From 9376bffd79d4debd60f7841a268fabc80eb2f61d Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Tue, 14 Feb 2023 16:34:18 +0800 Subject: [PATCH 01/27] Add osu-data-csv as req --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index e76622bf..c3efbea3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,6 +11,7 @@ python = "^3.9" scikit-learn = "^1.2.1" pandas = "^1.5.3" pytorch-lightning = "^1.9.1" +osu-data-csv = "^0.1.6" [build-system] From c041ef22e348395bbf424a30ae4d8b5c5e10ee52 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Tue, 14 Feb 2023 17:02:05 +0800 Subject: [PATCH 02/27] Add basic train script for testing --- opal/train.py | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 opal/train.py diff --git a/opal/train.py b/opal/train.py new file mode 100644 index 00000000..67134564 --- /dev/null +++ b/opal/train.py @@ -0,0 +1,50 @@ +import pytorch_lightning as pl +from pytorch_lightning.callbacks import LearningRateMonitor, EarlyStopping + +from opal.score.collaborative_filtering import NeuMF +from opal.score.datamodule import ScoreDataModule + + +def train(yyyy_mm: str): + dm = ScoreDataModule( + ds_yyyy_mm=yyyy_mm, + batch_size=2 ** 9, + score_bounds=(5e5, 1e6), + ) + + net = NeuMF( + uid_le=dm.uid_le, + mid_le=dm.mid_le, + qt=dm.qt_accuracy, + mf_emb_dim=16, + mlp_emb_dim=16, + mlp_chn_out=8, + lr=0.005, + one_cycle_lr_params={ + "pct_start": 0.1, + "three_phase": True, + "final_div_factor": 1e6 + } + ) + + trainer = pl.Trainer( + max_epochs=35, + accelerator='gpu', + default_root_dir="V1_2022_11", + callbacks=[ + EarlyStopping( + monitor="val_loss", + patience=2, + verbose=True, + mode='min', + divergence_threshold=1 + ), + LearningRateMonitor() + ], + ) + + trainer.fit(net, datamodule=dm) + + +if __name__ == '__main__': + train("2023_01") From b53ba308f41275797574622ee6620218da181941 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Wed, 15 Feb 2023 11:50:22 +0800 Subject: [PATCH 03/27] Update pyproject.toml for 3.8.10 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index c3efbea3..7678d874 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ license = "MIT" readme = "README.md" [tool.poetry.dependencies] -python = "^3.9" +python = "^3.8.10" scikit-learn = "^1.2.1" pandas = "^1.5.3" pytorch-lightning = "^1.9.1" From 651c6e119ac22fed32ead5a2604ffd5117c05e93 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Wed, 15 Feb 2023 18:59:15 +0800 Subject: [PATCH 04/27] Remove replays dir export --- opal/conf/__init__.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/opal/conf/__init__.py b/opal/conf/__init__.py index 865c26bd..e69de29b 100644 --- a/opal/conf/__init__.py +++ b/opal/conf/__init__.py @@ -1,3 +0,0 @@ -from opal.conf.conf import REPLAYS_DIR - -__all__ = ['REPLAYS_DIR'] \ No newline at end of file From 0cf77b55cb2361592d44e576c5dd3b59fae7aeec Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Wed, 15 Feb 2023 18:59:22 +0800 Subject: [PATCH 05/27] Add ignore mapping for osu-data-csv config --- ignore_mapping.yaml | 92 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 ignore_mapping.yaml diff --git a/ignore_mapping.yaml b/ignore_mapping.yaml new file mode 100644 index 00000000..e3ba9493 --- /dev/null +++ b/ignore_mapping.yaml @@ -0,0 +1,92 @@ +# This is the default ignore mapping. +# For every column that is NOT commented out, it'll be IGNORED when converting +# Try to ignore those that are not necessary for your use +# This will heavily reduce storage needed and increase processing speed +# Once you're done, reference this file when calling osu-data-csv via +# osu-data-csv -i path/to/ignore_mapping.yaml + +osu_beatmap_difficulty.sql: +# - beatmap_id +# - mode +# - mods +# - diff_unified + - last_update +osu_beatmaps.sql: +# - beatmap_id + - beatmapset_id + - user_id +# - filename + - checksum + - version + - total_length + - hit_length +# - countTotal +# - countNormal +# - countSlider +# - countSpinner +# - diff_drain +# - diff_size +# - diff_overall +# - diff_approach +# - playmode +# - approved + - last_update +# - difficultyrating +# - playcount +# - passcount +# - youtube_preview +# - score_version +# - deleted_at + - bpm +osu_scores{mode}_high.sql: + - score_id + - beatmap_id + - user_id + - score +# - maxcombo +# - rank + - count50 + - count100 + - count300 + - countmiss + - countgeki + - countkatu +# - perfect + - enabled_mods + - date + - pp + - replay +# - hidden +# - country_acronym +osu_user_stats{mode}.sql: + - user_id +# - count300 +# - count100 +# - count50 +# - countMiss +# - accuracy_total +# - accuracy_count +# - accuracy +# - playcount +# - ranked_score +# - total_score +# - x_rank_count +# - xh_rank_count +# - s_rank_count +# - sh_rank_count +# - a_rank_count +# - rank +# - level +# - replay_popularity +# - fail_count +# - exit_count +# - max_combo +# - country_acronym + - rank_score + - rank_score_index +# - rank_score_exp +# - rank_score_index_exp +# - accuracy_new +# - last_update +# - last_played +# - total_seconds_played From 5782543c778cf61c44b05a6591e3772245f4dea0 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Wed, 15 Feb 2023 19:06:24 +0800 Subject: [PATCH 06/27] Update ignore mapping --- ignore_mapping.yaml | 100 ++++++++++++++++++++++---------------------- 1 file changed, 50 insertions(+), 50 deletions(-) diff --git a/ignore_mapping.yaml b/ignore_mapping.yaml index e3ba9493..c2f895e5 100644 --- a/ignore_mapping.yaml +++ b/ignore_mapping.yaml @@ -39,54 +39,54 @@ osu_beatmaps.sql: # - deleted_at - bpm osu_scores{mode}_high.sql: - - score_id - - beatmap_id - - user_id - - score -# - maxcombo -# - rank - - count50 - - count100 - - count300 - - countmiss - - countgeki - - countkatu -# - perfect - - enabled_mods - - date - - pp - - replay -# - hidden -# - country_acronym -osu_user_stats{mode}.sql: - - user_id -# - count300 -# - count100 +# - score_id +# - beatmap_id +# - user_id +# - score + - maxcombo + - rank # - count50 -# - countMiss -# - accuracy_total -# - accuracy_count -# - accuracy -# - playcount -# - ranked_score -# - total_score -# - x_rank_count -# - xh_rank_count -# - s_rank_count -# - sh_rank_count -# - a_rank_count -# - rank -# - level -# - replay_popularity -# - fail_count -# - exit_count -# - max_combo -# - country_acronym - - rank_score - - rank_score_index -# - rank_score_exp -# - rank_score_index_exp -# - accuracy_new -# - last_update -# - last_played -# - total_seconds_played +# - count100 +# - count300 +# - countmiss +# - countgeki +# - countkatu + - perfect +# - enabled_mods +# - date +# - pp +# - replay + - hidden + - country_acronym +osu_user_stats{mode}.sql: +# - user_id + - count300 + - count100 + - count50 + - countMiss + - accuracy_total + - accuracy_count + - accuracy + - playcount + - ranked_score + - total_score + - x_rank_count + - xh_rank_count + - s_rank_count + - sh_rank_count + - a_rank_count + - rank + - level + - replay_popularity + - fail_count + - exit_count + - max_combo + - country_acronym +# - rank_score +# - rank_score_index + - rank_score_exp + - rank_score_index_exp + - accuracy_new + - last_update + - last_played + - total_seconds_played From 018f12a32247a3f6671cf5562449c7e821993659 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Wed, 15 Feb 2023 19:25:57 +0800 Subject: [PATCH 07/27] Update training strategy --- opal/train.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/opal/train.py b/opal/train.py index 67134564..41441534 100644 --- a/opal/train.py +++ b/opal/train.py @@ -1,5 +1,7 @@ import pytorch_lightning as pl +import torch from pytorch_lightning.callbacks import LearningRateMonitor, EarlyStopping +from pytorch_lightning.strategies import DDPStrategy from opal.score.collaborative_filtering import NeuMF from opal.score.datamodule import ScoreDataModule @@ -41,10 +43,14 @@ def train(yyyy_mm: str): ), LearningRateMonitor() ], + strategy=DDPStrategy(find_unused_parameters=False), + # devices=[3, ], + fast_dev_run=True, ) trainer.fit(net, datamodule=dm) if __name__ == '__main__': + torch.set_float32_matmul_precision('high') train("2023_01") From acd776e57b34122ad8e65a35bbffc7d7943d2799 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Wed, 15 Feb 2023 19:27:13 +0800 Subject: [PATCH 08/27] Remove manual call of prepare data and setup --- opal/score/datamodule/score_datamodule.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/opal/score/datamodule/score_datamodule.py b/opal/score/datamodule/score_datamodule.py index 9a64fa1d..7cbac08a 100644 --- a/opal/score/datamodule/score_datamodule.py +++ b/opal/score/datamodule/score_datamodule.py @@ -1,14 +1,15 @@ import logging +from dataclasses import dataclass, field +from pathlib import Path +from typing import Sequence, Tuple + import numpy as np import pandas as pd import pytorch_lightning as pl import torch -from dataclasses import dataclass, field -from pathlib import Path from sklearn.base import TransformerMixin from sklearn.preprocessing import LabelEncoder, QuantileTransformer from torch.utils.data import DataLoader, TensorDataset, random_split -from typing import Sequence, Tuple from opal.conf.conf import DATA_DIR from opal.conf.mods import OsuMod @@ -44,9 +45,6 @@ def __post_init__(self): super().__init__() assert sum(self.train_test_val) == 1, "Train Test Validation must sum to 1." - self.prepare_data() - self.setup() - def prepare_data(self) -> None: """ Downloads data via data_ppy_sh_to_csv submodule """ get_dataset( From dc2bf76df5d5dae15594073f87f67dcc3d99ac96 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Wed, 15 Feb 2023 19:28:25 +0800 Subject: [PATCH 09/27] Revert "Remove manual call of prepare data and setup" This reverts commit acd776e57b34122ad8e65a35bbffc7d7943d2799. --- opal/score/datamodule/score_datamodule.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/opal/score/datamodule/score_datamodule.py b/opal/score/datamodule/score_datamodule.py index 7cbac08a..9a64fa1d 100644 --- a/opal/score/datamodule/score_datamodule.py +++ b/opal/score/datamodule/score_datamodule.py @@ -1,15 +1,14 @@ import logging -from dataclasses import dataclass, field -from pathlib import Path -from typing import Sequence, Tuple - import numpy as np import pandas as pd import pytorch_lightning as pl import torch +from dataclasses import dataclass, field +from pathlib import Path from sklearn.base import TransformerMixin from sklearn.preprocessing import LabelEncoder, QuantileTransformer from torch.utils.data import DataLoader, TensorDataset, random_split +from typing import Sequence, Tuple from opal.conf.conf import DATA_DIR from opal.conf.mods import OsuMod @@ -45,6 +44,9 @@ def __post_init__(self): super().__init__() assert sum(self.train_test_val) == 1, "Train Test Validation must sum to 1." + self.prepare_data() + self.setup() + def prepare_data(self) -> None: """ Downloads data via data_ppy_sh_to_csv submodule """ get_dataset( From 59bb65234bf36ff11f8fcb5f6d5fcc95e66b1cb2 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 17:09:19 +0800 Subject: [PATCH 10/27] Finetune hyperparams --- opal/score/collaborative_filtering/neu_mf.py | 24 ++++++------- opal/train.py | 37 +++++++++++--------- 2 files changed, 32 insertions(+), 29 deletions(-) diff --git a/opal/score/collaborative_filtering/neu_mf.py b/opal/score/collaborative_filtering/neu_mf.py index 7c1da41b..3c222642 100644 --- a/opal/score/collaborative_filtering/neu_mf.py +++ b/opal/score/collaborative_filtering/neu_mf.py @@ -7,7 +7,7 @@ import torch from sklearn.preprocessing import LabelEncoder, QuantileTransformer from torch.nn import MSELoss -from torch.optim.lr_scheduler import OneCycleLR +from torch.optim.lr_scheduler import ExponentialLR from opal.score.collaborative_filtering.neu_mf_module import NeuMFModule @@ -135,19 +135,19 @@ def step(self, batch): def configure_optimizers(self): optim = torch.optim.Adam(self.parameters(), lr=self.lr, weight_decay=0.001) - trainer = self.trainer - steps_per_epoch = ( - trainer.limit_train_batches - if trainer.limit_train_batches > 2 - else len(self.trainer.datamodule.train_dataloader()) - ) + # trainer = self.trainer + # steps_per_epoch = ( + # trainer.limit_train_batches + # if trainer.limit_train_batches > 2 + # else len(self.trainer.datamodule.train_dataloader()) + # ) return [optim], [ { - "scheduler": OneCycleLR( - optim, self.lr, - steps_per_epoch=int(steps_per_epoch), - epochs=trainer.max_epochs, - **self.one_cycle_lr_params + "scheduler": ExponentialLR( + optim, 0.1, + # steps_per_epoch=int(steps_per_epoch), + # epochs=trainer.max_epochs, + # **self.one_cycle_lr_params ), "interval": "step", "frequency": 1 diff --git a/opal/train.py b/opal/train.py index 41441534..4ce53eb3 100644 --- a/opal/train.py +++ b/opal/train.py @@ -1,7 +1,6 @@ import pytorch_lightning as pl import torch -from pytorch_lightning.callbacks import LearningRateMonitor, EarlyStopping -from pytorch_lightning.strategies import DDPStrategy +from pytorch_lightning.callbacks import LearningRateMonitor, EarlyStopping, ModelCheckpoint from opal.score.collaborative_filtering import NeuMF from opal.score.datamodule import ScoreDataModule @@ -10,42 +9,46 @@ def train(yyyy_mm: str): dm = ScoreDataModule( ds_yyyy_mm=yyyy_mm, - batch_size=2 ** 9, + batch_size=2 ** 10, score_bounds=(5e5, 1e6), + ds_set="10000" ) + epochs = 25 net = NeuMF( uid_le=dm.uid_le, mid_le=dm.mid_le, qt=dm.qt_accuracy, - mf_emb_dim=16, - mlp_emb_dim=16, + mf_emb_dim=8, + mlp_emb_dim=8, mlp_chn_out=8, - lr=0.005, - one_cycle_lr_params={ - "pct_start": 0.1, - "three_phase": True, - "final_div_factor": 1e6 - } + lr=1e-3, + # one_cycle_lr_params={ + # "pct_start": 0.025, + # "three_phase": True, + # "final_div_factor": 1e7 + # } ) trainer = pl.Trainer( - max_epochs=35, + max_epochs=epochs, accelerator='gpu', default_root_dir="V1_2022_11", callbacks=[ EarlyStopping( monitor="val_loss", - patience=2, + patience=20, verbose=True, mode='min', divergence_threshold=1 ), - LearningRateMonitor() + # StochasticWeightAveraging(0.0005), + LearningRateMonitor(), + ModelCheckpoint(monitor='val_loss', save_top_k=1, mode='min') ], - strategy=DDPStrategy(find_unused_parameters=False), - # devices=[3, ], - fast_dev_run=True, + # strategy=DDPStrategy(find_unused_parameters=False), + devices=[3, ], + # fast_dev_run=True, ) trainer.fit(net, datamodule=dm) From fddec0eb99743ab57018b61b5ad3950e29cd8eb7 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 17:12:27 +0800 Subject: [PATCH 11/27] Set ExpLR freq to epoch --- opal/score/collaborative_filtering/neu_mf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/opal/score/collaborative_filtering/neu_mf.py b/opal/score/collaborative_filtering/neu_mf.py index 3c222642..981b696c 100644 --- a/opal/score/collaborative_filtering/neu_mf.py +++ b/opal/score/collaborative_filtering/neu_mf.py @@ -149,7 +149,7 @@ def configure_optimizers(self): # epochs=trainer.max_epochs, # **self.one_cycle_lr_params ), - "interval": "step", + "interval": "epoch", "frequency": 1 }, ] From 2308842c15057375f492e9525e54698e76fa8444 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 17:16:47 +0800 Subject: [PATCH 12/27] Reduce LR schedule gamma --- opal/score/collaborative_filtering/neu_mf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/opal/score/collaborative_filtering/neu_mf.py b/opal/score/collaborative_filtering/neu_mf.py index 981b696c..e6613e3b 100644 --- a/opal/score/collaborative_filtering/neu_mf.py +++ b/opal/score/collaborative_filtering/neu_mf.py @@ -144,7 +144,7 @@ def configure_optimizers(self): return [optim], [ { "scheduler": ExponentialLR( - optim, 0.1, + optim, 0.25, # steps_per_epoch=int(steps_per_epoch), # epochs=trainer.max_epochs, # **self.one_cycle_lr_params From 7b8a7830dcc217355892aac591d159eb875f1b4a Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 18:05:37 +0800 Subject: [PATCH 13/27] Update best params --- opal/train.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/opal/train.py b/opal/train.py index 4ce53eb3..ff70941b 100644 --- a/opal/train.py +++ b/opal/train.py @@ -11,10 +11,10 @@ def train(yyyy_mm: str): ds_yyyy_mm=yyyy_mm, batch_size=2 ** 10, score_bounds=(5e5, 1e6), - ds_set="10000" + ds_set="1000" ) - epochs = 25 + epochs = 50 net = NeuMF( uid_le=dm.uid_le, mid_le=dm.mid_le, @@ -31,6 +31,7 @@ def train(yyyy_mm: str): ) trainer = pl.Trainer( + deterministic=True, max_epochs=epochs, accelerator='gpu', default_root_dir="V1_2022_11", @@ -42,11 +43,9 @@ def train(yyyy_mm: str): mode='min', divergence_threshold=1 ), - # StochasticWeightAveraging(0.0005), LearningRateMonitor(), ModelCheckpoint(monitor='val_loss', save_top_k=1, mode='min') ], - # strategy=DDPStrategy(find_unused_parameters=False), devices=[3, ], # fast_dev_run=True, ) From 09d55769d619f4eb5afb73efac4cf3f6c85b68f9 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 18:36:35 +0800 Subject: [PATCH 14/27] Remove OneCycleLR --- opal/score/collaborative_filtering/neu_mf.py | 21 ++++---------------- 1 file changed, 4 insertions(+), 17 deletions(-) diff --git a/opal/score/collaborative_filtering/neu_mf.py b/opal/score/collaborative_filtering/neu_mf.py index e6613e3b..96062d61 100644 --- a/opal/score/collaborative_filtering/neu_mf.py +++ b/opal/score/collaborative_filtering/neu_mf.py @@ -22,7 +22,7 @@ def __init__( mlp_emb_dim: int, mlp_chn_out: int, lr: float = 0.005, - one_cycle_lr_params: dict = {}, + lr_gamma: float = 0.25 ): """ Initializes the model for training @@ -38,7 +38,6 @@ def __init__( mlp_chn_out: MLP Branch Channel Output Dimensions lr: Learning Rate - one_cycle_lr_params: Extra arguments passed into OneCycleLR """ super().__init__() self.model = NeuMFModule( @@ -50,9 +49,7 @@ def __init__( ) self.loss = MSELoss() self.lr = lr - - self.one_cycle_lr_params = one_cycle_lr_params - + self.lr_gamma = lr_gamma self.uid_le = uid_le self.mid_le = mid_le self.qt = qt @@ -135,20 +132,10 @@ def step(self, batch): def configure_optimizers(self): optim = torch.optim.Adam(self.parameters(), lr=self.lr, weight_decay=0.001) - # trainer = self.trainer - # steps_per_epoch = ( - # trainer.limit_train_batches - # if trainer.limit_train_batches > 2 - # else len(self.trainer.datamodule.train_dataloader()) - # ) + return [optim], [ { - "scheduler": ExponentialLR( - optim, 0.25, - # steps_per_epoch=int(steps_per_epoch), - # epochs=trainer.max_epochs, - # **self.one_cycle_lr_params - ), + "scheduler": ExponentialLR(optim, self.lr_gamma), "interval": "epoch", "frequency": 1 }, From 8d8e58673cc3cce2de4eadb295c2be9267845394 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 18:36:50 +0800 Subject: [PATCH 15/27] Compress NeuMFBlock Intermediates to list arg --- .../score/collaborative_filtering/neu_mf_module.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/opal/score/collaborative_filtering/neu_mf_module.py b/opal/score/collaborative_filtering/neu_mf_module.py index d0a78113..381a795b 100644 --- a/opal/score/collaborative_filtering/neu_mf_module.py +++ b/opal/score/collaborative_filtering/neu_mf_module.py @@ -1,3 +1,5 @@ +from typing import List + import torch import torch.nn as nn @@ -16,7 +18,8 @@ def forward(self, x): class NeuMFModule(nn.Module): - def __init__(self, n_uid, n_mid, mf_emb_dim, mlp_emb_dim, mlp_chn_out): + def __init__(self, n_uid, n_mid, mf_emb_dim, mlp_emb_dim, mlp_chn_out, + mlp_range: List[int] = [512, 256, 128, 64, 32]): super(NeuMFModule, self).__init__() self.u_mf_emb = nn.Embedding(n_uid, mf_emb_dim) @@ -28,12 +31,9 @@ def __init__(self, n_uid, n_mid, mf_emb_dim, mlp_emb_dim, mlp_chn_out): self.u_mlp_emb = nn.Embedding(n_uid, mlp_emb_dim) self.m_mlp_emb = nn.Embedding(n_mid, mlp_emb_dim) self.mlp_net = nn.Sequential( - NeuMFBlock(mlp_emb_dim * 2, 512), - NeuMFBlock(512, 256), - NeuMFBlock(256, 128), - NeuMFBlock(128, 64), - NeuMFBlock(64, 32), - NeuMFBlock(32, mlp_chn_out), + NeuMFBlock(mlp_emb_dim * 2, mlp_range[0]), + *[NeuMFBlock(i, j) for i, j in zip(mlp_range[:-1], mlp_range[1:])], + NeuMFBlock(mlp_range[-1], mlp_chn_out), ) self.neu_mf_net = nn.Sequential( nn.Linear(mlp_chn_out + mf_emb_dim, 1), From d7dbc7fdcc5bddd7c80b19066f6603a1de03f0f4 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 18:39:59 +0800 Subject: [PATCH 16/27] Combine Emb Dim arg --- opal/score/collaborative_filtering/neu_mf.py | 9 +++------ .../collaborative_filtering/neu_mf_module.py | 18 +++++++++--------- 2 files changed, 12 insertions(+), 15 deletions(-) diff --git a/opal/score/collaborative_filtering/neu_mf.py b/opal/score/collaborative_filtering/neu_mf.py index 96062d61..40fb6d34 100644 --- a/opal/score/collaborative_filtering/neu_mf.py +++ b/opal/score/collaborative_filtering/neu_mf.py @@ -18,8 +18,7 @@ def __init__( uid_le: LabelEncoder, mid_le: LabelEncoder, qt: QuantileTransformer, - mf_emb_dim: int, - mlp_emb_dim: int, + emb_dim: int, mlp_chn_out: int, lr: float = 0.005, lr_gamma: float = 0.25 @@ -33,8 +32,7 @@ def __init__( uid_le: UID LabelEncoder from the DM mid_le: MID LabelEncoder from the DM qt: QuantileTransformer from the DM - mf_emb_dim: Matrix Factorization Branch Embedding Dimensions - mlp_emb_dim: MLP Branch Embedding Dimensions + emb_dim: Embedding Dimensions mlp_chn_out: MLP Branch Channel Output Dimensions lr: Learning Rate @@ -43,8 +41,7 @@ def __init__( self.model = NeuMFModule( n_uid=len(uid_le.classes_), n_mid=len(mid_le.classes_), - mf_emb_dim=mf_emb_dim, - mlp_emb_dim=mlp_emb_dim, + emb_dim=emb_dim, mlp_chn_out=mlp_chn_out ) self.loss = MSELoss() diff --git a/opal/score/collaborative_filtering/neu_mf_module.py b/opal/score/collaborative_filtering/neu_mf_module.py index 381a795b..d6373ab4 100644 --- a/opal/score/collaborative_filtering/neu_mf_module.py +++ b/opal/score/collaborative_filtering/neu_mf_module.py @@ -18,25 +18,25 @@ def forward(self, x): class NeuMFModule(nn.Module): - def __init__(self, n_uid, n_mid, mf_emb_dim, mlp_emb_dim, mlp_chn_out, + def __init__(self, n_uid, n_mid, emb_dim, mlp_chn_out, mlp_range: List[int] = [512, 256, 128, 64, 32]): super(NeuMFModule, self).__init__() - self.u_mf_emb = nn.Embedding(n_uid, mf_emb_dim) - self.m_mf_emb = nn.Embedding(n_mid, mf_emb_dim) + self.u_mf_emb = nn.Embedding(n_uid, emb_dim) + self.m_mf_emb = nn.Embedding(n_mid, emb_dim) self.mf_net = nn.Sequential( - NeuMFBlock(mlp_emb_dim, mlp_emb_dim), - NeuMFBlock(mlp_emb_dim, mlp_emb_dim), + NeuMFBlock(emb_dim, emb_dim), + NeuMFBlock(emb_dim, emb_dim), ) - self.u_mlp_emb = nn.Embedding(n_uid, mlp_emb_dim) - self.m_mlp_emb = nn.Embedding(n_mid, mlp_emb_dim) + self.u_mlp_emb = nn.Embedding(n_uid, emb_dim) + self.m_mlp_emb = nn.Embedding(n_mid, emb_dim) self.mlp_net = nn.Sequential( - NeuMFBlock(mlp_emb_dim * 2, mlp_range[0]), + NeuMFBlock(emb_dim * 2, mlp_range[0]), *[NeuMFBlock(i, j) for i, j in zip(mlp_range[:-1], mlp_range[1:])], NeuMFBlock(mlp_range[-1], mlp_chn_out), ) self.neu_mf_net = nn.Sequential( - nn.Linear(mlp_chn_out + mf_emb_dim, 1), + nn.Linear(mlp_chn_out + emb_dim, 1), nn.Tanh(), ) From d5ed3921d3fd9655626d3466448b4f92df16a86c Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 18:41:27 +0800 Subject: [PATCH 17/27] Compress MF sequential to repeats --- opal/score/collaborative_filtering/neu_mf.py | 4 +++- opal/score/collaborative_filtering/neu_mf_module.py | 6 ++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/opal/score/collaborative_filtering/neu_mf.py b/opal/score/collaborative_filtering/neu_mf.py index 40fb6d34..c23c23d5 100644 --- a/opal/score/collaborative_filtering/neu_mf.py +++ b/opal/score/collaborative_filtering/neu_mf.py @@ -18,7 +18,8 @@ def __init__( uid_le: LabelEncoder, mid_le: LabelEncoder, qt: QuantileTransformer, - emb_dim: int, + emb_dim: int, + mf_repeats: int, mlp_chn_out: int, lr: float = 0.005, lr_gamma: float = 0.25 @@ -41,6 +42,7 @@ def __init__( self.model = NeuMFModule( n_uid=len(uid_le.classes_), n_mid=len(mid_le.classes_), + mf_repeats=mf_repeats, emb_dim=emb_dim, mlp_chn_out=mlp_chn_out ) diff --git a/opal/score/collaborative_filtering/neu_mf_module.py b/opal/score/collaborative_filtering/neu_mf_module.py index d6373ab4..918604f2 100644 --- a/opal/score/collaborative_filtering/neu_mf_module.py +++ b/opal/score/collaborative_filtering/neu_mf_module.py @@ -19,15 +19,13 @@ def forward(self, x): class NeuMFModule(nn.Module): def __init__(self, n_uid, n_mid, emb_dim, mlp_chn_out, + mf_repeats: int = 2, mlp_range: List[int] = [512, 256, 128, 64, 32]): super(NeuMFModule, self).__init__() self.u_mf_emb = nn.Embedding(n_uid, emb_dim) self.m_mf_emb = nn.Embedding(n_mid, emb_dim) - self.mf_net = nn.Sequential( - NeuMFBlock(emb_dim, emb_dim), - NeuMFBlock(emb_dim, emb_dim), - ) + self.mf_net = nn.Sequential(*[NeuMFBlock(emb_dim, emb_dim) for _ in range(mf_repeats)]) self.u_mlp_emb = nn.Embedding(n_uid, emb_dim) self.m_mlp_emb = nn.Embedding(n_mid, emb_dim) self.mlp_net = nn.Sequential( From 93e11d445aa8d0f9f0e2701c09e7a1e762cc76dd Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 18:43:37 +0800 Subject: [PATCH 18/27] Remove redundant mlp_chn_out, use mlp_range --- opal/score/collaborative_filtering/neu_mf.py | 5 ++--- opal/score/collaborative_filtering/neu_mf_module.py | 10 +++++----- 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/opal/score/collaborative_filtering/neu_mf.py b/opal/score/collaborative_filtering/neu_mf.py index c23c23d5..6efdebc8 100644 --- a/opal/score/collaborative_filtering/neu_mf.py +++ b/opal/score/collaborative_filtering/neu_mf.py @@ -20,7 +20,7 @@ def __init__( qt: QuantileTransformer, emb_dim: int, mf_repeats: int, - mlp_chn_out: int, + mlp_range: List[int], lr: float = 0.005, lr_gamma: float = 0.25 ): @@ -34,7 +34,6 @@ def __init__( mid_le: MID LabelEncoder from the DM qt: QuantileTransformer from the DM emb_dim: Embedding Dimensions - mlp_chn_out: MLP Branch Channel Output Dimensions lr: Learning Rate """ @@ -44,7 +43,7 @@ def __init__( n_mid=len(mid_le.classes_), mf_repeats=mf_repeats, emb_dim=emb_dim, - mlp_chn_out=mlp_chn_out + mlp_range=mlp_range ) self.loss = MSELoss() self.lr = lr diff --git a/opal/score/collaborative_filtering/neu_mf_module.py b/opal/score/collaborative_filtering/neu_mf_module.py index 918604f2..705e7fea 100644 --- a/opal/score/collaborative_filtering/neu_mf_module.py +++ b/opal/score/collaborative_filtering/neu_mf_module.py @@ -18,9 +18,10 @@ def forward(self, x): class NeuMFModule(nn.Module): - def __init__(self, n_uid, n_mid, emb_dim, mlp_chn_out, + def __init__(self, n_uid, n_mid, + emb_dim: int, mf_repeats: int = 2, - mlp_range: List[int] = [512, 256, 128, 64, 32]): + mlp_range: List[int] = [512, 256, 128, 64, 32, 16]): super(NeuMFModule, self).__init__() self.u_mf_emb = nn.Embedding(n_uid, emb_dim) @@ -30,11 +31,10 @@ def __init__(self, n_uid, n_mid, emb_dim, mlp_chn_out, self.m_mlp_emb = nn.Embedding(n_mid, emb_dim) self.mlp_net = nn.Sequential( NeuMFBlock(emb_dim * 2, mlp_range[0]), - *[NeuMFBlock(i, j) for i, j in zip(mlp_range[:-1], mlp_range[1:])], - NeuMFBlock(mlp_range[-1], mlp_chn_out), + *[NeuMFBlock(i, j) for i, j in zip(mlp_range[:-1], mlp_range[1:])] ) self.neu_mf_net = nn.Sequential( - nn.Linear(mlp_chn_out + emb_dim, 1), + nn.Linear(mlp_range[0] + emb_dim, 1), nn.Tanh(), ) From 40a031156804bac330722079baad965586f276fd Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 18:47:25 +0800 Subject: [PATCH 19/27] Fine tune --- opal/train.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/opal/train.py b/opal/train.py index ff70941b..b503e695 100644 --- a/opal/train.py +++ b/opal/train.py @@ -19,9 +19,8 @@ def train(yyyy_mm: str): uid_le=dm.uid_le, mid_le=dm.mid_le, qt=dm.qt_accuracy, - mf_emb_dim=8, - mlp_emb_dim=8, - mlp_chn_out=8, + emb_dim=128, + mlp_range=[512, 256, 128, 64, 32, 32], lr=1e-3, # one_cycle_lr_params={ # "pct_start": 0.025, From c61f6fcc36a00fe38643da8f40e116d8637769cc Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 18:48:24 +0800 Subject: [PATCH 20/27] Add missing mf_repeats --- opal/train.py | 1 + 1 file changed, 1 insertion(+) diff --git a/opal/train.py b/opal/train.py index b503e695..d86a3d87 100644 --- a/opal/train.py +++ b/opal/train.py @@ -20,6 +20,7 @@ def train(yyyy_mm: str): mid_le=dm.mid_le, qt=dm.qt_accuracy, emb_dim=128, + mf_repeats=2, mlp_range=[512, 256, 128, 64, 32, 32], lr=1e-3, # one_cycle_lr_params={ From 4841cbc7672e17e1cd39082c61f72cdbcd604b3f Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 18:49:13 +0800 Subject: [PATCH 21/27] Fix incorrect dim --- opal/score/collaborative_filtering/neu_mf_module.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/opal/score/collaborative_filtering/neu_mf_module.py b/opal/score/collaborative_filtering/neu_mf_module.py index 705e7fea..e7734f27 100644 --- a/opal/score/collaborative_filtering/neu_mf_module.py +++ b/opal/score/collaborative_filtering/neu_mf_module.py @@ -34,7 +34,7 @@ def __init__(self, n_uid, n_mid, *[NeuMFBlock(i, j) for i, j in zip(mlp_range[:-1], mlp_range[1:])] ) self.neu_mf_net = nn.Sequential( - nn.Linear(mlp_range[0] + emb_dim, 1), + nn.Linear(mlp_range[-1] + emb_dim, 1), nn.Tanh(), ) From fb9271f807a2be91665e4c9ca5233b383e28ddcc Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Thu, 16 Feb 2023 21:42:47 +0800 Subject: [PATCH 22/27] Test Experiment --- opal/train.py | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/opal/train.py b/opal/train.py index d86a3d87..2f9e2be5 100644 --- a/opal/train.py +++ b/opal/train.py @@ -1,6 +1,6 @@ import pytorch_lightning as pl import torch -from pytorch_lightning.callbacks import LearningRateMonitor, EarlyStopping, ModelCheckpoint +from pytorch_lightning.callbacks import LearningRateMonitor, EarlyStopping, ModelCheckpoint, ModelPruning, QuantizationAwareTraining from opal.score.collaborative_filtering import NeuMF from opal.score.datamodule import ScoreDataModule @@ -11,7 +11,7 @@ def train(yyyy_mm: str): ds_yyyy_mm=yyyy_mm, batch_size=2 ** 10, score_bounds=(5e5, 1e6), - ds_set="1000" + ds_set="10000" ) epochs = 50 @@ -19,15 +19,10 @@ def train(yyyy_mm: str): uid_le=dm.uid_le, mid_le=dm.mid_le, qt=dm.qt_accuracy, - emb_dim=128, + emb_dim=8, mf_repeats=2, - mlp_range=[512, 256, 128, 64, 32, 32], + mlp_range=[128, 64, 32, 8], lr=1e-3, - # one_cycle_lr_params={ - # "pct_start": 0.025, - # "three_phase": True, - # "final_div_factor": 1e7 - # } ) trainer = pl.Trainer( @@ -38,7 +33,7 @@ def train(yyyy_mm: str): callbacks=[ EarlyStopping( monitor="val_loss", - patience=20, + patience=3, verbose=True, mode='min', divergence_threshold=1 @@ -47,7 +42,6 @@ def train(yyyy_mm: str): ModelCheckpoint(monitor='val_loss', save_top_k=1, mode='min') ], devices=[3, ], - # fast_dev_run=True, ) trainer.fit(net, datamodule=dm) From 3eedd1f8eedb0ff48e764444989b84112c0b9af5 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Fri, 17 Feb 2023 11:25:28 +0800 Subject: [PATCH 23/27] Deploy new model --- models/V1_2022_11/analysis.ipynb | 408 - .../checkpoints/epoch=34-step=34755.ckpt | Bin 6750554 -> 0 bytes models/V1_2022_11/error.png | Bin 41068 -> 0 bytes models/V1_2022_11/hparams.yaml | 11233 ---------------- models/V1_2022_11/overview.png | Bin 124485 -> 0 bytes models/V2_2023_01/analysis.ipynb | 413 + .../checkpoints/epoch=8-step=66897.ckpt | Bin 0 -> 9043079 bytes models/V2_2023_01/error.png | Bin 0 -> 64923 bytes models/V2_2023_01/overview.png | Bin 0 -> 183887 bytes 9 files changed, 413 insertions(+), 11641 deletions(-) delete mode 100644 models/V1_2022_11/analysis.ipynb delete mode 100644 models/V1_2022_11/checkpoints/epoch=34-step=34755.ckpt delete mode 100644 models/V1_2022_11/error.png delete mode 100644 models/V1_2022_11/hparams.yaml delete mode 100644 models/V1_2022_11/overview.png create mode 100644 models/V2_2023_01/analysis.ipynb create mode 100644 models/V2_2023_01/checkpoints/epoch=8-step=66897.ckpt create mode 100644 models/V2_2023_01/error.png create mode 100644 models/V2_2023_01/overview.png diff --git a/models/V1_2022_11/analysis.ipynb b/models/V1_2022_11/analysis.ipynb deleted file mode 100644 index dc277831..00000000 --- a/models/V1_2022_11/analysis.ipynb +++ /dev/null @@ -1,408 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Performance Analysis of Model\n", - "\n", - "This notebook will guide through my performance analysis of the model.\n", - "\n", - "We'll target a few questions:\n", - "- How well does the model perform?\n", - "- What assumptions are made?\n", - "- What does the model perform better on? What about the poor ones?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preparation\n", - "\n", - "As usual, we'll load relevant libraries and ...\n", - "- Load our test data\n", - "- Load our model" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Download Files: \n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000.tar.bz2\n", - "Derived Files: \n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000/___.sql (All SQL Files are extracted)\n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000/csv/osu_user_stats_mania.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000/csv/osu_scores_mania_high.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000/csv/osu_beatmap_difficulty.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000/csv/osu_beatmaps.csv\n", - "Proceeding to Download ...\n", - "File exists, and overwrite is false, skipping\n", - "Download Files: \n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000.tar.bz2\n", - "Derived Files: \n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000/___.sql (All SQL Files are extracted)\n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000/csv/osu_user_stats_mania.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000/csv/osu_scores_mania_high.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000/csv/osu_beatmap_difficulty.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/V1_2022_11/../../data/2022_11_01_performance_mania_top_1000/csv/osu_beatmaps.csv\n", - "Proceeding to Download ...\n", - "File exists, and overwrite is false, skipping\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "D:\\Repositories\\PycharmProjects\\opal\\models\\V1_2022_11\\../..\\opal\\score\\datamodule\\score_datamodule.py:76: DtypeWarning: Columns (22) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df_map = pd.read_csv(csv_map)\n" - ] - } - ], - "source": [ - "import sys\n", - "\n", - "import pandas as pd\n", - "\n", - "sys.path.append(\"../../opal/data_ppy_sh_to_csv/\")\n", - "sys.path.append(\"../../\")\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pytorch_lightning as pl\n", - "\n", - "from opal.score.collaborative_filtering.neu_mf import NeuMF\n", - "from opal.score.datamodule import ScoreDataModule\n", - "\n", - "dm = ScoreDataModule(\n", - " ds_yyyy_mm=\"2022_11\", batch_size=16,\n", - " score_bounds=(5e5, 1e6),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "net = NeuMF.load_from_checkpoint(\"checkpoints/epoch=34-step=34755.ckpt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (cuda), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "D:\\ProgramFiles\\Anaconda\\envs\\opal\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:175: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n", - " rank_zero_warn(\n" - ] - } - ], - "source": [ - "# Set to evaluate mode (no gradient update)\n", - "net.eval()\n", - "trainer = pl.Trainer(accelerator='cpu', limit_predict_batches=2048)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predicting\n", - "We'll use our validation data to predict." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "y = trainer.predict(net, datamodule=dm)\n", - "y_preds = []\n", - "y_trues = []\n", - "for x_uid_real, x_mid_real, y_pred, y_true in y:\n", - " y_preds.append(y_pred)\n", - " y_trues.append(y_true)\n", - "y_preds = np.stack(y_preds).flatten()\n", - "y_trues = np.stack(y_trues).flatten()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analysis\n", - "\n", - "We'll firstly do some quick viz to get an intuition of the data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "import matplotlib.ticker as mtick\n", - "plt.rcParams.update({'font.size': 14})\n", - "plt.figure(figsize=(10, 10))\n", - "plt.suptitle(\"Overview of Predictions and Actual Accuracies\")\n", - "\n", - "ax1 = plt.subplot(221)\n", - "sns.lineplot(x=[0.75, 1], y=[0.75, 1], color='gray')\n", - "sns.scatterplot(x=y_trues, y=y_preds, s=8, c=np.abs(y_trues - y_preds),\n", - " cmap='magma')\n", - "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", - "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", - "plt.title(\"Predictions against Actual\")\n", - "plt.xlabel(\"Actual Accuracy\")\n", - "plt.ylabel(\"Predicted Accuracy\")\n", - "\n", - "ax2 = plt.subplot(222)\n", - "ax2.sharex(ax1)\n", - "sns.histplot(x=y_trues, y=np.abs(y_preds - y_trues), bins=25)\n", - "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", - "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", - "plt.title(\"Prediction Error Heatmap\")\n", - "plt.xlabel(\"Actual Accuracy\")\n", - "plt.ylabel(\"Absolute Accuracy Prediction Error\")\n", - "\n", - "ax3 = plt.subplot(223)\n", - "ax3.sharex(ax1)\n", - "sns.histplot(x=y_preds, bins=25)\n", - "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", - "plt.title(\"Prediction Distribution\")\n", - "plt.xlabel(\"Predicted Accuracy\")\n", - "plt.ylabel(\"Frequency of Prediction\")\n", - "\n", - "ax4 = plt.subplot(224)\n", - "ax4.sharex(ax1)\n", - "sns.histplot(x=y_trues, bins=25)\n", - "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", - "plt.title(\"Actual Distribution\")\n", - "plt.xlabel(\"Actual Accuracy\")\n", - "plt.ylabel(\"Frequency of Actual\")\n", - "plt.tight_layout()\n", - "plt.savefig(\"overview.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Biases Correction**\n", - "\n", - "If you've seen my previous analysis, you'll see that our predictions for lower (actual) accuracies are biased.\n", - "This is because our model assumes a normal distribution for our input data, however, it's actually heavily skewed.\n", - "Despite the sigmoid correction, it was still insufficient to correct the biases, thus, alternatively, we used a `QuantileTransformer` to force transform data into a normal distribution.\n", - "\n", - "**Score Limiting**\n", - "\n", - "We limited the input to only have scores > 750,000. Thus, accuracies taper around 90.00%.\n", - "This may have caused the model to not be able to predict scores beyond 90.00%, however we're not that interested in those predictions.\n", - "\n", - "Despite that, it may prove to be more useful if we included those, so we'll circle back next time.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analysis of input\n", - "\n", - "We also have our `DataFrame` of input data, let's take a glance" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "df = dm.df" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": " difficultyrating diff_overall diff_size version beatmap_id \\\n0 3.39962 8.0 7.0 Hard 192345.0 \n1 3.39962 8.0 7.0 Hard 192345.0 \n2 3.39962 8.0 7.0 Hard 192345.0 \n3 3.39962 8.0 7.0 Hard 192345.0 \n4 3.39962 8.0 7.0 Hard 192345.0 \n\n filename user_id year score \\\n0 FamiRockP - Oni Kanojo (Entozer) [Hard].osu 437945.0 2014 795309.0 \n1 FamiRockP - Oni Kanojo (Entozer) [Hard].osu 623773.0 2014 931373.0 \n2 FamiRockP - Oni Kanojo (Entozer) [Hard].osu 1192936.0 2014 761921.0 \n3 FamiRockP - Oni Kanojo (Entozer) [Hard].osu 1326008.0 2014 878780.0 \n4 FamiRockP - Oni Kanojo (Entozer) [Hard].osu 1517607.0 2014 984948.0 \n\n accuracy speed uid mid uid_le mid_le \n0 -0.957327 0.0 437945/2014 192345/0 2557 2318 \n1 0.181849 0.0 623773/2014 192345/0 2929 2318 \n2 -1.298703 0.0 1192936/2014 192345/0 647 2318 \n3 -0.683942 0.0 1326008/2014 192345/0 1001 2318 \n4 0.642689 0.0 1517607/2014 192345/0 1343 2318 ", - "text/html": "
