3eoverMRRkjAeoMuU0dEx7dLHXXdwpUE0FYtljaVscC+OIwTMlwuc2MHGI/RDLOHSoLFamvNsjKgNjaqpVUVVWfTba/Q7XUIvIhQ22XTJPC85Weak6YKNwZBWJyatCubHGbquWdvdoudE3CjXKJWN4zVoo8DYLJcl0+WCh0/v0+70kNOU2ippuR1G2YL54xnt7SGdzZDBbsD49JwsKwjCNuuvr/OEQ7JJTtzvYTsCq2ywbJtKNhRG0fP7F8LGo3PquqECnKDi4dOP0UoBGieEVOWMZwsWyYKe30cVDm2/Q91UBK7HoDWgqi+EdNe3v49nB/zio/9EocFttQl1xfHxAZYTsJguCEKI/IjQcYjCLlGnzaDT59HJA1qWh1tushX/HoX3V+TWjFneEMoYieT0dEqdLbAdH4WNbjSuvYKtKqzGx5IBYTNnNQjorUdIv+Fvf/FXGHKG7ZDF5FPOD5/iixa4DU/Pn2I5NrbRVBYo19AYAbaFveKwdaND8pMR6tyiTZ++2aRJJhw+mrMYa9KDhrbTw205VEZRjObIBVTpEr+tcS0L/CW2EIgqIFsaDIZskRJKF8/2UI2FMIJWp0048BidnTKnRkhBkZRoJWi4yDywHMlGb8DucI8HR085a+asrbbotSMe7c+YLDNarkNuaizH0O50MOHFuLEDj/PRmPOkIohOEZXFXNscHx8S+IKsrCiyBnWuqE4MXuXQ6fgMN7sYVzNPpmTzJa6UlElGpxNjlAGrwQ18qvGYwG3R7w94/dZt2rHD0cH7WNUxe7s7tPM298/OWdyHJC1ZThscYyEtBw/BzRt3OHj6Po/ykkKnLGpBpjQdW+LbCnsQMh8vmVXHTGZLihRsx8G1JUJBntccFoecnBzSasU4smZqUsyWxmSCz+9/yt31H9JbeZPcLNGmzWf37/H06QjX85FWBUahG1AKhLBwZBtdGtzGwdQ1ptQI1SCkwO86CKHQxqJoIPACjGUhAolrbALp4+g2G903EVXA7u4GlqP5mw//hKaWtKwuy8kc4XloDCJoviAmNP/xP/5H5tM5Olf0Q5/A2OxtbdMKY157+x3y3GaRZfScdYb+OWHk0OtG5OOKxwdP8eyGrb0ewi8J2yFHjz9CNSXGKinSMbrs4sgWJTOE3cZUmlrlVE2F6zlopYgdm73uDlt3V9Dk/Oyjv2M+m6JVg2sbLGEAi0W+wJMON/Zeww5DXEfSilwOj/bBFgyGq/gth7IBaQTLyfxCcHg2x/ElQTvAcQLGZyMUDa1WSJoXVE2DH0a04i7SCii1Zr5IyMolnucgbQdhiYtsrqKiFYR4jk+ZVZRJySQ5pttZQQYSW1tUTYPQDZYAy/UodInj2eTlgs8ejTG6wSjFYjkniltkeUV3o43UFpUpsGyL77/9NtFam2WV4+FRLBa8v/8XPN3/nNybs9Pdoco0nhfRDQNEbViGGcaLUEhsaZEkOUQSKzK8dmub4ufn3Lxzh/XNDUxlobSDa1lk6Rwsge+HlOWMZTrhs+MMu6kxjk+aZSzynOHuKsVsQiYvHNvO8wzLtrGDEGyBKSq6vSFBWGNsUKbAmJAg8JDSZrCyQqNKmrMp+81PmZ3PKWTOMk3QGlzXJ22muIFkOGxB03Dw+B6L8xQsi8tXucH8Rq2Yv8N3+A5/P6SU7O7ucnZ2xtnZGbPZjCdPjtjYWKXd/naJmslkwWw248aNG6yvr7O+vk5d13iex8HBAbZds7HhsCgWaPOri44rpTg5OWE+n1+V60wmEzY2Nn5louYymHyR1Li0yf11o65rJpMJs9nsinyazeY4jsPOzjbb29vkec6DBw9wHPdasO553rUgUkrJ+vr6tXt6+PAhy+XyuW1hGF4re/s6uCTJlssljx8/5vz8nLqumc1m3Lx5k729PYQQnJyccOPGjV/5PF8HrVbrmobH5XN+FZyfn3NyckKapoRhwMaGZHe3d+05a62/cXnjy7C6unqt9Ok3RXJ9U+R5zvn5Oe12m36/z2AwIAgCDg8PX6nk8etgNBpd68+tVuuaEPSvA7Zt8zu/8ztkWcZ4PGY6nbJYLK60d35VXDoWXsFY1Nkav2ai5sVUHYFlXQiPXerTPKtf83w2jXyG6PnyOBf78YW48BdEjxFoZZFnivlowbLKiJwuQeOTzVNm5Yy2HxAGIau9AL8X8vi9DzGexfatPU6mY4wAafucj5eY8Zh2y2dzc4NOz2aZz1kUCVpKWl6PfFawmGbMywIpQceG4U4XU+dUk4xBtM48VRRzheNELOYl3RWP+fScrnQI/AhLaZbFnO31FbLSQDtgWlQIoWmaglqDZ7UITQtfRoyOM0aLBdPTmqE9wFIW1bQhuDWkU1eIpSBdZujGJm71yMoC22mhREFvPaKSguPTE5RSbK3sMD2tSBYpcWghpaDKM1wdoYVDWR+wf/iYyXLBMgWJj+dGDOIhW8MbdPoWP//xf6Hn7OGZNsdPjyiKDpsbPyB++CnJYsI7777F2vbb/D/+1/8Nz25B6JPlEwbdbWK/xa0be2BLutGQbthno71DK9zElJqfvvcxp6MaW/u4jgMKJAJpWiySBV7UodVZgTRiuThhtBwzMAJHlrx5c5dEZ8yLOX/75z8j8jqcH59y716NH2qGe21yZ4TvWjQTEDjEHZdslGAFkn7cRuuCw8cax2mjmdFrxdzZ7fHzzz9ntr/ENzGxa7AsH9eCulT0+kNWt9Z5qh+QzHKSTJMvCspaI8oWgdXHqiuawuBbHpHfomoV7N3dZv1mB62WZCyZLHKMNthK4gcxdVOgDYAmzRfYnRa9dshkNmHYWcE0NUWWY0mBJSRCusjYJeh6FPXyQvS6CSgaTT1TFFlCP+zheW3SvOBgckQNiLpBpuDkDq04ZrAZ4QwET0ePWDRLZEezTDIsX1y5VuV1hnAkb3zvBndu77K7vUYctxGtgsFn63z87x9wf7QgGzYs05y61BRFgys9XCGwjebu7Zvc2/+A45NHGNlD+JJSKtrdiLOzYw6fPgZXYgkPx66YzQSOaxM4ghubr1OogqPxUyxRU5UJ0+kIR1ocRWOOFweUIqPWkpPkIacn77G7scfW8F3q/AnCCDzfphENVd1gjIVlu9iWi6ksdte2iW2HNB9jKk2VFyyXS+zMRVLTGrYIwhbCi0jyjKzOL7SKZE2/H/PP/+BfEFgBiAU/+fDHFHWGqzuEpks3XKNSCbtbW+zeavPo6VPy2GD7CsfYtHo+N7e32Nla5a3v3UGLHn6rzdBr0cxstgYb1A9yGm9Gx2/jxinKquj6Q6JAMi0OcW1F241xyxolG1r0aMk+S5UwFg1x4FClFZMHR3RXQuyWZndrk360yXAlJLUmfPjZJ5xNxtiOh43AqiqiQY+8hrpqCDsBj48PqdIax1YkM0EYhIRxjHA9pAulUUSdCIuabuwzmc0o8hQ/8lCNwnNdhOPS1BUmyXCDENe2sWwHZcBG4wkBlk1V5Ph9H8ty8KKIJM9RJdS1ZHY+pWwK9l67y1s3vsf8dEEpNBbiwtWtKCmSkkbU9Icx58kRq5t9HNtDlZKNYQ9DhVPVOG2PbJGRqISj2T6esVmOM1bWd7iz/TrLo0c8fPApk2qC1bVI04y61EjhYHsWnu3huQFpUoCaIKXi8ZN9Nna3WZQz3tzb5dbdm8xVwdn4HNuPib0ho9NzTk6fol3Fzu4Oeb0kb1Jms5KW9BBBm1u3Xqfba5GnKaMqo92JWE7HZHmKUBrRZPhhG8v28MIATxmKPCFZZLiOj1ACIy2WaYWqa5CGyegB4+mIqBURhh4IQZFndFctdm/0CHzB8ZMJUdjjiwnpC4FCzcXs/A+/nOQ7fIffGAyA/WoEptBAwy8dQpfH1PLCnUCrL/51Qb9QviIlq6trV6UAaZp9oakA3/bC/HKpaZoZeZ7jui79fv9KJPOnP/0pQSB47bVbHB4dXgtAvw6MMcxmM2az2ZV+0HQ6vSbU+XXR6XSuZRldCgz/uqG1fu5+JpMZ83mFZTXs7gqGwyHz+Zw0LYmi6wLJcRxz69at57YJIQjD8Fr8VZblSzWXvglRc6kXMplMePToMePxkrJU3Lq1RhRFrK6uXt3f7u7ur3yer4MgCF7anq9K1JydnTEajej3+9y6dYt+v391n8/iMnPo24QQgjfeeONasJ9lGbPZ7Fs9168DVVVxfn6OlJLBYEAcx/R6PY6Ojr71cy2Xy2v92bKsb52oyfP8muOY53n88Ic/vGqTyzKsb0LSwIVr1XPW28bGofvL7bV/Cf7e/Y25tOK+hLhSo35WcfqSpHlRfNiYi0ld6wviRkq+UF8WX4gL6wtSBwvTGIwlWO1s4/iC8ekUoxW2tqmyinlZIoXh5Hwfp2yzFuwwX0x4/PkR8yanHbdpGoHxAiI7wpOKIl/SANKxkTJkPp/RjjWtMGCtt4nb9hmPjwiDmLPxhNOTQ+aHCxo/YPPmJiYKOTzfp9cOyBdT2o4gcF1k3WaWLBFWhe32sAIPbdtonZGkU6KWR+TavL63SxTEfPiTe/SCIbvxKnZi8GRIWkisWcTBL0rWvJBM1aBq8vEcWVuYxsNxAvKsQCEoqvJC+NHSnC3GZKnhrdd/j/HkEdNsia1sbm2/Rne9y3/66Qkn5yPKRNFkgtXhGu1Wi9Fowo++/we88f1NfvKff8boRPAH7/yI0yclvdBn++4mSfLfkR3f4/TwCSdHJYGwCFwPbfkgKoRx2N3a4403d7n38AGtwMeqDIOgR7I84/hwRjqt6Vvr4GjyLMM0CscLcWyfbtBiXhR4LZu13ZjQGvLnf/pvWZan3L25hxe69FyLB5/cx1cdknrJu3/4BntvhRw/yRjPc+ZaI+wIu+Wic3XRTwIXx/LxW20cwCNi97UNkuTHHJ9+zns/ecSomRN3YrIZFFaJ8Ctsy6fVc7l7Y4PEGpPqjJIKXBvTSGxho5QgyzNkbmg7Ic3Ewl33+cHvv4k0S9yVguW04mQ0R0mJqTQYh4yCvK6xbQutNWm55CwZM1jp8ehsn8Bp8fT4CapWBH6IahosLTGuRtNQLwukJVkuEhQCSzosx0uSwxO2Nhx6q30WTYJsCgyStK5pdwI6ay1qt+LRaJ88qzCmQUhN4Hr0Wz3We+scHR9faJ1gWFnvs7m7ycbmKsicnAXhhiC+vcO9j6aoukQLC2EkNjZ1XVCpi9TOo+MjGpFy8wfb+K0+WA6ffPCYKs9x6ohhdIODs0dEawGVyoi7MdISZLMJ50cJOAYbB89xqYuSpqmxHY+z8yV/8eO/wmt79IabqEDx4OAhdjNgu+uzsd7ld3/v+3z0yefoxkJaHpZt4TgWKEM3CNnoxdhGYZTHJLXxbQ9TFWS1QGpJMVHE0ma+nDNJJhhR0xhFKwzQFNjBkvn0KWV1wv7hI0zjEHoDfu+df0Hb6fPw4DMOxz8hKc8oaov3f/EBK9EGrZWS9fWYd3/0OnWjOB5ldNtbKMumMhl+q0MQddnLbrGffkaZa45OjmicgsFgyOnknCzPaek+e4Ob6LJm0WgWsxG5d869k4c4vRX6vSFKKipS0jIniFoMuh3S6gycGlmmFysErg/apq4U7W4XYyuW8xmODNHawiCo64r5fMrt9U12d/dob6xzfD7mdHqG7RmMVAQtn6aoGK53ibtttIGyKHE8D4TGc228yEc6PpYbXWiSqQZha/r9DkXhsMwlXsvHIEE69MMWHbdDnRb84niEFTlEcYdZvmC0HFGhMHnNfLZEa4MbtFhM5qz0YyzXBnEhwD1YXcNWDjQVeZMTtxr6N7pQGuqk4PHhIzZ3Nwgjj5OjpyTLA8JNj3wRkJU5n3/2Gbdv30UjmDcFHdvm7PiQvjugLGuMVni1zezp6EJA3j1Fl4pFkhBqQS8QaFOiREW80kbJmrScY1RBlad0u0OGUQ+/10PrguPJnIOTA2zPpixLJtMZjWowX1gRpHXBoN+n7bcolwWlNviOh3JslNaUpcYoTZFXJNmcRZKhKenEEaqpKPKCplFUuc/D+0/oxDG+6bB/OEVribkqZ+aLsuXv7Ln/KWOxWFAUxdWXzKZpcII58/wBiOdTwl9Ws3/p5PEswjC8pgvxVXhZOU8cx9dcn6qqYjQavdIxvxlsaFZBvQIrIlNwToHnyx2uERBG8vDzGf+nf/V/5Y//6P/G//J//1+YHHf4P//r/wtHhy8EQqLg048fMpkdo5RiZWXIm28K1tb6DId9er0eq6urdLtd2u02Qghmsxmj0YjRaERd11eizM/i2oov8Pbbexhzkfnx6aeforVmsVjw+PFjgiDg7t273L59+2uJ8wohiOOYIAiQUnJ2dsZf/uVfUhQFSikcx2FjY+Olor3w8rYvy5JHjx4RxzHD4ZDhcEir1eLo6Ojaav83LWO4FAq+fM6dTudKQPhZXIo2K6XwPI/t7U02Ni5inEtBaKUUw2HvQrPvhf37/T7b29t0Op2rewq8gL/5L3/DYrG4as/LDKEXhXe/ThncpeNWWZaUZUlVVbiuSxAEV32prjXGgO/b7O/vMxqNrlzH7t69y61bt7Asi9FodOGI2G5/I0LsUrz4WVwGzN1ul9XVVXq9HlmW8d577zEYDNja2mJ7e5tut3tVdnZ4eMjh4SHn5yN2d3f50Y9+hOM4uK6L4zgEQcDOzvMivVVVcf/+/VfSIFkul2RZ9ty2r9oviiKapmE8HnN+fs5sNqPb7V6J/7bb7SvntRc1h4QQbG9vv9Rx7MXz/zp05VqtFm+88QaO46C15uDggJOTk5cStGVZ4jjOc3NRXdcv1VwyxmBZFkIIxuMxs9mMp0+fXiOvXNe9Vmp1SR69ClZXV69lrV2e90X8yZ/8CU3TUFUVVVXh+z4ffvgxdV3Q7/ev7qnf71/LiEqShAcPHjObJaTpY548OcSyLFZXBy+MUZu13jq+tcNsNufs7IzpdEpRFF+LFPp7R7mU14WWtNZXbPuLqs0vEjfPCxB92WCXnxXiIhtEGlBGk5c1Te6y2upDK2CZZ4RewDhfYKocHIcsHeNZIbLy2GjvMbFz8rPHbOx0GRcLqjonsNuEjo0f2IynZ3i+z6A/BEeS5hNW22vs7WwzWk7orwdIAYWr2Lpzg0GU0RYeUdfDtfpEa5Lx/AQ39KD2sCwfW8cMO13mySHHxydM5yVu1GewuoqalEyTlGBtDaNhtphRTnOm6Zy6A6XWtAYxcW+AthQ0iuPxIcezBY0tQGvWBzuUZYVbFeim4YP3HmFJg7BdtnbusEwk0vc5n5VkmSYrMnpezHw5RkaaqL3K7WibdmsfgcVykeF5NpUHyaLEtbroWjM+PcGRFr/3e7/Fw6eP+eAnP6WYV+ze+D5//p//34SBot+NiYIup6dTLBnieYI3v7dH3HGI2wGR6/ODN3/EYj6j11/n8dETIjFECpiVJ6BsPAe2t9p47iZJtaQ6PmX/4CnL5JztwRtEnU3ORkf0OjkdPLbX1uj6fU6rY27e7ROvCzIa7h3v09iCRV0wzlOUtPGkT2C7uGjc0MP2YHm2pNVdYTSaYlsBo7MZi4ViqSukleMEIbQUdtggA5C+5jR/gpV0MbqhF7eojIGspmoURjQgbShtHEuyurICkSKKPUZnY/7mp09J5xmVEphaISpDbRRK1WjrQjDbETZ101BZDWmRoSvNeLwkLWqkbQENTVOBcTGVYplUNEtx5Ui1ttsnsCw+/ukD5vOUU3WKdDWx36LIJZYlMGGF64Lfs8moUJWmN+wiTMO0GFMmBbmTM/OXHD04wdgGP7ZJ8yn3H/0d0rrB2soa4/kpnz95wnHhkbkNsgpQpiHy/YsSG8tCNSWWJXm6f4LxBKXTEDcNru0TeSHLecYP3vw91rqr/H/+f4/QTYoxkuGGj0QyyjrMx0tKuWD9RhspBFJ6GFUhsKgqePL0jGjgsbp1i+OzKe3WOr1Oi/HoIe///O+QTo+qKdFGgJEYDbZlYZqGje4AWTdMkwnLMkUOIvIGZgr8qE3otZjPxxTjjFyXKASValBK01Q5jjnnweMPySdzdCURtU8vGhI5PW6/9jq+sCnEQz5+fIZVN7hWjB53WLl5kxtvtKjUkqdPpqR5zcZuzPqqQBc5h09G+OGMvChpd3y8ymWWnRG2+hyen/GwesztGxusxhHloqLqGCbLOQ0VjZGMR1OioIvrxCSLisViBrJAuiFNVVO3E9xWj5PHYz7+xUfUro/v+JSLisUoxW0sKpnR1ArpgnQuUp+VWbBIEkbpnHV7ncPRMVlakJYZwtb4noPjhCzqGa0oQto2lrRpavWF1WvOymof4bossgJZGaIgwmhNWuUUi5TZaILtOViBi+u6aFMRdF3iOGSaF6ysDMhMyWw2ZXI+wsJGWWAJQ6fTJe72WOYFwjK8fvsOnz36mPH5jDtv3KHTDRCVpMwl8+WC7kqb9cEKs9Mpi/EJsRPApKQs58zTOVokLJdL0mmGbQKWWYG+KbCMYDQ6xepXuJHNvSef0V9t49o2JgCnFWJ7FrNyQp1XiMBins9JjzPibg+/ZdOyA2ynha0FmRI4bsjW1i3SWcLp0RFFlaBKCyUNo/GIBoHEAq0wtaZWxYXrnGxY2+ky6PSRcZeDowPmRUKua8qqRtcVZdVQ1xrbdljdGFI3JU1jqJuSIPRxXZ8gaoFyGR2XpAsBxkEI84WY/xflyN/Zc/+TxmUwdmlZW1UV0lUXgaD4Mgi+DCReDBLOz89fGki8GFh+HbzM/vU3JnRsBKgI1Ku4mwgwZ89l1Aghrt+7sUDbeNY63fgmkbeNLSIsM8S1nidPjEiZLT6/Is4cxyEMXVzX/uL/IZ1Oh36/TxAEVFV1ZfN92YYvCrC+TCgTIIr8q99fakZc2i77vo/neVeB5dcR1ry09IaL4PvZModfZu37st83TUNRFFdBk+M4eJ5HVVXfyEb9ZaiqCsu6WGCzLAvP867sqF/83OW2l13z5fN03S9t2Z9FUVxk+bque/U7aV+MLaXUc5bszz7PS3wdgdJn9W0cx3nOLvzFn4ErQufyPMvl8kp09XL7Nx2PL2u3siyRUhJF0ZU1vOd5hGFIEARXVs/Pxp+WZeG6Lr7vMRwOrpU5CSGukXcvtsXfh8sqkq/z+bIsSdOU+Xx+9ex+GS6d116GbzKXviosy3qOIL18F7wMl8/etu0ra/OyLH/pdV7afl+22Yvnf9l5XrWtiqJ4KXn54nmUUtcytCzL+uK7bEYYhiilrizUX9zf8zyi6EuC2ZgL+20pX7BcN/Jinrr8nv3Me/ZbI2qeLWO6xGUmzVcpOX/VPpe4HGAXx7jQlTFCo3SNlh7HR1Nub95k4/u3OTg+ZRgP6NYp6fiU1jDi7OQBq7u7JLOUyAsYuIblPEPXNlIKAktjqhTH7xJ4EVJb1MuakoIbq7ucFscIKRjnI0xoE7cGyMjDTkpsYbG6ukIynnN8/hhFQ20MaVVyupjT6XQQNAQd+yLbqLbJigxXOFBBOcvpeV0WecriuKRoKpQyWKJFXgmW04RWt4/tGVpexuH8hGTZsDfsIhfLL0gBmzRXuG4bYxSOAVt71EWBsGxODwqEG2NbLsfjEaqa4UcepSg4XDzi0eRz4m4f349487XXOR+fUJYli3TB995+G1VXHB89xXEbxqP7LJZPqOWc+5/9goAhv/+7/wK77fD0eMTjB39LGCigpt32aCobTU1Ngh8OGfYGbK1ssrG+y+dHv2CRFPTafQJ3lfW1bd5/8Lfk5T36Q5+o1dDUDUJ7FElFZA+hUhRZzQ9+9Nv8/Cd/zqPDAza8kuJ0SWdtyPpuwa3XYsaTMdUswvgxVbFkUaVgGQI3xugaX1oUsznRqkNVLTG2wu4Kzh7OGI1ytNHURJSVwWkppH2haeL4FmVQkOuCdK4IpIVQFrHv09iCNM/RUiN8qHVJU5bgh+BWrK/3mY6mHB/nTCYCW/rYdoEqJLa0EZZFDkjbwrIkOi8QuuHg/IzIuLhBi8rYFEpT6Rrd5GilkGWNNb5wzoncgNXVFdZfW2F9r83jew9pSsVabxNlcqajMa1eF1UpqszQ7XXQVkJBRVosuXlji3e//y6ffPY5k08niMYwHy0Q2THVvEGEEq/vc346Is8sZqMJf/j7f8Th0YTPPlywPJO0rSFpWpJXcwJ/iFI1GEOe5eBIGlWRJRbNicX56JB+J2Clu4brd9i9s8liPCEzM8qxwPUDRqMJva7P7Ru3eZCd0R04uL4izy5KxBwnwAiLoqxphEFkmtliSqe1TuS1ODs+5OdPfsb+aISyxlR1hUHgSAdTVyTTJSvtPp0wRNaG2TJBBRYFFVlVQuCB54HnYzwP23dpFiVlqdBaIG1Jt9smbm9wcDDC0zUr8R2GfZdMpfQ7Aw5Hn/Hg4w+59+ivmYzP6ccrDAct/vj3fpe7d3ZIqyPQMb3uLWaLjJ9+/Jc8PvuE19Zfo9fe5aPPP6LT66CNzWQ8JdcZmxu3CYI+jqcolhOkK0iSnAfNMfkyY6UVEciIyh8iPPDdFrNFTjdqo4AoCkmzEhKbxl5ytD9iLdxl1iyZlwkSyJOMcWlIywWlrnCjjOFWDxpDMkvwnYhcaf7zj/+WZZbTi7p0N/t4VoBnW+gGgqiNEgY/6GBLQW0riryk1gXZPEO7Hr4boBEIrXAkaNsmKzWedlmOlnTbHbxeSEPDcjpGpzm2sGl1PFxcLAxllWJsB2E5hFGLOi9RaLAMfuix/2if+VlCOOzx9P4xaqMmDjocHoyZZOc8fPSAJ/ET7uzcwWv3oCzIkwLjVBRqRllnVE1JYPs0S4uttT06nSGRFRI6DlAxLVK8ocu4OKeuGtqtNufjM8JWSNyPsX2b2WjEdDGlMg299RUi38OxbQb9AQM/pt2OyZVhucguslEbQ5FknI0SmqJBaUHdGFSjUKWiLgrKOkXbhnbos7W9gac9Tg+PqFWN4WJRQ9pgWQ62Z9Pp97BdBzuAxWxCrxPjuBLHl1983qIoGhwrRDclRjgI0Tzn4fHfgJHOd/gNw3VdOvEqyC+/JAshuHv37ksD0heJmn88+PsGx9cLVDudDlmWcXZ2xmAwwPO8L9L//2ENQN/32dnZuVpZv1wF/7Z1Kv6poyxLTk9Pr4JCKSWtVutbJySNMRwfH2NZFr1ej36/TxzHJEnCYrF4pWOMRqNrBO1zZR6/Rniex+7uLrZ94e44mUyusgAvy3Ta7TZ7e3tsbm5eK3NyXfda+YtS6jdG/J6enl6bH6MoYmtr67ltWmsePHhw7bq2trauZZj9t46XZap803tcLBbXBLd933/lzM5XhWVZLxUx/3W5Z/7SvLnLDBr4knB5XofmS+Hgy+3Pfu7Z38GXgsSXE74UEikthDYoKdk/OuV8MmL9rTX6ok3kegStDuVeF9m28TZ9nJaPyUJQhmw85613f4fT7Jh0NCEOQrqhg+1pTDlnNe4gCbCkhW3ZhFEfV1gsm4xSS/qtmKLKsTOJ7zmMkzFFkzNvFtR5hVeFVDNNUuXkWcbaIGaZz5gcLFlpDXCMTztoMZpWHBw/xfNtpBWibQvPiWj1YpZhBdKi1e8Amr4s+cE7O/z4/WOU8JFSEtgOyICkhixTiLakGWeYLOF7t27zNx/9nCBsY9ktNBYSFyFcFIbzs1PcdkjVGBxjsyo7dNoCz/dYJg5+4CNtH208eisxnz/5gJqMihl/9+GPOT2fMgx2eOONt9G2zzJpuPvW/8B4es5s/ilx6GJLC8sKOT0/5Rcff8Tmxj9jdW0NVTf0+n06lcN7H37EMN5Gasnd298Dz2Xy3n12b/SxteDh0QmTqUEon+Fgj3YHXNdCk9FbDRnNEuqg5ig7pSVWGezEWI7LYpFxvjhC9rvkumCezAk8H1HWuK5BCQtRNDR1jRW6dLdi6nCBG7uEnZisnCFCcIyF7QoaU4ItybWFLmqUKhkEESUNrh+C4zCeTakqixqDbUv8losWUKqaoOsxn004vT8n7Hfp9jx0U5DNwEISRhENAiMMlYQ6z2myHEtrSqBQJUrbICwq3dCYBkWFEZqmrrErheMINl7bxLgJj/dHRJ3XefpgH5U3rG2ssUznzLMRpd2gywbpSOJuyLLKOJ3NkFZNlkx5+OAT3CDCD0O0KXEtj/loga6h04sp0pImSVnOJZHX5vBxRpL6ZOMWjlLoxtA0Ba2uzyKdoxpNXpToSiG1RNgGz3KwjE03aBN7kvFkn0IJ/vqD/0C2zMmFoW4Epmqo5w17u5v89tvvIJr7LM2YUXLIYrFAGIEQzsUfq0ArQZo0TEYLNrtvIQJ4+vAxh0djtOOSlzUIQ6fdRmpYThe4tqbX9lBNRlbUNJZB21AsMxoFUoIwoHVDY2ss2xB3YtREkS8Vusmpyxx/xSXNU9zAIi1H1HaOsAzni33Oj57w/l/+BNdWBI7HarjOD9/8EZubW1RM0LlPr71KN14lDBPWTjokZUZdKEp/iWMZ0skUGo9h1EX4XZJ5QhyG1E7DrDbYTki3N4QmICkKgtU2Vl6xnKc0TU2mMlzfxbMEWV4ym5YoabPa3eVsPuXO3i3kwuLR3/45hVPSasfcurGNVTocPKlpexGlLlFFRTEr8LVFO44xtkutFW4jUZMGqwOW9DDKQqkChEYLSVkqpGOj1QVhYGHTCdqUs5LhTp9o0OL4/Ah1IcWL0AJVNAyjPitRnzq/eK80skb6gk6/QzHKcYVEKIUUGq0KAs9BWCAtm6qqMEJheRZ5WjIYrnHrrTfo9LuEnofKG7LoYrxm+YJO0KapNH57jV7skmYL5tUZqmnIG4UMPDZ3buE3bWpZIzOYZVP8roexDG5tEVYOVeWhG8F8mlKrgkZp7NDByyUmLVgJYxZ5ynI0Z64UcbvFsDskLVLqqgLbQguNwGU5W5CnJapRaHEhUC6MQSuFbhp0XSGNZtCN6ccxH3/6EScH5whl0E1NLQxxrwtKIQx47sVqeFkVpGWCF4QgJcYyJEVCWZQoI+nGq2DbNEpdCGB/4dT44rv6O3yHS7iue1E+I69nEbws0/o7/HL0en3SNOXo6IiVlRUsy2IymfxGBDS/DoIgYGNjgyzLriyky7JkOBz+2oKRf4ooiuLK8vxyDL3MmvvbwP7+IZ7nXAkQ7+3tcXh4+EpEzSXR8+K4/0249sAFUXPjxg3yPH+uP8ZxjO/7rKysEMfxc5bizyJJkms6K7/Jd57jONcEkr8q88T3/WvX9o9xzK2srFwrqXpZGeTXwWw2u0Ye9nq9b52ocRznWmmpMYbFYvFreRf+UjHhS2Ll+QH6pSX3RYmT/ELPRlylCz4rLnyJC50aiVLqy5sxYIxESgcN5E3JvcPPyT6b4Ls+w2jIRv8mUxLOJhNGkzPkTKBsSPKC8eMz7mzcJIxD7JGLXWna/ZCKDNUoIi9gbX2dJK+YFwsWVUopHNwopNIln356wqC/yqazxyBeRenHHM5PKUyNaDQrXovf/u9/gO7W3D/9jMnZhMV4iWdbVGnNfNmQiimlFnitFq4fYActgpZP37XZHA44LRKypqLdsbExLI/GPB4d8tbrr1FUhg/e/xRjLEIRoWqDIz2EdljxeoR2C8oMKcEKfBpTIWtwjcf66l1Gk4JaJRd5LnnNIOhgyjnZZMlEWYTtNlHUEAQdOtEGaA/P6WO7AUpXjE8rWu5dfuuHv4/tamrVIKVPq2vxg9/+A/70336CJRts4bCxuUmtGj65t8/O7hp7a68ROj7z9ISD/X1QhlbLZ3Q24cnTJ7ieR+D6LCcui1HGMs1JspIo6hG2HDoDH8cRWBR0VtqEKyG0G7JlRZalvHXzNR589ohZUeKvhqxvR8zvnaAbg+VIjN2gHdjd2UT1+8xEhvEF83LG+aLk4UcnuAT4HQuiEqVLRO3hGhsjDVmjsYyk3WojLcGiXtIKWxgJy6xCWCGmqRDSwbYdClNgjODxvceUZYrj97jxRounB/scPjnFo08ceti+hRAOVVVQlwV5liPqhrqqMRhKbKQjEbqiNhplFFgC9YVlrtDQqJoknTOIu0zPUv7urz8jHWsc43G6f0aRVyzyjHZX4cc2tcg5WhyQ1gWlSrGRzJYJy/QeVhQSxB5NZXC0g7EFluviOg5K1tS1RKOo64YilTx9MiFNC3zL0G2HlFaJcBqMNli2BCGRlkRIB8cTSOHgOgqanJa9ge2GPDi/x6NygWwilHKx7IYgdGg0dDsDNrb7bO71+eDTUybjFM+zEFJgtI02FkYYLBss4ZLOGurc4Akbx7NplME4NkHgYlug6pIsySjSFGMbyiKj9ArmWULYiSgt6Ms+eSWomgtyOS2WVCpDaoNnewx6A8YlLNOG8emYbnyALQydaIXTyZLCk7h2DyM0jiNp+33aIuCt793m7e/fIQxjsvwY27dpxyGuJ6ibGZBS5Qts4VKWNfcev8/6YAs3d8lVhtKGbFmzOoipsoYnB4fgCcL+gJ7bw8kdqkXFomzoRi3+u/Ye+6MDDppDBu0OeXJOlicMukM68Rq5aIhaIR9+8hGfv/8Qx/VpRxHdKOCf/fbvcvL5iB9uv8HJ+Rn3Fo/JkxJLC/7ov/t9zs5OyCpot9osqoSObbMxXMPqBHheh6eH+4zTDMszqLpkPFuQ5DlNXbO9usIPv/d9sllBonNGizG5rpC2hWfbDNaHtLyATqsDlmB9sMY8ndE4FXVTcHp+RtlUOLaNxMIRDkiNK22qssI2Dk1TIxyDsCWtjeFFOacoOZ+OsYWPbSz8yKcRIXVdcHR6StjuEMuQRbHgZHFMI5bkeXbh8Bf36K1tEdsxbcdlenrGew/uoTsgI8XKoI9jOxeZmXg4LrRjD9/3cdouXiQpjcIojUxTdGYo85rD00Nu7OwxzeZ4vouvHFpBgBYKWzo4MmR9tcdkNr1I/bbBwsY0NcmyZHdvAy/2sI3g7PCE2eEZUSsiKXNWtzZYWRuiqhJjBEaZi8BZGTCQZTlR4JOmS4xdIawGoyxmsyXqtKap9EUpi+Ef2iL+d/gHBsdxGAy7IL4karTWvP/++9e+YH/btsH/WNFut8jznCRJr+xh5/P5Pzii5tKN6Pz8nMViwXQ6ZT6fXwkNf4dvB1VVsVwur0p8HMe5KmP7tnF2VhKGOXt7DVEUsba29srZNMaYl4r5vkyb6tcB13XZ2Ni46o+XAseXZYC9Xo/t7W1WBisXpX/N8+U6VVUxHo+vHde27a9VPvar4mUZMU3TXJs3pZR873vfu7Z/mqbfennff210Op2XEv7fhOh4WYbYq5ScfV1YlsVgMHjp+X/jRA08rztzQbpcZMAYI7/IoPkybfp5W+7nV+teLH+CL1dhjAHbEmgMlakZ7gyw1hzGk3OWRxPypoA4pFAZVbXELjUyilhMlwTSphc7vP/hR/SsiCgYoCpBhU+/HxF4Of0Vm/t/9znd1Q0svUA2LrYQtD2bWZmjFwXdzZDVwYCD4wcoXWOJBiUKulsW/lbNwplTJwml1IReBxnD8eEIy+5QuwbLs/C9NkJ4eK0AbENlCh6dPqByBSenJ5QPlsRrA5azkif1jLd2dlmczVC1hSUjbNNiGEXkSqEV9IY9/uDd3+FP/+z/xc7KJrmUpOkcWfhs33mbre11un3F4hfHPHlyj431PbqBz7DtsZyXODIibseM5nNsYdH1NtnbvMOiOuOw9zlyoekH27z72/89buxhRAF6DsIg7Qzbb4i7A5bzU/oth+FGCz/e42c//TEf/OIhobPK63fXeXL8KQ8/OqEXbvBbb73BT5qfUjYNWbbA1kPOHq3w9ptv8ujspyTFh6zutJD+kv3JMWsbEb2Ww5ZzB6NjzvKHBF7FWmdIEAasrg3wjc+UOUKWtAMf3R1y9+4tjkdH1FnN7GyGrQy5e5F+7Sqb+ULhRCFRHFHoFGUrTBlQpVDmBWFH0un5iEjTNDWztMYSBuFJhGnwoxZnZwlVpXBaHsoyeJbLdLSkURLLtWkNOywWOecnJULF+GGAQtFIqJqSoswp8gLV1JR5jmc7eJ5PrTRKKixVoI3GdVyUqlGWufCXlpLIjalnDifzhLyxmGQTItujTjXHZydILXBDG6/2mIzmTPMJnWGf/uYak5OEvKpYX9vE82G6HCMdG8d2cbWLrT1mswX5LCPquxitEZbgnXffIO43TH8xQZmcRtkMVtcYH52htUbVGmkktmUBktW1NUozp6wE0sqoVcZgsIOoAp6MHxC0fKq5R5XmeC2HwUqfG9vfQ6tztFjSHfoU5YLQj5AyR1UNjhOgTY3ULu12h8FwhapULLMTZCtins9pTIWuJNIobNeiLFLKokBKG8/1OD6ZUxU2UdSi2+lRLxboRuF5bSzXQauKus5QqqIqFMLWBE6Lweo64tyjUA0nJyNu315DOzPyxqUQPo4laDCUZcGtO2/zozvfJx42iCCnbFJMWZGXOXG/oqpLsB3KukAa0FlD1dTcf/yQvKx4fXgHXdRIXDwnoKgLpukpgXSxPZvF4hyvA+W8Jup1SLKMth/gW4rd7QFuKTjLZrhORNxexZMRES1m1ZLD/Rkf/N1nDOMu6xs3aFTB5maX1fUW2/4AP/X5XPgsTcNEzRjEMW/s3Ob2+hZhHXL/kycMb6yy2WvDsGEcJXSH24wnZxydZRcCvRXkeYXGoqlKeu0uZ0cnnM5mKFeRmhTpOhhjYVA0uiRTKZHbwvYChpsbjD6d0siGqq6pmobaVBhjuL1zm7xIyYsEoS1c38XkgjqrUKbGSJvj81N8z+VmFGNhYVk2SmnSImcynjOeTCl0gXYtimrK2eQIJUuMaOh2u3TDGN/vEPgW2ivRtsPWnRuMsyWfP/gFpig5zRocOyL027QiB9fXdLouVV0Rui1mkxkVFlVR43s9wGCyKbYQfP7hfQqds72zQ6sdk2Upk3JEOk6wvZCmTFjM51R1iakuSqOQgu3bO9x94zZ5lRNIH6E0gWvheBLfCxhsr2IFNtooAjfk/HwCWqGMxhKSpmyo8oqqKWmv+AgtyKY5yVRgTW1QNgaQwrqw5cY8px33Hf5potVqXTnY+L7P+vqF68t8vnhOTPhSL+BFoiYIgmuByNfRVFgsFteO6fs+QRD80n2NMYzHY2zbpt1u02q1CMOQNE2/kdDpzu4OllkjTdMLPas0vQqon0VVVZyc7tNqO/T7ffr9PlEUcXR0dO2e4jgm8nosFwl1XZMkCWEYXhMeNjLCDjfwgi91SvI8ZzaboZTCsiza7TZhGLJYLDg7O0MIged57O3tfeU9xXH80rZ7cSU7CAL6/T5KKdI0vQp+ut3utftP0/Rr6X28iJe1/ctgjGFlZeUq4+DSYeVVA6Oqqq4Fxl81713eY5qmHB8fM5vNXrr/N3HBAq70PsuyZDweU5YlnuddETZJctFPbNsmjuOXamW8Kra312m3bdbX1+n3+7RarVcOYoUQDAaDq2taLpfkeU632/3WradbrRZBEOD7/pVVeJ7nOI5DlmUIIWi321+It67SarWwbZuiKJiOplSqulj8fAaTyYSTk5PntkkpXyq4/U1gjOHzzz+/ek5JkqCUYnV19VqZU1VV18SELcvi7bfepm7q557z8fHxtblMKfWN+983wWAwIAzDK5HqNE1JkuSlzmQvw507d67N76enp9cINcuy2Nvbw7KsKy0lKSV/8Rd/cS17RghxTfTctu1r1/RV435nZwulmqvMrK8qj9NaX2u7y+0vIs9zyuSCWLwkGLXW1zKs/j78UqLm0s3pMkPmWRGnZ2/gWVLmkoh59vcvu9mrY1oGdIWQglo3JFXFxsYmx4sDpvMlSZrS761grcXYQjHwIwrl0LJbhC2olWBlZZ1OFFMKQS5qjIooHYEtCsaTCcII+r0hhUlZi3aoyoJpOsIpAa/gwdn7nBT3ub9/j267i+2FZOqM1Jnywf45SVFTZhphfGbnY6Q2HI2OGPYsWisrFHVBnS3xjOLNuzdRbsH+04dQF4zPRghLsrO3xfkyIYjbREHIB/eestXeoNvzOHhySNxrY7dcZF2CEpwmx/ztvQwddTDnC2wqWhLCMOD2rQ7L5pg/+/F/oEhm+KLF3vo6bcdgYRi0dqllxOH+PVyvBmpcN7iYWC3B1k6P+eEp/bUAK5yjZUHejFikZ5R1QpnmRH6XH/z2O/zNX/0n8jpjmkxIihnSFRwcnTO9c8q0iHj/k/9CNvF4e2eL4cBnddvn9OiEdrzO2nCXjf7v8Nabb2M9MSybQ/Jmzt7tDaxWhGoW+MZmc/0tdNWl2wTccGO6QYu2WuHujV3+6oM/J0kVy0XJWmeVlb5h7cYKBIrj/UPm5Rzf8Uid5qLkKAOXENFXKK9BahuMA1aBCA1+EKHrGtfYpGVCXpf4XoQtbU7PMqLQhtrCwSdPE6qqwQhwfE1L2ng6ANej3QuZTBI8u0PUkV+U7kiKSpPmCUorMJqdnR3uffIpXuRhCwclFUYamrLAkQ7CWFjCQlgNQmt0pdGFphevcHJ0SHfVZ6Ufc348pUkuFjh7nRaDtQ6jfM7J+RK77QKaZHqO57oUpmCpl2jpEHUjyqpGGUWlSpQyVKZEK41VKsLOhQjz/tEjPvnkA5LzkkZrBJr7936B9Cva3QHLMgMNlisQRrCxuc7jkxOCtkejNFXWcO/RPd65+8cY20HZDo00RB2BE/g4tuTtd1b4uw8/48c//w90ghUaNSPwIurSEAZtbt1+myenn5OmIUWWMRtPWV/bxvIFFXNOzjPyUiGdhNDqQKNB1TSqIQpabO7ucrR/xNl0ip9nBL2Q5XyCIztEcY+0aCjKkrrMQVc0SlCkOZldsrqyw3BljTx1EX6FMUucABy3RVPG4NZIW2K3An707m8xCFo4VornWOhaUaqI+eSAuLNGo2oeH93n5HRBnVu0dIgpJb495MHxU8q65FZ/kzRtiNa7HEwWnJyNiHsBjgkRtUsyS8A4VHoJVs3++YJTWzDLT+ltxPhtid14tKUPumQ2O+Fovs/4wPDO22+ze7uHLV1OTufsvXaT2lKEkcAqFO/ceo26ETzJnvC7b7zDOiFht0sg2twO14lbqyzGh/yX+3/NfK3i4eETohb04pDz8yXGuEipMVpcufbZocvR/ITuIEY3FW0/whU+llE0nsIZttjYXuF8NOdsfEJFibAVvuvhKIfZ8sJd7PRkAtpcuDbYHlmRc3vzDj/7L39LaZfYnZC0yti7sYvnBOhaoZqUqi6o6wphDKa50HE5OTtkc9CnKBd4QYDwQnobfVa6Q0LZYZpmlHVO6EVMFnOOjo8u+vbKNtLzaHdX8W2fRhmKZs4yG6GaBq0yTo9OqISkG/exOyFhu4MpKiZnp0zmE5pGsRHvMfRWee9nf8FMJxdEihY0dYX0JRsba0RuwMnJiLDXodOPOJqcIRAUuiKvCtZ211nb2+Vkck5RpJRlgdQWWVJSFAVNXdLUFdQa3/FJlyk1JSwUi1FBUdhYxqFOa4SxsYUEAeIinfUr38/f4Z8OoiiiLEtOTk5wXRchJEEQcH52/1rpU7/f/9bPnyTJNdFKIcQrETVwEYj5vn8VOAwGA5RS34io2draxrM2OT8/v8p80FpfIyqUUkynUyz7wm3kcoX/UnvkWQRBwLA3RCA5ODhgNBqxtrZ2zanESElNm8bEdDodlsvllcPQcrkkiiKm0ymu6zKbzdjf32cwGLCxscH6+jqu63L//v3njnkZ4L6IXq93LbixLAvbtomiiG63S57n+L5Pr9e7pk/0rKDur4KXtf3LcFWK98x+SZK88nnqun5pcPUyXBIYWZb9WrWXnhUITtP0KzPTbNu+IiB/Vbz22gZRFDEcDoELguxVS5eEEKyvr5NlGU3TMJvNrvrht43LPgcXWScvc0a6JETX1tae062Z8HIL70unnWfxTa2YvwpPnz59bjxcuk69+KyiKLqmpSOlZGd35yoz48La/YIIfZEE+K/9zm6321f3dDlOlsvlK4+xS5HuZ/Gy9r7UHpJSXriYfmFk9LJ3w6XY9It41Wva3n6eTDPGvJQMa5rmlZ2osixHFTPm8zmz2cW/Usqrcfgq+CWuT/IlPxtAX1l3Sym4zKF+MXPm8udny6BerIk3xiCwQEoEGmU0h/vnrD0Y4BQeVqRZH6zh2C7jdEw5n3CszqkaH9vtELVbHJw/BmMxKzNqS6AdAY7DtEiZZjNEluD7bZbLOXlaoZw562FMfqzZctfQnqZ0UvbPTpifLUhPK25sbhE7A7JEk6gKUdh4jU8YeTibLgcPnxDH7YuV9LlA1TYt2SKQPmqesfFmm88/nbO1toEXws8//JS8kniRz2AtIlmkeJ5LKWvquiG+tcV0khGULsaSdCOP2fE5k+k5k2zB6nAF2TTcWllnPkn407/838mqlOUyp+3EbGzFrG8POD28h9A2a/EttrfeZPFwTtI8xsgFyj9D+ENOHn3M6dk+2lLM1EOOkylVMcYWNnUZIX1FbUpOjx4wm03pb/QZ7Z9yOlpg+TWtXot6WnP/wUOmyxGTowUb3bfZXFtnvpyTZTOqIqDzZoi3EjOwJF7QYCxDY1k0asZkOqFjdRENeHKF0WSGI+cMbwfMpM10MqPdjpkcJ7TtDpE7xXMiAneAaS1JihlVVhG1IubZgkRXFEWFtGzyUrGzsc3J8ghhg1RgAdp1cCOBzhvKpATpwhd6P+m8wnddTo4ypG5oSkFZ1wTtEGFZtDwfX4DwoDQa4xmScoGqNZbtYtkSSxiKtKQYF2ipcCILxwvwfYdet4Nr2yynCdK1qZsSNDi4VFWDH/lImgttDgtIQIqG27d2MNIwHi2YHydYWhIFIUE7ZJotOJ6cITQEtk8YBGhZo7XEsyyUKhmfL9lYWcMTkko2CEfgtEF4FotsCRiktEiWS2aTBdI42ML6ImvIQtiG3bWb2J5NvdAUWY1lg+O6FFWJMgppamzLRvghxhLsnz7FtXxk1cJxaoLVFnUlyfKMp4cHHB3lPHm0JHTPL+aOSiEaj6KAJmtoR11sKZktDFLC6lofLI20LOaLCksLtoerCBliZIOQkKY5liPxIoeGmuWiYDSaUKoKx/W5tXeXvLnIhimrBK0rbGFo1EXwqqqL0pFW3Ma1Q/LiGJ0kNDMbp+wgaCNVhe11EI7HfDmnZbk0zQzZjhBWQLZYkI0KEq9kNltwPsuYpRW2HWJ5Lo7x6Lsd9rY20GXO9HxOUpbolsb1XTq9VTI9QlQ2LVzSMmNjuEdZGtJ6SlZUZG7B8eKM2m/YHK7SGENZlUjZELRDVuwdenGAsgqkV2LqipuvbQEOaQaBVUOd0pEtfrB7k9065rXhOia/yOITlqIVRVja4bN7n/G//dWfkPg1yrLZvXGDMIwQFJR1g7QlRivQmqPjIz75eApdlyItSdOMrtdndWOAb9ncf3If6VmcTo9RwmNWLHEih0bXCAS2ZRMFbYpK4zk2qqy4sX6DyXRBLnOenj5CO4a61tRJih85uL6LokZ9kYmD1ugqx5U2Ukt0UnL++Al7wy7nB+dM5zm99Rjv9W0Opodsxg4PHzzBX/Pw1gM++/knLGZjBoMe0nIQno12FU2dUxQNRVPi+hG+7WBJyc6tm7SimCiIybIGbQQmapC2IegGlGXJ08eP+Hz6MY5lUGVNmqQYy6LVDmgPewSdmMhvMVQSJQXJ4guySWtyleC1QoxvM1pMcHwfXWuaQtGo+sJWuyqpspyyqnAtl35vwDKZMT0fkyQXmZpx1KFKoW40lTBEQINGmC+kUI1BflcH9R2+w68Ex3HY3NjADy++L19aKb9qqcLLyjKMSDieHFNUF6uv7Xb7KqC/tILO85yzs7N/xCLO3+HbxGUAO5/P0VozGo1eWs70jxGe510LjH9dJU+9Xu8aqfKq2ROX2YFJkjAajRiPx4zHYyaTyTUy0/O8Vyayfx1YLpfXyK9XIVwvcZml9ixeRhwKIXAc5yJjajolSRLKsuT27dvX2nA8Hv+jLMP9pRo1L8uQudCieZ7E0fpLseDLTvqygXApTnz5uYvMHIGQFhKBMYLx2ZxYxPRuvcE0G2O7FpHnMz8Y4TSKvMlYJgtUvaAqQ3RbUFcWrjIYKVBljR+EuJGFdnqUaUOZ18wXE2wbbF3jpIodZwUtbEZyytPkHCEkoRMhEoGTCW7cfY1H832qec3ZgxGv79whKxekRQ3CYqU/RAuL+SQlH1UgNJ31Lpbn8t6HPyMtUwpVgyNxfZ/xaE670awODXWdsLmzQVGUNNg4oU92OOWHt3ZZLKa8trtFEXfwg4D3Dz7nOJ1zNp7g2xZpUjHK5ji2x/bGLm3PBjdnPDsmrVIGKzs8PttnojMaa0aez7H8nI8e/1tmo1+QnCh63S0en37MBw9/zPuPFf1WxPdvfZ+13i6Ho0ek+SmtqMPmoIWwV8jzDLtpYwcFZVGRM2U8TS5W3mSPO3e2Wd1sMatH6ErQGzgs0gnnoxMWumGRT5kWj7BDeWHrW2lsHTA6H9PuKqbnZ9x6K8KOFE7WoRU6LPIzvLBFpxdzJgKcok8nWmeSTzg/XTCblGhRssxLHKGoqpIgjHCigKReoGmQQuL5LlqVgEVdFOA0uANJqhPqSjM9X5LMKnSToJoLdn5lMCQIaqKVCF0VeLZFbFym6ZKkzvEtm3Ke4n3h8iIdSVVklFXBYpywtr6CURphBOkkpR8OyGZzXOXQlA2WK2i+0KdRqqExiqAdkGdLtIAw9PBbDq7lsJzljI5mNKUiavn0VzpoF8pM0e33COoKN5SEgUer32O8OEHWFsJYSNFmMSpwXYfJJKETx1jGYFA4nsF27AvGV4MxLnUDAoXv+SAlda1oeV2SZYLKLaSRuB40quRkdEin16cqSzynTSGWeEGAMin91iaVkpTugqjVxrUiXMvi/qdnLGeSwFkhzTLqNIe6wXZ9/Cgiz0tKXbGzeQNL1gSRpKlTbKtNtZQ4pcOg16bbHlJqQdFkVA04no82DU+ePiErC7Bswjim2+/huj5G5eRpTaNqinKOaXKKrMCICt+PiFsRlhIocrqdFSJHsjiXFKbP7vpb4AjOl4/J8gLf6iDKgORUoxZzzMAmXu0RBwLZ9zk/mKDwsU0fpWoaGuyOj10obqwO8LwW48k5vWEb5SgeTh/jDWPiOKBehGBsFklCFEYYVxIKG7vpsT9bsiyWZJmAvEvb9NmfHBC0fKS0kVZAHLWZJAnGrSlUSZkVhIMOvhtSpjkLsWTohuRljrdis6U30KUhT+e4nQ55muK2Qk4mE/76kw+ZGVCVB67F8emU0M9BC2wbjCXotTuEnk+VJgRhQBz3ScucImlQyjCZzaiSjMbYZGnBsN/GFham1uTLgqrOGa72qLXCcSMsof//7P1pkyRJft4J/tTUbvM7PO68s+6q7uoL6G6wG+QSXJLDFyPCGdkPsF9t5sXMruyOyMjIDodDDkECBNB3d91VWXnHHeG322167AvPiMrMyOrOBhoNAqxHxKsi1c3M1dTU1Ewf/f+fh3YrwUqP2zdvMZ78AtfzMEaxvrvJ0dEJaT4jSlqcHZ5AJaiqDI0CZdjZ2KLfGfLo8wPyecHulTUmj6aI2mU42KCsMh7fO6CoZsTvDFnmOdk4wzWWMHIZbgypy5I7dz5nVE5593vf5traLj0nYjotOHx8Stxts7G1jpPEpEqxmE/ASFwjMVZhJUStmFY7wbEThFG0Wi0Wezm9wYCk28E6Cm0sZV5RpQqrQSiBqsFxQjxXUKscZWvmiwybF6xvbhHHCbnKqBpFXZagDRiDrjUmhLPJBKUqgrCFcHxCP8axkkiGqCajcZwnYeErnTlxTtB8xdN8ha/w14Lv+9zcuEWjU8qy5OzsjKZpSJLkpfQ7qqq6JHRqWPLw6D7SKwmCgMFggOd5JEnCcrm8SK2YTCYYY/7GIpxf4R82ztPitNaMRiMODg6o65o4jv/BuQm9CFEUvTDN6XcthiyEYHNz89Lc92Xb+Fy0eblccnR0xPHx8QVZ83zU2mAw+DslaiaTyaVokxdFyXwZzs7OLqXevYhkOT/mbDbj4OCABw8eMJlM+OEPf3gpeuYfqn7WbyRq4LcLsXqRPs3TeP7fq2gbEMiVF4UV1LXmYO+IjSCiqnKUAE0Kdck7r3+NRycPmMwecuXKFbTbILYCltMS2Wgax5At5pgiQzsK2Q6orcVzJYh6NUH1YkwZ0V8fcjY7prORwKEhDiLam13cRCIcMI7FLGua05rFccWpk9K/HkJRE8mQJEhYpiXD1oB8f05WZjhXFGeLCQcnS+KkS1VVaN0Q+gm5UzMeTUl8j6gbMl8WFIuK2O9RzGtsakiUpBMn+LmiH0cEkWHU8/nw4SHjaUmTabrxGtc2bzOZjXBsRdlkJLFDXVUoLfjz//wB80XJG9/axo1raqVQywW+HHP/tOTNK/+MWoxg2lCpEmFjosEuEHE0+piqSRkf7xO9GtHdrfBdyZt/eI0N9wb4lsPpZ/z5//HnzKc5najD9RvX6Q49RovHjNI5HjEHo/sMwz5r7behXpKXIx4+/iXdKz2GOzc4fTzm7MgljH0m5R79q1dpWhWPjh5wdeMddLvk7of7hE2b/eNDTCBpx9tc37rB0S9/xehoQdDrk2UZYdDGxQEr8b0Ix/FI8wU4Bke4+KFHVVa42coGGh8UirrKMcrDSBAShLH0+i3agxbXr21ilOF0PKEWK1cWpCIzBbIboDA0RQ1ooqRNXSuqsqaqGkI/ZHY2XWnW1IreRo9+0ueDTw7R1qB8hew4OICRFieQGDTtTpuqyhCeYa3fRyAYnUx4dP8QV/q0ehEb2+v0Bl0ax6Bmmlo3uL6DG2jqMkOS0O1FzOcwHzfUhWJrbUBTG7qdDcLAwxOKvJjgSAPCPmG1BUYpPBkiA4nExViwSnO0f0wi+7xx4w3uHXyONg3GgGoMy5nBl306vW1acUrgtWjJHlfar3Pn0a8oM8Gbb73J2cmYfNxw8PAQ3/dpasHoeEnsSELfxQ0kdVNzdHKKJufKVkWv7VJWOZEbMh2XbEW32Q5z+i0XR7p4vmU8ytGAH0U4QiOMwXV8CBw6gzZJO6QdJ+zf/YxZrmkPh5imIF/OcUWMH8QrRzNrEI2mylJi2SZx1wk7A167+nVeuXkdL3D4y59X5NUDwpZL2+2zPFoQ1iGB38I6iqibkLQ2MYScnJ5SZIb13i7G0USBRzfxccMAx20T2piqXjAejwijkLLMCYKQQbKO9ATT2QjdQFVXgH4i4NyjEyZsdq5ybbDN5GhE6ER4xIRRC9cq4nZI1WiW9ZIk6BAnbZRucFyBIwuQKaX1WRZj1lpdOkFMs9BY10EYibSKWiuOxiP2ZxPcpIuHwLiGLCtZzhd4nkdr0GZzZ4tOu4trJZ8cH3Ftd5d20sZhRutqAo5kNJ3TFDlulNBq9fGdmFaUMDqZUC5KNAbfD2maCrSDKzXj+YTY97h7cBfjKRwRrLRvBSRRzGI+YXo25+DuCaIBKQ1ODFEr4urOdcajKcPuJju9bW7fvMLRwSHD7hbjZoYyCilCjPH4+S9+yeODI25//Ra722s0NehMs0hXtp9N2bCYLAhvvYU/Uex9dJ+fffIhNnHZvLJJrRrW1tfZ2trGdwM87WCUAgyuK1G1xeKQNw2+NSS9Lu1Ol6KqyKqKQW+AQNKYVaSLLlfRRb7r43li5eokI5RWpE1OVTZ4jo+xmkZptLGouqEsKqy11LpGCBcrJHErQFuBMvDWzVdJpiF/+eF7SBe0sDhWrBr0SZTrV4lPX+ErvCyevVs83+PK1StMZ8ccHx8zmUyYz+fcvn371+j0fHGMc4vmp2HEkr29PVodwc7ODp1Oh7W1NaIoYjQacXZ2djGBc12X7e3t3/VJfoV/YAiCgDzPmc1mHB4eMh4vuXXrJrdv/x6Imt/2AfMy259zIS+xbRAEl3SgjDHs7e39lhX7zVhbW/tri0Fbazk7O2M+n3N6esrp6emXEjWe57G1tfW7qPJfC/P5/FI0X6vVemmi5suiuZ4nuRzHIQgC6rrm5OSETz+9w97elPX1DcIwuLTv70Mc+veNX9ubpJQXYjpPO0DBs2lNT5ed69mc42mG6/wY53+fH8PaJ7bdxmINNI2hSBsG/g7ThyMWYk6eCHztM8tL/KRDd2PIwfiQ9d0BUQTutKLXTih9Q6vTpTnMSfwWP/3gl6hC8/rNm5hS0wjN48WItrEMtwWzdsokm1BRMztLGXrbuEGMjAOOpkf4Dmx2O0SvtpnOCrJTi1aW7fUdhv0Bd+4+YrR3RCACwn6f7qCHax2u7tzGDyCdnyCFx6CzTqvlUpYZ5XSB9DuUNZgMkp7FmpJr20PW1lpURcNkPEcnDsv0lNJWFPMMkUriKy3WtwbsDNfwworZ7Iww9klaEXtHxxyfzHjw6Iz1K5tM8zmialCVQ13P2Nro0eu18FsVg62QsdMjnyW8ee0d4mAdS84kf8zjo0N8HeDIgjgqELJg5/V1Om6+Cs8fw+7r6+z/dMxkvCR4I1pZw7oeqm5QpQVTMj9+yK03vs+izFkuDokiCVIhwoawYyjPpkRRl5PllNNlCqcGhzlC/JhozSM3DXfv3+fsbIyXBOjBY7KPp1SZJSSmyktcJ8QRLp6URFGC63lkRYrjGjzXw5ESqxt8T1BNFa7jIwJBg8JIRWMt2td0tlerz0kr4uqNLUIpOHhwiic1Vhp6/Q62boj8DjpwsVYTiYBGr/qtNAKpHVwtQUPotghNyDJNWagFqUkpZxUiFLihXA26UoD0cASgoCkaaASxG+JZn4efHzAdzanyhk7PpbfeovEapvWCxjRkTYawDp4rsFZjG8vocEq05pAvFOgAz5G4nkMcR7R7bfq9Nt3E40//wyMCP8aNJImboJRiOUtxpUQ4q3OIgxgrLYPuGtfWbjFJjyiqEcJv4Zg233jjm6T5hNs3v8l7H/wFb339KoPWW9SpS20LpPdzjGnoDQIc2eLDvQe4rPQyikwzG1W4rRZrrYTGNMhQIggQTUCeLRFOgbQCXXj02x3WWzfodxaUixGT6SE7r26jTIWVFteV+EIiakPgBTjAxuaQpswojMHzLfU0Q1UJgeegpOTalWuMZ6c40sEVAaHu4Ms2idtho/Mqnz76hGmnpP3OgH5/m37rEceLMSQre+/IBZc2nhgwO5kS+RLp1Cht0MaiFUgRMBi0QeV8/PlnrG9tELYKehtDOKnpdQecnN2jdBvKoMKYeJWL7vWIgohsXLK0lo21q7xy9QaPH33O1Y2EfitAVwlJnGBCn6DXRxVzivGEtaBH6LoEfkyml8jIYk2DpkCGApFAf7OD4/iIwKEqCrSx9IUkjlropMPAXdAP25AElFXJpJoT+D5eJyJMQoZbQ1rtNlhBWVS02j1OTieUYUl3o0tpKwyCWmkm8wUdV7IWD7EamsqQtLsUtaJuCoxxWE4zhOMSx6vowsYq7h08wHN9ev1NFosJ2WJBvsyoi4ZyVmK1REqL6zj02j12bm1z5foVItnB5HBj+wa2Vqwn17lzcI/x2ZLQi8lmJdOTDLTG1IrbN1/DDyxNWCIDQdJuobSh0Zb9ewdc3zlhee+MumiInRCtXaYPzlDSst4fosuKhoZaW1zXIwgCVGM4PVq9cOVVQdmoVW7/MsWRks6gR1MbTFODdKnqBtd16Pe7q+guY1hM5zSVoRHByhK9qHDsKt04iCI86TLPS1zPpZu0KXSNLyOiMKRqCpRp6Ozu8Mc/+BPe/x/+HCscxBO3J3P+dvsP753mK/w1ce6g4vs+ZVmyXC7hOa1Ra+0Lw9vPtQOehlLqhbozYRi+1Mu0UuqSWGRVVZfKvkwM+0XlL1rxtdZe1rIRljQ/opY1RT1FizOEN8cxBuSzjSKkRsrWM5HnLxb5tOTliEX2gKxaoDhDuFOs41CqZ9vO2ALXE/i+j+/7RFF0oXORZRlSSpRS5HlOGIYXorTnn99mZflF7ez7PnEc43nexXW11lJV1aXr7DjOJULKGPPSqV8vK2j7ItLry/rji/A30dH5bfFldQ3D8EIn6Fx89fm+ct5vz9vV87wLMWGtNWmaXugNvShqSwhB4Acs0yWLxeJCAPtcHBWgqhTLpSXPL197KeUlLRzHcRj0BvjeShMJeKFuypfBWEmZSbDP9h3pStxzkVjfx3NdPBGvonifgut6dDodlF65pzZNjdYGX25fIkWqqkI1T/U9YXFkg+s3wG+ur+d5LzU+nQurP48wDC/16RcJsLvSJQpXETGNbijL8iKtMQxDgiC4+AghMebZ+8nav9nDu67rS/eo4zgvnaaVphnL5QLf9y/qez5G+b5P4AdIZ+UCPVvMUErRNA1N01w8G54fn5MkuRCRLsuSqmoAyWw2uxBUbxqL1i0OD4/x/Wf7U6fTeWkBemvtMwLFrutS1/Vf27XphWORlbS7PlZ8UacX9Y/fhF9L1JwPFk+zVOdEzPOEy9O221LKS0LD5ySOtRbf95/ZXgiDxYFze28Ly7TAyJBbN15BUJGajP39I9779FNO58fgQFlULKsl22KLnonZ6naZxXO0BSECIt3iG1df42D/hCY3GOugHVDzAs+vufPJHXo3PMbzKa12jzC2ZJOKWjtsBB2moxPW1wb4cUDgV4yOx6RVjIw8VKrJZcnxw2Ps3JIvTviTf/3fsXXrFqUzRw1KPr33MZ4fcnXjOr4fE/djPvroE2zrGpMmJdM5Ki/YuXWV8fwUVxo+Of6Qs8kZamkJIstocYT02nSjATe2dgk3Y27dvk69yFHVHNmx5NWS/GBBo1wa5bCxu8Nwp0tRLVjOcxxliWLJctpw7bWMuf4Js7FARpa+7yDkCZVRLBanHJ4+4nQ84/vf+ya9foNoFGVTYBJFIQ45Od1j9shBlC5h4LH/+Ji7D+8ShLeRdU0QhEShZeh2OD19yL0Hlv30HugMvxPSbnfw3YZ0OeHVG19HK4fHJwtqU+IawfZuG7dVMzlccvBI0eAikw4ydjmcPaScCJLAIH2JG0gcx0MKhzjyVy8Q0sGNBFEoV+4nZYPvGtJ8SZFawlZI3IpI8wwk+K6PtTk4DcotWCjDB58s8XRExEp4s9XyKW1KLQ3K1VS2xlqF+yQtpC4MjnZQaUksAoSATtBBV4Z6UVLOchpdI0NI+jEkoDEYY1lOMyLHJQ4jju4d4jkug36fk0cTskmOUgYnkHSGCcopGM8nlEbhex6b6+uIBoxVGPvEQa3x8XVMN/QQSYQQDlk1pakLdtd7vPm1DdSyZvfKLvMsxUqN50mM1jhCEHgOpVYEfkDkB0jPZa3fJ2457M8m1LZA5x6vXXudV669xt0HH1LVB0g35e7dn/Hf/LOv0bvyNX7+0X9gY71Dli/RjabTGxB3zqCJEfhAjh8nVNpQa4MWDa4ISJIOkdMmTw+xoqYVDwm8hDe//iZtd4vjRw+488sDnMphPpphjcUPJMZoVGMo5jlJ0kVqg0pr8qygwGAdSZi00crgOILQD1nMZniugzaWxO/zj9791zx6fEhhD+gmW9y8mpBECY4TIhzJ9vprnEyXmOqMhZ6yHoT0tnZor91CFClJt83kbMZ0OWWZKz69+xk7zZCtq99kfz9jlgnEuGHL12BS4thDNwHtJCaKBZP5lPXhGq7bodOFq1fWmJwu2D8YoQ3ETkOr5VILy1md4YYrQVilKwKbEbuS+WNNe5isyJNhD2s1a60uZ/MRdV1Aq8PR+ID+IOPaztdRcyjLDKtrSpvjEeMIl4Ef8iff+EM6Oz3+/IOf8Zd3f4XbCXAih7gfgwdlWeLJEKNhbX0DXdWMj4+wC6ip8aMIlMXBEvguCIHSUKOQIqCddFguG4pZSUTAaHKG43WwjkMQeDSlIpsv2Ni4jq41pyen5MsClMAXEiMFsu3z6quv8OYrt4nWAwI/oJu02Oxu0+QKV0dcdddw4oD98T61dRgvUlwbIT2N8B3+7N//R+I2vPH6LdrdNrYRjE9n2MpBWsmHP/45JoNABrz19jtYZXlw7y5aGlRWYts1jdbUjcILQ5q6IpsXLGZL9h8fIAOXwi+oq5owCEhaLU4OjlBarwQ6B2tIx/La7Vto1TCfT6iKit2dK/RaA/YeHUBhWRYNaZYTRxGqUai6otPrUBcr94i0zAm8FnMc2r2YpNOiu92jdjX3Hz2icRys1jiug7H2wq3xaefGr/BfJ54W6AQ4ONgnqx7xjT/oXdr25OTk0svs+vr6pfD+qqouuW1IKV96FfhFYrFVVXF0dPRM2ZcRI91u9xIpc05oPA1jzKVjguLDT/8U7BeTYPdL3v9LZfjoI7US6BaC3d3dSw4vT36J+49+xn37y4sSEUAUR0Sd5yLNHfiTd3/wRANyhdlsdiEAq7XGdd0Ld5JzQuf889tMBJbL5YqUewq9Xo8rV65cuO0IIdBac3x8fIkYWF9fv6T/sVwuX2iH/CKsr6+/dF1fhNPT079T95sXYX19/dI1qKqKW7du8Qd/8Ae4rssvfvELfv7zn79Q6LQsSzqdDhsbG2xvbzMYDAjDkI8//pj79+/jui7f/e53+frXv37J2j3wA25cucGf/dWf8Rd/8Rc0TcPW1hbr6+u0222EWBGAw+EYa9Wlvt/r9XjllVeeKXMchx9+94eMliO00RweHrJYLFlffznySzcej+81WPPslLPf69Dt9ehvbLCzs8PacMhiPuf09Fmh1tZgwPe//cdMplOOj445OjoiXy7Y6P7hpaiKu/fuMX5O6FWLExoegfjN5OHa2tpLkRV1XV9KWQQuCJffhE6rwzuvvINGczI74eGjh+R5zh//8I/J8uyZ1Kdf/epXFwTZOV4kDP7bYDabXbpHkyTh2rVrL7X/ubD0xsYGt27dYnt7m3a7TRiGbG9vc+3KNXpBj7Iq+d/+r/+N2Wx2cT7nQujPj8XngtnHx8c8ePCAvb1jPK8F6Ivv/+iPvvOldTo7O7vUTi/COTnf6XQu7q9+v8/e3t7fSPPr7OzsWfLLSt5+7V8Qubd4/HgfY1bmMt1u96XHR3gJ16cvol6+iJp5UVQNPBuy9PQ+57o05zh/yJ9H6AhhERdmoasw/9HZlGyRMyv2kI7FNA7d4QajJkc6HkIYBu0e1gqygxxfCg71PuFbEhlaeu0u6aJiEHbpuiEy9GiuOFQzB3/dxywEoYxoVIacehTjBms9picFIispDyqUqMgnim6vy/xsjqk0BIbFeMnjRw+IwoQ6FTTWwQt7jE8nhL2QWb1g8WDJj/7sAwatDsttgwgsvZ02k9MxW9EmdTqn0Q2ydnn02RFlk7O2tcmp0thgiJaKg7ND6oUkdCAOIq7f3iW+KnjvZ38JWQTtGZ7XRmHw/Tau77G7uwo9G8+PMI3BdzzixCP0XQLPIy8WlEpTZiAjj04coFoVwh0Rt2paKmbjxhD/eobxoYk187LEFBLhl5w8WnL/T5dsyhv4fkjQiXh4sEe7lbC9vU1/0Mb3S5p5zkZvgykjXntng9lY0fY22LwRsJ/+gq3dHXQGJyczXDyk0PQHAZs7ESdnx/zH/+VT1tdu0RoYPE/iGJC1T2/YAvJV33IcPN9jfa2LlIYsK2hUTZC4eIFDlTVEngu6xqkUu9tDlLVc372FPmhYFi7Sg0IrGtUgHY/Q74AbUmcNykAceChdk6sC7RpAELoBZQ1Kgakt+XxBImNiQmIZIV2PdJrS1ArpOAjXEsQxIhIYx9LIlYB0OsvxmgQhHBwriU2Ci0QvDCbTGKWIOh6tfkQy8JimS6xn8F0PV0pkKOn1uhRlwXK5QDoeVvt4KoGyoJE5InRxI4+w7aJFiZUK6y1542vr/PinY4IwJgpjQj+kzktarQhTVjhaIyW4VhKHAWk15cH+Qww+TaPY2e5wcPgJN1+5yY9/+e+YF0cEasAvPvq/+O7bXaaLu6TpGFUrWt5VRosZftih9ivq2kEGLk5gaUrNLJ2vLNQXC/7xD98mcEOWeUVRugRRh0Kl4Alm2THF8ozlQQrSJUsq/H5AYyqkddCNxeLiuy6qKjl7cIhBEPVi/H5Cy3XptrvMpwdMZymba9fRxqHILcPOLa7u3GQ503z68B6v/OAKb9xq0xSGojQwOqWftHnrxh9wePwRTrVg45VNbBDjba+x4W7gGh9Hxhyd7nE0OiTpBQzW23x+71PqzDAcbJMvU8plQRU6uCJB1Q3b67uclnM6rZUIdLcbkVVTTsYPiFsJ/XWPJFGgJ7SHMC/HRG5Ey5U8eLjPUk343j/+NpQNt9avId0YH5DKpzYuTuli1BzlTHl0vCAJOlRaUDYZpi6xoWCha0RZkCQZgQ3o9SXf3HiVSTFBeppvffPrfPToE3TTIImRgCtcXOkRdVtEfkxTlWxu9fnw048wGJQyWAO2VjRVhm6WCONjhEDKAN+VtFoRRb5KSRXC0NSawHcAydb6NpPTCdPJKZPRGapcObi51iLc1e9fv32dr91+i7qo2T88xPMDNgaavGpohy3WemsgG/ylQGU5lUqJpMuN4U2W6YRxOSFrStzQ5/DRiG996xZH90cIJKppGHZb6CwjTRVjveBmKyaUHn4YsrY7xOv5FHWBi0PdFGRliioNwnhURYHvByilVqLJFnTdkE5nuP4TYfntNdYGQ9Y7PdaTHvcf3ENYQ95UxOsD2kkP7/gA2UAYBNRNg1Ea1VRUZY4MQvI8JU2XGCAtlpRFhdZdjNXM3v+Y5lHOwWyCIsCxAo1FiHP3RsMqdvwrpuYr/G4xGAy4evXqM2XnFuC/D5y7kDyNoigukUdCCPr9/rOr6AJA/W5vCwGrNNbnJnFCgvPcSqxYaUi9TAXORTbPUwR83/8ba9ZkWcYnn3xyYbdrjHniBvbVOPHXhZSSNE25d+8ejuMwn89/K+ehcxHgtbU1pJTUdc1oNHqhyGyr1bqwVG+32xfuSH8TjLMxs/kMYwxJkrCxsX6JJPkyOELS7w3APEuAnDuODfqbDPrbDHpruE6Map6LlGh3wAZgfLDPf56tQ7ZsmIyf1jqxyLAkeJbP+juHNppFtSAt0ot2dV2X6Wx6oT91/vnbiAaLouiSg9+Xp2pextWrV4AVIX5u0X0epVJVFaPJCNVR5xzLS+HcWe+cIB4M2njey9fpHype6s49j5g5J1yejrR5mrQ53+acvHl+1eX57Z/6AbDgCAcwGGuZnM44vHtE0RpR1inDzhX6/Q1mywXGEUgkruuRRF2y+ZJpkyLaMY7x8EVAKSy6Y8mylJtfv0JjKh7nR2RzwUa3T3KljRtGjFLLm8GbUEimkxyZBOR1iS8dHOmAVYzHY6qqBs+hbuqVO0zSwWhDHIQUWpEEPo8f7zOvp9S1oWoscuGTzjLuHX1M3Atpsi1OTs64s/8RYT/B+j7FpGbWjnClz9bGECl9pumIRT5iMV2wPFpiy4yt60PCNc2iGLH36IjbG69RZ4q8yRCxRFtLq5Xghz5VlWJKRS9p4XUSfCGR+PjhKjWpk7RJ01MW2ZQqMGxdXUOGimyRceW1V+h0YtL6HsKJkYHB0wl22qecZZipJHTPeLR3yvaNbXa6kr1P93i0t0+StKjrAmMUk8mCrc0NqjRDLnzisMNmfwNdjPF0SBiuMz7KGT3RvGh7Adm84dFDyTyDIovQsUN6lrGsYLi+zuaGi43g5HhOManp9Dp01zp0OjGL5YQ0W+C6Dq4T0tQKYzRh2KLKVtoOvU6PRw/2Obl/Ssvvs7GxxaI6ZmoMRZMjhMU2Dd2kS6YUSewzaIVY4XM2n7HMKpxG0g071FlKVUHgO3Q8H7dx8BEUaU5VK5Q2KF2jUQShj98OyCmomgqkpak1qjSYZUWn0yMRAYWqqYoSXIUwMFhrIToaG9aUApzAwSpDb9gHC2m1RFeaLC+fRDcE+K7DbFyQFjnzesLNt28hEw9tSoLQYZEfcvTwAFev43tt6qokzyZYLYij5ImwsCFwXXTToJtVNMT9/QdMswVJdw2J4OMPf87mTpdoPeRscYrjCywN73/2PqIOOT59jJAK3xXsDF6jVncQ9i6GBmVcXFeSJCFWWqw1LGYZQavFw3sP2NgcMhi2CSOf8XTCzf4uebVg7/NDxvuH7HS3+PT+XaJ1h8TroMoKrMVzXYhW/jXCGERjcdyQNG1YG4SEQcL25hVmoyO0gsgPCfxdCPoM4lt4rs+g34U7LVwifF9Spg0npyPWem2CIGYw6LOcb/Pg/h3eeHXAvTJnlszY8gTDTgftzjiefM4kPSJOAh49PiZPFwzaHWTLwQskaVGR7uestzRVWdPprTPOCiSWq5tXmUzPELrENAbbVPR9F7dWlNMKYzSYhkIXLDKF44ScnU44OThiPd7ClYamWtBrxYymGa0oJlISp9T0ehsE7S0CzyFsB0znMzzh43XasNZmUTgot6QlSmo94yd33udXjz9i/3RCKQWLaoFaVjhCsdUfIr0EhIt0JMt0Ruh7NNRESUSelZjaoKoa0Xg4DVTLFKsCgk4LKzW1LjFWEARdpqMJHm1862NKjRe6NGVDFK7s1VXT0Ik7VDQYt8ELDdcGr/KP3v0n+Mrn/Qc/R1yRuG5IXiqW2QmDbo31BL4F5SxoxQI1V7z92ht8Y/t73Lt3jw8ffchJdkYzrxktSv5i+kuwCscVvHLrBnESMDo5Q6kaYRSLbEktPHavX6G31WPZLHFcH1015FnOfLEk9lvYRiGdFTGYpRmgsdYQ+f6KHLEKoxsaU7O+0afvxyzmY6LExRWwbFI++eRDhp0+s9kptbEYI3CEXI0RaIRdvdgURYnn+VgDy7zAaMV4NGa2WDDothjtg6oMyhhcx0HY80kggOD58OOv8BV+FwjDkM3NzWfKzq19fx/Y29vj0aNHz5R9WTrU8za5f5+Q5znHx8c0TXNBpPxNbJxhZd386aefAl/oPnwlWPw3g+u6zGazC20OIcRvRZ6cr9afR2sVRcH+/v4LJ9vn7kGwihC5du3aS1sUvwjW2otoA6UUvV6Pa9euvXSKnZTOKpLuOaKm1+vR6/XY3Nxkc3OT4XBIHMeXjvvbiObO53OOj4+frj2tXsbGf2FEzWwx4+fv/Rz44h7zfZ+TkxOWyyXHx8cXESi/jZvSy6LdbtPpdC6Vv+z7wGuvvUaSJBdjqjEGrTWe55HnOVmWsWf3fqtUotlsxnQ6vRhvtra2vhp3eAl77vN0pXM8Tbacpy89/R18Qdo8rWnz9DbPW3Y7jkQICUJgbIMQDrUG4fYYdHxORo+w1mE6nlNkBTJwqaqMqigoSk3fX6PT7+LE5coGNTVkdUMjGgwuj6YzHDyUiinKM470nE6vg9OEzLOCtPRoN226rZDd3bc4PDhjfb2N3xGMlku0hdHRCFMqrBYkYY/pImV0OsGpJB2j8H2ft77/FvGORlSSoB7wh996lyAytDdrCnfKUhWU6SbzfYfZYk7apBRLTZU5rLXXaMUJeFArgRN66CTA2XAZtHu88a1tWlcV86OKa9euEUcJeu7S6nQIOwntcMAiyzB1RSfwSWWI50j6vQjPuuSpwnd9tCpZznJ8r4srQ6gkn398BxsahJAkrsdw+DZi1OLz44Krb/UoSsvA9qhTgWc1r3/zOnfkJ0zyDGUqom6b6XzKg4f3cRxLGPoIBJ6/jZjC7NEM7SxRIwX9gqXMUJnBpD5YF6Elk2mKTDxm1Yws1dQLeHCwR9gLuPLOFTauDeiuGWblDDky1GlFdyOk307I8znLxZg4CNC6oVwuUUYBgmk6xxcOrVYP320xXF+nKjN6ww67rwz42S8OyHNF3G0hFKzFPao0Z60bELZclMpQxiA8gev4aG1YTpbUC4WpXayvV2l1SqCwFGWJkQItVu5ks9mEKI5Yc9ZoaDCsogx8GxC32vi+Tz9KyLIlrhC4YYwfroSGW4OYvdlDXN9FOC74Ek+5uJ6DsQYjoNGKrMrJ8gzPLfBkwfXt2ywLBxBk6QJfWsLQYTFfsr+/x+JkwVpbIPE5PdonzWsCLyD0fHqdFmi7IpOqmiRIqOqKxXJGksSsDTosZzlnoymz+ZRRtsBal7qqiGLw3BYP9j4nDNtoZblxZQ0XRehUeLamLBa0Wluk84aO12E4GJItF1gDRlj6cY+NtXUcL2exyKkqRd0IssWE/YN7BInkO69/g6lekrojpHWJ6FM3Dd1BTNKOEBp0tyD2O4zGKWmVs9W5Tp4qqCJib43uzR6xv8uw8yrvvvkDhBaEgUe/2+Od179JEgVYs2p3z0+IOkOsqXHEyjFnPBlTZDUflg3buxAJxfL++/zsxz/l/fd+QVHkKNPFwaeqJIIK1y/YXd8inWZI6RMMXUI3xjeWrV4XpxNSVAukp2j7AS0ZYbKGXqeLaQxBt09mNHvzjKwp6bXaVIXgys5Njvam1O0YBpY4CKibHFWVxHGEj8RTXVpeB0mIlIqD40M++PAOsnFJWn2+9vbbeFFILhyOzib86i/+jDuHDzGeovQgVyVISTtq0407OAocF8LIp1EKRM18Oaepc7rtHjQZy+mcpioJvIRrG69R5jXtcA2aGuFomsalHQ5ZH6xzbM7Is5K8OsHHw9cOeZViBai6ptfq0gn71HnNwwefsLF9ne+/8yd8Y/cP2fv4EfrMQWy5BG5EIEOk73N8eMonH33E1lab4izFyoK6KqCGH//yJ4xmc0pV0++2mCxmnJxMebw8ZufqJp1BhOcHTKZzom4fL7DMlnOidsju5hbdToe8XpKWDUZbslnKcrbEanADF+usFMqbpkIphetJpOuAI7DGoFWFJ1yqLAWt2D94jLI1VjRYNP1uQLqoOT54TFlVCMdDKzDaouqapi5AGcqyWpWzEpOvq2qV1mTBdVzevfUN5F7DSXMfx3MwZuX0dOH2hEA6q7HiK3yF3yWyLOPx48fPlP0+ScF//s//OUn8bDrW3v4eP/7xj58p+/seJZJlOaPRF/a91lo6nc5FmstfB7PZgr/6q5/Q7bYYDocXqW3P65l8hZeH67qcnp5y794DtDbcvHnttyIIT09PCYKAa9euobXm7t27TKfTS9fEdV3KsmQ0GmGtZTAYcPPGTT7+5OO/dt2ttTx69OhiMt7tdmm1Wkwmk5fqE44j2dzaQthnCZfziJrNzU12dnbo9XovTIn5bQit+Xx+KZ1riGbjyt+dQ9KLMJlM+OSTT+j1egyHwwuS6uTk5OIczomap+VGflfodDqX0uaqqnrplJw3Xn+Djc0NZrMZJycnF5F3nucxm80YjUYXEV+3bt16qWNOJjOyLKXX67K+vn4hoP5fWmrj7xsv1fvPB/svUpW+IGSeV1l+WtPm6e+f//fTD+xVjrxcrfEJ8YSwsRwdnvDNrTcI1kOm+SlWp/T8hNoqjBV0+10cGTOI+ly/dZtT9ZDlyT6BiaFwKKxLllU0kyVx0KYOBVYYllVKoENcfHwRgi/IqhI/ilBug5YVWS15/HBEWTesr29w4/o1lrMp4+mI9Y0uw+0tqupTzELjaYkbW7obHfythkAGqKMEx43orJcMX5szlwaZSnS6wfqVAdPxmNH8lNlc4yxD2r6PDFPiTkx/a8DZxJKJimgYcG13iyvv9jirHrAcLxj21ij0gqgVEHkuiZegmwKtFvhS0It72A1FbTWqapASosQHHIyWTM4mOF7I5tom7XaP+6c/Rrvg2AgRLpm15zgMUfWS08cuVZkxrh8wmpwiRQfPdNjZ2uLB489ASAQh2WxEnua0eiFh4qIaxd7+HpPxAu02DDavc/x4j+yoJN52sEbhahdPwvrGkKW3oKhzlqM5VeMicBgdTgiXPb73LzdYlMekoxIlFGvrHTq08JKAulaUZUa3lbCclwhjKPIlrW6bsm5YliVrnQGtbof1ZICoKx49HiFIGE1PiIOA0POIvQAXwZrTwu05jO2YRTpDNwZDRN4ItJEox6KqnLwqsIWgHIOpBJ4b0B10cCOB23IRHjRTRcftgxbMz1IC30UGEqzAGhC1wBEWB0PgOWgh0MZSoWi3ErK6wDiSxrgrkdzII05iojAECfPlgvl8gcIgQ3d1jbViuB0zzyW6sJwejdgKuxTKMjo+QikXUQWIMqNRNU1tQUtqY2iFPiBJ4pAqnZN4Edev3eDw8BAhDLEfIZEIa9FaQuOg5w5Jt8eiOma4voYrXe59/ik72z2qEprQcHL0Gen0BF+7SCtQVQEVvHHlTZwGxpVEDn2C0CNwAsZnU4SXM51neEGbB/cPqKsxhydzgrTNg+kJr7z7Jp+N3kfnEVc33uTO53exbQ/fi5GhgSigFfUwbgATwZtX3wUT8tnnd7h99Vs0NmejdY1Xrr7Ldn8TxxEIR9ButXjz9VdXAs3aYbgeYUwb6Vp0Y3CDiK1hn36rx3jSkJqGok45nJ6w+OX73PnwLst5CQKSsIXWgjItGXQ22N4YcmVzg6pVMxpPGZ2NcSpFt+pQ+w1JR/Lpw4+JoohAgjcYsLW5gWwEQSDQosAUFb7v4pk+vhuwrKa02118D4SU7M1O6EQJsjFs9G5QpEuETTGVy/5nRwy6XT59eI+f3Huf+bQgcBy+/UffQCYCXeRIYkbpHPoJt4bv8vln7xPEoCqBJyCQEs8POT0+Ja+O2NzdRrhQq5KyzGl5AY7wWOtuYksYpzmub/j2u+8Su310Zrn/8OdY13Kl/xq3br/FNH3IG29c4f6nZzx4UNPvd5jlpyzzKW7ioxvF1d2rVAtFVWT4ruH2jVf5g2/8AFNZPOvRD3vcn+6ze3UHU8Hk9IxPPvqA8eiYTwNDKwio04Iy0/z0L96jaiyltdy8vUuaHbOcLinmBe04IfIi0kXJhx/cZX1riN/2idoh3fU1tnaHxJ7H0ekRZ2dHGL0SLpxOZjSVwhqHptT4XkjdNIAgShKk74AwBJ7EEQF5pnCsxTNw+PgxR0fHWNfQ7cVEYYAnfFzp43nQGItqnuhwGEsY+AitEI6gqTSN0VS1ugiP1kYRhiFXrl7ne9/+Y/7qs/9I3oDrrCSEtQBXPFlEuXgOfxVV8xWehbX2Cen37Dvfy+JFGghSSnZ3d3+n5Mjzi3/n6Pf6l7RTpCsv6d4YY9jf3//Sc3v6nfdvil/3G0/j/B35N7mmAtQ1LBYlvr8gjmOiKLoQm33R9s+fy4u2KQrFgwcLtrZW9uBra2t4nvdCAdDfF/6+Rf49X1/HccjznMePp2htGQ77rK2tvXDfp6/ReT9YLBasra3R7XYvhFmn0+klPZVzjdA0TS8s3btJl9Xy34vr+TJ9YjKZXKTXRVGE53lkWUZVVZfq+zwcx6Hb7V4iarrdLt1u94I0kFIShiHdbvfSMb4sQ+P58jzPL9K+VhtC0nOAlydqXua+edF2v8516Nk57+r6PHjwgK2tLXzfZzAYIKVksVgwn89ZLBYXn3OB3pfBizJXnq/XubB1t9t9Zlsp5aUx++ljPF027A65tnMN13WZz+fPpD7Vdc1kMmFvb4+yLLl58+alNnpRn8tzzXRaEIbBhYB2FEVf6hD1IrzstXt6+y8TpP+y4/02WL1m/c2O8RuJmi8uusBxBMZYpBRP3u++aOzVdwbx5AXQ2mcdn55ujBdp2VhrEI6DsM7q2MIwP50Q2haVKtCNoRW6vLrzKtbVLMwZXtvFd1o4tUtmRjQ6p16WdFprhMmQyeMjPNPjle1ddKNYsEAHglkhWcwz+v02nai/irzxGozj0hgDdUOTWdZaV5FIqiwnKyssPoPeBsoY6jqj3+uwqJa0kxZrG11GhwsSHRG3BOVUkcQRnSuCGfc4no94cD9FpD6iUTTlkrKyWBvQG7ShmDMZHZMuEoR0WOgaKwzKlljPIys0y3GBs3Rpxx6R1+DYNWbjGaZsIx0JVq0iIMoK42REXshi0RBGDml6hhQRZVVzOlqSxJp8vM8/+oMbuEWIIx2aSlDJggN1ghet4zgB48dLLBXz5QlaKLY6fa6tDTBrCY6/5O7DPZAe80WKNA6litCmJEkijJbcvv4KebUkbvXJ6wWO57J75RajsxGuBVMZjqeHRF5AfTrDSw1VXeFYS9B38NqG8eKYsG8py3rVd0yF9BVKu3jKo1k09OOEIGuwnksZ9Enziqyu6fT6XHv1BovRhL2jezRlQTot6B4r7n5+xJVbQ4b9GOkL6kqDcpmkKdtvXSPTc7QyVKXLcn9CY6CoMqyqCXseOtA4UUCdCkxlKKqaVq/DJB9jPYNy7IpMWtYkUYtOlDyJ9FmFPapaE7ZDlC1xAjgdj0jzhiCOOFuc8Y133qZeKKblAsdIQgGuL7GORmtL5AU4ccTcmTPc7OK6Blsp9s7uUuoUKxTt1hrVQlBkiiCKMZXGaVy041KVOYHXRiIQwtKJO5iq4bXXb/HRe+8jcNHG8Pm9u+xe6xH5PvkyIw4SROKyOJvjlAHj5RlOoNhdG3Dv80PSiaFqa3QNiyrlUfMB+TQndrvsDq6RG0VX+NjCIfBiqBf02wNuv3KNne1NfvzBT2kajWP91Xig4c7799FmHc+7xiSVNKpEap+twTVuDt/hla1vU8ozlvUB0/kJtS1pzBJcycZwixCfQW+LSbTkxu4t9g4fszO4ykZ/A9MojFip6AtjCX1JldYIodBUYGJMVSOFxZiV7tE7r7/DwekpYZ2xrP6Cn+5/RquAdLFkNk0RaGL/lNu3b7K93uPq7i6qSkkXc+IwoLsW4qmIXjvBCI1uFrjWEEjBbLmgrNVKb8cL6XkeipJJPedsWTFIdun5HaoaVF+AbGh1fRZ1Tq1duld7+AbasU9zqhABOE3JWjdGlw0ffPiA2ULg0sJ4FUVR89Mf/Ypidsabb7zFcL2P7+7w/sf3cTxDZTVCOlhjaLTi8GREU1Vcu3IN6horHIwt6XUl3/vam4xOLUcPl2jZQiTr/It/+sdsdddxw4AymLGrB6xtrdGOdvjlL3/Bv/vx/8LWtR2cvEdUe5yWh4zzY0xowG+jVc2Du3e5Iq+jZiWu51FWGV5X4lqP7m7C8r0xofY5vXPC3sNDjscjmrpAG01VNshY4LmSt17/OjfDt/jwgw9IyQlcybiuGY1neJ7PxnqXzV6f0XSC4wjKXLGxsYtUPr/6+fuYylAWM/J6gRYVRlcsZkuM8rCNQDWGeZXiBwZjDY4QuJ6P4wrWr24y7HQ42t9nmU6R1qLKioNH+zTGoLTGNJZot41xXOpSYTU4uAhhCF13FcUqIcJBV4psOaauFJUB17d4gYsnVo91X3r8+Ce/5Jd37yPCEKsNjnQw8onD4nPP96/wFZ7G4eEhn/zP/wc/+OEf8sYbb6x0B0Yjtre3L734vkhr41zo9mWwubn5Ui+zdV1f+i0hBNvbK/eX80juuq756OOPLq1CP+9Kel73K1euXPr93d1djo+PuXfvHp7n8frrr9M0Dffu3Xupc3oRzt1LnkYURS9Mbdjf379U1/MId2PMxeS21+s/mUSvVt1HoxGLxQKt9aX9t7a2LonczufzS1o+Qmj++//+n+A44LorJ6k8z9nZ2Xmpa/+3gbquOT09fabsRRP1vy202+0XEggvwpcJHGut+drXbgCrdnuRGC2s0gaVUhweHnJycoIQgrquOT4+5t/9u38HrNrDcRzOnhPOFUJwenp60df/7b/9t/zpn/4pa2trF1kSvV6PTqfD6ekpRVHQ7/fZ2tq6sJZ+UVSF53kXETVFUQAwGo24cuUK3/3ud9nc3GQ0GnH37t1LES2u6JMkySWiZmdnhziOEUJcnEen07lEsGZZxqeffooxhjAMuXbtGsYY/s2/+TeX9Fvu379/QXxsbW0xWBsQteYYnk2DdByHnZ2dS+f5olSjdrtNr9d7puzL+t65TtTTKIriQkz34cOHHB0dURQFcRxzdHTE6ekpv/jFL/A8j+9///sX0SRvvPEG1lree++9lyIrWq0W/+pf/SsePnzInTt3OD09pa5r3n33XW7fvs16dx1lFI8PH1+IHp+cnPDw4UMODw+x1l4SkrbWcnBwwK1bt7h+/Trz+ZyPPvqI+WKOalaEYV3X1HV9kbY0Go0Yj8ekaUpZljx8+PDCQXowGNDtdrl16xbWWkajEcfHx0ynU1xXE8cuaZrz8cef8tlnnxOGAZubG19yxs/i7OyMLMueKYui6FJ/Ai7cpQ4ODjg+PkYIwXA4vHTtlsvlpbTBL3tWbW9vP/edyy9/+Quy2V3quqHdbqOU+q2Ip9VRfgNWN/ZKfHBF1pw/7J5n7lZRMau/zaWTedq68PnjW2uxrF5qxRMRNSsE8yxjNDkhWfNY66wTOJp2b52snlJUU04Xx0Syh6MdHOGB2xAECcY1TIszjG64cmWXs5MJN3auM3D6GLPOotXjJN2jnC8I/BASl1IvWE4zrg2v0R92ubb5GmVpEHjUqmS8GJOXBVJrgiDhs4/fQxIgcOmttZFBjCti/DTGd0OEY/ADl9wWnEw1jrtLP8pYTkOaNKIpGwK/RxQ4lNmMZjEj0BLXhuzcuMnS5CyqA5ZFRtcb0HW7VGJK5k1wY8nR4QxrPMoyxXETYifBGlgsU5QD3sCjXCwwxmBcSe1kzCdjZosCxwmQStP3Qoq05Obau9zfv4PnCGptOTw8pd8K6K8nTOcztKdAK8piwbSAzxZLnK7PMivptjZoHAPSJy0qWt2YprQQC9bW12l0w/37j1jbLfE3WkRRxcHklMBxGfba7J9mSOGiipq1qE2raZiZGhElvPXPrhJvR+Q2Z5mVVKrAMZbhRgejS5zGQqNwlh52ahjgILoRWRhQ1Kc4xqAMfPb4c9oyxBElyq+pK2hmlqsbV+n1PWxWMs9SpBuwsJrTssGc5khP4fkO6SLH0YAxSKnRtkGjEW0H06mJ+glq4jE7WzKZzWhsSdgPcQIBwrI2HCBri25qlrMlcSvGR0LoYgRMlilx26O33qM6XaKMZVFOVlox0qXWSwKng+NEZE1FtVSossHRlvXOVQgqgo6lKDMEDlVhKeoKKxTCGtJThecmSFfSVKuBVSSCwPNxHYEXJQg0w1aXfJ5x94PPkNql2+lz597nTGZzhts9aq1RWrHe2ySqS0ReU5cFni9oKpfp6IzHD/ewuc/0ZIknNbEbUJQL6klObWqm3YCr77zGcn+JtS5WeHQ7PVodn3aQMOj2CRwJSIzK0RgQliy1fO/bb3Ft7U12165TF5p/+2f/LzYHfXYGa1zZvcV7n/+Eqm7TlS6T/BBtGxzh4LsJy/mcdrjGras3uLJ+g5bTo90O8JAYNOLJepMnnYsoA4vBGokjFA6glUE4DdIVdNeHpGXBcJxy9Okd6nyJ32ljPIvGcmV7mytXBpT1Aul3meRTVJqzAIbrPdxAkPQjlmpGWVdU2pCdlsjGQVrY3rpGlUJdWc7yA/ImxYkTwHkiDO7gIBmYLrla0OgMx88J3DalaIiHCafZI5r+ksJ6iGFDHId8+KsHLOsM21iSfsCtV1/jzkcPiHwPbEZnrc83vxEhZHvlpCUM1nFwnqysr7RnHYTjEYcRieeTNYr5fMo3f/g2oayIg4DtzhobdHFuurz76re48/gh799/n7BtcRyXvXTCIDriZz/7C/Ky4vB4wjD02B7ucjo9xkqDVhpVNjRNyW5/i+903+XfH5/S9Dvcf3iHf//T/53N3haf/vRnjPQYZ+ngzUCNVppUlhqNBGNIpyXdfshrt97mm73vcbV9g0+Pf8nHo19Q2Yrd60OSqM3ucIdXd29zeHTIZwf3OD4bcbx/yOwg5+d/+iuysysMdxKsrGicGm0q4iBBBjFlOsZqTa0bpGhY5imtbpvAcwh8jzTPWYxHzE7O8AIPYyxFkREECfJJm0o8qlwT+S6OsQgDwtgnUvtPLHKbBq1qmrxEG0VRFQjfJ0oCPNcDbRFCcry3x4PxPtKNMZXCcTysEEjEk+jV8//8fiY6X+HvF84nZEqpZ3QHX9b++Zw0eRm87GT/3O3oefi+f6lca/3SIfMv+v3zFIzzd1bP8/7GK6PnE92Xqedvqvt5+3qei+d9ce7nLlgvImpeVP8X1UkIiOPg0nYvcs36feLvMgXCcZyXSsF5Wq/jRfD9L47xoon+09fMGPPMNsaYZ1KNns9OOP/9p/8+t7Rvt9sXk9DzvvO05qjrugRBgLX20sTUcRy++93vXiL5FovFhaXzeTRXHMeXCC1Jh06nc4moOXcAfrotzt2An9lfykt6V8YYfvSjH12QRuc47/dSypV1dBDg+T7VCx5zUsoXkqEvskx/HkEQ8Nprr10qPycunkaapsznc/I8R2uNlBLXdS/GVGPMhXV1mqaX7rGXHXe01heRT4PBgLquGY/HtNttPvjgA+7du0cURbz99tvEccxsNqMoCqy1v7Zvn7eH67orZ2ZjuPvoLoUqGI/HHB0dMR6PabVaFzby54EZdV3z05/+9MJJ7tq1awyHQ6y1LJfLC4LdWstwOODKlWfJsy9z9fuyej6/7W9zj71o2193Lz9/zEtjo5U0TUNVlYDzpTzIb8Jv1KhZRcBwcfDnI2IuiJZLg8UqyuZ5sua8Uc4fgE/r2VyEQ9mV/9PKM73GWIEfrFYsXVUjrUEoQyuMSNoJjkrAsRR6iXUEo8kM3yZ04pgqz2hqRVOvfmcj7rHb77PdW+fBw0fsdm6jpebhLOc4nXAc7BMFIaN8xHa0SxR1mc6mBMMQrQ2iVpR1ST/sk84yaKA8hMyfEXXBlB4Kl04Ipqw5vFewpEfoJjRjl/NBhwABAABJREFUSbUwK/ax8TDSxaoKXwg63T5rnR5C+qxdT9DjkmLsYlJLOl7i3BqAv0qrGc9KsoXBiBolDPvLQ9ZljziOyW1J5dYoDUJYkBWzdEHjCIJWhJPVLBcZNpR0PYfJbEk3afPmq9/hw08+AuuglaX2RoyOxpxMjhns9hn0EwaDiOwsp7E1sddiPp8impB23OLWrVc4Oz5mtiwIwog0U3jOnJtXb7Fz5SplIKh8l9qALARnJ6fYrMR3QlRas9EbMpvtU5UZge+zvhYTRFA1OV4UIf0Ynx5WZShbUesKW0jK8YQrrV36wmU0vwe4bO9cYzwawdxQVhVFWeLGIcN2CzdyeKRmqFqw1g5xZE26KKgKSRREzMoZha4ZT3Nc15BlC5rSQWuB4+kLL1uFwAiDtA6Or9EJtGjhaUlVFjieg+e7YAwtNyZfzJidjdHGxUaGpBtjXZ/jsxFNpRm6Lr2BR6vl09gGx+mRlxVVWeEhEcojTQsqq3E9D89xEEownT1ksLtOqQqM0ghtqBR4QUykoVgU+CRENkAtCpSuiXoSt6txGkOkHGIZks5S5kdz6tpQ29V9e+fOXRw/RMqENG8QbowVGdL3SIuMySSjO4jwQg9T+dz/dI6pY4w1VKUCYTCeAW3puBG69vnZR4+Y5ordtWuYuqYoclqtkM3tTfwo4PP79zg6PUWJmmmWUmuN77ucHqX8/Bc/4l7rfRzjsb1xm7gbURQZ68O1lS282+H29jrD4Qbvf/YjMjPj7OyY9d4Wm4ObtKMB6zsbIKB7YxOlLK50EM65uOr5hNg8ifATSG3RZuX+ISVoNH4s6dLm4X7B4/0HVFVNU9VMpnukRUXSCQgSB78VUGtFpqec3t+n1+pzdesq0/ly5SiXwNXrW/iNTzk33PnwMYdHZwTdmPaWJNzwKEWK26mfjJfQSzxCWSFtiOM2RIlPz4koZUYta2ZlTl043Dmdk1c5nz+4w97+fSIvpt/pc/T4jJ3dbcaTCd//4TeYjKa4wqEuQTg+n98/QVNSzF0Oz47JqoqkE+IIWCXpaZAWzw0wuqLMNA8ejpilY+4O9vhP9065dv11/vD2/42d61c5nT/gf/pf/wcePX6MIxPK2rKoR9SiwPc9MIaw1yGO+wjPZVrOWORTgiCiKkuqskGwsju9fuU67q88am2wmeH//Hf/K24UIWuwRrDNNt9+81scHu1jrcFiUUpT5BrPEaSlYTDcYnd3B8dV/PJwhgwbNqIWgZew3tpmt3uNnd4mpjA8Oj6EBvbuP2B+lLK91WGt36WpCwpR4MU+rmzRCboszpaUhcLzfNpBRL7McR2XoBVTuwZPWerRgqyeI2WNJXxCvThoa2h3epjakjYlnhuy3dugqhR53VDPJ2idoYTFM5LAQK5rFDVRN6CJIHIDYi9AComVdkXI5jVG+2As0nGQYqVttXqrWKU9XRBAX6U+fYWv8BW+wn/1OI/ceRpCCD777LNLE9HFYnFJzDiKopVw8FOQos/W+mWNmheRA1mWXSJfHMchSZ7Vm/pdpiT+deD7/jMpPbCq01/91V9RluUz5ZPJ5CLKLY5jfN9/oQOdMYaTk5NLBPfLakMVRcFPfvITbt68yY0bN/B9nzzP+Q//4T8ynY6ZzWZ4nsfh4SGbm1tEUYjW+sJS+7fBe++9x71799Ba0zQNWutL1wigrjUffXTEzZs5u7u77O7u8tprr3H37t1LkXxra2uX6lGW5SVR+P/a8FJiwqtVty8suM9ZwKfdn54WHf5iJeULB6inc9JexPQ/DyGgUSXzxYJ4o8PxySPaYY9Kl9T6jKtvd5j5DstcExUekQw4OJswLqfYWrAR9PC0z1rUx/FT2n6IsAqrDdKGxLLHWqsiICTxEjJ/xv3xCa+9egPh1Ow/fMxET/HcBAR4vg9W0AoiXD9gfWOLraHD9CSl7SUsmxHpbISqXYQLG4OYRw/3aKTEuAEFmrL2yLKcwixJ2n3avVXoFbnG9wOSXpesLPjJBz+m3+7gaEu9aCiCBfcf3KV2SsZnDTq1WONy440tcnvK/sMRrkkYBOtIb4QSFbqoaKqU3jBishTY2sPFoR13kGg8J6BSms8fPMS1krVBn3ypcH0fbQyHZyNcK4i8GDMx4EUYtyLLCiySw3sPyOeaThjg4LA23CAKYx7v3WdZlHhuQClrFvMx61sd0tgyqhRBE0GpUfOaE93QiVuoZUXmFhhPMlc5Uhp0VaAWFX7QwlQKi8BYD2slSypU2eA7AisaZKtBe4KjyQLPGNx8Ap6lthWOdZHCZTHNcG2L9TimlbRBhVSpYenOMdpBN5ppMcMYjXQDqjwDPwTloxtLVVYkPYsrXCwGIxwMChfQVtAe9ogHCcuDOZ7TYr5IEbVB1RVpuSQJYjyvTyAFfugTtj3cMKYwJZ7jkUQxoS+QA0OuKhwcCpVhqKlyTZVldHZiWi0fz/VxtEQLi9IGazTSCBKvzehkQV4o1jtDnMIiXUkoE+rU4CWWXs/HbXusbSU4RpPtZzi1Ri81ChCeREgPq6GpSzzXIqREGYUvO2iVkpYLxvMFy7JCpClbGy3qBpoqpCxTDJDlDdYR+FFMUdQkXoDre2z315ksaoq4pqkrfBdmy5xF0cYJuhycnWCFwzKtmM8LKqUwVlHlsL83ZxxmGNvw+PSYbjvgv/sX/096ww6eiHj79a8jPY2Lx42NVzCe5fZGQzfqsLmxi1GGwPOxKHAUvuuycuOxWDTaauq6ROkanNULS1XWmPM0EcA6DhKYHO/zk1/8kvFiRhj6SFfSaw1oWIU0jiYZudrjW9/f5forbY4OTpEWjJqwnNc4xucXP73PZDbia99aZ3MjJFnf4Mp+m1qFnI5GNInGTSQyrMGUzGfHbHa7dNolFSNqq4ijBOso8nxGbQ1WCs7GM372o/tM5ylpXmF0gPJ90smSKI54/d1rZFVMb0Px85/eoyo0RV6D45KWBaO9Gbr2KZoZa1ttfE+C62GkoKoqpOfSaftc3d3gFz/6EFN49IMtPvzJEVpYrLPH/+Nfvk51pPmz//SfmTQntDoh3//m98knmv/9P/3/cKMI1WhkIFlr90nCBGMVZ5MRg6SL68RU1RzHVSs7b9/DhgI39pHSRxmBsBWR6GDdkM12n2ZWMJ3McTxnFfHpSOqiQSuNF0mkjOgPd1ioE47ze0TrhkEdMc8F5cylv7vF/t5j3MZhNBmvnPTabYyp2NwYEHsxnU7Eg4MTwjUPKSSzWcqsXNKUhqJsaIUdbA1ZUSHjgMnRjNooBt0YP3LobQwgK1Z6NsIiHIHjuKxvbuIsFZ+fHtBaH7K1cx1TGQ6OzxiP57jGQ0uNcMBWCqMs+C6Bdeg6LiCw2mBwiMIQoSqSyEOUDk0pcJ7YcZ87PoknUTWWFfGM+Yqo+Qpf4St8hf/aUdf1pdQlWLmoPY/NzU1u3LjxTFkcx5dcxyQ9tte34DmiZj6fX4pWSNOUPM+fKbPWXkpxstailPqN5/O3BWPMs1o4T3BycnKp/udlq1TFHq1WiyzLLhE11lpOTk4uHfPliZqSTz65w/b2NtevX8fzPB49esSPfvQXXL3a4Rvf+AZ5nvOzn/2Kx49HvPLKDkmS0Ov1aLfbAC8tJnxOsrTbbYbDIa1W64Vuc44j6Pc9kmSlnZXnOdPp9IXXrqqqS236t2FN/vcNvyGOTzxJefqCXHnayemcbHlec+aL/38hQvz89+c4D2t6hswRq5dI6bo83NvH39igFgXG7WIch1fevYkdnIDwyaeGcTpGTwuWyymNNaz7mwxlhyRpMYhCkr4lpECpiuNMIZcSLZ5YKFNTGI10u7zz5j+mF3aRtsQkHtOzlOl0jpQO1mqsFbTabeqmIYpc1jd7LBYzbnxjnflScLo358pwwO7VASN1xCKbsbt5myAMeHTygOnsDBH5rG0McIME329YzheovEZaSZbllHVBuczobG6xyOfURUlVzanKmkzllHmFHxle+8YAp90Qu30whvhsyLff/jYfTP6CcpoyOixYZJJsUVM1Eh8XRxgcEzPstxFW4BiHebEk6kQ0QmMllLoCR6Bx2Aoi1pMOn+w/ZlmU1CYnCFym6ZhC10gd0N/qoCqFI1z8JGZ9Z4ezkyMiV9EKFJPZGVtrO0znE9ruGt/55g+YnZwx7rf45OgBeV6TZUtUYAlCgb/ZJfITfGVYMKesSozWq6gHR2K1QkmXVq+Dow1OqNmv7+IqjzTQbA+6zBYzJtWMpl61qxv4tIKQKhc4vsewv0ZLbCPUnJPjCa1emzBsaNKMbq+NK3wWk4xyrleRWNYhlAbHCDwRUOYKKyVe6IOpqYqaK2sBX3v1Oj8av8/kZImpGqqiQQiLKyWVVfjdkMEwRpsaIX2qsqITBJjGAaWxvsD1XELpUCiLEg2KBqUERVmzFq3xyjsDHNciqgRpfJygoC5SlFWcHCxoiobABEQEtFortjyd5RihGWxFODEMNgek+YzZOMNWArGsiLyQeTHHSEPSbdMULk4doHSDDCxGKmqd47iCeTbhZDKmyAqu3LxO3A3J6wmqapEVKVUDjpDUrscxS3qOw7ATs5wvCUMPp644OtvDldBuRWRphn6gCJMIZRR50zBf5qRZQ6M0ViiQmryxOH6A50kaW2Nw+eDTn/Hq9W+wO+jhujFNU2OVZntth6Cd4AiJNICwWOcJ3WId0KsUTaM1aZpS64ZllpFmJcu8RGtFrRpUo1e6XK6H0QoDlHnO8f5DGt1iMIyQsmBnfZ17Dx6tSGtlkSKiyjSff3JI3NulkJr5bI9vfu02cd/js58tqacud94/obeuGV6T4HcphyVFNmd2Mmf/gwlOLKkoaSqNMQXf/PYV4n5NGFrKHN7/ZMaDe2OCoIMVLqdHGZPTDFUoBr0tWk6AG7i0WhFlnRHEkrxR3H24z9nJlHQOqgRHSoIkodtxkFaSTi3wRGg7DNHKYTRd0GjFxvYaVpeUucChhecYVtxXghcqUDVlNubgYEySeBR1DydsuPPgI9rBOm+++hYPjh6SmZS6bnADF98VqEoTaJ8/euP7pJOcxeI9rDUoV/Lg8SP+cvyXEDgEbkxlNZG2/JOv/TF1GfPKq9tMT4/4/N7n4BpcLI7j0Y5iPCfFxcPRlr/6yf/FbP9zZssRtat58913Of3sEYvljPc++Tm20SyqJQdHRxSOIlM5uqkRASRRyOODPcI4oeWFPH60R6MVTWXwgxaB9SnzhrTMqeoGXVboRtPyPfwoxnMlG8kuQSiZpcc0pmKZp1RVRa/TA6lwJ6d40mc6WzIfzZmOlswPM1zpUNqaoOOiHQ2exLWrsO1rSZ/lPGVeLLFWUJc1ApfQ86iFQdlz+0z9hG00gDx/wq/67N9z55uv8LcAK8BKwAXrgtVP/u+uxtC/kzo9qcMluF9S/szOTz7m70aSybJqNyt5pgL2Zer+6477gv2ty6pNnj5RC18iKPsVvsI5mkYxHj+b+iSE4Pq165ciPXw/fmpMWH0CP2Ft8KymiEN3tUDKs5pVaZpeImryPGc0Gj1TlmUZH3982bXqpVPhrHjBPeI8KXtuMDgf6y6VPbt/WSje+9VHl37q5Piyxs3J8ZiyLJFOwOZGwnBtizBIwd599mewTMaTSxGunue9VBppVRk++uiYf/Xf1Gz0NtBK4/s+P/jB19FaXWjIfPObX2M0GnN6esrW1hYbGxusra0hhHhpoqbT6bC7u8v29jY3btxga2OL5XLJhx9/+Mx2QeDxwx9+B9/3EUJw7949Hj9+fCnqCuD4+Ji9vf1nylzXZTB4eXe0f4j4tU8Ho1c5qfZ87c1ajOESYXOOpx2djNEIcTn39zxc7Vzo6kUOUtasfs84kmWek9cFQd9nlk5JcDFE1AuH+ayi1dmk6WRsbG1xtOdzcjrG1oJ+t0PgCupshtCCbKGwDhS1oS4Uri8JQkmWpUxyzSQFRZfRZzN6iUvo9lnbXF89VG1DWSxpnoh32cYwX+Qc7R1QLnJqXZDmGbbxWOtmLKuAH/3iV5yOlpydpXz/e99mc6PD2fyQVq9HYyomp3vYZopPQBx26La6dJM26+01Ah2ST+acnhzR3+iAX7FcpAgcPFMQRLAsZuja4rqrF/OdzZhooDm7s8f4aMFy7OF6A1RaIowgSnpUTYbQKxLG910cB1rdBBm6ZGWB145QdYEyDU2pmBpLmVWQeCybmnbSIQhcsqbBdSSRn6zC3VoxLpLFckHUTWAsSIuUMBB4fsLDe4cc5wucMCXqDNneHFAva4zr4Lg+sd+ilobS1ITdCISH7/nYLKdSBYErcBBoU+DJlUixdCXCTbGywhSKoqmp3IrpdMpbt97g4NHeSi+iKgikpBtEDNob2CxjOZuxceMVRD/DTjQ4hl6/Ta1KEBpragLXpckbuq0OWjekRYFRAmqBXUqUtbS6AY7vcLaXc+/sEHem2Fzf5PTgUwQK4TiESYgMXawUNLZAJCFn+xmz2ZQ0X3LrylWCIMLzLMKpUNZgdE1Z1HTChMoWhK2I9SsJ3/qj10jWDUWd0Y82aCUeZ5MTzvaWnO1NmU9rfJngWgenBlU3LNIU2zjEawHLZka9rLFhw/RkSrEAV4T4yuAi0WFNNPARbk0oEhrjUFMipKKqCoLYoSkMnrsivzq9Pt/57j/iaP6AsNVQaoU2rKJ8LGhleVicstGK2druc1QuOUsbUguNaGgnEc08p2kUegbFaU2jFO1eC+kFhIGDtRXKWKJWCEbg+ZLQ8/H9hF63xyKbM1mesda5SuB2CPwWxq1pSxeDAccAapVN6axyaxFyJRxsLVppykIzm+dMZgvSsiataoy1K3FXbRCAIwVWNWgB81lKUTmEYZdZekQcKR49esTpyYg47mEEGNWwMewybCcc3SnwezEnR/Bw7Yg3X7/OFeXydnKbrMx5vHfKf/zP99m9vsvp2TFKgy1izh4qlllKVuS40vC9H77N6YOU0LUYUXH/sxH7DyegPByZ4rg+pnERdUInkXTiLiosaUxN2SwZ9PtUdc17P7pHmlmmfkG/38EbhCzyBjeSrG8Zvv+9d/nswwMc18cPJWcnU2bjgsUkRVvLcGipSs3Pf/7xykLIuKx1u6T5StS2rCr+9Ef/nsBGLMUUEUgshnmesswtZaZXUS6sXpL8IKCuC97YfZV3//j7VPsZR/vv4VqD1hZjwWuFDK9vcH+8RxzEFPmIsBvy1tvfYPLZkh/9pz9j69V1ToszFialMBVRlJDEDtIBkflQ1fz8Z39OqVZEnBYw+av3qcoaVaZMqLE47J0dU1c1ua5wPJfQDakaxd7hPlXT0BEdsnlFUxq6ndaK4C5hUdbkVYbCUDaKThTxg2/9IUFeMyqOMKHkaneLttdj3A+ZLicrh0NgMZ1TFTW2URw92mfihthcUR3mfP5X91lkFcazvPODG4SbLh4rwf6d7R1eDdf5xcGvqBvFMs1oGkXoJ9jaw5YRDi6rCdoqjsY8SWd+OpX59yXI+RX+/sD3+vRbb5OOexw+krTbm6x11hHqd69R8nhvj8ePH3Nycozrely5coXXXn2V4fqzIpCOq+glwyeuNysBS6U0k+ME52XIRrkEOQXxRX8XQjAYDC4tIt6/f5/5fH6Rpr+/v//k99SFuGin08EYwwcffHDpp4RYRSDO53NmsyVpamhHG7SiKzw9OayNy+jw5USXX3hKbsJa59ozZdZaxofPrcILxeaGB/5fP3puMpm8VLrJ38YquO/7bG9vk2XZkzadURQV5gXRgO12QhA8KwraarVYW1tjPp8zGo0Zj5eUpeDatS4v03WGw+Ell6ZzbZfzOp3rkPxtRHo0TfOMjkxVVXS7XV577TX6/T5FUXDnzh329vYIguDSdZrNZpcm+03TXLhRHR4eMplMKHOXjd67nJP5sOrLnWgLx3n2mGu9NVxzg7ODAI8Qz72FH4OpntWysTZg3MyBZ4Vez+eLi8XiQmj7Re1X1/ULhcnPRZOfxrk2TVVVHB8fM5vNaHUE3cEaT+uxCSE4ePhFQME5Hj9uyPNn752yfIRS9+n3+2xsbDAcruE4Mfc/fzZFDMCUOzj22ToN2kN0rIm9mHLZ4dT4NE2btc43nm0P2TDcsOA8S/Ts7e1dEsk9d9x6tp00u7t9Pv7kY/7H4n+krEv29/fZ2VmJradpyunpKYeHE3w/ZmtrJZlRVRWnp6cM14b8t//iv+WDzz7gZz//GXme0+l0WFtbQ2vN0dERdV3T7XaZTCbEccx33v0OrXaLX733K37xy1+QlzlVVZHn+YX482AwuAjKOC8/j6opy5I8z5FSopS+FFGz0sS5TMh985vfpK5rzs7OODsbMZtVHB9XaN0wGAg2N7v0+/1nooXOcR6N9TyHkWXZJbH3x4+nfPLJCGvB9+HNNztcv371QtC9aRru3LnHZ5/N2d118bynSXhJWa4Dl1PCfhv8WqKmKmviOALsEzcn/Qy58vRA8LTo2rnr0+omPHfcftbi+/wF8XnCx3FWefQWgZAuRVVjhYsfuCzGE/J5yme/qrl26wZiIfFNSHF8yn6T0oo7bLYjnMLFDSO6HZ88TalVjULjxxFSFwg0SdhGeg7pfMGoUvyb//Nn/OqXd3CkR7fdoxUH+H7AcG1tRTi4hmu723TaLSBAG7BURHFAk0mEltRNxYd37vBwPKBsYsJEkuYlf/mzHwENWnjIXJPVJVVV0G8FuCrAxaGTtDGVIko6fPO1P+LB2fvsH90jSlyCtkttlzSFIIk7VJXGM13cRoPNQAhm9oif7x9y860bxGHBnsgYXEnALzh+nFMuwRUtsOAaF9soFArX98jKldBRYzSl1mhjkLHP0XRBNlkiPR8jLd1OC4OhKGtcJ8LicnR8iiuh2+rSWIMfuWzsbHJ87yHz0qFRDekyR7se1799lXsnD3gw/RyrMrTnYYRFN5q6KZGhQ2MqpCuZqJyKBmUsTm2RWAYbMXUtaJQizXOisCEvS3S5EuKqTEVep5wcH2MajYODYx10ppHWhaJidHpCXqVk3oRZ+WAlWoqi0QVJHOA7IQgH6YLwXZqmQPiWfiemriyT0Qy9dFClYTya0Rp2cdOA7Kzm09k+rbUFSStBAn60sn5X0qDRhK5DgyY3hngj5jvffIVO3GU6HtEUBemiRDWWIl+CFRijcL0Q320YXknob0mM31ClNX4P9h494KOP75JPahwbYpWgFYe4mUDlBcpCp9NFFS6+51CT0mrHHB6fEYqV+FteZvgtjZAugeugvBoqj1h6hD6UeUZdaBLpIYXg6ChFOj43r77Cq9e+y9e++SZ87mKFT+6NODiIkFTURlMaRZCEzI3mvYcH1AaKRjMvKipTIESXdrvLPF0wWS6p9WpivshTAt8jCiK0NqiiQivwPEm73UJqSyts4ToOYRxT25xZcUYoGsIgJAgjAtejbponxJHFkatUTIN9MmW1OK7FtZJWq81yWQEeta4xVlI2DcYKTGMwRmNNCVahrcVgiZMEasBEFEsHUzvE/g5rvTXKoiZfzHjr9tuURcZkkjKfKvrR22THNR+XGf1+i5PFHF37HDwIycfXeJymtNs9Xn1th739MWnLxw8S+iqhShV7H83YvBYhdoY8ePCI00OPyNmhPYipqpKqUhg0G+ttXn/lVfYfH+CLkFJLFsuak8MxddmglcQRCXHH549+8DYnB1P+/D9/TLuf0NQBd+485uGjE3zfZ2t3i8O9M9JFjTAOpmmYnE2JohDpQ6cLN65cQSiXycTh6HhJo+DTe3cIfEmrHRAAeZ2xtblJnQkep6esbYfIqkNR1xhV8Z13v8Yf3Po217a/wb3pXarsp2hfYR2BdFwOD0/4nz74f7OYLSh0wdbWgDdvfZuoG2GbYxaTMfd+coeFXtI4GmHAKItAEnod3nr721TpnOPliGV2iEEihGIyO8V1PKy2aMdBWXBw0DgEMiJKEvKsYpHO8T2J47qMxjN86dPqttnaHLCcpIxOTtHaAUfgSHCEwx//yT/lX//Lf0k0T/n//H//Z77+g++yuXmV+bKgPJtSVAVxmCAcwd7DPaqmQWiYHp/iBzG3WkPGowx3phE6QuUlo0cLNntr+EGAmqUMr/Y4PZvx+d0HFL6DUhrP9xFWI1WAMA7ifBX9yTupEM5FJM358/rvMtf/K/yXCVe2iP3rlGmP+Sgk9oZ0Oh2KsuB3LWk0PT3h7qdLPr9zSBAEmGqddHpIUTzrsBTHMbdv38ZYizUGawyOtWRz8RJBMhbcDPwcxBcTwXPhzecxGo2oqupicjudTi9Wyj3Po9vtsrGxgbWWTz/99NL+165doyxLjo+PsRZUM8chAbXJ05EtWkFaXtr9pdFut2m3niUQmqZhcnLw7IaiwmgX+OuTKM9PFn+fkFLS7XbRWjObzZ7YMKf4vntp/BLisp2x1pr5fP5k0h+wvi7Q2uL73kuNf1EUXZrwOY6D7/sYYy5Imvl8ThAELy2k/bLY3t5mOp3y8OEBjuNw48YuN27c4Pr16+zu7rJYLDg6OqJpGoIguLT/8+k4T5/DuWsPFtBr9NuvgX22/ar08r7B+nUcs8NympDGLdaGQ3zXoyoup+o0lDxPipyTL2VZMplMGI1GWGtfaEW9vr7+zL+ttSRJcmmyfd43zmGMwagEj+ecg+yLz0mXObZ+9oZcjM+YTqc0ucDDJ5Stlfvksndpf0mP56ns3lPz9DpbfSCgHT7rsISTgT1g9XL56xGG4aX+uIowWjk5PZ1CNZvNuH37Njdv3qTf75Omv8B1uSAem6ZhNpvRa/f42ttf43hyfNEvlFIMBoOLPg5ciEZvbGyws7Wz+i6d8+DRg0tRTlKuBHXP7zEp5YXj2dORRyvSU+K6lxcCnidPYJU2V5Ylp6dnHBxMGI0MeQ5hCFpzYfW+s7PDO++888y+Sik++OCDS33n6OjoEvk1ny84PFyQZRAEsLXlcPXqSmu31Wpd8B2npwtaLZ8wfJaooeldqvtvi19L1JRlSRRFYO0Tt6cvImbg2ZSm50WFVwrI7sXf5xDCPKV9wzPHOVeVX5FBoLWhMYb5JCfpd4m8BJKA0aQEd4J0XeaLU9Skplwawl6LMIjIljkP80MW/ZgokORVhXUdhCvJmjkgqVVJnlVky4IHR3PmY0WvPaCVRGytXaesG/aPjzk52aPMZ0Sh4PPeHu1ui42NHV5/9SZ+BOVygRQSZRXGagQRVsdIH6rykHYrQUooG8BpU6WaulEM+l26ccDhgwO2NxPKZU5/o4d1FDIUdId9lGioVUMxa3Aji5QdlmnJYG0Lmft4jiVsJUzyz1k0UzAN18Ihna7PO38Qsv5myDivWL++xYNfLpgd1bj4oFbRUso01I1CWYsRGisM2giMXl3LWLZI8xQTgYzh6GSPqBWsVjCsgx8GJJ0I1dQUTYMWhnS+pN9KCFoxaZEjfHA8h/56F1jpMhhhMdbBSIt1DCqvMU2N4zngeChVMq+mCLlScFWNQQhJ4IVICXU6IytS5oscpQReKMhtiVGWTKV8fngXJTWO55KEXTwb4KiQ+5/sk6kRXt/nzByS6RTreFSqpqqzVbRM42A9SbwZU4oClWcr9hW50isyAmqJp12q0mCCkHbgkbQiOmsdGr/G8yRCCmQgqaixpgazsn9G+nTWI/7Fv/4+/V3F2WRGMArY/3xKnRbUSuCFPt3Io98LWKaSqtGUTUGjag72H3H8KMW/2iU9E/jzPk0xR9kaVRa0Yw/wSXptjCdpGmhSQ2BbtOJNSielqRe40sVVLp7r0NtIkJ7kbDSmqTSeGuD6IZ6wCG0pFjWe66MDwfSkxDQVP/zWbf7v/+yf4rRP+c4ffIsP3vP+/+z9WY8lWX7li/323jbbmY/PHh5zzkOxilVkkbzNvt3NbvXVVaMvGnoR+r4I0DcQIH0NPelNuk+CILUg4DZaDRBNXk5VrCoONWRVZmRmjB4+u5/5HJtt762H4+4ZEe7JymJVschmLsDhEeZ27NixY7bN/muv/1o8ezyhGTWoC43jeiCXiTLWUcwqqF0BjovyBK6BJM0IwphWq83h8RlFrZHKgbxEsPSTaYQ+roSsKinzmtlkQjuOUdKwSBJu3r6J8AtOp4+JVI+waNAwPWI3QioPi8LgYnV97scB2OUDvkZTa01dl0SxR1e0qKSgmiWYsqQsK3RVYo1GWIMUNdpY4jjCd2Jub9/ADxpQK0y5vP690LKYLZAaOm2fLE1ptQoqDFrWJMUCM8g5PJqQlRF14RDbFYJ2yo1bDTprPrJR01t7nVs7Gk9p4lhzepAwm5S0VjKErVjvblJnp2AdTK3JkwKjJZWT8C//xddIzjRvvn2DfA5Pd48JZQ2eRdmKUhqshFbs8/DTpyxmJY4CXdQcPavZfTCgLhSel3K0O0Mpie8EELuUVYmpDAQGz4f3fv027719m2Rk+Pgjj+e7J9QaJpM5biDxgg1KCnAK3nx7HVeEbEwi1tY3cbMGf/7dv+Q4OWE8HZDrjJyMrbs7rD/c4OF0D8d3UEKRezWTOiEvS3xPcntjh2++/02MzejsNHl9+gbjj7+L8ARpkiG1pahKPOUiHcnN27fIxlOe/+AYJRycQFBXJUoZfM+lZqmwqvKCIqvQ5dKAV7uQZgVFVYMQeNIu/V785diVlxXj6QwtBb7ylypUBzxP8umjj/i//X9P+N/+r/4Z999/Gy+Iefz8CU7D5/DomKPTQ5xwOfMYBiFFVpHNE8JmA7/t87Xf/HU+Hf+AT398wmRhkHic7I7xuj51O2JxnPKD3b9GAtL4SHueyCNcFA5oB2vEudJg+WOxSxn4C88S1yWHfIl/7BB8Fg32yo/9ZZB6F9v97Gd//4CHDx++tNbS2yEliiKazSatVoswDH+lPgZKqSvxr0IIvvnNb2KM4fj4mKOjIwaDCftPLJOzq7P4Px+u294v+j3+vkLQ7fZQ6mVSpN/v02g0Xlo2nU7Z3d1lZWWFra2ty2jojz/++AuNf9f5b/xd4v79+/zwhz/kgw/G+D68//5b3Lx586e/8G+FL3r+vDo2/Cyv/fkghLg2iWpvb+9K/LHrusta9gsgiuIrip719Q3G4zFhGJ4b5gqqsuIX/TmNsXz8ycdYcZUQfZVM7Ha7vPbaay8ty/P8StuYtZa9vQPa7TZvvfUW29vbdLtd9vb2rpg2V7riaHQEAra3ty8j2a/DrVu32NjYuKzZt7a22NnZ4dmzZ3+LT/6z48MPP6SqNElSkmUWpWBlBaIIwlCS5zlnZ2c0m80rrWg/S3pcsylZW4Pd3SUBNBgsODg4wFou70GdTpPNTYcvEAr3t8JPJWpgOQNn0ef/vkyvfWlG7uLLvDAYvmhtuvjbq0lR1zHYLyp17GUcrGZ0krBz8wY+oJ2KVGkOJyfEyqekIM8qRO1BEUBqULkiSSrKRcrmegehQhaLhLPpKXle4GqPql6Qm5rxfMZgMGAxmRIJICtIpkPWNteJo5uUZU2aNHnvnTc4PNjjez/8K/b3TmlGDW5uriApqKscYUpc6eCJEN/xCVoO3RVDoCIOj44oU41jFaq0bPTbuJ7k7HCC7zt0/Ygb69uEHcnRaJejwyPcyKOz1mZxBnkhaEcxWSYo5svju9V6jfe/9hqn5ffJPM36RodUn2GSFu+/e4fE+Wtm8SF+ZIl8QdNu8XE1YHCcg1VoI5A4mNoSKIl2oKgrXOEghMKVArcpiPsx0hV4TcE0nVIuCpp+h1D5RJGL60nqWlLUBcJXVJWmMpqw02JyNEAhEb6D34moPLBK4IuIopYIkSEdkCikFctoZGnJqwyBQkmJo1ysFXiuS7bQuGGI44ScDQdUeU2n1UHLCi8I8QoJ0meSFsSNDtrUVDUYXZLYjNoH60lU1yWTEzQCJV1qq6krja9D5mcZTttDVUuZtfINpgZdaxypqSrNcJBhK49Wt8W7r73PPBnTaDRYFAlCLttkpC9p9iLOpmfoomR7a5PFZEZVGe6+2ae7JsiylGfPnzMbaEylaLSbpLIEWbN1e53VOOJwLyWfVUSRYHh6wtGTAV62gTfscNe7wc2NO4wWpxwnB5zpE2SlafTaOIFPakusFZgaKBXlXHByOsMPGng0WQwt7ZUOQhjG8wmuGyC0wTEOkYyRwtL0fNyeix+72ELT8Fp4XsBqu810/JTR4AFvvf8brPQ7fPpBhSMtjSimqHP8wCOZzcgqi/CbaCXwAwdfunjSx+QJ80VKFER0Ol2yi1YtK4hCnzBwmM0SpBAEbkRZ5cymM0xZ0Wk18d0GiIppeoQvY6wpiKI7aJMxmy6IohZGKwQSaw1Yg5Rq2X9cWxbF0vNpPJ4yT0oqBFobFBZHCsrlF4/EoByBpxyEo+h0O3TbTdbWW2xu7RD7bWbjGaPpkDiO6LRauI6D57pou2wc1VpT1xpTg9EVZZVTVoY8qairCm0r4maIkZbClqRZRquXIpyS3prHzRsl49kJp8MHHB8fkYwFgYpIs5o0yVFiadS2shmizZiffHTIO+/c5rX37hK3Y/I05cnjQxaipqxmuI4km2uOD5Zy6FazQ1VU6MLgqYBGy2N9vbX0CdIGKQSOrAmth+MLPE+xKGuOjib4zmOofP76Rx9iHRelPLzQpzQ5RydnuIHmjTdXMWGB1/JYj9p877vfoxn3Kfwczwt5fnzMfxz9Z7bXPua/ff2fYR2N8kOktNTGEEYNdu62GLjHvH53i6995asI43I82qe33uNmcYs///6f8fRon1E6p+VFqDhgdaVPGIZ8/PzHHD19SFUtI+9ra6mLGs8JacQtJtWcKikxhabMC7BLdUoxXmClRSkXKR1cZ9k+Z2yFLgOmp5qg7rMRCbRrkcpiRMUin3G8t8t4cMz/dHaGSQuSMmdlZ4NWHLP//BgvdOgGTbDQihpQSA7PFmSiYF6f8B+/88e8ffM2blvi5jm2kuRzzemDEUM74O1v/hq/ffMNgvGUP/jhn3PkFBgpkVKhWBqvW7P0fJPnbX/2gqu0S7+mi3vtl0TNl/j7hp2dnSuz6LPZjG9961tsbGxw9+5doigiCIJrZex/V3Bdl+3t7ZeWCSG4sX4DI81lpG0QNBifjJic/eoipv9rxObmxpVifXV19UoCTRzHy3vkOVGzvb1NFEUsFosv1Pr5RQv9XxYufD+tvQgfFdfWUf9YIIRgc3PzSktUu92+krpU1/UVUuLzEEXRFcuO+XzOeDy+bNNJ0/TKe/yiYK39LJ3xHNepsy46Ul5ddh1WV5dpStvb2/R6PaSUl9HcL2I8GfMXf/UXhGHIa6+9RhiG1xpLw3LcGwwG/PiTpTJlNBpduQ5/WbDWkmUljqNYXW1z69byPnARhZ1lGYtFymAwZzhMODw8fOn1Silee+21L3T99Hptbt1KGQwWLBZweFij1BFCLJWYrVaL1dUV7t0bMx6Pfyn3oZ9C1BTUdY3jKIyxlwPFRez2dTv0ok8NLA/Ii+zVi21QVyO9P1PqGGPBWKQV5PMaWUfoKkW4BpNrjIRaKgQhTqQIohauF7MW9ahMTW0NxXRKI2ihfI/kRDMfzyhrhWMFVlsKayhLS6fdpNtqUyQ5VV0ynE1YFDMcN6C/skYU+STpHCEl3aiDLjU/+u5fEH71XbbX+kwzjbU1jvCp8gxf5aysdHh2csjxeMwsmS+NpIzD6spt1lcihpNjhITbtzdxFhUnx/uQV4yqYzIKQtmgtdZkcpIj6pA8MZSVxRcSx5astAMmsyM+3P2QWsRsrLR5+PyMSGxwY+MWj7IfYlXN6GjK7OmC0weCauqhhHMudBAI6YEAKS3WUwgJptRIIZc+HrIiXo1Is4RyUSCsREqHZJYQtxvEscdwNmYxrTECmmETGcSkeYEXBFjrMk9q2o0ustUh8zSOcHDLgO3eOtobsjs+pLQKhbO8+PKUKrXEYQtb6KU3ibIIZah1TZXnTOYL8sLSiLtooXAqScttMBgNyRcV86xGbTv4sUeta4o8Z3I2prvaxHUcclWgdYauDK4fUNeCxRi0teiswPiGoHQRBpRxUHgIBFVV0+12GLlj0swg/QBDQdARdG54JGdnBEJitMD1BTIqWGk2sDZge7vB2ckMh4h3v3qHyPeZnEX4ukMkBeu3bzKdnKB7Fa2+pb/VwUw0vYVPeytk+02B2wCTrSOGK9gp9DYDuu1VnqMoipKxXtCIIpTnMF5McVs+aVaRzGvSeU5gCpq9NkL5RHRotgOS5JQ6rygWmk6jh+t7uMbFsw4Gj9jzCYIa4bkkieX2zi2UUZwe7/PJo+/z9jc3QCyoy4Sj5yOUFYSuR1WkyLomUAqjDY6SyzQeY1FGYYWitgo0LJIUz/XRZYUSiiiICTwXa0rydI4UPq4vkdriuzFVWSEQdLo9fK/FYpGhXUHc6qDcmrrOyJI52pTYWiG0sxzHPI84ipfEjTHkiWE+r0jmFdNpQlqWy/YrK9B1BVYjhMERAqMrrJJsbGwQRTFCGEaTGXFrQa/boy0bjJIBUTOm0W6cT7S4uM7y+pJWcmnmeC5qsHZJAi6Z72VTlpAWIwxG12BAG41QgiJPefLwD/jkwXSpzLIurmySLWZ4XozwDbo2kGZ8+v0xQrd49OSY1noDvx1yfDZFKp9eq4WpymXMtlFEQQMkOMJBuh61U+MoiXJK3vzKOlXp8uGPd3EchUhqai1pNiPyfIHQguO9hOFeSl1opIhoNmN0qVG+iysEjs15/73XaW+2aXRDVF3w5MdPGR3OGYcpOB5e1ATjMUnmnHz4Hb66/RqlzvGckNokuMrF9QKCVgNVWFZ6K4xOj/mjP/wzEmZsbK8jFiFPDp8zSabgKZQjkYCuS/JC8uxkl9W1DW6u3eMHP/wOqa1wnQDf85H4CJmxutbk8PkhUgi0EGhrURJc5eBLD993cZRG1zWFUURuh3/3L/53xGWArlKenn3KB49/xNwabAUOLrY07D7aR0nwIh8rFKdHZ5R1TeiFGCGWLRxWEEqf1d4ap+MB+Sxl93SEWZ8jOhJnWGK0i2c96qklLwuejsf8n/9P/4bpH32X7zz4Hitxh3mWowUUWY1nJMba5f363GPuovXJvuDrcN0D35f4Er9qtNtt1tfXX1p2eHjIwcHBpXfCrVu3Lk0qf1VEjVLqCqEEME2nFEXBYDBgMBgwHEzI8xL4uylm/jFAiKV65tVWn4sUmhexsbHBe++9d+5vVDEejxkOh9f6E12HXzWZ/fjxY6qq5N13OyglmM1m7O3tce/evV/pfv2qYK1lPB5fIQdWVlauRDxnWcZkMvlC2+33+1dar6qqYjZbTuofHByQZdkVlcYvAlJKXn/9DYR6uU1tf3//SuvaeDzmwYMHLy27TikihOB3f/d3ef/997l58yaz2YzDw8Nrj8dsNuOjjz7ia1/7Gtvb29R1zXA4vPbcPz4+5uDg4JLklFJ+YTLs54UQgps3t+l0OmxtbbG5ucnq6ioff/wxjx494vT0jOFwwdkZBEFFu/1y65TjOF+45bvZbLKxURMEC+ZzmE5BCI1Smul0jhCCbrfL1tYWs9nsl+JP9TcSNdpYqqrGcc7jtgXnJM3yS3kxZvvCZMpaw9LT5mWzws/8aPSV1KgLqZwQCliSBIgaoQTCeNS5Yf/gOUFPsrrWgZnPJE8o8xLXA4OLkC7awjTPsNYgpCCzNXuDAbgedQ4GH6UCfC8AR1Elc+KgQ7ftkd41PHp8iFu7VAJsWZLrjKP958iypMgSSquXpqtliusq7HjGrCxhpYGwLlHogZW0Qpc6KSgrqAtD6LfRxqXle5TMOTjO8AKBq1yePjwi1or111eJexW+qWjHIX5LYqqY3ScZTubBoqbOCmLhYIXhLx98m/pxSdjW+M05w5MRa9EbbHe3KGVKampct8PmSkR5MGExy/FNGyGhrioMlqpePri7rkBqB1HW1HmNrySlKKmsxijw/YA8N1SZRgPzcUpgM1qtEpNLbA3KE9RFRZql1GWOsB7GiUiNYePGKmVj2TUUVjFv9m7zG1+5x97ZD1mMDxDWZTCZ4QpNlVdMDiuCTocPPvyEd76xw9qtHrVaJvDUZY2uzZL8cS3a1NjShczj+fdH1Cbg5tduIgMHYQ1GGYgkctUyVxOU1BiWSTfVwuJ6isUwJ5sa6iBh/WYTE8LZ2RhfKSIvwpYONl+qfmqd0W5HtLsRra0miTrD65eMoxT3ZoFcSPQ85v792xwkn7BIMwJX0N+RkMulSfNil5NBzI0b73DzTsAssTTaPRZpwvg4Q3jQWbM8PBnx/tffIdpJeHjyIV4Y0llvc3a2YG9wwjw741989bdY727w+Pg5XugRtmMG4xnaOjgCfBvR21nFbVhOpwcE2kNJF50KOlGbxWwEU5cg6+NUEa2wR6gi0BLpWaxuoaUBJTFOhcCh14qZT0fUpmQymzKe7/Hw0x/TigLcKGCRl5SFi+85+K5A4SKdAOsYirKgqgt8r4HvtUDXpNkEF4krHWTg0Y4buFKAKjDS0gxb+G4A9Yx2s0Wn0SYkpO1v4tQBbh2gZICnYhSSWhSoWIEDxaJA1oYs06TZmEYrQnmCMAgIgiZlXrOzvUWvmzEYTjkezpgmGXVVU+sSi0EbTbPlsX1rCy/0sRiElYRBTCNqU1WayWzK+vo6/V7/vDBejmvmfCwUl6YOBiuWfgpCCKR3MQbK83HwfCbFCZbhPAjqsuLTR59w9GxAy72L8izG5oTuKr0oxHEijF5Q2wmT6WNEIYikRFWWgw81Rms83mKnH5Akx9j28nsoTYVyKoQwGGERjqHf7JMkc4xx2D/Z4ytfexPh38CPHLQ2PH14xlpvhQ8/+IDY99HGYIwEaQh8SavTJplNqW0NrkenX/M7/+o+WVXieRJZBNy/t0MUdfng08dIqXCEAKOxRjKdzjBuQtCKicYOQ1mytbLJzsYdBsdjTJRxdDzk8Og542FCoTNm0yk3W7fxdEwsWxivptEOaPXb1NpQlgWVrjnIC6rC4EgXUaUoKbDCUlY5jitxIp/Ad/CBeWmptKER+DSjGKwhKxKCyKNUFkeDljllrHjt1n0+/PGf8Mn+T8hNgvQFNzfvcHZ0QjnLqe2yVTlutpmdTkhGU8JOhLSSMq+pspLT3QH/7nf/Nzz46EMGgwEKgReENHstHBuQLSSDowWBdbBGo2uHhvH4g+9+m3T4jH/zf/gf+cEff4/HR/uclgXG1mAFjlCXd1jOJ1oE5wE056bCn923v8TfN8xmMxzHIQxDfN9f+rcY8wsvGq+bCb0wmb6IkI3jmGazSZZlVxQIvu/jvKL7rqrqCxczvu+zurp6mQJTliXPnj27xi9gejn5lyQJ8/mcyWRyrSLC87xX9slSC5/ylUNnrSVJkisP7Z7nXZlZvyjgPM+7NMYUQtBut6+8/0VxNZlMmM1mZHnOdYp7pdQVomE5Y/y3L3qklFdbdaQiWSTELY+VlRVc1yXLssvPXhTFZQFqrSCOX1aQCCFYW1u7QupeKA1ehOd5+L5PURTkeU6e5xhjaTS+mKmm67pXiuULM1KlFHEc0+/3iaIGQRBcOX89z7vy+osY4clkwmAwWHoOFSW//Vu/TaUrkiQhSRKyLMPzvCufc6mKra8sm8/nl+dpGIYYY3Ddq743RVFc24JRFAWe5xEEAUEQ4DjOtQX3eDzG8zzee+/1SzPh09PTS6IpyzKstbTb7WuNdy/+/ur7v7pMGkvgeVc8aq5Do9EgjmMcx6GqqkuD2y/aingxRmitcV2XRqNxfv5dncQ3xlyeV2EY4rouSZJc+Z56vd7Ppey4biybzWaMx2NmsxlpujTLtdbS6XSuvH6xWFzZ/yiKcByHsizJsoyiKDDGXBlfUJpmEOCHzmXcdRiGBEFw6Q9zgeFwyKeffvrSMsdx2NzcvHLu/c43f4dbO7coy5LhcMjR0RGLxeLKcbrwCkqS5NJwN89zVlZWrhznDz/8iMPDA7TWhGFIt9u91lvIWnutP9LnjdlfpMVQCMHq6irt9tIwuNVqEccxQRDgeR5hGNBoNCkKS7MZs7m5cmUbg8HgynGaz+dXzt3lOpZe77PP73mg1HL94+Pjy/P/l4W/OfXJWPIiJ26E2Mu4MnGpirnwpVl+1s+MhK9Lc/pMgSNeeDj87GR+0YgYLpQ5dqlkKAsaLejeFMyKmlC28GxIXp9SZsXSBFclxM0A1QzBCmqjMe2IaVog8wrfC2h4m2RZhvRcaq1RboDn+Wgq7rx5i6PphIc/ekin0yfyXHx/2edW6ILp3i7LOXGNUgKBw8HpkN6vvY51HMK4RRg7VEWG41foQBPfbOGnMWIhUGPoND1UYxkLXuYltnJwCZiHNet9j+fD52R5Qa/ZZNVzGQwHeG4b5Ybc2vA5OxtRGZfmZoDoz4m7XVZXPQwpz54c4eFT5c8g6FHomtPnCQ4xwvaI45xsZFEWXCegzksEAo2lsooyqRHCw/e9ZauHm6Bcj8BvMB1M8T1JmVh0ZamyGl3BfJqhPBffVQhlqXKLLhTWhhgLYSvCD12CToOCjNB1iSqfN27epdcMOTis8bSEpKKYlLi+jyxh/HwAA59qXlIWObNkhBu3EEphK43v+RhhQGuqeclkOCfLFJHsUPkRjW6LRE0BwfR4RiP0EY6gdirKusZRAUp4CMeS7OfUeU2/3yZeUzhdwTSZEzpNPBGj05o605RZhZQWx1Vs3mkTNlvcemeNuThiPE8xJiFLa1pum6/8N7fY3ury7FsLqmqZbvPk6RNOnwzRcwfnxOWdd96n2copnQWBpzgbP2Bwtk+deoSdkEzXjGZnJOkZTSFwnIrFvMLQILFzKrfmbGH48PkTkmHOosogErS3OlQKsrykSAVPPjzg7fstZOUwPizIno3Z3FhDWZeRmFAuDPPCEIYtAlqETgdXeDiOoDqf7UcJNrfu8PHjDzGyxlUSbZdKleMnUz5t7/L00TOaYROhFYEVNFve0uTPROQLRV4mBK7EcyS1MihjublxD1NX7J8+Imq0wLpM53M8p02ZpwQNh157lUA16QVdWrJJq9Fmc32Hzc3bhHHE8ckTMgNe10eUHsk0J62mqKDG8bpU2iMbabK8pKo1s9kY13fx/JRGVBAGPkoZ4sgFOpRa4Lo+k+mYOquwQK/fprfm4/kKg0FJn9CN2dnZIQwizs4GKKXo93ooIc9HST77bcW1tg4X9pf2cn27VMucD8tCLsfg/f1jTo6GfO0r38Ti85MPf8z2zT6/9Ru/RV0vi/2jg30++fjHywLfC0kWKY1GRK/f59ad+7Sb6xwfHPLxg2/Tboc8P9hD2BRZn0upZY11agTgeSFCGd75yjr3326zstbm6bMD2s0uvqeYDRbcuXuT4VlOlpXgZMSNBmUhaTYDHFsiXEv/Vo/utqX2IS/n1JVkMc6I+m32vv8JVSlwZEBVG1xPoqyLKeDbH/w1ogixdUHoRLR7MZ2VBmeHJ8znYzA1i0VCbTR+HPPOe+/w3339X/L7/+EPKQ5KdKMmbviEvs8iy6nKnLKuGEzP6MYt7t2/xyePfgK6QliLIwSO62LzFM9XVMbiVRpHCe7ubNNo9znY3yNuRBQmI4oCOr6H4/WYPh/wp3/+n/jg6Xc5s2cEsU/ohDhWoHDwvRAhFNKXyxbP2qKEYngypKo0cbPJdDwjm2d8+vARSZaTpBmVqHACn/kiI01ytDBErYA8LajrCqftMDs+4v/5f/+f2Lqxit9tsXVzh73hGSLN2eivMzsrlmOmvWgpXp51F/fs5YPSZ5MoX+LvH/b392m1WmxsbNBut+n1etR1/TP12P9tIaWkrmuePn2K4zgEQcD29jaDweDK+6+url4pbvI8/8JRr51Oh7fffpuVlRUODw8ZDAY8fvyY+Xz+0npaa5rNJr1eD8dxGAwGVFXF9vb2laKn1Wq9oqqwJMWCs5frHay1V3wdYKnAuK7gvUBVVQwGgysF1MU2z87OLo9To9Gg0egwPZsxOXv5gd7zPNbWXjY6Ncawt7f3tybklFJXtmlFzsnJB6xv3+Ttt9+m3W5zeHjIYrG4LM4ODw85PT0DFHfu7Lz0eikl77zzzpXi7uTkhLOzs5eWdTod4jhmMplwfHzM8fExeV7x2mt3v9AsdqfTuZaouTDKXV9fv1RbGWOuEHpFUVzZzwt10wUuCsv33n+PyXTC8+fPmU6njMfLVvbris5XUVXVZVvFxcx6t9u9dt1PPnnE/v7LbSRaa4wp6fe7RFFEv9+/JEO/KJ4+fcrTp08v/3+db421lsPDwyvF5AX59SJcR9Hr9r4QUbO5uXn5PWRZ9nORi8trpMF4PL5yTRljKMuSRqPB+vo6m5ubtFqtS8LkRVxnpOy67s9F3hweHvL973//pfeI4/iKNxXw0nV/gdXVVTzPu1TkXBBar44vVpd88vHHdPoet27dotfrsba2RrfbvUISPnjw4IrJbhAEvPnmm1f2KQgCTken7B/ss7u7S1VVL5kDX6Aoll00F9fzMonvhLfeeuvK8fvLv/w+SZKyurrC9vY2KysrFEXBwcHLJubGmCvjw+chjuMrLYufh/l8znw+Z39//8rfwjDk7t2Qu3eXBuiPHj26ZgtfDBf7vrnp8opQizSdv3Re/LLwNxI1tTakScbqSn9pAns+k3PxUPfZgGsvfWusFZfrvNritHxAvBrpbe3L5M2FizKAtkuPiKqoiHyfTx5NiOfQ6awgxJzFYkar3cJzXOoqp6hcxvMZRkhmhUEIj9hRlKbCMwptNIu0RmuL1jBLE3AcyrzC8RXr/RbNRohJS5JpiqlrirqiLDVCG6xnCD2F34u4/dW3UDcCmqFibaXPpx9/iOPCXPjUBVjrsUhKdFZTyppKsGy3SDXGKmI/RlpLUWkePT2gv+OjpMeTx3OKSmNNSqu7wmvv3sBLRoxOKioFm2/1SJpDrJPyfHrCwZMZdRIxPPoYW33Cnbsb3HjH8uCDCSf7x5B7+KaDdCRaL1skSm0RrkR5DpXWGGHxPEWrESGFIcVS1xW6gshr4bYcQtng9OQUyfKCdhwf/7xvd7FIUVYR+h55leOGDp1WB9c/f3iqly0knSikFfvUtaER9/Csj00ySAWzowrXUxTTjMPhAe/90x1MWJCWFS2vjfIFNq+QjsaqEpEpxAiy/QRrAjw3xF9tYIRBobCF5vTDAcep5e1v3ibvVmRzjWdjQi/CUpAvSpCKoOlDbLBKoWyAq9tUaYSpUkyVoXONF0qEr/Bjy9tfXUHEGbuHRxh8yllBXSryOOf58COmukncjFnf8nEdl5Phc+Kgz3a9STadsmpCrDxjmB4zPDQsBgHprEO7HdNrdBmePqXRhN2DB/hrdxkepiTznDhwWb/V5jCbkRwuePL0Gf14bVmgKw/Xc3FCQ7vhcPbQQxQBIEjmJbZ2GB3OKceGRtgk9GuKqiIIIRYxzbhH0+kCGqzFUw7tuIkXhux07/OkeIrxS6SVGKEQWiKLiP2P56gywvUCKiOxpChHYExJkeZMBpbeVojngpQuofRIRwXrUZuqKIl2XiOrNe3WBlL7REHEfHKGlRmbd7YoE4HNCubVjM3eDTbXbrOydgtrNKrtIJTFpII69TDWRVcui2yMwiEMN5DNACTEKsR1PZTyEEKSVTPSIkPXyzagMGzz2v0W4/GQomgyWsxJi4w8mfPpJ09Z2Vhnc2uHZtzgxtYdwsDl7OwMxxHn7vkvm7TCZ/+1r/zBWpbGrhdj4OU4uvTJcZzzlCqjkQq+8tWv0O13qbVka3ud1bUeUeBhpUEIcF3DZDRlc+er54ZnQ+7du7WcdWwGWC3JkgVh1CLPJ0h8PK/GcTzquqbW4HoOwjW8995NZsmAVs/HioS4K3i9sU2WanprEv/NVarU5Ud/9ZRnT0Y0uw5f/+Y7jM5yGk3Jxz98RtDwufP2FrlIeProjOnolN3HJxw8P2S9fwOd+8hzA1Hf9REGkllB4IZ88mCXW93buKXL3M5J8ynHp3vs7j1ikUyRFhypaDQD3HZIb