From 5cc4a5554c8bd839b2514aed368851eed93c69fe Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Wed, 29 Apr 2020 11:41:28 -0400
Subject: [PATCH] name changed
---
.gitlab-ci.yml | 4 +-
README.md | 2 +-
docs/source/conf.py | 4 +-
multiview_platform/__init__.py | 5 -
multiview_platform/examples/__init__.py | 0
.../config_files/config_example_0.yml | 78 -
.../config_files/config_example_1.yml | 78 -
.../config_files/config_example_2_1_1.yml | 83 --
.../config_files/config_example_2_1_2.yml | 83 --
.../config_files/config_example_2_2_1.yml | 71 -
.../config_files/config_example_2_3.yml | 87 --
.../config_files/config_example_3.yml | 84 --
.../examples/data/digits_doc.hdf5 | Bin 2782760 -> 0 bytes
.../examples/data/doc_summit.hdf5 | Bin 70624 -> 0 bytes
multiview_platform/execute.py | 31 -
.../mono_multi_view_classifiers/__init__.py | 4 -
.../exec_classif.py | 814 ----------
.../metrics/__init__.py | 33 -
.../metrics/accuracy_score.py | 45 -
.../metrics/f1_score.py | 31 -
.../metrics/fbeta_score.py | 27 -
.../metrics/hamming_loss.py | 24 -
.../metrics/jaccard_score.py | 27 -
.../metrics/log_loss.py | 25 -
.../metrics/matthews_corrcoef.py | 24 -
.../metrics/precision_score.py | 26 -
.../metrics/recall_score.py | 26 -
.../metrics/roc_auc_score.py | 26 -
.../metrics/zero_one_loss.py | 26 -
.../monoview/__init__.py | 1 -
.../monoview/exec_classif_mono_view.py | 255 ----
.../monoview/monoview_utils.py | 231 ---
.../monoview_classifiers/__init__.py | 31 -
.../monoview_classifiers/adaboost.py | 152 --
.../additions/SVCClassifier.py | 17 -
.../additions/__init__.py | 0
.../monoview_classifiers/decision_tree.py | 36 -
.../monoview_classifiers/gradient_boosting.py | 96 --
.../monoview_classifiers/knn.py | 42 -
.../monoview_classifiers/lasso.py | 74 -
.../monoview_classifiers/random_forest.py | 82 --
.../monoview_classifiers/sgd.py | 56 -
.../monoview_classifiers/svm_linear.py | 36 -
.../monoview_classifiers/svm_poly.py | 50 -
.../monoview_classifiers/svm_rbf.py | 41 -
.../multiview/__init__.py | 5 -
.../multiview/exec_multiview.py | 356 -----
.../multiview/multiview_utils.py | 197 ---
.../multiview/profile | Bin 1288343 -> 0 bytes
.../multiview_classifiers/__init__.py | 10 -
.../additions/__init__.py | 0
.../additions/diversity_utils.py | 200 ---
.../additions/fusion_utils.py | 36 -
.../additions/jumbo_fusion_utils.py | 83 --
.../additions/late_fusion_utils.py | 178 ---
.../multiview_classifiers/additions/utils.py | 64 -
.../bayesian_inference_fusion.py | 39 -
.../difficulty_fusion.py | 28 -
.../multiview_classifiers/disagree_fusion.py | 14 -
.../double_fault_fusion.py | 14 -
.../multiview_classifiers/entropy_fusion.py | 26 -
.../majority_voting_fusion.py | 55 -
.../multiview_classifiers/svm_jumbo_fusion.py | 36 -
.../weighted_linear_early_fusion.py | 118 --
.../weighted_linear_late_fusion.py | 31 -
.../result_analysis/__init__.py | 0
.../result_analysis/duration_analysis.py | 47 -
.../result_analysis/error_analysis.py | 289 ----
.../result_analysis/execution.py | 247 ----
.../result_analysis/feature_importances.py | 84 --
.../result_analysis/metric_analysis.py | 393 -----
.../result_analysis/noise_analysis.py | 56 -
.../result_analysis/tracebacks_analysis.py | 36 -
.../utils/__init__.py | 1 -
.../mono_multi_view_classifiers/utils/base.py | 377 -----
.../utils/configuration.py | 96 --
.../utils/dataset.py | 769 ----------
.../utils/execution.py | 426 ------
.../utils/get_multiview_db.py | 1311 -----------------
.../utils/hyper_parameter_search.py | 653 --------
.../utils/make_file_config.py | 39 -
.../utils/multiclass.py | 323 ----
.../utils/multiview_result_analysis.py | 54 -
.../utils/organization.py | 11 -
.../utils/transformations.py | 44 -
multiview_platform/tests/__init__.py | 2 -
multiview_platform/tests/test_config_hps.yml | 80 -
multiview_platform/tests/test_config_iter.yml | 78 -
.../tests/test_config_simple.yml | 78 -
multiview_platform/tests/test_database.hdf5 | Bin 8952 -> 0 bytes
multiview_platform/tests/test_exec_classif.py | 415 ------
.../tests/test_metrics/__init__.py | 0
.../tests/test_metrics/test_metrics.py | 29 -
.../tests/test_mono_view/__init__.py | 0
.../test_exec_classif_mono_view.py | 245 ---
.../test_mono_view/test_monoview_utils.py | 51 -
.../test_monoview_classifiers/__init__.py | 0
.../test_adaboost.py | 80 -
.../test_compatibility.py | 157 --
.../tests/test_multi_view/__init__.py | 0
.../test_multi_view/test_exec_multiview.py | 90 --
.../test_multi_view/test_multiview_utils.py | 89 --
.../Test_PseudoCQMeasure/__init__.py | 0
.../test_PseudoCQFusionModule.py | 22 -
.../test_multiview_classifiers/__init__.py | 0
.../test_additions/__init__.py | 0
.../test_additions/test_diversity_utils.py | 74 -
.../test_additions/test_jumbo_fusion_utils.py | 22 -
.../test_difficulty_fusion.py | 23 -
.../test_disagree_fusion.py | 23 -
.../test_diversity_utils.py | 42 -
.../test_double_fault_fusion.py | 22 -
.../test_entropy_fusion.py | 23 -
.../test_weighted_linear_early_fusion.py | 67 -
.../tests/test_result_analysis/__init__.py | 0
.../test_duration_analysis.py | 41 -
.../test_error_analysis.py | 76 -
.../test_result_analysis/test_execution.py | 139 --
.../test_feature_importances.py | 36 -
.../test_metric_analysis.py | 180 ---
.../test_noise_analysis.py | 0
.../test_tracebacks_analysis.py | 47 -
.../tests/test_utils/__init__.py | 0
.../tests/test_utils/test_GetMultiviewDB.py | 166 ---
.../tests/test_utils/test_base.py | 261 ----
.../tests/test_utils/test_configuration.py | 63 -
.../tests/test_utils/test_dataset.py | 423 ------
.../tests/test_utils/test_execution.py | 361 -----
.../test_utils/test_hyper_parameter_search.py | 217 ---
.../tests/test_utils/test_multiclass.py | 164 ---
multiview_platform/tests/utils.py | 53 -
setup.cfg | 6 +-
setup.py | 4 +-
133 files changed, 10 insertions(+), 13653 deletions(-)
delete mode 100644 multiview_platform/__init__.py
delete mode 100644 multiview_platform/examples/__init__.py
delete mode 100644 multiview_platform/examples/config_files/config_example_0.yml
delete mode 100644 multiview_platform/examples/config_files/config_example_1.yml
delete mode 100644 multiview_platform/examples/config_files/config_example_2_1_1.yml
delete mode 100644 multiview_platform/examples/config_files/config_example_2_1_2.yml
delete mode 100644 multiview_platform/examples/config_files/config_example_2_2_1.yml
delete mode 100644 multiview_platform/examples/config_files/config_example_2_3.yml
delete mode 100644 multiview_platform/examples/config_files/config_example_3.yml
delete mode 100644 multiview_platform/examples/data/digits_doc.hdf5
delete mode 100644 multiview_platform/examples/data/doc_summit.hdf5
delete mode 100644 multiview_platform/execute.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/__init__.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/exec_classif.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/__init__.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/accuracy_score.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/f1_score.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/fbeta_score.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/hamming_loss.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/jaccard_score.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/log_loss.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/matthews_corrcoef.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/precision_score.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/recall_score.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/roc_auc_score.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/metrics/zero_one_loss.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview/__init__.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/__init__.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/additions/SVCClassifier.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/additions/__init__.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_poly.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_rbf.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview/__init__.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview/profile
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/__init__.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/__init__.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/utils.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/bayesian_inference_fusion.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/difficulty_fusion.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/disagree_fusion.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/double_fault_fusion.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/entropy_fusion.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/majority_voting_fusion.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/svm_jumbo_fusion.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_late_fusion.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/result_analysis/__init__.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/result_analysis/duration_analysis.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/result_analysis/execution.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/result_analysis/feature_importances.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/result_analysis/noise_analysis.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/result_analysis/tracebacks_analysis.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/__init__.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/base.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/configuration.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/dataset.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/execution.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/get_multiview_db.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/make_file_config.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/multiclass.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/multiview_result_analysis.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/organization.py
delete mode 100644 multiview_platform/mono_multi_view_classifiers/utils/transformations.py
delete mode 100644 multiview_platform/tests/__init__.py
delete mode 100644 multiview_platform/tests/test_config_hps.yml
delete mode 100644 multiview_platform/tests/test_config_iter.yml
delete mode 100644 multiview_platform/tests/test_config_simple.yml
delete mode 100644 multiview_platform/tests/test_database.hdf5
delete mode 100644 multiview_platform/tests/test_exec_classif.py
delete mode 100644 multiview_platform/tests/test_metrics/__init__.py
delete mode 100644 multiview_platform/tests/test_metrics/test_metrics.py
delete mode 100644 multiview_platform/tests/test_mono_view/__init__.py
delete mode 100644 multiview_platform/tests/test_mono_view/test_exec_classif_mono_view.py
delete mode 100644 multiview_platform/tests/test_mono_view/test_monoview_utils.py
delete mode 100644 multiview_platform/tests/test_monoview_classifiers/__init__.py
delete mode 100644 multiview_platform/tests/test_monoview_classifiers/test_adaboost.py
delete mode 100644 multiview_platform/tests/test_monoview_classifiers/test_compatibility.py
delete mode 100644 multiview_platform/tests/test_multi_view/__init__.py
delete mode 100644 multiview_platform/tests/test_multi_view/test_exec_multiview.py
delete mode 100644 multiview_platform/tests/test_multi_view/test_multiview_utils.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/Test_PseudoCQMeasure/__init__.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/Test_PseudoCQMeasure/test_PseudoCQFusionModule.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/__init__.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/test_additions/__init__.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/test_additions/test_diversity_utils.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/test_additions/test_jumbo_fusion_utils.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/test_difficulty_fusion.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/test_disagree_fusion.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/test_diversity_utils.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/test_double_fault_fusion.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/test_entropy_fusion.py
delete mode 100644 multiview_platform/tests/test_multiview_classifiers/test_weighted_linear_early_fusion.py
delete mode 100644 multiview_platform/tests/test_result_analysis/__init__.py
delete mode 100644 multiview_platform/tests/test_result_analysis/test_duration_analysis.py
delete mode 100644 multiview_platform/tests/test_result_analysis/test_error_analysis.py
delete mode 100644 multiview_platform/tests/test_result_analysis/test_execution.py
delete mode 100644 multiview_platform/tests/test_result_analysis/test_feature_importances.py
delete mode 100644 multiview_platform/tests/test_result_analysis/test_metric_analysis.py
delete mode 100644 multiview_platform/tests/test_result_analysis/test_noise_analysis.py
delete mode 100644 multiview_platform/tests/test_result_analysis/test_tracebacks_analysis.py
delete mode 100644 multiview_platform/tests/test_utils/__init__.py
delete mode 100644 multiview_platform/tests/test_utils/test_GetMultiviewDB.py
delete mode 100644 multiview_platform/tests/test_utils/test_base.py
delete mode 100644 multiview_platform/tests/test_utils/test_configuration.py
delete mode 100644 multiview_platform/tests/test_utils/test_dataset.py
delete mode 100644 multiview_platform/tests/test_utils/test_execution.py
delete mode 100644 multiview_platform/tests/test_utils/test_hyper_parameter_search.py
delete mode 100644 multiview_platform/tests/test_utils/test_multiclass.py
delete mode 100644 multiview_platform/tests/utils.py
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index a874c2df..4e0f6146 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -23,7 +23,7 @@ doc:
- export LC_ALL=$(locale -a | grep en_US)
- export LANG=$(locale -a | grep en_US)
- pip3 install -e . --no-deps
- - sphinx-apidoc -o docs/source multiview_platform
+ - sphinx-apidoc -o docs/source summit
- cd docs/source
- sphinx-build -b html . ../build
- cd ../..
@@ -45,7 +45,7 @@ pages:
- export LANG=$(locale -a | grep en_US)
- pip3 install -e . --no-deps
- pytest-3
- - sphinx-apidoc -o docs/source multiview_platform
+ - sphinx-apidoc -o docs/source summit
- cd docs/source
- sphinx-build -b html . ../build
- cd ../..
diff --git a/README.md b/README.md
index 09aa84b0..9a15dc29 100644
--- a/README.md
+++ b/README.md
@@ -71,7 +71,7 @@ to read it carefully before playing around with the parameters.
You can create your own configuration file. In order to run the platform with it, run :
```python
-from multiview_platform.execute import execute
+from summit.execute import execute
execute(config_path="/absolute/path/to/your/config/file")
```
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 01c4690c..2ab75d49 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -22,11 +22,11 @@ import os
import sys
sys.path.insert(0, os.path.abspath('.'))
-sys.path.insert(0, os.path.abspath('../../multiview_platform'))
+sys.path.insert(0, os.path.abspath('../../summit'))
sys.path.insert(0, os.path.abspath('../..'))
file_loc = os.path.split(__file__)[0]
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(file_loc), '.')))
-# import multiview_platform
+# import summit
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
diff --git a/multiview_platform/__init__.py b/multiview_platform/__init__.py
deleted file mode 100644
index f51f7a70..00000000
--- a/multiview_platform/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""This is a test docstring to test stuff"""
-
-__version__ = "0.0.0.0"
-
-from . import mono_multi_view_classifiers, execute
diff --git a/multiview_platform/examples/__init__.py b/multiview_platform/examples/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/examples/config_files/config_example_0.yml b/multiview_platform/examples/config_files/config_example_0.yml
deleted file mode 100644
index d16f5843..00000000
--- a/multiview_platform/examples/config_files/config_example_0.yml
+++ /dev/null
@@ -1,78 +0,0 @@
-# The base configuration of the benchmark
-
-# Enable logging
-log: True
-# The name of each dataset in the directory on which the benchmark should be run
-name: "digits_doc"
-# A label for the resul directory
-label: "example_0"
-# The type of dataset, currently supported ".hdf5", and ".csv"
-file_type: ".hdf5"
-# The views to use in the banchmark, an empty value will result in using all the views
-views:
-# The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "examples/data/"
-# The niceness of the processes, useful to lower their priority
-nice: 0
-# The random state of the benchmark, useful for reproducibility
-random_state: 42
-# The number of parallel computing threads
-nb_cores: 1
-# Used to run the benchmark on the full dataset
-full: True
-# Used to be able to run more than one benchmark per minute
-debug: False
-# The directory in which the results will be stored, an absolute path is advised
-res_dir: "examples/results/example_0/"
-# If an error occurs in a classifier, if track_tracebacks is set to True, the
-# benchmark saves the traceback and continues, if it is set to False, it will
-# stop the benchmark and raise the error
-track_tracebacks: True
-
-# All the classification-realted configuration options
-
-# The ratio of test examples/number of train examples
-split: 0.25
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# The number of classes to select in the dataset
-nb_class:
-# The name of the classes to select in the dataset
-classes:
-# The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
-# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
-algos_monoview: ["decision_tree"]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_early_fusion", "weighted_linear_late_fusion",]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# The metrics that will be use din the result analysis
-metrics:
- accuracy_score: {}
- f1_score:
- average: "micro"
-# The metric that will be used in the hyper-parameter optimization process
-metric_princ: "accuracy_score"
-# The type of hyper-parameter optimization method
-hps_type: "None"
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-### Configuring the hyper-parameters for the classifiers
-
-decision_tree:
- max_depth: 3
-
-weighted_linear_early_fusion:
- monoview_classifier_name: "decision_tree"
- monoview_classifier_config:
- decision_tree:
- max_depth: 6
-
-weighted_linear_late_fusion:
- classifiers_names: "decision_tree"
- classifier_configs:
- decision_tree:
- max_depth: 3
diff --git a/multiview_platform/examples/config_files/config_example_1.yml b/multiview_platform/examples/config_files/config_example_1.yml
deleted file mode 100644
index fb9ab405..00000000
--- a/multiview_platform/examples/config_files/config_example_1.yml
+++ /dev/null
@@ -1,78 +0,0 @@
-# The base configuration of the benchmark
-
-# Enable logging
-log: True
-# The name of each dataset in the directory on which the benchmark should be run
-name: "doc_summit"
-# A label for the resul directory
-label: "example_1"
-# The type of dataset, currently supported ".hdf5", and ".csv"
-file_type: ".hdf5"
-# The views to use in the banchmark, an empty value will result in using all the views
-views:
-# The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "examples/data/mkljlj"
-# The niceness of the processes, useful to lower their priority
-nice: 0
-# The random state of the benchmark, useful for reproducibility
-random_state: 42
-# The number of parallel computing threads
-nb_cores: 1
-# Used to run the benchmark on the full dataset
-full: True
-# Used to be able to run more than one benchmark per minute
-debug: False
-# The directory in which the results will be stored, an absolute path is advised
-res_dir: "examples/results/example_1/"
-# If an error occurs in a classifier, if track_tracebacks is set to True, the
-# benchmark saves the traceback and continues, if it is set to False, it will
-# stop the benchmark and raise the error
-track_tracebacks: True
-
-# All the classification-realted configuration options
-
-# The ratio of test examples/number of train examples
-split: 0.35
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# The number of classes to select in the dataset
-nb_class:
-# The name of the classes to select in the dataset
-classes:
-# The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
-# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
-algos_monoview: ["decision_tree"]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_late_fusion",]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# The metrics that will be use din the result analysis
-metrics:
- accuracy_score: {}
- f1_score:
- average: "micro"
-# The metric that will be used in the hyper-parameter optimization process
-metric_princ: "accuracy_score"
-# The type of hyper-parameter optimization method
-hps_type: "None"
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-### Configuring the hyper-parameters for the classifiers
-
-decision_tree:
- max_depth: 3
-
-weighted_linear_early_fusion:
- monoview_classifier_name: "decision_tree"
- monoview_classifier_config:
- decision_tree:
- max_depth: 6
-
-weighted_linear_late_fusion:
- classifiers_names: "decision_tree"
- classifier_configs:
- decision_tree:
- max_depth: 3
diff --git a/multiview_platform/examples/config_files/config_example_2_1_1.yml b/multiview_platform/examples/config_files/config_example_2_1_1.yml
deleted file mode 100644
index b1a9e2e7..00000000
--- a/multiview_platform/examples/config_files/config_example_2_1_1.yml
+++ /dev/null
@@ -1,83 +0,0 @@
-# The base configuration of the benchmark
-
-# Enable logging
-log: True
-# The name of each dataset in the directory on which the benchmark should be run
-name: "doc_summit"
-# A label for the resul directory
-label: "example_2_1_1"
-# The type of dataset, currently supported ".hdf5", and ".csv"
-file_type: ".hdf5"
-# The views to use in the banchmark, an empty value will result in using all the views
-views:
-# The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "examples/data/"
-# The niceness of the processes, useful to lower their priority
-nice: 0
-# The random state of the benchmark, useful for reproducibility
-random_state: 42
-# The number of parallel computing threads
-nb_cores: 1
-# Used to run the benchmark on the full dataset
-full: True
-# Used to be able to run more than one benchmark per minute
-debug: False
-# The directory in which the results will be stored, an absolute path is advised
-res_dir: "examples/results/example_2_1_1/"
-# If an error occurs in a classifier, if track_tracebacks is set to True, the
-# benchmark saves the traceback and continues, if it is set to False, it will
-# stop the benchmark and raise the error
-track_tracebacks: True
-
-# All the classification-realted configuration options
-
-# If the dataset is multiclass, will use this multiclass-to-biclass method
-multiclass_method: "oneVersusOne"
-# The ratio number of test exmaples/number of train examples
-split: 0.8
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# The number of classes to select in the dataset
-nb_class: 2
-# The name of the classes to select in the dataset
-classes:
-# The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
-# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
-algos_monoview: ["decision_tree", "adaboost", ]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_late_fusion", ]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# The metrics that will be use din the result analysis
-metrics:
- accuracy_score: {}
- f1_score:
- average: "micro"
-# The metric that will be used in the hyper-parameter optimization process
-metric_princ: "accuracy_score"
-# The type of hyper-parameter optimization method
-hps_type: None
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-decision_tree:
- max_depth: 3
-
-adaboost:
- base_estimator: "DecisionTreeClassifier"
- n_estimators: 50
-
-weighted_linear_late_fusion:
- classifiers_names: "decision_tree"
- classifier_configs:
- decision_tree:
- max_depth: 2
-
-
-# The following arguments are classifier-specific, and are documented in each
-# of the corresponding modules.
-
-# In order to run multiple sets of parameters, use multiple values in the
-# following lists, and set hps_type to None.
diff --git a/multiview_platform/examples/config_files/config_example_2_1_2.yml b/multiview_platform/examples/config_files/config_example_2_1_2.yml
deleted file mode 100644
index 256e18a8..00000000
--- a/multiview_platform/examples/config_files/config_example_2_1_2.yml
+++ /dev/null
@@ -1,83 +0,0 @@
-# The base configuration of the benchmark
-
-# Enable logging
-log: True
-# The name of each dataset in the directory on which the benchmark should be run
-name: "doc_summit"
-# A label for the resul directory
-label: "example_2_1_2"
-# The type of dataset, currently supported ".hdf5", and ".csv"
-file_type: ".hdf5"
-# The views to use in the banchmark, an empty value will result in using all the views
-views:
-# The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "examples/data/"
-# The niceness of the processes, useful to lower their priority
-nice: 0
-# The random state of the benchmark, useful for reproducibility
-random_state: 42
-# The number of parallel computing threads
-nb_cores: 1
-# Used to run the benchmark on the full dataset
-full: True
-# Used to be able to run more than one benchmark per minute
-debug: False
-# The directory in which the results will be stored, an absolute path is advised
-res_dir: "examples/results/example_2_1_2/"
-# If an error occurs in a classifier, if track_tracebacks is set to True, the
-# benchmark saves the traceback and continues, if it is set to False, it will
-# stop the benchmark and raise the error
-track_tracebacks: True
-
-# All the classification-realted configuration options
-
-# If the dataset is multiclass, will use this multiclass-to-biclass method
-multiclass_method: "oneVersusOne"
-# The ratio number of test exmaples/number of train examples
-split: 0.2
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# The number of classes to select in the dataset
-nb_class: 2
-# The name of the classes to select in the dataset
-classes:
-# The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
-# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
-algos_monoview: ["decision_tree", "adaboost", ]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_late_fusion", ]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# The metrics that will be use din the result analysis
-metrics:
- accuracy_score: {}
- f1_score:
- average: "micro"
-# The metric that will be used in the hyper-parameter optimization process
-metric_princ: "accuracy_score"
-# The type of hyper-parameter optimization method
-hps_type: None
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-decision_tree:
- max_depth: 3
-
-adaboost:
- base_estimator: "DecisionTreeClassifier"
- n_estimators: 50
-
-weighted_linear_late_fusion:
- classifiers_names: "decision_tree"
- classifier_configs:
- decision_tree:
- max_depth: 2
-
-
-# The following arguments are classifier-specific, and are documented in each
-# of the corresponding modules.
-
-# In order to run multiple sets of parameters, use multiple values in the
-# following lists, and set hps_type to None.
diff --git a/multiview_platform/examples/config_files/config_example_2_2_1.yml b/multiview_platform/examples/config_files/config_example_2_2_1.yml
deleted file mode 100644
index d462bee1..00000000
--- a/multiview_platform/examples/config_files/config_example_2_2_1.yml
+++ /dev/null
@@ -1,71 +0,0 @@
-# The base configuration of the benchmark
-
-# Enable logging
-log: True
-# The name of each dataset in the directory on which the benchmark should be run
-name: "doc_summit"
-# A label for the result directory
-label: "example_2_2_1"
-# The type of dataset, currently supported ".hdf5", and ".csv"
-file_type: ".hdf5"
-# The views to use in the banchmark, an empty value will result in using all the views
-views:
-# The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "examples/data/"
-# The niceness of the processes, useful to lower their priority
-nice: 0
-# The random state of the benchmark, useful for reproducibility
-random_state: 42
-# The number of parallel computing threads
-nb_cores: 1
-# Used to run the benchmark on the full dataset
-full: True
-# Used to be able to run more than one benchmark per minute
-debug: False
-# The directory in which the results will be stored, an absolute path is advised
-res_dir: "examples/results/example_2_2_1/"
-# If an error occurs in a classifier, if track_tracebacks is set to True, the
-# benchmark saves the traceback and continues, if it is set to False, it will
-# stop the benchmark and raise the error
-track_tracebacks: True
-
-# All the classification-realted configuration options
-
-# If the dataset is multiclass, will use this multiclass-to-biclass method
-multiclass_method: "oneVersusOne"
-# The ratio number of test exmaples/number of train examples
-split: 0.8
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 5
-# The number of classes to select in the dataset
-nb_class: 2
-# The name of the classes to select in the dataset
-classes:
-# The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
-# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
-algos_monoview: ["decision_tree", "adaboost", ]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_late_fusion", ]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# The metrics that will be use din the result analysis
-metrics:
- accuracy_score: {}
- f1_score:
- average: "micro"
-# The metric that will be used in the hyper-parameter optimization process
-metric_princ: "accuracy_score"
-# The type of hyper-parameter optimization method
-hps_type: 'Random'
-# The number of iteration in the hyper-parameter optimization process
-hps_args:
- n_iter: 5
- equivalent_draws: True
-
-# The following arguments are classifier-specific, and are documented in each
-# of the corresponding modules.
-
-# In order to run multiple sets of parameters, use multiple values in the
-# following lists, and set hps_type to None.
diff --git a/multiview_platform/examples/config_files/config_example_2_3.yml b/multiview_platform/examples/config_files/config_example_2_3.yml
deleted file mode 100644
index bb8fb31e..00000000
--- a/multiview_platform/examples/config_files/config_example_2_3.yml
+++ /dev/null
@@ -1,87 +0,0 @@
-# The base configuration of the benchmark
-
-# Enable logging
-log: True
-# The name of each dataset in the directory on which the benchmark should be run
-name: "doc_summit"
-# A label for the result directory
-label: "example_2_3"
-# The type of dataset, currently supported ".hdf5", and ".csv"
-file_type: ".hdf5"
-# The views to use in the banchmark, an empty value will result in using all the views
-views:
-# The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "examples/data/"
-# The niceness of the processes, useful to lower their priority
-nice: 0
-# The random state of the benchmark, useful for reproducibility
-random_state: 42
-# The number of parallel computing threads
-nb_cores: 1
-# Used to run the benchmark on the full dataset
-full: True
-# Used to be able to run more than one benchmark per minute
-debug: False
-# The directory in which the results will be stored, an absolute path is advised
-res_dir: "examples/results/example_2_3/"
-# If an error occurs in a classifier, if track_tracebacks is set to True, the
-# benchmark saves the traceback and continues, if it is set to False, it will
-# stop the benchmark and raise the error
-track_tracebacks: True
-
-# All the classification-realted configuration options
-
-# If the dataset is multiclass, will use this multiclass-to-biclass method
-multiclass_method: "oneVersusOne"
-# The ratio number of test exmaples/number of train examples
-split: 0.8
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 5
-# The number of classes to select in the dataset
-nb_class: 2
-# The name of the classes to select in the dataset
-classes:
-# The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
-# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
-algos_monoview: ["decision_tree", "adaboost", ]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_late_fusion", ]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# The metrics that will be use din the result analysis
-metrics:
- accuracy_score: {}
- f1_score:
- average: "micro"
-# The metric that will be used in the hyper-parameter optimization process
-metric_princ: "accuracy_score"
-# The type of hyper-parameter optimization method
-hps_type: 'Grid'
-# The number of iteration in the hyper-parameter optimization process
-hps_args:
- decision_tree:
- max_depth: [1,2,3,4,5]
-
- adaboost:
- n_estimators: [10,15,20,25]
-
- weighted_linear_late_fusion:
- classifiers_names:
- - ["decision_tree", "decision_tree", "decision_tree", "decision_tree"]
- - ["adaboost", "adaboost", "adaboost", "adaboost",]
-
- classifier_configs:
- - decision_tree:
- max_depth: 3
- adaboost:
- n_estimators: 10
-
-
-
-# The following arguments are classifier-specific, and are documented in each
-# of the corresponding modules.
-
-# In order to run multiple sets of parameters, use multiple values in the
-# following lists, and set hps_type to None.
diff --git a/multiview_platform/examples/config_files/config_example_3.yml b/multiview_platform/examples/config_files/config_example_3.yml
deleted file mode 100644
index 67ef06ea..00000000
--- a/multiview_platform/examples/config_files/config_example_3.yml
+++ /dev/null
@@ -1,84 +0,0 @@
-# The base configuration of the benchmark
-
-# Enable logging
-log: True
-# The name of each dataset in the directory on which the benchmark should be run
-name: "doc_summit"
-# A label for the result directory
-label: "example_3"
-# The type of dataset, currently supported ".hdf5", and ".csv"
-file_type: ".hdf5"
-# The views to use in the banchmark, an empty value will result in using all the views
-views:
-# The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "examples/data/"
-# The niceness of the processes, useful to lower their priority
-nice: 0
-# The random state of the benchmark, useful for reproducibility
-random_state: 42
-# The number of parallel computing threads
-nb_cores: 1
-# Used to run the benchmark on the full dataset
-full: True
-# Used to be able to run more than one benchmark per minute
-debug: False
-# The directory in which the results will be stored, an absolute path is advised
-res_dir: "examples/results/example_3/"
-# If an error occurs in a classifier, if track_tracebacks is set to True, the
-# benchmark saves the traceback and continues, if it is set to False, it will
-# stop the benchmark and raise the error
-track_tracebacks: True
-
-# All the classification-realted configuration options
-
-# If the dataset is multiclass, will use this multiclass-to-biclass method
-multiclass_method: "oneVersusOne"
-# The ratio number of test exmaples/number of train examples
-split: 0.8
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 5
-# The number of classes to select in the dataset
-nb_class: 2
-# The name of the classes to select in the dataset
-classes:
-# The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
-# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
-algos_monoview: ["decision_tree", "adaboost", ]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_late_fusion", ]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 5
-# The metrics that will be use din the result analysis
-metrics:
- accuracy_score: {}
- f1_score:
- average: "micro"
-# The metric that will be used in the hyper-parameter optimization process
-metric_princ: "accuracy_score"
-# The type of hyper-parameter optimization method
-hps_type: 'None'
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-decision_tree:
- max_depth: 3
-
-adaboost:
- base_estimator: "DecisionTreeClassifier"
- n_estimators: 10
-
-weighted_linear_late_fusion:
- classifiers_names: "decision_tree"
- classifier_configs:
- decision_tree:
- max_depth: 2
-
-
-
-# The following arguments are classifier-specific, and are documented in each
-# of the corresponding modules.
-
-# In order to run multiple sets of parameters, use multiple values in the
-# following lists, and set hps_type to None.
diff --git a/multiview_platform/examples/data/digits_doc.hdf5 b/multiview_platform/examples/data/digits_doc.hdf5
deleted file mode 100644
index 61e452b9118eeabc4972c11803a8bba775dc3301..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001
literal 2782760
zcmeD5aB<`1lHy_j0S*oZ76t(j3y%Lofkv4YSPaVj59Tm1FeE@3j8M7*O0QsoFc=v?
zkbyx0!etPInvbq7B*@j3fq{_$W<HFDYGW{9fv{laKzO6%NC^QCSH}R5Un4*S0|SEv
zls*AXXHYIfSY~Ru0Rsb=HiXhf3=9lDiAkwB#S9F-sU?Xii6x0J^{{lS024r?85y`3
zz-qt*D1~Z(RWLF^1X$U@Tm}aS#emLdW{?2u7gVrk-~g*}U@+lkF!|36<}k1_aDe5R
zm>>pnKywN+BUCv90~a4eDVP+33WID<HZF?eVQ^poWer9KRt6CU4u-tM+*FW4Nk&F+
zP6Op;kXi;LeIR*|ID-(xWRSc7STzGf1uICFfuR6GF~Ib}xWRt@E+D&L;T54P1Czo^
zLqdyz!2udhAiF>c8}uOhGN9s+uwh_OfQAc54K@sRHv<F13#dD>sR4<>%LyHbJs`2c
zgn1Y|pz+4epvJ(#P+X8$RGiAdAi)GmV+;%e91t25{%(#w!LAGp3?V_GAPOu23rB>X
z;66qOA+bQo38cpZL@<DifW!<P(y<aGmC-&OgNg$BhbtR6|H48amX8(~K+>HBG`!Kv
zFIal-fTn*~R6&(9fD1dAAl+#ZXmO?mrIn!c2Yce20@J4l)u#fbVSJeR=%#U?se>1*
zP=#txc?Bp96^2s+P;m_?4VQp2VESM*R2WXd+=WXX77n=NVfMq^2{RZLZZLPr;BX(z
zzdBHPn0sL12h$HT7Z)u7)enmYn0{R9af!q1gP8+!4=(lS;;?WqfVu}3e(37Z`RM5Y
zrXS`nboDSkEL>sf45kl8!@?J)9v2OZFPJ_U4RaSReK2uc`U$z8TKZw;poc%H=>V3V
zVc`i&4>0p_(XeoWg&RyiEM4Q$2NQ?oFIc*R@nPn{)ZwCG;SURMnEkNuAfyjwAIyH3
zewewq^uy9O%$+cr*n9&EH<-OJ{V*Elep1bY>4TXAb0;o;pywl)dkFa-J>1a4AC{hQ
z*$c~`FneJ3!f2fO&?TVd4J_VZ{)d^1t{fL1rXH5=VKht}mp*iHn0i?LgM|~iIv5`&
z4+}3?e8ch&vGE5BHyBMwAIyFj4Rbdv{9xk5(y(*{^AEB5VD`e?4NFI`^Z}=F7znc$
z7XL71F#qFn4=kKv`2-eUF#RxbxO-p{P#P9rFneL@Vg7*eVg7?^!%4x)1DJW}?!l=M
zCIQU{FneL{gGu9}Vd<P?`=AEG(kaYd7!4DLD#J;khZnm4356dly~6B;(Xe!Z(_%CU
zSa`woQECoMKP(-hSqJCB(jm-#SbV_r!^Ghl&=@fDVd`NtsqqQ37Zx9A*5KsA+z+!C
zSAN4~0L(s^epo)hr5;@zmd;@2pvx2D!_phf|FHNcRPMmch1m;pKRTa~e_-~(^uy9K
z%w2@S6P7<<=>Vn=W)89T!|XwKKg>LsJurX6Xqfw9G%UTs?1zObOg$kQ-5t2>h1mmB
z52Io3h0(amZ&>`o+zC?;qhaO~axY9C7Jsn(023!fqsJRA|Kc(S7GI>=3o{R9KP(@?
z!jV{az~UFCA4bE(VfNsnVfLe!SH#8xEPcbmA7(Gi9k6)DWj`#QVftY-A${of!om&a
z4_Lh5G6xpEFn_}MxXi`nepvd2xf^C4OdMt|A^os$!=(;G9Id?pvmZvo{g0s$B7p8s
znEkNugU264IR**y7tB5wA7&0L956JZ3BcS5GY6I);rgNeMAL@Bh4}|20W${{4=`~;
zG)y1Nepvd%r4Oba=5LsP(A|SeKP=o}=D_SF*4;35u<(QB16ceKDt}@6VDS!fC(J!C
z8YT{-VeW?MM-L~MJ76?SAB={nCs#kZ{jl;ECJqZ1Lh%5LHy8~I2N(@=FD@EpA51-|
z@sDmUEZ$(|z|2Qiht7xj9~Pc48dv#;t{-MEOdl*f(AB~CFn7WH4bums3F(K$3rrol
z{kYN{%zktl-T$zBgsu)3A3Z(7+=EL!x;QL6VDSbE2begzdKe$(Zdf=FQV-JyqhbDp
zxfj+Bgvk@4VeW<53v&k{eXw)|i+`AUm^)y6f;3wBPHecLn+q%FVBvu-kBbjWXE1wV
z`eEYm@(azKF!NyP7*^iE)WgCR7M`&51=9x$KbSo*dtu=R6Ni}(*9T?5%!Anjvlpfh
z<{y|iR2xnT7Je}MVeY|YKe|CM|HJgd{D-a%Cm$NVFneL<;Z%=H0?iy)IH4<trFU5V
zgZUp8p0MzPiKFX>`41MqFdC+xRQJQ;3Fd#8`!MW96M(6Mg*(iCm^h3^(+20l@-NI@
zSbW3yxcmVtXJFw3<HPKOiNpK_w-CxePp2?<!~6l$4~rkDHaG>d2i^TJeK2$28VE38
z=E3yC%q5@=E)7!;3pZTp4yFO-FZ6T+vmZvo(iu!2jE2PvOdZT#Sa=h%AEpi#PB8Oe
z;Q+G-7Y)-7vmZvo%)_M*CJwU)mcL*$%p90HVrf`-!t}$^F)sZu`(gTE;Rj2Hxb(rq
zVc`qY2eTJfe8KeLqG90)GY{rIT<X!qVd;q2@Q3M#g%iyEuzU=YCq!e!8?^i-qz@(!
zvkzuJjE2PnEPi0>(P>zC!Sun(6&Ot@JYfEZse|c*#UspI7$4n!7$4>jboapW6-*sE
z4U1>=_=lAz=;~p7n7d)&1hW_B4;T$ok50qF72O^fAC{ib^`Y}&?tqyGa|g^ELg5Ar
zUtBaUcc6zGOdZVMu<(Sr58d79e3*M-;fXF!h!2Zrc={ov0V)r(7nXit_7iIkOdZS|
zSUAAqfl&Cv$}yPxVdlX2P-o(#VDSKpKbU@)I8KdF37C6f{)V{+Dov1r>4(_|;}g;d
zQx9`5ESzBJhmd}lJS?1G`e6A0CQpclm7g&Er1~FbKYI8RvKJ-~%O^1VVdlWX1Evla
z4GTwDxWj0eyKw0vBo5P0Z2m<z4;BwFbI|2se3<)T;SQr=`H@g~psRzahoxtj{V;oA
z_QGhG|IpRL%!R4LMZ?U8*^5r&ibt3~Vd`P!2+W<Z^gyhBboar`#ibvn9;OavFH9Vk
zj$r8%CXP<S$~l<(Ve+u>gyk=oI64i}2lFpFAKhK(`eFWqxff<HEPOH4;}U?jn_%Gq
zqhaRb(gqWU*$2}HqjBkn`3t5G=6;wy7$0UnIt@#oF!ks(A^kA(VCf%L4#UiW#Rp6s
zAsUuWVET!*A7(Broxt?NXk6h3(+8)a@dvXPmM&r83s;B6KzBE++(J`^!G*;iEWM$Z
z0~i``2*AP}CJi$W7B4VyTr^A_%ssGhgYj|cLl=kTJ6QOk%j4q1?1zOnvEc_Z50=hg
z`eE?_qhaR5)T7g|d;oJdIt>d?^zeZ3VfMiE!Q26J7q0XJa~CY$VdlVSn0YW7-Q76(
z&~yxQH_U%H)x#v9`e5#d*$eY0Oc@~>W-qDn2s0O!j$r1%#BtFu|G~ly7Va>A;L-;Z
zM^_Jv52DKrm_bk)7H*{Khm~J2_YfQIu=EAXx9H&kbrwzv7S6D6hbe@a2Xhxp97e;$
zVd`Mz5G?#);xP5NXjnRd>4&+8SpDecqPq)So)8}vf3WmKs(P3{SUlj;56k~Bf5K>3
z_z}_%OJAg>7npls;SSRe<HO_$(rD!c%>C%;h@d$z1yJ)~=D_?1lg35E(myQRVfta>
zxb(rqVc`u62U6V+%crn(3o{26zc71n(dh1mnM2V3XyFC(H?j7@)WP(@(ml*SxZDd9
zhou*oewe>u;xKi%XmtBw=AhHC{ETi6%$+cM(e=UjFn^)zhw)+VfT@Gk8!&ST=0m7K
zuy};&gV6-z5vm{NPgpq!a|bMaLX{Cs!R&{bhoKRgeqio~*#ip)Sop!@(e=Z`Vd`+v
z#D*Wte3(06{)fdgOdb~v%eOFhkZLc?-?02gti7=Cg}Iy9atG!Qn0}alVfMplLhgXY
z1I&Jyxv=;r);%!$(A@zu51od&3uZ4Y++j4jdUQTaKg>Q@K7_?1EL_m_q4Qz>gykQY
zdBp06g&)iuSh|6^58Xa=KFqza@Pp|`SBHxaOLwqvgVDs68!-Ja`(gTF;epFum^e%w
zOdpJfg$GO>(KNJNftdq~SEBVp6~e-mP&~rShlM*VJYepJxdT^tqU(d{htaU~4Rsa<
z1#=(FKA3u#yD&6B1z`CS<~~^b!QumE9<FqVt{>fhFn7TG2elSX!O}g<ez*jb0rMBG
z`kat{nEPSwgc=K{aOsEn7v?S)AFdI~fSHS~4(4xIIRI6TlY*&-nFEV=Lh%PT5Srd$
z@dxu4T%G^}7T+-YVKhvffOZC0{J{JRa|g^{g!IGYVd(^BKg>LsJgGD+{)rApGz(zv
zA=V!-dtm7TM#KDro<HE$LE{hRUs(9T+z;bp=!fcq$;0eHr*XL-7H=^1==lxieq8Av
zmOfzmVc`V}2e|)mFks~?Og}meGarX`2ADmt^bT_`%)hYk#HAly9Oh40{v%dD%wF_(
zAmn~ne8T(<vk&GUboZh2Vdlf^g~caKKa7U?3tc}<9%er-8fFi=ewa8+A1pp$;;?W*
z*9YUn)WP(_XjnMF`~y>ui-y?)QxDS*i$`4g(8Xct3FZ!%f6&#z_^@z=g(EB;VD`hz
zgQ-WSVeW?6N31?{b6|X!|6%0@EPcV`anUe;z}yeZKe*JRi^J@N`GZ({VdlWn0nGm}
z^I-D0XqdZU`e63M_%MIK)M1!|R(`_54W=HJeh8%l^mv4ogRt-<q#x!kn7`5efh!%s
z#9{u0r9&7E3s;!==rp>!VfMmknEP?XC(M4BdbmHK;fih_1|O;prXNPb`~x!|Lm!#|
zEWBa<B{tsBjA4MqJ1iZ*>O*vO=zLhb!Suo0k1O0@`eF9L)Wh_{#9`_P(J+6*^uyf^
z4PQd~VfMplSiHmdIO73oJ}lng_ChU$nFCK3P;nRq^CwI{EF59-uy{llhsncen0|D3
z!t95I4@^CbhS?AEC(It0dYFEgI+#9m8fHJNyupYEbPZ^HSo%dbhmgH6V_@!tg+H<S
zVE%>K3v)NjJuvfe(dgv|tlWW_gG(P=9GZS$>S6AH(Xe=ctH;5B`4{FMSbBqn3rrq|
zcAUa6{e;{B3rCp$VSJc7VE)5p5zHPK4NH%(aDa*9(hrL-7!6Yo(+>+5T>4?^VKmIY
zu>1!z7ngomx`WvR<HPa;q40-=8_c~h{kZIdiNo?cEPP@5Ve*7%n7uF>=3ZF*5z>$D
zUs!y@%p;^97A`P*VeW^8Ke75@_QLeT;*n6i!Q^4#hwdL({Nr*5%$>0GitY}Wzi{b?
ziNo~4)T6r#rjAq^<}O0@FfMap?t%FeT^%g_!NMCB@38a(3s)E)T|bNu^FK^I%sf~=
zfTa_ddUP7*PMCUFyusoVT|GJ<rXFT5%ssI5M96+ve8Kd?+ym2(ZXY@yW<E?kEF6e+
zKg{1S{V;c++k=Y_vk&GESa`trxb(r?2h$JZ!}Jq!2h88F@Pe5G(+@Kbm;LDCuy})|
zdzks?>d^Txdtl)Qvmad@E<VgYnEPS*4dyRcK0()yZZFLJ=;E;WMb{5g4|6xnK3KTJ
z#9{71*AL^Pn*-Aa_YX`V0UDP7Vft~E8@P-?cLyPN<1z;pe}u{%4E@md7|i`Jb1>AQ
z3Bb}BOg&6LEI!bbBe+oe(DlLe<BE5f0kH6e*#q-0EIq>HVd@FdFn_@8hs6gWeQ<fG
ze_;Ay?t;q`V8HSTOg}6=5zq#ghPfB!e^|J}72sgN@*AwYgVCg>e{|zv`4HwWba|M)
zFn7cJ3yXJ{eptNV(hmzSn7?6s7)@$AfcYDjyV2bN<HP(xY`DYp!Dv`|frURzoDdDO
z7v^7d8kR56&4Gn0%)hX3hs6WT92g&6KQ2DZ{V@Hw!VxA8Gau%E^mv4cquU452eSu8
z!}P=S!R$fT59g!hTbTRd@&p*@?tq0W0c~(;SUQJ=KU{)H273I%%p*44!_>jj2`n68
zG?C6=fQ17r{9)!1s~@HwmL3Vl8xDhE;Q@08x%DV4{SeB}uyPFMf0#a){e<+x)WiG@
zQx7u_hd1Hll`wl??!cuULmVyL!{Qr8!`y=_{9x$@rXJ=Gn7d&#u6TsSH@be9IEH(1
z2*B)xxfe#0nh#<2!rVh_Ie;DxF#W{F2h6=Nnpl14!2$CxEIeTO09_p}KFq%`_ruB&
zT<T%sF!#dT57SS`epq=2Qx9`Dj1NmcFnb8muyh8~4~rK<`e5?t`bo7PJ^jG^2Q!}_
z4b6|Ra3tFOP<^m+02XgB{}8el=1!P?SUkY^xb(x!#}(c%b8+d1r5l)j^zeq6k1h@~
z4<-%^FPM6mJj{HUI+!?&hN*}76PAC_<ze{(CXP<S?1zOLEZ$(^uyld055|YN6ISlS
z+ygTQrVbYkb2m&MEdF8Qxb(rqVg7@~1I*pH!VRVmCJ%ErJikNJBg`GR>JON`Fn#Fm
zh4~*w!|a98uyBRxhl#^zSp4FWhpB^w8@fBu{RdNzPQ%QHnFI4TEIe_!AKe_7K4QZk
zT|X>5VESS4g6=PLeXw|dxgX|lm^rZYiLMXEhx-p2@33%(`3F5-VfxT%m^zp~LiWP+
z!}P)OA1pp#^0;W2KVk6%vlo|obaC|XhM5C%2cd9BHxCw$uy}*H2Ns^_>e2Zyb+CAZ
zmA|m?L06B?hq)W359S|Y;{oPwn0+vJ!2E@7A37iAewckQ{V+bddUQU_9+*BDA6*@c
z4>K309_C+S(+|uXSUkb>!_0%31M?S5JuVvN9+*B@{)6RDT>4<<!@?it4j4@+9ANPc
zi+`AXF#RxbT=rv#L(2h}`(fsztAphqnE9~$2=hP8ewhEz^`onUsYj<_>d^JW(kXhl
z!}JphH+1u0{($)#T^<)7W<ShcSh(O)k1h_=4+}q1&4GmvOdre~n19giL+8WXiyqIg
zd_inE2vZO94=f)Ms~?v?(A|s9hxr#~A1wZ0{z6xe&WD8?Og&6L%pd6L(fP1+1~Uhi
zk74N(T|F*7Og+qg7>z6cpqm4WKUloO;sX{w=<3n=uyO+?PO5&GKA1VM_<;EfCJ&=w
z;R|ygEZkuFVDd2a=rnq`!@?gXj;<ajA6nkQ?1#~C`=RnU^+P3K{)B}a%ssGl1yzPg
zLCuH7H_ZP8^+8R7#XHPC7!5NA<}RpmI0Z{rF!eBVU^Gk|9u9Dw5C+VBFm(`71PSvG
zuJ}OLkFE|D53uxvFd4#v`3vSxSh&O73*)1k3(<`rVfh8-4;T$I7oidd3mShgb71zu
z+<`+EPGOk8VdlX6iBkhq0+x?3+z)jhR2c?^9^WwiFn3^RfC|9;2UCwu!`u&5jzPin
z!{QglhnYht-p~z%g%eCaEIiQF;o?)OA7(Br++Z|JoDdDOA65^-(i0(lFnL&b!~74U
zVe*7%n0sOI0n5LH^r6ec^uf$Qmq+Kr$^n@DFdAJQIv-{}%%3ot*mMI+Z?N!24?kG=
z!_=YMkBg6P9;}?jr5+{@%ZD&`z|sRu9-W5C!`ulAH(0o!tH<C&%LQ1t!SrLO!yy0*
z7npvSJ8&q+5JuMzD{o=>7AB9O5h?(ahuIIKVc`ek6G{)T@Pes_@nPn`(lgXroD@tS
z%pWismcG$7!r~7W?=bbSaD>UD>x1!O=?WHJFdC+x)cAzC6Xsu-dkDD$<{p^6Fnz?f
z`(gTF@ky?Jm^m<i!@?CNj*EtcBg}r7K3IAn<bPN?fvJbNA7&0Ndtu@*bufLf@Q1kv
zrVbYkQwK}u=<Xpl9$?`B^9QUP!(}hJI4qoC`eE)tS4W5s3lCU45>k&YkM0gw_~23p
z^Cv9*!`uz?2P|Ks>xc1S@da})EFNL`fl&Cv>_d+?m^jQHbQ(RrVD5m~4|6{(Jka&S
z_%MIN_^@&Wrk{}eVftV+x_+2DVe&8<CXVi2n7uF>7C$h3FdF7=Sh&Ib57P$=f0#Wm
zeYj}!_#+f<Fmqt(3uZ5&^a9fdqtV?B;}eR1n0}ajuzUk^2aG1x|FH0c>4VY4h65~}
z!1Te=0nDAm(y(}d#VgVFLoFaRK4AWar88Lg!~6jYU#RgI6wIA4^)MP1AGq8Pix-%B
z7!9)@W-l)NFmaf>VKjPvf`tc$!_fp__QBi%iwBrJXv#6TF#BQpVd()wAr1jpc);8b
za}N&XIE7*EfSH3!A<SQ}c!JR|_rqwIyD-dwwx7}C9~N$~bcLZ0hX5?U!|WwC-eCTK
zxdY~Jm^cpW;OmcI<s{5rV%-hP-!Of!@PpY0<HP&~6Nk~TaDwTFnFotMm^e%wOdp(v
zhCj@|F!ks(vFQjF-Z1^JaDm%LfB~}yrXOdz!C?-}->`7Qp$tP9-Ch_ULm>_USh|7H
zgwhSVF|hC_)n4@QhM5D4Z-VAQ;~y5@Fnus{Vev;OJYne$R_?;mKP*0A;^_WH=fl!D
z%pEX&Fmuq=qw`_zgt;4C9v2^GJ}ewy?!cuUT^ttfu=q!pC&Y*O6Xt$G>S6M*bOy5@
z7H+WkAh!O1>4W(norbv!W*^Lc7!6BjFnuudU^Gk|mabs>&}mpap!)}wp3v39_%L}`
zIKsq<rD5)b*-xxKm^zqyNiDBn;Ry>Ln0ds~82X|485SRe!W-sZnEkkDLg51QKa7Ui
z3rjaJb8-0#rXCi5F!iu_f%ylQewaASoiG|EPKZVi2V(WZ^ugQ>^Do>!Xner=XbhNo
zn15mE80KGCc*5ctO*;cDf5XZ<n0lCbu<{6}el!DN_QLeT{D-CtCl^+3!rXyVAr1*x
zI)K>^3lAL1;leO`Vd(|tZ<sh-AsPcF53>&z?=W*<;;?W=)6Re%UNHSI|HIsYt`D7$
zEB;{Nfvz4GALedYy27OnT^ttAF#nQjFD!k*^y3OIbbDcZSUkYO0VYl^4a<+P_#xID
zSbV|!0gF#U`eES>(+8so*$<P4sfX3uFg~GhgoQgSpTNQqrXR*9<bIeuEZ)i04|6Zs
z`k@YirDH<r0p@>LyutDvOh1f<g%8wtIEC(CSop(eLg5FCKbZY+gNbCo?18x(W)6%G
zGar|6F#p5c3G*k+T$ulH>Bl7w(?=-$;rgNV9L)W&{0DO%Tpbz%R_?&m!}1X!eQ3tO
z`{6MAVdkK#L+8W77nWYpX_!2^J{TVs&M<xG_QT>4rXHO}*AL4-FdC*G7Eb8;Vet)9
z2h#^r2a88s{(yxuOg}6g!OVgA3zz*cdtm7VW)3VIVESR|aM9@QfTagm_z=<u^C!$+
zSiZ)k4<-&%2XiMnO~~Jb^b?y7Vc`z5ALf4;ALd?CX_$VPJ4w|K3r|An2bNx7;RRC<
z^9RmwfR-Du_=C9@rVl0#GZ&ZpVeW*{@N@$;AD2E@{(;$xPQ&cSr5`=r5>k)L9GE_s
z`(fb+Gar{em^jS8q`DubA4bE{39Q`%GmlXC!`ulAPgwlH$^)1@%pP<amOfzi!Dv{%
zKvz$Q4|6{(JPE0X%R|!(%zhY6$o(+=F#p5shxs2SPb>{H2NsUR>Vw4-%wAYJ!lfT3
z5A!FChLz{I^r4Hx)Wh5hGY4iq%w6dEaPiT@55`AV4>K1%ykPN8DE+|9g}E1&4`JyM
z#z(go#)p{?(+_hGOdl*<VCvCnSh|9_7iKTaJec|D`q264`e1x?b?AJU{jl@`vzJ)?
zF!NygVE%^jVe*7%Sh%6P0~W7@^rOqe!X0KFtXxA^ht7w&1Ll4h4f7YedUQUl97fLv
z#OjCn7Z(07`*EcMSp32A8O+}>eXw+e?oJpVJ^jP<!Q2lsAEq9ihJ_a{f5Xg2*N2M_
zvk%=Lgu)LNUNChqJ}!G;;xPBa{Ebc%N;j}@hov)^d9d&x6#g)EFn7b;1)~YY10nq|
z_rcV`%!Sdg^oGlwF#qGy4>JemPIPs!ct972>4VWQcf!H}<{lUy<_?&8bQ&fPa|bN_
zpsPdY!{Q4Te=r)B4hW@Rn7?4|g_)0TE-YQc<Y6>SKg^#naTpDg$CdwK@`Uum)WOV!
z(dhAy?oOCK7!A`0r=jg7Sh&H$ALbsII4<|Y%z?QB7Va>8IPHaLM5AHuhq(h5FEDX5
z<rrL;I+#0PG%P;RHNe~rOK&iD!}P&ubbT;BEZySL2NQ>>Cq%=-3uZ4YUlGy=lZWM3
zn0{RThN(xV(d{P`@96qr{YY56!Tk?SXBg_C0x);N;vMFG7$2$(Ck3+)orakMa~CcH
zVCe=X4=Xp|?uSX>pkeCK^9{^?SiGQXhov8wI&>PAE@9z=t{=vSnFDhNJlqI`J4`<;
zd|>{CnFE(6z<}8g3xD+RB%mEG4GSljI|%t3rV*A;VBrS~H<&yj8s=`8y`;JyrjJy6
zVeW#t8|DsJJ|xy1F#BQVz-X8_p?nMrcbGm{`a$Qz(h1DnFdF7gSh|7P3)7D?T%hR_
z=5Lrjn0sL93T6*38di?O)T5h+%iXxlgSi*xewewq)T4{T^ufvlba_I2nEPSwAfz5$
z9+rM!=Ag^N_^^0`r4N`sSo{-oKQ#Ve>R|d{?uMBUvk#qy$-~?Ui+^I{3+8T^J{S#4
zhv@d9^I_^>{)VMHn0wIGqw`_u4pvUV!U0C3tH;HM>4&8on7?qThl#`Nhxr>u!@?D&
z4i^ox2WBr!Kg|6w8Wx}E=D_TM$-}}8W*&@&nS-t$oewh)rVr*nbalA+F!eC~Fn8fn
zk1h^#C$air^$xl|n0}Z%x;-#HtXzSmKXh|o@-X#;XjpoN*-vaff|(0T|K#e2rDIq+
zfW-&Qe4I429D#)wEFHqkfu##v`eEe`Ec{^M027CWFE0JK!U<*`LH#%khPfY>&(M{@
z!Uq=KxM-L<bp0@Q!{lM%2eTLEf0#N9{b&NPaE66H%v>~OIJvO=1`B_j3UNrl^ufY`
zQ2K|t7hNCB|AhPv3m=$$Fg~I9hS>`XCm0P2FPM2Scj53Z{N6#BdUTqQJK*L*^AF75
zFmqt;gsUUKfW;ro90JNQq$$-8D}P|-U|4`d09Nk6{0%b)hjI*ISUAJ<V<;dX04pb8
z>4$)F3~87?nEkNygsa>@HvncoOh1fItU0i91EwDqez0@^b050B=zLguhv|dGBO!ml
z)WhtDxtmb_huIIa7Z#7`=E2ejOg$kQT|X>*2<e06H<&+Q@jyr)x;)JPuy{b1C&Y(^
z1I!#k>S5snizk>qm_G>lA6-36A1wZ1;X=qAFm<qSgZTp{PDnp2y};~;(S-EF<k90D
z=6+oE!t}w^!R&+4Fn{3E538SG>R|qb=_fb-VetVo52yWT66oOv<D)6V$%UmOSU3{W
z50i(LFED?@Xjr(w!WqM2XuSth2TP~u=E2f6hJG{w^!l6B@Q3*a=6;xa(5%79Mb}TP
zyI}^x!U-1tuyhHNN2g)xVd(_sUzq!0d~|&<K6-e;^ug?hnGaKsi-wiouyg~9KbZNr
z^r43r%)KykVD7=CA0`e{4|6xnewcn@{Q*n=uy}{*hl#`NBSfS7i;#MBd6;_%*$-0(
z^EWKKVd(*;A68DG+XLgn{0lP&rVkdcFm;4zSbBx&hq;H4KA1c#{z=u39^S<2M|Uqw
zAB-jx?=b(t!W%u^5ej#hzhN|``qBMEs{Js3!2A#6!`z39hJ_QX9Ea(L#V0O(F!#ay
z4bzWK<I)cce{_3bd{}tk(vL0<%TF-(z|%jpo<dg-<HN!U=5KTw<{p@ObQ)Gq!1Te&
zGg!VvSC5Mi(+>+5T<XxpVd`P_qRSJ@hlMxH9k6gAq#u|6Vd;vHewaO^+6yxm=5LsO
zSh|GC6Qa?}O;~sm(g(8#W-mHT$Q)RDg{3ca{jhXQ&>c_(uzUwI2j(xRGzJB;AC^zh
z^}*bQp%E&89`CStgr$3!d4$pdEZxEU3)2S^hq)7KEu4ahqsKqYU2ugs7%=z3!U<+C
zA@{<JgT*h*-LUY2xf`aA5Dl}R)Nq5D3$qXAewe>t^2E|Gf5ZF(3twXO!{Py!4vE!|
z?r&JQ!rX()ewaKg{9xe#(+>*|SUyEJ2VEUZKa7UyCzPIG@-TnH^ufXd=1z3`(fP3O
zg82s)F6ioT@}cz{%zlFUq3U7w!SumsnEP>sAI!b5bPr2+u=E2n2PTfw-DnapeK2>y
z>_Jn8lMB;Ns=Y7+VeW^~FmYmO^z?(%{m}4-*#ir2nEPQg&h!J-4+|%ldYE~z^i9a!
zF!#aCfw_Z_KA1dAKg@m@O|1Q}bPCf4qhaO~qG9fZ>4WhJ>BD6&vHD^DMvre|^~2o<
zjYpXKVKiKx00ZU@SiHmZ<4iZW%!9gvWcNeOgXK3^I)Vxlq+sa;=581tSGj|3Ak02k
z`3nmVn0c`NCAvPCI7}VPKJ@kw%wOpGVSHHmhaUbgb7A(t)Dxm%;ZMk3T;{;^!|Ekm
z>R{rq@PdUOJp5q_2+-)^1q%m)=?|LTVBrMQ2g^?|f8ewpO+PFiVCrG!;R=74ILv;S
zJ{S!P512ZbIE;pg!}O#37v>Licfs_-XqZ1?_QB#E=6*u)2(uSv56nF<^I-7{vk#qy
zse|c*(J*(QtB3Jn_QKS|`~lNPFdd=AAIv<M`NY!b_QTR6vHH=&4Q3v!ejwHySUAJX
zgM|k{{m^iLr5Bj}Fmnj$N7o07PeS@&=@;fsn7uF>W)7}+fr-Py372_<(l5-Nuyg|}
z&k3d<m_{fKb1%#ss4&qK%pWjwh}H;I2=hO&`eEq>-CkIHLXE{q!NMOFUT}M%=?SMs
z91^f_gOxil|KLy#7lyeXmTq8txB>zUn0>^icbIXo@PpY)Y&wJKhlL+ZKa3{Y9nf%w
z>4Swo%s<4s2c{1ekFfYB+8t02!PLX@Gb}!#(l{wtc?q+h)O-iC59V(epV)K&3n!R<
zbbr9&8K(!J5-@+DhbO82ff~mEa|euui4#l1{15XNE`6|c2#YV6KVb0zqjBkn`48rA
z7!5Or*zkjy151Cz>Vvrp=1&+6Yfr=EanUe)VfhHg$E6-!92RcyaDwJbbalA+u=s_g
zTU_dJiNpL0b3crRxeJ#*m^du`!}P)84dx%1JWM?<8m122J;b^LW-iPeSh&E%iKSuo
z!`x4-KA3&5aDdT-^uzKQOg)SyWIrt5!PLRbf$?GPA!I+gJS-i-@)5c`AwJB0SUMo2
z9$g+5-!OB~<uUls`~q_?Ec}SA|Iy1Em^m=}36>*J`(f^a*$Z<AOq?JMH3!}Qxa>!_
z2WB6PhPfY>F3|0Vr5{*+K{pR3kFF0FA7&0L{BWtmB@T-RT<*oC59UvpzhN}YJe=h?
zG`wNzVdlW}!Dw9JfF543c*EsRba9w^nEf#OVCJH$$Hj-~gP8-PajC~84)Z5WKg@l&
z)WgCR7Voh9PON@dx`u^2OdpI7a}Q4Y(IjBy9ZWx(G)^uoy~6YnYcDLF!t{|FZ!r5|
z;Q_NB9{$jDitYiJeX#h4xdTSS!V?xR==#z5F#BNoV0>6SqpL^f!@?it9&~v^e3(9B
z-3v1h7H%;6N!1Ti4+}R~xWdfGMZ@fcg%_#qKA1f){V?-j;;?oIjF0XPm^e&7Ec{^l
zVB&<_1+y2Ho?-f6;eu{2PChi9!t}%3hf_V81T6kw?GH3*oLpFbf#oBZ{e;Yc>4U{P
zjE3ohnS;w>Sh<IuPhjB&iw9i#VdAiGf`vcK9Wed4;t!?|=1z1PrXS``n0+uB7Vj|k
z!~6|PM=*7SXqY)L`(f@Pqz@*K9uF|{VDbcMv~&ma2eIbB!Uq=aFn_?}gOL3&_ruJA
z>4$|YA^k9Um^m>0F!NyYxM-L^Vg82ExYWbMVdlfq3Cvz%;}4e3VftV+%zjvYhWQI-
zFFFmg50-z?;{j$4x;_{mrXS`WnEkl&4NM;_++gm8(XjLna}P{CE*ho|7T+*?Vdmk|
z2NQ>-GnhYN?uYSV>TuC8|HJHq>BprWT^yz!7Va?rqpQQkhxs2C{;>1}3kO{K(8Xc?
zh4}*(p6Kdu@nPy={($jusfUTf(kIMbnEzpXSop%!W6;p@2j+hCbVX1<nt?EP!@?0w
z8YdSP{xJW;{Dn&cOdjS>n0>^$7p4woA51?iJ;TBQm;LDCF!#g4A7&oR9(46EKFl6i
z_{01I3rCnbTr@2G!tw#SIk@z}#9{7*xdY~2m^)zNF!ks(x;)Gsuy90IkBg5Uez?@Z
z#i8jIW*;mb(cJ@AkH&!c6Q&OqUodyW<k7TYaAEer;sIt3%v_x12-N*Bb+GV;(J*sx
z^<QwMe^@xe(gDnU=njYZ6J{T*K1ElDix0CO=1!PDNHquMK3F`$(jzW=af!q73(P;b
z)S-*R!V9Jk7LVxa(D^X`!}P)25AzSYdR%;%{V;#S!VyN}ihr0rFm<qafVm&$E?7L{
zvLC~IXu5>yCzubQ>S5|&=?>;D4D-<(0t+{oedshSe9*MPx#;>~_QS#lt`G+UmR@1@
z5~~kp94x#Exf7-y7Y&PlSUeKz4w$_#_rv^;v)n;*04(3Y!V#tq<_<LFm|ST5!_>pv
z0i$8*0#hS)5tw?IKVUR=)wo4r=D^&6Qzg{>uyg>^haRrD^uyu_mJVR{!O}mW{0Fla
z7XF0t3(Orbcf#yLr(y1asmDdb(j81a%s()9!T5yq!{Q$vU(j&Hr4J?!Gar@?VeWy^
zFm=Szuy}*Hhmby4IKuS7!V#7a2<eCEgV_f&kB~led6<9U=>S~;4nEACFn7blABTFF
zFf4vy`e63L(gDmpxY8p`9;P2=FD%`|#9`qBvlm9g!U+~`F!eARW<E?EOdl>9W)93B
zxYXeihv|pei%T6Yaag#KYCo(U0gDfqJ7DpO%RZPmtbBomJB%jQ9WeW0_QLd$8V;~{
zgXxFmYnVH6($I1OrXN-g;8c$$0ZVtVbO!SWnlhYRm_K3Z3>JR4(mlFC==w;t9~RFr
zeK7lB=Azq&&WD8;Ec{?J%sh1U=zN&{Fnur@7M|$paq(g9hUq6Z{$c81`e5k?mp$m>
zuzUxzALehEztGjA^I`E1qha9*GY{rIbbT;B%pREg(P?7S5zJng`(fr2qS5V#g$E&h
zFn_`P39}a_4l9pvnFA|#VE%=<16H2E#9{tIHwPw;t`6o7m^h*EgSiu3AF<&8a~I4U
zn7zd2KbU@4IRpz&m^dLCmhNEo!oro1K3KTG^ufX(W*#ga2$=&5H<<rn`e5e5?7?Xc
zwETvd1Jeg{2TpxBBw*nPqj4z1DGbvGOJ6t@LM7npp#FfBKg7lZES<sp4WnV<2(<<$
z1yctLFPQsb`4E>zm^{orbQ)$3F8#3d0@DZcH!K~%#9{WJn}e<&J-lG%!Td)^KYD(H
z=|?vgW)93<Fn7Y^5vl-|9&zc1Dr11z15*#<!_39yewcn({($L&<$qW>!qwqmz`_yc
ze^|XsDE?r^!PLS0kDeZ3?t`gEr(ybF=?@kTFd7!l==xxMnEPS=CKTQ<eJ~nk56pg;
zI3XJ5E|`8;_!EpTG=pLG!t}%ZgQg587v^7BdLdOm%pb6LgM}k3UT|6rm4LY)77noV
zK&*RU>R|qWg#%0+mM)>zVNkI23UfE9`q2%9g$K+$LiVHUhw%xy9~Qnab+GV*`3ELW
z(0*vW0SkASyJ0k;{D&UyFnz?PBXobj+zm??u<*b|!|aFYgV|4PK7y5VF#W{38|EIE
zdYFDf;RVwNqtX2ja}P|O5Dg0_7!6Cmg!IAWVeW_NgQWwQJV6?oeqi>)(jP&6PzC7a
z2Fx6oyP(Q&Qn2_&R}ae%I5k4u4bu<Hw=nfEf56fgOdLkT@)fS|g6V_BFH9dg4f8L$
zI7}Zd^I_=>=6;y}Vd`P&5#3+t_Q3SP!VBgenE%k#6XL_ngSiXl4?_CT{R4A9EFN&_
zhvjpaeK3768WtYt`eE^dt`6p2m^e%wx_%fRJ$%vilNye&`~b5b-F%q6xM)~-k*go(
zepq_IWe=|SBDVa3r4yJtVev=MUZ{g$`q9fRV)a9ffm5(_52N7{1Q@XRf%$__dO<f1
zmOf$rhlMM;I&?lP-NWpKnFEVISUkYohpr!H56oVeIp{Pl|Df9s(+3Mb7!7j=hW$_h
zSpI;~uy}>VBg{Wg<pe2M{(;d1H9!@>?1%XS=0B)3(G<+zq}mU&0G5AY{)dU<qG9fY
z#S<((Vg4bc59VK(eT4K8YaYy9xYVPI!@>>b9$5NBSBK7rr4N`s7){6@u<(MZhlLxA
z4|5N$c*JEdOh2q#A~qag?t#Sv&U}j=08l<GpTX>h3KLDi!V6|UA&oHgF#FJHSb2oY
z|FH0d>4T+vSh&LEap{Md57SR<dV%>1W-d%W%s!Yru{12+VCKO5L9Bk5I+*=1a|p#h
z%>A%%fTdp;O-Mg_{E?boVeW>7JIr2~xo~*`3|Rhx*-t<@hBPd^VCf%20S*CJ_`&qU
z;t_{(3}Ki*VftY-EF56<7=}iu0J?sddYHRl;epFtu=s`LSM+oT%SSMCpw?ni(0&k1
z9n2pv8fFfrMyLo>Kg{1S8s-nEDx4I|e3(CA_QKKyPK`JuVESO@;ZTNC7+pUhf574q
zJ^#Y|fh+!C4uIJQQx7u-7QZlcxM-L<n14yl2k7pHnTN|hba7a^g}ED+j$q{ix_THN
z7OpUV!`u(!!_2|u9+<mf>S5_0#)r8FmwuQyx;|L?g~`L*i%!GjVeW+43!`D-g02t7
zNB2KWJ<Q!Ob71PxX_$VPdtvDe7LG7;(DkA7Vd({CAIv{6^U>9#^I`6WxdX;WSBHxa
zb0;hx!0d;W3%K;b#9{hi@d&FAVg7-s!$rgV33ES8AB={@GcNtO#9{FO3twF7aETKe
z4lwg!{(zZBwEfWX8)hHO->~pNHy36;jE2P@Ec{`7V#5vQPMEzgno#`1+yzq)vmYi7
zb3d+f2)+D3cQ4FbT<IAm4)ZT8JYX~-{V;dJ{13Aq-CZy~%v^LD=1-VAVCKPSboJ<b
zSh)ey52JB~8@hgUKFmB=dV+-$x;k8Zn15j5M<^Y@!VP90OdpJfg)=VuVE%=vN2hV=
zgSiW)9%c`W4|5ML{jl(b>4T|*>4&)sCQd93(@)TT90tPtMXdcWdtm7erXLnBIIO`~
zHwAMyjE0qKxb(xsVc`c$XE6ORcfiyUqG9C+Og}7s2<e0Q6Q&Pl4ypQJ;YdgyOdj38
zuzC<yUchLWdUP7*ZkWBW_=Ckej7HZ7<HO<y=5H7c%fGO40Hz*;hSobUcf;(1nFFI?
z{>0FaLjdLvm^;XgN0_^CSc6j-W<QL^sSt+*%)c=I6G~68^bZSf^l*fUqgw~l2eSu8
z!@>>be^@#}*N@JJxtmmf!_0@-3rmma=HueS!UyJlV)et^3)4rc{jl-{rXQV#nFn(p
zF89OoDXiTJ3qP3oFn8k84-<!}hs8fkoLCx`PGIgJqz~q9m_O0|597nsp_>O2hpB^^
z0}C&hdtm7UT|bNu(+3MTLh51qU^FazVeWw050fXBhS?7@hgf|ubufEj`eE*Z$)nS-
z{0oaOSh|PlhlMY?J{TWnK1@A~CO7@S>H}Cf5FHQDdH`lW%s()95~5+{6f7MOTi(OW
zg@qf8hUFtz_`uX*&}i<4`5P8~F#o{f30*%dd|>Ke{(z~2*$*=hCXP<S^uhd(u8)v@
zn0c`D2QvreA6UG?)S=Tb|Do%H`43$^Iv*D9uyBCUgzSZx154*H8m1o>k1%zFXqbCp
z`eEr1CXP!#tek@BL#GM30~SB9@Pp|m*8ebf!Qu;+9$?~xXjr(x+(F2{=;p!V9~O@=
z8YYge9?pm6ADFvg_QPmG{(!j~7GAJ$hNXX4c;a#g%%3p#!rTEf4;HU*_u^o{+zHbM
zGY@7y4s96yADF*k`eE)NWDZO{jE3c7n0pBM0~S6o{V;vRrhiyEh3SXsgT)Un|HI^A
z_QB!{W)6%dRzEDfVCKPSoZ$yee=zsL)T7g|^n}YD=;E;OgqcICewcYM{jhWm6URwI
z{R2}^tbSPffT@RtBP<=^3_qAdp!UMjFDzbR(zs}txiEb&`(ZRJ9C7JKcQ-5@!t5uM
ze$c}SrXOY>Oh2ypgSj8(UUYRZ_rbyuW-dAn(+_hGOdiIEg%3;|orbv+7H=>bW-cs!
zqw9n5Vc`YS2g`>r_ruf?OT)qeW*(t<L^l@}UNHB=!U0_!Iv-shOh3#$=;~qd2Ma%#
zzhU7G(+^XJt{)a|F!eBh!onM79=bk4d{}(~GnbHhba_~Kk*gmTUa)Y1`4in97$25z
zVCrH1hlK}B9XbuO2Nq5+f5XBZ#z)r&=R?aKnEfzun0|8oPp*Dgeu3#ncNffk=rnpb
zz`_q*9WFjBoM86C;-65w!OVlHhvipTJi@{cmpQO}0ka>b9%c?9|HJgd)WPh5xd-MS
zSh&L6ONfT0JD7f0_!80w>o>yO4=Yay>VqnPxgTaPsqTh_2h4t0JV1>nNWt`@#{({n
zuyBE?hvh?<zhUO#(vL2V9_}zcx;k8ZSh|OWA1qzrQV$bH4|kY;SUQ5K!=Rz%2Q0i{
z;Q-SQ3m*)9PyuxJ!pwo`htatF0W%+FFD$-c`e5!SIvk<?fyE=y`k?-Sg(J*8P+{B@
z)IMDPhWQ8PPjrJ|`2*%(n0lDMVKlCE2n&Chf6>h+#E0pJ*-uD4x;(o3VdWUQI$V61
zzhL1Fa|g`dxb(rqVfta}VftbIf~mtr!_o&#A1oYU=Hk+aOB`knEL?D@Ll=jI8%#ee
z9ipql#fRlDnEP?5!zB(2e;AER9WHU0y)gH~Xjr`9(gzcV*$dML%Lg!X37Lbg9~S=T
z=D_Sjr(y983rCncVD`i619W{bJ}kY#^ufvln7J@@xM-Mqn0^=y^9L?{FmaeYuyTe}
z{jho;-5;><fTd5EeJ~p4KA3$l8WwJ_c!8;d>4VWQ^I`VF^uhQrb%bb``(XCN{6k0|
zOdgg_2*oo@Jq8V}2VmyFXjr^ps7DikrEi$~Vd;U`asWNvVc`cemykIycf#z4(J*sx
zh9}hDuy}*{8y4?q-o)U-+znF?E01C3!Q6$R5lsLVUNHSIcM$S7%ovzHn7uIl#M%$@
z2TUKa_QLFk*$eXrjE0#DQ-_O&g&!=R!QvejkA(aIb1%&OFnutZkbYe5g82()`oX0U
zDh>+=SpJ3zV^Yv`h|B*l|6yu`h``K;g(EB+;OQTt3?~VTCzyL-`e5$CsSzpx3ujz3
zEPX<i!zq|JOdrf%Sa`$4ak&E~4pRp+4`u-@JYeYwCJv)v;xPT_>4cDebo*i9i0*!z
z@du4}nEkNy28#z+Ji^?Ii-x%arXQ{kYCbM~aB-+Qm_Ar~g3A+N!2E~q4|sSG&<_iD
znEPSj4&xKh4wr_dADBB}G|YUsLP88^c);|-;**dzba|Nj355eJ{9rUJ{9*Q^r)zY3
zVD5vZ8<;*=cwnf93c%b2Q;$x=+Bdku59VH&KA3-D_QLWPvHD^04s{+u3T7@W-NM`f
z^A|w_pbF5#8)hC<nrI5<Z<sq^;X}v(m^zrfF#WK2#i<{fPGR8=(?@JO5Z&J}{V*D4
zF3ex(?ts|?Qx9`LEZ$+}<BAVhe8cp^;uGB-Tzr_jVESR@ATIUj;;?)L^CygkiKDB>
z#fPbfxdRpsxYWbMVg7}M4@^HSTw&^P(J*x|eX#tBOFb@enEfz!!`y{SJ-Rq79%1^?
z<q7d&?uVrtLh51iFn7cB6U;9#jZhkv?_m02_QTQvEPQc=2fBWkIk516nS(1{VCKTq
z!OCx#xiCI1{ZMZ(z`_Bh9!A6R2h1LrdN_@v{DAob9-mNkaD7k)EWe?vhlL}IhUE{a
zHaG?ICrm$#4_81W1E!x)IN>r57GE%PaJd(keptGLg%`2zfY}2JCs;aw@p0M*lYqtx
zOdpKK8Q(B{Xf!PS!2D0FIWT)*>S6f>mhNETie??03-dqBUYL0>1u$`3=>k^2z|_I?
z!Dv`Kz|_ISVKgE8Vft~!2h3eC`(b>Txv+47iNoxL(J=qQ(hWKd%f~QvFn#DW%zT)B
zV)enzfu<vvewcr8g&SNSlmROTVD`djShzry;iO>tVd)1JelYzo|KTzKmVROO!or<c
z{V;Q3_M+!!nE5bu=rmj(nglHU;|fPuyy8*^OMkHNK{FoCh1m}?2j*`W4YLQX0gVB3
zA1vHp=>TRfnsN*-Og(z|!`y?R0Zjm=59VK(c`)}73V&F-f%zAv50;M6tV3|2=^myJ
zora|Yga%X=%zT(Wn0lChVC5jHE(8zeFPMIqKVbS{;epVBodt6TOdl-#VD7=L3n7Xg
z|1ds61%4LHUYL7f_7lptFjHarVCfu2!_0xH!$qTqBP={&G@<m5%UqbhVD7=C4wpDA
z{t5a6nhs&%05cE9CzOxS{SR|DEIkp72dMopcM?<&b0CyPPk%6XL!}8)uyBH<JDB@n
z{vw!;q3(z2gM}B2PsjnV^Z_e3VBtW}UZ}w^|HAaa?1h;NRfdy->4SwMEIf#g2dMvH
z?trCxQqwKWewcf3Iv7m?=1-WtFd9u6PA<%yFnur@rv|75%wAZ$!Sumss4|=sEWN?h
zqniWEKe*BXOh39ln0YW77B4s*0+oQNgXx0`6HUR=4J`c;tr1NjOh3#Wu=qh!hQWp9
zSC~E+4NF%T8lVEOc!cF!m_8VdOFzs$boava!DyH|sC94(W-lzC!Tbr+2cvP>4>t(L
zfaXJ(J{S!%7p4p+jYB`N?J$_RFneJ%OdJ;tb017SI!&lNfQ1h%zrgsgd;z0z*^e#`
ziw~GPVD3X#2jj!k!|X**N4Vl27OpUR&}mq@B4j_z9GLwub70~ycM_su;Y29B(anLm
zk6e3U`2gmASUCc753F25w-;6}!omrr9_D_SdtmOv&<_=Wm7`ED1B?$d7iJGk9v2M@
zZ<u>w>42bq9Qh9BA6U2$vKQuWm_CB`q8SL&4-0=ZX`EbG{K3K-S9(V`0HzOSA51@t
zhJ_=#J{TWn4@^HSoM7P&GY6JGVft{<=<b2XKQ0Yu;;?i>s=Y8{VCfX5ALc)pJRut9
zev<tUO@FX(f|a8%cfiEa?T6V1OK-67g2fvyeK2#-{R^`XM#Id(m5yQI15*$4H!K~%
z;sfSRn7uF>-JiJB!PH~W&~Ss<4+{raIe?)ahXA@iVBtuqdtmtjhqdtigRppj<$sue
z2>AmRzc7Ep^ufX%m%W6<Vetrye`3P{rVi!~7!C6uE_-46VBrqa2cvQ6$E6S599Vip
zw;vYoF#p2B8>XI+{pjYw_~`m!@dT46M5F76`IC@7m^>`rVE%%M<Dy~d1(uFrG%P%D
z>4V9`!U-1cFdCMgap_057go-|{0lP&7EkErz`_Zp4yGTb4pzRv)WO7IG)x@ke)M>P
z$-`)vJ{S#i7fe6QKQMV%Jiy!m6Nk|-f5P;`XqbOsd|dir=D^$m(+7(`Sos692c{ku
z4O0)(Ppm$e{V?~!Xqb7p?1PEJ+zksq7!7j=OdUE6i!YdcFg{EjOg}80!o*=T%-t|`
zFg{Ek7JumCFnJga%V#k4FmYJ=C!`;izF_`9r*XL-W)Cc$VCKN=h4EqPaM7^x6&4OK
z8a+JG^~20ZcQ1@jtbUk%=;jg9kM15=dV`sVE{}^33qP1XLir8d99VjV`5V0)LRXK@
zNB1|34@*yk+yj$`*$<;(=@M7^f%zMz4wesL;fU@|bUrLy!|X$+NsT|4dYCydKDxcQ
z_~_{v=09BO;o{JCB)Rbq3xAk?QsW=yUs(9V;vYuiia(h9VetjCAC~`N?jjV9=<x_k
z55)QdW<D&v!u$hs7tEc+(y(xVnMY6`H2z@jfa!<Pg!};uFPOVwG|c@lb8)!?7C$ic
zaC>oSz#$HcKUjR=P=+B4D}RU%FEkTi_QC9jg$K-EXv#6Tu=E1c591RX4lwmF8m13M
zV_1kL0COiS-eCG*;%Le-xUhTz(+BehA^ouO1*RSrj<9?S^AAiNhQ&|;n7d)=3`V1i
zLzQDtFneL@VKmHsLg^otZs6)MY``u6D{o=;!DyI!u<JsI!qmai8%#ee{17VfvtZ#%
zu6}fXz}$u3R)j1p{lL-z%smJd_*pP_!}P<<gSm@P{K4W4mVRO32@@yiA87drvmd4(
zzpD|lF!eBhz-X915GqkwFn6J=hm~hAaaevp)y)83_Xtx5%dfEThlK|`9MKFQWDm?-
zT<H;JA50xAywS~tiKAH$=fd0xGY6f<<$t&V(D;MJ11uhKxf`wzjR6ZcSa|?*7c8F8
zlw)vV@r~|&Li%C#Cb~WtALcG}3t{HM)ZwDh^}+Zsb7AJd{Eco7Og$kQW)8XfVdkT|
z113%^4bu+`S3>&G;|=ByLi*6tIn3X%^hZcPEMLO(!O|OyPpI63<v*A^VEGKLpO6F4
z0L<MmbI_!5a?$+(a}O+i!Q6?<0QB%DH6Nqf5A#1P|HIsY%YIn-0t<hb`*Ep97l)-Y
zSojf&H+20l_rlUGx;!C1EWAk74~rL={jhk4g&RyBr~S}!7p5N;elYzo^I-9d%N&?~
zm_K0g0n-noap{M}7cBf?=E3ygihp!_VD`gkn7M@PMfW#MKg=Dt^y3nT#UorlG@Nm%
zM;C|bgM|mWJRv?T++gMqOb2KN!on4%pHTe6;s>S=-F{elLbHYeme0_`8>XL7{K53Y
z!U;wb)Q9E}SpG+M2eI`C+&E}F6B}-DeNYB0o?!Z6=?E%KGzIeqEM3Fmhgbv9%VS*O
z05caB@33%(*$;~km^yU(Vg7}wgSi*(Z)kX;>qF<m^uuUax<FTliw{emF#T}*acRII
z4okN%f8bDtAq)#|n0{FN!@?0){)6d*sYj<_>6%dd!Q^53Np(NWeX#I@#VdwK(FLIK
z3$quNj?k6i;=|Gv%%3p(VdlZY50`sj{)YJ%#)r``^I+)%mpd@Tq5gvDhvf?lbvOiI
z;R*8x4rMrnDb){)2blc?-4FFIESzEC4olBC9SW6zg%eC4EL}sTaZ^xxVetoZFN_cK
z2QC9(^0534i+7kih;={Qe5iY1{vp~QPy=A@f%yZ*he{KqVEF;&ei#iCCujgn0n{JF
zx&vlDEdF8P2@3~Y<uObjESzEX!O|}-eK2R@qG9&K;uDuTba7Zdhq;4T`(g0}^Dj(4
zEIiQdLFdE#56fr7>WBFcW-rWK7#}8&%N^+U!R&?6uyBO=4`x0g8kYYEslz1?3lCz$
zA7&2BKQQ}o`5UGlPNSuFn7`rj1Q;;&uy7~V-LUiz(+A71#OjCn4;EkOG@<eb7OyaU
zuyBLr1DJaVnFEV2m^rX?2NQ?Y&jf;z0cJ1E9WeW0=Hb#03rCnfSUSL^9$g&fKbZSr
z=Ao;@#fQ0*Sbw6M19Lab9q977_~`KuvmX|YFnL`1Vd5}<!~6k@H)7odQwJ-zVd)B%
zFJR`v?8Bg;`446<%sntZ%sdQzFafB3m^)zh!lVh&uyBLfOKkkZ)WP(@{7=X}m^{p1
zF#UwmIZPjnhN*|?hlLl6hJ^#noiKec8r^<a_`$*h#)p{$(+8)a;SMXmVESPB59ThI
zI9wkZ1C~x<=Ai4t<$g3{;NuA}b720%P=`YRmTzGCiM1D|4i;Xp@W){dhA?_~k*Xh-
zzhL18b3few(0D?(7?xgP`f$;>;vW`ouyBLXFmqt?==S2`!_>pvk4qi8I4t~N`e6A2
zT^%|f7H=?p=rqhd==$J%Xt=`s3v&l7ykO}8t{w*iW-m-Vj1Myx=1v^i8DQ>&g+DC6
z!SoXw9x(G^=E3yC%!SG0qG9O`-F`y)Vf7v?-NVu|j8Dk_=<b1qKTJQmJT5-W->~q9
z#Ro3+=;APYVc`bjqpL&b<I0Dya70%R<HN!mW*^MI=zN%ZbQ<Pgm_0B)Oh2*iM>iLy
zpVaV!nFF&IM#IE0XtewXvlm9g?8lY<(Zd~PA1oik(k(1p(CtUpPi(s#W)93;n0sOA
zp49XXb0^FkSUP~&Lo5x`2Qvqjz6j}uxfd3GF#WJ_#HAl*K1@A~hWQ7VK6G)IIk0>S
za|g^_=<4BoX!?Pr6BtdbJ7DgF*$az*SiHjG6K)?G1Lkg+eQ44Qu=s$LkLdC+aYFiG
z?nKuQb0<tcEWTjj1`7{ZI!D(J<HP(9GY6f9g$qnQIt>d4Sa`wgg}Db^Jvtv&e!<)U
z3m0^C=zN&HF!#djhov7Fjjj*IhuI6$4~s9DJ7D54dtmxtG%WmK`d~E7|Ah3z%t5Eo
z-G$43bbT;BEWU}gA7(y`hWQ^x!^}meVd)I!Z<u-*9~M67`e1yReK2?8qH&e~u=s`P
zgXI&LdKe#<JJ7?KP&$XX8%D#-h1pMRdWY$U(J=k6c!P-(OT)q&W)7+TfVmr{9~Q2J
z>_?A3m^)zUf{=cgJS-ex`4?smvHpPNH<*4{IwaN~=;aM8-N4L&xszBL<_=i65~~ju
z4zPHDg)g!CVfGU1Z<x90_QJv+77m2s6J0;dewcokJ-BFCK7_d+mpWYHu=t0?7tDTK
z>e0nv{)Oc~Sop);4{c|l>x1!O<qgaoF#RxhkQ#ol@PL_5h=#czW*^LcLi%9tf~kk)
z0~jCXFGA*^+Y5_FnE8bC!|Z{DAIv;L`e5>~c!#+g#)ruhqG90;i*J}Y#JV45J}ke(
z`~edumWIV6EFBW759UsoyJ7i}SpDe!hPi{-ctB4#F#Rxbg6@FkFPJ_UO>FrOi$9qE
zVKkv|goP7KJ&cBh2P_@I)S>$W7T+*^Fg{EjEFZwkgNegvm^e&7dU+4CAEpkb4@SfC
z4NN~w9W4A{;^^*z>4(uUdtmBeG)$aOJix*S=3kioFmaeYglJg4AvWE>(mgDmVeW>}
zgzSffH%vV&{9x%GM&oif%zT)Bn7h%<h4InNf!PPskFE|T4pWD&A3gqI`2eO479WKC
z0aFLF51odEFU(vF8k&D$_QT90l<qLhfeOIFk<|2n9v?7&5OP1vov`!{(+8tr@=)i&
zDfIA%sYj=A<sVpl!{Qf4W9Wz5h@XMW|1fd<x*6c-fxzMq<_<#o(B)zNfQ2WzJUSny
z9%diResp!X_~_{cW)3WTap{AJ!^#m@xdqb?a|cWurVgeLM#J(KOdpJfrC&nn50-Dx
z_2IG)W-mHTtbOSEVCe&9KdJV@!iSJQ(d~utVfMq^597n+3DK~4g3*N3qsznm4NFJp
z^0@fu`JYsG!_=eO4^Owyaut{TFm*8fxM*Ddg2gi|++q0@W-csU!^Cm90~QW2b71a(
zr3+l;C#+l~Ss&CPF!#gq53C$PHxH%`s*NB8GY{@>0`UMh5ULK{|8RK%4D|E>3qJzd
zFr;De26G22oG}za1z_m|-QR@#4|6Xp-@)vKnMcT8sPzmmdtvS+wj6<_Q<y$<8fHFD
z8X8})aE93n_cu<B*d<{8htb$oVT!`iC(Qlu@WWJ!RRrd4n0{iz9cCEJzcBr<d<4ta
zFm<?Sn15mV(P>iM1M@%3U9j-MWk0$&x_i*&3Grd>hm|9Q)WhV_(=p8bFnL@wEd9ak
zh0!p7;L-<+ADDVrISaEFW-c!MFmd$oh3SKt3sXld4a=7>cfk0#^rNR=Sh<2rJ-Rq7
zoM7&S#T&Z6(DkA7(aj;`Zgl;yd;@a_EIr_I2TUA2ykYubG%S43?Stt@cQ=ertovc<
z24)|z`e5#X*$1Ox;>6OhbON&<W)89G0Onp;c*D$t(Zreqb1y9Yz}y4NXE1qOG)x^#
zA1q#RsYe%wxf7-z77nm@Mpuu)ht_AX`WR*ouJi-b4~u_Tc)|3++z)dXOg%ac(+3MT
zSouw~yU`4U*#|Qh<~}rKIJvNJgZTsI9-JDW60mTB>4VX*^aWLhLBY};Og%b{OFv8;
zrXJ=_^l}8I4&7o{_@c|ByNBHR7v?UQJ?Qr1;=|&PkbaoCuyh8aVd)nZ4ls4N?1QO;
z#UCu)!0ad1|LE?9g(EI|(Zyl$2eX&dbc<eokZM27J?QR$@nQ1lG|b<yaD;^)EFN%%
zAGExI<$IXFVeTdrZ!mYD=U13JVDh+VSop&1g_#3$H!MBi(hmz4n0}Z#m_BrIm^_?@
z`X3fvu=ESl2cu!(3)hFnfW<q^Jun)kA3cAgX~*Eg%!BEN@i7!a1z_$&w-*+kP-QqN
zSa`$igXtqS{$U2g{0U1>Fq)A2VfxYI4dx!0zhLUnX_)!2c!cSL@nPYEt`Fv3m^zp~
z7!6Ag==xxMn7uId=rkeyFmqruOg+pTSiBG$53qa((+{Hwg&)j*Sa`tV4Hho2d<s*C
zPQ&!U)WhtB(Xenr*9YUn%!kE4Ed9as!}u`uglJfN!}JrfA7&m*AI$$S{jl;7rVgEk
zg)dA!jE1=zW-huu7#|jIu<(P$J4~Js4f7w&UYL6b>4V9`)WiIZv%Y|7z(K?O0ZYfk
zrdyagnEy%D4@*BV{}T#tbmznT57Q5G54t*ZK1>}<AB={DBP?Ff^}+Zs{V@N+^uzoE
zGY6J0VEQm<Xg!9mAC_)m;fkRTO#o&u%p4dEOUG!+F}N`MVE!RC9$@JPW*^MGF#ls%
zgeCwBXPADNJ79cRIHPHUbJ6v~^uf%*r5`2^QxEeuj1Th{+#)mvx_(%A!OByZIWRt&
zb~qPiFD#y5>S6kcO+PUI!onR^4#CWWTZn@J%SW*IgZU4KatvXZKA5|S)erL@OdpIU
zSdO9P8<;*Ajp0Bv0a$p!)Z?OYr308a%-=8?7XC1KSo%b>9)k;ue^`3JP(VNcmj7Yy
zAQ=A8@PpZl?ths3ahe0w4|6Xp9$_@hJXkmra6VibmR@1{VdlXV;9@}C1M@#jKQ86y
z;;{S+(+7)3bajOIuzUwI2WBoVeK7x`#{(?<ap{AZ3-d259$@C-(uYeN7SAwu!`y>Q
zJ-Rr$ewaDLx(DW8m_8VdZVx&imcC%>(P@~w(DlLc3rsyMzF~Y=xT5QW@nPn|;sKV=
zVftb50aH(ihJ_Qk`eEfI%-y8gkFFmUAB5ZulZT}jnEPS=fyv{dVfh=T4@Tos4-<!l
zJIuc@eJ~oP4i^mzADDV{8kQbl=>**zm^jQFn0lBwFmYJAK-Z7XhlMw+{6Lo{$cL7H
zFn7SxCn0^Xc!IeXmY)dehlM}Pzc6>h;sa(5F8g8O0@Dvme=vJt{(#woZVsXFg4qvK
zhf6=q-?-!n>4&)!T|X>8;L;EC7tH@Kb70{J6NiNhy7@3Z%zT)7n0{FJ!qlPDF#BQg
z1=9x$516^I_(0bWOE)lkVeW_NhtY)G57Q4b58WO@e3(73a)gk2ba`027v>IFxS*>e
z#E1DG7H)*p!{lM{1d9h)x`oN(q@np8rVr+RLg9^W9?X7NdWD$-qY3#3W<R?BVetZ!
zN4FOzAL=idd8EcG%pWlOaiw2$`(f%}{)UAMx;k8Zn0`{-4bulp|FG~P*5Byq7v_Ib
z(*aB$%>B6hi7pOHudr}~`2$@YIv?h5n15mRqpQQkhvie4y|DO(r&nk?!lfTQ+|bQ|
zg#*kUbaP<-gz1CZ3sndU7hLfO^FPepuyBB>huIG^2Wl)P1@$LP9jqLOg(u7&OpOo`
zSUQK<4+|HFEMXGnPFOg=XqdT#On|u;W)6&oxrdN`Sp2~J3)2sC2TY!jIWT!xyAGzG
z)O>`VE{F|JSUkb}0ka?G9+>;kX_$Sm@Pg@sr3ZBN=zLiC!1Tk+Lsy534@<u=dx;G%
zn15mVVBrK4$7LT(9OiDAewaR3c)|EE_2@KAKg{2-{0s9BEM3CXq3ehF6J|e*4^s#8
z56nE6I64ip2bS+);Sb}(+=s3Y#)r8NW)93fFnz@4LzsE6bOSRF<`0-WE*fSJ%zhXR
zOV7CU!TgJ!Z(;gz*$?wKE`2clF!RyvhnWjghl|FgA8sDB{Dp-Vx;Zd+!qwwoz`_Y#
zKMv)X!Z_SPC?BAwGnoCb_{6jbRRpFUorZ-6s%qRkSUADVfrSTd9Z(slztQ6pW<M-F
zLscUvs6JRef!PbAVfhwU{KMP}vj?URr+o;UQCR5iMlVND6l3AQ+zE3B7A1IvVESP0
zhnWX+7cR}PaDnNAg(FNqjE031x;g0jiA`@Xb71De?1!aem^`sG%-t|^NDY5<f56Hk
zLiWS*CCuG0eQ<XWFaV|>J^n~de=uuc;R!PjW<E@w5Dlw8VeW^SM@S#c9`tyCg)1Tb
z==Q?whnb60KQ#Zt)T4(#EIr~f2Ub48^y8v&#RtrMnETPqgZUqq{TTj2^AD-%7-k+U
z9$@JbCQgWk`2!XXF#i$K2lF3HAB-keKYF;q^ux@B(dh1gwG(0L(P={A04pD1>d^JW
z;vJX$F!#XZVdlZifr-P^5u#!0VeTO|{9yLN^ufY~kbQ7@Xu5;tADB6$razc_Vg4u8
z9q9Vu=@;rgxO<@tn0lDIVfMn@4@+0Dd<fMBr(pVF;RmblVEW(+2{2&h!|a8{BLQu2
zX;?ZV)m~Wq!0d&U`*4E^Fkt?Lg&T|}w%mt>AIx5uepq-AupTarUhdFaKYBR89Y!Pr
z=5KTwmM(GS16Vpi*9YUn+yzUYIGqE{A29VW8mD?R37GjXdtvDbmj7VxMAHW6!u$vG
zFDyO4%psJXVCKW@htb5Q19bPp+yQqW4hGC#m^)$qh1m~F-#E0x&s&E17e>SM!}2vw
z{ZI**J7D=A7JtOr4|6|^Ca4c;FH9ZGy)YW)4yZDm6fC@8_QLeT+>cWunglGKVfta|
z2~8PJE_(R^D;IHUfJ(r^9hP2T@duSANTItMW)6&o`3Dxx7zRS~ExLM`J7D1pb0>y=
zr~u3z=;px0p~^5QSiXe06P+d$59r|zi+7kguzZPOF;oC%KP-G;`eEq_s*Gp~rVnNg
zu^M6igt?p8^aC>=S2)1@MQr@R?1#}X|KN&8Sop#6D@;Ai|FH1KWk1YZn0>Hx2Qv?r
z{$Sw)^Dm5s>4(`16DO92xgTafEF6i|4^szoFQw+d{0|F1nEAM9Sh&K%8D>AsU%2$a
z#9`?JW-qRAhN(xVVc`$c4~s8YI1+LP%zl`=VdV$R|1cVtyI}Uf^uxjf-JQ7jF!#aq
z!{U)tbI{!Z3kO{G;u43|*D&|sQU?=<xf4di!V4CTFm<?SSp31%!)REz;L=A(9F`tn
z;Ymn6OddTPVd(=VkBf%67gi3y!Vl&@T>4<*FniJU!{P%LzA*JL8fHGsK3Mp{_%Iq*
zIH2cSSh&I50W%NgKbXCQXhQZ9(ho~VxZI7dALc%odtv^-6>jMIVfh8-ZWs*{M^}%|
zhs7UEAI#mba3tss9PJ>OenRyztbBsm52In?=<Y@5!|Z{%8<y^1@qn%##)tU}W*<yH
zjE0#DQ;&;=<uh2k!Sun>CoX+(acKC#+yQex%zbcmI2bT{VESP+A^*dSgQY*1J{S%2
z2TUDK8qNK%bc|CynglHU!R&+CPi*|d!X0J~EZxJz(X7MZ!omp_?&#r(p#e<*7Je}M
zVE%xaho&4S7nY7-{)d^1Qv*~2mQG;y!t}$+SEw?a6wIA4eJ~nkE-sBQ_rcV|XqY?T
z_CwPLx;eP)g_#4RVex~mALc$-e8JKkEFNI#2NtgA`qBBYe2>dMboJ<bn15mZLYK$I
zhq)i-Uzj^!{==mYCJzg5n7h$wT>4?<GR$5W9~N%t;^^kU?1!m?>4VYe;<y01|6%@t
z>4)WKT>5c|!@>a;p19P((lLxicR!)@11oo6?uDg8m^rxYg^NS;2TUI<{leu5FktBo
z7H%*a<}RGtq4vVu4T}d@I)<5pOFv8<Oh1edQxEe8%v_i_jE1R)>4&8!m^{p1xcq^x
z9~MtAa|p#hOdZUh=;04@7c5=C+=WiV<YD%~{0(ytx_VrEn0}ahh)q8*dtmN`*$*=Z
zmwj+?v~&P72j(7FIKkE9V8HSjOg}6<3F(KW512lfyJ7K<!#enRtuTG?bOQAc%zjw-
z;4%m1PndnMbOjTKxeu3qSh<Pre^@-g(g!a6FnO4Lgxm`=2Ntfd@Phdl79NDs4@@4W
z9%e4OIE)XIhtV*1!t}%ZkM0gwc*69d)3A7hg+DADVCJH$N9V)B0Y<~(3l=Ugaddq!
zKDzxdcfjq3rYD$s3>qpAi*K0!VeY_Ck0t;MN0>gCyJ6`HO*sY^-CkII3G)Yr1~dVf
zeXwwV*$WegrB5_%a4sy~VetmDALbvJd2kJA3|Kh9!kbuqFk{gD4>N~Qx`X)(=1-Xa
zVfGUnez5!jvlm9g%!9cT7Y%bKEF96@gG(Pw9Oi$Ry)biN;;`_AsfW=p|HJgb;twVd
za~Cdup!*w^j)~O|a~CYV!1Tdrm^e%wOdLkT+=mfQ(EJDsM_lO-RxY6XpVat=xdWyT
zM#Id9xt9<Pi!YeFafLg&IWT)*{(#XK;exIa%EzUjSo>lAhuI6GafLh7S~vwuuP}YE
z@PjMB#el{$OdpIU*55E=Vc`zb4-0={-46>tbaP<g0<#Af4a-L`e-j(-Fnut0!~6|1
zhmifSa7WL#FmXcqVe&9{z`_gW9vB~&ztP2E;fE`~qpOGUVd`Q2hKUnP!^%Ba_`$*-
zM#KDx%RF@ZVeWv@Fn{6F53?5*ZZJM9UE$IP3wM}3Fh0y2Sow}iKP=v1{)6%1=^vLy
z9OAHWgt>>1`_YYoxfhoHVD2E*9GJPVaD>GpOdOqt)iW@4FdAk*EL_p`!T2!!F!eAR
zCJs|ah=zq9EItUShsnd-2eX&dd<RP}u=t0W3v&mIhKa*ym^iw<uy}-t;|vFA`3+MC
za|b#Nvj=7`1`Ra_7H=^9Fg}KQ90D-)F!$h4hEo`pZgKh-hfY}h!R&{n16V%7p^X6+
zUoii|+zrzYE0=KThuH_y4=bk#hBwSWXt=}t4KoksZkRYsIjJ<vKjh|rm^)zMgl<2~
zTv&X;+z$&sn0}Z#bp0?stek<l7p5PU4`AwW(J*ze@Q0-bT<X!q(d{QX|3l*s=5Clf
z(9Orihouu(IN?%<OB`K4%pCOeh|51P_rlb};t>{KF!N#Yfy*5*dtu=SOE0kW3!`!A
zhq(h5f3Wa`r6*kaVB)a&hWP^*4Raq%AB=|Shsne2h4~9+9?YFEeYj{?xWV+n%)_M~
z7C$iiVfMnp0p>nj`eE`g`_O4Z<rvIdn0>Hx023!fquUST6VeBhhxr>8|FHB2lP5&O
z)T5h2$UK-nm^zq#Sp31_0mesn2P|G;`eE|0@FtXRVDS!92eS_r4={O{xwvRpeuL?Q
z(ZuS9`2*%|SUQLK3&zJ~KTJQ2hN*+;#}$4sb+CAVl@sXcmykbT=?xZ+FneL)0<#B$
zhL+zj`(WuF<_|*Q2vY~M7iK;zykPoa>R|FP8Wt`vbue>a?t%FO7Ctb27&O!$uyBIu
zhlLA<dK>~Udtmxud>qO#gkj+g(~qG5hX5?RVESPB0Eco6VOTiA!V6b;V`zj5!1TfF
zhs6ghKR}gXQ0VT4r5{+h!_0y4(G7sP6PJHs{(`B)r5~1V(B%o~hsop856h3Z+z+!4
z=6+cD2{R8SPADD0!WkA`FneL?8D<Z$G|XO@|A^Ix9-nY`5NQC+|FCofOAkb9XFyMf
zuy7%y9~Ry)dto#o|HI-1W-q!qu>4HOewaMWKA8Qa`X6QvEdF5O2@}Uf!_>j-B{tq*
z`3jcqVCKO1gwh|(9vBT%4|4~h@PoxKOdTwr!Sut@BQAf##9{FWb2rQ%FnK~WOg+qA
zm_G>VgUQ3v4J_VZ;R-Vs=0BKvbQ-1~W<NR&^B1~47#|jHFm*8fF#BQZ&}mrs!qmg!
z5oQh{{jl^7QwO7A=E3CA?M3Iq)WP(@?1#AzT|GJ<7XPsPhHeh2@d>jRW)3WT(d~ut
zVd`P_qsJ$q_=o9(*$1Ox=?G>IAsW3LBcvWC5Az?)URb__$rGYs@dcw{@d2Y@@r!N_
zy1lUU0}BtBd9Zwit{<ij=5AaxF8g8W0v68b@dh&=-CkULSop)-flD1OahN}0{)UAg
zF7+^Rn0lDKFd8O~%OB|B4|6{(9AGr8T!4ia%wAZ!huH&jKe{@YdR#QRI|$_$T;{;+
zhr1V-LLA~S|H9G(4rOp*Sp2~3h0(Bd3|B~i0Sk9nyureifHn+im^rxO8$$zB0Ons<
zJdzszFn`0$ff`Ggg7({R`Wq@w$RL=0nEzpX7!6Bb=;pxU52g;5ZeZmFtQ>@;3v~T3
zK1?4>9W4A{?jaN(Fn7T8!{PyEE}{Gcb1zIk%s<4YLzsOq{V*DCKg^>zXqfw9(lGzR
z!Ve~ni-x%$rXJ=VSop!j(e=aP73N<U4bu<vKd$@)b2rR>n7?t^hi*Td4^5{q`w7Jt
zTpyGH^9Rg+s4(#q)P9(Lm^)zpCu9&zAI#sdaDjzCEPT+-frTr~KA1eL9Dtb%6Gx|E
z=EC&D(i^Nifw==+A37iAZkRc+@I+UKiw{!|b1y9Y5F3v$|H9lsDBRH90SiZ%KA1mX
z;^^wp`LJ+=g&Ryi%pbVI5mrvY)WiG%GY=*X^B20kF!eBZ!{pKR!@?CN52In`!t8;$
z7siK~ODG)B;}51E<_?&BglJg$!{uH=`eF9M{6$DTOdjSxn0{i@9ZWwg9%11Jb3ZJ8
zVCrzuuyBUi4=Xod;xPB2>xbD3(+4Z}Vd`M^<BA8EILsdObPAJ)nFBKyMiY{U>4T|*
z>4VWQ`(XJ7CJv)v=>eDjVd`Mv4f8*YhQ$X=9WEN?KA1aT`e5R?^r4Hx)WgCPMx(34
z#fQZ^EIwg0ESzxZgM|;w{V;vR+7F8-m_8T{OJ9WSg@qSPAB={D2QK|Ed6;^18dk30
z(htk0F!!Uo1C|bO>PM4+xd#@Guylo{49<nc3oM+`-48Plt^th!b05szF#p2LgZT?h
z8=MP^A6UA9Nx<BVE)J8&MZ@fe`2%J?F7+^Rm^zp~qV0vYn_&82`5k61AsXFY^l*cP
zA50y(d9e6{xfd4iF!!L-==x!Nm_0E4Fq%;O!`uanFPM5*{RI<;*@I5Q)Wh-ttQ>%a
z1I&DMeYp5A|H9HEEPQaOM;C|L4@=jua3Iwjn7OcUfQ2ti9G!;Q3kwez4GRaDIq3Re
zd|0|e_dl`W2lF4y9GH7x<`be};RUmwkb0Or%wCv2boavKVd(>=4@RTscbGbuK3Mp{
z@&`;GjE03P%zl_ZVCe^@AEq9IhUR~mK3IB(nFn(RhCUnuu=EBq4~H^LVW>M`_QL#y
zsSvveEZ$-IVE%xG11^8S#9{WLyB`)m*llHCfaMFAeYn)&5{IQ1n7uIf!f2R0F8wg~
z!`unWcd+zAay&pSf|&ygSEw*f3T7WnA1vL$!Uv~Ds01whVDSJm59U9pGMp65UYI$=
z>Vp{w3xAlqVd4a7v~){qxdThDB-;yh7)(FR-{^c;x`34{P;GDu7H+Wg2BTr-!Q6!_
z9l*jFrVbW=uyhO42Qv?DF&YD=9^D+6ewaOI+TdK6KhX8V+ykRw=?bn9%7EE}%l|O_
zFn2<=5u{-LhMA8`1G@iU_Q2eOEB;{Sp{HM%J{S#82QUq2G)x`LpD;eW{D7u&G;MG$
z%>6L^uyhDlK!5>rAIu+w{EJ~6)P1mUhPeY4{)Ehf>4Ui&mY!gIT;&)no?z~V>4Uis
zrVm{lCJ%ELdU(Ua1LiKcdK?T`xdZbzvHD=f!QvT~e@N923ujpVg1G~hE@1ZHq@nQ#
zGY6(0<}aN3pb{|m!}P(-frT$r8BPkO4`v@MUSa;hsSzpx3pZFi!_ouHT$ngiIR*uj
zhp9)WVd)S<Bborr{V?-j?uYTwloRB_(l0DM;?e*MZ&-Z8Xjr(w+=WX&%pO=ifcYO5
zuekKV#nI9a%p7!g!PP?<Fn7Y@3!R4L1DN}u+Tau{oxt3S;ZLZ$aOsDM!|a6{3}wK=
z9i|Q{45wi63iBsi0?L4eA50xo7*4_b4|6|^52Io3g7M)R(HO9Bg4qY-<8n8ec8qg`
ziM1DI4lLYZ_QKK~EPi0>aM7@Q0t-i&K3KTm(gzcV<xiM7uy})|LtNnx(+?}BVD5(5
z3kwIBd2sbaGGOk4#S4+z;L2d`hJ`C!0*wJPA7&p+8W#VA^uyv0W)95%Fq)A6Ve&Bh
zU^FZ|Vdlf!1*2i<8Wzql8kP=V?!l!W7Cta{!t8~`C(IsPG`fD6dtvDYJv?!lgDwtp
zFU%Ztc|v?r^`nPB%pdUZAYdUZoMG-IRv*k*n0sORVCKPSm^ySC=5AR0!Qvfe4y+tN
z*9YUn(lyM#FmYmOnEPP%!^#gr_M)4Q?thqjVCKQnIl4YVe3*Vh=@9N-XnhAu*D!a$
z!V{N$F!N#I0LzDj^r4#vQx8i=#O6cvbO84cx&=7+=;;QBGMvJ&aDur5r$QVOF#BON
zdN|_H1{a2fH!R#>?tq0SES<wOKpC)f2eTJu4lMj&=?CUNs5YDwdVYZEgPDh}5tgoD
z@-TP8`~gdc==xxMnEznm1+x$4e?sX8W*;p5!ptR>hUtgd52In?gzSge1M?@0hJ^<#
zUSRPAQ%@`nGY9S-92#Nq01r2)I#~P@(hu_|%)PL1fW;$B9%?KGg`RF;`e5l1=00?d
zFmafA7!C6`%>B6H7v^7>dtv6m?1AxN=?o?gqhaE(bO#fM(XjZ0se_5bXqdZT?tqEI
zXqY&RhKa*ym_K3R2NQ?UFn7WH0TV~3Vg84i2a87-jjkS@5Az=^{9)lv$X=K^uyBC6
z7v>(AI7}S|4Xv+W>S5u8?hjn<fW;5GKVj-&=EBsW+YeI*(+{K3{XuO0h1mlOPnbFA
z_7dX5%pq1k%s!YqVeto}3E2k=cUb(xXqb6~^uyc>(+A7<F#BQQjLRHYIzi6|Fh0zG
zxb(xyH<)@@c?AnUnEAN$<8nVt9G5<rIIi#`<Zqbyu<(MZC)V9Cf5Ftl^uzKGOdRG;
zTr|uam^)zk21dio#ibwS4_N+z>4Wic>4UiwrXCj0=;p!V6Wtt``LOUumxuWuW*)jc
zOdrgCn0go=M#I7fT|bNu%O@~<VBrPx4~&MXCzghp2lFQ`eK7OU(?5(4qjBkng)h4Q
zVfh*6KV14@@d$G#OdrAg4=w*;=?)goFn`0`4HHMVA0`g755|Y7gXxEb4@?|J!{Q0%
zewaD1@&pzhg!~Vahv|o<8<@E;`*6`PeK7aJ!V%p+g!IGggZUR`4laE#aaef6;sNG<
zSUAAc5u#!4g~dA|^)Pw#@PqjSCQpclxf4di)kD)4EPcbpp$wQkFn`0;!O{gR{$c)t
zYJ*cSaajDp^uxj*7LG9g!8Jk|FmaguF!eARW)4(2K?>#`n0|s9&=jDThcN%3DZ}8x
z?1iN_SUSc~h(iFT59V$d4T}dH+Tg;lc!%kO(QpL>7`W^wpbah!OD8aUVKiI;0S3AH
zVc~--{lMf2SkC~<|FCd?(Zt3hdb)x6pIH4c_ruh~?1#lGA@`&E6J`#~UBv2#sfW2A
zR*u2MahU^C2h#`h56u0rat4=vm^^wpf=fNRIIJ9jr6Y8CLVQ^MCAGYPnF|Yln15m6
z0Ha~y15=Mq!`uxEH<&w!O$V^_i_2VG^62Ved{}tG;{zsvi-y)SF!eBh!)RD~#HAf(
zA1oYT`e5Mz3twFNVdAiOBoy8-_2@LrewcgE<#F+0_M-bA7Js<(!QutxUs(Eq>4VX@
z^uy{WnEkMDg82hx9!~ve5-@!*_YlhWXxbTI_MwLZso@Fp2h1E;IzYD%#)p+3Fn7bk
zjadD#@Pp}x(XjA<g)_`PbQ%^;u>1?7Vfta=g02t7hpC6T8|EKa`oLvBEL>skh0$<*
zFbi<e(0mJXAB={X12Z3&c3k4H@FX`M!r~w19$fao#9{7)*$WFtLg@ykAEpl$4={US
z?trNyM8o_Gi+4im(dA+KVBrr-2k7c>@nQO4G_mOb7Je{&Fd7yvxa@<8!_>j_!DvF|
zK1@GM9ZVmLhJ`0g9WENC4yF%A!{QT{KA1Qxo?+n)^AEA*2P}WW;t%E?Sh&LM!9~N=
z!|a2lUzm9?_u<kH^DlaQ!^$Oeakx3q{14NQ?p~O^u<(HEgEC;@1#>5i50!?mtHG5I
zVeW$Y7p@P=fVl^keyB3|xm7UxVgAPHE|^9r4bulpFED*Ddtu=KRgOWy^uzSQXqY&L
z1~dU!_`_)Q^Z*M7G;J8?2gA}K%)K!8!@?O|KTI6vUR?4pbI|2s;<(}u77v8d4=mlI
zn-9|mqtWGId|0@_+ye`Dn7O#Z52hcc9v1&FeXw!~rVfLK<{y}Pm^%o$7iJE6Jj3`f
zb71iaQ;$x=+y|?#VE%yXN7q1r4~s{bc?6Whr7`S<h6`K)4hAe<VBrUgKOD+2gkkDo
z`eE*X(J*<KJ?I9	{hj;f7Ab;ssqlj1RL9W)6%d6n`-LVfMk|6UK*`3$q8EhRMV9
z!SX#!Ka7vA4;LTiPndbQ)S-*R?19+}^B1~0Tzr^)FdCOST;j0w3d_H+{0A$42*m?T
z9nAeOn$&oNxdWyjM#IWkT<(YY8<ziIG)zBC9+!S}arF3r<pXqe=zN$xuy7>Sesud_
z`e5e3(kZ%q=zR2eAQW!s`eES@vlm?+7a!(+SbW3M4=(lS;xKo@>QQw6<4k|hauQ}Q
zESzBG!TbT^quUFMSD1cS_@Vm;#z)r=GZ*Gw7!C6WtUN&1ht7wY4@*BVbBNUsa|g`7
zu<`@OhxrTLUKk%{FU&reewaUC;RI8UPQ%p0!V%_vSbBo_4_zOO4@)mFeJ~nkE=(Od
zjjj*o517AU^62_te3*Y>`e5QP{jl@|6Gx|E?t{^=c!0SdXSzi<2+D_*Td;703KOJY
z@e8vT<{p9?p$cI3!r~7W-!Phxd9ZMV*^f@6hcDDxI0XwgSbBr`pHTe441mQK%wCu|
zFg~t$fW<FN9n2jtb7Ath+yM(8nEfz)FdAkaOdU)dM#JI_SNIUp5Azqye)N0>vlnI$
z%wBXF7GAJ)0CNv4J;3Zi*9YUn(g!RYVdlWX52lV-8W#UBf5FTrs2^G%z}x|&3ArC;
zF3et-KA8P5d0aHieK3768XkT)G{WKw7XC1OFd8O~LpuX3USaCd<BgDhT;{^!4`wdR
zow(Gai^JRj3qPFtq49+t?lAjt>VsMcvj^r*bUrNqK$T-qF!#aoH%vXuJVN%v!XK8-
zVfMq!#jp^E0DAg?nTJC;hA=FiDYX|C53q6r77s9gVOR`}KUg}4g&T~9xeLa}6`nBv
z!|a3cG3>)30J8_~f2g}~D2EHf?19x^a0xOQF!NyX2jk;14wl|v_QS#norbAHHwTvf
zVCvD;!Qv5T{6P(b#UCvEVdlW}!Nj4;F)3(x!{Pzv9$0+9%)`(K)dx#=Fnuuh5GuD}
z?t-a@@nPoRv={0SSp2|f^zbBPKhzlbI4!z6;PM0*F#BQdhS4zp;L11X#=_LW`~h<h
zEPbJ?hw)+RVfMk&4J`g(@dHy2qha>J+>1`b^uhcG(}zyO%3qj$Fmqt~(bdEG(DZ|@
z9;Oc_PADE>?t{^=aD%0PxP1f|uyBUi3kydA+ThYK`(fb#a|a>!!wrBk(BlObzffrm
z3T7`X{$cr;So>k-!~6|12UZSZScoP7QwP(Ji-yH3ns#_Q2o_&3^I$a0AEd?y%siOA
zuy94UAI67;BYJqi#9`{tX_z~4`5Tr$(DlLiuyPLOKbSc%aYF8f$;09umX2ZO6QW`M
zgV{@TdWGg+n7uIlFd7!_xa^0y6Q&O4Us(Es(YW-({0*aF`eFGG7M{5D!^NTgfVmq+
z6RjU=5G<Zy>5g1;pvE!4!iP{ez|tSgUYI_Z{V;Kux#%=3f5Ftl;uGc{boDSkEZouU
zgP8-%M=<s1G|YThe8Joa^Dm4>*9YUn?1#~?aDe#-#)qlLMWd&Gn0sL2xb(rqVfMq)
zF-$+P;Q%uSord`zW-husj1LP(SbBxUAIyG2;Q$LSn0lDKg!IAehxs4oZ<sq^@`PxZ
zdYJvNd`w6m%wCv2So(qKgURFa2fDp5bufLfaDasm%v>0ap6+4lVetiX7p{B&OJA^X
zgQ<tnF!#aiBSfRy3-bpdeK2`gx`DX|CQgusmY*>Hlj?4mKA8Qmcpzji%zv=(g7FFI
zgUdtRO|pMs20-=0?1#A<mM&m4q4W&X4|4}B{9x$@7A`Pr(P@}GJpDu61+y1sF1kJ#
zAC?Yb?uEGrM#IV%n0gEvY9GuUFq+u-fVmr{4;GFX_TUg8S0Btcc>ad^113%^4bu+`
zKbU`DG@<qsESzEa7Z(38J}iHry9XA&u=s_khl#`D9~M67`eA%n{KNFa+ynC$%p90{
zbQ)$K%pOAO(e=aP8)hG&@Iu!Q3m<~%6kQ{f4+}q7euD}VO~Jwo<_?%YU^F3j!R&|8
zFn7Sh5he}`XLN_b(iu!0EI!fo;|f=pxiELa%z^m}mM+lkh4Eqbz|_Os50fWEqlX*J
zJ%seZ)WPf}<W6++VEGKD9~N#fb+GV&`41)zqhaob>4*6n<_?%R%p90L7!4DLse{q5
zbc605m_BqG=02EySUH5Q4i_I?Kg=Jv)Z-F|r9+teVeWy6<I)FnFM9a_3tw0`q3eg0
z8?f{WOAoMg4s!>taD@3ArXOY>%-t~k==P%XVfMmkm_Km28(lw)4|5O9UYL0>c|tTS
z-NDi|A@%6;F#p5cfi91W4|6BX-7t5+#Bu3^i$m*OSh&MzV#5!XUSaxRG%S6<?ZLr-
zg%?a8j3zc5VCfsC4;KG8tiuq7g%`E-!_3352!{YHo?!U^<_=u>2E!O={K4`uOh2yh
zgV~3!9;OeLj&SLR*$-0>qhaR3%!jGNMZ@fY>4(w8raM^rf`vDX4>K254&kyNmpIHk
zSh~Wc4kiwZCz$_X{vcLA%$+cOq`DvGZ&*HpxgRDDb0;numS14@!^$BTjY}Uao?-gX
z{R`v6!Ux?Pm^e&7tQ>)<gYjWBOdLkT^uzoMa}P`&=6_uJVd`-C6J{<U{jhusvkzt-
zEFRI_1+y1M!_0?;11vsZ=>nz>7Yz#sm^)zRz|sRDeK2>z)Wh;UOdOZ{Vd`P&9%c{B
zJ%ro=QwOsL=5Lrjn168D53>&zPO$KY>4*6Pu8sf$<{wzR6HtyJ4f6-gJs1jb2|&wl
znE!Dp!zB*$H_ZL8^hYQhVBrr_2lF?q{3R6bF#WK2f~8|(!yV>snEfz!qnn4z-*9ng
z_`&!v`(ft6#EGS0`q9%hF8wfn!@?itepoo-(gzcVse{Eoj1QA1M8o0@7LKrV2n%0a
z`eE*c>4Ui&#)rw{(hsv2J)DTu4@*C=bOsAYn0{jO5llbK-{|ha<$jnr%pWlQF!NyY
zglL$1Vc`c$cQ6`PIKu3K*$XoVM#Id*r61-lSbBw}ADDTt`WL1SordXy*#irISh|6^
z16?1C4@;*of5PJnnjZ=2huIHP2Xi;fJuvfO_Ti#o{)CwWqhbEQr4J?!OD{10!@?Wp
z9=N|?8qjE1`hmFv<}X-$z~s@iA-K@+g83Wfewh6*cOf)DSx|j2b?EvCxf|wBn7z38
zP}?vlSp2}l8R{OGJK*9NI<X7D(h)4&u&crpg}ECR4zPTMsS;HLrVgeL<{wm5xOuR6
zhWQs3|1kH!Xq@o?4KJAeFmuppLg4``M`7s<rXQBBar*!w15<~t50)-)ray>w2AKQN
z!w=>jcsM}S!zh@$VfLbj6HFhBhUtURu<(ba8<;qZMvo7eIE;q*3+8@UdVtBp%*ACt
z%>6L^uyBN_hl#`72{RX+hUF8OeXwwcxgT9UIv*D9F#BNoVCJH$N9V)rgT(`k4>J#4
zJ&X?vFPJ`<zhVA`g)fW`(+8tr;xKz)=E3A)`f>RK7XGkw2Ga+lVc|lmewaJp`3{;-
zVdlbUbagQQ!}P&un0j0^%%8AufcXbzJ}!M2;!t<O?1iNR40SjJVBrOePndgfD8~?n
zxf`Y*W*&w@90D-=VEWO^1032IVD`ZD!O|}-_2}ZT@FG_~EZ#BPk8TkGJ}f=M+ygV0
zfHwI0SM>0Qxd&G|f{CO18&;0N#Bs#~EMLLOEtorD?nW1f$)nRScfe?v|A|e%=>CSe
z8)gnJ_ru%)(+^8GuyBL9ALdV(dR#P2AF=L6HwUJlSo_fR6Dm*9)uZ!a;RTC7SbU(X
zL+8WFFIae?%M;?m?1QB{Lh8}wVeW?64~tJi=E1@XmR?}t4|5M8b70{ED}P}6Vdlc}
zGt6D+?ndXMn*(zf%suGpVSJc8disUM3rrn44Ra^V9+*2}=D_@qt`EkC>4(`53lErn
zn15jE3DGcpF!zv}4`A+v>4(vT@;|z}VE%x`Cn0~p!U1MKEIeTTA*3Is59VGNO(^}s
z+znF)(+8so*$Yb_Fn_|zEttDu=>cXBIt@$T=<x*0kFan+*N2M_^9M{nE_E2<&~^{Z
z->~?=P=`YR=1)@H3o{O8FU;Mr_=K4U3n!R*bQ)$q%p7zYJ$%viqw`_m2y+LzJRv^J
zewcp<sfWqK(i==aEIq;G3DGe7VEPG#8_YbIe_{GyG)$fl4NLzpeK49(I)b?m7Jsn(
z1k(@W6S5y&9_Aldc)-FHT^)=Ma|cX4%zk3yA3eXr_%L%|<sr;IbQ;~CF!#d36<s|c
zJ}f+mbw4bhq5B`^4_xsG%U3Xe!f2R2m^*Okhxr2*{xJP8K1`kvjqZLz>M`V@<sZ!d
zFd9Q04gpv`g}EO+K5=M+3&X+-rXRh&#T9<&;SWpqu=ED=2d;DkOJ^{9anZQ!hj{=N
z{xExB=D@^Z>Il)WaDe%Pkb0Or%)c;uiB0z~{jl^#sy>*0SUM+FKg``Qf5Z4Na|zPW
zbPEf2g6h!}z~U9AA4bE%5ynSPk1%6k?uDs`#W&1c7#~--pzDXJgQZJYI)H^Q%wBXF
zmJiU?!`u%u4_zOO4>KR;4;T&0KQMQ~)Wc|)KVkO3$^mrqaD^Yt9CUeDIKa$>t0TZb
zw-@F<0@~oxu=pa@|8R|H3|RRGGY7^;QwHb4+zC?$qlwjz?r)erSbV@OBEW!|0}pp-
z{vx0aE)7dBF#BQQ30FXX0V@Y!`bl*^y8W<lBw#JZ_#e!ESh<KxKP+Fs)T7fdd0hHo
z;;?W+*N>hrVCr$vu<(MJLnz<F%!id5u<(cFKSJ(@nGZ`Z=>CAEYh3PynGd63@dYyv
zCXP!#x;RW9EF93~aq(gHz`_mP9GE;V{V;KuI&^=)!Ud)dPDAq-Odb|q==x#d4A%!^
zK=q@idvy21l;fnK=D^YcEF56w;?f7pA29VWf56feOdJ+2=;pxGW7q?A4@^HSJka&S
z#9``S<rj<(qj82G%s{Apu<(bu3uYcJ_rtWqXqY-!_`|{nCQpclrCV4$5K<45huI7B
zFRAvz^uzK$!SI4v45iWI0p<>vxlm;|DVTjQeX#JusR2y_-F|xOhou{s`(ZS({>P;s
z<}WnwVsK&h!2AufAC`_WG@uE<>_OL0C>_D%VfMlD3(Op1?T7gv<}6q`gt-eQPLPJ?