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difficultyratingdiff_overalldiff_sizeversionbeatmap_idfilenameuser_idyearscoreaccuracyspeeduidmiduid_lemid_le
03.399628.07.0Hard192345.0FamiRockP - Oni Kanojo (Entozer) [Hard].osu437945.02014795309.0-0.9573270.0437945/2014192345/025572318
13.399628.07.0Hard192345.0FamiRockP - Oni Kanojo (Entozer) [Hard].osu623773.02014931373.00.1818490.0623773/2014192345/029292318
23.399628.07.0Hard192345.0FamiRockP - Oni Kanojo (Entozer) [Hard].osu1192936.02014761921.0-1.2987030.01192936/2014192345/06472318
33.399628.07.0Hard192345.0FamiRockP - Oni Kanojo (Entozer) [Hard].osu1326008.02014878780.0-0.6839420.01326008/2014192345/010012318
43.399628.07.0Hard192345.0FamiRockP - Oni Kanojo (Entozer) [Hard].osu1517607.02014984948.00.6426890.01517607/2014192345/013432318
\n
" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see 6 columns, the first 2 are `str` columns, for `LabelEncoder` to encode, the result is the last 2.\n", - "\n", - "E.g.\n", - "\n", - "`uid=U/YYYY` means the user, with user id `U`, with the score in year `YYYY`.\n", - "\n", - "`mid=M/S` means the map, with beatmap id `M` with speed of map `S`.\n", - "- `S=1`: Double Time\n", - "- `S=0`: Normal Time\n", - "- `S=-1`: Half Time\n", - "\n", - "Note that our accuracies are quantile transformed." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Performance of Model\n", - "\n", - "Now, let's figure out how well the model performed on the prediction." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": "'The MAE is 0.96% with RMSE 1.59%'" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f\"The MAE is {np.abs(y_preds - y_trues).mean():.2%} with RMSE {((y_preds - y_trues) ** 2).mean() ** 0.5:.2%}\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sort of tells us that the general error is around 1%, which is acceptable.\n", - "\n", - "Let's find out the errors of each percentage" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_5460\\3748057135.py:5: RuntimeWarning: Mean of empty slice.\n", - " return np.abs(y_preds - y_trues).mean(), ((y_preds - y_trues) ** 2).mean() ** 0.5\n", - "D:\\ProgramFiles\\Anaconda\\envs\\opal\\lib\\site-packages\\numpy\\core\\_methods.py:190: RuntimeWarning: invalid value encountered in divide\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] - }, - { - "data": { - "text/plain": "
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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def get_error(y_preds, y_trues, a, b):\n", - " y_preds = y_preds[(y_trues >= a) & (y_trues < b)]\n", - " y_trues = y_trues[(y_trues >= a) & (y_trues < b)]\n", - "\n", - " return np.abs(y_preds - y_trues).mean(), ((y_preds - y_trues) ** 2).mean() ** 0.5\n", - "\n", - "\n", - "errors = []\n", - "bounds = np.linspace(0.7, 1, 31)\n", - "for a, b in zip(bounds[:-1], bounds[1:]):\n", - " mae, rmse = get_error(y_preds, y_trues, a, b)\n", - " errors.append([f\"{b:.0%}\", mae, rmse])\n", - "\n", - "df_errors = pd.DataFrame(errors, columns=['bounds', 'mae', 'rmse'])\n", - "plt.figure(figsize=(14, 4))\n", - "sns.lineplot(data=df_errors, x='bounds', y='mae', label='mae')\n", - "sns.lineplot(data=df_errors, x='bounds', y='rmse', label='rmse')\n", - "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n", - "\n", - "plt.title(\"Errors for different true accuracies\")\n", - "plt.xlabel(\"Actual Accuracies\")\n", - "plt.ylabel(\"Error\")\n", - "_ = plt.xticks(rotation=90, ha='right')\n", - "plt.tight_layout()\n", - "plt.savefig(\"error.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With domain expert input, it's logical.\n", - "There is significantly large deviations for lower accuracies, which is largely due to the undersampling of scores within that range. Refer to the diagram above to see the distribution of scores.\n", - "\n", - "Fortunately, this isn't detrimental to common business use cases, as the scores of interest are those of higher accuracies." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/models/V1_2022_11/checkpoints/epoch=34-step=34755.ckpt b/models/V1_2022_11/checkpoints/epoch=34-step=34755.ckpt deleted file mode 100644 index 3dc31c89addbd6e4378370d12a97857b2b7bc253..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6750554 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index 00000000..f6e41410 --- /dev/null +++ b/models/V2_2023_01/analysis.ipynb @@ -0,0 +1,413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Performance Analysis of Model\n", + "\n", + "This notebook will guide through my performance analysis of the model.\n", + "\n", + "We'll target a few questions:\n", + "- How well does the model perform?\n", + "- What assumptions are made?\n", + "- What does the model perform better on? What about the poor ones?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparation\n", + "\n", + "As usual, we'll load relevant libraries and ...\n", + "- Load our test data\n", + "- Load our model" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Download Files: \n", + "\t- D:/Repositories/PycharmProjects/opal/data/2023_01_01_performance_mania_top_10000.tar.bz2\n", + "Derived Files: \n", + "\t- D:/Repositories/PycharmProjects/opal/data/2023_01_01_performance_mania_top_10000/___.sql (All SQL Files are extracted)\n", + "Proceeding to Download ...\n", + "File exists. Skipping Download\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\Repositories\\PycharmProjects\\opal\\opal\\score\\datamodule\\score_datamodule.py:74: DtypeWarning: Columns (15) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_map = pd.read_csv(csv_map)\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pytorch_lightning as pl\n", + "\n", + "from opal.score.collaborative_filtering.neu_mf import NeuMF\n", + "from opal.score.datamodule import ScoreDataModule\n", + "\n", + "dm = ScoreDataModule(\n", + " ds_yyyy_mm=\"2023_01\", batch_size=16,\n", + " ds_set=\"10000\",\n", + " score_bounds=(5e5, 1e6),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "net = NeuMF.load_from_checkpoint(\"checkpoints/epoch=8-step=66897.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "NeuMF(\n (model): NeuMFModule(\n (u_mf_emb): Embedding(29006, 8)\n (m_mf_emb): Embedding(12397, 8)\n (mf_net): Sequential(\n (0): NeuMFBlock(\n (net): Sequential(\n (0): Linear(in_features=8, out_features=8, bias=True)\n (1): ReLU()\n (2): Dropout(p=0.1, inplace=False)\n )\n )\n (1): NeuMFBlock(\n (net): Sequential(\n (0): Linear(in_features=8, out_features=8, bias=True)\n (1): ReLU()\n (2): Dropout(p=0.1, inplace=False)\n )\n )\n )\n (u_mlp_emb): Embedding(29006, 8)\n (m_mlp_emb): Embedding(12397, 8)\n (mlp_net): Sequential(\n (0): NeuMFBlock(\n (net): Sequential(\n (0): Linear(in_features=16, out_features=128, bias=True)\n (1): ReLU()\n (2): Dropout(p=0.1, inplace=False)\n )\n )\n (1): NeuMFBlock(\n (net): Sequential(\n (0): Linear(in_features=128, out_features=64, bias=True)\n (1): ReLU()\n (2): Dropout(p=0.1, inplace=False)\n )\n )\n (2): NeuMFBlock(\n (net): Sequential(\n (0): Linear(in_features=64, out_features=32, bias=True)\n (1): ReLU()\n (2): Dropout(p=0.1, inplace=False)\n )\n )\n (3): NeuMFBlock(\n (net): Sequential(\n (0): Linear(in_features=32, out_features=8, bias=True)\n (1): ReLU()\n (2): Dropout(p=0.1, inplace=False)\n )\n )\n )\n (neu_mf_net): Sequential(\n (0): Linear(in_features=16, out_features=1, bias=True)\n (1): Tanh()\n )\n )\n (loss): MSELoss()\n)" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n" + ] + } + ], + "source": [ + "# Set to evaluate mode (no gradient update)\n", + "net.eval()\n", + "trainer = pl.Trainer(accelerator='cpu', limit_predict_batches=512)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predicting\n", + "We'll use our validation data to predict." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "y = trainer.predict(net, datamodule=dm)\n", + "y_preds = []\n", + "y_trues = []\n", + "for x_uid_real, x_mid_real, y_pred, y_true in y:\n", + " y_preds.append(y_pred)\n", + " y_trues.append(y_true)\n", + "y_preds = np.stack(y_preds).flatten()\n", + "y_trues = np.stack(y_trues).flatten()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysis\n", + "\n", + "We'll firstly do some quick viz to get an intuition of the data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "

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7du0i57J+/XrVb7nye6r8Pdi9e3exxy7ut/d5vyHK/M6ZM0ftOubm5kpz586VFAqF1LlzZ7XjTJw4UfU9U15jSSr8fHbs2FFSKBTShg0bJEkq+dqPGjVKUigU0ueffy4lJSWplqempkqjR4+WFAqF9OWXX6qW79u3T1IoFNKgQYOk7Oxs1fLs7GzV+vPnzy+2XARB+G8QNdCC8B+nbCr6Iv06K1SoABQ2U1O+eVfWABc3VZKyGZ9yHYBff/0VKBzkysnJSW39fv360b17d1JSUtiyZUuR/bm5ueHq6qr6Wy6Xo6+vT+fOnZEkqUgt9OXLlwkPD6dmzZpFjvW0vLw8fvvtNwB+/PFHtUHS5HI548aNw83NjUePHqnOs127dshkMs6cOYMkSar1z5w5g4mJCU2aNCE+Pl6tqaayqWFppkv5/fffARg9ejQdO3ZUS2vcuDHffPMN8KQ8y8Lq1av5+uuvVf8mTZrEmDFjaNOmDTNnzgTg888/x83Nrci27du3VzXxhsJyi4+PZ/v27Whra/Pzzz9jaWmpStfW1mbOnDlUqVKFq1evqmqxbty4wdWrV7GwsGDWrFlq/dTbtWvH4MGDS30+GzZsAGDmzJnUqlVL7dizZs3C2tqazMzMl+pHqxy0zMPDgyFDhiCTyVRptWrVYuHChUDhdSxuzIC5c+eqffesra1VrTSebharbOpqa2urtn2FChWYP38+CxcuVNXUPUtsbCxt27ald+/edO3aVS2tXLly9OjRA6DYliQA33zzjVpLFVNTU3r16lUkv97e3qSmptKxY0e1QezkcjmTJk2idu3az83r09LT09m/fz8ymYx+/fqplhsZGdG5c2fgyXV+2u7du0lOTqZVq1Z89NFHatenQ4cOdOvWDYVCUaQJcll41bIuLeWo5MpaZyVll5rNmzer/TZBYdP6gIAATE1N+eGHH9DT01OlVatWjc8//5waNWqomuZv2bKF3NxcBg4cWORchg8fTsOGDalSpQohISGvdC5Q/G9ITk4OdnZ2NGvWjIkTJ6pdR01NTdW5Pl2WcXFxHDp0CC0tLZYuXUr58uVVaebm5nzzzTfY2to+cxrD27dvc/LkSSwsLFi0aBHGxsaqNENDQxYtWoShoSGHDh1SdTtS7s/KykptQDJtbW2mTJnCnDlz6NKly8sWjyAI7wERQAvCf5yy2aeWllapt3k6mFH2/3J3d8fW1paQkBCuXbumSr98+TJhYWEoFArq1asHFAbtygfWpk2bFnsM5eixxTULLOnhu6QgXtlE83mjUd++fZukpCSMjY2pW7duseu0atVKLV+WlpY4ODjw+PFjbt++DRQ+uAUFBdGoUSPVgGxPn8eJEyfQ0tJ67gjW+fn5XLhwAUDVbP7funfvjlwuJzg4WK1/9qs4e/Ys+/btU/07cOAA/v7+aGlp0a1bN9avX8+ECROK3ba4PqL+/v7k5uZSo0YNteBZSUtLi2bNmgFPykn53+bNmxc7Ku6/XyaUJDY2luDgYFXT6n8zMzPj6NGj/P777y81mJyyeX5J16d58+aYmZmRmprKrVu31NKMjY2LHcn+6RdUSo0bNwYKA+7Jkyezf/9+Vd/tpk2b0qdPH7WgoyQ1a9ZkyZIlfP/992rLY2JiOH36tKrveEkDBD794upZ+VWWS3GjQMtkMrWuHKVx4MAB0tPTadq0KVWqVFFLUwZP+/fvJzExUS1NmY+SXlYtXryYXbt20ahRoxfKT2m8almXxsOHD7lw4QLlypVTvUhQ6ty5M8bGxoSGhnLq1Cm1tKe/X8V1afH09OTAgQOqPt3K9Usqxw0bNrBt27ZST5X3LMX9hmhra7NgwQLWrVunFsSmpKRw5coVDh8+DFCkT3NBQQFOTk7F/u60adOGw4cPM3ny5BLzcubMGQCcnZ0xMDAokm5iYoKLiwuSJKk+aw0bNkQmk7Fv3z5GjhzJ5s2befjwIQDVq1dn8ODBZdqlRhCEd89/b2haQRDUVKpUieDgYNXDeGnExcUBhYGPiYmJanmfPn1YunQpe/fuVdX0Flf7/HT/L3d392ceq7i+Yk8f82murq5Ur16d4OBgbt68iaOjI9nZ2fzzzz9oaWnRs2fPZx5L2Qc3OTn5uf1en85X27ZtuXnzJn5+fjg4OKhqo5s0aaLaz/nz5xk+fDi3bt0iJiaGFi1aYGho+MxjJCUlqR4ISxoZ1sDAADMzM+Li4oiJiVEFM69iw4YNJU5j9TzFXRtlWd2+fbvU5arsT1uxYsVi1yvtSLnKlwpmZmavZXoaZT6fNX6AtbU18fHxRfoIlzQdloaGBoBareGwYcN4+PAhXl5e7N27l7179yKTyVQDXfXv359KlSqVKs8FBQWcPHmSffv2cffuXSIiIlT99Z+u2fs3uVyOkZFRqfJbVtdPSVnLGhYWVuxAXXK5nOzsbLZv387o0aNVy5XXv6xHoy+tly3r0lIOUCiXyxkzZkyRdOUxNmzYoPbCTlkuVlZWpTrOmyzHkn7fAe7cuYOXlxfXr18nLCxM1QKquLJ80XMsjvL3yMfHp9S/XXXr1uW7777j+++/5+zZs5w9exZANW5Anz59VC+TBUH4bxIBtCD8x9WtWxc/Pz+uXLlS6m2UI9fWqVNHrebaw8ODn3/+mQMHDjBt2jQKCgpUTeiUzRXhyTy7+vr6tG3b9pnHUg6G9TS5vOTGMX369GHx4sXs2bMHR0dHjh07RmpqKu3atcPMzOyZx1Lmy9zcXFXjV5KnHyTbtWvH8uXLOX36NJ9++qmq1qJJkyZYWVlhZGTExYsXyc/Pf6Hm2/9udvm8fOvo6JRq/depuAdZZf6sra1xdnZ+5vaOjo5q+ympDErbYuJ1T29UmmtU0vV5kQBKQ0OD7777jk8//ZRjx45x9uxZrly5QmBgIIGBgfzxxx+sXr36uZ/b7OxsPvnkEy5cuIBMJkOhUNChQweqV6+Oo6MjkZGRzJ49u9htXyS/z1v3RWr7b968qaq9DwsLK3HEbyic0urjjz9WBfW5ubmlPs6rKG4At1cp69LIyclh165dQOHLtmcN4nXmzBkePnxItWrVgBcvl7L8Hj1vsLuSPjtLlixRdbGpXLkyjRo1olq1atSuXZuqVasWaWFUFnlW5tXe3h6FQvHMdatXr676//79+9O5c2eOHz+On58fFy9eJDIykr///pu///6bL7/8ks8+++yV8ycIwrtJBNCC8B/Xq1cvfv31V86cOUNoaOhzm4Hm5OSwY8cOALp166aWphwd9/jx45w5c4asrCxSUlLo0KEDpqamqvWUzek0NDRYvHhxmdTEKHl4eLBs2TIOHTrEtGnTVP2hn64BL4kyX2ZmZixZsqTUx1QoFFhbW3P16lXS0tI4d+4cVapUUdWyNWrUCB8fH27evImvry9yufy5Lw6gsCZGR0eH7OxswsPDVQ+/T0tNTVVNoWVubl7qPL9JylpxZZPW0lBei3+PzK30rH6LxR07Pj6enJycYmuh9+/fj4aGBo0bN35m7VdJ+VQGdSXVQisDvue9wCmNypUrM2zYMIYNG0ZBQQE3btxg2bJlnDt3joULFxY7BsHT1q9fz4ULF6hUqRK//fZbkX7TxfUjfhmWlpYEBQURERFRbPq/a+OfRTl11YABA/juu++KXSc1NZVmzZoRGRnJ0aNHVU38K1SowMOHD0ucOuj27dvcuXMHBwcHtf7x/6Z8aZefn48kSUV+s54ehVnpdZf14cOHSUpKolq1asVOl6Y0cOBAAgIC2LRpk2pkbeX3oqTvUWJiIocOHcLW1pYmTZpQoUIFIiIiiIqKKvZzfuHCBSIjI3FxccHGxkZVPsUFy8nJyS98rgEBAfz2229oamqybNmyIl0AihvJ/HnnmJuby7Zt27CyslJ1zfk35e9QgwYNVOM/lJaRkRE9evRQvTwODQ1l48aNbNy4kZUrV9K/f/8y+U0QBOHdI/pAC8J/nI2NDf379yc/P5/Jkyer9WMszoIFC4iMjKR69epqg/koKadROXLkCAcOHACKBq+VKlWiatWqpKam4u/vX+xxfv31V7p37642RVNpmJub07JlS+Li4jhx4gR+fn5YWFiopr15lrp166Kvr8+9e/eKnYcaYM6cOXh4eBSZNqdt27bk5uayZcsW4uLi1KanUv7/nj17uH37Ni4uLqV6cNLU1FQ1cd+/f3+x6+zfvx9JklAoFGovKd4lDRo0QC6Xc+HChWIDDSicF7tPnz6q6Y2aN28OwKlTp0hPTy+yvq+vb6mObWVlReXKlcnNzeX06dNF0jMyMpgxYwZffvmlarqfF3mho+w7W9L1OXXqFElJSZiYmLzwwFlKBQUFjBgxgoYNG6oFgnK5HCcnJ6ZMmQIU393h3y5evAgU9o8tbtAxZV/ZkubZLi1ln/aSArtjx46Vaj9paWmqsvXw8ChxPSMjI1XQvGnTJtVy5RgEJX1e/vrrL6ZPn46Pjw9Q8rV/uiWMsgvL04prwfO6y1rZfPtZ5QJPfpOfntJKWS5+fn7F9sH29fVl7ty5qsEJn1eO//vf/5gyZYqqdZKyvEpbVs+jLMtatWoV23/+6T7eyvJ0c3NDJpNx9epV1UvGp12+fJl58+Yxf/78Els1KbuynDhxotga7dzcXPr378+AAQNUeVy8eDEtWrQoMpiljY0NM2fOxMjIiLy8vBd6iSQIwvtFBNCC8AGYMWMGDg4OXLt2jUGDBhX7gBMREcG4ceP4+++/0dfXZ/ny5Wojtyq1atUKMzMzjh07xqlTp6hQoYIqGHqasp/itGnTCAgIUEs7deoUq1evJigo6Jm1QiVRBuzz5s0jNzeXnj17lqrJqI6Ojqpmb8KECQQHB6ul79y5Ey8vL+7cuVNkNG/lfLHKJoZPN6VVDpS2bds2JEkqVfNtpY8//hiAtWvXcvToUbU0f39/li5dCsDIkSNLvc83zcrKiq5du5KWlsa4cePUaoQKCgpYtWoVx48f5+7du6rBdWrXrk2zZs1ISkpi6tSpZGZmqrY5f/68qpxLQ1k2CxYsUA3mA4WtKb799luysrJo3ry5anAqZVPrkoL9pw0fPhwtLS12795dZMT4u3fvqmr8hgwZ8kID9T1NLpdjampKUlISCxcuVPWhhcLyUw6S97zm8YDqJcuZM2fUyjQ3N5dly5apXjIoBwd8Wb169cLCwgI/P78i12rt2rWqwfGeZ//+/WRkZGBra1vsAGZPU77Qu3DhgqpGsn///ujr6+Pj46OqyVY6fvw4+/btQ0dHR9X8VzmgVmpqqlrzfBMTE1VfWuXI+Eq7du0q9oXA6yzr4OBgLly4gFwuf+7YDl26dMHAwID09HRVk++GDRvi4OBAXFwcc+bMUQuiw8LC+PnnnwEYNGgQUNgHX0NDg82bN6u6oSht2bKFixcvYmpqqnqJoRwIbMeOHWoDu4WFhfHjjz++8PkqyzI4OFjtOwyFL2lWrVql+ltZntbW1rRv356cnBwmT56sNh92fHw8CxYsAJ4MQlccd3d3XF1dCQ8PZ/LkyWq157m5ucybN49r164RGhqq6n5ibW1NTEwMK1euLBIkHz16lNTUVMqVK1dsiyJBEP4bRBNuQfgA6OrqsnnzZubOnYu3tzeDBg2iatWq1KhRAy0tLcLDw7lz5w6SJOHg4MDixYuLHT0YCmtNe/bsyR9//AHA0KFDVf0Rn9avXz9u377Nli1bGDRoEHXq1KFKlSpERESo+juOHDmyyMiypdGyZUssLCxUNXKlab6tNG7cOO7du8exY8fo2bMnDg4OWFpacv/+fVVAPWPGjCKDwLi6umJiYkJSUhIymUwtgLaxsaFy5cqq5sjKYLs0mjZtytdff83SpUv5/PPPqV27Nra2tkRFRXH16lWgMMh+3gjjb9u3335LeHg4/v7+dOrUCQcHB8qXL8/t27d59OgRmpqa/PTTT2qDTi1cuJARI0Zw5MgRrly5gpubG/Hx8Vy+fBlXV1dVbdfzDBkyhGvXrrFv3z66d+9O/fr10dPT4+bNm8TExFC5cmXmzZunWl/Zl3Hr1q2Eh4fTsmXLIlMEKdWoUYOFCxcyffp05s6dy19//UWtWrVISEjg8uXL5Ofn061bN8aOHfsKpYeqdu/IkSNcvHiRevXqoaWlxd27dwkPD8fExISpU6c+dz/Dhg3j0KFD3L17l7Zt2+Ls7Ex+fj7Xrl0jMTGRGjVqcP/+/WJr7F6EsbExS5cuZezYsSxZsgRvb29q1qzJ/fv3uX//Pm5ubqW6fsqg9+kxFEpSv3591UwAmzZtYv78+VhaWrJkyRImTpzI7Nmz2bhxI3Z2djx69IgbN24gl8uZO3euakwDa2trNDU1SUxMZODAgVhbW6u6HYwZM4bZs2fz119/cfbsWapXr676Xejbt6+qa4vS6yxrZe1zgwYNnjt4nL6+Pl27dsXLy4vNmzczdOhQZDIZy5YtY/jw4ezatYvTp0/j4uJCamoqly5dIjc3l759+6qmW6pTpw4zZ85k3rx5jBkzhrp162JlZcXDhw8JCgpCR0eHJUuWqEaqHjBgAFu2bCEsLIyOHTtSv3590tLSuHTpEnXq1EGhUKhN7fc8nTt35tdffyUiIoKePXuqvsNBQUGEhoZSqVIlUlJSSE9PJz4+XvUybO7cuYSEhODn50ebNm1wd3cnOzubgIAA0tPTad68+XNfPi5btowRI0Zw8OBBTp8+jYODAwYGBly/fp24uDj09fX55ZdfVC+U+/bti4+PD35+frRr1w5XV1eMjY1V9za5XM6sWbOKfQEtCMJ/g6iBFoQPhJ6eHj/88AN///03/fr1Qy6Xc+bMGU6dOkVmZqbqAcbLy6vE4FlJ2WQQnh28zpkzhzVr1tCqVSuioqLw9fUlISGBli1bsnbt2lIFBMVRBvEALi4uaoO7PI+WlharVq3ixx9/xM3NjYcPH+Lr60t2djYdO3Zk69atDBs2rMh2Ghoaqil7ateurTbnKDypha5Vq9YLj0A8atQoNm/eTMeOHYmLi+Po0aNERkbSsWNHNm7cqJoL+l1maGjIxo0bmT17NrVq1SIwMJBTp06hpaVF79698fb2LtI009LSkm3btjFmzBgMDQ05fvw4UVFRfPrppyxevLjUx5bJZCxZsoSlS5fi4uLCjRs3OHXqFDo6Onz00Ud4e3urBSEDBgygT58+6OjocOrUKdV0QyXp0aMHO3fuxMPDg6ysLI4dO8aDBw9o1qwZq1atYunSpcW+RHoRlpaWeHl54enpibGxMefOneP06dNoaGgwfPhw9u3bV6p5oB0dHdm+fTsdOnRAS0uLkydPcvXqVWrUqMH8+fPZtWsXxsbGPHr06JXnRm7YsCE7duygV69epKWl4evri4aGBj/88AOenp7P3f769evcvn0bmUz23FpWJeVvz759+0hKSgIKu1d4e3vTq1cvUlJSOHr0KOHh4bRt25atW7eq5rEGKF++PN9//z02NjbcunWLs2fPqmpQBwwYwIoVK3B3d+fRo0ecPn2a8uXL8+uvv/LRRx8VycvrKuucnBxVq4PnNd9WUtbOh4SEqJo729jY4O3tzejRozEyMlLlr27duixevFhVQ6s0ePBgtm7dSseOHYmKiuLYsWMkJCTQrVs3vL291aYktLS0ZPv27Xh4eKCtrc3JkyeJjIxk9OjRbNy48YWDRwMDA7y8vBg6dCgVK1bkwoULnDlzBj09PcaNG8e+ffto0KABgFoNuampKV5eXnz11VdUqlSJM2fOcPHiRWxsbJg1axZr1qx5bsuQihUrsmPHDr766iuqVq3K9evXOXfuHOXKlWPo0KHs27dPrXWEpqYmq1atYtKkSdSsWZPr16/j6+vL48eP6datG15eXqV6ISQIwvtLJpV2GFhBEARBEARBEARB+ICJGmhBEARBEARBEARBKAURQAuCIAiCIAiCIAhCKYgAWhAEQRAEQRAEQRBKQQTQgiAIgiAIgiAIglAKIoAWBEEQBEEQBEEQhFIQAbQgCIIgCIIgCIIglIIIoAVBEARBEARBEAShFEQALQiCIAiCIAiCIAilIAJoQRAEQRAEQRAEQSgFEUALgiAIgiAIgiAIQimIAFoQBEEQBEEQBEEQSkEE0IIgCIIgCIIgCIJQCiKAFgRBEARBEARBEIRSEAG0IAiCIAiCIAiCIJSCCKAFQRAEQRAEQRAEoRREAC0IgiAIgiAIgiAIpSACaEEQBEEQBEEQBEEoBRFAC4IgCIIgCIIgCEIpiABaEARBEARBEARBEEpBBNCCIAiCIAiCIAiCUAoigBYEQRAEQRAEQRCEUhABtCAIgiAIgiAIgiCUggigBUEQBEEQBEEQBKEURAAtCIIgCIIgCIIgCKUgAmhBeEMKCgredhaEN+hdvt7vct4EQRDeNPGbKJQFSZLedhaEN0QE0MJr4e/vj729fbH/ateujYuLCx07dmTKlCncuHHjbWeXX375BXt7e6ZOnapaFhERgb29PXXq1HmlfYeHhzN+/HgCAgLUlg8dOhR7e3v27NnzSvsXirdr1y7s7e0ZMWLEGz/2hQsX6Nev30ttm5CQgKOjI/b29gwbNqyMc/ZqeSsLyt+B6Ojot5YHQfiQifvzE+/b/Xnq1KklXrvi/q1YseJtZ/mltWnTBnt7e3bt2vXcdZXn6+/v/wZyVlRgYCBDhw4lJibmrRxfePM033YGhP++7t27q/0tSRJZWVkEBgaye/du9u3bx8KFC/Hw8Hg7GXzNRo4cSXh4OMOHD3/bWRHegKioKIYOHYqGhsZLbb9z505yc3PR0dHB39+foKAgFArFO5E3QRD+W8T9+f28P1tbW+Ps7Pzc9ezt7V9/ZgR69eolWjF8YEQALbx2S5YsKXZ5QUEBS5YsYd26dXz77be0aNECU1PTN5y7kllaWnLw4EFkMtkr7aekH9VFixaRmZmJpaXlK+1fKF779u1xcnJCX1//jR43Pz//pbeVJAkvLy9kMhmffPIJq1atYtOmTXz33XdvPW+CIPz3iPvz+3l/dnd354cffnjb2RD+nwiePzyiCbfw1sjlciZOnIipqSmZmZkcP378bWdJjZaWFnZ2dlSvXv217N/Kygo7OzsMDQ1fy/4/dEZGRtjZ2VGpUqW3nZVSO3fuHGFhYTg7OzN48GA0NTXZu3cvycnJbztrgiB8QMT9WdyfBUEomQighbdKS0uLypUrA/D48WPVcnt7e5o2bcq9e/fo378/jo6ONGvWjJ07d6rWSUlJ4X//+x/dunXDyckJV1dXBg4ciJeXV4k1bdevX2f8+PE0bdoUJycnBg4cWOKDwbP6WOXk5PDXX3/Rr18/3N3dqV+/Pr1792bTpk3k5OQAT/rgPnr0CABPT0+1PjrP6mPl5+fHmDFjaNy4sercv/rqK27evFlk3RUrVmBvb8/WrVu5efMmn332GQ0bNqRu3bp069aNtWvXqvL0tEuXLvH555/Tpk0bHB0dadSoER9//DH79+8vtjye5ciRI3z22Wc0b94cR0dHXFxc6NKlC4sWLSIhIaHYbU6dOsVHH31Eo0aNcHZ2ZtCgQRw7dow9e/aU2HfrRY5TXB9o5TXt378/aWlp/Pjjj7Rr1w5HR0eaNm3KlClTCAsLK3LchIQEfvjhB7p3746LiwsuLi706NGDZcuWkZiYqFpv6tSptG3bFiis7VX2yyqtbdu2AdC1a1fMzc1p0aIFmZmZ7Nix45nbxcbGsmTJEjp37oyzszPNmjVjxIgRap/t5+XtWX2TlX0m27dvXyTt4cOHfPfdd3Tp0gUXFxfV53X8+PFcvny51OcuCMK7Rdyf3979+XVQnvOSJUvYvHkzzZs3p27dunTp0oW4uDhVXrds2cLy5ctp2LAhzs7O9O3bVy2PBw8eZMSIETRo0ABHR0fatGnDjBkzePjwYZFjKvtsHzt2jG+//RZXV1dcXFwYM2bMGzlngNDQUGbOnKl61mnYsCGffvop586dK3Gb0j5rKMtMqWXLltjb2xMREQE86cet/Ex269aNevXq0axZM2bNmqV6OX7w4EH69++Ps7MzzZs3Z8KECcU+i7xI3uDJNV+0aBFBQUGMHj0aNzc33N3dGTx4MIcOHXrpcv3QiSbcwluVk5NDSEgIgOpGrZSZmcnHH3+MhoYGrVq14ubNm9StWxeAsLAwRo4cSUREBObm5jRs2JCCggIuXbrErFmzOHr0KKtWrUJLS0u1v0OHDjF58mRyc3NxcHDAzc2NwMBAxowZ80J9TBMTE/n444+5desWBgYGuLu7I5PJuHjxIvPmzePEiROsXr2aqlWr0r17d44dO0ZGRgbNmjWjfPnymJubP3P/Cxcu5K+//kImk+Hk5ETFihV58OABBw4c4J9//mHWrFkMGjSoyHZnzpxhwYIFlC9fHhcXF1JTU7l8+TJLly4lMDCQn376SbXu4cOH+fLLL5EkCWdnZxwdHYmPj+fs2bP4+fkRGBjI119/XarymDFjBjt27EBTUxNXV1dcXV2Ji4vj6tWrBAcH4+vry+7du9HT01Nts3r1apYtW4ZMJsPV1RVzc3MCAgIYO3YsLi4uZXackqSlpTFo0CBCQkJwdnbG3t6eCxcusHv3bk6cOMHevXtVTfeSkpLo168fERERVK5cmcaNG1NQUEBAQACrV6/m0KFD7Nq1C0NDQ1W5Hz16FJlMRrdu3UpVhlD4gHrs2DF0dHRU/RL79++Pr68vmzdvZuTIkcjlRd95Xr9+nTFjxhAfH4+lpSXNmzcnKSkJf39/zp07x4QJE/j8889fKW8lOXPmDGPHjiUrKws7OzuaNm1KVlYWt2/f5siRIxw7dow1a9bQvHnzVz6WIAhvlrg/F/Um7s+v27Fjx3jw4AHu7u7UqlWL1NRULCwsVOkbN24kJCSEJk2akJeXR/ny5dHW1iYvL4+vvvqKw4cPq+7D5cuXJzAwkB07drB//36WLl1Ku3btihxzyZIlRERE0LRpU5KSkqhWrdobOdfjx48zceJEMjMzsbW1pVWrVsTHx3Pq1ClOnDjBl19+yWeffaa2zYs8a9jb29O9e3f27dsHFHYd09XVLdJ17Msvv8TX1xd3d3cqV67MhQsX8PLy4v79+9SvX581a9bg4OBA8+bNuXLlCocPH+bChQscPHhQrevEyz4H3b17lwEDBqChoUHDhg1JTU3l0qVLXL58mevXrzNlypTXdAX+wyRBeA3Onz8vKRQKSaFQlLhOdna2NG3aNEmhUEgNGjSQUlNTVWnKbT08PKTMzExJkiQpPz9fkiRJysvLk7p37y4pFAppzpw5qnRJkqTY2FipT58+kkKhkBYvXqxaHhcXJ7m5uUkKhULauXOnanl+fr60ePFi1fGmTJmiSgsPD5cUCoVUu3ZttXxPnDhRUigUkqenp5SQkKB2jI4dO0oKhULasGGDannr1q0lhUIhXbx4UW0/Q4YMkRQKhbR7927Vsh07dkgKhUJyd3cvsv6+ffukOnXqSLVq1VJL+9///qfK/7x586Ts7GxV2tGjR1VpDx48UC3v0KGDpFAopBMnTqgd4+LFi1Lt2rWlOnXqSPHx8dLz+Pn5qfJ7//59tbT79++ryvzAgQOq5QEBAZK9vb3k7OwsnTt3TrU8Oztbmjp1qiq///vf/17pODt37pQUCoU0fPhw1TLlNVUoFFKXLl2k0NBQVVp8fLyqXH766SfV8tWrV0sKhUKaOHGi6jMoSZKUmpoq9erVS1IoFNKff/5Z5Bj//tw8z5o1aySFQiFNmjRJtSwvL09q2bKlpFAoJB8fnyLbZGZmSm3btpUUCoW0YMECKScnR5V2+fJlycnJSVIoFFJgYOBz86Ysl6ioqCJpyu9zu3btis3bmjVriuRr7NixkkKhkD755JNSH0cQhNdP3J/f7fvzs0yZMqVIWZSW8p74799s5bV7Oq/79+8vkr58+XJJoVBIrVq1koKCglTpBQUF0rp16ySFQiE5OTlJISEhRfJrb28vXb58ucg+n0V5bQYMGCBNmjTpmf+U+T5//rxq+/DwcMnZ2VmqVauWtGPHDrV9X7t2TWrYsGGR56CXedaQpJLva8pzcHJyUjv/mzdvSvb29qqyefr+npKSorqvP/1ZfZXnIIVCIfXt21ftue7ixYuSi4uLpFAopDNnzpR8IYRiiRpo4bX7d02mJEkkJSVx48YNkpOT0dXV5ccffyy2r5Gnpye6uroAqto3X19f7t69i729PbNmzVIbUdjCwoIlS5bQqVMnNm3axJgxYzA0NMTb25vU1FQ6duxI7969VevL5XImTZqEn58fd+7cee65xMXFcejQIbS0tFi6dCnly5dXpZmbm/PNN9+waNGil56iZ+3atUBhsyd3d3e1tG7dunH37l3Wrl3LmjVriqRbWFgwbdo0tfJo27Yttra2hISEEBQUpHrrq5xqwdbWVm0f7u7uzJ8/H21t7VKN1JyRkUHHjh1xdXXFzs5OLc3Ozo4mTZpw+PBhVXMmgD/++ANJkhg7diyNGjVSLdfW1ua7777j0qVLRZouvcxxnuebb76hatWqqr9NTU3p1asXy5Yt4+7du6rlymtpbW2tVgNsaGjIt99+S2BgIPXq1Sv1cYsj/f/gYVBY66ykoaFB3759WbFiBRs3bizyZt/X15fw8HAUCgVTp05Vy5+rqytDhw7l1KlT3L9/v8xHY42Pj6dRo0YkJyfz0UcfqaXp6urSt29fjh49Snh4eJkeVxCEsiPuz6X3pu7PpeHt7Y23t/cz16lcuTK+vr5FlmtpaalNkfjvlk2VKlWia9euauk5OTmsX78egO+//56aNWuq0mUyGR999BE3b97kwIED/Pnnn3z77bdq+3Rzc8PV1bXEYz5LQEBAkWnGSmP9+vVkZGTg6elJnz591NLq1avHpEmTmDlzJmvXrqVly5bA63nWgML7+tPn7+DgQM2aNQkKCqJz585q93YjIyNatWrFxo0b1ZrFv0retLS0WL58uVpttru7O2PHjmXx4sVs2LCBJk2avNA5fehEAC28dsqmLUoaGhro6+tTpUoVevTogaenZ4k3juL6N505cwaAxo0bFxvk2draUq1aNR48eMDVq1dp1qwZ58+fB6B169ZF1pfJZHTo0KFUN2h/f38KCgpwdXUtdnTONm3a0KZNm+fupzgxMTGEhISgqalJly5dil3Hw8ODtWvXcuHCBQoKCtRuQk5OTsWWR4UKFQgJCSEzM1O1rFGjRhw/fpyBAwfSo0cPmjZtSv369dHT01N7gHme9u3bF+kXm5+fz6NHj7h9+7YqeHq6/5Sfnx8AnTp1KrI/LS0tOnTowO+///7Kx3mep29mShUqVAAKb1RKjRs3ZsuWLaxdu5bg4GBatWpFkyZNsLKyol69eq8cPAOcPXuW8PBwqlWrRoMGDdTS+vXrxy+//ML58+e5d++e2oOL8nPdrl27Yh9IJk2axKRJk145f8WpUKFCsaPAJiQkcO/ePU6dOgW82DURBOHNEvfn0nmT9+fSKM00ViWNml69enXVi4/i1K5du8iyGzdukJ6ejoWFhdqL76f17NmTAwcOFNu3uLh9ltb333//3OeS4l4Qnz17FoCmTZsWu43y83b16lUyMzPR09N7Lc8aUPzzhvL6ODg4FEkrV64cANnZ2aplr5K3+vXrY2VlVWR5x44dWbx4MWfPnkWSpFce1f5DIgJo4bV7ujbvRZmYmBRZFhkZCRS+XVS+ES1JVFQU8KTGtWLFisWuZ21tXar8xMbGAhT7Q/SqlHk0NzcvsR+vMp9ZWVkkJSWp3SCNjY2L3UZ50356moX58+fz5ZdfcvHiRVU5amlp4erqSseOHenVq1epp3/KzMxkz549+Pr6EhwcTHR0NHl5eQCqH2NJkoDC/sTp6elA0T51/z7HVznO88jlcoyMjIosV5bV0/vp0KED48ePZ/Xq1fj4+ODj4wMUPgi2bt2afv36FXkb/KKUg4dlZGSoDXqmpKOjQ0ZGBhs3blSb0up1fh5L6+LFi+zatYvbt28TFhamevkgbsSC8O4T9+fSeZP359J4lWmsirtuz0tXnv/Trbb+TXn+yuvwIsd8HZSfxbFjxz5zvby8PGJjY7GxsQHK9llDqbjzV+6ruBcdJd0/XzZv/25tqKT8rmRnZ5OYmPhOTVX3rhMBtPBOK65WTTmCp4uLC1WqVHnm9sob8vMe5jU1S/dVUP5QvQ6l+UF++iaro6OjlvYiAYu5uTmbNm3i5s2bHD9+HH9/f65du4a/vz/+/v788ccfbNmy5blzYIaEhDB8+HCio6PR0dHBwcGBpk2bYmdnh4uLC1u3bmXXrl2q9XNzc9XOpbjrW1w5vOhxnudFg7tx48apRgk/c+YMly5dIiQkhD///JMNGzawcOFCPDw8XmifSnFxcRw7dgwofEhRPqgUZ9++fXz99deqt9NPl+frVNzDnSRJTJo0iQMHDgBQrVo1WrVqRfXq1alTpw4aGhp8+umnbyR/giC8eeL+rK6s7s+v2/OaT79I8+qnKctIW1u7zPb5KpSfxXbt2j13cFHlgHZl/ayhVNrP8LO8St5K6pL39Oe6LPL4IRGlJbx3lEFdp06diq2tK2mboKCgEvutPCtoeZqyiW9Jfahyc3PZtm0bVlZWtGrV6oVuGsrzevz4sao50b+FhoYCoK+vj4GBQan3XRJHR0ccHR0ZP3482dnZnDlzhvnz5xMREcFvv/3GzJkzn7n9vHnziI6OpnHjxixfvrzIW3ZlnzGl8uXLo6urS1ZWFpGRkcW+zVbWSrzKcV4HMzMz+vfvr+qjfPfuXdasWcOBAwdYsGABPXr0eKmHhJ07d5KXl0ezZs1Yt25dsesUFBTQunVroqOj2bFjh6rP8fM+j+Hh4Zw7d46aNWuWOLq5kkwmQ5KkYh9Ci5uH+sCBAxw4cAAjIyNWr15dpM+f8qWAIAgfDnF/Lrv787tEWbYlTa0ET87/eSOZvymWlpaEh4fz+eefF9vdoDjvwrNGSV4lb896RoDCMV2UL+aF0hHzQAvvnYYNGwJw9OjRYtOTk5Pp0qULgwcP5sGDBwA0a9YMgH/++afYbUr7sO/m5oZMJuPq1avFzm98+fJl5s2bx/z58184mKpYsSK2trbk5eWVODefsr+asgxexsOHD+nZs6dqqiQlHR0d2rRpoxpcRDk/5rNcvHgRgBEjRhT5MU9PT1fNA6x8M6+pqfnM61dQUFDstXjR45Slr7/+mqZNm3Lp0iW15fb29qqBUlJSUkhNTQVerKZBkiS2b98O8MwabLlcruoDtnnzZtV51q9fH6DEuVK9vb2ZNWuWqon4s/KmbLIfFxdXJO3KlStFlimvSZMmTYoEz4CqD/TruCaCILybxP351e7P76q6detiYGBAXFycaq7sf1Oef+PGjd9k1kr0vM/i+fPn6dChA2PGjFG9OH6bzxrP8yp5O3/+vKr73NOU37lWrVqVcW7/+0QALbx3OnfujLW1NRcvXmTBggVkZWWp0jIyMpgyZQrBwcGkpKSoBj/p1asXFhYW+Pn58dtvv6ntTznoR2lYW1vTvn17cnJymDx5Mmlpaaq0+Ph4FixYAMDAgQNVy5WDdaSkpDx3/5988gkAixYtUv0YKh08eJD169cjk8kYPnx4qfJbHBsbGxISEggKCuK3335Ta8KTkZHB4cOHAZ47QAk86btz7Ngxtf0kJCTw5Zdfqh5inh4IQ1l7umrVKrVzzM/PZ9GiRdy/fx9QD/Ze5jhlpVKlSjx+/JglS5YUuYbK5lK2traqG5ryeufn56t9Porj5+dHREQEhoaGRQYH+bc+ffogk8mIiIhQBcxdunTB0tKSW7dusWzZMrUb5/Xr1/nzzz+RyWSqz+Oz8qZ8Q//nn3+q7ef06dP8/fffRfKjvCYBAQHEx8erlhcUFLB582bVqOKv45oIgvBuEvfnV7s/v6t0dHQYOnQoANOmTSM4OFiVJkkSf/31FwcPHkRXV7fYebDfhhEjRqClpcXatWuLNG0ODw9n5syZhIaGUqlSJVXz5Zd91lB+joprrVVWXuU5KDU1lWnTpqmlnTt3jjVr1qChocHIkSNfW77/q0QTbuG9o62tzcqVK/nkk0/YsGED+/fvx8HBAQ0NDQICAkhOTsbCwoIVK1aogjBjY2OWLl3K2LFjWbJkCd7e3tSsWZP79+9z//593NzcitwQSzJ37lxCQkLw8/OjTZs2uLu7k52dTUBAAOnp6TRv3lztx6h69eoEBwcze/Zsdu/ezciRI0tsTtuvXz9u377Nli1b8PT0xNnZmYoVK/Lw4UMCAwPR1NRk+vTpr/SGVy6X8+OPPzJq1CiWLFnC9u3bUSgUZGdnc/36dZKSknBwcGDIkCHP3deoUaP47rvv8PLy4uLFiygUCpKTk7ly5Qo5OTnUqFGD+/fvq9UGNGrUiDFjxrB69WqGDBmCq6sr5ubm3Lhxg8jISGxsbAgNDVXrj/Myxykrn376KadOnSIgIIBWrVrh4uKCgYEBwcHB3L9/Hx0dHebOnata39TUFBMTE5KSkhg0aBC2trb88MMPxTbpU9YMd+zY8ZmjogJUqVKFJk2acObMGTZt2kTbtm3R1dXlf//7H6NHj2b16tWq78Ljx48JCAigoKCAL774QvUy5Fl5GzVqFJcvX+bw4cN06NCBOnXqEBERwa1bt+jbty87duxQy0///v3ZsmULsbGxdOjQgfr16yOTybh9+zbR0dGqa5KSkkJubq6qj5kgCP9d4v78avfn0rp06VKRKciKY2pqyvTp08vkmOPGjePevXscO3aMHj164OrqiqmpKYGBgYSEhKCvr8/333//yoNqlpWaNWuycOFCpk+fzrRp0/j1119RKBSkpqZy5coVcnNzcXd3Z/LkyaptXvZZo3r16ty+fVvVXPzrr79+5oBrL+NVnoPMzc3x9fWlbdu2uLq68vjxY65cuYJMJmPGjBk4OjqWaV4/BCKAFt5LtWrVYu/evfz5558cP36cixcvoqmpSeXKlRk0aBDDhg3DzMxMbZuGDRuyY8cO1qxZw9mzZ/H19aVatWr88MMPaGtrl/oGbWpqipeXFxs2bODgwYOcOXMGSZKws7OjT58+DBo0SG3AhilTppCWlsbVq1c5ffo0jRs3fmZ/1Dlz5tCiRQu2bt3KtWvXuHnzJhUqVKB3794MHTq01H15nqVx48Zs3ryZP/74g4CAAI4fP46Ojg7VqlVj1KhRDB06tMggKMXx9PTE0tKSP//8k+DgYHx9fSlfvjwtWrRgyJAhGBkZ0adPH/z8/MjJyVENLjJx4kRq167Nxo0buXPnDvn5+Tg4ODB37lxOnjzJxo0b1UbJftnjlAVDQ0M2bNjAunXr8PX15dKlS+Tn51OhQgX69evHJ598ojbCpUwm46effuKHH37g4cOHxMbGEh4eTq1atdT2Gxsbq6pJLu0AZH379uXMmTOcPXuW4OBg7OzscHZ2Zs+ePfz222+cPHkSX19fdHR0aNSoESNGjFDNb/m8vLVs2ZK//vqLNWvWcO3aNU6ePImdnR3ff/89Xbt2LRJAV6pUiR07drBy5UouXryIn58furq6VKtWjREjRqjm3gwKCuL06dMvPX2MIAjvF3F/fv3Cw8NV/VefpXLlymUWQGtpabFq1Sr27NnDzp07CQwMJCsri4oVKzJkyBCGDRumGsn6XdGjRw9q1arFn3/+yfnz5zl58iRGRkY4Ojri4eFB79691Z4XXvZZY8GCBcyZM4fAwECSkpJ48OBBmQfQr/IcVLNmTb788kt+/vlnTp06ha6uLq1bt+aTTz7Bzc2tTPP5oZBJLzoWuyAIwisIDg5GW1tbrdnU00aNGsWpU6f4448/Spy/URAEQRAEQSjZrl27mDZtGo0bN37utHLCixF9oAVBeKN+//132rVrx48//lgk7eDBg5w+fRoLCwvVAFmCIAiCIAiC8K4QTbgFQXijhg8fzj///MNff/3FsWPHsLe3Ry6X8+DBA4KDgzEwMGDx4sVl2gxbEARBEARBEMqCqIEWBOGNqlWrFnv27GHEiBHo6Ojg7++Pn58f+fn5DB06lD179rwz02AIgiAIgiAIwtNEH2hBEARBEARBEARBKAVRAy0IgiAIgiAIgiAIpSACaEEQBEEQBEEQBEEoBRFAC4IgCIIgCIIgCEIpiABaEARBEARBEARBEEpBTGP1lsTHp/Kyw7fJZGBmZvRK+/ivEWVSlCiT4olyKUqUSVFlUSbKfXyIrl27xqBBg/j9999p0qSJWtr169dZsWIFAQEBZGdnU6NGDYYPH46Hh4faeikpKcycOZNTp05hZmbG8OHDGTZsWJFj9evXj1q1ajFv3rxXyrO4L5c9US5FiTIpSpRJUaJMinqX7ssigH5LJIlX/kKUxT7+a0SZFCXKpHiiXIoSZVKUKJMXFxISwueff05+fn6RtOvXr+Pp6YmOjg7Dhg3D1NQUb29vpkyZQmxsLKNHj1at++OPP3L69GnGjh1LdHQ0CxYsoEKFCnTq1Em1zqFDh7h37x4rV6585XyL+/LrI8qlKFEmRYkyKUqUSVHvQpmIAFoQBEEQhDLh4+PDjBkzSE5OLjZ91apV5OTksH79etzc3ADo378/3bt3Z8WKFQwaNAgjIyMKCgrYu3cvQ4YMUQXV169fZ8eOHaoAOjc3l2XLljFs2DAsLS3fzAkKgiAIHzzRB1oQBEEQhFc2evRoxo0bh4WFBd26dSt2nZCQEMqVK6cKngG0tbVp3rw5OTk5BAcHA5CQkEB2djY2Njaq9WxsbIiMjFT97eXlRXJyMqNGjXpNZyQIgiAIRYkAWhAEQRCEV/bgwQO++uorvL29sbW1LXYdOzs7UlNTiYmJUVseGhqKTCZT1SSbmJigoaGhVpOdmJiImZkZAOnp6axatYrPPvsMI6MPs5+5IAiC8HaIJtyCIAiCILyygwcPoq2t/cx1Jk2axPXr15kwYQLTpk3DzMwMb29vTp06Rd++falUqRIAmpqaNGzYkB07dtCiRQtiYmK4cOECEyZMAGDdunXo6uoyePDg135egiAIgvA0EUALgiAIgvDKnhc8A1SvXp2xY8eycOFCBgwYoFresWNH5s6dq7buzJkzGTNmDD179gSgXbt2jBgxgri4OP7880/mzJlTqmOWlkz26tu+yj7+i0S5FCXKpChRJkWJMimqLMqkrMpTBNCCIAiCILwRc+bMYdu2bdSqVYshQ4ZgbGzM+fPn+fvvvxk5ciS//vorhoaGQGFz74MHD3Lv3j0MDQ2pWrUqACtWrKBq1ar06NGD9PR0FixYwOnTpzE0NGTIkCF4enq+VN7KYmqTD3XasucR5VKUKJOiRJkUJcqkqHehTEQALQiCIAjCa/fw4UO8vLyoWbMmXl5e6OjoANChQwfq1KnDjBkzWLNmDZMmTVJto6WlRZ06dVR/P3jwgJ07d/Lrr78il8uZP38+Z8+e5ccffyQqKooZM2ZgbGxc4iBmzyLmgS57olyKEmVSlCiTokSZFPUuzQP9Tg8idu3aNerUqcPZs2eLpEVGRvLNN9/QrFkznJyc6N+/P8eOHSt2P1euXGHEiBHUr18fd3d3xowZw/3794us5+/vT9euXXF2dmbQoEHcuXOnyDpBQUHUqVOHy5cvv/oJCoIgCMIH4u7du0iSRI8ePVTBs5KHhwc6Ojr4+fk9cx8//fQTbm5utGjRgvz8fPbt20f//v1p3LgxvXv3pmHDhnh7e79U/pRzi77sv7LYx3/xnygXUSaiTESZvEtlUhbe2QA6JCSEzz//nPz8/CJpcXFxDBkyhKNHj9KnTx+mTJlCXl4eY8eOZd++fWrr+vv7M2zYMKKiovj0008ZPXo0N2/eZMCAAarpMgDS0tKYMGEC+vr6TJ48mZycHEaNGkV6erra/pYsWUKrVq3UpuAQBEEQBOHZlEFzcfd1SZIoKCigoKCgxO0DAgI4evQokydPBgpH5c7NzcXc3Fy1jqmpKVFRUWWcc0EQBEF44p1swu3j48OMGTPUpq942sqVK3n06BFbtmxRBbK9e/emX79+LFiwgLZt26Kvr48kSXz33XeYmJiwbds2TExMgMLmYj169OCHH37gt99+A+D48eMkJSWxefNmatSoQcOGDenatSsnT56kS5cuQGEw7ufnVyRIFwRBEATh2dzd3TE0NGTHjh14enpSrlw5VdrWrVvJzc2lWbNmJW7/448/0qlTJ+rWrQsUBsva2tqEh4er1gkLC6NixYqv7yQEQRCED947VwM9evRoxo0bh4WFRbF9mPLz89m7dy9OTk5qtcC6uroMHTqUxMRETpw4AcCNGze4f/8+Hh4equAZwNbWlvbt2+Pn50dsbCyA6o21cpAS5X8fPXoEFL4dX7x4Mb1798bOzq7Mz1sQBEEQ/suMjIyYNWsWkZGReHh4sHr1arZu3cqkSZNYuHAhNWvWZMyYMcVue/ToUW7cuMHEiRNVy+RyOV27dmXz5s389ttvzJ8/nxs3btCrV683dUqCIAjCB+idC6AfPHjAV199hbe3N7a2tkXS7927R0ZGBk5OTkXS6tWrBxT2nQa4evUqAM7OzkXWdXJyoqCggBs3bgComoClpKQAhU3DAMzMzAA4dOgQ9+7dY/z48S9/coIgCILwAfPw8OCvv/6iWrVq/P777yxYsIDr16/z8ccf8/fff2NkVHRwl/z8fH766Sf69euHjY2NWtqMGTPo0KEDv/32G76+vkybNo3u3bu/qdMRBEEQPkDvXBPugwcPPnNex5iYGAAqVapUJE3ZbCsiIgKA6OhoteXPWrd+/fpoaWmxcuVKPv74Y9atW4e2tjYNGzYkNzeXn3/+mWHDhmFpafkKZycIgiC867Kzs0hIeIyeXtW3nZX31vjx40t84dygQQMaNGhQ6n1paGhw8ODBYtOMjIxYtGjRS+VREARBEF7GOxdAPyt4BkhNTQVAX1+/SJqenh4AmZmZQOHAYCWtq6urq7autbU106ZN4/vvv2fr1q3o6Ogwd+5cKleuzKZNm0hOTmb06NEveVZFlcUk4GJy9SdEmRQlyqR4olyKEmVS2E0nLi6a7OxMvv/+B9LTUylXrhzz53+PsbHpS+3zQy5PQRAEQfiveucC6OeRnjH+uDJN9v9PLaVZVy5/0ord09OTLl26EBYWRrVq1ShXrhxpaWmsWrWKMWPGYGRkxPXr11m4cCEhISEoFAqmT59OrVq1Xvg8ymIOsndhIvF3jSiTokSZFE+US1Efapnk5uby6y+/8vu69bg416NePQfy8/O5ciWACxfOMWjQwLedReEDd+ph3gtv06Lae/eIJwiC8F54735dDQwMAMjKyiqSpqxNVo7s+ax1lcv+3d+qfPnylC9fXvX377//jp6eHp6enqSkpPDxxx/TokULpk+fzvr16/noo484fPhwsf22nqUsJgEXk6s/IcqkKFEmxRPlUtSHUiZXr15hx45dSJLEgAH9MDQ0wM/vDOXLl+Nu0H26deuKtrYG+fn5SBKcO+fPyJEf8fhx6ksdT1mugiAIgiD8d7x3AXSVKlWAJ/2bn6bsH63s3/z0unXq1FFbV7l9cX2plWJjY1m/fj3ffvst2traHDx4kJSUFL7++msqVarEN998Q8uWLTl58mSxI4Y/S1lM5l2WE4L/V4gyKUqUSfFEuRT1XyiT3NxcUlKSKVfOGC0tLbW0P//cwLZtO3FycsTOzoY7dwIpV86Q9HRzNDTkZGamoaNjgiRJXLt2g8WLf6R+/UbvfZkIgiAIglB23rlRuJ+nevXqqqbU/6YcfdvV1RV4Mip3cetev34dmUxW7GjeSitXrqRq1ar06NEDQDXllXJkblPTwn5xyimwBEEQhLcnLS2VmTO+o22bnqxd8ztHj/oQFhYKgFwOqamF42J06NAGXV1d6tSxx9nJGTMzM7S1NTExMeHx43giI6No1ao11tbW6Ojovc1TEgRBEAThHfPeBdCampp06dKFK1eucOXKFdXyrKwsNm3ahLm5OS1atAAKA+hq1aqxY8cOkpKSVOuGhITg4+ND69at1ZprPy04OJgdO3YwadIkVT9pZc12eHg4AGFhYWrLBUEQhLfn2rWbbNmyk46dWhEaGsrXX8+kbdtunDjhiyRBt26dmDx5AjY21gTeucP6Pzdy9OhR4uPjMDIyIi4ujvXrN2JmZsGUb2bw+HH82z4lQRAEQRDeMe9dE24onB7D19eXUaNGMXLkSExNTdmxYwf37t3jp59+QkdHR7Xu7NmzGTVqFAMGDMDT05Ps7Gz++usv9PT0mDx5conH+Omnn3Bzc6Nly5aqZS1atMDY2JipU6fSq1cvvL29MTU1VVtHEARBeLZb1+4ReOshtR2rU6dejTLbr5lZebS0tMjIyCQ7O5PY2DgAFixYSkxMFLq6OmzcuJnPPx/NunV/YmxiTCWrihQUFJCSkkpcXALdu3cjKTGZ3Nxc1awPgiAIgiAISu9lAG1hYcHWrVtZunQpGzZsIDc3F3t7e9asWVMkmG3SpAnr1q1jxYoVLF26FD09Pdzc3Pjqq6+oXr16sfu/cuUKx44dw8vLS225iYkJa9asYf78+SxevBh7e3t+//131aBlgiAIwrM9vB/BmKELycnORVdPmw3e87CpVvml9pWXl0dqagqGhkZoa2tRp05ttnmt4+HDB1y8+KSFUo0a1bhy+QpyDQ2+mjieipUssbauQu8+PTEwMCA/v4A1a9aRkZGBXC6nV69eVKtmS+PGDcvqtAVBEARB+I+QSc+a60l4bR4/frVRuM3NjV5pH/81okyKEmVSvP9GuUiEXwwnJigOa+fKWDqodyOR8guICXqMhpYcixpmwLMnJH6TZXLx7A3GjVyk+vvXDdNxbVinyHoFBflEPorCwMCA8qZFu9pkpKez6Mef+XvrbubP/4aMzEwKCvLR0JATHR1D1apVSE9PJzQ0FCMjQ35bu47MzEwWLvyOcsZGPHpU2BUnKyubu3eDqFnTnsuXr9C0aWNq1KiBrW01mjdvXCa/1cL74V2+L7+v01j9N35vy5Yok6JEmRQlyqSosiiTsrovv/1fV0EQBOGFxNyOYdO4XUgFEtp6WozeOpRylY1V6QF7brHjOx9kchnDl/VE0bL41jZvg71jNdp1bsDRQxfo1L0xijo2qrScnBzycnPR09dn84ZdLJi3EktLc9as+55atRVq+7lx8zZ//rGVwYN7kZiYRHRMLPn5efj4+PLwYSiDBvVhx44d5OTkIJPJ+Pzz0ez23kduXjaPHiX//15kRD6Kpl49ZxYt+hkDAwOOHz/D/Plz6Ny5+xssFUEQBEEQ3hfv3SBigiAIH7r0+AykgsLXrzmZuWQkZqrS5HIZ57YXzkggFUjc9Xv4VvJYknLlDJm96FP2nviZ6QtGYWhkAEDIwzBGDPmaNi08OXLoBMuX/QFATMxjzpy+qNo+JSWFwMBADAz00NbWxqpyRdauXc+a1X+ipaVFYmISAOXLG5OTkwOAq6szNWrYMWr0SNLSUgEJkLFu3Xqsq1qTkV7YdDsxMREAQ0PDN1YegiAIgiC8X0QNtCAIwnvGql4lHDvW4pbPXRoPdsWiprkqraBAol57BY/uFE67Z+vycv2LXycdHR0sK+moLTvmc4arV24DMGvGzwwe0pO1q7cCUNW2Co/j4ti2bReRUTGkpaaRmprGn+v/xxm/M0RFxQDw29oNjPxoEGvW/EmVKlYMHtyfzMws8vKy8fb2pmXLZmhqahIaGk5eXj49e/YkJCQUW1tbpk//mosXr1CjRnW6dOnyZgtEEARBEIT3hgigBUEQ3jO6xnp0+7YDHb5uhY6RDjIN9cZETTxdqVzHEk0tDao4W72lXJaGRHJSCjq6OpiamaiWVq9emUGePahkVQGT8uVo0aIhy5f/yqqVf1LdzoZu3doCEikpSeQX5Ku2c3Gpi5mZCQsXziI2NpKLFy/QpUsndu7chafnQAAePgxl61YvcnNzGTv2M7Zu3QHAhAljqVVLwbhx49HR0X2ThSAIgiAIwntEBNCCIAjvIbmmBromesWmaehoUr3Rk77FMpkMkN7YQCSx0fE8CoularVKmFmYlLCWxO6dh5k7awW169Rg3g9fMue78dy6GURqWiqnTp5lwfzlZGZmsfD7qcTGxmNsbISnpwc5uTksXLCMnj074X/hEgMH9aZCBXPS0pK5ffsmlpYVSEhIwM7ODjMzU4YPH4qGhpyCAom//95Obm4uNjY21Khhh5VVJeztFVSuXIWq1tbcv/8AhaIm2tpab6awBEEQBEF4r4gAWhAE4T8s4mY0h349h3EFA9qPakR5q9cz7Z4kFXDm+DVuBtynnIkBMdHxSFIBfQa3xaZ6lSLrxz9OZPb05eTn53Pt6h3OnQlAR1eTv7fuBqBAyiUjo7Bv9+pfNzL8o76YlDdi7twlWFpaMPmb8WRlZeK9ez9bt+5g9uxJGBrqEBx8Hz09faysrKhVy4iszEw0tTTJy8vj77+9mTZtCnfvBpGamkZaWjofffQRiYlJXLx4lYCAW+zccZCp0ybw+eefvpZyEgRBEATh/SYCaEEQhHdcRnwGsUFxmFQxxsTaRC1NKpC45XOPiNsx1GhoTY0mtk/S8gvYPPsw4YGF/aFNK5Wj3ehGryWPd64/ZOrnq5AkCQ0NOd36NWbHVh+uXLzD/9ZNxdRcPd96+jpUt6vCvaBQACpUMENPXwvHugpq2ttgaWmOs4sDdjWskWvIcXFxZMH8JUiSRHR0LA8fhHLe359Zs75GV1ebSpUsGTXqM9XAYZMnf0lBQR6aWpoEBd3j8D9HcXZ2Ye7cBcydO5u8vHyMjcsz6avZALRt25K8/MIq+pUr1jF8+GAsLF7PywZBeFHvwpRUgiAIQiHxiywIgvAOy07JYseU/YQGPEK3nA4f/T4AMzszVXr41Ug2TN4PwMmNl5m0Yxjm1U0LE2WgofWkf/S/+0q/qsz0LIKDIjA1K0dKUhrS/7cRz88vID2tsPY4KDCUiNDoIgG0vr4By1bM5ORxfywrWdCyTSM0NTW5fTuQH35YSdWqlencpRW//PInAImJiTg7O3Lu3CXc3OthW60K5hbGbNu2ncDAQMaP/0x1/Dp17NHXL+zHHBh4l+3bd+Hm6kpERAR6enpcvXqD1NQ02rZprcpPXn4ej+OSAOjUuQ36+vplWlaCIAiCIPw3iABaEIS3Ti6XIUlvro/um5J4N4b4oFjM7S0xUVR4qX2kxqQRGvAIgKyUbGLvPVYLoLMzclX/n59XQG52nupvmVzO4G87cnLzFYzMDHDvVvslz6So3NxcVi7azl6v0+gb6DBual+69mrCeb9btGjrRGDgfQAUtW2oUtWy2H1Us7Ohmp2N2jJlkF/e1JiMzCfTcwVcucmSJbNxr++Ev78/ixYtxcqqEp98MpTMzExCQkL4/vt53Lx5ExubwibjmpqaWFlVYcKE8dja2hAQcJ3GyNi6dSeWlhUYNGggHTq0Jjs7m0oVK/LVV+OIi0ugceMGaGiI26MgCIIgCEWJJwRBEN4q7dBQcvYeQWZmgmbntuSVN33bWSoTqaEJ7Pvci7zsPLT0tOi1zhND6/IvvJ9yFY2wa1iVYP8wDMrrUdHeQi3d1s2Kdp80IOCfu7QY4oqlwlwt3ap2BQYv6FTmLyeSE9LY63UagIz0bB7ei+TC+Rv09WxNZORjuvduRX/PDrg0dMDUouh5y2SFg5sVFDzJmN8pP3S1tZg+YwIPHjwkPPwRzZo1ICbmMdOmj+fEydNYWppz7pw/AJGRUVy7doPAwEAGDOhLSMhDVfCcmpqGgb4h167e4Pjxk0z8agJmZhZs3boduVzOp59+RPD9hzx4EMKcb6dw/Vogx4+doXvPzpiYvPh1EgRBEAThwyACaEEQ3hqNgnwy/9xG/qNoAPTMTaFD27ecq7KREZtK3v/XBudm5pLxOP2lAmhtIx16LejC4wfxGFcqR7l/DQKmpadNxy+a0WFcU5DLit3H66jZL2diQIt2zpw6ehVNTQ2QSTRp4UR4WCyuDWqhqGODnaJqsdsmJMTz66oNnD93hYGDe1DVxorAO0H88P1K8vPzadTIlZ4endC9p42JsRHV7WzZunU7Bw8eoUOH1tSqpSAwMAhDQ0M0NDS4cyeQ0JBQqtoUznn9+HE8Bw/6EPIwlFatWuDkVA8jQyOiomOZMmUiiQkpLPphFR99NBg7u+p8O2cpTZo24O8tezh58jy7dv+Fjo5OsXkXBEEQBOHDJgJoQRDeGgmQaWo8WSAr2z66b5OpfQVsm9sRcjoYuzb2mNa0eP5GJdArr4e1W9GRrJUkiWKD57iQBI7/fRWZXEbrgc6YVy27mlVtHW2+mTeUbv2aoaUpx/fIRcqZGNLHsy0WFZ7dimD//qP8+YcXAHNm/cRnn3sSGhpGfn7hnM7nz19h6NA+hIdHEP9Yl6zsLCIjowCobGVJQX429go7HBzqsHLVL9Sr54B11cL5rnNz83j0KJqQh4WDk504cYoFC+Zy7fptTExMiI9PIj4+GdtqVfH3v0wFy4qEhIRTs2Z19PX1iI2NU+Xjv2rz5s00atQIOzu7t50VQRAEQXjviABaEIS3pkCugc6IAWgcP4PcpByyhq5vPA9SXj4pl0PITkynvJstWmU08rJWOT1azu5M46RMdMrrI9d5sz+3Mhkc/M2fkzuuAZCfm8fAGe0ByMnKJSE6hXKm+uiXK34u6dIwLm9E45b1AKjfzJF7d0OIiozF2MTomfMo5+U+6aetoaFB9epViYmOoVw5Q1JS0mjbrhk5edk8evSIjIwM9PR1cXCoRUpKKklJifj4HAPgypUrzJo1leTkRGQyGZcvB3Dx4hW++uoLDv9zlKysLKpWtUYm1+T+/XBu3fRhxIhB7N9/lAYNXHBzr8uiH35FU1MTF9e6hDx8xLz5U9DT+28PIPbTTz8xcuRIxo0b97azIgiCIAjvHRFAC4LwVuVWqQJDB/C26vyS/IMJmLcHADOnqjjN6wPaZfPTqKGrhV7FkgPJF5Eam4aUL1GuklGpt3l6QLGc7HxkMshMz2b9/H84tj0AhXMVJv2vH2ZWxmRl5XDr8kO0tDSwc7BGLi/sn+znE8CNS8E4NaxJ07b1kMnkSFIBZ08GEHgrBCc3e9wbOXLm1GXGj15Afn4BEyYN4eMxfYs0HY+Li2Pv7n+IioplxsxxXL58g7btmnDv3gMMDQ2YPWciJ06cw65GFU6c8OPatZsAGBoakpCQgKdnP8zMTPD23oMkSYz8aCgpKUnIZDJu3bpDfl4BMTExnD/vzxdfTiAxMYHo6FgePYri+rVbAKxbt4k5c75hxYo/+OLL0bQ82BJtbS1sbG0YO3YUcrkG/3VyuRwTE5O3nQ1BEARBeC+JAFoQhA9aZnSy6v8Tbz1Cys5FVkYBdFl5eCGc38btIj+3gJHLelCr1ZOmt+E3o7nl9xBL2/LUa1cTjf9vEi9J0GV0I7KzcpHLZbQf6o4kQdSDeI5tDwAg6GoE965FYGZlzLY1x1nxrTcAM/83lKad6nH3RgizJ/wOwI6NJ1i7awo161Tl9o1gvhqzFEmS0NLS5A+vuQTeeUD7To3Jzc0j6lE02dk5aGtrq/JZUFDAhr+8+GXlBgDatmtK7dp2LFv2KyEh4Whra/PDDzNo0tQVDQ05CQkJqm1NTMqRkZGBmZkxhoZ6LF++hOiYaCRJ2eT7Aof/8WHud3OpZFWJ9PQsLC0tuXcvhPIm5tSp82T0cQsLM1LT0unSpQ1GRuWoWLGSasTtDyF4Bvjkk0/4/fffUSgUuLu7I5f/d7pOCIIgCMLr9m49JQqCILxhZu7V0NsXQGZ0MnU+a4PMUPdtZ6mIKwduk5tVWJvs++dF6rSpQUGBRFp8BstHbyctsXC6pwlr+lGnRTXVdhY25RkxvzP65XSQ/X+QVL6CEWYVjYiPTkVDU06FyuWRy2Uc2uav2u7qufs07VSP3OwnU2RJkkROVuHfqcnpqjmXc3PzOH7Un19XbkHfQI/O3ZqyZcteatetTp/+XYiLiyM+LpGw8Ec8CA5T7S8hIRmLCmaEhIQDkJOTQ0REFKZmRhw5cpIePTrj6dkXbW1tqle3pVw5A86fP8+FCxfxHDwATc3C80lJTkVPV5+OHTsQHh6Brq4BQUEP2fa3N5cuFTZfNzYpxxdffEpIaDhaWtpkpGdSpUpV+vT6lDp1avLdvG+oYl25bC/aO+zixYukpKQwfPhwtLS0MDY2RkND/eWBTCbj+PHjbymHgiAIgvDuEgG0IAgfNB0bcxqtGkZBZi4apoaFnYffMVb2T+aQruFurZr6KTs9RxU8A6QlZqj+PyU+nd9nHODSsSC6f9KIvl+0REtXC5MKRsxeP4ygqxFUsTOnWl0rCgokOvVrwMrvdlPTsTKV7Mpz5uhVnBsqGPN1T44euMzAj9tSy6kwOK/nak+Pvi054XOJtp0a4HvsHPUbOhIVFUd6emF+li/9i4yMdO7de4h11UrINWQ4ONYkJCSCzIxMhg7rRV5+Lp06teaff47TrXs77gTeIisrGwcHex4/fkxaajIZmVno6MiYPv1XAD77bDQF/1/z/PhxAps3eZGcnEzHTu05ddKPek51GT7ck2XL1qrK4vHjBBwdHXgUGYupqQnV7aqx4LtVZGVlczI2Hh+fk4z8aPBrunrvngcPHmBiYiKacQuCIAjCSxABtCAIHzyZgS4aBu9ezbOSWy9HylkYkJ8voWhqo1puWsWYIXM6sOunk9RubENle3Mu/RNIFYUFEffjuHQsCIB9v5+nSTdHbBwqAmBlZ4GVnfqo4IM+a4OdQ2VOHwvAa/0x6jhXIyM9iwGftMe4gj5rVm7n+vW7tOtcnwvnb1LOWBd7R2uiYmJo2sIZ7x0+2NWoip1d4Wjhilq2IJM4fdqfsLBI6jdwoqAgh85dWhAUdI/x46fSqnVTrCpZ0L1HOyzMTbl46Qq5uZnIyOfmzRtERDzC1taGatWsAWjWrDEVKhTOc33yxGnKlzcjObmwCf7hf3zo1dsDe/uazJu3hJEjh7Jv3z9UrFiBmjXsuHXzLrt2HmDw4N4cPXIac3NTIiIKR/Y2KW/8