32VzlqHm3e3GJkxeVBQioKiLFGeh1OWlPXS5FubiiSf4/kujmhgrEFqQVHXVKbEDTy0cHGqhDiISBcLTk5GzPNkua6wvHb3Lo5rCBotbtzpMybnnriPHDjM9ZS8LBkfHNKLuzi+ZJbNcT0fXddkaUqeZaChWuQURkGhQVv+5DvfRioBjsX1fbI059mzfRajFM8GhK0QN4CkMMhGwOZqh7LMGByf8sd//j3+u9/6HSZ5htICm2uMtshXfGiEuGgt1i+RM1/Gc3+Jv28Iw/Da5J6yLOn1evT7ffr9Pu12+1dKNC4WC/7gD/7gyvLV1dVXyCOHPNdwJRPmHwYuWk1eJcReJUn+IcEYw/HJMXmeY62l2WwipaSqql+4am0+rzg9fZnIcBx4Jbjn7xRRFL18jVkI3D793gqYn05sXEeK/NeI27dvXyHg6rq+QvAKIXjzzTevvUZmsxnW2nN1mbmWqBEqZOvOO4SxvSQrL0iVV7Gzs3Ml9UkIcS3B+J2/+A55nl/G2l+Qs9cpXa4jGkej0RUi/pfR+vUlrsffSNQIKSir+lKiZcQyEvHVakSIz0gZIS7Mgz8jcy5ef6Go+ex14lpDLCEEArVsMZAahcf4VLOdNqhnM6ajhNB4uELx7v1vUAvJbDZlMR/z6PCAWZaB8skKjee5ZI0Yx3MIbUYhJcY6lCXLIkXXDAcpTl2z2tvmRqPPahSxsoDHHz3kjx9+RNlwMEIQxjFbOxs4fk2r2Ub0FOENl1Yz5vmzPTQlTigYMcHVDRpilRKHZ+M9Vjc6lOmCk8M5tRMifEkmZrhSUxY1yTxnMUlxg4haR3iygxOD08vQzV0mZzWh28GPDO1+zUKnHBycsv/ojL0nHp6pabpNHMfh8eMBR7MMYVpIHaM8B6V80IIqrUgWOQaDLnMcK5dEmZQYFK1uA88RFOOCoqjJkhzXeCjh4fqGZrtJki4JsulkTt/3EUoQBT6NRoO0zGgIn1m+oLAFrhuzmM+pTEqnHxL6CmwFxqGuBA2vQ8PJsVnJYrFsm6uykvZ6A79TMsgndOoe+SCjv93h4Sd7rN9uEXQMg6OUxV6KnguEq3BDh6DvUssCTzicnYzITkpU5jB7ntHv9rBliltHaAOUBk+ECAnWxuRJil8rAttC2BaObDEvwGh9bkgLzahHdyvC78LB8AibW2xuMLWPFBKpHJIkQ/kuW1trBJGhpCZ9VtE1m7x783VGi09pRCUHR/vsDweYaY+bnW/itR1U85C4V5BrS9GSTI4SPvn4OWfTnHxSMapSmus1UTfELlzu3bxJYELUosQRlmfPnuLGCld4NFSb995YY5HPWd/Ypt/eYKu3TTbL8P2IoiwwVUHkeTi4uLh4ysdKTV3UdMIY15EEQYyvFTZxmE9T2k2PrExwXBclHGzm0Q/X8JRkVmQIJfE8H9cR+I6mHccI3+L6IIxCa4Xr+tzd2iFwBaeTI57sHrPV2uKf/O6/wpYlx3vPSNIJ/f4qDb9DOpnw7PFj4qiBZySeFPh+zORY0Ftdp5IGVwUEnoMbtqhmC8oCpElxA8FK1CKMQmDpzyEQlMLDaL1MQCstZVlhjKbd8Jjogk4rpBjMGQ5OEMLleP+UtZVNtjY2aMYBw8GAuqro9VYuZ3w83z+PP7Zw8SMuxr3P0hAu2k+EWCYlGGsxpqKuL+T1y6JaKcH6xhpKKYSwuJ7k5q31C0dYlnFi0O21ef/X3qWz2qAqa27evkm7Ey23L5b+S3EjpNtdo8hihsMFRjngaIxXYI1PEETIyPDG/beZJWM8NSLPcmoWeC4IWZNlC7xYo4Xmxl0fK2Nee3OTzZ2AtU0H0ATRLaTrU8k5TCA5gycfn5GMPVTd4f5r96Fo8cEPfoLnKigs6bygzms8N+S9t9/kn7z9W/z+f/4WJ4cfUUQVkTdif++AOqlZ6fZx3IBC10xHI779p3/O/dUdKqfG9SXzqkA7BlsDVhJ4PlLJc5PrjP3j50glKOuSVqOJrQylKZFC4lSCwBE4LbNskRUeQWgotEOeFnj4DB4N0M0S4Y85O96jyHN+7+v/lMRMyScJOSWdVoOVVgdhBFYZjJbM0wJdGQI/oOn6uK4D1mKEi/WgkpaizPEcZ+ljdN76ZoCknNFZbxLHHQ6Oj8hsxmB8gpICIwxFvuD//Z/+IyYtiQkxeYbAAyEwxr40wfJi68xnqth/vKaUX+LvJ5rN5pVC5kWiZmNj41JpdF27wd8ViqK4Es8thKCqqldmoR3SNAZ+hZX5zwFrLaenp9e29PxDhdaa3d3dy8jgbrdLv9+/9CH5ReKtt25y9+7Lnkuj0eTnmu3/edFsNq8U5oGzwUZv4wspav6xEDXNZvMKKVJV1RXyApZKvFeXp2l62f500b5XFMUVg2rhZNx9cwNkgtb6cry7rk7udrtsbGy8tOyiJe5V/Jf/8l9YLBaX5/lFi9mryp/rxtCL6/5VMvyXZab8Ja7ibyZqEFRlSZblxHF0/iVe/CxNhY15Oa3pxZPpVX+aC1bwoufrxeQJ+CwNSkq5bMsxevle1mM8KKgTyZ37LZ4+OKIuIay73F37GjrwKNIZ0+MDdudPaAaCRHg0PYFSGsfxcSSUxYRauSgVEYYRQjn4wmDIcfKKol6QTJ+z/+whq1PDzsGAuyrA+epXaa1tkGcZ/dUAGgUngyGFUzGcTDg5HBBLn856l9QO0J6PaxxUpdho90Fb6tIyGyQERQt8F7wK33NJJorFtKJMBc6xQxy7bNxo0vB7+H2XVltTDifs7eUE4iZf+eo98J8gC0nguKy1O5wog6p8wqCBoxRFaUnOXBzl42KoqhwpzfmssUb5Dr50KOuMoiqWpqJhgHAVZ9MJvquYznKqSpOmOVYXhK7BQSMCSWety/D4jN2nz5guUtqdFq/d2+Le61s8ePwQLRwmRcE8K/EbHrPRGNexDPNjXM9Q3n+LMnCoCsvm6m1MKpiNZpw+3MWgCAKfjZurZFlO4ISM9xZkJwVFZth7eorjQSOFyXEKtYd0JIXWBL6mveaSqATwqNIKgcT3A453B6zdeJ3b0Tp5UrGYL6hyhXI9lAqYnliiXodK5URxk7J2ydOUupZYFFqlKKHY2tyhdmccj0+ZFBl1bpBYemurNNyA/kqbUswhLqhtxuDYEnQEQd6n6+/wtDyi3hgjOw57u2OO9nJW/Yhf/83fwGskHBaP+OT0Lzg8XZBNPdqtDcaDEWVpaXKHOxu/zpn+AfiGxs2AoT0gnxS4gceimmJDgxe2MNqj4TVpbvY4Szy6cZ9mFHFv/Q62tty+e4tHzx7yrT/7YwLfw/ccPE9hrUbJZbpRt9nHkQIpXKQR9DtryKKBJ2N8pXEigfQ8ZO0itEPD8UltThQvk7kEGilAF4KiAms1vgopLVRVgbI+p4cHtJsBv/P+1xgtxDL/zgpWVtYYfHxC4s/wlMPKyjpv3nudvd1npHVBHEl8T3JjewPpRGilmIyOwRiafUW7G5KmBbUokUZRLARpVixnHJSiKFNGi4K8KJfeW8sBC1e5xEFIHLfYf/qIw8NDNje3ETJEOnD31mtEQcDhwS66ho1LEgUuNTLCno9vy3MDLMK+2OZ0IR9UWMtS4VYV6LqEc9Lb81yw8vz8VRi7NDATwlyOn8v3lIDE82F9q4MVFs9zl6kQ0nDheSOVpNfv8fVv/BaffvwpxycTVABaJ0h3ju+5xOEWN+7eoBvdYHr6FBucsihPcUJBox8ROoYoXqEuc3QxZWOtj6tCZuMZdT1lZS1GCH/pm9WwxEGDBJcP/uxDyinEqkXYqXjtjS560WQ+XGXtxhofffQh7331HT7+8SPGScLpbMjD008YJSM8FTLaX1DFkirVmMrSbPWY5RlVXSGtJhlN+Pb3vsd0OOPk9AS3vVRlGSHJ8gJrahxvOVFQ6+qcnANjNdPFgsgN8XwfU2ru7WwjtOTp4S5GOlirKKmpq5rVVp9/9Y1/zrMfP+Rh+ghNha7mWFfwnQ++TZ5laGVwgW4Y4TkS6fqo1CFPcky5TB2TaknjeY6LqWvSczm6UALlquUtrzYIA1JCGPs4DR83EhwNDijqEsdd7pdF4DoustbkeYayCi3ksm/OSl5sVb44b6QEYz5LZPwSX+LvIxqNxhWT3leJmosWiCdPnvydtINdh2v9YLjGA8EqTLHFP1SiBpZtTq/iH/IYYoxhd3eXZrPJ+vr6ZevScDj8hRM129vbV9o6dnf3fqVEzUVb5YtYEjWbYH+6ouYfS8usMeba8/xVs/MLvNqmdOGdchF37TjOtYoa5Rb82jsrVGbCcDrk9PSU8XiM4zhXyJ/rWmAdx7nyfcKy9SuKItbW1lhbW6PX612aab+I8Xj8UhsdLOv4k5OTK0TRl0TN3x1+auq3Npo8L4gb8ZUozxeNhM0LKRIXs3QvJj9drP9ifPeLLVGvtlNJKZaqB7FUMpTFAqsXuP0J4Y0xxz9K+OC736cxq3nr13+NsMppjubseCskjQaPspKh51GFHraWOKHElk1OdncZDI5ot3u0Wl2iKKbpBRirkI7A9zoMZ0/JR1NuCMXv3n6X6q1vkAQBus6481rMab3P0eQI322w//EBroHm7ZjalkjRoh31WRzPWNnsoZRDPipZZJao38bZsGzcbxJ0K9K55fip4uBwhm3UOMzY2eqztRNwnB5htMKWEt8aGj1Ffy2i92bASPoEaoXbt5ukQc3g6T7P92ekSQ0UaBTKiZGeh3IswroUWUma1biOhyNdqAzNqEXoVdS6wvNdXN9bxs8lNVYLfC9ENpeqqtLUVHVF4Ct8G9Lp9zl6fsRkOMP3GszmM/KqRRQ7PN0boOsCKxSYmnSRQF2TG43b0JycHuK6m5SZ5mD3EN9xeP32Fvunp5xNc5qtJovxgr1HY3779+4zGO4yPigoPMudN7YxtiIZKaJmROlaRGGxhcFtu1hVU5UFjvSIVEin26RaGPJ5xXw/x2sq/MBlvbtCnpWUaOLmGvOFZjaaMMwWRB0fx/fZ2uyyut6gtyE5PHvIyeGQdk9yMj/h9OSYFINGYMuCtqN5/+07RJHgLNckdYWXNBkfz5mdTWj5bZoNnzlzZt6C4ycVyUzii03CYB3pBsz1E56f7nF8UDIZgO+ErG2tsX+4T1lo3t5+l3e232Jo4DiZsrv/V2g7RroKoV3wwHFd4mYbV7VRixA5l3RaHeqy5Gw+w3ckUeBzdPqYvcNPQS0TYZTjIKQAufTr0Naga83q2hq6BuEqHN8lneTEaYsoXKHWGbHXoywzpPJBK1rNNjYsqbXA2AIpBb4bEVTh8sYkI7TN2VrboK4ddven3Njq4IaWqs5JZjNCNySZjlC2xpSa2I+oJBjHodnt0vE9Sgl5kbG6sYbrdTg8m5DnkuFwhvTAhAV5nVMVmmqaIesOURzQajVwnYA0MZSFJklyXFfRaASsrHSJ/IDFPOX07ITB+JS1zVXeeetdDvcn3Li5Ta/bYzwcU2Qlm1vbl2PZpSGkteSLlOl0jK4qPN/B9VyUXMYkW7u8aSvlIKSDqTV5XqJ1ufTkUgqrl4rCpfpn+TqlLlQ5ZmmWLhxgqVy0FqwwCKHP15HnxflnbaQCQdQI8T0PP4h56+33CRohu0+eYMQpN3ZWuX3jqzR6PTwvpOGvcXj8mLOBore5gmqFJKMJJwdzpHDwvD7J0KMsfNJZm2cfHxI2Fty45SJcKI8HrPQbVJMUW5Vsr29wY+M+o8WnrPQCvH4D332TTx4/ZXUz4Dd/+012Nlf49p/9kNGo4PvJx6hQsRqsMJwuSIcLPKmwvmT3+ROEOk+vEwphXQ73j0nLnKAT01rtkCYpWZotk9xMRVmVeK6DlBLHcxFi2QZrCk2aZTiuQGM4HpzSD1ZZjzaZlxmLqqDMDHld09/c4Pf+5T/nT+Yl6anmJDtFF2NAMEOBWsa6x3GTQPlUWcbobMB8njEZzzHG4gYuRZ7juYpGFKF1jTY1lTXLdkKWPmICqKoa4QocB8LQJ68zrATlnStulKTUNaKGIkkIpIPjBgjro0uFkAqJxPLyA+aL9++Lh89/yMXWl/jFw7JMpjNKIByFksvUPZXXxBXY0qK0BW0QxtJWBVHuYB0BSmEdQV2BWxkqCVpYNGCERZ+bqSsDjgHXQIDGSQ16YTAL8BOB1xY0m0sW/WISzxhJnmtaHUmjLYiblqhpESrBmldMIFWIdORLn0rqZYrpi6Lwz0swlVJeKUSvrGvBcQK2Nu7zqhHZwcHBlYLNFQJHXBjHn+/n56jZPm+/rlvvC5FUEoz2qQufPFF4jqJIHWwdoUQbRYmwc6TNsCztAXhl7LjOkNb3/SutHhck8IvKvZ+lqP8836xXx6nPOz7Xvf6642mtZTKZXIajuK5LHMbLieJXjunP4uV1nQJiMBhwfHz80rEZDkeXx+byRzhIGlfOJ2PPc7n/llCijRUvXyOhv0ojXF/G2wOOcojDNZrNJnX1WTfEFdPbc9R1ffmdXBgTv1jP/W1xMcn/4uT9xb68uO3rrp0LE9wvguvue47jXNlumqYkSXJlvVcTwy5iql89dxqNBmEY0mg0aLfb1HV9aSL9IqRT4IpleEbdCJiNNdLWKLFM7H0RWZpe8fAKgoDbO1cT6NZW7gJw48YNbt+6xfrKBqPJiNH4ZaJm/+AZz58vPV8uiKDPM1sHcM+fqT4vyflvwnXXSF0vA4p8f5nkV5bl8jnomiHSdd3La2n5A57notRn/MIvw3j/V4G/kaixWCyCJEnpdNsoR13KsZatTOYy3emzG+nLg/OL7U0vKm8+70K+9KhBIIWDtiWGgrouOHh2wkoY0Go1mZoTbrqC5h99l/L3/wxHWFwFoeMjvJA3tjb5ixsr5O9+Bel4DPNlfPDug6dYW5DNx1TdVVK/RSsKUcKgXEPHqXDzBKZDHLrc9dvk7TaTjkIGHtJLmZ4sSGcVulVh54LtO+t044D5VJPXHjChuaZIixmyCLl18zZnx2NMkNDZdGn1NKNByenzFKoOd2/0CNyQdluQFhnDRYHsNBidPGcxMbz9RkTPm4N7xuGgSaNzi/Hjh4yex5hZQF+tE2xbTFliqUAqdFkQuOf+E1bjWE3kWxwpkcZQ6pSg8vAjlyQbshgmGFVR6xrlOvjGoSgTtC6QjlgqDpSHEQ5RowGVZHW1ZDKeky5SJtOIH/34CVqUGGNoBA2SLOfs6ARdVNhaY+qEiQ745JPHJPMRe88OCMMWoZIUtebWxirD+S5JUjIdz0nmhmokODsa44ctWr0QjcGRDspC4WrKukRZTaPVRDYdRpMRNQpsRhSEhM0cTxkC5aJLS54lZEWBkQ4Q4voOi8mY0GvSXFtBDws8t8lvfv33uHd/nXH1LVRryKDK8DJLYWfMFxnTogDXxQ1jJAWDwYhPfvID1jYauN0QvVsxfzzlKzff5czNmbb2GBVPCVttgrDFs6cn3Ox9nVvrrxO2EubJx4yzRxw/1HjOJgFj6tLy6e4jcAKqWYbjVrQ6Hj/64SOm2idJLUZMaQQd0AZlFa24TbvTYnOzj+zAT/5oiMMaWi+AOa7jo0s43ct4+JPHyCDAcT0CP0YbieN4eK5LqUsKnZMWCzbWt1kUJcPJjNFkuvQlmtTErZBPHj2l3+uwtbpNVlbL9j9hqYopYctDKJCEGN2k2V3HlyFunWLSmum8JDOGJ2dnjCvBje07KBdyRszzITgSr+ER932iboQwOXVW0Oq0MFlOo9umEce4Xkx5fIrr+pS55nD/mMaWi3SWJEnU7rDS7VLXS6+e+TwjWSTkWY2wS3WDH/hUJqe0grPxCZ8++4hWv8VXvvoeurRsbK6wutJjOpszm6a8dv91hLLUujw32vTAWsok4eDpY/YPDrB1jbGGSheUtr68CSmlCMMAP4A8K8nzAtdTeJ5Ds9Oj0+nS663huj6e5+M4Psp4WBQWi+PIZVvM+Y3bXqocLyLADSCxdhkFvlxqEAqUL3nj7fv4vktZWYrMkmYB9197ndX+NlYohDCsrjVoNN/gTnkXS7EkEIKE/g1NGPpkecV0NqO75WGM5cmjQ1zPZXuzJJ9P+dGPPiXveVhZsHZnjW/8xjs0wi7HA4kMIvJU8xd//QEHRwu0LfnWd/+SrbVVhKxoSYPOUiqrqGSJ1SVK1PzGN97j408+pqxqfCckKTXTRcb/+p//W8bDIY+ff0qiK7K8oq4sZVYhlMT3vYubC912j2azwcnhGcUioxU0acYt8jJhMZ7ylW9+nTc2v0JrEfHk8cd865PvMS88ml6D6WLM/+X/8X/l5uY295o7HH9wTOU7WFkSussxSUhJ6LSgsAwHA2ZFisHHc32MWD781KVBOg5FXYNQGFgqroxGAoHnIazFdRSNZpOiOk8LkYJGt0Ge1xR5SVmW+EqdUzEVjvWw2lJZu2zjEktFFlZgrQFhL9ugXowzvu6B6Ut8CS1BdRrcab5OWEF1MuYn/+mP+PqN+zjjkmT3mNn+MYuzEc+zOZ4X0r+5xfobd2ne3kI5FcdnIyayYmhyprpgIWryQOEYaJTQzaGfQKADVnPB/dTSyjso0+SviwM+KH5Ee7XD5uYWq6urSCl59uwpcExan/D0EDgUpGV65YG8vfYe21svFEICLD3gN18iarTW1xpSXmcmfOHx8BJMhC1uXmkV2ezffWWLAtcJcVTIT8OLXhY/DXVdXzEVvRbCAgHf+qMT/vyP/+Pls/v777/PG7ffp+kdYosn2HyfyfwZe3u7wGeFvRCCGzdufKEifDqdXnpyuK7L9vb2z1Qw9ft97t+//9KyLMvY3d29su6Fv8yLaLfbbG9vv7QsSZJrPUUuVFsHBwccHh4ihODo6OiKouYiYeaLYGtr64oC4uJ8Gg6Hl4bZaZqys7NNv9+/VIg14zWy2SqYl8+9k5NT8uJvp/IRwFff/idXDGGbzSaHe4d873vfoyhKvva1r/Ib37hFWWj+7M++zdHREW+++Sbf+MY3gKuF+BtvvEGn02E2m/HkyROOjo7QWl+bhvRFUZYldV3T7/fZ2lpe92EYkmXZS/eqPM+vNRF/+vTpFzIxN8awv79/5TP92q/92rUpbl/0Htnr9a5dviQgfG7dusXNmzfJsuyKya6QFiGWre2dwNK+/zXeOb8MXn335W6/vO9plvHtP//Wlffe6v/ucvzLBc8eCnYfjhiPR8xmLxM90nX5+q//JpPpGT/84Q85OjpiNptx//79K8f57t17S9KbzyZ9fpY2yPX19Sutcz/60WNaLZd//+//PZ1Oh//wH/4D//P//KdE0cvEm5SS//6//9fkec7u7i67u/scHRX8D//Dv+Ltt2+9dI39Q27NvMDfTNSctyYVRUVda1zPf+nh7rMBe3kKLb1pxEszdRfrvvi66/794roAUgiEcqnrEiNqpHE4fl4SOS6t19ZYv1fz+MEzPpq7dIOYprHE1uDOF1TVlFDBs8c/Idy4QXPzDvP5jDIpaUUN6trFMYZqtmBlPWKlpYh1zfpWg/UNSTC9hVtbyoViohwmi4Ko3WSRJ4ymBTbrsOpvkpzOWeussXVrDaEziiIjbLr0tz16vYjqgwX9sE1ZZqysBbhtgyZjcWbo+usU3ROMXvplpEnBk+cJiJhSa9oNuPVGl5IpVaDxVxqA4PDTOebjJovpDsWZi6MDXOnRagFWn0eme2AyHEehLWgrsbbGF4JW6BN6kjhySbKUxWJO6XWomoZSGxZJTm0tZZ3iOhG4mqKumBc5qdZUQpDVObYoiBoRiIq6XjCdh2TG0uxGRH5ElebUSUpRZjSbEaXIMHZp6nx8ekKZzfCEg6hr9j7dxxeWm/119odTnuwf40iPpl+y+9EeK+1VWr0W1jjMBwnzSUIQB3Q2QpStUVIjXElSpwhlcKVHJUqaKzHrcoViUJL4KVrUWBSuUlQoXDfA1JBXyxuA1S6uY/H9KdtbM/zA8Nff+RY7b/Y4HQxwvIjMjpiVM3Rdc6OzQ9xrkM7OMDKk53XY++vnEK/ilBF+oFn9ikJmATqHT35wyNdW79EJBc8mx4igQHQPOTzZZa6fMJoMOXw6ob3RRskAIRyErfGMw2Zrh6PBx3zgebhRk/TsBCM0lalJ6wzHWAI3YqXTQsszJtM51cKjt7FOy2lQeCluFLDSWefphwuK4xqLh6tchJGgIfRDAj9CYHCkotfr0mo3qEyB1jVpsozVnkymCGPZ2rjBaTUmm1cUjYxG1MUWBpNpIqFwTI4VGmM8jHYpC0mj3WB9s8/J7gHSMWxurnE6PENbhRc0ePZ8wA9//D16DY/XXrvP4dkZbuygj2r63T6LaUZWavYPjmn2ety9c4+226C/1qcua4r5mLpuUqYKXIPnSZRjKcoE1w1odjyYF2QFyFxgjaXIK/YOjnEdQxD6HB8f4PqC+6/fxVEetTGsrvfJy5yzs1PefPMNGq1w2UaT1ZeSVl3VLKYTZuMxplqqiaQ1VFVJoUusXTL+aZqS5znSatK0oMhL5osZRZHgxx6e79Ht9Wh3OnT7PdrNDpHfwViQniZuerhOgCOaKFcglVr6I4nlb4GLwC4VUlacP6DrpbJCKMKGC8bgO5LX37hNmnbp9TaWE3hCL42PBYRRSBiElFWBEIpWc50lCSTQNRhrELIGC5vrr1OVGtQQpyXw7OuM05JS5Lz9ek1vxaHWC1b7O6hK8KO/+oTxKYRel7xMef70jMUkozQGKQxW+UhPIesa6Rqihk9ZzVldaVFVFmk89CzB7/dZ766RjBbnBK6hSEskkmajTW01xi4T7iSQpyXWLKiKijyr+Mob95G14vnBU4QIOB6P6HbO+K2v/Vt0YfjOh3+NF4cgBJP5BFdabJZR1wVB5IAJqVJL4AmkH9CKuux0bzI/HDEl4fbtG+SzioOzAwpdUpUVwigEikWanZNoEoxGGIvvO8RBiBWWSteXUmmrLIalukqqJTEDmjgMscZi0EgkSiqsdrD63BFJSC6JO/R5cfbZ/fZqMMCX+BJLWARGAlYgBUgDTmWIKvBKULlFpBYnMXSICFRMWDn4s4L6bMJC5CxGY4qGRMSKKGqgPIGrLK6GZgEdBX0L7/R22Kl9VlJo5SCM4dRN+EDsg/QRKkM6+XLyT+Uv7OMSjZa6cg67fo1wXpbnnz9dvvw5P8ej6YsSmEq4tBodsD/dr+MiUvZFLBaLK0SREIJms/mFyI3Pa8u4AisAH1vDi/P9ydwyHVeUuYOr2rSbGsOC4Wz3pUP1qpL+p77dK+v/LGNMWZZX1ALXmZcKIWg0Gle/+2visT/vO351n39RLaGvbltKealOefFvr05mCyTChmBfbqcTNgLztx+nhY2RvEwmZomgyBRxuIrnVOjKZzxKaTQkvu9fGnVfl2wGS5+Wi9hy3/dptVq/sJjiF20zLo7d563z0mfKss/d3xehtWYymVy5xj744IMrhFa/37+Soua67hXfmp8Fy1rtlXN0OVWz/L283f9McJRDM76+HetVHOyN2Nt9mdBq9UruvdXB2IL0BcVOu92+cvyvi3b/WXDddxcE4HkOaZpeXifttlpOhL7yWmMMUsrzFtkerjun1XJfUvj814KfTtQYe5l1H0ZLA0StzSuDr3jp4e86IuZvGuSvU9yw3CpCuAhrkUjKBBreLWZ7Z3z7//WXvLP2Fqnv8seHQ25IxdsyZL20aGN4evyM0eKM2bf+kHd+798yH+1zu+fgbW7y4MfP6DW6rPQcbuy4xHXGHd/jtZ6D3E8Ibm4yPlvwcTXhUTkhEhWrbk3/ZpcffDTGrQX3NrZ4//079FZXGOl9Dg+nrL/TJOxYuv0WNrFkVUFnVTHTKc3GGiJSjCYJycBSNxIamzCcnnHwUUG+aOC4LTxfIqVLay0mjFPajRaTaUI+3KLd9glkxOgkpuPdoNuXCGNwLFTaUGsLFnStqXWT2hZIV2GMBFOjpKIRebSaDr1uk0rXzGYJAkVWVEyTDH+eUmqL1iVKWExdUWlNu9ZUZukJISmQYkZZDdBlxGg4x2+CEytcv0kx86mTCkoXW6aUeYEbuBgVUCY14+mU4fEhq80OrpToWUanu8bWzbvsTWfsHp2iUfi+R504rHZazKYJZgHlOGOwP6a11Wd1pYUuXDAWGwq82CFsOeRTi7GGWTWhmhpm+wn9fo+qysmtxI1jhFXEjSYGl8lsjvJ9jKi4dXMdbMqf/+l/odKaaZXRWjXoQuCGMJoNqdwK13iseA1a8QrByhpBV7I28ugc+iziLs27IXuLB+zNplC7FDPBdu8eJ89OEGHCjcZ95gdTfjB+xMqdFlmSn0dI56RTF+vm+HELpMWVkm6wzdn8E46L59y8cYNnw11c18MRK9TaUlaK2A3xZMDZ/IDjozmNcJN+J6BKMlq3XG6/dwNRBjw9mLAebPG+gf3dRwSuotOI2V5fJwwa1FVFnma4yiHJUupkQTPu0W13iIXPfDEnDgU3drbw/Q4fffRDfE+z2vbo2RWMdkjqjIPhM1QYUBsfXYNWFUVYEzc73Lx3m3IxoZhNiazAq2pOjvaopWD32QHVShNHhQjX5cHHTwn9gAc/2aXb7bG2Jni6u8dqrWl22ijPY31tjdVel8Mnily3CNclFTk6EQz2R+RzjVQOUjrnypRlUev7iryypFmB346YzMdoEja2OsxmQzy3Rbu1geMGHBw+ZWNzjXY7Ii0WVIU+T01wwS6lqLNkRkWJEzhMZhOEkBSmQilFnhcIsXwA0lqjtEJoTZ5kpNOSotQUeQGyYDJKaXYmNM8G7GzfIvIz9vb3aPYFrZ5EORJpIhzXQbkuruPjOj6e44NxETgEQYiSy78paZfKOOUhpbOMEZcO3VWXHltIoQBz7rHDueHykkbwPO+8BdUgxLLol648T20KEIDjWMKoxthNpFG8/tYmVS2pTYFSS1VkKec0nYJ0PqEtNGtRyKwY4PmW2HfIpiWbW6usxis82T8jZY7MFLoTEYUuVV0QBSHS96m0pWFC3rz7Jjv9DR4+eIjreqyGTaZJShTG1NpQFzlYSZamxFFMHDRIixyDJIwbjOcLikXJ6XCC8DzCUFD6Ax4MPyBzC4LAw5cJwgpcGXD35n0iJ+DR80cgS1Zb69zZ3GEyOWHhpqx1Nvk3v/fvePrjT9n//f8P2+u3OEj20GWNtjXWQOCGKNfhQrMqxVKpGgQ+vqcoihyNxkpJlueYymKtpKxKtF3OLDUiD9EIcZRPXVUYLZCOg9QKarFsmeOzyRaEPfdGevk++7MWX1/iS1yHjbV1onaLwpMskoTZQcrjYsLu/BSx3SNsr9NdX8NvxmR5hltDXEGrgF4DdrZusW1DWjmoEjCGYPYQvlgHA+vr61cKiet8Y34ZcFyXza1NMD+dqNna2roy4356enotUXOd18R1mM/nV1U+PwOGw+Gy9cVawjBkc3OTsJkwnP3133qbPy/m8zlHRy9HWX9eUfir/O7/oeMi+efiXDPG8OzZs0u/nosC/dXv4gK9Xu8ylSiOY6IoYrFYMB6P/y4/xkuYTCafu78vwhjD0dHRlfPq2bNnV4i6u3fvXo0xv4YM/FXDcRw2X82Q/hw8ffr0FbXJUpW9ubmFH3xGdsznczY3N69cY4PB4Bfu49TtNvF9n5OTEyaTybmKb2MZvPACLszapZSsrq7S7/cxxrC2djUu/b8G/M0eNUueBq0NeVbQbbeWM3b2M+b5sxm5z6j3F42D4bM+wM81G76011yG2BqtsXK5vqscLAZba3RdoWvJgz/7kPXTgI1izle//uucrO7w7Omn/OjglA3hUtiCB8WYKlScPPkE7wd/hh8Zfu2rbyBDjzC5yWqjgd+dU9sTbqpN7lYu9X/5EfZgxvNZzrhT4vzr9zFjS7cJmysOp/kuK60Zpc24cXML4S149nCOiQrW+mvQn6GDlMluSWR9Gjc1WXvIRmObfmuDSX3IdOQShz5yo6Dqjmh2S7akS3IWUi0kVVLiSgdHC1TVwWVBPlScPYjJAoeVzjpRv4GvArTVNJsN2qGH63qUeYUSEmENZe0wTcYUdUVRWaRwqO3Sc6gWFZ/u7lNWJZblTHyalUuiRyhcR+AqhUCiFQhtUb5FooECK1O0NNzYaDE+O2CSJzQ6ik63zWRoWO1sIdpdZosTposa5YExoK3BCM3R8Ix+4JHlGdKPsMKhtOCHMVsb66yv9DgeLFDKWRaQlaKYaQpT4AtFoxkxGgxZOQkIhAfWUM5zbr22gw4KqjSlmmiycYoeCBw8FIq0yNHCRyKpi5q6NkvflSwnbEXks4JK16TJHFsb4mbAjZ07yNwSyxahcplPJohcIYyhTMc49Rq379wjMXuIqmbt3S5RQyEbBeq5RLkh0mp2eq9zkp0yXgwRU4hFQLMXoFd9KrUgLw2O36CzWpAuMoIgJEsLUAm2imk2VyiKObqqefr8+dJUMbcoz0UVEptGxNEmq41NxuUhQiTUekbtwfO9CZvJTdyTkk60gvI147MBnu/g+S7tRky32ySOPKLAIytgNjpjNgU/auN4iizPqIoKx28QOw08WZIVKffeus08O+Po9IAyL3CUSyvssr6xyWgyIs1KKiMxugKpQVc4jqQVdXjw9Amirrm31Wfnxm0+fLLLg2e79PsN3v/Kr+E6PtNFwrOnD7mxc4tGs4vnRoyGM2otqUtIk4JkliH0jF4npruiME6AjqulIbSuWBQLPLe7bKWs9XlCU4WSYtlWdO7zMpsvKKo5ndWYpBgzW2hWei5B7DMdTXGUQ6fdodY1dVWdG8Q5l4T2bDTmbHDAcHpKWVUMxhOKssRYi60NSi2H21azuZRrl5ZkkTEezSjyAmsNjnEREhSSYq7RVUK7kTG3JXlZEJuAs7MxQlkaXhPX9zDCIiS4jkKiMFpQ5aAIEVrhqQDPdXBcuSRpHIWjHBzXx2t49Fdu4HkXJofn5bxcplFZIbBGki4Wy/Ys3+Oi2LdWIC4Trc5Nk4V7nj4r8ByBZ10sPtDAtx2srQlVwVfeXyWf/QQGe3S2T3j//Vs8333Ga29tovIGZ6MxdQaO6xEGLVwJK50VZOXTcDscHu3hr7j8xvu/wd7DffbOjqiUQRpDGPq8ef8+jx89I61zsiLHdTziuEGeF9RlhVM5OIFLYXKsa3CVx931O0SpYbh7yh8/+UPaskfm5HiupOf0kMYhn5XUIbheiM7n3H/tDf7Z/X/Od//4j7j57l06nVVG4yl/+v1v4zUjPD8gLRZ4roOpNNaVCCmpS43jSlzfWbZnuQ5h5GONJk1T6togHENaaZTwcDwHXejL79kVDq7vIqRDMq8RFrzQQWQOdbY0mObinmwvjKcvvjN5ef+9+M5/Hu+DL/El1tbWaPa6nKYzTpIRR+MZT+cn7GcjWk2HprPN+vo6/X6f+myCV4NfQ1RBs4R5knBQJEwq8GoQVjOo0ldtOv7G9391FvzvKpHGcRx6GxtfiKgxxlxpv5nNZlfWuyhAvkghKIT4uYia0WhEkiQ0m016vR6tVotgseDHH//01/6ysFgsrrQsuK57bevRr/K7/4eO8XhMnufcvHkTpRQHBwfs7e2hlOL27du0Wi3Ozs7Y29sDrhL6zWaTqqpot9v0ej0ajQaj0ehXStRMp1OOj49/6npa60vPoBex9BB8mZSYz+dXtum67ud693wReJ73c7WIXQel1OcaHL+Ke/fuXSGfuiuajY0G3vnlY60liiI2NjaufNbFYvELJ2o6nQ5SSk5OTi6VMTdubF87DlZVRRiGdDodut3upQLxVSXefw34qWbCQkisEMu8dW3P5VjXSxkvCJoLudqLF8AFSfOizPRiHXmZgmLPo5I11p7722CWxYAEa2uOP33EjdSw1dqgdXzC5D//L/Rff5c3fvuf8KOPf8yf/NVfkRrN2BWIIKLb8rjR1bx5f43bWy5noxFvNwN2mh6FsJyOU6LyiEa1wujDM06nC/jm67i/vsLvf/Ah3/xvfxeXCkPO/idPSM2Cu6/3SewT/uT/t4cZNvnf/x//R/AzHh+PcL021V5OeDPGfW3IRJ+SJpY6DcgmA2QCd++1yG4/59SO6eiIjW6XarTN8FnF7icz0C522CDB4EpLcVajpzGLgSBMBZ6TkhYjjBBMJgNOfUUcBsSBRytuEochnijxYpfTYcJ4PmO2yChKTRhG1MYyms4QUhF4PlrXeI5HGLh4rkcjivGUYbKYM88KKm3J0mW/dG0rVCMj7lUkukK7MZ31Faos5S++9Zco2+LmjsvK2grSE+BbiqrEEQ0Cv42lIEtPmOcVok4xlcCUNc8OPyC8sYLQljtbmxTFHnlZ4ghBmWu6UYfMlLhNl7XXNzk9PcXWFi9y8cKQSubMTmdUKscqgSt8innFbJwQuh55nqJ1jcCnrivmSUIuT1lbcxCyYjYbk9cFTT/GjUMwhpIMIWPSeQaOQ1Vo/LFLz7RJvZzaVJRFgmZBmi5objQ58PYYLeaYgYcMO5RmyHw6wrereA2fftxnq9Xk6OM9Dg+mxL0GjpKk1RwvivAbIVrUaCOQQmK1oBP1ubFyA1vOQTocjw6QBLRil+l0jJi5rPo7rLi3GT6fMV4khK0YoWsWYkS83aA2hnQqGOw/Bcdjlg3w6JKXywh1Y2GRzalKS5JnjMdDQh+UiAjcBqPRlMHZKe+8v8Pt7ZtoU/D0+Al5XWClZpLUFNWMwPOoa4X0A9pxF1EUnI1nlNkcV9TkvgvNFlVVU5/7bEzGJ9y5scZ7r9/lBz/+iPXNNRqt9jJCu6rJFgV7B3vsbG+x0V9lOlmw9+yAZrPPdJzQiWukTghlimUIzhgrwSAYLU7xOx6UhnyR4jsNQjdEqZK8yEirCmOgFTfxYofBWcV0NCZoOKyu3KHfvYGxJaPRkK2tzfP4bUUUxriOA+epOovZnP3dJ3z7O3+GEUsjWCMUWV5ejnG+51HkBUM1xmiDqS11rbFGIITC2GVCWRyFREGIwTIcT8nXKpothXI0Bwf79FYb1FmFNjVeKHBDBaLGeIayyojCCCl98mnJYpTRiCJajRa50VhjsMJg0UvPEt+Bt3w2t8Jz/cxnygvLUnqra8OzJ09odRvs7Nw+V9dcrKcBhUBgrGTJ79gluW5BIkEIrNXL81mECC9gZSvmG9/s0nr4hP6dU4LmMfdf38BVkryuwFh8EbJ2e423Nt9jODjlrbfeI3DaiNLhr/70Tyi7kJ5WPHr4lFpZpBTkOscPQ0aDU+7evMHswQM8x0FJxWw2pywLbt+8xa17Ozw/3MeLXSyaOze+wb/+jX/Fwd4uf/L9P2WSj3kye0blGKyxfO2tr7O1eoO98TGDYsRgdIKLy48//DG//fY/5Xf+6T/nxvYtfvzgJ3z7B3/MQbJPaTUffvohJUtljsWSlylGCEQtEY7Ad5dtaq6SuI6L6wo812U+y0BCWVcUVcV0MVsSq46LIwSB5+N4kqo2OJ7A9yMazRazI4OVLsY6GCOW91PM+b33s3vxhZecQGKMxfwckvov8SXa3S7dfp+pLsiHOYPRkOP0mLNyhpMsk/G6vS43VzdwZxJfgXLB8Zdmwj/67l9STOY4emkwjDA8ik+w/Z/+3kIIOp3OleL8Yqb/lw2l1FIl8wWImk8++YSDg4OXln3ePna73S/kB/OF/Gn+BlwQRY7j4Hke/X4f5f/qCm1Ytq9cZ5T6KlHzq/7u/6FjPp9jjKHb7eJ5Hvv7+5ycnNDtdmk0GmxsbDCZTBiNRte+vtVq4fs+vu8TBAFhGP5c5MUvAovF4gt71IxGoytEzbvvvkur1XppmZTyyjY9z7viYfWzoNFo/MKJGiHEF1aTvfXWW9y4ceOlZW4wJWyfgFh20RRFgeu69Hq9K9/rq0bIvwg0Gg2MMYzHY4wxRFHE6urqtSbgw+Hw8vrf2dlhc3OTw8PDf3xEjTl/GBdCkGUZVV3j+OrSkHCJq31mL8ZyXzB2rxI1L5E25+9jjMVYixTipe0gPlPeVEenbI8LGtMat5a0pEP+yRO+l57S+WdfZ/HkIU9OTshqge+V/N7vvM97v/U6/YZh4U7I8j3erzZYy0NK7bHibqCjEcP0hPIbq5QrG+hv7lB4a/yT7VtEbcHzo6eMJwv62/d5/vC7mMEQaQS319/g7d+9j9NasHu6y+ws5V7vBr/5u28w9HbZqw9Y5BYzOub4WHP/xiob221kZ8hCPEfphOHpDDOQqEXI4gyanovjh7REE5Eq9JFHmGtq4eKFHoHv4iiYzwqSrEApj0IpRnqMUuAHDrUuQVcUdYHyIowIKI3BdSVKaIxdOonXZUlSFhgrMF6N32ySzkeYMsH1lgkl/cijLErqwMfxA6bpiHg1ornh8OzhAlV7xJ5lPB4QOz2KouCTj75HUb3NylYDN/Qo6oJOu8XWytuYsqRaGXP0+GOMWOaR+GGAkJK9/eeUsiYKfDbWe5yNlm1DptI4jkS64LcU3RtNgrakGldUosSEgAmYT2scFHmZYUxJO+pRRYIiT6itQqqlWe5FAeP4oDyN9GtSk6EaHtorMI5BKagpOVkMMaaiKnO6qoE/09RpQeVX1HGDZ4fPePK/PKDbbzPpNplVY4ajjMBtsdL3SYuco5NjhM7ob26TV1PS4XPcTsx262u4seXD3T8k6MH2zjq7ZU5WKHReEQUN6tJSF5aqzFDWRRRdQg3CKrJ8gF+1if0+N1dfZ2vtJrPqkEissNaNGY6OmE7HrK1us71zm173Jg8ffUQdDth6r8OPv727NGHEo9KSWZKgLORZiq4qek2Frue0vU2O5md89avvsrbVodN2CYMeYSPkex/8BYfHpyjHQwsfowL6N24TBgF1krDR6RMEDXYfPWBntcdWp0OgC+q65L137zObrTA622OW5ORG0+11SRcJ08kUgWA2nbPSX2E2P6OYTRkeHnB4eIrSmiJbUJQNlGepigV7Twd4wRDdGFJlgqqWFPUCFbaRqkQIhdKCMq+YTBNqU2IcSbPZxDuPxV5Z2+JgPwV8bmzfwvc8jk+OAEMU+efEsVrO4JllkVuVBYf7z5lPR9xY32GRlhRFSVUbImXPe+sNpq5RtVr6h9hl26JEIaSgrjWh49GMWjhKYWtBZSyuDPDdmCzLOTk8wwstjSDC1IbFUGJqg60dgmjZvy6tQeiIxSynzgTWKDw3AhyUcKlMQaUNCJDKoiuDrs2SdBEvG9ZdDOtlWZEXOWEVcKGfwZ6TOkKxWCSkyQIpl+1ljuuilIOj3KVnjhRIyTI2+nx8F1KwttOlvfI+k8UptZYIO2FxUjE+y2iFW7zxxhb33rxDmLbZ2XgNL4rJihKhSr7y1a+xOzxmdnZGki09aIQxGGWpdMV4NqIsS5QniWRIVWvqWiOFotI1d97eQUWSwWhEjUa7lr98+FcMRyOsK1l3VilTgzIaYwpajRZVXXFwvMfh9BBPKtprd0mzhE8e/4iv7LzPn3/nj/nD7/wRo/SMXCRU1lAtShwhl6qqJf+F57m4zjKRz3M8jK7QlSZ2Y4SukEKjdUGRQVGVlFVBpSsacR/fW5pWF2XFysYm83RBLTTSESTJAl37WCOWCSHCfpYc8gJJg/3sNwikFD9zL/yX+LvBRZvk2dkZ0+kU13W5c+fOz+WN8PPAWMO3//zbNGYV4azEXRRIrTk7PWUyn/HoeJ9PRg95VJ6QWMOt7Vt89Td/k/tf+xrSDzh++pzWBwes+DGdfp9Or0fUauD85m9Q1TWu7+EFPspTvBbtcho/R3nLRD3XdSnLcjlp+AKEEHied8W8dT6fX1nXdd0rEckXxr2vFgNftNCvqor9vf0v5FEzmUyuxNp+XrhGXddfiKjxfZ9+v89sNmM4HF6mzty9e/fKs3m/379ynPr9/uV20jRlb2+PtDxhfWMDxMu+MF80zSdN0ytKoc9TCfV6vSuKmOt8Yq4jAKy1/OhHP7qyX81m80oRfV0y1sU+7e7ucnJyQqfT4d69e4zH4yuKnutUPlLKaxUMF4Xmi6jrmkajcakE2NzcpNlsEsfx0uPu/DofkZNNNVGwQq/Xu1Q5KaWukHLpNck/n4fBYHDlOIXh0tj64ODg8jy8d+8eGxsbl0qrPM/Z2dm5dpuTyYQgCGi32zx69IiDgwOGw+Hldl88Tl9UIXahjnBdl6IoODk5wff9zzVyttYynU558OABT548YTgcXhpEXyDP8yuqM2vt5bn/ItI0veKzE0XRlXHDGHPlvP1ZUJYlz58/p9Vq0ev16Ha71yZOATx48ICiKOh0OvT7feI4vhQ6ZFnGcDhkPB5TVRXb29crUF7FRYrWS8usIUkSlFKsrq7SarXQWl9L/rz77rtsbm4ynU4ZDAZMp9Of25/owvfmxTFKCMH6+jrz+fwyUjxNU1ZWVjDGcHx8zHQ65dNPP+Xk5ITxeHxJMgkhaLVaP1crZJIkV647x3GuPXcGg8EXS+D7GfFTW5944WE9TTPaQfvckHBpKimEvIy2e/FG96rp8HX98C/9/9zr5vwpcjkbK89l2gIQy2LCVw73Opts9lvUVcriJ4/Q6zG3/unX+OMnDzkyJRMFpSuYuxVr72wi2hUiqDg5fszmdMjaM0XU61FvVkw7GYfrFs9vk6uQUZCTM2HFaXHrbg9XVTRswGyegDGsvbGCKgXlYc17b73H67+9RSaHrIdd1rcahGXNnz/8fRq3Ag6ORjz8yzO6tk8kNd/4b96lsS0Z6T0qWyNsQOlMaLQsFCU9L0I2fKLQo9+ynE4ykilEImBtu42SEhywpkb1PIz2MRY8JUgzmKcLrK3RJkenmqgRc3RyRmVdSm2x2pxfnC7K9ahqA+cFaq5zHHLCwKOqMspaQLb8W7/XQXoa17W0GzGzLGW26xHka3hKEcYRjbUN3Drk5OQxi2rMgw9/wM1qk+Z6iPQcxtMJDTkjDjs47gqdlVuko6cITxKGMWub61RViRM5aFuzttrDCMF0lC9TqCKXxlqIdQ3pfMHZyRBZufS32qRFhtIu1bymqCrm0ynpJCN3SgQOYdjE9SVUEuX4YAXNZkzt1YyzU7Rf4IQufiMkrXIUCuWAVA6FcBFW4ikILJwshoRxi8BrsPv4BN016FnJ0dmQT3Fpr7aJGzFSuiRpytrNG1TmOa2oS7+7wWRWczqb0OlE5LMRjapFljtUs4KPPvqUqixQfpPVVszzR0cEKmYyzPne6Q9oxCHdaJP7a98gLU6Zp0eoZsxG+xbzQcazx88ZJruobsSTj59jVYW0IeiIwPUIA587d1/jxx+f0OzD69/Y4kfHH1OWU2wdEDRaCCupU8nN7TUwKdIKVla63NI3KPafUpQZWZniui5ZvkAYRavRprQW6bigPE6GIxphiDA1yhgajsSXkt1PHrAqXqcfhpzORwzLkrDRpr+xSS0DkrTmjTffJa/nBJ4iSzMCTxFurHBzvcvO1hZr/XU2u2c8eXrIk93HrG830Dol8prkQ8HJYMbQ7ON2A0pTY2WNH9T4yuC3Fe3QI59GJGlEqB3ilSbK91jMZyTzBa4fUCK40dug1Wyiq4qT4yE3b21R1SWqBj9wz8epZavm2fEpDz95TDFfUJeGIk2JGw1WVlY5Pj5GtSKsXRrHCfvZeKe1pTYGYS1VqXEcB8f10JVeEiFFSe0a8rRmNFpQpT51WrH/SbKM2XZCfDfCtx5+vTQzlo7GIFmMM2wl6TZahKHP+uo6k9GYIs+W2zYWbQ1Ru0273b7er8Qu86SyLMUYe36zE+ctr8uWWGsV89mcweAAbQqM0bheQF0bQj9E1zVu4NJdWQHpIa2LshDFAY4SBNH/n70/e7Iku+87wc/ZfL1b3Fhzz1pRKOxcREoUW2q2umU2NmZtPWat6TEbm7d5mP9rHmQ2DzPWDyOpJdqIkiiJJAgCBFCF2ivX2CPu6utZ5sHvjYrIiCokUABJsOtnlhmZHvde9+t+/Pg53/NdDKYcIps3UOKYZODZHaW8+bom7eUIA1Ea4doAOkZGHsWSVs94OH7AgZTw14BWOC+RQeAbz7yqmJzP0XEHbAQXiKKEgKOsl/zoox9y7/ZDmtAwny345KMP+auz/0rrW95+41t8/e5bDNMtFlXB7v0xW1tb/Ot/+2+ZuikYi5E5b3792yye7WPnJR/85Gf86Z//R87DjNLVFK7ExAYfHGVTd6CJgDg2pGmEEBIhOvBOrOjFcSTxXpKQgC9omgopBYkx9PsZcawZDgbUVYVIFOezCWVb4IUHPHncx0mNpZO4ddplfyEsFit3Qo/vkqAIK1nUV0DN39Xq0jUDdV1fTBJfTLP5G60AxbJAV47Ed8BHLAzD4Yh8OKAygkUiUMsxqV8Q7exw+84dhsMhR+WC4slztg9m9FPBIBoyHCpSk+Bv32ahHI0WNBqs8ohkwCgZg7g64X1xEggdA+NFs9nZbHZtgJ3n+bUJlxCCJEmuATUvC0oE71lWSwg/31BnnZj6MvWyZp1xHDMej/HeX0zaP2+yEMfxF66EO+dYLheUbUGaJCB/OXaEc+4aIPV533vNyPhl6yaZzct6h6yvvffdBDXLMuI4vvHarxNvL5cxhiRJrlk6rH1/LldRFBcTUK01Wney6cvtsa5rglOUyyWSHiF0HnFZlpGm6bX9/yIT45vMmNdAwzp6WkrJcDgky7IrfU6v17vxM6fTKU3T4JxjPp/z7Nkzzs/PuX379pXX/SIsmyiKLtqo9/4i/eyLErfqumY+n3N6eoox5hojZrlc3niuPu97vVg3Abxr6dMv61NjrWW5XF70Z2sw+qaazWYURTfuftGM3FpLURRMJhPqumZzc/OXPKYAvgVakFevw001HA47lYW1F6bMX5bFtn7/ZaAmhECSJCtJuL0AgdfmzmVZXkiwjo+Pr8SoCyEwxnwpQO2m1Kh1v3GTlcuvo36O9GmdGBIIAZbLgsFocGVQf/HKF2RN1z7p0u8vs2pWhJpL/jWXQJwgELKTXgnZuaGbfED+asbio0fMPz1ADiTD/+5Nznrw5m+9zui1DT759BF6GKE3c8Smp1IlzydHjM4X3DtRmNMZbnhO+dCxvwtnqaY9nTNIxxTLinm1YNivoLfg9PxTTo72qV1ELnZ47/s/5fXxPQZqE5UvWWYTzjmk8CUazWTZ8mTyhFe3brP/kylmkpNkA8gtnz4/4JXtPZrYMz9pGPfvk21vE6aBk2dnpEEwiCX9VNOLI07qklwOQDm0nxKcwTUt4MmUxEtJlqbEBupexrIyWO+ZLwVZHhGlMcV8wvmsJDeGYCLq2pFmPWaLJU3TYp3HA5ER1MsJWgS0hLg/xLqaKFH40xlN05BEgiSKmJULWhFoSsEw3+7ioZOAoWY5e85MKVxjqYoCXUi0NoBEBIsQDocjG2bUS4XFYbEkeYo2spt0OE9/0KNFgC44OTpFRwLfBOYnC/ppjzB11NS0y5TZ0YTYG0ysSfoxprdFEpWcPj4jUgmbvSGCbvUaLxmPxrSqZmaWyJ5iY+sWIYk4PJ0jZd51OKJBaYX3CmE7FkPUVIzf3IY0ZjFbIlPD0i2IZAIIvDeURSDPBWfnJ8znj/G+wNLStDWnz57z/Pk+YqPh9sMdnr8/YXtzm/QgYlbNqduC+/d3kSZjkKQcPTvDFiC0pHUzluWcUbzD0b6jrCxJb0CeDannAuFjJCnC9SmnJYvWY5IUVykKEuZ5S1/OGA40snWE0PLKm3c4enBMNVlwPom4/+A+s7NzZOQoasm7P/mIV1//Fm9vjFHTQ+bLGbOziv3oGKM1DsF4a5PywKKERGnVyRitp5jNSFTHpoik4u6tPT49Oebo8Iy03+fk9IzH+8ds7uxx7+FD3vvwU5rWMdzeJM0NztWdV5JUnaSyaKiLgrNwxHDQ4zvfepsnJx8jQsNsfoLJFVEaMXteU8sIESeU7QIdC7wrCEpBFFGKmH4/52tvb0Epmc5mzOolMoswjUWUkJoxt3dfX7FFJigp6ff6SCFxLlz4fLTW4qxnsWx4/OiI7Y0N0iwDmXYPOB+RpSOcszjXdpKT4BBCrh7ynXtLCJ4sFSvasMA7ixCKIATzogTh6fd7bAxyenmC0RKjFFGUEMVdEl/woWP4VJbz8xnKG4yJ2NnaYW9vm+Atdb0ghAYRuj7desGgPyRLc1ZzduSFRGZlAu89ZVEQCKRZftFHBxxCStqqpW1qpPQ0TUmSpWgFdVVBLKnrBefzCq8EG1t7VHVBPS/RU8nu7iZJ1CNOFI3dRqPRcUFkcpSK8EBrW4pQEekUYyKwNbZaUrcnyCShP4hJlEAjqKVGe0lZl6ggiU2EiSI8HusqnHOkeYQPlo8/fULTwmZ/hNaBUS9F+DGT8znWCVQ/43e+9zZPD55xXE35yfs/pbAFZVOSRRGuLfivf/bv2c0GbN/eYzQe4xNHMatovAdh0KqbfFhr0ZEmQsCavSItAbDeQfCIlg5QjA3LkwbXCsbDMUI6wCKNIsjAfD6haRoGww0sDmnAaN1NdAqBc3QyOh8g+JVJdLjyvBaIjrUqVmANX5kJf1UvWQI2tzYZZYFR1RkCxxY27t5l89YeY3efu/5tvitqfnj6lA+LU/q9HtZaTo5PKJ/tMy4yNoFR3XnUSAcmiagpeVpNeLqYcFZOyAYTxhuLlYF5V58HQKxp8Jfr5OTkGlDjnLu22r5e7X9xTPtlBvd/k5Wm6QVbwVqL9/5X7h3xVX1VfxfrckpXr9fj9u3bKKWuAQxf1lz6pmj2lwVyv6qv6svWz/WogW4iIaVksSxwziGVvHBdvhyftvan6d5znUb6opmwuPC7+WwgefF+Vv6GKzBHCElAcNxafqJPGD+w1FXM/d/5Bv7NIbM/f0T2nTu8/mDEK98Y4nNFnSUIoYgHA1Tt4a8eMX+n4XxyyvB3tlnqDbLhLRZxwfPnnzJ5dsbk+Zw//fP/wlv39tn+H3+bg9kJn364z2LieOutXXYG96lCjRdHUBie//QRtm+pTitSO4AKhjrj8GfPKA8sVIHT5hRRGz5575hX33yASkcsjw3ZU8HOaAeVZuTDQPCSQZJwcLhP6TYY5huk+Q4tZ1TtBGc7bULrLE3T0FpLHPdBadqixcgIjUBnEcIr6rJgkKbYNtBYiw2Wfp7hfEtsJEZFtK1DmxglHLEOaBlQUpBvDDg538e7JbOlw3ZzO3a3dyiqksXCIkVEsdxnMRfsbo25f28E4SFNPePw6IzcpGivkE4S6YQ8NbT2lPPFETI0eCOprEc1FbHS2MaBAyEDuICOFSHyZLsZ+Thjulwgo5gQFIPBBhUVs+MF84M5PolJ7gwRWSAyhlE6RDaS5aQgSWL6eYJsPb6FYX/IYCfjo+IRflOhBxGzytGIQBRL8lyS9DXW1izOahAeZzR+SxNSz/P9Z2AFVkFTCxICWZzjLQQnqOaa5bLhbHLOk/gJkYw4efIxT+YxkRkzHqRoZekPFMJMqeolR0fnDIabHB5OUdkMNnpkA8XCWprlgrJaEqNw44Lz0xm27bMoYeIbfDsjMSleVCjdQ4YFOgzoqw2QGYunER9P5rQzwdbdiHYuqdOaW1/r8ep3d3jvL5+TbwnkoETWDtfOKJaK2/ce8uDVB8SjAVZofDDUrcV5SVAOFxyiaJA6xvuARBGspS0bZHAEGaiKkjRNSJI+/+AP/hAZHIuqJR/ssesyhIkpa0HW32Bx9JymKXFtzeRkgnWOna1tkjhmWRTUVcWgP+DevVdASLZHWyzPl1TjklN7SDNfsKiXWKkppiBURttYVCpJNjLaUlM2GV7V9PMFST5ieThjUdfIwZAkyVEhsHf/NuPBJtY2nJ2f0ev3iaIEYwxSCqz1ncF6s0RJyd6dHX7vH/0+Z8cd7TEbbRMZg3eegc5ZLudY24IQKClJk5Q4iTuz7tbS2I6iqaQk+AaEW3nWCMCTpyOUsuSJZtBL2NkcsLExpK47PxatFLPpgmePntNaTyw1cR5x+9ZtNkcjlosZx8cnVGVnWLwGynUUs7W9g9bRyo9mJYuRnexV0rF+FrM5WZqSpukKVF+hOqGTbC2WM4RokdIRfEvVVijpMcaTZoppUXNweIDXCeONEcJHtGXN2dkRG0NB3kuJVKBaBJxKCMbggqUqpiSJwRPwwWFbR1u11IuGtq0RERzPTyltgZABhSQ4jxaKJEvweIQApRVRbKirutM9pwlYyfmzM8KwRSmBDRW1a5AYnj3Zp7WexLWUxYxCBnpJnwaHD2BrR2Q088WM/XLBxE55/OxT5nZG6xtAI4QkTTPKoiBJE/ABEboodecdDnsBikkJ/UEPaRxBKqIkJU8dm+Nhx44MFrRkWRcsll1sZdOWyEiSJIYo0RAkrVWEViCFIgiPkBKJXA1mP5vgXiyCrJ61X3k5fFUvW0IIbt26xbgSDCvoNRA7MCHg24Zk0OPO1i1ujTPkyQ726QeMRiPmbcv+/j6Ljz5lKN5mJCGvQbZAAJPE1G3Jo9kp33/0Ph/sf8r9Xcu3HkQIPmufSqlrsq8Qwo3pLTcBNdCZaF4uKeU1BgB0UpdfB439V115nrO9vX3BBNFaM5/PrzE6vqqv6u9blWWJtRZrLaPRiCiKLuajl2u5XH4pT5WbgBrn3Jf2h/qqvqqXqS8EapSUHU0aQAiquqGuW/JeivDu2s1wLV57ZVp4FZy5vOpxnZWz+hdiRbHvDA9XwbEhUOuGZaMYpQPy379N9Y1N5tMzzLJB9Q3t4ScM0gFRq5k3DSex4tQLTj8+58O//JiNM4UVkjenE9xP4e2N7zDuSw7qlu//13fZy+9w/Khk+slfs92TyEFAuphe1tBExzz87i0eH37M8eEpn/6FJeqlZHcM7XlFUpREMsKbitbXVIVAVJD0PJFQnO8f85f/4Ye8+U9us5d+C/u0oJ40bN29xfZ2gh/XCGWx2Yg83sGImDzboRWKJ/tnFEWL81DZiufHz5nNZiilSbOMtrbsbt2il+U0bYXRPeJck4sEEWkWxYKqsWhlcT4QfM18UXReGa4liiWDXkKexjR1RS9V2CYlaM35/JyynBNHfZ4enlDVFpXqC2r+sD8gTlsmkyN0rHjwymtYK5FBoqzk+OgEFZa8ee81ltMz6vaENDJkgww3ryibmqhv0DpGykCUxpzMZ8x0S6treuMeRBBjEMojhcBXiuKgwRYVcZYSbaSIvqHyFYuDOWaiEK1GC0Uv7RMbzenxc4zOWMwWfP27b/Deh59Q+pKydCyWXYLNaCy4fb9HHSpOj2e0YklAUooe+TDnbPqc1ndmpw3dIC9PU7Q0mDRmNB6wu3WXv/yrI3rpkNj0CK3DNg2+EeRZxuK85OnHE6q5pWKIDAlNVREGjsVsjqgCx88eMUh2ifI+ptI0lad0AqtbTO4ZmJhARRwJ+tkAEVLqKiHLY6a1Q88W7IzuYicKGfVofcXJp1MWkyXOpqiNlOViDr0lgwcCMyrYtz+jkdDGM7KdHd7ceYV7d7ax7QmlPCekgTj0kCIjKI+kBe8xiUIiEdbh2hYfQOkIvUq0aRFoFJPGd5PmwYBRnJIPCibLJVXjMFHO1s4dhBZ4GzARCGuZzRuKwhGZjNK2uEWDf34EQTMc7lJXDc8en2ESRbVcEskUo/oIrwHHsqppKkcpctJoBCGhY3cJZosZRV1hW08oa4gV/c0cmUbMZgts3XBwMGewMebk5JQ4jj/TEAtAeaKoAzzf+MZruOYuzoHUBiUEVVnTNi11U1HVNVVZoZQiTRKkkN1xFWUn+dPd6q1tWpz1+BBompbGRmzv3OrkVsGSRpLtnQ1CcOw/ecr9B/dJe0POzpfoKCVCkeqYO/fuoJTm6fPnVEVFUztc0B3LYtWfDoZDBqMRyHW/6wkrS+E1c6atW6bTKdt7m2ilr7IgpcB5jzFd351nQ8qqJokipBRIBZPzM4qqRriYR48eYyLJ5mBEIxSEhvPJAQOxSZ4NEKJPUUcUjSWJJF5AVS1AViiVglLoSCN7Y1TT8uzggD/74Y9ZaDBCEyOpqAkInPBYb9FCYkSEUYqgNbSOSMT0hjnjfECzqLv2EEEVW9KB5v7uKxyfzzgpS5SRDLf75CZnUZY0tsa3AdPrUT2fIjLFWAuErRn0U8q6ZlFV5HlGnhqqsosz7xY0BNa2hBBQQnWsFh/QoktxKgtHL4+IDIw3cmRoO8mvkizrirqpOoZa3AGGSiriSKFkoK4DrhIEJ1GIzrBYiE7iFLrYTS4mvOvfdXwaKQU+fAXWfFXXS7yovBGC8eYm40YyvMSoOX7+lKPD5wzv7DCM7hMNIwa9nNu7O6R5xqyaMT09YbJ/QLT79S7ZybEOi0PHEa2Dk2LG+88f84MP3qEuDXv59iptsqskTuj3BrxYZ2fnOHcVmDg5OblCgwcYDIa8GCUlpGJzvPWZKdeq6rrB2avynS7V4kX22d8uGy1NU7a2uljatm07Bp/WHB8f//p3ftE+XjwHf/fO029OhU7uJ1Y+Y/gX/rzw2sseFf87qhACh4eH3SKXUvR6vQvJ001JTl9mQeImoKZpmr+nQM1N9+4NFcTqdauUScTLv/cX2Q8v7ufSn2vv/0X2/7L1OftBvgBhiEvnZFVBvPCaX66+EKgRQuCdAymQSBprWRYF/UGOFIooUp+9bmVMdMU8mI5KL1aGhd7//CO+YjZ8+f9Sgvf4UHH2TDAcWba+2efJ5JCDP/+A8ceeN8LXsccFxZ9/yPn5FLkxpP97b/NkUPCf/+JjimRM+d0xe3cfIO4/oHUNf/lnz3nl4S0eJt8i/v1dPv7JB7z92its7fXIRpuYUOOocFsRtnfC0kQk2xk7SUbPvsnWzphSTHj35AcM8h6iVoze3GVu53i7ZPHcdt4IwTNfnPPJ+xKTxNzbStgZ5wxiSJQmMKXy58wWZ7RhSOUTkl4PFZ3j3IK6XnB4WIBMqaxFqARHxbJoOZvMEMLTNPsYJXDWkeUDkjShqi0Bg3MlvTQi+AalNFlk2B6NaVpLnERkmabfS6jLklp7msWEvc0es+IE1Y9Wutk5ZWuIkowsN9TlAkIXKVs1FTY4FnVNlOW88eabnE7PWSznNIsSguPHP/kL+sOcjdEWaRJhq4qirjk7mxGAfhoTGUljG5Z2QaMduqexsqWpW4ITEAsaKnCdtKHxBf2dPnKosalE+ZjMeGaLM1RraGrHdHbOHEdRLDHa4wUUVU3QBqk8xaKmXsLmeMTGWDCZPed8vqSat+RZhshipvOapEkpygBEONsQa0Uta+7s7dG6ivfefcL5WcYgHfDqw3u8//H7CCeRXlEtluTacDx7yit792mnglFvj3ub/4BPn75HGuUID1pLbOOxlUSkEUEEosQwaWYYEfPJkw+76HR3RJQUCGHZDncYZPdwPuH8rKVyM3qyRy+kpIOUemnwTpJmfRrmzE8nJMMhP/h3HxGPBIPdiMYUpL2I2i14PH3Ea7ck2V2Y1EdEzZz+Gyc8iBUf/7gllBInBFIGlJAgFMF38dMdGwKcb2mC7LxFhOqSZ3DIINC0WCLKZU1lO1kl3iNN3ElBUGRpSmsb5EoDXDYOhCaJMzA5cZwRVIV0nkVRMV9YEAkyzUh0jvWWXqoZZClWNYRWUltFKxxWW4JztK3DukDTBtIgCK4mH22ytTOmZzKUM/QHW5ShRbiSum5ZFlXXJwJIjVQBJbuI+zgSSK0QrUUrhUk1g3GvAxZXJBRjJFqt4pF95xXjfcA7aJuWprG0jaVtWxbLgr3Goo1CGokWAq0UXkQ0bc3m9n2CTDmflaBSBuNbuNaiIwgy5ex8gfU5QRpQLd46pOrkrErD5u4u8RW/hy7G2V0C18ui06mPNkadDLXrpFdAOjjXkiSCLMvxPhAnEVJ4ImN4+uSY6XmN14aN3jb7h8d88P4HyK+9zijfwi4Vwiw4OXtOINBPN4hlwnJRsChbsniE8CWucdi2RSd1l2aEw9qYn/3VxyzPKvrZgJ2NESYI3nn6IRKFdaCExCDRREAgy2K8DaQ+ZWs04nfe/g4sJO+/94iJPWXv7i7zwvI//KN/zvt//T7HzTk+aVmIGcX5gratO98IoKhrYhXRyzNs1aIC5HHGMOsmSS40nJ0do1REZNJOkoDEOY+RklhEqDXzBVieLNFpBzZtjcfYCmKTcjI9ovIFvizxDrSJCcgLqVtqElpbYbyhaTQGA6skMhHkhawYuHj+diaEq2c8XSrUV/Hcvzl12etiXVEU3ejd8mVKBohc99MLqAxMReA9P2UUJWz2MzbinA0TcfiffsyoXJDTYmc1y3d/xrOTQ/bPj9n5dJM7m33+4eSUT1XD980Z7+iWqKiR+2e4KmXo73LkSmzTspMP+cbeA9J6xqfvl6RJTK/XI8tSvMiYnl71ikBY0mRA4Lr56xVfigBtbXjyaQvhs0VGowW3t7euSacW53OWy6veGnWloH0h6SWkEK6+LoTAZDK5xshZm1u+TN0kR1wul9eYMmsPo7Xxq1KKNE1v3M9lP4cvKkvxc1/TlQQ3AP+CtKRdQPuidMxB0CuE7tdbZVleO/dlWTKbzcjznOHKmy1NU2azWRexPh6TpimLxeJGNlJRFNfMqeM4vtHn5CZQIM/zLh3sUq3lakmSXPgnGZVztO+IozkyEhRtAYsDGqZgrrZxj6VpmyuTQ6XUF/q5XK66rlFKkWUZZVny7NkzrLU3GqVaay+8WvI8J0kSmqZBSsl0Or0wgd3Y2LjW9j5PWquUuvAdWS6XlGXJ1tYWOzs7OOcugMfP82paG26v/xhjLhb4L9dgMLjmMfOLVJIk1+RTX3RcL9bp6ek1c+0kSbhz5w55ntPv979QSrW3t0fbtvR6PeI4RghB0zSUZXlxDfv9PlmWobWmruuLc1pVFcPh8JpPUJIk17yhlOyRJa9fvUdDYL5cXBsjpGYbl86p003maY8m3YR2RvZiG/WeyWRCHEXkvZwsyy++exxfZTmdn0+ueU5JKzl8KinKjLbYIhYRvbjipz88vfDsGQ6H3bmRPZy+eo+2bXutzxNCdOfxhfM8nc2QUtDr9VefmZOaU3z19OoxKYV0I7a2tsnznMV8zsnpKaF1cKXvkOB7fFkg9edIn8LKj7CjUcP6QdEZlxEEcjXpuMyiuZAx0UUMrz9KcNlwJ1x5z+Uvso4SFStJVBBilRQiCWiaSDK4M2Txs0eY1+9xMp1ye/s+AySlk6j3C4Y2IjmTPFsc8EHP883v/Q7yvqGsJyg8H3/8CTqyfPrkkB/+qOaf/P5/RzaUtOGMP/qj32Xezvj0o8fcTlKc8Ji9AWkeOD44ZnECO7s76O2WtnfGcJDwQO8wf3zObr5HHZ+jei133h5RbFfMFpZ+v8fR4zlRbLCTmidnn+I2BlRJwk6kQbQsa8tsWXHyfIrzE7JYkZuI0bBPOa9oqxJjNLZu0Tpm2NskjRwKQxSB0ZLZdIZvBVpEFLOSqmqI4pxEGyIhiVLNclkQqwiBQ0hPFglm58fMzx3BBQSK4FP2NnO2bu2yf3DAtIZYGXQkca5m/myO9IIqFBilmcxm1HaJCxYhIUpT7g76PH32aGUwmiGE53D/HEmKGib4EDo2TJpwNp3QNgn9NCbOI3rjDBE3LLyjrloiFVMpT9PUeGtZHM/IRR8dGdIsoVUWoxWKCB0JZN8zP16ACEgVGA36aKU4Ppnw6PlTDv/9Idk3d6AFX86R1qPxnB0vaPwCZxWjaIsk9Tw/OEKqAYtpgW+gqmA83KVZzNA6YG3L8dFzlNIsZ4HToyl7t/sMshG+keRxj62NiGLq0ZHEYBlFCRuDIeOxY293ROVvkeQ5ykiKWYGXluAkRbtEhc73xdMwaye40DDId6jbCYmRHE0/5uxsjm42yIxmtNFn1N8kFymDxBBtD1nM59imJEFxf3wXO9PUxylJUERjSyGX5Jt9Bv0xp82U4+KIxyePGOk95gePmKQ/ZOvW6+TPHUkxZjpvKG1FQCKUwnmPC11EstERsTAgJFVtcd7Teo1S3ep96wLzaoGONR4QIaCAZTnn8OQIJdSKyh1RtzU6MgQjyNIUEWlqaqqyRSmN0BExEpoaZTSD0QYSiasKiqImeIlKJcG1KB06nxMMy6WlriuKZYvJ+xgE05/+jPnzfcbf+xrN9pgo3SbrxaRCo8RnA5+1h0tkYoyBunYdCBNLvHM0bYOzDucdRVFibbsyj+36vW4wpjrQRIFSEqE8yhiyQdzJPenYKvhw0Xl2viNcLKp513neeO8YjgW+DTgLzje0tiVKh+A81nbH07puYKEIJLlh6/YYlEKsPns9llp7uwcH08mMyETkWY5YGUuuV9lDCFjbEMcKHyqkNGij8bYh+MDJ8YyqUESjPrY1uFZSVS1Pnz8jfmVAFGtcZZDacXZ2TLQpSdMBIcRYG+F8DQGSNMM2BSE4lOzWMpZtw7e/9jY9aTivJ/R7fZxT3M4rAoFZW9H4lkQrahpGwz6xkuxt3+burVfY3ByxuzEk3olZnjd8fft1iuWcx0czNvYe8rVzyej0Ec/cEVIHpuUpRgiyLEVKQHru3t0kMgmboy3aqmQxX3J/Z8zp/JR5O8Xi0SZBSkVDgxWWtrU8uPeAW9ltDvcPkUDb1tRtSZxATycM0zHxIKOsK04nJygZkaU9WsCLgNIKhWB6tuD3fu+32X/+mLOjChqNDLprJ2LVVi4Z9Hfy4bC6uN0PKQTW2a88an6Daj6fX5uE9vv9XzlQowKoS7upNFQq8Hz6lEGvz63NPW7tDbk77BM+sbxeNtx6AMEfcfjuu3z01z/l0cEZ9Az37u7xNSHIlOGDe5JZ5AicE8pzfAmbzTEq6ib239i9x9u79/jZuz/l0QefsLk1ILq9zSjfIRIp54cvAA2y5u5rd9DR1e39fv8FI0hBW27w0bvLDjBYlTERd3blZ2PVVZ3sxytG3OVKgRcnwevV3qs1mUyuGZju7Ox8qUjexWJxbdJxk5mulPIaKBBC4Pnz5y9hQBtAzeGllCIS3AjsVd+fDqh5wSBYlEAL/PqBmuVyeY1NdXZ2xtOnTy9SmgaDAVprzs/PiaKIvb094DOD3BerqqprMdVroObF/vMb3/jGNanNTX3s2ow1jmO2tra4desWed5D6g8JfgksWTb7LNeHcwX7EgQVQzDAZ5NwKSUbGxsv1adPJpOL7358fMyPfvQjnj59eg18WqcL9ft99vb2MMbQ6/XY2NhguVxeRFcbY9jY2LjW9j6vjDFYa1ksFjx//pyTkxNCCBdGsV9UQogrfd5NZs/ryrLsRnnjl60XjX0/r9q2vQbU9Ho93nrrrZd6/4MHD65tu5xktY7QXpvnLpdLzs7O2N/fZzKZ8Morr1xLQdvY2LgG1Gg5YDzY4TINJITAcvoM76+e216SEFHj6hPK/hhfn5BHNVpdhRVa2/Jh9SHDwZBbt/bYu3WLjY0N9vb2iF7wAfvwo4/4uP34yra6qdl/ug8YBAMGKfSTwHs/+Y+kSUov3iPfvsfOaIdYzljIqyynZbGknF1lFwopGW/uXcNPpJ8iZZcEO+wP6eU9fH1C2b/a53sfEFZzZ+fb3Lp1i6dPn3J6+BNC4+BKG7yKbfyy9cWMmrX3TLBAQEhJWVQX1Eq/8jtwzq8YM/4KUBNYm1OGCw8EH/ylCYHkM9reVdlUICBepBYJQSsMM+PRusfoI0HYzvi93/lttqaK83ffJTpd4JWhqlpa1/Jsf069mzM5Oifppdy7c5tRKnG0OFfgxIKzaeCn7/yI+3e26I1S/l//5t/y7NMDNnSfvX/6e5xVU+KDms1bW5x88ITJ44rqtmLwwPL1N9+iaSskMa1yzNUZwWmsdQx7gtdf3USaHnEsePZezPzxnJPzOYoZTVuT6T7L2iMiQeUs1mukkEiVEhxIkxHpIXdv52i5T112qLYNgs3RBnGU0tYNzjY4a2mXhszktEVJXVW0dUvPZDy4fYc0jZlOZ2QmoT8YYrRmNp+zbCt6aY+z0xOayjIcjMnzAcJqTp+dU84sAzPA4TC6S4rSISHPe1jZULqWyltaVyNNwHqLlxAR2NgYYCJF0zjaxlMt5zx7fMhsMGOwkRMrwfbOHk8WH3cSDCnJ8z4n9gyZAY1FO9N5IhVLYqWoS8nk2RynIM4SdDDYZUMUd+1Ftpo45JRtjVQRo/6IJO3a8d38DuV5iWg9aS1JRIoIjoqCcrokEIizIdoFNodjJovHTE+fM9owDKI96qLEKhhv7/K8LYm1RumIxVSSJ9vMyppyZni0mKP8BrHM8ZUgzxJMqChrRzVtWcYFkTzl3dkfM1t8Qpqq1XxcMcpHpDKhagWnJ6fEIkYIQ+sCZVWAsIx6EOtsNcn2aK2IhQTXsDg/xy0qTuuWRFkcimXTUBQlbVuxrDy7dx+QRwOev/sJu9/ewuSKslyyN36d1+4/RCwCsc05Pjxm0S94Lgsmk+fUlSDUHfshDxGNbVBa4ICirJFCkuiAlgYpIZGC1gaa0OBtiwsWaVpaDyqJ6PViCAHfwn/+8/f487/8M7a3Rzx8+BDnPcvFgiRLqaqC4bBPHBuUkjy4d5+7dx+SRhH9JCWuA1VboWNHnmaMdY+mLJhNXcdoWfVDUmma2lNVNVJrdJyC6gyW3Y/eYzqf8tFf/yXD773B5m/9Pr2dV0hlgo/AtqsVFNEB0D7UOM/KU0YjpSIyMXGsVyC2Qq5AkOA91nXH0smnPmNFehdoG9u1XaNWoIxfyaAcWit0py9C646dCBBJDahLkdlruWjnOROcIPgu8Sh4sG4F+niPiRUm65KBrpgHd9BZty0EmrphY3OMiczaNGzVF4N3HghIHWFdC0J10i9i5ouG+czifYrQSedr5DSRjGiawHRxzs7WBtINwS6JYjg/O2a0IUjSAVaBdeAtVN5hkoxycUJqIoLtjrI/6pMbTao2KGxgtLnHnb1XaJqGT54/YWNni7ooeP/Jz8jjmLe/9gbf+uZvMRrdwpWCanZE65bce3iHuKcwZpfXvz2ivzWC7QWHjz+hqhvoOzKVUasu+U4qQe0bXnv1Db55/7sMkm3e+9GPaIY1Jo350c/+ChVppDEorVlWc4ILGKNxzrO3eYs70T3uDh9y5/ZtPv74A+aLOePtAXVdcntnl599/C6fPHkPIkGc9BDKUFiLxyFF6GJhb93i7PiMpnBon4LtQHZBJ29DdoDfGutDSAT+wqNmtXKCUuorn5qv6kpdG1qKz0QWToDtQhRpJNQKgg5UxuG0A+9pleP1+1u8+XCHH//kPX78zifcv7fBne98j8FvfZtZdBVU+vjjjyln0yvbymUFqI6tcvnPtWFre9MRc+/evWur6Mf7kvf+urjyGd4LTk8m1ya2tg037Ovla2Nj49p99eJk6Te+AnQgleLKNbjxOr0oFfibrTRN2dvbI89znHMX8dMvprd80fvXErN1fV6a0Xw+v8Z6Ozs7uxalfbOPUKAza/w5JysI0jRma2uDy+f6y5rcnp+f88d//MfXtpdlSa/X49atW+zt7bG5ucnt27e/tFHvi7WWM6VpSp7nZFn2G2Ps/XnV6/WutZ2XZdb9zdV6Hv5imw5cu7/hs3v8Ut+cxPk1ILptWybnc4bDIVtbe2xt7nZR5CrmRYA7S/tsbV2Nu6/rmra5Pj75F//z/4U4jhkMBhcR3I8fP74ADdeVLpcEf3U/Sim+9rW3r9334/EYay2np6ccHBzwySePV4Dvi32Zv/5cung+/eoXvb7wKeSB7jnTXUBBoG1avAuoRHUTgBBu9KpZAzb+4vHeAT8qKAKX3yOugDsvpkd1WU9dRHf37wa85vTpOZkNbJeaext7PD74C+pmwuRZQTqIkcOM46MFz9sFveEWe8OIt7/3Bv1hTJIkfPj4U/JkAEfw5NN9NgYNvdGAxhvODufUS0fv3pCTsma2rFGPG1zZkLghu8MNRKtYPFqQvZEzLQrOHy8Y9jfRI09RF0gnOd0/4OD9ktt37rJ5r0+2CZPDBdODBb04BrfktFoyOStQkSCOI7IsIYkMvV5C2zbMynNOPjokNoYojqiqkroNCGUIkcI5j7UO17REOmGjP0TpCOcDWilc60mSeLXmI8iTCKlihHJIaRluKHSlKFWEDEOKRUkSG3o9Q5oqhMmoQk0VPLZukEIzn83QJqGoPY1vibOYJIHKOUBiW4uKSoL25P0UvGdWTDHSMEgSpI5oS8fh4pimrvjuN94mMilGRxRVzekHH9MmDcO7I6rTJVr30H2NLj1xniBjRZ718QtoS8v0aEnb1vhJIEsTQm2JlSYoTZLG2KZBpIbBQON8wvPHhwzUBvlxTTrI6ffHnDaeumjRWjEcjWhsQVEv2T+ZoFSPuqjxXiCV4NbGNvc299jeyCjsKSdHZ8hgeLh7lyfuiNn5jNzEjPvbGA91a6nqmuAdwtUcH84pljPu+ZbhTkyxtIjE0LoWW3lckCgvGeoeS18xX5RYp1AYYhEzGvQY9wbkmWJZTpAoUmWQ1tOWAudarHDdlM1ojJDEIsebiKZwlIsJdVUwGm9QLhrqskA5ixIpk8USb1rKYsHQpLjM4SOPaAZomULeonxJLvpEMiYISWQMgUARSawN9LKEJIrx3tO2Fg80SKbTunOf0h6URquAFJa8F/HRR0843H/G9vaYjY0tJpOCp087uqFSimJZYp2lbRtaW/O//M//J+7sBSq/wClDHBlGcY9mWbGc1WRJiokNac8QG4WJelRNjSbu+iTRElbJR0FI6pNjssMJg6IiOviIgx/8NYfvf8p3/+//D6LkFq5ugE4LbZ3HhoB3FqchSWOcheW8XIEwAa0VWEsIrjO4UwolwBh9xTBdiK4XNtH64dgBJyEEvNF495n571oK6v0a9O761nXcN2LdU6vuUaE/e2CEAPHFXi+63vVfn8lj8J1vlRDY1gKOwcZgFdO69qfpemLnGoSOcAzxpLjgCEGAg6eHT9m99zpt4zEbCZEaMj2ZcvTkGBU3HB/u88bXX2V7a4c4Mehg8N5zdjZlc2uVZiUkQRh8o6hsQOgMLwMmS+gLxdnpY4IXCGEQ3hIrQz7I2etts7W3zXBzCxckextbeBl4+zvfwsQ5KhlAJEilxJ0eEOmS2i5Z+ppektIWx5h+4Ovf/Sb7/+mc0c4Wt9N7vPfhE5J+n9n0jGgk+ad/8D+xlWxT1hXf+V6KSjXvvfMTvn7vG3x6/ojbd24zn57zqK6RkWJjNOI8KXjj3jf4+u7X6Y82Mdrw6v23qJuWNI5pq4rTx5/y0/OfsjW6Q9tagjI0xRHaKLxzaKVovOX2gzu89uAujz9+ysHpguDaCym3QH7mbxD86klK50EkAki/agIrOdT/Dj0OvqpfTQUBtYZFBG3UNbdFDM/rBZPzGc8ry87dEb/zP/5ztv7Z/4H/cGqx+ipgcXZ2drE6vK4v6/8Qx/ELk0hBHHl4QdbjvWd/f//aoH1tTPrLlBCCnZ2d6+CPtb8RBsV/H2vNALG2kxYfHh4C8Oabb770+1+chIYQbmTfrMGGy3VThPyLHp6/aOV5zjC7fUV693nH9LJVVRVPnjy5tr0oCvr9/oWkZjKZXEyQf5X1+PFjHj9+zK1bt3j48OFFZPpvcq3ZW5frN/073VRpmnLr1q0r29q2ZblcMhgMLkC+jY2NG83a+/3+tfvh8+Kx3377baSUXbBO29K27YWh9OVaLpfXzr0xhu9973vX+vcsyy4i09d99U3Sqb/p+kKgZh31J+RnMZ51YynKijjpDA2d61ZZQ/DXABfvP5tkrE99IFwDY26K+l4bIV5U6KwuvRCYAMeqZvztHjSfsHucs1gecPv/+C0OXUXxqEL+tORoPmX0yn3+2//pj0huBSZ+n/c+nRNEig0xB5/s8/4HZ3z6yTHtbYOIjphOplQLh5EpSqdUDSiZY6RCtEmXzCICvvHEIqI4bmgAU/VJt7qJe6YyVBxT25b6UPLpX0748AfHjPdycA7Zdud0Mp/hmkBjPHESo1VDEzvGwz6tcpS15Xy6YDqdoZUhjjRRFKFNzPn5DGYLhhsbpGlGr58jhWRzc4SQ3WRSCUlwHQ1eKJCipp9IlNIU9RLnK0SAdjlDesXOZsYsCtR1hWXGyeSEsi1YVBXCSLQUzIs5LQ3OO5TXLIsSS0zhllhfo6RBSyBYXBuQeDSBSAhc2xDRTWKV0UymJU3V8PTZPmnWp/UNg3zAyfEpPR1RHc4oTuYkqURaT+Y09bRkWbfEKqawFUJ4zg4nEDw+80SjCOEDy6aibi1xmmDbFqNT0tzwfH/B6fmUugmoSLM9GEHlyESCURptNImKWZZnHJweUTaQ5GOUd9SLCVsbQ6p5ydnRY9781n3+61/9mMlRye3hQ5598AmjWwMOn50zHrzGw63b9NKIZ4fPqZcLmrbCe0fRNpydzDmaT0kTxc6dLZqmxYWWyGhcDe0yEEURuR+iVEa22UN5TVM23N7ZYTjI6eWGRX1OWVRsj3bxJdQLT6IDw0GnVxUeZBA418mFjk/6PAj3eXLyjMUs8OrD1zmZf8j80xM28y2CmFJPLUrFqHGKaBvs7IxcpIzMgCYu6YUcuXRk2jPcGNOU0DQWbcCqCmG7CHlCIBJ0YI5wpIOYtvU475GRWMkxFKEq0cDZyQmtcMxmBW372YDWWYdAorTGGMl4PCRNYoRvMVqSpwpjJHkaEycxysRIJJVtaZsGbyuK2uKcJNCgtERKS5TE+IXFNQ2L5weI8ylhukQ5yzi27P/4Q1xR0KQOIwVpliGEZDZb4r1HKoiiLoq7sOWqvwyrlDyBlKC0RKkuklsp1SUmEVbJSapjE3XISzeR9h7nHVKqDuzR8kIKenOtgZbP/t+xfl7oV28YC774QAyADLI7liBomprWWdI86x5ofqWXWT0NnPPUlUNow/HJOdbVmFSznBdUzpFHAe8bTJzRSyKyRNPEBk/L7OSM9995j8XdGa++8ipJmpHGMZn3LJfzTkIXaYRQndeKE5RNSfAtIlJESY4NAnS8Wu8xSKmpyorxaMRgkKCjmNFozHg4BCPRqSHoBCcVHoHOU9I2pQ4SYQyRlAjbMj89pZ/kDG9v8vrrb3D7zQcsns8Y9u6z9/AhP/mz7/Pmdx+yO9rh8PCYjw4/5vCjj6jxfO/t7/HKG6/xWvVt7uze4v/3//3XHPhT7u5tsRONeK4Lfvs7v0+/v0kQEdoJ0t4QEHjpkSGQm4Q3z8/Yur/N/Ok+P3zvh5xzRGQEaMPW1g6z0wn9rEcxLzAyxtXzDiRbNQYZLrWFFTuqWxULnaT4QhElPiO1flVf1S9RASg0TCXMkm5xbxrD43rBpCl57fde5dvf/iY733qDWaj52YcfU6irQM3z58+vsQ3quv5S7ICDg4OLyfi6FpMea8j64vhXpqQv1t7e3pdiwGxtbd3Iqvj7aUD6d7/yPGd3d5fT01MODw85OjqirmveeOONlwJL8vxmtsD+/v611373u9+91nZu8ilZS35+2crSjI3+7hWgxlr7pT5TKXWN7bH2n+n3+xcMhsFg8Gthujx58oTj42O+9rWvMRqNVl4mv9mgRr/fv8bu+/soN07T9EJauK62bZnPO0bN7u4uu7u7pGl6o49Ur9e7dp7qur7GTBRCMB6PL+Rfh4eHnJ6esr29fW3/y+Xy2nMkjmO+/vWvX9v+zjvvMJvN6PV6vP3227z99tv85Cc/4a/+6q9+qfPxq6ovBmqcQ66YLCEEpJA455hNZ2xsDLrVWyFwznU+Czc0vMsn4iLF6QWg5nJd2S66Ccc6ZaTbpkDCVAvKtzYJxTnL5+eM/ul3eDZoODKWkCUM+il7b99m583v0qQx//o//yn/9T/8F06OF9y5d4//5p/89/zbf/MfuHvnDt/+5vcoq5J3fvoeZVECgl4+ZHO8Q553EiETdf4uAoXw4NoWE0mOPz3DiRrrBO1I0tvcpDyfMi9qbBnQrk+zqPCN52y5xNqGTOS0XrExug1e0vpOhhFCN5EI0iCEWk1WIpJejlEx8/mcRe148OAWk7llsVwAS+qyoZ9laKUoFnOSNAUcwXryvIdSinKxJAi38hQSNG1FUSyYnp9Rzhr6w02qrOLg9ASpItrpMbZuCCJgQyDPcrySXdJN3hl2aSMZJTmNrXC+IukbXNMigkCFBElndtnrZwziHovJgrq2xHHEoigQSPIsZf/wkDSOibWkbVqyNGeUpnhq5r7AlQ2Nc2RJTpZmPHv+IcvCEqseUhiMgrPTU4L1pFGMUYLJ2QQpO7mItQ7vA7aBum6RUqG0RimDkQnjUU4TCirbYGLNZDmnLi1V6cn7e8RJgq1Oqcol2eYGZ/MZcZrxl3/1Dof7C8I8xouM84MDylDgheb4/Bgqy63NMVGQbOQ5ovCcVXOWtsJ6qBY1romQZknRLrC+pd8bcHu4y3DQZ7e/x9JULOZLBsMRedpDysDWaIDwLVW9JI92sUmLEgpnLNnQ4IRlUk3YnxwQvKeX5/SjhEgJXnm4hfOwuZHx4aMnbO8MONhfUJcV5cGSMKhQOiLKFDaGWbHAt5aej1EzSVTFyEpilwVCarY3EkavbGJrz9nphNPzKfPlDN+2EEJnDiy7iaNwgVHao7WB2aLECfAotJFsbYxx1lO5mtZ6BoMB3/jG20wmE7z3PH70iPliAQSaeoprl2xvKaazY2bzgNaGYi5RkSTJ4k5yqQRKK7I06VJtnEYET1FbyjqmqBdYJ4llTtU29B7eofjpx0gX6Ec5mR6idIIUkPcylFYslyUhOLTSaN2Zw66jUC8nInV9n4YAVdlgIk8cRyjVyT09XZqdbS111axAq44P40MgSSSXge+b6qbn/GevX83Ef5G65D0jAGUMD197SJqnn32p1bxfBEFrPdaDtNDWgaqy9AdbTOuK5aLm7OScfi/DCg+5Yj47ZTmfEiUSZRWh8BzvHzMablBnFlpBZnqkcUZVNfSMRiuNxOGlQvbG1NUCjEZ6iJIeeW9MY1tiHREnfbSR6Cilsi1pmtKEQNzLEErQeIui81HSQhE8CCOJezFGd6ai3kp8FCGVoBUN97/2AELG5lCzdf8e+dYWvUYgdeBP//iP+fF7P+S4Omd2fsDg1i7/3X//39LXPUwdcXr6nAf37nLr/l22b2+xIfscTM84ePqY7y9+wG//1h8waDujY+882mhsAJGmfOcf/AGDzREn0Ue8/8lHhIVDCxCk5H5Iawu2ehs8+vh9lMjQIoGwfOF6dqbPSkhcECs8RnSsmkvt6u/jgPHvc60ZAUoptNYXP/+mqjOjDlhrqeuaRbmkyTRLFXOeCYKXFBsx5uEmr/Ye8t3vfZfRaMRffPxT/urf/TE/abep5NVJ7NHREVV1NWEpyzKGw+GF/846de9l6/Dw8Br4o/xtjHntikdNCOHavtfbX7ZuOq61seflenGi/quoderN5eosCa4zd7TW176X9/5LSR+tc4T2qvzspn2HsJL6+PaF7S93njuG7s/z1+lqfV9cLqXURRsKIdC2bSeraNtrE7ab7qnrDK0u+eema/+zn/3s2mfeu3ePN95448q2k5OTzmj1Utv+RdqdVLJjEFwCar7IuPfF363BpPXvyrJjBb/oERNCoCgK0jRlOByyvb19o7QQuon1+fk5SqmL83h5zqeUumIAvD5PYbVoNZlMePbsGZubmxRF0S2KfUk519923RQZ/qv4zJvaXlVVtG17cd7W7f3Fe9xae415JaW8do+FlTz6JuXM+ndxHJOmaeft8oJvj7WW7e3ti9+tQbeb7rGbztPaMPjFOj8/5/T0lE8++YTnz59fMLyMMRffK4qiC9DROXfBklFKUdc1WZZdMHC89xwfH1MUBdvb29y+fZvxeMxsNuNHP/rRle+9vo/Wps1fhsH2MvWFT72wcvtfK9dc8EilWK5uHiXVijnTSV7WD/D1l1nf/GuA5jIII+V1YGfdkC5PUHzw+NBp7YUQaCvx2lGVkoXXqL0tcin50ZOnDPI++n5O4xuWu5J5vuRf/bv/N8VpzMHRlDSMSQwcPjvlX/4//yVVVbM93GDZzDg9P2dZFCRJhESSRJp+L2U8HJAmCUKDMhK8RPhAEsVYW4MI9AdjfLCE1iKmnlAaVOHxdUtZF8T9Ht47gnR4p0ikASURJGRZzmAQ03poW4GwoFQEdCvqkRFsxCOyrMfmLlRNw/H5Ah33GccDosigkXjXMRfSNCLLM6SsULJbxW/alrotmS0XCKG7CUxd07bQ2m5yMK8tWRKj8j5SGYRvqZs5aZpjRKB1nedPGzyxNl10u/cM8h493cMXkqKt8N6TqojUpGij0dKgkMQ6ZnNjDFoyXSwRwhNHiqbtNLon51OSyNDWFXuboy7dpHVkSca8doTaozUMo4zX9u7ysw8fYX2L0RFRlBGZAtsGiqIheIfzAqMVWZ4RQkVZVCAigldEUYxWupOQIBn1h1hnQAUqWyNiSekKqsQRJyOQHqFSUpVx+PSI1lue7x/hrEWpDZpG8Kf/8V1eub/J/PyMeKg4LSakOmde1Qz6CeN8h+cfnlE0FVprMpWwt3mb6eQYUQu0TWiWgTQZUU5LxuM+wyxhM04R2xukeYI2kqpa4tpzloslk8mCxsV4WrQW4D3BeWonO/NvIfBBMC0qioUl04pe27I5HPG9r79FbjJ6mwNOpnv05YhdeYdECiZ5xXI6Y//4KSDRIaCWgXZWo1pFYiLiQUrw8OiDR1Q7E7QWaBlzb2+DZ4ctrmlJk4SiLjupo1BkSYYxMW0biCPNdLkgTjJ0pDicdMaxxhiqesliMefHP/4xvV6OMYY0jZnNZzRNw+52n9t7Y04ODymWc+q2c/43ShOwOGrapkbGmu3tPXpJSh4rUtXrgJvIcHR8ihea0ko2x33mcUr58Dbx4Yx2UjJDkD14kxD30ELjQwd8htABLlJ2aQVVVSFVd9xOdLKl9QMoBE/TtCgl0EpeyJ5CcBAE3oNtfZcI5QNKCrQxGCVRat2XrhMMPns4XHiAhauDsl9kgPf5nT5rczGiOCJO40uMjHDBiCSs9y9xPrC9uY0QgsnpguVpQzVzGJ0yOViybI4Y9KbMz2b4FhoXiFUft7CUzjI7K/nko6cIL/lH/+AfMhj2EUFQFjVJHKG0xLYBIWLSTHN8eoSrWsa3XmV77yGVbfDOEXxAykAUacrFHJNmRGkO3lJWS5qmIU0lMkiCbLCupUUjTIakJlAQpRlBGJwTBClIN3r4JkGICDMagjJkgwEffP8v+eD9n9FS0jOKkOYkIaWe1vzpn/0Jnx4/4tWH9/jn//ifI6oUZQyLxYTF/Ih/96/+N85CwZtvvEXPjWh81359KQhKd5HnsWZ+csy8OqeJSxA1kVIkose3Hn6TH58t+cn3fwSyWzhpzhKU0t2ka+WjdNEewprR2jFjRRBcpN6sFmLELwrqfVV/a/Xs2TOSJGF7e5s7d+6wtbXVPed/zYPFdcVxJ2s9PDzk8PAQGVr+4Gtj8vGIA7pj6L31gJ2NhPfef58nP/hToOtGFjblg08+phVXgZqDg4Nrx//P/tk/44/+6I+ubPtFInHlavzzWQnyrMf28M4VoObLljHmRqNSa+01D5Jfh+yp1+tdM2+11vLs2bMr29ZyrBfr/Pz8SwFIZ6enFLOan0fL8851xySvgmIvy1yq65pPP/30pY7p1q1b19gv5+fnF8bLxhheeeUVgBsZMXt7ezeyOG4C9O7cufNSx6S1vnb9R6MRf/iHf/hz9/F5NZ1MmR5/wMt4Ke3u7l5jwCRJwgcffMDGxgZnZ2f8+Z//Od77a69bp5jN53P6/T7/9J/+U/7xP/7HRFF0zbT5/fff50c/+hFbW1vs7e2xt7dHv99feSlqjDGdMkBrBoPBBaCwHk+FEIjj+AK80lr/xgM1v45ap29drrIs+ZM/+ZOL//f7ffr9/o1gdFmW19p+nuc3grZ37969dg1OTk6Arg2v77W1vPDF2t6+ajYuhLi2DToj7+l0em37TebUf/Inf8KPf/xjPvzwQwaDAd/5znfI85wPP/yQd955B4C33nqL733ve7zxxhsXvjP7+/vM53P+5b/8l/z+7/8+3/3udy/a91tvvXUFw5hOp1RVdZGQlSQJSZLgnOPx48e8++67vPvuuwAXNjC/Dr+/nwPUrMGSz/4vRNdh1nVNptIrB3UZjLk8qbgJwX/xot8kf7rYv/edP42UaCFpCLha8OyjY6IHA8IExu0Oi5/OKIo50+Wc6rHFbG+xnFvaKcQyZjTo8+x4n+lkTpqkZEkPIxP6I0UIDucatBR4a2nKJftPH9EsJ6RpQtZPSbOYNOl1ifHeIUTA+pbZNJBEmrYoaUPLqLdN6j1OVCgjEZHpVr+lIEpy8sjQ+pbBcIN+1kdbR9laagO9Xk6/1/l3mEgzmU4om6bz0WgsSEWvFyMCpFHCoN+nlw/oAgsscaw6x3bRUJYFznpEXZG0CitSiqICkRNFOUkigR468RRNw6SwZP0N6rplPitxNqYoJHk/o6zmBMA2LQqFc7bz62gFm8MxFYJ2NiXPFBESKSxCdqk0rWtobUMIkuFOn3SUEJ8rTk/OuojZJMILRVUWNFZQNjWmgVR2iUJIh5YSZSTeWW5vb2FbzwefPme5XKCkRmqNa1sa61bGSoLWWqx19HspAklVdJHMcRTjWkdd1cyWc1q7gQgOv2KGSesxTjOMRp3cLrQYGXNv+zXeeXpOZDyuCbhKMOiNsKlk4xt7bI8GTIqEyhdEcYKRhrZxnJ5OWTQVQSqk1mRSsZlvcmewQ4rFOs/GaJvgFJFKOJs9Z3I+oxgU5CoikgpsCRLiCApncQKcFKv434SyWqzQc8OApGN2eE+kNf0kQ6AwMkKGTnLjvOXb33odY1JOT95kWs/Jiz55FFPWj6mlphItoa4RQaAtmFagg6YpK/qjPg/vP8A7wdnpCZPTOS44+oNeZ2AbLFpnJBisb1d9giOEtksFiyUjlTFfLOn1x9TVGUkc0dqSddxoUSyZTs+7dKTQSW0A+nlOP4+RCLKkz97eiOfP9zvj2cqSpAmxUpjIcGtnl8xEDBPNIO0zL1r+P//6P/C//qv/wP/yf/2/YbIR3isefOPb+GDZef27pIuK1lt6b7+CMmnHfLEO5yzOeYIXhNC1rc6PpkvaWa8GdJGSzYX0M4kzkqQDPLroa4UPgbZuaVrfMRwISG0wkabD2D7z9uq8fjt56RqcEZf8ZS73mZ9XL8OauBCoijUk09FnxIrWGC5P5gVEkSbPPN4LnPMI35lHb4+GbGQ51lmK5YLE5rRNQ783oK0t3gsUkmW9AO/56J3HFGXBa6+9ymSywIgYozVlUdI2lsEwJ4oUjfUEIcj7WxzMjllOz4k17N7ZYTTMV1iSJ3gLeUac9giik+dGOkajwHpsU+J1jVIpab5BWXi8K0FYWtetwigZoZTukuTSDJsBUYr30EjLeXnGeHeLycEpaS/m1Tf+AV9/5bf48C/f45N332dqSnT/m/gkJ9YJzbTiL3/wZ7zzyZ/RhBlJEuO8JTLJyldGID244BAClLe896Mf8OMPfsBR8xxHoF60/A//7A/oVRHFtGAmFzgsTR0TbHLx3L18rddJYeJSW+m8keQF4+ar+qpetm5kQQv42f5jnpydX2lNdV2zfGH+vawcj54/xfmrk+AufvsqW2E6nfKTn/zkyjal1I1xyC91vKvuq+s6f/Xt/m+Tmfay+/5NZs8ppV46MetFn4oX66Weh38D5/TzVAW/rvoyx/qyxxVF0cXkff1nPB7z5ptvcnBwwDvvvMO7777LkydP+Bf/4l8wGAwuIsrH4zG/+7u/i1KK1157jW9/+9u8+eabJElyAQx8VV19noLlZaPZnz9/ztHR0ZVtm5ubFwDmy+zvpu2/jvvmmkw/BAaDwUX7uvxva+1FPzEajW70UPo8Rc9N+1oul5yfn1+Ajnfv3u1Y/o8fv/Txf9n64tSnVVyhD57guvU4ISVN21AWFf1er/Mq8O5zfWbWk5XLSNOL4M1l0OZFn5tOWuU7b5gQOlsHwAaN8BH145rz4zmTg5Lp8pRsGSN3U46eHHP6aMkw32P74SZHx0tSHZH3NikKR9tYaiom5+d87Y3XMMowOZsigyRNMjYGA4a9AYmO6ad94ihGBk9b1UQmAiSobmK8nC7xWYTDoYWmPuvkEbUtWCwX2NYz6g0pq5KWc/qpIUoSQuOx0RJjA14KnBCQCE7OFjRNQ5amNFVFYx1F0+BFhDEpMqyiJC1Ir0jjjCjWaC3wvkFJidEjIj3COU/S1iTxiGFVslgsWC4qVGSITESSbtI6TxsCy2qBkNCqFh0ShBUgFPkgJ4ly4kRTlEt8CDRt07EYpKKtBInssz3M0QHqZUHrKtqmW90NvostDqLFrzxqbt++TR7nHB0e44MkihWF0Qhf03hL2QjyPCc2NbqukQSCDDS2Jsli9nbGNK3lo0fPccF38cPWsixKshXi2ctSmqpG9WMUimVRUi5Khr0Bi/kC7zxVXdJ6S7AV3gccgul0xrNPjmgC3P/6Lazz+EqxnY+5O7jLaXGMl4qgBbfjXZahYVLO0UjuDO4xr05Io5REZSgLQkUM0oSgFXXdkKcZfdODypHKnCSNyU1OkmXkcc5GklAuFmxEG/TjmNRolPJYKhZVzWxScTYrqFpPZFqcF7StRyLwwuNDgZay80OKYwbDIXmc4Syw8npZzOcsFufEKmK3v83ueIvdwT1EpMjPLR9xxrJxpMHgbYstA75qyKIIhOBsNqM33aeXJYzGOXkvZzKd4YUkiBobLOfzcxAw2hjQz3tdgpEyWOeJg6Ysq46JRst8OaVuK+r2Mz2qlJ10SQhoawsrsHZ3e4udzQ2UsAglMFFMlt/HtgFvw0qW2LKx3WN3b4tISmzlaGzJD9/5kH/17/6Mg+MpP/npu/yjP/wjgnXEvQEyNkS3MtI0pRfpzrjae1rf0hQB5+wKjFErL5qAlF3SEc6j1GerqOuVXLEy+W2dxbtACF3yjm0djWsQQqz6P4GQAik7MDysM7gvusl1n3hlI1+0ivnzpFPX37D660Ke6hBSsXalWffjnYEvZElMEkeXgHzPeCMl+F287byFrHXUradtapqqpq5aqsoyOV3w8SefslwsEJWnlw2hlCznDbEoGI36JHGKtZ628USxROlAWbe0rUBHPaKoR1vNeLZ/yCv5A6LEgFBINPnFCq1AxgLvWqRS+NZS1RXO1sSJ6WR+UY9ysURKgxQK6eKODm88RiukFlTWEAU4ePyIH/7wvxDFntym5HHKnCliGHNrZ4+P/uzHxErze7/7D/nWt/6AKNpCKcfZ2XN80TDo9QmJpZSKoEFmcZcOZjSqBd12RuCxCIzH22xt7VGeVNRE7G5u8dr9t5h88AgRabxlxYwxBNRaKXzhCbe+8kEIWEVzO98BQT50iYzrZ+9XBqdf1S9bAXg6O8WLq6uzSivi5CogU/pODhteWHXc3Ny8xqoIIVxjhaRp+gsBNV/V358yxlxEaH9Vf3drbSi7ubl5YR47Ho95+PAhdV1zeHjIf/yP/5Ef//jHfPOb3+Thw4eEEC7YH9/4xjfY2dlha2uL27dv0+v1/sbYgr/ppZR66XtkbStwuX6T0h/XflOLxeLCqPjWrVtorTk/PyeEwK1VFPiXGd9kWcbOzg57e3vcunWLO3fu/I23xy9m1ACsQZUQUEKsJt6BxXzBznZHuUVwxTh4LYG6SG564ef692uDqot40HBlzRb4bAB54f8gHMILLJrl0nBcB9wipYkU6WaCGdfEW5pb//g+vYWkOTOU00DSGxCLPm/ci7i/eZe2nnWDdwG2LMl0wmv3O1PLXt4nTxL6WYJRqvMyiRRZL6aqaqxzOK9oS4cNnkhkpL0By2qKChDLCBcCVkiyPEKhGPX7tEcH6JUMKDY5oZaUVYUIgo1RHy8E97Z3eXxY8ux4Cm6K8J13QeUszpZkscO1jshEuCgilC2xhziJUJFA4LC6m/hJrbHOsSjm2LZmUZUUVc1iXiKFwTnXxaqHLkWqcTVKCvAwjBOi1JCkKSISDDJFU1tEK4iSmNa2CCFJkwQdGZK2Ic9zkshQLJfUTUPrLLP5bLWqG0B0rIS6rjEYdsZDxv09nh/ucz6fEhnFbNZQNS1ZFOPX50oUSAStdxg888WUqmy4tb2BdZ6PP31CcBCcRxLhnSMyhkG/B66lXBYokSBCl4JlJMRRTBInHVPCB1rraVvP8fE5J6enaJeTZTl93QcZkyY5Ztnw9vh1Pq4Nw+0NeibDOMPR7AwqyzjrIVtI0p1O5uI75koIAtcEYpeyne+itEKJGI8m1SOUh3ZpEaogk4Y7m1uo8SZ7m5skWqIklPWc2azi9GzO2VlB1QoaL3FN0RnyhoBSGts4nA5Y5fHWIoCDoyOyOEGGrhMe9IdkaZ+6dVR2QRylRFlAigVaR2yPd9g/qxi1LcxqTDSiDY6qbFi2nigxtAE+fnLMeJAxHPZR2nBeTaHu9KpeKrQyBAHzqqGq90niCC012phOziIqrHA0dcFkOmGxXNIIj3Nu5fXSpcytDVFDgLax7O3uMMgTZtNzpudzotiQ5CmSTjIUKUmWxsQKXF2hkxwV9/jo0WPe+2jCcO8VrMl4uv8cZKB1NUE62sZRJTFZCBipcCsGS2stznUC0ChSwAp4XOl+ve9+SiGRSlxos51zOOep6rbz6llJmIL3BB8QqovajiKNVp0nVViZwApU52KzXgWWNwEuv7pVwcsfeWFAe/H/9WexOv5LMlbRSbbgkl/tRVxrBAEGIUDoX/T53sH0vCZKY2azGYvlnDxJiI2mmM1Q3qOVZzDoEyeGuq6xviXJDEmkKJctQkqEUSTxABXlBCPxK42uCAIhwirdNOClRMgIETxCGxITIcg7I+fgkSYizrYJvsI7i7VgqxpLjVJgkpzW9ZgcLfnhn/8ZR6dP+b3f/V3qgxkn58+Z2imn0yNMFvPwjTfZebDNK7/9Lcbb9zFtihQ1W/du81vZf0PzpwVKJ9x+7XW2NnYIXoMUuFUSolix7loCr3z3t9l58JCfff/7sLdBc3rKv//j/4QXNSGR6Nbgao8InReSpJO/+vDZoGRtRB2CX3nUdEwpyWfS41/nCu5X9fe/goB8e4zMO2bgupqmYVF0CUufAYcR3/3t30L6q/HcvV7vmqdIr9e7Bsp8XhzyV/X3v6IoYnNz86VeO51O/9aTWv73WlmWsbe3x9bW1gVQMxgMOD8/Z39/n4ODgwsj52fPnhHHMRsbG2xtba0inLcYjUZorSnL8sJv5av6+fWLADXe+2vsm5/HRPu7UmsZ5/n5+ZVEqZ2dHbTWTCYTQggXsrsXEwV/kfrGN77BnTt3aNuWpmnY39+/kFD+TdUXAjVt266mAisavpRdigyBxWLZrZDHGn+Nbt3dVJfNq9Y/O3BAXtneja/FapDdPfgF6/jZVa0/XgqMl9jgqJoYYQZkY4+gRQ1LwvaUNvak+YDeWFDfTqhODelkh0yO2c4TYu27CW5wIARlYwlS4QP4IDsTUA9aCWJjUMqQZAlCOTx0rKLGUtY13gcSYWitJTIDdIAgAwRJGuVkmV6Z2sLO7RzpPdpIlE7J8z7DPCISDamRzKZTjk5PmFYt2nQTG4UjNQl5qvF1w63R5koGEUjTFCECg7zz+UjzlLapsG1DsI5qsWBjZ5Mk0swmJyTRiIP6lDxKWRY1Td2QJjHa+U4fWhQUZQlBsJEPMEnK2dkElSjKquhWyYEF4EVACMlcCbI8p20dfllhk5jWtSyLEqUVqYqoXUNwHtEqYh2hVYR3ASE0cZRy/1ZEpJ7z9OApShmEhKYNzMqGQRIzHoxYVg34riOZTc8JDoyJeO3BHdq64vnBCRJJFkfdftMUYzRxrJFYFBKkhABNVZNnWRdf7QRlaSmrhidPDpifFwwGQza3NhhtjohMwNmaXPWo5hYhYDPbYqM/RDpHXXtiZdgeboB3SK2QIUKICGUUdbnAe4H3Ek1MZHKs75glPmhC6CbrxnTxzZPJnLKZo7VnUhyRRApna5raUS4DtRVULdhgaW1L5TxaRxihyHs9rLJUTY23gbZ1lCEgBaAswVvyPGdWF1StJc5SgnIEPwevabxhXkyYViW5jBjmtzg/PaW1CqUEMo8oyprgPME52kXF2VkN4hiTaBwepMQIicQghKa1nbeRlJZh3oPgyftZB4A4h3caoQzTxZJlVUMsAY9z65jq1e0vPErFlLZmPB6SJJq20fiQInUHnDStRQndxRhby8ZwxMawTyQMQUdoHfPu+x8zLzwixMRRzuHxCfcfvEpL2xnMapBKoIPA2kAdAjhLGySRUljRJRmFld5arLxmQmAV+y0Ivus714ybtg20ziKFR+CQwqMEtNaBDQSn8ZFGSUEc5x1IEwSgrrqHrECcm/GZwMsANy9TlyfwEmDlveMRSB84PT5nMV104LCR9IZ90izugBwhV7lCq2MSdADJJbBHGcFoK+WVN+9QFlvUVUNouyhzi0VJKBYLImMYbSaoVdqURCI1jIaSJPYoOj8vHXceQGIVbf6Zq9olCdeKVSKExOgIEQLgL4yTtcm6dm0DwjtCsoH3Ja7tQKH58ZKq9Ozs3mHn1javvvlNlvKQd376U1gINr++je4bXvv2W9TlgnQwIPiy83FDk41G6LzPK4+/xd1By9e/+1sE0ccFg3Jd23HKoaTHrxYyVBwxuH2bb/6Df4i3lv/tBz/GNw1T5pAqpFAoF2FRCCXBdTeMXD1D13K1NegpBCuWK59JP0JYMXF+cyURX9XfbgXg3v3XGNy5zOOCw4ND3n///SuvjaMef/iH/w3SX/eoedHE8vbt29e8X5qmuZ7QFAQEAy/IqQgxHe3s8raX96a5CcB8Wbr835W6ien+c18nAOTq/L1gIHrtjZoVOv8SJSBEn3kprMvH16/dDaVkwmi4+3NfB1AuPeWXmdzfcExfGs4OMeElvifed/v+uQC6APRqQeeXuM5fdKhfAryPoogsy65In+I45vvf/z4ffPABZ2dnF3PD8/NzJpPJhWH4mlm3Nmit65qmaVBKvbQX0OVj/1V7+P1dvc/XJaV86YSsBw8ecO/evSvb6rrm9PT013Fov/IaDoeMRiM2Njbo9/uMRiOSJLnYHkJgNBp9aX+je/fu8c1vfpPT01O+//3v85Of/OQK0/NvYqHr5wA1DVKsQZWwosQDQrBcLiiKgn7Uv6qLv1gB/gyIedFM+KZ47tWrL3wRgvjMcDismDYBaIMn9QqvG1ohiTIDfYUWFcVojtML+qKPkilGGPJejzY25GmMLB1aVezsbNJLt2jbBq1ilOk6u6puqeqWumooirqT6UgJaIL0eBytDyipUZEiVylJnGLiCKMVrra0taORtpuQeVBCkSY9Ih0wRuKcpXUtjdeUKlBXM2QA3a4mK42g8T3yXKPTwO7GiH6/R5QkRJHCaEEIEu9t53ljNEGCNBodKbTvoSSIVtC2LSYx5EYQ5ymL2ZxhZal0gwia3e1dtJIk2lAFsDJwcnpKuagZq5hBL6XtZ5RtTbMs6PdyXAiUbUPjLQho6oq5tbS1pV1WuH7GfDnH2W6C1K4i3r3zRLUl72e0riVohfUCETTeOzZ6Y4rBkiAgzRO8dzw/OSCMeoxWhlnK0iU7pSm2sUg8vq149f4dRoMhjz59fuHT4p2F4IjjTioWPERxTC/rMTmfkacpcRQRLBzsn/L+R+8z6I25s/cqm+NtTBzhQ4NyBukzgje0AqS3CCM53i8xIaLxniAzjIgJztHogFSSpvGIYGnrgNKaJOmtAExB8A11UdOqBqEFVmqqqiXRMVpFFNMarSVae5TpYu/rJtDUYBuP8wLbOoQWtEFhW4+TktPpgtTE1GWL9RYhwTpP01qKwhDHCaWvaVyFCy0DO2AzG5DGgbNFwcQW6NyRxj22NjYxImInG2FbR5QmaJNwNpnyaP8Z09m0kwahmc+XnM4K3Oq+1zKghemOy3dyiyBhWnq8c3B8jnVtJ6X0ChMlPDs8Wg041nKdNVCzBiA84MizlNhIzk6P8I3Dr+LnvXcdo0Z40kQxHORIAsv5ksmyQUeG88MZx/sT0q0t5ofH9F55wOHBKaPNPfK0T88kpMrgYk0pwBDRWgi2oys0weG8hRCITLRyrJd47/C2GxR2HjSfwdvOeqxrkUagpaMsJ+w//5Tnzx5zdjahqSu8t2gj6Pd6bO1usbm1xdbWDhvDHbKsR5zkGJ0ghLo4LzfjNZ8P1lx5logrP668e/2LNWum259YpVR17IymbDg/mdK2DUF7XnntIXmeEEIX632hoMKzzhoK4oLuQiCgTGBzp0ddxV1f4UIHnoSwSthTSK0Aj1AaETRr4/o46lhI+A5gCHIly/WsvH1WSwurL6hDx0xaP2UI63jqFRuUgNABoQUiFnirOolaSFEBbFPRNFPuvHKfvVf3EMEhTUxyV/H7//ifY/7iPzOMbyPjnHioyXpjKtm1zSKUmCRFuYgsgm/+o3+E0xKdRHgREZRHhxbnWloJRktcaAGHl2CFRt/ZYvrTD9l7/RXeHn6dP/7Bn1DFBREGZSOq1XkLl56thFWymASBBOe7aykA4TsM7cIUOly++l/V3/FaLBZMJpML+nVd1wyHQ9I0/Tnv/PWUCIriyEB9dTV2GL3FP/nG965sk8GQmQ1EuDq539zcvLZqboy5lgqilLoG3gjhUa6BcJXevpHfZ5he/kyBJOZlgYU1S/xyrb0QLleSJJ/r6/Bi5Xl+bTW2qirOzs5e6v03naebWEYhBMqyvHb8Dx48uGYUu1wurxmN1o3l5Kzhcr8ghODhw4er5ML1jiTjoWbUe4mYZmFB3gZx9TqZcB+anz+5DCKhnr9g2hw6kM/5q59p223w1w1IX7qaWyCvHtNivmA2m/7SH1nNYgb9n89YkFLxu9/9P6Pk1et6dHREWV1mCQmePznhow8/ouNTdqW15sGDB9dAiu9///vXwNA0Tanrmp/+9Kc8ffqUvb29juX6gmH32uB3NBpdsBa+//3v89Zbb11L5MnznI2NjQtW8eHhIW3b8s4773B6ekoURbz66qsMBgOapmG5XKK1ZnNzkwcPHuC9ZzabEccxeZ5/oZ/ITVXXNc45oigiSRKiKEJKyeHhIcfHxzx9+pSnT58ym81uTE1aLpekacre3h73799nd3eXfr9PlmXkef533tj4Zc/Vmvn94rabgJ6bvvPGxsa1vvDLAlk3xZh/Xkkp+c53vsPOzg5nZ2c8evQI5xyj0Yj79+8DHTHk6OiIg4ODLugmTbl//z4hBLa2tsiy7CLK3jnH5ubmte+/xiGiKOLu3buEELh37x6PHz9mNptxfHzM8fEx8/n8xpCkX0V9sfTJu9XADhCi07VDJxXxgmVR0h8NIPgLVsx6TUVwM9L0oizKe49Yp0Nd2FVen4WEEOgccyUikggv8cKysdfn9sMxRXvC+5N9los5ERm3sg2K45ZybknjGCM90OBazWQ6o6pi4iRB4fCuoShLjDadn0c/JSkbmsZ1DzAhcatjk2q1KmsMuK5T7CUJaRIhB4KmsSyqgnplZBucp7IljfMY353ujrnjadpuQK6lQoku/sw7gcDQy3vkScJ4vMFwNEQZA8oThF3JLrpIxdq6biXfNt2K6soDJlaAFrTa41xLQ6BwgpaIRd0QZSPaIKgaS2FbWhcIQiJ0RtJPqeqWYl4y3Big45yRyfHeoYWgWS5QzpKmKedn5yyLGiE0vg1kMsWFhtY52qbBOYFSnczKBYFzGtd2k2pCN8Hu9/rMFhOG+ZjRcJuT8xNKv0SrhMpKFpXtDDmbBUVZrFJ1Oo+dtm0RATbylOVowNlsAV4jlGJjY4x0FuEgCE/d1qRGk2yOsc4TCcFiMefx4ydkWc7uxi12NrcZj7ao6oYgDEpqdBR3Urh+Sl0W6FgxXxa0tYemIklT2rbpJAzC0zYt2kBTFQTrqevA3vYOftXeA9C2NS50hstGa2xrVyAFCB8jMeAlvu2avWtKvG07E+SmIYQE6UQndwjQhABO0tQWb0PHCrNdtHpQErRiOauwvkXGiiSPmZcOGZYsWoFQElsWyMaRRo5F05BmCXGkMSkoBWkvJpMSM4XYGpIooyhqhptjqtbRVjVGGqSC3d0xm5tDpmczzk5m2KBpW8+sKjtPoQBNW+GdwMRdO9ZGYj2rO00gpGQN0UppgI55pGPDdF7RVBVSGWhWE3QPeZbQeChbiZ3MyJKM2MR4oSjahjg2pJFhvDUiTWOODvYZb464s72N1pJ+ZpDaEscpiUlxnpUfjQc8PnhECBgTYWJD23qcd7gVaUOGjg3jCLS2oakb4ljTLEp+8Ff/lb/48//C40efslwuCc6jhCAyish0aWxZr09/kNPv52yMNtnd2+T+w1u89sbX2dq6h1TRqj9c97Z0E++ud725H191w5fUTJ/T4a9f0BkXd331+pM7yEVKwe7dbTZ3xqt72hIn8SWIKFywNTogYP2by3vtnhLGCIyJP9v3ZYmVCCufns/Sibr9ywv/HiEv80BWkFBYM2i4eBCF1ZPp0rB1xVhaH9X6edMdgDSra2g9CIlta/bu3ybJUupQEEIEQSIGOa98+1tMjk85tw3BKTCGoCVm7TXkPN6BcJ3MKqQRSEUIGkJAaYEICiU7tpCyHtF4pFAIPEoZvFAM7tzit/a2+PH3/wtapCAM/bRHqDXnvu7i6FffzotuX2uWaggdYNPhMWtJmL90Xb4CaX6Tah1TvI7p/tuWBggkvjIEddW00fR6DAdbn/emK/V5q8A3jSFvpOeHpKP7XioNn9clvlTduXPnpdKIlFIvHRu9HvBfrheTob6oXjaeXAhxI3C3Tty5XEmSXDPc7PwHr0qHhBDgRrwIdGl1bdPnVARcN/YUfgPcSwA1NiK0V0GBEAJNOfvVe2y5Ebir1941grb65ffTljmt+fn+SkIIDp5WN0z4ekSyf2WL0Q1aX72eLwJx67op2OX4+JizszNOT0959uwZx8fHTKfTa0CNEILhcIgxhjiOEUJQFAXL5fLa/m5KoFvLRtZBMsaYC+bMfD5nsVhQVdVF3PmXkThevmfXfeTZ2Rk/+9nPOD095fDwkIODA2azGdPp9No5WcdI7+/vc3h4yHg85s6dO9y9e/dCnnXFg/DvUf0iMeIv2xf9InUTePRFte67ZrMZdV0zn8+vvL+ua+B66tS6hBBX+t91KtmLtW4jxhjyPKeqqi50YrWvddT5+t74VdcXn+kuv7iTjNAZYa5XuAOCxWLZ+Y9cOq7LQEzgs87hsncNcIVZ04EwXGxfDy5fTJDywaODosUihSHgKcoF8yoQjxR7Zo8nzxpm50u2tGUzvkXdNogy0BaByBvwgsn5EkSBiQ1IgTKdQ7ESLXXtEEpQ1y1KGjwBaxukkiilEXSr5m3TIpEEFXBNi48kUWzQRqNUQlFb6tVgKgho24Br7QVDKQSHIOBcgCh0g3cNsTbsDjc6M80kQiuJzhRxqvECnBXYFrxd+VyIjm3QtjVVXdHUbZfAgusYpkqCD0gEde2wwpCPxggkznuCbSnriqZxCMDEGXWoIdN46zgpm9U+JN518ewy7iFtS+slaX+TrKfwrBgePhBlI1TkMbYDlORqhV55jxAQRSlKKUxwXWy5kAyGWwidIJUiS4ccnD4jiXIEFi0sbbWkUy8pqqqml+er70/nIxRFbI6HlHXLsq4xIsJ5T6w1UoEVDuEDG9tbxCpiURY0ofPXuHv7Pr10SD/tY5uW06NDtFSMxiPOziddfPJoiGtLtDGUdYNzgaZpacqWprKdNMNoEAJbi87Q2fSRxJydnDA9W6C0wYXunISVP4ZWCpMYjDAgRBf5LQ2JToDO4yZLUxLtaOqaWtXkScdSaW0LBOSqs5BSYK1DJQPwASU7AMB7Ty9L8c5RVCUuOGIZ4a3jfFoTBBgtaCqPVpoQKlp3jtKKJI7I05jtjR7jLUmc9Xjl1a/xyQdPODg4JkkTer0B7CiOj09Is4zaLpnMzomMY9DvMR6M6Pdj8nzA+aTifFpyfDrh0dMnnFczpsv5qv158KtJtVAr8GPFpgkKa1v6oxTrAsfn845VJkqUll03JSRVY5kVCilnECyRicizDEJg7hxvfv11jiZLxHjExx99xPn0jF4v4a03XiOJDFVpaCc1/YFD9ALOh85QN2iQHVKtlUKqjskjFURJRBRM1016iQ+C2rYsigIpJIeHB/y7f/O/8p/+059QFMvuNV4QKUUvzzEqQ0sN3lAvoC0ayqlnelxycnDM4cEz9p8d8J3v/A7DzU02NveQMupm3v9/9v6r2ZIrTc8EnyVcbHH0OaEDgYBWKStZLGZXFYuii8UekmN9QZuZX9BmNJsr/hQa73nDGWuj2TTbujgkbVjWxSqWSgkgEwkkgBAIebTa0t2XmItv+T7nxD5IBBKJrExWfGlIBDy28O1iLf/e9YoZeyeeBTrODuRPbGvBk/MmZIE1vPdpEjoBXdqPsLnB5mYe+TlvfkqG9KcBlXN3cg4vULRm9k9+uELPgJ3AqdW2NOe0DFCZQ5gxfGbH54kdVbRsm9P7qrFWDPC10ZRlhxhEbhTJ0AFiriDTPPfqi2RHmxxtHgKaotNFGSueRUqjdRQPpKrB64gpAipq0NAEj1UarQz4QDCWopSH+RiRmHYF6mLB4/c/Yv9gyNLiKo2fMHETDndH6GBRWryPxCQYQnsMECbNKaXTid9be8k8w2qe1bOaq2vXrs15OLRN7elyzs3LsT6lPg8o80Xq07wqzmuuFhYWuHjxrKSojUl+sn7V2QT/PVQIgY8++mhu+7Vr1+bYBq0fzOn6tEZxfX197pzevXt3Bli0/x4MBudep+ddD4eHh7NmuK3xePzUsfT7+/tYa7lw4QJbW1tnop5/3lpZWSHPcwaDATs7OxweHvLuu+/yh3/4hzPAqK7rGaviyQohzDyONjc3ybKMGzdu8Prrr898d9bX1+n3+19aY/6sfr5qAZvTtbCwwOXLl89sizHOgZG/yvUzgRrvgzTHkB6sAwFpAGMMDEZDnGvI8iwZGArdOiKrdu2gfhqcmRlKnhPRfTbSWx4424fqmFZKQwigonhmRM10UlN7S6w0uepz7eLL7Gxucrh/zMryTfqmL7tegHIWgsO2+6HEj0JjAYXVGU0tvUUMioBINbQSSn9MXgkxBGrvsGjGjQdXE2mYVoqmdhKJbSyRSG6NvOeMKbKSx+eQGnVjiV5MVKfOw2LEh0qaDmOYVPtMvSIrCqILSS4RxDjWi7nzZDplMBjiGk8ICqVzAmCsmJuWeU6WBUqtyPKMopT0nrppGE8rqnFFXTe4EDB5iXcek2d4H/Be0puU0rggv61TlDjvUT4SA9K4In82mcZ5j/VBWFkxYG1GZkxqJuRayTKLQtHUDT5GlssePgSM1dii5MHj+9TNkF5/gTGRwfGIGEVKVdc1tizpdkrGowk6sygFC/0+U39MVhQ457Blj25eMGnGaKXolQW5yVAZTJoGh2F97TKrKxdo6imT8RCcZ7HXZXVxiWo64eh4wOh4QDXJMFnGdDrFp6aoqRsBWHzEBbk/NEqivrWmSVH21bSiLDV7B/sYo2YroCvLyxwdHOJ9wFpDnosZcj2tqaqG6XSKNoosy/BRwAibWaH6Kok/1560gi6NXQgZSmmMLej0ZDJZ7ncgOKqm5uDwYMZQ8PTIikL8QBYNhEBT1XgEMKqrBh06KJUTfJ/oeix0uly6ULC4eI3VtSW2H2/TTA2XLvYIMTLYrDiaNEyGE3q9QK9bMZkErl3TvPra84RQMJkoHm3v8uGtW3x0+z4fffSYMi9QTcD5QIyp8QwyoijtaVxNv7/BcDIhywJaRZEVWY01BqM0UxcwriD4Bu9rrK7JsxqjIlNXMJgMefDwATYveHjvEZPpmI9++lO+/VvfYrFXYvUSXmcQRToYASWxTniJvsNrjUIJG0JJ8956fxgtTKA81/Q6BcdHx/wf/9u/57/96f+PqpoQQmBpcY3LV66zurpMv99neXGRbqcky3IIFU0FBwe77O7sUI0j0zHsb3/M8HhEp1/wm9/+e1y//koCQRSS/NMyJOZLyCLxRGKq2878vFV4GRu995+9YvKZzyef8wFm7uXnIj/p/0/ktGfFDZKeNfNA49SDa/rZM/KOOnHTOfVq+VN6i9aGPC9QOiNGhdF5Gu80wYEymmyhy9GPN9l6vIOyBabokOUlWmdkhSXP5Vre3tli93CPr/7G17l54wWM1qgg+yCSLfl+rTQ+yH8ntSTTqgab8bW//XfY3H3AT94fs6csytfkCpwWmVn0J4lp/pQXxJk5+NTBODkWT3WGntWz+htTVVXNGdKex5xpmuapfR263e5TsXS+aLVSkifr6Ohojn21uLg4x2iaTs9jdDAz6XxWX14ppc6NFD4PJOt2u3NNaAs0PPmZGxsbc2yF9lpopZOt8eqT728/48lqPWZO12AweOpUnN3dXbz3rK+v8/jxYxYXFzHGsLCw8Nlv/pTSWlOWJU3TMB6PefToER988AHvvPMOvV6PhYUFFhcX6fV6fOtb35o7rnVdM5lMGI1GMwCrrmvG4/EZg+SNjQ2+8pWv/Nz7+ax+8XVektV5xuJKKdbW1n5tQLbPSH06AVVkRVUjbr/ycDwejxmPx6yUK4R4Er19uk4zaU4zZJ40GT79tnYVVCs9k1C10irSdmG3K5ppZDppyLuLZLGkm+V0Lyww2ptAozDkArIQRWJlbPpehPGuwBpJzchNRlCRqCJKGWEXBI/RBqMtdVPjQiDLDGWuKfMCHaAoLajAZDKlqmo0BqW9eHMgE7lPUgKtjTAglMZaQwieqpKVf62lez44OGAwjhSlJc8MxoKjoZpW+Eaif/MsZzgYEX3EmgzvxIskRo3SGZ2umCNHAiF4MbpVCjILTWBaj6hdjfOeGDVGWfm+1BTIaqugZdoYfAiQywpxRChgjWuYTiqMMUR9woLSGHp5RpHlNHWNc04aBC0AhvwjHjdaW4qiQ4wIuGGFtVSWPTrdBe588hGNn9Ltr1JVYybjIdE3GK1xeYYLXlaTY6TX66FMwaiqaUJkPK1Y6nax1rBULojnko6E4CiKDJUZTFHS7y9Rln2KoqRTdnF1g9WKR9t7lJ0Flm1HTKM7HYbjkbCjgqIoCpwW0MU1FcbmLPb7WGspC/ntk/E4pRhlRCwaQ56oqiFGlhZWmEynAv54D0HjvSezGcHVBBeoJg1jVRFQs4Q0bQQs8K7GGomwNkoatMCIum7olCXT8YC8KNjdalAxcDwcJPDHzGSH/f4iZdnBoCjyjI3VC3T7JSjFdDQlhoaiu4KiAJ/T66ywsZExnlYUec5zzy3hr3jqSoCNK1deYm/3mIOjI2o/oiEycTl7A0t/1JBlEZ1nPHfjCo13HB5PeevNr3DjhVeYVjIhTiYpln48El+npuHgcJ+FpT5HgyEx1kQ8WoO2AlTY1FSjxqkPlWbXVg0mKKZNzbSpOBoeY3TOdFrjnefWrVs8eHCfzEb6nZJ+f1X8pJwDZYkEjPZJyqPwPkmBYkArkTlBao5NkHPgFQTPd7/zl/yn//Qf0UrYTxcvXubatRssr6yTZZYYA8PRmLoRCdHaco/r1y5RlC8xPp7w/o/vcPfuXXzjufXTT7AdmEw9/5d/usjaxtWZ90qI7dh43kjeMkVOaaAiwhA6b9w/NVafHqN/1UopJWy9VuL0JOLQerCcedPZz4jzm+bK6MSmsuI51sqpMhTaaIiBMBizNzqkt7RMubhCNZ5ibEGeWXYO9zg4OODDn37AYDLgzgcf8Ft///f4+te/RrfTASVm2OICnBSsIOBQEOjXZBmXbtxgcnzA1u4WNzdeQu3cY6c6ILM5sakJSerUWsq11+uTZ1klRmycHYFn9aye1ZN169atObD6PJmQ957d3d2n+swLFy78UoAaY8y5QM1oNJprorvd7ly61nQ6nWNFxBjPlYo8q19saa1566235gCE85gunU5njmXTNM25QMvy8vKc7G11dZWXXnqJ3d1dHj16xPb29rlATQiBP/uzP5v7zMFgMAdePsloaH/TeeDL4eHh7P7Z3d1lbW2N1dXVLwTUtGWMYTqdzuRdxhgWFxe5du0a169fZ2Fhgfv378+97w/+4A/odrs8fvyYTz75hK2tLfb29vjpT3/K3t4ex8fHHB4eMhwOnwE1v2I1GAzmQJm6rnn77bfPbCuKgn/2z/7Zr02K4GcsmUYiXvT4yVNEHohFu1/XDYPBiJWVlTMGwbMH+5aHnepM0tMZo6vzmPBnN8w+G2F2hCT7id5QDw2dtWUy1UN5Q69YordUoSvxssFHlI5ELfIt+X5NnmcsLy9RZgajIbPS+B8OjxkMR7OUIIUWVorp4JUwWFSIdMoui70+Wa7wBEbjMV0fyE1BnhusFWbJ4eEhk+mUumlENuUcpEZPKSUyltoRfSDPc6ppRRhHvHPkmRZTTRPxLpKbEoUiyyJKLZBnljwPlGUHpQ3j8ZTJVJpYazVZZlAajG79HgJ17ZnUFbWT1CrvhBESXEBbg5pJabQYmhlNpk5ieWMMRO/IrUF3CjGxTDHtTd2QGQvaJJNORWYz+Z2AIUNF8M4Rg08MKVlFzpXQCGOIaKVZKws6vZJbt36K91MWl9bEj2U6pKobrG0IztMpSoo8RxuRL6wtL7E3nGCMxYdIxNApCghikF1kFqUMCiR5a3kVbWTV3OYFZV9AjIBn5BXogsbV0Dh0VtBbLMjyAq0UC0omseA8vV6fvGjTb4QptOQ902lF3dSEGFhYXhVZkjW4pmZ370DYL22kcwAfNeNJjdY53W6GD56qacR3JjFhmqbBGkNuRB43nUy4/tw1yiLHuSmbW1sopTjc3RHgJy/F2Nd7irKLbwJ1M0WbhiM/pJo6mqpmod9jdWkJ5yKj4QjvG3rdgr3dHabDCTcuX+Zoe5udgz2WVpdRKggg13iauqHT7dLvQLbRZWmpy+7+Dlu7DzliyvG0YVTVrK0tUE3HXLvxAtFA0Sl48eXn2R8MmVYVMYJzHtc0NE58fLTSDIdHFKVFmYjTwiTJioK8tBirKLMSIuwdijGjtSLPGVc1xpdUrqG/vMjLr73MZNyglcX7hs3N+7z/wftkBjSBy5endJsVjO2jVAetDV4Je0JrAY0DIl1EC1MuBmE/qCAxyzEo7n3yCf/7//7/oakmaAOrqyusrK0RlWIyndDUNjXTAa0n5HlOPa5wDVy9fJmr1y/wwgvX+Iv/1ucn7/2IQdCYseaj5kPeff4v+d2/+38F7CxEQ57r4gz0nhnLzsZeYQmhwBiLjmfjdNvyKXr8V7lmPjSopK467Y92CoJQnNrObPtsfkkgPszPQW1ppfBRPKiij4Q6QJZhrCE6z9HmNvV0RL9Q+OqYo70Jz734Gusbl6mHE8hy9nXOpW8sMKlH2CJjuLvDxx+8z4uvv0Y3L9EeXBgn4+mMLCsxKqCUALJoRTQQleL1N75Cdfw8u//HNoXPUKpGhj6VgCSfUvUEqFHhyevg7MJIS8x6Vr8e1e/36XQ65HnOZDJhb28Pa+1TmzB+Wfv0pGTh07wyfl3q+Ph4bhxszVdPl1LqXJnRzs7OnORjPB7Prfh+GXIon547z9t+Xj0JxmdZNif7ijFy8eLFma/IYDCYReM+DfiklDpX1rK6uvpUscCfp6nqdrtPnX5zXp3HJpWU1Z9/oPTez5lGZ1k2B0qEEPjkk0/mvstaO7et9WA5XTHGc8/Hzs7O3DbnHNPplMlkgnOOsixZWVmZAyNjjPzu7/7ubJzp9XriUXlwMNcYe+/nDLOVEjl1VVWzmPV2HLPWsry8zHA45M6dO+fKiWKM594nL7zwwtx10fqF1HXNvXv3+M//+T/z0Ucfzba1vl4t4+bJaqWA/X6fjY0NBoPBLFI8xsh4POb27duMRqO59/5Nqv39fWKMM88epRR1Xc8Zkyul6Pf7v9AFv/Y8jMdjDg4OZv+cx+TKsmxu3LHWsr29TbfbZWFhgYWFhXPN609XWZZcuHCBXq/H5cuXWV1d5Qc/+AH379/Hez9jaz0JsI7H43PBy89TP5tRkwCZNkkD5HmulS3FGBkOhgJcaC2+K6ceBBVCwGljts8zsxK2ztm47hS0euZ1Z4Cd0GruFaHRZM0yHb+GiRYVDJNhg1UlJlNol4vO3wQxBA725JlUGY6Oh4x0IM+M+BAQaZxHGUvd+BlBPCCeDVUrYQqRg6NDJuMxRTfHx0DjPFobKg2FN5RljlaKvOwSlcHHkXgWJEPZGCPWWmIIOOcJytO4gLFWWBkdQ1XVgCEGRVSO2nu0tgQfsRZc3VC5yLSeSvqJkgd678QENYSADwKK1HVF4xphFmmLS2CXD45MG6KKVI3HkhEaL94cyfir1+uhVJS4vErkTAQ9uzkjyUsBmDby38YaYvSzhwOlDVqL/IkoNH1JJ0rXjDZoJSZlTd3QX+iT5x1eeOl1Nh89ZDKOaDvGZA0KxWRSYXpdsqLE2oymqcmNYrnfZ/dwSFU1jLOalV4P5yIEMTaunCfX0O32iVlJ1umgdUmIjqAcISiwNsW3y34bLT48ESRpyAXZb+VRSuNVZOoqiSLHUJQlZdEhxkjjxUen8YGi6OCbGkLEFhnWmNk9EYm4RiRH4pkkDVZuDFnwMzCncW7GUtJWo2KgXFhg5DzTMEX7SKe7St3U5IUMGllR4gPUdUOeF/K5pYdYo41lbXUD1zT4umJrcw9tI94HMpszOBqLZ8y4YiVfJPiGyWBCZnLKLGccJ1RVxdFgAERWVhbpLy5R2MjgaJftx5t4rdneMXz8YUNmYW19ge/+4Md88vAxo4mnCYbaewJBDJwzS5EXFGWB0hZCTZYrAg1WW4qyJMsysjzj4Hifo8N9XO0xypDZgqLoMJ2MqeqabqdLWViqFLW8vr5KNW5YW14mBE+vnzMeT9na2aOuxlTNhOeuv0xeeGCMxGQbtNHJTFyRGYkBV0rL9SwXODqBjlXV8J2//HM++uA9sjyj2++xfvEiWpvZeGe1FhlinpNlYqC3sNjh5otXaSaGw6NDVlYLfu/v/x2MCfzVX/0Ak1lq73j/vbf5jW/9PZra4GMkzy1FUSJKS2FenaTtyRjsYyREJcy6yJluvfUtgSgg4JdgFPeLrNgiMJxIu2SwDsmnhmSaeyJnOo3YCOGqnQnSKH/OgkH7XQqNikoSqqKjtclxwxGj+4+IOlIWGpPl3N/eZ+vRA1zjGR4NKPMC08npdDOK2GN1bQVKmDYOXTnyfo42FtUETBCJVbuYoI1OslNwIdBdWsBoTVF2uLZ6mQ/iR1gV8Sqe/Ewl4JV6gsV6xh+Ok1QbNUc7ela/ynUakGmbj79OkAakMX4y/eXXvc4bA/M8PzcV5Lzjf3x8PNdctufry64QwlOzfEKSGD9Z5zXrq6urWGsZjUZMJhMODg4oiuKpgBqtNYuLi3MN23nSqy9a57GEvmgVRfGF9nNvb4/BYHBmWys3Ol0xRh4/fjzXK125cmXunJzX/LbGv0/WO++8MweqHBwcnMu+Oe8zX3/99bkm1Fo7ty2EMPc9cJZpUxFG+LMAAQAASURBVJblLEq5rRb8a9Oinvye837To0eP5r6/TWc6ODjgo48+4q/+6q/Y29tjeXmZuq5nqXkxRm7evDl3DFtTWqUUKysrrKyssLa2xsWLF9nb25sxj/6m12AwIIRwhmVorT33Gj2v93+SsPF5azqdMhgMzngsaa3nQN9ut8uFCxfObIsxcnBwgPeefr9Pt9v9mUCsUmp2/6+uSprc+vo6d+/enRn79/t9lpaW5q5H59yXC9TMdjoCKsgDf9LOxxiISjEcDHHOkZX5LH745E3nG1C1cqr2O5Q69UA5+955M2FgxsAwWla3g4cwyenqNULT0NSBju4RdMTHhqhsSjMCq7WwNbTGN42EBERwDbgQcbEhEqkc+KDxSS5krIAaEndqxdsjNFhtaEKkmVZpW4QYyKzE2dbBU0+nwg5wTtQGVjxGYgQXAy5GtDLScBuLBmG1aGGpZEqYEPKbDSE0RBRN9Ok4RvAGE5w0kiltpMhLog8oLQlJIXhymzEdHBMcyZg3J4TIeDwltwpjNIJBBPCOLBcPhbpuQE2wGozWdLsdkRT4gIuexmuqxhMRM1wVA0pL7HbdnEQ8tpdC9KeMkLXIs1BK2AhKuihjNePJhKgUxlguXrrO0b4Sveh0jLHgnaduApOqIbM5RZ7h64DtGDZWlhk7R1M3VI2nq0qMzpjUE3zw8n4f0ZmkZPkkOdDWokOAxHZqJXKZyUUSh8LVDT5JzlrZlVIG7x1aG1yMUNfYLBPPnSJHx4Jca3xTM0nHTikggVOtRFCZWqQszkGIlHlO7T02ioeNMQaMRVuHDyK9MUZT5hneO3zTgO2gTCQrIv3uEt45iZBuajq9bAYUZJmhU1hCjIynU4LzomxsFFYpAbJ8pNPpgQ2MleP+wSbdToldLBi4MYNmwNHhIa5xjKcTooo02YiRGjKpGg7G+0y8p3FiWK2Vpd8r2T484tHmY0ZTx7T21C6KuTVRTLsTaBCQJCwdImWZc+HCOuvrG+zu7vJ48y4HBweMqzFGGxa7C/Q6C2gi+webOOfp9vrYjR6ZUTx48FgkXiZHRS2eMCry3PPPk1nD7u4hw8Exg8GQahK5cu06nW4XazOs7tA4BU6hMNRolEKuJW0gRozNpKXXhscPH/Hnf/bfcKGhzDry0BhBKU1mDQvdLlcuXmJhoU/tGobDIb1+l2vPrXH5+gIP70zZ32uIqkatdHjra2/y45/8lL29Y4IJ7O4cMBwds7x0Wa4bLeAM8cT7q/WacQ7Gk4YqNQhaCxRutaxSlmUhcdenwfJfddPIlsEJpKztNMBEgk9yzxaYQABnY9uVt7QQMJuaThKt5qvl32iIStLqspjkZoHq4IjJwQFYyIylrh1F3qVb9PBJ0tp4AVZjDFT1mMHomE7H0F1dpbe0SF6WqKjI7GJKGHuC7ZPmIW9Swl/0kgIYFLiAVZGpFn8wOQzzwMvcw1I8ee2vOnvqWT2rX5Xq9Xpz7JmmaWYRr6frrzOJ61n9+pZSigsXLsw1jS375HRNJpM55lQIYQ4QAtja2poDCT8PUNPpdOaeC3Z3d+eYJUVRzDXrIYSn9jfKsmxuPxcXF/nGN74x99rzWGPj8ZjHjx9z79497t69O2uy19fXWVtbY319fZbedJ5PyTjZFZyuZ3PkfGVZRlVVs/MvabzziV1t3PqT1U82Ec/qs+tnAzXttanShapE9mK0pY3LHg1HTMZTik6BaZtOFD4mE1ASzVydpl2ffTiM8SSV4gToaZc/z74vREm5iYrki2OoRoFCL5AtZUwnU5Sy1L6hCY6olRjQxpgYIEEa3CyTVWfvcS7iUIRGkni8UFOIShIfPSp9n/wWpTUm12hARUUdJI7ahQgBshDxKqPQBdHmKBXodno0zjGpKppk/IoWMMgoRZblKYO9PT4eFyPKaqJ3hOhxMczAowgz81qCsJC0F+ZMUWaEKNKYqq6IMVK5mugD46lP3juQ55osz1kuSjKjsZnBZjmT6ZSmFvqa9wGUIShNUPLb20ZfqUj0YPMCp5wAH62oICIMA5PhUqNiU5xuiMzMeG2WZGzpWnExYBJrIXiE8p8al6WlC1y4OOb2YF8mjOgYTyuMrVjqL2CNRqkKk2mubCxzZ3MbHzyj8ZiyzLARqqqmCQ06yzEhYGJkPBwQVE5QUZLOQkQjwIjSImnR2szSPxXijUHwONcQvEiThFkEtfMoben1+vT6fTKbo40mRofzcl4CIqnAaIy1lEWBDwFlFFpZoneodF9olaGNxagoUiAi1ubk0oICItsriy7OVXiTSVpRCBgSo807gndiGN3U4qvRpLhpbYnKUHRLup2CxjdU9YhxqPCuZm97wMBNqeqG6WiK947pdMJkOqHMS46Pxaxr/1BkXBBF1hYhBkMIFrT4JxHFiLwF2kK6plGaEIMYlRMTeEsy3RZ50OryEgu9Zb73nXe4d/8+MSYEv9ehY7sMjir2Nh+TGcvCwiJLK4t0e13Go4rbH99j//AQZZKMM6QQcCXSQq00mkBmNEZrvvvdd1laWaDX77C8vMj6+hVW1lZZWlpgaXGBTtnB6i7aaJwGrQyNl2Z+PJzy/vs/5ifv/xib4iwFWJCHmAsba7z68qv4psFmhrXFVR48aBiNB7z9wz1u33V87Stf53jg2NquOB48olOUvPrmK/zJf/1LYrQ0dcD7mrLMIIq8UcAHMxtLT5g1gTxTKOyMqShsuzhjvEVsYlnE2fvOjs+/YtqYKMCMQoMP1JOKg8N9efA8PGA6nkhyVWIE5kVBb6FPr9ej2+/TW+xSdjoyP2lJtzrvd6YRC5gSg4PYl3nAOQJwvLWFCw3KSIJfVXmKvENRdNHKisQxNrMxQyuJedeZ4uZLL9Hp9gBhP2ldoFVEGXXiraME/Jf5MuCCFyDqcMDmo8fCYlQCMs0S0mTHz4I1Ss3ALWICylHJB27ex+ZZPatnNV/j8XhOQlLX9blAzXmN7bN6Vp9VMcZz2VCtxOJ0/fSnP+V73/vemW2yCDcvO7x48eLc9v39/XOb6CerfSZ48nre3t6ekw8tLy+f6+P08OHDz/weEPPXJ71jzmNiKaX45je/OfebDg4O+Pjjj3n77bf58Y9/TJ7nXL16lStXrrC2tjYzA15aWjo39enw8HBOTtbtdn/tpZy/6CqKgqZpODw85PHjx6K2qOs59ogxho2Njbnzd/PmzWdAzVPWzwZqEun7pOS/Tz/QhhA4HhyzsNRPzXVI2nhZspNmSH2q/OnJVT51GtChjVtl9tCptZo9wCptUN4wHo/Z3T9ieX2VBlA0hCRJwSIpHSisyUAJzdOc8mEJOiaDYp9WTdPKckzifWNkW2yTp6IYh3qPihEfFZgMCJhMmqFJ7Zg2noiAFwWKunEkiEYezFFomxECRG1R1sweuKXdDYDHoKiaKY2LTJJRWAiOgCMS6WRLLC0uU2YFKM10GvCMKfIcX0t0dFDQNIGoMpTN5RgbLSwRo8WQVUHdVAQCmIyyLBiPxlR1gzeKgKQCpcOfgDNP7R3Og81yMd6MEkUeImgjMh5ldQIdFNEECBnRe0xm5SJMq+POe2kiU9pUniQhWkNeljx/4yW2Ht/l+GCH3FhQVmRcLkAIWKPpFRn9pYJpDGzv7DOejFh0XRa6iwTlGNfiB5LlOXlmqKZjlI2YXMxdY4i4GBOIIU2T1hCi+M0sLy3T7XUYDoaMjgaMxmNUkiMYo1lYWQIUJsvwzqGROOZpNcY3TgCgKGwRZTJicIzHLq3oC7AIHtfUuODJuz1sZkTCpwU0JLFwVIwih/KSphWNMGTa/ynkmOs8UZNNTVaWqCjH1xg7a+CigiZGpnHK5v4m9x88YHtrh4P9HYpuH5RlOBhTV47xZEQ1nVDkJcF7irJIyVsBgkFFk1gNjqo6Wd0ZjcfUjSdGASezXKO0sIsULQjYxgrLbe89aGPY3z3i/Z/cptvtcOHCZfoLfaaTCcPRkIPtXXJbsLG+wcULa6yvb3Dv/n32dg4YT0a4OmBVQVU1QCCkiTimsUmOvYBjRGFSPXjwEKUDzjeg5HopOobFhR79bp+FhWVWVxdZW1tjbW2Nhf4SnU6X8XDKx7feZzA6Zv3CZWyeY7OMXr/P0uIir7/2GtevXmNaTekvdhmPJ1y+domiyPnop3d5+wc/pGkqvv613+SP/+gjrNWsbyyyvrFG2elgM6Hgl6VFq0gi3BGTjLQdl1v9evABIgKU6jb22gCaLBMJV+tvE4L/coGZM+DBz/e+2M4REYLzPLx7n49+8gEPHzygcXIfWa0l9lprfBRAUBlDp9theWWF5fUFLl25hM1zllbXyPPO/E4lsCR6RwibEEcYfRNtekLDJLD3eBNnITciBXYhUPY7CSzzaAVGyXVNDCkNUdFdXWZlY0PYnpwwmNq5M6rWc0cBGq0gs4Xcr84zuL/D6OAIZzUO0I0AjSEBNyrNV23KUzzFYBX2kMyL7VLKM6TmWT2rz64HDx7MJTx9GgPx2rVrz4CaZ/W5y3vPn//5n8+BMltbW3OSou3tbe7du3dmW6fT4dvf/vbc555nBP15gJqiKObYEjs7O3NAzXleQiGEcz1yzqvzZEV5ns+BN0op7ty5MwegbGxscO/ePb773e/y4MEDNjY2uH79OhsbG6ytrbGxscGFCxdmjJon6+HDh3NMnY2NjS8cG/7fW2WZeI8OBgMePnzIzs4OIYQ5Kd+neUtdv379l7Gb/13UZwA1UjFGOBV52jJg2gf6wfFADHutToDMKdlTy0qfPQk++Tnq1Hekd87cMOW/pRE5sYhU7euRmNToHfvHB3RXlglRoRAGinfSquZFRqY1vo6poY0i8wlB/GGImPSAG5VuO4HEHEIa6gioiFaSTuRDTCuxLZgV6RQdQM08e1zTyGCrFOPKi4QiO4G+QoyzmOXGS+T3zOhYG4wR6vtwfMT3f/gBo6pib2+fO5/cpZ4cUdVDGhcoTMnv/g//gN/57b+LIqAjNEQxC/Yp6txI85xlFhc8sV2dDX4mHUg93oy5ZEcSn62UoplWTBM4IOyk9nxGXBTJm3OSeCPeRgZSjK02BcYEfGhwMcqxU4agJF6c2PoeiaxEQmoCioAnSbFCgGjodRdZWF7n3v1PuLpxEZMXQKTxkW63JI8SkZwXOc9fv4rNDKPjYxSKstuhjlN6RhGCJ7O5sF3yLl7leO8QckeWmFWgE5tHBYVRim6nh2scg2ZIXdV4D9bkWGspshKTZSn1SxgjNsuoqikhBol7jhLDnmei7bVKU1c1MQEu3ovRNK38Smsa10jjpiRhqj6F9iutyQSNoUlNdqYsKoKra1SU16hMpesvo5o6oZc2E+p6TLffpyxKXD1lWk350Qc/5r2fvMfjx9vUVaCpHZpDYhR2k2886xfWee7mC1ijOdjfY3VliZdeeYml5SVslmF1niRBnuWlHnt7YwbDI0aDQw6Ojjg4HnJ8PGFz8zHTakSILt1JVq4DDUWRk5elsGy0YXmxz4WNDdZW16iqho8++ojNzcdYa1noL/Hqy6/wyisv4b3DmIzDoxHZdELZ7aGjTWyqFDfvnTD/gqd2DZk14lHkHd5FJhNhDsXg0drjvcI3MG5geDBC6zEh3kdpL+w3F7EmhxjplDmTSU1Zllhj0UpTlCVLywtcvXqRF56/gW8cV65e5P6DhwyOD9Em8kd/9Ce8+trzvPH6b3Dn1o+5cWMfbTWbmzv0Fgxl5ypLy8uMp3ssLvSYjsbshx2KvI+2FqUiLjR410pthDGkojAndPKmasdOY9UMpJF7VuFcdcq7hZPx+/QD3gw4V+eALfHUvPHEfPLka09Jj05oeE/WKW+VU9uUh/FgzHtvv8t77/yIwmZcWNtgYWlJ/IuUoswyYQFGhwuOQTNhsHvAzsNNth7fZ/vBFsPJmCvXL/PKm6+xunGRdtKZfVcA1wxR8R461mAuEHWB1jDe2WNvZ4vGOBSOqvF4BTa3eF+TmQySzFcn83KlQWeKy9evkXWKNOnIwYqhTl+vBElRJu2P7I1WJs3DBjV1uGmNtxrfkMDFBLqgZmBNnM3Fp45dQqDamTo+eX6f1a9MPWnMCPKAfJ6B5nkmjnme/1LYcK0haZYYhFmWyVz1S/Bj+bJqMpnMyZc2Nzf5+OOPaRKDtq5rrLXcuHEDay15npPn8jxQVdVcs33eufsyKsZ4ri/CwsICIYTZ+arr+tzkoE+r1qehLMvZv9s/f1Z9nt/t0hx2ulqw4Lx9evI4t7/x5608z3/hIJu1dm7/zzNR9t7zne98Z+7eOTo6mjunGxsb/OZv/uaZbb1ejz/4gz+Y+9w//uM/ZjgcntnWmp82TUNVVZJQe47hdPvs+WRpredYEef5OCmlKMtybp6p6/pcc+0nz/1kMpkzYgZ4991357b99m//Nt57Njc3aZpmdt3keU6v12NlZYWLFy/O/JZu375Nr9djdXWVe/fucXh4OHft7O/vz8xiDw4O2N3d5dKlS08tHT7v2LXS9J+3jDG/FJbP6d93ei5pmoZpSqydTCZMJhNWVlZ49dVXz7y/BemePE6bm5szX5nWjLjX650rmztvHmnfU1UVzrlPHYO894zHY2FWp/G5jXDv9/uUZfm57/WmaXj8+DHj8XjGNJP+7st5jvqMeO6TVbnTD+vCqkmyG605OjzCVw3GSoOrk6nmCUUmxdmmTTohAhJbrJ74zpPv0G0U6hkGTlod1BoCKONRGobDY7yPxCD+BJEAQeGjomkUQUvDKbHYFh8jzkW0NlgtfgMoM7t5tNFEBIjRWs/8d2KQ1xmTElNSjxFDkmgg8gHnhMkinjYRjBKOQFDCxgk+HR6TQAH5fGssoMT4NdRYYxmOK/7ie/fo9DMGoyOmqks13aMeVwRdUJSa+/c2qZtAlotvhicNrgqUthRlFx0iWkWca1A6pdOApFDFiDaWuqkkRlxpAQTS+bHWgo84V+NDxKTBWY5VgqqiSM20VgIGhYibNhhtUDoQVJAY2iA0/hg9ToF3ARRYZWasEpEEyY0YlQKlqb2nOq4oeivookNQmjLPaeop42lFWWZoFbEYrM1Z6PRZWx3imxHBR0xmyWyOahwuiqQIY7FFT24FV6OsSo18oCjK2QOPSYlSUSnyvBCJnc3oLi5TV/XM2NZ5T1U3KCN8tGldC7ClxOsoeDk30WR4FLm2hFhT1w2ZDhij0JkVRoQXj4sYA1U1xfmQ5HHJzwYFLtC4SiRiiWngjMaoSPtclJfiV1RNRzjv8N4Tohd2hjaS5tU4cpMxHo740fffYzydsthbwy5mKYHJEUKk3+vz2muv8cZX36Df7xNdIDOKw/1d7t69zdH+HsuryyyudCmKUjyUtKG/3GdhsYflMi54MQ720sQeHh3yp3/+Jzx8vIW1kmo2GAxoXE0nRHq9Lpkt8BFu3b7FT37yPuPRhKqu6XR6XL16gxdfeIFut+RoeCRyPRTLa6v0nBPTMw9ZKYbKoGm8S0waTVNPBZi0FqNhMpnSd342zvkUYS+eUF4i7UOgric0zVSuOSPHaTqdMpk2OA/ayHlsarkHrIVLl9ZYWuzz+P5jRqOCH7/9Y65cWeG111/mR++u8ODBY55/7i1+8r5nOK5ZXV9ma3OHxYUFLl3coGkqHty/z+/+3ldQ0VFNj3DTSuSE3s0kY1rrZFws/ipRG7AZMTqaEMjzgjwTWWYIYQbgTMYjur3+rJGfNfsxzQhyo58Wtc7hK09OVS07StEiwYqoWm7hKaC+nXVUu71lTyaGphK2ExGqccVf/vGf896PfsQLzz/HxoV1McnWmiYEonM41xCNJc80RmmWLqxwaWWV4+Mxj+/fZ/uTXcbTKYP9PY6G2/zt3/k9lpcvtXskSV7O46fHWD3l4NGQ2j2ksz5mYXGNxz/5mM3tx2RLImuc1p6yv5rYmYgpvY6ceLdFoo1kizlr6ysp4l0mD+8a6mqMzSw2mX0HAmrmVyNHOyiF8ZHjzV3GvsLZgA7QqPacpPOTwO2Y5GEq8euECYosiiRAPjD/EPmsfjVqa2trbtuFCxfodDpnto3HYzY3N89ss9Zy9erVL3X/2tre3qZpGjY2NlhdXWVtbY2qqs6VBP261HmyjqZpWF1dZXd3l4ODA7a3t8VI/+iIlZUVLl26xOXLl1leXqaqqrnGutfr/VKAmhDCuak0L7zwAnVdc//+fR4+fMjjx495/vnneeGFFz7zM5VSXLx48Uwjlef5TEbyi6zBYDDnnZLnOVeuXJnbpyeNQoFZJPPPW5cuXfqFx6gvLS091XFqzYCflN9cvHhxrqG8fv36XGMMzLG+gHOjpPM8n8Vjtya5bfN53mufrJdeemnuOC0tLXH58uUz25xz/NZv/dbc+x89evRzg7kxRh48eDAHdrz33nuAeKD0+/1Z+lOe51y+fJm1tTVeeeUVrly5wk9/+lP+zb/5N7zyyiv8w3/4D/nX//pf8/LLL3Pjxo0zn/nxxx+zuLjI17/+df7Vv/pX3Lt3j9/5nd851wvovJLnsXnT5S8CZJcpUOOXUacDhNra3NycSfQuXbo08+46j6H15HwF8Kd/+qfUdT0D9rIs46233uL1118/87qmac6dBy9dusTDhw/58MMPGY1GXLt2DWCOdTYcDjk6OmJ9fZ1Lly5x4cIFFhcXz/Uneto6PDzkD//wD2dm6lmWzb7jyxjfn0ogdpYBo+eYMNOq4ng45OLiAniH0Xr2WjjFpmmJMkpkMCeGwtJ8nqRStB4oYsp7ej/aSGsQhDcmDvdkMpGkoF6HxmkCjuCEhQER59sEK5Vi7iAaWXlXRuKAZyQRJK7anzLCbVM0NCr9PvlBWou3RgwSZR7TQ3mQHRbpS3KiFi+IiNY51hhcYtDY5HIeYiA48WipXQCVE7ym37/CP/kn/5RhtcV//bPvUFrNODTExkNWo1ROZg1NU1F2JWFKq5BAJE2Zl5RlF0Ugz7MERjmapmYyrdBWp4ZaU5RGgBylMVYnUEmivT2OoDTKaEIUZlHEQDBYa8hMkm4RsIQECtQoqwE3a7KUMun4GaHh2wSIGStXS5QWMUaVmm65znTSyfYWVlhdv0jHKMpC4qmtVbgQqIOXdKAQKIxmcaHHwZ6h0Bkg7Icst0Tn8dGjfWA0nhJNgTEam2UEHMqLd0c7QCXdFsZm6KzAjyq0NtTOUfYXyIucuqro9HtMp+OZlxDBIJHyrcbXzPydlFKM65qAwiZj29p7rG8BgSBMJC9eMo3z6XN0Ag8jRgnooBQpPloTA+JvhDSJYTpBRZUS1uT6j+n+CUqDCgQUmbWsb6zzf/+//T8kGcxY6kpiDKOCunZ08oKy2yHqwGQ6RgUwyahteWWR/f09dvZ3ePz4EWUpyQtFUYpML0SUseRFTq/s0SkLtrd22LuzjTGRojB84xtfZ3d3j/W1dSbjMds7m0ynEw72dnHOEbwksS30F7l08QovvfwKFy5cFKNwAkpFtPIp0cZjNHQ7JaPxGFfXOB8EIIoR54VT4FxDBKbTCRATkybOAOWQTMLlOAnzTGlFrjQxitm193B8PDxhKaTxy4UGbbpYK55O3X6HxZU+mblG5TO2t/axJjAcTXj88JDX3ryOdw6jLUaVeFeTZzl15Tg8HHB8NCDPu2xvHfC97/0ZS0vrdIoFOp0eRZFT2i7W5mibCZsq3ceoQOMmaARkVijqsUj7xIg4x/vA6HhEZgqUr9BppSBE+c1aiVRS5KjhjCS1HTdP8TDTfZsGTyC0oEPL9jirMhJWyKlPmyliE0gTo/ifNeOK7//V93nn7R9y+eJFlldWQCuqphI4wiq0LihsjjMBj6epGvyjGlcU6FzxwssvoM1DPvzgI5qDhgd3H7Gw+h7f/OYCedYRCCoEmiZgsmWUe53J8DGPHu2gHj1gdeUi7//wXfZ3D4hjYQ52F/oURUdYcLVLMt0opr8mEFVAFxkXr1yjLBcBkb0SBKgR4DCxaJSwoLyr00KHnvlJhemUx/fuMY2SNjCTOsaThC8BepLZvHBCZX45beKfmLHq9Bz9rJ7VswLEF+PJBuPo6Oipm7PLly/PsQ3quv5S4rifLGvtudKCTqdDlmVcunSJoii4cuXKU0VjP6tfXuV5zr/4F/9ibvujR4/mgMMvgy13XpxxjJE7d+7MMUPW1tZmKTht/aIBri+ziqLg5ZdfZmNjgxAC6+vr5zIzut3uDNAqyxJrLbu7u3z00UdP9T2t9OrJ+kUnk31Z1YI0VVUxnU7PjeH+vNWC+VVVMR6Pqeua/f39OaDlScCyrbt37/L222/zzjvvMBqN2Nvb4/r16+eCQr/u9bmcfFQCJ54s7wOD4YhLiUkjK3Tx1EN3u3aa1miVrOx9Ft0oJsPIllVzJvq7JbSkBrqqayaTCYv9HmVRYPIOdd0wmTSgIxaNSwbHPnoxAk5MGleL7CKGOFtlNEZjtU2ghVDJjdGoILR0eZ0APC14pJWm9dWJpNjDGNGcgFLOe0KsMSl2lRixWgxOVQyz5B2ro4BIKKJWvPrCJUbR8Bfft/igMVrhosYqOS6j4TG7e9vY4iJWZUQQJovRTKY1TSONsI/S6DrvqaqaummE1RMkdSezBqUNrgloLd4nCjESjglkITFxlBZZRUxSMW0t3U5Bnlms8lTNlFFlaYKn9gHjk9l01CiV4bxILrSWbqEFr0Lwp1gjyWA5GXHECJ3OMnneRzOlyHPxACosITZorXEuiIQoBvIso1OWdGxJDE4Si3LNzt4BtWvIU2R4FaIkFlU1MYp3UN20517jvAcXmFaO0aiSNJfGY8uCvFsKZVTBpKloQhCj1tQgyXGLQAsgnEo9MxbXiGW1pE9FmmpKG93eOAFpjLFk2UnksvfyHZ5kCKyUmJ0SqdNx0IhRbxYBNNaamcxBK4VrxMMkIua/Y8EpyFKKkBh3K3p5j0DAmobMZjhXExIjIobAeDLG5xllJ2dlfZ1Ov8dkMuHo6JiDAzEYtllI0dAZMUS8r2iqKdYW5HkGRFxTc+XKRRYXenSKksNDzeLCTXb39ohOcfnyRS5cuIBzjl5/gaXlZbIspc1Fj1LCKjHpfvVRpIpZpsmsplGSUhZCMtANJ35QzosnUIzCyAvtOUzHOwaV/FtCYiFEdIj44IAa5xpsAb45GRO0NsKocbUcMzwuODq9kuHxMWUPfu/3/i53bt3HV4ZXXnqTl1+6xve/9wM21jfIswUePPyQunHsHwy4/cknlP0e5UKHDz68z6072+mhwdDrF/QWSvodibzslB3KskN/oU+3LOh0S7Isp+z2JPnKdglaJJ0GjVKevb0t6mpIZlaIscH7Bu/AuZBYcSrdg6qdDE7u0wRMcebfp0f/2ah+rgZKtRNMO+Srs7NHiBA1aB+4/9Fd3vn+9+n3+1y4cIGgREIaYmShv8Ti0hI6eOrpGK3ErLu72GN87NjaPcKUmuXFjOdevM7O1hb3PnlIzODOrVusr19iefEC/W6fajoVOaeKFLpgYeUG5d4nTCdb7A3vEadjjA+MRw2dXo9euUAnL+RneDHPbllJCkfUkazT4cKVyxhTIt4zSgyuVUZW5PimIXoB62OS5kbdgq4eNEx393h45y61CoTgRA6VcPAWK2u9bZSKp46jmp2zmKRrLVDzrJ7Vszpbq6urc41wCOGpo3nPY4z/spKgPk1W0fqZKKVmMcZtvOyz+tWp81blb968Odcz7e3tzTHpvmi1LKkn67nnnpvbtrq6OtcY/7okCcYY6XQ6fOMb30Ap8Rx9/vnnzzW4XVhYwDnHo0eP6Pf7LC8v8+jRI95+++2n+q7zJGWno55/lasdw5xzM+bVzs7OTE7289aVK1eYTCYzZs7Ozg5ra2vnsuaevO5jjOzv78/eNxqNKIqCjY2Nv7lAzZPmknMHDdg/Op41tt43ksyUGqGTt6ozN/Bp4OXJ74ucosOffmg/8+e0byjRDE+nuLqm9jW60qA03nm0VShrCMGhreyDCz7tT8CfGlhE4uNTQxuSfACSHQ3KWHzwM+2jNUbitU9F4sa0nyoE8YVo2UJKEbWh8Z7G+1kcrEjFNL1enzLPybMMa2pyvYCnJqiGne19iqzGNxOMClibM6FEUaOUZlqNuHv3FvvH+/Q6fZpqzNLyEv3eAnlRkGcZ46l44bSymdb3R2kITlbX5Vh4SVxKA0skEhph6GiliD7FputWGgZRRSpXUR9XyUDTE1XD7sEeo/GIvYNtJoMJRMVCf4m19StcuvIcIHHG7SDW+vvYFPXWPnB4Wg8bhVKW/sIa04NPCCFSFiVZkTGdeDyRwhq8b1Aq0jQNC70FCl0CKWpcZ/gYKZQVGQYiUfPRJfZKxCg5FjpdI6DE/0iJnK5qHEVRkpcFtfOza79OUqeY5AezSSuKd04kxQDHSNM4tA1JZuZwXkxttTIp2UsRlca7cAqY1AnIMmSZxvuATZ/rfHOG9RaUwnlQToBTAUpOJ/oonJcJxBojUeKmBeISsySK3A2lxDMpOPI8k/j5ugIl31e5Bj+RpKw8K8iygm53Ae8cdV3TNFPGVcO0DnjfgK+YjIZkhYBWqytrTKY1Dx8+5I3XX2Pr8RZNXVNkGVcvXuabb32DlZVlOcZNI4bgWS4AGpGmliQrr2VCUSmJyTWOGKAwhlgUKCrqGJjUntA0KHSSQTYz75oY5Fr0IRBbcDcBfzIuhZRaJsBi+xqieEFFJ8ckEAjRUddTFIG1tVVJP8stK+srfPLJY567cY3FxS7KKrKiy3e/80MGwyEvv/Yam5v77OzuUxY5zsPtu58wqSd0ul1QHeqmvXY9o8kAvX+MNgZrt2aswyzLyIylyHKKXNhMRW7pL/TolpaizOl1u5Rlj0/u3GVpzXA8uEe3uygG0QqKsqAouiJL0wWaDKWyE/FT8PgoKW4hsWJOhnyJtFYyeCQ2kklgThonOT3OJ6Dn1JygSGNlhKPdfb73F3/BdDzi+RduEpWwbXRmWFtdYnVlDUXJ4cEWU1cRYsHDO9tYPeTizQ0uXOnz8P6II12zuGB48cXnuf3BJ+xv7jNhyOD1t6jGMOpOZmy9qKGOEVUYVi4uY7xierRPuLRGbMY4C2jL0tIiJsshaowhyVsj1igiDUFHLl65TtEtZ3MEiQ1DGk+VboE+T2YzsJaoIwY5jjFExjsHjAYDplq80sIsFUqOYUQYVGJSfNKwCQ6mErNReDQqgci/Dg/Wz+pZ/TJrbW1tbnX9adk0AD/84Q/nAJBPW1n/RVdVVfzgBz+Y2/7JJ5+QZRlra2tcuHCBlZUV+v3+M1bNr1CFELhz587cmHzp0qW5JvTLANhaidDpUkpx/fr1p/LyGI1G58Zm/ypWt9vlN37jN9je3ubg4ICbN2+msJazgOri4iLOOR48eMDy8jLT6ZSHDx+ee4+dV48fP2ZlZeXMtpWVFd54442fe983NjZmcp9fRjnn2N7e5v333+fWrVu88cYbc6DK56mrV68yGo04ODjg+PiY+/fvs7KyMscEzPN8Tt6olOLq1avcuHGD+/fvMx6PuXbt2q8NQ+nz1s/2qEkAzYkkSZ3Z3v45Ks1oNGY6rSlLO2MMRC8yjTkPgxg5ex+0D4p6ttrfNsXt6uzpfVBpNRCSds7I3x0fHbG2tEhUAY8WaUPURA91cChjZw+lwmwAEBmPtZYsy8RzRevkFdP+Voh4qmmND0JpD8GhtREvivQ6nxg0cGKy1QI6rXwmJhmM9158XiIomxpnpQlA1TTiSaINOgu4CGsry/zoo58yONikLBeJKkOXK0Q7pPIRYxXT6Yi3nvsqn9y9x7//3/5Xer0uV69c5fLly1y4cJGNC9dYXF7DOY8xFqOTzCkEjBFJVNPUYoTbSj9Ue748AZNW1sVwOXqAkKRjqalNwJmKntt3PuS//skf45qGo8EB0BADGJ3x3HMv8Y//yf9Mf2GZGP0JgKC0AGvKUDufwA5hPOhoIAqY0O2vMj1+ROMdvU5PpHRR4SJEq5lWNXo0wgVhBDX1lKwocM6jnQY0CwtLFJ0uh2OHKjpyTSiJnycEsqzA+5CumSCNFBofA73+AmXZEUaFB6XEj8SHJOnQWphJdY0xNrGYHISA0nKs5TgKC8soMW7WSst2RD7jfAISnZ+BRgIK+JQ+pkXKQxSfpAge2V9rrUitkpSi8Q6TZCitjAgiRhsBg5BeuY4upWJZJpMJCgGuMmsJzhOcw5P8LUJAZSb9Do3zQJTULgBjMwotRsudvkn35YThwWOMDozGNZ1uh1defRmTG65fv8bG+jrrqxvU0warBGxYXFg6FZuokgGZpdfrkecWpRRNXTGejJkkdp0PgbqqcY0jtxmdvENV1ri6oe7U1FVNleIErVI4EzBawAnnG0kfi0E0t40XkCZ46XgTmyn6kNgLSdKGjF9aG/EYURrnGjGy6/apphUfffghr7/+OmsrF3j0cJud3QPKXo6ejFlcWub1t94gKsUH7witNssNk2rCR7c/YjKuMEeZyCkzQ563RmgRpRWZyTBGk+WZ3KtKHuS00pKEZC3WKLTRGBswWph3xlhc7dB5Q5bdwdqczFq00RSZpdMpKcqCstOhKERT3OmUdLrd2cpKUZTkRUmeDDOVFgPjGPN0jE5S8wgWgsh5iO1koFBp3FUzlg5AhTUWP/X89O13uHfnFquXLmAzhbaQ5Rn9xUUuXbvG4f4RVy5fwsUp0USWF1YpTJcPf/R99pr3+cZXv0avA4PhBGsNy8tLLPS6bO7t43KJba8mDePjXYrCYq3G6ByVWYxxrK9nrHSvUw2WWF5Y5srz14jac3A0oL+0RgiG4+MR01DThIi2Bq8CQXmKTsmVq9fQJiPGOknc8gRWCdvN2AylT11LWsabEOQeVwGaozHjuqIyJ7JQQpI9hXZufILHlOZMndg0Jz2AgDX6S6DQP6tfbj35nPbLMBH+WfXX/f1ftFqT3dPPukVRzDEG2mfkJ//54Q9/OOcTk+f5uZHA59UXOX5VVfHd7353ti8tk/fjjz8GJJHq5s2bXLlyhRdeeOFcn5fzvv9J8GD2jP4F61cNKP48rJAv8vvP+w6l1LmMlsFgwMHBwZltnxeoOX0tC2s4zF3LWZbNeem00qcn9/fmzZtzAEQIYQ6o+bTr5Gmvn087H097nk7fB+HUAmqWZVy7dg3nHPv7+6yvrzOdTuckZnmeM51OZ54kvV6Pra2tucStT6v79+/P/c6NjY05H6Z2X5+m3nrrrXOvk087nk9u/zzHHoQheHx8zNbWFnfv3uXGjRtzc87Pen87Trav7fV6sz65qioODg5moM3p+jSGzKVLl3jjjTc4PDycedQ45+bG3Fa29fOOVU+ej9PjaVu/qHHw0+pnM2oSC+T0xH/uydaaxjmGx8d0izWiF9mHSQaV4veQYmFbOrwCSZE55UGiT3/16QN7mp+tZmwbIYSLf0LwHrzj5vVrKBvZPTpiMJjOkpZG0ylaS8KJ9036s0n+E3IRtnFj7Ykw6qRZ8B6KPKduPM6JZi54kVuElNykzSnT4Rhn6LO1NvmCQGxc8nxJcdTB4yqRSlR1heZEFqOZErA0vuTm8wVNPSVWY6Iu0bbH61/7JkfjTbbuf8jRaMjm1mPu3rnNK6++xv/yv/w/OTw4YHt7h3v37vGT9z7m+o2bvP7Gm6ysrNLv9zFG2CnKJANlpdApRlspod8rRUojEp+a1D8kloE0WcEneVoC3GrXEPGsXVzHGM1oMMVNa0bTCcYYLm6sEqPh+GhIUfTFgForohA30ErTkExvQxudrMQTJ3gaF+gvrDIoF5hOD/EhkNscmxWYaOl2OynO3NPr91FFoJuVDMcTur0+WZmh7KGslsVAUXSoUOKRoxHjYWPpdMvZ+RUnemGRdHp9ym4vRR+Ld4axOcoHac4U4uPjPTYrZteC1jb5RLTMGfmtKsm+VGIM+eATsykkyZeeATAryytUdY1LrBJIGk4VZ/GJIcTkDxQgNeFKiwTEaJVAFIVTSuQ+TphjMURpnlVkUjmMAZsV5MayuNhnod9HAVVdc3h0RNU0+BAS80QYOyoyA59sllLRlPhkZFnGizdvcP/BxxztTTg4OuSTe3t0Oz1uvnCDK1eep64Cu7uHPHf9BkXmcbUAnePJhKLskGU5dVOT5Rm9rsjZFpd6aKXwviAbGtRI7vtWxhhDxFVuNrgqmB2/qq5oGkdVNwnUi2TpuodIt9tle2ubB/e3GQ6HDEdD6qaW46WiSCERVgcqiIeVzZLsymKUIRJ4cO8hS0tL7KwssfnoEVubj/nqV77KzReusrq6zmA05miwT7cf2f5EkkXGo4ayyCgLw+2PP2Tz8SO00Shl0VoAFwFjUqITEsFd5EW6twXQiunfmRXwRivQ2mCyjDyzqa0X8FIbTZZFoEarRqRjJkOpEUp5lPFo47FWJVDzxLE/s5bMWmxmKQtJA8kLAXA6nQ7dXpesyCTNq+hSFD2yPMfM/LAMJrETY1A45yRJpizww5r9B1v81Z/8KdqAyTVRBWyuKToFV69foyi7rG2U3L/3gMeP77G7u8n+zi6//bu/y80X3+D77/wRRy/usn7lOkcf7uCaiOlollZ6HA0ti0vLGDK0yWVcrmt8I/Ha3ni6Sw5yRR3BLq2xsnSBJVfhpgMuRtAUhCYyTb5OddMwnoxpQk1DxeqldWxpGU+OKfIORIPJNEQj50idmPXpdP0ppYleWIzGZsS65vGd+wx9TaW9LA6EOJtzWt1tK29qFwZOe8v5GE7JzBQ8MxP+ta+DgwMODw9ZXV3l4kWRiHa73b/W1KVerze3Ouq9/7UxGP6Lv/gLrl27xhtvvIExhlu3bvHOO+9w+/ZtYoyUZcm1a9cYj8e8//77M1luywReXZXnrNPPzMaYuSbwvNJac+3atc/VSJ2utbU1/uW//Jfcvn2b7373u7OoYq01S0tLs7SmsiwZjUZz0cfdbnfu3IUQuHXr1sy7b3V1lZWVlXP3sU3ROV2fdu43NzfnPuOvG7jZ3t5+qmPf+v38vE3adDqdi61WSp177peWlr7QcdnZ2eG5557j8uXL3Lp1i//wH/4Dw+GQ9fX12dywvr7O6urquWDi2tra3Pdba+c8RfI8P9cf6Tzp1J07d+bef14NBoM5mVGMkYODg6cCq3Z2drh16xZ3797l6OiIuq65ceMGS0tLs57v+eefB4T98qQJ+IcffsiHH344e+ZuAZ8HDx585neDGEE/6fujlJozfY4xcv/+/ac6z++++y7/7t/9uzPbsiw71xj88uXLc8bD55kmf1odHR2xt7fH8fExeZ5z8eJF9vf3+eSTT1hfX+fy5cusrq5S1zWPHz8+896W0XvlyhW+973v8dWvfpXj42M++OAD6rrm4OBgdh+98MILvPLKK2fe/2n31tbWFkVR8Nu//dvACSDzaYBea+islHqqa66t4XCIUmomE/3e977Hd77zHT766KPZd7Vz7i86Ka6tzwZqaBGwEzTsvEE1xsjg6JiNtRXxvwiBEOtZ9BYJiBGqfmogYhshewKOxBilaU0gCZDWYk/qydMWgkfryGR0jKvGxCaQGVhdkjz32lfE2Ejii7LCzpBM2ln0d7fbTQkwtTTLzhGQC18bg/dykjtFRsgNdV3NGl6Tm5ScIr4ArSlxe9J8m0JSN4mhodJKrTTzOrc0zlFVNRDEs6boCCOgySgW1jio7vPR7dtsLK+ysz+mv3iJ515+kybepJoMqQ4fMRoP+eSTuxijePGF17hw4RKvvvaWIMTDMcpERpMhm1v3iY8j6+trrK6tohG/EpEZORrXMJ1MZ1GMdS2GlnlmGA2PaZxjcXGJumnITJaaPmHohOjxoaEONTu799nZf8BkMKWaTHAxJ89Kur0Fup3uzKxVGFXp3CoBKoT+3yaO6Rk4Rgz46DHGUPaWGFXHuOBRTSNgho8ok7HQKQlasbZ2gZ1Hj1CFgGLUgSY4tNHU9YQ8MwJ05J10bWqiD7gQGY6naI14wyiN8w02LymKDtNaogwJM+FGaowz8TLx0gRpY878fWEtLhlMB+9FzhAD1mpamZfABYqiyAkh0rgmsW0Ux4Mh2hiMzSAEtEoGgdbS6XbpFAUuyopGVTcikyLiY5yBbUpr0JHQCLtBTL9FDlF2+7jQ0KT90GhcjAwGI5rGo5LUqfFRjE1DQ4giMzJGJFszH54kHdQGGj9hOpqwt7fJgwd35Z7UhmvXX2B1dZ08t4kBpLl0+SJKWaKKmETJDtEjciJFbjO0ltj2IrE3irKQsSYvwY6ZTqZyXaX98bXIUGIUA2bnXYJ5I94J+BcT1cC7BhAQguhZ6vV57vpN8jwTqubhPtvbOzx6vM1oNKJuGvIyY1pPxGMkgDIZaJ3MwQ1Hh8f86J33uHb9Ci8+f42f/Ph9ptMBFy5cYzzybG4fsL2zzfb2AXu7W8QQ6BUl1mg2799nd/MxNkJoxHMLHcUs3eiZh5RSCgfU08nMcFpAV4tWBmuMADgajNYopSVlTAmDLURQVpNnBa2jiUkgojWSXmdthrXCDhRgwcn1qDXGRIwRxpM1kwTggEZS5vLMEhRyfE2k05NYV6s1WW7Ji4JOb4FOVwwvq+kU7wK9/hp+3HDnvY/Y3t5iaWMVHx02N2ij6S300EZzPDzEVZH3f/QhWel5/bXX+NDf5uP7t/nK9a9j64ZJPWR5IYMgSV5eRzaurJLlgf6lVQgmmXhbslyOjyHSWeiwurGI1Q5rczwWbXKUzzBFhrEWbQqI0DFGGC5EqvEIE2pcdNSx5ujoQIzDo0Jj0VGnMQNsnst8qGXGCzGiQut6lgDjwZDNe/cZaZ+8sEiAzInkTCc5X2sijBIgbeaLRbtQcjKj/npzH57V6QYCOHnu+msspdTcPvx1N+Cfp9r5uI2gvX//Pru7u2cMuY0x5HnOwsLCLCZ6OBxijOH3f//354xVt7a2ePjw4VN9/+3bt5/qdcvLy3Nyqhgjg8GA+/fvs7W1xeHhIc45NjY2WFxcJMsymqZhMBjMYotPV9M0c1HOIoN2J2Dyz2hKPs+5/1X0NHnaffqinkNt33O6nmQetPVFmsAYI8YYJpMJW1tbbG9vM5lMsNbOMRb6/f653/W033/euYfzfXcWFxefyny41+vxG7/xG2e2xRhnxrafVYPBgM3NTTY3N7lz5w6Li4scHBycy9ZowZzTtbm5eW6S1tOW1noOHDg8PJzbJr2hfSrgz3s/99uttXPAHzCLpj5ddV1z9+7dp9r/hYUFRqMRR0dHOCeMe3XqWf+0V+F51QIls3AWhPUXQqAsSzY2NtBazyLTn6ba++bLTr46PQ6I6kRiyc8EM3zJ7NGffUTSql48RU2PcZ5WG2OAqBkOh0TnUUYlT4L2dZETeOXU6t+nPB7KiY+zP7e7EmkfYpnbB01kMhpRV2Ou37zK/vERJuaYLKMJDbbQHA0rfIrNjUTqxosHTQg0pxBUoxSmyGeDqK8D3gnjptMp0TqnspqmcRRZB2vFzHI8qYhIY9PKUk6zkWxm0SacuRmNMSKniLJKbbMS7z1VU0MweCzlEvzJf/uPvPv2R1xYv87hUeTa9ZcYV56jYYUtlxjymOPBAHgEODyBleUV8rwUw98QyFSG1RFXj7l3/x5//ucP2VjfoKprjo8HWGOpqgmD4TFKabJMosKn0wlZlmGsoq4mhBCEGqkU3kX6/T7eB65fvzZLNbr50qsYpel0ugyPp5i8g9U5l69eoigNo8kR9+/fYmGpT1F2Z9dRC8gorRIbIQEcZYGrHZL+Imk7nd4yx3v38dGTaXnwMCmpRduMxjfs7R2gMGxubrJ28QI+RGrfsLa4zHjUcHiwQ21XiOTkZYe6drMGxjdOGl5jaOqaGDWdsguI0W7jHErbxMhyM/aC8+LsaYyYMWtjJTY+eoLShCC8sBAiGtlOFNZMiBEXZPCRhXJNnhcC/gRZIW8aR57AFeedeAVFiNOKuqlT02jJMllJ73Zy6rphWknihA8epTV5t4uurUiYEug4ntaYzKBNWpFXihAiVQzUkwkqitSndk7uuhaMsQarRTriZqCuxgcI0VG5mh+99y5/+P+9w87OJjduXOHy5Wt84xtvMhxOcK5mfX0drW2S0dUJ5NH4dJy1toRGgAFhvSgmjWO8d4xSQgOO6BnwZG2OMorGObTNiV7AP61AB4u2OjFMDNW0Ji8KmqoiRkdmDEVm6Xc6oMDT4J0HBcYKoDuZOA72D9k/OGYyrdne3WEwOGA0GDEaVxwNh3gfqKoKpTSH+0dE5ymNRkdR/PzkvdvsHwyZVOI3Ep0w28qioJMZhscDdF1zcXmFGBUu1Il9YYi+Rsy2DTGKMW3UrbxS7icBapLsyDUEL6CLV5GgamKCBaMyM8Zjparkb6JRBKxJUZBFzvBwMFsxPv3gpbUmy3KUOjulaK2xRZ68mRphx4VACA3GTFEx4p2DKOwQjzAVm8YxTWa+mdUi6UKT2ZzSOapG0uq0UXT7XZSGuqqZHgc27++yfqWgmo4ITcNCf53GOQHOgMZN0dqDyaEw/J3f+zvoRvN49wCvS3zUYkptNEoFuv3I8qIlawScdLEiEJLRMBLNrgw6ClwSvEsSo4gtO+jYFb5ScBQII9NHj7UZREXT1NRNTR5LmY9as/qEwqgYUT6irGFwsM/g4JCxVQQXWwLNLFFLISmIKrYGzWd94Z4suTbMz3zNs3pWfxOr9ac5ODjgwYMHfPe7353F/J6uLMt4/vnnZw1IG9396quvzt1Xd+/e5c6dO5/53SEEPvroo6cCAm7cuDHndRFjnKWojEYjyrKcsWQ6nQ7WWo6PjxmNRpKk+QRQc97KePu5z+rXs4wxPH78mL29PR49esRoNOL69etzfjS/TDPWls3zWaW1noshb4GaJ9kv59XDhw8ZDAZkWcbx8TF37txhb2/vXJCnBRBOl1LqC0fQP8mkk8CNs4CQ1pqXX3755278nXPn3rfyfHYW0Pjxj388J6U7r07vk3Nupj75ogsBVVWRZRkrKyssLCzw4osvPrUs9G9afQajhpnM5eTCOQdgST4mk7GsLmc2JwY/i3clppVr70nao9lJVskl9TQq1f77NPo226XYyqjU7LMT3wetAuPhkG6nYDTNMCqj2+1R+SnDaoS1iqYRZoE2iWpvDJqTRrtTdoTNkyRSdS2rz1oJGuibGlNIFLXRhqIosVYiyrVuMIifivc+AQ9yYRMj3U7njEbSGE0IEtGtETPJ3GZUAXxdo2OHxZUFjgd3uHv7fUbHkfvTx+TZAgv9JW7fv0/tAstr61RHPcaHA6aTTY6O9ni09Yg8L2hqh3ce1wSMkQjiyXiU0pUc9+/dEYaRkmY/RJGISBKPJHr5EGbHoEX7J6MapTWuaeh0S0IIPH70iF6vx8svv0JmHDefv0bnf/5HbG/v4zyMhjV3b99me+c+S4srPHj4MT46XnzpdRYXF5PESWhyIUZ8kMkly3K6nS6+9kzqEXhDxBCzQoASJauHxhgWen2KsqTXX8BFh0KR93P2dzex1qCC5+DoCNdMmUwcve4KvlE0MeKaBuc8YEAlY2ki4/GEGCNF1mUynWJMCxsK2FKn5CRSykNwAaOSX45SKRlIwMu6Oomai8lXJMaImLskVkeSOslc0QKWp/5BzIe1FhmDryp000CMGJ2uJSUeLihPp8zJ8hylDdNphQrij+SDR2UZ2gMqYHWWQByDtmJUTAgkBxxikBQtUFht8U48TqShj6ANwctvDZDAH5HQTaYVR4fHPHz4iNFwRIxbHB01fO2r3+bBgy2y3HDx4lViVNhMQZTjr7TGB080Okm4DDEEau8x1hBTLHcMEYwhuOQRY20yRVZobTGZFZlXkCQsZRTKZsJuADpZLqOIAoKlU+ToKIwcaw2KnPH4UGRPRqMBozLWVzdYXb1A7Wq+8c23UEQmk5rj4zGb2zsMhjImHh8fcXR8RD2dcLB3RLfocHRYc3h8CNpIKluZU1jF8uIKz129wnK/RAVhzjSNF8PpxFJsGoevJcEqRFlZiSFQVQHn/ex3xhDwnBqH2gEbhUfeL9wLIMo2ndhVKkqqUFQSGT08HtI0iZnE2RWUVtse4ikz62S+rLUlMxaUsCo1Kv2dmFM3dSXXWQhEdeL30HqWRe2ojQAbHZtR142wD4VsJ/47JqNTduiZjLfeehWymiLrcf3adS68cImf/NWPcYWh21ljtD/BGDCZJet36K8vMdqZ0OktErMimeAHMHJ/LiwURO9wzQhPBGXQaopXJgGymmAMKsWHR0FikyRNPGa0tuj0+63SWJNL8mBEJGOZ+FUprYiCrokc1HuiFzCNGDje3WM0HVHrE8mTGKKfmp45Wdxo/2L+YTjM5vJfxorQs3pWv27VAjKHh4d8/PHH/PCHP6RpmjkJR57nXL16lfF4zOHhIXt7e0wmE/79v//3c81My275rIoxsrOz81RNbBtxe7qKouDNN99kfX2dXq/Hc889R13X9Pt9nHMcHx9zeHjIcDhkZWWFq1evnnl/Xdfn+mesrq5+afT+Z/Xlltaax48f884777C3t0en02FlZWXO5+SXycRbW1t7KgZFCzw+ue0rX/nKU0mfrl69yvLycvLW69DtdjHGcHx8PHePPffcc3S73TPb6uRl+PPWaDSai7Ou63rOj+WLzsNthPiTJX3UWaCmTVt6mmrZR0tLSywtLSXrjF8cULO8vEy/3xc7gi9wnP97rc+4Q1oytfx55hdzzqsCnkndcDQcst5dQ+skg5+BEuYsr2ZG7TsL/LSmu6gUl62YcbRbY2EgGb8m5k4EH+XB/ZPH27wwqpl6x3g8gaNj8rwgBkuRF/gQaJzCGEvwYQZEKMT81ceATgwDHwKN92SZodvtkFuJOG5cTe0ExJgcH1JXFcZYtDHiWwPk1jIajQgh0Ol0RdLjI+iU5JMAp6hEKqVtLgbC0yneIWBX7BHtkB+/+3/y8Y8+YbV8id/67W/z7o/e4aOf/ACz9Bxf+/pX2P7kr2jqcZIKeCbjCfsHR2htyIucpqpE4kLrpxMSw0MSh6JSRBWxxornShCflplxs5fjYaySJCEnyVBGB7xzNFWD1nIpHe0PuHzpGpsPdnju+iW+9c2vc3R8zNHgGKLhjTeu8f/+t/8rDx48oq48+wcH3L17i7/1rd/gxo1ruGbM4dEu+8f7TEY1oOn3luh2lllZXiHLe4RYgnKgcozpCaijIeuURBXJckO332U6rQi+4dHD+xwfHfHwwT2KMkPhIWZYZdA6UOQWnyK4jdHUTQNEtMppmlpAmqKcJYY1PuCDE0aMSf46MTFQgjSYyti0mi2Xt8RlQ4yeiLBXCGLkSjQSL5xSl5xz1I34HxltU8pTa+ws5so6RSs7L5I0iesWsNFgUCqgU1x3tX+AtRZjLHUtoJJFS+OuxFtJtV4nSbaoXEhx9ZLkhBIwNGqTwCyNYHuRmKK8g5eoYK2MDC3ezZgdu3v77B8cMBpOyPNS/Gpeeg1bdHn9zbcwVuQ7RovBrNKKJgRpZpOXlIue1g7Gu0iIkgaktJjexuApSvmzaxoBaoSSJb5ZQc6TTiCW0Zam8SJB0yllDEVQUDuPVpF60uBjJERDXQeMLTDJ/LtxwlzxIYC1NEFYhKbIWb3QYe3iBYw2WKvI84ym8bja4eqaGD37+8cYI4ypvd19rDUUVrO+tkavV1DmRmRtxjIeTggxUJQ5xloigYWFDr1el6IsIMiYUteBuvbpegi4xlFXFXU1EQnkaEQ1qaiahtrLw4drJMWumtZMnfw5NJJ+5b0DowWsLTTeOJGHBeHiaKWF7RgD+ADK4b2whUJiDdWholIaCJx5DlGCPDrnRAYZI5p0vnQrGRSGiImtJLKmamqm0zFNPYbQpZqOsVmOG05opkPe+s3XcTRoIvkLGffu3OLgcJcXXnmdlfIi9z55RN6xlN2CtbVLZL0+3dBFdSsa71KqHejM0Ol1sZkhNDWGQOMcISh0bAjOI+bIaVwNcTZHlWUhc0jj0Ebhqym19ySbcJEopUUKASAhzjTUrTeaFo8lEzExEhvNzt2HbFPLXNICi0oA7hDBE8RMXKDjGbrbPorOJMYkUKxlzD5bKX9Wz+pMtfT+4+Nj7t27x4MHD1hbW5t7XeuvsLu7y+HhIUdHR+zu7tI0zVwTur29/VRAjVLqqSQhIB4KH3744ZltKysr/PN//s+x1kowQJZhreW9995je3ub8XjMeDxma2uLmzdvzn1m0zRzUg+l1Jxx7LP69SljDLu7u7z//vsMBgNefPFF+v3+3DUdQvilxbW3fkmfVcPhkFu3bp3ZppTi+eeffyrA4OWXX+Zb3/oWzjnxj6trmqahLMs5oObb3/723DFpF4J/3jrvvh8Oh3MyyBgj29vbP/f3eO/PlWiVZTkH1Djnnoo9FULg/fffZ2FhgRs3blCWJSsrK18YsG2DdhYXF7ly5QpFUXB0dPQMqDmnPiP1SYCZE6eYT0H7Et7iQmDv4IDVjZXZK1sJUKtRO+2Qf0a6dJ4RUCu9SvvQGhMneGYWoxyjyBFiiGzv7XP7k4coG3BRERxY7bF5htUFRe6Td4LG64DRIcktxMujbkJqzqVRVlo8baZVRV2FtCqscV5AnBACymS4EMitpm6mQnm3FmUM3U5X0oF8SCAJGKNwrqFpZF98dNJYKpH0GJ0DJZ3VDo923uaHf/oX7G9XLD6neemlV7j5wnX+21++zdApqsEWWw9u00xHxOCJ3lOnpCRrITMR707MgmPyg1FpJdbFKBI1YySNO9r04C7NccRw4rUScC6KfMwqkbk0HmMDoa6xNkMpzU/e+4ClxQWm4ylN3YUYGQ+H5EXJ0mKXSxcvcf/u+xidUXQKjo+O+OD9gudvXGZ1pcfKasb7/+k7HOxNqavI/v4h/d4iqytrvPzqW7z86lcJzuMxdDoLOHdA8A1ayUMJ2jAaT2iqBkNgsdcjYx2MeG54H+h2utTIangVlDSzPiZDaQELR5MRRtvkXyQSBaUErJJjI4OY937GItBJXhFiwJpcmEhGjFxJDIUQnMgbgkw2jRNJSAgpWlopAXfS+RIPmBPQU26LdO/ENs43SHJLVHgkClwAJ1BKGmvvp/jkv1FVzcwryaUEqVaSJybb4eSfSGJ/eWKU68E5j7XCdrGokz5Ptav3rUxSZIHb2zuz1YuyKDDacuP5mxibE3xIzDqo6oa8yGdDjUKkZ5kWKVmR5SigrhthhcVWSpihk/+TUpHE10MpsKYguMQiSkw3o42Aj1qkQDMZYlQ0VWTqopgV60j0ck5yk+4NpYnRg0lMLpAxA401WrCFdI8Zo/HBoVSO1oYit/S6kiSysLAs+wtcv3pNjKBdQ2Y0zleAjDHaaLqLHTKbkqS0Tl5GAmRHoNvvkOUlJm/9vSRFTMZZk6Rzgp7EIMbPUZEMc8Ps4WVSVTRNTTWZirxrPKZxjmlV45uKuq4ZD0dMJ6INr+sa7wXcaVw6JyHSuOQzFSJNFMAnhHQNAyFd1zKmB5FstWxDSGwWSSwTa/qARdE0TsbjScVwOGZl2TMaHTEaHbG2tkI9nhKVpxpPyIzG+ynKa958602WVpZ5/GgTkxmKXsHS6jK9Xh+PoVjoU/S7Mp63UrEYUyKfQhWFgDdNkji6hixdF2l2wjuPtnYmFY6Nk0QtaygLMbCzRpiLdTUloESGFkRaG2JIQOcprXdEpJ0h4qaBBx/eYRpcAmDUmTlT/sxsMSNdhCKH4uR1WitC1ElGGtPU/oxR8+tcradIm2RijKGqKsqy/KWwpY6Pj2fNXZ7ns/148qH7i3p6/DJrZ2eHV155hclkwu3bt+n1enzta1/jjTfeYGVlhdFoxAcffMC9e/f4/ve/z3Q6ZTgcEkKg2+3OJKLtONk2hk9G6lZVdW5KznmN0GmPmLZCCCkN8Wz95V/+JW+++SbXrl1jd3eXn/70pzMvxtazotPp8MEHH3B0dMSNGzd4/fXXeeGFF1BK8fjxY/b391lcXOS73/0ui4uLDIdDut0uy8vLZ1bBf9HntdvtzrEaQgjn+m+cV+1v+6yKMX4h7xHn3FOzEj7t/eft0+7u7i/8vh2NRozHY2GeLyzMGCZPnrtfprxtf3//qQCQuq5nUsTT1TTNKaaw1MLCwhz40xrJtiCUT6qHixcvzv3ejY2Nue9q3/809dJLL83JI3d2duYYauclFLXeUk9zDtr0pdPVNM2519RgMHiq89z2AE9uu3DhwszXant7m8FgQLfb5eLFiywtLbGyssLa2tqJLcapquuajz/+mMPDQzY2NmZj5FtvvcXS0lJKbhVQvNvtfumeM+1vetpqj0eMka2tLd5//30++OCDXyoL+elce07VeSZXJONDoy3D4ZDGSWJM20S175ELo41GO/sZrZfCWRBHpWfNBC7MPkto5e0Dv27/TsFoMmZvf8DG5XWia+ShGpVo9qBiJEseMlFplNVoZcisADKymg+KPMkKxJAzKkXjhHERo8a5gNLiAxJipKrr5IUhUWNGKfKiQCnDJDFa8jyXh+Sk8fOJ1SI3kEixiIYYMmzWJ+h93vneH3P/vS0US/z2P/h9lM3JFbzx+sv82V9+j+n+hMJavGsE/HGemBoArTRN7QgpPlrkNGp2rFppkIoG7xQYnRZgIz6I+ehkOhGvj5Reo40BLSwLl4xNmyadKxcxRnF0NGR5eZGdnW129x4wHE44OBywsNjn4GCPrc1dQogMjgdMqylFkXPnzh1+8pP3+cY33+LFl17glRdf4rs7H/Cb3/wW//b/9W9hLVCPaw72jglB8fzzLwnzqOjh40AkAkFRLi5gsxxrLBM3ZePCOke+pjDgdGQ8OcZoTV015Hkfk1l0HQnJB8V7YXNUTUWMsLy8eNK4hUDdNATizDiwripc48iyXK6daASksRKTnBe5AIMIu6BJrJsY2zhsQ0zXXUjSqahSw6Xb2zPMrn/TSqVS45VnFq0VdV2Jb0mIRCWr+610UCKiJYpbqbT6D8n7po2pJyVGBbzjBJSKHmuZJXA5V8u5d44QEgBgxaumNY8Nad98COSZ4eDomP29fQ4ODrBWZIXrGxdYXl4nhPZeEhNxMAlMMsKUip48zwXA0mbW3BubiXgjAS/SnwrrKEQhd5iUohUBm2XJwwayTNKxXOMEOE3Mm4hCGUNZdiRhTBlsZjFEtIozk/DcGqxWVE4YZXVdS6wyINazUfydokzwRVake8+nyHthDeVFjrWyX9pYtLVkhUhiMnKcF2NedyI+Q2srMdlZRm4spjDknRJbWNDCkpBxOkkIQyCaCJmSMQZp3C3CKnKu5LT0VAsCLqwZ1wg4QSbHJ6XcNVWD947aCUgjDLBGDNgr8ZappnWiC9eMpyPG4xGT8Zjh8WBmwNw0cv9MphPx8qlrnBIgzKe4aa0tYabfUfg6UI8rmjxnf3uf9eUlumXB9uO7WBXp9pbRRtPplbhpzWQ4ZOPiRSbjMY82HzGZTun0SxZXFrly9QraCrAVE/AajcKodtGgva5m/4fNMqICZTQ65rNljBgjOovpnhEwzZYn4KpWin4hzEpipAw+nU89S1Fp58bT8lgdwLkaRWC8vc3xwTFWGWJs0sx7Mi/LRHpqIyQpWkqTSw+0CdZJktsW4HnGqPl1Lu+9JKRlYng+Go3IsuypWRlftKqqIkZJyWs9UfI8/7VeHV1aWmI8HvPuu+9y7949Xn31Vb7+9a9z4cIFrly5wuHhIffv358xU9pSStHpdGiaZnZvCaPVMBqN5uQOWZZx8eLFp9qn84Ca84yjjTEzBlCWZTx48IAPPviAhYUFqqpia2uLzc1Njo+P8d4zHo9naT3teczSnNl6m7SJVgsLCxhjZvKHL2PsyPOcfr9/ZltVVezv7z/V+zudztz7z6tPYyA8bcUY55rtX0Q9TTLY56kW/BkOh1hr6ff7XLhwgbIsf2nsmfPqScPqn1VPAnfAuR4znybLafvKLMtmYMCT3kxtfZFxK8/zuXH3067F8xh6T1tXr16dkyxqrVlcXJx77X/5L/9lbtw5r+q6PjeJqkkpqcPhcHbOiqJgOBxSFMUsZXBxcXHumFZVxa1bt5hOp/R6vdk5u3TpEgsLC2dem+f5HMj1112nAbrhcMj29jY7OztsbGz80sCazw3UwDlgTWQWLToaj6mqik6nbMnXtMaipwGb2X/Lkt8ZB+Unv+uE1cPsoXMG6sjXi0GrijTBcXB4zNLaBj7KKrj3QdKAlDTJKrETFMKMCMphs4zMKEJUmGCIQQF5Yg84YZ1Y8axQWmNzg0L+LsQTXw5rs2TuGpMpZGu4qsQcVct3ShSzTLyyoi9pQAqFC4rugubjO9/nw/feYzK2/O7v/wNee+NNQiNJRFeuXGR9OWc43OZof5/xcIhvGozJZkyopnFobYlRQxQqbafsgNbYzNLtycp+PRW0+vKlS2hjmU4rlleW6HZK3n77HV544SYbGxeI0VO7SGYtIVbs7e3x3o9/wmQyRStFp9Plueeu89JLL1MUsLW1zbQacng4AnIePdrmzp3bbG3uzMADbRXVNDJUiu985zvUzYSi0Ny9dY8i6/D44SZlluGqCu0NwRu2Hu9y+dJNtMnJOz0OB45MRTp5Sd04lhaXIDoW+4tkxrK0tMhCd53tw13qWvx5Ygg43yBqlSieSpkMrnVdo5Sh1+ueRZhDoNCa2onrd4wRjRjSxuAT68tjlCbPW98J2W60kQbdCyOnHayyLEvXophYO9eIPC4iUpDZ6r7HaCupSvEkKSDQ4F3yaVLCStBGgICWkeZ9JAQvXitppe40E0ipEw2wsKKQZKgZM6Zd4VOJkRJnptGgBNAMp1f2k+5FRRoCm5ubHB0NqOuaTkceWK9de54sL3HO06QHwMqJ2XEWDcp5uc+1JtYNMQaqZsyYE5d8pXSSnUVCSt8SaZjsSzQAChMhzBLnrDCenJsBLy2bSAFBRbLCohNghJZzZ7QiRAG3umVOZgy1C4wnE7QZY4zFGpExRh8SaOYxRs0eCqyVL5SkJSQFzjmU0XjE5NgngMsYRUDuUxlbApUTdtLReCDeWjGgTaDf71CWGVkuPi1l0aEsu2gj7w8q+Z5wMu7EEKlT+pmo8BKTRInMLQbwWqRoOs/kWERJDMuKDEPAapEqhXgi5VH+dIJf+kwvqXchCNNlOBrhG0dwkeFoxN7uHocp/vHwSJIQJpMprvE472lCTNdX8sepPc2kZnA0YH9nj263xFjF9tZD+otT8qJAW4vVGUordvd2Odo/wjUNZZ7TW+ywcekiNrMzQKudd8Q+OaaVg1aGy4x5ologJM0DMbbb0t9rlZLcSPeAhiQNNCaNeVrPCHEe0LmdSaIgEHxsJ1VMkHMfY2D0yQGV9zQxcF5rJKCaeDa1QM/JHHpqIaT1sImke+YZUPOsntWTdePGDb7//e/zR3/0Rzjn+OpXv8qNGzeeKtq1nU/zPGdlZYXFxUX6/T4HBwdzK+tlWT61iealS5fmGtY2Ovd0aa3p9/tMJhMePnzIeDxmbW2Njz/+mP39fXZ3d2e/oyzLWWz4w4cPsdZ+YePUZ/WrV1tbWzNJ3tra2rmN8rP64nV8fDw3RnwZgPXly5d56aWXzmxrY6SfrE6n81Tj1sHBAT/84Q/PbAshsLe3N/eMMJlM2N3d5e7du+R5zs7ODjdv3pyLZv/rBAL/e6mfC6h5smYPq0r8Bo6Ojik7J9q/J2O5TkpWeYlpvTieyKNEMnVKdHXqQXrWiMmSK9JTBmJU+Og5PB4wrTxRBYKTVY1MSSNXWCNeHQkhRCsynaFQZFZMap1XTCuJ89bKoLTBZNJkR+9pnDTfwQvg0zgxTgYwJsNamahdSonJ82Im7fKJNdE0slJhTUZMrBrnHZk1FF1Low754//zj3hwb8CVF17l27/zP9ApcpyBGA2KmqtXL/Kf/+N32dk7xj9BfZWHfU0MipdffZ03Xn+DjY1L9Ho9TGaxWUZRliitCI0jy6zIR7Smrht8dBhjuXL9Brdv3+bSlausr19IceYRY+GTu/d4861vMZ1MsJmlLEuWlhZRWjEdH/BXf/Ee7777Dq+98hYhwjvvvEdVTfHBYoymyDIWFxZoXMNkMmVzc4u/+PMRDx/cYzQ45uaNV3jw4JFELyuN0p4YPVplKJ0RTQAyXFCMRyN6eZ+qmtK4mjLPKApLr1uS9S2+GdM0VZKiKAgOTCCzFrAUWUSVJZNpRafTZXllVaLKmxrnGmG8OAH0iqIQgCVGdFQi+2hqrLUpulgTnBgfRyLWtLRLZnTotpESlpWefQ8APkmoXEMTQmKaKHwEd6qpslbuJ2HJRIL3BB8EOFQSuyyL6NJ8+hBmRtyyDy3LRs3AlxaQKZNpq8bM5CAzxkwQU9QQOPVekVe0r41EjNZMJmO2tjY5PDwUD5miR6fb5erV59A6m8WwxyjsMmGX6KTEELac+CvJ+OFPATFKtSuJMTlztNIakTS18K53HqXC7N5TKkV0J1lmu+/W2nQcxUPIKAE4iSmGMDXgo/GU1jxZaU1vYRFiSACyMITKPCfLDEqMutL9mBg2VsCRzOSEGJlMJ1ROzJEVEJsGbTVZ1kEbi08MoeADqJBYSQqrHFFFhmPHcDxF6YrMDsR0t+xSlCXGKPIkhZB/5BpVRiLnldEJkAigFCEI8y6GiMlE7tMEN2NmhOBQVgtomPySDHHW9Ifk4+TDCU6RFTkKkaZl/S69uEpEYTKLUnoG8lWTKfXxiNFgwMHhEUcHRwyHA46PBozHY44OjxgNB8LAcY7JYMTDTx7i64aLly4yXawZHo5BC1uoU3aILnJ0eAwukhtLQ83axgplWVBNpvgYMDbHKjmm4jdzAoTODOu1ePGcSISS71okzVMC6PhwstCQLGjE50unyGzdslz0DPWRaPV2zFagTxn8akVU4iEzPB4yip6pgdgmPs3Gg5P9OjPDngZndNLJhYQ4te+Nc297Vs/qb3xNJhPefvttHj9+zJtvvskrr7zyuXwqWunZxsYGL774IteuXWN5eXluxXgymTy1pOfSpUtzkp7j4+NzV8EHgwF7e3tsbm7S6/W4fPky9+7dm1sMbaVyx8fHfPzxx2xtbXHx4sVfuZXtZ/XF6t69ezx69IimaVhaWuLGjRusrKw8a6R/wbW5uTk3TrQMw19k5Xk+B7R57881AX/99defiv1xeHg4J31qpZVPXietlUGv12M4HPLgwQNaP9on3/+svlh9hkeNPIBzSr503mtmrzUKpRWDwVBoQeakKZTXQNsYtv+tVPuM2L62Xb0/ecjk1NfOGsEZ1bv18ZDmkegZjcY4F7C5Sc3pKaDH2Fk6iXMOFSPBR3zt8I1IkjKTU8eTKF9UnBlHotI2FFpHGu+xWX7y+bptbPWskT59jEKQ4Kv2pg0pTrqVHUTlyHL40fsfcuvWkP7KS/ze//g/srS6QD2VGONoFDHWXLxyiYOjI+ppNTO0lYbSYHRGCIpv/sbf4g/+8T+m11/Emny2QqyNEWZJjNiOgFh1XUtjoTVaSRO5ur6BzXM+/ugjJpXjueduUtUTfIxMa8eFSxsYLY2ntZYss9R1xcrqCoPBmMwUHB9N+Xt//++R55Yfvv1DhsMRWsWZca4xlm7XMp2O2d8/pJpWbKyvsbm9xeHggKgiSpfYLKPTW+bKtWvkRUnlpkSVkecdTD0lBI/RDdPJkG6xwtLyIijH4PiIrc37BBMlxSZ6stxKjC6RzGgKC2MnEoROr0zSHlltFw+JBqtF7hUTgChgiyLPc7z3ybgvYzydSlKLgiwTppRQltWZaOO6rhmNRqeADpVkD34W3x5DoAkBk2XJl+IEpGzHP2mgJaUshEDQUQxvlYaQQExiYgYoYpMkLVpT18Jm8d6T5+k7oyTKiC9NSPsiwKkw4cRnJ0Gos/tVjknE2gyffHeODw842NtjMh6J/40yrK9fZHFpJRmzClvA+3YwD4mxwKxZlX2R5rKVihDlnrdagBp/qrF2yYiZZM5c17WAXaeklYnCMANPxZtIYU1OkXXQwHQyFWmUkXhum+VUVU2IXtK1RHNJ09RopSQWXQNaJFsmajplTm7FuNil5ICiLCiKHKNE/ojW+MlYfqfWmE5BJxcwsKobVBBmRbSIn5GxeN8g3uQZSlm5p7XBhYZQizH2cDxBaygyhU33vEoggLaaLM8EMMot3W4nSfTkeMYk1YpEVEiG2SEBF15AKa0EOAtOQB6VwHABAzUhQWVBa5GMmji7L4gi57JGkVkBPW2R01tZYjkErkRwjUSiq2lD0zQinRoMODo6YnR0RD0eU00nTKYTDjcPqY4byt6IrMgxxjIxGUoZyC2Li31ymxGMIyvE9F0mN4jOE3QgNHHGMiN5JoUkA9NGrre2FBEVfJITCdDSmvO2LLWZD1VihrXXcyv9tZl48LTX4UmKVmI/KXlvVArlAoOjAdPg8LrFVk4Ay5nULQGZszl29rpTk7ZSEAVFSvybU5zVZ/WsnhXAe++9x4cffsjGxgb/6B/9I/r9Po8ePTpXWnBeCTNXsby8zPPPP8/rr79OWZZznhotw+FpqpUgnK6VlRU2NjbObPPec/fuXR48eMDDhw+5du0ar7zyCq+++iqrq6usrKywtbXF0dERRSELiePxeOZzNBgM5iK/n9Wvd21tbXFwcEBZlvR6PS5dukS/338qc+tn9XQVY+Tg4GCOfbK+vv4LB2qcc3MsmU9LTWpZc59V3W6X119/fW77eTHq29vbs8CcdvFZKXUu6Hw6IfRZff76TEZNu+J22mum3X76wJ/+83A4FtCjbTZPsQBOv5/opelVbbMZZn/vfZiBNSFIOpGs+J9i06QHYx/Fk0Nri1KBuhoxOD5m7cIazldYo/HeiYfNuCZ2umIo5ZyYvhmNb5pTK6aBTEXKMkNrS+0aXAwEwLmITc13lait3j9BLT/FnGlX/MV/IBmZmvysD4HJiE7illGB3YMt/ut/+QFvfuWfstDv8sabr0BoqJ3C+wajClyIFL0eS2trjAcP0mquAAdKK6LO+do3v84//p/+KYtLS0RUSmERZoG2iT4fAw6SaWxaWjVa/D2UJsssRZGzuLDA2+/8CJTm+eeuimtGVGidoYywMAT8kdVnazX37z+iVy7yt//2t/nw44/5W3/rb6MouHXnFsPxAaGZiqlXCGidvEGCZzicUFXbrEwqev0CNTA4Dxcu3OQb3/omF65eARUoKAFhwCimZJlleWmRbqeD957hcMB4cARhwmA0pigzrLFYm8v1ZDOyzJKXGfd3DplGWN3YINQV1aRBaU3wwnxa6C/MPGqqRvw3QggQBHRrvMdNBVRxjSDPmbXkufih+BAYTSa48RiXorUjLXsDrIKYwBqlZdGbCGWvh3Me7zxoyAsBBZtplQyBJYksRpG1ZDYnKPmz1uK9RIhM65oQPLVr0BGKIsdaTZ4LS8RaS9OIHCsEl1YAiiSfk/vn/8/efzZJkp35veDvCBcRkVpUVZZsrRtANzAgZkhwl0PODGdIXprtLvcazfiSn4CfaG1tl7bGu+QL2nLFvRw9Q8wAg2mg0RKtu3RV6syIcHHEvniOe0aKBgqqGyIfWKEroyJDuB8/fp7/+Qv545PUj9S4m8QIEm8fpcVTw7UtCs3W1iaHB7u0bUWWyfV05fJVinJAIKKUF28VEHNq1/QGzNpIA++jp7SymOxkX1EpTPLwkZjimPyejhbB3aK2+70jZp7qH+/mpU77r1EE0ybwsAAUdduI+XCAum3QWmE1uNBK7HQ69oUWgEQYcy2TasrBWFxyMmPJTIbS0Bw05HWGjpbWOZwPuCQTCgqslgQ5m2Uom6HTe7Rty+HE4X2L0khyXVCgkiFtSIzD1IiH4NERQOONQgUBwQLCCLRarhmXW3zjmZsbkWUC4iklTCST2FwSZqYSGUNRh5DYOBCNTh5lChuAlKylk2TIR520ZwkYSVKziBiTozvZq5aId7GpwWZWzA6HkYLIPKtcJLGmlPhKxSBm6c55XNOifCsgfOtomwaUYn55kbnhSCRYNuKUJ4kWe+BPYVA+pmh78TRSkK7lFHmvde/ZQGiJrjPwTol6iFEzOomLujk+HIElxhhhEgG+nfGMUUC6xx2V3E8M4Mc1D2/dFQN75XtmmdxLmQFwj99jOzAndAbH9Dhop4wUMOicUvMrXVmWMTc3128ajMdjtNafW0qPsC0VdV2zs7ODc465ubkzfSXOqsFg8EhmnSGER6Lw/6RljBEweDplOp3SNA0ffPABZVnywgsv8PTTT/f3iBhj780wHA57GfTJ9fDLL7/MhQsXeOyxx1hdXaUsS/b29tjZ2Tn23kVRsLS09Eif8/bt26c8UTpj39nqPuvt27fZ3t7m8uXLHB4esra2xtraGjdu3ODmzZu8//777O/vM5lMjpkexxhZWVlhb2+PGMWTTynFjRs3mJub6yN6u3GnlOoNWmXT53RT+lmyjLPqLENRrfWZvz+ZTE41kSejkD+rfl13+7sNqtnxrJRifX2dxcXF3vh1PB4/sk/MKFkl/LLXo577z6rBYPAzJTw1TXNqPE6n0zN9pB7F8Pqz6iygBs723ekMfGerUwbMltb6TL+sy5cvn3psb2+P8XgsvXBd98bOJ99HKXWmEfSjxLL/MpTWujd8v3PnDuPx+DNJK2fNRWcZvf+k9eOP1I+4MGfBl27hrVBUVcV0UpEN5o79O9DvnGvdxQ2nxWo8An9Oa+ZnGDRptzHOfjQlC1Jp2DwhtowPD1i7sIaKAec8xmpcalQOJ2MyI14Z4s4fUaogtxnjgwPKsiTMDSRO13lc6wltA0a8K5q6ZXw4piyHWJvRtpU04973C5ZuwdTJS0CRZeKXIzIP2dHtGAwoWewbm/H2Oz9gZ6fl6pMXKYoKFQMhQNSRzJTiiRJhd3eP/UMxDWydTMRKW2JUPPbEM/yLf/mvmZtfSAa56VMYcWFQSa4Q0o6+mNiqJAcxR3Hcif2xsLjI1772Vb73ve+Rac9jj9+AGMWQNjrEjwJ8kFjfpplQVVMGWcnS8jKqyPnjP/5TvvrV3+Fr3/ht/j//v//MnU8/RkwupelwLmKtYTSco6obtrZ2qeuClZVVnnnqJa5cuU4xUOzs3KQoBywvXmRpcZmw2HL34zF5XjIoh+RWU7eOw8MJg3LIdNwwml8muIr5+Tl29/chirfMoCzIM8OwyCnm1yjKkmDk99tWWDRGG3zb4kM33kX6YbQcK1moSTJS2zgBxULAuxpiFLaCtTTO0bayA2+MnLNuPLdOoqe10WhtBSBRirb1/bhy3qNaAf+0lfhrbSxKa9qmocjFk8M7j8HiYwAcw6IkRJhMp/047HbROlabABeWEAJZVuJ94OBAbuCdEbG14tvSsWtCiiWOsU2gZAeIKFCRvb1dHjy4x/7eLtoIOLSwsMT6+gZN05KXBZnNUyKQeOpYo6mmh5TDESYxeQii9++uMZUAFnlv8S7p5Evd+el2Lbv5RGRNoZu4ZlhJR5HoXTJanhuRzuU5xmgW9JC6cRyMp6jEunJRDG+Dd2gDKir8ZJqMZA0aYf8oInlu8a2ndp30LBInU6yyAqokY3SVpF0dGHU4mdIkSZQ1IlcKiETMe4/zkhIn7CiLj54YpdnxCChsbWIckY5ZD+gZjFL4oFEOtAqMD0Q+lcgk+AQeZVmBzXPyIu/ZXnL+k/eW0egkI6PzNtKdgXZERQE0O1AA1c/gAuSEgM0yOd/aCFPHe5TR4s9iErslARJoi9KRaISxkg0ycp2MklOEu0keShFJ7Yo+EK2krREMmckF0PYCoIcYaV2bmC8CyNgkBezlgYCxJo1VneSYHYuxG30BgkrzamKNRjEiR3VpfwL8yXydZE5ao4ztZXfCuhOTY4tiur3H5r0HeBXSeT4CZrrNlNgB7TPsGEVEpXtLiEf+NP1zlEJ1k8B5/crWYDDoF/3OOba2thiPx9y4ceNzef9uwT9rNnnp0iWee+65R/r9LoHmx1Vd178QoCbLst5z4d69ez3Y9Oqrr7K+vs729jarq6s888wz3Lp1iwcPHvD000+zvLzM3t7emQ3Xv/k3/+YYUHbv3j2AMw2ez5IrnFXj8fjU97fWnjp23nu2t7f54Q9/SFEU5HnOe++9x8rKCjdu3OD69etkWcZHH33Ezs5OD850tbW1xVtvvdV/7oUFCVV4/PHH+/fqZBZPPPEECwsLYirfNGcaHoM0pieZPz9JZVl26vdjjNy6desUS2l2HP6mVdd/TCYTNjc3uXv3bp+4Mzc3x8WLF7l48WJvgP2ojeTPCmB8XvWznvtLly791N9TKXXm9V1V1SkAaXbO/mmqi8meLeccd+7ceaTf74Dmn7aWl5c/t42AL7K01kynU9577z3eeOMNHj58+JmbCt194+ddPx6o6ZgrMyDKbB3fvQMQn4n9/QNGS0OM0Sd+T/UMnbNQqa5x6pk0WqOTl428ROjZNDMfUv4/pEWpahkf7hGCMGaMUbSuEUPLKJHCbTNBRZhOJ0QVMFoxNxoRWkeMMFcOCLQ0rmaQW5SBg3FFVUnTPDeaxwWRnxR5RiSiVdrFNslIVIv/QupN6Lw4IpI041NctyKgjKJtJdHkh+9+yGBxwPjwPi89+wRaaVyAEBuRWcWIiYGbn3wku8pKFuEoI2lDOuf3fv+PWFpZJ3gvgIKK1K0wALSxhNSYyjGP+NT8oUw6tFqYH0aJPMN5yrLgSy+9wNtvvUGWyVnJs4zGSYN4BLAFptMDFIGdnR3+y3/5f/L4c09RDkegNAuLi/z2N36b/8ubb2C0ZjAYpISIBt96VCZNPRjGhxWwx9vvvMG3/uYvQcFgbsDcaMizzzzN008/w2OXV/EhUBQDRA8CS0vL1FVDMSiZjA8ZjhaJLmNpeZXJtGVleZ0sGzC3UDLer7iwusxksCDskjIjb2TR0Znmeu/ROop5rOqinOXcGttFImvaVkADa6XZcz6wt7ePNjpJpsR3om5arNWYLMN7B8TebNoFj9YiP/IdoyWBRD6ITCjPM/K8TCwyyBJy6YLH++Qh473EJ7cOkM+X2VlD6yifK3RyDYlyFsCmJcuMeClpRRfNrpOMRT4v/e/1c0EIeN+SWTEn3N7aZDqdkGXCXtjYuMbq6rrIDZ1DGUORl4LIOwFjdJEznRwKQJV8k7yPvZFxF7Mtc6V8Dq0VzofUuPrkxZL1O0mzE2vX0PaGzCFQFIXsCMTIpGqYTKbC7Enn15pCjMaNFXlJAjSBBKRB672AnoGe8ePaFl9LxLbRhhQ9B0S8lnQgI9pMieKOhrqtMYA1RqLWiVivicpBFAkSSMyzgAYRqzVKR4wyaJ2MkKMY0eIV3iUAQCtUkLERjCEz4ifjg8f5BAJqReNa4QEGj1FNYmBJopW2mqIosdagk7+BMomdZMXXJ/GX5Hun7xySaTbqCOTvQA8fRX5qcvGX6eSUPnhUTGyb2J2zDpyS1zC2Ow8BH5PcKjFIBGzSYCEEjfdg7TAxv1o0irwsRd7UbR4kSlEIAmyo7nFSIJZSWJtjUvKdJwGsKhk3BYjJJyoS0zmX53QgIz4IKBPlPmhQaBVxXlhyIQSil8RCpTQ79x+yd7iPM/QMzg4QPtrNV8dAyNl7tNLCqJJodgHpe5FVFHnkeZ3Xef3m1axf3mg0YjAYnMloCSHwF3/xF6fW7a+88gpXr16lLEuGwyFlWfZy6vM6r/P6xVTbtqcAqV9Xhthvev0YoCb2XOkuVpQZmvTRhJ1+RiMZOorxZEr0QLcwVdIsdIvqY++SGqdubemTBEqlpiTozgcxdDzvtDvbecCQTBpDMht2TKaHxBDJswylA0pngKZxrqeH65TG0Rlfjic1hc2YVi1aQZFp1heWsHnGwWQirJTQpIhWeXejBUTK85ymqYkSNUPbOmwy8Ox2/lVKjpENUNllVUoazCLL0Dpjc3sX10QuX1zg5eeusL40ItBFMGZkxhK9oyxKJpOpUO0VKGVQSMTxxuUb3LjxGMSAzRSaKCk0SkxXjdLCnvGeohjQOmGBGCM72p0EzVrxtAi+89bRDEcjnnnuGV7//veYThzPPvuCMDISvb6LOq8PJxA93gd293Z5+813+Nf/0//M7dt3uXBpiRja1IxE9updNi5dYfnCNe7du0PdNGSZwVogwOH+mL29XQFMXGR7c4u8yLl76zbf/bu/50svPMficMDFlQsiTVCKtm45ODygdY2Y5elIDIrDgzHGliwur7B+YYXxeIfxfmRQGh4eHOBjpLCGoBTGaqwVU1JUJNOGg/GE/YMDxpMJzifDISXjiC7lBpXiOK2YryoloByAMuLjExskNl52mmK6jEKEIi+OGAHxyNy0i/gMyUfDWJ3AiYjNxZRaoyiNTu/tE1tGUpm00RR5Tkwss85Q21ibIqpj8hgR6UgEuWZN8tJQwjpwXvUAltYSEe6cSJ2MNbRtjfewu7PNZCIa1tIWGJtx4eIG+WDEwXiCDoa2baibVmLAjabIC3xo0dYmRo2MQaOEUaQ5Mie3maRgOdema7oDYRvyXIASAXQUTdMmHxo50N0isvOUappaQAmTIReUEXmJUrjWU9cTQlSgdZ/UJek6Akx213XWmRtHMQfvJijnnTDHEjuDKIlY2mhh9GkjzD8T0wyq8VrGR9sGQgI3QgeqIedVpWhW53xnSZSA4u59AkkQ1UdvC0gOUYk5dfAeYwqZRKOhaRw+SuLTIBuQZ4oYHG3taFxLCJG8cCijsEZjtKScGWuweYakccm5QZs+4UjGl8iIxFCX5H0TiV7mMdc6sGI2jpLPIIlqSSKrk8EzUW4K6QalUCijMMjn6bxaTEwIizaoKPKmmICpXsqUwJioJDqeBOxFJfevno2iOoYWuOCJLslNj93HRLZF6L80QJpbu8Q2yK2A+74HCyNEmSsyK8yaEDzByPsdPNzm0NXUOhBb8bg5ugcn5tCpe2q61xwdpiP5WWLXqDi7yXJe53Vev4mllGJ+fp7r16+zsbFBjJG333772HNijLz//vunNmtDCDx48IBLly5x48aNPtr7HKg5r/P6xdV4PD4Vud2l1P4qSNTO69HrRwI1XepSz4AhmRcozhwIqv+jOBiPcW0gL6z4w/RxobGne3c7fydfKib5xJGxYgJp0k5q39XGSHr46Dla3q+uDtnf3aXINC5MZddSl5jUqHWGsDFGnA/kNpfYXhWpXUMYN6j5Ab7y1PuVGDqqgM00aImodq7Fp4SW6XRC1wAKOCOpUQLECOVeRZ92lRVN24qnglIokxFiy7Q6ZHdnm+g8v/3KS6ytLAksFh1ZXqLQtI3IbHShmV9YoK4rDLJJH5U0jXlekuUWrTzWxN5/JgSfWDyatvEobfFBFukiZ+jSiRJjSQVheyiFUoamFWPmYjDkiaee5M0fvMX4cJeimMNrQbsKm6EJWB146qmrvP79j5lbLLB6yPzSKpdi4M7tj7n56XvUVc2gGKHxbG1t84+++S/4rSLnj//0/8X+7ibW1miV4R2pKZZTHZyj8oF62rKzPebi+nXcguLgcIwa5BibMZ1MmJsb0rQ11kCuNY0jRWYOGIxGzC8NGVcPGAwKUBb2I9oqBqMRIUDbNmSZZW9/B+9ahkWBthlzwyFKG4mib6W57yQ5Osl/vI8YqzA6E2qpNj240RmHdjv4RI2KURrsxGpRXbpPkj914ERIfj4KMTztLC20OTIUzkzHQvEJ4DEUZSaJSlqa6KglFhslO+wJZyJ4T5ZJhDVAVmR05qioSIwS1RxTUo8PgRCcSGyiNJV5XjKdHPDg/n3G40m6bjWrK2ssr15gXNWECK0P5FmSPkVwLtC2FXlhBeAlGfVqDfboBtRr5jVYawjR470jy8R7yChDUQyoqiolWmiyrMQaQ11PxfMnHRvxaA49C8kYnZrgmNg4AtYRTU/pbpM3lbHiTNK2TQIdNHUtoGxZDkTO5sMRw8onWVwMKMSPxCC+U0ol35/YwSoCjnVggk9eROJ9ddTcZ9ok8EbkV6hAQOR1VmtJplMCviktchox8M3J80xwnBjIM8NoNCDL8vQ5JBHNGgsquf6Pp9SThsYFDuupAMPWCDNMk6LITb9Y0OmeEVQUlowRGaEx4sFjkheWTOOyCRB8i4ue4PXRrm5iV3YbAlonHy5IptAC6gjPq2Mw6j6Jit77zBBUSHOwSb5nms5LrJMGCbNQo233vmKQ3DFXDNBluytzxLjr/l1rjTIJSImdwXACRILI31zwib10xDgVlV+krlsyI2mD2mioHLu3H1BHTxs9+G6/4ujGGdMRpN9QmbkrRznHKnYbHAghKwoBqAdyzuu8zus3sro478cff5znn3+euq5PATXAmVKJDz74gIcPH/ZGxRsbG+R5/jN7hZzXeZ3XZ9dkMjnld2WtZWNj4wv6ROf1i6ofK33qAJlOhqT0aYBmVokkzY+lbirG40OKMqdL11CaGSDjRDpFnH29ox1CrROdu1ukJ6+AY8ZtqCRFONrF9L5herBPefkiVVtTNzUuBrSVxXEXSTiZTDCklA4lDIXgHbWPNLWXFBWEgaBNhs0KfGI5GK3RNlI1wkwoy6JP+AnhyFjIZjYBNl6SppQWc9joUwqQYzyu2Nvd4bt/912ef/4ZNi5ewLk6pdWkLPrYGVkKaDQYDCjyXFJXCP1ueVNXgDSVdd0ibCVpyJ0PqK6518kLIflKOOf6ZtgHz6xxc4weAiirUdGwvnaJxx4f82d/9sf8s3/2Rxhb4kNLCDUYT3AtKyvLPPd8Qessz7/4WwwGQwblJb7/3fe5deu2HHOtyPIBw7lFrl17jMXVZRbX5vkff/kXvPv264xGBS7USMiQJsZAWRSJRWXI8pK9vQNyDc53ceg5PoicyFpFdTiGPMPoAuU9w3LA4d4+k+WSIiuotKMscoKfMK0Drnb4GHsPisFgyO7uLg+39yTZJSqi0oQk8ZDGXhN8JCoZ11mWJSbLkQdKSEbOswkwEj0NZS4pWy44SUNKXk7GmD4pqrteROYWybQiBnkdaeAlTj4GkaeIREkaR+c6wE1+btI47Bhu/fh3Dq2QJj7G/v2bRkxsRWokxyUqhXez0iKVQImCvd0t9vf3GI8PpSG3OatrF5ibm0crIylOUfV6TqNMf7za5sgAuDMMnk6nPaNIzIvFULeqAk3jGJQDubZI/lDOJ+mOgC9N0+CNTjKP2F//acJI7+2pqhqthYWj0oUSgnjndOesaZoky+rOocx53TymlfgW5VlG8J48z+T1fPLa0RpPnJH4qL6JJyp8Yjx577F5At1cS9PUZJntpU26k9/186eAGWLqrGmDR6UxE7V4YUXrsRnkpWZuKCwao0CbSFGAtV0ktZhaeV8n0DLigyIEhfMxgZEOaLBWrkujlRgKa0uWZ3T9v7YyNxtj0ufR5OWAoswF2NXiv4MCjRf2ToAQGoy1gD2SS4UoYGYUkFOAlNCbHkMU6VICflTP3jka+yLR65LWju5zRh/dW7rnzj6nuwemEXPsdztjvBDDkSQt3a+0EtCqv4f21+vRvbV7HUjAvhIQSAHV4YS79+7SIubjHYNxlgdzliy5G9uzzLw487OMObmDnuM0v34VY0zyXd3/+Tx3WmNaU5x8/7OYFt298cfVL4ra33mUzYKteZ6TZdmxHWqdpNp1XYtPWGcufkZ13/loDXWSif6T10kZb/fYyWPXzTWDwYCiKPrP2M1Tnelv9xk7A+ClpSUuXbpEVVV9QlW3plVKsba21q+frbVYa3n33XfZ3d3l8PBQ7stnfMbZz3qy+rkzbUQdnw+PqvOUO/nY2fPeb3Z1117nGQTi8dOte46FupzXsXrUuQj43Dx7un55tuq65uDg4NhjeZ4/8hz5k3zPR60fde3/qlZ3LXWGyfD5nfeufiRQ4+PJ2M6I7hb+M4OmY2F0O73ei1ni/v4+i8uLstuuxCuguxkeSQ+6k3p84uigGlnQHrFwXJKQKDRaxWTOmT5PFP8FjejxD/a3yazCZgMUkaaJ6fPJTam74YTYohAgJQaPNULhr52jbYP4N7Re4raVJypNVU/IMyOeH0pRDMp+kHbMoaMdUzFk7dyvQy91kh3ww8MDdna2ef21H7C+eoF/8FtfJcs0g8EceZ7hXEtdi1TG2gzfup6C37Zt8s9o5Tx4z+bDe9y69Qmry6tJ3iX+Kp02wkWJUCbt7mfGysI/cOI7iJRD/q5QWTIq9WI+ur62zvzCLd5+521efvkVRoMh1kyYjLe4c+ceUPDbv/MltrYcX/rK1wnakduSy9euc+fOx5TlQFgDJqMczDFaXMTmBZevXOdf/x/+Z954/Vneees1Pvzoh+jEWokx8vhTT/Cll15mYXmFhYVV5heWmRxsUZgpw9EoeVu0aJMzzEc4pWmmFZgcY1qWFhZYWF5kNJwHX7O4qDn0Hk2DsQMCKW5aKfb2D8WINip0Puhj3WOUqHe8pN90DXEHQsrEmgDMBHj4GJI0JJkNh0AMCqPEd8IHn7xoBDzzyVi09xzh+OLFB5E6eeePLW5c9NgQKMsBkIxnW/HEaKOMb0m9EgZQ27Y451Mqk6YzudZpARicJOlI6pq8d0hMDG0FyAnOSZOqFU095fatT9jd2yZGT5aVZDbnyaeeJStKIoYQHcE5bCYGtRhFUZRpAZxifzqmSALLQJKPugV/Uzd9tHZIIKXRirbxCbRTvcyrW8x216QP4Rh4prWiKMQwt23aniXVzRFaSXKXSwtR7z0RRVkWFEXRL/B9EJZfCB6tDcO5EqXAaEuWJaZJkjl1s55PzYwAqw3DwYissLimpa4FnMnyDJVAPrTCGmkerLGEtJju5JHOO4EsfErDM2KmrjONUpa6jkQ/pTCapaVlsswmvyKIIclNQyS4yLRqqKqa1gXq1lHVLa3v5mf5Bk2bjqERFovGY6bCIsysxViSmbQlT4vyum7Fbyn35FmGIZLSzo/kUSHg21oS6hKw0C8Egu/BPOE1kcB63bMv6ViZ6viCtPNs6ZhtvdF9oux1N+GTZvmz96s4cw/srhVhxaTmLDEnfRqHOjFx+u+QgJpIBC+SXo28t85sivYOmKjYufOQu1tbVFFMkQnidTabHBhmXrs7RjJfzty9VZIXK4UOkTRUiSEQzqGaX7uaTqf86Z/+ad98X7p0iaWlJeq6/lwatN3dXT788ENWV1f79y+Kgvfff//Ucztj0y+inHO88847fdM/NzfHaDTqZTz9/OA9N27c4Pd+7/d48803+da3vsUnn3zC9evXz/z8y8vL/fGu65qmkVTFnwWoOcuM9+DggJs3b556fGlpiX//7//9sflrMBgwnU754IMPuHPnDktLSywvL7O5udl7XnTGyf/u3/073n//fb7//e/z5ptv0jQNN2/eZDgc8tJLL/VpWM899xyHh4esr6+ztraG9/5Mw2et9Zkx5C+88AIxRh48eMCdO3fY2tqi88yZraZpuHv37k976H5jquuH3nrrLb773e+ilOLq1at885vfPDaefx5pNL+O9eDBg0d6Xp7nbGxsfC7g98OHD8+8ps5iuJ01F5xV4/GYhYWFn/mzzdbc3NyZ1/ivcu3v7/P666/zJ3/yJ3z44YfMz8/z1FNPsbm5+bl9hh8J1MzKNE6a/87+PMuM6QCESORwfJB8MmS387PQti5et5dDIU43fSugj3auY+8DcpTbLkyBI5GU1gLiTA/HtLXDFiIzGAwzfPTUdYNzDd6Lt0VmLTbLQUHT1CmuNRBURGcG7wKZyXGtoGo2L1AgKSGInGB2R6ZbBDknTV3njaEAqw02l9jfiGc83mdr6wEff/AJa6sX+NKXX2VxYQEfWmEvKfG3yHIDmCTZcDRNxdLCEqPRiMm4wVidfA0iBwe73Ltzi0sXLkrKkNLEKN40Lsa0u+tTQ+Fpkjyj8/sIQVJyrJHG0nmf0n4ADHmeEdqK5ZUVrl69Sgg5H37wPi+88Ayta7h/5z6Ny7h27VmMzaRJNAZlAt451lbXMdayurrCvbub2MwyPhwzGe/3CVZWW1758tdYXS755JN3Cf5o9/ndH77H/XsPWb94kevXn+BLX/4qhZWkpBjB2oyoAs4FDqoxozxnOJjD5jkx1MzPj7h4YR0yxWiwwDCz1Hvb5JmnbiX5ipQk1EU8K62pncjttDKQWEedqXAH0Mj5Fm8U2VxPu/tR5HwmGYVmVhg3zglI0HnZFEWejGBjkoXExA5JYCVHu/HScNKzP5QSxkhZCPA2mY7R+shPBlKMai9xC71JL3CU/NCzwNIufJQmWGnos5WUgpjGnIGQZJHOB+rxIdtbWxwe7Iv3jLUsLK5y4cIl8qykTZ8/z3OxGtEGrUnpbBn4iE2SKO8dJop0Sic/KO/lPGdZTp4Lc0jADydGw4lNE0MnO7L9blxnRtslsmmtyfJcFi1KjJV9CExSnGKmNZPptI9YFqmiTlKvSNN6tLFHzDQE4PZOgLgQA4O8THMidABCN7a6Hdlu/syLspf6mMyCFvnXLDurkyl256q7NvM8xzmZA4JvkoeWQbQynuC0mDMrgwaapqKuD0EVGKNQ2qAoaJ2jaVq8E3+cxgXquqGqW+q2Fa+edCvQPUtJE7zqgZYYIPqA8y22NUCgmniKPMNajTYK1yjaqsFaRZZnWJtT5GXyTFIItBFoklxNPJA6FlKUCO3EBItBUq5CYgLFmTETetbRzI52kgAdW2R1iWkzuyXdNd3dt+TeQE8h/cydycSO0VbObeyBVDlvOnnwqMgM4CTbIsIWTJ/fKx58fJODeorDC6gy814idk1MrnRNn7XL3LFoul/qfJKI8Vz69GtcP082x0/7/rP1y+id0PmznKx+42KmmqZhf3+fuq4lDXNhgeXl5TOBmps3b3J4eNjP2T+PXdif5Ph1G0dn1WeNh26sWGt7oGlxcZHhcNjHDldVxfvvv8/W1hbf/va32djYYG1trV+TfBYIqJRicXHx1Htaa3vGzi/j+PhVq9l70iwL9NeR7fBF1i8DG+lnuV5+EZ//l+GY/Lxr9h76Rd1HHynIvANiHuVDymQrO37T6YTJZCLUf3N8YXvy9WalUJ/16kcH6sTKsv89AYmMUQTvmdZTHm5tMRgNCHgGwwytNEWeyWJVK2ESJPNKn5ohaaKa3ljSaEnrsKaASGoMFU1T09Q1pmsEE1NoNga4a6q73U3vpdlqmjp5Yljm5+b5ypdfZThYpCgGNHVNVY8hsQkkHUf3jJngxRz24oXLvPjCi3z7O69RliV1JfHLNlN877W/57HHnmBhYZU8z0D51LQptFHUdSXgknMJyDmShGHSeUySENeIp4iystPrnXhsSJKQ4fLGZT748D1u3vyIuUEBccSliyssLa5T+322dm5hjKZ2LTEErM2pq5b5+Xnu33sIMbK/t8P+7hZXr1xhMnHCIIqBtZUl1tdWuHt7O3lXiEHD1vYOh5OKEDJsNuRLLz3FsCjk35XC2FyOGxqjcxSR4fwCqysjRsMB2aAkKE8+GOHqKA2kcri2xUVhIRmdWC8eSdVBjFcxyfchRRWDSHi0Sbv5UXcijH5s+hD6KF+l5JzKeBa5Sze/9ZeW4B5JNiW78ybtkgtzIoF2SXrVRW0DIp1KwI/znkwLkDF7/cXuCkqLsmMUyKgTY02J30n3+vjErNE454lRFoKtC0QvxriKyPbWJnt7O1TVlMyKifeljavEaJhWFb5jyliDUUaaz3D02qgusl0OhPjX+B7QaFufaNfinyMLYdN/L2UMOoEawrZJXkv9vCOva22ewA+bGl2J8M5ziR898mxyaHsk0+oWPJ2krWnaxNZJ8i+lGY1G2Ez8Y6q6xjkvqVJe3q9rquE4HV2YFQqfgB9jTA9cdPNfBwh3x8okMKBuGjGa1ooszbeZVmiVQfAoH8G3GCtUiv3dlvFhjbaaLLMsLiwwmheAMURPGyKTasqkqiQmPiTQPAoTz7kWG1Mzk9gwnZeMSwys4AXsE0BCwJvMJIDRKEajAcpY8JE2VPi2ljjZspSGwRgKa8AH9vcPRQ5nZGxmuVC581zYRSEGjO0MoxVgiCEB+R2MH2Ly7Dm6/3RztI6a6IOwotQJ9ppzAgClUabTWJzdyIjEBGzG/vN097auEn7T/V//nP4emBh6znuJT69aHnx6h0lsaRMYFUU1SOicaWbBog5/6f6XUgY7OXH3PB/kdehApPP+6Lx+Q0vSCE/7Opy1zt3d3UUpxf7+PkVRsLGxQQiBd95559Rzv/vd77KystLHIa+urtLR6H/ZqygKFhcXOTw85P79+9y9e5e2bamqCu89W1tbbG1tARITvLa29mNf0xhz5nHOsuxX4pic13md13l9EfUjgZqT6PjsjeskaBNjSJ4noX/MO89kMmFhcYHo0i4ixxk4s3Tt2deUxWRnoiq+BulT9L/XI8YpNacjvIcYCQQUnt39AxZXVnCxSalQSIx2DLLznuQMdV3NLFpFVjEYiHt9EzumgRhBkgAMie/VfUM4TRo2Y1LjkiQWRZ6nxlh2vyUdCnwbGA6HzA1KYjAEJwaqHaPBe5ca+kBoPU4JUOCdZzAYMDcqeOH5F/n7v39dmBPGCoiiAx999B5/9if/nf/j/+nfMhwMqZppAsNEvtDFsWqlZOFPoG3d0Q51DBRlx2oQRk2InqIwGJNhVEYERsM5vGt58cVnef373+O5Z17g6SdvEHXEO9AOmramaZvev8FHUMoSY6AsM4geouPmpx/w6itfocjmaZuW4Gu2dwLLS4s8uL9LNa2lWQpihgoZMSp8AG0tymgC4Jw0L5kpMCYwNzdPkecsXlxlZXVeUmSsxSgF3hOiYzQcMDew7NVafj9JBCIpEruuRCrQOqJP3hVamEkxxLTT7ekMgrPMEKPuARBpmIQdA2KCK54bYg4sfhuethE5VESMm7W1fV+nlIxBOQYCWrlAMgzO8D4m41ukMT2icYg0S5veo6KPpk5sjI4loAASIGi0TXHDGo9PvjKz10Fi1YQE2qAJrmFne5Px4QExePLBgLIccPnyNbKypG4cxhpCYsvoTORXeWISOe/QkbTDJmPRWos2mqquehlZlnT33geatqVJbCBjwFhLIKUnKSV+P90cMyMR68AWabYlpctYnY5POm/IdSXpWAICBJ+sbwXJTSyuxKpJprki4bLE6BkUJdAl/8j8pBMAI8DTke41KqjbBoMiprlPEu1AaZOkW0aSrgioWWAYIASC98zNzTE3N0THiG88udUi1cySHMlkaC2f+XByyGQypWl2GE8zjLa0PjCZNNR1I15MShgeITEejdEEr9N8rwneEaMmKt/dIOgSxYrcYq2mLORaV8HT1C3RKabjKd5ljEYlg0HyUlBK5BkhMA0Ro0AhoF7bNjR1Lcd+MiXPk1l0nmGsQdsEvFuLUZ1RvT6656jT97az2KLHdk+6ZKr0szARIz76/rkdUBNDQqRm7pHduZXT89n6cWU0KhzJk1SA8fYumw83cSr0vjRB9TBMf0vswBm5aGJiqR5tZ8j2RpKBqeTjM3Msujjz8zqv37TSWveMkNna2dk5JQ/Z29tjPB6nUIKcixcvnrmLHGPktddeY21tjeeff575+XkuXbpESJLbX/bK85xLly5xcHDAxsYGly5dom1bPv74Y7SWjYjRaERZloxGo0d6Ta01Fy9ePHWcz5J0nNd5ndd5nZfUjwVq4PiC89jCtAdcEgyR1okdidv7yMHBIRsbJ3Ym4tGf2eVk936SQpNSPGKEEGfSTo+ze0IIBCWeFjGZWXY7jT46Dg4OZNEdIo2rj9km+l5KoGmrOm0Jy45rXdXktsQqRaYlejvPxc+mbqa0bdeACRW0rmtCYidoo4RhoFRq7HwCkyTJJwZwjeuBHkhRtSb2/hzzI9GSTqdV38T7KKBSoxpA09SO69cf4/KlDe7du5UYBoEQKlT0vPb33+bJJ5/k61//bXwbsVbjo++NT9uUahPTQl0bkWnFCEUxYDgcUNfT1OCCsh3woJhMa7Jcs7i4xCcff8LL6y/wzDPP8MYbb/FbX1uknBsQVaQoR8QoEjFrDMFHrMn5B7/9Df74f/1vzM/Ps729Q2Zzvvt3f8NTTz3JK1/5GpcvrjEaWf7qrz9BKYvzrTSDAfJsQJYN+KM/+tdcuHCBlbUVijIw3nvIyC6QZznaWDSBteUlrm5sUA4K9KBAZSkq2xiin+K9Q2lFmeeUuXjvZJnBO5LXR8RoIz5FrsUqldJTOlFTIIYgZsAhpBhuSbMRGY6wtnSSQ3VyAxUjBE9uDV2st1H0scUoAUCqusJo24MSSolUr/fBSPTmzsPG+5ZM2xkZjkejBFSKnXxNxhOd3CIKQNP5r6goV5CLKa0tSKNrs5yI6bgAuOD7Bk+na29aT9jb3ebwcJ88zynyAZcuXWVlZZ2kOkJpMKl5bl2Dc548E+PGQKTt5UBdE+mpagENhsNBAk4ibZQ0oi4NKUb5TKpt5XvGiHPiaeN8ICbZijazAM3RmO7ASOeP5C8xzrIhUrxymrKc9ylNLWAREEx3DKXOrNmYxCCKGJthtaZpWwErxQ1JkoOCRL1rLcwd08lYlEJHkxrrZF4N4pGS5rDOmyWEIB5iKKpqincNc4MBc8OcleV5MpvhGgdREbwiRvGVWViYJ8sKxtOanZ2JGBVri0cRfIa24rGCApUYMx0rKUaYTiuyxFLs+I4okU3aLMPmiiI3FIVmkJcUuYWoidGmRDyoqzE7O3toI74EdeNoWwcoMhuIGIzOMXnWvzZK2GPjyQQUlIMBRVkSS01GACum34qQTPA72efsdTBjEDyzadBVB6yc2lzQwpyaBXU6sKYzte68cIDeD2n2tU5KigPI/IJIDFUI7N55yPbBPlHEuHgiPnZMGYFqQkfLDR2j5vj9umd5ajlBwv5MyW2k7x5+/SjL53Vej1JKKZaXl08BCAcHB6eAms4wN8syiqJgOBx+JvBy69Yt9vf3WV1d5cknnzwmcf1lL2stKysrrKyssLy8zMrKCoeHh3zyySfkec6zzz7LSy+9xFNPPcXNmzcfCWxRSjEYDE4dg7quf1Ff47zO67zO61e+Hkn6BEeLv9kF68y/QrfBnnbaIwqtYDKe4Jwjz3OUiigMHtmBj93Of/f7dEwZEImO7hu1TjJy0hG+kxwJVcOnnX7BXHxoGY8P2D/YoygskBaoyK68MWIa672wYJRSeDxEGHQymtTcFpkhs4px1aTEpiRVSGwKl3xMrDE9u8g73zNSsizDi1svvo1EHxiNRrSNo3WV7Eyj0UZ2u10roE0IQZoPjBipEtNra6ppS5EP+J/+1b/i//4f/6/EoPFBMZ62+LZBa8N/+S//Dz795BN+/w/+FXmWMRgVaJOMJp1PwI04+Pv0XXViHlTTKc5JjLicKy276VozvzBCKViYn+fjjz+kmtYsLqywcXmDDz95nxe//CVJyiGjLC3bWztcvrqedqYdj914nKeeeoofTN6gaR5iSsvh4T7/8T/+3yBoXnrxWcrS8eGHHzIeV0yrWhpibdHacuXKDV548SsUuSXQUte7TMYT9mNkYbTM+toCF9ZXWVqYo8xlURWyHGdAFQPxoghO/FsMWJ2xOD8k3qslFStGyjxnMhnLmEp9TGYtRhtc8GhrybMcpRSuabCZRWsBbJq6QrfSqAcvRq+5tWRWzF/b1IznWSasqcQMMyAeOUokHjJGDa5N5qyZlWPohdEVOUpwiDGS5RnRe6q2Jc9zSQ3rrhkSW0qBSvIsOwNU2EK+y6AoGA5LrBVz3Lb2TOsWH1yS7BmyIhd/m9hFvzusVexsbbJ/sE9dVZRlQZYXXL58lcFgKNIfhClnjcZkRsZVCDRtjfNi4musGHDr5E3Vti6ZHed9jLb4KB2lanSpV12z2kkZQRGDI3CUOEcIcuzikYbb+0Bd1yhtCMmotQNzOkBnloHhvZcEp/QcF4KwaiD5zgi4Vpal+F6JBogYBAiySUbonJO5gQQMBpG4hZjkQmpG5hYCWaZTPLpIx0IIWCMa/6IoxLCWzhhY/LhGQ0OWa4rcUuQZMWim44rWS2KWNhalNINyiFIZ48kEHyLOy1xcV21vViwSM3ncJ7mq0Zqmcb38Tyf2jVaIxLKBKtPsOkduDGVhmZtbxOQ5xuaUeY7NDW1dczhuaJuW6aSiaRzGZDSZeDY1bSOSMGtQKlIUJVlmaGphe02rQ4yZohTkRcZgUFCWBYNh0XtEWGuJISAKySP52ex95aQ8t/OgOub9EqMYO6fqwB6jpRnTCajpQJRZf4BZwEa8n4QSExSEkFhtKRVt8849Dl0tMtXgjsmXSOdaxnGCbY4pgmNvWh4Se69novbfJY3pc+3Tef2cazqdcu/evWNgx1nml190hRC4efPmIwEInT9Ydz8QBrXhy1/+MgcHB70k6PDwEOccu7u7vPvuuyiluHv3Lq+88gobGxs0TdMbDHf3ms+jyrLk0qVLPP7447zzzjv85V/+JdPpFO89d+/e5U/+5E/427/9W5577jn+7b/9tzx48ID79+/jnKMsS5599lnm5ua4ceMGa2trDAYDLl26lEzvM8bjMXVdc/36dV555ZVj76215v79+6eO88OHD5mbm+Pq1at47/n4449p25YLFy480ne6cOHCKe+Vvb29XwmmTlEULC8vP9Jzt7e3e0PqrhYWFlhYWGBvb4+trS22t7cZj8c8fPiwN+1++umn+frXv/7IzKfPqrPO3dLSEoPB4Gd63V/Vcs5x7969U4+vr6/3cvYfVXVdP7I59i/C+Hl/f5/JZPJzfU2t9Skz4yzLePHFFx8JpD44OGA8Hp/6/c/LoHh3d5fRaESWZezv7/O9732P733ve3z66afs7e3196+Tsei/6HoEoOZo5Re73X4AugVt7HfJjwvdIyjxpJhOK4pComC17sCWo0VinP2/COJ9ctpYbHan8NguZjyigfvO/FFBaBuqyQFtVTEq5/C+xUWSua/IVtpWGsymaQjOSwqNl0ZuOCzIjCG3hsXFoSTVbDc4N8Voi1WyyykJUobGteKPEyKubSU9xtoUqSuJSya3OOUh2J6B0QE7bdugoqf1LeMk5bA2o66mhOh649HWObQqE4CgePyxx/jDP/wj/ut//X/T7XKDpPxk2vC3f/vXXLl8ja+88gqTiXij5IWkXuVZjs1E9tF6l3aLNdF7adg6g+QA4FFEiXYOsutuiKytLbO1tc2Vq9d5/InHePvdN3jv/bd4/pkXIFiuX7/BR+9/zIVLS4g3iGE4mOOZp5/l9e+/iTZ5YkB5JtWEP/njP2FulOPjHnfu3WN3b4zzknCU2RyrLddv3EBbK821SVKnvMDojGFRsry0yHBYYKym9Q3GK6AAbVEmQ9ES2jSOjSKzljxTBD/FI9K1EIR9FJLZ8qAswYceGNDW0tY1xmhhW6Wd+hjDkYGtd5DMZZXJ0vlrhFlDJPhWgDfvca0TRoIW+ZIxmhBIKV+IwWqMyUw3JoAgpuY5MbMSI0V3yoZOaqGSMWpntqpEmoeSKG5rDKPhQNgpTS3Hx0PbtLSNJ4ajnfkQYm/+55NsyBqLa2sODnaYTMbYzFIWBasrK2xsXO5lVUYJAJtEfum6FSmQfFx3ZHbsO8aQpq1bSNKfzkw4puQapURyRJddI+QTiiIneGhaR1D0O5qdb0c3d3QyEaLMTSGGYx4yWhtUaohJv6OVInR/77xkEkgRgkcrnTxpBEBywdHi6GLMhXkhvlXy36PodZVAmhjBN43IXZK3TQeYiB+KjEWX2FSdGbMxYrbrQyAo2N0f03onqVYJsAg+JuNhTwjgXBCvHQwqef6oIMdEO5Xm/c5LRcYPKXGqY/RomyUWU0BrMVoOIUCjqKqWQZljsyF16xhv7VG3TQK4DINBSWbEbNuYnHI0JC8j02mNC5oQoRZcnBgcxMh4ckAkMBoNMAlIdq1PTJvAdCrgbp4ZijJnaWmBclBgrUEri7UC1oun2hEjVICo0AMxs5sUPVgXICbpk9FG5os0LlSkl/mabkOhk0UhAKTc7o7AlRiiRJR37ErAVTX3bt5Ksdxejnk/v6fL+hiodPxeGUJIcigBYuKJ7yL/FWbNLMBzXuf186gu2rmua6qqoqqqR2pgvoh61Ka+A2m66jYQNzY2GAwGfeJR0zT9GraqKu7fv4/3nvX19bQWProGP8+o1/F4zOLiIouLi6yurrKwsMBgMOi9Z3Z2dvrUpe985ztsb29z79497ty5w8HBAVeuXGF+fr5PZGqa5lh8+eym0eLi4qn3Pwk0AFRVxdzcHGVZJkN89xM1pnmenxpXJ2OLf1lLa01Zlo/03LPGibWWwWDAZDIhxkhd14zHY7a2tvqGdzQasbq62p+bn7bOOnePGgX961jd8T7r8UepEMIXyib7WcfDZ9XJuTTP894H8MdVd7+Yrc/ToLgLj+k+y9bWFrdv3+79uLp55vOWr/4Y6dOMnGkGGJFV3VEEKokZ0+/KpXVfNHKA93b3WFpaTH4yCqWPkko6XwfpWxNI0LNq0kmKifYfff/+s/8evEeZI4q5JNYYQmxp3ZTJ4QEX15ZxyqGVPYr8RqGiJjjxA2m9l2jxzKBVoG1qXGjI5waMygy05rAq8M5T5iN806KwVK2n9Z6oxG8iuCl40GhU58viAzEqtJeGwmaG4WAgjaG3ktyRGCtaaaJv0mF2tK7F+1Y8b2LEKEVeGEIEVE7A8ewzz/PC8x/x+uuvUxYDQlamRlEa/r/+6z8lRs+Xv/IKNpNY6TzPpYHwjqqupOnPMnSM1I1cbJIalAEilTJGWEO+qWlDjW81i/Mj3n33Q5aWlhnNjbh+/Rrf/973WJ1fZTRaYH31Cu+/8yG7O1usrK4l752C1fUNrl5/nKqJ7GzexWsPGO7e/Ihv/81fY3Tggw8/ZHvzIYW2ZEXJ4tw8lpwnn36SLNd4LOiAVTnBFGgyLq5eYHlpXrwcVDqqIYAKGKXxbUN0FQRP8FH8arIBo4U5dPYAg0GpIIanyoJV4tURPCYlceWZJc8sTVVLs2mEBeJdS5EXwobyEemhg7BXrKFxjqhBK0P0LpnLCljkakeW5Ylp4WnbSlKiohizuuAJOnmZRPHiUanRMslM2GqDzWSHfpALMKSseGp0k1DdNCitaFwgZhnKGtCBMhtSZIbWyLio65q6qqgqR+PpvTB0khcqhH2lAkSj2NvdZX93h8PxOEWAG9ZX17hy+RLTVmSFIZ2LNt0gjJF4e62PGk6ljMQQ9/ONEkNyrTEavEueQKTfizF9mjRfKPm3pnF4J+dBZ/rYHOZ7aaIAN518bPamIJOxQmsZQ50xMVGApSw9R2uNBqwxmMSSaV2LVgZJvROPF+89WoE1wl7zbYs1Ei0eQpCGv5euqD6FCOeEWWMzSYpqncg1XTLXTu+tDEyS11bngSSeJpr2oCbsVthM5toQZExpa9AorAooFXFEgmtFkhUTgJlrjClwXqRmSicJVgDxiIoQZRxrZUVWlwCdECLOI/NxFZk2Yxm/BKomIoqvyHhaJTBSY0wjs2G6DpTS+CBy2M6bRSWD66Zp2dnbY21tRdhtKAjQ+oixGoL88c7TNnuYLFIOMwb5HIMyx9iUyhchKpNkbWmcJJNdYTsmsCoxyFQC9Yw1BCXnK4RITOAnSqRxs2y8Yx436TzLS6akwygpURCxwPhwwubDTQHzFX3ymLyOTvNKAvg4YsvM/pHg7UgkpHu56sew6qV0M34353Ve5/UTlbWWCxcuCDMyNT+z0dJZlnF4eEjTNLz55puMx2Pm5+d7oCTLss8lKjnGyL1798iyjMFgwGg0YmNjg2eeeYa9vb2eEXR4eMh4PObdd9/tgZrNzU2892eaAZ/XeZ3XeX1WhRDY3t5+pOeeR8afXT92e0Ol5uxoN/EMVExJ8ky369r9f4iSYLJ/sN9TRL0PKW5bfrXbUdRKH9PXd8ybnnquj1I2mGnKvPdEHfvXlCZEiQeJUjjXUFUT5ufmaBqYtIFmWhF0xJqMPCuIKlBX4n8ymhuJ0XD04mGDYW//gGk9RluovcfqnCLLUFmGdy0eaOoGrRSt89KsRE9mNNp2u/SyK96GluFgILsQRHJrCVrkLsV8SVM3eNcQjJirVlUtUiS0sCmaBptlBN9QDucYzc2xt3OfEOAf/+N/TFEW/OCN1+UYxYqqnRJ94MGDe/y3//ZfaV3LV7/6Kk4pvG97+Ya10jCOhkPm5+dp/ZDxeMzh4QSlxYfHO4/KrIANIaCUoZoK8yLGwObmFjYTM9LHr9/g7bff5stfepWFxSE3blzjh+++yz/47SVCgLpqKPKSx248ztVrj/Ff/8t/wuNo6pqLcyPWreX2rU/ZvXmLTCkyq3nhhWd46rnnCVXg+tUNyixj7Bo8Hh2E9aMyiVuWtCeRScTWY6yiDRO0j70nCzrH25yYF8SsYH5hiYHRWDKiltjyoAJZlkMTCS5Q5gVKCdBYV1OIkpRlTEaTjFfrqpYx5JO0QakkufNoLbIljUZbjdUlICyOPDNobSmKnEbVkKRBVVUlnyMBFaWxU3gXBLxEJFIonbxRLN61EGuUVtjcpqZQ2jGbC1hpjQEn5rVeK+5OakDGqFKKvCjk2leKGCX1RnDZ2O8kEiIWg0Kxt7PF4cEeblphMzH4Xl5dJS8GOOWgdUQvAJBSmjyzEDwxdB4xYsZsM9P71HTeHp0XT0ysJZ08R5TSONfiE/usmxeIiDk26fdmGtE+YQ4o8jwZ9AqLr26aI9ZMJ49Kx74DZUAAKhJDRwAcj3eOdjbhR6v+3BttsNoQorDtbJIbOe/EtDlKo28zYXl0QJKAzqrfvfHeY6wRFk0gMZFkfnHOEZPPjncSZa2VJniDxhKcxweJXVdpTmkrAcFbpSA6goloTJKQBnxdJ4aRwWYFPsrxa11LjBqVvH1UAjBjlISx7miHqEQy6UXOF7wnNo0A7UqYYEHwsDTng0sgvnN1nwSm01jo0stiiHjfpHE65HBcsbRYYLQVP6m2xU9EimXE7IyiEADS70wpsjFFrrEG8iIny0oyaylykUdlWZaOn0KpjM4kX1vTG3BD8p/qtijU0Q67SztIziVfKujHWAghATLdPbBLfkp+M8m7aPf+Fjt7+2JmHhOIKIf5iIDaA4vHAZrjNfNvM4+GeJRSdV7ndV4/XWVZxuXLlxkOh/013pntVlXF4eEhu7u7TKdTDg4OuH//PtevX+e5555jcXGRoig+twbl008/xRjDpUuXWFxc5MaNGywsLDCZTPj000955513mEwmMq/mef+nYwif13md13n9JOW9586dO4/03LIsf2OldD+qfiYeai9P6ubvRBs/MpyRxfd0MmU6nTIaCQhiE+jS+85EYTr0EaXxyNSx+5lukTrzHkfSBaHwH5lCys8R8cM4PBynXWuI3jEclNRNhfMOqxweRevFJDRG2Y3tNy2VNF5t5YgEia7O4cHWNq6pcW5KVs6RZQVGabJiII1SaIjRERwi1QCszsmKnLnhgLpuONjfxxotsh8vqVhGKVxTUdcNRPl5MBigdKSuxigdab2jbSJVVXOwv0+MNc47BoMhv/M7/xCbWf7mb75FjB6bpV1pHYl4/uZv/4rhKOfJJ56maeSA1q5F1zWZzTk8PGQ6nfYsm6qqO10bNstoncMaiQVWSqGNIVclV69c5dade6yuraIUrK6ssbm5x0cfv8+XXnqRa9eu8+HHn/L9732PF154kYiDWBFD4Mknn+If/+N/wl/9j7+gmu7z7LUr3LAZywtLHGrLODim04r999/n02rK0sIa9oWXCUVDG8RkuFQKGzSF7UxNRQISogENTllhIGEwWUYILUpHDHOQGbTJyW1JhsYFj7GW1jVoY6hqATzKIiOzmqZt0oLMEINQQiMCJmoMrq2lMcwyfJAkI6s13tW0ydBW5CcSG20SG8dYI3Ie32JTRLqxhjwbCnsmGQxHpJkNqvO+UP1/pQnMJKJaRYyK6BiYVpPkZ2J6IEIpg3fCQGnS7ruxGhdErtX4VsA5DCpKsxxjxFiF1UbMfGOLwuGblr2dh0z39imUIctzRstLzC+vcDipoWPMQJJlRUnfUmLK6hOVMHhHm4BZ7wQwikFAUw3EBJ4YYxIzIH3/9F/vXUq4OkpwCwjIJolY0phaI3KVpmkS6JO8RdKYds6Blp9NZshtF+F9NO9oLX5UMXnadJ4fXUKd947QRZEjC3rftn00dOdbYo2Rz5H8rdo29nNf508jxy6l4Tnfg9LCnrGghOVT5AVaKZq2FgaSEplZmeWoLEOnMWOMoaodB9MabXOcUmhdEJwjRi+gSnQi5fQe8MSpw4WY4lSFgSO2Wzp9x26+FgAphEDberRKMhsnY8d0LKIosfUhSadiiGibUv5Sip2KMb2+SJSICpNZYlA0rfjRZDYnELh7f5vlxYX+tdDCLMpLTWjls8SgwWQio/IKowKHB1OUqsksDAqDNjrR8MW7y5qCPC9Q+ug+ZLTBGomH79hcaobh1d//lOqvN7k3yfjzbUhsMp1kSZI0FmMUMCgEtm7fY9rWeJWAGmK6jyXvGe+FL3MWkybMsMNit9kyc6tWqpf8day18zqv8/rJyxjD2tqayJrbNhnciyRlf3+fqqqYTCYcHh72/1VKcfXq1R7o/7zqwYMHLCws0LYtCwsLXLx4kfX1dUnZi5Hbt2+jtWZuNOLlF19iZ3ub9dU17q7cZX9/H6u1SDsFd5c/nK2cPPlY99yT1b2WJv1JP8+2Er/q9VkQV/wR//Yoz1XMnIeZ83Ls/MTPPvbndV6/6AohsLm5+UjPXV1dPQdqzqifCKjpdv1O+sec9pNJQIv087Su5fBwzNzcPHAExHQLyS4K16S42JO7gmdp27odzp6Zc+L3tDZEFWnaloPxAfcfPGBuWKATrpTlBU3TymLflAyGc1TTKWiNFYRGpABtjVLiwwGatq3wboJrA001BeVR1pMXikGeY4zFx0hjK2Joadoa72NqYjwxelzb0FQT8J7GtTiEtZFpw6DIsIOC6FqqWpKojBX2RSjmKIqS8bRm9+BAmBDeoZQ0oZnNyPOMr331a3z80Yfcvn1bDIvbhqqqaJoJW5sN//k//y+8+spX+cZv/zaDwRCMBS3mqx17QyUfj+CFBZLnBSr5gATEDHna1Cg0RZGztr7C7Tt32NrcZHVthYjiiSef4LXvfY+PPvqA5aVFnnv2Of78L/6cubkRN65dp2lqWtcQQuTrv/U7hBj50z/7X7l6YYUL1pA3nieCwWtLnWXoJqBvPWCitni4fJEr/+gbqOAxvpM2BFwrRqtWi+zHaIWPnqg0WTZCmQI0KBKYZ0TSFpUmzwoGo5LpQaCpm2Tmqsi0SC98UzN1NTYTbw7nAypJAF3rITWg1mraxhO87KJnVmQk4mGREq8dwkjQDcbIQi0oAUfaRmGsYjgoMdZCJtIrUNRNi3eOPMuw2uKjRusjuUxZlrSNw2iFa2t0ZuQzaYMLQEAMh0E8f4qcw8lYvHiUxCnPDUbU1VTYK1qacRdIUjv5Ht55tDVkxlLXYza37nFwsEc9bcBqylHJtWtXmV+YJxJp60YYB17kGpI8pnrtanc957mAAElXI9c/kGcCvBVFmZKRfA/W6JjSyoC6lmOslAKjBACK4h3ik6+OlgOWYruT9KRn9IUU/24EHFJgrfj/GHMUt66UMJrEXyT0yVM69bxK6dTUi7QzhEhdNz2g1q1CRV4VezbGyeoW0IojL5XOJ8F517MUjU0/x85zSFhTg7kB86OSsjAU1jIY5FgtZr+uNdzf3GVz50DkSwp0gKb1tK5FWQXKIMbwKs1jujenVTGgMCL/CUL5N8n7hSTtK4qSLjmrAxCMkTQkFTVW216Sp4xObBpJzevOVQji2UMCRXyKsM6KghjFLBcUxubs7o+ZGw0ZDktiaLGZfG9lxPBbotUDISgyLPOjkXgLGQ1RzJ5jjDRTx/Swlg0Aoyly8bsaDgcUpaTHEQTs8MH14FdnUNxtQlhrj5kHi5eREWP50AF7KsWSg4oRp8RvaPvuA2oCbXQJqOHYPa77OUTxI+oW8iHOmAV3bFd0kk0dAYrQgbvnrJpfx+rGn3OOvb29noJ+4cKFz8UrpmMk1nXdR10Ph0OuXbvW+wROp1PG4zGDweCUB0dZlr2fS1d1XZ8yq1RKMT8/f8pQ9vOsGCNFUbCyskKWZayvr/Od73yHuq7J85zV1VWWl5d7L5jNzU3efPNNdnZ2WFtbO9PPZTqdUhQFo9Go93Dp/F9+2rp69Spzc3P9eGiahqZpmEwmWGu5cuUK1hhWh/OUWxNWxoEsDlgerVNbMa4ty5L5UDK/78j8AWGpJA4S2JQ+WptrJvG4j0MZNevFEvrEba5cNAxNSbHdMLdVcY0h+2VBntYpXYmZ/mlQ6/79+2d+15PPzbLslJl1jLFPhp2tn/U4n/W7ToM70cr46OBw95Fe8zC2tDOXiAKqvQPGD8dMHm4yuXuXyf37tOMxGzpjZUlYXs8sX+GSz3kQpvy8nTUmk8kjscG01szPz5/Zy/261cHBwam5aDAYnJrL8jxnYWHh2GMxxt4P5cdVXdc/k2H2WZ/pF1VnrW2XlpZO3YeWlpZOHRMQc/BHqcFgcEx2+pNWlmVJcu948OABn3zyyZnnw3t/pg/WL8qz6RGkT+rE33W/q9ztxImPyfGBKSDNkZ/MeDxhlgXTVbd47xass3XseRwhwscBmaMkjc4ANKQEJqMzPI5pNWV3b4/FhY0UvxvwQVGUA/b2H3L39l3m55ZoXUsIjtyWDAdzLC3Os7o8Lx4Kk6nsYkbFwf4UpQwmK8gyg9KayXhMSAsRm2cUgwKlMnQtRrNaiZwDpXG+JS8y8kIYF20yH51OJ2R6wOrqKovDTJp6QBtL1Uw5GE84nFRApCwLuqheYmdKKjKRQTngX/7Lf8Uf//F/59NPP03Hp2uIPcZYfvDG93nw8AGPP/EETz71DIuLy2TWQpBjWWQ51mpp6mMUnw/dNW6B6FNz6iLOO0LtuLxxgU8+vcP62gqgyDLD9WtXuHnzExYXXqLMM7766lf41rf+Bt+2XLm8hiJyeDhmeW6Fr3z5FfZ37qF8y/7+Djt37zBsW3TUoDSDzFCSsfz0k/DgIe6ddxhevUyrCjCG5bUV2skuTbWP8iN0yKgnFeDRKsdkBpNZ6qYh4lBocm0JRpq/fFAyHOTsHbZEJYbKhclRRlO1jfhEEJJUR2O0EVYIsmPRVFO0sXggRkn7UkEAAGsyMlPg64qyKHCtnLMYGlwTe2mNNYboNaGNOBto6wowNE1FwKM1GB0hChNHI1IW7734tqhApoVNVhQCNkwmlVzsNiNEJC5aK2mM6ymZAuU9Vmti46lV7H1gVASrLVkuTKPWS/KMyJ7ARrh15xZvvvM6D+9v0kwayIDxIUsLC1gdmdaHELprP0lnmoA2NskXj9LGFBLzHRFPH4VCJVBH5JOSZiUNp8wvzjkBF7UkDR15aXWm5SQzXtOn94CYMJtEVW9b+V5FlotxtdLYvEs4ks/atk3P7jNW5Eo6UVu8a9M8pVCIGTnKJN8ZYaWoNHc1tchAJd0qyTcjaf7yOOePUc2981gjXkwdAyekJtwHMSJ3lYAStUuyJCKEwO6+5+Bwh7IwDMuChfkRw0HOoCjJ8pwLF+YZjjL29g4l2jwWTGtN1SjaEFBKU2YyB6gYcD7imxZjk5+WFpBUaZUYGyKvaVsBYLVRydtJo7QW3yYfEzAckmwvIyKgmHMerVUyhyYx9+Q1O6N40zHLOnaUFe8grQxoy/buPiF6hgOZI4PXFJllcWXIoMxBQ3XY0DQVmoZBUTI3P0RrS0zeNyJd9CKrS95UYnQdCaEhxCMpnDVyH4wx4BJz6wiAPA6C9CzSGdPN7l4qMjdQWjPe32fzzgOcVrg2EOj8cmbviWlzInRgVX+jllE/8/6dbKpj18yyURN1lPP69aqOreG9Z3d3l93dXQAWFxc/F6CmA16apmFzc5PNzU2KouDKlSunrouzJHtFUbC0tHTssb29vf57zNZoNPrCgJoQwjHj1tFoRIyRH/7whz04m2UZk8mk9zmrqoq33nqLt956i7W1tVMJSSBNw8LCAqPRiPn5eVZWVtje3v6pmzOlFC+++CIgiS/7+/uApAl1f7906RKXLl4i35ty7ztvYtO0Mg8sKsXjF4U5xBSYjnF6jDHrmOJ4opCb1uxuHm9wFoJluLyIOuFfOly4hDKGSMX1uYssP/Nlfjje5JNwmAzUpT4LODlrLJ8FtGRZdmo8hRD6737ssy4s/NTXSIyRnZ2dU02bVxwDWgDa2DI52D2hSuCzfzb087gCqof77H+8zcHDLQ7u3uXg3j0m1YSl5SU2FpbYWFllo83h1hZ6NYfi5zvPn0zo+awyxjA/P/9zfe9f1jprPGmtT4Ei1tozx+P29vYjgYR1XZ85Fz5qKaU+F6Dms95nbW3t1OPz8/Onxkld158Jxp4sY8zPBNR0xujOOe7evcv777/Pw4cPWVlZOfa87p76edWPnImOUlKOkkkA2aFTHevkeJxp99+Tfx+Px7R1g8k7L5lZc2LS66aUigT+SE+UFrIzn6s3RETNLDrDiRs/vRSr9S0Hh/vY8gYHBxMOx2Oc0ywvLaKV49NP3yPLFtDaUJYZC3OLGCxTqynySDkwlIWirh3j8Zjd3V18q/EusL62Rj7QBBRBGVwMNPVYmkiF7OiH1NwrLbvyMdLUNTHKMbVZQetaqqrC1RWTg30yWqzVzC0skhWF+C74yGQyZdq0SULTJX3omXMjF/va6hp/8Pt/wH/6X/4Tt27dQmsjOxRRKPbOB25++gm3bt/izbfe5onHn+DypQ0uXdpgcXGRrLDy+b34PFibpfMRpJFWiqLIiLmiacQIdH5hRFkYtrcesr6+jmtaVleX2dvZ5vatm1y+coX19TW+/lu/xbe//R1c+xRFUYhPTpYxUEP++T/9XZoP3sMejJk2tTRlTkx2bYgo36IP96m29qn3NlFP3IDlNQbrqxTzlnpvk/e+9edsvT0kHxYUZYYyUA4KKAqG86tYk4kZrB2SRYUuM0IwWNtitx8Styq8MaA1rslQRicPijQOo5sZ/+LfEXxEKWFU6AQYiN+ES80nBNeilU8sD9U350pp2sb1DZyMdTH/VVEkKMLckISfLMtQ2ogMRItHS24NnSeGw4n0JEaIisxkKCOeRDazREQ+5WOAJKWLrRgShxAJVtgFfYpR9ATncT7Jk2JMxrkG4xzu/ibtrfuMtzZp0NiBRTnHu2+/xc7uHo89+TRzo2UUGmsthcnluyGAUVCSRCXR5wnMSX41IXiMEmPiMs+pUpSosFICZT4gpLkptI6syHBeDLdb16LUUSMszLgEBiiN9+KzAvR+MkqBNZrWtVgMUQXxVfFHLAkQFk2UE9jPPxLdKuwSiZDWaGV6EK2TXKkYJS7aKmLUxKgSMBBwrRcpXBpvwuARsMgoebxj9WgQBojSCRARGVHq6YlEmqqV+Ow2Mq0jB5OG0bBkWFZoHEU2oMgLLq7Ni5G7slS14+H2Hnv742SYa4WhpgUEck5YNCQz3xAi1ti+UXLJJFsbm7jZkg4WE6Mp0T4SGygmFloC0JQkeblGxmr3fUyfiCRJaH36VZzxEgoRYzKKcsDu3gF1XTMcDjikxqhAbmE0LCnLEqtzlM2ZNlNqP6Fyod+AALlhZ7mwg6zJsVal6wE6s2pxChegQ2uFj3RuNQlMPJIgSbS57ZPTZu9/R1G/DVYQSsZbu+wfHNCoQNChZ1x1DJqQ7nc+gTTHmDY9AHNU3X21qz7pjOMbJ+d1Xr/ICiFw584dDg8PCSFw4cIFrl27xv3796mq6thzz0oledTG8IsupRR/8Ad/wP3793n99df5/ve/z8cff9yDLrPVAVmzpbXmy1/+MktLS2xsbHDp0iVWVlYYj8efS+x0J5cxs1NDjOxubR9bswcFw0xRHEyPb6buRw4nx1GJNmg+Lu/IHDdTl15+mXwwSGsfxXA0DwcP2NzaOgbU1HV9poTixo0bp9hYDx8+PLXjfRaY17GbTtbly5fP3Nl/1Nrf3z81B5sAP30Lebo0YGUJh0l/dATvWz7cfJ/B1oCtzYdsPXjIaHWJ5d95GYpHS5g6r198tW17iq1xtsfcr2ft7u6eAkPPAlh/EclUv2r1Y1KfUlMys/BTWkxtj4MsRxr3WVLMLFjTxcYt5nP9DrqwYAydp8Osrt6HRNfu3+so2rtj8pwEe9Jmd/+JgneEGGjbms2dTd54+w1u37vHnXv32Nk85NqlS/zhP/8HHB4+ROmG1eWLFLllYX5IZqFtJkynEZRlMj5ka2ubqm5pnWJ7c58iL1l68ikad8i4dRALpm1AIc14553T7bPbLCd6+TfvfUqGUcRQE6JIUoZlgY6R6dQx3Zvw4KAmACazZNbiW4dOC36U7PyW5QDnklQpdGk2mtXVC/zT3/09/uIv/pzbt2+hlE6NQkwGwqIK3tl6yGs7W7yGYnV1lY2NK1y5coVykHPh4kWWllYwOjWdrpWd2hBomkBmM7QWk1Ci4fLGJT766EOWlxb6Xd5Lly7x7jvvMDc3z+LyAuvr63z961/n3Xff4GBvl6eee5mwLg3qwJRMqorgInsHhxSILwnpfFofqd57nwyF29uivXOL2hYsX7/CThGw1ZTb9zcpgazIxBMj06AjpjQYWwjbKrOS6ITBZhZrBgRVc6AjfuESdnEROz9HGJWEsmRaV4yrioPDfabTCQcHBzgXMbogs1mfViY+IQMyK7dkm9ggWmmyXExalVYYkyMeNyGlnon3R4yeoiiwhSF4x9z8PJPxmKp1hGQaHBEzZyKY1EB735FaFQZFcK1cIwGGZY6KHoeSuHCjkylrRGc2gRbiLZJbATC6BlIhUpgooVloI026axpQjjw0bARNzOeJ9QMmylOFBt843n/nPd597yOcD/zW1/6hsLIS2GWtwSgrLKAgBriaSFnkVHXTMxJUkiOFFCHsfEtsEnhgDFU1QamuYY+0TSPSHK96k+BuASjMGQEuunlJa7kmMmt75oswjxRtKylnxKM4vo6JoBKTJuEQiKnu0XP6OVBHOW6J7RcTABajxHN3XiNai05UAOookj0fCAmQEHDaE6JLYIAidh5FQcaXtQYXJFnPh9AnUGFzSZBqA00baV3NZOrQ0aGZiLTMynWgjCZEjQ+d3wq0TU00gbpuk/TNY0hAVxDQoK7rI6PldC34IBLK2ce6xKFOFyv+M/FU3GEIgc6TpwfFUCmeW9hT3U1donCTT45KJpiZZTw+QKkaOxqAjzR1ZNxMgRprMiBiNMKKHLfoGLFGI1ItlZg9Ij0ty4zhoGQ4KLCZxWhhwekE2Kav2d/XZsdJ991Pynm7jRCVvm9QCqKkqDV7B9SuoQnumE9bTCDNLNjTsWW6+O9ZqRUgzxf6HvHUGvAorv68zusXXd57PvroIz744AOapuGb3/wm//Af/kM++OADHjx4cOy5k8mEyWRy7LFfpVSQxx57jNFo1Ee8Pnjw4EzpR13Xp8w2jTFcv36dGGMvAevuX19UxSipUcfmMeBK5Vgsj0uKwmCHrXcfHnvMq8ibrj4GvgAU/9v/RpZl5HlOURSYPOO2bbhrKsIJoObu3bunPtebb755ChSZm5s7tVsvNgDHj18Igbt3755qDq9cufIzyRhO/q6KUAQwP0ftkYpQuLO5kIFAHWuqumI6naKrkqVfkCzjvH66qqrqTPZNWZa/ERKxzc3NU9fJWXP+WWyk37T6kUBN27Y9o6b3lUl7c1EdsfFiAiK6UkqB6vbwjkyBp+MpC4tzKBKqLVz5lBjSpVB0aTIeMwMUdTumRywE8c6IoZP/aFks93eBbqEcqZuKnb1t3nj7+9y9+xCUprBzDJTmiatXWF8d8fa7t8l0iVYtrq4o85K1tVW2bz6QdKS6om1aQfujJKjMr45YWBiwd3DI69/5ASsXb7Bx+TJL83PoOKV2E4jSrMcg8osYYp+0JNIZkWEYYzBFjjIGRSTXcwRT0LROkoOslRhYI6GsxlpJFkEaOmkuo4A5LiVDhcBTTz3NlStX+MHr3+dP/+zPGNfTZISZGoQoxrFGS+LQ7u4OW1tb/OAHr2Gt4dKlDR577Ale/tJXGZZDondMvaS2KDSNTQyN4CmLYdJSF3z44ftcvXaDtpUI4ZWVJd774Q954aXnyYuS5eUVvvSlL/POmz/gtde+S2gDj1/foLAtOw832X6wS+sk1jqAcEC8w9SOURtojCYoQ147isaz98MforQnB3IHLmqCESmbtuJXhMpATcSIQ0vTYsiY6oBeXmPu2gr62jpmeRVVFhx6z82PP+SDj29z++5d9g4rssywuLBIWYqRaJNMnXd2thkMBpRlSdNUNE3LYFD050Hoz3LcjdGgLc5JwzUaDgledsqzPnXGkmWGpcWFlHADxlryIuulVHleptQo8U8RfWbBSOdowLkGXMtIDegsMQIRbYS5YmyOd4Gpq8gxeGIPnhibPJUQQ2MVhUAQNRgDLijK0GK2N1mragoMmAI1HLDpa+rVNdoLF7m3t0tph8QgLBKtlFwHETwugSldw66YTia0rRMA0zvxCdIQhK6AUUcNv1fQuJo8K4S1RqRuGsrhMLFYJBGLECSNzEhKWYwC9CitMIrkoVMnCacleoemY6h0UhYnbKCi6NOXMqMxHdMhSS+9azHWUlUTSAyijg1iugQ4L+CM2NgEOl+TzosGBU1bp3QvOVatayUmXBtiPJIZqSRziYl9FCKYmORRXiSKUXmUMXjnMXmWACJQxAQctwK42hbvW7TJcB4B75DzbYyjqV3yiaFPIRJGkMy4vawniI8VUeRIVtsejOhAJu8dPgQBTBIYI2Qbmb87AK0DOTp2kSElqIWOQSOsHNlQjbRNLaBoZsnzATu7u4yIzA0HAp47UMoQUtKaBmonUkEdtbDP1NHGg9YBYz0H4wajJxRWXrsoDXOjIZnNKIsiAX2CP5FYL0op8b/iOHjSb2DAsU0PrRW+bSEodu89ZOrbIwZNYkv198IYccmXRtg2Zyc+HfnSdCBNMso/sbz/DdnEO68vuLz3fPrpp7z22mu0bcvzzz/P+vo6BwcHp4Cauq6p6/oL+qQ/e927d4/JZMLS0hIvv/wyGxsbPZV+tuq6PvXdlVL89V//NQsLCz2jZnV1lZWVlZ+J2v+z1sOHD481VybAxYNIoY9LnyKwd+J3D4znO5NbNCd60LZqGQ6GXLx4kY2NDZbXVnkwiNw31SlGzcnj1IFHJxu+5eXlU340w+HwzN36s2QV3/3ud3+m5vDq1avH2AIKGDgY/BxxNhVBO2hOzN1GGS4UF1gaLHHp4iU2NjYYrS5RFzm/eC7WeT1qnTWetda9yfive21tbdE0zbHHzprzi6JgY2Pj8/xov3T1I4GankXTp250oowZ2koaT8cZNgI+yI8qATma8XhMCGsSM9wbLpp+lTib+NRJl2Z1/jF0ZpomSUfEgyKE4+ydmAAgiGnBL/4eeZEzGi1QNxX1tKa0JasrK3zjG6/w7e/8R+qly8SQ41tPEx137k+4fec+SmmG5YBBkdNM9vAeltfWmJ8b8MN332JxZcDe/i63Nqfc3zzgmSeus75QkJmcpq1xjfihKCXWs2VRym68zVKDGTBZLr4owLSuyIzGZhpjC5FMWTGFlWbQ4X1LbjKCd7jW0TSOcjBI8g5DWUizqo1ibXWNpcUlnnj8Kb772nd7EztjND6I8axvwZTC4hDgR+Hahrt3brG9tcP83ApffvnLuBgobYnznuhjoqXJDnRVtdhMs7Fxkbfffof5hWXKcoRSmpXlJbY3d9ja3OTixmUUmsWFRV566WX2pw0Hh/v86Z++xZce20ChubW9CQbaIOlJTYjo1ks8MJGoFcaBB0lJ0lqYTEozRdHgUal5JwiPV9H5MUQBbrA44zFPXGD43HNMFgtCZqgmDTc//IjX33iXBw+20xiKtK3jm9/8HW5cv8ad27dp6gbmZSf/4sUVLly4wGAwoKrG3L17j42NSywsLvLDH77Ht7/z97z04os88/TjTCaHKG0xWcF0POH113/AaDjiiSefIARHCDKG9/cmfP+1N4TxEgIxNfZaC9ss9BIS8SIoSwHA1tdXuHbhAutZjtrZ5eHODnktyVhZkWMyMY82RYZSkZHSYhRtLVGBzoYSbR88GIuxOcpYgtEoq2mahkGEsL2P/+QOanOb5sFdLqpIESMbxTzDL72Kfull9pxnaKwk5CRGnMhdBNho2waQcdTRlzujydnEo5jMZo0xdPv/k/EB1mQEYynzQsCUTAAKYkQrkW55BU1dJ9BEy2ulqasDWbq5SKW4CaMVo8GAJsWEd9rVYykd3hFjpMjznhHiXAuJDZRlnb+TJ/gk1QlBjJiNpP0oI4ymGALOS0OtUOgka9HQs7BkDvRYm4tUTqv+e/i2TSwlQ2gdTdvKLmUyFfatmFFX07pnFOnM4JsWbQytF0AVH9DaiSl6YgA5H9EmoE2GUuKj4pyctxhi8kRJXjQdBb2XhHm8p2fHSLR1IJkJ9eyV7tahVceapP++uh8HwtIBhbEZsXVp7hfGk0rzvxisA1EzGMxxuL3DQMHK0hIhwOGkxrfpLqUUEfHdchE8InWbNc43XmONRgVPjcMahxk7gk9yQecp8pxyUDAYFuR5PpNAmDYfoGeJyffUCcya2YjAkNuMtmm4f/MOY9cQok8R9vK5QhBGZCehEjZaODIPnmXYdCDN7JZKjP3GS3fzjkkod16/GdW27akF8udVnQdDVVU459ja2uLWrVscHh6eWqBPp9NT1Pizdlc7xvZJVl6WwPmTzz3Le+TnzVSJMXLr1i3x+ysKnnzySZ555hmUUjRN05+Dtm1xiWF98vdv377NYDBgfX2dCxcucPnyZV599VWWl5dPffaTv/+T+E90fmiLi4vUdS3Mnc0DaJqeKdh9prqtj4EiNkLcn2DTplpXnoBTx49ppTwPuU91ogd9fP1x1pfX2djY4Nq1a6xdvMDu5qfcv/3xMUZN0zRsb2+f+vxPPPHEqfM8mUzObAJP+kp0jNaTAM7bb799JsvzrON81nhaWVk5BhTpAPqgJT88fpx+ksqT0els1eOWw7qR+19ac8zpOW5cu3FMNjdaW+Y9M8HZL0ZGcvL8/KaV9/7M8XhSxtjND4/it/WzSoLO+kw/SYnU/8d/TpF1n55fnXOnrrG6rk8xajrT80eps1hwIYRT79NdtycBsY61OJlMGI/HvzSyqx8J1Bxp3jtjRAXJHLVLrOhid49mnxPMmn4xGBlXU5xvyUyGDw4fPRqhex+LEyXJHfBpBzYKe0R1C/HOSDKkxtX0Mil0onoHMRiVm3WkbSpC0Ny794BykDEqlvn07kP+5jvv8On9bfJ8wN7eAYsL8+SjHA/s7e6zt3dAlhWUxYimBR0dc/NzrF9YwVrNzs4+4wPF2soad965hQ+W3b0xz9y4xEvPXkdbmFbjtDMtjIJBmYsMycmOsY9ODI5di1LQuoamctgsI8tz2e02hqZtJRpaifGk7KxKQ1sYS55JKhNWPDbyPKMocmLyzNi4fIl/eeVf8ru/+7v84Ac/4M033+SDD96XpssqptNx2uk3uCYmb5WMyXife3fu8+pXRBKhjMagiBoyrambCkVKbAkwHM1z/foNbt+6xctf+rKkEoWSG49d4oP3P2Vt7SKLi0OcaxkMBswtLvM73/gdtu9/wEd/8y3U9gF3t7fJbE6uYD7P0C7iAwIqhSApTdKNYI3GxogNSBMIoMG4iEkSi0BEqTa1IwqdUml4bJ3hk9e5Yx1MAtPJhLfe/oD3PvgQVErXcRIDba3mwYOHTCYT9vf3EltKs7AwT57l3L59kyzL2NjYYDAouXnzJldC5IknnmBnZ5fnn3uGtq3Z3tomywuuX3+MucGAtmn5xj/5ByyvrnD71m2Mybh29RqffvoJr333+/JZUaAjs/OKSAYlJjkpcfj440/xMTDIDddX1nn+ymWeWF3FTSp23v2IUdtgERADpfAmoqzpJRxaG5w2fRQ3SqOMhsyI4Z+WcUyITHfG+P0Jvq6JrqVUMB9BD+YoL19hR1mGeY5REdd6vHfMzY8IRvxYQNLXgvc4p/tJNoRIZiXqNMuyJEk6koBI7HokM5J0VmQZ1XRCURaURUlTe0IrzBmXvHpUjHjnyDKLLXIi0iyLJYhMg13zKyokTZskNTaBJK1rcW3TJy2pzidkZjKPKdrdWGFPocRDyDuHm53fPIQohtsCVIlk0aenOJd8aLROHlfCJAFEepQMb4P34hWkDQaDTeahdVXhokjMus+ljbClnEufu6kE3PBi9Ku0xM1rL6leAmp1nioKZQONn4pkh0hwHh8cIUn7QpRUO2sy8EEi041IssTjKgrAnubz1vkevNDJzzYkYEappFcWiCct9NK9RHXJYeaIieJdOhYCJhqjUEZg2eHSOpsPNhkfiMmiDwFUQfQBr6KAIQoivo9nF4aKFyCFQsaB0GVoXGBQFCijWF6ex5AAuICYqyvx9RI5G7jgZB6K3UYCaGXEz6gDTVQkuoBTgenOPvfu3qdWAfxxkMZHjwsBF73cIzsZFN1+RwfSpHEGct/u/10e7c9pAtS66/G8fv3rwYMHXxgrowMlO1PbTz/9lP/wH/4Dr7zyCteuXXuk1zhr0d6FJszWxsbGKbPOwWDAhQsXjj3mvT+VJPWzllKKtbW1U49/85vf5KWXXuLOnTt8+KEkc04mk1ORtJ3JbWcCvbW1xdraGg8ePHgkE8ssy7h8+fIj7czHGLl//z5/+Id/yFtvvYW1lj/++7do7twhOzEtLMwtHHtNGyAcRA7j8YbTKkOujwNFhY08Vj5GdaLreOzqY4mJ3PDBBx/w3ocf8K077/GtO+8fA2rOqt6I/QRYsrKycur4TyaTU+dZa33mcTo8PDw1zg4ODs5MnznrGP/VX/3VMaDMBLh+qNg41OifEhO/dv0ao5EwlxQiHtje2+Hmw/ugxBft8uXLaK1ZX19naWmJCxcucPHiRYarS4yLmrnsHJD/Impvb+/MsXNWFPW9e/c+j490zFD8p6n19fV+PP6o8t6fknaCzM+PCiaf9ftn1VlmxNPp9EwPsKtXr576fWst9+7d44033uDv/u7vzvTR+SLqkT7BMTZN5xnQafATLV3+7ej5IFIm0+XVomidxAAu5Ut0BpxC6Td9A9rvQCqFDzP+DIkG33k1yOsn+j2ILCiInABA+pyIa1uyzDAeT1mYX+UPfv8pPBVGD2lr2Jq0XLn+DF/56gRfG5aXlxgNDa1vsEVJUQ6wNmd+fkF8ZrRifn6OucWRMBsy2N874KmnHmN/WjNcWMMO5ijnBmSDjCzmaBuIaAFXXAAjLIhu11kri2od2ghAVZYWpTKsTTtCEVCRPNeohEx3C54sL/BtatRpkyxG9wyTw/EeeW7RJmJyYX8sFvP81te/xiuvvsLtO7e5e/c27733Lvfv32U8nuJd2l3vpRWBq1cvgXbkhTTJSitkuyWQF+IFkqH7yOTl5QU2tx5w5+4nPPbY40Q0S8sjVtcW2N3bZGl5Dm2gKKWhLXLDjY1V1p99mvGtLW6/9TbT0FJ7T9O0KJUlMxCNt9K06hjQNkOjMDGSeY0BjJIxmWmF9gGbWDUWhc40LgRsl9O+MMfERapJDR7eevc9Pvz4JlqlRCInDTSpcXz77Xekge2uiyBsBxCvixgD341vYI2mLEs++vA2xlpCcPz5n/8Vw+EQFSVB687tB2xuCv3vjTfeJssyxpMpg7Lk008+4YMP36dpGxTS1EX8MYmCgDNHO/8d0KmVpWkVH97f4tP7WywMB3zjxZd55tVXOHjzHcrNXbR34juE7H5pIiYqvFbSvMqQ6xkbsftBK4l4VBodNb51eNdi85xhXjDIS4Y3HqNZXYZBwUhbnHLYKHIgkXElJoTWySg2T2CwmKZaa4g+4hqJe8ZqMX82OZ10QymFKnK8c7RNjfdOYs2NReEYFBKL7hKzhmDEM0XpXibiE+MudKAzXSqVgEEdIONaidBWianXpqh0Evui85exJgESPtB6Yc6EdK06J4bQxhhJ9opibNxvSPiAT3MWvQFyxLuml3uCmEfHIKlRUUkUvFI6+dH4PlFDgNyAtQKgRmbnYSdeOEHmTk1I0iOPtgLsaqWJPiaJUSAEibb2MRCD+IUZpfEI66XpjkMUdpaASkakasGJZk5GrZgzp3uDUnItK8RE2GZyjo022MzQpmPfyZ+UIkXPO0nCUhHnJN47s1rAlSRbG5Y5ZV7iGs3y8jKT6ZQHW9ssLM6jVY02CqMTkKfEED6iemmZ1pk8ogJt62gjPWhkWsN4WpFnMMhHlGWO1VZApvRKMSaJmTYJnFFJCgwkSVsU2mg6MhBDZO/BFlt7e9ShJfg2AYhHQE1IGxQh/RHmzBGQ11XHnunuzbOG84CMgf7nX3+q9Xl98ZXnOd/4xjdONbdzc3M/9133X0Zfg4sXL3LlyhWuXbvGk08+ye7uLpubm9y6devY87p7xp07d2jblmvXrvHEE0+cApk+q34RzUWM8ZSZs4lwx8P+ienDR/FgnK3GKQ5DQXtiE/7u3bvHPm9UsLW/xWQ64cfBx0opbt++fWpn//333z+1i95Jl0/WW2+9deqxJ598kvX19WOPzTIlu3LOndmAf//73z+28ZwBS0tP8IK9hD717Eer7OEhav8EM8FPzvmQ53Veqdq2PWVK/6vka/ZZ9WMYNbOsmCPPF/m3R5sa0rOTYann8HDC0tJyD/4cyauOS5fEr+GIQdPtdncGobNGii7dEJRWqJhigJWAPc5Lws208gxHOX/wh3+ELUReoqJB6xavGv7p7/8zohepBLQo1dHJQaj5sojovCtQYtY5zxzLF5awNuPy45dBGbTJ0GisDoTWkJdDQIMmSQUS9T0kbw4iRJtAp+4Yq+RX0R0Xjh2v7sgqpQlJBqYA1cH1URoxpWQ3ODkLpd9ViS1juHr9ApFXqKa/y717d3n//Q+4f+8+77/3ITu7u8QYef75F/jH/+TrDEYFxNh7V+gEtvWfSWuUltc2cYELl5b46//xN2hzjdW1dRQj1jfW+fu//z6mUKwurWBNy9xbsLwyh9/ZxitHfbjPlaJkrBS1azDaMA4RHxUsjrj4xDWUUkzHY8YuUDeOUFXEqqWd1rimgsT2GVm4lBeMgqbGEIwCq0Un7SNt69G1w2eWZjxld+eAIh8QfOhlBjLW5U/svn/aBVfRorwAOU3tUEbOqdORyfTgGLBCiNhsN11XHbtAzvQbP/ghKETSEVu8b7CZoihyoDPczo4ZlaaDnnyOVC93kHMfCCoStGL7YMx//+7fc/vpJ/j6S89y8NrbhIdbZCGQuUiQoYmRYTMTai3gjYkKo8S8NiqRiwQdcdGnsaBxEQqbceA9155/iptKgFSTabTOxQDYaCaVRJTmZcmgHBIjKZZc2BM+ePEVMhpjJb7eRNM3mvL9dZIfCSphjEksiECWWarpAfOLc+SZYVw1aGPQmezYEXUCKrSwGGaAYaNNb1Lbti5JaHSiPDt62SWSUKXUUex970EShImGUsQE1EiDLtdg8IGoJLJbTK6FXRSix3mfktkSEO0DbduQZfkRkE2KjfagrcbmpcxViOePTpJTZTTESOPlO2syYgDnoxhdK2ZA8JBSqoStRhQPlM67xlhD07SSatSD8gGfpDZynI6S9+pmSmeOLcct9HN0Z6qstLBWumMX0/3Ax1pAKOIRm4kUzY3EJkoSkxibF0UBQaUUN0MMHpfOQV21YkCuImVhyfI59vb22dncYX11nsLmIk3IC4gaFw2ND/jo5LoPGaAJatrL6LRSoMF7xXTiaKb7aKYUZSZMzdGAQSFpUbKo16ioe3lYB34b4/r7V3+vDBELbN19yH41paWlTV4+3THwIRkDw8w9EDrJ8HH58FEIQDzxOyrJ6YLv5MRfTLTxef1mVZ7nvPLKK6eAmro+Lqn5da3hcMjy8jIXL17k2WefBYTh9Hd/93fHnhdjZDQasba2Rl3XvPjii3zlK18hy7IvTLamlGJ9ff3UXBGB6sRzD6cHPNzdPAb/Gm24dvEi6gxQ+Ng8CAzLIRfWL/xYRk33uyf7kaqqTjVsRVGcYpLFGDk8PDz1mp988smRxHnmNU/KMowxLC4unvr9k+M7D7BIzkLNT82o8Xe3mX33qKAZgBqpUwbN53Vev4m1t7d3plzpV93z58fA7gLQdKlMR4/9+OpMMYFusxqtNZPJlKqqxUgz0bSFih17487+l2ICaZz86Rb/Ju1Id4tRochHAWmC7Ib6KDHG0Ys/Ttt69vf3ORwfsFRmhNgChojDo0BFtNWSpEKUhjuE1LEKiwgQ7oESDwWP/FcXBlTExJYYaiyaqAyt8ygd0SQ2hPJEIwfFAxiF0uLy2nlTKBAqQ3ekZ5hMIM0ZnTdEd6S1yAP6X0rbpNp0YI9H9Qt5Oea5PWImoQ0DXfDUM0/w2OPXCSFwsD9he3sL7z2XNi6wuDgk0PYMqu7zdF4pMcaedVGg0QGGowEvvvw0H33yLleuXkDbISOb8cwLj3Hr1sdcuvwKxVCzsGzJhorDT3cZZIoPP/mQhRgovCJkBVle0CpLMzfH1/7P/4KNV59HKUXjGhoP1aShmUypJxWTwzGT/QOquqJuK7LxmPajm6jdCZe//ArXnnwMn4s/RAyBmkg7zFFFjgrw8le+xP7hBGKXzOOSlrMmkuKpQ6Ajkmltk+wjHEk4MD1A45OEZTqtaFuPtdKwu1Z0xa4V/WTbttR1Iy791QTX1mjTGbAm6VNqoEkNWIiRGEySRcg15NoWbWS33GYZWoGxmqgzfvjJHYq84GuvvMzut19DVy0uiwQCOgRMEIhGY3AKfBo/OnmMtFG8VVrn8DGNYa2ELWYEBJsfDmF5nmJpxFw0ZBnkNqfRLTrLUXUkyyzKAMoBAaWF0ZMXBc4rqqmjbRu0UeRFToiBzJp+kRpCQJuYAJWaEA0RGAwKqqoGpTk8nGIVkqSjA8qkGOjMYmzyaEmsve5kOu9RQTygjBVGikmAB1FYJx1zRrygFM4lQNi55NGSJYA3oJI8SRubyEiJoq1USoVLxsqAtTngjuK5Ez6e5wUxQNsErBUvGWss2ihh4CGsjBhAmQy0QhsrUrMQEpjoCHh8k2K+yxJrMlonvxcjNE0r4yrBCcZIQplzjqgjSmuyvFu4yuDuzH61EpBcJKlaItYVPXDT6X5jEABLoutJoJDIg46Yg/L3LhHJ2kxkoV68bEgm8i4lTrVNJ50ShlzbOiIa7yK1d4xGA0a5yJLGk4bRaITCsLd/wPr6Glkhfj9yHixBKYyJCB4njBir86Rz9slYOuKbhqYOaBUxynNYOez+GK13KYqM0aCgKMVkOCsyijxPkrokd0uMxUBiqyrE4LwKbN55wJQUhR4cLkR8EMZRCMJUjYTeYLiXhM00LGf9ffYxpTogX+Zy/UtA7z2vX//q1msnF86rq6s/dxbI7u7u5xJl/ZPUgwcPODg4YHFxkQsXLrC8vMzi4uIpkCqEwNzcHOvr61RVxYsvvsirr77KwcHBqdjps8o5d8q0+GctYwz//J//82OsFK9hp4SDE0SVBw8fwPsfHHtsQef8/nOvkp9AXzY3N4/tegcFVwvP84V7JKDm/v37p45f54E0W9ZayvJ4PPVZLCGAnZ2dUzvxh4eHp0GqzwB6Tlo5FBHicIVR/OmBmu3pNrU/8nGKQLUwQo1OA0XndV6/ibW3t3dqfhwMBqd8vX7V6ie+M37WAnCWEdMtqEkkP50ibhWKqqqo64aizMS0UXcQQ5JLpeZCa03btP1rnjT8mmUWBJIEyicGTIS6bSmKAF4aD9cGDva3uXv7I0bzV3GxAbSAKlp2jWNsRR6BFu1U9CIt6UEn2RnvQ6vS7qj3wjIgBIxKfhjREVVHb5eFtbS2R4tqAU46Fx6JLo7QR/QGEpOGlLYVJZkkRgGQJBJaGA3dJ0oHDBIjSBg7ikAQo1EVj4Ag3TGlAiZThOBQFgptKQfLrF1Y7BsukONhuqYiqp4BZAVDEPBjNqc2wmOPXWV76yGvv/73fOWVV3GuZf3CMh999DG3737KE09doTAB39RMH24zV3umOztYBdZ5MGKsqrziid/7XZ74xleZDjWqDVgzoFSKRdGXIJCBwqQEsNY4XF3D3j6Mp2SrKwwX52i163cgcmepYyAYjY2aAOK1Q3d8O5NOZGccWQTFBJp00pPewBSRIxmbpcWDgAC3b93hh+++z5e/8mWWlhYJocV7OSfeH5nU+RhwLopvUAzi5eQEZPDJFMs7J1KMtqVtA3Xd0DQtTd1wcDBmOq1kXARp9rxvhSHReioC1doi5snrhNsPqUOgrqfoaUPWybyKgsYq6kzjkOuqbVoqF7AqEl2DV8KoUUoYPdprSjdlJTTw9htcWPkGy5cusrS4hMHgggBTgeTtrDRGk+KmRU6nlfgBuXYuMSjSUOrBAJlHmlZYViRAJHg5LsYYhqOC4OdRITCdjMms+NG0aazG4MjyHG3EFFkYGC2q88kS1BFjS0JwCQxqkt+VgCnC+klghpbzm2UCalRVTVTi9WPQKYpbotKFrVODUgmIGoosznlc7EBAGTLWGtrGJ5lUx3FSvdkuaPluTjyEcpvjQyDTIr+irRNjBnxsgCCeTETqekKrDcSjRKrOwLdHIEkpdYmxYWwmnlpKiSSpqXtGjFYd2CByKSWKNnoWZlTJoDfND1YnaaWcT60kqr1uKolpT55jsiiuiUrjg0crQ5ZJ2p0khYH3IkMLIVCT/I2yAoWArONxS2zAB0WMGu8Co7kBLmTcvPuQ5eVlRsMhSoU0jmXnNypPVElKqoZ4nBgKo1BewLEQxLcJbVA64oJDOZnz6+mEGA5kfrCkxDfDoCwoByW5tRIxby1ZbjBWAKzp/iH3bt2jUWJMXfsWn679WSbNUUT30b15NrL7x5UA6yp5H0XK7ItLkjmvn7zOOse/bLuGZ31G7z23b98+9VkvXLjA3Nzcz/X9J5PJmWadX2TdvHmTqqq4fPkyIODBYDDgxo0bx57XGXAaY6iqiscee4yrV69y7969R2K/tW17CqgJgNPCslUJMNCJuW4iWB8pnMh0Bk4ShWwQ4CSkDZ//3R/8AaP5uX4N7Azcn4OdwXH5za1bt8h+8INj77+qS3731f89hT9+Dj796KNj7Jeg4GDBsj+f/VimSIzxTJnT6urqKf+MyWTyyF4Vk8nk1Gvu7e2xs7Nz7LFZW4jZeu21146ZY2ce1u5Fqs1HAWrOfsKmGjOO42PP8lGjWOARpvzzOq+fe/2y3YfOMgAOIZzyKuvqkZVBX/AF9iOBmlnwpQNLPquO0ac1/WL/6MWOZAaHh4cUxbLsBgcxA46pQwnRz/gihER1tz0YQ1DEFBsOkgLSPT/GI1PE1jlc7cRTQIsR6LVrKxRFw/hwG1sYtLKEmINR0qwpn0CSLhnmCAySmODkgRBT9HjyHDAoSF4hHul10CExMDpgxUN0gOobqHRY+hSb0DeTSRCjpOmPM8ewi8WVgy5ml7rbioYjBhAnmS+JMdCd1/S+XeB65yUZEbmEfNeOoNPdjETiolIz242Nnn6fQLrU5vUA3Usvv8hf/uVfcvv2pxI9ZxSvfuVF/vqvv82FCysUeUm1t0+zecDW5gNiGwgRSqVRUWG9Ji+G3Pjql3CDHB9aiggOSdRRqclUWo4PShMN4uGRK9TKAmpxjmAyJsH15qFaaZRKoI2S1Bdp0nV3IPuEnA5oVBjyMu8fEwaWQqlMwDglnJTOLKkD2p54+gaXr12iKAqs0RAzeYHQ+UR07DJFiDo1ZUfXm4wyNXMP7yQPMu6Jio6GEUKbGB1yzp13ZEoBRqQvWmP/6T9CVzWubskbxwf/4+/4/v/3z1gsBowvr3LnYIc9AiHLyW3GYp6zdukCdz/6mP0Pb6FDwCqFJWK1eME0zrG7f0D1l99CRdi6sURucq5ef5Krj19mtDDs5YRKi1GtUhJzni4o+WZhNmHu+BwjWEKR8ARNl6YWZ54ZkRyfGDwqarq8r84vpqteJdiZAod4zJupN1iN3eNdopDp5TndTp7EZgfquu2NgJVWaYwpdAKffRBgKYY8edIkfxYf+lSsssgIIaKTZ48AuCn1zViBizX4tk7gEbh2Sgjg20Du8yQXDRiTZJZREzsQIkJmckLwAk675FdktPjxxAhaWErGGowVLxjvfALSQvLwUSjE7wYCWgu43CQQJ+QCVsaUjqSUeOd0KVrGZLStJ3pFdIFhOaANTvxxZiU6ylBkOc55XNNijBiZhxCwyQ/HuyBpZsrjXYUxmvnFkhg9NhbgA3Uj170LDTEYlpdWOdjfo6kmrCzPYxS0QeOdAOjGKvJMjrMmikEwkBlN0OYovSw3wrwiEwYTijzPUUEMsH0DVRPROlBPK+yBw2qR+elMMxoVzC+UzA9K7t+6y/0Hm0x8TeNbfDoOnfypuyiOdmyPpML9XHGChXnW3zt2ZUxSQmPOGTW/KrW5uXmqWR+NRp+5GP2iSkDu46wErcUv6uRi/u7duz+1/E79/9n7zydJkjS9E/ypqpk5C/fgNEnxLtJdzWZ6MDsUA7Zze7crtwIBVuT2j8A/cnIf78PKyYnc5yOACACZ2cUOBoMhPd09Xc2KZZGsrKSRQZ27G1HV+6CqZubhUVXZxbK6O1+RrIqwcHMjqqam76PP+zxCsLe3t6Rx0+v1lsCfy44hpeTKlSuf6ti/bOzu7pbM8SiKODs7uzSxsdayubnJ6uoqgUn+1ltvlRIAnxSXuqxIy389fp+Dl5/nt176BvZ8wpt/80PsfM43rz3HW3/+15zdvMPk6IzrDzKutK441kYMkxhm7Yj3b71P1mswSdz2QkJj3CZKFsuEZudDErt4rzOr+cHDm6gL6Ev3oEukKlaIxTG+V6NHS/i+/e1vLyVSgZVZD2PMEiAGlctkPS5LzEJOUo/gTnkx/vAP/3Dhs9JCe6ppzxZdn4LGzdHREXfv3uXOnTv0+32n73PhFL71nX9OjOW9d9/l9bdf52x8xvp4ys4Ho4XvNBjevv02f/KP/oTt7W2m0yk/fvMXrP2jV2BjEbxaXV393AHSJ/HlxHg8vlQf6VHjsrZP03QJzPyoOD09XQIu2+02GxsbC9uUUpeOr1+EjlYcx5eWLL777rtL53QZyDqZTPjZz37G3/3d3/HgwQPW1taYzWbcu3dv4XNfNnDziUCNo5UHEV/zsSdYJpu22neBaSPcZ+bzebl6rU2BQHsGRkhWK7ZOAIeM1yjQ2lTnY91EwBg3OTbGc16kxOSOHbPSabGzvcbu3rbTSREz8iwmjrue8WKAAtBYab0dYXDowFNFKo2Ai6OnDBozQmCscKsPCGzuxFGFwotpGnedRgS2f5nUW58xhv+6e1wl+gv3t2wbHDgEHtipgBN8YloJPYeM1B+zBsrU/+9OybFHrC0cjGOdiKk1JUnGlZixmAwY414QhHYH8sy5ncRxzCuvvMLPfvpT1npbJI2IVrPJ1YOrvP7620gR8fDWHTaSDnfuHeNFINBC0BCCBoKN61dRqyvMigLpE2cTKgYISbMGISmES7alFQELwSjlxD49cIJ2K/3ai7MIKzDCMXKcElHFHHLXaUBorAhcAen7c2U/bwKdSwhKVddag7VaTd/wgfkElBbLDpgzgBIC53ETeYDTfVjYitlVfa3EC8d4jM4BNq5PGJyUcuIAOQHaKif6LFvMhHbMJAQ7wnBFZ3TiBmvffJnfbjdJeivEzaYTCJYRqpNw/8Z7/P/+r/935HRGs9BEhUFZQa41SgoSKVkXEVujOf/od7/Hkc748S9u8P69G3znO99hd2fXlcoJ7coLbR1o8cw85V14POhIeVttCY66NgFLlcCW449w7klCibLPCwyq/CpbJrnCs1TACVKXgBh1pqD73YlKC38uiwCRKB/KStw5fAbrdGMCABGuU3qYNOggOUAFQDrtG39d2jMnAtAdrKhjIZGRA150YUrgVSDIc42x2pc3KiyKoij8MVz5mdaZ07spZDk82NjrIKkKrHQsIncNjWbkRIV1Dlb6MlThynS0Jonb7tpLoMkQNRzLxz2fzlVLKYhjZ3udewAoinEC1VpjtGOrxXHkNH2sA8PyIjBzHIBTFOH+CWxuiGLl9LcsZLOUuBEByuubOdA8SiLIBVmasdppMh6fo1PL1uYa2kSkM0meuxLaCGg2hS+FsjSSGHRBbgS5cMLMmJRGs03cUCCcblGzGbPS6qJzzXCQkmZz197akOkcI2Mn/pvnCFuQxNC0glvvfMD5dEQmMwrrWjSA8wHQDf039In6OFxnt158Vy+Ann7M0sYQxU9Aml+luCxh/Crqu0RRtDRpbrfbpc5gPYIO4acN59a3+J2XJeuXhStHjD/xc59HXDzOx7WblHJBU+WX0aa5FGiQkK8kDBpwYuZENsMmiiRJaBtBMRwQD1OiswmdmWWriDECJt5wT0vJLJ0zyS0DBWMFOXA1XqEdL9Y+2ZUuZmd3YZtSitbuxlJylPl/nzYete0u3s+Pi8vAs0ftT8CCNXcZXZhe2JSmKeedglFzlXljTtHOUbNV/skf//FSIru5uclwOGT+5hb3VxWnN96hGGbEOkHWupG2miIvSNO0LAMpWa8XQkr5pfX9J/H5xmcVX7+s7S+yyD4uHvU99GWOrx8FxF/s+87Nt1ja7ljxTosq2KSHzz7O+MQZWh2sCXGx5r0+Oby4L1yYNFpXalEUBTKSaAOFF6MM5UxBcDLsF3RC3Iaq5MYYXd7skO4qITHaOSRd2T9ge2uT7e0u7ZUWMooxNiXPMwrtSqysFCikm/yDF7V0oFD5rhMBzKiKjNz1eb0YCp80BsFISktlY9wKvANArGdFeNcsA07ppg6yBHBmcbW0LiDrfq6AE2uqkqrqnDyAUJ47NUZGxcCxWAc+2KCP4/5SubWGjFn4a8S7AQUmibvv0pe6mdAnPAPHlUcYdnZ2uH7tKX784x/zve/9DtZKnn76WV577ceMHz6kk6xyfuse06NzlN/PRK4v2LygtbNBplxy5UrSLFiJFa48x52mREUKB3I59lJgNSCdfhHCIgMIZnE0YLxjihCu/QlaHQ4MxG+R2KpP+HsobIQrW3PtH3CaxXlSBcQJWyfFhHb0Cb9Pxpx2S4WxVUlYvX/Uw12DDaCGCEwe/xcpcEbeAdCzzrrZCqyKyKVg/fmn+P3rByip0I0GRjgQRODsJKU2pEJz7fmn+cPf/V2G799i/OEdTDpFCsVcWEePtqDmcx688Sbt/7LJtf/+n9L7kz/i3Rs3+Kv/8lf84R/+ETu7uyVbzJ1ZdUGOmWXLMg9CX/OsG0sYk8reWd5rf3cwyBI0sWWfNhc+H+5OGM9844SnIiTEtSZ0IJryZVr1ciF8H5DUm+YiYh8ApqCpokXF3hGutstBhB6ow59XJL0GC95VScbuWAYvoF5eZDiZ0DuxeJ0tG3SUHFNOKTdOauMYgsa4chqMY3pYvKCwCWV5nmEkJNYWXl8mgIMWYz3jR8YlmOTOx5V/uUq14Kak8cMDTjAYZGQRIgdTuBYUFqkUKIvRHqiXEhV5dzAbYa1EawdIumfdPddKOAHf2SQjn+eoyDvHSYGKnaNYu9GkmUgsmtWVbSIKGpFlc2eVWK0wHmWMhzP3bpIWlSjiRNFuxigsg4FmOi+gpcjyMfl0iJERURKRNGLiyNJsSpJOwtpqF60LNwYJS5pmTMeZv0eGOHHMsHQ85/jBMVNRkBVzNE4Xx3inp6DjZi94oVwEa8LPFUuvGnPCCBDemdZahIy81s2TeBKfX7RarSWWT6PRuHSu+CS+nNjZ2SXDlWCtzA3dbo9GKmH+ybo3HxXzecqkYOHdZ4xZsslVStHtdi9dxf4qAo1fRhhjyLKMZrPJwcFB6TT13e9+dykRPzg4YDabsbGxQaPRYGd7m+L9Q7qnc5Rd/M40TZlOp3z44Ydsbm6ys7ODiWM+PRT6JJ7Er2Y0m82l99Cv2jvokZfSQtJR0dHFwnYRVptrwIzX3qRMRgLTIs8Zj8e0Oi20d53QthpC6sydoE0TKJ9SKK9j477XWuvLpzyLQEo21td45umrHBxsEkcRUWxQiXWJmHJwS9B/MUKjJD4BoXJlkra2Sh5WwSl1GcI1ug8Fl5KQGlpXvqFwoskm98BCtf4vcc4vbrsp2RlVB5JLE++LP7vE3YZ0lLDyjweNXDJsywQ0eAxVs3a/pxRBVKKqCTZRiQi4dLVym6oSXevhjPAXn2hbSrQiiDAL4Jlnn+Xo6Ee89fYNvv71V1FxzisvvsDNSUrjbMzZ0RH5cIw2BUpEjhGBE0XOhXNiwVpsJMmtJjYWZMH5oM+d2/fIc0sSN2m2GkSRpdls0Gw16HQ6NJoNcj1HKVGtjAjHRhBGl+CSlIB1LjxSuBVn5Uu4TLhKW12xA3JsWboXvg9x0dtAlPfe1LYJGRE0jCo4x7ehrZ6neiyulkl/HiEFA6uNc6nxfcBYx/LCCiI8EALE1pU/GAGFBdVIyLAI49Fjq32O785bW5ddr3ZaGCFIZ3MaSiCUwmjDTBpyDMZI0mzKnb/8e3ZefIHWKy/z0te+TjPp8KMf/oQ//sd/RLfX8VcWdDZq4KFxgCmBmWLxFscBhQr3s+qTdVBY1u4kta2iRCJtjbxnq6/07RPYC8E7W9QRWw8uGP9chUchaGW5YcnDpHYRqKmfjzCiVntVB558n6+Nt+jKnjtcq7WOoeNGswqYK1lyNeBVivKhDPAqWuOfJzyQZ9z3KElEYG/gHeXc2Ki1EwkO27QBW5ZiSv95x1KMrSzPxgoNeHAmlIJicWIvwttku8/HBKaI8J9LnYC6Fb6asFo0COWuxqGKSOXczjpJB2EEWeborUVeQOIYOMYadG7IhSxL4bJME6GZTk45enjC/v4Oq71V1noR/fOM0STFzDRJolB0aDcaNCJJiiaOBVvrG0gTMZ1m9McDsnxOnkUIk9GMHXuw0UgQSpEkTbrdJnrNercvp+mTJDHTh2ecn54zQ2NMTmG0L3lyz3dpx12ysvw9/higxtqqT5Rd3CPuTjdIeur/E9enJ/H5RrvdZn9/f2HbF0F3fxKPGq5E7MPJGbdv32HTxnx944BmrpgefXqg5q233iLrL+7faDSWNGKUUgu6LSE2NjZ+Y1kdzqjCidzv7OywurpKkiTcvHlzCbza2dmh2+3S6/Xcv24PNnO2ErMA1Fhrmc1mTCYTbt26RaPR4IVnrnMSx0vuXE/iSfy6R7PZXHoPWWvp9/uP54Q+RXzsWzPQfi6yasL26p9f8RXUKAMB4GBhcgju5zRLiRsxeZ55AMRNSB3hJCSrfnXXAyjauBKlOvPDGIOwmrgRsbGzzlNPXWNjY51Op0GS+HOWwlH5feJnjHHioFEMwjgBz5r4MbjjGVsNlLJ0MgnAiFsddQwKVTIipLsYzwBwehQuh5RlaUtIqcOqvayBXouJ+eIq5zKbNSSk4RuDQ01170NyWkIL5d9r8IoxWBOAtnAQiXeQ9kmnA2VCRhhWYynZTV7zJrAdQvImTQkcyUjxW7/9Hf72b35Ip9Pj6af3iARc29mF8wmz0YCiyMmtpQEkInLt324y7A+41h8SbXaZKINGMJtn3Hj7bf7hH37M/fuHzGc5Re7YUM1mQtJq0Flpsr21yc7WFkYUxJGis9JhY3ubq1euEsUxOktdX4gkRAo8i8BI6TglUpT3MmgGBXaS8OVzgUllqUCAenO5FnCMC+tXsx3bKS/3c03l21GE9g7tKBbavyrdCW3jHz8B3lLJMbZ8BYuWrtTGgQhORNBIDzzgyvG0TxpF6JfSOVRpYVFCkkjFfDxiePKQfDxEZhlRM4bIYlaafOef/WPS+Ywb/+tf0bCa6OiM9//Xv+HF/SvI9XWefuZZ5rOMv/ubv+ef/LM/IYpF+Wzgr1UI6dloAVysnouSkVKqedfAqcDqsxbhAYASOHaoiwO+SpDHt1UA0EKv96AU1lQNKKwfayxFYcr6VhBlG5XC5tbhnmVb1+g+VakhYCgBkFAqZS0o75xmvDNK0OKqdEg8G0dbL65syr7k+la4L9W+gtqYVfYld2DhRLIoXeesAM9oDOOTIFhKCwwaa7Qf80H7sTmwiwQ4lo4J7SdcSamttWi4JiE9w6cCHcL7Q1hRaxXj+4QpgWfrXbdKHDo8Uxgn3qsSVOJb1jYojIO0LJDOU3KbYqwgzw2RksgoclpZsyE33nmHgyt7bG/u0WjHZAhm6ZRcG86HY9JGjs5hls7JjSFOOrTjDu12AxmtgxJokxPHEY1mTNyInV4XzpUtShJkpF1ZrPWworHMBlPOh0PmReoc4Yxzc7No//4LAsKeeSpCH7ZLY0MF2iyCN2EsCe9BpSL/nD+JJ/H5RqPRYGtra2HbExv4xxubm5t8ODnj4cNDZLxC59oLtFK5VJLzy8T3v/93vP+Ltxa29Xo9dneXS5+uXbu2tP+f/umfLula/KaEMYY8zx1DZmeH69ev0263OT4+XioFHI1GnJ6ecu/evfLfVgpbWwdLQM1kMuG9997j/Pyca9eusbGxQT9OgcdbwvEknsSXHXEcL7H7jDG/PkBNq9Xxk3rt7WddkpIkyQJoUy9FgCrtCYVAwgaKtU9mjKEoMudGo4tyEm+xaGsxtnAOHLiVVOMTiUJrpHfHEAascS5EvW6HZ5+7xt6VbZqNGBVJVIxjZEi3SiyUch5PUiKkd3TSCqkkQsoy2XGEmlAuUznLuOtxCW2dLbFAoXLLx5RsGAtCRD5x8uvttVKFxftWY6r4hHIJmLEuqQqWheWxSwwsJK6B1r5wYs6i24Y1eJf0GOFKelyiJGpJjynvRXkywpUX6EKjCe4s7m9OVFeVyaEvSHJJpxBgFUhLo9nmm9/8Jj/56WsU2YB7b97g+ZV1WnnO8PzMJVpCOnUVFaOiFl//vf+GB5MRb/6HP+Ol//OfYteb9M/O+OlrP+MnP3mT8WjihEZz4zWMBNNZzniaMuiPODnq8358GxVJGo2EOJIIKdje3mJtc50XXniBjfU1wLLa65JlU6w1GCPorHRdUgx4akfVt60rXANcsusbwnrpWltvZw/sGVHBZgLnaEXVjD7BDX0SDzz4PrUE/5jyuQHpgZggD+2fqRL0lE5DqdZvhNGOuWKNb/cATCp3nsLp/AgrsUIQGTh68ABZpEwHZzSUJFGSaZHy3B//AS/96/+B+XjMvQ/ukr13kww4euNdun//ffb/2Z9QxIoXX3yOw/uH3Hj3Pb716otYo52oqlQErR0h8aU/i/03dMOqjDL8vf68CISR5TNQwpFiYYcSRHHtWgMFyj6vqgfQg5VS2sVjliydaudFBk3YvwJZyr0F5fMfWGiuBE540ETWK7EQVKCxW2izWBPGDP98Q0CJ3O9lOYup41NgHSgentXq3HHOZjb049DjFpN9ayvXPYkHHkvNbTd2WumBbgvWg09uFKzdP49YOZyuKm0N96ze/iXMUwPfqm3hHrjvTYsZs3SKLirGEyg3XokIqyxY40pepbsOIyVzO6PRi1GdNc7GY2b6PkpGRFELYkuuNdNJwWAyBl/OKjKLGRmytkZYi1IRwgjPiIkphCSOrNOvwblZFV5AXwiDEU4ri6zg6P5DzmcjtM7KMieDLplKdUZNGI90YJ9aLnlh+L4j8I5jjjnohI+deH0SJ07E2prlfZ/EY49HtRV9VO0NcO41jwKYxHFMr9db2OYYaMuKIteuXVv6bLfbvcD+ZOn3zyv6/f6vHJ3984pms3kpe+Ui+DGPYBzHbGxsML9ylXgw49atWzRPp6SnZxSnZ4zOThmfn2HSAlRrYX9rLXfv3mU0jBk0YZxALuH0+GRJ1HQ+ny/ZXgshODw8XDr/V155ZYlpk2UZxhg6nQ6rq6tlovXgwYNf7uZ8jtFqtS7XnvkM0Wg02N3dpdPpYIzh7OyM4XDI+vr60rNycnLC8fEx7733nvv3/nsMZ5LtXdjf3uHatWtcuXKFpNnk+3/7tzwcPWQwHtC90eXbv/s9JiuWxnaPtbU1JpMJH374IVJKVldXP9drehJfTjSbzc8EcDYajaVtsR8fPm18FZlxzv1zsrS90WgsPWN/9md/xs9//nOOjo6YzWYcHh4+dn0a+ASgJo4jiqLwgr0GpZybRZIkPqkwpaU0UCYCAlHPSdy2GmvEvexz8izDWkuhfVKPcdoYwYVFGKeARiWYKvxqt5SW7lqHZ566yu72Jt3VNkIapIIoiTz4412bAGMkoiw1MBido4V0zk9IwoIuwUUmZEmiTIHwSrS19C+sV1eTXLeQL0vnpipDCsKjAZwR1d/9Z90E3K99+0MsCjj75KYG0JQHXVh1tyWAtkiL94lLSP4FIKRPg2R1bJeWVrkUfjXaGqQFbVyfaDVbYcm6Nkly98KIILIbI0VK0I2RosnuwT6vqld57633aMYdRufnMJkxm6ckQlFYQ0MKCgztVps333ybsckZTeecqoj86ga/ePtNjs77pKlwLi2FK+dw/USVJXVFXmC0IZ2nRJFiNpvTiB1gd37WRxvNhzfv0O22uXr1CtevXUHrDKUErXbHWSQ3YpJGQpzEZUKKKHkdXmvo4oS01kc8M6H+ESE+7jkx9YyWAOLZix8uW9Qlnqbsmar2N9dvRO3YVZ8IvbICnxC+nEtZfw6O9VAg0DJnNjtFriXMZ1OSJMIqiZAJ17/7bbI4Qq52+db/9H/iR/+3/wc6zRmmGR/85Q9Zv3qF5jdfIY8Uv/V73+V//4u/4srmFhs7a1COIU5rhJpeRlVeKGrnHvr/ooZM9anAJBG1/vsxCUJgJIlFACBEACtFDSxyf6jAg0qnq2IHIWrj3sLhwnMYVsxqTD7/u9OWqfWZC6Up1gqvTbUYwlbHXLw3tvxXMStqQJPHbKS/H+E8hQhjE4R+GK7GlIDBYl8OVyytd2/yZVFgHDCJax1TnksAjGwF1NROq2TglCygGrpVu0RrAwvMAeIoXWnbqHD+ujZW6fKdYPzBpkXmALRYMslnTgsom7nztXggMcLYgsiDnIPpnPF85LAlWys5RRPFimazSbPRREjH2rHGOUgJASKSKBkRF4I33nuH83zCLE/JTe50bQReLHk5ya3ruFFrtwUBf99OxoM0rozYvxeNJU6cfb39DU1yv+pxEfz4POKySetl0Ww2Lz3+ZeVLW1tb7OzsLG2/6D70WQUwPyrG4/EX8r2/KnERqJFSLrVdEsEIS7fb5eBgn3H2gPt379M4mZCMxhSjIaPhiPFoSGKBRZwGa+Hu3TsMOop+DagxWLq93tLs5LIE56JLDMBrr722pCERRRErKytsbW0hpaTVaiGlLMVxH0dIKb8QoCYkzGmaLlmq12M6nTIYDBgOh+7fYIAYFjykzUZvlV6vx3PPPUe71+PtN99EWkk/6/Pg4QPu3rvH0UbE5nqb1dVVlFJl2dWT+NWMkIt/nhFF0RfyznmckWXZpePOZfHTn/6UGzdulL9fVqr5OOJjgZo0TZFS0Gg0kNKXPCkv1GmdCweA1bqaQH9MWGudS4kXm5ynLnkutBvQrQg0bj+Z98fBWqSVRCpCCkOzmbC3t8nVa3tsb6858UgBUaSQSrgkWjrwRYWSJyRSCKQvCzHWIHThSkKErNg2QlGmar7kYeGyanbZC6VKYa5cSyjc5NrUmDMhgRLlT2E/N+muMWwWkvrANBAX/4QlFFkEpMkxOcKKWV3zRgdtCKq/VZdWSxaF8ElYlXkJL5QqgCSOiaOo1POp9IuCiktwnXKgg7B+9dm6Uqjc5ly9fkA+nvHXP/yPfPfgCuenfXRhyRUYIcmNZVrk6OmEeTpBJzGit8a7d+7x4PZN5vmctLBo60RRi8JS5O74jYak2WxgjCZN594u2ZW7iVyi8wLpHWCKouCN198kiSTv3XiHg4M9rhzs0VtdodFs0VtbZ319jThWYB07SnhR0KDN4ekZ5X0UoaSEWv8om7taAbeicvxZLEuoSmKq7NcnnzWcwiXHod0DmOAYcMsIkDuBCjCqAwi+vNAX5glrHG3DghYSKyOEEEznZ6SzU0bzCbYwqFigZETn+h4r16+gjaWwho2vPcvu7/0WH/7539BuNJg9OOXuX36fZ65dxa6t0u51+MZLL/Ozn/6cP/5nfwSqEhJH1ChjC/etDh7Ue//Fj4ZyMHdddYC0diPKex2+c+FgF+9d7X5V/6/KnRb2t8v7V+0ZgFNbntXyZ8MzLj0CGICSi2NrdfyLifnSNQWQtuxj+PGp/hn/P2EX9i+FzGvbgo5ObTcPYF0GhlVwWXUbDcJ61ysR/uKYiyWmVN/HLn7fUrPbUFplMdYJ80LFzNImR0iQMuhuCb8gYGv7L94Ka0JZqvIi5JUwtcU5XmXeWU2IYFQm0YUpj22tgcIynI5dgmoFQoZE1Z2lBiQRUSH4xXvvMNYpGYV37HLvReMBoHpfu+z/Zc+8hMWwBFT6EjcpHdNOyIv960k8ieUQQrCxsezc83knDE/iqxiW09Mz+jPhgJoG5ALS9LMl+6+99tqlwrnPPvss4PqWUuoruVr/VQylFAcHB7z47IuMXh/RaXQ4PDzk/lTAZpfr16/T6XTY3d29lFXxJJ7Er1NkWcbp6ekjffbiwsJXJT4WqGm3Wx68gDA7Ls1wBeV2l7guukKVCYuobcNN293k0JLnBVIpNxH1CWyYIIeDCGtRUiFsQRzF7Oxu8Oyz19naWkMlAqkMsRTuM14kVQjpqOV+7BdWolSEkgolPSADnrnjLbqtX8334rCICrqoM1qCyGUQTxal13aJ1LjPhTvlGQG2BEUuAWOsLVfChf/d+vMR9QTN1HRiKNNyD8wEsOSyZE2UDVYShTwAVqWVpva5AJT5dvQizcLWzZA98OaEe/xnHVAihfTX61g0wiYIWYBJQBlAE0tFR+Q8tbfBG2/dQJ0PsEnDCZTmTh9mYi2DYg4rXWy7Sb8JJyZlmKdQGGfNKwS60CXQFcCvXrfHaq/Hnbu3mYzHTozWeBttE+yIBdYIGnEDrQuGgzHDwTt88N77bG1tsLa+zvMvPI8UlkYjJpKSKEkotUgQEGyUPRnAlXs4JoIQcoHJUiXoVVIlfFlInf5gTFGWIlRgQri8oAPkyresZ8+UMI30bCjXEmU/CcBj2cbl82m9Jo2/COH0hqRxQKPxK/qRtGQnh2TzAfffvUlPxCSRQmcpW9/5OvQ6ZEVOFAFxxHP/3Z9w74334cEpmpyHb7xH86//noM//WMy2ebpZ5/l4f0HPHx4zP6VK2jr2QNWe1euOqASANALoIasfyr0YOvveQ3IoP6oVWCFMReYB2bxntcQWW9Pv1jieTFZDoDGUsobxgT//6rk6ONXl63wukIiMFIWw2nH2Bqod8l31EGmGph+kVXl/1y6bdWvqX7E6scAEJZ3doHBd+HioQQBq74mFvYPnw1goq0BdPUxByovzNTUAAEAAElEQVTY7rLrFRUI5cWcpQJjnVtcWeJJcMXz4511oIxj4gXXP4PxJb/gWZZ+3JfhPeBB6aJGTbIGMBWg68qVIrACq4Ojm2tbbS0SzXg056h/RiEg0wU6z6AwJS610I4l6CY+4p7XALbwLr7Qb7XRxHHincDi0jHrSTyJjwshBPv7+0uJdavV+og9nsSvS1hrefDgAWdNWwE1EtLZ7DN97+Hh4ZIeizGGRqPBfO7kb4uioNPp/MaWt/0yEUURzz//PH/we39Aq9Hi/PycW7du8WEzR273+PrXv87m5iZPPfXUV4Yx8CSexBcVs9nskRk1X1WG2ccCNVEc+wTAYE01iQ9sjcCgkPLiijGXZg4uwTUlE8X4VXsnbBjWGA1G++MIUAJMMWdjtcuzzz7Dzu4aKytthHIU9kgpJ8sgNFb4MiknTIPRDuRxc1xn51oYjRAWrUEq5yQjC42WHsggnL5LphbcMDxLBiGcK4sU1Kn6FRQTPhsSP5dRKql8Ai5KDMt64KoEjgj3kVKkuSwPsO7zZfLvE3Zjvc6B8Ofg26xc+7ZhXb46R2orsG41tb5S7VeY6wmAqWkjEJJWgdUeOAjJk08ovBcRWOUdhxxbSWtIGg2mJ0fc/PFr9KxE7ezyxt37bK32aMuY6dmAuTEUcUTWjMkaiolNOZ+mTDwgIrVBG0Fe5CVLIZSCGFPQPz9H+fuqtSYSEVK5lRnpSxaiSCKFJM9caVZe5CRJTJrl3L3zgOlkxtbmJlEkmYyGrG9usLu/R9JsuORJSJem+fYJ5QPGaA8iVHbLoS9U/QMP7iyyaYJYar1op2y9kKRa175WCyz1Ug7hxF3LPuD7FZLADiuBGg/cmbBV+IRdAJFCaNdXnKaORBhDce8Ecz6G4wmxSFyiGUesf+cltAQhDNY78XT2tnjuX/wB7/2//h2NWJKmKQ/+9odsPP8U0SuvIGLJ8y8+z70799nbOyjLWawJ3kkVeBCeuQBKWK+SXL+nZddjoXLKd/WqvKvaFr5HlGMXJRhUgcrh2bO1J6j+HfU2DQCEAwAox8BKgavW/hf2qgDe0NxhfKFsq0Xg1VL/QlP+GL6zDk9JQjlOBRbV71qtj5XaSnVQxKv9hOc8DPGwcL+qM7uMZcTimdW3LQAN5lLGVGCTVCWcFdBUfdr9zZXj1tpVOnczrb3jm1TVedcs763Rbl8bikHD1Tu9GG21x2mEf/YpQTzjmV6hnKwasz27zrr7GgARbS1B/N2Ygvv3P2Q6n1DoDF0U6Lxw70ZZ7+f1xRD/faLmDlh7B4dtdfCxfp+LoqDd7CC8wLJ6AtQ8iUeMra2tz8G96aOg1q9i/HqBA49yNeVboTYttMDJ6QmniSmBmkJCQ0PyGe5Rr9dbAmCMMRwdHWGM8a50gjzPH1mz6Vc6PuLREB/3mdrURCrF3rWrfMs75b722mu8det9bsczNo6fIs8yVjorxHHM2enZR4M1v17d/kn8hsZ8Pv/YksJ6/EoCNcJbpmIlSONWCVmc8IXJfHiqqzIYWwIKYXWvcppworNaG7IsdyuOgJKOoSL9RF9JQW+lxc72AS+8cJ211R6GOUJVjixCqNKdxq1QK5qNNq3OCsPBkDwzRCrCzXs12KIa46RzmKkLdS6OgKL8/uqv1jFGqiuGUBYkQkITkjzv/uSzTamCu8biSr4tP++vWykP7EifCHg3nCBeWpt0SyH9pL921kJQFE6QMlIRSkXloFva6foVZNfOPgEO1uO1UodQfhOaOZxzWFELtsGBpm+9mHIkFVakIBqISFCYHMuARqNDWyb85f/+n+gMU+KZYnxyRhEpjmxOT0jyhiIXEboRw+oKpybndDZFGYFQMUZYtDUUKHeduijtjI2xzGZOPGo8HhJFEVmWobUhUtBsKlrtNo1E0WgqZvMZ47FBGwNGkRWWZtxCRS4ROnzwgHQ2ZqXb4eToIcP+Oc88/zzNVtMnStaV3Pm+HcAsYVxypkPfx6/ch/5hw2QoTInKbkE9EQ8sqZAAhyTe5asGfKJWJr7W20SLIFYdwMvQ82R5DrUe7PuNEzHWGCJcO1oBsbBkp0OOfvIeswf36IygEcfoIqP13FU6V/fJ0xRhDUa5Di2N4dnf+w7nv3iL4Y9/SjOOyO6fcec//4DndncpVldYX1/l9s0PufHG2+wc7NJd63hGUEjCbUn0kkKU9yUAxiWwtBSi2p8A1CzPfsoypIUktgIiyvturZeeWTyetVUJYQBpFsCZGhhRP3yJr9QAtvL/9VmxsNX3loBf7XvKe1J9poSYPEJbZ0OKhROpzrNitlTXUZ5G6IMlBPQIs7cALpU/V0wyF6YC4AjD4+INKgHnqssvHGBxPaB+0wIjp9Z2OgBzFqNBBxH0hXD3cgF4qp+SLiCI+QockzB8QCoPhjtmpvU3rhI+NqEFyzbXxr0fjQGtod9/SCQL0uGQosiQQpYgagVSmRowU7e196fBIkhTB4CD6HDZhzzr1Fi8oP4Xox3yJD5bNJvNpW2j0eiRBA6FcGXrj8JCyPOcoijKfZIkodPpLCXGUkqazeYjATVa6yUquSVjlt9DCPMRe1URRRFx/JjsvK0C0wH7yaUhxphLk912u70k2hzucz0+q1aItXapPYwxS3ouuYTJvIM2GeKkj+pPsfPCCf+mUwpZMF1RTDdamMLwi2iGEZBGMItgLCzHds6oMMxSKDRoCZkGbRZfxcF05OJ55nlOq9Wi1+vR7XZpt9s+F9CkaUqapuR5Xt6T8XjM6ekpWmtGoxGj0Yg4jkttlyiKlmyswYmaft5W8HmeM/uM7KGLoZRaYqMpA+3ck89rcevDh0zu3EMc9lmbWQ5kh95ui9Vnr6OubDLpxjxgTpqPSNot0s02HKyT31lh+lAyPTrn7Mb73PzhT9jRCQcHB5BJHh4NGI/GGGvpdNpE3TYTZS/KxV0al40Fl5dAXx6Z1ym9eE8ete0ua/sn8fgiaNp+lWI+n5fMvIvbL/a9LMuWxufgbv044+OBmiBqKcAxIi6slF6Y7F686PoEISQzUlTb3SpnJYjoVqK9OxGGTrvFN7/5Mvt768QxSFGACHbJAqliby+q/ARW0Gi06az0iOKEyXhOnqZIGbtyKzQS5UQsS/0KUCVrxk/0/UqlO+8wAcYDGNpNuK1LOwLjRQiBLJMSN4mXZRLt7pTO9SXporjAqgg6B2HFtErGKrcon3T4QSqUI9Un5sb47KS0Y/FsAWEx2jAYDIhURNJoYLR2dtTG+zlblwQulYJUGb27n0YjhPLXZzDWIkUMxrVHo13Q6jQZjad0ug1EPCZNC8anIz64eYtvrFyh1+lwdnLMIJ0xywXpRozsJmQIonYbWg1GgykGhVKWLE8x1qJ1Tm6FB7AqW3DHliowxjKZZKX4W5alaOGAqxeee5ann96n2VTcP7zP++/d5Kw/ZDAcU2hIc83m6gqdTsLg/IzB6RFbW5v01rpMJyMGgyFXrlyh3Vlx9dORIopCH3S3qK7xI4VbyTfGODDM6z1ZcJpKOMCthkWgPatMKZ9A4trPeiwgCNeGZDSU/ZQJp+/Prp+HZwwQrt8LHBArRNAjwn2fUBipS4coKywNKxi8c5P2RDK6OaZhExpCkmU5W19/HoNCaI3CkFsJ0mK1odVs8OKf/hE/vvEecjalAB6+8R5rf/v3bP+T30M1u+xtbfPv/+Of8Qd/9Ie88urLoJw/Gh6sLYEpsVi2Ux+LAi5QXf3yDCNMOi7TdPmoch0RrNhlBeQsAgQhua/2r+dE9fOTl52Trc643L++n/bn6NlQlwPki2NE+JZw3qI8xiXnJUSttwRwIqCItZm3CKWCF67dA0EB8Ar3TtQdmzyQ4sYIW4GVUH4ujPvWmlLU1yPFtYs1VH8SpblVyXiyF+9RBZKZMAD7PmXMsg5SADAq1lGAVtzxKxjLAXCOOeaPFJwLrUGbAm1yrB9Ly+fTBJDEOdMZC7YwaK1RUZPd3XX0LGX+zoR8MCO4zmFljSlokTJcoym3WesBcyH86dnFvi1Amwq8tVik8gsc1jlVfQTi+SQec1wmXnr//n2Gw+En7htFEVeuXHmk44xGI4bDIa1Wi4ODAzY3N7ly5Qqvvvrq0meLoliazG5vby8J2gY74XoYO+Xo7E0Qn1xysba+xlp77ZHO/3MPG0N+DfQnAzVFUfDw4cOl7c8+++xSwjkYDJYAlNlsdun+jxq9Xm/JqaUoCu7cubOwzQJGiWAqirAgLZwcH3N+eoa0ILabiO09hrMZP7//sHyvGu1eCWatgQEaAhL/t5OzI6bTRYPvIARcjwAebWxs8Oqrr/Ltb3+b5557jh/84Afcu3ePo6Mj+v0+JycnrK6usra2hjGG4+Pj0q46yzI2NjbY29tjf3+f1dXVSxOura0tVlZWPvU9vSym0+nSdX7W6HQ6S9bBjQL2MmhewGL/05/9De+88w4AO8D22lX2v/MSX/vHv4sRMAF+MTtG3D+h0Wgw6Pd5MD9lvNthJ3qJ+//bHd75u//KnR/+iA+++yf8z/+X/5mt7S1GP3+X1994nTzPefHFF9l6+VlOkoLiEbD7g4ODJTA5TdNHFiyHxTwRfjlWQwD6nsRXI2az2SPrwXyZcREM1VpfOuamabr0boui6KsN1LhkHKoSHie0W2UrgWFTMVzqUWoAUJtAy0UGgLXW3wSDkiAl9NZX2d/bYWtnld3dTaTQWGlASQQRwgMzSkVlciykotNeod3uAr7sScVYnSIiiUD5qah2wAtQst/D5SA8CBEm7rY2ufWTdhF76ruf2vqSjMBCCQ6+AZQKhkBWuEn1wgJ4cFYq70cFGAmvARES83KXkAMFG3CBy+hsdY9dSZlPKKxDN401WCO8BoOb+OdFwTzNKQpNnmd+tUdT5NqDIboEa6zXXAiFEFHk7G4bSQMpFUkSYQzEURtERHdtm2ee30apCT957T4vfu1rnPWnvPWLG7z79oe0TZOj8ZhoRdK3Kclal9QasnZMI2kwG83Ii5RsMGM6TxFCMEFTaIMucjAaa91KsFDSX64pxXmN0WhdMJvNkFI4e3ksSdLjay88zZUrm0RRwWpXsdZt8Pqb7zrUtTDowjJLc9ott6rYiiO67TbKGnSacfzgAcJCt9ujkbRRcVQyjBwJxN0jKZUvv3JATUiMhKiYSEjhP+tBJ+n7ZgDJau5d1aq8Sz6tMSUO54lQKOH5Ydb6FX6qPmbMAvjmSu4USroSQqViBAqrPEgrBRhNMcuZvn+HPBOMbUK8qmgXGYiIzrXrzMYzMIWDk0QEElQEQuf0Dva49gff4/5//EtsbJCjEbf+yw/o7u/S+sYrmCLnW1//BrGMaDc7zNIJwrMWRJkJg5FOgLZiWASQoXouwv9q8A3hgTPe/vmiAG+I6vc66OD2DyBt9fePnhhctppUMW4ubA/fHUYXfzFLY2Y52Fw830+eoJTjVu12LLDkwtfXSoDcqFU5QpX09xLQW7wXDh9YBGsW7qE1lO5RJYBiq1sZgB4hkAuOZfV/rszQem+zGufnAnAnWDoPG4q3qnFcB8etcNvL0tH6zQpRg/5sKAG25bHx98ZojS5yCpN5ICeIhddBd7cNCzorKAoDKkdnhu5ag5e//gwPD485vH/CdJ6jgcJWAJN7X6oLrJnApHGnYoyplScvXov143oUx6gowhh1oe2exFcpLibbIS6CIpfFL+OwFJgyGxsbbGxssLm5uZRAfj5hQRQgH2HVVeSP9rkvIkz5n08d/X5/qQ3Ozs6WgJrpdMrZ2dmnPs5lK9hRFPGtb31r6T33gx/8YAlsmM1mWAG69tG5gtkn6EOHN1Cz2Vw6zhOR2keLPM+X2r6ZQ2cI2QWgxs4z4lqXDKqMhfTmuD6EACUdg6pQ7v9awk5ni9EkQxhBNppydnhEt9EiMhBpl0ZExhl+PmqMx+Ol/pdl2SMzj7a3t5dEogNz6pNCCEGz2fzCnOSexG9eXFaGmef5Y2cJfSxQY4yjaIeJtCiBDTfRFNKV5VAHE2qryPUpcwihbZmc+lkvUey0QjorTQ4Odrl6dZ+1tRWihgMVhAArJbZ0ZwoqwQKlJFHUoNVcIY6aCBEBEq0LIqmYz+e0Gh2qlMWBQuX5SKcxUAm8Vgmg/4S3mnY/25IZVE+ga44bNVDF1FAW68/XlHoD4Wbi7221oluJjYZ2CKuhYfJdP5YtEaeweluVUTlXI2Pdym2eafK8YDafMZvPmac581lGOk/Jsowsz8jzgizNcUK7lS6Esca7k7tEQSrHvGk0WigVEccu0W+3Vmm1enznH/0x0/QW0kz43m+/wuD8If/lP/2Iv//Bz8lzycZKB9NZZTCdobZ6rEQNOo0mudWMZlMmWcoszynCirEVGOVMxLVxQsUh0Q0JkVISJXGlUF5cuShyQvmQFc6WPYkkioL5dIDOJ2yur/DUtQNOzwaMJikznZHlOePJhM21bdZ7KzRjSasV0+z0SLWm2+7QaXWAGCmUFxd1jSO9kxjWrUQ5LM2UTBoAja2xsYK+T0FIfKvBohLZs74/Sc+CMVo79ol/Pl0/dCvx1rgyDWO0B1LriV0oD3GiwQinnxTFCVJECBERCwVSoDCYh2ekp0OO5yk3leHb33iedv8cPZohmm3OHp4hdI6NBBSCQmcIoRHakkpQu9sUKyuo0QTRUMwOz7j3w5+yv7HO7vom81HKw8MjXv/Z6xxc2yeORTWg4BhJzmEspNtOvyUApXW+Rym2fQFPqVhXy8i4wzw8MGGtA6CFCBqz/pt9eaWtjw7+50vAGZf01xHWxcG/HB+o2FXWa3aFMr6Fzy8cYxHIqTOEAoMiHKP+LU7Lyn9eVJo/YdCqWCoVSONu5UWNlDrrMIyaF5P9C2BXeRdrfw/MG1H7/vKa/FjmGyA4kjmmoam9Z4xjFPp2K8f2oD90AbCrgB3KvwWQN9yX8HyEBQjrbcbLIds6HaoFsCjAs8LZbVtReLao9QscVamSEO40pRBoXTCfp0gUCkt3rc3Gxgtsb2zx3nsf8vC0T1EUXotNlg6J5f0R4XyqsqjSQa3sI1UvCGCPUpEbV5Wqbt2T+MrFZSUAnU7nkZKTOuv2k6LT6bC6usrW1hZ7e3vs7Oywurr6S5/vk3Bhrb1UF+H09PRSoObo6OhTH2symSwlEu12m29/+9tLnz0/P18S1gyLNp821tbWlt5XYZx5Eh8faZpyeHi4sK2ZQzSE1gWg5rPqZ1w5uML5VHM+OWc0GnH37t0lW/RfNvr9/lI7b2xs8OKLLz7S/jdv3lwqS9ne3n6k/a21TCaTJ/3sSXxusbGxsbQI0u/3PxOQ/nnExwI13d46eeYsuoV1q5l4tkIAa9yk0CKVo1ta7UpuFtZXF5JESSR8GYiAOIpIhGRre42Xv/4C69urRInwmhQGIRwTRvg6MSFUKeQbRTGNRtMlmCQYGyG0coluaedt0SYHEfv5qgEK7+BBuVpeMhasAOtFVIVA1tyMAgMnUNhLFxhbJQO2lrxZP4s2Huyw4EuS3GdkAKwEBIHUCowJAFl1LJc4qCpZFBpNgSBx1+RXiY325T9WM5uljCZjhsMhk/Gc8XDCbD5jnuZkqWY+TynyuWsPhCu78udgvKPRYgmUJY4dk0kISRzPnc6PNN7l5Jhud510dsj54B7d1irv3n+X//y//Vdu3j5B0iKSAhW3mUhJWsxZ3VoFFTGeZTw8OuN8MML483BCxM6iUWLJigK8AKYQoCKJihJaSZOd7S3W13tMxwNufniLkTXkhXVAnDEQQX/Q5/z8nO6KYD47YzSZURSKs7MBcZyw2muyugqjwZh5ljKeZvS6hrjpSpwkkkYkwGriuOnaWzqQxrWzIKxwS1mD/qTyQr/4/hnSTlEKQQcgwqB9si4phVmNqJUNup9lFGFwLJkAyFjrhLmdw7aTRDVeD6NafffApAUTkEUDVjuhbWNzZiKioRXSFExvPWAymnKY5cw31mg8fQX9bkGyd4WZlRSzDGu0KyksLGmeUeRzsnRGWhQwy5DPP838p2/TzTRNoP/+PTbvnTMWU9oqohm3eOfGexhtWFltlwmKLly/dIwHz4rBsbmkoNLREm4F2djwHLokpdAZRruSlCLPmWcpk+mEyXiCLhxLLDSGVIookigFjUbCWm+VVqtFnMQkjQ6NpEnSaHmAjap9PSjnjnwBbAigWPn8uJBC+CpDBzoG8KPwyF4Q+A74QIB7Azjr2m8RlLJGg6o0rVyzuhLMMKaEMjd5kS4sAtRSsx33fVcgy7LL8J0BIy7HqwBAUAEIAaAyoXz2QoTR0pbf4FlgIuixBLCmDlRZXyrqT8BapAelLZp6CZTxbn5SGOpvJFvdsHIbVOwXd3Lh/ebQVm3ceclwn0ztneDbDiGQUYS0EmNiEO7Ybiw2IAzlsI5BKEUUN4iKDJ1rMpMjtMZGhu3NVYS5gs5SzoZTsix3jNIAEnnqphLKX4ZwbS+sG4+ogCd3T9yBXUWbIopif/u8zPaTue5XMn7v935vadv5+fkjJW1FUSwlgR8V3W6XtbU1tra22N/fZ2dnh1ar9dhXEn+V4+joaMnN6IsAasbj8VJ/aLVa3Lx5cwlAuX//Pv1+f2Hb6urqZ2JPra2tLZXoTSaTR3Za+U2O+XzOycnJwrZWAWroAJt6fFaHpqtXr9IYZUw/mDIcDrlz5w5Xr179TN95fn6+xNCaTqdL/f6j4s/+7M+WkuBvfvObfPOb33yk/Tc2Nh57WcqT+PWJzc3NpW1a6682UPM7v/uPyNO581ESwf1Co40uQZswCQyT4zzPwDrqY64LB1QQyi7chLbRSJjPZ0wmY556+jqDfp9nn7nO+uYqVhZODFU6GnwoPZIe9bfWC5U1YpSM3ETUKKxQvsSg8KvqBkRBswVGj4hUy0+yLcYWnurgV65FtS5c6sCU/69W9x22o8pJrdWiPL9SZLnEV7zvkfGUdQAMIrg+QVluIFE+QbAIacsEHVwiVCqOWLDGVm1hBYoYg7OeNUZjrCZLM2bzlP75gOOTU05Oz5mM56TznGyeoq1rQ6NBFxqtc68XZCkKN8CqKEL6Uhwn/gzBytwJBwukjEjTFBV59ywZARn9/oif/+QH7BzEvPWz92m34O7dc7Y2d9jc3Ob09IzJeER/OmJno4NRgtFkyPlgTL8/IMtc+qoiD9I0GiRJg/l8VrJKXNmTQKoIgaTT6fC1F57j2rU9GrFg980t/vN//WuMDQ4rri3SLOe99z9gc7MNFibTlLt3j7lz99gJTgtBp9Oh0UgYT1IGoxFJYil0i167xXhcIGPJLMuQcZd2p1tLYoVfyJc+SfQCx36lHeHAHFX2wyCySsVQEJWTlzXaJcmilrQHdpB/Rq0XB3YaSYVP/IuKgeAZO+4MZVkaRmmBLTzopjzIIImxZL6vmXmKmmccvPg8v/jF65xOJoz6I7LzKVdf/jq5ih31Tjj7dqRFqVB+UaCwFCoi2dthsn1EeviQXpwwPRmwqSPmsynTWNFpt2nnM0bjMVESk2UTptMpnfYKUkYUhUXIwjNA3GqyMc462VhLXjh64ng8ZjDsMxgOGA0HnJ2foYuCdFY4MEEK0ixDFx5sFsFlSJT3EZw2ULPRcMm3lLRaLdrtFQ6uXGV3d9evPm/QbLaII4Xyot1KKpI4RinHftCmQEqJ1g68cJpavr1k5PpwAOQCc04KRKgmtR7M8edYll16FoQUCqMdiKCiuATlQGC0A+5QjiEjw1hXUWfKcc93JsKvgrAiWmNmhN8D88SCtQGsDt9jy++yATSzomIghpHNH9ufkv/q0FdtDdixUGP2lVvDTsKzWazG2ojAFHMAj8HYAGCG9q60j2ytrNcBe9U1LOkB+V8N1fbyOsM3BEA9nBrCuzYpP/745xjrNcAsUkU0GpK5maOLHIMhIwNpWFtf4VvfeoX3b93l3r1DNILUaIqwGGLx5ajVe6w8T+vaMIxFjlHmSy+lRCpnyV0Cghet0p7EVyK+973vLW17/fXXH2nimOf5UhL4UbGyssL6+jpra2usra2xurpKHMcMBoNf+pw/r/hlGEFfwMHxqy9cRDEvruB/FHtkOBwuAV39fn8JqJnP55eWelyWgF6WADuG3CLDajab8e6776K9/mA4v36/v6QfEmyvwxh7caz9pGg2m6ysrJSim1JKlFKMRiO/iGQqowV/HGMc29sZPeiP/MzF66yfpzFm4doufvZR+87jTPS11ozH44VtRW4ZDuxS6VMQ+w4hoVosvuR7L/bLtdVVstVV4igmzVPOzs6YTCal4Hhg9YcFi6KoFmjDv4sxmUyW9LLef/99Pvjgg0e6/rfffnsJ6HnzzTf5q7/6q0/cV0rJv/k3/2ZJi+ijzvWjytIfV3zUef4qx2e9psfNjup0OkvjwWWlnV/2eX4sUNPpdDCtJsJalHRTTG1yP7n1wAKeMaDrbk7asS6iqkRJSkkUKaLI0XlV5BglURSRZwUCUHGMVE3/WpSlwKorJXEDUqQSVCSwVnuBRoUUkV8Rt1i0B0cKCjNkPL7HbDwilm6lvMRQBAglXIIqa64yYTWYSjMEUbERpLhQuOuTjwD6lIVTwjNzrKlcaoTjgmit3aq3v3fGVhoDxrpB1uIACKUiX5MQVtMFShiEcYkJRFjmpLllNCmYzjTj4YyT0xP6/QGzaeoSEi3IM1cOo22OUu6+qkixurpJb7VLq9mm1W47tX4BRZ6TzlPm6ZxGkjCbzej3+2hdoI0lnedIaSmKOVoL3x8sr776LZSMObiyyl/9xQ94/507rK1ustpbA2vZXF+jkUgiJVhpKbJ0zHw2xxROP6gRK6RSaGOxxhJFMUVhfHmTK20r8twDV5Jca8Cyutphc6NLuxXz+6u/Rbsd8dd/+32OTkZogkCv4Mev/QzIef75fY6Oz3n3vQ8470/Ic9dWa6trxElMls6ZR4bxJMJqw3yacn7eR6qEjZ0dchtz/dqzRMrb2AvpQBghCbJNomRJiTJZDL3EWEsk6volrh8Zv0IuPWspJKpIx5ayAYYJg0VgFPjk00kUiZJBUVPS9ccJAtMCa4RL5kQF+KA9e8dq5g9P2FYx90bnfHB6n6euXac5L4hUk3hrh4kWGBzAZDwLwiXPCitiDBptDTaOkM8eMB4OaI1SmkLw3n/5G+zGBqexIPna0ySRYL3XQ6kGg8Epf/7nf87e3j7ffPVbxHGCMTl5UaC1ZZ6mzKdT+n0HypyenTEcjphNJxRZhlASFSmv1wTYyLWHCGVUIMokvwJFg6ObRpDNUg+owHQ85dSe88F7H7qVLQGtdov1jXV2trddycD+Pmu9Hu12mySJUJHyE2iBJTAC3T2OogglFMKDEyKgFkay7CLnxXNDMo4ox0RrnXsewhDHyoO+1XglhKBQjr3jxtKKXSNxoJD0Oko2vBLC+FcyCW0JGEmpSlDeoQEO4DDWOD0mb0tvqVg7sqa3hb8uKV2/NtozP6QotcM85QvfcARB5boOS8WscZ1eiKC14sfrsC8WbbISoHQAqcEKjRCm1PMy1jhgw5dWOS12z7jCeG01s/DMlaBOOCP/DFbIjoelRACUdMmuLEwFKCEtzVaTKJakeU6eZVivyRQ3Y77x6gusrLR49/3bpPPCMXa82LhzTLQIFUpV/RMeJt3WYD2QJ6RAG0sjcZpa4V0lA/D2JL5y8bOf/Wxpm9b6kTRq2u02//Jf/stHmjjXk7H6v16v90jn+UUkHIPB4JFEk7+QsAlkCorFJHI4HC4xUoJT1sLu1vKjH/1oCRS5DNTpdDq88MILS9957dq1pfv64x//eAl8Gw6HS0DP3t4e/+pf/Sv+/b//9/y7f/fvOD4+RgjBn/7pny6xZ5rNJsYYTk9PefDgAScnJ8zn80fWmXnzzTdZXV3l1Vdf5fd///f5xje+QZIk3LhxgzfeeIMf/vCHvP7661hrOTg4YDKZ8P3vf58f/OAHfhFDL4AvSilms9mSPlOj0WB3d5f5fM6HH37InTt3EELwO7/zO0sOU2dnZ48EZiqleP755x9bwmytXXJWa2SG05vv0cgW+0kAUMt9gZWtbZrN1oJGzXQ65S//8i/L+2mtpdlsUmjN1tYWL774IoeHh9y4cQOpJC88/wLNZpNms8nRw4fczYecbzV57Y1fcHp6yvXr13nppZcuFTZ/5513llh75+fn3L1795Gu3xizJEbc7/cfCSAWQvBv/+2/XeqnQWS6HpcBd1LKJcHrLzNarRY7OzuP7fhfRHQ6nUv7yaNGv99/fGM+l7PWWq0W165dW9iW5zkPHjz4sk7r44GaJOn43MAiBY7uLVxSHFYpweA8IxYHlSiKELFCKVlD2hWR8mwZn2hIJWg22+S509uQMiGKYsdMkE5HwZU4ANaUbkMltRuAwiW01tlES2nJ9YiTk5sMBrcx6QwRR1hlscJ4O1Lp1NOEc6mRwmtd+JKoQPkvB0C3REpBNakNAI31PIZS4NNWq9DW+hIoP98XphKmtJ6K7sRf3b7arxIUBscaCYmj0S4pstqVoWlcomNBF4rxDI7Pcs7PM+ZTQ5plDhXPcnTugLA4brK2ts7qape1NWeN2Gq3aXc6XhzYXazTn3H6CU4XIccYw3yWkhc549GYotBMJ3OEhPF4Qhyvsr29z9HxPX77e9/gypVdtL1Fms0otMKKiK3NLa5dv8p0NOS8f4rWGZKCLFFMpymj0YxGHGNRGGHQmXYWstqS5Y5N4RJFRW4LlF+lbzQi1lZ7tNtNdJExn2fEkeIbLz9HOhvz53/xt1grsdqBINNZyms/eZ3heECSKPLCOFKIkKy0Wnzrm99CKsGNG6+TZnPOzgakrTbb2xt0emvcu/cQLRqsb2yQpgNoOEFlx3aSCFU9Vm5BTngbeJ9rW8egkEJijC9lEwIhPEsN6/1iKtDPdTlfHiJk+blQ51RfCQuQWek1JADrWXD4SXVZPleeqdvHWvIAKuUZ+uSU8TTjg9ERjZUWvUZCc56RbK+TtRK0segyKQ2lkc7uXFvXPw0WHSn06gp6b4vx5D6NNGN46xbN8ZQsEpzKnLvzMWcPT3j5u9/jtZ/8lN/+7e/xwx/+ECEkB/sHZFnGYDSg3+/THwyYjqdMJhMKrUtwQlhQMkYbQ5G5MaLQGiEyP5Z5BpFnEQgp3diGfxaFCU+2KxMRkJugL+T6XiNpENzTjh8ec/zwmCiKaLfbrK2tsr+/z97eDjs72/R6PeKkgVIx2gMfUkisceWXUrrvdgSV0OreRU4FQM3rsMhqlDXGjaE2sECscdpSJoDawmt5WTdWeEAwiDC7vYTvXhUbUIgI170cYOHwRw/iSYHwQrYCr6WDAwN0oZFKIqOo7HOBEamLvCxddSCKceAeIFAeKPAORB5wKYEW3+/9jaixn3yfd8Vd7nfhnrWgyRReD0q6EtbwnW7cNdSFfi2uXNR6VqLEs3GoSqpcrZquwCRq5xIes4DQhhV5X34VLsEd25WtVQVZ7h0SxTFREjPzgIrRxpW2asvWzjrzNOPevWOmk5Qs930i3E8T4F9Y0GGygZ3kSx4FJI2mdyR07a6NQT2x5/5KxmeZ9DabzUe25/6oeNwrvo9thdWY8t1ae0mWDJV6WGsvtX9NkmTp/Hd2dpZ0QVqt1qUJ42XlbS+99NIjlcCsra0xHo+ZzWZOksBaOp0OcRwvibcGNk5o68BuedQQQpQW5f1+n6OjI5rNJtPp1OlrRRFxHJOmaXmMy+5j/fvgcuZS/W9h/49i1DxKBCeqx9XPp9PpEqMmzyyqZD5XcZGtYICiyB1zqTbkz+fzJeeaahFalAtGxhgm08kSKFLEGrORlIynoijQWvPjH/94qc3eeuutJZcfpdTn7rj1UfH2228vMSDu3bu3BGQfHBzw3e9+d2n/x2nv/bjZI19EfFZGzeN+33xUXOxjX/Z5fixQ0+tugQdpsNXKoJu8u4m8xCCERpQuGqIsmZGqlmyIsLLqfpHCTfCjWGGMJWmKMnmSQiJj5fUjnKYM1tX55yZHyHAeEonECl/GZCKkzZmNHvLg/nv0zx/QjAuiFYuSGiksJriZ+HIQKd3KbLhOKWvsF7GcIJSr8rWVVO2TgrJMRYCwwW3FYr2mhZUuMfGzazBBcFjWHlqBJfITaIs2zmo6y3MHWniLZ4PACEU6yzk9zXhwPKbf1+S5AW0oCqdB0Gy22d5f5+Bgl83dHdptB8oIhEvsrCVuRDSShDSdk2VzVntr6MIN5EoKdBGztr7uAKQ85/TslOPjU1547nms0QxGc65eeYXvfPe3yIqH/ORnf02/L0jPTrh774jV1V2uXD1gbbWHsJpet0OjAZPJmNlkzHg0dJMSK4iUYxQU1hf1KFW+HNyEwq0mOxqsRElLqxFRZHPu3LmDLjaJI0EUWZqxYn93m9Vul5P+FCEcK6XQhsk84/bdI1bXOuQaX/YgKQrNjRvvYGzB1158luOjh7z//odMpppMw8HBNs32Kv3zEYP+Ga1WipLVQ6ukRCrlhY3rjDKFMS5ZDavqzr0lqga3chJCyXJwybNEKln2PcdcUz7pqxLHciXUBme1CphxcIp1tsQePBLSr6oHx5/Q2y1EQpCd9VFZzmk2Y5Jrok6b7d4K8t4pyUvPMTUzrAFtHSjjGBIaowuKInNMBnKMyEmFxiqJ3dtkejagdTrEzqbEwyFREvOT73+fhzF85ztN3nn7LR4+PGRvb5f/7v/4f+BHP/wRP37tNcaTCePx0Fu0ewaGB2/D/MFXciCVJEkSkiQhiiOShqTRaJAkidPFihOiqOnubyg3tHi3sAytDVnmxLXn8zlZqsnznCwvSNMUazTat40QLvEfG8NkMuH+/QcopVhd7XFwcIVr166yt7fH2tq6096RBqMFSMeEkRKEijyDyukEuWtTpSm1a9cgFixqjIkwPtVdnHyZXSmsKwFVMkocUzCAQDgmRhirRF4BDOAsvI2b5Dm7cBOIJI6pAaWweF4UiBpTUAiNwDEsQ0lXOc55sEzgALAwJCrhV5xF5cwVSvdsrY+73x0YGYAjgWOcGetKAZQfO6rxOhzbf18Y3wMIakKplHt2jNXOycnkjpliXemv0U4bbIE1Yx1jxtl+e0aQNR6nse49hcDawkNLTvDbmiDwDHgB8DiKiHALEqYo0LYgiiVPPXWFtdU17t875N69I9K8oNTEQrj3YslECkLrAZBzvysVkzSaWOvGJG1DW301J0i/6bG/v/+p972YkD+JLyY+Sgtoc3NzKRl75ZVXeOqppxa2XSbma4zh3XffvXT/R3X8unXrVsm+WV9f58qVK0vMk88jkiRBCEG/3+e9995jNBrRarWYzWYMBgOiKGJra+uXsmz+ssIY88g6Tl9EXGbN3sxh/xGT+MlkyvjwwYJj1y+jKzUajpaYAVJs0H1q0e7dGMMPfvCDJZDw7OxsyeFpd3d3qY9/EWGt5datW0vg0Ycffrj0PvvOd77D7//+7y99x1exTz6JJ3ExPhaoWd9wtNcSqPH/CzoCboVeI4VBUAkMC79yGpKmMKENDAHhmRBBjNM5CtVqLYPIo3RoRprPMdolEI5Z4yamSuDo6kEA0qaM+7c5PHyLbN6n25AICoJQb0hqHHNA+slrSAZCnuzWN6V3FQrZq/Afkta7ZJTjgL8XBg9Y+RXh8jPCV4l5qrvIPOZly4XW8Hk34Q9JiAIrXeJjLNJYMCkS0NZdy/kg5+6dCcfHc6ZZQVFYbGERGrY213nq6Wc4uHKF9c11rNBkRcZqt0en08Eaw2QyZjgYEEuLLmYYPUeSM5uek2VpuSpcFAW66JM0WoCg3RLEMmfUf8jTTz/r6NFyxO27P2d1LeHNN3/B/u4V+qOHTMeGbluSpjPS+YyiFWEoyLM5eTpjOpliLax0uhSZYWbnSBlh82Axa73eSdDJqexnpZQ0mjHdXpvxdMQvfvE69+712N5cp9VQbK51mc8zpFCL/dZCoQWD4ZhCFwgEhba+LxX0+wOiWJCmKds729y7f8xonGLOpwh1SrfT4/zsDCk11owdo8RaDwQKJ+YsHJ9FCuGAOiTWVECl0y1ywGHFkgj0X8/AEV4Y2oT+ESZ1UTmxC6U9gYVRahqJqs+6cOwsow15pv39kC6ZrOWrSggiqUgKaD2csd/Z4kgUFHmDa8/ssx7FFP1TTrM1pve90K/v5zKI4hqvH2M1utAUhSbLLeQFmTKo7Q796QjyHDWbY4QgSS3j4Yh2r4uSltVel5PTI7q9Dr/929/lf/lf/p8IGWNMjqVwbeoRA2MctbfVatJut2i3ElqdNo0kodVqeaAmIYndCmcchbLCoJtiPHgGUoSyzqpeO89z8szpOmV5znQ6ZTqdMB1nTMYTJtMp89mcLM1QUUQUJ2htOD46od8f8u6777K9vcXTTz/N1atX2NnZIY5jTBBINwKhNUJKlHJAjZCydKrCA9IQ2tr3FeHaNYxD2lgiJUAEpkYYdU0JBlhflmOMLgGQgLyU3xf0dMO4H8Z+KrAEaiufpgJQnMhu5bYkPEvI2VXXGFweNHLaKLrcrsuEpablJaqVryqhqcAralcbWGOB8h2udyHhkRUAKvzuxq+cO8aRZ15aiRURUiuvh1D48dkBycYUhNKrUA5cavn4U3KslQCKSQSxe0qsdqCtcICX28WV7Sqh3LtJOX0rd+2ufTY2e3RWGrTbLd67eYt5XngHKeXej4TlhHCbLUjpy9AkcaNJpCJAIWWE0YUXgX8C1HwVY29v71Pv+0Rk88uJPM8vTfaff/75JbCs1+stgSVZli0J737Uanscx48EthhjuH37dsl22NjY4Pr1618IeBeAmuFwyM2bN0tGQ7vdJssy4jhmd3fXMWIfo+bRZWGM4eTk5LGxGy4DatpasBdtUmdyfVRMpxMGh8UCUPPLsESGoyG6WAQ6mk24TFo6y7IloCaO46VxZm1t7TONW48a1lqGw+ESe2g0Gi0x3M7OznjrrbcWtimlODg4ePLuexJf+fh4jZq2qA0VfkbrJ7eC2mo+TtTX2CqREKIsvPBRiTZSUvOrZLMUpcR4WnpBXmQYnaJNhrWFt7F1SZawwouYFkhRkOdDBsNDzo4+YDY+oZMk9DoN0ixnnjsNA+2TaTchDyeKd18K65JBL8Inu34mH0oGFp5pgWMm+O8JjKDwoZBChKTECEsRylNqegKEtMZCSLykyJ1DlP/uJInQhXOFKazgww/nfHBrxGxeUOiI2cxgteDq/gEvv/gcTz+zT7vTxaKIkyazbM7hw0MG5+d0V1r0ek20nqP1OcNhSpbNMaYgUjCbGoQqaiwNGI6BGkNTJZrJLOPe/TNU1ELEbUS8z/lAEUcNbt26xYe37oKRZOkMQQ8lDCZPmc7HnJweM+wPsUbQbDUx2pWPRVHkqLpGB28nwJAkDbQO1rKupCJpxGxsrNFqJhzeHzIZjzk+PuF2q0G7FbO/s0VRaHJtfLLp3XB8aUeaZhRFThInBAHfosjRaDpxE6MN7VaHa9eu8c67t5hnmn5/QhIlrPYSYpWTRMHdxQFBDuxwfUfaGlNGFH6hXeAc761L0nwZkyn7iXueRLDzDSUXYT9hEeRlElo5/IQV+ipZtD7pdrocvkd6PREHFgonWFyWsGi0cXLccRYTjw2dvR4f3HmfUabZTXPsYMxEDxkUJ0yPB748xH239LQIbUUJihnjBaq1wiCQRqO2EmTWQX44IE1nqJU2240uD9KUyXDMZDDk7u0Pafe6SCnY3t5CKDCFhx1MgVCC7kqPTqfN2lqPlZUuSZLQbDQBQaPZIIokzVbTuUFpZ5e6vrHO/u4ukVJEKnbMNevYRsYIdJFhtGOxFXlBlhXEMiePp0CMEC3s5qoDHYwTDJ5NZ4xHEwbDAefnAwb9IWmaOeabcX3qzp3bHB4+oNls8uyzz/Lss89y5eoBrVbHQVzC9XeDciLpVji9c+nYiVYE4EJAqTNTgctBn8Sx9lx7iHIEdsydMDZX4IWv7UL4MV35MSkwVfzPIrgGeXZWaVtdJfjGGP/RYGNeDmjVf30ZjgMcbXmW9VHQ6Ti55zOAQkqpGsAUQPkASlaAUX28Dt/j7tOFiZirIvXHq+5F+UzVfgfjGEXa7Wc1mMJQmCDIrr2WT+WKWOr3QPleDHpRznWp/p4QCBG7kivtxvBCu+fbV/NihURIi84zrM2JEsWVa3vEzYj33v+Q0XgOwpUTCF/uVujCl5pV7CODIE6aWKFQIvIgoS8D/DWkYf86xObGp9dQsNZ6puqn/ILAZv60eYzI3b+FbYGJ9usSrmTy9Ozhwn0SQG/11aXStdl8yJ27i6v4k8lkSdNDCMHO7u6SO9/xyX3O+/Vpu38n2MXSxcAUCXoPvV6P/f39sozl84w4jjHGMB6POTs7I89zut0uBwcHZanVxsYGxpjPH6gRuD4lPh0oadGcnh0+th45m045PV90/JprAdvdR7qmeTrn9KyPrn1UIEgaycJjayVo2XD/V1BI9282mzJJK1DDAqvjNtcvOdaVK1eWyvHiRKLUYh9dX99gc+vRtK0+azSaLy29ux48eLBczqVT/uIv/mJhW7PZ5F//6399QYg7LBx9CT1CRMvj4+MOK8F+LCzwxYawX849EXr5OBYIFS+f+qX3xcTHtog1k3IAq1BHQfCpdhPyoG3hbXLLnYP1r6fb+12DtXdg1QAEkUhXO2kpigxLgdEFuc58OYBPhMMXYYhkjjAjppMTJqMHZOk5jTijkKEoSpPEEmOlK80QAIrgwBNyaCV8UiOc6GLQeKhCUMoE+9IoGzAEQW3lm5JOfxGkCavhIXWyMqwGh7QIAj3d7Va4fMxT242xGCGYpTFvvPmQe/dn5FmMtTFZqtnb3uIbr7zEM889RavTYjLrc/fwNgcH14kTZ5m8t9Pj7OSIB3dvc94WxEojZI6ShkhZVCwQaK+ZoXwzepFQBMZapAdJ4ggSpWg1c7TJKEzGrVsjphPJoJ9z8+aHnB4NkSIiiSSdVgspDPP5mHQ+Jp/PGQ5GZFlOq+1sQJ3lusIYjRLSuVtFklgG5L5ASkVRWNI0RUrJdDJjNBxgrGU2m6GUYjAcMxjBbJojlaLIjT//KrEtgUIr0IUFoUmSiCSJieOIRqOBMTCZzlhb6/H0M9d4++2bRKpNFBmuXtsikrNSKDWwu0pNJSgBFMJz4p2gAs9BGAEycn3fVombtbk7xzJRdgCP9D976ND1q1BdhXCaThaMqRJ5B9oINMYl2xKUqlsK+f4qyh1c/z+fsNHa5tb7NzGFZq3TYh1BdHyG3FtBNy2KgsiLmfpqFVfiIVX1orOgrUJqSR5BU0cQN0lWErJxQVbERM2IZ154mXtvvck7P3+dVrfDZDwhLTRRFPPmm2+WxI44iXn6YI+nrj/jrNqFJVKSVrPBSqdDHDc4PT5l1D/ja197gZVeB2M1eZYyGcPx4U3acU6n3SYrZo7FEJgqUiG9OHkcJ8SRotWKQMRkeZM8z0iznDQrEEiiyJIkEe1Gj9XeCvv7O2htmEynnJ/3efDgkPOzPrPphDhJAEGeT3jjjTd5//0PeObZp3nppVfY390jSSzCFERe48hp50gkQSw9aLpIKoFqoCy3tAg00noQ24N7rj088EEdbK4YWK7PuX2UHwNDSY0IjVgvAw0dpjZeIatSz3KXiiLp/xuYhg7sEYLSdageQgToOoAzogRtrA3nXjuPEuiugN2FMO6Y2hrP4gxMnuq83IHD8xD0noLLnSlF4UtxeGOdsWDJqAnW8e6/pnb9Wnu2DrK650KhjV9AMJYoaZDE1msODNAm9+8b1w8QIKOE4CwVY7l27Qrdbo+79x5y5/Y9ZOnqBkqqMMr4d63rF3EcO9aXiJzrlJCeyfVEo+arGEXe/OQPfVRYC+YzMChUH+IHCxNaay0npyfOtv4TQhcFxBd0VkTuwZqvfly9dg1pthkMBpydnTEcDrl9+zZ37951ZbBZ5hJXmfL08zGr6x12d3fZ292jt7rK3Tt3yItFpk1eWEy+eO8Km9NZXywfEQJEknJxbJwWQLE4N43YIJLb5TsCXDlWv99nZWWFRqNBq9XiwYMHpRtQPQIQnqZpKdg7m80udaK6LEKiLKV044v/vrOzs9IBSinHSjw+Pl5KrNfX15eYPp1OZ6nESwjByckJZ2dnvPzyy3zta1/jweFtTgdvouJP16estajW6LFhh+1Yc3DBh7tZwMnRazQu5JFra2s06mwqAaJhUK3gXFltRy2mdkZFnDeuu3Lv7j6bV9dZyTLef/99Hh4d0+112dzYYHV1Fdlt8eaNt0tB6cFgwFtvvcX16xfhG41M+shoUWMnisYU8uanuyFAp7NCp/No2lxGnS7154NGwXZeu09WcHKU8s6b9ygnzDgd1f/wH/4DvV6Pvb09dnd3WV/vMs3vUpj+wnfqoiBf0v35bLGSr9DsLdqjCyHY2tp6fCwfvQ75Btgvvmz28PDwAvBnyDmB+B5Lc7jPEPN0zmCwKFAshGD7SnthoRCrQPfArFAfd4NkQj2MMWxsLJYHWmt55513GA6HnJ+fMxgMKIqCg4ODS22/f5n4WKBG1G6WCLYhVCuPlTSHZyv4n8vpvZDU7U8FwWI7TKjxAE01KXa18xJjFVIalJVYq930W1uUzLHkRNKi8zPGw1tkkxMkGbFIkUmEXYlIp3Ny0yRJJEoLTFH4VePIrRSFZBqLsBYpg86M30Y4RVFeW0iOw6ptuDMhsQhJUFnG5P8timOGxL2ayAc+j/HJuUtoBEIoR4s3zoL84dGcX7x5zOlJgbUJOjf0Vlp899Vn+farL7Ox3sNEkkJIUrFCdtbnJz/9Ec0EmglIr3+xs91AF1OELMBr/gjhwQarXXmVKUpAy3EhnPuNEJXQadSUKJEipOD4OOetGyfcu9d3OjkGGg3nwnRlf49Os4XROcYU9PtnDAYDr+MjmE2nRFFMs9lEF7rUYMn1DHwpQpbNiePEg3lO0Loocs7OJuzu7tDd7nB8fMJqt4vWhsF4Qn88J4oijNFICdZEtTbw2hh+Zd7ibG+brSa9Xpc4jtDGMhyMiBsxL774HO+9fwuAdjti/6CDMBOaDecaJnziKYMTjQc0wwo9BBt2lw8GkV3HHKqeBZ/t4soYQlLp+qQNO3pgsVr9tx4UxSXp5c/4FX6/vwdV6pMSl8j58xMCISEpIDvss/rM13jv7ruYpmFnd53ddpP8+BTz7S7WZiAUdaZG+EZsAUHsW0qklUCKERobRxgkup3AlS2Gt/t0Y4ld6/DP//k/4//7//7/cDKZYHKNTWBjfYtvvvpNrl6/RjaXTMcD4ijnhedf5O0bN3j7xlvsX7/KzvYGkZIURYqiwd27D7l966d017sIAUlsWO1auh0JPGA8Ns5KPHL32CXfAoPCWkE690wMKzzQ2yCOI5coW+3GERKscW0RR05vyCbQWWmxtbXBU09fZzadc3j4kMPDY/qDPnmeo3WE1pa33rrB7Vv32N3e5MqVVVbXFM04RUlKC283yY1RKnFivTL87uzUtTZOhFY51o1SjpEjVYwIzIkoRsgIpRwoo30Zj1QSJf3nZIJSkZNY8aBCsIoSIvL3IoDufmwSwoPJTmvLAkr4+1aWsvreH4bMGtASAMswGDus0H1QClfAE9goCy9UhANHhBuXAiDhunslIlx92o/n/v0ClH2+DsxUkM0iy8bggRrhnh+Ndkczxj+/pjzfUmg5AEIlky8AXw4YCSOCEAphodXusdrtkc6nNJstCp26RQrpXirB5Un7UkdrDFZb9va22d/b5creNm+88S6D0Rjr3yXWBit3d25RHLv3TGA2leCX/dxX2Z/E5xO6nqzVpx2P9LtgYXX0l94/h+gURK2MwFpm+R2MfsSJ9GNcnP2sEccxFIqiKEq2yPHxcQk2hH+tlZRrz3bZ2Gyzv7/CwUGPtbVV7j38CfqCaxRy+TiRgt6lBkuzyzZeCEFRKHTapc6qyfOcoihKNx/gkdxUoihyRiBC/NJATQBk6udQj6IolqyYgSWHHnD3/iIbKZQpnZ+fI4Rgc3OT8/59ZtkdlP30WiPRo5lbfSERAY0LkkNJZpjdu4O54PrUFbsQtaoNAmQCUdNLo31MGNvgJF8DJaEtoN0CWsxOmwynkni9BQfrJN6F6OSDW27urxRpmpKmKdeuXbsA6OeQHEM0WDreZ3mbyGSNRnvtEz9nsRh5iLaLR0ta7l8VgvEEoniRLWKt5Qc/+AGrq6scHBywv7/P5laX1e1zVGNAfTCs2O2fY8zbnI8W+62Ugu76FPEpGWKfOXIN6SYL6tRfUMymOVmaVe8doSEZQuOYz9aDFkOnE2bZ8cI2qSTbvWsXgBrv9Kfb1DOay0r+gCXBbGst9+7dYz6fY60thcIvAjqfJj4BqHEnG9Q0nHuNCDhDyVAIk+RyBTR0aEGNYRJWdCtx4Yo94vb1X+1LnwxCCUzhBuhIBXp+CiYlm/eZTR6Qz45QtkBYS6xcyttqRORp6o7oV/eFZ8rUVfwrBxTrE21RA2eorT5Xq9C6XLW4mDhUHw6K6uEFZsuSFieivJiQ+9smCmygnRnt7psWCKswOubDewN+8cYJ/VGEtTEUcPXggO98+xWuXt9FGEFWGFqtNgiLnBW0mwK9IpHMiaRzQpLS0mwkzI3FUnhwwelfuOv14pzC+H9uFVt4611rCwSKKEooco2QltHAcOOtM07PCjY2tuitdrE6opkIuisJvW6Pte4KlozRaEyeGeKoQS5m6CLDWksSJzTiBK0ce8pYS5Rl6HD/tMYqQ5rnZJmmyAt0YUiShOFgxGDQp9lqkmtDOk9dAhvFFEHfwZcQKVW5W1WaLh60kS4xFFISxwmTyQxtNFGW8tIrPfb3tpjPNN2VNq3EIkWEUholZaXTEdxnwhNhvCdMOcaLeqf3YtwBHrROvFWIUvTXdSkJwv3NOR/4PlhSTmWVSBt/UHy5k/8X+l3NgMbvSfV9HgiSw5zNRo9cCCazlFTBaNRnazaj2ZAMWpJECOZeYSe4moVqQisMQtYBKotVmkRatC2gSHh4OuH8+JxkltOYZdy/c5tXv/FNvrZzhdfu3sTGkkYcs7a2wge37pJmhn/6T/8Fo/MT3r3xMw7v3aYRF7SbBQ/uvY0pNmm3GiBypMy5/lQLIS2FHiOEIo4t1uaOMYL1fcAnz2HcQSC8SGzo81iJ1gJtcqQySGGIpEAbMLqJ1qC1II7abrVKKG8z7fRier0VOp0WTz31FKenZ9y/d58Hhw+Zzx3waIoR6WzCcNhgb3+NawcNVlYswqZgNEUOOWCd7Z5L7D2ghlUYrYkihYocZVNK48FMJ7ztxr+4ZM4I3LMV2CVCREjhnucoipAidqwiKb0WkkKICIEqB0QHBCksqhzXKnFgp8kiRIwQCVLF7ntU1UECc9KB2t7ZLrCAPNItXS0hGOPeGaI8eyr2TAC0/dhK0EuqjeE2gEIBsKm/qy4M4/Xx2gb3L/fu07ZwQtNWo23hy36rlUy3p6n/4kFSs8BcchisxXoXRB1cu1AUFlqdLtZaZvMxQitynboxWkFwtHIAWQzKoHMNwvDsc9eJVYOf/uznDMcz5xplvdCzly9Omk2EckyatMgJZZla6/JZfRJfsZAXEtuLCdkX+btMcXS0S87rExLDX4fo9/vkM1OCMycnJwwGA7TWrKyssLq6yurqKt21gisHOUnTPUPn5+dMJhOnafVF3yfry1f6xwtAzeN0s/nyw/569sela/qY6/yE67cWzs/O0NkieDadTcuFlIU3wKPcT/ERP38e8SjfV1vn+fjPWRqNpnNWqzFFtNbcvHmTNE0ZjUbcuXOHRlPw7d9ZZ3Vz8T61Wq1HEvH+ZSLLUk5OLgAIUtJqtR4fo6aQkLbBBvSyIiQ82u9h2yf/XpgpyGBChANqRMbn/jx/XB+5ZP73WWJjY8Obm2iKokAIJ+fxWePjS5/KRqjbj4aSJZ8BCpfgOIHf2uqhX2EMrIKQEAeNmnAER9qQtbZ0xQiuDCYmLyKnpWJciY7Jh6TzE/LpIRQTYqsdYIRAaFdaJISg3Wq45BlJFDm7XoHA1MAU6ZOesE84P7cgfFGfx5229BmpuxfV34MwclhBLe+gPzc8UEBtFTN8tWPSG7Rx7ANhHHvCGigyyfsfDPnZjVPG0whhFHEkeemV53j16y+xstJCxE3650NmJwOMTZHKkE4PKfI5cRQcrzSRSABXUpU0muTZlHLlW0AQIRX+/gDeIt3dF2GDG1GV5BqruPdgwt17Y1QjIWk0WO31aDU7JLEhUZKN9XXybM7J2SnZPCObW9JZ7uy5pUs+rTWu/AloJLErDZtMUFKi5zlCKow2zOczsszQarS5crBDb7XLvfv3ODsbOD2azH2PVEHKQ6ALwIvHBhHi0AdKWpgUNJsNoihiNp0R+US+MAXKOJbX/s4WP3/9Jp32NRqxAwGVElAmm9qv3YdJkrubIYEMCabTQbJeercCQ6vnQteUuCvr5PKc/f8ryrHfz7i+aa0vvcCCdx8rWT4yJMUeNDTaJfwYjLDEUmFORmytXeXuyQlTNJFqsNvbQN36EL0eUTQcs0QJ4xg8Ad8MVy2DI5Prx0IKrGlghWQ0yrl754wHd8dMhxlr2hIPztlpKe6+8y7f+PrLWGl4+/ABush4eP8W3/7uP2Y06PPzn32fb7z0HO0GDPq3ESrlYC9GCkkUzxByjJJgrfYMB6/TYWKicrshgMJ4RoYTFPbFjaVQukCIHEGMsaCEJok1RO5+IQTWFBgjKQpFXuQYPUbbGCkTkBFKNlzrK0kjEnSvX+Ha1QPOBwPu3LnDzQ/uMJlOyJOE4kwzHOecn7V4+qlV9ndatJspwriSQCMMGO3LYAxWaISVyEggZOZ7m0KZouw3JQIVnO48YCgQKCmcBIX/bFFAAQ4MCmVMHkAI+LVFujFACBCOZ+eGfom1EmMEeaqxIibNBLOZJSscuNBsxnQ6DRqtBkkcO3aQcIK2QjhNHsfQAiFBqchp9SA9mOSADXf9zlkt6PdIIVBCefcoDxcJVYFaYXwXXnTXP3ehNFBK6RkolEwfiStzcqLv1ms/hX8WS172I+tBelf65wtdw4p77fkMz6uxrpxLIf39xrNIoTDQWlnDSsVsNkJIhda5WyiRtgTMrI3K94vVmlk6ZXt7lVdefoEb737A+WDkZLGMJmrE5EYQN5pOo8a3e6SUv9fyNyyx+xWK6OzxHVtO8IPEb2ScnJww7o84Ojri/v37nJ6ecnrqSi1WVlZKrbH1bUHcvsM8GzCZTDg+PiZN01/KfeezxGQy4d696QJQI6VkZWXl8SV8T+IrF0dHD5lPFllVF63Bf12j3Wpx5coGdUaNMY6BPJ/PmUwmnJ+fM09HbO6PGc8XGR07OzufO1Azn885O1sc30N+8vhKnyaQz/gySp9WVjp+gTGEATnnAmT4KxW7u7t0Op2SENBsNpeYgZ8mPhaoUdIJ0VlP+QaBsF77QIQVx5CklVNUgHKFNKyyytoCZqnn4hMHt3pq0dqJskqrEUKijKIhFcZkSDFDp1Py9AydD8FmTsNEuEl2KYhqDFZJojgmT3M3UfYW4bZ27GBnGxgjIY8NcE39QXG4k61ADQL4VH0gJM6XiTJWTimitm9YirUO+NENn7hnDqSxkswo3v2wzy9eP2eaRtjC0GknfPPbr3D9qeu0V7p0uus0mglKKB7mdzg7e4BSBbHSJIlFSYOSFiUdAUNbi9GGZrNFnrskRwrKtqsYRBU4tfB7mZK7ds4Ly8OjAZNpRlMoBv0RWMvWpmZ1tUPUTJimKSfHDzk/P0XnmvlkShK7koxESkAxz3Kk0iRJ4mqcc9920tU7F3kO1jkxJXGbZqvJ5uY215++gooEeT7n9PTMJSax8mUerh86m3NLFMxXbM2lpmwjdz3r6+voImM+nxJFCms0eW6ZDMZcubLPP/zkLUbjKUJ1EcIACVk6pyjmJIkiipwjT73/1EE5ailw+KGkVZZgoesnoky0Q85tlwbw0OdE2XO920etLy6cS6mrLFwS6hNZJRyY1MwVpJLmRpfDm+8xaQmuXdmjOZ5SzCbIa2tIa9EKYiHRBIqOezaC3pO1FqkExjth5abFg/tDbn5wwqA/JUudTfGZNMTTPp3zmEQ0mLS6vPr815idDZl32pgspy1Toq5hOr7Dhx+ckc1PiRrOca4RG6Qyvi2KYOhDEKYVInLPV3kLAphsy3N2Cbzwib4fDHzpkZSR02aSiiRxoJw1QeY6B5+4a6MxVqFtgdYp8xSyNEbJFSLVRsnECbwqyc7OFqtrXa5cvcKtD27zwc1bzOcarXMeHGaMhinnB12+9kKHXs8QR07FNlg6O1tvwBaup0gnZmuEJLIBpHGdxnpEWYY+ZG1p3x2ARFHvl8I92wFcdxTUwIySpX5XxU0RniXi/q8iB3JFETSaEBmnexVFDswuctCFKLVUrHHfJX15lSnHIff+wAqM8e+SMIZaB7BKKUvHIomf4MjYn6PybCBFHLUJDk8qci4VUrRdaZCxNJquBrl8jgL64s9HW7f6Zq12Au+6ADOrjYf+PlItYsjAohTW26R7LSDhvdFs5IS4hSv1ROIo3Mbd6Vazg4okaTpFa11qS1g01rpvtMa9L62Srl9Ehs2dDb7bXeHu3UM+/OA2aaEBRSQljahBJF35pcUxTJUUjqknnpQ+fSUjOn18x5YzPk+tgF+1GAz6nJ8oTk5Oyn/BerrdbrO/v8+LL77IzkHCaF5weu5Ak36/z/n5Oe12+0vRfprNZpycLAI1cRwvUfOfxG92nJ/3mQwXS23yPP9cVvy/6pE0Gp5RU12rlJKXX36ZwWDAvXv3HBh79oDJZIwWi0zGTufzf5ayLOPk5GRhW0juH5tjn5mAGXFRnPyLiObKVdRC3aFdZpD+CoUQgo2NDZrNJkVRfK5A/cc+oUV+u/w5JLKgEH6V1W0Pk3g3eQ6Jo7GBLeM4BcZP6BGqnOIHSorGehq+RBeGQufEKqIoLMLOMNkR2g4piinWpkhy8mKOsRDJCCGMXxEU5WqwlAJjNUWRI7wNL55lIITy4I6/JJd/hR9LMCLkBcInPAscmwVmA5QMIxZ/rgM3YuGnWgaOREkFRqMBowR5FvP2u0P+4afHGNPCZpa9zQ1e+ebz7OztMp+nJM0CplNGoxMePvyQ+eyMWKVEsfGTcHzyDEIKpNAuqTI5ndY6s1mEENqzPNx5hcQVUbsHVKcayFTWWhCG2VRz/8EZUraYjCdk84x0NkGYjOmkw22d02o1mM/HnB2fIbHs72zR7XbJ0pTBaMJ0OmM2T1FJg2I+RynFbJ4iZITWBq0148nUl1FImu0mQsK7770H0rC3t4eKBDfff5/RaOLLIaTX4rDOHEy4sqcoinzSZUtqmlvN1uRpxnw257lnnwJy+uenJJFiNkuRAjrtBtYa7t49Zm87ImnCbHyGNSkb600S4VfhA1pQ6yvOCaYCVqrtwrNrQgvYkvUV+kqwDA5aE2VrleDfIqMlbCiTznpI4WoWLR6sUljtkmMpJGKckmQJ88wyHE8YdOBAGHrTlH6sma8qpLAoKZ2uEw5ocywNdx6SCKlyx1qQPYYjzRs3Drl164i8iMkLz8rAYBtNTLPFvJDMBn1sDHJlg2988xVO52Ps3irDwW26HcFaZ04x69NpanKRUgrmClcaEhgPAQw2/nK1NUih3Jjk+7YQ4fo9zCzdd0jfyZ2WlqvRLpQmSWKSWDqHH+HEyaURAaPyDA3jisGspWsFRgvm8ynj0YQsTRBSEcVNLAWxijnY32Bna4OXX/4av/j5W9y6dZsiURQ6J789ZjhOeeGlFV54pkdLFgiRIkQONnYspXJMNiA0RlikqVTr3bXKhT5kbHAYq3DDsgeJSmvG1ktEw4dsCTcAnvVn3T4SiVVeLB5BHLtSeABhFcIDZ6VmmXSdxWiD9mK/ZWmPB4yF1wty1DhJECO21lP7TTUoWQSF9WVReCDJY4jGC3a7ssHqmTE463UpJXEUeR6VF971n3FgfvW+C6LKtmj4MSVyfVBKJK7MS8hwz924YwVEMqbQBlBIFWGIUFJhbIFQTTQZOrU0kgQlBBpLErVpNVewGIwRFEUYzzSmyMmzjLxIKYoMbQqMzWmuNGi0Ba1ml2bU4t2bt5kXhrXeKs24jVQxhdGgBM2GA6eMMZjPsSb8SXyOEQUx2vBCEZ/y97Dt4u/uM2makuXZJbsuAjUftRj1qxyl5qKUpUaLFE1+/MEd7t+eMJvNybKMJEnY2dlhbW2t1FUZDAagDMP0iOHojPF4TJZlH8lQazQaS8K5RVEs2Ql/VLRarQvgj0TKIaPhiLpQahzHZFlWznmCfkye50vtl6bpkpvPZUmGtc6wYWVlxc3fvK24UuqRbb/DfKu8zx8hZN5qtdjY2ChLCIIF8/b2Nqenp9y7d4+f/OQnnPXv8+xLhovf8Fnv869yKKXY2NggTVNmsxnT6ZR0nnPeHzEZLM4HO51OWWozm804Pz8naJYFEep2u02SJPR6vcX5pMgh6UI04/NkQTyK/Xz9/D+ZDSpQpkdk1xeAGqUU169fZzwe0+122draYjDc551bf8/RWU4UwcqK06TM85zpdFoyJMJz+Fn6U8hFLsZjLX0CYEhZQ/4xr5fZfE5R5Mt/uPT3sK36fZKmZHXmjoXAaP884zJ9GWsto9Fo8T7bhJbSS6BIkiRLoLeU8lKGVdABa7fbbG1t0Ww2GQ6Hl9ra/zJA+sdDqeZBdQ3eejRkJmHVH9xFG78WGlbrEBa0W40V1iUPLtGsykIqKjiuREJIjHalR+NRis4yrMlQ0llwSwdjOFDHWwIGcUusKBMv47MQIdxKqNNm8Toi4UbjNUHA669Y75biV1WrS/XhJ/+iumZ3FdVnKqvXKqEIyXhInqgdA6pbKoR3DpGKrJB88OGM1356l1z3QEueurLBb33nZdrdDq12j85KRH94xuHDD6CYIMWcKNIkyqKERMTuXJS0vrQLBAblAaw4cc42xs6quZioXb9cHCiET6zLEgq/LY4jGnHCyMzA5BRZho4yDh9MQDSQUUTckLQ7Cb3VdfZ3t4mlZTweMxiMODsbUlhLlhck8xwlobvSYDKbo41lnhakWe7tf53uTLe7ghSC/vmQt9++wWi0z+bmGusbmygVM53OHACi3T2VUqKiBp1WgpCC2WxKURjneiJDOZQTHT45Pgar+eM//j1eeuEZ8mzG2ckZK+2EqKVQUnByOuFv/vYGKytNrh502Nvr0WonxLETfl7Igmt9pdxSQwidzIwqyySstY59VgMCnf5H4Ik4EMgQ+pfX2AkdniohlSWIWAdUPYzok2xt3eq/sAaJQp9NWFvZ4/7hETqKUVazsdYj++AuyfUeacuBTpGUCOXKN1QoIZSANCghQLbJteTw4ZSf/+wBD89G5NpS6DlKOSZEt7fJ9vY2LQlmnjEfz2iN5owbKV//3d9m9+SMeTNi1j7D5H0Ec6TMwGhiab1Nty9jsw6wMVSC3yaUXApZgYuerVEXVRbSjwWl2LIoRR+lkK7kyAtuB7chx8JpULoDCUA4LRAhwFBAYmi1IlpNwWg4IS8kWqegV8BatI2Jk4StzTX++I//kOefP+aH//D3DIdjjIU0T5mlGdk05pWvdVhdE0iULzlzxy/7mAArdQlOhL5TMlOE6zBV6WnoU6F/QEXgMr78U1ZsQw8UllpHXjw7lMcaK7E4S3Hr3c+c9JF1KLitDmQ8AyewUUKZkBu7ncV8SCod60VUv4f+a2WVNJZjcdjHgbPOstyiKZy+lgosIeFt5Q2NCK//k9VYkqJkCOFZN1b4/4frNjGO4YfT6vIAkFKuLKuyBHdAZqPRds59xjH8ijwDpTAWeusHNJqrRLKJRFLoDKUiCu3KKxvtJnHUxBiFxekmCZw1eKELsmyOMTlZUWC0JZun6GZBHK2hmht8eOc+Kk5oNjoUGoQoKHzZnvULF4gnrk9fyYgefimHmYzOGV60TrZNMB0uVcD9NY0SSJARb7zxLh+8+3BBbHR7e5tez1kPu1KoE+eOldwEX4IqpbxUIBdcYhn2DzGdTh854et2u4tUeis43YzZ2s6or4IH56W6I9NHrdJPJpNHEhoGp9uztrbG/v4+/X6f27dv0263HwmokVKytrZWukPFcezNHpYT7U6nw/b2drkyXRSujHl3d5cHDx7w1ltv8fbbb7O23uCpr73AxaN3u92lRGgymfzGADUHBwf0+33m8zmj0Yjzsymj4YzpePFeb21tsb29DbiE9vj4uGyPbrfL9vY2+/v79Ho9ZrML4tYih3jqysEfA64QGAyfGBYo1iFbgQs9JTBaut0u3W4XxBVef+fnHB+f0W77eZ409Psj2u1jtra2WFtbY2tri1ar9cjPzUfF1atXP/lDX1pYEBnIMY/CpDw6mjGdfnoR7+F0AAsEGgGmDbbFF92hgivdQpiYvU0H0NWj3W4vlS/FcczBwcFitYK1/PVf/zXz+ZwkSdje3mZ7e5vxeLz07CRJ8pHviMviE+y574efykm+EmESKkrXHIu3dMVWQJwI4E7QBZAVSheQNQ9kGM9EAEfDzvOcyeiUJFFuhdtYgvWwELbUnhHCrdKC8JoHbiIv/e/OYrmmQ1GiL5T9IFjbhjQgCD/aoMtTQ2IcjiHKhCA0TtVQ9RWnCtQpxVrLw1eN6z6isVZhpcHIiLv3U3704wfkWRNhLU9dv8J3v/087U7M+sYuK501hsM+Ro+wuo8UGZHSzmIbSSQiLM4xRHmGkQNqfDJkXMLabLWZzmal5Xhg/IekaNFe15bbEUH3wdJZUfzjP3mOh4djzzxRNBvQ7SUUZo133jvm7HxEI97g+rWnwOacHD0gy1Lu3T9EG4ERkrzIWdEai0JFDdLU2eHNUveSNj4hi6KYp65fI03nzOcZ6Tzl7r37jMYj4kghlBM4lVISCaebIWVBu9XmuWevYzB8+OEHTCYzgqWuMQ78CzWrx0enfP/vfsS//B//lPW1fab72wgsqhlxsL/F0dEQ0Vul0WizshLT7bWJYg3Clci4vqBZHGwc0OXAutB/AqgQAD0PxJSr/qImeuuT6fL+hz4XABj/fbbS3FhoQ48I2gUQyYF4USQQxiJz0IOMznaXX9y8g17r8NJTV4inGdpk2N01UBqhFLGVSBUhEETK6WUgrXMVQDLPYt5485A337rHeGqw0qD9/W00YnZ3tumtbbiSjESyvbGOeHBGcfeE9PSMh/cPeen60xTacq/okyaOxaKERGhJgUWpoBkSbkbo656pZEM5ivRMmwrMqtJ6Ud4XBwZ6xEK7Z0WbwNYLgID17WwxuihBL3wbSmsc40h4EVpTEImCvZ0mViRMJ4LBcESezRBqA2shihJEZLhybZv/duNf8POfvc7ND95jpg32XPHaTw45O2vwve/ts7fdJFIzYIa0sX+uIxAKKzWUrJJKr8v6MS5A0258k15otwZaBbpccEQSDnwpnYxEVeJJWT7oQJcSw/egozuv8B7IfN90bYSo/UwY+107htKncowtnbdCawVQrWJuYis9GGuEc2gyFqPBGO+wZvxxfH+R5TvHnUclIGw8MFqrE/ft6/W63ZPjS4WUEUTSg3vCurFU4FzYLJ6NE2Ns6jRoCue4pXWKTg1WKMaDgo21LaRsYK2z43bHseRFRjEtaCSSdnsVS+RYcBaIILHQaDsHLEVcjgEYQ1Fonn5pznA4ZjKdMpmmzOYZKopdCVdtDPl1Y0n82kR8/Mmf+TwiOoP4QtKhe2Ca/LoDNW7ByTFQzs7OHANhZkjTCSsrK2xubrK5ucnW1tbnojfwecfVa1dZbb9CfTqvtebk5IQ0TZlOpwwGA9I0pdfrfSnlWJdFHMd84xvfIMsyptMpk8mENE29y+diYjYaOW2gwGBYXV11rqBas7GxwYMHD1BKsbm1+fjKRL6iEdyx+v2+L9nr0z/LaTRjFIssgLW1tSXr4DRNPzMA8ascv/vfXOHkfMLZ2Zijo5ybNw2TyZgkcf0sWNCvrKw8Xj2ZLyLE3Dv9PYIVeXzkgLrPK6wEvQlFk8eC/H0Osb6+vsS0ee6555ZA4/l8zunpo5c1fyxQY/SUKkF3YX2NOwisdrU1xjtgAGWntThKvJtYKz9jFyUjYEGnRVAmmC61Kmgk+OTAT89LsMP4ibs/K2H93LpyDAlfWi9zkVI67RD3hQgrvKVdSAhwCYQI5QwhaamSu+ou+CwkrL6XnSpoEVSgRsXAqU6txH48KOLsXwVGxJyeZfz4x3eYTAXSRlzZ2ebb33ielW6bze09lGpwdHyfwwc3SefnRLFBKZc4KukstpUwTldWVGVghHtpLQhNns1YWekxnp4QidBuduF8y6stnxlZrtIL3P2MVMHBXsLezg5SWrAxUhQIVTCcrHH/4ZDReE6smt7tZkIzlpwPRkSNJsLAcDTGYsmKgizLOTk7J801ea4pCk0Q0zXasL+3z8HBPg8f3iNSEpPE3qZ7QJK4MgStLSJgB0KQxDFr3S4vf+1rIGEyGqD1CdPpzGtl2NJNyfVLxcOHp/zoRz/hf/jv/ymddkyaTiBSvPTiMxydvEajGbO+2WZ7u0WzqYDc9UEtStbMxdwnlJBUDI9y6yJw6IWELw5VpQNXib0IrzFD+X2VllCtr/pvC2UnoixR9MCGtURSIcY5ncYq0/mcHMtYataKDM5TxFpC0ZHOVUi5ZDIWeDCwcJbDUoGMGI0lP/7Jh7zz7jFpBkZasAalJGura6ytrbLS7iAlrK31WFttI2xK7+oKhR0zudVndvuQO8Rcv3qdNTr05ZBCRmijXZmgds+bFQZdMip8UaW0joUhXWJfglZS+me0ehBD0i8ECOWd7KgAMK0DA8RSlRp5MXHlhXsD+0I41yUB3rY7Jp/lxElMHBUYq+l1GzSbkuk053x4Rp41MLqHNIYokbSbLX77t7/N9naP1177GdPJFN1KeP+2JtfHfPebGzz7TINYFg4bkEWpaxIe1sBVCQphhor/WP4kwtDmAHCE7xcL45f0wKEDDaoJcVUqWf3zN5S6Vkttq5SO5RIGP+vYSq6sx7FUjDWufDa0kXV9vBLOrsDIi//w7aitxhhnZ62Ncf8vvMaLNaVGkwluUX4MtsJ6LTbjQa3gNmXLBYcSeQLXF6wvsRXhteTZVbZ6L1gr0Hbm2TjuuNZaN+ZKjbaWIrWcn95gaxPiaAeLZzEIJ1hsdcxoPERFDZrNFb/eESM8O0dIgVBgC7dA4SBhi2pAt9VjdWMPkBTGkuuwcIHv314c+QlO89UMdf7lHCfqgxrUNgiwTt+LX/O+IYSg0WgwnU55+PAhd+4ccnKUI2WjZNHs7++ztbVFKP/5KsXa2jp7m89RZ9QEZ6qTkxOm02kphPziiy/SarU+5tu+uEiShN/93d/l/Pyc27dvc+fOHUajEY1GYwk8Ojs7Q2vN3t4eGxsb7O3tsba2xmQyYX9/n6OjI+I4Zm9vEyV/c3WULouiKDg8PKTf73N4eMqDBzn9c1hfWycSawuf3d/fZ39/f2HbaDT6jQZqnn7qada3NPfv36cojsgyzXyecXh4XsolWGtZW1tjx9uZ/9qEyEANQSyXCy1F1Af16Rk1y6E8g/NX94Wzs7OzVM4WymXrMZlMfqkSv48Fahwzoy7E6UNUJRgOTAlCwiH8Kr8IE3rPLgj4Rn3VtjblDyusSkEjlmS5/5uknDAL6y1hpUUYn9CKWrpQJq4BdJAUhfbuIsaDRwbhdTXKpGz5ImvJRhDf9GdazvCtTzAcyyck/eFBLksJykRPLBzGTexdUmmA4VjyD//wgPO+wWjB3s463/nWK6yvrrC1uwcq4fDwHifHN0nTcxqRc9cJIp9SGIR0dq1O88eW/y/xJi2JlGU+n7C2ueVWl5UoQQB3Cz0jgXJD7fKr1WbhhTEVFisKpNRghQNsRIfZRGB0glIJ9+/dIysy4kSx0mpRFHBw9Rr37z+g2WohJMymU3ShmU5m5EXmEhvjxI+FlBhtWFvfYLW3wtFR4Y7jz1cIwXyee1DOvaykkMSRopk0eP7ZZ9jf2yOKBZPJyxQ//znz2axsd124RE5JgVQJxsLtuw84H4zZ2e4S25jpdM71qztsb3XZP9hgZydBipw8z50LVJnYukTN3UevnRSekJDwhfOuHoEyyUaEflKtFIXWqNtrV4lmvd3swufD9joIF0IKnP2xkkQGirM5m6tXObx/RhoJtLB0YoU9PcE+30IrV86iACKJNBbpQUKpFFo0OT6d8Xd//yH37w/IrcQqlwg2kw47O5usdFtIoVjtrtFd6dBIDA1mrK1Yeh2JXd3g3sk54zu3kasrPP3cU3TyDmkWkbdi0v8/e//ZZFtyZmeCj7tvcWRoeXVqASChUUVWUYBgs6xpbPbMcIYjzMZm/kb/lfnQ1tYz/DzGMRrZLLJYBVQVVBWQkImUV98bWh29hbvPB3ffe5+Im8gLIJEK8abdjIgjtnT37e/y9a4VlWAhEgJrFaYqYgzDh2NkGA9MCZyQtiUw5dy1bRb+VMK5Vlb3LgAExuDAC1+yU7FUqMtohJAOLBaO5WeMRcmYMnc3u5WmQOZc6+yENJG00ohuHwZnhpPjAqN7YGKEKEmShGefuUmn3eOnP/s5O3sHGNPl3v0x48GUolznxRf6JKpAkxNJjTUlSgnP5AijrKj+C1RWUb0e2paYG4tDOZIbq+W5NgjBFcm3IPcdKwjuME7UuQbWg1AwKOde5cGxsKcahPclsjLADAFMc9fQekancx20FbgTmDlBYLfhLUg4Ais0VhpHr1Ee0AxACnUi2izndYO29t01AEH1gB7YNeBLFWUoR1RYY6rSRFeMp93fFasJrI1Q0mJ1jkVzfPQ2pszY3Po8SesK0EZGJZEv2VVKMJ0OQUCr3cWKCKzECmfBLqRExU5zBylASee+JiTGOEcppCRSTvcr3G2J9SDaZbLziQz5EbmyiJFzeZp7rccfiphwmqaUZcne3hFvvjlmfw9euLnJ1toW6+vrbG1tsba29kQa+8cdRZFTTOYFQIP+hrWWBw8ecHJywunp6cfaz5Mk4Rvf+AYPHz6kKAr29/fJsqxiKDTj5OSE0WhEt9slTVO2trbY2Njg5OSEra0t9vb2KqBGyt332eMfZmitOTg44PR0yO5uwd4eTMbw3LU1Ouna3Ge3trbY2tqaey2OY+7cufNRHvInKo6OjpiVI3q9Hq++2ublly13757w4MEJRXFWzbPLsvwMAjWle+Y8DVAjRh+u+K9VT7ffT3Csr69fYCdLKS+4q1lr2dzcfOrt/lqgRgo7TxjBumTGr9Va0UiUw0NCQJhoyzBVbhy48KBHmHjXoo11BqsEJLGiLP3KqqhXWa1UYUnUgQ9eqNKz02tGiHHHLqXEFroSBw0Ag8OK7FxyG44ysFFq6gJVWuHygApxqsud3IWotyMD2BGm5pLgQu4vGhY8pV+hifnFL3a4f7/EmoSlhQ6vfeEVFlf7LK6uY5Ds7dzl5PgxeXFKmhiUkEic05Dy7ieuwsx4O9vaUatSFZKOFVGWGVhBHKdgs7kk3pVw1L+H+1qdY6NRCG/ZK4Qr+cADYVrHWNtieWWLWW7IZiWJcg4FkYxZW1+n2++xuLDA6mpMUeYcHh0xGU+x2iK8lXYFfvhkLY0TFntdFhe6lGWGLh3o5+iz0q8Qa9rtFosLC0QiYnlpmeeevcViv4tU8OILzzEYnrG3f4AxEl06BFT4exk5ihF5XnJ0NGBhoYOUkmyW0WmlxFHkRfmcPXcSKV/XaLyAtq3aRtVzGtjkXDeucD9RlZZUTW0OxXFsH8dOmd925V7VYNTUfctvM7AmquzWtYtIRRggMs7ko7eywuP9d5n1W2z1FljMNUrlmOVlEM5BTXlRYyEFVgbx45jd/Yzvfv8tdvcK53QjXKlSr91hc2OTXq+LkJbNjXWWFhYR2ZhuV9DpGjqR16DqR2y9ts29v7mL2dvh7r0OfdFicHLC0vML2LbGSIvVmlAK5iDcoDgFGA8k+n4phaA00jncBP2UcH9EKBdTFWCAwOtWCaz2QIMM96LW55LCfcf1K1uDIFIhbURWjOj2UqSyCJFgvVMdgDEFnaSkvdah1xEcHg2YTWNU2iGbaVSUsLGxxJ/+6T/gpz95k7fffhtIOTxt8VffeUhebvOlVzcRSruSJ7yQr5Cu3Ca0Oz/2CKG8ELJvSxVzpjnAN8YABLXTUvNdB/zauc+4bQZAvmrDVRv0IDuycuUSoRELB/5YaysRbusZLQRiStCFsRYjAvujrHqS25az0sb4tqGtAzWNey+A6gFAl6J+4jTB1Lp3uv3I6lN+W02mkD8FNy56sW+/iCEqtg84i3NTcbksBqMAb9FthQFZMBg8otSWG8+mKLmKJcEY5eAwa9E6ZzobIoQhSh04E8kIi9N2K6wFGXndJlfOaAl9Ndh6W1QcuevkGa/GWtQfgPPHpzJ0/yPZjcTNJ5phZQfzGS97AvdMVEpRliVnZxmDgdMv7y9GLCxDf8nS6ZWknYxZPgF5jm0gA/P83MtPKIt4UplO2P/THuv5mEwmjI73aE7n0zRlc3OTNHWuKkHjJYjzBvvYIOwrPVgbGOhPiubrSimSJCFNU7+gYSph4HDe57eTpinr6+tMJhMWFhYqIdsgdtwMpZTToLR2bl9xHNPpdKqykyiK58bkEMaYSoA4xJOEWz+ysBAbiM8dgvT3Plx7bQyy0Iwj94RrxiRhzjXZCjBJhzTukJUleZ6RFyVlITjaLxkMDMMz0LkiiQT9xZReZ/5a9Rct3YX5gypmmpWkYFFM6ZQD4mmMlDNSXc5NPK3QaJV/OmToRQ5qSFNM+MmfK9jde0Bpj1hfX+f69eusrq5i7W1u336d2cwwGIyJolPA6WG2WmklsxHMCT695VCRZ7U8BdvDzEB/mGWUEmz6wR/7ECIIZs+/KCjNiNKezB+VdBUE8x9to01B5ULko9vtXhjjj46OGA6Hc6+12+0PD6hBRm6a5yfdATQJWWdoiq6kqFE2E1ZlrQLrF0kR1VdpfCoko8Z6kU8v2BhFikhpdGkQNqL2G3H0dBtYC1aCrVdga5aCn6CisLbEGNCFpjCFc7fx2hVOz8M5lVTMkcrlQ1bYTK0VMs/i8GutFy6dW3kO+hk1Y8XgVubDtTCmABLeu3PKL988xNge7TTlq1/8PBsbW/QWl9BSs7fzmOHZY/LskCQSld1uFINS2p2TH/SlFK78SAiENN5hxx26ksIJneIeDosLy4yHezWA5delHVtFVOcKAZ9xukRCKF+WpjxzyOlQCBmBNGjTBtHi4GiXwsD6xirj8YTJZEra6hDHKa1Wi2vXr7pVXq8lVExyJtnU7UzhZkyNh34+m6EiSa/XxtoShPCuLTFxnBDHEa1WyrXrV1ns9kmiiHarzfVrV1noddG2IIolX/jc5zjYP+T+/R1mxp1rFCW02gn9xR6dTpc0Sbn/cJekFXHr5haCU/JZhhIRp8cTnr26TDu1RMonikKAKLHGYL2uxpPKChwuI7y4drM9SaQUXlh7PoEOrC93h3zya73r0vnmJ/BMhQBw1e3V3UgP3AlJhIdVs4K2ThgOJ4xKw0jBSwvLiIf3UMsRuh8hZemOTVrQGh0pJApDwv0HY/7qO28zmri2r41TtV9Y6LO9tUHScoPd9tY12i0BHLGyrmmlBSK2lGVBZGPQmu7NPovH65y99ZiDriBafY7laB17OKNzrU0mZpgEJzyOS3yFBePZHtJGYKnLI61EZ8LZrWOQlR5Ks78GoNn1eSUdU8FohYwEStXjipBOOFnh+oD0orlSCDQSaxXTcU6r3SJNFUaUfmyJPADhAFaFwcopS0s5vX6f/T3NYJghlAMPrVC02wn/8B98hcWFDq+//jPvihfxl9/ZpZxpvvTldVSSOe0e7/xUlzR5pgoSFcp4rBfGrcAUqomuAISVvv258S+AfU0ou07/q9Zc/S6IaDZd6/eP8YwmOydDTw0W2apf1GVcVGiI9RsV1jg2jA3bdpNyawxG6+pfDdLQYMJUZwloP0ZT9Qlb77T6rdmp6j5Zw2D1z3AOvowqnJuVWD9GSmsrIMfIoIGmHPPLCIQtGY0e8eiRYmn1BRZ6z4BKCW5mCIsuNGOrSY2g215CqYiiLJ0ouIqQyoN0NvBlLFa5Z5HTK/L9RbhSM6Gcdlxd1ncZn6iYvfaR7GYxdf/mdj0t2ZscP/kLn8HY2dnjwYMpSQI3rsMzLwi63VPgjN2T99gN8/cLuYTmSZoO29vbT2V93Gq1fidR0Xa7Q3tjgyajxhjD66+/zunpaXUsp6enPHjwgF6vx+bmZlXOdXp6yqNHjzg8PGRnZ6dKKp6UbAYwZnt7m2eeeYalpSVef/11ptMp6+vrXLlyhbW1NeI4fqLTyV/+5V8CTsvhW9/6Ft/61rf40Y9+dCGRWV9fd3bKwGg04rvf/W4F/hwfH2Ot5dGjR/zqrdf5p392jdY56aDj4+OLYqEfY0gLizNYns4/V/r9Lle3rnJ0fMTu0S47O7scTSaMVmKKc6BKsQDzVWuCGze/wCtXv8HPf/4G3//Z93n99beYTp0Tj5KC1bVFXnpmi+WVLleuRUTpfBKqOoKpnn9tc1Hx//zmcwwOj9ndfZOdd3e5Mxrz/AvPubJzH1paxDMrsPLRJNe/U6iBB1Q/CECxvPalZ0HcBFwfODs7Y3s75lvfusnh4RE7OwPefXeXLNvl7bdv86/+1bf44z/+Y8bjMb/61a9YWlp6ahe0T1yUyx6oeYr5wPQAZh9m6ZPwSOTvf3HAGMOjR4/OgcmCstS00vnBZHFxkeXl5bnXSr3Aw4cW7Dygde3atQtjfrvdfmK56W/CzPz1jBopGyfSmFRXbT2sWIeX3Sp9WLEPE0W4CHrP/d1ITK0XTJBRRBRpirx0K+DCr50b3LYrhkJzFdQdhw0lJxbOxmOKwrDY79JptZnmEm2dOwvS24ZXxyPqBMc2D9Ih3dUk3tYHb0XzgSYb5+Umx83raCr7XpfMGm1BlByeWn7yk11M2ceaktdefZHr17ZoL3SRccTh8S7D4WPK7IRYaKzRHsiLHLAlBUJZrCmdNocQWI9zCyNq0xfA2AyEQZuMyXRIp7PE4OwRSigwCiNmWBs5thEBvPFiobZAqhht3Kq6030oUEogKZBWgCkpbY6RXQajMZub6zzeKYmVZDw6oCw1pXYlBb1un1Y7JZtldDsdijwjn0zYLXOmeYmKZcUMKkunjXJ0tMdsOqHf7bO1ucbp6Zg0bZMmCctLy6yurrK4vEgaSVqtmFacoKKUdreFiBXCQJooNjZW+frXvkgUKXZ2DrEich2qk4C0RHFEaUoePH7McDxExQlL3TYP9x5jpQMWTs6GrC/HPnH399VrZofSQJBVOzAh2bPuwS0EaM/cqi2BAyjmfw+6Hp4FZuZzyzqtDAkpYf+uHzonptBfHC9NeaaX9McZC0F2eEa/u8nDnUeYboqIBAiDyWckm0tMvXW1A0kFWLfSr43k4eMxf/3d2wwmjjmhjBMYXltfZ31jjXZLsbqyxsb6OkcHD2nHlqUFTZJkCF8yg+8jEldytfLiKtnOEaO9Y+6KBb7y6gsM3n2XtKVh2zARbgUPa9BIIg/UOADMIeYygKQemFIiqoAWo72ldOgXDZaIA5YN2CBM7VacK9aTdYwzN0yYqv8LFSONpMidKG230wYMkYgda8LVR7k2ISXCCKRykEciCq5sJ/Q7BXv7OZolrHRAbhRLXn3ledI44u9++GMKDzD/7d/tIZKUr35pkUhqIiSl9BokACIGYRwnzcoKY6i4IxcGZQAVCprm3qiYkL4lVyw9Ecr0/PUJgLaoRYidhI6onxXV+OgPyFoHADv0hcDUCd93ZT71cwUR2DHugRvEg7VxAKHBgaTGhnIlUQM+BBDdMofTNI/H/x7AlpqR1gCn5sTCa2DHYUONaydwTCZpPYsF1wa9R7oQIKUTYC60Jk0jJmePmY7HxM9E9Ho3MH6hQhiNUAmUMB2ekAqFjA2jswHdXp9ItjzRqD5mGTTZhHfQCqCMdSrLTvz8gyavl/GxhXl6C8/fJZ7YAsyUz7qQMLhnhVv1HGEtdDqwupYQJwYhfzcq/tOsrP+uq+/Vt3/L7bRaLRYXFynLkvF4TJ7nF+y6gScybYQQdDod0jRlcXGRpaWlKlE9D9SEefDcolFjO814kqCtUoput1sz4OcojZ/8iAyk5wySUi1INCRl+GddGbqC/Fx2lilQzdcsHA/G5NkOu49PGQ1KTOlqcKWUdNpdFvsbLC1ssrTQQaqji+1ZzkCmc9uUKBIbk5iZ/zcl1jNSM3N5go/SgrEl5UXk8pMVAhyY+nTcH9H4f4g0TVlaWqIsSyaTmbc+d/Oz3d1d3nvvvbn2/ekMgWPUPCW71kzwQq+fyrg4nlmc69W5Z55MXT9pfAzR4kkDz/uNkb9rfBAPrHFk4e/AjAlpDfNt2jaaeZjE23obNf4hqgll89TCiSqpiJOYLC8bC5ceVpnDZupvG6xPytx2jLEUhWPTCOFoknlZunVPp8TpBh4RRk/PShGOCeQSDNM4rgDUhL3KuXNvsiCqFem5ObubPAvhaKYqEuSl5M239jk5sWgN166s88pLz5CkbvV/MDzm5HgXiiFJ7BIug0ZIrw+iguKEKxcSlTJoA1BrPhz9tZdKMJ2O6PWXEDgWjvXJiHA5OhDEMT2A5ct6pHBlIVrjbbyD7oL7W+cKqTq8d/uXnA1nJHFEiUMQtdYkSczCwgLLK8tY45ycuu02ayur6CKjLAvuPd73tNf6egohOBuccnx8xOpanxdfeI6HD3eIo4QkTtjevsaVK1dpd1pgC0yZI6UgTbsoJV2JTxRRljlGG1ZXV3j11VdYWDxgOM6YzWaUZUaZ5UzGE6x1dNTB2QAhJP/0H3+V08GIwXBIEqccHZ+RX9kACuKkpv+6agvdaKc158p6kKbuz+LCXMP47zmGmS8eMd4Rx9afCds4jwoLgqYHFZtG+osYCQ/SWJ+KWossQR/NiPodTsf3KVsJK70ufWPJIku53sHGePtrBUYihcIYxeO9KX/zt+8yGAd9FEmcxKxvrbC8ukKv1+X6tausra4wGR+wuS7pd0vSuHTXqMkIEgKrHBCS9mOufvEW73zvLvnojNs7O7x05TqjX71OvLCI6juYJY8EsZaUkbtOkSkd+FVdC+n0O6RPilXkSpBkrb9i/I2qykSEA2CwoI3TuHEN0LO7BBgRrI3d96WM0FaiZMI0O6PTbfvhQfq2K/0pOpDYeoYLFUBniKKM1dWEtC14uHPMZLaIIsaojCSNePnlW0hh+eHf/5jZTKNtxHe/94g0lnzpC22UFCjaIAoEilAPKoj8NQ7wi3DgnZCNkd2PDVZWY3ctHezGsoCOCBks4T3zRgQQOzAR6z4bGIUOsPDAB3VOYX05VrhjxlSdxrM8wrhrqjYdPmKMdWLPxgsHGwdWWhP0tuozCGynUHqGldV+5h5fjZ4YNM6aek8eEZ37RvPzUjgQ0DFrwvPR+HYT9u32qIQDnZz2DihpSZSkyAtOjx9zW/+UF19u02qtY3ULYwQI7QS9LRTZEHROPj2jnWryXCK9gL5UCl2WKFWX/FpZ64spoSjywpd8aFdCcBmX8QcYWmsePnzIwcEZ1sLqquTWrau0Wq0/CO2mhYUFkiQhSZLK+fLs7OypBWWXl5eJ43hOdDmO4wtW2L9JwjIcDi/oOrTb7QuOKn/o8fjxDqcHAw72jzk8PHRjv5SVjfT29jZbW1ssr3TIOPu0YFqfuOj1enS73YopI+U+43GGMQXvvPMOo9GIra0tFhYWPsVAzWV8UuMDgBo7P9kOE/Mnhn8jAAXV9PfcyqyfyFssQZTYJaLB3Ud4dxZXxqOURBd1IhV0J+ZwGlv/SlVu4hIt5etydVmSJJET1/Wsk3A+lY5MWHl0eYxTcWjo8ASHEeETMBrHMQfINFYMhKg/ZSxECifgKgQawdExvPXmKcL06LYTXn3lWaIErJDMpmOGw0N0dkY79RN7671chHCJmZQIr0XjNF2M16GJXOokLE5U2Ik+O3zMoGREns9QUhJFbYSYuhVWnJaBitQcmODOx63sSiJXFmKsu6ZCO6jIaqSSWFbIcd8vi5Jup002nTIej+l0elhrabfbZDO3ctPr9YiiiOWVZWazMf2FPunhqW8XNStJIJlNZ0zGE5aXO1y9skUsnS6NRLGw0KPb65K0Uqwp0YVCWAfOBAFY67eVpm06umRxMed0MEXFHR4+esRwNEaXBUWR++vmSuQeP9rjjV++w40bt/j5G3eQMuHoaMjBcYvV5Qhd5E5MGu+U4RkGobXXCZkrBQlEEiPwr9elFZXsq8UnndaDnnUjCyKolXiwqNkMwkoKXWIJ2j2BsePuvRCCRCoi5Vx39ElBTy5wOh5xkmVMk5grSytkdx+RLKTkHUmhcqQBqxVCWKyQHB4afvDDu4yGTp4bYYjjhO1r2ywstVEKNtZWSVXE3s47LC/FdHugROFBk8T1Kc80qMAC4UDH7rUlVp5d5+DePmdpylmckA00/YeW+IUUKwussJ7R5/pj5BqLuxe+b7uSMsc0UVKhjbunmJpFE8CDIDYshPTCzc5CuXKWEg2wQ1g0oaRHIlXMcDwiaSUkaQK+xMQNAR5E9WWXJoyFIuineACcnE7PcuNmizu3R0xnCVK0iKSk0+vw4qs3kYngh9//CflMM8Dy3759j+7CDV59cQmlIw8IG8dWEhKBasDk/tyFwArZYJkE4KYByldtzm9HCKwxjkHmmUUQxIPt/FDfAL2qMTqMhdV47b0CG6tQNljYQyUObXz/MNRW29bi7bc9WOM/B06w2DQWFQKYJirgJ4BINQhfj9P1/gmLCdW1a74cylmDzpn7SGhHwtZ80nDtg26ScESwylDHaTxopBDESlGYHF3kjAYHPH78JtevK9J4G2MUxjrB9MjG6LJAGEOnFQE5GNA5aGuIVIsoUeT5zF9yBzQKqSi1Aek0i8rcU/bk0zsQXMZlfJZCa829ew84OMgwBpaXW9y4cYPBYMBk8iGKZX5Co9/vs7S0VLmkunm3emqgZmlpiYWFBdbW1irR5ScxaoJl+NPEaDS6ANQsLi5y48aNpzupP5DY3d3lF6+fUuSa2WyG1tqLLDuXrCAWvLzS4dHhuxSffdzx9xLdbrfKU4zP387Ozjg8POOdd064d2/IF74w5Utfeu0SqLmMDz1+PVBTgQ01k6QGZPxnfBIZgJywalslBiL8HQj1Qfi0uXIaJt4GJ4RZT4CllOhz9b9z8/7wd0g4DA2quqDdSinLCUVR0EoSR/XW2lsUu29XYE0jq7BzE/dmQty8NP58z60i15evvhLhb2MNwjqNjMKk/OSnj5lN2ihh+aOvv8S1K+teMydiPDplOtqnm2qk1G7bXk/F2SILb/DhQRqBu34CXzLk3pfCggi2rA23G1Ogy4JWq8d0NvLOUapKL5yLDefOseGc411FBKXbtlQURqN1h1xbrl29AWIXa0qmk4ljb0hBUeREUcTR0RFp2kZrg1IRvV63AlUc86V0gnCB4iol0+mMoijJZxn9fszW5iqz6YyiMCgZMiXHWFBR6tbDZeTsoz3YZ6yj/Ecq8Ss/U0Zj5940m+XMZmPwx+omL1PGoymPHi3yL/7FP+S/ffsHlKUhR5AVBXHSJXY5MBrpKTPGp8W+ZQhnw+5C1jobPsE01jM6rEVaDzQE2ox1JR54sKVuUPOgYOgN1oL0pSQSQVGUVVu3XnQ191CjVBYeTthov8C9x/tMIoVJY2IBUZlR9iST6YhSzohJEDrGqoLj0xnf+94Ox8MMKxPXDqOIa9c26fe7LCx0Weh2mQyPON59j9X1CMkis0mG1VOE0A549CfSFLIUXg8mszFLz65y/GAXe3zKrpJce+0L2MN9OvuW0TooDKVxTDqtBMZKp2kbGrnv5w7o82wi6XWWlOufOng2E4CYBtPPtwMb7p0Hc2pAR2CNRKqIPNcURUl/uevBXN9PhPAUUVUB2a6/NNhqYTwRFkmEImd7O+Lo2DI4nRLJlNmsoLfQ4+WXn6MV9/iLb3+HUsNgIvnLv9qlv9DjxoZCWYWQBicI55g7FeAekIvKoSy0T1mP48K5yRH6fGhnCA8C+78tvtzOa+GI0ALD6x4aqsYcDy5W4KRx9ys8W6x1ttmE8dzdO12WhNJRax17xhrtWTQBmBfVsSJl5bNeSWpbB+jNCduL8PgKQHoDJKqedQ6MqsUxq4blt+HZmc1xnvp6WZwAcui4MtR+SX8hhMBaSVHkJIlAF5o8L0kiRdqNmU72efjQsL2pabVWgBhrYsqyJI5iB+4k7mer02E0HDt9o8iiIkUUngseoBNSkhuNFJYkVkymGWVRXFrcXsYfbFhr2d8fc3bmxoRer8vGxsYnzt3p9xWdTodOp4PWmul0SlmWv9G593o9lpeXWVlZqX4+iVFTFMVTAzXT6ZSTk5O51y4T4IsxnZQcHsxr8cRxzPLyMktLS9X9WFpps3siL4Ga3zJarRbLy8to7QCxonA6QPv7Qx4/dvO+tbVDvvjFT3Pp02V8UuMDgJrG7zZM8MNbNWBTrcJa0ZRvaXxVhNm5//38DvyquHTlI6GEJCjSF4Vz+AgLopXLUkh0jRNbnFPbDivskbNottaLJ4aJsrXVCnJVV1i9H0CpIFLskqswyXfMhXpi3lC3qCbzQdulBrDcr8IIhLeqffhgzP0HQ4Tss721xIsvXCeOJRrJaDxmODyik2iSuEAbjbECJancgaSQFVgTihikbJZjhXInX9TRuDfGOPvqbDah1eozne4ipEEQYQLwUoEzNWtDIDy7wCd/JpRfOYBHlxIVLbPz8JDpLCPPcpSyDAcDlFREkaLb7ZDnmXdOkggxAqs5Otzj4YP7nJyeUhRF1RbqC23R1jKdZYyGI+JI0WknJImrm41jJ+5qjHGWtdZfIy/uVLOiqJLSsiw5OT3h5HTMdJozHo+xuvSJdkSRFxhryfOctdVNWq2Uza0VHtw/QEtFaVKsjYkiA8atRtWmYLXblsVirPbtMgjZWMfqEDUkaAKLxgRYiSoJlULRyLh98zIejKqZVpXwtk9sKxab8EmpMb4qRmLzAl2WmDjh4PiEcqkNSiKzjDIqaF/bQMiSMm4hSSE2jLMWP/vlAw4HBUIpUAVSxDxz6ya9fkKn3ef5Z5+lmypGg8cksUBFBYKcPNMIFEqBttqXw7jzqCyYS4u1GotCyZjtlzY5eH0fE0vSq1d5rrfOvR/8HeJry0x6OdoWSC0pIsU0WDUH7SZfb2pNjEASxQJEWTGLXLNyYJmQwgOdjvlijKHIncYL1vUJo8J1VsjIYoREAaWwTKY5/V4bo0sk0p2bACNEpe/j+qH0faf0QIIC68sJraCcGaSA5UXBwkKHu7enjIZDCt1BW02/2+WF528yy77MX/71D+gstNnZUfz5f36Pf/u/f5mlBYmwEmFTDxV4MeOK3uPBlwokEgQw1lYDuLgwlobXatwhjHO1flforR538WyR8EqFh1VASA1yWI/H1iLcxgvLa126/uHHg/B6BdLMw5fOdSsIJ3ugUwQ2YvW8stiq/KkJsodxPYz3onq/DleuVYM5NWBqgxuhf6644wntrMnW8QsAxl3fOI5pt1oIIvLc0O52iRLp+l52yO7Oj2l311lcfIY02UCbktEoJ1leddu0CmslrXYHsgxjcUxDpdBF4VlQBlNqnLSTRQhFr9tjOptecDW4jMv4LEZwJXKr4YccHR1xdjbl0SPLK68s8fWvv8ba2gJnZ2efaqCmKAru3LlTOR+tra2xvLzM/v4+xhgODw85OzsjiiJu377NzZs3K52a27dv8/DhwwvbFEKwsbFBFEXs7e2xvr7OtWvXWFpaqtybptMpjx8/ptVqXRDQtNayvb19YbtxHDOdTjk+duU7RVFQlmWVDIeQUrK0tMTzzz/PD37wAx4/fsyLL73EyckjOqVmdXW1KvmZzWYXGD2/SRwdHV3Q6en1evT7v50Lm7KwMYbVCXPaaMPhkO9973vV+WqtabcTtre3yOL5587zzz9PkiScnJywv7/P6emA0zN3jFEUsbKywtraGisrK3zta18jiiLiOMZay3AwZHV11Tt01vEksWtjNHluKpHrzc1NtNacnp7OLXT8IYUbJ86q/rS6usrCwgJKxTx8eIe9Pc2PfjTm6Ojb/E//0/+DXq9DlmVkWVa14yc5vn3SIssyjo6Onuqz513VPk0hpXzfseh8DIfDC8+CTir4yuee5bxLVRzHvxeg7gPsuaO6YzaSmiZI4aJebW5OeuuJaWO1M6xEUk+Y60RBImVZ708KoiTGTqYVGAJUzAPrM1tHRaNi4OA/ZYzTJui0W67MpggJeFgnrZN2KYIjhqPxC7/66hJlqnO+cBNCLiPCkYWV1ifYMHqOhRVgjOTtt47Iy4Qo0rzyyk1aLYXWTvtlMDgkUiWtlsAaXypSOdPMIWgeLDPVZNthTaJKYqrb4kEyIZy+hxWWWTZmcXGN05MIIZyuimpsf54tJPz9apTACVPR9q2MKUxC2lrk+PQOB/snSCEZDo4xRrO0tEgUxyilOD09pSw1nY67/3EE+wf7nJ6ecnxyitam2mfNZnLMk6OjExKnHotcW8DakrIoiKRAYNxPGXmxTOkdxdzd0aV2ejVaIyPlhPQW+ty584C8KNFl7srk4ggV+1VoIUniFjdu3EQpwfPPXWdv74Qi09y+c8rKUp90VaGUceKwZr5EUBhXiCGFAunAlrqkyXqLdvcdhcdwZKNx+dYjK+ZauKcBWKiTbdccGskmgb0QkmoHDCkrESLCnubEdJhmBVoK2gsLbF3dRt27R7TRRi9ExBgipTBCUVrFT3++w73HM0zkyl+UlNy4do2Ffp92O+GlF1+h3xOMB4/p9wrSlvagQESuBGUJcdJyluZWu7ZbtWXhbYUtWkJSxoh+n2JUcvjOMcO3H3LQbdNfvkI7Txh0NVpmlFaDdaVuBhzrAve7tRajncivFNK/ZtA6gAeBTQMC7a6ndCCCKTVGC4ydubGlDB90EIgiQqLIshwVxwx1hkTPsUuEAKkESkHQhTG40iRBhCQONxwrEkyZ02m5hN2qMWvrKdPpzFObe6RRSjtt88XXPsfe0RG/evM+slty/17B9773mH/yzeukkSFCI631JDwnAh1AXteTdQUxh3YegIu6j4vqv6rdVXbXvsR07nkgqpZXgzW2MYYGYfL5aw+iAmAcSKPRuvTlhOG1ANTQ+G4DCLfMTSTrMdiEA2k8R+qxbO47HjwSfgx1cZ4xVAvwOhyocf7VPuavhsCPRabZFw1WKQ/caeKkxdnZjLjVptXtYr0mWKQ02FMm4xHCapaXANEmilMsGmMUCklezLDSImMHLGrrxkgrIow2lGXzXmmKcYbFUOryUz3puozLeNqoWNpak2UZw+GQwWBKHMPaWofNzSWUUuzu7jxRUPfTEtbaOUZLHMeVDXawrg59PkkS1tbW6Pf7WGuZTqfvC1IVRcF4POb4+JgkcYzkYEtcFEX1vX6/X1mDN+NJrzVtuo0xjMfOSeZ8Ymut9U6STlMHoNNuczYYUBhNu90mz3OMMU/ltvXr4knJllLqicf/NCENJAWk5fyszrW/+RIzKYU7v2T+/IUQVbsdjUacnp4xmbgFpk6nw/LyMuvr66yurtLr9ebOoSwL4iSG6IOP31pXUhz7Erg0TbHWcnZ29gcL1IT2HSJY2ne7bVZWBIeHMJkY9vfP2Ns7IM9rQO/TANCEMMZ8qse9pw0hxFP35QvXxEIscmfvbecFqi9Yfn9I8YFiwrWlb1No0/qJLwEnaIw+nh7QBGioP1DpUECDaOJek9JgrdP4CIloFCmiOKLItf+c274xTuhVhqS4WrWsUg8/sTYoVYv6ShkhRO5KTITwTjHVUTQApHoVtE5mmudi62NvnIsllE00kwgB1ni9FYEREfuHU/b2pigRs7bSZfvKCnlZIGzKaDSgLAYs9QVQeqBBuZ+E+yDmrrljIPkEC1xijQNRUMKDOBapFE5d2bFwSj0jSZwOhhChHlvNJxvngJuA+eATtaBdgVAouch4UnB26gR4tS6QStLr9zHWPezyPGc4HCKEotXqsrS4SL/fZmt7G6kUh8enRMZceChYb8O+f3hEFFkGgyEqFkjpyoSm2YRoNKBtod3pE6ctVyqhS7Qpq3PSpaYsC7Q2pGnMrZs3eO+9u+wdHGBMgdYlshUjpLuOIHjppZdZWFjwJQMxrVYLScnBwYzvfv9dvv7Vq1zd6iDt1Km/N1xdrHRaIJXLkE/uTOACVKQCW/eJ5u3FJWzC6w3N3RMP1DQ+OO+GVgGt4IRstbNYLAXSSmYHM9a7G9x57zGq26LfbSPOBsizM5Jnl8ljJ37rBLYV7753xi/fOoCojQCUkGyub7G0uARAr73M2fEB09Epm2uSSM3A5ggUgpxW21IUhrzIgIhI1awvPE3BaagYYgBlyGPJ6iubnOwPOTk7QC5d4fOvfQFOdukNR4yXLUYWxFpijQN4lBWBv+LuuXZ9ME5id+2N8OKz9kJy78YTCTZBWE07SZERhBIl4V2XrJBEIqbMNRJYXF4CCgQ1C6wGgUKSHPRUnJaQNY5NU/oBPkpKRJSRlzN0ASiNsYa19TZHx5Ysizk9HSCWBWmU8I2vfpGjwyFHRyfE/YQf/2SXtGt56YUWsdQoKwMC5fElx9wRVTmXA2mUUkghsVZUbEblf0IY7iVKCKTyTljNpUExP04YW4+BgQQWxJqDc19gQtZleQGM0e6fB22s1c5+O7DhmuAM0BQNbr4ehNCDkHbQdSIA9BdCMEcJDZ2zYv80GDEiPLfqZ4XfMtWzpFku5a9HrV3mhOGdU5X2fUCSFQXd/rIXhnf9S/llBUNGkT1icDpDxuu0213GYkCiOti4gzBtz2AqsWhEKYii1OFUrrGjtXZi6qYkL3LKsqDUGl3+fiYYl3EZn/RQCq5fj1ldXUQIwenpKQ8fPqLdbv3WiflnNU5OTsiyrGImaa0piuLCdbLWVvbaHxQrKytVIhQ0cqy1FRgTYmlp6cJ3LXBwmJOOM4Q48GC4qfREPkvhFjdL9vcP2N8/4fAwZzYTLC0tsLa6wcaG+/ek63QZH344KQfJM88sIcQpp6clWsPPfvYzVlcXq7KzhYUFn4dcPmM/K+H64f4FRs3vK57C9akGJyqQxVqfeIh6MlsBEv536yf5PimdAxh8VOK8HkQQttaJqdyflCBJXTIUWDRFqSnLslrjra2NpXPJCeU+fqIspUtErLetk1J6PRwHbITSE+knxNYnjO4YG5eiOv7zaPv830I0UzV/Pn6FVqAptOLO3TNmM4kSkmduXPcPvpTZVHM2OCSJCiIpPcAgq2qXGqSRNXDls3r3a1j5NSgp0AYPpjjAQQjpKm/8SndZTgFLHPfReupXmMO2n4yeV4BBAxRDQKkFggUG46kDacoZ/YUuxfEYqTzbwa+Uu9URJyYrlWQ4HNJKW14g1AE6UKPRWmuKskAKwfHxqQMdipy0lRDHklaakBclB3tHdLoLXLl6k82tq+TaVKmbimIEgmw2oSxd+YtC0Ou2+aM/+go//8Wv2NvdZTgcEscRSRyRtlLWVte5fv0ao+GIfiemnXbIZgVJnBKllqOTIX/z3ft89Ss3uXWjRay0T9ad4HNoSNZfstDewNlCh/yxKS4KlqBIU/eZkFjj+osXg3U3M7j1MNd/wn4CMBmATpDYqcaeGtpLHabjM7KFlM20xUKhsTHEyyml0GgEUkTsHU74ux8/RkQdhCwRJmZ5eYmNtU1iZWi3E4pixnh4xuZzXeJ4jBIWa1x5nlCu/DBOnJ5RnmuMEbRaCdaWKEVlYSykJDaCQrn2li4l3HjtKnf/5g6H4z4HRcF6e5nxG3dIv7aC6Tl6sVF+nPAJetA5EbGiLC1KSRCR1wSSnmxxDqxREisF1kRII4jTGCnCeIEHNgTIGGEF+axgeXmJOJUImTbgIaphEW8JHVyO3HijMEaQzUrSBJIkQkQl2K5jjVinP4VwZY8L/T7vvacpSsPJ8JSl7gKddo9/9s0/4T/8x//EdDbD2h4//ckJzzzzPP2lksjaip2GPzIHgJjqeKzVmFKDEBirz3V73z5NKCOyXry8AdRUoEVjvGzY+jrgOrC7gqC5BzMqIMjxjGpx3lC6ZDx458dd/7PJ5KnLTJ8Uvv+LClIP5JoawxTBStyLLFeMoXnQPgycFQ7a+G51rs7zvsFEbRZmCYIrn/FsTSUFpbakaYI1mlY7JUkjJ2JvmwxRhRIWyYwy38cUp2SZ5IyIWHWIZIqzjHQMTCixpiROI4zVblHDQlnkZHnmXeNqdtEf6krpZVyGlJIbNzbZ2NhASsnx8Ql37hzyzDPbXOI0dYRkczgcVqB6ljm3zPNAzZNKCN4v1tbW6jm6B2iMMReAml7vyVb1+/ugYrB2hLWuTP3atWssLCz85if5CY7j42NGoxH7+yc8fpxzeAiJaHHlyjarKxuVw9NvW551Gb9ZBImOl156nmvXxrz55m3eemvMD3/4JhsbbV544RZpmrqSM08suIzPRhRlyePHjz8ZQE0oq6hXMf1k3K0Hh09BRZnHgS1+JhyS/coxmmDFWqEe1DbCAWyoRX69+qOjVzLDGudSoauJtHRaEGHyTmO6bt2+pc8eIhVRlBqIKsHcsOopqpKi8F0/k59bna2PTcytol4Eaer5b3PlVnrdBM1wPOPe/VMgYaG/wPbmFkpGFFpwNhxTmjGLbbAmdxuSjq5asXpCmYIMGhMhpbcerqour3NhsgatDVEk59lBAqwtmU7HdNp9RqN9j/m462NpuqHYCggK5I+6VUikEkzGBk3EvXv3KfIcIWA0HJBlzg45y3IHjqhQAuDuf57lSOnEQc/OzpAyqga2kEA4mpkBETGZZpiDEzAlt+8+REWW1cU+nXaL6XiGNZKT4zPanT5R0qIsc6LIgYjahoTb7bssC6wuWVzo8eUvfp6DK5vs7e6jtXPPiqOYTrvF0cE+sbQs9JbJZgVFXqJkwsbGGlneY39vn+9+/zbt9Atc3W5h7RilGmVbIRkKOGajzTSddqpWHFbemY/5qjeX3LobaTwcWvfXarvCeXVVrC8jSKKE0dER3ahHPphhy5xZLjCjMbPTM/pbPWgLlNUoETPOLH//40eMM4vGILSk20nY3Fghjgq2Nre5dm2L8fiYbsvSbWUoaVBWIFSMsa6ESMgUsETKotoxs6ljNsVxhJNbdmV00oKWDhiJEFihWbzZY+X+EscPjtl56y7p9hXkNEE9nqKeb2GVRVvtr7HvL0EwqAm0isAwU9jQRwMwAFgJVjpnJi2tBxOiWkjXO60JGZFNMuIkJmnFCKUrS+5wN2tmX2Ms8Q5gFBF5ntFKWySJAFEiROyoxx5YUNbpzVihQcNocMTx8IyNa6ucjqGX9lhZbvHH/+Ar/Pmffx9Uzv6h5M03Bvyzb24RyxnCRnP7dyBVnaTXPRkqO7IG0CetCfi8/54ffzGEMqbAjHFsJeMYNSb8rtHalZppY5wrkx8ka42xugzpPJg/P6YH1acm4y48A5rjVBMoqsHOpkta8+/58KBa1VFrVlG4TtbqahypgKXG88M9wjyYVB1b0Cpy/dEB6BJEQZJGlEVOr9NGRsIzrpylt5UWaRVKeAYagkgUXitMYHVBoSW61GT5FFvmWJuD1SBrEWZhrXcy9IwnGxZQLEZfAjWX8RkPC3Vdsap+SqnY3rrK0tIqAslwMGF3t+TqFWqg/TLmYjwec3R0VAHq50GV1dXVp97W0tJSxc7J89yzAi8CNa1W64nfzzPIJxDHEEcTsLC+tgn98yUnblz/xN/P0Ebt/PEPzkYcn5xydJRzdAjjEfRWW6yuLbO2usLa+gprayt0Om2Gw+G5jZ4vUf41IZz3QVN32D7htcoawz7dlhuPz89EWOvkQW5cv0UUR4xGGW+//Tbvvqs5PR3R7x9y/fotlIoRwoDNPlPn/4ccuiw5HO6D+QQANVp7MS4bNArAudfY+QeYmMc4Akhj64Xc8C7COvvUOWZKxcyY36h7xYltGm3RpVsJVkI2ElZ/bAHcqVAhPzn2P5SSHqgRICOsLKsJfW0NC1gnMGzOU2mqpKbiKDSuVLg6YQ0/vO+TZxv5rzpa5+PHA0ZDxzB58bkbLPQ7gGQynXF6ekivJ0kigy7DQ0VUyUYou5Beb0IK4ZxEpK0HQikR3uVJCbBSobXGaIuKPCBGBDih1Gw6odteYDSJHD4WTFPmBhXhE9BQUGK8k0qElM7PaJanHJ8OuXv3Nv1uj+FwiFJuVbgsNWXhrrkDbiJaLefwlJcFrTRib3+fidcjCqs3QUzY2aK7ko1Sl4zGOUpJdvaOUMpiipJ2klDMZkgVESctdh7eZ2Nr293PEkTk7IAipVDKWVFaITHWUuQleZYhECws9MmzDGsN3U6LleU+sVJQDnlwf8Ibv3rHuV1Zy/b2FeI4QRDz6NEDvv93t3n22SVefmGNXrvEmhkIJxIrfLYrqJlmoSSkbmmNix66WRNkaNwPK5yeS+iMQczUVslh6F/+d1OiIm9zrA3F8YRELbJ79x7ReMoLm2sszQqmo1PiL2xRRgapY6xIePO9fd67OyBud7DakCZttrY2iVPF4mqfF158jtnogE4ypNfOUUJjjbfttsaNCRV4KxDCYKyh23X6K8YY0jRFCImxpcNoFU5820qMKimF5uqXrjHYe5vR/YfcyQq+/sWv8+CN79LfajFasERWoYXFKIMMhmHCODBRuNIoEQUwpcEk8ABynWy7exVEgAM44Hq5xBiJ1ZqsKFhc6GFxfsuyAtikd0QLpT/WgUPCWaLnee6c6Dptkihy5yxjwGnKxCrCGlA2oswVd+894MHDB3Q6Gyyu9JnmU7TokMsZQimee+YGX/jcGT//1S9IF5b48U/2eP6FVV58PkWa2DN5SrApEdoDHkAD0CCU6ZybdkmMY9ERHJpMxXRx8HsAZILIrytR1AGoMRalDdoY95pt6s2YRru+uO/q0EwokfJjUeO4axA9sHA8kBKAoAqcqYEZO/+/ii0UHh2ub/p92rqtBHFii6nG4woICudRHaj1MEiYyQYb73Dd8OOBcOORKYli6QF+i5JubHK6GhZnahf24dg4br+Owadi0HlOacdICoL+VS135cZsrA0ELxfmcv54GX8YEalFpF1i9/4x77w7ZTSStNsr6GyLk/22KxEstnnmGnTaMU6U7NeHlJJ+v3/u+fzRaVMUZcF0OGQ6cSXlo9GI6XTK6enp3Ofer/xiMpnw8OFDTk9PGY/HCCHodruVVsyTwlrLeDzGGMNoNCL25eALCwt0Oh0mkwl3796d+44QgoWFhQvX6eTkhOl0itaaTqdTvR5Y1SGiKGIymXD79m3Ozs4QQrC3e8iffeuf8eDRfe7de4fdBwWULR51YHiS1WA60F2AtY2YebjhydHr9S6IIf+uZXDT6YTT03xurH3SNTZGcfDIsHj1Kuvr65RFwcHhIXffOeLo2GBNxEK7x9pSh82NHutbmk7nFKM0g9kpmUlQcTQ/posS5NOJK88iOG47AKYZ+fbihYWNs70jzM4+3W6Hft/de6XcfH/uuwqGKeiP4EHT6XQugHwfeljIM0U5XcPk21gbs7X6Jb722io/fv1tJmenvPerGcu9CdtrKb0FC4w+cLO/l0O1luFwOG/M8j5xXsD7Mp4cVs6watdpjn4E8WuBmiKfEWZz0jskuWhOPj0kMVc+4AdH63krTTFJW6/4n6esn19JDclmnheu41tZDd7Cu+YIn+w6PMMXWfmJdkVvFy7BV0rWk3ohPOAkKvilOjtbnWF9LhWYY/0iaXOlXMxNoKsyB6PBKww4zMiSFXDv/og8l3SSiJWlLkQGKxSDwSlC5Gyur1Bkh2hPwzfWeIFfdxwOoHGlB4HlEkAqK9wqbFUmYx2QI+IYU2iM1igV+XvqEuYimyC7fYSMvXW4nmMghNvqthvEkv21sTHGzpiViqMTw97+EYuLPUaDEc7WNvIU2RlGG6I4QqmoWjkvygKE4Oj4mP39A2bZDGN0DXc1HgwW5RJnf01MaRmOJrSSiOFoRhnnpErS6SW0WxGT0SmD05g4iTG2JEpapK02pVH+frifWhvKImc2GTManrHQ79NdX6HXabHQa5NETghWCsk79x9xOhghI4VSkpPDA5597iX+z//2/8Lffvc7/PDv/p6DH91jOJzyldc2WehKrNUYKxHSegch4wFBD9LYqmV5kNPWbki+Dc5HIyGsGAh1AmpxzkogKpDGYlBS+gQTTGlQhWJlaYX7Rz9mwYB6vE+uDa3NiGglYUpBrBLu7U/50U8fEqUtdGmRSrC+tUa3v0CrFfHMM88znp6iswMWuxmY3LU+GZymqPu/n/gKIVECQNNup2Szgsl4SrvTQsgYS+mt6N24I6Sz1ZZLilvfuMnb33kPOW1z2kq4tf059t/+GcmXl9zqjhIYK5BGkPsyPYFBCuXaoHLXuSrlCddOeAAnXHtjnW24NR6sMe54LFgrmYwndLpthBJoq1EWgsaLEso5LzVANBAYbZnNcsDS6rRcMm4MUvo+IY1L4S1gJGfDMTs7h8ymJV/68hdZWIpRqsPbb56wezyjEClxGiMsfPUrL3Pn7ntk+QxrYr7z1+9x/drn6bUF6BQjDEqm1P7l8+P1+RYWICeB9m0y3EPPqJEBiPP31Tr2jPAgh5DWfdeCUE48WUmLsAZpwnUMTdmPmbaeTNSlseH3+ujC55qlR8HFqcmascaBbE0RfCHq0qNqP8y3hSA0754tUX2MftvG1MBMdZyN36v+6bdW/e1LyIyF0i+ApEkLKSRRnCAj5xyobIASa4g8sJ2EtDXw6R8uQnjWjXIgbGN0qZ5kgf1UAZDhWOXFEeYyLuOzGML2yMcr3L+tee9XE5K0TffaNUbHfcYnApDE3ODl57YhuQccf9AmUUqxtLR0YQz9qKIsS4bDIUeHp+zs7LC7u8tkMqHVaj3VMU2nUx49elT9LaWk1+tdABHObyvsdzAYIKVkeXmZbrdLmqaUZXkBqFFKce3atQvb2dmphZuVUu9buiOlrEClIMC7u3PIl774VdY+t8n1rWe5d+8+d9+9w/7DBywtLbKwsMDyygoLC31aLQ0UPA1Q8/soH5pMpujT4dxYO5lMLnzOmojBcYv1qzdZX3yBs7NTzg6nPLidczbQrK4sc+XKFdbWV1jZKOguDkAMmRZ7TE8coHX12rUnHMHTeXNnCvLO+VcFdBfnXokMnL5zF7l/Qnttjd7VmDXRJY4jstk8UDNOYBKD/giwy06n875lch9aWMhnHSbHCcMjBwottF7iq198idNDwZ3bd9h5MOWd7phnrmluPh8jk+ZC/0cbw+HwEoT5sEKAlBlJ56O7nr8WqImTpJpsnk8b5xPoGq6oVywbk0FqoMYYUSUAYXrYpLs3LZTdJNIxQKRyZU6EldXGcTU1IWqmjvXWp3WRVqBXCqgnuNbJNIZJbVh5rGjucyQGUSXY9RS6eVXqleE6yamvkBUwnlpOTqYYE7G8skS73UJFgsl4xnh0RqdtwGS0WimqFEyzmdunMUgpXOmF98d1OJet9hNScuGvQyhxEjhtDiUjyqxACOMtWp0TkbUlZZkjRYIQmU9Omm5L/iykW5HHRlhhsNJiBAilODuB+/cOGYwks2zgLbIlM2+RKBBIJYkiJ0JXFAVZNmM6mXJ6csrZ6RFZllPkhae+zuuGWGvBgKYuOTDGqdPnuXMbWGgv0m5FdNsp7VShdcZsOqQsJIUuSNIOEkOStLw1uCBWglhYiukYTMFzt66zsNCn3UqJIoHRObYsnLCq10sJuh55ljHLxuT5lDffeINXXnqFxYVFvv2dv+bt9w6ZTSb88ddvsrgQoWSJ9e5kxhp3/YTBGlm3IZ94iXPX/WLYgCbSbKDVS42eUTEBMBRCooUktYLZ1EK8wNnxkRPMlQmRUFhdkNzawMYpypYU1vD6zx8ymfpjk5bFpT4rqwsoBUmcsPPwAZEacGM7RZuc4PIjTGiv4VyaSXQNEmAtaStB5gWzmRe3ls4m20GvBmHcmFIKzeLVZZaeWWa2c8SDH/0dSdlGFTnyxgyz0kJgiMsEKCmikli7biKkcOLa1fV2eisNUg0N/LnSsbLemcoa7wxnLXk2QSpBFEnneiaNS95xJZnGg21OY0ZgrKT0zgFxHBNHkbP4Mh4Il8HG2gFSAkmuS/Jixo2b6w7AEhYrplibc/1ml5PRGdg2k2lGnETEseRLX/wC3/nr7xMtdXn4cMjtOzM+/7kEUEjaFdMEaZgTzmUe9GhcEX9NLBWTxCf71jrIx3irbINBW1dqY6x1AsBec8gY29xRDX57FK8SbRfK/R2aMKHUMqAm7vPG1OBSExxpMmuEEFhpG+83nhPW0qSV2MAa9c8dL/3rz9/DHqYe+ZVSdTsx5sK+jTENhLJ+CgSAW0nn7qdU7BMb7VRNzz0XIQDq7ntC1NdPylqXypX5aT+214pACtANQCyAcvMC2h/P5PEyLuOjjulkyuHOPuPRFCkjVpbX2Nq8Ag0nNxf63N+X8UkMa+HHP/4JL774Ijeu30LJmN2dPQ4PjxiPpyiVcO1an6tXrrOwOgMefNyH/IEhhWR7+yrLy2tIGTEcTtjd2Wc2y/zjSrp/VgGFA/BFA4QRBjxr9jeOxnrzB0WQ1pf2Cf/On9Nn8hHjgF03dviwls2NbQZnI4rdXcajKXu7B6xtL9H/PZN8LuMjDI8TfFTx6zVqpH/b1mCIqLpgQ7DUA4Vz+jIEuIT5/9vm9+e3E6ISORQuuRJCMJ1k6OJ9xJi8ILCQss49hGBupVw4oMA0nEDmLrOoUgI/gZ+vEa3FLOd3bXEDqxN2DYmarRxWwsTfYrACDg4ztE4pyhnr66u0Oi2MtU53Rc9YXGxRlkOKwqAiRafTZZZlmFJXU/jAqBHCOrYMfqJOUCmpy6KEZ9hYWyKRxEnsS5AU0msQGeOEddtJn2k29QBbnWdUcJON3eq0AHC0YC1LdBlx//4JR8dTjI3cNfHuTs4KUmOMS0ajKCLkZ1prJpMxxU6OtZqycKKpjmmlG9e92S7qkFV7c/XL/X6XbkvS77dptWMwBbPpENAIJVFSUeYxZT7ziaOAskCakpYSLKyt0Ov3mM6miJYiiVNybShsQaklSsRIJUmTmMkkw2A5G5zyN3/7NwxPp9y6dYv/6//t/0SaRvz7f/8feLRT8jffvcdXv3KNrY0YYUvPTHP3REiQDaaAuPB482mbraC+Wui1gS5UIKhotNPGZ0J3sv7CCxSlbLO0scXe3/4diXaJY6QNeSrg2iKltERRzIN7Q969PQDZxlpDkiq2tjcRAnr9HiuLS0RMWFqMkHLqkmiEmytI0wAfotAtG0m1rAA9azVJqhDSlaElSey1YTRCgtICIzRGGnSqufLqFe4+foPRz37OfdnnygvXmd2f0VpImSmDMgml1EgssZFo4UATXVrXr71+lRWhOKVmb4TkVxtDpKRn0jh2nDUSbTR5kbO0uOiYIzhwQhI1GCJu7EIotDbkRY42mrSVEinli4hsJT5rrHHtQSh/Ty1xLNncWAURmCrStREh6bQFN661uHNnjFILZFlGK4159uY13n5rjbPhgDTq8r3v3+HZ516hHYEUKZYpiMj3b3kODD3f+vx4IgIIaOufBEDClzJZjfaMmroMygMW1RjY2JejPV5Eybygbw15gyu39I2nGpMaAGczpAie7FUPmp+vhrbXGO/9WBdAutCHasZkOH/RALHrp4eUkvOgdlX2YJ0WV3U9ZVgcEESxF7hXEdlshgE6HYVUCUF0ulrWCECNbDxVG+3VWkNRzNA688LX9WJHKFhtrES460yY4As+yknHZVzGxxWj0Yi79/Y4OTkhiiK2tra4devWEzQ9LuPTEpubm9y7d4+f/OQnLC0t8c1vfpPbt29zcHBAmqbMZjMOD48wsmB58+M+2g8OISXP3LrF0sYGQgiOj4+5f/8+0+n0U2X1/IcaN2/eZDQacXR0xNnZGffu3ePas4L+8kdTJnMZn734QKAmFAZZ41YoRRNoaU5kRXNCGV6v6eD16qf0E//arrb6hq0noG5i7/YdJQlxEmG09mwan+hWc/HmZDygnPYcqOISwsgnTkJ7nRsrq2MEXScTVULstmmNcNhNmIhXu/ZMA8Qcgi0C8i3cdkFQGsHj3Qlax6SJYXNjDakcy2c0GNNuRaSJRtgCU7qEFRXR7fYwhSHLpu48Aoglwv4DwOQAteCBFaj/zuhJ+hIqSRRFlKVGyAgVKYQqMaak01pgmh34+bzL7ufBA6drg9CA9ABQxP7+jOOTElAYWyKkrMrMjLZIoShMSRxFSOkAGuttbYo8oywLjDHMZpl35UqwNsec06g5307cOTrwqd/vs7y0RLctiSSuVEwYinyClJJExUg0VjvG0MnxCaenZ7TiiDiK6LRikjQCnVHMxswiSydVWKMpipIoTtyKupBIqYhURD7LOD4dMZ0UrCxtsrmxxdnpGb1Ohxeef569vWPuPjxgOrvDP/yjZ9neTIHSlxo47SLh9X2g0s4ObsoVQlOnuG5lX841UdfWTUgivbC3MW5DonI/EyhrkEZhTISOuwzHM6bHJyxJ5UouTIm80sMuJwilmWSav//5LppWKOBjfX2NJEmJ44QXnnuWNBLk4xlpVIAusEJhQ/8z+HK5GlQMAGC4j4FpEwCcOFZIYcmzgiiKXDJrdf1ZBYXIaK0mbH/1Onv/7Q2m/Rhz8xYLScHw/ntELyxUSzuJcUw8g0VJi6PZuZKk2vWm0ZcDKCmEtx8TYDVSGDARVgpms4xWu+VXsuo7YY1FSuXHOIkQitJYZjNXK9/pdKrk3wqQXvBcyGb5TkiuPdvHiytjJE5oWFQAy9Z6i4ODKeOZA7CMgSQRfP5zL/Jf/uK7tFpdHjwa8s47Y177vKsvF1XjEXPjct2vwpjdbH/BIcoBUqE0y9hah0bjdGu0rbVnqm1aqhZc9eIKvAiCu34MbbB8AhjUJNPUwP78vavBSdfGTKWr9oSlPGHnPttciPANocI03L41QbwdE45FMNeYq+t37ogCKm3CuYbxWmIMRCoC4cZKbQxFqYmEqdgyIuwLW22qGvyte6YpIdBGY00JVlfXtKn9IwnkLQ/nPwG8uozL+KzHeDzm4cNHzmWy1WJ9fZ2rV6/y1ltvXei/l/HpiP39/cq6+sqVK9y4cYNbt26xt7fH4eEhDx8+5Oc//xk3nu3xz//lsx/34X5gSCm4du0ardU1Tk5OGAwGF/SGLuOTGUIIrly5wv7+PnEcMxgMePT4EcPhKrD0cR/eZXxK49cDNdXbLmkQVSLoXnP/DyyOerpXTQ+DiGj1okvaw7eamjfuIzUDBoun0WuwliSOKbICo50+SaUtY8OKqKj2HSazIREL3JqqBENIJ8QqImygtVsDqCpBEYFeOLcOHNKDAEp5MUnCdQnJT1idVwTwxwrJdGZ59HjEbJqwtrpMr9dBSJhMC6bTCVe2U6QcQekSA4vFmJLxaEg7bdPtdsmyDNCOSVNN3E2VkFdJMHUiLKpr71b5BZYkjsizAqkkKhKUZUG/2wGrENLU97FBk5dCo40TuSxtgS4jdnanHB5rpOoh5IBW4pLJLJ+idYlUEbrUKKmIpMBqjRKCwl9VrZ0ftytFAqUi4jiqQKamoHDVLsMxCSpbxyhOUXFCqQvSOKHV6qC8iHIrTRFSMBmekShI0xbdTsrJcUE+yyCJ6XU7FLMJaSvB6hJhLKZ0wGCeGwpdIGPIc+01RqS/tinGFmxsbmGF4vWf/oSXnn+Gf/tv/g+cjab8x//tz3lw/y7f/bsHvPbqOs8+t4RkisKBnkK6+9I8tzpDnG9v4Z5YD4KAt/f2b7j2ElbIzwEQQiD9VT8dFQynE5ZPxihnvo2MFGWZEV1dwMROK+W9B0Nu3x+j4jZYS6eT0uv3sAZuXr9Fr9tmcHKfVjxBUoJVFdCE72fCGkwleWSrdtrsTXVS6bJmFUEqI2aTEmsj4kQ5kw5bIqxFCANRwfKzPc7ub7P/YAan+7zy/Mtkb9xjZU0xXiyJrHYMGOVAKKXw/UbhtHJqwK/Rawh29s6hzLENHItCMJ06MDGOI6zVFZtCSemE0o1AejAlmxUURUmapiSJmmNDyHlct07GER4QhIoEgcUtpAUhagemREJw9UrKL988QcU9ppOcbkuxfWWZ9bVVhuMJGsUPfvCYl19ZoqWci5wxGmtlba/diDpZceC8pAbVA1ATgLNAXDFBM8qXPIVypwC0VNi3H4+bhtVVi26Ar03A/lyrJ7BzAqDjXgvPElG/Txj//EFyblvWg0I2AEo0PtMsvWocu9d7EvVtnGNQhW2H/dRY1/y+wdnd66Kg00qd45IVKBUjXX0l1uJLXT3oLsNTzFbtJoojsAYpLFmROxK2Hz8CAyicT/NkwqtO+DpoaF0mqZcxH1LKJ4pyBj2R3yastU/9faUUUfRrp6i/cWRZxtnZKdZa2u027Xb7N7KS/rgj6M4Uvoy2LEuyWUY2U5RliRCCJEne1wo4juM5cB6c1syT5lhPK8j6JO2LsiyZzWZEUeRKfeP4d76XbjFvRlk6nbskSWi1WhwcHNDpdPjCF77A2toau7u7vPLKK6ytrfHmm29y7949ZrOM8Tjh8eMzFhah02ljjBP1Py9a/PsIZaGnJGmaosuSPM8py5LpNGc2MygFcSzddWq3WVxaZJjn7OzscHBwQFm6eUSapvR6Pfr9Pv1+j05HkiQTv3jq9/URnE+I1dUV7MxQFJqzszHtdvv3rw/zCQkhBK1Wi7JQVX/UWrO4uFiNLYPBgLOzAXt7e2zfgF6vW/VPrfWHPr693/h6CUJ/uuMD7LnrVUr3QnNC2khzPDAizn1fBmp9eKOaN4byFr+VOfp7PS2XLkfCCmfRLZVC506jxNHtff1+OJKqbsHOTUutrcEKV1rjVr2n0xnWOEcm5z5SrzjLAFYI4a14JSIISApbOVc5oVLOJdtUYI9LuizawtFJRp5HCCxXtjZQCiyG8XiGlCXtlmN9mCp5MCAcq2MyGZMmqZ9URGhbVtbb1WrwXJLsU4IKqHGCssaU/rWItJWSZTmJkCBc6UIcdUFMa9CscduwOVEUUWhLXsacnFgO9gRRuspw+Jg4VSgpmU0cqyZNW+RZhhSg4pg4cseilETIlKIoycsCEJSFAxHS1D3QjTHosvSOUe7BbIyu2k1wi7HWUJYlewcHrC522Vjt0+l26XR7TMcTx7iJE6Io4ti7IcRxQrfTZn1tmelw5JN3QZ7PaLcTpBQoqSjygjwvGU8zoqTN+PSMvd1jysKA0cSxottpo8uM926/xYOHD/j617/Ewf4BP/vJL1he3+Jf/+v/kbfeeYeVpT5/81f/iXGmefWlKyg1AaZY62rj68lTw+rXdx3r2QZVMtXsg7YWvQ4JWtUGqw+7diQ8YFjOFNnDY4Zv3qdlLSJRCCnRHUX/2hIzIZjODD/55QFaxES2xBpY39gijiP6vQWW+n12H96m3RpRmBO0hTjuIpTTLsIYpBBEUnkdmADnmoqIUDHDcP0lWBdjXX11q50yGc8wRtJqOd0aY0qfYGpEKtj60nXOjt5j79EjkqUVXnzlGwze+T6tr/YYuYaHkRbl64irJFs2ijjDdQ4jR0OoNpLOvSlIjhRFzuJivyZKGF2BNA60VaAFs2yGRdDt9lx7F07Atz7vJlwR7qBP0ANhwt/fMOYEO2xjDUZr8olGCcNiv+ToZIaQMbm0JKnktdde4Nvf+QkpinfePebh4yEvPtNHZwahIhwIrZ/wADc1aGECGFMDNdaacy5PTYttU1lBU50RDcaQc/wLjbQqqaoGmfMaOc22HAAWGiDNPBNpDlmpegjVsePbmTUGYVX96LF4EM0/cPwGQ3toKoDh74k4B8zUui8BxMcJBzfec/c06NgIhIp8m9bO+l05C3jHhnPPD6UCY9M9R9y2PUgMKCnJsxmmLBA4HTO3PiIbLDt37LX7kztGYwLzBieafRmX0Yg0Tbly5crca9ZaHjx48FQOIk8KrTU7OztP9dmlpSWWlpZ+q/28X2RZxmw2Y3t7m62tLcqy5N133/3UJDLXr18niiIODg549OgRh4fHHDw+5nS/C0QkScL29jZFUcwJBIdYX1+/AMAcHx9X4rwh0jRle3v7qY5pf3+f2Ww299pwOGR/f5/V1VW2trbY2tqi2+0ynU5/sxNuRJZlVduRUrK+vs76+jovv/wyP//5z/n2t7/NysoKf/qnf8r3vvc9RqMRGxsb/Nmf/Rmbmxu88c7r/C//y7/jj/64y+c//wKhrMi5TZ7PXj7ckAa+tLjNtfYaR57lc3h4yMFBzvExrK7C1laXzc1NWqvXwMJf/uVf8u///b+vrtmNGze4cuVKxQJbW1tBtQ7I7e05oOajjP/+X/4ZZ2/c5T//b3/B22/v8cwzK7z88ossLi5+LMfzUUaaJNy6dYuT4wm7u7scHh5ydnbG6ekpWZZVffXoeI9vf/vbDKbb/JN/8o9YWVmpyqLW1tY+1GMqy/Kpx9fL+PTEBwI1zQmwm+MFZ40mMHIxmiup9Ws0Vomb6M38il94PSRw1mqiOCaKFAV46n/pJ9PehchP4IVsbL1yvGmIPFqLNhopI/Jck2UlSRp5EMABL678xjtpyOAyVdvIShkEhb0dr7We2VBP3K0ofWJlsaLACMX+/pg8AyEtC/0uUjq72vFwQpIYoihHa6froo32x1r6ayGZTkuyTNFqtYnjiCIvcC4+pVt9FRZtnLK6FI6jLwMI0GDgCOGs12OVAJZZViLLjGk+RaiU2WyElBdLQyyO5VKaFkcnip3dGVq3mQ4zX/IBRVEiFSgr0bYkUopIOXt1pdyKkJR+Vc5CUTq9GGOC+Jkr9AlMmTrJEVgriSK3+jWbzbxbjiDLC9JCk5UGbSXTWUHaKphlOd2uY9bEUczCwiLT6ZQ0nXlNmz4YjS4KtC5RkdPXUZFzpipLTV5ozgYTotRydjZkb++AsshptZxY7ZXtdYbjhPfe22E8zrl3/x7T5TXee+8Oncc7PPvsLf7pP/5Tdnf3UMkyb/zqMdL2efH5Nr1egS2VS7yka6muRat64lj99P0hsC2aSWt1i0KSGOzrQxbqwE5jJRQx8qTkVtzjdHDmtWCUA8Wu9TELERrBo0cT7j8copIEjGFxcYXl5cVKt2Xv8V067QkLHel0ibQDumxpEFoQqwihIscw8WVyWjsgVCnp21Non81BAq9rYxBC0+2lzGY5szwnTWKUiMFqrDDkCJKtiBtfucK9H+3wzs9/yiv/5v+IfDNCPZgibqVYJYj8WGUxBFJL6LcCgT439kivgBwEW924EjGZTGm1Ug88eTc2JStWB0KiS01hCqQStNtt3Kk6LRDHX2D+ZBt3rsl/cKU1wmt0uWS+1Jqi0JRFgbGGNGqz3FNICYOzGSJqkeUalUi2ryyxvNRnPBthtOAXP9vl2euLTjzcWCxFNZ5dTFLe/2+D16SpW9f8pxuglxu/n/yAaJa/PWmXT0ycquYsLhxhBZQEkC2Mw+bJhxCAsDBs18fjy+IE1QPL/T/coQAM2SZe+mvCPvGnDmWUKnIr2tZZsotKN6xmMCk3ODRYZ2EBwV2JsshJYkVZFDiHMm/pjcSE54eohbDnrkEF5FzGZVzGJz2GwyHWWo6Pjzk+Pubk+JjJpAt0P+5D+9ji5OQEKSUbGxt0Oh3G4zF37typLIlXVlYwxrK50eZzn2vx+PGYk5Nf8OKLm9y8eZ2Tk5OP+xTmwlozV84F1HORT1gMzgbosmR1dYEsm1AUGYPB4A8CqLmMy/io4tfzrkJyeP7lahU/rDb++nBJki8HQngxDhpMncbSpnvBTyKFf88gpCCOEzKVOap441gcffsJg5gX+A2TeG00k2lGURi6vR5CKooip9OLPOtGECa1yrsruaSNaqLs7LFltTLeZKwE7QghnEGyIK5YLiUpw6Ehy0p6nTZJ5LQ3JtOC2XTKxrogiUuMlpQIrPZaNMH9yBqcgKahLDNUJImTyE/q/TErz+wRFmsLv1wNEMqytGeiuGtfFDnSJ9P5bMpkMiRN2pydFUjlVtYbmploE5PlirOBZmd3yvFZQZo41xtjLIKISCmszJmNMwSCVpK4lXnlNEq63Y5Lco0vLwGms5lzvbGgdemwQIRzVvEAm1IKpSLSNMUYfKmU9Y47iqLU7OwdEgm4cXWL0WiE1iVB38VaS6vVweKAnfDQc/twJVZpq01RGlQUY7BoXVJqw3AyJTsbMx6OGY1GRLGi20spCs1C13DrmWtYW3Dnzgn379/DlHDrmWfYWFsixvDDv/1bVNLmtde+zH/9r8e8/vM7jMY9Xnttm8U2FUBlwsp/KH9yl8H3GzzJIaTA3pfGul4432+ov+/W1R0AJVqM93PU/gwzG5JgiI0gkZLSZETXV8jbMNMRv3rzCKRCWqeXsbK2AkKwsNDj5o2r6PyIxW6EEFMkEpGAiiOK0lJozSwvkLYkiWLiOHG2wZ4ZEPqVCKdenajwIEqExYAosVbSbkdkuSGbFbTS1A0fAgwKVMHisx0WH3U5vH3E3R98j+uix+Tnd+msXmW6pMFQMUOkVBVQY3zmL8NQVLFdPHupcStmmbN5ThLHiHPq2AEUdh/K8xJrNGmSkLZipLSA9sZ1lRLRPHDQYDiAl0HxQtfCM3nKoiQvciwFSsa0Wh1kJB3oY3OWF7osLWacjgoKo8gLTZLE3Lq1yes/PWJhcZGf/vQx//QfPcNSV/lzcmVl8+Oob1Xny5AsdT9qfHZu6Be1iPl8xdJ86eg8KBBQnSZMdS78c6GGKKoNX/hoXVHnwUlTP1POOx258tmapeLRMYLGU72nAPTPP+kCU/TXAh3+MOdYpX7rxlgQCiskRaGrMc49byRCWLDatwVXLiq9RX2YtEshKIscrUundWOd6HUNgwbnqeae62viNKOezGK6jMu4jE9enJycMJvNODg4YG9vj4ODE/RslZZc/7gP7WMJay27u7sopbhx4wbgrlFgY2mt+cUvfsEvfvFzVrc0r732HN/+zhvcuVPS652wtbXxMZ/BxTDG8uD+/arkCWBlZYU0TT/mI7sYh4eHdMuS7e1tjDEcHBxxdDTg+vWP+8gu4zI+O/EBGjX+/yFZDPXv1cr9uc83Vmhr1RpR/XOJS0hAg8ZM9We9XdzvbtVbVWhBHMeoKMLowr83f6QBWAhOUWGVu3InMYZSG4oy2LtGaD1zexNu4urhF59Q1kutQjiaeQ3OzGdbTbaN+5pFeytyqSKywjAautXNxYUeSRqjjSHPSzCaXkeipCsDUp6xo4329r5el8LWE3WLE+9N4hSpEq87oXEEIMfkkdV9CAmIwnp9G2tq3Q0pU6SwzKYjOp0FVNJCSQM4QMWiMDpiOlXcvz/i9MxyfDah1AJB6pxGpAOmlLJkswmxUnQ7XcqyoCw0QhviOCKJY7TVHnBx11h7oKcs67IvKYS718ZUSUwcK+I4pijcOSil/D12dsanpwM6aUKaJGBK0laKUBGtVptIKVSkSFspRZ6Tl87pajQeuX2pCHAJehwnFEVJqUtyrZllOUcnQ7JZhjaGWChaqSKJInQ5JFEpLz63Qqxi7tw74v7Duxhb0k5i3nrrXRYWlzg6PuX6jWf51//Dv+Q//+f/yNu3jxlO4Asv9un3IqJI4/RbcImadCv5EtfORWCEGWf/TGAaWVmvoPuSCInrNnNtU1hsASe3D7lWLnO4c49YRqRExAbKliLaWmYiBXfvn/HuvWPn0GUk7U6HTq+FlIJnnrnFUq/D8HSXNC1wZUoKS4nBEEUJSilXGqMNWhu0niGEJUlj4tjboDpCgWvXosHCEAJjJBYnbCOkA3HTNKHMS7Isc2WQcYSyYLVAJyVbX9xktHvM3g9/zNLzn4OsR/TmMfFXlyg8oCJxfbjUolFuU48/dfWHA0hcOZakKAVlVtDptYDSA6ZOtttapykzLWcoGdNutbyujQVhEMLUDCcbRk+fSgsHnrrbUyFFCKGcs1Q2oyw00rf9KFZO4N1KZ+1uNdJalDBc2epw+s4EpRYo8oxYKa5fW+X11w3GaIYjuH3nlK9+cQNriprxgq3HUevAwqo0CQcfBm2UAJoSvvcEcOUCaCFceWL17AjsuAAWhK3bsI/6q03AqEY9amZLpYHT+FRNc5lXwpk/buYQaNEAaxxDkRrkDiWsc9uoLlf1bLwYzWv4hHeDKLS2Tsuh1UZFdXmeJHj4uetSudRRg8vWCLQfV53tvBuznQi1Zf6Z7QGvDzjey7iMy/jkxvHxMWdnZxwcHPD48SFHh5pUlbSWPu4j+/hiZ2eH69evc/36dcbjMW+99Rb37t1jOp3S6/X43Oc+x/PPP8fqZsm4eJOXXlqm3x9yfDzhu999g9deu+nn/p+MsNZw7949Dg4OsNbS6XS4efPmU2sGfZRxcHBAKjpcvXoVay0PH55weHT5LLmMy/gw49cCNTas/oaVZsICpwzpv/+cmVsZdxNbL/rpoAr/yfB32H74v6l0OCocxwJGuimydBPTKIqIk5giL6vPBt0OtzIoqE1sPVCDdcJNxmKQRLFklpUU1oDypUXWeG0ag5L1iqpLfqEWDQ7lQzUwc/6nmwxLwkq9EG7VVpea8cTpWXTabVQisFIym5VILO3EefpoYcBK/MK/n6D7e+ATbpe8OpHlooRYtkmS1DFITOE1IUx1nCEJdNiWcoKsXjciAF5RFJGXkGVjpExQIkcI0DIiL1rsHpYMziKEWicvTxgOMlSUMhEzWqlzTLLWIo0hiRLaaUqSJOQS4igiywrn4NNguAgsSgniKELH1pUb6cKvwjstiVALX4kGRxF5nvvV5agSz1VKMptOebx3yPHpGf1eB11qrm3P6HR6REo5JocA6d10yiJnPBqgS0O/v+CYISpCCEleOPvtUlvK0pBlHnASCikFnXYCtkCXGZGClYWI9IVlIgVvv3vAnheB+0f/6E85PDtiob/Mg3u3+cY3vsH/+D/87/hf/92/43Qo+cGPd3nl5TW2152dOEhX0CzBaOf4I4zE6BKLxhgPEJgg3qocCwmBku7YlXVuRQbt+oF0aZ8cjikGBVk2Qo+ntGWEVIoSS96PmWaaw0dT7t4fMzMapRIMlv5Sj0hCp9NhdWWVs4PHpLLAlBkanyAKl0QHLpnAoiTISHomkCAvDHnu+nHaiohj60tNpLcO98lkpBCkbmSpSlk0cSIRUjGbFqS0ESlQShAlrdWIK69e4/5/fYNyc5nnv/jP+OV/+Hcs3ehRbrXBOrFVaSTaQiIl1jirdON1nionHx9KgZWKLHP1yEpaB3JIhRUKTIqxlqyYEScRcRpjZACdLcrWds7CO8AZ466LsRZpPLuFUF8uyfKCLHMuaHEU0e62iFStseXYGbqCKgQKQ8biQptOZJgJSWlcP+t1umysr3F0fEbaSXnj7TO+9IUNpM3BpA4MayTothrg8aO1A5usB29sNTADNgDa/nPSORdZHZSj/YOjKluswR4RQHbrx0vPrLH1Q6H6vDtLD4iFD7xf2VH4Snhdhs/XH5hnA1Wy8P41A9YEQo4/Dg/6+Adhpc0h59uK33p9IJYK8HHldhLjOVxCKnJt6cTKA5aKpJU4YDAAY8JL2Yv6KVl64FNFBvdVSaRiSluidQ5SO7ftgDGFayosmAD7NK4/tgbiLsufLuMzGBXYKwLkqUHkIGf1P6Hcz2Z/FgX1uPwxhBVgY/Bz6TC0jIcDzk4y9+9Uc3oCCx0BS5+s/htA/aaw+2/D3Gt+p7mN5nz7bHjEzWiT1bUuQmVMZieUekycWkoz4tHj95hmxyzuwPZ1xauvfJErWyd8+9u/5K37mi98LvXs/TqjQZSAvviM+S1DANZEWJtidQqm5f5Zg7AlGAnGvW50zOHhUaUb1Gq1WF1dJcuyJ7I3zz+3f+1xfMjj/HA4RHdTlpaW3O9aMS2pF2TC/bIQv293mj8mLUGLCy8/dfy2bW3uiJ7mOgkDYjY/lpz7J2SGkDmhauTDOLYQl0zYP5z49aVPogZWQsM13u45CLna83M8TycP5TXz66Hhg/UgS/ichblCqoaaqvCAkZSSKI4Rcoq22ukthLTQuuQnsq7cybgX0DpovEAkJSQxcQy6KFBKesp4LQgcSpnma/lFhbg3O/BFZk24BNZrwjhdDCzkheVsMMXomE6nQxxFGGuZTGbEkSWOwdrwYHCr8NIzDkIpS2DquNKR4KCj0XpGbmNaaQpIiiJDiNQ94kPpgDBY4UWKK8ci/1gyU7dqbzsoUiJVUOoMTMxopDg6MpyNUvICTs9O2T84dOVDhaXX7njGgCQvZnTiiKWFVfLcKf53Om3OBgOi2DGbtNYIrz0TEr1YRchWhAGKIqMsNWVZus8KQRRF1QAnpazsf9M09Z91AJiKIqazjMl0ymSWk2UZg+GEVqvNC8/fpGOVd+sJrdE9TIbDIUVesrl5hSiSvgRL+8QIEJJSlwgp6HRSotjQaknarR6joUFJQbud0GoJkleu0OrE/OrNx5ydZfz93/+UF198jgf37/OVL30FowvW19b47//sX/Llr36FH/3ou/zwu/+Nzlevs73ZJZYSi8Ha0ncl4dgTxgn6Yl3Sanz/NL6cJzA2sCB8+7PGIgwYIZjZmMHjCUneZTg6Q1AibIRRgrFMOVSKt3/ygOVnl7l7Zw8hY6yFtJWwtLiAwXLt6jWkNYxHBxTihOFw1JgfNPp0IyFW1b2OPVPEYrQmSoTXnHH2xE4XCAdmMPZAI77EQyJt7MGBCGsihqcz4o4kwrGPZkxpbS+S3trmrbvvkmxvsP7KVzj99t/S+dbLDJdalLbAWWaXGC3AKl8VKDAYhLF+rBGUxiBEynRSYAUk7RhjCxARhgiswmqDRZOkLZIk8n21dt0Kfc8Y55onfamfNtKXXuHukZBkWU6ezxBCOJeoOHGMGd/P7Zx4p9MbMdaBlaXOiFPL6lrM3UcjiCOyrCSOIq5d22Jn95C4HXH/3jHjWcFCyztUNZ2TQpjmGOgEg2UFmuD1bRq3urr3fkIma8vrptDvBTZK45vGNnCbMH7aJuxh/DynAYSEJ4rw0G8F5NQKNjU49IRJV5iUNw7r4rwnAFHa9cO5/YoL36nBM/+3tU238Zof4883TVNm0wmtVsu5sVSf9GWvTYFtfz7GWmzpHCNimXA2PMPoGZ1u7Po94Gil4Xlr55g/QXcsLEeIxs/LuIyPKzY2Ni4wBn5XTQ6lFLu7u6RpyvXr1/nOd77Pn//nHTAlr1zr8PyLgjy/x8ODQ1ZWVs5hlRZE9jvt/3cJQYul3ucYDzV7+3sc7B9wcnrC48e7TCYTjInQepVuDN10kfPqZ1EUce3atQvb/X04Aq2urlZzs+FwyHg8Btx4c3BwwDvvvIOUkl6vx7/5N//mwpz5Sfc5jmOCFs/u7i5HR0fVdhcXF9nY2GBjY4P+YsqVWzlRMuJk8vfY2PD1P1nm6Mh4HZ8Zu3uPubvzmBvXtrh5/R+S2E1efX6NP/7K/x1Q/M//8/+LNEnY3NrkmVvPcPOZq7x1+9sMhg8+8NyjKGJra+sDP6eM4ud/9Yi/fv2XWO1kGIyOSdNNnr22jpSgp5Kdu4qxmvKzN95iYuHKlStsbm6ilOK1116j3+9Xi5Zaa86OjzkZPeBpxIS3trY+dIezra1N4kyxv79PFEX883/+dXZ2D3jw4BHPP/8sr7/+U87OJnzpa5+nfLjP+Ufx5uYmSVz3eyPgtAVnrXPzkt8gTk5Ofic7842NjacqM8uKE+7v/DfHvk41m9cN61cFe3tvI1oFm9cNa1faGHuN118X/Pl/2eXd9/5/fPWrt3juuWd/ZyHh0Wh04TwvwZvPZnxA6ZMX6m2+JgJIU6+O1kKO/kMNeroDGPwWqmXQBghSTavPgTjnZ9FCglREiXJuR9WirPAlBPik1CB9qZDTeXEHHVZUlJDEsWKWzUjShChSVWICzRWYMMmHUMf/m6HRjiVgrEXiBG5Ho5xWEjlBUs+yyfOcbjsiipVbfZUuccGIisETwDDhtor0IIPnAyCFs0fMZ5okaaFERGmEX43VHvm1/pzm7qa7rsqxNZAFSlracZ+dwZTx0DI8E5SmzWAw4XRwwmg0chbh1iUlFtDaYEtnnS5biiiKUcrZOxpjmMYzR+W3oYQpJpKSyXjsGCCBxWQMsS9B0sZiPFhjrEX5e2zdx7zYZijpcg+p8HA3RpBlBdZKBuMpv3z7PTq9Pte3V9FWEyuJ0YaycPW/URQzHk8YjccsJi2nbSElRV6Q5d4e3K9Ax3FEqy2q33vdjkvOpCSW0O/B888tE0WCt9865uDwjDd/9Su+8IVXybIJb7/9Fu12j699+ctIFXFyNuJ0ZPju9+/wtS/f5NlnuiihfcLs9JG0tVgpkR6kEajG6mAAD127rSi81pMfjMAKhc5jVm5cxbQKhu/9grhIMSbFpIqBhYejEfsTzb6dMJ44NyolJe12G6kcU2Z1dZnB6T7drqYdxWjdwUl9hJWCkLgar/HirosglARKzyxxoIwQCqOF02DJcqRSRJHECq8/ZOvrLnRwUBNYD55NMrCFa8IFU0SmWPvCdfZ+dp/DO7d55vozyEGHyY8eM3m+z6CTI0RMZiTFzILVjl4lLEI1yxchKy1l0UJKTdo2TGZjguOYUhZnFW5I0xhhDWWeu3O0oC1IpRBWuV4qneAyQvgqRldiVJaQ5RllmRHHCZ1un0iFHurHPuMGNpewu0HPVOCtQnsnOiNy+suS/M6QVrJMqQ1JJFhZXXLgJJq9vQH7+wMWbi44kVnPgqmSeTwLMOzeA4XVOE4NHDSH+w8K4dkrzWdGiCZ43yxlam7bViONrYatxjeqTyBktbWnKdN6muN27BoHvkncmBrOvdnv/E6re1eVT4XVcA+UBGAqihwzryhL2p02AuXGcq/PVTsZ1idcJzQO7CytJi9KsJo8t0hb4ASZNBaLqcrNmH++BQC4AapeCgpfxscZvw8rbldGayoQIc8LZzSQgownROmEXE/QnICKzq04frxhrWQykowGlsEJnBxpjo4KJsOIomhXn3PjdYvz41tY4Pooogn+hMW4EMaYSmcFYDweXxhrnuQgFnKK8P3g1hXKqsN+41jSUhorJxgmoCDtwIIBIySlheEEsgLK0jIeQisWdFsxS/0FWq0WLzz7Fe7evcvtdw7ptrb5/Oe2SaKnE2d22pDqA8dPaRTLS5t0tzf9M7W+XtX5a9DaOffkRYlVaq5fxHF8DkBwC5Za66cCan4fSbxSEULULqztdsrmxjIHBwe88cYb9Hod+v0uOw8eEcuLC9udzDJ3RgImvyOW9Ns60oV4quskwFJQ2lM3B1eOgS2tBTVA2BKloOoZ0jKdGkajKdPpFK317wxEn+9rl/HZjQ9wfWqg79YCprbcDqCLDZa6AZw5P2A1J4rh/bCeF2xw5TlmjgdOvFOKsQqEQClBFEdEScRslqGNxVlt15Nqrd3gJcE7IUnHLghzZgGRFBR5hpJuIKxXLANAc/48QtYSjq1m18wlHXM0zQCrWKyQjMclZakQLVG5RpXaPYDS5Q5CGkJeKoV3omnSJBCeQSMq0EaIoGeqK1BNl7lnMDhNFmMKjCmcC1TFYQnnBK7sK3KosNRMsillvsTebsxoWIBNGYyHHBzvk+clRaEBgYokZWG8i1JCJ2mBdewBC5UzVbDXnk6ncwmX9OVZUrhVCWkMtvCJvRRoYyiKsGrtHmZFYZnNsgoUKIqieqAHgU2lFFEcM5tlBH2io5MBf//6zyjLV7mytUavkxCriLwoKbVGRZIojhiNRiTtDipJyIqSvNBMJlOmk4kf/A1CRMSRxBpNknSIREmkXFmclAahDFEc8dwzq7TTFr/45SMODs746U9+xo3r17h+/RZJ2uatt95keXmZb3ztHxLJNn/7t9/mez98m1l2hReeW6GTRAhrgBLrE+oa0gy00sAMCu5Ark0YQFqc25KUWBHRmUrSpSWS7VVOV2LMyRlM4OBoh6nQLC0s8U9vrfDjdx5xJzul3WmDEKStFKNL1tfXUFKQT89Y6muUshgdI8Mqvw3JsXtqnQdynTOZswGXUhBF0vcdidOEcQ8doy0WB+ZVQt5YFNqzSuqku7QKnTnmkE26SBSySFjLrvLGX/2EYlrwpX/xL/jV3/wXeisZRT+hNIZcSqyNXUJrS4zVFfgTmCtZIclzQ6/XZjqdIJRFigSMQCrnsqYwlPkEMESRDEMXUgkPTCnP0Ak/pQNuhRPOnmUzksSBtEVeMBlPUUJX18Ya1z+Ukk4VR7prVbn3eOBLSoMhJ00jOi0HDpcllFFJqx3T6bbJywllIXn8eMyzN5dBFBV7Jowt7vANYdlLVHotYKu2Nzd6nIv3m6wK7FwbaQAvAXz1Lbni0TSeBRW7pvl8OH8AHj26CPNcfB6F962df/dJLBmE9UwV0fhMhbzM7c193S8O2MC/8cdUo5j4W4fFMstmSKXopu2q/waMp/l3vR93HaMoAetWl62ZYW3mxguvXWM96GYa5xKS1uYx2+oDH/4k/jIu4zJ+u7DWsre/x+C0ZHd3l52dHY6Pj4HfnWn0cYUxht3d3QuvN4GcDyuWlpZotVpE0RFan6GURYgZOzu7FLn7jBCCfr/Pq6++ytnZGXt7e+zs7HBweEhZFh/6Ma2vr9MuF+eAmtFo9EQb9U9zLC0tcXh4zK9+9R7/+B9/jdXVVf7qr77L1at/mILXF+PyWXsZv1l8AFBTr7iCQ/kDLiGEr3m31tP3A7uhOdFtrmQ2HWnCJuXce/XwFX4zBG0C4YVSI48w25Bg4MqLjLHOIUk4wdBQelTXSdYzYBUB1tncClztv1TedhwvYsw8EHM+UTkfc4mE/57xq91WKKYzjbURSrrjlxLKWYExGjfnNgTr82byJGS4SnXSK6uk3OsBVZN6C5SA9BouAqUSyhK0LpAeeHN6PF6fwIJzxoopTMLe7pTTkxJDDxkVDIYTTkcDijJnOsspC00UJQgkSkFRllhCWZV14JB1SVeeF/R6XZIkIejrzGZTrNcdSdPEMVtKQ57nREo54MQzbJSSWOuSfqM1RgiyLKsQ81Aa5UAc5+TkSgLc+WttKgbO8ckpP//lm2j9PDevb5NGFoRCKEmUxAgZoQ1MphOUFxCeTjMGwxGzPHdW6dqvzuH0YwSQpLKyfBZCInHsh14bbl7vYuwG776tePD4jNt37gARh0cnvPD8c6xvrjCbjPjaV/+ITn+B//Ln/1/+/qePmRWaL7xylW7iSR/C6VuE/hWYJlK4BJ6q73k9DCzC2/Qa6dqzGGXc//kP6S5fpYgUExnxuT/9KpvZlPfuvcv6lT6GXc5+MECp2AMK0G6nCAGbG5vMxlMiLK1EAMqp0dgSRVhRcaCpFXFjVcMlllJGDoTzgJqUTocpJKNKRlgbY6ygLIwXcy78qllELCVY40VT3Xci4YCoIjeUtqTTSSkiS+/aIks31hkMBtjnttg++zJ7v/gr1q4/z6TjrmksWyRSYHFW1xbHWnI6MjF2IklSSbslkVEHgUVrgRSKJBHOZjyUeGKRyomAS+VPivqfQFJ4IeRQshcpRbeTIITF6IJSF1gEGk2Rg7UGbbSXLYZmSZVuskWsxNmVW5AJcdxjWhRYKyh0SRIrlpcX2NmbkKYp77xzxHPPpSSiQPnhX1RjZQDjFUVZIiVEkSs9a45rDkSyBPM98z4TjwDUNYVw57S8aGimGNeJKoWwasgWzt0rsG3mBmB3vBUGYt3RvP9EyDNVqmfCxfctbpyqjzGA7jTuqUTacNa1I1v1ew2DVN8yFYjkHAqVdOycbq+HMZBlM9K00SF8vzlfjlGxX4Qrn/NX0IFrxoN4olELXy2QNJ5nNWbE77b2eBmXcRm/j7DGsLe3y+lxwc7ODru7u5ycnLC8vEyr1fq4D++3Cq01Ozs7F1gVi4uLHzr7Z3FxkeXlZaIoQmtNFI0YnczY3d2lyOvn53g8ptVqcevWLTqdDtPplO98+9v0V4+IPmSTpSzL0MNhtagKMJvNPtydfAIiz3OsNSwutsnznNFoRLv9yRNC/vji/bLIy7iMJ8cHjI7NCarXOWnMos/rtZxvgBfYG81XhJuCh7nk/Df9Sqt3S1HB5QaLkZK03UYNJmhvXR0cnYQFqWQF6rj9AEg/OXWTZaUEnU6bPHe/G63BJwNPvgzCL6Da+if1TyHnV2L9XDgs8rpErXRsgyhy5R1SCreSYCGOpd+u8qv6ujp+KSRGmnBJqv3OsXpQDrKqEkcvNJxrVBSRJC2KIqLMZ8RKYE1ZCQ67DSoK3eHgyHJwCEm6QJ5PGYxPOB6cMp7MGI9yyqKsgC+nG1NSFDlFAUWZ00oTiqIgV4o0TRA4N6VOp0OWZa68CxiNR3Q6HSKpnJMPOUVRkCQKk2mXiGKJIoXWXhRZ1ho3AQRoJltlWVb1t7oskBiX+/nmZLTldDDi3dv3iCRsb62hgChJiWOXSBktmBUZ+XRKnpVMpjmj8dSDNNqXPDgAyRiN1Zq0HREp4UpdoAIrlDS025pbNxZI4xbGwN7RiDfeeoMr29e4ces6f/3dv+bG9k0Gk3f57/7lv0KoiP/y5/+Jv/vJIeOJ4etfukavlSCtRiIr4Wzh7e2rNNACRiIi4RkAJVIptAUjLEprJrvHTN+8Ty52yLpLsLLA4+SXxHEbm/RRvZTB4YzTwYwoctcxipzdepLErC0tMz07pd9SRAK0cTba3h/cO9a4C66tE7B2Sbr2bS3CaohUhDVgtBezlk5Q2hjvaAbEiSJJnZNZWRbkWUZuIIljoqiFkHi6b4mKBbFKKErJdDSk011ikhRsvHade995h5/+xbe5sbaNGkaYXx2SfG0LpXKE1Z7h40EQ6ZhvSIs2bfLTnOW1lCgqEKToIqOVJCRxhFQaKbUD5oJdsnKC1tILNAvhKNpZllHkBWmS0O93USqA3Q5kqJLv8FJIsrFgvVaJRyEC+UH7X4RnTxjjrd2FZGkxZnpQImUbrTOiyAE1d+7eo73c4f7DMcNJxmJiMCZHmxrYsNagHbXIr3J6pppSFfMirOYGMNbaczosFUgQWCFuWxVF3IOoQVfG4QhB1NmdU8XuCTpfRtVjrt9XJdROowwSpxH0ZBZ6zdS5wMZpfOL8W25cjfzY3izxbYBKfrvNP+rtzCmvIaSizAxpt00UKVrttgeiLXk2I00TV8IpggacvxbnTsqNNe4aaFMgbekPod5z5QAX4KMK5AF8yaL04+plXMZlfHLCWsvR0RGnxwVHR0ccHx+T5/nvXNbxcYYxhqOjowtjWbfb/dCBml6vR9uPrVnmWNj52LCzewg2Io5jkiRhOp0ymUz4kz/5E775zW/yF3/xF/y//z//K3/yzStsXvlQD4mHDx9y+tPJHKMmSRIWFxc/3B19zHFwcIi1lq9//cvcvXuXe/ce8uyzNytx5Mu4jMv4zeLXuz5ZPQcKVIkFFXTjVz0DQBCAl+bEz/pPOtr+PJHbeAzEQTW100fNhHF/aV8+4AQ50yQlTlJ0McNanNaIB3QqgAQ866Zx5BWppqSVxmSzAm2NxzVqHZuQAIfjqlYkoVridBPlAFbVDJemYGM4TWucc5D7XACRDNqUGKN9kiuxqHofjWVi6RdMpQ0WzP4IvNOVS3S8g5OordEFFp3n6NIQJyntVoc8m7kkUpdImWKJGY0F77474GwMy2tbDKdjDvZ2ycsp48mI0Sgj98C/lN7FxxhKrdFG+xVjgdYFWuf0ej2EUrTTBISi3ekihCCOY7IsdxbOMiZOE2Z2iraW1Dhdk9gYbKmRQqKFdsCb9kCNFxFuTlZCkhHYNdZaSu0tj7GeGeKS6aLUnJwNeLSzS6eTsrTYR0aO3YBxCbCNIrLBmNLbhRdF6QE6lwy2WwmtVkSk3L6VDIwHvzrtk223KC7opBFXthLybB1xW7C/P+DxzkN+8csOC/0uv/zFG3z1j7/E3u4dTk9OePmVr3JwsM/Pfvkrsuw+f/pHV+i3FFhvWe17hMElWZrCuTNZl7BKBVK2nBCy9M1oZhg+PKFTSITNSKZnmOGY492HHJFgnt2itbbO0aDkbJhX4m6tdopSgn6vSxJZxmZI3DIYqXHEhFo6tdKkERKsREVO7M6iagQTKE2JUpLSQCwV1jiF/yBTpGQEAtcvlESplCRJKQtDXuRMJ06kOo5jlO+aBkukUtJEMB4NidtdxKpk9aUrPPrb15EvnbF261UO3v0FazfXSDcSdAkiTrAUCGk8wABCSiYTQ5K0SVJFqafoXNNpp8QqQghLIM0IGSGlQkoFwjpnMakwVjCbZpS6oJUm9FbaKCnm+ywgKBujTQCnhW9rDpwVKJyKtHGaIzaMPharC9cWjPMKEkKyuCB48HiGShcoyxkoS6fTdiVltmQ4AiHbdLvWsUKqEjXXnrUtKUsHjroyUg1Cep2mmqkR2nw1xhHec4wnB574t40AoX17CM+VJmzvYA8H3uh6vJW+bxee2dk4/eAq5valCc6CQoRyWlFpJ4QxQgbmkJwf4UOIxoIA/pnmSDrN55/AiaHVdJeadSn9damfIuHp58S9HZAbfMxMQ7g5VhJjYnRROhaVBz6fJLwZnjsKi7ElwmpEsObGYFxNG3MIWrWIIOpzqJ65n85Sisv49IWUktXV1Quvj0YjhsPh3GudTodOp/Nb72swGNDpdFhaWuLk5ITRaEqrBSsr0Ot1McZwfHzK/v6Ml15a/a0tmn+TUqTRaHSBRZHn+YVz10WLnZ2CbOpA6oWFBay1T7Rons1mHB4ezr2WJAkvv/zyhc86tsM8MBvmZs0oiuKpE+vhcEie5x/4uXCd2u02i4uLLC4u0u12fy+20ycnJwyHQ2azGUmSsLq6iihjKCJaaRdrrW8TI3q9Hnt7e9Uc8otf/DydzoS8mL8ni4sLLC8vz72mlKLf73/g8SgT0WoNiKJ8DqiZlTOOd465unaVbqfL4dEh9yf79No9ZL/D6upq9a/dbp+7T5aocAtD77vI3IjhcHihrTbFn3/TUAZORrA8nV9oT5KU7e0rJEmMMZbBYMTe3gHPPffMhW10Op05jSMjQEXeceu3jF6v91RiwO8XTwsaKqWeOJbt7OxceG1jQzAYwGQCDx6csLV1wubm5lMf02AwuNDHiuLDL8+7jE9m/NoWOZ0Nz63ohd/nO7sU9aS7BleaK8fBsropuhVeb4hTWg8y+O2FfRtrEdZN5IuiQMiIKIopywnWa6YEx6bw+cA4EBWbxh23FRqEQCiB8m5CRe6EZ4X4/7P3X222JOeVJviamcstQkccmRpAAqAAWawhizXToqqfeaqv53/13+jLueyZnmd6alhd00WySJAgMgEkMhOpjog4IXds5cLEXJiZu++IkwIJgEgQ8QGRJ8K3bxfm7ub2LVvfWq7LBHqWCt35+4F7nJUUnSzPcCzdaabgdUIIiVurK9LUAzEulG/gPBPIaOM1YlDeISa0aadFEWbYO4gozFQj/GC+SwYGP8L5ZE8l3trctBUiScnyEdo0OKswVnJ51fJP755S13scPjhksZzx8cfPqdYNxmnqpsa0hrbRni2QZoCgadseMHFgtEG3mrpa07YtDsnuzhZ5UbCztYVUKW3rxY7L0pEmKUpKdNtSUyMVWOPLCZSUJErhrCVLEoTzjBkTQJrhPRmPIYrOSSmJ3lYdpBhBNLza/rKquZzNGU/GpGkJDpJM4bQmEYrpToqdLVhXmjTNcXXjy3acJs8TtqYjilwjpEG4NFwS65NsRzcTnpAgnKDILG+8tkOeZfxUCU5erPj5ez/j9ddf5/DggP/P//afsDhOTs/ZP3yFb3/nzyiKjF989GOMfs6f/+kj9nfxjA8iq016kWDn0CZhtWrQ1tNrmyZhUo5YVku2yhHpyRJz0VC4hMQZRgpUqriW8Em9YjY75lG9zZPnK++GlHsQdTr1g+T79+/5RFC2OCXRNumwRIT09tZxpl5IlPD3sVIKKQPrDdPd09b58rBWewtqLxIcs3oPOAqVdH+DI8kFSV7iHL6MqG2QLSAlWXCPSpMEmSYsVg1apOz90UMWT065blf8yX/4v6H+o+Tkv/wU9T/+IYtVw9o68jLF1GtGowKBoGk9uJRmKXVdIyWUoyI4UpnuHKWUIFMswjOLcKzraNeeUBZj8iIFTM+YAZywCBG1vlR8oumYNSKCrDLctyo8/zbCuKG/dIHJ5/ssYX3furWlUGqNQ2AC8DQajSjyzFu7C1ivBfn9DNtGRk/PqpEkJIFhpqwL97YXZu9Fo90mEEPUKfo8oCbC+/G/NvRSHnwdwhqC5AZjUSCU6rcfjsda74gFDmFDn4jomExR12oIRNmAX8R7zffFwwGu7NoWEZk9AkkSWz2sZ1HBHWyY83TXOLZRPOZwLSwgrC+xTJJQXqhk0EpyfpmNmm+9velNTTQhBEqAsxqcRkmHsMa7w9FdFCIU1YNrsju+4VHfCQnfxT9XSClfmtg+e/bsViLiXSO/PlBTVRXj8ZjJZMLTp0+pqoY0helUUBQ51lqWyxWzmWYymaDUbx6wrOuaxWKxsWy1WvHixYuNZVaXLBYZzoxIkoTJZPK522zb9lbSVpblS0ukXgaKtG17C7xZr9dfGaipqorVavWl60kpfdlvkpAkCVtbHvj4TTCFhscuhKAsCnZ2JrTLAvBgR9M0VFXF06dPee+99/ovi4bZB5+B2gQw3n77DR48eLCxTGvNxx9//KXHI61Ca8nuzg7D0qez+RmnL065v3OfJElYVSteXL4g390hn0yYTCZMp1MmkwlXV1dcXl4OtmrJxjVHh4d8FTHhl0WSJF8JaHrpdy3cW8DeenN5fObOzs5I04QkcTx/fsz3v//dW++amxOvVoBNfjWGZ1EUX/i8/LriZSBdZKnejK0tydYWvHgBFxdLlsvlL3XPr9dehPgufj/jC4EabZqAcLi+tj1+6OKANtT93xxcC+lHxsTBY1Rb6NKSwZSsJAq/xm2IOGUbn2shsM5SVS1CplzPrmlbr7OgZK+d48VsbZihxSeK/V5ABM2OcChG25gL4mcYLdYSZmtFdyz9ed1oJDeYWQ2bESGLFWFWU4T1pOwH23EwrqQMoruuO13hTCj1CQCAP5IAFNmgQ+DdrnxxRAS0BDIwl0QoAXBBt0cIaHRDkgiEyhASltead3/yGVeLgldfuY+xDc+fn7FetLRReR5F21icIRxTZAbEJMhnP740yICzrNcViCsPFClFIn35TFVVFGWJWSzI0szrzjif0FunkEpR14Y0TciDirzWhmj/HQcUQ0eXXoPIvzQ9Gh7KQQLwZ0PC6dtfYqyjaX25m1QZum1xLiGKHftSDUmrNYvlEqM9u0MgaNuKJBmT58q3pfClHEiv0xOJUM4Jb+2Nt9BWAg72C95sHpClSy6uKk6enzKfr1jMr2nqCpVmnJ9+Sl1X/OEf/2uStOD405/y2dM12+M90qTFSn8djJUsrhuePl9SVQuePn3BbLbCOIM2UKYJVdswForvqpKjFrCWBIFUllRaZAvn1YrPPr1i/f9qqCvh9ZvwIECWpThnGJUl1cpycb5iPq+YjHOaqmY8ymnbhiLPsVZ756tEoPWaPM9pmpYsTdHGkmQZtdYBPEwxRlNkKe3akKS+5C3NFEp6ICdNk4CJSIzR8eFFCEWSZQhlQSes25q6cr48TECeJSSp5HrlEKJl7weP+Pj/+3Pe//G7vH7/MT/52Ts8/cdPcaMtZutr9nbHKKk5Ohqzmi/Z25swGhVczxcUo4Q0KxDCYSNLSsRuU2Gt7z8qswoCyQmjckyRF75cyGnAhlIfApggieysyHqI910PbBP6VRH6xr58xuuR9Os44W3brZflIs8V43HOsvVlWdY48jxnNC5otEMkluVCI0TmgT8R+rkI9VoPYktCeV8AiZwMrlsvBWqCbhIO54Y6ZeGYbWSp2KDVEthCEaEfkD+imHP/fIfvW9u9SeJ7xzkT2kYG97He9SlG3z8M3k/hWt4EapwT/fYxA3A8tFFYDi60xUD/jN6Ny4Xz8LJpXntMG0ejDUIJimIHow1JmnTn31962QHQUc/pdolSBJoc1hmE9a59zvWfdUc1BJIi8+dGUnZX+nQXd/EvK4wxt1g24LVbfhMW3b+r4ZxjNpttLhQN030vUzDsQOv6NnNpuaz427/92ZfuR1nBG+1jHnHbMv2rxieffHID5LNM95bs3V/zdRkoe3u7X7v0KrGwncB2zsZ75mUW4MZYzs7OvnRSwApY7ZcwKr9wvbu4i9+n+EKgJkvzbvazC9GXejjXD/56ynoEHAS9zKSld4dyg4Fz1EmJ1rMD14/BPxEsSaSiLAXaONIsQTcG5WQAKvqjdAGssS4KBIfjlMFi1imU9FaoWjucduhWk8mk247rHKkCCBNAlDiJHTUSnCNM1UIH5Lj4fS/qKpUkSxRGWxKVIqXyWh34gbhP+AO6KiwYi/XUg9gyWLz4ZCyx8g4wMXNU3rkmiIr6JMQnVk6KUKICSgis1RhnWK0SfvbeBcbt8+jxPsvVFavVitXCWwXXgTHjHF5AWKUI2dPxvYi0RhvtRVRF0K0QDmM0y+XCT6VbR7VcM51MyfKEre0cZx3VekVReLXcRCnqxpEkGWnaorWlKAqPOAtoGotSiS9hUn2y4VwEcXw7RMFfoWSvW4RDqiSIMlsPViQp67pFW8VWNqJt5qxDHXNrDcZ4do61hla3LNdrRGD5NI0vh8qCto1PkGRIOv39LQCjobEOYx1SZqxXDW0DW1u77C4F43zM6/cPmS9r9GSEA1qjSQs/03R/d8z/8N/8OzA1zfUTmhc/h9kJaiw4WRt++JNPODudcXm59qUTSQYoRCJRDurK4lyK05Ykl6gAmiIcqcowzrFCY4uEP/neW8wvzjl+eonMEnCCNM3Cfernf5paM7uaMZ+f0Wo4P18wHo1YV2vuP9hluVhjjLdrtG1LOS5Yr2qyLKNp26DFoVmva9I8o20bjg53OD+9ZjItqKqayXhEXuS8OJ1xdLjD1eya+/cPOD+/QuWWREm0dhzu7/P0sxMO9g6Zzebs7O1wvVhyvbjm1cf3sY1ByBKjaw7SgoUTvPNXf0W9fcibj77NX7/3DuqRZHa9Yr6Ee4db/OT9E0xrsXJM2bSURUlRWJxbhX5K4aQDJ2l0ijENxhmUFORFQlFkvlRLghNBoNgFseuu6xThHolgJ13/FG7q8EhHMMaFvjOyUWJpZ3CTc6Jn5bigYWRACosNYKPRBoGhyArW1QLh4OxsjRWTAJj0+3cb/aiHKayLQMwm+GI7RlU87sDk618TPZAjQqlUZLcFlqPA9TTw2H/KBOdsYNoEVlwAWAVD9k2H7Ph/pbiljNvjWTIAQbGPjcC6C9TFcL5h8sF1gFMEi1S4DrHPHYLEdvO7LjgtSelZN873AUpBkaSk2QihBIvrJWmeMp6OPbgunC9ZCm5QnT1tnKETnvEpAsLlm93iAkhDKEOMt41wAmF928eIItSOCHS5QXnwHVhzF3fxLyW01i91E7pZavLPGd9EQDhNU/7yL/9yE0AQDW98VzLe2mQwHB8f8+TJk41ly2XLkydfXoKiLByO2y9VBf2iiCzRPixPn77gJ+8/HaY/v1Ts72dsb3+90rPEwr1lYNQMLu3jxw/5zne+s7GutYZnz5596TatAJkdIQ7ugJq7uIsYX9htSBkp3zejdyaCMDwPCUMEY/zvriP5dctFP9T2CZAZDIBjsjDcZy/gKKQgcYbEwWQyoq000igPUNDP8ELATqQNfPOe3h1phwKLUmGmUspQeiRDaYZDOD+wVZF9EyniITlxzvlepQOqGI7hN5IvAaRJirNhltx5kV0ppQdcgq2qBJzQIBxSuCAi3M1197PyQm7MREfHLRHbz3lGUaujppDBWMhTX1awqgTvvvsM63bY3jnkcnaONo7lYkXTtEjpS8ys1R74kCBVAsFiuJ9djqpDrtNOMDZ8Zg1t6xXfdd3QNpokU0ynE5RS1NUaIRx5kXsGjoX1akWaJLTtGiX8jIafWfaAjVIJQvbUQi80HO4SIRHGePaKC8K+MRl2URhXoo1hva7I84JV3XCQpDgk66pGJQodS7hqL7SqEu/SVdet1xoyiV+uBFKGxCnee7H0zhqsS2jaBJmMOLr/Gh/8/CMury+YTKdkWQq6Qq+W7KclxWSP7cmUsigpRwW7O1PuHd5jl5bJ1hbX1TZ//+4xbv6Ms7Tm7y7WfHpZUYiUrJxQFBKJRGuwaFwrEBhyZ5kKRS4dzhqkc6RKoJykERI3HZO1kldfe8D+H7zF+5/8r0TwL0k8oJjnib8Opub733+I4Ihl3bKuWoTNqdZrptMCo3XHJLIOilFJVXkB6bquKNIR1mpWqwVpnrBcLplOJ4zHJUmSeLZVnpIXOUIpL7ybCbZ3JkjltXfA0baa0ahgZ2/KeMczW3YmCZPRlJ2tlO1xicshUSXH54bLWtDubbM4u+D42VP+zfYBP5je551148vxasPF5ZKrxRVvvvYGy7VjvmqYljAeQzkSBGIMjbZUlbdBLoqUssxJE4lUFucafCmi2gA0el0u23UR3OzjwrMUGWDEexefjHswI2xHDPpKK7wNt9Y4a7AOr5kkLKZtQYG2voQnTVO00QgnmM8bDyMPmSaD+7hnrQXrc1wnZB1ZG52TU2TVxVPpAI3hud1gcAz4QRHEicBVx6ocfCuWnMYO0JfHDjt7F/rZgXU5PcguXfj+gLnjdxLdBl0A110PbIh47fwf3TGHaxtPMTojRjA8nq6zntnoiWB+4kBI72BmrAfj59dz0jwlS1PAMy47/OlGeVJHLhX+3rDW+hLaAPC7DjiLxyn6W3DQ8rEgFNf9FkCbm+vexV3cxa813I1/X/bZRnzN7JvPZ9S8+eabX3ubv2p8E0sso9jwTaCmLPbYmm4yQ549fcbZ6WabttrxEpmSW6EsFAgGsnSbIT7nFhjEK6+8cguoqUzJss6698AXx+3216bGmq+ndaIs5C3kN8g83oyizw/Bj9VPX3I/3gwrYHpvyvhrHdFd3MW/zPgaQM1wENyDFAK1MbjcLG26GXHuVhBp8JufuX6m18VSgQAOBUAgyw1SgjMByIjslo6xIzpdG89CGdDS40BeQZIKTCi7sUHoU8jgNOViORFEh5M4UPaT4qHspTtWBmCN8O3nDM4K0ixHSjCBpaG17kqwbLD9ddaXhkgBSIvwfH6EEzi8HWtnlUtfKiFc0zFLfNGCxElNrhJwfibaGnAy4eKq5Uf/8BmwQznJ+cVHP2c0nlLXLfP5wgMVpt247tGpJiY7nQaE84VZ1liENYhBkuOdfjy7ZqW9IK/KEp4fn7C7s81qtaSqa6bTCUhJmmWs1mvvDAMYrYM9t9csMaGEJEkT0jSBIJ4Z215K705jWm95boxPJqWQaG1QraEoctoWLq9mIARn5+cc7O+hrWO5bsjzDK01q+WK5WLp9V7qOgBtvjRKSclkPEbKIIAbQEDjPBgmSLBOsFw7xuN91hW8OF5ycblmvlhRNY6iKJmOCpplwvL4guuzExbLloKUMhtznjqeKYUUCukk337tdb432ee94+fkW9u89fqbnFXvMU0VjQCnNTqwGlLpGO+NSBNHsqzZXRpE7UcHEkcpM5yDlYPzpoG04N2ffsgbjx6C8hbaCFDSaxuVZYGSEitaRoUjUZbRROBEirIJgol/zq3sxFJrQWgPgZQWSJG+DoxEeVFEqSY453jl4ZgosGqxSCX4FtvBHWg3AGI7KBeeO+cAiX7rEJTFNQfQQFtZ1ouGq4s5i+s1F9fPeDZbkO8ckE5yMva4ePKCp88+5l/ff0C5k3GyXfB3f/1fcXaHVx6/xdNPjqlMw/b2lEkBo/Eh+4wxZonVC9JkRJlPyQvj7zfR9mWOUvpbMgrxEFkgsgMBbqO4N8Oz47pyqM7W2m/HOoe2llZrrDFo7TVzPCsvJQ8zpVtbI84ujX9urAd9syzDWYtUHhTT2njXqA4k6nVqOh0oF/u9UObYtX/vQrc59naDzz9nUO5ufmfQHi5az0dAIbZZz6LzzTRgZQYgvB8T9u3csz37Bu/4I12HTcdQ8X2cGHwuuj42AlhxI/22Rd/nh+s8PB4HSOUFn5VKkULRaI2Q+NJC6wWb0yQJgM6Nm8MFkXAZJ0f828iYFmc0DBg9XRsNAa2u0T22Fe9XR2QE3bxAd3EX34xo2/aW9omU8leyp7YWVitH0zQIISjLgsmkZr1e3dKWyPP8V2Kf3P6upK0LVtdBlDzEai1ZXW+ep7Upzkm+bjjnXirwe3FxcUvroq7rr70f8Lo3N9kyWuuXip0q5Sff1us1l5eXtG3L1tbWrbZqmsY7JrYtSilGo1HX50aR6dFoxKjMqFfiZq/prbDNAEFwUNcpsHnvGGP4yU9+srFMouF6ys5oE/wQQvCt/HtU6zXL5ZLVaoURlt29nPlijbOezT2ael2h4WsuMZKDs12OZgIroJXQKEhQiDTnzNZc6znPM8tiVKAV3JTDvXfv3g2RXMveaJfD7TcRX1b65BT1KsXaTfCprirqG/eJNYbqxj0hgPFksgFoKQfFvCWZ16zXa5bLBeu6oqoFP/nZFfPrFZeXlsurjGXjyNJpr8sfYjIZ45y/XlVVUbU14+MF25MXpJmjLLNO1+g3ITr9m4jx2JunxHNqmoaiKFBKkud+pHt+Pqeuv1yAO8bGvXwXv3fxhUCNkvHjIQDjox/wugAQ0M9GxpWk/6Prw12YcR3MqAokQrqNjj4CIh2LhX6Q7MeghjxPyQqvewEDAWHnsNF6NB6GdH4wLl2YkQakQkmFUg6rUupW01rPJCDYvYIvI1BOkiiwQiCDXk+fA/SlQLHkyB8P/ayzUF7k1rW0pqVualqd45z1Oi0WRLA6FqFcwgmJsL5UhZCk+pn1mITE9gvHhAAbXU0EQmmstUjhzydNFZfXhh/96DMWy5Sd3W1OXpwhpERry3w+xzlB02qs9TPwSkmMiaCHRSjlgZooHguB8RIdVQKkJQRpLIeyBmcsVQ2ZyHl+cuLV4J2jrlqStMZBYKkotNZkacKqqpAyCbPdXvtGSIJArUDrFmMMEq9dJJUvC/ElSwPBYfpZE6m87oo2Xon++fMTdvf2sMZwfnnFqCj8izAwCQS+NE1ai5POH6OUJKlEJSCEL/fzZVXCC/GS0rZw8uKSJD2j0ZbVcokxlraBql6R7OZc1XOKVLL7aIrcyVg9OUfMKsx1C1LSmoZRJpmWYy7e+zmT73yHN/7oj1nv7PCn3/ke43uP+Jv/+p/YKQSLK1hVhrwU/Pf/5z8F0ZCNUsaN4uP/+I/ka4tyglQIUgtaKWa25Uwb2kRxdTLn2WfH5HlJY01XXmetJc8ylJBYJEr4Ug7hrC++sTUkvX26dBIpHFKZ8PCnHsOxYBIP1hltwCl0CwIThLUlTnhmm7SAtCiXIEkRKGQQmBXW4VpDXbdcztdcLSpmp1cszq6Zna+oFjWmCoItwlHlitzmOCe5XmqOSsXPrp7xg7Lg+0dv8eCVh+xv70A54upywd/97T9B4shyRZGPeeedY7a3FY8ej3h0f5ekY3MtAF9qZp3XvHLWP7M9YusBpY4I0nGvhkP0CAtIIijg3OA71vdl1mqsCfbcASBO05Q0V6Fyx5cBWqs9eKwEDkMbpH3SLCFNFbGcU2vr+yZjBoBQ7Hd7Ad5hf+xsD+AIEdCWqP8SgeqO5TI4ywCoBAy9Y6/07wD655SgXxNB940p6KhbFt4hNgBCwhL1fIaeff33PZvJt2+EQYLt+RA8G5RudRpp3ZUL5zYAX/ojc8RSNv/GGQ7u+217y3bpgXbr29eLVHt2pG5bVKa4OescASnn4vb9dqX0JVXCxcLfWPbU3Tw4hsnP8Kjx9+ygjb+Js9138fsdy+XyliNNlmU8fPj1fZObxgt6Xl8vePjwAQcHe2jdcHp6eusZuH///q8M1Gxs0ymW1ykvnuYMndZWK8uLFzfAJwfw9ff9eYDWhx9+eGvZ7u7X1ykBXvrdxWLB+fn5xjIhBFnmJ8NOT085PT0F4C//8i/Z2traWPfq6qr7fp7nHB0dYYyhaRr29vY4Ojri6OiIrZ2M2aeSDU14vJjwxr3jAC2AzWOVUt5y30mM4+LHT1lebwJaf/EXf8Gf//mfc3x8zCeffMJnJ5/x1K64znM++MV76MWKbGvCW99/RBlMKuJrRgLba8krMw/SLDKY51DKnGK0zUfVFQt3xWoEZrSHXdwWjo1mGV17Oku7mtE8f4r6MqDGZizOdrDtpvjtdLrN/niTv1JVFcfHxxvLhBBsHx1ugpkO2rrm4vKK58+v+cUvnvL89JiZWdCwCdKZLOO1V79Nozafsdd+8MeA4MWLE06fPePs/AWX7z3FffSU/QN4+HCHo6MjdnZ2fmeAmocPHzKbzTg+PmY+n3N+fsH3v/+HZNkZW1s1Ulreeecz/viPv8cXzNrdxV108SUVk1FMaxOoERt/RQBFcvOmE92yWBfvl/ZD9L50ZTjAvQmC9PvqB/RpkniBzPUCqyObZrhGmIkUPslw0nUOI35Q62c529awXHuL3tnZJQpFWRZe2FT5wX2iJCrxzlKp8oCEH+N6UEIbHexr8S4eDpwwqKTA2hZtBU3jhXz9un4wHQcBq6plsWo8SJP4dvMOQhZvTe4H5S4ANhEUkSE5cXj8yc8K26BVYUFKjNAIUi5O1/zwH59xfmk5OrrHYj3HOseomLAIIE3UWEhTX9YRX4zWtsQsyQuM+uTcEdxhwjVSyosGp2mCsM7P+luvm2MwNG2DrR0np6c4Z2mbBuscZVnSau3zHWc7Ny5rPSsA541vo32mUpIkSTzKHFgzQsow8ywwpr8PoygnwLqqMdYipWJdVZydn/P+Bx8ymUy4ns2ZzxdMRiNGRe41VfBlDat1hTYW6cJ9gGc9JdIzmJwzWAyOlPF4m+tVhXYT5leGq/nMC0Vbfx9mWcZy1Xib9HHB1tGEnYf7LMuE2UcvMBcN69qzp7S2LOZLxpng45/9nHvffpO9e4+xGv7iB3/GweGY9eoJ//H//bc4YWi15PjpC16cnSOcY2JTyiZhJFqchUQoUpWwcIbywR7t6orlYk42Uqx17kumhNfc8UCdIk1SpACZGKyrIbyAfbsqX04jfdLnhGeVpEE7yFqBJEUK6dkyQpBmgXkDGOHvc2scWOEZbRaMLtDasW4NpqpYXC+wSkFjWM+WrJYVJ+cLrlcGpy3CthhrEVbgpKRx3shcFSOqRjIeTTlZPaEYleyPp/zi+XP+hIB3xwABAABJREFUYG+H8bOUh288pHzwCmUyYbK7x9//w1/z8N4uzbpi2SwZb+1ycXXOVjli90CCaMFlYBOQQfCYwKKJ7D/8fez7idhjyq577O7Obl0RxHBdAGWCLbbzzDQlJSpVpLG80EWnBBMEbMP6OCQKb6dtQPkyLGMCW8aBsw6jrWfUWG/v3Yvi3ihj6sqaosiuDc9YSOytZ/QMWTYdsB763c2+OwIlPbi/8UJxAy0bBkyRCA51lMiomzZ8N4URsfCOdx0TBg/Sy+HfjgCAux5bEpuz3JthO7DGxRJTGwEs0ZU/eaStf+fFk4tCyFqDEjlYhwqgsQddJM5YdGuQSnRlpBBYLyhwEfj3TD4pvAaSL3ONrNGoyeZuwFbd5e8BuMDUjOyloRPJXdzFXfxq4Zy3f95gmjhFtTbgUoZADS4Ny34/4+rq6hb75rfrcCNQVqFMuvFGqGY1F88vaRYthSi5t3MfqVrcjuTZ9AWXq4bdrT3ub9+nrqvw3vWRasgMpAFPUa6f17ACjAQtoVV+DuKr4ubSea2YL4X0rODx0WOk3dtYvFgsmL3YdPeqqurWMuccP//x+7ecig4PD33p+tpQFlsc7giy5YxZNdtYrxEpmhQjN09s//5jsixDFWNcUpCOJrTHS+brTaDoLu7i9zm+EKhxsbxpWM+OuDUM7OjesVsLtqb98HzwWRA+7LfVz7DG+vo40bdRXuWnOgERnJwsRVmAWoAeAEmunxkF7/gjlQgz+yGhQCGk1yO5ni3RVrGzu0WSKJbzNXlekuVJNyD2ZTW+qkEFtsFwIK1UMphtji4uEqlUYMk48kyR5SK4pfjzUEohpGC51uwYD4oJK9GtwBpLlikS5fDuMVE3gsB2F11igwMpA0AjHEJYhMuxQqOF5PxixXs/u+D6WnHv/itYZ7lezCiKgsvLK3Srg2aOd2CKAFKaplhraVuDC8yVCHwMrWOjnkWSJOR54UsujKdOeh0e39HrpsEJWCzm5HnOar3GOouxXlPDaIOSXtshSVOauiHaFSslMM6LIXsgIbS58aKpllD+JL2o8HBie3icbatJEs/0qeuaF6fnLFcVWOMBp8BKKouM0SghK3KEnLFarSnTnLLMAojnfFmPC/e/8An1crFgsbCs1pb5ogGXkGQZ1arCGct6panXLUoKrvQabTXV7pit/X2mJmVhz9BXNataI0x41uqKsXGc/exDdGOZOEl6/4jvPv4enz7LyPOf41iRSsmnH72gQZMYhcun6DwjXZwyEYJUJVgBlTKo3RF/9mev8w8/fp/Pnp340jjbIqVnTflyN0Gapj4JlGAwWOMBNeE8MAICK8GK4HoFSJt2CbxD+3VEaCunIDBvnJPoFkylMXVDtapZzmtWV4a2bjBVy/p6ialb72iUpKQyRSQ5u5SMJxKpJMZZaizGWZqmJTGWSgiyrSnrqiVJBaPphKPvfZf0bM7sxz/j5NlTHuxssbya83T9EdeLlgdvvMrO9hF1VZHlCfVFy/X8itffHHF8cs50+wFJalAUeHFXgqtaZOsZrAvsDuFLuQCwvbZTf0/aAMhYDyx2faBDSUGaJB1zTzi/rVgWFP2PnAlpuev7UxtAMv/MeOcyZUNfGvouY4xn20U2zKBMyXYgRwRq+r/js9yJqIshqE5g2cQTHDx7gwXD/r3HaBydavHwy7iNAWsU71Whj41wjSSAOL7X3RDQ7aTmoyX3cOeDfQ3P8/MBm+H16/vxfuHNY+3LbSOabqxFty1plqJkLzQtpcQENqgQveZXnIAQof/1g3+Nko0/bxHFpmV3D0XmUXedBrGhGec33LfJXdzFXfza4vT0FB0Y3wA4xWo1Br6eHfK/1Dg/P2c+n28sk1K+1Ob4txlXV1c8efIEpRR5nnPv3j2mpSLNW652X/C8cjzevc+39u7z2ZPPaJt+EDoEaX5b0TYtwm6W21xeXnJxcbGx7GWMGucc73/yPtZsAjXXi2u2JltIKRmNRkynU7YWW5Rnm2LAVSExL3mtRifXuq79T1N71vVd3MVddPEljJrITrn9hFk8w6S33JbdbOzNpGRYvvT5QHHEhOMoc3gM+MFoV1ogESiKPCfNJKbRntnASwbOQachCEgghC9VaBrDbLakaSzFqEQI2N3eYr1cohRkqUQKPyuulPAindJ159MlUQgQqgMD/OdhdjxYZRcoWtuSZY66dlS1Bgdp6q3Ft7de58G9EiUNs+Wc//yff0jbNPzBHz7g3sEU49YDrQTPDBrargiUTwydiRPkCBStGfHJx5e8/8Epzm1xdP+INJc8ffqUuvZ12fW6IcvyjpqvlE/k2taRZenGfuOMu3MBqLORNWWQUnmGhQ4uUIlCOOl1cnBhxsQDJevlyie4znFxcUXTtBR5Tl3XJJ3DiQfSwDN1nLU+mQm1mtEC0NnEf5b4W9knoW1oHjeYaO+TSGNCuZy11FVF27bkaQoBiCpHGYicvCyZZglFkXNycgLSsT0d4x3O4v1s8bpC0pvCC4szLfV6gbOWtmlxwlvnqiQJgIhle3uPUZmhm4aTixUvmJHVlrQYI7cE5rrCVc7PtrgGozUTARe/+IDGNOz+yZ+S3H/IG/e/x//1PyT85//f/4OdEnZ2p+SFxDUltdtmebmkOT8DCypPWTvDtXQcnx5z9Moub775mHsPjvhP/8c/eZexUB7oAtsgSxIkgqoyyFi6oTzzQzoPJDkLVsSyFUEbWBgylINYrbFW0daWet2wXjTUq5p63lBdr6mul9hK46wAY0mdJVWCFMm2UKQqY2RBV5barVmYBY2SrJ2g1ho1yhGTApfA3r0R+w93+fh0xqoVHG7vMclKJtsj5HhMevQA2pbPfvpTpof3uK7W/B8vnvHxxSV5VnB2ec6b33qFP/7B97m+viYvEnZ2p7ha8/TpJa+/uePPHZ9Ue9ZdYKwIhxAZOOkBtgA0O8Ba4cscTQBc8OCLEF4PS0ZxceGVi6Mjk7WeTWIJ1u9EBk0EpSOAG/teX9rEoC+KSXicDXP0gG8sf3lZ6al/1sPzLqIIe+iVRexrB/umZ20MQQwh+u0LQIb7y7kALrsbeirE/jruKz5rgTkS+iJB1NUReHtSz8TpXQADP0fYHljuAKPI74l6LfT7C20dEMkB6Ojb6yZAs9k3hkmKAJp4CEXQOsjSlLbVOBxlmfs2tBYZmKMylJP5y2Q8YB4YQgTmTXSiU8IzqkRgqvVEmcGkSodHDaZRNt5R3a1xF3dxF7/mOD09paqqfoFLsPUD7oCazXiZbfPW1tavVI71m4gI1Ozu7nLv3j0ODw9RWyPKdsFy95SdCh7tPuC7uw9YffSMddWzhDLjRXe/IlHm1x4O+OEPf8h6sal846UNNoGRlwE1ALnMuUm8PDs/YzFfsLe7x4MHD9jb26OqKpJkM7VcZYLnLzmuH/3oR1RVxYsXLzg5OeF6PoN0ztbeS1a+i7v4PY2vYBYXh9/i1tLNz92NztZ9zu/ixno2AEHRRoOXDh79QDsMnIMLikoEWZGwXtbdADQOkIcbcXHCFYmzgtZa5ssVxhrKssDbnhqUdKRKorDevjtYXctAne8JPgGk6RaI7rM4JI4JhRNe06MsJKMyoVoLqqrGGBuEcVUQJ9MIKrStefvtR6jEsTVNcNTBSlx6y258EugH8H5fPjdwJNIL2Tqh0Mby4Ucn/OKjGmunjMdT8lyxrtbe3alucM4DLBC1XXwy55NDNUjyBnPfXfmYb08PWHmQx7OLUqSk016QIREtywJnrRcRrhraukZISdu2LBZLnPMCcs5aGmMpigxtTMcccq4XohPCuzh5YUzjGUZCYPOctm1Jkr5EC+e8Zk9g2/jJe19SZa1F6xZpvbCqEILZfEZeZkymE5I0JS8K9vZ2UYnk7PwElYDWDXXlkFiE9CwfqRKsbREYtqcF9w9Lrq/XzK4tVVWh0hJna4z2tvdb2zs4Y0gmE8rskHW9pJ4vWK/niKYiKRRWCtqqYiwdGIPSjS+x+uwztIb7f+KwD3Z5/ZVXSf/yv0OsnqGmhiyVnH624OcffMre9hFuPCJdVTjhqAQsleJ0vuaTv/sxeaJ49PgVVJribIM2xgOTge2lpALnaBpNlvoyQIcNwsHBy8h4/SQlUqyxuFahW0O9aplfr7i+nLO6XKMrjW4twjikhQRJjmQbhSIN97WlcD75VBIa3WDbNcvEkk1Kdg73OdiaYNOUH/7wPepmzf5WwaO3Dzh6uMt0muOyFFvk/PidJ2Rpyc/ef4/t3RH3Dx8ipyVPnSZVgvqjj1gmiplZcb2cewZTIri8Oue1115ld3uL0xcfYBrBwb1dfv6TY45WE0YjjXECQdJ1MwKJcBJJgiXxz6YBbbzdvAtaNlJKlPIi5E76Ej86AMF6xlgsCXWuA3X8k+iC7fwmMNAl584DtLo1XpsKzzJD9K5NAIkKFtiD5D66UkXovX/ub/cBEdiJ4MdG3Ej+e1B7oGcz2Hf37oj9aGDHeGku2/W3xOmCASXHu+R5OMS5TVDq894/PdDEJrEmtPeNV1V41wSAK4A0NuC//flH2kvXoPijisxTX9okcokxDVmWolRge0bAX3hAJ5aURhcvGcEaQEhHKoIosdO+L3aubzlnB4fvuusZlw2vQTz5TaWgu7iLL48hozjGy6yXb2pdfd19DeOXYVlsTBAK7yKZprI3Q7C2W2/IFB6WHn7VkFL2fXH4mc/XmyU8TpFSk7hNIPpXbaNfNYZtMVz269juV4nVanVr/3meb1wT0b071cZ17da50Y8NmeE+BM5KDF0ycCs6VrwF9ZJ1ogByURQ458jznEk5xgEX6ZQsXXBfjrjnMnbWkK0GpU8G8rZLIL5W3LxOfrLIoo3GuU2w5UZzgpO8+/5PuLzY/GBva4+t0aY+UF3XLNeb2lBCCN547Y1bz99HLz5iXa29nmGes7W1RVEUtxlSicXaVVe+G+Pdd9/l8vKS6+trL34toNw8nF86XnY//3PGhkZmuG/dQPTfOUgS+fLx013cxUviC4Gaz39ZdUO/jWXD9fsB7HB7EAUOeyiln3kebufzvk8Y+PrZekM5yplfrbAmzo/2xx6HyiKgLNr4unxtfXlAmikykeCsRAmHs5qD/S3/PakDgBM1kQVx6rV7LUSgJL5M4plFFofvSoPat2A8zjg91SyWa6qmZassyLKUFyef8fDeNuNSkyQ1+wcCRIIUDUroqMnsmQ6AkCpMOocWlD7B9XhFQVvDuz875ZNPF0i1h9E1SoBuW64uZ1TrmqbWKKVIUoHWulN+dy4I6AbXpvgi9KKjtn+ZxesZkkRr265TAs80yLMMGco4ijxHtzUmy9DaJyFVVZGqhKZugVUoIYPFcoU2rU/6jSVRarBdGcAki0qCw5X1NsRpmlAUBVJphGhoWz9jIOjLorx2ShA5tsEa3vVJjhB+hmdUlkymW2R5TjkuefNbbzLZypF2QZ5bihxfdqUcznk2kXCeDZQkFvbgjVeOWKwbZtea04slqwoa4TCm5vj4Oc5atLPkaQ4OqrZGC0fVGmTbcliMKGUGVYPAer2j1pI4WD35mBdtxdEf/wGyfY1viz3+4W//CnYtJs84/viC5ckSwxNeEYpRorAY5tby9LpiPspJJmPqqua//JcfUckEoStUMu6f/UEynucJRjdgnBeVdhlaK5w2tEuDWRmq6xWL2Zz1dcNquWa9bFAiJVMZiYWRVCQiRSEQVqMEZAhUsA/XOKwQNIlAZAJZSEa7++wd7lEcjFHjFDFK0aGk4093BXmWcnB/h2QssbZG6hatHdXxBeVVjW1POBLgzuaIqxnv/uM7nB8fs5OlHC+vqbCIyYjt8Zi1sZ6pZQV/81/+ltdfe4Xvf/cPmIwN88WcPF9RrTOSxGtk+WdE+uuPxBlFa0XnoCQEKClJs6QDdgV9Pm/CM+M1fqxnygk/4LLWedHZMKD3DtSebeFshFJEz4yJDBIHbWuQsvBystJrKOk2CBE6yPKMqDcTj9OHBacYaJaHzwYgeGen7cHQzVdE1D2J/Z8LDJEbJUzd2h3MPfh+1KmJ+j2hkDRSQIasGOkFnJ2zvBxw6FC07vfYd3cwxueMnTdAH9t/2w37dwFEIfwBoOaZn35dKSQY14HM1hnyohhoZwUgGjzYTRCnBpyznqGYJF4s3VmMbQHTHZ8gauuEloxY02BSIRILu1aJbXg3RryLrxGPHj26tezs7OyWQ9NyubwlKPt58bKEZXt7+xar4pcBUHZ2dgAvLDsajfj3//4vePz4mL/6q3d48WKOUh9w7949Hj58yDvv/JTpdMT9+/c5ODhgd3e3Myb4sijLkrfffpurqytOTk44Pj7m6uqKFy9ahoQanEHoU2j9GPaLzv2fM66urpjNZhvLftVj+jzg7mWx6WTUH9N8Pmdvb4+HDx9yeHhIWZas1+tuTPL8+XOOX9Q8fOMeKp1sfP+VV165sT/B8WeWH/3dJbi+HE0I0QELx8fHnt2jNT8ojyhuOERZa7m+vmY+n/P+++8jpWRva5v/8b//H3igM+ql44O/+mv+5n/7X9gRfvopHkHiwGR7IL8+m+r6+nrjnASOi5MP+PmTv0cNgBqtHU+f3nAUcnC08z2OblBVXgZylWV567mTUvLv//2/v8WUeXH6gtVy1YFpxhjm8/mtvmAhDU+un1DfyDittYxGI8oylEoJS+vOgIqvG+fn57fKuf4548mTJ+jgdLu7u8vOzg4nJ8dUVcP5Ody7l/Bv/+3bjMcjFovFb+047+J3J7609KkvJ+rr3eO7cthl9MKZDFZ6yaxmsBx0N5d3L+AeZey3MFi7G3v7wWyRFyRJQl37ciARs+3B4NQF221ng6pBSNqVAiVd0MR0YC1KCpQQmBuzHkTIxvUzlf6fOCMcz6sfAAvpj8cGR6Pt7RRkQ103tI1BCUVRJOh1Q1MbxmWLFL3NtZQgbHihO9UP4EUUfQ3HIWOpQ8JyCT/96Qs+/VRj3RQRhEaX8xWrVcVi7mcvYvmD0cHqFUiTNNgi+8TRGLqZao8IR1TYdoN/n1D4e6RpWm+FrSR57q1onfIgmDYuCJ/6siUTLGlbrRE4z2wJ+ihGW1brNcL5jj9PM69JE3bqmTCaTGbetco24DyIM52OqevWC5xVFboN7leh5jlJEqxVaO2TZCWVFyJ2tpvRVlIym804OjwKduWWcVFydHBAWwtStUQKg0yTkFh7++RYHpIC+V4BoiYvHLt7OUcPCtZryXJRc31d0dSW9bqhWddUTY1tDLUzVA5m8xVbecpcAXnqZZ3QGOtQFhZtQykMq9PnzH7o2F4Lri8uGB+vWJ8tWWU5ybLmSKWUVrJtLLmS1A5mTrNUjr2jLUwmKbf3SfOck9mK5bz1+h5CdG5d/hFQNHUNZ9eAQ68MVdNg1o7l9YLVbI3UEtFapPOlLaVIGMnUv8At4AzSOKQ1pEKRyKgeYr2QXuJwmcIVCcn+FtuHOySjlCRPEJlCC0sjDa7x+0gRPNidkiuJrAymahFVg1s2uNrydjIi45IGixWOLBujP3vG1rKhSnOudUMtHbUA19S4LEXlmQfxTMbJySVSrinyI0bFQ7a37rO3KzBa0zagG4EQtS9fTCTOatJEkWSQJIokVd7NLibT4Znpk2YXZsFEyPGtZ+CFH+ts0Ls1we3JCwNb44WpCQxDjxEEcfNQ9tPqkKVbP3gzxlu1SiFx0lEUstuXuGUJHUCH0K/5Pib2wuGY6PWyht/z5yZu/G2DM5XvvD1IEN8nA/2awesigvQyYjOxT3XxBRCOJ3a1bhPovxnDSqX+bUaHmA3fVPG9Eo/fOTpnpchs8d+4mcDdsIgVPSBnHSiVepcmSTdx4AHwHhDdeKNGu/fAsvJsLBWYexbbaV9IohW6L0kNjDci2NO1cAdIW1TQt9lozru4iy+Nl7FpPi9+VUbNcOLn635/+LtSKsxk+/s+zu5H7aubs+BfNYwxLBYL5vM519fXzGYzZrPZS0GeDUbbNyh+3WCRUqorTx8ue1m87H66ee8MWTWb69mvdr0cZFnC1taUoXBzBGrAC+suFguq+fwLL0/3jjYWs6oxz87JLlfsVtDakrGe+smowUYkkCebExa/bCyXyw3NI+Es+XpFZixi8D4yxm0IGQNepiCMd79K3LwmQgiWy+Wta7parm6BMi8Tgnb04/+X7eemacyvGr8t8HPIqIP+vu306sJh+UnjOzbrXXy1+GIxYaAfAIuOLNIPUt3GAHdjsNzNSA5YNmHNOPDuQZhNTDeuJ8MObyLjcS2JIEsKRsWIejEPM5LeeUUbDVIG22TC7K9ASq8noqQiEYl38FHCAxZKkSjpgRLrmTIIMCGhidoHUg3ONWQLLjqhDI7VYcKA2SGdYH8/Jc9hXVfUFWAFeSG5tpbVWrGzNxAitRrhVGh32Q0mfLmFQEiNExpJjnEOQ8KqSvm7f3jGZ5/WCJUjMEjry1batkVIgTa2mxW2ge3jrJ/5F7JPFmQQ9VVKYbQlSXziJ4DWGowLrjPBM9i6XlzYWoWUiiTx4pe+zCsnL8aoNKXVFVprjLVUTQ3Sg0Fa1x7scAar47YcrTEIpTBmyNoxJCkBzAkuTEqR5t5WXClJmnj2Tdu0QbeD7rN4qfI8AzyrSDjP90oThQRWixVZXrBcrimKnOl4jC0ahHZIVoG50+sxeZaRwDkTcscwoy4cqYKtscPuZxid09SC6+uKy4VmObdUyzVVXXN9usQKSLZG1ELQzCu2M4lKCkTVomixLlgsO4s8e0b9wzWvv/Ut5P2HnJ48IV8LCpsyr5cUQpIlgbEDnNGy8/oOb//pY5aLBZ9+MmNS7nB2MfeAFuCkReFngXTV4oygnres/uYzD7ytNNoJrHYoHLtpEgS3ldfTcMoLRDs3KOlQCCWQiSBPFFJJ8rJA5BIxztA5kElQEqsS6qZi1ax9H6AtrmnRTiBVirSgWkNa1eTrhsQ4tDPYqkWtwbSaFs2uVGiZYqTE0TK/uGClDbXVrLC0eYpTCi3ARlRAeBbZulqztTthsuU4v3gK6pwsO2BrK0O7FyihfGIse40o5xxtbdCtQ9QB6A3AZyJBqSQ83yL0DTLs0gXWhvAmm0L0ltlWBJ3dqE/lBX9dYNv4AYAM/WqLdJJGpyAltvVubXWtWa9qryOlYGcnA2s7oKjrcUXsnV1g88RuPw45Q2lNxE2ia5V1g20MQX3Xr4w/J+tiqWvcfkRnTFgvgk4MgJ3IrhHhPK1n0oRdCPp+9yY7sz+ODlbuirxeNpRzoTO33XcDZ2X4GnQWb2MevyS7f21g5snuLP29JYRAN5q8LAPQPgCHQpIRWTLdcSmJEhJjW7RuyZQvQ6xWc6RowJpwD4T2jdcOiwtAHp3WUY91gUS4cAzGMyXvBo13cRdfL7TWPH/+nMvLS46Pj3n+/Jirq7kfc/yeRlmWL2XK/DajLEsePNh+KVAzHvdM4nmSk7QJ6M/bUh9u3XD+3sckrWPHCabjfdxoj+fXzzG2Z7kIIP8VsYOLi4sNBobAsd9cc/CrbfYrx8nJyS1A7PT0dNMGnd8eSHIXd/EvNb6CmPDmPwJCYgDRXtuLTw5fSsPfh6i4By+IA8ruo35WsNuXiH8MS6pcKF2JSYyfqRyNR5yfXfnBZ9isVB608eU7PfggIJQORBTauzbVVY3LEhACExgndOwe0Z2WlApr6ayi/bHZl3ROMfGLjBTDzk5Joma+1KhqMQbKIkcqaBqLcylCBm0VEeyqhUTGgXjEhoT0yYFMQWYokVNVhp/+5BmffTqntSlK2GAJbTHWC5QqpQJwYnDO2+xKKUEoYp12rAO2xiCkQwU72jLPOqckY6RPjK3dmJWKbSylRGvtLcmVQklJVdcgHKNxQUbKYrkgCRbQfua3n1X2QFVI8qVvO2tNRycEr1EjVUKiPBDkRASXBGnmmRxppjxQ4gSr1Yqqbgczgi6UcVmSJMVa0c06JCpBCNmBN9YY2qZGZIo0UQiR4KxCCDO4N3utia5MLtzIUvqZc++oY5GZJEtTRuMpe1rSNJLlfMF8seb52SV5mpKmPpluhOCiqUhHE5981RbdGEYOaP21FcsZT95/l/3HD8nVa1xcXmLOT7EWUinIlC97WtuWdJLy7T96i7RQ5OkOmG3+7h8+QvjcMzhfgW0NumqpFhXCCEaNZHVWkU5yCitolUQmvsxHWOdn+MO1Uc55a/jwpEspEcpBJsjKnHJSIrIEhKRar3FNg6l9+ZNyFmEgKQtkUSKyHG0sQvtr0hpBluU0SQso2vMF5brFYjFCeHKZ8yLMbWPQwmIErBLHzFhmwCpRNEmCkxIbAEsnPBPIj85apMjIs4RqvSIVOfX6mtnFBdpMyfKave0jmjbl+vpqoD3jgUMpwAlL2zY0jcYaR5ZK0iwjSfy1FYCV2gOPBCFZwAmBNhprjC95EYpOXFiIDqjBiYGqTOhnBWgraXUvk+uwtLqlqtpOl2lrKw+Akeq76A4QF8Tap15uV3bX9iaLpgdjIvgRwBRnw/dF93k87r7eyHUuePGNIsRgm+Fv21FnBrwV0YMwNvQdsjuM2JeIALBHxkk45s35g43wgJgetGyYLGBTMDkWHHWMlSAg7cvRTNCx8aWQ1sU+3LMWO3BK9IdxGyiJ7x8Qwust1FXDanmNMWtS5XUJXHddBEJJvF239c/e4NJ4LNADNsTyVtFr1sjPa5C7uIu7+MLQWnN8fNwBNcfHM66ubHBw+20f3W8nyrJkOt0s87HWfuVSuN9EFEXB/ftHLwVqRqNR15eOVUZ6soSvALTZtuHs+FN2xZid8Q67uweMRiPWy1Mqu6kbk7rPfe18pbi8vNxoP4EjTa45yH6FjX7FcM7x4sWLW8tfBtQURcHW1q8oNHMXd3EXXXyxRs3Llm28eXwS0dttx47t87ojP3DuBvvBOSSOzeOmP+/bceAZ2TsGn5SNt8YkuaRda4TzAp1KeYed3jnIL+8SmFDT4+1eoSzSMEAXNLohSVSgrgYR2jBPGkbtOCvwlS6iO++h85NfN7iSOLAYtrYyylKyXDrmyzXaQJrlpFlK3Vi0TUmVwmvwRHaGxKJCIpAglSJPUpTcRWU5SElVS979+5/xwQcLmjYlyRTGeGBDBYcVpTzwYIzuaMBKKW9nbXpHGIIoXtvWHuxIUqbjkjTNaE3jEyS81kIbmEYe8OivnAdwDG3boGThZ5O1ZrUypJkXUM7SgtHIz/pWVUVd152IX3+cSSfSFxlRsezJGItDBhvjaJtuka0J2jsJo7T05SgqYTXKubi8xhhDlmU4V3iHKGO8EG6Wdba53gGLUI4FiRJY2wApzjUI6XwCLVSfOIbr1blLheQolo1ZPBgmAiPA4hkOmYAkSZmMc8YLhbMNWT4hTVIWyyUtltq0PLi3T7NcYa4Mxklsq3FC0hiDsyucaGl/8THf/qMfkCSCi4tTkiQDZ1HOoYVj5TSvf/u7FKMRJ6cvmJ1ec3Hecn12SWs1mXMoZ3CAXq5ptGV5cUliLKXLUeSIVvgSQeufG+cMSiYgElApSP90msBwMkCWZRghaLE0TqKXhup6DVIx2d7GJSogcnj2lHO4ouCy9hpGaVFyfvnCl1sZi2s1R7s7jJAIA8potGlolMIIiVUSLQtMkeLllzW1hEomGAmNaTFSeKtxHCa6lin/fGtT0bSQZyVlmSCM4dPPTtjaKhhPvI19286Zju6xs3VI3cxYVWc4qVHOhJIlhxKCLDh9WbwmTbuuqNYKz6YxoXvwMIALzIeqqmibBqXS4Pzj3bhEQAJFRH+JYKZn7Qhp0Tqnbkoc3k5dIKmbhqrWpLlEYMlSTdP4fjg+XwRALSbsLibxPeLY98HxTyF7FCD20R3d3wOkHcPG+wR2AEgEwkV8J8SeeSCI63+PIsKbrJmOlTM4oG7djZdGZMmEtVy3+MYv/R/DUrBYhhp3EzW5PHY0rO/vr0d8Q+E8qKaNQTqFUik415fndvu4/XvP+fEC1aK7MoLJZAK2gq7N4vmH0iZnkM7hNY3iO0xinfOTEDYAW6JnQX7TrHDv4i5+7eHoJ1Gc8Am7S8G+5MelgPazgV+UXTtfqn1xfs3l5ZzzsyUX55arS9jaBnXzsXJftLF/OZHnOfv7+xvLtNZfEagJE7/CgGhAtCBbP8YQN7RXgkviV4ksT5jkW2ykPUJQFgVp5mjaBa1ekroWc7lGV5Fx+vnRtC3n9pwRh+Rmh8PxFgf7B/z0owTX1sMzAgGNglb68ZENt6J0oCwkNlh4O0haGDUwHvxrzudUJ732isDRblWI/ZtHdTucACM1rWq+fOXPiRfX89vLZi9ulT5ti21KtanvY6QB2XwFpCpKLHzDo8t31O1n2nqdw824e7/exdePLxET7geewGDg7n96YUbR3axuMAK+NUsYZzvjn274SzetS7/HfgZzcFQIoegEJrHIRDGejLmqfPmTxJfMxDpNKVU3W2yt9U5MIszOOrCmJcszqnUFbZh9HOzUhXOJAI8I1PXYJi+jjXtQIZpXe2p5WUoePpzw03cXLBdr2kYzLlPyMkMbQdsmpFkS9AMUOC+WmyQFWZYjRIqSEpxmXVWsVg2Vhvd/fslHHy/QZoxjjbXOa6tojRVeC8Ra29lSezaN7Cy1net1XKwxCBxZosiynDRNyfM8uP1kHhRRgjRNWa0qqrruGDDWaqKWjRQZVgqapsEGcERIwXy+pChTEL5cqii8iFgUCI46MuDBJX+sYYZeRAcAv7xpms4OPAJcNBqVOJSVjIsRRZ5TFDlZllHXDcvlkiJPKcsSgQg25RU2lk+lKUmSkCQJ2mjatmEiw/nbBikNHQAXU6fARog2xS6wBjzA12te+P/69vEuQTb89Hd8qy3lSJFmGVndUu4WFA/uk+/v06YJs8U1kzLBCoFtNaV00FqoBGUm+Mk7/wRo8sw7EiVJ4llUTmBdwpOPn/L8ow9YLiv0qsE5Qa5zslDOIXRDYRPK+ZrRqiURF+iPn6HWa6ZFAcpSWAvKAw1WKgQJWkjqNEEnklQqtJRo6dBSspYSZyW10R3W2coEpERry8XVFdoatra3Wa3WWCfJWkNrDUpfkqzmYA2JcEzGJdI58kQiRI09EIhyiySTWJUiZYrKp8xXgtOTC4w0aNdikIy2txHVmubiLAj4yr4/i9fKCYTIaVrJYrHm1UcHPPvsCVjN41e2KTKDFAkKjTZnGATbOzvsyjeYXc+wZgauCcwFIDhk2QDQGRN1ZQSQ+OcmJPQe8BRk2QhjinAfSf95ULT1uEhgdzhw1tAaP1iVUrFeG3ChFMsYnPJ15NoYlFOMRwprKi4ua38PDgCZzbKheE9HQEV6BkikjYneHUoIgRLeJUx0favvuT2LMVqSi66v6Lblhu8ZAsskllLG/jeC1v5d4a9deBcwYAV1sIl/j2y+SQjvFjFY/4aGhgsAktjUfekZNIJYGhfZNregobA5G9rFGuffA9IFpzrfHw/PN0b3Hhm8c+Ph+EWWssxIVIvtJgJ61qfFa7AJJwcDyaS7ltJ5x6/+MP070rkhNHUXd/HNiMVisWlvjde3uwkA/DJxuP+AP3o74dPPPuHjn7fQZLz22mNeub/lHYV0ztVpznJm2dlXpPlm/cv+/j47OzvMZjMuLi64vLzk4qzmR39/im6hrke4umCcgdRsJqYOsMWNhb96rFarWy47Sin29/d/rSWNzjnOzs5uae9sb293Oi8x+nHc5vdfFnVdb25TOEbTmr0Dwf5Bzb2HFffu1RSFd0zdDINKvkKNEuDEnJaPGCbN1louTpdsb2/z4DXLbH3KT9/7Gbv798kON++z7e1tJpMJ19czzs8vuLq6pG0Mb5gJDsOL6pjFp9dkxxlbTJjmvcCxFfDErTmpjmkl6AaaFayFJTewV8GkAS29jffDec5kbsgvTsk+uCZNU/6bNgHbC3k7HJoztD3rqm99O8NNmRgj4cezT2jSpxvLJ9OE8ejLQQQhBA++9eAWoP/W6DHJDd2aIs/RxvCLX/yC1WrFo4cPuX94wPJ/OUWbzXvg3r0RWTbUzXGcnsNiBVUFVVVTVZV3hPqmhS3BHDJkaOGAVoEe3pOGy7Mr6hru3YMHD8YYYzg7O/vGlQf+rsdsNrulkVQURScs/7saXwjUGNMMBt0EikBwHhqkn57+rRiQCYizpeGL/aA5lgMNkgTXsXLcxvq97k0/a7pZahI1XCyTrS2uL1e4FppWh1xCbJT0+FOIAp892EKng2DRpiXLC1/SQVx3eD4+meq/v3mMHXAT6fpRsFL4MpSHD0a88+MrFoslVa2ZyhGj8Yh6aWnqlMnEkSbOs0k622vv+NE0C4zWPgGTjlWT8ZP3zvnww0vaJqHRFmslImgagC9NccbguOnQEhNTX34Q2Szxxw/oHWmi2JpMUIkX4LXWd0BSarQ2NE3dS1dG9yQbZnJDUtlq42d5lRcRvp5r0jQlSdIAtnmhTGMsQkjSNAsuTd6+27kha8eHtQ5nPTvICEHrIMs0OQWJg6ZuSGRCmmY4JyiKEWVRsl6tsMaSpSl5XjCZjFmtKpbLOU0jAztHUOY5aZbR6haEDTNjvlxOCEssd9ts0+FLKII4/t+OfOUCQBBKJRQRtLKs1y1toxhvpVSVH7woYGd3B5RClClLaXBSYvIELR1NXTFRCdY45k3NrnPkiQdolEyYbG/z8JVHvPvue+yZnNnZJUqv2RE5yo1JlEHmOYlSJMozqHLtSJwgQ5LNVpz9zT/glGEigvaOtBhncVojHbQY2rJg7jQmy0nKEeumYdlUrJcNWZ5RJBnz9YIiyxiXBXWryZMCpGRrPCZNPYtsujthJS0ygSxPSRJBlijyvCQf5ag8lI4Ih3QjyvyQZKRopECIDEHG5XXL//P//r/TtpY0gzRPSIoRjV3z/OIUnEUZAS7BhdI+gQdFtfWaL8YIXhwvuHewxWpl+P73v02RV0hpSfDOXkKtECLlen6OlCO2tvbAHrBeX9O2M6D2DAfr02iQSBnKN43EhtIVGxyCjJUDMCbBdY5mUTh4CCw4pHNYp3B4gFSSslrk4BKMaZHOAwWL+RqhLNZIjg5LxqOcRLQIkdxgzbgAVLHxrEVwaJjQd0B2BI2I3xv8HdaWohdINgPnuAhOSSHI87Rj+AFB4Nx1g/2+LHLwDhAEVkj/xEnYsNd1zoNwkfjj3wvOT9YiCCbf4dtDB6V4juGlFt8ELj7N8f03AFc61k18J3rAKW4vCey/rofozqMvzxr2HeCJZv79BUkqsBKcbbs+aHCVQjvHI+3Pxx9PD7r14I5vDwYTD3dxF9+U0FpviKcCt0CCXzaKbIvdrRGfNAsWsxnr+RjX7jPOd/0KxifQzbphug1pvgkMxNJuYwyr1YqrqyvOzzRXZwJcDhRIvNwa5ubefzOhtb4FaN0ESX5dUVXVrWswmUxurde27S1HG2Ne3iBZdqN2R1iK0ZpyWlFOM8rpgmIypiw1Ivn6bkCOFYZN9ofBsqjO2Np7RDHKUNkl82bGaOc+WbkJqtnDLdTuLubMsdILripBqhyyzdEI5qZivvDH93j0iFT0CbyWMGtXfCoq/5owdPdHKiBtYBzWHbeCt4xiZBw0NSz8PXivOKJMRv35CMuxmfPEbd5qQsAN3IxWwWm5Zn4TF9hWyO0vFxgWQqDv794CavKjKeNRubEsSRIvqH16wZwFewdb7D/aI522JDewukV1jbiBvWnrjz/PBXmeUxTF7XvkmxAuA70Xnvu4DNDVLaCmXqcY489rNPLjrqqq7oCaX3M0TXOrL/yXwBb+wt58vZr32gndaFj0Y9cIvOBnfvsyENENAAkJfJwp7RyDQoJkXRhUCjkAV+JwUoaZ7iEa72d3OwUE4TDWkCQpSqXMrhZY6xiNyjB47rVPrB06nLiIMhGzoyLPaY3pB/9ic77UN0UsPBbdwD+KeQox6IXiZ/Qzx1LA4UHOaCRZrObMZit29grKsmS1uKauIFUTUuXLolrT4JxnuFjrmRzCU1qo25Sf/uSUn/70gpYEayq0AYHqNGhiu7nOLrinukeHIucIWhgDjRj8S1UqX9ogpWBUllzPG7Iso6pqpHCkqS9jci6wlPCCxIlKQm4TEzTjAQ9a0tQDQHXdkOd5R793jo4FpZQkSVL8jLzCWtNpWcRz8la3HmByELR4DMY5stxTorS+xjmBMY48z/w9JxPq2osLT7cKBI40TUkTyXK1xBlfBqOUYGtr6kVBhaAoc5S0XjtE2MDyifcP4dr3yWRkHAwTV9clZF67wlmJxfh0y2s3I5VCJYlX19ea9fKa0xN49bW3KKcTtv/g+7z345+yWK3YyXJGIsNWhkpacgQaxwTBKFGkTuDWFR/84mNsq8mM4UiWbBUZwiUUMkPIBuEkKiZr1lNxUyeQxmv0ABhhsK3BKoHBYaQIosoSjcOphEZZXJ4iE4luLOOioBSpLwFSMNoa0xqNMg37WyVGOlTekKWColQelBll7BeCNM9I89T/pAkyBYel0TXaamSiKFSOEKCFRQiFsA4lNOVI8fobjxhNd9jZmzDdKpkvKy6vVlgUz58d+5GNEL5vchJroHGVt6DHO39dXr4AjvjO269T5oBQyKAXgmg84OJsuEfXzGYNWbpHOdqlcCXL1SVG1whpvRW8UEhlwzOnEKE0RYS+xOsx9TC4t/4G6QTODkDtwAaROJzzTmnWOqzOaFuFkqkXD0egW8v8eoVUlqapeXB/j1GRgksQBKbhoJ+LPEn/3HKj741/h65TCIJojgdN6PtZKTwM4vVnwnMresA+ggnW+r7Ki5r3ZU89CxK08a5ZPQCzOR2wAZBa1zHZPPAEJgiXxRIoIVynGdO9q25sx4XyqiG5Jb4yvKBRD+B3TJ9+NT95bo0fr4m0E0B3Tg5KaQdgTffN24iJ8NQppLDYoKEkpOvOz5cTR2DGBXC/B9gQ8d0V33XdArrTuwNq7uL3MsTg5+by27FarWjblrOzM05PTzk9PeXywgH5537n9zGqquL09HRj2ecxanZ3d28AS8YjFxsWzQM0/uvE537Pbf4rBn992b6EB2AqbidS6wTSQX6oRZC8eUk11c3dOKAGbsIntQI52JEVvozqZqsqBYeHm1ttFDRTKG7gAru7E7a3bwNtLwvvVviyD24ev+smN+ISKeHwUN66B548cdQ3gJrJBPb3BQcHY46ODjk8POzEnr95cbPvcC9Zxkv+vou7+OrxhUBNkgwpgt1c6mCNvhzID9bjyN7/xznXd3whGXcbSug+EbFOEkUZoyMPfmy6Uf7XlxmJQPL2482qrVguljRNCwJGoxGx2MAFkKSfBegBp8jYEeEwszT1CY8xATTyTJbY5zhcR+/vzz/+Jm5sv/9UuJDUWMl0DPsHGc+errk4v+L+g21Gk5wsVzibsV5ZXFZhhRkALA4pLLgEhKV1jo8+qXj3Jxcg8wBcCKyrUDLxdr3OhNl8SxT29EmUn9kXwjvHiODWI6VASYHRDVZrWmtRiaKua+bzGUXhBVCja5KU/oWQZWmfEOBZMGmSBoDEa/747Ud2jA3lD16MOGolxFl2iDPiASwKyZm1jrZtu/IQrXVIhgJbyjl06x2RjPFJk7WW9bpmXdVMJmOapqLRGqUS5qsV050dtiYT2qYiSxWTSYFpW5IkpdGG8bikKDJUcG0py5K6muOMAWERGyyw/q6OwN/wTwBhHS7WtIYb2zrjHXqsBqdx1B3oo43hlVfuM5+vyBLFqMxxbcbSOiqraVrYTjO2RgmN0bh8xDpJaXPHGkGpBXljyGoDSmC1YSQkI6VwxiGtxWmHQCOsv79dKKBorCMN4GtMTo3wDllCOBQCEu+IZiSs2xaZZ7Rtg66XZIDTlkxJUpVQjEAVKaosScucZJQjS0FepljpEJnfHkKgHEiZoBJvyd46Q9U2JFKQpgllkvtmtf4el1agLKgEkGBawx//wR/zwcefkaiE6XgX214z02vefP1bCBTPnz2hSGsQgjQtKIuU0WiMdTVZUXDvcJf7DwoO70kSVhirgrU76OAU54zXqTKmxTmJEIZ19YK6zUmTgvHoAIRjPp+h9RJEghAWhw6lTAlR/TyWw7hBe7sA+EnnZ89ullmKwO4Q0nOz2rrwAAeKVq9QAhbLNdfzpe+PaXn11W3KLMG4lK5siNh3ByA3iLCbG7d31GbBdd5Cfv9Ddob1a0ac3jnny/vCeQ37cyEiAOWBT++aJAK4EDRZhCN1Aq0bLyAuIkA+eM5cvz2ICcGQXRJPoF8u3XBdut+dc0F012+7uyYR4wjr2qFQZNy/A7ChlM0PzKVMA3vQs2k8U3NTujcC+d1kBpshA+riXbp83xMFn+P5+MOwAeBz/SQJtgNm5MY59B3Ty9Onu7iLu7gZ8/m8AyGePz/m5GTG4koCR7/tQ/tGxWq1uqVHI4RgOp3eKsc6PDwMY/YYhutqTusu+KaHFrDgBqMFmGaQqP5vI6Ax4L4Cy8oIWALtjeVFCm0AWmKfXSXcwgCSRPHo0c7GskaBmHKLUbO7u8v29vaXHpNzjvl8/rlg25eFUpKHDw+5+baRck5dbzbKaCTZ2xuxv7/Pw4cPuXfvHmmacnZ29rX2fRd38bseXwjUKJl2E2+um4LsHZEAD7A41w0vRZd0227gGceh3lK2Zagv4AfHIiQFLnyHPmEQN5B4Ee1dA1AjBUWeUq0kWMOoLLy1rewQooiUEBOSTkgxFP875yUbBd6mujUBQFDBPldGcci4nQDw+BMmbqQrc4kD642BsE8w0sTx8PGU4+OGi9kl1foRW1s5ea6oK4NpEmxikInukqeYbjirEUpwOdO889MTGpOTJRJdVVhNODaLDToYHfgRZuJtcDWKn3mNGq91oKTCmyZJUA7datqmgTRltV5zfn6OEAKVKF+qkKXeDSlJgv7NAMwTYLTGOoNQfjY5gjKR/ipiOYQx6HB9lVKdWLCUnvUjJR39udOpCPdHd1cM2t5ab80egSNTWy4ur6jrBoelaSrGkynWwny+Is9yRkVBlgrSRIGxWKBuW4oiZ3d3B6lcSIAtKkkwNtzT9uX6RAGR9G3ZHWS0y/X3ho12xM6Gl7cjzxMm4xxd12TjXXSecO/BIfsHlrapWZiG9XKBUZK1tVjX0mKw2YSH9x6xUpALCdsFVVujjERqRyFT1rNLLo+XoEEZX7okhQpa4CYkxp71Y6V3QtPOIaT0NtvOy+46LCShVFCCSBWqyChzRTFOsIkkyRXFdAyFQpUZKktIUu05vhKUFGgZ720TthUefKkwIsECbVNjrQdQ8ywhSSSpAGP9PeOkwDmF1JBLSZoKVlrjkhFPTs74m//69/y3/81fcG9foUTO4cERSSbB7fHWG2MOD2TQDylQKUwmEiFaEiUosgQlJapL9L2+jFIKgw1MFg/WyI5Z4tPstl1g7IqmWZGnu+ztPGZdXXB5dYVzDd5syQbnsMgJiQ5qvh16ed0wQ4UHBkXf9RF7Uf+3pGoshozGGFZNRZklzK5XNG2NFbCzU3CwXwIalaQv1+wTHsi1eIHDm+OyoSB7z5ihB6+jQLyIzJ8ISwSWznBXOK85HN4RxG7b+T7JBfDKOP/sKOlQ0oM7sVTWuaFW2nBioN+LtIP9de+t+O7ZODssnm0T5hr8NRbG79MFxy0EMogCROFo3w6ia49WWxIlKYoRTa2pqwo5LhHKu3Wpl0yrxr4kvq5imZcMz4l1sc0tkHSTG90GOiQs0PNufhZ+714rQxT5Dq25i7v40liHsdD5+TlnZ3POzhzrBYzTuznzYTRNw9XV1cYypdQtJyiAnZ2dGy5BBn0+Yra6teo3LrSEa+VBmGFMchiShBzcYo58XhgJCwHqRp9cZNDcKGmqkttdt5SSg4NN0+7IqLlZafPLADWLxeJrAzX+mG5rS73yyiu3SlMWiwVaaw4ODjg4OGB/fx8p5R1Qcxe/t/HFjBqVdkO5ruwkmO7ii0IAgbAxgfaJnZR04AAM2CYSHE3YluwSb+ccKtiKEvbl2R6yG7D2kIfFBAYJ+HIBIRWjsmCZLjGN87Pc1iD8gfQzq90gPlq+um4wbowhSRPyLMNUjafMBx2L1miPX8ggYiwlIpRzeYAnzGAGMKib8R0O5lHgFALNo8e7/OzdiuVywcX5Nbt7BaNRydULzXJpKYoOHfNvf6sQTiGkZbFM+Ju/+YjnpxXlqKRuDc44nGtxTuBsgnMeqNiYLcZvKk1TjLFo7Wd2iaVHRN0eQZam6NawXK7IsgylJGcX5+BgVJYx/8JaE1hXGW3b0rZtf/3oNWWcc95hqStfcgPdIL/fuI4HaWTnSuW/7/WIvL5HsOOVvlQizngLhKfPilAeEexncR7omc/ngf1ggCVKKqqqYr1eMx7l3k5yXJIqhXGeVWKML6FTSiCVQes1RvvSLOFCiVzIMF1neCY2Sg/iWzQ+Cb1rTNTw6cGnPEmZlDnXC/+MrNZrZotriizl8nLJxcUMKQTlaMz5xYzRKOPgaJeiOOC7/+bf8d57P+JbbzxkPCqwRqOso1kuqVYrmrMUbWF5PkNXa4QwGN16MEL5xNgKAQq09ICDCjpBSZZycDQh3crJSkkySZCJQ6YSkUjSTOEEqMQLmjokToJ2XjzYuRahwEqvq+LxUoOTAiMkigQZgFtroTYtWjfkRUY5LpHCQwcSby2P8EKF1nj2lEp8uVUra6zyYOjOzpQ//T/9CY/ffB01ShiJjCTXLObPefvbBZMwsjamYDHXyARGpfFW2YCgQToQ+DI+65S/TkIjEjA26ZhqTrhwgYNuiLM4k2ComOtjlusLxqNdjg4fsVxds1xddKCmw4H1Tj1xoD8gifjecwg+iMjoc7FjDs5NktXSYISi0g1IQaMt51fXnS3241e2GY0SBK3vjG9kFh7U9WV5HVQUUfYBEN/HAPyOIEVfU0Tf+YVrHkF4XP9wiAgpeGDfY5kRHPOApxCOLAE/b2m7fh1nA4jes2siKN6X+vTA+caxhneKnzzw7egi5uVEACY9IOSZlP5ZlS6CTv3gUsSyJ+f7JISgqqvwrlA417JYzDEYJlt9ojLU24mtKqBjHXX6QQHwtcGxadjXdF2883o73SRGKOkbQDFhzsIft+ygQDb+exd38XViMpmQJAnr9boTAb4p6vjrCGMM19fXX2ndl2lbpGnKwcEur7/+Oufn5yiluLi4YHt7+9aky3K1orGb+zo9veKzz05YLpdcX/vSxt82QJNl2S0A5DelyzCZTG4l61rrW9dEa31LfyOyqm/GdDpld3d3sMQisgdMmwWTSdmxbW7qFf26QmvNbDbryq9eebyPNOmt9S4vL5nNZhhjyPOchw8fMtmzjOaSPAA1sWVmiTcGGY3GjMcjiqLg6mPB/HjzHLTWLNeb9tbCCUyecJNdef+VA4od304eZ7cUqWQ3BUSPFCmlbrSnB2rWU0huADWj0agbZ39ZTKfTW/pEaXq7nbxBx5rxeOxZsFKyXC5vHRN4ce6b27i6umK1WnXlTr+JfuQ3GUop2rZltVqxXC6p6gVXV9eUpeLx4wMeP74f2Nm/A+5Wv2NRluWtvu8bqW/0S8YXK47JNOYCfsDqLEIkfflRV94hwixgHMwPJuwgjo/jsJmoFRAZ2i4MMQkzic55cdVuhTi8DwmDhE6IUwS9jzzXyFSiWxGKQf0B+CSrd+nxehI22MiKrlxfW0saZiPb1u/38mrF7GpFmvuyCikSpLSkaepLhoRApRIVEvOwSy+KGdKPmOwIqbwzitAUiWJ7W/L8ueH56TmPHu4z2ioQ+TWzdUNelWRBZFMlBqy3Z22s5Z9+esYHv5gh0wnWaHTr28kzVsLwO8y8O2eQQ9ZQYNDEsiiHQWtHoiRp4nVZ6rZCGy/8WWuNdo6szDHaoJuWpm0pyxHGGIxxpMrPDWvjG1IgvE6EUCBtTBtQUmKlDWweD9horYkOVDHJctF9KoiORutYpdJwHQPxIjyMUgikTBDSsx3SLPGsB+OFkK3VGGNo25am8SCjbhvq9ZIi83o5UinSLEdl+cDhyqfdKpFkqcTReJaQMTGb647Fi0uHOyz8x4LXMLEutHm4DyNi5okL+NIRn+QqBa8+HvPDH59grGU02ubTj0/40z95k3q8Zr0qEUgOtgtef23KgwcTJqMJlxcjnp2dkpQjPn1xwcXFKaO85Lvf/jYP3ngTaR1NVbHz7UvOnx5z8smnrK6uKIqcum1pNFR1w969fY7Pzzi/WoKArEjYnk7Z2p7wg//2zxlNE+bzF6BqEBrr1WnQwoF1GHTQDPLcm9gBeCHlwLzAy/YKpzxnQngAUxuHMdqDf2nKaDLy4qmu8RiICKCJUkiZettjrUlTSaJ0SFotiZSYpOLw4JD9oz10I0nUnMneCuHA7GfkmQJRgXUolWJsTSLzQGMIfMEA9lkiU0yjlMRacI0gUf74/XHZAL6ZLmk3zoMKzjm0a5jNahbLgslkm729V1ksrqnWF51Gi9+X7fCX/uYKzKvAePIfSmLpXfC4o9KGZVXQYmlbjUSwXq+4ni2QUoF1vPXGFCW9yLgnL4YUPQIxATgMjzAdXBLBlg4+6X8X8Z6Pa3dgRzjGsFYEBQKGHwBO74TV40CDk49lTEJ69onwDJgOcwnL+twqlOm5ISAfNblcOKihoLwlurN1765YvokXeO7YlxFM7RAPBmCR6481ahFLR5JKpPQTHRKFsf6c21aTZml/mjd+8bbosZ0H18lprK0RzvNxuvXj6ePLKmOv1LOLeqBNdCzTwAySoQ3sppTxXdzFLxu7u7s0TcPp6SlnZ2ecn5/TNM0tN6BfNYwxXFx8tZKYg4ODW4P0PM95/PgRRT7h+PiY58+fc3JywtbW1i2gZn59DcuLDSTmyZNzPv6433+eQa6A36IpTVEUv/Z2flkIIV6abL94cduieTQasbe395W2W5blDUFiw2T3sRd5EX0y+5sCauJ9CyCl4O23H/D804J6Ez/h+PiYq6urjZKcvbQkv6xINnJuxz/+4z+SupT7uwXZ4YTt/QPuJzC6obUyu57x4YcfbizTwrHOPbNmGOVbDzl49HiwxFKICYUYIcQX11Q1CsYTNnRvh+f/VeJl1/5lEcHanZ3tbtnl5SWPHz++9YzVdU19g2qUJEnHsFosFrdEqb/pkWUZ6/Wa2WzG8+fPOb84YVFf8K3vZXz722/w6quvcnZ2xmw2+20f6r+4mEwmLxU3/12PL7bnlkNXkDA4FzLM6gpkoHxHHRhPV7cbL7ZuCBgH8CT9poJLRXRX8uNvPxvqt21vPdg2zFxH6WLPXlBkuWU0GVEvl0ghUV2yMZwF7kb53cDb4WcqlUppW8N6vcZYQVqk5AUkqWVrqwBpSFSOcw3gQmmOz7il8O0Sz0Jrh5K2R8SFF9yMs+ZWVDx+nPPsueXics7l1ZJivEc5ylnNay4uDVm6QsnGb1+mOCd59mLFP/34BCknJEnq7bZNZC5F9pAhCgh7ECFYXktfZrFerzHGoJR3cVJSIsNtEMf1bVNjjAdKjHWsVmukAGMturZYt0IIz8QoyhHX8+sOKFJSeatrpUikFzYeloTpAYocRYG7e0UE8M/2zCpvGW5RiUAKRZKmXiwZj+bLYF8shKAocrZ3phRFQbVuUAqSoP+yXCyoqgYpIc+8ZkSWCJxpSJSkLEqyNCdNvHitCGCNkBYhPNPHmLq7t739sm97EY7d30t98iaECyyy/vaLiWqgayBkvM8dAsuD+xPkO89ZV9dMJlucPG+5vqoQQjKdTMjynL39kkePC6RaoBvHaj2jatYICX/z13/Nul6hSHj/g4/4kz/6Hm9/6y3KyYTdV1/l4JXHvPb973B5copuW15cXrKcV8yePWHy2iMS5Xh+ek6R5yTOIdMUg+RCO3Z3D1jZFVW17mzKPZNCBHFVhZB+Fj8ChOGRHiC1gZUgJCboDrW6RQjpXZ5U3jGnnNOeTaMSpFRIkSGEwmhL02iSJCFNJbg29CMgpaEYGZ48eU5WTsEWtBfHvPpKCtaSKgNOoI1n97RGe2BVKKwxJKkKMFMsx7Shu3JYpxEkoYTHi21b4+9Viw0as4MyHOdCC1mgQmvD5eUalY6YTLbYTg+pqhWreom2jb8fIkCADSU+aQeSE+7zAT6OT+cVq4XE2hyBRLcNWMP8esVysUSlKdOp5fXXdr2AsXP+/o73anwew00aGT02MFtcb/EU3gSBt+Fkd54BVui72LAsihUPChV77GAYnfbM5rHE84yH2Wul3W6Lzb6+30oElrrtDDfooNNZg048vwM1BscRS5sig6/fQ78/KQXGGaTAC61bQ914EfYszb2zXduS5EUHbAvoGT7xeLr9BsFl0wLas1VtLA27eaa34ZZoKX5zze6BCW/S28o4d3EXd3EXXx6j0eiWy9RkMvnKNuq/KYeqf45oFVT5ZqGpc/D3Lz6ibVoOr19w//oFR/sH/Mm91/jBn/xg46VxcnLC1eXVxjZrBb+wc5obRJfLEm4YLLFtvJ33HdB+F3fxLze+uId0fvTrE4+egr8haBkT1Ih6CNkNDMXgv1HbxedqkW4+/D0krh0V3M8yD21EnfMCts7F2dI4EPWlENPpmNnZ3JdNRfjH9S5HUa8lThB34pNIjPEgx7qqSdIcY1rG4xTsiHIkcUJirWeo+FKRMLCXKgynQxYuur/8IDxob4jQBgJFqQyvvZbxwQdzzs4bnp9esHMwZTIaUS2XaO2YjLcYj9cIp2hdy2qt+Pn7x9SVAhJ00wYACLrZbBfZAPQAySBhapq2KzmKtsFSetFf3dQ45R1JvBtLsMRGUNc1Svq5YQe4RpOmHjSp64a2NQihyPMEozVSSZLEA0NRcyZNvQ11O5gViRo6ncZDsL6M37POC97GM4y6NUqJ4MwUdFWcZ61MJmMODw4YT8asVxVCWMZFjsSyXq/QdRMAnQwhJXmWkeQ5iXA+sVK+XEqpBJmocO0M1mhvieucByCsBLxGCcJ4C/Q4ky8HTIRo9yuMbzs3tMUVoVLOf08ERsje/jbf++5r/OznLzw6vDXmw4+OuX/vHpdXV9x/cA+VJKzWmsk4R0nB7k5BMdrl8uoK3TakWYo1MFvOOT47ZVWvyZKcvf09jo4OmJQFWw8e+Gu8s0NxvaRKwKQSEoFNJFr59K02gpSETz97zptvvEo52ma1PkPI4MRDGhh0Ail7ppM/p0HiHZzRCG1gtMboFilgXGZBbNUnlZZQXhgt46XEW0l77aMmOIYJCdbqAIT0gLKjZbmsWdeOIm95/vwzHj16A2zjS67wYKaxklZbIDDNrMNoLxQMkYkXWFwylD6Fa2psi3WKDrYQCudMp8ES0/nQ3eFLlDyQ3egZl1fXFMmUYlQyTrap6oqqrkG0ob+VHeDhq+t6l7bhHeacoW1TFtcJDoHVFiE02mrOzi5BgG5bXn9ri+lWGu7HqOE06FjjM9mBMYMI62yk+7ED75iPA+Q7ljcO0PDhfd+10I2+PeysmxzoxYH7zz4vXlY7338/vHs2T2rj9+H7Anr7cREZN851mlRSBA7VjX1KEY7D+feCkglta2nahq2JnyGUANbb14okCdpXoc1DGSXdmlELy2C19oLyoe2GDdMxjELb34Rlere8G1TrrjQq2pHcxV3cxV38crGzs3OrfOaXAWpeVj7zuxJawvImS8XBXFlWrvYmAmaJ1SVtJhmNxxu4+fb2Ng8ePNj4+jJxvHO6YHVDpOY6h+lgX8JB3vKVBIrv4i7u4nc3vhCo6cUKZTcIdZhbs5oQZzPpf/epW7+xSNfogBs3GCtvaqR4IVYxKOGJA/c4qJeBjTHcpqAsC/IyYW00sdA/6gB0CUIU7KQHmcIfpFlGOXLUjS/BKPIUnYnAHvACt97GOop6hoSyAwuCYxFAcIfqYZtwHsKSKMveVsprr025uJhxfDLj0aOKyahkPJmymM9oasX2tESqFlzKp+/P+exJTZKMvMCrbRFohEjABmaH6N2OupYWonNY0roNYIjoEmpjNCpJfCmYFGRJiksNVa0920VIz64xDqkSn7CGZmxbTdN6bZyiyNFaD0oRwnEIujpVpzXRIjy2fwRroi5NBG0In5kAwjh00MvpBwSeCeHC7wnj8ZiDgwN293Y8g0k3jMuMer1EtxOE8+BVlmfeQlwKhMw8IGM0ImzTg0FJ0DrStG1FvV5h2gZrGoxtUCFR84K2oivRkoFZFTIgpEoCE0KG8r4A6nSAhkAoD246a5F5ymuvH/Duu09ZzNZs7085Pjnlw188Jc9TVlXLcqFJVIYzgukkYWc3x2L51rde4Q//8G0+/OQz2tZy/+EjHr36KkoqFrMlT54+58nxcybTCZPxmLIoUEpR7m7z1tbbJFIzGo+gKHn69JhqvaapK7KdXa6vF6xXmul0n9nsGCdapLAIFwAaIZCqZ9LE2yCCOJCijU9arbYkiWI0KpDKX1//fAcLeZEEQDf2IT5ptcbS1BV5nqMUILztdQS5nPCCvK0xXF0uKEYJR0eHvPtOxXxumIwFOC8K7azCOIk2oV/Bi8EabfAlVgLvsLNp32ydGYC+YXk4Tl/SY3tnox6NRpBgjA2n4uGQqp2xupyTZROmk33KXLJYnWN0jcMEgM+ERHxjzi78z7MXl0tJ2xYkSc58fkmiBLPLmqurK1SiMK3me9/fRymNM965y5epSm7iG27wE3GXl4frge8htNP9c5vF0QPyEZCI64hwDWPfIML2Yx/tPMAXN3fr+zeO7BYoJAafDfcXr00ASbqSo/hfEWcFQklj+Ny6XlM+7ivuxXnhbSl9P9U0LUJJ8jwD4Z8PAVhjMMILBYtoJ961zfC4AxiG8X3HBigZPnZxKqVv+3gObmPVvnTUL1fhw7ta+bu4i7v4erGzs3PDtcnrmfw+ADVGeCvuDV13oDjcpVAH3Lt/jwcPHnBv94DVouH999/fAGqcc9y/f3/ju9fKUM8+ZHVD722V+p8Y0kE7NAC8i7u4i3+R8cWMmpCAdIlQNwtLcKDw60gxGGji6EQl5KDuw3VDx8/dlx+nDoQk42C647WH5fjBqR9iB6CEYN07zqnXJrixEL/UJVaR6+JtUn0SYIwhzRNfFlOkrKs1DhmYFI0HKaTtBuvdgDwkeHFmMs7LspFYhE8CWORokXhK/BuvT/nJT05ZrWuePz9jb3tMWZYsVwtWa826yignLReXNf/0zgkyGSOEpNGNtyJ20b1KbNiPR22X4d/9MheuZXB+QiDTlCiwLKREdG5Qvt2MsV5jxjqk8Na1xjjatgYHeVHgrO3EaR0CpQRam8DskRusmd75SQRgRHRMmuGMcBQY9piGoWm8Ra8Kdt9Z5vVGhBCMRiMmkwmjsmB3e4ckkQjbkkioM0lTV4jobqVEB8j40hJBkmYhRwrsqw5QtGhdo/UaXTf+d1MjAlNIWxfKokwPCkqvyYOQJKoXDZNS4IwHzQilVUp6u26pfHtoDYlUvP7aNh9/dMJ4+gYPHz7g8uKSpmlYVSu4NLTtiO1piWk15TglSR2L5Zw/+qM/5N6Dx0iVs7+/59lDTrK/D21bsVgvmC+ueXF+SZ4oyjxFpil5npAIy+HhPY4ePKRa15yennJxdkGrNUoI5tcLjg5eYTTeZ7WukIlEhfMSQY/Ia9T6JFWEpNhaQVU3WGtJUuUFvwQgTJd4CklnWmxx3TON8O2jW0vTtOR5gVIhMRWhvNFJrNOgwBpJ20qkyinKksnEW94/e3LNt7+zg2lbvPZIEUr7NCoR4FqkEhgjwrVMOv0qZwNjRIbSG+H7JCnDZx3BIRxz0JqJ/YR1IF1k5dkgMGvBaRCKulnRXGjybMxktIs2a1brK4xp+iS+68eCLbzzYIF1CfNribHencvoBovjxckVddMAkkcPdrl/fxJAQk3UZnEDpguOIKArN7rsWN4ZV9rsvSPbp+uWPeD4knInN/zX9aACgz7yJngTj8F1n/WvAUEEKG5sZiCi3gvgc3skHZb1luebYE7HGOomHEL/1YH8g7Gxtf25ChHuEX98TdNSlCVeQSa8saRCOIvRXn8oTYNofwdwhnebCNfE2u690gPZrjvOyBiKEwgilGd14tCbJ03EpIbsLCm+mqDkXdzFyyLel0opsiz7Z9FM+TrhRI0TM2Q6Jy1WFJMGJ2sW61PyXFKOSgT+XSldCnYrmC/4iau2SjyNYhg2sEWH+wljl6/ikhMnqb7K9+PY5zcd3snz5VSNOG6LP+Px+JaY8XQ6/UpuQsBXFrP9TcWt0iunQK6wYlP4ON4zMstx8grtEjATjBZYNq/Jq2+8QZZlHB4ecnR0xP72Ltfv/IIXz59sADWTyZhHG7ozIIQmL0v0Td0Z59mx3Xo4apeyTraRQ7akc7dEm1th0bT8upmTJo5nb8RN8E0IyXy+Js8TisLTguIzdTNe9jx8E8PrbLbh+e8jTVOEELRt6yevEYxGBaNR1k1Ox2foLn698bL78Zt4P31R//qy+BKNGtm/FJzYGLTfPO0oGBpH0n4gHQe4thtg9yKIEcaIs4ZxcB9WtD5p6JL3mATjMNrg8Hoo4KnpNiRC060JVxdzr/UQhvT9zKkIGgJeQ0UbQ93UoczCs4XSTFGUKW3rS3qcszTrltG48JbKREBJhHxsoM3u/KylCDo+hPIs7xJFYAgJnJEgNLt7Gd95e4d//Kclz5+/4PGDQ/J8i3JUsl7OuV44jEz5x3/8jOuFQiQCozVCOpQQCFJ8OVIsIYrsFDV40VuiFowX3vXtaUIJkpMydCZeX6HVpmPCRDFcYzygI0ISIoN7kpSSLM+wRtM2a7LEg11JkmIGdHsdNEiEEB7AcK7Xggj/ynAcccAXhaojSGitd36KrlBJIklTxdb2FolSbG1tcf/ePbamE8ZlTlFkKOFwukHahkwJrPD24onyZTZSqXCN/LaVTHx7Sl+mZHSL0TW4ljx3ZEmDMV7MUyLBJUGrJj4wdODf0IjYP5QCbBqYaXjnsnDN/PNqPNgAWN3y5lsT2rbh4vSch6884ujeHs+fnnBxcUpyeMBsXmNNhbYj5ssLHJb1qsWR0zSa5eKMarXk8OiAMp/4ZzlN2Cl22T/Yo65qqsWKerWkrjRV0yCAi/Pn7O5O2d3Z4Y033uStN9+iWi+5vp5xeXnJ8cmYsthlfn2JswKrbMAwHKnyekHWeNaW1hqrvTNTVuSM8hQhHM6a4MPsyz4G8AY+kXREi2FrvQCrMYaiyLwujA1CvdZ07AqLQ7iEqrEgRki1Ji9TDDV/8qff48OPfsarb0xIpcU5gzEtrRZIJZHKv3BTFFIlaG0glgga3y/5yh5fruZLYELZlQh20fh7qB+wuw7MRTiM0KHf8PcWTmGJ/UOLcy2VXrC6PGcy2WFUTmnbDG1qjGmIOiuejeUBI0nCcqFYrCxJJpjNFjhrWK0bzi7OkDIFo/nDPzz0bW8MiBbh8gBw9eU7m+UxEaTsEKgB6NKPMnv2nAv/D9cWf2/3bLnNZ73Teel202+7T3f6dUOXcetYb0V4T3iQZABAITaeUVxkErlwzcK1QgbXsuExDI5I9MCOF40OwHecEHCESjDZaZ7Vraac3nYd8KLboJ3Dtpo8zUG6rhzSH7fFWY0xDWCQ+H5zeCVcaDVJEIl/6VTI5pRrx1RyEQ6VCPu774xwF7+9iIKkOzs77OzsAF589b333vstHtXtMO4Sw5zRLry2C699Z4/1asL/9D/9r3z3exn/4T/8JUmS8PP3PmI3eRtXP+D89AVPnz7j/PzcO/5kRze26scCN+P4+PgrDcbLsuToaHObzjmePXt2q687OjqiLG8IlfwGomkaTk5Obi13zpGmKffu3evsk6Pj1zCm0+lXFhP+bYaUkkePHt1YKnjy5IfU7mpj6dZBwt6DFDijdmc8PQVnt3HtY9wNld4xEtaaq0+fc/XpcwTw+PA+B3/29sZ6xhrerzddjZyAf/Nn//o2z3Gtufj46eAoHWpni+Loe30OgmdxfvjhB5vblA33poKEX6+D0vn5+S0h6fF4fKuca7Go+J//5/+df/tvH/Pnf/5nCCH47LPPXioUPOxDvsnRas2zJ0+5qdD8r/7Vv2I+n/Pxxx9zdnbG0b09/i//7ru8/u0SsMznc5RSvxPn+LsWFxcXLJebKuDj8ZjDw8Pf0hG9PKqq4sWLF195/S9V8fq8wfFw0O15LdAhNCFiItLRzOP0q4gATdzJUP8mgj1hqx1yM8wbogVyYGoEcMQ5SVmWZHlGW4XkYUBvR/Q2yVE7Jc5890wYS5YpqrWjsRqcxAQbYOfoqOoRBxK2P2cHYDfbAMDYOGvrZ+qtk1gMqYRvv7XDh79YUK0XHJ9cMN0eMR6NMW1D3VrOP9V8+lmLUilOGJyyKAdSJlgdRVd7jZThvz5R6mdlhtcs/i2C3oJxjrppaLUOwMimRoSLgJe1WEMo8/GfG6MZl4W36nZ4F6VgURcBJHC3ypYgCjmrl7Jt4r+9jo0XTU7ThKIo2Nnd4mB/n92dHcpyxGFgkORZSpYlCGvQRpDnBVYl2AA8Sl+jE8qRZHChAicExoKy3gVMKuEBu2aNFG0Acwiz1PE+1uF30bXHzQTPOYVLw/3rXMzDcMKbQVsnsCi8FbTXYrFCs7+3w3/6q494/vQzDu/d497hQ84vzlgsV+xsj9je3ebk+Jy8SABBno8oyjHr1QuePvmYi4sRWSpgywND62aFtYbJeMSoKCnykqIc0WiNdpqqqrFScLmouF6ekKaKNEspi4JiPCVNUq6ul9irhuUCrudLmrZBCM9ES5TAOkOrKyaTkmq9JFMpUqTIVHjmgbNkaUKaJqHsKZQMqjDNH9pNyQSHd0prjSUrEtaVQUjTlVM5QFgVyqUyrMtYLAyXVwvOL+e8+uabvP/Bp0iTcnK6YLFwTMfhGZUS6/y9p6SHeqwLAtJCoVuDSgUyUaG8qgeshwCwZy2IjikiBSH5985yTvpnxil/7/pnL87ARrAiglMOlTjW6xkgyNKSLJ3Sigpt1gGskIHQIzAm5/zcoLKC5WrNcrlA2IYXJ+dU9Rzlxhwdjnj7u9tgHAiJtXUAGIeJ+7CP7yB1wHng4SWpf4BlgiG0CMCuwFkvxEwENMJ5xlmOvgx1AKCHNmDY9d84rqHwuNcyus2Q6Q4/PIMOBrOXQ+emIWozXL/XFYoQyJC107VPx9QZ7NB50MeawAcTiuVqSV7k/ridQKjYHnEPoX9Foo3t+lS/1cCiEQOgsGuL0B/TC+YPXdZ8NxOdtsIVcINzGVDqnbMol6Lr311Bz7v45sRN84dvVAgglJRuHKVsGE8dedFyfvEUKSVNO8ePPwuwBdg8/JhbidnvS3xeDjAej28xqEaj0Tf7Xgjxecc4mWZYt3lO3ul04FDkQNB08mxfFotqhVOb+5NSvrT0y6tZ3ohb+xCsa8Pp9Sb4orWmkcXNVbH/TNZkl5eXPH/+fGNZVf3+COlcXFx0Nu5lWbK3t09ZjsL7fHPS6i5+8/FVWI3f9PilRmdxFu5lvZLrRtebn4oIuHxOHbwgzDJDl9A74uB0OJvpG1y3vnRASm+t7JxDCXAuwTk/wz7ZmnBezVADOnccpFqcdy/S3tJXBNvb6ODtAnDQaovMJFIJUlH4ZMzZ4EokiNatUUenS9FlSL6CC1IHNIRzxRtAIZTEGsP+Tsrb397lJz8549Mnxxwc7TEpdxiPpzw/OeP9D2YYO0IqjbW+rZSSYL32jJTiJUmQDSBN754Uc6Fe+HagUxNmvlvnUMo7Zg1pt7H8wzjtbckH+ipKCsrpiK1JgRLQtsa7+RBKYgaT88OSrKEuTZIkG+5QJtinx2PsklvnUFIyKgu2phPuHR5xeLjP/v4BZVGwNZkgpUNJX/ZmQxItVeJLskQShGllKBsBY7yzlQWE8bo2xjmETBDBflpKhxTWMymkQDrV388DGvJNcCmeZ3wiohNXBwv6Wjoc3gY6llbE8822Ev7y377Oj390zAfvv88rr7xOWUywWObXa37RfIY1AuNKrs6vyEclo0nJuJBs75QURYnWFRcXL7herLicXeKwjEZjtsYTlMoYT8dMpxOKTJBnJaDQrWZd1azqGruuuLxeoJwhUQlZljMalSg5YTLNaHWLMS1KgUVjdcNkkjGZpJQlXtOnDaWEwj9kranQOpZuuEhG8AwdK7DGp//OQjka0TR1uJetf+4CNOwwOBuBNzCm5JNPZnz48ROm2/uMxtucv9vy0Yfv0ZLy8/fO+NabW+BcGLT4srr/P3t/+iVJcp53oj9bfImIzMi9Kqt6qV7QjYUkNpEih6I0HF2OSM2ZT/cc/aX6cHXn8Ej3Xh4tHElH5JDgAN1Ao6trr9wzVne35X54zTwiK6sbBXQD3Q3mC1RXVmSEh7u5mbm9jz3v8xgj7AXXOWGPGWGGuRiwQRgXWq/up4yz9XuuQAkDyAcw1qSHslB7rbGJdqz6PD9Pi71rVq+pFVJZqaFpW7QKaAPGCHuu65wwsYhcXEDb1BR1yWR6jtae6aTl+fOTNNu0/PCH99gaB7Q3IqQcFZFcjnhVs6Sf2a+IzvaSyH0/llfXV6erMpxcXtn7GSkBHVbjeA08v7JYWZ1AZrv0v3kBaF6/Dy+P1aQTE9B/5Ztivnern69eUwbhYN1hUI70Amy1dgABaTRd67CFxofAaLgp7M2uw+pSKvnSGSqtMIhItQA1CotG63StMfQMpTwHR8RxLipFDJFV5d3aRfXXsr7BsQYix7h2r4BouDj9Ev2Fb+ImvsQoCsUPf7hJjC337z+QF6Mlbn+pp/W1iZ2dnd86dsDe3h6jF620Ly44Pz//lY95eXnJ5eXlldcGgwG3b9/+lY85n8+vMVq+7JjNFjx58vTKa1139Vn72xwPHjzg6dOndF3HeDzmzTffZDyugIsv+9Ru4msanwnUZGHQ3tGmj3UAZbWL168NrwEsKXlNZrUvQ4blv5mpsL5rKbFO08870usiwwLEaFCGjfGI85NLolslBCpRzb1zOO/RxhB9NjlVBB/BKjIpyBhD2zms1fjOo5JNro4qOVxFUAlMyECNUqjgySLKKokNrxpPACtPhwoajUMrxTtvb/PgwQUnpy0PHjxjbzxkuFkxmTmOTydEnXZFvZbkPiq8S7vMXpJBpVfJy4pNs163KglTLzMZ44qWmwQujdHoCK0PV5gtWZfBJGBFASoGjFKUhWVjo2Y0rKgLy2y2IEbNPHjKsqFtXapFlXPLTJp8rllzJp+ztZbYdUncc5UeKaVT6ZamqgQs2NzcYDgaUtcVg8FAnIMQ8IqsGaM1KibbcHQP1OTSDOcDy6ah7RzBRxZL+dkWBRujks2Rx5pIjA6l0jFjSp6kVVhPknKamt19Qww9kKcykyKu3HUENLKpHwZUlPaNIYAKHN6uqf7gTYajikePn7JsS157/R5luc18fknTLZhM5ihdYU2FD47bd+6gdGRzY4xShovzOZPLCxazBYPRgMlkSts6RqMRR2eP0T7w3d99m83xiBA13hbUdU3bFrRtYDFvIRY03tEuOy7nC4ySkjbXtRSFYTiskhPXkNJqBnVJWQ7RNJBqrWMQu23Jf4VlkLU0AlK+GLwiBEvw0lutNThfIKmnAHghkP54QhAAz3lHMxVb7rfe/gZvv3ePSOTtt7/FBx98xMVsxvOjC+7cKdDK4GNDYSzBd2gdMFajopRZpQ6OTbXGMZVareYpsbgWIFKBsklEXBOU3OPRoIYouk2ZYJWZQLkfkz6vEkoi5Xh6DeDo8LHDO4VSRdInEMC4WXrOTj2zueLy+XOUbrFGcXR8znQxw+ghh7cs33h/jIodWpV436FNhQut6LXn0RV7fhK5aC/GDLbk7r1ikcRMVVkDYTNdJWb9nBh766Rc8nk11pkrL75+9bVX3xF5CXDTH279uAJmr7uw5Q2C+JLvy25MmQmXj6CuvE8YNYt5Q+cVppVyT1sUwpLxScfKmLVjqLWSXunTPoFaucbddw1K+SvnHlGEtHGQoaV8v+jfma4tZl0dtQZMyUZDjAEVAzFoTo5uFpE38Y8ziqLgD//w+/zoR/83//APR4QAt/ZrXrJQvYmXxPb29jVB3MVicQ2U+DrF7u7utedACOFzATUXFxcsFlfZL9vb258LqJnNZkwmkyuvGWPY39//0lgbw2HNnTtXr6lpPE+eHH8p5/ObjBgjDx8+5NmzZz1Q88YbbzAeL3E3QM1N/IrxC8SEV4CDLApXu3QyB6zv3qVJLS/i089xTcxQJfbA6o3pp7h6R052e72bq/wclDFJvySBEyGRxBUoZdEUVFWgHpbMLxoUBVl8se286E+k7zBWI0LAaUdbpb3T6CkLw+VkidZD2q6jLCWBDjGg42pfVWtNNKFP42IEnWy8e20enTUEFIGQfFxNLwK8PdZ8470dTv7PY548PePOwT6H5T7Hx1MBlaxoqfgQUWZlaw0ihpoX5FeAGi8lOXmyDoSUYOXd2nVhypVmQuccnRP4W2mNSeUa2dkqBC9uSeie0VMYcY3qnMcYS1XV6OBomwXzOel8SJpHq3sao+jZGGt6EE0nceGmbYXB5Dy98qVVmGSTHEKkLAqqsqKwBUbrVEaje8vmGH1yixeB5EKlNkdJwh8dy2XL+cWEyXxKs2w5Oz2lacUBandnxLtv7bK9HZMtvE/9PiTWgULp4iobYa0fA6kcKpJdhUAux6BTAijns5KjzuVjmoDDqMD+vuWf/ck7fPzzI/7rf/uYT+5/wPb2AYeHh2yOt5jP51hTMptPWEznfPDhkrZxnF3MRAixg2owwBaWSKTEsLkxILLk7mHJnYM9NkeBqlomlpHFeYVVBZWtKcyAEDVttyBEYWu5rmE5a1g2DWHmeXZ8TrNcEEPH7njEH/3x76ONQ4UZnWuorCPqiDZe+pIXx66YEmYfAW3xQdE1kbIwGKOSk5NCGRGxDjI80xygibEghMii8YwQAeA333qDe/fu8fz5KYPRkP/5f/4X/B9/+ZcYoxkNhxAdUWkMFq2MMHNiQGtLWVmUEtad1qK5I5omKVmOXvpikPsZggCgwaWEOYHFs+gpbUGzbPvyMEVcgT4J4Mmvk5l5SvR9VNKdIoM6sQCk/zoPi4Xh/NxycnKKc4G9gwHnp3OePX+O1QUxOL7zndcpbGQ512iCsMSCTdWZhmyt3QNOiE5XglzWYI/0SgZ7E/CuVLgyPYc0qHsIJqE8oWeS6RWgnccJ9HNQr1e2BuKstGbWRtc6Ta8/P/nC2DOTMjCajrM2P1/5XLqmDASGNfaJIrtDrdpg/TkX80Wv7T2ECPWgZr5cUJRVD+iZoiB6t9r0UEnQPpeHaSVqY0HGa1EYlLKgWrkupchqTj4xrvoWiUAMPbi9rieUn9BKrbUB4h6logiNN0vHJw++WO2Cm7iJr0sYY3j//ff5+c8/4eTkCOdgaxz75/pNfHaMRiN2dnauvKaU+loDNZubm9dEm09PTz/XMReLBWdnZ1de+7yOV03TXDtmWZbs7+9/ruN+ntjd3eUb37iqTzSdNvzN3/zVl3RGv9k4Pj7m/Pwc5xzD4ZD9/X2Gw1Muv1rEp5v4GsUvsOdeLboViqBiby+af39tt1OtFvWgMUqnvCasFs8q18vn3dm1B+La4RIfRpabKakIfQohy/qm6yhsQWGMpLuxxJaa4caAxeUSgoAvne/wAayRZFWrCMlBxTvZ6Q8+oIwkRnVdcHmxxHURYyyd67Ba6OYms0yU6ItIqQxrW9Rq9cen9KRPhLLVq0+7qREVG96+N+LhoynPnwU++PkDFs5zfHKJ1pJM+tCBCqnkKRBCdsxZEXZyeUEIXlotOzf1iYg0qsr3ti9DCkmnJaw5FAlgkkEhF8GalFAYYaYEhJWzmC+hKnDGU1qx0C50YFCXWKvpOkVQsmPsvBdoQikp69CGuq6x1tK2LVlw2FiLDx3tEmE7hZVNsgBuga5pUagepImEZI8NIXqC71AqSCmKtqikQRJT8hUItF3H2cUFF5Nz5vM5k/NzlsuOjeEAtWWIYUHwkpRKa6YSnDQqJAlagV+Sg63kcXOX0GlHO/dfhUJFk9hSkoLJWAh9YqYwqFS+Z3XHe29v88brv8+P/u9HfPzJCR99dISxG4zHm6gKtra2iIyBQFmIqPRiuaBtGzbGm7TNXAR/w5yd7cibb9xBM2M41MTogA6jNKUVfZjpwvGzn31MNbjD1uYOO1tbBDzN0nE5EZCw807AvQ5iqLh1cJvd8Yjjc8e+GtDMZjRLuHNnJMyaBK4aLaWKxhicj5RFSeNECK8uygToBZk3tCJGmUs0gaBF0ScECBhcDJTlAGsq3rx3C200H/zk5/z0w49YzBfce+sN/uWf/jMW8ycQFVVlCT5gdEQpYfmFqOU+JsaKzTNQFIA1KiPMhph4cVEYh1KGlO9wKmvUNo2vwMbGCO9KvGsF7FErgfW+EE7pVSmVjsnHThMDiYOoQAWcj6ArOldQ1XvculVjzRDnZswWEx49eo5zAbznnXc2ONg1PHtyAsEIg0bL/JzHSR7/xigpp1SREHrahQAMWlg84i5HzxIUomBAR5nTo5JRYRILswcndQbfVRKa9unzMvddAWMQlkoGRmT+l3ugExUr23UL+BP7502eB3UuNe3LrGScq4Ss5JHpw+q8ZB73xNQHRGQ3RVidSx7JGaTVSuMJyRVM9K2cD5gYKAqbbOQzgKywZSXvJRKNSk+xfA1Z503ue9cI8N02AasN2hjpozqVTUaIQZ6tSkeUNmRhe+874e5lxlYG4BLgHRVEjDANKZhOWh4/u+oSchM38WWHtfa6I89LQinF4eHhtcT65OSER48efcqnVmGMYTgccu/e6/zZn2mOjo5ZLhb8j//xP3DLLbz3dF33a3NcWi6XPH78+MprL11b/wajLMtrgrAxRp4+fXrtvc+fP78mmvzLOJr86Ec/uioAqgId99HVE7a3x9y6dYvd3V3quqZpmk8/0BcYXdddY6TcunXrmpPV7LLkv/+nU66WC8P+/v41pxnnHFV1Vd/o8PCQN99885XO6eLi4tr17+3tXbNG/zQmjU0yA78onHMvFfnd2tp6aZu86LLz/PkR/+k//fXV77YF/+bf/DGjUdFfw/b2NuPx+Nr3fNUcegAmk8k14FH5HarqXS4vGo6Pjzk6OmIymXB2doa1ljt37jAcDvnoo4+49VpDvfElnfyXHN77l84bv45wySTni4yX3fvPG0opyrLk/Pyc4+NTjo46XtBAvhKfOWqdl0RYp4U3RNGkXNPigNUGZc+yiULnj0qlHen0OupqOQ6QcJs+8o/9UllpKQPJZVFp8S+GL4qiGCTyjSxaY7RoFGU5xBQzfJvcZ9KOeYgx7eSvPwhlh1GckQwohVEFWlvmsyVlKSVLyuhkOR4SaSKCDul7Ve9uElH43lZXDPF0FoVUJIAoabig0F5TlZpvfuOA6eSIycTx05+2dF1AKSOsIS8sjuDlnL33YlOssn7CSnA3JlcrImkSXTVw1svpE5ncAi/o3Gite5BmpS2jKYoSY8ShqW1bpjESamEJlIUVcGixwKtAWVaMRyPaVsqzgnNJiDgCBh8CbdtQDyqqqhKru5RWVFUl7kEolkvRT/Ah4LzHOUfXdVKmkpO66JMDk7RTBp2knc0KzUL1jJzg5YHsXctiMWMxnxJihy0C2jSMNhT1IBJp5V5HnXRqjNgmC5FpDZhJe9hqvWWzuXBKIFPiGqNJpVmZ3ZSxvbXt+R5ok/8Yoxha+Ce//xbf/f7b/Oznj/n4o3NOjh/zbB6xpqJzkY2NCu+hLAumszmXkwnRdcTQcnh7j729LcrKcX5+zOZgwLPLc9EmMhrvAvVwyLLpKOodTk5P+OijD3jjzTfZ3Bhz6/Y+OzsHlEVJiI7gDMPBBrfffI1bB4cE13DncI87r7/B3/1f/wUTazoXmS1hMBixnE2xxkqSmxy8OuewRcVkOqeshzROgMbO+QTARbpOgNi2a6nKoTzsVcR5T9c5ItC1geWy4+L0nLOzC378D39HUViUWvDP/vh7vHbnNmXZEX0j/Vg5mWmUlDMGD9auWBkSwuDyUYk2Fkl/Bil1ubKUVnmGUj27Q2sBoYjiJBeiEzAggw0ql0/lpF1ABRUz20MAuxCh0JplU1JX+yi9wdnpOcEJlHN8NOHk7DmKLerRhO/98BYH+2ViLtkMZRDxeB/SOFifMwTBjVETUumjgE2+l2GKCFie3QBVao0QOtGVAoxO80tIQtE6aWrFSNt1dF1HURQyp6rVPJjnJq20/DF6VTK4huJrrdI87oQVorKwfLoFieXTF24lwDyEFUSqlSJqAwhjKgaIKj2IYujLg2Rejz0z6ErpbXDonl1kEnyrUaYQV7qiABXQKvIihu9chwllWpBmBml6ziUHuq5zTCYzzk5OaZbp+WUUVV2leSNgjMVogy0MRWEwKiRHvohNgDpXQHrTn78KiqgcIRqePZ8yWdwwam7iqxVdmi9e9b0vJniv+vkQAl3XUZYl43HJfK5pl6L/4ZYi1A+rtdEXHTFet1P+skNrTVledYL7NF2wrus+F4CyWCxe0FnxNHGK6WbUddknX7/JUp6XAWXXgMMIuILhcMm6wHSMkY8++ugaWLW9vX3NCcsYw/379z/Xub4o5PyyiMD9Tz4Bc/YL31vX9bVSthgjP//5z18JgJvNZoTwYn9W7O5u9Ix/eIkt+lc4MljbRwQdHWXKk9q2ZT6fM51OsdaysbHBzs4Om5ub6Tp/MwDjVzG+ivPbLxPX7v0XEFmbddV3Ol6CjfbxmSNlvpiu/El6od+0uFybNMV+OK5ZdK9cdIgaoyzGarTOzj1rDz2t+oXxuuCkzo44uJQQxx6UEHKN7KiLV63vWToRQwwaawdEpWi6lhhS+RCkUqlsJ45oKii5vhgCUQk4EIPDaMVi0dG6grrShEKjNFit8SqglUNFLYlQAIw4yWgFKogddHa1yXqhKp2yRgnDI52zd47bBwPevrfFP/xkQtOFfmc+eA/BSTlT1D0rZp3xkrP7/mEahVqfAaneWjZ9br2kYP1xpPskLOMNsdfUMMZQllUCb1ZAiVGasrQUpcWFQHSO1rVUVc3OzjaL5ZJ5XEKMyT1LAA+tIp2TBVZRFFhrcV2L9y4lZKKIHzy42OF9pO0amqZkNp/hg09glDB7lI5p194J+ydfhdL0uklKvtt76bNyJwwGg+8cwXuqUnPr1iabWwVllYC9dF/7EpXUXnk8rMDIdCtyB0swDan8LqZfKpV25VFrr6ektBcZXoE0RHGFQkU0HYVRvPfuPm/d22NyuWCxUMymHY8fH6G15fx8RllGCqvZ3tpCcYlCMb0848mjKcNBwXS24ODWDucXF2gl4tRKGcYbFccn52xs7vLs2SUqRJ4/fc5keM7Pfv4h3/veP2FnZ8zh7bt4rxlvbWPNkOAjT548I4aW1kesrfBdi7Il86Xlo4/u8/D+J5RFgQstSlm0tni35PbhAU+enDAYDbm8uKSwBW3XUlhJOC+mS+q6ZDZbcvv2PifHp9SVZTgYojUslw1np1OULjnYm/DN925za/+fUg8MO7tjNjZi0sWJYtOe+nneCTNaHHtUEnPNt00YgCpJsii0MmgVyBbictvX9GtSV1BJMyvGVTJfVRUxFmSGmziQSPdU6+BeDBB9Ars1YIkoGmdx3YCm1VxMnuOaOWVV8vTJlEePjlDKomj47vfv8NrrNaXQ+YCuB/1AEa30yxDVlblAKYWPQUoFEeah/D/3xLxQjwmAT6BHEGZhPzFl0DjkfizlpqbQVKHo56sMGEefdIqCsJAEIFkBzNkJKST3rcx6SbOotHcaeDobBfaL7DVNMRA2pNaI3DmrdlGpzRMAl+6mAM9a9W2Qn1sxqLTBID9HlUAfdJoThDmp8jlrlQB+hbFG7NWTaHDWrlLK4oO444XQUpYF+wd7LBaNJJ2uwwXPctHiWgW0aKUpq4KqKqkqjyZri0mFLTpZ2QME6UcqGkwowCygq3n40NG4L54pcBM38XliNpu9dGf/esju5MuAmpu4iV9nGGu5dfuWOIKtRVVV10CNl7ljLRYLPvjgg1f6rtFodA3cGA6HL2WlvCzuf/wxjf/FOjFdpzg/v3pOSsHeXre2afLpsbExvGY3/3UCZT5v7O/vs7W1xe3bt7l16xb7BzvYwVMi51/2qd3E1zQ+W0zYaAiy4NSptsevLdrzjiMxLQpjXC2wFYSg8N7T+CWKVHev8+K/3+dLVrGyYw2xt44NXo5jjKFrZfc7l9AEr2hbh1IaaxVVVUJOsggsFgtSQQnG2swrl9IYEZYg6w9kJkb0gahkB9V7h1eB88s5bWfYGI0gdLjQsLM9YjS0/WLY2oC2huCd7DbnpEtyCbQiWQBrVDTJ4lvwJXlfQfQeayNvvHHAo6eas4tWXD6ilAsIqyaL2YJSsvMd11RBr+yO9/dm5fxEv4MerzFr1t2YwtoDRq2BNNZKktW2Lc75Xig5pHvWtI5gImVZ4FtHMJ7haMjWeANrpGRpMpnTdQGUJCshBKqqwlrLoK6Zug6FXLNKCy9bWJxz6KhxIdB1LYuFYpoWcqONQZ/waK1wrkMrKwyoHixRSbBW2tS7yGKxZHI5ZbFY4lygbTtKaznYH3P71hY72zVaOQojZQVaeWIqfVApaQvRJ92cuLb1nxPa1VNNEqTU/xLzTGWgJoE1uc/E1ScSkJcARVy6imQFHiGqjp3tkt0dC2zy7rs7xCjiusaIgxMg2ihRo41lsWixRtM0DWVVsljsg1J4JwwFozWL5T7Glry/vM102vD/+6sfM9y4xcH+AZ88+Ail32S8scvW3pBvfvN9Tk+m/N3f/j3LZkbT1sznU8rK0kaNchC0ZzQueePeNtZqHB1KVcKSaSy39ocUlaceVCyWpbQpIZXlWLp2SVVZui4wGg1YvDEkBEVVVRTWYI0IAVfVgEBkOFQcHOylhLxFIXNFiNnxLJJ1rkII6MScERBN5pBMjhKtpkjoIirR35X2qf3lM/kuyxyT5sJUuBRjkBJMlRzHcnmiycn/uli7MD3kf0lPCYMLBV1bcXzU8fGDHxNCw1tvvs50dsbH9x/RLEWA+95bBT/8J3epyiVFGIBxqQ1W/TF41rDF/iJkvIf1d67ApwzMZNZkxIveVgZAYu6zZvVcQOapvmWiXFtm93ClTOlFofkVfJxPr6dYRwT8IZUJxnz8zE7LDJJV+VTMwsaQwFzVX38MnoDvxYFjdAm4T8dZB7lTOVHwwnQkGpaN43K6oCg1d1+7hbEFIXRYne9vaslED5JSTUNIcmVBxQTWKYwRwBQCpdU4AuVWgdKFsJzQUvbpUnvEfA891pSYVBrWF19GKZnNZVIx4YOaDodiOg8cn3a4K3D9TdzElx+TyZInTy5+4ftUWiMacxVsHI1G10pNbuImvsgoioK7dw540bL9j/7oj67pzxwdHV0robi8vHxFMBLOz89p26vufAcHB68M1FR1jQ6/mH0zHFq2tzdfeDUS48UrleNtbW1x9+7dK6/9Y7KjzqV6h4eH3Llzh/39bWbdJdPll31mN/F1jc8EapQSYEVFhY6kBavrAZZ+Ma3y3qYsvvvFvMpaC6Ff6K7lrkkQVF6LIYsVC5VMQIgMFIApE9smiPZKDMkZOekFON8K24TMHtFsjLdw3RkqyOK6847VarX/akDhvbAstApEregCNB0cn844O20x5jJpgCqGwxmjjYJBbRkOKkl6u4bGCX22HgxEcxSwhREWhNZsbA5Tsh8Y1BUmsXO66CBonFtyPltiyoIQ23QPSAvukHZh14R9oxfQxsA6AENc7XzHlyzAQ8gWwKvIE3AGg0DYNdba1d/K0LYtPulMZNcmAWvkVjg82imC86IhMxywPR5TWIMLkoydnk2IeHwImGhSOYTt/3jves0cse3W9AKY+XxDZDZb0DQNbdsmWKPogY0QAtoU6bOSALoYCFHT+UDTtTRNy+XkgmWzoG0XKB0YDmvu3t1jZ8dSFlGgSZ9Kv5RK7lFJqFhpjCaBNiucRjQudGI8pf6KlIGAvIYSkCYqYXaIHbm8noVk87n7zDRQTiCBKAwhqwxKm5TYAaoDHRLTQYCdolpz9ImBSEtZRmJsGQw1BBhUdRprml4kVpVEDRpDcJscHR/wo384ZfzuAdoK0LVcLihrePbsEWUxoqg05xdTjk88e7f2AYfWAW0jha25fWuH4es1Snsc0Dno2o7K7mCtYv92nUCB0PfazKYgDHu6ipSrVCzmHWVZYAzChlCWGFvRicEl/CsBxwkH8y5QlDYxN/J9W+kMid6I2LCntJ4MjCkjybWxuQwz6YKzAqhVFqvqbcTTHGPUWomRSgLarv9eAG2ECSf6RWL3Hb0GVbFcFii1R+CYjc0hdT2max0PHz7n9PSEwlQMxpE//ef32BqBCoWwRUzRjxmVLO5VEvQWQCKzYKRttSn7+aDHmxU9qyyXD6ENKn0+vy//LtuNq8RI8RnIZ53oIsVCsWe7rB8ngTZ57kv3b02Xnmwv3osHrzmqxbhWYrtW8tiLGkdh9cmYyBsGVy3J82PiiqV4zEBvIFqD60TXpoglnZswHG3ifGAyXTDaqOjdC/vjqN71LM/hzjusLUXLTcVUfqshdnRugdVhxbjSMi50AcYmsCndP6V0Yn3JMyC3t4jBZ1aQgLUYhY4lbWN58vyUi1l7A9TcxFcrIszncPIqRjEKBvWFlIKvhTUVVTl4he/SgFn9ncDyK4vVm/gNR9rljPl+pD9Rf/bHfsNhTCHCvS8ANW3bXgNV6rq+Vk42Ho/Z3HwRFHl5/NVf/RUnJydXXntRn+bTQqF4//1vYcrJL3xvURTXtHhijJydnq5ytj6uSiuAXOeL1xRj/LXoh3zhITszXB/7L+t7qbRYBVC+/3tnZ8z+wS77Bzvs7++wf7CNP61vgJp/lJESjyu6pgpyJQb5mdO9OIyuxGczarQVsci06I5KYVLnzEkorLE48onl3VQk2dFJryUvdrOgpu6TlCC5Zy570looKPlilHhcxMSGIBpirfpFv7hSiXhqLvPxvqTtlkwvL/FNEGAnU/tTQqbSLnHwkjQHH4lWifOML3n65BTnCg5u7aLQzGcNREU9rBhvb1DXhroqCc4xbS5xwaBDQdOYHqBCFVR1hbUGKGVHNkaCt5Sp3KdzDQSLNoBqOT17mpLOlc5KLjOAlN70wr8REwXE8t5D9KmETIANgHUU/MVSh7i227z++8ykWf97VZ6j+hIpoAfWgokYVNKaEfDNOc9wOMAYTec6FIr5fMFi2RIxBO9ZzOcUVlx+RqMhikjXiQZODAFtNUVhhcUjWTlxrT+4LjGr0k610apPmKWLigtW5yNN51ksl0wuJzTLBcvmkq6b4sOSutbsH4zZ3h5Q2BYfnEzAGIxSeJV36sXDKetgxH5yz0Mg6Yr4lAT2bmK56EX1lsy9wa6Q1zCJiZN1mPpEWuXEMoM5QEo8s5OWT8BdZhGkm5/aKrlnISCm2FtHYi41SQCFNhoTSAAtKOWprOUPfv8t7n98wZMnz3jr3de4uDhnlPSHmmXDcu756Ycf8ODBz6lswZMnz/mXf/qHuK5hsZhT2yH1cAhxJsmkA9d21EWBVsJm0Cm51n2fjT1LQgf6f2s0y2VLqTWFkWuTMpQI0SWHtSjlKUr1gEkQFdWUzDp6AefUo1YsqDzHreYX+UEAHec8ZWERgdY8Ra0AgTxG+j6hFMSQrLUzi0Q0RmJfqpIZHy6BFBYfDKgB84WFuEcIFYHAxuaA6BQPHjziwScn4npmGv7ZH7/FnYMK5aQdg/IJ2tQrK+moJQVRCCDImvAuAu5l+dkeeMxXpmMPjNMD7yppiPWeTn1bZttpncHz9VGiVppj608olY+/NpwEGFr9W4ZEuk89cpu+OzHTcneRqVPaXKfnUcxlSGv3K6q4Yuak4+UuqBE7dhlLUurkiaLp5EFZw87uFqPRkKbpmEwn2HKfYiC28iFGYUilMZwh9BWwmwBpnXXLIkZHMAJw9eALSZQ/LRhN1hFLz1LpS1dLM3WUzQ+tFT56QONCycmp5W/+/ilPn05pvVmDwG/iJr6Y2Nra4r333usthKfTKW3bYq29tsNeFMULrylG9Q7bo5L1ycBYy/glie3mpu3ByRyl2oTuFfQ7vOb8+ZDLS8f58yHnz4ecnXS49tXKNbz3LxWbfFFk9ZeNl4m3vmoJidb6pUyLvb09QghMp1MuLy97bZhXFXDd2trq136iRRI4ODigKApGoxHj8TitC9prLkkhBC4urjOklsvlCxo3kd39Nzk4/CaDQc1GuQHdCBctfNWS/TgAff2enJycvPL9f9W2/8EPfsCHH37Ij3/8Y2azGQcHB7z33nt873vf48mTJ9y/f59PPvmE+XzOxsYGGxsb7O7usru7y8bmHqPxLbRtf+H3OOe4OLqqbKoU7Ixfe6E/ejAXoGdceYa/hD3z9WHUGPDbsjG4FluDuwxtx2w+Z3J5yWQ6ZblccHr615yeNkynMwq75DvfO+C1txWj0Rw7eM7CzTi+qFk0z/nMTPy3OLTW7OzsvBIba7lcslx+tRCtuq7Z3t7+1T6sAov2IY07lzVqKCAMmE5rnj9bsph5ZpdDwqKiCJ8OQn/mrG90gYppBzXlTBp7LYHpNQxSUpw7ZA/dZJZH/14S20bYNzGKg4bSqx1XAQySUwgpaYSUxb0o+Ck7kcasXlfaAgW2MIQOohOmggrpLELsTzWEKDvtuiL4gqULPHx4Smm3+MH33+P09IzTkxMGlWFnb4fbd/cZbdQYq1Ax0HWe3dv7FGWZEuzkBBJl9z2Lz4ouhu3FM7MKydAMUJUFXXB08Zxm0WBs2hWOPrFpQCkj9zoEeuvvlLyHEJL1b1z9+4WB8aKAcIzXGTcZpDEmOa2khb+cC/19LItCFv/eYYwAKRBp2oaqLJJGDDRNy2i0wcZGSedaiqJgPp/x7PkJnZeSqeViyXA4oKoGUsLSSBmZd07aTkkNunOi8ZDthJtly2KxYLls0LrEWzlnbU3vgiL6NY7lYsF03jBbyKLAuS5pgAQWzRxlFDvbu+zt7eBDpO0UoVMYU1Bog07vLYySUhhtCV6YLj1g5X1yFZM+7b3UNqgMEKRET9kV2BQySIIwboR1lMFOlSypI0oJO4is86RCnzTn3fpc3hCE0EGfBMeY8I5cSqcIUSzVPR3ZgU1pwCdB7ZDAI4S5tT0u+Rf/4lv8H3/5E06OzzE28Na9t7h1cMhyseDi/JTHjx8xGFYc7t/m0aMHfPiTTe7d22Nro6IqA8vFhKqCrunonKcqC4qCVGqXQACl0yZN7quSQmZyglIK33nwkWpoiKoTFljS71lpL6X5pdf/kHYW0DE1jcr9WxBvEQcWICzGzIohN6YIuGpN1wWcFzZcDyitlb2lGayfx+QHj0Lc0mJQOOdp20hRZsAniGSNCoSoUt8d0DQ1TVMzmbQcXzxFGYeOhqfPz7j/yZOkq9PyT//oLr/7nV20iuImphxKdUQsyfi5H79RJaAw7wixXoLk0/WHzGvpy8UEL1zpyRCFKRYy6G0S+BJXNyzGKABPgjV77KrfLc3sqfSMWENzVhjM+hy1KmuSwyVAJek8ZYBSp/G1Aswiyuiefba6W2tzcRa2T+eQ8VeVHaZI32dUD5Lr9JmyTGBy47FFxXTWUNsCUxoBgchAVTqfNGZz4iXzqwBPzXIJIYk4ZwaPPAXTmSYHOmXkmZnHee6vffOnc+5Bz4AKhpPnnn/4YMKTZ4H5osTHDlt+9Zw2buLrHePxmK2trZ4FMJvN6LrupWDDyrAgRdQMqm0Otm8jG3YSZVVy987da5//1HgFmZqgFBfHNZeXnvPjEefHIy5Ok+D8KwyLTwNqPk8opRiPx7+yy5TW+qVAz1tvvYVzjqdPn9I0DdPplBjjK4EFSqkrTIvFYsFiseDZs2e9RtDe3h7j8ZjFYvHKQM3Lvn9v523eeu3t1QsduK+o5FCXy3DX4mVMl5OTEyaTX8xo+bT45je/iVKKR48eMZ/Pqeuau3fv8vbbb7NYLPjwww95/Pgxx8fHvPPOOz0bfmNjg73dPZbLOa59NfBoVF5/zb+oN686KOZguysbK1/riBr8Drhd1i+qriuGhaabH9PMnnD23HE+OeLjxx9ykrr5N79Z8v7vbLB7e4lWDYFTLhdwuYB/rCANrOaiV4mzs7OvJFDzKoLdL48OP3M0y4VMEV6Bc8zmjp//9BjcWPqb36CILxl0KX4Bo0avgJq8y7puXZq375HFP0iiAawtf3M5Tloux1R2szpIKj3IK/RchqAJpJKrENfGzGoXnHQ8WDE/giAaKBRFaaXkhoCPAqCovEYGSeaSJavzgWbpQRmOT+cQS+698TqT6YyLs+f4tuXe62/x2puHFLXFhVb0ZzAYUxJipEgTo1e+X3jkkpkeeAqBEAQICYhmidUFne9wznF6ciE3U3m0YQW4JEDLx6sAjNIacTxafd9nIZfZRjqSNqGzCCj0jB15n7R1Tmxj0vBRKAprKExiLWiZxCSBbjFG3Iaic8QQcc7TLFsGo61UR67YP9hhsVxycdkQo6JppXxpvDkSsMNauq5LGji5xCFSFAXOy7Uul8vkaDRlMKgpyzExJltkFFlAOBJF02Y+4+mzU05PT1Fas7W1yfZ4g0UzJmCxxrC/u43WgdmsIwSoB5sQA8O6xnVLum5JWVaMRiM6H1AEmuYSo3Pfk2sX4NDj5QbL9TQNBPAE6kGFVhGjVXIBygwzLaUnTsoLM/tLq1T+pLNDjhIbZcSdzEdhjIWesZPGb0oPY8i8HUmqg/OE4KSMw2fMQmGMdIdOe3SyidZa0cWI8Yq3397me9+/y9/87SM2x1sMhiN2dnf48T88pm077tzZ52JyznI5YWd7g9fu7jLeNMKWCxNQHYtGWFLDukSbiPctiijsJVLpUUgKGz1YowiaVEamWLiGajBEaU8InVyXKlJiLcCYzrSRBGjGKELfxhbpiCtGh1RUJTe5K3MNiekU01hIbW4sXefRRRKJ7QEP1lpfRGnXxxI940FjrMF3gS6VYskUGSBawBB8zbIZcHwSuX//AafnZ2xsD9jb3+f86IxPPr5P6zqMdbzz1hbf+/4dKp3gEKUFRIkCJpGc42LMoNcamNSXiyawL32ODCj086z8W2U76tB7X5FhEee8QGOJNUgvCKwSyLaGwlwBPxJgrABrIIotewYzVA9CvIDgpD6d0ZV1+HvV7vT22QF6ds0KwOlRzdV55OdQOlbIYt+CihBjv1+QNI4iasWVoygqrC1YNB3aVtjS9ILGPWNO6snkFoQsCq8xUYBAq+JaSev61a4zIlfPWjnrFadpdffi2ict3pU8ezrhfOrxWkAnoyPxM3ZzbuImft1xfn5+VXw1apbzXWDAVTqdvvrvLyIi6ZiyZiDmOf3qHHgTL4/JZMLx8TFKqV4T6JexFt/a2romvDscjvjC7/PXPM7OzgghcOvWLdH+bBoeP37M06dPmc1mWGt7G/Pf+73f60uQiqIQdnrfr7+o+C0tD7wy/iUW84bFYsHx8SnHRyecHJ9xOTtHK9jagtEI7t6t+02/lXnDTdxEirzjrF54sX+mfTpY/plAjTUmOVHnXdjQi02uFtA5RcnLwjWaPWvL5ryZqnSiY8e1z2QGQX40qmSZCln4MwtE5gTrxQfBKinSSRdBSgtsUeDDEjD9ghaS49JaaZB3kemi4dnzIxYNbIw3OTk+YrFYsLszJgZFWdjkiGQIXlPaEq2k/CKopKcRIwShxYujVUx2tma1C2t0nyhEwHWe1rUsusDzZ2eURUlMDe2cl51mrYnB93o/mQovopf+F06XV9pLrcC2PqnPoBL0x1+3Y+z/nZIL54RJUxaigeGdQzRrDKQyLB88Abi4vGSwMaKuLNpUjDdHbG9vsmwCi4UAL4vlgrYdJnFmQ1lYAWacJ3r53rIs0F7TNC1KKTrX8eTJU1zXYa1hUFeoUmO0IZfqSRmcp3MO5zwXl1PqumJzY4N6MOD27Tc4iDCoawZ1hdWBEDydE4tzozVGgUKs0a0tKMsKY8SFxpjL3MB4HxgMRoTomM9lp0pFg9Y1zl8yvbxg0TS8/voeyiieH50km/lIUZYE7xmPN4ghMJtNhEUVBaCpatG8uLg4x9iC7a0d2qah7cQNyYXEvIohuWUJY6ZrG/qyQCC4iHeuT7xDr/ekKUojDAECxmqsEVcyYaEB1vHd77/GyemCD37ynIcPPmH/YJftrTFd2/KDH/wetlTcvXWbu4d3mE+ecHr6MT60hKBYNg0RzbDeTAKuLoEogRCMjNvMLsqTmnRXccPRBYtFIzCLUQKEUSRHHSvjZC1JjTEBIxF8SnC1kjbVKpWzpDkmAwkCKosoSGZuxKQbItOWRxy4hBUjejfp83odRFUvXEfs+4nYNkt/9s7TNJ2IJlsL0eBdSdsMaN0GJ6fnnJ7PqQaGzc0hz5+d8PMPPmG2nIIKvHlvzJ/+y3cYDiF6TbQeH6V8SVMmLMolkCaXxUS5r5gEgKyDS2k+Dqv5QOH73ydyJL0uj8plegHvhZZe6ELGSwypwunFhGfF5MtlVj14TEgaWsIAklKgPG0JwBYzKNM/R9bLmDS5uKgHiHQClUJAKRGWXxcXXl1+7P+t+r8jYZ05mgBzhbBNfQi4LoiWUHLaU9pgrCUSaDuHMqUAZmtCPZGIoSBk5ywiXeswlRF2oncipk8SAZaOutZ+oT9ObkeVu1d/US+USirDcml4ftRwOXNM5xOCK9L9vyp8eRM38ZuMo6MzFov1nVRNQUnJ7pd2TjfxanF6eopzjq7rUErRti2DwStoA6W4devWNebQaDT6ok/zax/Pnj0D4I033kApxUcffcRHH33E66+/zmQyoSxL7t69izGGP/mTP8E515eSNE3TSxjcxC8f0+mUZ8+ecXx8zOPHj3n27Bnz9oyNHfjmNyveeusOOztbV5yLb+Imvqj4bEZNNH3Nf66pz+UwskgMQpzv9QHSgjyXUqTj5AXuardfvbCwXO1gZpaOgASxT7hlMb/a2c0inqpPzRLsIZkGCrFLHYy2OD2+RCuH0RHXn2tI5S+WZum4uFxyftyxnCuqasjk/CnDzQH7t/cZDAdoa9HGoKzCFAYbC7Q2aG1QKmIU5JIjY22/GZOBGqM1ca2dIgp8JPjAfD6j3tjg8uyM+UKhLCLc2zl8shTOf3pzICUpyUofaNUK8tdq5zWsgS79wr0HYLLOie7f04NI+fNrrJ7+mswqAfLBi5OJhqg0jXP4tkMlhlMg0jRLqnpMWdQQAsOqpiqmtK3He0fbtcwWC6qyEGzRGsrCkPVqiGCtRhskSVIKcJxfnjOZTgHNwcEtlLK4ELFmJWaK0kxnC6azWcrtNFU9RKmSjWFBQAAYoaxYtI6UCRw0xvRlH0UqqWqco9QlSimKYSW6Q9KYKG0oNGzW+/3uu1KGwfA22zvywByONijLkrK+TQzyMC3LCueETeSDZ2twiFKK5WKO6zqG401iDFzOa6p6wGBjHxcnFKrBFCWEQOc65rMplbXEoFkuF0wnLaisY6Mobc10csnk8pJ6MJCkUpeMRgMm5wva5QJdQFUVKB0oy4JBPcT5BmUCyhS8+9brPHlwzgc/+ZD33v82jx4+4fzslLuv3WFvuMMbr7/Bu2+9j+ve5u//Ho5PP6btHJGCYV1jrcFHkt2agBbek8p2pA9Jrhn6uUNHi3OKrgsMByUxNNJ3lQDDMbpEUFjBsYTkqaUUIRQY5VAx0Gt5pA4tWltyLlK2WIBeCdrKSLMQRY/FO4dWFh802iuBgLN7UlTJGSjNfWvjTsaWAAbBB6IKAoZHhesCWhV4P2C5KIAdYhwQ9TmHd3Ypisjl5JKf/fRnzGZLIoqDHcuf/E/32NmE6DocJgmnB/qyrpS051IgKRsV5zGv/GoHObGPlM6lRKoH10PMduSpJCqKbZS49KVnhdao5HKhdNKG6l1Y8gIxsXpy2Wue19Yiw2wqu53lUlZFJtj05av9cyl/Nm8q5Hubxnp+b2YM5rJXeV/aeUjlZvlocXUmkEvHEPHiGJPlN4Gg5ClYlmUSQXdUgwJtggCEXgAYW5dSUhh92myTdjeqkKY2Cq0DMbRo5aQkMZ+N3JoemIoxCBylcjlxmvIj/XOiZ92k5yAqopXl4rzlcgZd6oNyTLNu/HUTN/Ebj7OzwGSyNhdEz/aopbzJ17/ycXl5yXK57FnPIQR2dnZeWSR3Z2fnxp3rFeLk5ITt7W0ODw9pmoYf//jHnJ6e8uTJE0B0ng4ODtja2uKHP/whFxcXPHnyhCdPnjCdTqmq6pX1cG7iasxmM54/f87x8THPnz/n9PQUrxzjPXjnndf4wQ++h/eehw8f3gA1N/GFxy8AakLCXGTBbxCXmtAnMLGvf+9ZNwj7QWfRTOR9a3vKsvBMC2mllAiapn09vb4LGI3sqgJg0i53MgiP8dpObey/SfV/6uEGKjkJGVWA6lKZiAcF80XDbB54/nzG5MJRl5t479naHbN7a5fx9hhTWHRh0aaHl6jqUpKNLFIASTxYo00SjkxlO9lNh7RgDmRLctnZHozGmLLi5OQ+EdFt8N7jnU9A1ouaMxFCIKqslXA9rriVwJpWxvUV+TprBliJBydmzLXPr5VYiYWtwqYHgPfC+lkBcQFjxN5b64LhcIBVUFc1VVXRdpHWOYL3TCZTltYwqGuqsqCqa3xYotzKBagsCqqygijaLW3bcHkx4WFR8c1vvi8CzYVOTimSkBkjIMx0OiWEQFGWSchV3HfKrDFDTFV2qi856vWZAJL1t7SJlDt5Qt9eiqynlCEznYAiBdpQGEsx2JSSsKApqk1U9FT1uO8jWmtcENHkGANVvQkRjFb4EHj7rb0eSKuqMc456SNGxtS4bdEJEm3bhp0dhwKcF7eu6B3WjHjw4IjXNg7YGG8xGo4wSjNVU+Zzj+oUVT2kaRdoXREZMZ056qqmcR1Q8K1vvc9Pf/aA/+tv/obF7JJnT5/QNnOif5O//ZtI20Tee+cdvvM7f8B/+28tz44+YXO8A9pyfnHGcFjjOklitbG0bcB1nqqsMIUkwbYoCdEjGlaWtnEoBjhfomILdGRB4B7UTf1X+mVmXkCb2FCBIHb3RiV3nfVyFylh8sFjjZY5IzMaFDlbpqwKNJoQDZ3rBJhFzoFUdrQCNxKTgwwYhTRPZP2XDqUNVm0wnUDbFoRQ43zD8elzYuyoajg9PueDnzxkOllgbGC8bfmzf/UtDm8ZTHSEaFFWE30CVHUe/1wpn5GuI0CKuAAlYMZ7Ydf4vJBLoA6gMCn5z8BjBsCuzjX9PELs2UtyMn5V7poBmwwx9HQZGXfZvNsnjRsptkrPniSGG5NYdJauubolIMy+kFko6CQUvGLL5J9jXCm/5DLJ/tHGmnpOVKxK6DJgtTpvazRKWeaLJVorrFGYfv4QsKZtPaa2aT5Y6fWoTIVV2WHNE6PvAa3+Jvb3JPZ96cW2V6I2fIUptv5Z33rOTmc4p3ANQIFOYFqMXzGRzpv4RxXOvaATG6Eoa8ZbW6y7rZRl+YUn9blkx3vPYrHAOUdZlq/MPlgv+VmPqwK5v3w0TXPtHIqi+Mol2845QghMJhNOTk76f79oT+2cYz6fX0tki6K4pgHhnPt6uAT9EmGt/Vx9N39+c3OT/f19Dg8PAbh//z5VVTEYDKjrGuecrPVixFrLYDCg67peJySzbNq2pXdu/Uca1zSglGEyn+GWV/vu8fExT5484fLykqZpxMFroDg8FHBsZ2eH+Xx+rc9/Vnjv6bovT3jJWvsbufcxxleeC18sgfyqRgjhmqvbS0N1n3pN3gM+yA9B9Gg/LT7zLjlEpyLm5XNazIrm4RpIElMmG1ZwjPyURW9XiTY9jX61Qo/Z0pZEYk9jJzNtYuTaAr7fakzLe2GQZO8KndKDFm2grApmCwfaEEJL50Sc1zlonWIycSxbxWC4BQGKQcHO/jajzYEwirTCBy+Jvc71ZAGlLTqVWSklgIAxKg3+Ym3HOTskxb4tfIgEFyhLjbKaxrWcXyxRWuFc6IEZ2ejOQI2APjn5kqZfsWDUWit/mpAwsGLWpJ/XFwMZLFhn0wC90v8KfJPPeB/EZSfddNFrgKKqCEGYMEVRUJUlZVEyHIwwBIqipCwK6jpAI8ls0zS0y4hrHW1doY2RdkoJj9FijRRT6YhODIWyKjk5OeGnP/sIrd9hY3NIiJGyrAjB9wOqKCzGGMqyIARJhrx3mMSOMUrurySi2Vp7lRQpYl/eEYOXdDO3SbpufHYLUqv7oqSsRyW9H61t3346af0QEQYS0o+k3TXWVn1JXS+2ncCf4APaWDKzIMZIVVmCc6AipSowtSJ4YS2FKH1oNNrhD/5gTFEWdFHEgzWaW5s73D58DVOUFIVF61zeVbC7J7RmbTSd89jfG/DNbz/j3/27/xfNfM72eMSTRw8praYoa37ywU9wruOdt9/hD37/f2V79yOePHkEOjIYDTClxfsG184oB5bF8oKyGnAxbyAuKUs5h2WzJACV0TSNxmrDYhGZTC5R2qHzeEssAmM1dV2mhFnutQjjlvgu2Ux7TexSGaJCmHFGtGF8cOB1z+aIqZRGysdUYjMk+1ZtUUbTtI6yLHCeBGJKH+oVgzIwEFK5EQLY+Cg6NiEWOFfTtiUnxy0PHv4UHxz1aMBgWHP0/IyPfvYJs4nDaMPuruH/8a++xeuHBVa34Cwoi48dBiPQRu8uFYlR9wwQmTySrbWS61BBAEoRY1+NbmFRCnzez0FkMW/S3LwO4LCCyXswV+ZagKhW9tQrBD6sQCQyIpZHRy57yt+RroHVI6dnzsjkRUxAnGiDaYi+P7/0rZAA2V4IOutTyMAmz3D0nxMWUsiIXf9cktKnmHTOIpHClkKuCjI2tZa+5H1gueyoCoMubb/RgUpP2cQqcz6VtpLF5ENq26vt8rJdu5D7WXoW5PZS2hC8AOxlpYn+UsroVdLfCSGBuzdxE19O3L6t2NpaAyWi5hv37vGNez/gs2r3v4hQSlHXNffv3+fjjz8mhMAbb7zBdDp9pQSjKIo+ac4RY+TRo0e/cuIRY+T58+fXXr9169YvVVb0m4iNjQ1ALKkfPXrEo0ePrgkJA3RdxyeffHLt9ddff/1XFk3+OsXW1tYri6q+LO7cudP//Prrr/Nv/s2/4R/+4R/4z//5P8vGrvc45xgMBkynU7a3t7lz5w6Hh4e8/fbb3Lt3j8ViwSeffMLHH3/M+fn5byxZ/6rGi3bpxJKf/vRnnB1dJQHcv3+fn//851RVxZ07d7h9+za37hT88H/aQWnp98Av5Q7UNM1Lx/hvKnZ2dj5Xf3zV8N7z9OnTX/v3/CYjC7L/4uignl+rLA8Bug5RSHcd4nj06RsDnzlCO6V6S9G8YM1JQE5mM6kl9oyPtJAmgQhoxKEmLSxTmYgw6Vfk8n5XNNIfI6jkbKTpk45ea6Y/n7yUX6PnQw/aRBWphzUnx5dUtkAFEe50ztM0cHnRos2Ad79xF6MrmsWcra0Rm9tDTGFBm1VJlxKx00ztV1GnRbrpnYC0NsmpqBAmB/SJGyoSg6dzoidSVQZjLB7HrGmZTjuMsSIKG7r8lWQ9iH6xrkgAgVj+9iBQDKv7EFcgwsviajlGAg207kt98s7xKhlb0elFEDn2yUDU9MlELvUKgDYFMXrKosQog3cBYwrqwRBjDKONIaYo0dMZi2WDdx5CpIkty7ajLMsrJVnBr0STq6oihkBRFqAMC7fkRz/6Ec61fOMb7zIc1lRVC0RmsylKaQ4PbycwzVLXFUVhsaksLaSdau+dJO7KXGmf/DsSvTe/HtbGVv9j7P+TgBUvu/pJDDevSYwxqX+LSGno29BIAh980i9daV+oNCAUCm2jOCappEsapa+YwiQIM1n+GlDeSVlLCKgYqQYbhBBwWQwqQlnINWtTJYA1oq0k9Pl5poyhKMBHz5tv3uPP/9Wf85f/73/H5eWU7fGIhw8f4bC0zjGZzphOO771/u/w3je+y/bOa3x8/yN00QqgUsLOlqLpphSDXep6RIiKZjGjLC1FmRgrpmA2Oef2cJO27aiKgvHsAuddv3vnQ0jsokDbegGYtEKbDbmnhWU5X+Bcy3LecH5xhopQDSqqqqSwCueFfaSVwxYaayyb43HSh2rF9t0o3Nyl8aBQqqJtA4NBidKWokjzHTGBSOKaFYPrsWWVy4+iIcaCpq1QahNTDIjqjI3NLULsiMCzp6d8/PMHNE2HNpE7d0f8yT9/m1sHlug9rTPghTEWlCOEXOIj55f74ApCz+NdJ1vt2JdzhZg0irJ4LiGVJwkAK2MvEuJVppk4Xa/BxBFiTBpdAVZiaSuGpZA91jRhepAngyQrkF7ptWeDyjD5C/NaBlQSi0ZcpUhaN+tAtAAnSmUQLTODBIARaRwB1FV/XSLCvQ5iZSaX96JNFfAYY0RQXq2eS3muVUoAToXC2BJjYmKSZtE3i3MdTddCcCgVhFkTxJXL9HP8CqBfn5N7/bB+0snnKSJ1Shm0Cbz2xpjzqeej++ecLyNdMIROofyrsQdu4iZ+HTEebzIYrLMqzAv/vomve2Sb3hfjHzNQ8Hnj3XffZXt7m/v37/PjH/+YTz75hMFgwOnpKSEEqqqiLEtijD3bxjnHcDhkf3//S2V0fNkRY7wOKMYS5zrgKoCjtaYsS3Z2dnrL862t335w8SZ+PVEUsLNbQrcjrk9hE8Kns+0+c4aMVLLmi7kuHkATgshLSglRWrTKerK3GhaxxGSFmxQSIKKiTevjLAwJWR9CkUU9JTHNJVYxijZO9BGNTYSatFyOgJIEWpKIdLyoAEuMgY2NHbS+oHOK0AZiMHSN4vy0oVlqbh3eErHVqCnqEUVVoIxOZU2yMysAyoohIfo0BUoZMMKQEMDGipitFkBAKVnoG5PEhHWkcQ3Gaga17Ix45VienDCZtiKiFkkMDdnPTwa6aKV7N5QMiqzKlKRNeoBMrQkG99vJa8BDBmfS3xkAWVlyr6j1WXsHVlo2nXP9znn0gabthJFixII8hJCS+pLhcCR3Oymh98BVjFgNdVngXUfjAz5Kwu2CJEei/eAJqpeR7gElLZQPnGsoCkvbdTx7fkxRlNw5vMV4cxNbGFznqMqKsiqxRYk1WRA49UC55L40JXifK+16ZkSMqzbK1OOstdHr96y19boGkDgJ6cQcAx289C0CIci1ZD2cnJiKQ5kkm1JeU6T+lcpnUjuKjXv+zvT9/f3N4yNilJRSaZt2+xUYIqYH31KKqyWpjAmoQimUtlKio2SMCINMQKY7r73B//oX/xv/4d//JWcXZ2xtjXn05DHT+YJ3336Pn310n6b1fPP973D78A7lYMTjZ884PbvA2kCMHdYM0D5gzADloKg01hphSxUDlk1DWe1QVCMCDUVZMVS1lDIlxoFWipjLn6IwZUJwKCUP4/F4zPZ26rtNw8FiAYiVuzUapcXyyrmOrllQ1RXOOUabW2htcF1LCB1KR1q3wHWdaEg5R9dB0xW07YLRqERpKArDYFARCcznU1CeuioTQy7gOod3LUrVdF1FBGbzC2aLCaPNIctly6OHT3nw4CHLpiUqz+5OzXe/9xaDQcViElhSprlTE3WEZCMPCaxRWacpMzySg5pM1WtjP5EhA/ioUSqilRyXpEcjQIWgKSqBIjJ5677/Z1FcgBg8OirAJBHfVSmkUiH9DvryKpUYS2nOCmusEZ2eLQolYvZ6XQA54RJp/lNRkW2ulZY5J2vSQD5OHvKiNdOXySVgJtVNkZSQ87fIn8QqDd4T0XStJyoDuH6+58pcl+6DMmilaV1ENZ7B0KIIfelVdn4K3qd+Ic5reXoKwafn8AosXweSZW7S/amqNCEI4CvgcIieauj4zu/uc/f1HU4XA9qwyfRiydHDI27iJr6s2Nvbe+EVw8Zw40s5l5v49YQx5hrzCG6Ams8Tt2/f5t69exRFwdHREY8fP+4ZDMvlsl+HNk1D13VYK8+ozc1NxuMxp6enL7VM/8cQIQSePXt2dTM7lrRtzYtAzWAw4Pbt2+zt7XF4eMjt27fZ2g3AdebYTdzEL4qqKjg8PIDu1ucHaubTmhgcWkUKI8mHJ9J2HhcSZT2KzkcInuA9rhOquTGW0uakMflCJTZ6NhGFlVOS7tk1iT2gDNFnpocsnlMJ/lqktCAII8IYTXQ+ASti5WyMpigMMVqWC4/2QldfzDuUqTm4vU89FNZMWdQrAKGqcV4cXqqyxthSSpm0BbSUixgpb4pKhEZ7poM2iUkvr2XbcGMNrguyg1+UyIJeoXTD6dlUyrG87Ir3gpiJLRHyLnnIbZnZGQLUuDXgZF1j5mX7pDlJU32idhWEyYBNSPc401JXejgKHzuI4gymFXRdwAFVWWJtZv4oqmpAiEIVq+qa5bLBOxGea7s2lTDFlJRbnNYiPBulBlDQ7QTOlFViTomTUwaVYvDYQs69bVvm8wXRxx68qisRsNVWYUyBTiVrAgImUU2V9U1MKvNS/bXmyrAYoehr19f6sDaA7Kyv2GFSuuB92rlXsoOulCTDBPBKksrMYpJky0BwAtRoLe5EAdRaItbrAyUALd9vKeeKwqBRQNS9G5ywF5Jmks5sN4XJZYw6X4sWYCkior6pvExbQUyFUZZLPSSZf/3e2/zF//a/8x/+/V9ydPScqCLTyVOW88A33nuXeXPJ6fkJ3/r27/L6G2/z7rvvM3p2wvHxE9pmglIVw2GBjhptFVYXiGaGWD63jWO8vUVQWko6oyIqI2BxmlN8TEy+EFGYVHRkBDAoapQeiB5HBDMcMtzYTw5TibERc8IbmU4uqAc1y6YFPcIUFUWpEmIQGY5JwCPJ7UhKyxaLGVVV4bqOjc1R6kOB4cijlUlgKzTNkqmf0IVL5vOGJ0+fM50v2Nu7RT2smE4X/Pyjhzz85LGAGgbe//Z7fPtbbzKoDFqVxBAwtsTHhhCdlLhGRehaLi7OIAaqshStI+9YLhuGw4qiqJjNZyxmc2L07O5uUxQlWpeJOt0RY6CqCsoiObgFERDOfUyr1AdDvKKXkEHpmEq6VtN0iyKDnAJ8Wo2wdVRmC0axnE/zb1hn6WTSSQI9fFiN7b56Ka7hK0oEdKPz0p8zC1SvgIyIEXBSC/CfqXHZESsm7Z6YRIZDKhdTkNh3mhA0TROoBjWt61BFYsj12j75vQkc1wYdoe08qomUlRZtr6jpOodrG6rSEm1JxMnV9xsl64vJSNBrQFAa3yED7P1nIEbRHvIxlQ8rR1kpbt0acHfjHVR5QDOLPDp4zE3cxJcV+/v7L+g7GIzfXBm63cTXPrTWN0DNFxxN0+C9p65r3nrrLZRSnJ+f8+TJExaLBSDPhuVyyWQyYTQasb+/3+uqhBD+UQM1T548uQbUuPa1a+8djUYMh0P29vb60qfhuMHdADU38StEXVfcubMP7ha4zwnUHJ1v0jUNFxdHjAYlh7e3Ea/ixAJQ4k5kygIVPN18gSk0l5dTLi7OuX1LkhWRaokQHSiPVHqExNKRxaXVBk2ijZsC37m0wBXr7AxCeCtiicE7iCElX6Us7LWR3U0F82UjC3ET6brA5vYuwV8SO8t8ssRFw87+HmWxBcZRVwPKsqYwFaCwRZWyAy2AjDGJRSA779pYrC1QKHxylxFF18xI8KBCKncxoANN6wCDtWUqj9JoBS40PH58wmA4IHphAWSuUa8Ik9ECMlV/pRtzVWj46nvXYx3EAV7ymXVavVp7Xc5kRf/3K9q9AuNlFz7GiDWh/x5rhV20XC4pigptFEdHzwm+pWkaKU+xlsIaSqPxSvSE4jzSLT0hOHKJd1EUvUBaCG0SKNZoazBWBFwptLBxui6BHqTzqCgKAc90Kk/TKZGMSbg5JJ0OnazUMziUWUghRrSxV8rZ1oGrFRtpBYyINtHqHvaitamsKKYSEuecCBMnbQ1jC3FBEpQMrQSw8cGtgEBI5x3S+Um5j4qqL0ULXqgI+Q6GvnQirhgKQvaR8iEfsKZYudL0DK2ASnbkQlZImlJRSgC9j+zu3+bP/vxf8//5D/+BD3/6Y4we8PzoiNYveOfdN2jahsvphOOTU+7de4+D3X12tnZ4+OgRpyfPCX6J0QFjlCTsSFJ/OZlQDWqUsUmEV77a2AJU6O+B6KnI4BDNIGnjrm1TqVroRVZJrJGo6ZP/RJYABV30bNQVhdIoa4laEZIOljg6qdSHQBcKm1hLUVsgUBYVypSopD9kdWJUEJhOJ0wmDV0LZ6eyUEAHhsMh1hacnZ7x4x9/yOnJpZQhGc8//cM/4If/5PcZDoZJRDmgElsQHEE5mXsoUdExHs8herRJJUDB0zRzvBfQM4YFi/kJy/mcyUWNsYq6rogxMl9MiDGwXOgkmB5o2hawGKNpW0/XtXjfEaOnHtSJSUMC2RP7UkWMFvvvrlsCkbqqQQW2tzek2ZNGFNFjTAKN15yMMggpmjN5zkN4MFEh7M7MHsy93AuQE0AbgwuZjxhR0fTjJb/mM3MtCb/H6BOjLYEsStz6XNbZSQAIXuO9YbkEZRXOB8oigTsIQyZq1ZcjZnaaTKuGxbIlYjGDEqUszXJG8E5K53RMAGuap1mBs/38sjavr0qgcklufpe0R1SemMp08/NRKU1ZlDgiZa24+8Y+N/EVjPDL6JHEX/yW32SEIqGVg/6PigFCfe1MN4YH1OulTlHjFht0i5evZb7IeFXR4K9CrJc7rsenXcPLtAo/rRz+i44X2cXGGMbj8bVzzY5RN/HLx8nJCdPpFKBnekwmE/7tv/23aK0ZDoe9UPNyucQ5x9bWFlVVsb29/fn0jiIQS4gD+Erdv1ccC94wuXBXDVkilCqi1vQplVIMBgOKomB7e5utrS3G4zH1qOV8WtPvcv6yEeIvOb9/wREGEH5xeWns//PCq8rJny8pPkvW44s+5q/6vpwn9ZbXwUC0WFsy2t4ANwBfyrMyfroW22cCNbff+mOmk0tuvWWo6wKjFYUpyOKSq5NuiGHBfHGKUiWmaNk/NGxsD9POvpQ4eN8RQ0MInbjnxI6uW+C6Be1iiUIzGAzQSujxwTuMFvaH1ZIkdXEpu7rakrUUtLJUVcnlfC6iqT5gjMFoI04CSuPCnEUzA1fQecPuwS2qaoj3BRvjMcPRCGsLAV+STbM0s05AS7rmgFgaGwtaduB1puirZEuLShozqt/ZbJZLvNcMBzVRKXyMWC3ipdPZlGdPTzAq0sVATIm7TBIRl+pIQ0iJvFFrv2eV4CudWDmrutO8yAcR412xL8KVB2nWuRFAJu0e9ywPcVyRHdpUapEccIyxqQ9IYm2LQrR6iKio6bxHa0NRVZwcPefx04dUVYEtDKPBACJs7Y559uw509kMW1aMVI22mrZ1tG0r4Iz3dEpYQ0US6TRGgLHSWGFH6IBzLYvlomeMkIBAYwymkNKdnJHHKM5Z2hYiwZruVQhQWLsqV0PhQ0dR1Un3R/qUlGvkXfqISSynftpPbAHRDApppz2VWqTEKnURuraRtlSKLrEDLIYQJNk3IYsRZztp6WeZ0eA7Txafxqs+m+ur3uRGJ5Aof6+wGBQKF6JoCiXx054VlGfomAW/Qz8uQghE71FWdJW2tvf41//6f2frP475+7//e1zwXF5e8Hd/N+HNN+5xcNAxmcx5+uQZ7733TV5//T2+8c63Odo64NnRI2aLEyKOUhdEAk2zQBvLYLSBoGyJYWd0L2INApYGQjq1KPcUKRMMXmELKSnTCZBRiUmlM2utbyc5flUaNI66FMBORY9ohuSGFEttryI6O3sBG5sbnJ+fUViDLQwZVWqXHV3bcnl5TtMsmVxc8PjxY9quZTSqGW3ULOZLPvzgQz755BMBd4rIxnjIn/4v/4LvfPu7EAUgdK5CKQfaoalAaXT0qa8XhOilfDIBV9llqhrskKA6vPPsHryF6xpCaIkJKIkobheF3GXv1gBdT9bJCqETweUYCEFAMCL4pHfTOYf3wgbRiKMfQTGdzDg9O+POnTuEqOmcYnI+YTbveq2corREjACtUc7TB49NAt/OOaxVjEY12joiHSqhkt4HKX9sF1J66XNduUVrjw8kcNemceJRJgnZR9bYh5oYEPBdy3t1AgBDcL0+V3CWZhmYzqALHdZCNdBoD9qmUjpEOl/6YoZXBNA1phIWYmEpjKFpOjZHBYq2d8vKZZNhHVCPccUayowwpft7vc6+EYZTZv8JO06QVospNzFmg0iNUoFydFNv/1WMN2796Su9bzKdcn529us9mV8yTo4uePr0GTEMCOEeu6M3GFdOdrFfeO9W/UPGm+O1VxRxs5AF7KckXl9UvFSr4isax8fH1xKRjY2Na9ovzjkePHhw7fMxihNQCIHt7W3G4zHn5+d9sv9FxcXFBR999BE7OzsivHrrFqPRiKZp/lGAMm+//fY1B6Bnz5594eyVuq57J6kMKuzs7PAXf/EXTCYTjo6O+Pu//3suLy95/fXXuXXrFqenp1xeXnJ0dERd17z//vtXjjmfz3n48OErfLsBdwB++wu9ps8bh4eHjLfGV147Pj7mg598cOW1CHzvW/9PnPd0bUvbdTjn+av/73/l5PgnvP766/zRH/0R3/72tynLkslk0ucSy+WSpvXAPX5VgHygI2/c+vIog0praF5hbo2RR48fXwW0cFCcgDnji9wgyEDYerRty7Nnz66998GDB9fE2vf29tL691eL58+ff263vquhod0XweCoEhhTUNUDDvYHEDuIF8CUdXfDF+OzS5+WkeHmvuyERg8Rurwz2Ds6STrXdXPOJw0qBvb271APh7ggZSmogFZeSjJQED1F9KA8zjU417AsFmhTcLC3h/OiB+GiJ6YbEXwHIVL4Fh88isBsNqXrGgaDIYWtOX7+gLYjCewKXb1tPbPZhIuLlvHGa5SDglt3dqjqAU3r2dk+pB5somwqM4lBypmgXyDHCC6V2lgtABBIuYVR0IslK4UKq89oLaVQbdfiHIyGYyCkc8spsBaku5NkkBATCJLKcNZAlJgAhhUzRsAbay3ZOSgDC3nwKGRA9roqrCb0l+mpQNZiMf3uy8t2ZWQne/V7YwxFUYh9pDXUpb2i6bJYLJhdnjObTkENKcoRRVHgOkdd1wwHAxbLJYKEBarSMhwMmM8XdJ2ja+W+a60x1oowrkJKgmKkMMkRykW6VuigJrdLujZjysTQycl1YqlolRtG2haN1rJANEbRti1aWZTK9rq5dEzAmlwGIuLOGqtW2kuZYUDMGhhJJLsHUAQAs9ame6ppvccnHZmQxl0IiVmz5o4gbAO5qd75voxQmDxJnyQvchOZB3T6OznL9L8T5kDWYSKXdyWglahTwur78rvoA81yIcwLbSBCVdX8i3/+L9naHvPf/vt/ZT5f0jXw0c8fcHEx443X3yT6QLucc3p6zJv33md79w5vv/ttzi5POT5+gp9NiEDTeQajzZTyGzJvQ0AXncCiFqXA2gRk9OCuoW1bcfrShuAiRV2sgMdcVoLqWVN5DKkAvnPoVNZHiHJ9Cpls+2aJvYyJUiLOuzEcM51OYFDgfaBpWi4uzumaBbPphKfPHjO5vMRaxa3DbYias9NL7t+/z/npqVyribzz7hv8sz/5Z9y9+zpijx2IaExhhAKFRUeFopR7EjsBuKNOU1Ek4sWhShu0EbCRGKUMy1i0LkQHhWwJnTDMGBNzLGlkBZ90kQK2qBJQrnrgGBBWEZEqiWZn0Fel4w1He2AGbO/dQimHUZqq2mU3eoiezrUoFQk+0iRwNoZA23WJGSYP7BgcHk3nkvjusk19oUBrhXcVoHny5AhjNFVVMt6sRGfIL/Eh4l3A+Y5IoCgMzjtAURUVbePousiycYk1aRnWiuFAgHmjpXZ9uVwym3W4LjKbz9jdHQoLToGJoCMJ7GbF2up/XLH2mmVHi6NtPGqjED0fzRWBZpUZS/1/5M7E5OLW98eYxjehB55iLIjpeU0QMXGvPUUxJMSKEAs8HmVf3Vb0Jn5zYdT2K71PRw3hq2VnbFAMyquJiHOOyfD6jqRR22g2rx5A8es2fAJ+md3RLz8+bT32sriaWElk9i6sGNm/DsclYej6/tyMMRhjXs3S9rcgMntlPX4d1sMvu3fGGPb397HWpufUjOVyiday2XF5ecmTJ09o25a9vT2Gw+GVz796oqoh1vLnKxTtsmJhrqa2s0vNxdnV8aCUYjjcZHpxzqNHRzx9+pTz8/PeDWtzc5O6rimKAmvtNeAtBgP86jpaCilQ+dKi33j8Re+LhG54dT5RXVqH+i8URz85OeHo6KpeXtN0PH16fO29xmSH3fVT/XxzuWzEfZHgmYY4XJld96HQJgBN+vPZ8ZlAzfb2Tu8gkjO6mFaF2ZJa0jmp1a8HW+zt3cYkW2QR+NTJ5UaSVecVWgXZ5fQebQZUVjEYCPjgFSgTUDpQGt3vEkLEOwc+MLDCphltiV2y1ZoQPK+/9VpiNIheDRqWc0naYwjQ63wYptMF27dHVOUAokmOGRGTAChp4tjvQGtCsm5WPZNGhQgafPBpsSxuIVorjBUGhOs6OhcZbY4loUhJsooidmoLxayZE1XAaAs0wpIJQvfPWjIrnRhJxDObBlS/Q+I6l3aLZbG+LjicS2tIdruQtSTWrV5XujRK5deuj2Z5fyopSJ+1haGsCmxhURpckORnuWzouhnz+ZLp5AKIjBJ8YKwheEfbNIkZJa4rXdsRoyROu9tbdJ3j/OwcksZIdjgyUYkQrBVNpFAqfISua1k2C8l2spCpStbbKJwTxoiPkWxRnGcbndy7VGKWKMQhrKqrK1RIEJFdHXN5m+yiK5IWRgZMpAfJAitlU0prVBLBzULCRVH2bemRsg2tjdB7MqgTM/CT7p3PzJHYi0wbpYitp2lbXJcYAArQmtHmJrasiCFKyaBSq0uPWkoZdSqX6WdfASliXNPHUQIeoTVVNcCYUlx4ohTlFKXhhz/8p7z2+uv89V//Nz762cd4Hzg6OmI+n3P74BZtu2S2vOTp8wccHr7F4Z132d69zei195hNL3l+9BhdeNAaH72cUWI/KWMTrmYosj15FCDB6AKjhOHhQ6SsamIQdp6MoQT0EKTNErPoii0znq5rKHWZxF4TOyYocUb3Pt1HcSLTieUkDC9Q3nD05AjnW5bLBfP5jMvLC87OT/C+YWdnk8GwZjJZ8vjxU548fUbXLlAqMN7e5vs/+C4/+MH3GAxHKEQTCyW6KIq1MRuSx3I0kB2E8j1KOl/aJFBKCfArDLyAtgVee5zv5PVEKwrJaWgdpzPaoEIuC4KsxbUSz819MpUV9S59uU0DtjSMxzs0bWBQVyijKAY6sRE9VfQ9aDmSiY4MooUe/JZrTMQofBA7Uq1ERHi1EDZsbO7jnGd3d4fCyPU77+i6jul0gU/XWJZlKqX1xKg4PjpjPp/RdjKunGtwbYNSHUpB02h8iISwpOtgMKjY2LBsbFvaTpzZTFDoEFEqYPL9W1vNKEWW4iGimU7mLNvI86MZMTYU1mC0wjmP80FYisakZ0ZqV5V1eow4QqnMdlsJJ8tYNgScaHJFk5iHNbrYFfacy6DQTdzEFxvD4bDf7c/hnHtpwvpiEnQTN/F1jheTTXg5cPbrCGstd+7cuVLWVNc1w+GwZ49dXFxgjOHtt9++YvsNXy/g8mVxcXHB5eXlldeOjo6kzHwttNZsbGxwfHzMRx99xNOnT/Hec+/ePW7fvs2dO3fY2Nj4tQBsN/HyOD095/j45MprbQtPnlztk0rBu++u9FB/2+MzgRpjRUxSQSqRyNUTAZs2mIMPTKdzgtfs7d/GFjVZL8LogDKqZwSgIsZKGqiJuLYFXWKLQhKRVOojTHgFUfX6BznRVVaBtVLWEAK2GuC7DlTAlgPR94iSwCijqLRLKpNRnFZiYD5r2Nzdpa5LnG8g6AQdhP5PTny9d4SoKGyVktuQyp8ixkpZincK13ZYK+UWOiXrznsWTUtVD3qRVkl0IjokBosOXEwuhPGhjIhtRnE3CV4SVYnMpoEVAGNSe4XephhI2hC6T5zynywQDFm/ZcXQAUkcQgYdcgLLVSp9LrXq/52AoqIoQKlUhiUOVYvFkvl8ibUFbdPSOcfW1pi93X2Mhvl8itGG+WwmC7gQxKXFh16AeDgYYBSMBiV5f8IosEpRGo014hAkTJsCtOVyOuuTMmsCtU6lUlrAD2EzCRq8DoRl4AolYrRaa5p2SdQkjSIl4qwIoOS9gHJ9mVUaIIlLQNZYkiqngEklOz7ZQNvCJh2cnOwKFGpTYhdiwFqzxgpKoFn6K3gvGhgx4LoWvMfHyNmzJyxns7SrH9GVZbZcsntwi52D26iiFKFirTHKgJZkXmdb9dQnQm4PrZNLj5RQaJNAGxRWicOZCkHwBBVkE9Qr7t65x5/92S5/e/C3/M3f/g3T6ZTFYs7Hn3zMxeUFd+/eZXe34fL8kkcP7nN4500OX3uL4eY2tw9v0zSbTCaXtM0C17UYrYnYJDorrJpAriXO5XhZi0fho2JQVHSuI/hOSj+UousajFY94yoEEa/O2jzaiDi6tLmAY1kg1iorbELvMQjDSquID57ZfMZ8MsN1Dc53FIVhOj3l/sf30cYwGNbU9SZts+Tjjz/h8aMT5vMZHk+Inu98+1v83u99n3e/8Q3py0nIVoCyzJBb/R1V9kGLaUYVgeN117dc5pWZVkQwiRVmi8QOUqnVYkQHI5SQvhenUhsdiNEQiQIoxyxOm8v7QpqnhQ6iEnsrhsQ01JqirDk7O2VweKvvZ6iQACiZf1Qehzpm4lea4zx9+ZpMPmgNZbkaf0rnuRH2DraZTWeUlcVqAdRN0JRVwWA0IsZI13W9y1meOzc2RnSvB4KHqGT+b5qOxXyOd5HFMnJxMWc5n2JMwXQ643LScH7REqNlb6+UcuMMq8WIjvTgkkQkA+naaOphjXORxaJhuWxQRMqqwHeeZdNKW6nkJBUjznt5SoWQgDTds/syWKc1WGOT81UQa3kqugiDzRFjtZEEm+V59WvYVL+Jf+QxGAzY2Li64yyC5deZPzdAzU38tkSMkaOjo2sJ/mg0+nyaMK8Y2V2rrut+3VDXNd57lsslJycnXFxcMJ1OaZqmX//nKIriWvnJ1ynOz8+vsYKeP3/O06dPr703b2BvbGzw9ttvo7Xmu9/9Lru7uwwGA4bDYa8FeRO//qjrkq2tq8zKpvHM57Nr732RTfPbHJ8J1OTFcgwqgQIK75ZSokNgOZ9zcXFJYS1bW9soVaxKOlJtv0q72yptIwbnhG1DYNm0bIyHvWtUXH0xKjMs1nZqbb+DSyoXEYFZcc2xKbkUxw6FIrhkG55fU5bZ7JKNjS3KoiZGn1ybdJ/8RqTkKQvnRjTGGkyRxEwRrRxC6MVnC6sS/T5iEjOn6xxN01KWFUVZijAmUXa4k+aDNgalYTaZy46uSXoqVr6r67oVeHCF2SJcj94qu+vE9SlRnKSEJvlGvVDilNsvAzf5fq00a2JK7vLvVqPhRZplBm7yd3jvMUpB1Mxmc+bzOTFmTRXRsNnf22M02uD5syeQ+Apd11FVFXXd4H2grgratsN3S7rWUlhLXRc4L+dYFuJIU5UFSinRBDGig1NWNfWgxlqNdx3BOQaDKiU64tpl0062iklfh3jFOjwzL6wxLBfLBLSloRITmykl9TEDWlr14sNyb1QeCOlnnY6d2zfdf9VTWhIgGlBBHrZuTatoBdZI3wkholPpmUHjW8/5yTHzy0u6+QwVvDjKAEp7aKY8/uiU87NjDt98B11W2LIUoEZJgqyCsIgKI+UdLtmjR58T5FyKIWPNeY/VBdnLPKZyj6gBrfAhMBxt8T/98T/nrXe+wV//9X/hpx/+BI3i4vyci4sZ21t7HBxssTmWsqdHT37K3sEtbt+6y2i0w8HOLdE4mZ4xm89oFh1djFhroBC2XgYgFTrNGYGmm4vGiDLCcjMVURUEHfGhpW07hkMRuxTgKfZzVmFDYmQl4hByTNHG6XLnp3OOrnMsl0vOz8+JETY3h6Ac88Ul85M5s+mCwaCmKA1Ka548ec6DTx6yWCwIsSUquH3nLr//+/+U3/md30nAhEUrK/yICL2obAYksgJ0z3RM4zQx7ERsd8XeQ6ke4Oh3y2ICm5Pgb1+mQySXcsY1sCaz9FaaRTGxwjKYpwBDQFzLVpR3OUYIkboeUJYVTdMx3BhC9D1TS5iaCWxb2zxZn1/k59UvpS2M6OAkdpoIECu0tow2RrRdg6lqYnq+KK3BCyhZVeXaeer0t8UWEdd5jDVEPEPv2NmpISpCsLigmF9e0HVwcTHn2bPnPH80YTGpscbgA6jhBtZUaO0TaCb3TPXjJIjOGlCYyOamZVAPibFM9FsvAK4SwNZ70SHSaQMgrs3Z4nAX+2OHpPmTS2Y1IbHdIr7TDIZ7WFvjujW9rvVGv4mb+AKiqqprttvee+bz+bX35rXOTdzEb0OcnZ3Rdd2V16y1vzGgZm9vD2MMXdelDQnN5eUly+WSi4sLHj16RNM0nJ6ecnJylcEwHA6/1kDNdDq9xqg5OTm5dp0xRi4uLrhz5w7f//73+e53v8u7777LZDJhsVjgnEvOs45cmXATv944PDzk3Xevasw0TXMNZIsRjo+P/tEAaJ8J1GiTKOVhtUMKoFVkPptzfHzMxmiDzY0xShtmswUxivWUJK1plzaxDrquJZfthBAp67oHG1L2SnYbiilRyGUe6wlr27ZCWV9z3WmaVhg2RYUti+SAk8oxgohNtm3HeLyN0SURhfcxWSDLTnHWe4wqrspYVN7llbIOqfrJNqiySCaBSpmNEqNoU0gyUMtubhIjzgyL0O80R5r5sk9ORFslsRY6epAK1hHEFVAjOhExuaFEMCo550gSlgWB11HhPOEI0KLX3IPk2CHEpKGwKgdaB4vWWTpKSflH24ZkBezxXUfXCegSY3JqUZqdrR3Gm1tMpzOatmVjc8RytkAlF7HtrTFN07BcQpHYXG2zQKmawaDE+0jTNFirMQa0Fj2h0bBmNBzQeU9RFQxGw96u3bWeZtlQV0MBTZQwgNAWE2PP1sosIkVOHOU+FUXJcDDswaaVfpDuQcTYt5TcpL40ag3xjag+mbK26BkCq+QtWdQnVpkx5kqfXwE1svuvdEQZRec7one07ZL5dIIJnqIusWp1TkEFKgNRB3wzx3dLVGlx3gk7JJXF0J+3wTkvFuHJESffwxBT6RaKZecojUHbZK0MoCxtcChlUGblsnP3tXv8q7/Y5/33vsn/+df/haOjZxitODl9zsXlEZubG9w9fI2IZnJ5weNPHjDe2mF//w67uwfUZcnO1iajQcVyuWSxWNB4B0r1jCqVgSRt6FxgOBygTYFFs3QBbQoIAVMUci+MFaAGBdGvrNSjwihxB/M+JKFij3MCRjZtQ9u25BKxsrS0zZz79+9TlobxeFPYZQ7AoLXl+PiYR4+eMpnOUEqS7a3tDX74B7/P7/7eD9je2qOwFYv5grZ1ie0TRIsprnpYTOO2B7HXBGV7/ZLezlo+oTO4TCqdIcqborigiYVzYthpEqgRyZbasS+pyazDdCaR/jsFXJfxE7U4lsmMkcux5IR2dvYE7MrueDEQMf3cIoCT75k0CtXPWxnsl7nXC2dFI6ARqtewyu+11uKcF3FpbftNA63DlWOugFB5VpWF6QEsn8pls8V2CAobFZXZwHUw3txka3PMgwePOTs/52LcsrG5gfMRHyI28+MSiB7XgCfyuI4ObRoK3JrQuFjRByJFfv4EaVOTmFZyyjFtRETE2SmLlSeQT0UpI9fgoqeyI7bGh4RoCFGegdbqK3PVTXy14+Li4prWx4tJ4VchlsvlNRFIYTBeX1x/mSBN27b8+3//73n27BlFejZMJpOXMn9+mdjd3X2lRKJtWyaTyUs//yoOJp+XjTQajSjL8nMd42XH3Nvbo6oqvPccHx8zmUxe6vq0vb39pVl0hxB+LULSGSBZj3WHphx1XbO//8U67nVdx8OHD2nbFu89m5ubVFXF7u4uTdPwxhtvyEZRCDx8+JD/+B//I9Zadnd32dnZoa5rnj9/zvb2Nrdu3eLWrVtsbGwwmUxeaZzWdf1SJt3LGC0vi9lsdg3MLcuSW7duXXvvycnJtXM6PDzk8PCQ09NTnj59yunpKdPplL29Pc7Pzzk7O8M5R1EU/Pmf/zmbm5vcuXOHzc1NnHMMBoO+366Xan7R7nBt214DlL6qsbe3d7WdVQfFAop1AsHnj3Vx7BwvY6LljbsXWWuffLJksZiwuwuHh9vs7u6yufmC9tnXMD6bURN02r0PyYkiQHCcnZ+zXC7Z271FXdc470U4bjCUsg6VWTByA7P1LyDJslEYu1IqXyXLshj23osdsspAgelvSgixfzDlkpXFbIFCUdWD3hmHCNEH8IEQYDFv2ByPRVciRIzJGgt69f1pJS37kHJeuczDe09kZaEsDjJZtPGq6G7TOpQ2VPVAdorTrrgkXV52uK3sfCsNy0WLdw6nXUqWXkz013RJ1hgx0rar1xWC2q9YRys9C6Vk13hlTysyEAIYxNVuOrk0Yr38KeNoWcTY9Lu1ossRRAxTK5xSlNaKCC6r8itjNId3DgkhMp3OxeralizbCwpj6DpHWRqGgxKjV4lp27VoY6jrimE9ZLlciqUvwqoojLBrikILwFVK4u4cVGVBu5yJVXPSOtLZ0UvbVbJLUpOJ2ZJX2mC5XGJshdIWqyVJlIkhJtZCxIUgfc6YfrrKJWE9w0FlR5l0XxIott73QwigV/dnfTxkoHK1+2/zF2HKgmY2xwfFcGNMt5hDaDHWyjU0czofCFqjy4pl19K2CwZbYxnbvoHkkISS8jDfNdKXtMG7DqUMQaWyr0iyHBYBYw/JvlmLoG/UKGWJgPMBpQoICkfAFiO++Z3vc+/tb/CjH/0dP/r7/8HZ+RGL1tGetpydT9keb7O7O2Y8HnN6eszjRx+zOd5kZ/uAzc1NNjY2qOuara0SF0Q/qOsc7aKTcW01PnQE1whjKkiRW2EVBIfRikFdomth/4k9c0h9xON9h/eOxXKeRM19YjoJwDGZTDg5OWFzY8x4SxadFxczvA+Mx1tpFwaaZsF80XB+fsnz50csFjMiYEvD5niTb3/nm3z/n/yAnd19xLnK4oPCFhWLZcsQk5g0aR5IQIKIuAtoDAlU5gUBaK2TxksCFwGVRaIJZOcvAWpEC0VrYcKlF5Mo+gqolTkolThFQAWizsi2sG0yAyiD7VnMOqZxF2Okroecn5+htRNGC17OKQHf/bNHyfND8A1pi9iDK1K6pQJkzR4U6FSSmjVtjDUMR0O6TkAdEdgWEF6H1XhaFz3PGmAoL85rxkJMTlhZrDVC1BVVDUWIFIXBlq9RPyk5OTqiKCx33yhFXipmAC2NZ50ZNYKVOdehYocxnQBURoApAZpzKVsqVVWrZ41fE5aJMQNP6bkXVyLhAvbJuLUmUhVbaDMSeSM0ojtkrzCVbuKrHRmo/qpH27ZfC/FY7z0ffvhhz84GPvd5i1Dp8Be/EXHZeRGoUUoxGo1+I7v4VVVdS44+b2xsbDAei/NOCIH5fE7TNP1r65ET4y8jvPe/FqAm2zm/GC/2q18HQBVCuMIeySYfL3PDmU6nfPLJJzjnODk54fLykhACTdOwt7fH3bt3uby8ZGtri83NzVcCK6qqugb8/TI6L23bMptdLXVxzl0DvjIj5sVjd11HCIHnz5/z8OFDjo6OaNuWqqp6i23nHGVZ8nu/93v9mJ9MJkwmk75szBjzhQOY6zGfz78WQE2ei66+2EExAjta26X+YuJlfSVbzOeIMb5UP2i5nHB6eom1sLVV0XXdb4XG0GfOErLQTCVMRLquYT6d4n1gb+9AmCiR/m8BAwxZaDYn+pKISuLeW0Rn5yPh9l+ZAHLOIetoeVA559MuqekZC0Ba/A9EUDGVW8T+T6TrPMu2ZWNzTFXVvUgvkNx+dNqRJYFESd9G511U2f1GGUleC9n9jcEL0yaIm0bwAWPFiSSEyKAerpg4KfmXIyZhUGUAsbptmzYlBJJcBe8IwZFLi9Z3oVY7xpC1ZvodWrWy7V4HZFalN6sSmnWGjdDwk9W4vIDK1Hm12tHP37USN1aURUFwDhe87O5rTdclp681Rs/29jZ1VdM0S6azKXdfu8NiMUMZgykKSaKNpigstjB4JyygqiooSouxlmFZUxUm6dl4bLLp1lrshI0V15QQPM7TlwitJ7q5JCAqTRbGlncmUV6EiRRRhCA1k2Ayh6kXxvY+4EPAmjLlx1IGBRmoySBXLhvJjJx4BYRc15/xXgS2i6JY/T5b7q69l9QvM1uiqgdsbu9QlRXzaclickFI5Xpd19JFKDd3oWkovCciwsraGKJZK1mSL5SxExMY5Z30Ha1xrbDLtBEBZt81eF1CR39tWguoo0iOVBm+EgERWufRdsh3fvcHvPX22/zsZz/hww9/zNHzZ7TdnJOzJefnRwxHQ8Zbm2xubdJ2LbPpDGMsGxsjhsMhg8GQ0WCTsizR2mB1IKhIXRV4H0XkuWtYNssEsoLrFgllir2bXI7lYrnSbyKgjOHy8oKLi3PuHB6yMdrs7+/JyQkf/fwjtndkTglBEYPCB0Xbep49O+b8QiwwW+dxLlAUBbv7u7z3/nt853e+zcHtWxTlKAGtCtIcam0JStgOylhyAZJSoBILJvWufixC7IktPcAKPcCuMP25a2USwKJkDlNZYFlBD1EnW+1UMhMTa0Pmgqvi4wqNiAALeCJzR3boWjEMc39VQFmUnJ9fcPv2QWIFrsYN+J6sk8erlLaKmHHsAYp1NzwZT9rKuZB1odIxMlChlBEb8uj7frlOac4lo+KapglRwBodCwFp5DZBCEQjAs62UNhKYauCwXDIxz/XPH70lPF4g0Gt6XxAofqSWMhzt0Gh6doOox1a+xWAlt60uqcZ812BWRn4yvOKiJIn5lNIUHKI0p4JhBKr+y1QhXzeKAplb/RpbuImbuImvsDY2dl5JfbJl8UkyvHtb3+b3d1djo+PefjwIc+ePWOxWFCWJcvlkrOzMz7++GPquuYP//APX+l8l8vltWv/vIy5ruteKtD8Mi2gDz74gIuLC3Ganc1YLBbEGNnc3GR/f587d+5w9+5dxuPxTTnTb1ns7oIxMBhoFosFz58/J4RwjeH1dYvPHHUxxCS2qFguppyeHjOoR2yNhdIZbRZ+pAc7QNJesSKORC+ASQz0wq8xJRLrDJG8KBeKdxDNlgTS5BrLsizXkpM1tocWx5rgVyLARGgaR9N1jDe3Keoq7YqvrXfXkEDZWe7SzybnHAmUyAvsNX0Bk5IKrYhtwBiD95627ajrQToFlfaZZRdYgB3RN9FK03VLAoGu8xRFiYpiA+46J+2Zzm/FpsklN7KzLfaHof99BsFyuzon4Me6Fs06M0OAl1zCsxKR1UZjC5vEW1e3J7eZJICKqqyoq0q+KwScd2Lnm4AbcSOhnySbrqFpW8qqZDgcMptN2dzYoFkuRZC4LFOJjSLGFhBgblBV0j5AWRbYxAwyWtpUSts8ShtJ+rQkiF3rsGUhJWil7IoLmBTFslatLMtJbAMVRbzVtS3K6LTbbPoeihIAZ9mIO0suV0pb/yky6yk3uojR+jVxZ210LwCtX7hvYoW8ft9XANwV9mGyqFYK6tEmg41NNrZ36ZbzxBzQ7NzJbkCa9VO1ZZF29UNfoiZ9ATxeDLNiSL5VwjaJMeJdJDscBd8IGJM8wkP0iUmUyqiyGC6ANhBFbyM7Em2Nb/HDH9zim+9/l4/vf8hHP/uAx48esJjN6S47LqcTzFPDcDhkd2eb0WjIxsaYqiwpy5q6FMenQV1TViWDwRDXDijLgrq0Ygmqhf2liyIBGOlafOyBBa00R8ctDx484u7du2zt7KGUZjEfMZlM+PGPf8Jrd9/AWCMsK12wvb1LwHFxOaFpOuazJadnFyzmM5bLKc51aKspy5q33n2T997/Nu9981tsb+8CAhR6b8kUJRGfBRFrtnRpx0cJpUL6gDLE6HvNnAwy9oAKKybWCjTUay5MgpfpbOGt5b1FUSbGY9KhSX0s5HkCldptNVPniUHAgQwq5klV9cwflQCSkMpFvffU9ZDLiwnLRctgWCbgeiWqLfdFjpGg5rQBoHs2ncxNipUVdRI6j5llkzcBEuARRaS+sAURTVSreVPrbGkuV9svKgOoVK6qtca7IKpaWhOSiHtASgVNUTAYWd56+02U1ty/f4opNAe3SpwyPaRF0qbJ5UraZNbc2ubDlTkpAb8JrEWpvoUjqncukzeGJN4sLxkjAKzSCe7TNVW9QcD0H9E9gHTDqLmJm7iJm/i8oZTi8PDwlUAA59yXyo7b2tqiKAqqqiLGiLWWpmkYjUYsl0vm8znPnj3DOcdrr732SmV2GxsbLJfLK69prRmNRq/EyHlZdF3Ho0ePrr3+6NGja0DN06dPmc/n1HXdl+CNx2Nu377N/v4+d+/e5e7du4xGI37605/+SudzE1/NuH17m/FY5BGm0zknJ1OcM7z55ptf9ql9rvhsjRotNPnlcs7jR4/Y3BgyGo5k8ZccJXqgIDlQKK0SMBP6shrvPXVV92KrPrFoIqxKiEIQm+t0DElMdC+SKzvnK5AhR1/7H5OVtvcYrWi7ltZ5trZ3EgMAsgCnUpGYdvsza6XruozMiKtVUeI6YR+sJpdV6QBKdnG7rsM7h7WGtnUURSWJvUoL5pDtk+VjWcnBOY82hsV8yWQ6w9pC3I66kGzN5RszmyamUiJp03XdGvnbGEORgCwBcNZ1TVZtll9fB2v6UgGjyWVK1pheJ6XXVjFWBHARamedNIbatqXzcj2VLSiLApMm/JPTE7xP4qIazi/P2dvbpW1bNjc2mM+n2NKys7VF1zXCGtBS3ua1giBuXQZNWVcJJBIgSSvovE/OJsKG6kLE4iirIVFptrd3MVqEYFVKlEhJ6zoDZtUeCawIwpCKIP2VZIGNWGBXZXUFyMpJXiI7rB1vlVblvi1lVkk4uk/UMmsqpp31FSMnRk0Wds2ASmY35XOWajVFOSipBwN8309SWUpMZRTegYr9uTvniCTB0+R+o1NinXV5FAIg6ghBgScQY0cMLSoavHdph1+Ap6iSTXVPjQugbAL61vutCBFX5Qbvvfs7vPXGNzh6/oRPHnzExx//nJPjI7FH7homl5eURUlViSDtzs4O480hZSH01LIssNb2oFdZlmhjKAt5fTgcUdYF9aDCKCNgRwJqQozYQqbCTx58ws5sRgwwXyxFnBzNyekZIYo+1nwxl92a+YyLi3Oapk21zAHvO7SB0XjIvbfu8e3v/A6vv/k2G+MdYctEg3MRhV0BEz3rSEBua61oo2iNMuLuJUAdSJmRSi5cCeTJKEMCwjJQncHDXueoZ8uo/isFKDQJrFsJt6skji59LqzNIWsi5LkMiheuA8jsHIi9/lU+hFKa0XCD05MzDssDTGF6Fo2MvzznptkyjVOTGHTZWS5rEmUWUPqRDF6pxJ7SRhGc7zXEtNAbZbzlOT+3GarfeEBnQMf1QJkLAR9BJ6YKUc43JvCsrAy3Dw84+4cJDx4cMxjtszUeIfhJVjUS4Md1LZCc4/r2Wm/PfAvVCrzpp5k8p+gk8RxRa2VW/VuVzjgexgzRZqM/F0hD07B+9Ju4iZu4iZv4HLG7u/tK7JP5fP6lAjUnJyecn59jreWdd97h/fffpyxLjDGcnJzw+PFjHj9+zOXlJWdnZ68EPr0I0oAwh15WevWq0XUdx8fH114/Pj6+BtQopdjb2+Pg4IA33niDg4MD3nzzTd59992+hKrrumt6QTfxS0S/7ltb2P1GQ11fcgK7O7t0mx1Hz484Pp7y/DkUBVxdQP0G41ObRn3G767HL2DULJlNZzx5/JidrR12tneEUcBq4S+0d0lijZZkwXVdYuNkSrlZJYFru4Y56VfotK4XcMMYjSfQtrJAHgwG11g0sEpUQRJO58RutWta5vMl4+19sbnNbaLWlvV6lakH78WJSsvZlVWVtCtSEi3ZTr9zi9GpPMnjvSiDN21kMBB7cEkAIDu09HNbTC4lqeQgRM/Z+Rlt57CxFAtVtdIk6V09opTEZMegfO3rYMA6K2OdhXHtnr7QfvIeEe4tjJUd9+SIUlhJco0xFEWBLcSGO+s5dF2H6xxlWbBZjxmORgzrAWVR0DmX6l09bddS1zVd17FoFsK0QZgVnXPcun1AWRScniwZVDWdawjBY4xo3QTvcQg7xlqLx1OXFQQRChXdIVL5SaBbNAQszntGmxu0iwneu2RjW/RuZJLTrXR8VkykrgdqVjvs0jcFUEtOYz1zRvXHiz0NKmuE0Cehffsr+uQ1l66Is4sTcGzt3qZ8UcDFF/q8Stmv0qCC6q2QMYU4XPX3OwgAhpSBoEilNQZlLVpJOaCKCmUEJNIpCc87+9avxrkPnhAcbZuApiRGG4NPQFgn1vK5XaKwMYKPKCOsMrRGxywArHDBo5Xm8PYdbt8+4He+8zscHT3lZz/7CU+fPmI+b2jbJc45JpNLTk6O0UoxGA6oqpLRaEhVldR1jTUFZVX2bBqVgFxtoCgLrC5QUeOiw1orNc3epfsdePjwEW3j6VwQwLdtaRbPuJhMWC6XdG1L5zoIwjZCOYyJbG6OuHPnHu9+431u37lD5+DNe+/QevCxQgUrGklKUusex+ofdoqoIkVpWTZNchZb3fMeMMnJeO/UBCs2RrppCeRc7yu57C54DyFgzerz64CsgOtOAI4r/VS+R5hiaeZ+cY6J9KCiSh0/BimFzHNsjLC5Mebi/IyLiwt293auHCJb1isVE3vI9wdXKpcfRsADRs4nsSW1lrksAycokSrWRizFgxdmp/MOUH19+qrELAEmuUQOncpoRdMMBfgAthOAJmh8FFAzmEBRlOzaiu/87rf4H3/73/nkk1PefceyOa7QCINFpzZs2zkhtuJElftD3hiIkGipPZMxobdyn7Tq2zm3e2b+wXqJmE7PIMtotI/WA4KX9hVHvnxPb4Car0tUVXVt3HVd97XQg7mJ62GMuaZnkxkIr5IYW2tfWePlZeBBWZbXmBLZyvlVoqqqa8dVSnF5eZncPGsGg8Gn6n0sFotXEsM2xryya1LTNK90zF+XiHXWhVmPtm1fek4vAhgxxpc6o/06IoMWo9GIW7dusbu7S1mWXF5e9nqX1lomae3zYohm5Is6Ictr/TbrvbzKc+ZlYsKz2YyHDx9ee++6xEOON998k8PDQ/b29jg8PGR3d7cHas7Pzzk+PmY2mzGbzV4KHv2myqGMMa987182xr7cOV+D/3JFeoelvSbWXpgCfEtth2zWQ9oNR10Mmc7mrIwwPj2+eBcpRWl3KcyYdaSoNCW4rVc+ymcCNbPJJacnpxzsH7C1tS3JFrJIFLFJ0UAQiQUtzijeS9JjpOQkgzEyH0pik9ehRmVb3aTZYQyESHBRynZsQZEmixfFdNcnWO8dy+UCrRVGlzgf2NrewZYVWosbSE6Oe/vXuHIU8d6La5MTfRCxY6PfkY4BTNLUQYkAadc1aKUoSku7aLDW9nbh/Q4tqmcHyUkL86PtZBe1bTrqqqRZNNg6ATwh7UC/QElfb4N8/S+2SUjJ9FVL59CLZK4W76SEZ6VtoNK1WCtgzKAqe/GtwWBADBEfZVc6K6EbbdjcHDMYDHuGjbWWxXwBMSbh14rFcsHWeMzzo2dsjkbivGQM52dnjEajnio5GAwZDodMp2I9bZSi8Qva5QJPh6lLrC0Y1DXBedqmRVmNtZYYoO1Cyq8ibdvRNC1t02KMFQV31WK0RUdPMrjJ0ht9GytWpV1aSUIV+116EV/OfSejySpP7PleKPrkqxdfJSaHrtVgzQCGgCTy+XXtmlzu1jMcFH2C1R+bkO6tAJ6iRa1AmeRYk3fOfbJTBqONqEgp0bFRWhJ3lHw+O/UQ1ZW+lSAlfPA411FVjrqupc9HUlmTCJ36LjM4pOQk4ASgCAofIHqFiqLF1CV2WXBSQqWUwfz/2fuzZsmRI00U/FTNDIC7ny22jFy4FauKt0umR+6d/v/v0yPSIv14W7qnq7uExdxjO4u7AzAzvQ+qaoDHCSaTLJKZyXKlJDPS4xx3wGAGh372LZHx5Mk1/j//5/+J21//Cl9++Tm+/eZbvH2nEZO1CkqNuH94wMP+Ad9++y1CdKnTwgZLKWHoB6ROAVtl4GxACJjzBDHgaRpHO8+Mw+GIUnQO5XlWfob5o5RSEKMCy10iXF5e4unzG3z8yQv8/Bc/w8XVMwybK4TQYz/OGEeAQw+ImsuClOGh9yGVDbkHTLVbjsaA59VDCMENZX1uKHOGweoKu3wFGFDCpD5bgoY5wGAAGA7SgD9xhkfOEFrNwaoPl3pvCwtw7fPfkCS/17El2znzDGTeOUyoNSNE+31jtjx7/hG+/OZzXF1faXIXCGLpUSBaWB/i+MhsAC7DhWJkzEVBtTQ+PWEyiWg1cMrXTskFFYx5zjZPVD5HDmzZ5+vaM/YlBjMytjlFANx8nCz+GhVd6iA14FhnPH12iX/6p3/C//yf/x0Xu1t0/TMQWRw6BFRnzHkEpJiHTkVgQE2UF5lU+x6x/wn0nNguoIPNDGiKIXy+GPjjTCUkdOkatSSIsDLF2rx4zFQ914+3bm5uHr12e3v7FzFFPddfvvq+x4sXL05eIyK8ePHiezWNl5eXj2LQP1Qigs8///wRWLDb7R7FMR8Oh+8N1FxdXT1qON+8eYMvv/wSz549w8XFBZ4/f47dbvdB9sjbt2+/1+d0Xfe9gZrb29tHhrR/zRrH8VHT9+rVq0em0ZvNBi9fvjx5rdaK3/72t3+Ve/Jnn32Gzz77rP333d0d9vs9fvvb37bXnjx5gpubG/z2t799dE5v3rx5BCyM4/hBAOFf/uVf/uTj3O/3+Od//ueT14gI/+k//adHAOB//I//8eScbm9v8c///M8nDJrNZvNXiUr/rvrQuv991/76+voRmPvD3vMZKE+BcvODfDoBeHb580evf/Pttzjc32MTnuHvPvsN/u5nAsRv8e03vwPo35bi96cV42L7M1xt/qERQ1r9ERjbdwI1b169w8uXn6IfBmQRSDAnBbHIbVuzTIRSM0pWX5LQdeoVENQ0mIAGjkDsUVIEJLbTT2zyEkKFIJeKEBM46i6oy0KYtcHTh+8CDgr85DIjdREENeG8uH6mO6utmV2a7QAdL0/WEDuXeS7oUq8gS60QUiYLk8pOSgN5lLkjBCAw7h4e0A8DYogG5DhAoiR3wONlxUCRDARgfxix6QYcH/bY3++x7ZU1ZHaQCwWj/d4CkGi3HVYE+dM6TQ2KNhYVIktqlcqc/OfVOyilDpvNgBgZm14j9jabHZJdh1IzpllnVwgdhmGHrhvQ9Qldl5aYb1bTaKoVlVQmtN1sse07PLm6QNfp7k8XE64uLhFZAbrtdgsixtBfIIYNai0Yx0lBtlyQpxmZIuqUIaU0w2cpAg6sjCPoHIxJPYD2D3s8e3KFeTqAYgKzQPIEomS70xU1LNILQUWZR8SYzLy1rsAY/SFlvHDb6T4BVWyeGbxhYEw13xmfx9pEMqlXDUSQs8og5nk05Fxs/gIuAUFrrFxOIhaJPmtjywJhjdXWn1nNDpG2xjyAnCCQIubXohIOCC1MDvMBEQGqmVEza4ISx4y5ELph09anwNOR3JBY17lL96ooA6/kipoVpKnICLPeQCvDTJpnzKMgl4ySJwRmvHj2BM+eXuHVq1eY8oT7+3vsH2ZMY1GgZdJmWf2KMqbJwQTgTdXjWryalEmgjTGA1UqiJh2h9t/EjMRATBFd3+Pqaoebp1d4fnOFq6sbXF0/RbfRtQAeUCmBubPjmNEPAdySi9RIXDFUgTSWnwGBAvWaAjBPE7q+h/M9arV2nRxgqcooM+SbSFOKREqbY2L3VxKFyZ3pKPC/FwMe3avFpU26NjbDDmALooMzpOyI27xfeY2xs2AUiKwwCRfFBnpSVHbV9vISm4dLTLnawxapf857dzVxjxW/lxF0HBsTSYEfCmJzwAA/KOCxxMorOltE0A+b1TVWyVAl2G94CpWtFhY13fb5AACI6uHEYswlBT9KLeiHiHHM+OTlR5As+N//+/+Pvk/46JMtUgy2NidEDgqaZDVArsAiywJW7DyfnuI3bKw3MR2II/MFcnYQRAEy5gCmDVAHcIgo4ol/1YyaT72vznWuc53rXP8+K6X0CPhzpsf7QE0p5RHg8T7Q8+coZsYnn3xy8hoR4fnz54+YSz9Ugti/ryIAwf75EZVEqKWC/7dA7QJ+wBK24/rTj+M7gZqnz56j7wd40ofTwl0Kog0rqQQol4b+l1IRU1qisoH2QA1nKjRTUd+1t1jSKraDG1V7L2qQ6DKgWrWp0gdMIOeKGCKmaUKtgt3FpbEPVgwUkZPnXKzYKID6kcQUQR5fzeuko2UXuZSCPI+WRtRhf9iDmRFSZw17sw5WAMgAqFIKui5B0z8CHg4jmAM2mw77hztA9Jz93/O8eIbkPDeWDFvK1JruJ/C0kjVTQ9rPvC9p8GsUozZ0JesOdQgBfdeh7zo1yTUvCLGxOxwOmOYRu4sd+q5HrcB2szEPIMKbN2/w9u1bsMmiSpnbLvx2swEzYzNscHGxa/F/m2HAZjMs4BEzShVl8gRgnI7ohw0CBHWaUXLBiBHBjr8Z+UJBhRgjApcGBuZ51p0NAzuUBVTBXEE1A6Waqat6uei5VmtqlT3T4oVhEqNGX3ATVW0O1x4eIhVSSms43X/Jr0MpFhcetFkGxIyFC0IgVPd8adfSvJsMQSGQyXT0y6oY+AMYFdTApEW6B7AoAKpeQbY7v5ZaMOsY2PewA0HBpEMUaPl8M6thTgCiGY6bEbJUFClNdsPWOKpUQ2dsrVVNxqHmrNUZOSZBLDWjFl378zyhlIx5esA8z4j9BkUqpnHEOI7Y287f/mGPyaig81yQc2nAoQK1rJ4jUpV5xOrtgbaK/M8FMVKjgw+bAZtNj+1Fh6urS2x3G1xcbLHdbbFLV+j6LVK3AccOKQ0AR1RhpDgg8AwRGIXYAD4s7Be/7zkLC9XSi4jRxQ4lF9BADfzlBshIm68LwOQEQAUMFLwIcM8lBc0ABAMAFZqx1DplRjJrwp0azAYFhow4Rswr2aquE/2MBaRsQE5VyZFKcdQAuTHCbKzJQMjrmxu8e/cG281WAVtbU+JUkNX3jkv9TuO8Zfl3OwZurB1i97kRMyCPOt8aX8fvme4EI+0e2r6b5FQa1lhIoqC+mzVDqn5vZUHXdahF8POf/wzv3r7F//wfX6Lvf44UEjZdRZ5nw4LEPIgEkKSfbsyr1R1dfd9sI0DvKf5Xtf2cm0nrLLALxwxBQOq24NijiiZfFTun9787znWuc53rXP9+q+s6fPzxxyeviUiLvV5XCOERc+ndu3d/dknOdrvF3//93z96/ZNPPnnEOvuhmTLnOtefu74TqBm2O4h5CgQKzcTRywGBUgpCTMjTbCDG0JKTCCYZclAkZwg7Zd4BB2McCJBSgHskCOkDp2vx9SbBJmsgSwABDvsDpnHGk2fPWzOpzXNpD9CemuEsmjVQo6CFWEOhD/psABSw0OtVs1HRd3quJITddquNEUF3rq0x5BAMHFrSmggwWQVwebUDcMTd/bsFRAGQUlRTXVJtaM6qIQ1m7pudtbM6l3VDvj4vPXZqfjf+b016ScgZCJ0aBxMpks6sTWrfD3h42CPnd7i+vsY0T0hdr4bFrB49t7dvUaVis92g6yIuL3YgUtPPWgtiDLZra0BQr9TVcVzOL4WIcRoRicGkfgp9P2grmTP6foNEDOkycs2YjkcUYlBglS6wvrdUBcaGocc4afzuPE8YpwkP+yN22wFVZuScwZwROms3S4YUNXt2+UcIBEjR2HUDtwjBpBHLfr+YlE2JNysDYHHzVAVZqslTRKSlKenPxBV4M1pkvb4XhaDMi5NrujTZqN7kVwQOCFGRbWa2pB1Niqmt9yQEjvB0nRBUlpjnrI0tFEiobOflfkdYDLe12RXUUjFNk74Xx8Z40rmWQIitkQcMIDAgR6yZZzYWiVRQEFBrQqVJX6RU5DwjxAASwlwynh0P1ssLiuQm9zsej+adUyCoyDljmpWBJlUwH4+okg2ArAic2hEqGOFslYoYGYFVArjdbtVcrwvouwEhRHRdjxQ7MIJ5YAWVsHEAB5VixpCwu1QwISbVZqsfip0/ZMEW/P8bcwsYtlscDnsFwXSwAFAzbYf7xtqcYFvrEONf2fVaQBRYKlMxkpjNY9JmHaTvEe2HY/PIMmmVgd8eCy2ygGDUgEubO8Fjrh3g1uPkwHpPNqAGVXB5cY2H+wfMc0XsNfXNWYXOSiOs7ukG8LBNrGVduMRwff9fWFUupOLg8kbRBEK7H1ZjUTpbx46+/d77nmCEoMfCRk8h/UcEECqQ6j5WwG/+8R/w//3/vcX/+G/foIufgp+yGv9yXbygjM2m9wlq42kUmYUd52u73eC5fbeypYdBfOxtbnFAN1xCqDPmzeKPpBsvpx4D5zrXuc51rn+f1XUdPvroo5PXnNH/IaDmfenTy5cvPwiq/Fuq7/sTOdP689+vc+T2uf7W6juBGmI367HdSGKUmkHsu9++q6oRyRpNrT4lpe302e6pRU/4A6/LefwGkHNuRrUKCK0Xm8sW2Hb+tRmSKri/vcP93R4vX36MFCNyre14TswWoY2QS1EU6Dn1c1kexi2WHCYZAiA1A1KxGQaM44iSMy62O/t980OoDvQsLCHUBZAajyPGqeDq6gZEgiIzbu9vVVqWM7qUwBwacOR+MQou8EmzsK7mW9PMh6U1H+8DNf7zOSvIFUJAF6M1RhW77RaXl5cACOM4oZSKaZohIGx2O4AI727vlOVUCvaHA4bNgI9evDDZlB57FUGXopkgk3lQ3CClaDv8bnjsDZQBexwwDBvMOePiIuEYIqTrUMuM+/t3oEDNR8UZESEEixJm9H1CkRFTrijzhIf9A/phg+ubG8zTbHMig1HQRcac9bpSBQQqI6KgDVy1Lp4pYZ5GBZ5OfH6AuSh4wUSIKRkrzL1o3BfIm9gKd5rQftKjs1Uy0cWkRtqTm0drjLv/brW5DSYDU0JjWjBxO7bGXGmNpa9Ck1oBEFsjORfEmHRdMUFyacCAJt4sDK4QYmvA0ZgiC6NJd+hJ1x4bs0JW580asbyABIDGFeuxO1NB7zfag6Z+AFFFKUDgHh06hBiUAQdGzrWdo0p6VCIJY1Xo3xHKtEeVgmHToxb1ykFcxa6LaJ8ssLFU1kIzm+WCEJJKY4TUg8ubYamgqJ0vUUUIBUwBmyHicDggcLLjYDMTNmjGgJYTAMDYNSkRDoeDgeRsrBqonFSkgQr+HuTD6cwTLIlf7R5hjBuNizdwgFTs4zHbDu4wBzAvUho/Ph9r9WuhNjcdVnFghO2ARHwMpYE2BNFocLvFv3jxEu9u3+Fid2lMS5+tJrWliAadiBiQgYVdA+cprQEbgn9v+dwIQX8vMONwHFGKJtnlPOE4juiGpOuDCc0wyFlCa0BcFu+o5buJwAa8mjc7RASpY2y3T/D/+qf/N/7bf/u/8eVXt4jdJfpEiObZw8yQYnPfl5dvbngylY+un6Ylw9HKIM89okiWzQ99ywTiLYqNn4OiLdmLlmt7rh9/Obv4/fpQg/LnN0f8aZc/H/lYzca6/TF6NP2+6wycsuF+X2P6odf9+ewP/RwbQ/lDx/R+feje4cflf1dKeWQ6+6fUh97jp8IK/NC1+q5r9+eek9/3XsDMj1gpIuo5+f57jOP4SGp0cXHxvTyT/pjyJM/3S5+RTsdpmqZH8+SnBN783nTj9+q71v73qfc39f8Wyu+LSwnAwRJZTsfqp/Td+J1ATTaT3WayWrI2yFl3utc7jVIrLi4ulgZctyZRymljS+QDxHAZiEqDuia38Z3nCt8FXh6SPSKXmTGPI+ZpwmeffWaGsUq5Z1Y5k7JZDLiwHVZNhtImTlOWvLlZmpX2gG8PzQQFJYIbbopgt9tBRNOmOOkN5JQer8egzJKIkjPu7+5xffMUMXWYpgfkaY/f/eu/YrfdQkQwjSNKzXCZlYM17iPgu/LvM4KAD0w6+/taa0PC/R9ljYTmK4OoMcabzQa73Q53d/copaqh3DipD08/YDZPg4f7e2w3Gzy9ucFmu0WMCSl16HplDkjVuOMYIsbxqAyFwOhSQskZ85yRLEHKjaBT6qypiuj7QQ1uCWAKiEHw8HCLPk8IIWAaD5jmCZxia6b0AUSb4aHvMc57iGTc392COeKTTz/FMOwAUSPcaTxgMwT0PYEm80OZRyQmsFRls5DFoZPu6deSG6CwsBRcZgSU7CZ9YvNeECOjVn3AMOJGYzDAmkiNLy+oVUHIKhXs2k9ayWUIANsDEPTf82zR5yU3qaH6eZiHiQOQUGNrDqHJ+xQ8MUmQMzyYIHVJdQuBUcoC+HjschWYx4Y2rQ5o1LpqYu1YHHQVoJnYihCkFAAVir+SI0ogCQruikCMwVGhspJcs4J6zGBEhCi2zpUtAVEmEhEr2wHGpkjB7i0dYtT1r6w3Y9oZOwRi0eXWJ2tiHSFDjWCJ1F8FBNTq12IBdwGGVIZHm+dcGqC2dq7WdWL3m9aHGzBg87jafSClDirNcYRHDdirsfDgAK404o3OSzFwbHUfbKCOmCGvs5NKBjEhsN4TFKMMjX3RwFUH+0Ta/FpL/5Zbkp4/E4xFoxefyOeaylwDB/T9Fqj3OBwmXGwH9R9TmomBUvrGZPdmAqvsWAxww3JeYsa+agJcIQVmFC0GGEd0MWEzbDBNEwIH3B2OeNjv8XS4McCazMxdAVCSx1/ogmrAss4vETbmC9o9e9kUqPjNb/4BDw8P+Jd//e+4v7/Dr375HJeXCSmKfcfFxmwhMSC1VJDwCVtqmUJ6vsU3B2CG1/5DAmWklQqgg/AGIhpNzrbONV3LvHt+Ao3OubS++eabR6asl5eX+PnPT80V53nG559//tc8tB99HQ4HTPbMeDwe8Z//83/Gf/kv/+VH99AuIh9MuTkejxiGAc+fP8enn36K58+fI8b4SGoSY/wgA+FnP/vZo9fevHnzyOT26uoK//iP//jBY/o+Y3V9fd0Miud5xu9+97t2TH8qqDJN0yPvEyLCZ5999r2isH/oevLkCZ48efIHf46IPnid/q31ISPpD9XhcMD/+B//49HrH2rqf/GLXzwyuf1rgmbvf/ZPvf6Ya39xcYGLi4s/+bPevHmD29vbP/n3f4z19OlTPH36dPWKALEDOsHaTLiU8sH764+1vvPu1pBqqSYxUEmJSEVqciFtBFPXGzAjqDWjqMZjxZxwRkhpYI2n1fT9AN9xbawPyOqhnxqI42yF8TjisN/j+vpGgRD3loGg1Gwdg9PF9QFYWTgVHBcJQqPOty1u8xipiydCsdhvQL90hmFA4GjvWdTjJtqDuTcsUB+CFBOYGK9fvcKm79GlBEhFDIJ3D7f4+stvMARdbNN4tBupvo9KhGwE7A9rZs16/NevKYPG/+yMD7bmsp68T993GPoOV9fXCCHgcDhiHEccDuoB8nDYK/ixzThMk6LaAsTUY3d5hWEY0HcDdhc7bRxKBkdC7Hr1N2GyOHJg6HtMZM1S1zVGRuoGpK5DnjNECONxQuoGFBGkIaBP3rTOOO7vcTg8QKSqZ42MCBwxbDaYZ2V5DUOPKSvQJIVwPB4wTTMunlxiPu6Ry4zKGSIZXUyIAXjYTygy2i5zVANlItSq15U5oJTafHG8WVIWhyb1iLO4ZL1+uIEQDmiuy0G5GJPNfQKHaKCAMjva9x4pQAMxOSGK/a6BVFhdX1JZln8aOUokjvc4s8YAHGOFOagzm3cRYJHksvj4OGrNvKwhIme2+MHaWKwi5+GfJcBcC2qZQBCULBiGwQ8P3uDDzgEUwCEY+0Fj1pnYgFbjWhiA4ewA8ibfQA/mgFIVNFEE0Na7xW6xSb5ENGKcSMFFSAGHBMkaJ64m0H5vIWt0DYQ2ZkljOjEhRmXEnbCPDJCRk/Gy6yQa0U3M6PoBJds1aQiMfjKTcbPEk84Mc/G5yZpFxA3ccDbGAvw5SK1Gu+ZFBEIIWN3HFuCtFmdxKNCo4E1pY6xzHnBWJAQ2T5c5QRTMmFjv1876uL5+glfffo2L7cbWANv3R9F5r0iEAnhwcI7BXFGrRm3rQOg9R/VdOt+6Tk11XV5bVeeIEDSa/WJ3id3uEsIFQu+BZ1g2I9b/dinfmmHj69LlVEQMtVOeEfiIv/+HX2Aue2x3A96+/QabzRYCQs0Zh4cRCGisUmXHEHabHR4eHjDPCq7HGO3vdNw6A7hFKih0ek7ThFoK8gRkEVw/vwDxxpKeHEBb0rWYfho70uf67np0Lzlf0w9WW8P25x8bSPNdtd6gW5jbv7++z5z4vrv162fNP1Qf+pm/xO79T4kR8H3X419i3f6x4/R9f/7fyuo412n9Mdf+fH9/XB8cE+vZ1ptRP7Wx+wMwtH2JSWl0eTWf1d3znDOI1NNE4A+otptfaksYAU5v8kRkgARhGIZTgKM1CEDbGoY06iQT4TBN2D8c8OTJU6SYNDYauvuo3QFaRKw2r9rAz3m2ptZ9CfR9OcRlUxsrxoQZwLJRxg/7PbqhV48Wk3PoTrzS3oug7caP04SuU3nE2zdvQQAuLy5NUjMBNOHtm1dm/An1DDETYx1XnIA23iCvmz2XNK0bQZXMsDVuDIiCHEJoprbeaHddws3NDTYp4dmz5/j222/x+s0bOCvGIwbJWBgOrj25eYqPP/kE252mNKV+A1AwiZbGlBMr6yGEilwLUAGO2mwP/QCQNrExdQCzsqRiAiGgCIErkLoBIUXUOoFij8vrpwAB+/0dSp4xTSPiJpp0zsdJm5btRhv/w2FEmSdNf3r6BKnvcTzuUXxHHyaxkIwyH5FFNHEMhK4fEAK7Ly6IAkBsThmwhlCbWQ5Bk8IgKzPq5YGKGzPEQERiUAhAzSCoZ5B4Ipo6u1pz7m4dCvq4OSygDafTo33H3Sb8AnL4UYhiE5Zpg0DGTWhfskv8c139ubFBzKS2MS0MhFukJ6GBV+4TQqSGxbJQD3RNsiAwEFJAjMF+XcGIEBi1oLGYyAkTtj7ck8gbcw7wlOtWzCvTZk89YtZ1B52biumor4gPHRtIpaCaSYcI8MhkTfwxUIpcgmPXDFA5Dxa/KxBMulhOkgm0V5aF1eRAFlaASwVS6nB/d4eLi0XCpGAH6XBWKJPD5U1wAE7nl/+8WmEv4IwCIO6TtMyTagwfgnvMLA9rRAqe+H3AQUUdoIUxRVj8VNYP9W6aTbQwj2pZ5Jjb7Rb3/QaHcUTXd3bNVIIlUKaIkKhPVFtbxtZq3xmankR2TJ4yBRGE1BsAX5b1wBHjtFfAejNgLhV0kmKweMGsQRpndgKykhzahoTP99XaLzWjSwU3T7b4zT/+I0oFwM/Q9wBoRmDG5ZXKp3IumPOM8Tii1gIOV+iHC1QcNc2u78AgTPOMOs+YJsFsPmap77AZttgf7vH27Vvc3d4jy4z/66NLEHqgOjt2ebD274KfUrN6rnOd61znOte5zvXvob4TqBH3vDCfCqAiRIbUglKk7fCtEX7dZK7LDr6/jkVz21g4Jhlyr5vTz14ZOhI1UOh4OGI6Tnj+/DlADCHbgSYFdfKcEUx7X2u1hleapCqZj8j7jBQHY9prIm0XlpkxHkeEmBBjwjRropGggrsALipjYJMtlJxBIuhixLu375CnCU+fPIX7RNSSwWHGq2+/RqROWREhIMWEHGczEzbZzYknCi2mwivfmvfPp4FHzBCCNax6PsF+xw2Dc86YoE7t9w8PmKZZx3ueMU6T+YqYDK4UbIYtXr78GJvNBXLJmmbFEXMuqEXAVWO8+z60VDDdcSeUrMyUvh8wz9l25gl9v0EICeM4QQRIMdmcYTAnzPOEYXOBWiK2OWO62mN/d6sMliot3cmTwIgIIQZstwMCB0yZcH93h3GccHmxwQO9Q85FDYRFAbsuJUwxYBpHSGHM09HmMENI44VD6BBTh1IFsyUzUWOMAE5tIIKZ2i7SGQcxQ9SGXVrDWRFCUnNbZ3G59MLXDzxxae2PofM6xrg0zn7dgQbYwRrVBj6SRQCTsp/UmFtNXtel51DNO6oDwO34PE3JAUEFGX2+6rGF4IIgWZleLw1iDAxOsZ0H7GeZ1ENFDchhaVOkqAQEHATBMA+X5VRa6/lJCXQsYDCEVe6BIKBJQAGNFQXr+zms7lPCKEUlJYGVVUUEBC52LV0upHHifiTLfcvvf3q+KaVmnr0AMgZpEK2gAAezdSyqAYY+Pmswxz+H7N5n2qD3dgyWpCSihmW09yo5I2fXl4tJYfT8DZZrn6G/r28QLMpe5xE38+Nq6VGwSHqXgS5APRlo1jhegM3TGBOIgJsn1zgcHpA6vTaBg72nA0LSTJSJpCWphZDsO0oWMLCNMhuOZIyuENoaJQOL53lGLcVSp/ybyHauG+Nq2XAQUclWKQXFz9VOKYQA4gXYFui9w+/x1zcXuH84QGRAiAxg8fASIcRYsKGAunPwTbDZAJfXxkIVXSM7vStgnmeVIRO1Y4jdJbr+BrurA7gLuH7yKVAjgkWJi31XtiTBnxir4FznOte5znWuc53r30N9N6PGmgoHUjgQUKo1HqGZSGkjqrIo+0U0mMVBHOhD7jzP4BDMVHbttfKYakekDB5nj+zvH7C/P+DFRy/Uk0bMArdW6DO26IN9oPbwGVfGTM46WRgIasRKtTZJRGnHVJtPjDNWNtsNprw0t828sTrDhTBNkxoDDx3u7u7w5vUbfPLxp9qAVmWXUKiYpj2++fobRE4tuYYDI6WIcVxM4nx32o+jjfeKAuv63LVhMDOf7ID7a33fW3Ojkp7b21s7f2WOFD9Gj3IWbaameQYqoe97HI8Tbm+/xHY3oO8zxmPBOB5ApPK0YehxeXGFzaYD1aJMEVoAG+aArrMu1yQsMXUIsVOKv8mMxFgRxFEBEalI/QZXl0/AAuzv7zHPMyQKHh4esN1ureERgARDSogcgX3GdDxg/3DAbtOBQ8BxP2LoC0AGqNh8JsCMVDUNS6UkQQ2Gweo7ZGCEMqlCuzZrEJBDxMIxkNaUk1FEqnlolFIRYwdAk5iIWH0nmMEUtD8FnVx3b3Jdb7wGQv3Y3Vi6AT8GFARaZIg1F5BFqLv3DACE4OZ/FfNckGJnBsOLObWupVMzQ7/ObI2qHrs1+XU1Fm3ukrmtuJRHx5W4mL8Kg1mPrJh5MjMAUWkYjEGhGNXCRlKmjNgYGmDgqWLQBC9yIMGYcX6/qdbEa7Q8jH8EEJuvT4uclsVoeM0GlGqSML2fpLT0YmkAAQAASURBVMAY6/HkOvmHOQvq9Fqh0WoCM2JQ/4GudyM9ZTEtIMoybsscQZO1tPmyus8SLFFuOuDwsEfqE1KKFgnvDBi9Vnpt5fT4/HPayNnPGcPM//sEwG/HvpxisHM73N3i8vLSANwJpVakkAAQxBlC4uALtbcJ7D5M1UhkwSlD+o99pSgrzK+zg0kGDHIAUcHheETf97Y2APng99F640HlctOkoH2MyZhoxn8z2SMBmgQopGzUGLHdJLx+s0fqd+DYQTlsAFBB0eSP7xkKVqkQNfxBNSaRVEHqOoQUod9dzpiMGChgc3ODq5tn6LpLkCQAWQ3CV+vSv+PO9PVznetc5zrXuc71t1UBqOlkIxMSAOmWHcy/WEX9/D/Du/zeqjVrlK1FIVfJyLmg6wYDBzR+2NkBTCp5ouC7ud6k6a5knmdlD8SAavGujIjIZA2wuiq4n0UtszZixHh4eMBhf8DzFy8t9jkDJNboBqPcV5UE2M5hiAFTyYAAKfVg1nQdUAWJ7irXClBiRCbM46jyIwDHWVN+3BsgdbrzCiFNvyrKGlKZijonTNOs5sEpoMwZ797c4umzZ6CgDT6bZEFIMJcJh8MBKUYcc9FxTREhRIRoQIUsTYYDM9M0tYdsb8ZcxrVunHWXWXee3YA4pajx2uQ79wpIKRtFp4I+vGfdeYU2CNVkSxwi7m4fcHe7hwDY7XbNg2R/f4cYO3RdhxcvPgLAmKYOm02HFFjBHwj6EJUJwYxiIFRgBoWoSTqhYJwmNbuA+k9oc83KOok9+t0las3IZcZ0PKq0bcqYAoHCFgxRU+AYsR169IMmqz/cvcHlRcJmu8Htuzsc9g/IpWgDzgKOA4Y4IE8zAilQUlvTl81NnhBSBxAhcoJItohrBythPkrm+UGAp0LZ5VIzWmuJK2wOAdpPK+rXkoIEzvTQXfxGxHEAwRlEITYmAwBjOjivAI1NUK35JSEUIURReRxgsqNaIZIRU0KIgtRDKQzQOGNgAQhcKuP+Tcxudq0SpbICdjQFygANIoiamTQvmiafsbXPxvBQ4EXAlQ2oKcYMUu8lZ9xppDocoThhsJBHg9eiBsYxGHDmnjZ+xyMEJoBdbiYnzb5ShKrOeV5oKi6xITsXXZ8GWkVeZGz+WStPkLXssx0Fs5rpAohJ7ztdn9o1VRDIwVdqcdvKEHKoRQE+afdhtDmoyijC5uLCgOW5gb2a6qcMkVoFHZtYjs3TRBSEr/b/RBoH38C6qsCZ3v+XtCcHA6tNYDe+3gw7xJSU5QEgxg6H4wF9p0wfDurL5KACPE3QLreAbKzZADsxJhsbG0avpVSxeQwDOww0jgEddciF4eRFIf0e8flaGyNtLeXSf5JdnxgTVArrk5BAVBFJ11sp1VhiBdwFXF1d49XrV3jx4gU4AEQZUt2bBgacSwOY2jkbA7CWxR/LWW7K9GQEBpAiOA7o0sYmnoJFIFa5oKE11a/PX/p55Vx/Un3xxRePXru4uGhGrV7jOD762Z+Sf8e5vl/55ujbt29xOBzwL//yL6vv4qU2m80HjUYvLy8fRRpfXl4+MmX9KQG333zzzaPXvo9p7h9bu93OElH/cP0YzY2fP3/+Z78nrCXd5/pp1YfW/R9Tb968wTiOf8Yj+gtVuQDk51iTQRiCj59++pf/bCHEcIX1JuWfUt/jbqInN88zcpnRpx4x+C78wtbwBBP3aPGdYo3BVkmSp2GEGCC5WtOaNdmkS8pUYKBkewS3pmf/cI9pnPHi+XNwiJpQQroXrx4VS/IFM1v8ru1SijY7Ihb7zYsMQ6U52tzmkq2pLiYJoWbU2nUdcs44Ho7YbHa22x2sUSHMJQNSkEJScKdM+Pqbb3F9dYPtbquUf4uf5kooQYCJkPOsoIgQ8lzQdxbjywzkRYrSmoNazWdmMW4la8CJfBdcGyKX3JTijayzmJQdNc+zAVe1JdO0L3wBQlRAZcoWdyuEcRzx9ddfI6aIru9xPB60mYBgf3+v/jObLeY54/nzFxiGHjf1EoSCEAibPoEAxEAtKtdNbwkEhIRN6hG6hOP+0CKqY+zAHBRA4Q6IFd2ww64qi+W432OSCeM8gucOw2YD5tiArr5Xw815PODwcI+nz5+i6xPe3b5F12/MK0evcz8MyKj44osvMWw2iF3EMKix8X4/InUDhqBxb5rypABdSsmJCydsJ4LJWOzhiLAY17pUgkOwSHq93oEDcp6tIVukJDHquOt1ogaMrKWDzqBYAxVsCTbewqvHhgE2jbkF1AJreG1tGJtOH9oCzHxjYQ2tpF9rtoFKXIBo3hdS1eeprm6UDmqIszEIKNV8W+yZk7yBdKBFFHzV35d2RmQAy8LM4wYeNeAGhMjBPISrgVPSxlzBMIc2FibImkVSaoFBJXCQ0+Vf7X7VAJ3lDuoGsW1sDFHysfH480eMlSpIKeH+4dDe0E2BFZzwH4ZFbfsDtoI2DuIA0sBA98pVOZmgH3p0NTUwBGtTaKL2qSC0hCnAGB6re/Ry3MFnvc45IQClvacfn4PKFAiJOr2XgtD3Ax4eHlCyAt5NauXzthSgKlCkUkM0cMzvezrXl88ECBQiUAuqeKpeRrEUuxgTWPRzXHJ6KlU7ZSuuZXx+ThpTOtiGgX/2AtAyqVSuSgUqsNkM6FKHb795hZcfv7DPJjB4uT4mnbIza8cS9AaCkmeEENt9ILV7i/onpa5vkeEtDWo1r9u1I7S/O9ePqz70IHx9fY1hGE5em6bpp/HQfK5/U/k9SH358u/9uXEc8fbt20evb7fbR0BNSukn3XD/teZ9jPHRuvupFBE9itE+17/v+reu+/fvIz/akgEop+uWAAw/Piz199Z3HmpgAiEY6CLoU6++IOTcesA9AAS6C0zWfPnWfy2Co9HKY4ra/BWVSWkqjTJwqlQ1lwQQyJoRALe3dxgPB7x4/hFijBinUVki0SKKAXjD5aBRzkUfkkOE5eRYo+JeHApQiACp60wiVFDFEpKCMmkEenNzeng/9FAF09JyCpTBk4Ia0M55xuvXr9F1CbvLnUXdKvgRQ0AlbwKB43hEzno8VUR9U5q3gXZ77gtCxKBwKnk6kXF5o2W0eTbTVd8YUW8bxjSNcHmEDpdGNpdaT8ACAI2xEKNKj3KelNUghHkeMdblXErJmKcJ0zxjmiZM04hhGDCOT7QpiVGBuy2BOeoufFyaMJe/cAgYYgAJcDyO7XwUcAgWu86Iscd2c4XIEST6nuPxqFHk6upsTSKalxKQMU0j5nnCxcUOr759ZQyxHmEMKH2GT+3dboe3t+/QD4NG5UoFB8ZmuzE20wKe1ZJRA8O9nLRZtevTxtGSoZrfk3kJ2T+QajIqZfDkPGGaJszTqOBW7JBjNIPm0ObA2l/CJQzvm043/wpxOZBCHMyEKsXSdHxuwRo3sYbRmDmyMCPcg4oN2PTUoPdNSZfdG4c+/P8rXALW5EMGWkmtNk7a6OoYW4MaCHmuJhEzxsoJc8aByvZRq7lc2piH0EK5FZzx3/fjlOVtvNpnNZgGJ+O8/Ix5vPDivaWeUjOIIjgsaU9rj6wFMDvdGU0pNQAB4owll2F5Mp5GnC/GxDbm7WRWn+csIcVpjLUBXZ8ixiCz+4gBt8qkknbNTDe0zA1ahsslWcsrDtYsx/F+Ld5FgpgiLi4vMc0TUhdb+l67t4Wg5CuTIMKuo4IOAg0bVFCCiRobBrTcNzkEcGBEUdaPHhIjzwXZQPqF2cUNmPRaWFqweameYe/PfSI/Fj93tnUElDrjxUcv8PnvvsDd7T2urrdYZFl2nHbfe38XVGVjhJyNcUMJQEWydDYhASMgpV5vzidTytmXyyu8Mig/17nO9dOvUgq+/vrrR6+/fPnyBziac53rXOf6IepvYwPqD8RzA9NxRK3QdCZUNXMEW2S1+j6Uotr7Ok9GybZ0GREcxxFd16HrO2NIRDOeXEVJQzTiOgRUM/0VQJMvcsGTJ0+VFVMFIZphIzkdvioRX3zn0rxFIJa4koGq2v8YA8ZJGTyBGTFF9N2AuaqZZK1qbFmKgid91zePmL7vUWvV+GkmNcNl3cGNUWn2tQJv374BM+PJzRNjFixNffGm3JhI43FUs9FpHd9LyliSigLBPGfUqmAJE0NYU2ec+aIyM2/gsh1Pag07c2oglp4XoA/rwDyrf0wwvyAHZQCAqkpdutC1axaCSmy0KTJ3Edv9DSQoklGy4HCQlrC13z/g+fNnxkz6CEUIuQiIgS4l899dgD/fre66Ht5UuFdPJQZIzTpD6IBa0fXqddFPI97JWxBrtHTX0bJrb4yiodf/PhwPJtuqePfuDbbbHVJSH5ZiqStgxma3A5FKvjZ9jxBVb0gUzMuooJRZx8KimbVBth11qPwpBtYdftv913mv4BZBUEs2RkfVLox1rAkFzBWB1HSUyOWG1Yx7F8aLM2vcbHrVOrf/d18RBenMF4Vh85QMIFFGSq3VfF2omQH79cl5htSiII+ekjEJThvV91kI7yMgHBY/JUCbWo7c5oTLx/yYFdASZefJCiRZNcOt3yRjvti/2e4ZRMY0gZwcjfvFkJ/QB6olDAkWE+m6NNXLfVNZI+xrI+j9Ytj0xoYxULKxS04ThdbSMmXCdXoPCP4Z1E7bPhGGnAAudWxyVPfTsabfgSapqNXvNxYDDzeXNVlVcEbOMpPWAFnzcTGA239K54MxTmByTwfGjEmk50zt/MnyyAT6XTONo6rMgjIyfVwVKzLjdihDUISW60+kOkdWiWGZZ3DQa84h6BKDAy0+J82XKAXM04xclIXmpKzl2rr0TOeRAtQu/2PzMgsrlttykdaSKYigipqRP3v2DJ9//jliDNhsk73XqbH96e8vUjb3xHK5lRp363HGlJBSZx/PBoZZQthq4jtAWN4DCM91rnP9dKuUgt/97nePgPH/8B/+AzabzQ90VOc617nOda4/tr4TqNk/PODt23d4cvNs2eVrTcWSFqFsFSfJizUJFfd397i6ukJIAbkWbcxEWTPZmi6ypiKYZMl36o/HI0ouePrkqW7oV21oQ2KTUymY0hgDFjm7RIJrY1ANACrzrI0fLBY6MFAtOaRkTNMRTNzMLIdho34HDiY1ecliypmiymuqPejfPdyBQHjy9ImOxCr9xP/R5lVZRUTAxcUlprdvFRTpEkqd0KUOmQghaDM+jhq/ypZmpbHb+lAeEQyciQ3UaIay4n4RTt2nE+2sJhczcp6gHjapsQSoCtCShwR9n1YJQWqeO88KbNVaQSjoIkHjqytKmTFPI+ZpAkC4uLiAIGCcCtI2IsSox0nqG1QKlJEF6LwgQtf1mHNW1ooxIkJgFDBEAjgkgKkBhjHtMZdsSSgFKSnA4wlIMag3iRjodHl1gbdvvkDMnYIshwMAwn484H7/gFoFL19+gu3mwlrI1t000gmggBZ7D29GvtrMioEojHnO0IQnl2RxMyB2IKkxVqqyr3a7LaR0egkoIKQerVn0eWXyGB22xfCVmqZhAWzEhBQCj2c+ZeU0uZC81xhi6e1KKZin6QRcYHaASn9Q/Tj0/bwp99I/NtrOis1iHiqrz14/YyrAEsC8eLMsnA201wR4pOIQ877yuUsNoFqAHAUUnJl26oPSxsjmEfnn1HrCBnHZDaDSGpGKGNQs+5AzQmBjpUU7XgUnmBYmzZpd4/cMB4k5hZNr7NKjdl5EAGp731oUlinOYLRjV5BcgScFnOzziewc2ACQxQtnzdhbjzrBA9r14jOtJFmNzWJ+MS22fZE+nbB+oPcODsEkUHs8ub42/5pTeZh6DCsjRn2rZQHfiCHO1kqdgdoG3DJQpdj51XY8DrY0iRixsVWW/7ZTan/n/7hc0NP1TmRssgChPpkUhFOPrZQ6vHjxAl999RU++fQ5hsFSuMjBzfUaWM+LavJRNqluNFBVyawdq5F6LmbU7ewni15vax3L+j/Xuc71t1GlFLx69eqDr5/rXOc617l+OvWdQM3bt29wc/MEXafNIrElxTSQRtklnvKiu5IR4/GAaR5xcXFhO7kLyDFPFisN221mmPwD2kAR4fbdOwiAy8vr9tA7zxkUVIOfy6TGi2agG4I+uM9zAQm1Zrjmgi4GzKXiOB4RY0QMUZtnJhQAc56Q56MehwimPJsOVZDLbA/GS+NYJduDORlzQt9r//CAUjKeP3+mbKBpUnYDq7lqS14KjBAUyAohAIVM+qX+G33fIWeNa1WfEk3eAczfAMqiofbUD/MvUR+eKgIYywnQZtQ9DEJg8/dZRGOQRWvoTKVSigI1IGOR2EcZaFNrMfZHASowpA4XwzW6rsfD8Yj9YcQ4ZQgE0zzi9evXAIBvX73Gi+fPcHN9gc1m0+QjArQkJU1uscaNCVQJuVRLVClwUZh6VDBA0RpdAYeEPI6odUSXpjZ3dP4uEiA3/Xz58iV+98U3eDge0PfAVCqyqB9TTAN22x12F5coApDJkvw9YmTkrAbRTatJ3vxyY5gIYNHyyxg6uBEjN5DlNCXHvWMiOGnjpgswtLa5GHNNU6kExQCp9XstYI1/rgkBDdhUICU35kY1CZYUM3vlAPGGUfS1lCL2ezG/KY/kdlaERybruCzgoTbkpazMjpX80RpGac20M4sWIELNptkArmX8YOAJsScT2Rgbc2l19gs0VDOYVKtd21i/L2GyppwWA16VBVJbI9VgAeJVM00KRjjgLMZiY47KzqhrYKDCTZpBmha0Zt+tpVAhBuz3D7pmagUhwCVGJ+AFKVOulNLur/56yRWULO2L1KRXVuDBIuPyucOaEORzktn8dFYP+rTc1xqmQA5mwNAUk4OdyJ/WgMfCGCk1m3kwsNluTcao5y9i3yMEEAdL2lvGnhwMJzFGlgHRFNoGgRgTiCtQWhz9wvwSqWYgP9u4RrjMj9bnZb/nALAD8ipHyo3htowbDPxzFqKx16pAJGO32+L6+hqvXr3GZ599cnJtHXnUc1tAIma7Pwe9l03TZKlVyhrqh42jWTrqa8YWL2Cazo1yBmp+pHV1dfXotWmaHpmlHo/Hv9Yh/c0VEX1wnB8eHn4SwEbXdY+8U0op7blrXb/97W9xf3+P7XaLi4uLxq75vuv/8vLyEYC83++/0y/nz1UhBGVC/0B19ng517mW0pTdUwhhnmccDocf6Ij+dus7gZrnz59rdLBoV5XzbHvPglImAABzRCkKKPQhYhqPOB6P2O62IGIUKfo7ApScIVJQjIlBhCbj8Ibt9t0tSil49uyZ+cHoQ+jikzIbcEEWa6zmunnO1nxqoy81A0SYZ03DCMa6kFqBAORSMI4TmKKaM4Jxt9cvMGJ/MDfTVA7LrmMVgAUu+SECpnnG7e0dnj19aoDR3HbUa60WiW1UoVIAAlKMCmqNBqhgaQY3mwHH4wE544TFohKG0Fg+gIFdYWHT1FIVGLCd9fd35+d5NgaMAhilFLCl/lR/KFH6AqI1TSKakkUokCLI04g+dRi2W2w3G6Q+IZICKmRsp5zteoSAOU+4vb3Fm7fv8PzZs5ZQBGgjAzPGhbN5iAEzc16YQZYCVYq2yJbyIpWaBM3lLfM84eHhvjE9NGpa5W5EAVV0XlxsL/B3v/o7vH77DkwRxBFX108RmNH1HWJIIAqaimVsodaxwVgClvplA9cabXG2mGij1KWkEedA230PrAbTZNfyeDxawtjGrp0l2DSgxj7BwDRnOHhGUYzxPXbFqgRwbxhqjaOZfzdOiTboGVkbWwHYgJ0CBWYVqFCQiEQQQ4Rm6Kzno14LAC0pqlb33/BULGO0OIsIBthCUKUY+LswbqI1rcEAGgaZXMPlK2hznGh5X2ce2QBgnmf0vUoE1Z/EwUc5kVQCxh60YdT46QUcbZ4+boDsTbABvOM4opSCrovYbBLY7l3r66KSE10rrbFfMRXbeLL+XSlZ55j7o5jf0GlRA89cutakOyvwi4wC1qyTm99U41xBWUho6WwLkGNvZowcTzsDmXxtfUjka8LkfzDwhFmPp3nHiK5BNAEhum7AcS7YdknTugwFEriZ70oS5YxFYZXnCmxWEiqKEnqkNqCXG4Cu77sGSTU2PKPvUwMFF58aS+DDhyPLfWNChE/u0eRcNint/PQvVQb14sUzfPNNxv39gzLp3gNt/T7iwPwiRawGxBOmaUJInc1r83AjBfhPQDGQybTeB+HP9WOrp0+fPnrtq6++Oj8M/xmLiPDkyZNHa8Dv4T/26vv+0TyZ5xkPDw+PfvZ//a//hTdv3uDly5eIMbZkqO8D1Pg4vV9/yNj4z1UhhA+uh3Od61x//fpQqtzd3d35u+kvUN9tJhxUM+9UfuNxW8S1tEaj1oouJozHI0bz/1CD2tKaSsBAmaKmh9qsqhwgBobUgrfv3mG/H/Gzz36mzWFWv4BSKro+qaQm+w6+NsHFUqNiCBDW3ftxPKDrOlRrgmBgi/W69jszAmvqVIU+5G42G/R9j9FiutkaL1P/q4eNeYB4Q1lKxetXb3CxvUCXektyUplXjBElK0hQTe7CIUJkNFPYCKBAoF9CXddhnqsyf2KH43GEiDa7CuIwhmFArbWZV6aUEKN68ZRqZsgt2UofzB2o0PEqTQ6m8hcFn1Q2YrvehkMA1JpnInPCIDUevb66wtD1CvZIwTwd1QMoKzsgMAFRG4bjOOHhQXd48i9/Ad/F7vtOGUCwZsuNZSGWfLSco88dZ93EGFFzRgwmmLAElBCjRtVOR0yTNlrTpKBiTAnJ4qOlAPOU8fFHH+HmyVMcjlnZOaFrTBSx3edokjCR0sAygqf+BGu52Fsvk82c+o7AmD2waz2OIwIzcilgEbvm0YyP9T2JGTUXFFEGj5MSuDFAFuNoNHnDqZ+M/9tlZiFo9DspCgMmYJZiTb2ukRAXQ2I/F4hH0itgEg1kSkFZN8RswFowIE5Oru1iRKxNubMUFBNU03IPfYbI6u/YGkobXWdONKbDKTD1IV8PH6vUJchowDFBmWzNWHWJgHZGhEuyQIsXjYggUFBGl+FnDEItKm8h1mZ9u90YWLscWylF168IcplAIaosEysHffG46ZXZMBQsmKYJyaKsxcxy1+Cdg7I5uzH4qYzKx+J9/xOxNceyjIOzR8SAIUDHi2EAi4ilhglKNhYWuQ8O2jUUERS0vLEFoKiyugca+CFLUxQCY9hs8ObNWwyb3iRZdtyoq2vmZ6CLQxljy/rQNCkgG6imY2CMThAoOLC1RMmrJ5feS9NKbuaJgUBFAKspMZFaAFujo8y003no4+6goDLUqH2m3h8Knj59irfv3qKUimhrUGXE3kAua2pZ1+rboz5I+r3QbzdtI0HBbGljCjgTdpECvr9Dfq5znetc5zrXuc51rh++vhOo0QhrZcWweToQAajaSPuDeAiEeRpRclaUjfSBfC65MULyrGyaLiUQMeZ51vQWqPfM7e07lFzw/NlTiAB5nlBFk1JSipBawIEAizL2B1FvrBSsichm9ltr0QdUWkwo2ZqaXIoSIZjVD+dhREwJm83Q/Bxg/hK5ZOQ8q3wGsmp0KsZpxP39PS4vr7EZBm1uQEscLxNiiPCGwpsKEUGep5aqwqyN3W63w5s3Bzw87A1QkQbUKOtoiUPz3VpvKsdxRC61/cy6QXQWjT7Y6/EFY5vEGCCFTnaOdJeZrRGzOFiXCBBh6HoMnca0MwORI8ZDRp4mTOOEvutQM/BwOGDKI0quQASmcULXdbbbr41mNgBKmuGlmi6XabY56LvZbtYbmlyoIiIwIU8HlFKRUofNoPKQMvt1U+BN30cQgsbASyWNoxbBphsQguA4VYBCS4rx9jJ1yeRSql6JMQKSlY1i0i03DnZZXzWfJm+GND2ns/HNqKWgiz0CL+ajm436IqHF+jp7YNFeuLzlZLeelrQfIgaoNnmXN90ioqyJsBgJK+jqzDE9txaXTjZXAWtqF7YMQeeEn5ebfy9pVNqkpxAVrGUCCzV2DbGNIZbUJvUO0XOMzSBbASJbWI2J5Oe9zNdFUhV4MS/2cxG/l6WIWoLmkBujw+9z/j4aW2/vZefl4Fxepf84T+RDzbgb96p0Rseu77vGslPD8wixtbuWL6kZ+8KicGmNps/NCKG3v1vJnhw4EWlAwwlIVU/H64PsCZtjzn7Ra6hzWOAeOjbOYmwaY7gQBwQYCs66MpdgQPMpYlvfBnYKpKVrt0mGU0lOjAnDsFHz7+22AVJL2pOxzt7DGRxgJDKwFWa2bfd1l1TCWHr+feH3ZvfymuepSe20FKRR7zN7RaitD4GsmG6n4Fg7Nps7FXZtDUgqpYJDh5ubG0zTCJWKPb5m1P57AVn87VMXIbOyGKuBbupJVJppeC4ZMTpAZudNy7if61znOte5znWuc53rx1HfDdTAdgrFd0statpAEthOfLamWHc+qVHlmZeHTTcvVWlMRojKShjHEYfDA/aHI15+/DGIFiYFGbtgmmcQR1DV5iWw+rpgZVga2XxbrDmupSBQMH8RaiwYEY9f1d3Kec7gGLC7uDCTSdt9TBF51odYQIEMNQPNCObJ880336Lve1xcXqDmCikFxLF5yayjf5k0Npy54ngcQWB0KaHKiL7r0PdDAxPmOVtTB7hBKXOGGmdWOxYHZFR6tbAp6qr7cQPjsjITViNlNhaTVEGVilwWthARQZiQy6xsJyGUbB4JIMSeMY0jaghg7gEhTOOEcRoBjghR5Qc5TxjH2UAMlUmM4xE1F6SwQcmlAQzeBBFUrjHP2RhZKmMgk3LA/HhqLaCgMie9Xh06yZr2UityGEEMYzhVBcwEep1gnhlCGuHOEUNUo9pxyjbHGNUDUqw5VTNbbcTHo4I6McYlAhjOcAACOS1JU2vCSgJUS22yA5VgwBhfmu7kTB1t+hSA0gVpLAGTLbmUqa2z6nKa5XU3EXVmlDLBdA0Hk7atQVf3hKmrxj8wq/SNdB6nqL4fHkXtPjZ6DmtGT7E1WfR3g90LsBxzi9euQKkan951EUQBMXUgOEtjMRGu7wEPi/9SacdVi3mShGD+RZoYFkIESGVPYIZDn7WqiW3HnbGBKlgsEceAAPdTYhufRYZIcAPbakwfN5ANq+s7TZOBaTrGCPo7DgwpAL2k/pSS1QAbgj51uJ+V6Uc2NxeAY7ljE9yYes1hWRg0apqs9w2Bs4scPrC1Rst6FFGTbyn6YbXofQXiZu6wdbnMHwU4/T5kt8K2RhQcodU5KltTcZNg5wcDGjebHm/e3mPoe5WvBlZfL/L3dcZPg0v9LxqIofOFEWJS4EmK+tyYRTign1tNpgsxAFtCY/QJgABPTANc6tfGnQNqWXxD2hxy4LDxFZcxU7DUf75CU/vUuyrPM4gMIIYxCUF6Hwxs56S/7/efKoSuTwghIXA0gDGCk4FbIkhR14AmN+YlUYvW53Ouc53rXOc617nOda4fur4TqFHSuj/gB31IJ3/4rYhMqJbKMwwKNBQpqCZn4KIPoKVJIAgFFRTVxFRywcP+HsfjiOunz8GxwzROqFnjvgVVZVZQsIGEFkq/ZFQRlDwjcATIAYmMWgSBo27wEpk8xXwXSIn4UsWAGGDY7tque+CI0Fu8LTEo6uM124M22W7qt9++Qi2C588+Vm8X21nWjUxyTQSqaIMcAGMwTKhZAGFM06TNQRXs9wdMR6DY3zFFYzFJo+LnvEgpqh1fLhW5aLKSKiGWJjlngYhFBVvcbAz6c7UU89Ip6vvSwAljLEDjaruoY1dmbbzYmB3qFXTEcToixQgixtAPKEIYiyCXbE0noxb9vFoK9g/3YHqOkvXvi+3mEmkHJFikHjnXpZlRJ02kEMBgu9YmwYsBLBEBHYYYVS5kZsxzHgGJmOasoByNiKlHnjMmViO+YKlZIQhCAI4G1lBwJpSYl45twpOSU1LqQVwQKbbZISiARVvbJUeXEkKIzYuklFlNmmkB8vRnrWknWQBSWzeefqwt8GLgys0YVGUcEGf38MkuOQcFcAR6XoHYUstMwmONbjBja/9sbVxtPoQAkWLSKFvPdnmWVBm9c3j5+bnBMrCKBsfCQqmoiCma5COYVHCRA/lbKpBYG0ji8iJn8diHIiQ1XqYYIOaXFFlBudQPCmSQA0c6JsRocdbNuJYUJBQEUGi0I1AxBBE4GWdPP1peV4DTjZWXiGf3MhGAVnHOcFBKAOQFXLFGPZesEiXiti4aQFJtvraZAuSSzbR5kYgREXLV+2eKAQ5X6Vyo7kG7MF8Y5lHV7i463nBoiAz8VHAMHhctYgAhgBbJbX5AZvcUAoM5reaKe8bYLZQZQ7fB8TBit9uY7FZN0xd4KbQ5y+2aKuip66Bi4bxAr5vYa+LG7Axu3xI6TimllfeCAqSAmgbbTxnIrPPTvcya1A0qE44GmtT2o+qxZFBYO3egAGJx6QXIU0bqO/0dcicpTa2CgeY6941dJQzmDkwdgIRaFYDitvbsZygi52IbB2cqzbnO9b45M3DKlvyxlDPU33/th6wQFpazl28ofp+KcWGA/qGfO9e5zvXjLWZ+dC8Qkb+Kh9Xfcn03UCP+4K+7kCHY7iS0aZsnNVsb+sF28VfMmwoEVulDlYLUBYyHEYEIIUXMpeL+/h6HwxHPnj1H6nrUKii5oOs6dF3SRCYzPVxo3mweJBOCxVBzYNTiBqhiu9CwnUhtbdW8lc2XQiUOIsDlxaV5DBTzaVFKvn7JMKKBPExKUe+6AXe3dyAi/Pznv9Cd/JxPJBfaNNludIDGS1sjMR5HiDCGzQ4cAx4e7jB06thvnr66y0nLLrj7CZRSLN2ptjQdbfBcqgB7gF8af03aMUnGKi65GFAjVZupYHKLwBon3XUdiIFSJkt7UV+blDowB8zjCGJB1wfzydEGdMwF98cHlLKYKM+ii7TkxUw3l6LGlw5wiJpNA8qCWBpwk6qZTM2PfZomcBB0MaBkBUY49SCpKJstckmQwx5FRkgVlDrjcNgDZE0lCgQdhANiCKCoBrhDCAgh4zjPGI+zGpIa84RjQAhk5yFIKWKeszGIXIpAtlu/jPXayBNQWVTXpZPG2cGLGCMqCkouKqGT1ZwS98BYxwarDCbGAAgh59JYUT4HxKQq7pvkbDW2KHVnXOn7wcyZT/0rnBUmq+OBSYv4BODT67f+XWYGm89SXXmnuAOUs0CIVFYlIqCVkfB6DNe+K+6b5YCmQ2VkjIlg1wCyAELzPGEYBnBUQKqKAEwGQgkc1KxVT3ZJa8PCXjC2TM6erObjttw3DY4xexFqYODCwIGyfkQgvMic2r0X5qnCaNeeAOQ5I3UuO0KbCwqGOUgj7V2CMX2WubfMnVqrrg2U5nsk5mOiP8EIpMytEHSdreehz/E2Rswo4iBlbX4qxM7e8GsvBrTQe2bIy1yDzSaVhV7g7u6NslpCtM80gHC1rtYP+y4JXRKd7LvDf5HJJGEKjKv/jMs+T99jnmaV34ogxFVynt1wa3WwxHy/2rqs9nd6LguAt6T2qRm1HXtdrlSXBtzd3SNXYHuxQy4zhBXYodWkK6VYOpsCViEkBedF11SVAq7mxWPsmXkecf9wQD/Yz57rXP+Oq9aKzz///Ic+jO9Vz549a0lNP5b6kMHv/f39B+PBP1QvXrxo/n3nOte5frq13W6x3W5PXiul4F//9V9/oCP626g/wKjxB3s0ujwbrX8aJwSO6IfBGukA1e5b0+9EbwJSisjTBIIgECFPM/YPD3j39g6ffPqJJUBps5FS0odjY4+sU38UeFAmTIuUZoLLnUou1rBiYQJZExyDMhWkVEyjmvTuLi6Ri0Y+qzRKH2ZJNFIWdq4unUIVPNw/YJ4zPv74E3CItivpHhdOqYcyj2DNWlUT5JwLcqnoUkJKAZvtoDumvnsti/HvwkSX1sSXUhGC0errkuqkG+Gl7QSfeioAa32EN0ENRGCNgQ6B0KVoMiy7BtMI5oDjXLSJEaDrOyRm5Czo+4S+HxBiQClAroK5VOyPR+SckVJntHy2axuw2/QqS7DjKrU2SRFIPTpKnS3mGfa5/ge0hDFAWiy37nIn1KpN4LDZadoYs0kStPnKpaBIwWHaY6AesYsoecY0qkt51/cga6i3ISAwY87Zro/u9NeqXjvOKnAwk1bMBxHnVRigwdQatvcjtDVJKZocbzFUbX4vzqR5rwn1f3vSE1Gw+R9PdvZbfDKUvZBS0t1DWWYFh2CeLdJAGvfaaMAIUZuLbu4cOShbQ5xRcwrknIA9VQFQNgkcoGlCzIvHiFSTNSIAFg299lay1nwB8PKMaRzViDkFk5Vl9dDqOqSuV54Eu/FuxTSPqJIRKRgLwo9nxa5x8Cl7ShGpr01j2KhEL4RlrRERgoM/RWPrlQ0VFBysBZtND5ECooTFSJbbOpCVhGeaRkip5o2layOmhFILOh0FTVji091IZ1e5B9HyOjWQ0yO/OZBFRC/yKADNu1YZiUvUe2N/ES2fsS5SGR2qAhREBtoBEHYw2cGNunqv9b3p1AzaweOu63F3d4er66s2942whMZ+gkZh+3w/HRc7D+iXGcEmbgWUZbkewDU4qXLXu7tbXF1fgNrGkDE0ofOHoCQdX+e6ZhjCPn8Xhptfe5FlETr7SEEcpRv1/Qbvbm/BHNB1UUEdW9P+DTvnDKCi22wADiAElGrgsktlRb+TY9A5fX9/D6GgiWhY7ifnOte5fvz1fdgnf836cxzPj+2cznWuc/3xdV7Hf5n6A2bCbljpD74AIBgPR3Rd3x5idffXGzONcA6KKChVO6sPQUyagnR7d4v7+z0+/uRjpG5AezgXtOQbb5KWXViLvc4q64gxgkkwTSM07lV/VwwsIqYldcb70CqYpwklF2x3F7rjaWBSybMyMziYWSs1CUBg3aE8jkccDhOePHkGQJky9leNdRKsgSCCxZmrJKCUrJ4sYPR9D6IJT589xZefv4HMSg1zkAZkspKy0Ma8wVrMKn1HXJui5oHQduY9IWlt8rqkk4iIJvZUIFBATMoeGvqEzaZHzgXHWvCw3yPnCuKITZcQg3oxJCZcbLfWuRIO84y5AIfjiGmaAdImQuVahBQjrq92uNhtADKfDYEZ3hooUw2oquqFlM0XQ4hRxHe8NYWLQ7RxryAKCqoZeBU4IbCAQwJzQplGa4oE+/2+NZ4xBQSKmMcMYAKHqMbBBEQmxE1AFd29z8YcKLUgl0lBqFrUa4mpgZhuYOoSh2ZUK8pIWkeOL6DaacwvG+vLd8091cn9XJwpEKN6TRBCY1esQZylQTRhkzE3pmlCChEUQot+hjE4tOmkR82yNvL6k87YyTmDW9SyQ28r0EOWaHTfMfP3jTGCipoZVwPrdP6ah0drbD2BaImZVrDDZR26VgLr/WYaM2rO6AOD0CtApbS2dh/R9zWDWwOVl/NttJjGZFNmTbGEH13nBLsv0BKH7UlMRAEIsjB5mDHOcwNK/H5Hdk2q3zvI/WP0/dlo7pq2BfRdj8Nxr3NldR7McQG7xN9bgZoGvsDXv8kcLb2pzUWcpkyp3GYBaZzd4l5dTUpDKyNysUvvc2bNxgoaoU2Edj3XvwK7ZzrQ4Z5agN68h2GL/WGvTLMY0eBHHwfxs2Q4aiLEIL30xv7Jq7lpc5UISmU02RZ8/i+eSF1K+PrrOwAVT58+AZOau+vBWwi4gd7Acq8Flu+OUwAdEFRQYIQVUAPyGO4KhoKqm2GDN9++xYuXz3ROEZo5NwBNETNhLkE92UoVdM5yEyCbmXBKEaUK8jRhuLw6ARm/Tzzvuc51rnOd61znOte5/nr13aLP1U62x1Qfj0fTpEZt4GnlAePsiWqpMyaZmqYjCIQyz/jiy2/w8PCAv/v132MYNs34FMDKX0AZEy57gskzYDKHwBFM2tT3XcLhcAQs3njOalSsD+r2OF/16POsYMx2szUT2NqMO3OeMU8Zu90OQtpY6464+ibMc8W7d7e4uX5qqRrqkSKo2giQmlFqAySQWgDR9KF5mn0TWgEuVoPZ6yfXSH2HOU/6syWrBwV5jHJGrcX+XRvg494dC2iz8oKANnp+XiFEO7ZqO/v6U4EWQIkDWYpPRd8nZV2UgmmeMc1ZrylVTPOEnCNSIAx9j+1mAwFw97DHeBwxVzU2tqmjEiFrSPu+w831JTbbwUAMb3CdZeKMFTXbFZOruYxIG+wAQFlL/hnE6uUz5xFCAeIpSlLQ9REMxsgqjaMYcDweUZscDKAaETihFsE8ZYQgSF0HZmXv6BhVoKq0LhJh0/cmHVL746XTdH8LNlNZXRvetMYYbf2sE1sWUKM1c4YQsmvPTZbinhkOWrTdd/94crnVqdRKDYYzIunnozXMS9wvGbAkVU2B3VPFNfEuCXHgtMmdSP1cVIq3Zg0s4MBaW65pOpboBfXEcSNsZQp43DhA5DIkBx6swRf1IlEtrI+hIMaE7ZbBqKCQ7H0YpbpUC6tEJgNVVkbMeu9R41wHwlTykw0TcJaKG7BGW0C8YqQs3izOEssl24/pPaJUBTtQFGiNIUCqAQwEOLFGgbWqPlaySOR07HACiKxBEU1nQzu/WgqkVE2PM8Ba1I12mXt8yt5SCah5g7F7fa0kWrIAvsGT+GyuoQrEo6wNNKc2PxeWEq0myzqa20sMfAwhgDggxQ7j8Yju8lL/3uaFmre79NPpQH6M1NYPizPw/HtGGpCmb6Fwt6ee+ffZMAz49NNPMY4HeEw42XeL39gbeGbz2oE4n+c5Z1szalTsx9kAVUMQnTUKUinx5eUFjocRr1+/wfOPnkBomWc6B9W/iiiokX1dfHOINEmNwOZxVpDzjGQsPL8O63+f61znOte5znWuc53rx1F/AKixB2DRqNbRQJoudY214mknuczmwxDUEBcVtWYQquZ81IKvv/oW9/d7/PrXvzZWiT7Aphgb24B4MSJzkMibU5cnxcgoeUJgo8WnZEko1iwQFkmSCIQUpJmmCV0/IHUdSq3NlLWWgr7v0MVkzbdGhgOs6R+14vbdO0CAYdgg51n3bi2BplpzpB49ytphIlCtGMej0fRZGQCWtkEccH11hS4lZJoxzZPGqLbdXKPzAG0XXpuHCk90AVwq4ZGx2mxpwtASaw2Lji6W7BSDymRqKasoZiBGbb7vH+5xe3+PcRqXXX2pIKpIKaBPAcPQox86lAKUeofjNGGcK8ZxNA+HxUOk6xJ+9rNP8atf/kL11dZwmlWuXoMV00ekotSsY1E1yUcEJoeyxCPz6AHpDrKn7OiI2C65FMTYo8aMqVSkFBHiDrfv3mG/f9C5IhExJoSYIAUouYC5gDvdNT8e95iMhSUi6PsNYmCkrgMx4zhNFpvcaYy9NbnAwgJxBoazD1Lq27V1xgutGzfS8/Umbt0MOmvGWVKLLAXtM715P2XYGAtmnkx6l4xVFVEMGFv/vv55BR7ZvFo3dDEGlFLb9XZfDv1dA3XM82ieM6ZpwsXFRZO9aJqWzmNvPsnW/Bp8ICIUa3R9jgA4Aa10fBghAiTBGFdsEsnaWCp63t7YuzlrbU07wKBgzCIDTtSjyCSgNldTjKg1t8/2UrmcsoiYgkoSLQmKCZoMB6WPSa0KVEOB3WCR5QKg65Klsdm9b3WOJSs7sV10yIoB4jHYayBQTdf9832thdSdXG+pxX7X5isMyGugmzNodG06w2uRUbIBFtKOAUTIYnIrWuRPvi7a/HVw4wPAgRjgsttd4KuvvsR2u0NIAQUmpwNjmifE6Mlpzv4yqg2ZtxAHTU0yAEzE/YYWf68yF0zzpMCR1GY2vtkMECkLo8jplqu0JAXBF+DT1w1oYYQqUK9R4eLyrwaQrZhGdt9mFjx99gRv371RjxlGmyNL2f2BA1BJjfQbzYgUrGcFtEJM2O4C8grgY16+a87146pH8sLf89q5frj6ruvx5wZA/9j3W99Tv+v3/5pz6i8xp89A87nOda6/1fpujxqLi4XUFoOcYoI3Jr6LWKruEjO5NEX9IyAVggIpGd9+8y0eHg749a9/rSappOkVKSljYp7nlddLtXjUhY7tD/Vd6DBPoz6qMqEUsSaIUKE79t6UFHOaDiFiKgXDdoMYu2UXGovRaC0VgQOmcVKZkQg23QYQwe3tO9y+u8XPfvELlTOtWAQLkAT0Q488TwBq837wHWHd8Q/I1vDnMiFysIdqlTcwYDvggnmezfDYoAdS+ZCyaHz32XZNbWw8mpvtsnqzrFIV7xn0D0yE1HWIQb0qnKlzf3/fGuDtsEUV/RJNXcST60tsuogUgKEf2o53FZg8SY9r6DoonAL0XYff/OY3+Kd/+ifcXF21nepsYJV/v9ZamjmrsivMN4IYao4J5LmYF0kxxlTQJLA8Q0h/twqpmawI5pyVWcDqlZENkAuBMO5HjByAegdvgKvF7mrqUMZhf4dxPOh8yAVdl4Cqc+P+7hbb7RZDDJhg6UMQIAZjonmErnskxaWhJe9h6dHONmBMCNv19mbqfdYE0cKOcqaCSLU46rWhqXlpiJi0bWjNWa0eeY4T1kkxSd263JDawT5nDrBJh0pRdpmyMQwIsKSpWlUutd1uW/KVrgtu6T3MDLA0w+vlHJRhQ8bK8TFcpDHKuAkxGktEDcNB67E7NdBVJpAzVxq012RfAAy8cmBU4ObF+j4KUsPAQgIh10Vi5Gw6EZX7dClCirP7CmqeLQlP2Usq3yQ16+UIAdSgusC8nlKTSHWpwzQpKJGL+kCdgHyi9w5P+wEUPKK0TlaStnbX4JeDM2JSKzEwQe/tKpMjGxuPKvc5scihYABcbeOv9+Y18LKASD6eDgL5PXORqTngABAH7HYX+Oabb/DRy48UhhGgFDGmyyK3WpsI++sOYmkMdjAwCwqYkJpyMwdwrwlQteQlEhyabHg4HNSM2likTXZqoB9W63iZD/595p5UyqghZlCVhicBK2aQzb1SNSHu+uYSh8MBm+1GGUCia9oBYGaLnReLIbcKrOy+auufAwEoQFnWtwOX5/rx1e9+97tHr33fNJ1zPa7tdotSCv7lX/4Fv/zlL/Hpp5/i9evXjwCNP6Zyzvj6668fvf7y5cvvlVQ0DAOePXv2vT7r+yY8lVLw5s0bXF5e4qOPPsKLFy9wdXWFzz77DF3XNbboPM949+4d7u7uvtf7ft/6kJSylPLB+dx1HYZhOHkt5/zBJK73i4jw8ccf/+DJV+c617k+XCEEfPbZZ49e//rrr7/XGj/XHwBqYuDmgUIgo0wHFPNU4GCNDQFdn5TmDsFcNBWHGahzwddffalMmr//P0AhmhTDd5oZnl4UQkDOU5M0rCOpdXeeUOsEgiYqBYog0ofRXLTxL7kYR0MaLTzngm7o0XUdctGH0hAZXG1X3uQWRMCcJxAR+qEHEXC432N/d49PPvkETIRSZ3BgazILPH2JmeyctNmUrM1IigHekMUQQRBM8wFUdafzeDhinqe2U4+qUodA3jB6sszix+CMDZGl6RGplhbstPuFvu8SFcAe6gMjJvWbCUSgANSq1wFCzaBWhHQ8A+P66gKXFxvUPKFLQUELqEHzNGdMs16nYejRdwnVotR/9atf4R/+4R9wdXlp5r+LSSyzWMPjnUoxJgk1OQVBUPMIOzNIyZjmCSEyQo1ArQqOBVjjpE0mzPBVpKLmrNc1MOZ5RC1Zm6CcsZ/vIKJGzF06YJ4UaONIGI9HTNMBNZtEax4BIXTdBgzg3Zs3IFRstjuE2OvnrcCTlLR59JrnGX3f27VbIqbXkpa1t4zXGrBZTIhdfqNznYz5EYIzjPR3c87guBj4+lwSKDDhnjmBg5nZ4sRE1xvfwAG1VNRcwDGdMCBExNPTddef3JzbOF/GyHMZ3hpwWsokfLIwLvTclSUhWFKDGrhlkiOPI+cYTxplbo0+GrCVc0HXOeilAAFb0yzkoNcCIhCRJWNJAz39mGDzLcSokscVYKIYkrK8mAjFztdTt5gtGa6uZ8iyvv26rL19RNRsfZqPYKABwJ7a1c4VwJKSBQOVawM+iFVyhtW8CzZ+1UBqZSAJILoGHRBW7zEd2xD8fJ0NsgIsQApGGVDoIFh4D0h0mZWPw3qO+7yoksEUQSBc7K7w5Rdf4PLyEv2mb0AFZGFn+T1Q7zXL2IHcZwdNFbUeff0u0gh6JgJT8CPQMbL5Nc0zhn6wdaf/wNYTsERuLyC7vhJTwMP9Xg27jfWoAVnSpJILaiP2ThWAbmjU2mvqV0ru6q/jD0IgRhEYg82+c4DGiCJSw+pc1bB9Pb/9/M7146tzrOmft/y7wzcGo21+/VsMtX3j6U8tlzX/OYuZsdlscHFxgaurKzx9+hRPnjzBxcXFI/ColPJXm2cf+py7uztM0/To9e8LnJ0ZZuc614+3ft/97cyC+/71nUANAxjnCSEoyGAb87qTx7xKgiKQPQzPFl1MAObjiK+++hyvXr/G3/36H3WnjxkhRg1ILi5dIHRm4kpVAY+6Mt2staLvVFpS5tlMPPUBuZSinjgcTX7l8gtBCAnj/giOAX3XtRv6sjMuLXYWRJgmA2n6pHKNnPHq9Su8eP4CQ9/jOM/GWND4cQVU2EhH9sAuKhFLxqBQsEiQYmweObUUDBuVYL1+8wpBQmPSqNeMNCaB+2E4CNF2KoyW4Tv+1XyBHPAJxjZYeyAQE/q+x9D3KHMGiWC72yJ0jPF4UOYHaQIXE+N4OGLT99jttohBr/Ow2yAFf8DRh3+VQmhk+7DpsdtuwJTw4sUL/OpXv1LQC2hMJW8ip2lsPiINqIJAhJHn3Jggy3oWHKcZIgWlKqhUpgnHwx5CKrPR3SJPTDLz3PGA/WGPYABgnuw69GpiW+714aHvN5jnCXOZEbuElNTAdZIJ0zhBSsFe9qiVMAwbhE3A/e0bvH39Bk+ePVc2hDCw8vJwllCtgnEcsdkMp/4n9pDo13btOeLNsT9E+kNOazwFaG6k0OvjDIK1YXEpBSkmAxEdxjQwx5gjZAwMAUAnm1O0MLGs0VaAqUJgEciiEhQhgrD92ZgG7p20Zsu5r4n6mSwNrV9jmFzF/XcULC4t3roBVazgizLGljHSe4p5CJVq46ItcNf1uoaLIAQHtGwshVBXgKb/EzhAUJtnkA6bfo7vCFQoa4tWDDhlCdn9khSYGfoepWSVPGa9f6qURudIqRkws/T3v8j8PdUceWE5id0PHMjycVyzUfzfISoA64qdaiy8SiZFgwIuvhb92J2B5e/TGFEG3hRL/ltDDW6CXcWuwWoutxXtiU3GUFpL98TXRFzOI8aAq8srfPXVl/jFL3+GIqIgYvVzJGNaRfXXIfds0fuP5Rzpn33t+NsbYFdzQYWaO6vcaDEF3wwbHI5H889a5glI/Z106pon2GoN+fUIMeB4OGB7eQUQmp8bU7D3eP/3FKgRaOLedJyQ54o4RLiFvP6uMg/Zv48MPPVRqbZBQXBzcQWfyWlJ52emc53rb6ZCCHj58iVubm7w8ccf4+XLl3jy5MmPEtR49erVo++Fi4sLXJoX2bnOda5z/Xuu7wRqjscJzAEhJJUwiJm+MsHsQeBJGppqNIPJGQIFX3/5Nd7dPuDX//AfMOx2QIhNokTECBwBMwzNxtxQW5mCEBS4mMYR291WvUoAZU4YkyWbdEnBkwxI1iY/z4h2zGBG329Wu6pLqglZalWBJsgUEXRDp8dYK169eo3tdofNdquNYiBtYmTZASVmkAE9Oc+6uy0F46wOOEyEru8xThNqLsi5ous7iGTc3+0RIlD2WZuuEDCbQbD6x4j6yVT1bHHg6v1d5/Zvb17hLIau0f2Zg/rLDBvEqEbQITI2u60yUmpFCgEX2wuUUnE83KHvgnn3MLabAdvLDbpIyHPGNGUcxxlZgD4lDJ02mNvNDjfX13j27AVevHiBzWaLCtEYdE/wYm/8q0nBHDjQrW4i4Hg8NGAhhAipAEcAohI7iLOMZhCp6fI8zUCdgb6HMAFmCns43ENqaelZ8zhjPGaUUjEMG3DWsR3HIxi3GKcRw3aLrksYhg6BI0odkUtF4oppnEEICCHi6uopjvOI/WGPrt8hmHcIkUsdFHAElsaWyIGLBdA5oQqTznElUy3JUJp25Ia9bgarzDNnB0jVOc5mbJwMxGDoGpvmufmb5KzSDgcGidkkOtq1OcME1uxyDAgpgmNArcpW8gQaCQswWHNGkYLIrOCNgRjvn+f6mbEZiVv36BK91vya3JA5IZfF4FYBGzf9FszTcYm0NiAp54w8TwoI9327D0h1GWKx+1wAxQhZsYX8eqiPjaX4oEBQ1Mg6Eippc+54zxpQItFYewTPw9LxICZwDOZjI6v7h/nerIAaBb3aQAEOmhhbhswbbJ5mRE56TNXT8gLAhBCciScGsBcwA5EBwJOtqrGQ0Py9PERpbfwd46kkr6VjwYBjA6d97pDJi9pgNgPsxexd/aZ8jvu4EwKCnSMgXFGl4snzGzz89hbjeETXJwXXAqMWZZcssipng6kUK+cCYR2TKhNQ2Rgxi2SXSO83yu5b6E5iAHOKCdIBuWZlsgQ3Bl5YQoBJCtscN3CrAJt+wJs3b1HpAbvdBrW6YT4paGn3NgdGCWyAta731CccjzNqIRCpDLlSBAkD4AVMFOXJBA6gKraRgMZgqsZyE3t/+vH1b+c617n+xAoh4JNPPsHNzQ0++eST9uevv/76R8fS+vbbbx/J+T755JMzUHOuc53rXPgDQE1KyiwpzXBRH7zd+4Xt6W6eJwUyGAhEmEvBt99+i9vbe/z93/0G3XZASEkfZa0Rr6Wa/GNpVksuarjYJ0gV3N/dYdgMarIrFZGA2QEXYwREVip9LRnMaH4PtWqSyXa3a7v+QsWAnQW9V5mPNhIpJvtzwes37yBCeP78I01dsgZCWn8s1sDqGEzTiHme1LCWGFMuGKcJ1zfXaDHaIiY7iMj1ASEkpBQhrKlQ1Ux0RSpmlx5UZcMIs+2Karl8xYugD/mLrIjbLoXu2kf0fY9aBcej+h5cXuzgpr8hRHQpQaDMDybGxaUav1JgPHn+HCEIapkhUzYJwIS5CDabDeYsSKnH849e4tPPPsXV1TVCYOSyMH7WchwiM4eWjHmeGguKAzBNE6TOiCEiBMJk8dodJcRArXlSsK8Dk1KZo9HrqiwsjDrr+/d9p4bI0CY9lwkPe238utShiiaiMHSnepqPuNhdQGpuG+739/fYDoJ5nCEVCkJ1EX0/AMx42I/oKKIfegPSYHKVqE20+S7Rqolby5o0Xh6NPbKWiOTssh+PQzYZR1V/JGUVKPCgsiQYa0SlOhqVbuyPKpr0FYKlfXnT734baHObmZdUHCxskxACUHI7x4VpI5hKaQ1zMLCG2JKEyE2u/RyssVyda0UFiTb0pWoK2vp/mooWG4Mkm5eTH0cM4cQMmBrDSRkf4zwihE4NxscR85yNntmh32xBYQWSvCebcYaGN/Uc3IybjcHlaV/cWCYhhiafIgIqL2vXTdBrUTPhBAf6lthrXb+hvR5DRKkVXQgaQW3HGmMwwOvUHFYqViCZjoMaves4uW5tmW8LUMCsTMpcMuCGyoEgtq59jEJgY/bAeB5ozBjyN11um+13gzEP1dOmruaHzmO2+VNraRLZ1CVcXV3h3dtbvPz4OSqM4alL29gii0eNA3IxegLbkqrn3z82UgqAiaWMwe/xtf29sMZmz8cZOZcGvoqUxnQCFoN2ZQ86+KmSt4uLC3zz6jW2fYeYwiLfMtDM2UQNsGrfeQUxDhg2jPGY0Q8DaiUQq9RUaVJraszCJtJUKQcKT+9B6+S4c53rXD/9CiHg+fPnuLm5wfPnz/Hs2TM8efIEr1+//tEBNe/evdM0ylVdX1//QEdzrnOd61w/rvpuj5qoBqywB/e6krm4dGKapuYrIwDmUvDq9Ru8fXuLX/3677HZ7UBuGtp8MtzgUx/YS53N24LQdx0AwcP+AV2K6Jqp8AJAuG9NWMVUM6tnTNd1IAooRdB3PSq0iU9dQq1ommQvj4MGfFeccNgfETjg2ccvVK5T1cNkzQgQURClGnPBqe0EAoeI/bs7XFxdQkSBDwJbeowyGSoEV1dXxnZR8ELjr1Wry6yiE4YlvtSKbM3X++k37xvh+Z89EjaEaN4QamYZgyYdsTXqMSZ0fUIMKkXp+g6bfoeu6zDPM549fdpAFeKgDWXXQYhwe/cAIODi4hLDcIFf/PxXuL7RL9k5azNTq7GLTEpRDKiJ0RrqQCDR1igmjW8OMaonhAAc4wIeeHS1a83LDFBE6AI6ZgP/IkotyHlCBiF1A2JKIEurqbFiGDocjxPevXuL7WaDy4tLzLMgRmOz5AnjcY8869y+vLzAdtPjf//v/4Wryys8e6brA5GQy4z94YjY9ej6VXNvs8qb9rjypPGGap1+41H1vtb854ol9Sza8qXRCgY6Omih8sOMoYsrLxU048CUIuaSMaQNFlYOrEG1deAgnwE1MIDieDx6+2o/F2y+OZuiwKU8Yj5WmqoTTAFkXjpudEvA4p0EW0NsXkbKQnLGSGBllo3HQzNmdnmNr2ciQpc6rJtVkYoYGDQMYCZM0xHzOIK2W11fTAqWil+3amCyJro5aKH/qMdUkxqanExBIPN+odjSfBavJwBgBE6tCQezjlf1+GlGrn5fXKRwbgSuc0VZJ13X4fbdOwQe2rH5dXAGh7MGHdT2OaZgk8oLF4Pl0gCzugYlZHkfBbfXqU3BwCr7OV4BMY2xSA0EZGdIuQxM7GfbNCBotPQKYILHxysjxL1fiAjPnj7F119/hTxnxC4q45IClqQ7HzOGSDCGEWz2KoDYyG4CnXMGFrk/j7OLSgPIK4CAQAEpJhyOezgbsJkZ23EvrBhfHwQygK7vOzx9coPDYY/LdNGYYW2eAajMBtbo3BEpqEVQoEbIXRdxd3eHYbhECkFZoeE0lU2BVwPIaTExF+j5rkH9H6Mk4lzn+nPXOI6oteL58+eYpglv3rzBxcUF9vv99/r9w+GAw+Fw8tqHjHN/X2232+ZT5/XH+NNcXFw8+vn9fo/9fo/tdourqytcXV2h73tsNpv2Wbe3t/as/hik2W63fxUz3lrrB02Xx3H8oEfN++c5jiO++uqrRz+33W4f+e7sdrtH43yuc53rXD/F+k6gZponzNMMCrT4IQhMAlWRc0XOylbgQKi54u3bWzw8HPHLX/0Dhs0GQrqjGg2syaW0nVIiIJcJNVd0KSGEgHEaUUpB3/XogqfWAHNWgMgp9TFEfdi2RA2VHFUkShDRRKNg5phECihx4KVZsB3/WrQxY9uZvr+7wzRO+Ojjj3EcR2W0BIbUsmqGa2vGBDoW2kyo/8z+YY/tbofNZoP9fo+UEmrR91FZVodaCdOku6JlP2OeJ4zHA9bUf4KaqUbWiGEmRkFtzQ5EVtHg1Bpr/9J15kUIClblPCOlDkOf0KVOI4ZFsNvtAKo4Hg8YugFd36OPUa+FpS/NuWCzGRAZKHNGqQUpJcQYMc2C3cUVPvvsF7i+eQYBUMqku7xGrRcRZIv/5ZWp7FyyNe6dNUlAiAnBGnAiRj9ohDSTm+KqGXTJBSm5EbRdFxBiCMh5Rog9OIzou21rFqfpiONxjy7NYH7A/f0Rt3e3CIHRdb0yv6SglhnHo4I+IgP2e8aw2SB1Ea9ev0LqepSa0R13mEvBu9tbfPTyYwgEuWRE0qXFzJimCdM0Im621jB5Yo4zDAghRpXgEBk7RhkYDsS4VEqvtf9ZDbsBtCYcgJpWmxTFzUUJbJK9spiNvp8QtWoWQSuTXRjjQqr9twIgijeoMW0pKiVzs1pmBTVKLShSEeCeKsv9hbx5BNwVGQsIp0AFB1KgBYQpq3yt77qV9GdhuwCLhCXAQYqKIkB15glZtLvfAO0BzyPvmU/j1cnkZH6fIah/jHrTLACpiPplVVj8Nhx80veoBgYAaggMAgIFk0v5OShgafrPxlIqK78ugsWiV2MkmfyJyQFt1844KCLtOPz8HLxrzDsbXwdgBB4bLa2pZ4aZDOv9KaSIPPna1Xu5Xm80UHWpZTwW8GIBxvx1ZWEt46lMG32HECKq5JUBN+Niu8O7t7d48fI5ijiLR05YOw24IjQ5WK16VRTw8/utfSfol4zes0jXnbPb/OeKFMQUEWYdcw6hgZMqK1tkYUQOxq4kYgIMQ4+3bw8Yj/r9ecKEMbBGyI7K/kPN9JWtlkxeN44zQsdwFlezcl4oPW38nfHk3l1+76nvGVqf61x/qzXPM2qtuLm5weFwwN3dHTabzSPw5ffVNE3/poSkYRhwdXX1J//+drvFdrs9eS3njLu7O3RdhydPnuDjjz/Gbrdr5+TJTu/evfu9x/R+6tJfonLOeP369Qc//32PmsPhgM8///zktVLKBwG129vbR0BN13VnoOZc5zrX30R9J1BTRVT2lMVSftSbJZDS9UuZMXS9yTEy3r67xbvbO3z22c8xDFu4mYjS+LNF0yorQxNxtLmJTOhixDjPJh9SQ9tcspoDV/1M2A7r2lzVH4Y1ZVh33UMcwBxRbce8aGdpUbMABAjW7JBoYgYzY78/4N2bt/joo5eaflItMptwsuPZ2EAAqAJC1Wj0jHmakVLC9fUNDseD7lbWqpIu6lBrQegCxqli9uhdgjW6GZ6cIvYwXQGUwqilNLnB2qPmhOGzahBElka8lNpYRyKCw2GPJ09uMM8jui5C7PNrLRjnCV2MyJVxHEcwE47jEZvNDiFGe85nHMcDQCpLmOaMFx/d4PLqCQQq0ymiSS5CjJASAhS8AQQMT/oqoNWObi3aGHZpqwlG7dyAENUcWv0jXF5ST64L7Jx1Psw2jozNMCjoQcB2W5DziOPxgM3mArud+svM04hpnjHld5jzbGa/CX3XYy4Ft/t7CIA3d+/QdwMyKvbTEQ9Tsfjcp9jtLpqszlkeLtfgEJq5p5oNW3NK7kGh/bk3dQtLxa6jeRcBC7vNx80/a5G8hcZyUMkRGWPHj8kNRZU9o0wyS4IyUAZQxoyHNgOwJnnNEvDUNmUteGx8WyelqOypNeR8cg4EhzHUzNY39dfJWe04oRKmGM3YlyOqFEu6Cu3nAW9AXXIiDQiqdjalFmXDGUtJf295UBRUSKXTJteYCC6hEpn03gQlIolda5KKdiLGHBSoHC9yPDHk1eQnu48wgdS8awVmLO3zIpEDhAVdSioDgoKt6vUV2rWsJi0ju2fGoMBnyQUhLswLlwRJNSaUGaWcxHbbzyqWFk7YF7UKYmJUk2yKMNzWnHzgSAx8WBll8ykbUMTvsYsUUIG15arQyuWaiHFxcYV3d+8UBCG9nzgDSe+l5gkFauCkfqZLL+MJ+4ZIPW4qLYBRBRoo7/5Qfv5d11kS2CIbwzI0ftSovtb9GkLTAK+vrnB/f4/OvgM+zGmRBowqswa6aSDAxcUVKpKtRWc4vcdKeo8ps2b6NfZO9ZVxrnOd61x/mWLmD8aQf4hRc39/j2+//fbkte12i9/85jcffN9znetc5/pbre8EakKM6IngRonqnyKgGADRKGaGNj73d/d49+4dPvn0MwVpVg+11QEaUrlSKcVMO7V5nsYRLITbuzsMg3qOaLJSQeTUGoVSFvlHLUtCDrM+DKvh78LWEFmBKr7jah6ZpRaUol4rIQZM44hvv/0G19c36IfB4r7R2A1tx5Ko/bmYb8hs/gnjcQIqcHPzBOPxiDLP6LsOx3FEnmaVcln6VIWyZR4eHjCOR5ScT5oGb1ZzrWADjVy+0DxfsOzeAktjt04Kcp8Mj6MkIkjQBKppGhEYSCmoJCyrd8U4FjUwlYoYOwUZjH10nKfGqhjHSU2JOeLy6gZEAeM4K2sBHnfsTT3AnJThZJ5BVSpC2ugx5wIKCTEk9L2Cgg2AEJWRLDHq1ggym7+NNpnadDNKnhFoRuwimHtsdhvkeUaiCEFBqSP6zQS23895wpxnlFwwTrOxj9R/BaViKjZ2kRG6AR998hlubm7QdR1S2iDGTtk4MWrCjbFY3Di4FJXkLTKdhVkiTY4gH2ywRHTHuzEvYONJCl80Jo0BVW7CW1HNiNVBPP0cNiNnlSIyPAUGYpHRCyUCBG5zzMtZAaUs0h7AJYXL3CMiNRIuBZXUn8nlTy7RWDeVDnApe0XBkoUxIhBLLCvueeRgpAFFpbrJOVn8sIE13v4yq89VDM0jaQFCFgPaWjU9jCioN0rx66lMCGV52XvWBZJQ5glhzrWxoNbsETV3Vt8koEBNgwMCq3mxM4rW6VgNILEG3+cUM2MYBkzHo96D7XD82ruXFpOyZyoUMANgkezV2Dow4PZgBszKrmnXxKSEDiqKMWoAnYtEpLJO7pq0SJlWxh7xkTlJxjMzW/fr8XuVOBB4CorrGjApmiyyJhHzqrm+xsPDAZeXFyBayYfIo8A14ajAwSKTCEGBshiTpc85i5ERo/nkuFk9uzn06XHFxmoZMQwbgBbfHp//Cwu1gtuaVrZOSgmbzRZv377FzZMnLbVN7PeVQbPcQ0VgqYZuBkzKzhQ1pv4Q1vL+d8P69fX8rPUsfTrXuc71l6sQAj777LNHrxPRI6Dmyy+/xP39/R/8ff/+Oks3z3Wuc/2t1ncCNfCmbpZm6ulNfyACqKLmilffvsbDYY9PP/s5um4w1otT9cWYDoQARqmaCuKyBDEZ1evXr0DM2G6VkppShIRggIoCB94A5qyJG0vChZpdsjEAtCFVr4spT4jMCOR+A+q5M43OmtBd12+++QYXF5d4+vSpRkDjVMffGhXbEZdSMU8T4oq1IrXi+uoGtRZMkxoLT9OkRqEGTqAK5qqsm1Iy9ocHzPOMOc/LQzUI7gEkIqhq1XLCnlkay3XayFLrhmL9ezmrR81xPKiMqWRIjej6hEOpIIGZCRM4ELpem7BaC3Jh2/0YMU4zShEAjKsnN4ipwzTPAAW9LtZcaGOmkgLiqF4UUNAtxQ4hJEgFUg+TEPiOvUs2tEHquk7lHfDkmWANZ2dsBkaIyRrtiNjrtd1edIgpIqUNghC0Se6QUsZmu0XJGdN0QD/0yrAinaPK5go4HkfMU8HV9ZX68ohSdUNQUIgRlHki3pBzA2r0OCPGccTFxcVqV/7DgNpJY16VzcHR/Vi06dYdfF+cq8Qm0ua5CoFJzHNjSbKptbToXkChV5aF3dG4Lax/dm8QNlaCN+YeBx/CWnLmIJMCPCJ6LWFkCvH1m5SR46yJ9ZRtgJPHNMNYH1SVxSUKbmmMM8yEV9q5KN/Am9o1YKSxx/p7KtnxdRbMV8SBsxASyHx2IIufjs5lakAvtfhsG6tGprB7g4Eqfl6ghRGhII+YN43OJ2fRuLzU58BqMesYGLOQSKWd97d3Cg6VGYF1HUgp7X0W9odJlgIhIKqptKzZLM7cOgXlGmBjwN2JJMwMeVUO5mPoTLkKqmLriRoQyQZwB1IARde4AUE2B0E4YUgt8iW0Oe2ASS4F2+0O727fmf+WgHlJVSMiA548nXBhtkzjaOvUmTXFRWNw37BqZxYomPTV/R2WcVPvJ/Nm4oCKgnW+lV/LWgxcsTEsVaWcw2aDN2/VTHN7sVMAy1LA2tiTSr8cbC2lYCwz+jTomsYS2f5+w/L+98JSfnaw9XFudM51rnP95YqZ8fz580f3pHmeHwE1Xdfh5ubm5LXLy0u8ePHi5DURwb/+678+So0617nOda6/lfpOoCbnWRkZMC8HEeTs8gpBniZ8/fmXyKXi009/hm7YakPFgDZ5UCBGpMXvRt3GBWrBVEaIFIyjerM8e3KD8XhcUeCrRmyTeQdAjYQhgmC7mb6rm3pnLOjufLGGJVh0t/rK6MPzNCnTRb0ZBG/evgUR4ebJDfbHgzI4kkorIMvOP1sTr5SFgsDAPI/I9neXZvR2f38PIpVmHY9HS8+yppCUqQAoADUMHabbg4E9uisdmCy9REw24Q2pNsNrgAZYgJtm/GqsBzeq9Z/tuk79aFCRUgChIKWIPGdtw2pVYGLO6LoOF5cboFYcDwdc7q6UGVQy5nnG4TACYPTDDpthhzkXEAtCXOQgHMhYG5ZswoIYE+ZpsmYrAgg61rUgdT1qmQGTCzgzQX/WE24AFKPqE6lXxAqwc8Bg6DeY5hFdP+jvMMBC4JBQakQiMrZQRew2iMkZQGTgBLDdbvDu3bvWUDXgyca6lALiYCamBmwYuNkMrlcmwNrYLQyxEAKkkjHBOnBUkCoQYy5zS4FaP9iQsSRIdTLmRQMA1EAFGHCofjuwnXoDT9hNfnU6OlOnmd6S+1ysAEBWkDXL3F7zRtjPa/26vubMDLbjDWCOJl0zLxRf51hYYiBPK/O2Ge3fygICBDbPlStivJ/V+IQFyBRndMAlVOoHBCZUxWztOtmnOOVO36yBHaVkMDECqYFulQyQghTOxtB1FQ3UUsYYrU5DxyeejFfDt967zsvxU/sMqrIANkENlqUWiww3yCnwilih19DvvQ5eB4k2V/UabTabRTbnIJ2xsJZSY3NpUj1pLDY/Vgez2MCnBjY5CCU+tAuzxGVWzsZZpHjLGIQ1y8dZXqjKcCNl4b17e4cnT6+VBUMECs4GU5YMgSG0YinZPWPOBXOeQCRIMSJQaAllnkSl60PnrYiZy0ttDKCUEg6HA7a7YYE9GxhroDP7vDKRkXkMAYQXL17g9ZvX6IZBP5uMgbNuaJyBJyb35KiG1DriumL/wK7yMu/0DcnZfOfd6HP9O6kQArquwziOeHh4gIj8Xn8WNx5e14d+vtb6QTPcv1allLDdbjXQQwTH4/Fko+7HUkSEYRgeHdf19bV5vp2+9v7Yd133aOz9erwP1Pw1zJHPda5z/enV9/33ki3+0PfXH0P9AaBGzRuDdRtkxqHTPIKC4KuvvsTd3S1+84//AbHbgjlitpSkFAPIvFmUrq3UdW3U1Ki4omLO6kR/fX2tNPrWDKuvx/5wMONg9ZzxLyB3r885o4oYw0HlRCBCkaryFRQwPGlIo1U7OGuhYpwmTNOMzz77OaY8Y84Zm+1GaedTsQbSdkFFG2A2IEp3xgtAjBTVWPd4PMCp6qVkDMPQmqAQAoQZuXrTznj67Cnuvr4FwY1idYdB21DdWZ3mGWysgFpOd7zXqTfrHfAQQpOteBOuOxLPIVLQdwl5PqIfOtQ5m2Sk4Hg4YOg3ev2gO9HDZoM5T5hzNumG7VhzxLDdYRgMxDADW2W1BI3JLrprTrYrDyizJiU2FgqDKSBLhogaF8cUME3qMROj764D7jnR9ZoGEGMAM6lJctdhPB61qbN0nJgSUhcMXIPFdxdwUCbPPGcFSIICZ8K2g85BiR2ZQIjouh6EgMBJ2V0iEHCTKlSTh9RaEWzu5qxmy9M0tgcoT2FaP6jMtsaAx3IXH8+c1Qen1AoKAeR+G+SAlrGQrDlW9pkyBYgj3G8jBN3xd7kg4RQo0L5RTcE5LFHUrOgIjscjLi52rUFldh8dlw75TXcBFOF+MlVaupqyAirIWBwcAoo0GLJ57LSkIoKakEdCCmlpMP0PWDPI1kwdDYpejHb1yErOEIiZxDojyRKGBHDJJGRexhrOWFIPGmXMJY0Ct8QgxYTMlJlZmRF+r7GGGlAZmPtGkSVYOVjhnipN+ubXxecEkc5nVmZXLmauDGWqeYLQAt7anMDyfiEE9WEyNqFKAHW9kdJqGhNnAZoUBHRPpWpMx5SS3q9ImToki6F5e9BmWyt2XH58ChaFBoIvtURiNzDr/XllgBoHxsXlJV6Zn0ED52R5L58Phgs1RpQDQamLBsShHUeFGLCkcLmn0/tcESkmodPvFE3t03RBWq1lBfHsvucACWCSRwXFQorY7raYzeydWb87PYVNwZSFAcPM6FIPJl3bIbgkd2Efve9V00bQGEncpJPOR/txNXXnOtdfona7HZgZr169wpdffomcMz7++GN88cUXj9bLmzdvMI7jyWtXV1d4+fLlyWvTNOGLL774ix/77ytPegL02fHrr79GjBGfffbZjwqsWYNI69LNw+9X78d4A3jEsjnXuc71468P+VV9qMZx/EHvrz+G+m6PmuCsDA9mLaA6Yd7f42GakCfBP/7jPyF2HZiBeTpqck1g1AygZEgpKKIGjUyMqRwhRZDzjHE8AgRcXz+xdCVlEAQOIPOE8Ma2VI1W5kCoJaNW9ZkRAfp+0xpHqfrwmaKZZ5as7BvbvcxVU6eKHcM8ZTx//gK+Xzv0G20mslLTp3myhpItppUAqSh5tp1ta8JDAELAPE7oUsKc1V9HuxtrvphBFEGUQRRxeXmNX/7yl/jtf//fiDEiZzXzzCU3mQ+RAEXjvNcSE2ccOcOi1lVEN1uj4TvxxLi4uMCz589wfX2hsqF5BvqEEBiH44gUI/KsEbfb7Rb90OkciAnDZovD8YCal+YpxAgOA3a7a6R+q/IfIhTRhrzrO2NEFRCUXRKYzYyaIGSADoIZ/SozouuipT7prrruptu4M0DiEgs30s1g1ihqDgElzxiGDeZpxu5ia5I4jSAXiyFW1ow1TMYs8CbdfVtC5JU8S5tQNjNTgWCaxuY7403hkhBTGrvEG+NSbFeenC2ijVRMC8OGrFkH6Vg5IlGqaPKXgSGKySyGsETc1mopS2y18UUUnDAWRi0ORFizSA74GPBodKgAZYBVMfATgmhSNQe+WJwlYrI+Y+KofGPd/LnJs4MwylTwSGgYmNJYBA6U2DwHARzNs0M07cqQOyW3rSRk0v7Kx8b5QQbIgFFFwY7UDZimGUIMjgZM1WoSFU+J8iM0EKhdPX9tuQb6wc1UxFgbxn4jUq8eSLt+RYDUGn8y5qG+Dwu1cwFWIJ7F2FMFYtfheHxATFH9XyxZD+zOOXo+ymBygNDj2mO7l0GAvtP1XlHb0LpszllXPtEJj4EADe9WkETnIhm4Qc3gV2gBBorJ1/z9dH7q/a/5CmGJsncmDpGCb0Ri0ksF3EIMOBxGbDYDyK6TmOm41Grrvi0puASNCKilAMIQcg6jA49mBl4M8mv3VwIKGeNPgaCh71Gk2P2JDCK02eGsFfc28nMC2rludzscDwfkKaMfesCkfQSXG/rAK4BbwYCx1E69r05Zbr4G3n9NfLEAdk8617nOda5znetc5zrXj6m+O/Wp6m44LD1lnkdIPuDh7i3mGfjFL36NEBjjdNCdQ3uWr7mgsCCSRiUfxiP6vkcVIFf1t5nnESUXXF3f6I7kNCmgQgBYpSN1LogcwMaQCSEA4jHZDA4BMSr1HYA9EKucohaV8/Sp02Y8JkzThD5EhBixfzjg4f4eT588AXHANOfW/NesGilnCQRWQIDaDnBtqSK6q6z+EOM0g0PE/jhaA69snNR1thsvLS6cKaDvtthudqhiYEKIKHVaB/zqw3R11sYC0ujOtkq6ansQ93+vAJWgD/PPn7/A9dUV+qFDYGAqs8ZnW8Neq7KdYlQ/nRQTjtMR226jTSaWeOCQEraxB6iDIKCC1LMjMI6HESlEkCgQ43OHOEBKVq8IDgiR0HcBMRoQyOqTk1JCihFAj2k6QiBQtZvApRy+I69JVYKLi00zTQ4dN28hBYY0BQmisddr2U4yCQzM5LpJOExCNOcJIQZwYLAEbeQ8TphPd+7dC4OggCRFS1oqFSl1CxOJFqPZEyBgxRxQAsMiD+r6ASEE5KxMJd+pXwCh0D6LiMBRgSuYUa1LSsQ61RjTAmyQHwsAKMtMrJHUJp1AwshlNmCFsER9OwOGAaceExAiw8OPiDyaemFz1Kqpa8oiW2KCCfp53qAvtEixplv/vkKMuYTWpC5sMhvLKgb6WqMeGEzq83F1da3Xg1zKs+zwUQirczEGg42bSNHrXjIga0BhuWYisLQsHU99H25nwYFApM115cWY3O+dp+DPci8WXoyd2fxIYkq431d1iBaTuDE1CYyCR9zuBy5DA0GBwFohFCyxjhpDhQwAIzsIZ/iszc59vgNAIDVdDsai88jvGJb0OoCacbbKMknBCANSc1Zj98asIUDMoFglks5kMxBGqLF2pFYM/YDj8YjNZtPeIxdpcsBmyg33OgsA3FzaQaiqzCg466eu1trqatPaCFvsnKCbBFUQzNhXUBeociU7Ilt4are0AGB9N+D+7kFToFivnTKsdGLrWmaAg46JsUfhoPNjAs2j3Wtd63ruzsJTBtXj3z3Xuc51rnOd61znOtcPV98N1BRAqIKpQErG8XCPh9t3yHPFy48/Nco1GqjBzAgCM3nVB/w5F8QUUKVgPO6x3SbM44QyZ1xeXioIUzSFpbpkQASH4xHRmAKe0iFunMvatA7DBt7UqKnjbLhGNkBkkWQwgBQjUlBz13fv3uH6+gr9ZsBxnAxIKcsDunGIUopAVQZPCMo6AaGBLn2/Qd93mHMxiVJAnjO6Tpk2upscWsqLP9QTCMfDhK+//gZMAUQuC1sMNt2AtAE2evaIIeouujCmaYRKi/yB3xOWFLCIMaDrBjx9+hSbYYOh03jrUfbGEiGV6IyTfUxtfhxSzLijVARhbRZrBVNA7Dfo0g4gTa4BSJObpCKGAJEZtarumEkgpei5QZunGCJiJHAQnSsE/Z1SkVEgQihFDZZjSCpVKlmbSwOyPI2HmTBNClptthuMx9EYSnodSnWzVAcYKhiMmKJKat5rZtax18likLUpVIAQxhpY/nu9a60eEiFqE+n+IUykfxaPJ2YDMZbr2mLnq8scqDGlFonOqT8MrZpGX4PMEURurC3t+PXcQjs/bZ+5NY4wFkWIS/KQCIGCy/Kc4VKbMewjY2tv8v1nAQgrmKdMKgKFAI8hdzTCgQ4dQ2k7/i5zaSHdtJyz/7ynSJVSUEUwdAmFLJGJfAyghqlC2G63qDLp+/p7rWotP2vNLAMoJj4hBTxqraAIuwc5Q4NOro1e19O5Bda49VoyEPxzYKzC5TMbG+iEMeRgrY6vS3hCUK+mAgHbNXd5KFscvF6nZW6pP5DKykJMtqYCVldE54H+MpaZuoB7UnUu15zRxw4VKqNCVoaPyBpwW+ZFAyxbApPeW0JURhEJUEl5Jw7kNeDLYWybg6UUXN/c4O721u69nYLUHCym/JRNou+wZpww1EttYc4s186ZnYCDG4rliI3rMv9VQuZm3zo+OSuDU5Pz6upYFoDFXwshmlxywmazsfPWhDcCoAQi86jiaObg3N7Pr/PJXLP3Pj1/+cBr5zrXuc51rnOd61zn+jHVdwI1XegQWFBrxjTe4duvPsc8V/zyF79GSh28OQnmh6GRxr4Vi0ZiV3Bkj64LmI4j7m5vsdlsW4OlDbiCGlIy8pQhtUAYJhlZvBQ0dUoZOt4I+s7q4n1QrYFBi6VWmYlGd9/d3WMYBux2O9vJXcyOyUAnTwkxbQWI1LQ4EKtXiwDDdoeu65HnERAgBcbxOCJGbaBKVelMMamDShuMzl6Br7/6Bhe7a/T9gOkwW7JRj3F2c2EzoxTADSO9Iej7hJR63N1VYJpBqOoJIwKioLuypP4Zu90O2+0WTAEMk9WI7lgXAmJkZTiVGbWWFk8dOKDmqrvlxBrLzoS+HwBOICYzSmZAKg6HAzbDANQZpSrrIDQZhvW7zOCgMoAqFVKyyZsKqBKKNRGBA4IBMIAykLoYMdeMAmrym5RiS0xyW4daBbvdVmN3azA5Cdt8dJYLI1vsedd1q/fQeTZN03uAiMW/x/ebeiM91YqUlNmlzZx5y4TQAMZ1+XxV9ssSD63nSih1SU5b+xCtm16Xc7yf1KOMILKmzo6HHEANxvAQa0AXxoUDG97I6exft7RL+/6+CVg7hoVkZO8r7feK6D/OhrGW34zCjX2D5c8wFg4RgfyHsDS3nnYFqSpf8d9z8AmPx4aJMY4TYkQDgvz+0AxxT4AWZwXJEnEdWFlyfl0dp1nLYxoYYufYri+1s2hsPFG5oHvdNCNhB9NW0d0nzCkm84d6LHlp7MAmQVsABTgbxECzUBM8sUkMsENjfqyZJKdzgAyMcMDMPcSY3biXH80TkEl+bLzcz8e9dRzkE79P2gc6wNNmpSz/uEfTZrPBfr9H3/cATN5V5cQDyFkyy7XxeWQG5cLtOqoBtkWds19HGOjn0kNNLhR7nxDUR2EYBgg0PUpI54ylfesxiCzn6nNPgM1mg+Nxrx5IXVLT6zyjpbtxQK3OCFquuZ7Kst7X4ON6rIBlXS4/x6ureq5z/e3W+wDlh4D69d/9W1770Of8PvPM941z/5jP+lD9vp/7feDs933fc53rXOc611+3/gCjZoLUjDzv8cXv/hWHhwN+/qu/R0g9ADVxLTUjRm5N+5wzyjyDg+2BF+A4HpFCQJ4n7O/vsRk2SKlDyRkVKmvquk6BnpLV42UaIQTEmNpuvppgZggRUuoxzzNi1IhbT9nJRRvsIlUfalEUSGKVWrx+9QoigmfPniHXYh4m3sjU93Y9jSFhjB9mRp71oTzEhM1mg3GcGttlHidE36WHmt1q38O6g16LpiKJ4NtvX+Pduzt89tkv8H9f7DAdj9gOW1xfX+Hd/RscHu4xjpMmD4jR1cV2sEnd82+uLwEp2B+PKFkNbKes/jZqShkRQsJHLz7CzdWlRvdCQEb5rzVaskHGlDUiMeeM/f4BROrKnXNp3ivuVxNiAojBIdnOr5i0JKFPBEixxBtr5Bz0av8tQPBGR9q1rZ46NU3ouw4hasR1wgCEihQSuGfsD5M1kAUhEHLJKGVGijqHatXEr67rzICXwCwnO8z+IKUx6aWxToLtfFef19bciXADcoIxQgD3DlFD0QXo0bk2jzOGzeYEaFnvwOuQKGOklAx2RorFA7fUqKT+OrWqgweTRoIDMFNQrWJm1NXUMPoBfowBnrwEmPeGNfFL478khYl4coKxF6xRddmhMlhqA0udDadsJQUR5rmgVl07IXV2vJquVbxRdqQLUHmWqA9Jk4W1Jj0aY2f10NsehvXYPCWsElQC5H4zpCa+InoepbHtnJW1AL1ea9BGCAoUMaNKAQsjdb3GNRuoAGdShQBnfpgyBp4cpJ+jIF4gbgw753co4WqZG+s/eyocsyf8VJAQui4BqJY8Vhuo4wBGYwXZYLZ5a2BirRVgwjRnDH0HpQiJHbwzpPR918fizDA2SR2HgJwz+r5HCB2yxZx/CNAjYrAAUkoTeqWUkGc4kqHvb+ADsALhhCFYeUCtAZuojJRxHJuJO4Rt7rvptR8JO3ylGwYaUQhBbhsOnjZFUBbZmq1ouj8QePVzy/nmkvWeGVSq1xhpgPnKWKz8CjgSS6YaNhsc9gcEW5fOVBPzpaEQFVB6b2xXS2lZkx9swFZm1SY1RD0DNef626/b21u8smdAN9x9/vw5fvvb3z4CSzy1c13Pnz9/ZF47z/MHU4bGUX3sPvroI3z88cd4+vRpe991ffvtt/iv//W/nrzGzB80A3769CmeP3/+B8/zQ+teRPDFF188+vybm5tmRnyuc53rXD+m6roOP//5zx+9/rvf/e6DAPffYn0nUAMU1Drj22++xsPDHr/8+a+x3V6hiIIapRTnweuP2y7vZruBQDCNE0JgbDcDxsMed/d36GOPlJSaXmpFjAkpJo3EjhEIhJLLardxeftpmpBLRd8PmlJizao3MKWWJh+axhndoBr+mAIIjPu7B+z3ezx/8VIbelrvPOqD6voLt5Si8berBJNaNaWj22y02RH1SJinjDzP2Gx2ALmPhprHluyJM/p5b968xVdff42PP/4Umy3j5ukN5vmITbfDL37xSzwcr/HN119gvz/g9eu3yMUbMaDkgmgNycVuh2m6VE+fXFT+c9Av+RQTYupxsbvEs6fP0Pc95nFEmSdILei7QRtNHDDP2c6tGtChjdk0TwgxgUPEzfWA7WaH3cWlGsOGACAsiSMMbPoBTBWoZCwiM362965FJTwhJEQBRH8I85xRSkUMEW6MfDzsob1xAFNEKRHTNGGaM6apAEGvq0c7g7QJn8YJXdetzFf1Qarrutb4OvPFGV3etHiUth5jPGly1zvsOunNMNWbUSLM2UAJ20bnEE4emBbGlzamxBr3TqLyjlIXUGTd4FYz1tW5zY3BQ2R5LW4AY2tw7XMiAuRa1LQYy05fWUm+mnyrGRA3ikhrgkvW6OxaBTEq82ItuyADYNBYCc42WMYZgDb1vLBKXNolpi9ZpC76E+Jr1K5dFWVjnfB8SH136P9h71+XJUeu9FDwW34BEBH7krcqFtlSd0unZbIxvYD+6Z31IMeO7JwZ2ZwZG1OLrW6ybpm5LxEBwN3X/FhruXvE3pUsNdliN4lFS1bm3hEIwOFAYH3+XQgVdBOQUsyVc0lVPglmZa7JHHf8+sqn/R3qcQMy2VMAgWECIfOHEbDRXdyvemqKMfZKKeBc4ENA8KGCHWKSizrm1qD3sppr+VMDlRWcaiNy8b7+PPbH2X/JlSwyN2GGCJDjFUczQLV/rwFMVj4EnE4nRPucep6pG4/WQBAk7asPExeZUQOBCl+e+7b/8rrCl8AaE2EYBzwfj4gK6tXxquwyAe0E+EDdV/HvYRAHiI/9FWCv41yZXmxgIep5ABG4ZDV5PmHvzXy8zad6XyJ0qIqNk8xtIi+ebqVJGe0ewIX0e0+Mse1abWwtvtzvn1hBr5/LfDlxttrqT7zsvmf3+hAC7u7uXlwrp9OpsvOspml6AcrY82df/bZsMcBk1NefE0J4kXxERLi5uXmxXWEw/+OjpxvL8vV93Wqrrbb651S2kN/XNWv4T72+CNQQMj59/owff/yEf/NXf4PD4R4UAtI6I6Us0ckkjZ7EVoskIOdcVxk8EXJa8fT8jHEYsZv2cN7rQ3hrSEjNLdd1BWdh2AAaYUsWZyurjTEOWNekYI+l9qhhqkZoS8IIyRctAafjSUGar+QLk7vmRZ/VPUnDuayLrLjEiCUnRF0JJ21Ad/sDMlgSroiQSVbpx3HSpt3BRaGmL0tCyhnTNAJrwuPDA/773/53vHnzAXd3b+BjxtsPHyTWO3v8xV/8JTK/wzg4zPOMw+GAb7/7Ad4H5FSwzOIfE33ANI748O49QIR5mTGM8pBfVEb29s1b3N7cY7/fY11XLOuMp4cHjGPEbjdizCPm+VSNeIP3gKa/AECIAw6HG7z/8BXevnkL7yXFaogDxt1O5EhDhPeEzBlcxHB5Pp9BGkHePwKklJAyI0Y5n5ZsZW4p+/0OwzBiGCIePj3h+XTENO0QwwjnEo6nE06nM6b9LRwcYhhQCssKEUt6S84JOYt0KQQFW6gBMfZg1jNiANSmVoCathJNZPMEiEHAnpQzYnR6/EHBAWUKOAfvA87HE2K3LR98nWvMLNItQgUb5fd23UkJG0h9achfNOsAaSR0hoODGbga6wBwKIzKKKlMi+4P7CeuxYyDgbSmyhIwJlCqD6OkDeSlN0dmKJPJgKQGfDrn0bXUrekW8lAVAzEXTVfLNaWqGsOuSRlPIn/pZTK6Vb2hUwdCyb+pKGigOyH3G5PWyPzx3le/F746EUTi09PYHto0w7xf7MXyvv4cCRiV6+cK+0yAX1eBaKrnp2fT2EO1/SyEUEEtA4mJgHUtoCjglYergHAd8Q6Mln3Rex4g0iMFZErJIrXJBYZHXgMydXtX4IvzYu6+poQQRc5H3fH1Ups2vhcfIqycNdcGyivQyvoekyKWUsQEuqTOO0nfo9f2sorPSykZVIwZp0CoeSfZeezQNWHIBDCXChSJZ5n4iAlzhgGNfNdssnp+QeLxFOKAeUmaptX5O9kkrEMgIBEg17tzYqrtfABz0mtNQPGUgcIOHgHORbQEOKrXT39iesaRVft7S6cCb2bCW/35lnMO33zzzauAxTWj5qekS79PTdOEb7755sXn/PKXv3zRjFwDR1tttdVWW/1p1xeBmu++/x4PDw/4q3/z74RJQQ4ZWVfyC7yXpj6nDFbUq6gBrSMCSsZ5SZjnM6Zpj3EQRgCcUylHQcorQoiYTydhZ4Bk9d9JhLZFCueSMYyTrpxrRKk+SOdSRGrFjFyANRXEOILUbDalBR8/fsL7dx+kyYNEHkMfxgeVlgjYRGLqWR9yAUeyyvp8PIKchw8R67rAJALn84IQJcY5xEG6TweRRYHVT8ZjXQo+ffqE/X6Pm5tbjOOEVI549+FrfP70CaeHGSGM2I8Ob9+8xZoS7u7eIhdpjtOacI6LxF8PESFE7HYTUil4ePyMlAtKZjW09PDO60oN4XQ6IqUZLkiM9X5/gPOEdT2DiHDYH1BubgGofbFzeP/+A+7u7uG8TBMC4fb2DrvdDjFGmExlXRfkvILUd8Z7V6N6pRlOwqxw0iguy4J1fUYpIm0bp1HNRIWFNYwj3rx7i/U7SfhKuYBSwZIycgHiENUjQiVD5BGHgMeHBz1vGcwthSnGqEafa23GAVtd4so0MclSUYNUKy5iUhucSNiCFxkYQdgs1qxbFDqRw5oTxnES6ZbGB7fkGAOqetPSl/KbtK5wwet8h/S0em4ceXH77o4jhFDZH7V9ZDWotWNRBpw1qwBgPtSMtvpnAIzJZ6xJ7h8crxtAAWdkQ76i4NoUF/HRMFNg2UzHhlK0wNK3WJDU7vOsGRVgT+ZLYz7AdYlB6NkLThKNiGHmwTFGrOsZlhBnc6Jv+qs/TLc9JQmJRIUhUeFZEQ+0hti24+w9JMcv2zGAUMYjq/G0yb5K4WrUfAHagIUx6OS1jhmAMQhTO2b1uDEJW32/yjqFRSezwysDxHsvxr8VaMsVQLT0LAM6LgHMBsYSEeIQL4AWiUPv5i1asY6JgCB1kIV9YgAgCUtyWZY6JxvgYQBGK9u3cRxxOp3qz/t7uYxHAzcqYlKnsqUiEYgzAGFltrh4eQ1351CaN/PCoep79fR4lFTC0H2XKNjZmxZbMhYZy0c9ddg5nI5nuT96kUsRBhAF3ceLHUfP5upZYdcNaH//u+RKbbXVn18ZUPNazfN88e/j8Yjj8fgH/fxxHPHLX/7y4mdEhG+++eYFMLQBNVtttdVWf171RaDm++8/4a/+8q9xc3MLHx0Kr+DM2ihpQ8XyoBk1acOYNaUweFnw+fMD7u/fYLefsCxn5Jyl4WUo4waYz+f6sFsbM0aNzfYuqvmw7O48z0ilYJp24ieAAsCDmbAsK+IwIIRBV2hP+O1vfoubww1CiEgsq/WObOW0X1FsK9cMYFlXEDnMi6QRxWHAtNtrlLcHMWNJJ5DzCMOAIUSsSSJ8iU2u4MWPgBOenh9xOp3w/t3XCCFgmiY8Ph9x//YrvP3wGcfPf4+npxnvhgFDmLCuJ9ze3uCbX3yD87xgTSvm0xk5ZeynncgVvMN+t8c8n8HzogAKqZShYLfbYRgilhW4vbvBOI3gVHBzc4MhBuynUfx8iBCcwziM4p9Bkm6SRdsjsqf9DYZhhAANWZksRQ1BpYEqynxKa0JWrxOTr0iEe8EyzzjPi9KBA4KXsZpnSWsCCoYx4he/+Bqn8wxmYFkSfIzYBTFJNj8ZIsKisikAuLk5IKUVrICRxRMXbUhDCDXOuabtwJpKiQqf5xXjOMJ5kwQBHsCaBBixRC9jRngfu0aOkFJSZpUAOS5otHJtngTIMnDI+qgmtTLWTUb0g0Iuch7IBZgEiAEEJwyClBJysnQgAUOJBSaxsblO3+n9cuzf5jUlnijl4nWDsq2a+a7GLOtx2fYNdOHC6ruiR8AmHxQPIUvPCTHqXBJzWSdaITlP6Fh3yjwxoAbEwh7qPG1k3jdD6l7Z0UAxJyln6M7VxWugSISxvRpIYdtylcWgHiZavWcKE3fbbsyfokw8IohP1xC18c96X/IXgJ2Bc3L8AmgWNeq2eZxSegFCtiZdeWvMlT0j5yNL809yDCklZaSYjS/VvcbVOICacS30vzFGEFNlNxllNRWTsrYxllOnHjJoJd8dGejggx4wZG5AEcjB29dFd9zDMOB4PFaPiN7U+rKuwBr0YEeTCFamGpwCXgwDV/v3eufBKNXceL/f4zyfEcLUjZ0ZYF8xWyrbx8ZWmIt5YJxPMw43gxw/Aph93dc6nlcMqp5J02/7mmGjaw2vsgm22urPoYgI79+/f/HzdV1fMGoA/MGBmmEYXpU+ffjw4QWjZl3XPxtfhq222mqrrX4HUPM3f/PvsZt2+jzLSFmMFgmilV0XYTOM0wg4SUUqeYVzHnlN+PzxE6bdHrv9Tqn2Qk1nQCUmGSmtGMahygMAqPeNNCXLvOLmZgeLFT7PZ1mxHKf24KkNcVpXWW12vrJ7TscjDocbHA439QGYs5o4lvaA60N7+OUia55BY4XXZQYz4+bmFuR9TUeiUrAsC4bpRsyQlwSAKhsjhAjrY5+Oz/jxhx/w9s1bicweByzLjNPpjDDu8Bf/+q/w8MMRv/nttwjhLYIfwekZ5+OMw/4G4yQNQk4LcioYQ0Ra19oQPD094fnpWKUq9vARnESTv3/7FiDxKtnfThiHATkGMB2qh0fwItXJ64o8LyJvixE3t3eYpj0AwjyfZQ4oY8A7BzhCKgnzsmBdZ/Wj0SQUH6Qh18ZvHAcMw4D7ytzojXlZx3bFeT7papaDcysAD5+FtcTlWUC4JSKVBAfCj89PGCdpysZxFDkIOTVNBXwQAIJAdYU+xqiAIFXpEKtJqFNNpDSbxnZIFxpzAyXFLBhVviIsCTEXlnmuJCtlOYj5sMgrxH+i6mcak0IbQ0d6iVIz7rVm2Ycg4IayhpIBUGouUgELZfPk0tgLJpswJpCt5AMax9yxQHLJmOczdrtdvUZLKTpGAqLkIlHklsZlE9/wT0myMuZNgYOkoM3n5WJfUxKGnX1GlaRp/DRD5SakyUJyMDCZjZ2fnDPIN48rbVUBADEOCOpTZGbSOeeqg62SPWpQRS0CakyV/kDkWAr2VhCksWGqREsxAQFUiu5vY2JY1/xa03zZjBvjRq6rEEKd01wE4Ms5wweRjglQKVxCUoCFlO1GweYBK4CpLB29X8OJmbKxcoyrJTHjrCyxUlkqzIy0rhicpAIWnXN1/BWkYGXk2O/r/SQEJQgJ28M7Dz+MdR/sPmLj3TCPBimBHPb7PR4fH2tUt3jQGBDTxtiAE9tWyyrs9o8IcH2yEgFqUAzlaIJIkpxIrh/nHOCFFVQsba3uoBxb0eMxuIig11wl+Ygh+vPzM47HM8b9LRgiMQPbfQBtnuElg+Z6LvXzyNhSlwLVrbb6061xHF+YATNzZcp8/PgRnz59wtPTU0sx7cp7/+L98zzj8fHxxWf9x//4H7Hf7zGOoy6YyULHNdBigPZ12bPc9b7+nMo544cffnjx87u7uxcsnWVZ8O233/6s7f7cmqZpMyjeaqut/snq/fv3L+6HDw8PL1iQfwr1RaBmOtyglASmAuQEVllTjBGOAQf1hYEwFphXeegtCU+PD5h2B9y9ucO8noXOzgSiAu88nBdz3yFEbYbUzFG45GAuOB1nTNNe2CtEyHkViZUm7NjqwrAbwVyEUUAa9ZoTnh8+gZnx7s07HM8zQMp2IdRGiQChSziNd2aG+Up4R1jPK5aUcXt3C/aExCJnIi5YzjO8i8oCkW149SEoxYnRZMmY1wXff/cDpt0tbu/fwPuAdU0oJSG4iJvpDoOLuHv/Bv/3//P/hePzB3z97l5kXOejECTIYZgmTNM9lvMJ0XmsZwHOpkPExx9/wM3NDe68x/5wwLs3H/Dh/VdyHABu93sM0SN4YUDllOBdQAjC/FjTGfN8RFbwJ5DHtBuw293AB4/zfELJndygAIkLVqwq2TFmk46ts2bHUoIUQDNJBVoDaAbGOTfpT8pJpWMWvw6Aos4DMVQehlHOfwg4zWfcv7nHsswSHU8BUHnAOE3i15IFlAg+1lSywgnMYgg7+Ig5iQG2sD40dSpG5JwQY6ipUkTG3hoFzMsFESJPWNekFGVNxiGC97H6tqheAgRh2tjx9KwDYaOE2vAb68cABVKJRtHxTZzFY5W5Ji8RkUiwwqh+HlyBpb4KAyCGC+IZJNsBXJTjLwCKIyA45FUkW/BiHszaqBJncGYEH7CWLIk+2nzrCa8NqIAs0rTHMcAkMmueRcqm8hAiElCECWHwQJFz6L0XDw9kHWNt1plBTlxaiEiZLhoDDyB4b3urxyymzwSHcYgKVNj4F3BpvjClZ12QpTQRNIerHqIjAaBkDikjCVwbdHk/KhDkg4fjAmKVC+r5q9QXAA5mWOy0QTdfGaeMmgYCOvLwKpMTwED8jaSpj3AOgEqcej1SKRkRQSPlY7fPaPtBXiZL0fOu5rdi1MsKPBKeH58BLhpRvSKb8bQOAetfBDTwgJki6+4Y6GYgQpXpqYk0GfLTT9Zacu+fph0eHh7VSHysYwYnx0pOt8sMcCcjki8wPQdejbMzxPcJCs4AUKkicwIgse8EYbFxWeV6ch7jEJGWFWGUBQoiS8pijejOMteZhEmm15QkhQmbZ3+Y8PHzEXF/ayOk487tOlG55u8CXS4ebBQ3K9yd6622+hOuEEJ7XtMqpeB4PGJdVzw8PODbb7/Fx48fXzXuvb+/f8F+iTHi7du3Fz8jIvz1X//1xeLG/2y9BtT83LJjuq43b95UwMhqnuc/OEvon8LLZ6utttoKkPvrfr9/8XNjUv+p1ZfjuZW+zzmBOQkzQVfzCjPCMCAXxnmeVdYiA/jp00cMMeD29g7LOssqrckldAV+Xddq3rqmpA1xqNGq65rFSDYO2uBS9eHw3mOeF6Q1YZqEVq5rhBhGoYg/Pz3hfD7j7dt3koYENLBIWQuctf1SqZAl0DiVjazrKs7/6skihrlmeMwIPgJeVuaZzSOEsa7KANHG/unxEWDg3bv38N5jmiY8PDwAAPb7HZZ1RSkJ92/e4PH5CZSl8d1NEeSANK8gFxD8iHEYUXLCECJSKtiNOzgPDHHE2zfv8eHDB9ze3cFrI0FEGLxD9BFiypxBIESVEuRSsMxnpLTAB4dpL542gYIa2YpMLYRQV99TSgIGOIKlqcjoW4NhzUDnVUEidUlF5CwWNW1SoZQk+UkMh3NljRiQY8k4BHkIWfKCkld4F/FwPKIAeHx8qg9iPg6Y5xk+RqSSVbLl4SEJNSbbK0xAFh+hUhjLkmpqlESTe8zz3DEYtGl1HjFqHLcHiOXvoUqyxD+HvBmDtnQIX820ZUy8E5NmkDblxOoB1YxODay0uWZyE2MTXEuYivqvmBRKzkFbdbf/Nm8WtN8RgCyvDSFUs11jixGRxCujbS8MAwgOOUsjmvU9Ne5b23BLfLKyOHSRzoSanOSM5dR5ukAZDoUZMZiZqsqrSjMGRzcf9S/S2MJiyqGyvMsobKt+HKHzzQyX+9dcx0OzAcCFL7YDA2Yk5/viXFlktyc5pwJCeY1apyYjvVo54CL34MK5+kU1lpPMT/OraTHXvWmsMf8MYPEYBsI8r+0zXlu9VQ8v883p5xMzI0RJujsejwIi7yewjrmNpfnPAAx2BOT2/ssY++48uCapkv1ysNju/pzY9kspuL29xfH4jGEYdRyh90AFVozxxVw/s5r9Eur9LSVlpnmvIH+T/QlIbfvTwLPMBSHIggQlwrI2plhlAF0Nb39lcB1XwjBOuLnxOn/aPl9LniwB72dJnrqfb7XVVv/4eo1lYz/faqutttpqq9+nvhzPTVn6G5KHZudcfbgXuUdukcQ+YM1JAQhJW0pprd4l5MRe1hJZzNNgWVcxtDT+OTNOx5NInQaPVKSJSF0CjhTX1W7iIoDKMCA4h/M84/j8jDe39wgx4nyepQ+uK8PU5B2VOt5WAXJKWNOCeT6hFF0ZZonVLTlhXVY4JsQhVAYI6TLvPJ9B5DCOAUTAPJ9xOh1xp+CJV08RaUTlw71zGOKA3X6Hd+/f4eGHH/H4PIGxF5+VwphGYVxYIxyHAWFZBLgpGe/fvcf7d+9wd/sGcYhwAQCrNEdXXr0T5oL5y4h0CgjkMO33uphcapNgMc91dUSIIAjUYnWFDUIAPDIXpLQgJcKSVm0wE8TcV1beS2GQlzhrax6WZcW6yh9p1NQ8F63ZEIAjK0DkdSUYCM4j5RXjtIP3ATEOYs5KIm/zAM7zDBDBu72kdXHWRCGZmzGKMfO6rjDfGOekaU1rRoE0XaHzuCmaSkSd3sU5Lz425ITZQQIKOe8V5LQGCvWYLH3M5BFyvTBKBkLwjWGm15wlVgngCQWACsh59eFxIBIJTJMOEUpWdgsDnC0iWJpuU28IAUWkVFwAp9INRw7TNMFmRF3VZ/OPEW8b8U/J8C7UiypX0E0ZJhWs0TSdzgDYjqeP865zgGQ/vfcVpKqMlQLZM3LK6mpgiF2bRY10nZPjaslfjbnCV0wYY2+1a+ESvLBrv6bOaWNdD6n9BWa0DW5R3db8lyygVs4Jpu65pMc3UMiOxxKHigIm5PSeVserf7e9v1RQDDmr5I90PxnkApxr0knblh1xBQ+cJnT1wGABTEY0jiMcQdO7GCFeSvYMvKpeNhUwugQWSvU4Yv0eavcM7q47sv2s4K78exxHnM8nLMuCGEMFaXzwOoP1vMA8cXT89O/kCCjNM0gke66ebwP+vFdja1ZOofqXGYNtmAacjrMCtgXBTJb7M0wWl34Josr8iphGD3a+srfsNTI3de9/B+jy2u/762Srrbb6n68YI/7iL/7ixc83oGarrbbaaqvft74I1EiSj3oHqG+BU23+uixIOSFEMfFd5wVPz08opeD+/h5gYU5I4yMPiGvJF0k1Eu8tkbxOV5eTCzidTrh/c0AGNFVHmxFQ9ZMwCQdzwXJeMYQAr5Kn0+mIu/tbxDAgpawpOageHHX12bm2AK8MgZwTSkraizrsDjuEoA/qaQW4gFNGHCeRYYUBzhOyplgBwBAlsnqeT1jWWSPFo3q6SBM9xljlCcEHkBNa7d/8+7/B//l//Bc8L2eRsxTGzW6P3eiATChrqok4MYp0KbiIsq7YTzvc7PfykE8tctiR0P1LZuSUAS4gEYDIw3sB0pyRVdITYwQ7aTxzSShJm0ygSp9cJ8UxrwYBalLVdjMDy7IKM0XBCx8CoOdRZEQyN4ZhEJ+VlHVFOmFdk4IimroUXLfiDpVSHbEsCWvOWNaEcZTzNexGTFPU5g5ImXFeVmVwJIwqw6LMcCTSqsIAqWeMgxNZUy7IakbMvrVz2QAtBY1I2RCnk/jqFMj8cRpPzcwgb0bGtvJ+2ZC1psmYNE6ENaZ0KF2jq828MG0UMIGAXGZknHLumn6usrMKLuqxOKIq9apNm6XTsEi5doedXSm1KU45V2ZLZSeQsXtEAliPqTbhjY1iAVXCimn+PWbGakwcO15jFgkjS5grBjbYvaH37ZDPMyqHsgMhaJA3Zk8H4AIiMbQ4dxurPgGsLyK6OGdVsqWswWq6/IogpYJAClTGGKo/kcnlSr1P+Yt5IudazpvzDinnZsiMRiqqDXz9TDvrzZ+oSqpQlB1HFYTs97W8wsiw+dEzpZhlX7gU7KZJSSCXQIDNB7ungBobsx/rwqxSMtk3AyMu2CEdJtWYI+0zd7sdjscTYrzRa9SJTLNj6xiTp0pvbftsiXAOzjX/o6IsQmM6OfXp4lJQVMoEBSbhAHIO425CzgUxDmK+XpHRDuAzRhY3powBcQVqEi6jfjGf+tNyzaa5GPOrv/fjuIE1W231j6sQAj58+PDi55vp71ZbbbXVVr9vfRGo0Zax/lselNsvB2XN5DXh+PyMeVnw1VcfqrkqYJG9rXHx3lV5kzThDAdGIYnRPh6PuLm5BbOtdNsaPiGlrPHNQ004WdcVwZgiJeN4PGG334t8al7E+4SLrPI7AIXFAHgYuodUh5RXmCwheF8fsPf7G+QlyTgwYznP2E0TnI8oTHA+wAdCXrKyb0Z5PxjzPGNeFrx//wHLvMJIDJaoYpKwDMAxYZp2+Ku/+mt8+vgRf//rX+Pz4wM4FWnSszB7QiSAM1IIGH1ADB5DHADeIwaHnBYMccSyCsDCmv6U04p5WTGfZ4mbzrKK7L0XY1dAG7QA5zyCMqZyUWBBG+nzeZHo2wJN4Sr6mhbLCyIwtQSgGCO8i5VJYz4r0zQJOJdyBW5ksV+AnXEc4Can/jVZzhFsJVma4HWRn83nM9Y1Y9V44HHdgRCQi0SRxzBKs8WAeLsQcmH4GCXZKmf9nYB3a0oACcPFwSGDRa5UV/YhseEF8N7YBCSgxm4vzS+pxKtkZX0A0iTLvC5J4s1N3mJxv+SUkVNZFgyvx8t6nmQ6mpRHmVk+oGIiLBI1GCB0JY+q1zQ335r2c0voaolCpTAylcqCyFlkgMbeKEVYL5J4pk1zMe8SluQschdgRP9ZBhDU5pF0TDt5kTExjEEEGAPMVZaNI4MnUBELk8+JgXBRn6LQxsNR9fSx+WX74ryvIPF1CQsm188iBS4MgLiWodghFz2fcu0RVrcqSNiZmytbixUsqPO+jlprti/ZLxZtTWK7zKwAdQOwDGyVxD7o7/ReqwbcQedbYxt1oJteEw5UmxE7VjHl9ng+nWrseFFm3zUo2ZhLwqgBizyvKILXjttV411jy1SQBcAF3mNADgzMjjiffwQRsN9Puu0CQE2+qUnQUKWbBiZ1oKaCNVnNrgi5Al92XiU2XSRnxAxyJg9jOB8wL2tL7xIH/CtQ0QGWlFfBXLtfCGuH3CUIY4CSo9f9afqx+imp02uyqK22+nMpIlLWtNx7drsdvv76a/zd3/3di9SnT58+CXt7HLHX58wQggQTXNVrXgnn81ml8b97nw6Hw88CUJdlefFZPwUSvebh8HP250t1Op3qIkG/zevtxhjx5s2bF+/f7/cb+2irrbba6ifqi0CNxC6bKaUwA0h9FnyMWOazerIIIPLhw4fqaSEP37pyCckYsYYsxgHSoImXR8kSmZ3WhMPhpv48BK8R0WKq6MAibfEB3gektGpakXhiHJ+fcV4W3L19i9P5DIsOJqjko1w+5NaUHjWIdeovUtYVYGDa7ZCVkZNzwrouGGKUxKvENbY1rQkESegYQgSXjNPphNPxiDdv3qCUgjhEEDuAi8hQnLBx0ppE8wHGYb8Hl4S/+qu/RgiE//u//r9RSsacFszzGV8P7/H0+IT9NIITY5wGeEcYhwBHA0IgnE7PtcmQmOwZ6zrXhnLRZKasMiNWRo938rAxDA45STPjNKp3TSvOx1nNeNXbJiektEBMM+2cNqmEADWEEPR8uQAmQqoyj4zzWeLa1zVdMHEANWq11WpYQ9Li30Mg5CQAEUBIeYUHyVxk4Pl0xvPTCXHY4XBzh2EYME0jDjcHDDGocbEwvmIM1bOkJlDVRtLBEpfIeTjRlOl4mgSwwHsZW2YIo8CYHVztXtS1FxcNaIu0Zl3xD8rGaSvtzBBDaVBtWEHG4Oh9R1glfbLNlDOmIarUCdXrp4/RrqyPfpwV7bmW/5jHrXwSV2lWFYzo+NVHS2NwsCRC9Y2keeaIr4qAST4EAV4aAUcYdSbPUmKNMPAcCmfk3D2QMoM8CfuiAwSICMMwwocmLymaeOP0+IAGFlWWjYI4tts2Hr0cL6VUvZZM+tObQDYQRYAHAyEs6Yl0OzkLmFzBC309rj6TdR45HffKwFEgNSeJPrdxqUwrtmjolpBlJd5fcv6dV8NshZNlfnQgLFDTgoRNxtWnxQcPSQdcEWJEYVRJa38MRTVu9ZiIlcXWPJOMVSXjIt8RIOOSGGeoryYFFClh0vuzw83NDZZlEbAxZ4TolIGlpswQKZddSyKjI5DjluAEoBQCuSIpeU7Aqx7IYRZmGshp6JkyNHNBCIQYhPU0jaP6GBUYDGdMHnLO1hMgElAPLiSgMVw97n4uVqbRleTPXnfxndexiDbZ01ZbyTVxd3eHu7s7/OIXv6g//8//+T+/MNn97W9/i8fHR7x//x6/+tWvKlBzPp9/1rX0/Pz8akLUa/u0V3b076rz+Ywff/zxd74OEKDpD12Pj48/y4x4GAa8f//+xTENw7ABNVtttdVWP1FfNhOuLBivjRyBszSU6zwjpRXLecbp+Yj3H76CI9eQdenPRPLC0NQUr+wLedCWBlAYDvO84LA/6Aq6xCOTc1jUNwSQh9FhiGKUWcyYlWoSCdjhzZu3mNcFVY9CAKFrUIgw+EFXqqmyAizCeF1FbrTf7VBWOf4Clm16LwlCBSCnkghdXZZVT+kUzucTPn78Ebv9XmUvwBAHcC5IqYAhprOkFCWGJGGlkrDfHfCLb36Jw80O83nBr//211hyxnx6xs18wPl0QvzqK5xPM24Pezg4BO/hSCLOc05YFjF3thjt0/mEH3/4EdNuh9ubG/jgkIeIvK54fn7G0/ERIQw4uDtQTqBECMMkAE4I2O1vsFsXzPOC81mMdSdNXBIGiPp0qDmz9w6FzARVxl8kA9IoFWU4zOuCkjPSmhTsQ43Clr6sNaHee6BkBJYoYmuuGYz5PKOAAEognLWhCgBrbDY8yl5Wy4P3OD6LUe/h5gDnJL43pSR+MsGaz36FWRr2UoClJG1eVa7ggqSfkawsxWFoDAHt8L3FX5s/CHAxp+0Y+0aWFOhqfhjanDFLYpJWY8mg8T503IyxxiyAVP95Jk9qLKjL/TKAwoy/AVFDOSKsaQFzhmRLsYImLEAoMhjCnGPiaqxrOpyeWUKOZKOKaBHk3682kwo+ojbWcj5J6GY6z1qyl6UaMdu4QRvpUsGnogCUgSeefNsXvXf0Y1LPgf3bjk3ZDza+ch00ZotcE5YS1Bp/K+ccWMGJa1ZRTWeqN+VLNoXcl2V+xBhBhSS6XkFsQOVc5udjqF9l2PTeLipp6wA9u1dfSK90Lme2ez3DBZ1DJAy6IUYBcqFAf3c+Zd+7QyoWC18uzru9V/x0XB2bOtbgNjZXwIOBF6UUHA4HNSrPIrdlVHBQ6DgGiHbb1jl1cV3qdonknm5jasAXERBCFNCHCVC/ppzlO8U7B7AXVuigK/iZ4Txdzg0D0shBhHMie+Iu4aoHagyY58L1Ouvrmsl0/WerrbZ6Wff39y9YIT8Vxb3VVltttdVW/xT1RaAmKKPCmClCahB/mqJymtPzE25vbiWDI2f4II1W5qLNW4Enh+CjPLsm9dEgjxgclmXGsqzY7Q4Iw4Ci/jMAsGZZrbZtkHcK0hQkjUsGhDlyPJ9x9+YeBYwlZbjgBUBx3JgMTjxiiAxwatR9M4k9nY642e3lQVtXWJd1ETlQHFDgUFAUSKLafJcsTQBzkS9yBva7A5acqj/NcT5DWCZBV+NXOKIq45LEH4f97oAQHP7iX/9rvH37Ho+fP+Nv/9v/D999/AH7uMPtWRKv7u9uEMMOORcMMeA8J+SiXi2DxzyfME0TpmnAsp7x+fERRMDtzS0Ohz0Axs3dDR4ePuH5ecZ5OQsotcxgJuwQMDqAikMIA5ipRmKnZa3eJyBNQaFQTTfXskLAOAYXAYwKSVOWS8Ga1m41mrXBuWy2LDWoynIcI4QRwxBUKlWwrsJWej6ekPOKXMQwdF1lnk27G7x/J2bA0xhxPp1kmwjwIGncAEmzsabVOfhIICVGCFiSL5toQAGeAuaMKURt1AJKFpaINX/mo+G6RhzAiybJjjvnouMhcoqqm2J7TcdigcXM92CCvM53njPCUBv08zUVqIj0yqu8p8pbHFXgg1lMWYOmExXkjn0gkjSLBZe+n5UFIowZvtxT3Z9Lr51ecmXjILhwBy5dvMJgMJl7Xj2AoPegUqiCFJU1lM1vRPbNANQ67iRsJQJXCVK56Hip/q82w1SURdJADrJ95Ip9CTus35cK1sgghxCQcqq/bx5CDcx7ySBRXEQBZzDgNU4euv0e6OTawHfgHJp/kcyLZo6cUqr3qX6+O90fMzK2/ZNrRPaFOgaMAV/X1w0RwNQSv+weat8ZGSJBJSfpfN459U3qrhtuIJUBM1XGRIB5JDHLPXZZZgzDXu9RWVl7MnYyHxxKaZKmnJN8bp2X3KWQyb+JDACTbXrvkdYM74B1XmR+OKpsHgHQs4Iq3TmtU43qObK/y7+9AkQCCvfj6VTTWeedneu6QHI1bxS4VJFXJb5ttdVWrd69e/ciyvvh4QG//e1v/0h7tNVWW2211Z9b/Q6PGntgFDZEyUkeQr3IfY5Pz7jZ7zHEQXwXjL5I0NVDBxSN2fURqayarpNgLc2yJMQ4Yrc/yGq3V/o4y4O+pApJ5HIuGeu6qpRIdn1dVxyfjrg53IDIIacVIUoalEX2mseE+VFYM5lzFi8GpbGv64rgA4ZBqOkODrlIFHDRBk5W7IPKpqg2qyEEcE54PB3x+PiIr7/+Rqj1yiZZ1xnrusD7CO+drtTIw7sfAriYiSnBU8B8XvHLb/4V3r97jx9+/B6H+1v8t//v/wdPHx/w99/+FmMIuL3ZA0RIyQG3eyxLwjyveHqa8c0378BccDo/Yb/f43BzwPH0I47Hs3hyhFuRcQ0D7t68QYhnnE8LyBOOz0ecjivu1oz7+3vE0YGcyBDWdZU0L1ZjV85Iyk4wnxUQoZCAXCWzyoyKRnOjRh1LCpAxZvqo2+YbkXPGPM8yXi7DO48QA7wnbWwHrGuC9+IzJE1gEd+gxDgdn/AP84KHxwfc39/h9vYOb9++RQgey7Jgr2NoD2SXsiCT54nsCJ46cEmYA6Uwcmo+L+b/QawgHloSjTXiwiRz6KOqW/NMahrsOrYMtflLwiLKBnBQUcDhstOSa2S4aLR7HxprLtvf22sYaiiroEEIQcEX8duRUkCnFJQizBFHYrIMh8pI6pk5Aqy4iwaVuq6SukQbmwP177YtLUkwE+aIdq6tSe+2ATQggcHwLoBY2Ceg1txbkhYr40+klipRokvJiRy9+fG4K7BJ2BrS/AoQY4lX/Vj30jgASGtCHBurwnyZPPzF5/b7DCirw+5pet5zhjIE5bojpsp6s/Tpxqgo3T4JkyvGiOPxKKBjKXBG5OgYNYQG1IjnjsyFZV0xBANUUGVz/WdWOWPhyrA0sEz8vQJY7/8GdF5KkJpfDrNIE3s8wkBKez+RpBDO84yUSgXUbZ+ISffFQBA5Su+jMNhQqtdZM7d2AAUQZb2XyXuYBWByABBH+X4oBc41UCvGoHHdNu52L22gIRcBkSyBjSrwRg3s4Y6J14FgF1InXAwNTGLFgKFbPVyz1VZbab1//x77/f7iZ//wD//wR9qbrbbaaqut/hzry/HcLMaqTAKrpEUMcbnIysJ+f8A4DPKQqSvQjqhKYHIBgnOAd0h5kQdLAhwVpHXFaZ7BDEnEKLmtyOtqIIgRvEPw2ikUxrKKETCRMA+eno6YxgFxHHA6nUDOIwAI3mPNKzy16Fxh0BhjR5gB5CMIwJpWEAg3N7faFBBSLppa4mryDjkGUZeIAgcfCIyMeV3w+PyMw909wjBiTStCkJjm07wghIhhmgDnwdZgO0kc4pLBOQkQBYlk/fDhPQDGNO3xv/1vf4Obww7/5//+v+Ph4xOoADEOePt0xG6MuLs7YBojTucFT4/P2O8ngAk/fvyIDx8cdvs9bu5WFIisKGcCgvj7uMHB+wjnj7KifSr4/PlHrCXB+Yg9E0KANOAsKUBQjwWU5pFQWHxRUs6SiKUJPSlLelNmafLzysgpIaUC6AoxIM03K6jhvcc4jlXOBeiqOuS9zklSGBzghgh3d8B5TVjXjPm8YE1cPY7GcYe3b+/w9s0b7PZ7eC+L8+M4IIQoAI+DJlK5mg4moIs1MB5JfYwcJOpcmquCEAcwIP/VRkuaZ5LxYWFVkBMD48xqIkykpqOusjuyyjz0AmyNuGvpabB5TE7lOkA2oIVM0qGyG/XREEmZgQ2NdVPYmDSt+SX1w3GOlIwmjWCpDaACis6Luap27kwiu2EuKNnOK6OkDPIOJTNCpHqOUUe3NfJKhLgkG9QmVBpKkbwUcC6VrWR7ZlLF+t5OKlQqwCEmwcE3vx4hpTTfKWMmWAS1UmLq/hjziCCmvd5HlVEKw6qwJSc5TQgz8MYOlwEmSXaDMBCzsmrMB7JKW/r54AQEtHPSjI51Pwvj8fkB027ENE71XKldcmM4XQE1Ahr6arDrXFCzY4jvjTJJMucq55L7ObV5DMa6nOB5qCdFJF1RY+BFFmZeNHZp2fx0zq4HASiKzofimhk9ALAjBcALSP2LdMrDoCQyFhXJ6AQfsJ/2SGlFDBEapN2NnVMwxUDSgsKp3dvs1R0LTLAeOa+O0CU+Mdac4YNHDAOWtAjLSUF7770kCxaId5f6NBVmkKaM2TVS2EncNxlILMBOjbBnAMUMmOtFIvcwJ8zRdjGgAjr2FWtz/zXmzVZb/TnX4XDAMAwXP9vtdnXBoxRJuVwWDVi4qnEcLwB8oGMDXtW1R0uV5/6M5KjfN13KAP3rz/99fGNs8aOvzYdmq622+qesapPRVa9i+JdaXzYTVjr9kld5gGZJvfnh4w+YxgnTbicD0LEQiFAbc4CAwauhYtaHR6DkjGURw8rbu1v4ELQhzyi5iIwoBm36zLhV2DNDjDAmyvPzETEMOBxucDqfEWKUk8RQc9qMYAbB+qXqgxNAoBDIh7qy6F3AuJ8ANHmORvzIsamRo62ggp2uNBdlg7CyfYB3d29RmDGMI8Yh4HQSo7U4jLCoZpMXOKX0c5eMUnLB3c09hjDg8fERu+kAooKbmz3u3t7i86dHpFzwP377LX78/BneEfbTiK/evwVBmE//7b/9He7ubnA8zvj22++FOQLCsq6YJkZaCxYSFoyP8qV8OBywrAvef3iH0/GIx6cHTONBZWFChoA2+Eua8fz0iN04CZNJgRoB6CRSmNESZ5gLzrMkDpzPC56enrEsC1AYQ1Qj43HEOE36cMRYV2G67HaTSjraw4TJ33KRmOhlXTGnVfxpvMfgCEQe07SXxC/OOB4fwZQx8Q7jOGC324lXzjJjHCW1QcxYLSoadeU9hAhyYqrtSZqzdU16YwhIaYH3QZtFrlHnYr6sKVWEKksphbHkBaP3FWiIYUAuRbxyatoRakNmRy8NrTJ2igCkgiPoCbKV8sLKpLBV9takUXe9wgAQZTkJm0Budssy1+ZeVvsdglMmg94TJGFIRRrkQHpPyCWj5NRkGaWxZsT0V28y1XBcQAmSH3XsH0vPKvCOxBNHQRHXMYSanMpAJZGqVCaRMbo0wpyLeqtYqbmvYCCsIBApKCn717NZjCVkXlAGCsj+2EgDFbUgMw/W1xZlYMSAcpT99T7IkFywqQAQ1/MvWACBvKYHoZIEEWPEjnd6rbBcu2wAHnfn2MbMdzPC5krz1AneIxWGEHvMz0jYMzDACgI0oxRMMVYQUMB3ql5ABqpdS7sa06vNE7vPgAgOBWnpjLAJADmQxtUbVOWoY4xBAEHbNyKPIY54fn4GgTCMQRl/pcbW64fCYrCNTScAVr44J8wsLEhjYDkD8qEyWfGqyVxaGlsRANN7DzcQjscjdrudfB7LlbHOCxgFFEYUFqlt9cThFtVeq2fSGAio92HYNd+BooLrqCTRgJ1uO1tttZXU3d3di5/98pe/xC9/+UsAwOfPn/H582eklF418/1P/+k/6fXd6rWEJUuZ6ouZ8etf//p/SYPx6dMnPDw8XPxsmiZ88803/+htHg4HvH///uJn1zKyrbbaaqs/ZL179w7v3r27+Nnz8zO+//77P9Ie/WHqi3dO5xuteplnIBU8Pj5gv9tjGIYas8y6CikNfarAyhAjYEALy0N7LgWFpZnf39yqv4Q03YA8bJJziMOA8zzXh+N1XeWB3kmyyvF4xLquePfuDmuy1BZXpQolF5FAsXw5GsIvcdBJGxY1j7RmGKiNTKnsCTN1ldjVi94LXB/kU1rxfDzhzZu3IJKV6BDkPeua4DT22hphKPvAynuPlAtOpxkuBLy5e4PHp8cKTHnyABM+fPgKj59P+PzjA4JzOM0noDBOx6PIXYJHyQlcMp6OtwgeAmKNAz59/AQGYYwjhjCILQsCkjZS3ssK8DAM+NWvfon/8Xe/wcdP32EYBcTxEPkLUOCDSLV+/PwRwUU1VhZPmZSzNBskAJat/g4xouSE1QkPgVDUhkTSUZYV8NHDFzlPJWfMOSPGqH4ZYph8Op2RUlIZVkJW5lMuXD1jAJGozeczQoj6b8KkUrZpmkCEytDy3mlDGbpmLKMUTeVR/wmqK9aXsdbi6RHBheGDSu8gwI41SJVForHTUU1kyQACEoBxmEKdiybNAFk6UBYWl/coWZrlrI2vGQYToUqUbM6ZrKUBNq3Zu5BK9GwJ5+t1B0JNWoJJNUpBiB4uBKQsoIlT3yYiQoZJNlg9Wy/9MEop1UTXZG5Ft9P21YyqU2W9oDJ/XAULbN9Ln8QD85DqZVRo27xaRXRXspF+bLrRevE7mwP2x5MkBJlsyDknshljM3T7S17m1vPzM2L0GIapprHZa9rH9uetAVSNEeWUGeYlQUtQISBbVpI0572nj4B7Jn9qc6DOL+8lZltZJtb396PJEMCDOyC1ponVayN0+yv3zcKXwJdErTfgSW9Q9TrL5l9WxwIVZHRAM9nW7TryEFkU6f1iAXPB8fiMYXrTmDh6z5d7lvrb6G26StTsfOk/kvrXyLxNsr96zzAj9AaSyN8NgBN/GgcfIpZVGD5mGD7rPS0MHi4MAh5SY9zZ2BDZPKonDcAlCNe8jZrH0wbHbLXVz6vr7wxAWDL39/cXP1uWBT/88MOL1z4+PspiVFf2Xfe7Putf+gow8Pr4bbXVVlv9U9Vr95w/hfvQlz1quoYvpYSPv/0et3c3KkkpdQBsdT7njKQrBkMc4Mkii+XBOcaIXIDT8Rlv3r6plO5lWcEqBQFkNX3WZJ/m6dF8RM7nM+Z5xv39vTAgvBMZRsrwPmjjask3pb63+qKQxOEaQEOOahysNP5ZPSvMm0L+mH+KD67SRZ0D1pTw/Y8/Yn+4xbjbIyc1GQWQs0SZEwudvRSRVwjdPWizaj4HjALCzf5Gml49DzknOCaMYcTb+/c4/mLF1199AyLCD999i9/8/W+QSsHnxycMMUiSUlmx5ISvP7zHfJ7x8PiM03nBfF6wG/c4n8+4u7nBu3dvlQmUMY4T4hhRUsHtzS3+8i8Dvv3uezw9fwY5whA9uGTkklCYEWLE+viMp/MJOUsT6Lx4kKzrGUTSyAzDhN1uh2kaMQ0j3twB97e3+Pz5E47HU034YrSo97ImOO+RS6OueR8rndiYB7kw8poxpxUpZwDtQUjYIozdbsLhcIvD4YC7uzvc3N7i9vZGG0/xyeGiUbxePFcAa9Jci5xXWYwjhwJj3ghwJc2Tgwuiq7L2XsUUgLExdOndms+SU2VYEQFrXjHSTpps6UL1WGRbpRSRQ2jTTd6B+BJwKB1wwxBmGNGl9OWCGdADB+4S1JjnGdM01ebQOQ+oDIXMQFaNg80gl/T4jVGEwsjKfjGg1fQihdtnVap0EXmbjbcBq2aaKwCYRHSbY28PgpBui9DkIsbOkI8V1hXn3uuEq6fVqzd222f7uzbhBmpUWU62pDxCKSZfudym7avJzkLw2O12Cih3AB1deuSYFEg+vdumEUHAgCOkkuAQYQwrclApnuviz2tGGCwFqi/nHNY1SRKTHrsx5ooaFnvnqowpl3yxjRrprmACW7oSNW8e+954rSlhtMQwAlUGou0bVwDk9bJrjeDAVJDyimEYcP/mHh9//BE5NwajgXbMuBjfykgBKdh3aeBbPZcUsK/G8t31Z+eQWUlOIDXrJoy7nTB8jOnEGRQCvA8IYQT5CIZrIFl/bD/xQNKPpcnzuEeJ8XoT+KfQGG611T913dzcVEaN1fPzM/72b//2xWu//fbbFyySd+/e4fb29p90H7faaquttvrTqd8Rz52QSkLKKz7+8CNubm9we3cnD3V1FZThgqy8c5GmwzmHkhLW3KK1bcX86XjCtN+DfEsIioOsKKaU4LzETec1VfCHSIxbhVmTcD6fcXNzIwye1FgutvJvC+WsaTlG+y9FfAOakW1bsTUWUNEVfqGY689J2Bb2cF/EVAZcCtac8fB4hCOP3bRX2UBUw8qs24va1Lkq5+rHBY4wn89Y1oRp2uH29g7rckYIXn0rZB93uz2YGX/5VztlNKzIOeG3//BbnJcF8zzDe49pmpDLipwz7u/vkHPG9z/8iHVZsS4LTucThhjxm29/i2macDjc4Hg84vn5hD0DMQYUAvb7He7uDvj06VFiuWnCOAwoK7CuZ6xLFnPkwHCe67E5T2BewSx0V2OilCIsoxgjxnHC4XDAw9Mjno/PArKFNieISJguYagyjNN5xvl8rrKi4ANCDBjGCbuSxTuHDKgRwGG/v8H9/RtM0x7TKICRbNchJWkuxQRXUqKMoYAq/9HkHO8a48LZ/OLaWJu8oXlJoK6EE3EFoqSxl1VyEEAVpBOjZuc7w1QIqYAqssFdY6nzlfniYbA2tTldmO/218iFkSpdAjO2Bl+ZAzX9R0FGMqmeNuCFUajtl11zMjBFvU9EFlcjnytY5Ew12RpJHVPvPcprDAFrPl/hBphcySlLzqRKbA12/VkD4XqQ6ppZdF1NitVMau2+0XanMVNsDoIlevpas9+zWHa7HR4fP8Pi1JtBLEnSFszXRQEn6gy5lZXGbPcuZex13kc9wCCvJkHECbhu7qvECIycMij4F7/vz1kpRXyjqLFa7D6ac5b4aWV02c8tEt22KcCRSfjanJTPI5hcTe7HHmKu3ca94n0dg6UfX/E1IwQMIvFcFhwOBwWZ1KeHqF7DThlF9u+2LZmfjggpZ4RoBvqV19PYNHUOCDhZgA4cbEbdWVmDBBLJVAGYA0Rv1iRrvUQM/eHC5nabm9fzmrtzdQ30bCDNVlv9vDocDi8kQZ8/f371td99992L75FpmjagZqutttpqq59dXwRqlnUBEfDdb34L7zy+/sXXOM9nffiWB9TgxOdlnReEKKyVtKxYlxnMBbvdrrJVPj18xuHmDvv9XppS8qAgD6LLstQvNTFUFQ+HXlZQSsHpdMJu2mEcR41ozoBTWVPwyJn1YbcIwKIr0kYDL0UMLW2B15oGM6tsFH0zXzXWBNeV86Jx0yDgfDpjmVfcv3mj4IkAFQAUcPDK4G/HYA/Ky7Jg1AZCkqMidrsdckrCWhC9C7iIee1uOiCGCbuDyMk+f/4Bz8cjQAXzckZKwlqYlwXjbgCRw8dPDwr0AKfjCWOUKOubr2/AzPj2ux/wr8YdQhxxfH7G8XjGOI4YxoCVEw43N/j88ITvvv8O4zDi7vYW0zRgXQtO5xUgjxBHBSsMhAKm+4h5XpBSFllRKciJ4dUI1DnC4XCLYZxwuLnF8/NzAz7MpNO1KOMQAsbdVMfO0r+WJYmlBRF2uwnkPGIcQeQwTRP2hxvEOCDGoXpRGOOmKOCTUgKI6ryxz5RVdDnf0HMRvPhFZAgDLJDI3gxMceRVJhWEtQXugKO+OW+SBSICMXA+L/Axmq5BXq/sFC65JquJzARVMnXZPGuT7C4BQetq5VpqspTmx3PJ3oFud1LPIGuM6zkiAYWaPEQaV1KZXpWIsaTgmFmpmGgrHGRgqTWKhRXQUIZbzrUlNSlNYZlDPRBj+/y6bImRs95r9LgcOSzrioKC6OKL9/fj1j9ol+ufs2XXtfNgjTQrMHv9vv76b+AWEEPAcjqj5FXniYBgjuSeRe4yKrtnbsg5zxXQFWAkwcFXA2rne+YI1/1PSeaoc5fHSkQiySkJgUJ3P7MxbK8VQEb8WlLOVbo1jqP4TRGqtMcbqKgAlt0zzO+n324//iZbLaWgx/bsOKrBcQfsmc8UWMzgDUDcHfZq/tkkfAa0VHBPGTlOGVuhgsgN0AdDFyiUN0cC0gMJdn27KjMTIKpde3IOY4wV7CO9sFyIKFniuEu5jHbvwae+GC8Bl/77Bt3ve6CxHstWW231O2u3273wQHitmBmfPn16cU1es3G22mqrrbba6kv1ZXevkvD9Dz+i5IxffP0VzudzTb4hbSCWdcX5dFbTXoe8JoALYpRNex/hgsfz8zNu7u6x2x2kCYjS3izLgsyakBEjqoa/ayTNV2ZZFgxRVkSPpyMAEqNYas71Anqk2rgSqcmirqJzaYapMY5qSqtMGWUjgCRWWvpJQvOv1KbAkXitrAuen464uXmDGMREM4Qo+5KFIRRj6JrZJgkwltCaVuR1BQOYplHMO5cFKa8IwSEvkpZEziOGESE48LKi8AnH5yOenh6xLGcMU8T6eMSSs/iIOEZwHsfzGWBGHDzOywJyDokZHz8/4t3bN3h+esZvfvMdbu/u8PD0jBiCNoZ7LGkGo+D+zTv88OnX+OE3/4Dvvv9eZUwT5nWtaV1OO6FpNwkjKhccDjc4n2esa8I4em00PFJiUC6I0SGEETv131lWYQGxY5QssibnHdas6S46L4ZxxLiLcOQwloxlTboKLY2fADMjDocbhBgFBISsqu92O0wK8pGuoNt+iYltJ21gSawK6jlj0pukfilW67pWgI9NFqTNdk0IqyCUNIXWWEKZDfIehxhbyoQ1+lA5EV2thps0xBot57xEyCe9fiBJS+Tk8wwUkt1pDan4TMmu9G0y6TiEIHH0rpMp6VYQAgGkHj7O43Q+Yn4+4+7+Xtkv4s9iyWslU2WX2JiyHosBtHJMV/HDOlq+8z5pzfglyCIoEmtTzzWxCAouMSCyuiWhb2avGS/X1bNSJOpa5EO9VKmODBv4619Izur468/SsmCMA6ZpqkwTm5uoxwoQF92WsVH03KoPkN0Dgw9IOakXiu3fZSNv40xsZu1dklCbgDCJUq5zT7aTWY2kO/kQg7EbxzonKxNFeVo9O4VgMfSo1wtzYzkaK0a2JffcBtYJAE4VqNPtQ+bipUxIIBKRSmYY+827iFLlmmKoXKVW/ZhRUTNp+z4S4CUEQkrAui5qTi+MywCVzJKNecMhZd6IlNXixm2BQBY/nEgBi6RuleJ0/9t2+tPTzzsj2vwUO6Zn+r2QR70Ccm611VYv6/b2Fvv9Hp8/f8b333+PH3/8Ed9+++2rqU/GAu/r7/7u7/Ddd99d/OzDhw8v0qUA8cO5rt/85jc4Ho+/cz9DCPjqq68uPp+Z8d1331UJqdV+v38BIL3GKP2p+rf/9t++2H/v/WYevNVWW/3Ra5qmF/e3Ugp++9vf/pH26H++vngn/f7777DMK/7Vv/oLkHNY16VKMwAxkJznGUTypVJYfQn09+M4Ac7h88MjxnHE4XBA0fjXVDTamcXc1TknSTalgSlG5c8543w+o5SCu9v7aiI7DsKwcF5iitsKeqkrtbUZ1USQlBNyKhjHSUEdM9psrUFtrOhaGiKfhQIUAo7HE4ZhxDRKXLisfifklGuDJck80hCsq3htDMOAlDKcI6QkjfIwjojDiOfHR+zGAQyVzTjAQ4AMgjTULktSEXOG94QPX73H58+fkXPCfJ7ByCJxApCTJCoVFKRSMC8r1lTw7fc/YF4TbvcH/MN33+F5nmviyHm5QWGCHzwenp7hlc1SPgLH+YzPT4/w5BGHAcMYFUxTWv4Z2O92cJ70GB2ICj5//ox5+R7TuMc4DtULZb/fIw4DvI+IcFjWZxyfn8WEOASAqIIvhRnLsmJaE4IPmHYT4jDAuYhSCNMkCQsheJWfBcQ4YBwnOOcwxEG9imRurasAf85Zk9fOsTU1jWF1uUpvc1wa/JbkJWSZnukB/bzWWDnvOg8aBx8kwUbOtwOrr4g1Xo0U01gR+hOYcW/P5Cjmk+ICmKR5bzatPaOovHggu1hl17EPIag8RPe5ey0ZIUavr+ADVgV+nBmNa5PpvJP4a4sJ12bdxqWUguBCHWcDWMi5Lvqa1dSYq8zrGrAxuYzXC9rZuFljrwwM81qxek0K9lO/qzKsDuiJMWrSVmM9WCKWDVsFG0pRFo6ATyVn7EcBakxCGPS1uYjxdk4Ja0qStqfztG6LqAP2NJXJM4ILIusrGsVtHkSsIEWVwllMOyko4sHskKkxiMx7xxgzSkqUsQ4BhUkBIAK4VKmfItUvmCAWLV5ZXh3wZXNNmC3dmMNOmSAYxMZeqrOynSO9pJmpfpa9NcYBOScxfDcWjiNlq7FK0DIYuX632Gc0KRFJGpy9t6hMST/cm9SPgVJWvR8KoJoLV8ZgrcIY4ojzIvOXuZ0nOQeX87Fdgz8NtFyzaV6TRG0gzVZb/bwyVrWxek+nE87n84t0p5+qZVleGAwPw/CT8qnrmuf5xftfq59iya3r+iJ56nA4vAoK/dza7XY4HA7/6PdvtdVWW/1T1WsG7tWy4F9IfTmeO2X86ptv4MhV/wx7qMvKGPHeV4PXshaQrtCL3IXx+PQA5z2m3V4eTkm8QUrOCD5cfPEREdZlVR+C9mC6LAvOpzPef3hfP3+aJqRVV4ItqQNttdt6EWugze9iXVaRwfj2gN7T7a9p4dagAy0iGFxwOknq1Pv3X0mjryvNJSfxwiGPoN4OSYGBnBPGcRS6fF3JFlPOcdpVBo7R64WxEKRhSozgHXIpCNljtxtwe7vD2ze3ePxc8PDwGdMU4RwL82ldwTkjJ6Hsr9nLl7xPiMMzuBQc//63eL6/Bwh4Pp2R0wrvHE6nGfOccXd/CxdGfPr8CYkzimOcjicxaAZQkLHmFTFKitIQ5TNKKbi7OYAI8D6IdwwYKa/4/sdvEeMA54Q9Mi8LpmmHMER45xBCxLQ74On5CfPprAbBwP39GyyzePhM0w28Ex+hGCZlZDhM414mdQzwziEOESFGOOfFz8Y3Q+l1WcV/IwI22SRWWts5klV4kBpC+5bE5IPXCHKHtKQq7SqlYFa54BAH9bWpATJgaMR2ba6BAjHFTatEpecs0h5W0EgYOm1+itwvdQa2rUkrRVga67pimnYKWJoPDF/KOrTxtmMilYVcrLaX0j3AXUGZyk4Rc2OBgQpkFXG4H8BgBYf0Pa+ATYSW0tRLO+p1CAYKwLgcg7YrL6UeVX4IAjllSlgzaowOCPjjnK+I23Wze7EfBgopK6kCcVdAjbE9HDf2h5kdXwNAPbjmnMd6npGWFZmAw+1N9TJBm54gR5jPJ+x2E1LOyhIR7xcyU9sKyAlYU5CrNAcAOPf+JCbNE7DGjNZ7kNG8WJxrX3S9p5edDjvNpZtL9XcwuV07JmJhURl41oORQE1Er+ybi2LxHaKLH/Ugjn6ADpwYHst3BQw0dA7znMFgxNh5UblmEN7vT9t2+xznhDnIaJKnnDMctXS/UgyMMsNmqr+z64JIvsPOT0eAAxwNkMCty1jwyyFovjX9v39qHl8DO8Amedpqqz9Eee9fxGsDwPfff/+zrrF5nn/26u48z//T+7fVVltttdW/3PoiUPP111/Dh4BVPTwY+iDOErdb6Y4s8crQB1ByHiDC8Sw+AHe3d5VpY19cwzjCKwCUi4A2tlIbNZKaSECa5+dn3N3docZve4scTogxarqNrrBerTAWlbiUUgRgcBKLWrgllXjv1efEPCTU+JP7h1xJzxFJ04rHhyfc3d032jkYJWcxpIwBwQkLpoDVpNIDEW3VGBaTS4gxwDvCnFbsdhPOp6N6TshDeM7NFLWUgsN+h5SOmMYR7969gyNgmHYgcoiDx3/9r/8XPv/wVB/Il5TgWWRqy7IARJiGHc6nM07zijgECCiQQc5hdzrj8+MzwrcRv/zlL7HmgvNyxjCMeMYT0rpon5wxTXssi5hIByfHsq4rjqcTDvsb8aK5uUEBI3EGiDHPK9KcMKRUJW3jNFXmzDhOmNTH6Hg8oRTGNO1Ryor7Nx9wOOyrHCkE8XSJQUAZAwmHGLGmhFwyplGAoZINeJBV+BiDgAyliIkPRHLQ+DLQuG0PIuj8BIgcvEoxVjNNLRl5zsgsjS+4SNKX6Rag8hGIh4vEcjs1XhbvlRijGsF2F2FlDMgcr74bgJwvEkDFwBICiY/TbqdsDGmog5dGsVysrBviAU3IAbI1eYzKaqopavLKunu18Wb5OxScBcs1amk2pHO9SpD0uIoCG1AQIq0Ja1rlelAvHq/gmlUDUt0LsMb2RblF8ArYppxFLoOWOiXGraH+/eLIjDHFXEEYA+oqgtCxOHqwxprwmvjDbdSIqI6Vc7YN2UiIASmvWJ9OuLk9wHknQCJJyhir/EfhNp3DuYJ1dvw5rRiG2HkPGVsGdQ4TtZQ782kSsCdXsCznJMwj57BmkYhxycgNO4IXYxs7nUpy6UGs5pvUBhUCihoAzs0MXoCbrKhUex+zXJ8GShTTOeq2uskB8tR2pi9WML37UQgeKacLoF3wPAXa7GPIjljYXAxJMRNjZAHWa/qZU28phgKfMgAiHWvniSHyWtJr2rmAEAc8PDzi5s1XAi4V29t2nVoZWPYq08ve1V3rPXOm+dxskqettvp9K8aIX/3qVy9+/unTp5/Ffjmfz/j7v//7n/VZNzc3+ny81VZbbbXVn0N9EahxMUqTRkBn8yAPh5XWLSwS5CSafycSnWVNWNYVb96+URBFGhiJ6NWobb6kf4upq3yW9wHICU9PT9jt9pimHQqknzb5wziOrVGxh/36QMqaQhXAhZGWhHVNONze6GetsubPBqRQNX+V7ciDuiWqyMqueJY8fH7EMEwKVMkDOKuUq4AB75BKaSlB2ig5iNQqBI+yJhAJ42IYR4kxLxnLukiD7IOmOImZsK2aExWgZFABDocb/Ou//Gt888tv8HRc8ObdB5xOj/j73/4dXAlIq2iRcy7CatFxPp3OIHgwEY6nE9xqCUcyfud5xRBngBw+Pz3j/v4WIQIlrbi/vcfn8hnLctZmSwdJk1/IOdzd3+E8n3BczgjZY6IJ0/4GTMA47TDPZzw/P+N8PgmjQmUXwzAg54JhmuAhjJb7u3fYHQ5IKWN3INzdvqlMGe89nIIeIQT4QShu3gmAs6YEBzEALjnDhQAfPObzDOccxmlEVpCjrAt8HBBIWAqFWxPknK2QF5ALENCCkdakwKEDeWFQ7TRVzJHEhxdlxIjxb4uE75smFEbKC+IQlZeijZ4yDQwIrEAkHIhzA3vq7+R13htUUbTBd7UBJTTPFyKGJMsAnEs1vc16rVq0PdB8nsBQHx/dBqjrlZVBQqgyyMosYQNqL9kK5m3kPMHDg7LJYFAZcRXk0CISWYlcr6jMAmNmGENOemTdXlUFmTOMQyZGkagicFZ/FLK2WNkO2swKwGHbqy+qHiPGqJL9EyNqARig9wEoyANlvRBKjXUnhCFg2k84P3wULxoKFYQQnEBkm+M0QIhCwn4RQNlVs14ZAAdPVCnuzlk6ldNfy7k34Fs+gXSfxViYDSRyHq6ISbR4cwnw58hdzOOSs54n7oAZkzLZ94ZK2phB3sHpkJABCoWRSm6eSXZ94KVMr6SkwAjBJmcPz8huFBBXwRPYzh0UvIxeXkN6rphBTAreNo8ydOATqhk6o+h9Wf5eOhCNwI5QINc9K3vM+YDCCaWggvoi0ZPrM8QBw6jfJc7LdULCXiTDn/RY+pX6Xv72Wr0EOvu/c52DW2211f98hRDw5s2bFz//XZ5nVsuy4Hw+/6zXDsOwATVbbbXVVn9G9TviuUttgMyg15oia05yzvLQzAVrynDBg9eEeZ5xd3+nrBF70G7Rw3UFUFcUz2rIKg+lspr4+PCEOIy4v7vHPM9gIqRlwTAMF/uG0mJdTRaScxEJiX72+XzCMEztAddW1UOASTisoyDIgzRDDFTtGbYUxvF4QkoZ9/e3Lbo7ZXiovwEA5wY9skupiTW367rWJni/PyArs8SRUwBH9seSRojEeDitCdFHLOsJwXmE3R4xRKSU8O79gNu7N/i7//Hf8O/+3f8D/yP+Go8PjwAzjqqjJhfhSJq2NS3wTkxixfNnrelEADAvM0oBnp+f8fj4GTe3exAxdtOIME6gEME5gUmYEmsSKUZKCSllvHnzDo+PD1gTo5xOalQqbJfDIcL7AYdDxsPnR+SUcSpnAIQwOFBKIBeRU0LhghAnMBhv373vItA9fAwqaxDfHxdIGkASM1rnHEL0IkcJXlflvYwFSfqLD5KMk9mSvFrTU2UFKinyXlhSOSU47zGva12ldyal0ZV3WfU3gI2UlaG+SWoyazILZ014mGpT5cipbAK6D2IobQCBAQg92ElE4r80jSAHcDJ2mOGkFl9vjbpuu1t5b6vzdGF8zbWhh7JWWBtkuWAccMluQNu2HLN5tyjwS83IWTcpjKoQgGLeO837B+iaSQagXioG5jSZEElEtFIhxOajMWUu9q0IM7ATt1RQCdQzh+S6LQp6CdOGOyAW3TV++Z7KdinmD+Ng4Fj1IVK23zhNiCdhKSYudR+aWbRTphQUXLax4/p7GbsOBERjthgIY8dapZyARrC318E18JsApFUkjmzXhTKl2F5fx6tSYbpxsFPTGdq6xtDiyqqRce85HjaCF4wQPXcyjPp+O169JorKugpret7lXglgWAF68QuTcypMocyWxOQATSoreu22e7kxlIyVmeWaF8fgyk5iAI4CoMBcAWHJC4KZGNdrMiCOkpDmQBdgDDpA9Jo107Nprlk0fV2CMb0R8+XobLXVVq+XST7HccSHDx9Egp8S9vv9xeuYGf/lv/yXFwDo6XR6wbIppbww+GXmVw2KP3z48Ic4jH90CbP98pheA45ijC/G5Kfq2j9iq6222mqrVl8EaqpvzLqCHOqDJ2APgvK6GjE7SmrI+TRjt9+JiW0pwgAgkQOk1AAfq6KrqJK4JF4Gj4+PmJcVH776SlaGSdIyajKUrnJbKpT4MRRbopUmn6ThnedZvDPGqA+7pLR1X1cYK1DDPeDTgAACYTnPOJ/OuL29g/dirCkmle3h2TmJdDUqvBjMqnSEWR76i7EexIQ35SINKhfkrPuiJspWp9MJMQQBqTjhcLjBmmaUxJjGHe7u77HmhP3+Br/65i+B5HA+n7Hf7/Hf//vfSjrUeUZWOZA0qQXOM7wdozbQ3pOCHUmo+Tnj8eEBcITzvGC/24EIGIedMquEkVMg3iwpyX4fDjd4fHwEF8CRx/F0QikZwzBgHMV8z4cBYEZaVxQWA9oQInKGJELtbip7K0ZhxIDl85yXOGz5fEIhiCQJ8u/xZsCyrgCJzMUpi6twwRgFTFO3ZngWeVMp4jEUQgQpyGjyE5E9SDNo53uIg7CoKvjQ5GpgRgj271Jj2+26ct4jpxXcxWVf+KnofCJrIp0kKJkMhpyrWIn0uQ6prIg+gEqB8AUYTlfiCxcw9cCjstzcJTvCEQFFVvq995JupIwdm+/SmMscNlDSGTACvGgS7bjlL/IfSf2xMdGmnByYFCjTxr4Z3ur72hZVtiLXawWrNEHHfG2Ymp9H/TxleuSctZEXlkcp4hRORK3BZwaZRKgowyJnFHLKVukj3S+P24EAR+gfRdlAJO7AXAiLbr/byREyt/FQxkT1kslZ52YDZBr7S42pdfwMfLHkPAMVAHSSH2GLmaTsNfnMogC5gY0GQNhrJE69yL2OlFhVvyvkrIlJMQAUFGVYVWANylYpOlIqq2JmUOdtI+824+MucYpbRHfhn/Kvseu2gfw2LjlnNXUuVcZ27efSzx8zrJfFinSRWNbPNWOfUrFxRgXec04y80jvn5ngnUfJFW+q31c23/sroD+u14+zA0ov5qX9zPbn9et1q622uiwDT6dpwjRN+Oqrr159HTPj/fv3L9KQfvjhhxdmvq+ZXTIzPn78+OK6/GP7St3f3/8sYGUcR7x9+/Z/wR5ttdVWW/1p15fNhJmFyYIiEpOumSxF04xA4uEBj8JiNjtNE8Zh1JhtXXF3ThsFujCjtOagN/dNacXT0zO+/uoXF6uojhqIEoJ4oRhwk3OWVeHgKjuFHHA+CVNjv9/rQ3OTdPTeNeIdw7U5HkIAeV8flJdlwTzP2O0OdfXZPE5yWWXlm4BlmeE56pcZ12amLkJoU1Yy4+bmgKRf2rZGK6wNi6BlbTZZk4yaVEFkUx4hDBiHGwE3SoKHw93tG9z8+1ucTicFYzyW5Yzvvv1WWT+Mp4fPSOuKNSWUlOtDu1cJTylZQTNpMIp6LKS8Yl0FTDnPM6ZpbE2MAgPTOCCzpm+FiHmeMYU9DvsDfvzxRxyfT7i7u8M0TXDkcLg9wIGQcsG03yHGEcu8AvDYHw4if9vv6sqTd5rEpJIZadCMHaBsBSKRNZWCYRrBJMd2Pp3k/I6DnhSVe3iJOCYnJ7Iw4DowsvdG8Q5InJUR0kCUNp9JZSJdw9eZ4VaGg8rj1mVp0jNwPR5h+PRmwb3BqUkpLMZax8Q5RDUhJZLkIBBrLPhV/DIu97ld+KhzoTb1FyANYP5NL28abdb3ja5JhkoXyWxsEgErdIzpkiHQWCG9x0tj3bVkIT0iBbZAXSS2I/P0raArObmPxBAAL6wqdA/BJWdNgxcfl+AAYoeUFxA5bewDWM/lui4yn5VpKIwgBTFgZuYCEhhA5ZwI3ZiaZEoke1k9azJSSWAUxCGIXMY5iTw3ZoqyIhpzqSAD6uPFKHp/EU8n1HuljZWAP+brlevcamAMwyRedp3b57LKYgszPBkLiYwYA+PYCPOpMQuNiVnZWgY86NyintkDOwdd3PcVi8Tmmv4A4CZhtflo+2IARQNVxbD9eDwi7uT85SSMR6F3CjBmfjC2nwQZR/u5HaPtyzU4ImycBkaG4DDnVaWTYrjuXZArrLvm5V5/eaz2bWFg0/VnXf/9p0CYF2O31VZb/cHq7du3L9KUiOhnJSyVUnA8Hl9cu1vk9VZbbbXVn1d98a6fs6RiSPKQrkqrr0FJYvpY/UBWiR3c7faIgyTtgFqz1a/49g/sQFvZBcSn5uHhEe/ff0CIUQEDeX1wHiW3B1T70jK5E9AxW5S9M88zbu/utBkslYHBbGCReihoM2cGqBKhmut2n56eEELEfn/Q1W1paASwkodtsQ5GjWUVM9YGDBVtbLgUjZJWI0mSYyilIHhXG1E5TjOwtZVpaTByKup3M2CaJuS0IqUFMUbEYQSBcX/POB6fEIeIZVnw5u07BB9wPh/x+eMnfP78Cct8xjqfkVJW+UVU5tOC0+kEUglOjZgGZLkcGSUz5nOGjwGUnMiLiLCmhJGF3LSkjALC49MzPrx7i/u3b/Dxx4+YlwVwhDiMWNaEMY4CMsQRIUQ4cghxwm434Xh8xjAEPV8r/GhgjLFOdPKonMg7NbLNBXEYavNXWJgucRjgfECNyQXV2PkqcYKZCxujges8NfaVmamaP4Xsk/p6EMGFplEntIbIwEAA8ORwSgnDNAq7x1bglYVgwF4/tw3AkR+2Rs7+aywuUhaaNKTCkmpNINdza9c6uv2y47xYPVOg0wxyYSDKxTgVYUbQZYMp0dC+nitLg6oJxBevtXsC1TlnPlJs+6HXR9Hut+63U4YPq1RFX2tMEDvHzDJBhSllV67NpcYMsoZepocduxiZS3wywUGArJJWAc/UpNya7NYss7yngga9J5aMRQgBa84Ig302a5y5zgMFt5oPju2PSktVTuVISCkpSZQ9uMh8tXsWcwVeeo8dsvujXVx1TgtDb5omnQtyz2xAtNBo2HZAqwdY7HX9mDRWYwNBLFGt3jOvmhU712ZOfcEo4Zcrzhdcku61FfBURmBKGV5BPmGxZYC9GMNHY0DpsDBg6XxBfanMx8jAxgspXN13D2IglwUhRpW96VZJ/IZapHdpfkvXYKrNmVdAmH58e4Ds+nfX29pqq63+cPX111+/SGkaxxEPDw+/8712P7lm0Pw+MdpbbbXVVlv9y6vfwagpyhhoD9s552ooaY+N67rU2Os4DqgNDTcqfb8NY9fk3CRPuWQwBBC5ubnFqNpf89iwh3b3CuW/en9wk0NxYTw9ihExKb0/lwwUkbC0xAw12CQCUYGDASWS7AMA5/OsvjRvID4rXqQ2Rc1AVYJC3mG336GXD0jvoCwaXc2XdKOI83mG917TiYoyaaQZFtaRNK01cUZX+J1zSABKIdzcHBCCx3mZQeQw7XbiGwN5bYgDxmmP0+kZb96+wzAMOJ2O+Hz/CT/+8D3GMeDp4RMeH5/hnUccA5wDzucTzqcTSilYFokTD904pzWpXEmbLU2kgTZv87JiHCfgdK5sj+fTETe3t4jDIA8rJPIf5wMKCCFGhDgiZ/GmGbzDeT5jd9jBeQfvojbKOo9UWuacsI3gffX0kP0KCgQI8LYsK9aUMQ0DfIg1Ll4katYPsnrNWGS8pLyYr0yVPqQkiUgqubFkHibUeWMAhe1jUd8LBrCqHM9Auulgem7S4zNAAY2Nc7UCLs21pkyhHacdhwCB4r/hbE6yyfDsNejeqw2wSkB6+QcZ2AKvAEMDi5hLA5bsKLqm0gBKUqAIHVjRIZDtvR0YVAGci7+3xtOhAb91zF8BtuzeJfcK+d0wRBgAIsCA7U4P2gBcjNUDjTu3dB8Bv5gZQwiaMNeAiv48sc1bPeYmPeE2niCQE6NxLi09iFyLjuYi9wgfCGDZj6LpX5V55ACGeHRliO8KOtmNd06SsDpAxMCs3j+s6Nz1IWC321XPBAOVvRMT7euxtrl1zXbqz8UFeE9fYIZcvV9ASOHIXIMusu/KijMQi5rXUA8o1s/S6TcMA9Ka1Hh90ekpYCe5IAsU9TtNUbzK9NLjrd9BRedKB0IRKZgvSWxFgV7y4htFTCjFOJ3q0YRLALYHtF6r135/zUB6ea5+mnGz1VZb/ePrw4cPL7xngt5Lf1cxM5Zl+aNLnbbaaquttvrj1pdTn7yrFHUztjUzxX411CKfQ4gwxoc1Jq/RtPuEFKDFrR6PJ4zjKAlPmkKj6+n1tfK82Roga76MEWBNwPF4xDiOCFEOUfxvAGteDaSR3wU4NVclkGiY9AF8XVes64r37z9UkEhYBroCDZJmhWxFVB/eGQCXGjFsjeJ5nnF7e6uGwkkZSQHOSwNs8gYZHyc+Ofq5maXp5iw8hmGaMIwTUlogwEKA88Buvwf0i94aBB8GxDiAuSCEEcyEw+EWITgc377Dxx8/4/b2DoUTiArOx2c4IszLjNPpDC7S2IpvSMbxeMT5dEbJBS4EgQWYEbykEsQ4IISA+/s3SEklG5qQ9fbte+x2B5zPJ+z3NwAI3gWEMKiX0AguBTGOWFcx6tvtJkQ/gIkQw6j+R5IS5r2HD16bNzkVzvdxviKLW9Yi4JGcdTDnem4qi4VspV7mC0NwBVapjHOtEQ/a2BpTYVUj6OBjZUMojwLWyNn7KGjTmFNttC/BiH6+v7bi3SUq6e9tBd7OBaMow0RTp0iYElyZWe1aMmbFdbWmXdkyXpOanNNmWOVGUIZBUTqH7cNrzTfbXeHyM16TYLT3GpuhB4h6QMaYe6WOY29CfPlaZfFAEutCGFHaB8pdq5f4wPCty8+DHnMPPIhE7OVx2L2QoUblXEDIIO/hlUGRcgYBmJcFw7SrwBZVOZhIwwRzUdNaNOPrnDM4rRjGod635dwV8T4pzUtHAJKWaGXj5FxLKmNmlRo6xCgSRgN0oCNg9yvovCM0UJ0M/LOT3Z0TOw8C4nXMD51rL1kgcsyliFGwI6B0ElaTkhlMRx1AU4EJ5xCcqwq3dn9IOv8JOWXEOGBNsx4H1zF+TYJkDLAemEkpwdKk6tnX/ZE0KVajcFaJrgenoqFgcnFce+LUuXg1j+3v/Ry0z6vj29VPXZOb/Gmrrf6wdXt7+yrQUlmJXyhmxvl8fvG9//j4+MLj5rXqrQT62u12L3xzdrvdz0qSIiIJbXjlOeG1z99qq6222ur3r9/pUQOgMlZMCpFT0ijlXD1ept1OmlxnUhmugAMRq6fMgpTbSqF5veSUcD6fkHPG/dt7WMCMyB8k6aZkkVqllC/258UqIhFOp5MkIb17h3VdwDCmjZjaFi4ioypQRo+XJszkG7ZCmxPO57mCDjk3Q8yUijapASWLV44LXlOwNPUH0vyG4NRfJWMa93DkseQVwzDU47DlfGPmNICnGWkK+0RWw10ImKYdEjPgCUiEIe7gYgB58YrwWbxLmBnTdABY4sWdC5h2GbvdDvNyxloY//rf/AIxDsh5Rikr0nkRps58xDyfQQjV08KBhXEznySSHYRsUefaw/pgzKPGwhljBLOwEg43t9WwmZTyH0MEE6v/h8c8LxinHXY7AWqcNqchjPDBISW+aJq8DzL3IAwbgzw8eaxLxqePn/D27VtwKQLsZDT2DISJYA0xoKalCjI6EiCHudS5ZI2ZNac1ickAShJzULuW7PU1sagUpDUJwGkgkQFBdlwkXW5lHxjgqUctpqTilZGTNncxILMkOzkfkBIDpYh8h6z5kw+8NhLuV+BrQk9tdNs1Jkyjdp9gbk0orh4uAajRLNVr3/ahB6cuPouah1QzFDaehnghFWMxKBBWmRNXwI8Bp0HBiqKSJ+8cSsnqB+Xasb3CyClFfFjq9KAGcBkolktGzgKMcceCMfBaGvsMhkTCS0y1sQSp3ncAabDFK0qYMXaLcDafdCzJmQm23AuXdRXghxr4Vtk4XYPuiJCvGnaZX77zolHQQc3NQ5BUN3uoF3NrHaur81e3a2NFAAz0UhZLZUkS1RhrzgLw8MXcUOP2yoJp8raeFSKAq9eP0guKGY5bJL2w4i6bGBtz7wM+ffyMu7tbjONYAX3zjbFrvbL6dP70wF29Rq/mNQCNDLfjCUBhpGyAjO6jjhOg4ku6BHr76/O6ievTwXoArm+sGrtrq622+qcqIno1svvdu3c/6/2lFNzd3b24Vn/961/j6enpZ23jOl0KAP7Df/gPv5d86le/+tUGwmy11VZb/S+s35n6ZGyV+pCs/00aKZ1SwjTt2uqkyoHs77JC2FY9Sy7VD0Rew1jTinVNuLu91VVbL0wYXXkE9w1gv1qvTQ6hGoguy4Ilrbi5FSCAnGQT2wqxsWBqA15X/+3BWMASR4TTPAMg8YDJpTbRl6uWIt8pLJ4otov9w3HO2hy6gN1uj+fjs/qbwDoWNfVkmHeO801mU+UJjpBUIjaO44VkLI4TvItY0ior8MwgOITg2z5w1mYhY7+7ESNmEKZdxts37/U1K87nZ+yHO5ADhnGPlMXnQk6BjPuwW3FbsjRXyqqAI5SUhDFAkvbFCjqIb4urzSYzY1BJhgOhMOHmcIPCRU1eB5y/+x673Q3GYRDAqWQQnJhXK1gQY6gNJsjDOXUbUUCCIIDgMs+4vXuD4CNclGbJOerRh7pq3zdLdXVeY9idl3lcE826xt6MrY1C0LdpAtCwyidQG7Pz+Yw4jdqk18ulvU/3oW+AmaHgwmWzVlkkDBAFeJXneMfgkpE4wZNXMMJgLGn57VgMTLKUoZ76Igv+akpcf1yFRhBA4jJe2fartH9UQKs3DrZjs9e/aHL1mmVmnXOXTatV9cAC2v2CLlk1cioY9QTlguIaM+jiU/W9uXDHuoF5pNftepVzVvBYWXk5Z2FoKVjRH6MTjZKyNnSjYIzDgJwSYgxVeirDK6w7uWWIWXnRBDkBHxj7/R4GgdkK6LquF5ImGScvjCJuLEc7DqcABCDsL1bmYggBp9NJjYmvwLCrcyjsodJA6BdzAh2lqvu5IxCr1K7+zF28zCDCaxYJqDOQ7s+3zp3CLOBY974GzAqQent7K59BQe5nRb5DDISsYCRQ54ZckwSgtO9K4RsJYExmfN0VO/U5kytQ0v4Yzsu4mam8gZOXY3cpWbq8Vtr3nP27MYEuz8FrTJytttrqn2/d3Nz8LPbLT9UGsmy11VZb/cuqLwI1npQhkotQ/G1FHoy8JuRUsJsmxGGoBpD2AFkbDAiLJeUVKSf4EAGiGnud14y0Ftze3csDMa8AMXJJYJU/1YZIH6YtmrgUkTpYE1Ryxvl8qukr3K0wmiwCRVb2GdAGB7ZsaWvUYFeQFDza7XYXK9JEDLGTYXubddLyYF2BIAFniCTNiBkYxhEprwhBVsFTWqUxZoCzbMObFwLUr0BlBYDsZ06yfWH4FAQfkYoBFfJyGTAPH33zp2AAWVathzDADdKQpcJ49/ZrDENEWlcsS8FuukUgNVMOkzS6ziKSGSllBI2z5VKAIr8nB6wp6co51wjhkhOgzCrvozIBCIwMLgkoyhCIIyitGKYBJTOmww1u799U9oVzwmDycZRzAgIFD++MFdWkegwGeQ+nrJEQGXfjDjklnS+u7r+xCMg5acZVUpFVRsPkUSCm1jkLgyxGYXjAORD/ROOIZpKtfRvSuqqJqDTC53XBdNi382RR3cwAdc0qkU2xClb40NhKRASoPweTpXDpdUYFcMIGArcmXgyW28o7us9ak/gEEYBCDOaMzAUOXhJ+FIYDUAOgWDBRifP20FQ44SsVUnCDusaTenmKjdilFKMHn8AiLSr2hq5BNa8pMFCSzBXZlnry0KU8kiGSuFwSAgeUlOHjYG2+NuHyd2b1HWKTTSorwUOb4izm0SqfLLmASZLEjGvhFLwRsZJGoHcgsYBZzaQ6lxUOgx6rXntsshWoZ0qu9xjnApxTwIA0FlxZM5kLzBfI7kcxkMReK/gZKsClx1A6ABCkkfAOOWVwLhUUMaaYoxaRbs1AKYzCkGhqsBrjcmWWZM7VX6lnV1WRmwEjAQbTATonZZyovkeSpzxcD8Cx+ZWhgqkFRcyZ63XLOqHkPI/jiGVRiReFTmYmc8lHA1By3e+cU52PJsU1wIS4vwGg3ssE4A8CvmcGK2MU0OuGutSt7rp8TfrUAzGXIE53/zH4jtvfe7Bqkz5ttdU//9oir7faaqut/rzqy9KnwshJY4g1HYWIseaMh0+fcXd7j2EYATTvAeiDPtAeIB0RliW1ZkxX1XPOWJYV+/1B4mg7SZVRzC1e2jlC4WZ8KY21Uud1W+u6YogR4zDWpsapMW/1Z9AGprDIDzx5jWyRFW0BXhin0wmHw6FKDey45N8mx9Bkq9JYF2RNMHmQynhSTpjGHUKMSGmB80qZh0iRUFSq5AFAJFdhUDDCzJhZGxxmhBBh6SzOe4whyIpxyWrEKqlU0vcT8ioNHYgQQ0SIIuOSFfcJcZjgCViXFcMwyao2PAKbWa02XpxQOMN5Sc+yiBmqNBBG7Eya2ZgaKCgpAcqyIUiEMrmCkhZIIxTEnHddEL3Hjx8/48NXvxBJmfMV3BLjX6+sIzNxjjATTh+8gEqQc+11jvgwwCQs1sTY+EpjXAAuujIuTaUn2Zb4e2SVpDFIzY8ZBVSuGh2Lgu5W84VJ0YGGQDUI9l4YQiVnSUW68kGpjVfpZR4FlorUXtuYIjX+Wz1kUMFOQS9k7jfw70IipH/POVV/kSbhASyyvaXrGGvF2AHyeQLultbId/Hj/X7bPaHJWqCeP+3n9l8DbO1+ZFIhA65Q7w1yTTn9O7EXEOmCfSOACNcUKjGIJjXGNqDCWFl2pNeSoX4uMazXlnNE6Hx/1Ii5oLELqfdmYYi0CsLoy8xY10Wuczh0/ToYjGVd9Fpwei1LYlOp57KN33UTPs8zvNvBcanx6GaULaexHZvXOZPVn8yYQ8M4VHNdLgXcSejafGxMmJIL2Ml87+U3dg0a66mCaYq31G3Kza+bA8I+lP03HE9ltQpYGFtOpG+lmXLDrkeurJXKulLAxgAwMSRXkKc0kE+vbPlvlQHmKjczeZXch9o8hw6vmVoXNk6byVxtxFTw9Mr568e4/7eNt9zjLj8P6ADdq/3Zaqut/mXUu3fvfi/p0nfffffC4Hirrbbaaqt/vvVFoKZwUUPh1BIskEWuEQbc3t6KOSkaKNNr/0W+oxG6RAhOKfPMyKXg4eEBt4dbjYNeIauTtp1rureDo0alNyr/tbHZNE51JZ+U8lLNi9n8BqCAgUk1ihLQ5aH3eDwCgHqeFDVSzvUzzcTYjFp9CHXV2VY1m1dPQcni0VOyfEH2iVPCCAJ80JhgBSDqKrF3WFcx9FzWFeQ8htHAMV8bVpNyrWtRY1xZjV3XBDDg4yApI84h5aTN3ooQB8QhIs3S+PkQ5Fx58c+ReF8BhdZMQJZmLoZRwAOn7CE9phhGYSsUiWMGBAfL6wKJGqd63D4A2bnKhjEAzzuPaXfA7d0bjZcmlUslgCRmG0TwXppF56I2V6V6dggDAgJMlKxR7gJymWRP9s0a/SwgBGxF3lhKZlDcZDEWC1+0ubb1emu4bR7Ja1j7QTk/nkJt5teUMKqxoAERNU2H6AKIsOvGopMNsLHr5EI2BOvr288EV7FGV9tM3Tf0DaxQbkBE9TjJVuaJWtOHBkJVFIGEKVVSqddcBWo64+N+f203YO32RYP5GmugyXQMsMk5Y15mOOfUW0SudVJ/JHqFNcDKgDB2HTmHXBhRG9n+enfOCRgstDcwGD40k28fojDDDPxTTxlLw5Ij88rKaFIxUpYMGVIANVaHABsw3xrHDVAjggfw9PCAEALGcdekkQoWm1uu3Zcd+QqGcCmYxkFMjyv4dzl/+vjugsvode89lmVBVH+t/o/MPUZOa50fMm0bi+RSNoRq0G3zG6wAWjuCCgLWWd2Nl91rAIAzgbvvA7u3Gx+n3u+53adtX0BN1kSOcJ5nHA77V71s2hyyeeSqj5CUq/Mm56x+UYLo2xia/1BaEpZU4CjWY2pgyks05adkSo3taUb93Rj1gObVezcmzVZb/cupw+GAw+Hwj37/Dz/88I9+73av2Gqrrbb6X19fBmq0KXFqJMml4On5DO8Dbu9vUXTV93Q+YZwmxBgvVucZTS8voI2tXGfM84xxHDFOOzUZXvXBVkx0vQ9Iea5ATM4JuWTEMGj8aXvwNDbNNE1gVlmBFqNIpG01Hi4gdjWZR/ZVfB7AwDqvWJb1SvJkD76tIZGkJgdmQsnGcmiRzhatnVLRNCXZhj28y+e6bgVWmigmwMUgUjLvVH7kkbOsHA/DIBG+RQ1QtQEQg9vOU4iEFVKIEOIA76MCJQxj+wAZwzgJiLQmTLtd3Sazh3eyokzKEPA+gijo58R6nguLHMJWu3POYC/vhTZv7L14BlFQ81BGLisASQtzXoCzHFYs5wV3b94gF4aPEvdu4IL2cnCWrFRE7uGcNMKua3K8dwpuNDYIEcTHRn9OgMqfCCF4pJwBEHIRPx/7fOYM8XJhxCgGzcE7gLzuH6OmApWfaLAUBGKIUXbKqSYwyFipXKQ0KVIpBTklNR/OYBIWl8whp00w9NwCZowsTAdGfiGdEDlh0ChzFG3YDRxSmUhvBG1MBPH0aebIrbfuQCIDNi7AA2UydQyhen3aew1Y67Zzsd+51LjonMWQl1jG0K4r1rEKIcBV8M1AgZ7Fo39ACs4AcKQAgQFVZmYu8ylzqZ8l3j0ip6rONeTgQxS5mDKObK6Wwko+M2NhkV1BE6N64MCRAzsgpYy0JrnrhADvqUqJJE3tXu8Bvl5zFaOogBr0mvDIa8I4iZTKO6rx63JOmr9YnzTUEppa6lGMUSVOpQKOvRyMVXooEe9ZPXAAYxmtqzLLvIMwDhNKIcQg15nC55UtV9lSNi+urqsmSW3gfR1T3XfDeGz+NNCQqyeOJQx670FuFN80BXNtKvbznFlZgZDvD5szOTcwyV6/qt8Q0My7BYAWphmvAvaYvOsf0xDZMQvIVuq8tXlwDdD0kqctAnirrf55FRHhL/7iL1783DmHeZ7/0dv9xS9+8SrQ+3Pr5yQ+bbXVVltt9YerL0ufYA+cBVwczscTGMDN3R28CyKHYrwK0gAiE0pprYCEbLRgWRYQEW5ubqThcgQPX1dKxX8lgbyrbARyVE3USso1dhuQVdkQgrISLldU+yYEQDOGdK1BsQ6xlILz+YxxHBBCVPaHMXJyjTiGNhLynqzyJGVdVIYGsCwSleu9x7JKk1clUip7CMFXPx8m3S5JXycNu0pNIMBH8IO8VuOChRTRInclfjvq+4HgA7wLYFg8uQBAj0+fMYw7jVQHhmkSnwSI8WuBF3Nlp8BBESmQ943F5EiaqZIl5Sk43xojlQ0xs3iWkCQxBR+18S9I5wSvcd6knhtUvDSjcdA5FLrmRZsZtohgSeDKuTTJkbJhoIylwgVejX9lLOXnkiIjzWUuWU2ltbkXipOAF3C6/66evza3RI5XuzThxCg4ZXOwzcU+pYy5YFnmGndpD0/eqZeSNqwoMn7OCegGNtNXKHhjEkGuYE4BK1jUVtVt30vJABMoqMwGRVlAKhXhl40iG+JAxpB4ne1i5ZzNadknS4dzoYE8aEfY7SMu/mUMCECabAM453lGjBHeOwVthU3gdEzIeVDJSEV8lJyn7iPkGDx5BbnmalZrhuP9+e1LDL9Ld61xBf8KS0NOYAwxXOy7zLECRyaXJFBun2Hj2xpnGbvT6YRxGJC4gEjS0QRcKnoPacaQ0qTL+PguFalaeFEzcq8gC1H9XGMHSRKb784vV2C8Z3yklOCDqyIdAwnqbLvyB2ssKEvySiqDElmmd0HYhGTj0sAHA95J73U1ucu1/THA/jLFTM3oS4ZFgOeSQbHNsDpOjOqHxmCM44jj8SjmzB24WMGgC+NpmV+OGDnXS6WCwTK2up9F9kF+YBJEBYouppvsyTUL7DVWTD8GL3/HdX/se86u4a222uqfZ/WM1uv6fYCWzUx4q6222upfVv2OeG6NiM0O5/MCZuD27h79A6RzhMEPFw+z8oDqqwFl39yuiySQ7Pd7pJRqE9A3tOu6arPQVlIdSTR0WlNNBiIinM9nhBAUqEHXIFqqz6VhYk2K4vawajT98/kM5xyGYdT9MRlE2w/vvT5od6uY4NrkmRylZIYLsq1SukjxbI1RG6eijb6xBSyuVqLIlaHCQPDN7d9WrdvDN1XfHO8d2HTITFh1OxYbm1LCumbc3A3C4IGAR6UU5MKwRCRH6vGSgWKr9I4umlBrgHwI8M4jJZb97EyQ07qi5II4jMJ8AZCLgDGkPjsEkXuczjN8iPAuyjxzXo11BQS5aFwK4LxBcwL4CVhHYPUbSrqabRIPIpFHuK458xTFpkj9eJgcoN40Bm4QoN4/l82xdyTnT1fwDfCx+VFpIt172jXiaoNcH76UqWLytcJZ53TW+HD15SgyNjmrpC9nEAU1NrW52q1+GciSM5yTRLWcC8xxQxA3buBaN6d0hy4avOsG8gW4YxYgxtghEqPsTtJRGTdoDAi7IKkbq+ov5Rrga42885bqpjHUkkEullNAvdYBA6May8B5152Ddo3bf+289KuIF8dPjc0h12NQCdQlaOsIyg4RsLAfqQosoMkq5R40YFlmOALWtCBEeV3wAQXGcmlsGvl8ZS5ViaYYQHudY5IQR3U+VRCcmo9XG1+bxwJ4tHMmSWtcMohVomSGxlUK1+bF9TbJEXJJck8oXpk3CShZjLZDk8k559SEuRnrNjYUBGgjvkgmtM8TQMXw0wZ6pJSQmDu5XzEiXJ2HgDQ0pZSL+0c/Z5NeO7ZAAN0f5wKMgen0HijnWD9fARlHHrnYd5XvpF6XbJefquvrz35mx35dl75Al9t47frdaqutttpqq6222uqPW1/kMQb1pVmXFfM8Y9rvhUXjLo1Br2UOlTLfSR2sCTkdTzjs9vDksCyLSHq4PUQ657QZ5vpgL74p0nTn7iF0nuf6elmVttVMYzu0z5VIWXmfNFGuxg8zE5ZFwIRJPUNMjtT+TkKf11XrCgoQ1Wf+nFfkLLKAzBnTNLbX9w/CrCR/awY1KaqtWMv4RWUNiT+Phw8etavQaua49qDuARYfEwMVamNjf2fG/f09vJ5HIonVBkRyAUgfE7xJH1CbZEeuO8+yv0MM9bPqOVNfEvmv9EpexzRX75IA5wII7mI1/nBzkMbGAcwmX2mNej/X2vyzRliaJLHqaA18a5zbijjUs8ep2S3pv+1cSfOnbBD30hdJmknxGCmajsZMldXVr4BfN6ylAOO4g3ehzQmG+KmwtPNOAUoD9rwyVWwcihq89p9lkpYQAhiSXsZEgHMKKBoDCBf7VptE9bjo52sPpNpn2cQwNkgFm7huqquuIUQDQey4rxvNi+O58NxAlWRZ8pvzHj7GFitugI9eC1S7deg80n0BK+NPGFT2uTZv+/1pYwTILVP/8GWzGzQ2nruEJYfm69PmSb4Aa4hILjhHel+RNKR1nhGCq/HYllJEaPdmO5+lpAooynnIF5IkAbdXAWxyESCrA1+uwQEDSezvPdAtzCJuXkBXr2vXhqsgSkoJIJPLCdMlpSTfJyCN//b1fNc/jQ5Sj9fpeVdo7WLf+wSz3DHYWjqWqx5jAl7QxXS1c19KaX5H3e/qPHFXsqEi7CUZgw7sBOT7y/bTaSw7KYDItphgRsJ9te+EV4gydZztvz+10n597b7GGvt9Vum32mqrrbbaaquttvrD1xeBGluBPJ1P2O131WiWIQ/BIGFD9LIEe581JgRpPlEKnp8esd/tQERV/iQP/bo6CqdNPV88fDeQRAANRw7rulYJBCCroQYc1BVZci+a9aCgTzWwdbJiezqdEcKIlEqTwXCp7xNwylXavYXeyP6K54Ad+7LMiNFLSlJJyCWJqWTuG31CTtKsSHax0/0bdPvyuTmLzClWrxYS42EHQJk8wQeVyQBGJQhevEwKsgJJXEGKaZrE6waQONsOKCOQRobrSnoRA1Vr1gU8EHCKIIaonnzlCVSzZxa5ATlrsKBxwgk5LzBmkwBmHt45rMuKw37fnSNjPMi2DaigHqxiNUt1rXElbeLXdUW18eEMkBwL0ECrAoB8AJwX6Zlz1czZVsaJWJrckjWZx45bErpMWeNdY4dUoEMbYmlWc52TPUOk2v+ynKOcM0pOHdtB5XKQeORm9poRooPF2TtPCNErIGbHyRegRm3ckyaEQYHSkiWuWT/XGlnoOJnRqklKZDvC7GkoIOrrr+8j7b39Nf2yuZTrJNfmvpkSmycKcDodu3GRZpZBcg4hJt6o4JRcEq0xb58n/0Y1rO7/XL+29tTOiUk2NaCslAKUjKhgX4xR7kt6TyOoLM+QZB1Du5faPcupR44ZSue0IKcVMRDyvGBeFuR1rX5QjgFOMie9jqec91LPk3gnNaBRxtSkmUud5wKa5w50EdAIHRhhQEyL326mw/ZvYe2Ueu2DIACWawybCigXqPFxwbrOAuKCkRVwTMuKdZ6R1rXNW72fB5Mmkqv7FhRAMgmUAUrOOYTQmHAG6rWT+1JS1FhNC5Zlqb+3eVTnbylI64w1zXK/YAfz0mrbaszOSlKCg5Ka6nH1CxZ9MffXlMp5meu9HnW7r4M1/c+umTh2j9mAmq222mqrrbbaaqt/XvVlM+Fc8Pz0jJubG2WakHotkDJUJAraWCz9gzhb06ANyvF4lPd5j+fn5+rN0a9mC6CSu22grn6iCOWAixhQzvNc/QNk1baor0zbB6t+xZe5IIYBpTSMap4XxDBcUOaLepX0D7HOtXQfLowQBWTKa6pmn+u6wDlCjB7LKma58l4xY87IFVwyEMgAoCo3KOrJkSTSPIax7nttwsgACHtg5/pZOWVkVyqjyDuNv4U0Z37adWyBgpLFrBYsRrlg8fsgbY7FS8JWYeV81nEpXH/GlfmgRse60i8f32LNheTgFAAz+QtwOj7jw1dfIecMr9IrOSTS43UqEbFUJDvBTlgo6n1j53oBdya1MrbWNIHRGBeQOGRH3fmtPjjynrSuiGqsjMIKGKImmsUonicy5m3eMQzkkD0opTVrUb1zrIcvJRu+UOcHVO5BgHokOY2ibqviAGsjar4dhJzUUNqOF9L85pwRfIWGajMt8wratLYm3EDSijNUGYx4ETnWSVF0TqLFV/eAVQVB5F8NvLmKN2+r/e0+1CR+rVkGoOffADVcvV+8aoxL1jOFKjukiDl33whXHAVNKid/BDBl23cAYLkXEgiyRzJQ5DxQCgonoBi4YNehGhZDcBzDt+xwnSNkBcHIOSzrjNClV8l4tTll9wLbefm3nGtSsFPAup6RoilQbGloHciq423AAXrQER0IfnHP7gGOa68U9YXpAB8BZj3YCwPIe4fj8QhGqfdGkHiRmXF0KQLqm5zIosELLP5aAJwYXfXZ8aF5nBlIbdecXMZcx+wCsKB2rDFG8Qoax4vXlZwv5iMBYNK/m0SRDeiE3ofatbCuCvjDvgdwMTevARX5rOZn1M/1eu7R5v5PAS/99dH/ewNqttpqq6222mqrrf551ReBmtPphP1+j/1+jzUlTf2xB0qlv+cmZzBQxVYvxShS0ktKzri5uUVeV8QY4bzHmtaatlRqQ2A+MgIClZLBhetDemFGYcYwjNVs7ToFyvsgjI4KKNj+scp9rE0FTscTlnnFzc1BVrKDxl1zb9DbmrjmsaENd2lgALOknOz3B32Ylybeew9CSxVKOdWHbQGXpAkU4+W2wirsCcYYdzVembl0QFZBykWaZWsuiUDGcHJO5FJFGmgBkZqnhzACVNpDANhkZIClGBF6804vhp7aQwhzoZOqQNKzUkrwEOBgXZMycbSBtWaRhBljHhVrSiCn8eCltMZDmxhjBGgnLHOFjMUlEedm6iyNvMjdnPMoSeLIHXkd36IAI9cIcQMoDGxwytjIOSMGjwzuTFazAlGNfeG9rzIkVIAJqNAEaaw3FzgfsBxPcKM00SZ9cQpcGVumpbcARg2qCWYOF34yAjwFSR3yBCoC+Kypi4KHMLPqttUHx3ymTX6R04qKZ/WNujW1Kr8xJguJjsp2o4I5dhzQRpn1+q07rIyEwo1pRnVTHZPIADFuzLWUEoIT1hiDlAEGmBEzGzjVRSL3DTApuNRLXQBNDTNUCjofyBgx9n5trL3ZIdt1qdeuyjn1isWaVsTo4V3U8ZO5WmoKW2NcBAUHLImMS0bROeFVFsVwSGmBc1HmPEzagyaFs6bduXaPIqrSPmbxlbL507NkKmBs92UDgIsk8hExQoxI69qNX26gKoxd0xKXSK/zwqV6vjgXYd4t6yqyrBjUJJwIqxoBe5NEsd2PEkACwpWc9JgUNCpQEEXuCzKnSYEyuTct64JhHAU4VfDLOSesNDL5mezXbrdDWhfM5xkhBkkGY2MHCgA1DIOOu31/sYBJWeafSEFlTjoSOdw5Cwhv99KeOXdddk00eZScM/vOtJ93Lk+1esaTldP7TanALIPp5Xu32mqrrbbaaquttvrj1ReBmjhEjNOkMidXdf4GeqSUwKV03gzir8EaMWzAzTKvuL29RU6pAhf9ynnJ8lBvdHVr7OXBM6svhzTE57NEE+72OwiDwCHlDFcTUCwKWMxCuWhTybZKbyvCIlE6HU+YpoO80xVd3XaaBHNJYZcGuWt2ShFvAk0XWnNSeQmwrllbWAJpo7AmkUIxSxyzNO6MlJa26h50bBKDecUwjABQARrBWRggkTEAktAEZlmxZ9bjtmBmSXGCgi3DIEkrxA2wUM4PnG9R09I8agPKmoTEMg+4MFKRJlNWwBXY8iTmtmRNJcN7aZzE9zOoKbSdfwZBmAePz4843N7WFWY7l9r71FQUgpq/guG9IVSW6CQNiRl8msE0k0rduEv5MjYSSvUIIul2BDyCAhLd3GMIeCUkEvObCG2l3QHBkrBqpLHMNzbGCTNKXgEIG0B2RyQjXhk4RB7kGMxZQUCR2mSQni8GF2mfi5MmzfkgDJKsc18lX44EuBp8kHmqDWUuK6LTa9rpir+yNNK6II4RYEsd6yk1+sfABZ2PF+k+4ApCOAV+cjatoK/sLWJhQ6ScFUSQc95YBgqCoAEOxu5JhREqGGbgjkl/Oi8iXIEz3X/RzbUG4sicdMTKnrHzTQjB/Gf0HNSeWprwDGFWiJRK7zeOEIYgLCliAVLhwSA4ujQq16lQwUPxSEkYvMx35yUC3IcgZup2vMwKfBqfTOZDzlkBXlImD5ByRgiEgiwflhvTxoCo63lbmVAKQGYF5wWUD42FYtiW3uMNxM1FZKgUQ0sAI9LEI7mGvfcC1qZV2IdEcMEh+ij7VjTZiQ1QK3qe2rkzhp3zXiSbzut80XQlxVJcB0p5L+dC9snBh8u4dLmfRHz6+BFfffWV7m5Fxi+YQqgLDXaNOJS0wlOQeaPMP2NmGUBqCxQV3f1i1UGub0kpXzBxfoodUxczZCqiKOCIjkW01VZbbbXVVltttdU/j/oiUDMMQ2UakDXwkMZbjFYJUanz8ywAiqV0APIgvC6pJjyZqa/JRWpzp+aZwiaA0vILiEnNVmWbyyw+Cvv9XleJ9WFbU1uEwNKSNAiEVCRFyXl3AQLknHA8HhHjgBCEDcHM9eHVUQOTesq70+SaZHT3UpCz9CTLknA4jFUmVLkD2oiXLK25d0H9Vi5NPG1VO+csyVIEjesu+qCvdHm2/4rhcM4m8ECV70gcspPVZfLipeKd+lBAAZLSDHQvntRVTnJl8iyNZwFnWU323ksvpGyf2oRVg1NhU1jCjEl6eolFVrPllGSeWHrLJfvBzkFLzWEFmJQY0TExZA7kbElbLU0HBZ3U6pL6T0TInE1PA2NUSRKXMj58k1UYoybnFdacyfkwKsolMOCCyrzgsCwrgkY455LqqnZRNoiBdjaXrZlj9S5hFnZDzhnBOaSUMUy7CiiQduyVSVNKJxdiPX8FhNDJDUnHx9frEiyAizXqcsgaCa6sGi6ssejGrCrVhFYOXuZmCGY47VRKB42D12PTeSGbip2kRs5V0esnxgHLvNQ5UtOlDDzq2TA29q55l/T/NrmcMbvsOufSgUOyC3psgA4sDPm1X4uMziPTDAGMxDCW9NgLZ2VUiNeQHVtvwNsmu7DVpt0e63pGHEd4dvA+qDGt3EOYC56fn2FJTCJzanOuN1gupSCGgPP5XOeSJJ1dJanxy+vCmJHzsuJw2CtzR36/KqtG0re4MsQacNYlSvmr44TNb5MQJXhuYI4YR1Odx1WeJxsQNlE9N3xxX1nWBSF4xBgri9H2JcZYr29UVoqcU+YGEjLLYkQcBoBIE6AijscjhjHCgHxW9JCcyYCVdckFITRwWO67VEHufpzb+F/O3X4u97+73keTIvevu95WL0mz61txvhaCtdVWW2211VZbbbXVP4v6HfHcXD03SMEaR4RcGHlNuppaBLSwFT00psb5dMZuJyCNPRi3FVFZESxoq3mW/MOaiJHNJ4YLci5YlxXjNF34IogXhQOn3sxYt2cNJNlCaFt5Pp1O9aFdOeAih9EVxgKJxH4pf7KYW1kdtyZimRdM06QAj4Er2oATQCxJK+4KoAFa0oiNgX2O+PiQshNkzNZV5FE5sz6cexBl5ZU0hhE5Ya6kLEan67piHAcBwCASEgPO2vI/YM2VjF8DqMSsWXkgXOA8xMjWjEN1jvRzx84FuR50uWY4UJXYvWQ3tG0xMzgXMLFIQdSElJzTeHFSa+zeCLZtrzUpDajx3uuYCSPJAKZ+ldw5h+U8i+TDO3BqK9Fm/NoSwqTZhPMX+94YC20+GRPJmAqVtcDaQNXxkevOZA0+BGSVyIGcMHqQO8mayidI5roLXg1b+7lmMiDxB2lSpSJzFwY+KENKmUW1kUXXCDLjeJTzR2rkG9SMGZ5QSlJQSrZZIPcUY4Q477oYca7R0j07wORWTRKWBa4zQtJVs2tmsv0curyGzUuJKghYf89tu6gj0f+L9HJRLx79mQA1ci4zs/jK2DHbXKw6l25r1LFpmJV9IpKyYZzw+PSAuzdvkHMRpggYRuCYz2esKYlBu2tJVb2cKedcGTcGTHg58ZUpZuxHux6vwVIGcDydsK4rbm9vYAbPBiw7Yzv1uAJ15wLND8s+g7tzRyRgVi5ynwvBd+fU5G8ELsoE6j+iG7tS7wkiX0oqrXUk0fBKiKs7KSw4pzIzVw3fX5sz92/eVEDkfD5j3BnTERXYcwpYU2W6COPRBz33BBQmpJQv5uxrTJjrv/ffCwYM29zt63pxof/TXtSuXfGeLsgo2Gqrrbbaaqutttrqn099EaiprJLSzBhlZTshRvEb6VcqAVRJzbqu2O32tVnoV3edmtua3MmMUIuaBmcFA4QdLg/B67JiHIZmrGk9j1Iq+qauSTCcejPoaxUwmM9n3b8d5PBEqmDbcQ5qgKlABNQnQ1dCGQxYokjwmI/qrxAH5FSqVwIruFHH0718IM85obCwNEQqVlRipmlI3iGltY2XMlhkxVxAloKksqmg3jTC3JBz4zEvszIaCEWsa1CKNeni/eBqUoxIfRj5Yjylz1G/Gm4pL+bTUJkqJI2RxZIDLVJZvCKsoRTZRk4Z8zzj7du3tUHspXF92g/AIqNJwg5yutouPjcCnpFzEAPjtg8XLApqjQ6zzDkfxTvFe0mnyTnDh1BlM6UUBDOdhaXKeAUzHF6ACjmBVBJS5RV6PBlAYmC6Ahul0Ve/DK8JZo6rcbE0eWKk7JzTJXBpYodhhKOAzJLoE4I0+syalCWfog2vLJ8HoMq2ql8RDGQVD5JSChyAXBLM/FnwVTE2FUCJLsZXu8B6LxAAzYEN8NHfV3DCruGr/77WtDIzHIBxGDpcsWu65TAv3gOgO/f2iW0+2WsvgV/fgZRmZCygSv92kTBV2Epe4wjn81nYiKhQHAjBYEOdn65upS/n1d3ZezgCfBwQQkQuxtyCXPOlII4D4ij3xFK4yhh7BlE18O1uRHJNeZRMF+NvY9Ubwtsu5pxxOBwqM0vYO8KoMa8wOf4rQJQa8EUkCXUAqkcU1KDdE2EYRqwKuEvKncDPDgKGSNqcLA70qVzt/KnsLhdhvy0zvHc4n4WBdXNzo/OkAcHrumKMg97LSAGV9n1n9+VhGDCXUhl1BryQDLgCkQZ2NkaiMA8HWSzIGkveAad2/b3G6mnn65oh00Asu1965+t9+acAmjrHnYLCmi6YlhVLXrHVVltttdVWW2211T9d2bPxsiyY5xnzPNeFvdfqi0BNfYDXB75cVqVyiwnrujbPGaDJkObzDHKEMEQs8wynsbX9wyNp055zBmvCT6XNF4vUFkbNuiyV/dIecdtquOxfWxnXndbfdWQRAPN8xjzPmmKF7oFammADgsCsTBEHi01NmsI0TKOsP5KsPs/zjLu72+rJw6z7Vhi5ACHa6jWrjKPUk5RzwjBIvPWyJGT1Sxl3Y/1cr8bLIivxSHlFjKMdZgd+aHPkJNGFWMCK8+mIt2/egoiF7MEiG3OWXQ3AErXk4b9Jklq1VV3zppDxKBqD21hC2m7A5EHeefFzgddGE8rQAJZ1wX6/xzAMWNf1Yt71MdaAvCfn1mwYC0qOU+dMLhUgENkXQM4Lg8u7Oi8E8LhkTBkHh0kSZqyZkQSZZkZr++W9A5MTk1WoF5BjQWOKiIPaNQSAs7ibvLqaXupxkdAskPMqAJIl3eQibB1y1d/EOfG9gKbKSLx3ggWumaG1ATzSI6pPVEb9bGt+Y/SwZK7a2DGaPxKqaKwmvu12Y90XH1pUux2fXU+OmmTKWDT9WFyyBi7HR7kscOQwxAhJYRIPnfr+jjlSQUNlkTTAj9rf9ffG7jGT2uPxhBAjwiDm6bU9ZgXSSPEh18yEqdvfy/hqqsC0q0jPJThj25Zoe1S2F5ix3++xrEmAPJViEQmLrDJBunG7Zui0ucXKoPN6/faeNAoS6sVp4Hobe4ebm5sqGZLjBKAMycLC9rH7MEg4YDYeSlSDxaabmXhvls31niS+X06TyYSB0hYDSHF3Y9O0/dH7Nbo0N2bkXBSQb6CFASKOCEtulz3rAAALYElEQVTKWGjV/dF5SRAPKD1NBtaEEFBMeosG+PT3BMDMzNUrzBK8WOSSAgg1Vtpr86CO7xdkTxU4BBC83t/gKgB2fQ31iWc6ieS/hZHOCz4/fXp1f7baaqutttpqq622+sOUEFQCHh4e8Jvf/Ab/8A//gMfHx59+/Zc2Zs1DziKdYQg1H46Q1iZnsgdD5xzWRVYvx3HCuq7KAnHqEcOIIbYHW/X+KFlXSPVPbbScw3mesayrGGt2D599r2MeDBer4kKRQTX+1GbxfD4LiKRJIpb2cTqdaqNo7ApbXTbUK6UVwzDU5pOY8fjwiGGI1f8lK5uDSCLMx3GsjAoiaTokwpWQ8moUJMzzLM2/8yIpK92KKEmzEEKsDI02RtJJeteOx/km+2D1prAEI2ZNwtExN7aGjamZOQPScJTcGCiAxHtbMyqTLcIify/kSlDpGhGcFzPN6rkJ863IWFeRjNn49DIwk8wB5qUhk1vGR82U2bxrLhkZpPtfG2uTK9kc4gZ0VHaNgjVxiCBykhDGKjHrmv8WTc9VzlbAdUW7OYZaY6j/x1xlgmzgAQxotOQeZUOUAnIe3gsTQxLJlMHBknTGLKlaNUpZG1UywBMy5oRm9GvNX1bTX2vkCRIXbuwo8zJiziAyJoyAmgSJmvbeI8ZQvURKyfj08eMFYOp1XrCBahCARaR312CgzBzFqfTzFUTKWcAUztqA54vPabMOVz/rbheMOt5gAyahf89y/4GyMdKKUsoFyk3UxrYxbXRbeo8JIWAcLMr5JbvhQoJyvY8dyGnMverv4oQ1UwlLV2AmcCmhvG7UDVAVwCZXIMDu8Tb3+vf22zDWjPk2sSImIQQkPRdyrV9+pdRzQT0oecVg0nu9ADhOzb0VuGOuPkI5J6S01nNvjCFWvy3ZtwaOhhgrWHrNuHQknzWOIyzy29l9Vdln4AZwGitQpKcNXLo4BzZpu1lHzl2MbwWyuj9tW5dz47W5cg3YkP7Pvidem2/XqU+yzwIAU2Ecn57ww7ffvXjNVltttdVWW2211VZ/vPqdHjXM3BpEp54yAn0gxqEyRUDSxJeUJSkqJwCtqZVVRmUvZPF58c4hOI+iyTqoC87Nj2JNq6xgEi6iZa9p76W0pgZsvirC3OHCIO9wPp+QU8Z4GEXa5JS1kDPiEDWxSR647YHcHsJj8HB+FHNKTU06n0/gUrDbTRp93a/Iqu+CmfeS+H1Y7C5YGQEkCVGpeuxwXZG3hrLkzjeFqTbUpeTaNEMBGDH1tCYHwh4ax7ofttl+7BjNLNh51NdKrLivTV1tgEJoTSGR+v7ICq9zRt9vjAbSRC4DAQwYWtdFWRyxNsT1/JYm45AGu/kByYq/JI4V9SNx7rJ5skppVSlRW222hBt5g54TBb1k3jRPmHVd4aMlO6E7JmtCey8UMxOVfzkIOwcsUg+n26uyGI2eN7aZU7YMK4BhwJwBAo4c0pol6UZlNuuSAK8G3WCEoI2uMmxKAbxeA7m0OSApN/5ivJxzEqeuTDdA3uc9YMwyOW6ux2djLk22SFhSzggqAyEX1L+EAZRqxk1owMD1eSsldWCIeVqp3Iu5RSNrkpL9IQY8CTRrYyaMOeCyCZZ7WGUZdNhCKQXjKAa09h6bd3JfAczUWiassiMUBCI4cEmAg/p5Zf04utyHq38COrfQ8XP0c8UbJer4uwo8yz1AjiXnZuJ9DQoxyz2nKLhXgSpl2TRWSJP8MAtI5ZzT+crtvgEAuh/GSLL96T1e2nm1AzXApDFKeuNpZgGAe08zMzImMgZdBufLuSNAIukxyR6yOYxD0vaCsZwU6fLGMun3rgNyzYeHCIiDGjgrYJtzxhDHKgO7BpjXdRWANwq7LOckn8OSgNWzBNsYtfvy9fhd39MacFcuXmcX90v2zdU2geoFVVLG6fmI58enlx++1VZbbbXVVltttdUfrb7IqKkafWNhcJFmyUmU86qxu6TAxvl8xjAOyClVM1sA6H0N1kU8RoLzGOIAR2royyYiMOp2xnmZsTvsEIcgLBHXkkz6JkfSh9rnUf3D6mXikNcV59MZ07Sr7yUCyAmDIg4RDGDNDTBxJN4OMXh4pesbI6bkjNPzCfc3t0hpAXTFN8aInFZ9QDapAlXgyXuPcRykySEgKLBjrBSvEdmcM0qSpBsUBidJ0BIGi3j65JywLLPIbFhXSBXQcN4jpRUEYIgRII2D1qbC/lRwgi59Rvrmw5qWdVkQo30+1QhzSWLSRBtqBqkxDvDezq2mS1GTPZzPZ0zTVFera/PCAkrEEOFJhDSua0IY0jSN46SSKjlPpUaYuzp34xDrds33CCSgiCMvRtLaDNbm3sBIZUQ4svSnXNPLzKRVYuqz9b7qMdQ8NHr5YAWKjNmgr63Aor7WqcSlpofpPgDW81MFCIydJUCIsYu0OXaurujbddqzjuo8V2mOyaZ8d95J911AMd1/mdmKmzQ/EyLC/Zv/f3tns9s2DAThIaUYSNOg7/+ELXppC9hoRYlkD/tHyml6C3yY7xLYsRVJJBXtaHb3C5ClVbK4zkQ88XbPun++jhHBZLgMzJwwCiPaZap3HPuO3g5PwZJjSCJgZUm1CceIOC5im2OdJBGF6jG7A+WaImO0ruHSyCm9GWCPYk/qQK8daECrB/YiTjxo3a3Wm7uiRsZjNy0zQdJaWq26DRu3LvPWOkHl1X9Oa9u2qVpW2TaseXGXk6elwcZH6mjJ8QKXyxMulye9dsHXOpKsf5uffk1s4Wyb1rPNpcFVk4bRG8/BJKJGkpOf3KTr19OmfEzlf4IJuXJNkmtqbVXqOJuYixBL7tLkTuNqgogJJi8vLzLjerguvf6aHb9OQCnqnbEsT1jzinroNf0fgsyZswto/Jy/P3x+dFfdz6s5DbP3jtQ6atnx/es3CjWEEEIIIQ/Gf1OfjuPAfqirQN/3FA8Jz7GXHaVIrZFFxQYLVqP7ij6l7w3PGpwDcpPfNOgdb0r3fY+nqr1Lt6g8uEBON7AmLpgLw4KNBIi9+3bDuj7J/rfxRlw64+Rl8RbCklKyT8FFrRVl2/D7z28AwPV6xafnZxWyGkrZ9PPiXNi2P75fFtxLyo8UJT6OXZ4Oa7BtaSiyj1osdF2B1vDrx08Jspa5m1DOGbfrDbfrTc9D8nNQW0UpOz5/fvHxSrCgMiMt4lBIgxPFnqQDlgJl6QiyP9tWkNMC6e4jBYntdZR9jqfnEjOEMBAW/a7b2/D6+uo1KFxE0DkzpSW1hr0e6ClSjizdoXcpCnx+wpwXq8vSEKky47FGwDZ2pvK/t5e7gDZSYWQ/zO1jLbVlflknnaotqEUgs+DOAr0ETIKFCaJISVNNxBFTa/Xvxzhnr8kipYaSr4HZSaJD0rt0rkrRfcrWsgXZ4SIIn0FeTukZOsS9qdvLBCmYVqTH6PpUdFqT15YSNtfqMUaRywReW4/W+rvbbPaAXYS67MVjgnOQOgblWYXfPJxb+ViTFCsM1xst8i3v6Rzpbf6e1rQKYbbFnNNt9l5RW5tqiUznoUdHPH+rdbSjwtwyov6KAwU9a3qbtAcfvziKseZcmx2O/U5kOI/BGV8L+rucF+2WNLecnr8f7evfGpMRm5fmaJT9B5BayDcp1lm2+mbaeUnceCbCyDmx/0eT4GHHOqVN5mnfbIxtfdRavRj+tm3TuZIUrKSFuC9azwd6DdXW7U0KdSfcH7cP/j+wvzG+9m/ZiR0EpvcIN1ZDPSpKKSh6PIQQQggh5DFIb92ME0IIIYQQQgghhJCP511HDSGEEEIIIYQQQgj5OCjUEEIIIYQQQgghhDwIFGoIIYQQQgghhBBCHgQKNYQQQgghhBBCCCEPAoUaQgghhBBCCCGEkAeBQg0hhBBCCCGEEELIg/AX6faTR6tPk50AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"BPP=\", bpp)\n", - "plt.figure(figsize=(20,20))\n", - "plt.subplot(1,2,1); plt.imshow(bgr_image[:,:,::-1]); _=plt.axis('off')\n", - "plt.subplot(1,2,2); plt.imshow(transformed_bgr_image[:,:,::-1]); _=plt.axis('off')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a394209", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/encdec_nb.rst b/docs/source/tutorials/encdec_nb.rst deleted file mode 100644 index 8628b338..00000000 --- a/docs/source/tutorials/encdec_nb.rst +++ /dev/null @@ -1,98 +0,0 @@ -In this chapter we show how to create your very own EncoderDecoder -class. - -In order to evaluate your model with our framework, you need a model -that works with the ``CompressAIEncoderDecoder`` class. This was -discussed in the CLI tutorial. If you need more flexibility, you can -write your own ``EncoderDecoder`` class and this is quite simple. - -Here we demo a simple ``EncoderDecoder`` class that encodes & decodes -using jpeg. - -.. code:: ipython3 - - import logging, io, cv2 - import numpy as np - import matplotlib.pyplot as plt - from PIL import Image - from compressai_vision.evaluation.pipeline import EncoderDecoder - -In the constructor, instantiate a logger and save the provided quality -parameter. - -.. code:: ipython3 - - class JpegEncoderDecoder(EncoderDecoder): - - def __init__(self, qp=10): - self.logger = logging.getLogger(self.