zW0-s3`k~^u^uz3j*$dMTqY3!~=3kh4SbD}~AG$cqoiP3A@`U&>`(Wh<A@#WAVf8-T
z9R$J+W)94Lm_B%T5vd;*elT-j<`PT8?1jZ6EM3C#1uk=7@-Y3lXqdUU%z>pFSo(*l
zhs7&Q9G8Arc@MK6y*~#FA6)uj^(}h*!R&>{2Mz;Z;RSOCOdpJfnUAg=7Jjh$8>SxS
zZ<shP`(ffRf5Z4N8WyfFd(iENg%eC2%p4dE^B1~4bUw`8Fn#dwCXjw%=EL;C!U?7y
z7M|$#!T7Lr0@DxkH_SaS8m1l>4a@g1b71zv+=ojax;V@nSUN(N$H|9=6U<(iKX9r?
zlYrR=(+`VpnEP>=2TNx#f5XBPW;`ri;0i~WIJ$nAdtl<QbOtjQM#I7jrVgEknFCXg
zi-!3FrXFSvspTOoy}|UOhX=a-uy95X2bekN>e2Zy_rcr^<HN!oW<I(;I3JpBVEG0X
z-!T1fbp#mb`e5NnKpR{d-T$!gBibC8KcFVU(hDp-<1z;pKCtkE#TSewRDQz34HjN7
zcf)9yewaN_=V4H=@Pp}txd%fb0RfnP7>&!{xQv0h7p5PUo^Yv07l*kUrk~h!3^NBk
z9T4k&^zeh(4~rj|`M79UdV%SKr5jx8(Zyl*!@`eL{V;dJ^ufXb-5y+g^l}j9AGmt7
z{DCeGvll(S(Bl(59mCYa!VQ*hVDSbMhpB_x3uU0IgQ<tnFn6Kb3)K#%VCKNWAEqDX
z9=JjR448c|a|kGhOT+Ak*^3eWaFy5@u=E2fM@Y?gFnuulVSHG+z-}u8{N7TSJ{S#)
zcUXA9+=p%s%%3oIuyBB>gM~k?_5(~DM#J>MXhQm7_QTu@(+8tr?t#%Tada9M->`HF
zGZ*GB7$039oDa=kuzU*(e_Z~8>w_|2=EM8}iwBr_P-QqNboH=s!xe8ZgJ9}l`eETj
zNFTa>SUADVfzgE0AIuzd8fHJt9GJV%)e+*u!joA2uyO_#Z!mws#BtdNGau$&Sb8F)
z4`v=rKTIFY99VjUsY9n>@dwifOGhwqm^tYBaPeX8h1m-W2VCmW#bN0cW-lxr(beJN
z!_>pdKVrilmwB-CN5~&Ad00Ha+yP5JF!$iH2NvG2d=K+KOg}7Kap{N2!{U*Uedy-E
z)WhtDwI|Tk;o`&Wg{2={>Trp}{0j>=T<XxpVd`P}Vd)N*j$kxQ9U=X&@P_#V7XL7F
z(DkE-J4`<;K4IYk6Gzt%<HO8>*#q+rTpzT4fT>5PVg7@uhoyU%{W#+jYA#GYOg}6=
z!_osReW2Tmt`05_HJ6b6Fn7b$!Tb%QVeW#358QkL44AuN@kl^9TpH$2n0~kf0S3%{
zF!#dZpMW-mG&H<m{)U-{P=KEWQx8l3q`DtH9%1PK7C-oHXJCN28=lUf{vo6fW<D(b
z38_byhlMk`epopS3lEq*==x#euy})+1Ivdn{V+bdei$F74i*kDcfryhvGD;bZ(#0$
z>4S;G>_ewv@dvXXrXMDbt{%pRr5l(&bQ&I?Fb(K5G@jA*!NLPwIR+nxenR05GY1yW
z==Q_n5vC3ojm!VA^p8s)x;VQ1MEf6V5UkvR#T$%<N)x1D@r>>cnE!AY0F#G>H_Y8I
zb71Kgmwt3{Sbl=(hs6)NI$V61dYFDf=?|9AVD5+I7npvSI4*y{<YDSzG|U{BJTCn(
zf56;LY`TN#gVC_`28&0SI6)eke_-x}*$?vvEFHn(8QnaXeK7Sff5Y4ZOPA>SV0?7_
zgw(_I!DyIznEfysW<M@>!}P;xXgI*)0p@Q)<tAJ|8Uv;eW)7M(1{dZ|bpOEofuR8^
z0MiHaH;jhGBTOEuoFE0uzp!{Dr~#?~W)IAMm^n~sf)q?WOh3##LK<QD7p4!Eo?!Z5
z<q59*2eTI=o}lRgCJs{vGZ#j~@(s*>Sh&IX1mgi}4=kS1^~2l`vyTuB^FPdfLggR2
zd9d^c^B1~2E<VhiF!#gq5ia%U;xPZi+>b7gD?OmcKg=C4^{{w@(dg#F?1P0D%-=A7
zz~T>PF1mhL_`>wT?17nwE8St30~LUU8%#e`m}m;7ALeeNHA2%NOdrgC7@v?iu<(NA
z7Z@LA4k7*M;RFjun0bWsqsznG4WrTJ(fP1+kM3@m`(g6v`e1x?eK0=EA2`zw)Bu<~
z%)jX20ab>Rg5?94ew+$%NWlCDOUE!8mcDUlV}Q96W-l!L!Tb%Y2XX0#<$IX>Vd`Mw
zF!NyI=rk<c;NsA9408`5_rvtT$_rR}A!IL1AB={%7e>R(gUREfVd~J+87w?tG$DV$
z?1jY}%pb6H2$RR<4p{ud^ugRiNFOZxVd`P&0p@O){kYr@D}Q13z`_HjpHMu)?1%Xq
zW*^KwF!OQw8zv4@2h#_$7v>(AI&>N)4^t2GKg>TcccJTp@nP<V*$dMTiw~GM%pRCN
zbQ-1)-TyFm;R=74{V;Vf`(WvSkiD>c4pR@y?=W*<@rdqT7$0UnjE0HB^plzoVeWzX
z2i;tl`RFuEAIx4@xS*>;=fl*&^ugi{T^%kyEZ$)HVc`f%H*oWy<r;=LP;r<(bQ)$Z
zEIiQl!`u(k2U8D=M|Ab*d|0@_%!io+<Ks$qFmqt)VCe)#!`wk^{K3pa4^Kk&!}P)I
zf#n~VKVb5NXjnMF+zoRFA${onh1m-$k6`A);sfSRxVg~yhuI6GVg7)nBb@r72Eo!9
zdbtHN2Uj@4+zGQ6W)I9?u=EAB27`k650(yy4L6uUF!N#lgt-IeZkRh@>Iu@&@Px$!
z%sgEFCuAPXU$Ah&sUB)C%)PL1C00Ky9AWN-xgTZ@PJ5veuyh4;C(Ile4GU+eatsO<
z-mvrovlpfx#>dbI6@d90%7s%f|C3q|z|u3!enRetnU9{1VESR<0(Tgc0Sh;nI#@Ws
z_(Z2?X#B&%38o)v4bc?L{V@L%tr4ma=3ZF(fw=?bE~qjL3MP*pUodyT!UaPk)ZZ|B
zVBra)Vd)8$zR~r=(hW>M%zhXjM#JJ4T|bNuGY3Y)!U1L;%snvmglJfLfrSep^>BG;
zc#vx^%%8Zz3vLgT0rM|RJ<MKMxWQ;#@dt|^bagO)!@?0)`3Z9`Og~H?)Okcx=<X*q
zzF_WyxdZNQqFn${2+KDx_drB(lQ4T=>S5sw<Kxx|k%5(KFn7b^4<-&#MVN$z7fe4a
z-xAgdQGp(h#M%$D7v_F+f5YT)nFk9$n0}Z#m_Ar}2=f=jwFnYD{ld~8EFNI`VCEup
zLRjejhPeYq!`uf^jhlqI7iKTaUAT3i$w12un7uH6qp8Blg_Q#^dtvT?iR01;^B*kT
z!_>pdKSJpUCJ&2u7!5NImM?MH4+}S#eK7yS;*F4gn7Occhq(u49<kwwt`6odSUAGM
z31%NU4GVWzdV<Nr+yiqTOdNxT#y`y8=-~j17Yuz+0a*Hm`2%JS%>6KVsB$<3^C!$c
zbbrF!4Ko+65z2u17p5N;4zPHGr2|+vK(!$#sJ~(H1#>sdJVO440hs?_{)OqsX)jD8
zl!n<4^FPdAF!w-};iO>xhNXX)y)bvc!ULB9F!#g48Kw^wez^3Zi^JRv(@)4=xPE9n
z!2AoN;qn9+F!eBdVg7;H59gzqgC0&WK1@B#Tv+&`X~#JC0~Y@<8s;8YyrS!erGJ>c
z=;05GPjr2__%L%|_7a<ZVdlZ?gZUdKPBaaTM_77>xdUb{E^}b+hS>|t_b_o-xS;EY
zg&#~GEZ$)1VDW}aKg=Fje8JoeOZPDM!_>jdh0!qkVeW*{Fn7SrAvGRg=>?`AW*@OM
zEWBa<Ayywu9V|SEbvMktu<(ZIgM|;wTwF9P{$Sw^qhaRZ(g(}mF!gZ%Len8E-eK-R
zHwRW;;R<J%Jd8%yj~>tH^007$y9-SLOh3&1XwnQYdvT>dn0lD|Vg7}whnWjYmoW9{
zG%S43&4HyOboJ<bSUkh@!|WxPj-c@bi)Wa-VeWy6<8nVNd|>v$!V^Zr+yhG|=<bH`
zVetcVFN}t{1Evla4RasN9mIwk%sg1U!Sun*frTqf9WELcPB8tj{6wt#VfMiM4bu-3
z$7L_NI4m4t`eFXUWe!Xn=6;xZSiXUogUcVV_=mY0-951Qhs6)NJj@<gc*E?6(J*mb
z_QTABr8ijmg}DdDhp8h-L-Pa7->~o@s1K?D7XGkw02L;hg6V^qLr5dceK7Sfcf-OB
zCXUM-Sp31l8)hCXzrxJHr5`2^*9R4Vg*z-Bpuz+xnEzq^fW<38jZg)!bOzH8OJ7iF
zqA6H<gQZ_scwiU+O)oHeVftV+%>RV^0gESCc*AH|cwpFzCIHh<s=s0Gg6V_NF#BQt
zK(h|cg}D#rPnZPEJ?P>vc^C~#C$RJf6Nk|-aa{2T(+{Iz`d~E7eYoUd=HhZE%zj+@
zVfMhn3ziOG`e5RO!Vwl;F!iu_fSCughgcexZeixZ@;NU3uyg>c2VnYOG%TFZ^~1zr
z>TuDx{0|d{#S?lsz|4cGL$@CnAKm|?>L)e+;O>By2QYWTXkz0LR*u5#howtcIK%Bh
zW5CQORy|BR%v_i|VKgiqz|`TSq5g&03o9q#e4HAwOQ5?4yDHqGuzU*h2X2+vWnlh>
zg+F#xn4&O!FneMChq(uqzA<$|L}2j;vlpF)l?Sl!h3G<%FniGT!Q27M-v}M3ESURY
zG|as)|KPG0)f5B|mXBfi5Fvw~1#>UVUQ+eL!U0CZ%psKjafJ^d{jmHBQwQ@mj3yLs
zF#WJ_hxs3-50|@eh9WdS!om$^K2G&$62!V2O&iX0AYk!;t{$BaOSdrlVc|fi-h!1E
zF!eBVVg85ZD_r3Kvkw;DFn7S>6Gr1qKhW@o`4eUyOdl*C;j$m*UKkBi2XjBM`3~l8
zm_8UEorc+iPQ%=bt{;{zVKllv7$03fTs<^=VCv9mSo((Phs7g|4~su^eT4Wh`(ZR8
z_2}}j_#~ts7GE%T!_>pl6D&QTn~%<ir4yJruy{jPhl>w$2h4t$Ik?oLi^JRr3wL7e
zhuI6$M{0b*)Wh_{@-vLaneU(;K-Uj52c3qQ2i1l_!PLXjKg?bjA7&4RMyLRK_`$*-
zW-iP;sB#Pn7LMrg2QwE#1G)e-pTXh{mVROGhL@vIb?Dk*_Q2c=OTREajK&p?uyBI8
zA7(y!x`v6P(=dIo@&H!u!Q4S8{BW5E^B1~2E<P;WVE!f5UYL7f@qo)7ba9wHFneMC
zM^}f74@*C=c!v1{=6`s&;xGUff9URq#S1JQ;n2<i(+^V*QwOu3kbYRYg4vHw!_31)
z!~Bn)@8IEyOCwYq=5CmKp~CQU4PfTO#L;P(K3IB((XenqSC7tz#Sex%aO78X{jl(b
z`5$I4EFGY$hw)+lf`vCsAIuyW4O5SchUr76Vc`$+A1?i{^bfNSW)4h0OdgkhT;i~N
zNUnaEdvNK8g*(ih=;;6!uDJ3uEc{{d1oJmcA50vV|6t-U_rdhT{0%c7rVfLKrZ1R%
zF#p2*12YdpA5;KV-oVtu+<`6*%imCKa0=!>nEmMPh4}-f4z3Z(fQiG@L51NIOdO^T
zE&*l0+=cE>m^)zNxb(x+!Q2na_b_{5;X)|=qlXvFURZcSor{x#xdY}-n7y#{iBls~
z0_HxLJ{S%22UHmb1+y3Cf0#ZPO-Mh?UYI&`b70~a7UK{=_dhH?a45$RhLyiC_rvl7
zhC&<y==x#iz`_ZKHn=c)dL%afVCrD$1{SVxg9$Lu!yV=i0@~oxuyBI8A7&o0?trC#
zSUHJq9^4`#8L)H%OAkb9gU|QF;tiJnU^FgsVBrtbhl|D)9x(sI!VSiU)h93-S2}{F
zbC|s_J}&pd#9`qB(+3N87#|iNF!ks(%s()9qnihlN7sjo4@-A2`(fdVOFg<c%s!a^
zVeTU2514v%^I+it^B1~2EIwfFg~c08JuE-M__*}L+zB%WrVr*Wm_6wF(fP3OBa~jz
z^})=C*$>kX3r}?Q=zN$vVd)gz9CY<?KD7M-3ujpQ0n<+?-eBPh3olrDf$4{Z2QG79
z?uMyHr(y1bg%8}_I2bVdVD7=845u(mKg=IE6+$Io?t+B_jE2Pn%pEXss5S%z4S#fh
z!}u`&!^}fygs@=g1EwBVI73uplIZabGY6I*VE)6@2@!$mhs7hzpD_I}d5CJ_Nm#fM
z8xJu1VD%2nAH;?SEZ$-IaD_KcA3);~W)95#uzZYDA5;R}-7x(ye?gVuq|n_Bi#J&M
zz@-u9KA3)3`H7zHVCJEl1B)M+eq1!nTy*^~b7A^m_M*EBmafqC!}z$u4`wdGbO4P%
zSUQI3gZU3;4=x(!512b)G%Ov#+=t6Nn0lDKF!iu>029ZRpJ3?@J)B|Y!sKzeAC}Hw
z=>_H=ba7nzVd`P>uyBO=A0|$ShS?AEH@dlm^uzSw3TIsUVfMhn3C72z9$g$3FEIVE
za3|yrn15mM3)2q^H<&mqKcL$S<HPa|%siOAu=s<i!$rf~4^t1LVdmk|2NQ?IH%uSQ
zJ%qv$<}X;h!OVmCALb93I64h;A1vHp{(zZBNI%RxSiXVz1ICBR6QW`E!r}|&9zyzH
z^62>;=6+($fvJb3JDB@n?t$5Zi-v_0EZ$)HU^FbAaOsDM!_0xX17;pf9HtJP#$`Ut
z99a0E>w~2)Sh|CS7fc^4Twvnp`eA%n{K3@0?1Q-<md;@6(P@}GOdre~nETMx!}u_H
zm_0BW<{y|pVCvCnn7y!gg4J^{|G>my_Mq#Bg*!|gx;)H1u=Ip3Pe?y39B}yqm-}J%
z!15z5{pjjo;RcI$n7?55z|^DDu<(KDC**H*{V+Z(++pUx+yOHmrVfLKwm)F@!rTp`
zVd0FS4=Ml)ADDV{8kc@pyu#dzt`Ek?<$hSW!tysPzF_)bG%ow0&WBU5^a9fdqjA{}
z*9c|6@*hkej3!n;EIeTG05gw}epr14b3ZH}LY;+^LXS6edvR)nO2F)eg*$rsgDS&G
z!PLX_!{Uk5aD!O@GY^)YVB!R6X!?ic7nr|b;YcX_VeW^y8)iQ&JYjrX?trCVn7uF>
z7XPqt!KELT4`A+x(S+O$GY{r|m^)$qfVm5%4i^mzcbGe1=D^Iwr4N@l%zl_TIQ;>Q
zH*|Nx!V6{&ES;m<2Xi+pywK%g@dt|!ba@ybrVf_>VCo6k57Q3|H&}eb+=I*AFn#DW
z%-=9`(B%p7Vfu)5H_SYky)b>Scq8O~SiGXEhlMA4yu$2*(XjFoW-rV>n0^=yb016}
zjE1F8n0|B`77s9Y!t~*!q3IE34la9f>4&L@*$XRwaH)rh!@>dPe^@$%`5#8Z)Wc|)
zzhLf#(J=dA>48u@!1Te?!OVr(gNugw7p5OZ!_osGb71nYd<+W*nEPS*5SRThahN_B
zALegj^~2Porze>IVCLeYVftY41dB&p>e0nv;ea0R=>9_22jj!s2@8LiJ7D54b+~Al
z|6%%xO}8+0Fnur@W*#hFaoGz?Z!mqhXju7*ZVpTwmfq0qgNeh^6S{sF9~SN~`(fb+
z<HO<?rXCj!^Eb>sSo(v-FD`wsaD>?dqhaR3`~ee3HwR`9%)KyuuyhF1k1IW)yBB6Y
zEd9XDL-z-a53?5*A24y4xiJ61%tfcs;|=B?So%O$kBbkpA7(Gi9k|q^i^Kd$s=Y9C
zVCjaCe_`rjG|Zi_bO<vCJzQYwVKhu1Ec{{mVDU^S9AM!L(}(VEn7OcY0J9fH!`uh6
z2PO`)AEqDXPMAE5hKa+{CrmwzhQ$X=JuVvN4wygC(*Z8`!_0%Jhq(h5|1fvJ+zC^U
zi-yG?Odl-&;ZhG1huIHvKP;ca#9``i(J=Ke{jm6e`G=7EVc`r5Cm0R$2aJ!)epr0N
z?1RxT{e;2+=02D@7!C6ejE~EH^mGeT2TR|uc!7zd(=d0!!VzX4EL>nTx;_{mmR?}$
zVD5&|u<(JYN2g)o3sVnsH@Z4pd{}(L`~jn3{=uaWW-m-Vj1S9CFmYV&hlLZ&-7tG#
z@d--@F!NyYFdAkq%pRCHjE4CO<}R2xjE0HB?19+}iw~H&FmaeZI1O#@!}P=a0n-OF
z2d*9m1Lh8xdr6HySopx~hox7X?nZM5%)PMqhM5C%51MieE=)ZvAHm`kW-ctAFf>91
zVCfzfUNCpS^ux@9D#xH;{)efD(Xe>Ir5_ePuyPW{hxr2*j=16jrXFSw%%8CMg!v0*
z4u->_0x*3rcf-OHrXS`$sB$<3vmd4&7GE%ZFn`0s2d)vyfVmT9FU*}V{V;#Q;svS=
zPQlEF>4(uUcfi~S*MP==`3n}lFnb8ui)IW47v?UQxflx21)%vDCJc)YSUAAManUe;
z!|a9WgM}w9eK2vDdRRQd_^|kasY9n>;froB%sns~7Ov>}Vd5}#FniHyLg@z<->~>Z
zw+9y==0BMIFdAkaE`4xuXuQGv4~sWg`hoG`>Txh&;Q*6{r5ji{z{GLUFn7b^8|Hpu
z^~1s!T^~#wm%T7?SUP~Y8<vh>?t#g})T7g|@)j0uFmqt;!Q~EEe8c>Y9**etpz~qo
z!t|rlFn6KrgYjYZ!SuoW3)2S^huH(u2dAOw2bVfn{K3)}Tpt<(W*;p4VBv<#Tr}-)
zE-c((_QL!JSAc_o?r)fW^mKqjJA9uL%)RI|E_cAv6}oyDALb5R_QLeT;|CfaF#o{9
z6PJFNILsUvA69?E+yNIyW5Cicx;tUwgzSfvFEDkmbOMWim_N|0#o)r?9Ts0OKFl8&
z8qfsL(;v)TXv#3T(C~(Z6U=@XA6LA?3_>rzVg853C(Im-_<<S#Q->b@P-zC3ILus_
zIE;pg!@~<E0i_A)$7Mgvy|8$Nr3a|7a0=#sSbW3yFneL*a1A&ZF!#g43#Jcd9u94A
zVRZelbO-YXTp=0*=6{&Iuyh3D!^}m~2Is=k87$qv?1Slpg&$l48Uv;trVeHv%>6KT
z5XuLzbP7`sGY`gx$)j11!G*;$%v~^d!2E-*0j3`2f0#NL4HJi@2Xy@~K1?3wUKkBa
z2QYhJ>d|RfI)V9{SbZ>mqT2`K!_0%JL$?<eUodqrd6;`(=D_$cc^D1z7fe4aykPoZ
ze3*J%G|YUMJ{S#)KV14?;xKivcz~5FFmqt}1g0LHhN*+)ADB5XahN~R^}+bC^a)cB
zi$9n+OdUE6ix-$Wn7uIl#M%o}j~<_d!XKs{=5Lt$VCKR60~1H5VeW^ihtaU~0Mids
zhpr!;4@(Cy^|0`O#V5LYLVRNN!_>jt4@)nw@FQeDOg+pTn7?7^66POV?nT!RD}M;-
zgP8}j52g<m{xCjF9R>|;KfwG6vk#UYVE)0-hb92C2WAe;Juv^HDaYW#(mBlCF#Rxd
zF*KkFz}yLQH_SX(e8T*NrVY-8xf2%8u=s$v17;pv0~!OCZejMr%ps%?%^3K7p0IF4
zmnX!B`4^@i7A}PJ!O9<)eptBx<Kxs1O+V=21PcdPxZ=_evj?UhW*>|XqhaxZVGh)O
zSa`$I3rru}eyBPO{b&NP_=4$2lP1W8g(I=yj&2|<ox=3P%tKd)i;wPqm_K0VqU(c+
z!|XxNf3S3ft`EkCrF)n?xM-MqLNt2#!^|V34=xXlXP7=14Ko+64hI80J;L<kP>vxC
zvj-NAu=I?f5KRCU53qC$OSdrnF!NyMqiM&uM*)`qU^L7h==lg{FU%fv^I_=;S2}>H
zhq({#E@(W!#9`?KE)QkE+ynDBRG0zgZdf?L#0k<w+KbB^m^*O!7neSmI4m4t`3WXY
zh=!#*SiXnRu<(P$A50zGJT(8p)WO^j<HO8>#TQIJj1N->Gap8yi^KH6XqZ1?=ELF{
zW<R0$gQY)M{}2}bF!NyUgV~3RhM5C%FD%{SQjac<?q8Tc(ADAM!`uzCpICRp%!Anv
z^EWK~VBrE&hfc%t56oUzc*FF=(h<5o7$2q|rVr+Bn0YXHn0i7q%wCv&So$EO4<-)_
zM_B%cnFDhdOdX7d#XGL}g83hpJ7D54dtm;B#Rs}L%w8A`vmfSeSp32C!R&{rgXu%3
zVg7@e153ZKc!7m0E^}b&VfLcauyP6AUKk&y9_D|TJ7Ds-Xjr(z^rO?TaKxn_mcC)`
zN9V)R0Z#omBw+4`(KwXBg<<Mo@dXPnSa`q{;$Xnk!Suo0jVr!z7z1zr!@?2fE?nwi
z;<)00pne?n2eJ0U?1k9_3qP2CSh&F4Nr;A}7npun`X{6hCXcQk<{p?lAsSsjEI$()
zkFfB7>4Uo;8qPTFhswkB5uJXZ`e5k}=3iKO0nb-BEJpV)%%3nCW)3W0q8kH?cUXL(
z%ftMED;>b}<FW@P52Mk|hq)iFA58&FAB;wmW<d8REc{^VVetX82VFmm4=cZ6@c;`C
zm^m<eVCpewXgtE~gXzOiheH5XkHNx$SaV?dVCrH1fYC5<9M)o-w}kFbSo*`|A6R~Y
zg*&=EFnur@T^`1V>4&)+T^#05m^eBOb0;kRU^LAC=>9_250i(P19K0qbdRndR!+d|
zgM|yaI&?nFd{}ye>4(wi>T&U5=?3Nwn0dJJJuY)#?uO}yxeu3mba8b3gz_D_epo!i
z?1z;<=;;DoAG*I``q61z?m^cNvlr%nSbD;xAC_KV@kYqKF!#gEgXx2r1B*|XI$Sg?
zykPExrDK@CNsWJ)J+SbAnGX{uNTZd5#D)XRTv#~)^FPc#FnL@wEZkuJhVfzM!NLcZ
zeptSTsfWckEM5qP1JvIzd(ml_`MBZ(CJ(a@rXS`Gn7?rO1EwD4ewaUDd{}yhiNk1E
z_`=MG#TSf*xd)fKVfta}VdlczLonQ68lW_+{DAozDoiv5(+3MrVl~3t5A!dKPpW>H
zy|DC4$p0{VVD`fD9W35p;<&;e7GALUgQpK@dW4A+atF*Dn0>HtBvwBxoM7b{OdpJf
zr4yJs3>xZQnEf#S!}JsDewcc6{}9p-a}O+^z}x}j!{l+%F!N#IhAY0%^~2&DW*;oy
zz|BF|fX0WZhuII4Mh^#=I64grCm0QrhoxUwJix?>rD6Kf-9<=0%pEZI!}13qeXwwV
z>4VWQaag{@We!XprXJ>RSp38MgG)cm-RSX1Fdd+!dsukE(gn<ZLiWP^i>?ml9$fhk
zmS1t%0~3dZ2fFz%aTtv*4>KQ^JgmHeg*&=_SUBL)4+~$I|6$<=Qx6k|sY5p(#)r8R
z7G5xYFmqt)aM3V(Vc~^K9WHTL`h(evOC2t8nEPS+Vd08PJ-Rr${V+bdIzoJyJ7D<*
zSGvP#4m3Z&`~k~XIMqWXVD`Y&!|aEpE0{P;9aI~fg2f-qUR*S;aE0lIr5{*0z%9VR
zfVm51A1vHq?k8COLBk8C9%c=!{vg&J==lSd@5oKhB-;;lI4pc&;Rp*?s5DLr7H+U`
zM5kfq;W7XgPB3*adtvbdlgE{QVCKTY4VLZ*sfX!<l`}AR!~6rYAEpi$4NE7)s)w0_
z;XY`3g1HAhUBL9gXjpu~+>K7d(<Rhgm_9fS6^H4A`5WdRSUQ5m2V5T-0~YQu`(WzP
z%|+7&=fc7Pmfm6J!1#p10p?zqdScxTvlnI`Odl*A!o=YY#le87hs6g>A1pj^Xu}YO
zxgQp8F!L}J;t+s^JIsEVIk50S*9J?k81|s03z#}|{pjw6@nPwgko_?8VD`iEEzJEe
zb71Ol(J=qP^ugj0mwLE3nm=Lj1#>r{bO#F;n7d)_fa`}_go6RKA7(Es9l`X&!V{KG
zahL)ZhUG(8xWUYUm7j2hXbfEPFn__!L(>N5!onTqURZp>?1!aGxCS%^EZku62Ga-g
z53&A-*$;C!dU(RjN3$M24i8fg3lCWQ!u$t|Cv^QVdtv%u@-TnF^rNRIn7?51Fg{E_
z%pd6H!sKBz%pMpY7XG;W3sZ+q!|aFYLpK*)JvtxePMCglc|v?xIKa#yq#h;@izk@9
zFmuqu8>SwehQ&K99%1PY#z$8V<HN!SrXOY>vGIm(4lJL+(m$bm1Je(qVc`Hvr!aG1
z?t-a9r(ynt`3EKs3r|Av0ka2|?qKeQiKCkfGap97)Wht9(XjA<sl!FX?1jZU%>TI5
z!^C0f9p+A$y)ZsZ9Znire#7*^!V%^UochotVDSdC7e>Rv1x-077wQg}Ik516nFABY
z)QDdM<_|*dfaMohe8Fg9?T4v{g(ECK!NL>2D-p6Vf5Xf}$lzze(gC`?F!S*1W?+D&
zH&{5p+zq2~>W9V~%wAYNg@rqzd<XL<EZku3h3SWh<BETndtvc}PQ%P0lz(CVgQ<g=
z0~05fhNUxt;fBLNSh))ePguO-P!1P{g%8Z#uy7-!A0`hACz!pkc!XPkgMn@@Oh3&3
zu<*d49b?@x%>A%%fbnsqBlP%!`5R^*Oh3##m^eC(o{nJV5UU>+j<9?L(+{&37Eb8)
z!ue?V2&NzAKe##^446GI_rlyk$X=NF==yM2gCUHrAC`__?t$fJ42@6$SpJ9EkBf#X
z$Dm;5!}P(@A1uAV#9`rqZU8LaVBrmOKa3C45A!Fwei$Dn5A!$799TYpsl!P_;~%CU
zW-rViIQ2m#(8B>{4x#vmYG;6j8_eA>ae_3|-7xpU^uyv6M&mLE=02Ey7!3=5n7eS9
zgRUQzk74l&3kMjD%N;O%F#BQZVg7-+1DAf7eK2>z^uuUk;}PZ`T<H~;{V;KueK7yP
z#0k+bdtu=Y3kR4uA^k9WVg7`L8zKELd6<1Lf5XBX<{nu7LU#vDKg@m@4Koks4w$>p
z_2c5h^ux@@r4B<JEkEFLH@ZGpIRUd5=5JWOMpqBx!|GRb^)P?K!ULurorZ-s%zl_W
z%siMmFmZGm7Cz|l1@i|?oM1RW!wnWsF!eC^!}1ln{e<|$>W8U=*$)eMnEPS=#AP2W
z9AV)G^FPV%hK2)7Kg@nuc;K=hCJwU?rXNNV>wcJe7)>btVd)R19-W4TE6ja_Xmt0J
zsvq4wr0R!-6S_ZO<`Qy0OddTQ5Yi8`7v^4c`{Cs|G=0GAfzhyZ0<#wujxc>NKCGUC
z>4UZN(e=aZhv|c>M`OV93rs!Cei#iChuMRs9p3(jxf8Aq>Q9({m_6wFaq-dfFWg)l
z8esVsW)IB$=;a~~?Qnm<@(WBqMtI{e0G7XD`eF7G)DI0Om_AtgfyF0G9G5xh{)IUY
z-95O{0W6$h_QLeTXjr`B(hp1bFn7ZA!DyJdxbhFoUoibJ^I-0Wr5_j{W-dAnb05q;
zbQ)bf1|J#^uyBXPA0c~T=D_?1^9M{nOdO^TorZ-!%%3p*=zMg2Fh0zDSUkbRVfg`O
z9xR+;@-P|}-Y|9O=E2+nb0<t6It{ZA7Je`q77wuaLDz@Qhq)6*!}P(-M^_Ky!`u%G
z2Uz&S+(Ae`Odgi+VKht}W)ChJrVgeL<{p^+FmYV^Vc`r@2MaeCjVt_N@dwk7%Y0no
zu<(WHhtaTbgvA3c{V;i0x<NM&=6+oIafzdcCoXj`ahN?Y`(b=oI)JIeMZ?s={11x<
zV)H*tKTIE?`U>40F!#djhna`24i_I5{;+fhGY6M?oZ`@O5T+j%zc|&SOOUA_mX2ZZ
z2g^6;#^d6{+>1`b;schRap{NI3kyFOAEpl`jw?K1;;?vt)ypvbFnL_|qq`I4f0%z@
z<`UA6ZZAwfEPQe4hoy5;?S+{KGasfO7G8wh4GR~TdYC(4;;?Xm*@Hnt(;F=R!15i;
z9Jn}!MpOZqeXx87i+`9ns%lIg%$?}|z*LDU0t+`-Ji)>bmTpj0WAb3(3-c$sd6+s-
zMPT6sOE;wIM>P#G4+V1%x;!pEESzEC2lE#$^)PXCeXwwZ#UqS{r3;uobQ%`kFn7b;
z0i$8zgRT$8hlLv~{$S?7+y_&Ki-xI(xdWD-U^FgsVDSgjk8VC8_ruaTF8wg~z|^6e
z4-?0wA0`i@(e=Z^73NQLd6@mU(gQ5qVet-A2a9)Dd=QF%m_C?0;QoP@FX-+<=fmOw
z<_=i<1zjCFALf3TJ7F};9GJV%^}+bCaD}OdxdWyT?hlv-G#VEE==x#GVBrBX7bXs)
zVfMqq8zv5;VfMq+!Ng%SEM3F=jUG?1aD=4;m_BqG=1y38hq(u4F3cV1`e1yR|6uxH
z;Q=!bCJ$4Oi-xt+Vftb2$E6-74hsjEKVbS{@dHzbi$>Q6(+^8uxb(r?3kxThJ{S#?
z$E6=#96kJC=@4BVE<P;0VD{rmH^l0Pg$pkA=;AQ{!r~p~9&~l+d|3R!(kU#x!R&{b
z2QwdCKP;SJ@c=UiJv?!x2bemTIdJo#<rgeG(eoF~-7t9=AEqDX51jD;H6P|)m_As#
zgZUfXT$nm^8m11W4`vR!I$V61J7M~9slz1>3xAAo#H9gE9F|^S_7m%VSiHjG0T%9r
z^uxjnrVl+H(42?Kg~lIDKg{36mUl4s!SuuY4dcVoC8os?5tuzN^|0`V>4S;G!Uv)Y
zlZ1sAOg+pWu>1#$7fhWH5m-3F!VhK+j3%TX-9DH(5Yus!Fn_`9f$4+saqEQ0z`_rf
z?qTH%vHBsVFfhQ(frURzKg@kFb%bbGIKbQkGY94_bp0^%Vd~N4Vc|h2{9*RM%!ARe
z^a$gln-Al|<k9^PONTIZxM-L=VKjO<i%TCY++psA(YV3`-5i*^Vd)>H4@RS_$Hj-m
z54w4{)Z-F|`JYgH;nEKaf0#R9;RACYE`6|k4hugRA7&md_rUT!Ec|fMFm<@>hl#`7
z4RZ&K50fWE!`uziPi*>w`46TKMia6h7QQg`=rpW+$K`HVyrAoY@d??FZZ5h%i1k0r
z99X)C*$<0<m^>jG=3iJmz|u3J{0#Fa%s!YqVD5*dD_rKl)WOOt7$2q&<{w<?5EgDQ
zeK2(}f56IRT>gjo8%D$253?U;9xnZGacFx277noV0hcGhfQ2{Aegewj(&*_JE<q#%
z7Cx}>CsG?uWiaz#=>n%hGzpk`Sop#CXv#3Su=s)LgM~ZHKd|t@&<GWP#WO72Vd(&t
z?x4yrD40Dkb?E63CJr+X-2j-oVet-g2h80t8kc@_^I&|KKVURWoLCy>9#}jQs}JUX
zn7eW1cMS8O`34qVuyDXoheH6C?_v5$bvH~OI!!42VDSRe2lF>fA1poMN`J8Uhp9tP
z@344)(Kx&hKW`dl4$S|saD=%JmwuQ#VDSet2S&rf7ngpRyJ6}Hxff;*OdrgBu<{zl
zhpEF!L(>h+zcBqUf8f-ICIO2dn7uH6!16bma-3Y4IWYgj%p*D<pqUKQ5Az?IG@J`l
z2eTIzUode(`eFWrsfYQW)Nq732$t?(`2Z$Ph=zq9%pEYAkUm)Yg~dNiA1pj@>4(_|
z^Dj&vj3(CoFmqtxOsamE`_Ri1V(rJJA66d0<cXzW`e5M#OCPxO!_o;X-4UDKVg7@K
z7mS9RgUdpwI4rzjG<x_E>kg=K4CwA5<ZqZh7!7kb%pb6DB-I@-^)US~|H14*r(x+F
zW<M+(VB)a+h^`ODhxr?3AI$x*_=lC#F!ks(%s!a?uzU#17wGC?e3(9%eK376KFnP(
z_2@J#eqio|$;12s^FK@+orbv&rXNPb?1#~?@&#Q#j1O}MEFZw!4RZ&K4^vMp4U0EI
z`eF9M+z*QfnEiy(4=f&F_QCYQ!w*`|5^^`p9$2`+XqY%5{V;oB`d~E7eHi+o?m|yD
zFnuupVCaJiz}y8>k4_WP4>bl(!NMJ84xxC1YlJdj;SJLd^A}W_Acbx}%p6#GLdXDE
zI)dqg`yU$4xb(x~2UbqN`~izkm^iwAm_C?&FdAkrj1P+^bp0@MVd`N1gymP5I4qx|
z>xboAm^zp|OdRHZm^e-vEqq}9hs6g@edrQUb71)x7Cz|8aPeX34c$Mu)T4{T!WkC+
z=<<a4u=s$58_Yd~^ug4@+zqoICXP!#EM8&yVD`eqap{AJ!`utgk50ql5tg1{`d~E7
zeK38n{0fUVn0sL938oKDL*pB!AC~`M@-XvZ;Q<pzr(x*~mTqAFg1HCXTy*^~KFmIt
zyK%V-rXEJa{0p-e7H&BG4buqqKg>Q@`h(?Tn15i((P@~yFn6QN!@>n-9=be?53?U;
z4vdC{AFlX^>4&)!W*;ov;QoOrM5AHm!t}wU(Zyl@gvq1R@bm!<PnbL`K4AWViKEjn
zbuf3p;t%G2n0wImq4Qz!O>BCD>xa4<77s9*P`ttP!`u(k2Xj9>Twn^(XjnM`i$542
zrXLm_XxiXhSh&K}!z5to2Cg6KKbSm>hRMUy3p$PNE|@-a8kRm_=E3x#t0TmR`G@HE
zgZc~RUzq!0?jd9!EPl{wV%-l*Z?JR%ORunWK*)ZWJS^T}_QGhGc`$XjXqbP|{Q(OP
znEAN$;}R$24w$(xd(h(#CJxIV==Q+!H7xzY+z$&sqSFD?U|4v;?1h;JmBvY->myb_
zy8mJNU^FZ|a9NDYUSjp5n+pp^n0{jY4>JedJ+SzP$rGYs@eH#cM#J+70Rv$AVfMmk
zSpFiQ9lnncW)30sFnL%ygxLpkH!M70>TuDpc!rfjFmnjG13mq~_^^0`*$;Ccy8W<x
zkDgy(_QU)Eb004KF#BLMOdqT~CpP?H@eI?CZXY2&x_=0%hsndj4VL~1g*!|ijE4CW
z7LIWDL+clqI&>OlFD$%a@dR@Rj7HZ7<HP(53wM|}EW8Q11C~B8^g|7Ti4%%{n7^Ug
z;P*Pg{Dn(D%$+cGFmqsh7!3;-nEB{5EZxBJ56m2xIJ$Z`9~wWfaDw?4M#Id3g%eyK
zlmUxBn0{D&1M@G8hQ$+98-hZ!AC``Asz>Mqvta6A_QU)S<HO{^it!LIf54Q${10;v
zOdOqt<r8%Muy7!heqr)38m15Cespuu?T5+3+zU%DxZ(?4Ka3Ca2Q0n``5Rpy%)K!E
zFg{EjE*h3TVfMkofmr?M=AwrmEL_ml5#qzb73L0H;R#D$u<(PKi>@D*4`A{z8s>jk
zyrH`vmYxaaf0#a)Ik@bD*^7&Y`2!Y@gzSZx15*bJFIarS<O$I*f0Al1to(%83yW`<
z{jhkzWj`$ZVfta=2XhBZ9OgfCb71Df)WPhBse_qEC_d2BJuE(9<`U8mvkztt%w8BD
zm-}J*VDS!fKTID?9w&`fe#62IMiVjz<}a9gVeW*9!~BoSepvX!^uyv4W-m-XOdOXv
z=;~qS!TbSp4=(+%asZYdVeUc?R}B4V;RnkHu=ESF9~Lhd`k?|a_rcV`%z@E_{0}vT
z0X-aG=E33xrVgEknGXwZnEPRTc=(`eg!&&A-Y|VIWiWr?vLD@ESiZrf9%e3#hM5m@
zCya)f15<~ShPof759WRt9~S>O^+P4l!yTp{SG@z(2B%>Dh4}{-?{Eb~GGOrpOLs6D
zCXUM(nEkMLgt;F^!}0}&erWi?+zV5WPGjhU3c&n}t{$BaRgOtP?T3XkF7sgi!!!U@
z1Qt$2+mEW90jGYbJJFP5aMAU{;sYj*p#dfUHJ?yCz{&%dHW&@dFEIOH;Rq9lg$FF0
zVftV+EZ@Lrm^du|z|tXlJiz!c^I_!yx;P>KqsznG4GRyLdkE1mb70{Ba|a=PFnO3i
z(Zc~IkBf%YH!yo)?!cuUCJu8SEWThgtUQ95hs!-Mdtl)Yqhb1C=>-;#FneJ%x_%fR
zCQpclxgTaP%)Kyw5Yi8mhuI7BC(Jxt?uO~ZMZ^3Jqj9N&nGbU}db)$T2bX@B|6%DA
z=581t<}Y0OF~p(e0L&ax^~2l?(+~3>hCOHkFneJ7VBrP}7c}JvF4Vm+|H9%O7LKs|
zgwTM>f~kYqi;KqT52*bx_ruB`n7stukLnya52g>6kKhtSGSI_=NNo(TdK%__qV+))
z!r~tmu25lu6f7OX{0)moV*L+GFR=Or#)p}M%K@<Xg2g*bAIu%FaDb^pHwR`uEWBa<
zhM5Nwhn3Ih`f>4L`eEq`mpWYHF#R~wExJZ%I)?cZM#I7%79KEhbnUQkgV_rUH<<rn
z;^^T3i+7lMSop!rhq)i-4wyJP4Kp9+KA5>M`w4|3Og+q8SUMtPFHAk0hK3_7JYe|=
zE>C~~%RexhfN~6JLh*#I0p@R*KVjiTth-_9A7(F%hQ%AYedv6cKVk6>(+5ir=<3n=
zu=E5o2NusTKDv5Bd{{WZ;-8RuxI8r8Vg84?3oeg~0o4Z!cUU@x@nP=46(6wpgQYig
z8s;9jIXK(_b1y91VBrrF$Ds{ly*11pm^m<i;L;BZXPA93bufRy+>guuFmYJ8!O{_o
z52In`!OTaeVc`Z-53?6W!|XxV2jj!^!~6{shvg3#4HHMFVeW>R153xS@PUb=>x1*5
z=?E77Fn_>knE7z^PzEeqVESP8z{H`-a8fXT!R&$Q$7LV7K`{Tp%z^P?=Af%b=fm6y
zqhaBOo}SS4;o`&U4_J8MQin?%rjJ<v!`uyXFDx8kG%Wq0rz>>(VfhSZKP)_9;RxfS
z>qF<m@;l64n7J@_qN|7Vq3I8%59WRtO(_4s?1A|c77sA<;P&8P!0HW{J?P<pLmP%L
z%p6#Jz|6x?2o->Z7c6{X{)O?O$}lLHKA3u3G%j;s@c~QkFd7zau<(TW2g3nq0<iFc
zg*S|bxeHf%hS>{K2MaftyJ7lZ{zbDEKHddOr|9y8_%Q#$?1#A<mwo8w!PLS052ML-
z4=laF+=Ff(1|OPlVD`bni)j5&17Yrl>4SwoRGKgabq7ozIt_Cl%zx+x!sKE0!_ona
zhQ$jk-J<J<se}0+CJ!?g7SAwobQ)G}z}yFOKP+6()#Kzt!vW?VSo*-J9!&z~ewh7e
z(ge9Mdtv#Cpa!S{m^)$N3>7Atg6W5aBg`Ezaa;z$+zE?!Sh)jp2chtXg%eC4EPcTE
zFmqw?itY|rx`3&Jxf`8^sYBNf<HO2LSo;I!4w(C4?u4m_(J*x|c^Dri4-<#cFmX5y
zO>Z#wquU4b2h3e?eP|4r`(gP7rVo}L(cOin9e#ci%$+cIz~TXB9=bkQyuj4K`~l;m
z)9CtP?t<w<mnX!BrB9fCn7d)=7-l{$bI|p}{0XCB=HSu~6NiNx%sg1Q!`uT?hl_^A
zFD&1{>?c+~EFNI-4-0pgI4*l}iNo~6{DVs!x;V_eF#WLd0bLzBAC^C1{)Xv;#VgKu
zgytWZKA3ya%|o{j24L!7_Q2c^3r83YQ;$JI?T5u5%pEZEV0;XH1O#CA!qOE1<rvbi
zbOZ|z3<YQcuyBB-cUby`#RHmh3@*%{F#Rz9!)RRb2Qwe$UYI(V{V@F)7D5GJ{)4GQ
zr-=<eSa`$y0W$|yu0pNDNx}4^yB{WwQzKLYW*;mbVD5m015_DK3g%CkeK2!i?!l=M
zDgpB+EFNGqEL}sDVN%fa4|6{(|G@Y#8s<I>1JLxt+yP4$g!IGw2lF>fJ&cB>M+|$>
z1YrJz>4VWQ_n;}q;KITS7GJRVBi4SHdUSu_3^z0vz}$&$4lI1ol;h;W%z?!tPK7um
zVE%=b|2UN46o!R6jE1FSoEo4Kuy98YKbU`@$}lOk^oO4AVCe!=BSZvdFU-BL@Q3kX
zG_0J0=t7V%aacUV!X2g$CQfWT!u(5e_(Q`17Jo4NVSI#(v9n<QhJ^<#-eBgyXzaQf
z7+~@+eK3E(%!P#uF8%20VfxU`#ibuz92VX%{jhjKR|n(6<YD^I(-BM_rXEg1!yQ(B
zz~T+&Ul<=|E?gfP17<!f{loOZ+<~SXlM8hROg}9Ah}92sKTJQm`!Owqh``K;g)@u~
z^DoRDuyg^@g&<+-VfG?qAS_t^fzc3A1PL=2mTnO;5Ed+5;nI&D?l5;lbTcr(+=WX&
zOdU)frXJ=VSh&FKfvLwu!}2RE{)yEG^CwInjE1=jmwhmCm^;zq9VQM7XP9~z4GS-r
zI+#0P{T5jK!@>im51od|!_0xvuyl#89-R*hH<&({y)YVGJ&X@?FU%fTdW6Zt%){k>
zm^jQHSbYN%hv`Q*7iK<;hUtU31Evq=FPJ)9G|V5cc!!w-a|bScuy}&mhaL|w^Kj{h
z`3vTMm^zrdVdlc@fyu*Yn0}ahVd)RXhv|o<GnhVf8Ww&qdtvzo=6*u?0Hz+@Tv&dF
zg)h21j1RL1=6;yJVD5+UVd~LonEPSj2-62MkC6Xi{)4H9@nPYCZXb*f^EWKKVftYH
zhPeZm`(gTF>e0=G>BklSF!eBZ!`usV4~!2}hl_@}4`v^XCYb)A2ExK0mj2PrgDS&G
z!Tb;N2Q0n7+<{XgR00-`F#p2b0TYKR!%4yX2@5w^dL$I?uyh5}2eTjM4_JD{X)(0j
z0rMx!y)biO;;`^UHwPvT3qP1VOh3$im^zp|jE0HB+zk_l(J+6(+yxVd(J=k!_QT`}
z>4&)!7T&OUgM}Z=|AgWJ-5oH0!_pDVJ-BFC`i1EuWFLk(Xz>V3PZ;WO2|)G3!V4A-
zxRhgvL-oVL3s-z#=z|HM>4%v^Z1};9fu$2ze8Om$JT4j*UNCpV;sNFkm^d!?z{Fwd
zanZ2w#c&7I|1kH$(jSI890D-)Fn7cF#Kt4cU9fbB9v(QXg$u*#9auQP?1hCJTp<Al
zEI-2h0do%lZE$IrdRX|wB?vHJ_QKo?qha|PM#I#h8wV4Ig(J*<bQ<P9T>4?*1~Ug1
zp6Ke)`LOVU#XC$NjE}A!#)tU}W*^KSFmaf<F!i`-SbBl!hoxhfe{ku;B@U|(VCKNg
z#ibsXI4oVl%)_M)7T+-U!u$`TVc~;IKg@kFdtmVn(+7(Om^yTGVB)awhLvOJ>TvO4
z{)Fj=sUu_#%wCv#Vg7~r0~RhY^U!HndWPEzEyrN~hNVMveK0;Oeqs7y;RJIJjE2Pv
zOdpJf`5UGVM#Id5`3I(+5DjxTF8grmhx!|4KOujkn**~C=5Cldu<#+&9)PKXg$GPM
zEMF1|2bg^@eK7aH%|mxEln+Z^uyBLXuy};|2bcY@aDv$nb1&3*`1lRXT`+MN4bu-x
zhcI!NyI|o06URlPn*-B_?jBtFVfh8-4_Nr&QjabU%ZIS=f`ucla6{J*vkzuJ%snuF
zp{s}SVfLcygUJ)3Vc`tZ4=XPS*$Xol=0BKzbed55gSj6TUa)k4ZZ3=uvlpF)rB_(I
z!_>pv0}EeteK0=E9WejH^ucIC`eE`gf57zNO7}2(VKmI$Fmqt~Vg7)r!%0KSFIakq
z>4(uUf8o*(OSdrfFg`5)VDW-WKP(-=^uhdrOFg<cOh3$i7$037E<P;3!1Uu%hf5q5
zKCpCzOC2F`SiXU!16aHgn+{;^fQ2{AJ+OEsWIrt2VdlWx0ZZSw^uy#~@eK<{Lg^0{
zPcVC6`r-b@Wg%1?=1-V=p~CR{JYe!Laa=S^KP<dp=>g^*T>8+(Vc`sOKg=9-b?AIp
zIKb>j4-a(pFg{ElEZ@NFLr+Jz(htmkFn#Fe5{gHdeptGJr309GF#WjV0j3`2UzmEB
zc`zE6ewcf4(XeoUxf7Q<SUQEpA50%C9tqhGiwBtdVg7;H3-c!~`(ffR|H9%E<{p?l
zE*cizF!#dz1LNb0f0#JTK6LY8=HhY(%pRD#(e=ah!NLJu9v05H<YD4C!yj62!SW4y
zIK$ipvj^RN7$4?Nm^m<i!NLy~zcBUaG|XO@epotyl`H7#(fKg@VfLZR6XL_d31%-W
zUJ2=g$;0f2=_ll0m_8T{Qx8k`F!NyY#L}?z2BV492UCac4wyJ08kW9b_QLW5EdF5W
z2Nuuh=E3|63qM>m%sh1cF!N#hVD=M=N0@tH=?10`W)3V}pxX!I!}13#-eBPe(+_hO
zOg%ac3on@c==x!NbbT;BOddwV@(;{Bm^xx<m_C@hh}8!RXPCdyX+q|q$3H9{VDSwL
zXI$=q`5$HvEIwf7;?f5zr(o`d*@G?)^B=l7u=D|QFH9b04$M6;adaA%{$TdO@)68E
zxa>tY2hN9<=P>`n%!A7l$$+^F<}Vl@mv&e@!NLQk9+n;nxdRqYF#Rz9!~8)gAHm`u
zrVkdLxZH~_4s$;&{lmhYki9T<FneMCfW<q^{jl&rw-=U9VCvBQK`8uS?uOY9qhaR3
z^yBge%s!YqVESR==<dbIho(Dn^~1sqmfmpMgF^xqZ|F2Ed~l@)SiHd84GSk24GR}s
z`eEWQd*RN5h6l`ixHuXEW)IBYu=s+x2W~FZJT&bLF#WLjhJ_<c9nAf(d<7Fnr*XwM
zEFI(057P&8Kg>O_{0?&$u5f^f!`ulo2c{1ezUc17#YYc6^zgu?59Uu;xWo9k)T4{T
z^ufXdT^<)77G5xa!_*ON4m4fB?1#~?bOTHOuyBT%52vB}Vd0H#FD%|+@d?+5#(=4V
z(J*OPI1<tia~~}JVD5&wi%>d%g(FNqEF54oOdjSg7!C6;%pRD2n7d*63E7V>57Unx
zuP}SjX;}Ec+yS!}M#I7bT_21O%ReynFneL{f`u<kJvt4Ohv|d42VEU5KFl7N{V+Z*
z_2}ZTbOY0mE>A2UJsgP5|1kH$!XKs&#wTPy%>A(R19JzAPpp2J|6%a}YhMu353>iR
zAC~_K>4W(b-9A`)gZTrOIk0>Mvlr$Lm^jQGu<*rY4lEzS!V#tqW-cLjz|t?w-7tOV
z{=#KH%s!a=VeWyM1M?5Mewe@D_Mj<%r5{*0qe(Ns#9{8n<$qlE!_0$)56m2xyI|!I
z%v_jybQ<Pgba7aC5V9W@POxx*sfYO=<{otOVSJeVF!#gEgV_&@UzmDy8r?paIfU$m
z<u8~!Vc`U`7v>IJ=D^g$!XM@?SiBOeA7&0rKg?Z(;t!?|S9uLf*Rb$`n-60^>wj3h
z!GvKn%>6L;z{JsMn0}ahVBrUIKe~E!J}i7;?uD6$t_~L;W-m-1q4a`o4lMj&{)MGS
zbalA+=<yBX<5G_<4pR@aA3Z&xtH<C&;}NDG77iHd2nfK!5oQjt;Q-5TFdC*GM#IEm
z@r>>a^zeu2gV8YkxY9Aqov`!+%g=<;0n9xx^{{Y*@d>40n0+uBmQP@OLh%T*58WP^
z{e<+x{0F09_QS#r=0EiC#>I!(2eTh;E-no)acKI3>4VviPQ%hUOdFiWp&u45u=vO6
zAE<s<xdT&A(EU(-uy}y^A4bFS1Kgcx40QLwq+#)o)4x!KFnM^ohPnr)9~Pfb<pe31
zdtmM-r~yp@EWFUofr+CjhjU^6faPPD1S}q4@-T698fHFBJ$kx<hYz|&G(If;3B?<l
zHu$-*Fnfu$4`vR`-LP<jrBfImrVgEkg$pd-z~URmC*%*9`7rxo_QL!Diw~GR==Q_(
z!_>p<h4Eqe0+)VRxeIeAOdq-F4`v@M9?{*4&WEKNSbl-&hq(jhA6Ph`>xYFGOdZTW
zFd8Ngvj>-cnEkMH0}BrrA6*}e4+~eAdtvbi(+~3xEM36#q0=yTqKm`K!zGWd9>$0H
zm!SR7@(N}TEZku6M<^Y@?18C=g##=+Ve&9{;iRGNfW;$>hPel)KBxrDKA1jO_!H6(
zi(i<#VettymLLVQ2WBtKJa~LT{e{aQn0}c3=rp1DgV_Vq2g@h0aEFBp%pP3sfW;py
z9l-R%#9`*((hpM)(+_hGjE0p{xb&m@1Evo~!^|b5AEplGZdmvcaz8F}Vev{RAHecA
zESzEfg}I-QeptN0+zHbMb3ZIy;c_QT9G2c;`ia#C(+_hu%-=9^LNvO)g#3$P9yFi8
z{15XtvH20^ZkT?UzhU8kVIP_R%)KxgmL6g5KvRyvg{g;y8;pjz149Fv08Aasy)b)W
z`3g-r1{bCt7GAJ)2=fny1~dU!xWUpP%wAYNg1HY(8=MP^f0%lh|6%@t<x98*GzQGQ
zFm*8Zz}$hR9L|Ng3#Jd24`JZ|GY75#jRA8n%p90BOh4RQm;@RP3olsugQWwQy|8dV
z(+20l{0DOf%s!a8Fh0y&xJDuwFnxs50nGjA;Q)(Qn166tiymIEc!%kOnU709%$>0C
zf~9AeI4*s#@Pny`r6ZVqF#BQZ(9MDQ7p4wf9_9~NIH1d;`x{*zW-c!M=<$rMALcJy
z`eFWpxsz1=F!iwTLQjV<`(f&E(J*(Q>&NA840E96Kg=C4|G><HnUA3lDgX;-7>#ZY
zEL}jA!zoz$g836>4@^C}I9wx?0n-mlx3K&Q<HO8>D#uB|(h)2@!0d&kPh1*d?uC_m
zFm*6@5Yi8GAI!Zlcfi6C=0BLZxM-L?u=s+-AF=Tdvk#^ZorcMy+YfUeOg*fegZT^Q
zE|@xW{V??~|HHx`rXM|A(e<PI7v>IF{K3qH#}k?XFmd#7g1HxF9*mEs9VZv&ewaIO
zDuhbF`~lMkvlo^gpvo{PnEPS=gz1CjKMV~x1Yq$1vmcgzVet;L2VFZX++g;@^b?AA
zm^m>2!R&{{H%uJHhp9)WVd`N1hJ`oGJed1&#vioYgQYuk^I_sJf1%40;=}Y4iVv9i
zuyBL<3+5kKIODPpW<E?kEIwf7;nD{ehlT?zykOx8a~E744hAg!!t}x1PbmIi@-ThG
z+6Qw6%zrTbuz1InUSaxRG`fD6Ik0emxeuluorc*1(+Bf6%pb7$K-Y)PhlL+39?{K(
zxf5L<j1Nm6uy}{L2S&r>Vd~Lon0sOQ9;OdQ<IF!$gJAZ;{15X7A$P;fh1m-WZ<u>v
z=0UB&pkV4?;S8f;;;?wc<qlZ5!15`&1u%19G`c(?KDxhfsfW7{nh#-oxPDw3q2e(2
z!{Px(!@?7)3^xU}7nTkQg&WKuSUQD;J1qaf!V4x2Q;$x=;umHg%siO8Vd)RXN7oPY
zH;hJ?hlL+3JkjN0`e5!xmxrYfT=Fn?!R$fT4@=*$a7CAg@nQO5;Q`YJ3qP1UVd~Lo
zm^)$N2n#oy?nW~JrXQAXVd)2EKg>O7+Tim(Fm*6@!t8~)1C}1p^~2P|{10;n%siNW
zbbW9>G~J@BM-K<MdJ-9g?1!aSn7h%9hq((~9L9&GOIUcr!wKpyLiWSVfyECjox$|M
z%!R2VM8nb_OdrfVSUSh0AEpl$?lAYj_%MIs(ho}qaQmPNVft}}FH{*k9m3oV(+4vb
zt`3a>GZ&^FmJVUzN66o>_=4FBOAoO050gi;7K00OCoH|d^ugQ#GZ#Z6ngA@EN%aTJ
zpRoK7a|eu%W(}MRa~I72Fn_~nLhgt88>Sv+FpLlL2TUBDhUtgVu=s?z2S%gogY%*J
z45ki7!^|V(ewaD1aDas)OdRG9Sbl(sqtmc>gQY{5dRRJunS-tm#)sJti!YczVD5+I
zPnddKG|V2DJ{TXDdYCwRxWoJdGY6&)7Yz$<SbU@Nap{AJ!~74k4@Se>0aJ&QhNc6U
zK3MvPnTtyw%-=A5=<$VKp5ZbF=5O@)hl#_?#ibu64pRqnKg?Y)K1>}h8W!Kgx*KK=
zESzEXz`_MRKfu(Z)3EdkQx6Mw4D--5!t8~G8%!F;hlK~sJeWL;hRMU!!`zJ?Zn*Lf
zOdRHZm_Bs#VD=HB$qj#)xv+SH<sX<hAsQBru<*teUbxJGxf|wxSowuZJ-Rq7ol{Fc
zEMC#=!No^+KP((^smCP_vlr$MSbD~#9wrV;cQF01^aM*sF!#XZVftV+EdF5j;-X>o
z156)`hQ$|59%etx9GE;zJvt3@CoKJ-hbPQ`==$J%X!#3EcQF6J+yPgIg8?%i=1z3?
z;ED&BJurXbqH%>g%s!ZZVd)qa4lw`Va5h{R<_?&DVetT0K!5=&hhXl9xr=}{xHK&M
zVD`hzfzdGY;Tq5wF#Rxp!^$6+c`$i2Z8*6w_ruJ?sSqXsO+T=BfY}RkKOu8q?nPG*
z_czphn02^lSUQ2xxYS{YL(PZTk1M@m=z|Kt(hn^CL4}E?VCfW=?qJ~u<Kr?2rVp0i
z(90=;?t%In7H=?nVCKQYA0|O04GTw@`-s#AR|ZQjF#nTk4op4FUodmv7N9X;{)MGO
zxHL3;VE%!NqcfoDVBrsoKbSs3@c`2Ya|cX4jK-CJVd)MQUa))%qv8I6x)0siFnL(`
zz`_ltAI3-52jj!?8O*;hcfi~Y6UUjKpzeaX17<FahM5C%4@{g84Rbdv{BeaJOdZT0
zFm<r-fZ31BKA3)(I+%ShK2Cq58wd>tn0{EiqASD2hlLMJKg|8O)T4{T?1SmY6(8vO
zV0>8k!SuuO9Za4O4NE65{jhXIZ1}<43v&ldKe6Eei!YcvVEWPBgVSHo_=DL8%f~SN
zFmYJ=LJtpEI)d2`D-U4e=<3n=Fn7WH0SgBhA6-3+4>KQTFN_Z>_hIhG84pl@quT>B
z2j&l$c`$onG%Vf0!UrY}qhb1C=^G{vr=j-4!W%9QWx&kE74A@F3~>EW^I+;>;xIl;
zJvt5ZFHAiw+|a`nT_21O3kP(4IQ<RP2y++A9+>?w8a-a1+Tau{-@wv4%pWiXF!#XR
z2NQ?UF!N#Q4<-&X2Nr*@c!0^nXqY&RhKa*ynEPP<fr-OtSh|AwA0`f?3FQNrJ7NBZ
z*@tc}%pWjw(P>!tz}x`~H<-DwctY0)<HO`(_Q3oLb016{It_Ct%szDeFnM%+Fmqx0
zVBrK)2Q!CI`h&^CXqbMOxiCJlG%Orp_7kfQrVi#0^z;amCq$#CXPCcW?!u)X7M`&9
zhQ%MuJXpHGX%4j9f$4|&1DAa;aag*6>4W7XSop)-1yhes!@?PEAJiQ%_oAyu=flDo
zrXOY>%>U@>(fKg-Fn_?(E6gA0>Iv~-?t$fFLh8}wVc`ff2NwS@^I+~o*9Y?lOg}9A
zVKgi}VBv(WA7&rAeqzG`rVbW<uyhBr7u`NWe3(8MO(@;M?1QBnn7?6sn7=UWg{C){
zewaR3c*E4g!WYIzr(y8|vkzUK5FZvVINeFW09gFN@)H5&IHh6n2XhB3{o&LAm4K;(
z#TTr-1`AiHGMp65|1kSu3W?Q^ZXe7Xm_K0V;i6&j3$qWF4q)MmOCL-e=5JW|!|aFg
zVd`+vFni$Yq3IHq4q@SgOFv8;mQP^$5tbfd?t`gAr(yQM)WO0H7B1-OVSJcAm_0Cm
zz|4Wk!_=eGF!#dr!}1Z#zc3nIA37i1ov?U>nUAg>7awL0%>TI5;Sz_1J1qWjslyP5
z#y2b+VD5(HD;SNT4=Mn&50-9WG%Oxqe3&|@HVg_DUod^J_=JTEh6bnrOdrhsuy}*<
zVg7+Chf^?fVd`M{7^VOgo-p%Z;xHN(F0lB9iNk1^ILv+M0Ol^3Khfo3=>V24(dA+G
z!_=Y6!}Bw`1e6c+KP-M={)Ewl^h1q-uYZB5hq(vlKe##^3|KhB!V8BooWd~o!u$dA
z7fuam60q<mS3k_%F#W{(8|DmHdWY$Ug$GQY5DoJ$OdmWw6VeEkhow(+f57-K8kXLm
z+TavSKg|8OXjuAyYlJdj=^GXfF!P|&I4PJun15mE0G9r6YJ^I_!V9Jk7LLT~hv|d)
z3l{G%e?YAvnu6(vg*VX}p$cK{fzeQ5f)vc(FneL)0*eQjyU@!=xWUl+0~T&D8fG4h
z4>J$056XbWBTPLk{Sc}zpxWW%S1^4rb8w|sm_C@jFnzFc047g}hPe+G->`5ZWG~EI
zSUkbP2i-k5!y9S<%pREgVSJc-aD_KiJDh^0UvwJgF1SJ*44AuN;SO^TEMMZ#1{a2<
zKUny~^uxjzt`Lm@^DoSuuyhOa2aJ!V4bFwR7hN4J{0P|(lZS;f%snvo!_ouXLLwP3
z^I-0Sg%6S1;mTm?3*CQk1yBY|9n9ab_=HL`z`_?@93~H=VdlX652JCJ3)2S+H<*86
z`e63L!ULurPD9Hbn0i=zz~o{2VCKTa(P@}Em^jRzgxn2N52Io6gv&iJb%bbGJ|N_7
zxH-^pfb*dYnEP?1Lzq8d?uCT|%pWlOVetbs7K4JB15*z(51#*VxDRF!Og}9Bz|4W^
zhna_~JcOx-r59Lwhxs374$MAu8s=XZ4YLPk9(uY$*AG(<b3e>pn7d)(82X?Bu=s|l
zhtV*9;EF$}b_STcVfMhn111hrM~H^`7iJD2^>BG;{KDK#$h~lVXbf05!{UpOKVaz+
zrVeI5Iv<w4(5z#C*#pxDvma(IOdOYfm^zq$Vc`uk7v>IhdvWn$`446<EdF8U;?f6q
zA2c3d?t-}kW-c!MFn7Y#!{Q%SkHXRgF8wg~!Sun*f%yYw4leyLahN+{`3L5Im^>~T
zrVgeL79TJ^E`2cjVE%`xgZTqS!^F{PSbBlcuy}y^9~Lg?`f&20@dtA^tX#mUo`3`_
z++qI2r5qMsF#F){hPnqmd~lfq(+~4MEdF8Qxb(rqVfMh{6DCfGhNX9yeK7wJ(g%}=
zxfdQzP=CYHJv=;M8lW^R-eLB_>H(O4VdAiOfNFzNFm*6{V0@T9n7d&1z%@b{Fn_|-
zqtm$D4b_gIp#Fuq6PErE3W#ID;*VVYuy}|01DC(iT?2DBEF59sg02po56iDG`(W{h
zt_~L;RzASojY}OaahU&L_QTu{^AF5@xb(x^3)2Tv2TRAq#skd#u=EE@H?Z^vvj=8A
zE*cgdF#o{x!_32_4<-(?AEqA`J}`M)G`hX8bVx8AK+`?UKA8Qu>_HcYxeulv#wRzt
zVCKMRSUjTJ3+JP`AEq8g!_0w|k8pix44A)R=>?YVVCf1bkERWSiyjX!`(f_E(10cY
z(+^8Guyl#041){P2Xj9x9tfp7m^{p##Ks%U0kCj|<yV+Eu{2CS%zjw9h0*YEhsHCy
zxiI&`!VAWSse|b!6pk=?n0lDIVBrZ%4={6a(XeoUxrbo>gBk=gAEpl$e^6<h6wJS{
zbP97X%>6huLM348VBrsQFN{wp-NMoxOg%au7M?J3q1M7FSh&OV!~6l`!)TZ}aE(v~
zEZkt>4pR@~6PupV!vW?Gs5JyBnEzn$2Q!bLMyLW<Ji_dSg#*kynE6oUa0;dmrXHpr
zmpEJ_lmUxhSbl>FGr+`Q;R!PrCJ&=w{)E{NGY3Y);tiL6m^m=_!r~JqPON^IdUP6=
z9|+|qn0sOA5#}$L|Iy9G;6uwNm_K3qVBv_N9!&t|Z$j~nrVY-8r4N|BFn_=m5MaQ<
z2d1A`cf;}*Ec{^lVev-5S_bs+g83hoo(SoO$-~?a3rCncVB#=!xM-L<n0sLUfw>39
z#~H5B_=lx8nEfysW)8euf;t42?qT5tvj=7lx_YQKI0f@B%zRk7g^9yxxCS%^EZxBD
zg~dB8JkXS5aAEpk{)ELF%>6L)Ff>91VE%`NJ5(4xpAK^uT%1@2G(KSNA*2oNU#LD<
zJj2Ba7yyeWSUAA^5Az=Z?Qm(Beq!x~X+*alW)4gorVgEkr5AMdF!vJ*KUn<2;tA$o
zSh|CmgKjU353?T@-Y{_(4RaTak3mD@3*DVCcfj<+_!#=30<ioC^AC)VZZ1?g1_d)8
z7T&P@2nz=c4QK+e_=f3+g&Ry9O*xzkb1%%DF!eBTm^p9_Xbf1m!_>p1VSJc8OdOqt
z#UIQbSUQ0DpOAi-d9d(<nFkYxnFr&OO2fhvW<D<c@bU+mo?z;TwI3Gluy}y^pIH5{
z^ae|>Fd7!FuzD8eE;tQ!FU&reI#{}c(Qx%>3|KtF?1xFi!V5h<VDc~;7H%+q!pwuY
z8x|fgahN_B4GTAze_`gq<YDy-x;RW8=1yGxg!v0*4@@6A4bu<vFU(wcxT0%7<HP(1
z^9Oo(pee`T!t}%JgM}Z4LNo!G|6umQ^uzeD@I%uE=fd2Ht{!F%p>Ts41am(u{lVN1
zb016{AsQC$F!#XCC8QB54|6X}KP*0>(l{xYy)b{n^uxjjCXUNJuyBUygQa_zei#ia
zk8n8z=6;xdV#`lh`i0pKb0;hwVDW;>URXH6^ux@9xdRqHxb(xsVg7@eLu~xR!VhL2
zOg}6<VCJLKFn7Y@2j&l$dtv^EiKFX>g$vAHnEPSwfzhz|!_bc=0CO)a-eB&6(P+xy
zTv#~4)WO^hSAc^7b05t8q{chUUoiKghZ_!S;li+Rh1m=9KP(*K3W;RE+yOI(NNw=>
ze^~ets}H6Q7XIk&hPeY4&M@`pG)x~X-QuEQ=@nf+EFHqkf%^kmzhkIJ6M)4d%)Kyk
zVE#f=4(Gz+52hX_0gF$VKVahMG%Q?T_QK?0=?JDDT^^<mrXS`Gm^h4vr3aXMVB+XB
zOdT$9m_6w7Fg`rLLBkv7URb!o)ZwCG?uYporVkblxb(rqVd`M|VEGwmdVy+$>4Ui+
z7LTxahlxX#V^FYgh3SJSfSC*P56pisc^D0Am%+jdCJsx_@N^89KxM$ph4~v*mI1y#
z1zjAS53>j6ZWs*{M^}%|hlK+y9l`j7!VhK+%s!ZUSh&H=fvLkq!_p^AKP>!ksYe%w
zg(Io<!puR}597n+3DGe1F#9pm0|5hI=@q6Q77qlpA*7-C7M6}-@eYd@ghK2rn0>JD
zhq(i04vY_TC%P%Hd<sj)F!eAR<}O_E3JWh-zK7|9xd+`obUw^o=rqjz=;{gaVeWvL
zM@T))U$Ah5xgSQu!UdN(Fn^+}hvgqw_~OzJlZUw*7M{4&ql?4T!|aFo2VEU5KFr@R
zf8bI_NF3%4m^rZWm5@GIIKkWx(+A6!xb(xq4W3S*`3RSKba9w_Vew1IK6L#s^)PqC
z{0*aF?nBoH=R@6#?tWN#3QG@g^*9)?bPkJ0Sp37y#i0*f7#8j@eXwvrSBK7rl~b^I
zgy|#XfAsK!>4VX5|Dan4<-@`U=6|R#1FXD)`3EMBPQ%;_3vZY_%>OX=!NdvD(D;P8
z3uZn+edr3H=D^Ysy8qCX!`u%GFLZaq+yhgGt{)bEFneJBhox&6jVnB1_QLeR(jU4#
z=zLiCz|4oyuzZQG9-R+MC$MmW@nQZzSC7tz`3I&RW)93<=;{gaVeZEj&bai${0j>|
zSbX48k1h_g7Z%U3_=mXz=1*Mt5hf2a2Nw+sCv^K^?uY4v*^jOc7ayh`rXQC*xb(x~
z3#K2I4sfYQ7l*|=Og}6h(ADAM!}P=S!OS5xAHd@qn(txez~Tj$y)bcD_`|{tmJVR%
zz}yE5Czw7M4NE65^)UCtXqY&z^aG1Om_0E2Vfu0DgV_&rKdjt_l_xO$@bH0YAV9<X
z33CSl<qWWJgV~2p6G~68@PYXsrXS`GV$%UEz6sff;U8$af%z8}o*3$I2*B)xxu4YV
zhWQT`4y4)-i*K0wVa|l5OBf#)4Rbdv9$_>r9&n{Um^;zkO-LWi9WZ-f_QT>0W*&?W
z3tyN%7!A`8vkykY!hulwfyEO{9ZWxr53`3L4NX6k>W75`%p6?)hPemkesq1X^gzfT
zu=D}54;G#<^9j)~^I`g7d_wwQ@-X{g`e8K8KQRBn)T7hr?uVsESp37xN7skWhuH^9
zZ!rC^bcn7Voe#?&F#Rxpz{+EE^|<&j_ru&nY<R-r8>SCt4zccsxf_<=VD%A99G831
z#bNOUvlkXF=<0CsVg7*Whn0)C)T4{T?1!ZbV$FfYH_U#Ry)bhK#v?TU!{Q4T4>0>-
z;fn5FI3KDXrk>PrgT)st-NE>9^9e9u>S6j}{wAOeE)5Gmn0{FLgDW7wfQ1{(|1drQ
zZ3t;-_`%|j*nAH&0OoEO4RZ&~9GEz+aDl}aOdm`=j1Th<EPcZ4MW<om2Ga-QqpO4Q
zVdlZoCoJ8;(i==1rXHP!`46TKCJz&bg(plLorc*1Q;!}FFh06ITzpvg!qOA5`q9<F
z%z^t0nm%yZ3v)lLyo2e7`5zX4xb(xsVeths2NsVo^I+=GX_!1LzF_W#nTM_(#)suw
zm_4xgfVmfz4$;F0rXO8BOdMt|Og}80!Q^2yOdZVKFm<qSgXxE<gXx3Q(0GBlA4bF6
zMNmK7AnXi6;Rtgt%pO=efSCr1M_By8#L;P(|6t(&GY>|?>JxN*Fg{E_%>OVxOr8)8
zi(i<1=>8z2ALc%oewaT9>O)fi(+>-GG-=#iXgYxDhtV*1;ns*M1B)kEIKt8)swzw#
zEWBay4J&_O?tu9RMq`)&bw5lVrVmEL+yhJ3==x#y!_>j_!Q6wc9-R*>Ct&do(?_iT
z(anSDgPDgekHLrfAKf1?b711Q^r8D3=6{$ttUSZe4;6rgD=hqpb~jW%dO3j8y-@Y&
z6fFE<_M^)a%ZKGVnEzqs64DRzFH9fI976dAz1)E7BV-{|9_Buny|8pgZ1}<A6{a5+
zez15a<Q`bO!PLXj0lL3n<pR{%7!)l0VEG=V9_B7sI>yk5CIB-BrVmCFs~;8~uyBC+
z2hAExE;Rqa?1Slpg$t$zR1sKw!Qv05pHTS2O+m9C<_=gqz}4Ykpoce1KcVyslZUB?
zg&!;)h;=_qJ<R{GbOlQfuyl^Y`EX&FI#{^FXqdThg=h?P`(W-tQ-+fZi!WHZfrUFP
zJYeRd8vqlBg&)lQ=<xwF4_!aZe3-p3d04o?!XKA@Sh&FSquUE}7q0Mt>4T|*(Xeob
z`3L4MTr|vmF!#XRk4rr+ahSct=69GmF#BQQ1v3X0uP}A!G%Vl3+zV3=3m2F;x;_{m
zrXFT5%pEZM356dl{lI8gJQ1=NW<M?(=6+Jc5oR9DJuq`%=@cf9PQ%;{3lCVi0`osC
ze9-m5_^^0@xfkYtSbBts!_=eG=;<D&G>4QPCr{V@ArG$DV(jDgt)(+8u8wVzV`
zu<(W13!`D-0&^!W8Wz4VeK49>_v5l3<}X;jz-2!yoxtoxrwQqYnG4Hz#JU@%4;D@^
z{jl^4OaCx=n0j;?rVf@5VEWO`L)QmOcd&4R<vUn>z{*pYdFc9K>R|d|{zeZ!m^xhg
z(bdD;O~~JH^Pue)n0sOFfXfrffVqQcd!gpS!Vwk^=-~lXhC#v76-+&h4~qwwdtl<|
z2EfV(c)X)2fSC(R-)PeC{o642qsznW$0ZL7XI$pP^rM>#<HP&~%fB#jnEzq!hl%5&
zVet-gC(J$Q=Hk*1i*J~FVfMn@flD7u92O2Rcfj<+_%MIM)T7fdbuf3p<O%76se`#2
zmQP{fK*;?tbuj(t?uO+<m^zq!=rk^QSh&DwbbW-}2a7-SaDkZvi)Wa-;pRch1DN~a
z;xLV9G|YW4b70|t%U@{P;rrcT`4CnPz|6%^4;6s93uZ6Ozc76;aj0?(3T7X=Iv5`o
zJ{TI&1)$*t^Divi(3KJ5!|a2lXBZ7jmxT1A%ftK!qtWGIe3*Y>;STc$tloizD@;8)
z4a;XR^)T~b;eadsp_>D<AEpoH4s><se3<)S=?>;!Li%Cp2c{3^Zglf;@nQag>4()P
zxYVPIqw9y619Jx<_ruZ!EWBX;hPeYK4zmZ{ei$DXez5R|*$Yd5FmafAbQ%_pFm*70
zz-U;yK-UN7L-P+Vcfi%5G0^RW`5Tu1Vg5(c2Is=^8_Zsq1T4H@{(*_3)9B$1iw{`*
z!Dw`SFg`5bz|_Iqi|&4yKVkZarD6Vp`3ILim^xVa!PLRx4P6{252In}1*RV64;T&e
zCrmvq8s-k7(+$)hSUjNXBjj$FyI}fY;SM#HAcbx(EWN<OgOCBR@ItpA#wX-{m_0D{
zF#o{x!Ng(d5|?{m?uOB@aDeG2lzw386FvT5`eE|8?1zcN?1z;*FmYmOSUw`vUYPq}
z>6KLd=<xv44~q{%?uUghy8W>9MMyuoJS_ZR=E3}jt`5eB$;09gW-rViFmXcZ2&N9E
z4;Kxy7e>Rv4;KG0eX#h1sl!FX^uh8I%pEZQ<I;yq9Oi$RIk?op#9`qFt50C=hWP`Q
zpJ3|IX_!4Q^)PqC^uf{xx;{7`t^S0C16-Z}1Ll61yJ7wypbah!b05t8gwhFIBa{Ji
zFD%`{;**ehuyg|pFPOVw;xHO&4F&~sH_Uzv1q1|O`eE)wr(x*`r!i>p1}iUMG%P%D
z>c=GkH5V3+xRk-fVc`Tz2QYUN8!qVnhS>`<4`vS`8s=YO?S+{Kb3e=-u<(QV1ICA`
zN2g)w9#(I{(h<x&T<IBRE-d}Q)WO^jqtWey@nP~Xcf!I8W-p=efVmr1e!%!J{V;Ku
zxiEWSG|XO@dYC_8;xKi@(lGmB<rcB}VCrDu0rNLZoDhv3|FC=siys(`%N&@$Vet=B
z2TRAWaD<7&XqbAKzhU7F6NiN>uJC|`Gt3^CyJ7BuiNnl=*$bm#?t<BaPUF%C(+{Iz
z;RkaM%p90Ju{6v*F!vLy4;Jq*dx_N#b2lvfVc`rjhgko^)WOVy>4&=?TAsrBXbf2T
zg83h&A4bFC5ltJM3rp`X^)Pc_=?GS?z%`&TFv=@5c|u&6J;a7Px_L17!`zK7Plyk5
zKg@nuIwYhIrVbWvuyg?9!{QZ}IWYAw_ru~5rXQ9baOsEH5A!#=ei$E@ez-U^eZkCw
z*$<Z|k^xI+F!vCtjREFQn7y#@fVl^jKXLg7J>S91L8oEijmurIbO^H_<{v`w2(usN
zZ<u<RyJ7OU?1k9}%V#kCF!NyU!lfT(4~&NSlaReIdtm0n!V4Dexa@<8qw9n5VfN#S
zFPMH94buk;Z&*D9GY6)Q5RIOX;rgNJ8<&1qxWW7nqhaY1mp)iH!PLXz3uZ5&@Q3*q
zT^&q6%s;s7M;C|1C(J#B+>fpw<_?&<(B*OQVfMn@4U11)>e0nv=^y59Sh&K(afJiA
zy)gagG|XM-_QLoub71}^*8MPj==BDS50i)4gHFTz2}?IHd6@fQe4I2iUBl8ZEWZ)*
zFCp{L-Gfs-nglH0lB*vU9x(sF(l3mLm5*rFVw^t*3qM#qz~U9gN7oOt4@Sen4`vRG
zkFF2Khsne858S;l1q5iAJuqdk^bQM0Sh&LEVKgkhVftb2fcXPP!@?D&4@SfE!}Ous
z1Jeid7fc@^8s>gjxD!$ja|g^mm_C>|A^kA-!SoTzcj)H9{EcouEL>s!LDvW4!^$Zb
z4J$uj`eEk7)Dxm%_QCWM3I~`x%zjuphv|pKFD`r0-3!wXb2qW!2}^G<8kc<-;?Qyo
z-ChiJI0RtfMaZ8xw8Pt<u=E29N0_;U^r5>O=6_iD6VeY;2McFdzK4n9(hpOQZXe7a
zaQ)EmB?QpREttPx>5Gtlba_}fqMM7Z4i?TZ8YU0RkFfj=6Gx|E=@e!k%-t~m!_qgp
zKA68?`d~CH9l^|jsYBNf^A}7VjE_#E>x1#p-3=3m(*)uTrXOZM%>6JvjE1R$n+s*Y
z#9{i-!ygvzFdC|jXbP4tafLrD{lelIW-v@WEFNI$VDfMp8a^<8!r~jI4_Caw^+Opj
zbue>qwKJf~F(_F0!DyI!VgA6-026?^ALedYx`BlUEFZ&^qtmc>ft44q_=A}Ta|bT}
zz}yRqCm0_VelR}Fe02L^;xPMQG)zCt|8RGp82}T9se|c9Q$~;rOUE$%FmnlNL{k7u
z2Qc@;!XGA%rW}I{3olsw!Qux-V`xAVfa!;+hlMXJUeT0eaADyHb3aT!uJC|~!|a3E
z56cIzaK*3)DgbjA%s!|vygr4Q2NTCd!`uy14|5mH9k|jjx_Pj8g2fxT`eE*X<qLFs
z(D^X;!omR-E^zzNHQ?aG(l1OuvG&7^gV_%=htzO`>4&)+7M?Kk(P@~!VfLYyU$A_D
zt`D6L(+7(;7$24{VCfTGA37iAURb!n(m%{UxWWUb52g+l{^;i8;=}w2b3ZIxVCKN&
z(e=aZg@qrC57P&853YO&3m=#|n7y!ggxQNrKP;SJ?uMy{xd&!0%pP?2!^C0gVKgk<
zVeyEr51kM5H%vb+_oC~A@nQDB^ux@9$rGYs_QAppM#Iw`w4O#c2WAf}oM3#II#{^E
z&4EckX_&pR@&+mlUylqk7ngomdWNZk#UIT5FdC<RsD-d}gYJHqyI}5wDkqwP>4(J+
zx<>SL4)YI89nAeO_rb(rG)x?p4q@tH_QJvgrXCj!b017UjE0Hh(gzcVg+DAm!Dv|g
z!_=YEuyBRxhs#6L8%!J)PH=fB1Li(l_Q3psEB;~T;tD5NIKsjgYAu|Cr5Bhzu=s!}
zfa!<%6DE#M!_>jt0dp73|LExgT|c@zVD5pLgRUMY9~$nk@PyGg)uTzk`~i!9Sb2n|
z41)^`UzmNc_=1(A7#h$7VBrh14@Se%2byvWE-d_D`d~Cc{m}4&g*Pnz(EWp+E?^FT
ziNpK}^Eb>rF!NyIFnzdaSUAAKAC`V#{=uaWW*;p4Vg4cLf2e^leX#h4=_h0_%>OWT
zFn6Hq$K?)~ewcb#c)`qr(FF4WG#+8$46_dwFEDeV-o>P#`qA?#EIeTGfvFK90&_o%
zCe?nJdYJoR?uPlFp!*@t!05NY(htmhs4@%+J>FpIVg81>3qvDR0Hz)mUa)iw3lH>g
zg=&LSF#Rz9!t}x11y_j1fTbUpy)gg6(leTJ3@$8P!PKLh2b0Iph$a9FCzv_t{)d^1
zrVWD&3kR5au<#%h{;={2rXCg!F!L}hLKA?6H%vb)y}``E6@M^)!PLRx0VWPJ2bcR{
z?t|Hj<}CO<(y;KrB@YXCba|LQSbjp6hlLx=-Eetmc>wblu5<uPpD=YWb70{Kqha9z
zHy@1wb0;huVESO;gQgsV3rlY>b70{OqcJq#5P;baqhbES75^|}VBrYU2cvPt3rru3
zhPfN24@MK~e^@$$>4VWQ^9j+o>?foTCJzf|n7#0DgDD_HL(>~9ox=P>NE<9(VEQoZ
zhnkDa99Viq*9YSh41cKkuzUb>57GM390rRASo%kk#>s`b6J{?gKj74WCIJfv7!C6e
zEL;i22h3hvG_LXk<_wsBVd)0u4r2WQb1zIE%pEXuVfLWYFnut4VftWvV)eu9g_UzK
z{jhw2ZZ9rAESzEPAXPss{lN6YXrj%5re|2V!^$s$`l0$@=><l^!U1L;E`P(~0p?y9
z4GRxk`f!QE?1h!fxYVJGqlX`%bPLlD3m=%huyg~<moRncG|YUMK3MsI&WEKdbp5dK
zfrS??8Wtbu`eE?`^EWIV!_0yC3zz-q=E2m#-H)z-0H0j_uyBN>SD5`Uf8g>z%sDXs
z!_>k24WnV<1XB;Eq3IrGFI=2R1}uHR?1iOQg7!kqhpC786PE5^{(<pf=?bb1PQk((
zW)6%GQx8iIu>1+vh{k}056s;#dtvc{rW}I{(+9H;W*&w@0s^pjAmm<jV_^2f^uxme
zT_FxWx$cJ91G5*0@i>KH`eF9N!W)*JVC4g@@PO$@&o40ZVBrEY50^t=_Q3rC%@;8D
zz|toyUEwkZW)Dm~EFHnpCoX-kd<0APF#RwZmws5dz|_IqkM0g!?uLaQ%p92eVD5m0
z3rrjr4NLE^@P_#t7B4V&;nELN2lEF^KYBdk(hqY#Og*mf!lfT34s!=AK49X6Xqf#l
zcf-P!kUp3^%syDW!Q6o>-eCG*^007&#TP6dV0^fGGzQFGxP4H0SbW0Fh4~LEjX}ZO
z4WnWD2-y!a2<8r$KA3xm4M$k|g4qYlx3Kg;$Q+n`=>CM62MZ6Fd(ml_Jj@+18fHIC
z9$g<gA7(#HKP)_8;fk&v#)pL;%pWlIuyg=32WCD@A1wXA^uhcK3qP1UVDjktVfMnz
z$3?^31Gg8A0n4v2_rmOlnFn(xnl_wVn0{D(#HkR61WX?+Ja8z(5Qc>h%-yi?!BB`M
zfbM@-x`FuvO*xzkOV=>>!onG*9~N(L4QLFQdvU3Q)dRTFFU)_q!U-0iFm-6w!_SX{
zsY92Cr9X6W7#|jH==Q_-u<(GX$DpD0JS_ZR{)O>z`5WCFn0>HtBouGx`eFWnr5l*P
ziM1c59;P2g6VeY$KQQ&^G%S6fyBEfX>4$|EOg}6fVCrzuF#RxnFdC*GCXY)$OdQ>Q
zm^rx89ZVmLhQ$|5AB={@3rrm@8fFj79kBR-nTtywOdO^T=5H8H$p0{VVeW<53**D&
zVfLWYu=EbkC(!%=(@&_}g}Dz#!_0$)1G;^<_%Qu2eX#Jrr5;@zrXHprW*)2@gym0k
zeK0=E|FCd}nF9+4Li%Cl7fe0OUPAc~W<M;R(CsCpAEpmxFFFl#7t9`X8s<(I4U>n3
z10nse^Z^TRn0sOFfTc&6Jj{GtG)x_Oe8Bj)^x+bR=|lG)hI(kahq)J~9_9~1<p8Xl
zgsCSw{LmZ%^FK^KEIiSa;pC#b8>d1v37G$2?uXGZf1xRdb7Ag-sfVQ(bUrR~VDSS}
zkM4eSak#}$2D*M&xWV+n`~^#=P;GDu7T&OY1XBmo4_8Pm1DcLt`IwM4nEkMF6Xt)I
zKVbgDWe$3H!_qITyn_1+hXJtogV_gj4-VxR!Z7<`?#EC-NB}LoVBrprH$wWM@-TP9
z{0j>QQq6(YBd~CX(NJq}Qn2ubxgQ??Q2*i72$g`j9~S?xbO@CuNWt6<^EW{aXbNEd
zgvBSCG)^we-Q=bpm_K3qVSHHl!TbY@R}6<h!xyF>7XGmG2ut5E^Dy*71z_=qZXZmX
zkbbB!3^03P=D^|;SNR9i2lF>99$@}Jr*VY?%%3p*uy};gFmqt>1-F*~1Lkj7`ogIk
z8V~6HhuI4Y4_x|T;RI6;qha9>3m;tiVe+tWfVmr|dK?n4@PpfnLme(*n7avv7n%Vu
zccX^`q4Eo+59VK3`hd9~mM>uH(5!{8H-p6w%-t~aV0>8kqU(pHCzyJe|8c2D*AL4V
zFn7ZI4|6}ddUQU_Uod?zd009m6n^OT!@>{dewe#p=HjAZ?tp~@j3#71x_Pkhgz1CX
z4@;Nm>e2Zyb+CL7^B*jJ(bdEFu<$~UZ&<j(%!R3k(J=SI;vJ?Q#)r``|G@OYX=u3t
zQ->aIF!NyX0oO->0rNL5cN5SKU$;hVIz%@QrVk#T=nByIu=E3qZ&*0M{DGz%gA0pa
zn0}c3Fg~vEgQa&^_`|~+Y6C{PfQrK?SUQ90huMoR4>JcQ4x?e|0_ILwIKbp#?!gt0
zF#Rz9!}P(-fw_xNJiye!?1Q-nW-ct9!^}sgVdkTogDc$8^`plF%zl_Sx_WdzdOX0~
zL9Bk5zhU}d{zkV4gO8SfVft~!H@bdU`iAL)(J=p@tH<Euupj0h4D~n!VCfcSKa3_e
z{9yio*$d0RxY8}UGhqIO#XHO#Lg@~s4;EiAK1@F>U836u<HP+24M&(b%p5}cVd`Nt
z%)K!85S#vC`eE?^%hxb*LNqMD!|jD?fcXzbLxnLYSbBwp8_XTB_#hOIF!iu>1LMQ|
z0Sgxli=hIraE7@bW)3VKpvo{Pn7?7_VeW;cKbU_oG(rVn?u4s@rURHcgzSeJ1E(;|
zgUb-dfY}T44=g-j`HPS#Fm*6@z|12Qf3Wz0`4<*`Fn7SjaoG=xXPA1JeptNX(uYeN
zrVr+RV)GGtdVslykbYSHfT@S22N)lhJ7M}@?u5A;79Ozp!=)b<FEITu8fGtyhQ%kk
zIWTdUJur8}_^^0@r3-ZZxcD%03B@}u{V;tn8kag)x`VkF=3f{M3m=#|baP<l!_>oQ
zn0bWs!{lM=aoK}zFN_Z>H(~k-r8Ag5I1MdtVDSWte^@$#t0R(u9v;M+15*c!cbGe1
zd_va4{0|F%n0{i@9W2~o;Yr9oba_~QgQb6Tc|v@cKA1U#)T7J8;*VJWqU(o+7c9JC
z?tzB~G@YR9gYjYE4vPm^I)KrzbPiLGPQ&!U?1PnSF!#XRhprFChxr?(AEq9rpV)i>
zvlr%Gm^m>2!NLV*FPw&k54t*-{V?-z>4&91T<(O$FRXrn+l$74r5~6*Fnut8pecuQ
zVc`dJKa7U!gZdAy0S5!-514;p@sC3}rZChVn7uIbFco4KfrSq&9bi|5DGKvHEFHqa
z4VE4-b)bsC+zazBEFNItfT|jk2Mcd>8JK>U`_ScKe3*Y><tWVkFq%*}!om&a4r2Ae
z?1!Z{m^)zM027C)!$rgFL-!{v|G><{r5_egFmqt>0ka<_k4ryH92QQn_=A}TqhadM
zX_&iV_QLeT%)_Z4n%-gI4huh6e8BYK@;}U9FneL<!2Av4!^}f>H;fOHhxr5MZkV|+
zb;Qyzcfs62tUj1JSop!h113%^4bxAo{V;#S)WgCXCQis+n0>JDhLs~Q8Wx_o%z>$c
z*$Yz#OaHL&go&fmFniJK4_J8sqtW%j%!T<IT^<(xxWWM@j!QqxU+C^aw+E&l-90e-
z(bc2#Vd)nZ4#e7vZVrqOiw9W#Bi0?T_=2Se7$4?8nEB{5OdZT#m^@4$Odci<qha9%
z^EXUCOdm`fSNNlc8%#aSzr^N$m^)zZf|(1ezhL6T(lCGE@;4!KVD5*}gw(^r2j*WG
z4GRxK{(!j`T|F#+5V9YZUP<*YESzEX!t8^Y19LY_9NitT@PYXkM#Id5(J*t+^~1^)
zm^zqwu=EcThq)77Ka3A^A1ocg+yM($m^xfEx;~iwxYVPI!@?itUUV9#JJ9NHm^m=@
zFn8f9|6%Tg*@sIVx_e=KSiHc(3zqK*g*Qw;OdZUAbQ%^fF!h9J^z;DBSA_Jz?1A|M
zmVOE8gUQ3f0ajka#EGS0@dUFU#wS)k%)PMiBi7$A^I`g6`3ELWh(-@@SU3{W2a|`{
z2lF>99AWN(rDt5>fu4S0`e5e3;sZv*?1j^4`3I&CmM&rLf~9M?J~Rf*-!S`OG|XO@
zJeoE*7u_D1J7D39D?VZ20<#a@9GJUd;S9GB%7BF*%$=m>f0(&2dtm+~)*M(l1#>@)
zhPelqzoE{D-@5=a2S&rpf$?GLanUgM!onREj)d%onFmX!Fni(g22((QhNU-{epq;t
zYCkO8VD5+c2bc9Qcf!=e!U1M4%ssGlhHef_Kg?cqd04rGD;{9tuylm39~Q1Kb?EXi
zcfr)b<YE4RxeJ$mn0lBx7!3;#7!6C;F!ks(EdF5e26GP~d(q8-xf^B<Ec{{aLRSyt
z!`uf`2eTjMei$F79!A5$4`vU#dYCv&9ZVmLhS>{Khl_@(gXu%3VdW5vhWP_VqpK&x
zhlL-Z^pDFNSa{(|m$>x7#9{hj?tqEoqS5maOh2x0hnWMDhlM-Lzc6>h;uEGGordL4
zm_C?1%-!hXFnJgai!YcuSbB%Khfuu3%z^nArVr+Rm^>jGrXNPb+z;~~A^kA*F!#gM
z!@>`iFJa>7G%Wwa+z+E+;exK75Fh3anEizO0dpUQKcMX!LhgWtH@Z3)ALbr(ccc3o
zmS17+hS50F0n~jkcf;HPOV=>-(CsC}hvg?i;fBi`m_C>}xYXeiCsjYpzp!wCr5{4}
z!}P(_!@>_eUSR6b-3yCXSiXVD!}P=A55|Yd!)TcMVD5&M2QYm&;~i!Il!obt`2)s>
zrE91%oD?jb!u$^lf0#H<jZg`geK7w(g&APsLdYL5eK7Sf8Wz4V^9a$fc!Gr=vF?Yp
z*I@c!;RtgFA$wu!U^L8LSa{;n4~uVDK81x7jE^f^VdlZY2j*UM8s;CEI+#8f4ND&|
zd04!|%z=d`Og$kQ=6;xdLh8}wVftbIN0%qWhv|da5A#1PK5*)XmZLCz=ro~tgoP8j
zJ{TV+kIQ~oJj2w(XqY%IeK2vDI+#8f4a-+Bb+~Bsa3fVeEPP@5VBrqSSGeqjiNn$%
zOdpJfiNn+pqG90-%ZEg}2O9q{dtv%vG|U`a;R(yfFdF7gSbB!(hq(_Xk50qnVKgjX
z!r}*AJ&X^NhuMQp<8n7lKP>)W`5%_vVd)B{j#wHNZZQ87s}JUHnEk}+gQ<hr2lEGd
zIN(Z;FneL@VeW^83rrkWdW6}FUeCkA9cCWPoj7S|I)tSoSiHjggG(Q}`(f^drDvG?
zaOsDc4@++_f0G;kFnut0;j$M)9O{3VyK#j#x;~gXm_K0pVKhu0T_21Oiyv5c!@_}(
zKVb0<3op1mP=CP8g^R;1gwil^^l*Zi2TMOt<wR33f5ZF(vxis%VCe<seq!AL^9L;d
z!qOqGe24BHSa`$ig_(!04xNu4Uod-NG%Q`9>w}pOQwO7A_QUl-;}cy!ES_ND3^Na1
z9WFjBpTpt{7H*`*7cAV+-A}4HF!#gEf$?#<A0`gV2Qc@;^uy8-OdT#77S1qzFmrIJ
zM;C|r6Q&Oqp0N0Vg)_Q77$4?-nEmK9%p90{bQ+dF(DlLiFnM%+Fg|+x!SunzVd^kw
zXu5={N2dwd3(J=<dtmxud<^?=2%yIw%p5}D4l@pxPGI)J^uyc-Q-_O&#Xl_H!pwn*
z<I)Ec$K`&QIWTp&XjuM74{w-zVE#kb4^t0Ix9IXPahN)Ed06<u>_L}@`3qJ)qRYeF
z1q*Ljc)`SB{({+qt{=vS<qw!S=rqh+n0j0^x;eP~jjkV7PNCZi6GvB%&WD9FtlWUb
zJIw#+>fwB7{)4$2E)HeD!XKAEVC4=jd6@fQ>Y&ELDVRLW9=HUQ0gE@7I;b$5f{DZY
z50`-Yi=h9Z8er;R;RlNcT<H#~4Nk$_0ZYdi?t!bs&VYp<%w8A`^FMZ74Dj>wVCvEB
zhl%6T53?6N{9)p-@W7=XmpIHF=;q>54-<#k3v)NI`3|NI=3ZF5!O8(#=?`WvOg+qA
zSbl)<3HbwNK1@A~CKP`#`(gfuxgSQu+z(TSPNUlk3wM})n7`2V!T2zFm_C>|j1QBC
zi4&q>?uGdu#wVl?CJzgDSosALCq%>Sf!PZS7g)Rz@&`;F7H;S?%zRw#fY}GLALf2o
zy2PaqCJs{v^FPcS7)>Z0!Q|274W=LFKA3&DXqY=-G)zCNe8Z&=*3Ln92TVUKJkj;T
z?19O{)Wc|4IN}O_n0{FN!Q2Vc4|5l~y}0<W^a6_qT<XxpVeWv{BQSTMtHa5M)>kn9
z!rV`&9)X1mEc{^VVetSn2bX(b{)L4f%zZHb!_0$;qtod2!}P(z0TwRk`p^N)9+-P!
z?m}0Gix0C8=5JU&z~ygrb71zt^ux+yn7h!`qw`_@M2|mW^BpW4(e=Z^1167dFD!k-
z)WK+2Jiz$4!T}cVu<(PK2lF>fKg@sV_QU)KqhaoZ>BD6&x;ZdD%-yhXfQb{L(d~t~
z2j(A`JT7x!@-TP9?1$wmSbD&vA0`e{4>JcAzA$-0G%Ov$%pulZn15mVVD`iKgzSZ>
zgVC^fgP8*h9}IJ#<rXacz}x{-2Mb4B;Q;d=EWg6^!O|PdT$nnTJdB2^gQZuPdYC^5
zx*uu~%zrTTFdAka%pRz6oD?knVE#c54_q2y_QS#nmJeVwA^TzaVeW>}u<*xaFI*fN
zkLck>w0@{TFneJ%tUQHE<D_8zL$@CmuQ)YAC1Cnt_QGgf=?1C|PT>kaV&e~1&Y-)W
zSo>k_hou{s`w96UT_4PTSUQ541M?r;!%zlH9ZVlg9W4LD#G%SDD46{)eJ};EbOSRV
zCQgWk*-L7^gP9A<FX;Bd%!R2#r(ynpg%?aeEIwfQ8eJbcALeeDIWYI2tHa<!^B+t<
z%sm+Da0tNM3k!D~%5Vz9^bwujVJ4xu8zxLDjh@b7`37EoK<z;{7Z$HD_ru}~mTqC;
z36n?H597o1!PP<SgT)`r9GH4^8kYWH=D^|~#wQe?F#p2rf$4|Qu<`}nURZp=)WPh7
zxf>>qt`D7$9?r1vgNFyY1{{2tIWT)+?jhuVbmL&*0dog>`aoBYix1OJDF4FZ4;F5)
z`~>p{%ssg5LH9RIAIuzF`e5M#(+>+Tn0}ahaOsE1!_>p{!~6{s$E6?UZ@4?5=^mz!
zP`II+3o{3%AC^DS)#2i!r)QWsFmYV^(8Xc?g5@VzIH9Y9@nPy=?uY4v*$)$ksYj<_
z@dr~6%QrA_boJ<bSUP~EGnjsud4%EtmOo(e4&%ep10nse@&)D&nEPPi0F#G_<Dzl7
zALcGx`e5-3^Dm5s*-t1OVetpEpOAgH+yS$PV0hs$0G`gE`3Q$H3}IM4fyEch|8Vm#
zbU*}P?u4m_@nP{#C>&tn4znL(Dgy&7{SmSsrXEJa%!9cHW)6C~f~iNRVd`N1fVrPg
z{zEqh#)s*HxdSGSPQ&blr5~6+m_J~AbbT;By1!xWhWP_#K1@A~hKa+%8Kw_rFHAp7
zJvt3b_b~Oac!$w2ahN^m`r&+NdWETj<vW<W;OYo4VESP8!txUVZE$Jy^ba!!7QS$W
zgcwl&!1NP}KlFHqxdRp-gzSf@gV_g5Z-n%rdjsYUn0c`HKv##(M^6W^bOSR7CXcQU
zW<E?EEWTjwhKZx=gYjYZ!2As>M_}m&mL6c~5vC7Dqw9y6gHFTL!SrF!(D;Vg57SR<
zy#+H57XC1I!^DZDVd00%zc|f-x*ujQEM9P`N0WesH!R%|s~@Hg7SFKoLo*&H7nV+7
z`30v!91^hbg1H+;!{QHaE;M}5je)rrrXHpr7LPC*7Cz|uVSHG)qo*@s{RhimFneI(
z2{RWK&M^DXX_!38=>}>LEF56=LWPN@VD`e?Pqap;LRdV(!VeZ6#O6Pkepo!hXqbOs
z{)1YFLBaIHXjr_%^uf{vhDN9W%-t~m!{Qw#4s!=i|3K3x%pREgVSJc-pw?kfFneJ6
z0Yd=+0hoT6`w1w=kcRmW7G4+%a0x*D4f8K79ph4tE)EN4n0{D24a+w$f1&Gx@zM3e
z`~fourXQBRVftV+ES<vaM~`1vd=Ls(m^zrdVdlZ&pV)ALse{=EGZ*GwLNrVt%zjw-
z5Yh(=Cs_EwXhQbG%!k>B9-f5K6D(d}`4;95n0c`9gvBSiKVbI3+zZnOqhaO}%75tQ
z!t}$;N0%qWhq)U)JrU9ei+@<S!_p7TJeWMXIWTdUe_`PT3xAk6x;_{m77s9g!{PxJ
zuCR0kQxBtI_QLeR)Wc|)IH7QanGdrUrXJ>Rn0>_3uyhGCk63*$b+GV<g(FOySQ@5}
z)N}`n2Uxtr%z=e7F8g8rg@rddjY}U)9A+=NK7!!~E&tKu8=l^v@-TbQX;}J1w-@Gb
zboJ<bSh%3u4~su^^|<&jf5O}WOOG(~ap^-Bhs8H6{$PA`b?AJUI+*?FG%SA5^}+bC
z@P>s0%s()5Vd~Inm^{orSbU(X!^MaB6J{@5KY@6MnFot!nEmK9%v?h80P_dTJedDs
z=E2<svlxwrnGY*>(8CiZkERXIg{3!`eK2>!%!4Z=z<{|MW<Rm|Vg82Miykk8>LHkV
zm^m<K!py@JelYjJ+zZnOqhaY57Cta>7!9)z=5LrdjE0$uOFztCuyBXD6Q&*}4l5sF
z=AzTEdIm<r^uhd3DE?vcuyBN#19K0$eT4Y1aDn+B7S4q9!R&{XUoe`G|6%H2_QGgb
zIN-Dwn$KbC&}mrs5vw2O4p{ub(hpAkI3!^1hna&z8BSrCy)bidD#RfHOV32xj~0%w
zaD&l=+yS!(7XC1Ou=D_vM|UnPUBT4D?180cboJ<bm_K3qVd))4!}2G(J{TXCE@AFQ
zr(xz2YYuun05cEfPnf;9XqdlY`4^TBaH)rjqs1RA-@?p;tHZ&7g(J+pFd8O~LmLBn
z_`%!_3pbcKINbqtH!R=5)WQ4>GY6Ob=;AQ_FnzG}gsu)3AEq9rA7&md^)PW*yu<tp
zOYboAV0@T*bQ%`UF#Rz5Vc|(A{h{k8HU44g6P90L@d3|A=njVRVe&Bd!t8~a1M?45
zIR*uDH{73SDq!Xi@;}U8n7d)&4RaUFe`v;IaAE3U?uMCzp%6^~Js-f*FWg_ybdIJC
z&V{)f<`0+zEd9V}m^cOvH6IpUF!#e~LjHioBh0-peK3Dv*asDW>4T|9rwO?m7EZ8m
zhouLYIWY5~)?rZa{0a3xEc{^pz|eq80IDBmFDyUfQjRVT3tyOim^>`}VDd0=bQ%^;
zF#Rxp!1x5+19d+v9EsKkbqFk+Vd)2EKg|75WjHBVc*E>Pr(x!!8vyec%syB?hs7%_
zU%<i#mwuQ!ba|LQ7>zCua~I6L=<=}iNvwXDc`)@ba|x9{Fm>oOEI!cPh0cfB3yXhP
zd=Ro1W)4gpEF59!6&4RLb+~9)IHB7Qa|bScu=s}g8)gp7UYP%3{=+Z_n(krxVg7)r
zgXx3C2TUAB!_qg*9+)_chNVwj`qAA9(+8tr;R!3(VB+XBEZkt~VBrQ6huMR!55|Z2
z6Q&+U!^C0g2+^?chlMB1{e<j?nG3TQrXR+K#WPGDIt?=)Rt~`Q!@>bwJvtwzA7&p+
zKdyL1*AL^v?1SmY74I<h=rp?fVd)E99XcQ8esud`;^^ve@nP<Tg#)SjVfh~29fbS=
z(+3MTSh&OZFn8f{H$0s}^E1rdF#T|O90tJhJ<MJh4bxA^esuLPeXwvO<bRkvjE1EH
zSowj&*_gsm|HHx`QvrSvSouN7zv!mG!W-sp7$037E<P;&Vc|urewaF#e@PB6sDohf
z1xp7|Vfgv%Fg`jBvmd4(W<SgyFd7!Gg!IGw0ZXqi_rTH@x_%fR7Je`qCJu{FLi%Cq
zVdXW<KQR5c%3qlOVg7^J1Je)l4=(+%bOuw8i-v^*%$+bA7T)OYgt-Go!_*U^Vg7)*
z1Li(L`e5>~@POq5m^pCw!4wjpVdlWX4_2?@(gsrxi$7SpgV8X5;nEKaN0@tI;YLUw
zEM8&i(e>l94<-&vuP}Y+;f5>TVftY-OdZU<F!NyYglL$4Sa`wQMMxh^9v1E}_rt^q
z(Xeob>4U{1A$>4;n0}alVeWvXTNob}E--!QG|b;Hb6_+~92P$4`e1yR`7re`|G?4(
ztUQCMN2g)#gxL$DVd)=TJvtwz9-W4T1G;(`ALcG}eK3Cz@;5B~qw6PRFU)=z4bxAw
zy)cbX8dg5Q%z^m}Dov1rxtCn~VHUvBBP{-5?t#&`%0ZYum^xUzqo)g)KVkO4X=u9#
zJ-%S!0P`1&57!4{K=s4i2TQjw_rUxEQx2!0`e5e6)T7hr?t<xuxd&!1OdZUhFdC+w
z5DhCoVD`h(Cn0?>d04pza}P|MXc`(Gu=pd^ewe#q;S38;Sh^r&KP<jr`e1xm`oX0i
zR&K$<3l<+Ra|qcFvj?UgmXBcO!Dw9WfSC_dhfc%HBN&d*a7Q-}m%nj}L(PH5A5MkX
zC1CD_rDN=>u!utKh4~+54i=SIg<$Cw77wuWgjF#XL0CMJn%`hX!NLoce_-Oo(lC2r
z?uUf~jE0$y%U`f`3sVnM2NQ>x3riok+y%=IF#BNoVD7`E4`wbb9$@Jm#>b@(mfvCS
zhS`Ts<I)fFAI#mbd;-%Ca~CfC=;E++ORj#Hc`$n^H3#M|So(ySkBf$>M-Nv*@c{ET
zOdrfXSbBz~Cv<yZ;;{Hek4Km}F!OQghuI6$52IoF(e=UkXypb>AB-lp9D%7vr(x*{
zZXX&0W<I(;SpGp%j=_b6FS>q0@dYyo7XIkr2jj!!Vd`<w=>8xW?q~+V`~gev#OjBo
z3z$BbJK*65jbAkDFu1UE4~s8Ydc;tOLjV>|Fnut0z|6y;4K55*j~<UOf5FNrxCS%^
z%zn5!sJ~(12rEy}v>~`q^)UUg_=o97D8$Z!g&#~GEd9aUg<ThJQJ8r!{}ZY|U?#!B
z5f%?H{jl@}Q-@B&!UyJln0>JLz@;CS{$T20=?)fdF#BQQif%6~d|>W{xgRDDGmqGG
z1T!DzE|`B{;>6Oh@PLH_vHD=@VDXR3-!OGB8W!KMaD&n4;Q>>RPQ%;@iw77D3x9O=
z=zLiC!}P&unEmMLVSJeXVCvCnm^@59It_~_bbT=YpsUBlhs86xIWY5a>4S;m(hrj-
zNJGmHn7d)|M^GP{0$4tSrF)ovVCfM}Ih+f#7p5L&KP>)X=@G60jRA{icz8km12Yfi
zE;Ma$E-b&o%s~%Nn1A3JU<{}|=;~qi!u$a<AEpgP!@>oo4#tP+hx-SnfdCE5hcNdL
zP>vxD3ul-+FcjbrfVmTv4{<2NDGalhTzA9L38DOo;Q(m-!SuuAam5!*9!A6LgXxE*
z6IlGg{R6cJrVmEL!VRV#7Je{sSh|I!512l58fFhH-eCG+=^5rObbT;B%pWjyF!#Xt
zFn7Szqtod6VfxV3q4Qzs6=okg4U@+eAGp#DEIeT5;i`XO;RI6$(+7)BnEmMPM(4xA
z2i+WWb-4Jj@(*S|Ec{^R;?f5bhv|o<9~d9z512Y!G%WsL_QTA9g%2)$=;E++3QKqB
z^0@f0bPEeVn7uIbVD3ZL4@(y?eX#g~*$)dxbbT;BEd8K|6D%BH;fO2!z~o{2VeW;w
z2S&r}f!PbA(ftim4>J#@59UvpK3p`+zp!w?r4E-k%wAmbxWr-pfY}dAKe*Jx#9{Fa
z^Eb@DFg{EjE*hpEmQP^*hQ$Leedyw_@P)Y_mVR)>JGy?DI+(lBX<X?S!yGgLSUAGn
zgC>v8h1r9iE@AFNR}bUE!VTtbSop!rgQ+7#!_>pv4U12h`MC7M<YD%s(=d5l`eF8=
z>x0>kOCQX===xxMm^?21=;;QgkC44E^I+z~@*PY+EI+~25u#!C!SoZGUeL`&&qwI;
zxcKPd0J9%P!{l-4hox6oxeL<=^AAq_&~_C}9V|Ry=E2ehE_-4Ag@r#X++pPa%v_ke
z(9MCBKQMJL8fG3WUI^tsm^m=_!u6qBNPtgnzJ(bJ(+}elO+(`W7Va?r<BT_`!7zWr
z!VN~l!W%|Im19t_asj3f=6;yH=zMeoVDS&L2WB2j9ZViw93~HoS6F(1i$fVO{jhL>
z3S&?(cf!nrDS(N?%z=rc)3EdcQxBtI;R4GS==#w4Fn7ZA!SVyTI&?m|KA3)3xT33v
z@nP<QsfVQ-nEPS!F!ks(dO1NzJ-U9FdYJvN{0H+lEFGciL+7Kr17;rF|Iqw_t`D6L
zvkw;Euyh3r4|MhDe3-jo_QKo&iyw6LFh0zDn0lDKF!NyQ&}o=^Vdlg1!)REzpzDM2
z(e<Ib8|E&UdUP6QKdc;q(J=dA`2t-ZE<VhDnEzqs<5CY7hsH0=UKkB?Kg?Zl^*9)?