+i7eO8jX1/dtZ0EQBEEQ3lsigBYEQXjHaetp4dBeUWS5TC6j2UAnGnSvQ3pKFt/2W09SbBpGJrqMW94bmUyGJEkYGOtiVP75o22npKQSHRmPXW0rfA76k5KSSvVaVsydshZJkggLiSY1JY0zflfo0LUx589cp32XRqxbu5OCggIeP06ilkN1unZvyePHCSTEJxEWFgnAxQvX6Na9FUF377N7zz8AnDh+hu492rNn90H69+9J1apV8Pb2xq66LYmJSWhoaBASEkr16raMG/cZZmaF03DJZHIyMjKpWdMMLS0tcnNzadjQnQ4d2rJ69QbS0zN59CiSBg1c0dczIDs7j2rVbBjz2XAK8mHb3/sZMWIAhkZGWFtb0aFDq7K7WO8ZSZJITExEW1sbQ0PDt50dQRAEQXjniQBaEIQPhkwG8mLmSy47EpGXIogPfoyVc2XMnmp6/frI0DbQ5uHNKJJi0wBITcoiL6+AGesHExEUh717VUytnl3LesrnGtM+W8MnX3XjlyW7APD3u03nXo3R1tEiOysHAGtbS2yjK5Gfl8uU2SPR0tbg7NnLJCQU1gQbGeqxZaMfHTo1pXIVS4yMDEhNTcfKypLklDRMTAzR09MlMzMLK6uKuLs7kZyURGRkNB07taJ8eUOcnBxIz0jF2LgcWZnZpKamqILnc2f9ca/vzoCBAzhy5DizZk8nLy+PpKRkJEnO0CEDCQi4iZGRMeWMjLgacIsjR04BMH36BB49iqZXr85UtqqEn98VevXqjIGBwWu5Mu+yxMREFi9ezJEjR0hPTwcKRzvv2LEjX331Faamz57LW/hvOvUw7/kr/UuLauJRUhCED4v41RME4YOQG5PMw7/9yYpPo+6oVsirln2AEHcrhh0TdiJJoGOkw9CNQzCwLP20U/+WEpvG/QvhGFcwpJpbFeQaJQf/VjXMqd2gKncuhFGtbkU0teUYlNejQ5MGpTtWcjr5+QWEPYhBT1+HzIxs5HIZOdk5/Pzb15w5cRWbahXZtvUAsbEJOLvWJCk5meU//UWffh3JzMympsKGrJwMvpn+Md/OWcbD0BC+/mYU0dFx1LK349atO9R2sKdV64ZcvBhALfsaLP1pBbm5eUyfPglNTahQwZy///bi7NlzAHz99ZeqbumHD/twwf8SIz8ayfjxk4mPT+DIkWP0798bJydHfH1Pc9TnDOnphX3B+/btgaGRATo62mRn53D7ThB7dx+l/4Ae/LFuJwnxSezfd4wJX37y0tfofZSSksLAgQMJDQ2lcuXKuLu7k5+fT3BwMDt27MDf3x9vb29RIy0IgiAIxRABtCC8ZsqH/3/PiSu8WVG+dwg9eB2A7Ph0Gq70RJKV7fQ96XFpquucnZpNVlLWSwfQeTn5bJlzmFunHgIw9pfe1G5ZvcT1jcwMGL+yD6mP07np/5BZQ9bj3NyOGs6V0dHXokV3J8wrGasGIPu3pq3r0b57fR4ERdF/eBsiwmLRN9DhzKlrTJk7HNcGdUhPy2D5sg0M/agr/xw6SfcebZDL5ezcfhgNDQ1qO1Rlz/YjWFqakZeXh41NJWZM/x6A6tVt+GbKGALvBrH85zXk5OSgoaFBT4/O7Nixm8eP4/j55+Wkp6fTsWN7bG1tUSjsMDDQByDo7n3u33vAF19OIC01nZo1axAffwEAO7vqpKXlULOGHY8exXHp4lWgcOqsqMgYzC3MkCQJu+rV+GP9T/xz8BQJ8UkAlDP68ILEX3/9lbCwML799lsGDhyolrZt2za+/fZb1qxZw6RJk95SDgVBEATh3SUCaEF4jfQLEtCNDwDkZJq5kCk3edtZemvk8sL+uG/jRUJBWhaypw4s19Z4LaNtV3Sywq55dYJPP6D+EDfK25Z/6X3lpOdw2y9E9XfMwwSs7C3QNdJBx6BwfuXIe3E8vBmNtb0F1rUtueYXzL5156hiZ05N58roGeuwZYUvNZ0qE/noMWH3Y+gysCFNO9YjODAc/9O3SXicQrM29ejk0ZDpi4fzOC4Jn33n8Dnoj0wmY+TYrgRcvk3j5q4YGOqzcu10xn02l7jYBFxcazFv4Xj8z1/DxLQcN27eJfJRNK6udQDIzSscLdvKyhI397r4+Z1HQ1ODnJzC5uD5+fnk5RU2GY2Pj1c1JT582IepU79CR6fwPGNj4zl//hK1atVhzep1jPlsNB4e3XFzc0ZbW5uc7AKWL18FwLBh/ZHLwMLCgkePonF1rYdHr6qcPxfA7t0+DPLsi42NDdbWVhgY6tO9Z4eXvkbvKx8fH3r27FkkeAYYMGAAV65c4ciRIyKAFgRBEIRiiABaEF4TmQx0H19GnlA4ErKeXINMsxZvOVdvh0bQQ1K3+6BVuQK6PVuT/wZHPc59nMqV7/agY6CLbVcnJMB+QAMkmQyeEcwn3okm+moEpnYWWNavWqqAW7e8Pl3mdyU3LRutcjrINTWeu01J9Ix16T6+KXuX+2Fkpo+RmT67lp1CS1uTVoOc0Suny/whm0hNzERHT4tv1g1g+VfeSJJE8M0o2vR1Ii6usF+ylW159m0+C8CtKyHk5+cTEhLFX6sOAXBs/yUUjtaUMzHin72niYmKp1PPhljbWrJ/vy8r/hfGkBFd6dytOTXsbaloZc7Qkd25F/SQn5auY/RnA0lJSQO5BcYm5ZDJ5EyfMY7snCwGDuxJamoS27btoGnTBlSoYEG/fj3Zs+cQnTu3xdm5Ng4ONalQwRxNTU3y8vIYPXqkKnj28TlG167diY6OISQkFAMDAx48COO33zYzd+5UUlJSuXH9rqrcAgPvY2xcHhNjE+rWdWD1L5to07YpNlWrMPmbzzA3N8Pc3IzPJ3z00tfmfRcdHY2zs3OJ6S4uLhw6dOjNZUgQBEEQ3iMigBaE1+kdnFP4TdPIySHxf1vJT0ol+85DNKzMkXdo9uI7ys8nL+wxcn0d5JYmpd4sNSiGpMBoAGSacpr9zxNzxyo8fpxa4jaZ0SkcmriD3MxckEH3VQMxdahUquPJtTXQMdUvdf5KIpPLaPNxfZzaKZAKCtiz8gwXDgX+f6JE496OpP7/HNDZmblkpeegoSknL7ew1remUxVq61dF4VyF5KQ01X41NOQEB0aSnvVkzujU5ExuXA5G4ViVqEeP2bP9BO06NyQ5JYn7QWEAbFp/gMePE2jXoQkdOzVh4fxfkMlkTPrmE1at2oBpeWMGeXanfgMHTp3yo0rVCuhoa+JYT8HUKXMBOH36POPHf4KBoSFLls5l//4DzJ5dmDZv/hx+/nkxjx/HIUkFSJLEkcNHade+PUZGRiz7+UfCQiOIjIxh0+YdFBQUcOdOEPl5MqysKqGnp0teXj7dundg/nf/A2DEiAHY2FZBW0ubNb9uwXNYX/UylkFeXh4aGh/WrdDY2JjIyMgS0yMiIkT/Z0EQBEEowYf11CAIb5AkQZa5G7oyDZBpkFneqcz2LZOBTCYrsT/rO0UmQ6ar/eRPjZeplZVI2HuRsD99ketoUuu7QWjXti559bx8pNx8ZHra6FUyRq6tSUFOHjom+uiYPn/E5ZyUrMLgufDQZCVmPHuD10RDQ4MK1U3JTs0mOyNHtTw1MZPKdhY06lIbmQzKWxpRUFDA1NUD8DtwkwqVjbl3N4K9m89iamHE+LkepGdkkhCbilvTmsREJeLkXgMNHRmJ8cm4NqiFz6HzPHgQRqv2bpw+HsCZk1f5es5Q1TGr2lSiio0l94MfcvqUP1A4BdIF/2vkZOdw9+4DYqLj2Lf3CAA/LVnD7Dlfcv78JfT19cjIyEQul6Ojo42ZWTn09XU5cviIav/hYRHo6mohSQUUFBSw23svN27cws3dnQ1/bebzcZ+TnZNPVnYuPXt2Iy8vj2rVbJn77TI8PXvTqVN7CvILCLh8k/+tmE92ViaaWjpcunCLPd5HUdhXI/jeA5DgcVwiFStZsHHdXo77XGDEqJ549O+AXF62feLfVQ0bNmTLli306tWLatWqqaUFBwezdetWmjdv/pZyJwiCIAjvNhFAC8JrlCErT6ZFa6DsBhHTzk1H//ZRZDEPya/XnpSKDmWz49ckX0sLk/GDyDp+AQ3z8mg0rEfBC+5DlpVL5I7CJsgF2XmkXg/BrIQAOicyiZs//UPKwzjqTeyIaTMFTZcNIi0sHmP7imiaPb9mzaiaGS7DG3Jty0Wqt1JgUa/yC+a4bOkY6dBhZAMSolKRgK6fNkbXSIe6Larxy4y9aGjI0dDVoHJNM4wsdDGzLscfKwvnWk6IS+X+7UdkZmdiXtmIzJwsGrWvTXhoNFv+LFwn4GIgfYe35tb1YGJi4unZvwU3rgaRlZHNtDkf8zg2CStrM6KiYsnMyKJOnRpcvnQTgJo1bTl+/AwA2jpPXpTo6uoQ+SiKe/ce0NOjCzKZDHv76ty4cRO5hkRk5CM+HzeG/y1fRYUKFbCoYEpqaiqSBJcvBXDjxi3Mzc2xsLDAo1dvAgKuI0OOvcKe779fjq6uDp06taduvTqkp2Wyb6/Pk/LS0adWrRoYGUoMGdabWzeDSEpMZc8uXy5f+IW42ATadmhMVHg8EeExzJ+9lnqutaihsH0zF/Qt+/zzz/H19cXDw4Nu3bpRo0YNAO7du8f+/fuRy+WMHTv2lY9z7do1Bg0axO+//06TJk3U0oYOHcqFCxeK3W7Dhg00bNgQgKioKKZPn87ly5epXLky48ePp0uXLmrrZ2dn06lTJwYMGMCYMWNeOd+CIAiC8CwigBaE16ysB83SibyF/MZJADR916PbdyZZ2m+uT/HLyLepjNaIXgAvHDwDoKOFWdNaxPxzFQC9apYlrhp3/j7xNyIAuLr4EK2dbdCvaYl+zZK3KaJAwnGwO3X6uaKhp41M8+3XTNo3tmHW7hEAyDXkyOUyTuy+Sn5eAfl5BeTk5bJo4t8AdPVshEuTGsREJhIVmkBiQipH918uTOvfkF+W7aT/iNaqfTvXV7Biyd/k5uYVBk8T+3H6xBVc3O25fSeIDp2bsXG9Nykpabg3cMDcwpjJU0eRmpqKpqYmAwZ0w8y8PJYVzZk8ZSxxsfE0bOSCzxFfhgzpy507d7GxqcyMGXMAaN68CQ51apKRmUGvXt2oWMkSDQ05kiRRqZIV5hYWeHj0JDs7h9TUNNLS0klPyyIzM5OEhEsAZGZmcdTnJBZmZjRo5MrhwyfJzs6hUWM3bt64S15eAdpaWhzcf4ruPdrhc/gc3bq3JS42AYBjR87RtXtrggJD0dbWQvepVhL/dXZ2dvz+++9Mnz6dnTt3qqVZW1szf/58FArFKx0jJCSEzz//nPz8/GLT7969i5OTE56ensXmT2n69OkEBQXx9ddfc/XqVb766iuqVKlCvXr1VOts3LiRvLw8hg8f/kp5FgRBEITSEAG0ILxvnu6vqaFZ5lMxvYskmYxKw9tQzr0GWkZ6aNtXKXFdHZMn/Y8Nq5RHpvViTcaT7sbi8+1BcrNy6DC3G+b1rF4632Uh9GY0J7YFYGZVjpb9nTEyK2yCXlAgUaeBLTfOhQCQk/2kiXdifAq6xlropWsxbl5P1izeo0rLyipsmh5yP5o2ndyJi0mkZQdXDh08/f/7LUBLW4su3ZuRkJSAXENOcnIqF/yv49G7DX/9uYO+Azrj43Ma43JG2FazJvh+CC1bu/Pg1H1sbCrz+HE0K1esoWvX9syd+wPp6el4eHRW5eHKlWs0b9aImMBYtHW0sLAwJz8/Hz1dfb6aOJUmTRtRvXo1LC0rsmXLbiIjo5g542tOnTpPbGw8I0YMZP36vylXzogD+32p51SHKdPGEReTwMWL17l3L4T6DZzR1tJm6LBemFuUx6pyBSpUKK+aE7puPQUduzSlklUFGjd1wtqmMvB2Rol/02JiYnB3d+fw4cPcvn2bsLAwJEmiatWq1KlT55Wbsvv4+DBjxgySk5OLTY+KiiI5OZnGjRvTs2fPZ+bz7NmzzJ49G09PTzw9Pblw4QI7d+5UBdDJycmsXbuWSZMmoaen90r5FgRBEITSEAG0ILxnMis5IG/sgSwphvxqrmRrvdw8w+8dA10MGjy/Vsy0aU2cJ3cmOz4Ni6Y1QefFfuYC998gOSIRgAu/naHbqv5vra95XnYuv3y1m+iQwlpTPSNd2g5xU6V3Gd4IK1tz5BoyzKzKceLAdZIep1GhqglbfzsGQMLjVIZ81p7tf52gjpMNiQmFQU1WRg6fT+nNr//zYun3fzFmQh82/HGARk3qUr68Ie26NGDZ0vXY1ahKRFgUhob62FazpmlzN8wtyuPsUouYmMcoFLYMHeZBaGgY2toaxMbG4e19AICQkDAaN65PQMB13Nxc8PHxJS0tjUGD+nLa7ywtWzZGLi+sedbV1ceyYkVycnLwPXaCK5ev0q59R0JDCwcxu3f/oaqpeGJiElOmjMfLaz8GBvo8eBDGgf3H+PiTQXTu0prWbZqQ+DgZAwMjQkNiiItNoadHB6Ij45n7w3h0dXRwqKvAsqIFrdo15t6dUGZ+8QvljPUZMqoLlapYvLFr/DYMGDCA3r17M2HCBBwcHHBwKLtuIKNHj+bkyZPUqFGD5s2bs3///iLr3L1bOGp6zZo1n7mv6OjCwf9sbGyAwjEBqlSpojYA2urVqzEzM6Nv377F7kMQBEEQypoIoAXhPZOnoUOy3Yc5Hda/FQ5yXji/tJJcRwuLdi8fEBhUePJCwrSaqdq+X0VKTBopcWlYVDNVzeNcMolHd+PIzc5DW/fJz3R+rnpzWH0jXZp0c1T9vWr/F2Rm5HDW94ZqmYGhDtlZecxaOoyQkEckJabj2qQmIQ8fcXDPaXyPFPZDXbNqJxMmDyAiLAoLSxMio6IxMtbj0EFfOndpyTfTR/Hjol/Q0tKia7fWhISEYmRogJfXXipamREaGs6ZM/40aOCsOraurg4dO7bBtLwBp0+dYuzYj4mPT6S8iRFdurQjMzMTSZLw9t7PjRu36N3bgx8WLeSv9RtwcKiDVeUntf/6+k+1LDAyoFKlinTo0JKsrFxyc3L5bOxwLvhfQ09Xn9CHMSQlpWCvqIHvkfMAuLrXITYmAXMLEyZ+87FaOf684G+uXiqcbs7MwpiPxnk85/q83+Lj46lUqXSjyr+oBw8e8NVXXzFy5EjWrFlT7DqBgYWjySsD6IyMDHR1dYvUfJuZmQGQkpKiWpaYmIitrS0AkZGRbNq0iZ9++gmNlxqcUBAEQRBenAigBUF4L+VGJ/Fwmz+5KVlUH9wYPbsKZbJf++510THSJT8nD9s2ilI36c1Jz+HO0XtkJGdRu00NTKo86ZceF5zAr6O8SI5Lp373OvSZ1Q4tPa0S9xVwJIjl43chSRIjZnckJiKRAhkYmOqRm5WLlm7x2xqZGqClq4l7c3tGfNGJh0FR6Opp8cf/DjB/9Uf8uXof6WlZdPZozJ4dJ+g5oCUADnWro6htTVxsAtq6Wty4cZfcvBwuXbgOwLVrgUTHxJCYWBjInDp1kfDwSOQacurUqYmmhgYJCQnk5+dRvrwJo0cPIzo6jm7dO3Dy5Am2bdsBQGZmJlHR0Ywc6UlmZia5ubmEh0dy48YtALy99+Di4kqnzl2JiY7FwsKckSM9sbAwx9bWhglfjCI6OhZnp7pcu3oLSZKzZdNuGjRwwd3dGW1tXW7fvIeTkwNJ8WmkpT4ZPV1WOHQ9hkbqo7DLZBIaT/Vx/xBG4m7QoAHHjh2jb9++heVShg4ePIi29rNfECkD6B07dnDw4EEeP36Mvr4+7du3Z8qUKarA2crKCltbW/766y9q165NQEAAISEhfPHFFwAsX74cBwcH2rdvX6bnIAiCIAjPIgJoQRDeS1FHbxN2sDDAy07KwH3poFIPUJaZnElBTh4yraI/gdomeig86hWz1bPdOnwX7/lHAbh/PpQhKz2Q/X8wFnYziuS4dAAu7rtNu9GNMLctX+x+ZDKIuBeHpa0p0Q/jCbwURlZeHucO30FLRxP9cjrUqm+DnqFOkW3johJZ8PVfXL8UzJff9qNVdydSEjNo2dkZ32P+PAqPAyDgwl2atKiHpracEaO7k5ubw/p13gB06NyUkJAwuvRoiUxWWLtfkJ+PXQ0bLly4BoCurjb2tWqw7e89zJozkeioGLy8dgNw+/Zdpk77ghMn/WjW3J38vCe15vn5+Qwa1JesrCzy8vLYvHkbLVo0Vx3H3d0dTU0tfl62GgATE2OWLZvP5MnzmThxDH9v2Udyciq7dhzm0zFDcXFyoFIlS06fusys6T9Tz6k2devWxmvLEQYN6UJSYirtOjYmNzePps1dCQoMoXM39dYbkiTji2kD2b7hKEbG+nTqqT5a9H9R69at+emnn+jQoQMNGjTA3Ny8yIsDmUzGhAkTXnjfzwue4UkT7sDAQL755ht0dHQ4c+YM27dvJyAggO3bt2NiYoJcLmfhwoWMHz+eTp06ATBw4EC6dOlCYGAge/fuZePGjS+cx5K8yrsE5bZl/D7ivfCsc/6Qy6UkokyKEmVSlCiTosqiTMqqPEUALQjCe0kmf/IrKNeSgwwoRW3x46sRbJtzAB0jHVrP6Uq5GqXr7yoVSETfiCI7LZvKLlXQ0levBc7JysW5Sy0Awq5HUZBXgIZ2YVBS0c4MTW0N8nLyqeZihZG5fpH9A8jlMq74BHH07wAsqphQ2c4M17Y1WbfwMBqactoNdGHh+C0Ymxkw4xdPqjkUNnFOT8nk2P5L5OXl0a67K3XdbQl5EMnN6+no6ulQqaobUv6T1wtVq1XE2EyP6OhY/M9fpX7Duqq0iPBoxn45GE0NDWbMGktkZCyVq1hS0cqcnJwc8gsKePAwBCsrS/r178Yxn1N07PQkKJXJZMTHJ1K7VnUmffUN/fv3oVevHiQnp+DiWheQkMnkxMUlEBoaxp49+5g+/RskSUZWVhYaGhp4eHRFLpezd+8hgoPDsLAwJyoyloyMTNWozllZ2SQkpaKhoUXA5dsAXL92BxubKsTHJ7F61Tb69OuIzz9nGDKsB7bVqlDN1ob8nKIfEjt7a6YtHPlBDCAGMH/+fKCw6XR4eHix67xsAF0agwcPJjMzk1GjRqlqwDt16kT16tX54Ycf+O2335g8eTIAbm5u+Pr6EhQURIUKFahYsSIAixcvpmXLlri7u/P48WPmzp3L5cuXMTc3Z+zYsaqA+0WYmb36eBJlsY+3qY/569nvv8tl54XEF95HnwbFv3R8ljd1nJfxvn9WXgdRJkWJMinqXSgTEUALgvBeqtjegbzMHPLSsrHu6UJpxvmSy2Rc+PUUmYkZZCZmcP/wbVxrtCzV8R6cCmb75H0ANPJ0pdWXT7aTySA3K49L+++goSlnwHed0NB+8vNa2bEiE7d4kvAoGWvHiug8VXscde8xJ7YFoKWtSQWb8mz43ofsjFwSYlLpO6EFbh1rkQ8c/Msf/xN3yMnOIy4ymTP/3FIF0Kd9rhFyP5L8/Dz2evlRUCDh0lBBYkoS9wLDyc7OxrWhPUYmBuRk52LvYE1uXi7xCYkkJaZiYmKEuUV5srNz6Nm3FTOmLKZ8eWMGDu6Gto4GSclJ2NW0xtm5Ft9/v4oqVSrRqJELWdlZtGzVkMDAIGbNnsztW3dwdKyNgaEemzdtoG/fXlS1sUZXR5vomGgMDPTJyclBQ0OLuNjHDBniibV1FbS09Hj0KAoLC3PCQiPZs+cwFhbmLPpxLro6uvTr15O7d4MZNLgX585eokbNari5ObHi5404ONSknLEhKclpGJUzpCC/8INgZm6Cvr4eIz7uw4E9fmSkFPD3X0fQ1tZk+bqvcWlQR+36fijBMxTOs/w2DR06tNjlnp6eLF68GD8/P1UADaCrq6s2bdW5c+c4d+4ce/YUji4/ceJEkpOTWblyJZcvX+bLL79ky5YtuLq6vlC+4uNTX/pzIJMVPtS9yj7+i8qyXB4/Ti2bTL3l44jPSlGiTIoSZVJUWZSJch+vSgTQgiC8l7QsylHt45KDXyk/n6zYNLQMddA00i1cBpjYmBF7JwYAA3PDUh8vOjBW9f+3jgTRYmxT5P8fJBfkFHDWq7B5c35eAdHB8Tj+a/uK9hZUtC+s7VYOBqaprcHW749x88xDAHqNb4aJuSExYYW1JuaVjdHU1qRJdwdquljx+4KDRIUWjshdrrze/5+RjNzsXJKT0kAmqUYMD/APonU3Z+4FhnPz2gMsrMohyfLJLcjmUUQ0GzfsZcCgzvTp34GdXj6M/MQDJ9ea/PD9ahLik8jPz6egoIAjPie4c/senkN6kZGRRqcuLXBycuTvv3fRrXt7rl27gbW1FTExMRzzPYbX9u18/PEwxoz5hDVr1tK2XRtq11ZgZVWJvLx8wkIjyMjMJCoqhstXAvjoo4/57bdVAHTs2AZNTS0qVbKkS5f2TP1mIZIkMWXKOMzNTElNS2XgIA8kSSL0YRQJ8UlERcXRoUNzqtpakZOTT05WDoOHdCUzI5eNf+ynSTNnmjZzYs/2wrnTc3LyuH7lfpEA+kNy6tQpWrVqhbu7+9vOihptbW3KlStHenp6ietIksTixYvp2bMnNWvWJCoqigsXLrBo0SJcXV1xdXXFy8uL3bt3/x97ZxkYxdW24Wt9s9m4uzskQYK7uxeXGlKhpfLW3VvqXqqUAi3u7k6wIHF392zWd78f24byQYWW+lx/Mjsz58yZM7Obueex6xbQVuvvf5FyI/r4N3Kj5vbP4M88jnCvXIkwJ1cjzMnV/B3m5N+fLUVAQOA/h9VsJv3rZDZM/4LdC7+ltdQmSK1WKx1u60GvRf3p9cBAQob/ehEV1Mkficz2k9l1WgckPyqPJZZLSBwW1fbZL/anE5oVXSznuXFf8ty4LynLqmlzSQaoLKgnuqM/Q2Z24rbnhtFxcASZZ4u4dCKfnNQyVE4Khs9IYsDEROrqGtj0zREqS+voObg9nbpGolDIsXew1cIddVMPcrNLEIvFdOoexecfbWDXthPkZJZQW9tEcIgfSz5ajUql5Mnn5uHq4YDZYsXHxxN/f2/69O3MO29/ipeXJx06tmP5N+sxGMx8u3IDL7/0DvMXzGLjxq04Oqp54YVXefPN95g6dRJKpRKDwQAiK3q9ntDQIKRSKUajic8//4rlK77FZDLh4OjArbfeirOzM15etvlqbGxCq9Xi6+tNfn4xVquVESMGsX3bQVau2IxSYUdFRQ2VFXWERwQxYsQAmhq0WMwglyvQNhupq23B19eLiynZBIf40rNPB2Lah9B3kE1MicVigkK9f/V1/zfyzTffkJKS8pccOz09nZEjR/LSSy9dta22tpb6+vq2slXXYuvWreTk5LQlEquqsr3Ycne/7Hvs6upKeXn5DR65gICAgICADcECLSAg8MfRqqf1yCVMjRrU3WMQ+3uA2Yy1thmRvRLslX/IYfXVGs5/bStf1FBYT1VKCcF+trg2Oy8Hus3rTU3N9bkA+ScFMnflbAytBtwj3K9oa7Va6T+vG6FJgSjs5fjHX7tEkEgkYscXyZTl1hLVJYB9K87g7u/E+Lt7UZZbg5ufI1UlDZQX1BGVFMCZA1ksvm8VANPu7o/VamXTyqMMGJvI1g0p9B2eQNOmZvZsPY1YJGLWnUNw83FArzWS0DmcsFgfTGYT77/1LUajiZamVhyi/PH2cWPXziNIpRICg7wpKS2ntKwSrU6LXCllwk1DeeN1WyKvg/uPM3rsIESIaNcuitOnU1Ao5JSUlOHt7UZqahqNjbba0hs2bOG+++5EqVTi4uzE/fffg0wuw2oFb29vfH198ff3JzGxA2fPXuTQoRMkJsYzdeokSkpKcXd3xcXFlabGFtzcXDl44DhyhYK0tBwAVn23lVdeeZQnH3sHd1d31q2xJW0rKiznycS7+Pab7+jYOZpRo/vx2bL+pF/KZ9/20yR2dqAkv5pho3sglUlpbtL++gv/L8Te3v6Xd/qDCA4OprKykvXr13Prrbe2xTRbrVbeeOMNACZMmHDNtgaDgbfeeotZs2a1tfvh7w+x3GazmdLSUiIjf7lmvICAgICAwG9BENACAgJ/GNrjaZR8vA0Au5OZBL4wh9pNpyhZfhC7ADfCHpuE2Nftuvs1Vjejq9VgH+iKWHV11l+ZgwKXMHfqc2sAcPB1/l3n8QPOQT+dXEamkhPeM/hn21utVlx9bLE3Lj4O7Fp+BoCASA9mPzmYV+avsiUaa+9NVVkD+dkVbW1P7Enn5oeHoFTLMVmMdOoRyZ4tp4mI9SM/uwyRSETysVQ2rj4MwKmTgdx8x3B2bT9GQsdIyktrGD2hDyqVHEdXexbeP4Oy0gpee/VTps0cgUajobGhidraerKy8lAqFeh0egA6d46nqbGeF198k/89dDf+/l4UFhZz6NAxZs6c3DbGiPBQWpqbycjIJCYmHJlcRktLCykpFyksKOK++xdRUV6JWu1EXV0jSZ07snKlLfv3uPEjcXJy4c03lmC1Wnnq6QdZsGAOavVlN3v/AB++XbmFSVOGkZ1diFgsxmKxJUfLyy7hocdvJT+rjEVz36Rn33g8vTzYvuk4VZX1tGoN7N5yCoCBw5J+1fX+t/LAAw/w2muv4ebmRvfu3XFzc7tmHeU/oqSXnZ0dTzzxBI888giTJ09m2rRpqNVq9uzZw4kTJxg9ejQjR468ZtuVK1fS3NzM/Pnz29Z5eXnRpUsX3n//fYxGI+fPn6empoZx48bd8LELCAgICAiAIKAFBAT+QEwturZlQ1UDNLVSstwWi6otrqXlUhGO1ymgtUV1HH7wO3R1rQQOiiHhvsGIFFdmxJbYK+j73GiqUkpR+zjiluiPpsxmJVX7OV2r2z+Mipxajqw5j4unmo5DIvGP9KTv5ASkyss/v3qtEbFEwqxHB5KbWo5MJeXTV7Yxc9HAthJPkQn+lOZXI1dL0dbrUDvaodcZUH/vsm21WlEoL79MUCrl5OeWsn3zEbx93HBxdaKktBwXV3ueeeZtAgJ9GDdhAP0GdCYyKphWrQYPbzd69e6ETmdg0X23U1lVQ3CwP2lpaezeY7tu27btZubMSaSnZ3HHHbdhZ6fkoYfuo7GxwZYErKiIhIRYLBYLzc0tfL30G2QyOfMXzEOlUrPy20/Iyytg2LBBSCSXr1vqpXScHJ0IDPSjsLCEzMxc1Pb2bN60mzvvmkl1dQN6vZHtWw7i7u5K6qUc7n1gNsWFFchlcnZvP8FR9Xm83DyxWCwc3p/CxKmDADhzMoO77p+EpllPYlIkXXr9/wj1/xaffvopOp2Oxx577Cf3EYlEpKWl/SHHHzduHJ6ennzyyScsWbIEs9lMaGgoTz31FNOmTbtmm5aWFj766CPmzZuHo6PjFdtef/11nnnmGd555x3c3d15/fXX6dSp0x8ydgEBAQEBAUFACwgI/GGou0XjeCEfXVE1fneOxOpghyrUk9Y8W9yiwuv6xWx9Rjm6ulYAivakEz2zG0p/16v2s/N1Juh7y3PZ8Xy2PbrJFlP70hjcxyX89pO6DkQiWP3qPkx6M0VZ1Wz+9ASdh0SRvCuTLsOj6TYiltryRroNj8EvypMvF++ksrge71Db+ZQV1XLTHb2pKm3g3IlsgqI9uHAql4tn8ug7LIFbFg6ntKiGyTcPQNuqQySC4WO7o1DKkSlEePu5MmJcL0QiESJsZbLWrN6BUimnb//OvPXG5wCcOX2RoFAfdDpdm0UY4PZ50zl/4TxurpfnNywsGLHYSn19Na+9toGZM6e21eJ1dXVl0aK7vk9AZuXI4WNYLFZGjRpJY2Mzp0+nkJdXAMC+fYd47bXn2bRpGxqNlj59e6JWO3DT5LGUlpRz7NgZunbtwOgxQ2lq0hAWFsz779iyR4dHBOHu7s67by7n1tsm8tVnm2xjiwzEoDF/P/ciomKD6NIjFjuVkovnCmhuaOXOhya3JVr7r+Lu7n5FzPAfxcKFC1m4cOE1t/Xo0YMePX59zW21Ws2JEyeuuc3Ly4uPPvroN41RQEBAQEDgehEEtICAQBviFg3Ww8mYa+qR90nCFBTwu/oTebng+/g0sJixSG0/N6GPTERzsRC5pzOKdj+dLOincAh2QyyTYDGacYvzRe7y8/GcIhGkb03FYrK5+l5af4HEP0lAYwWdxoCrryNHN6cCsO/bc3QfHYdIKiYztRRHZzv8ojyRK6TUV7fQUKOhx4g4MlOKCQh3o6y0Fv8IN0QySD1fgH+QBxfP5HHmWBYoTFw4k0OHLhFsXmdz3RaLxcyeN4zqmjryckrw8nalorwGL293Duw7ydBhPamvb0DbejkOuLy8ioSO0dQ3NBEZGUpWVh5yuRxfXy/y83K4cD6VBx+8CydnNUVFheTm5tHS0gLYagkDODg4MH36ZMxmMwaDkY8//pygwEBmz5nNsq9XMHrMSEJCwpDL5RgMBm6//WaeePxV+vbtTlJSIp9/vhJ/fx+GDBnAqu+2ADBv7ixefM4mjORyGU88vZCUcxls33KMXn06YrFYCI8M4PYFEykvrcZosOAW5kRS9zj0WiM5GSUkdIxm86qjNNQ189zb8/7z4hloe+EhICAgICAgcP0IAlpA4F+CWCz63eLAeuoCLd9tB8BwMQv1i/djkl0dY3w9WMQiEP8oY7WXCw5ePx1L/Es4RHoz4IMZaCoacY7yRmxvq6lsMZjQlDaicLZD7qJq299qBZ94X3IPZAPg19H/Nx/7emht0HLhQC7dRsfS9L3FHEDlqCCqkz971qdQUVBHBXBkyyViugez8JVxfPLMFiRSEXe+OJq3n16DTmtAqZLTa2A7LFYLEXEBjFP3wMvXhZbWVnZuOolmn465C8exac0hIqICCAjxxNVLzZ6dx6ipqadz1ziMJiMJiZEEBHnzxeffMWhwTyKjQsjOKmDWnPGUlpZjsuiIiQ1j9JhBhIYGcOlSOv0H9ObFF95kzZqNhIb5smvXXgBuuWUW586dp6ysktmzZ+Lu7oqDgxqTyURhYTH19Q3U1zfg5+9Hz57d6NixE1lZudx222wUChk6rQmdTsfOnfs5e/YCERFhhEeEsHnjXsaPH0F4RFBbTWcAg8FIVkYBm9YdAECvN/LYE/NZtWwvEVFB7NxyEoC49mFIRDLSLuQzZmIfNC2tfLD8QcRiMT7+Hn/KtRcQEBAQEBD49yIIaAGBfzhiiwnl2eOQfgFRYhKt7ZKwikS/rTOzqW3Rqjdg/Zta61ShHqhCL4shq9HMuU+OcGnNOdReDgx9fQLqwMtux1Fj2uEW7oGx1YBP58Br9mk1WxBLJVi/T6+trWulOr8OFz8nHLwdrthXJAKz0YJEJr5mJm+RCHZ+nszWJTaX0xHzujJ4RkeaG3R0GhxB/IAIMi6UkHWuBAA3H1tMZ0CkJ7c8MYQv3thORVUdOq0BAF2rgdBoH/QmI5++u4HmplbEYhEz5w1h2LiuqNV2VFXW8fCzs6isrObxh99m1Jg+nDhmq01tsVgYOboPvv6uiMUiHnpkHukZOcy6eTzl5RVcvJhOh45xZKRno9G08tpr73PHnbN5/70lDBrUl0ceXUhgoD9PPPFc2znq9Truv38hCoUCi8WIQiGnvr6BlpZWEhISqK21lQ4LCgpi6NBBXDifwScff41YLGbOnGlXZILu178n3bslUVxUjpu7C5s27mHM2EG4ubowYdJQ9u87wcRJw3ByvOzyHxkZTE5WKWmX8tFqDYyd2A8XV0cSO0Vz6VwOM28bTscuMTg4qa+ZIOu/xMsvv8zYsWOJjb2ybFtLSwsqleqqZGHbt2/n6aefJjk5+c8cpoCAgICAwD8CQUALCPzDkRflYlm30vYhPRXFPV7ovK/fNRpA3Dkeu7JqzCUV2N00DJNCcQNH+uswN2ppTC9H6a5GFf7T9ZR/jK62hUtrzgHQUtlM1YXSNgHdWtXCobcOUH6pnP7390Nqd2XCMavFyqVNqRz+4iQRvULoc0dPrGYrK/+3hYJzpTh5O3DbJ5NwDnC2jc9o5sg35zjybQqdR8Uw4PYuyP5fnyKRiNxzpW2fS7NqyEstp7FGQ4+xccgUUvpOiMdkMiMSwaVTBfQubsf+rSnotAayLpYgk0to3ymEi2fy6dYvlqiEQFIv5dLcZLNmWyxWSour2b7lCABDR3UnIz2X7OwihgzvgaPT5ezVri5OVFZVkF+gw2gyEhYWhEIhJ/nkGerqGggND+TVV95DJBIxd+4MZswYj5Ojmm7dOlNTW8XmzduQyST079+b+vp63NxcMZmMlJYU4+3jiZOTE3q9Aa1Wj+P3IvfEidMAXLqUjo+PP6lpmYhEInr16srWrXuxWCxMnjwWLy8PYmIiqKpsYPOGfegNBm65ZTKeXm7Y2dnz1WfriYuJZuPa/dw27ybGjB1AYLAP3329ixFj+xAQ6EV+bilarY7YmEjsFCoW3D8FkYi2lxutzVqO70nDZDTTbWAsTm6X5+a/wNKlS2nXrt0VArq+vp4ePXrwxRdf0L179yv2NxqNNDc3/9nDFBAQEBAQ+EcgCGgBgX84IhFcaQT9jdZnwOzshGTOBKQiEaa/wPpsNZi48OZOyo/lIpKI6bX4Jhza/7LLtdzBDs9Yb6rSKnAOcsE1/LJ1uuBoHjkHbXWEtz29ndsT/cD9skW5oaiezS/uBuDU6vPYOSpx8HKktrgBgMaKZiqya9oEdHl6NRvfsGWk3vnJCcI6BxDe/UqrtsViZdhtXcg5X4ZEKqbj4AgsMvALdSeova1ubVNDK3s3nEMiEdN1WAx11U2s/uwAHXtG4ORiT+qZQtonhXDzoqG0aLScO5VFi0bDsHHd2LHhBB27RRIU7kW33u0Ri0V07R1LdU0NQcG+ZGfnkZaey5gJA1DIZcS0C+Hc2Qvs3n2YHj06kptbiLe3B45OPrz33uc4u9rmY8TIgSxf/h1NTc0olUruXTSP119/m8cevY9XX3uLgwcP07t3Tzp2TESr1WBnJ0etVmOxWHB1dScjPYel25bj6OjI/x5cxPETJ4mIiMDBQc3uXQeYM2cqXl4ehIWGU1FZxZo1W7jzrlvYsf0oZpOZoGB/EEFmRh49e3XGbILmZg3Hj6UAtuWtmw7Rs3cHNBotMrmUqbOGUF3ZRGFeJUf2ncditnBw+1mO7LlA78Hx9B7agV1rTrPkZVtcdWFWOfOfHHtdNcD/rViFSRAQEBAQELhubnyRRwEBgT8VfUA44knTEbeLRzL9FvRevy/xl9XKX5Zoydyio/xYrm0cZguNedW/qp3EXk6/Z0fR/+kRoFJyduVZNKUNACgdlW372bvZI5Ff+d5QppAiU1xe11DRwvrnd9NxeLStjbMSz7DLpbZkdlLEEttPp4uPA/bO17bSx/YJZfSd3Rl+e1eWvrqHkzszWPfREc7us8ViR3cKpPvQGMbN74HCUUL6+UKS+kRRWVbPxNv6MGp6N/zDPPji7e001DbTUNtCTlYJCUlhTJjdk+gEPz54+1vs1DKatY08/tA7lJXUIJZaaWrScDr5EhvW7UGn15OeloXSzo6hw3qzbfteLl1KI65dBKtXb2bgwF44Oqi5fe4M7FV2NDU1ExwcSEJCHCo7FYMG9efw4SOMGzsKs9nMuXPniYwMxd3dBbVajcFgQGWn5tFHn2bHzj3Mnj2DhoYGJFIZI4aPJDIyColEzOOP34e3jzfu7h588806kk+m8Oyz/6O+vgmJRESPnp3Jyy0hN7uY0WMGsWrFbh5/5B2mzRiNp6cr/Qd2xcXViU5JsXh4uOHr54GzkyNvvLgcJyc1yUfSkMmkDBnVjWcXfcneLWd45t4vycsoobSgpu26ZFwoQfRbQxwEBAQEBAQE/vMIFmgBgX84FrGE1o69EHXq/Y+3KEkd7Qgdk0jephSk9nJc4vyu2G6obcHQpMM+wBWR9Mr3fyp3NclLk6nNraHiUjmuwa7EjI0nsEcwQ58YQlN5E6F9w5Cqr0yKpvZxZMZ7E8g8mIO+1UTG0QIAgjr5c3PXQFwDnNuszwBeEe7Me388VUV1ZJwq5q15a5iwqA9dJ7S7QpiJJWJ6TUyg4GI5Bp2xbX1pdjU6jQEHV3vufWMSm74+wom16XTuG8G+becIjfLB3klB5YUaLBYr9z43gd0bT3PLvSNQHBdTXVlPbU0DO7cdtx1HDKdP2jJ8NzU10axpwsPTmc5JcUikEvr278zddz7B8y89wKOPvARAZmYeuXkFDBvWj2XfrKampg6xWMz7H7yCq5sjp06doqAwD5PZQFxcFE1NjTQ3tzBh/GgSEuMpKSlGLBYjEoloaGhm8+bdWCwW9Ho9lVXVvPLqCzQ16Xnm6ScRi8U89dSDpKXlsnrVZhwdHbh74a2UFFeg15vRafV07BjPsSNnqKqqBWD3rqNkpZfQ0tzKZ5+s4cXXFmExWZBIZcS3j6Wmsh6RVcr+nad49Llb6dEnkUHDuyGWSqgqqbvi+losVgaO7cChbRcw6I3MWjhIyMQtIPAfoU+I8JgrICBw4xF+WQQE/gWIsCIxGjDL5DfENVXc2IwhNR+JiwOiqCCbSvszkEqImtsH/8GxyF1UyH9UJ7opp5o9D65F16Al8ebuxM7ucsW4rCJQOly2BmvrtWx9bAuhvUIJ7BZMxIgrEyj9GN8OfnjH+3Bu7UXyU0oZcncvQroGIlFc+ycysncwDZs0nN6ZCcBXT2wnoksAbj8S2gBOnmoSBkZw27PD2bPyLO5+TmReKKW+som8zDIyzxfj4ulAfJdQtq9Jxj/EncAIT04dS+fQblsCMKlcgpu/PcXFZVRW1BAe5U/n7nHk5paQnVGEX4AncrkMg8GIWCShsqISLx93fP096dErAb1Rx+3zp5Cens1Nk0exepXNlVkikWAVWampsQlOi8XC7l37sFcrKC4upa6unldffYt+/bqzbdsOunZNolu3JFpamrCzU1JeXoGbmwcrV65mwIB+pKamAdC/f3+kEgl799pEtcVi4dixU5w9Y9ver38Pqirr2bxpH5s37ePJp+8hJ6sItfpyUjE/P2/OnbZZ6t3cnNHrDJgNVtJTs3FxdmTXNlvG7Vm3jWTk+H4c23uBkoIqrGYrBoOZOx8Zz4mDlxg6tivhMf6IxGI+3/M/LGYrjq4/X/ZMQEBAQEBAQODnEAS0gMA/HKlOi3jbVsznUpAP7I+xTz8s4t+edVhkMlG/ZBOa0xkAeD86E3Fi1I0a7i8fXylDHe1z1frKc8XoGmy1i89/fYLI8fFInVQ/aiii9wMDydqWilgiprFag0UkYt/7h5EuOcbUD2/Cs93V/f6AWCKm0+QEOk9J/FWWfPsfu4Y7KpD/v0RiP8Y/woPIJH8aGzSEt/ehuqKel++1JX5L7B5G14HRmDBRXVXHudNZtOsY3NbWYrZwZG8KRqOJo4fOERjqSV1xPeNv6odIZGXtml2MndiXmHaheHq5IZGKOXH0LHl5+Rw7dgqLxcLqVVsB6NO3C48/uZD6uga2bNnByJGDGDZsADt27GPw4L4MGtyb8vIyfHw86NEjierqepqabMmkNBoNzs6O3y+34uLiTllZJffdtxB7e0ckEjndunbhjdc/pKGhifHjR5F88gwA/fv3JiAgiIaGRoKDglj13da288tIz0UmlVNbV8+4CUOwV9mR1DURrFI0rVq8PN1wcXYCi4hdW78ltn0oU2YPwcHBnvGTB3Bs73meWfQFAEFh3jg6OtCtbyxvfn3vFZZm9Y/vFQEBAQEBAQGB34ggoAX+M6gs9YgsOvQyd0zWnxY7/zTEeTmYDhwCwLhuI9KISAx+vz0OWqwzoDmX1fbZWFqD4k8U0D+FU9DlslR+XYKRqK6OPXYKcSPprj5UnCulbv0Fir4vE2UymKnMrPpZAf0Dv9YNPrpnMLcvHkVlQT3t+oTi4H5ty6ZBayDnUil11c0c3ZlK75HtQCTCTi1H22KgrLiWgEhPausb2LP1FABmk5URE7thNJpp1Wpo1ehQ2skZPbEXJ49f4MTRCwDc88A00i7l0n9QEls27eXSpSwmTxmBu6czqZuycfNwJqlLPJGRIWRl5VOQX8KQYb155plXAbhwIY1PPlnMrNmTWLZsBXv3HmD16jVYLBZOnTrDc889yXffraZr1ySGDBkAQElJKTExcdTXNWIwGCgtraC4OIVDh47h7e1LZaUtbv3ixVTuvnsudioVSz5ZQVFRGY6Oau66O4pevZNYu2Y7MpmU8PBg3n1rKUOH9yEwyBcvTw/On81i3ao9AMhkUpwcHVEqlXy+8hlSz+fg6uZEdLtQPLzdqak83zbXpYXVhA70uyKO32K2cP5wDhVF9cT3DMUvTKgFLSAgICAgIPDbEQS0wH8CtakcZe5GsJqReyTQ6N4Di/XfkUNPZGd3+YNUCsrfV3rKrFLiNmUgtSt2I3FQoYwL4e8QMerROYhh706mtUaDZ6IfItmVVvbq1ArqCurwbueDV4IvVosFvcFE1oEcZHYyfGK9b+h4JDIJnUf9tFs4gNVi4dt397P+s6NIpGJmPTiIneuS2bX5NFPm9aW+uoUmXTMP3f4hsxYMaWt37mQ29z09kZzcYmqrYOjortjZywiN8GXr5gOXz7mqHgcHe5qamjl+zFbG6/NPV/PGu48ydEQ/3Nyd2L37IBIZ3HzLJMBKZnpuW3tnZyeOHjtBcLA/W7ZsZ9q0m4iICCM3Nx+TyURtbR2zZk2jqakRsViETqdn2bLl3HffA7z22tsAqFQqHnnkQeLiorFT2hEVFUFmZjYGo5HU1Exi42IoKioDoKmpBalEjN6g57bbJxMcHEB1VR0zZ4+jvraJ08fTyUjfxhPPLqB9QiQ5WYXcMncCy77Yxphx/XB2cWLyrBFXzHG3fu3YtekUeZmlzFowFIsZhoxLatt+6VgeL8xdDoBPoCuvrJuH2smO/xqnT5/GbDa3fdZoNAAcPXqUysrKK/Y9e/bsnzo2AQEBAQGBfxKCgBb4TyDVVoDV9vAoqbmIzKMzeqvyF1r9MzAEh6GYeyvW0jKICEfv9utqJ/8kYjHKUT3xS4pBpFJg+d5t969GJBbhGu+H6zW21WZWseqOVZiNFhw81Ez9Yho+nQIYFuNF56kdULmrcfR3btu/tVFLQ3EjjZXNmE0WAjv4IZFfn9u71Wrl1JY0CjMqie0ejKOHGrPRTGCMF9bvrZ8GnZEdK3+wKluoLKujINsmVlZ+vJ97XxrPSw8vA2DL6uPMvmsopYXVyJVSDu49x/YtR1HZK7l5wQg+eXctiz9axIJ7JnH61CUc1PYkdo5Co2/Gy/uyVVXtYM/F8+n4+nmRmpZB8kmbsFYqlMjlMvoP6M79DyygpUWDj7cHa9etp2vXDtxxx+1UV1cil0u59947MRpMhIeHUV5ejFQqpaCgEIsZRo8eTWXFZcHV2tqKXq9n9eqNPPPMIyxcOJeq6hp8fLw4dCgZN1cX4hNiuXA+jaAgfzw83PBwd2fgoF7fvwBwQIyEXTuOc+ZUGlarlcb6FsZPGIiruzOH9p1j5Jg+1FY1s+m7Qzi7ONB3SEfcPZyRK+X4B3vx3vL70OsMOLmqsVqvzLBdU9HYtlxeVIe2WfefFNCrVq1i1apVV63//PPPr1pntVqFTOUCAgICAgI/wT9aQGdnZ/PWW2+RnJyM1WolNjaW22+/nb59+16x34ABAygtLb1mH3v37sXf31ZnNiMjgyeffJLMzExCQ0N5+OGH6d69+xX719bWMmjQIJ577jlGjx79x5yYwA3HrPREigiwYnGNxmj5fVbaPwORiF+VEMwqEqFrnwjtE2/Ysa0SCfh6/C0sz7+GpvImzEYLAM3VLbTWamgobeTI5ydxC3Sh/ehYjn+RjEwpI7xXMF+9uJec5CKie4dSV9ZE9ykJdBzXjpbaVuyclMh+Jp65Mr+O9GMFyBVSvn3zABEdfWmo0RAQ6cHXr+zh7tfHsurDA8jkMu56ZSyDJnVi89LjSCRi/EMuC92gcC/Ki6uRSiWYTGbqa5sRi2HXlpNEtw9C7WrLFm40mLBYrEyZM4jvVmzj4vlMaqrrAXBxd+S7lVsJCPThwYdvp7KyBoVSRm1tLXkFBXh7e9C1awdOnjyHu7sLk6eOZM3qjWzcuJ3/PXQ3efm5TJ06noceepwBA/qwZs06wOZ+/frrr1BbW4lUKiUvL5+VK1cxdcpkBg4cyHffrmHEiCEcPXqSYcMGs2LFWm69ZQbaVgMXLqSS1CWRTz5aQVpaNuPGDUMulzFm7FAkYjGnklPZs+s4UomcNd/tZNbscXz9xWaUdgqmzhhBfX0Th/efY+CQ7jTWt3LxbB6N9S0kJcWz4jNbve78rDIKMqp4bPFsgiN9UdgpUNgpKMuv5cLxXLwDXWnfPQyxWET77qFEJviTdb6E2Q8Nxs3Hif8ad9999189BAEBAQEBgX8N/1gBffHiRWbPno3RaGTq1KmEhISwb98+5s2bx2OPPcacOXMAaG5uprS0lH79+jFixIir+nF1tdmzLBYLixYtwmq18vDDD7Nv3z4WLFjAjh078PG5HDf5/vvvExwczKhRo/6cExW4ITTL/LBETEFkbkUn98LC39u6oqwoQnx0Hzg4YureH4ODy189pD8EQ00zZp0JO19nEP/2a+IV6413rDcVaRXEjYzFwcuBT6cvo7laQ3VeLbUljWQczgOgb30XcpKLAMg4nEf80GjKM6vZvPggh1aeIzwpgJmvjMDBUw3YrHGp+3MpzqgivJM/S5/bSVmOrdzSpPv6sP6zY7Q26Rk7vwfBsV7sWXOW0jzb9u3LTnLr0yPoMigaByclvmEeOLiqqKloIrZTII/e8TE3LxxGXnY5Kns5+3ee5dFXZmGnlpJyNouodqPw9/dC5aBk86Z0lEo5dbWXLaoGvYGRo/uxY9thTh5PYejIXhQVFnPy5Gny8mznePu86YSEBNBvQA9OnjzDxo3bAfDx9iQ3N4dTp85SX1+P+EcZzSMiwmhoqEUsFiORSKmurmXE8OGEh4eTmprO2HFjqa9vYtCgQZw7d4GysgosFjGvvvI+/v6+JHVORC6XYbFYqKysJioyjIqKKnr26EJJcQ09enTCZDQzZcYIDh04i9Fowmg00dLcSm1VE24ubrz2zDLc3B2564HJGAxmDu28HOvc0qKltKiafVtPc2vkGABam3S8dOdyirKrAHjuq5tJ6BmOh78Lz349h1aNHic3e0R/Vkb5vxGCgBYQEBAQELhx/GMF9NNPP01raysff/wx/fv3B2D69Oncd999LF68mD59+hASEkJmpq3MzMCBAxk7duxP9peSkkJ+fj5Lliyhb9++jBkzhm7durF582bmzZsHQEFBAatWreKTTz4R3Nv+gWgkbiBx+6uH8YtIrBZE61ZgKbEJIKnKHkOvob/YTlZQgKWmHlF4CCZn5z94lL+fptQyDjy8FpPWSJcHh+A/vN11ta/NqEJTq8G7nTcqTzXj3x6Ptr4Vey8HgDaXbIVaQWNVS1s7Tb0WiVSE2WRF5azEbDThHuzNmlf3A5Bzqpj8c6XED7UlTss4UsB7d9oss3I7GfH9w9oEtNlkplP/SJs79+5Meo2JIyetrO1YCjs5Cjs57bqFgNVKVXkdtbUNqF3tCAzz4oUP5pKbUUzPQe0oK64mPLY3rfpW3np9FbU1TYhEIl575156D+hESkoqq1buYMHCKaxYuoXo2BAMRj379x3j4cfmEhDoxeYtuygpLkevN7SNobq6BqvVzLvvfoRer+fmm6fz1VcrqKyqZPXqtcyaNQ2AM2dSuPPO+TQ01BMeHoJIJEKtdsBe5UBsbCyFhSW89PIbjBgxlLNn01HIFRw9epLw8FCeePxB3nv3Kxobm6msrKFb905MnDgKLy9bia24dtGYTSKsVgmrv92J2WxGJBLx/MuLaKjWkJ1ZCIC3jzuJibG8/vwK2zWuaeL0iTS6dG9PQJAX1ZUNyGRSXJwc0bYa0GmNtDS2onZSoWnWtolngMri+rZluUqOXHVl/W8BAQEBAQEBgd/CP1JAl5eXk5qaSpcuXdrEM4BIJGLBggVs376ddevW8cADD7QJ6IiIiJ/ts6KiAoDAwEAAHBwccHFxoazs8sPwm2++SZcuXejVq9eNPiUBgSuxWNoWf82rGllOLi2vfQQWK9LIEOT33o5F+feO8S4/VYBJawTg0tfH8R8cA9JrxyHXXCyj7HwpHpGe+HQJpPRkERk7MhBLxZSeK6HrvO7IHBTIflQHesJLozi75jzeMV7otSb0Gj12TkqkKimdRrfDoDMS0SUQpBJSD+ehdlXRUteKSCxCU6+lJLUC/zhvynJq2vo0aI1EdfLn+NY0guO8UTgoObDBZhm97ZlhFOZU4h3sypR7+lFZXE94gi8A2ReLOH00k5TkbE4ftf0m3fFIMxaxiU/f3YiXjyuRsf6kp+UzdkpvBo/ohpurE0s/30JmRgEbN+zBYDIy+9YxeHg5ExnnR3FpMU2aevwDfDh77hLnL6Ti4eGGyWSme8/OHDt6Ch8fLxoaGlHIxaScuwiAo4MDTz/9P0xmPVOmTATgvvsWolQqKC4ubBPPIpGYlJRUFHI5vn4+rFu3EYDw8DCk0jI2btgBQE5OHkaTiZCQQMLDQzGZTPj4eHHq1EUOHzqD0WjizOk0brl1KkUFZUyeNoxtWw/RWN9MaXEl5eW13DJ3PDKpFItJxJ7tp4mMCSQrvQixWIxMLsdkMHN030VGTuiGt587uenljJjQjYun8qkqr0ftpMLV05EZiway/O29BEd5Ed899IbdqwICAgICAgICP/CPFdAAUVFXl9YJCgoC4NKlS4AtrhkuC2iNRoNKpbrKguzmZrNMNjU1AWA2m2lubm5bf/78eXbv3s2aNWtu9OkICFyBWSTGOnEG4gO7wMkRY3zSL7axlFfC94mrTFn5KDWav72Adgy47JbukxSMSCa5Zsx3S0kDG+5dg9lgBhFM/Womp745TdHpYgDaj4rD2GJA4Xrlz5lnrBfDnx5Cxr5c1j+4icB4X/RaIy4+zuz7/AQB7X0J6RZMTUkDZ7ZnkjQ6FrWritZmHWvfOIhULuHJzbfiE+ZGuz4h5JwppfOwKDqPjCZhUAR2TkqWPLYZAO8gFw5uuUDqaZsldeL83iT2CccnxJXTh9L4/M1tKOxkVFVetopaLBa+/mw7rRo9+TnltO8YxtDRXVj8wlIAIqODuOv+yUikImpqnXFzd8RsMXL86BmOHD4N2F4aPvzYPKpr6tDptHy9dDWtrVq6du3I8OEDyMjIwsHBG6NR33ZcNzdXCosKCA0NZvXqtZjNZhITE+jduztBQQGIRCL0ej0uLm5oW3WsWLGauLgY3njjVdLTM1EqHYiPj+P0qRRKSyuQy+Xk5BQyddp4nnhsMVarFZVKRVy7SDZvspWimjBxOG+8+gVWqxWlUsF9D9xCdVUdIOLc6Qx0Wj1Dh/XEYhbRWN+Cf6AXIWG+REQHIpGIKcytZPiYHqz8aD9yhZTRU3uwad0xEruF4+lru48kMgkT5/VhwPgOqByUqBz+3ve/gICAgICAwD+Tf6SAVqlUwOUyHD+mvt72gFpdbatFmpGRgYODA6+99hrbt2+nqakJR0dHxo4dy/3339/WV1xcHM7OzixZsoRHHnmEzZs3o9Pp6N27NwCLFy9m+PDhxMXF/RmnKPAfR+cThGj6PMD6qxKJiSJCEbs4YqlvQjmoFybHv0fm7GtjxaIx4t0rnL6vTsDYYsC9Y8BPnqeuSWcTz7amGDQGmsqb2rZLFVJkjtdOCme1gr2zHVYrFJ4vQ24nQ+2u4tnDC2nVGbCKRDh427PwqynUlTWh1ejZ/uxJZAopcb1DaarWENUjCETQc3x7AmI9UTmr2vrv1D+SI5tTUahkaJp1bev1WgPOXnYc2pGCq6cD7ZOCaWpspeegdnz57nbcPB2xU8vx9XenvrYZALlCSmVlXVsfBXllaFq0ZGcVkJtXxIAhnXnv9a8ZN3EQjk5qmhpbGDGyH58uWUFQsD99+iTR2qoF4OTJs3h6ubJx4w7eeutZDh8+ygMP3oFGoyE0NJji4mIaGxvbyhrp9TpcXZ0B0Gp1eHn5oNFoWb/B9oIgNTWdkpJSGhs1HDm8icLCEqZPn4CjowMKhR1yuYLMjLy2Gtq7dx2mT5/uzJw5HolUQmCAH1arFYVCzuQpo/jo3TX4+XsxZnx/ps0agb+/J+fPZOPk4IxSoSQwyJva2gY6do0mPCIQo9HM3FGLATDoTcgVMl5feifBkd6oHS9fD4lMgruv88/efQICAgICAgICv4d/pIAODw/HycmJgwcP0tzcjIODQ9u27dttCXJ0Oh0Wi4Xs7Gy0Wi01NTU899xzmM1mdu/ezbJly7h48SLLli1DLpejVqt54YUXeOihhxg0aBASiYRFixaRmJjI/v37SUlJaev7RvB7Qqh/aCuEYV/m3zknNjHya87J7OON6qn7oKkFi5cHFpns7zknVivFO9I49d4+PNr70eWBwSg8HX62iVuEO0mzu3Duu7NEDo7GJcydwY8MYsfzO1F7qGk/rh0S2U+XoPJP9GH222PbXLLDewejclSiNRq/n2IRwZ38CO7kR3ONhszkIly8Hdn59WlO7c3i3o8nEtfn2u7AnQZFMvvRQRTlVhMR78vWb5JxdFUR0ymAbz7aw9mj2Qwe34mdm05iNluQK1J48IXJiCQi9AY9Sd1j6NAlEr9AdzIzC3B1dcI/wIuy0momTRtE6qUclEo5gUHeHDpwiqBgXzas3cPAId3p1iOBLz77loqKaioqqpkxaxx2dkq0Wh3dunckL7+IGTPGs2TJV4waNYgXX3oNgHbt4hgwoBdhoZF4enri4eHKxIm2/BCtrTouXkwlMVFGa6sWPz8fSkttHj8ODg4MHBiFTmcgJyefpUu/Y9asKaxY/g0L7rgFi8WKRCLBbDZzxx2z2bhuLwcOnADg1cUPM+GmoYgRsW3jEVo1OrIzCykpqmDdd/v55OsnsJqkvL94NQD5OeU88vxsMs8W8/QdX3HPExPp1DOKneuSAQiP8yex+8+H5fxebsT352/13RMQEBAQEBC4IfwjBbRUKuXOO+/k5Zdf5tZbb+Whhx7C29ubAwcO8MEHH6BWq5FKpRgMBu6++27s7e2ZNm1aW/tRo0bxwgsvsGzZMtasWcP06dMBGDx4MIcPHyY3N5eAgABcXV0xm8288cYbTJ06lYCAAAoLC3nmmWdIT08nMDCQBx54gK5du173Obi5/bxo+LP6+Lfxn54T92uf+6+dE2NjK5W7UjA2tODRvz3qcJ9fbnSdNBbXcWzxTrBCWXIBteeKiZ/xy9+fwQ8Poef8XigdlUjlUnyC3YnqHYZYKkam/OmSUz+gjfKitrQJq1WE0/cWy/8/L2aTmdJLlfSalMCpnRmYjDbr7NF1F+k1pv01+005msunL9terB3bacedL45i6Xu7ePl/Kxg1vTvnT+RiNpsxm20x7Qa9keYWDTs2HyMkwpdOSdFUVFbz3JMf4+yipv/AJMZM6EtIuB9FhWWIxFZEEistzRIog+joMDp1boevnwc52XmUl9uSZkVFh3HmzDnmzZ9BRWU1/fv34OiR46xdu5nGxiaSusS3jTktLZ2QED+2b9/OCy88RUlJMSKRiHPnzrN58zb69euLs5Mjhw8fZ8KE8ZSUlBIbE4Wzswu7du5n1KhhREVG4uHpxvq1tjhok9HMurU7GD1mCHFxUaz9bidGk5npM8awYvkmGhqa0bYYCAnzJzDYh0sXcgCQyqTMnT+RrWuPERLm2zZGuUJKcU4NBq2JwWO68MHzG/EJcGPOwqGERvky4qauyGR/zr+v//RvioDAH0SfkH/k4+dPcijfdF37T3T/gwYiICDwp/CP/QW7+eab0ev1fPjhh8ycORMAT09PFi9ezGuvvYazszNKpZLbb7/9mu3nzJnDsmXLOHLkSJuABlCr1SQkJLR9XrduHWVlZdx5552YTCbmzp2Lr68vS5YsYcuWLcydO5etW7cSEBBwXeOvrW3+Va6510Iksj3U/Z4+/gmIRRYUolbMyDD8Qt3m/8Kc/Nq60D/e/3rmpGVPCgXvbQWg9kQWIS/OxPoTSb1+K2azGXtPBzSVNrdlsb2CmprmX91e16S9emWL7up13yMWi9A16fhg/mqq8mzu0RKFmK6j21Fb24zZaCFlVxYtDVqwwDfP2+oM95oYj6ObiqbaVkITfMm+UIqL79Vu8XKVDDt7OdEdA3Bys+fonlQKsmwJCS+eysMvyJ2CzEpuurkf6RcKGTaxC+fOpqOwk7H+u/2IRFZ27zqO1Wqlvq4ZqVzKqVMXOH78LENG9OCzz7ZRV9vAQ4/Oxc3TnoqKGpKS2nPvPU8hk8m4e+EciorLSEiIpqS0lOLiUpK6JFJdXUNqWgbxCXEcOXyCkOBggoICKSwsYurUiWzfvo2AAD9KSkoQiUTU1NSxaZPt2ldUVGAwGPD19eODDz7Hzs6Orl27cebMJaqq6kk5l8oXn6+0eencNxeT2URMTDghoQGcOX0BkwHy80sBKC6u5KVXHmTLxqPEx0egbTEyfFQfEhKjkMlkuLk5k59TgcloobK8jrkLx1JaXINapWLV5/tR2MmZdtsgGmo1NNRqqCip59HXp9PYeI374AZzI35Tfujj70pRURHV1dVYfpS08MckJf1y/gUBAQEBAYH/Gv9YAQ0wf/58Zs6cSWZmJkqlkqioKMxmM/fee+8VIvha/JAc7Fpx1D+g0+l47733uPXWW3F1deX06dMUFhby0ksvER8fT0xMDN9++y3btm1j/vz51zV2q/X6xNAf1cffFRFWHA0ZSOrOYJXao/MYiMbi9Ivt/q1zIk9Nw3jqHNLwEMzdkrBIf9nq+gO/dk5MDa1ty7qyOjCZsUpurIAW2yvp+9J4yk/kYe/thGeX4D/kepl0Jo4tP8vFPVkMnted6vw6RGIRPacksv3jE1w6mM+A2zpTll3LpYN5nN6TRULfsLb2qUfzGbWgOxaLlfMn8tmx8gzjFvSk2+g4dBoDaafyuXQyHw9/Fx77aCpvPbGW8iN1jL+5Z1sfke38iUsKpqK8lgM7zxIQ7s7br65g8uwBbFp7EIDkY2nEJ0ZwcN8ZANQOdjQ02OK7s7PyqK2x5XT49OPvCI/25+iR01RWVuHp5U5VZQ1vv/U5zz7/AJrWZj7+aCmzZk/ioYeeRKlUctNNY3Fzc2HO7MlkZWdx++2zaW3VsGHDJoKDAxk9egQAhYXF+PsHEBwcTENDA5Nvugm9wULv3j04efIcjz/+IF8vXU16ehaTJo2ipsaWldxsNmMymRk0eABarR4xEuLiYlAoLpeLcnRQ09ykIyw0ALlcQUlhGY4OalYv2wdAeFQAYaHB7NtxBl9/d+YtGoumXs/JgxmMvqkXtVVNuLg6oFTJ0bUaCI70JjjS+0/9jv8bf1Oqq6tZtGgRZ8+e/dn90tPT/6QRCQgICAgI/HP4xwro7du3I5PJGDRoEB07dmxbf+TIEYxGI126dOHgwYO8/PLLTJ48mVtvvfWK9jk5NhfCH7J2X4ulS5disVja2lZV2dwlfxDfMpkMR0fHtqzgAjcOhcSApNImKkQmDVJ9Och+WUD/G5FVV6N57wuwWDAcO4PKxRlLXOwNP45DjygczuSgK60l6J7RWBR/TN1cdYg7ESE3xn/NarGScyiPyuxqgjsH4N/BD4CCsyXseO8IAN89vZMpzwyhLKOakzvSaa5tJft0MVKlhKB2PhiNZkx6M75hbqgclbQ26eg6KoazB3Nx93fk5M4MwhJ8uXgyj+LcSjz8nFn50X4qvq8zPOfBIZQX26zb50/mMXPhIFQOCpBaOX8ui8aGZkZP7cErT9uyaxv1ZlxcHaivayapRwxV1dUMG92D4FBftm89SGy7MGLiwjCZbLWcOyXFEdc+HDs7BWlp2aSn5bLwnpspL68iONgfTWsrra1awsKCOHr0OGazGY1Gg06nQyqFnbt2IpFISErqzOuvv8Xo0cMIDQ0GoLlZw1dfLQege/euvPPOW2zZsptjx04ikUiYOHEsd9/1CNHR4XTr1ona2nr8A2yu1p6e7iiVSi6czyY0JIBRoweRnppHQodY3D1cqa9rIjDQn4/eWY1OZ6BPv07ExIbS3Hz5RU14RCB7t9q+52UlNRj0Zrr3b4e7hytfvmlzDz99KJNFz02kprKRPsMScHL7OyfI+2fw+uuvc+bMGbp06UK7du2Qy4Ua2QICAgICAr+Wf6yAXrFiBRkZGXTu3BlnZ2cAWlpaePfdd/H09GTUqFHU1tZSVFTE8uXLmTx5Mmq1GgCTycQ777wDwPjx46/Zf319PZ9++ukVmbq9vLwAKC4uJiQkhObmZurr6/H29v6Dz/bviZ2dEYlEh9msRKv99RbRX4PJKsGi9EKsqwTAKvt7PDSLsSK2mjGJrv7qKJuqEFnM6Jy9sf6q6s2/kv9nArOar+1u+XsR+7gR9PxMRCbTHyaebzR5R/JZ8cAmAGSKU9y1ejZOfk5If5RULKSDH5cO5VNZUEeP8e3Z9UUyVouVhsoWNI35xPUKwSfcjca6VsYs7EFYoh/rPzxKyqFceo9rB4BfuBu7vrMJvfD2vji7q9sEtNlsRiIVYzZZ0OuMOLmpeOelVRiNZjp2j6Sqso72ncLw9HahqqKebz7fyUvvzmf/7lOYrUb27bElxvLwdCE00peszAKiYwNY9vVannlhIQcOHOOzT1cAcP+DczEa9Tz26EsAzJ8/E6VKjqZFQ7dundC0asjIyAZALBZjZ2fHypWrAEhNTeP+++/GbP4hVk9EfV0jMTHRdOrUEV9fX44cSeabb1bRvn0Mffr04p23PwUgIyOHqOgIgoMDCQsN4bbbp+Pm5srB/cmEhARRXVXP8q+3YjSa2LH9MI8+sYAVX29n+Ig+6HS2FwGHDpxh9Ph+VFU2cMv8MZxJTie+QyQZF0ooLa5GKpXg5uZIU50Oi/ny/W6xWLFTKZhwSz+UKgWF6ZVoW/SEtvdB/ivi3wWu5uDBg4wcOZI33njjrx6KgICAgIDAP45/rIC+8847ue2225g1axaTJ0/GarWyevVqCgoK+PDDD1Eqlfj5+XHXXXfx7rvvMmnSJCZPnoxIJGLz5s2kpqayYMECOnTocM3+P/roI9zc3Jg8eXLbuvj4eIKDg3nxxRcpLi5m3759SCQSRo4c+Wed9t8GpdKE0i4dkciI1SrHao1Gp7txD7MmixSNSy8UxgqsEhUteN2wvn8rSk01ykOrENVXYuo/lWafy1ZgdXEa4tUfgcWMdPQcmqOuP7HcT2H09EC1YBbGvUeQtovGEv3HZR+2SsRYJf8M8SyyQt73taABjHoT+habWAvs4MvUF4ZTeKEMpaOS7R/bskFr6rWMWNCdmpJGKosacHBTERDjxaH1F0g/WYSjm4rJD/ZDrzcCUJZXy7h5PWjVXq6jXF/dwuhbu9JYp8E32I2CvHKm3dmf1hY9Lu5qqirrMX6fgOzs8Sz6j0ykorSOBfePp7qykdj2oZjMesrKK2kXf9ltPDTcn6ioQHQ6PdXVNQwZ2gtHJ3uOHD6Fj68nUVFhODurqarW0aNXEseOnOLIkWS0uhays/N44on7cXVzwmQyIpFIOHPmDD4+ly397u5ubeL5yJFjZGRkM3bsGIYNG8qHH37OyJHD2LjRZvU9c+Y8Q4YMwNfXm7IyW0x3VGQ4xcUVBAcHERwUhI+vJw72jhQWlNHUrMFovJxER9PSyphx/YmLi2D/7lPo9Ua690pAZLVSkl/JlvVHaZ8QRn5WJRHRgcTGhxDbPpjTR7LYvOwECx4dzbiZvci4UEhSnxhaGnUoVQpSj+VxbEsazQ2ttO8RyqAZna8r07VYLMJi+Zf5Y/8GtFot3bp1+6uHISAgICAg8I/kHyugu3fvzmeffcaHH37IO++8g1QqJTExkRdffJH4+MsZZ++66y5CQkJYunQp77zzDmKxmMjISN58882fFL7FxcWsWLGCxYsXI5VeniKZTMaHH37IM888w+uvv05gYCAff/zxdScQ+zcgkeoRiWwiQyQyIJEagBtrDdJbVOgl1y4f9Fcgzz+PqCgDAOnur5HPeAaDRAmAOPciWGyiSZS8B0lMV26coViEsUMi4k4dMP3Wh3+zGd2xNLS55ag7hiOL/23zaqpppu58MUo3NQ7xAYjEv6BeLBbKj+XTVFyPT1IQjuEev+m416KlVgNW6Da1Aw1ljXgEu+Ie5gqAWCohflQM8aNiOLHqQlsbB1cVHYZFsezJnZgMZobN60Zrs470k0UANNW2UppdQ6/R7fALc8fOXk6/KR0w6o2UFtRQXlDHvGdHIldLuTt2NIsfXUVFaR1isYg59w2ltLyK6PggFEoZep2RASM64unrRHFJOV9/uQGJRMynK57i8K5z9B/UGRc3R6bOHEpjYwvVNXW4eTiRn1dEqG8Ihw+fwmDUM3nKCJRKBUuXrsLNzYGTp84gQsSAQb0IDQngyy9tLtgGo4GPP/6MwMAA9u07xPDhg4mPj6NTp454errTpYst1OX8+Uvs3XsAAIlEjLu7O0ajEY2mBTc3V2prbe7oMpmcm2+eSk1NHRqNlq++XE1rq5b27WPR64w8dP9raLU67r3/FswWMwvumsr+vScJCw+itVXPxrX7CAsNYtKUIYjFIjp3iaO1RY/JaGXg0K4Eh3iRk1qJyl6JSAQNta0YdDYRXlfZwv6153H1cODIlossfH4CAFnnStm7+hwAF48XkDQoEmfvy94pZqOZ4tRK5HYyfKOuvNdyjhVy4JuzBLX3pvfMDigdlDfsXvynERERQX5+/l89DAEBAQEBgX8k/1gBDdCjRw969Ojxi/uNGDGCESNG/Op+AwICuHTp0jW3hYWFsWzZsl/d178Vk1GBXK5CJGrFalVhNP58lux/BQq7tkWroysW8eWvj9U78LLTdnh7/ggj1++xnBnTiih6awMANVtPEf7eHYi8Xa+rD6veyIVXt1F7wWb17fLiRJw6h/xsm+qzxex/wuZibbfqDKO+mIXMRfV9h2DWG5FcpxtuVUYVWYfzQCQi/XAeNUUNdJuUQNLkeMTXyBqeMCQSk95EU42GDkMjcQ9x5bktt9HQ0IpVJELbpCM03pe8C2XIFFKMRjOtTTpmPD74in4e/3wmFrMFi8XK+q8OUllaj0qtIKFrGO4+jiz9ZCsqlYKSkgp6DIoDwNvfheqqWnZvPw6A2WzhQkomGRn5HD12lvTUXAYP64Gfnydmi5Hjx0+zf+9xRCIR02eN4ovPvwWgd+8uqNUqVq3azOgxg9i4cQeTJ4+msakZqVSCu7sXarWKtLQMDAYjgwf3p337GHbu3EVAgA8JCbYyXEVFJXh42ISlQqEgICAQpVLFoEED2L17Hw89dD8lJeUE+PvR0qTj9JkLJCa2Y/26ndTXNzJ33kzq6xqpr2smrl0EBfklLPtqPbFxkZxJTmXObZN4/ZUvGTt+AHcsnIaLixMiqwRfX09OHc0iMNib7r3iyc4ooaykFm9fF9Z8fRCJRMxDL0yn1FILQEuDFl2rgbJC22dXT5tIVvzoXjHqTYjE4rbPIhEcWHaG1YsPIBaLuPujicR+X7+7pVrDpws3YNSbSDuUh0eAM4mjYq7rvvs3MX/+fB5++GGGDh36iwk3BQQEBAQEBK7kHy2gBf469HopVmskUpkek1GBwXBjszXfSOTmVuSaSsxKJ7Ty6xONP6Y1uAOqfhZE2mYMoR2uiIPWRCZhN8URjEb0AdF/XtZeiwXr6XSM5TXI3J2xKORI40Kw2l1pXbMajJeXzRasBtN1R2lbtQZqL5a0fdaU1P+igG6tamlb1ta3oq1qRuaiwmIwkbLsFBfWnSdmeCydb+/+q4S0vlHL1/esR1NrS0TVcUwcNUUNtDS04ujrjNVqpaGkgdZGPSoXOyqya3B0t6fn9ARObcngrXmr6TMlEaW9HKVaQaehUVQW1ePio6ZnWDsiEv2pLKmj87Do/3/2NNQ2Y9SbyLxQRFpKEVarBXtXJfk5pfQYHMvencko7OTU1zVz9KDN6j12Sh9mzR1JaJQPqRfz8PB0RiwR4ePrzpHDpwHYveMYw0f1ot+AJJ5+4k0AJBIJGs3lZFv1jU2oVKq2ZE9qtT0Zmdns2rWXp595kKbGRurra3nqqYc5eTKZyMhQdu3czeAh/ZFIbCKzttbmWh4TE8ubb76OVCpn69Y97NlzgOjoCD766G1SU7P57ttNPPHkgxw9fQpPT3eWfLycgYN6oVIpUcjtsFpEODioCQzyJyg4ABdnNVariL79u1Bf28jM2WOwUypxcnLmtWe+YeCwLnz10Q70OgMSiZj5iybg6GSPXmvk/Jk87nhwHCKRGJPWilKhZMGDY/DycyHzXDEleTVMuK03Hr7OAPQeH09lcT05F8qYdFdvnDzVbXNkMVk4sNJmnbZYrGSfKm4T0CKRiCtu+F/ynPiXc/ToUVxcXJg6dSr+/v64u7sj/tHLCLDN2TfffPMXjVBAQEBAQODviyCgBX4zBoMEg0H1Vw/jZ5Fa9KgvrkVcnolVZoe45+1o7H5bPLVJpqIpqvc1t1nEUjQBcVesU2gawGJB7/DbRfsvklNM5Zvf4jS4C9XL9wLgPKIb9jdf6XGhiAvG66ZeNB7PwGNcN8T+1+9KLXa0I/rmnmR8eQSluxrXDoG/2MbBzwnPdr7UZlUSNjgGbZMWR6A2s4qTX9jiks+uPENg12B8kn65P32znta61ivWOXra4x3hQXN1C7UF9ez+IplLh/LoNTmBisJ6spKLGTi7E2f35SCRick+W0rqsQIAjHojFaUNnNhhc83PTa3glS23X+FBYNAZ2PDVEfRaA6eOZJJ9sQQPH2d6jojluy9s5Zg+fHUDE+b0ZeVnu7nlnhEYDEZU9kq8/V1YtWI7K77ehp+/J7EJoWzffpC775vGhnV7MBpNODs7oNPpeOetpdx86yTee2cpKnslEREhxLWPQteqY9asCRw6eIyJE4dTXlHB40/cS0FBAZMnj+PkyWTWrFnH+PFjWLduPRaLhR07dvHss49j+T6sQKm0o66ukJUrV7FixWrmzJmJr68fBw8eJSYmkqioSKqr6lCplDz++H2cPHGOuPZRFBWU4OHpxt49R3BycmDW7CA+/tCW0KxzUnuio6MIjwjlqcfex2KxMHxkH+pqmomICMagtWAy2Y6v/z6RmNlsoVWjo7FOg7ZVT3ikH1+9uQs3TyciowNI3p8JwJR5/fDwdKFdxxB6Dm2HVG77V+XgquLWZ4cjEsH/L10slorpNrYdmz84CkBIgm/bNnt3FfPeH8+x1Rfwj/UipvfPv/j5t/Ptt9+2LRcXF1NcXHzVPqLrCS4XEBAQEBD4DyEIaIF/NXJjE+Jy20O5yKhF0lQGv1FAXw+qwkwsX3wIFguq2XNpjYj/5Ua/Acv3ya3Mrbq2dZrkdBxmD22r4SzS6kEqxXl6f1ym9cPyWzOEi8UETErCq08UEnslEie7X2xi7+eCSCHFt2sIRWeLiJnWGQC5vRyxVIzFZAERKBx+OQRAU6Nh06v76XpTIpf2ZOET6UF4t0BqSpvY8cFRPMLcqC5q4NKhPABam/VkHC8EYPeXp+gxsT35lyqor2xu67OhSkNglCfdhseQfqqI0DhvyvNrcfd3RvK9O/ipAxl89eZOxt3ck+zvLfDV5Q3YqS6P2dXDEbWjHQNGdqS+rpmgUC/snWVkpOWxY+shAEpLqmiXGE55WTWnT15i5pzRSKUSysur2Lh+N8NG9Ean1/G/R+ehUMp4+snFREaGMmJUf/YfOMSm7xN8AYwdNwQnJzVarRYvLy9mzZpOQIAfDg5qGhubGDy4f5t4PnjwKHl5+QwZMhTAZqVvaMBqEXPbbbPJSM9h08ZdpKSkMWb0UIKDQxgx3IPvVm1iwoQR9Ovfi+ysfLy8PCgvr2kbQ2ZmPn379uT8uSweePgWMtJzCfT3w05ax8WzuQwd1YNuvduTm13C0NFd2b31FD36tqe2qomwSD/8AzyxGK3sW3+eoDAvMs5dFnFVZQ1cOG6L0Q2O9iEi8XKeiZ+qy2y1wpDbuxLeyR+lSkZgvO8V28O6BRLePfBfV9P5t5CRkfFXD0FAQEBAQOAfiyCgBf7VGGQOKD3CEFfnglSO2eFPKjl29iQYbW7T1oO7EUcl/CHZf6VRwTiP7Y3IakUkk6II8sa+Wxz6czkoEsKo3HGW7Lc2Yhfogf/C0Yh9fqc1XCpB7uvyq3dXuNvT/cFBNORU02FuT+z9bW2dQt0Z+8Z4yi6U4RXjhWu05y/2VXqpgqyjBRTYyQhs74NbiCu1xY1kJxchlohw8nLAYra21XJW2MkQiURYrVZUDgp6T07E1S8PFy9HVr6yD7Wzkrjewaz98AgXTxQwbEZnrBIrdw5+l1sfG8bIW7oBIswmC10HRKNpacU3yI2ywlqcXe1pbmxhzt3DqKtpwifAhcyMQuQKOc3NGurrm2nRWnFxdSA8KpCcTFuSsh9Et4eXCzt3HOL2eZORysTcfe9MtDoNH37wFY6Oah59/G4kEgmZmbl06hyH1WIlMNCPoqJSbrllKq3aFlauXIVWq+Wppx6murqcTZs28b//3U91dSXOzraa6Skplzhw4DAAzc2NyGQyLBYLoaG2esytmlYOHbLFZxcVluDgoObs6Uu0turo168nB/efZP/eE3TplkBGeh7jxg/FP8CH8rIqbrllEp9/sg5tq4677p1GeWEDie3b8eWHWwHIzijixTfvxmIGOzs5M28bQXZ6MWIRKGV2FGRUoW3Rc/8Lk9iz8SyDJnRg3RdHkSukeH3vsq12UhKV4P+r7zeZUkpU96Cf3C6IZwEBAQEBAYHfiyCgBf7VmMRKWhJvQt5SgcXOiVbFjcsC/bP4XH7oF4VE/GGlcyxKOXbThyAymwkY2oX6LcmULt0DgP8do8j/fC8WrYGWtGKak7NwGvvnl65R+Tih8nG6ar1350C8O/+y2zaA1WLF2dcRiUyMQWukNLMS32gPInqFMN5RiVeYG64BThiNZmY8O4Ty3DpCO/gS3SOI8tw6zBYrGz86yoR7+/D23asJbO+FTmMgLbmI80dtFuttXycz9b5+AHz3/gEGTu6I0l5Bp77R5GaWsmLJHrr1jSWivR+J3cP4+M0N9BrajvXfHQRg/LS+DBjaic1rj9BnYAJ6o56G+kb8gzwJi/AnMMgbFzdH7n94Nh99+A2NDc08/cQ79B/YhV59O/PSCzbX6KamFlIvZTJr9kT8/b0Riays35BOXFw09y6aT2FhPk7OavR6PUOHDmTduvVcvHiRUaNGkp6eRnh4KFarFalUjuFHse9eXl4899xTSKVS8vKKsFqtxMZFERsbRVpaJlKpFJFIzPJv1gOw8J5b2b/X5maffOI89z1wOxKJlMlTRhEY5MuyL7eg/d7zISujgIy0Anr0Smw7nlQqITutBLMRju65iF+gO32HdKSxRsM3H6zD1d2Bzr2icXBW4entwoS5ffH0cSUzpZiqkkbuemE0AWGehF2HgP61GDQGMvZmY2jRE9U/HAefv0ed+T8Tg8HAp59+yvbt2ykpKUEul+Pj48OwYcO47bbb2uLtBQQEBAQEBK5EENAC/3oMUjUG5/A/9Zj6Tj2ROzqB2YwhIu6XG/wOrFawiiWI3V1pvnC5NI2uqApVsCct6Ta3Y5nTnxuvbtGbaMirReGkxN73agENoKtvpS6/DidfJ+y9Ha6xh5VL2zLZ+8lxonqHcMsHE6jKrcM90BnPCHfK8+rwinSnOK2KlL05ZJ8tpaa0kfu+moJnmBtisYhDc1dz/kAuAO4+jnQdFkNVWSMO7io8/C8LJ3snZVuiqXZdg6mtaMQvzBN7ByUdekawYskeThxMw83LkabWZsZM7smhg+fa2rc0aXn12S/p3jeekycu0tzSzJAR3WhsbiIg0AuZXMr5lFQiY0IIDvbDxdWJzPR8Ro3px6W0LDp2as+e3TZrsX+AL2VlZXzxxdcYDEbGjhuGwWCgurqSiopKwsNDuPWWmUikYnbs2AaASGRpE88bN25m/PgJuLi4cPPNs3Fxccbd3YvHH38JFxdnnn32MS5dyqS+rpn4+HYMGtQXdw9XtBojo8cMQa1WYadSolQq0On02Nkpycst4cvP1/HEU3fT1NhM56RYLp3PRiqV4OnljtFooqGxhUnTBlFX00ivAR04eyyHUwczaW5qpaK0jhHju7PkZZuFuq6mGdP3taP9At2xVysZOq0LiT3CUTvb4exxrfvhxpC6LZ0tr9ji14vOlzPx1ZH8l4zTBoOB2bNnk5KSglKpJCAgALPZTEFBAe+++y779u1j+fLlgogWEBAQEBC4BoKAFhD4AzDLFOhiOyIxGTFL/5yHUIvViseEXhS/tQ6RTIK6azTeE3tTse8CMhc1qu7/P7P0jxtbaEkrxaw14tjeH5Hy943ZarZw9ovjnF1+GqlSyth3JuEW53PFPvpGHRsf3ULxuVIcPR2Y+tEkHP2dr9insaSJ1U9ux2qFYyvOEdDOh85TEhCJYNcHxzj4zTnCuwRwbncWAN0ntKfwUgVFqZV4hrlhtYLd9/HVgTGeRCYFsPHzY2SnlAHgH+HOjAcGUFvZRESiPzq9np6j49Ab9BzddREnD3tKi2ro2jeG5z68mbqaZhobWljy5ibOncxm8m19yc4oRiIR4+PvyrYtpbh4OODl40r/IR3IzS3C1c2RdWt3UVhQyl33TEelUtCq05B5PIsnnr6HjRt3sH37AR5/8h6io8NoadGAyIq7hwsFBcVMnjKW999fAoCjo5rhwweRk5OPRAzto2KRSqWMGDGUDh0SsFqt7Ny5m06dOnHmTApSqZTCwmK6du3C8uWrkUqljBs3hvvvexqxWMxtt01n9arNALz08uMsfuUzDAYjUqmUN958gocfvYOCghKiY8LIzixk2PA+GPQGziZnIJaKefmNRVgtkJ1RzOzbR1OYXcmxgxexVyuJT4ghOMQHD3cX1nx9ALPZgtFsIa5jMCkncgAICPUgtmMQAaFeyJUyrFbwj/hld/7fS3O1pm25PLMKi9mKSPLfSZq1ZMkSUlJSmDt3LnfddRdKpS1rv06n48MPP2TJkiV89dVXzJs37y8eqcC/lUP5pj/lOH1C/pzH3Os5jpCfT0Dgn48goAUE/gAkZiOyvbsxHTmKvFsXTIOHYZb98bWy5T3jCAv3RSSVgLsTMokIkwkaT+bi5eaEov2140PrjmSR8pJNSIVP7UbQLb1/MV7U1KyjeH8WBo2ewH6R2P3ITdvUYuDct2dsyzoTFRfKrhLQTaWNFJ8rtS1XNVOdXXOVgJYqJMiUMgxamyuyXGUrdWU2WjixPhV7Fzvqypva9tc0aLF3VuIb6Q5YaW3U0X1MHH4R7qQczWPXt2eoLGpo21+rMRLRyY92PYOxc1Sw7vPDXDiTS2znQEqKqvn07S0AVFfWk3I6g7KSWibO7MfCx8ajN5lobmlh+LhuRMYG8MFb3yKXSxk8ogtNTc3s3XOCXTtsGaHnLphEVXU1Wp2OoqJSMtJsAvLF596jY5cYpk0fw9KvvsXX14t+/Xvw/HNvkpSUSKdOCeh1+stz1tSCwWCkXbsoLl68gEKh4JlnnsBoNGC1WtHrjcTGtENlp6J9ez9eeeUtAgMD8PPzYdKksXz33TrOnbuI1WrFbDaTl1eIvb0KjaaV5u/7BjCZTOTmFvPJB6sAcHFxZPbN49m8YTM1Vc04OToTExWK2SCiqLCcrRuOMvvm0ZRJa5FIJPzv8dkkH87g8J4LSKUSHnhmCqkphVSV1DNkbBLuHk44u6k5sSuDoRO7cmjdRXIulDJ4aifa9bRZ0ZtrNMjtZCjVN/57EzM4gtQ9WTTXaBjxUP//lHgG2Lp1KwMHDuSBBx64Yr1SqeT+++8nJyeHzZs3CwJaQEBAQEDgGggCWkDgD0BaXIRx23YATLv2II2Mxhwe9SccWQRerlhtS1QdTKPwa1uMbt2pXBK/uBMcr3blbs6pbFsuP5xJ8JxeWH/hNXnh7gxOvrsfgJrUcnq9MKbNDVZqLydiYDRZu9IRiUW4hV8de+7g44h3tBcVGZXYOSlxD706wZm9h5qbPxhP5qF8PEJcCe1mewFgNpnpOj6OqsJ6gmK9qcirRaaU0XVMHKMX9cY92IW9y86w5u1DhLTzJqKjP5dOFCCVSRg8rSPHt6fhFehCYW4ly97bw+S7+qDTG2is09B/TCIrluxhwJgObeMwmUyUldQCsPabA/QbFc/OLSdwdlHz6AtzWPH1FvoP6UR4lD9rvtuBh6cLpWVVl6+KCIwGPWWlFTg42LetDw0LxNXVhb17D1FUVEpOTj4BAb5MnjIWX19PQkICcHJyQqvTcvLkaUaMGIxUKuHgwQM4O7tw4sRxIiLCsVqtiERiNqzfQlZWNgATJ02wHSM0mEMHk8nOyefWW2eTn1/MxQvpACR2aI+rixuenh5IJBLGjB3E1i376dU7CYv58huUhoZmPD3dcPdwJiQkEIVMzlcfb6a5uZXhY3oyZFgPHJ3UOLs48eBjs/nsrS00N2kZN6036745SEVZA4d3XCIyzp+Y9iEc3HQRgITuoWScKmbZa7a4/dP7s/lg791cPJjHF0/uwCfUlXs+mIh3qNvP3ovXi0eEB/OWz8BisiD7FRng/22UlJQwa9asn9zes2dPFi9e/LuPc/78eaZNm8Znn31Gjx49rtiWnp7OO++8w/nz59HpdLRr14558+bRu/eVpQLLy8t57LHHOHPmDH5+fixcuJARI64s06fX6xk2bBhTpkxhwYIFv3vcAgICAgICP4cgoAX+c8hMrUgMrRiULljEkj/mIEoliMW2YrUiEdj9csmnPwTr//vwE2Zl1w5B5K07jdVkIWR8Zyw/I54tBhNNuTVoa1ra1jUU1YHFCmJbO5FUTM/7+hE+KBKViz2uMVe75Spd7Jjw+hhqcmpwDnC+yvr8A/6Jfvgn+l2xLnV/Lls/OAZA9pkSntxyG3KVrK0cVmVeLcue3w1A2vFCopMC6T2uPSaTGaPRzKsb57J95UlWvHsAgOR9mXQeEMHRXalMu6sf42f3orykju4D4sjLLCMk8nJJpNBIXxrqm4mKDaS6qoH8vBJOnUgFUhk2qgeFhWWEhgfQJcCL3JwiQkL9yc7JZef2g4hEIhbcNZO7Fs6mubmFsIggLFYjmZmZFBXZrPFWK8TGhvPss6+g1+u59daZeHu7MmbMEKKiIti6dSdJSZ2wWEw4OjpgNpuRyRS4urqRn1+AUqlkytQpSCRSZs+egdls5duVtsRgLzz/BtOmTWTatPG4uDjT3NRCdXUjGzfsQyKR8Ozz9zFu/DBefO4DBg3sS9duCeRkFzJ1+ki++GQ9SpUStVpFS5OO5mZbPe7tm44ydGRPUi/k4evrxaWzedRU2bwCko+kM3JSDxprNbh5OjJiUjfadQnhhS9vpbayiYTuYWSdLrl8Ya1W9K1GvnpmJ1arlbLcWi4czL1KQOua9WQczgORiJg+oSjsrz/kQGIn4w/69v/tUalU1NXV/eT2urq63x3/XFBQwF133YXZbL5q28WLF5kxYwbOzs7ccsstKJVKVq9eze23387ixYsZM2ZM276PPfYYWVlZPPjgg6SkpHD//ffj7+9PfPzl0oDLli3DZDIxZ86c3zVmAQEBAQGBX4MgoAX+U9hpqlHs+gxRXQWKTkNojh/2h4hovbcfygXzIT8fgoLQ+wX8cqM/AM++sbSU1NCSXYHP2CRwsr/mfg4dgun58S1Y9CaUIe4/3aHFSvrXJzn/TTKRw+Nwi/BEW6eh230DsYqvFN0yByX+PUJ/dnwqDzWBHurrPi9t82W35uZqDeU5NUT2tFmnrRYrCns59o4KNE22/dTOSpK/yqSlUYtcKWXojE6onS9b4hO6hWIymFj44hiy0ktIPVdAjwFxhEb5cOaYmktn87j9nlE4uzsSEx/EpnX7KTlcztgpvSnKLyMo2IfCgnLcPJyJjQsjMz2P/gO78vrb/0OhlPLUE2/axma1omnR4OPnySeffMP9/7udb1euoV37WPr26469vR0eHm4kJ59Cr7eNfefOfUyePJo333yXGTOmMnv2VKqrq9DptFitViQSGZ9/voyqqmpuvmU2vr6+vPzyu5hMJsRiMc8/91jbeTo6qsnPL6K4uIxHHl6EplXLV19sAMBsNpOZnssbr37Ow4/dwfmz2VSU1xEZFcaenSeYc9sECvMqcHV1JsBf2VYirH1iOPm5ZURGB2E2WlDaXbbohkT40NygJalnNEEh3qQczUVkFdN/XMe2fez7KZl2Xz8yz5YwYnYXXH0cCY7zJve8LU7d1ccRo87EuW3p1JU3ET8ggtSDuWx93+YeP/6BvvS9Jem676H/Mh06dODbb79lxowZuLhcWZaurq6O7777jg4dOvxE619m9+7dPP744zQ2Nl5z+4svvohYLGb58uUEBNh+GydMmMDw4cNZvHgxo0aNQiwWU1lZybFjx3jqqaeYMWMGM2bMIDk5mbVr17YJ6MbGRpYsWcIDDzyA3V/1olJAQEBA4D/FbxLQa9euZcSIEcI/K4F/HNKKHER1FQCIz+xCHtkVnepnBOPvQBcZC5Gxf0jfvxaFqxqPab3xQIRFb8BYWofESYVYrbxqX7n/L9eINrcauLT6LBK5BLPZQsykDrTWt6Kpa8XVZLbFXv8YqxVjiwGpUopIduNeVMT1C6PDsUIKUyto3z+cj+7ZyEMrpmMVwdJnd6Jt1jHvldEc35qGUi2nMKealkYtAAadiT3rznF8TypzHhpEQVYFF87l0ndUe15/whbzO2hMJwoLyvn8/U0kdgnHP8gTkUTC8AndOZOcyoqvbe75n36wjpFje+Lj50F8h0gSOkaSX1jAyL59cVCrOH78DDu3H6Br90Tc3V3IysxHp9cjk4mZMm04ra0a+vbtybZtezAYWtmwYSv29vY89dT/2s41Pj4GR0cHpkyZSFhYEFlZmdjZKbFardTXNxIX146ysnKcnZ0xGCwUFJRiMtkS9FgsFqzAtGkT0WhaiY6OQKc3MGHiKJYtW09FeTUDB/Zg795j2NvbYTSaaW7W0NKiQa6QolDIcXd3Zey4Qbzw+BdYLBZEIhHjb+rP/56YhU5nxKA3odXoUTuocXV1xGQ0M3FWPxQKGdHtgtj67UkqSupR2Sk5uiOVkMgrY+HtHJSMv7MPYrGordzbvFdGcXpXJi5eDnQYGM6F3Tl8/cQOAI6uu0T3UTFt7YtSKxCJhBrP18OCBQuYPn06o0aNYsaMGYSH26oUZGdns3z5chobG5k7d+5v6nvevHkcPHiQ8PBwevfuzZYtW67YrtfrcXR0ZMyYMW3iGUCtVtOhQwd27txJdXU1Xl5eVFTYfquDgmwvxyQSCf7+/pSVlbW1+/jjj3Fzc2PSpEm/abwCAgICAgLXy28S0I8//jgvvvgiw4YNY8KECXTu3PlGj0tA4A/B4nDZFdTq4o1Z/ueWdvqrsOiM5H+2n+ItKTiEehL/1HikXtcuLfVziO1khA6KRiyTcGnzRdJ2pBM5JIaz35xigKMS76TLScqsFitpmy5RkVaBnaMd7Se0R+FkR3NFM2p3e6QqGa3VGpTOSmSqy+6irXVaLu7Owmw0035wJA5eV1uoHT3V9JvdibWvH+DAdymYDGZ0GgOF6RWExvti0BkpSK/AIrLQ3KLFL9iNxF6hmExmwuJ9qa5sQOWopKy0Br3ZSHC0N+Wll11aU8/mY+9mS1iWkpzD0LHdUKolvPHiUtw9Ls+bTCalc49Y0lNz8fR0pbCglAP7ktm35wTjJg5kzSqbeNi8cS8L7pxBeEQAgUHevPrqezQ321zgp0wZy+w5k3n55dcB0Gg05Obm8/DDixCLxYSGBvH555+RfDKZ+Qtux93dDavVyrJl35KfX0jfvn146aXnMJnM5OQUYG9vz6hRQzlw4AhDhgygrLSa777bjEgkYuvWfbz19vNotQYkEilFRWWo7O146JEFtLZoKSqsoHuPjogQU1xQTmxMJDs2H+XU0TRGjO3JlvWHsVqt6HQGigqrsOhF7Nh0EqvFytSbByOyiImND6a+ToNYBO88vQ6T0czoqT149/H1hMX60HXgZfF7xT36o1rpxzdcYvunJxGJRDi5qmht0rVta6puIaZXCHu/PoNYLKLHpARBPF8niYmJvP766zz99NO8++67iL4P2bBarajVal555ZXf/H89Ly+P+++/n1tuuYVPPvnkqu0KhYIlS5Zctd5kMpGZmYlKpWqziru52X6vm5ouJwqsr68nODgYgLKyMr755hvefPNNJJL/qkO+gICAgMCfzW8S0O+88w7r169n06ZNrF+/noCAACZMmMC4cePw9va+0WMUELhhtHqGw+i7EDfXYPIKxyj9BwloqxVLfgWIRYiDvNtqFv8aDJUNFG9JAaA5r4qm9FJcf4WArjtXTN6eDBy8HQkdE4/CRUWHO/pw8ZtkLEYLAEXJBXhEemFo0V/RtqWiieqsalI2p2K1WGmuaUGkkHJ63UXiBkYgc7Tj1NoLBHf0Y9JLI7D/3pU7ee0Ftr1nc8+tLmpg3OMDrimQ/Nt50XFoFCKRmB7j40g9kc/RremUZFcDMO/lkez47jS1lc3I5BIWLh7LN+/t5cySbPqNSSAy3p+jhy5QWVpPcKQXc+4eytbVJ9C1Gph8ez9OHLmESCRCIhFjFZl5dNGHdO/dHoVCyqKHp5OTWUxCp3Cee+p9tFo9dnYKnn/lHp57+V6K8ksIiwhk88Zd6PUGALy83WlorqehoYFBg22JkmprG/AP8MZo1DNgQG927dqPSCTC2dmRmJgI1q5di6OjkuTkZObNvw13dzcMBgNHjhwnLCyMgoIiwsLCeOSRpwGYNWsaq1dvoH//XjzxxP9wcFDT1KRBoVCg1+sZN244Sz7+hqioSDw9XRk0uBdqtT0KuQKj1MreXaeJT4igIL+MUWP788wjHwPQUN+Mk7M9Xj5udOgURXlJDQqpkgO7ztGjXzz7d5wFC7zzzDqUKjkTZ/ch9UwBvYa0Z9zMXoRE+9GpdxRSuRSZ/Of/7Zh0Jo5tuATYBF3+xQp6T2pP4qAI8i+UM/nRAYQkBfD0jrmIRODgef0hAAIwYsQI+vTpw9GjRykuLsZqtRIYGEjPnj1Rq3/7nG7btu264qfr6+vJzc3l008/paCggIcffritva+vL8HBwSxdupSYmBjOnTtHQUEB9957L2B7FomLi2Pw4MG/ebwCAgICAgLXy28S0EOHDmXo0KHU1dWxadMmNm7cyNtvv817771Ht27dmDhxIoMGDfrdSUgEBG40VsRoPCPBM/KvHsp1ozt8kaK3NwIQ/MB4FEnRWH9BjPyAzEmFys+F1tJ6RBIxdj7Ov3y80gaOvraLlgqb9cekM1JTWEeneb3wiveDFacBiBwai1Uipjq3FueQOhyDba7gJq0RRDZLNEDGgRz8OwUCIJJKOLX2AgAFZ0spuVBO1MAIAOrLm9vGUJlb2xZr+/+RyqX0n9OZhMGR7P3mFPXVmjbxDNBU30ptpa0vo8FMekoRxTm27Qc2nefxD6ezbuVBeg1tT3lZNW89v4o7Hh2NXCnhqyXb0On0LHrqJpxdHThxPIX+Q5Jw93QgK6uAb5ZuYeYtoyktrUSrtb040Gr1FOSVUl5eiX+gJw8/9DI3TR6J1WIlJNSfjIxMHJ3UlJSUsXatrWTY7DlTePfdj9FqtfTq1Z277roNuVzGiRPJFBXlsX//AaKjI5k/32Z5NpvN5OTkc+TIMWbMmI6rqyslJZfdWTMyMpHLZbRrF0t2dh5xcXHYKZW8/fZznD+fxtEjp/DwdCM/v4S01Gw8Pd3o2qUD9ioVi19aCsC5sxl07tKO+vomOnSO5tzpDMRiMSaDhRmzR2CnUnLiUCrb1p8AQGEnZ+iYbpw+bMv+rWs10NKkpfeweHz93TFozFjMFlQOdte8jlddV6WEvlM6sOn9I4glYsI7+OHk48jtb4/FbDQjUdjuecdreCYIXB9qtZqhQ4fe0D6v9//+7Nmzycqy1XEfOHAgkydPbtsmFot56aWXWLhwIcOGDQNg6tSpjBgxgoyMDDZt2sSyZctu2Nh/T33eH9oKNX6v5K+el7/j9fir5+TviDAnVyPMydXciDm5UfP5u5KIubq6cvPNN3PzzTeTmZnJzp072bt3Lw888AAODg6MHj2aKVOmEBn5zxMrAgJ/NqJWHebCciQuDli9r4zLFotFNBy40Pa5bu95THsv4Td/GCIv56s7M5hoza2g3ssZ3NSInVQkPjeJpvRSVH6u2EX5Xt3m/9Fa04JZb2r7bNKbKDyci3OACwnzejH246nom3Q0lDSy/60DABSfLmLcBzchkko4uew0CpXcFi9tMJM4th2IJTSEe2A1WVC7qWipbUUsEeHk7dh2nG6T4sk5VUxYZ3/cAp3JPl5EaBf/NjfT/8+ZXRkc3nCJrsOi6DkqjqNbUnHzdaSpVkNCz1DOH80jMNIDO3tFmxj3DnChsqyeWXcPobiwgoyLRQAs/WAnc+4ZjEIpIz+njG2bjjFwREe8/dzQaltZ8fU2fP09GDi0K2aTieAwP/oOSOLgvlN06hzL4UPJREaHcO5cKgaDkeXfbGD+gmm88MKbmEwmbrt9GlVVl0V+WVk59vYqtFotR44cp3PneBoaGrnl5ukcPXqUhXffSV19LZ6etjJgX3/9LfX19dx66y307NkdZ2d3HBzU7Nljm/+YmGiio0VIxDK2bj7AsqUb8PPzoXefrnROiicsLBhvb08OHkhGrVYxevQgUi/mkHzyAp5erlRV2tzYXV2dWf7FTiZOGUjfAUkU5lawdf0xtK16nn51HlazFYVSxuhJvejeKx4Qsbn+OHkZ5UhlEsxGC4729hzfmo6rpwPnD+VSmFnFkKkdSegXgaHVgFFnwt7VDhBhaDVSXVSPo7s9Du72DJ3bhZgeQSjVcnwjvy+BJha1iWeB6+Odd95h2LBhREVFtX3+JUQiEffcc88fPTQWLFiAXC7n5MmTrFixgsmTJ7N8+fI2N+5OnTqxb98+srKy8PT0bPNyW7x4MX379qVz587U1NTw7LPPcubMGdzd3bnzzjvbBPf14Obm8LvP50b08W/kqnnJq/9Tjuvu/ve9HsK9cjXCnFyNMCdX83eYkxv2NCKTyZBKpYjFYqxWK3q9njVr1rBixQqGDh3K888/j4PDX3/CAgJ/R0QGI42fbab5yEXESjk+z94CIZdLN1ksVhy7RtOUkg+A3NuF2u0pOJ3NwWH4lbGKIquVom+OULAmGbFMQoeXJqOKC8BkMKOp0WCVSFGGeiL6Beu1ytuRyJHtKDtdhL2XAxabxzZSpQxEIlxjbcmgqpadamvTVNmMxWRBKpPQVNFEWWoFsQMjcQlwpvBSJRmH8mg3OJKetyTRaUoiF3dkIBKL0Xyf4AvAJ9aTO76YzNtzVlK96rztQf7LyYQmXZ3JXNusQ6aQ0lijIeVgHr3GtefZ72ZzaPNF1n92jJjOATz55XQqSurJvFDMnAcGo9HocPNyQKc1cOLwRRK62CzfCqWMgWMTefOllfj4uTFgeCcsZivbthxi4JCubN5gq6ddVlJNx6RYwqP8adVpiE8Ix85ORk52IZkZeXTtkYCHpxO7dx0GwNXNqS2p1+efreSdd58jNS0TrDBy5BCUShk7d+7jppvGsHfvHnr27M7DDz9KZGQEfft54unpgcViYceOvRQVFQNgNJo4ceIMubmFXLqUxvz5txEeFopYIiUrK5eiohLq6hoAKC0tJzo6gjde/YL5d8xg/55TBAT6opTbYadUERDgS0NDC488PpdLF7MJDQvgo7fWUlPdwMfvreHhJ25lx6YTmExm5AoZTXUaSvJr6dEzgeSDGVQXtVBb2cis+UOJjPXH3sGO/ZtS8PJw48CG8wwYl8jBDba6z2cPZvPyd7fy1bM7KEqv4tbnhtN1VBzfvbiHI2sv4hHgzL2fTsIj2JXQjleWLhP47Xz00UeEhoa2CeiPPvroF9v8WQJ65MiRAAwePJiAgABeeuklli5dyqJFi9r2USqVV5StOn78OMePH2fjRptHzn333UdjYyPvv/8+Z86cYdGiRaxYsYKOHTtyPdTWNv/meHqRyPZQ93v6+DfyV89LTU3zL+/0J/NXz8nfEWFOrkaYk6u5EXPyQx+/l98loOvr69m6dSsbNmwgNTUVq9VKbGwsTz75JKNHj0YkErF8+XLeeecdTCYT77///u8esIDAvxFRs4bmIzaRYdEZMOSWIQ+5UkCoBnYg1MeFptM51J3IAqsVqevVPwLWVgNFm8/a+jKaaUovwy7Yk9NPraP1e3fspKfH4toz4mfHpPRyJHxcAgH9IhHLxORsT6PTbT0IHxF3xX6h/SMoPV9KQ0kDAx8ahFghxWKx0u/uPmx7YSfVebX4tPMh41AeAJd2Z9H79m4UpBRy5LvzAJzZls7ClTNROiqRq+XoNHqqCxts52O1UlvaROiPKhUZtAYKLpaTvDOThuoWpj8yCE1jK91GxeEV4oq9i4rgWC/On8xj8zcncPZQs2ONTej3HRXPqcMZTFvYn/BYP2RKCXPuGoqLmwPvvvktrRoduVmldO4eQ2S0P3kFTmh1OoJDfSnIK0MkEuHr58bKlZuJiQvhxLEU2rWPpKyskhGj+nLwwDFi4sKYf8dUfH29UCrl9OrTlaOHkxk4uDcaTSszpo+jqbmFe+/9H48/fh9eXi4kJ5/Cx9uLDevXU19fT0RkWJurka+vH56el2tp+/h4UV5eQ5cunXF3d8fe3pFHH30FgJDQIF544VEO7D9Bfn4x8QmxaLU6Bg7qyScfrcTf35ttW/fTt183tNp0Nq09hNFo4uTRC8y+ZSJ6jYGWlssvNHKzi3n0uZtJv1SATCKnqqyRytI6SgurGTauK2eP5jJ51gAWP7Aas9mCg7MdT7w7g9SThciVsivvc5GI9OQi8i/asit//uR2Qtv7cGSt7d6vLm4gL6Ucj+Bfzggv8Ov5+uuvCQsLu+Lz35GxY8fy0ksvcenSpZ/cx2q1snjxYsaOHUtERATl5eUkJyfz6quv0rFjRzp27MiqVavYsGHDdQtoq/X3Z3O/EX38G/mr5uXvfC2Ee+VqhDm5GmFOrubvMCe/SUDv2LGDjRs3cvjwYUwmE05OTsyYMYOJEycSE3NlhtUFCxZw8eJFjh49ekMGLCDwb8TqaI+6aywtJ9MQyaTIQ64s9WOtbsRY04gsxBdnNyekHs7I3B2x6xR+dWdKGb4D4yjZdh7EItThXlj0JlorL7+J1ze0/qpxyd3UyN1ssabRN3UkbcMFTn5ylHYTE3GN9rLto1bQdW4P7D3UKF0vJ2XzbOfNxDfHkbYrk8bKFiQyMWajBY9gF9TuKuydL5fBc/F15Iv7NiESiZjzxmhc/JwYMrcruz49SWCsJ+E/sj5brbDxvSNoWw3sXmF7UVCSXcOg6R3wCrEJL69gVw5uSWHfhhQAItr5cdP8Plw4mYdSJccvxB0rFnZvSaZL7xiOHEph6pxB+AV4kJ3xg5XXyIpl23F0UeLi5kCv/on0H9IZPz9vXnn5YxwcVPj6etCxcyz7956gT/8kOie1o1nTwI7te6mpqUMsFrPgjlnYq5SMHDWQsrJy0tIyaG5pwmg0MHhwf5ycHCksLGDIkIFoNC2YLSY6deqAXC7DarWSlZnLd9+u55FH/0dkZAQikYiqqno6d+7MA/c/Se/e3bFXXX6RUlNdS2lpBYEBAYwYMRi5TIaLkzNajYGkpESyMvMZNXoQZouZH2eiMxhMBAZ5c9etrzBmUl9amlsJCfUj7UI+ra06Ksvr6NkzkTXLDjBoVBLxnUKpLGkkILgZrGA2f++iYIHMMyWU5dUy497+5GdWMPXefuSllTN4SieM2sthAf6RHji62hMY60lRWhUSqRjvEEE832i6dOlyxWd/f39cXV1RKq8uZwe22srp6el/yFjS0tJYuHAhw4cP58EHH7xim0ajAfjZEplbt24lJyeHDz/8EICqqioA3N0vh724urpSXl5+o4cuICAgICAA/EYBvWiRrbxK9+7df1XCMJVKRYcOHX7zIAUE/u1YZTKc54/FYUhnJG6OWH0vWxstJTVkPrYMU2MrzknhBDwwDsfRXX+6M4mY4Nv64dEzEgdvJ/B1xWq10vF/w7jw/l5cY3xxTwq57jHm7csi+Qtb4qjyi2WMenMC5ZfKObfmPCUXynENcmHiO+NRfx/PbNToydyXy673jiJVSOgwKg4nbwcaqlvQNumJGRDGHJdxVBXUUZxWyentGQDkJBfTeXwcw+/uQc/JCdg5KlCoFW3jMOqMHFx7gYS+ly1qcqWUsPgrLfZisfjylMjEePo50bFXOLVVzYTG+NDUpCG2QzBOrmr6D+tMXk4ZQaHehEb44ubhhE+AK9W1tfgFelBRXslXn60H4I57phIc4kvqxRzmLpiMxWqkS9f2pKVlodO20q17B3bt2m+7dhYLdbUNTJ8+/nuXaivlFeWsX3+cS5fS8PPzZfDg3ixd+jWTJk1k69atzJ4943vxDLt27sXH14eKikpqaup5//0v/4+9swyQ4tja8DM+6z7r7r7ALrK4u7tbkEBCEmI3xF2IQ7AoECQQ3N1ZfA1YY93ddez7MckSLsS4RL9+/sB0VVefrumZnVN1znvQarUAmJiYMPuhKbg6O1NX30C3bh3JzMxh9uzJlJdVUlffwMWYBBLik3B2tmfm7Al8sXobADnZ+1jywnwqK2pZ8vI8rl25gY21DZdjrhPWxpdtm44B8PgzkwkJ90EikjBsRA+unE2l/9BO6EV6ygvr2fHlOSysTSjzqSKwjRtJsTmMmtGVzZ+cRK/Xc27/Dd75/iG8w11a8881LRpmvtyfwswK/Nq5YGxlxMPLRpIZX4CdmxVuIUIlhz+a3r17s3TpUoYMGXLP9iNHjvDmm28SGxv7wK/t6elJTU0N27ZtY8aMGXc4vj+WverTp889z21paeGjjz5i6tSprbnQP/6bm2tY+NJqteTn5wvaK38ipzM1v9zhAeU7d/P8/T9Zf9W2B3QdAQGB/1/c17fEI488wqhRo3By+nUhIjCIfQgI/BXIWhqQNNfTYmKJTiz79RP+QvSmRohDffjvqJSG9EI01YYd46rLt3Aqq0Hiavez42irGqhLK0KpMsc2zP2HHDARdn2C6RXti0guAemv10xtKqsj81gKej149va/I1zGxNaEU5+eRiqXkpdg2OmpyK6kLK0MUwdzKjIq2PzkLtzCDd8RmmYtN4+nETUuggtbE/Bo68rFPTdIPpdF//mdqCq9vTuu8rJC3aRBppRi6WTOfyNTyhg0qz1G5krkxjKcfGxIjc8l5vBNrJxMybhZSFpiPgERbgyf0Yni/EoUZlKO7r5GvxHtaN8zkNq6Op6cswyNxuCMPvzUSHQ6HbdSc6irbUCta2HtV7t4cskU3L0c+OR9g+K4RCJBBAwY1IUu3dpSUVlOamoGx46eob7e8B69+PJjjBw1kB3bD9C7d2ecXeyYPHkujo72jBk7HAcHG3x9vVEo5Fy9Gkd6uiGvXSaTMGPGFFQqFbW1dTTUN3Lh4iXc3Nzo1Kk9pqamODs7kpOTR0hIIH5+vuzdcxRfXx9y84oIDQ0mNDQU9BI8PN2YNMmR5555F4D8/GJqa+tb51ChkNPQ0MwXq7ZjY2vJs0seIiu9iNqaBkaP7014hC+W1ubotKBu1iAWSdn69SluxmVjaq5k4pzeVJbWEdzGnay0YsRSMW06+tC1XwgyqWHnXCqT0GVgCMVZlTh522H0wyJISVYlG984hl6v5/i313h2/SR8olyxdv79dckFfhu5ubmt+cJgCIM+fPgwWVlZd/XV6/UcO3YMmeyP+b40MjLipZde4umnn2bcuHFMmDABIyMjjh07RkxMDP3792fYsGH3PHfTpk3U1tYyb9681mP29va0b9+e5cuXo1ariY+Pp6ysjBEjRvwh9gsICAgICNy3A11XV8f69esZOXJka83IDRs20NTUxOTJk382NExA4M9CWVuKfNfniErzkUf1oi562N/eif4RXV4Z6tJqFF6OGHnYIzGSo21swSzYDck98p5/RN/YwvX39lF6NQuxXErPFdMQu9m0touM744U0Wv1lMflUldSi31bV4zszdG3aEg/nETMyrMANJTXEzK+HbVFNVTlVtFhTie2Ld6Fna8dlk4WVBVUozRTYOlicILSzmZSnlOFwkRB5MhQdFod9n527Hn/JCIR1FTUc/OHnOgDn53jka/HU5hejoXKlE2vHaW6rJ5Z7w3Gp4PbfxmrRywWofKw5v1HtjLxyZ5kpRSh1emprqgnZt8Nrl1IQ2Es5+j2qwycEsXubedorDeUmors4kf6nkuUV1TdDjkGcjOL2LXtNAufHsWXq3ZQU1OPytGampo69HotHaJDSU7KZOykvnz++QYaG5tZuGgqOTn5dOwYwe5dh1rHSkxIJjjYjy5d2rFv3xGysnIJCQnk+vUkrK3NeeWVN2hqaqJLl2g6dIjC0dEeS0tLPD09kMmkNDU1UV/fQFOTwebBgwfiYO/CiRNnefbZx6moqGLH9gO89eYnLFw4m9de+Yj27SMoL68mJdkwp++8u4SKihpGjR2EXCYl5vxVqmtqmT5zJLW19RgbG7Nnxyn6DYzm2pUk0lPyWbtmPwC3knPxC/Bgz7HzWNtaYKeyxMPDhZtx2QDU1TTRUNdMt/7hqGZYs/ur81w9mUZ4tDdHvrvGuAU9GPdId9SNGo5uiOXCniS6jQxj5hsDf1Do1t5RzkrTov2lj4LAA8DZ2ZmDBw9y69YtwJCLfvjwYQ4fPvyz58ycOfMPs2fo0KHY2tqyatUqVq5ciUajwdvbm5deeomJEyfeU3G/rq6OlStXMnfuXMzN71xYe//993nllVf45JNPsLW15f3336ddu3Z/mP0CAgICAv+/uS8HurS0lKlTp5KdnU1ERIRh1wO4cuUKBw4cYN++faxdu1ZQ3Rb4S5EWpCMqzQdAdPk48tDONFn8/cNDNRmFpP1nHboWDeZtvHB9ZhT+H85CXVyFwtMeTH5+cUpT1UDp1SwAdC0aqtKKsf6JA30vSq9mc/gZQ3iyY1sXfAeFcX75KWy8bPDo5ElWTCY1hdUY2ZnScVF3RCIROp2eXot7svel/QT08SOofwAVeZXUlTdg6WmDhYPhs1+QVAxASH9/fLt5McpYjpWLBZqfOK+m1kbYuFnh3s6FPR+fIfum4ZxNrx+l7UA/lCYKOo0K4fzO6xz+9iqdh4Vg5WiKTCHF0cOKyydTSLpqKEM149l+1NU1cu74DfqMaIuZuVFrHWo3bxWIQKPRYmQiZ9ajgyjMLcfB2ZZO3YPRS7WcOHaZSTMGkpmej6mZET7+ziQnZWJtY8HiZ6eRknKL+nqDwNbe3ceZt2A87y9dxbgJQzl25CztO0TQpl0QiQk32Lp1F2q1GoAxY4Zx/XoSBQWFNDU1AXD27Hmee+4p0tLSeeyxBQDU1NRw8OBRDvqAYgABAABJREFUUlNSefjh+bz99hvs2HGAgQOc0Wp1vPbqB3Tu0oH4+BsArFr1DW3bhmBlZcmFmHhcXBwpL68kLS2TnduPU1tTB8Brbz7Oay+tZNDg7hzaZwjDHzKsB/b2dkREBJGTWdL6fqQm59KnfycKMqtoamgmKMibmOM3CY/yJv5yOkYmCrr1D8fYyJhTOxPYu+4iAIXZFQyZ1oHASDfQw7ENV1H/UAbt9I4Exj/dE3NbE1wC7Zn55kBOfRdHu/7++LQT1Lb/aMRiMatWrSI3Nxe9Xs+sWbOYO3cunTp1umdfW1vbO0TH7pdHH32URx999J5tnTp1uuf1fw5TU1MuXLhwzzZ7e/vfpCwuICAgICDwILgvB3rZsmUUFRXx9ttv3yEa9sEHH9C3b1+WLFnCypUreeaZZx6YoQICvxe9ha1Br16vR29tj87I9K826TfRlFWCrsXgeNTEZqCvbkDiZIPEyQZtSQ3ajGLkrrYguzsMW2ptgnPPQPJPJCEzU2Ad/OvOSWX67brERpYmHHvjAHqtnobyegKHhGDpakWbKVGtu4Y//uvZ04fJX0wgYe8NmprUmNiZIf3BJr8e3ox+fQDFaWVYOpnj3dkTcydzzPKryUooxC3UkTkrRlOSUY5Pe1dM7UwAMLe5LUJm62JBVWkDjRmVmNuZsPaNIwB8/+lpHl46lGEPdSQtMZ+ctNvO362bBahbtER08ub47ljCo72Yubg/2hYdjm426MXQpG3k8+UHARg8qjNiuY6q6mocXazQaFvYu+M0z78xh7KyUp59ciliiZipM4Zjb2+Nm7sjHaMjcHNzQCqT0tzcTH5+IQf2HyMsLIhu3dsTF5dAXf3tcGkAlb0d4yeMJCDAD4VCQXNzM506deDAgYMMGzYQgOrqGkxNzdCoNfTv34/klDT8/P2wsrLkZlIKep2esrLyO6J7XFycKK+oJCe3gOdffIwDe0/SqWMkHh5urc4zQHFROcNG9CIo2Je05Dw8vFy4cPYm1ZV1yOVS3v5wEWKRhPOn4xkysivF+VVcOWfISW+ob0YikTBmWk8mze+Lg7M1Lp4q3nhoA9Z2pvQcFo5ELCagjSu1FY2g02Nha4rSWI5MLkHdoiV6aDDG5oYQbpFYRPToMLqMDUenE6RF/yxcXFxwcXEBDFFk/fr1E/KEBQQEBAQE7oP7cqDPnj3LlClT7soxEovFDBo0iISEBI4ePSo40AJ/KQ0O3hhPeAxRZRkaZy9a5H9fB1qbUUh9ch5GHvYY+TohMVWgrWvGqlMAzSU1qNOLkFuZkvDS92gbW/Cc3Bn7iZ3hv0IdRQoZ/o/2wW1YG+Q2ptiGuPxiHcyaW6VUZlZgYmdGfWktLlHu5MYXUF9iOEdmIkdhYYTURHHP840dzKiraaaxppmUMxlc3pXI9JVjsfWzI2hQIEE/6VuUUsrqedvQaXVI5RIWb5mKXxePO8aLHBKIWCKhsaYJLXq2fnIa9Ho8Qh3wjXAiLa4AlZslGcmFxJ1PJ7K7L33GtGX31zFY2pkgkYlIjsul35h2dHwhgIy0fKztzCmrrGL/vnOYWRjTvV9E6/Uunr1BTq4lyz7YxJiJvdm/+zQr176Et48rjy38loYGw25x8s0M1q3dyshRffDz9+DLLzYDMHPWOAYN6sX+/ceRSMSYmBgjFos5fOgU06aNJzX1Fn7+3piYKCgqUpOYcJ1582Yjk0lJTLxOjx7RhvehpgapVIaVlQ0TJ01CJIKc7BxSkm/RrVtXZFIpCQk3kclk3LiRxMyZE5FKpfj4epOZkUN4eDDPPvkeGo2GuNibyOVy5j48kfXf7CCqQxhxV5PJzCygoU6Nh5czbdoGceZoAgD9B3fm/dc2UV5azbipvampaEQquZ3qoDSSY2FhhlatJbJLIGUFVVw7kYqLly06jY6T++NpalBzK6EQGztT9n1zkaX75tFvWhQ+ES4ggvDu3sjkUvSAXqujvrIRhYkcmdE/I6Xi38YjjzxCYWEh77zzDg8//DAWFobUi+XLl1NZWcmCBQuwsfnlyBUBAQEBAYH/r9yXA11eXo6rq+vPtnt6elJUVHTfRgkIPAj0iKh38AWHX653/FejL6nk1gvr0Ta0gESM/9KZ+H84G015LerqJhKXbALAaVgk2sYWALK3XMB+WCSY3h3OLTZRYhLk/N++9T2pya8i9dBNXNt74NjGBSs/FR3mdqY0qQixTEL6+UyqcivvmZMIUJ5RgV6rJ+WMIfe2sbqZ3IQCbP3uFjmrr2xE90PotqZFS31lIz/tVZBayrqXDlJf3cSc94ay9/MYdFodHsH23IrPJ7SzFy6+djj62PDFWwcAyLxZxKPvDOeJj0Zx6kA8h3dcRSwW0aabD9+uOkRaUh7eAU4oTCWkJhlUel097DEyktPY2EL3PhHs33saK2tzkm9kMXnmIA7uP4lGoyGqQygXzscB4OPnjqmFHDuVHWlp6a02X7uaSI9eUYjEvUhIvMHxE2fx8nRl6LB+qOytOXU6j/MxZxk1ajh+ft5cuBBDeHgw1dUVREaGI5VK0Wg0mBibceHiJW7cSOPgQcNO+5IlT1NWVsV77y5j2rQJiCUSFj+5gMaGRoKC/UlJzual5z9CqVTg7eWNRCJG84PgrIOTihs3bvHeB/8BxBTkljB4qCm7th+ntKQCN1cnuvZow5mTsUglMspLqwHY+u1xZswbgl+AO8bGSqor6nB2tePSmRQURgoqS2p5deY68jLKsLA2YeCEKJoaDGHq+RlleAU50NHdhq+W7KemsoEZr/THyt6MCzuuc+VQMsMWdiYzrpC9y8/h086FGe8OxsJRSPX5s8nKymLy5MlUVFQwZMiQVge6pKSELVu2cOLECTZv3nxH7XEBAQEBAQEBA/flQDs4OHDt2jXGjx9/z/aEhATs7H5eJVhAQMCASATq6gaD8wyg1aGuqEXp5YhMZUXFt6db+4olotaQdNtoP0RK2V2K3b8X20AHbHxVlKQUEzgygkOvHMDGy5aqvCqcwp2w9rQmekFXTF0sf7z0HZg7mKHTaXGPcCY7Lh+xRIS1i+UdfUozKri69yZGpgoGPtKZw6sv0HlcOM7B9nf0O78jkfS4AgA2vXWUDkODMLMxJuF8BsX5VVg5mpOcmEfjD3nFP2JspuTVR9cx9dE+WFiboDCW0djQRFpSHgBVFfX4utyuqy2ViXn8+fFcvnATO0dzeg+KJDsrn8ioYDR6Neu+3glARJtAnl7yEFKpmLy8fL7fuh9zc1NeeOlR6usbSU/PoVefaGrratm92xAS3qljOzIyM3F0VJGfX0BycgoymYzvv9/B8OGDCA0N4quvvmb69ClIpVJKS8tYt24jU6ZMpnfvvrz99vutdhYVlZCSkklzcwt5eYXs23cUAH9/H6ysrNmzy1BqqqmpmStX4pk6fRQ52fmYm5sTF5tCv/5dyM0p4uyJBFxdHdm78wz+ge7MfXgChXmlRLQJxMTEFCur24JMoW28sba04K0nNqDXw4LnR6BUyhk9rTvtewSReb2QvIwyAKor6vGNcMHWyZyyghqCo9yor2zCJ8CRkowKnD1tOLfzOg3lDcSfSKfziBDij97i9OY4AG5dzSPtci6Rw34apyDwZ7Bs2TI0Gg3r168nJCSk9fhrr73GqFGjmDdvHsuWLeP111//C60UEBAQEBD4e3JfDnT//v35/PPPadeuHWPHjr1jd2rHjh3s3LmTadOmPTAjBQR+L8alOUjizoOZBU3hnVEb3V0O6a9E1NRCzdFYGlLzsenXFrtBkZTuv4JlBz+U/oa8ZU1BBcbO1thEeVN+OR1jb3si3p+MurIek2BX9L+lFFV1Iy3l9cgsjeAndZF/RKkyo9+Ho6nOLGf7wi0AVGSWEzAgEE2zlr5vDqO5rpmr316hKKmEiJEhOEfeVsY2czSnw4S2nPzqMtFT2tFY00x9dfPtC+hh+1vHSL1oEPka8XQPXjkxH7mJnMxr+WQmFuIV5oS5ygSFsZyuo8O4fDAZGydzuo0J46Vx35CfUQ6Aq58dAW1ciTmcxEPPDyD9ZhF+ES74t3PFwdWanKwijuy5CkCXviG06eBL7MU01GoNfQZG4uRqS011HWlp2Vy8EI/KyYqaujq0WjWWVqYUFZchld2eo5KScjIzsti0YRddukUREuKPg6Mthw+doKysgslThlFRUYXKzorXXnuKuvp6ZFIJLeoWioqK8PX1ZNq0CZSVlREUFEhMzEUaGmqZMWMqZmZmtLSoWbt2A1KplLS0TGprGxk2bBAbNnyHubk5QYEBaDR6Ll64io2NFcbGRmg0WoaPGIiJqTEdO7UlN9ew4GBkpKSwsJTLl5Kor2tEp9MRHh6IlaU1k6YO4j9PLAMgJSmbzFv5pN0oILydL8cPXMHSypTp8wZhaWVGdkop+VnlmFkYU11Zz77vYvhi37OtCycOHjYEtnUl6Vouzp62uPmpmPfaYC7uT8Yj0B5LWxO++M9+NGotadfyGTK3I1f3JqFRazm1NZ7Ri7tjqTKlqsSQn21lL+w+/xVcvnyZadOmERkZeVdbREQEkyZNuqPslYCAgICAgMBt7suBnj9/PidOnODll1/mgw8+wM3NDZFIRHZ2NjU1NXh7e7Nw4cIHbauAwG9CqlMj2fkNlBrSCIwsrJFY2qOxskOjNPlrjfuBxvh08r80lJCpupCM/4qFqMZ2BlMj9DIpmpxSivZcRdPQgpGrDW1mdkfqbActapR+jvd0hu+6RmE1x17bR1lyEW3ndsV7bDtEkrvPk5krMbIzRSwTo1MbQqyVFkb49AugNr+K/IQCTiw7A0Da6VvM2zEbI1sT9Do9hTeK0ev0GFkoSbucS2A3L1qaW9BqtEikEvR6PTVltwW1GqqbUJorKEgt48NZ36HVGPKhu44L59C6K4jFIqa92Jfgrl5IlTJsnCzIuGF4H7VaHUbGCsY92h3vMAf6TopCKjd8hb26ZganD8a1XiczrZhJC3ri4mlLXV0j8dfSEMtg1/YTABibKJk+dzBV1dWsWbH5h2NGvPz6wyTGp1BSUs6ch8fy8gsfAHD29GUefmQKVdWVrP3GsNBQXV3D0OG9OXjoGHV1tcTHX8fd3ZXOnaP49ttNTJs2kbVr1wNw8OBhpkyZiIeHQcSpqKiYoKBgnJwcycvLx9nZEa1Wx/HjZxg1ajjBwYGkpGZiZmrGo4/OQSaXMXfuNJQKYw4ePEVgoA/R0e2wtbXCwsKcluYWbG2tycwoJDX5h7rSUhlL3/waCwtTJkztx6Z1hlJbHl5ObF17Ch8/ZxYuHkttTQNVZfWYGpuiUlmTHJ9DUIQHIjH4h9xZRszCxoRnV0yiMKsclYsl1vbmpFzO5cyORM7sSKTToCBE4tsLqlb2ZjTUGhZULFWmBHRwI7SHN2kXc7Bzt8IryuVXn2OBB09NTQ1WVlY/265SqSgrK/sTLRIQEBAQEPjncF8OtLGxMVu3buXLL7/k8OHDpKeno9FocHFxYcKECcyZM6e1NrSAwJ+OHvTqFkSAKLAN2oN7EVeUowgMhdHT0SiMf3WIP5yfqg/r9AabrW7vxtXG55C3Px4AEzdrVL1CyN96gczvL+PYPQDPWd0Q/0I5K4CSazmUJRucz2trzuDawx+Fgzl6jZaarApkJnKMHQ25j6aOFkRN60B5Rjlm9mbYBthz9bs4EvffJGri7Xqqegz5yxlnM6krq+fkl5dwC3fi6r6bAOQnFRM+MBBjC2P8unshEosY/3I/tr5xFGNzJUVZlSQeuYXCTI5WczsfuraiwTAVOj1yEzk2rpbodHomPtULG3szGhtaqCqvI6KXN+cOX2fH2tP0HN6GsQt6ACJcvFQMn9KV5qZmmtUaFCZimhqbEMlALNNzYPd5Js7uS2iENzlZxYyd0puiohKqa28LrDXUN1JcWkZdfQ2h4d5UVlYhFovQasHU1JiKigoamxpb+xsbG1Nf34CdnQ3nzsUAkJ2dS/v2EYChbu2P2Nra4Onpil6vR6+H+vomiorKUKmc6du3P2q1DmdnB6KjO9HY2EBubgmVFdWIxWI8vVzZvu0wcx6axBuvf4ZWq+XmjTSUSgVubs4kxKWgsrdl1Wffsfjp2RQVlmFpac7xw1cwMlZSXV2HVCal/+Bo/APc0TTpCGvjg7W1FV98sB+9Xk/nniGI3cRcOnmT9GTDrvb4OT0ZPbtn6+5zU10z8WfTkUjEhHXxRv6DAFhYVy+6jQzlxoVsXP1s8Qt3Ii0uH+8wJzoOC8bawZyynCqCOnvg6G9I7XHwtf3FZ1fgj8XV1ZWzZ88yadKke7bHxMTg5OT0J1slICAgICDwz+C+HGgApVLJwoULhZ1mgb8dGokM/ciZiI7vAis79NdiAdAnJSKtLEPj4PYrI/zxKCO8cZrck7qb2dgObg92Fne0a5pu5/k25Feh0+hIW3sWgNz98dh28Mai/S/XaTWxvx22buZihdRUAehJ2R7P+WWnkCqkDP5wFDZhzoCetFPplGaUodfqaT8tisT9Bqc4Lz6fTlMjKUkvJ3J8BDcOp3BkmcGWqNFhNDdpWq8jkohBD7Vlt51H97ZO+HZyJ/ZIKmWXcrCwN6XL+DC6jQ3n7PZE2vbxxcTSCLFYRPfx4Vg5mKFp0SKVS3DwsmHcU73ITi6iprKOtxZtRKvR0X1IOMd3XWPYjM7U1zWSmVqAo5st9U0NVFfXImuQkp1VSJee4SxfupWgcE9s7czpOaAtjU1NFBeVYm1tgZm5CV26tSXpRjojx/Zl04bdZGflkZ6eg4eXC48tnkVZaQWeXi48//zbdOzUltFjBqPX64mMCuPNN97n0UVziI2NJzs7FysrS0JCgrl06QpKpYK5c2eTmHidPn26o9fr0en0SKUKevToTkFBEUFBgVhaWlFYWMLKlWtpamzi7Xde4nriGc6evYhSqeCppxcwceJwsrPzkMtlNDZqDc+IRoO/vzfNjVrWrNqMVCrFzc2ZT9/fxJz5Y7G0sMCvryclpeWIRCIO77tAh46hZKWWENbGDwdnG0LbeZFwJR21WodYL0GuuP0nwdTCCJlMSsz+61y/kIWxqYJdnxsWCma/NID+U9sbni0bE+a8OxR1Ywtl+TVIpWLCunpj4WiGwkROeJ+/t4jf/0eGDh3KRx99xEcffcTMmTOxtLQEDDvT69at48iRIzzyyCN/rZECAgICAgJ/U+7bgf41Ll++TFRU1B81vIDAL1Lv6I146pMoSvIQnTkFLc3g4IjW3PKvNs2AkQLzMV2wFHe9qxaupqQapZMVpp521OeU47ewL5gqEculrfWhZWa/vPsMYNPWjQEfjaMiqxxVW1ckpgr0TRpiv71suE6zhoJrudiEOaMXiej5TG/OLj+NqcoMr24+pF/NReVtR1FKMUEDA+nqa4dIBEc+O996jbqyBkpyKxj4aBeK0ssxsTKiOLMCn2iP1j56PVg7mVOWV027gQFcOZbKvq8uMvmFvjy7YRKJ5zKoKqln6kt9Wf/+MQ5suMLoR7oy8pGuiCViCrPLSI7PoaqsrnXXOvZsGsNnRtPc3MLLi77kRmwm5pYmvLV6Dvt2nmPHZkOo9sVzN3j+relkZxWASM/WzYfIyiygTTt/OnePwNHJluiuEfQdEI2FlSkV1WWEhPji4GTNvr2HcXVzwsbGEgfHNoSGBnD+3BXcPVzwD/Bkz+6DLHn+cc6evcCoUUMAPTY2ljQ0NBAaGkD79lGcO3eWHj26IBaLaWhoxM/Pn4qKKs6du4hWq+fbb7dgbm7OoIH9KC+rACAjIxsbGysGDOjFoUMnqK6upa62kcyMbB6aM57ColJsrK2ws7MmNjaJwrwyFiycgrmFGV+s/J7/vDiHlR9tpbbWsKv/0IKR2NtbM2xUd2qrmqmrbQKauFx8E4VCRsfuwfj6uPHNh4foM7Id9s7WOLvb0ntoOzIS83n/sa2oXCyxsb29IJMWn8+AaXeKysmM5BibKli5cAc5N4tp08eXqW8NQPkbnlWBP5dZs2YRExPD6tWrWbNmDVZWVohEIiorK9HpdHTo0IG5c+f+1WYKCAgICAj8LbkvB1qr1fLpp59y/Phx6uvr0el0rW0ajYb6+nqamppISkp6YIYKCPxedDo9jbbOKB9+GnF5CVpHV9TGfy8xsf92nvV1TVx/bTt1GSVYhbjQZulk5P6OgIh2b42lKjEXMy8VxgG/Hl4pEovw7hOIRVltq6MjUkjw6u7DjZ2GGsBW3rdDaW2CHBixchwgoiytFL1ESmFaGR7tPZAYyVtt7TChDdtfOohYIiKkvx/mTuY4BKoQS8U01zQjVUiRKO78auk4JhRnPzuSr+Rybu8NADa8cYQZbw0gJ6WUgoxyZEopjbUGNfIdK88xYFoULWoNL836muqKeh56biBdh4YiV0gxtzDGzMaE0qJKbsQacn5rquqpKq/D0up2nntkR39efm4FVZW1jJvcl+LiCnz93dGh5qP3v0YulzHn4XGcPHWONm2C+H7rPvR6PZOnDCc9PZtbt7IYPqIfX365iYGDejBh4nAuXbrG4cMnmTJlFIWFBezcuYsJE0ZTUVHGwYOGvPapUyeRkBCPp6cbcrmM6uoazp69yPtLP6VHj26UllYQ0SYCuVxOTU0NtrbWABgZGSGTKti75ximpiZMmzYWYyMTtm89Sp++nVHZ23H4YAxDhvakqqKBK5dv0KVrJF+t2cH8R8YTFu5PWXEV4p/kurc0aTA3N8PewY6WRg2Hd14CYPTUHuRmltKjf1ta6gzf4Ud3XMU32AUfX2cUytth9mUF1XToFcCtxELEYhFdh4XepcgOkHO9iJybxQDEHk2j/9wOuIU63t1R4C9FKpXy1VdfsWvXLg4dOkReXh4ajYagoCD69u3L6NGjkUh+XaRQQEBAQEDg/yP35UCvXr2a1atXI5PJMDMzo6KiAkdHRyorK2lqasLIyEhYvRb429Bk6wS2/4x8Pk1NI3UZJTj0CEIvElF2NRMHZ2vEpkpMgl0wCf4fRZdEItrN64JLew/kZgpsw5zvaNbrQSTSc2nDVbKvGuomK0zkWDiZEzzMUO4maGAA1m5WaFs0OASrkCrlrecrzO/ebdQ0azi54RqX9iXRdXRY6/HQ7l5kXC/i4qFkfMKdURjJUBrLaKxvIbKPHwoTOZU5dVRX1OMX7sKBbZfISjXkdA+bGs3mNcf4ePMjRHTwJe5iGta2Zrh5OeAV4MSNhAyuxCTj4GJNVaUhz3nX9ydZ9NQksrMLWL92OwAtLWrQ6enaLZKysgo8PF1ITkonMTGVMWMH09jYgJ+fFyIRZGTmcD3xJiNHDaJX787k5GSTn1/IgAF9sba2ZOfOnT+ZRz3Ozg6IRCKysrJpbtZw9co1AE6ePM3IkSNISUnBwUFFXl4B1jbWLFnyBGq1ls2bdqHX66mtrcPIyBixSEJIsB96nZ6MjDwy0nP4fPV3PPX0PCpKa7iVms3MOaP5/LOd9OrbnpgzMQwY0pm8nGKcXVVcj8ukpUlLdXkjLT+EfwNkphXQUN9CaDtvrKzMSY7Lpry0lqguAWQlF5OWmEdYJ2/mvDSIlNg8rGzMGDU7mogePvhF3TsNws7NCrlSSkuTBitHcywFle2/LSKRiBEjRjBixIi/2hSB38DpTM2vd/qL6Ob58z8lRSKwtTWj7CcLuX93/oy5Hi3IQAgI/KO5Lwd63759+Pr6snHjRsrLyxk4cCDffvst9vb2rFu3jqVLlxIWFvbrAwkICNyBzM4Mn+ndKDiTSk16CQBSCxNUgyMAqEsupOhiJhbetkiMFMgtjDDzVf3GHyZ6iq7lUX6rDIdQR2wCHe7dSw9KU8Vtm5QyjCyMWl+XZ1ey7uk9VBfVEjk8mGHP9UaqvPOr5Kc1ozNiC9j63kkAKorOMv/DYTTVt2DvZcOrE9cBkBqbR+ehwTyxbDS56WV4hzoilUtx9LBh6uN9SYrNJieruHX82qoGFEoZphbGvPTRDFKuZ1NaWs6Gb/YR3S2M196fx9GDF7h5PQNbOwvKSqtxdXfg9KmrzJ43krS0DC6cj8PYWImji4q3315GRXkV3Xt0QK3W0K9/Z1Z89jUlJWWcP3+J555fxGOL/kO7duHkZOdzIeYynbu0o6Wlia5dOrJly1YGDuzPtm078PPzxdvbHYCGhkZqamrp3r0H27ftQq1Wo1Kp8A/wJyw8lPLyKkxNzcjMyMbcwoK2bcOIiAjh4EFDCLqDgwPvvrkK/Q+TufjphwDo2asTH767FrVag5e3K5vWHqCpsZnD+8+z8ImJfL5iO+4eTqjs7Ui8lk7KjRz6DuhIYJAHF08bIgA69wojLNIXv2B3RCJ49JXRfPT0NratPoteryeisw8SqYQBUztgbmbMymf2AHDxUDJv7X4IhfHthZMfcfCz5ZlNkylML8ct2AFzlSAm+XdAp9Mh/oly/0+jxn4J8W9Q+xcQEBAQEPj/xn050Hl5eSxatAgzMzPMzMwwNjYmNjaWIUOGMGvWLC5dusS6devo3bv3g7ZXQODfjUyKw7C2ZO2Jaz2krmsCQFNZz5nntiGRybAJdiLndBoisYhu74zEup3Hrw5dnlTMzse24drOjZK0MszsswgYFISpkzk6jQ5dkwapqQwQETmpLXIjGS1Najwi3agqbyD1ZDre0R7kJhRSXWTY1b2y6wY9ZrfH2s0KiUSEVqsn+Uwml3bfwDPCiY6jw+6orVxX0YSDpw3OgSqqiuswszKittKgbG3vac3X7x8k/bohTPitDbPwj3RnzPzu1FU3cvZgPJtXn8DR1QYnD1uGTolGKpNSWVnD8SMX2bPtLJNm9+Xl/6xAJpcyb9EoGpsamb9oLJcv3KC8opJbKTmkJGUgEusZOKQrAFlZWVSUVwFw6uRF3nrnaTQaNSUlhjI+paUVnD93iUmTx7Bxw/d06xaNrZ0lL7zwKjqdDlNTU5599gk2b9rK/PkPYa8ybC2UlJTy+Zqv0Gg0REV1ZMzYUdja2FBQUMrSpZ8xb94MVq/+huHDB+PoaE9FeTVrVm/E0sKcRY89RFVlDS3Nt8XkAJRKBYOH9CS6aySxl9MoL6tCJBbj4eVMYlwaGo0WrUbL6+8spKa6kXdeWgtAz77tGD62O+89u4mBI6IB8Atyxy/YvXVsG1szslOLW511a9XtdIeGmqbW/1eX1qNt0cLPiNk7BahwClD96vMo8OcRHBzMe++9x9ChQwEICgpCJBL94jkikYibN2/+GeYJCAgICAj8o7hvEbEfVTvBUBIjNTW19XXXrl1ZvXr1/2SYgMD/W4wUBD3al+RVxzF2tkbV1R8AbbMGdV0LNpFOFMXmAKDX6alIKr7TgdbpaK5oRGZ65w5hQ3kDIrEIpbUxiT/kIdeW1tFxbicOvXWEnNh8ej/enZBhwZg7mdN5QWd0Wh3fPbWXpFPpAIx/axAqT2skMjFatQ63MAeUZkqOf3GJ2COpRI8KZf+q81QW13FpXxLWTuYEdfNizvtDSb2cS0hXT5wDDaWMLO1NeWL5aM7sug4iPRVlNaRfL/zhFvTkZ5XjH+mOSCzGzMoE72BnBk/qQHVlPfYulgREGO45PSUXIxMloyZ1JyujgEkzB5KbXURaSh4njl8kPi6ZYaN6okPHQw+3R6GUMGpMPy7GxKPT69DpDDmhGo0G/wAvvv9+H+PHDyUkJIDr15Px8fWkqKgEC0szjIyMUBopEIl0rbt4dXV1xMUm0LFTFA4Oduj1epqbWnBz8+SRRxZiYWmBtbUVnTp1ZOvWvZw7dwGAxMQknn32MUSIaW7RoreSEB93iMFD+rBm1XdoNBosLc158pk5nDl9mZBgPzRaPcbGJly7fJNJUwdTW9uAuklPhbIa9xFOODjYYGZmRlJCDs1NGmbOH4ZSKWPUpF5UlNZRmF9BYb5BrKxDt+A7ng9XHxWvfDmNS8dScPdTEdb5tsp7QJQbo+Z3pqyohs5Dg8mMK8TayRxHPyEO8Z9AZGQktra33ytB4FNAQEBAQOD+uS8H2tHRkZycnNbXbm5upKWltb4Wi8VUV1f/79YJCPw/pPpKJlfe3IOltwqXgWHInKwAUNib0/7p/qRsu4prV1/S919HJBVjE/wTkSadjqQt17iw4gy2fiqGfTgaLAx5yapQR3x7+NLSeDu/q7akjvzYPNLOGoS4Drx1BLmxnOOfX6D9mHCCBwSQHZ/f2r+yoJrgAf4sXDeJyvxqXMIcybyaT9GtMkqzK9n82hGihgZxYbfBQddp9YgkYiKHBBI5JLB1HL1Oj1gMeZllHPjuEoGRbojkIkbMimbnV+exd7EisN2debZ+Ye5Y2ppRX9uEysmCXVtO0tzUTHlZFbYqC75cuR0XN3vqGuq4cD6eyPbBhIT6cCstFyOlHKVSSlFxIfUNjdxKzaBjdDsO7j9JY1Mzr7z2OIWFxcjlYgoKijEykjN/wXRu3kzh2tU4Tp06z6JFc1i8eD6ZWZnU1zfQrVtnTp8+x6BB/XFytsfS0hy9Xk9q6i0SE5MwNTXl4sUryGQyFiyYz8qVXzN9+sRWB7pDh3Zcu3aT8LAgjh05S1T7CDw9Xaivb8DC0ozyskq8vNwQiSQEBvqzZfNBZs8ZS01VA917RnLiyDVMTYw4evBK667x0FHdkIjkODrb8dHr36HX65FIxPQe2AFHV1vmPTWUdSsOMXhsJ/RNerZ8dhwTMyWd+odga2tGSEcvgjt43THvFfnVLHt0O6W51XQYGMChLy+TfCEbmULKsxsm4Rpy71QAgb8P69ev/8XXAgICAgICAr+d+3Kgu3btysaNGwkNDaVPnz6EhYXx2WefkZ6ejqurK3v37sXBQfhRJSBwP5RezEDXrKXiZiGaDRfo1MUfnUHdC4d+wTj0DECv0+PWLxi5mQJjj9s7S42l9VxYcQaAstQSss9n4j7Q4LgqLIzo+Xw/KlJLqSuvR9OkIXpOJ2p+UrPZxMaEnPgCvKPcOLnmAkZWRgT39EEsFpOfXIxfZ08AHAJVOASquLLrBuuXHEAkEtF9UhtObYqlw7AgVB6WWNiZ4hftzn+TejWXNS/uw8bBnG4jQmnfL4CsW8XEr8tgzNyuLDvwCOZWJpjbmNx1rsrJoFZ98vBlyksryckqoqW5hRNHrjJoZCeamprYv+80ANlZBUyY0g+5kYiaumqiOgbx3jurqKqqYdyEwXz8wZf4+nmwcOFUevXtwupVX/PuuysAOHjwOFHtwwkPD0Zlb8vixQ+jNJLy2utvAdCzZzciIsIJDAxALNZjaWmOSCSiurqG777bzvDhQ9m5cy8AarWahoZ61Go1x4+f5qWXnsHY2JjsrEKqK+s4cTyGSZNHkZKcyZSp43BzdUIqlxIXe5OkxEw+WvoNRkYKBg7uTmZmPuYWpmz69iAinYRSsYiQMG8S428B4B/gyYl9cdTVJjBqUne2bTgJQF1tA7Gnb7HtizMMG9sFGxsLPn7OIKTWZ2RbmhvU+IfcFpRrqG4iZvd1aisacPGxozTXsCAqkYhJvpBtuK9mDXkppYIDLSAgICAgIPD/ivtyoOfOncvRo0d59NFHOXfuHGPGjGHVqlUMHToUhUJBU1MTixYtetC2Cgj8a9HVNVFxLQu5sQKznzjEjtE+6PkvhTCZBBFgGXqngjaAzESOpZsVVTmVAJg5GRy7H3coRXIpNiGOjFk9DvSAWIS12pYRbwyiOLWUmrIGzm+MRSQW0XdhZzY9tx/dD6WMZnw6EpW/Xeu1xGIR57caymHp9XqqS+pYsHo05/fcpLygBmdfW5z8VdTXNZN5vRCfCGe827qw5oW95N0qIy+tlJCOHrh423L28HUAvl9zBmcvWyJ7+qPT6UiKz0LdrEYsFZOdXkhQuCdlpZVkZ+Tz+bKdALh52hPW1hdff3ckMjh16jL1dY107BzKh+9/DcCJYxcZNa43RUWlmJmbkpCQQnCILzm5BVhZW2BmZkZVdU3rvZWVVWBqasrVK3GcOHGWl15+ivT0NGbMmIyZmSlGRkZUV1dgbKzE2FiJSCRCp9Nz4cJVoqIiSUy8Tv/+fTh06CgmJiY4OjoiEokYNmwwJ09cJDk5jVmzJpKSks6QIX159eVP0Ov1yOUyHn1sJhMnjaKuppk1n30PQGNjMzqdDqVCgUgk4lZqDosWT+Hbr/bRpZsnPfpEkp9TRlZaCTcTsgBQGimY8fBg7OwsMVYa8dnrhvk6tS8ev0DX1nstKajCzcf+jufo0oEk1r1uKMnVf1okdi4WlOZV09ykJmqAP5cPpmBiocRDKFH1j+C555773eeIRCLeeuutP8AaAQEBAQGBfzb35UDb2dmxZ88eduzYgbW1YUfoq6++4sMPP6SqqorevXszb968B2qogMC/FZEIMjdeQNesJvvITRSWxoQ92geFygyLMNefVdjW1DahqW9BoTJDJDYIAklNFfR9exgFV3KwdLOmIq+amPVX8O/rj1cf/9Z+iETww3/FMgm+ff0RyaWcWLzb0CwWYetp0+o8A2j/yxCdTk9YH1/SYw0h3iE9vMm6WczZHYkAlOVXE9DJjQ8f2Y5Oq0Mml/DWroewVpmRd8sg0CWRign4Sai2ytmSmpo6PnhhM0ER7qz+YDcDx3RAq9fQ3NxCwpU0gtp4UFZazZDRnWlqbsbY2IjgMC+8/Jx4aOqLDBjcBRDh5+96h73mFqZ06x6FVC6hvq6BXn26kpKUgYubA3v3HKBdu1CSbqZy82YqM2aMZ/36Lbz40hO0bReMpaUpNTXVtGvXjoKCQioryzExVmJiYoRYLCYh4Tqurq7Y2FiTl5fPww/PRSyW4ujoiIWFBQ4OjsyaOR25XEFxSSkVFVV8/PHnDOjfi5YWdesCR0uLmuamFr76fAtnT18hMiqEK5evY2SkwNPLjVMnrzBl2lAio8JoFxXEoGHdkctk3EzIYsPnR+nZN/L2XDpYUZFfx5YVp3HzsmPec0M4tO0K2WnFtO8ZQNy5dBBBWEcv5HIpzY0trec2/kQ07ML+JF7aPI2y/GocPW2Qm8joPT0SS5UpVs4WP/9gC/xt2LFjx13HfhQR0//X5/rHBTfBgRYQEBAQELg39+VAb9iwgY4dOzJ16tTWY2FhYXzzzTcPyi4BgX8NutpG1CXVyFUWiMyM7moXAWXXsjHzsEXbpKahqJqba8/RY/0cRMq7SwUB1GWVc+S5XdQWVNPp0R74jIpodY5NXa3xc7WmOC6fA28YdhEzzmUy0c0am19QR/bs6M7gZ3qRd70Qj3Yu7HjnOJ3GRpBxNZegnj54tLu7BnXXiRE4+dkilUnwaOvE8bVXW9vEUhEgwthUTkNdC+oWLbWVDcx8eQDn9t7AxFRB5+GhmFoZ8+pX0zl7IBF7V0tWvrcLAJlCAoCjmzVfrdxNc5MaewcrHN1saNs+gDdf/ry1xnP3Ps+Qn1dMU1MzO7cdA2DRk5N58pmZnD51heAQHywsTPHyceWLzzcDUFxSxgsvPcrJk+d44XmDo/Dqq0/z4otPcC02jldeXcynn64gKSkFKysrZs2aQlxcPPHx8XTp0hEzc0OJpri4eHbt2ssrr7xMTEwsAQEBWFpac+7cJdpERGDv4Mjjj71AebkhKmDcuOGk38rC0cmeNu1CqayoYfiIfhw/do4Bg3rSvn0Ezz75PhUV1US0DWTeggkUF1ZgaWmGXqNHqVAwYFB06zwX5ZXy6pNf0W9YR9q088XCzBwLK2PsHaxY+uxWAHIySklJzCOysz8hbTzZvTaGJ94eTU5KCTRD+q1CrpxMo/BWOQpjGW16+VCYVYFCISOqvz+2blbYulndflba3B39IPD35fDhw3e8zs3NZdGiRXTr1o3Zs2fj4+ODTqcjOzubL774gosXL/Lll1/+RdYKCAgICAj8vbkvB/rDDz9k5syZPPLIIw/aHgGBfxW6ijqS39hOTUohFsEu+D8/ErHFnfV/dHrwndSR8vjbwnwO7b0QKWQ/O27BlRxqCwx5qRc+O4Vn3wCkP9Zq1umpyihHp9EiVUjRNGt+dpyfIlFIaTs+nMiJEax/dAc1ZXXoRBDSPwD3CCfkJnfbI1FI8Yv2aH0d0NGd6OHB1FU24hKoQqfX4dvWGXMbE1QulrgH2yM3kjNmUfc7xono6ovSVM75I4no9YZdMUtrU8RiEZVltTQ3Gco51dU1IpGIKS6soKqyFplMik6no6qyFt8AV5xdVOTnldC9VyRhbfyYPf0Zxk8cwtkzMdTVN9CjV8fWa4rFYt5+62N8/W6LZn3zzXfY2FrR0tJMRkYpSUkpAFRWVtLSosHGxooJE8bQ3GwovdXQ0IS9vSMTJ0zgi8+/YeLEcUhlcubNW0yfPj05fOg0zzz7KFVVt8PD7R3sePzxuahUKmpr6vlizXdMnzGGJUsWsW3LYZ5/9iO6dI9k945jxF1LIjDQl707T6FQKAgJ80MqkXAl5joikYiwdv7U1TbQuUcolhZmfPPpIfJzDLv7jywZhb2zJcX5VcgVUmQyCZmpRWTfKKGmsoH6mmYaqlvYv/UyAGmxebh7q0g4k8Hcd4dg52DO7hXniTuWyhOfj8fpJ+H7Av8s3NzuFOR75513CAsL46OPPrrjeGBgIB988AHz5s3j3Xff5auvvvozzRQQEBAQEPhHcF8OtFgsvqOMlYCAwL1pzC6lJsVQmqn6Rh5NOWUYh7rd1c+6mx9mfg7YtfdC06zBKsLNEGb9M5g53a7RqwpxRKK87dwm777O0feOIZaK6fdMb9LOZODX2w9rv9/mAOl0eiIGBKDysiFm53VqyxsQiUQ8tmESziEONNU009KoxtzehNY48B8oL6rh3L6byJVSJEZSdn4Zg/oHB37uG4ORG917Rx0goI07AW3c8At3Ye/mGJRKBY++MBonNxsuX7yB0kiOi6cNp05eZtDwzrz10QLiryXT1NyCX6AbaamZ9BnQEW8fF65cSeDIoTN88MnzHNx/ilFjBxJz4QrpGZlMmDiM5qZmxDKIjU2ge7cOlBSXcv78ZUaOGsTmzd8zZEhfjJQKgoMDuXEjCWtra9zcXKirq6a5ufGH8FcRlpbWLF+2mqamJhwcHJDK5LS0qFm4YA4NjY20bx9JYsINFiyYybZte/H19cLE2Ji6ukY2bdxDmzbBvLt0CaXF5Vy6mEhioqEcoEQk5onFsxGLxcTGJuHoZIfK1oba2gbqa1p46unPkMmkvL/iMXZtPMu5E4nMWzyi1XkGqCyvpf+oKLRaPXqNnp3rzrPolZGcFiXiGeDIlSMp+PxEOKwsv4agtj88mxodu1ecB6CqpJ6USzmCA/0vIiYmhqeeeupn27t3787SpUv/RIsEBAQEBAT+OdyXA/3QQw/xxRdf4OfnR2RkJGKx+EHbJSDwr0DhaIXMwgh1dSNyaxMU9j+XMypC5mCBpcNvyym1CXamzZQO1BRVI1XKaKpswMjBHJEerm6+BoBOo6Mit5LBS4eh0/1MIvU90Ov0NLdoSTydgV8Hd4rSy8lPKaGuooHC5BJWLNhOVXEd094cQNSIkDvOdfa1w9LOhLL8GmwdLdD/5Lp11Y2t/6+prOfMgQTUzRq6DAjF1tESgMb6Zjr0DKbbwDY/5H7r0ethWYg7h/edY+nrawFwcbejrKKMhoYm3D0c2b/nFF9+voXJ04fy3eY9XLt6g8ioEIyMZVhamVLf2MChgycAkMvlrPniPdasXouPtxuLFj1HcLA/zy15hKKiAhoaarl08TLffbeVhQsfZuzYkYjFIhQKCbW1WkQiEfn5BVhYWFJYmMHcubOJj0/A08uX995bjpGREWPHjuDb9VuxsDDnscfn4+rqglKppLCwBHWLHplMQY/unfhizXdcvpTIzFnjCAzy5sC+0+j1elQqFZ99/B0Ak2cMZviwXmz59gQJ19JIis+lW+8InBztWf3Bbjy8HPAPdmPXd6eZ8FAvNn9xHHcfe3z9XTi+Ix6ViyVdBoQwYFwH7F1t6DMmirdmfsuNCzkYmyhxD7AnO7mYEfOiuXwgGUs7E9yCHAjo4EbyRUNUhIOX9W9+fgT+/iiVSoqKin62PSMjAxOTu1XwBQQEBAQEBO7Tgb58+TI1NTVMnz4dmUyGhYUFEonkjj4ikYgTJ048ECMFBP6pSB0sCVs6mcbscow9bJGo7u0gV17NpuBsGpb+9jj1CkIkN3yeRCLuKSJWV1DF5XUXW187d/TAxcEcxODbw4dL6wxhuQ4BDneJBP0apRnlbHrxIACFaWUMXBCNW4g9Hm2cObUxlsoiQ97xlrePE97PD7nx7V1lW1dLnt84heKsShw8rTG2UhJ7Oh1XX1tMrBR8t/w4KicLsm4Vs/WLUwBkpRXxxNvjOHc0jneeW8/QCdEYmynxD3anXadARCIxpuZGmJkbftArlHIamxs4dyYWMKhNOzjZMmnqENq2C6C2phpbO2tEYj3Llxkc7ilTR2FiYkJ9fT1arRYXF0dmzZrI9OkLALhxI4Wu3TqxatWXuLg4o9UYQsY/+2wlQ4cOwj/AF43GIKx1+dIVGhqaGDhoAH5+AZw/fxkraxUHDhhyrxsbG6mpMYRsV1fXkH4ri9rqBpYv/4aJE0fy2fL16PV6wiMCCQ3zx9XNmaVvfYWzi4r/LJmHiakxF89db53Ta5du0jYiGG8fZ+xUVhw7eIlHnmzPlx/uR6/Xk56cT79h7Yk5eZ2hE7oQGuFNZnIhSxd/3/reR/cJpiS7mqvHbhEU5Y5PuAuxp9K5fCyVqU/3Yvzj3ZGKxZibGRHQ0R3XYAceem8IqZdzsbI3wzvy7vx3gX8u0dHRrF+/nu7duxMZGXlH24EDB9i8eTOjRo36i6wTEBAQEBD4e3NfDnRGRgaWlpZCGLeAwG9A5myDzNnmZ9ubi6o5+/wOdBot7IEuNqaYetmRui2W+uJagsa1w9z/zjJDJiozLF0tqcqtQm4ix8LVsEOo10PbaVE4hjkhU0oJ7OlPVU3DL9qn1+oQS8WtjrpEJkEiFaP9QYG7qriOvvM7EXckDb32tiq3T1tnpHIJmmYNyRezkcjEWNiZcmp7PDEHkhg4rT1dRoZw7UIaB7dfoXmDmr7j2nJ8Zxy+4bfLH+XcKqG5sZl3nltPWJQ3O747SXV1PSKRiBUbnsXB2Zp3X/2Sqspa5iwcSUFBKVL57UUBsUjEpm/3oLK3Ie3WLc6evoK7hzNde0TdvkZOPosWzaKishIfHw9aWppJSLzOuPHDKCos4+zZC7i5GZzEvLx8+vfvRUZGJlKpFDd3F4yNlQCUl1fSsVNnli1bhVqjZ/z4saxf/x2Ojg6Ehwdz9KhhUcDDww07OxsGDeqLu4cHSoUSiURCfX1jq1MbH5fEq68tJjE+FaWRgjZtQ3n/7bWo7K2ZMmMYp45foaVFw6hxfbh8IZmC/HKCwzzp3D0cvwA3pFIxarUWAHcve0LCvbBRWbJnXQy1VbffcwtrE+oqG1n25G70ej2mFkre/n42VrYm3IrNJ2bPTXxCnTm33aCebm5rzCu7ZmPpYE77wYHofyGVQOCfyRNPPMG5c+eYOnUqAQEBuLu709zcTEZGBjk5OTg5OfH444//1WYK/I9087yvn3j/Ku5nDk5n/jbdkD+b+7Hrz7p/4VkT+P/GfT3xx48ff9B2CAjchbS5HmlpMTpzS1rM/70hpHqNFt1PHNO67HLqSuq4vukKABW3Shn05RSQ3o7yUNiY0GVxb3IvZ2PhYonC5rYwmdRYjmu0JyIRSOV3Rob815W5cTCVg5+ewSvKjaiRIWQlFGLnbsXE1/qTeDwdE2tjks5lEtLHl7XPH0DlZkmXMeE4+9kS1tsHsVTCie+ucvlwCvW1jTh62nB6t8ERW/vWYfzaOhN7Nr31ik0NLdRU1OPqrcLRzZrmRjWznhqISCzCzdsBhVJGdXW9wTq9nrLiSrIyczl17Co2thaU+Dpgbm1EeBt/mppbkMmkVFRWUl5eRXV1LZOmDeLs6StkZ+UzL3ASZmYmiMVioqPb0dhUz4oVX6DT6Xh4wXTWrPkGtVpNmzahfPX1p9y6lcFjj81v1Xh4882XOR9zHhsbg/q0VqOjrLSCXTv3MaB/H77dsJnu3bsjFospLCzCxcWJ//zncSwsLHB0dKJXrx40N6n56ovNXLhwjXHjh9I+qg2FhaU0NDTi6enG0UPnePSJWYSFBfH+24bd8pLiCszNTenTLxq9DspKamlqbMHWzoIdm0/ywpuz2bH+NE+/PoVrF1NQyuUc2RFLeXENXfq2oV1Xf5Y+9R3DpnbCxsaMW3EFZN0sxr+tC8lXc6mrbqK5SYN3mDNrXz4EgLvf7QWamrIGsuMLSDiaRnZiISOe7EFgj9tCawL/fFxcXNixYwcff/wxx48fJykpCQALCwvGjRvH448/jpWV1a+MIiAgICAg8P8TYclI4G+JpKUJ+bZv0d+IR2JmjnLOYzTZOP76if9AlM5WdHhuIKlbrmDmak3G0WRcOvvc2em/orDVNU3sfm436gZDqHFfqQSfgUG/eJ260jqyr+RhZmeKa1tn6krq2LRkP3qdHoeqRr58fCd1FYY85TnLR2FqY0z6tXyGLe5OU0MznUaEoFZraGnRENTNEwtHc0QiETfOZ2FuY0zC+Qxc/W6XyZJIxRibG/HYeyO4eiYNO3sLFGYyAqL6sG/jRZ77aDKu3ioUP4SA/+etqVyNScLEXMm2TSeIiPTF2FSORG4oFxXdPZQtmw2h5Ta2lgSFeRHRxp+LF+MQi8WMHtePtLQswsIDADh69Ayvvv4ENTU1JMQnYWyiRKczLFQUFhajVhvmLjY2keLiIg4fPsC5czEALFq0gKKifDw9PQCorq7lk49XADBjxlSqqqp/GKeQadMmUFVVg6+vN5+v2ci8edNQt+TRUNfIhx+sQafXM2hwLzZv3E1wUCAysQKpSI+7qwvZ+nzQ6amrbSQ03I+4awZHprmxhYN7YjA3N2HwUGsO77mIWCxm1MQenDmSwIXTSVyNSeX1ZXN4cf7XAEhlEopyy2nXzZ/3N81Hp9by0uS11Nc0wfFUhs7oRMq1PEbN74yjlw1SqYRHPxlF7Ik0Ivv4om5Qk3guk87Dgkk+n8XFXTcA+OKJXbx2bB4m1neqxwv8s3F0dOTdd98FDCrzgOA0CwgICAgI/Abuy4Fevnz5r/YRiUQsXLjwfoYXEEBWU4H+RrzhRW0N4oIc+Jc60IhE2HfyJmlbLDkxmagbWnDr4U/I+EjqiqsJGh8Fsv/aSdbr7xDo+rU8Z22zhl2vHCYtJhuAactG4hikQmkqp7GmGbmRrNV5Bmhp1mDpYoG7Xo+FgykiqZhLh5Npqm/BxELJ4IXRrdftMjyEK0dTkcklxJ5KY9DUKGQKKWFdvbhxNYPrl7Noamoh7nI6YrmepPgc3vriIXxCXSjKL+P6sXQcXW0JjvCmsqqaTd/up9egNkRE+nLkwDlsVJa89+njpKZmtNpXXlZFZFQoMedimTF7OOfOXWX37sPU1tTx+luLEYn1XLuaSFxsIlXV1Rw5cpKBA3tjb29LcXEZISEBnDhxmsrKKrp1i6aiopLr12+2jq9Wt6BS2QIgk8nZs/tAa1tlZRUBAf48++yTXL4cT0VFJZ07d2T7tv00NTVRU9PAB+9/AcCUqaPZsH4HarWa7j06EXctmWtXDdcxMzfh/Y+WkJaSw5ULNxk4uCsDBnahuLCSyvIaZswdSvLNbM6eMHwOfnT+T+yPA0AsEWNnb0nvIW0pzq/EP9SV/OxygiO9cPFW0VDdeMdz4eRlw+cxT2BqYYxYahB+jBwYQNQgw4JD95HhnN+VyPqXDhHe4/YCjpGZAon0lyIZBP6pNDc3c/nyZfLz8+nRowc1NTU0NzdjZycorgsICAgICPwcD9yBFolE6PV6wYEW+J/QmFogd/dEn50Jcjk6ldNfa1CLmqabuQAYBbuhlz3Y4A2RkZywGdFc+vAo9uEuOPfwQ+lg0fp5+m9kFkYMWzqS2M1XsfO1w73zL4fYtjSokSulhA7wR66QUlNSi1dnD2Z+NorkUxnY+9ii8rVl//Jz+HV0o6a8ge/fNYgAXjuSykMfDqOpvgWA+uomKopqsXG1BKBtPz+cvG3oNCSI8sJqfCJccPFXcXJXLB8v2Q6Af5gL6mYNLq42JGoySbuRj0+IMy8sWkXKjRzEYhHLv32Kc6diyUwvIDO9ACNTGTu+NwhzdewcxoDh0YRG+HEzMZ3Z80YxfGQvZsweTWrKLd58fTmNjU24ujmhR8dHH6zGP8CHtm1CKC0r+2HHWEdIaAAdOhpz+vR5lix5gmvX4qmsrMDKypyFC+fzzjtLGTp0YKvzfPjwUTp16kSPHt3IyMjE1NSU6M6deOutj5k1ayrt2rZl//7jnDt7ld69u1FSWsb581db5724uBSlUkFwiD9NjRqKC8tb24yNjaiqrKWkuAKdTsTxw5cpK64hL7sYgOdenUVJYRWqKBsO7r6ISCTCxNiIPoPbUV5aw8DRHdFrRZgYKakoquVc/g36DLstCGVsYcRzqyex56sY3P1VdOgfiJn13crKej1oWzTEns9Gp9UjU8jISSqm/5wOaDU62vT3R2muaO2vU2vJupRLU10LXh1cUVoa/eKzJ/D35PDhw7zyyiutu89fffUVarWahQsXsnjxYmbOnPkXWyggICAgIPD35L68gK+//vquYxqNhrKyMnbv3k1paSkrV678n40T+P+LRmmCeNIcJAU56K1tabL9ax3o6iNxZK85AoDHwwMwH9D2gV/DJsqDgetnIZKK0P9QX/mXdpbt27owsJ3rz/ZprGykoLQBqZUCubGMmsoGsmILAHAONuS8uoQ64hziCCJD+ar2o0KRm8qJ+T6xdZzmBjXWjuYEdnAj6WIOXUaHUV5UQ8Y3lwjv7o2Dpw0O3rY4eNsiQg8iEXo9lBZWt46Rn11O98FhZNzKRyqT4OJjy6oPduDl64SDkzUtLRqK8itw97wdZaDXaVv/n5NdyJaNB2hsbKZHn0jKy8t5bOGrLFv5On7+vixf+Sp79x6jpqaajRu2M3hIbzZv3omNjQXdunVCqVRQ31DPkSO39Ruio6OwtbWla9cOzJs7HzMzM55c/AjGJoZQ5YMHD9PQ0ISnpxdL3/uQxx9fRFVVHXv3HmPMmOGYm5tz9uxlUlMNOd7u7q5UlFcTFhrAjesphjzmPl0J9Pdj1/bjmJqaMHHSMLRaHTqdjsaGZk6fvESHjuHEXkmm/+Bo4i6ltdqn1eoYNLwrUqmEgcM7I5PL2LjqCFdjUojuGYKzix1PTViNk4cNbTv5oXKwZNvqszzxwVjkCsNXe0CUO4HtPX41QiH2QCprl+wHYNST3Qns4omtuyVSxd1/IpKOprH1ecOOfNSoUIa82OeeSvH3g16tRd+sQWyq+PXOAvfNtWvXeOKJJ3B3d2fcuHGsXr0aAJVKhZOTE++99x6urq706dPnL7ZUQEBAQEDg78d9OdCdOnX62bYRI0YwZcoU1q5dywsvvHDfhgkItJhagp/lX20GYrGI6mu3w4dr4jOxGNjud5eH+k1Ixf+d7vyL/NSG/Kt5ZFzIxs7bBvsAFd8+soOqwhp6zutE+4ltKLp1e/ezoaaZjIs5HFhxHp1ax+gXeuMUZI+RlWE3MbyvDwWppeTcLGb4412xdDJnwfJRVJfWkXmziGWLdwLgsTOBlzdNQyKTcHjzZfZ9e5Huw8IZNLUDEZ28yB0RgV6sw8RciV+IM8bWMuY+N4RXn/mS7HRDHdoxU3tw61Y2KSlpaLU6Pv3qSdRqLZXlVcTHp1BRVsOs+SN5+41VNDY2k5KcyYjRvbBTWXP58jWi2rehvKKCkpJizMxN6NIlCmMTIxobm9i6dQ86nZZz5y7w1NMLSEi4TkZGFrNmTSY0NAhTU1P27NkFQHTnjq3Oc05OHg4OTnTt2pny8kqmz5hOTU0969YbajMnJt5g+fKlxMXeaJ1TCwszThy/QG5eIe9/8DLXrtzg+JELqOztuJVmCJ0fNKgnVhZWVFXVIEJNfm4JWzIPM3ZiX04eu8LgkV3Yt+Msbp6OhEb44h98Z2SBt58LJYWVqJxsSLqchUatJSethJy0EnoOieD8oRvMe3kIcjuzez4jP0dx5u1n4/R3cfSY3g6RRHzPvqUZt/umns9C16JDJLt3399DU2E1F945SFlKMR2f7INL30BAUAD/I1i5ciXOzs5s27aNxsZGVq1aBUBAQADbtm1j3LhxfPPNN4IDLSAgICAgcA8euIiYSCRi8ODBrFix4g93oNPS0vjoo4+4dOkSer2eoKAgHnroIbp3737PfrGxsTQ1NREWFsaiRYto167dHf2Sk5N58cUXSUlJwcvLi2efffauxYLy8nL69OnDa6+9xtChQ//Q+xP4e6DT6bHtHUbVVYMTbdM95I9xnv8Haguq2fTYdjTNhl3bAc/0oqrQUIv4xOoY2o4MYeTzfdjy4gGsnS3x7ejOoTUXybiWD8DhlTHMXD6SxtomTqy7Sl5KCZEDAnHws+V6TBZFuVX4R7li72VD3Onbqtq5qaUUZVXSolaz6tW9AHz70VHq6xrZ/s0ZBk6KYvv60wB06hnEzcRMamrqaaxvbh3D2s6cKLtANFoNCqWMd9/4gpzsIh5dPImOnUPxC3Lj/NlrTJ05FJ0W9OgwMZWzZvVGdu0+SJ++XZg2bQSpaenk5uTj4emKt7c7U6eOZt/+43h6uaFWN2NsbExIiC9jxw4hKyuLvLxc5DIpHh4ejB4zkpAQgwhbSkoamzdvBaBnzx6Ymsp47bWlDBx425kQi8Xo9XpGjhqMt7cnOp2OLl07EuDvT21dPVcvXWfb9wbBMx9fd4JDfEm6mU5FeS0b1u0zjN2nPRfPX6ewoIzdh5cT3saPm9czmDF3KOjFfL/+OC+863XH7m5pfjXlhbUo5Qr8wlwJ7+RFfEwG7br4kpdRxqDJHTC1/P1iX8HdvDix4RotjRoGL4imua6ZlLNZyBRS/Lp4IlXe/lPh29mTmE2xNDeo6T2v0wNxngEKLmRQnGB4Hs+/e4hRUR5I7+NeBH6duLg4Zs+ejZGREU1NTXe0mZqaMnbs2NZdaQEBAQEBAYE7+UNUuBsbG6mtrf0jhm4lMTGRadOmoVarmTBhAp6enhw/fpy5c+eyZMkSpk+fDkB6ejqTJk1CoVAwdepUTExM2LBhA9OnT+err76iffv2gEGg5/HHH0ev1/Pss89y/Phx5s+fz8GDB3F0vB1Wunz5cjw8PBgyZMgfen8Cfy+MOwUQvOwhEImQuPx8Tec/muaqRvRaHUqbO3NZNc3aVucZDLvmP+Ia5oTcREFQf3+ebe+GVCmlqqD2jhJXRuZKQE/swRR2LTuL3EiGSCLhwj6D4FVkf38u7L/JE2vGEtHDhxPfx5GfXk6vcW34/rPTjFnUFbFYhO4HYbOq8jpEYhHVlXWt16itaWTw2I5UVNYwfmZvbsRl4uXnyJaNBygurMBWZcmoCT3JSDc4Ucs+3MikGYM4dfIy+3Yb8rFHj+3P6PH9OXLoFFVVhgWCo0fO0n9AF3JzDOdlZeYSGhpAUlIaCxZMZcmSV5FKpURGhVNbW8vnn39JaWkpGzdu5uOP3ycmJobw8FD0ej1VVTWMHDkStVqLXC6nqqqG1NRM6uvryc7OYcyY4VRX1+Dq6szNGym4u7mzbdtBSorL+Pqr71m4YAZrVm5m8tSRrfctlUoZNrwP/QZ0IzOtsPV4fl4JxiZKevVtj62dNQOH96ClSc+WdcfISi/E2dUOrUaLWGJ4n/LSizm5O45T+xKwd7Fi7vNDeHHFVKorGtBrdVRXNuAR4ID0vwXnfgOe7Zx57+QCqirqsXa1YO+7Jzm7MRaAMS/1JXJ0aGtf5wgnFm2fQUuTGkvXB6fabGR1+5k2d7FCrJA9sLEF7qSlpQULC4ufbZdKpXc51gICAgICAgIG7suB/lEN9r9paWnh+vXrfPPNN3h7e/9Phv0aL7/8Mg0NDaxatYqePXsCMGnSJJ544gmWLl1Kt27d8PT05O2336a5uZnt27fj6uoKwNChQxk6dCivvvoq+/YZdoPi4uLIzMxkzZo1dO/enWHDhtGxY0f27NnD3LlzAcjKymLLli2sXr0akUgILfx/hUiExPWvVaYtuV7I90/sRN2oZvhbQ/Dodju819LdisHP9+H82sv4dfXCv58fM92taahowDHMEamxwRn5MUTb2tUCj3AnJDIJMrmE9iOCubwnifoqgxK3TC6hse72LnFjbTNl+VVoW7TYe1jTe0IE1y9mc2pPAoGRbngEOPD8yskkXMjAxcuWdZ8eRt2iwTvAmeLCCpqb1Lj72KPRa9j2nUEYbMa8wRSXlFJcWAFAWUlVqwMO4B/owZTpQ5n/0O1IFo1Gy5OPv8aEiUMxMlLS2NhEeEQgNjZWuLg4kpdXiLOzA/UNDbRv34Zvv938w3kaYmMT8fX14ejRo4AhWiY/P6/VeS4qLCE9PYvUlHQio9px6NBR9uw5yNtvv8m+fYe5fDkWIyNjPD3c8PPzpaqylubmFkqKywDD919VdTUODnbU19UzcfIwiovKiGgTBCIRPXpG4+tbTEpyNlWVNSxaPAUjIyNU9jbEX03DzcMB/yB3aqrqkEolPPLM2FbnGaC6ooFDW69QX9tEeXENl08k4xPSB6WJEgAHj//t+bL3sEZiKgM93LqU03q85Cch2z9iojLlbjmy//H60V50fXEQ9aW1OHXyQmwkONB/FJ6enly6dImJEyfes/3YsWO4u7v/yVYJCAgICAj8M7gvBzooKOgXHUi9Xs9//vOf+zbq1ygsLOTGjRu0b9++1XkGww/i+fPnc+DAAbZv38706dM5c+YMgwYNanWeAaytrRkzZgyrVq0iPj6e8PBwiooM+Zhubm4AmJmZYWVlRUFBQet5H374Ie3bt6dLly5/2L0JCPwcqcdv0VxrcGrPrj6HZzev1nxpkVhE6IhQQoeGIJIawotdo1ywtTWjrKz2LpEniVxCxwnhhFc2IjOWse6FA1w5kIJvOxe6jgkjN7mEDoMCqS6rQ6vVofKwosfENih+EHeK6O5LYXYlnQYGMWhae3Q6iOwVQGSvAEPIu7MFN69lYedojlQpobq6jphTiXgG364THXs1FTsHczy8nMjKKMDN0wE3d0fe++RJykoq6dIjEp1OR3SXNqTfykFppCAyKpiGpirEEhFz50+kpqYOExMls2c9yZ596zh44BhiiYj09Cw8PF0ICvLn5s0UADw83HB1dWDGjKmsXfst06dNwtjY4HwaG5uSmnaBUycN4eZKpRJfXx9UKhX79x/h44/fJi7uOokJNzEyNmbzpr0E+Htz5Mg5OneO4ty5y4SE+FNSUoGrmxO1tfXEx8fR2NCEr58X5hYW+Af44B/gQ+euhvsyMjKivKyKx2d9QHpqHtY2ZqzcsISN+99E3aLG2s7yjvfMyd0aG5U59bWGnUEbe/MH+Xi1ogf6PRzN+qf3IldKCent29omEgE6Pfo/YAFRLJfi1CvggY8rcDejR4/mrbfeIiwsjL59+wKGv5/l5eV88sknXLx4kaeffvovtlJAQEBAQODvyX050FFRUfceTCpFpVIxYsSIXxQa+18pLDSEQfr7+9/V9uOq+fXr14mPN9RPDQ8Pv6tfWFgYQKsDbWNjCMutqTGEhWq1Wmpra1uPx8fHc+TIEb7//vsHfDcCAr8NK9fbIZfO4c4Gb+Yuz/jeZa/uhVgixsTWhOb6Zm6ezQIg7Woe9p7WNDS0UFFcw/PfT0Or1qJt0aEwu62MbOdqxbTn+9/TBLFYRNuu/pRXVLP87e0MHhdNS7Oa4DYeVFZVc+ZEHHq9nvbRQXy+fDsDh3fisWcn4RfogcrBpnW8lpYWTp88z/q12+nVpxN69JRXVnDq5AX27D7KU0/PZeeOfZSWluPgqMLExJiAQB/27zuCg4OK1JQUkpKSGDduBE5OjkRFRbBzxw6USiOWPPckUpkUvV6PpaUVn366Cm8vz9vTKBFjb2+PXi8hMfEmNjZ2tGvbhpqaOuQyBYUFxQwa1IudOw4TEOjNsOH9cXd3wcTEBGsrSy5dSKSkqJzuPdsTGOhDh063VdsVitvzWJRXhpuHPT5+riiN5ORll+Dq4XDv919lwVPvj+P84RvYOpjTddDd32v/jVat5dblXDRqHX7tXZH9xl3doD6+PLf/ISQywzMCoG5o4cLXl0k+nkbHKe0IHR4CYiES55/IlClTuHbtGu+++y7vvfceIpGIhx9+mKamJkNt9y5dWtOg/hfi4+OZOHEiX3zxBdHR0Xe0FRQU8PHHH3P+/Hlqa2vx9/dn3rx59O7d+45+hYWFLFmyhKtXr+Ls7Myjjz7KoEGD7ujT3NzMgAEDGD9+PPPnz/+f7RYQEBAQEPgl7suBXr9+/YO243dhbGwQlqmvr7+r7cealqWlpa27yj/NYf4RBwfDj9S8vDwAgoODsbS0ZM2aNfznP/9hz549NDU10bVrVwCWLl3KwIEDCQ4OfvA3JCDwG/AfEIjCTIm6SY1nZ88HJmSmMJEz/LGubHrjKMYWSkQiEU31LYR190YkFiNViJH+TFWhXzIhumcozc1NfLN6L3b2VgwY3YHOXuH4BblRmF+GRtPCyvX/wdffA2NT4zvGE4lg3TdbaGxsZMr04djb21JaWsa2rXvp3qMj6ek5WNtYMW78ECwtLQiPCGHSxFkUFZWy5PnH+eSTz5gyZQLp6ZlERobz0Ufv8/XXlixatBCJRIxWq0Gn05GZkc3JU+sZOHAAdrZ2qOxV6HQ6XFxcKCmpQi43QoSc9PRMJk0aj4eHO/Hx1xk7dhhrv97GnLmTuHUrkzYRwSgUxuzcdoQ58ycy9+HJKBVGpCRl88qSz9i84yOMTYyJORVHfX0D0d3aYG1ria3Kksy0YnIzDbWfu/SM+MX3yivYGa9g53u2leVUkXI5B5WbJb5RroCIK3tu8vUPJadGPNaVAfN/+8KmuaPZHa/zruZx7ptLAOx76yhOIY7Y+Nr+5vEE/j6IRCI++ugj+vbty759+8jKymp97vv378+oUaMQi/83cbisrCwWLlyIVqu9q620tJQpU6ZQVVXF1KlTsbe35/vvv2fBggW8//77dwh0LlmyhNTUVJ566ini4uJYvHgxLi4urYvgYPhNotFoHojTLyAgICAg8Gvct4hYXV0dO3bsYOTIkZiamgKwYcMGmpubmTRpEkql8oEZ+d/4+PhgYWHBqVOnqK2txczs9g+9AwcMPxabmpqoqzMIGP3ocP+UH+1rbDTkfJqamvLGG2/wzDPP0KdPHyQSCY8//jgRERGcOHGCuLi41rEfBP9LBOSP5wpp2Lf5/zAnzTVNSBVSim+VIbmci29vH8TSnxeMumNO9Ho0Ldp71vUFEd0mtSG4iycKExk6rZ7RT3XHyOK3f4ZLCiu5cS0TlaMlOvQYGcnx8nNiw9eHKMwvpzC/nEN7Yhg0shO+Ae6cP3uNZR9sRCaTsvyLJbSPjrhjPI1GTVFhCRu/3YVGo2HMuIFs3rwTgLS0TJ56eh5r1qwlLS0DsVjMx5+8QUZGNiEhgWzduo3w8FCqKit44okFnDx5khHDh1FQkE9zcxMKhRyNRsOWLdsIDQklPz8fMzNz3nn3Y4yNjXnnnde4eiUBY2MT1C0aysrK+c+zr+Pn50NUVCQOjg68/OIQZDIZNdUNdOzYHpXKnicfexuAJx59nZVr3mTXNoPw2cDB3Yg5k4CVdRZvLPkCtVrD0FFdmb1gNHlZJa3OM0Blee19PcMN1U18/PBW8tLKEIlELFk/Cb8ObmTE305BiTuWxuCF0Xfkmf/Ib/n8/HT3WiwVI1VK/tWftwfxnfJ3nZ8NGzbQsWNHBg0adNdu7oPgyJEjPP/881RXV9+zffny5eTn57Nx48bWahijRo1i7NixvPnmm/Tu3RtjY2OKi4s5f/48L730EpMnT2by5MlcunSJbdu2tTrQ1dXVrFmzhieffBIjI6MHfi8CAgICAgL/zX050KWlpUydOpXs7GwiIiIIDTUotF65coUDBw6wd+9e1q5de4dj+yCRSqUsWLCAt99+m1mzZvHMM8/g4ODAyZMn+eyzzzA1NUUqlf6mHbqf5nL37duXM2fOkJ6ejqurK9bW1mi1Wj744AMmTJiAq6sr2dnZvPLKKyQlJeHm5saTTz5Jhw4dfvc92Nj873PzIMb4t/FvnZOcuHzOfHmBm8dSaWlQAzDTbhwh/X49Z1TfrGPjy4dJisli9FM96DWt3T13l1Sq35ZTe+F4EnHnb+EX6kKPoeFUV9bz2qKvuRmfjUQipt/ISPZvP8+KTU/StVcYxUUuaFq0VJZXM6rfk7zx4UI2rt0PgFqtIfZqMoOGGSI9qqtrOX/2KuYWpri6OaLRaABoampGKpW2vvbz9yItzVBWTKfTUVtTi6WlBcYmxvj6unLo0BGUShlarQa5TMbuPXuYNWsGCoUcrVbH5s1bSb+VQds2bXjkkQWsXbsJgIaGBhobG9m79yhVVYYf/88//wTLl39FWWkptrZmKJUS2rYLxdPDle3fH0Gn09GmbRBhEf4kxKVgbm5GSJgPffp3QtOiJfFaJscOXEOhkDFyfC/QiwkO9WbeuHdRGskZMLwDB3ddxMPbgc49QrC1/f3PcFZpPXlpBjEzvV5PZXEdtrZmRA4I4My2RHRaHf1mtMfa2vQXx/mlz49lLz/GvTuU/BtF+HbxxKet2++285/Iv/E75cMPP2TmzJk88sgjD3zsuXPncurUKXx8fOjatSt79+69o12r1bJ7927Cw8PvKCWpVCqZOnUqL774IidPnmTQoEGtUWQ/pmZJJBJcXFzu0CZZtWoVNjY2jBkz5oHfy9+Jbp5/SNGUuzidqfnd5/xZtv1Z3M/93M+8/RkI76eAwB/DfX1Kli1bRlFREW+//TaBgYGtxz/44AP69u3LkiVLWLlyJc8888wDM/S/mTFjBs3NzaxYsYIpU6YAoFKpWLp0Ke+99x6WlpaYmBhy9+5VjuPHnWdz8zudBlNT0ztyprdv305BQQELFixAo9EwZ84cnJycWLNmDXv37mXOnDns27fvDpGy30J5+d3CTr8Vkcjwo+5/GePfxr99TtIuZqFVa1udZ4DqsnrKyn6+XNyPc3JlfxIXdl0H4Otn9+Ee4YiVy90lbHJTSrhxPgsHd2vCunsjlty9fVaQVcbTk1ZjpTIjKT4HI3M5JuZG3IzPBkCr1dHcpEan05OSlMPZU3FkpOVjq7Jk+LiuDB7ZmfirSfQfHN3qRPsFevwgdKbnvXc+48s13xlCTJe9RGRUKFcuJ9LY2MTceZMoLCymS9eOdO7SkclTxrDh2+/x8fHA3t6W2Q9NQqfT8f77HwKwa9dennzyUdatO8uECWNxcnJAr9ejkCvp26cvU6c6UFZWwdGjJ+jWLZpdu/ZjYmKCXKZodZ4BkpJSefrphRQUlLY+X+8tfZ7vNu1prUgQe+0mHy97mRvX0+jXvytW1ta8+d5i4q+l8MjsdwBoblZjamrMhs+PYmFmTlVlHVRC/JVbLH5+AiamxphZmP7ie/pzKC2UDJrdkf1fXsDZxwbPMEfKymrxi3bn1T0z0Wp0OPjYkhaXR8q5LCzsTPHr5oHkhwiG3/r58e7tg3dvH4D7svOfxIP4TvlxjL8bYrEYS0vLP2TsjIwMFi9ezMyZM+9ZSzotLY2GhoZf1SYZNGjQXdokYEjT8vDwAAx51N9++y0ffvghEsnvL98mICAgICBwP9yXA3327FmmTJnCiBEj7jguFosZNGgQCQkJHD169A91oAHmzZvHlClTSElJQalU4u/vj1ar5bHHHiM8PBwXFxeA1lXsn/JL+dE/0tTUxLJly5g1axbW1tZcuXKF7OzsVvXSwMBANm/ezP79+5k3b97vsl2v/+X80T9rjH8b/9Y5sfO25cyXF2g3KoyU0+n4dPLAvb3bb7pXQ41nA8bmCmRK2V3n1ZbX8+5Dm6koMjhFS76eRHBnT2orG0g4n46JmZKQaE/qaxrxCnLEyEzG5fNJOLhbMnlhP4ZP6sKujWexc7AAkR4HZysQ68hIM9RmLiupQq/Ts2v7cQCefWkWyz9fgqm5CaERAej1hl3mXdsP06VbFObmJuRk5bPmq/fZvXM/GzZs4/CREyx8ZAaWlqa89OLr2KlsmTp1NM3NzVy9Fsu3325i/vzZd+xUBwUFMW3aZFzdXGhpUaPV6Hl96XsA9OvXh5s3k8nLL6B3754899yTqFQqPv10DQsXzmbNmnVERIRQVl5FXX0DZ89cQmWnonff7pSVVVFSXIG1tSUVFVVMnDyUXn26MGBQL8DwDIrFUm4mZhDZIYgrF28SEOxJZVk9Pfq2oyi/ApW9FSXFlVham3H6cCIJlzKwc7CiTae7xRF/DYlMwqgnutFjfDgmlkaYWBq1vsd2ngYnRNOsYdPzB8i4ZnhPZnwwlOC+fneM82/9/Pwv/Bvn5KGHHuKLL77Az8+PyMjI/znf+afs378fuVz+s+3FxYaUhd+iTeLk5ISHhwdr164lMDCQ2NhYsrKyeOyxxwD45JNPCA4OblUSFxAQEBAQ+DO4Lwe6vLz8F3dcPT097+m0PkgOHDiATCajT58+tG17W+H27NmzqNVq2rdvT2hoKGKxmISEhLvO//FYmzZtfvYaa9euRafTMWvWLABKSkoAWlfFZTIZ5ubmrargAgIPitLkEmqLanAMccTI1gTHcCfGfTCCuvJ6uszpgJGNyW9OsPTv7M70dwZRml1JUDcvTGzu1gRoqG1udZ4BKksMO8LfvH2Q4zviAFjw2lB6jG5D+14BfPmhoX761q9O0W1QBPOeHc7QCdGYmBtRkFNKm87eHNh9Dg9vR7LSC3FytsXe2YqHHx9LSXE52Zn5TJ4xFLVay+WLsdTV19O+fQSPPjGDTz/5kpLicsLC/TE1NcHb1wM/f0+6dI2kZ89OjBgxpVWYaOzYYWzZspNnnn2UysoqzM0tWLXqU27cSCYkJIS8vExc3VzQ6/WIxRIqaipb7/HGjSReePE5jh07xfnzVxGJRDz99GOsXPkRXl5eeHt7cvz4WdzcnNm5/QDdunXm8cdeZdny1zEyMmbfnpM8/Mhk5HIZ0V0i73IaigpKWPXx9/gHuTNgSGd69Y0iL7uczNQCju65QufeoURE+uLiquLblcfoOySS+LMZFKRX0H1oBKYWvy+fUyqXoPKw/tl2daOG7MTb38sVBTU/21fg383ly5epqalh+vTpyGQyLCws7trBFYlEnDhx4neP/UvOM0BtreF75l7aJD/mMP8YISYWi3nrrbd49NFHGTBgAAATJkxg0KBBJCcns3v37gcqaipok9wfv3TP/5/n5df4u87JX2GX8JzcjTAnd/N30ia5LwfawcGBa9euMX78+Hu2JyQkYGdn9z8Z9mts3LiR5ORkIiMjW0PR6urq+PTTT1GpVAwZMgSlUkl0dDSHDx9m0aJFrU5/RUUF27ZtIyAggKCgoHuOX1lZyeeff87ixYtb/9Db29sDkJubi6enJ7W1tVRWVraumgv8e2jOKqUmtQhTTzuMfB3QNTRTeS0biVyGRYQrIvkflyNUlFjAhvlb0Wl0uLV1YfQHw5Eay7ELUPF7P1VleVVc3H0TuUJC9xlRKE3v/ePWzsWSiU/14rsPTxDUwZ3gjh5oWrTEnr3V2iclLpc+49sR2t4TkchQLkuhlGFmboxCKcfT36AOrXK0Jm7VTS6dv0HHLqH4BbnTuXsIFy7EsW3LIQC8fVx59aVPMDMzYsXybwEYN2EQLq4qiosMubwfvP85w0b0Z/ETL5Kba9g1tbS0aHWebW1t8PXzYt78GXh4uPLSS09TUlJAZuYtRo4cSWpqElqtBo1GQ1JSGsHBgYwYMZydO3ZTXl7O7IdmcvTIWUJCg/Hy8mLAgL4EBYW23u/4CWOYMHEsKckptDRDdVUtAwf1oL6+njZtQ3hs8UxWf7aF5mY19vZ2BAXfuXMsEonoEB2ChZUZN+LS6dqjLRNm9CMpMRMLc1N2bDxNeKQPfoHujJ3Rg6SLuZzdcxMAiVTMgAl3ays01TYjEotQmPyyk3IvqvKriB4dytnv4rF2Niegi+c9+2nVWhrLG1BaKJH+xrJXAv8sMjIysLS0/MPCuH+JX9Im+bHtp9ok7dq14/jx46SmpqJSqVr/3i5dupTu3bsTGRlJWVkZr776KlevXsXW1pYFCxa0Oty/B0GbBMio/PU+/8Vv0W34x8/Lr3Ef8/a75+Q+rnE/3I8Ox4PiX/+c3AfCnNzN32FO7ssL6N+/P59//jnt2rVj7Nixd/yx27FjBzt37mTatGkPzMh7sWDBAmbPns3UqVMZN24cer2erVu3kpWVxYoVK1pVtp999lnGjx/PxIkTmTFjBnK5nA0bNlBTU8Mnn3zys+OvXLkSGxsbxo0b13osLCwMDw8P3nzzTXJzczl+/DgSiYTBgwf/ofcq8OeiLq7m8tOb0NQ1I5ZL6bhsKnlHk0jbfBGANk8NQNUv5A+7fkN5A70WdqGlUYNELqGxqhEz49/vMIlEIja/e5yTm+IAmPB8b7pNaXvPvmKpmMEPdaT76HCUpjJkCoPjNHBSezZ+chwjEzkqFytEYhEBEe68sXoW6ckFhEZ54+huKGVUX99AUkIG5pbG1NbW4+Rqx8Vz11n07HguXIhr3VUCSL+Vy8Tpg0hMSGo9dvb0FcZMuP2jt127UKRSKY2NDa3HCotK+Ojjt9i6ZQdBwT68/vpbTJs2keLiIsrKyrCxseHcuXNYWZkjl8tQq9UUFhbj7OxMWVkl+fkFTJs+DR8fL5Yv+5KgoABupWWyYMFD2NnZc/jQMcwtzGjfvh1isQS9Xs/xYzFsXL8TgM5dIinIL2X1ik3U1zfR2NgMwOervic8IhA3jzsjc2QSI07sj6f/0A6YmZsgV8gJj/QnpI0P0xYOpqywktiLt+jWL5xzPzjPAPU1d+s2pFzI5uNHvkemkPHEijF4hjv96jPwU26cSCfuYArtBviDCGzcrO7qo2lUc+Lj08TuSMCzvTuDXx2AkfXdO4UC/2yOHz/+l137frRJlErlHWWrYmJiiImJYdeuXQA88cQTVFdXs3z5cq5evcrjjz/Oxo0b74hO+y0I2iT3x2/R4vj/OC+/xt91Tv4KfQvhObkbYU7u5u+kTXJfDvT8+fM5ceIEL7/8Mh988AFubm6IRCKys7OpqanB29ubhQsX/s/G/RKdOnXiiy++YMWKFXzyySdIpVIiIiJ488037/hD6+fnx8aNG/nwww9ZsWIFYrGYkJAQ3n33XSIiIu45dm5uLhs3bmTp0qVIpbenSCaTsWLFCl555RXef/993NzcWLVq1e8WEBP4e9NSXoemzuAY6Vo0NJXUUnwpo7W9Or0E1QO6ll6npzq7EqlSiqmjOSVJxVzYcBWFuZKU0xkYWyixdLKgpUmLmcoEuenPFGS+5+B60uNupxeU5FT9YneRWITZT8K7RSLoN74dNg5maDRagqI8ABEikYjI7kFEdr8dvaHVavn2870U5Jfg5KIi5mwC9k6WBIW5Y25pjEIhp6VZjYurPXm5xcyaO4rt3+8nKNgHuVxGS4ua6C5t2bvnGC+98jgKuZxu3TthYWHFE4vns+W73dipbKisrGDkyOG0aRNEly59GT16ONu376CmpoYOHaJQqWwJCQlELpfR2NjI+vWbKCwsYuLEiWzevB2JZC1LljxDTk4h4eGhfPed4Qd4hw5RJCQk8+36bQCsWv0eAwb2A6C+7rYDX11dy7EjFxg2oje1NbePe3m7cvTgBdq1ryW8rWFe8rLLuHj2BgCH9lxk+Phurf0lEgnWthZY21rgF+oBwFNLx7HspR24eqvoPODOBRqxWMS2T0/TUNMMNHN6W/zvdqBt3a1oqG4i9mAKUcODEcvuznutyKwgdochvSXzUjaF1wvx6ub9u64jIPBL/JI2yY/50b8U1aXX61m6dCnDhw/H19eXwsJCLl26xLvvvkvbtm1p27YtW7ZsYefOnb/bgRa0Se6P33K//x/n5df4u87JX2nT33VO/kqEObmbv8Oc3JcDbWxszNatW/nyyy85fPgw6enpaDQaXFxcmDBhAnPmzGmtDf1HEh0dTXR09K/2CwwM5PPPP//N47q6unL9+vV7tnl7ez/QnCuBvx9G7jY49gig8GQytm09MPG1x2d8e669sx+xVIx9J58Hdq2bu65z6N2jSBVSxn48iuTjaZjbm5FwKAUw1PfNSyzk26f34hvtzqg3BmJk+RtzY0UiprzSl49mb0Eqk9B+SOCvn/MDV04mc3T7VfzDXTGzUoIMbBx+vsxVQ10jxUVlHNh9DoDnX5/Fju9PUlvTgI+vG/HxSbSoW5g8dQh2jlaUl1WQEJ/MzRsG1erQ8AAyMrIIDfXH2VnFxx+vJu3WLR5dNJcOHaK4EHOF1NR0XnnlWaRSGbGxCQQE+KHValoVehMSEnnyyUeRSCRoNBrWrdtIUZHhB7lWq/vhXy11tXV4enmxb+/RVvuvXYvnzOkrt9+Xm6kMGNiP4qJiunSNori4jLTUDKZOH82F8wl8tXonPr5uzHxoNOWlVeTnlhJzPp7vNx7jwxVPExDijY2tORKJGK1Wh5GxAlt7Q35yS2MLOp0epcmdiyHhnX15e+0cirMrUcjvDJ3W6/W4B9qTciUXAJWr5W9+L38ktL8/RmZKmuqa8Y12v2cfY2tjjCyUNFY3IZaIMFP98d/jAn8OKSkpfPzxx1y5cgWNRkN4eDgPP/zwfZVh/F/w8vLCzMzsntok8fHxAL/o+O7bt49bt26xYsUK4LY2ia2tbWsfa2trQZtEQEBAQOAP474TOZVKJQsXLvzDd5oF/h38GOX/V68Y/RbEJkr8Hh+A1/SuSC2MERnJsesRQE8/B0RSCTL731Yv+dfQq3XErDWEhWuaNWRfzMbC0Zy8xEIcA1QUJpcglogw/iGENu18NiVpZbhH/XrEQ31VI7s/PcvN81lMeKE3YT19UP5EjfuXKMop5/WH16PV6jh78DojZkZTWlZFY30jliozuvWPQCS6c/fS1ExJaXElYrEYnU5Hys0cHnp4BO5ezji52GNpY4Ja3Ux6Ziavv/EJKpUNM2aN5svPtxBzPpbwNgGsW7cFd3dnnF3scHd3obCwiJjzMXh4uPPRx+8Ycq4VSsRiEcnJSYSGBuHj482xY+ZoNGrmzp2JRCJBp9NRXl7F9OlTSU5OxcnJmerqWkQiEX369CQyqh3XriXStWsHbtxIBiA8PBQvLy/eevNTFAo57SLDuXY1jheee5+M9FweeWw6Tz49j+NHLuDj7U5GWgHxsSn07R9NRUkdXl6uOLvYsfrSDhLj0wgI8cY/xJOPv1rMreRcvH1dkCAh/UYebz72LfU1jTy/bCphHW4vxpTlVfHq1HWU5FXj5mfHC19NwfL/2LvvwJrON4Dj33tv9pY9ZQqxiSD2ihFixh6lLTVLdWi1v9KBqlFUd2kptVfsItTee0TIFNlk7zt+f9y6miYhiUiC9/OP5Iz3POfk3us+57zv89qouxmpVNBrfCvs3CzQ0pbh3a0clbp1ZNTu4PbEbYxsjRn6XSD3r8Zh5WGBVR2bMh9HqH7u3r3LsGHDyMrKwtDQEC0tLU6fPs3Zs2dZtmwZXbp0qbRYtLS08Pf3Z+PGjVy8eFGTLOfm5rJmzRosLS1p165dsfvm5+fzzTffMHLkSM1T6kf/3runvrmkUCi4f/8+np6exbYhCIIgCM+q3An0jRs3+PHHH5k1a5bmzu+XX35JTEwMM2bMwNW1+AI1wqtHN+4+ygMHQFcXSZcu5Fs83wJzFUGiq422rdm/FkjQcSg6ZvSZjqEtxaOtOxc3XQbA0t0Cp5bOSKQSlAol9f08sXS3ZNsXBwDQM9HFzLZ04zZuHosg+M+LAPz64R5mbxvN5Q2Xyc8pwLd3PWyeUK35QUJaoUI/KlQc3HWeYwev0CXAGwsrE+p7eyCRqL+sZqRn8ceKnWTl5DJwZBe2bzhCi9YNQKriyuWbaOvIWLVyG/Xr1+L27TsAJCY+IDc3D/+ADjRoWJubN2/Tr393YmMTuHMngp07/6Jv3x68//6npKSk8vXXnzN8xDAA9dNbPT02btyEVCplxox3USrl6OnpkpmZSfPmvtSs6UJw8EFq1fIgKSkFAwND5s37goiIKC5cuMq3y1bg4GBL4MDetGzRlK/mfcf0d8ezZ+86cnJyOHTwJH8fPkNNZwesrCzYtH43CbGp7NhyCIBBw7qjVCo5cvASl8+rewv0G9SJeg3cOH74Cl17tsbUzJimLbxQ5Kn4ZOxKZDIp3fu2IC76gfrvMn8X326bprmpdD8smcQY9fzT0aFJxEc/1CTQAKZWRnQa7l2qv/+zsKhlhUWt6v8eFUrvp59+QqVSsXz5ck2yfPnyZaZPn86CBQsqNYEGmDJlCsHBwYwdO5YxY8Zgbm7O5s2buXPnDosXL0ZXt/ihKuvWrSMjI6PQtJE2NjY0b96c5cuXU1BQwJUrV0hOTi4yzaYgCIIgVJRyJdDXr19n5MiRFBQUMGnSJE0CbWRkxOnTpxk8eDAbN27ExcWlImMVXkBSqQTFjiAUN9TForQNDaFX79Lvn/wQVU4e2Fuj+s80Ky+DVuN8qenthI6hDvZNHFAWKDmy+jzpCZkABMzoxMhv+5MQmoRtHWtM/+m6q8hXEHbuHgq5Eg8fJ7QN1F1+Y24mkJaUhZHp46fNOvpaRN1KYN0CdeGg6NuJTPshEKWy+O4AkaHxDH+7M1dOhVOnsSPH/1Z3tczLLSA3J5/7UUm41rZn5ffbOXLgPL0GtOH3n4PQN9AlLTWDH1d/TFRUNL8u3cKd0Chef7M/4ycOJuxuNF51XVnx60YkEgkKpZKDB44jkSrZukU9LVafvt0wMlIXGcrPz0epVHe9/umnlbRp64uzs/rGXKNGDZFKpRgY6JOXl4OxsREFBQWYm1vx+phxtG3bhsXfLEZLS8aJE6eYNPF9CgoKGPXaEOLi1N2679+PZ8vmXejr6pOams6WLbvp0LE1EeH3WP3bNuRyOWF3o+ndtwu167hx7uTjgmc6ujp8Nvdtpo1bQP2G7jg62WJrZ4mLiwM7txxFW1v90SqRSNi/5SxyuQK5XIG2zuPXsIunHSAB1H8HBw9LrB1NNU+gbWuWfJNDEMri/PnzDB8+vFCi3LhxY95//32mT59OfHx8pc4mYWVlxbp161i0aBGrV6+moKCA2rVr89NPP9G+ffti98nMzOSHH35g3LhxRYqMLVy4kNmzZ7N06VIsLS1ZuHAh3t7P/2aTIAiC8GoqVwK9fPlyzMzMWL16daECWtOmTWPAgAGMHDmSZcuWsXjx4goLVHiBSf/V3bcME7BJQqOIn/M7qvwCzEd0Q7tnG9QJx8tDx1gPtw6Pu/EqC5SolCp09LVo2N2L1MRM7BVK6vcsPH75wq6b/PmpekqogLfb0HlcS+6evceiMRtQKpQ07+nF+MW9uXcrkQYd3Ll45PF0VCmJGahKSJ4BbGvW4NDOcJQ6cmS6YOdkTtjtWPT0dajTsCbaOlpcOHOTNSv2ABAdGU/d+m44ulgRcy+e8IhoDuw7DsCwET1RKOUsWvArCoWCzl1aM2PmBJRKOTExcfTw70DygyTNsXNyctHR0aJt25Z06NgKqVTJiRNn8fLypH//Qfzxx2/UrVuf9u078cEH0wEFurq6qFQq6tSpy1vjJtOzZw8UShUnT54gIKAvhw8fIz8/H4Azp8/zxhujyMnO49Kla/Tu3Y0Tx9VjnwcN7M2Z05eoXdsDfQNdMtLldPFrQ8MGdWnh24janrWY9/nPGBsb4N+rPTraurz97lB2bDhG8J5LtGovRyqRMeatvhgYqsepq1Qq6jZ24e996rGdDh5WvP35APLzCmjdtUGhJ/2WDmZ89udo4iIe4uBuWejpsyA8i+Tk5GJ7hTVq1AiVSkVcXNxzSaCnTJnClClTil3n5OTEkiVLSt3Woxv0xbGxseGHH34oT4iCIAiCUGblSqCvXr3KG2+8UWz1aScnJ4YOHcratWufOTjhxadUqtDqHYDExASJrg4qX99S75t3IwxVfgEAaUHHsPFrgUKn7NM5vUi09LUZ+nUAd89Esf+HU6iUKk5uvMzbG0ZhYvc4oYq4HKv5+frf4fiN9yX6ZgLKf4plndsTwpCPO9P9teYkJ2dgYKpH+LVYMlNyGPmxHyWlz0qlipBrURzedwl3LwcK5EpadaiPXx8f5Ao5D5JT8KzvRPw/czUDRIfH06ZjI75fth6Am9fD6NitGWF379G2QxMyMjI0czcHHzqJs4s9Dx+m8OnsaUTfu8+5sxewtKzBqZPnadasIT//vIov53zI++/PJDn5ARMnjuXSpcvcvx9LSEgIdevWJyXlISYmhuTl5aFQKLh06Rp2do4EDuzH3r3BPHyYwr59h3B396Bhw/r8uXYzAD4+TYiIiOHsmcu4uDixf9/fzP7sfcLu3mP5sjXo6erw4ccTWfrtbG6HhLP+j30cPXQZD8+a9OvflclTR2Jrb0n9hrVZ8+tu7kXGc+t6FAAn/75Gt16+zP/kD5q3qUcNC1MA/Af7YuNoDioV3q3rFCkeJs9XEHImivw8OXV9XajfqnzDX+5djuXqX6FYu5rTqJcXOmIOZ+EfBQUFaGsXfT3o66tv9OTl5VV2SIIgCILwwipXAp2Tk1Psf8aPGBoakpaWVu6ghJdLvq09kqFDy1xATLvm4yciBj51UWqXe8j+C8WuoR3pD7I0T4lz0vPITskulEDXb+fOmR03UCqUtB3SGKVShXsTB3T0tcnPKaDT8Cbomz1O1Ow8LPnw92HqNqXFPcVXcetSFAn3H3D+ZCj5eXJuXY7C3smctPR0Aoa0ZsKIr8lIz8LZzY6lK9/li0UTuBsSjbV9Da5fu61pSSaTolKq6NjZh0sXr9GmnQ96errk5ubRb0BXanm64N0sgMlTPqSuVy1WrdpATk4uU95+k9DQMBITk7l+/SbJyeqxwr//vpbWrVtgZWVJgwYNuHDhDAcP7sfY2AipVMqSJd+TlpbOnj37WbhwPseOneXhwxTy8vKIjo6madMmzJv3KRcvXePatTt06tyGpk0boqeni1NNB/T19FEolPTw74BKJeGdKfNo0NCT198cRFys+un43dBobt2MYP+uU0gkElzdHHFxt+Nu6D109XTIy83H2MSAGjWMGTisC2u//ws3T3saNq+Fo6s1rf0aFnPN1U7tvM6PH+0CYMCktgyY1q7M75XshzmsmLiV3Cz1k3YDUz3qdRVFlITSUb0I1R0FoZL8HS5/7sfYfCalzPu0dyv7d6CjEc//XAThVVSujMTd3Z0DBw4watSoYtcfOnRIjH8WCinP9zNJkzrYzH4TZWY2Wl6uKCVF5619WTk1sqdOG1dCjkfQZnhTLN0tHq9UqbgcHErT7upKzAX56v8gnRvZMWv7aDIf5mBfxwrZf8aMq6CE5BkuHAvho7G/oFKp8G7tqe6OHZmEuZUxSYkPuXs7hoz0LACiwuNISkiha6/WdO3VmqULfufmjXD6DezMwwdpBA7pys2bdzAxNeDK5essWbSC7v4d0JJJefud17G1teXWrRCOHD6BtraM1FT1zbY9uw8SGNiTn3/+BrlC/UTMx6cpPj7e+Pv7Y21thYmJKfv378LY2IgHDx6Snp5JWpp6GitDQ0NOnjzDgAF9+Oab5fj4eJOVlUv3bgMxMTGmV6+uXLt2Cx+fply+dJuMjCwWLJqJg6MDb0/+gvbtm7Nn1zEArl0NRYUKWztL4uOScfeoSVqKely6b+tGXDgdgoOjDf0GdaB124akp+dgYmrI/fAH/PGduuibnaMFUXcTmDCz3xOHLoRfezzdzsXDoQS+0x6VSoVKqSIvMw8dQx2kspJf+5Hn7hFx4T75uQWaZQW54kuTIAiCIAjC81CuBHrgwIHMmjWL999/nzFjxuDm5oZEIiEiIoJVq1Zx+vRpPv7444qOVXjVSKVQ2wUpoKzqWCpZbmYecpV67l65QolK+fgKKORKQk5HkxKfAYBMW0bzvvWRyKRYOtfA0rns1cLDb8dpnkJdORvG+A97k5jwkJBb4Zw6fg0zayN8fL04d+oW3i3q4Oj8uHeAta05Hp41SU3LoEFjDzIy0wgLC+fK5Vv0G9AVuVzOrqCD6OvrMeNj9XhIW1sb6tevg/G/5ov3btaIgYP6s3btn2RlZfL994v56quvCQqKwd+/G/r6+mzatFZdmE6hJDwsEgMDA774YjYpKRmEhUVgbm5B69at6NevL8bGJnTp3A+A9PQMcnJz6NChNVaWlri6OnHx4nWWL1vFVwtmkpGeSWpqOja2liT80z29Rg0T+gd2IyryPrq6epgYG5KTnYuetgHLv1Z3Cf/fV2/Qo28bzTls/f2I5uf0tCxysvNRKJTItEougOfd2ZNDGy+hkCvxH9MSpVKFPK+APd+f4uAfF2je04vADzqgZ1x0GrKclBxWTtkGQJvBTbhzNgq3Zk54thWzIAiFhYeHc+7cuULLMjLUnyG3b99GS6vo1wEfH59KiU0QBEEQXiTlSqAHDx7MlStX2Lp1K7t27Sq0TqVS0bt3b0aMGFEhAQrVi1QqKbF686tAJVcQfy6a7AeZ2Pu4oP8cCj1lJmdx+3gkN4+Fa5a1GNgQa3d1tXuZtoxOI73Zuuhv9I110THUQVGgROsJTymfpkEzN/QMdMjNzqddtwZsWHWAdt0bcur4NQDW/fYXXy2fSJ9BbWnoXQdTs8eJb8tWjfg7+CzJyam0ad+A6VO/BGDw0J7cDY1i0tsjuXzxJq7uThw6eIQOHVuTkpLKjz8uIjT0Do0a10cmleLn14mtW7fxxRfzMDU1xcenMZGR6vHFP/zwA02aNEAikZCYmMTq1X/St28fWrTwoV69xnTt2ltzA8Dfvwe1atVGoZDTrp0vO3eqi621bePLjz/8yeJjv9KosRfe3g1wc3PG1saKxk28OH7sPK+/ORAjQyMaNalLM59GLPhiFeF3YwCYOG0I3/76MYO7z9Sce+y9xwXQAFp3bci1C2GEh8bRvltjmret88TkGaB+WzcW7h2PvECB3T89De6HJLPnZ3XBpOObr9LEz5N67YrO4SyRqCt952UXcGLjZUYuCsCro/sLMd+6ULl+/PFHfvzxx2LXzZ8/v9jlt27dKna5IAiCILzKyj2odO7cufTr14+//vqLe/fuIZfLcXR0xM/Pj9atW1dkjEI1IFUq0Lt4CtXZk0iaeJPbvD1K2Ys5JlmRkoUyT462jUmZqoIDxJ6M4K9PdgLg0NSJLl/3RapTcdchMzmLFZO34VjbGi0dGfJ8BfaeVhhbGBbarn5HDzIeZvMwPgOv1i5o6RWNIT7yAWnx6ZjaGpOWnEXi/RT0jXTRN9DBws5Ms93DxFS0dGX8uHU66WnZHP3rAvejk0h9kKXZxtzSlPPnrrMn6DjjJg1g0Ah/zTpXd2e+X/EZSqWKz2d9A0BT73ro6GpTv0EtYu7f5+r16xw5epyhw/owb95ikpMfMnXqOKa9Mwktrcf1FHJz1V23c3JyMDVVT1WjHvvshUQiITc3j1Wr1pKdnc2JEyeZNGkSGRnZhcZwKpXKf8Y/3+PddyfTsVMbrKwsyc2VExmhToavXL7FvPkf0rFTW6ysLJn89miO/X0WY2MjBg3tjYWFegqpGf97g4+mL8HS0oz2nX0wNDJkygeD+HzGCswtjGnTqXGha25iakjrDo2o38CdFp29cHS3KdXf3VrTa0BFekImOrpaaOtqUZAnRyKRYGymX+x+emb6vL68HyfXX8a+tjUuTRxE8iwU0a9fv6oOQRAEQRBeGs/0zd/Hx6fYLl5KpZJDhw7h5+f3LM0L1YhO/D2Um9cBoIqOQsfWkVzX2lUcVdllhcRy+qPNyLMLaPJed6z96pVp//TYx8Xx4q7dR5krr9AE+kFUCjE3E4i/m4x3Dy8c61rj1cEd/f8kUOb2JnQf3wqkaKotx4c/4N7tRGrWsSYu6iFfTViHUqHi7QV92fzbUcJvxuFe1w4Tc0MGjG1HQ18Pou/G8d4b35Ecn0bvIa2ZNHMAxjX0sLQ3xc7RCr/ezYm8e5+c3By+WfAHAIu/+gM//9bUMFdXmZZKJUgkMqRS8G3VBLMahqSmpXH79l3OnbtM796defgwFQClSkFy8kMAli79mVGvDcPAQJ99+/aQnp5B+/atGTSoP6Ghdxk6dCiNGjWkoCAHiURCXFw8dnb2FBSox/oOGhTI4cPHkMlkjBo1lCtXrtGlS0fs7GxZMH8pv/yyBnsHW1q3bk7Xbh2xsjTHyMiAzMxs3Nxq0rFja6ys1E/127RrSZt2LZBIpIWScR/fRuwK/h6pTIbOPxXgu/VuRdPmXujqaWNiVrgHwom911gycwsAZw6HMHfVm2j96/Uhz5MjlUmQlvBU+uyOm/z20R4sHEx4a3FvYu8m41TXhpoNS55iyNnbEZdmjiJxFko0b968qg5BEARBEF4aFfoIMSEhgQ0bNrB582aSkpJE96+XiVSmflr7z7d0iezJ3VKrq6SzEcj/qVQc8vtxbDrWQfWULrYA8qx8Yk6EIZVJce/oSdjhUFpP6YiWse5T9y0LcyczrF3NsXAwRSUBh3q2mNqZaNZLpRJiQhL54Z3tFOQrGb8gAJfG9iRGpzBr8O9kpOZgamlIt1HNUMjV46b3r7tAdkYuAGE342jXqyFHgi7T0NeDG5ciSI5X3xQIWn+CAa+147OPfuH65TC0tGT8uHYmA0b48efvQZoYbO0s0NVVJ+1XLl9ny6a9eNRyplOX1tR0seeD978kOzuHuvVqoaerS3T0febMm8G9e7GYGBto2mnbtiW5udmsXbua+fMXAeDv341ly75hz569XL16CZlMgkQiITs7hzVr1jFx4gR+++1X5HIFX3/9LZ6etdiz+yBaWlo4OtqRk5PL/n2H+eWXNQDE3o8nLzePj2bMYcCA3nTp0halUolf17ZY21j/J+mUFFuNWE//v09/JVjZmhf790t7mKn5+d7dROQFCk0CfeNEBD9+tBNrRzPGftkTWzeLQvvmZxew8atgVCoVyTFpxIQm0X186aZ9E8mzIAiCIAhC5aiQBPro0aOsX7+eo0ePolAoUKlUNGxY8rQtwosnz8YB/VFvQMgNJB61yXYqOh7zRWDo8LjAllVTZyhF8gwQtvcGJ5YeAaBe34YM3zoWbXPDMncBL0xF7PUEHtxLpWZDO0wdTDG2NmL04t6sfG8nV/4O5+KBUKavHY6VmznR1+PZvPAI2royHDysOLsvhO3fnWD6ikEkRqeQkZoDQFpyFkb/KjjVqJUbezarx+pa2JqQk52PV1NnAGq62yKTSVEolNRr6kqBXMH1y2EAyOUKIsNiqd+4Fv0H+6Gnp0tSUiqdurbAwNAAubyA335dj0xbiyuXb5Ceri7ClZ2tjuPmjTtMnTYGbR0pD5KTSUtLRS7P57XXBpKams6AAQG89dYkXFwcNbFeu3aDQ4cOsWzZUgYPDgRk3L8fS1e/bpw/PxYzM3NkMi02b95ESMgdTExMcHWtSVhYJOHhUWRn5XH1ygk8PFy5ezcCiUSCoaEBtTzduHUjlDOnrwBQt+7zmeLJ168eZ4JvcS8siXe+CtTM+SwvUPDzx7t4GJ/Bw/gMzuy9RZ9Jj4uP5ecUcO96HK4N7Lj2z9h3CwfT5xKjIAiCIAiCUH7lTqAfPnzI5s2b2bhxI/fv3wfUxWz8/PwYPXo0TZs2rbAghaqnkkjJ9moCXk2qOpRnYtWuNs2N9ZBn5WHe1JnSPrhLj0nV/Jx4KwE9a+NnLqZ2+0g4x9ddxtBMnzuno+n5Xnt0jXXJy5ETG6quBJ2dnkd8+ANsPMzZ+FUwIefuAdCylxdSmRRrJzNUKhVOta2p1diBO5fvU6dZTby71GamoylaUimePk60CqhHZEg8UpkEFdC0rTqBrNfUjeUbphMf84C6TVwxqWFIQGA7dm4+ipVtDRo09gDUT2H7D+lW5BwcHO1ITExix3b1U+BFSz6mhrkZKQ9T6R/YnQsXLxIcfByA4cMHkJ2dQ1JiErt3/4W5hSmXLl2mTh0PbG1tSElJ5bXXRhAdHc2gQQOQyWRER99j7dr1mJma076DekhIYmICa9ZsoHv3zpw4cZb33p1MfoEcpVLF+j+DuH8/Hj+/dvTt1x1LSwtycvJ4a8IYoiJiCA2NxMXFkYA+XZ7pb1cSe1cr5q4ei7xAjp7h45sYUpkEm5o1SI5VT7ll9K8u+RIJBP92nqBvj1O/nSt9326LnYcF9dq/mDepBEEQBEEQXmZlTqDPnTvHunXrOHDgAHK5HJVKhZmZGWlpaXz99df06tXrecQpCBVDW0aN5mVPTNz96hB6MAR5TgE+Y1s9c/KsyCtg29dHeBij7j7dvHc9ctJy0TXWxcLRBM/mNQk9G425vQn2nlaABL1/dRc3szJi0Hsd8OnhhUoFplZGvP/zYJJj07ByNMPQVB9zOxMsLY1JTs7A3tUKe1crAMJvxxC0/iiOLta0aFef2g2cqd3AWdP29I9HEDi8MxaWZlhaq5/YZ2Vm8fBBCpbWlujrqxNDmUxGi5YN+fCDrwGQy+VcvHCdDZt+4OGDVOzsLGnXLkDTbnpGJkaGBty6dZv+/f0BOW+/PZ6LF6/w5ptjcHZ2wt7envPnz6JQqKeeWrNmHQX5BdSt93isem5uNvb2dty5E07z5k2xs7fD0NCE0Nt36dnLj7VrtpCVlU1CQgpLv1nNl3Pfx9XVBVdXFw4f3YS2jjb29hYkJ2c809/w3yJvxRNxMw73+nbUrG1baNwzgFQq5fXPenBm7y2MTPXx7VW30Prze9XDXa4fjcC6pjmN/J7PE3JBEARBEATh2ZQqgc7IyGDbtm1s2LCB8PBwTdLcrVs3evXqha2tLX5+fugXGSsoCC8H83p2DF43BpVShbZpBb3O/zVw1aiGPsbW6qmhtPW0eG1BTxLDH2BkbkgNBxOUShUD3+uArYs5eoY6tB7QgBr/GhutbsMAoxoGPEnqg3Tef/M7HiSqE/dFK6fQpKUnfx88z93QezRvVQ9tXW1sbC2wsKyBUqEgIT6JTz9ewsnjF+nZuyP/+2wKxsZGnD1zkQ8++IqWLZqyZ1cwAPkF+fy1L5gCeT7dunfi7aljWbTwe4yMDDEyNKBT57Y082nAggWLuHcvBj09Pd54YyRffP45zs41GTNmFCpUODg4MXBgG5o0boapmSnNm7cC4N69aCZNep9LF68ydFh/atQwo3Xr1qiUKg4e+JtdOw/Q0teb1NR0oqNiAVjxy3oGDe6DVCrDwNDwqb3u/zXUv1TiIpKZOfRXcrLyMTDWY/G28dg4Fx0jbetqQZ+JbYosV6mg00hv1sz6C6lMSt02LqU/uCAIgiAIglCpSpVAt2vXjtzcXExMTOjbty89evSgVatWaGmpd3/UhVt4tWilpaE6cQZVTi6y1j4U2NpVdUhlkhmRTOKle5jUNMfSu+ZTxzNr/Wtc8bOS6WozYn4vjvx+DnM7E3yHN0amIyP6ahy/vrsTpUJFqwEN2PbtMfpPa0fXN5tj425B4IyOZTrO2aO32LP5DI4uVvj1a05WRo4meQZ4kJjKpfMhLJzzBz36+PLT8k2cOXkNZxc75n7zNt8vX4uJsQEnj18EYHfQYYYO70UT7wbExyfSvHkjWrVpgrGJPnm5+ezaeYDAQT1JSExg1ar1fPDB23Tq1I7MzAxMTUyp36A+V65cIiZG/Znh4GDPsaPHcHV1YeiwQahQ4ejoTI8evdHV1aF9B1OOHTvOtm1bMTU1JTEhmUsXrwKw7s+tnDp1EHNzdTGu0WOGEh11n1u37jJ9+lv872P1lFp+Xdsik8memhQrlSqC953hePBlfNs2wK9XS6QlFMuLDInl5y93oVJBr2EtyfmnMF12Ri5JsanFJtBP4jugAc717dDSkWHjYfH0HQThGdy7dw8nJ6eqDkMQBEEQXkilSqBzcnIwNDSkf//+tGnThmbNmmmSZ+HVpTp1jpxt+wDQjo5FOv0tVKpnKapVeQpSsgh+fws5/8x17Ld0EOYNHZ+yV8VybGjLyG96ayo/SyRweM1Fkv/p1h12MYbGHdzZuuQozbrXwcqlxpOaKyIjNZt3X/uBjLRsAIxMDejg34Tx7/fl50U7aNLCkya+tQm5GU7rDg2Ii03izMlrAERFxnHm5BWCD5wmoG9HdHV1yMvLx9hEXTwMQE9Ph/37DrN/32E++HACy5asoFdAZx48SCInOxstmZSUh6mFCgqqVNCwYWPmz5/DrFlfYGdni1+XDuTmZaOtrY2+vgH+/n2IjAzniy/mkpiYSKNGDVm9egPjxo3Bw/1x93tnZ0eMTR5PI+Xp6cnqNT8gl8uRSGQ4OjqSnZ1LS1/vUj1RDr0ZwafTfwLgr11nsHOyomHTol2pJRLYvuI4189GAtDAx5WmbT24eOwuPh1r4+JV8pRTJZHIpDjUtS7zfoJQHn5+fjRv3pz+/fvTvXt39PQq7uagIAiCILzsSpUFf/XVV2zdupXff/+d33//HT09PTp27Ii/vz/t2rV73jEK1dU/8/ECqLJzkKgodVGuqpafkatJngFykh9PPyTPKSD2dCQKhQLHlq5oG1XsVFX/9u9pk1QqsHR8XHlZ31iPlAdZNOtaGwOTp8cQG5nMrUtROLpYYmFvSvCO8/h2rEdiXAqXz9ylIK8AmZaMQa/70aO/L/qGuujo6mBgrMs3X6/Gzt6Kms62REfFo6evi4OjDQD79xxnwMBuqFCRlZVFQsIDJBIJ27bs0czJHHzoBD17dSQl9SFBQeqbKn5+7cnNyyEs7A6Ojk7o6j76ki6ha9eueHnVRqlUcvHiebS1tbGwsCQwcCgymRZbt25l//4DAOTn5+Pp6UFWVhYSqZRVq38kOuoerdv4YmZW+KaCVCpDR0f91Ni3VfNHV5bjf5/j+rVQGjWuQ6u23oX2yUjP4sj+c6Q8yMC3XQNOHVXfRFAqlIW2iwlLIDbqAe517TEye9xVPisjl/eWDSbqVjyRN+M5GXSDNn0aYGAqkhKheho+fDi7d+/mww8/5IsvvqBHjx70799fFP8UBEEQhFIoVQLdt29f+vbty71799iyZQs7duxgz5497N27FyMjI5o3b47kmabzEV5EkpbN0IlLRJWege7AAPJ5cV4DBvZmNBzVkqurT2PbxAnLhg6adXd33+DoksMANBvVnKbjWpfrGDkpOZzbeo0HMam06N8Q+wZPfzLpE1AXhVxJakImyQkZ3D4bzVsLAzA0Lzq2+c7le4TdiMWjgQOW9qb8740VxN17iLGpAa3967Jz/UkAegxojnvtdrTsVB9QzyVtav74ya2RkSHtO3lzNzSahk3d6dKjOemZ6dy+fZeFSz4gPOwexsb6zP3yB7y83Jg89TWiIqOwtrHUtNG8eWNc3By5cOESQ4f1Y9/eYGRaWixZsoygoD28+eZoPvnkY3R19YiPv8+QIYNRKOSaatuJiUkMGDAYmUw9v/S/6ykYGhry4EEaFhbmZGflEBURQ/2G9fD0LL7Q1uWL19gVdBgXVwf6DujOvaj7TB73BSqVCplMxsYdi7G0bKTZft+OE3wzZx0AHbt607lHM5r61KF+k1qabaLvxDN98PdkZ+bhXteeGQuHYGJmgEqlomPfpmhry1j95QEib8YDoKUto9NQkYwI1dP//vc/PvzwQ44cOcK2bdvYvn07W7ZsoWbNmgwYMIA+ffpgY2NT1WEKQiFHI+SVcpzyPAiQvjhff56L8vxt2rmKnqzCi6tMr14nJyemTZvG1KlTOX78OFu2bCE4OJhDhw4hkUiYM2cO4eHhBAYGUqNG2bqbCi8euZUV0vGjUalU5FfCo2cJKpBIylTgqcS2tGTUGdUSjz6N0DLUQaKrrVmXfDdJ83Ps5RiaSSXlqrp949Ad9n2rnsIp9GQk07ePQVtfm4f30lAqlVg6m8F/bjoolSp0DLQ5d+g2ORn5GJjqFvskM+ZuIjNHrCA/T46uvjazfh1J3L2HAOjqaxMX80CzrUKh5M0PeqGl/fgcMzOyyMzIwtrGAoVSiZ2jBZZWpixZtJpOXX3YFaS+gTBkeC8+/fxtcnPyaNuhBTVqmGFqZsKP36/g4sUr9OvfDR0dHfoN8Gfx4m8pyC/g2LHTvP7GcDw8XBk3biIAv/76O6NHj8LdvRZhYWHo6uoQENAXqVRKQYGc+LhEbt26iY2N+kbGwIGDyM3NJS4uAU/PWkgkUm6HhLPy103k5eWjra3NipVL0JJp4+3TCD09dcKdlprG+LH/Iz1N3aPArIYJVlaWmif9CoWC9PTsQtcyMT5F8/ODpDR+Wj8TlUpCanIGDxPTsK1pSfTdRLIz8wAIuxlLfr6cQZM6afbLz84nJeFxVe+cf7YVhOpKW1sbPz8//Pz8SElJYefOnfz1118sXbqUpUuX0qpVKwIDA+ncubMYsiUIgiAI/yItz04SiYS2bduyZMkSjh07xscff0ydOnWIjY1l8eLFtG/fng8++KCiYxWqIaVSVSEJ7dNkX4vm0sTfCJ2zHXlCaoW0KZFJ0TY3LJQ8A3h280JLVwuplpQmw33KPWXVv7sAy/PloFQReiKSLwNWsm3+YS4E3SQnvXCiZeVihqGZHh0GNaZlLy/aDWiEY22rIm0/iE8nP099xzcvpwCJVEqPweouyyY1DBj0RkdsHc2xd7ZEogNffbyK7MwcAKIj7jNm8P/o1XEKq3/dwe4dwQQfOM2NG3fo2bs9WVk5muPExyUhkUjQ1dPFxbUmpmbqyt9GxkZcv36bLVt2c/NmKDExMRwOPsb27bvp0aMTrq4u2NhYIpWqP2I8PWtpin1JJEp69+6JVCpFoVCyM2gf27fvYuWK30lIUFfOtrW14/33Z+DTrAXJSSlcvXqLms5O5OWpi3UVFBSwZfNuRo2cyi8/r9HUf5PLFeTmPL6mCoWCjNRMBg/vgbm5KW9NGkT9Bo+fLAP49WyBRx0nRoz2x97alqA/j3M/IpH3h//ApL5LWfrxJlw8bbF2MAOgWTtPbBwKFwnTMdBh8uK+2Lua06yLJy26ez3xtSEI1UmNGjUYNWoU06ZNo2fPnigUCo4dO8bUqVPp2LEjf/75Z1WHKAiCIAjVhkSlqrj059atW2zevJldu3aRnp7OrVu3Kqrpl05ycka5E0+JBM38vpWRvFY1Sb6cc2/+TO4/8/Z6vN4B28AWhbep4GuSl5yFUqlE39r46RujHsscdiKS+NuJuHg74djYnqzkLI7+fo640GS6jPelZlMHNs76i+y0XMKuxJKSkEHDDu6Mmu+P3r8qfKuUKlLj0slIycHC0RRDs6LTZmWmZvP9/3ZwYt8NOgQ0ZNzs3mjryEiKTcHUwggjUwOunA3hp292cO1SGAA/rvuAek082LJuP/Nmr9S0Ne7tvny3VP0FedDQ7nTq2pxvFvxGQYGcL+a9Q/2GRZPBtNRUtm/bTfKDhwQEdOOvAweZN3cxoO5+fSg4CGdnF86ePUNkZBTNm/vg4uJGaOgtDh7cC8Dly1fZvWsvvXoFsGPHTgBmf/YpEyZM0vwNFy9exuJF3yOVSunVqxuGhkZs3LCDrt068CA5nSuXb+Hq6sRfhzZqkvVjf59m2Ter8G3TBG10+e3nHTg4WTP5nSF069UeiURS5LUSERrLuL7zUfxz02Pax4P44csgzfl+v+MdTGoYkBSbhqO7FUamxU8XplKqkLyAffletc+U0qiIa/KojeosNDSU7du3s2vXLpKSktDS0qJjx44EBgYikUj4/fffOXnyJOPGjeOdd96p6nCfK/H/csV3xa3I61JZXbjLc8+8Mj72y9PlubK6Vj/rcV6W909FEtekqOr0/3KF9svy8vLif//7HzNmzODgwYMV2bTwCpLHppB6JRJ9a1MMHM01CbRMX/spez47XUvDMm0fezWe1W9vQ6VSz+P89sZRmDmZ4f9+BySSx13AnRvYcvtMtKa779UjYSRHpSDV1cLczgQ9I10kUgk1HEyp4WBa4vGMzAyYtiCQ1z/yx8TcAB099TVxcHtcydnYzJCQG1Hqn00MsLA2A8De4fE2nnWcyf9XMbi8vHxatfGhecumgAqZrPiPCFMzM14bM1zze0RkJAMH9SY/Px9bWxvs7RwACc2bt6RFC19UKhUhITcIDt4PwMWLl9m5cze6OrrIZOrE18HBgZCQUFQqJY86x/TvH8Dt23dJefiQXj27ER5+j99+X4q2ti6vj34XgOEj+2uSZ4C27VvSvqMv6WkZdGs7DoD79xI5e/oGPXp3pLh7hjKZtFBPAwtbE3T1tMnLLaCmhzXmNiaYmhthYWtW4t8EeCGTZ+HVk5yczK5du9i+fTu3b99GpVJRq1YtXn/9dfr06VNoCFbbtm0ZMmQI69ate+kTaEEQBEEojecysElHRwd/f//n0bTwksi/eJfM69EY1LJHr0XtIrdvVVl53JizlazIZAAazOyDiacdOmYGWLSrft1js1JzNHfDCnLl5GaouxGrVIUrbXsH1MWohgFXjoSRl5WP74AGrPr8AGFXYmndpx59JrfB2MIQXUMdCvLlnD8QwsOEDJp2rIWdq2WhY+roaWNpX3KS7V7biRWbP+LGlUi8Grhg66Dev0WbRnz764fExybj41uf9PQMsjKzkUqkDB7uj0oFshLmPy5JZmY6GzZsAqBHj65I/0mKL1w4zY4d25HJZFhZqbtw37kThrubO8OHDaNvv37k5uaCREp+XgEtW7bg3yNLXFxc+eWXb4mMjKJr58Hk56uT/bXrlrNz9ypy83KpX6/o60GpVKFvoE+b9k04sPcUAI2b1kGpVBU73XdNdxs+/WY0Ozccx6e1F83a1mHxhsnERiZRq4ETEiUkRj/E0rEGKqWSkDPRKOQK6jR3RqcSbugIQkVq3749SqUSIyMjBg0aRGBgIA0aNChxe3t7e+7fv1+JEQqCIAhC9SUqgwiVThGdyJ0vN2r6SXnOfw3t2oXnYFZm52mSZ1BPO+U4un2lxlkWzk0c8OlXn6v7b9N2lA9WHpbFbqelq0W9zh7MWDeCxOgU8nLlHNp4GeMa+qAtZWqX73H2smbq0gFE3Ixj8bTNABzZfoU5G15HR1+nTHE1b10Xt9pOhbq6SKVSfNs2+ddWdtStX3xF69KKiorW/Hz69Fny8nLR1tZmw4YNXL9+jV691DfUIiOj+fPPDQBcu3YNa2s7AGrX9iIvL49atWoXaVupVJGbk69JngEyM7PxbdXyiTHJZDI+mj2Ozl1bYmZuTLMWDZ+wtYT2PZrSsae35km0m5c9bl72RFyP5aPXfyUjJZtxn/WkhoUxV4+FkZ2RR0p8Bu0HN3lCu4JQ/TRt2pTAwEC6d++Oru7Tp8h75513MDau3l3RBUEQBKGyiARaqHSqPHmhQUbK3IIi28gsjPAY04G7vx3ByMUK00bOFR5HQVYeEYfvUpCdj0s7dwxtTcrdlq6JLgEfd6H79A5oG2g/tSuvtYcF1h4WxN9NRtdAm7q+LgRvugxA1K1Erp0I50Fiumb7e3cSyc8tKHMCXVk6d+7EypWrePgwhU8++RADAyMKCvIxMNDTJM8XL17WFChr27YNxsaPn547O7tqfk5OTkIqlWqKjgF41HLjm6WfcfnSdezsbf41x/OTmdUwpWvPtqU+j+IKxl0+epeMFHXl7rWLDhH4ZlsOb7wCwI1TUbToWRe95zhXuCBUtD/++ANQj392dXVF+58K/adOncLIyKjI02gnJ6dKj1EQBEEQqiuRQAuVTsvNBqc3/UjacwHz9vXQ9XIsupFUinV/Hyw61EVmoAMGFZ+g3N1/m8OLggGIvRpL1y97lmv+x0ckMik6RmVLcG09LPl43UgexKYTEZrAvTvqKbQs7U1RocLRw5L4qBS6j/BBz7BykmelUsHVq9dRyBU0atxQM4VNdlYmiUnJ2NhYo69fuIhWo0aN+fvvg2RnZ+PoWBOAGzeuYW2triB+/fpNAgMHUqOGBYMGDaJOHS/Wr9+IoaEhPXp01zzd+vvIUca+OR1tbW1+X/UtPs2bASCXywkPi2H92j109mvNgMB8jIwq5XJg5/I4kW/g64pU63H39rycAuT5isoJRBAqSEFBAR988AH79u0jKCiIWrXUlek3bdrE3r17GThwILNnzy5UW0AQBEEQBDWRQAuVTybDpFdzzHr6oJI+YV5nqRTZc6xgm5Wcqfk56W4SKqXquZbSzE7NITUhEwtHU3T/lQw7eVljX8sSlVRF+JU4HDwsadDWjTuXYzCxMMTRwwonTyu0dJ4+1jYvN4/Tx6+Qny/Ht23Dp1YaTEpM4uSJ81hY1KBlK2+OHD7OrVuhLF70EwBz533E0GEDefAgmWlTZ3D48DF69erG/K+/xNS08PhrCwsrLP7JNS9ePMvp0+o5sJs1a8Fbb01BJlPHL5HAhAlT2L59NwA5OTm89toopFIJy5b9Sl5ePnl5+WzZslOTQIfdieDH79YAcGD/MXr37kLXHp2oDM261ubjX4aTnpJFozbugIS4yIdEXI9j8PQOGJkXX5FbEKqrlStXsnfvXvr374+FxeMbRBMnTsTY2JiNGzfi5eXF0KFDqzBKQRAEQaieSpVAd+7cucwNSyQSUYlbeCKlRMIzPfJ9Rh6dPLlz5C5ZyZl0eKfjc02eU+LTWT5xK1E3EvDu6smYuT3QM9YjNTGTHT8dRyaTcu7IbWIjHlDXx5n6bVyp08yZKV/3Iy+7AHu3R3MoS4qtIv3I7m1/89Xs3wB4bVwAs796q8RtCwoK+PSThQQfPAnA/IUfcvnyVXJycxk5KpD163awY/t+ho8YxLVr1zl8+BgAu3btZ/SYEf8U/Crq/PnTnD2rbtPHxxcfH9//HFfOlSvXNb/fvx8HqIutNWlSn/PnLgPg4fG4W7epmQkmJkakp2eqp6GytqCyaGlp0bhD4bmj35zbUxOzILxoduzYQUBAAHPnzi203MPDg88++4yMjAzWr18vEmhBEARBKEapEuiCggIk/yldm5KSQn5+PsbGxri4uKBUKomJiSEtLQ1TU1M8PDyeS8CCUFHMPCwZ/NtQVHIVsufcPTr6ejxRNxIAuPBXKP5jW+Lc0I6z+26yd/U5WvWqS2zEAwBunosiNjyZWk2csLQ3A0AhV3BoxxlOH75BG7+GdOjZtNjulXdCHhfzOnvqxhMTvJycbE4ev6D5PSwsilWr1JW0dXV16N6jAy1aqotq2dnZoaenR25uLiYmxhgbG3D+/FlcXVywsFRPi6VSqTh37hTnz58GoEWL1nh7P06yk5ISWb9+E6mpqbz//ttMn/4xFhY16NWr+z/7w4SJb1Crljs6Otp08eug2dfewY6Vfyzi/JkreHi60KRpyRWDK4NInIUXWWxsLKNHjy5xfYsWLTh8+HDlBSQIgiAIL5BSJdBHjx4t9PupU6cYN24cn376KYMHD9ZMeaNUKtm0aRNffvklr7/+esVHKwgVTKqrDZVQ/8nKqQY6elrk58qxsDfBzEbdtVr2z3haqVSKvqEOOVn5WDuaYeVgptlXXiDnwokQ5n/4JwB/77+Cg4sVtRsULazWpUdLdm0/hrxAzuhxAUVufP2bsbEx774/ljlfLMfCogaNm9QloHcXsjJzCA4+Qb9+PWjbrg0AtWvXZsvW1dy8eRs3N2emvj2V69dv0KyZN7/9/huWltacOXOCixfPAuDr244mTZoVOt62bTuYN28RAC1aeHP27BH09PQwMnpcvM3CwoLBQwYUG2+DBnVp0KDuE6+zIAhPZ2JiQlRUVInrY2Nj0dfXr8SIBEEQBOHFUa4x0IsXL6Z3794MGzas0HKpVMrgwYO5desWS5cuLVfXb0F4WUgkaMZ329e2Yub6kcSFJeNc3w5TG3UFrObdvchIyyEpJpV3lw4i7WEmtb1rYmatTrAVcgV//nSA2Ojkwo2X8ADUu2V9tuxfhFyuwMHJ5inxSRk+KpB2HX3R09Vm4YLl7Ni+DxsbSxYtnkW79m2QyR5/RDRq1JhGjRoTHHyQ69dvAHD+/AVCQ0O5cyeEy5fVT7OvXr3B7dvh2NjYYm//uEBcbm6e5ufExGRMTEzR0Sl69yItNY3ExGTsHewwNBTjiwWhorVq1Yp169bRt29fTQGxR8LCwli7di3t21ffaQOF56eda+WUxjkaIa+U45TnfP4OL3tsldEpqTzXrDznX1l/myLXOSzlqfu0dxOlm4TqoVyvxNDQUPr371/iek9PT7Zt21buoAThRaaUKzm36xbXj0fQtEstGnfzRCKR4OhljaOXdaFtjc0N6DuhTYltpaVksurbfdg41KDXIF/uRyfj16cZteqpp5XJyc4l7E40Fpam2DmoE2Zr2+LnoC6OTCbDxaUm4WHhbNq0E4CEhGQUSmWh5Pnf3NxcsbKyIikpCScnRzIyUgkPvwNAcPBRjh1Tj3+uX78+Y8eO1+zXt28At2+HEh0dw6efflhs8hwfl8DkiZ9w7UoI3f078OW8D6rF/LOKAgVSmQSJqEosvAQmTZrEgQMHGDBgAG3atMHV1RWJREJERATHjx9HW1ubyZMnV3WYgiAIglAtlSuBtrCw4NKlSyUWGDlx4gS2trbPFJggVKXsB9lEXojB2NIAx8YOT53X+d8irsTyy4xdAJzaeYNZW0fjVO/JT4NLYmikT+OWHlw+fZd9W8+y8PeJNPBR1xdIiEtix+Zgwu/e59ypG3z/+8fUrutWruNYWJrTqHE9rly+gba2dqECXv/l4uLOjqBt3L59G6WyQJM8+/q24bff1mq209fXK7RfzZrOfP/9t4CqxLv116+FcO1KCAD79hzh9TcH06hx/XKdU0W5fTKS3z7Zi5WjGSM/64q1a+UVMBOE58HJyYl169YxZ84cjhw5QnBwsGZd48aNmTVrFq6uJX8GCIIgCMKrrFwJdI8ePVi5ciWurq6MGDFC84QoMTGRH374geDgYKZNm1aRcQpCpVHky9nx5QFuHgkDYPTy/ri3din1/kpF4exQqVQCkJmazcP4DKwcTNE31itu1yJ09XX4cP4Irp69i5WtGfW83QFITUnng7e/4cbVMEzNjPBt25BrV+6UO4E2NTXjhx8XcvnyNZycHGjY8MlFulxdPYiOjuTmzVAAOnbsiqGhER9++AFnzpzFzc2NXr16F9nvacW3nGrao6enS25uHra2ltjaWpXrfCqKokDBb5/s5UFsOg9i0zm/9zb+E1tVaUyCUBE8PT1ZtWoVqamp3L9/H7lcjqOjY6FprQRBEARBKKpcCfSUKVO4cOECS5cu5dtvv8XMzIy8vDyys7NRqVR06NCBsWPHVnSsglApCrLlhBwL1/yeGJZcpgTarYk9Iz714/y+27TqUw+nujY8jE9nwcQNhF2Lw6ezJ5O+7ouBSemSaCvbGnTu7VNoWXxsEjeuqhP8tFT1fNYenjVLHWNxHBwccHBwKHbd1auXePDgAd7ezTAyMuHIkQOEhNxAIpHQqVM38vPzWbhwMVKphJiYGHr08MfMzLzMMdSuU4t1m5Zz924kDerXwca2fE/uK4pUJsHK0YwHsekAGJmJwkrCy8XMzAwzM7Miy3NyckQhMUEQBEEoRrkSaD09Pf7880+2bdvGwYMHiYmJAcDZ2Rl/f3/8/f0rNEhBqEy6Jrp0HufLgR9OYmCqi5tP2RJTmbaMDsOb0mmkegoogIjrcYRdU893fO5QKPfDkqnVxPFJzTyRvYM1TZrV4dL5ECwsTek/uAuNvb3K3d6THDt2mMGDh6BQKBg2bCg9enTn7t3bSCQSunTpgbt7Lfr1G8CZM+cAGDx4AFFR0U9ptWR169Whbr06FRX+M5FIpYz8rBvn94ZgaKZPs57P5xoLQmU7ceIEhw8fJjMzU9NLBkChUJCens7Fixe5cOHCE1oQBEEQhFdTucvZSaVSBgwYwIABxU85IwgvKolUQpsxPtTtVAs9E12MrI3K1c6j5BnA1tkcPQNtcrMLsLAzwcLO5Al7Pp2JmTHzl71DxJ172DtaY+dYvie1d+6Ecu7sBdzcXWnZsgVQdKz3lStXUCgUSCQS8vNzuXv3NlKplC5d/PHw8EQuL+Dhw4ea7SUSKa1bV1w359CQSG5cuYNH7Zo0aFy7wtotLWtXc9FtW3ip7N27l+nTp2uGVEgkkkLDK2QyGQ0bNqyq8ARBEAShWnumevDXrl3jwIED3L9/n7feegsDAwNu3LhB165dnzj/rCBUd1JtGZYeFTcW0KGWFXM2vM69sCTc6tlhbqtOoFUqJWcP3+T2jRgaNHOjia8n+Xn5JCWkYlrDCCPjkqdxMrcww9zCrNCyq1ducPnSDby8atG8ZZMnxpSUlMiIEeO4HxOLVCply9Y/8PHxKbJds2bN0NPTIyDAn7p16yCVSunatSduburpb7S0tFm0aD4ffzwbOztbxo59g1q1KibRjbufyIRRX5CWlom2tharNs/Bs45LhbQtCK+qVatWYWZmxldffYVSqWTSpEls3ryZ3NxcVq5cybFjx5g9e/ZzjyMiIoIlS5Zw+vRpsrKycHJyYvDgwYwaNQrpPxXv8/Pz+eKLL9i9ezdGRkb079+fyZMno6VV+OvL1KlTycjIYOXKlc89bkEQBOHVVu4Eet68eaxevRqVSoVEIiEwMJDo6GimTp1K586dWbJkCdra2hUZqyC80Jzq2OBUp/CT4pAr0XwyYQUAWloyftwxna3rjrB17RHqNnThi6VvYW1XukQ+Kuoerw2fTk5OLlpaWqzbuIyHKQ/Iy5fTpk1LMjLSycrKxsXFGalUxoMHD7gfEwuoC51duXIVb29vzRfXR3x8fFm4cD5JSQlIpVK6dw/AxcW90DbNm/ty4MBeJBJphc6HmZyUQlqaeox3QYGchLhkkUALwjO6c+cOI0eOpH379igUCmQyGQkJCXTq1InGjRvTv39/fvjhB5YsWfLcYoiJiWHIkCFkZ2czYsQInJycOHDgAPPmzSM8PJzPP/8cgBUrVrB161bGjh2LSqXi559/xtjYmDfeeEPT1tWrV/nrr7/YsmXLc4tXEARBEB4p16Sm27ZtY9WqVfTu3ZuffvpJ0/WrUaNG+Pv7ExwczNq1a5/SiiAI2Zm5mp/lcgWJcSlsXXsEgJtXI7l+MazUbT1IfkhOTu4/bck5d+4yY16bzvixH/Drz6vp3m0gHdr3ZsWvqwEVrq6ujB4zDIBatdw4duwYp0+fLNSmQiFn376dJCUlIJPJ6NGjT5Hk+bGKTZ4B3Gs5EdC/PQAtWzegXgOPij2AILyC8vLycHJSzyUvk8lwcnLi9u3bmt8DAgK4du3ac41h5cqVpKamMmfOHGbMmMGwYcNYuXIlzZs3Z8OGDYSFqT/7tm/fTpcuXZg2bRrvvPMOfn5+bN68uVBbCxYswN/fn7p16z7XmAVBEAQByplAr1mzhubNmzN//vxC46SsrKxYvHgxrVq1YuvWrRUWpCC8rOo1cSVwTHtqWBozZmoPXD3tsXVQP3GWSiXYOpa+G3kdr1oMHa6eOqpHz/bEJyRq1p0/fxWZTAbA0qU/k5mZia6uHv7+nQno7YeJqR5//XWIuLh4zT5yeQF79wYRFRWOTCbD378vzs5Pnxs2MzOD5OTkYtelpKQSdiec3NycUp2TgaEBH85+k6BDy/h6+buYW5qVaj9BEEpmYWFBSkqK5ncHBwdNwgpgampa4nu4okRFRQHQsWNHzTKJRELnzp0BCAlRzwcfFxeHs7OzZhtnZ2diY2M1vx85coRLly6JqTMFQRCESlOuBDosLIwuXbqUuL5Lly7cu3ev3EEJwqsgPCSGG5fDGDG5K2sOfcKwiX5Y2tRg0S9TmPHFSJb/8R51G5X0tLcoAwMDZv7vbY6f3sJXCz6mZcsmyGQyZDIZHTq2JCnpAVKplGbNGqGvb0B4+F1mzPgEiUTF2bMXaNnSB19fXwAKCgrYs2cH0dGRaGlp0atXP5ycnJ8SAdy6dYtufgNp4dOVXTv3Flp3LzqGkUPfpmf3Mcz5fBk5OaVLonV1dbB3tMHAQEypIwgVwdvbm02bNmmSZE9PT86cOUN2djYAFy5cKHZqq4rk5qaes/7OnTuFlkdGRgJgY6Me7mJpaUlaWppmfUpKimauaqVSyaJFixgyZIjmibogCIIgPG/lGgMtk8kKTXvxX+np6ZqnXYIgFHXlbCjvjvkWuVxBj/4teffzYTyqgF3TzZ6abvblaldbW5v4hAROnz5Pk6YNOHU2iOzsPDIzM5k4cSSJiQ/o4d8FmUw9BvrOnbtkZGTQu3cPRowYhr29AwUF+ezevZ3Y2Bi0tLTp1asf9valm3Jr65ZdxPwzrvrT/31F5y7t0ddXF0K7fOkGd++onzpt2rCHkaMD8fQs/Q0CQRAqxptvvsmgQYPo0qULwcHBDBgwgJUrV9KvXz/s7e05ffo0/fv3f64xjBs3jhMnTjBz5kxmzZqFk5MTR44cYdOmTbRq1Qpvb28AWrVqxb59++jTpw8Af/31F927dwfUw8nu37/PxIkTn2usgiAIgvBv5Uqg69evz969exk9enSRdbm5uWzduhUvLzFfqvDiy8/O5+bxCFQq8Grjip6hToW0e/t6FHK5AoB9284w/v2+mJo/29RWAFevXGNA/9cpKCjAy6sWu/asxspKn61btrFs2c8A/PVXMMeOe1OvXj1Gjx7J77//gVKpol69BuTn57N79zbi4u6jra1Dr179sbMrXTIvlUpQ/evGmmdtd3R0Hl8vZxdHtLS0kMvluLk5YWlp/sznKwhC2dWtW5c1a9awYsUKzM3NMTc359NPP+Xrr78mKioKHx8f3n333ecag5WVFVOnTmXmzJmFvks0bdqU5cuXa2bymDp1Kjdv3mTo0KGa9dOmTSMvL49ly5bxxhtvYG5eMZ8lzzJ5yKN9xQQkhVX1dXnV/x4v2/m/bOfzJFX93qmOKuKaVNT1LFcC/eabbzJu3DgmTpxIjx49ALh37x4pKSn89NNPREdH8/7771dMhIJQThIJz1zU6tiGK6yfHwzAqNldyczIIzs9l1Z962PnblmmtkKvRXHm75vUsDCibmNXdHS1yc8roPeQNhiaGJYrvitXrnPu7CXq1PGgTduWhN2NoKCgAIBbt+4QF5eIk5MzObmPi5VlZGRSUFCAmZk5n3/+GdOnT8PU1AyQsHPnFhIS4tDR0SUgoD82NnYAJCcncfBgMIaGBnTp0lnzVPnflEoVNV2cGDS4N3l5+XTs2AaZ7PFHTMNG9di8/Qfi4hJx93DG3LxGmc/35sVwrp0Lx72OPd7t6iCRlGsUiiC80q5evUqDBg349ttvNcuGDh3KgAEDyM3NxcTk2W/mPc3PP//MokWLcHR0ZPr06djY2HD16lVWrVrFkCFDWLFiBdbW1lhZWbFp0ybCwsKQyWS4ubkhkUj4+eefUSgUjBkzhoKCAhYvXsy+ffuQyWT07duX8ePHF5nq6mksLIyf+bwqoo3ibDmb8vSN/mNA87J/xj4vRa5LeDU+n7Cyx1YZSY6yHN9njoTJy7xPec6lPN+1AltUn9dndfa8PlOepLp/3lTFNfmvciXQbdu25ZNPPuGrr77i8OHDAMyaNQtQFwF5++23nzhGWhCet8hTUVzedQPHBvY07lsPLb2yT6kmkUgIu/K4WE3M3ST2rT4PQPi1WD74fRil/T8jOjyeXxYHceFkKABTPgnkl60fkpqSgWfdmmhplX3IQ8y9+4wcNoW8vHx69+lCeHgE9erXwdnZgaio+/Tr74+bW00