__class__.__name__) - self.qp=qp - self.reset() # not used in this class - -Define how the image is encoded + decoded and how the bitrate is -calculated. We are using BGR since it is the default input format for -Detectron2 predictors. - -.. code:: ipython3 - - def BGR(self, bgr_image, tag=None): - # bgr_image: numpy BGR24 image: (y,x,3) - # tag could be used to identify images if we want to cache them - # BGR -> RGB (as PIL works with RGB) - rgb_image = bgr_image[:,:,::-1] - pil_img=Image.fromarray(rgb_image).convert("RGB") - tmp = io.BytesIO() - # encode image - pil_img.save(tmp, format="jpeg", quality=self.qp) - tmp.seek(0) - # calculate bits-per-pixel - filesize = tmp.getbuffer().nbytes - bpp = filesize * float(8) / (pil_img.size[0] * pil_img.size[1]) - # decode image back - pil_img2 = Image.open(tmp).convert("RGB") - # back to BGR - rgb_image=np.array(pil_img2) - bgr_image=rgb_image[:,:,::-1] - # transformed image, bits-per-pixel ready - return bgr_image, bpp - -So, we have a compact class that defines, in a single method, all -necessary transformations and calculates the bitrate. - -Once you define an ``EncoderDecoder`` class like this, you can use it -with all the rest of the infrastructure provided by -``CompressAI-Vision`` library. - -Next, let’s see ``JpegEncoderDecoder`` in action. - -.. code:: ipython3 - - bgr_image=cv2.imread("dog_512.png") - -.. code:: ipython3 - - encdec=JpegEncoderDecoder(qp=1) - -.. code:: ipython3 - - transformed_bgr_image, bpp = encdec.BGR(bgr_image) - -Print bits-per-pixel, compare original and transformed image - -.. code:: ipython3 - - print("BPP=", bpp) - plt.figure(figsize=(20,20)) - plt.subplot(1,2,1); plt.imshow(bgr_image[:,:,::-1]); _=plt.axis('off') - plt.subplot(1,2,2); plt.imshow(transformed_bgr_image[:,:,::-1]); _=plt.axis('off') - - -.. code-block:: text - - BPP= 0.16878255208333334 - - - -.. image:: encdec_nb_files/encdec_nb_11_1.png - - diff --git a/docs/source/tutorials/encdec_nb_files/encdec_nb_10_1.png b/docs/source/tutorials/encdec_nb_files/encdec_nb_10_1.png deleted file mode 100644 index 59d83366..00000000 Binary files a/docs/source/tutorials/encdec_nb_files/encdec_nb_10_1.png and /dev/null differ diff --git a/docs/source/tutorials/encdec_nb_files/encdec_nb_10_2.png b/docs/source/tutorials/encdec_nb_files/encdec_nb_10_2.png deleted file mode 100644 index d3207361..00000000 Binary files a/docs/source/tutorials/encdec_nb_files/encdec_nb_10_2.png and /dev/null differ diff --git a/docs/source/tutorials/encdec_nb_files/encdec_nb_11_1.png b/docs/source/tutorials/encdec_nb_files/encdec_nb_11_1.png deleted file mode 100644 index 59d83366..00000000 Binary files a/docs/source/tutorials/encdec_nb_files/encdec_nb_11_1.png and /dev/null differ diff --git a/docs/source/tutorials/evaluate.rst b/docs/source/tutorials/evaluate.rst deleted file mode 100644 index cf33c97a..00000000 --- a/docs/source/tutorials/evaluate.rst +++ /dev/null @@ -1,14 +0,0 @@ -.. _evaluate: - -3. Evaluate ------------ - -:download:`[download tutorial as notebook]` - -Evaluate detector results for images that have been gone through the encoding+decoding process for various quality parameters. - -Produces ``mAP(q)`` curve for quality parameters ``q``. - -.. include:: evaluate_nb.rst - -For more information about the ``EncoderDecoder`` classes, please see :ref:`here `. diff --git a/docs/source/tutorials/evaluate_nb.ipynb b/docs/source/tutorials/evaluate_nb.ipynb deleted file mode 100644 index d6396047..00000000 --- a/docs/source/tutorials/evaluate_nb.ipynb +++ /dev/null @@ -1,643 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7b41d479", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "source": [ - "## 4. Evaluate\n", - "\n", - "- Run evaluation on fiftyone dataset with Detectron2 results\n", - "- Show how to evaluate with VTM as well\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "bc06938b", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_69474/3813857106.py:5: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "markdown", - "id": "aff7ef99", - "metadata": {}, - "source": [ - "In this tutorial we evaluate mAP values for a dataset with Detectron2 and a deep-learning encoding model from the CompressAI library. We also show how to perform a baseline evaluation with VTM." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4d171492", - "metadata": {}, - "outputs": [], - "source": [ - "# common libs\n", - "import math, os, io, json, cv2, random, logging, pickle, datetime\n", - "import numpy as np\n", - "# torch\n", - "import torch\n", - "# images\n", - "from PIL import Image\n", - "import matplotlib.pyplot as plt\n", - "# compressai\n", - "from compressai.zoo import bmshj2018_factorized" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1db77483", - "metadata": {}, - "outputs": [], - "source": [ - "## *** Detectron imports ***\n", - "import detectron2\n", - "from detectron2.utils.logger import setup_logger\n", - "setup_logger()\n", - "\n", - "# import some common detectron2 utilities\n", - "from detectron2 import model_zoo\n", - "from detectron2.engine import DefaultPredictor\n", - "from detectron2.config import get_cfg\n", - "from detectron2.utils.visualizer import Visualizer\n", - "from detectron2.data import MetadataCatalog, DatasetCatalog" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e246d463", - "metadata": {}, - "outputs": [], - "source": [ - "# CompressAI-Vision\n", - "from compressai_vision.conversion import FO2DetectronDataset # convert fiftyone dataset to Detectron2 dataset\n", - "from compressai_vision.conversion import detectron251 # convert Detectron2 results to fiftyone format\n", - "from compressai_vision.evaluation.fo import annexPredictions # annex predictions from\n", - "from compressai_vision.evaluation.pipeline import CompressAIEncoderDecoder, VTMEncoderDecoder # a class that does encoding+decoding & returns the transformed image & bitrate\n", - "from compressai_vision.pipelines.fo_vcm.tools import confLogger, quickLog, getDataFile" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f386b4c6", - "metadata": {}, - "outputs": [], - "source": [ - "# fiftyone\n", - "import fiftyone as fo\n", - "import fiftyone.zoo as foz" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d503052c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cpu\n" - ] - } - ], - "source": [ - "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", - "print(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a9593bcc", - "metadata": {}, - "outputs": [], - "source": [ - "## MODEL A\n", - "model_name=\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\"\n", - "## look here:\n", - "## https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md#faster-r-cnn\n", - "## for the line that says X101-FPN --> box AP is 43\n", - "\n", - "## MODEL B\n", - "# model_name=\"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "42a20652", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "expected input colorspace: BGR\n", - "loaded datasets: PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000\n", - "PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000\n", - "PROPOSAL_FILES_TEST: ()\n", - "PROPOSAL_FILES_TRAIN: ()\n", - "TEST: ('coco_2017_val',)\n", - "TRAIN: ('coco_2017_train',)\n", - "model was trained with coco_2017_train\n" - ] - } - ], - "source": [ - "# cfg encapsulates the model architecture & weights, also threshold parameter, metadata, etc.\n", - "cfg = get_cfg()\n", - "cfg.MODEL.DEVICE=device\n", - "# load config from a file:\n", - "cfg.merge_from_file(model_zoo.get_config_file(model_name))\n", - "# DO NOT TOUCH THRESHOLD WHEN DOING EVALUATION:\n", - "# too big a threshold will cut the smallest values & affect the precision(recall) curves & evaluation results\n", - "# the default value is 0.05\n", - "# value of 0.01 saturates the results (they don't change at lower values)\n", - "# cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5\n", - "# get weights\n", - "cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(model_name)\n", - "print(\"expected input colorspace:\", cfg.INPUT.FORMAT)\n", - "print(\"loaded datasets:\", cfg.DATASETS)\n", - "model_dataset=cfg.DATASETS.TRAIN[0]\n", - "print(\"model was trained with\", model_dataset)\n", - "model_meta=MetadataCatalog.get(model_dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e909bf9e", - "metadata": {}, - "outputs": [], - "source": [ - "# model_meta.thing_classes # check class labels this was trained with" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1ab5cd0a", - "metadata": {}, - "outputs": [], - "source": [ - "predictor = DefaultPredictor(cfg)" - ] - }, - { - "cell_type": "markdown", - "id": "d2ece99f", - "metadata": {}, - "source": [ - "Get handle to a dataset. We will be using the ``oiv6-mpeg-v1`` dataset. Please go through the CLI Tutorials in order to produce this dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "cf1def5b", - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fo.load_dataset(\"oiv6-mpeg-detection-v1-dummy\") # or use the dummy dataset for testing/debugging" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "bbd34b76", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Name: oiv6-mpeg-detection-v1-dummy\n", - "Media type: image\n", - "Num samples: 1\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)\n", - " detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " open_images_id: fiftyone.core.fields.StringField" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset" - ] - }, - { - "cell_type": "markdown", - "id": "638769cf", - "metadata": {}, - "source": [ - "Set some loglevels" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1ed28678", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# loglev=logging.DEBUG\n", - "loglev=logging.INFO\n", - "quickLog(\"CompressAIEncoderDecoder\", loglev)" - ] - }, - { - "cell_type": "markdown", - "id": "253c442f", - "metadata": {}, - "source": [ - "Get a list of labels in the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2a9dc4b3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['airplane']\n" - ] - } - ], - "source": [ - "classes = dataset.distinct(\n", - " \"detections.detections.label\"\n", - ")\n", - "print(classes)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "03d58ded", - "metadata": {}, - "outputs": [], - "source": [ - "def per_class(results_obj):\n", - " \"\"\"helper function: take fiftyone/openimagev6 results object & spit\n", - " out mAP breakdown as per class\n", - " \"\"\"\n", - " d = {}\n", - " for class_ in classes:\n", - " d[class_] = results_obj.mAP([class_])\n", - " return d" - ] - }, - { - "cell_type": "markdown", - "id": "c945360c", - "metadata": {}, - "source": [ - "``CompressAIEncoderDecoder`` is a subclass of ``EncoderDecoder``, i.e. it's a class that encodes an image, decodes it, and returns the transformed (encoded+decoded) image and the bitrate of the encoded image.\n", - "\n", - "In particular ``CompressAIEncoderDecoder`` uses a CompressAI encoder/decoder to achieve this.\n", - "\n", - "You used ``annexPredictions`` in the previous notebook to push the dataset through a Detectron2 predictor. Here, we provide it with an additional parameter: an ``EncoderDecoder`` class that transforms the image before the image is passed to the Detectron2 predictor.\n", - "\n", - "We run the ``bmshj2018_factorized`` model over various quality parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c9ce7fd6", - "metadata": {}, - "outputs": [], - "source": [ - "params=[1] # debugging\n", - "# params=[1,2,3,4,5,6,7,8]" - ] - }, - { - "cell_type": "markdown", - "id": "17947ef2", - "metadata": {}, - "source": [ - "Detectron prediction results are saved during the run into the fiftyone (mongodb) database. Let's define a unique name for the sample field where the detectron results are saved:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "5db9adc2", - "metadata": {}, - "outputs": [], - "source": [ - "predictor_field='detectron-predictions'" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "5e84407f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "running the detector at 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n", - "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.)\n", - " return torch.floor_divide(self, other)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sample: 1 / 1\n", - "Evaluating detections...\n", - " 100% |█████████████████████| 1/1 [11.6ms elapsed, 0s remaining, 85.9 samples/s] \n", - "ready!\n" - ] - } - ], - "source": [ - "xs=[]; ys=[]; maps=[]; # bpp, mAP values, mAP(s) per class\n", - "results=[] # complete results\n", - "for i in params:\n", - " net = bmshj2018_factorized(quality=i, pretrained=True).eval().to(device)\n", - " enc_dec = CompressAIEncoderDecoder(net, device=device)\n", - " # note the EncoderDecoder instance here:\n", - " # before the predictor is used, the image is crunched through the encoding/decoding process & the bitrate is recorded\n", - " # you could substitute CompressAIEncoderDecoder with VTMEncoderDecoder if you'd like to (see also the end of this tutorial)\n", - " print(\"running the detector at\", i)\n", - " bpp = annexPredictions(predictors=[predictor], fo_dataset=dataset, encoder_decoder=enc_dec, predictor_fields=[predictor_field])\n", - " # .. now detectron's results are in each sample at the \"detectron-predictions\" field\n", - " res = dataset.evaluate_detections(\n", - " predictor_field,\n", - " gt_field=\"detections\",\n", - " method=\"open-images\",\n", - " pos_label_field=\"positive_labels\",\n", - " neg_label_field=\"negative_labels\",\n", - " expand_pred_hierarchy=False,\n", - " expand_gt_hierarchy=False\n", - " )\n", - " results.append((i, bpp, res))\n", - " # save to disk at each iteration as a backup just in case\n", - " xs.append(bpp)\n", - " ys.append(res.mAP())\n", - " maps.append(per_class(res))\n", - " with open(\"out.json\",\"w\") as f:\n", - " f.write(json.dumps({\n", - " \"bpp\" : xs, \n", - " \"map\" : ys,\n", - " \"map_per_class\" : maps\n", - " }, indent=2))\n", - "print(\"ready!\")" - ] - }, - { - "cell_type": "markdown", - "id": "c0879934", - "metadata": {}, - "source": [ - "After the evaluation we can (and should!) remove the detectron results from the database:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "57335fd6", - "metadata": {}, - "outputs": [], - "source": [ - "dataset.delete_sample_fields(predictor_field)" - ] - }, - { - "cell_type": "markdown", - "id": "3b9a9914", - "metadata": {}, - "source": [ - "Load results" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5f7a329c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'bpp': [0.10060123042505593], 'map': [1.0], 'map_per_class': [{'airplane': 1.0}]}\n" - ] - } - ], - "source": [ - "with open(\"out.json\",\"r\") as f:\n", - " res=json.load(f)\n", - "print(res)" - ] - }, - { - "cell_type": "markdown", - "id": "11491cf5", - "metadata": {}, - "source": [ - "In that loop over quality parameters above, you can substitute the ``CompressAIEncoderDecoder`` with ``VTMEncoderDecoder``in order to produce the anchor/baseline results. Let's first set some variables for the VTM program:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "ceaf58b7", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [], - "source": [ - "path_to_vtm_software=\"/home/sampsa/silo/interdigital/VVCSoftware_VTM\"" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "123bf61c", - "metadata": {}, - "outputs": [], - "source": [ - "# NOTE: set path_to_vtm_software\n", - "vtm_encoder_app=os.path.join(path_to_vtm_software, \"bin/EncoderAppStatic\")\n", - "vtm_decoder_app=os.path.join(path_to_vtm_software, \"bin/DecoderAppStatic\")\n", - "vtm_cfg=os.path.join(path_to_vtm_software, \"cfg/encoder_intra_vtm.cfg\")" - ] - }, - { - "cell_type": "markdown", - "id": "46180813", - "metadata": {}, - "source": [ - "If you'd want to see what the VTM is doing exactly, enable debugging output:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "d366617c", - "metadata": {}, - "outputs": [], - "source": [ - "loglev=logging.DEBUG\n", - "# loglev=logging.INFO\n", - "log=quickLog(\"VTMEncoderDecoder\", loglev) # VTMEncoderDecoder" - ] - }, - { - "cell_type": "markdown", - "id": "b26df80b", - "metadata": {}, - "source": [ - "At each quality parameter in the loop, instantiate an ``VTMEncoderDecoder`` instead:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "5b5e99e4", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "VTMEncoderDecoder - WARNING - folder /tmp/bitstreams/100/47 exists already\n" - ] - } - ], - "source": [ - "enc_dec = VTMEncoderDecoder(\n", - " encoderApp=vtm_encoder_app,\n", - " decoderApp=vtm_decoder_app,\n", - " ffmpeg=\"ffmpeg\",\n", - " vtm_cfg=vtm_cfg,\n", - " qp=47,\n", - " cache=\"/tmp/bitstreams\",\n", - " scale=100,\n", - " warn=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f9089b51", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "vscode": { - "interpreter": { - "hash": "104d1e3c0714c39a49e0363d53a772ca68ba2a5370285cfea1720d3aa41a3850" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/evaluate_nb.rst b/docs/source/tutorials/evaluate_nb.rst deleted file mode 100644 index affad866..00000000 --- a/docs/source/tutorials/evaluate_nb.rst +++ /dev/null @@ -1,334 +0,0 @@ -In this tutorial we evaluate mAP values for a dataset with Detectron2 -and a deep-learning encoding model from the CompressAI library. We also -show how to perform a baseline evaluation with VTM. - -.. code:: ipython3 - - # common libs - import math, os, io, json, cv2, random, logging, pickle, datetime - import numpy as np - # torch - import torch - # images - from PIL import Image - import matplotlib.pyplot as plt - # compressai - from compressai.zoo import bmshj2018_factorized - -.. code:: ipython3 - - ## *** Detectron imports *** - import detectron2 - from detectron2.utils.logger import setup_logger - setup_logger() - - # import some common detectron2 utilities - from detectron2 import model_zoo - from detectron2.engine import DefaultPredictor - from detectron2.config import get_cfg - from detectron2.utils.visualizer import Visualizer - from detectron2.data import MetadataCatalog, DatasetCatalog - -.. code:: ipython3 - - # CompressAI-Vision - from compressai_vision.conversion import FO2DetectronDataset # convert fiftyone dataset to Detectron2 dataset - from compressai_vision.conversion import detectron251 # convert Detectron2 results to fiftyone format - from compressai_vision.evaluation.fo import annexPredictions # annex predictions from - from compressai_vision.evaluation.pipeline import CompressAIEncoderDecoder, VTMEncoderDecoder # a class that does encoding+decoding & returns the transformed image & bitrate - from compressai_vision.pipelines.fo_vcm.tools import confLogger, quickLog, getDataFile - -.. code:: ipython3 - - # fiftyone - import fiftyone as fo - import fiftyone.zoo as foz - -.. code:: ipython3 - - device = 'cuda' if torch.cuda.is_available() else 'cpu' - print(device) - - -.. code-block:: text - - cpu - - -.. code:: ipython3 - - ## MODEL A - model_name="COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml" - ## look here: - ## https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md#faster-r-cnn - ## for the line that says X101-FPN --> box AP is 43 - - ## MODEL B - # model_name="COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml" - -.. code:: ipython3 - - # cfg encapsulates the model architecture & weights, also threshold parameter, metadata, etc. - cfg = get_cfg() - cfg.MODEL.DEVICE=device - # load config from a file: - cfg.merge_from_file(model_zoo.get_config_file(model_name)) - # DO NOT TOUCH THRESHOLD WHEN DOING EVALUATION: - # too big a threshold will cut the smallest values & affect the precision(recall) curves & evaluation results - # the default value is 0.05 - # value of 0.01 saturates the results (they don't change at lower values) - # cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 - # get weights - cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(model_name) - print("expected input colorspace:", cfg.INPUT.FORMAT) - print("loaded datasets:", cfg.DATASETS) - model_dataset=cfg.DATASETS.TRAIN[0] - print("model was trained with", model_dataset) - model_meta=MetadataCatalog.get(model_dataset) - - -.. code-block:: text - - expected input colorspace: BGR - loaded datasets: PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000 - PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000 - PROPOSAL_FILES_TEST: () - PROPOSAL_FILES_TRAIN: () - TEST: ('coco_2017_val',) - TRAIN: ('coco_2017_train',) - model was trained with coco_2017_train - - -.. code:: ipython3 - - # model_meta.thing_classes # check class labels this was trained with - -.. code:: ipython3 - - predictor = DefaultPredictor(cfg) - -Get handle to a dataset. We will be using the ``oiv6-mpeg-v1`` dataset. -Please go through the CLI Tutorials in order to produce this dataset. - -.. code:: ipython3 - - dataset = fo.load_dataset("oiv6-mpeg-detection-v1-dummy") # or use the dummy dataset for testing/debugging - -.. code:: ipython3 - - dataset - - - - -.. parsed-literal:: - - Name: oiv6-mpeg-detection-v1-dummy - Media type: image - Num samples: 1 - Persistent: True - Tags: [] - Sample fields: - id: fiftyone.core.fields.ObjectIdField - filepath: fiftyone.core.fields.StringField - tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) - metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata) - positive_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications) - negative_labels: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications) - detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - open_images_id: fiftyone.core.fields.StringField - - - -Set some loglevels - -.. code:: ipython3 - - # loglev=logging.DEBUG - loglev=logging.INFO - quickLog("CompressAIEncoderDecoder", loglev) - - - - -.. parsed-literal:: - - - - - -Get a list of labels in the dataset: - -.. code:: ipython3 - - classes = dataset.distinct( - "detections.detections.label" - ) - print(classes) - - -.. code-block:: text - - ['airplane'] - - -.. code:: ipython3 - - def per_class(results_obj): - """helper function: take fiftyone/openimagev6 results object & spit - out mAP breakdown as per class - """ - d = {} - for class_ in classes: - d[class_] = results_obj.mAP([class_]) - return d - -``CompressAIEncoderDecoder`` is a subclass of ``EncoderDecoder``, -i.e. it’s a class that encodes an image, decodes it, and returns the -transformed (encoded+decoded) image and the bitrate of the encoded -image. - -In particular ``CompressAIEncoderDecoder`` uses a CompressAI -encoder/decoder to achieve this. - -You used ``annexPredictions`` in the previous notebook to push the -dataset through a Detectron2 predictor. Here, we provide it with an -additional parameter: an ``EncoderDecoder`` class that transforms the -image before the image is passed to the Detectron2 predictor. - -We run the ``bmshj2018_factorized`` model over various quality -parameters: - -.. code:: ipython3 - - params=[1] # debugging - # params=[1,2,3,4,5,6,7,8] - -Detectron prediction results are saved during the run into the fiftyone -(mongodb) database. Let’s define a unique name for the sample field -where the detectron results are saved: - -.. code:: ipython3 - - predictor_field='detectron-predictions' - -.. code:: ipython3 - - xs=[]; ys=[]; maps=[]; # bpp, mAP values, mAP(s) per class - results=[] # complete results - for i in params: - net = bmshj2018_factorized(quality=i, pretrained=True).eval().to(device) - enc_dec = CompressAIEncoderDecoder(net, device=device) - # note the EncoderDecoder instance here: - # before the predictor is used, the image is crunched through the encoding/decoding process & the bitrate is recorded - # you could substitute CompressAIEncoderDecoder with VTMEncoderDecoder if you'd like to (see also the end of this tutorial) - print("running the detector at", i) - bpp = annexPredictions(predictors=[predictor], fo_dataset=dataset, encoder_decoder=enc_dec, predictor_fields=[predictor_field]) - # .. now detectron's results are in each sample at the "detectron-predictions" field - res = dataset.evaluate_detections( - predictor_field, - gt_field="detections", - method="open-images", - pos_label_field="positive_labels", - neg_label_field="negative_labels", - expand_pred_hierarchy=False, - expand_gt_hierarchy=False - ) - results.append((i, bpp, res)) - # save to disk at each iteration as a backup just in case - xs.append(bpp) - ys.append(res.mAP()) - maps.append(per_class(res)) - with open("out.json","w") as f: - f.write(json.dumps({ - "bpp" : xs, - "map" : ys, - "map_per_class" : maps - }, indent=2)) - print("ready!") - - -.. code-block:: text - - running the detector at 1 - - -.. code-block:: text - - /home/sampsa/silo/interdigital/venv_all/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. - To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.) - return torch.floor_divide(self, other) - - -.. code-block:: text - - sample: 1 / 1 - Evaluating detections... - 100% |█████████████████████| 1/1 [11.6ms elapsed, 0s remaining, 85.9 samples/s] - ready! - - -After the evaluation we can (and should!) remove the detectron results -from the database: - -.. code:: ipython3 - - dataset.delete_sample_fields(predictor_field) - -Load results - -.. code:: ipython3 - - with open("out.json","r") as f: - res=json.load(f) - print(res) - - -.. code-block:: text - - {'bpp': [0.10060123042505593], 'map': [1.0], 'map_per_class': [{'airplane': 1.0}]} - - -In that loop over quality parameters above, you can substitute the -``CompressAIEncoderDecoder`` with ``VTMEncoderDecoder``\ in order to -produce the anchor/baseline results. Let’s first set some variables for -the VTM program: - -.. code:: ipython3 - - # NOTE: set path_to_vtm_software - vtm_encoder_app=os.path.join(path_to_vtm_software, "bin/EncoderAppStatic") - vtm_decoder_app=os.path.join(path_to_vtm_software, "bin/DecoderAppStatic") - vtm_cfg=os.path.join(path_to_vtm_software, "cfg/encoder_intra_vtm.cfg") - -If you’d want to see what the VTM is doing exactly, enable debugging -output: - -.. code:: ipython3 - - loglev=logging.DEBUG - # loglev=logging.INFO - log=quickLog("VTMEncoderDecoder", loglev) # VTMEncoderDecoder - -At each quality parameter in the loop, instantiate an -``VTMEncoderDecoder`` instead: - -.. code:: ipython3 - - enc_dec = VTMEncoderDecoder( - encoderApp=vtm_encoder_app, - decoderApp=vtm_decoder_app, - ffmpeg="ffmpeg", - vtm_cfg=vtm_cfg, - qp=47, - cache="/tmp/bitstreams", - scale=100, - warn=True - ) - - -.. code-block:: text - - VTMEncoderDecoder - WARNING - folder /tmp/bitstreams/100/47 exists already - - diff --git a/docs/source/tutorials/evaluate_nb_files/evaluate_nb_22_0.png b/docs/source/tutorials/evaluate_nb_files/evaluate_nb_22_0.png deleted file mode 100644 index 373f61a8..00000000 Binary files a/docs/source/tutorials/evaluate_nb_files/evaluate_nb_22_0.png and /dev/null differ diff --git a/docs/source/tutorials/evaluate_nb_files/evaluate_nb_23_0.png b/docs/source/tutorials/evaluate_nb_files/evaluate_nb_23_0.png deleted file mode 100644 index 373f61a8..00000000 Binary files a/docs/source/tutorials/evaluate_nb_files/evaluate_nb_23_0.png and /dev/null differ diff --git a/docs/source/tutorials/evaluate_nb_files/evaluate_nb_27_0.png b/docs/source/tutorials/evaluate_nb_files/evaluate_nb_27_0.png deleted file mode 100644 index 373f61a8..00000000 Binary files a/docs/source/tutorials/evaluate_nb_files/evaluate_nb_27_0.png and /dev/null differ diff --git a/docs/source/tutorials/fiftyone.rst b/docs/source/tutorials/fiftyone.rst deleted file mode 100644 index c989dce4..00000000 --- a/docs/source/tutorials/fiftyone.rst +++ /dev/null @@ -1,7 +0,0 @@ - -Fiftyone and MongoDB --------------------- - -.. _fiftyone: - -.. include:: fiftyone_nb.rst diff --git a/docs/source/tutorials/fiftyone_nb.ipynb b/docs/source/tutorials/fiftyone_nb.ipynb deleted file mode 100644 index 78bcd9d8..00000000 --- a/docs/source/tutorials/fiftyone_nb.ipynb +++ /dev/null @@ -1,671 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "bc06938b", - "metadata": { - "tags": [ - "remove_cell" - ] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_34697/1348678174.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n", - " from IPython.core.display import display, HTML, Markdown\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# https://nbconvert.readthedocs.io/en/latest/removing_cells.html\n", - "# use these magic spells to update your classes methods on-the-fly as you edit them:\n", - "%reload_ext autoreload\n", - "%autoreload 2\n", - "from pprint import pprint\n", - "from IPython.core.display import display, HTML, Markdown\n", - "import ipywidgets as widgets\n", - "# %run includeme.ipynb # include a notebook from this same directory\n", - "display(HTML(\"\"))" - ] - }, - { - "cell_type": "markdown", - "id": "657c143e", - "metadata": {}, - "source": [ - "CompressAI-Vision uses fiftyone to handle datasets. Let's take a closer look into what this means.\n", - "\n", - "You are probably familiar with the COCO API and the like, i.e a directory structures that looks like this:\n", - "```\n", - "annotations/\n", - " json files\n", - "train2007_dataset/\n", - " image files\n", - "...\n", - "...\n", - "```\n", - "Then you have an API that reads those json files which have image metadata, ground truths for images (bboxes, segmasks, etc.). Another such example is the [ImageFolder](https://pytorch.org/vision/stable/generated/torchvision.datasets.ImageFolder.html) directory structure.\n", - "\n", - "Fiftyone takes the obvious next step in handling datasets (metadata and ground truths) and **uses a database**!\n", - "\n", - "The datasets are saved into a *database* instead, namely into mongodb. The interface to mongodb is handles seamlessly through fiftyone API.\n", - "\n", - "Using a database has several obvious advantages. Some of these are: the ability to share data among your research group, creating copies of the dataset, adding more data to each sample (say detection results) etc.\n", - "\n", - "The complication in using a database is that you need a *database server*. Fiftyone makes this seamless as it start a stand-alone mongodb server each time you type \"import fiftyone\". Alternatively, you can use a common (remote) mongodb server for your whole research group for sharing data and/or if you're working in a supercomputing / grid environment.\n", - "\n", - "Let's take a closer look:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4d171492", - "metadata": {}, - "outputs": [], - "source": [ - "# image tool imports\n", - "from PIL import Image\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f386b4c6", - "metadata": {}, - "outputs": [], - "source": [ - "# fiftyone\n", - "import fiftyone as fo\n", - "import fiftyone.zoo as foz" - ] - }, - { - "cell_type": "markdown", - "id": "72f304c3", - "metadata": {}, - "source": [ - "Lets take a look at the datasets registered to fiftyone:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f8765e0e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['mpeg-vcm-detection',\n", - " 'mpeg-vcm-detection-dummy',\n", - " 'mpeg-vcm-segmentation',\n", - " 'open-images-v6-validation',\n", - " 'quickstart',\n", - " 'quickstart-2-dummy']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fo.list_datasets()" - ] - }, - { - "cell_type": "markdown", - "id": "0490a0aa", - "metadata": {}, - "source": [ - "Let's get a handle to a dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "864e00fd", - "metadata": {}, - "outputs": [], - "source": [ - "dataset=fo.load_dataset(\"quickstart\")" - ] - }, - { - "cell_type": "markdown", - "id": "359be67a", - "metadata": {}, - "source": [ - "Let's see how many *samples* we have in it:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2c568438", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "200" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(dataset)" - ] - }, - { - "cell_type": "markdown", - "id": "fe8d2673", - "metadata": {}, - "source": [ - "Let's take a look at the first sample:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "745c5969", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ",\n", - " ,\n", - " ,\n", - " ]),\n", - " }>,\n", - " 'uniqueness': 0.8175834390151201,\n", - " 'predictions': ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]),\n", - " }>,\n", - "}>\n" - ] - } - ], - "source": [ - "sample=dataset.first()\n", - "print(sample)" - ] - }, - { - "cell_type": "markdown", - "id": "c5650c4c", - "metadata": {}, - "source": [ - "Here we can see that there are bbox ground truths. Please also note that fiftyone/mongodb does *not* save the images themselves but just their path. When running mAP evaluations on a dataset, detection results can be saved into the same database (say, with key \"detections\") and then ground truths and detections can be compared within the same dataset (instead of writing lots of intermediate files on the disk like with COCO API or with the tensorflow tools).\n", - "\n", - "Let's load an image:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9bbefa1a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(Image.open(sample[\"filepath\"]))" - ] - }, - { - "cell_type": "markdown", - "id": "bdd643a8", - "metadata": {}, - "source": [ - "Let's see a summary of the dataset and what kind of fields each samples has:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a38e5a34", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Name: quickstart\n", - "Media type: image\n", - "Num samples: 200\n", - "Persistent: True\n", - "Tags: []\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " uniqueness: fiftyone.core.fields.FloatField\n", - " predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n" - ] - } - ], - "source": [ - "print(dataset)" - ] - }, - { - "cell_type": "markdown", - "id": "c0dac1b1", - "metadata": {}, - "source": [ - "You can visualize the whole dataset conveniently with:\n", - "```\n", - "session = fo.launch_app(dataset)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "4a0738c3", - "metadata": {}, - "source": [ - "For more info, please visit [fiftyone documentation](https://voxel51.com/docs/fiftyone/)" - ] - }, - { - "cell_type": "markdown", - "id": "a299c313", - "metadata": {}, - "source": [ - "Here at the final, a small recompilation/cheatsheet of selected fiftyone features" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "7d433c1e", - "metadata": {}, - "outputs": [], - "source": [ - "# Access by sample id\n", - "sample=dataset[\"634472860faf93a9a586c9c4\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "b1dd3b01", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset: quickstart\n", - "Media type: image\n", - "Num samples: 1\n", - "Sample fields:\n", - " id: fiftyone.core.fields.ObjectIdField\n", - " filepath: fiftyone.core.fields.StringField\n", - " tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n", - " metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n", - " ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - " uniqueness: fiftyone.core.fields.FloatField\n", - " predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n", - "View stages:\n", - " 1. Match(filter={'$expr': {'$eq': [...]}})\n" - ] - } - ], - "source": [ - "# Search by a field value. You might need this one with the with open_images_id field.\n", - "from fiftyone import ViewField as F\n", - "tmpset=dataset[F(\"filepath\") == dataset.first().filepath]\n", - "print(tmpset)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0562292", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "vscode": { - "interpreter": { - "hash": "3037665f174b3a6fb0f50efe07aa50417522d3f7584d9a5dd4e8c45d17b52a0c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorials/fiftyone_nb.rst b/docs/source/tutorials/fiftyone_nb.rst deleted file mode 100644 index e5ac5f0a..00000000 --- a/docs/source/tutorials/fiftyone_nb.rst +++ /dev/null @@ -1,476 +0,0 @@ -CompressAI-Vision uses fiftyone to handle datasets. Let’s take a closer -look into what this means. - -You are probably familiar with the COCO API and the like, i.e a -directory structures that looks like this: - -:: - - annotations/ - json files - train2007_dataset/ - image files - ... - ... - -Then you have an API that reads those json files which have image -metadata, ground truths for images (bboxes, segmasks, etc.). Another -such example is the -`ImageFolder `__ -directory structure. - -Fiftyone takes the obvious next step in handling datasets (metadata and -ground truths) and **uses a database**! - -The datasets are saved into a *database* instead, namely into mongodb. -The interface to mongodb is handles seamlessly through fiftyone API. - -Using a database has several obvious advantages. Some of these are: the -ability to share data among your research group, creating copies of the -dataset, adding more data to each sample (say detection results) etc. - -The complication in using a database is that you need a *database -server*. Fiftyone makes this seamless as it start a stand-alone mongodb -server each time you type “import fiftyone”. Alternatively, you can use -a common (remote) mongodb server for your whole research group for -sharing data and/or if you’re working in a supercomputing / grid -environment. - -Let’s take a closer look: - -.. code:: ipython3 - - # image tool imports - from PIL import Image - import matplotlib.pyplot as plt - -.. code:: ipython3 - - # fiftyone - import fiftyone as fo - import fiftyone.zoo as foz - -Lets take a look at the datasets registered to fiftyone: - -.. code:: ipython3 - - fo.list_datasets() - - - - -.. parsed-literal:: - - ['mpeg-vcm-detection', - 'mpeg-vcm-detection-dummy', - 'mpeg-vcm-segmentation', - 'open-images-v6-validation', - 'quickstart', - 'quickstart-2-dummy'] - - - -Let’s get a handle to a dataset: - -.. code:: ipython3 - - dataset=fo.load_dataset("quickstart") - -Let’s see how many *samples* we have in it: - -.. code:: ipython3 - - len(dataset) - - - - -.. parsed-literal:: - - 200 - - - -Let’s take a look at the first sample: - -.. code:: ipython3 - - sample=dataset.first() - print(sample) - - -.. code-block:: text - - , - , - , - ]), - }>, - 'uniqueness': 0.8175834390151201, - 'predictions': , - , - , - , - , - , - , - , - , - , - , - , - , - , - ]), - }>, - }> - - -Here we can see that there are bbox ground truths. Please also note that -fiftyone/mongodb does *not* save the images themselves but just their -path. When running mAP evaluations on a dataset, detection results can -be saved into the same database (say, with key “detections”) and then -ground truths and detections can be compared within the same dataset -(instead of writing lots of intermediate files on the disk like with -COCO API or with the tensorflow tools). - -Let’s load an image: - -.. code:: ipython3 - - plt.imshow(Image.open(sample["filepath"])) - - - - -.. parsed-literal:: - - - - - - -.. image:: fiftyone_nb_files/fiftyone_nb_12_1.png - - -Let’s see a summary of the dataset and what kind of fields each samples -has: - -.. code:: ipython3 - - print(dataset) - - -.. code-block:: text - - Name: quickstart - Media type: image - Num samples: 200 - Persistent: True - Tags: [] - Sample fields: - id: fiftyone.core.fields.ObjectIdField - filepath: fiftyone.core.fields.StringField - tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) - metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata) - ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - uniqueness: fiftyone.core.fields.FloatField - predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - - -You can visualize the whole dataset conveniently with: - -:: - - session = fo.launch_app(dataset) - -For more info, please visit `fiftyone -documentation `__ - -Here at the final, a small recompilation/cheatsheet of selected fiftyone -features - -.. code:: ipython3 - - # Access by sample id - sample=dataset["634472860faf93a9a586c9c4"] - -.. code:: ipython3 - - # Search by a field value. You might need this one with the with open_images_id field. - from fiftyone import ViewField as F - tmpset=dataset[F("filepath") == dataset.first().filepath] - print(tmpset) - - -.. code-block:: text - - Dataset: quickstart - Media type: image - Num samples: 1 - Sample fields: - id: fiftyone.core.fields.ObjectIdField - filepath: fiftyone.core.fields.StringField - tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField) - metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata) - ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - uniqueness: fiftyone.core.fields.FloatField - predictions: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections) - View stages: - 1. Match(filter={'$expr': {'$eq': [...]}}) - - diff --git a/docs/source/tutorials/fiftyone_nb_files/fiftyone_nb_12_1.png b/docs/source/tutorials/fiftyone_nb_files/fiftyone_nb_12_1.png deleted file mode 100644 index 8c2cb3c8..00000000 Binary files a/docs/source/tutorials/fiftyone_nb_files/fiftyone_nb_12_1.png and /dev/null differ diff --git a/docs/source/tutorials/go.bash b/docs/source/tutorials/go.bash deleted file mode 100755 index 72eddfcf..00000000 --- a/docs/source/tutorials/go.bash +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/bash -./compile.bash $@ -save=$PWD -cd ../.. -make html -cd $save diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst deleted file mode 100644 index 41fee5da..00000000 --- a/docs/source/tutorials/index.rst +++ /dev/null @@ -1,58 +0,0 @@ -Tutorials VCM -============= - - -Fiftyone -~~~~~~~~ - -CompressAI-Vision uses fiftyone/mongodb in managing dataset. Here we take a quick look at fiftyone. - -.. toctree:: - :maxdepth: 2 - :caption: FIFTYONE HOWTO - - fiftyone - -CLI Tutorial -~~~~~~~~~~~~ - -CompressAI-Vision has a rich command-line interface for performing all necessary operations for evaluating your deep-learning -encoder/decoder according to the standards set by the MPEG-VCM working group. - -.. toctree:: - :maxdepth: 2 - :caption: CLI HOWTO - - cli_tutorial_1 - cli_tutorial_2 - cli_tutorial_3 - cli_tutorial_4 - cli_tutorial_5 - cli_tutorial_6 - cli_tutorial_7 - -Library Tutorial -~~~~~~~~~~~~~~~~ - -Here we take a look how to use CompressAI-Vision as a python library. This is advanced topic. Normally, the CLI is all you need. - -.. toctree:: - :maxdepth: 2 - :caption: LIB HOWTO - - download - detectron2 - evaluate - encdec - - -Input file conversion -~~~~~~~~~~~~~~~~~~~~~ - -:download:`[download tutorial as notebook]` - -The MPEG/VCM working group provides annotations and segmentation data in non-standard format. - -We convert this data into OpenImageV6 format and also register it into fiftyone. - -.. include:: convert_nb.rst diff --git a/docs/source/tutorials/run.bash b/docs/source/tutorials/run.bash deleted file mode 100755 index bafc5333..00000000 --- a/docs/source/tutorials/run.bash +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -if [ $# -lt 1 ]; then - dirnames="fiftyone download convert detectron2 evaluate encdec cli_tutorial_1 cli_tutorial_2 cli_tutorial_3 cli_tutorial_4 cli_tutorial_5 cli_tutorial_6" -else - dirnames=$@ -fi - -echo $dirnames - -for dirname in $dirnames -do - fname=$dirname"_nb.ipynb" - echo - echo EXECUTING $fname - echo - jupyter nbconvert --debug --to notebook --inplace --execute $fname -done diff --git a/docs/source/tutorials/templates/tuto_rst/conf.json b/docs/source/tutorials/templates/tuto_rst/conf.json deleted file mode 100644 index 93bcd13a..00000000 --- a/docs/source/tutorials/templates/tuto_rst/conf.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "base_template": "base", - "mimetypes": { - "text/restructuredtext": true - } -} \ No newline at end of file diff --git a/docs/source/tutorials/templates/tuto_rst/index.rst.j2 b/docs/source/tutorials/templates/tuto_rst/index.rst.j2 deleted file mode 100644 index d453b570..00000000 --- a/docs/source/tutorials/templates/tuto_rst/index.rst.j2 +++ /dev/null @@ -1,111 +0,0 @@ -{%- extends 'display_priority.j2' -%} - - -{% block in_prompt %} -{% endblock in_prompt %} - -{% block output_prompt %} -{% endblock output_prompt %} - -{% block input scoped%} -{%- if cell.source.strip() -%} -{{".. code:: "-}} -{%- if 'bash' in cell.metadata.tags -%} -bash -{%- else -%} -ipython3 -{%- endif %} - -{{ cell.source | indent}} -{% endif -%} -{% endblock input %} - -{% block error %} -:: - -{{ super() }} -{% endblock error %} - -{% block traceback_line %} -{{ line | indent | strip_ansi }} -{% endblock traceback_line %} - -{% block execute_result %} -{% block data_priority scoped %} -{{ super() }} -{% endblock %} -{% endblock execute_result %} - -{% block stream %} -.. code-block:: text - -{{ output.text | indent }} -{% endblock stream %} - -{% block data_svg %} -.. image:: {{ output.metadata.filenames['image/svg+xml'] | urlencode }} -{% endblock data_svg %} - -{% block data_png %} -.. image:: {{ output.metadata.filenames['image/png'] | urlencode }} -{%- set width=output | get_metadata('width', 'image/png') -%} -{%- if width is not none %} - :width: {{ width }}px -{%- endif %} -{%- set height=output | get_metadata('height', 'image/png') -%} -{%- if height is not none %} - :height: {{ height }}px -{%- endif %} -{% endblock data_png %} - -{% block data_jpg %} -.. image:: {{ output.metadata.filenames['image/jpeg'] | urlencode }} -{%- set width=output | get_metadata('width', 'image/jpeg') -%} -{%- if width is not none %} - :width: {{ width }}px -{%- endif %} -{%- set height=output | get_metadata('height', 'image/jpeg') -%} -{%- if height is not none %} - :height: {{ height }}px -{%- endif %} -{% endblock data_jpg %} - -{% block data_markdown %} -{{ output.data['text/markdown'] | convert_pandoc("markdown", "rst") }} -{% endblock data_markdown %} - -{% block data_latex %} -.. math:: - -{{ output.data['text/latex'] | strip_dollars | indent }} -{% endblock data_latex %} - -{% block data_text scoped %} -.. parsed-literal:: - -{{ output.data['text/plain'] | indent }} -{% endblock data_text %} - -{% block data_html scoped %} -.. raw:: html - -{{ output.data['text/html'] | indent }} -{% endblock data_html %} - -{% block markdowncell scoped %} -{{ cell.source | convert_pandoc("markdown", "rst") }} -{% endblock markdowncell %} - -{%- block rawcell scoped -%} -{%- if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) %} -{{cell.source}} -{% endif -%} -{%- endblock rawcell -%} - -{% block headingcell scoped %} -{{ ("#" * cell.level + cell.source) | replace('\n', ' ') | convert_pandoc("markdown", "rst") }} -{% endblock headingcell %} - -{% block unknowncell scoped %} -unknown type {{cell.type}} -{% endblock unknowncell %} diff --git a/docs/source/walkthrough.rst b/docs/source/walkthrough.rst deleted file mode 100644 index 9b34fc94..00000000 --- a/docs/source/walkthrough.rst +++ /dev/null @@ -1,519 +0,0 @@ -Walkthrough -=========== - -This guide provides a full walkthrough on how to use the CLI of CompressAI-Vision and configure the different parts. - - -.. _configuring-paths: - -Configuring paths ------------------ - -To evaluate a pipeline and the performances of a codec, we must tell CompressAI-Vision where to find the datasets, and where to output logs of the different runs. -Override the following paths specified in ``cfgs/paths/default.yaml``: - -.. code-block:: yaml - :caption: cfgs/paths/default.yaml - - paths: - _common_root: "./logs" - _run_root: "${._common_root}/runs" - - -It is recommended to use paths that are somewhere outside the source code directory. - -For the test data, you can referer to the directory data which contains the information to retrieve the source content and label for the considered datasets. - -The paths to the root of the generated folder structure is then configured using ``dataset.config.root``, default config for datasets is as follows: -: - - .. code-block:: yaml - :caption: cfgs/dataset/default.yaml - - type: 'DefaultDataset' - config: - root: '/data/dataset' - imgs_folder: 'valid' - annotation_file: "annotations/sample.json" - seqinfo: "seqinfo.ini" - dataset_name: "sample_dataset" - ext: "png" - - - -.. _running-evaluation: - -Eval --------- - -First, activate the :ref:`previously installed ` virtual environment. - -To evaluate your pipeline, run -.. code-block:: bash - - # Train using conf/example.yaml, and override criterion.lmbda. - compressai-vision-eval --config-name="eval_example" ++dataset.config.root="/data/datasets/fcm_testdata/SFU_HW_Obj/Traffic_2560x1600_30_val" dataset.config.dataset_name="Traffic_2560x1600_30_val" - -.. _output-directory-structure: - -Output directory structure -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Logs are written to the directory specified by ``"${paths._common_root}"``. -By default, this has the following directory structure: - -.. code-block:: none - :caption: ${paths._common_root} directory structure - - ${paths._common_root}/ - runs/ - ... - e4e6d4d5e5c59c69f3bd7be2/ # Aim run hash. - checkpoints/ - runner.last.pth - configs/ - config.yaml # Final YAML configuration for reproducibility. - engine/ - src/ - compressai.patch # Auto generated git diff patch for reproducibility. - compressai_trainer.patch # Auto generated git diff patch for reproducibility. - tensorboard/ - -Each experiment run is saved in a directory named by its run hash inside the ``runs/`` directory. This directory includes the respective model checkpoints/weights, and various configurations and diffs for better reproducibility. - -The default directory structure may be reconfigured by modifying ``conf/paths/default.yaml``. - - - -.. _aim-setup: - -Viewing the experiment dashboard in Aim ---------------------------------------- - -This section demonstrates how to start up the Aim UI for experiment tracking. Aim allows users to compare parameters, view metrics, and visualize results. - - -Navigate to Aim repository -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Aim logs all experiments to a single directory containing an ``.aim`` repository. By default, this is located in ``./logs/aim/main``. Before running the ``aim`` commands shown later, navigate to that directory: - -.. code-block:: bash - - cd "./logs/aim/main" - - -Local-only -~~~~~~~~~~ - -If the directory containing the ``.aim`` directory is directly accessible from the local machine, navigate to that directory and run: - -.. code-block:: bash - - aim up --host="localhost" --port=43800 - -Then, open a web browser and navigate to http://localhost:43800/. - - -Remote host (private) -~~~~~~~~~~~~~~~~~~~~~ - -If the directory containing the ``.aim`` directory is on a remote host that is on an accessible private LAN, then on the remote host, navigate to that directory and run: - -.. code-block:: bash - - aim up --host="0.0.0.0" --port=43800 - -Then, open up a web browser on the local machine and navigate to ``http://REMOTE_SERVER:PORT`` or ``http://USERNAME@REMOTE_SERVER:PORT``. The Aim UI should now be accessible. - -.. note:: Anyone with access to the remote host may also be able to access the Aim UI without SSH authentication. If this is not desired, see below. - - -Remote host (public) -~~~~~~~~~~~~~~~~~~~~ - -If the directory containing the ``.aim`` directory is on a remote host that is publically accessible, then on the remote host, navigate to that directory and run: - -.. code-block:: bash - - aim up --host="localhost" --port=43800 - -The above restricts incoming connections to those originating from the remote host itself. Then, establish local port forwarding over ssh to bind the local ``localhost:43800`` to the remote ``localhost:43800``: - -.. code-block:: bash - - ssh -L "localhost:43800:localhost:43800" USERNAME@REMOTE_SERVER - -Finally, open up a web browser on the local machine and navigate to http://localhost:43800/. - -.. note:: Other user accounts on the remote host may also be able to bind to remote ``localhost:43800``. If this is not desired, please configure the firewall on the remote host appropriately. - - - -.. _custom-model: - -Defining a custom model ------------------------ - -Ensure that the model class will be imported at runtime by adding the following to ``compressai/models/__init__.py``: - -.. code-block:: python - :caption: compressai/models/__init__.py - - from .custom import MyCustomModel - -Then, create a file at ``compressai/models/custom.py``, and define and register a model as follows: - -.. code-block:: python - :caption: compressai/models/custom.py - - from compressai.registry import register_model - from .base import CompressionModel - - @register_model("my_custom_model") - class MyCustomModel(CompressionModel): - def __init__(self, N, M): - ... - -Then, copy ``conf/example.yaml`` into ``conf/my_custom_model.yaml`` and customize the YAML configuration to use the custom model: - -.. code-block:: yaml - :caption: conf/my_custom_model.yaml - - model: - name: "my_custom_model" - - hp: - N: 128 - M: 192 - - - -.. _custom-runner: - -Defining a custom Runner training loop --------------------------------------- - -We provide the following pre-made runners: - -- :py:class:`~compressai_trainer.runners.BaseRunner` (base compression class) -- :py:class:`~compressai_trainer.runners.ImageCompressionRunner` -- :py:class:`~compressai_trainer.runners.VideoCompressionRunner` (future release) - -Begin by creating a file at ``compressai_trainer/runners/custom.py`` and then add the following line to ``compressai_trainer/runners/__init__.py``: - -.. code-block:: python - :caption: compressai_trainer/runners/__init__.py - - from .custom import CustomImageCompressionRunner - -Create ``conf/runners/CustomImageCompressionRunner.yaml`` with: - -.. code-block:: yaml - :caption: conf/runners/CustomImageCompressionRunner.yaml - - type: "CustomImageCompressionRunner" - - # Additional arguments for CustomImageCompressionRunner.__init__ here: - # some_custom_argument: "value" - -Then, in ``compressai_trainer/runners/custom.py``, create a :py:class:`~catalyst.runners.runner.Runner` by inheriting from :py:class:`~compressai_trainer.runners.BaseRunner` or :py:class:`~catalyst.runners.runner.Runner`: - -.. code-block:: python - :caption: compressai_trainer/runners/custom.py - - from compressai.registry import register_runner - from .base import BaseRunner - - @register_runner("CustomImageCompressionRunner") - class CustomImageCompressionRunner(BaseRunner): - ... - -The following functions are called during the training loop: - -.. code-block:: python - :caption: Runner training loop call order. - - on_experiment_start # Once, at the beginning. - on_epoch_start # Beginning of an epoch. - on_loader_start # For each loader (train / valid / infer). - on_batch_start # Before each batch. - handle_batch # For each image batch. - on_batch_end - on_loader_end - on_epoch_end - on_experiment_end - -The training loop is effectively equivalent to: - -.. code-block:: python - :caption: Runner training loop pseudo-code. - - on_experiment_start() - - for epoch in range(1, num_epochs): - on_epoch_start() - - for loader in ["train", "valid", "infer"]: - on_loader_start() - - for batch in loader: - on_batch_start() - handle_batch(batch) - on_batch_end() - - on_loader_end() - - on_epoch_end() - - on_experiment_end() - -Please see the `Catalyst documentation`_ for more information. Also consider using our provided runners as a template. - -.. _Catalyst documentation: https://catalyst-team.github.io/catalyst/ - - - -.. _yaml-config: - -Using YAML configuration ------------------------- - -We use Hydra for our configuration framework. The section below covers some basics, but for more details, please see the `Hydra documentation`_. - -.. _Hydra documentation: https://hydra.cc/docs/intro/ - - -Basics -~~~~~~ - -``conf/example.yaml`` contains an example configuration for training the ``bmshj2018-factorized`` model. - -In the ``defaults`` list, one may import configurations from other YAML files: - - -.. code-block:: yaml - :caption: conf/example.yaml - - defaults: - # Imports conf/runner/ImageCompressionRunner.yaml into "runner:" dict. - - runner: ImageCompressionRunner - - # Similarly, import into "paths:", "env:", "engine:", etc dicts. - - paths: default - - env: default - - engine: default - - criterion: RateDistortionLoss - - optimizer: net_aux - - scheduler: ReduceLROnPlateau - - misc: default - - # Imports vimeo90k/train into "dataset.train:" dict, etc. - - dataset@dataset.train: vimeo90k/train - - dataset@dataset.valid: vimeo90k/valid - - dataset@dataset.infer: kodak/infer - - # Imports current YAML's configuration, defined below. - - _self_ - -One may also define or override configuration within the YAML file itself: - -.. code-block:: yaml - :caption: conf/example.yaml - - # Create configuration for current experiment, model, and hyperparameters. - exp: - name: "example_experiment" - model: - name: "bmshj2018-factorized" - hp: - N: 128 - M: 192 - - # Override dataset.train.loader.batch size. - dataset: - train: - loader: - batch_size: 8 - - # Alternatively, one can also override the above via a command line argument: - # compressai-train [...] ++dataset.train.loader.batch_size=8 - - -Creating your own config -~~~~~~~~~~~~~~~~~~~~~~~~ - -Copy ``conf/example.yaml`` into ``conf/custom-config.yaml``: - -.. code-block:: yaml - :caption: conf/custom-config.yaml - - defaults: - - paths: default - - env: default - - engine: default - - runner: ImageCompressionRunner - - criterion: RateDistortionLoss - - optimizer: net_aux - - scheduler: ReduceLROnPlateau - - dataset/vimeo90k/train@dataset - - dataset/vimeo90k/valid@dataset - - dataset/kodak/infer@dataset - - misc: default - - _self_ - - exp: - name: "example_experiment" - - model: - name: "bmshj2018-factorized" - - hp: - # Qualities 1-5 - N: 128 - M: 192 - # Qualities 6-8 - # N: 192 - # M: 320 - -Modify it as desired. Then, train using: - -.. code-block:: bash - - compressai-train --config-name="custom-config" - - -Specify and override configuration via command line (CLI) arguments -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -For example, this overrides ``criterion.lmbda``: - -.. code-block:: bash - - compressai-train --config-name="example" ++criterion.lmbda=0.035 - -The above is equivalent to the following YAML configuration: - -.. code-block:: yaml - - criterion: - lmbda: 0.035 - -Please see the `Hydra documentation on overriding`_ for more information. - -.. _Hydra documentation on overriding: https://hydra.cc/docs/advanced/override_grammar/basic/#basic-examples - - - -.. _resuming-training: - -Resuming training ------------------ - -Loading model checkpoints/weights -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. note:: This starts a *fresh* run in the experiment tracker with a new run hash. To log to an existing run, see :ref:`continuing-run`. - -To load a checkpoint containing model/optimizer/scheduler/etc state, override ``paths.checkpoint``: - -.. code-block:: bash - - ++paths.checkpoint="/path/to/checkpoints/runner.last.pth" - -To load *only* the model ``state_dict`` (i.e. weights), and not other training state, override ``paths.model_checkpoint`` instead: - -.. code-block:: bash - - ++paths.model_checkpoint="/path/to/checkpoints/runner.last.pth" - - -.. _continuing-run: - -Continuing a previous run -~~~~~~~~~~~~~~~~~~~~~~~~~ - -To continue an existing run that was paused/cancelled during training, load the config corresponding to the run hash: - -.. code-block:: bash - - RUNS_ROOT="${paths.runs_root}" # Example: "./logs/runs" - RUN_HASH="${env.aim.run_hash}" # Example: "e4e6d4d5e5c59c69f3bd7be2" - - --config-path="${RUNS_ROOT}/${RUN_HASH}/configs" - --config-name="config" - ++paths.checkpoint='${paths.checkpoints}/runner.last.pth' - - - -.. _additional-loggers: - -Additional loggers ------------------- - -By default, CompressAI Trainer logs experiments to both Aim and Tensorboard. Additional loggers can be enabled as shown below. - -CSV Logger -~~~~~~~~~~ - -Store CSV logs inside the current run directory via: - -.. code-block:: bash - - compressai-train \ - --config-name="example" \ - ++engine.loggers.csv.logdir='${paths._run_root}/csv' - - -MLflow Logger -~~~~~~~~~~~~~ - -Connect CompressAI Trainer to an MLflow experiment tracking server: - -.. code-block:: bash - - compressai-train \ - --config-name="example" \ - ++exp.name="example_experiment" \ - ++engine.loggers.mlflow.run="example_run" \ - ++engine.loggers.mlflow.tracking_uri=http://localhost:5000 \ - ++engine.loggers.mlflow.registry_uri=http://localhost:5000 - - - -.. _tips: - -Tips ----- - -Single-GPU and Multi-GPU training -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -By default, CompressAI Trainer will use all available GPUs. To restrict to certain GPU devices, set the visible device IDs using: - -.. code-block:: bash - - export CUDA_VISIBLE_DEVICES="0,1" # Restricts to GPU 0 and GPU 1. - - -Quick sanity check -~~~~~~~~~~~~~~~~~~ - -To quickly check that your code is working, run a few batches of train/validation/inference using the following CLI argument: - -.. code-block:: bash - - ++engine.check=True - -To avoid filling up the ``"${paths.runs_root}"`` directory with unnecessary checkpoints, we recommend adding the following variable to ``~/.bashrc``: - -.. code-block:: bash - - TRAIN_CHECK="++engine.check=True ++exp.name=check ++paths.runs_root=$USER/tmp_runs" - -Example usage: - -.. code-block:: bash - - compressai-train --config-name="example" $TRAIN_CHECK -