zat4+TVdlX2IJ7aq$_?~%1Jh3^9%1Ig)WgC7<{q5(qDjE)ftiOUO^^%A|FCpHPy<u}
z%-=Bmu<(FN6Qp4NhPj`h2B-p9yu$Ru+yM(W7!6g9Nui}1Sh&N|EsRenJYo3^rXJlL
zuyBF77t<jS5tw^n_QA}9rDI~l72O<S^AjxIVfMoO1#>?x`(f^dg&V9~hw<U=hj<fC
z!rTdqKU_4SbOCc8Oh3&1Fg~GhhxrSp4p#2K%!NCcNCwPbFq%khaAk!23s;E7fY}F&
z4>W0<T$q1h=>!&UI5nV2z~URGA7&n!GMrpkx`U-RoC<MBz~URGALbr-I70I|4($xE
z@Pefmn0c^t3*+O`4^s#8Kg@nue89|wr89JMVBrOGKTJK$URZp<)S>H#nG16VjE0$q
zQy<JAX!yX=JIsC<4KoL(9Gyn@CrlqKJYe|>T_21OGasf8M#IttOdUE6GZ)?6uy8|H
zkHLrf8|F@!y|D1cP>(|ZJ^hfX9~M6_eX#JvVI77rx_(%Az|sLB^KgYfEIbH>CoCRd
z?tsy-a3wY#Vd`M^!R&|m2gBpg_=BlKr(ybF?nBoP6NmX9mXBce!^;nJjX3zQaE9r}
zp$w-mEFHnj!Kn~U0;Uh$|LA;}|6tl-_Modnk2hF(2Ga+l(e=alFnK~WES<pO2^Rl^
z^r6ec;tA#tczmKOB*2H|KbZdsD94b7`5)#ESo*?Hh$aB@H%uQa{lLNpO*u|3%>6L^
zI2EEvz}yLo2UvK*$`3T<a4yV!Fm<qShlK;7aD>@|t`24%tQ`%v5RC!T2QvqjzG2}A
z3lErk(6lqa)WO0FM#IEm;enq1Vg826!_>oQnEPSwLAMvihlM+ghS`s9A51+V8kUaW
z;Q`eE6NiNtR2WXd!Wrftn7d)(Fnut8!!<$~FnzG_hN**z;|dR$KVjhpb0>Ov2Gb8S
z7iulM-wo4`PQ%;-SC4}MOCK<MVD7-79H%fW{$S?eRER?Y7GALQfI}HhVOaiy#TU#x
zSUlp=h#n8HaDeHDnTJb1EM8zVOdTv9VdlZaF=%K#4s!=A{le^pxdUAvOg(zMqw{ga
z2Q0i`?t_IB%wCu{y1lsg=<Xpk9%11Ha|bSa(8XcuVc`U$arqmj9~REA@Phdloexuw
zPQ&6GW<N|Grk~V&1XB-lKg>KrG|U|^d(quPNI%S8SpJ2X2Ma$KAD20>aD&k>b71};
zln!9_!`u(kPe>oSdtm7qW-qM#BjkQqx<gO*Fmqt$qT2`K!_>jT3zn{6=E3R{n0j;?
zrVd8K%p>G(m^>`JVBrU|AKe~wK1>}<A1u6J=Ao;{;6uwTn0lDIVdlZg0}Op=0x*BT
z?1z~{DBNJi!1TfFh0%oK4HjQ8^)P#3@c<Kt*@I5Q{0FlimR@1u0m~2Q`f%}K?uEGn
z7B0Bd!^NTT0CO*?`5!&~!|a9m59TkpeP|4ry|8eDxgRDDGY3r@oC^y-n0lCgSp2{h
zqA_6ZgoPVSKa3{iZuImIi!YddLhgpyj~?E{>PPbq1I!$l|Iy_M@nPW)3r|AoVe+u@
z7p5QPFPQ%c#RGbJhovW&KM2JGdbq>*g!IAO4~u`8`(fb^qhbDmsfW?%@eflEGmnt{
zF#BLME_-11q0_K%hlLX?ywTO+;=|kr(+_h$%wM?lp^L-Z4f8k5JYvgVm^xTGfa^!M
z2nQb)4lwtV8V)e^FneL<z|tWOYvIDMd<V-nFdAkKTp^JRn0sL1K}Z`c{9*Q@`vVp}
zxXgjsgRT!2pRoABr5~1#VBr8W50`p$ahUsI`e6P?SBHxaORvPn7cO&P?tu9l<{nu7
z#2F6I`V*!e77s9g!1TlT==P)QgUQ3pf$4|24<-*Y50=hg>R>cX92Pz>eJ~nk9xU8o
z=>!&!Fmqt)iKSuT2cwDA2U7=g521PkrXHP!>4&9Dba@ybCJ*yBEWFTZT=5PIADDU=
z9~N&g^I`U((=dIo`U+j15Fe%wW-lT2=<=|10dof-_oC~E@nQZY*Bn?nhWP`QA7JL=
zq@no^XSsz_BX$W`dV{4Om?~H}!T9JjEZku3faM>UK3KTG@*BE-n15m62vY|Whna`2
z4`x2hURXNC5J%St<-@`omj0o_3@~w6`hxinCJ&=w>R|pur(yEA^rMG2Og+rsF#o{p
zBSgc(2^Nlo)WhUq=?~^#m_G=WUoiK>)WgyZEIeW2F!N#d!DyKIaQ{Lj(BmJMeptA|
z?17mBOAjzUOdZsC3<?%rFmqruEIctZpb5a@4W=FzE--h%(j%HSI2RUPu<(bO19Lx2
z9<Bk60rMYBJ&cCwgM}xWHaHg+zA*Q~?1wAB!GM_yvk&HeSop!yFSMLOHwI=8EZxB5
zVSJc*FneI~FdF7=SUQ4*Gt51(aDk~ur(yCi_rm;-%RY4dF#o~y!_ooF9T@7-1kmk=
z#TU#Rm^_*`OfEEhVd)qaZba*cng|PbSo($OhlxX#VNkGmgt;3QE--h%+=ZbL>TX!L
z!@><_FDzVP?m^cNvlpfgmJVR?4-<#+3F(KahtV+e38e>EI)ud^F8k2!N9V)B3l<LO
z@`U&>{e;xvl85;d7GJP%fbj|GgQXLg|6%C?<}R4|FmqshSp38E!NL<}Ka7T@3z&Xb
zI)llh(=c;k_QUm|F<|iwOFuCCVeZG3{?Lqpb7AUX`r#5pGGOi?6z;f;gZY=}bOg<Z
zFnzFa!w7F27Q)oSXjnYL_&Bt|=U-vr1hWs8E@19~iKCkXizirkq07U>VKlltEZ$-H
z7F`||{;>E&mxs9%=3kgPn0w*=hlVe@ei$F-4p{nyg$G<eOd$ap<`0;DSojdo1|J85
z`2%Jz%s;sF!~73Z57Q5G2TUB7e)Rkc3pbcJEc|imhf2WG3Cw<&|6$<@Rfdy-xfd4x
z#M%q<KTIDiykYub<snQRE(gHE3GRQWLRh+m(NJLw3Z@Sh4lwgzd}6}^-5i*|VCLcS
z2TUJKJ<R{;;fmpKr~oX!VDSfYFU($;IZ)*Y3YxEA_Tr*p=?S3`%7W^Lg%ix(Fn16N
zcc>{C6wKX(@(;R3^l-!#-st*Ze3*V%coM50mTqC`73O|eJiyGyprPRhQxCHj7LG7+
zT<(B{FS<Hdy#<R;T;T{)2eTIzZ!q&<@)+)h3c%b2vj;{K(g)Sf01G#medyr_GasfN
zorcN7(jP4TVBro6M|6GYe3(06;XtZCVD_QApIG~0{)f>p_rT19@zLE4^Dj&tMm$5)
z7q0vdb2ls<;W8hWJS?7Z$-~@@OCIK4T=Fn+m^yU#p~pY0oPvqN(lfd~bUw^{SiHm1
z0W5sc)uZ!a{)Fj+#Rs}NoP20J!R&|8#M%!t7iK?9Kg?e+c|tTyAB-l}epq<J(l1Ou
zEItU?3sVQvPi(xw%!P$N%wAH%9~Mq9{jhL^`3q(b1`Uljn7d*6VdW8qdNcu8`i8j|
z#)p}MrW}I{iyxRfVfmTV@Pio)D<@#?fQb`J!@>a;p2X^dxf`aRSbZ>cu=s+7BdlD4
zy8{|eF!eAR7H+Wc#zo^Q$6@+m;SJLdi+`9r%szA)CJ!?o=3baNFnM%+xcKPi;8KS!
z4$}vVe{^|5d{}yc#UIT5u=pU=AF%QO7LKs=fzut(_=S}Nu=t0$2bNE8>4&8sba%tz
z1D8H@ahQ8yG`c)4KFl7N{V;#vQV$b{g)7Ycu<(Y-6QW_^1hW?&PSEfqq#u^<Vg7}s
z0~nu>epvj#>__Lr%*PcTFmYJ=g@psmJc8*4&4IA+gV~EFjmd?E6HGnKUUVAfFIc*U
z83YS2n0i>a!Ng(ygT*sUAB;v%H!yeLQV-LIPQ&bjxdX;WS4W5sD?ec75K@mW4~s`q
z^~2IF%>S@(L^mIU4~;*VeJ~o9FEG^O5P*d@Ed9a!fkQclFiamToxuEqp%6^~W-rX$
zu<%DyhLa0RKQQ}Y;RkaEA@gAA1ZF=heZ$-jGZ$tLx<g>%F#WLf4pRq<M?&!db0^IG
zFn7T0C!`;y9%diRJ+Sfs7S1qtq0=ybz`_ltA7(!x{V;oA?uVrxm^m=>(e1^>hpC6@
zhxreedUSD^{V;c-%cJvQ<t)touyBBdCn5jC^uxjnM#J=@rwerZVSJcASo(vB6QW`E
z!_pBU_2}|2`(XZu<qLFm=zN&JVDSW_Vg5i@kIsk14~&Mz8?pX?xf5m|EZ$(|pxcLw
z4+}q-`(ft5(jhK=Fmd#7Ko3ts?t$3@vlnI_EIwf21G5jEhNVxKei#i4A42&DW<Sgx
zn7uH6pxcMehlLwVAIyG2{)YJzrXOZ5%p4dE6Gx|E{)V|5W)94L7)`7{(9MVCUs(D;
zmnX!BmD@0LaH)r>gM|kyzF^@8^B*Dou<(L~1B`~5Pe?yZJ&cB@A6y#I+8;3WFn7bu
z!4>{6^)Pj?_=H&pGY3Y)#9=f{99Q_l%*Q1UD`#-Y!}P<{!R&?6FnL(G!^B}U%zZF*
zFmX7IBm7|TMQr+onGXv`m_C>|+&&x(Sh|CS1I$0T;tkyxSpI;O!!UQi{Ex047awLn
zOh2*ihnWWpH<*4HA0|(ThPjtq{pju|HvC}jf`vQGep2<r;vJ@+P`Lp!AEpl$f3W%h
z<}O?`Og%acGY^+Om^dtcVfh{wUoiK>%0ZZVLNt2(5vw0&Kg=J5!VTRWgzSZxkFF1v
z?_mCgg&QnfVCKTa(e=aP52hcThUvo<pD=f!r(0M$Bouxyb7A2Ii$9otn7Qcg#>I#E
z2j*^Axq?eQTpUL^3YRCqz!eU#bO4hlU<_Ou7B0m40~T&D17PU_7OsTC0av`k(h;%l
zhpC6T8|Dv~yKvF4c!RkE7XQTB3ri<3{e;pT%pEZI!Sunx36^hQ;RsWYPQ$_%rXS{R
z7#}7N^B=l?Sa`wI!Sumsm^iWifw>!|AD6x8?tt-O=@o7-OaccDiyxRjVD_TZgxWta
z^)P#3=E33zW)HeE(EUqnIKj<9%lEK!OR71raE9q8lx|@5!~6x)4@<|y>W767EZoSo
zAC_)m_QTvqEDg&yF#Czs2U7>r2cu!)glJf}!O{=R{jl(Zg)c61VC5!EKQ0;;&gkaA
z(kV<IEF56=!u$hMhpr!{9;P2%9wrX+54t=oox;??%!B1OSa@LQLlc0R4|6XpJ;1^j
zO*sY^W-rVgFdA35!3;nTC-m|M#)qjVmWKHoSGc2_1M??LJuE-L%pv3tn0lDMVKgir
z2<eB#Bf9<Q?!?7M4@a0exYVPI!_>q40W%L>9YH>{JcXqrg8qgX2-OEmhp=#ig+ELg
zIt`0An0+w!!}P=ag{}|AhsneA!~6|PcQE(B)T7fd_2}ln;vZc-AwGI}1oJ=49zyzI
z?uCT|%wI5ZLi%Cp5T+j%4utf<+y{$qn7d)+2qFD2dtmBe?uLaU%zO-Upy>wYUYI;A
z{$cLG<$suaVfMn*!_pOu4>K1R4KoK8e=v10{kZIhsl!FX!T}a9xYWVShq(i$4i<he
z|G>m?(J*sh;Ry>DT<YQC(0G8Uhq)V;FW~BMFktZovlqsPr3*sl!2F3Toxs|O=+?u+
z1?Fyac^Ds;{jl(Y#WPGjjE1=fmws4$;nEKa2bg)dX!P)g)yKr@hxr2*{xBNmeq8p#
z#9``S@rCXlm^wl<Odl*hafKT$b71iZ(@(7bVdlZ~!D#gS3{!_eL(@M@AB-keKP(-<
z^ucJDIEH;_0x)$jeXwwZ#S5BpOfJ;@Fnur@mOn5xKty2j==PH8Z&-SR(J=EMwh&Lk
z?1h;_yiSNpSo(pbV~8kW5<UE3;X+6!EPi0_B{jXm$~9Pc!SV}?hM5O*AG&*B`5vYp
zorbv!r+=X71{Pj0^)P#3{zJDH#)rihto(zy8<zfH=D^ewOT+ZR+(oQDnEfz+5UO|4
z&4rb_uyBO=gHSl2n**~KmL6g5!le!tzi@k?@d681n7h#R!_>pn!Q2g_Vd03b4;LTi
zURb)sr4C&jmJVR{!u*e}4i_Khehhn|;ebmYEL_mz2^OB{;^^kU(g`e_VEGA_?qKf1
z$Ujhb!TgJhhJ`1t@PL_%ZZFJT=<X%Nhxr#~9wGHGcftG#b1%AoaG3`SCzyJeeptM~
z^uf|8x_e;i$@MSHT$sOM{y?{f5Fb7L!s3OHK6H7Qe_;Mcmq+Kr?180MSU8fZA7(Es
z9MJ6{mJf?hn0YXF5z-Iy2h9C2{V;Jt`eFGGrXQ9r3F(8SFPJ_U4YQw6IHK!^`5#tZ
z!^|V(9$2`;^ugQ>6NjZQm^eBOEB9de0Ol^3ei$EJAB+#n2k7Al^AAiOrXHP!xf|wR
z7!A{pt{$Bab05qcSop*IgRY(sAEqB>9wGIxbOy`+F!#gtL(6Mi=D^|yrVpKl#Sbq1
zFmafFiM1D|59WTDy@cWmrVmEL;tA$|m^i64%-t}55bJN4I#_yz`2)ts<qlZ7fvJO)
zH|Xxbr5{}#EZku3CzStT>S6Z7(jBSc2-64iH;j+V-RR;l`(XJO<{m=shnoXU-!T0!
zcfi6Et_~LihkjVTz@-f)j&3i^epo!f)ZwCG{>5buq4F0NelUAsd{{id(g`m6Vd(%C
z-Y`DQJ+S<Rt{)b@FdAJRW-rV==<=|9kM4h%e_{6G(hoBaW)94~uyBCUFmafAbQ<Pg
zSa`wg!{sk@{V;dJ?1SZNn0YY&pzDK)qw9mIgM}Y1cfjHc=3baS7!A`8qhaDO8kW9s
z>4({iOFt~!VeW^S3!`EBVD5y8!)REz!{Q&N9_B8XILscHJ`5U~e_`Pb(+4vLLp{0x
zR6i`8z-U;!qAN$|!_>p-0T_)dT+sESmlH5|!~B7+9-R+!C(Iml`(fdXu8$BO7M=vd
zABRD(bcIVldbq*T8_YPE{|UMu8h<eV!@?crewcZ<XqY;fdtm7hM&nAquzU`)4;Kyd
z2Q0s%y8~t~Oh0-!psUBlhxr2*?zq(95+^m?p_>B>FSviv6`=89@d~pS77oO^AEqDX
zZ*s#AW<Si`FmqshSpGtDJ_F2tm^xVcgM|Z(CN}+$8t*W3VeW<5hwe^Xe3<)T{v+2M
znE9}9fSCsqCzghVAF2A$%R5rT5f=ZjbVRIsVd`N1CN|x|^ucIYxWn856DLH&%2k+t
z^zb7T4lsF`ewh1V;SUqX<t~^wOdTv9VC5W!I1UfN!X4&*SUQI3hw*V}$2f-urVnN=
z%suG(Vd)>{Z<s!G8YYjfALd_}dYFB%aDllS7S8DUVe&Bjuyh2AS9JBb_^^5i7Jjh!
z!=)aVILx0g8kag;;xK)%aKNPwT^#0LnEy$2KZd=~_5iv)IQdZZu=)q)4p{k$Qy)|U
z7VhZ!V0>KZ4;F8*_=kl9)L8hvx-fMxaTpB~huIGkhtV+i!_pZ{97e<BVc`c8htaTf
z3{wXahtV+mVeW_JCzw3UT$nnTIE;p=hq)i79u^O{%!TQP(dg#D(jTsT2h#_mVc`yQ
zC(M4BJT4lh4;Ig`aEAE@CXY)$Og(z|!t95IKQ8?+^I`b~W**EvFmYV^VdAjxgSi7{
zE-V~je3*J-X_)(A<s>eBFnO4In0sLAVd((I$3?^33G+8hAB={{W9WzGe^~m1#UCs@
zG1Q|8z`_Ye!@?hC9-4AE7nVL@>S5&svGEU64|6Zfei$D;9N`w@V8Fr+<{p@Ngv^1-
z!}P(z8RiaHy24>C11#Rr?SsV|EMMZ%53?T@-Y|VI|KQRG6GwM1Og}7L!qnlSVeUqc
zH<&rN{11zNm^zp{(Bl;rKDg|M*$XoVohB51xXguxC(NC=)Zr3`g$pkGVg7~36Eq*f
z?1QBzT;{{XVdlf!3-doLTwv;O(olP0>S5`YVEjY%!PLY2f$k1m_QKo^b1y6%!_*V>
zKh%C$IK%9Pr8k&3%sgB)%wAY|2Xhb1JY4$F#bN$|g$K<2FmZJC=zN%aVeW^SL#%$7
zdYHYi_(Zn{7ax`$Vfta|5SMzGI4t~N`e5k*CQpclxf7-zmj7Yy!KEMOK3IIg!U<Mh
zz{FwhLpKNAUYLGZx`*k9xd&Z8EPcS#q0_MV#HAnRE||Trc!!w-6Gyif7ayh`9*(#a
zqKU)w!NLO;e=u=0<!~;{{V??~cf)8{dW360W5CRZsYj<_;ftmX&LyND7M^eoXbeL7
zVdVjuHaM4%enR%c(l;!=NcBI=A+US^3ompU7Edts=rk;y!t95+2Uog<>4(`1i)R=g
zrk{}iVdkLQ19LyI`eF8>=UZa+!{Qm{e{>p_FW~M)XF%&Am_As%qASD2huIH{2VClK
ziIW`OXa>RD3o{4i4;Y_N`hkTztQ>&xVdlWhN3#~rMK>R&o>={GgP;tU{jhWi6~;-y
z@-fW6FdAk*tUkeM05tw#=@zCRmj2NFhszvT{KL`%E_LYQF!#g!ORRk`b+CAY`4{F6
zn7Qco!T2!!=>CPd14hHt!SoTLVc`ukmymv#JS?BW^uf#_WFM^jfa!y|8y0_r^uz3j
zse_pZ3r|>jhRMTdm^xVa!_0xvF#Uw$0o~s)eK2vDc`$p?X;^-N=|iVs=Ai3?xeKNq
z7H+Wg3-cF@M%ND$hv_FaykX|R!Vea2Fms5FcbGY_@PWA>W*$tQSQ_SUm^+Bo2Md2#
zzJs}gSpBea2WBt1;Rv%AW*#hj3AqF2ZkYWr8s;xT`q9$?we%Ak4zTnMvlm9Aw@YyO
z1Kt0mrXN_i!|a8bhs!>6ahN}0`e5e3(j&ThbUrM<z}!u2yur-D6@M`Iz|`TQafLt3
zA29!+>xboAm^yU-!rX-}4l@U4E-d_E=E3A)?!u)XCJxIlFnur@=6_f@2@{9Wu>6JY
zepvX!^uyHSqG9m|a|cWx%zRw>;NsBs3rrs@9l*i|JzU`WpbVHgm_K0pVSHFPLX~4s
zu<%D$57P&uF*HKm4bz8S&%)vdMx*P8g%8Z%=<=|5z@;A+?zr^B;sf1XaC^|)4>J#D
zFHV2M4S+CU=EK4fMiYuRh;9Z3m^)$N2;;-#anUe)(e=UjFnL`1VeW&4FD#!Bs~_E5
z^mGU_A6*?TJ}kV6^*_uUbaxY5Z@~1!$|+dBfrST*CS)GWy|8eD(J*mb;Q)&tn7uHX
zkbN+BqlY)lURXMYsl!FX+yzVTr0R#63$qWVA7(C0o)ArLyrYL7JiZ872$hGahq)V8
zj=<spM#IV}s5Ur-p6+1gz-X9xFn7W=qA_6phv~y*ADTA!`Y)J%Sh)jBcj)Q~@nPuz
zmLCYIhs#6DQJ6kr{SDI((+BfEIt@$bF!ks(EPP@5VDSPAM?&clW<Jc_=;jmR!}P)O
z0U`Cc<YD0tGY=N8gxn9)2a9)@{|TjEm^;wJ4JM9m9!wlY!@?bA56m4faaejJ<bGIr
z2h$I856u5C`v}o6f5YsBxeI1KA^kA>VC4eL9GHKI)emz&Ec|fkgP99+CrlsAUSh)y
zW)Cdgz|4V#2QK^3#bN0erXS`$bam)_m^)$qfQ2KvI$V5odx>^8wA>-rUYI>F|H8^4
zSp38IxWWM@4%3fL!{P;|4wwI7<qymsu=t0$1D8JZaEF-#EB9dL!1%b#f!T|0A1oeW
z=A!F|`3vT6SUkei!TgVIE=(TgE|`Awd`d_^di=rM0}B_JxiEDwdto%X{V@N+!VP9W
zOg%0d=08|C!f15=;L;DX4`x3ub-2V~_QCQ$E_LYQu=EWJCm2nvyJ6uCb2m&sEL_p;
zBgCgP9ANH%nM24vm^`fBf$4+!0~S8$=^CaFM#ItxOdl>9W)DmsES;k3CpP?G`4SfY
zF!#gU4YLOq4NJGMbOQ@Nn0s*PLl;N)2dVmD=E2;L9!@ZMTr@0y!@?crFPJzkeK2vD
z|6%&kX`JqX);}=yFmqtx57P(BCoubP(J=imnpk}>f5O}W^FJ<o(8Xcygz1OHC%QUZ
ze3*M-`iV`yuyO{b4@Se%0n8t`+=K3Kn0{D(h4Eqj!{vUMJuvmK_=E9b`2v@InEPPv
zhUtTa3rrqeKTI5EKTJO?Kf}TYT_2neO@A<ZVE%-e12Y${o=65P|H8~8s13~>F!vDD
z2UP$|=P-Z6!VeaYP-PetEc{?JOaaXOuyBBhqtmc-3Ue=v53?8M4s`Qj=D@^Z;RLf6
zW*$r&rXClK?r)fWm^*OkgNeiJh0!p7!_0xHBbJ7xe`4Ja3kR4!n12YSU#Np%=@b^;
zFdF6`nE6oUa0(_4(+?{TVD5&Q1500UjZg+GzG2}7t50G19p(?1Jy2~33YzX=?u6L~
zGmqH#gV_hu52IoJLRg5M1@i|?JuKc~G<IFMMPcDbvVLfI!1Tew1-DI58K^#3c*Fb;
zqhav^RgIH^*$bm#;es>%peDiOVetmj2bCsBp@$dDJc1gb3Sj<&`4^prr4Oib3<@R>
zqhaocg+I(ZV)G-+J+Sy7l#XHkg@rrJJ{TXv!8in9_M*F+ko_3OLDLh=9R&45)x+Wu
zR*u2a1I&LI_CN(-;SIA77H%+cs4|=sEZ@TPqtm#`FPMQa|G~_G(J*(w(gjRCAsQAB
zFn1902P}MG_Q3SP!jq7FFnO4JVfta|047g}hPeZl?qKE+azD(!uyBLLKTJQNd;<%A
zn0gouGl!77Ve&BhVc|f?UYI_ZI+(q%c!0|jV8GHjEZkuJA>@9TI+(q%bP6*EM#J2P
z?hKf{F#YKMhl%6z4=jDa)S=Tb_ruZ!y8SRdOdgkiVCo3bFn7ZIMMyomJk0-u!VO&?
zET6&r57Q5e4|Me~KFr^+^agV`OdO^TCk-t(VfMnp8`cgZWDd-IF#RwZ<_?&<aHSuZ
zI7}bR-!OY&;;`_+<qw!V%sv<&rXN;5;L;Bhhs7UEAIv>4cf!=6(=dI++Jmki7Vfb8
zfG$snkM3_m>S6LQ_rm-Oqha9(O9wFZ=rpWchUtUFJIp^YahN)E{jhL_se{olb+GV&
zg)dATord`zM#It%j1Map(DlLiuyBC6ALb5N{KL$HsYj<_@-X$Ve1WbG7a!ezSh(O)
zk1h_g7e>Rv3um~)41nf;SUACGSh&OZxXgjM8x~&Z=>=vE%snt`;WV24F!NyYF#Uw$
z59U5}_oDM*=HjAZ;Ro{%EFa)fk1h_YKVUSvJT5-We=vJtd|c{b;;?iFOSh!j3kz?U
zy|8o(%fB#rn0@FpEPi16Vet&(!@>{dPIUb+_rlcUqKOR;bo0>dBgBX4hxwn7dYC-S
zf3Wa^r6ZVmFm)I-H2uTW!{QM}!@>hSU%||QiNn;v@&S6h5Gqe#;S2LOx_+3sFnfum
zVfhc1F3|PC!VN~F%frG2<}P%37$4?dm_4v?f$4{d!_?!VVetoZH!NHU#Um_yVD5ma
zhq(ineK2vDewcmeG%R1j)Z?Pj^}+bK)WgJK=EKy(Xju5c)ZwCG{)Fj+(XjBqr4J?!
ziyxRi7!4DLsl!FX!W*U!M#JJ0mp+&{%pRC~VKmHNFm<?Sm_4xYf|&=)SGe@S#9{75
zPj|3#0OmiKdKe9JA51^Yeq8wprVmEL)WOV$nFA}gVd{vbVd)s=4r2Ae)WP(_!T}~u
zh=zqXtek-H3F(8$!~6^LKP*4M<O$NyavY`~=6;xe2<b-;e^|W3%ps&7rVeHfOg+p!
zu=EKNhtcTi2bLaS^00h>%O5cPF#BQpU^F59Fmqw<M9(iUahQ3yXjpi`(j6@Rak&fK
z99VqA%z@d9?jCe~Fg`4NVfMqq1wA}r>R~iY9_CJ%ei$E?o?+_IX_&n*_rlDBnFAAt
zxd&Z8%-=9|u=s-UVKmGhbp0?s%zT)8(c={sA29U<X|((VGY^+On7d)|1<TK{_=JTE
zF8wfl=;21lURb*drVmEL!U5(ET=v59D=eJRX;?adsY5phW)DmsA^Xtv!{Q%i56m2L
z;|t~<Sh%CxM~Dxz7v>H^>S6M*_=VXIqha#6XqY~jeXwwVrDt6FVB)a!fvz7W4pWDd
zhNg2^`i0pKGY_Xe91^f_htaU~i9<O;80rp~y`-iiSa`$q6KgLloMHN4@sDsAeikgh
z!Suu20W$|i!_qmrX|Q|`(~pZLRQ|#2g@rpTzrxZNy1nRpn7y$0hnWNOAG&%NA7(yG
zJ-UBk<`RkznEznvVfMoKg!IGQ153v+eZ;0Cbbr9~!_0%3kBf$dH%uQa9ASK1`e5QP
z|HHx&rXLosFm>oOdisUwgZT$tJvtwj{$TdP+zT@oT|FT_Og}6g5K<45hvg5Lewe#p
z@`PxZdYFD<{SPx2mfi@3H%vW@Mt3*NeptGJr3+l;F)sZub6_-~@Px$|%wAZ0;<6X!
zei#k&H_SeC8s<)zdKe88hpC6z3(K!Caaj6>>4VWQf8kOG6NkkwOdpJf`5UGV#z%J-
zOdO^UM#IEm`eES(i#ND`U>b1HFm*6<U^L7e9NOT*Fn7SrfzhyVge%0sfR%GF8fG32
z<ru=Sasx)g?1!Z%3=L2LSUAJ{iJtFZ`eES;)dr_v;;?i8mw+;0;<(};CJ&>b+TavS
z97e+>pbSFx!~BKIepooe$`2SHrXD5^wGJl*Qx8iwu<(b4FD{L+@P^p~qhapGr4J^K
zt{-MUEFHkq5u#!74AT#z3F(8$!_>oQn0YXHLNv_1F#WJ_fccM9{jl<okUp3^%wBXF
zmcC%>aM7^%hUtg-2bOLL`UBd2hUtURu=I$_UYIz{zp!)za}TUMfvLkqqq`4g9<lmi
z=EBlDsqTl_4+}pS4f6*qf5Gg*MZ>}ymVRLRVCLh}2NQ?+7gv1;Q-@B&%!m1#*m56k
z4mAE@{)YJjE>C~~QxDS*^EUx)aA}ykVd)1hL4W~EpD=q#^*6e^VdlVSSp4HM7hOLr
z-q76zQ-|&ySiHgPgT+5A{9xiR^U(Ff_^@;jEB9dfV0@T5Tr|u+n0lCgLh%7}7cBl@
z{)CwW4?kQM;}D1CADDSKlwk<N`~kBU7QPq?aR|W5TXg^6P>x#|nxA0iz|6&^0p?Cv
zc*4}f^b-mPn0i>e!Tb+PPcZXw*$-0((+{Iz@jysFEWTmtVfMq!f#nNa_M?l#;t{4F
z7B1-OaPiUILCF0ud02Xf*$Z<AE_-0&F#o~y5sELEK3KT|i)WaASa`zBg{#NGfW;qr
zxWmkY*@Htn156&KALbsS!yTI5VfMjjSUrx@UYG<9{jm6eDZ@phr+=9J#D*Wt-!OeJ
zJ}g{t*$WFFm_8UyZ2ZH@ZCH4dsvqVqnEzqsz|ub<_oM5F*^f@c(icoUIt|M|u>1lG
z7g#!mhbOv50(_W%2-y#_4`x3s-NF0;b0042VD`Y&qtk@!hlMjNy~6B=*+<BJn0}Z$
zVCe#;pAZeR4`x3h^|<6=?u5Am7OuF|!^C0wVCfDP-!O5QI$Sg?d|~#&(lado!f15;
zF!#aY3l{${dtu_Z@*~WhFneL<!_0x{hs6&}9!A5$9i|T5|1fcwJ7N0JX_$Xu?u5Aq
z=02Ez(DmWs!}P=QA1-xpacKDqb3f7gpa#I)4f8Lq_<}0OpkVgG^b-m%m;o?#Fn#DW
zEPi0>(P>ydgXx3iADB2S9AW-L*AL^v^rNeT`3q()Og%ac3vZZxu=s}YVfh1HADj=3
zKbU`E?t#&`(l5*&m_Ar~gV_g@huee3fQ1vx9+>}Od|3RTX~W4yHxI_gsR1ehb0^IG
zuyBV;6HUR=EzBH3@rKJ_n7avu1I*np^)NoM;Rw?Qb0^F`7!5NAmpjqLVd)U2AC?Z#
z)nV|V=?|t4M#IWc4E1ONFn_@G!Dw9R7EL<?%%3oQFn^${!^MZW6Q&<V<5G_<jvoH7
zd<%<DboJ<bSUAJ%h2=+db-4KG{)f2-mwI$@n0lD~Fq+u>3v)lr9kBcdGZ)=HTzr^)
zu<(P$3oiBO;xKz*?uX@PbalA+uzDEX9Ae`U-CUSEVE#gvhw)+VgyjR6J7D?=<pWrH
zfvJbl#D+i2f3SFj`5$I3%$>Msm_0D{u>1#e2h4n2`eE@63nx<bqpOFx17<EE`*FDo
zRuABk$0ZJnM?(71{R_)K=;4XW9=JHP{Ds9IEZm9BKQMQ|^ufXnmM-D;;9$V)f$4|Q
zFmW8(;KJzX9i|`VF1SJ*446A%`e5$Ip&UaPmR?}`3B?<_0Wg13OFzsUSh%3uhrx%&
zKYBiZr9%w$XaeZ&hld~3Tr}kvTv+(S$_<$NiH$#)fv|iE(@(7XVftYC2Bsh8FPJ>c
z9&{Q#-@x?4Xjr~P*9YUn%tu#Gtp8#03=2P)xx~`2^b3mzV)db`Bi9^Q_`uQ)sqTRJ
z8)go=y9n`N;SS5UFmqt>f=fRv++q4*=?6x`%)@C8G=ISSjh<d%`H@g~!0dyC1I#{{
zI4oXp`2!{nQxEeG%v~^l!qj2VP=CPehv|d414BJj045I$Cv^RU(m%{yF#WK04$OY2
zH8?3){)Xv?#XHO$I5nb4z~ToMZm@WR`43Gw1{bCd=5AO#z}x|o$Iu8BfQ1uGJvtxe
zPpER76fB*<`~eFWLiWQ9gxLer2NNem!`utA7iJD2eK2{LeXwwYyBC@+Vd~Inm_0Ci
zVeWv%AIv;-eK0=EURb!n#9`*a(i2P^M#JoZsl!F%vLEI@n7uIf!f2Rzu>1kDmk<qe
zFHAqoorLtk(myQy!f052B%~jfZej5O^9PJiNI$wfsrJM4!`y)@++pf)(XjA?*$?vv
zF7>eT9u|IZb<lJRGY1ymaCsO5O&u(}U^FZqaq5Sf1B+*veK36(?t-dEr(pWg^8?I(
z=<3n=F!!O`5Az4QdR%;%yJ0k}U4~0NOdMuE%>6L^#JV5mPMAIzO>Fvyr5l(&SUAAk
zM~H^S6HGs>Tp*+mCJ%Ez%wAYHz~U9Ajt~tCM_4{0*8ebbVfh7SKTMnu4Rb%K`e5#b
z=_9qggrzT-yJ7Bx@nP`-6Gx|E>R|T6`~izcm^jRx==#ys!}P&uQvCx<?=XAO?Zd@~
z`4blJu=GqQ{lNSMOLwqz3kz46IIeUAGau$(Sa`tP33CU`eK2tt4bzXSyoc$7nFG@Y
zqY3GU>4VWQeJ~mpKd^KF6Nk|-ahN)oIE;qH7c87$>R{mqk4I=d0n>+0!`uf`53?6$
zKDv5zJ}lm0?tsN3%p4dWT^}s`VDSgj597nkh4}}TUSQ_H+y_$+QwNhLmWH{LTIRsQ
z5#7D$?t#g}XjpoKxdT_ahnWM5H<<fj;SAFc3m=$zbQ)$aESzEbVCJE#$Hj-`JD5J0
zc`!aMeK2uYc)`@8hXc%=F!ks(%zhXRGmq5t0MiExM?&_Zy91X0VBrYkqpKstNB0LI
zcfiUoSbBiPJIoz~^uzKY%>OWbu=pV4UYI;A-eB=hDE-3p!DyKMFq&BXuyBUC8|ELF
zxwvRpxWM9@RQ>4o!t}$+F=E3HmQG>%VeW#3BP?8T`2*%(n0{FLfw=?bPF(t7;xK)%
z@Q2Yb_rTO)(9rq?rXJ>QSbD@zk0t;MCzw8TKALh2E-e0F?uXH^@PLI6hDN9WEPP<<
zVc`bj6YCFHdWG2sa~IM2q3(fEF#WLf0TYMQ&~S%^8!Vl`;tQr9RzAS>p);W7z|^7p
zALcJ~Z7@E}K3Kew>R*^UVfMn}17<!>8tQIXJQ1xAY9LHKOg~hZXbPqu-QTc$0Ha~$
zF}lI9bON&<M#JJ8-CT72Fg`5)(Zd}UuIS=0eJ~mpe=zsL+yT=MlZUBCr(yCidtm7Z
z#>eG<m^m>0FniHym^m=@xM+0u!^|Pqepq_MWgko&y}W_h3kwIBI$ShN9ZVn0URe6T
zr4J?!3m=$%SUAAs3DL0dhnWMTVd8}J!_q0b{iM1d=5Clduy7${Ke{~399X_UmnX!B
z=_6=AwETg^7tGx-8fFfz^al%HSa`$25oR8YPcR(O+yOI>kUwGaFneI`g^3fQVeW^y
z7v^tt8dgrCn+L0}(AC4-11kq%_Mq#BxfiA%S2&~VgYjYRg6W5qcQAQkX;?aenFA{i
zh}94CA1oY+bq7ozEPcSzJuLhP*$)dZn7!n>A7&3M-NDR*i4&sH-2vkh(g%}=`4<)s
zFmYJ;!1(AiEZkuJhVfzH52Mlb!T2zFxPPJf5Ec(G_uz5|disUA14hH-ap{M-1Lkg6
zIKXI_Jh3#)Kd^j9tUj1JSbl<q2h3eCd0aHie=vP8ci>WwE)MfAEZhmTS7G{L?uCUn
ztUQ9zu=s<iN2g)-!t|rlFnM%+Fh0zmFm*8Z!_0y4Vc`SQM~H^$Cp8{n@deXQY&nJ=
zjxhbOa3thjm_0D}!qPn~d<f}Bmxsk0EL_p$(fP3Og}DP}Kdd}MSC7tzse}0&M#IuG
z%v^MRFh0zFn15mM4l|e7^bB)1vEdH0A3ffQwGTbsVc`w)56paAG)zCtzp(Iv`x}==
z9O5u@VDXPb8Ky9_+=j&)so@7x57SR{{ROp`TzA9L3(Q_v{(*%bjK<|2m^@5BE*h3T
zFar)E0*h~$c`*OLXk6(DrVo~GaoG<`ClK2ZBzm~R;vGiA%){k=Sh&E{!Q2Z=hp_aF
zun{{8W)4g}%siMou<ODUg~cCCKa7T%kEsJy1m<p-J{XP5|ERhd7|_!b%s;r|52hbx
zK1@B#UKkCNhp9)WVg822CrmvoK49je>m$U6`3GhWA@wkMSa`$SPpJHc0rd1tsy>)M
zVfhGVE+HCb56r!=@WhpG(9MJS7iK>!Kf=sGSC7tzrE^&NgXxEb2fBI~9~RFrcf;Zz
zrXR+KsmGw9<tHqjVe&A4z`_M4PKbuNht%`}^C!&zq}mG$XPCPQxf3Q2b1!=M!NLs|
zj<EQJ=|iVs@dq;p<_<#oVE%^L1JjRA!_0%JhuIGcCzyIzxWm-L;uU8)hQ>cE++j3M
z^)QRkXjp!O*$*=Z-MwhqFu1Vrg82vLKMaL91km*p>kn9bqsJpGec-STE)4T8%-t|^
z;0g#ZVE%;ZhtV*15zx*6OFyu9hq;GXcfi68<_?&7uzW?xepvj&+zq2)?tqz#%N$rb
zf!PO3M=*1VjYn8|hna)RK6G(dJj2`#3lDU4xcIPehS9jx;S`7FLzsS8_`~9pkb7YM
zhUtTa8_Yk1(h<xaSh&Gxn0}afxZDqmUv&4w;*r?+LpLAhE?oA*#9`?NT|X@S;c9=u
z;vMErn7?7-F#n^Qi@}G+AI!ZleK2z|)I$Ye?uO}u(J*(z%)u3ZFmaeV7!6AYuy}#l
z1GN@T!R&+SBb3hI8leoBdYC_9?#5*wR2v2bQx9_o%wHG^2?)UKhouVw${Aqk5@sK%
z_QU)SvlpfxW-cN7(dA+34;G%Va6wmx!H1?lnEk~1A7&0L9gu1-%%3oOVfhA@-w3%M
zW-lzg!qmgu0rLk;9OhmaA7(Eu8Wu0Gc!KGN(J=SJ?17b|Fnut4VCo6cu<{S)9$faK
zn**~4W<SjTFn8cG2c{1eelUAs;R@r!{D*EYOdO^T7JsmE111g&Cv^QVahN+{?uWSt
z<_~mz7<_2@MRzyM91Qh11Yqi6?uOAY|KQLD7lx@vr(xv-Tp<nyy8mJRfyEOJZ45B?
z!NLn>KP-H4>4W7nn0j=Ypu3^r4vRmSei$ESK2H0g5-|6p>nCVGR6oq$u>6B=9-;IP
zvlm?*Oq`HAVD`e|4@Sen0o`1vv*8p>9_A02y)b{k6%t^;@*m7zQuV{qKa3`n?qJS=
zg%8Z##Kt?idtv1X%v?h07Z%R2aD%xQ=5LsL(9MCVgUQ46!}1$U97e<92j(sq4U2b}
zJ{TXS55|Y7Cq$#Wo7ixK`3F}2!{Q$nKDgWua}Uh@F#Ry|VESS1f~iNRVd({y4q)nG
z;eZ~m==x#l02W>__rUxClSkJ_hz~Od<{otQFn7V+jV=$fA3goR)WO0DW)IB$F!#dz
zPbmCh=E1@drVr*mm^>jGJslI9eqiRp!U<+C%siMsVd~InSh|6w7nnTEKe)mV=005h
zg}DPp!_0-zFmqw{z{FuROdqcJh4~kkewcq?@d+~*okov;n7Od<LsyT^hlMZ9KA1jS
z?nc)S<HOP)%s;U3fytxOuyhHt4;BwF|G?5ex;_{mW*<x+EWBa<fQiG@qth_;F#BQd
zh4EqO3SA#M9~Mq9eK31r=Ao;{#YcBH%p6$!;L-;ZN4FoQpHTXN=|{I8#)rw{qG9I4
z)WhNr<{n)7VB)ZNM%PEI`(f%}@de|<(jhFJ!R$k)VetgBAKf3Y_(j)8h!0C&Fq)8h
zba`0%#$`V&pTqnKiwBrFu<%D0hsncenE5bsVBrVTPcZ#K+u<;O!pwu2Pl$&31C|b8
z_QT3&T>8=V!Qu_3pOC*{@eWfD3vU>oSbw9t8y1eZ+>Id)4L_K@uyhDZ&xF!1%%8CM
zf|(C92PTfoKQM8adRRCR8;>ycuyBBx2XhA@8a>{K)eln#3pW@I6DMRZOdghQVfta|
z0wzx^4GU*jco3@(rVgeLW)4i85DjxLEF59+0Lve+^noi}VD`fNkBf$>gSiK09?U+N
zK6G_3_2@LrT`+yH^bX_0<k9uP`Otg=OSiCafaxccZ{X%Y8L;poRX=(-kedI|(;q>9
zL){B2A7So=*$dMTqj81<H2h%tVCLYWVfMkShtaTbgP8-14_xYD`d~DyoPp7>^aD2s
zrjP&)iwBrKSb8F$4K59{ALf4;AFhB%1}q)I@-fU^=-Of8xa@(6!}2vQ{V?~!;v1$8
z=6_uJVd`M+MUOw2xj54g%m642vmaI-5F7unaDdqdb2rpjf)q?W%wCvzFn`13(G7z6
z3l>kXd<atq6NjZom^h4viNo9tOLs6DCXQ|{Og}8VVd`M^!TbRehs6&}A0Zm1A7(xw
z^)Pu@{K4$S6>c!~FdF7=n7?5(%zjun!_=eGF#BNP1fyZ)3M@X+^}+Zs`(gQo-0*^h
z8_Ye#reAb_!rTq>2h5$s(y;J?nM+6?%)hYsgV_s<2VCI?6NmX5M#J(A%pHX6hnWM*
z4=|coeK3E*^uf#{q#x#ASo{-Gk1h|3Cs;V4%M;?m!WkA`uy}y^1165kKQQ&M^acwD
zn7d%+!0f^09+*1x{0hqtFn8e64|5+Z9ANH&hby$5fJ;9toxs$?+zayuES_-bhq(`C
zFFFlNmoRt2%z@bpQxD_A@-0js%pEX&Fd7#AF!#d5Vc`h#4@?{v4GU+OKA63*_$1_i
zSp32K0i$8&!2N~G0Z?(6eK7Yxg$Yuy@PXL}3qOJyp$cI3!Sutz4Q4J>83qLlf0%z^
zeDv_Z6@D=DVfMn@4bu;E2YR}JIRqvS3pbcM2<e0AhtV+mVCKNwgPyKn>d|SKdYJoR
z@eLD)`4e3qj1N->(+9H`CQe8{OdjTcSboG6?l5~{G%WsM;ReeGFnL@wEc{{iqQ^hX
zTwMBL;;?W>4?nm#Od$>$W-lzfVD5p@Fn8k6jv)*)50);7^#_JQ&~g_Rt{C!A0eCz?
z)kB5h6ih!XeZVE4448Xy`5#s<;0gzrxv=nt(J*nSH8?4l`(Wu77G5y-5V98*Pv{oF
z(jhE;qszm@VeUnjhlMN5UAW|7_TZAor61-mT=v7_1($x9eX#I@g&Rzq*nA9&Cs=sH
z!V_jL%sgB)Og$lYV(5paSD1cW;eeqJCV-|NW)3V~Vaf>6Fn7cB!}2X5edzMA@Pg$R
zba_I2n7uG}z{)d1`e5k<W-m-XEdF8sgT*ttdFc9J`q63hbPmfOFm<r>154NF_Mr1&
z?t+CI%p91x=<3n=Fn`0$Ayq%Rd9ZMRnTIZqix2ZJ%>U^55SKofILsd~_ru}|mX6WG
z9i|Va4rVWmhPfY>pJ3uJeJ~mp&M<kH{pd6y|G?Y_(+5k(uyBFdhl_@}7v>+BIWYI(
z(uXb%3rARbA~yfP)WPh9g$E&jquUST!~6j=2WCD@9!A6b2a9KzIE;qHA1-&m^ug4@
z!W$M2F#o{Z12Y$$hQ$|5J<Q!ObI{e}<U`A2Soo8wALb93{W$GGlYsdT=6)C-mM&rW
z8ciDmEWBXug{4E7I+#0P?t+QKXju6QvlkYQFmuqwVftY1gUREfVfMq+!}Q~%q2UiR
z50}4j>4&99n0{R84yGR#elUAr;RK^$>TuC8`(WV+OV7mm7v?^geptA}%z?QNm;JEx
z2@7X*f57;-^b-<?g#*l=gw&(U!~6rI(dA*~3VOPOg$vAIxWW?_J}`H};vMF1SomPr
ziza|>4w^JUF3i0!e-P9FRRFUW=5APcK&1&%uy}#_pP&Xb1u%ER!V6{|EIq=?ADBDQ
zv@^ie!`u&xFIarT%!S#5t{>)KSosADKUjRj{Do6L%s^<l0aFii56m67+z-<Z%Wp7!
zFdF85n0sLA(P@~yF!eBhz|4X96I~z7T`+Ypdtmxu@qn%m#)s*Lg%6C6ZXQfMAsS{5
zEFNL;3o{p&ewce<_M+1;b8zX0g%3<0EZ$(@fm0t;0zLkS)d$ti0P{C29%125$low~
zVd)nZ|1dr*|G?})r(xw2Og+rqu<#_LA7&mbykPMLGY=N7Fm<?Sm_K3qVDSL+A1-|`
zahN+{G|V3`_YjJISh&H$1LhuBJiz=1vk#qy#UISwFdF6_n0e^>V0@T<nEzqn50l45
z!}Ov16J{^W{kZhQ)WOmnOdZTzm_K0R=rnpb!_=eGFm>qq(fP1=g3+*eAr$`T=D_&q
z=D@_!X;?VH;vXgt3kUS@fyu*Ym_C>~nEzq%Nhlq|<YDSz<`5fhFnzFeL#(@D;RCY|
z<{uah3x8bhfcYC1elUG78s-j||6t-U8fHH({V;i)@ei$cVeW*P3!`E7!f2Q{It|kg
zw+Cu2EL;hdV=#BZ@*yn#VfhQ)K6F0J|1fu;%M;?m;tysoA@wkMSiGXU8|ELFI$Si&
zUUdD0!W(8D%zhXRGY6KAVd{vbVc|$>IKbi;W-m-X%pWlK;c^Eoox}9uqG9<3ZVoh^
z!NL)y59SWIIvfmGdVz%-EWN?Zf%yxEb_SR|uyhZrr(pVF{>P;srXEJa>?2k`y16j-
z!2AO<4_zGw9~zD@eXwxAP=_vnrVr+Bn0{Ee;0gzrztQ6lW)3=yk&dADqw9zH17<$D
zvtfK#x`XM5g&)jbV)HS~JeYrB`iTvHbbT=WF!#XB$3?^Z33D$@AIuz>`MC7s5{K!B
z#V3Y3X!yhO5iFizG)zB?kFFo)ZdiQ5)Wc|)y)bp?`Z4%W_rT19#V>|B90IWN52ha$
ze;DNsntqr(%-t~m!f2Q{nl=WQ`7n7{yusoTmM&p5x_+2AOg}CfW-hvZnEPP%!Dv`G
zz|4b*qwB}Tht>Nq8kahBaajI_*-LEv!O{sV9m4H}rU!I;(D^WZ=;48`4i_J$AC^vG
z{=lUkT^#0qn7?5(%v^N!=zLiGz-X8}Oh3$DFmYmOn0YXBaOs1&9~NIQeXw)~OHYLK
z!{QC*e;6N@zi{b?g*PnU!r}p!dUSD^|41z-VfMoEA-cJQ_^@z-*-uD4x;!jg(8C!$
z{BVW`G=IR<!_0xX3l^U+adi7(d|0@{!w+gM%wKSEm<BW&7LG9WF!vDB2QvnyAC_KV
z@c}arCJs}NPQ%hCOdU)g%wMqdjjj*Ihq)IPA29d8#9`$;Og)T-#S6@Q7#|kiFn7Sz
z6QW`91+y3CA42&b7T+*^Fd7yvFn1BM9~NG)a3G`}CJ%ETEZ$(^#L}?vg83K5C**FJ
zzhU~&-2n?vT<(CWM~^3%J{XNlKg@lw@P@e?7LG7^T>4?+u<(YN1Je&nhcI=xXjr(z
z`~#z5=D^HH*AFuvrVb_#3s+dU5DEvFI&}9F%KxzNK@T^WdtmN^xeJ|!`4i@Un7d)-
z!Q6$e55|Y7hvgHPILyB=cfiCkXlOYJOMfu`!)TZ|uJQwB9xNPS?t%Fm<`0-ZVd{yd
zq3$CT4lw&*G~9fsepvXz#nBircf$OQPNRn}nsx@5y|D0xm8a<HaPeW`3`=h?`*5Xu
zm^m<eVeW<TVetVo7gzkj%*PeJFmYJC!t909(C~$+gNs8Muy}|03o4ADp!(tNM96?y
zFmqw<1PfvzVCrD@z|_Oy1B(_EAy|09(h1BxFmq58W8uK!8)hF&Kg>N?w19<R;RB05
zcsN7D1tt!QSFk1|0_HxLIv5{D!^Gj{BI!kD!rTv2k4_WPk8Bn)8)hz{a6s0B%!cWM
z(a3UOHZ+`I`4^VXVett|7qD;!>p>!5>d@mI7JsmO2vdio70iT*!|aFI2cu#7!HTgE
zuyPt^AIzUH8kf6aM#1#M?1Axdr5Bh!7!9)r7T>UNfYC5{n0j;?=6`hiVB#=;!O|nT
zenNa$x`Tx$F7+_~qq_q=UU2D$se^?BjE1=%<_<#oVeW*{u=0SAewcl*@P^T_a6|VO
z%pEXuVfMiE5lS~O{V;Vf{V;dK{0%b?rXCj!i*L9*G+)B}1s5lg0dof~|HF)f#Xl@O
zVD5(bi;#Zw_=o9(g+I(5Tr|x8Fn7T8<5G_<j;@bbdtv^9*#~n6y7{>HFneI}j!PXb
zarAJ8xdRsO1mh1aoxs8YR*u8UGeYiz*#pxLO9!y}2Uecp@(0X5n0}c1Vg7;T6LkHs
z@Q1k%M#J0#a|gOU7#|j2F!#aK!)TZ~Tr^A_x_vNr;nD{ahs8h4esuT4@&!yisWdD+
zafKft^I-mir88XW;pRir6HFaUAB={H!)d5`nEzq%j!whU2V5T-17<%g9$@~4#V?w2
z3@$94!QumE9!wlV15^MOJ}~z}h2az|{o_gxuyBE?gKLB`VCKWrL51NIEPvvXC#0W{
zJj`7%b#RNJ3|RQW;tv*2F#p2h1F9S+1q*kWdRVyQvKQSTSbU-DhlLNidKe$(PMCc#
zb71~}nFmt`(+8tr?t}XuDgg@@Sh|3zgGysiuzU_nx3F-B>4&=uLnlN4W*;maz|4cW
z9~Qn4)x?u9^I+~m*9o&1R&Jr&19Jy1{V?-j@dOJ;n7?5(q40z0hxr#4pTz1zR}Tw+
zn7?4|M5kf#0@Du*5173$_n_-T=fmuS`5&ea<}Y;h7<_0s3JWh74RZ&EdNcua`(WuE
z<}X6#!0dzRgBcI=4=f$Q#L;P(KVjy;XjuAzg)6!~7$2q|W*@QoVCKU733ERzK49j+
z>_Mkt>S6AMg%ivjgwj3CJXkt{r8gK2GZ&_gR2pUudcGvq9GE^_{(yxaTppSZVdlf)
z1uhShK%-&mVBrSyH!NOY;fkgW&P8_zOdl*<;R?|iQ1`;r!{Qs=espaxKFnTN{(*%T
z%w9tNhM5D?2Qvpo!_pT_9WEN(9WeL9+<{9UOdRHKSU!d6gM|yse3*K48kS#RG%UVg
z?jYn3nE5dKU^L7f==R~_!_pDVAGp-v5{KCftN&r@VD5*x6DAI$VfMko5he~Z2Nn)6
zK1?2F9?V`?xWVj$g(oZ@!}P&un7uIbanZQK5oSLwdtvDiW*@OM%s()H5UUU7UyN`h
zU;xY>n7uIn5YUDp4bx9-zC||xrXLm$=<$uN9-R*hZ<u~q{GzMF#fPPTSUCza50`pe
z;;`_8xdX<>r5+aku=s=757Q4zzp!!+-5i*H^zeh}N2k&C!~6|X2lF?~9fZ;oOdjS=
z7$0UHA^*eN4f7u?-eC5lyBEfX<v*DFVSJdquyBE?$DpD07c9NP?1Slt`3G12hxrSp
z4(1P7_`%F2l#XHUgxL$@W4ITG0D8K?p^Ts~%>6KTz-XBNVE#on2qq5mH%uOu4q*CW
z;^;IizR}|m<{p^&==yN+VdfFC7p5PUZeaRg{($Kx6d$nkfu4R~d_wULa|bNEU^Gme
zAPr5&u=pa@KA68@_QCYS!W|}$%YIn6z|^CgLu|ak?1AMMn0dJDMHh#~6T16h?n76P
zi;v44FmZH!u=Ij%4~!3U4=i1y>xZQen15mE2xcx!KTI55Ka7v=f0#a8_QLd`yA!4l
zMiWX$u=s`L7g+qm%5j)IxM-NaVD`aim^m=_;nEMY9~OSFaDcf77QVRj!^NTb2gZlR
zH_TjEe8A+1rD6JTxdY}ubbrI*4OXth<k9Vg>4%vQb1zIijE1=bT|bNu3vZZxFmYmO
znE5dK2&EfzbJ6v~(gnIYTzpu1gV_%Y4_G|n(gzcVse_pVOFyK#17;q~Kd|x)CXS1S
zsfVRsSUkY|19Jx~y`!50OD{0>xM-NaVESR{2R+@u%!8@JMZ??+3vZZzVfN$F2NQ?s
zhlM-Lf5hsCnFFI?{(<R-nFo_6M8m=vrXOY=A$>4;QvDB0cd&4P*$ay&Sh|3P56m7o
z4K24|;SbY~i^i2NVeySiAIu$ad(jxM@&o1%7$0UHE_-3dz}ycDf0%n<;xKi%XqbJl
z@Pz5Z8E$9>!SWl-9GJUd{>GIKVD`cMjh>HS<`8lR%p90{Sa{*G7tQ$$F!#gK4Z1ub
zJ}kUp`eFWoxeJ$mbbT;?!om#}p1AbG)WPx%%s!a8g!IGI!~72mKUh2voBv_v!2A!R
zVc~$w{V;Kudtv^7iNo~6<YD6IG)x~X{9y7haacIP#L;P(KVj~Mg#*lfnE%lA!Ng(t
z1*VUXdtv6l+zZnOvkw;jq^4h3{)g#@#VgD{Vrf`75K2F|%!BENg+DHJ=;E+=f`tbx
zUSQ#jt{%pR`4grOM#I7b#)pMBOdpJf*$WGQ^!SA7$K@YbJi+wC+ygV85Dn7@qha>I
z+<{X+wElv{H>}))sfU?|%ipl@h4~+*4@Sen50^Yl99McLWIjwEjE30@qhaBQOCA>P
zuyBIe2U8D=H}r6UnGd63{)DNAg(t3XgXx3OuyBBx0~05fhUtUl8)Egr)WP(_+z$&E
zm^>jGmd|1OVc||lA50#WKhfPmDBNNC(P>zG!r~EK9-R-1cUU~Y!VyNp#9`{t^~3xN
z^FJ;c<}P&ou>1<sN36SH;Re$OGY6Lb(CxwCL-RjOKP<h%__+Lw%N$sI!Ss`wj?nGL
zu!n#EEFNI-MnE}S8m15CZkT>pJiyW=Tmu>d7OyaUFdAkajK*mWG#+5(EzCV=*5KsA
z^uh8WEM4H#0F{8nFU-AAVWKHmz97}TFbmN2!{Q&7j$!gJ_2@Lr{jl(b`2!ZtuyBEy
zi>@EWhs7T(zG339@Fe68SUQK<4|5NUCS*U%d|1AP`JdQ$gt-$I9xy&E9AN$-M8oWZ
zg$JqnVeW_d8&)0=vKJ-~b3b}`z}y8>2cu!)Fd8NfqhaY1Jzin*F#BNsg~byrK4IYq
za}P`(It?=)J^o?gK`0zx=EKq}x_i*=gYnVb597o94NFHb^@M0xe8cRAnMX(;Odb|q
zFn^P(A7&0rAIuzF;Rg$MSop#8!)RD|!qmaUVKhu1EWBXp2p0dabPVIe^r6!*f5X(n
zXjpint4HUf`xmAkorbv&T|dlzSiHgf1q(kIA6*}e4@<Y`=^ti4uJDKHM|T%2+=)(q
z(E1N%Kg?eAc!Al6PQ&~O(+8tr{vs5AF!#gEf!T|04=z3|eZlM_6kh1&!0d<Vhq<3n
zyur<Zh94{)z`_w`9$XzE2Gkswe_-K3NE=KZmfm6Rhq(tPPb>|KFPQsb`2&}JSh`1-
zhv|c*M|61@AEqDXewaR3_`~F3>T%I9dtm;Dg&)jZT>4<)4bulR2bL~y>4S;G^uh8Q
zj1O}^OdT#77T&OQ57SS`-RS1Q!U?7y=6^!|fXhSEAA0)5<qvdq=<b2}2j&iRd00Hd
z+zX2bn7?8Efu%!q{V+aEAIuzd8fFhnJt3Ohd;`;m9$x5tbo*iMgZUR;enH~{r~lF9
zVd)kY9x!`h?n1X07C-3Wh0ceWhprzNAC@j)@eWU4xHO=N!`uz?51KSVF3kNfa|mgG
zxepc|uy}xp<I)e4hq)6LPcZ-E(gzoZ#vjbRMC*eZ08<YOXQ(jzJOdaX7Y!?SVD_Wa
zFmqw*Fw8-7Kdk(Ng$pb^Vd5D2p#m^@Sh&OTKP+5f@=)bCDVRQ(KVa^M=O<h`q5goy
zAFlj@OFPV+r0RpYi)eSEIRKU~VfMqq9af&e!UIhkoD0(r3ojTAa}SIrl#gKgVDSgD
z0Ok&u`(WbeG%Va;>R|d{_QKqOt`E+K#vd%5z}y4V2g@gL^=J%OxWV+p+=HeJCl}^#
zQuV>|6-+-)3vful`~`D24rMrnVfta|9v1#E8kRnA8UU4mg*Qwe%v_ke2!#tw9;O}^
z|1fcwxlrpcC|LZ#)S<fv7S0$N(F9=XVESSC6HOUTF3i2K_=K4UOUJl0!rTwD7Z!f7
z@)IVGp&y#=Vg822BTPRm{lm;b*ALSNGana?D;>b>fzhz?2IdZ!dFb}y;={rL7Je}I
z;Zlz-4hwIXy|8qIt`40Kb3ZJ+VD5*RhpryZhlW2)JuIAH_QL!DSC7ts>WA42a|euu
z`2!X%=-Od?n7uG_VESM*L4QCEg4qL84>K2*uA$0^r=b3Wg%2)`u>1i_r?BvY<v*A|
zVD3XV2c{lYe!#*VrVl2Lt`EkCg*PnxVD`e~3DGcj!R&+ihmbyWd004+n(krlh1n0I
zVeyD=4+b9^Pq6TZxdWD;G1L<ffa!z90|Djmb8KPh4Ca5BKVb0-qtVTSiNoR#rVkg5
zt{-L}jE4CiW*#hl(e=T^Vd`M%4Ca4Wx<c0n<HOX!^uz3j`3I&B7Y)-7vlo``VBrpv
z$E6>aILu!#b8x96Bo0eYIMX3C-ND=mvlqsP#RD$=F!eD1!{Qs}ZdiB_awkk3%s!ZT
zuzZC}Kg^vldtv1zOdRG8m^;zUf!PbU4^06qKj2CaF#BNUplN4-r9YTDba|LQa?OW@
z56qqD=ELm8B@a^%3wKyN!`uVQ7cg;{dUP7*Pgp#_Xqf%z>e2bIaDeH9#Rsl<L)Q;;
zKP)_8?t!I$boJ<bn0}ajF#WLd09`#gAC}%=?uXIn=A!FE=flDo7H+V3gYnVT6XL_-
zk&r)N?uYpwZZ5QZfcXP1j>dq6GtA%UG$H$8@-TB?G%Q_UxC>@K)P1mY3R90x!_oyz
z8;pjjgV_(`!{Q&N4i^nm53?WUZdiE1__*}L+=cEx7$0U1EM4F<2ZsbKUSatOT^Y<C
zm_K0Y1?GR4{kZ%A(+@Kr7Vog|B$RGp`eF8<>qmDlIv?gvm_C?0VSJeX(e=UjuyBRB
zA0`fS56nE6II%P=-@<5O^}*D^>?c(}Og+s1u<(JIi$SC1ADBLv{jhWai%(d3MK=c)
zelY#$G%SDMat}-#W)Dn1Odl?LVet!72a7LQIKaeV=^EYLu=t1h7sf|Vm$>x9%!Ap7
z?ruWu514**f56-gGZ&@~7Y$Pnb3ZJ+FvM{f0CP7izDe~zEPP=0!OVlD3z&OwSkC|p
zCk%bKG{D56=@(rej1O}cOgSzZ7XPsH1M>%rk4qnhIGTH4;fbLRhXBmIq`Dtw9L&8i
z{jhk1$>XBY^}+lPb3ZH|Vfhr@9GHGs{zZ>Zn0sL6pzDXFa~KUXhfw_iGY95gn7uG}
z!pwp3Vd`NtEWBa*V0@T7EZxGw0WJ<@z{D~1LFHlj5vC3*45wh?xb(yPkIVhAasx)g
z4Tdt{;SEifP+>R)OCK=*!`us_Vc`eYfP(>ZCoCRd@eLElp$#q!i+7lMnEh}CI2bVZ
z!|a8*7nYxKg$v9$Sh&LU!Ng(l4ht8!I2r>MZy5Ta>R|FPe?f)e6fFK>?tn``88H1Y
zbx>hA1=B}J9u~fEjc5#5c)`LQW-m;fP&mT;2a9(YO~_uDGhptAr&pK+ku<vfFn<uK
zodM=QQr!(RALdV(epq^hrB9eTf;2Rr!pwoCLxTFy6u|6<mCI<-7+jeDVd)rVKg=9h
z{J_#1x&g5C0#gsukBdgv4~s9DdYC(4G%SA6^}+Zsb+G&jqhaR3{0CEyK|{kGCJs{%
zOaCx&Tr@16Vc`I?AC@j)^0@TF;tA$nbQ%`^xb(y9g@pr*hJ_m;`(ft7{0s9xOq>u6
zvmfSeQqwKWeK2>!!h?{#FnL(H0Sj+p?T5J==5LsJ#L}?*K&pFT=ELF@=5CljVBrXJ
z7c6{W`rtG)-NMwv%z@=6Sp37xh3i9Oz|t$sJuv%Vd|14oX+v<K?trO-xd&Drz`_Zk
z0hI-FFHAj5Kg>K-)tEe3dWGqO@nPWy<74VX6@j?}=6{%bh}93%2eTLE9+<hPwlToR
zhhgCiOSiE2AXYz29lH6jbO|#LT^{CMm_K3Z1m+%C_`}SHsYf>l=09|Q!}P)2flD9E
zei#ii4_!SvALdS&y)gH}@&USfbUrLy!u$=3XP9~D>Iv~-`HfutFn7cJLC8LId06=2
z3U74vu=s@e9~NIQ`(fb=3nz5_FmaeZLNvO57$0Uo%>OWPVrf|VhWVdZeK2(}|H9l4
z^A}7W7Yz$Pn7yRxhnWjY?=XLm8xAmiq^3WZe_`<e^9L*)!Q^57g}EC>qlX_%KROLl
z2h)d6!|G#Lc*4p<balA+u<(P?xYVJG!@?itURXH*QwIxILjH&O52g-Q9>Dwq6Ni}#
z3s;!EFn7S@VetsFA0`e97nnYne_{C*M#JRM%|(}osY9n>=EChIz<~J|W)6Bd63`Cs
zhr#@f?rvQAVc`f1XP7(C!yA`=Sop$dT<UO%!{Qz04qWPRiNpL03pW@I%a5@5#?TLK
zcfr)d;tLjTuy}^K4?{mx0HzKWelUN)@*~WAobG^{15=OgADFwK$}lKcI)UkfsfYOk
zLj#%s+<(yUfa!yoho+n$7Zx5ccM#M7RRD7bEWBXufJzfhq1%ru9mB+7=^fnym^eHg
zp(%j55AIKB_`#IH^uy(05@<9my}-g7M#It*EPm0n!MU(}2eSv}FSr5%446A%;SO^T
z0c~(;nEPP*VSKm(0t}dXn0}Z)2x!BQMmL90enU3^7N0P8z|s-AI&?lP{9x$>rXLoM
zuyz0}f1~S%xf>SWF#p5!!_0%pqw9zH6DAK+53?6TJ(>W_-!Oa8{eh+&Hy4`zVdlW{
zGj0v2GBEeT+z)dHswxBz=581bOZVvEi7Osq=EK~JFd4#v<ttqNfY}RkFH9XQJ;20q
z*$=UW0lwb_J^o<!!_?uTVeWwW14hHdap{AJqq`fX4`vQb9WEN?PnbNc{D7GY6URlv
z{0XxUJ>A37H!l6Kc!z~MEdF8U;mUU~eYoUd{(z+u4Ev$!9p+w`y)gH}(lbu|Py=E1
z!@>a;o-i6F4^@syLEQoKFU((<3h|4;(jVL(_?2VG!t}%ZfuVqa0L=X`f0ODDn7d)_
zfbj`fiy;k5x3KubP=F=?OD8aWu=D`)2izQJJfUfauOEi#N2g)of~y>Xr4N|DVfMq!
zA(Z}L`39yRW*^M`gyIt>4>JcA?lAqZ{D9$ar~phKEWTmxhS5-EI4M~8!@>t<FHAqo
zTyz6q;RaKWPQ${Hko#fg!om+0-?;3BsfW=p_rvrPYadKMjE0#5^EZr!$>XA7`e6Qt
z#UHWmhq)VO4laAp#bN4U=?&&@SbBiP54t`WAEpjwKg|6w^I+nz`V6KIM#IEm`e5+}
zlZW}AP&mTUKU^I&pToit<{p^2xM*1Vg}EE%E?79>(g*VgET6;l!OX#>4<-&%2h#_$
zkD$Au{)Fj64?lQ#z$`+eVc`qY2XhB3JYh7NHk@2oc*EQSvma&;PK{81pyzj(Ik51+
zr5_e=F!#djgPBJt9mC=e=6{&IuyDa;FS<C)UYI#BbJ5k|;=}xb9`7)B!15c0erWi?
z>_zu4OdKN|q55F<!NLocj$z>m(~oX1OdU)fW)3X;U^L7ebp5#auyBLv$8a|;jZkq|
zc)|Pw6(&qU!vkh7EIkv_2(u3s4sd&+<`JtO=5LsJu=GGkKe{~3-K2&)Og+pWu=)Vq
zd<;I+pD_LC=>nHIF#p2x4Z69oa70hfFm*8dVCfE?KcMb`l|Q)j!_0x{gV_g*f0+3&
zb+~Al`(gTV`4^Xdm^jQ{Qqwz3Kg@n&-3`+Rqhav|a|g^{FnK~WEIwiGfTcr1`q1TJ
z=^hq;=<?`%n15mR!qNlGA29P^?nBoPi!Ye}VfMoE0n8j2A45M>02W?wb<lJOl_p5R
z;vKFZs)JDag(`!uLx%Ym<{p^)arqy^UZ}e<#9<aeX>@s*J?J#dJy30M3Kn0m@P^Sa
zahN%<_<?IAz<`+pqX{TSNJIS%vlr$+gaYg=SUQL4gV_rUf9$#t<4!PrF#BNTKP(;N
z(hpM)vkzSzOdRGuba`03z``3noMGi6OdYy@m^{oLbp5b&i%UOD9<CqF0$4b}>_L-;
zkK3V#A51+if1#TLQx7u-7Vfz63rs&u9#;OKmme^H5gYz6{jl(Y(XeoW*$bm#@-X{h
z{(!j)rVbYk(+~4EEWBXl4=#J*=0Woddc4E@3yVLvdLkJxdtu>+OB*bI!TbT!2MZ4v
zjY~hw-7x>c!T}clFnOH%p%SoggXxEvM{GR6!V8w4VfMrP1+|7C1yc`m2h2Qz8lehc
z=?|u#Q24>ZAKjm@cz}fi%w14x;1tZ?Fm-SVC<B&$aLL2`2Xils4>K1QFEAQvES!S5
zAKl+D^Wgem=^U;TjRBR1sfW1(O%+Zq%%6nPJGucd_rt;m#z$9&iw}!8n0{R9aEZgh
z85UmXG%OwCbQd(-VESO`1eSha<ryq~aJd5(ZZLH)`(Wn5#Bu3|g#*kSn0sL10&@?#
zewaPz`e6A6CXcQU#)r8NrXS`Gn0sOJF!ks(Odl+qVetVIC!`-{AIzUHf1}fc{DJN+
z7$4nTFmYmOn15j5flD9E9&~wFK7@q_x;%^zlSlVAj87;W(aph?USR6bX_!A@<p-(Z
z4bw+#eFe84n*U(>VfMn^50!_jM`OU$!NLiaj$q*l<HO<sO*=;W4HgbCbufK|;vZIi
z!qmas1xpt&b71D8(=dO-^uzoEGZ$SwIv=JFmhaK!3Gre2Vc|tcJ-R$hKg`{P{EMz1
z7EUnzF!RvW;p9Wp39P(^nL}xML{AU6?1!m?g+DADVg7>UUs!pEZVpTy=5AaxEM24P
zhov`|y)gYS`(ZT9edzjO=Ax^EnFq_C7~u&GcbI*!aDb_U>4S@-Sqw|}=;;NP?uktY
zuy}|01Lht=_QRY53xAk@VKm%+s6S!qF=(hf%wCvz80v5cz}yeB7Z#s5lw%0P!VBhq
zbed53qlXjBei#jlPjri6@-TP9?1i}p7Qg8FV0?7_uyBKgJB)^@$3^3^2j(xBJ-GDa
z5{Km%n7at6Lw5&EAB@JO9~NG)^aG<|;YzIgVet&pkIOz>;;{IGl>@lcp^L-vFU-BL
zat`Jmm^ivVSa`wW2|d1H?tszg`eE?{^Dj&t%pWjunETN6!|a2phtV*7Fn8ff|LEqy
z{0-9&i&u1YxcIPi0@DvO50`p$ahQFua6p&G$tO^L!OACCe4(2IiwBs!Fm<r_hxrFR
z9?<1s@c|15Soq^gKQQ}X=HsGa;ezgNc)UXW50i(v17;q~KQMV*G%TOO^ufXnmwI$@
zm^<PAMROOrdKe!)9AWmuXjr<2sYj<_;RuUISop*AqsJReKP+5e?uGGT{vc#8%p6#_
z!T2!u5F36lcf;I^PQ%;-GasFX*#k>&Fn7bkflxk#$-}|{S9qb@3**D=f$4{d6HCMN
z!`w}*{V;dK!VBFTSbP$42Q1uR>R|4GnGX}kMWd@DRX@yJm^tX~fyoo1Vc`a|pOAW(
zJWL-f9AWtpmabtmOg%acb1zIkIt`OY*9YUHr+-*H!sKz$uyha0Z!mYl?1RY@s~=XL
z!t{~q4s?IR!WEbOu=ELwFPJ}I`3M#dxb(x)3(Q`a`(fb#GY^+JFm*8fFh0y(uzCdM
zK3wjAg%`}-F!NyX0CN{E{jl;4rVnNwF7@c*FneJBg_%RhUYI#B`(W;X`GeT_hnWKl
zH<&wM=E3BNrD6Voxr<nRu=s=dpHTXPnG3TArVmEL{EsXB!1TlX2@5w^dV!e(qhab`
z`e5l7rVpkLW-m+~E*fSYOdl-0!ptKS|1kBi@&`Tq2<eB(!}P(z3FaPHdcfs=ba9wF
zVCe^49w#5#&Vr>IV)etqALd?MG|W77|DfxK#XBtiV0>6Oqw9zH2bTU|>S6Z5!WE_t
z7Y%baEc{_KEPin5gP9Mr59SV-`w8iXsfXDQi$53*iw|7>hMNzKKXiA(%z^s{T>$|;
z%pWismvUV31GAryewaKg-=WiRf8uZeEFNIt2s01nE?79@(9QsJCrm%gei$ED_@Sp?
zm^)zVVdmhn7p5QPUYI&qdO{b+1z`5U+zX>&=D_4}>4&)kmfm3cVC5rB9+!TYI7~kw
z`(Wx}G%VcU;e{hT!_=eGF#FK+KcRF3^A{|<VCKN`8{9qU7DD;3`Vpod7H%+iz{GL7
z1L{whKA3u#ewh8R@PS%OGzCjPFdCOen19jbVftV+x;%RLq5Bh-uL$KoSUAG+D=Zvf
z;eyM3F#BNYanZQK4<-)N2aA80e_`&%Wk0%pn0lDI(8CLzkFFo)4j2t{54t{dJ}mrT
z=E1@pmVVIH6XL`COE5n}(=SXN%zhXR^9L+E(d~t$A6PuY)Wc|)`(f@u*N@JJl~*wH
zVDhkVB;*d5eK2>y^uxqq=EKBc?t<9|%eOH9!_osxKf1Xvd6<83(XjZ0*$bm#{({*H
zQwNI=7)@+?gqaKT56nE6I3XJ59+*E!wHM|OSUEt*K6H7Qd(q<wT^%|fmVaRJgf0&=
z52g-X9v03p`(g6v@e4~AFnO4Hu<(MZM;C{MD@-4ZhJ_PMKROL_7fd}m4NH$OccGhy
zE5D$d1LMQo4|4~(@efM}FmnmfFn7cJ3kyF&`e5M-vkw*yu=Ee(<1z;p-!K}c4yF(0
z9+)^f4GS-reK2>!_=Lg{R{p^3hxr3W!|W$D9%1f;xesO@OdOqtxdWFtu6zYk4~svT
zJ7M7m3lCU0;IapnKheVtrXOYxIt@$zF!Ryvhna`255|Y3A6WRKyAS3rT=@XzKA3-D
z=E7*0ILuuzaTpB~hpB^!!)TZ|OdU)dM#J0>qhaDO8m1ra510fR4G$-1djKX4OFy{m
zhpB_f!_pbdJb1c6GZ&^0PDABk=?FdDqsI$OA1wZ1>S6wbxd-MBSUAA+!Dv{x!R&?E
z15*z(7bXtVhfc%ng{g=68)iPbdR%;1_`%G9rAu7uVd60N!t}$^E6o3}bP7|CPQ&!0
zhXafc3r}=?Fg`4NVBrAEk1&70+yhe&qhb1B_Q2c=^EXT$rXHP!xeMlRSb9J=2VEbG
z57Q5`ALdRNALb8S?tqEI;v1$OorakUvloMgrhk}wVE%xGC#mTOW<D(c!pwn*!)d6!
z=-~&Ghv|dK!^B}U%>OWT=-~j12bh0g`p{{ZzhU79Gl!7BVd)x{&S2>nM#IcOw-3gL
znGbU(OdRGfxPE9j!Q^2yOdTw}z-X9xuyhPlkBf$x1M@$OCKPTk^I-7<iw9V^z}!J7
z{lV;mse`!(rXLnRF#FJHm^>`JVCKQ%1!f+)J{TY7PnbVoG|W9Pb+~9)xWmF5mhNHp
z6EX*;AKjfWf5FldhW*fVfy*9PJiy$8t{-MTEZ)%NVd)d54qYA=Zm@VlmxqNTF8g8X
zVetmD4;CIU{V;X7Xju5b%z^1g4^LeBVc`U`4@Sen1?Ep&`eES%OLyor%ze1@!|XxV
z2a8ui{($)xT|dkpxa@_A!_<+hpHll_;X|(duyh0S2aJY^!~BU(!@?DoZeaGq+=H$j
zosS-GuyBCI8@hUQKFpo4as!qQVeUXzkIsj=6PCYV=@3_bMAr}FqlW{baD(ZC(HQQA
zmP;^sTr|x6u<(b48!S9v;Ry>Tm^;zUgZUR0ZZH~VF3cQU`eFWs*#k2V7Qg8B!T7Ln
zhq)VO4on>84qW92%>OWbuy8~-4`v@B8s>hOewg`$^r6ec+zT^@Q2m9jAI67;A1pjz
z;^;KYKJ@s3>4&9jbbT;Bdbq*D0Tz$Mx*ui_+}|(@2+%P5Vet*~FU&tMf57|+Hx8Ol
zVCfX*ZkRmG{V;RU;}d2sOdghRU^Gk|mi}Sp!f2RzF#BQZVKgipVE%yV!$rf?!OVlX
z50`p$arAJ+2rqOEXndGIVeUqg#^A!-3yXJ{yJ7x^(YX8p(+5)ri$9n+jE4CO-2pIp
zn0sOA23;L4KFnP(`(ZT99k}$t#9{Ws(iw~oGl$ghhlM9hKP-L0>?1_O?1#kz%sfK+
zVE%>a!xi4R%z>$gg#)qn!pwu&Pp*Dgc##_3F!eC~F!vBkqw9y6151yv^o7$rX!!-x
z2a7+LxiCH<b71zNm(wtFVCLhB7g+rXi+`AUn7Od{#pMr}epo!h%1@Ynm_KmohuI5L
z4@(CycfkCCt{>(fn7uH0Sa`w8GnhCb8kXN+_QKr_jVE0CVfMlFq0=yVT>4@01`7vR
zc;Zx#CIJf{n0{Eepee)2h53`z@PZi#D|cb`!~7337p4vu4O0&@2j+j6c`*N>>qn0-
zm^{oJSUQ5KgUO@QF#BQg0CP7iJ;M0t`e1xm`h%&5(J*(w)ZwCG=>z5tnE!F9M;C|1
z8!Vh)_QU*xt{$Ba%il0_VEGqDqpOGUVd)H}59WRtA0`h|k50q<2~!W#52Fc%AKZLs
z{zMN)n7Qco!ue2ru<(M#Bg}pnAFdt;0~YSEbOK9n#HK$S#=_Ud!OX>_4rU+By)gH~
z`~?eVT>4?^VD`Yw!=)ZW94#DS@c@q>3=I$gm^{qgFd7zbF!w-IW0Ej;!rTut52g|p
z{;>FgiNk1^`7m`baTpCt|G2^rmTzF;jcz~8d{}tE%tfbR`q9N<;Xp_~%>6L+Fn7bs
zPgppin-Al|!Uv`v<{p^;VCrzuu<(J|3-dS3TwMBK;xPR%dto#of5Y^{)WP(@XqdS$
zb+~9)IRn!NqhbERr4J?!3wM}47!3;tm^xfEF8g8r!le%;4s$2WUKmZR|6%C^W*>S!
zBvg;U{0U1xFq)7)m^{qAuyhQwALcKZKhbGe`hwX9%YQI`z~Tj6A1*#jKg>Lszi_EX
z7l*kQ<`0;8=<3k<FneI(080<(>d^VH_=V|%(J=dA=D^h9at}-$EWg9TA7&mbJYeyK
z?hcrKn7uH4F!RvW!}u_F!OVx50~3dp>oE1`G%TKB_Q3oHiwBrIx;|Wd^mu@oi%UJa
zILv;S`(gfqiKDA0#E0pF#Ro3+u>1>mFSI^}@nP|TOFzuLFneI}2Md3gIk@!0#9`);
z>Tj6&FneL~2QwEIFEI0A;;{4x(}$klVet=>N7oNC7bXw$FN}tX!@>_1A29o2=E1@Z
zordX$l~XW%glL$3=>8$359S`2y|8d4qz@(!3wN0RVdA)GSa`tlEiB$)_QPmg=E3ZP
zse}0!<_}o7!R$eI2h3iWei#jl2Xyu5eDwH->4TXA%U9_7V0@Un(AB}>4`x10J+U+_
z97)xW9v-m#LC9X1dtvsHnr>kBz`_p}E`;ob$;09urXMCwkcO6X#Ksr8d9ZW_(+^9J
zFq%*}z~o{2Vc`e!2Q1yg>_N95mTqABVd)*F4i^6~^I+oWG|V1Yc?UBW7Ot@PMb`)8
z!|a8b19K0|UYI;gJvt4Ohxs3-A4bE>LDvW4!{lK!dO9W)elUG7^I+jX$loyau=EO}
zVftY1fT_bp!{Q0159V(ejm!No^I-0Wg&&L$(+6`GOg%0drXQ9raj8QWhs7Vvy|Da@
zt_~L;J>9^<1DASqahScZbPEedm^m;ST_21O3olrF!T2zD5eg5Oy|D0uxrdNGnEfys
zW*>}(i4&q>{)3e}uzX8wJix*WW)IB&uy}=u<Dy~ih4~+*pV)K<QxDS*cMr6D#$_+e
zeDv^!g*)6F90tJj!{QxXJuF;d@r!N@EPuhm8>Syd!~BCwKg@nuc>@av7!5NArVibH
zLVTFJVD2QO9$g+5j<EPemxu9T=?>;znEPS=CuBb?ox$|M;sa(rAsXgBn0^?IE8OAc
zLDLsZKTI4J-Z1~b#L;P(I+%Uv@-TnE!Vz5_mOpXY19cxPd@=Mx1z_O}3rDCh{CrS!
z|H0&8G|Zhaf56f`Ed9X5Vd^nxs5@cmVfhiphxrpjA5;M5Zdf?L{0j?Ts4|=sOh3$C
z7)>z#p(euYf$4+Mgxn3)4!^e=rVmEL%!R4LMZ??)(+8s|)ej37T=v4mVet>M7e>S6
z3DVH`hov)^ew^tZ%|KYZ!}P)Ug!~UHS77>J=?0b`2-yz{ADDWWvta2KRxZQDanZ2&
zgUQ443oIYO#EGS0?jcn_Og$_-(A|a0epooc^ucIYJix*gSA4+K!SvyxafK%=9?{be
zEFN&V8(kkP9mD($a|eu%ZVpU6jE2dhhX=9c0J{5O=D^G)M8n((3r|A+CuAN>KP+7k
zQV;Vd%pI_Jhoxs+`eE@33vU<=vmchvaOsDM!`une4-+Ru!_0^2gPB7}A1pn?!W-S6
zuyBCM!_>pfg{423y|8eAr6*WEhJ_<cAB={nhv`SBVftb50@H_1!`umTA1oc>vKL)H
zjE^3Uq^4h3e8a*EW-cKbW)IB0F!#g417<FUIna6s7EUl4W*&OD;L;DX2NoX$^}!5+
z+7Hu@PUCbpntqtQu=s;X!@>dP9+)^G8fFemKP+Ae>4T*+Sp31#F(G|0bujy2d_wwQ
z@-X+p!T}bKFmaeVTr^A{%zjeC3l?ughZEExu>21TFQ_ol6wIA4cf-mbn0sL6;&c}@
z{lUTurXFT4Odm`fm&0J;2QN3F=EBT@xrb1Ez|4X914hHm#o+*0eub%r*$3mp+<~qg
z<{p?jSh|Cm2Qvr8N7oNa?=W}6<YDeb7l+AX(9rOLg*VJSF!y1ohYG;_1yc`82e9~r
ziNn-EwZSQvzhLq({}QqnZUB@4QxCHbDoiwm?r)+sLc<N+zcBX@G6&t?uyhWi3F(K)
z!@?cr4|Mmy!WpI>orcN7!XHKxTmHk`3!`D~fu&D$`(S*Sy)b<+aag$oOUE#AbQ%_Z
z=<bAtKP(*3^}+ej_=BYzn7?5(EI-24<6yw@8O$Fr_v28GAq)#In0+w+z~U1_15^O!
zf0#a4c)`LI#)m4$pkVT_b_*=LVD5qW3qvEC0J=M1`e5M!iw`ty1i3JOz`_Hk253CO
z<)M67yb_8JnEzq^hS9Kag`10m0dprT{9yjap&UaPrVr*$n15k>3=L2LnEP<q2lEG1
zIR*uDH!l6K^nuGAuy}{XGr9#Zd6+rq^00IP%WvrNF#WLbK$nN{Vc`c0517AT=EBT@
zsmGw9=>=vEjE0$mp&m^Dmd;?|jVrv+v@yWSN0>Wd?uUgZEL~#gLlc1MhnWWpSC~13
z{11zNn0i=zp__|lEu0JUFHAj*hM5OfNQeQA2UxiWGl!5in15mJfZ0oI_`%e}!U5)g
zSa=e$AEq9bZeZ?)(J*&mm;?1MOdTwp6PxbP!--gZFn7Sz!|aFAu<(GX!$rfw7p5NO
zPgpp>%*Ul4=6+cG!PLRR0T!+>aY8iAewaT9rF)opuy}{*BiCM-yJ6<QXqZ1?_Q7aa
z_`uW=8*VWD==Q<%6Ps>f<rYjIj1Qw>_TZ$U=@%A%u<(ZY2d6#)5-|NR^9U$MNJHHN
z(~m3M5gH&Y^mu@!XNWBEBrN^G^b>18Ec{^g2P|HRwHa1#z~YhE_=lMf%g02=J4_>#
zhWQs}KP>!V;;`_AD#xH;_QUkS)WgC77A_bXp#tdshv|ozOGrP|7>xNwn0sL5156z{
z4YMDn9-W4z3v_)jJ}lhP!wZ%!VCrzuuyBXDgIImA^oj0Xm^*OU2NQ?I56pg8xWmjN
zlz(CBVeWvr8>Sy74zrIC4Ra4HJPE0X$-}}8<`0-VVDiM$FnbBbH@bPS`UYk{Ec}TL
zKUjFd?1hCJ%suG#!T2zL!}15ZzhU7Cizk>q7!9)rrXMB_qhaR5@*zwdM#Job>xY)#
zFnL(MgV8W?bQ&fPb1y6$VBv|&{pjYv+zAU$n0e^x(D^WZuy}*H2j)L?^>9AazcBmI
z!wZ%!;OcQOVDSv2VdfBWKP>)W_M_7<b8uJ-7lwr|%)Kyoz`})KIKWLpWx(7|tUFM3
zGr-GpSUNzLhw)+dp!*-jhxrSp9!A6b1&eoF=@_OSord`frVo~GVBv|b9-R+M=P>(W
z@d*omm^`j>1m=F2{V;tnKFt5P^uyhQmX2ZiVE%)v!@+=sKg?cOc*FdKLmLCkT`>D#
zd|3MjW*&@>ZVt@<=>CS;3kyGVedv5xe8JKg%wAZ0psPpc!|aEL7c@L!;^^wp`7r;Z
z+XvGR^C!AK7$2q%77ws|3-b?59ZnkRUUYr1a3o|uE^}ew2#;4>3Zde#_=5Qd?tZBI
zpvo{PSh&O7ORRk`gJ9}m_QB!<<{p?jTr|vHSop(en0dJL!Ng(eVCKO1Fn7TG2UCwu
z!{lM=VKmGgxbhFo9Q5=L(@#hrOg~ILto(z;AKZSJLUbA$uQ2_v@P?HiFmZHk=zN&}
zVd)JPjxcl3)#KvB@*m7zSh(O)4-<#!gV_(uM=*I<xS)pzEMLLgi!Kife^`1z_Ych9
zF!!R{0~3eohuH^n7c9JC@-TP6;t!@DW-d%0AsXgRn7J^25Yh*8A50(2e}weG<YDmz
z(@(6u=-~kKKg>Ls`M7A9e_`PTqha9*^B1Z5VeY`C4<?Swe)Mz*Q;$x=+>1^V@;^)-
zT_223$ltJh1B)-1J{V1G{K5PQ(+4vTW<I)q(fKfcz|4W=Ls<Obj0b4>1<Uuac!1F`
z{V;RU?S+L8Ed9acVf7JAKTMnu4Kokse`3QAW-iR%F#Rxpz|4iIgVRv|!_>oQm_9=J
z7iKQ3e8Cyca0{_BVD`Y=k6jfWQ8a&`rz1Q%!BQ}H!~6kr2TUBS2nzvAcd&4SDS^d1
z%wI5ZbQ-1~mVRLA9hM$p^2DYCnEPS=htV)`bo*g^m^(4lL&FuO4i^ne2e5E}`2&}F
zba9w^nEmMTg!nM`!@>>b4wyJD{jhL{*^8d;VdAj-jBXAroxse2g#)a-fVl@Ij;<eF
z9A*#9UYNP)`f&20@da}iEFGfTgHu161k4;l;RuUAnEPSj2=hNo9?f_-7ZzSH_rmfW
z%sjY4GzQFHu=EWJHy8~|2e|Bqxf7-yW-m+}M&r^CiyxSNm^*N(M{_;{%zT)BbefR=
zVfMh>0aFhPM_Bx!+e?TKa~I4ULh51V6igp1++p!bNI%S-F#p5&u=pjUA67oX^b>0@
z%v@Nw!Susun7?4^aM3V-!}P&uSp4A92NQ>-8<;*AO{{)O?Z>5`*zkvi6U^PPd=K*<
zAsS{6EdRmGA=dve^I`E$YWjiM4@<YO@PN@U^U-OTzhUVIW<N|F%s((bIt?pVVfLWw
zhsne2LDvt<_vrfI>Y()jEPcVnp$wS6aprTVepq-ywP8>&cfi!4hX+g?LnE31%$=}!
zfTbf`{)gpzm@%+$gqZ^q$3?^Z4Kp8RAIuzB_~X(Ki(i<2boUT)2h4o9el&|<>7S51
zq4>uoPbmIj@-Tm*IS<Z-xeq-&Vg84S!!_Vwz|_O?Ka7Tj6Ao<{`zK-c!Q2gtSD1g$
z&4J0o(k+aJr9&7WT_4Omboar+8(kb-KL#I~-(c>CnUA3kCID3r3pbcQVBrSyKTJ6~
z4f7W)|HAwWOUE#ILhgX+C)vMH2f@M%W*=0TXbR?kn7?7}A!Gn7f5G&@{0Za3+yyHS
zaG3`SH<-OJ{e<+v%tLnvvE?O9Kg`{*_$L(aFnur@=5AOx!tIBqFPJ)9G|WDjJ7DP-
zmwI$@n0}c1Vc~(U4i_KhURb!p;)9SmuyPBg59SV-d4&7{Gasf7=6{&EFn8c`H!g8>
zci>WoE)KIF<{wh?BP_j>>TZ~OVfMmkSUAGmh3;M$9~OQv8kTQhe3*GKahN_B4NJc;
zbujnC;sK@(orbv!rVp0>VD`YwfvH2+56dqwccQC<g$vAm==x#qgPDUa4-<#Q3%Wea
zT$nm^d6+mX9?<1s=>z6}boayb!Dw{-u=Eep4^syV512SCJkj;T_%L~xdKe80KXiA&
z^r6$R_=C|f|D&rz=fmn9n7uF>SGc3=NB1u*-N5QmboDSk%wI6|FneL)2vdiPMvp(3
zeptG~r4J^K9)B?XFmqw*2+=ToFd7!0g!IAWVeWv%H;jhK6QW`946`2=|Ah3x<YD??
z?uNxDvEhL3UYI#B8fFi%G|W75^~3x_Y&w9s3l@$r|HH%y(Xf01vlo`0Vd5}zVd)*+
zJeYrB@dmRWW*&^j<sX>2F#Ry~#D*WtoiKf{{0A!+(A|s9huII)2jiowL+8WP!Tb-)
z2k7c>@}ccBn7d*AhlL-^|1f*d&4Jkm(+{Iz`eA&SIq3Rfe3<)T>S6f?mJVR@F!ks(
z%zfzg!ptLNKP=o~>R~i2UUAt63on>@SUN>F59U6YIWRs<KP((!@-Y9x<YD53Xjpv?
za}O*W3F$+ZhlLl+99;PZT^~9h<_}!q2v-kHhp_wtvk&HfSiHj3qcPCK8|H2p4Ko)_
z8=MP^H<)@D4NHHx^rMFp%p6#_!`u&ZAKXGH1LjW{4NDg=|HABti9@yFq+sC;b3e=-
zI5nV2!0ds!7iJDF|D$Qg;KKY53nvT(Pytx_huMRkj$z_Z<royq9+-P!G$DIo2BC)=
zEc{{V7Zwk&c!KGJxeMJsm_C>|%s()FFd8NfQx9wJ!SoS|KUjRh+yzU2u<(G%!|X$+
zVe&A0VD_TB2VEbG4+~$IJ{S$t4|5kxJvt4uAC}%=@qn(55Fh4WSa=dr50i)44~uV@
z`(ZR8_rUxQOCRWbm^jQnbQ+fKVftX<1+$-2|DdNwSb9LWmk=LjFU%b<cM;NuE)UZO
zGY4HB#)tVIW-p9}#S1Jw!PLWOSp31l8D<ZRhNTNwIK%Y8XqbMO`7nJjno#(`(k;w>
zn0oa5gv)-I|6uYk_rTH}%v_i_%-y(Xn0sLEhlMLH_2}ZT_=4FBGY2M)uAUGd<`0-Z
zajA!e7cBk4+zoRFEL?HvhnWv^FN}u8E1~d%$-~kMEF5v!hb|6tAI!b5avY`(<}Vl@
zPD9&0F!#dJJxm@JE--m?b7A2GlZWYt(J*mXIKa$>(Xe=hsY9n>{(+@Wm_BqG7Je{)
z!pwoW4_!SjJ}ewz`eEkcQjabUb1zIkx;!pE%pO?$!@>`jdUSDEJi+XTnTM_p7ax{>
zVdmgchf5r$9~Pgu)Zr3``5R_G%p6#J67ml$pToi(<{p^2FdCQruy}{5gV_hOA7(DD
zbO=)qOXo2AVftX^!_?t&2h9JZ=6{&EF#BNPfy*9radPd2nFDh#EWg3r1EXQ;aM7@E
zgSj6@!_qS>J>b$0i#M1%VCrD@!_0w+qtmeb2GbAI2a9i*`RL^zx;Zfa!`u&}Vd0Ce
z9-R+MH!vDq9v2^GKFmItyK$*U7l(yE%>U@}g!r)VfrTR>^)Pu@IK$ixa|etMa}Q4c
zpw(Znc!uRS7!4E06@M^$VD`h@1+y3C9$fx|sfWcIOg)T-*$-0(^FNG+*#lDti%*z5
zE*f1Q%pWj&U^FiC(9<h=_`v)Fa~HZh(DlRA!_ouH|Ag`{%pCOif|-YIFD^dJ9GJc6
z{=uaWCJu{#7!4CAM8o0%W-qMXAfyi_4~rj|z3A}>Q-_O&>4TN~u<(Y(11^2A^b8AU
z7!C6eu6T!;2McdlyusWJ3kR4yOg%acOP?@(F!!UI2UCZxpAaABADBN0sYjQGxd#@$
z=<+Z=%%3oIF#p5sCpI0z!W(7|vHpg|H_TobO|-w!%2AkpSUQ0D6DN&BKe6tIxf5nD
zJlt?vh$;cI2c{oJ6C3U@f5P;^;sGX(Y8!4IOdre~n7eT6K$U@|Bba^|4J#i|RU>%l
z_QKLF%sgD~fcXz*4=mlm(i6f4>@1l1F#YH>%zW&+8Q|;3Vfh~xU$FRxl>@l+;}VCN
z2QvqkI$YwgaDbVEOC7p6Og+5Zh2{%%b?AIpc*DXE7Ov>(aPeXB4KoMkeptT3r4Lq5
z!QBf}2z581@Pp}x*$>kWqhaxf%Reyp;*y7n!)Tbfa2o1PSh&N*p$wS)gu(%qe_-lh
z_QAw)<sYau@N)!U;ROqSLg^o7E=)blK6rRS{RguLorZ-!EdF5T!TbR;7nZNk^~1^)
zn7uH0m^m<WVfMh}VKmHtF#Rz5V0^egU>b1H=;pxe$DtgjFw7iSJdkS+%-yhX#AzW^
z0+#+@{)gF*E8L*k7+~&$>4W8ebQ+gAFmafAn7d)(F!NzF%w8A`^B>H;FmYmOn0}c3
zFn_@EH6e3g=?@luFdCMA2<b<chxr@k9$e`jT^~#xEF56rh@lQ$0GdBx_QBkbu8a^L
zW*^KPSouRpAG&{G?uVHJ%MXOifyEmvoxuD7i(f+e(dA+05$g|h{jhR}*mMNb4@*xl
z|HIr53n!R5bQ)$4Odl>9mpQQTf~8wny#w<<%pK_V!onNoUl@%`J-U8ax`nwH-8`5)
zhCVa_So(v7AB+!k2QGiZjDgt;i+7lNVE%!HFHAi;4NIpm^)UU!+6xPJm_0Ce!`y>z
zAB+z(ALd_}ILtpVb7A7>G)x`L9&{S!9&~*$K1@F>{lNSUqtV?3(+8tr=?kVFW)3VK
zVd5}*VESM*OdU)gOdRHJn0|uk2wD!p^ux@B#V1UhSQ_RYSa=ev50;K#?uMlULi%C;
zg~c~aKg=D3%z^n6rVmyQ!)TZ|%pMH)K*JAa4vZ#bFU%ZRI)S+#=581tSNx%e7mN=x
z7Z(1w+yPS$EAL?XU^Gk|=02D>jE0HB+zS(j(XjLla|cWuM#It>EPT<^Im}#GJizp!
z)39)Yg*(hXm^jRx==xxMSUAA+!`uV27iYXf4S>nR?1SmU6>m^&@b)82AI!h7aD$rz
z(||_9?1i}-O_~8F4pWCN4~t)zf6>Dq7C$g`xb)+4C(K@$JJ9vR(k0A(^zee|hlLNi
zewccg`LOVV>4S;G(gTKmr~pho%pRC~V0>8mfGWqJVD%78J={KMxqzVoO#l|YFneL{
zhWQUoIR+PIA51^Y9T*C62*AP(rXOY=dV0X2odKu&acMvkhviRn`_Yu)<Wg!sEdF5r
zhS4}JM3aEI9~SQD`4^VX(X_$2u>1pycNh&b4_7*X#SdH^)I^whu>1=Z#-yO>6Q&M5
z9AW0bXiNhjA~65M;t!UdVCLd-KSVbJeEbEbpOAW(J{S$NhmbpA>d|Rfe8bWKx;(Lb
zSop!?0~)@B^ux*@nEzqn2a8`q`eFWt*-NZGSU98GOUORBJk)>a`eES+qv7h&8BqN&
zcf-tq(Xe~~OJBJB1q*kWK3IBymD@1$3FSkWKA3u#c`zDXKg>Na_rcr?iw{Enhow7M
ze8bWKto(qPhaPM&K1?0VpRo9ZnG2JLsmDdb^uzoOqY1emW*#gYVCrGv2y;KoUoiE=
z(lB#i{vlQ$%ssGhh0(b5!_0@pGmM6%E0}rc`eEX*@Pg@sr3ZBN=zN&}VESSDVCJB!
zN9V)B8|HqPdtmxu^62_7_|SX^O9wFhF!M0f;}C$^2lEdOWjKXl@dWb^PK9U^Fnut0
z!_pm$hPemkKbUry|6uYk{kUjc=@@1%%pAD;q2UU%4;Kw{2fDrJ<qa<Vuy9A$4=WFF
z>4S;G+>frGV*6nh5TIfHhlL-^9hBM+3l{>`W3)d=wI7!LVC5~$URXT<GY=NdaQ8sd
z2TUHFhNWLz<q*0(u<(MJ1Cxi_hl>Gq2Q2(y<q$6AFmYIZK=&ui-GuT1EWBX$!f2R2
z7)>Z0!}P(z3FbaxX;^r{%41^np{s-04|g}Z0vvo;Ji*)n3s;zOSpI<V;WRYfVBrn3
zA0`ho2Npi)=D@-qW-m-X%>C%%Fmquv%zT(SSUNzbVd@FdF!#aSOKN<eha=29LiWPs
zVg82EFmXaO%pRD(38hzb^I-7+^Eb>qbalA+u=po6ykO?R^ux*zm^iUCEdF8X0hW$n
zG)B3HW-u(gVdlWX5vCvJK3w5~9)2+6Vd`P}VeW&;!)TZ~SUC=J2aJZf2c`~PAFRBD
z#UD%`%siNSbQ<Pvm^xVcg~cPx-RSyYe3&}8yP)L(%)Kyqn0i7q%pWlKlbar3?t!@<
zSGW^$56u0rbbwPm)ZZ|9SbBqnE6g02JUWdYZ!rH8(g!mK7Tz%b!orc*asU?2FniHy
zm_3AObbT;BEM4K!50i(fhtV+q5b_5s9mDj&XhPu!izi&+4|5kTcf-;d%sv<mb2m&L
zmwuQyOg&6LEIwiGgQ>$s!_>jj8_YadxdxNR&<{<wF#BNX2Ig-VA7&0N{V;pc;|Z2;
zU^Iq)Gy#}9VfMmkLg5IDXIS{bXmt0WSqJCB?1c%y!Vg^>CXb7Txf2$igzSfz4|6}v
zUQ){|Sop)^2^z1s+=(s@OaHKZM6CN^=D^$!a|bNGVKht~It@!_Fn7afSp31vMb`)C
zL;VR$CougmdtrQ7yukGl$$;4p^B0lYFqFakgDXA2%3qj!VKgir2-y$wA50y&=?In%
zVD5#Phv6YK0a$v4>4VX*^n|7ygNsW)OdLZ4ngA>v!R&?6u>6Il9D@r>FED*D8drRw
z8vyesOdl-#(beJPL(?%VzG3FT+=WvgR08IIbp0@MVCfjD9D{<zJ1if;)Wh_{(igf$
zm^xT`g~`MCFnO3bIt}w5ES_QU1+$l6_(Q`DmQG>%U^F3j!0d(j8(kkPUSaM+cR!2|
zlZS;PL3hJ6LTOn3Cpq0f^}{Gw{KL$Hi4&q>;Q;eL%-@9c!Q^4#PpUqcewe#q=@=$X
zEDZ}cSop*I0TYM$50`l``*EdLm^`k0082mU;SRGG<_=u;!_o`P9$31C`G-)r!0d;q
zCp8{m;RUk?7N4+m4f6*s_rv@LQwIx2n7d$nT>4@5qWd4_4qW=+;?Qyc<_=i+!R2u<
zVDSml2XiM(Kg?Y?w8PIIg!vPu4i;`Ob711=G|YaOewcq@`e5Sd`f%}K@eb3EOC7p6
zEZ$)00Oo$UI%xR9`A`PT{V;tnb70~y_rua9OdP5mW8DTUykYeM%v@N!!1Tdrm^e%w
zIt`PDsmDn}{Ray_m^rX~iBlh%1kAs%bc`lVkPC}9m^p+rz`_%z59V%|{e=7v(+5j;
zu<(b`uz17e4p?}hrx#c`2+LQ*h9i2s!_3EJKe{+9JYeY%77pm{L)V9k4+}q1-2pQX
z<{v`w36qD>Fn7bk59TkJx#%=Z9z8x`;^^wp`7m{`^aG<|=>T0lIv*CkFn#cJ1I<_H
z>e2bIaDwTF`2$@YE<Q{>Og}9Caj8cahlMjtKP>&CtHZ@dcR$JL1sYDUd<ctwSiIn}
z7hN3Y4wyOU^0@f0@Pfq~(e^^!4GTB)^a4v4Fn6Kb56fS$@Q2C6?1zcN;uj_lqhaR4
z?1!m?=_A%Zu=s|>8%!Te9A+OO8s>kPdtm+`qz@(!^A9ZkVd8{n^!S5?1F`nQ{0*aF
z{)U-@(|&0D!|X>_2iFf(hb|8bFIaq`%cHvoT^^QxaHV%xc*4}7>xZRJT;UCKKTI9E
zei$DX->`6i*$eX*p?HAlgQYu|e_-JQvj?4qnG172%pEWqW)8YO7#~*Oz|`XkCzyJ4
z8r|K5+>5Rs)-HhA4+}4txftrv1Yq$33pbcKFd8P0rVWD&3rCm)EZkr;OdJ;t(+_hu
z%ssGlfJ+}t96kPEe3-c~b?7uq9;P1VZx{_rzc6*^`eE`g_oC-}m^rw@5oRwe9l>ar
zK4Se33pbcPn0v@gPcV1D(i1NKqlX8K4^t0w521Vk3m=$1bpPNA2bg-8J7M<0XjuA#
zse`!>gNBx`Fmqtx2XhyOdNcu;|IqD4Q-+fZO9wD}3Az_*5=<Sr_M(>`xWXA~9VP`0
zH<<nCG_mHv{14L)OMfsvEPOE?01<)36D<5-`d~Dnd;rVGF!eBVVEGwh3vLpo9%c^A
zy)bcDJfWKa6NjmT(J=im_uz6rOh3$Cn0gouOV2QM==P)YVfMhn9~Lj@>Im^+_QCuE
ziyvJ2FyztF6`^>5xfAAZ7!9)@<{n)3!{lN9hJ`;a_2}X-_rctOE>DOL(+4Z}2&sq5
zqlFX9A8>P^@^JNN3|P3MhZn5egymnjd1(64%MVyMz?DJmhowWfI2r>M?l2nNAFy~t
z(+20l)Wh-tOg+qA40pkGLKx`b05b>XKZq*aB+PtRIKpU{xwv&gWMJV8(+8s=vV=)o
z`eETh$OK&a3F(8$qw9yIXP7)88kXN+{w7sFESzES4KoKOPRM>(dWY$Q#XpROiNo9p
zGY@7yOdb{vuznXz9G0$O`d~CHe$eG%=?Uf^Lg^4@4$NLy`iIdl|H14<r_uch(+Br2
zG#{hugYjYY1}xs-`d|tO&@lb5@PdUa0c~(;nEPQgT!L5z)PJyefVqR9c~E_@@Pfq`
zEFNI-g3}pL{V@Ar;eb;;nglGJ!1TlHhs6V-a76bnEZ$)0g`oeT?m_n_%pWj!L6s3r
z!Q27Y4-F@r20-Ou{)g#<#RE=#XcDk`4rUITG)^we|FCobGY6*zGznOE!^#gdX$D;J
z2n%PJdtvT?>4*6r<}R2xjE2QOOg}8XVd)1Jj<9qF(+8tr{)Fj+xgX{qT=v5B<Dy~u
zVdmjdhb~SiJkZsn^I`VF!VO&>7ayh`?q6tngsX@0(c=eJuEW9^7M?JDF#o{h(bEA;
zJxm_v4wye+;<&;QW<Jc_uylYP|1fvL+(n3nr7IXsNIgs*=0BLbVSJc8AsXgSnEwf>
zhsndj4Q4Ma9AM!BQ-_O&g%`|Tm_8UEmp)kd!|a2l514td_{F6k7Cx}@65Ss#^I__6
znFF&Q=6@Isa|g_PT>4?|gsCSry}-&BQqv2}JurPRb70~GX=uKL#TSew6kafMVet#o
z2jj!aLzp^TG%UVg?uV&^g+Gjsi-x5axO<`gAyz-Sxv=y?tovd5VeWwGgXKe*JRurA
z{lU@^jE03T+@CN5q5g%bgP8+!2aJY=Gt8YZ?JyeVUYLFujbR^51C)k^7tDT``7m)<
zdVng&pkV4@_QCiteXx8-C|$$C2j&i#e_;M6<PVs=Fn7Rc3<u*7faQOfKA62Ylw%0P
z)Wh5Xb3e>n3=L=ku<(MJ0}BU2{)QO?vj>)MVBtxuewckQeXwwV#UIQbTr^BSEZxBP
zuyl({A1-m2KVbgCr4C&j=3bb7ba`BSSo(zd9~OVO)WgN0`4?s%It?=i79P0b1MW|#
zc`$u2^I-9TOFz25VfGPQkHO4``5&eq<_@@jaWG)|Vet*qhh8t>&<;O`3g%vzepr6M
zr4N@l%>6L;;8KS!4vPm^xWm#jx;k_|disIshna^?<I)e)hl_^!3tc}foMHN4@d)=n
zG+m+VgM~9pA1u6K`e5?7(jCk_F#Ry~==R{^qo-S#IWYg=(gzcV>4&)k=5Cld#Ks@Y
zJXrY+(+`U`m^@Az8osdbgM~lLJe>NV5-|6}?1iN#s5C(e=5JU%0^`HN5j}sx41~oi
z%)K!EFn_?@0}BUq|G@Y#bue>b=D^$m6URlv%z@bhGY`hcr4J^K?hlxLn7?7_aM7@I
z1k(pI50`p$aaer8;t^e*5FZv^Fn17Ak1h`jXIMNEn-5{(4)ZrGy}<Yc&4H$SnEzqr
zH%vc_hS`JeZdf?O?19lReWb=8EWN<eFN}|FFCjiG9$@C+az9RUpy2}Z2cdEZ=5JW~
zf!Pa-4?_0C<YDSz_QK2~<W5++fzdGcz~dE%0kH6e*#~n64&@lauyhO44~uV@xfmMI
z04(3Y^uzoM^9L+_p=pD2VeUm&2QwEI4{!~H7|?i!xdRp-gtWoj0rNkx?ngHl7H_cd
zK=&8AdKe#;-eLM+e3(2=8tPw|dtvbjQxEeOF7sgSh4~w%4_1!A!VyCrst{%$%-t|`
zF!#X3aiwE)_o16dYW&0e3A2~jbPV+l1I#`c4HG9s!~6@=M{K;q%!TPkj}KV*z|`TQ
zVfMn(JIp*{^~1sm7H+We5@sGOTyfbC6NlwnSUQ05Ve+_Wm^)$qfTcTP^~3a`r!!c1
z!u*5Fepq<H)WO07<_>goVdlWx4f8K7{9rWP{m^_0(}zyO^uyc>%YU%&hns`05z2?T
z7iKSv4>K3WhbqTO!NL({4vZ$GAC^8~_Q7aae#T`XdicTe4J^C~`5P8r==Q+)g!IG0
z85Ykl|HIM)Odgl}Vd5}<z~YNk{jl(Zg*S{33lCi350l5G4<?Q)9AN1U7GLQ8fQjP@
z2bj4qd(dfEc)-F5gNEh{^mv4s1M?q-J{$rt|G?rChccYPu=t0WgHs{81k@ko>W76p
zEZxB3pHz3i;sa(LEdF48bZ5i(uy{hxH?VLdq#x!^n0>JLgNeiJA(n>O2Xhyx=>TRA
zOdrf$xY98!f5Ftj{0-y7+yQeJ%zQYF7XPqxfKxq8BMyDUmVYpJ!Q2Zo7DmJ5arqlP
zzG3=cG|U|^8YT{-38hn5zQ&afVDW>?{V?~z!V_jMIt`PD(J=d9=@8~lbbT;BES_Nc
zVftX^z~o`-VKmJBuyg`TM=*JUG&KHU`5s2Y%!Sztqj8xBiyxS~Vc`x7S6DdW(hn1d
z`2$A7^b@ilmcC%_CsrTKT$p_@cf!npxeFH!3vXDw!PLR{uyBNlW6;p>huIHvH_U#5
z`k@BG)Wc{(`3|ZLPQl_0<{wx%z{+#DLL3ZOI)(WgJzQbw0f%-5^z;LB56nNX_{F6k
zCJwU)=5ClfVDiM$u>1<6iPZ;J2aQ*lJK*9t7_j()g&!;(5Yi7zAF%L(#RIY7g{~iF
zFU%b{oQok0Qx9`5EIq^g1&dz{jW7Xdc){Wu<{y}TLjHl753?6$4x#vm`5&epmVRL2
zNGSec_QJvqW*^MmFmqtu#Gs-6hJ_m}9AW-|xd%fZngC24OdpIU6mGEm4hwIXJ7E5X
z`2)>5I2UFwOh1edQvfp$7A`Px7!9)r=3aDlFnO3eVftV+%syQ1gz1OTFnur@W*;ov
zVd5|vCJsv{FmZGm7SFKs0Ha~)0$n{WKFlAm@PPRbmwI$@SUADbGt7RNKhf30`OtI-
zvlkYhFg{!z8Uto7%pRCN7!3<QG;MG$EZkt~VfMlJuyBNHKx4q-1?F#<dYFDv!w**P
z!SoZ#KWNUuIL`#;K6G`s_%Qbp9e+6D4Hgc#?172H;s+M)Fh0zFm^xfEEPuno2^M}Z
zb8zXyB@QzWM&nY4Ar1`(SiHf)5knmg0a*C}OUF2rVF<(0KP>!U`eES!qhaP_7yxxQ
zEWBavhtV)`m^tYBVfMrF5llTyKcRF0b1zIEto(uLCln8`d<RSCuyBXz$LVi$k3jh_
zf0C;o79QySg1HlF4PgozFEDcmg##?T!|Z{D2TVUKec-YfJ=|gbhv|cvi%UOD92S1C
z_=JfQqG9I4>?M?LVCKQX8|F@!KVb5(_=nLjaTtwmFD%?(=EB^AOFzuNFm<qaf`vOw
z9A+-eUKkCt2c{1eZZLf?b8zX0nFFI??u6-wnGcgEM8nb%dN>l&2Mb4-ewh8Rasiiq
zboavSCDy;N@P~yHE_-l^!`uf;Kd|zH*z^Ze57Q6xKg>Nan$&oJxfhlmVD`Yo3DGe1
zF#WJ_#-$IY9^L)0@WrJMCJqZPn0+wwVDh+VSh&E#kB~dj&4JkivlnI_x;k8ZSh|Jf
zKe#$*`Af(=nEkMLhJ_z2{lmm@rDvG=F!iu@3``sr{;+%jcOM!9W-rV=Flm^-VDd0=
zbQ-1)=3bb3SiHd8g{}|Ahq)7`9_AkyO-Mh?ov?HSO9wFhu<(G{M~H@n7t9~H)Wgh$
z*$=Z1oyKVnG@ZcYVc`d(3Hcl5E|@!D;RlNkm^>jG7XC1I5K@mW57P$=cXWANe3-jo
z;SDQKVc`kOU+DT_`e6QrxgRDD3pbcK==x!Nn11y91=EMiewcokJuv^nXqbMuIzkL+
zI)If!Fn19fk1+dS?uCT|%snu7;&KK&y`d?9xr2~?SiHf^fu(<#IGV8-T$nnTK3F)Q
z#|yefSbm4;gM|YuJ;D8tt`W+IrB|4JuyBLXFmYJ8LbbsunEPP%!`y@J9~d955sd*e
zALf2oIKsjo<{va|1i3JMFq)tSGzBnsz}$r<4d=qlhlK--hJ_z2T;Lke7_e}J`4<*m
zFmW{H7+je9VD5#L2Qc$+reA2jgSi*oKA1aj<wID!;Yw#PeK7Z+I~*nsqtWGI=@XYc
z%zl{v;rgNef`vc2zu@Yi3|Kh9%!7p^%zmgcoD@tSx;Z#C5Rib`k1JepX-7|oF!$h6
z56kB;dtm7RMq`94nn5smn0j<`aK#(U7+Ai5r4v|q!_0%p!_*U^Vd`P}VevvpA50$R
z4w!yI?uY3^r(x+17Cz|m=zN$un7y!YgvAfKdKe#;u3`FM`4z?|*8i|@gV_hu4@*xl
z`v}o6`(XNE<`U8elZUw<7Je{sLNqKKVERb47v@ixyJ6`7<{wx(fQ1jbdtmt$rXLrL
z%U!T^56iDG{V;QJ*^3@tFn6N+2WCFHyJ7xAHy`E>7>zEEZVrqO3pbeiVd)qqPmqR|
z+c5nw{}a@QrT`YsFmrJEAEpf!jxcrT`eE`g_2@J#9AW7XW-lyX!_p_ZK6E}ToMGk=
zvKL)HEZ$-IiM1DI56s;#|G@Oa!UNqtm^zp~So(vhgT+6tc!bHr(itrMz|4V}3v(aL
zTo?`04^xMWhN;Iz!|a8%lVI+F#UCzx=;E;WgSi8jyJ7lZ>T&r47LPFX=rk<7qT2`a
z4=n!B^}+b)=>_H<m^>jG7Cta{!r~3)E`sJk^E)hk!2AKDVdlcp86o$e%frlt#V5Kv
zoDX#e%-t}3Fn17&N0|9A^|1IP*4?mpLbn&@9=JQv7_jh#*$1;977u93F}YBGldKPF
z63iZ$eptGLN)x1D;R@3aOOLR0MaVo@xdhV>iwE>@goQ7<gJ9tYvkyk2mjf_&q3eh9
zq2UNiuQ2z(_%L_E)uS<B=>*-~Fmnj$hs8fk9W2~n=E1@l&03sXSh&F4k1PBz41k6M
zEWBX$!@?7ndYJoR{)N#naaj1E>xa1$=5Lrk;qi#BfdC)oUzoWBl*6TA_LFNbEWBX;
zhS9M61-A%|0SgzHdYC(4<pwN2plO41Vetyn597n)4Hlko4LBGu^|0_GRv*kbbpMkY
z{xJ8$?1zN|%zRul%pRCNSb8Hh9?|0g<{w=4qKm`g8RieTerSF|SC7tzxf>P_uy`ld
zA29o1`qABkGyg&z0xNG|;RvH){(+eXRgRN_r9YT@nEPSw!l@C51bV!{+=;FnmJZ<l
zgoZmTAHn<qGY?%q%-yhXhpC6rFn_@9K{Ehm4@@1*JY4Ew+F|nO_QJvoM#I#j(=dBr
z?u4Zyn7Ocg1f$XQ!@?7meqel<Iv5`opD=M44HJjaFmV_S3lCi34huJo@&+1zuyBEy
zhfc%%3G+A1y)Zt^U+DVK`LOVT*@xjCbPYK8uyBCI8!UcsD2EHf^ugj0M#IG63egxa
zf5OzG)A0O&rVqx2`4dLN^b;FyFmqt`z{(9m?uP4wGGOrrvmcf&pwa{>SUAJ<lk9$|
z4KRCQ@dxu4R2q|lrc;=Gu=E3S2P}RtH9|yS`e5o{_M?kKR1;6a+z&H{Se@wM2;&p0
zpIm!k<pV6-VELL@dtu=YOMkHNgqepc9ANgq)WO^j%MXP757Q4z?=X8{?t$@f`2*%o
zm^xTF1fyZ@!j%qjxeu0(VD5mq6PG*C#bN%3mEY*{1o_Z>0dqg8_QTAFg)1zaVB+XB
zto(qBL+yvTALbvpJQ@RLKFqzaaD$0sn2)9(#)a7ji%*z1AsXgSnEkN)Ku8}f{$cuH
z_QKo?a}O?aVDhkZ4^syVFPOP7ada9MJ}~_-cf-ODrXS`%bp0^*!^}a~2Xh}x9lCyW
zKFt3xedzLp_%MCA+>5Rs=3n%32j(9b4T}$S{V?~!{0&nNqhav?%g^ZgVd`M+MwchV
zM|VHWJ%se3%fsRa7T+*Fx;jF9SUBPeFLeE|as?JXF#Czs4^s!T7Z#rA_7Ka5mD{A6
z1B(Zk`(gee)?S!>u=GN#K3Mp{?1j05T>Y^0gW(=%d5p_kSos0ZcTn{(_rT<F>4$|o
zEFZx91*<n<{v(twVfMq!f#pjWA7&ngJD~1`g+ELm%wAafLe~d#4@@1*pD=g8!V@Ns
zt{>f>=<+ak!`y`~597n!0aFi)KbXBRahQ5^8m1oI-!O5U;f7`q%pEX$VSF@YIJvNV
z0}D5t3UNrl+z(54IF!MKVc`$+CoG;}?uW%EOdVV!lmRP$VCrG|Vg7*eVd|jTFesRQ
z^n3u*PbmCh=>ujCOdlb8VGe<%SD1bnA0|(ThS>+B38go<dC+_ZvlkZcu=s$h!^ME=
zhs6`RIk>c;i^KH8(hn>=am71KKP;WW?1i}-oyKJjEWTm-;qeUh7fc*x56oT|4a;BX
z>d?hu{(<R3r(yCibI@s6yrAnt=fnJip8jFxqpQcohnWL&2QL4^^uyc-QxBtI@c>hY
zi-x%m7XPqx408uAeX#gKw+|NIuyln>KP;YL?u3OKOg$_g;>yP`_rdZTx;nUd(DVc|
z7e>R>!~72uhtV*1!TbXghtV+iqw9y63v(AtJvt3D7iJGEo?(2L`RMx4`LJ++#UIQZ
zbam)_nEf#K!{QwlZ!mwL>x1!O@dI-&Og&5-M#I#j(=dC{!wKeoSh~U$4lw&*?uFS0
z(+8tr;e&2Jj1MyhrXQBBVeW#t6Q-V68kR5N=Hk!@Pp43Mn15mMNyz;${V@N+^bxWb
z7XL8)Fnus{VDSo52X!8tg6V^)hlLZ&y|D1Zr5`2_vj-LrxYWZfhB9FKVD`gkSUQHe
z1F9T@f`v28-7x#n-G!l%fB-DqVeTQI977rw53qENp#V()=1-V?F#p5+ho&6Pg~`M8
z!R&+Shl#_&1FjLuK+mTzcf;ZZ77nm{0M&+|pye{m9+*Ca0^(Tc>5y9Hz}!!qD-fz+
z;fO2TU@BqZ4~svTeX#I=`3t5VgNBA5EWThgEInhW$0YzY2Nr)Y8kcf(ahN-ab~l;<
zu<#-^-eBbf%wAad!SXSz97nSb&V{Li=|`tw;R%Z;xJDQQ8g4LsF!#gEg~cmO9+!Jy
z{)f3AW*@qK7$0UmoQApsW)3X=VD`e?1q&~jKA1f)^)MQy4@Tq4zp!)$3nv&KmLAaU
zgV_tCVdlc@f#nxM`e6ED>R|T4+z)dHOdYW_%p90|h}8#E2a6wAc)-MQ(J=SJXqf+D
z;etyaOdJ+}F#BNchNVB4Izlwe->~q5nFC8tF!$jy2WCG^J<MG&J}h0M>&FN$XgY?e
zgXx3OFmV_S(+_hmOdLkT<YD0o6Nk~TbOLiHOdLkT#9``S;xHOk|H1r^E)H`SOdU)f
zM#JI>rVeHgj1SAVF!i`-SoompgT(_beK2uYIKk|Lr30AzVCpbvXneu_59Pzs3tSvS
zBUAvY9%dg@6n@_$%s;rwO_+LEJi)>lW*&@&`46TZgNC{T7LG7~!}0|zUNH0#5P<1}
z(YTbu+zpe5@nP;K6pt`>!omR-k1&70%tfbR@dVQcb0;huVB)yKA68Dm>_;~r<_=u?