KCsDPrzPDR1zl/PnLfPjhO1hZqccX6ujoYm1tS25uLhs2/E5WVhYSiRR//z6a5FkikTB//kLWrNkAwIIFXzJixIhiYxoY2Bc7W1u0tGS0at2y0LqbN0L5dOZSAD6fM7XM5xsfncyHY34mL7cAiUTC0vWTqd3YpcztCMKrbuLEifTu3ZsPPvig0HIdHZ1CvUael8zMTL777jssLS3ZtGmT5glyly5daNmyJa+//jpfffUVixcvBtTDxjw9PTX7p6Sk8PPPP/Puu+9iYGDA8uXL2bhxI19//TUqlYoPPvgAbW1t3nrrrTLF9eBBRrlvukok6i91z9JGRUtOzqjqECr0ulSH8xEqzr//ntXx/VPVXrRrUhnvz4q4Jo/aeFblngd6+PDhdOnShX379hEZGYlSqcTR0ZGuXbsWqpgpCJUtPS6dNe9sR1Gg5Mq+25jYGuPZ4eljbWNuJHBq2zXMbIxpFdgQwxr6tB3QkEuH7qBSqdDSefx2SU3ORKlUIZGW7lZtbGQSCfcf39FLin+Is4cdztiV/QT/kZKSSlZWNn36+rFz5z42bdqBnZ0Nq9csJz9fjqtrTUxNTUhOzsDKypoFC75CpVLw39qBubk5bNmyjqysLLKzs1m9+k/q1KmrGfesUMg5efKMZvuIiKgSY9I3MMCva6ciyyUS+P3XrYTcDAdg5a+bWbR0Zpk+ADPSc8jLVT9dV6lUpD7MKv3OgiBopKen4+r69Ir6z0tERAS5ubn06dOnSPfrVq1aUbNmTY4fP17i/j/++CMWFhYMHDgQUE911a1bN00F7x49erB169YyJ9Aq1bP3WqqINipKdYkDKu7aCi+P4v6e1en9U128KNekMmOsDtekXAn0uXPncHd3x8bGhtdee63I+tjYWM6cOUO/fv2eOUBBKDOlCtW/+jqpnlDw7hF5npyVH+wiPvwhAHqGOrQd1gSvtq7M2TcWgIJ8BbHhD0hJyGDk/7qWOnkGsHYwp3XnBuzadBJTMyPad29SxpMqqpanO2+NH0FsbCy5uXkAxMUlkJWZTeMmjYp0w1LP1144ec7JySYoaAtpaalkZWWxetVaEhOTSE9/fCdRKpXx0UfvMn78NAwNDfD371rmWFUqsLJ+3L3H2tqizB9+Lp62jJzsx6YVf9O+RyPqN6vYBECRryA+NBl9Yx3MnUXXMuHl1aVLF7Zu3UrPnj0xMCg6HON509XVBSixGKlCoShxXUxMDH/++ScLFy7UdNFOTEzE0vLxkBpzc3Pi4+OL3V8QBEEQnlW5EuhRo0axYMECevXqVez6EydO8OWXX4oEWqgSJg6mDF3Qm9PrL+HazBHXFk/vEaFSqcjNytf8Ls9XaH42dzDV/Dztp4HluuvlVtuBgCGtadutEbYOFphbmT59p6fQ09Nj+nsTiIqMJDwiiqtXbtKjRyfc3EuXWGZnZxMUtImHDx+gr2+AgYERUqmMESOG07Zt+0Lb9uoVwJkzTdDV1cHS0rpc8Y4c3Q9zCzMA/AM6PnnjYmjraDN8cjf6j26PnoEuUlnFjX9WqeDo7+fZu/wEWroy3voxEGdvhwprXxCqE1dXV44cOULbtm1p0KABFhYWRWbOUA/dmP9cju/h4YGDgwP79+9nwoQJODg8fq8dOnSImJgYTX2V/1qyZAleXl5069ZNs8zW1rbQ1JnR0dHY2to+l9gFQRAEoVQJdFRUFD/++KPmd5VKxYYNGzhx4kSRbVUqFWfPnsXQsHxFkQShIni0d8OzozvKUlbd0NbT5s2FAWz/5ih27hY06Va72CJkz9JlxN7ZGnvn8iWfJdHS0sLdw4NVq78jMSERRycHDA2NnrpfVlYmQUGbSUl5iKGhIb17D6RGDXOGDh31z9MhCQkJ8Whra2Nurp5z1cGhdFNZlcTWzpo3xg1+pjYkEgkGxhU/H7Q8p4C//7ig/jlPQdi5eyKBFl5a3333nebn06dPF7vN80ygpVIpc+bMYdy4cQQGBjJ48GDs7Oy4ceMGW7Zswdrausj4bICbN2+ya9cuVq9eXWh5QEAAP/74I0uXqmssHDx4kClTpjyX2AVBEAShVAm0s7MzERERXL58GVD/x3ru3DnOnTtX7PZSqbRSqnBHRESwZMkSTp8+TVZWFk5OTgwePJhRo0YhlT5+OjVy5EjOnj1bbBurV6+mRYsWAMTFxTFz5kwuXLiAg4MDU6ZMwd/fv9D2eXl5dO/encGDBzN+/Pjnd3LCMytt8vyIWzNH3ls3nKgb8SybsgV9Ix1GfOyHXS2r5xRh6amndFGVmMDXqFGDGjVK1+04MzODoKDNpKamYGhoRN++AzE1Ve+rq6sHwN69exn/1juYmBizavWPNG3qXRGnUW1p6WnRuFttTm68AoCDV8Xe6BCE6uTQoUNVHQK+vr5s2rSJ77//nvXr15ORkYGVlRWBgYFMnjwZK6uin7sLFy6kXbt2NG/evNDyt956i4yMDDZs2IBUKuWNN97gjTfeqKxTEQRBEF4xpe7C/csvv5CSkoJKpaJbt258+OGHdOpUtFiQVCrF3Nz8uY+riomJYciQIWRnZzNixAicnJw4cOAA8+bNIzw8nM8//1yz7e3bt2nUqBHDhw8v0o67++PiUjNnziQ0NJT33nuPy5cvM336dBwdHWnYsKFmmz/++AO5XF7s2G/h5bB27kHCrqqrbx/dcpXBH3YmIyWbE7uukZeTj2+P+lg7Vd4Y2ZCQUL779jd0dXWYOHk0bm7lH/ubkZHOjh2bSE9Pw8jImD59Bv4zhdVjSqWcuXMWIpfLefgwhT17/nrpE2iJVEKPaW2o284NfRNdHBuVv7ibIFR3/+4yXZXq1KnDsmXLSr39ypUri12uo6PDzJkzmTlzZkWFJgiCIAglKnUCbWxsjLGxuuz3vHnz8PHxwdHx2bp0PouVK1eSmprKggUL6N27N6Cex3LUqFFs2LCB1157DXd3d+Li4khLS8PX15c+ffqU2F5CQgInT57k008/Zfjw4QwfPpyzZ8+yZcsWTQKdlpammTpDX7/iu5EKVU+lAlOLxzd/9I3VxW6ObLvM7/P2A3D3Whzvfze4zE+4y0MikbB82Qr27jkCgLGJEbNml693R3p6Gjt2bCIjIx0TE1P69BmIsXHR+V5lMm2aN/cmPFxdbdvZ2anc8b9IdI108WxXdZWJBaGylNR77L98fHyecySCIAiC8OIpVxGxfv36cePGDebPn8+sWbM01S+//PJLYmJimDFjxnOfIiMqSv3lvmPHx8WIJBIJnTt35uzZs4SEhODu7s7t27cBqFWr1hPbe1Sx89EUXDKZDEdHR2JjH88D/GjqjMDAwAo9F6H6UKlUDJjWHjs3C3R0tWjVtwEA6Q8eT5kUG/Xgn8repa/C/SweVZoF0JaVb+a5tLRUduzYRGZmBqamZvTuPVBzQ+y/VCoVMz58F+9mTTEyNKBzl87lOqYgCNXTyJEj/xkW8mS3bt2qhGgEQRAE4cVSrm/j169fZ+TIkRQUFDBp0iRNAm1kZMTp06cZPHgwGzduxMXFpSJjLcTNzY3jx49z584dmjZtqlkeGRkJgI2NDQAhISHA4wQ6OzsbPT29QmOkASws1IWS0tPTNctSUlI05xAbG8uaNWtYvHhxkWqlwsvF2sWcvm+3K7Ssbe+GXD0VTtqDLN78pAeq55g8h4beZd+eYGqYm9Gnb3emvP0GZmYmaGtrM3zUgDK3l5qawvbtm8jKysTMrAa9ewdiZPTkSeStrW0YNmxIeU/hmaQ9yEBbR+u5FAsTBEE9Zvi/CbRCoSA5OZm///4bc3NzJk2aVEXRCYIgCEL1Vq4Eevny5ZiZmbF69WqcnB5375w2bRoDBgxg5MiRLFu2jMWLF1dYoP81btw4Tpw4wcyZM5k1axZOTk4cOXKETZs20apVK7y91WM2HyXQmzdvZs+ePSQnJ2NgYICfnx8zZszQJM729va4uLiwatUqvLy8uHTpEpGRkUydOhWApUuXUq9ePfz8/J7bOQnVV83aNszd8CZKpRJtXe3ndpz8/DxmvPcl166pe05oa2kxZFh/Zn8+o1ztJScns23bRrKzs6hRw4I+fQIxMKi+FfLPHLzBvKnrMKlhyKwfR+Jev+qGiQjCy+qdd94pcV1KSgqBgYEkJiZWYkSCIAiC8OIoVwJ99epV3njjjULJ8yNOTk4MHTqUtWvXPnNwT2JlZcXUqVOZOXMmo0eP1ixv2rQpy5cv19xdf9SFOyQkhA8++ABdXV1OnDjBpk2buHTpEps2bcLMzAypVMrcuXOZMmUK3bt3B2DIkCH4+/sTEhJCUFAQf/zxR4XFX4rec0/d91naeNlUxjXR0pEBz7f3gUKhIDbu8RfX9PSMcp/Tw4fJBAVtITs7CwsLS3r3Dnzuxf2ehUKu4Jd5u5EXKHiYmM6pAzfxaFDxCbR4/xQlrklRFXFNXsTrWaNGDQYPHsyff/4pimUKgiAIQjHKlUDn5OSgrV3yUzhDQ0PS0tLKHVRp/PzzzyxatAhHR0emT5+OjY0NV69eZdWqVQwZMoQVK1ZgbW3NsGHDyMnJYezYsZqkunv37ri5ufHVV1/xyy+/aKbc8vb2Jjg4mNDQUKytrbG1tQVgwYIFtG/fnmbNmpGcnMxnn33GhQsXsLS0ZOLEiZqEuywsLJ7chbay2iivvPRsUu4mYWBljImTeZn3v3P0LtHnorCpY0vd7l5IZdKn71QKVXlNKoYx3y7/jBnvz8OjlgsDArtjaVn2c0pISGDHjs1kZ2dja2vLyJEjy5w8x8YmEBebgLu7C2Y1ihYbq2hKpZK6TZ2Jv5cCgJ2zRbnOvbRe/NdKxRPXpKhX8ZqYmJgQFxdX1WEIVeBohLzM+7RzLV9tjrL6O7zssbV3K3ts5dmnrLEpy3yE8lVdqc438Ypcs7CUp+5TWedTWa/p6upVP//SkKhUJc0sW7LAwED09fVLfCI7ZswYHj58yI4dO545wOJkZmbSunVrjIyM2LlzJ+bmjxO4kydP8vrrr+Pv7//ELuT5+fk0btyYWrVqPTHOU6dO8cYbb7Bjxw5q1arFyJEjSUtLY/bs2Vy4cIFFixbx559/FhqHXRoPHmSUOKfv00gk6i91z9LGs1Dlybmy9CCR+2+gZ25Au4WDMHC2KPX+GfdSWTvyDxT5CgAG/jgI64b2zxRTVV+Tf0t5mMamtfuJjIhjyIhuNGxap8xtyOUFyGQyJJKy31hISkpkx47N5OXlYmdnR8+efdHVLdt44ph793nz9XcJDY2gZ89OzJ0/s8SiYxXpQUI6Zw/dQt9Il5advdAz1K3wY0gkYG5uRHzkA2R6Wki1RU2D6vT+qS4q4po8auNFkp2dzahRo0hNTeXgwYNVHU6lSk5+tr+1paXxM7XxJJWV2Fb0cUq6LuU5Tnmua3mS4fKorgm0tJISzsr6f+NVSqCf92fKi6girsmjNp5VuV4hAwcOZNasWbz//vuMGTMGNzc3JBIJERERrFq1itOnT/Pxxx8/c3AliYiIIDc3lz59+hRKngFatWpFzZo1OX78+BPb0NHRwcTEhKysrBK3UalULFiwgD59+lCrVi3i4uI4e/Ys8+fPp2nTpjRt2pSNGzeyffv2MifQKtWzf+BURBvlkZucQeT+G+qfH2aTcisO/ZqlT6DzMvM0yTNAbkZehZ1HVV2Tfzu0/ww/fbsFgHOnbrD9ryUYGJXt6a9Mpu7hUdZzSUiIZ9euLeTl5WFjY8uoUaPIzCwocztXrtwkNDQCgN27gxk3YST169ctWyPlYG5tQvehLTS/P4+/pUqp4vCKM+ycH4yrtyMBn/hhZGNU8Qd6AVWH90918zJek+HDhxe7PD8/n8jISDIzM5k4cWIlRyUIgiAIL4ZyJdCDBw/mypUrbN26lV27dhVap1Kp6N27NyNGjKiQAIujq6t+KqVUFn8PT6FQoFQquXXrFu+99x6tW7dm5syZhbZ58OABKSkp1KtXr8Tj7N69m7t37/L9998DaIqqPKo6DmBubv7KdXXTMTPAqoEDSdfuI9WSYexs+fSd/sXcw4rWE9pwcd0FanXyxK7Rsz19rm5U/5ofWqlU8t/v3rGxcZw5dQl7eyt8WngXqQhfXvHxsezatZX8/Hxsbe0JCOiHnp4emZkFZW7L1dUJXV0d8vLycXF1wsbaqkJifFZ5WfnItKRo6Zb/7nBGfAZbP/8LgNCTkYSdjqRRn/oVFaIgVHsXLlwodrlMJsPa2poRI0aIBFoQBEEQSlDub6Fz586lX79+7N+/n5iYGORyOY6Ojvj5+dG6deuKjLEIDw8PHBwc2L9/PxMmTMDBwUGz7tChQ8TExNCjRw9cXFxISEhg27ZtvP7665oxzSqVikWLFgHQv3//Yo+Rn5/PN998w8iRIzX7Pfr33r17gDpRv3//Pp6ens/tXKsjqYEuPp/0IvV2PAY2Jhi6W5dpf4mWlEYjvKkf2AiZrlb1HqRTDp27tSAhLpmwuzGMejMAw389fc7KyuLdtz/n4sXrAKxYtYA2bVs+8zHj4u6za9c2CgrysbNzoGfPfujo6JS7Pa+6ddi09WciwqNp2NALq2qQQN86HsGP04MwszbircW9sfcsX0w6BtqY2BiRnpAJgJF59a1KLgjPw6PZKQRBEARBKLtn6uTv4+ODj49PRcVSalKplDlz5jBu3DgCAwMZPHgwdnZ23Lhxgy1btmBtbc0HH3yAvr4+n3zyCR9++CGDBg1i6NChGBkZcfDgQU6fPk1AQAA9e/Ys9hjr1q0jIyODt956S7PMxsaG5s2bs3z5cgoKCrhy5QrJycn07du3ks68+tC2MMKqlccztCBBpvf8poOqSjUszJj07nAkEgn/LTGQlZmlSZ4BIiNiaNP22Y53//49du/ejlxegIODE/7+fUss8ldQUMDhw0eIjU2gffvWuLq6lthuvXpe1Kvn9WzBVRCVQsmazw+QlZZLVlou5/aE0KecCbSemT5jfx7M9UOh1HA0xdW3ZgVHKwgvhtDQUFxdXTWfF6dOncLY2Jj69UWPDEEQBEEoSakS6Hv37mFpaYm+vr7m99IobpqriuLr68umTZv4/vvvWb9+PRkZGVhZWREYGMjkyZOxslJ/ue7bty/W1tb89NNP/PzzzygUCtzc3Pj0008ZOnRosW1nZmbyww8/MG7cOExMClcfXrhwIbNnz2bp0qVYWlqycOFCzZzTwqvn0cPz4sZIFlefz9zCnCnTRvPtkt9xcLShpW/hsfNyeQH3oqMxMjbCysrmqcePiYlmz57t//QAcaZHj97FJs8ZGemoVEpOnjzL2DenAVC/fh22bP2jWs8L/YhEJsWhliUJUeoqnaaWzxazaxMHjJ1MXrqxrYJQGvn5+cyYMYN9+/YRFBRErVq1ANi0aRN79+5l4MCBzJ49u8KGlwiCIAjCy6RUCXTXrl35+uuvCQgIAMDPz08zJVRJJBIJN2/efPYIn6BOnTosW7bsqdu1atWKVq1albpdIyMjTp8+Xew6Gxsbfvjhh1K3Jby8Du0/xS/Lt9Dctz5vTOiPaSmmetLS0mL8xFH0CuiCqakJNcxraNYplUp+/eU3vvxyIVZWFqxZ8wv1nvAkKDo6kr17d6BQKKhZ04Xu3XujpVX0LX3x4kXeeP1tcnJyGT9hjGb5jRu3ycrKeiESaIDAd9vj070OeTkFeHevXdXhCMIL67fffmPv3r30798fC4vHBSAnTpyIsbExGzduxMvLq8SbzIIgCILwKitVAt23b19q1qxZ6PenJdCC8DKLu5/IR9OWoVQquRt6D6/6bvTo3a5U+2ppaePi6lxkeVpaKl99tQSApKQHHDt+ssQEOioqnL17d6JUKnBxcaNbt17IZMW/ndev30piYjKg7l1Ru7YHt2/f5YsvPsLSsnTdoLOzcjh94jIALVs3xsCwbNNiPatrB+/wy/SdGFsYMPH7/uib6FXq8QXhZbJjxw4CAgKYO3duoeUeHh589tlnZGRksH79epFAC4IgCEIxSpVAz5s3r9DvX3311XMJRhBeFNraWujqaZOTnQeAnl75C3Y9YmhoQNt2vhwOPgaAm5tLsdtFRISxf/9OlEolrq4edO3aE5ms5LmMXZwfD6XIzcllR9BaMjOzsLKyLvWNsM3r9rN0wVoApn4wnFFv9C3dSVUAZYGCjfOCkRcoSInP4PKBOzh4la1wnSAIj8XGxjJ69OgS17do0YLDhw9XXkCCIAiC8AKp+pnCBeEpJCoVSCXVaryqpbU5y375kEP7z+Dm7kCrdk2euU0dHT2+/vozLl++hqGhEa1b+xbZJizsDgcO7EapVOLu7kmXLj2emDwDDB02GFMzMwryC/Dz64hSqcLGxrbUcUkkEi5ffFy198rFUCRvFi2Q9rxItaS4NrTjQWw6AOZ2xpVyXEF4WZmYmBAVFVXi+tjYWE3NE0EQBEEQCitVAj1q1KgyNyyRSFi1alWZ9xOER1QKJWe3XOPw6os07upJp7E+6Og/+5PeitKkWV2aNKtbYe3t27eHDz74mLp16zB//twiXbLv3LnNwYN7UKlU1KpVh86du5eqyE+NGmYMGzaYyMgo3nlnFrdu3GHuVx/RK6B7qeJSqVT0H9SFE39fBqD/oM6VljwDIJHQf0ZHPJvXRN9YlwadnqX6uyAIrVq1Yt26dfTt21dTQOyRsLAw1q5dS/v27asoOkEQBEGo3kqVQJ89e7bIsn9P0WNubo5SqSQ1NRUAfX19TE1NKy5K4ZWUcOcBGz4/CMBfP5/GtbE9tduVPO3SiywtLZXJk6eTlZXF338fZ+/e/YwfP16zPjT0FocO7UOlUlG7dl06duxa5gq5wYeOce7MZQDem/4Zbdq2xMzMrFT7tunQjG37lwJg7/j06uAVzczWmDZDG1f6cQXhZTRp0iQOHDjAgAEDaNOmDa6urkgkEiIiIjh+/Dja2tpMnjy5qsMUBEEQhGqpVAn0jRs3Cv1+8+ZNRo8ezeDBg3nzzTcxNzcHID09nV9//ZU1a9awePHiio9WeKVo6cqQaUlRyJUA6OhXzIiDqNB47oUl4F7PAbualhXS5rPS0dHB2dmRmzdvA2BubqZZFxJyg+Dg/QDUqVOPDh38yjW9jIXF44rf7u7O6OqW7Wl+VSTOgiBUPCcnJ9atW8ecOXM4cuQIwcHBmnWNGzdm1qxZT5wjXhAEQRBeZRJVOfpivvbaa5iampY4hdR7771HTEwM69evf+YAX1bJyRnlHtMrkYClpfEztfGiuHMiktunonCsa0PDbp5IZcUnjqW9JvfuJjB14LfkZOVh62jOonWTMLd5+vRTleH27Vvs2bMXGxtrevfug5GRMTdvXuPIkQMA1K3bkPbtO5e68Nd/r0lOTg779h4iISGJLn7t8PBwf56nU229Su+f0hLXpKiKuCaP2qjOUlNTuX///j9zyTsWmtbqVSP+Xy6foxHyMu/TzrX6luA5El728ynrvDTlmcjmn2cJz51WOaZ/r6zXvJgAqOyq83utPJ8dA5rXqBb/L5frql65coUZM2aUuN7b25sDBw6UOyhBeKRWaxdqtXapsPbuRyaRk6WunB0f85Ck+JRqk0DXru1F7dpemt+vX7/C0aOHAKhfvxFt23Z6punj9PX16de/1zPHKQjCiy8zM5Nt27bRr18/6tWrB8DatWvJy8tj2LBh6OmJqeIEQRAEoTjluM8ExsbG3L17t8T1V69e1XTrFoTqxL2eA2517ABo3r4ODi6lmwe5sl29ekmTPDds2PSZk2dBEIRHkpKSCAwMZO7cuURERGiWnz9/nq+//pphw4aRkZFRhREKgiAIQvVVrgS6Y8eObNiwgaCgoELLCwoK+PXXX9m+fTs9evSokAAFoSJZ2dVg7u/j+G77VD5YNAwjU4OqDqmIy5cvcPy4eg7WJk2a0bp1e5E8C4JQYb799lvi4+OZN28eXl6Pe70sWrSIxYsXEx4ezg8//FCFEQqCIAhC9VWuLtzTpk3j9OnTzJgxg7lz5+Lk5EReXh4xMTFkZ2dTr149pkyZUtGxCtWABFAp1fMyv6hMzY0wNTeq6jCKdfHiWU6fPg6At3cLmjdvJZJnQRAq1PHjxxkxYgR9+/YttFwqleLv78/Vq1c5ePAgH3zwQdUEKAiCIAjVWLkSaHNzc7Zt28aKFSs4dOiQpju3i4sLPXr0YPTo0ejoVJ/5eoWKkR2fwfHlR0kMTaLDOx1w9HWp6pBeKufPn+bs2ZMA+Pj40qxZS5E8C4JQ4R48eICTk1OJ611dXYmPj6/EiARBEAThxVHu0myGhoa8/fbbvP322xUZj1CNhR8LIzT4DgC7P9nNGzveRMtIt4qjevGpVCrOnTvF+fOnAWjevDXNmrWo4qgEQXhZ2dracvHiRQYPHlzs+qtXr2JlVT3rQwiCIAhCVSvXGOhHEhIS2LBhA4sXLyYqKoqkpCSuX79eUbEJ1YyOgbbmZ0MLAyQlTCkllJ5KpeLMmROa5NnXt61IngVBeK66detGUFAQGzdu5L8zWW7bto3t27fTtWvXKopOEARBEKq3cj+BXr16NQsXLiQ/Px+JRIKvry95eXlMmDCB4cOH88knn1RknEI14NahFp3yFGQlZ+LRsRYyfe2n7ySUSKVScerUUS5fvgBA69btadTIu4qjEgThZTd+/HgOHz7MrFmzWLRoETVr1kQikRAVFUV6ejru7u5MmjSpqsMUBEEQhGqpXI8QDx8+zNy5c2nSpAmfffaZ5g62m5sbTZo0Ye3atWzdurVCAxWqnpahDnX7N8RnXCtq1BLd+56FSqXixIkjmuS5bduOInkWBKFSGBgYsGnTJiZPnoytrS1hYWGEhIRgYWHBuHHjWL9+PUZG1bPQoiAIgiBUtXI9gV6xYgV169Zl5cqVpKenM2vWLABq1qzJ6tWrGTp0KOvWraN///4VGqwgvAxUKhXHjgVz/foVANq370K9eg2rOCpBEF4lenp6TJo0qcQnzQ8ePMDCwqKSoxIEQRCE6q9cT6Bv3LhBz549kclkRdZpaWnRp08fIiMjnzU2QXjpqFQq/v77oCZ57tixq0ieBUGoNk6fPs20adPo2LFjVYciCIIgCNVSucdA6+qWXH05Pz8fuVxe3qYF4aWkVCo5cuQAISE3kEgkdOzYjTp16lZ1WIIgvOLS09PZunUrGzZsIDIyEpVKhb6+flWHJQiCIAjVUrkSaE9PTw4fPsyIESOKrFMoFOzevZtatWo9c3CC8LJQKpUEB+8nNPQWEomEzp274+npVdVhCYLwCrty5Qrr1q1j37595OXloVKpsLKyYvjw4QwZMqSqwxMEQRCEaqlcCfSIESN4//33+fLLL+nevTsA2dnZXL16lW+//ZabN2/yxRdfVGiggvCiUiqVHDy4l7t3byORSPDz64mHh2dVhyUIwisoOzuboKAgNmzYQEhIiKYIqEQiYcKECUycOBFtbTHDQnVzNKLsvfrauZb9K15FH0ciAUtLY5KTM/jPjGllVp7YKkt7t9Jfa4kEDoeV/VwkkjLvQjl2QVmOv1N59ikPrXKcUHled2W91pVxDKF6KVcCHRAQQEhICCtWrGDt2rUATJ48GVCP8QwMDCQwMLDiohSEF5RCoeDgwT2Ehd1BKpXi59cTd3fRO0MQhMoVEhLC+vXr2blzJ9nZ2ahUKlxdXenZsyeNGzfmzTffpH79+iJ5FgRBEISnKPcY6Pfff59u3bqxa9cuIiMjUSqVODo60q1bN3x9fSsyRkF4ISkUCv76axcREWFIpTK6d++Fi4t7VYclCMIrZsiQIVy5cgWVSoWDgwNDhw6lZ8+eeHmph5Hcv3+/SuL66KOPSpzyct68efTv35/09HQ++eQTjh49ioWFBa+99hqjRo0qsv3AgQOpU6eO6P0mCIIgPHflSqAXLlxIhw4daNasGQ0bigrCgvBfCoWcfft2ERUVjkwmo3v33jg7u1Z1WIIgvIIuX76MoaEh48aNY+TIkRgYGFR1SID6qbiTkxNTpkwpsq5p06YAfP311xw7doyJEycSHx/PnDlzsLa21gwfA9i7dy937txh+fLllRa7IAiC8OoqVwK9Zs0azMzMaNasWUXHIwgvPLm8gH37dhIdHYlMJqNHjz7UrOlS1WEJgvCK6t+/P/v27WPJkiUsX76cJk2a4O/vT7du3ahRo0aVxCSXy7l79y5du3alT58+xW6jVCoJCgpixIgRjBs3DoCrV6+yefNmTQJdUFDAN998w6hRo7Cxsam0+AVBEIRXV7kSaENDw4qOQxBeCgUFBezdu4OYmGi0tLTw9++Lo2PNqg5LEIRX2Ny5c/nf//7Hnj172LJlC2fPnuXcuXN8+eWX+Pr64uPjg6SSK9pERESQn5+Pp2fJBRUfPnxIXl4ezs7OmmXOzs7cunVL8/vGjRtJS0tj7NixzzVeQRAEQXikXAn0u+++y9dff42FhQW+vr5YWFggk8mKbCeVSp85QKF6kkjUVVuVlVV68QVQUJDP7t3biY2NQUtLm169+mFv71jVYQmCIKCvr8+AAQMYMGAAUVFRbN68mR07dnDs2DGOHz8OwKZNm7CysqqUoVkhISEAmikvc3Jy0NHRKfRdwszMDJlMRlpammZZSkoKFhYWAGRlZfHdd98xYcIEjI2Nn3vMgiAIggDlTKB/+eUXcnNzmTlzZonbSCQSbt68We7AhOorKzmL4BVnuR+SSLeJrXD1carqkKpcfn4+u3dvIy7uPtraOvTq1Q87O4eqDksQBKEIZ2dn3n33Xd555x2OHj3Kli1bOHz4MEeOHOHvv/+mbt26DB069LnOpvEogf7777/54osviI2NRVtbm3bt2vHhhx9Ss2ZNtLS0aNGiBZs3b6Zdu3YkJCRw9uxZ3n77bQBWrFiBnp4ew4YNe25xCoIgCMJ/lSuBtrS0xNLSsqJjEV4Q14PvcHTtRQBWTt3BJ/vHomusW8VRPRu5vICcnJx/nmKUrStjXl4eu3ZtJSEhDh0dXQIC+mNjY/d8AhUEQaggUqmUDh060KFDB1JSUtixYwdbtmzhxo0b/O9//3uuCfTt27cBdYGziRMnYmZmxsWLF/njjz+4ePEimzZtwsnJiU8++YTx48drxkl36dKF0aNHk5SUxG+//casWbPQ0dGpkJiepRf7o32r09yulRXLk45THa/L81CW83vZr4VQvbyMr7eK+Kx+VuVKoP/444+KObrwQpJpPe5ip6OnhURaNe/OxPhkbt+MxMnZFleP8neVjo9P4NNP5nDy5Hk++uhthg4fVOrhB3l5uezcuZXExHh0dXUJCBiAtbVtuWMRBEGoCjVq1GD06NGMHj2aq1evlji9VEXp1asXDRs2ZPz48ZoE2M/PjyZNmjBlyhQWLVrEkiVLcHd3Z8+ePdy5cwcjIyNq1lTXlPj222+pWbMmvXv3Jisrizlz5nDs2DGMjIwYMWIEw4cPL3NMFhbP3g28ItooVnhKmXextCxHLM/pOBVyXcoRW2Up87W+W33PRXi5lOtzoLKU8z393D5ny6DMCXRGRgZyubzKKncKVa9ep1oEpOWSHJ1K83710TGsmLv/ZZH6MJ33Ji4i5EYkhkb6rNgwG0vLeuVq6/ixk+zffwSAmTPn0rZ9K82XtCfJzc1h584tJCUloqenR0BAIFZW1uWKQRAEobpo2LDhcx8H3bdv32KXd+3aFTs7O824bABtbW3q1q2r+T08PJwtW7bwww8/IJVK+fLLLzl58iRff/01cXFxfPzxx5iamtKrV68yxfTgQQaqcpb1kEjUX+qepY2KlpycUeXHqY7X5Xkoy7V+GZ8ICtVXZX0OVKaK+Kx+VqVOoE+cOMH8+fO5c+cOAI6OjkyaNKnE/wSFl5e+mR7txvhUaQwJ8cmE3IgEICszh4g792nhW74E2tz88c0gS0tzDA2fPkdqTk4OQUGbefAgCT09fXr3DsTS0qpcx68OcjJyuX4iEm1dGfVau6KtU67OKYIgCM/MwsKCxMTEEtcvXrwYb29v2rVrh0KhYOfOnUyYMAFfX18AgoKC2LZtW5kTaJWKZ07yKqKNilJZcZTmONXpujwPL/O5CS+2l/G1WR0+T0r1LfnSpUu89dZbKBQKPDw8kMlkhIWF8dFHH5GXl8fgwYOfd5yCUIiDozWt2jbi5LEr2Nhb4FnXpdxttW3Xmu++n09EZBQd2rfBwuLJ4/uzs7MJCtrEw4cP0Nc3oE+fQMzNX9yaABIJ7PjhBDt/PQ3AW3N70S6wURVHJQjCyyo5OZkxY8bg6urKsmXLCq0rKCggKioKJ6fii1NeunSJgwcPsmnTJkBdlbugoKBQXRZzc3NNkTJBEARBqGilSqBXrFiBiYkJv/76q6Yb1f3795k4cSLfffedSKCFSmdkYsTnCycRFRGLjZ0lNnYW5W5LW1ubXgE9SrVtVlYmQUGbSUl5iIGBIX36DKRGDfNyH7u6uPz3Xc3P926X/ORHEAThWVlYWJCfn09wcDC3bt3Cy8tLs+6nn34iIyODcePGFbvv119/Tffu3WnQoAGgTpZ1dHS4d++eZpvo6GhsbV+uWhTtXCunV1BlHedohLxSjlNZT6mOhJftfMrTi7s6P0gsT7f08vxtyjNzanmudVmPU66/ZzV9bUL5zqc8r4Gyft5Up+EPpYr8ypUrDB8+vNAYJAcHB9555x0mTJjAvXv3SrxbLAjPi4mZMQ2a1K6042VmZhAUtJnU1BQMDY3o02cgZmYvfi0AlQr6jG/Dd+/tQEtHRtNOtao6JEEQKklOTg7x8fHY2tqiq6tb6gKKz0IikTB79mzGjh3LqFGjGDZsGNbW1pw+fZq//vqLFi1aMHr06CL7HTx4kGvXrvHVV19plkmlUnr27MnatWsxNTUlISGBa9eusXDhwud+HoIgCMKrqVQJdEpKCvb29kWWe3l5oVKpSExMFAm08FLLyMhgx46NpKenYWRkTJ8+AzE1NavqsCqMb0Bd3BrYoa0jw9zetKrDEQThObt37x5ffvklJ06cQKFQsHLlSiQSCXPmzGH27Nl4e3s/1+P7+vqybt06vvvuO/78809ycnJwcnJi2rRpvPHGG0WmplIoFCxevJiBAwfi7OxcaN3HH3+MSqXil19+wcjIiI8++oiAgIDnGr8gCILw6ipVAi2Xy9HSKrrpo//g8vPzKzYqQahG0tPTCAraTHp6GiYmpvTuHYiJycuWZEqwcXnxu6ILgvB0cXFxDBo0iKysLJo2bcq5c+cAUCqVRERE8Oabb7Ju3Trq1KnzXONo0KABP/74Y6m2lclk7Nmzp9h1xsbGzJ8/vyJDEwRBEIQSPf++WoLwAktLS2X79o2a5LlPn0EvYfIsCMKrZNmyZeTl5bFt2zaWLFmC6p/BeK1atWLz5s3o6Ojwww8/VHGUgiAIglA9iQRaEEqQmprC9u0byczMwMysBn37DsLYuOonbxcEQXgWx44dY+jQobi7uyP5T1WWOnXqMGTIEC5fvlw1wQmCIAhCNVfq8mfnz59HoVAUWpaVlQWo54hOSEgoso+YI1p4UaWkPGTHjk1kZ2dRo4Y5ffoMxMDAsKrDEgRBeGapqalFxhH/m729PSkpKZUYkSAIgiC8OEqdQG/cuJGNGzcWu27FihWFflepVEgkEpFACy+khw+T2bFjMzk52ZibW9C790AMDAyqOixBEIQKYWtry927d0tcf/nyZaytrSsxIkEQBEF4cZQqgZ48efLzjkMQqoXk5CSCgjaTm5uDpaUVAQGB6OvrV3VYgiAIFcbPz4/169fTq1cvzQwaj7pyBwUFERQUxLBhw6oyREEQBEGotkQCLQj/SEpKJChoM3l5uVhZ2RAQ0B89PZE8C4Lwcpk4cSJHjhxh2LBhmnHQS5cu5fPPPyciIgJbW1smTJhQ1WEKgiAIQrUkiogJApCYGE9Q0Cby8nKxtrald+8BInkWBOGlZGxszIYNGwgMDCQ+Ph6VSsWlS5eIj48nICCADRs2YG4uprUTBEEQhOKUegy0ILysEhLi2LlzK/n5edjY2NGrV390dXWrOixBEITnxsTEhNmzZzN79mwePnyIUqnE3NwcqVTcVxcEQRCEJxH/UwqvtLi4+wQFbSE/Pw87OwcCAgaI5FkQhJfaqFGjOHXqlOZ3c3NzLC0tNcnzwYMH6d69e1WFJwiCIAjVmngCLbwQUlNTkUmlGJuYVFib9+/fY/fu7cjlBTg4OOHv3xdtbe0Ka18QBKE6yMnJKTQt1dmzZ/Hz8yt2KiulUsnx48eJjY2tzBAFQRAE4YUhEmih2jtx/AyT3voYPX1dfvr1Kxo1bvDMbcbERLNnz3bkcjmOjs706NFbJM+CILyUMjIy6NmzJ7m5uZplc+fOZe7cuSXu07hx40qITHhVtXMt+9fPv8PlZd7nn+LyZaJSlX0faRmPU65jlH0XlOXYR6Es+0VTVsI1K+9xKoNMWvbAynH6lOf0laqyH0kmqZzzORpR9vf0AMtyHOg5EAm0UK1JpRKWfbOC7OwcsrNz2Lp57zMn0NHRkezduwOFQkHNmi50794bLS3xVhAE4eVkbW3Nxx9/zJkzZwDYuXMnjRs3xtHRsci2MpkMCwsLRowYUdlhCoIgCMILQWQNQrWmUqmoX782ly7eAMDZpegXvrKIigpn376dKBQKnJ3d6N69FzKZeBsIgvByCwwMJDAwEIALFy7w5ptv0rlz5yqOShAEQRBePCJzEKo1lQomTH4ND09XdHS08evWvtxtRUSEsX//LpRKBa6u7nTt2guZTFaB0QqCIFR/wcHBVR2CIAiCILywRAItVHuWVpYMHd7/mdoIC7vDgQO7USqVuLt70qVLD5E8C4LwStq+fXuptuvbt+9zjUMQBEEQXkQigRZeenfv3ubAgT2oVCpq1apN5849xFyngiC8sj788EMkpaiuJBJoQRAEQSjqhU6gIyIiWLJkCadPnyYrKwsnJycGDx7MqFGjCiVIsbGxLFmyhJMnT5KRkUHt2rV56623ioz/iouLY+bMmVy4cAEHBwemTJmCv79/oW3y8vLo3r07gwcPZvz48ZVynkL5hYbe4tChfahUKjw9vejUqZtIngVBeKV98cUXRZYpFAqSk5PZt29fidsIgiAIgvACJ9AxMTEMGTKE7OxsRowYgZOTEwcOHGDevHmEh4fz+eefA5CUlMSIESNITU1l5MiR2NjYsHnzZiZOnMjChQsJCAjQtDlz5kxCQ0N57733uHz5MtOnT8fR0ZGGDRtqtvnjjz+Qy+W89tprlX7OQtmEhNzg8OG/UKlU1KlTjw4d/ETyLAjCK2/gwIElrhs7diwDBw7k2LFjNGnSpBKjEgRBEIQXwwubTaxcuZLU1FTmzJnDjBkzGDZsGCtXrqR58+Zs2LCBsLAwAJYvX879+/f55ZdfeOeddxg2bBh//vknnp6ezJkzh+zsbAASEhI4efIkEydOZNSoUSxYsABLS0u2bNmiOWZaWho///wzkydPRl9fv0rOWyidmzevERy8H5VKRd26DejYsatIngVBEJ5CV1eX/v37l3qctCAIgiC8al7YjCIqKgqAjh07apZJJBJNt+yQkBAUCgVBQUE0atQIb29vzXZ6enqMHDmSlJQUjhw5AkB8fDwAzs7OgHouTEdHR2JjYzX7/fjjj1hYWGimAhGqp+vXr3DkyAEA6tdvRPv2XUo13k8QBEFQ//+XnJxc1WEIgiAIQrX0wibQbm5uANy5c6fQ8sjISABsbGy4c+cO2dnZNGrUqMj+j7plX7lyBQALCwsA0tPTNdukpKRolsfGxrJmzRqmT58uqjdXY1evXuLo0UMANGzYlLZtO4nkWRAEoZQe/V/n6OhY1aEIgiAIQrX0wo6BHjduHCdOnGDmzJnMmjULJycnjhw5wqZNm2jVqhXe3t4cPXoUADs7uyL729raAuqx1AD29va4uLiwatUqvLy8uHTpEpGRkUydOhWApUuXUq9ePfz8/CrpDIWyOnXqFMeOHQagceNm+Pq2FcmzIAjCf3To0KHY5fn5+aSmpqJUKvn4448rNyhBEARBeEG8sAm0lZUVU6dOZebMmYwePVqzvGnTpixfvhyJREJGRgYABgYGRfZ/NIY5JycHAKlUyty5c5kyZQrdu3cHYMiQIfj7+xMSEkJQUBB//PFHhcX/LHndo31FbvjYpUvnOHnyGADe3s1p0aL1K588i9dJ8cR1KUpck6Iq4ppU1+tZUj0IIyMj3Nzc6Nu3rxiqJAiCIAgleGET6J9//plFixbh6OjI9OnTsbGx4erVq6xatYohQ4awYsUKVCpVifs/WvfvJMvb25vg4GBCQ0OxtrbWPKVesGAB7du3p1mzZiQnJ/PZZ59x4cIFLC0tmThxoibhLgsLC+My7/M82ngZHD16VJM8t2/fnvbt27/yyfO/iddJ8cR1KUpck6JexmsSHBxc1SEIr7ijEfIy79PerexfWctznPJ8e3jC183ity/HMeTKyvleI5OWPTqFouyxyZVl3qXSKMr691SUfUSslqTs11lWjoG3BeW4ztJyvAv0tMp+PtIX+Kv6C5lAZ2Zm8t1332FpacmmTZswNzcHoEuXLrRs2ZLXX3+dr776il69egGQm5tbpI1HT55NTEwKLdfT0ys0bdWpU6c4deoUO3bsAOCdd94hLS2N5cuXc+HCBaZNm8aff/5J06ZNy3QODx5klPkD9xGJRP2l7lnaeBmoVCrOnTvFuXOnAXVBuXr1mvDgQWYVR1Y9iNdJ8cR1KUpck6Iq4po8akMQBEEQhJfHC5lAR0REkJubS58+fTTJ8yOtWrWiZs2aHD9+nPHjxwOPK2z/W0JCAvB4LHRxVCoVCxYsoE+fPtSqVYu4uDjOnj3L/Pnzadq0KU2bNmXjxo1s3769zAm0SlX2O5bPo40XlUql4syZE1y8eBYAX9+2tGvXjuRkkQD816v8OnkScV2KEtekqJfhmixdurTM+0gkEt5+++3nEI0gCIIgvNheyARaV1cXAKWy+H4JCoUCpVKJm5sbxsbGXL16tcg2j6pvPynx3b17N3fv3uX7778HIDExEQBLS0vNNubm5sTFxZXvRIRyUalUnDp1jMuXzwPQunV7Gjf2fspegiAIr6YffvihzPuIBFoQBEEQivdCJtAeHh44ODiwf/9+JkyYgIODg2bdoUOHiImJoUePHmhpaeHv78/GjRu5ePGiJlnOzc1lzZo1WFpa0q5du2KPkZ+fzzfffMPIkSM1T6kf/Xvv3j1Anajfv38fT0/P53m6wr+oVCpOnPibq1cvAtCmTUcaNmxSxVEJgiBUX6tXr67qEARBEAThpfFCJtBSqZQ5c+Ywbtw4AgMDGTx4MHZ2dty4cYMtW7ZgbW3NBx98AMCUKVMIDg5m7NixjBkzBnNzczZv3sydO3dYvHix5mn2f61bt46MjAzeeustzTIbGxuaN2/O8uXLKSgo4MqVKyQnJ9O3b9/KOO1Xnkql4tixYK5fV/ceaNeuM/XrF53jWxAEQXisefPmVR2CIAiC8H/27jssiqttA/i9NKmKFLEAdlCMoAj2EsWK2Auo2AN2TTTWqEmMGl9jizVq7FhBUVQsqLGbqFGBGImgooIdRUH67nx/8O3qugvuwi71/l2XV8LMmTNnHpY5+0w5h0qMYplAA0CzZs0QGBiItWvXYs+ePUhKSoK1tTX69u2L8ePHw9raGkD2dFe7d+/G0qVLsX37dmRmZsLR0RHr169HmzZtlNadnJyMdevWwd/fX2GQsSVLluCHH37Ar7/+CisrKyxZsgSNGvHxYW0TBAHnzp3Cv/9GAgC+/LIDnJzqF3KriIiKr+vXryM0NBRxcXEwMDBApUqV0LlzZ/ZpREREuRAJuc31RFqTn8GuRCLAysqs1AyYJZFIcPZsGKKibgMA2rXrhDp16smVKW0xUQVjohzjoogxUaSJmEjrKIoWLlyIHTt2KEz3KBKJMGjQIMyePbuQWlZ42C9rXk5xycv0Uq2rF8w0VgXx+ytp01hl5GEaq6L8d6L2NFZ5mPapaE9jpb6CmsaqT+PyRaJfLrZ3oKl0kEgkOHPmBO7evQORSAQPj85wcKhb2M0iIiq2QkJCsH37dri7u2PChAmoXbs2JBIJ7t69i7Vr12Lnzp1wdXWFp6dnYTeViIioyGECTUWWRCLBqVPHEBPzH0QiETp08EStWo6F3SwiomJt165dcHJywpYtW6Cn9+FrQLNmzeDu7o7+/ftj165dTKCJiIiUyMtdeiKtE4vFCAs7ipiY/6Cjo4OOHb2YPBMRacB///0HLy8vueRZSk9PD15eXoiKiiqElhERERV9TKCpyBGLxTh58iju3YuGjo4OOnXqhpo1axd2s4iISg2xWKz1ffz333+YOHEimjZtii+++AJt27bF/Pnz8e7dO1mZd+/eYeLEiWjQoAE8PDxynJKrX79+mDNnjtbbTERExEe4qUgRi7Nw4sQRxMbeh66uLjp37oaqVWsUdrOIiEoMR0dHHD16FEOHDoWurq7cOrFYjCNHjqB2be1etLx//z58fHygp6eHgQMHolKlSrh16xZ27tyJP//8E3v37oWJiQkWL16MCxcuYOzYsXj27BkWLFiAChUqoHPnzrK6jh07hujoaKxevVqrbSYiIgJ4B5qKkKysTBw7FiJLnrt06cHkmYhIwwYOHIjbt2/Dz88P169fR2JiIhITE3H9+nV89dVXuHPnDnx8fLTahvnz5yMzMxN79uzBN998Ax8fHyxatAgzZ85EdHQ0AgICIJFIEBISggEDBsDf3x9z585F/fr1ERQUJKsnMzMTy5cvx5AhQ2BjY6PVNhMREQG8A01FRGZmJo4dO4S4uEfQ09ODp2dP2NraF3aziIhKnO7duyM8PBw7d+7ElStX5NYJggBvb2/07t1ba/tPT0/HtWvX4Obmhpo1a8qt69mzJxYsWICrV6+iT58+SE9PR9WqVWXrq1atijt37sh+3rdvH96+fQs/Pz+ttZdy99mpou6/UVhUUFNS5YUoD1PrFMSUTHmZXkqch6mvCmq6LHWnigIAiaB+2/Kyn+Qs9faTl+mY8nIsefnN5OWjWU5f/a2EPPx5GuVh6quiggk0FbrMzEyEhh5EfPxj6Onpo2vXnqhSxa6wm0VEVGLNmTMHXbp0wfHjx/Ho0SMIggB7e3t06tQJjRs31uq+9fX1ERoaColEcYLSV69eAQB0dXVhbm4OXV1dvH37Vrb+zZs3sLS0BAC8f/8ea9aswZgxY2BmVjTn2yYiopKHCTQVqoyMDBw9GoynT+Ohr28AL69eqFSpSmE3i4ioxHNzc4Obm1uB71dHRwd2dsovkm7cuBEA0KRJE+jp6aFJkyYICgpC69at8fz5c1y9ehUTJ04EAGzatAmGhoYYOHBggbWdiIiI70BTocnISMeRIwfw9Gk8DAwM0K1bbybPREQFICEhAefOnZP9fOrUKfTt2xc+Pj4ICwsrlDYdOHAABw4cQKVKleDt7Q0AmD17NgRBQI8ePeDv7482bdpg2LBhePnyJbZs2YKJEyfCwMBAI/sXifL3TxN1FMd/BRVrIiKgcM5Zn+IdaCoU6elpOHz4AF68eIYyZcqgW7c+qFChYmE3i4ioxIuJicHAgQNhY2ODNm3a4OHDh/j6668hCAL09PQwadIk/P7772jevHmBtSkoKAhz586FsbExVq5cCVNTUwBAzZo1ERoaiujoaJiamsLePntsjFWrVsHe3h7du3fH+/fvsWDBAly4cAGmpqbw9fXFoEGD1G6DpWX+HwPXRB3FjpJ3nD/HyioPccrDfoio5CkK51km0FTg0tJScfjwfrx8+QJlyhiie/e+sLauUNjNIiIqFdasWQOJRIJx48YBAA4ePAixWIytW7eiXr16GDRoEDZt2lRgCfTKlSuxZs0amJqaYv369XB2dpZbr6+vDycnJ9nP9+/fx/79+7Fu3Tro6Ohg/vz5uHz5MhYvXoynT5/iu+++Q7ly5eDl5aVWOxISkvI8GJRIlP2lLj91lCavXiUVdhOIqJjSxLk6v5hAU4FKTU1FSEgQEhJewtDQCN2794WVlXVhN4uIqNS4du0aBg8eLJtL+cKFC6hUqRKaNGkCAOjVqxfWrl2r9XZkZmZi9uzZOHjwICpUqICNGzeiTp06n91u2bJlaNSoEVq3bg2xWIzDhw9jzJgxaNasGQAgJCQEwcHBaifQgpD/0ZQ1UUdpwBgRUV4VhfMsE2gqMCkpKQgJCcLr169gZGSMHj36wsLCqrCbRURUqrx9+1Y2iNe7d+/w77//okePHrL1JiYmyMzM1GobxGIxJk+ejJMnT8LBwQEbNmxApUqVPrvdzZs3cerUKQQGBgLIHpU7MzMTVlYf+hILCwtERUVpre1ERFS6MYGmAvH+fTJCQoLw5s1rGBuboEePfihf3qKwm0VEVOpUrFgRT548AZB991kQBLRo0UK2PjIyEhUqaPe1mhUrVuDkyZNwdnbGpk2bULZsWZW2W7x4MTp37oz69esDyE6WDQwM8PjxY1mZR48eoWJFjqlBRETawQSatC45OQkhIUFITHwDExNT9OjRD+bm5Qu7WUREpVLjxo2xY8cOGBkZYe/evTA0NESbNm2QlJSEvXv34sCBA3kahEtVjx8/xubNmyESidChQwf88ccfCmUsLS3RsmVLuWWnTp1CZGQkFi1aJFumo6ODrl27YufOnShXrhyeP3+OyMhILFmyRGvtJyKi0o0JNGlVUlISDh3ah3fv3sLU1Aw9evRDuXLmhd0sIqJSa8qUKbhz5w5++eUX6OrqYs6cOTAzM8O1a9ewZMkSODo6YtSoUVrb/4ULF5CVlQUAWLp0qdIyrq6ucgm0WCzGsmXL0K9fP1StWlWu7HfffQdBELBx40aYmppi5syZ6Natm9baT0REpZtIEAr7NezS6dWr/I0gZ2Vllq86CsK7d28REhKEd+/ewsysLHr06IeyZctpZV/FJSYFiTFRjnFRxJgo0kRMpHUURRKJBP/++y8qVKgge1w7MTERFy5cQIcOHWBoaFjILSx4paFf1obzD7LU3qZ1dfXv3+RlPwVF3d95Xj4iedlGLFF/0tu87EeSh42yJHnZj/rHI85D25Kz1NuPTh7mFs5LzPIyhXFefp/l9NXfSk9H/W2M9NTfpl+T8kWiX+YdaNKKt28TcehQIJKTk1C2bDn06NEfZmZF84skEVFpo6Ojgy+++EJumbm5Oe/cEhERfQYTaNK4xMQ3OHQoEO/fJ8PcvDy6d+8LU1Mmz0RERUVSUhJ+//13hIWF4dGjR9DT00PVqlXh6emJ4cOHw8DAoLCbSIVA03eTNXlnvijftRapeWuwoJ5SyMvdV9083ObMy93k9DzcHc/LXduMPOwnOVNHrfKpYvX38VbNu9wAkJGHOBvqqr9NHjaBY1n1Z27Iyx3oooIJNGnUmzevcehQIFJS3qN8eQt0794XJiamhd0sIiL6fy9fvsSAAQMQFxcHMzMz1K5dG4Ig4PHjx1i+fDnCwsKwbds2mJiYFHZTiYiIihwm0KQxr1+/wqFDQUhNTYGFhSW6d+8LY2N+ASMiKkpWrFiBJ0+eYPbs2fDx8YGeXvZXgYyMDGzbtg1Lly7F6tWrMX369EJuKRERUdGj3jMKRDl49eolDh0KRGpqCiwtrdGjR38mz0RERdDZs2fRr18/+Pr6ypJnADAwMICfnx969uyJo0ePFmILiYiIii4m0JRvr169QEhIIFJTU2FtXQE9evSFkZFRYTeLiIiUSE5ORp06dXJc7+rqirdv3xZgi4iIiIoPJtCULy9ePMOhQ4FIS0tDhQo26NatLwwNmTwTERVVzs7OuHTpUo7rIyMjc02wiYiISjMm0JRnz58/RUjIfqSnp8PGptL/J8+lb+5QIqKiTCKRyP2bNm0aLl++jPnz5+P169eycsnJyVi/fj1CQ0Mxc+bMQmwxERFR0cVBxChPnj6Nx5EjwcjMzEClSlXQtWsvTntCRFQEOTk5QfTJPDuCIGDnzp3YuXMnypUrBx0dHbx9+xYSiQSGhob45ptv8McffxRSi4mIiIouJtCktidP4nDkSDCysjJRubItunbtBX19/cJuFhERKeHu7q72Np8m3ERERJSNCTSpJS7uEUJDDyIrKwu2tvbo0qUHk2cioiJsx44dKpdNTk7GoUOHsHfvXi22iIiIqPhiAk0qe/QoFseOHYJYLIa9fTV07twNenpMnomIirs7d+5g9+7dOHLkCFJTUwu7OUREREUWE2hSycOH93H8+GGIxWJUrVodnTp1k5s/lIiIipf09HQcPXoUe/bsQWRkJARBAAA0b94cw4YNK9zGERERFVHMgOizYmPv4fjxI5BIxKhevSY6dvSCrq5uYTeLiIjy4P79+9izZw8OHTqEd+/eyRLn9u3bY+LEiXBwcCjkFhIRERVdTKApV/fvR+PkyaOQSCSoWbM22rf3ZPJMRFTMZGVl4eTJk9izZw+uXbsGQRCgo6MDd3d3NGnSBKtXr0avXr2YPBMREX0GE2jKUUzMfwgLC4UgCKhVyxHt23eBjg6nDiciKk6WLVuGAwcO4NWrVwAAZ2dneHp6wtPTExUqVEB8fDxWrVpVyK2k3Jx/kKX2Nq2rq/8VLy/b5EVRPp68tO3/H+JQmUTN8gCgk4eB8fMyln4empYneWlbSpb630HfZ6m/p8hk9W4Uxaarv4/09PdqbyNOz8P4FOJM9bdR9wMN4GxZK7W3GWlXfG/IMYEmpe7evYPTp49DEAQ4ONRFu3admDwTERVDGzZsgImJCSZMmICePXuiSpUqhd0kIiKiYosJNCmIivoXf/xxAoIgoE6devjyyw5MnomIiil7e3s8evQI69atw8WLF9GiRQt06dIFNWvWLOymERERFTtMoEnOnTv/4I8/TgIAnJzqo02b9hCJ8vKgDRERFQUnT57EtWvXEBQUhJMnT+LmzZtYs2YNHB0d0bVrV7i4uBR2E4mIiIoNJtAkc/t2BM6dOwUA+OILF7Rq1Y7JMxFRCeDu7g53d3fMnTsXR48exf79+xEeHo7//vsPACASiRAeHo6WLVuiTJkyhdxaIiKioovP5RIAIDLypix5dnZuyOSZiKgEMjExQf/+/bF3714cPXoUw4YNg6WlJQRBwMaNG9G6dWv873//w8OHDwu7qUREREUSE2hCePjfuHDhDwBAgwaN0KLFl0yeiYhKuJo1a2L69Ok4d+4cVq9ejS+//BLv37/Hli1b4OnpWdjNIyIiKpL4CHcpd/PmNVy5cgEA4OraGE2atGDyTERUiujq6qJ9+/Zo3749EhISEBwcjODg4MJuFhERUZHEO9Cl2PXrf8mSZze3pkyeiYhKOUtLS3z11Vc4evRoYTeFiIioSOId6FJIEARcu3YF16//CQBo3Lg53NyaFnKriIiIiIiIijYm0KWMIAj4669LuHHjKgCgadOWcHVtXMitIiIiIiIiKvqYQJcigiDgypULuHXrOgCgefM2aNCgUSG3ioiIiIiIqHhgAl1KCIKAS5fOISLiBgCgZcu2cHZuWMitIiIiIiIiKj6YQJcCgiDg4sU/EBl5CwDQurUHvvjCpXAbRURERCppXb1kfV0racfTpobqxyMSAWfvZWmxNR/o5GFcWImg/jaZEvV39D6rYAatfdV+osgAAMv3SURBVJ6u/njJ/71LUau8OOWd2vsweBWr9jY6egZqb2P06r7a2+i/uKP2Nsh4r/Ym22u3V3ubCW27qb2NNpSsMxgpEAQB586dxr//RgAAvvyyA5yc6ist+/r1awTuO4CXL1+hV+9uqFevXkE2lYiIiIiIqEhjAl2CSSQSnD0bhqio2wCAdu06oU6dnJPiQ4eO4KeflgAAzp69iKOhQShTxrBA2kpERERERFTUMYEuoSQSCc6cOYG7d+9AJBLBw6MzHBzq5rpNenq67P8TE99BIpFou5lERERERETFBhPoEkgikeD06WOIjv4PIpEI7dt7onZtx89u5+XVGbf/uYOYmAeY+/10GBkZF0BriYiIiIiIigcm0CWMWCzGqVPHcO/eXejo6KBDB0/UrOmg0ra2tnZYvWY5BEECoGAGdyAiIiIiIioumECXIGKxGCdPHsWDBzHQ0dFBp05eqF69llp1CIIAJs9ERERERESK1B/bvQiIi4uDo6Njrv/atWsnK9+uXbscy8XFxcnKRUVFoV+/fnB2dkbPnj1x5coVhX0nJCSgYcOGOHz4cIEcq6rE4iycOHH4/5NnXXTu3F3t5JmIiKigvHnzBj/99BPatm0LZ2dndO/eHUFBQXJl3r17h4kTJ6JBgwbw8PDA9u3bldbVr18/zJkzpyCaTUREpVyxvANtYWGBxYsXK113+PBhXLhwAR07dgQAJCUlIT4+Hl9++SU8PT2V1gVkvzf89ddfQxAETJ8+HWfOnMHo0aNx/PhxVKpUSVZ+9erVqFatGry8vLRwZHmTlZWF48dD8OhRLHR1ddGlSw/Y21cr7GYREREplZKSghEjRiA6OhoDBw5EjRo1cOzYMXz33Xd49eoVRo8eDQBYvHgxLly4gLFjx+LZs2dYsGABKlSogM6dO8vqOnbsGKKjo7F69erCOhwiIipFimUCbWxsjB49eigsj4qKwuzZs+Hm5oZvv/0WAPDff/8BADw8PJRuI3Xr1i08ePAAGzZsQJs2bdC9e3c0bdoUhw8fhr+/PwAgNjYW+/btw/r16yESFY3HnDMzM3HsWAji4h5CT08Pnp49YWtrX9jNIiIiylFAQAD+/fdfLFmyBN26dQMA9O/fH1999RVWr16NHj16wMbGBiEhIfD19ZX1wxEREQgKCpIl0JmZmVi+fDmGDBkCGxubQjseIiIqPYrlI9zKSCQSzJw5EwCwcOFC6OllXxuQJtC1a9fOdftnz54BAOzts5NPMzMzlC9fHk+ePJGVWbZsGRo3boyWLVtqvP15kZmZidDQg/+fPOuja9deTJ6JiKjIO3jwIKytreWe5tLR0cHIkSORmZmJw4cP4/Xr10hPT0fVqlVlZapWrSrXL+/btw9v376Fn59fgbafiIhKrxKTQB84cAD//vsvRo4cKdfZRkVFAfiQQL9///7/B8qSZ2lpCSD7fSsge0CupKQk2fLw8HCEhYXJ7mwXtvT0dBw+fADx8Y+hr6+Pbt16o0oVu8JuFhERUa6SkpJw//59ODs7KzzN5eLiAiD7TrO5uTl0dXXx9u1b2fo3b97I+uX3799jzZo1GDNmDMzMzAruAIiIqFQrEQl0ZmYmVq1aBXNzc4Wr0FFRUTAzM8PixYvh7u4OV1dXNG7cGPPnz0dKSoqsXL169WBubo4NGzbg8ePHWL9+PdLS0tCqVSsAwC+//IIuXbqgXr16BXpsymRkpGPnzp14+jQeBgYG6NatDypVqlLYzSIiIvqs58+fQxAEufFFpExNTWFiYoK4uDjo6emhSZMmCAoKQlRUFM6dO4erV6/K+uVNmzbB0NAQAwcOLOhDICKiUqxYvgP9qePHj+PZs2eYMGECTExMZMslEgmio6ORmpqKV69eYd68eRCLxQgLC8OOHTsQGRmJHTt2wMDAAKamppg/fz6mTZuG9u3bQ1dXF19//TUaNGiAP/74A7du3cKxY8c01ub8vEJ95coFPH78GGXKlEG3bn1gY1NRY+0qrqTxLCKvphcJjIlyjIsixkSRJmLCeCqXlJQEIHs8E2WMjIyQmpoKAJg9ezZGjx4tG8Okffv2GDZsGF6+fIktW7bg+++/h4GBgUbapYnfNX/n8kpLXNQ5vpIeCyJtKwr9colIoHfu3IkyZcrA19dXbnlGRgbGjx8PExMTDBgwQLbcy8sL8+fPx44dOxAUFCS7et2hQwdcuHAB9+7dg52dHSwsLCAWi7F06VL4+PjAzs4ODx8+xA8//IA7d+7A3t4eU6ZMQZMmTdRus6Vl3h83q1WrBt6+fYMuXboovYJfmuUnriUVY6Ic46KIMVHEmGiesteoPl0vfbS7Zs2aCA0NRXR0NExNTWXjlKxatQr29vbo3r073r9/jwULFuDChQswNTWFr68vBg0apHa7NPG75udFueIUlz5W2t9HX0vt74PyQ/nFvZzl5RdaPQ/b5EWLAtpPwSkK55Nin0A/f/4ct27dQvv27WFubi63ztDQEF999ZXS7YYOHYodO3bg4sWLco9/mZqayt7BArLfrX7y5AnGjh2LrKws+Pn5oXLlytiwYQOOHDkCPz8/HD16FHZ26r1/nJCQhM98h8iRrW0NuLi4ICEhCa9eJeWtkhJGJMr+g8pPXEsaxkQ5xkURY6JIEzGR1kHypE+KSe8yfyo1NRW2trayn/X19eHk5CT7+f79+9i/fz/WrVsHHR0dzJ8/H5cvX8bixYvx9OlTfPfddyhXrpza001q4nfNvyF5jIsixkQRY6KIMVFUlPrlYp9Anzp1CoIgKJ3jOTcfD0KSk7S0NKxatQojRoyAhYUFrl+/jocPH2LhwoVwdnZG3bp1sWfPHoSGhmLUqFFq7V8QkO8/CE3UUdIwJooYE+UYF0WMiSLGRPOqVKkCkUiE58+fK6xLSkpCSkoKKlbM+dWkZcuWoVGjRmjdujXEYjEOHz6MMWPGoFmzZgCAkJAQBAcHq51As1/WHsZFEWOiiDFRxJgoKgoxKfaDiF29ehUikUg2qMjHzp07h86dO2Pz5s0K62JiYgBAbsTuT23btg0SiQQjRowAALx48QLAh+RbX18fZcuWxdOnT/N9HERERKWBqakpatasicjISIV14eHhAABXV1el2968eROnTp3C1KlTAWSPyp2ZmQkrqw/P3VpYWLBfJiIirSn2CfQ///yDatWqKZ3ColatWnj06BF27tyJ5ORk2fKsrCz8+uuvAIBevXoprffNmzfYuHEjxo4dKxvoxMbGBgDw+PFjANlXyt+8eZPrlXIiIiKS1717dzx9+hRHjhyRLZNIJNi8eTMMDAzQtWtXpdstXrwYnTt3Rv369QFkJ8sGBgayfhkAHj16xH6ZiIi0plg/wp2RkYG4uDi0bNlS6foqVapg3LhxWLlyJfr27Yv+/ftDJBLh8OHDuH37NkaPHo2GDRsq3XbdunWwtLRE//79ZcucnZ1RrVo1LFiwAI8fP8aZM2egq6ubY0dPREREioYOHYqQkBDMmDEDt2/fRvXq1REaGoorV65g2rRpsLa2Vtjm1KlTiIyMxKJFi2TLdHR00LVrV+zcuRPlypXD8+fPERkZiSVLlhTk4RARUSlSrBPoN2/eAADKli2bY5lx48ahevXq2LZtG3799Vfo6OjAwcEBy5YtyzHxffz4MXbt2oVffvkFenofQqSvr4+1a9fihx9+wJIlS2Bvb4/ffvtN7QHEiIiISjNDQ0Ps2LEDy5Ytw6FDh/D+/XtUr14d//vf/9CzZ0+F8mKxGMuWLUO/fv0UXr367rvvIAgCNm7cCFNTU8ycORPdunUroCMhIqLSRiR8bj4J0opXr/I3gpyVlVm+6ihpGBNFjIlyjIsixkSRJmIirYOKB/bLmse4KGJMFDEmihgTRUWpXy7270ATERERERERFQQm0EREREREREQqYAJNREREREREpAIm0EREREREREQqYAJNREREREREpAIm0EREREREREQqYAJNREREREREpAIm0EREREREREQq0CvsBpRWIlH+t81PHSUNY6KIMVGOcVHEmCjSREwYz+KF/bLmMS6KGBNFjIkixkRRUeqXRYIgCJqpioiIiIiIiKjk4iPcRERERERERCpgAk1ERERERESkAibQRERERERERCpgAk1ERERERESkAibQRERERERERCpgAk1ERERERESkAibQRERERERERCpgAk1ERERERESkAibQBSwuLg6Ojo65/mvXrp2sfLt27XIsFxcXJysXFRWFfv36wdnZGT179sSVK1cU9p2QkICGDRvi8OHDBXKs6nrw4AEmTZqEJk2a4IsvvkCXLl2wdetWSCQSuXJPnjzBtGnT0LJlS7i4uKB///44ffq0Qn1Pnz7F8OHD4ezsjC5duiA0NFShTHp6Otq2bYvffvtNa8eVH6rGZPDgwTl+Tv766y9ZuZIQk+joaIwdOxZubm5o1KgRBg8ejHPnzuVYrlmzZmjYsCGGDh2Kv//+W6FcSfjbUTUmJf18Eh4eDicnJ1y+fFlhnarnDQC4ceMGhg0bBnd3d7i5uWH06NGIiYlRKPfXX3+ha9euaNCgAQYMGIA7d+4olLl79y6cnJyUfvaoaGC/nDP2y4rYLytiv6yI/XK2ktoviwRBEPK8NaktJSUFYWFhStcdPnwYFy5cwPDhwzFjxgwkJSXBzc0NX375JTw9PRXKd+jQAcbGxpBIJPD09IQgCBgyZAjOnDmD69ev4/jx46hUqZKs/I8//ohbt27hwIEDEIlEWjvGvIiLi0OfPn2QkpICX19f2NnZISwsDJcvX4a3tzfmzZsHAHj58iW8vb2RmJiIwYMHw8bGBkFBQbh9+zaWLFmCbt26yeocPnw47t69i1GjRuHWrVsIDQ3Fvn374OzsLCvz+++/Y9u2bTh58iSMjIwK/Lhzo2pMAKBx48aoVq0aBg0apFBPixYtYGVlBaD4xyQyMhJDhgxBZmYmfHx8UL16dZw5cwYXL17ErFmzMHToUADAvXv34OPjgzJlymDgwIEwMTHBzp078eTJE2zevBmNGzcGgBLxt6NqTEr6+SQ2Nha+vr54+fIltmzZgubNm8vWqXPe+OuvvzBy5EhUqVIF/fr1g0Qiwfbt25Gamop9+/ahZs2aAIDk5GR4eHjA3t4ePXv2xIEDB/D8+XOcOHECJiYmsvr8/f2hp6eHtWvXFlwwSC3sl5Vjv6yI/bIi9suK2C9nK9H9skBFwp07d4QvvvhCGDhwoJCZmSkIgiBcu3ZNcHBwEPbu3Zvrtn///bfg4OAgnD17VhAEQXj37p3g5OQkrF+/XlbmwYMHgpOTk3DhwgXtHUQ+/Pjjj4KDg4Nw6NAh2TKJRCL4+voKDg4OQkxMjCAIgjB37lzBwcFBuH79uqxcamqq4OXlJTRp0kR4//69IAiC8OzZM8HBwUEICAgQBEEQsrKyhBYtWghz586VbZeYmCi4u7sLe/bsKYhDVJuqMXny5Ing4OAgLFu2LNf6SkJMevXqJTg4OAhnzpyRLZNIJMKkSZOEevXqCffv3xcEQRBGjhwp1K9fX3j06JGsXEJCgtC8eXPB09NTtqwk/O2oGpOSfD45efKk4O7uLjg4OAgODg7CpUuX5Naret6QSCSCp6en0KJFC+HNmzeysg8ePBDq168vfPXVV7JlISEhgoODgxAdHS0IgiBER0cLDg4OwtGjR2Vl/vzzT6Fu3bqyv1UqXtgvs1/+FPtlReyXFbFfLvn9Mh/hLgIkEglmzpwJAFi4cCH09PQAAP/99x8AoHbt2rlu/+zZMwCAvb09AMDMzAzly5fHkydPZGWWLVuGxo0bo2XLlhpvvyY8fPgQANC2bVvZMpFIBA8PDwDZj66IxWKEhITAxcUFjRo1kpUzNDTE4MGD8ebNG5w9exbAh5hUrVoVAKCrqwtbW1u5mPz222+wtLRE3759tXpseaVKTAD1PyfFNSZPnz7F7du30bhxY4WYjB49GpmZmThw4ABevXqFCxcuwMPDA3Z2drJyFhYW6Nu3L2JiYhAeHg6g+P/tqBoToOSeT/z9/TF+/HhYW1vDy8tLYb06543IyEjExMSgZ8+eMDc3l5WtVq0aOnTogIsXL+LFixcAsmMPfIiT9L/x8fEAAEEQ8Msvv6B3796yq+NUfLBfZr+sDPtleeyXFbFfLh39MhPoIuDAgQP4999/MXLkSNlJFPhwIpb+Yb1//x6CkifuLS0tAQDv3r0DkP3BTEpKki0PDw9HWFgYvv32W60eR37UqFEDQPY7Ix+LjY0FANjY2CA6OhopKSlwcXFR2F76qJP0BPxpTADgzZs3suVPnjxBQEAAJk+eDF1dXc0ejIaoEhNA8XOSkpKi8C4WUPxjIj0xOjo6KqyT/t38888/ss9AXj4nxe1vR9WYACX3fHL//n1MnjwZwcHBqFatmsJ6dc4bt27dAgA0aNBAoayLiwskEgkiIyMBQPb4pTROb968AfAhfseOHUN0dDQmTJiQ94OjQsN+mf2yMuyX5bFfVsR+uXT0y0ygC1lmZiZWrVoFc3Nz+Pn5ya2LioqCmZkZFi9eDHd3d7i6uqJx48aYP38+UlJSZOXq1asHc3NzbNiwAY8fP8b69euRlpaGVq1aAQB++eUXdOnSBfXq1SvQY1OHv78/atasiVmzZuHKlSuIi4tDQEAAAgMD0bx5czRq1AjPnz8HALn3PaQqVqwIALIBFypXroxq1aph27ZtePDgAQ4cOIDY2Fi0bt0aAPDrr7+iXr166NChQwEdofpUiQnw4QQcFBSEFi1aoGHDhmjUqBGmTZuGhIQEWX3FPSbGxsYAsjuYT0lPki9fvpRdqVXlc1Lc/3ZUjQlQcs8noaGhGDVqFAwMDJSuV+e8If3sSJfnVtbd3R36+vpYvXo1Hj9+jHXr1sHAwABNmjRBZmYmVqxYgSFDhsi+UFPxwX45G/tlReyX5bFfVsR+uXT0y3r5roHy5fjx43j27BkmTJgg94K7RCJBdHQ0UlNT8erVK8ybNw9isRhhYWHYsWMHIiMjsWPHDhgYGMDU1BTz58/HtGnT0L59e+jq6uLrr79GgwYN8Mcff+DWrVs4duxYIR7l51lbW2PSpEmYNWsWhg0bJlvu6uqK1atXQyQSISkpCcCHk9PHpINqpKamAgB0dHSwcOFCTJgwAZ07dwYA+Pj4wNPTE1FRUQgJCcGOHTu0fFT5o0pMgA+PAEVFRWHatGkoU6YMLl26hMDAQNy8eROBgYEwNzcv9jGpVasWypUrh3PnziEpKQlmZmayddLPd1paGpKTkwEo/5wYGhoC+PA5Ke5/O6rGpCSfT3LqoKXUOW+o89mxs7PDzJkz8fPPP2P37t0oU6YMfvzxR1SpUgUBAQF4+/Yt/P3983hUVJjYL2djv6yI/bI89suK2C+Xkn45X29QU755e3sL9evXl3sxXhCyX6TfuHGjsGvXLoVtfvrpJ8HBwUHYuXOn3PKkpCTh1q1bQkJCgiAI2YNRdO3aVfjpp58EQRCE2NhYYdiwYUKTJk2Efv36CX/++ad2DioP1q9fLzg4OAjt2rUTAgIChLCwMGHp0qWCs7Oz4OXlJTx//lw2OMDu3bsVtk9NTRUcHByEESNGKCwPDw8Xnj59Kls2YsQIYdSoUYIgCMLLly+F8ePHC82aNRO6desmHDt2TLsHqgZVYiIIgrB9+3Zh/fr1gkQikdt+8+bNgoODg7B48WK55cU5Jlu2bBEcHByEvn37ClevXhUePXokbN++XWjYsKHg6uoqdOrUSVi3bp3g4OAgnD9/XmH7mJgYwcHBQZgzZ47c8uL8t6NKTErL+WTlypUKg5Woc96YPXu24ODgINy7d0+h7NmzZwUHBwe5QVsEQRBev34t3Lp1S3j79q0gCNlxa9q0qbB582ZBEAQhPDxc8Pb2Fpo0aSIMHjxYuHPnjsaOl7SD/XI29suK2C8rYr+siP3yByW1X2YCXYiePXsmODo6CuPGjVNru0ePHgkODg7CmDFjci23b98+oWHDhkJCQoKQmZkpdOjQQRg6dKgQHh4uLFiwQGE0xMKSlJQkODs7C82bN5edFKQuXbokODo6Ct98841w+vRpwcHBQdiyZYtCHa9fvxYcHByEr7/+Otd9Xb58Wahbt65w9+5dQRAEwdfXV+jWrZvw999/Cxs2bBAcHR2Fv//+W2PHlleqxiQ36enpQt26dYXu3bvnWq64xETqt99+E5ydnWUjO7Zs2VI4deqU0LFjR8Hb21vYvn274ODgIJw8eVJh28jISMHBwUH45Zdfct1HcfnbkfpcTHJTks4nyjpqdc4bP//8s+Dg4CDcvn1boezx48cFBwcHpV92PrZ8+XKhbdu2Qnp6uvD27VvBzc1NmDx5shAeHi588803QrNmzYR3797l70BJa9gvZ2O/rIj9cs7YLytiv5ytpPbLfAe6EJ06dQqCICid+y030pfhlb1fIZWWloZVq1ZhxIgRsLCwwK1bt/Dw4UOMHz8ezs7OmDp1KoDs9xQK24MHD5CWlgYPDw9YWFjIrWvevDns7e1x8eJF2NraAvjwPsTHpO9TKHtHQkr4/9H3evTogdq1a+Pp06e4evUqRowYAVdXV/j5+cHOzg4HDx7U3MHlkaoxyY2BgQHKli2b6+ekOMVEatSoUbh8+TJ2796N4OBgnD17Fq1atUJ8fDzs7e1z/Zzk9h6WVHH625H6XExyU9LOJ59S57yR38/OixcvsHXrVkycOBEGBgY4c+YM3r17h2+//RbOzs6y9x/PnTuXv4MirWG/nI39siL2yzljv6yI/XLOSkK/zAS6EF29ehUikUg2CMDHzp07h86dO2Pz5s0K62JiYgBAbmTQT23btg0SiQQjRowAANkQ79I/Sn19fZQtW1Y2WmBhKlOmDAAoHaESyB5tUCKRoEaNGjAzM0NERIRCGelofa6urjnu5+jRo4iJicGkSZMAfIiJdNQ+IHtKheIUkzt37qBr165YuHChQpmEhAS8efMm189JcYoJkP3+0KlTp2BiYgJXV1c4OTlBV1cXV65cQWZmJho3boz69etDR0dH6edEuqxhw4Y57qM4/e0AqsWkNJ1PPqXOeUM6+mdOnx2RSKR01FCp1atXw97eHt27dwegGCfpl+6iGCfKxn45G/tlReyXlWO/rIj9cu5KQr/MBLoQ/fPPP6hWrZrcAANStWrVwqNHj7Bz507ZC/QAkJWVhV9//RUA0KtXL6X1vnnzBhs3bsTYsWNlL91LR5x7/PgxgOwX+N+8eZPrleGCUqtWLVSpUgUnTpyQzdUmdfr0acTFxaFly5bQ09ODp6cnbty4gRs3bsjKpKWlISAgAFZWVrKRKz+VkZGB5cuXY/DgwbJjlv5XGhOxWIz4+PhiFZNq1arh+fPnCA4Olrs6JwgCli5dCgDo3bu30n0Ut5gAwK5duzBz5kwkJibKliUnJ2PlypWoUKECvLy8YGVlhebNm+PkyZOy4wCA169fY//+/ahTpw6cnJyU1l/c/nYA1WJSms4nn1LnvOHs7Izq1asjKChILp6xsbEICwtD27ZtUb58eaX7uXfvHoKCgjBlyhTo6GR3rZ/+PT169EhuORU97JezsV9WxH5ZOfbLitgv564k9MschbuQZGRkyE62ylSpUgXjxo3DypUr0bdvX/Tv3x8ikQiHDx/G7du3MXr06Byv1q1btw6Wlpbo37+/bJmzszOqVauGBQsW4PHjxzhz5gx0dXXRtWtXrRyfOnR0dLBgwQL4+/ujb9++8Pb2RqVKlXD79m3s378fFSpUwLRp0wAAEyZMwJkzZ+Dn54fhw4fDwsICQUFBiI6OxrJly2RXiD+1e/duJCUlYdSoUbJlNjY2aNy4MVavXo3MzEyEh4fj1atX6NmzZ0Ecdq5UjYmRkRFmz56NGTNmoH///hgwYABMTU1x6tQp/Pnnn+jWrVuOv+PiFhMAGDt2LEaOHInBgwejf//+EAQBgYGBiI2Nxdq1a2UjMk6fPh3e3t4YMGAAhg0bBgMDA+zcuRPv3r2TdUzKFLe/HUC1mJSm84ky6pw35s6dCz8/P3h7e2PQoEFIT0/Htm3bYGRkJHskTplly5ahUaNGaNOmjWxZ69atUa5cOcyYMQO9evVCcHAwLCws5MpQ0cF++QP2y4rYLyvHflkR++XPK/b9slpvTJPGPHv2TKXBNY4ePSr0799fcHZ2Fho0aCD0799fOHLkSI7lHz16JNSrV08IDQ1VWBcTEyP4+voKDRo0ELp37y73Qn9RcOfOHWHChAlCkyZNBCcnJ6FNmzbC3LlzhRcvXsiVe/TokTBp0iTB3d1daNCggeDt7S2cPXs2x3qTkpKEJk2aCBs3blRY9+zZM2H06NGCq6ur0LFjR+Hw4cMaP678UDUmly5dEoYMGSI0aNBAqF+/vtCjRw8hICBAEIvFSustzjG5dOmSMGjQIKFRo0ZCkyZNhFGjRgnh4eEK5f7991/hq6++Eho2bCg0atRIGDp0qHDz5s0c6y3OfzuqxqSkn0+UDVYipc5548qVK8LAgQMFZ2dnoUmTJsLYsWOFmJiYHPf7999/C46OjkpjfuPGDaF3796yff7zzz95P0DSKvbLitgvK2K/rIj9siL2y9lKar8sEgRBUPuyAREREREREVEpw3egiYiIiIiIiFTABJqIiIiIiIhIBUygiYiIiIiIiFTABJqIiIiIiIhIBUygiYiIiIiIiFTABJqIiIiIiIhIBUygiYiIiIiIiFTABJqIiIiIiIhIBUygiYiIiIiIiFTABJqoAHXs2BGOjo5YsmRJvut6/vw5UlJSNNCq3LVr1w6tW7dWufzBgwfh6OgIFxcXvH37VostIyIiyh/2y0SkLibQRAXk6tWrePjwIYyNjXHgwAFkZGTkua6goCB07twZr1+/1mALNWP//v0wNjZGWloagoODC7s5RERESrFfJqK8YAJNVED2798PXV1djBw5EgkJCQgLC8tzXX/++WeBXOVW1+PHj3Ht2jV4e3ujXLly2Lt3b2E3iYiISCn2y0SUF0ygiQpAcnIyTpw4gfr166NHjx4AgD179hRyqzRv//79EAQBLVq0QLt27XD//n389ddfhd0sIiIiOeyXiSivmEATFYDQ0FCkpqaiRYsWsLOzg7OzM65evYp79+4plH306BFmzJiBVq1awcXFBV26dMFvv/0me7SsXbt2OHz4MADAw8MDgwcPBgDMmDEDjo6OePjwoVx9Dx8+hKOjI2bMmCG3/MqVKxgzZgyaN2+OevXqwd3dHUOHDsXly5fzdIwSiQQHDx6EoaEh3Nzc0KVLFwDA7t27lZYXBAF79+5Fnz590LBhQzRr1gyjRo1CZGSkXLn3799j2bJl6NSpE5ydndG2bVvMnTsXL168kJUZPHgwHB0dkZWVJbft5cuX4ejoiFWrVsmWtWvXDiNHjsRvv/0GNzc3uLq6YseOHQCAFy9eYMGCBbJ9OTs7w9PTE2vWrFGo+3PtWr16NRwdHREaGqpw7CdOnICjoyMCAwNVDS8REWkQ+2VF7JfZL5Nq9Aq7AUSlwf79+wEAnp6eAICuXbsiIiICu3fvxuzZs2Xl7t69iwEDBkAikcDHxwdVq1bFtWvXsHz5ckRFRWHFihWYNWsWfv/9d9y8eRMzZ85E7dq11W7PiRMnMGnSJDg5OcHf3x8mJiaIjo5GYGAg/Pz8cPDgQbXrvXTpEp4+fYrOnTvDyMgIzZs3h7m5OU6dOoWEhARYWlrKlf/uu++wf/9+uLm5YdKkScjMzERAQAAGDx6MHTt2oH79+khNTYWPjw/u3r2Lbt26YdiwYXj8+DF27tyJv/76C4GBgShbtqzax3/jxg1ER0dj4sSJSExMRLNmzZCUlARvb2+8e/cOAwcOhL29PRITE3Hw4EGsXLkSaWlpmDJlCgCo1K5evXphzZo1CAkJkf3epYKDg2FsbKywnIiICgb7ZfbLH2O/TGoRiEirYmJiBAcHB8HLy0u27NmzZ0KdOnUENzc3ISUlRbZ8yJAhQr169YQ7d+7I1TFjxgzBwcFBtnzKlCmCg4OD8PjxY1mZ6dOnCw4ODkJsbKzctrGxsYKDg4Mwffp02bKePXsKLVq0EN6/fy9XNiAgQHBwcBB+//132bK2bdsKrVq1+uxxTpo0SXBwcBBOnDghWzZnzhzBwcFB+O233+TKXr16VXBwcBAmT54sSCQS2fKHDx8KTk5OwtixYwVBEITVq1cLDg4Owq5du+S2Dw4OFhwcHIStW7cKgiAIvr6+goODg5CZmSlX7tKlS4KDg4OwcuVKueNxcHAQLl++LFd227ZtgoODg3D8+HG55W/fvhXq1asndO3aVbZM1XYNHz5ccHJyEhISEmRlXr16JTg5Ocn9PoiIqOCwX2a/zH6Z8oOPcBNpWVBQEADAy8tLtszGxgZubm549+4djh49CgB48+YNrl69ipYtW6JOnTpydXz77bcICQlBjRo1NNKmwMBAhISEwNjYWLYsIyMDOjrZp4Tk5GS16ktMTMTp06dhamqKNm3ayJZLj3nv3r2QSCSy5SdOnAAADB8+HCKRSLbc3t4eQUFB+P7772XlypYti/79+8vtr2vXrggMDETv3r3VaqeUvr4+3N3d5ZYNGTIEly9fRocOHeSWv379GmZmZnIxUbVdffr0QVZWFo4cOSIrc/jwYWRlZeW57URElD/sl9kvs1+m/OAj3ERalJWVhZCQEACAi4sL4uLiZOuaNGmCq1evYs+ePejbty/i4+MhkUhQvXp1hXosLS0VHrXKDz09PTx58gRr167FvXv3EB8fj7i4OFln+nGnqorDhw8jIyMDLVu2xMuXL2XLK1WqBHNzc8THx+PChQuyTlwaB2VfPOrWrSv7/8ePH6NGjRrQ1dWVK6Ovrw9nZ2e12vgxc3Nz6Okpnv50dHSwefNmhIeH49GjR3j8+DHev38PIPvLlbrt6tChA8zNzXHw4EEMGTIEQPZ8nHZ2dgpfFIiISPvYL7NfZr9M+cUEmkiLzp49i1evXgEAhg4dqrRMZGQkbt++LRsM4+Mrv5rw6SAbALB06VJs2LABtra2cHNzQ7NmzWSDfYwdO1btfUjfJTtz5gzOnDmjtMzu3btlHXVmZiaAzx9rVlZWvuKh7NgByK7ofywiIgIjRoxAVlYWmjZtilatWqF27dpwdXXF4MGD5b68qNouAwMDeHl5ISAgAPfu3UNmZibu3LmDiRMnavz3TEREn8d++QP2y+yXKW+YQBNpkbQD++qrr9CwYUOF9QcOHMDp06exZ88eTJo0CQDw4MEDhXLR0dFYu3Yt+vbtixYtWijdl/Sqq3RUUCnpFwWpJ0+eYOPGjXB1dcW2bdtgYGAgWye9Kq+Of//9F3fu3EHlypXx3XffKaxPSUnB9OnTcf78eTx9+hSVKlWCra0tgOxjdXJykiu/YsUKJCUlYfbs2bC1tcWjR48gkUjkOlexWIwpU6bAzc0Nvr6+csf+8RXsT489N8uWLUNycjJCQkLg4OAgW56ZmYk3b96gXLlysmWqtgvIflwsICAAx44dg1gsho6ODnr16qVyu4iISHPYL7NfZr9M+cV3oIm05NWrVzh//jzKli2L8ePHo3379gr/pJ3zkSNHYGhoiIYNG+LixYsK02gEBAQgNDQUZmZmAD50yoIgyMpUqFABAPDPP//IbXvo0CG5n9++fQtBEFC9enW5Tjo1NVU2bYRYLFb5OKVfRnx8fJQeY/fu3dG+fXuIxWLs3bsXAGTvM23fvl2ursePH2PLli149OgRRCIROnTogLdv3yI4OFiu3IkTJ3Ds2DGkpqbmeOwSiUQ2rYgq3rx5A0NDQ1StWlVu+Y4dO5CWliYXE1XbBQBOTk5wcnLCqVOncPr0aTRt2hSVK1dWuV1ERKQZ7JfZLwPslyn/eAeaSEsOHjyIrKws9OrVC0ZGRkrLODo6onnz5rh8+TIOHTqEuXPnwtfXF/3798egQYNQuXJlXL16FUePHkW/fv1k7/FI37v6/fff0apVK1mHuH79esyfPx/x8fGwtrbGH3/8gZiYGOjr68v2WatWLVStWhUHDx6EsbExHB0d8eLFCwQHB8vek3r37p1Kx5iRkYEjR45AX18f/fr1y7HcsGHDcPLkSQQFBWH8+PFo2bIlunfvjuDgYDx79gweHh5ISUnBrl27oKenJ5sb09/fH6dPn8bs2bPx999/w9nZGbGxsdi9ezfq1q2LQYMGAQB69eqFQ4cOYfLkyRg2bBiMjIxw9OhRJCYmqnQcQPbcnWvWrMHw4cPh5eUFQRBw/vx5nD17FoaGhkhOToYgCBCJRCq3S6pPnz746aefAABLlixRuU1ERKQ57Jc/YL/MfpnyofAGACcq2bp06SI4OjoK9+/fz7Xc2bNn5abTiImJEb7++muhSZMmgrOzs+Dl5SUEBATITQURHx8veHt7C/Xq1RM6duwoW37hwgXB29tbqF+/vtC4cWNhypQpwrNnz4QGDRrITc/w8OFDYcKECUKzZs0EZ2dnoX379sL06dOFR48eCa1atRLatWsnm8Yit+kyjh49Kpv24nP69u0rNx2FWCwWtm3bJnh5eQlffPGF0KJFC2HixInCvXv35LZ7+/atsHDhQqFt27ZCvXr1BA8PD+Hnn38WEhMT5cqFhIQIPXr0EL744guhefPmwo8//ig8fPhQ6XQZyo4nMzNTWL16tdC+fXtZe3x9fYWwsDBh2bJlgoODg/Dnn3+q3S5BEITExEShfv36QqNGjYTU1NTPxoqIiDSP/bI89svslylvRILw0bMmRESkce/evUPLli3Ru3dv/PDDD4XdHCIiolKN/TLlB9+BJiLSsl27diE9PR0+Pj6F3RQiIqJSj/0y5QffgSYi0pJvv/0WKSkpOHPmDDp16oQ6deoUdpOIiIhKLfbLpAlMoImItCQhIQE3btxA27ZtZYOVEBERUeFgv0yawHegiYiIiIiIiFTAd6CJiIiIiIiIVMAEmoiIiIiIiEgFTKCJiIiIiIiIVMAEmoiIiIiIiEgFTKCJiIiIiIiIVMAEmoiIiIiIiEgFTKCJiIiIiIiIVMAEmoiIiIiIiEgFTKCJiIiIiIiIVMAEmoiIiIiIiEgFTKCJiIiIiIiIVMAEmoiIiIiIiEgFTKCJiIiIiIiIVMAEmoiIiIiIiEgFTKCJiIiIiIiIVMAEmoiIiIiIiEgFTKCJiIiIiIiIVMAEmoiIiIiIiEgFTKCJiIiIiIiIVMAEmqiYkkgkhd2EYqk4xq04tpmIqDjg+TVvimPcimObqWhiAk3Fwl9//QVHR0el/+rWrYuGDRuiU6dOmD59OiIjIwu7uVi7di0cHR0xY8YM2bK4uDg4OjrCyckpX3U/fvwYEyZMwM2bN+WWDx48GI6Ojjh06FC+6tc0abs+/VevXj24urrC09MTs2fPxq1bt5Rur+24fU67du3g6OiI69evy5bNmDEDjo6OWLt2bb7apIqwsDD4+fnJLdNUTIiIirqePXvC0dERjRo1wvv37zVWb177BE1Rt89mX5o/7EtJk/QKuwFE6urWrZvcz4IgIC0tDVFRUTh48CAOHz6MhQsXomfPnoXTQC0bPnw4Hj9+jKFDhxZ2U9Ti4OAAR0dH2c8SiQTJycmIiYlBYGAggoKCMHToUMycOVMr+y+Ocbt+/TrGjx8Pe3v7wm4KEVGBi4iIwJ07d1CmTBkkJycjODgYvr6+Gqm7OPYJAPvSvGBfSprGBJqKnSVLlihdLpFIsGTJEmzatAk//PADWrduDQsLiwJuXc5sbGwQGhoKkUiUr3pyegTpf//7H1JTU2FjY5Ov+rWlY8eOmDBhgtJ1hw8fxg8//ICtW7fC0NAQ33zzjWydtuP2OVu3bkVmZiaqVKmSr/3nhVgsVrpcUzEhIirK9uzZAwDw8/PD6tWrERAQgEGDBmnk3FdcH+dlX6o+9qWkaXyEm0oMHR0dfPPNN7CwsEBqair++OOPwm6SHH19fdSsWRM1atTQSv2VK1dGzZo1YWpqqpX6talbt25YsmQJRCIRfv/9dzx48EC2Tttx+xx7e3vUrFkThoaGhbJ/ZQo7JkRE2pacnIzQ0FCUKVMGw4cPh4ODAx48eICLFy8WdtOKLPal6insmFDxxQSaShR9fX3Z1c1Xr17Jljs6OqJFixaIjo5G//798cUXX6Bly5bYv3+/rMy7d++wcuVKeHl5wcXFBa6urvDx8cG+fftyvHoZERGBCRMmoEWLFnBxcYGPj0+OiXtu79pkZGRg27Zt6NevH9zc3ODu7o7evXsjICAAGRkZAIADBw7A0dER8fHxAIBBgwbB0dERf/31F4Dc36e6ePEiRo8ejWbNmsmOffLkyfjnn38Uyq5atQqOjo7YvXs3/vnnH4wZMwZNmjRB/fr14eXlhQ0bNsjapElt27bFl19+iaysLOzcuVO2PKe4ZWVlYevWrfD29kbTpk1Rv359eHh44LvvvsO9e/dk5VSNW3h4OL7++mu4uLjAzc0N33//PQDl72197NSpU+jfvz9cXFzQpEkTjB8/HhEREQrlPveu16f7GTx4MIYMGQIAePToERwdHdGuXbtcYwIAaWlp2LBhA3r37o2GDRvCxcUFXl5e+PXXX/Hu3bsc95uamopdu3ahV69eaNCgAdzc3DBy5Ej8+eefSttLRKRNhw4dQmpqKtq0aQNTU1P06dMHABAQEJDjNtruSwHI3j1+9uyZ3PK3b99i7dq16N+/P9zd3VGvXj00adIEgwcPxsGDB/MbDpWxL2VfStrHBJpKlIyMDMTGxgKAwmNCqampGDlyJF6+fIkvv/wSenp6qF+/PoDsk2qvXr2wZs0avHnzBk2aNIGrqyuioqIwZ84cjBkzBpmZmXL1HTt2DAMHDsTJkydhY2ODNm3a4PXr1xg9ejSOHTumcpvfvHkDHx8fLFy4EPfu3YOrqytcXV0RGxuLn376CWPHjkVWVhbs7e3RrVs3GBsbAwBatmyJbt26wcrKKtf6Fy5ciJEjR+Ls2bOwt7eHh4cHypcvj6NHj6J///7YvXu30u0uXboEHx8f/PPPP2jYsCGcnZ0RExODpUuXyg2OpkkdO3YEAFy4cOGzZSdOnIiff/4Z9+7dQ/369fHll19CR0cHQUFB6NOnj+zigKpxmzFjBs6fP4+WLVuiSpUqqF279mfbcPToUYwbNw6vX79GmzZtULFiRYSFhcHHxweHDx9W9/DlNG/eHM2bNwcAGBsbo1u3bmjfvn2u27x8+RK9e/fG0qVL8fDhQzRu3BgtWrTAy5cvsXbtWvTs2VP29/GpmTNn4scff4RIJEKrVq1gbm6OixcvYvjw4Th37ly+joWISF179+4FAPTt2xcA0L17dxgYGOD8+fN49OiRQnlt96W5efbsGXr16oVff/0VT548gaurK7788kuYm5vj6tWrmD59OpYvX57n+tXFvvQD9qWkDXwHmkqMjIwM/PDDD0hKSoK5uTm+/PJLufXv379H1apVsXv3bhgaGkIikUBHRwdisRjjx49HXFwcBgwYgBkzZsgeMXr58iXGjBmDc+fO4ddff8W3334LIPvu9pw5c5CZmYmff/4ZvXv3BpD9btCyZcuwceNGldv9008/4fbt23B3d8eqVatQvnx52T58fX1x4cIF7N69G4MHD4abmxvatWuHlJQUjBkzBm5ubrnWvX//fmzbtg1ly5bFunXr5MofOXIE06dPx7x581C7dm2FusLCwjB48GBMmzYNBgYGAIDTp09j7NixOHr0KCZMmIDq1aurfJyqqFWrFgDg4cOHyMrKgp6e8lPUzZs3cfr0aVSrVg1BQUEwMzMDkB3/H3/8EXv27MHatWuxdu1auLm5qRS3Z8+e4eDBg6hataqsrs+JiYnBkCFDMGPGDOjq6gIAdu7ciXnz5mHu3Llo3Lhxnt9JHzNmDFxdXXH58mVYWVnl+O7/x7755hvcu3cPzZs3x4oVK1CuXDkAQEpKCmbNmoVjx45h3LhxOHTokEJsz507hy1btsi+aIjFYkyePBnHjx/HunXr0KZNmzwdBxGRum7duoX//vsPlStXRqtWrQAAFhYW6NixI44cOYKAgADMmjVLbhtt9qWf88svvyA+Ph5t27bFqlWroK+vDyB7kNONGzdi6dKl2LFjByZOnCjrK7SJfekH7EtJG3gHmoqdb7/9Vu7flClTMHLkSNkj2YaGhli8eLHSd4EHDRokS451dLI//mfOnMF///0HR0dHzJkzR+79HGtra9n7RAEBAUhOTgYABAcHIykpCZ06dZIlz9I6p0yZgrp166p0LC9fvsSxY8egr6+PpUuXyjp8ALCyssK0adNQrVo1hUfFVLVhwwYA2VeEP+3ovLy8MGLECEgkEqxfv15hW2tra8ycOVOWPAOAh4cHqlWrBgC4e/duntqUG2knJQgCEhMTcywnjYeVlZXc71lHRwfjx4/H7Nmz1R6ptUOHDrIOX1rX51SrVg3Tp0+X+0I0aNAgtG3bFikpKQgKClKrDflx8+ZNXLt2DWZmZvj1119lsQSyr7ovXrwYVapUQUxMDE6ePKmw/cCBA2UdPgDo6urKRln977//tH8ARET/T3r3uU+fPnLnYh8fHwDZjxN/PKWVtvvSz7GyskKrVq0wdepUWfIMACKRCIMGDQKQfRH/9evXWtn/p9iX5h37UlIFE2gqdg4fPiz379ixYwgPD0flypVl7xrldIVP2Xsuly5dAgA0a9ZM6ZXhatWqoXr16khNTZXNryh9l6Vt27YK5UUikezxqc/566+/IJFI4OLiovTqart27XDixAlMnTpVpfo+9vz5c8TGxkJPTw+enp5Ky0in+rp69arCVWIXFxel8ahQoQKA7EfiNe3jd6tz63RdXV1hYGCA69evo3///ti0aROioqIgCAKsra0xePBguQ5MFXmZB9LLy0vplX3p42HSd8MKgvQz2aZNG5QtW1ZhvYGBAbp06SJX9mMNGzZUWKbN3zURkTJJSUk4duwYdHV1ZY9vS7m7u6NGjRpISkqSe69Ym32pKmbOnInff/8dNWvWlC17//49IiMjcfDgQdkoz9oYP0QZ9qV5x76UVMFHuKnYyc8VPHNzc4VlT548AZA9xcLWrVtz3f7p06cAspNTAKhYsaLScnZ2diq158WLFwCyR9DWNGkbraysYGRkpLSMtJ1paWlITEyUm/br46uuH5Mm1dqYAuTt27cAsjt86aNkytjY2GDFihX47rvvEBERIRtoxNLSEq1atULPnj3RrFkztfat7LPxOTn9nqXv30t/BwVBuq/cPnvSdcrapez4pV9oBEHQQAuJiD5POniYkZGR0vE2UlJSAGQ/4iu9u6vNvlRVDx8+xJ49e3Dz5k3ExsbizZs3ALIvqkvPoQV1LmVfmnfsS0kVTKCpVFF2JVY6wnbDhg1ha2ub6/bShPlzcwbm9L7Rp7KyslQqlxeqnKg/ToLLlCkjt64w5kW8ffs2AKB27dpyj8Ep4+HhgRYtWuD8+fO4cOECrl69itjYWBw8eBAHDx5E//798dNPP6m877wc76cxk5LG/uPH3z8np5HeVaXO7zundhMRFTbp49upqam4cuVKjuXu3buHS5cuoUWLFlrtSz+W03l6586dmD9/PiQSCaytrdGgQQNUr14dderUQZMmTdC2bdsCnXeafWnesS8lVTCBplJP+rhX586dMWzYMJW3uXv3LuLi4pSuV/VqqfSxnpzey8rMzMTevXtRuXJl2ciYqpIe16tXr2RX8z/18OFDANnv9ZiYmKhct7acOnUKANC6dWuVyhsaGqJjx46yR+afP3+O/fv3Y9WqVdi3bx98fHxQr149rbU3p9+z9HNRqVIl2TLpl4qcOndl02KoQ/r7fvz4cY5lpCPXWlpa5mtfRETacOPGDdy9exdmZma4dOlSjgnKlClTcOTIEezYsQMtWrTQaF+a27laemf3Y0+ePMHChQshkUgwZ84cDBo0SC6JTE5OLtDkGWBfmh/sS0kVfAeaSr0mTZoA+NDhfOrt27fw9PTEwIEDcf/+fQDZ0zcAwPHjx5Vuc/r0aZX23ahRI4hEIty6dUvp4CJ///03fvrpJ8yfP1+t5BnIvlterVo1ZGVl5TitlnR6CGkMCtP58+dx9epV6Ovrw9vbO9ey27ZtQ7t27fDbb7/JLbexscHYsWPh4OAA4MPj+dqS0xQhoaGhACD37pj0AsXLly8VykdFRckeS/yYOlfymzZtCiB7BNCkpCSF9RkZGbIBT9R9JI+IqCDs27cPANClS5dc7+5J340+d+4cHj9+rNG+VDpNk7Jz9Y0bNxSW3bp1C1lZWShfvjx8fX0Vztvnz5+X/X9BPMLLvpR9KWkfE2gq9bp06QI7Oztcu3YNCxYsQFpammxdSkoKpk+fjnv37uHdu3eyaZt69eoFa2trXLx4UWHKqg0bNuDq1asq7dvOzg4dOnRARkYGpk6dKhvlGwASEhKwYMECAB9GHgUgGyVclausX331FQDgf//7H/7++2+5daGhodi6dStEIpFshMjCIAgCTpw4IZsibNy4cZ99h7xGjRqIj4/H1q1bER0dLbcuPDwc9+/fh56entwVc3XipqqLFy9i8+bNsp8FQcBvv/2GK1euwNLSUm6EdunI7CdOnJC7sp2QkIC5c+cqrV/a5qSkpM9+8ZLOeZqUlISvv/5a7jhTU1Mxc+ZMxMfHo1q1agpTvBERFbZ3797JLvZKB7jMSdOmTWFvbw+JRIKAgACN9qXSQbCCgoJk7zED2XcdFy9erNAW6Yjfb968wfXr1+XWXb16Ve7x5/T09FyPKz/Yl7IvpYLDR7ip1DMwMMDq1avx1VdfYfv27Thy5Ajq1asHXV1d3Lx5E2/fvoW1tTVWrVolu4pZrlw5LF26FGPHjsWSJUsQHByM2rVrIyYmBjExMWjUqJFCwpqTH3/8EbGxsbh48SLatWsHNzc3pKen4+bNm3j//j1atWqF4cOHy8rXqFED9+7dw9y5c3Hw4EEMHz5c6aiPANCvXz/8+++/2LVrFwYNGoQGDRqgYsWKePDgAaKioqCnp4dZs2YVyFXUkydPyh4ZB7Ifv3r37h3+++8/vHz5EiKRCKNGjcKYMWM+W1erVq3Qp08f7N+/Hz169ECDBg1gZWWFly9f4tatW5BIJJgyZYrcgDLqxE1Vrq6u+N///ocDBw6gZs2aiI6Oxr1792BiYoJff/1VbgRPT09PrF+/Hg8fPkS3bt3QtGlTiMViXL16FRUqVEDjxo0VLrzY2dlBT08Pb968gY+PD+zs7HKdw3L58uUYNmwYLl68iLZt28Ld3R26urr4+++/8ebNG1SpUgVr1qxR630yIqKCcPDgQaSlpcHe3h6NGjXKtaxIJEKfPn2wfPlyHDhwAJMmTdJYX+rt7Y1du3bh0aNH6NSpE9zd3ZGcnIzr16/DyckJDg4OctM4Nm7cGC4uLggPD8eQIUPg5uYGc3NzPHjwAHfv3oW5uTmsra3x8uVLJCQkyOZoziv2pexLqfDxDjQRgDp16iAkJAT+/v6wtLTEtWvXcP36dVSsWBGjR4/GoUOHZHefpZo0aYKgoCD06tULycnJOHPmDHR1dbFo0SLZyKCqsLCwwL59+zB58mRUqlQJly5dwrVr11C1alXMmTMH69evlxsEZPr06WjWrBmSk5Nx4cIFREVF5Vr/999/j99++w2tW7fGgwcPcOrUKSQlJaF3794IDAzEkCFD1AtWHt29e1du+rHjx4/j1q1bsLS0hK+vLw4ePIjJkyerXN9PP/2E77//Hs7Ozrh79y5Onz6NR48eoW3bttiyZQv8/f3lyqsbN1UMHToU//vf/6Cjo4MzZ84gMTERPXr0QHBwMNzd3eXKGhkZYffu3fD19UX58uVx8eJFREdHo1+/fggMDISVlZVC/eXLl8fPP/+MqlWr4vbt27h8+bLcHZFPVaxYEUFBQfj6669hZ2eHP//8E5cvX4aNjQ2+/vprhISE5PvLGxGRNkgf3+7Ro4dK5Xv16gVdXV28e/cOBw8e1FhfamNjg8DAQPTs2RMGBgY4d+4cnjx5An9/f+zYsUNhPBFdXV1s3rwZY8eORbVq1RAeHo7z589DLBZj+PDhOHz4MDp16gQA+OOPP/IdJ/al7Eup8IkEjqlORERERERE9Fm8A01ERERERESkAibQRERERERERCpgAk1ERERERESkAibQRERERERERCpgAk1ERERERESkAibQRERERERERCpgAk1ERERERESkAibQRERERERERCrQK+wGlFYJCUkQhLxtKxIBlpZm+aqjpGFMFDEmyjEuihgTRZqIibQOKh7YL2se46KIMVHEmChiTBQVpX6ZCXQhEQTk+w9CE3WUNIyJIsZEOcZFEWOiiDEpPdgvaw/joogxUcSYKGJMFBWFmPARbiIiIiIiIiIVMIEmIiIiIiIiUgETaCIiIiIiIiIVMIEmIiIiIiIiUgETaCIiIiIiIiIVMIEmIiIiIiIiUgETaCIiIiIiIiIVMIEmIiIiIiIiUgETaCIiIiIiIiIVMIEmIiIiIiIiUgETaCIiIiIiIiIVMIEmIiIiIiIiUgETaCIiIiIiIiIVMIEmIiIiIiIiUoFeYTeAiIiKN5EIEIlEam0jCAIEQUsNIiIiojxhn/55TKCJiCjPRCKgnLkJ9PXUe6ApM0uCt4nvS1WHS0REVJSxT1cNE2giIsozkUgEfT0dzNofjtfvM1TaxsLEAAv7uEAkEkEoLb0tERFRESft06fsvYnEFNX6dHNjAyz1bliq+nQm0ERElG+v32fgZVJ6YTeDiIiI8ikxJQOJKZmF3Ywii4OIEREREREREamACTQRERERERGRCphAExEREREREamACTQRERERERGRCphAExEREREREamACTQRERERERGRCphAExEREREREamACTQRERERERGRCphAExEREREREamACTQRERERERGRCphAExERUb6Eh4fDyckJly9fVlj35MkTTJs2DS1btoSLiwv69++P06dPK63nxo0bGDZsGNzd3eHm5obRo0cjJiZGadng4GD07NkTDRo0QMuWLfHjjz/i7du3Gj0uIiKiTzGBJiIqgUQiQEdHpPI/6TZE6oqNjcW4ceMgFosV1r18+RK+vr44deoU+vTpg+nTpyMrKwtjx47F4cOH5cr+9ddfGDJkCJ4+fYpRo0bB398f//zzD7y9vXHv3j25suvXr8eMGTNgbm6OqVOnolu3bggMDMSQIUOQlpam1eMlIqLSTa+wG0BERJolEgHlzE2gr6feNdKy5iZ4++Y9BEFLDaMSJywsDN99912Od35Xr16N+Ph47Nq1C40aNQIA9O7dG/369cOCBQvg4eEBY2NjCIKAefPmwdzcHHv37oW5uTkAoGPHjujevTsWLVqEjRs3AgCePn2KVatWoVWrVtiwYQN0dLI/53Xq1MG0adOwY8cO+Pn5af/giYioVGICTURUwohEIujr6WDW/nC8fp+h0jYVyhlhXo8vIBKJIDCDJhX4+/vj3LlzqFWrFlq1aoUjR47IrReLxQgJCYGLi4sseQYAQ0NDDB48GHPmzMHZs2fh6emJyMhIxMTEwM/PT5Y8A0C1atXQoUMHhIaG4sWLF6hQoQKOHDmCzMxMDBkyRJY8A0D37t2xdOlSHDhwgAk0ERFpDR/hJiIqoV6/z8DLpHSV/r1+n17YzaVi5v79+5g8eTKCg4NRrVo1hfXR0dFISUmBi4uLwjpnZ2cA2e9OA8CtW7cAAA0aNFAo6+LiAolEgsjISLltPi0rEong7OyM+/fvIykpKY9HRURElDvegSYiIiK1hYaGwsDAIMf1z58/BwBUqlRJYV3FihUBAHFxcQCAZ8+eyS3/XFljY2OULVs2x7Lx8fGoU6eOysdCRESkKibQREREpLbckmcAsrvAxsbGCuuMjIwAAKmpqQCA5OTkHMsaGhrKlU1KSoKJiYnSfUrLpqSkfLb9n8rPIHrSbTkQnzzGRRFjoogxUVRcY6LN9moiJppqHxNoIiIi0rjc3qWXrhP9/7cZVcp+/L6zJsp+ytLSTO1ttFFHScS4KGJMFDEmigorJvp6utDXl6hcFgAsLU212SSZovA5YQJNREREGie9S6xsWinp3WTpY9i5lZUuMzMzk5V99eqV0n1+WlYdCQlJeR6BXiTK/lKXnzpKIsZFEWOiiDFRVFgxEYlEsLQ0RWaWGJmZilMTKpOpn33BMiEhWauDkGoiJtI68osJNBEREWmcra0tgA/vN39M+n609J3lj8s6OTnJlZVuL32X2tbWFrdv30ZycjJMTU0Vyuro6MDGxkbt9goC8v1FVRN1lESMiyLGRBFjoii/MRGJPjzpo1r5/D3jXBC/v6LwOWECTURERBpXo0YNmJmZISIiQmGddCRtV1dXAB9G5Y6IiEC7du3kykZEREAkEslG83Z2dsaJEycQGRmJZs2aycoJgoCIiAjUrl1bIbEmIiptRCKgnLkJ9PU46ZKmMYEmIiIijdPT04Onpyf27duHGzduyJLltLQ0BAQEwMrKCq1btwaQnRRXr14dQUFBGDZsmGwu6NjYWISFhaFt27YoX748AKBLly5YsWIFNm3ahKZNm8rumBw6dAgvXrzAyJEjC/5giYiKGJFIBH09HUzZexOJKRkqbWNnYYwfetTXcsuKPybQREREpBUTJkzAmTNn4Ofnh+HDh8PCwgJBQUGIjo7GsmXLUKZMGVnZuXPnws/PD97e3hg0aBDS09Oxbds2GBkZYerUqbJyVapUwejRo7Fq1SqMGDECXbp0wYMHD7Bjxw7Ur18fPj4+hXGoRERFUmJKBhJTMlUqW85ItXKlHRNoIiIi0gpra2vs3r0bS5cuxfbt25GZmQlHR0esX78ebdq0kSvbvHlzbNq0CatWrcLSpUthZGSERo0aYfLkyahRo4Zc2fHjx8PS0hIBAQGYN28erKys4O3tjYkTJ8qmsiIiItIGJtBERESULxMmTMCECROUrrOzs8OKFStUqqdp06Zo2rSpSmUHDBiAAQMGqNpEIiIijWACTUREMjo66o3AqW55IiIiouKMCTQREcHYQBdZEgnKlzcp7KYQERERFVlMoImICGX0dKGno4NZ+8Px+r1qo3UCQA1rE0zr4vT5gkREREQlABNoIiKSef0+Ay+T0lUub2FioMXWEBERERUtnFmbiIiIiIiISAVMoImIiIiIiIhUUCwT6PDwcDg5OeHy5csK6548eYJp06ahZcuWcHFxQf/+/XH69Gml9dy4cQPDhg2Du7s73NzcMHr0aMTExCgtGxwcjJ49e6JBgwZo2bIlfvzxR7x9+1ajx0VERERERERFV7FLoGNjYzFu3DiIxWKFdS9fvoSvry9OnTqFPn36YPr06cjKysLYsWNx+PBhubJ//fUXhgwZgqdPn2LUqFHw9/fHP//8A29vb9y7d0+u7Pr16zFjxgyYm5tj6tSp6NatGwIDAzFkyBCkpaVp9XiJiIiIiIioaChWg4iFhYXhu+++y/HO7+rVqxEfH49du3ahUaNGAIDevXujX79+WLBgATw8PGBsbAxBEDBv3jyYm5tj7969MDc3BwB07NgR3bt3x6JFi7Bx40YAwNOnT7Fq1Sq0atUKGzZsgI5O9jWHOnXqYNq0adixYwf8/Py0f/BERERERERUqIrNHWh/f3+MHz8e1tbW8PLyUlgvFosREhICFxcXWfIMAIaGhhg8eDDevHmDs2fPAgAiIyMRExODnj17ypJnAKhWrRo6dOiAixcv4sWLFwCAI0eOIDMzE0OGDJElzwDQvXt32NjY4MCBA9o5YCIiIiIiIipSik0Cff/+fUyePBnBwcGoVq2awvro6GikpKTAxcVFYZ2zszOA7HenAeDWrVsAgAYNGiiUdXFxgUQiQWRkpNw2n5YViURwdnbG/fv3kZSUlMejIiIiIiIiouKi2DzCHRoaCgODnOcbff78OQCgUqVKCusqVqwIAIiLiwMAPHv2TG7558oaGxujbNmyOZaNj49HnTp1VD4WIiIiIiIiKn6KTQKdW/IMQHYX2NjYWGGdkZERACA1NRUAkJycnGNZQ0NDubJJSUkwMTFRuk9p2ZSUlM+2/1MikdqbKGybnzpKGsZEEWOiXGmIS3E5Nl1dkdptFQQBgqCd9nxME5+T4vJ7ICIiItUVmwT6c4RcvlFJ14n+/9uMKmU/ft9ZE2U/ZWlppvY22qijpGFMFDEmypWGuOjp6UBfX1er2+jq6qi9XVljfWRJJDA3V35xMjdZEgn08nDOzavS8DkhIiIi1ZWYBFp6l1jZtFLSu8nSx7BzKytdZmZmJiv76tUrpfv8tKw6EhKS8nwXRSTK/lKXnzpKGsZEEWOiXGmIi46OCBYWpsjKkiAzU3HKv9you41YLFF7Oz2RCHo6OvhufzgS3meovC8LEwMs7OOC16+TIZFo95enic+JtA4iIiIqOUpMAm1rawvgw/vNH5O+Hy19Z/njsk5OTnJlpdtL36W2tbXF7du3kZycDFNTU4WyOjo6sLGxUbu9goB8f3nXRB0lDWOiiDFRriTHpbgcV8L7DLxMSld7u4L83ZXkzwkRERGpr9iMwv05NWrUgJmZGSIiIhTWSUfSdnV1BfBhVG5lZSMiIiASiWSjeUvLSkfllhIEAREREahdu7ZCYk1EREREREQlT4lJoPX09ODp6YkbN27gxo0bsuVpaWkICAiAlZUVWrduDSA7Ka5evTqCgoKQmJgoKxsbG4uwsDC0bdsW5cuXBwB06dIF+vr62LRpk9y704cOHcKLFy/Qu3fvgjlAIiIiIiIiKlQl5hFuAJgwYQLOnDkDPz8/DB8+HBYWFggKCkJ0dDSWLVuGMmXKyMrOnTsXfn5+8Pb2xqBBg5Ceno5t27bByMgIU6dOlZWrUqUKRo8ejVWrVmHEiBHo0qULHjx4gB07dqB+/frw8fEpjEMlIiIiIiKiAlaiEmhra2vs3r0bS5cuxfbt25GZmQlHR0esX78ebdq0kSvbvHlzbNq0CatWrcLSpUthZGSERo0aYfLkyahRo4Zc2fHjx8PS0hIBAQGYN28erKys4O3tjYkTJ8qmsiIiIiIiIiqNRCIR1Jkko6CmpdSGYplAT5gwARMmTFC6zs7ODitWrFCpnqZNm6Jp06YqlR0wYAAGDBigahOJiIiIiIhKNEN9XWSJJbCwUG9qyswsCd4mvi+WSXSxTKCJiIiIiIiocJXR04Gerg6m7L2JxBTVpqY0NzbAUu+GEIlEcmNMFRdMoImIiIiIiCjPElMykJiSWdjNKBAlZhRuIiIiIiIiIm1iAk1ERERERESkAibQRERERERERCpgAk1ERERERESkAibQRERERERERCpgAk1ERERERESkAibQRERERERERCpgAk1ERERERESkAibQRERERERERCpgAk1ERERERESkAibQRERERERERCrQK+wGEBERERERUc5EIkAkEqlRXvWypB4m0EREREREREWUSASUMzeBvh4fHi4KmEATEREREREVUSKRCPp6Opiy9yYSUzJU2sbOwhg/9Kiv5ZaVTkygiYiIiIiIirjElAwkpmSqVLackWrlSH1MoImIiIiIiAqIqu8zi0Qitd99Ju1jAk1ERERERFQA1Hmf2dLStABaROrSSgJ95coVHDhwAK9evYJYLFZYLxKJsG3bNm3smoiIiIiIqEhS9X1mfT1dZGZl51F8n7lo0XgCfejQIcyYMQOCIORYho8hEBERERFRafW595n19SXIzMxOoPk+c9Gi8QR68+bNsLa2xtKlS/HFF1/AwMBA07sgIiIiIiIiKnAaT6AfPHiAr7/+Gu7u7pqumoiIiIiIiKjQaHw27rJly/KuMxEREREREZU4Gk+g27Vrh9DQUE1XS0RERERERFSoNP4I95AhQzBq1Cj4+fnBy8sL1tbWSgcNa9asmaZ3TURERERERKQ1Gk+gvby8AADx8fG4ePFijuXu3Lmj6V0TERERERERaY3GE+hx48ZxmioiIiIiIiIqcTSeQE+YMEHTVRIREREREREVOo0n0FLJycm4ePEi4uLiYGBggMqVK6Nly5YwNDTU1i6JiIiIiIiItEYrCfSJEycwZ84cJCUlQRAEAIBIJIKpqSl+/PFHeHp6amO3RERERERERFqj8QQ6PDwcU6ZMgYmJCcaNG4fatWtDLBYjOjoau3btwrRp02BrawtnZ2dN75qIiIiKqAcPHmDFihX4888/8f79e9jZ2cHb2xtDhgyBjs6HWTWfPHmCFStW4PLly0hKSoKjoyNGjRoFDw8PhTpv3LiBlStX4vbt2xAEAW5ubvj2229Rq1atgjw0IiIqRTSeQK9btw7lypXDwYMHYW1tLbdu4MCB6NmzJ37//XesXLlS07smIiKiIiguLg4+Pj5ISUmBr68v7OzsEBYWhp9//hn379/HvHnzAAAvX76Er68vEhMTMXjwYNjY2CAoKAhjx47FkiVL0K1bN1mdf/31F0aOHIkqVapg1KhRkEgk2L59O7y9vbFv3z7UrFmzsA6XiIhKMI0n0Ddv3oSvr69C8gwA1tbW8Pb2xt69ezW9WyIiIiqiNm/ejMTERPzyyy/o3r07AGDAgAEYMmQI9u7di6FDh6JmzZpYvXo14uPjsWvXLjRq1AgA0Lt3b/Tr1w8LFiyAh4cHjI2NIQgC5s2bB3Nzc+zduxfm5uYAgI4dO6J79+5YtGgRNm7cWFiHS0REJZjO54uoJyUlBZaWljmut7S0RFJSkqZ3S0REREXUw4cPAQBt27aVLROJRLLHsqOioiAWixESEgIXFxdZ8gwAhoaGGDx4MN68eYOzZ88CACIjIxETE4OePXvKkmcAqFatGjp06ICLFy/ixYsX2j8wIiIqdTSeQNva2uLPP//Mcf2ff/6JypUra3q3REREBODx48d5+qdNNWrUAABER0fLLY+NjQUA2NjYIDo6GikpKXBxcVHYXjpuSnh4OADg1q1bAIAGDRoolHVxcYFEIkFkZKSGWk9ERPSBxh/h7tq1K9asWYPVq1fD398fBgYGAICMjAysX78ep06dwpgxYzS9WyIiIgLQoUMHiEQitbYRiUT4999/tdQiwN/fH5cuXcKsWbPw/fffw87ODmfPnkVgYCCaN2+ORo0a4fz58wCASpUqKWxfsWJFANnvUgPAs2fP5JbnVpaIiEiTNJ5A+/n54fz581i9ejU2bdoEW1tbANkdWWpqKpycnODv76/p3RIRERGAnj17qp1Aa5u1tTUmTZqEWbNmYdiwYbLlrq6uWL16NUQikez1LmNjY4XtjYyMAACpqakAgOTk5BzLGhoaypVVVX5CJt22iIW90DEuihgTRYxJ6abq710TnxNNfcY0nkCXKVMG27dvx6ZNm3D8+HE8fvwYgiDA3t4enTp1wsiRI2WdGxEREWnWokWLCrsJCjZs2IClS5fC1tYWkydPho2NDSIiIrBt2zb4+Phg06ZNEAQhx+2l66QXBlQp+/HUWKqwtDRTq7y26iiJGBdFjImi0hYTfT1d6OtLci+jrwsA0NPTVXkbqaK8jf7/b2NpaapS+Y8Vhc+JxhNoIPvq77hx4zBu3DhtVE9EREQaFB8fjypVqmil7uTkZKxZswZWVlYIDAyEhYUFAKB9+/Zo2rQpRowYgUWLFsHLywsAkJaWplCH9G5y2bJlAQAmJiY5lpUuMzNT70tWQkIScsnLcyUSZX+py08dJRHjoogxUVTaYiISiWBpaYrMLDEyM8U5ltPX15Wtz8rK/u/ntvlYUd4mUz/7AmdCQnKuF0Q/ponPibSO/Mp3Ai2RSOSu8kokql15UPfKMBEREeXNvn37cObMGbx//16unxaLxXj37h1iY2O19g70gwcPkJaWhh49esiSZ6nmzZvD3t4eFy9exOjRowF8eL/5Y8+fPwfw4f1m6ethz549g5OTk1xZ6fbK3qXOjSAg31/eNVFHScS4KGJMFJWWmPBRdXnq/s6Lwuck3wl0vXr1sHjxYnTr1g0A4OTk9Nl3r7Q9WAkRERFl27lzJ3766SfZzyKRSO6Kf5kyZeDp6am1/ZcpUwZAzhfYxWIxJBIJatSoATMzM0RERCiUkY6+7erqCuDDqNwRERFo166dXNmIiAiIRCKlo3kTERHlV74TaDc3N1hZWcl+dnd3z2+VREREpCFBQUGoXLkyNm7ciMzMTPTu3Rvnzp2DIAjYuHEjdu3aBR8fH63tv1atWqhSpQpOnDiBMWPGyD0qfvr0acTFxaFLly7Q09ODp6cn9u3bhxs3bsiS5bS0NAQEBMDKygqtW7cGkJ1AV69eHUFBQRg2bJhsLujY2FiEhYWhbdu2KF++vNaOiYiISq98J9A7duzI9WciIiIqPLGxsfD390fNmjUhCAIMDAxw8+ZNdOrUCbNnz8adO3fw+++/w83NTSv719HRwYIFC+Dv74++ffvC29sblSpVwu3bt7F//35UqFAB06ZNAwBMmDABZ86cgZ+fH4YPHw4LCwsEBQUhOjoay5Ytk93NBoC5c+fCz88P3t7eGDRoENLT07Ft2zYYGRlh6tSpWjkWIiIijb+IPHPmTNmjVspcuXIFI0eO1PRuiYiISImsrCxUqFABQPbj23Z2drh7965sfceOHfHff/9ptQ3NmjVDYGAg3N3dsWfPHsybNw/nz59H3759ceDAAVSuXBlA9nRXu3fvRqtWrbB9+3b88ssvMDAwwPr16xUeM2/evDk2bdoEKysrLF26FJs2bYKLiwt27dqFGjVqaPV4iIio9NL4KNzBwcFo0aJFju8eXb16FVevXtX0bomIiEiJChUqyA3MZWtri5iYGNnPRkZGeP36tdbbUadOHaxcufKz5ezs7LBixQqV6mzatCmaNm2az5YRERGpLt8J9MOHD9G1a1eIxR+GLZ86dWquj0/Vrl07v7slIiIiFTRt2hR79+5Fu3btULduXdSrVw87duxAQkICLC0t8ccff8DS0rKwm0lERFQs5DuBrlq1KsaOHYuLFy8CAG7evImqVasqTFUBALq6urC0tJRNVUFERETa5e/vjxMnTqB37964dOkS+vXrhw0bNqBz586wsLDAo0ePMGzYsMJuJhERUbGgkUe4x44di7FjxwLIfkRr3LhxsmmtCsuDBw+wYsUK/Pnnn3j//j3s7Ozg7e2NIUOGyM1B/eTJE6xYsQKXL19GUlISHB0dMWrUKHh4eCjUeePGDaxcuRK3b9+GIAhwc3PDt99+i1q1ahXkoREREamsatWqCA4Oxs6dO2UXt5cvX45Fixbh9evX6NGjByZOnFjIrSQiIioeNP4OdFRUFAAgMTERZcuWlSWrd+7cgbW1tdyUV9oSFxcHHx8fpKSkwNfXF3Z2dggLC8PPP/+M+/fvY968eQCAly9fwtfXF4mJiRg8eDBsbGwQFBSEsWPHYsmSJXIXAf766y+MHDkSVapUwahRoyCRSLB9+3Z4e3tj3759qFmzptaPi4hKJ5Eoe/AnVenoqF6WSgc7OzvMmDFD9rOHh4fSC8VERESUO40n0ACwZMkSbNu2DSEhIahevToAYPPmzQgNDcWkSZPg7++vjd3KbN68GYmJifjll1/QvXt3AMCAAQMwZMgQ7N27F0OHDkXNmjWxevVqxMfHY9euXWjUqBEAoHfv3ujXrx8WLFgADw8PGBsbQxAEzJs3D+bm5ti7d69svsmOHTuie/fuWLRoETZu3KjVYyKi0kkkAsqZm0BfT+OTJhARERGRmjSeQO/evRu///47mjZtCmNjY9nyPn36IDExEcuXL4etra3CdBSa9PDhQwBA27ZtZctEIhE8PDxw9epVREVFoVq1aggJCYGLi4sseQYAQ0NDDB48GHPmzMHZs2fh6emJyMhIxMTEwM/PT5Y8A0C1atXQoUMHhIaG4sWLF7JpQoiINEUkEkFfTwez9ofj9fsMlbapYW2CaV2ctNwyKi5UudMsEolw6tSpAmgNERFR8abxWxp79+5FmzZtsHXrVtjY2MiWN23aFBs3bkTz5s2xdetWTe9WjnT+x+joaLnlsbGxAAAbGxtER0cjJSVF6XRbzs7OACCbz/rWrVsAgAYNGiiUdXFxgUQiQWRkpIZaT0Sk6PX7DLxMSlfpX2JKZmE3l4qQzMxMZGVlyf1LT0/H8+fPER8fD5FIxLE8iIiIVKTxO9APHz7EgAEDclzfvn17LFmyRNO7lePv749Lly5h1qxZ+P7772FnZ4ezZ88iMDAQzZs3R6NGjXD+/HkAQKVKlRS2r1ixIoDsd6kByObPlC7PrSwREVFRIu3vPpWZmYmjR4/ip59+kg0ESkRERLnTeAJtZGSEFy9e5Lj+9evX0NXV1fRu5VhbW2PSpEmYNWuW3NQcrq6uWL16NUQiEZKSkgBA7jFzKSMjIwBAamoqACA5OTnHsoaGhnJlVaXGeEA5bpufOkoaxkQRY6JccYtLcWlnQcoeVE37+/j4v/mpo6jS19dHz549ERERgeXLl2PLli2F3SQiIqIiT+MJtJubG3bv3g0fHx9YW1vLrUtISMCePXvg6uqq6d3K2bBhA5YuXQpbW1tMnjwZNjY2iIiIwLZt2+Dj44NNmzZBEIQct5euk456q0rZj6fGUoWlpZla5bVVR0nDmChiTJQrbnHR09OBvr5qFx91dXXU3iYv+8nrvvLaPr3/H0jNwsJU5W3yq7h9TvKibt26CA4OLuxmEBERFQsaT6BHjx4Nb29vdO/eHV5eXqhevTpEIhEePHiAo0eP4t27d1p9VCw5ORlr1qyBlZUVAgMDZXNetm/fHk2bNsWIESOwaNEieHl5AQDS0tIU6pDeTS5btiwAwMTEJMey0mVmZup9yUpISEIueXmuRKLsL3X5qaOkYUwUMSbKFbe46OiIYGFhiqwsCTIzxSptIxZLAECtbaTU3SYv+8pr+wx0RMiSSKCn5gXLTLEE7xLfq/X71sTnRFpHUXf79m3o6+sXdjOIiIiKBY0n0E5OTli/fj2+//577NixQ25dpUqVsGbNGtkgXdrw4MEDpKWloUePHrLkWap58+awt7fHxYsXMXr0aAAf3m/+2PPnzwF8eL/Z1tZWVtbJSX5kW+n2yt6lzo0gIN9f3jVRR0nDmChiTJQrLnEpDm0sKIb6utDTUW9EcgsTAyzs4wJAlOvTRDkpLp+T3AQFBSldnpGRgX/++QfBwcHo0KFDAbeKiIioeNLKPNDNmzdHWFgY7ty5g8ePHyMrKwu2traoV6+e1t9/LlOmDABAIpEoXS8WiyGRSFCjRg2YmZkhIiJCoYx09G3po+bShD8iIgLt2rWTKxsREQGRSKR0NG8iItI86YjkpJrZs2dDJMr5AkKVKlXw7bffFnCriIiIiietJNBSdevWRd26dbW5CwW1atVClSpVcOLECYwZMwZVqlSRrTt9+jTi4uLQpUsX6OnpwdPTE/v27cONGzdkyXJaWhoCAgJgZWWF1q1bA8hOoKtXr46goCAMGzZMNhd0bGwswsLC0LZtW5QvX75Aj5OIiEgVCxculI3p8TFdXV1UqFABjRs3VnscDyIiotIq3wn0r7/+is6dO8PR0VH28+eIRCJMnDgxv7tWSkdHBwsWLIC/vz/69u0Lb29vVKpUCbdv38b+/ftRoUIFTJs2DQAwYcIEnDlzBn5+fhg+fDgsLCwQFBSE6OhoLFu2THY3GwDmzp0LPz8/eHt7Y9CgQUhPT8e2bdtgZGSEqVOnauVYiIiI8qt37965rheLxXj48CGqVq1aQC0iIiIqvvKdQK9btw41atSQJdDr1q377DbaTKABoFmzZggMDMTatWuxZ88eJCUlwdraGn379sX48eNlo4NbW1tj9+7dWLp0KbZv347MzEw4Ojpi/fr1aNOmjVydzZs3x6ZNm7Bq1SosXboURkZGaNSoESZPnowaNWpo7ViIiIjyo27duvjll19kg2d+Kjg4GD///DP+/vvvAm4ZERFR8ZPvBHr79u2oWbOm3M9FQZ06dbBy5crPlrOzs8OKFStUqrNp06Zo2rRpPltGRESkPc+fP8eVK1dkPwuCgGvXriErK0uhrEQiweHDh3McN4SIiIjk5TuBbty4ca4/ExERUcEpX748fv31V9ksESKRCPv27cO+ffsUykoHFuvWrVuBtpGIiKi4yncC/eTJkzxtV7ly5fzumoiIiD5hYGCAVatW4c6dOwCAOXPmoG/fvkpni9DV1YWlpSVatGhR0M0kIiIqlvKdQLdr107p6J6fI+3YiYiISLO++OILfPHFFwCAGzduoF+/fpxukYiISAPynUB7eXkpJNBnzpxBeno6WrZsiZo1a0IikSA2NhYXLlyAubk5+vbtm9/dEhERkQp+/vlnJCcnY8eOHejVqxdMTU0BADt37kR6ejoGDhwIQ0PDQm4lERFR8ZDvBHrJkiVyPwcEBODMmTMICgpCnTp15NbFxMRg0KBB7KiJiIgKyMuXLzF48GA8fPgQDRo0QP369QEA169fx7Fjx3DkyBFs27YNZmZmhdxSIiKiok9H0xVu27YNvr6+CskzANSqVQu+vr7Yu3evpndLRERESqxatQrPnj3Dzz//jLp168qWL126FMuWLcP9+/dVmoKSiIgUiUSAjo5I5X95efWVipZ834H+1IsXL2TzLCtTtmxZJCYmanq3REREpMTFixfh6+uLnj17yi3X0dGBp6cnIiIicOrUKUybNq1wGkhEVEyJREA5cxPo62n8niQVYRpPoKtWrYpjx45h0KBBCldYMjIycPDgQdSqVUvTuyUiIiIlEhISYGdnl+P66tWry6a8IiIi1YlEIujr6WDK3ptITMlQaRs7C2P80KO+lltG2qTxyyWDBg3C33//DT8/P5w+fRr37t3Dv//+iyNHjsDHxwdRUVEYOXKkpndLRERESlSsWBE3btzIcX1ERESuT44REVHuElMykJiSqdK/d6mZhd1cyieN34H29vbGo0ePsHXrVly6dEm2XBAE6OnpYerUqejcubOmd0tERERKdOrUCRs3bkSjRo3Qr18/uafDgoODcfDgQQwZMqQQW0hERFR8aDyBBoCpU6fC29sbZ86cwePHjwFkP9rdoUMHVKpUSRu7JCIiIiVGjx6NP/74A99//z2WLl0Ke3t7iEQiPHz4EO/evUPNmjUxbty4wm4mERFRsaCVBBoA7O3tMWzYMG1VT0RERCowNjZGYGAgNm3ahJMnT+LevXvIysqCra0tfHx84OfnJ5sbmoiIiHKnlQQ6IyMDO3bsQFhYGOLj47F8+XIYGRnhwIEDGDt2LCwtLbWxWyIiIlLC0NAQ48aNy/FOc0JCAvtmIiIiFWg8gU5PT8fQoUNx69YtlClTBhkZGcjMzMSrV6+wc+dOXLx4Ebt27WJHTUREVMj+/PNP7NmzB2fOnEFERERhN4eIiKjI0/go3L/99hvCw8OxYMECnD59GoIgAMgexOT7779HXFwc1q9fr+ndEhERkQrevXuHrVu3okuXLhg+fDiOHz8OXV3dwm4WERFRsaDxO9ChoaHo0aMH+vTpgzdv3siWi0QiDBgwAP/++y/Onj2LWbNmaXrXRERElIPw8HDs3r0bx48fR3p6OgRBgLW1NQYNGgQfH5/Cbh4REVGxoPEE+smTJxg+fHiO652dnXHo0CFN75aIiIg+kZKSgpCQEOzduxdRUVGyp8JEIhHGjBmDsWPHQl9fv5BbSUREVHxoPIE2MzNDQkJCjusfPnwIMzMzTe+WiIiI/l9UVBT27NmDw4cPIyUlBYIgoHr16ujatSsaNGiAr776Cl988QWTZyIiIjVpPIFu2rQpAgMDMWTIEIV1sbGx2LNnD1q2bKnp3RIREREAHx8fhIeHQxAEVKlSBQMGDEDXrl1Rt25dAEB8fHwht5CIiKj40ngCPWHCBPTt2xc9evRAmzZtIBKJcPLkSZw4cQKHDh2CRCLB6NGjNb1bIiIiAnDr1i2YmJjA398fgwcPhrGxcWE3iYiIqMTQ+Cjc1atXx5YtW2BsbIzdu3dDEATs3r0be/bsgYWFBX777TfUqVNH07slIiIiAL1794YgCFixYgWaNGmCIUOGYM+ePXIDexIREVHeaGUeaGdnZxw5cgRRUVF48OABBEGAra0tvvjiC+joaDxnJyIiov+3cOFCzJkzB6Ghodi/fz+uXr2Ka9euYf78+WjWrBnc3d0hEokKu5lERETFksYT6J49e6Jfv34YMWIE6tSpw7vNREREBczIyAh9+vRBnz598PDhQwQFBeHQoUO4cOECLl68CAAIDAyEtbU1nJ2dC7m1RERExYfGbwfHxcXB1NRU09USERFRHlStWhVTpkzB2bNn8dtvv6F9+/bQ1dXF2bNn4e3tjT59+iAoKKiwm0lERFQsaDyBrlOnDm7cuKHpaomIiCgfdHR08OWXX2LVqlW4cOECZsyYgVq1auH27duYM2dOYTePiIioWND4I9xDhgzBDz/8gCdPnqBp06awsrJS+t5z3759Nb1rIiIiUkH58uUxbNgwDBs2DBEREThw4EBhN4mIiKhY0HgCPXXqVADA1atXcfXqVbl1IpEIgiBAJBIxgSYiIioCnJ2d+R40ERGRijSeQC9cuJCjexIRKSESQe3zo44Oz6dERERERYXGE+jevXtrukoiomJPJALKmZtAX49T+REREREVVxpLoP/9919cu3YNYrEYzs7OcHNz01TVRETFnkgkgr6eDmbtD8fr9xkqb1fD2gTTujhpsWVU0mzZsgWtW7dGzZo1C7spREREJU6+E2iJRIKZM2ciJCQEAGTvOLu7u2PNmjUwMzPLdyOJiEqK1+8z8DIpXeXyFiYGWmwNlUQrV65E2bJlZQm0h4cHZs2aBQ8Pj0JuGRERUfGX72cJAwICcOjQITg5OWHatGmYOXMmmjdvjqtXr2L+/PmaaCMRERGpSCQS4Z9//pH9HB8fj9TU1EJsERERUcmR7zvQR44cgbu7O7Zt2yabrmro0KH44YcfsH//fvz0008wMOAdFCIiooLg5uaGPXv2IDQ0FKampgCyB/hcvnx5jtuIRCKcOnWqoJpIRERUbOX7DvSDBw/QuXNnhbme+/Xrh8zMTNy/fz+/uyAiIiIVzZ8/H+3bt0fZsmWRlZUFkUgEsViMrKysHP9lZmYWdrOJiIiKhXzfgU5JSZFd4f5YpUqVAABJSUn53QURERGpqEKFCli1apXs5zp16mD27Nno1q1bIbaKiIioZMj3HWixWKxw9xn4MNdpVlZWfndBREREefTzzz+jYcOGhd0MSCQSBAQEoHv37nB2dkabNm0wc+ZMPH/+XK7ckydPMG3aNLRs2RIuLi7o378/Tp8+rbTOGzduYNiwYXB3d4ebmxtGjx6NmJiYgjgcIiIqpTQ+DzQREREVHb169QIAXL9+HaGhoYiLi4OBgQEqVaqETp06Fdi0kzNmzMChQ4fg4eGBgQMH4sGDB9i5cyf+/vtvBAUFoWzZsnj58iV8fX2RmJiIwYMHw8bGBkFBQRg7diyWLFkidxf9r7/+wsiRI1GlShWMGjUKEokE27dvh7e3N/bt28dpvIiISCs0kkBL7zaru46IiIi0b+HChdixYwcEQZBbHhAQgEGDBmH27Nla3X9YWBgOHTqEgQMH4vvvv5ctr1OnDmbMmIE9e/bA398fq1evRnx8PHbt2oVGjRoBAHr37o1+/fphwYIF8PDwgLGxMQRBwLx582Bubo69e/fC3NwcANCxY0d0794dixYtwsaNG7V6TERUMolEqucvzHNKJ40k0OvWrcO+ffvklkkHLlm0aBHKli0rt04kEmHbtm2a2DURERHlIiQkBNu3b4e7uzsmTJiA2rVrQyKR4O7du1i7di127twJV1dXeHp6aq0Nu3fvhomJCaZMmSK3vGvXrrh//z6qVasGsViMkJAQuLi4yJJnADA0NMTgwYMxZ84cnD17Fp6enoiMjERMTAz8/PxkyTMAVKtWDR06dEBoaChevHiBChUqaO2YiKjkEYmAcuYm0NfL91uuVIJpJIG+d+8e7t27p3RdVFSUwjJerSEiIioYu3btgpOTE7Zs2QI9vQ/dfrNmzeDu7o7+/ftj165dWkugxWIxrl+/jiZNmsgGHU1LS4OOjg4MDAxkSXVUVBRSUlLg4uKiUIezszMAIDw8HJ6enrh16xYAoEGDBgplXVxccOTIEURGRsLDw0Mrx0REJZNIJIK+ng6m7L2JxJSMz5a3szDGDz3qF0DLqCjJdwKd08AeREREVPj+++8/TJgwQS55ltLT04OXlxfWrl2rtf3HxcUhPT0dtra2OHnyJFatWoW7d+9CV1cXzZo1w3fffYcaNWrIBhOTzuLxsYoVK8rqAoBnz57JLc+tLBGRuhJTMpCY8vnp/coZcQrA0ijfCXSVKlU00Q4iIiIqJGKxWGt1v337FgBw5coV7N+/H8OHD8ekSZMQFRWF33//HQMGDEBQUJBs2ktjY2OFOoyMjAAAqampAIDk5OQcyxoaGsqVVVV+Ho6TbssH7OQxLooYE0WMSemm6u9dE58TTX3GOAo3ERFRCebo6IijR49i6NCh0NXVlVsnFotx5MgR1K5dW2v7z8jIfgzywYMHWLNmDdq3bw8AaN++PerVq4fRo0fj119/RZs2bXKsQzr4mfQVsE8HQ1NWVtkUm7mxtDRTq7y26iiJGBdFjImiohQTfT1d6OtLPltOT09XrfLqbqOvr1sg+ynobfT/fxtLS1OVyn+sKHxOmEATERGVYAMHDsS0adPg5+eHsWPHolatWgCAmJgYrFmzBnfu3MGCBQu0tn/pXWIbGxtZ8izVtm1bVKhQAZcvX5a9g52WlqZQh/RusnRQUhMTkxzLSpeZman3JSshIQm55OW5Eomyv9Tlp46SiHFRxJgoKkoxEYlEsLQ0RWaWGJmZn38yJysru4yq5dXZRl9fV7Zem/spjG0y9bMvcCYkJOd6QfRjmvicSOvILybQREREJVj37t0RHh6OnTt34sqVK3LrBEGAt7c3evfurbX9S99ptrKyUrreysoKMTExsLW1BfDh/eaPSd+Plr7f/HFZJycnubLS7ZW9S50bQUC+v7xroo6SiHFRxJgoKgox4WPkBU/d33lR+JwwgSYiIirh5syZgy5duuD48eN49OgRBEGAvb09OnXqhMaNG2t13+XLl4e9vT1iY2ORnp6OMmXKyNaJxWLExcXB1tYWNWrUgJmZGSIiIhTqCA8PBwC4uroC+DAqd0REBNq1aydXNiIiAiKRSOlo3kRERP/X3p3Hx3T1fwD/3GwkISIJrQq11IQgkYglETwSawRBJLZYG1QtT1HbT7VaW1u0RZXaisTSBEFpUaRVS9WWKFqxxBJ7JBKyZ87vjzwzjJnETDKTyfJ5v155te49955zT2buN9+Ze84pqiIvcrZ+/fp8l7AiIiKiksHDwwOzZs3C999/j9WrV+Ojjz4yePKs0LdvXzx//hxr1qxR2b5161akpKTA398fZmZm8PPzw9mzZ3H27FllmYyMDISFhcHBwQHt2rUDkJdA161bF5GRkUhOTlaWjY+Px8GDB9GhQwdUrVq1WK6NiIjKlyJ/A7106VLY2Nigfv36AABfX1/MnDmTay8SERERAGDEiBGIjo7G0qVLcf36dbRs2RIXL15EREQEGjZsiJEjRwIAxo8fj8OHDyM0NBTDhw+HnZ0dIiMjERcXhyVLlqh8ez179myEhoYiODgYgwYNQmZmJjZs2ABLS0t8+OGHxrpUIiIq44r8DbQkSfj777+V/05ISNB56QhDkMvlCAsLQ8+ePeHi4oL27dtjxowZynFUCnfv3sXUqVPh7e0NV1dXBAUF5bu29dmzZzFs2DC0aNECHh4eGDNmDK5evVocl0NERFRqWVhYYP369Xj//fcRGxuLzz77DL///juGDh2K8PBw5dJT1apVw5YtW9C2bVts3LgRX375JSwsLLBq1SrlJGMKXl5eWLt2LRwcHLB48WKsXbsWrq6u2Lx5M+rVq2eMyyQionKgyN9Ae3h4YOvWrdi3bx8qVcqbinz+/Pn46quv8j1GkiT8+uuvRa26QNOnT8euXbvg6+uLgQMH4saNGwgPD8eZM2cQGRkJGxsbPHr0CIMHD0ZycjJCQkLwxhtvIDIyEmPHjsWiRYvQo0cP5fn+/PNPjBw5EjVr1sTo0aMhl8uxceNGBAcH48cff1R+A09ERETqLC0tMWHCBEyYMKHAcrVq1cLXX3+t1Tlbt26N1q1b66F1RERE2ilyAj137lx89tln+Oeff5CVlQVJkpCbm4ucnBx9tK9QDh48iF27dmHgwIH4+OOPldsbNmyI6dOnY+vWrRg1ahSWL1+OhIQEbN68Gc2bNwcA9OnTB/369cO8efPg6+sLKysrCCHw6aefwtbWFtu2bYOtrS0AoHPnzujZsycWLlyI1atXG+NSiYiIiIiIqJgUOYGuXr06li1bpvx3w4YNMWvWLJVvb4vbli1bYG1tjcmTJ6ts7969O65fv446deogNzcXu3fvhqurqzJ5BoCKFSsiJCQEH330EaKjo+Hn54cLFy7g6tWrCA0NVSbPAFCnTh106tQJ+/btw8OHD1G9evXiukQiIiIiIiIqZkUeA/2qBQsWwM3NTd+n1Vpubi5Onz6N5s2bKx8pz8jIQFZWFiwsLDB58mR07twZcXFxSEtL07jMhWJ5DMWyGefPnwcANGvWTK2sq6sr5HI5Lly4YJgLIiIiKgIfHx/lE1dERERUNHpfB7p3794AgNOnT2Pfvn24c+cOLCwsUKNGDXTp0gUeHh76rlLFnTt3kJmZCUdHRxw4cADLli3DlStXYGpqCk9PT/zf//0f6tWrp5xMrEaNGmrnePPNN5XnAoD79++rbC+oLBERUUlibW2N5cuXY8WKFWjZsiX69OmDLl26qMxoTURERNrRewIN5E0itmnTJgghVLaHhYVh0KBBmDVrliGqBQA8ffoUAHDixAls374dw4cPx8SJE/HPP/9gzZo1GDBgACIjI5GamgoAsLKyUjuHpaUlAChnE3/27Fm+ZRUzh+o687gk6VRc47FFOUdZwz5Rxz7RzFj9wt+DcUmSbr8DfbxOSsrvfM+ePbh06RJ27NiBvXv3YurUqfj000/h5+eH3r17G/WpMSIiotJG7wn07t27sXHjRrRo0QLjx49HgwYNIJfLceXKFaxYsQLh4eFwd3dXW45CX7KysgAAN27cwLfffouOHTsCADp27IjGjRtjzJgx+Oabb9C+fft8z6FI/KX//fXz6gcBmsqamOj2NLy9fWWdyhvqHGUN+0Qd+0QzY/WLmZkJzM1NtS5vamqi83GFOaY0tK8wx5mZ5R1jZ1dJ63peVlbeP87OznB2dsb06dMRHR2NqKgo7Nq1CxEREXj77bfRt29fBAQEoFq1asZuKhERUYmm9wR68+bNcHZ2xvr162Fm9uL0np6eaNGiBYKCgrB582aDJdCKb4nfeOMNZfKs0KFDB1SvXh3Hjx9X1p+RkaF2DsW3yTY2NgDyHn/Lr6xiW+XKuv2RlZiYigLy8gJJUt4fdUU5R1nDPlHHPtHMWP1iYiLBzq4ScnLkyM7O1fq43Fw5AOh0XGGOUSjJ7SvMcTk5ecc8efIMcrn2v3B9vE4U5yhJzMzM0LFjR3Ts2BFJSUn4/PPPERUVhSVLluCbb76Br68v3nvvPTRs2NDYTSUiIiqR9J5A//vvvxg/frxK8qyszMwM/v7+WLFihb6rVVKMaXZwcNC438HBAVevXoWjoyOAF+ObX6YYH60Y3/xyWWdnZ5WyiuM1jaUuiBAo8h/v+jhHWcM+Ucc+0ay4+4W/A+Mq7O+7rL1/5HI5jh49il27diE6OhppaWlwcHBAQEAAAGDnzp0IDAzEF198YbAPuomIiEozg4yBfp3cXN2+EdFF1apVUbt2bcTHxyMzM1NlkpTc3FzcuXMHjo6OqFevHipXrozY2Fi1cyhm33Z3dwfwYlbu2NhY+Pj4qJSNjY2FJEkaZ/MmIiIqCS5duoSoqCjs27cPiYmJMDU1Rbt27RAYGIj27dvD1DTvkfjQ0FAEBgZi0aJFTKCJiIg00PsyVk5OTti7d6/GJDk3Nxc//fQTGjRooO9qVfTt2xfPnz/HmjVrVLZv3boVKSkp8Pf3h5mZGfz8/HD27FmcPXtWWSYjIwNhYWFwcHBAu3btAOQl0HXr1kVkZCSSk5OVZePj43Hw4EF06NABVatWNeg1ERERFYa/vz/69u2LjRs3wsbGBlOmTMFvv/2GFStWwMfHR5k8A0CVKlXg5uamnDyTiIiIVOn9G+iBAwdi6tSpCA0NxdixY/HOO+8AAK5evYpvv/0Wly9fxrx58/RdrYoRI0YgOjoaS5cuxfXr19GyZUtcvHgRERERaNiwIUaOHAkAGD9+PA4fPozQ0FAMHz4cdnZ2iIyMRFxcHJYsWaLy7fXs2bMRGhqK4OBgDBo0CJmZmdiwYQMsLS3x4YcfGvR6iIiICishIQF9+vRBYGCgVjNud+7cGT169CiGlhEREZU+ek+ge/bsiZiYGISHh+PEiRMq+4QQCA4ORp8+ffRdrQoLCwusX78eq1evxp49e7B//344ODhg6NChGDdunHLpqWrVqmHLli1YvHgxNm7ciOzsbDg5OWHVqlVqs3R7eXlh7dq1WLZsGRYvXgxLS0s0b94ckyZNQr169Qx6PURERIV1/PhxWFpaQi6Xq2y/d+8eqlWrpjZnyasTcBIREdELBhkD/dFHH6Fbt2745ZdfcOvWLQghULt2bXTp0gUtW7Y0RJVqLC0tMWHCBEyYMKHAcrVq1cLXX3+t1Tlbt26N1q1b66F1RERExcPS0hJbt27F8uXLsWXLFtSqVQsAsHTpUhw6dAgff/wxunfvbuRWEhERlQ4Gm0TMw8MDHh4ehjo9ERERaeHnn3/GJ598gvr166tsb9u2LS5evIgpU6agatWq8PLyMlILiYiISg+9TyJGREREJccPP/yAZs2aYefOncpvnwHAz88P27dvR9OmTfHdd98ZsYVERESlBxNoIiKiMuzatWvo1asXLCws1PaZm5ujV69e+Pfff43QMiIiotKHCTQREVEZZmpqipSUlHz3p6WlIScnpxhbREREVHoxgSYiIirDmjZtioiICKSlpantS09PVz7GTURERK9nsEnEiIiIyPhGjhyJESNGIDAwEEFBQahbty4kScKNGzcQERGBW7duYdasWcZuJhERUamg9wTax8cHffr0Qe/evVGzZk19n56IiIh04Onpifnz52Pu3LlYuHAhJEkCAAghYGlpiTlz5sDb29vIrSQiIiod9J5AW1tbY/ny5VixYgVatmyJPn36oEuXLqhQoYK+qyIiIiIt9O7dG127dsWxY8dw+/ZtZGdnw9HREd7e3rCxsTF284iIiEoNvSfQe/bswaVLl7Bjxw7s3bsXU6dOxaeffgo/Pz/07t0bbm5u+q6SiIiIXsPS0hIdO3Y0djOIiIhKNYOMgXZ2doazszOmT5+O6OhoREVFYdeuXYiIiMDbb7+Nvn37IiAgANWqVTNE9URERPQSIQROnjyJR48eQS6XaywTEBBQvI0iIiIqhQw6iZiZmRk6duyIjh07IikpCZ9//jmioqKwZMkSfPPNN/D19cV7772Hhg0bGrIZRERE5daNGzfw7rvv4u7duwDykmkAKmOhJUliAk1ERKQFgybQcrkcR48exa5duxAdHY20tDQ4ODgog/TOnTsRGBiIL774An5+foZsChERUbm0ePFi3L17FwEBAWjSpAksLCyM3SQiIqJSyyAJ9KVLlxAVFYV9+/YhMTERpqamaNeuHQIDA9G+fXuYmpoCAEJDQxEYGIhFixYxgSYiIjKAkydPYsCAAZg9e7axm0JERFTq6T2B9vf3x7Vr1yCEQL169TB8+HAEBATA3t5erWyVKlXg5uaG6OhofTeDiIiIAOTm5qJRo0bGbgYREVGZoPcEOiEhAX369EFgYKBWM2537twZPXr00HcziIiICECTJk1w8eJF9OvXz9hNISIiUpIkCSYmxm6F7vSeQB8/fhyWlpZqs3zeu3cP1apVg5mZapVcUoOIiMhwJk6ciNDQULRs2RJdunRRDqMiIiIyhormpsjJlcPOzlqn43Jy5ZAk4H9zYRqN3hNoS0tLbN26FcuXL8eWLVtQq1YtAMDSpUtx6NAhfPzxx+jevbu+qyUiIiINVq1aBRsbG0yePBkzZsxAlSpVYPLKR/6SJOHIkSNGaiEREZUnFcxMYGZqgsnbziE5LUurY2ytLLA42A2ABMC4GbTeE+iff/4Zn3zyCerXr6+yvW3btrh48SKmTJmCqlWrwsvLS99VExER0SuuXbsGU1NT1KhRw9hNISIiUkpOy0JyWraxm6EzvSfQP/zwA5o1a4aNGzeqLJXh5+eHTp06YdCgQfjuu++YQBMRERWDw4cPG7sJREREZYbeh21fu3YNvXr10rjOpLm5OXr16oV///1X39USERGRFrKystTmKSEiIiLt6D2BNjU1RUpKSr7709LSkJOTo+9qiYiIKB/Jycn49NNP4e3tjWbNmuHPP//E6dOnMWbMGNy4ccPYzSMiIio19J5AN23aFBEREUhLS1Pbl56eju3bt6Np06b6rpaIiIg0SE5ORnBwMDZv3gxLS0uI/01f+vTpU0RHR2PQoEG4ffu2kVtJRERUOug9gR45ciQSEhIQGBiIH374Ab/99ht+//13bNiwAf369cOtW7cQGhqq72qJiIhIg+XLlyMhIQHr16/Htm3blAm0r68vVq1ahbS0NKxYscLIrSQiIiod9D6JmKenJ+bPn4+5c+di4cKFkCQJACCEgKWlJebMmQNvb299V0tEREQaHD58GEFBQfD09ERSUpLKvvbt2yM4OBgHDx40UuuIiIhKF70n0ADQu3dvdO3aFceOHcPt27eRnZ0NR0dHeHt7w8bGxhBVEhERkQYPHz5Ew4YN891fv359bN68uRhbREREVHoZJIEGAEtLS3Ts2NFQpyciIiIt2NvbIyEhId/9V65cQdWqVYuxRURERKWXQRJoIQROnjyJR48e5btURkBAgCGqJiIiope0a9cOW7duRb9+/WBtba2y7/Tp09i2bRt69OhhpNYRERGVLnpPoG/cuIF3330Xd+/eBQDlZCUvj4WWJIkJNBERUTEYN24cjhw5gt69e6N58+aQJAmbN2/GunXrcOzYMVSuXBljx441djOJiIhKBb0n0IsXL8bdu3cREBCAJk2awMLCQt9VEBERkZbeeOMNbN26FXPmzMHvv/8OIYRy0rDmzZvj448/hqOjo5FbSUREVDroPYE+efIkBgwYgNmzZ+v71ERERFQIjo6OWL16NVJSUhAfHw8hBBwdHWFvb2/sphEREZUqek+gc3Nz0ahRI32floiIiIrIxsYGLi4uxm4GERFRqaX3BLpJkya4ePEi+vXrp+9TExERkY5mzJjx2jKSJGH+/PnF0BoiIqLSTe8J9MSJExEaGoqWLVuiS5cuMDU11XcVREREpKWdO3fmu0+SJJiZmcHc3JwJNBERkRb0nkCvWrUKNjY2mDx5MmbMmIEqVarAxMREpYwkSThy5Ii+qyYiIqJXHDhwQG1bTk4OHj9+jKioKJw+fRrh4eFGaBkREVHpo/cE+tq1azA1NUWNGjX0fWoiIiLSUe3atTVur1evHlq2bIkxY8Zg0aJF+Pzzz4u5ZURERKWP3hPow4cP6/uUREREZCA+Pj5YsmSJsZtBRERUKpi8vkjRZGVlQS6XG7oaIiIiKoSHDx8iIyPD2M0gIiIqFfT+DTQAJCcnY+nSpThw4ACePHmCtWvXwtzcHGvWrMG0adNQt25dQ1RLREREr7h9+7bG7VlZWbhw4QI2bNgAZ2fnYm4VERFR6aT3BDo5ORnBwcG4efMmatWqBSEEAODp06eIjo5GbGwstm3bhlq1aum7aiIiInpFp06dIEmSxn1CCJiammLcuHHF3CoiIqLSSe8J9PLly5GQkID169fDyckJXl5eAABfX1+sWrUKEydOxIoVK7BgwQJ9V01ERESvCAgI0JhAm5qaonr16ujZsyfq1KlTbO3Jzc1FSEgIzpw5g4sXL8LM7MWfInfv3sXXX3+N48ePIzU1FU5OThg9ejR8fX3VznP27FksXboUFy9ehBACHh4emDJlCt55551iuxYiIip/DDKJWFBQEDw9PZGUlKSyr3379ggODsbBgwf1XS0RERFpsHDhQmM3QcXKlStx5swZte2PHj3C4MGDkZycjJCQELzxxhuIjIzE2LFjsWjRIvTo0UNZ9s8//8TIkSNRs2ZNjB49GnK5HBs3bkRwcDB+/PFH1K9fvzgviYiIyhG9J9APHz5Ew4YN891fv359bN68Wd/VEhERUQkXGxuLFStWwMLCAllZWSr7FE+wbd68Gc2bNwcA9OnTB/369cO8efPg6+sLKysrCCHw6aefwtbWFtu2bYOtrS0AoHPnzujZsycWLlyI1atXF/elERFROaH3BNre3h4JCQn57r9y5QqqVq2q72qJiIhIg0GDBul8jCRJCAsL02s7nj9/jilTpqBt27Z4/vw5Tp06pdyXm5uL3bt3w9XVVZk8A0DFihUREhKCjz76CNHR0fDz88OFCxdw9epVhIaGKpNnAKhTpw46deqEffv24eHDh6hevbpe209ERAQYIIFu164dtm7din79+sHa2lpl3+nTp7Ft2zaVx7CIiIjIcJ4+fYqkpCQkJiYCyPugu0KFCnjw4AFyc3MhSZJywk+F/CYdK4p58+YhNTUVc+fOxQcffKCyLy4uDmlpaXB1dVU7zsXFBQAQExMDPz8/nD9/HgDQrFkztbKurq746aefcOHCBY3jpomIiIpK7wn0uHHjcOTIEfTu3RvNmzeHJEnYvHkz1q1bh2PHjqFy5coYO3asvqslIiIiDb788ksMGTIEwcHBmDhxIuzs7AAAmZmZWL9+PVauXIm1a9eqfPOrbwcOHMD27dvx7bffwsHBQW3/gwcPAAA1atRQ2/fmm28CAO7cuQMAuH//vsr2gsoSERHpm94T6DfeeANbt27FnDlz8Pvvv0MIoZw0rHnz5vj444/h6Oio72qJiIhIgwULFqBFixaYM2eOyvYKFSpgzJgxuHnzJhYtWoQtW7YYpP4HDx7go48+QmBgIDp27KixTGpqKgDAyspKbZ+lpSUAID09HQDw7NmzfMtWrFhRpawuivKlu+JYA3xxX6qxX9SxT9SxT0gXklT414q+XmN6T6ABwNHREatXr0ZKSgri4+MhhICjoyPs7e0NUR0RERHlIzY2FlOnTs13v4uLC/bt22eQuoUQmDZtGipXroyZM2cWWO51+xSPlWtT1sTEROe22ttX1vkYQ5yjLGK/qGOfqCtJfWJuZgpzc/lry5mZmepUXtdjzM1Ni6We0nCM+f+OsbOrpFV5QzJIAq1gY2OjHLtkLFxvkoiIyrNKlSrhypUr+e4/d+6c8rFufVu/fj1OnjyJb7/9FpmZmcjMzAQAZGdnAwCSk5Nhbm6unDMlIyND7RyKb5NtbGwAoMCyim2VK+v+h3hiYioKyM0LJEl5f/wX5RxlEftFHftEXUnqE0mSYG9fCdk5ucjOzn1t+ZycvDLaltflGHNzU+V+Q9ZTWo7JNs/7YPTJk2eQywv3QlG81opK7wn0jBkzXltGkiTMnz9f31VrxPUmiYioPPPx8UFERAQaNGiA/v37w9Q071P8rKws/PDDD9izZw9CQ0MNUveRI0cghMh37pM2bdqgZs2aWLlyJYAX45tfphgfrRjfrBgGdv/+fTg7O6uUVRyvaSz16wiBIv/xro9zlEXsF3XsE3UloU/4GHnJVxJeJ3pPoHfu3JnvPkmSYGZmBnNz82JJoLneJBERlXf//e9/cfz4ccydOxdfffUV3nrrLQghcOfOHaSnp6NZs2Z4//33DVL3tGnTkJKSorZ94cKF+Pfff7F27VpYWlqiXr16qFy5MmJjY9XKxsTEAADc3d0BvJiVOzY2Fj4+PiplY2NjIUmSxtm8iYiI9EHvCfSBAwfUtuXk5ODx48eIiorC6dOnER4eru9q1XC9SSIiIsDOzg5RUVFYvXo1Dh06hFu3bgEA6tWrBz8/PwwZMgQWFhYGqbtJkyYat1epUgUA0Lp1a+XQKj8/P/z44484e/asMlnOyMhAWFgYHBwc0K5dOwB5CXTdunURGRmJYcOGKWNzfHw8Dh48iA4dOqBq1aoGuR4iIiK9J9C1a9fWuL1evXpo2bIlxowZg0WLFuHzzz/Xd9UquN4kERlS3iyQ2j/rZWLC58LIeCpVqoQPPvhALR6WJOPHj8fhw4cRGhqK4cOHw87ODpGRkYiLi8OSJUtQoUIFZdnZs2cjNDQUwcHBGDRoEDIzM7FhwwZYWlriww8/NOJVEBFRWWfQScQ08fHxwZIlSwxaB9ebJCJDkiSgiq01zM10n+mXyFgePHiA6OhoJCQkoG/fvrCyssKDBw/y/Za4uFWrVg1btmzB4sWLsXHjRmRnZ8PJyQmrVq1C+/btVcp6eXlh7dq1WLZsGRYvXgxLS0s0b94ckyZNQr169Yx0BUREVB4UewL98OFDjTNn6gvXmyyf2Cfq2Cea6aNfTEwkmJuZYOb2GDx5nvX6AwDUq2aNqd2cX1+QDELXdSP18TopSe+9jRs3YtGiRcjKyoIkSfD09ERmZibee+89DBo0CLNmzSrW9mzatEnj9lq1auHrr7/W6hytW7dG69at9dgqIiprdH1aTJeyVH7pPYG+ffu2xu1ZWVm4cOECNmzYoDZrpr5wvUlin6hjn2imj35JycxBckaOVmVTM/OWaTAzM1Gu66gNU1MTnY8rzDEKJbl9hTnO7H9PCRR23ciy8P45cuQI5s+fj1atWsHPzw8ff/wxgLyhVW5ubggPD4ezszP69Olj5JYSEekPnxYjQ9F7At2pU6d8P70RQsDU1BTjxo3Td7UAuN5kecY+Ucc+0Uwf/WJiIsHOrhJycuRar1+YmysHAJ2OKexxha2rpLevMMdZmEjIkcthpuMHndm5cpibmujlXm1sa9euhbOzM9atW4eUlBRlAl27dm1s3LgRAwYMwJYtW5hAE1GZIkl5T4tN3nYOyWnaPS1Wy84Kn/RqauCWUWmn9wQ6ICBAYwJtamqK6tWro2fPnqhTp46+qwXA9SaJfaIJ+0SzovQL+7P0qGhuCjMT3R63t7O2wPy+eZNMloX3z8WLFzFu3Djl+s8vMzMzQ69evfDNN98YoWVERIaXnJaF5LRsrcpWsdSuHJVvek+gFy5cqO9Tao3rTRIRkSZPnmfhUWqmsZthNC/PYP2qrKws5ORoNxSBiIiovCv2ScQMietNEhERqZLJZDhy5AgGDx6sti83Nxd79+5FgwYNjNAyIiKi0kfvCfSgQYN0PkaSJISFhem7KQXiepNERFQeDB48GB9++CHmzp2Lrl27AgDS0tIQGxuLZcuW4dKlS/jss8+M3EoiIqLSQe8J9NOnT5GUlITExEQAgL29PSpUqIAHDx4gNzcXkiSpzWxtjCnjud4kERGVBz169MA///yDtWvXIjw8HACUk3kKIRAYGIjAwEBjNpGIiKjU0HsC/eWXX2LIkCEIDg7GxIkTYWdnBwDIzMzE+vXrsXLlSqxduxbNmzfXd9X54nqTRERUnn344Yfo0qULfvrpJ8THx0Mul8PR0RFdunSBp6ensZtHRERUaug9gV6wYAFatGiBOXPmqGyvUKECxowZg5s3b2LRokXYsmWLvqsmIiKiVyxatAj/+c9/4OHhoZwYk4iIiApH7yuLx8bGwtvbO9/9Li4uuHTpkr6rJSIiIg3CwsJw/vx5YzeDiIioTNB7Al2pUiVcuXIl3/3nzp1TPtZNREREhmVtbW3sJhAREZUZek+gfXx8EBERgfDwcOTm5iq3Z2Vl4fvvv8eePXvQo0cPfVdLREREGkyePBlr1qzBzp07cf/+fWRnZ0Mul6v9EBER0evpfQz0f//7Xxw/fhxz587FV199hbfeegtCCNy5cwfp6elo1qwZ3n//fX1XS0RERBqsXr0aGRkZmDlzZr5lJEni8CoiIiIt6D2BtrOzQ1RUFFavXo1Dhw7h1q1bAIB69erBz88PQ4YMgYWFhb6rJSIiIg0cHBzg4OBg7GYQERGVCXpPoIG8cdAffPABPvjgA0OcnoiIiLSU31KOREREpDuDJNAA8ODBA0RHRyMhIQF9+/aFlZUVHjx4gCZNmhiqSiIionLv2bNnsLS0hKmpqbGbQkREVObofRIxANi4cSM6deqEjz/+GKtXr8bdu3dx8eJF9OvXD3PnzjVElURERASgRYsW2Ldvn7GbQUREVCbpPYE+cuQI5s+fDzc3N8yZMwdCCAB5Y6Dd3NwQHh6OHTt26LtaIiIiApRx92VJSUlo1KgRTpw4YYQWERERlR16T6DXrl0LZ2dnrFu3Dp07d1Zur127NjZu3IgmTZpgy5Yt+q6WiIiICqApsSYiIiLd6D2BvnjxIrp3765x7JWZmRl69eqF+Ph4fVdLREREREREZFAGGQNdoUKFfPdlZWUhJyfHENUSERERERERGYzeE2iZTIYjR45o3Jebm4u9e/eiQYMG+q6WiIiIiIiIyKD0nkAPHjwYx44dw9y5c3Ht2jUAQFpaGmJjYzFmzBhcunQJQUFB+q6WiIiIiIiIyKD0vg50jx498M8//2Dt2rUIDw8HAIwbNw5A3gQmgYGBCAwM1He1RERE9D8HDx7EzZs3lf/OyMiAJEnYtWsXzpw5o1ZekiS8//77xdlEIiKiUknvCTQAfPjhh+jSpQt++uknxMfHQy6Xw9HREV26dIGnp6chqiQiIqL/OXDgAA4cOKC2PSoqSmN5JtBERETa0XsCvWjRIvznP/+Bh4cHXFxc9H16IiIiKsCCBQuM3QQiIqIyS+8JdFhYGGxtbeHh4aHvUxMREdFr9O7d29hNICIiKrP0PomYtbW1vk9JREREREREZHR6/wZ68uTJ+OKLL2Bvbw9PT0/Y29vD1NRUrZyJiUGWoCYi0pkk5Y0B1ZaJifZliYiIiKjs0HsCvXr1amRkZGDmzJn5lpEkCZcuXdJ31UREOpMkoIqtNczN+KEeERERERVM7wm0g4MDHBwc9H1aIiKDkCQJ5mYmmLk9Bk+eZ2l1TL1q1pjazdnALSMiIiIFXZ8W06UskS70nkBv2rRJ36ckIjK4J8+z8Cg1U6uydtYWBm4NERERKfBpMSpJipxAP3v2DJaWlhrHORMRERERERWF4mmxydvOITlNu6fFatlZ4ZNeTQ3cMiqPivwxTosWLbBv3z59tIWIiIiIiEij5LQsJKdla/WTkp5t7OZSGVXkBFoIobYtKSkJjRo1wokTJ4p6eiIiIiIiIqISwWADCTQl1kRERERERESlFUfiExEREREREWlB77NwExERERER5UfTklSSJCG/lae4JBWVJEygiYiIiIioWOS3JJW9fSUjtYhIN0ygiYiIiIioWGhaksrczBTZObn5HsMlqagk0UsCffDgQdy8eVP574yMDEiShF27duHMmTNq5SVJwvvvv6+PqomIiIiIqJRRLEkFAObmcmRn559AV7HkklRUcuglgT5w4AAOHDigtj0qKkpjeSbQREREREREVNoUOYFesGCBPtpBREREREREVKIVOYHu3bu3PtpBREREREREVKJxHWgiIiIiIiIiLTCBJiIiIiIiItICE2giIiIiIiIiLTCBJiIiIiIiItICE2giIiIiIiIiLTCBJiIiIiIiItICE2giIiIiIiIiLTCBJiIiIiIiItICE2giIiIyuH///RcTJkxA69at0aRJE3To0AFz585FSkqKSrm4uDiMHTsWnp6ecHNzw9ChQ3HmzBmN5zxy5Aj69+8Pd3d3tGrVClOmTMH9+/eL43KIiKicMjN2A4iIiKhsu379Ovr37w8zMzMMHDgQNWrUwPnz5xEeHo6TJ09i27ZtsLa2xrVr1zBw4EBUqFABISEhsLa2Rnh4OIYOHYp169ahZcuWynPu2bMHH374IRo3boyJEyciJSUF69evx19//YUdO3bA3t7eiFdMRERlFRNoIiIiMqi5c+ciOzsbkZGRqF+/PgCgf//+cHZ2xrx58xAWFobRo0djwYIFyMzMxI4dO1CrVi0AQI8ePdCjRw/MmTMHe/fuBQCkpaVh3rx5kMlk2Lx5MypUqAAA8PT0xKBBg7Bs2TJ88sknRrlWIiIq2/gINxERERlMZmYm/vrrL3h4eCiTZ4WAgAAAwKlTp/D48WMcPXoUvr6+yuQZAOzs7BAYGIirV68iJiYGABAdHY2kpCQMGDBAmTwDgIeHB9zc3LBnzx5kZWUZ/uKIiKjcKbPfQP/777/49ttvcerUKTx79gzVqlWDr68vJkyYABsbG2W5uLg4fPXVVzh37hwyMjLg4uKCCRMmoHnz5mrnPHLkCFatWoUrV67A3Nwcbdu2xZQpU/Dmm28W56URERGVGubm5ti3bx/kcrnavsePHwMATE1Nlcmxq6urWjkXFxcAQExMDFxdXZVlmzVrplbW1dUV586dw/Xr19GwYUN9XQYR5UOSAEmSdCivfVmikqhMJtAca0VERFQymJiYqHyj/LLVq1cDAFq1aqWc/KtGjRpq5RQfVN+5cwcAlGU1fYD9clkm0ESGJUlAFVtrmJvxoVYqP8pkAs2xVkRERCXbjh07sGPHDtSoUQPBwcEIDw8HAFhZWamVrVixIgAgPT0dAPDs2TMAgLW19WvLaqsoX4opjuUXa6rYL+rKWp9IkgRzMxNM3nYOyWnaDZuoZWeFT3o1NXDLqKzKe+Kh8MfqQ5lLoF831mrevHk4deoU+vbti6NHj8LPz0/jWKuVK1cqHxVTjLWaOHFivmOtZs6cCQsLi2K7TiIiotIqMjISs2fPhpWVFZYuXYpKlSpBCPHa4xSPfhZUVrHPxES3b8Ts7SvrVN5Q5yiL2C/qylqfPM/KxfNs9WEamqT9r5y5mSnMzV8cY25umu8xZmamGo8pSHEcY+g6FH1SUq+/OI8x/98xdnaVtCpvSGUugeZYKyIiopJr6dKl+Pbbb1GpUiWsWrVKGXMV3yZnZGSoHaP4Nlkxh4mibHp6utqH14rjK1fWLUFJTEyFFjm8RpKUlxAV5RxlEftFXVnrE0mSYG9fCdk5ucjOztXqmJycvHIvH2Nublrg8ZqOKUw9+j7GkHW83Ccl9fqL85hs87wPRZ88eQa5vHBvHsX7r6jKXALNsVZEREQlT3Z2NmbNmoWoqChUr14dq1evVombjo6OAF7E3Je9GrNfLlulSpUCy2pLCBQ5odHHOcoi9ou6stInZeVRdCo9SsJ7p8wl0PnhWKuyjX2ijn2i2av9wv6hgujjXk1Abm4uJk2ahAMHDkAmk+H7779XS3CbNm0KExMTxMbGqh2v2Obm5gbgxZNisbGxcHJyUitbuXJlvPPOO4a4FCIiKufKRQLNsVblB/tEHftEs1f7xczMpMDxVy8zNTUplmOKu66S3r7iqsvspdlk+f7Rj6+//hoHDhyAi4sL1q5dq7KcpIKDgwO8vLxw4MABTJgwQfk02ZMnT7B9+3Y0bNgQzs7OAID//Oc/qFKlCsLCwtCrVy/lY9ynT5/GuXPnEBISwqVyiIjIIMp8As2xVuUD+0Qd+0SzV/vFxESCnV0l5OTItR6Hk5ubN8eCoY8p7rpKevuKq66cnBdzaOjjXl3e3b59G+vWrYMkSejUqROOHDmiVsbe3h7e3t6YNm0agoODMWDAAAwbNgwWFhYIDw9HSkoKvvnmG2V5S0tLTJ8+HTNmzMCAAQMQGBiIxMRErF+/Ho6OjnjvvfeK8xKJiKgcKbMJNMdalU/sE3XsE80U/cK+oYLwNVJ0R48eRU5ODgBg8eLFGsu4u7vD29tbuVzkkiVLsGLFCpiYmKBJkyb4/PPP1Sby7NOnD6ysrPD9999j/vz5qFKlCnx9fTFp0iTY29sb+rKIiKicKpMJNMdaERERlQwDBw7EwIEDtS7fqFEj5aSfr9O1a1d07dq1sE0jIiLSmW4Dd0uJl8dahYeHa/x2+OWxVrdv31Zuf91Yq6ysF4vEK8ZaBQQEcKwVERERERFRGVfmvoHmWCui8kuS8NoPsxS7TUwk5RhoIiIiIiJtlLkEmmOtiMonSQKq2FrD3Ey7B2vs7CoZuEVEREREVNaUuQSaY62IyidJkmBuZoKZ22Pw5HlWgWXNzEyUMy3Xq2aNqd2ci6OJRERERFTKlbkEmojKtyfPs/AoNbPAMubmpsoljeysLQosS0RERESkUCYnESMiIiIiIiLSNybQRERERERERFrgI9xEVCJpM6P2yzibNhEREREZGhNoIipxdJ1Rm4iIiIioODCBJqISR5cZtRU4mzYRERERGRoTaCIqsbSZUVuBs2kTERERkaHx+UgiIiIiIiIiLTCBJiIiIiIiItICE2giIiIiIiIiLXAMNBERERFRGaPrcpAAIISAEAZqEFEZwQSaiIiIiKgMKexykNk5cjxNfs4kmqgATKCJiIiIiMoQxXKQk7edQ3KadstB2lpZYHGwGyRJgmAGTZQvJtBERERERGVQcloWktOyjd0MojKFk4gRERERERERaYHfQBMREREREYC8x79NtPyKTddJyojKAibQRERERETlXEVzU+TkymFnZ23sphCVaEygiYiIiIjKuQpmJjAz1W3isVp2VvikV1MDt4yoZGECTUREREREAHSbeKyKJScoo/KHk4gRERERERERaYEJNBEREREREZEWmEATERERERERaYEJNBEREREREZEWmEATERERERERaYGzcBORwUkSIEmS1uVNTLQvS0RERERUXJhAE5FBSRJQxdYa5mZ84IWIiIiISjcm0ERkUJIkwdzMBDO3x+DJ8yytjqlXzRpTuzkbuGVERESlg65PculSloh0wwSaiIrFk+dZeJSaqVVZO2sLA7eGiIiodOCTXEQlCxNoIiIiIqISSvEk1+Rt55Ccpt2TXLXsrPBJr6YGbhlR+cQEmoiIiIiohEtOy0JyWrZWZatYaleOiHTHZ0GIiIiIiIiItMAEmoiIiIiIiEgLTKCJiIiIiIiItMAx0ERERERExUiSJGi70hSXpCIqWZhAExEREREVA0kCcnLlsLevZOymEFEhMYEmIiIiIioWEsxMuSQVUWnGBJqIiIiICHnfEOv6yLQQAkLoVg+XpCIqvZhAExEREVG5J0lAFVtrmJvpNsdudo4cT5Of65xEE1HpxASaiIiIiMo9SZJgbqbb49W2VhZYHOwGSZIgmEETlQtMoImIiIiI/keXx6sVJEmCiRZfXHNGbaLSjwk0EREREVEhVDQ3RU6uHHZ21sZuChEVEybQRERERESFUMHMRKdZtTmjNlHpxwSaiIiIiKgItH3smzNqE5V+uk0zSERERERERFRO8RtoItJaYdbHNDHhhClEREREVDYwgSYqp3RNhiUJqFzFCuamfHCFiIiIiMonJtBE5ZAkAVVsrWFupnsyPHN7DJ481259TACoV80aU7s561wPEREREVFJwwSaqBySJAnmZiY6JcOKRPjJ8yw8Ss3Uui47a4vCNpOIiIiIqERhAq2DpKQkLF++HIcPH0ZiYiLq1KmDIUOGIDAw0NhNIyoUXZJhJsJEVNIwLlNBdB+qxDk7iOj1mEBrKS0tDSNGjEBcXBwGDhyIevXq4eeff8b//d//4fHjxxgzZoyxm0hERFRuMC6XL4Wat8PGqlBDlYiICsIEWkthYWG4dOkSFi1ahB49egAAgoKC8O6772L58uXo1asXatSoYeRWUnml6Q8LxT9NTCQIoVqeM2MTUWnHuFx6FWcyPHnbOSSnaTdUqZadFT7p1VTnOoiofGECraWoqChUq1YN/v7+ym0mJiYYOXIkjh07hj179mDUqFFGbCGVBYVZJup1s2Pb2VXSR9OIiEqU8hSXCxMbhBBqH56WhHqKMollYZLh5LQsJKdla3VMFUvtyhFR+cYEWgupqam4fv06fHx81AKLq6srACA2NtYYTaNiUJg/KIDi/aMC0Dw7tpmZCXJy5GplOTM2EZVm5SkuFzY2ZOfI8TT5udZxqPjqyZvEkskwEZVWTKC18ODBAwghND4KVqlSJVhbW+POnTtGaJl2iisBLA2Kc+3j7Bw5UlPStO5DExPdZ8YGCp4d29zcFNnZuWrHcEIwIirNSntc1kVhEk5bKwssDnaDiYkJhJZByJD1SJKkHFakiMFMhomotGICrYXU1FQAgJWVlcb9lpaWSE9P1+mcJiYodHL6Ymzr68+RlwBaFy4BzJXjmQ4JYB4BQNdkvTDHqB6n6BNTU/XxvgqSBFSyKVwyvOzXK3iq5R8UAFC9SkWMaFuvUI9PVzAzQUVz7dto8b9vC96qUlHtODNTE+Tkqn8DXd2mQr7H5KcwxxRnXboc83K/lMT2GaOu6pUrluj2FWddVa1efMCkj3s16V9pjsuFPXdebDDV6hibimbIyZXDzs5a5/oMUY+9vXosfMOmotb1VKtcocwcoyj/Zglrl7GPMTczQbaGJ+ZKQtuMVcfLfVJSr784j6liaQ4g755oUsi5AfUVlyWh7UeT5djZs2cxYMAAjBo1CpMnT1bb7+XlBRsbG/zyyy9GaB0REVH5wrhMRETGwrn9tWBtnffJan6fZqenp8PGxqY4m0RERFRuMS4TEZGxMIHWQs2aNSFJEh48eKC2LzU1FWlpaXjzzTeN0DIiIqLyh3GZiIiMhQm0FipVqoT69evjwoULavtiYmIAAO7u7sXdLCIionKJcZmIiIyFCbSWevbsiXv37uGnn35SbpPL5Vi3bh0sLCzQvXt3I7aOiIiofGFcJiIiY+AkYlrKyMhA3759cfPmTYSEhKBu3brYt28fTpw4galTp2LkyJHGbiIREVG5wbhMRETGwARaB0+ePMGSJUtw+PBhPH/+HHXr1sWwYcMQEBBg7KYRERGVO4zLRERU3JhAExEREREREWmBY6CJiIiIiIiItMAEupjduXMHTk5OBf74+Pgoy/v4+ORb7s6dO8py//zzD/r16wcXFxcEBATgxIkTanUnJibCzc0Ne/bsKZZr1dWNGzcwceJEtGrVCk2aNEG3bt3www8/QC6Xq5S7e/cupk6dCm9vb7i6uiIoKAiHDh1SO9+9e/cwfPhwuLi4oFu3bti3b59amczMTHTo0AErV6402HUVhbZ9EhISku/r5M8//1SWKwt9EhcXh7Fjx8LDwwPNmzdHSEgIfvvtt3zLeXp6ws3NDUOHDsWZM2fUypWF9462fVLW7ycxMTFwdnbG8ePH1fZpe98AgLNnz2LYsGFo0aIFPDw8MGbMGFy9elWt3J9//onu3bujWbNmGDBgAC5fvqxW5sqVK3B2dtb42qOSgXE5f4zL6hiX1TEuq2NczlNW4zIf4S5maWlpOHjwoMZ9e/bswdGjRzF8+HBMnz4dqamp8PDwwH/+8x/4+fmple/UqROsrKwgl8vh5+cHIQSGDBmCw4cP4/Tp0/jll19Qo0YNZfk5c+bg/Pnz2LFjByRJMtg1FsadO3fQt29fpKWlYfDgwahVqxYOHjyI48ePIzg4GJ9++ikA4NGjRwgODkZycjJCQkLwxhtvIDIyEhcvXsSiRYvQo0cP5TmHDx+OK1euYPTo0Th//jz27duHH3/8ES4uLsoya9aswYYNG3DgwAFYWloW+3UXRNs+AYCWLVuiTp06GDRokNp52rRpAwcHBwClv08uXLiAIUOGIDs7G/3790fdunVx+PBh/PHHH5g5cyaGDh0KALh27Rr69++PChUqYODAgbC2tkZ4eDju3r2LdevWoWXLlgBQJt472vZJWb+fxMfHY/DgwXj06BHWr18PLy8v5T5d7ht//vknRo4ciZo1a6Jfv36Qy+XYuHEj0tPT8eOPP6J+/foAgGfPnsHX1xe1a9dGQEAAduzYgQcPHmD//v2wtrZWnm/UqFEwMzPDihUriq8zSCeMy5oxLqtjXFbHuKyOcTlPmY7LgkqEy5cviyZNmoiBAweK7OxsIYQQf/31l5DJZGLbtm0FHnvmzBkhk8lEdHS0EEKIlJQU4ezsLFatWqUsc+PGDeHs7CyOHj1quIsogjlz5giZTCZ27dql3CaXy8XgwYOFTCYTV69eFUIIMXv2bCGTycTp06eV5dLT04W/v79o1aqVeP78uRBCiPv37wuZTCbCwsKEEELk5OSINm3aiNmzZyuPS05OFi1atBBbt24tjkvUmbZ9cvfuXSGTycSSJUsKPF9Z6JPevXsLmUwmDh8+rNwml8vFxIkTRePGjcX169eFEEKMHDlSNG3aVNy6dUtZLjExUXh5eQk/Pz/ltrLw3tG2T8ry/eTAgQOiRYsWQiaTCZlMJo4dO6ayX9v7hlwuF35+fqJNmzYiKSlJWfbGjRuiadOm4t1331Vu2717t5DJZCIuLk4IIURcXJyQyWRi7969yjInT54UjRo1Ur5XqXRhXGZcfhXjsjrGZXWMy2U/LvMR7hJALpdjxowZAID58+fDzMwMAPDvv/8CABo0aFDg8ffv3wcA1K5dGwBQuXJlVK1aFXfv3lWWWbJkCVq2bAlvb2+9t18fbt68CQDo0KGDcpskSfD19QWQ9+hKbm4udu/eDVdXVzRv3lxZrmLFiggJCUFSUhKio6MBvOiTt99+GwBgamoKR0dHlT5ZuXIl7O3tERgYaNBrKyxt+gTQ/XVSWvvk3r17uHjxIlq2bKnWJ2PGjEF2djZ27NiBx48f4+jRo/D19UWtWrWU5ezs7BAYGIirV68iJiYGQOl/72jbJ0DZvZ+MGjUK48aNQ7Vq1eDv76+2X5f7xoULF3D16lUEBATA1tZWWbZOnTro1KkT/vjjDzx8+BBAXt8DL/pJ8d+EhAQAgBACX375Jfr06aP8dJxKD8ZlxmVNGJdVMS6rY1wuH3GZCXQJsGPHDly6dAkjR45U3kSBFzdixRvr+fPnEBqeuLe3twcApKSkAMh7Yaampiq3x8TE4ODBg5gyZYpBr6Mo6tWrByBvzMjL4uPjAQBvvPEG4uLikJaWBldXV7XjFY86KW7Ar/YJACQlJSm33717F2FhYZg0aRJMTU31ezF6ok2fAOqvk7S0NLWxWEDp7xPFjdHJyUltn+J98/fffytfA4V5nZS29462fQKU3fvJ9evXMWnSJOzcuRN16tRR26/LfeP8+fMAgGbNmqmVdXV1hVwux4ULFwBA+filop+SkpIAvOi/n3/+GXFxcRg/fnzhL46MhnGZcVkTxmVVjMvqGJfLR1xmAm1k2dnZWLZsGWxtbREaGqqy759//kHlypXxxRdfoEWLFnB3d0fLli0xd+5cpKWlKcs1btwYtra2+P7773H79m2sWrUKGRkZaNu2LQDgyy+/RLdu3dC4ceNivTZdjBo1CvXr18fMmTNx4sQJ3LlzB2FhYYiIiICXlxeaN2+OBw8eAIDKeA+FN998EwCUEy689dZbqFOnDjZs2IAbN25gx44diI+PR7t27QAA33zzDRo3boxOnToV0xXqTps+AV7cgCMjI9GmTRu4ubmhefPmmDp1KhITE5XnK+19YmVlBSAvwLxKcZN89OiR8pNabV4npf29o22fAGX3frJv3z6MHj0aFhYWGvfrct9QvHYU2wsq26JFC5ibm2P58uW4ffs2vvvuO1hYWKBVq1bIzs7G119/jSFDhij/oKbSg3E5D+OyOsZlVYzL6hiXy0dcNivyGahIfvnlF9y/fx/jx49XGeAul8sRFxeH9PR0PH78GJ9++ilyc3Nx8OBBbNq0CRcuXMCmTZtgYWGBSpUqYe7cuZg6dSo6duwIU1NT/Pe//0WzZs1w5MgRnD9/Hj///LMRr/L1qlWrhokTJ2LmzJkYNmyYcru7uzuWL18OSZKQmpoK4MXN6WWKSTXS09MBACYmJpg/fz7Gjx+Prl27AgD69+8PPz8//PPPP9i9ezc2bdpk4KsqGm36BHjxCNA///yDqVOnokKFCjh27BgiIiJw7tw5REREwNbWttT3yTvvvIMqVargt99+Q2pqKipXrqzcp3h9Z2Rk4NmzZwA0v04qVqwI4MXrpLS/d7Ttk7J8P8kvQCvoct/Q5bVTq1YtzJgxAwsWLMCWLVtQoUIFzJkzBzVr1kRYWBiePn2KUaNGFfKqyJgYl/MwLqtjXFbFuKyOcbmcxOUijaCmIgsODhZNmzZVGRgvRN5A+tWrV4vNmzerHfPZZ58JmUwmwsPDVbanpqaK8+fPi8TERCFE3mQU3bt3F5999pkQQoj4+HgxbNgw0apVK9GvXz9x8uRJw1xUIaxatUrIZDLh4+MjwsLCxMGDB8XixYuFi4uL8Pf3Fw8ePFBODrBlyxa149PT04VMJhMjRoxQ2x4TEyPu3bun3DZixAgxevRoIYQQjx49EuPGjROenp6iR48e4ueffzbshepAmz4RQoiNGzeKVatWCblcrnL8unXrhEwmE1988YXK9tLcJ+vXrxcymUwEBgaKU6dOiVu3bomNGzcKNzc34e7uLrp06SK+++47IZPJxO+//652/NWrV4VMJhMfffSRyvbS/N7Rpk/Ky/1k6dKlapOV6HLfmDVrlpDJZOLatWtqZaOjo4VMJlOZtEUIIZ48eSLOnz8vnj59KoTI67fWrVuLdevWCSGEiImJEcHBwaJVq1YiJCREXL58WW/XS4bBuJyHcVkd47I6xmV1jMsvlNW4zATaiO7fvy+cnJzE+++/r9Nxt27dEjKZTLz33nsFlvvxxx+Fm5ubSExMFNnZ2aJTp05i6NChIiYmRsybN09tNkRjSU1NFS4uLsLLy0t5U1A4duyYcHJyEh988IE4dOiQkMlkYv369WrnePLkiZDJZOK///1vgXUdP35cNGrUSFy5ckUIIcTgwYNFjx49xJkzZ8T3338vnJycxJkzZ/R2bYWlbZ8UJDMzUzRq1Ej07NmzwHKlpU8UVq5cKVxcXJQzO3p7e4tff/1VdO7cWQQHB4uNGzcKmUwmDhw4oHbshQsXhEwmE19++WWBdZSW947C6/qkIGXpfqIpUOty31iwYIGQyWTi4sWLamV/+eUXIZPJNP6x87KvvvpKdOjQQWRmZoqnT58KDw8PMWnSJBETEyM++OAD4enpKVJSUop2oWQwjMt5GJfVMS7nj3FZHeNynrIalzkG2oh+/fVXCCE0rv1WEMVgeE3jKxQyMjKwbNkyjBgxAnZ2djh//jxu3ryJcePGwcXFBR9++CGAvHEKxnbjxg1kZGTA19cXdnZ2Kvu8vLxQu3Zt/PHHH3B0dATwYjzEyxTjKTSNkVAQ/5t9r1evXmjQoAHu3buHU6dOYcSIEXB3d0doaChq1aqFqKgo/V1cIWnbJwWxsLCAjY1Nga+T0tQnCqNHj8bx48exZcsW7Ny5E9HR0Wjbti0SEhJQu3btAl8nBY3DUihN7x2F1/VJQcra/eRVutw3ivraefjwIX744QdMmDABFhYWOHz4MFJSUjBlyhS4uLgoxz/+9ttvRbsoMhjG5TyMy+oYl/PHuKyOcTl/ZSEuM4E2olOnTkGSJOUkAC/77bff0LVrV6xbt05t39WrVwFAZWbQV23YsAFyuRwjRowAAOUU74o3pbm5OWxsbJSzBRpThQoVAEDjDJVA3myDcrkc9erVQ+XKlREbG6tWRjFbn7u7e7717N27F1evXsXEiRMBvOgTxax9QN6SCqWpTy5fvozu3btj/vz5amUSExORlJRU4OukNPUJkDd+6Ndff4W1tTXc3d3h7OwMU1NTnDhxAtnZ2WjZsiWaNm0KExMTja8TxTY3N7d86yhN7x1Auz4pT/eTV+ly31DM/pnfa0eSJI2zhiosX74ctWvXRs+ePQGo95Pij+6S2E+Uh3E5D+OyOsZlzRiX1TEuF6wsxGUm0Eb0999/o06dOioTDCi88847uHXrFsLDw5UD6AEgJycH33zzDQCgd+/eGs+blJSE1atXY+zYscpB94oZ527fvg0gbwB/UlJSgZ8MF5d33nkHNWvWxP79+5VrtSkcOnQId+7cgbe3N8zMzODn54ezZ8/i7NmzyjIZGRkICwuDg4ODcubKV2VlZeGrr75CSEiI8poV/1X0SW5uLhISEkpVn9SpUwcPHjzAzp07VT6dE0Jg8eLFAIA+ffporKO09QkAbN68GTNmzEBycrJy27Nnz7B06VJUr14d/v7+cHBwgJeXFw4cOKC8DgB48uQJtm/fjoYNG8LZ2Vnj+UvbewfQrk/K0/3kVbrcN1xcXFC3bl1ERkaq9Gd8fDwOHjyIDh06oGrVqhrruXbtGiIjIzF58mSYmOSF1lffT7du3VLZTiUP43IexmV1jMuaMS6rY1wuWFmIy5yF20iysrKUN1tNatasiffffx9Lly5FYGAggoKCIEkS9uzZg4sXL2LMmDH5flr33Xffwd7eHkFBQcptLi4uqFOnDubNm4fbt2/j8OHDMDU1Rffu3Q1yfbowMTHBvHnzMGrUKAQGBiI4OBg1atTAxYsXsX37dlSvXh1Tp04FAIwfPx6HDx9GaGgohg8fDjs7O0RGRiIuLg5LlixRfkL8qi1btiA1NRWjR49WbnvjjTfQsmVLLF++HNnZ2YiJicHjx48REBBQHJddIG37xNLSErNmzcL06dMRFBSEAQMGoFKlSvj1119x8uRJ9OjRI9/fcWnrEwAYO3YsRo4ciZCQEAQFBUEIgYiICMTHx2PFihXKGRmnTZuG4OBgDBgwAMOGDYOFhQXCw8ORkpKiDEyalLb3DqBdn5Sn+4kmutw3Zs+ejdDQUAQHB2PQoEHIzMzEhg0bYGlpqXwkTpMlS5agefPmaN++vXJbu3btUKVKFUyfPh29e/fGzp07YWdnp1KGSg7G5RcYl9UxLmvGuKyOcfn1Sn1c1mnENOnN/fv3tZpcY+/evSIoKEi4uLiIZs2aiaCgIPHTTz/lW/7WrVuicePGYt++fWr7rl69KgYPHiyaNWsmevbsqTKgvyS4fPmyGD9+vGjVqpVwdnYW7du3F7NnzxYPHz5UKXfr1i0xceJE0aJFC9GsWTMRHBwsoqOj8z1vamqqaNWqlVi9erXavvv374sxY8YId3d30blzZ7Fnzx69X1dRaNsnx44dE0OGDBHNmjUTTZs2Fb169RJhYWEiNzdX43lLc58cO3ZMDBo0SDRv3ly0atVKjB49WsTExKiVu3Tpknj33XeFm5ubaN68uRg6dKg4d+5cvuctze8dbfukrN9PNE1WoqDLfePEiRNi4MCBwsXFRbRq1UqMHTtWXL16Nd96z5w5I5ycnDT2+dmzZ0WfPn2Udf7999+Fv0AyKMZldYzL6hiX1TEuq2NczlNW47IkhBA6f2xAREREREREVM5wDDQRERERERGRFphAExEREREREWmBCTQRERERERGRFphAExEREREREWmBCTQRERERERGRFphAExEREREREWmBCTQRERERERGRFphAExEREREREWmBCTQRERERERGRFphAE73GsmXL4OTkpPbTpEkTeHt74/3338fZs2eLrT05OTlwcnJCSEiIctv06dPh5OSEmzdvFuqc8fHxemrdC+3atYOPj4/W5aOiouDk5ARXV1c8ffpU7+0hIiJ6VefOneHk5IRFixYV+VwPHjxAWlqaHlpVMB8fH7Rr107r8oyvRPrFBJpIS8HBwfjiiy+UP3PmzEH//v1x9uxZhISE4OjRo0Zvm4ODg07HpaSkoH///li5cqWBWqa97du3w8rKChkZGdi5c6exm0NERGXcqVOncPPmTVhZWWHHjh3Iysoq9LkiIyPRtWtXPHnyRI8t1A/GVyL9YgJNpKVmzZqhV69eyp++ffti3LhxWLNmDXJzc/HFF18YrW1ubm7o1asXrK2tdTouKSkJ586dM1CrtHf79m389ddfCA4ORpUqVbBt2zZjN4mIiMq47du3w9TUFCNHjkRiYiIOHjxY6HOdPHmyWL591hXjK5H+MYEmKqLGjRujQYMGuHLlCh+NKqTt27dDCIE2bdrAx8cH169fx59//mnsZhERURn17Nkz7N+/H02bNkWvXr0AAFu3bjVyq/SP8ZVI/5hAE+mBiUneWyk3NxcAEBISgq5du2L79u3w8vKCq6uryjfUe/bsQVBQEJo1awY3NzcMGjQIhw8fVjvvkydPMHv2bHh7e8PV1RUhISG4dOmSWjlNY6CFENi2bRv69u0LNzc3eHp6YvTo0bhw4QIAYMeOHejcuTMAYOfOnXByclIJqkePHsWQIUPg7u4OV1dX9OnTBzt27FCrOz09HV9++SU6dOgAFxcX9O3bFydOnNC67+RyOaKiolCxYkV4eHigW7duAIAtW7ZoLP+661J4/vw5lixZgi5dusDFxQUdOnTA7Nmz8fDhQ2WZkJAQODk5IScnR+XY48ePw8nJCcuWLVNu8/HxwciRI7Fy5Up4eHjA3d0dmzZtAgA8fPgQ8+bNU9bl4uICPz8/fPvtt2rnfl27li9fDicnJ+zbt0/t2vfv3w8nJydERERo271ERKTBvn37kJ6ejjZt2qBWrVpwcXHBqVOncO3aNbWyt27dwvTp09G2bVu4urqiW7duWLlypfKRbx8fH+zZswcA4Ovrq5yjJL/5SW7evAknJydMnz5dZfuJEyfw3nvvwcvLC40bN0aLFi0wdOhQHD9+vFDXyPjK+EqGYWbsBhCVdgkJCbh27Rpq1qwJOzs75fZ79+7hiy++wMiRIwHkPQIOAF9++SXWrFmDNm3aYNKkScjMzMTevXvx3nvvYcaMGRg2bBiAvE/H+/fvjzt37iAoKAgNGjTAyZMnlftf5//+7/+wfft2eHh4YOLEicjOzkZYWBhCQkKwadMmtGjRAtOmTcPnn38ODw8PBAUFoX79+gCA8PBwfPbZZ2jatCnGjRsHExMTHDp0CDNmzMDly5fxf//3fwDyPjAYPnw4zp07B39/f7i7u+Pvv/9GaGgoJElCtWrVXtvOY8eO4d69e+jatSssLS3h5eUFW1tb/Prrr0hMTIS9vb1O19W0aVOkp6ejf//+uHLlCnr06IFhw4bh9u3bCA8Px59//omIiAjY2Nho1Y8vO3v2LOLi4jBhwgQkJyfD09MTqampCA4ORkpKCgYOHIjatWsjOTkZUVFRWLp0KTIyMjB58mQA0KpdvXv3xrfffovdu3fDz89Ppf6dO3fCyspKbTsREelm+/btAKC8n3bv3h2xsbHYsmULZs2apSx35coVDBgwAHK5HP3798fbb7+Nv/76C1999RX++ecffP3115g5cybWrFmDc+fOYcaMGWjQoIHO7dm/fz8mTpwIZ2dnjBo1CtbW1oiLi0NERARCQ0MRFRWl83kZXxlfyUAEERVo6dKlQiaTiU2bNonExETlz71798SRI0dEz549hUwmE5GRkcpjBg8eLGQymYiIiFA5V0xMjJDJZOKTTz5R2Z6VlSVCQkJE48aNxb1794QQQnzzzTdCJpOJnTt3qpRduHChkMlkYvDgwcpt06ZNEzKZTMTHxwshhDh16pSQyWRi0qRJQi6XK8vdvHlTODs7i7FjxwohhIiPjxcymUxMmzZNWebevXuicePGYvTo0SrHyuVy8eGHHwqZTCZiYmKEEEJERkYKmUwmli1bptLGsLAwIZPJRIcOHV7bvxMnThQymUzs379fue2jjz4SMplMrFy5UqWstte1fPlyIZPJxObNm1WO37lzp5DJZOKHH34QQrz4PWVnZ6uUO3bsmJDJZGLp0qXKbR06dBAymUwcP35cpeyGDRuETCYTv/zyi8r2p0+fisaNG4vu3bsrt2nbruHDhwtnZ2eRmJioLPP48WPh7Oys8rsiIiLdXb16VchkMuHv76/cdv/+fdGwYUPh4eEh0tLSlNuHDBkiGjduLC5fvqxyjunTpwuZTKbcPnnyZCGTycTt27eVZV6NzQqaYm9AQIBo06aNeP78uUpZRTxds2aNcluHDh1E27ZtX3udjK+Mr2QYfISbSEufffYZPD09lT/t27fH6NGjkZiYiI8//hh9+/ZVO8bLy0vl3z/99BOAvE+8nzx5ovxJTU2Fn58fsrOzceTIEQDAr7/+CltbW/Ts2VPlHKGhoa9t6/79+wEAw4cPhyRJyu21a9dGZGQkPv744wKPzc7ORrdu3ZCUlKRsY1JSErp37w4AOHDggLKNkiSpLKkF5M0KXrly5de2Mzk5GYcOHUKlSpXQvn175XZ/f38AwLZt2yCXy3W+rv3798PGxgZBQUEq9XXv3h0RERHo06fPa9umibm5OVq0aKGybciQITh+/Dg6deqksv3JkyeoXLkynj17ptJ+bdrVt29f5OTkKF8vQN5j/zk5OYVuOxER5YmMjATwItYAwBtvvAEPDw+kpKRg7969APIm2jx16hS8vb3RsGFDlXNMmTIFu3fvRr169fTSpoiICOzevRtWVlbKbVlZWcohYi/HEm0wvmpuF+Mr6QMf4SbS0siRI+Ht7a38t4WFBapVq4batWurBJuXvbqs1I0bNwAAgwcPzreehIQEAHkzZ9arV08ZPBXs7OxUHhXX5M6dOwCgMbA3atSowGMVbZw6dapWbbS1tUWVKlVU9puZmaFOnTqvXc5jz549yMrKgre3Nx49eqTcXqNGDdja2iIhIQFHjx5VBn9tr0vRd6ampiplzM3N4eLiUmCbCmJrawszM/XbpomJCdatW4eYmBjcunULt2/fxvPnzwHk/VGma7s6deoEW1tbREVFYciQIQDy1vGsVauW2h8YRESkvZycHOzevRsA4OrqqowrANCqVSucOnUKW7duRWBgIBISEiCXy1G3bl2189jb26s9Al0UZmZmuHv3LlasWIFr164hISEBd+7cUSa5Lye72mB81dwuxlfSBybQRFp655131L5Rfp1Xk19FAFy+fHm+S07VqFFD+f/5rUn5ukCanZ0NAPkm9gVRnPuTTz7B22+/rbHMywl8ZmZmodoIvBiDdvjwYY2TqAF5k50oAry215WTk1Ooa3/5eE1e/X0CQGxsLEaMGIGcnBy0bt0abdu2RYMGDeDu7o6QkBCVftC2XRYWFvD390dYWBiuXbuG7OxsXL58GRMmTCjSdRERlXfR0dF4/PgxAGDo0KEay1y4cAEXL15UxgJ933c1xZjFixfj+++/h6OjIzw8PODp6amchGvs2LE618H4qhnjK+kDE2iiYuTo6AgAqFatmnJSMYWbN2/i+vXryse33n77bdy8eRNZWVmwsLBQlnv69CmSk5O1qufGjRtwdnZW2ff1118jNTVVZZIUTcfa2NiofWDw8OFDxMbGolatWso2xsXF4cGDByqfBOfm5uLOnTuoVKlSvm28dOkSLl++jLfeeks5KdnL0tLSMG3aNPz++++4d+8eatSoofV1OTo64tatW5DL5SpBOTc3F5MnT4aHhwcGDx6s/KQ6KytL5ZNvxR9X2liyZAmePXuG3bt3QyaTKbdnZ2cjKSlJ5dt5bdsF5D1mFhYWhp9//hm5ubkwMTFB7969tW4XERGpUySW7777Ltzc3NT279ixA4cOHcLWrVsxceJEAC+ezHpZXFwcVqxYgcDAQLRp00ZjXS/HmJe9GmPu3r2L1atXw93dHRs2bFCJ+Ypvy3XB+Mr4SobFMdBExahr164AgGXLlql8CpudnY0ZM2ZgzJgxePDgAYC8cdJpaWn44YcfVM6xdu3a19ajGC+0ceNGle23b9/G+vXrcevWLUiSpAwyL3+K27lzZ5iYmGDlypVIT09XOX7hwoV4//338ffffyvbCAArVqxQKRcZGfnaNbEVf8T0798fHTt2VPvp2bMnOnbsiNzcXGzbtk2n6+rUqROePn2KnTt3qpTbv38/fv75Z+V1Va9eHQCU16PoC8VyJNpISkpCxYoV1b6t37RpEzIyMpRLmynar027AMDZ2RnOzs749ddfcejQIbRu3RpvvfWW1u0iIiJVjx8/xu+//w4bGxuMGzdOY+xRJM0//fQTKlasCDc3N/zxxx9qy1uFhYVh3759yvk+FAmjEEJZRlOMAYBdu3ap/Pvp06cQQqBu3boqyXN6erpyOaeXY8nrML7m3y6A8ZWKjt9AExUjT09PBAYGIjIyEkFBQejevTssLCywe/duxMbGYuDAgcqxOsOGDcMvv/yCxYsX4/r163BxccGZM2dw+PBhWFpaFliPt7c3evbsiZ07d+L+/fvw9fVFWloaNm/eDDMzM+Xak3Z2djAxMcGpU6fw448/ok2bNqhTpw7Gjx+Pb775BgEBAejduzdsbGxw6NAh/PHHH+jQoYNy/Wg/Pz/s2bMHW7duxaNHj9CmTRvExcUhMjIStra2+bYvKysLP/30E8zNzdGvX798yw0bNgwHDhxAZGQkxo0bp/V1jRo1CocOHcKsWbNw5swZuLi4ID4+Hlu2bEGjRo0waNAgAEDv3r2xa9cuTJo0CcOGDYOlpSX27t372m/4X+br64tvv/0Ww4cPh7+/P4QQ+P333xEdHY2KFSvi2bNnEEJAkiSt26XQt29ffPbZZwCARYsWad0mIiJSFxUVhZycHPTu3TvfOOrk5AQvLy8cP34cu3btwuzZszF48GAEBQVh0KBBeOutt3Dq1Cns3bsX/fr1U8ZsxXjoNWvWoG3btspEddWqVZg7dy4SEhJQrVo1HDlyBFevXoW5ubmyznfeeQdvv/02oqKiYGVlBScnJzx8+BA7d+5Ujl9OSUnR6hoZXxlfqRgYcwpwotJAsYzV9u3btT4mv+UbhMhbDurHH38Uffv2Fa6ursLd3V306dNH/PjjjypLRwghxLNnz8TChQtFu3btRJMmTURgYKD466+/RNu2bQtcxkoIIXJzc8WGDRuEv7+/aNKkiWjTpo2YMGGCuHbtmkoda9asEa1atRJNmjRRWYpr//79YtCgQcLNzU24uroKf39/sWbNGpGZmalyfHZ2tlixYoXo2LGjaNKkifD39xcHDx4U/fv3z3cZq7179yqXy3idwMBAlWUstL2up0+fivnz54sOHTqIxo0bC19fX7FgwQKRnJysUm737t2iV69eokmTJsLLy0vMmTNH3Lx5U+MyG5qWDcnOzhbLly9XXn+bNm3E4MGDxcGDB8WSJUuETCYTJ0+e1LldQgiRnJwsmjZtKpo3by7S09Nf21dERJS/bt26CScnJ3H9+vUCy0VHR6ssc3X16lXx3//+V7Rq1Uq4uLgIf39/ERYWphLjExISRHBwsGjcuLHo3LmzcvvRo0dFcHCwaNq0qWjZsqWYPHmyuH//vmjWrJnKskk3b94U48ePF56ensLFxUV07NhRTJs2Tdy6dUu0bdtW+Pj4KP9GKGgZK8ZXxlcyPEmIl541ISKiEiMlJQXe3t7o06cPPvnkE2M3h4iIqExgfKWi4BhoIqISavPmzcjMzET//v2N3RQiIqIyg/GVioJjoImISpgpU6YgLS0Nhw8fRpcuXdCwYUNjN4mIiKjUY3wlfWACTURUwiQmJuLs2bPo0KGDcpITIiIiKhrGV9IHjoEmIiIiIiIi0gLHQBMRERERERFpgQk0ERERERERkRaYQBMRERERERFpgQk0ERERERERkRaYQBMRERERERFpgQk0ERERERERkRaYQBMRERERERFpgQk0ERERERERkRaYQBMRERERERFp4f8BB3u4kqP11BQAAAAASUVORK5CYII=\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.ticker as mtick\n", + "\n", + "plt.rcParams.update({'font.size': 14})\n", + "plt.figure(figsize=(10, 10))\n", + "plt.suptitle(\"Overview of Predictions and Actual Accuracies\")\n", + "\n", + "ax1 = plt.subplot(221)\n", + "sns.lineplot(x=[0.75, 1], y=[0.75, 1], color='gray')\n", + "sns.scatterplot(x=y_trues, y=y_preds, s=8, c=np.abs(y_trues - y_preds),\n", + " cmap='magma')\n", + "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", + "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", + "plt.title(\"Predictions against Actual\")\n", + "plt.xlabel(\"Actual Accuracy\")\n", + "plt.ylabel(\"Predicted Accuracy\")\n", + "\n", + "ax2 = plt.subplot(222)\n", + "ax2.sharex(ax1)\n", + "sns.histplot(x=y_trues, y=np.abs(y_preds - y_trues), bins=25)\n", + "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", + "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", + "plt.title(\"Prediction Error Heatmap\")\n", + "plt.xlabel(\"Actual Accuracy\")\n", + "plt.ylabel(\"Absolute Accuracy Prediction Error\")\n", + "\n", + "ax3 = plt.subplot(223)\n", + "ax3.sharex(ax1)\n", + "sns.histplot(x=y_preds, bins=25)\n", + "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", + "plt.title(\"Prediction Distribution\")\n", + "plt.xlabel(\"Predicted Accuracy\")\n", + "plt.ylabel(\"Frequency of Prediction\")\n", + "\n", + "ax4 = plt.subplot(224)\n", + "ax4.sharex(ax1)\n", + "sns.histplot(x=y_trues, bins=25)\n", + "plt.gca().xaxis.set_major_formatter(mtick.PercentFormatter(decimals=0, xmax=1))\n", + "plt.title(\"Actual Distribution\")\n", + "plt.xlabel(\"Actual Accuracy\")\n", + "plt.ylabel(\"Frequency of Actual\")\n", + "plt.tight_layout()\n", + "plt.savefig(\"overview.png\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Biases Correction**\n", + "\n", + "If you've seen my previous analysis, you'll see that our predictions for lower (actual) accuracies are biased.\n", + "This is because our model assumes a normal distribution for our input data, however, it's actually heavily skewed.\n", + "Despite the sigmoid correction, it was still insufficient to correct the biases, thus, alternatively, we used a `QuantileTransformer` to force transform data into a normal distribution.\n", + "\n", + "**Score Limiting**\n", + "\n", + "We limited the input to only have scores > 750,000. Thus, accuracies taper around 90.00%.\n", + "This may have caused the model to not be able to predict scores beyond 90.00%, however we're not that interested in those predictions.\n", + "\n", + "Despite that, it may prove to be more useful if we included those, so we'll circle back next time.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis of input\n", + "\n", + "We also have our `DataFrame` of input data, let's take a glance" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df = dm.df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": " difficultyrating diff_overall diff_size beatmap_id \\\n0 3.39962 8.0 7.0 192345.0 \n1 3.39962 8.0 7.0 192345.0 \n2 3.39962 8.0 7.0 192345.0 \n3 3.39962 8.0 7.0 192345.0 \n4 3.39962 8.0 7.0 192345.0 \n\n filename user_id year score \\\n0 FamiRockP - Oni Kanojo (Entozer) [Hard].osu 75777.0 2016 928747.0 \n1 FamiRockP - Oni Kanojo (Entozer) [Hard].osu 140578.0 2020 947180.0 \n2 FamiRockP - Oni Kanojo (Entozer) [Hard].osu 242795.0 2015 916065.0 \n3 FamiRockP - Oni Kanojo (Entozer) [Hard].osu 255854.0 2017 820569.0 \n4 FamiRockP - Oni Kanojo (Entozer) [Hard].osu 437945.0 2014 795309.0 \n\n accuracy speed uid mid uid_le mid_le \n0 0.253264 0.0 75777/2016 192345/0 24400 3494 \n1 0.469655 0.0 140578/2020 192345/0 7226 3494 \n2 0.200091 0.0 242795/2015 192345/0 16329 3494 \n3 -0.938611 0.0 255854/2017 192345/0 17189 3494 \n4 -0.837020 0.0 437945/2014 192345/0 20213 3494 ", + "text/html": "
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    difficultyratingdiff_overalldiff_sizebeatmap_idfilenameuser_idyearscoreaccuracyspeeduidmiduid_lemid_le
    03.399628.07.0192345.0FamiRockP - Oni Kanojo (Entozer) [Hard].osu75777.02016928747.00.2532640.075777/2016192345/0244003494
    13.399628.07.0192345.0FamiRockP - Oni Kanojo (Entozer) [Hard].osu140578.02020947180.00.4696550.0140578/2020192345/072263494
    23.399628.07.0192345.0FamiRockP - Oni Kanojo (Entozer) [Hard].osu242795.02015916065.00.2000910.0242795/2015192345/0163293494
    33.399628.07.0192345.0FamiRockP - Oni Kanojo (Entozer) [Hard].osu255854.02017820569.0-0.9386110.0255854/2017192345/0171893494
    43.399628.07.0192345.0FamiRockP - Oni Kanojo (Entozer) [Hard].osu437945.02014795309.0-0.8370200.0437945/2014192345/0202133494
    \n
    " + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see 6 columns, the first 2 are `str` columns, for `LabelEncoder` to encode, the result is the last 2.\n", + "\n", + "E.g.\n", + "\n", + "`uid=U/YYYY` means the user, with user id `U`, with the score in year `YYYY`.\n", + "\n", + "`mid=M/S` means the map, with beatmap id `M` with speed of map `S`.\n", + "- `S=1`: Double Time\n", + "- `S=0`: Normal Time\n", + "- `S=-1`: Half Time\n", + "\n", + "Note that our accuracies are quantile transformed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Performance of Model\n", + "\n", + "Now, let's figure out how well the model performed on the prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2: 0.7777448070587558\n", + "MAE: 0.012337327\n", + "RMSE: 0.01907213816646561\n" + ] + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "print(\"R2:\", r2_score(y_true, y_pred))\n", + "print(\"MAE:\", np.abs(y_preds - y_trues).mean())\n", + "print(\"RMSE:\", ((y_preds - y_trues) ** 2).mean() ** 0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sort of tells us that the general error is around 1%, which is acceptable.\n", + "\n", + "Let's find out the errors of each percentage" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\johnc\\AppData\\Local\\Temp\\ipykernel_1648\\3748057135.py:5: RuntimeWarning: Mean of empty slice.\n", + " return np.abs(y_preds - y_trues).mean(), ((y_preds - y_trues) ** 2).mean() ** 0.5\n", + "D:\\Repositories\\PycharmProjects\\opal\\venv\\lib\\site-packages\\numpy\\core\\_methods.py:192: RuntimeWarning: invalid value encountered in divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "data": { + "text/plain": "
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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_error(y_preds, y_trues, a, b):\n", + " y_preds = y_preds[(y_trues >= a) & (y_trues < b)]\n", + " y_trues = y_trues[(y_trues >= a) & (y_trues < b)]\n", + "\n", + " return np.abs(y_preds - y_trues).mean(), ((y_preds - y_trues) ** 2).mean() ** 0.5\n", + "\n", + "\n", + "errors = []\n", + "bounds = np.linspace(0.7, 1, 31)\n", + "for a, b in zip(bounds[:-1], bounds[1:]):\n", + " mae, rmse = get_error(y_preds, y_trues, a, b)\n", + " errors.append([f\"{b:.0%}\", mae, rmse])\n", + "\n", + "df_errors = pd.DataFrame(errors, columns=['bounds', 'mae', 'rmse'])\n", + "plt.figure(figsize=(14, 4))\n", + "sns.lineplot(data=df_errors, x='bounds', y='mae', label='mae')\n", + "sns.lineplot(data=df_errors, x='bounds', y='rmse', label='rmse')\n", + "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n", + "\n", + "plt.title(\"Errors for different true accuracies\")\n", + "plt.xlabel(\"Actual Accuracies\")\n", + "plt.ylabel(\"Error\")\n", + "_ = plt.xticks(rotation=90, ha='right')\n", + "plt.tight_layout()\n", + "plt.savefig(\"error.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With domain expert input, it's logical.\n", + "There is significantly large deviations for lower accuracies, which is largely due to the undersampling of scores within that range. Refer to the diagram above to see the distribution of scores.\n", + "\n", + "Fortunately, this isn't detrimental to common business use cases, as the scores of interest are those of higher accuracies." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/models/V2_2023_01/checkpoints/epoch=8-step=66897.ckpt b/models/V2_2023_01/checkpoints/epoch=8-step=66897.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..1025bbed19cd991f9a6ea174ed53980cd9022be1 GIT binary patch literal 9043079 zcmbT<2b?5D`Ty}d;&J3GISCx;*s4igAW4$2#3dccWAAR^4ld2Jvm{ya9XUu41e7F* 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z6>CSiARt2VB@lLxhe6c{*9p7ilVIo-7oVAxF;w6m+O;y7b3FOnd|K+h&l;Hf3mbq2 zlC%o9?$}Y1dYZEOyvM6MmfYVBv|tJ*85kMaI5j(s2k-}~ka410pvYX$$+C{#Pyne! zUwdg7&_`NNYJd0)RqAm02eN?;?1&8_OrqA?0Be0l=OPdK?1DSt{oRhvgC$kfRdVGk zgjfs8J-^u4*l)(>%hQg;t#GLrnrzkJ>45@QN^=XQAY(c93(1l1sJ>q)^s5Q9%h59%LawyB+@m&Q-{it1ZF>@vqih{ z0NEVeYeDg&^q3H*6P|hbN6_<2-ChcZlGh-gf8#bu0>SG)(2jp{ME`5Kk?<=2OJ&mk c_qSHmG|TyP?V!z?mI^N`%aIl_=D)A{Uy7GWLI3~& literal 0 HcmV?d00001 From 3691a1e705d44ecd45ceadb413dd7916ff9edb3b Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Fri, 17 Feb 2023 11:26:49 +0800 Subject: [PATCH 24/27] Remove unused ignore mapping --- ignore_mapping.yaml | 92 --------------------------------------------- 1 file changed, 92 deletions(-) delete mode 100644 ignore_mapping.yaml diff --git a/ignore_mapping.yaml b/ignore_mapping.yaml deleted file mode 100644 index c2f895e5..00000000 --- a/ignore_mapping.yaml +++ /dev/null @@ -1,92 +0,0 @@ -# This is the default ignore mapping. -# For every column that is NOT commented out, it'll be IGNORED when converting -# Try to ignore those that are not necessary for your use -# This will heavily reduce storage needed and increase processing speed -# Once you're done, reference this file when calling osu-data-csv via -# osu-data-csv -i path/to/ignore_mapping.yaml - -osu_beatmap_difficulty.sql: -# - beatmap_id -# - mode -# - mods -# - diff_unified - - last_update -osu_beatmaps.sql: -# - beatmap_id - - beatmapset_id - - user_id -# - filename - - checksum - - version - - total_length - - hit_length -# - countTotal -# - countNormal -# - countSlider -# - countSpinner -# - diff_drain -# - diff_size -# - diff_overall -# - diff_approach -# - playmode -# - approved - - last_update -# - difficultyrating -# - playcount -# - passcount -# - youtube_preview -# - score_version -# - deleted_at - - bpm -osu_scores{mode}_high.sql: -# - score_id -# - beatmap_id -# - user_id -# - score - - maxcombo - - rank -# - count50 -# - count100 -# - count300 -# - countmiss -# - countgeki -# - countkatu - - perfect -# - enabled_mods -# - date -# - pp -# - replay - - hidden - - country_acronym -osu_user_stats{mode}.sql: -# - user_id - - count300 - - count100 - - count50 - - countMiss - - accuracy_total - - accuracy_count - - accuracy - - playcount - - ranked_score - - total_score - - x_rank_count - - xh_rank_count - - s_rank_count - - sh_rank_count - - a_rank_count - - rank - - level - - replay_popularity - - fail_count - - exit_count - - max_combo - - country_acronym -# - rank_score -# - rank_score_index - - rank_score_exp - - rank_score_index_exp - - accuracy_new - - last_update - - last_played - - total_seconds_played From 6493abaa0c962365015323df37f14a9dc199e305 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Fri, 17 Feb 2023 11:27:01 +0800 Subject: [PATCH 25/27] Remove old train.ipynb --- models/train.ipynb | 669 --------------------------------------------- 1 file changed, 669 deletions(-) delete mode 100644 models/train.ipynb diff --git a/models/train.ipynb b/models/train.ipynb deleted file mode 100644 index 52492381..00000000 --- a/models/train.ipynb +++ /dev/null @@ -1,669 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Download Files: \n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000.tar.bz2\n", - "Derived Files: \n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/___.sql (All SQL Files are extracted)\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_user_stats_mania.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_scores_mania_high.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_beatmap_difficulty.csv\n", - "\t- 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D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_beatmaps.csv\n", - "Proceeding to Download ...\n", - "File exists, and overwrite is false, skipping\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "D:\\Repositories\\PycharmProjects\\opal\\models\\..\\opal\\score\\datamodule\\score_datamodule.py:76: DtypeWarning: Columns (22) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df_map = pd.read_csv(csv_map)\n", - "GPU available: True (cuda), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" - ] - } - ], - "source": [ - "import sys\n", - "\n", - "from pytorch_lightning.callbacks import LearningRateMonitor\n", - "\n", - "sys.path.append(\"../\")\n", - "sys.path.append(\"../opal/data_ppy_sh_to_csv/\")\n", - "from opal.score.collaborative_filtering import NeuMF\n", - "from opal.score.datamodule import ScoreDataModule\n", - "import pytorch_lightning as pl\n", - "\n", - "dm = ScoreDataModule(\n", - " ds_yyyy_mm=\"2022_11\",\n", - " batch_size=2 ** 10,\n", - " score_bounds=(5e5, 1e6),\n", - ")\n", - "\n", - "net = NeuMF(\n", - " uid_le=dm.uid_le,\n", - " mid_le=dm.mid_le,\n", - " qt=dm.qt_accuracy,\n", - " mf_emb_dim=16,\n", - " mlp_emb_dim=16,\n", - " mlp_chn_out=8,\n", - " lr=0.005,\n", - " one_cycle_lr_params={\n", - " \"pct_start\": 0.1,\n", - " \"three_phase\": True,\n", - " \"final_div_factor\": 1e6\n", - " }\n", - ")\n", - "\n", - "trainer = pl.Trainer(\n", - " max_epochs=35,\n", - " accelerator='gpu',\n", - " default_root_dir=\"V1_2022_11\",\n", - " callbacks=[\n", - " # EarlyStopping(\n", - " # monitor=\"val_loss\",\n", - " # patience=2,\n", - " # verbose=True,\n", - " # mode='min',\n", - " # divergence_threshold=1\n", - " # ),\n", - " LearningRateMonitor()\n", - " ]\n", - ")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Download Files: \n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000.tar.bz2\n", - "Derived Files: \n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/___.sql (All SQL Files are extracted)\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_user_stats_mania.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_scores_mania_high.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_beatmap_difficulty.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_beatmaps.csv\n", - "Proceeding to Download ...\n", - "File exists, and overwrite is false, skipping\n", - "Download Files: \n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000.tar.bz2\n", - "Derived Files: \n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/___.sql (All SQL Files are extracted)\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_user_stats_mania.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_scores_mania_high.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_beatmap_difficulty.csv\n", - "\t- D:/Repositories/PycharmProjects/opal/models/../data/2022_11_01_performance_mania_top_1000/csv/osu_beatmaps.csv\n", - "Proceeding to Download ...\n", - "File exists, and overwrite is false, skipping\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "D:\\Repositories\\PycharmProjects\\opal\\models\\..\\opal\\score\\datamodule\\score_datamodule.py:76: DtypeWarning: Columns (22) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df_map = pd.read_csv(csv_map)\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", - "\n", - " | Name | Type | Params\n", - "--------------------------------------\n", - "0 | model | NeuMFModule | 539 K \n", - "1 | loss | MSELoss | 0 \n", - "--------------------------------------\n", - "539 K Trainable params\n", - "0 Non-trainable params\n", - "539 K Total params\n", - "2.158 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "text/plain": "Sanity Checking: 0it [00:00, ?it/s]", - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "252cd459e717487a80fa061a5a9e31c1" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "D:\\ProgramFiles\\Anaconda\\envs\\opal\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", - " rank_zero_warn(\n", - "D:\\ProgramFiles\\Anaconda\\envs\\opal\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", - " rank_zero_warn(\n" - ] - }, - { - "data": { - "text/plain": "Training: 0it [00:00, ?it/s]", - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "33bc1f155e3548f0ad7d0b2006635a61" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "Validation: 0it [00:00, ?it/s]", - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "aa1c33e614d743258c8a8b5bedc339c0" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "Validation: 0it [00:00, ?it/s]", - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "82d7ba0bcdb6413b87f268f099d200a2" - } - }, - "metadata": {}, - "output_type": "display_data" - 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}, - { - "data": { - "text/plain": "Validation: 0it [00:00, ?it/s]", - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "87f8b7ec13b2442396c82fc938b41f53" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "Validation: 0it [00:00, ?it/s]", - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "2cbe7d6a5c0b4859afcb05eb1b7da104" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "Validation: 0it [00:00, ?it/s]", - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "d171e3e35e3e49b19f88fa2e82da88ab" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=35` reached.\n" - ] - } - ], - "source": [ - "trainer.fit(net, datamodule=dm)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "model = NeuMF.load_from_checkpoint(\"V1_2022_11/lightning_logs/version_17/checkpoints/epoch=0-step=993.ckpt\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "data": { - "text/plain": "array(0.9585964, dtype=float32)" - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.predict(\"24331923/2021\", \"1001677/0\")" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 025db4cd0886359fbe61393e04b0540e472602cc Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Fri, 17 Feb 2023 11:29:22 +0800 Subject: [PATCH 26/27] Update README.md for new model --- models/README.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/models/README.md b/models/README.md index 21bdb7fd..8a28d603 100644 --- a/models/README.md +++ b/models/README.md @@ -2,15 +2,16 @@ **See below on how to load the model.** -| Model | MAE | RMSE | Error Distribution | -|------------|-------|-------|--------------------------------| -| V1_2022_11 | 0.96% | 1.59% | ![Error](V1_2022_11/error.png) | +| Model | R2 | MAE | RMSE | Error Distribution | +|------------|--------|-------|-------|--------------------------------| +| V2_2023_01 | 77.77% | 1.23% | 1.90% | ![Error](V2_2023_01/error.png) | +**Note: V1 is deprecated due to incompatibility with new model signature.** ## Limitations The model cannot ... -- predict maps not played by at least 50 players within the top 1k -- predict players not in the top 1k +- predict maps not played by at least 50 players within the top 10k +- predict players not in the top 10k - predict players who have not played at least 50 unique ranked maps in that year. The predictive power (i.e. accuracy) is dependent on the number of players associated with each map. From 82bf0c418d3f50e597db6d3f7ffdf4d8dba4ae40 Mon Sep 17 00:00:00 2001 From: Eve-ning Date: Fri, 17 Feb 2023 11:37:56 +0800 Subject: [PATCH 27/27] Add performance graph on README --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 3662ddf6..71995aa9 100644 --- a/README.md +++ b/README.md @@ -6,15 +6,19 @@ opal is an accuracy-prediction model. It uses Neural Collaborative Filtering to learn associations between user and maps, then using those associations to predict new scores never before seen. +**Performance Error Graph** +![Performance Graph](models/V2_2023_01/error.png) + ## Project Status Currently, it's in its early access, that means, it'll have many problems! However, we're working on it to minimize these issues o wo)b + ## Dataset Used -I used the top 1K mania users data from https://data.ppy.sh. +I used the top 10K mania users data from https://data.ppy.sh. After preprocessing, we use - 1m scores for training - 120k scores for validation and testing each

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