zVfLZN4=!<Z^U>W8a}SJ0SC5Mivya&H1~&(qZead|#T$$db01ti8Uq$TF#BQBFh0yY
zm^eBOGaqIjtek_zAG&&UK1@F>{$S~i*zkj?gV_&rFU<WgbJ6XE@nQPW;~&N+RDQth
zgZUdq!~6l$5A!F?UJM!<k1+Sb)Wh5dvj;;T0RdPz5Yi8mN6+W5{0EcAWgRRX!Tb%Q
zVex`1|G~_I*@LbR7Je}EVd~LoSiXU|6Go$pqw9n5VfMqqpICcg`eE@7vyYH}VftX|
zVD`h}8<zgy>Tob%`3&Y?Sh|6^ABQ%$FiaoJ9GHH%Kj12{Ghpgr?uO}unTK5$Zc$kJ
zgP8*hAKW@nWnk$IMx)B&=E1@N-CmeDF7sgSg@re|zhUVD7Qg5&faOn^y|8o+ONTIV
zbaP?mpzDXJN2f^*N0@tY`5R_GIt@z?F#BQdLsy5+NB1wxepq;*t4HUf>nGKIba#`g
zAC?Ya`d~D%{)fdcOdpJfiKF`$osXXXVETy-KbSjV`e5ZSEL_m-L+8Wnfu$Fieq!AZ
z(+9I3M#IF>?L+6o%1f9#VBrBvN5uLAW-d%0EL>pXxM)~80aFiiKa9rZ4_xU6R&K!L
zaoGzKhoyg*yJ6zQ(lGmojW2X_Vg4l6y)gZ-c!7l*ESzBG!Dv{#!t}vtn0|Ek!@`4*
zewaDvG%Os54NqA7!`uO@4`4LRf9UqW_%L_E)WhtBnM<(zhPEGJ=D_vAEFeI`+y~3I
zuy7@y4K5AS2g`SG2|^60yJ7lZ{)hPkM#IcUHx3rhuyBW^6IgiwGY{rJbp7ajbbYY&
zh^`Ku4@>tj_Y!L#%p6#J!Sut@A55ND8Ws*Pe-lhMP=jId4f8+D9fb75^ug4_XqY)r
z;|WvHbN~x)m^m;S7SFg0f~6l=zJaNSg(EC}F!V$1g@qTX?uLaoEPcZ4g~cZ<d@$^V
z3c%FC{E1Ez>n>QjN2g)#f`tRrTAUP2Kg=I6{V;dp)QA?2Fn1Hwhh`wmewcn(dO%Z#
z!G+la(+3MjSo~mUfC|9E1*RUIhPel-9D{;|56m7I4Ks&O{y{eo7VdEU=n4q%VdV~7
zKQvwlXoE|m>&KaI;5twluy}yk596b%LgGQ)2a5;v_=DwZBpoPB^zeqMhs7JrJY4w@
zmcC%+28!Vf3=A-FT>gOB4^xM(A7(!+UeV=Y{)UAYx;#uju5t+`4$}|wC(OMtah&N8
z8vZbKF!Nyc!ommLUFdw6|6u08@&(L2=<3n=uyBCc3o{26zUb=Vd^Go?$3M&+u=D`e
z2W7zg3$qVBoM7&P(NJx03T7X!aDurXW)EB=lmW|cFm+I2I0Xw2T>4?|Lbo3lA29tm
z;{h6eFm>qqVD5uE2nPce4zO^9xd*4cP;+4Nu>1ql4|5MLe?zr1!0d&ohq)IPo-lEk
zdUP7*Uzk3aJk0$t8YWIG4YQY!`_avV#V1TZEPT+_Verx75f&~O>Tn3a)WgCb#)pLq
zOdLIbz>LEcf3SGKmH%M+(d~ixpHTe+i$9op7$4?d7$0U21`Q22bbrD0ql;tcM-za>
zH_Sa~(geA%aD;_FK@Cs^uyBI88^(vxu=)V19D{<z6Rf<1*$*=h<}VD5Pyv{Jn0lB$
z2-yqO4xev@<u`P5VCv9mSpI;y8<vh>?uWSpT^|M?mJXm2F#RwZDvXnYg)=O@!Qvn0
z4?^a^)WK+&ewev9Erv?K)WQ4<a|cwKXbNUOsp%e;zhUks+Cr#8SUN$Ee?s8~OP4Tr
z!~72m514yk=>%#WoPwoWSh)*x2aG0UKg{1Sb70{H^9PI%w-5&d-5)SM4&|7_(DV!Q
zFU&uf3bBj8)WgCL#>cK2Qxq0nF#BQQf~gW!1f~wA4?P}n#T(2NSbm4)e^`9M%pnwx
zF!NzFOg&6LvGE6U4=mhZ?tz&Llg9;M;Rmx9#>b@|T^tr~FniJEaq(gH!0d&EC(Iu(
z8WyhT=HQAyn0YW7W-hvZ7#|kyF!#g!Ln!=U`5a~s%$+cQ!Sf}|A~YJN4yGSX8s7gy
z7l-j-?uO@gm;{uDiNo9p6^2u=avG)%E&*l0!U30lm^ivWVgABpF4TB91+xb|K4E;g
zLL3a3eXwwZg$oYl7{ai4hUrK54~7OD0_f!k%v>DGF@$0IVd)S<0WJY(`3rMDsrq5=
zgz1O51DAEU#9{Wr;uDuTT;ec&uzZ9|9WHTLdLuUeVE%^L2lF?KhNUZ9_QC9dg&VA#
zfw==_E-w8rd(hJ>EF59^6PJE;ahN$U_YiVFOg}6fVD5qW3nov9hS>wNm)LxZ%U)Qy
z4+}?Fc)--d+ympo+zqoIM#J>O+y~Q#i-y?)ONTJ?ajA!i!~6@gm(=isxf7<JSbJgW
z(EShd2TUI3PIMaG-7t5chd<7A2rYMD_QUkS?1AxN{=(Isf#nmJyJ7hVW-g4z<sX<h
zOh3#%7$29tFmZJIVESS9!qmb11*2i{45MM`3}zn899TUG(?^Jg>4(J!A@%6;uyO$A
zepvXRt0TmRg&)j4gw&(U!`u&}(dA)$Sh&H$3EllT(;w6TnE9~qhWQ)jZbJTtrBj%H
zVdfAj2Vm(IW)Cbs!Q2ls2j(xRb8%8Icf!nt*^g5LnglGIVE(|BZqc;CxiI}OdtvT@
zD<F~q3r}**frS?={9xgV%X*l9Vd`P_!r}#&KA1Snd{{n)nFFH<g(J+rFdAkr%v~^h
zh^1lffTe3#c%bWtr88K1hq)I<!^C0g(DlR2g~dC}URd~{t0%;V*+(e6(9MCl1LkgY
zc|v?x`2h<rLh8}w(c=r|4j7HgKd^9v`5VTEr6*YY!_o!1y}0-=|G><JnTJa~x;V_;
zr0Rp2L$19r{jhK#Ha=kX!NM69Ua;_knTv~tm6tGcVD`e&6)t@+`(WuE7H%*aW*)9^
zME57m-7p$v4$M6`-GL^7t{-L(nlhYRSh&H$1*bwB5-|6|{7*0((anLj3t|3;l`H7V
zV0>8k!f2R(Vfu0U18NV<oiOz<8s-mT>oJ%*Sop#6A<UhGXqZ1?G$Hjcd06>Fs(WGX
zgZUrkewewi^bHGFxHyyn^Cv94p~7$qmM?IHKg?cOc;ljB?t~i*Wx&!EEWN{Mn7O#@
zg=)v3VCKQ}!~Bn-5QhLP9$+*MWf;Qf_Tma}42@6$m_K3h4;3bwg6V^WAIyH3y9gNs
zO9!y{gP8{t#}$7tb7AUX{(y<Y+yP5RFmZGm7T)Oj2G(wYxeFtGqZtJAKg`{*bN~}a
zQ;xw!*GI@cm;vbSME3_w9WELcJ}~!^nr>m?1B(Y-_MnTy(g#dGIt`0YoZ$d1e_{Gy
z_QT>G77j3Rbo*g^m_4xYhlMAM4-XfZ1~eMxK3F)y+ykRw=A&uD;KKC7?SuLcLm`>~
z%)c=EVE%@=3r#r&7u{ZB^}!5+xfhm?VB&;mba$hNH=%q3b0^H-Fn7T8!Td$YU9j*%
zk0+Qo%zT{wfyNWe{V??~8s-k1`k@lA@PPRPSG@>T4yR!G2&RuvdWUI*g&)k_82X_8
zf`tQ2AB={D7fe4)97e<ZjbScS0nA)<cf<Iw@WWO9!@>*Z4p_Xy^uf%BT1S|I`WL1j
zW<Ft!5Eba<21JxF2}_?adtv?|wj74(gT({P-*9tqH~|*_F#Rxhz~T!Q4>+{Lg<<Iw
zrXFS<jE2PvTmudU%)PL1gz1Ng<Isj74D%PvJurV^C`1#0>4W(PW<MeKqq`TT4;CLV
zbJ47WbJ6v~;t#HX00ZVfn7uF>SH3|v4(30YJ{V1G`iI32Odl-YquYav4^t1*4@-Zz
z)T4`|+Ybv5balA+F!eC|Vdmjdk1h@iCs=xenFA}A(bc2#VfMlN4bu;!Vex~m4`we+
z9gK#hA6Wdu)S>G~x0hJ=!pwoW7iJ$!oLCw?-NW(=F8|^(7v@ixe_-K2tbbtP1GAS@
z{pjhJ)bN0nH!y#|!U1M3E`P(sVeW;cKUlcJ(lxGn1m<s;ewaI8=ECG*G)x>u!|Z{j
z1N3qZ<{xx(Vfta=jY}S8F0OclxeI0v%v_i_Og~H<gND}kFm*8Z!om?|4u(E70a!S|
z^uhRO%5ZXF@dS$p7>z4j;08h64^t0IcQALu+(T^q!_}dgOUOU4bPBT{#)r8JmM`J%
zMq|Lz4NM)nenR%683$ia3$veKIKm8o`Wt3HEFZ$$2UCVl!~6{kZx~IiyUC4zn7?5D
zgr#GcILuw}ctvwK%%3otkU!D1!}lM+?1#l4%zkwBxcIRA3v)lZdARi95{Knum^rx0
z514**f5XBJrXO7%rVmEL{0mbLqY1`8G`*qQM{N3m>4T|<r6X9ng~cn(o#-^o-RS;<
z>4TYvt`8R<W)95%uz1C#9wrXc2lEfiewa8+9WEL@zF~Y^>fz$h@JA0nSbl+(qc9q-
z55_>#5A!!J8pA(O{jhL`>4W(nM#J0#vld3f!V5;j-3c`pmLK8bFpW?emOf$m4rUKb
zJzN~B9v1~O2P54<-GNIVOdJ+2F#W{Z3)2rvXR!1P4>y<s0yNA%Sh|7n321{$!|a2(
zpHO)N(+Kl7%syEB!_0yC3#J~OhQ$+1A51+g-eKvQSogrphlK;oTw?X3`v+z}%v?e=
z%-t}1VeTNL52g;L4`vP_eK7aJ!W-sZSbBz;1Iu5y%!9>0%zhXRGZz*wF#FKWfte3e
z2TKnyf1#^~@nPn{+zm4a=6;y{uzU^E2cu!>2c{1u4x?e^5=<RT97e;$VeW>B!)TZ|
z%snu17!4DL<y)9IjE1=jSNem656m8zKA1QxUSQ(rG|XI>I+#9Kc*5k-^`Y}&;RFkB
zSh%38!^MZGhq(i0KP-H3>4S;G)WgCH7T+-U!1yrrFdF7Rm^xT`g2}_uA<TU+aTpB?
zFPJ)*IE;pc3rrnM97e;$VKht}M#Ig;QIEjf1+xdH59VK3c*6K_c{B#PI#_t%N|$Kb
z8DQ>*<$GfF!OVfh157{6-{|oHQ;$xg>xY>Ga~HaL3_dh`VftV+(fXkV!t}vtV)et~
z5vC7D6N)#ewG8O`Veto(Cq%>Y8O&Z%^~2mrs(x7f!qOj1Ka7T@16=Nh#XC$L%pb6F
zfMkC_ErR7In7L443<~Ccn0i<_1am)(hRI_X08Mu=dtmxu?uMlw7>%JHDgcv*>4&)s
zDov1rg%?hD5-<T~FDyO?D94b7xff<X%sg1S!q9*w0P{aAykX@J%v?0(7+hF*qt~Mt
z3ULU)+zU&8Fmqt}5{EXpFibzp{jmH1a~I4%a1Ce-m^$?Ehw)+Q3r!oG3v)Lt{9*RN
z_%QduHQ->t+KVvvz-V0Yi0(d^J+N?q(J*!B*23HkQwMV|jE0HB)S>H#@zKqP<p-F1
zh)qYZ_=edF3rCneglL$0n0}afg!G}y!_p7TAL#Pve3(73d<641JpRx%K>4t6gSi(*
z!}JphcUZW>+zZnWqhaoWT8BZw!V8w}VE%yd38i}s1EB)2aD$l-6(&qU%X66jaix1)
z2Ep7*s=csqhUtfeD=vG`#bMzMi%*z-Sop)l(e=U9!Tbv|2S&rhap{Md2Qvreewcn(
zISNyUK|{kESNb8OA6*|Ty~FH>xd+2ur~oXTz``FEZ!rC&#y`wFn0{EeK&>N4!TbZG
z32H!70E;hj^~3bRXf)$-a$)5)%zhY6DEwgQ3Knj#{14L)ix-?0<B))bKOy^J^00IQ
z^EXUCjE1=n!#ZgCh3SX68>SxSenRO077j4`Fzg{90L$mF^iM!JhBVB*u=J0ifLH-&
zc)<Jt^B1w&Vc`gi2V(WX$_ZHb!{U`txWnXO`e5e3#9{7$g%7%WVCf#_Us(9T`~ee(
zrE7Hkuy}*n15*#9aoLM*4xEn`{xI|5@&p(#cf#z4g(CrNaA|b^!zBnX(CvlA0|9Mt
zX;^rZ99~cpVDS%Ycfjl?Hvhu(!@>=wAKg5tbr=*Z-@?KVT>;F0=;AOw%szDg!{Q(2
zE<*7MOV2R(z-X8`F#FJHm_K3mz-V;$pzDL>GnhJ<|6$<{Gl$f21m+%CyrA1lh!4{T
za}Oc)=<+cAu<%5eC&Y)@3o920sfWc6EFZ!A4+}p+`q9<H^ufdl>4(`1OQ&#mL(3yV
z`eE*e*$bm#;^gXwr6WT6VetgB7Z!hn^ugp|;RMqU%QrB2LNqMgVESQvLi%9xuyBIu
zhvg@jJRuquPB8uO_#>bZmVaUC6&7wVb70|$ZVXHx%pO>H!OVg2VKlma7$4?tn0c^r
z5N1D&4^xkehLtZceYo6>OFv8;7VfZcKzAQZ9U&SP4lr|I=Hk)^i+@-+z|_I?!Tg1*
z{6|l}F!eAR<{p^2FdAkbOddTxVc`l>k50qvh3SL259S}3Ji0y@AC^yG<qu38<{p^)
zVdCgCEPi12z~UX|9$5Ip{E4m~&WDzB=<a}}UvzP}J^~DwJ7D30OF7IRFnL(`!R&{L
z<D${k!}P(#ap{AJ!{QkhA21pw4pWDVhJ_C-zF;&=9G5<rILw_eeJ~m(4pWDVM$ad(
zc*mt4T^#0Ln7y#@Lsy534~u7*epouhr5;@zJ-x%yGr@EVH3$|?Fn7S*PipwX^ufaq
znlGT%U{J7h3-dS3-7x)x%5Rwe(A^2s2ct18MiYRUgC34BcM&oN7H%;0FdE%mXx75H
zuyBC+pVat=83c0&vG&6B!Qvkl?u5!4m_BqG=5Clf(B*OQVdVtOep1~J%YU$ZjLRNe
z;;{IF>4(KPvF?Dm8)gnnKg?aQ@Wo|6EZ?Aq8!TPI%!l#O%|W*hCJ%Ez%w2@y72O<|
zdtm7jm;JDCgV_U12Qc$s?!iT)`yZAcVE!iLZkT;AeJ~p4Zd~@l#9{tIcLz+K5Dn80
z3ol~*4Ko*(K49e!EZxB53DGcnVd{wWH%vWDKg|E6x*ui^EZkw~0LF*O<Dy~yhv_5Q
z{m}XamQG;qhuH%QPgwlG#9=hdeK31r<q(X9iNn$tOdpI!Pxr8JLl=kH1JehiVfMh>
z53>(O!^Cm9A7&1WhM5P`5A#1vo)C>54>0qH)dx$rF#8F`BP_gN{)E{NGY6I~3E2;m
zhq)JKKdk(N$rGYs_QUkU%p;@^E{~QTVg7^56U%^_153}O#v{xcSop!xBTO8fM)x<&
z|1f=o!T}}^(+7(`V$&@woxt?NXmoet;=}BN>4WicsYe%wg%eCa%suGpaPeXKU^KD*
zhM5P`2cu!)#L_VL!t|4BKdgR*g#)hgj*xp`=^d7C38{z6L(@CV{V@N)<q0rg@eR`t
z^FJ(I!qPXoF)(qMK6p8e<{n((0m~mS{V?-k;Q$lIm49LWgsDTPVdlg912Z2+!^C0c
z!`uloht%>9<{p^6Fnb8muyBOM10nV3^04#*vmeGMXdYTT!Tb#if4D!O>d@_lr4N`s
zxO%8USa`zJL51NIhQHD5fw>=7Jiy`)rVef}lmW|+xa@~2gP(H_OTRGx!}P<{;i6&Y
zqlY_89G5;=x`TxuE*d>tVdlZy3DXA)Z<s$|>R{mn<HKl}`(XNEe3(43G|YXt;(?Gk
zu=s_!1DAWz&4KBIiNnGlT^uHlPQ$_rM#JnUHXLB-7uNnFHod^y0dprzAIyFj4Ko*)
zdtmt;7Vfb0gq|*7e3-qsXjr(z%!AnvtKV?xgNeiZ19LyjeK2`KG|Zha{V?+g>4V9`
z+z-=F$bMM-!NLt@AI#sdc!8OZPQ&!W>_ewv=A!Gv#YYc!n0s)khl#`Nf!PmB&&0+X
z%wAajftgDz4YL<k4&X|!=;p!H!SuuIhlM{ZJ)rA@@zLWOz1)NOi;y`mbubzh?y&fW
ziNowgr(y1Z`5)#V7!5NIT^}rdVE%@whtaTj#n1;8fcX~|Ua<5C6NkAUsvLuYxeKNr
zrT~_HVD5mK2a|`>(E1u?4@^CbhM5a953Uc50SiBvdtl)L(@!Y=(2Qe%nFn(RPWPi}
zgz1CDH_U#Rd(f1_?ZFj)#M%op7iKTa-7xpS<Z;okaD(ZCr9)im(Zyl*!u(6Dy)biN
z`e5k*7GE%T!PKGC==mRJ4$S>9KF)jqO@A<T=;b}kewaMWK6H1$+zSf_m_Ati!NL`m
zztHu=!VBhZn0gou3m0^K=zLf@LiY!{Iv5}3ADBL(?SqCJOg%b{%bzfN(ftdv9~RCq
zd6;@aG%WmK?jTh^EParxA7&rS{e=9F%RjJqhS9jx!Ng(q!u$_Qe=vW*XjnYM^ucJD
zJ7M7uGY6J_VE%!r$4Nue1G;%I_uwl3VD5t119LBo57Q5G7rOm0{jhun^9L+G(bePR
zL){Ov7Z$EK)uTzk!V8x>(UfCwVeW>N8yE@*2*BJ8OE<7^gVDIcA7&pc++j3KA1qu6
zSkC~{2Q!b5dUSbMxkqxmK@EcGgM}khm}Cm3pICpx{EMFMiA@KvbOwt*O2ZMBA7JLe
z!iCs?fTbT;IKa#!q#qVuFneL?4JHnwG0Z{Bcd+n;se_q^D?DN1u=EPk2Qvp2p0IF+
z$-`(^x`X)_W)4grEMLRaW6)6d!`uN2Z<xDb?!eH8LjV@uF#BQQh(kGsFwB0Ke=ro_
z5P-Q8W-lx~<4}$v46_ecI~^8|F#o{}fVmeIUNCVO4KojyJS@Io^00V-*#{Gc@nPn|
zXqbAKI+%H|aDee~r5~6%Fd7zaF!Rv;3sZ-ShWQtk4{@nO7l+vkb1%&OFd7!l==xxM
zSopx~houKt{(zYaQ%@`n^9LdOVd)&^4p{iX+z&Gcm%Xs`2U7>r2Q!aQ{K4W0=5LsO
zn7eS<i!Khc9~O@2^0@f0@)4#VmpTk_X!!%v2Xi;f-x%uA1kmk;>4TYrrko%bmY-np
zK~Mu!0W5xC`bZ5wn0lCgn0Zj+aZ)gKFnzevDNG%@LFnlPmJea(!14{ue02S=bcdea
zVc`#p4_x-c#L>-xnTIp|LemY*9vBUCFN_aMAL#Ce*$cA==3kh4T>eEj2Nqs1^)MRd
zZ*=vz_%Qon?uPMksYe%wxf|vmSbl<~XLR-Ge3&{|_`&Rlxd&Z6Iv<vfVD`iG!NLPw
zJ)94XM|6EKJ}jQ#>d_dmbOS5rVetX8ALcGJZ3MY6`(fr0)Bsfgvk#`9ko%$97+~of
z7G9*<3$u^ZbO*Bs7GALMgwZhn!1!?YLK!f5n0{O|EPkNc5EL~2Vd)iJKTiKZ^}+m&
zo^E0KVCEq#L}kJJg&t2Z^I+~m)rFe}GY@7jEPQb5K$U^n2dfWIWpVS+^FPcTxOJe)
zz}yQ9H<)>_asfu8hZ8K_!r}pD4vdC{BaBAZ4+|eyc?0u5Oh1f<sY7)&0|P8w!PLR>
z4?O&!=>%OLj1N-}%Lg!VVriJaVBtfoKA1X~J7MO)#EGV%{zDHpn7M?^fyD<by}-f)
z7M?J1T;{;y1zjJ^J-GCti^Kd6vlpF)r7v`SFh0zkF!#XHB`jVD>4%vE(+4vLmcC##
zTpbz%mS14@pt~RDKU@Ho{?N^V<qMd*aOsDM!}2ew;ScjK%-zJ=2eTIz|AfK`rXEJa
z(jQDe%zjun!rTQ@k50qnVftX{9_9|1Ik5DBt{)cOF#Rz1z|sv&9A|hy^B>Ipu<(c3
z3)2s?2i<-cALc%oyJ7aj%z>p3n0i7q%pWlO3F(K~52Io3fY}c-50|~L^aisJ79Ozp
z!KDx8PnbSfK7rW_6Nl#mG=p&23kz?U{kYr@vkw+;XvP!d!omyYPIAqGxfkYdSUM)?
z05k<K|H9mZGu=Y<!NLWm59SV-enRO67B4XMFq+(OfQ2V4{lMG<buLZ{T_4PTnE!BT
zgvrCw5lkO0_2}ZTc!IeXW*$r(EPQZ<BYHlA<p-F#gu)YMK1>~qhNUAIO(-3}+zX>&
z`2yxXm^dLC77s9Y5Nj{Y9+*BD4GULX_QJv&rXJ>BSbW0xFm<@hf!PC74>OP0^aqPK
zm_As%;Ia>0oKpQT|HAwM<D=Vyiw|=*EFNItfJ;5PI4r%u%po-&!Q2be52NAkfY!6<
z_QLq+{)YJjW)7ir2vZMp2h3h#;~$p4VfK;gf0%lhKVbI5{D+H%rE8czSpI|QCzK9h
z{)71+W)Dn1OdckVPQ&bng&WNOF#Rz1pzFinL*p0b514rv>Tn3a^pR>W%zrR<!_o=N
zU$A(^VJ-ar4|Mm!^r4%JZVoJdVCrDyK6-vaj~{e%VD_QwgQY8&ztHue^I`g7G%Os@
z)#2j9`~h<hE_LYQFn`1J!)Uns(KW#M&~yV+4>JcAUN9P_92X5sH?a7C`5$I3p>zz>
z2U8C-4@Se}aoLYc9Oi!*jY}OaahU%J>4&L<r4#gg0psJc2Nv!y|HEjQxrEXY%zdzY
z1k2B`dJ&hsFmaeZm^)zU4;GIwK1@A~hP8WP`eEWQ8YYj+9hAz$%!Qc)vlm9g-33kO
zFmV`-OFt|eVBrc=k50qvg@qH0hS^If-eB^uc!tq1adi7|@nP`|OE)n4Vg4Xw9?ab^
z|HAaa%p;V4Vd`P-fY}eD3E2-bAEq8=FQI$`3lCWOgz1C%2j(uAI$Si&URZj7`Gb)C
zFn_`9g~cDN+=9vDvKJ-~vma&-A$!rygZUF?FU&kxxT33v^P%|y=3n&k3l^?$^=J%O
z`iI$%Cd~kI4@@1pJi7m3_QA?~qQe8~512j}4f7Yla6oey%v~`3Fg}_xI2UF<OdWbY
zf-A(qKu<TY@PwI%LmONe7T>V&g6YQ<?r@Dz1}vUn`d~CnoRB+U?uY4vxd$c=wFV~z
zi+7m&i4Av{fw1_7>4$|sOr8)8vk#^ZW)3Vr5Yi7*4-0QtxWU{-NIxu`VCKMRSh^yl
zA0`hg_h9D1#0k+beJ~mpkFfB-sUMo}VD5#57c9NN+yUdGn*$Svg%`R!EM36LWpsI%
zeX#gLmxt*m<PTVSg_#F82kJk#JX`{m0gGo?xWQy$;;?XliQ}SS>R|Svhbu09FmYIT
z!t}#vm^>~TJ>A0M18zSKjj(uuse^?xtp36kFEHcK)x*SL<`V0Em_8T{GnZWbFn#Fp
z2{WG%4f8iF{t2l^mxqNX(fXn36qbKs`eEUKZa$n3)d!1jn0i?J!NlR}(HT(vFn7Yz
zA1pn=!U0_yAwDeJVeTQM9$g+54y2Z!uyjYNyJ6u33wIbF=6+Z?gziol9~N#f`(ZS!
zJb;-Cix-$a^l*l$gV8W|V3cc617P6<vj?UQW-iPeobHFlKh!w*em0nSF!N#ZglL$1
zVfK<5e=v8$%z=eJA$!r~VetV=2e{0Ese`3|m^m<a!}P=A6DAL%(cKI4H;jgbBTPL(
z8X6xk|HI;wpgyPqSbV`~s4zhamOfzaCe>b;KheV(7LK^W58Yl^e8B97g#%0-x<_E;
z46M9|#Um`<2>AzQF3cWuedzu{w;zKKjX#*ZF!M3gp$Wj^3#Jd9hWQIk8$7?l+zs;w
ztek<RPk6YZ832nHn0>HtM^lEA3k!dkewaUCG)x|s{V;KuJJ4yEe@Ko8sEc9g0_Gm5
zFa`y)7v_Ih_`&>zD;{C)hoyg*dKe8i7sE#E0x)x6?#8YPQxxV7n7xGji*5oeoxt?N
z{Ew~<7ax{BVftY-OdOX!m^dt)VESM*EM3FG1*RUIhNUx@K3KT}i#M1!%%AA`Vg7}s
zH<&({{V@Ho^ntD)7XC1I!qmge!(}hboiKIi`e5?t_QCitd075{g(J*7Qquv<T$sOM
z=E1~q(J+6*(k;v%FdCORuyBL97p5M@ho>KCxr)nvSbV|s!}z$=!^NTI!t}xX0hcGh
zfQ3IyKhgH0nF~uFFn`0s5oSJ`ayS>J52hX#Z?N<L6NhU+W5D7C=6)C-MibHxGY;l%
zSbBr;i49L!{)4##W-ly#!0f?A!|Z|C3!`!6S9Eh=?uPjrrXR*fSC7tz#XC$N%pWju
zboJ<bn15k3vHphT514&0{V@N)_~`b*_%Q#$)WPgU_XjMU!Sumsm^e&5%p4dWM#Iu0
zOdmQ8i+@-=!qPpgT!)#9u8$BO7XGmCL01p+FDx8k@-T6j{V;#P#L?Bk+y}D<ord`j
zrVkel%fGPrL$?o?KJ<8i`G-)r!}15ry)b{m!VTsQm^yU(VfMiEqsznM6J{Q|JWL#J
z51Im)ILv=&((v{#%-!hnu<*p?ewcb#Jj3D%<}Y04!t}vtSUjPp16closUt+g!V4Cj
zuzWyBA1oYU`d~Cn9G8BWe_`rj{)O=g*$;CsjE1E*n0{Ee;IbcG9Ht-U4s>}!e3(06
z@jysDx;)H&nEMH(8+84!bVI6s^mrgO-N4kt{0+08Q2fE&4{Zm*@*6B1VCKTr5y^ms
z11vs>)CN}u%a1U3z$J)ez`_CM4no>s=@e!kEc{@6SUBJcPgwZ`(+8tr=@`a`(dh1h
z`4=V+qhaxZD;>hz1M@E|zr)-EEB|2P==S2`!@>>beq8pW>xbC`b3eK94-0RYIWYI2
z+ee5G3m2GqxYWbK3+8?p4f8)t9+!StJi*)x(+4vLmp%+}Xg-9+8!TO7sKX%u(?`g^
z=-OfGVeTf?URXH8$}yPvF#qF92e9-DvlpfhM#J(SEPuenVKht}W<M;RVCe-GA254h
z`d~D=I+%JG4Ko*}9v2Na9~yr!{pjI=t{)aYF!i`-SU8~Thxr#K596cL==xxMm_8T{
z6DO92*#mPoEWBXhN_0Ach8rv$!qPE}Cbs;6xf^CL%v~_|z~o^xEZ$%=%pO>J#g#5$
z`f<e{tUQ3Z1E!u34buk;M?&i1^3d>u`4?smT%G^}Jst_ATXf@K=?7*%EFRI-;o`&8
z!}P;wSop)jnOOZWbufRx%)u2Ou=D}57Zx7q{(`3ym<3Q8W-ct<z|4WsgyI9{KUloO
z^ug0V)E;ySW-m+~EF59x!OVfBYjpiEKFk~#AEq7_ZZJMv9v1_e4q@(q>Bkjr=*Ga*
z!`uxE514uA>e2bI_=bfCx;!C1%syCpAfz594^vOBy|DBK^Eb>KoZ$uyXP7#eK3F<~
zrCV5fgt-%)hWQt!4_2ST;sIt3x;_{m=02D^U^L7<Fn__+<Dy~q!t@av-!SuF?uWS>
z<_?T>4by-|!}P;wm^+AdKTI9Wewcn(_@G&b!G-x3rVnNwhC(y}SbB%K8<tOD{)fdc
znl?BW7Va=}VBrPR52N84&>7Hl2uo)$ccA+R<}P&Yg!nLXVdfE1k1h|(moR^#%ftCl
zf57;#aDe#-7Cta}bQ<P9m_4v?huM#=9v2@L|1f{U+<{9yOdMt(Og}8&!rTK>he1RA
z4bz8iFFgLC>d^JW(l<;$x_e>af-4+g=^W;Mbn{{QaruLg{lta?OdZTVbaP?uM0X#I
z5A!!H{9xe$^A}7Wrk)TDtG{98390(g-9fB=nEzq+!u$aX4-9uh(*?|*Fnuuh!}Me5
zg9^axLr-Tg`v~cW8iPT>;tS>;3<U%PVD6_>Kg|7v!XM^dn0gou6DQzY23R=-i+5Ol
zgYgOJhvzS7e8T(>GnbHlba_(kN7oNaPlU={bbYXT1{QvV!X2g`rVbY0Fd9}az~o`-
z(P>zC!R&>lYgqWhXmourb7B64>4(uUbI|p{_%L%}`eEq>#)riNOg%aclZUwj#)sJt
z6GzvF&PVq@Ed1c<3S9#lAC})?@eT7g%p4dWO&gpG(}(U}n15j50oMR!z`_gWUzk5&
z=?JC|mJXoW;1tY0SU!Q71EXQ_15*drh=T!B2h$G=7aYnlgkkQ4*^8k7hXBl8SUF6p
zy|8eGg#*kQm^p;f0nA=lxWn|r`~x!|CJv)v@eYe8Sp32K4>K3$KbSrk4O0gTZ*-cF
zyW!?S%S{-cki9VbVKmGiu=I}Z4p=zC)T7fd`(XND=D}!~yU_K)%!TQPg&(>)Tzpvg
z!Q2hYr?7m5OCL-e<}O$~!_p7TA29P^>WQUc{(zZ7sy<kHf`tnq{V@N+?1Slpg)brf
zuyha84|6|3eNY9kc!ueRnFp07NWs!M%-;kxpecZ*1DJkTJi^2Yxd#?rF!#gwuyg_A
zqge~*!t8~W6X@X%(+>+LxJD=g-5ywc!qOkiT`=>Z+7J{p|HJHosYlNj2o0z#^zeu2
zhtV+ipz30PpPLB_Z&<p4sfW1-#>dc)LjYzDEL?FY!w`mrJ4_$CyD&7M3BdHh+zrzY
ziw8927+jcsT=9&d0V)6sADF$catSI;kb>#MnLY@Z0CO+QpRjZT^A9ffz{Fwc1V+R1
z9n2h<|8O}2rVf@4(cM8P|HASQ%zhXR6USvQOdRG<QuV?71#>^lK3KYenU9l(=3AJ4
zSh|Ptaq2^pfQ1*S?uW%6EZkw~2xcCdH5go&I#_(e@*gZ6!o*?rpc?=SCzyJeJj@)J
ze_-JZlZVkTb+Gt@nFsR+j7B#frk@ZE%NOM8hlMMQCS)I69-1y-{>K%5F!iu_fQ19h
z->~!qQ-_O&xesO^EZ%Ubhl#`71=9z!AC`Uzg&RyBrXLpGu=s|F!{QlcFN}t{8|Gh_
zIE;q*1C}pg;xHOL++gA`8s<KjzhL4pnpk<5KVbS`G|XQxd6;=H8WxVQaDnMVr(xj$
zGY3ZF@;ADEnE9}9gxL=hM^_Ky!{Q&N52hX#UNCpS)WP(@XjuA$<y)9Ix_@Bd1e1r+
zFmqwy1ak+>epvj&;u)roSQ@4eMiZ+KrVbX4xb(x^4|6xHo`Z?Q!Ue{MnTt-t@(ax0
zu<(F|FS>d-AFZ5$#TPnFDEwjSVfMq)0nA-+`_LG~#xt5WI2T<%%zjw7!xiFS!2AVE
zCougm|G>=0p&h3%%zl_TIO7i*{xJJt@db-_c)Gx06U_ZEd(df^c{sGgg<<IvT|XiJ
z!~6x)2TOM_|G><JTZG1d>4*7~*zyWy40<@j^uy90OdU=d8vd~GhWP_V!_32}A58-0
zADFvf;en<Mg9{5USbV|KA&kb*fF=Ob4|6w+CORCT=E3yA%!3LOq+s^I^phHHF#BLM
zK?~6oz~T=U-Y|1u;fbakg9}T4F#Wj9!_Wv7fT_n7U$Ah&<sMl0!R&{Xn=pUC^uxrV
z)?!dF`(f%~{=$`yVd)j74yF%A!^C0gFf4=$z~UQbKg>Ub+zTteVE%^r7bXrh9w!A0
zPndhrX_z~38h|DN^C!$dFn6ITBglpM14a|l04pzG=?LZznE!C<hn8P3cfx3b`p^x8
zngfe(SUkeoBk0P}`7nRN+zoRF+#GZbXndGD7!8XDn0bWK11#KN`eDYy+<_|{pxXnB
z2Xt{<=@?xdEZkxB9?bnPbuf3K(=d0!+>g#jSC7tzg%3<0j3zezVESS9;|ecydtrQ7
zx`CwwnEkMDfT_bp!_>pV31%L#;Rmx9=6>|_0E-t~_QU)Sb1%#tFg~n2z@;CTIIP?y
z6rM13uyh7<56u6B;t{3~7H%*amY#9B4<-&%2lFS)UYNUJ>TuDp_=D+##RE(n7M{5D
z!|a9WgYjYJ5%NFGe3*I|4NDie?1hQL;u)q7MiWXmF#Rxp!ptEx{9*RN;sM5o#VgDn
zTr@16VESP8<5G_<4oh#ad;_Cl;fk&v#)s*H#TzW0!`uTi2c{mKhN;5{XJ~x{3s-b~
zFg`52VCKNW3uZ4YJP7#%rXQAGVE%%oLqh2f<{oq!-8^)87$4mpn0sO23R8!RhPfA}
z9_DXYc;M0p6Nl-Cg*VI`V*L$sKg?cO_`}VCS%iazse{q5_#ieN!_pnh{V*C9{y40K
z3&X+*=6;yHuy}wg#KC~s1JehyA0u7k&=2!3EF59xz{GKAgA1d(A7($X@dq~uE#JY)
zGq^ks1}vOl_QK*3macGUgA2p_3DXBN53T?Q1EvmUFDxCvl*7Uw7C$g?7!4~wVBrlD
zM>iK&IKaeV_QU)E<HOPuu5f_a19KltJ&cB>2bjAE(rEDq^FKj-Pz5l1VetSJCYplz
z8%7hY5vq_}`(fb=(+>+zsIi18Xt=@j!^$th8X+ps?I%?~EFF+*Kg1Ob@O7B5@Q0ZT
zlgCBF+z$&km^)zR;3`MZ%N<yHMHh$h(d~!jKV111<_=>00W%-w4pQR*mVaUP!f2Rz
zF!OQIFn_?p4aO(d{jhuoa|bSa(8Xcq!|cTsU+DT^e3(07{)N#nahN)E8dko*+zsQy
z;uR*2t`EkCxf@2q#9`*Z(j!cq5Dn7@E6-sxtehrRKg|DdebDlOkbanY7)?k$OdjTc
zn0sOQ5hhQFhJ`n&=^bV+EWN?}4WkK~2MrgPdYHe_&4by4PQ&~S(+87>r5jjyz{CmB
zFnuuhz|sRDeK2{LJ7D(0@)N1%z|4b%1I#>_I64heho0YH=^y3}bbT;B%-t}5qtoc-
z!t}vtnEmMbV0>8mf~kkmuyhJj2Xim1yob@Sc!ueN(J*(x^y5+oQ;$xgyBo%b*^90o
z7a!&hSUC@K4=h}9>4S?y^8t(x(?=-YVd`P&78cH=>PL4s%>AT>1I)d!c!ar+SQ=Jd
z!0acr9->q~ET6#aCpA66^ucIi^`qwlm^+9KH&{Hu?1jYxvHD@|gz1Bs12dP9IWYaO
zbO<vCCXP!#OdghAVD`e&BP>2~>4)Vzm_GDygSiJ5zUbz_@(awp=;bslAHm86bp0^<
zVfLWQ!}P=ahb|8bUvziF!Ug6oT=v7vgQ<hr2Mae?xZv_XOg~H><_;JQb016{P8wQ%
z!Q250512o2>Vrza^ugQ<(~m3MpxPK<?t|%rsfXDMGY95Bm_8T{i*J~Ibak+Fh%5XF
z#Xn3R%v~^h;WRYdVBrlDhq)gn4vSZqJUR_C9~SR0eJ~nk9=bj_AL<TRc)`L07Cvxw
zI2f>Sg6V_NFmW8(Fyapuo-p^q%pp_{pxcA4A3a`ixd$c=^EWK~VB*Bmu=s${#Oj0j
z6J{?geG}A=BmQ9Nfl&H^#XHPin7d*6VEGePUZT4P7XGmKf~kkaC%SrcK1?6XKA1k3
zIHCB1<uh2k!OVyG3uYcnoLCy>516}%)d!0=nEPSzO00gEyJ7J_P#-k@Vc`I?7Zxut
z|HJYHx_Pj40@DZcH_RO{aa`d5OBb;815*#<quU4PL){Hm2j#=!5oQi9cf-v=W5E0k
zb3d9iPA)8+!Tb*of2g}~0a*CL+>1`*3OAT}F#p2b3ky$}JRut9Zdf>y8(y$<K*&CH
zd6@rU;Rf?ROdMT3j1QBCsfW?9@PhdVrXHP!g%iwP7!C6WjE}Aloev9lSh|6^1Li(-
z_2_)`{0=h*T^%kyES<sJ0kfajasw7luyh3D!`y|-UYIy6ykYjj^b?zoVE%>ahxr4R
zE@AfIqG90-vlkYBFmrI}Ll=jc1EXQ?L03nJ4|6w6Ke6EeGZ#Hw!o-QCVg8223n6{5
zc!q@^%-!hbz|sf0dFbH<i%(d60*eor`RMv#>S6Z6^BsY56Fr<^_QCuOHwWE9C?Dn@
zn7uIbVEG2-UYP%&+TavU{ZJJ!f5ZF-6^2uU;u)qNmcQW|p$wRQnEPSr4CW7*ILw_;
zZEy+}?=W>R^U&>unG4s5#(?<`W)3XeVeUXvj*|<s2Nv!yci_~3CIM3qa|bN_qAA1R
z!onA(4@MKxkFF2Khv|dS7#2bWVBrG`cc?IY9t{?bFmYmObaxP|52g+l{)F5QQxB)1
z`IS`tF!#d39Y&*v3)~(U18N^kJuDr<%!9cDrW_XyQwPfrFdAkqES_-bhsnceSbl@)
zhox&=`q9N<<rt~q53>hmFTwDGSqu$FSpI>92dvzKg$K-hm^K&<3wM~kxM-L<m_BqG
zmfm4BF8k2+!NMJ8FHAqWI$V61yI}fZ<v%X<=;APaFdA0>z}$td9>#~s!}P(z9i|`d
z4`}?r^r6!*{jhWolZWwP{)365)3Edgvk#VTVCE9i4>J#zeqj1w<`IfVn15mVU^L7;
znEAM9m_0D{F!#d5ap{AJ!{Px(!}P;wm^wl<x<6p$03m%adGv6H<v*A_E*h3UVe&9@
z2>KtIu3+H}b1%%_Fmaf<xM-MtF!eARCXP!VOdO^TrVmCFbU)Mpn0oa1hvg?i=?~^#
zSUAGWgNZ|}AxOdU5llZU{}Hku-C*?afTb&Rb%gjZcficUr5<KJ%>OWd!@`eHc?b)4
zn0+vs*z}7_KP)}sayKl$!s<zwKVazw=6+JcA7&oRAGqv?`46TJ7Y#EH-5i*HSh|6w
z7nnIPaYEq$GZ*G>m^m<ULNqLW!0aUy-{|JS{0B=n=<<a4F#o~g3+4_OA6NXr?1QO?
z`5TrFVB#?I(cJ+{cQA8c_Q3dr>_;~b-Cme`VCJH$N9V)T!R&{*AI3*lkHLqQ=P+@M
zc!f!z(J*(z;t>{JF#p5C2~8WEi>@B#ZdiJR<x{u@GzQFnFnur@CJqZ1G;J93Kd|tI
z(J*sx*$*=pmd{{(n7uIn!^F|;N9V)B0cJ1E9CUSr_^^0`*-xoCF!x~C3r#Pu_=VXE
zs|R552XhCma6k`ln0+vPF!M0%M-zaV4@-wI_n;}m$%Taz%-t~aaB6@`z|sfI-LQCs
zN)x1D?uCT|OashZSpI^E!)RFf4+|&sau?<gT;TvS7v^qQJi^=q6UP<)F!#aS4WnW1
zftd$$FR?VtURb^&Rv*m2Fq+u-gM|;wURXLn4^Ld~fY}G5iM1DI9!ww1ei$EDxWn|p
z?1AZn(dh9?$R9BKVDSetAC?XX#RE(pW)3V}!rTK3UzoYLXqbMOJ{TXDdUSD^dKe80
zA9Qu-e02Acnr>n0VeTdr-stwj^r6$Ra79-S<HP(1(+A6Uu=t0C10i!@>R|qWnGfT`
z(g92yorZ-U%p6#J!`ubWPv{z<e3(CA_Q3SP_%L~>a-0;*y)btWs}ELQ!|a8bgVQ23
z37G$2;ZLl+Fk@iuh3SXK7qotbsY9n>_Q2c?(~r(4lpbOFVDSbs7ZwgM|DoFt<HO`(
z{(zNRFmqw%z|`ZSVdW3H`(g2eOCL-eW-rVgg!~QD2lFq?y|8eA$rGZ{^BY_}H2z@n
zaB(OD-ClHm!on36E>LZ73O(P!%!7%;!V%qEm;tbOfvH2M356r9{)d?Z%daqVVDd2g
z2+=Ti!~9K1J-R&19WZy}@;ACZn7y!YhNUA|dO%l?&WFV>EZ@TT#D)VboM7&Tg(HlH
zxd+`|LVQ>_lA4}j=EC&B`~wq5r(y1dg(EB-!t}$!1zjJE4^t0wFDxA3;SbG6F!ks(
zOg~ILj3ze!!_0%FA6UG>+z*o{M8n(-(@$*rgP98pN0@%1-46{Pm_C>}Fmnmfuy~`E
zepo&tWG_q}7JumBM{M}R)WO^jqhaX@W)ChJW-lz<VCE6Zcd+<{*#`?pT=t-g!|a8n
zS6DuSnF9+KbbT;B%zdzQ3!`D~f%%8%bObdRW-rWLF!w;EF(_C#z|_HLm^)zZ#Lx&8
zfQ2_qJ&cB>Bba-j$}uRIJk0+v8dk1iXh0Kyl~1tng82s)KWNG^xUg_Sw-28Fp!pMB
zBTPNazv$rva~HZe%p90F%-t|?7!A{pEB;~jqw9x-C(K_k^Kj8H{jhk4rDM4LxHQ7V
zq2U7yZ&>`{ayLvHjE30<3rCncVE%)t!=Rz|!rTvwcUZi^+=HPHDgX<Am^xUv!Tb$#
z4^%l$3g%y!J7E5S@o{QIlR)<`EdLN2j%dcg*O3s?4+{sF|6%5!hYL&`orc91EWN?P
z4HggR>e2Zy`(fb@3pW^zt{%pRxfiAmW<Sh5g!~ON2WBs<9D|7yOT+Ak#UIQ+1ocDH
zFD$&!X;?bKX%19BES<pYhpC5!KSB3E&4J|~n0i=v!onG5E;<df7nToT>4uQ|VdlcZ
z59WVZe4yKd&WD)~b0<0<<{xx@Fg`5Y(Cvfy17;pfJ+U;*T$1%e;{g^9uy7^TURb(^
zg$FEr3F(KWdziiGG%OvVn+M~=)WhN(rXJ>hSa`tHW4H&+-!O9s<zJY2u=s+x14hH#
zfngt10G7^S>S5-f(@^CY6wH2Dc*Fb&GY=LY7#h(8VCrD{VEF+}8BQ)N{ldZxM#I7%
zr$(p*ESzBehPemkFQ_uy6g2)|?uPjTW*%;h5E=CJ2XjBM=^x$yuy`QW{jhjMFE3#F
z(ZwOICQQP@fmHo4f5Y^{(kUSuVg7)HJ1iVw;<)s~+y~PKvlm7aG6$B<(AC4z6)auA
z{DtlgnEfzyu=pe7ZkTy6dtv%u?t%FS-98u}W-m-XOdLkT+yk=*CJv)v`eEu|;xHPP
zk6`H=CJv)v;;?XniNk1^KVa%$;xHPPF3|0Vg&Qp1VD5zJgV8W`=;<65Zn)e9(+{Iz
z?t_IROdrgAm^xfE%pWj!!2JU)R|x5Y*$dMTa}O*%;L;BZUsyQ7Xqb99ABQ_&_Q1*?
zSh&Ib12Z3{4rVTlhNTZ&?u6-snGe$kr=jkG`5QgFVetTS2V5T-17<%=9nAeOb76cm
zZ3r&Z9kB3%#RtrOghKo*SU!X4hq(v8E`%)1A29tm!wsqhW*;oxVESM*A^TzJ6s8_#
zAB;~(KRmvm;RkgV1F`yH>ftoh9GHHB@dnojWuWPYrBA3VK?>#{nEkNuz@-tU9#{Co
z(jPAUu>1jw2N)j~?l5=Y(hn1dxdX<B>4&9fm^dyPW-rXWFneJ%EM21OhowK5K3F)w
z`0)6H#tXWB7$4?-n0}ahVDbcMsQEDW5^8s&n+FSjLh+5R51kJScbIwT@`U)X_#!pk
z!qmgUkB~j+^62h{@reybn15m6597o915XF&7DM^4^o4FOEPcVugDS_MVCe;B4y?TZ
z3lEq$hDN9WEd9aM!SoSwH&i<V%zZHb!)Su(2`$~i`~jn3=D_U1MWeeD<_}`)FPQ&f
z{(!|BEc|iVi!KgxH!T0d+yOHmT|GJ<t_~V5F!vDh2Q1yd!W(7|EFNL*K(`OZhsndj
z8%D$Y0W%M#9-W5y8>SCtFS<H(KFnO0KA1T$e-j)2Fn7Z8D=fTV<pR3B=zR3_19Lwt
zAEB!!#D}>D=08H}Ve&BhVE%`R6QW`MgxL#Azp!)x3s+q3f$4|Y3-dpWhM5cFqq_%Y
zF3esS4T}$S_2_(b{jl^vZ2ZH_gT*^cKe6`1^rPnkSbV_b3DK}{g4s`R{pkLO#TP7m
z3ArB@Z!q`6!ULB+n7OcY2Ga)%2VDAK;;?)NOLwsFf|&zTM=DLKewaUC`2-dYuzW!%
z9l^pGW-m-X%wMqZ!Q~H_JWM|<TyUvJ7l(x#vEh!cALd?|JJ98c<)eomvHD=)kINk}
zd0ghf+zrzYQwOULaiwFJxv+49#WzeGW*#hG!OVrxFneM8(Zyl$2{R8S52In`!t}%3
z3**CRn0j;?J>FsNh1m=954t{Fd<_3U+ab8rql=SkFRYv=S3fM=VEWPBMTn1{e_?!>
zxwzsFW-ctAVD`h}0ha$^=E2mX(=hcgb71)hrVb{KD?VZFgqZ^?KVas-^uyv8W-g3|
z`48q^m_J}N%>6KRFnur@mX2ZSVKht}rVgEkxd-Mxn0sOVg!u<uAB+!k4=nw_#9`*a
z%!i2+qGA4og#*ldLi%9xF!#gkhs6U-o>&^@ZkT%r>4SwAOdU)gq40;Phxr#4Z!q_x
zhbv4y1`RDIVDSZ`G1TD@fQ2*6UYI)w)w}4%!R&?UC))i`gJ9tW(+?|Oq0$5?Sh&O7
z0rM9@jZg)!c!1GRVS*IQ9+<mv)ps}zg2o>#{9*pUsU9i;vlpfwW<SjTgyIiZeu4QP
zCJ(g^HwAS+OaaV2MC*rYgykccKVbO~Dov1rg&)lQFg~n&CS(9CJYni!_QTA9iKEjn
zb+GaVW)3VIVCfoNA50z0y)b{nXqY%m9lCxPA7($gK0@&ilZWYpnFC9|uylmWepvp7
z>4&L<`5PuqC?BJ%hv|d)pV;_=g+I(+Fnut8z`_fihUr6hCoDW*?m*WE^DoT3uyBKk
z!}Jl#2QYhJ=?rEbj1Myp-F`S9n*U(>aHTi6dNc-lJi^Q&XfM<pSh&IT5ejdpHaLZD
zKP=qf3J5S@?u6M73pWDV;L_;&VdlUU5MrQ(Kiqz(I3evYd6+(!{jl@|lgCBF!Vea1
zF#Rw-%zx<mVftY9!qmgk56m1`enQs|%QrCfFnO3bOg}FBVd`Nt%snuF!_yIgaD?S^
zn0@FpEPui5L8oE*(aRxpb-4I2|H1Ub+>c8=x;V_=Fn1GjFS>ph9~KX!+7HtY3vU<=
z6DLHY$2ZJBg!IAW(d&JfKX9cxm_8T{3wK!f!T7LnfR#@$_2@KA9ju&&yC0hF2!#jC
z{jmH4GanW%=;APWbQ+cpVg7*SOG564*$?wK%s!ZZ(CtCz!|a2FJB$xA4_!SvAKgBf
zewcsI)uZ!a`3L4-n0e^xaPpz?1hbb^{V;#S!VAVHHvPfuLyvEGI>PC2hy=`DnE#0l
zH;8Ub5*BZ8e_$%XDgw&~uy7>S{V;#S+z;bpwFHYGF89L9L0tZTrF)pYuyh2YVd5}z
z(5;4rJIuc@bujy2;xKjS`eFWr*#lD#qhaX+T^}JnEIf!!2e9%1W-p9}`2(kYX!!<K
z&Y{zU^uyG{!UM)9Xg`|4u<(buAI3*hMvx2B2QvqjP6%p5QvizxLh%SoAF%v`Za+*M
z&3HH$T|La*u<(PWXSfD*23kDA+>Ndb7av#pA!skmKs5Vd`2i+Ph=#=v%-^u|Lr5P?
z9zFbs%{MUpu=t~v{e<EhW-pwEh94~42&Ff;J~Re;IKud7$}qUFavtV?V#5!XK49*J
z(J*mX{R!uz*^D0UuyhDh4@(EIe1N7MzCR1*Zdf{irGJ<>%pK_ZVd60Lu<(cJBQ+jj
z_QUkU%0mo$aR|Wl!`y*G8HO;-9+<r_|G>f#Ljz0zn(kowU^L9%F!#Weqtoc=20i{^
z@dpcMbp7b=Mwf?~2UCYG4->}~UNHT*`~eFen7?6sm_0D}!_p;89G!-xPnbQhbPaPC
zy1D53Vc`RFFU&nK^Dxw-3Bc@w>4Vt|3m-J)7+hF9!t96ZgQgD*4QK+e@&ZPqNfYG4
z!Wrfdf*Q~iz}yY<Ka39xS2X1~xiEhdoBq)ag2gk;eqzgCm^zsIVc~&pJ}y2i++q4*
zG@<Z>xf7ONVCKNe512SEdtvzk7H%+gFmquvOq>u63wN0NVdfLk2a`wF56hRZ^bJ!-
zh=%zG77m2`0SjkX{KIINenRmH3wM~mVKhu1vEcxZFCz0TdN{!Bh4~*{9v2^$eqsK=
zr4E-kESzBW!)Ta!u<*pC9~Mrq_=l;3m7g$kVB+XBOdre~czS_`BTO7!AB+#P2WAc|
z{9yWE=>w)7ord`j77j3duy93JkIsjwgXxE*1DL;I=A-L_r7M_yFn7bmVetSD4>SW{
z=EM9CQwPg0F#Rxd(X=zb)Wh5hqha9%%fB%5(e<PA(ftVv4`TJBn+J<eLiGsDy)b{m
z!VeY>=;osHVd`P=01F40dFblV`LJ+=g*VI`^zcB}2jj#13DXZtPcV5xG|auQ^gyh=
zxZDkM2Q2<@g+DBOVCrH1hJ`!KUodf8{($L+#RJS;uy}>Z!^F|ef!PCdFD$*m^uf|G
zx;{7`t(<}BgP8|chl>H#57P(pKQ86y;^^TBi%)cQxcKP$iFP;CK=g1X7+z3)FbWoL
zF#p5SAxxeSjcz}TPe>n39+oc1wI7yWVBtn;_@k#cn0{Eiz}$&W!@?QnZdf{or5~7k
z(DlLiFnRRwg6W633#J|y4f8+D9GH7x=ELF_T|Z15=3kgT7!Aw!IMV|(-@x1nGY1wf
z==R~_qq`edd4)?q%syDS;Zlc792Rddf5T{)I4*rKahUxueK7wJ@&_#5VES-|Gt43!
zG|b;Hb71j-Lpe@iSbB%WBaDwz0}csTx*^sbu>22G5Az4H_QULh=_5BBVetWrcUb<x
z;Y|!-n7y$0fSHG(5KRE)Z&<j)!UIhiCKsCjVE%@+J7D(0@;9z@2=gDzy)bvc_%L%}
z{z7*EOddwV^ufXpCJu8Kx_+2H(c>AW9~Q6Z`f%}K{)X8L^B*qt=;AQ<!0g54Z*+Yy
zKDz&5?t#hUqG9&H?8T)HT^tr}F#p2%Fm-S~G@Zc2VKgj!U^J}Ug1G}`F06cm>4VWQ
z|H9P4^ugi}CJuK8Oe2hjxeG=^#bNqk=0k-EQn2`g*$WF-LK<QEVD`cE!)SOuLNf^F
zFIc{X>4$|EOh2yl4@)1gd<Wyh%!TQP@zJb@b7A^m_Q1jqrXS`WxCS%^%-t~cFd8Nf
z3m-IXa4yVzSo(qKC**Fp0Z<0a-7tHh!UQRpJurJ=;Ym;<Q~}K0=;;?0u25wd6fC@9
z@ekv}%!7pshDN9W%)c=8P+<mGc@4{#aB(ySEM3C<50i$un~;8(dUP5V-^Au0bp0@Y
z!OTII$Hj-)2akVfxj@JqSop!>9~TXaA9VX+=>-;#uyg=37hOF%9~RCqdtv%ve025b
ze3<{x?T48Ii%)cY=zLhX!|WwiA1ocg?1Rzh=?dLG7$4?;Sop%i6Gp?-q0=z;!Q2Z=
z_po$8Z1}^}!_0w&1I&DMd(rtY_2~N1)uHoY@rJGs7LTy-K-UN3!`ufeCt&8m_%Ir#
zo>&?d9^~qW#V5=hV(o>cBUpIA__*}L{140TFnur@CQc|F!Qu}_!_>p{!_32#o?!lj
zse|c*xeu57VfMiE!T9Lzz@;Ci4n3Y==AetCn*(zndN{+(h0*Bx2mzS?Vc|kZJxm@}
zUckZwmY-qrxM-L@Sh)ksKd|_M$;14MZVoKGVD{poVd)xOKg^%F^ug>$*9YUH+YgHm
zLi%9-f%zBa4w$)wXqbAKewaI8;RY*DaG3*>hlLA_hQ$LcJYfF8We&PH%wMqd2QwF4
z9gGiC4@)mFK1@HMd;kjvn0wJ_Lh%5z7nbf}`eES-b0;nurVkeGFd8O~OCQXiF!eBX
zFg{#Av>gPKN2g)tz`_aUei#im2VEnS538qO>R|d{;R02LlY-d~ODC{+!>Iv>1T38p
zj4yQk(C~q!TXbn$e3-jo@eFe}EF56&!KEMOUs(Esse`#67LG7`VDc~;7SAwqVB#=7
zOh1edlZVkTb7Ag9cRws%Vg7^ZL#JWo0L<Mmb76c!`q9mU@p1VNCJ#$rF!N#d!o-P<
zPndr6aDe#>ZXXT?%pO>Hqth_=plgTu52hX#PjG*rYe3_}?1i}-W*)KbhQ$voox;q4
zxd%O6qFIlV3rjb!dIhIK91<{lVgAFR3@!`{H<)^u`w4{uESzEPgr$F2xWFwSmVs7|
zz}!VhJIoz0`(ZR8eK2`gyu<ttGY=+@i-x%mJ-lH4hs7hV^b4~OrXQUT6Nl9+=<a~U
zGc5hV`~l04F#Rz1pzFuQhoxhfxwzD!i^I|jEIq*ekFF4%4~=)2J{S!%4_!Ho50i(v
z6Q&OqUNC>a)T7fd{jhL?g+DC)!1(C;(D|@%hnWMTVex~m9-WV#jtIFMT|Z1c%wCxL
z(beJNqw9x-BQEtA;%M;)3x5oCI0Rtn6J{@rhKb|Q#sEuSFn_~nSb887f3R?Z(J=F1
z`e5eZvLBXyVCrGv2;&o5j==1JxgVAuaM=qJhnWx42eY5pbO$pBrVkcxu<(b;6QW_^
z0MiGf3F$+Zhxr?(A7(DPIt)HE{lUV4P`W|a4=XQW@c@^Hnu{S0RRD7z%wAZ0K&2UA
z=?WIEFmV_Sizit4!^F|eg@p@D9!A6T!NLpX4j7Hg-7x(yb7AUW=@zD+)Nq95ADDY^
z)q61eVKgk<VBrOG4=j9O?u4mFr(y1f*$*=h<_=up4|5;PKA3-Dd|0`TOFzsWSUkY=
z!Qu~Q9xQ#KyPFW7SpDes!OVrZ9~Le!adaByZkT$Qd9e6ESC7tzsfUFFEZkw@=;|@}
z(0m5d2Qvp2KN#xK1YqF{OSdp{VE#f=j=_bcGgvx;#VgDlV$&bo-B24~;RiDxDh#J!
z;RACwOg&5i%>B6Z!@>uqA7&o9IIj4I`4i@DnEfz+!@>jR9$Yjmox<D!(@!YeVCKQ{
z7tEh9dtu@*br>{Sx`EjXa}UfMSa@LQhYG;_4|6Zf9kB3+D#J;^?1TBASbJdx!onS9
zA3=Y^G(u^ZzhUtSGY2Y-lY*rexO!-K!omZmMl=anc)|P+D@V|j;pD=?4;F4Pb70~)
zH9{p|?uV5FF!#X3p~^5RsDEMMkFEfw9~MsN@-X+|@-Hks!Q6$eAC@m+=Ag^N#Bu3|
z$;14Mt{-MTF8wg|uyBW&1JegfSA_Bp%pI_JgXx3$A7&3B8Wzql{V?-j=^B@QSoovs
zgZT@WK6H1(?1Py@Y(9X8FEl-osvq5b=<Yz5C&Y*8hoxUa>S6M*aE9rJ(S+h3rXM~2
zVBrTd7p4wI!^C0b9n3zMIWTp&XjuA!>4U{LJU(z~go?xLh2?*kK4QZS=6)DWs(z?5
z;1tYWm^p9>LJYKY2MZUNy9mV}EF93o5f+{>cj0mdOdU)=jE2QOEPvwC5A!cfAB>Mn
zJ-RqdA9}e3_dmJ@C?A&pVg82-!}nzq3OAT~7!C6$%ss@WW0-zeyureaTytRN!O}G>
zTwvnpG%S8$<v)yu*-t1OVDSZ04=ev*;el=+Iv*BJF#YKAg!r)dgVBW4!{lM<17<JG
z9kB3+#Rp71It}wTOg*}JF#qEUe^|bO=|>NDn7eTK3l_gHbub!cF3dcbztG(eb1y6(
z!1TlXg{~eaA6ovv^yBg`x_+4bu=Iyc!_pBh^I-a6{)NRCjE4COrVibH7$0UYj87~L
z3n!SrVKmI$FnL_&!Q2INCya)<ALcGtI>KcRE^%1;fSHS-4jON8^PzlLdV|@Ip${ei
zRgcTxF#o}n<Dy~eVEF*2dvO>5b0<tc%sd>*;leO=F!vKG=inNl3|KtD^uyvC?tW<a
zK$T-q==x#l7-k+U9pTEqFmYV|hUI@4jo|>O04$xr!V%_vm^h4vD#xUt{)D*$=6)C-
z7S5O&QAJ?(!1TjtSb9WNjo`uLVeW_d7Z!dnK0*g73+7H({J~^l=E33#CXP;{mrt<p
zgXu?CkHLqAAI!Zlf5T`(?uXeA^9Rfvm_A~|AEq8gW4IHC0L*+?`i1!)hjI*In0i=v
z!Tdoe++pb-=3iL+!O{av99O=E>4&)+t`03eVCpa&4i$je2a7Kl4KG)r>IhLV^)Ua#
z+)v1Un7d*52NoZ&@FWzDFneL~4%3ejE;u{@i$9pZVKmHK9NOT*F!#aS3-dpW4_AoB
zfSC_d4>Jdr4q)MjrVY-8xf|wB7$0UH%pZipAC~`M?uFS83s0E2aEpm#z|4WsL~4U8
zgSm^4epr0N{RvI~uyBBx12-6r0h5P?7mSA43o{>08=MQX7d;;l3OAU!uy}^q3!`D?
z!7V~#z}yX22lY41J+O2Evj<H(1I&LgbI|2s=Hl`POdMtox_<QZj4lu3!~759!)RFg
z!(~299W33#{0nn8EF59s0aK4o!~6l$4@);N_oJ&P#D}>Xm%q^U!{P_s-7xpV<k9uP
z+y_$!vk&GEm^ivV7$0UXdibKd1E!u>8di=IvLBauF#BQt!KDtDI4nKD>?aie=;pw}
zgOGlhxiEDw_rk&z-CT4&%%AA`apgyJ{jhWjGY7_pxgT9UIv?gvm_K0g1~U&`Jt02K
z|HRr4GY^*kVBr9>AC|sh^(0I^jE1=%=5LsOTr^A{oQ9T{u>1mZKP((z{(yxKTpt<(
zJ-xxqfw>P&Ih+ggAI!Zlf5Y^{?1yVWW5CRZg%8Y~F#iynj$p>3`vYb!Or8)8vmZS^
z2)P3m|LEq!!T}Z^=<=|3BYOD3)WgIH`2!a2F!eARCJqZ{4ExapVCrGvh$fAb3-dqB
zzr@-HGY}TuF#F;9q3Ii@4xNUjKbXC+@PzSU^%&edGy`Da46`2=&#>@=(J*t+wBx)t
z7p4#9ADFw)^~1zr@e6YY%v~7j2?&s@A7&rSy|8=>O9!~Z59S=0e_{5+^uf%7sY9n>
z@dwNAu<$@vhl>vjH<<k}8s-j|KXB=XiNn$_jE3ohnFq56rk+?DmTq9?;nD|F2eTh$
zAIyGOIN{O{3qP287$0U1E`4xuXg-658(f@71}wf|G?Cii`@CW9CN=(H;RW+AES%x~
zhWZPadtmBe<ps?BuyBBh<8&9)e3&})d;)U^EPvs02h2X0dtmVm6NjY_T>4?|gQ<sw
zC(K@SccGgDlZV*@(~nNW)S>H#xf>P^uzU}T7nr-y^}+Zsb+Gc6kT^^pM#J?%;{(Qr
zxgS=q!_?!VVet>s2cu#B!KDu-j&3h3TwwBqXjpn76kfQ@!4;0U)WgJK;REwOEFNL$
z2bR8I>d|SKI+(lB{edeSVCrD@!O|Bj{lmmz?nAd1rXJ>QSbV`~m^tYBaPeX8huI6G
z3Hcu;57Q4bAC_(ixdT>?!NL)p4@*}tb1`UWx`){h^FJ(HFw~<7!0d(TgVBWa!{P;|
z9u}XlbO;lN#S@yfa4syIVftWvn0{jY4-0QtIR-Na#)n%-Bm<@oW-gK1;L2d(hb#TV
z6vF%miw77V=6*uy2j)&#c)@6x{V;oQ($Mq+GY1wA#OjBsgV_rUFPQsr#Xl^a!|a2l
zCs=-hg#(O6cRws#VCfEB9%de{@Q2xtOFzurxb(x!hxs3-9_9|1`*7)pse^?V%p90|
zFv10<0gZ;)3)2sa2SWCu83X6S(jlSvgKK~?VDSXAA4bFC6RHeO!NL!{+=A(Y(QplD
z3|M-FsYCZaEd0>4!MU(-gSi7H0W%NgE|@s6G|XOd^`rX(mX3(E9~SR0f56fcx_(&t
zhpB_bH?H_X*N>h~VES>{hprzMf3WlqOFyviLsw6T53?8M4npeD<zev!a}T;aE<P+B
z!NL*d4tO|1%U5*$Fn#Fp4Rb%tJQyEcKYIMZ)Wc|)xv+Rb*N<*L%pPLX8_a%~K3KYg
z@zL`Yy8SRdOdZTUu=t0W4^xMWhN*+;gZUq39xi<_ahN(-yu-|a`2%J?Og*tQEc{{N
zg02r{E=(TAhq(jh512T*epvc~*#lD#OUJPMgrN_I0L&bC`o*CRT^ME$EWBXh38T@~
z!}u_HSa`$20p@=gAEq9iMz<Fx5A#3FKQMJLc^D0|7v^r5dRREZXjnM~(+8tr?u4m>
znFHg)+z<07Odl>9-8^FT!OVlXAD4S!>fkiAT!MuU%zl_TgzSffA1u6K`eEe<A^kA*
zF#F-|B9Z~q53?WUZWs-72TtRl{)fdAOg$|9!~6%6$7v1@37B~>_ux<l9|wcwXIQww
z{0F0P>4(_^OLs6D=6;xYxb(xqpIG(i=D_?%$erl=VC6o{y@cF}t{>)an7d)}@bVHG
zzc6uh8Wztm`*G1Qd(id6?1AZng(FNIEFa>ke_`ri?uVr_n0>Hx4KoL(9-T&44|5MJ
zUI>LNOdjSAn0^=yE7xG^(Cvr$52hdH519L5^62_te3<#@;RiDhmQG;m(P^0fVeW(R
zVfMqs(e<J8Vd`Q2hS7w=AC_-n>S6wY=_A&DSUAD-p}P+k9~M3^`(f_Er5;@z<~~?>
z!@>cUzR}gg_%M56{)MGCm_K0RF!ks(ES<pA!`uPOmoRsr>x1!O>S5|&?uUf~Odh75
z5Dn9hZXUt#gqAZf|HJHo#Usq0FmZGm7LG7`Vc`xl4?SO?>xc1S_M!WOP&mNc2eTih
zAC`|`=A+Xvd6+(!yJ7JOa}T;c7$2q{rXS``n7?7}fT_nt!`u&353?U;4$K~0`f-WF
z(jm-zT<UO%!|WxfADVv9(;3VkFn8dx2WCDj-NR^DxT2ejVGh)OSa=b#4_!YjpToi*
z#wV0+Ve+tc11!8??uD5LOBd+&!u$=RammBdDY`t&pSbkH+=ELV-JR&>!|a9m6I~w0
zhvgGk_`~dl`5#t3z|^DDu<(J!H!S>N=Ax^I@nQO4=?tb1W)94MF!ks(EL>pfVg7}=
zn~?o5f5Gg9xd)aWVBvvoFFGF<k1%s!_QUc4%pP=og!nLj!Td=`J-R&1{W$eQ^Bqhc
z7Vofdgt;H)KXmh9;RW+IOg)SbGZ&@~T|bNu3on>^(P^CN7wUd=`(XCMXqdlX_Q7bF
zKVkO4#EGS0=EM9AGY6KQafKVqd|0@^+y|p!=@#ZMLgv8CL#JWxgVE^nF#BQdh1m}a
z7g)RyvLEJeSUA9Fn7uH2(CvriZ<v2!?tqyG^FOZmhl#`NgP8-f7e?c<ALd_JKErSy
z0RfnPnEfz!!~6jYM_k6h+yzqyGY{r&Sop)_ahZcl9A*xTMpp;39~O=<`(gIM!XH=p
z1rvw)ADzZ!FS<Kmd|3R!?1i}xCXb7TmDjL%g!u!Pdbl`R`9Z9GF#WJ_g2gw?UKkBi
zhl_@(Lk~A}K1>}hb71OW`3a^D7A}P10VWSizcBk?;Yld}!@>;~e=zsJ!UZM|a~C=d
zvmd4(#)r8F#z)r&<HOX!@(a4ZVetf052Io3g6TsKKbSgPG%Vl1{0nmjj3(r6nEkNu
zhS9L_hUEhoAD26DiNnGHmwuT2F!#djhs6WTewaF3_Q2G`!T}bZu<`+yKDanqKExGY
zaP?>mSUQ8*52Io6gQgtLg}D#iy|D0tnFCV?*9c|6)ZvO}m^h4vYJ*cSf8vsd#RnmI
zSp2~ahB9FKU^G-1gM#@V7LPFXF#i+E7qIYwnFC8Vgu)-@5SYC%f56HeV)et^33E4$
zhPi`~`(gTF`32@4m^dt+VD3eyVetfWKP)}M_=NPMn+M~=`~jn3;>6N0f56fyE`2cb
zVg7)rhxwmS_`$*rrXEJa%)w<ZOdM9ez`_eg!`uUthp9)WVd({(hNVZC|Iqcp(ic3P
zpeca41E)Wr<rd7pF#BQpV0=REhgl1AFU&kx{K4dL(J+6&;sF+4FmrI}gNeh^D=eH~
z>6KXhu>246H%uSSaD!S53m0^M!}u`wK$T-qFn7Z2h4Eqf35Fju{$TMBQxA(bn7J4h
zqY1$L4|6Y!hJ^zxKG3ug<ig?w7QeVO!14*YJj@+18eJZy4;Ef9K6*Ifibq)d!NLpX
zUl<Kbk1%!U_QUzmc!2pAW**EPaCJBs==Q<%!^Cm9A7&iP|1kH$#9@4xyI|tzG%Va;
z=D_TQxr3npp$5Y2f$4*#KSKIp@e6Y=EWN?p152M!YcMF7J{S#C56c&X?1$M0QxEe$
zp>&Jx5LkS}%z>GUt_~L;=3kgUh}91ZXPACie8Sy<!$O$<VE%>agOwvN{jm5!HwI=O
zOdTx#VSJdmF!Rv$!_qZOJ&cCwgPDh}4;LR6?=bx^|KU=PE)G)<(@$*pqniWs7t9=(
zx#;T9`LOVZ>4VWQadh=C_rbymJ^Wz$VeUcK4@+k-b+CMkE{@B7SiXXVJIs8TdYHMe
zd;yauM8n(*(+}el(g%}=r8k&<T<I649-T(lPbmIi{>K&mg!~T+Z&*Ht@nP=AX+N~y
zf!Pa74>0$@^uyu_-Jfti)Et;TnEkNy05cb^9*qIh2eS{IhM9+^4TB5wKg?cOyc2Ri
zOde)0&hWyp5h4JKA6R)t$iEO>2oh#L%-t||5bJMP`h?jJGY7^;*o4Z0*$az*m_J}N
zOdM4gf(Hv<Sh&MvVCf%LuE506X_)=6@Pes_*$WE?m_6wFVSHG5fvykc4!Auq4LE36
zc*ER*Lm7rJ%zT*rgxrg604)As@dgV|SiV44597nqDZ0Hdf5XgysVA0(xu4W<K#vDP
z?nieIEc{{d4)X`PIzoJOcfkCEOCQ{y&~l8__=LqD%-t}W*zkwtQ<!=f4NDKW+zoRl
z%s!YqU^Fa!;L?vCPB48iaa{V~;?VGdxfe#m(j(0MFn7ZB5n#a7!}P=4ML-*dG%Q|m
z#Xqc^gM~lL-!Szsf56HobPHhaL{GOceK7OU^%3I3@(s*gFg_uDF!#Xp!O{gTcfi!c
z@)^wCFnur@7B1-a!^C0n4huJ!dkN`BHxI^#nLAMWVflt!`(fcttbSN{!NL)i-eK_r
z3m058dVIs|g~cZ>b71zu)WOm@Og+p!u=qf?9~Mrq^n)%BOAj#rkSY%gM_l@0?t-}!
z-F%q4VD3bhhw)+lhm{vFeXwwZg$qnQIt>d)n0}afF#n^gN9V)z!^%6D{jhjISC7tz
z>4T*MnEfzuboFpPwETwYgWCrchv|ofGh82(0Tag+Kg7l#F8zeehsndrU#PR-6e0Uz
z3Ss_-#UIQ(m^jQFn0j;?<_=gogoPU{9?{jq_^^0~#TP8S5lXKx{V?~!^uz3j@nPz4
z($IK@r*9}9<_?&@aO%e)L9TvSIK%YA(<2Uh(1p>%1Ll5Q=D@^Z@eQ*Voral<ZXe8i
zn0i=xfyDzXUBluTT|dlzSbV`~czmF1K;y&87nr@oraPE1u=EL|Vd)NL4on>`8s>hO
zJj}l^|HH(Ir76`9izk>rh_x3Mj<9${r(yEA%!8?i#UIT7FdA3<qniiwKg>LI8kfId
z;;?i8%SY(u;YwF9|H0BT%v~^ZVg7)L6HCMN!OS59VDX3UZ&>~$q#tG<Oh3&3g!IAW
zVeW+KhlvwQ!|aFUCt~%%)WO0Z7LPC*J)gnU!)RFgf~kkO0~T&Db7AUXG)x~XzoV;z
zg&!<jVESM*Og}8$!~G4dM_}TF+z(5aF!#d3ALcHYeZ<l*|HHxq-CsD(fw~)}ALbvJ
zKXK}VNkH|(+z+!KCXG(R@+nL`EWC)d50+nG_Q3cs8r?p0J}iD=>S5soa}T<DLVTEh
za^nr=9$5GjvJYJzW-lz>VBw1^{lUax{)hP+ora}Lbo*fD!_>p#0p<@_zJb|;t{=un
zw;yI7A$>4;Sh|7P597nkh1r8n!^(Y_KA3s1c!c>2S9*l$hlM-3`7nRratAD)aK$$)
z9C69R!XM^-bo&Xp59VK(KVj~LnF|X?m^;wThw)+dz|4WsF#RxfglL$1VD`bxft8QA
z^uyc>iwBrK7>!FGEM8#hVD5#5H%uIteptGIxfiGVaT$O^9A+NOewaNtlw$~^>xZRh
z423uZ(Bl)v$DtfU7?zG;?uWStT_G&IVBrp<iS;kcT$nysyy40Z==Q_(!)RD}1#>sL
zdP01dIWYGSQjabVb3cqmm&e72g(J+}u<(b`u>66pAEpmizJZws%O~jiVetf02Xi+p
z9ANgs>_OL$&WE{!SoJXdFn_}ACsjWzy}<kfGnWtz%U3Y{F!eBTnEzqo=rk-}!NL!w
z4<-&X7sf}|4|6X}9lAWsJXpA(%ftAv@PpAXeK0;D{jl(c)n_pOz}*FH7sBj=(@=4k
zJurPRnozk9(+A6M==Q_>0k;p00gE44IAY{~sQb{gVRE7BVD5mqA7(zL22>GP_`&=G
z3pbcKF!!PALhxYrz|4Wk!15E!T$ngI4YLPbJ<LD2@;}TRm^{oJ7!5Ozko#ff1x!86
zJaqrS?1Ry;_=UL>77noV3X4yedSYo<c)`pkRv*khnEy%D4|6{(9AN$;6n?nE307{v
z!UNqOFnut4VDW&i50`$Jc`$#Yr(2l0Fn__+qth_|!Tb#iKNt-YN7o1A!_o`PewaUC
z_QT2rn0j;?W<T7W(EJQDk5D;;ZXV1&n7d%%jIIvOhuQ~oFN}uyA7&n0JvswTKg`{*
za3I$GF!#dJKe{zI`DpeNo9<!Z0<#z9Ul>iu99TMqsfYO&=6{&EINgmV0rNM^JXpF$
zQ-+g^9&e=ThZzj>2h1I~;tv*n@N^3^07}Ec0ayBgg)ht=nEzqn1{25S519S1{0UPJ
zqhaPjor6Ka^ux**m_8Vd%iVB;py>|gPgpp>^ufg8>IpE=-2)2;0@~ox=>EVJZg36g
z45)u#@efN+Fn^&dhx4KO;Cv_p=6+asK!xEHEdF8P1mnYKm^dMSz}yLoHy91G9~Q1~
zi*Ybu=><mPP=+B4b05qdu<{&3Ar1kUKA1VM@FwJLSos6f56h=8|HIq?GY{Q*Sopwb
znEf#QFmrK*2YPyg>4VX*bcD+vuy}^4gXLqGx#;dC#E0p}mELgbho)bcy|~<qOFt~!
zVD{ruhf5r$4;CJ{)Zr3`g*U10hNTObesmfZ?>Nnch8rxMz`_Bh9;P1_zqs5FQwO7A
z>R|B*qhaD0G}Ili_=EWa<}R3fF!VtMVBt=zdZ;!y1#=&|`{4?3FktS2*$d0RIFw@u
z!`uz?H--WN0<d_2m9sE^z{Jtx6Wusi`2*7r<HOPw%v~_^(DlRo3yUw9e_-JP(+_h8
zhJL63%s!YqVCKQfHK;P&6g2(7^x<?b4ijMNVetra4-VxBVKjST;fGLwp9L%5VeW?M
zhuIGcM_i`B)Wh_n(=h+Q>_ImNW)DmsEWTjz2+I$!ctO_><HO`(<si&ln0}Z$VCvCn
znEPP*VeWy^Fn^-!L+8WN8O%NC^0@dg|H9k}<Kt2f6NkkQ%-yj32h&f;-7tBWesuF-
z;Ro{<%w8A`^CygkrDIsQ!DyH{Fnur@W<Si|F!eARCJ%ELOdl>9=1!P?T<UO%!`ulA
z4_xYC;xKoi>x0DutbBs0$DpD47d_p>_%L%Z^q~pB!XK9IVdWN#MpKT-g}MV4e=r&r
zF9iL8X(Ctz<`0;=z=9+YuyhPF2bWfuI#_(b)WQ5gC>&t&uyg~XVc`lZA7JL<qG9&K
z;vZ%XEFI(02Xj9x-NE$1Xk6hB^B+tdEWXgq!R3CKIJ*1L&BbLuF8`qGCpG@j^~1sk
zmwo8s==x!NbalA+u=EBCSM=~f*9Vh_*#j$wV0=REhuH^<Hy9sA6AC|=K6G~x;={~^
zr5l)iu=GnP-eKtn=1=r+fSF6k99-^14@W}kVDhkh2n!cN;SX~+%)Kxg7Cta@anUgS
zu=E1c4|4}DeK7N3`e5#b@nPYLOFt~0VC5G~9n8Nlb6|X!JdB3LH!gc%=E7*0J{S!X
z$E6=84zmZQ4@Se}Vg81R!)Ta3ob@2I-h%lP79KEtuy}x_Q<%BvG|U|^b71a)#SglA
z7#|j{FneJ21uR?$rF&Sqf%y-XUSRrRG|WD98m1oRUYL7ee3*X-xdRqHF#BNcf`un6
zAEMh&hz|=tSojiBk1kJ0Kg`{*_=f3+iNnGHW)4gqPD9fVOdTw|U^KDrhJ_o<9We7?
z=>ZlmaC^}hF!eC|VCrEsq4<N@k7g`EE-YMN?uNxPEc^%=fUX~A4$MD9y9*i*==Q<P
zK^KSdVc~;r56u0rd<j#Ji-x6hSiHk%Sa{&l2a6Y&Juvse@+D4vPzjiNSh&H`3oQRZ
zmEojd=>_Ia7!5NQr$!tSu=s@e2Zu78!mxONm1j5=qDw&I9~SN~cc3f7#fRAo(+~4M
zEIeW1i>@D*PGI_A>S5suqY1_b)cvq@1hXG*FH|1gepotz*$dMLvkw-Iuy{q+597o1
z!Q2Zo7iJEOhN(xVVc`HX2j(6ajjo;$AC`V#G|WAO^ufXp7XL8+!1#pp!`u%m7hrr?
zJ|$K^%-w{-9cB+K{lI8gcoMP?mTqC;4CBLSV)eu07n;ss?jSbYVfMh>4bu-3Czgh#
zS9rQ7QX{&(=<yDd$7LMMKA5|4(dhbN=EBs&;vHr`EL>snjIJNXhxr?p-iS@F==Q_h
z0i$8&!t6n(VeW*fhs8H6-NM9ixd)~nS9*ixM_Blx+YfUe%p7!im^d!|FnL_|6Ou;{
zKXmuN;uGc%boasdFnL&b!DyIyuyBQ`$DpD44(1P{^+64SsfVQ>Sa|^BLzUsAaH)ft
zgDd`F;Ro|Sx&<(CSb9R2hnWjghf5w7kLdC+a|xvnLirEoPgwZF+yxUS6o0UAfQ2JW
zAIuzdcN5~n`~^!#uyO=lAIx1adtmN|>4VX@!Vi~vm_G>Vhxr@kKA3x8;Y~<Cx_@AN
zn186HALc%o`GjbgdYJvV(kCu+VBrnZ598xf4{KkbhdZ(M!qPvCMt46fp3v=w#TP8S
zqN{_s9~PhJ`qB9?b+GaXrXQA`(bc2#(ftq8Pi#2A(hbZUnE%l2!NrH=2V(7mnFF&2
z=3ZDhz~qUgVevq!epvXD>u*>+N2<Lr^)P?K(j813macL6AC@m*`q9H5<}O&gqMHK~
zhlLv~9$@NV>5x!-!t90RADFqY@PzSU=AzRudtvHf{vl*9Odm`g%)c;m;NggFA(Rhu
z7tGx-^)P?K(gjpGK?;`tVeTLlf9M9n^ugQ@^B=l8oP20I2o@f&cqP>xF!N#Y3QK1&
zada9zykOx7(+^A6==xxMnE5dOqsKq4aDc@hOg*XLh8|9^@PfG?=003BOh3$j=;;WT
zK3w83_Y-SB+#G1U!@>bqc)``9F<|b8*^efTlMAy4J>FpMfYGpYfNl_a{K3K>mY!hg
zg4lcr%YU$VfYF4~Kg|EQ{DJOn7#|i-FnzG}0P_dT|1fvK^ucLpyutZU2D*NzFwqpH
z`eFVdH9f!_2=h109k6r)lgCBF!U?7yZa<E2f|~=ChpC6@C)R#g`h=wySbBh&19KPL
zJ~Rd_-r?$??jcq`nsE#;^I+~mm&f2k?SrXDk3YD(Ff>90(9<!@-LUW^q#t4m0|P9)
zVfhv&PKbt;12A{P+(Sqox;!l2VD3hjC&Y)jpV;ySW*)4(g3++@hLAb1aD|08j1P-{
zxIdur3$qVK!{Qs3ZejMp`~fo;?k<={C=JsOQ-@B&^ugQ()rO#;?t;ZPOg+pTV)H%B
zKn#DwB#5M8;f2$^ILv{C8!Y|dP=-?&7XGmGhf^UA30Qc+!VhK+q40yb8>S!T4p=zA
z#9``iSkC}cho0|X`e5eb(hrk|xgVDPVd)DNuekKX{E6;vSbPwQPjr1S|G>%>T=v4`
zVfMiM3v)NjTwMC$;?R5m^EXT%EMLIY;b6eR9p-MBdtm0_&;}QVg%8Z#F#Ry|VE%+_
zAjE+B2WAe;TwK~<_QU)Ei*K0yuyDntA7&3sJ&cB@CtMnFh@;yNa~BTfaA8<Dp}PlH
zJiz=7OaCx;!)RDJ47UJ{0rMYBJ-WRxKAJW-7nX0({RcA-mJZ<>h-AR*C09Rsc)<8X
zTF(Ge5Az41^aD$uF!eC~xZ(}w4p=!0(+9H$7B58mADUla=EBki%v_i_AsVI+=6+Z@
zCZrE04|6xnUYI#Bd0aHiUYI<L4@(EIaDvIhXqY;feK2*faD=6QT;(7v{lfeSvkzt-
z%>6KRFniHySiXhD7tCImIJ$Z`9~y5kdtvT|r6-s;F8g8PF!#djgZURGj-Ef^_Cpyk
zd6<7;;Q{jx%si-aqA8d@7)`WBs6tpcz~U7uj6uQVVKgipVBtq7AEK*=g&WK~3=0Ve
zpqmFvrv$Vyz|_Iq4RZ%9e$e&9)WhNlrXH65V0>6QLDvrp7nna`_Q2u`rXNEe0RfnP
zm^%n4$B>4(2WCG`_haaU2*A=WOg}7NL1YP&F!#ga0Txa;bwcwodOX1RuyDntALd?I
zdWZ30?uUg3hJL8M81BR+j%ENX{lI9LG|c}n_Y?9zEPi0>VeW>}uzCSzJ}w&Of0#M2
zd;~Kemp+&{Oh2jd4f8)nIKm7jl7^L^#QGOzEG&J%?8h13Fb!xlOdre~G-;e%Sh&LC
z31%-$9H&O81k65|epr5o@nP|Z%RHDkOg+?C2ADW39AWValSijv_QLWFOg}6g!{P;9
zADj=(FEIOH?t#&;bO=|E#(<?Cm_0CkF!!J-$Kb;B!PLXTjgUDodtvI(-ASzfVfMnz
zfrSIi9fa~f%tPqu0p>55JRuqu&*<(Uqz@L~uzUb>FVXfx;~(aK7)@$90t+`%^`VCw
z%>A(VCFFltJi+1{mXBfi0+)W6I(WE26~fGe=|@jjP-XD(R+xU6dRX|uXjpv0^ucJD
zJur1Jb6|X8!x0w$FneI`fZ0PV4bumUM`HEC!UvY#VE!T$f9UZJb3ZH|Vdmj<2Q=NH
z+XHhCEI;AYk0t>#2d0nIaDc@ZEF59+2{RYXI)YqS`h>X$mj;+SVfMqq6&9X^^uzSQ
z(jAP3`2&_8aoLY94hw&n|6%5%t3&6*+zrzQa|cWuT|F#ZVg7@uhlK}>k4ryH9Ht*;
z9?Tv>`eEwOX;^+FS3k@hF#Rw-x;?n~u<(cJhxrqidUSDEe8J*@SpBf{0?WVX=HlYR
z{0DO{jE_q_x;U(yfTedB4a-01>S26zf5PGo<_}o9fT@G&gVC^Z4yGRFZ<s$|G%k1G
zQV-KdY&gK&4RbHdJur6?OT+ZRXkzuj(g!RaVCj!o{V;tndtvDgSGd8=g_i#?eXwwV
zl~=HEgsVqmz}yXs2Q+C0SUCbyhb|9uA1?i{bc0JCW-ct=(9MU1BQE_g^)Ua#%z>pF
zSo(yS15=MdL&FiK9~N#fK8AWU0a$*6#UqS{*$)dRG;MG$EWTmxgwe3@fyF0W1C#-i
zhv_F2Ur=ob3eDdz|H90J@evwGW5L`5^ABmd85m&U2BXpS!_0-b6BZsYaag*5=L>WL
zpnO>T!QvejZ?JR(Rfdy-rB7J6!Qvn09$XsH-3e0<^A9e6<1!ECPnf@nbw4b9!Q2ni
z4>J#!edyw_aDas$%pd6Lh~>k~frS$;eX#I``2*%}m^h(ugxLd&e^@xc{DaG0ba7aE
zCsiM;oP>ot%>C%*<Kn}@nN)jW_Q3oN*AFchaM=eFhq)W3ADxDUBg`I{K6DzU4;Ftg
zd6;`(@dy(~r(ynwse`2#ba8ZjFg`4u!|a2(2j+fscfs_*#9{Wp(h-aglZUHEW5CiS
z%-yj34>Jem9yD!mE-d_E>S6wdr6ZU)TmudUEPi0_hLwjfKFnMk+HnfQ+z+F1D#Rgy
z?k*h47|_ER7N0PG!15)!ewaE~Jj3E0M#I7p-F$R&V0>77!pwt-6QW`92-8Puc){Ws
z=3heg!R4X(9OhqGJj2o<EPUYV(HJoGF#YH>EM3Fm5luUMoEPSAbbDdp05b<&Ka3Ak
z2a7M5yI?fTe3*J%G%Oy_{Q=7dxb(rqVc`W!Z!mLU?t`g=(J=Ked6+w4`d~D!cty7d
zmJVR<h3SWx53?6W!|aEJJ4_tiTy%MuI$Sg?zF_v_Qin^N+WK*&KV0^s`yZCCajA!y
z4-0>ozhUtSOINt`!}P&uSbW3Gfw>2lewaF#KA1XKejsE&OdYJ8gQ<tPA7&mQ8a=<k
z^ux>}q#x!Fm_E3_q2&}I{jl(ZrGG-|Ve+u_4YQw6e8cp?Xjr(zXjuA%(J*zmXju5e
z{0qykuyln>A50wPZ&)}Js}Eg2%>S_Phr6H1@Q0ZPqhaX*7A`PxqG_nTaChL)2(t%P
zp25;FEFHkY6Nh&AI0wxCFdC*GZZ0kZpyDup!r~DsOpt=b7tCH*dL*b3ssI)~aDPI>
zjZiv<*#ol|<_=hTg3&N>sC94(mTqC{VD`hzgVDIc1EwFQ4xNUDC(iJP`V;04n0lBw
zIQ7AOfXaaBhoyg5Izm;2g$H#9Odl*gVo`}z2*X~eDq`Ia3rCna=rq(g3<^EIVBrW0
zKUloN(ige`FnO4IbQ%^OxY8jkUBTP|GY@VbG+fc`#l?q(1FrOjOCQV~F!$qfFE0JC
zc!IebW)6&oiR01_vk#^oR_?&`!QvB_eptB=GY6&)79Oy8g^3fQVdlZy0}CHQ`e5M>
z(+8soxgTaOEFHko4XODErXS`Gn7PE#F!vH`FDzVO?uF@xxr30su=s(cdzian<`61>
zVD5yu1E!u>{pjw4nM1CASUkhhExP%*_^|u~vlk=$2$Tab^)UUgaE0+<@q=MM)LfW4
z7!6Yo^9Rgd82X_CF!!U!AIuzBJVBLXP%wL7G|WC24Ko*ABP?IT+zoRFOdYH|fYC5<
z7!A`83olqYgoP)p9D=FGMZ@exFE?Q3!sK!3hl!)x3)4?1{9yWF;RiDZ7XGk&0aJ&I
zhJ`asA0c~T{(^-YEFOr}4-0>oK3F`#{0;LDF89LnD_k8kJ;UM+<{w=8af!q1hlL|9
zb?D+S_rUxE^B=l8bUrK`VeW>7C%QUZd{{n#*$Z<&j1LP}bp0@UVd`KsEdF5OiZdNR
z!yBd^?rx|!x_#(;n7d&1!t}$;LsyT^hlLl6hPe}F9-;7u`5#83y9XwYZZ9qX3on>^
zaH&HVhq)8xZdkg5(S-6Z%wCv&bQ-67pz#C?Z&>)l$}yO`2!#VoKP=tB_^@<|?q761
z%>A(Z3X2DHccANoxeI15p>RUikDgx8-2wAIx;_{mCJzf=SbV|wuyBCc1Jg$=4T}$0
zc%bWp*$<0vSop#0hlL9)oY3{d#9{hi=D^$yGap?a1|OP^VD5#P1EX=dA7&0L++p^@
z_%Qu2_rcVo(=c`D=?&(8SUN)22jj!aZ5SWsZ<x8TaDvI>qG9fY*$dN8to<<cFd7z*
z#HMSQ|6%syawmp3H2z@WPsqLK`eFWtrGFR=3rBSIFg{Ej%wAZ$!`u&}Vd~Lon1A8n
z1XTcY4=fyUxdRrjFmqt`!}P=WFn2<YCz^ukgV985geru&gHZl~`5zW<F#p2B6~>1e
zi$Rf_Z(-pF(+`U;Sh&OZ==lrg0GPXA>R{mn^Di!aF#Rz9!u$^tCzghl%P{xC!V6{|
zOdh9s(0l`PFN}u88_Zms`f*6W{7tNWn7uIf!r}ww4;<FOg<<}N*#~n!EPmk%2{2&x
z!txUV<#1_OI)J$Y=6|>X0t}cxVfMn(1<YJr+F{`Z^Eb?1m_K0Y5mz|C(k)Cs%zhXj
zM#IF>-2p3KVCv9mn7?4|Le~%D!~6*|59ThIJh3!PKP>)<)dve7Sa`wWky!V`)WPhB
zg%`{pFnJ93K=U6=9lAU$9%1PLT^^QiVD5#vAKe}3`q23>^I`VE;t^&Zx_VrEn0}c5
zVeW#(FD`vBaddlO;X)|=!1Tky8D=lh?uW($Oh3$CSU3{0AEq8gqx&D1J7D=2W)I9C
zFmaeaaJ8Rc?t+CI%>6KZFnO4{xM*1Vhq)hS9<lKTGZ$trhWlX(aL_ROVfMoEFU&tU
zv|$Lt?1!a8T;YbH5h?)7PcV0osvl-QsqTiUhlM{ZKf=Nh>O2ez7C$ie!rTuthfp}c
z!WpI?rXI$J(J*!B4uGX=n7?86qU(djFS>qoJ}lqB^b@NOW)4h0%ss^FhlMxHUKkCF
z7nu3zG|YUMdRTbCXqY(6e02RVKDv7Ja3t0pu=D`S_po?_nGds%SQ-|duyjSNK3KTG
z`~l<R(hqYt%pRD3VfN$F2NQ>>gM~ND--PUi>4)VrSUC(ck68UMf5K>3K7!FOd(dfE
zJi+XR*$1Ox`eEUNt{)a&xZ)R<?r_P	=hL`LJ*yBo7O3bp5b+#N~dNKVkV57Va>8
zF#n^Qi_V9I1I!#)coJQHL+gK-{pjYv!U5f07$0UI%-t{=T^yz!M#KCMOSdpSOdb~Q
zF!NyIFdCMA(A^7*512TiaDdqhvj^rLSa`tfBSgdW!|a9mpO8M7JWM@IKg|Cyc|tVI
z-|%z;jaNeYVE%;ZgV_%=4;DV?=D@^Z>S5^wmL6f@2op!w597o1!}P)Ih3SXoW0-mj
z8tPA&ei#iihtzO{nGe$kqhaF2(y;VE$p5f%6Xs8tyJ6{pkbSWDgM}ANA1r<d>4&L<
z*$Z<wp?m`~7iJ%fhJ_a_UU8LwF#p2sgBl2P56nDRd_tw+6wF+hzhUZN_QLeTHQ->t
z;s>S=M#IV%9NI91Vetra2aJ!Q5KRE)514yl`eE^brW}I{3olqWz-VIK53?Vp59WVj
z;~y4(F#WJ{1V&>x7%G77ZdkfO7sureSUAD#g{3E`@eD9`!OVq;!)SE<uyBCM6VeY;
z4+}4tyJ0j;9A*#9Tyz>1PO$igr5l)jn0e^>V0>6S!}Q?_514vfG)zCS=?-QN%zl`?
zFd8NfQ-_O&g%3<0jE0Hh(gzcVg&RyCjE3cFm^xfEdOX7Pqo*fa`eEX*c!cSPi4#l1
z!XM@zV)en)!Qu@@!^8>EuyBI8pV)ARnG16-jE4CGSH6YmgV8X3=zL<sAEpkLPhk0m
z*z^ap7iJDD-^0WS(XjA?`I}UGVfMk&4ZOU9wrgPS!euTj{lfIY;vZ%nEPP?%gYF-g
zewca~4Qp4Rt4HU<%!R2(w;v{tt`D6L3rAS|!Q4Ttesp_b=AqkzlMfAVn7y!Y$EhBN
z1k4>U8iz8tFf9II@dt}9Sb8Glf0#dE`4{GXSUAEh!oh%<4^vO7zhUl%*-LD`hdBpk
zFDx8j;;?cEmOf$PFdE%nSa`$S4|5MJUBdLiXmoWj_2@J%`(ft5!XM^-n0{D%z|;|<
zVet+N4_x^jmpL$fFn8cmhb|6N4@++__rt;yT|JBs3qP2>u=s$96B}+Yf0LSSVESP0
zg}DQko?-69MZ>}u=6@Isb3ZP9xWr-ZArx-t`eEq@<_}!y6<r^U4@+k-{V;#Q<VmGr
z<`El?F!N#g6&4Sqx&!8ZSUeD$9$@JVrXNPb(la4{poc#!9m8ls@d=ABn7uIfz`_L<
z9xy%z4Xt-z=D_?9iwBrLF!bRNfVmgu9vB~8IV^r)_Q2G`_~`1<`7rmw)WgCHW)3V|
z357pQAIzUH{V*D44!Zp?K1@GMJv_Xj;R;iSlZL8;>4&9boa)geVCrG|(c>RYIR+Qz
zURZcyD8MBEbvH~uEIx55$0ZJnA6PiT;u9v0OCL-eR$jvV52FeB2WCFZ{V;tn8dthS
zw-=@#mVRLFfvLkq!@>&|e=vP88katpIL!TUcS7R>mVaUD2+^?g0@DWze`3o&nE9}L
z0;`8$e3(2T8s;BZyurc~7CyN2!|Z{nhv|o<CwTav8vqS2m_As%qlW`5p3t?!#9{hy
z(J=R+>xc1S@eT7QEF56&fQiG@6QW`IVetraCn0?>d6;`(;Rh2Zmc|+0xD0^C11$Vt
z=?CTyT-spbuyBIqLzur|;xKg>G@8AzbOTcdOBcBE0W9C3yBnq-W<Rd@N7oMvcbI-y
zI1q||n7?7+1*2i&=>CG452Io61ycv(!}R0wKTJPN9v1Gf@E|t*pt~0q?l5t<J?IQ*
z{Gr<mv!779hs6s_Jvt4GA9QPBe3(4CJ{X^%`=JKG;uoe5<{zjuK?>a;uyjXIBUAyp
zKVb0>l_pF<;{oP=QtgMOADF$cbPp3J<N%m`F#BQQ2Q!b5eptGP#RJS<c={(`06d*R
z!vV%8RzJG&Fn7afba@Ou)LxkTVc`JdW2i?HfcYP$4;Kyd7n*kXy&JG_hnWY<|FHCh
zt`EkCnG1_Ym^rZUgUQ3x!)TbfF!#dB4VXDFahN+``rtG)++g_!CJr+PW<Rm{7^WX)
zFU%b<abjthKBD6v&0v^+;rR$n9-RwQ53?8MZ<si`dJH}^9AWms!Vg!xq3ef*FHAqN
z=@w=V%$+cMVdlZ)3DGeBqT35|KYIFrnFkYx#WzeGmM&rD!uT+G7>%wUW)6&o>4%j^
zFn#DWEZ$(@2h$G=7j*Tw_~`C|`5%{hba7a|B{ki`!Uv`w=6+awqT2)KL(?5h9ZVlA
z9AM_c)uS=c?SuIrO&LxuEZkxC!u(Ba`h(dAi)WZQFmap~qf0=;0cH-Y{6$xWix0Dp
zT>Y?cCfof`hr!yvq`DuLKVa@BHQiI{ewce<=?+$o!1yrtLA{AV!Sutz59SY8`h<lG
zhDN9WOdTvA!0dyG6VeaUhi)&-J+SbBT8BZw(mO2u!}P)U7#eU0z`_ltA4bFS84hg>
zF#o~S<DzlthnbH{A1r=w>4$|kOdZTTSh$iJ4lsLQ`e6Qpi4&q>@da}SEPe>-gZTsI
zZWy1SKBxkieWcbKu<{2M{xJKAbvH~sIt_~tsPix=nE9}D4-03QI4u9d_~-_}{0Vb6
zx;j|=!Qum5Kf1qRe3&{|c;bu)Xt@uwAEpl7T`==-(J*^p<pnGpVgARZ4<-(?4;GFv
z8r?rI^%ykNA259|`_aP_LmyNC=6+Z>z-V0I4%Nm0lZUBCrwQ4QtGt4_A7&3O8kRoM
z?S=UVmp+&{%zdzUgP8*}7p9I74T~q3ei)ySKDazI{$Tzl)qYsIg{3!GzJZ%hfB_3H
znEf!CkUP-REi61?_7l<%b2ls<z?=!wPii{^W*#gaV0@UlglL$(F#WLdjF3K<`(gTE
z=?NB&FmYIZK{pR34$Hsj^04#-Q->~(?rxYlFn6J=hw)+lgSijJM>h{wIKtuwrVr*$
znEkNy2(yn64GTAzdvMu{%N&^fF#Wi~3zvSFIEMdW5(H>i`iJEwm^)zNxU{3Ihv|cb
zFCqQt`4Gm(WgogYdO9U!A51^YeK7aJ(gC_WEM8#ZFdCLVVD`hrVd`P#!t8;`!)Ta(
zSbBq*1JegfPcZf9G|Zo{@JH7Va|gOUbUrMd!Q25$Kj`Xk@nQO5?k3h=n0YY!VESP+
z%s()7=rp>$F!eC=aD_X}99a0m?1QO?g)2-QE*chpF#RwZW-g)p4@>7Tdtvnr%p6j~
z5oR7tKYDt`<qw!WF#o{P0U`Ip%tO})GY=+DEDZ~HSop#Cg!IGI!Qvn8enJ|d^5lja
zR6D%?2@409II%P=-eK;CrC*r42$=&*Kd^X5r(xlP%N%t5u<(PKLv(yX+kvq12u2gK
zALd_}eXwvKHvPfU3yg;8huM$I-!O4lIRXm@nEzokOdh5lokn**%pWlK5Q;}w_`uQ+
z%>6KNbo(&)&~S&@3-bqtIvfHpcf#x^+Fq!6F#UwuYf$AF6fAwg?1%XS7XBC-paQUP
zg{g=68<wA7;!x!n6f9n0>R{mx;}h}^%-t~cF!#d5F)TzAfVmf@kJ$1WW(>^zFmqt;
zhshJ7Vd00Keo2jgn7d*AAU5BkyAKxL=;;7Ge9`rx$2ZJBu<(KDhl!)>hlLwV9V|V=
z#9{8i6)x!Zz|4WsF#YKE65_-3<8n7H{jhL^#|JKjXyP#c!Sun*K~sj43-c!|J>XP`
zLjvYcSbXA8h9L|~$1r!p;*Z#T2vZNEVc`JdV_1kL0E;K|@Pdh>DaYW#)WPa2bpK#z
zKofwaQ<(iQ^I-9TrW}I{3vZZzVc|$>Ji_$D;t|6l0s^pn1Pce4J76?=xdMwPn0+vR
z!`uN27Z{DMAC^8~`f$;3{W#nY3qM#n45MN0hRNg5jv);57tB5A?#9r7CIAZ$7>y>4
zlZ$RIsqqg>x1_oomTqC;1=A0sVeUeAI4oRY>R>d?->~>a*9YUn;uWS3M#J0zQ-_O&
zrBj$WuyhAAhtzn3g*Qw;E`Q^44=lgKXk6;h#nJ7B#Rs}NTzpt~!~Bm+9lAIypTP9N
z!X4&+7!6a0t{+$Y!_0%l53Y28?r&1V4HiGJ_=nNx?j*#A`48q_Qqv7gKP>zR*$0z{
z<yV-yVc`XnCq$!%KP)^6>BA)t3x8bkg-ahSykPz(HT}ZW!@?gHo;dA8lYrR^qhb1C
z^%<IS3@%I`%zhXR6NkkUx<**~gQ<t5dzihj_=M5u`eA&SJS-i+;uU5;Odh5lorbvs
zrXS`Wn0`X(0v6ve^)Pc_{(;3Wy1g(yEZxBL!`u&xSD5=?>R~j@UYL7f@dlG8M8oWX
z>4(uUdkN`>g$vC6Fn<%PAKiRdJi*LGm&eIR8$ZC6-f-!MxfkYsT<UO&L+yv@hs8Uw
z?trO>(Xe=dg$GW1p%S>_4Hhp@Wf&C9eK7s7^aN7~s~6G5Ve&9>n11wh3QLbLbufJ}
z8r^<${e<j?nFF&Q-9A`+!0dskN2g)s4a^)E4T}eKccJTt@nPn`!X0J~p?HMldze0$
zd9ZMUxdSE+qha>J?18C=<sX>2xcm<jhpB^^0}D4|{S7k*7GE%P2$g#<f1uMa`^nXh
z9**ed<Kn}@lU)6<c!K#K79P0lgNeh!85R$)c!Ze)3tyOebQ)G}z}yd$M>h{9j!whW
z!O|%#{9*RviU(MJgQ<hjF!#Xxfo>m+5Azqyei#i4512YaG`Zo9ZZ0goVD3Pd$Hj-`
zH}r6WnS;we=<bH4J9KeaIHB7QQx6Mw^!S6t1G+vKA6D<d+zB%W<`0-Uf;6=LLr)Kc
z;vd~ySUAGmfu5ex)x-Hv`(gftg$rDs00TX|am5z_V;ErmhlMAx?uW%M%-^td2#Zfb
z_QJvq=5LrjSa=Z953>(uKa7Tj3oiYzaDu5vx1V7Aqs1HC9;i4W`(f%~;SLK2Sa}6=
z4@?~{8s-m}eX#To<Kxl?6Nl-Cg*VJSFnK~WEFZ$c5tbeZ>4VARaxctW7!6Yoqha=7
z*b5DRSUScP4lr?;ewe>u;SSRevk#qy#TQIJI*rTS=;pxufo?A>UeML!;=|$(<}Y08
zaEVi@9~O_Wa3k0KuyDlXPF&)!a)Vs`=;a73AL6nPT^#0rnEj;s9~N%p>PJsM=<dVC
zhoyUT`(fdMOCL-emd;`7VKmG<m^xx<n0c`DNUT0seFcjLSa{%y2Uxy?g*Q44(+@Kj
z-5oG<Vg7~bhq)hJJuW^h{3*@1F!$iH2SXfMPNKUX=5Gx3XaX>G=-~|u2V(0_n6a>M
zhUtfeBO!mm^uf{_Oh1f<`446fjE0#DQwO7A{(`$78cr~M=rqh;m_C@f=<3k<Fm<r-
zgZTqp9U(r<-LUjbNIhI0>MxjnbQ<O#xOy}O%>A%*3kxrpKA3sLmZPxx6c(Paa3Q20
z<_?&BFd8O~=4_l?ba#`gALf3T{V+aG3(+Ld?T3XYnld;S=02ExFd9~F!sOu^&=|1v
z2GfU5!{Px~x<apSVethk&tNnzcfkA$(}&9*G-t!PF!N#VhWQ7sfDi*M{ln5RA#LdL
zu<`}wA9Q(Kd|0@^{0|FHT<X!qVd(?re^SE_-8@*jfw>1=o)90VpJex-#RH*qhwcto
zyu<7z)%~z=gXt$V9?;VvEIpvR6Bi#=PQdJk)qA+qql?4R5zO7>rem1>uyhPF7u`N|
zKDzs1;ZLkTVCrG<4zm~LA9VZB`7n3G^uy#~=EBT_iNk1^I7~l!yutJns()bi!qPu1
zJ;B6be3-rHG%UVg?tsxS{pjj(@nQDE^uy96F7+^RnEf#QuyBWk4@?~{8W#Vsd<ZiS
z7Jh`xfyu+lGnhRvcfibriKEjncf;(1$-~kgy16iUSiHdW!Q26phouKx_M_{E>4T*g
zSp1=zi>?mlepr0L@*m9qaP>GCFnut8z}$_~UNm!H;R8!IFn7bm(UfCwVfMrP0gHc_
zIT#vn2%v{EOh2*iKsOc^j<EEJE>DOLvmf1E==xys1PgZ<4bzXV9-R*hcXS%29~Q2-
z+yP7HF#Ry~Fn7S>2bNx8_QUwFaDwTF#UD%^E*fSJ%$+cMV0>6Uz@;A+Zs_p?a}O^6
z!py^!zG3#m{0CD{h=$n*3kO{0<1z>4ZkWGtsY4fssfXDMi#M2iVBv$|9%#D>=3kh8
zTr@^_;LuNMeuKFa<_}`i4|+Jj(i1E_!_p6izo8mo;;?iF^Ea#<gz;hFi4I`mFm<qS
zgxL=>A6*|hA7&rS9q977_%QQf_G82kE{!m8wD^OW2a_h2hWQufe;5r*ceu=f#XHRX
zFdCK)VCLY`53?7h4;CLV8YYg*U$F3k*#}b(qY1emW-iQ~Fnuulak(2N4$CJneK0;O
zJ;2oAqG9&K(ml*SF#RxjT>4?+FneMCfYGr015<~KhS>wt2Xhax`eEk6(m%}o#D*hG
zKa58A56pcqd4e=FU!cbeL48mKFn_?pgOL5Oc!T*D7Je`qYAj9)W*^MGFd9~$!15U`
z17Plh*#~nsEFNIy!SWj}b71zt@)OKlT<T%sF#p5sfw=<~9x!#dXqf+D^#;sdn0s*P
zgQZWHy)gYS^)UNk=?Nwdqha9+(+_h8Odb|q=;APWm^)$mVc`IihuIG^2PTeA!_0?;
z8_XXtbBQg-VD`cE!OVlX6Wv~PJ}lk9;vZce7a!(6n7uF>7C*T3!Nk$s4bxB1-Dvq7
zrVmD=#|O+l7!8XTn0gou%QrA}glL#Ou<(QVhmbyWd6<5fyU^tc@nP<UrE5a!Vetb?
zCoumL%y&?OVd)g+ZkWGe=D@-QsvLuY<x7}<VSHlsqZ<gb9~KVi^0@fu_QKqQOFg<c
z%s!aAVd)m;f0%hNahQIXJWL-f++qHJiNo?Ex_+2B=<x^R<FXf~51odULoj{l@;Lcu
z`4Cnf;8c$$fgb*_bcv=6Cl?kzuyhCuM_BsAsSzpx(+>-8Sop#8!@>os9D_pF4|6|E
zAIuyKjZgvfbN~wvm_K0Y0jeA)1q*kWKVaz+<{n%cVeW^8A51?Xdtv^AsfY2=&B0|a
z%%3oK!|X#h50`$JIIO&b>4W(PSAK!%gVWIT3v)N2_=4+0W5D#m+(9VZ(X_+Y-@)|5
z!UtU)Iv<u^VftYHKv##shx!{9Ua<56qhbER&<7QOrCV6M!O9I7ALbsII;b|B6f7QL
z{(+@`oEp$1VEW+xhx!jq8BQ)tA0dC^G61FzW)3cO=;E++3Ufcq9CUT)e3(9%eK7qn
zadh<<d}#Q<^ugi_W*(vR3sVP6FR=KBnL{Z3!qN%cy-@eS^uy8vhP%-OVESR{7^WZQ
zK3KVgrVY-8se|c*g$G<8)Ln25XbhM?VBvr!O^^$7H_RM@8qgHL+)r#iL(|RxOK&j$
z!)RjN1JjRA!`y{#A0a-hJ|H<fq45N>7nU9g*#}EMF#BNsCpP`T(m6~YjE1KlLJok+
z!~6}?Pptj0^aImJYPkt>Crm$#hUEjO^KeqIbOLh+EFHkiAykgQ42Jm^rjKZUK;s>z
z4;KHhc!imdlZNVt>4W(Lr+OR`=<X+!A7S!ve?r{>ONTJ?a99TyMh|aTIKa{+Tp<ny
zOdU)g(cuO)59WRt4HYI#LH!F0cUU@vX@unmnEPPjFdE(8F!eBZ!2APK2h#_mVdVo%
z9Xbtj7oqTg#Um^qz|4WU2WB5G8fHE$-eKn8QjabU3m=%h#O612b71KnJs!~2q4Ux0
zhlMM;I$V61e_{T|r4E-kEWTm!3=2<Kyx`IY6NlLgqhaw53r|A+fw>>19;P4WE*K57
z51odk6PSKjxeId-A^kA(VE%{M52J|<2bepEwGU=5jE1=zM#IDj(XjA^`JdGE3k!b?
zcSFk;LiWSl3kz?UJ79c5`eFG7T_4O`Sh(Uc2bO<e>S6H+GY=+@OFv8;W)CdhVc`MG
z4={DOXqde)|KL)GE)Fvv=3Y|u!`uzi4>J$l9$b8weJ~nk9xPoEG7lE+u=s|BAIv?3
z%0pN<!_0xj7mS99<8nXDy)gS>_QBi%Gar|Jm^dsSz}yYjM<5@-^ug?hnMa6*g)7WH
z^zer937G@)Crmxe-7s?rl?O2Q!_>oQV%twJ_rcOXso?<A57P&84@{g84NE65cN6k2
zx_Pj44+}SRd7}BybOH+pSh|I!Ph94}?1#l8Ed9dF!4)2`dI{!lm^xUz!R&{H54!!Z
z^arySW)6%GqhaX^T|X{9%v@M}!uYt<ql?4BgHX7k>x0<?^EbLYAwDdeVBtqdJ-R&1
zzodp2%p6#F!)Ta)VDdO=Xu3m>KVtR6!WmsZPW#X#VCfX5pVaV&rFWQqQvDB$FPMH9
z4f7Y8b8&KE?k2SyfT@Ss3!`EFfaOzkiwU|L8n3wG5tiOy_QA|Uw+9y=J>FpE!14hu
zedyw_^bYecEI!fI5#qzb5tjc6sfWqK+zWF*%snu9LNqKKVeTiS9+x~UJYoLF6>sSJ
z(cKU82h1FF^)NmxzG3QN?uXe6<HOXW)3E#j(+>-Om^v6AW*$r&Mx(nMJ>Fn^m_0Ck
zFdAkq%$+d*!u$mj$E6=;4!S#F{(<R-sUt+g(hV#<z~YmTKA1mX<p-=DhKUo>4-03Q
zKVj~GnL|iFOdX7dg*&nShq)8xZ&>)jXju3XatFFR%sns~U7iphW-rV?IMqYTNm#mp
z(J=oIs~@Hg7H-7)ALdS&dKitX+(-8ZES<sh!QvlX9WFjB{lMH!Z2K1$?=XEZ`(b=o
zIO4JwCJu8yEd9gGfr-P^5lh434dyRG`e5M?QxCHrR-O>j4~r+5zhN{id<f}><y)A&
zFn18EALf3Tepon?nr~p{z-X912)Q3!9u{uo>W8@>Mx&dLi;o_Tu=E2<hq%HM7Jsnt
z!$so?Pgr=t^ufvjnEfz!5z1FE^I_%?osXgA7c9JC{)OcOLiWPK6BgdE_=C9vCQpb)
zcR$R2T;UEg58XbPy@dP^GZ*G>SUAJXgXJTbJ?Jz{Kg{2-c!cSL#UHvpbUw@+SiGaV
z2VFfn9~NIQeK7yS{DZC@oextFb2rTYF!RvW!}zf9h3SX6111mi4=h|@;xHN(-?;37
zxdT@?!t}$^3Cvs=4bu;E2TUAB!@`G<`*GP1i#M42Vc`L@7iKOl|HIq?D?ecVfa!zz
z4`vTcJuVs+f4JO>OCKzM!|a98IMt&`z}yeh2cwB~H!R)2^uh8Inl(7N==S4^Z(Ihz
z;s<6wEZlLa#}J2>pJeNY8VGYYss4whJM{De^B2^3oD|I6u=E2<&oDktjZg_#_(Qo2
zFn17}4q@(u*#~nE%pJJm13jO?^ufXpmcL={LZ@NwhPe}_pO8Lub71a+xu4i{4>Jd*
z4;BtEaZ+hmc)-#FvHH>TA1oabs~<i5NcBI=K3KYe@nP`@E1z(=2c`}de=u`kG%S8_
z>4(X~(i_bEu=s$Pk4rx+y};~;#RJTKSp4GB4-<#k2h#^L4;DW#bub#{PFQ%s)Wc{(
z_QKo=QxEeej1Qw>`2=P!jE30{b2lvA!SuoW0aH(iM)wCUf8#U<S`NeF5tc4-s>dM#
za|bLua43Td!@>vVf0+Hm`X8na7H%*amagCyp);WVg}D=^59SVZ<>-7^`he+&xdX;W
zSC7Gm+7A<lr6-uVFmYmOSh$lLZ!mYm(j&3<!rTpuH$vqNy1B4$N2lTbKno|Bepvp2
z=|c~Hm^j=Vm_{5lrQrwD4|4|&Yj6s~^uyu-r$QVOu<$2WKTIEt#$gRkVVFK*!yDZo
zSUm!x(d7y8VfMnpk&t>=`hls3r56~VkbamuVet*q2Q!C|ewaR3euI@)uy`b-A69On
z`vYb!x;%z?(0m1pH<-K8X;`@8(hsu_rVr*Hc>M#l2SYzp02XdA8Y)aQ1+y3CU!pZa
z6~f#L3kRq$(G;w_hJ_Q%-^7{+b3e=-uy7z&KP<h#)Wi71)?+aD!r~F8pXdO9Dun5W
z)dx^v!W1-pz`_CM9>N+SDq#5rrXQ9cAhN`hFn7c9HL*Hj=?)fduy`a^KP;YL@kgva
zm^zq#Sop*2CpH|=!w(jYFdF7Rn0@Fpx_+2@VE%!HE4n^79~%F#@Pp|`=fl<GV4&-R
z<x3pO;lk+dhuI4YN4P><45<5I{)NQ@jE_qjhB(w5n0^>dY&^o^2^Mdprem0UVD^%$
zAEpmx4$NF!G%S6<;tv*YFn{3E2NOq+KbSt4ImCt^Og+pUF!PC}Vg7}sA7b^v)WP(_
z+)u2#VftbA!t|5c4u_>zn0`|2r_}wF>PJrp#QGnm4;FqfnovCi(+3MLbQ<O#Sp1;V
zuyh8~2U8E@!{P&7A37hF@6pYJxer}EPCm5!f!Pmh&)`&#LjvXxSUBQP1{a388&+<>
z?1z~HSBQfF(+{%`MiYuZbmL&<5X>Dg`(fdWt{#>@VDSW_Vc`ZVPhjSv>xab;%)c;o
zFd8O~%O9}%38oI6hPeaXUUWXp9GJZ@f5Y65t{xX37EUmCz|6&^9$g$3Z!mYD%j4q1
z;sNenXgbEF9+x=G-LUpIE_E2<Q2Su|VKlMj4|=%4^uy8zhJ9!P==x#dNUZ%ZbufEj
zG)x@LIt(r>eZus??Z;4wT>ut;==x#e*mYrw!r~uh4k7o$+zoRtOdpI7lgG3PA_7Y%
zFnzFm1Je%^hp5IRVetl|VJcwyVCE6>2P~h%^r6#)@)NpyVg4dE{9)>0<sY&7VBrgM
zFRA4POh3&1u<#?7hJ_=c@Ip5q=3kgU2*nq=epo(-$2YnH0(_XeVetS{j-IYy;^;Ii
z-eCTQ@nJNqK1A0C<D=(0m^m<cm^rX;f{DXunEzqn2TKPqf5G&_)Z?OI_QKKwEIeW9
z1eZRTILtnnei$DnkBdgP7v_FgxDZMYF#BLMEZ$)LhVgOP4~utL_`ztHeq8!s;Q~_+
zi)WZSaOs1I!`ub;CsYC!9<X*9R2U}((+|@Ja|g^{I5nb4z~T$$Z<znkl;PyU(hDs9
za4JNTfT@RtBaDxx3?~=nURXH56vFZgESzEDFdF7RbbDd?VDX469AV)MQwK8-<{p^2
zxa^0;7fc;G4Kp9+UJM%AZi2ZF=6{%bVE(|+heH5nKP(;KP=+B4OQ$e<VdfBYKhz{x
zx`EjX(+4w$pg*AD082M8cfibrT0@Y6g(J*fn0W*>!W2N=0n-nQUzjwpG|d07bVNuW
z%-yi?gZUrk4npAvb0<tajE1ErLiWStVc`vP2TYt;8m6CEf1{fV3kR6{3Dq0u`eF9N
z^phI@Fn7cB!^#76dvNh#`d~D%`eE@4(+8t**@H_Q=6_P-9cCUZ-H{q@Fn_|_4J%Jz
z=HjAZ{zeaXn0k0Rgyu__IE;q56J{?g{h*7(&4Foz(lB?z^uxjr#)r``bx>_^3g&*8
zy|D0u>4TL6a18_)=;45?-a$78<_?&>F!!LVBgBWf7v>IP?ME+vVBtt89m33qxf^CL
z%$+cEVE%)N!)Ww)hP9(%@-Y1{bue)l4O0)ZA0`f?Vett|-!O4F4NaFYcf;HXvkzu2
zPW@1WVBro^52Io3hnWjij!8k?0gHE-dtm;<)PO1iOW&~gg87?}ewh1U?j=?q%yyVQ
z7!7kbOr8*p9&RxAz|tcj{cw3`_`&p%>JFGWF!#X10VYm}hNTafy`<`g#UCsjVSGaN
z!sKE8hS>`<hfw^(^uyeZi-xJgNkhXQW*^LcT;(<{bI{8ZQrizOb7AoaqhaF2(y;sw
z3r|A&VDSY@2e5n$3kO2^AEqCs9!A6Bm5}`~d6>Il@kq#Cm_C>~n15mA2uz+34f8k5
z-7t3$(g%}=g%>RU!_omvo)8Vw53?Ue!`wxzewaBh_rt=GkbYRYhpC6fCoDV(>4(X~
z?1hB`vH1sPAI#q{dr9>_x_)BQ0nFVn{V*El519W5(Xe=jrF)n^VCECjk1h|(|1cU|
zo)90F9$+*s^|1T{lZUw%=6*u)2unXO^|0`R(S*uDnEkNyN31@Wdtu=X(+7)pn7J@@
zxM)~B!}Jq!FS<D}f5Xgyg%=_FVe&A0(P?7i9TsjdccAmp-HXmg4?mbcU^L8~==xxM
zm^>_<!TbR;7p9I74U0Eo{f%xO%)K!CNsUKXxWV<KTR?!1?tfT$KtLN@8s=}9{iNzg
zx1Uu1QyP9S`(ft5Jw|{5^C!&TuyhP7hX`nAz*T>c>JC^sMmG;<J%rY7gynmfy)gga
z@&`;kOdUE6%MUPj;R*+sIk0epnS;~aXa>Q`Z<sz<JfbPX&4q>o%zk3+g&7Fb2g`rx
zG)x^X8s=^o4Rb#%UE$IP6NiN}%wCu{u{11xVfGViFS_|Kcf-m7ba_I2m_AtgBcvWC
z4@*BV_ruJA$rGYs?uNx9q4bK&JXrXXT5rJ0IdbiXxgXu%aCbn{H@drF=^Peruyg=3
z4<-&b2h9NVat5XzoyM7-pzeZ&8+ts#(lwg3IJxNVft3q5HK0kr+(BwQpob%==?CUd
znEfzk!OVftFmV_S3s+cp!{P}Rk1%mu;ea0AFm<r}2h$HrpD=r2G<rILsfW=p^I__7
z(Xez23wIce%U*PI(8GaPdtv6FhZoE|m^>jGrXLn=Fq)wIq3ItMUg$I|JrlAYrVc$F
z!qOEX{jl@}3paFkz|s}EIk0eo#TU9hSiIs22Xyzs^uy8>x_!9#=<bHa2QKyK;xPT_
z`43$kE<P;$VeTi@ewaQOO{)K4;S6&>%p6#};&L~-I7~k*KGEd~@nPv6W)30sFnL%y
zg}ED64#Cm~EdAl~56oVeeq1!nJea*O8a@7C{)dUf(m6~$It_Cly1g)ep{qyd!}P(z
z4Hlm;aYFXP?16_5TDgMD-{|gu@nPW)3kR4uAsS{sOdrf2g!IAWVfhfIA6K~nQxCHb
zmS14u1q%;YyufIfdYFC~jjkVN4vdDGhf6;!eqip0rAHVaW)C_ID<@#~!1Tey(bc2#
zVeW#tALb5pb-4Jj^aisZW*#noqMHLtS1|iw=E1@nT|FT_EWg3bBcvWC4|6Zf{V*CP
zPmqS@7g)N3xr2~Cm^zq!u=s<8C!uhF$-~sc^uydiFdU%nhO2|}VdlZ|F(H4z<YDO*
zrXS`$T<H-e4~uu0J{S!P7hL*b;fJoC*mMj_e=z+pKFmLaXqbOt?j)ohmpr;ZVD5p1
z4=#N$aag>=+yRSEm^e%wE*hpDmTqA7!_32_4<-(aADDg^O=@_;;tggmA@{@bEqXlx
z3s0CkVD3bxVftbAz|uP`J;L09t`D6Lvkw+-Fd7!F=<3n=Fn`0`0n-oTqpOGUVftY1
zgz1B&8<_uK>d|Rf_`vkR%t2R&iw_HTn0^=yvmci}bN~xKSa`wQMaccQ%z?RsSp6{f
z!t90VC)9p`*$+$SFnzFc0VYp~hWQ&74usUh<YD;^W-lx~z}yW>KX7rFMkoz)C(Pea
zVFp+_Ko^I}!)RE3f!PmJ597n!2UCxWhM5D?4+{@m>e0nv@eH#U79Z&9aPeW`0P{C4
zb-2V~<pnH1;8I6O9OiFWdL*PCCXXI|u=t0`6QW`6N4FOij=0h#EPi3(4!0NDj)0j9
z3tx11z`_d_@38QP@nPu~7Qg8FVg82M53>ixhq(`3A1*%3JVN&3(ht*zE4*;&gQ<u4
z8|Hpkc;M0plZUB?r5_j{7XG;OquU452lEG^@Pnlnm_C?$aM_1b9GYHW`eE^iQ#}p|
zm_9=O#?TJ6ALb93zcAF{5P+p)nEP?1R~*_IVCrG+hQ$l9?tsNREZ$)HVCLYm7nWaP
z{zk7SVD5pbLpKMOK5*Fs3m2F=bp0^<VCvB2Vd8}J!`%rr02ZDwcfss|N@Gy4c!Svo
z3r`r0p#e<*W*^KPm_AtfiKZNr3r&CM_QTA@)PO1i3m;hg!~6?#52|WR9<1Dhg(FNq
zEc`Hapo*ZUH<&(HeneG`#Dn@9mi}PjMo=Hj2&jITzhUVV7LG7wglJg#iyjVy^ugp|
z@dWcX%p90JAsVJ1=5AO#z~T=UzqrhSsfW?9bO)nh=Ai3`#WO7YVc~$T52t^i;ScjS
z%p4e>kh@{-f%zX6?=W*<@<h{U?jc$qR3R+=;q)(59WDyyZ(Q=Y#L@i^GY6I)VDh;1
z!_qx0o?-bB#)rixPW?~`nEzq+!T7Lr0#$~Sg4qkBVgA5rKU#dj;vGiA(j!g_(e%U8
z8_eA}^`S|?$`6=3Vc~|R3?~;BJ}`4&=@_R5GznOE!R&?6uy{aI4(Gz`g@qT4Ce|IW
zbPkIrn7d)-!rTE%UvP_|3|RcY^g)H;6ighZ4lV&@z{FwxhYG_fLh`WqhUF8uMkoWO
zALb6IFa`w+FIf2rGY=M?uyDfA2o-?21EwFA{$S||sthLuQwMVg%w8BDXF7nUf0#O$
zJ{XNFTwxA@*$Yz-qhaR3(g938I*m&|Ed0>b<Kn~887%x@d|c{b;;?XmxfkYsSb8Sp
zewaBh_rmnUXky&~%O9|G2D2Y#4z75BxgTZ^Og$_;!Q6+7hNUx@J{S!PM_l?~;;{Sy
zOW!biVc`k$7fd~|G|WH5hCj?)Sb8GX{V;v7aD?fD`2!|Th(?cp7#|jIFn8fH2WBsN
zyu-o)7S8DUVeW>7KTJK$976tpnG1_A7!6AYFnM%)G5BcZ2+aQ&>Tn34r$ds{4b*;g
zb70|t!&;oeu=s+72f=WF+7HtQ3wL7m!{QGX?l66@^n=r(I3!^DNR0=Waj^6Va~DjU
z5DoJ;Og|y@aCvC>!Tb%&_b~UvXt;VP1D5__`e5lErXCiaxcmXr2cu!>1!fLRKd$r$
zb0^d}a0(XwFmvG&L^5FE2=fOaZMf<Um_Km2AC_KW{)E{J^A{|9aoG>kPpLm)`eFWn
z@d>#f=3ZEOgXxEbKTJQ2kINsp#9`*b?7^iDCJs{vD~DkIfXU;cVd`P&4_4j~3V)b+
zFm*8Z;)-vWdUP5VZgBTP^CP-CbUrMdz|s%Q-RSCY@hR003vZbHFd7!$uy7${KP;WV
z;sF*8#D*s<pTg`%=flj$<qnuTVftY9qo)U4`eES?vmZvo?1hCBF8#3hhuH_y2Qvp|
zJ}&*};xO}I=D_@it`5eBnU8KiOg}6>Vd`Nt%v_i|(P>!x5%NFGJXpBH;sGX3h=!RD
zv!B%b2lF4yewe=q*$Yz#OSdq0!_0%p!)SE#VEGRg&M<Re;;{I@Re!?FgVh5t|HEjQ
zewaE!G|YaOept8?(g#xqi*J~{Fq)8lm_ArKgoPK(9Wei)n*&n^3qP1Tm^rZcMArx7
z!}P<{!D#gOgQ-WSVfta}Vg7)*2No{q`e1yRKA62Qcf<S*6Njlsr(x+07S1sDz{FwZ
z;LH!udIqK+W*^M`F#WJ_MYkWF4^s!DVdlX6iLRayA6AaRXk6-H^04qj4+oh4aOsET
zFIfD+{0(zIOdgkhO8pD72R;0WO+PUA!qmgc514tl+znF?%lGK&VCe{^4qYB54hw&D
zd04!|XmoiPAD25}_7kfg7Jo1r7A~;#0J9H+hUNp9J76>{Jz=QFApmnHEWN?Q1!_JH
zZ49t*gt-@;hNUlD`eF8<>%$eVFmqt(1YIAD4|5kxJvt3@7fd}m4U<RL2jj!y1(yEN
z^~2l;Q;&;=g*&=_Sh(QQhb|6N4~suS`5vYp-F_G!CQpclrE_%m6YGCi`3?(jSo($W
z3E2;mhq)W3A0|$ShNTmj`(fq~(g$-lEdF8k!txy~{BeZ?y1g)SVD5nNap{M-4;KEg
zas%#u0{Iq}eqrii`eF9N_%M6WX_$VPI#{}cxf^B<x;|WdSa`tn!`y{SJxm;3AB+!^
zCq%>I3Fcl{xdZbbA^os)01F3na|r5(DS*ZoEFHnjA(Ri$?S+LOEFHp(Cq%=-0Tz#h
z)WhYW?uOY*YWTzK!-#LFxiELZ?SnF4;R3T4DvYt73Bz2dLYTR*aE8SrOdJ*uxY7a4
zT$p}z8fFgEIt&VCE-bvz&4bBfXv84^D<@#-7l$$oVVJ!z|DyW?Lj#%sOdl*A!pwuw
zXvz^>X#B$LBNSc;jSv=0Jxo8$esn%WH6{rQFPM554NH$OKB0UBONa3Ig8B;<E--gu
zx&R^q(+5)rvzOR-gsFq&16a7j%!Sy3hlIKprXS`GJUURMVD`h@4GRwxMOZlK;ZG=>
z(T#xF1G5+AZkRYB|HIV7^uuUayutV|ccR-5D=%Q`VeK!NI#{@%i^Jq$G_3xHnFEVI
zSbV_B2bg+v8s<Lq^ba!!T|GJ<7G5y_qSG+*(e=Ujuy}*z8*<$NGY=MTFn8ecKTI9W
z{V*El9+-ZZI+*z|8W!#_b+~9;@dtAkES}N*4Kp8>PGI)pqS4L4r4C&j<_?(sgyIXX
zADZuB@ditO#Ofy$&an8v75*@Hz}$@<{;>2<NI$weVE!Pa4`x5i9+*CuJ7DrSX=wPt
z)Wht9nFn(hF8#20gXxErN5txf*$2}Pb3d;1ipw3aass9wmL3SD1DJbZ_QUuvb6_+s
zdtvHf@efmn?jD#p1`Q2=m_6t;%w2^10n4AT^a^tqOg}81FzhEFK(2n6eK7rm+z(4<
zF!eAR7LEj*2|vFJ7VofhMMxh^9+qBV_QLqE@Pw(uprP>x(+5kpuyPYt9%AT&3c%tC
z=5DAkoPvqt3O`u*z|_GtLK!ggVd|j5a0(`mo_=8pVE)0S9~RCq|HI6Ig&#~Dm-}J%
zz}x}T2eX%uK6L%Ca3B=!Fnur@rVr*`m^)zdglL$*VftbD7?v+_>4$|M%s!ZYxci~$
z3zvSFI4rzi`eEr0CQpb)*AH_)EFTln53>(uKa7T%Lr6a?pTYbMiw|7s4&6MM{jhj|
z>4SwYx_Wdz%)RI|%p7#}=zLiEh1m!52P|CC)f3{Q`-6~rm^>`LVCe}Kt}uCAG|Znc
z|HHx)mwK2ux;~hG7){U}(DV(n4;F5)`hZaWf%y*>UNC!MG~67RgVAW1`(fz~rVmEL
z%th0N!G*;$OdpIUw)}#H6HFg0Jz-dcLjXPeVD2FlelX);=@u4VF!#Xy4~-|7dUP7*
zPgs1w%z^m_=00?NF!#aY9cC|#4~rj|It=~j0#J9t%N-~mT^TMuOds4{Tnf>|Vg7@M
zJ5(MP9xxgz45wh?FneL~0Ha~yfy+HG{jhL`<$qZC!NhTeA1s~0)S=TbbKnlf!GO5~
zCJoC^F!N#JglL$*Vd)OWC!`OS{$TMyDBRG^gSi(L53ukiWIs$E7H%+ipqr1A4^5vi
z{jl@{3wM|}E^}ew4pRqnKg=9hIKc8Fx;tR*hna&e57Up!esuLPb+GV+g(JE;3_dg*
zVDU$^KBz%3f57Y|HQmAV!Tbv~mLLTSH(2<?+)q#=Q~@kr(DlRo1678Tf`t=IKa7U?
z17<!h17P6<(+5j8Fd8Nfi&tFcpt}Q>e_`%`rDt6FVg7}MA50%CJ;TBomws5e2U8Do
zH;g9aepooc)T7fd^KsdaE{+}^=<?`%Sa{=dKP)`a^})(@boau-59Tje_+sdX3c%!H
z_QT=<Dov1rg)ht<q=pyFojCnTzy&b>qU(pb3r6EI2BsfI!{QIdC*&WPdYJ!VG|XSP
z?1hQL)WP(@Xjr_!)ZwCG<rj>GxtmZt!ome+AIyB1KA1T;-2pAPVfta=1oIC}Kg=GO
z`7j!0F3jC9b71;l=D^hBqG9C&EdF6MOdOX!m^jQ{n7?5(EM8&iaM7@Q19LB|TqRWQ
z!OVlHhxr>u!`ubS=P>oe(lGmB@j|RVm^zq$VC54`97e<94;Ica8s;BZyu!j0rVmEL
z^uyG{+zazROg~H=OdmQ8b0<taEdGfNH(30^^ugjC#z(gg#)qYASUjP-6XqXW?tq0C
z%zl`;Fd7zbF!#X3VKk-kF#BQdhnWj=FU(!A@P*667-;z)7SAw!uy{ld7npV!4U2!6
zJ{S#iA50xC8Ws*PeXwwc#SbohFn7V+2@7Xfyu<uMtbSNNfzdE?Vd06(9GE?@ctEEK
z`2*ctn7?4=5F3u@=D^%fNIxunz~TYsf0+H~=A!dq@sFN=VdkK#N9V)b39}Do4$M91
z>S28JaDtfwOFuAw!PKMEuyBFdORRfg=D^Y$Og}9B!rV{DewaMEe)RN2DE+{~0p>1r
z{V;#R<k4xEesp~>KDv5zJ}ke%!k<vQq3ega4;KEg@Pma5ES%Bx!T2zHVKhuVjK-CJ
zVfMo8fu$RmI4oRX_Mp=+^I`hYX;^wd*N4uB*$eYOy1D4;3Gva>6(RL7d31YW@c@&@
zMZ?l3EFHl7K`8uS=E1@n7G9*LADF!`{V;P0(m3irg8HBeVD5&+J1pFx(gZ13xe3z`
zqhaY7W<D;1VDd11uy98g$CdtJ@dL9D7LMrdf~8MF?ttlsxd&!0q4<P_8_XV<Ik5By
zb0@m}FmaeYF#p5!!Tf=)55|X;Z!q`5#EGS0_Q3R`(=hiCG7siXSUADl1q%m4`eE|0
z@POq5n0sLIxM-ODFnur@mX2`ggV_gjFH9e<^oGkEm_0E4uyg<`S8(ZriNoxN`4?7h
z!OVlX6Q&+U!^C0gVESP4u<*y_4p@A_?1hCROg}7rpo^o+!}P<%VKglLz|4V(!)RE(
zf$4|26I~qUPMAIz4O0)xKQRBo%!7#&iceVl!PLXU5n8^&?88OF)WhtB#Ro2b!^}bV
zCoCPn^ux@9sYj<_?uMy@r8{)}Fm>qqVSHHlqU(d1OUNHEbI@sW^~2l?(~m3t!QvGb
z{;>20(+7)BSUy2_A50uwA1oYT=?h&SIv*B~FnzFiKv##(huIHvFD(7S`~{<7>d^JW
z+y~PS^9L;aVCKR2==x!NnEPSoz-X9%VCrzuF#p2z!DyI!aOs1IqpOGI16cgQ)S=U`
zaDnNEg%>P6!^Da8Kg>RuJ{S!PXLS2ve3(CB>S5uAGu=Wp!sKE4VD5(bA0`e}j!8kw
zCz!io@rbUUko#fb1Iy>I`~<TfCXeX={30;@Fn8fsjgUo;SD3pHDo|PI{)4H9*$*=h
zRTqMX9$v)e8<@GU@Q1}aEFB_jz|MljKU^Q0GPu8>@r)+T0J8_?PjvM#f562t41fs0
z@-NICF#o{PHAEF|5*EKOcficUtpimCW*^MGFn6G;!oq`w3rs&Oox<Ef$lvJk2h#^j
z|1fzhHe(fn<zJY7xFV=KahV4bhxr@k4|H)@x`vwzWx&!ex;!lYVBrHZ52_8m&kI8y
z>Ry;Qy16j@F#p2B4VIo^@edP+rBj$b7>%wUrXEJa`~_2wi$?bs%-y)`#bpl6Juvs<
zQim=MOJ}h7gV_%Ye{}Wee3(CB{)W-8{D7_=&WFYmOdU)gjE1EnxOyB6SbBx&gV6-_
z548PI|G?56%-!fTOdhHnlY;sKS3D3(N3i(E6^^j<fN3#A1eQ)<_QT3kn0c^t1yM}|
z3H1-mUV{EdGZL2mVd)A@8qS5~JDB@nG%WmK=D^g!H9{FM^I__7(S*_=%zT)BSolG$
zAxOc(2d1A;e4`r(vmfSPm^m;SSNRDu2ff_E6^=0V1Zime!|aFo50^feJj`BLx`XM5
ziKFX>`5UGVora}bSpGoQ597n+Vc`HXhtzn4g)_`vnE8Zgbbr9yK}a7=9^GD;`(ZRJ
zAHwW`>4VX*@Q2Yb`(g0_qjBknnFC9=uyhF12Mb@gIy45%K3KTnif=S+a4t+e%)c-`
zTmb<F%wI5f!^|N%{-Nm|rk+&&F!#dBBUpG4a5h{T7XPsDhQ&9`9fZt-mHRON!{U)p
zyulnmuDfCOz}yLQ2h1NZ_rT%@W*>}(iNo~6+yM&@SbBoR3rrt64KoK_9A+M&aDeHD
z<sX>)Vg5lk7o87N2crqO7hONhewaR(IfUE|GY1x4uyBOYFn_?*;i4(k4|6ZfUKmX%
z-NM`h^EXT%jE02^OdT#7mQG;$VEKnw{jhL?>4(w8>W8U=l>;#IV0>KehKa+%52g?1
zADFo?b+~Ald(ml_dARh!;s>S=mJVR)4knIEKTJI=pP{RR$;09ompm?a!tx``99;U*
z?T5AZVCLe|4|5mHpXfBYx#;%8_^@&g7H=^7h>bs3Jiy!w(+BfE%pP1c%zl`Dm^zq$
zVC6JS9G!-#gSi7H4>J!Io-lD34NE7m{00+;(Xep9r5_glFneJ7U^Gl0OdU)dPDAT$
znEkMDgV8W^(ftM24`smoj~<RNb7B63D#xH;?u6L~qY3GQ83apb#FiH@eX#I_xgX|U
zm^>jGW*>}(xr0!8!DTKi{D}28%p6#J!R?1h5J|(rpH%(m_QJvs7Ot>(g!v1Xb71OV
z@c^S?;<&;SmM&rT!t{}v?_v62G$HrG`~kBER{y~C!OAh1I$~*9K85**SbZ>cF#BON
zOq>u6OK&j$!~8`^A50#WZeaRh?uW%6F89Fff$2l1Vdlcz2eS{IhJ_m}++pUx(jmHf
zbUsWUjE3ol@zK>2;=|Gd%>RVc!}1@DhS>`%S8%0&n7?7}#YMx^p}PYX{xEmK^uyu{
zmcP;UVep~#2uwer_(InYa|bNG(d7y8VfxX-4OTu73O`tSg_#3W4+|Gu=?G>%Og~H?
zOg&5-S2)7NVfMh>4+}?FdLZO~m^m<gu=pdS4<-+@2bOMO<`5f?=;;&|kFaon*@KIQ
zg(J-WF!vL(ALedYIK%Y8!ULCmFmagsVE%>CFmqw*aMI9p4@)<s`WxL`n0sL1fi6#o
z5A#3K?uQx(vk&Hfs4!s)>VKHOVftZwSh|LV54wTq_QK*3W)4h0p>zOCe=vJt;fQV@
zE<Q{j%w8A`vmci}Sbm48hn2H1|HAx(%l$BS!qPpuJurD(`eETmNI%RRbo0^O4+{ra
zI)a&tD?P#71@k}5zc4;595C!f6M(6M*@x4;X!>AWSUAAk4U0FJI7}Tb8m1oR4p_Lr
z{D(^)OdOW}VE%^DxWWyl4@Sf6hv|dSLtQ_y@dyidSUAAq5tg4|;e<g$%O6;H!Rjvz
zbvOiI=EL;EXkz0XrVeH=%>Bgr8{Jtj_rT0UmnX!B>4Uk0kb0Or%)PMu3!`D?;LJy8
z;SUQxbQ)$JEMLLwh4EqXu>23R7e>Rv1+EW`0juv}<rgeJqAA14h4~-mZesHt%s`kv
zSh|IY6Qt3?4;Fv0a3Q207A`RR(P@}@u<$`Q59UvpK3G14*$;~km_6wFVSJdsVetry
zHy9rlzA*I|G&CGw_QCYQ-GiY4RRESgVet?14<Y}<`~_2wi-xH~wH3jG*$)dhSa`tv
z1D8i=!ODWUmsJ15(mTxkuyBC62diaBg0OS|b2rR>nER2GVrN3*4`v_Cewcf3r9YTq
zu<(J|2eTjMFPJ)9G%TIK+z+!C<_?(o82X{^hN;Iz!_qT`ejEZY`(Wn5!WV~fxG;MB
z!_0vzz`=lp6U;tXy@W$KhA=FgVESQvLh*)f5X`+Wb71j_t`40K(+5)zi$_?zqN~T?
zL*o&qA7($natCS<EPi0|4AT#D2UHo+6ih$NJYqG%;ti%B7Oyz<L;Vd4KUn&K>4)17
zRfld4OdT#77M?J7q3ef*8;pjjhue#;0T&<YKA1ZQmAB}|!0d<FOKSRs>4(`5qhaBS
zZXY@y7EdsJuy{aMht7wkPnbR!4NG@0ccJS;=fl+DGLO{ogq7p4_=M5u_M-D)?tqow
z=>9=h597na8Kxd)AIv{6b?7v#d_oU@m^rZY08@vqAI67;56nJT{K3qDnGaJ>h=!?$
z>4$|2E`2cn!_qB`#+C2T&4H<h*$49nx;jF9Sh^$C9GE*{{(!|FEFBQC7d^aT?jRIy
z80JFD5t#iL@&p86=>-;l1eC+2Vc`wa50`*4VDSSBAE+>#g2}_ofl0vJfy+Oz@PWk_
zjE1=b#)qkcnG2&~;xP3v8WwLbK1@9!8kWys`eEi1(g%}=sfVQpm^dLCrVr*{qT3Bn
zgJJm}rXN>4L6u`ruyBIWuyDXoh(iEoFM9mrP!1PH_ctuxz{+p9LL3a3KVkNx)39`j
z(?8Js2Ga*ir?B{kiNox{;SBiwYcPE<b71j;OCL-eW<Ig@!SumsSiHgP#pz#|1~eMx
zZc@`NtlWgT8<yXRwI3EfF#YH>%se#b!nrVeVESP0howW9IHCN69)7U+fY}dAM{tYL
z7_f8<3r83YGZz+4XxiXhm^``h2eSu8!wn|DfZ0oIIH4N{3n!R<V)HG`99X=d>xY?3
z$UIoMqw9x-11uhJnFC8duyBXRGqhZQ@!{nX%;9J>EF59#VD5qGhq)6?8wMBV9zyQM
z&<GWPg(u8EP+_7enEPSr9;OjyE{u;(!{lM^hWP_#9!wlvA1t0>;RSOyEFZ(fVfh)R
zA7(zxT$nmsG%S8#`e8Io999m&+yjd@nEPSsiKSugf|*Z9AG&{G;ScjCF8wff!~6}?
z2Mb3+?uUgdOg+p#n7J@<T=@qk4pWCt6CDoF{07S>FdF7B7#~+S;0kA0Ji@{SLqF7B
zm^)$N0i$95f$=f)Lj_>+Fmqt`!r~RG3?~JPCzyU%`2~v)oEo7Lu=s+77mS99LzUsA
zVD`cEq0@xi0rM}qKA1Qxo#1i+ES%Bx6N-13x#;>}=HaprT^yz!W)6&xt_~L;7XL8)
zFn<#YH<)>_ctVc{oaq*-5f(l$dtv1;%sgEA0;-(>T|X>5Vg4bcA7&5Cepvd2`5z{Z
z%N$sF3rqj7@Pg4W{V?~y+)ao^cNZb`FnL&hh1m-WPnbL|8m15C4_LV3Qjac<t{>)p
zbam)_m_0D}qSG*Q(e=Uj==x#)g~bajK4IwsrVmEL;sus&Vd5|vW)4gpOdLkT#BsTk
zkbanZVd)K)ZeaRg`54BB*^5rY)WOVw`5P9WFmuuM!T7NFf`tRjUod$>G)z4#{$TbJ
z(g%wdn0wL9fw>EpIk0>Q^Eb?&=;47&KTI4Je=zr>(=dBr>T%N0`WO~pFn__)DNcPj
zBw+CYOD8y#!Tawpb)?!4(+6`e%>OX^Vd09)epvXRha*fsE`2a@So~qAhlVFi9XbsQ
zSD1TY=?PsOAwEn$EZv~<ahVGXH<)^u`(fb^6NmB9-2n?%m_8UErVi#FLiqru9u^O<
z@PyIm=EBs&XqY@qJuDt!;xKjSG%WmK=>|r_@-e!4bUsWSES%73n19gq!T9LzgYjYR
zz!mN={kUjY`2~w#T<XxpVeW<5k1O4v>q8GmSUAAqAD25|;xP3v8fHJtA24^J+Y3uq
zFneI?(bW;+!_0xDV_5njqz~pUn7h$wSo;wcuei*^m5yNkA*3JXUUd6$*#{FxcRwr~
zVdlW%6{a4YhRMV79ZVh;p0Id?iKEjn{V;ps=?z+L!rXzb51kK7N3igMg$Kd>1GNXH
z4`v_SKTvse`(S*SKA1mY=?7*GEF56!(P@}G%>6KVSa`tF1x%a}4NE65`_auMq#qV;
zFq)8hm^{oMFneL~3QJ!w8m1mb!^C0X2h$JZ!)RDI!}P&vX#Bw93l{#cd<R!YfB|zq
zdbq><K|nhL%-yi~g1H-J9xPta&4HN@(+}gr)WOU{7e|+e*#i@Y(J*~58YT{-Vdlf^
zhl#^zm_A(j2o`@Z{pkLL#T(2Vn7QaQEWBXmz-U-}qN_*e!_qk{9l+ufT^%kyEWN<o
z0W%Mmdbl_={lVOeP7`tmEL>pm2McFdxWW7bw-1d0Gapwt!Ng(qplOG%uZG!&E)O#w
z<}P%3n7J@_!r~uB!^#ny;fWT0u<(b`uyBEyhi*TN4@<`|eK7yP+y_&Ki-xI#g%^y5
z`5%`)m^ga8!}z$u4W<u9!_?!VVd~Inn7eW5gZTqpAB<0_epvp8g*&=CVEGDWA1)f^
zPIUdSc*Ug;mM&oN1TzN~|HOtrEPcZKL2SB%xeJ#5(cKSk&%hMopkd(%^DoRCg8HHP
z2j+g5|6%%J;eyK?T<(VHhtas=5thHv&4Jkqiys``h6}^o4-0p=1d$AwJ7D2Qq&5s?
zFn_?p5kmnE0hs$?;RVZAIFuuVq45XP2lEF)0e%)NeUqyn7GAJ)52IoJ!EZYQ11#Ob
z?1#l4EF9tff%+4dd9d(?nFG@g<HO`}>4&8=n0+vPF!vCO4_JDI>4VX*@PLIAF8g8b
zgQ<tnFmqt#5ib2Oaaef5+zm4aSN?_RgZUq(59S`2I7}Tb8m12BUYNbG^o2_wx;V`J
zF#BQVpsT~hhs6_2Kg>Tc_u<k9i+7lPFh0yYT>4<*u=s)bAEqBBkBf%o6PUd){V*CP
zk4ry>I5eNY+ye^-40QwqVC6NTbc=2bEWBX;AvWE??1Slp@nPu!-5zv4EWB{hu>6Ux
z55|YZ7p&ZX`5WdRLg@#l4rUH4+zHtWvma(3y8B__0aJ&IhJ_cpewaIO>4S;G+=;Fq
zW-d$}P8yo-VEGMZ4lErK3O|^+Fn#DWOdcl<wI5v{vHpj-59VK3JmRzuO#-GL7GE%$
zQ2K`%154*H_rm-^YW|0%LzsRTA7&3O8fHIwIwYhY7EUnxaM3Vx(d~zc!_o;X{lI8g
z{Q(PKbp7ajSh$lKZZPv<`iYG<bo*g^^l}6yPKbv27Z#4Ncp;<@CJzfYSiHde0ZZ2~
zbub#{Z&>)l+zpe5nF9|Om;{uDrB|3d%>A(P1j9X0eK31r`e6RWMdM0`FmV_S3on?v
zVfI6<C7Ob{3+4}EHNw&}Oh3#$g!H3_2P{5d@kdBMEZ$(@2Q!b5KA8Jp`bZ5gbp0@Y
z!_on<_QUkS+yV1H%sqtS0j3_NAC~`NG|YTL?m<sCu=EX!S6upG`e63JXqdZT=@VT)
zES_NcVc`bzFU&tMd362ge3*H#aD&n4>TvO4?uDf%T<UO%!`u&ZH;j)<J-Rr|{V@NM
z>V8;$frTF|9$@Jd-98u}mQG;)hq<3nyu<Xv;uoe5MiZ+aW<N|HjE1=%7A`RR&}m%u
z!_qIhdUQT4d|>v%Xqf%z>e2Zyf5P;^Xqdmy)x-EOf5FllOdpJf`3t5VohImRXuA+y
zA37iAPFQ%sXjpneSC7tzxdYuFgwj9EURXH8>S-7smX6Ww!^MZ02Ma$~KEkCQCJwU)
zmfv9dVg7=t!$rf~4bumsVdfEQ4qP2H-eBQC$RDtD0t;_g{Gq!8m-}J*VDSfYH!NKf
zN(Zp;gV_fQ4?_B3_QLeR?1k}RG|YeK?uVHV(+87BPY1a4!}1@@9vBS^cNiaL9?X1P
zG|V0t4GT}0J8|j5B~Gk<SbW369lacf#SbofVDd13!@?6Lj!Pd*92Sl+cfissJUzn{
z;-F#nz``3w!^ClDgA2p#h3SLQ#M%$@FH9edCY0`B{)UAcOdpJfrEj=HaWK&J!}x^k
zhZ%<+-!S`$O?NPVz``3Ap0Id?*@KIQ>4&L@*$<;(^0@TF(k)CqEZxKSg!H4E3k!Ex
zxS-49;=|Gl%>OVNW*#nmuyBL97e>Rv16Cg3(hsv2=3bcpajC~84%1J_y}0zl(;Y4a
zxWu9L70lf*n%H=Qg)=PNVCeyub?D+SeK7x{%j4q1;svIk*!%)B4^}V0^uyc@i%*z3
zTr^A_Oh2p~f`tbx9AWOnWe!XqEWN?h!T1E@6&imqdtvT@*$+$KFmusqSbBr$hv_5a
zZgl^{+zTuBVCKTY72RGKALc(;IKa{YOdMtpOg%ac^B>ILFnz?PUzqtY{V<x4|6%Tc
zr8}7WVc`c0PnbGfG)x_ghUtfe11^0qadiK~^+D4GOdTN_77j4`VdfIj2McGI|Kau%
z(g>A@`5Q*V%!8#Pn7g3L;S|h$=<x;94@(E|bP3l9VZg*;?uLjmFrb?Yi(i;LjE0#H
zvmX}?a}P`(jE1EzSa`u`n7J@MOg%vw8s9MeF!OQggSiXlUs$}u`~l<R(ht)IQ;$xg
zhby`{==Q_(!{P-dkFF2KhsneA!Tb%ACzgiAFHAqo-MG>nE^}e-f$4{tN5~wQK3I7P
z3wM~mVBw6*UUc`v!Vgxz;nD{ahv|pqKUlgWRX@zVFdF6$SU!f?2dAOw6&7AFeJ~m(
zPDnq@9+*BD4f7Y=J~Rd_f56nE)0FCm#RHnPa4sy~Vc|`x{V;=I{(zYSi$_A~2BsfQ
zL*oPH4w(Dk@&p*LdLCv!0p)OMnEPS*1!fLh0S*RCAI!bz_QK3T7l#=KlZWZYMdOM`
zSopy52}~U<-NMX+xf5nDAsVJ1MiWvGlZTZvFnfuOe^|K#(+Beh%v?e=ET6#i!)Tbf
zg!IGw38P`*iYwmG&4Z;Iba%u2k2Czy(mPB&+<d4!ES<o`VGO7|%)hX3fJwvB1uS2}
z#9=hLKVkld(J*rfg(J*=uyBCsgSiK056oV48s<M(c*E?6xgT9UIv-{qvFhRaq2UK}
zH(Z=Z2F!mj{V?~#+ynC$F8{;CVdlWy4|NyJJdAWfr2Am;PO5&Gdtm;;S^q)Z0dprT
z-eCSFqz@(!%QrA{VBrUI2h<u23YP9*>R{;<y*|d!h(iDtPB4GN_&Aiqg<<gqb2rRC
za0Ns%VD5*R1B*W*wZoO6y9ZV-!xa!<z}ydW2h3d<+Mwwa7Vj|iuy};00}PGW1z_%m
z>4)*Lt44^z@)0cFVD5p5BUEB%q5Bt>p0KM%h{DV#H@(5)1r}~F8W!)c^o_6)I}4@{
z7fmP~!{P~-epq~9w;dr0i!Ye_Vft{UAE;KCJS^T|`U%AYEPP<`4l7U4{Q<QGgMyh4
zb0<szj1MyhCXPWv(?869n0gou6NkAILqD1TOdWc9g!v0iIZiIj9GE+B>4&91So(#9
z2P_`o`f=I>k$|Nyn15m62a8vTDm)}K9AWN&`5%uC6e*Z`n0^#VA~-OA!qP7hTEI$R
z;Rn+X3qM@p4c3H2z`_ltADxEf10*ddOkD8{6Nl-C<!=<t3=9nD<rhpn%pb6Lgwg2w
zVSJc5Fm*70z|s-SJuvm?G%Va<`e8Io99=y+ALeeDy)c^C^Z>IDrVmEL!U5erbUsWS
zOdpJfnTxI-&PPl4FnutZkbYRW!_=eGuzUfx4+jHXAB+$47Y=O*VW>Y~@c^@zP`two
zfW<S+UKkC_UodsJXqY=;`d~Dn@IyBb=3ZDi2J;WPI&?lR_ru&lC>_G=fvJblu<{cY
z&*=7|^I`c7R<5AS<Kn~o4YQY6`(Wn5+z+E+;>6Ohc!JpvqY3!~mOfzWVd)ND{zB6w
zE_a~&8y4QU)T4`|+Yj>xjE0rFxWW@A4ohz^f57y^#9`*3+mDM6a~CW<5Q-O=dYC;h
zcfe>u@c;{Vn7d&#EPQdf6GI#tUodxL$P*BN*#`?Z0?IL@Vc`Vx4=g<p@;^);++L_Z
zVEG=GewaP5aE6s*Fd8NfD__tZ3{wXSZx{`;7v>&VIz!iwiw|=bjK-x7T^wc)%zjw9
zLRW{2kFFo)KV0h3#bN0e=5M0yhsGByykX|R$`P2m(CvfC!_q6v-!Szs^Kiu<EZv~1
zgZUdKj_wb1^I_`HX_$L3+zl0grAzd512c!z_=g(H01JPZ`7m)@G|Yc6`(XCNXjs0+
zr5{}!7Cx|eA=VvmbD-$~<_|*l!{lK!EIeWQVdV(id?*9vPMA8Fe_=E%9m33oYQv<U
z?uDf%Lh+7n0L&dQdvUoJT_21Oi$9pZVd8{nm_ArM!NLJoenB@67OyaU=;olS$KXT5
z4Q38{eulXZLmv(SSUAGmK``FX%z?QB<{y}Q(3D|tVetjihi*U2UFaHN?t|%r#XmZY
zD?ZTm!_plr9l+8fF8g8bLC^Ov_rv@JQ-?uA;}6}vu<{c_Jq`g_K0`N$p#4zuVDW~o
zpV;&R^Dm5sg(EEdpw?heF!#aig@rRLzF_f+p%E$ob2rRhSUAA!hv|oz3)KdvVB#=$
z!rTGV2XhBp0~!OS9~S>Gdx=f=Fnut4VKglLp;?E)h4~jo!_p1R91INv1YrJ!>xZUK
z0@~o?FtBihg&!e(FnL%$gxO1KIKtu|rXQV#n+LNHjfRCEOdZT0u=s(=qiKV4Vg7~b
zhouX+0s;(}`(ZRJJP2rmOT+Ag=_5A%zzu*hVDV3`e)MpI8cUFZxgQp9uyhP_53HQP
zWe_afVfxW&Sh(WS4~u7*IWRuVenR;f=6;wyn0+wwVDh-^hxr#4?l5~{@rg?xOdMt(
zvFc&^VBrc2FPQx>cf<HF^%yj?e1OF}jE4D#TzA3jfw_xRf5E~JmM;m#AIv_Oe~EQB
zEM8&p4s$QD_QLeT?1hCp%p6kv4f6*~KP(<#;<#v7Jqn9In0}ahaG3)WhpER!!{P(O
zerWnZ4?jZXExI|db_&dXQo{{qKe6$KZa>UESh|6kgVWzo1JLsy%>C#zR5=DktbSPd
zU>E=sfW`wXK4I|=a|g^Gm~uii%p910Lg9dJ9xOaz`eEjwtAp`j`3PnYy8B_`F!ks(
z%s!YtSo{&24q@pB=3bb7n7h#JL+8Wv!O8)m!y6iJF#Rz1!u*474>}*F4yGTShNWj*
z<tNO4F#p5MgNeh;gNdWtkCTt)FIf46OFc{;mQG;qhS^Uj{ln4+EdJ5`gUeoYahShh
z@dz^)T^%kyto((gA6(&%t{>)an15mR!rX()99X=-^uf{(Odrf0FmZHyVc`W+2eTIz
zelT-k;^_Ke;RI8Mi-x%eT|Z157XL8+z~Ud~K6HJA_%Qvr{Ee<3J^azr8_b>P`e1yR
zzhU;m@;{6Z3n!R*bQ;}$n0dsyALd?|KA63P+zoRF%zZHZFdA37gXx3OF!iwX597n)
z6Q&NGMt3(%AB@J8?$FIaPp>dOvHpj}7c3lL@d2}kkU8l7hM5OTzXbgOEiYmD5axfF
zdkEPNb0^H-F#QDWg=&PEk8VFKUqY4Pq+sC%vlm9g!Ud;Bs02(MOdpIUHvPl=2@5Y6
z4NG58YY0*>^{{Xtlzw3b!qmas4YMC6Pl$%8huI5r2h3a;AKe^S{J`vmr8}5<n0c`H
zLDvsUpD_I}K1?4>99KC2b2m&KIt?=q-ClG)%%3p#!_2|uZgl-HJ}e!>^ux@B$>XA7
z>R|Z?=5AQJ!le)0K3KVn9xkx>KsN_wFU-9#`(Wx};xKjS`eA%@`(XNDG%Q?U>T%I9
zdtfw7KdJEmGar_Y2-y#lhoxs24YMES9+<fpG_?GLg+I(*Qp*pReX#h4l}oUA#;_ME
z0MiGv7d<|3=|_)en7^UMGQivcOBXP47!3;-nEfzu7!6C$xZ)2cj!Qo*JaO3%i#M2l
znEPSj4ikr|gSiVv!@>m?ZZP%eG)x^#A36<lA50%1ccbfvnGcI^n0sL2=<3n=uy}&m
z4+~dVxk<<zm_C@juy})s;|f2RJ7D&~)Wc|4e8AM<qS50ErXQBRaOs1I!_>pV9ma?G
z2c`}e4O53s!_2{@4=xVP|1f{R;scg0;OcNOp!#9qPHecrj6?S~%-t~m;EI1(_`=k~
z>?Kq#!0d&k3z&X%_rt_t_Q3R^(=c;j{(#ZM>PI&Z#)s*L<pY>FIt_Cl%>OW&Sp6{f
zqnEodKDvGAd|0@_{0+-bxZ(}2ADV7p?u6-wr8|Q9p$5Uy35<r>5933X;iO>xhq(hr
z!~BI)BUAzwPB8mmG)x?-41<Ej8_YhKzj2A98vyesOh3%M=;AOwx_(&r!sKD@g~bDm
z4^zj$;O^}2!w51)07Nh_fY}TT3{WmZN@jXyNqlmCPJDV%VoJOL6GRy<c|&IW@<vc;
zh6(&I)+jw10;3@?8UmvsFd71*Aut*OqaiRF0;3@?8UmvsFd71*Auuo@02@DmjZ?wK
z3t;02u<;di*Zr`Eh`{8<4nX)qP#Pu<8xMiVB9YMXqX+vTVsbEjP?`ry!|Z{mLXa?b
z!qk0$ngdgT&WDMk(=hvC;t!zyf`tdVJWSmWsC}^UN*1VpVE%@=TL9`0m_9fS6=#9c
zQc(RuP+9^i4)Y&eAB+K&SAfw_eK2Vas5p#Ih=zq1%s)8e4b32!KhbG;IHIYCae1Kb
zm4ni-@Po?}>3*2I(d~o956nDt8WyfHxa@(t3tb<vd{}r8QV&aCuyg=dPo(`Y|BImc
zgV^|k#W!rckW~9&_Q29DEL>sv2W}n?1}^s#ia+%Dh8Yh}S7_k@lZVkT_rlU2EZ@Sy
z5muhT)T7g|@&}e~Vet)f7tB1EJi2~ZISI?ZQc(B8;saJL!`z9kp98913yp@kA7(!+
ze$e&9%!iFzD?!bH#T(2W==xxMnEzq!fW;$B9+s|Qd~_OCF2ceY?oX&Y2*p3lKA1hQ
z@)FL6<{wympxY1Q!^}aie_-yx6@D=NFnL(~!_q%2d|>i0_2@J#y~4_WSbV_z0ShN|
zeK0=E{jm8zSiHj00X$w|8gS4sdtvd3Lm7rJEPP@5VdWUi|1cUuBUAw9e^@xd{7WeO
zVfhM{e_`nb=6={b8Z13StwT`I^Z~OM<_}mrz}y9kPlQGY3+7K){)MT7xd-MB7!A>d
zL_*U!EZxECAy_&>PtQm?u`*%l02bab{a6(v3qr#I7Jsn$C|LUsSt&L)EZkt_1<e1j
z@&_i5O)p3c7H=RnGKPgGvKTfttlWh87nXit?t#(R^n%1-<qs?#VdlWnAB+a6#fD+&
z5GIGt$K^kmdYJjJd<%;QSh|7L6EOAYG%WqX>Tehy<_}nSpzDM2Vetgh2P-#V@`Pwu
zeu3pPQuV|94+|$k?nQSGEWI;8^ApS+aB*}EXna^X3(I#f|G?q{CXS{J&V{=dnvY=i
z!_0w&D_jE_0~SxP^bT`3tb9RJj=_cLhm{{N`(W-OHa=kG2dut;>4T+1m^``zVD5(5
z2Xi+pK4I>Hg)>|~G`wKt1B@1fio?PYW<OjXlmQDTn0i>b11q;-`3A#YsD4;_fw>zN
zZZLnt%4MAPL;HW|;R5q7%zT)2xM)~9ft5qB@WZ7ZT^#0a6=?cLcMrOHbUu3d2rGYJ
z>%q{~<K#o_hlMA>`WI>tES_NT33ET359^OZ)uU6e@PhdRmTzJ80<L&~<!e~@!`uzC
zA7{FOIsiSr!qN-OUYP%3@dMQcr(pR5=6;wuSp7lB9WZ&AyV2LJz|t{1J;H5<GNAbk
zz5IvO|FCsrP}LX|Ec{^Zg(-lU2lGEn9GylFUs(9T;tLl4==xxMSopzcSa|_67ZyLb
z%z>$c)%!4Yu<{g^?_uQ&%wBXF7T+*)(B)y~qKm`$u=)>1!_p(%erUdesYj<_?SAz7
zA2$92OCRX^V0_rRBUpUF(hDp<!O8=edKeA!7p&Zcg*Pl7Vd)d*FPJ_U4O0(GKd^9z
zxfkYtm_0CkFdAJPrXD5_i(eQG6URlv+ykTG=^I!0!^EL9EdRpNKg@kF|Df9oa~CYU
z;NbwxmoR<s^o?!+l&=Q0AC_-m@dk@Ws4{{S%-yhj1TzQb4?+gO(hp2M%zrQ%T|X?G
zVfJF=GpN5{=?GmvEPimMPeS1aa~G_fgw?+=dtm0n_~<mud|0}LnFCV?i%%GzAPw~|
z%v@Obz-U7HVd)=6!{QMZAGq|x@;^*HuJFXAAC~T6;fE2AI5fiC1*`vH=@r&~!=VjB
z7-lalK4JbK6mGC|1&cRWdMBh0-2pK7!rC1$cfi60M#KDzt{;}pVCe<co`%%}@c4k1
z3+VdM(;qAxVfMmkSh&FTL(PN9!}P(z1D2m*`4g@W%7BF*to;U~q0$Vn@PzR(XlQze
z)t~6$0CNwl9K_I%E&w$T9)D0iEI*+uN9V)R5iC4m;g1n+P>nEqVDSc12MZUNIIeJj
zr3YC2!f1H@fw~`7u0ySbQ!xFoc!KGNDS*Wz%s()3bQ)$3TtCcSs6v=Ll#4;Z(g!Rb
z!0dy$3+^8@oiO#V_5&>4!}P<&q3IM&I|Hm;0SjMPxWnv&#SgqZL^A*;4x?fD0A?@D
zJY4Aymfm6QKA5{<;RQ1nR=%NG&j2eYVDSV~hc1q;4;LTS4urWI<{n(?Vet*~H!S_a
z`~z!0;!1z8{0j3g%wAY~24)US9Niu0`4S#qu=qwxhv@oY=>--)u<(P$AG&&UK1?6X
zov{25^AF5_==#w4u=s+dM|6LotB3QU{)D*~mTqC?9wBpJ=^j>p!`uyT55g@XjsZ&t
zu=s+}gmlBg38o(wuCQ<+q#x!FSo$NBZZXV*h9gWI77wuU8&(d(<k4xEdYCw@T!M+i
z$}yNc1`SQ8u<(MVOIUh>#Vag+(DlRe3#>eWg$K+X1k(Z3eptGJ<!jjZJ*<2|&&M!(
zVEsmzKVklW#V5LZVSHG)!omp_@34G`%fB%FFn__^2cu!-0L;C(%z@bh^9QW`1>?ih
z6I%L!*$=Y^<_}nS!_qA*9AN5T`p{`u`2j2MVg7=N<4Vskdtl)P<HP&~6Nj0HZZA3?
z7EUmI=<>MuFn_|r4aSG1BUm`%(hu`DOdZU<F!$in2NQ?67u|kXdWN|NrXHP!rBj%F
zF!#XnAG&&UJ}iI2@*&I|SbU(Xhw)+Q0_HxLeX#ZrOdTN_W)G~qf|VbH^ug?fxfhm?
zVdM9N^uy%g?uF(Pc)Y_D5TIf9!<51Fql?4j(P>z|N6+uDctKZ>&WEKRSbBlwJ6O2{
ztDn&I!T7N7hvick4NphVaDk~ur(x*;mj2;1v|NV8E3WW>l{>KV3r54zHOxM^Jum?%
z4GV8r{KIINI4pc&=>@6{PQmPh<v&<@fVC&#{(<X&GN9oC^B*k!VD`cMh0EVCb7AE#
zEZ$%=)E3+nG<;zSVCeuBpD_Qz`~_17r=j{`>S5^xCJ!seVD5y8qtmeb3JV9AIWT|2
z<k9uP_~`v#xH(Yw!O|g2Jvt3b_ptDXr5lpxgP`$=ZaywPJUv47!O{&}J=FcMcz}yT
z8L;#LOMkHR3YBJn`3IMNSiHe#bak-shnt7SfW-?ey}{BCnlgf1cshal7p4JbKa7Tn
z<D%j53^fN{Zb8Ermp+&{%>6JL9v(RK!`uanH(0(Ub>0tV4m=#8_QBl`b08WGi#M3P
zFdC)|p1xoLIB1wYSiXaq3y&8Z`p|`8?O#|u4l@T`9XcPT4_?kd;}4b(355f!e1W+a
zmM>uW0ahO13J+NRhov8wy)YWRe+YL!G@fAYhNT;rd9eHk%b#$4PzEe~VESP7CagSz
z$>Z`r%)hYkhPfZ+f2cJKFnJiC5DjZLz}yAP=Y;gZ@(auzu<(ZY1DAf7dtu=X(+A5}
zxb(rqVd)1p4gkwf#QGbiA4bF64_g-svxg83OK-6B2n!EF`e5o|=>*n)fu$=#`eErC
zmQG;l4;CJ{%z=r++zs<L%ssI9AY?x*oxtJ^<{p?nxVbO^C=C;bm3y#q2<CsNGJ+IL
zJxo6=y}{f?&;T?Au<(Gn3r!l%g@qq1oM7<>6Nf9r!GNVx7!Av3Fn7cFIJCphUxlSJ
zSbB%ig!IGAg~b~zy};@Lm^>~T7Vfb01}l$X;Q;d&F8%24hWi^@uENqGF8wfZSa}7D
zH<-U+=EKzCqG9<SrXS`Xn0s*PgNeiR!{QxgFN}t%L#JWk0}FRp{)NRKEIiTm!T2!s
zu=EG3Z(#KZA^TzaVdlZq!`uZECz^)F8_YkjbN~w%40E9MJi5KG_=M?)#S6NASopx|
zOPK#*`5$I3EPbQvhsndndtvbn6Ni}xD_79<WALH=ftv$ecLnbUVrW1Wfa!y!KbR~$
zeqjPo8WvA5d00OXCJysAES#X);1o<BES%A4m^rxg!@>#XZdkg7g+Gjj<wv;1PzFpN
zteizx2OG~t7l&%cpkV0)=5AOx!rXzO0ZjmwPGR*DEFYmM!^wq(CoH~U>%U;`!KD!v
zj&ONsI)aregvvo!`h=N-ZXYiH!_x;eox;)&EFHkY50($$?t?O5`eFGU<_=i+;Yv49
z?eO_KSUCjK4|6Y}_8&|gEPcSr4Olq9(htl&Tr@16Vg81tM`FhT;pqli{=wp(P`tt1
z1M@$O537G+=EB5r(XjAFmxtvCba8Y(%>S@*6c+EW@JDwax_+4buyBLXu=WAW9CUp!
zKFoYrKLDm47LTy@Bzn4r>4)iq`4>jR%!Q>>n0j;?mTzJ9!sVg)5SA{{^8rjB%s!ZV
zVd`Py0Jz)@OCPX!hQ$M{9EF(=^B>$@Fa|We!ML#agT)8jy=dxS>S6Z7_%Qdw_^|LJ
zl#gNRVKgkfVg7=<1Ev5*L&F!wh3SLkV;CQoepvkhOJ}hB3)2q^4_G+?vj;}Q^uyu{
zw*D6uU-0>Rm<Aj)ES_Ngg}DQkE^ugr3&X-0W-qK9hWP`o5C;Pm-{|EXtQ^6i4K54|
zKiK>YEZku3g3)jdI2f>U7?%HG;Y+MP;Pyb%D=c0J<$qYX!RjHHdRV@J$-`(2=R@s-
zjr+sOZ`imGEPT=R!QvZ6!}2ezJqb%cuyJH`{jmH8^EWL1VBrr7M_74)OFv8;<_=hR
z!~BaY{loG#ES}(eXn4TF6J|a-4RbHdov`!+%ddpeAIv^<^|160Gaubv7$4?8Sbl@I
zm!Rbe+&wT2=rq)yuy}{X2h2aP_(azR=R@_w!W~xL!`uU-G3ph#0T2c({$cjR%VDTL
zaK#^79mE(o2~!VCH#pNT)O@%<p?p}p!OAtb!DtMaxv=nprCWHp4GkYOZE!BSI&}L8
z#Xl^(VBriaZ(;5rq#qV<F#Ry|VEGp2Ke&T&FwoN>Tpu(%acF}J!_qq}-@@VpSNy^?
zLK!gqFn7T1hsxukVBwFRK4Il2%snu5=;pxegO#(ebO+1Nu=s$bCv^QV^{{jblZTZ@
zF!RyV1xy_*e9+??R&K%a6`}Be*#|4PVdXz8J;34vW-p9}xerFe(h-b?mq$eA16Vl2
z+zX>&_My`-|D%h;!WCA3;!+1Ie_{Cq#)qW`T=5384`wbb|HH~#nEkMH0aK5QhM5D4
zXIQ+!+7r0+!NLm`->`HGOAj#r<I)d{H&{InQ>O#Vw=fzefrEyn7npglas}4^hw;(1
z!_>pV8<uZi?uO+*T=4+&Ka7UygZUp8pD=qc?1%2Zgo)z{Z<skSb?7uKzG31p|H9k_
z^A}7W7Y&PlSiXVTiz^?%?1klDSa`w8Ls-6rsl#P2te+2y2bg^@aa{V~>Y@1?=6+bY
zhf5z^9I6kN-eLIwmX6`-a4=xu1JehKXIOfH#RCrQ@bv+(d<%;wSbqTK4tPAG83eNz
zHeLWL2VgWT{}XfvG#p^%EzBJ-8s-i(>)~9OJdB2w2e5F3xdW~NjR7mKVetto4`AsK
z=1(+ja4syqVKgi}VCj;O|6%Tg#V3r0#RH5Fw-Aj1R|g%(gM}k3{o~Bn=<1>I04qme
z^)jyf1558P=fK<nqhaE>Xn4AUma{N-qMM6LKTJI={9ySWZZ0m3P;pp3hvgese8bWm
zR2fbR7T>URgWhh0iQ_T==3ZF(K=%(U9&zc1sfUF(ES<yB3(Q}*^uzoQ%m1);04)E)
z>?dRnOdl-0z}y9M2h4xy?tsM`EZ@T79TqOIbO%$1t{=vSm3OfCgP8}@4+|fddKeA!
zKP;cZ;uBULz{FuROdmQ83m=$2Vdld6l`wg9eQ-WB{lokTb2rRAaCJBsuyBXzhlL+3
z|HI6~p`8Jy4@Se>3r`2QG(yE;;SEa%u<{aS9#k171ubu2=?vYyFmo|ALPTKs9G1>t
z=^rA?zyK46@zH5mJi%yKxerrEsQ!nkgQZ(o`h%GV3kR4wTr{k{g2fxG{RS&PaE2o^
z{9x$>mhWKc6_$=+=AhdT%QxuZh~AFEnSP+@7G^Ij{9*2a`5#uFVAv1U4@+mT_=5Qt
zrVnNgx_%fRmhNEgfQiG>6O2!YhNXL$I#@Wu_ylQae4wWXLi%9g4+}R~et?xHxb(x^
z3)2Tne=zlg!VP9VEPcSj4;J6J%1w0hVc`s`M_}?Wb6|XQ8kSGs<rlO+0t*jVen8iU
ziw_GAm^>l-Vd`Ksx_+4ZVDh+VSbW3U4Y2eKi$`4g(8Xct7#5DOaDe3pboDUvVc~?X
z9_D|TI4=FLaspNk!T2zHVKjz*X#Rz{8=haF`2d!GVeY`C9~NHl@P?X;A&y}HssOCq
zf~8|vd4j464-d`VcvOO=VCe;xe_;6l=5DYe1_pxrxMAT3(+A_j^yA9MF#BQQ28(A{
zISkW}EB;{V78c(y8YWIC|HHx;W<M+)!qNfEKd}4_a}SJ$se{olaajKerVnNhOdg$v
z#XHO$u=qz;hl`IMf4IUMmwuSNu<=7!xr*MNz@;A+->`6i#VgKygJuxSewZ-K{jhcf
zOq>u6vma(2EL>pjA*3IcUSRPDOGmK!38#K&zJsYpw-4qHT>4@053B!R_QBE(EMMZ%
zkFF19Kg=JnejU1gm^v5@YwyF(e}e5Fg_YCj`eFG6J>SB@0Y>A>zv$wy@Q39anEPSr
z0o{IBJj3+C%!8$0SU!S<BRYWTgXMFWd9e6EFNe_e!}u_BVBrX(VeWyc!$rg557wTA
z#W&3Txb(rqVdWmI{)WXjEI+{15u#z~4whbF@kdA>EPcTGJFs*?NFU4|xci~`2&NAf
zt}s4M8d~4N(hW=<u6T#Z!|Hcf{F549FmqruEZ$*!Sh&FX1Q@XL5|-X!Gy!dJX;}J&
zr2}I90}p?gNzm|wl?O0kbQ+ev;prEu4_0my%s<fhM$fOXc!cExT<H*&&S2q$9^bI=
zz@;A+-Y^<heuK%Q`yV}gVfMiM154lN?t+;E6NmX1R_?&sv9R#P<t~^xu=N?R^aq>I
zfu(C$I)|x8r(yXXrXNPb;uT#zj1TiK%-yi?hWQ8GT$nx>4ND&|`(XVB^l~2-o-lnd
z8Ww&q8rBYhr3aY5VfMiE!Dv{x!pw)&V=#G`yI}G#aTtwWKEdJ*Rt~~wnEPP*&}o>z
zVd01#?y!7-t`EkCkMltDAI#mb_$Q<vW*@A*2UicxpM=UWSopyF35$PNc;d=GF!#ga
z0T%DDd<}~~Sa}9hk50ql0aky)+yg6zaM=$_zp(NTmR@1{2-*t`A6PuY+zktV7>({;
z7#~)Cz~TuO4sdrs&4-l(Fnur@7Cx}~Ui5MRww?zT9x#2lXjnMF%!RpwSpBf@M~_EX
zxZtuECJqY^SUG|#-eBs{X_!7(dWMw;FmZJCuyl={Z(;6*<x4{802cqS@*ZXnEZ$%=
zOdL)_^DE3Ac)o)wfcXPg`2|(R0NdvWYxlv-fw>2k&SC1&X_!5*auZ&jKo!EwfpRe@
zSiXnZ3yXi4d9ZLs*9Z$|SUCYR2R+}w(gnJHSbBq{TNn-Z4|IMIXF7t0KP=zD+yM)3
zSU!Nc3&Vb>epvp6l|wLp!^C0f5M4jIyI}Uh%1@l>7R?}7{KDc1J^s*?V{l>mVBrUI
zH!S~SXh0Kyg&Qp1VESS1f{CMPgL7fw0}p3txWdv6A^os$hlMB1A24xPe84S4W1zbe
z79OzpC!`-{AFP~!tB3gm%|3K4Og%gtq2tIf^U&3!^I_#ath|TmBR2iO%!9QXVftY1
zf`tc69WEMHPr&j&EIwfQ9G5<rI4rzj?uW%YEPuk(!D(py2#Y_MJ+Sf|macG>A29dA
z;txi{+zl(2VSKo~Fa|V!Vc`W6hNokw0L&jyE}VktgXI^PeXw!><{p^8;2O~xP<Oz_
z?_l=9`fo6QqHBZkVfLWsH&}ZRW*$sEjE2=~u<{lbZ?Jp^YtO>Wh3SLQFn__^2MY%n
z4GVXeI+#8fjo~h6IH0E^m^)$m;51Y{%pO=h4s$O|9IhUX0rMw}hUF7jc*5+(m40F7
z!`hp${06JnVCff@PSC7}b7AE@%-=Beu=D^|NPq!zH!MHF!T}?k(ab^jH%uSQe=r(N
z8v{%ohP_aQu<(G<P+<fG&Hu3c25W!76u`m(#>b$c@efOHu>1#82a8u2AA^SKhs7_<
z9WZr-!VRVlM#IAiYA!5%VDf}$SUADTJ9s=2(g>A@l^3x542xH&G(ifM-(l{6rGJ7N
zp$aha0W2J#^0+AU@&U$&)kC=Sp_jApbO^N<7O%MU!{Y~<4q^2-OdJ*-Fh06DF!N#Q
z4@Sen4OSk(_~`m!@eIrVuzmpC-%xkK;saejEPcY;4Ne1uCM%p910Sh&IXuy};0
zXP5>w8s;xpe8JSi!Uat^1{Y=@Oaf*eJl#Y61Cu92!`uN2cbLCm^%I7EXgYzF)3Er5
z`2$vt!}1d@{jhk4nFI3|%p4evp&u#$i*HyufVmr%?qK3j<p>HEK2U$a%z-I@rwf<>
z8Vz$7tlojquy}y^8x}5T+TrV)VCfj959V%|dFc9Z@nP<P*+*<Tgqa8HcfrbWcshb9
zz(K>^4V8!08-)B1RnCB(-q6j1sl!FX+G()z8&+-;n~!1YVCe*w?_uV_;sclcuy}%*
z153ZK{Dey%TpXJIVBrSyKg?X1d*JGEFktZptM_5%;82bs3`;+-b^t6KarqyX&S3c*
z?q8^XVESP742FeJ0a!YL^|xVsSa{&_KTI6vPMAJeKModtFdAwdoPw2Wu=E3qH<-ET
z;&6><3|P3q@*6Ba!`uZk7fl;H|HH~ZSowoq{^QgS9p8c311tB?^DVCMhs75x|G~l!
zrXLpm=<bH`Vd)K)USRznSa`t1VdXkZAB={X50i)a6UIj`k6`-HX_$LqG%P$}@rbS-
zoev9lSpJ2jTUh+V(h0gg7$2q|=3baTVDSRWCouKsG%UVh=>{eb<HN!kCXS1Sg(oZ?
zh}8!(4;EgqaDas;uJi-b2TMn=ascLkSouRJ{9x%1mVRO70IVE?(J=eaX_!7(`h~?G
z%pWisT^}r*VDSf|VeW^OBe>iT3tw1zgQZ)Tept9**b5ba<rA1bs4xR8y};Z9b0<t5
zCk-ukVdlcp1B{PTA5;S74p@4G#S2s#gMzsemd;`61SSqk53qEAZU8KtVD`Y`5vCqj
zxeYT1rXLnRuyg~<H?aOcq4WbY2bO+d`GAl<m_K3t99THO$^k<02FqWt@j6&~f|r}n
z_9@JNFn_~nn7y!c2J<&8{lmf&7A`P-FdF7RSo(n31JeiN!~6-;2cu#63YPw1{)MFj
zSo(vhgXx3OuyBU^7n*)y^04|8mL6f^xM-MrVe)Wy!31#7uy}{juyDbl977nEeqrGb
z%SRXraR|WD0ZbpU{)O2G%P+8W3`-ZVc)(#T{QN;!{KC=^%v_i`uyl=K4m7@C=?<2T
zVD5*x2NtgA`eE@8izisP!@>jRFPORL`eA&Sei#k259V${<p`{thvgGO>S6Z6Xn6jG
zw#VT4AEtl+4GVXe|6w!%ZSZ;mrVbWPu<(P0KTIAM4T}d@{(<Fx7!3<YSUQ231Gf*F
z4q)j9M#J>M%z=r+^uuUa`2b5dFmqw$39KA}i^DWRX;?i8qhbDlN;AOf0a!YPiNk1k
zdV%=^CP5?(^CzkC4i8UgIS<SKL|O}vf0#M2@PX%hsJXbzLr)*DctWRP^*FjYF#BLM
ztQ>^ZW3YIDnTM_)rVbYWuyhDl2aOL5akv5q0~Wt9b?E(eSic^k8k2;%6P6AzRbUr^
zl}|8zuy}yQBYOCvn*vKWuyBK=ADBO2<qFJPbp5b!hlL}o9D#*5to+8MA6Cx6%1xMi
zVet)%7nnWh_QTu>b2rT2F!iwd8^%Z1kIskr111i04~$QchNTy1ISdP5T>4<;J-nX>
z(+{10hKb`Uw_y1UW)IB$Fn7b`aoG<m=V9d=EFHq^gM~jVoYBpJm6x#igQZ89d9d(D
z*N4uBm5Z=+0<#xZ{-Uc#=fm{D+zm4iT^%kyy1R+B7iJzT{9);tRQ)h>VCQ|n`~jz7
z3UJUc`(gPG7XLVu!-Zk#1EvoaZ!iV0_=WM&X;`?!^uhfPRY)k`!OVxHAB=D#P=CP8
zhuM#zAL<UGDQNzKnFA}&NYxKZf3SGLr5~2xU^J}#0Mif42k7AnGY_T@#)tJ|VESM*
zJbYjZVKg*+;o%77!_oyTeqqYdX;}FIi$_>^!P<YY@(o=dj1TiC%-yi^0#=R@O8+o-
z!{Q5OFDzVO^#aU3bQ+fLVf800zF_)bG`c<*A7&4%zK4YeY#tC-xe3z`vln)5B+NWm
zxWVLM>d|SKe_{1Bx;!p%m^$?Mg{40j4Kp7WPB48i8WvBmcz}f$EWN_yVd)8`4@Se%
z4NO0Je51P$rVmEL(={~T!rCz~8fG5M9+*BD4YL>KUwFF%nh#*<6Xp+?K6Dzk{tG4!
zD}UkT8cYI>hQ&85oMC);dV;zGO&gpGj~A#q7$25?VCfmI0i6L2H&}eZq+#h69*!^x
zbQ-E3=1+7Q<{wx*plgTmVfhB89~K|5aL1K?VCe%E-Z1~e)WiG%cL&TuC=JsG3olqb
z2+Mb{^Z_ehpxWRROh2rh2CKhe`eFKD{()<RGGOrob03U`xdUb{R5=EP?thqjVd)m;
ze+-RK0rdO>3vXCE8kV1-$}uRIeenDRO@}b|!@?CqBborLo`Ts63s+eA1&dcSZE!Bk
zewce<{)Xv;g%ex@8Uq#&F!#dJ4=i86!UatmoC_PjfzQ`N!vWUsgSiK;0T%<R50-vl
z=>ZlVxU|8<Vf8PJPpm$;dT74`Jse^23{!`ThWQibZdf|Rr5+Z}u=E0>Vfu098+7wv
z{Y)5*9^bHZfUXagZeZmOjE4CGmi}P%0=j+}AJ+ee?E`_i8|EKax`L^P(J*m%x`CFb
zu<{Tl4^xj$!~6yFH!R*@@r9lq(DlRO2bR8J{)UA=%pK_ZV0@T7jE2=0Fmqt;fvLwu
z!_q0by)bie>4S;G^ufwoI3K0}2Mr4+Sos6v!}JphKbWzw_8v?hOdM9O!1#n{n0}Z(
zSh&E_5h4Asb_6V4!14i1oREH)edyti&WD);Gar_ZVfMnzfu$>0_`>wTXjs02`45(k
zVeJK2zW|s0FneI`fY}c-AC?~Ad^85EzJcX?n0+vJ!}33_d<b(FOg}6g!0HWHISy;j
zp;-?<*8^rgES}K$==xxMSh&N|Da=2x{0GaAF!ks(Odl+qVE%y7uyhCuUv&L2aaef4
zXjr)d3m=%j(DlRA!^}aKhlL}|pXl;1dtm7R-5!kefMx)!{R49+EZkw?1~(5{E~9CO
zb7AE@Og*d}2usH>cfvJ58L<2gEB|5sfQ37(T>({&LBZ^U<s-N|pzejGUtHk}i+@-=
zz|ub~{?W@r3<p33VCtY;__`uk{faC8VD`e?2cu!;z-U;w!1yp47H_b4hUGU{Ji_dS
z<p-EP7!8YmSp2}k3r54#!Q^3lI1LSFc>fRDeuKp;EIx3Re{gfq8EEFf(h<5cSp31t
zO_={-;ROp9^!S0B1GNX{Ul<LOhv|dG6HFXN!@>_%&%@#yCJ!r*V0;W38h)^J2a8Xb
zI+DW;rV&cR+z;gvq+sz3^DitN!NQY}0WkN%`~lCu&~V13AJ)%<rBj%Gc)SzP57P%r
zZ?Jg4m7g%&0gVS(c%l0jR?cAPgSrcrZeaB|EZ@QE6<9jN&<_=Wr6X9l!`uP$53C-5
zDo0RIcf-`d^uhcOOP4Tn5gKu@p#Fi4L&CxphiZf{R3FU0u<{e306z<sZ(->TmJZ?R
z0Kazl`ej&o01FRd{SAv>SUkYOjexaqX;}Qi!V4DvM5jNfNig@r(h)3O!OBypG7Jh9
zPB3@C)Wh_{!WTVV!wiCj7cAYt(h<!4Fn7V!qtmc{KFl0+dtmN^r4P7%X!?bv7g#?M
z=6+ba0A3EjH9{D$@P?&VSUC!FH!MBj(ho6(fdLlpF!#a25te>n_Q2E=qGA3=H<yq;
zm^>`~!~6jgN2g)(u<(MVW0*LMM%M@9!`ulACs;ax`5#uE!_=eG==x#iz~UPgpXmBv
zd{{dVJ$_*BfT_bp!~6>iXBZ7@58%=V6Nja9m_K3h4f8)t9WEMHzr*Z>#RDuK;nIgL
z4huJ!y~LJ3F#BNbb(nu(;xKb@h6gnN!O}e}ox{ovSUChU2i@JUdI?6u%z?!VjE1EX
zbp7ajn0lBvE_-3}=rp=NVc~$T4#tO-(=dC`^~3xFQ;$JI>pgUL!_*N<2QYb9`h)Rd
z`eEq`#>b$c@dqm}VDSf2ht7wI<Dy~tmehOzGY?k&!^|NR{xE&8@c~$Q01FRTx*@ha
zfQ28t9)OPL!r~cbA36<lH_TpGzJ}!|T;T|-7h&#$<rf$YE7xG*iEb~<e=u`k?u6%O
z=sFr)=@6DaVD%@uzhUJFOdW>((C~rFL(?<NK3KTm@;|zHuy#Bw9O2~xx<V))=6_f}
z0Tzz1^aFDTR5=C(Qx6MoSUAG+11|lrdJ(1{mQG>e1uKVP@quA6Q~;Jvpj`NRURZem
zqhaze8kVkL@dazYpsR=b1Evv;hQ%8!{$YGrdLyJCW*jVjVKhuX%$+cGw4!0*4=aaZ
z?jY7aSh&H=fyE=S`eEZoFnbB5TXb___QJ$r^(VSGOdb~v%MY+{Al4jMxd$^BmJVR)
z7naUp^5`@yzrxIc<qur>2VFm`oPg1Ae?#jhSo%cQ2jj!iDO?>?AFN)0n+MZ?M#J(C
zOg}6hVc`f12Q+PPE-W9z{140jF#p5y16%_E1}q+7<rvId0@~pBIm5yO?hXPPVd~Js
z3C1Vne^@$$m7}oy1oJ;UJ>YN$ES_NHKg|EId<08hIJCosVeW<54|5M(0S*SteX#h3
z*SpaA6IVF~(+6w!!S%!Jfu=)T`eFWr>4)Vzn08$H(B)y}6)gS2^+D~2sYj>b=^UmG
zMne_C(jSydkb<Qnn7Od{C#Vss0H%*<dtvT{(y(y^n7y!c0BaXQtwB&weXw!|7LG6l
zF!#X26D9$rVe+u_3CriOc!iY<#QF!r9;o}E))Ay&_QTDAwa0Migr#3tIKlD{TsZ;#
zu=WxxpTWujn0{FPh4Im8n7uIlu>1oHS9pA)YlQM)`eF4GESzEb;p0D0^*AZ0KVk6(
zQwPhJuyBHj!)a*z!15ieoQAmrrVf@bV0?5MR{y}-bLim)k56=sXna`wquY<B49<o5
z3l?v%_=J^zFoo#h0uzVPu=)XJ4vdC{2h3eC^)MP1Pq6TVmH)8x0Lz!K{0!5Fi-wtp
zOCF~<H2&fK$Egri0+v2t?nTc(gxmwS4{ATUdR*ZKa|Nutf`uQtenRO6R^Pze3()=?
z%zRR5^mK<F9)!$8*AFWXaO#7G8+ty6`3F56qniT@e^~m3mE$mX!14#WJ{TVs-mr8E
ziw{_SfyEz;57P&uVc`!m2R$9Z+=sLNf*J%1XIQ?2#XHPg7!6g9LBYcdDg*NeEM1|O
zM^I&O3T7Y7zp(NQmjB@jaWG*1gM~XG`_PSnr4w?^fz>0h@P>s4j7GN)oexVtFmYJ;
z!NLP3j*CXuPpm#zx<apCVD%x)Ke+6LrEgd|gsFp-YcO+Q;^;IiUBS``dN~b?4|IJn
zK1@Gs{vSQOVg7@uN2g)t!`umT2aJZL3t0X@*AGj#FneI`frSe?jjkUSZm{@*r6ZWR
zFn7S@(e=aB!^$a`J7D<-mX2WNq3eg~gSiuye+b1p%pCOahP78=<A><!3Z@T6qx&Bg
z&ba&w(}zyO+yNWMgM}N+Kj`Xl@nP);^mK?zJxm;y|6u-xg##=;VCv9mSopxoU0C{o
zm3we;Xg?BNKP;YL<43UYgoPu_Jup7Hepooe+zU$wu>1>?hl!)>huH@UZ+N*2tyf{@
z5{d_yJX}B2BA7VL9Z+GM6fC@9G){$330Qc;?1lLQ7XPqtgOvkNZEy;vAJ*=M>xZg<
zxdT`JgNeiJhoxhfJ{TWHLyaX!!Q2HiA4U_@2vq=!S6KOiEB;~bgq5GL^ac}$(Xez5
zH6BjE<YDO?)_#GhCs=+#O@f&Ni!WIG!RCKq=?JPECxvbftbV|$0V)Br7Zy%1_d}%#
zQt0Ummfm6UfXe__xdY4pFm*8fFg`9C=3ZEO!KDtDIINt4wO?WCi1j}#UBc2o%pEZO
zFdAkqE*ho}7Vj{1uy}#-3DNL)fR<l`)WhUq<uEKC!160BJ;BuBqG9PDRv*K{6P6!f
z;u!j&?u3=sFnuufaQ8sfVd#enz|tAay>RzH<#AGI;}`IFfZ7KO2N;dZ99aCr+z(5?
zFmd#F#bpkLIMjU@;%EY}aE6t4uyzEY^n)H=Fg{EltUN@s4$g(Chvj2fIRy&`SiHjG
z6Rr`;fa!;F8Q}V${)Xi*V)euFGt4|#Jiz<|6Nl4KdtmBd`3Dw`F!#Y|xIP#Isvp)~
zg@pqwK49e&tQ`T<hEBuU|FCod(+}gr(-E3Rn7?4|g@r$?9*6PKw86P>e?ikb%pWlQ
zgwj1M+~5X688Cmt@+DLlgM#HZm^)$V4d#DXcw%To6M(rFHhuxKA7&n!attoapXlx-
zw)}>rb6B|p3s+cpVpxbK0L!ni`WogQc)CDS597kZ59V*!{V}lcgr^H={KE9XX|(hU
zvlo`{VEGAGJj29c>R|T4!VwlPF!#Xi#le8t3o{pH4i4oQ!Z7#aDlgDAz|sjUox$os
zm^;wbqw`_u0~YTv|KrN9==x#qgr#?wJfU%am^v5@t0!URz{*j0c)%3mpkex9=@zCR
zR{r4720y<E)_#Ne8<rk$>4S;G!VhLItUQC|1DHBoG%WsL@-Y1{|G@Y#d2||<&S3t8
z#XHPgLhgX&KUg}4@nPn`{Dp2WIv<vAVc`c$7ch6Bt4HU<(jClxcsjt9@6il^g#*mp
zuzUb3KVjj4rVY-8m2<HC3Nr`hE|__64QLEl`2b6IuyBC63zojnv|(^z^(?GBgr$E%
z`eEr1=3bb3n7d&#hJ{c8m^`fg0Ha~?4+|fta!d*uPcZvo=@#Y>*uG#)ji@59dIY8)
zW<RPbOdibruzCy@AFz1D)PX7jb2luVz}yW>kEp6Kc`*B7@eNBym@2W0!2AodAC~`N
z?!vAMNfcTS!qNdO{$b@h%zPvrC`|Np3-dS3ei)6S35f#>FIasKD|g`W2TdPHTJSPq
z`5$HuUd2d~F!eB+kiTJCVf81>Ubwqq3UJV{@P>smEdF8X4wepaXom~K>Kj-&3^RvN
z`3X}88~=it12Y)r4j7+U8lHYhj5nD1u=E5gM_}T(X!LPkSosN?7bG@5VB=M=^hm7z
zF!iwTgM}+D_rk@Y;RDOBu=E477Z(1o^aa-kWx&!8ES<vq3CnLVcfrhoYC}-ad=E=E
zuyBLLAFgx(GXUmKSUQE-3#*4<@-X#;XqdlY^%f!Zuyz8>-|%t=>Mxjnm^_RRr=j5w
zD>q^G!omX<elT<4`k)M0xI?+{eY-GoVES=}1I(RJ8Ww)AbO19K7Y(xq=3ZF1!^08k
zKV14@;R1_4So(#jhq(vFN2g)o1}ned?Iq|s0$4gi*9YUn!WC9;!}0-49F`to>T%Jq
zd=4wOVeLs=>S5xrd<AnqEF56z9;OZ#4GU+OJgnS;@nPWy3ul-(jE1EP7!7NGz}y2%
z2eA4W9l+8TtlWb62Nn<L>M{7xbO$pB7GE&;pr<Pg{pbSF^ae{uu=ELwPjuxNe5ig{
zJYdM<5P*dfEdGg2H|XgX=6_f|!eJdk80ubFxTD8EEIq@@DTGD{3s!zZcnl11bD-wJ
z`~_<t!qmZOX#By_E37<%rAwH9aJB=W`eE@53pZGLMNb!Sd(jxM`UU16So$G49HHjH
z{0obJSo(oVV^A>v!qyMN@*7M)OddlcQ~)Lq3tyOfVd)o^KA_4mD40KC`48q^Sop!x
zJ<NP`17Pwncf-;PEFHkq!`unu!)Unspy>ss4(2XcdW5F~m_`^4jel7BfeFLn9i|@^
zo-lbB4U2b}`7nRO!ULv`APsdtEIe@8hszvT_`&r<&4c9wT>4<*uyBN>e^@$3j|Z4~
zbQ<PfSh&IDVc`Sg<Dy~ihS>*;KUjLgr4JV0uyg|}_hIgUxeJ$mSiJ(PZ(#n0sfU%P
z=;APWnE9}9gQWvlJqF9qFmqt~U^Hx;4_05p%z>4cu=s=d52g=B!_pfp|HIV5!V{(+
zrXCj!3wKz2!NMKp4_x|S>S5&uEIeT82_}zAKTI4}-oVl^jE0rVFm<?SSUP}}GcbGM
z;fYHlR2=4im^)zZhnWXehLeJg@4(_87Jo2*;M53}fX(y6;uEGH<}au+oD@tQEWN?v
z0mdif4w$_#8dmPX^+C%MTn>QcS9p9u%T-u9#99AB4TPzK#TzUg!@?DpeptSQ`4grO
zrXJ=Gs5O`r)L*dt4~uVD`iHp-QzJwK-QTeIhlLy5Jy3ffx)3DH9zAGy!O|<Nynvkp
z1v3w!6O{$)|HIM&to(z83#w{N9xR{3@-a+5%wAZ2z|;v5fw>zNU$A(AnFmpYn?$z{
zW-lx~!O{h~39$4EE9YSG2s00spV9Tf`OtWQ>4U{TEFNL`5Uw7L0ZSh+`(Wt~Ru7?@
zi>93c7XPsEU0A+_r5p5q9lCy)|6w%DeptGPnGbU(x_+2{Vet!#M_9PS%)zA}W*$r)
zW)3W0z|6xHf3Wz0wZCBHFf2Y{=HsGa_Q2c;GY^)3aOp!Ahov`Iy2a&QbbT=OuzUs!
zPndtu)uZ!a`4tx5u=t0K1EZ@)=fmP19$wJ&43Br{x=TXg0Sh-+I)>$ASbW0lLAM_k
zPB8W8@ePxQ=O=UnpnO>Q0}Dr3ctE8YVBremqth_`@Nj@CfTasq`h*H&P%!_)Bw+Ce
z%LjzYF_`%<bI`*9W-cxo7C*$g7u_7#_z^4}!{Q&tM^}%|hpB_bH_X4VatvKPIv?h5
zSUAAaH7s5Tm7_5C!qO2e-N4E-SoomZ3**D=g@qq1-eBn&Jzc=`!Dv|ehPfLi4s!>(
zI7}W!!@>>bUsyVZ@nPW!Q;$x=+z<07tUV8NH>{t4t`8P(==Hx2v>brdAL#nf`7r;%
z+=1?InE%lA!Qux-!}P<-F+%=^nFs4H!^(45xWU{7a}P{CIt`0wSp37(!@>_;Jvtv&
zj>G*44L?}AMpuu{hxH?2<rb{GgSiJ?Jvtv2?=TvpKZvdYjStfYOAoN{M^lEug@rFH
zy~6wti$_@a!SV;X0Wf)Zd_mi>u<{yaE-ZY|^~3Ce(Xe=c>4&MuX+P9HSbm3<GcfaD
z;yBYGR6i`9VESO`8J4bKe3*N1(XjN3OP-K8EZ%VW7ngomyu<1Nn0{FJ<I;yN4l@Uq
ze_-t~bam)_SU$l>KhW?+SC7tz*#~nsEdRj6A6)|)AC~^n-2pQfO*sY^rXOY>%-=Bg
zU}!)SfW;duKcPtz<igS^th`1~hlC7(+Y2qXVD5(13xxE;<YD??@ra%eVd~InSpAJ2
z-mv-{T|EXLEuFyf5zc&zW&o`GfeFLI4;mgYaa=T9Khzzt^aty|!NLQVepvj&)WO0L
zW-g46OFyjqf#p9~zJ=+7(YW-((l1OO%ssf&!^C0kh4~Yfj$rWy<HOXW)3AI3^Czqx
zhouLYdtm;;r61-Fn7d)^PME)N`5%_wVeM^LJp<DZi$|Dy(Cvqb!}1?2-N5XFl?O0!
zbp0?s%-yhh0+tV8;Q>=eh=zp|%s!a=aO#7mZ<s#VI3g_E2*n@FT$q1h;Ry2wEdF5f
zF!eAR=1y4pg@r$?9)|N_8gS4s{jhL^r7IX8W)BYS3@~|gb720!r4J?!3qP1YVeJrD
zIRsOOi-x6tSa`w09hUxZ>4S;G)Wh2CFg~I9hq)JhoDkM7gS7`>_Mp?S@*S36VEGUh
zZm@CzE{<*hH2uMZVdXU}Kf%KhIt~F-hl_^khs8TA{9)mNp$}SK!^F|u3Fo7^6Q&*(
zKQMb>`34pqF!#g41Evo~!}2*S-NC{W7H+WkgOyV-edshS-@?){EdRj9fnec*t`EkC
zxf_;_Vfta|0LF)@N2g)_gz1O*1Evoij_4Ypd{{Wb@+~a9VCewvFQ|HS3O#;d;R&N*
z;f$^i<}O${1Gfh)U7+hj=fm{D%!B(MT_GAD<}O(L!om@jFVU3a<ig5b^l-qb0Zjtd
zZiMNFnFlM6VCe%*8=MPEudsN7g$FD>z|sL+0~!OCe_-JU3kR4oSUAD>=rk;yz`_w$
z?-J4nGY3{~z{c%h?H8ClE*j=PSUAD@?=W$ge{kuCiNoR>7S6Engt;FUo-p+=8s=}<
z_&=<?faP1bJ_6|(W<M<cVSJc>VD=HB(c=wnKLL&C>R|02SUeKY4zIsq>R|B)OII*)
zTr|wTFnMC*4W=Hw+=A8TFnK~W%$+crP<}x-4_3~>{6VO^N7oOl=V9>=qhaY0T|GJ<
zR^P$$A1pr+N<T37!u$*K2Rz=P^)5Vo(Jh9`!|D$h4O0el4=g{z#9=fn-NC{gmfv9V
zFg|*^hRMV9!^(RY4GT9|y#jLwOdmQ8lZS;DEdRpFQ&@S7t`8;;tLI_y2n$cxI3UcO
z82Zr#py3JY_ru}`Mx!f-@nPWuE4N_jAC`}B>4(KDto($<6D&Mn<`Z&1OdeK#!^%OJ
zeq8whW)G|!g@rTBKZNwd<YDOq&PQvn!qOK^o)8UF5Azqg`GoYt^uf{xoR6jvmd|1F
z15*c!4>aW%Tv#~4^ufXz7LOPj&;(%l6&C*J{zg-d!G-CEl@~Doz~U7{0}cULeueo5
zmJV<zhYQ2h!_qy>|FCoqa}SIU*9c|6!W~v_z|_IY0a!f1@(ENMoPvcrjE0pPFnzFm
z3=@ZIgfd|6g_RR98deU&$`LpX)d!<s@db-N7!6CWFdAkLOdke~);@uiAFy@+dVa>x
zk0t;MM>Ju=T&O!>=?CTxLK<P|02WVhKGZy1`eEu|@db-_Sh|MC0}caV=?m6>hv|p)
z2T2V#c>Mvj50<W=T$~gvzr)%AFolHT0Tx~`|HHxqrXQ9sam547UKkD22cu#2FS@xf
zd00Hd(k(2!VE%@sKNt<u2cu!?VfxX_4N}{ou>1rwAC`Y$e1bGAy}@V#8eriLQwB?K
zF#q7n_b~Oa^atak>xYF0y1!uRVeLd19~MuraD|oYxb(x!hnWLQ|FC$6nTv~tnG5$X
zR3R)}2&G?`dto#zzF^@(NIxum(d~n!8<=@eXJJsV`U76RL*pG*j>FP1hDJ02c)o|~
zhlM|yGMrp^xI^{B+8sDGKqX+|2CM%F))P>Da0=R9h4EqK1*|@XnG2Igr_tjF7G5xS
z!OAmOIfbqtmVRLRVC4y{9g1!ax_($V!SW5P9D~^l(~s^>m^m<eVet*qZvYJ!Sh|Ac
z6PP}98kT-w=E2exOh3AMbUsWU%-=A7;VQS$^~2H|EF56<AI$$S8eJcp5A`q1AFz0b
z`2$z{!Suu23G**3ykYqe7SAyC=rl|oW*;oPVdV-eU7+iO@!|5&`~$0R2<3m6epq<}
z3wN0PFnL@wOdo9g4^|$bi^KedVGcChVc`c02UvW;@*^xf(e=a94b1;Adtm;CmH#k4
zx_-EQ(EXC=@eDH;mwuRiFnzFahnWj2N6_ts@nQO5G^`$i`wJRAF!ks(%pO>L!NL(1
zUa<6st`EkCm6x#fU9fT-W<N|ErXCj!E5Bj+5S9;MG%kHGaaekRxd)d2h}91ZH(2_E
zr5{*%1`7|EedshS-eBgy!V?w_F!!PBgYjYU04u*>`4%P)OCK=x=rqiHn7h&C3Grd+
z2UhOG!VM-4D-Us*1B*9Ue-Bpv!}P=A1($wUxWm%{G~L4Zxb(sO3snzO2a5-oewaI8
z@^Bie4<?Tu{;>Fmr6;&PC<CSsR_?*<g~b~z-N4-m)d!<s<pM07z{FwU0pr8e!T2y5
z7B4XKVf7E(-_UXirVgeLM#J0*E2m)jALegZeF*a(OdmQ8ORsQoXgvaJ7r@1dWWdrN
zEM3EBT-ssb3`@VT_BSkCFv1tjAXvD;?1h;JQwB@lFg}cig%hlthUI&hJS==+G)x>u
z!_p5d-QWsGn0j;?<}R2%T=t;rgYjYEg02r{E=(P%G|W9P^Kt2i`5zv?(EJFC2Uz`$
zOFv8;7VfZcgvAS?aub%XVD5$G1DLsl(mhNa%%8CQ12czEIKt8y%)hXF4|5O9T$nxR
zG^|{Lr9W6chtV+e(DkA7Vet=h4=g{xXms^3KFoix@*NhBF#o`4n0j;?rXLo6Fg`3@
zz}geI(gDm~Sa`u`SiHj00n9vf`(fb=tG{71OdTv7VKhvfSQ_RIm_3B_!PLXt0gGQ)
zd4@|rtek_TcbI)Jf5XJl^~3bR;t58>!W-sqSiGR?hs8H+{0|miFmYJC!rIa3`eA&S
zJ7MhynEPS!glJg!!15m~90}=zg)c1s5K@mW4@+O@;R_2_n7J_bq3ef*FD!gud{{n&
znGbU(x_(%^z|uX84=cA|?HO3Ph^`;bht}7yasZ|uW**F4aP>GC@OXooheH{LFf9II
z`eE%3SpNZ*&oMNj*#pxL^EWJ9VE)0SAC})>;SN&=E5`|?CzyROeXw|k@d?qe@P*k2
zOHa7s9cCUZox$=ehC8A4DNH>&4Kp8>?_u`B!VBgubbW9>)O=Vy0n7KWbOWQ|>Txlk
z=D_klOdl*hacP5z!}2{WpTY7IMtHz9pwTe>u=EeJ7ZyHf%HdpCx`nBO*$0acSh&LC
z39b>!fTcH>Jj`C0|DnnlVBrhn!)Ta#SUkbZgQX)_Ji@{SrVmEL+zZPmF!NyP7p4zq
z{(~9>o9}^zJ1iby?GKnZR5=C(Qx6MIm_ArIVrW1UfcYDik74G((=(cSbS`Y113e$Z
z+yOHmT_21Oi$C;uf|cJe8m1ndhUtU#+u-uh@(mVGaB(ySEWTm!2TKpIctuZ7XxicX
zxMArZ77m2s9cB*Pe^B?r)`P(0anjIy4;%l3xgSQu@(&^XuznxR-!T8e!W9-j=;pw}
z6-L9-4J_Va`3GGeoDcOc++L`EVBrC)$6(<K*N4V{xew-Vn0i?HC6xYP?uO}uxf_;`
zVdkJ&3%_p`mi}S(z}y2f2VEaJA3eTd=@^#pVKllvSUAD*KP=o~^$4sziLMXMhlV$-
zoPfC>rXLpnFmbp(C<7MWuyhVn2lGFShL!J7Z3qe)|FC=pi*HzX!WBZ@3Fku@F#BQc
zWSBWH8mf!|<_;Ji7Y$1X=<X$?AC^8~`3)9-uyO@P<FXf~9=84;7Vfa{fYIpsVeW;+
zKP)}K_7B725nUgQ4|6xnepo!h<YDCkjE_#k^uy8}dOX0=6?%BW%t5ygR*t~(4NM+Z
zkHYl9XjnM~<HN!O#)p+FaCgEapft=rSiXnZ0}BrrAC|76+At_s{)B}$tlWT^50l5x
z2o->})1h1jm_K0R=;Z@U9WEM{4&d>EBmbc5hxr%Qo`bmqMx(2Tg$pd5!2FG#UvQ>>
zsJme49Twj(8Ww)Ac*L+DIz9_4FJSd9Odl*A!TIP0K+7Lk{J_!ytXzTd(X|oc!`%;C
zM+o&NA$>4;n0{Ee!{ZaC07}F17rdN@`3GtrtUQ7$$Dm;5!z5tg2usJX^Z=7br(y8|
z3wK!i5vC57K45%w8Wzql|Dxwpm^ivVbUv(nfu%o~K3Ke>t4HU<(mTw*uyO_FK6Lfy
zd{}yir85{Gmabv>5M3XP4|6Xpy~5%Z7Js<nAEqC@+=clYRxiNHGnjf94RbFn-eCG*
ze3(06;S19Tqhb1C?uUgJEd9XpJ&cCw!%0KaEv!6&jl00o8BTpr37C7JTn0kn4p#?d
z!1FsaAEBp5s4_SO3l~^C!@?QnURZd-HJ~wI;R~y;Vf8;OJhh<iK+}ek3$q`l0G57W
ze4I2|xWm-J{0HNs)3EXbW-p8nQwQ@0jE_#k{0|F1So(pfgQa^|{)CCcXjndj=|eAX
zVDc~;rVmEr$~Uld1S=0=`2nU6Mx)0wEWN_=F{~Vcrz@C7Tr@NuVBrR%aVdv|A1s~2
z)Wh1pF#o~afo={gd|}}R(+8tr{)hP!T|aub!qmg;gVC_?#Ly3|zhU;n$_;dNxcKPd
z4NLDZf8o*x6NiNt%$>0CgwZf{xM+BKhsGPspQM&|Fn7b!F|6H(%U*PGSpI>D!~6x~
z6QW`Hi)8mh4Ti@Xth|EJP<etBEc~GDC0KY68;@}Fq2|Eq4_JGEkU8l26PB)E<sZx(
zxIJhLSor|UC+PVCmcG%n!MU*UKUjW+x96b#AykiG_z$KJO2aL}!GQS}<_}o9hQ})o
zeK28|`!LcS)P7v%!0K06_@bvnSop)#!R&+4F!eD1!_>pv0rL-x57P&uVfhpm?l5s!
zIR^6=OdU)fM#Id9#W&0zbn{^9anZ2)6W-4!G5^5wKP;R{jX#+C;WV^dhKD;e9&x!J
zJ$%sZhw;(%!^C0bEi50x#9`$!tUg884~uVDI)KF+tQ>=-YgoC8t{)~2QxA(bczz||
zZ&-eZ*#nCQn7Qco;o`&cKdc=HQwK{=Fg`9C*3O588;lPNS6upF;;?vz+XwYGtQ>}i
z2TTJR4a*0xaD=%77B7VKqvtbNyu-{Rq#u?(VDS%Yzrf-FmQK)|4d=q#3kwHWc>=Q+
zCJ)zu#(?EJSp384ADFw~;fkgY#)a7ji!T@r3l~^;z|^DDu<?IbI}qj$SUm!BAG$tR
zeuu>q%)K!CVEtiOxS;Dt7e^0&n0dIuA6*=lk6`fz(+`UWbo<fyuy}&eu<(GHgRUMu
zUf|_0H2uTug@p&YeptB&b2lvBVBtxyJcpL^=<Pk2|6%0<y1f{DsC!}Y42yS|doa|a
z3Bd9hEZkt`!0Kf*<(OQkKVabr3pZFe!1$ONAtEsQVdF^X<uJ@$5Y@Ozm_AtkhnWjc
z2e`CC!w;?w#)qmW<bIg@VCe@|Zo=vZ7!5TJlY)jHtlWmtu>21TS9FcAbOIBH@d@dJ
zsfULTOaST*Sooor>o8?78dhJ!XjnMG{0qxB81}#nfZ7L(H(2<?%TJg(95l>aSh)iW
zCs?|JnTJC=To@K^Fn_?xMOeJT6{0a<^00gY%Qvuc0av*LHwK!{VC6SVKf!VXY5+_<
zl*<6~KP>)W<qAA}VG5x%x_(&xgXxFyap{M}H`F+|{jhk4xd)aWVCrBrtlWgfBP{>G
z!XKs%7Y%b4EdF8j6|w0D7Vj`~VE#c57hLwE+XwR}%ssgD!Ng(Z1T6kx?tsM`OdTN_
z7H%+gFn_`LglJfPfw><RAB6P5>u+c}hLsnD^ugt!`rv#h1D2m);R}lws5G2HcPA_y
zVC^56IWYghH9{G%{0=h*7Vj{B!2E}^9)*@)F!iu-hm{Mk@Px%H)LJ+NQ;$Bs2P^+!
z`3PMeW&q4xF#BNn4VHgl_QK?0`d~Cn92UN?dIP2(mX2ZWgz1CP@Ny0+08<BZH;jgb
zGgKLZf`%WgoP@<2vH2fn5Ujj}`3F~g!_>oQxIIvJz|sLM-eKx+(J=d9<qeDv3wK=l
zVB)awf~8xSILtpVJ}w$&A1uAV^ug?hl`FXP!~72mFIagGr(yXMMxz-73ny6mftBYl
z`(fb;%U5XH336fi2Uk4~3qQC)P<O)o1=EM24ypkr4ojb~^aqPim^tX_9IBlGo^GM(
z2UcFe!V?xBFm-T!PzKC?^zt4Sudw+am^!F73<|v*fcXQKE-*Bp3BckJ=1y3+!_0++
zBbqii7u|iZ^Z-`?jTbl{jR6Z^SosA@H?VlYFb_>X%zjvS!_qIzKd|sb(+0o49u{7(
z_=cGWr=jE0==#z5Fmqt_IxO5^;fbyuW<D(bVftbF|6t({E0@vr!}zfB17;q~URXa6
zrVbYk8-IX>7fc<@J@E7alR&4T=^rDUq2lPuVSJc+7!9)r77wuc2UqyP+zlItfW<#7
z{lLtH*@sTU?172H(jhDyVfh&*k50qtHy8~IFPQx>addq!KCGOC)wi(thS?8G7clj>
zXjpoK*#}Dxu<*g94<-%^cbI-yeuI^RFm<?SSh)`~2NtgI@F0+XVC^SZ_`_(JJ7DpP
z%YIn;fQ2KBhLtBUci_?wODC{$8>S9c{=obN6Gx|E^00V-g+DAEz`_SzAB+#HA7Swi
zTL%Ekk1+qh)T7fd|H1qLvmad@E<P-sz``Bo4w%1S=Hk*13ul--EZ&H92P}MG`3)9s
zu<(cF2blTjG`c>Rdtv1Vx_WdzEc{^phucda-NMX)xgX|EnEkMLg{i|u!^Zhw>S6AI
z@p0*ciNoxJr9Wcz!SuuO7c3pX@-Ivt7fotBqniVBCwlzB)WO7I{)36bXjuA$@zH5m
zc%bKNm^xUxh37A{`U*W>VftV+Oh2r>0pr8c0nA)j`3BR6i-zfg<$GM}aEZh6Ev){B
zslye1FnL%!!qOeOdYC*+J&cBhKP;c)3TIe;g6V_NFmqw)4OWiB<O$I*^{{#$Mx*OP
z_YX`SR?fh}8)hC%9!BF5hsncec>KWxpfs#pfpT$EXz3585SFfBd~_P-KbZev=??B5
zBHQ0Eedz5?LiWSL4<4RS`(W`4Yv-W58y4@dd<vuC=@2>(jjj)!54RU40kt1i{^N>2
zSh|Gy8)gnn92TAg;}2>dto(%83o{4iEL=1!|G?rA*LV+xeyF{${lhSGVC5>zJXpI9
zLqAjimXG1;py?169<Xu{svJ(i(ibe8VCe=X4oerf!XIuB)IM1J!^$yO`hoce7l5TB
zm_Atf2TONw_uw!9CJ&1*SbB$*>+o<Pq#xQ|Al6=(aj<xT`4greCXb7Tr8k&7EFHt#
z3kwf)bujg?_=VXA^DoT*uyBQ`N2g)&3eyh@M_Bm7(gQ3VpzBBH!~74Uaiu$SeK0=E
zJuv%V<p|6@Fm>oOEc{{d56kcH`V5-S(DlL6Cv2P<)?b2!D<S{G?1klD_;?M}KG^v$
zu=qr`ALdV(I7}TZ9bm)@Od*tp*$?G1z~UdKAJ%Sx$16-Bl!k>DlndWy1&aq*I)a4<
zOdX7d#V^brSUAGUF<AJ*)T7fdcfs5XD|cb>089Vq`e1yR`LJ++`3J^_(J=KGG&G*j
z-3<$O7>%J1DgaC8uyPN^hn4@Z@PjHRNWt6-%kMD%!}9?S6JX&03wKz4fz=BzccE)X
z*9Qv+SiHd0!_>j}FdAk)ET6&B0W2NE)ZwCG=?Yf=z|sLmxIya|baP<o6qZk6@sF+!
z!#s2aXnc6N39}bQqp8Eig@rdP9MHoPrVpkLR&K!5!DyH`Og%ac(+}%s!SW9*JYn$y
zizjsbFmafDuyBOwhm}vT^oXt>mR@1$1I~xG`(WV?4^K1$VDSah2TQjwWiWA=`7m)9
z4J+qid{{n#<pY?zap{MpA9%V&D-U4l6Bds!dtfvy++h8GSa`wQ4GT|Lx`ydPr_u8{
zOh2*XzcBy8?1z=haB*}CVSK26Vd(_jy|DCwEB;{eu<(V27cAak?t!U;SqrCO`3g$I
z#xY>>u<(V22TUAB!{QH?4q*O(mB%o9VdlW}q0_MVhowhY{R1-}Mx*P4@nQJ|7SAyM
zqw`_vVKmI0uy}^~6Gp?-;i6&b8%D#z0Tv&)^ufeo=^EXguy};UA51+u4bu<vKP>)W
z?jWQeW*)2@gVC^Z3?@&AhS>}AH_ZL8^Z+X#ahZdjzG3+WR-T~Cqnm>sU$A(EiNo?0
zx;|LE!Dv|d2eTJ0j;;~Phxrp`KCHb03rCoFP~{jDEM3FmA69O_%!TC#42@_4Fn_|r
zo7DJ+#ShHCu=t0$1I;=NE-b!a>R~j@JVNmYOE)lsVg7)*i%>Yi!XFk6u<{(nhtV+e
z(P>!u1dBhIyJ6`FT|GJ<Jsx59!QGFp0gVr{9~N&gKFr@R^U$=xxv=sQ=3bb3SUkYY
zfonixz``G%ZlUt9as}=ls5m->o?c+(IV?O0l|!)f0doh;UUYkK@!|OuT93i)#iapF
z9F~4z!Z805TK@r44=ev*`4#35SUQHO!$rgMJ#_p77JsmC#H9}=4zm}g4{kp+JYee3
zX_z`#_`&Rh)t|8R3sZ-#AI69I9~SN~b71KbSNemcPq;p4dkB{QVCECDAJ+bZ?f-z$
zu=){K`h&$gth|8P3**D=$3?^2H_&*4`4eUiE`2a@Sbm3%8^FY2`2ohqMZ?M+SbV|M
z!O{baPl$%O7Zx8dcM;MDlZU4hXuks%4zP3qOP4V9=rqi~F#BQY5k|wp8C@TY4~u75
zzJ>V*7LVxV8cZLIhUIgZJ7MZ!?LwG3bQ<O^SUQ5m11$Vt<uNQgaOsEnA6AaS(gVzV
zSU95VN6#0y(gQC0Vc`Wc7Z#5&dtu=L<HOX!XqbOt=D^|$?r)ev9OV!!ox;j{n15jI
zfW<4Ueuo){PQ%9e(A@)TSHa9d*N4uBxf_;laD^MXKA3vg_zf()VBv?W{DG;1*^4Wl
zVeyJ?FD$%Z@-P}h9V}m>Nx;;@!U<MxqbbA5h2>Y6IWYY&K2D7|Bw+erG%P-GC?_aP
zDBZx)D=h!Q3?ww}15*c!FBlEW2e5h{9{(^6Xf!PS!|Fd+I)TM2nsN*-yqtmNFStG^
zALbqmjZguY{cwHI`WNONm^iF_f@;H{VEGlM9!A6R1FSs8(1=3-mY>kW6^C;8I#QTC
zto(+RD=-=+k50qV0W7|VEiYi^!1Tkylhkwz4__kV4Q4M~A5;Jq?y&Gimxn5YQ!xL+
z)WK+&ewe>t@e9`oWx)Ik<-*s4!qPEJAIuz>Iv5R0pXligW<N|Fq4<D}_ruC-So;sA
zAJ#sE@!>Qyy~Fgu+yRp(SPnqr3s%m+>TkHepytEE8D=hwhNTl&`h@ut77wuU6{en8
z8kP=V>e2Zyaa=UaKQMi;c*Lb1rVbX4=;~ng9J)9xTwwhKSUm`fe^`9L`bX&cVfMk|
z8%D#_!O{h+euIg_X=wbx?180QSiHl^A-H-p1}vUo?KN2Z!}JjfH<)oS`(ZRp99E9O
z_=ISfdtmtz7Vhx)Az%PZA1u6K`e8J>cDT9FbdK&`xIDT>C?A&2Vf6&O9)_9+%NMZr
z08|@-f~teH-(l$o7LUZP|A*NFGni1jAEpjQ!{Q$nez?jzn0j;?md|1OVc`wS7r4?9
ztbT<18yX(4d<9E~F!M0%h4x!v=?0dcV0>75hNTB|{jl~2jE4CGrVo~$am61jeZbmF
zuyO}IKG5xj^P&EQr5ji|4=Y#U>Tob%<qV95l@~B`VBrA^M|5Lg{)PDymJVR<hs6Ud
zouliAg$qnQj1RLH7Cx|g0bM^XJ}h0p^b;E%F!SK<fC`}JYp5`s!tf_FUSRnUt`G+U
zmj7Yt9Tr}&a+{F*(bs>#(h;m*h{Ia=`F1e#Vet-2|L}Z;!vL5%Sp30gSh&OdfkQjI
z-3seB!O{~<AF1&U(+_hOEZ$)5!RZc|1hhVc`2${#!QvC94vmJH4@(Cy`(b>TJeoFm
zdl6<2EWBaq7v>IJ=?G>%to(<i1DJYP`48ix+mFtN*$>O_uyld04ucO3A6PnumA9~P
zz)+7S0P{b(dR*ZK(+;x-R(`_DA9y-|mhZ6i1k(qjVd(=_-oX3~lZTayFnO3bjE0pH
zFneL*F#BQhuzC$952In>3(My)8WvyhdIy>=VfqNtF!j)QA*ddz4%Y62xeG=U(hm!N
zSUn9Ze{lI5W*#hl;pHB5oCfZ1m_i&hEM8&h0A?PnK7{2X9NOXQ&0zXqG%Q`f+=a^=
zxc^|`4YdeXp26IOp01$E;N>4I-eKVlvmd4oJzv1YVKl6qhnWLwzro4}Sp5uBk50q#
zA1s{{Qjb$VG#p_5Kv##04+~#dyu<PjF7+^RSp36im^duoqKm`i(P>yX!NMID9)!{n
zEdF8f19vwx9ANH%r6Y8EVSHFQ57!UPk1%s#_Q2Gm)3Er5xgXXKg!zlmK0sKx4~u_T
zI}qI-bUrM<z`_Yu-oes8x_S&g)SobS!}P(!0Yd|t08~Fr6c*pG_=A;8Fn7Y#;i6&X
z7c9TR`~@=yT^}r7VD5#*7cBk5@;}U6bp5b$9@bxgxf2#1FnOH$5t`0m@egx1tQ?1x
z!-V1=<}O&c!T2zL!1%amn7d*6Vc`OEKQ4VRahQK$`30sQ7OpULxM-NYu=EZ~2e9-E
z6Gzt%ix*h9!R4Xp3YIT$#y_-u0ZaF=_9`qqVD5*d3%L1E2CTe;mD})i0IdgM;;?)H
z)dr_v@dHx_OD8b<VDfMcXbf1m!0HQ_G%WqV;sqv-PQ&!W!Vl&jm_AtkLDvW4!@>s_
zA69O{!V{(rM#KCG3pbd1VCKU3uzU#9hfc%VZ?JM0mTzF{VD%G>k50qVE3WhoPmkyt
zp?sM6uyhOyPndbI@dc=I3<?&`u=ENGe^`2i`3plMngCoMH2uT!51KNZTv$GVg*Pl7
zaB6@`!15tRd_&7Os4`3n>Q0!y(bEY!AKd_0yuj4K(h*D@OdQ6?prP)A<v;Xv151Ax
z`k(@^bOXz`Fg~n2fhr@Jf|*As|HIM^%mSD`Sp5(857eJ9bub#1Zee^_I)jA=jE~Dd
z=<b7s7fd}Y{ldZpW)F;pl{YYTF#o{v5lkH}8fGrcy|DBO>&FtC{$cqO=5CliSboA~
zKTI7g{lfS#|G@G;F8#3jQV*IBVC4qPKk)d&WdKwkth|Snr!aG1;fYH-%zjwDhvgrb
z`w7JZEWTkhEWN<o4+{_UbOv(|EWTj*6y|<dyu##R?u6-s(J*-!9~NJ5^Pu%R%zZF*
zFd7!`Fmqt>2%}-{M^EQ4{V;dJ;tA$YSUCs_M_4+C=|iVs<92X+p!pS+k8!0VSh&FB
z391h!4jWfOw-3gLr4v}V!TbZ$j~)&%eJ~mpZZP-3_^@&rR*%5K5vC7D!^$gIdVz%_
zJRiao;-F#fh3SLQ#Oj9|3(ZF`cf#Ba^AF5_aP?3IES<r^A6D+b{13}dxb(x)B`lm_
z@dZl<uzms5It&Uuf5XBNmY!gIT=v7_9cD0k{)Oqs6`rv44bu<HzcBSMd00Hb)WK+2
z{KNRL`~%|?qT%L3(>*Lb!@`x2ewaMW9WeW0<snQSCykbFVBri;H#jvyBw+arR(`|6
z3!d*G$_SFMbOTcei&yl10Zbl)hWZ0Ny~4^d4E1ONuyg~{kDd<Dlw)vV<uEMYz`_Mr
zzX`(tr~u5pF#BQo2r7+}g4qkx4~s`wc>v4jxD0^lhtV)~Fn_@K#L_VL!_=emVd5|v
zZVxmb(bEG=9WEN4U!eI2Rvy93h0EhG09L=j(mkyFgykbb;Rn+X%YU$VgXIT8{)g#<
zmj_UH!qN}ST~J{-1#=fH-eK;5<sX<o;2Ll+VCKWr!`uNgABQ#un7?4<4=lf;`yZEn
zm^iHbfTdShyusuN(J*(z?1k|OxgTaOtp5%32P`~b@`PxZKA8Jq<pHtgz}yKd|6%b?
z$X=K{JiMU!2bPav=@+IB7Y$1<FnL&i7?v(!;<#v7IzX3)rGIpBbUu21h4}})eSoeH
z=1;gfsQ+Q*A1q(M)S>H#`5Q*V;t|$vfW;@wJ?Q#j=?o?gYrmoU8(ketJ$m|pl~b_x
z7|h*-!WU)^Odl*fVfMiC1&j})VeK4Py@&2jm^d!|Fmqt-JeWF||6$^=_<*U0(J=qQ
z$_-d}!}zdxfvLx!q45p#FU)?JIfT+b%zjuphn1hOc!Z@Nn0dJDhl@k&A6Wi{g(t4`
z1G68d9~R%R@Pd`YF!#aK!Dv`G!_?u5e^~hf(+8tr@egbNz{FwxhLvM5|G?zYX;^z5
z7H+V3fVBgO9sh^LH!S|);SP;wbbDcZm_Atl1y+8+!WWi5VCrEsEPi0=2j*{>JdB2g
z1B?%+q3(j&gG(JOJmC7E3|RQX${SdIgG$3GT=54>C$Mw?vmcfo;06%Mfa!yo2Maft
zI3Z(T<rJ(Oh4G2i2lEdsK5^w6bn{^ThUtgpBUpUG@&(RtM2kn5`LOyJmY!hZ=rmj&
zW)5`z1ePCQG%P&P^~3TPEWBX;f#pkBxrD9{#)qkg@nPu*mwhmGFd7y<u=GgCKA1ij
z4a=`E_rm-ElP5&O+J7*8xbh1wb71id3kPD;D=h!R(hDrz;<5)_9HtLl99C|@$^{I0
zr~rEW!2AI#k6<*e@*fsnFd99-z|tkGT!dPSpwQ|om^p9-P<eEB!sKD<VdXoFPwf0Z
zOg*f;ft7=>@Pnrd=r}0MUKkD24@-A2{V;Ku|8d0w%wMp0how8XdIJ83xdTSS(gUpg
zf~7;4dtmBt(J*zed<Y8%xO;GDgryG{4f7YwJ-GD4<YDO!-d=+G8y2oGccEJk^CztQ
zfY}3!2SV%rVCfa6A7(zhJr5JWLBs5W(Xjjva~BS62w|u{VeW;6JB&ss#Lj|+3#|Nx
zg+DAkW7mZ#3QMQ3@`%*(7d^aS^&HGROdC-}VBrT7g_#E{f6(P&>R>dwe_{II<2cZI
z5vHCH4NDg=dtu>BNFPid7M`$t0Ha~@xM)~?4+}S9^}*6F%-=BcVfi1%N2g)-!1Y1X
zBg|ex`eEk5(jP1x!`eYG^I_`HX;?ak>4TMjF!NyL1FRfE*AL^P#}CXrm^dsxVd^nx
zwDbW>H?a7}P>&`6%P%nhz{)d1?uHoy^DitN!|a8pBOLV)ET6&RA3eNb;xKby_Q7bF
zeptB+3n!RAVDSM<4={Z&8YT`?2NQ?2H(~Jr3m=#~jE3ol@nQDD!UL9$VDh+VSiXVP
zqp)zqr5;@zz5fUc2Ut6TP<aj;zk~S~mTqC`06kvN?T7gj=6_hc!15oA#?Xf*0J9gC
zeqiRo%tup>!G+laqhaY6SNee&01F>jdV`6>`~h<hOdOqtwGUzXVd)7LuDH?<%p6$z
zAC})??Lk<)!pwuICq%=_Lzuf^G$DPkaDv4<-2c%27%u&=^b4~WJ-xu=1Kj{<xdqb)
z^FOiaALc$-_`uRHj1RL8W-g2mr=j%)OdqWMgPyN(xgV|$Y7Wd?m^m;$Tpdh+08Om_
zVg7=thx;2nTyd4Z(0GNFcd&Gdo(~DRA7($yJ+OFzm1i(<0^VjIHl5-!2Nv!yf56fu
ztX+vqAI!fn8s=XZAD6pe;^^%?m_Atkhq(*GerP<w>T6g&fQ1{O@n2YZ1M??LAB+zx
zS1{~_3c$hzmS17{02Y5R_du0nQqXh{3on>HnEzqs!}#b1z`_MqU&7R5q!(y7q3egK
zgM~9J9mC9nsfWcAjE_#k%z=r+%0Xi5A6R(_3olrG0&^GK9GC@YG_0Qvi!WF_!p48m
zl;h;W;vH7+VbC~q!r~v+j)H|3EI!f02W|{BUSRbfEdJo~(DVU|XSg_&fo?C%KBzPU
ztlWjsFmX5yH6Iqv=<NiUc`zET56XbW3oPBk$}y-ke19c|zu*c`88H2@@PdUS%%7;L
zF?lfkFn_?!ftJ&lI-nxZcz~sUSo(p=GQi3S7$1X13rE=aCx$v40<d}qMx*C<SowfM
zI|D3yVfhD^pJ4F=3kO{KVeMU*KVkO4;uSqS(9J<NA69?B$`PFD4{8w1J{S$t2bCsF
zLE{e=?}XfoZXhh3!RjG&c|v?xc?AncnEzq^Ayz*upTYc3NIy(Hyxf9X1S^LK<r|oO
zbagO3EIeT4In;PK1#=&4oDL=d%ZD&?VC5T39!A6Tqvt!Ae_-(fqhb0mXlVX}mG`jv
z0G6I%?!nLp6@cX%n0i=z!rB#3WjHCAKVkY{`2m&>Vd0F+0QCF;R}b|UoR4MzOdJ;9
zu=Ede2h88Fa7NP(-+v9$4=cA|@c}ajy*z@M12-4uPG~s`(+|%Nu>1+r2&G~AVd)1}
zj=|y|JzSvLFeq5O!@?aF|FH6u*zy3D4hW_<G#9|~2aJZr3yeln4(Gz`f!T|$9u_`u
z4QLElIKjdVO_~9ge_`=}E)R=em_0BW7LG7|u=I_t9~Q2#dIDD8z|tX19F`8z^~1zr
z?GJdl3GHve+C6Y{(G5W3!`uV&7c6|ylwojT>R{;*M#JI-)~-a?2n#1zc@6U~disZj
zBQE_g^)Ua!+z(R^^B0Vdi-zS>SUCoBA1qvP>4S;G)Wc|)ILuraAA^SGZ*+gc@*jqJ
zr~u4dSp5KVH_W}T@WfRPz`_YuPQ&66*6)MqhgwH61<POP{=#JtdOpOJZ_xF_?16;?
zx;!EO!_p}%JYf1^`5DGXr(xv{EZxD<F<c#5xegbHGGOrw3up9nip$+l?F=yY!PLQM
zm^e%w(KOV(=;@bO{jhuvOGmKsiI9F+xeqIEVc|+hKYIGdNH0+T;Ibc<U(nMXOg+p!
z=;;C`53?T@?&#qLGatiUPy=A)7A*c@{)L$bD@S4J1gZ^A!Ng(az-X8|VCj+A@*l1r
zT7SUe5pE$G0~Ri@aekP3Sbl`12Q+PPE-YMN;RH+nu<{Gm{)2@xTqBeL(+>+TSo;fB
zA3~Ke;3|LM=0ej6EZkuE2Nu3Cdtm0F)3EePtT;>^JYJyXKP<h${0(zA%pEX&FdCL#
zVDhkX45l9@4pRry2cu!~u=EEXuYoFnjsL^iBT!*D1&eQV8m0i|ewaK=97e<94HkZ|
zc!#A!n7d)|0n-Pkq45TDA1pj!`e8IIUg7%C7;t-`@c>U3XzFlrVfMh%3C!QP)T4{T
z+z0a~uJQ;&A2grB>OELE!{QZPJq91@Us!t|rXQXjFf?Enfa!;o2QdAx_=BZu?7A5k
zVDSbkuVCdKJY1mu#HAmW-(mXD;}0f|OFv8;mJVU=g{2D^4O54UhUtgd4^Q9FejTy;
zVCeuoKjP94OE0i=0J9g?F2SV_CJrlqVdXeH-eCC)rVs}Wi+@<T59?3jP!1P{g%`}-
zFbQJg3l`3>bO$R3VBrQ!&oFb*X_!8kJ7D=279Oy40J8^OKTIE792#D*@Pm~Du=WjH
z9h3pjr%-*cc!$LwR2h7H62^zqP<^oQg83I#{=?h{a}Qh}lmW|Mu=s-2zc6>h+y|Qv
zhHAs0VD&uAei$E?9xybZ3Bc@w`5!$!qbbMW!paR;IRf(+Y+MFc{J{)@wHsjJ2n#P*
z_`&=IQ;$x=;tS>uSUAGfLCaY<9|r?wA1ochl@Zbpm51dcSo(#<AE9yp7XCU=b71iX
ztJh#O+&yRvSbW3737Rg^lwojT{)MGiSh&N&6GH=<06boy?uOL^u>21*A59ya3v(|l
z-NDqu(hbaAaC_i7Aq<#42#*0i9taC}Sos2r512X_4f7`~y~6B=g#)ZS!=)dl50-vm
z?uNAkVev{R{$cqarXE%g!0d&|!`zLFhNl~7e8Bt<GZ&XWSbV|CTbMpv;SMtgCJ)mG
z^FK@;M&t57tek=Q6P6xe`d~CHo?v_!4HJju4;UXN52Ml32}~U3E?9dBR(`?c;WUB#
z1WO07@(0}=FnPFnI2f>U9+rM#%5a4XOdb~g=;~nU5T*_n4QpS)!V#tpmJVQiTr{lQ
zhM9w|4wf#_#bNG+m7B2q4pRrqkFfL!6Nk~T^ao3KuzDUAez1IrQ6E4JfW-?uzeB}g
z{(*-Fj`)YgGc0|<__*wY8jnH2!UyJVSh<6t5KRD<uVL{7^AAiJ++1jR027DPP;q!T
zLj4KzH!Od^)uS<B{)P#|+z-<aqha9!QwOJ^_QA}7#Um_Tz}4Ykz|4i&11nEp`2mME
zxG*ez!Q2fCKT`F>(jDAjsK4NR0t{F@!qPd+KLoU4NMpn|G=4D@q6xsl0agyf(gTb}
zQ;xxfxgSQu6u`n0#z&`N@eIrVuyg=Z2XhyUMhCF;f^IKN9n4%<xWL3=G|Ydn{0~b{
zFnM(Mz{FuRth|TSAFy%*CJ*D|Di2`dF#WLdAEqBx4iE}Qn0i=w4+~eAzhLsXXju5d
z^uzct`*G=miQ|k1Xg+|O2h#whVdWMqo}j{T3Knk|`3tH7mfvCd1}Y4vaOs07faQBw
zyA{3ug2f-YI7~mRe1z$T(Xey_Yfr%VFn#DWEPi469>&L&uF>_w(h*D_tUrJ&T+sEy
z`~fou7Vfb4L06B?huI4YCs=&L;s=%<(e=UlS+MdJmX2WI1(V0+512g69vBS^cbI!%
z?!&Mjnm%FvhS>{?cUb<$l@DO{z{(Mrez-n#3vuva<t{A0;&Ly#cG$QNEPP?{1*6f`
zqw`_zhS>+>;|gzd{jl^3b2m&L7XL6lE*choFdCOSg5v1)95h}D>Vqmk&u6gsfTc@Z
z;R*LAn*FeH6lM-AT|%vcQ?Pi03!pQg{vf0dmS181fVme|4iM50i*J}Zn7?4<GCW<N
zyAR5Tm7`EDydMsWS6IG)$)nRS`!MW>`X9YshUtgtgT)i997C6f@zH6ReX#I?*#i@Y
zg)6!~7$4>@Sa|`{hpW7R>4Vcy|H0<_VftYC305w_)uS<B?uOY5qhaX|O*sY^Jzc`W
z6I~obBUAtuzp!!uDh!`bfbntBu<{u_-U#^{W**GFuy}&SA9{L(smGw9`4*OMVC5hz
z{9ySQR?nd8huIGkhvgGkKNi;hKvxG-4~uU&4RsI99k6r-OAl~;PzEgiVdF)xd<>O_
zuV04q(HJm)!~6@g7p4s69~d7O4X^K@=^2(U2*m@;U$FKs+&-u|=;;F8epr0N!xtKU
zuzo+fdR%;1IKa(;Dul&5x;RuCPN9bztUQ91pBV81*9l?3!U4i#fX|o1>UUT?!{lK!
zEWg9jKP(-=-2)8|xI9cFl!p5Yst(qEh2>{h_`~88strz|i^IwTSbBivZ=C4>8qY9y
z!}1#}KHwJNV8G%ZmVRLI1xwF3w84ep{)f2(Dg*Nel#7#s*$2z-u<(L~A5M)>379`&
z?uCu>;tVgSJ{Sdye^~l}#WT!YSp5$(AEpmZL*pHm?_lW=M#I7vt{#m6PiIhZn0i?J
z!@>cVe_-~&Xjp!Mm77pwaZ)h*VDS%2k1%s^YJ^I_;vW`ouyhEESEw@F6g2!``5l(e
zi7kI%@dm&532GjE-3M-)vC6>m56oOx_+wR!BnXRtSh&IX@Nhv=i;W3OZ|Lm?SUAGM
z8AC7Bzp(rRi$9n;Sh|Jv3t{3g8fG8Nepq^hn*%i;mQP^%;50Pf!_p6|zJ<xd$^lq<
zhKa*ySopy7!{Qy5F41Y2K0-7sKM+y}lZS;9Ed9aaAC?|q>TuDpc!Gr!EF59!8fW}L
z4TOaQOdnL30p<>vxiE2b8eJdEJ+S%%9-rtMp?p~SfrU4$yn*r2&4+4(_fufyCM>>T
zG_mCnti1~JC(QjYb71zN(=d0z^E))X!qPJ=U7_nk=fm6&6NjY-*t%kvI4&AJeqr$j
zi+^1DVB+Zhg!zlu@&^`P=<y7Tf0#YEXju6G%QrChz|sXSeQ<GTxWn|p>_rb(xOy}O
zEdF8sfQ26{{h=wx;KIgzVftYCme}xvr8`)A5oQ5Q9LC2*!~6*gN0>TTJi_?sG_0P0
z)f=#MgdUH$+yS!}=5KWKVdlcpGrBy?eX#g~@!>Rd-5SiD==x#t2Fqu#_=mLvVB)yq
z0p?Fw`3Ex(7Voff2yQOA1JL;J`UhGsp-E$KVc`l(FR=6v6DO4aVd`Mz9L#>0dtu=Z
zQ-|RIr~oY9Vg7~D==Bj)Ih=yU6D<B<<qga|Fom#s9>#~!u<(b)6U<&14U0!uzJ%$+
zprPRntG{9Hfvbbc!}(|oczF#iM_~SdxdT`G0cH#=AHdpyu=s@a2Vm(6rXHP!sfWiC
z)EzK!boDSkOg+rKuyhRbKP>)X>M>}jJ+S->^Czqy2@7{z@dvXXW-cte;PD0xcUbts
z)ZwCG=@3rC)_p?ViLMWpPhsf@R$stqn7?580bM`Lov?HR^Czslf`un6T+sEy(m(q6
zA50yrT>uXkm;@RPOF!^%f`%Kc|A(d=gA0!bXgI;b59WUuA44Nl02V$l8dl!I!j+(Y
zsJ&3*;QMM|;R7=VCJ&=w=EBlBEIwi77)%|k9D?)F7_f8-3ujn-!NLU=uV~uf?u6L~
z^EWI%!Q2h=2fBV3AC}Ky_QA&UV0>7)4pR@KVe&BbFn_|zeONk#sYj<_@e51uuzC(X
zTw&_a^~3CgsfUF>%)hX72~&rz9~Ry)|H0Iw+XqvJt{;}(VC4<0oP^~&Sop)-g{~hL
zALf3TJS?1Hd~_P-e^_|K(hJOAFn6HqgYjYE4)ZrGAH&*(uyO{b9-W4zf0#JTJaloG
zJUR^zcWAi*E6-r*0+z1P^}+bC@Pg4Waag<)3O`sl!Tb#iN0>VZ#UIRk7!3=5SbD&4
z7u10;f5Gg9xdSQ<ABTnUanZ2!2D2Zo4y{~+i$fW(bPbDVcznV1LE{6e947@U=V9>(
z^9PI%E053(fcYO5-mr8G%TKU$2MZr`{jl^3GY3{~!f04K0%k6{epvc|g&!>4!qmaa
zWnB3O7O${ygp~)dc!2e1VDd0?VKht}mR?}t01GdeyJ7Bw>4VeI_<@-N(+4*PDi3of
zTpyGH6NlITuy}{c!_ozmi<5%+7Zx8dJ}i83YJ^I_{0WOk7#}K4kb>z$R|rdo=;G*n
zSUQD?!@`A-ewaUD;SJLdiw~GNFmYTo%p90J%>U@(FnJgaGar^;VC57n9*7NpSp37?
z4ehrQ(hu`Dtltj{FPJ#4d<%0wEPufA7kWE}ko_?K!^&Bh{jhL@$;0eJr(xv;EZ@TF
z8(6smr_nXS_|W_Xi!YeJVf{fu_QQ;U>4U`|%shDbq2)`MK3p`c9Dvbqf8o#o4?n0q
zF!#V{Sow~w9VQQRC#)R_6DJgZu<{j_?%?qObq6e6pxX=M!}P(@4J_Tm!W~A#)T7g|
zc!$L^Ts<^iVf{~7e4y(`=fmm`n0sL32(a)$R}bUE)WO07mJVR@FmV_kordX$xfe#m
z!Vi`&(DkA7Vd(`%!~6rI(bdEFu=s|BKP)_9@`Pwuy8~X&LBk)OexT_A!yKqM%>S_V
z8Z5qG;Q*6I*AFWvVBrKSk6`YB`3qeioDX#;%wCxPVfhW_A6PpCt`Ew9r8ijl4T~q3
zdtl`Vq40#61FHvN_QK4AT1SwAxt~yc!wiDeo3Qd9mTqA3uzUdHqtmc_0W$}d&SCB*
zq#u?~VEGv4Z&<v-(+|4EP(I8aSUAJt5f<LC^aD#zP;GDu7VfZe3uYe7-LUw8`3tTQ
z%7B>*OP{dtfTcTFI)Ez2Nx{Mo=3bb7n7d%%gKhxK{jl&tk3ZP>4@@1pei$FF51Jog
z;Q*7zMZ@fcxffOs!OC@9`rzVF`(W`5%SVLV3)2s?9~RE&`5%^#Vd`M|U^Gl0Odb}G
zuy}>J3+6wVJ~$06KhXKGc!T)^W-eSElmScs@N^3e5A^sTl%HYo49owp{0xf+m^{>4
zoD|F+Sbl=#BbWwQzJT#zG|V2DKA1T$d02Xar4N`moJI>rm_K0g0MiExFStG^1EwBV
zxetqHsB#Pn7EiEz4$E(_^a%4GhDIC$F!#d316Ho#P|g6$|1fvJ!V8yrba7a@19K<L
zesp!{d|0}NkN-gx!1|3ad06;DrQsCJUReHs*$Z<AEZku<TqBeLi(j}rH2uT!F+3fi
zr4Oif23R=3Xju6Pa~I4#F!ks(EWBaq92QS7dtvT?)dT4IVSJc8Og}8$!1Tk)5tw>(
z8m14X4pwf#%!P*!x<)7;mM>xPWB?6E7$4?8sB#1aRfk?)z`_kC4s#bmBZLJDXP7;(
zaD(ZGrE8cvh%N*POJ6Ym!TbwT4~rj|JVGag1&eo>eK7N2;RkEC!PG%?A(7B@1&=Q%
zAEpB4A6)js)WO^db3aTRmJeWjTr^A{Ed9dFBV;ekJXrX{#_e&&H%tQ>4eKAm%z@QE
zFd9ucoC|X|EdRsog}EQ*ADFptjZg+Go?+zxth|K92dun@se@{RQ?T+HW-lzhVc`z1
zhhgI(aGg*FG`?Z(g^I!{*ti}pd04uFr8`)9hlK}>56|Cllc5Z#{qXt*njc~12D*Qs
zx-ckM{)gEIb1!UM2*$_Ih$aA!C#X9J#Xp*ML_G!#Z+QHmE5yNvxgRbK4Ielk2Ll$~
zaQ8#i!NL{F#h_s64pv^E=Qm=*4OULTXqZKW{13Aa*8YR(huKR=Kg=E&4NH%(`~XXr
zFnM$u)?R{zJIsD~euJh1bbT;BES<u_5ta^N?uOAY_2@Lr|FH5K=5CmIFdAJSj1SA-
zF#WK2gynxi@ej+FFn7Yj0p<@_`2({L7Yz>w=z0NI{K3qFnU709dOX4M9V~ue<tQ%w
zaB*mS!SuoM1-u@ID}*p$`2{8qOFuC8Lsa1*q4vSr2k7Aqiw9gLz`_@n?$E;t7C*T3
z!_o_^oP?z(n0i>epocF^9!^8c8Cd-Ta}PW{!Tbr=fXcuyA8H;f{le-=R9y%jtbYiL
zH<-P!_#hOHuy}^08(98@`3IIi5H_N+VESR{fslGs-3$z{^a%?GSiXe$AC^AQ^}+3f
zhA+&YFmqt$!u*A<9~N#fdtfw7AB;xV2j@fM2^P*UdtvoH%ssI30Im<pfTcTFIK$F2
zRGI-M4ojynaTpEDXXy6B(lJaNrk)TDb3e>n7$28DSiHm3!Tbxe7siLjA2gm|=Aox+
zn7y#@g0<sd;RMqMqhaR5(i_Y?SiXnlLs)u(nGe$s3vZY`FmYJAfbj{@Fn7S>3FZz$
z`e5k|J$=IBkC1+tJS@M%!Wq3Bg{ecQVc`LzVfta=kFFjUA6D+b^uyvGmwI$@nEzq!
z#ueY_`e5k}R$js48)gnn9$g<gAEpjgf57|$qhaL?x<2%Hg1G}04lwgz`2<}*j1O}!
z%sg0ng{4ni=?SJDmVROB307~x+yToUF#FJH*t{J~99Eyh(lep-0Pi<K4S>ZzEL@?&
zI4PJvVetpczc6u}8le)f@PdUSEWBX$!^&f*aySKxS6DfZ9^Wu?U^HAKlmQEGD3<{i
z-!T8f+T}3+z|_HLSh;~7zA$~Tc*7MAu=t1h6J{<h`(X0uG%Vf1+zpF&balA+u=E0R
zuO75sgoQ86e02S=aE0Z6Sp37>f$lGK{jl_nPQ%JWn0dJT0}EGJc>{}oSa}H7k6|%X
z0P0VezhUVCorbE$Nx{^^+yPIoX!Rv717QAunS)N_(gzcVxgS<e!Qu@T4ls4-G^~FD
z8~=rc7ku0R+K-2si>@D*E@1U7EFZwkf%yYoo}n3lo<3mV2D1+qF1X?!md;@L1*Q*9
zL*p0b9yIG2VEQoHX;6Kzd<=I7x&cr=tQ>%)V^})C<!-1pI0ef`u<{2MpKt{P7_j(;
zDI--sJl{a$8|H3U`am}yW)IB0u=s_A14ek`$_HrTuzC>Yepood?19+}qhaMIEFHkq
z!{iCkuy}=~Q<!-$eX#ThtCwKna2lFU;PDRi4=g{x^uxjvt`Ew9*$4A4tQ>{aXR!1M
zb0<_A1_evMu=s+de`4qTU<ShU!QvZ6qvunYdKe8$zcBk@e3(2T8di?L?1QO?wF_Y3
z1QUnRFneI`Mt3K{^Z<=7nEfzwVKgjV!1(Ai%w4c}g7IPMaHU(AJS<(n;t!T@VC6P?
zc)<0+7|`?w(+`hts5l}0uzU|ozp!>BEFa?v7nt=heX#Tia|g^{gzSgK4=mrq%!S1(
z%v_i_It|O0FdF6`Sop#66}mncAC_KV`e5+|^9Rgan0j;?rXE)Q!Q2b89~OQvb?EwG
z=>-<vFn`0`gHEIChn0KqbOSBF(e<IX?_uV^@+-`pu=t17<1lkz>M>|&{(-q4J--sl
zKd|tHxgVB~VeWyMhhZ;F0GiHW;SCdpr9W6XOvoKDeXw|krAJu$g}Dc&4xNU@8_XV<
zzu@r;4JUMcFg`5(z``3={=@tQi)Wa6bQ-1)){cY4H_Sh<_(azS<HPjB(gQ3$Ve+_W
zn15mJh3SLk1DHH4pP`!r3tw1033De*AFLk$a~CfCaB*lj!`uT)Pq1)=tAjD1=?7MR
z!|Z|UhsxtBM_~G3;S9^?uyBC2A7R#^(=d0z%z>4suyPTWe$e${@S*;O>4&9%40UJ%
zuy}#_8`l1SxgWj`1Uj#QrX9Y&1r}d0f57rHEFa<Y2h`oLaDbTuOBXoxK^+3K59VH&
z|6%@t<$tJh3<{P{Vd)O$URb#Ua|eb-r~ur3P=7#$8Q|psj&OsigVC^b3d@HudvT>Z
zm_9fSwI7!MVc`Mu7hD|<2CV-NOAoO4z@Z#23{wwtKdI>!mhWNVNGQL+Erv2+`3;`l
zq2T~?4=fyE?u2TCQ?POhCJ)c|P;+4A53GEKYlJdj;S5W+u=EB?moWFi(j!zGf<h}_
z;NcFnAI8U(4q)=I@(1P~SosgL2;MG)382xid=JY%u=)mO9!xndcfi6Q7Jo4Jz{Fws
z43~bGI4u9c!VeZNuyhPF2j(6a4Rb#%zF_eP%ZIr1q3eg`KUjRB`yZwrord`vW*;p4
zVE!Rw4lLim;uB^bx;VPMFh0z^u<(Y3Crq9Y4O{;Mb2n`K2WAc|KcSlgOW!bgSiGUf
zKP(>6^~1$s=^fgBMHh#;3tc}fUSZ`m%pI`&3fBkCXXyH2;SG-;s5@Z(f|-l151kLo
z_b_u{`eEe)x_WdzEZkt_AWT0jJ)x^d=fm3Vu>1>`hx!N3$H9Q*A1!D&!SWj{Kj6@Y
z5Qf?VtM_5?2QwGLT?n0E7EB*3|H9%AR(`|k39w=;1T4S6!Vi{iVex@Q3#t&*->`HG
zqhaX>W-h8G1P_+(VCe}~ufXDukbYSI0#<Ip!X1`=2>Ao%PT2iFdeHcQiR01_izisS
z0Txd%f52#XJR-adX2H@aOdeKV!OA^YJm88yba7a^gVC`34Ymvm0S{Mb{K3K<rW9^I
zR30V{qha|D77noRhS9KifvJP(gVRuRVeWvH1F-Z96Ni<{aD7k)OdjT6SUQ5`1N3wa
z)rLXA+UYR&!txEwTnr6p0<idlg##@8qqif_v>~|A^ak?}EWF_L5>y_c0hI+yr?7B`
zg&!>a!Tf`&3&Dfi2aOL{dWV?{OQ#4Os4SR0u<(ZYAC`Uz>4(KTJiS4UhxrE<u25kN
z3RX_Q%z@>9Sb89o4`AgdEdRpRf5Xgy`2*czn7d%=VD`b%5iI=B^}+ejc!kF^R00<M
zu<%6Bmr!N!eiF<bu=E294_Lh5(htj5u=s+x7Z&cY@)Twt1`SOwF#Rz1!om|44jB5-
z1YqeCrXCg^uyl#09D@sscbI!&=?zwnVrW1UfY}er$FTT@`3p@soC}L@*tt<K_rt;+
z7M^eoXbf0Bh4~kz9##*)Xf$nbE-ap50x)}F@l7b*!`f#s|H9l4D?efGf~5zTJun*P
zK3F)x%z@bp(~q97VftY-Ed9dLJ<ME~{|M=a$;0v?EFNLuK`<Rb^C7JMhxO-S{(<=q
z=3aCfmJVR{!`qY4{DH0>oe$Fw6NjZ+nEPSkxM-OFVeW;6KQ8q+#i8K<^9L*(ajJ((
zz|sdSAHwnzEIq-}IaE2Eg2gXP0G1A5=@M2x!O|m49XbuGr(x*_mhNEX08AWRA50v*
zzXq$fVgA4s|FH0ZsfX1cu=WJZ{pj|>_%L^(`x_Sjg!H3_AB+$4H_RSfG%TE9<ps?B
zF#WJ}2{RYn9GHGs_`qnGI7}a|_=lMXvmX`?u=EQ{|FCofQ;$x=^ugQ<OTV!2g_S4h
z`e1x?{jmBUrXL=kFbz-|T_4Q7u>21z&tdL@YJ*d-^aabmuy}{ju=*4x57!7~z{Fwp
z!t}wy50<`R`4g%QPQk=s<p!+%08;=<2Qc$t;xHN(Z@AJatUSO~{=&pz;S9467GALM
zf%yYwFN}uy6XtGM{SPw_)((Nm!}OukuyhM6_hJ4aH0}q>FR<|+SUCjK2XhB3ouJzb
z(+~4EEWTm!2n#=$I&}STe?jXRSa`wn3$#4PX+KmSESzBWz|s|=_=VXI(+{Iz`3<HY
zMx(nQ#)r8VW**GFg!IAm!~6}4e^_|~OV==UIB94&!20p9c!R|!PJK`bnEf#S>p;ze
z<r`Q%0acDc!NMDs&SB=j%1@a27#g7huyBH@gV_hmAFy%=svLuYse|c<)weKr!15`E
zMris%4=-3e!rYIc4=MojH;jgbH!R*@<p5MUCIt;=nEPPvhS>|tKd^ER-2j+<Fn_|@
z@v!<F79Ozlh^`;zZ&<p4g)^)@0ap(VCpaI<fcXz*KFl4k@(Av39Qxt%Q1f8r2+Ukq
zeujxdmBA^PeX#TcQx7v2mM&o8aE(v~EWN_q3oE~1e3<#T?1vfyUpEX>2eTiYhN(xV
zVc`dhe^~twD+gfZ5xPDYALdS2IRxtmz`_wG4^t1Pq45GM|6%DCR{q2CKg=AsJ}3j0
z?qTT&7T&P(AC`|`>Y&=-6igoGKbZUB>Y?s~)n{;xPzEgD!2Ac(4+}q-K3KYjYJ*cS
z`(f^fr3YBN!T9j>1=oqnfQ1h%{9yLM{eh|+g9mGG!qN{sy~6yDp#xO_7GJP<g!vy9
zf2gW4d9ZQ<rVr*1OqJM0VCfX*ZkT>px`xr{x?%o>>4)V%SiHgV1Fmp{i$lW+JsrX7
z5m<bp+m9YTF!iu{7?z%4=?q;zOdOWpVg7-qLumPqEB(O4VdXe1++g;=(jSaZh(`Ah
zEItV7gQb6%yJ6vgE8Z~7gN8rMK6t!fC?rh)mOlyUBHEoe48oN@a43fh!}P&uxCE4e
zOB^Z<r(o$8mhNFRtp0~9L}S3>8@;~<D}P}58BH4o7p4xD4q^IX@rj`UO#qfpVD%;}
zU%=8gnsN*-EZ@V`L(>y1{$b@4hDN9WESzBefaODU8mb(Vg4zc&2Nn*n{75JtqWc%7
zA6~9Q%K=P_AtJE!3o{26uMk<nBrM;;+)G#ostQ>94;Id_c!Z?`nEOz5A$YL%D$Kty
z{jm5X6n?PyhlLv~J)xV6uo0C73olr?4NC_w{jhX^stdt`se{!wFd7yggz_=Wepq<H
z{0j>Qc>YFMiNb<~6HE{m9x(G@`2Z%5i-xsBVBv^M9ZVdSZeZ;On0+uBrVba4o-Sba
z!u&(f-Ozji3n#dF(EJDsN1XOU*Vn<~309B6%z?!_EPipB0~3dZJFGs3l?SkJf~iBN
zVfMgibpOM|(e=Uju=)~~Zei&dmaYhe19~|PcP})2VeMm>edsjIURb>Y8!tqs(e=T?
z6+PTx<q#}9VdVn4epo!AhZn3p2unY(a6s1&(+5jGFdE%m==xv)W-iQqFd7#B@Nk4_
zz(K?8gT*5(zG3F$&;}QV>4UYqVBrZ1N4P>9446A%`eF9M@*@sy7{ajfA4bF6fuRtG
z08Ae&JkZl64sGyxV7NZ0LRk8Ma&c0y@IzMsiwAUZbUw`8=;G+^L6?W|aj7HLepq=6
zOMkHX0;V4pzcBmIX;?mm_XDB%9u|J^{Ee;=%7@EC%UxK!!N!we=0KHWP_T3db2lvA
z!ovk-E}Bl5xv=nul~d?6Odd@;eB1?A4q(*N&~SkH2VFnReptB&GY6&)CXZn*Q~|7=
z0_DOfn0sO67`nM|g;4jw>MvM0!rC9O_=m~E^`S9f<sPgahnWM52Uz+-(+20Fj~Bw~
z8(93q`~%kjWx(7A^Cv9*!s3rm{KMh}7Ctb4z}yWp4`vV4S~vwa9~zFZau2=UfUAS+
zgfL+73X5lmD1wCPgPDhrfw17=3Qgazd<t_fto;B>Ul3gg5|(~o{)NRGy#9x-hb0vM
zuzU|||H0x777wuaMA!^r!Sg+Y%K%dkOGmKyfl6ag@NyR#{;>Fh`x`?CssPO2u=*Aj
z53q27#V4vR1P@l;!2AbGhs4%<u=s_AH!PlD{y^A-odt_;SUm~T2QwEY594Du1tAJk
z4~rKV4Kp9162gLoAB4xi0L#ZP8m1qXu3+k5G%R1k)WhN*77s9an0j;?md;`61QzbF
ze1$9Dz|29<_b`31^bSi0F!ks(OdggGVetrScM`H6W*#iP!qmgk6-=HG4T~RmdWDt;
zu<(P0CoXef;Q-4|uy}*{hmd}FI)K^>%fB!hm%T7?Sh)eS2Ns?%d6@rTd~_P-epvd4
z<!87$sJq~NGzKiZVdlWxfu;;67nW`?!VTI#gqe%e0H_2^9n5}Met@|TstkjIr5jj!
zgrx&met?N%XoL#D+z(5KaQmR;DpVOx3g%u|`2n*Rrv{h=G+to&A6EYoJO2l>4_5BN
z@+~a>Vg7=J56oH^4Rbe49n2h9IKawdI1SSXrD5q5=3iJj3(GgKaK~jo%v@MJ!{QA+
zKf&@b)LIM*77j3fz{($(dtmYy8leI(_rdi;;{}!;Vg7(B$Dm;Dhvie)JTT0Eu<$|G
z2&)%h<pf+EG~E$$H_Sd*Ji^isEF59t==Q?-P<vqE1@k9NAB+!IkH&z-6D+^L$}wWY
z4W<qjPcZ#3_rUytW-SI6rVnNg%v@Ob!Nf5%!UUl44dcSv|FH55o*!Te&}nEm!om%v
z4@Se>hpr9AhqV{s=^W}FSh|4K?=bZ+8dm?q%!kDTtR8@cAFN)3>4VX*@PYAR=?516
zuyhS~7fd0PhUFJnxdTh@P-*!6nlN=RaTpD2H^TTB>Y(8O^9M{FjE3uns)L6Eln*P<
z;Obx+p)|}~Sa`w8Etom5bc9Pk)EEZ%{14P#Sa`w2(ZdC%4}*s4hvjQnyu;jyp&m^D
z=3iL4fJwvr0pp|7u>1ilk6`k!auJvPu=s-I7j%1J`eEjw)39)Y>4&8wm_A(g!pwoG
zgQZtk`h|rz%wI5f!t}vtSiHjY!SWAG9HtLO!}JlNVftb2z@;7*-mv(FmH)7O2@4-w
z`eEr5W)CdC!qN>ceK2uYx`3sBm^eJ#q2(G(9G!-_7j8c^{ld(H#SgkZ7#|i+F#p2R
z58PasLL4+KeqrecR$sxyVc~<T{Dzg+=rpX{hM5cFqo)H{IKgOGJi^q&%!S1>jF0Xe
zSosf&H<)?o>d^Txf5OUlSp37{6<s|pJ}m#h;tggljK-x8Jv?CkMyFx^LN^DNzG3QN
z=E33-mL71$A1vL$)WP_$aDbI3u=IstKQw*A(i_Ykn0sLEgSiJ?Ka3CCw+oA3*nBQL
zJwWY&sfW?9c!#+U)}Dr$gDc!%`eES((+6vh!Ng(eaM7@Eg2g{PzG3c#(Krl1PiHXy
z!j!}Kgxmv5@344)l?$-?7-kO4Tyz@U-LUY0g(EKgFn7V?8^b=R|6%C}-Cmga=<x_M
z59SY8{erF^7Qe7|9!xzf9^vkWma7>0VFFNh!tyVy+=YcJOc^>2lZUwvrVo~%;Nb}E
z$D`|q@zK)(%p6$$frS%HJuVvNZ&?2omM>uD;L-<+e^~y7*^f&-x;V^ySiHg7QLylY
z#S6MV7$2q|=3ZF165IZRg%2$K!^V?gG|W6)?tqm~uy};guyhNPhlMZ9-7p$vE{uld
z3s^Y7$_ZF{fa!zLu=D~8KNt<Gmtf^G&Uk^QZ<sl-bPux^=6{%d7&J8f!_pzlAL!wL
zp${qma~CZBVDUiE-BA6od<;tmFneLrxM)~7!R&#J`@q5(mM(DVhq)UTPB0o4Ua;`M
zr5~0aVC`O5dkN+rn7eT4huIIKVfhf2-eBPgqj8x7b1y91U^LACaChM_0G4iG>S5^@
zrW~CQ6Nk~Tavv5Suy!9T{9xe<Q;$x=%70ip50(yK^TIG1T_4O}u<(JUJ6Qa~<k9t^
z^I`D^E3aVj0i)5?!}zfB0j3X@Ut!@0GZ&^Foklkw=5Lrdto(q9qw9z9Vc`smcUU;U
z%5#`GF!ks(%zT(RF#BQZ(D^WN7>yqPuy98YADDV{8kTNgG%P*C%5PXcL)Qo6!_o~b
z-@)P^CXb7TrE8e`;rR|)9>U~d`2*b?Sh|DJuyg<mM_4$(%tzM`iyxRcEdRse3#Jd|
z4wyO^4RbFnywLT*${$#J0HzN{!@?DoZeZ~Y^Dj&vtUiG0gV8X5!qOXz4|5N(@c=Uq
z7Jo2z!_0xp<6ywb4_Lm#<z95{uyBQ?A6Wbm>u#8RFdEhmfW-^Cedv6c{cv$;Jr1kC
zVKh8k;0hrOSopx~hxr4R9%1<!RvtriAxM~hSiHgPf%zX6j&OG%w4$(J{)WXH%zl_Y
zm^mn#kT|e*9?T!G{XejNAS`_$X+dGa{0$R?rDvG`VB)xFSiXVz8|EKae!!&<CJqZ{
zSo$G0{$cuI{)Xi{Sh)bBVe&Bb=rk-H!PLXTALcJuIfP3e%%3oGVBrAs2h1FF{V+Z(
zykLBo{pjflCJ&=w@dGR8VdAj!pJ4NPxaxnHdUX3>=@2GQh=%zOo=&0VKOucEd04o^
z>U)^KVDbcMXugNVKdjt`(Xe&}A#-5i085Xs@Q39OSp1@!1FNTD=?vChgQaJf{V;iS
z{jm6lnFDhNEZxD%7g#z&*AL6DuzU`ScUb->6ffxQ23Wm@ZVx&i79KEn!qPk3K6DLe
ze3(CB;S38mm_K3R29rnA&HzhyFmaguFmaf_;O4*-LTQ*iP%Z<koQI_wcs{_9o?-5V
z(XjFwW-csVVDSpG2S&rhVfta>uzUcEcbGV_G_3p~Rz0l#hs6i1Jb{~o!~d}S2Fo8X
z^|15|3kRI(0h(`N@e5NAOHZ)&94wsB{R4|{n0+vLSp37>gEJhV{(|Kjn0lByVB#=y
zanUgKu=t0$3zxfbnF9-dSh)c+7ngc;aaj2R^9Q;-E<Q{jjE4CG77w`e!Ng(d09GEr
z_^^0|sl!FX%Ud)BIO7dn9dw-}Oc<sQ77s9STr@15!Q^53;rSmXfrEyH8%&zi`U_Ux
z!t}x11EXQ);-X>gKbU)A@rNs1VeW<HdsurB7T+*;;Ia=U4l7?_`5z_@>-WIi0~3eQ
zuyhSiCol;p4Nt$&{0SAtpkVm~7SAvm<{p?oF*HI2VD5yK(=ZyAZei^bsB#PnW*^MG
zF#o{v0j_!n-5{7dVc`k0pV;yP7GLP`M{M~8vlm9g+ye_oboaveFn_|zTR0yUZ_x4`
zS2~7?!)RDOfYpbvdV^3pMvn)W`(Wu0W<FdT%0SQOF#Dj=@cjiaJ~|E44>up`UsycA
z@*&K9==x!Nn7y#@hqXgs?EsiOOg%acOSiCgA1wah^MW|$PhjB+YbV0O9X1XOi+`AT
z==Q?uD_A;*r8ii843me^==#z5u>1oHKUg_|t`5eB<vSP+(+>+jn15jMFnur@7JsmE
z02WU$8s;BD?ttY#SbW3u!O|bhK3p^`d|~c~m20@vV~9i39o#-x{=-m+DgcXrSh|7n
zVfhet4<OF)gVwvSauOB}F!iu>33nH&^$ak7!-Qew2P{3o!UHCcPQ&647SHJU1eT7_
z^}+bCc!s$XW)95%uyPot9w!YAe^@$)r30AxIQ2m#VE%=rTUdDlcL!7*E(#WoFneL?
z4@TqC2P<!2`e5+{GY^&?aOsDa`_S?Q7LKrZg!u!PepvXx@*gbSz|^D1Crlhh!}1BN
zoP_n;VEWL_gXx3Ou=E4-KP(-?+yhI`F!i`-n7y!a0v3L-^ao29==x#d1WRWyKFmB=
z`HM?G%pSNq(b6HzTy%S3e3-egb|5Uhz}k~A`(f(QX;`|2nFEUtSosf=N7skWhxr2*
z{&1Q=IR<MV!Qv6-UKkB?54wGDKGb|zdV!@Q7$4>@nET-R&=@dzn0sL52uwdLpP*@j
zb7AQerXEJa6+q(^&PQXw#`j?97*?Od;vZKz0!yE;^aIPkFg{Ek%{n+2RzAbbhov7_
zIwq9=Vg82cgSi)$k6`j}3(**`@P^p~D?efR7goNYX@hfN?u6wxn0i?Hg)79xfW`|f
zzF_GFmhNHVu=Ig$49tF*I4s@6%4HZ0^A}7VoQ9@fSbV|KJxm{rhSl$IeP|4rK3Mue
zPrroH0bCyH9#}ep`41KjXx75_eZ%S*SUP~ECs=sm3O`tSf#rW#dVtvj3l|t4ora|w
zSbBreaCOl5h4axEF!eBVVBrVzFU()CdKFDO1I&Lg|H8@%SbV_zg)1Mx^ux+)Sa`tV
z10IjictN)x#)sJl3ul-=VD$n_9Xbt*Us$?@g+DAG!Q^4?Le~!yhpB_*TUa`PmB+Ah
z23<eQy)gUW;RN+B%v>0at{)bEu=s)TVd`P|7#0uc`eEq{<_=iA!}2RE{$cJz*AFun
zCJ!rzVBrsoKbSq}`eEX*@Pfr7%s;Sx9J)SSe3-p3cf-;VF7@c*uyg>+4>0$ktHZ^I
zg&$0w*m4*aPq1_Xvlo^gVdmkYVetivcbI+{jY}U)9HtMJeqiYVCQpcl`4dLN%s~$i
zT;{;aTUdO<$^}^Yk4qoSov{64@OX#jADBFhMmGl*-mrLwwYOmT5SG4S{z2Cd6Nlw{
znEzqv2Nr+0;sKU#VCKO53kx?`yx?*_%ssGl4@(Cy^)Pq9{0UQ!PQ&~Sa|g`bF#q6k
zKg=AserSIhRv*CfKg^#n^@M0x_!ClxE)QD=3@cC2<#F+0=>`_=u=s?92QGauahN_>
zdV#s0SpBg44>J!|kHX>=CXPWv>vx#_uy}^?37G>+U$FcJOE0kahtW98QD{Dc<zJY;
zVg7-+2Uq%miNoRxW)7^q2g}zmbJ6_|^Cv9d!0HW{xx|Ja%v@M{hs7H#d|>hzG@Acm
z`3^?I!WBawOaQ7M=0BKwVfta^5{wVihKq)UBP{*G%!9=TE`6|e3``#89#}ZS;t?i~
zPQ%g}EFNI(09d|<r6Y8GFg~o_hq)JK4$K`ef5FtlXjr<0>4U3-hAT`QrXHP!rFWP+
zVd)Q+j|s&;EPi3(4O0(GKd|x--Ch_UW)F;p`5V@5g!vz)9v2M@2UvW;(lIXe=;E;O
zhq(imUSRnP7Eb8;(D^X+Fn_>kLg@$QPgpq&i#M2l^z?~tFFGF<9;E7n`5WdASa_nF
zkBbkhe_`^ldJx9PMZ^3H%kMCCuyg?9<Dy~t4LzP<=@FMcn0i?Gg6>{eIN;I`6NjaL
zSpI?K3z&ak;R;g^qhab`?LJt3fzhyZ38P{9&}mqG0go@V{15XVx;_{m77nm{0}pp-
z{R&fui-wguFn7b;0V`Kv<pD1JF!#aI4=mi^`f#`dmaky>1V+QmgU3G({V?~y+=(6z
zFn_`7RhW7h4bun9zp(NHrVkb#xb(x!fu(;~dVtX|c|tTSeZbNy%zi@pVDhl?0_J~M
zdln{-s~mxaKP-L2+zksCSbW0rC)_+J1EvoaUr=ENn15jL2@^-BVfMhnALbsIeK2`g
z{zTUgtCwN<7M5;d{(y-Sia(e+u=EDA7v?T>d(ruD|3lL$oR6*$2Ork%$03bV80KDB
z`vtxH!>JJ}0dptLbOTj~i-PHcxdT1kVetsF2i+W4`2f=otAAnX6~>484_!Yjzrysx
z;u%){!Qv5?KhgEW?1$wSm^)$i!qPX)Ty*`g^afk62jj!+gVWIR99=)m9`t+<OLs7F
zn7h#R!}zdxhoyIzzhU}d{)DNA(@=N8@)s;VVc`!e|6u-t>qBF}<YE4S<ztvQJbuvh
zp>tvO!TbX&*I?m*t{%>Z<vZv+C`>=hKQQ;e)uS<B_Q1+3SiHf?1z354rVY-8>4S~e
z!f04I4)YgW0}ci(zF;)WT{x6u2*dQj!W*7`F;o&K0E<6Ds$urP?1!l*6mBqg!om+0
zUoic!bPVI;qG9Hs%i~P<P=zpgSiXV9H&mK|V7h}Ugz1OHH&mDbmX2V2bQ&fPqhaX;
z#wTPy%>A(P4pwf!@-Mo5=zN%eVfh~xk1%mu<sQr&nEfz5%-t|PtUSOKKQMJL8Ww)A
zavfHGz|4WEgVC_`26Hzo{9xe$3qKeQ(+8tr`qA4tu=Xn~{ldc)rV)*X`5QLx3#-pz
z?t%FWO&dIY!PbAm!W(w~7tGx-ccSZug$t~Hgq3?R`(XJAW)8Z3m^@q@T8_f%ahNzR
z{jhL>)t|6*2UicRUvTAPSop&756m1`x`Op*;OP<OAT%0QPQl7~Sa`w0A7&n!HaHiS
zK4Iw%rVo}5;R<muVD`bn4Ho`5lw%0P(hn^BVCe^DE`|m)0a$p$)WgyRnlhrfu<(M>
zgftSX4wpHw_=CyA;sZv*#BtHM^uxjhmp+&{ES}N*0V`Kv>TuDpbd2r}SbXBt2hDG=
z_5!*&@N|L80H`?3URXH}<74D+sCpO$^EWL2!O9O<xWmkWr7M^|Tr@2G!_>pvPbfXY
z@*PY+y!?iiA24^|vKM9_jE4CKM#IV}T>4@1uyO`Q<5G_<4zrikd=E1R=5JVd!Q2g#
zCq%=-4_1G`^b-m<n7Od}4p#2L%4_ue08<a6Vd(@$!_0xT=U_CUd;oJNES<p2gO%sF
z+QBgQ!SW5vU9frtCJw6~Ve;rS%-=BcVD`bw4`@0>*9YUn!W))<VB>i(f8$CQuyg~<
z|1kA1_rT;~?t$3{qha9%Yrnw45ta^M`eFWp>4VeI@&^{)F#BQY5au6P{($R)GT`FS
z@PWApR&K)lfy;iFdtvT`g%eCY%p9n7m=x6gFn7b;11r~H{=n1-5rL_L<#$*<gvc^5
zz{FvEbQ)$K%-yi`4of$%b%E&mF!)e^z~UK}ZZXv15P+p`n7y!ggO&fVctO_=QxEeu
zEZ@NWh2}1F{V?~!@+nL|EZkw~5*E(r`eE*aiNnl;g(FNKEFHnr!DyH`%)hYo0&@?n
z9)Xo-Fnur@CJ)QM=;2N%{$b|8+yP6^FmZJAVB#<u=3kgTSbBlQ7c5`E)T7g|d<*kG
zti24&59sRA`7r;%;u{uzFg`3FqU(e4VeUqcZ<xDb=?kVF7Y(xqmXBb3SiOeJ9GL%M
z>S6H!qv8I6rek#bVeW**11#Oa@)OK|xY7?yKg@lw{vV8n*$49%jE_#ErxRFs!rX<f
z9-R-%Come8{$c({SC5O2;ZGdlhD$#z9$@n5e3&>o4J!v=?uN<3(jknGi-zlix))|I
zdbr}!4-<!lA51?i{$cVsX=wdNs(mo?VC63?Jz=C5sJ~$9VKhuVtel0#A1wXD(lIPu
z!}1GEKP;WW(g{o-OdU)gtUQA0gVRuV!PLX@0ZbpP9D?;rVBreW4=cA|G_3sya~I4#
zFnO3h7!3<=SosYz9~O_e?1kw^r(x+3R*%8jQRwQ>`LKKniw~H4(ADAMW3(5c;R+iE
zz@-lszOeX**$)$km7g$wp_>ECH?a7Fg)=Pu!O|0<_=n|Fn7?7+2cu#3pxY1U<H$cS
z|KQ4pFneL<!_p0m4@>{B^Z>UH%7FO~)((KhBYJ*>r8B5D3<@^?0}FRpyurc|7Oof?
zp#re@hxr$_o&eS!fGWeJpy>qWPndsT?EqN*!PE#9f$E3l8<_iH;Q-6eP}P_eG~QtG
zjvh|1asa0}Q2nrc3Nr^r!@>inel!=s(lIRlVg7=J3z~8`7iK=py|DZQi#K$C!8Jk|
zu<(MN`vXgduyI_R=@%OQu=ED=H!S{P=0dH(Nx|F)i*Fbom%VU<py3ZoFYtN*>Moc#
zTs;8>EWTj*4Q3uJUI=K1-+u)Q2Uxt|EPtQ|!sOxQ9MoU1@*8FjR5>OE^)EagpnRBr
zVC`f~jSvx7IRr~TuyvmhSq8YjV0?5M7SAyIVCfEB9WFjhKg=D3+>34wtlou{*96lI
z)Bsp`!)RE(gSiJr!{njb;1s$zEFNLzz}y8h2d)vyfVmTv-eBnr77lPeG(Mr);1o<B
zto(tghn1&<;sa(sEWg6k!`uJRbcnMYhq?={9_lh!y#X^1R-Qnm;S?;qVD5#*AF=rs
z=3kgUVBrok7bXuYPv90q8L)7Mxfd$T0E;h}Jj{HUJdB2gKg_+bdKji29xu>&eV9IU
z8s<(|dk2<(2$lOVb70{NOK&juz~l+hF#RwZS9swv2UhOD!X2icV7Ub~5aw@Kc*E)e
zs5DLrmj7V!4vR-vI)cRyP6MFn7p4vtZ?JX*EZxHF!DS9C++g)CEd9Xr!}u8bq4^7D
zKdk(PxdRseu>66pAEpnM&S3Qg+`VZ2L)Q=E!}kBd!VlJ-gS7);=@F(LM#KCKb2qv;
zp?HMphou{sewe>u=>aB=PQ&sIx;QKxVBv@%4-<ff4~z?=;o%1pz(K>>XXw(n#L?Bk
z@(H>)EMC#Y(fKfYVC4>s4@<x3>T&U5<vT3AVCfc@dYCwR{GrD;dc4B)q0_K<MlZKu
z`5Ik4j1TiK++JvYg2@vMM`--R@+W#egOy*n+yT=EOaHKNLk~w>`eEr5mJVS4fwdoD
z{(|KPn7?2&x;!i#Vfh$c9;OeShPfM8K1Wv%<HP(9i)UDRfSCjH2P`~b`d~CHd|~Mo
z77j3duyPF+E--yC8eJS_KTIBG9*mDpqlXvFJXn1Mt54DO!T7LtD$Knw8s=YEy$Dl}
zi-y??E6-pwx_{91!_>jjAB={DCoCPH>x1!O=EM4Zu<{-jZm@a*rXCj!GY6g?(DETJ
zeK2uY`h%qh^l|{E4ugi~Us!y>;uDtsVC6Wj{0FlaCJ&20Sh&K>g{eaauyhX-hq)gX
z4{&`r!UN`BSUCVoFEARGe_{TC*#o0t@-TP9;teJbYX`y77fc*ZL)`@%|ACnYlZT~a
z7#|l6^EbM?aj8cahn2gqa7T9sOdMSwES<yJT`>Q`%1c=I!)SE<u=s<;Kg>LsKVas<
z(l@$(c=$lW5jI``OE++LK;_Z(!{Q%3{$TdO@&T@T3}!z(yrA~L)WhsYx0etfJslBJ
z5AXj$^CK)h!_oyI{jhur^DoSPSU3>U4=d-;>kC*o5Yi8`2Nv%zcf-;hEM9S$1Jeh~
z_b`1h`(ggWr5_evFnzFe3`-ZVdIpz%Sor|U=dkdAiNoB1OFzv2FnO3h7#|l6%U3Xe
zqo+eyxZu(c%l9z%!SuuY0n3lL^uy9WEZ$+^2+Ple!VMPAF#BQo9hPrk=@6IwFnut8
zz}gG2@Q1k%mws4$!^#VoeQ<ry{DdAqgv^80!?5s&xer|*%pO>L!{QUx4uGxef$`Dx
z!}1AC9+qBV<4G`iT=57ie_`^l_=nLjaf1GbmV2=9hxs2PJkdM=D}Q0}2#W_;coB+6
zSop#6H>@89OFuC8qge~*!t}wy0hS(N?t-}omQLUrp$u3)fyE!p|FHBAGapy@!NLbt
z|HI-DM#KDvOFt}q!SWp}ykPMTqoK~lpkU_1^uf{(%-t}5U}%I2!2AuXH(=oe3l~_t
zLX~4sFm<qSgOy`2^I+kFp%E$ob0<t6%syCo1T!D19D{=S4;D@^{V?~y>K$C+4>KR8
z4`vRm9D}(FmX6RJ0P`P=hRMV1gQWxX`2d(Yc>F`lDVRC1{6{GL!_>j@4J_Zo{0)mw
zn0@FpEdF8P4a+x#^uf%5>4SwIto(!VVc`o?kBf%sgOwYwd<;w9xb(ro2Npjt`(Wh<
z%v@M{K=&6co?!6~vj?Ug9=_1{LDvt<w=jJ$b71;l?t{sr)3EdfOD8b%VEGtUuE506
z^~3xJEC1l>2U?!P$^~5E2n#2e`LJ{YGZ(|ZXbwP6Kd^X5&p)tmM$^s!Qx8kGFn`0`
z4NDKO_(0bW<HO8{(J+6((gVz2F!ks(%)hYsfVm&m{(<E$bbT;BOdqTr4>J$u57_u1
zOg%acFCU=k3>FSBbujZ_d~_OCj>6mt(+~4MOdeeyj1O}sEZ@QO!Q^4#3FD*FuyBW^
zdsukD(hn~CVe&Bbu<{TV-!S*U(h0h~a6UA>!_qNK92O3+aDj=#XqZ1?>R|Z<rk;?$
zVdlW>h2=v+@eS7pWx)IoYcIm$17;2^T%pS06fE3f`eE*X<tLc?Vd~%-VGL+`gSi_f
z3`=jYc!1RlFnJga3on>`@OXxr3oHL%;R(|Rr=jr%3x8OB0?T(Wdtv1oTpv0EY7Wdj
zF!#g45f;zr+R*v1@*9>eVfLe|!^MY%BdlD6xd)eeSbB%WFHAiwJYeNNtUrKm4lG__
z?uLaMOh1f<g#)^NSpI^AFU;RCbKvO*$NVcyAIyB1|6%Pibo+4e(ZdUt?r^DxiNoxH
z*$WFlm^e%wE*h3zVfhCZo-qI8(gzoZraO52LC=qag$G<60R}9+!t5tD-NE7u*8YLT
z7tH-Id6+-ZodF9cnEkMJJ1iVw{(_m0t{<ikCJxgFOV=<OCXY_T+y@hf)w?kJVE%xW
z3ovys8kT-x^00gWt1n>UFg{Elj7CqtFmYHp3JXuTI1UCZ-eBb&oR332Oc)k^u=XD;
z-N5n#%s(*o=rpWd15*bp=U_A}KG5~S`Ox$W^DivEVdfF?KP-M=_Q1m#YA(!waQo00
zFn7YzCwh4Wa}O+?qG^Y7Vfta_!Ri~B`(fn+Tmu>d7T&OQ2cu!>6J{=&HaHhnPs8HT
z09yaS$`xGs8J139?LS!j!|r{6)vGXb;TA#}uy}&y8<;y``54BBrAw$bI0f?`EFNL;
z1#>q%T|na#t`W+B<sX=Ruy}ywdzd`Te5f`!1xq(D0a!f1XjuCnCXb7T$1l{Ku=t17
zBd~DBr62B2XuQF~0o{GL^uy9EESzES1v3{Gj=1!r>x0`1^$$*cPzhLig}ECRA5dxd
zIo~iojE0pTu=s+dQ&_r#nTsnPVESR{2<C2>dYHMm!XFksu=s-Ib69$Y<zHAi4|6w+
zhNUZ5_`~=x`(XZnnFG@Yqha|2t`FMIfr-P?Axu3X8kQen;Q$LyLi%9g1S<z%`eEe?
zEPbGx0}FRpJi++z@PwuxSa_i8hvgesJi_7w77wueLn!^i{Rz!KuyO!qFI*nY0Wfh`
zxd(GMOdM99!1!p|8DQ}Qi$6FGbqCzt&~QN4kBblUH_Sb_)Zr3`g)6b?2iEU{<zLu1
z0!$p2J+SbF<u_QmfzdE=T<Hfry~6SljE3bKm^yTKz|tEmo?zhxvmX{8u=IqkAEpnM
zUeM)Xe3(CBd>9SW597ny3$XBq`5)#Um^v5@(+?{rVC4?X99X#FN`EkOVEGj0PMG~L
zb7AQlrXEg1(<v;TVd(|d--G2xT;UHBhvk2mepvYr%OAM>4-<#cF!NyP5Eg$h8t!f=
z1Li(hyu-o|R-VAz0W%+}4TFNmKQy1h!Vwlf7#h$8py3E}4?Mj=#nF|c^WpUiR6Q)*
z(ADAM!`Au0;vJ?A<{lUyord`nwq6t#{xI`kd>D<cA0`f~KVb0y>-WI&Crll>epvd3
zhZj@<EdF5np~4sx%zv=(gZUrk9$5K-EB(OaVdV`h{ld}%%pdS_9K!_=0a!YLyBC^{
z2<2OdZUhN)Kg?gGrUO`f5=!qd8)5kj7C*54LNFQ@jxhD;G|YW4f5O5G#z&7=bp0@Y
z!pbRFy$8$3u>6az51kKl2Q1ydXjpngSC5Mii+@;pf`tz(T+#Kx#9{Wq(g93A%s;U7
zgsvaPhpB`0dtl{1%sia&2~B6Pd<3hvVKgirVBrk24@Se{9cCX)99AB{(j!b9orZ-E
zES_QU4|5Np^aIle^CwI{EI-2Rhp9ui9~Lg?{)5LmG<;y@pzDX(3yVi|c^Ds8{K3K(
zW<D$(z`_$2zPQQ{Sos5^Vet>EPhsLPdtmNCr(yLwyx#|{M_}y&Li%CugvC3I53>gr
z&M-cVhQ$la-7p$Gym0A<r4v}V;Zg_7-!OY&G%S2z=><l^;uWS2Ck;&>uzZi6ZgA?u
zApuKA=>A9N<Iv6kuRov)VBvu-4wc5BVCv!V4fP+c^asnouyg~94_NxeumDW}=3iL+
zp_k)m$}za;@dqn6Vd)A(1DXKLzp(TVOSdq0p(!WGh3Ur?UbqZ^wew-+0L)!5J}jJZ
z>4$|MdOXA80ahO1@)s<=VKmJB81_TU1$1}7{0(zAOg}6=z{SxuLisRzVet>kf3SQD
z<3p8WP_TH1<rA2DVEPEz4>J(vU%0){`4E^tVCv9mn7y!c1TRMj=!eA@tlWd~;WTtS
z2bcR{>R|4M)t9jNhPQuU`54{dP(G~uf~7N9x`FlM;PDAnkBfrY4+}3`=?z0aG+n{;
z!@?7mt})c33BcS76Nbef%pEXsbQ)Gp!2Au5H)y!S%z=e7x_(%@3+7%J4f8k5TwLye
zrB7J<1DAQ|_QLqE`UckSf#(CL`(f!3XZ%6a1<c<t8s;7tA6NQ=xdWE|V0;)2(+8tr
z;uthE{$b*<aEGN!Sh)a`htaU`hVkL?1TzonKUlcH)S=Tbf5P(%Q~@kLpj-?J7Jo4P
zu<(YJXSm`WW)RH1uyhZr2VnXLh8xr%SUQ851Jh5)-!SuG?uC_`u=t0y|De|3q+sP2
z%pEw>8=46)|HH~Fm_ArIqAADV!pa924f8L&zYa?W7&;*WF#ke$3=FXNhWQ6Z!^#Pm
zIv5SN2Py!|2e9-DGY{rIs4_SO(+{Iz60mrI@nPn{_~<k&|HJg5#{;_g==x#l7na{(
z^00V-r4N`mIt@#AF#p5&uy!GAd=NdnVdlWnJ<Ojl`(XJSW-iQsFnur@R^Gz$FN}tn
z2P;2d?M0YA7!6YoOF!uDfrSH1Jvt532TLa~|HH&#=^9-hIv<t~VEF=8zN4$d;6u|n
zES%u(g_eI9>d^#X`e5M*8^1$ShLZ~$FM!22%sf~*1n1*00aky*!VebzaOF_{!TD$m
zboH=w1GAq{`iG?tm_As(hvi#B`eErBR&K)bBP<?aG@7&FTzL3E;|pdEtbTzj#KD01
z9~OVGatD?UVEF)tcKG=&F#WK623x;{OCL-<EWBX)VB)ZN#ibu64huhYcf!I27B4XM
zFdC*Fmd{|}3=@aB1C|b8`p{`u{zs=_`4(m_x;{7`8gH=hhM5Bk54bus1}uHS!Vea2
zu<{$`Z(QcU+yx6K*tjRG9D$`Hm_N|0hsQgt{D+wXvmfRkT>4?*3LE!<g*VI`Sh;|1
zFN_b1cUU@sm3J_A!~6j&7hw8eG)zB?4-0pge_-hV<{p?j7!7kT+<(w`gxL=hhs6U-
zAB=|OPgwlG?1RM%jE1R4r(xv^%zRk5!@>g=zPRcwm^jQFSUQ5~hlMAsd_cD!=5AO!
z52g<mF0lR|JUq}1fbpT_Gt7RNFw8$NJ~|BxA6R{f9#6R99cB(J++g_z7XC1KLNrW0
zth`1KCs_F4G6xp_u=)d*USR5B;RxdsqGA4lse`3&SUADNanZ1Ng1HNpE^w(w7l-)+
zW)3V}5DEuayu;iJ<D;98iw`RYVc`!;hv?yfOFt}~!^$g|K0@gSW*$r*EI+}*k68V%
z@Pg%6SiHm17fc*xA36>5KRn!^`3aUEVB#=;q3ef*J1if-+VL>=!)RRT2o}FE8rJ@U
zg$s;EPv0<i!Qu-h59dSuf$m-yALbvJ`(W`8^AAiOrXHP!sfU&Kuy}*1gP9BCqth_;
zu>22;e^`9N`~izkbp0?stek_jLtyTK#VafxVd`NtEIwf22=gy29$+*qJYf1@G)zCt
zpD=S^?EqMO5Gn^?_Q3jSF#o{90UrM_i_vIU_`vkRq;a_$CJ&=w`e5#Wg*%~q1G5+A
zUs(9V^uzNX%px=z7BA@G4pRn;KbSo*aTpEL50i)W8)5QrcfiJhVG>XpmVROF7MOow
z=@RB2Shzs7!6{fihov`I`b94f355gPKqv!N4?wv%DVV=u=?+H2>T{Spj7B#ICJqZn
zSbBrG9~LgKa6;D)i#K#}n15mVVdkN$gV_(W7nWWLwL4+vz``3A{xBLAUoi7v>d|SK
zepvp6#Xl@x!_on|K6E}T-NC}2*nAH&2Nu4tc!1}3XnPo@4i}AHZot&R`UNmPE*hR*
zpySrC^aqPaT>4<)1*2j40TzC+c!lxN&4J}_m^h3Ni+@<Tf-4@;;|CUgFn7SrgV8X1
zU^Kj3Mhj<jb7Aob(+8tr;e#GuuyO~M?qTr@(}zyO>U&tY!~6?t|KYM9W)3X=VCfGQ
z4(K#4bI{9wn0sOVgVFHtf>{iuVc`qQ@9=UHTJFNcVfhWJ4Nk%03zlAB?t!Hnn0asw
zXbf2U0VWI!e^|U=#3xK4l!o~amfm3MVeto5hLeK16BgdEc!ZgYQzKLYRu01A2c{n?
zjgx|<6ZCuzGY?}t5Qj;yb}M?k!~BCo8+`vYJbs|<O_;l2@ecDBE^}b=uy})|Cs_R9
z)CaW>=1y2TfTbf?xr9qUtp0+<A1och;tl2xT>4?|hUE)bx`m}5m^d!|Fn7Z2h3SX+
z8%E>O4-<#wf0%hNf1{@hn0j;?7Jjh!hvidPd4jGUoextF3rASEqSveF`e1xmI)Q~d
zEd9djaaekQsYj<_;SCEfSUUjbADI8p^}*Z=t8ZZ822%%f54t!^9!A5=fwco*=@^!N
zaFru4|G~<Cn0YYw!omUOADBFhhRMU@87)7+(gjRCIt_~_Sh)vFhp==63m2IGaOsD+
z0~Y@9d<b<9EF94Fqnis04_JDF(YWFt9zRfnVeWu(8DRMnLmZ|6TJNL#7Zx6{^b2zb
zOdE`br8}5^Vfx_V4mBT^4q^JxX?Q&km4LYymM&oKgGysk&~S#8_b~miaEH;D8X+RE
z^ae}6uy}!`4;T$mjUZv>!u$=h7oh^ef`t==hah3*!s3yjKM`8NESP#&_~VKvuwpC(
z%w4c@npF3r8ws-?mX2ZR4_zHDKD^#1GTp$+XIOfJ#RsfEfXg13IIP}>#WyTGVetu5
zhl_@VA50&t{D760FmrL~hn0(PeYh0j5Ql{~EPuiL1B*|X{V*DbG4Ok~Vd)>H4i>Jk
zau6ntPQ%Ix^!SF==dg5+t`EkCg&(Z^L{A5V<^f^ugoQgS9l+`#m_4{?Sh|PB1B?#~
zPh9$7;xK)%d<YYVl`FWyA7&4XhNUN1dlKdkm^m<WVKgk<(bFrezJ!%4xb(yFDXe~h
z#Xrnkm_4{?So(wc9~Ry)_rT&6md<gR1B+i+IKbQs^9PK^r5`2^D-YoMq4fex9w!Y|
z2Xj9xy~51Fr4JUb@caQSw_xE43x8btVet#I7nYA<`e5>~bd7EfEPcY<0dp@b9AWxl
z^62_u@dFEA7$2q|7O&{}8fFem9;P3bZ_(2+%s()FIB94+z}$h$UYLHEJ7MKLEdJr)
z1&t3_xdPLNPQ%J6m^)$qg6V_N==xyk1YI2FE?7Q>mB;AnVDSP|2eTiR9$@i{t`E+K
z`VZz_SUkYOAC?~A>d_c5`(XJW7H%+q!SXknHcT!w-r(gh)Ln$~J=`FudYF4*{($8N
zxH=pRSUQKrBU~BOTsR*G1HIgXrDJ$H#Gwxs&#?G{DTC*K==cvz9GylFcbGgZ9AW7h
z79TKq7!6B5uy}-}BbfVP;Q=d;VftV+EM20<GtB*Pb<p^Q@nJM9ox$oon15jQ!NL#b
z9+*CK8dm<p;u~f^EZ?E`5770)!V4Y`Q1`;jgQ-X7!^C0n4+}?FeuAlo`4c@o!@?i#
zZ)kdh#RIG!h4t@Y;Q_N3M#JoZnGehNuy}`sAIyC)eK0;Ozre&{;Q-SI3s0ClIt`0|
zm_C?#So%O$kBbirN0|R%?!%=XLmV1!u<*x_M-zbMKiK^NuyOzv9<claQ-`J<zTOs=
zUSZ)6OLs8;z{*8*{V;J@`he+!se{EIjE_#k;vJTbVCfj<Zg~9%O;_mpVetkt2c{q9
z9ypDo9D>CUES<pkuyBC+59U5}`(b>TJ7D&~!V@MA_XkV^8V$=AFn_@M@vwM=rAIVv
za4yW>FacOPfUW<+75^~xu>1x~cQE(B`~eFGn0gEv8b7dbg4qY-!^#H?eNX{deu3Kq
z%?EIQL-|nUm=x50SUC)%VfmL(I)t@fVeW=mK&<_+at>C0z~TcY4o?R#gVAVs`406b
z%p6#Fz|tq0HaHiSUSR5B_QTqRaE0g$H2Yxj4@<AGd<aWV=-OfJ6<9pO(km?d;r@r(
zgRUPvykYjj!V8w4;PHTN05slV_QPnHdtrQ-x#-$q;;?)V3pcpGq2|NX;nELFzp!|N
z`3n{vF!OQght>Zuf5OU97>%J1O#n9j3oAch{)gp%H02mvSpJ8lGg!F8@&(L142>`W
zXgI<2!NLoc?_lW!rW~DymAA0)hpC5!8!SB0^}*s7rXN;r!NLKSj$rC=>4&uwVd)<|
z9AN&y&<~w2hsndj3ziOG_QKqSt{)cPu>23puP_>>9+r+^d>9Rn7g+v+#sf?q7M`$j
z0VWQkVdXL`d}028xf@n~!om@z4@Se{2c`}q-l6V+%fmE6X;?hL{0F09_QBEtR5=C(
zOK&jqVKgi~z{(#Cjc5X}{0_Gd8lJFlK~s*wg{2=@c*Eia=06M#XaX>Guyg~HCY1kS
z>S6AO>4TXEqhav^lZVl;_=bfOEZku60Ha~y3eyLpVc`$s!_pZn{lM}OEI#4tpbS|0
zhsnd-1ItISaD*zyq@d{w-Q6(%!omkrBSZvdKP=v1;Q$jSXg|al2AFzSdkN+bT;T@Q
z2B%=<KP>!U<`Ig2m;tc*ALd_JIR-Ni=0BKv7!7kLEWFX}h1F}Yd=AqGqha9%3ny5-
z!^#y{`hdkROdp(v<}-9YEI+~0HOwBkIw%8XK9tJ<b1y92!tyUJ|HI-3UOq$J0}B_J
zxwvRpc){$0g*Pl7VE%^ZLmURd=J{ax1!gX+UVz6Z4*jt3g4qlAFVsJ{?1#k{EPP?{
z2=fQb9k6gkw;rY+CJ#%mFn`0+4J@27^h4`USbGr`jxcjz?m*WE<HO<?mfm3Q#pPd^
zJ{S%2FRa{#g&VBj3-bp|Jvt3be=r(m4$NHGxCgpE7#|kja2hSXVDS%Ak3mD#!{Qz0
ze^~ng<{k`vFafB3n0sOI1dBIV{)5FcOdE`b=M!i-42w5dJi^iyEL>sw;55{Jn7y!g
zgNHlRTv+_V^$}pe+zWFb+&pxBQ1jvG4C)?Oc*4wqxeHx8%v@Of!@>)uAC@m*^62_u
z=E7)LdmWap3B^Aw-^1b=mX2WJF!!L_3**D$9Trcpd<aWNFmqw*F=%M`!O{z?-i3t=
zEZ$)LLDvr}cVY1ltA}9jf`vcK9CZCKdtvT|g)^+YM0W?eepvc|nGbUZ%w4c>fQ1jb
zei$DX->~{0<`0;^VdlZq!)UlTG#p{!4WnW9<4OlG^I_=*7XGmCgNeh`!R&?6Fm<r<
z4i=8E_7u!N@caSO2&G}>!r}oIUr=fI`KmB^m^h4v`5%@~VCfYWp0N0X`3t5GM#JJ8
zCJzfQSbl=JkJ$Kwxd&z*Odl+r!0g4Kq45U`Czw81IKusbp%Eegi(i<(VeW>B!`%f@
z4kuyi;WU~%VB-RC4QLFQJ7NBVl^-x=@c4%bpwZ~=g5@KaGMGQm(*;Z(rXQAHVc`dN
z2h=~f>JM1Dg4MGy8kUY>@rdp&nEfys7JjgC5STk)?uV(vMZ>}oW)3VJ!`ufeS8(Zv
z)tj*N3yU{c_`u|G=|@+O?hah~VE%*o8<r1Y@dYavaOsDM!~6#e2bg}C`7m|pG|ay+
zdtv1stXzWUZ*+}ld|3L0#WyS+!0d;)2TdEC3rk-xf56-WOE+-y;W|(mu<`~b3JV8V
zx`w3#m^?ZSk6&o}1Ll7%X!?hx19W}pd|0}JiNnf&7#|l6^Cv7EVE!SL|6$<^vlr%H
zSUeE&H_V@~^apbvto;HDcUXAB<k4xEdtv6mXqY;fJ7Ihn4GULTdll|}r~;TcuJRj}
zeqr{*_%Q#&(h*!7YA!AcJ^XR0!zB)j2bejqaRFTZhvf@c{KN7eto(zC<FXH?4rVVd
z^|;ajOdOW);N?ElLR{{KO2gOp!r}>@kKhU*3|KmX@NkpFs>5XhEIeWUf|Xw|8katp
zI4nKF(hH0algCA)m-p!U;o%4^Uts3I+zZnWOXsk33rol7{(<R-g%>Qn!QugyA7J?w
z7Oyb#Vfta|2PO^+Cs_W6xf^G^qKzNG)Wgaxbbr9@g)(6F!rTk<2h3e?{ZRFUC|G?7
zGY=Mig!IAWVgAPDUzj>L4UJD&K1X*yEIiQD157_G{$TdP(i@D1r4LxS2os0ths7_9
z4~u6Q9~Qo__82UFVe&9{!TbwzFRVU*)#K>)!T_w?fa%AXj$s<mXjr<2#WyVcVDXOb
zUNr4+E_%6#%e`<7=nQB)!h~Vr0^{SNVetkV?}4W~s0Nrgl#4;Z;txi{;tv)Nu=IhU
z5h?(SUs%3}sfX!@$wQT6P%wE|c*EQQ3qM$Xf|-kM04#mN;t!S%VD5pLhaNvLb711I
z`U@W4&~_J09n3#4J_ZeqKUlvXW*$r(%s((bjE03b%)PMqg3++}gwZg4=rk<d!|a9C
z<FNDrOHVKwT|cb6g!vzqFJSQk3s;ypTtC#`uyBNnLm9Ag1D21W!f*-}uCROzw;#G*
z2j+gb26P5Ao?zhzqha9%Gap?Wj1LPBn7`omLHz|wpD^{fXjnRfg(J*fn7?r8gNeh^
z2TUJ~5Az319WEMHZo&G2xYVJG!|a2lTUdTWSBH}i4JTOsL$B{(@rg@6Ec{{h11!D3
z)Z@zcFnO4LuyBHf8%!Qox`fFSq@n(Umoo%3!2APCPcRx5&IGi>+p{opVE)3XAL>3>
zyu<ttOAoN}0GGY6eiE#_faP=8xD|SOMmGnh9~Mur@(gAUEZkuE16@DNUoii|)WgCJ
z<{y|n82X{@KbU@8@-X+I%cJK{SpJ8FA1wXgG9TtXSUCv`FZ6Inw+H49n7Od|Us!m+
z^uxjtJzv1|!)TbhVdlZ&6J`!f9Xbv3AI#mb_=A;uuyO%iAB+!+7xeZTEI-5I6BeE@
zeJ~nUKH#EZ;f6~;Ec{{R04)6A=0ocdSbBnq!)a*!0ka<#4lo)PJ}`H{^`S9f@ed0R
zSbYrB2cyxn!MPaq7BpSM=7nML3fBN-z~UW7LxmY&<pGROh=%76Xn4Tfj~<@r=D^Yk
z%>A%%fW<qme2;DpES+P76SRCoSC5Mi_Xkt~%zkuns5G2{)q601!z5tg3d={Z_=U;C
zXqbLjK7gf5Sopxq!KEK&4vdE7Ls+>0GY=*YqhaoanF~`#Z2Y6AZ&*CQ(m$d2huH_q
z?=W}6@)OK_n0wJ_SbW3636^eP`4z@T*N2M_D<@#_4RaqZ^)PW*IKs+JnEPSr2S&rx
zqtmc>hl#`74>ONYIKbQq%SW(qhv|p6uV5CS(J*zebO4h^HwRrFrVf@qVdW?+9$|b~
zJfN3DFmagwVESO~2e|#vas?L8FneM8Vd`P+9awz8!Vy>f2a|_|8_YbIe_-hn-Q95e
zq2(+rzG3Me#)qX(n0j0^EWN?X0bK6Ir5_$I(DDuzj=0pr#9{j2<NRp)VdWOi^aE87
z^EWKMVfMq)H7tH%_QGgbIKj$&m_4xk0*enAAEpmZL(>I%{K3*QOdTwL!1(Aidi<iN
zGj#PBd}uhp(hbc2u=E8BXAFH%0hs+Tdtu=Pb2p)M1W)(S@PL)yuyh0~m!a0dDVRD~
zJj2`p6Nf9r!GPs6m^7@P4iA55xWMGmX_&uY@edC-XuAtm9;54n@nPy==D@^Z{($8x
zm^cOv4KG-_g{41OxdV$241H(<uyBBfBh>w{bPLM|XxiXhm^{qCu=s?f19*7Cb)Yg}
z=>(<^mLFjLf~8ATT?`B`|H9%MrVkc=F#llag9$+67v_Ihc?pXbSh)bp$1rVh8mb=_
z&aildr3aWgSh&FWa2l!~=5M$-lmV+3VBrWAhEuTo35##I1eAf(-B5XWxI^6mvj^s0
zSh&N~LAAjtn7?82u>KM(++pP-F8{&e7uL>)>4Veg=^Jh{8UyNnSUjT166C_d5mwH@
z(mg?qI26G00}g2nVOaRV$~%~TLh%PP2$oJ@<s>W~Vfh254i^ox2bOPO>R|Ob%p90H
zm^_Sz*@vFbVetWrPh8;&Q-@xU!_py49L9&)3**D)e_;6*W*;m*Vd~LoSiXU|AC~`N
zd_wwR=@%C6uy}x_dzd-s_QLqE@P_G!iIYmh%z=k5G(W)HLC8E<dV_@<tloj;e^|c8
zWezNyVCrG{9cDjF9#{H-rGJ<|VC%kM=fA`Fu=qlE2h80t`(ft6>_u0P&WHIIR(`|M
z5v*K5R}bUE`~&kZEFNL{VE%!rN2g)&1*>OZ`2gl0SboCgZ<zfs^|15?E0<yU7bcEj
zKeT*>sfW22mX2Ze;?fUO56d?&_rUCh#Xrn^bo*g^SoseRM`-$m#RDu{Vd`Nt%zd!>
zA0FRO{jl(ZsmDdb!X4&Lm_K0V<I)F<XPEn7{)Op>g(EKgFmYJ;q1y)wcUby_sfW{0
zf5Orctp0$x2Nn;o@*A!X%7En;So(q41Is^9Ww<G5`hh8cg(HlH#V4$sgsFqkuy}`!
z^T6B*(+_LU;dBqw|5{KQ=702b0J9H+hU$Zrf3S8NEF3V@;}C$A8*qD}>R|N~EImMl
z5fn81VCfvD9u{t}ctJN8W&kYz!O|Tp9l^p4mY!hg2&NC6hUF7jdWM+?Q-{<2Pz5mc
zuyPFMZkTzn@PR7FpkVP2^Cv7n!{QSbJ{TII0<iP~OV2R%Fmqw)4;HRaZEy;v52g;L
zA4bE%8J0d^8e!oAi#J%lgQZuPILuyL<pC^wVEW+s8Ja#}=EB?ow;wuw1Cxh|!{PyE
zAIw~sJUR`FKUny~+zsmwqpQcohuI7B56oO#>e0nv=?2|D=<3k<u=E1+Cya)fhpryR
zhvjovISGqjcsN7DAEq7`jh;?m@khx2FnL(Mh2>Y6I4oVl#BtdV%ipm23zq+3@d=}G
z>4$|AEIq^h57ht@hjK9}boav20n9zH_`uKz6@azxVD5nBXShG0<s?)&oPvc5EdF8c
zhLvY<g*X^6{jl^7%m1+W!=Vi>3@hJY@efN+Fa@x9f$`C4n0sOA4i-+Zd<zQ?SiOj@
zAI^t{4=lW3G|YY&4OfrGfYpC6d(i!XrW}I{3qM%A!}|+Rcfra742@6$n7?7^9TtDE
zaD|nhP~{jDEFNL&hG71HnTMePO#tS9SU!Q7M@T<R9n8J(@)BBZ5Xv{OaD%xQ77j2z
zJRYFtp*b6Vei6(bm^)zZf|(CD2h9LjxWe2GOOG)9FmYJEK+}$~-x!u|Vg7)X<FN7p
zT|YV>=3ZF5qs!ys!}P&uT<%5Jj~;HYaDw?8mcP;U!THd5ft6P<^)Ua!(i2=g8UrQ|
z3vZZvVCfE)|6%ITw8Qt8!Ng(l2WvOM(lyL|Fm*5*mM>xDG)x@k4j3Po{V;#R>MvNh
z!^!~|AC}Hw=AqNDegG`Jz~o{2VC5N19G!;cdssNa^uhS(>S26Xe8ch$EZxH5ALc)p
zdUP75AC_NX;SUo>4@Y$U==BV&oQB0aj7HZ7<D>fzW*^KSuyzeB9l-R#XjuHi{0VnI
zv|R~P2lEe%52In}20grB`4Hv~Sh)bx2cu#3!Qvm5USR%$l>;zyVEWK$So(z73yXi4
zdFblV`LOf~vlo`$2-yo$4-03Qf8h2)%LjD(;C!e&EZ$(|!Q2C@M`8Yg>w_|2>Y-ft
zeH5^Gg{4be`5)#^SUkeaft6!08kR0$@^Bhjzr*rBEFHnZ1E!8pIKtu|7Va>8F!#aQ
zhj4Km40wKlhAR$bn8Hx?uzUjdFH{^;1F8rtd}02C=NCfyq4F^OF#p2B9p)amd8pRH
z=f7aWuzUbZ7qEH+CJs{vr=jr&3wKyPg{4Or9~Q50eJ}=8KP;cZ>K&Lgdb<`Tj+2Il
z2TUHGpI`!LG`!q~>Vx(7Vg857qiKV4Vc`oCfQ18$k1h`rhtaTbf!PPkr!YP&-e5FL
zAB={DFRZ+R#S<(XVg84i3)2Uuq2Uk9f3R{G<`0<r(CZnvekcQ$PM}-{boay3B}^Wj
zhJ_QX+=AyrsQY04L)Qo6!}1L*zF_@1SbKnwewaD%d;}GM`2$8ng)u0Y`(gIO6u|N!
zEF59tFd9})!2F95Pf-683_qwkbbrF)A7&pq4J+qi@eNB)=<0~(L)`&a56xFFf5OFa
zFktS1hZl+ZVg84u2OQR52*c6~ES_QE0n0})^U%W+rXE&b!OA_D1u*x($}x2Pu>6Xy
z50>9xG|YZjdV-k`lZT}bm_6`x4-G$<JurPR8fGrczp(rdb2qwuFnur@7GH3GLG6Qu
z2h4mJ4bz8C!_o&V|G@GA%pY*`(KW*O&~yjOM=)W6^*+?Su=t0CBdk1w%?rTd8D=hw
zhJ`CkJ<PqZaDkO)F#o{x!Dv{y4)Y(Z9)y+0uzUauADBKE4bz97eqj3G@@VaGn0}al
z;r@e$2h3ft^o8EOL)Q<BUs(AGqhab`;Q`~rXjuA!`5TsQVE%=rN0@(L`p{{Zepr0K
z{6#4KVetl=|A(0ib3ZIS!o<;Om^)$ZWLW&e`~{Ooj~|#hFm<qaf`t<-ykYSIiwBrK
z7!6Yo(+_L!!@>b34s!=gADo8f8<@Lc@dzvTVC4YJe{g+h47mHC=E2+#OGmi;4>JZ<
zf5O5YM#J<Gs+VByg}D>ve^|O8lnyZB8R`#Mc)-d>n0w(gG{3;Y7nWXO;R(yfFmbp(
zC<DD*fQJ_}zrxCMSa?FU!6{h2fQ1jtJXkph3rDyHGzKhwVg7^BuyjL6KP>&i;~!=|
zH2h%Zz=UzpuzUz}H!L3E@rp|$OdM(-Og}8&z~TX>3?~hZM_9UnnS)b3nglE!VCfYj
zJ)x;b=fct{%)Kyoz|2EekIsjc12A)7^2F+gg*V(@X!?W2FU&k#G%UQ({S8Y$uyP%j
zesud`{(<R(nTyLmuyhM6_tE(<f5FlPx;xO<hr#LtSh|Gy16?1C50i(57c4)(Xjs06
zsYj>b=^UC4Vg7{WYgl-o>x1!O>R{z4%s!YnEL>pfVKmGhSUAJd53JmRg&RygIt>d?
zSh)i$=iucQ)IaF@V0>73!Sun(Q5X$#4@^A<4bAtkemg83!r~1kj-d}K0CP9YpD_Qx
z%z-MyNx|F+^9L**VeLMg8le*C`iKoTsCEWex`2ffEWP21Z<sz94J)@`^$RTA;OPdY
z5C;uQKd}4>3lCVi4>J!It~iW=-ya9l2Qvp|E-Ze~^~2P|>S<Vg28&m88eKn(4~uV@
zewce<<pPWkQxBtI`3z<cEZ@N51C|bOxgVwvW*#j5Vfg{(KUjFc)WK+Ye8U9LXjr)5
zazD&`SUjR>hjU@+6fOW`K=U80{D!#`7Vj`V%pEXoFd8Nfqha9%D^FqR5SGqi`d~CH
zzF_S;m_C?$VCR3~$`7#chsQIt-h-t(7!9)zordX$xf?y+;O;@!2<5}#1s49WbOWou
zVCe>y&Y;>bC|LNx@-upP!Q6?i5ti>^@e8Bj>Y(w9OFu09VeW&47cBl^;;{IDn+s*Y
z?1T9mW)Cbs!qPX)9;h}93O4=^QwIxoSo(&g4-Ab^0a!f1?1c(5z`_;A$3?^9A6AdT
z)WO0R#z&`N{)2@ZJiMR^Vc`Si;-p~d4&A-z=Ajz^b04hSLQgk@^rM@H?p|2>MOTND
z4^3~dc!T8&oa)geVDS&j?{GevI&>~9y};5R%zk)yLfbL0bb_uQ*6xDY3ro+i@*JiQ
zmOo+oVg7^ZhqeFF^}+lBcMsG6n7J@>;PD4_FU()C_=WiksvSQ64=ZnA;;{4xvlm9g
z<Y6?-JeYf7;RnnAF#n*Z3z&X%8fGutAJFuGt_~L;rXOZ6E_LYQuyBIuhs7hhI&?lP
z++pPgEI#1&6L3FFJuJLn^&N~R<PP-w3FE`u12Y%h-7r4P{V+Z({$TYs%p9Eg80J4H
z4f7Ao{V?~y(l^W=7!Au0u=EM@FS>m&b71<=X;?cS*8Ya|-_XrL*9VJtn0lD~gu)GG
z4$NJ!aD?R(Sh)^Uhl_^gXIS}-UQZHoKP>!U_QJv+#)ripF89F9hsnd@0ahNs#Bs$R
z%wI5jVEG(YF2HD5_`uY|Xqf+D>R{uS=;p!F6HFgE4XgiQ@d#Vb0}BUOd5o?P=6_gx
z!)RE#Kv$2>hv|p;4;CLVadh?Qe3*Jz`3XzE=;;7mAB+zRKbZf}(<4kBAsW^WfW<S+
zT$p*Vbb!koSo;UA4qE@f;uB^rx_(%=!NMCB9<cC(>w}gr==x#l9hQDz@eSj{`~geP
z==#z5@bVuj0V@xoT$~ilov`$SQz4oJEPP?<9ma<#gSj8ZN2g)o4J)tF<q7d&;SCEP
zc)bhN2#W_O7lVSO8<>7r_`?*!!Ue`BM8nL3xeJy)VDU^yKg@nuxWV{@@)IncVd)00
z9-59Z<k1`gi#M42Vd01_4mTf7Kg_+b{Ew~<RxZQJS2XPmu<{Y+UsyVZ@nP;k*9YfA
z^A~zLf|V<{?1kxv`4eU@%)PMifT_bp!|a8b19LYn_2}ZT_<_~m=<>w!VdW4kKN71C
zmQG>i9W4G}?t=LXmw7OIVd)%RA3)O|E`6~453?7Rj$q<2^I+~nHwPvTk6);LuyPFM
z4|sk+GXNI9u>228hcJEcc!Z`eH0|*I2h6{)^ajiS@N|nqKe~A^KCIk=xdSGSlZN^m
zmVV*sh)8>3;RQ>_Fn1GcKipoJ1EKDLr5~6u1`Sn*?q68E!qNq<^aoQ1i+@=81B*|X
zKJ;-7xI3WX150=4=E33-);@sigEC<D!um@v`(W;dr8}59s5T4=HogN(Kd}4@3m2F;
zx<**OgS8i7@eYemboJ<b^!x>jZy1fP9-R*hH(2{0=6{%fVfh$cAB+#P2bSJo@dtAk
zEF55bm_BqG7EiEvhn3&3c!#BHbbT;B%pO>Jg}EOlPskrId6+q{_=oumM#IcQr{VoS
zXnugjA1qzLXmow(d{}yc`2*%2bam)_Sh|O&bErFD@rkY;#)rwn{0r9)RRAwPp?q8v
zEPi3>38oGfuP{D34U>n(H?Dkwt`D6LGY3Y)<YD0n;}fKz<q14pL&Fi4J_zZDhZoEo
z=s85N^nk7(7Qe9a7gip?#L?BG^I`b{=3ZF%!_0%l3%WiSALd?IzJu{$;YP^cuzUfd
zVftX^!~8?Y9WeDU`(gP7)_#Zi6Q&NGhLsaAaacNnr3YN$4^t1zZ?JHK(Xf1lUY@}8
z!Dv{x!_0xr=fL<d_rTP{XqY@qJuLiSG$D7x;s@q`SosTcH_UvPedsjIUoaXyo?!Z6
z=Ar9{wZCBY!r}`S9<cHSW)8Z3Sp2~9Ka7T@M_7GGDE?vfG%VcE{Q>hIy8UoIG@rxL
zD?I+8^Kmf$z}2HMVE%`xgZUT6hsmR9!{EZ~MVEk?gDwv9C#>9tg+E*!v>bxvPq;Xg
z0Si}Hc>^;KDh)r+8Ws<5aVP^;?!ej)8197H4^@sq!Q2IlZ&-SQDa4iTVDf}$SUh0(
z3x`ISe_+zEbVIIwSa}9B51od&50?L7?LfGF&~ypsqZt58@33?WizirmgXx3iGc@fC
z=-~^C2Uxto!UM)f*AI(tSa}Uo4=cA|^0@4W#W&1en0sORVg5k37o88Y7Z#qda*<g5
zF!Nyg;O-@meqs7xG~68|>VxG!Sp37{5oRtf8dko-^uzoQ>(3HOf3Wle(+9T)n(koc
zqo)&?I#_tY_^|u}Qx7Z8(AB}zqx%z<&S2>Q77wuc4XzK$fcX!W&S2>WM#J0#s~@1+
z;1n#rVD`b(!`uxE7nnQX8qpZA@qbu8fW-$ae9@F6xX^Hh#XHR1u>K@W9H9Y?1=SDp
zA1oYT@ed1kn7L@W;rE`y#9{FUb2lt}VB#=!a2lG<Vf6?s-@^P43pbcKaD7k)++3(R
zu=s?f1DJkTI)rM&pkV4?`qASP7LVu}Vet)1hcNfUXqdS$8eKn(4{P7S{0-aB0Sk9n
zx`3(2prP>yn?Hit4~tg}^=JaH_=45{Fni(k0-Aal7Z$Ft_5&>4!2F9V++g})^00IU
z^C!%HSiHmdFnur@mTq9_0Ok&uc`zF0PMAIz4U2DBdVz%-%syE84|4}hAB={{!_o`7
zd9d~<EIeTP&}o=HSUQ5G2XuAld|3X5xesOzEd8LXN9V)B1r~3x@lRMdpsPpc!`une
z2Qv?*4i-N!J~|DnzhU7Gi!YdeVEF;vTy%3_;R_3I7!6BbxZD9V7oN_c<p|6?m_0Cc
zFg}cir8`*t0y76z{=>>sSo(tLgVRv^VCe|vURe0U@&hcK;rh@RF#BNnA59u37v?Wm
zIK%QkTm#g7a6TFX7OoijVCo3yhlMjN|G?4%Ogo|S3l^?0{jhWh(+_hWj8BNhWj`!k
z64DQ=4`4J*JuH30_=IR!`iIf5bc9PE%zRip!pa3$y1=CmCJsw?==A`M4^xLu!_p6I
z{12W^(c&GJPtf(l;umfYnm$-QfVmG{KipiXK6pBW)`u|n!PTJ~0M!S}XRz`YrXH5A
zVC5>hc9?pYzhUZN@dnG+Fg}KUGy%9esJqaladKht3=2nC{KLW*mqu9lz{)L{dtvDv
zR<7aF4>KQ@{$Sw<r=jrxlgBUzDi13MVEG1CAHm`QSNy}wLpL904vdC{3rszXhUIry
zIKaXYmY!kauyO{b4^Bhl30CgF+8;0)7M`$l0oMm(K=s4g|FHOn(dgnZZ7>?$->`B5
zW*;nF!o*?vaM3V(Vftb5h|B-5_<@@TjZb)dLH!5ICvbI82FyO#_zyh1pyt5xKU6se
z1@k{lKa7UCmr(f+OW!c}qL&{qb1*E13c%7WOdl-W!2AVE$57=M6s&xK`2&^@Vf{Bk
z@di^5&+kwhVB%0NP70RKVfh&5A6)qb-5{9zVE%yRCzv`y?trO>#XBs#VDSj6&v3>g
zG@ZcQ3(tqJbO)7(#V^cVFdF86SiHgF5te`9;Q=!brV&O%#bM<H%p91xuyBFpCzv)E
z4f8iFpTfcmrVqCN1D2j(`p{`u`hlebn0<uw!OVgA7Zy&i@Q0P3Fm>oO%wCwk(epVh
zT+sEw_^@z-se_d#F!#XXA0`gd2cvO`!^4$O{6pgpmJVU&!T2zHaHT(3`hdk3EWFUe
zgHSmFpZ|rr2bK<C{(zOkF!#V|sQ+N`4$H@|{6MH52#ar6x`*XIxWAz0!|g+3z`_gW
zZ&?0=>4(KHj7HPW086K^bOQ@FSa`$IFU+0j`eF8<yBlUMEc{^Rq3egGUzqz~=Ap+w
zEL_p`!}-u~g4I_r8fGuf_=m<fEd9Xz3yVJ(A7}hS9RRZrM#IVzSh&H=gDQtpF#p5C
z9Ttx;_rT%>t^th!3pZFj3iBr{9S|}H<}O$|g7IPL5f+ayccEDiKkp4D4l@VlADDSC
zd2||PK6-q^;|&_G==xxMSp2}o8(@4`I)c@YF!i`-SUV3EUNC)x;vHrlEFR(c9p-Kr
z4NYe-^)MQy4_4m7${m<EjK-A?VCf4M-mvh8nGd63;R55M(=h+S;saeCm$@+YuyBK=
zdvyJ{_%L_D(k(83;<6u?xiE2fx`o99OoB)n<~~B@53Jt;QwIwV^z;KO57DiM#Um`;
z!t}$`!`uNYXJPtb;R4GqF!iwTgo(rA1tt#D52Io657Q6J|1kY9f55{Nsu9MAxfjNV
zg$FEO;)++OG4Oe6m_C@lVC^8dIvfmG`vq1G!omsW9vs@>!mx4*mfv9E1y_KB0n-O-
zC&6f#a#*;+@(D~FM#J0<i+@-+!Q2lEKUloL^ucIYdV%p_{)LId`~h<ZOdXttmP>GT
z(0l^(2h4wP^=J&3eX#h4xfhlXVC5N_HaHh%J}ll~=E2+#GY75#jR8+5XzqrI;|h0}
zG4ODOh6619!Q^4#0^<{+Vc`Z>2Xz;mkBb3S2e%*U9$2{G(gsVfu=)d5PQdaxJYC^1
z0G7`&;tA>=SUAGs35PN8{R6OggSAUw`U&ZSr5_j#O9!xa0j~55^Divlz|_O^!@>cV
z{W!&;;g8E+T>4<~4^LOP6rzd4!U-0SFnbBbKg<|d{K3K>qg;V0M5AHx1ZzLQ^ux-1
zm^m<YXxbTI=?mt5SbBuzTbMqWIq3Rf@db-_Sbl?<2Mb4-I&}T8{0*xoVC`O*I#~R}
z`~?#ynug{BLh51tKUn(@J`V)d4-Xe;x`LSp^Cv9+;Nb>U2lqERA7%iIhS~$GH(=ob
zlZJ&WdN{!H2~0gK{9y49i$9pTF!Ru9Sp31vhoxJ%KB)WA^}+Zscfk5ju=E1g4^v2h
zhUFhvyu-@}0{URmF#BQY4knI^hLzW_d;p6-I3Jfrs5q?rg}D>vZg_fts>4OW(g%!&
zhZi(G;L-;Zhxrp0@38oWxdWyS7Y&PVSU!ZsJF)s<`2rSiF#BQk4a_{4I&^oy@((QD
zVD5p1E390DnS-t$7Va<_<_|a@8jkS%jAj6=yhlIh6J`#qTtL$X=fd(C%zd!<faOP6
zI>6<BSUmwV5aw=J_`&iKESzBKU^FbAVESPGfu(DhI$ShNAFTd`*#j#NVBrAsCoXef
z@dh&omX2ZiVeyMgKP=qf_Cw<hmJea#xb(yNH!%Oe)^oz*9X*_3=D@-U-F>idgsH<t
z!{Z6MP8pt$q3*|}4<-(aKUljP7GAJ?1XG8LhUtgtgZUp;9^=vn6NjmT(Xe=i`2(g7
z7Y&OSSUAG+0nGid{EHsG=;p!7512U2J+S-(O9wD{7!C6;jE1EnSo(#>JAwKOR_?>n
z4J^OH;upq;*^5rY+z$&sm^)zlVE#eZ2j@f63#{CLg+F?{!quZOVESPB4_1D{#nIHm
zxUljC=1y2V!tyiBewca~4bun9Comco&oJ{~;Rn+PqhaX=M#J0<D>q>22bX@Bdf5H0
zFd8-wKy19j(iPm@(0mLlPhd2x{RMMBoQB35%pb6P45MM`5LQ3K^+6f1aDsB->%n31
z57P&W512X_4NF%r8kYZH`39yA7Yz$1m^)$V1(u$1>4S;G?1lLsmJeb138sz^4Rbdv
z{ozV?80J9pHOyX^ewcqS)Z-9<)$_1)3=2OP4NHeGd(e%6se^?B-2Kq>0Sjkb`eEX*
z^afK8i+5Ohf`vD_{V;v7`T!OVu=E2n4_zOO4=b-=<rhpGmLFk!LNqKKVCKQxPe>oE
zJb{HXJp2f0gv!J8!SWNa`eFGW*8W2`2bLb7)?iRD{jl-_7T&OUg5@KayU-0l7l)-E
zn11wdLDvuSCya)<A68z$$`P12x_+2EOg}9D!_pHh-JrV@W)4gomVaUS9+r+^?uYpY
zrVmEL#9`?KW)3X<!omZVu3`G%G&KLi%6*u7(9<C-JmC6Z3~2sD&;Kxgz?8xACyWoH
zVeW&)Gb|io`35~aVftV+Og}7~VDSM9516}Q=D_s9XqbOtd|0@_Xjr<0$)nRSdtmN{
z(Xe<SRzE!5L-*Ul?1A|Y#>Yj&(iyDX0t<hbxw!Pf#9`$P%p6#J!PWu6)Dfa#<3BKU
z=-~?!N2g)=3l{D$cf-;Ju5f^v57P$=e^`1a6#g)KVd~)V2~F3qb`mT;(cKN>!|a3Q
zKbUzid0aFsykYe%EZxJ^L-Qw`kH&z-6U=^CxdU@AhIwfEadF|{Lco5Qe)Mu479Z&S
z0hoF?4Gm{lx`o9%OdeJa!T9JjEZ@V@0nFVnb+GgR3lEq$oQC=j7Voh11}lFFg&#~G
zES<pG8L;?(<wLlAxERoIfVmeIuSDyI`U95!VD2L1ewcG$=?@m(Fg}cisl%Y5?uO-i
zxci~y7;O9iJ$=B;fyEol-LP^9=5AQJfVC4~`d~CXUZMVn^?P9Qu>1vc7fc*R!@?Ps
z?qKeKg#*kTxb(y9howVUyu-|e*+Ynir8ihQf~kj@3*+OWVc`bzKg@oZzi{b;iNniz
zXnex_3oB1x>Il*Bat~?_A@%6;u<(cZ13f>ZtB3Jn=>(RZVBrQc2c{04hN*|8Pgr{a
zrVdxV1Cxi@3yVLPc`$jHe_-x`iNk1^`LOVYnS(A5QwI}=(XjZ1<x?0Rmj7YmFg{El
zjE3b8m^zsM(Zylu38oL7hL`iu{y%Iz5iB1W5J(TO`T~~zVfhYL{=vc#-Ch_U=5JVh
zqn8)3`~g#sPQ&~U^Eb>Lu=s?<54t`W9~M5ab|$)gxWXT1K5U#1R{y~A5lkP<e3*T>
zXjnMG(j_cDVdh}ygO;1H@gKN7(D;It3vh7^10Vvh{vU+L06+H|R{q1pF=(hgu=t0C
zGdvuj@;J)@s65PnF#p2R53E0co*rQCfQ3J-+=j(FtXzksE0{W%J{S#C2j@fUJs2OB
ze&KvH1}we7%z@<>Sp31_6Xp*z?eO)7Fn`1J!O{`T{jhLF*AL^v(m%}qu<(Md|Ax5}
zrXHP!)&DU4FnM^n3XLb2IE;psYcPMp+zE?6SiHmh1=EL4!{QH?|6%1VEL?H9A6*{i
z4p=<E?16<Nj1Qw>_QBi<iw9VE!u$u5hv|dSaB*n(!0dsA13cY9<zf0@G%Q`f)Wh5l
z3wM~mVKhu1jE1=vR?oxq!TR$sd6@YyeQ+A;E?E4-)WO^h%hz!A=nSZSSp35BJ<MOQ
zau8-7x^|d2EFRJM@N|HlFJT5j%RgB9f!PP+!^#zSJi+wAXsCWzIKk2_tRDc+4^V%?
zw4u{5^I_=)M#J0>HwRrKj1M&*7T&OUgoO(s{V?~#+ySf4Vf7fU^aE25vlpfgR(`<p
z5sVLWE{ulz59)qc_`%{IW<D%lVftV+EPi1AgQ<g=3v&-l9;OdQ!}P<_EiBw%<tNPl
zgz7(-`LOVY`2*&Dn0>ftSopyF4bu-xkGS-~#i8jCmTq9>5-dN#)!|^E*Yhy<z{-Cd
z+TiyT!|Hihc)`L0mW~MNhxrqh4q-GbUE!)nVet%$N0|LE8g4H%eZl!q1}we6(h1BQ
zSUjTBP;CS$xISpP1LLD>gxLr42do@~mE*AZ#HAk=&am_g%Wtr7gM|ak9(4O*=^7UQ
zu=EO}VdKIud(id6_%M55@dQghFnK~WEZxKWi{1|+q#s=#=6-Z>LVR@lVfl!VK3F)y
z@*B(?SUEySKP=o};S4hmmJVR?ipxDPahQFu^aP_}<r=J?f$kodeeifg%P%l-Si1mS
zKP>;l@)JxRCJy7{qG9fa<wIC{fcYDjKA1QxeZ$&MF#o{(4^xMWhSk?F`(gftxdWFz
zm^du|!Tb+Pzp!!)rVbYkOE0kahPxlypN6Fmm^yTGVB#=$!{lM{4x?f2g2|)PuzUkE
z2UdQ=Xk76D@Bcy54SN3{-5xj}svcHe!NL(%|G~;bxOyB6m_ArJ1am(u9C2ua@4JA-
zKdhXEg#(Na3twF3z|sv&J-i%%#viPH!=)b<t}y??%!8$8Soq-54|5;P9`yKz$-`)L
zb718>Ed9db8&|s?T|c_}VD5*7Bf5GRALbrdxde+pSUQD;3rsya4O0iR2bSMp=E3}f
zt`EkCnGZ`R=;p!l9V}j8`d~E7{V;oA;Q&hqFdC*Fora|oSpN%VAFTfY>qo-U0lI!z
zx`*qBDuAnpwhN)cxG88pg!uzzE^ZB|GO+vx3x6G`y>NFy%}3RR;K9NjrXS{iSosT!
zAA}AF3s%0v+zm4a7GE%TK~!UsuzC|N19c}n{Gfc8`EYem2230lUa)cm#)rp0G(4c%
z;1tY%u<{O;p3v(<T>gjYhlK+y-^20)EPcT(#KC}-8?gEs=6+aw;LrvahS>{Czp#7^
z3m>>bGzQGSu>1;Z|G>f##z)fz=feCAi+7lMSh&DwxCS%^Og~H*7H%;A5K2F=c!9+;
z%sv<&W)3V|VD`XiXu5#ugSBH|;SRGOmY(7IpbVHjuzUtf&#-b0s*C|94&$TKu=*P2
zA6WU1t_~L;=1!Qq;p(9I2hPXAfaQ01zJscRm8a<bfl9+ESUQ5G1DHQx;SLjrYd~Yb
z^uzoCa}TWi#qbxJepvp63B&lXd;~KOCXY_T+y~1q=;45_9-R+MAL!`_T^&w7G@fAj
z7N<G_60rD!g&QnC!orn+b_Q5{!SVqtJYeY@mws6M!R&*D2Q1uS?GbeSu>21ze_-~&
z%!8FfFmZJKuy}*D+hOqyvkzuIx;_{m=1({c9WQ|K356TXf3WlkE5Bg&!f2R%xM<k?
z4=ny+=D^|ump+&}SUVAx?_lK#EIr^d2j)L`I6})qn15jMxb(xyHCVcb<r|ndEFNKe
zbbr9Y4<-*QuVMaz$>Rz~SbW3mg{2c%IKs*Sm^;wzhpC6DgXx2%V_g15HwWfEnEPSr
z872<%2f98OA0BVeasbxPhq(t9A29XkG)z4#zKD%~SpJ5k6IeLH>NA-CVex@(FH9a5
zj_~#lG#$bGjY~f)d|>$%-F#TM;EF$(Jj@^H>R{%>@-w>muyhME2Ud>2`~^#w@brLY
z08Ae&o?z`bSUP~&53>hNJDdyC4-<gZ|1k4$g%3<U%)Rh%hNdI9Jdq5Tdto$@+8AK|
zgQWwQdx+H!^B;_cl}E(ths7@}pTO!LV)bL#2Ms@1y9bsI(9;*pd|0@{`VBC3uyg>U
zVdlZ~!Dv`K!{wp&!}2q%e239+ebD*}W<ETfLiNMVgK0pcVc`Tzx3K&R%TI*T0Zc#4
zSXem<<HOPgjE_#k;u{ukF#p2hlaPK`zJa+L<`0;;FmurDh4EqXuyhNHXPCcX;R-Vs
zrVpKl#Xn3REd9g6A3dDV^~3xP7l)Rgu=)*_KH&0D223Ap{12uMHl75l7hvvzYJ*cS
z{jhQm7M`&1!=(>q04)4q@dvXX=5JU%0uzVngV8Ym!PP<2Gdw(@<99Ii=rk<eVCf!K
z-@w|9Fn7T6Bf5SVAEpjwFU&nK|6`O7FpW?erVkdLu=*cvFEl?wm19t_@PkR{LDj>|
zgZU38k50qJ4dC$xO+SRvEi9kI$}M>OL+!`#2b#sObPY@QF#WLh1T0)|g&(^6V0>6U
zg!>!n9yIIW>zrWzhWP_l9>B^YSh_&h4-0pAd_xt$#9{hzl|!)f52In}9L9$lN035~
zXPAEoX@tceESzEeJ{V2V9JFvn4>y?q;qid(AgF$rzhUlxl_#+L1CvMB4$B|t{(z~6
zg$s;E*AI&qm^)!K%>S@>fVm4@Kg=FjIRc|$=@q6ASNeguAEpkL?qT@`7XGku4&8oO
zxWe)cEFZ(_9auRIb0>y=r~s^-hvgqw`i0R@WjHCAeK2>z!Vl(tTpHo|6q*iT`f!C8
zx;e0Thl#`UFVs9(xeN<um^v5@i(i;LtQ>-g!_o~bK4JP`G%Q?U{)W-8d;pV&#V<@B
zjE2d>$`6=*Fn7c3g}Dc&51oeThuH&b|HI-FS3JPf!{Q%iE_%Gf+zFFMr(x+HmVaRO
z!SuoM8M;20zhL18%kMCE!PLXT1;$6GVd)5#4q@iN@&zn@(e=UjF!#dz0qe)Y(j$6!
z!1TdrbpOHZh2>9}xiIzUG%TKB>6zH{4>JcneBj{;Q$T=*>4&8|SpOeZo)FLuKSv4O
z{jhi;q#qVvuy}&G2bPZr>4)W8xP8$44@-}@^uy8_jE3bSSh|J93oiXI``|RR-2@AN
zSa}5VAG$fPbO&=M%pI_BfY}dgC!y<yr8C$%FPM3-d;l{KRt})+hw)+M1<c<t^I-0V
znFCV~r=j5t^Cv8y!14{uA24&^`k)M$xv=~JD-Q^{7pfgT{}0Q5u=ESF4<-&vmvDVh
z1}wZ_`5hi_(0GLDhbqTO!Qu&K56s`NbO9>|a2f!WfVm$Qe^6n#KjC~d20Xr??uPaA
zVg7;n6HOa@ydGBmqo;pZx`5?Jbp3E~XgYxPn_%|A`~!;*bp5dShn06Q_rl@>W)3d>
zF!NyP7RHC^gQX`}e8A*kG)x`L9WZg2c`$L9I+#3+hPf9O|FH0YwO?WO<4Q*`dtmt(
zmXBcVKv+J4se{>zPQ%J;SiXU|8zzpf9-R+29~zH@!W)-<So(*jdvt|3_^|vBlZLq;
z#wSQa(>+WbEFNHdLNqLX(c=?VuMpA?3wKyPhlK+y9}~(4=;;S$A1pn<^y3OwSo$I4
zZ<xDa=EKy(@;SOZq52=@KA8Vu^%yKY!OVx*hfc%v!SV;J{DG-MPZuz87!9j$U^Gk|
z7H=?fVdCgC%syE7q1Usp^nk7p#)r8NR=&W(111lPH&{Brm9Amo4wHwO2Xhb198&Wo
zEL`CJK+7jE^I-80OTV!E04s-J{z0c<@du+}@egx9uJj5w2U_01%6nKn0CN}29dPw%
z3|PFu{0Vahx<Amg!MQN=Vd(@GpRjTpmM>xcfonu#z~T+259S|Oyuswrw86PB^I`P{
zOg+qgSa`rSpfO<K0P{a=za}jI!SW-THUt+M4>0|(a6u@*&w}|A7Jjhshna^<H_V@~
zd;`yqQ1`&xk4rzg`7n3G+yjdbm^~QgK<$BrKg=C4K1?5sM%NEduh4u2vlk`~qhans
z*AI&~SUnF5KUjK%g$FMEFmafEShy2PFEDq&{RIsN*mx1FJqhE(@)t}WjE0r7Fg`5Z
z!^#_&|6t(&QwO7A<sK}&VdWjH+<?U|Odh5WPD8^TW-lz<VD=NrKj`5H^CvF%!tI4J
zVD5*dQ&@Pw@-Zy^!Q%z04@SZK1yc{JCt&Fa*8jzoe_`S<8a9sui+^~!fLVxxhV>U<
z(y(*~<Kv=X@dT?UVDSL!2f+Q0!vI*g!NL)iUSQz`%MUoTGr+<dCJ#$5uyBOA3uZn{
z9h`=yA6PuV)WO^hix-$X;rgHqSUQ8HJD9uC{SPw-strLw?T4v{g(qzM7v?{hyAT>t
zS?KM2*!&MHJYeY@RTm}?<{$KUAY?zx->~!sqhav}3r|cNAtJE!3o{3%4{k3soFJ+(
zNmxFImEW-VhnWL27gHxh1ZF=h9%1DotUUuu*AUg1B&@uE`4eU@EFZzl#ncHAfyFb-
z{V?+g>4WHIV1T(7R&Kz`F_=6d8WvtKb+G+puyFyHI4&9%{;+U{r5{}R2HhN3`h>+d
zEZxGwA6-2LADTX3?ttlsm7f^u(F9=XVet$Le^@z+rW}I{^CwI{%wCweuzZc75h?)l
zCrlm8A21pguTbR}6fFP4+@S-l&tdk%%0YCEFnO3hm^rX?3-1@8YlQM)<r*yA!_plr
z{9y7><row!zG3+f#)tVE#>dbI6@aBP^z;ua&!NiT<6Lk)lmT-m%p6$yg~cB%USRDK
zs5UqS%jYl}W*&@&r3YO4VFsbQAC@0s`Y`+h(+H(u<u)vyVeNld{)Oc)sB$<33tyNz
zSiHl`gZ2Mm>fjoo3|M-Fxf528!OVfBTUhx9)dr_v`eE@8vlkv8P?fmshl#_&AF3Qq
z!OVpVpfTX(D>T2LNyE>hMHh$hVet&J2bSJp{)VZ8<qsGiM#J0<3paHAu=IyZKg=9h
zc@5*kXjuCl#>Yj&+JEqL2rW-x<pV5TVd#g38_d73^bU(hT=5P!2dW=E9%1PMW)6&o
z>w__%?u5z1+z+E+?tz6fOdE`bg)1z-!^$t1IE;p=N2g)sCM^BK>_Ml|^}+Zsb+B-P
z#Xqdxfu$>$dUP7r&VrTOu=s-cpOAi-`(f%~`4(m_EdAh0Uod~e;tv+yu=s$P2Mb?x
z_ruD0m^h3Na}UfvxZ(k34=mnb`4Lu*!_zG^ox$vZ(Xjjl^Dm5sm6x#ifzdF1Fd8Nf
z3r83Y%Mb8!44O`1`d~DyT!*#yVdXw7JYnGnQ;$x=)WPh9>4${}j7HZ7<HN!k=3baM
z%wAZ$<4Q*`{V;v7aD&mX@P&mFOdX7drF&R7z|Dik7c70j@&imCoQ8%g%zZE#W<R_f
zftm-`2W7z2!Qvg}Z&*5p$-~_R)d!<s_QBi_vk#^omY(7D3``>$4NK>+^a@K4F!SN*
z2u&ZH3-uqYzJ}A#@PYXQt{#m6Qx9`DtUMu<eqiYj=5CljVc`kOpJ>*>xv=np(Xjjr
zGY95>xCS%^Ed9WQVfg^okAk@mCXYcw!v`h~D`#Nw2J;t89-W4{6Gp?r85V9Z{V@Nb
z>xbnFSosJ`?=XMB+>h>0baP<g4NFI`{y$tDnm^&<PzKDMFn7afSox1L9--+DmOo+X
z6s8`=hn2@r>);g3pRjO%@nP`};}Z%$Sop*BLH!F0KbSdii=hmdJ7M`AM#IEm?t-NY
zs5UqS6NlLgvk&fGX!yX)gKI=%z|tEm-@|BF_`%Fa(}v(e?SYN|!P@h%e2h?t%7VEU
z-p_-E8_Zr<xqzw*!Go0-uyzYfAFSMmiNowc=!CFf`eFGVR_?;W3+8WlctW(nNm#iH
zOJA_~g6V_hD_Ho#H9{G%bc4%$m^)zZgldCRFn7c9CtMvgKH&bxu?`*PPME)8<rb_S
zhq((Do^XrN7_fR8mXBfa35yR{x<=Cm?-#?{|Iqk{<$qYd#+83y;R-9aVBrCe2k3fy
zSo~nvk0t;!2WBr!8Ws;QK8%Lh2XikhKjQQ+Oe2(rxf2$?uyPcZZeZ~PRgOWy;s=&r
zVc~=;y`vidvmcg@Vd(`H|1fcMeK0;OeqrS_Ed9a8e+aoBW)7_V2TMON^I-7+GZ&^F
zora|Ycz8h55iA@C`2&_dVCrG%09K#E@+rE#==lz24=ns(@d`^9==x#t1<QBvbOUui
z%sg~`Fh0y&m^)$ahox6oc;Ye#rViHshlMxHUoiK<%!S#DPQ%;{D+gfuVCrz`hl#`L
z4On`Cr7KvvfT@F-2cu!}4x?fEVDW`ZA51?iUBSWu7LG7+c>cgqe!}d5sfXo5m_K0Q
zi7WhJ<p<1On0>JLfQiG*fw>z-!|Z|i8^(vF3z)yq;{&E2orbA{`2!ZNgu)SK56r!=
z@*I{AVB+ZZ!T7N7gT*&2|HINROdT#7=1y3;h1F*;J}!MQaag#);t5s`!tyUn9WEM{
z4q)*Fa}Oc?F!#a28y1f6bOEi$aoGzKhqb$5@eWH5Fn_?}6{a3W!_>p-9hf+*9EH(@
z;sJgCKdk)+vlkYxF!RxAnEPQg+&x749~OSFaD(|D=5AOxquUGfAIzPwat!7USh|Fz
z3v~T3edy^2<{p@OT=v7vh2<|;eu3$Or7Kvu027DNu=E0JFTmUj(+BepuKWiRhm|)l
zeK2uY`heL-kcP$=%v}W4LlwZn5te^o{(wpoq+t0C7LK^W8<#;C>Y@I?r5;@zJ)B_f
zL05+hz}k<naD#;h%s;sF!Ng(l1=9};M_9XpP&t5}4`AlP@((Os!tw=79!^8U3#Ja{
zZdg3R^+DCa^+6f1ejd!<u<(FNGr;l}jE_#k<YDDLEdRsv4YWOlt`EkCsfW?9@PnBP
zHwUHxO2fhp7EZ8y4-0Qt{)YJrstrLw!vhvhFm*6}uzU@32SOu+1<S9ndIsiBSbBh!
zqwsVA(S{(Q;Q~*;Q1`&X0cIXT19lcHzF{<a{9)IHDGKu^tQ`sC!~BD(10n)*Kdc;s
z*-xx~m_K3Ri5?HI_=MPoNy73wEd9gMJ1pE`;ex3XA_8+K%pI_L3>JT|avY)>lZ3e&
zCIj<7EZkt`!{pIvSUQHKdzib?)#2j9(=RkV!TM9U)WgJK;Q|{kfbrq}g{DWCdUP75
z9u{A)bPM+{%suEDp?p|)!}1v{AHmFn@uA8wD40F4atEdWmVaU43FpHU5TIe{80HRG
zdcvg*R?frHAuRl0@dzuIaOsDIBP^Z6$}^aGFnL`1VeNI8dtu|huyFvGJJ9vR`~^!t
zuzU|o_ZabsW&q56uy}-}dzgK&_=A-XXxicH_h9h@a}UfPFdAJSj1S9qF!N#VfTe#}
zc*4>FOdpJfxf2%Ouy}*H8|E$;4bumsVfMk~VeJ^0KVa%$?t$~6446Jx`y5t&!Q2gV
z56pj1Z5R|RoM8QUn12b4|H2JI%jX#GhbzR+fcYCHODMg<)WK;q`(fb<mnXnLcP}hm
z66+3_eX#TZE0595#pMiG{J_kCse|QX7!4cOg2}^ZSUQHe7iJDDU&7ph9?mfRuz1Fm
zUt#`&<xiMC7!6Bzu=)x{!^RO{>d<Le`i1!qmR@20ft7nOf1&G#iNpK_b04h!MGpsb
z{jhWnvk$HwTJONbVfJ9?hYG;_0p&8l%z@Fc_=U;CXqfph_rvT#=fli_>4VX*`U~c7
zSUm!Z4_G>cse|c5r(yknSbU+E1Gvf`So(sE7r^2jrXJ=Fbo*d@SU!WLYgjo7qhaM5
z%pWj)FdC*0W-qKA01FRTxWUptOdpJfg)_{(F!eBbSi1mLKElLdG|XODIKaeV=D^||
z7OyaQ7!5NYmJVUz0W%L~FRu85*$>ONu>1lGcbGYFdteqrX_&e2dK+4f!r~Jq53A3i
z+Tau{e&Fc^E(2lU3U^q3fw>bFzp#1}W-mN`AjV;kFm<r_hUtgtgM|;eMp*p7`~@rD
zVD5mchn53yJ~{&?52a!81uK8hl@sE_;veQOSh~P)7n(t^@Px%TOdl+MVCe#vIq>ue
zjenRwVDSS>59sj;vk%t(hlL|dAKY9t_rkcab_&cKSUkeQ0j3TY4bumUXIMIi<vUnB
z<I)eyH?VRT7H)7pE{#xen0+vR!@>>b9;h;$6wG}v_rmnU#9{7%g$FEMqZ<f|e^~s&
z%0*Z_!0H8<`RMx5;|W$D!{QZ|j?m*DW)4h0dVYe%4@?~{8WxVQ`X3e^F#p2*g{~iF
zFN}uy8y0RbahQK_>4*6b7GALUgViUn`T;{fbY2POewaKg9ANbr%wI5h7!A+o(DDPu
zM=w9%=>et@jfRIa)EqQvf?QZQ!Te271DXO@x`Kr>tUUn>Pc-EiT$p~C1T4JJ#bNU3
zG%VcU;RG$mVE#f^kIsjMH!K`s?uMm9boJ<bSUCo_AF6;@{V;#R^uzoQ^A}7WYAgl?
zOQ-PogZdwq4`BHpLnBlGR{z7wUzq=4G_HIPi*J}ZSUCuDH!NR4t-+vR`5NXwSa}Td
z2dvz~&<GWP#TzWVVEGUhjxcvXm19t_avqjXVd`P&0OlVIjc5We`(WV&b3e>{H02mv
zSUQD;C(IwP_=D3JIw1lu_rhqHJ7Dn+iyxRgL>GdDg+I(4uzmn+Tpzt2K<I?9VD5(5
z1B*vk{KL$Ls78>mdJEn@ftFve^bC{7nGT@o9F~q?@d%4ISpI|g3t=;q1=SCWFIfCT
zW#Q|a2*o2V^I_=%mQLa3!WdBh!u$sdCm0QLKa7THgVRv`uzU+EhhXsm^A}t_8UyBE
z*nKUq^Z|=En7L@$Fu727z}$^1JTNsvL}1|rOXsk7hsY8^LgNvx5}gm1z`;OM4{t}{
zP>wDP>vzDy8<zjj)#2j9^ugL081VpeAB;vb2&NuJ!@>`iA7L~seV}QFb7AoZ6M*qy
zG%Q`g<YD%~$_H4!M^^_^k50qF36_6h=?#`2(AA^!VftX<2FoY#^n|VfjSq7#th|7w
zKUls%Q;xxf>4U{TyuAv|4;UJ72*BbC=5Lt&Fh0zmxbhWD92Ori8kUb?@c}ar-5D_b
zF#p5i8x}vXdH}{p*AMF-!t}wy1r{&p>S26Xc){u^Sp31-&+vQ!(||_9{0S3=#RIYS
z!^0KYj)&O?%P;V7fmwh?!{Q0%4_LZ`l^?jm8)gj5oiKmH!Vgy8z~T?49)pIaGgy5I
z(+_hGjE|uYO#l|&F#p2h8OBFb4(GzmhmHTh)Wh=?j`9j-0L*`|@Pf5_VBrB%hl_^U
z2diIU@dt}nSUBR+4=ZnA?uDs?nFC8-u>1*=htV)~F#BQY9TuLjcz}f`OdmQ83rAQ!
zfSChxA50u(9=d)QA7&0L{lfS#eJ~oP9!A653kwHWe8Tnt!_qlSJ)DNdFRc8Bxz_-i
zE@AC*LjH%j3#K2Io?-b379MbWp$wS2VD5*-H_ZPq{V;Q(+At`1{6S^l@eVB)2&G3@
zI)vE=a}O;4!_p(vcsK>~H_W{-^{{dQt`Hi|a6TFXmOf$m31%M5UAWv2OTV!5dSLMc
zoBxG{1FrCgIRjQd!0d;sho%EqJi*0b8lf~y9u^O<bO;Mi7$2q%str!T<YE4Sg)5AP
znG4r|#(?D$SU&_-U&7oAa|fC>I2T<W#z*guz%`&TVC5dnf3Wz6`3sh=(X_$2u=t1h
z1Lj|tJKzd&F<|KnO2fhtmvWdm%syEDhqVJ>`eE|0b_GlyjE03bEWN_SVf8pnA1oci
z<k4xEe_`Pbi+5Q3;Y$B7^|1U33rAQwhWP^)FX;Bd%!kRt^uyc_a|bM6W9Wwp!0dyC
z7cBl^=E2H;sB#PnW<M;y!rJ*T{jhMv&<GWPt^a_P*Rb>tD-U4d09B4b!Qvg}4_J7@
z+=HP3DgX-?m_ArM!qPj;Ur^;36x@7hIRnctFn1GM?!(l<(iyD0hKC=9%@6@txWH&w
zI)kMvm^)zpgy_N~VfMk)!Q2B24_NqM>O>WRwWDD93T7_MU#O}PJXpBFL}2TFU^L8s
zFnM$uo^GM#2fBIa>e2ad_d??lmi}Ss5LZ0F;vW`1u=EQ{@33+OmLJgVg@qTaoP>oF
zy1lS)MAr|~2NQ?sgT*5(9l**1m^v5@EB9gX4YMCT9pMUpSU!Q}H&}TD(~s^xn7tS@
zG~8kKW5^Q_fQ=Wz(gCbIf%yxUc3AlcizisT!~BEG-!SuF_QCuKi+@<Sz|`TQVfMr1
zVd)5#UeM)X>R>e7eb96d6Nkk=p>i9hA4bF64a+|;_uz^bSosL^H!M70=^CaFSN?&y
z7q<QfW*)5l2a|`1!}#blEdF5W9~O^<^ugQ*(+@M3*!YL#f0(~v;Rd5&;R!PrordX$
ziNoRz<{lUyCXYcw^9@WK7S6Ep0%i`ZJc6l%(XeoWmGiKC3+w;D?1#lCOdpJfg)c0;
zVD_V{CzOt1^FOd~gZ2O5?nd(`%zhXR^CzsngP8}TVd~Inn0sORVfg@7Zo=Gyt`E+K
z<~x}GVBrQc2Uae?)uS<B`e4E^_rvlXEF59!6s8VF!{Q4TPB8z#<YD?@=EB6`G&Edc
z?uD5LOBcAzh3SX+4<267@PO5)u=;>dIKuKj%syCo4og=s_rdIi(XeoVsfXDIr(p_l
z(NOoo%1M|zVc`W!|G2cn<YDfB<#+V>gXKqbbI|iYEWTjsV0;)26Nk|-`(QMz9D?y-
z`5MND>4VWQ|HA4?m_K0g4-0=-I{~H-M#ItzEPi0=0cJ0(e1NIPMZ@fcsfXE*OFc{+
zrVn0TKqX+~2{7}}!x^fK0TzF-`W9v{%>A(Zgq}}f`Z2_z=?P{Yu5gFxhuH&bpTW$7
ziNk1^dJGzxe_{16EFNJrEI+}*0Yg7j0Onp;c);|*_^@z+D#xH;@c>JI=;bLaePC#W
z3c&L})IYHPJfZR%svSO03Udc6|H9=7Fks;hi*J}Y1hg^0)WO0DrXH3LV0>IOEWBXu
zh3P{#50`#eyu!j4R{y~C<4X51^I-7`D+gfd4Q4K^9EX)>Fn7ZA!)a)|q07VgFmqt(
z46YB#fV%^le_-JP3tyN&V0@@Hc)Y{h2P>~&=?TV%t4C)*?SsWXEWg6S0hTY(wZZr>
z_rTH(%)c-imXBcSF=%MG!RjlRy)Zt^Ul{tJ0x<W&{0S=uVD5&c1E_Kg3YOkr@eT72
zEc{{V3PU5B04!c%`5wlHrAIX77+hF*!TbBrc!8x$3=L=k@cJJr4jT_b7l%qCD5yO!
zcf-OH7H+Wq1VRHU3#JYh|FCut%-^u^MAe1KgM~9J-iXx?b1%#u^!S9OYnb~mY=)*k
zm_C?)VC@Z9y1>u}6@baZ+zX3$Sh&H&Vdg`%VNkI23rlyfbPwytV`zX1!0ds=A1uAV
z>OYt~R5=C(OLwsFfrURTJ_zMsSo;U&KUg^h^9M{G!(yla%)RLL!O8(xyh4>@QqcH=
zg)_|GuyhFX7mUU<03rg5cUXKvM2RP1{)UA&th|TW3kx5dCPMuItM}3S|2XwQ^DivD
zz}6AL+JCTm09Kx&n*;MVOdOUDVc`dB_u>i{n7?813~T4Z(iJQ}pxXnp4;C&kd04uE
ziNnGVCJxgFqhaX;=1v$NmL6c`1I#>_J{S#4cQEy^aDv$j^9QVchUtURaC4#Y0LzE4
z_<)5Q%pRCNbQ<PASp37vZCHGutB3Jn^L{WICJu`inEPSk=rqiou<{O;A7S|pW)8YO
z7#|kyFd7z)u=GHv{D9?GxIdx!02cl*8fG6Z8W!&``(WihEdFungNeiJf#qMAI4nKG
z@-r-*!sKBzOdTw|Vet-&S6Dd1@&imCjE1R$g*z-A!sKD;2c`}t4x?f51grO9?I4&q
zEL>pfF=%M|g}EPA55V-n!UsbiQ~(y=u<(YZCzwBA;Q>{SLBY(2se_G2!15hDpP}i5
z#WOs9py>xze!<)ir_uDo!W-sZm_K0thuI5@KQ!$OF!eC|(9MJKam70<d|>4^EdRmc
z0T!R|ctmpmOdX7d=OdUpXuQI>=rqh<u<(ba8+3KJ_^|MS<v$o77LPC*mws6Ohxr>8
z4>0p^>4S?y;~SPgVESS42Ukac0n-Ny2bg)VaEIk@T*kn{AEqBx51`Yq^on5)G@fAQ
zz}Ef1;vZHYz}$nbAEpi#j<9@#UY?=rgXIgD|6$=qtbSOyz{3j~A24^o%1d1ChJ_b;
ze8c!Kb7AJ7yB}8G!2AJ|howW9ei$Dn52IoFVg85ZSD1TYe3*M+`p{{ZJ+SzO<ttdd
z2(t%WAB+zh=YjSAVEPHQ|6%TfwHsjh4Ho{ebOEymorZ-gEZxEKBdi{WmBZ-a3o{3%
z9~M5a^aJC=+y_&SPQ%JiSUP~o!{Q4@!^%mRJdB2=Q&{}L%z^8JDa1uX(<>}~VBrAs
z7c4w+X@`l!%1v0j!RjNJ`(WyD(Xeobr8`)A4knIEA50wPPMCg}KVb5NXjpi`$|pkc
zMyz>+)WPIo<p_)q^9PJih=%ze7LVxZfslS!x`CxfnEkMLgyn0Px#;G>^rO=-{jmBU
zSGdB`56mAh^I_^?`5C4TCJ&=w?!{0Ctyf^`aM7^x3uZpt9nk!NOCL-emfvCihvgp_
zAC@m*>d|RfIRgu4nEfysmM+ot!T2zHVC57n9ANDsLiq^hewcYM{V;J@x`4@}({TSn
z*Q>(96I~rHKCE32*GFWy!_0%#^RV(B=6*uu4=h|^=@eGZ!peU_`eEq~mVaU44A)1%
zAFz0X(Xf01OAj#j!TgCqL(PY&gQXXkdtl*$p${ei)sG&}uzHM8x`o*XYyZQ-16KaR
z<YCs~qG9zvEc{{fdGPdrLnAERz{ZPU_QI4C@;^)-W)3VIVD$kk9AW06)39)dhZi(F
zVd8|`4=ZnA?Ic)v2h$I82fDp5KFqzaavvrROTV!48b-t9(P@}JVc`JFKQQxQ;RI8M
zt{;|uVfLV>Us(A7Gap?)EWg0g3#`2l>j%KfHCXzB>4&)smQG>ehsz$AJ{S$t2Mcdl
zIR|quOdT#7=5JUy!Sut#ap{AJ!^#U-e8Kz)%Lg!ZxM*0o!R&?kpHR9(HxIqufu%cG
ze8a*M7Eb8;VfMoO4J%J!=E7)L`astYtEXW8hqdQn=>Vo57GLQ4(ZdV99KcYICIE9c
zOc>^07@rW0?l1IoN=QFUJuKW|;Q$L)Li%Cp2v(lJ)D!CuSo(#f8(2Ps<zpC)Ghaa4
zSul05@Pye9OAk2H547C?k6);HaQ{R3u=Eb|ADo8D!_onUI%v5JSC7ts%ER&@EIeT0
z0Lx$K+F*Q`zhL<V7N0PASiKJ8qtmePfrTF|J;B`pyN3c@Ba{!TKj8j``UjS;VCe&@
z9D{=S8y0Wq<v%POFf>91VDSv|Kde54N)x1D@eZ>GW<NoVPzA8^0j3}3Ut-GvSp2}!
zKP*0B=EC?;>);eDo?-rmg+ELm%sp@oXbf09!-Qe>z|4iEXPEggbub#H9v*Mdd;qr>
z#>bI<VD5$a8&>YX#9`qG<HPKQ(Xe!fUhcrsEsTb{2c{89!^BCAZ>V<o`Wsk10Sh;n
zyJ6<T)T7g|aD}xaVBrnRH@N%_GY4iLEFNIt0rL;c|1k9!G&Fu-;;`}+Rt~`86()~P
zqx%n5f57~Mp1#oa!`urm-=O6N%wCu|==xxMSi2Kee!#*5R&K*+n0j0^e4ZAj02(fY
z!XKs_*6)M47nUAj`4FZK7Y#3ep!ozAF0k~0OCL-e7T>UR2Mc!?AEpi$4O0gTFIag5
zGZ&XWT;ec&=>Ec`9u{sed02eH(lyLIFnM$u=5AR0!|DT=I+!?&k50qRYl4+Su>1^*
zSC}}uJ~$tmUSRHk(QtVJ448UYx`*)zXoE|m*E6K*hlMjN|B|X7mVRL21TzorKmrU{
zdWHEP7Jo2tSU$p)A7J`m^00CY)~<o^Vd~JG16!{FE6-s5hoxWG_yBr&08<A`FR*kB
z%m3){f^IL&f3Wfv7Je`qRv)74gYjYhg1H-(?_lES?uF@t(Xes`mfv9E2crqqA29P_
z?uC`(Fn7TG4+|%lz34P79ANH-xeLaJ(dhbMe3-qkd=66&vzJhMgvBo`9mC=g=6+bc
z46_eL!_p~wIRP^VE)FdpVESM*Oh3$>uy!lV|1kS;rCXReF!#aY3*EoC^uyu}<_=hX
zK~Hz+=AqjQb2qH~fW<#7USR%%>w_|2>S6AKy9e41fcXn157mZALBkhTPr%9xxVvHI
zq3ML>8(8@d^EYhV2No_cccE#Ab7AJd{0WOUxB>zUn0}Z)Vd<BEHn=n_oZ$k{@PPA4
zWWe%2%s<3xhou9UK4QZGrXCjGu<(H8Ls<C;qtV?1(+`U;SUAGU16aC-$)oFs+Y4=f
z!O{U-9W*>)=^8E$V?gC$`5sm-!pwy!!%0KaCwv?m%EyRTTn0eZ!`j2}bPrPxbsvli
zqha9&3m;g%frT54CX^mw>R{zKEWg0R3+g_Yy)YUc&uH?naD;^itXzZXL#JWpz{FwY
zCyY;shNV-OyW#OiNF!7pHeU=g2j*`==D^|!7Vc2v7-0QBn0sL2=rk<c;qe0vM_72F
zhYPxX^mLA%9$@7uhCZkOEWN?XaaekX@nQagl_yYba0=#57!Av}u<(QFgM}+xBa{KN
z2bNA?`q9e?m^n~w7!<7jhuI5j&tWLUB>>G|@N@u;2Uz)qOB-AqsvqV~Sh&K{2V5PI
z3|Kh_%SVK?!NLue9$@+j<s(>nf%y}r9u}W4a|qcFvlo_rVDSlSf5Xx_x;e1)4a+aE
z^Z-{64Of^s==x!NSp37n3$_jj=08IEVg84?2WAdj99FNuEQHdqaEFz@@bUtheqi|r
zS2}>@dsw=Gwd3LW7V3YPKcLpaDVRD~Jp&6rm^e%!JUn3nP#Pu<b05sTFn__*F?4(#
zstr!T!U>k|VE%!HBP?EF=?bn9%7FP7rVi$Qn7xF`PpB~rFm*8hz~cd?00#|A->~um
zW)3VJ6G}HQ{csu@?=b6N;Q-^K)3A7hg##?yVKl5CimngFhxrrMZiATzw-=gzVCvCn
zSa`!|SUkbp11pDN@rte=#)q{VVBr9B4=fzf(<4kDoQAp+R!_j}g{4av4OfrGfTcfJ
zIRu;kgZ1Cw;en<P#>J3_x(7oXrU8wH`5z{XE5E|z(P@~!(Zylu09_oN4~utV#nIKl
z;t4~XLIGH~5Q;xs_QTQ>A^q_9M|UqY9?^x-`RL&fE7xG_NYLfc^~2IDto(q{uzCS!
zdVsnQ7EZAI4)ZrGU*d{in7d%<1g0KVZ^6W2=ApYAR$ju~2Xim1K7zFaaODG7xWmE=
zmj7Vo60BT@sYACPCJs{vlZT1J>H(NIP8wS7!u$yff1K)}5-|V6$_dy!0&F}HRv*ID
zLAAjtSh)xbFIc$=i$|C~7!B75Wx({o{0oa`SUf_NF~H&x#)r``ahN`sIE;qHE3WW>
zg)ht>Fnus{;qeX)Uzj|MhQ%}7f6(*{<HO8{sYj<_?uNM!7T&P@1Pf1eedv5xc*Dwl
zm^rZcL|2c_hou{sIk5dCuy!TPedzjNe3*V%Ji=&LJqYtBOg)^2h8HZ~z{&wwy8}kU
z<l*|z7_j_@9$&C{g}Dz+8+`u_EdRm$4>J!|F2m9Tx_($ZqnAUlbO*B^R*s<Shoy6v
z`(Wi3EdF5naHS)dJ7F}eyoR;^Ve9|l;ezG>m^)$V1{Th+_=AT(nm!m8J-%S=Cs=sl
zvKOWw=6{&`Vg7}M8!SFx>d|SKeK2*f_<-39=i}(Vz|4cGhs8In{|56PjE1R4r(y91
zb2qG9fSF53KdgR-r6ZVmF!#gUg>Em54~u75K82O%F!NyH3sa9y!`uJR{76VWOg}81
zVd)jdhlL*@cf-nm7!8Y07)?k&EWN_q153}a@I#k}se{q5atW3WVftb5h~6H6>4VX*
zd<mmr=E3q0EMLRQbC^CD4ND)e^b2z@EWN<O6Ba%&edsiLIS<RvF#BQY0$m@>zp(g+
zrCXT$VdCieV0>8mgt-@1-owm+iNn;R)9CJlg(J*dSiGR?gYjYE4a+~U_=53a{)2@h
zOdpJfr88K$gB~9+^KpegOddwV{0*bg{R6WXgNEiiSbl~11C}0O?!wRq6@aB5Sh%6D
z=Z3ilsvLuYkNZL00n7im(hbZ2SUAJ-KP((z^(ibJ!PKMEu=D}TZ!rBZdtvc{t`EkC
z>4WiMG|WA?<Y537KQKPbz3Bder6ZU+7!6ks9Up?F6Igh|{09?<>4VeIb|tL5gyjdA
zKA1W}`2aru2Q?4of0%xldtiKAG|Znccfk0#)WgJK`3tTdnvP)NFm*U-sCu}5s6uo;
zR2Y+j>PMG=`2$@XgAWTgC=IK3VC^4*{y@_QOSiD}2OIw(<R6$iSiXnZ3o8%M;}zx)
z^zs2-|3MYN$`x3<5Go9(VEG@G?_lYgkbB_<K>ZKvufY5d%g=CiI2f?<1{UtH{0H+t
zp?HLa8!UWa<si&{ocRyx9+*9Fbx{An$}vLu2OeI~asXDZ!1SS)Gf->c6wJRc`(gUv
z3J5UZ;RDSF#OjCXgQX8xJiz*W=;aE!Ghpt8iNo~6(g7^|(9<VOJuKW|G~69ff1sNW
z(+8tr@e9)r3r83Y%fB%7=rk-|VD5y`u=*6157G6(_%MIL(mRX~OMfsvOg%0dmTqA*
zEIq@*6PG@iIIO(~tM_5?3X4aWI$ShNJ&cC=A6_m%;{%s|Sa|~rPgp*N`3F}y0*fb@
ze_{5*%1wB@Lh}{6{jl@`vlm`|!Q2bY$1s1P>xbD3b1y8tz~T>PKa57#53849?uCT|
zEIq*D4^}>)>xcOl)^3K$!}2do9A*zp9!A6B7j|C@EWN>Km^wl<EPP=8hq((D4lo**
zIk5T(mLFmMhV^^k;f!VgEZku62x|wy(hE#Ku5tj@PJ;OtM#J0#vkq7I!}2%GKA1k3
zxiI&@;sa(LjE0r-FdAkaEWN<WL0s_%i(go|153v+^)MP{A1)g1K4`j!g##}2FmYJC
z!)Ta0VBrCyVd`<wF#p5i9p-<SdYtA!^Cv7EVC4Z!A1pp#=>R5<PQ%g{Ed9d55thEt
z)uZ!a_QKK`EF54oEFRJI!T2!yVDSa>53C#@6d$ns1v3X`F0nK$|G?7$ks4v;J8ZlN
z=1y2ShBMxv=EKy%+z*R?c(_B;BUCv~3g&NExWN<>v>%#&VEG&74w!yexen)}F<|Ky
zW-lxpVeWyIXK30mxG;CZ{S9>&tQ>)v3rlC{2Eg(kOdg&Nq2ULsM`7WOOFv8;W*$sE
zEMMRn&w-f_(}zBu2r~y}J~|CE9~N$~@Pwr&jCesa02Y3*`~%DHF#WjFHLRS0*$Z<g
z%pWj!!15uQwea(+VeUhZZ<x90`e1yRdtrRIerWu|+yj$Gr(y1bl`}B&VE%%YYq0P^
z*AL^v>T4Jc%g-=*SiJ=2Lm9CA4~suoK7!>NSU5tJV^HYv1oH<h-UzuHmj7V-VD`iH
zLBkQlVyFPDT!y6&n0sO22x|{QmE)vf;SKW-%w4c>z^M@`0SiBv{V;tnaaj6-Du+|(
z_QL!PD>q>I5?4BcsYj1rn0lCdVCKLrhB9FKVdDa@aD=Ic$wQT6P%wAG$~Rbiz{D{$
zpb5bI56fq;_=3eBj7HN2=fdI%md|1Cg_#Feh=T!3r|9Vpo{w?p!zBzW|KaY2mfx`W
zfw>P|KP((z;Rq}L(c3>TKDvIGxv=yGqhaMeET6;X2hsJz(g`fx!sZiT{R-H8FuFb%
zAC|sh;Rs7duy}yQ6HGmfhShg4_ruJA(XjBqm7d`Gq5VTxxWUp9EPi3?VD`djSUkhZ
zA6WSb3kR4!obHF(3rl~nc!rfDFn7Sh17<He4GVvmK3KTH@)NpxbUrMcVCKNaZD97J
zt4HU<$_rSy!`g8$b+B-R@zH6Rdtu=OtJh%aVBrkoqth^bFmaeaV0>Kh50i)GJ6O2|
zQxA(5LhgsT52g<0AB=PXvk;AjsfXozSUx}(N7Dx9!onSveqj3H3J5S@;RSO)vHD^9
zVC4kNzp(U=(T+fO2ef>Hl{c_-0}ltNJi0a*9~SPgaDugWVEGQF4pu(E_;4E9K7hF!
zrVkeHFn7Sj;rgHqm^>`JVCKN{9n^fB_Cw8q#UHvlSpI{ROHkz)6igl#j_`6G8h*Ik
z4>JfB?=bzaaX?tUhRMU!!)Ta$VeW_dA6EXO_XA-1U^GlWj1P-{SUCy{M_4?=)WK+2
zy$9pN(jzP#z-U<bz|`TQVg7*0!}u_BVd5|vmhNEVJTUje_%L~Nf5P;`@(Ij;u=s|R
zM=<-*%R`ucn7Od{hLvBic!TMK(J*~58m1o>@34J;F#WLj!xjH9b71WQnEPSsVdlWp
zq0=z?Vf7X)-@*J36Gztv<HO<wmJea}!@>i`huH(u2cu#29!wp~JXm^xnF~utFnur@
zmakyyVdla74~svTIWT=N8s=YE{KLv`So(*hCzv{zJ{S!XhtV)`SiXV9J1+ZS?u4a7
zn0{R01(U}`!_on)JcQHGatN0`m^iH7g~bQVeXwwbsl!FX@*6BZVESO;fUXZ#e!$`#
zCJ#%0Fn7Rcm^_Sz#V;(K!|Z|i8)iPP@P~=RXqdUM_=l$_9P7_u?t-a@`4<+BFg~I9
zgz1OP2f*foVD2GA!_0@Z17PVE=6*u@Ve+v24)YgGoDhv3elUAs`GAmqcsN1Z<FN39
z#V0QPu=s_QyRdkN>4T*sT>4?+Fmqu3fahmuzCll)aD7ns!s=a^ewcZ%c!8CNFg{E_
zjE40uVD&bvK7iQ^OW&~a38oK5!_>pd5m-3D#9`$EOg)T-g*z<XVd(}Y4|6vxKf}af
zG%S2!`4FZbW**FZn7d&5U^GlWto((U14}<Jb-2O}CJzg5n0sO7!OJ6PI}oNGM#I7h
zM#J0#i+@-<0+ya&`d~Cn9OiGBI4nMK`5UGVMq~I7hkIe_VKgkfVC{TZdjZzphlL|7
z9l-R#Xjr_${0%FIVE%!L!|Z|SgVC_`4&%er!O|-%USRU*G%S8#;SEbK@OlTDzhLo+
zt{<ij7EiE#D9j%)d0g>;;a;eHFni$ff+Id){)2@R%-^u^hPxYweptGLl^?KjA4bFE
z;r@U*42_1B`>=EeqjBYXn08qF!`fG{aD&A^OdJ+YFnw?uT0X+u2XilshJ_ET9E0nF
zGGOxP^$pBCSop!zLAAjtSo(v>!)TZSbaP<hFdAkrEZxEC9hiS%;;?iI(+8tr?t`UY
zSh)vF2Qc$->4%vEtM_5~6;_|Y#Brrxn0^=yD+gfq!u$b~$3?@^FD$-c?uCUPE`2a@
zSUQ5$PcVC7=?JC{Ck@RXu<(YZ7g+g<OCKzM!r~349##&(#L?3Q%siMl%syDYfwfy;
z{({NF^ucIY_`vcFES_NgfVmG=FTwP|XqbLjxebdqSo(pLBe3)U(}zyO;vHrlOdeK_
z!1(Ai%>S@-3(L1Kbujy3@d^`%(J=im8kQem{)VZ8r3V-vM#J(iEc{^M4RZ%99m2$6
z`d~CnKP;WW;vZI?!r}v$`(ft5{E5q*Fm<?Sn15mVVfhGVFU*~|^uybI(0l-ke^_~d
zOCL-eW-lxq!2AyjM_lO_rXS``m^xTE!rYCXuHgEh3|Rcb!U5)Pm^rX~1yv5GVESP8
z!txD_4@(!g?1%Xqmd|1B0GN81d2kES80h|k>4Ui!77s9YqG^Zk+lQ4GF#BNUz`_CM
zA9VdNJ}mxW<vmOvEM8#lfT>5P(dA+J0oE>nnS-tm<}bMWVCfG^!}1+m9^C*~{SWOQ
z!_pnB{Dp-hF8#3hgM}B&-LQCpxepeu=+?u<q3(sr!_pz#UNm`_I#~GtQx7W#;Nc1N
zA1oii^+6f1^bK=Atel3K18Wz+%5|tVI0X}jnGbg_R0S+uz|4mVV^A>tFneL{K=%iR
zMyLSH|FHOgxdRseP-QqNczQ)s0rNj0_rvT*)6M|PSLotI^Kr#HngOu%1`|f-!^CmX
zuyhWquVCo{mwI$@Sh|PDBa!ZgmG`jlhlMjdJfZ0q-9B_a%>A(P7MAbe=AdhU@?q|V
z<$IX_VeWwCFQ{@13Z@QbA1uAX(hVW~Fau%f9_C({epo#Ws~=$M(P@}|m^e%yEFZ!0
z1xy~DhS>`%k6`w}@;9tJMArxBL*pA39<ca;wF_b43|9|jz}y9E=fUzV%p9mPoD{ly
zVc`Z#|1kICG62@zgVnpRbPtP9V)euP2Maftewcq?G%k0*#9{Ws`~fS!2<eB#AIx4@
zI);S<%%3oO&}mpW!qYD--$COQ7N6+)V0@VUuzU=oVc`NR7hvjf(XjFWW*)KW02V&5
zehRVvhNVB4KA1mY`eFGO7Ov>-hQ&9`ewaF#`(gPKmLJje!|Z{@7cAUh=D^&Ct`EkC
z<x5!k1xv>;b7A7J^aRrfqv7^K;~Az7rXM3bU>d1R!@>_1-mr2U=6+Z@Lbnzc&anPJ
ztiFYX157{6U+DT_?Mhg9!|aEtgXJ4oIKjkWG%SCj=O0)+!txW$oiKec8fHGsURZep
zqhbDnnGe$kqtVL?m_C@lVDX15AHm`o=6_f^h1mxScep!Y7NgOybP9_{SU&)!A5A$1
z7gkTfC7|gM&L_Zt<ujOn3Aq<$9L(Rac!Z@NSh&E{;i6&sVe&9OEF59w11vmY>d<Le
z`hbN4+&|EA7-k;2KA1aU{)MF%Sh)ZbhmDWG?17mB(+|rp@N^Ef7Z$Iu^a<04PQ&6G
zrVh5A6XqXud6<5f`LOyQ#z(I&(D^X^u<(ZY1Evm^4`BHNM#J>MXju5b$_JSLVeths
zmr(e_;tS?qSpI{V1IrgMd(mll{)C1*%)PL9fu&1yeK0;u9n8J3aDe4MSiXR%$3?@^
z2Q2(>sY4fs#T(2YMC*t87Z(4p^aCsZVdW>feQ-WhKP-M=@d)!jOg}6h;QF8pn0sOI
z4)Z^u_=0L@fVmfDFD(9H<v%Q5VCf954~+q{7v>(A`(f^erE@fGa4yXMuyg{m7v>LG
z`3Bd3#(<{}Xn4ZP5x6p_IGm5hfc5iX?KjvuUs$?8Q;xxfxf4Ad!0K;Uc%qvJ3pZH%
z4JHrEKk#sex*ryva0fueVfhyp&am);xeJy);O0RMfW;Rq{9ySHD$M}14;C-z`eEq@
z=1&+6i+`B;u=E8p7oCRr7v>I_yJ6xmbI|pn^I`6Vg*VI{=<0CsVc`!e{|M=a#UCu6
z!u3NfAT<96b1%%lF#p5KJ(xRS@d-5+PQmoU;ujXqF#WLbgP8-@2xY*`g{y;#!zgs~
zVd5|vCJ*DoXpD9Pj`k3&oP~uOEZ$(@2@4OHx#%>yyJ7hVmQG>efvykEhq?<E&oKMZ
z(=l8<4hAg$!K7jSf$?$Cu>1-OFPJ)5xDrbDF#RwZrXQA%Vd(-UPAm=6Ptbm}`Ffc7
zaQ8sVCzv=o4YLOp{xEr%eqzHBrXS{BSp38CC(K-&G&CK+$}?EHgqep+A1vR(>J6BF
zn7xGD4@+;bd<ZLNVg85Fxa^1d6INfs_;7c_{Ds2+Sh&GxSUQIB;pX7b4~svT`(fn_
z%pWlG(9<`%aj<ZJ`4<+hu=WTc{jl(c(XjRetbBm=C(!MM@!|0eokxMiBW(N+-u{Pa
zfYLB`!s~ss^iC-LVc`Qa2i9(cg)6KafLez^!QuxN|LEl%EM8#Yif#a`o`BJCb<lJJ
zYe&Jt6J0;dewce<<qfR-gvsO54>J!IZ?N!yxdTSS;sd50orZ-IEWM!D12F%g>x1!O
z_QT=}M#KC8ix-%BTr_OH4`v=LAK_9D6NiN}%-t}5!@>`y4xNU@FU;TQ;RH)pu<$|G
z4~rLg{({CkOdYJ>4T~3;IE;pc6O0e@2aFF(7cd$o4x?e=1B)LRA0`hAf0#T>97e;^
zJ4_v{+<}D~EdRs!Fnur@CXUh0Knn+$dUP7rPJx9FEZ$-6C75oY<pnGrVgA9DpV94y
z@nPi`%>VH6j(|U4^#v?`VCfG%9%1&N)39)Z+XwXzEZq<?2NwRYaDvA_)Erp)M7I~6
z4@>8;c!1>}Sb2o59-R+sr^D)TSh~hl?xX96)nBmqN6&}o>e2Zy{V;v7a6wmx&WHIE
zmVaUC4qY8CKHR;~@P?%$Sh|MABdlIWHwR`vOdOVeVE%!nYnXX3bub#{Pnf;1_=cwg
zX!?bj2h#_mVftbD1!fMcyo04bSiXYkL#JW!4AT!w53qCqYX_j~gYjYNV0>741fyZ%
zF!N!27!C6W%$+cOFmd$ofa!zL==m5H?y!7;E)UZOqha|5R*%4Fc>aOL157<G8m1nW
zE@1wEl}otv!O|^EKdjt_l`F7tz@;B14@*C=atW7uT;i~FhAW=X^}*D`(hJPLu<{HR
zjxcrT`eFGB7EdsHVg7-Y%kX@PZU9=mz~T`Wez1H23s-dQ=;p)HF-$$Wd(id6+z)dv
z%>OX|!15o4J{$tDc!RqiS}x&G&H!^KOg+4uA)pUt9?XB}<A1RBJj_2Z^@M0x{Q-+F
z*gkStIN{O{OCPX&3-cE&-{R5-&u38o!uYW8hn3$jf1;ZMb0;j`VftY1fT@S&Ll_@M
z!_p5doM8DFmfvCifW;3?AB;v%H?VvIlZW{qrVb`fh=%zGW<MeI81m5Y#1(Gn>S6gD
zCJrmFVd)5G{6p=5m0K|X!omTTe_;NBnGd63@dc~zVD5#*7cAUi>R|d{G)x~X9%1TX
z^04%Qo*rQGFnut0!|a8rhue!*PQmoU?1QCSn15mI2$(o5JYo7^G)zB?hQ$NS99THQ
z!Ud)eordLqn0sOBVf8+t^as-iGZz+aFg_s~LqBX>4M(`a%!Ap3t{>)pSbGwt9-W58
zKYIEkJKsPpfVm6G#Yw@^4J`g(3gP~N<`0-SIt_~#Sa}2UFU)>ezCzcB&WDv-u=)pP
z4!k}=*8t_i(i^P&hNU;CGy_Z=#>b$c{)5FAtiFcX56edw`k(?Z_rm-S3rASKftdqU
zj!8k&7c3lM{RmjThPew<BdQ3@ov`)?%ssGpKu=$&x*6c%1WS)FeXw$XU_3&@5vCvJ
z4;UYo&d}|J@nQalr#q+wtX_ka4^Uw^1&d!;y$|;%G+fcmgKLB`VD5yK@38hiEc~I$
za8j^*3UenczF_`=rE6RUz{+cwI#_!FmVRLI3JXVE=D@^Z_QS%P*!Y9l2TQ*&_rvUk
zxdUboIt?rDVeW^ihxrc{uITz;d|0}L<!6{UEZ$+^3lqmhqo)U0I>x0QE)Gq<F#BQY
z0TwQ>@PMm_F`)Wj;S5s`^EWIWaOsETD;Nz=cTjU-*1*y!j1Qw>;S4h$=581ba}O*Y
zVftV+EM8#c9*l;i16cV6^AAiPjE0HB${ScZhUE+N^a#^Oh=%!xkUCiX0P`<QJ<MJh
z9~TWvFEDwS|6%D37C!KJfhmO2u=E9sXIT9M3lBp2Vc`dhCtUu4T8BZw+GQ~NVBvtC
zjxjVs1z_O}i)UE4!O|OyhAPKN!SX$<yoH4qEL?DEgi65t4WpsL46yVB<Kv=X<p;d{
z$E6U5I4u7W>t1wYVC5eyKGEd~@nQKBmY!h#fu&EFI4*Nw@-X$Vc!1F`|KZdRZSTU;
zAuK(>;t>|Vxb(x)A1wS};Q=#`P`JR#d6@aIc^nuYcAp=Nk3mDrOIUvo79Ozl1Is5E
z`k(?Zcf#@mEZ@S)1DH6hT!d<aQ!sgSf5OUVSh|F{2d)vyfVmfz{$cqSR<FSH!OB&r
zHVg`ue_`<k<HP)cp#drYi#J&L1=A1X!~6$Tj+=tU157_G9pTo1Dg!IOVDUw)y|8$P
z>4Ui&7M`fKVDez;8<vh?`eEr9mOn9dLPTKk0&_3AK3MvKsKz8==?xa|Fn7cJ1B)L_
zoe&XN`hlf0SiHmB1B(xcYD^MV&ZGMuR(`-}Oq~!BcsxVnALf6EECU0~U2r}c1D5|`
z`5t!u8!SD*%}3J*<HFRz)(ykN36&eLaD#<AEF56r1~Ug1o-jU~hWZy)Uc=%UmhWNt
z7rk77>4(_^i+_0e0`0%R%){w_s5)5whP7MZ>Y?(m^a-;UorcN7(ito~VdCiO3GoT#
z7ee~s^3d>w^Kmd>=@Ul7>Ook)5Ejokw8Q(qu>LwM{9xfgDE-3H4XoUN<s(>p!qNkV
z{ZRkG@+Zt*m^xVe!{lM&Fd81t(0GQWCzw1uA426};&2*W9O@oedWNZkr3)AzM#IEm
z{)FWtSa}Ld*XZto>4(v<aD$cOu=s+dXP7!dG;AISW*@BlBsTtF=?4}MFmrH)Kg?X1
zzhUZN@eK=qxI551fUXW^KP>&i;ulx>0ZXs2_5sY@u=s%GUzk78tcTCvz`_|u!_?y}
z_s|VOOGhw&!NLPwIYB-&9m3K9A@#8I3R4dY2Uz(J8%M@v4y=6)^DlaN3QNc6`eFWr
z#TzW&!@>_H4vR;0{jhk3xf@pAz|s?phPe-2KdgL!iNn$x%zl{rVCKWr!Dv{zgry&t
zKVk6(3rCnZOdpJfg%?ae%s!YrEIq-}Axs>dhPe+`|HJgbXms^(J~W-c!VTsgm_FG2
zAFO<V>w_|2;RTBy^mvDv0}BVJHaG<<_hI1$qhaMDEPukp;ToX~SUf?w46ybBEZ$-6
zg2|)Puy}#F8|HsldVz&2x;_{m7B8@L3v(x|{DAudrU3^Hvk#WuVc~}EKOEW_VE%=v
zhph*}r5;@zJs-is9bFwRKCJwJg$J?vVd)N*j$q*k^9L^bVB#=$!@?bwpJCwvt4Cq=
z3``%4hN*|~Vd`P=0qaM=XqY+}4NKSP=D^YwEd9XZ6{ZhH!_o=N-!O6XasXx?Odd`{
z>kU{r4GRxgIRFbsT<HPkE|@!E@d4w*;uRL2aC@N)SiHf)ALdRN4a-k3cR;nlDVTk*
z^a3jfVftZwSUC&V2xY*+9p+wG{)fdEOdP5lgMzmUpy3D$cbGY_@WIds6@bM*to(qb
zf0(<V%5YM!aD&+kvk&H9nE5yjfJ(sHeX#h4$2ZhpP-QqNc>F@u!{P<j9>k>)rVeH=
zEF56&hs6V~{0Gwy3olqb12YGfZeZbp?haV|!owXp4hPc*FMpx+9EN_VdRRQb<YDfC
z#S2UvorcE`)O?tKVd)3medzjO=^kbdEWg2MSpI>@W9WwpV8k~x9%11LGY6_1gM!5a
zY~LWv-LP<k(HI(`{)VL=nEzqn2U8DA4=_HAhSkroau;SVOdMtoF8#3ZgXLpbIKlM6
z-3<*7n7uF>mOfzhEsTbl3rklpbufMCG^`wl(eU(y!(Md#=zN&_VKllt(R^t9;8G9E
zSFnCQES<n;SUrMEKdk)*%O|k(2J=6xe!!(47SHJ63`>u&@*kIeSiHm14J;kP;vH6x
z;L;C^FPMIqKVb0>a|bT{FmafFVd)Q+USZ_`p?HMpgM|mIyo9+6W)ChJ7Va?r!|nlq
znF|XKT>4@5!0Hj0I#{~`<{p?hIt?rTVeKZEy|8eEg%7$uSa`$23FZ!1x`CAoaP!a%
zfT@SkaC@NX9u}Us^uy8}EZ@Vz5tgoC`2bh`hlMLFox<WB<}Y0KqIm;@3v)LvdoeVS
zDFDm=@OX!oSFrv*tXzPpL$?Md4l4&>;Q-5bFmYITpzDXl8!Vl`{0mD@@OClOUFiB@
z;R}m*So(*hA6R_C;u#&l;uoeL7Va={nE%lA;o`&M9~KU<dK_jhE`2cf!PUXiFSLDv
zOCL-eW*@Bnh1KIQ_rUxEQ;$JI?Sqx~@OXiWW2i?HfaOzIe8b`s7OrT@;apfe!NLjV
z4_Ll}>Bm(L!{P@PZZH~_-e5FL9o%9l0~RiD8tPw||6%D1CJ)sHr(p81@≤p(<eQ
zSeQDfFr0$<4<-+@2j+iRIKnj$%fOLeaB7G88@-%|g#+9@xD0^mhpC4tgVlSmdKM;*
zPQ&s$x;V`Luyl+e4;8=_zi@p7!UNsiP-Efev%>heXjnYL<l%e*_QKS|(hW=>jE0p9
zFm<?S*!&-QJfZV(>4(KTEdRsQ!P6sjUooz5hUH^e{)71kM#Itpu5gCA6JBpX!wu$t
zm^{oqF!#Y|nEkNw2UZWj$|XYS0M>4Sl^5{x2AZy6^$5&9bQ&fPD+gfp7|j2;+zm4a
z=5LsPVdAjz5XL7&!{P^KAB-keAFSShnF9|ur~$BefO6p!EZ$-93u`~Y^uxjfmws42
zhlL}|-7s@u<p(TZz%7O{VCf4MkFfZMr3aWhpvo~Qm^$=$hq)K#9}JB!0cd)K#XrnH
zuy}>}AEq3ghM5oZH!MHG^rNfC$%m#_m_Oj=;nV<;fVKZ%<uH0Yz|4cFMv$=l0@DYp
z$6)Cm7JdjFs4Q5#!Tb-i7Z&eu_d~-MRW}2C{tXsRu=D}T_b~I(^}+Zs^|1JYiNpK>
zD_3y&AEq8oL&G1Ik74<NkUL=dVeKGTc*4R39u6>tIA~b9hq)Ive}nEnn08n`hv|dy
zVfMk|14hI2!Dv{1gV_fQH&{M^(J+6&^ucIYJj3DvW-qMVgXzPSj$rW)lZS;LvGETp
zS77ZlnEkN+C@des+>48bnFG@g^A{}sap{AJ!^$6+`(ftbiZ_@(7!8Xzn0{FJ!Oejw
z#6hF$hq)V;4q@^*v@^iU0a!YKrFWP(EPiq6hvhR2d1$_e#WT7*x;xS3VdXxqaDeGY
z*9QxCSU6(HLj_>z4i^7VVK@Z~2e<$l1EwFA&SCb#(j89sp_v17CoKQLq+$Mt@o~|x
z{11yiSbU?m2XX0#<u_P3z}y2%kFflJt{-L}ESzBF1&oG;4@@1peptGJr7w(lhNd4_
zIz-nGtCwNs!`uOPFSI;|iKFX>`4{F+SUAFHnEPPr(DlRYhxr$l-(cYhi%(qoVdlZc
zYtZ8xSHA$J4@Se{3FZ%&y|D2gm^ySCmOf$rg!uzT!@?aFzUca4^00IdD~Dm}2j(BR
zIy3`d_QTwbCd~j7htcTruyg~<Z!me7xv=<v)#osIIE|LRVD$zp{^98cst&FX#(;{$
z(k(pRq3I4+y$jP0qhaw33rAS^!Qug?4xNU%6Bf@fcfj-$JO2mEFEIOH@dhhL(Cvls
zVd)H}57usknMWwy!`uriKVj(%7H`CcA1t0=@dt}<nEANUAKV|%_=4FB(+@KbR=>m4
zW6)6jFn7Si4Jr;Z4?`bR0On387rq`7HZKUHVe&8<=3ZF*!_p7BJdBS{!`unW|FHZE
z%Ws6j4dyRc{KL`>EF56+==Q?+u=E2<H?a07EMLRqVd`<wu=EDYf3WrjEIq;eiAz6R
z9vWXT^I-80<Kxl~D{o-(u<(SrA4bFE(P>!x!_0y4VfQ$~_~`mzd|3GpD+gim0h32h
z7jSVX0~X$}_=BYbs5Aqto`dnxX;?hN+=-rUVCfWHA1s}~!Wo^0xf|v_T>4@0409(e
zzF_4Cx_@Biz|4io!^$g|e_-V(%v_j07!A`8(+Be>Odl*C!PMiTVet;r4|6vz^)PXm
zzhUD(u<(MF7qD~zPv0<&P#RXQ!OVfV8|HtgGNLJ1e8SQ{EL>skLN^Fj?!){KtIuHJ
z21}3V`e1xmyujiCW)F-HO9wFZxM-Mqn0sL6!r}v$KA1SH{DP%>n7y#@fT_bp!}2T4
zK3I89$p5hX4vT+SJfrJ_g)h4OF!iu<7p4!Ek74Rz@d4w*XqZ1?<t=(Rz|s>eUSRrQ
zG^|{NrFU3*gt-?MF0lB4>4VX*bP97Hdif6%hq)7`51od|!{QGn4=ayg`2!}7PQ%g*
z%$>0Cgz1CHqw9n5Vc`vPFDzbQ;Q|jwm<BW&mTqAA9Tt9s+J7)((ESOEPnfx|@PVm^
z(J*^p@disjuy}*{8|ELFJ{S!PADF*k<poS1OdOZ}u<(YZFPJ-E{(<EWSUQ2(3!`D-
z4tF=S{)g*_D#WE9W<N|nOdZVKuyBO=2WlJ!1#<^1zF~Y=ISw-qLnE31to($<8!UbZ
z>4*6PR&K-c0j&Ij$)i~Z=i>4oEIi-}aWHV1PsrUc<6!QD@nJMBb7Ar@8Wv9I<`C_E
z==>V7_QC9h(J+6)Xt+4kTo@mnhPfLy{trv<u>1!r4`BHWT|X>e!qy$Y(lM;Ofu#d<
zedv6cK3M*RxdT?d<4PYed6+&}_`}jKEI**z2jj!S8)iPN+=Z0~F!N#Vf$2l1VfMnq
z3)-)T@nPi(x;}J1tek>{E4q8o)x-Gkc!$~x%f~Q(z{&xbdUP7*PMCVQdFTpp@L}N%
zYyZH)6^C-TFf9GS(ibe<z!eb5fVmfz-(cp!$|IOP=*GdsVftb9Kdk(QrE7Tjpc???
z!{QCD4jK=zbORTMs)tihb+B-QiNn$pEF59=08Ad8hK>Kg`~%;|3$2gf>-f+#!r~3q
zUWA1MEI-1PL){7IqcLFag~cDt-GtH;EWTjn4XoaRl^d{p2{RwfS~wThe}cIm7M`$p
zf%y}z0S5zS4$M8U@Py?@9NOUbjKK0MOds6c1i}xNPGR{LM#J0#^A9fjVeW(Z6Q&>L
z4_LgQ>xacVdOHsmjxh7k^}+b){zOlwFmqt)(P@}^m^)$V0Tyn!+zm4amOfzRG%UVh
z^007)r6-s;E*fSItRDjNKP+Bx>4Vt|qtX2h6UU_==1y3=!RiZG{KLWtmws6M!}P=Q
z9jx4kg)c7su=t13F#WLnh)W+#92Q@&_=Ck8EF56|fT>5PVdV`hox{=<EZkt_Il4X=
zAEpjgZo|U?s(?`Z!}LRy!}BvN++ghsm^?ZS^Eb@>F#p2ZA22?;J{TXCj$!VEr9YTF
zu{10l!0aWY4;J6B_8%<$!{QHS9xiiW?uE(2-A$mpgQ<sw7c89N=?+?N!NLWWzF_)b
zG%Va<`e5o{`e5}hA%CEohwdIi`5$I4EIeTKA50u(J}w%j50-9W=@_P;*!&MG&tdHr
zSUP~w81_TM8CK50@*yn#VdlW{Bf5T=KVk6*vj>*%ap{AZ1B)kkcthO-%kMCAVfMiE
zq0=z?VC^iJy|8eF(dhcn`7rmxXjnN+DE+|P3rmkMe-Uyw%zjvW!{Q$nAFz0b<x7})
zbQ+eAVfta=0c#h+;uT#Vj1O}^Ed9XZAJ&e9$-~scXjuA$xgW-d*#}EMF!ks(EPP?@
zZkWBW_=ANXtbRn-4~rL=y)g4&`4wFpT|X>+!QvYx4@)mF_rUxElZVkT{jl^4QwIxw
zm^e%wOdmQ8QxB_$VD&Cc99=ywJ}le_r5AMlu=E2fN73bR@nPW$i$9qEVey7bA50t;
z&M<ew!kbwAuyhH_Z?N<L%Lg!b!OVx*3!`E7z``9?e!$Fy(XjLc(+8tr^*YRam^)zc
z4vPm|`eES>t2bfpfu&=Ze_-JXvlpF)t^a`4cd+xHVDSoH2Z*i_%7^(AR_;KBiKbxg
zg{23gH9{4_^uy9UEIwf23RQ-af~kk4Cs_Q$$|YF1;4}ax0j+;v=E3ZTr3;uc3>w<r
zftCNTavSC!Sp37xL)Q;e4~u_TJiy`~W)8YO7#~)C!OVxP+k(X_tRDbNZ!mr6G|WD9
zaYB5UJ+SmoNIgs*=3ZF&PAHwj^ucIYe8bZnH2z@q4lG^4)T7g|c!r6?^ufXbrXLm`
zFm*5*y?lnn6Ffek`?p~657P&uVd(`{KfvN2W<Sh*FnO3h7!9)z7Qe7|JZ%3b%-yhd
z08Ae)8dm?q^btxgFmqtx1Pec6^BXMwVEGkRUc$_Ug$Im}PQ%>^jdxghz|sROUBSW=
zT|X@TVD`Z33z#~XzhLPbCJv)v=@nLfqsJ30U!(gArXQAHVEs#2y#aF<Oh3$DFnt7R
zXn6!Phmd+$K7^$o7$4m|xXgpa1FXD->4&9Xm_K3R408`Gp3wEd;tf{r!onG*51odY
z1FMf=;RUk~7LFMDp!pN#Pgp#_?1PyHb3eL%7$0UnJpR$rBTOB!G<vuZs}H6Q7XHNM
zUs(LX!W))fVEGbPyu<8+>4)bNsQ+Q{0*eQjdKe9JCrm#~92VZNaug<xK|}3<+Y40y
z%Wv@bfQqA2xZDLxKZN`Z3tw0|fte4BKbZev;^;KYe)Mz)D;LrIg{~jQhn4@ZavoQ>
z!qlVFuyPybUs$-q%!ASB`e5#Z#UHHw4$Dt48kV2X^~2H|%)PLD1Pf1CJfQ1?@nQO4
ze3*H#bPFq2aK!`6z3_AbO;51$2WAeg`V5xNVDSg52Vm(J<}N}s%zdzY2Mbr2d9ZYb
z%N&@$Ve5Ec?uE7cVeyJfKg^#nb71OW^#RO0T>4@9VCfm=ZkRYO{jhujOYg97gvAT2
zeFT$7w;vW>u<(Y(2TUEzA254h;xHQKUbsA3{KMh_W)4grjE30<a~~`p!NL=kK5?aE
zn7J^2!~6qFf9T-=vlpF)&+kF&eOP*d^$TJ4pzA~D!{Qs}Z<sl_;t!@D*1m<gA0`e9
zS6KYO#9=fnd|=@Pvj-NQa6ZgmFpW?erXQvrW*;nGafLfnJA8ittek?;uy}>hF!ks(
zEL>swVC5Ig9WZ%xeT4Y1@_>*!T=Fn`VKgpvFmYJ<4dcUTSoq;G2j)*$yBQW<u=)d@
zu5rX4tbGkjH?Z^$3rASJ0jm#S?t;;<_=klT%p6#`52In~VESM*tlWp$16Kzv2VnUb
z=1-VD7!50*VDS(0FD$%Z@-RM3A36=oAFz0UrDvFVFnM%+a6Z(%uyG%leXw=`Tpbz%
zW*^L-u>20w4-<!n3z~kIIE;q57nW||<p`QS7#9{ku<(P`8!&lTdV=L=m^h4vxf7ON
zVC5z({9x%2rXCj!b0;jl!0d&ohnWlGqtoc=7$y(P7qIYwiKEl#=?mr_Sh|PN==yN-
zq4^D?UO>|T%P+8UALdRN4HHMx2Is>34J-fA^DoSNn0auGFb33}uy};guyg^7cl7Xq
zX@})sSUkhx4_2SR%z>E;(}zyO;v1H3VD`c6htcTzV0@VWVfMh%GkUoMQ;$x=^EWg+
zVDS$NH<-K7^}+bCd=9f8HvR((2befaJ&cCM4?Lcs<p@k3<_;JioraYkF#p5C8>Ws>
zxWW7li$542=3ZF5z|2FZVdXZg+<@g%m^iFFL)Qo6!`uf8Us$}u+yfJbnG4g0PNRnt
zOg$|8(dE(g!@>>b9+*B@`XQA6VfhXgZZP{`{Wh39y1g(yY}^l)9$@-m<H#^^n0gou
zQwJ*tVBrq44<=7YKP=v1`36SA;~y<u!t96n7Zz_YJ}ewzG)z4@4O0(`XIOg^=3W>L
ziyw6T=;;d<Z!j8`f8qW>HvpR7Vc`YKN3d{%rE6IDplgTmG5UE>b7AEIOdT#7R?oxy
z3**E4lQ=ZO{0FP&VBrm`|6%@tg)6!-F#Rz9!omTT|6u6}<}P&ouy}{X157=-J8;<#
zQwP(J9?r0Q49kb;_QTQz%pI_N01H1DA6Nc`*$+z}u=)!|!}P)GIduDBd{{pZ*58JO
zD@+`g&SC0t(XjFyCJzgDn0YX9bQ%^uuyBCM!}u_N!o<;On0|C|SbPw2Kg@h|b71KZ
z-CT4&Oh39ku<%D$kIqN87bZ`vewcb#dV;wJmM&rPxM-NWVfhEf$E6-!9F|^T^Tp`)
z!ty7&J~$s*KEvD(E4N_saR0yr&}nFSg>EmpGF*IEI)(WImVaRO;))-bd9d^ii!YdZ
zSa|}=pD^|4G%Va<<rXabVf`UkxS;EU@!|4l;Ye)yg_#5MKg>NaaY8h#+`v^%;?fTb
zCs_Fp4?pO<3NC%<;;?cO=5Au+4Q383{$PAq{KIH?{SH$ArD5)eav9L;E10?P@PR3W
z(lB?z%4e8)uy}{XAFLjMYJ*cS{jl~9th|HS2g`S`bO_go#(<?eSU!NY|6$5t?LZhG
zM#Itz&UPL&J;K5PrVmEL!xgFy?oMcahUEho4bumsVfhN?e^@^rT^<%LFmV_S3txEn
zL(?5B9$@JimcC&6aM7@Q2#-IQzo0ZOeK7lA^(QR6VDU?+T!Y0ktlbDphcJJ^!xe`E
zVD`djSh&FA9hZKXdRV%F>4WnL_!}0lFn7Y^kAOayH(=!jtiFJS8_Yah@eb1uqhaoa
z(XjLclgCBF!W|Y4uzUn_7cPA;ahQHsK7fS>%pI_JgvrD7!Dv``!~6{^uVCo_#)q||
zVESM*th|KT3-bp|Kg=D3(jTn6fw=?bFIfJ8<x`lw=rqiquzUb_FSH!MX+Jcbpob4E
z-@wxox`j|a%)L-9oPxO*M#Cl088G)j#bNfs!j(|^gM~XR9-+oC;PfwC0V;!_J5iNm
z@}T~Jxd%pLD#R`Vi+|WS04!Y5%MF+~x+ySmSa`wmGb|io;R-8X(e=Z^3zqL-?uVrd
zn7O#z18aZ4XjuA(`3vR_m_2a&q3I7+Kf~e?W<QJ%^AB7flmSx*t1n<QRGI-+p1|A>
z6Nl5#c!QO9u>1xKFLeLH_2FQ^{0p-e!@oH6!Qv0r4uJ9D<p&OZFkzUzu<(b43!!=!
z7Qe7^4jv9Lg9*?udtv1bEd9X35oSKDd_p%4R$jrxVdlZaVc`IyVd`KsEM39U2h86v
zd00J!s~m%+FPJ)5yur+Yl?yOF%v=l_nvP)ViH&!-InaCn%Qvuk1TIg20gGo?J|gH|
zH1lBg!NLRX4>WbSxUhJJ>4W7TT<X!qVeW;Q0}BTj4QnUE(gnJHSU!b?16&<6Tw&=O
zUaz7V0MienVeWzZ0~)_@KAL`*zhUD&uyH$BeukL~ORq5PFm<qUAC~@M`5wlHsmGw9
z{)4#-J^$e>_n-#B@(nEgqq~n_c>oO;SiJ%ZH<&(HeBcUqs59Uc%)PL39Of^A;Q%!O
zmVRLQ0G1A5<r7pH1_hIcm7g&CVC4?3@&jfN%zv=-3JV9=_%AGcVd^nxXgtEiVfMoK
zuzU&Q!)TcQVE#rIhm}LHaD~akXju5d{0|d{(Xez2<D=6sf1``T{13|)=<+Z=ES_L|
z7!6CuFmqty=rl|ny8mGQf`t#dJ{TXSAKjg}!ULucM#J2P9!@ZE7!6a8PQ$_vmTqA6
z53K%#)94z}_%M55`5ESaG-ViESh&ILh4~v-dc!aPDge_5E2m-U5mxRJ>kn9b!0d<D
zOKAB6YAu|CsfU$cFn_^lSU!MjKxaVBhm}{bbP3D%F!Ry1!T7LvgpJ?A(i5ycfT<%y
z!_on){DG+_)_z#|1q**z`iF-D%s?m&i#M3PFn_@GL6zY=KM<x4W*@8`fR+ESbO{rO
znFpg`?t+z%uyly7A6NXr(m5=iVBrpP4@@7-9WZ+d(XeoUxr2~;ba|M5Sb9X4N9V)R
zIjr7?(XjkP$o(+;;Q0)io?-UD!U@KQ(@=GA{cs5g0~W3j9wrG(Kk)E|DuJa}SbRW*
zF)3*K0hSKX72tF?R3S_sjD`v`;PMYl9-W5y6XtGMx<NM=T_21O%jdB8Cg^XNMkozy
z|G~-+nEkMLC*&VkdV%SKxdUoE11z7y_~<k&ox|J>QxBuj;}@nM)=q)R!^$66x`nw1
z9<DHrP#WehSpN{_Us$?=)n~AB0jdp7!PLXl!Qv4XZ?JH{m5yNUf~kYq3#-pz=D;l^
zz<|XAF8AWn4lnPa`4N{oT;eeOu=*31I&^VZ{K4V@7LKrZM^}%*M{_T%9D~&-u=D`4
z2VFnR9vBTz2helVVD&J}f9U#Q=?j()VCfg;4_Li}OFt}r(c>THZkYWrb1>|O3c%a>
z(C~ou>tOccN)NE`!dU+WH3ue-EBs*X6&MY(2bSJp@qkM|EZm^pfY<Xd{V;KKK1>`|
zZou*p%s!YrtX@aA2PO{-f0%xlILtm=?uMy@(Xf0Aizk@5@b)K6Ar2bdewcn(`of_N
zE(}XQu=*F~ZrHjYxI!EZSUQ1~AF%QVmM&o8=-OfV1{Ut<`5#^mqiclnVd(}&!{Q5`
z?x6E|xZ)3{4;Ejr`~q_?EF56!pw_}ESU98m1EvpVE{uk2gfd{^1RF1a>4Vh+F#o{P
zDO4Mrg2g*b9n2n>c`$u2akxet44C~eci~V57lxI)u=*dS4;KD#g*X_nbPo$ZxO<`H
zCk}0JVOaRX{0pncVdW^yKX46b448gczJrzDuzUbZmoPq>b~qOnzOeEOW<M<5z|s?3
z0~!OCK4JL~7OyaKVe)9&;9Qu$VeW<HM_7J>xd*NRjRA`<Sbl~17uGH$RF0tQhw%yJ
zUzjss;SO^*%s()BSp2~Fa2i_A!1Tk#p$u4f;?fUGcd&E_)y4n|H&}XviQ}SS@dT5H
z<pV<D2GfV`KA1SnJe)MN{{c(?F!vIzA8IhH{)f@9^aqtDNWt3u=;;)e9&s4}%b&1%
z4i;`Oaag?I@(--MhNTx+yur#Pn7y!c1>?hLSp38IFm*8VVE%!*2c`~2!|a2nGiZ8*
z#V4#>hPCHl`d~Dy{DG;1r59NF4~tKjJ7D_IX_z@M|G@O2t0R_AsJwug0}E%^IZv?g
zgZTqqp1?Gq(Xeoal@qXVhLxkRd=2v#nsx?QIR*1SEZkt>4NJ!`d(id6^ufXx7LTy>
z28%~nIgYL$7XL8yF!NyQVD`fN1rvwSFneM4z`_qk!|a9m1Evq1hPfM7&%p8>Odrgh
z==xxMSiXnF8_XQo`X5~B0j3V-UYLJi>fz?XEJCAU`eEq^<_}nYfW;%4Hr!ljc?k12
zdVHX3gt-$&!~6jYKUhBsSNy@k3+7%}`2|a_F#Rxd(d~!vVfMrH!`uxk|6t(+Q;$x=
z%!ieuuyPS*E-YNp^}+bCd<tvF!Tbx0e;5r@52In_KFoetxWnWLxf`YqmOjzr8y1c*
zb?7uKykOxElZT~4Sa|>wN2g)=7DmJ33#JYh{xBLQj*EtcH!S{O@c}ExaOs1I!^&-#
z`{Csqf%Y)Wd>9RjKbZevG|V1!8WxW*8fHJN{YNPMz|2F}4-+Ru!_q6Pyn}@UA$>4;
zSbl}+gQW|YJT4j*uP}Sz@dpiWSow`hKP;SK^6376r6*Xtz|_HLSUQ2(3o8fF&4r~S
zn0sORVete@|1fjV^~3TJx;V@nn0^=y%eSy_h2?9QeK39KG_3r<&<71qT=v7l2Nn;o
z{0OrTM#IEm_QK)^ZXdM$1q&ybKVazsW)4h0oQCQ{k3TpcTCc#>Lm9Ao8`fTdm0z&-
zILy7U@Plf@pkV6Z;SaSBmTzI{1VbZK0Ol{4I#{^C%!Mi=nu5g>vH1w59+v-M^&-(0
zLc<>x|HRr0Gasf87S6EvhRGA6(e=a953c-(ZXPUu!O|Nn-^1!f7$039j1NyI&~QK(
zM{gg)<Y6={ykPks=6;yFVDSOx!!$x^^l~279)RU5So(&gGpIHU3Km|laDeMa^B1~C
zn0sOM9gK#V2TNb*`e1yRI&}ZR(gQ49VCrEs%wE`d1za3DZV2-?EMLLoVKmHLF#BQR
zu=WfrUSQ(rG~8TheuvAW^WhSx3|M@?${m=!u=qw*jhhE+-@*KgE4*--085`R8Wt|-
z=>V60xO%9+VeWv5!^$&U`eEfTEWY943e^BJ2g-$0FmYJ^gw?Y!8kU~m;R@FYVZhQ4
z%zfzL28(}O<uEM%VC6n~c@NWv?jMNl4Dk65n7y!a4916r2TVO78dhGx+(Sq`EZ@Lr
zxO!+j!rG1Sc?769OdTxTU^LACF#FMISa`wo!_qmt{DhhhOFyvihpB_>gEC<02Br?y
z4uPdxs4|=s%wAYL!T7LzfKwxy1T3Cl{)gEQOJ8WpF}SeshqYH=?trCV3=L=kuyz}4
zy(i2)xWWxhJAAzYEd9X3A6Ac`tB3Jn?t|%rxf_;#356r9+=ay(EFNIuu=s`9hfc%t
zJ<Pu_8fG3$KP-Ks>xYFq%-t~aVD%R)9$|c#ewe>uG|U{hf1&9ZR_?&u0n-noVdXw7
z{ldgy`3@Ey=<x{4N3eVeJLd)Fe^|al*9S8norbv^<`4AvgM|ak9(4Wi@)la}!u$&p
zhn3SXf5G)b+ZC{K52g>shtV+q!u3HJFmqw?0Lwoxdtu=Lvlqi0XgtC4E!;e)KVabq
z(+^80Q0p<y4}+ESF!P9Q$HM##D=%Q_24)Y@G&DSkjdz&2u=s_w_t5pj(g#dEIt>qh
zXn4ZP512W)@*m6`SiHc(85VD_@P?TWQxB)1{)NRijE4CGW-qSrg!vnm?qT5%6NkAA
zo)6&`Lm5#2qNfw6ECVcm!{Qev4x?fD4CZfGIRkSqTpXr>01b;L*tjh$9C2xbmD{j>
zA51+gAHdARr5`<BVBrK4hlM{b{V@Ar;Rg$ESiBSRKTI9m9B6ug<va9p6_-2E;~$oO
zVeW_d2bX?WeFIDHu=)#@9%1PUmwuQyOdTv<(8B|!4ugiKLs+_n$;0XgSopxi(P>z_
z0cI~uJ&aE%9l-qwEjMA|Pi*+X{0(y#j1P+kSU!Qt!)RE%z`_Y04^Ra#{V;i`Fr0$L
z6D+;M@+nL`EPcT>pfO<f!-Qe#Vd)8$pJDRoG%TE9`35Era}SJ$iDS^vc!$L^EWg0q
z4@-|2`k(?Zcf#TqT_3C*g(}CSp!p3Z0W0rd^&lbt!@?Vu|6%1GEL_pU1!fPNhWa0-
z57vHzg)1yP;OcQOVCe>y9$@JPmJVSw4($x+;Rnly@bE(mCv<aQ;SP&$n0c^pfQKi#
zMkpWVUi9#WjSJz*Kd|_Rxf5L-%so(Ra8j^z4~s{bLYVtud~_NXPcVJ3^Z+xDQ2K?b
zgT+5A++ch{`eE*Yg#)bph#sD>atx*(ora}5^mv4qi_r0NbbT;BESzC9EIq>f2MZUN
zdUP6QFD%``%z>2)uy97#2P@xU@efNsu=s$r_hIIv>xYFmEZ@NLKTJQ2hWP_sKg>Ru
zJS@M$(jlRA4{K+`(itqgVeY}O4^05pK0uFem^rX;MAHW6!rTY*H;fPKx4;z=V8GPF
z;t%F8T;Yyx94!7|{)XjybalA+FnzG_hlK|$U*OUQ7l)R^u<{ya4x#Xa=|@kWF!#XZ
z3AqE7|6uV3OV_Y`L&zO4d02SEXjuA%r4yKY&}q1Tq3s_SAC`_`=Ar9@@!|Cgw4Q*a
zCzw1T8Xi8-c!1eUNFQ7tYA<ZO2NoW%egwKYI3LP@g+DC)!tIB;2daz#md{~)SpJ8V
zBd~l7D{o=(h|@f1xe5zkn0{C}4l@Uqo-yo)x(^o5Fm<r>2(uT)hr1iffSCu&ukdsN
z4Nq7*7^<9T3YH#V>6d7Y=nA3!Ll=he(cAs7`Vtlnu<{=!4$}vtVet-2FR*Y#r(y8}
z(+8tr;xHO!FD%?(<ta=(I*lIQuy8|Hhl>x(r?7B_m7~O(14|b$b71v0EL?Ee3loQh
z8+v&MOSdp}IB94)gT*5(Kf%KVr$$r>nEkMEWLUa~<v*A>%)O|(85rRHg}Mi3KTI5!
zFVXeE?1k9_EB9de3RW+|(i2P^W)7^}fTa^yx`EpZEst@fUs!nsb05q+Sa}Mw4_A7C
z*@vzlrVkblF!#aS4WnV{9p*ond9d~?OdgkhnE9~!9;O}^f4KY&vmfS9c)1S^FIakp
z<p)?e!Sumsm^dt+VESO~0C+lvrVE%p7!Aw!a6Ys=hv|d)8y220bub#;pD_2r+5xcq
zi7Omn=EK|pQx6MQSbE15|1fvJ;vZ%YEFNL$8y3zm_2@LL{(z~6g)6LFg3;*uV0>7-
z!`utYPcZ*r)Z<W%F!ku;e=t6*Jb}|teJ~1E&%$U}yu;MP+yM_Cm_{fK3s;!`VD5#*
z3rrs@exTYA6tsSWr59Md!{QH?9&x1~n7Od<hS9M62XhC)Li{Y4KVan#%zwCa!R&{{
zA51?i9T3tF3ojjLIKa$<r9)iyqKm`IJ(#^PbK&U#YCgI?So(p5JIwtscfi5{W*)kJ
zSh|6gA24;WdK8w9;53>6uy}{16Iecj*$c~uxWWS#&#-g?^AF4#Sh)<Vmtf*B8m1qn
z4rUK5{lVmMr9YTCFnuulaoGn~Pk;gQ4*_LxX_&pRdLEV!VD5sY3z#~XMp(GR;vbgZ
zVd(&-4i?Yo=^17oOg}6>Vd`M!!^}YsM;ISg51`u%ONX%X6+J)0<YDm#qhab{<`W7}
zSUkhr0ZXqi_rPeFKM2vV@PeC1NCTQYEdF5m2~8S<3)?RYi)UE8!`cNH8lVEO_=Cke
zEWg3ZGg!Wc#V1r7f`YaSVBrU=KVaz}CJyrtLL-C)OD{0@!r~VeZ!kX0oe*6J5+)Bb
z2S&r(3yTL_;Rtgd%p6$z1HC-O75^}I!u$)fA6EXs(jhFJB3zEjf`vDFI)$|hQB@;&
zuy8?-XIS|KD-RGlP+2hju<{nB59S|OK0wul;KBS43qQCFG+hu%|FCd@#ShFKuy})|
zN4U9Y40!nhwGXBq9$!##xH>fbFfJ_KVE%@g1539s8m1mb!~74^2U7>jhcJ6#e3(9T
z8aB=gtFK}AK*7v|g%i3y7$0Unyc~em_ptN?i)UQsz|uRc{)L4f%zx;7n7uG_VetkN
zhs7VvU9j{GlZVkT{cv|d-3`+R%b&1x3DXCoVftX@K8%LBn^69P>4$|AEFHqj52$-!
z_7S3C`e63M$~8jzFyx7~7n;6d;|H*CgZT$mp25Ny-5yx_f|XaWaE7ISSosgjAL#mF
z<pa#WFdF6#m^m<Ubp5dSh2;}iI|8N-7N4;62NQ?Uu=s=Nhw)+Zxb(r~;WRY7Vfh$l
z9?bu6b?6MJK3F<~$3I#>7+o8T4|6ZfpRjm@l>@N!2vd)XhQ%{1KH>ESG=0Ft(e=aJ
z3yVjXeXx2Q7C*3Z0bM^VpTpF_^uf|Sy!?l{16@DNepvm19$&C@3kxT7{kX(o>R{;>
zW<E?DM#Jrcx({X_di=xv0n-SjVfta_!{Px}p2Fl|G*la$g1H}7KEn3#!pd>jx^TD#
zTnuPBgvBE)zF_$fmo~UKOg~fr7H%*aDvXnY`2(i{0ur$BhS4zPuyBR(anbO2gN}c}
z^ucIc`e5R)bO^H_7GK2r3td0V92gC=2Nw-<KTIALZ|LGMc^D0Ax5LsWtQ^6W-(mV-
zG|YXl^an4mq2(7$9WEMXFRc87xfdpmOCL-e7C*510p@OE^~218l|!)fN~(TXeub4=
zuyBEy2lE%qJ{S#C2dgJwd{{XEqhazeeJ~nsF0}lC<y%<09abN}-3QZ%M#KCIEB9gH
zh^7pK3k!Exy$37DVKhu0LnBlGT^<%*uylwn57mZ2!Q2Z=_ptT|jE03PhDN9WEZxG|
zFR*Zh<quf;fGWqJVD%m>9mC9lm8UTCFf>91VDW<<->`6m(NN_W6s-RSqv7cQS}w!f
zhoKQF08<YOM_BknrEyZQaD}-a7GJRTBTkJ_3H11ar6Xd?O<4TE)WgaHm^joLoD_P#
zg6V_#2i`A%<}Y-EVCe>CFU&u%cqf!@VC@}vcthh0W*#g&VC4$B{jhus8{db;GmM6n
z%jo)Ge3*Y>?uMBMa}TUv2^(KVcL%KAg459Y6Ww1hJ}jPK_QULh(XeoX$;0e}r7O5N
zlmV+}VeW;68+tzgsvIW;vmchvVEF*21~dt{|6%nWG#=5EVQ^vP0xaLd(hI!&z|esv
z08N*$_8u%AiOqj7(_s2x?jcq`EdRs98|rUZzJQsJi-wi=FnL(|fTc@Vxdf9(r(xj*
z6NkkkEIwfQ7~LM2dKeA!H_YAe^a_nnSh&K}!)Ta&u>1zEZ=m@ArVbWPFg}cirC(S+
zg{3=KdVr;Sm^zp~bQ+faVc`es2f*A#C_TZ#8<yW-?t$e;n7h#Jh4EqTgM}Nc9)yJ}
zF8{;y!_>peJE#QATo?@%#-L#709IbZ(+xB|!om?lBUAvUA3eRm-3`rWP~{jDtlWU9
zhvgp_O~`(@fl&9t!XKs|7B6shXbhOU(bFTEG7K&(USatkmd@en0Ye9>04%-3%z?QR
z7VoI4vGAbb3QM=Ja3U1nFq2^E24*iT9%1<%M#I#j)3ES@<quf=!SW3(J)yS~(9J=2
zKg?aQaDjyrx;_{mR&T-l4-0=-IKa{uOg%ac^9M{E*6xS-3)Vh>$)nRS^|1U7qhanq
zR}bey;}7OPn7?82@O%gpK%-&d4f7|g+=eNGrAKsem^>`rVdWo899E9N!V{J*VDc~;
zCJ%Er%p6!b2CEMU#XroQuyBK=e^|W2#9{WL(=h$8^ad;cVc`c$kLdc)`LJ<6Soset
z7hvfCT|LZQu>K;<|FH50<_?(q(DlR8H_V-|aD$lxi(g#f2agwMxd%(fuyBX-q3I0W
zepooe(g7?!Vd`Lfm^)zNFq)t|wEl;g57P&uVeW&4BP={%;RW*_uKWwr4@>{B@P@Ug
zVD5vdgV_tGq2UG#Z<u~qI)KF^q4WcDFU%h>KEeDCa|qNOFfJ^=5-NXT>S5u7?oW9B
zfhi<FqpOFN%LKH+rD6Vt380$?6URxTrBhP%!@><_J}f=K_ylP*`(fz;mp)jzj~-62
z@&K1Um^dtcVd)GOf3W%ho<Ct4&}f)G7!At@u>49$Kg>Ruv9R!hnS<^xm^ySCRzAV<
zE6hDG{V?~S>x1PNcz%VJ7qEDTnG2Ig*AI(tn7uH0Sh)??hZe6ebujnA`B3{|@ea$s
zxaI+1;RW*tx;jGX09HQ1+NrR57Zz`@^Z|1pIt_~_n0{D2f-a7(51kK-A6R(9_%MIL
z`RE#<d{{Wb%28PS!NMOsexTam6wIG6f52#1{GyjDaE(v~EWThgEF56vBBAmd7EiEy
zpJDkL<_;JQwGM-VxesPPtiD6fUl<yp0<d%jOHZ(L0E<VcG7Jh9Zm@O_tlo#&4+}pG
zjZgttK7qwEEZ$(^FdCORFmYJ?!_p5dzG3kIOV3bi;S{W#fTbIlyI~4o?Mj%tVB#<u
z7QQfZVBrOG4@?}!hv~yf!^#P0eM~6bz`_ST-eLZM%?IG>KfvM}M#J0-(+{I@wLj6_
z2g|20aaef3${BQj!_o^Zy}{BEEIpvBN9V)Lh3SXI6R!A1*AGj-u=);No>)FCox#;Z
z;}6cq!GNV7So(m48%!LBHVk2S{A0-A7l7p-SiXUUA1wS~@rlb6Sh)jJ2cu!`fzhyV
zMmGmt9F|^S?trClSo%cQ53>&@4oj~v8YT`)Con#YhJ`nb52Mk;16O>&%6C}#46AQo
z=@^zS(aRN>Juq?DI6W-9VBv}@-@^36(m6~YJfA|-AFN!5smDdb`~eR)sD4=bz@-ls
zt}y?@;ti%A7C-3m43me^uy}^~1D5_^{v)Ix<_?&7FdF7ASh|9Vqtmc>f|&!0Z&-K|
z(hrL-7!5NI-5y+gnEzqr0IVFwr5-~ZR&PLQn7d)-VyK4-z~UE{Zei&K=3kgRR5=C(
z%a^e94vT+Syu-o~LnBlG=3iL)gXx2XBO(1TdDyrGES<n;nEPPnLal{U=<yA!?_utO
zg$pb_!ZqSzK=TbuKg@g>jY}I`9I7A2hxrdi!^F{PSo;x{e_-JUqha|7T^~#w7JsmK
zM|VFi|HHx=W)94suzUwA-(cbx_Cx1cVC?{ydtvDpT|GJ<=3ZESgV_s9hp_Yub04~X
z7$4?77!4DL`5QwVt^kbz&3~|b2Mb3SjiwsTg~cyS9c=s$RzJaLxCS%^EZ$-MgoPh0
zJYn)^+TdK6dtu=R%TF+O!RkY}1}Fm-zAzeAZo%w_<tM0eObQxaxY7wM-{J~?n16A}
z!`fvqe_%KOn%`jVhm{ww{sYY4xb(x)A50z=jxZXQt}*OI6M(hTVdXDOKcV^`W(+Ld
zz}ycD7nnRD8Ww&qeXwwc@d@dN$;0%)_%L_D__%18J+OFytAoZ5oR5P6D~HgfVg5oF
zN9Uv43)2USUv%~8d{}t{(+8u8^*=0sW2lGa4|EG*e3(2moM8H4^#M#7E*j=;Si29F
zUSa77mp+&{EPP=39_DYDI7}TO8Ww-B{7SIgfEox(Z?OCV^AEB5Vfh=@|AXaYboapc
zP;227EZxA;FM4?kcNcVA53Ui)fR&R_E(5HbhSBhNfW|LO9gK#_!|aEZ->`6m%_G3n
zqtmeT0t+u#Ji^q$%z^RIX;`|2*$b10r2|-a!o&&DuyPj`j_~>yNBE(e2eS|69(cUM
z@(G#-Sh&IB7v>*WIKa{q+&yUeVd)Q6Zo}dY9`Dfh4a{9=+TrJq!qmg|{lMyfSiJ~y
z54wIBA7($yorKiG^ucJDewcl*asU=SFm>oOEWTj*2-a?Z<qKH*85U0H`eFWtr9)V`
z0gE?S`h%GV(+_hO%wCxJF#BNoVdlc*VftY-EZxK69p)dHeXw){O9wE0Fmqw$CX9xq
zdze00yA-A#rXNPb?1iNdSbjzy$A_s$r(yXUrVl0$Yd65g6=3$j<Y6>CoS^v&=3bb8
zU^FaU!1TdrSosC>H;jh$=V1C^;xK(M8fG8N|FHZ63xAk8Tr|vFSh|D7Gb|o)>4S;G
z?17mB6Ni;Ma6S$OOh2su1WT82|KreyE)4TGES->=e_{C^mS5oJ<ES^_@=yj$AFLdN
z<qKGTget?Npyeve{}>9O@)+VU0jNC8{V@Are0aD((<e+DoQC=j7JsmG1W#9J=EL>D
z7*PGN_=3eJdb)+hD@+?Y4T~?BI+(j*=D_@gt`EkC<ujQ3Ve7zQd|de#rXQvbW)4g}
zEF59#aM9@cVdWi+#-$G?4hw%+zJY}!%pI_BfvHEQVeW*58%#f}T!WSK==xxMm^)$i
z!2AQZA6gH?)T7fd{V;J@c>)VhSbYeSN2lTCDAXR9{jmIut{$Ba^EavbVCKN;0a$$s
ziwAhU4pWFuL-Pl$-h_n%EZkt_0lGFA9~M6_b?|(E77j4==rqhem^jQlnEPSo!Q|0t
zSa`wGAIu*xJ}h6O>x1!O>R|4G)6nvU*m?w3Uc$l+mVRO30<#Ynjb7iw(lM<3z@-l+
z4og4i@eLD)sl!F%D!*a%0^B_~48V|wmaDMvg~uxn{jhk3g+DC+!NL<){)dGxES_Qc
z71sWOm%Gq(j&40Ho?!U~#)q{d&}nr2F!N#g92Q=%aEJLDCXcQk=08|F4JHq3|HI59
zln!A1eRw$v4S$$BVEGAVK8%LN8!X?z$~jp0!NLO;&M<v28kRp`;RFkRn0sO219KNl
zADo883)H<ZeK2`gdVskDCJv`z`e5e3B~Tf#bcia-zyNa}%zRjU!Q2ZAf0#OS{V?~z
z(l0C@!NL<3@38cVt{)~3JEsw5AFMowwc}yw1YJLj4~u7*c`zDg9!woB8dh$=+Vk*u
zAP}xF^I-9hE1kgPVd~LoSop!jVg7~r0~S9pd2||9{=&?GrDK>sVCfuPA1r)e<qNv|
zVftY1K-UlB!_>jb5m<b{{0&PdF!eARR_?>x4U2c!Iv}|Fq4g<DAB;v%udsN8*^f@+
zDhFWhgQa^IA6CEOO207o!SX#!AG$cKJjWG|uyhBfiA;B}aDdUUd`c+2z|_Oy2j*{B
z_`v-EQ;36x>4)WeSbV_zfkPXHFf84`;t!S{FchK*z|tKo-N3>Z79TKkVDX5i9e#f|
zEFZw!4bu;cH(35c*AELfSUAA)EzCX`jVt}Y!WkBCuy!;o9MSDV=fli}xffO+!OTHd
zkIskH3o!q|!jIVW12Yd+exQc~Or8)8i$7TUgVn!;^ugp|`eE*ei4#l1;vW{C#Oi~E
z6U^VR^au-o^zszlTy%L@dV=YPm0vJ%bp0^<VdAiK1S|hx=D^|yrVd8K)Wgyt%>6Jv
zOdU)d#)r|c^bU(Jm^)$l8D<WlaD~<LFni$shPH2D=ELlT(J*ze`~q_itX_fT3s^Y-
z(+8tr`308lVEF+yege~v%YIlm!{lM<02Z&X{0|Ffn7uF>CJ(a*7GAjG3#Jc)hK3U?
z{lmf$LmdtQSUQ5yuyDYk977mZZo~W!D^D;K;t+u4Us$~fOAk1dV+g~>i(vT}7M`&5
z0gGP@jnH})mR@1$6jtuT^+DC)(hn1d>4TXAi(gp2fT@Ggu<(ZY8y5dCd04uEtAk0P
z(XjZ2g+ELhmJVU&!^B}UEWg0?!{QMZjxZXg4yF%IL(?71->`N7jE02+F8g8W71sWT
zg*Pm`VCw>4>frW588CIQ_=lAPFd8NgRgOWy)WPgUcPAnJa08*?0OP~r0d77_0*!{J
z52$`vc?1hL7#~;s!_o^ZzroCd#Rn|?5Gqe#?u6xgSa`$AL0EjEIUj=yb0@6cfu$cp
z{($9oSUQ9C=i&VTs7<)y1GZlPmX2XG%wJIL7!)jj!2Abm|H0A$EF3U2LIq&<z~UP!
z48JD=7B4VybQ%^vu=s+-6U<$(a6>mA24L=k#S_duFneMCf|&!;hfc%76{a6nU&7)O
zMx*OP=flz?%w8BDmY&ho!}zdphxrFK4-6BBrE8dabQ+dkVCrD?KdfAb(dhbM;S6&x
zEZkuJhsndj2VFmm5A!EX9W33W%ft9E8W#Vs`U++aEF54oEMLO(q0_K%f|&!OVdkN$
zhw)+Y3d^ss_=ou$CJ%EbOdkdftsi0a2dv(O+Yc?@(DlLcJ1o3m@d(S$Fn^)zgY%*G
z!peV``(gPPSG=R^hpB_L=V0!E#V1@n4hAfKVEGglUU2{8&<ArTEdRsuDMt9=&<{%=
zu>228|1fjl;Q<XN9LB)QD_Fe4!hu-*Fmqw)9z7gj{)DN+MZ@fa`5)#V7)?kYEFHkq
z6HGr)17YrlrC(Th!onA-41<D&7i_!}R(`<ZALb8Oc%T~qvlnI#Og}9CVC51lT+sEy
z)WO7I?uVHROMkfRhs86@URZvInG5UJ;0gzrJgmHd@nQLaP&mTOg~b=FJU|zR`463j
zg$peIqWd3Ko}lZ4^P%|yrVp0iVevtz{QwIun0mOmPzIrT52_3|g`?cWt${QdSUkaK
z4BgQ1!4QWDK-~vRKk$4G6^DfjuJi|sZ&-Z|O9wC-W(_Q!VSE@3OXo0oSbqUs9>&K-
z!`u%`$FO_@b3ZP9uzU{7N3ebbF8|^(2bS(&<ps<fSbGrWPh9$8?uF%BSiU1Q{lUzE
zg#(NxRF1;RDOfnb%)#Yeba8n8g{B*Hc|v?x{)MF*Sp5x47qIZeWe%*ofz`vX@Pox8
z+#DPR!2AtMM=)hDa|wk9tQ>*WBQXEK%z??n_~<mueK38n^aP84m_N|<!THc|hq)J~
z4;KHhd<s{O#(>!mqha9>GY4n+0yPI#&Z6swN)x1D`eFS@m^rZWAI3*F2<9$Wyuthn
zb2qGAj4S+L`eFWs#S=_DEIq>1q1zAR!^#<0{KNc1tbUj|uzU)qq2)KsT`+Yp8fGsn
zy}<kdOaHL)hfw~3se`#67H+U~14~yhdto%poiKUWyg#h}2WwBj%z^2H(XeoWl^?MB
z2NsWT^P%fmVESM*%$=}u6PAx*`e8K89GE^B4U1=3{KC>1%s;Sn1*2j5U^Gk|R_?&u
z0SiZ%I$SiY{(<R-r58f=1-f~#aD@2-Mx(34#fOzUFmqtxh)X@XI4u3aXjnQ#SBHy_
zUT?zO0i$v0gNeiJg~cB{y+iX4OdT#77T@UdxWv)b;R<h9IfSkroey_6R03umvHIcu
zh3bdZSH!v>mOo+oVEKaB@Q1}W%p6#Jz~dF_E?g8Wy~Dx*Rvy641;nKfE)I=nn7d*5
z5N1D&4_6Omz|_OaQCR+il^d||!xav2bD{Rb@-a+5EIeTLK&^#SaQ)Ew8)iRD0W3UV
z<swWRM#J0(QwK8-R-VJ^4Rmv1`eEWQcf!g6SbGpAkIQ~oxe9OpL&E_UUa)Y0*@ugU
z#Xl_Fz|_I!m0&bX9G!-_8|GhFxeDXM!UJ6&EZku7uzC{~k1+qk!WmsZEZxBJE6l&J
zaDj>A(htkGuzUxj;r2ko6XqYdIFtcX5A!E1{9yipr8}5EpxWRREZ$-6h2=XK4XfW^
z_P{m57|?VJoBx63H&}ea+H)}F=rqi|u=E0RCyWo1N7o1A!~6$JpRo1@JX~?K8({ij
z{({9DdN|{ZH>d%y_=UL}?p|oPz|4m#$4x=w85VD_@)Tx1ZjBHbSUU)o9%11D3rAQw
zhp5IRVd`Kqu=t1ND|k4=6rj;C^I<eB9l*@R6^?LuXgtC6!^F|7!QjHog{5P-JE7r;
zp#e<*R=>j1KTH}u9l*rVX;}Qg?S;lW%suGp(fP1^3yXJHI)a&tt{$Ba3ny5*gSi9N
z{)Ex+bb)37Og)T-nFotMSUmu9Cz^H!n0sO3Fn7S>5ta_&G)y6shO2|}Vd`M%4<<~A
zhQ%K&K4I=4qz@L(Fn_@ED=a*4`5WC_SpI|6zp(Izg(FNHT_21Ovk#^Z*8YZt4@@4W
z9)pIaJDB@n<r$2Ig$ss0r~s_IhJ_<69$@(Z7EVy*7!<6WfR$Sq3J3_m>UUVY!Rif|
zyI|=Omocz#hPfA3kHf+bS3JVRVfMnp59VJO4GRxk_QU)Qi$9oqVC54o{V@Ar?G2dw
zVd)7L?=Tvd{V;oA>R{=a*m@9VE-W12;Rao|3o{?4jt~v=Kg{2_%0rmBFnJgab2rRA
zxa>m@KUn&OxgSR3(hsvA*3N;chr0usuW;#yiNo9pORunY2+Vyjb?7wAK3I7Ni+`9u
z(A8t`q4gZhUYLGZ_`%W<ES;n4hou)74f6*q{$TkUmws6I!OVrZ1EwG5KbSl^4U>nd
zhm}{b{13AqW-hvZ7#~(Xz{)vTeuSBitNew@!~74+@33%$`45->Vfh`VAD(WY4nsE|
zDh#J!`d|XE^a2-$#xE=%!PMcRVc`HvPcU`x@&G0PqoL^=CJzg5n0{C|;<6u>PGRu|
zizk@BVf{jwb+~AldRY3yr4C&jmcG&T!}#dx(fP1=g~d0V4-0p64QPB=xdHP(EWN<u
z0Zlmu7Z%^JaD=5FSbGpd0}cUn^{{Y+g%6C6LpuX3-eKxs;Rnx`(C|RF2WB2jAFSMg
ziNn$ZY#bj}&%@-=?S-W?m^e%wE*h4vVfh6XelT%d`e5Q1<ps39gP8|Yhl_^g8(4b-
z=6_hc;?jpM4oi11{jhL=^U*b+@nP+BSiHgf4~sus@di^53vZaaVeWyIZ!kWZwebBH
zu=t0CKP)}N$`4q5h@l@U0Lw2hb71;m=0TO=q+tGoxf5m|%s)6aLM34Op<D)H^})gq
z7Tz%Zu=s%a3uk(Q<|~-HVfta}3C15Zy~5HVOh3&1F#FN{2U7=2XR!7sto(xITevyU
zdIGKwjRA{in0YY$u=s=3=V;m(VBrT-4~uu0J7DP&T_21OtFK}Hhq(jhZuEG8>4VWQ
zdtl`_EWe?f3saAahQ%MOzYQ}7<_=u?VDSvI4_1!A_^^5ymws6OL=Qh$IKsqX_TVxH
zmQG;d4O0)R7jf!?rVDuZL(?<N-LUw8g)=U5VCe*A4lG^3%)zA(CJsx#==CrxUSR5Q
z(XjZ3g*(juFmYV^VB)awgXLFPyuj>-nFmu3qhaE(bPB7FVBrDtKg=AMJ{S$N2Uc#v
z#tmTlap{BUhuIHHudw)snF~utxY9Aq9$3DI)$g$IfaQBa@ei{H7Jo2vVd)p<UR*S~
zepood{DDg!OdOWZVfMnrVd(+J$3?@^D=h!R;u9ABFg`B*FmYIW4aSH010GK>2{an!
zP8bbKx3KVpnS-Vc&PA^uVD%>~TwvzHHJ~wI=^hr(uzZZB3?~<seqr{))`7vyfu&1W
zIz=}K=0BK!Vfg^=ZfH4$t`EkCsfWcEEFNIt4l@_#KbSsr8Wzv6bO+Oqt`40Kb0@6)
zhn1T!_rSsjT_21OOMfsLW**GFuy};2N2g)#g~cPR{Q^rz==lO&Kg@lw@`}{_4=X=l
z;S8f;?uVI+ZZC`vOD8aWu=s?@!@>o|N2g)o15*ccKg{2-@&#QVIv<w*;rR)g?qKGl
ztB3Jn@da}?th_}}PcZf9G)x^VAHeN{*2ge^q3eV3VetoZKP=tB%z@D`_2@J#o?z(*
zR&K+?4H^#U`rv%1JK_3a>R~ioA$A5VeZbN&EF9tSfL$8{%-=Bmu>1$}KP(^M3O|@W
zm^>_;VD`e?0}BsyXTa=-iNoxJ*$)d>So(megVC^Z0@nV4tB1~~!TbY{2be}E4J&V9
z`39EWVdlW{KQ8;B#=y^kgykDpeukL`Q-_O&wHsjZ4^syVR~R3ihLx|dbPEeln7?5D
zK-UN3!_0@}A9(yh(;cz;VeWvL2a89TyI|rldvVb)b+G&o(+^8e==xylVEG;vPcVPL
z+yzUA==x#)gT+5gAIx5K^U?Lg_^@^!EIq>V1uXt?r9YT{SiIrNCopxmXjuHg!Vi}^
zba9wIc=$leA$WL0`RMv!e3(A;aE7%XVCfmA4yF%A!@?WJhtaU|3znZ?G)x{w!}P=A
z9p(;Lc%zHM^ucIY{KM>n@nP<UnFmW>Fn#DWEd9Xr!`6So$^n>r(DlLiu=X7+{$S?7
z<O$I*{V;W~@P^SaadaAHA1r)f{)MGOSU$jI4lMn^%4=9S!rCJ+d6@a=_M`J*`e5Z3
zEdRmWg{~eJFR*eNmhNEgg}Do64!V9=IK%1>SU!ZsKTIDiAEWDs@!|0cEjMBN|6u-r
z%fmFF(XjY~(XenpQ-;BXg$t~_fhizlA50xAeqiwpt1n^U2a|`XN2g)o4NFI`@PvgQ
ztQ>*46J0+neqiFTdIM%ZOg}7MVCrBrEPUbl4_fcT<YDO%Rt~_#VKgk9VESS43@caA
z#bNpg(J*rfse|?BVEg`H@dyh~czVE94nxIZ`4krZF#p2B4Q3v?dtmBe`3RQYVd60L
zxZ)31zQMu?7H+V3L3antJQxi#7p4!Ek74>@?t%FOrVpLQ2q$Q`p!)+|AIyC)8kXK+
z;R#EhFnM(SFh1=3KbU>6{110G)IBiuxM)~83G)vuUBJ!3p%E5-u=E5|4>JdcHn=d%
z-EaXY0~Y=;8Y+xQ;qo7}JjB$1Dgw*zsG^AT{m{kH`7r;Yi^IbOT>=*$=3aCfmwK2u
zR6VTRhS9L{1f~oZ4Qv0y;ty7S!f4q1A%=cvxWmdxm^>^$6S5zc|6uB1e3*V%dlqMT
z2K6^A|HJ%EY`TNxE13DP{EsU>Vg5s>VfhB8kJNaFg&(XwgOxw%_Tb{f%2!xD3iA&x
z_2}ZTc!r6?@*~c8hbe^8uzC{~kFfR(%s;Sl4XPYLLDLT`++g}(@ea%Pxbi0~y}}H{
z@HaGl!2APK2cu!>4aUc?2kJhY@d-5_mX2WI080<B@*9_am^iH61JehK4-Egn9EwK6
z#%p2e0Hz<-FGN!g=c22JNx<q4Lh%oaS6KRmrCS&uR*#{_6U-i%{jhj|g(Hj)iw~GR
zFnur@R!+d|g{6O3IRI0K%YIn;f|VaIb71KYrXN@Ofyu+-4Hlm;`(XND?u4mFr(x+I
zmVRLEFj#p6Hy2$aln-||G`+&~!{Q(24ybZC1xqI|`(ZRJ9AV`su5<v4XIMIiwZ~xY
zhP8j;7NRj=;R1_ym^3W@VC4a>d;qft7GJRR1oJ=4JX|y^{$cjO?1jZAE`8|Y=<bJ^
zi>?lx4@>8;cz~sISbU<ZN9V)*3+wO0+zks4boJ<bSa}WeH%uOu|6zPwG%Q`h@((N=
zVCe$pK3w`?=@J&du<(GT3talp#bNaf%syE7!OTZj59g!h7g#tDO1Cimu>1-ur(od$
z%SW*IfaedGMkoz)FO<swQx8iIFdCLFVCrBrdOXAOEleMbhM5b~hfc%v!Q2B=4{QIU
ztB3Jn@dfiAEIq;E4_5xe%!TPgr(xj@GY^)IVCLe|4^syZ2WWi4;sxdoLg@hJZkRfl
zy|D5OCXViI7#|jXuyP;fE?n^j(+8tr=>^7z>4(uUaa=Ua{V?~!)WPx<jE_M>!waSl
z7S8DI!O#a2fVO|&`5!v|LA1Z2`eEfb%vhK^V0=O}x_e>qPDme29u|M_{0ns#Odb~v
zOD8b>Fm<r<2qq3IA7Jt@8m1pc!_>j@EsTccN0>fBG)z6L{zeZUbp5dU5~dH9j$!ox
zF8wff!|Z{@8?5|=g#)hi0J9I~Z&*11OHVL&!t8<ZVKl6~fbn7J9bFt}eT){*uzU`S
zHy9tDzhD-@XsCXeI#@Ws${)Bss5+Q7I1Lqtg%_;egoQs$9IhS*1HR51Y91^<qlY67
z?eO)-F!#gSdxXk=SiHc(8&-e9?1z~Li(hp6VdAj-3TubK%X6rIaHT((epvWpl$TKb
zFn6NcPlyjo&p6dV&4;-I7H=^3!o=a>f@Tn`yn(qNmfvCd5f(1+bcCiKW-cteVDSYr
z2c9m_^x@*d+zm_rxYWVKVc`v<Vc`rj2c`}ejjj)-4rhLaDL|uP_QHf=`eEr3CXS1S
zxdSGT&WDNPqS5Vvy9ZXk;?f8e$FL8o4^}>+n-A57prGjxCICxMuy`Ppj$z|}u<{e8
zpVV{!%fGPv4@>_rcj2O8^*=1V!~6@&AGq|Pi^I}A%w8A`3kP)d=zN%ZnEzqn26GR(
zdKe!Te=zsM>M>Zp#Fc;H`qA1=u<(HCgP9Lgk50qf2`g`5@rbSt7ayh%E{~R;;NmzK
z==B|}T*9FoA&i#Ji48B90qFSy7SFJB04qmf;RDkLr=j-4_^|kgmE$mTVD`Y&!Dv`~
zqw`_m53?6W!_>iOSUADP|6umR;{zHGFm*6}FdCLlVdp=<{12<YVftV+OdpJf`3si+
zVd~&CTKK^9q0_K*3Tv;x%3qlK(AC5FQ2)Wo4VZdZc)`L2uATq`7Je}O1e9Y)qx%Dv
zeqiQeXn+d9$_H3Jg~dOt-3tq6sB#PnW<D&v!Qvn09$0ybp%E$oGapv}!{QSbAFzA{
zRgOWy(ha(OFmafBFf>91VC5aGy$SO_tR95XP~{jD%$>0KhJ_<6J;2?Cp%Wqi3x8OB
z1*f6ui%|H%;t3Z1uzCj;ZZP*iY(tQ+bOuW&u<(M}2Xhxf2X+?B9GHJ$@d<MeOddlw
z)PJz>gV_ty4@(EI@(e>ix&So4!|a3k3tbs5K1?6X-|+Cjr2#6AGkrk)2UUiXLQkLY
za3rK3>R(tt6=ngf{6jC_Ve&8<=5CmOVdAj#3Udcc9D|0s4_4p9(iyBgfQ2W9KBxf9
z9#}gcW*;p56C3X^|H90HttWtq!_9-*i%!Av3oQS`XqbP{)uZ!a;Rq9l<wuzNVdVl$
z9-W4bGs5x<j7E<abbT;BEZkuB!Ng(yfbntBF!#ay3**D|!^%fo`r-Y4r~+7efO6sI
zi^KTnG%TOP%3WBxKv##04@+mT`U4go#F_(hKdiifxdRrj@c2Y`05t!>;t!VJVBrT7
zhm~jO+F|y=${AQY0v7Kucfrypx_)$b!T7NDEL=S_pThZ22CRO7`4{F+n7y!Ygry6p
zHaG=~XIMIc`5WebSa}Yo;W|+nF#BQVz-6KChPeYSj>dqMpRjO*nTMtfCl^+J!r~iN
zZs61alYquQEZktiu<(PK3lm4DVfMjjSbBwpH=*={9<DHV!@?1kFL0HAuy!uY9C*16
z-4_gVFFZbB0%$ZW9l`toE5BfTn7h!l!MU(<8>S969|&_l%zU^87y}yqF#o~Y5$O2}
zrW~Dyg)=N2!)RDOg4vI*55|XuA1vR((jQD57OpV$1Zk*$VCfy^4nq21{(yxOEd9gc
z0li*;nFotkSUkbfADo7|2bX@BK3Mp}?1Pm*u=vCo4p8%9@db-_m^rX?3^xbnaC91K
z4thR>`2$@!Iv?f_bbrFib9D9Sd|150#i8XE$@aqyfG}X`0K&sfqNh`se_-*8TPH*Y
zrXIp0e%&BUJ-Rz#^%X2#!qnlS(e<PAap{AJ!_>pl3oP7W^#V*CE*h3!VC4tQ-RR+p
zOFyiA1S>yb;R!DXaA`ynhs77nKd^iS6Gu~y!G+bouyBCYL+I%mT_dcVf!T|m-w4?c
ztLNbP7MdSn@qjb`LmdLkx3KeiVe@vd_#mVo)_#Qf6Xp+C_z{XnSowpVZej6B$p0|&
z;p(CFCoCO7y@`{8)gLf_!u$>M2O)D{>S6H?i$9n?7>(foXgtC42`nC9;YMuwh4~+r
z-Z09442vNGFn7Z0f0#Ki_rUTcL^U1~>JOL-m^rZUgNfs!(c>4!C)WQk^)T~c;SEp!
zFbNzqdi?=+H?$tXp$#q!vkxYK&WDNPqG9PCCJzfgSbBzu<D_BkgwinmuyPQmKBxpN
z9l&U)Fas<<!1*{BF#BNP4dcVg1034m!Z3ZXd<ctA7!6m5g8|czo?c+?AY=|K-@w}Y
zFl*rX2rYfX<k4wZI)a5GEFBR{572mnxgS=Jz-X9x==Q<*u<`;{55m<$&4sDMMZ@wt
ztQ^Co4wpEr-hq{0xYXeihlMlD|HQWYVCKQ>g@rRLzF{;>9XbumU$A@vOE0kSgw;#9
z%z>$g*$eBxz``F^FT?704Ev$+15dxuc|2G=!tx)?J?Q#j=?j)`VD`Y`1MV+qeTc3f
z#)p;vFn7TE0Wf=Ur3;w<VdFk9^I$YAKfw6tG%WmK=?@luFm<qSh4Im8n0}ZztQ>`f
z3oQI#^5`^7JuJP!;v1F@am72#99Ve4+I_I_hv|o<YeMG0)Wc|4`hkTDp>TwU6D&PJ
z%PT_m!u$cV4;Egq_=LqTp>_Z)|H0-BVdWOgd_puV++p<vEIeWM<I)cc7g#vK(kV<F
zmVR;RhuI4YcbGq6^#IHq4E@mXMK>Sj4wyeM^g#t+@dC3K#)s>Ns)NZxxd;jx&#-WY
z(eQYLs)VIaC>Kt_@&|0+2l{wDOd(7ijE{?kxf`}$0A?>NUEtCO6NlwLSUCn0Czgh#
z7g+v-xf|vVV$DH!FM9l<n*%FfVfMn@0gDG%I)cS7x_(&thN*+O8(#iH(-|)Pu=D|=
zVdlWnHOw9G@&Vle&~yY#2e5t|EFZ(-6Xq^-?JzzpoMHZfg&WTN1Jekjq3(d`hn4F@
z>xY_y?oU{_K&5d~xa@(YOPm^^60rD(xgX{ZV(mwdUs%0FDBPje!_RMl*$blyl|$(I
zVfMko13f>$)Z?OI;|8#BfYm3s)WgJK_QC2)SU!Te2c{04hJ_m}y~E-QHV%uf9-R+s
z_rvUm>4TMLuy{q+2jj!qcQ8K8J+Sr{%v~^Xm^v5@Pan{H4$}vVH$wi0)psy=!_0%}
zgNeh_1I%J54byJ`&3~|UK(Kg&)rU~!7!)j?;qHc-0~3d(2bg~_41oF@77wuagOyt_
zeYoNQR{p~B3#|PJD?ef8z~UFfey9Mf{DRR?VK{{={$S!T|H3su8L)B;7B5g?I0cIr
zm^v5@3lEsPak&fTZ&-Z6(i<%O!omTr4sJ7q0W%ktPGIFf%wAZ%05cz=3qiul2bg-8
z46OWzhYMP|fT@GUAB={%2NsX8^a<m`^ucMUxiI&^;schBVC4ub9AM!M(+`UmSo;a)
z4p=zC%!8>%2QYb<`(XCK(ktBm=o+DXSop!>3+6tUJ{S#Ej+27vhou`>c)-d5TpD5V
z4zmXqZ}5Br3twCYK*iztAIgWt8_a(&^P$o>DOmi&!W9-jI5j{e(ER}`H(>2aShzx!
zV^DB=q5WjIdx`Wv%s`ksVEGMJpTYbCQ-@B&!X1|WVCfcCzYx+7^Divj!pwukKOz6a
z;tQ6JVDkX5bO194-QDPXnEmkZLsx)<k6!M<@)03>Vc`M`Cz!pkdW(>Ln7uIlFz3Pe
zu<(G1qtmc(MYji*Zej5cD-Y21!_o;X{9*Di|HIM)%$+cKbQ-1}=5Lt2u<(JUZ*+Yy
zJ}jPL<3O<d4D%OE9;P0hhV>U<>R~i29ipp8=flb^m_4xa8&-b6(l@$37$2q{7GCHy
z+#Hw&G#XZZqnDeo_#tFJOdYHqft5QjaacT{Sqncu9Ts1(c!s4Pn0i>chVjv9m^>`J
zVf{dud9d(7*9Y@2te%I}<FI>xVdla7g`poR0E>T^enRODstxXcn0sOFgrx(RI$Si&
ze=r(7+;QoHiNn$fEF55bSUCoxVd5}-FdCNsVSHG=fu(0y`GG6_;YuH{dIXjZVE%*I
z1E-<&6U^VRd<QFUVD5v3D_kEM12+BxbFTrkT@F(Qa}SJ<i-!3hW)6%GOGmi$!Q2T?
zKT!K|g&Qt&(8Xcq5=u`ndtvzqW)3VJ!{PxZj!whk8zv637hbNwByiB^_QK*3R-WU~
z1{a2v|1f{T;+v4WVFtkB7naXq^%1(eVCvCnn7y!k1S>CL=>T0lIv-{q%zl_Wdbq;G
z(P^0dFn<v1Z<zmJ<qs^q!o*?r;LQKf`5IU{h4~kzA4bE%17;qKhQ%|iy$4G-F#o{B
zarp<P9%eqQo`IDIuyBRP3(R6P8diS8)S<@%nsPW7-M=sinEPSz0pr8uVKhuX%-t~m
z!t969uyPWn4@Sen55|Y7gZUelFW}(>QwXJD_QCQwtR9Ay2T)}=DVTk*@P@?$to?{n
zBUA$BZ<zfs{}5|GEM3CtDVTXMcR{VeO+m|9^l=(k_~F)wDg$#b%pI`$1XUGo9xNTe
z@(V0HVgA9X6Y73gyur#_SbiiF4={OHIKt8?%pb7w2bViw@-X+o`~j=4VgA9TALd>d
z4XY<%=>SH346`2<$}!_qe{{V;dK{0*}gmL6g9glL#QVdV(SJQz(#KP=o};R$m$
ztUSV{9~REA@*8d+v|R)XPh9$8_QLWl%pb7$hs)zI0G94y?u50wVE%xai!1(N`3x3+
zFdAkZu5uqH4@);Nb71KOCJze_Lh%n*56w?7KC$Tl9&XTZhv|dmLzsEEXjpj#^FK^K
zEPiq6gNeiRqlXtdAEq9ihPe|~|HI-HRxZH!==#w4F!!RzH!OV6)x-F(c!IeXR&T+|
zL0EXe)T7fdcf#z4r3aX~uzZcK55|Z2ALdW=bOcj}lZM4RH2uKx4^H(^30S<r;vE)F
z@caW+hl_&ghxr#4?!=~dn7Ocg3$q_qe!}E&(J=L}asn1l#OjCDKQRBo^ugj07B8^)
zMRx}*zF_Wv@nPv0M#IV>4E@k}gNei94W<u9!_0@NgV8W?*m@C|dboYi^Z;`gOdpJf
zg*&YLhM5N|Phor*4bumsVdAjxfu(bJ{6qZ*OD8aWFd7!#u=)mO9?ZS4_=LF!rVmEL
z@+&O;!t8;iLs&S#+y~Q#PQ%PWR|j(k%p7!m=zN&_;o?yL!@~nEft>*>FJQ88KGYnT
zI64ihXJO?Ay!{8&2TvF18ex2>I#@nN4^No+Fy-hpth|K9C(OUF@PNl7x<)iUEZ$)4
zNmzWs`Y~wA;ar$LSUCX8zc7CgvL6=DuzUbZZ!mwrEyBTo`4c7$vmaKjz{GLUF!#aY
z8&>YXXk7YW@-TnH{0mbL6NmB9X_&iV;RmblVE%^r16?1S4^1~PdtvzqW*#g(z}2HM
zVC4lY-NM2j79KDfO&bOm7S6EnAe3I<20+V4n7y#@hq)h?4$;d6xPB-DrXE&Kz`~u7
zKA3&5d<UEVh2=Ncd;qR+fT@F}Us$?@nF|Y7Sa`s~8|rKZn7iTW06PB$YuCc!73Mxz
zx`642xeI0=OdRHKnEkMP43me^F!#XH9n2nBy$tskOal%YW<Si`Fn__~1BW)aFsvSg
z(J%>^`(S)rG%WqV(hDqHaQPeE99a1bOE<7^fcXnuJvtxOZh)zWxtrMVgXLRTe8Kpz
zbb)RkIv?&&Xnul)Kdk&9<PMm7Vd(~@AC}%==AhdP<HP(7a|f*4fu$>0e8AMB(=dBs
z@efN!=;os9gYjYc5awQ3eF&2$M5D($A@`!22P-#V;S7sMSa}Ge(e=Uj=<bJ=Be48K
zDEwjJ4of#M^{{w_(J=eaX;^xPg&T~9y8~SVln?V4%zl`^VeLs+I)b?istrLw(<?0h
z!0Hj0zhN{i9UwG9STJ#zJ+OF$<!4xWz~z6Kepq`A7JsnzJR$$X?1O~^jE3oh)gQ3%
zg}53)!pd2gKVjhx%Qvv{AC{jGI-x9R{SWgW%)c=EVCfhp4%G#xVCrD@!OBlqK7=d8
z!GOgptbT(jBUV2wox;K$rk_|ER&Kz`e^@$zxr?AV(C~w$7r1(;IGj(2ftHS8;R=fv
zLfT>J2o@gj`~z(d;L;CM56eF=d*Sg8ou9#_A7(Es++g;?(g7?z!{lM+!1(a=3zdMG
z2XhaMhQ$+98JvQpYk2tq4M&*yFnRR!3**D`2aJaK8)gA4JaDB4Sa}804~s8&y$f{@
z%sg}&rVdtKz{+u0{)frK#nB9ag)c1q!u$&>mtf%lGY3sO1FYPD$-}}Ct`C}iVCJFg
zhl#`TFU&nK_rk&rW-hvZm^iHbfVl$}-!ML*{0$3tn15jQz|0}GJb{@5b2ls=VD5yO
zi;IS(2Ut3Sg%2zpVd#UV57@Xry8AKsXa>N-2UhRF_^|X1_XnCj7#HSWn7d)(F#Rw-
zE*hpErXJ>RSUp52|H9H4EWN<W4_JDD`HNuuLCag1d9ZQ~<}O_Bhsne2g_TFJ@*EZ}
zuzZEf99a5?rBj%HVEaE{;ety)OdQq@grz%}{V;i4G%S2z<tNNP1nq^UKUnzzOXo2C
zgwh`@ykPdj;ti$`7LKs^fVm$QPcZkw;sISB%zRirhUtgVFnzFe2um-p@Q3*y<}Y-8
zuyPw7f6#OUQwI~rm5*TUGg$i%mfvCeVdlg5xM=wNF;syLnmb_W3M!33!NL(94p9BD
zaEH+t8leI(cf;ZZ7Vfb4fSC(bjzPiF3oM_&!X1{5ap{NE7qIw+rFZo566P*+i(%q0
z|HI-P=582`EB;{qhPfNoZh@s+m^ga+fw=?bFIfD;;t5ut!{Q$v9x#nKXqbAKyI|(P
z%5@yt7+~oh=1*Ao!Qv5D{KL$J`4`5A*$;C!EPtch4>uQDj==ieFm<qSgP98xhtp7f
zFn7c3g~`Lx1uPxG#9=hdd{{XEi+@-=z-U-J!t}vtnE%kj3s!Hz?S<wim_BqGw*C|5
zPgr=u$`5q)Fh0zEu=S#_bPP*Z@OA-A0~!tUFU&reJ{S#)e>81yE<FCABCz;_`2!X{
zP-zSbmS17@J<LBab7AITXoL#D;u{uDF#Rz5Vd)N*&!O5dC|vOi_dhh9VQ3^EfS$ex
zC}%(qFIf1%{E16Hto(=h8&+<?XjnOkOFw$Lf$4+g3wS=lVF1jXFlktRgr$E%`eEf3
zEFHl3u<(PW2blTjG)x^lA42mREFGh(hw)+Q1y*ju{EaRT^9M{Eora||m^rZg1Tzmk
zUSaxS?uF5?_8a>8UtIcO=D^|?R_?&ef!PmJhfbsW9~Mur`hbvrSiHc>5m-FH?17~p
z7$1X%#uH2&W*&O_fytxOF#FK+KP<jr;R5p)x_(&tfR!^aKCC?g%NMY4#Z`{L{R#CC
z%p4dE_Yb-QpnQ1zLiNMSZCJj7wdY{wLAAjtSiXnRa0vnon15jD0LCYv4MQ5ke?*p-
zuyBLrcbNa+=?}vu>;kaz5f+Z5>PJs^Fg`3?VEF)7_`uRXOg$_fVC4XehN(mM1xy@9
z!}P<_0jwPd&(}}`(Cvfe16aC+rGKb$3<~BSSp32A0nA)n`37zfG`+&&4`vQb9u~ha
zJ~|DHZ<xI>|HI6I(dhcn`7nRN^ux+iLi%Ch4C}wb+ykqJVDXMC9ANrk=>_I4Sop)@
z9Y({{!_>iOSh|IUJ1o9oG;BX0%snuDFdAkq%-^u`50-vl>TuC8`{DYb3Sspcl#7!>
zZ$H8O0V|(zYDANOr5BiauyBQ?Q#9r9`D>UvVftX{5vCsAj)Mt6X_&dN_<@Bl%-t{=
zmad@M;1tZguyg~f|6%bDD~Dj^6I>&d0n-O-|HJY(EZ$-D4!VD!+Tj!|{$cvy@d(YI
zaD_M+Fn7Yj30984#9=hLc38fE`5Wd>SpI>zA6NLn#9{FV%daqZz~dh}j*M<UOh3&3
zuyhB{&(Qe?SUh6rhlUR<d|}}R^B2rLFn^)zhqV)6`eF4oEM39UBQE>V&4ZO=F#q7P
z7hNAL-NMwvXjnYL$^#62XaX>I!rTq>A1oZul*75Od;p6_m_K0l!sOu^&=_!eXnerJ
z4{i>cdUP&q+z>{?+zm@d=<3n=u<(M}3k!c(xWdHI^})np=?zB1+zm?)==xxMm_4v^
z8rF}4rDNFmFRYw^>4VWQ{jhuw6Nl-8@p017b{Z`I!rTj^aq5FgpyyARK6rdW)#0R|
z_QTQ_%sg1WhQ%K&oYBpJ+XFQR<_}o-!OVfh2fBWEyu->Ds5vlyz|t?w9CZD#`~pjN
zF!#XKL(PNLuju+=d{}(J+zsph!OBmVJWM@0jb4Aj_~_{hmR?}`VdWew++gi6n0sO6
zKDs`bepr0L;ty61!paX=x<wBkn0}Z(SbYKWKg>Na{V*D)4@SenALc%ozhUJNEZxBT
z3Dbv8!`utY@33?MD@S1I09_xP4=cBz=?GTt!u$>MCtN)m1J+)E*$+>DXzI|puyh7%
zH^Al-VD5*-Kd$r-3vZY_x_@Efuy91T9~Le!|H1Mr%pWlS!_=Ychov)EJi_$D(iKb|
zmaowD!^C0n4-0o#I)>#Rm_O0=!}u`!VBro+53qDZNIxw7!t8^E8_XRrf57ZRr(yXU
z*3O672WuC?+9l}vVE%^1Kg=AMILtlh`q23>`(XCK+zZQx=;~p7Sop*I2}_SK^I+u%
z&h!H{2$t?(`5fkcSh|A615`N%1xr7$^ad-3VCjL7KVaz<W-rV<czS@A<G9KZSo(q0
z|FCih7XC1Q;R*+sy|DO)mH#jro`0a_40-^-+6^%G!r~FuE`XVjt`FuPSopx=0Y<~(
z5k{lyhl#_|0j%7C<sVqOz!iV6@PMgDHy;)cxZDquN7o1A!_0^AF=%Lb!r~9zop3%>
z9lCy)zhU8y9{;fP4+|G`{V+Z(pTp7(EZ$+^i&H-|UBb+P<v$po5RD$LuyPA#FG2m#
z`VQtkSb8GX9k6@>%dap#vG&8<2eThmZo%}U`wylborc9f%zjvT0IQE+;R5p?x_(&w
z4YL;}56l0sc!i}0m^_Szse{=A6NiN(Oh3#Xm^@rPG=0MSi|!7XdUP6=KVabp<Kyxt
zx_+2GSUC$Xr_j<Rx;_{mmVaUSAC{kpwI6N{%swa$GZz*PxWXM~4=ns)=E1@NRxZNw
zHB25(L+yjb6HFYY4<-&zS1<)o8WvwrF8n+mSbW3W56d4gb?7wAeAxMsuyg{`kFFk_
z4{N``%4=BqhlK;WdUQT4-@*J%c05AUG0Yt>^U%WyZa*~qVeW;66RaHo^FO>Chiil|
zVDd0~VE%>K2jjzNh%O`&>R!10Q1`&n3oL)Y(l?S$FcVf!!@>`C?mMjA4yzYn@?cGP
z2v|D+=6+bcghvZl3Oygd!XM@@Sb730Mj~M00?R+Jd<P4ESUAAK7fCCa2`gV=@-TnG
z%41l(!O}Na69WSS%)K!4Vg7}chp_wuQ;#bgVE%-q8(4h7%z>3>==x#pQkXs%4U12h
zy)g4&>S5wIX=wU^$)no~6Nk~T^a~3gSUC*q2g20hqG9@B`4tv_u=EOxUtId(@=$lf
z%!8GCxY7;GJeWPOcz}f$EFHl0L(PZjgV8Ye!qOWo{9)}^Sp31`6Q&VL!_p1Ry)bp~
zbO#+jhJ_DQ8=QjqAC`V$=?>;@SiHmh1=k2=z~T)SZZQ2Y_rT&279LP-7!=GtLh8^p
z!rTwbN3eK6SBH}ijTe~v38hzbb71;l@dS$xSh)bJw_)x?*AL^Pmj|$TgoO(%K4I!%
zG%WqV?1i}p9$#qwgXx3Q(DEOa?_lu>vmfR!obdwHkG}p7)(?b*CoEmV?L}k2%3qkf
zVd`P-fVm4z8+`r@mfm3b2IgK^x`(A>T>4?^Vd)1Jjxh6Z+7InNz{*cpeFHE5q2Yro
z{^9O}=2KWYhPe}#FJb1w#L;P3{KL{4tXzVb52Mlb!TC^k!uT+M!~KOO4^s!DVetgx
z!{Qwlt}t<Q8kW9c@ed1USop)r6LftrK0KVE=^SP+%)jXFg6V_NF!#Y|SU!Zw!@?29
zN2g)=4;Egqd=GO!u5^cP4lI4Z+yP66#Ku3&URXOAR-eGahfp}c!W~x5!0d&&hmb#D
z;SOuR!P<ea@&i`Bp!*vZ@344=)fX`Lz|2F}2jj!iJ1o3m_Q2c=GZ&^FgGNi&u=EHE
z2Uz%_rwf=lu<(Y(KP;cZ?1%XuSNOx!!Qu;+-(WPX9EAB3W-mGoi+@-;g4qj8pD=%*
z>x1!O^00V^g&V9~g{1?SdUP7*PMAK}_zx_8Vex{l55|Y-Lofef{Ro&k3>up6VetjC
z2NoZ&aKjaUFn`110hW$o`5xwPSo*@SA1VM#uTU<Wg82t#4_pGufY}R=H>fy_f|X-1
zJ_Zd<zcBr<bOx&r2*n@F-7t5;>K|Bqz`_j{4jA@B1z_nF7T>V-+_3P3rAMf8ObQy0
zF!y6Bz%BxFA1vQt(Ac%XMPcb37G5w3Lh%h#2cu!>9~NK4>W8IMSpJ9QS6IBk%*RE;
z(+{*A0@Dw(A4bE&35P+j{DYoOVd(%yqszmLgSi)0e!|3I`4`?EgvK+vJS^T|`4?^u
zntGT%7!6A=Fn`0s5$10g4O5R!!|GX>y|8=_S4Y5pSo((LA6R(<O9zC~Jxm=e|H9HE
z%pHXC9W33#@-582Fnw@74u8PxMXx_#=>Zn+Fd97_!O9PGf5OTEm_ArKfVl@2zOZxy
z<HPb1%wOp0Vd`Ks%zjutg2e;OUoaY`9-W4TGc5g}+lS6a*AL^v?SYoZu<(b411z1w
z)T7fdcf-n27!9)@mafqC!NLg^e=t6Je89p9T|dm-u=E3qFBlCAADH{l^~21Cxf2%8
zFdCMxVEF`HKRO>a4-6BB=|fK^FnJgaE0<v54QrRd@)yiMF!eAR7G5xUn0i>b01H=K
z@dwMVFm*6<VeW;|u=D_vhttq}2}}1db+G&g%Wp9M!1X~HuyhYgFEAPwFEAPwj!<oI
z3T7WHy}<kr3s1Pc&~y#gh=T!32k7NF4&`uRm_ArI!1@6&cfl3nV8Fr&9&b?h!u*dz
z8(bJxkHX>$R{y|gxI!EZSpI;y17;pfKg@qPv@^iM85Vyq8fGrc99;Tg@ddLV7XC2*
z!{l-4hm|j|`~%Amu<(GD1Gx0V#9{hj^Y*ZO1B+LfI&>PA?_uQy%-!hf(D^X`!rTd~
z|6%DET|EXLn*U(pu<(ZQ3DL0f0~X(~asU>7u<(GDv*_l*{Rwpk%pb7$gOvktaWn&9
z^%g9CVeUW=N0>Qi+Tr*9!O9Vse_-hi=5Ac&2h2R!_#Z63VeNldc*4>%Og%ac3on>B
zEL>puhLHWRau}A6;Oe2_iYxuW{0FlS9uCm<G%P$}=EB^EPQ&~M3qP29VCKT&6I~xH
zox<V~Ru9Ac4U>nt6J0+#9~S<wauAjd;N?8J1}GoqZkT^z{(+eTa|cv81_etmu<{xn
z{unAz1z_%l(J*^q{)d~3stwMAse`#6=3kgUVDfMcXbhM-n0{D1!T5y24JHpOA7SYj
z<{wyipjiv&!on5i4w!madV`f8u>1|z2xXv`f3Wa_#T%?#$Cdw~#xTI#0V`+V`k?hF
zEPcY%!)TcQVKi*q2R3d8%m1)=f$4+MF#BNf1e-60r5{+m08@`n!~74+AF%QtW-p=i
z0}F4Mewccgdtm7RMx)yg(+_hmEd9X5Vc`Zd4_!a3e1L@yEdF3LtlWZy6S{sFALc$-
zJi^iwEF1{MKg@hs`i0T3^anE+W*<5YQwQtk!_on)9)N`hx;_{mmR?}t01HQ$yI|_j
zX;}Qj%3D}?!Tb$#7c6|x^~3TNEFNI`VEG3gZqRxJT|Ycrp!ozA?l5tfdAQ;qrXH93
zVD`b%CyWoHVeW?UVdW0YU$A@(a|cWvjE3ol)qAjTgoPWdT!W=!m_BqGo^PSy1k1PZ
zc09TUC?A&JVD`bxfwd=K=@3`=!Tbq}Cs;m&m7lQifTbs>wQvenZo&MC?jD#zT;UH3
z7g&CU@nJN~TwM0U%!j!bW)6&o#~U<UVd`NtEZxBJ35*YOFD!q+;u)q7M#I#h$2%-O
zVEW<yfN6x%Fmqw?18eug$^n=>EMB16;1n#pVetg>FO0^e4`u+&UYI{%;SLK=SU!fS
zN2g)>VBrQ!PcRxLj;;^Jhov8wdtu=O^EWIUVdlZ~!DyI1SbW3tA+$V!m7B0|g6V_N
z@Nx#4K49?$6Nl-C<qw!XbQ+d^VEGM3!^C0kMArx7!~6@2cUbuki&vQcVCvCnnEzn*
zz-U<c0W%+6A1q(N!xtKEuy}#B2VmiVt{<L$pzVE_c`$KUc*4qQbp0@MVf7Zw-7s-j
zIAZ973ZT0aR)4|FLw5&M8wLf7Cv^A2(gDmL7#g7hF#Rz1!ulbwe1y~gQ2ns@f#rW#
zx`)Ld%%4!@7!+*05SCtG`Y;rt3BddXvkz8Yz~dcFJvtYbZeZmAOdqTqL06B?hv|oj
z!`uN2M;IUGPMA6v4G&jn{)LSnz{(w1zJ=uvm_8T{OK-4vhNU}LI)eEF7QZlkFd7zb
zu=EBKhq)UTAB5r$R$s&P!_ooF-RS1S)WO^f^DnHNfW<$|e{l6^3|PFu^uyG{%YA5h
zfTj(>g@!v!0Ons<c)@6xJO&LlAMQUW9~Q5$_=M$Sbp5dSftCBPc!QY-3wM~i(DlRk
zF!N#I027C~16>>@4-<#g|FComqhav}a|cWxoQ8%UEWN<m`>^sKZVohD;QG)Qu=XNs
z{}(JD!^}fdj^IM=hs6^t++gt!3pZT(4<--uA1pp#d~|aV7DHGtb7AQYB8nhk=AoAp
za23#Sf#+Yi1cU+C2UQ0VMUXIe!qmay6Xq^hxZrXhEdRmM2`nCA<v+~*Fm(u<AuL$=
z0n4|rd=H~x`52-alZ2&PSp37xf$al;xd)~W!vtvj!^%lm{KIHi{|8ntqU(qG8|F_~
zIKs?B4^MRcuyBFpTUdI6*$WFFSUH6Yz|uP`y}{~TSiHg9g{~iFF07n^`3Ghm%zZe+
z4{9LHzv$%v%s;SpEmS$2g82_-A1uGa@;|KnfW-$~Ba{J)Uzk2vdVz%}OdP5lgM#@B
z=1y2S3JXVA`p3`+6@d8*mXBcZ1`BUkx_~OjpkVr7_QLWF%-^tZ!O(~%0J9Hf9?bnP
zf8k2MFk@ih2y+K4AHn<sHwUHxjfRB-Og$_e!2AzOS7_ScTv)k{-u{RA2Uq;V@)s<g
zVet=3SFmycZV?&-R?flP4a+w$aYFGAD|cb~VCKN|!_pU;wQw#hTwvi1%WtrFfaOnE
z{K7S&F<{{YOZPAumJVR~8%-O0{WDA+7M?J5u=pe74_x^l7Jjhw0W%jy!{P~6PQk|U
zVEG>wuejnLmabv-Jj^_pKM2J?EPP?<6&8-L_#_nWF#F*76k4Ca#9{FO^DjCLOFyvq
zhNT}^xWU37T^~9h=1!RVVc`iYM_~R$*9YUn@;A&rn7d)_fTd@cdUP5VUa)ik^9M{E
z=1+8eFg`5(!`ur?2eAAM^A}7#E*h3zVD5*hgQZ_sx`2tpXqf+D{)B}eOg+q87$2q&
z7Ot>z8<yW-;_z^QrVI3NfY}S9VeW+42P>Ch=?j*wVD`gkxV_ME2$(srbb%foFnut8
z!onX$!`ul=m$<?orVdta!R&?kA7{RWIRK4@<yV-0aoLNe4ddKcSh%69!^MY{!!UVR
zIz*3em^>~T79Oy0hs7%{_2}ZT_<*?w<_>gqxcIQ}hQ&ABerWo`r4N?w(fteK!`uP$
z7rHqx{jhuhQx8)I<HOQ5OdOqtse`2>m^{oMuyO$=4x?e}Vc`d(VdAjxfT>5PVdlf~
z5qi3VrAKsquyhWKFSveac*F7!EPtTuhlMXp9_9`h4NFfjf1vA!g*z->VCf!KPQc88
z+k<8RTphH32s01nZdkaXhX>pkm_BH^12Yen4q@Q|6Nl?VW5DAbY93De(DcE$aDPJG
zVE{D;=6<+3s5p!dqhb1C=>|5A2Md3gI4quE`rtIwT$sOM=E1@p-5qd!PzJ1h1WQk_
z_6RIp!Q25=jzPiF2TUK#ewcYMcVK9Q3c$>VtAqLn=5Clfpvo~RsCroXfrSGs9TAFu
zSUV1u4q){VrbTEX(0m4Kr@-6`^9L+k&~(AMFn`0^DX{hrOaV+D#z&`N;R2&!_QCYQ
zXmow(e3&|TIRgz(Sa}MIM|6F#c!s$j7H+U`fTagmy9`}FOde(rtQ>*)2WBr!99=&w
zU%=Wqu<{*dF3er9^o_0`HckV}@33)vm^rY1A<RGM`eE@5%a<^9uz6sZIJ!O<AC_KW
zd>9RL56u5Caa=S^9n3wj@PLH_E`2b2VEF|Wf3R?a<p)?iW0(U=Cr}#Zf0%i&^o+|L
zF#p5q4Ol*crDK@;aM=&j4>KQ@j$rWz(+6`0hWnucFneJ3!`gu`cfrC1svMJo`X8nq
zR-Rxg#4Z9WpJ4d}=6~#}F-2kd1ttUQPs7p$OdJ;t%O9}%5*BX6x&s!zuyBI$Vd06(
zUYIz{-!K|oKinPA@)o8KM#J=B#5+_yOdK9RFpW?e7XC1QqpO3J3osh0jRBTUVEG(Y
ze!|6J3USb|d<xSC%Rex29NOT*F!iu{4ptt*6+rzD=c6%T`eES>D?eb$VEGHiCq%=-
z53UZHFW`JM1}uHT${ARCfyD=`+=tO<+TrK*!16!3I#|5JXmtO<_%Ir7J~Z55`rz$;
zs5o35lmSbhu<(b4D^!{RoevX-(J=k6bO0-VVfh+nE=)Zx8kT=y@do3=!V{N1So(#P
zTd?#8OBXQn(DlRO4;Fr~d;?PtD~HhI1>GE&|6t*OUVp&C0bL)g-2#(`@nPi<jE4CG
zT|bNui)VN^K+`R(UVy1Xr(ynu(XjRc%s+(60eF6arYl%JhPf9O59s#7_%Qdu)WgCX
zRvyCSVd~Lom^)$V4wjE#`eE^kt`EkCg*PmH!Q27szrg$lGY_T@ordLmSh|PBKg@iX
zIq3Red|3R$(j7cLp$Z6v1I#{He8I+lVg857Lydz|Fn7VyEi61?_QS;C8qgRp|H0B7
zEFNIy5{iFVc*D{;tek<FODNo7&VhwHESzBaVg84yBSgd84bulV4|+ZbF8wfn!om%v
zAMQVB{=ij^!0d&UU$FE5t5;y{LRkJlcLyx}!r~9+ZuI&dW*)kJSh|6WL){CTPlAi1
zF<}0LnG1`5SbV_5arqnOUs(Eq#XqdvhqdeA;e%#BEPcZK4GRaDGMG3lU%|v-G)x?p
zUSQ%dnvgs!eqru_`3IJ6Vg8532h3a;4f7w&epvc}#WyTmVCrD{U^L8JSpJ61`@_s3
zwjT$x2c{2ZKCv_`++pP)EZm6I4+}R~dV=LAV)et~0haz?^#IH~Lgv8q!O9_+dtmWM
zNI%TKF#BNP0gHcl`i15zbn{^Tg@r%NJaloGztHu=!X2g`=6)Cr6GzYIF!#dr!@~iG
zJJIch>4)WSSiXh18)iScJ{TXC?qTT{R&K!BWw3C7sYj<_;S7&AbOq3M5V|lrA7&0L
zzF~Y=IgG9zoe%RbtbB&)hnWX2AJ8>I`LK9}g(Fm$0TypC8kafn^o8bMSbW3k0a$v3
znGd63^6+v1%^a9I3>unFVCKTgZ&*BHs7Dikxf>SFu=EE@7ih{cxlsFI`5zX3uy}x(
zkEszV0`(^>o?z`@SUAJ{1v3|_3r@l67g#!n<p)?kfbns<18P64-3&7rmLFl}!o+dW
zuz6mX`(f=rnEPS%1TOtBdtmhiEd9dWfm0(y0u~Oid<^q9u5g3sLXfa{hs7H#{9)-4
zp%Om}R$jo;11w*{$`f?mFmV_SYxl#-6Ii-{(dhbN;RegUFmqt`!)RRkVd)&^Us(L1
z=XaPn==Q_H9hUE4`5o47gT)_A9z#Dg|G?D2{0%o3Dvz!Yoexuwp3Y$YLst*yqosdX
zIKXIJ?H;&(GzQFmSos4>Pq6ferknv5FR=UxONX#@027C)L)Q<JM^9%kf5XxPj7HZF
z^B>H8uy}{jF#o{Rq3egm7fe6w+&5VK!^#DiIq3Rfd{{XF%WrUVq4R<0e3(8M4NE_;
z@Ix=pVC4cVTwwZOG%UX0`e5k}nm%CZ3#J~OMo-_c{0Iv-T=v4uLC<%vd<`p4U^GlU
zIt?=)77j3bVg7>2qw9n5Vg7@ayRh(r(XjdvrXHP!#UHwUbalA+u=E3S2do}|nTtyw
zOdRGvSUkY|jVs^4^ucJDyJ6`MmTzHvm^wl<x;Zd+!QvB_epq^e<r7$Xhw%x816)5e
z-NN!aOh2rAz-d1;ox|*b<$qW>z|6s=A2tsN(+}$h!qPP?9pTast2bcz7gnyp^yAV8
z7l*nZmhNHUO>F%S3vXCDgqaIVKd|tGiKEl7c!${!Yk$G~0i)6N!Q2P)C#?K|xd#^R
zFnM(SFn_}O>oD_R?uDg4Sbc`BA6Bo!+zpF&7!C73%s=S*Vfhs{ZVQV?SbH93FRUIw
z*N@JJ=|j(VF!!LVhx4K79+nSa?t;Z9uJA*bhw)+I28%bCINUxc1D3B~=^j>2!|a36
zu<(UygHy0{1Ir(<@(U&o3x8O72G<B>z{Fwc1xCaC4+}?J_Ct-qpkVnPrVkb#7z%L+
zz~T$;4jk%W;R>4{g4JIzf55^4hj#e<6)gS3{0qxR1l<iyH}H6amLr7hg}Dco&tQC5
zxWW7na~I5fm^v5@t5;#>!1TlHhovu=I7}amhUtgpV;CP+j>7n`bPUr6qha>I!W(7|
z%-yj33rlA(edsi-zJ{e^SU!g7hm{ZL`e1yRdRTfPw!DC)A6UMJ<sX>;VdmqaVfhOt
z5BDcDUBKKAlSik~%Uf7}hlL-E#ufgs`~@q=VdV%c9mC=WE{^5^n0{D20n-O7uVM1&
z{zNl|0TypC`(Wu8mhNEYpzDM2Vd)2^AHDu36rV8lF#Uwo!|a9SD_Hoz{7r29!{QHS
zFR}Vy_QTR2EdRpX0iy|pJ1oD!+yT=E<HN!qRxZNqh0!qmuy}`+cd&ef9zHOA=rpXq
z2@{9e3-cGD_=n|hSa`tV6PB)F@-Xvp(XjjjOV2R#VCfN;KA1aU<p3-lz``Gwewg{N
zaEG}U7Je{snEB}T!{lN493~G7KbZaSas{RUO2gs@7QQfhVdXx|UR?TN<sB^l!Qv5C
z?!(dr%zUV|a0-@wU^Gl0JY1mlAzT9?23olZa~Do+Q1!5OBTPLk|HJA9T>4?|gyj!d
z{)L4XEIq)&5#1bEzK4Ymtel3$D=dCsG%o$HaEFCA%v_i|VDX8rAI68-4>JcA4zO|=
zmY!hh(P@~wVKmIYuy}>lV=!@a{jhWovj-M#u>1&17a01W0<d%oGY1x*uzg*yc!7lr
zR2!Uv>4(K9Ed1dL2ryv&gq44=^guuxTpB%GVBv@>-r*Xd44AuN?tt-O;SN=XlY)f{
zEZ$+^MXo<!=E3zt^BF9DFdPDvhm~V6eK7yS%1;=LOFv8>Ec{^VVD$lvhKZwx6U^PP
zaDbHqFmaeZ7!3<wm^)$Ou<(QBCs?|G$-`(^JqydXuyhV9x6#uDOdpJf)l)EgVC6B)
z99TYrsYj<_`eF4IEFZx7FED?>XmtJP;Rkas%wI4*F8wfhSiXnFFU;MraD({=!+z-a
zEG*t(<G-+S0~Q`If1>M$<#U+5u<{NTf3SWb%%AA`Vd)I!KbU@actX=Pu5=IcKP=o~
z<uJ^>FmYHqL$@Eshv|dyVKhA4VD5lPKxy>y0xArrVE%^r4=w>^z}yE5f4DlReX#I>
z*#p%Er(pRCCJ&1T7!6Yhiw_tdorbmVVB`L<d=AUUu<%6J2jj!ifey620ZTXN?trOB
zr(y8}%ZD)gVCfoNJvtv2j<9qK(}$jq(DlLiuyhBDFE|a&AF%QSrXHP!xfd2*Fd7!_
zF!!MAgYjYhhs7r>J;MA&$bMM+1Qy;f8Wt}wdvMV(`(gPXHvbE&|6%1ThJI)|h3SX6
zA4bE}!@?8BhtaU~4dcVgE0}#S|HH*$3ZXR2UMLs7pB^R-D<9C^15*!+H+XpiO}{Yv
zVd)8;FJT&?G|YXlbPH=Q!qOA0{J<6eFn_`07aE@E<sHmFP;227%zrTR(c>L15AzpX
zCn^JGJ}lqE{0;L5tb9P##lQfMKd8H5>S5x9(jP2cz`_ZZZ(!oEbd7E=j1MpOp#Fn}
zKP+C+!x5$rM#Jod#Xn3OM#I7n#wSF>%5hkD!rX^TAIzVyb{j1IVCrG%78Z^$^)PuD
z4U2!6z3AdF^I_#7OdgibVd)qy4vkNkdtm0mXqY;fyI}o1Sh&N=f0#O4@c_$Luy}^W
zA1pt?@&_(|!|a2FGb|s${0R#`SUkhs17*O(Vg85t2bTU};RC0k`p_wueXw>PjE1=z
zMx*P4@nPu(R_?>{5j@<W@c~nhPQ%hKF89O25nUgQ4?F)2=5JU%01IE7{)f5?mM&oR
z9n3$lasXxzE*h54VE%{s8<s9`>4U{DtlWU5AGms```~;W3|M%>(h<x(Fg^}#7{ajh
z2a|yN2PS}nhWQf~fAIK%x*wLmVd)Ir7?^%oeuAZISiXnlW0*PU`eA%nK7hqHOdl*h
zVeW&ehtV*3SUP}(Crlhh!_>oRsQ+N@hNU-H`2h=mT<HiV4om;Ad<WA9OAj#f;r7B9
zQ2Sx(VE%?l!@?UDo-lD34NHHx@;A(VFn_`H!Dv|ez=%KSegT;MF#o{x!DyJdu=I)^
z-!T8c+zHbMqhaE(@P^g@uzmn6Kf~0c(=dO-;ul84`hB?43(Opt|6u08!ULB6Vf|Q`
zdUP5Vudw)m`2&{zVE#kTmoRf+`eF9M#9{hi=>isyFnJga3wKyJ!^B}UOde(rOdLkT
z@*8^m!^%Hcc*5KR(+8tr=>isBu>1<6Vc|<C9%11Hi+5N$fVmGAt}uJiX_)(9?uC^D
z=<0CsVfx|jh2~3K>e0o~!xxtBVBwFh9-R-%N3d{!xgVA;(bc2#Vc`p_hhXx!(hW==
zM#Jobg&#~Dmi}Spz`_S652vBw1<U_1f5Xx}EIq(zxIP#I+J1()111dfKTIDiU&G{K
zG;F*W7LG7|Fn7b^7p5MahNoYcJD@bo9Q6E&t{+zZ!}2Rk9_AicK7om&)9B#@iwBtf
zFn^%ygYjYM52g<0A6U5rQ-_O&xf>Qv=;;ZT4{_;-i$l`^Og}8#VD5pdLu0_)4J&tu
zwhw9!to;uw2VwaLDvg_hx)+wtVdV@gJaB7-$iVUgdbq*(5LI|csQoZ^!`er%^bTte
z!}1flNwD~Zr5{+jfu#qSILush{jhL`sfU>d>;J&gFE0Hsf5O5WmR@1u35!>lIq3Go
z_^|aKF!iwZKg|Cy^I_`IX;`@dvmZ9@4@*z5@)%toj1TiSEdF8shlLBwe3*K48s<Nk
zKA1mX?tz()t`C-eVCrG{1m<2?yc5z7OD{0}u<(NAKUlgzw;z`7VCe+b4uF?e&~gwK
zpD_2p%z>$cwR>Ul1#=I~T$nvDeJ~o9eqjASSUAJ<!@>pTADDWWewe>t;;?vtxdWyS
z7Y&OanEPP)9#?utHwTt~VD5v32Q1y8t4HU<)Whlzm_C?)(A5*-!@>(@E+O@>_=Ncv
z7XC1OaPwg06ifn2!_qG-zF_9T;vFUqqoLa16wG{BJi;Vk;xK)<>V24cSh%5^1D7Yj
zfVmsyf0(}rXoHXU!qO4U9Afpu%!j2rSUx9KKP<n)${kqxhvj3K`Gjbg`(f!077j3T
zVD5qW58XUic?Hu4GY=MjFdC)~T|X?pqQ@Uh92WmD8eKn(4{Ha)@-v)<DZoL)%6XXk
zVDSM9516|M`5zV^uzCcR5767iFlWHR6;{5&%28N6!u$_Y4>KP<eBthbme(+QVetoZ
z4@^HSf5GxS%zT(TVDSZ04{OiC)WO09*581+8>Sv+K1?4hUBmRl^uf;QgVE^Y{IGHr
zrVkeliyv70!}USK1D8ISI7~mR9D(UacQ;HuIt>eNn15mQE-XCI)no9X<tr@zz``FE
zKd|^l*9RAe>W76pEIq^cuyl>CAI68JH<){2^%2Y;uyhEw52_J89l-Jp%-t~i;pr2q
z51oRg2Ut507LTxT0GRu5<!6}xVC@K4`;Sn00doh;d|150>Q8vOfGNa5!}1qQJuDx=
z!V!lyqJ?pVA50vU{$TEag(oaM!{P(oLRdP1xfd3{uyzeBTwvjgt{;|OVCevsPGB@F
z{$b&Xt{-MT%)PMqhRyrJ@&PPf(DlRkFmqt}2WBr!o*)e^Ct%?LE0+oCgDQZ91C)!H
zmxKurpkeNZl^?Kl1d9&>+TrIP!^&ZpKZp$nSo%TtH!R*^^$09Ip}Pkb?lASRauX&F
zi$_>Jh^`-&eqio}g$GO>EIr}M&+u@9#v?2oVftX^;G$vn!^01{?gAE$xb(rqVg7}c
zPq1*rmG0sCpyTW?dtm7ZR_?;YVc`PP2cu#68kT=y<u5Eh!OVlj3rrt64bu-Z4_2<g
zXms^3KCFC$g*PmIVet)92P>Cgd>9RjXW0HfSbBk#|1f<p|G@O2)9C79<v)B}2wejj
zALf5p{KLvWc)CYZkIsdqADH`L;RuUAc=|-w2<5}t@34Fb3wIceE8oH7Vc`gKKdiij
z#Sbojz}yA%KP<h#(lLyNxeMxCI0aV^6@isou=t0C11vp5l_4l-euc#sES<y3e^~y7
z`3Ip9jRiFa7EUmCpsB*ig~cB%JaB~<P6MD4Fmqt`!{P@jO^|}+Us(A8OHTwfLKVQ`
z9~N)0@Pta^q|ohy#VgDou<(F|54u4xarANqmi}Ss3zvIf=@)J;G(KVZ5T*~^e3*Ll
zaDv4HOdpI7Q;$x=^uz6g+7C-luy{e&ht7wkQ&{-H;sI7ZqN|7TVf6$oy}-g9rXOw|
zOaqLDw)<i611o=F?uNw+jD~4Lr(yQP@;NNO!paYv_Cv!7T^%eOU^Jodhx-@mFk<yV
zl{3Kf!`cV1dIQ#<fT_bp!@~`#0Hz;CLxnLYSiXU!Gnl(!@d!)*7#g7hu<(X*8DQll
zEWg0qh07dRxWeRN=E33*)-J)7Z(!<R@efOHu=s?fdsz6w+zF#$=ELe|SUQ2Z8%D$I
zf$2l1;pIOxJ;COIVf8o6d~|&<KCIsWb0<9Bq2|HVq0_K@0Sj+fc*5+1`43$mj1P-v
zSosC>KP+7k(vQnNSpA7^4~!3^VfMko7siK`W3X}!<{p?nbQ+d^VdVy_Jpv0KSo%WO
z2jj!S0Y<~>ZJ4{@=?kU-jfRzvu=s<`|G>my{z21*!G+liOUJNyfwd3e@@P6?{)EZH
z(myQzVCfh=9AWxk=?50xuy}>}2PO^+5194n`5mSYmS17%02Y2Q8m1qn9#-$e${$#G
z!R&>FBTOHRhWQf~-!OAv?u40-OFzsU7>yntFn#FmhN;J(q4gOo9$@(pLmdtQ*!U03
zURXHdP!1P{m2<FgfQ27CU%*vjXTbEs%3oNx!_psiT?kQFxWUp3ES<q<m^?xUgayl&
zuzUyeH_RV!|3kwSq6<O7?1QC4SbB%~2gb)~KeV3=^DjK!VD`bvUl@(B8q9)~`>=Wz
zrXSY+hvj2fe1J7!Az<kgR(`?UgGDJ;Ay~NylO#6YVd`NtES<x`6BaKpc|tTSKa%SY
zn0{Dz!Q4T}Ui5ST3lEq$EIeT84c$Cg`heR9jenSWSUCnON741e@;NNOz~U1|!}P=I
zd360SKFpmkK0N(E-2)fL!GPr#SosGF2bldZ|DbD!<x^OA!OBsXI6Pd?G{WRz;Q})s
z<`0-SEIwiR5oQc5zre~Jm^{oMFneL{f$4+Mu=ENmFJSQi3pW@Ivmd4pM#JhGSpJ2T
zKk)Pb&G)c)gz3XcL*ohNf0+5W)Wh;QtepsJ_rcN;q4WX^f0#Ki|HJ$P3qP1Vx;tR{
zVg7@KKdk(OxdY}Obp5b!f{CN^Vd0K04pR?{ADI0x8YU0Z2TR8=aX1Z)A6R(<OMkHR
z1WV5_f57#@7*PGNbOg&M@b(Z?9+sbA+F&%yT-dxHy#EGG2e9~r`4grOM#J2P9=@<}
zgVDJ3!{QHS9!wo9{$S|<=1#aeGzKi3(ZdgB9-Kzg2QwGuPFOg>%z^dm(X_$2FnzFe
z3(GI?dH@=Za1Ce-Sh|3xZ<u;0O{o5Y`5RVF!Qvn0ZdiGMW-Xix(+?}hVEGm19+<!2
z8gMXR>d?~*EPde61{a2f8!S9w{)V{^t`G+U7LPD@!{U#SewaNl^)UNk=?><8^z;pL
z2FyNK_`~8I79Ozpg~`M8!D(nb!}2REKfv4#(+{%;t`Ew9<$GBAhQ&XOhM9+}{(<=y
zmQG;#A6A}2tzm$*>tTF!8kT-w?tqzxt`40K3xAmZVeW+c3ta;m9~N)0@PWk}nlhYR
zSopx)0gESC_`%!-lSelQ7C$g~cshZ`E36!c)nn-TVd)kYZZLnt+5<2?EFGfjht)T*
zaD&w&u=WScJ+N{OT|bNuEB|5X4%YvN#Vag-!qmfPm^_RRn@56~gDwwK2cu!(0t-J_
zx`FA3g&#~kIt>dSSUP~k7c5?3{)d^5t{;|OVBrUg512U299a572e9@AEWTj*9!A6b
zgDd`E=E2+z<HKlJ{={WJOdTw~Vd)k|!|a8*6Q&-WhNUCezHb-}D-U4qK-UN3!|aEp
zKUg@z<O$KRbOH-6SbiX+50*|~@eGScSbBiT<1`1FPhsj{@c{D=tUQ3F19Wp>=?bPF
zt`6Gng1G~h&e8S5{0YyWP<^oQhQ$kpKBxdJUSa7EW<M<cVetX0m!R4(C|G)hNx<3x
zFn7S?1*QN84NGr0qzMYc(mBi@1T{bvz``FEA2`Dssvez!wKrijOh3$AboJ<bSh|Ci
zldyP)`3GG+1|OOZVftY83d|j_^bM0o*AJ72<s+E8VetV=53qENt{;|8VDd2iF!#gs
z!^UyZ^~2<0=^0jj!pw!4hn~)0;xKz*;SUQ(SbW0r2YNb0mxsjzEc{^ZftiP%pJ05L
zy)gg6!V%q{=zN$xuy}!$7cg^R>S6wb@zH5mJq8OeSiXa~n^1WQa|g^^Sbm4mFn_}2
z(P>yZfvJPpkFE}#5AzpHKP=v1@eHGJng>ny=-~w`*I@3)<$qYX!_ooFJ+O8IjE4CO
z-Tmn41Ll5MdVtjjuz10xA6EXt{0Vb6EM8#w6GJ~#0M=iF`2#8pUss0HeyBp2epo()
z(eQE<TCYHrV^Fa0fwlW!^*<~h!2E@w5lsM=k8#BpnsPW7=1!PDVEGu9E@1fzt^vw`
zg%`}7u<(ZIhs7U^hH8UTuyhM6XJO$0a|cWwz1)Er0F#I5gSiJ5{xCi)J;3zAXqdUM
zd;;Uc;u}`(!OI7jMkoz47nW{e=@}}`0F#Hg4<?R_hWQ634>uPkfQyFaV^}=E{Q*@E
z6Nlwbs4$#@+Y2=ZW-l!MVc`i&CoqjLd6@a=;RF+h<v&>Zfa!;&3z#{u@&uONVD7>h
z572xCE2m-M4KoMkUzj}1UKkBar?B{k`5Pt=D~Dh-OdLkT;uS{2(k(3g!0bi$H%vb|
z4f8L|JoI=(R}bUE!Wrg%n1A5%&~SqBVKgj%z}yWhM`7U&OV2QKVfxT%Sopx)3ri<3
zarATn(+>+5m^>`rVf6+q9AWN*=|iVs@eK<Hn7J_j!Q|2P!T2!$!t}w?5iA^G{(z}R
zr(yQMXjpi`%57MEimngFhs8Ubk0ZWd@-P~fZ(#WoW*;mZVCf&G9v2NW2j*^A_!9~@
zn7?582xdRb9%Ai><rA2FFnuudVdmkYVd`M%4yFz!4&&pZVftX@5!@fp@(`8|ap{MJ
zH>@6msfWc2Odgkh^maXL{14U-gz?e!!_0-nA8ecu)((Klqw9n5(c>8w53qayE4N|l
z(P>z|fweba_QK*1R*s?TgYjYdVD`bn4`weR{jl@`D<5F)hoxhfIk517*$bm#`eE?}
z%jYn2VCe$pFPJ_U4NG4zeX#TcQx7W-VC5)GAB={jGuZeaY`h274urWIwoU-151odU
zAF%L*rzcqYMc07FhlMjNK4IYoqtTROaADyE3wKz!z|t3n1~dVfy|8eEr3V;|rW_|1
z7N0O0SGc1a0COiSzrxZlEdF5efUXb5howJQ`i0pG<HO8_se|c*)6jT=#RDu|!1TfL
zKRjNc8e!=S=3ZF+3(xmZbD_#HC|LZ$>R(v=!Ng(aVrYa4z~T+24`vQ5+z7=Vte%Cr
z8y3#6^aBeIsC5_=OddwV+zr!D$bMKlfrT^Ne(1gwm^dt7!~6wv2uwdLykYKug##>p
zVCeu|Kg=8$4Uczd{SV{A(i2P_It@z~uy}ywM?(5w<sQu6Fn7afn19gigYjYc2F8b(
z2cu#B$K`LB`LJ{VOHVL=!{QU>KDa$l224Gyod!!sFd9}KVz?8kA7(!+U*dEpG(AAI
zVNfu4!rHGe8fGud9T*y+0x)^lx-VGy4=dMU@d{OrLBYZqCIL&2F#Rxp!sKBzEd9dt
z!|Z{phn63(aE9rF(J=im^{{w=@nP`@^B+tfjE3b?SbV|Uf$nZx@ek7vORq40!_0@p
zC(NBNadaByKA5{<?t!&yVe;tuV0@T8FdCMgVf7izoiO$2G%WqV%3D~tp{qmZ!@>nt
zKfvM-)((WNBS+T<<HOoFu<(YtA69O{!V~5$m_8T{QwQ@O%p6#G2U7<t58!+#0~T+v
zbOQ@-m_K0Q0P`nQ8=Qj07fe0O92g&-ZlUQIt`W+Br3+X&kM2(xALdV}HcSd?KFr@R
z^I+i%cL$nISh|9(+klnhu<<{bzhVAC)6Re%uCR6@EF56%N|-(9`eFWqiNn$>tp0$R
zL$Dr!8UTxbSbBzq8;pi3Lr~Cq4wheF=?JDDR*oVxps}F(VeW*{uyG8SIk0d<(+$7>
z3|9Wb<YD0j%MUPfVDc~;mVRLE16aJn+zrzYOXo0sFd7#AuyhRT@4;x8I4nM3`p{{Z
z|6t`OEFHkq!O|7XKA1SneK7aK`~yn|u=)!YKIr~{$;0%cn+FRIm_C@fF#BQo1C~Bu
z{)MF@xcSg{hNTafeptSO#XF3Kse{EMtUQP5gV8W~c>aRg0}CgZILuuzeYj{?_`}rW
zvX_v4xIU<RaH)rh!@>(j!}2{$9v1&Fd6+nihL!U$J}jPK;SY;P^zsH4f3R{7R$jxz
zVftY40ZRukdtmxt;SEd2Fnw_QVc`Iyp$5Rh85U14_rc7Cxd#@{P;GDuR_?&k4b0y#
z1#oj=0%$bMU$F8Kmi}RUnEhzlaB^Ys1J{S<Uv!P|^agF8!ty;V{BV_9Fmqt`!SWxh
zoPqP9^%_h)It}wLIt^2Y?mn0}jK&CWsD0?}M(4xy!}33@-2h8pFh0zDm_K3qU^L91
zFm<r_hS4x{VCfL151odmH>mqz=@6DaVSHFPq3egW-_YX?=5JX3fYC7hu>1nczp!!x
z*8YQ;2UCwu!}P=41@kv7Kfv6LZZFInm^e%ydb)*$E6g7-eJ~oPAC^C1@d1;E#V5?2
zFmV_Sb2rQ!m^eBOQ->}Nb1$rZLpKLjAEN7n@nP<U>4T+rSbYdf7qD~+(}zyO;tiG#
zVC{BT{K3K(T^}r5VC@B1xdT%VOZPB!xb(yP4|4~M#-$Ht4y^ox#S^UDhvgTzzj2gf
zu<(PG!?5szxf|vm^mGBUA4bFMhvhf)d<%0IOg#n-&9^XnVESR{23PupnF9+47!Avh
zF#Rxbn0j;?77j4~!utQP@Q1}Gx;~gau=EEj|6%%I^**{j7#|i-Fg`3EVD3h5$HLUX
zXjuHh@(C>6!}u_Fz}yGZ2czNn51Kw;>S5wA^Kr!=EWTjveOS2#qhaL_x<6s|!`uZ+
zZ?JTP9$zqb!SumsSUAD*4=lW3`4*-R9=|Y+P#TtAVe+ti4D0{F(j9tx4yql#?;lp~
zz{-8}{v=F2It_ClEPP?%25Sev`~?dqbp5dO3JWh-IK%Y8!V^OuQ~>60So(&A8!Q}Q
z;;?XnYJ*d-_=m*<OdTx!z|tX3_d`vB*$4ACEIq-}1uPz*$}uUl^b0G;VD5ss6H_BZ
z1QzbFavzreVeW>x3!)m6goPU{K49fHEIq;MaZH^M5t#pA`5zYUu=IgTKg@lwaE66D
zEZ@TNA1r-AY(tPRdtvd09!@ZET=t`fFI+v;3RrrCaxo~Fdtv^B(XjY~@nP!F4S=P4
zm_K3Z2&Nwvp0Myh*AL_4%C9hSm^xfE+?~*R3>L2F^#d;b@bHD|hw)+Yh90iy=D^|~
zT^yDk;WUN<hyX7AFniI{1w<G1NL=wttPSvT4q6Vt{0l4J2$};8cbGe1G%O$D(htk`
zuzU#1_po*(E`2a@SUQEdAC_KW^$AQJE*ho}mi}S+f!O$h`46TaR^Gz=fy-XFI5fOr
z=>(SUVg7)t!^MDxJ1n2T-3=9og%2z|VC4<EaWH#f;xO~j%L8=%uy}^07g)Z7*#|Qh
z=0BMEFdAkLEFHkY6Gp@2VdVo%ADo8z6UK*?Q!sbJ;u9v1PQ%p0>L-{zuyPmX4xHvd
z;}4ckVCe}Ke{lap$2ZXJN9V)rgSiJ*e!<*>t{%pRl|ShIhPemkFPM6C8fFhHykYSN
zi%*z2==$J%Xgt8`e^|W@^FPdAaP?>mSiXU!1DO9|<pQi9fbI{NJS?5U+zZnWYtO^X
zg~`LLhqd!y`2-e^u<(SL3$q739l*q4=E2N|*$0acSbGefPoNrL<p!+%1mnZv1C~Bv
z=?SV0K|#|W%zSipFh0CoK<LEEg2e+&J<L5=6=M;EryHodVBv{HDOd=W-eBPcQx6Ld
z7!5CHz`76wEMLIV1xy`GAB@K34_N%bO@yXnSUkeaBb5GO;RQ1f<{wx(f}4-VfVm6i
zPFOs`?1$56`e0m``(XWjm^jQmxXN9aIv5R$H&}eY(hYigg{eoUVfh`VAJ&g2)_#~i
zbaRR2!}2ez-i7&}kbZdlK;s=2jwI`cS_CU^VE%!XFHmU=3f&%9c?okDjE~D4Sa}2s
zXIQxhqv81p>OWZeggFGJ4;F5)`~>p{EME~y2e9-DEB|5rf7p6JSU&(}AIyFj4J+Sa
z=^bt_)ZH-m!PLWOn15jEVf73wTw&n@%O@~>=rk<f!R&#DKeQZ!nTM_qCJwU)mhWNV
z2rI{N#UIQ(SUrNCj$!V`6>c#7uy}^K2j(AGyuivCn0j;?7JjgJhM9-14i_I*ZxHKV
zbaP<w4|6Z9{D9>nxcksGLemE<d}02E`4<-cuylp49i0y==V9>y3s1N?=o+Bu0~T&D
z_rl^I9*!_|==!02SUnHZ4~uVDK8A%WR5=C(b0@sqhN^)12g=1wLEQ&e4-HpXyyDgf
zk%8qqSo(*#7siLE!X$B-4-<#gOPD%QMPTs_3rARd!OTHbjl_fc8<yW;@d#4^GY7^;
zr(xj^D~DiwSa`tF5w7%y9?vj+uzUcMht&h<_QUuv`(gIL!VM-4cLz)Z8VyT#aC>0!
z4-Fq!e4=TCb7B2;m^#=vt`1Z`%wKQ~PzKChF#YiGhuRNw7gRYW1yu+0FRcECr3aWe
zJbuwkfaOzIIKkWr%SSNx;VOS%`e5qNjAwx5CtUhr;f2e6F!OQA!_0@(Kd|%;qhbDo
zsY7=kj1LPpSbqnWZ(-#ZEIeWA(P>zD4NE_;aEFUS(*e3ZbUsWU%zhXjT^%|f7Cx|W
zhPeYKj;<b^4-02v#nIK_iho?<==yQ-VdV)fd6+mZb+GaSrVbYkOFyvk6P6EQ?uXI1
z^uzKEdVK{;hp=)9Lq9bC!r~idFD%``#W6HO1Yq$COXo2AVftb10*Go%5*9wN@)qU~
zn7J@{Oq~!BSh&I49Z>(k{0nPOLR8}>Vd)i?|6t_;jK-xCmfm6E1mnZh!}u^7XF7+T
z0}3nmVD%o%T$sDi(*Z2JVDo$E?R;3f4n||>hlUr-9+-Ksas#Fh<{xw#7Qe7~g6W6F
zKP(>5^}+Zsb+Gt@g%2#<5-Rs#@)+eSw7eq}?l5=2Xjpv1(jzQC5G)6w;RyFH)P7j{
zBcvbZPMA7ae8BpF@Nxm>P;?q<Kdion?E`?tAKX9a`k;JRc*EKOFn7b^73Ln8`=Hw3
z>tA8=uyhNHPguT!$;12!(+`t}^}As9!NL)y4`wb#JVX5rizit6fw>b_F2U+oSbBro
z4~-{S_`u39Sa}Y!52ha`4$}`a4_CT}<!4wq1=EL%hQ$NSA29v6)T4{T!V?xAuy{mQ
z2jj!i2aJZLe^~y9od*V^VftV+EIwi70L<Mm`_aP{rVmEL;~Odf(~lkw=;01k4nJQ5
z7XC2%VCKNX1Ev9uhWQt!4`vSBK3Muf(+A_i)j`t{EWN?%FIf73smDdb@;R)Whs7Jr
zeYo_	`w;u>1n62Vn64Q-_O&#Xn3QJzikq=rpW8ft5Qj_rk*kT>}~)mVRLQ8)h#o
z{$b*1+TdJRIKu>B<p?bQ!`ub42c`~2!|a3cVdX3={9)$9%!8?e(XjFXW)3XAVc`JF
z_po#U(+8)a=>}H5z}yduM_4%stLNbQ&=|1zhJ_n!JQx-~u>6ds4bFwdKg|8G_5@4;
zEMLI*a2lFFVEG?b?!&?n=6{^}p$5UqIT#I#FPOh!?t&^uP|)y$rE^$5f`vCM{$S=I
zG@`O#;SNhLu=ES33CthD;u{v;Fn_?x6`byeIs@iUSh)pDe=vP8cR-b6P%!_&?1$;Y
zP>4$a8jrAX16VqOr9&8vOFJz6!~6kDx3KUel#gNQ0~Qakc!Q-MSoq?yAEqA`@33%y
z)#tE$ic3Gty)bn!8fHIEeNYKlx`WvV3lEq-pvrJku=s<86D%DNig%cSF#p2VeZj<G
z`2x;IW5D#m>Pc8R0ZX^A`2#d<a4yW9u<(NU8&;md`~?eNxJD=g=3kh8m^)xJ%ze1@
zLydvALt)_sE3aYZ<BCUEK7r*|n7d&#Ed1c^fLREoVfMk&JuDr<Xjr<1r4Oh!3<{Rt
zVCrD*C**IKL9li^tQ`O=|6$^=_=KrPr_tjX<{nr)psR=RVc`PHw=jRh!WHHpn0j;?
zmVRLA2BsfI!}1R-9?|u~_^^0D=fmO^<{y|mjE3oh*#o0t^04@a)o*ZdC<Eqhn15mM
z1`9WsK3F(GwP8@O^aj%hD{o<ZSa@J)gbKjw3z&Og`k~ScuzU^U<Dy~t73L2ZAD4QV
zILuyH_`&qS;uoe47Y);gE{`kUVyMGWKf~e|T|GJ<=6(!u9Oj~{!{vTb?MHV9p>zXt
z2Q2<!=@l*xt=D0EbQ(RL;pq&j50?JW^}+Zsf5Y4fD`#N-Afz9jZ=mfsm_K0g4l^HS
zA36<F2Wxl2<)Q8;HvD1X26r!1KTI5ME)E8)e1hdWSh|Oa!^$6+IJz;gauRMoQ~@l0
zVCKTg6R0$tg2}@LpbS_(L^lU2&44ZrYrmkk1JT_D(+_KJ!Tbf&4=Z<J{)eRxm_8T{
zi+`9tn0YXF!OX!G53qg)jE2>hF!#dR6|nROvlkZ)a|bLP!@>_{9xi<_ahN@@_5dvX
z!TbYLhfc%d6&8;$8m12B4;UYvhV|cI<9@LE9~SQD{W)~~Fn_}Ah4~*{9XcNt@38a>
z3xBwNXg-3;!_>pff!Pa_hs7f-+~DIOP;+4VU^FaV;N=8V9n2rFb_pyU!OVr}howJQ
zxd&^n!O|ft++pPsOdpJf#WT#muzDOO57P%r7cg-Q8dm;6-4AmoOh1Nt90D+Z!~8{1
zKh!>0I}uh+!O{z?9Kh9nfTb^3IHS88RxZQBABVFUVCe!z!_0%l1I%1p`eEq?7Jsnv
z0+y~|=@MN(EM8#o2cu!`hNUYQjY~f)9l+cVvlr$rn0wIm!}u_N!urdwd;u$uVD%hK
zJvt532g~=c^bFIFt{$BaOE<9ihm9w}!UtVFIv<u#Vd)MQeuUx;=1*Asz}*YY59sz_
z@S*7!=1%l-6c!J-;tv)tuyhPl4|5MpA6y*W0Z@0s$_bcxFn_?(1x!CIeZY)?xf>Q<
zF#WJ{4dx%X`7n)WG%UZu`~l0)uy}=~YnXj7?J)JQ^aCp|VDS%g2TUDIAB={JL*oIK
z?qK;4o^GMx1k(qjVd(>AFU<e2`~V9VT>gjI4~r+5KVbS{;;{IH*$bo5+b6Jif|XY=
z_rcU-&`^KD)WgCT=5APc!2E@-ALcK(Jy7%Dd~^-C_|WjjC5=-Yst*>9Fd9~l;M50|
zfcY2ZURZjE#RII}0}E%UHUtIrFD(3E@dcw{^#DQxDhuXbnEkNw3(kj{4-02h-3$yc
zcfry*ET6#ABRu`0YlQM)_QS#fR{z6kX!t^vV^A>jVeW<HS9E`1XoLws{ReXgY~3$x
z91!Lom~vb+%syCph4JC#GcJu#|H1qZ3ul;rVBv~OJ6s;>4;UX7{{-!a>O&7lSbm4a
zFM7EPa}TVXg2gv1-@?joSiU8g4x#HeVD&wWhJ^#n9WZ;*X;^r}@(qlJ#Vag-pzDM2
zVd)0F{SWgOEM8&i(P>zH0joz~;RnmV=;~p7SUC$*4+}r^asZ|torZ-Atla>kVg7~r
zA6Ghn#XHP?bn{{UhoviYd070y{ei9kn!aG}M3;u?hq)7G4op2f{GjHb>xZQeSbBrC
zqhK^F-NM2HT|bNuo1cfJ16X{*>v0_R!@>;~J}`H{(jUwmm_0aYXgL5&XRvYrR-eM`
zLDvr}H(>b%7SAyKFg`4N(e=aluy}^K8y0>rd00Hb`DhGSdWD5E%zl_Mm^)#7bQ-1}
z<_=i8gwg2g(fP1&LQg-i_&`^W&WF|4u<(MpgJAg&jW<}jft81_bYlQbH?Vj`w-;7U
z!_p7By)gS>`5Kpgm^duoz|4b*<Dy~V1q)|beu2e5%sgEBVetwpw_xsti4$@M%)hYu
z9%c{B9kBcj6Gx|E?uOX|iwC$kH2l%^q4QzmKd}4?^9L+G(bdEFuzClk59SW^_{SB0
zuzC?@4lJL;(k;v#F#o{ph0(D1gM|}J9ZVdi4i^o}x3Ktw<zHAifJ+}t9G0$O`eA&S
zKj1V>0~!rWCouQJ_%LO#`~l<RqG9P2J^l%mTQKur;Sa0-Veti{Vd`+vu<{jVKg?cY
z^~2%|)?bCi11vsq*$Weg>4SwIOg~JX5DhE$VCrD;4=Yz;;R}<;Nkhv+n0i?HhM9*;
zA9^^!+z<0V%zwD_qn9Hv^)Pq9>K9!4VfhU<UI!}=VC@f>`MC7M(htlZu>7wFEq~zo
z0*3*xasxd-!ty`NTy*X5`~eMj7$0UHEMKAPgYjYU1LMQe53Kwrn17-3d$95wRt~`2
z0i$8&qtmc>hUtT+Luk0c@)5c|7#|+5Q2)cy8O%Ib`hls(MZ@&L!VMOGxYVPI!}2}M
z{pjYQtB3Jn=@YIW>Tj64Vd)R19-W4j8*qD}`eE?_lZV-Zt{)cPFdCN5VD`hp7nZ)!
z^~2P|#9{R|j1MyxS2{ws2bLaT{(;2@%pEXuVKgj#!}2doJuEz7<uWW^!StciuznlN
zz3}=R8lE`wKQ!IJ;t7_|VfMn(0lIzYe3*Kee_-yw749(o=-~}3_h8`%GY_U77Y*w_
z!15a`y}<ON+XFKP7JsmE3Y~_<J1iZ-^r6$R_=UB%VBrXJ7ol*3r5jkh!)Ulapyd?0
zy)Zs({SIvW2S&rz1HsZ4Og)T-#XBtiVCe)F{xEg8Xqfw8^04v{7VmI#py>>z4o0Kr
zKUjGSO9!xUfW-q$AB=|ShuI6O?_uiU@ea)gFnur@mOfzV02U4~b7A7JbOqA~r=k9W
z#V;)0Vc`u+H!ySI`k)M$`(QLw7(OluOFuAibQ<PPSh|DddsuparDvS(hZ+cTKTJJ(
zI|8Z<Ck0Et=>CVrBf9@^8UzhbSUCtw4>;9BCD6kgR{s-gFU;MrbOg%>u=D{-Pf+VH
zC|G$13ujomfyFx^cfisKdisU=3uZ2c#ZUoQ{K05gJi^*<g!IGOi?DPA^FK@+Y7I^b
z7XPqtfQ1{Z9Dw-`mjSSJ3@cAyG|b(waKxn_=3baOSa`$2k5E2>rBk>%wDJPxFLe81
z_QCuQtEXY%2#XJNedv6cKA68@?uVr#boFpP)Sa;UA68z%!X1_l;OcQOVC6Kd+<@f=
zSh&K{0Zbld49uOdbPLP3FneL{hvi3@JWM|<UBL9i>QR`vF#o{Z2XhZhKP<dp@c^S?
z^%^YvVd)DNFEIVE_=dR`77s9Y!_qOVJqqK)^ux@Bwbx<c1}nE<=D}!~K3p`+Kk#sY
z#xJh)hi(pf_@ajwx_UStTJFNa4`vQ5{9)p7^=J&3JS^T|<t5A?Xv&G^qQ?U+jWGA3
z%fsRqT^z=Tg*Pt!gyIbr&*<p_W<JdQFg`jBOYg9B1e1rE2Mbr2I64i}haS(c^g>8K
zES_Nb8&=-J%!S(rO^0wklmUxx7!7j=dbq>#1ymcHg1HmDoQ9<v^!gaC5z2st6U@D^
zau`O#!WZT~s5UqS3m;hd1S>CK?uVrhxCS%^%v@Og0pr8U16X+jb0?a12AKb0>R{my
z<HOv6t`EkC>4)iq^I-~zq+#twSUkbv5f-kvjDdv@to(-g8<y^2{vf0uW)F;p<tv!~
zVey1+4lI6Q_QK);rXR+K(YW-(;tl2xn7J^25DHhAepo#Pt1r;&6?FUIe6;)pD|ZOl
z3)hcUe#62ME{}r&t0!Uk9~S>Gcf-VSm5;D|220;Cf5XBLmXBfKgYFENI+%U1bPS6x
zSUQA>qw9y24>142?19tJd3khwFh0!PFh0y4n0sL98|DwVIy43>y}{BcEZxA|0TV~l
z2Is=sYq0bOix*h>htY5iXbf1ng&tq9b(^s8LDR+n(+8tr`4#4WSh<X@55`9iADBB~
z=>V25Vc`MOhm(e;V|aRl<_lc<VdAiK0!vS@@PwsLT=t>+7v^u6I4m9F(hsvAo{pgI
zhs8Hs9G3x5ahN+{?JZb(g5^i3GMp4Fy}<Ot+z;y);M53{fVvyTg_#EnN0>M+8dmPX
z<Y9c6KVagxXjnQzmnXXXL)S<mA6GmPYd<U<z^sMoLl=k1qtmc_11o=E^00IS<Kv=X
z@eQM4?K)UI;L-;ZhuH&*Z&-N&D;Hqu&}n$Qp`{;KxeQC!==xxMSh)(LVfh#44_Lgw
z?SW~8(y;Os=1*8T0F`Ed@p0v2m_K3W!_>p{!`uTiA0`f?Vftb52KO&CJYeM*EPP@5
zU^L8su<{b-PFQ;smJi|fz%-)K@NxoLUc&n6F#F;1X!>C4VDS#K50-uj#Xn3REc{^Z
zgtcE_{z0=A&V{Lm3Bc+bSp37n6=pt69gK$A1Cxij8zv9SS1@<N#9=hdepooc#9=hd
zJXrdKiNk1E{)VL!Sp5xi4@@7fau8Si!^&}3I)IrEvlpF)rCV6I!QvGbf4JfwW)93g
z__!}L9l_E)tQ`Rh512k!{)5r5`V&1|VDSp`FHAp-hRMUshou{sI4m8&)Wc|)y|DZR
z^DoTZuy}x}$DpD46s8^)f3Wfk7H=?n(DlRI3-d25y}`nPP`JbW1$76^UYNNsd0gQJ
za}O;2!qPKLoDdE3H!R#><HfN4AdH5k4_xNK%z>ptSbl@K3zt4vxWURl^z;rh50<WR
znFC8FaQmR?mykY~d9Zkf#UD%@mabrYTr{lQhxr3ndcmb17Jjhw3UfC+9pKW4CJvkL
zgXLE=X$&qbo?tZ0UKkCFe+-RK0a&=f!U@*ihs7JLK7lI7pkU=1dVYb0A0hvvryrOF
zu=s%a113(0hJ^#nU$Fd0NFU6fu=ER856#yw|G>r37_f2&W-lxq!o*?u51zi!^uxj*
zmj7Yl32O(!_X9xP1Je%E2ajiHzJcW@m^e%wOdpJfmD8|#4i^6~8YT`)-!Oe}8tNXH
ze_{5*;tl2>m_2ZPPzEgiag~!Wd04)JYJ*cSf5F^|9#1fNba9vgF!N#hVd)MQZ!mF~
zI+#8f4f8k5zp(TJi*J~{u>1tm2cu!(1B-81`h|rz%v@Nw!t|lj@N@-@4_LZ^`5#?9
zIv?g=n0lDIVg5r`597ns!NLtD4l5UM`5UGV9=>SnBVhUnl^^Ktg_#RWPjEgC1}^`>
z@-Yr=aA8=wfYlo?dvT>#xJEPvZ2dnhzTxqSrVbYuR{z7&5iHzb;<)s|#9{u0>4&9D
zV%-7L2XjBHy#q@hFne&(=<O_+y|D1br4L;kR?fieg}Db^9XcPD@8J0sI_?OIM|5-1
z^~359n0lBz%sd#4E)P=&%dfEX1EXQ)!r~DYE^vKN25j6AW)3X>5bJ-aF$}QqhL!(>
z^uxj#mJeb10ABAx>m!)?=rl|ptlWg9Lzur|{)O3tOFzutuyPDW!@?bA4u*bcJj22p
z<{nu3hm}w0<r~Z#^zef5Vg7)rBSgd2d&2C6#V5=?=<xs(hpiWfrC(S*0gHcFIKtG!
z+yM(uc)Ehd6D%LY!UGnrFnur@mcL>4!SuoW4@>8;{yj`RF8wflFnO5$Fn7bugUjP!
zz`_sa9+*B@_~6h6KYtA7UzmGf<tVQ712YenK49q$M#Id5xeKNqorc9XY#$J;{}0oT
zYaAG64=f&G@c~m0b3aTT-CkJuz``4r?_vIjsYmC-#9`?Y7T&OQ1uHLM^((wQfN6x%
zuyz8Bh6=;yRbl24o1b9m9_Aic{v{NDF!NzFx_e;m!bQWv7Zx8d8W!)caKfb@7T>V=
zhqc>b{=lUl<}X-2hpC6fJ4_yy9$?`Ea|f)Q3Cm}&^n)G_uylbQF0ga~i(goL!OVl{
zgM|akUKkBC4@Sen5tbfd;xP5-G%S8$`47g2`5%^l(Dh;Pq3vdvJ7Mh+xH%XaAOf&>
zfvJOqFHAon_rvtT;sItJ#B{<WJiMUpM~{DE%RQJoVetsJ8CoyE`DhH7dtvbmi#J$0
zfQ2WTHaHiSeqiRn+yM(mnET=76kI0`2GqT<aDcf7mLGBGVu0lnSUkYg!Q2N+S1@rH
z4f7|g{Q|QGM#IWMSi1<O4@SfC2`nCA`5s2Y<YD<2rVmEL)WO^h%fB%7Fmqw@Fnur@
zR&K!RX;?hM;ti$`=1-VD7!6A=Fnuuh!u$=3S6Dd0^ucIYxWMJ1=@=H@aC@QRFnzda
zn0sLH4|5N!J%~#mEPi3_6j;3l;}a@JVCKW(9Y(|Khm{Ao?1#kzto(=Rhs6WTKe+V6
z)Wht9#Xn3vEPccHFdC*FmVROJ1q(-5xS*R4(+@KjrVf^lVBrPR2U7>r2cu#242%!U
z@96qq=>%3@!qmgu3r~O0_B_lSm^`lVhm|+5@P)-Y%zRjWf~QZI1R4!XC$M@5CJnP6
z#wSF>(g92zx_@Ef=rl|pEIwi7C%QUZe3*Wi|6%D4mwI$@SUCZ+7Zx7q>TvPl`5dYM
zmj7Yt2wfhk3{Jt~2NwRY{0J-mU^H9<8UvONVCfiEZ@`qn@&|f)g~`LyJ+%ITiNoRp
zW<D-|z|tSgURXH*qhaX~CJ!?oM#JoZ^&^N)H!%Hhe?!YzSpJ8}6Qa@c8!S9<`4`<h
zSbW21SpJ8FJ4_s1AB+!+7Z{D6{$S?7)T7g|^bU(BSa|{~H(>P{ES%Bx!{P^)4$<9D
zs2qUV19Jy#ydO3n1T!C3{KL{WEFTg(e+2UfEdRsO4XnI{$rGYs=?9koVc|eXA3S|P
z(;bWt3qM$XhQ%wod9e6^(Xe<zr(y9AQwK8#mQG;t4iksPKP=v0<q0f&Vd`Mv1yc{>
z!@>hb!_>p<gYjWBEWTjwLyv!$xv=ns>4UikR!+d&3rkNh^I+)%rVmEL>SdV!VDS#C
z2Vml`@Pp}t(Xf08i#M2k==xyphv|dSF!#b}Sa_k^2Mb@AJ{S$t2P;2e@d$GVEZ@My
zVfxT%m^rX?21~!N@IY6O&WGuPg(p1xq3scL_2_(9Ji_$D{DZCz7avys!paAj{jl-_
zmp+)iFd7z*u<*c@ZeZrY+zSgYn0{Ekg_UP8_2@J#USR11W)93fF#o{JMb{5=C#=4K
z)myOegQZWnd1wZ}@*_+iEPuhwgUdt9Cp7JFE-V~i<tVKDf-6947r@1#448UYK8Ef8
zg}ED6K0uXYP_X!irF)orVB#1W&;(%m;NcAQFH9UwIR+ONZ|D-R_(d1T;6v?$iNnl;
z(J*mbG%TLb<>CH8mq6pA+XM4AEMKE3$Kb;3htV(v=zN$sIt|MwuzUcsAC~^n)uZ!a
z^#v>)!SX*WAHewN`e6Qp>4UYmVfg{3A66fr>xZQ?^l*lyTUh+#3I|yJgsFq&KbX0&
zdK}$e7$27ZVeW*{aP!d86-*zDhUtg-7Z(07_rl`=rhxzrEB9dG3Nx30Hn=ow92gc4
z=>2%OLNo>}pTp*nVBrTVkI<AOxX|!{g)=PtVCfN7k0Ugov7q{4qOfp>r8ihOz}y2<
z2cu!>24){j92O5S^Kj{hse_pV(~nNW)S-*R%!jo*VCfcSA50uq`iGedlZS;DEZkxK
zg3;*q!}zdxgVnb%`(f)jVfMh(!)Ta$Vd({SUo<RT!^Q_;@-Tfc8m1o>Pq20ttp0=f
z1DAeSK7oZlEd9gM6U^N(8fGtyhJ_m}y`Z=IVC^?t<shuwMt3*NKA1mX_Ti#2%z>tB
zSoq-52NOs4H%uHBt}s3>8kX;2^*`KR0__HvI#_yvg(EE8z}AD}N`J8O7Z!i8{EtrK
z3V-zS5ypp^2Tvb3+QBe&u=s+7BYHZ(6>czp!Q2Z=_b_u{?!l!Wmi}Pr1g0KVF5uD!
z6NjmX)t|8P0_Gl=zhLSyXlOcv&EvzunNWKVW)3{wq2|ED1ICBaF!i`-m_K0o2$pVP
z=?j-Wn0sOVh4+`B=@yp0aO#Ijz|t)&y};~;@uA8HQn2ucl~?faClC%Wcf#Tk79Oy4
z1QUmui|!DZI#_(b(iO}c7>%wE#)p-&F!#dBJy^KH(gQ4g!}P(;h58S+j*O6dVfx|W
z3ym+ByJ7x>$3N73m_8T{b1y92z`_CME||Yy;S1A;PQ&bl#UIRGnE9}DhOQ4*e!%KY
zm^rX^Buqcf{0#LM%wAZyqx%QlUUWXJ{(zYSb3e>I=;~p7Sbl|tH>~`J)d#S6hp9)W
zVg81-|6u-xwHsmK4~q|U{V;dJ#9`?T)^3H#<I)dHkFaoq>4TXE3x9YzgGoSXSoomp
zgV_s<cbGX)Z5R|x9#(F^+8;3YVQ4@TfTd4Zy#wRJ+zodRnm!m877wuYA9{L#<wuyg
zFnur@K5h?P2aBFAVC^_qJj3+CXqfr1c!KGNnF~u_F!eAR7G5y-!{PxJpD=&JXqY}2
z4bu+`CwRVrmM1WGz{*9KJ{S$lFR=Ux3pbcNEdFuX5Azqy99Ve((+>*|m^)$OFdCL$
zVCKNW0hVuI=EBk!OdpJfrC(UR2aA97_#l)HVeK`Tx$y9Uh9lgaFa=N==08~afY}36
z4=YEY$}uQddW7W{Sp32K0}BV3I&=eI?u3Oq%wCu~VDS$#A6-8z++pblT^`mh!=)cR
zy};Z7GY1xLxY7?S{$ToG^%u;)FneL<z|4iwF#o~w4SM{;@++);1Jehmq45hVuVLW{
zi#J&O!sOxl&=@d#VCrGvilz)F7iKRk9ANncm%q^sg4+v~fa${(uTW)h3L_qI^cUb7
z&=|Pv!6lBSonkI59l**fQVm4UH>8>aOW&|?hvi#XI)LRzba%nx8<yT-`5(rI`3pv)
z>xacVOdeK_!^C0kf#oZ7{jm6f>4TS>Xz>p-4_!aZd|3K{mDjNP3s#Q7XmtHBK1@H1
zhUI5i`2e#ArXHP!<sVo+C!`)-KP>+e%IE0%V0@T<Sp5ZyZ(`j6E4N_j2o`>X^uyu>
z7Qe7~goPh0eqruJr(ybG<qa&|!}-wmIl4Y{KFl6ix`m}9Sa`s~2bSJp`eFWq#WPGl
z%zl`;uy!FVe9-m7@(rxMg6V_#52g=hK1@H1hQ&LKhPfX$e+M%MrXEJ)swZIO39MZJ
zQ;$x=@(axWu=ERaKdk&f*9YUn)WgynESzEH!rTE<k50q%!QuhtPgpu46mBr{VEGmn
z4=@@QkMQykrU8wH`xjat!^$(5xiEL2X@hfN=ELF*M#KCA%MWl3Xbf1t4>s=yo6mve
z2VDI?n7uILVDW)24wFZxVc`SwCoJ6H{w0wAVCe={zQXu0d(iV6OdL)_!yD#qn0wIU
z1Fjwi1D1YZ<uEMV;O5}a2NQ;sU$Ah4>4TLYu>22mA50(2e=vD?{)UDdEFHjTxIEMV
zSo(mw8!8UZcTjU+>fquqbubzljxcpF^I$Yg87>;;4;YQp9vlY1?8PBXwlFMS2^j!W
z2a7jC>WP+zhCd<ouyg?rKSCOal_z9BR329D!qOS6Jb}dvtUib33%K=A{V;W~aD?Rp
zSbYW?7lM@&F#Ry~F!N#I4=X=m;xKby@-Y4A;RW*t%v@Odg}D!=4@Se}VeJ=~I7~gv
zA22>l9!A6J8JIk*{DQd~RxZKZ1JehiVf7cxA22>lAB@JOA7&2BevJ5mDIh?@!U0xZ
z!Q2ni1~(rj0HtB#Fnv&A2AI2H<rS=agZUGt4rVVboMGVytN&r?5vC5N4@SfC4a^;|
zat9WlFnzFk2|fM5!W))8VSJdmuyhBbVfMmkSp38E!_>j_!@>cko)8VIM_~B`79NE3
z!Q|oL1uM_d`av*t=rlb3VELIyeK2!i;SI~TgwiWaAB={z7hvIs9#1fJxM*1X!|a8P
zyTE8zeBjg%^)IaafcXce9~M67=@(`mEZxBL!~6jghs7%_Kfv_CXjuCWmY!hlh2;Z6
z;Q`C9F#FKkfv|9a$;0f0`3n|)uyq8megrIDVCvCnm^{qCuy!0Q{SqpVVD5qWA8sBr
z9m3?%?S=7S`2!X{Fn_}G9W4BCg*(g~7!B)h!16OJzG3MArVgEkg&#~D#)pLij1QAX
zr(xwIEdRo2m^xVa!uaSkte*#qcNiZQez5d`t`EkC%R|E*RvyFWm0=2T(6D%i`4bk8
zu=E8hmvLx^@7G0_hs7t%Ty%LDA6Cx8_%IqCZqV`&CJv)v`3#mnVd5|vmL6d4fQiFs
zSo%P>AEq8w9>Ub4)3EdbOTRFAT;ec!7!9)rJ)gnFq45b*k3mD_Vf}oVK6rl-8gB4;
zAT$GD@dEQdEWBaq3FZ%2dO*_-k9S!5f#qj(^|0~*mwuT4Vd)K)e_-ap+=Z?m#)qW?
zn0sOA6{a6Oeqj1wG)x_=odGuorT_;GvlnI`%>6L;5;6xCPB4A2d=D$XVBvzpdbltw
z{$S+=Y+MNDF1SKm3}`xmg%hmYB~)(0%!m6EnjT@{La5w^r9YT@Sbl^#6PGwl9u_{Z
z`T|D7!w;$uSNQ<TFR*d~rXLo6Fg~n&hq)JOES!SHBdpwo(J*niLL3a3dRV^=M#Itr
z%zZHbq8kJA2P_@H;ss_dx_S&gG~QtG1@k8?{4vy{3BdHB#~-2c6J`u7-NE=U8rGhI
z@o~~n_oK_h(<QnDIv-6xEIf(T4=X>=)x+`;x^?J$SUQ7;7t}qt%58N0u>20w4{QIy
z)WOV$@nJN~eXw)`E5~8!43-aI{)6d*(XjFXmOf$XVB)ZPgi!p#<YDDLEd9dL70jJ5
zd*L*++=8itwfkZ2hvh$9=@=HzuyPaT4w$=P;Rf>;++HXHo<5=OfSCu=2QwEvT|l+N
zDVRH9`eE*eD<Hst^;=-(z`~D!Hn=pbe1!Q2mX6>G2ryvj2i@NUw85oe@dJxzSUP~!
zi?H~GYrw&PnS(A3OBd+n6I>lM{$S}HR)4_wu=EU9kH&ztZ(!jMD|cb_2Fx5-`a;vr
z0J9I~Us(AMYuCZsbLbkOe3*T({vXU8FdCLVpvo~RXn6z6$1r!o^ugi<QzJwK7Vofd
zf`v0IUBJ>6L^URft{)cPF!$ife=vJs?nF<wxb(qX0P`2jov?fli!WHX!qnrWq3H)b
z-@(Ekmp+)kVe+tY9A56j%!MhyLBsqH%QxudKf1eM#=yc4rVmz5!O}C#Tv$4WsfX!@
z#W$?{fcXdJZ<x8T_=M?$(J*x|KFl1LeK7aH+U2lx2~!V?Cs@3}{0WO!m^iL<027Dh
zKUn&K*$b10*@ugUm)p>M1TzONj!PrDIGTQRX>>j;y};~&ts{fQKP>#w^}+bCaDs&+
zEF56|hRMU)gD`zC8W!KM^a6`#SiXUo15=Mqqt}nH^aYD&I1ODFiLM`=kM0kcepvoS
zSC7tzsfU+W(EJAr2Xyr?J}jTWXjpv%(?=*A;N?6t{^9L@C?6J|F#B-PFn_|_fu6r`
z>4V2F)IL}~g2e|eeK2vDy)Zt!{Q?a?xHt|5tQ>(!!^(YFx`v74qG9O{CJ)PhFg_s~
zmfvCOaJ3^~^5`@yAHdQLEZ&I?KUg^h^EWL1;PHZPAsQd19+vM2wHIL8Vdlfi37Gp~
z`56`t=<x&75A!EX9#&4n<YDa@n7d%&FdC*G#)qXNbbYXL0;Uc|!}1kO9$i0792P$?
zeJ~omynv~Pg$pcRVC4c#A36<7Z?O1=rCXRdx_VrESUke?!OX#>9wrXU=P>ue{0ple
zVCv9mm_K3Su=s=Nht<dE>R{?&<pj)rSop!*4=V>?@^F1n1}xv=l85C>m^!F7I0cIr
z7!8+zF`(rc%p907jE1EjnE5aomY!jJxPM?8p)|}~n7?7|7Ff8!#9`{7+At_syB$^!
zz`_-lk8z~~SUV9Gez1DN09x+C%*C(}CIAf|Sp34`9o7zjl>;#4=rqiKFmYIZhJ_nU
z92br5FIYJNE01914<Y@qbPG%Wu<(Gz11|lrc!BAI`4^U+VCLY`4--dsH!M71<vC0p
zE*jR~gXdRh{K4V@mp-_9XgtEw3oKq><smFRp_>D9H!K{`(>Y8W79JS-q3(qF6IOn~
z;tiIbVCJCfhmAME^uyc@a|bLP!OB&1{V+bP-hzb}EIwiWgQZKDdUP7*UYI{&=E33-
z9*$@lVeW#}SFm&oD+gfa!2E-z9e(aGEFNL&{$PAqxWN2{t{=vSg*z;s(Zdm@4i^o}
zCvg8j;}uuD!OVfFhnJU7eXx84Q-_O&g)c1pVc`Ue2VDAK;xPMRd{}yc#RJS;FnM$u
zR{p^J2}>ui@PwHI6UU_=mX6Wg4c7<FpXmBwd|1APxd)bRVe+tWf$`C4SUClYcUXFX
z)eo?6M%M=mKUny~+z&Gc77nm<g;PIt{RK=NEZkw{q3eV3Ve+u@8<uWh?t#S%tQ`T<
z2cu#7VdV_W-!SuF{=?;em^m=}VD`ZL4-<!lFHAi;4eRg0(hDsA!_>jz6~;%WVf8+2
z{vS4P57P%L2hjDw_^^0_xf53Y!Q=_iu=EBiuVC#WLi%9g3bP*;53qa)lgDKaEPP=!
z%p6#G1WQLS^Kh916NjmT`2!{nE1zL}7!6Bbu>1wn2M=#(c?NSAOdpJf#SbjK!NMOF
z|FCd{r6-s^bQ;!fgXJGs{KN7uTpnE`ln=8P%7ve2Oep+e>e2PX+yV0+OdT#77JumS
z#Og=aM=2i`udwogka_6tgy|=w4<-*wKQMD(;Q^yz>TuEU^Z`w8u=s${xb(r~Vd)c=
z?_uV_+AXkr36qDJ2P+?8;RGu`;r@Y+yTSCKmkY3Pf~9|$Ik0*U#)sJhvlm9g<YDO)
zR(`_rJ-U4`edshSoMHJN=6{&GVD$>RJ{TXS9>#}-7tDTGyu#$sX;?VJ`~%a6t_~L;
z=5LrgVBwETJzN~xu7Hgf!rTFiM?(ESSh&N=N0_}Z{jhL=+lS78+K(Q;uy}x(kFE{I
zhq)KVhs7hT{D+mFFnM$u=3ZDl!_p~iTnAP!qw9n5VdXr`zwmg2=0kWn2+apDedsjI
z|FCd`xf>Rr=;|@}&~QXA_hIgWwM#JcK?Pvp3~NUasxP40;1n#pVg7}MBTNA-USNDU
z4RtpxoMHN4^(ZWUVf7(gACv(rw_xVOXqb7hd;t@OYJ*d-aEIkjn0+w+z{(+5`haVM
zGGOr!b2lu#!_0%F2Ut3TYJ*cSf5P;^_%Qdv%!O+pz<{|QR<6V2pMXACIKlM8+ymDJ
zjVD+-fr-QTF#Rxp!qY8OA50$RFPMH<K8M*03olst4bumU2Uz(C(+{&BCJxgFi+@-?
zf`ucjzJck7g*PmmVD5&Q2lEfCJb>wg)6jelOCPX$0_IOxzYdlj;QDYdVD5*dOIZ5C
zp&UaP=3kgTnENplq6xtA5lkN}Tw&pXrX0bA#xE?r!NLI+elYV88c<oVaDurL7C$im
z!_osx9ja~y2J~_bR!+dw!_yVio#^^u_QB!@mL6g01!g`>9lCxP9~Mq9f56fcEdF8s
zfT>5PVfMn*!Q6wc4i_KhPnbJ!sY4fs^$%d;F#YJ_FnM$u7Vofp1dD%I{zF%f&WEKB
zSa}1_XE@>uT|dmdFmqt_4=mo$)uZ!a=@b^;uzUcE2Xytg_~`XEEd9d50X<#9%z@=I
zSiXhvVd)np4vSxyxiI}O|H9l4^EZqSqtVkdOh3$A7!5oB3pSnvD}Q0?(P@}_VCff@
z-r@NL8jrAag03I#UTFIW=3kh3F!N#Ifvz7Ge=vJt;SEy{i$_?#MAr}F!_p7BeXw*1
z_YX`18VzeNz}x}L4={hADaYW#!Uq;#=<dMKfF=Ob2OIZ=#V0I$(UfCwVet$z2S&rq
z!O(#!08<AGFPMH}{SON_Sos0V2QYI`ZNucj^uyvEM#IVnOdY5qu<(cFQ&_rz#UrX}
z1P|s;Sop!}Z<q=gA3a^d<Y6>=I);hE%!QRNFn_`1Vet#=r@_u|h2?jcKA1b<`p_6~
zb+B<*s60#@%Ed{++Gp_ahPI1f=>n%ls01wjU^L8M82*K-N2g%^h1(A;cZf|tu<(c3
zhv6S|3(@%K=?7*GtlWU*0~n2_odKpFR*t~rVdWt#9l+8NOdg#^k7rnU0Ha~%!1TfD
zd6+%0@PmaX%sdzk(+8tr?t!@<mTzGB7-k->c)$>c#T&FffVBr;;Rv%AM#IW=m^>^V
z;r@i02UCwu!`usNufg06^AE20f|&!;4{P_s>SLHYVD5sc$3?@!5mx@d>LWts56nDR
z{KE1ZtpAP?zAz0?8s<;*d=4`QmM&oC!O{g(I|Hnqf~kjv11$Z+?1$-tr3aXPSopx?
zVettIN0@oAaDas?Oh1edD>q=_2a7kD|6uBI(J*ze{0B1+W-l&%FmZJM!qOAWUodqz
zX=pknl-_aahqYT_^)D`U=;E+^4a+aEbO%chu<`+2AB+#v4~u75xWUR#n7OcY0@DYh
zVftbD2o|3(d00Hc_~<muUYPl?c!1R(u=Im&F3cQQ`h$f#I*o2VOdmQ8b3ZJ-z`_xh
zo?z)2T_22(-cEq|8)iRD9WELcPq1-Z*f<_6J;3u7x&hF12OBqpr6X9nh0*BRV0@T6
zVfhXgez5cpOAj#h=rk<f!Qu}dPtf)QPIp5Mg6W5H8DQlV%sue*08@ZQ!{QC*PMCjT
z=EBTF(+20l(ihCXFg`5aVEF*90gVCkFHAiw-@(!gEIiS)VQ^vj2(}IgrXS`X3=L=k
zFneJ%th^@_{xExC;SKW-EIeTT!W9m%bPtn<>4W7XSiJ~y7n-x-?Iu|IfyEolJun*P
zE_D5{`UsW|VD%WxKA1jS;Rw?Yb2rQ!n0sL90arM}%!lcN#T%^rg5_UycjMy2@-NI?
zuyP2OdUSD^J7Mh!Sp1@^L+8WFQCNJ#@(nB;(AA^!VeW*b0~jAYeW2@u@nPWvvkw-o
zu<(GX!%0KS8(6r*?1h;Jj~^Tc!2AnKx3KgA3lDg_;?NIEZ?JfS(XjA`rE478;rRgO
zewe>u=^2;(aQ8tChPfNokA%s?>KCXoI0Xx5^l}Q8?qTIGjD~AOW5Dc%se^?F%-^td
ziKY$Cg@qH$y|DO$>4U`!TmzH=i(i<1Fn_?(A50&NhH67l(E1Z@4m2IX?1lLUp#hZz
z(+3YnsCh8=!`y?a3yBA{4;J6B@*n0OSUHZQ11l4jeql7MJcETR%zSjsF#o~ISy(v$
z%a1U3z}rD+2EhCY%lEMU7tCCkKA5>^+TrU|VB#?IVD$zp9$@JarVd8K@--~JVeWvX
zJD5ILx`OG0(J*;fc)`qrxff<GOg%acHy0X@uzZSc4!S;Ee3-jo<p(Zx=;APU!1Tk)
z5nTBmmws3}hS?8`e^@$4*9YUn;tl3+SUA9FV)et)4J<vv@-56=F#FJHSUkbp4dcV&
z5ta|o^}+bCd<rYaVCeyt{$SyN%N$rc8m11G4`Kd?rAu7l0L!1Sc08;f0ISDg{({9b
z%-t~aVf_V|I4u97i^Jq$G|XODK85Ln(Xe>M<!@MegT)U_A1vHp@dr}}vlm9g;s=&q
zVd5|vW)3X;VB#<uW<D<W!NhUd4@<AGbO(z!n7?6sjBtlK0Oo#}zhUap`^~U&2C5B%
zf{p*eBw*<XmL6f|!{lK!%v~^l!r~j|Uzj+IhUtURF!#gK7c3pX(iN;6hPOjt8lg1I
zeptB=O9wD>VBrB(j+=s(pD_LK@P~@S+=p%eEI+}*3zlAB;;?=p%v^N+uzU;42k8F5
z6>czdVD`ZHF!Nx1Sh|Ao(P@}_Vc`wS_ptawSC5Mib0=)x50;;ZE&pNefTagmc?gd`
zXuAfN{jl;D7Vq%%0@Vj|A1?hc`(W`5iw{`*!Tf_uKg|8GavR+|SUSa}A1)3}Z!rBZ
zcfjRwFktFn;RTOBsQ+<jgA2p*Ev)?qEBE0Fh-ARR9cCUOZLs);g)c0gVE%yl3+^8@
z17Z4M<rGXmEIq^Y!^P3`!?>{cgXI@kzJkRAOdh5lM#I#@$|+bm3oqB9^I^E+ALcJu
z{Q^sGuzCd+o-q5+X?VE;O*b(0uyOzvKIr;jd{}yig+DCbVdlW{6-+%k4GUjbx`Blg
zEIwfM5V}4%ADTYl@eTC{Oh2p~hQ$Y5ACv)$S6KMM+>0Lmu<(Ft!=Pa9hJ^!M0n|OP
zc!G;V88Ce?`{D5cm5132lZOf;D5&``eK32_X_$Wy8X+v0K3M$>iw{_R1dCrn=>XQx
zgT*^6-eBSo+whRkc!ZVr@OXjF!{PEjEMDRH2Wk#X9Oge5jqU<i`hfWtT^{Ctba5CT
z=1!P8So(p<!^B~HbQ)GZz{-7CdVs5g#xI<Y#(>2OEWg9b6PPkw{)Wk8&`|qe`e5}T
zEIcvPqY1$L4~s8YISvaaH02C1d6+mXykO}7CXS1S#UCvF!t}$^KQ4VRahN_>c>(h`
zMm~mVK%-&hHB1<m{$cKbi4&q><u@#x(9OlA9~LjL@(-3CVD%TwJuvgp&4JkmQwK{w
zu>1?_$HL@s>4%vE3wM})SiXYEqw9y2`!M&z(hp1>tUQA8F=%LhhvggeaD#;phCZkO
ztiFV`n_=k|Dvhze6D|&A!1Eono`!`#jE039tQ?1G!%4x?4J<vv!V{JcacP8wA1vNs
z;Ry2&ET7=g4-04Xc!Px>EPQe4hvgSo{SWI0z~Y@yJ%DaMA@wkKz}$zP&S3EgOZPAu
zrVmEL;sqAIu=D_nS6F!9(hn1d`4i?ISbYQ!cWAi^vlmW7#bNfr;t`hbVCe`JzHog|
z1}we6>JOMYSU!e@1Kb>_J{Scn?_l``rVbY0u<(G1!}Q^zVetqnS7GT7mwH&d!O{)P
z{jhbQgwg>lf5B*&y|D0r)vvhhhs7JroiKmH!Vi}|xHvTaVDS!%KUn<3XqY@)ACv)e
zA1uGa{10;nEFHk|HB=jdg4z$$5A#3FT$ul0<p@F}8Vg#EqPrj7ZiL!{rVGx6g+Hu3
zht=OOcfn}51}FpOK3G13#TzVLLzUsCpzem{Gq^nj+zU4lsvnlVVg7}=3s=0s^+Or3
z^aCqLVE%!{KhE?7btkMGg4+wNPhjN>EMGybLr_rtuzU;)H&{5r+yx6CghmJprVo~G
zVD5*}uyg}67orQ3gxLp+cUU@r>4%T&U}^=6z|_Ibf%*$9$-n?VryIscr(y1c#Xl_n
z!15C;9MJWl$1g0L(ZdTC9_adE@r9mmVDSyBhtc)H_^|MYse`!_79X(ifbn7aU^GlU
z%%3pxVC6Pk9;N{Y4f7u?{lm<|p&UaPmj7Y?z)*lD01G!*I{{`N%)hX59~Q1?+TmPS
z{(`w1R_?(S;9$VQ2_CM{asU<%u=t17i#Uv7fTcH>d9d;t7XL7DT>4@015*d{2P_<6
z{)dGZE^}b+hSm2lcfry#A^k9Sz~T|^4`?|AlgDL0EdRmc6Lt;+%pb7$Mb{7WAFLe!
zQxBtI<q5hz7#~(X!paYH|HI6Om9H>;FdA0w!}zdv7%V<u^)4(uz|_HL^!S2>H_Tp`
zIfU$orF&Sp1xv>;K6*I?vmZvo+zm^wu>23RpOAi-yJ7VfjE1`#tvrU=3!`EEcv!r{
zXju5c;uEGGorc*5OJ^_|-Y$gZPjr2-bPX#9VE%`PA50&NMl%5BKUny{!VQ+5VeW>d
zFEs7&buTdW=;;aO4_G*$>xc1S@dV?;`~}O`u=s_^!)TbjF!#ay0}Bsybud1RhM5aX
zXRv$-E5Bg*3Ef{X{V*C<Uc>S$EIq@@A(%QsG%Ov#{6jF@pyPwE{02)$F!!T}E4ux#
z@P+Avg*!|gEL>oGbQ%`kuy}x_JD56H_`>+;G%UTs!VBF#Lg5Zq56z$G>e2m!Za+F7
zW*#iu;PHU25C<O?4zP4jNIy&-R{p@!3#=Z3(KxJw3&X-0mR@1$23NTa(+CSMSoovY
zQ?POYrXCj!3pbd1Vc`cWKXB=TiNn$>EF56^VCKTq;i6&sVD%v^UBS~QwEV;6ADDia
zJd6*^7cd$YU+C^Y7l*a~VdXbWKg?X1et3BSbswxf2BTr-!tysvAB=|i4;D_a^aD$O
zF#o{Rqtmeb3QsrCd<3%>mL6dBJi2~Zx`V|xEWg2Mm^h3^*N?%6+7F9=SUQHqD~5Wg
z04$xu!ULusmabs(P~{jDOdqV>2a6|||6t*Op%E$o%lEK&hLz(mcfjHksvIW;OAjyw
z#Oj0j5AJ?wIKt8eJU(FtLur`5VeW>d8(4aPnTuYZLbb!!`@_tExfd4hFnusSOdpI!
zk5^dv0do&5{$cGBm_8T{vkw-|F!#g!3yVKkI)~|l(J=k6@(<>3m^`j@50i({u=t1B
z4|6x6bOX!RFn`1HFYFw67!9)rorc8^EPP?^$K@Y%{V;dJ@+XXjnFsd|hDL}0y1!xR
z4;FtgJ}iAebRkGsISq3+%sg1Q!@>vFE<@;quwd?knFEVwSiJ@>|Dfe7L>DFrGau$|
zn7uIfz~Ti{Cqx8R-ox@Q%w4c`VX*cgL^Xnhm6I@iuyhNHKSK7x^uzKQEWN|@!@?0^
zBPt7?|DfRm%MUPrqpHT_!SzF>VD5pnD`DviDvd$G!U-k;D|cc3fyD<bJ;Kz%XqY=;
z{)WXjEFHkigV8X3Fd9}q!onG*A7&qVet_wN(J=SJ;un^WVe3F);Q@0eOdmQ8OMftT
z!16uJeXw+at`EkC`5#tJ!txW`{m^j?n0j;?7Vog~3f(?j=?LaOSbl-U7cAey@*zwe
zy1lUQhS>*8PcU(qe_-K^t{)~2b3e?#F#p5EVc~<WAEq8A4s$20Jb{T5qGA4r#WT!Z
zg!IAEB}_kToEVm#VB)yUfu&Pe_`$*p7VfZc#HAk=E->}5`UDmZu=I#aKTIDiox<`h
zEZkxF5SM<KILse#K7sTKQwQ@OyncbUdtl)TQ%8t~#W&16Lgg0BJXk!#;tiJnVd5}#
zglL#PSUAG?uzG=zepr0L;sa(stXzS~<1z=HZlU7|uyBBdE6hE(^uyX;FdC)~mcHQi
z0Zam&hQ=E#-@<5Eyuj^;)>G)(VeW<HXIMDG$}?E{g82tsKTIALUa)cu=5CljVBw3d
zAC_KV?EqN$4~suoxWK|0rXLnBFneL?7S?}&nFmvkPQ%;{%eSy_fW<$|zp(NFmws3{
z!SXGv9D(Iuc=)3k05c!vFPMH<Jiz2(d^GJ0F!#gdq3Hk?F0k-`nUAg?UN6DY1(b%B
zn=o@>@rO%4EM8&p0IP=xm4C2shWQ&7Z?N<SD;Lo1Md!oP8;pj<7tH_Y>S27CKVkY{
z@q`hsFb!xldb)v0!`zQ6++pfq{)UAUEWBa*VBrH(k50qV7kd1_;saehE<P;X!)RFg
z!KEHU9GdQ6?tz6rvF5<S3s!H#^ux*p4Evx0uy}*1gM}k3T%pQvQn2ual`k;;u<(P)
z<1zrI4{jec9ANH;g$FME=;E;YA7($yd~|i_d{{XF(+?~EVdkK#N9V)xEiArZG%S2^
z<s(>nftB+x`_S_dx_vM{%pO?$!OBrsdVsCt!Bq~x#s^{gVfhBue}m<3m^{p07!9)@
zrVke0=;b2JpD=we8W!&`eK2>!$`4q+#FdU<;RQ1fM#J?%+Y>NxVrf{qgP9Kx2V4d~
z%YAfdm^)zci7pT0!_0@J7g&0Ng*z;KVd~Lom_FFNAFSSn#Sbie(e=UjF#WJ}N=Q9S
zA1t0=`eEfSEFNL$8m1ndhRMV7A1vL${0%b~T_21Ot3P1z1PgDNKVatK3V&F*!rToD
zcUb)kGY{rZn7uF>mVROF6_|Z6_rUxEvj?URM#I#>%z^P?>S66qm^e%yE*ci!u<*yF
z4kiu@CtUdpW)7@8hUtURuyBHvJ1~Es(=d6MJ{S!PUs(A7a|cX6EFZ$l6PQLQ4O0gT
zFIalP6<<(oa0+HWth|TCA1wXB72;sP+>NdtE{;PVTo_tzppRGJ3NN@mC<7KRFn`0s
z0Y*cW5u{-00M>paHvhv6hLvBiaEHYUOdb~v3ny4P088(<;v3x@n0{FJ!}u`wz{&?$
zzQE8A?dQYt7tH-I^|15+YZu_s4+}4t{pkLHl`Amw(anLCA24y4yJ0jeU%=c2QwO7A
z;SI~Luy};k12Ff&%z^2Hr5{*-5mrvZ%!ARm?1lLkW*#iR!rFnb`~`CtOdX7dg*(ju
zaQ8v;1<ZXgb71;lG_2f)rC(S$!}0^neT32z%pLH21GOLKE?9iR>_w+x;RLe>=3kgN
z++XM#(fH{30Ol?<WjMJo|HI-Nr$VR%EPP?{4i#pA^&?>U3Z@Pw52In>4^sz=e^`9M
z(hV#gVEW)Rw7m}tH<&t@d*I;?RR`CH#(>2eEFHkY4OXrb3V)dY;QoQx2cyyKgK=U0
zgSi(L@38QIsl!FX^uyc-^FQ1jxHO`P!|a8Xd&Jfsu<(NUAEpn+hxHF&?nJW|zWxeU
zuEEkfEIwiN8mt^e*AMeIOdO^UmX2WI2n%1BIv5S}H%uQa{ljROI$ZX{(h01c1`B_f
zy~M@?%)Kyk(DNTGKfv6BPQ&5>M#IW|nEPPnqw9n5VdXun{SV{A^98PS3@zv2;RRI(
zJO2qT4zmc2hQ&LKhQ$Ls-@*D{P<zm{Gr+<b7Ctcd!onREzqryLEWTjj1hXG*57b_m
zJX{>gfQiG?A=Ljccfn}5JX9Zyg6W6(AC}Hw`4ARwF#o{x!DzUCXgdTJ-Y|c`%RiU~
zG#chESiXae_rmI5Sooo7gL7f_!Sg%R|FHCnE8oEkfXTz+2Nv(Jc!Y%mEPP=4;55`e
zn15m64Ko);!`uhg2W7y*7Z&d@^{{Y-r7K+eVdWjHo`<;`mXBfK2lF4)S_}&2KbU@)
ze_-M;_h4v*3c&hNuyzY9USRPHGZ(5HCk4y@uyPxgE?_jeMp*p7(hV%$VdF(G`_c8m
z_^|Q{mhNHtVd(&7FRt_t(+4vT77noT4;Fv8^uxjhJ$zyAg82_-J~|C^A1uCMG%P*A
z%t6-&<HPKQr9)VHg2g*59l+G%qG9P17XPqtfTc@Z`e5QPf5PG)mR@1;2vdhaL(4~)
zI7}Zb-NV8IS2{qC7Z@KF|1kZy$}w2{!om+`Kg>O__=Tk_m^h4vxeI0w%sf~)!pZ}f
zdR#OtzG3FT;tkdw$E6Pz&#?G|>4T|<g$Im}PQ&~QYbU{ISUSQLZZLCT{)gpXSh)c!
z|6w#tJuVua&Y|gnSbZ?_VBrLF53%}T;RVa@u=s}MD_Hoz?7^U+?uEqztp0$-KP)`q
z@@NLY?1u|O%|qwI%)@XGwElv*7v_FgzJ;Z0*!Te~oxsdT4=<Q|VDSN?Vd()^dV;O<
zf~kj<|Ag9suyO<DFIYZ<_5bvs{=-#{z{)9@epoo5=YN>FFn_~nSa`u`SbYF950`&o
z`eEk6@+~Z!VDS!9hl_@p56k~Bcc8};PW{mKEiB$(=?_+Z;?f5bhowuHdtvcMDBr{M
z!|a2N-@x?4>NU7J0t|TihPFdt^F}ajuyO^)htaTbf|au{dtvbbE01C61f~y0!}P<#
z50-CW=?E4tuyBIugV8W?SU!T$FnL&df|(B!htaV3htV)`m_Ar|;)+k0|6uNbm1D5*
zgqaH~2VmyIXqbMOKVkY{;ROp1m^zp~7!6CWu<(Y(6HFYIk74RDXlQu{t3P1*92Wnu
zas)#kQ~;Jw(c>SMe$mSps5UqSi!T@r3rARa2@5}1I)Q71GGO{(=?>-&SbW369TuKY
zZ5R}KIK%2MSh&O7g`p8D0F#G>JIsEVy|~g3R67Gq9n9abaD&+o^9M{l1`Ulz*#1vg
zI)KF|F8g8e4huh6dV!@USomVt2Ni&YH!S_a+zIzTwEn<l4op2Peqs3yM#J1iNIxw6
z;rRz@FD`#Woe#gi1eVTV;SEdIFn_?*W6;oWhl#_&4HloUas(!iPQ&bliNnGHR*#{}
z!_>iOnEzn<VEGjmUa<NRrXHP!#Xl^(Vet>s4@*br`e1xmc?WYREZ@M~53AQ;>d|Rf
zc*FF=`~eF$So*>h4{-lM;~O6CP(I9jn0dJDhm}__8s-mJ_`>R0bp0?sEd9a!4f79N
z9HtN#4NXU|@P^Sa{jmBFmv&fu!Qvm*9)O9%{ei;(Sh&ODALf5pK7i3Uw8MpA<p9jx
zu<(S13tS-@0~SxPegw?Fu>1s*N7KfD?k<?UuyTcvy|DBJ3m;g!6;=+T+lS7F*#~nU
z%wAZ2LRXK@hlLj`eZuk!OdMT3Iv-Zwz|yB4G#$ah4P8AUKCC=|n+wfHg!IAO4+}R~
z`hkTHtUg0G2PO~mH!Qwj<vuJuz|2M04=aCQ>R|d{?tzKJ%tZ&VbPp?EVCex?kHF2r
z5e~5UhKDy)9n4&qz3}iva{#)2So(u0gQWvlK8A_oqG9;~W<M-l<5G`H9HyVxbc}8f
zEM8&Zhsz$gI5gbgd=eS3avxnAtQ<xcN9W_x56jo+>e2bI^aB%z#TP98VB)xFc=$oX
z0j3|8uW{*viNoqa^mGQRM_}r3(Xf02+xG+G!_pIsCZr#h4`AgtjK-x8CJu`ySbBk_
zKUg|~=VO=#C=HW`<yROD3qN?cLe=A<;Qm7^KVklXg$KHRnEznu1Q!0VdJiU#t`D6L
zE5~5*h%Qfv56h?I>W8H>nEPSv07CY{;tf`g!NLh<4&49Hbd7EvOg}81VDSknhhg%t
zbb+oP#)ri_tR8`>hq(hDzc3AGG|ZhaeXwwZmB%pup=pD2Vfh^9URXH5(giF%!Zn~V
zVD%#`9ANs;l;PyU;veo`=r|5e4NwV~K3Mp`#t~utMwq){;Q`eKr_kkL;RFi@Sp5fU
z55P4-8L)7Jxf>P^uyjCd_`~B3ntx&D!r}pH9Zm`!|Il;_D}P|&f>R?*0;&%dPO$V#
zNI$IJfTdrUKVabq3tyNu=rqiHSi284-V2LASoovsL+8WnhnWY<|FH587C-3vVCrG*
zGg$hC>4VX*_(0bWFV~^*2GbAoH%uQae$e&9(g`daVd)v>A6R(+i%)d@uyPs}Z!mjc
z=@;fNSbU=Ehvie4KVj(&7Jo2vU^KdZ7$4?eSa`r_m^rw@AEqBxe#82IF#p5i5#~R*
zdNc+s++g})@d|SnnsPW77A`RRVEGpoAB5ZuYoEa52bzyy7Qp-u<D=6s^I_qSp&rdV
zbp5b$36>5B`4?S3Iv*BpFmYJ=gz*W|XznFdKP)`p_CWI$oR7wU_4i=$2TLcgb|5TW
zp!*AE3``zYZo$fHSbV_T50i)KL#JW+4rV`kxdl%j=o+En1+xcT8s=_TdPJ9p<zJY7
zm^zqy;e4n)==x#h6-*rFPFT4E^B1~0xO%9$uyhO42Wt<)!V_n@gzAUob6EKgQxB`p
zVfJCr(DZ>T{$c)s<#TlX=<={}ba?zj+ab90!^NTgK#zY|_`%{4-F{fS!Qv6-Z<sn*
zzYbj-CXY)TrVm!G!pw*1gVC`138oIF4;GKGc!$w2eJ~o9Ut#Wm#RDw;!OAC`^#@wG
z!SXZAK3F`!>_ewv>S6AO*#~nMTpnE`ln;x4Sbl}s2a7kjJXAe81&eQ3I)~*0n0w%S
zbd69xEc{{WVCe~#E@0(4R5?xxmcC%|28(Z4IO5a@m4KClF#BQpVSHG;!t8-+gHy2j
z9$wx+^Ce6i79Ma7XbkjlhPel(4_2PT;t8f5R{z54ZCE)1b0^IGF!ks(OdTv9VDS(0
z2hMa0H3+5;7XL8yFd8NgRgOWy@(qlJwbx+r1{257h$a9_FR*Zfwc}vou>6Ro4bFwd
z6U^T*30S<N#}7;%Mx%!_%pI`sg{3E$dYm*g-N5XFmH#mRpzDK~4{P_q!VQ;tbp0?s
zEWTj+VCKNe0hl^;8m14HPhs%|3vXEX!@>hyKa3Ac$FT5*=_izaVESP+EZkt_A1pn?
z$|;ySbQ-1(CJw6y3HcwU9+qxk@j<G)Vd)1Je(3oeW)C_I%lEMOhuIIC|A&P;OdMT5
zEZxK63FdBCe4?vI=fm6!OW!bku=Wou9AW7MT|Z1c%p90|VBtw9-eBPbt3P1n2TUAh
zF1o$wd|3Fv;v2?CSBHxa3qQC#v|fkv2{2&!4OZU6(k}sR4Dk4cw$tGG7g~<M<Z+n;
z^DnI301I!Je_-)|OFt~!Vfta=4WnV{0+)Vxx<GR;db+@+A0`fqcUb<1xd(1OOd%Q#
zD`#No4wn95{)gpDoZ$#{H!QutXjnN0qha|0&3gE`{IKvsF9%`zVe;tuVg7~n8(`@M
zR-VDc(e=UjF#p5+2Mae?zJ--9F!ks(ESzBZ4VHdj@eWHraCK+~z`_frA59w0#ibvX
ze$nF@<}a85Fm*6KjE2QG%$>0O2Wv;d(l;*sFmYJ^g~b=lJXn5&*@ugU#TRV9C#)R@
zi%(qoVB)a!0i$8@2@hYGLL4+K{9rV$@W!DXK93LcH_RO{^Kj{diNo}v(=d5LG|WC2
zO{~2zb7AUX@d-=6FnMBWnEf!CSbZ>cFnz?v8!Ugr@+mC6!O|lv9l*>-r(xv{EdQam
z;|aL~R!+mxFD(3E`G`<B!t8^ogXKGzIk5bR?rwBGEWgA23kwIBx#;R)d{}xz4+oh2
zuyBRR!_=eehuI4ghv|dSF!#aaanUeyVC4u*9jrWnr4N`mjE2=yuyBXPCoc6ceJ~p4
zPFQ@y!Vi`&VCv9mSa`tdPnbNc{D8^B#4%`S{fX{w*f<Q#T`)enesuF;<t3~hgXx2X
zC(M6vbD-@sSbl-Y!_0;G0~Wq8ahQHs_`>Xi)yFXVV0@T7Og&6LjE2Q8j1Nn1F!iwZ
zJd6)h2Or;oh9hj=2yP#AeLKuwFnur@W*)4bgXKq9zJuihm^zp~7!Aw+FdCNMVESO`
z2bX@BIp{RZJur9UYCoatgYjYU33CT}`h=;2(Xjjpi*H!G!Qz{c`(f!67Vqfg7%Y5X
z_My|T_=c_jg~cl@AHryKeX#N$7EZAEhtV*9!1EWn0Z=|H-@w{^u<(OQGr-~z#)r|c
z_8u(0VBrVLFR*k7iwBrKTr|wRFmrJE8<&1qx`nwP7Vofl#H9}=4$}|w7c3qLg&!>b
zVDS%2Pq1<rSNOr~g{}95?f-+tD=hqA@dHzjPQ$_*=5LrjnEh}XT_cPSEeB!g6qb)+
z`f!CGEWTjnEX*A+K1?4>9n3ly4U1=(dRTnH(ladnait%ae_`boESzEfhl#_~!R&?6
zaC4#lhs7H#AHdWRqG9@A<`F6{FwBFN--O~BT^}sn!O9<)ztP8wVe;tuVSHHrh3SW-
z16X*%;sd50M#J(8ESzC{n0>H(M5@1F?I4&tVCfKMFCiM{A6R-Mq#l;fVg7{q1Lh7w
z`eEu|=D^Ye%pC;vL(?Cu{D-+4HvWrCKg?cOdV{sYVKmG?uyP6A9GE^>dWY$Q#TP99
z!tyD)epooc@+nLoEWg9@9ZViwKTbY0Kfux-sqqi959SV7xkAW3SUC?X|6u-yl~3sU
zVc`plcUU;X@+HjOuzY~7A7&qH{1)E-hx#8Dp6KmE40E6YuzCX)?l|p*s)tdq_7yCg
zVCexC4ls4-G%TIN{13AamTzI<1M?@kepooc(jhF~VDSzc*Mp^Vbp0@KSUkbpfgTSq
zd360Scf#ah`3`0dtUQPL3tc~~+=JyCSh&I54=Yb`g(EDUVEWPRfr+Eri_V9obM$f-
zT^%kyEF59^oY-;;W*#j4z{&%dILtg4A14h>@33?N^9L+_z|t8`{ZI*bdV`jyuy}=~
zBd9VA3Kl;w8kP=W`45&aVCJD408<Z3$1r!p^uf$U*9YUn@;59!z}kbbegG^Tz|4o~
zgVC_?f`uQ<-MG}l^ucIY_`v*!o{nMaaM7^z11s-f`3jeMm^dtbz-XBLu<(PK3loRw
zgVFH#h6%uEXnKN$KRkS3@=zL<zhU7H3om#+gqj0$4^$hRf~kj<12A{O?1QCSm^!#d
zGzKjGVd`MzI4nNl?m^QB<HE`<SUEs${KLW%R&K%E1G5Jg4GU*j{)fdstQ^Fp59U93
z{6pulVBrn(7cTv<@P^TF|3l*qmabvt6NWj^asr+|p?p|;!rTR`Ptf(l{09p!7$4?t
zn0}Z)(e=alu=t1RgY^Sp=^vIpVd~Lon7?7|2$*`9yU^9c_^@z84{w<NVc`OcUzk1^
z4Raq%9;Odg9>T&OmLFjHU^GlW%pEXsSp5rg7tB8}c^C~BhsGN${9y3~OTRGp!1Tdr
zSUQEd8<x*t?t$rpg#%0<oQB#9(+9H;M#Id9t0%yKxgS=J!tyZzZ49t*g6V_hV^}!g
z(hu_|tbGg1zcBr<_`{_iCJt*az|4b{d$4o~_ZLh9l!oOOSp5dm4~thAAC^v_+At`X
zyJ7JNqhaR5%)!uzCV=h_m^3UtVSIEN=1*97!_qIzenRmG%O9|P0I+%=7N3OjG0c9L
zyI|o3qhaE>Xjpv1<YDngsN907M-N|Ey^1s6z%)W>xP4GQ%-?VtCQd93&6mU~N4JMi
ze50ESPp{DW3znbY<v+RxG(LLy0ZR|)@rtI60hX^|>S27Cd9d*VT<H&%K49?=OaHL^
z2%~Z7huH^<XPA0exd#(R4_~-HVDSX4uVM8F%v`uS91K|b3=4mle_;8LQ24|2p_>D<
zpOAi-vta6B@dfiYEFZws;i6&r9p-LW{K4FZOCL-erXSXBgvA3q-k{+NQxB)1;;{A+
zEFHkg8(4b4RSv-X3yWWvKDfJ~@r#kaVGe-V4^;;%$6?a2aD|nlFmV_S&!<rRaCJ~~
zVfhf<T$p~CJ7MmF(XjFx7JfM65o#~Y9WZ^caD=5Rn0**DR6k4|EdJo_L8v^2K9~U1
zpRoELmTq9<zp(U#EB;{OuyBIKAB={j3wS)k?1j=Wf1`&FOdqUXfW;$J8-jxR4_05p
z)WOODghKo*Sbr5(?!dwUrVHJDFmV_SOE<9ihKa-c2g}beadaByUReCY%43*2aHT((
zIk0v+Z2T7%FR*+Ja|cX4jE2QKjE1=%o_?U|17;pfAB={#@1W{n@c^@zkh{^#M|5|>
z;saeAZa*~sVc`o4M_9UrmFF<^=rk;RVeW<TVfMkyL3by*IWT|1@*&JUuyhYg$LRWC
zd|3X0*$49n%sqtCA1u9~_xoV;xG?{~?1Ry;avzp&VEGoN9+oa(>d|Rfc?WYREF59_
z356Rheqrebmi}PnA1oZv?S=7S;SS@&!VSiUnFr(JqGA4k<v*Ca2<eBp6BbS|f5Y^{
z{SVC_==Q_(qo-4tKVazrMiWX$Fn7Sl|6ub0FdAkb%pI`!hvjcrxesgi!Rj}dJWL;)
zhWZQUPgwea)%&n=5f(0ReNYB0oM7<`3kR70;o%R>=TL2M3g$kTIk0>HOV6<U2-kpv
z0Sg!O^n}aZ=*Ga@3-dR+JRv?T{lUrsSbid;4<-+DKP*0B<u^<o7Y!?)VBrPJzp!wJ
zr6-s=baP<w3kzqMepoob%!Q>7bp5b)A50%C{9*oqsV7wa!PKFrA6U8|q#qXku>22;
zcUZXuGasFX<wsaJ!OVfhAB+!6AL#mFd{{Wa_^|MXnG2JL*#}bxb0?gJuGfOK17Z4L
z`2;;&VSKncXnuypCoDY?%KtF;z|sv&KU^QwT$nl-4NDiWc!$Lo%)KxgZVpT%l!mz<
zmX4spa0(_5OQ&!NC<8q_p~4KXc!SY!cfb@tX;`{~#TQIHEFHnZ1EvnD4Nk$zJ(xRS
z@eOk?tX&G%fX0Bi3uX^2zA(yfG=1n?So;wcj<EHDuzCkq_`~84mhWKk4$C*NaKWV?
z7GALMfyF;8J;TBWW)Hf%VetzqZ(!kup$<B(f-VkI53?U84>K3$ZghE=I+*)l`3I&C
z7LPD_n15jU;54jWgVM0@hJ_0({&DJu8U!mpVf8+&9){6SWkgdj|G;QM8e!oGR|m`Q
zFd8m_oq;QSv8%=ug@rr14AJJIxf2%Nu=qna7oCr8KTJQ2Mpuu{CsrN0J{TXC|6zQh
zX{f(p_QU!Mu=)#@{&ATHi+5N$g{4DSd5%jTOdMt}ES_Nfepvd4g)2-PrVmEL<YDbT
zSh)++4|4~M57P&uVeW^OdoXcW`33V2tbBpV!)TcMVdlWfV_3d}r5_j#(}zx@m!B~6
z(ADAM!}2Mt{KcgXmpDv6tlWb68|FV;`e5R)aR8XRVc|!tewhDZ=?+#O;wtxH_QGhG
zdYC_8?t%FS)~<%BN2g)#hNVMTeu9a^(igfu7$25CVCfB(4q)*Pa~DiKIt@!NFn7Y-
z4Qn^T!UbI)j1P-<Sbl)%hlMMwK8LA?(Xjd%=02D?F!!LVgYjWBEWBX;hs8V0->~q*
zl@4Ix1WRu)eXwwVnF~_~vlpF)xdWyTmfvCR379+3^<nU#^)SplLggP^KUz5ovkw;j
zaCK-5So((5`!E_7kFfAS(+20l;t$rphqvRPDq!IX<zi5<bPWq%SpOfM4xs+R&<GWP
z`4heThq)W(52$hs3YK4B=^K_#VDSeN$IysF0OlW9c@8rlhjO?uEF59+1xx=h_rMk6
zV8G%HJ-x#G0gGpJ?XYqSUcW)Z8&;3OXk7YX;R|yoZ2T8C4-D%EqT37SL+yj5A6Pj6
zmnXo0m9sE+!rTMb1~nhfM`OVJ4T~pO_`#IH(j|<KPNSzgc)o=ygynxI7lVSy!|Z{j
z1DH9maK+GwO91L$n0{FPz@?n(;?VMoSaV_ig_U!#ej`jjvFQO8Z?N(omabvu;G$vS
z15*!64=_F~Kf>xkbaP<o4VLd==@3@_!2AU>4?{n+UkGzQET6&3O&ASJC%E*((l5+E
zF!iu>1rtZt597na8Ria{II%R$|FCk4SbgZ~VC4nO-7p$m9XcQ8epq<`a|faP1B(|}
zyur$CSp5St4_Exd(hJOfSi23DZs6uZ^Ch~wVd(?rPMA3`eK2uYe4^`z#TP7{pr<>y
zJ{<WHrXE&~!Tb%g2WCEuPmo6QC#mrV8$W`DBSHJ13Sj9E7M|$-fGWdD!QvO)y|D5L
zmQHXP0L!<q`W6<Cuyg?9<I)eypRjTp=6{%bVD7`EAC~`N@d?up=R@NKR$if-1J5te
z@mpB?9Y({<N7o1A!_prt|G>t9VDSkr*U{1;%sns~Hg5nQ2Y|X877sA{&}mpZ4dxD5
zxWUo`x_THN7VfZo0`n&<U&GQrEM8#x2+^=~0Cxu=4QTSP_#@<Rm^PSwF#p2h9o8R#
zsl!FX(kZ+h2@Q8*{SDI(YsbOzBeD4q7XPq(0`oVF#^r99I6QyC+zm~~u<(GX!%0K+
z!Q^4-0v3)iaa=S!{Gke9<u<YL4=X>Q+8KxqcbIx~cao|f-JS68gjqm<hQ%LT8CrP4
z#i0zCdRTgal{2vX3oA!p=?bb1gMx)S%>A(Z3sVS-M;ISQ!^C0r9xVUE@;xj(VdWxB
zA36=Q7iJzT9$|b~Iv^CDu=s-cAEpo14j@$i!@>bp4#L{OFmaeUuy9BB2P~Y?^FQ3(
zXyrC8{jh!?`Z^F;xekk0n0{FO04pzG^#n{kEM36Vqtmc(frTF|K4Iwq)_z0R2j@fm
z1=9~JFJa{nEd1c%0M`g%z{)pRe8I{anEmMD5M2lomVRLI2CHvi{($MnWk1{`Sbl^0
z2j*_LJX9RcM`OU$!@?Uz!|EZJJJ7VjxiES3dLI^#aD_M+uyhFvcNh&5$Ds`_3|9vW
z2Ph2}hpWWSfW<pZ7M6d}(<@9KokrIW3lCU1g03E&kM2%bx<FTli;rOsG`<O@8>oS>
zc!uQzSbBh^XQ(pV6x2SLyJ7K1sQ!lMduV*Y+yS!)CJ*DI(=h+R;tN(@!}1>?`(f^d
zm6tI8!_0;G14d)m59_}}-4822;p(B`g02sp4-0Qt{Q+}7tUf>w7j*rw@&e|5Sh+-O
z`h|rb%pEZM(e1&-ht(hO^g^V+VdlZ&2^QbPmK!krFnzFm1Pc$CJRurhPr%#_jZe6L
z2p9m1H(2<=;veQNm^dzDVC4nO{jm6k#V4%(hNVk%bKvm>bw5l$jE2>dF!Ry%!}1NR
z9D%tTM#IF>^}+b)=@izkgV*m+cftG#(+8tr@ej+7Fd9}55sC+xxv+W^7H+uw3$qtS
z!_o^Zd|~A|tXzPZ4^xj$!@?P+4`wgSJ?QE&_|WtQi$_>~#88Jr02WU$f57a=p&UaP
zmR@1*C$`)`Hwae$ld2z9e!#~6VC5H#Mz;sXhxrqhKVbfVrAJtN!f2R27!6Bzu>1iF
zKUh4#%5zx10HzN{!^C0c!_qCxJeWML{0~c~u=s|RLoga94ohD!dto%J-vaY5ET6*c
zgS97N?t<xq(J=dA=^SPrOdb~gFn7SjanjIq43o#@ZkRkQd|~kpD~DkEVDSS}kBf%+
z8|Gh_I$ZvS$;0e{`5z_@D{qL-zfg^^aDve=bD+`$DVRTC;Q&iV@O(+Y1Xwu*^A9W?
zz|6s=9i|QzAFy&4mOcpOXITD)_4{BnOdl+L;<6uRA50#Wk753SiQ}SS`3zki9uMde
zXna`x4HG7mZeZ%*G+Mex&qr`|I2h>mlbY^Gb}yPcVCfAO-?+jPO&gpGi(i;KVCe|1
zfDi*3elUHod`U<fOdeMM!O{(^9EXJ~EPcb&!)TZ|Ed9a49cDkwJ-EUHp08l;hqiNI
z`4v`<!|ExRy)YVPKa3B{M=<xm$^)2tVCrBr%)PL359Ur-IKawpSbYZ52cu#7(c=$h
zFDxD7%15yL2}?gPf5Obg<!+e$FdEiQgq0Vtb_A>)2~&qo!|X$k2blk1;xKjS`eEq<
zCJ(E}Vg7;T2bels`eE*d>4W(jW**FZbp5dKgXxEbH(VVwUBUTi3|KtD?19xcF#p5F
zq3IG$JN#TsSo(pbJ6L{##Um^n(DlRku=yHTeu3E!6Njlor_sX&7Je{&22gjy_~`m!
z;RX|j<$G9qhS3<|3e^CMCs;Vb;u%(sL6yP73C4%fuyBFpGgy9yhdWFmE*cuYF!#a3
z11gS78%!LQ-_gqvxH&L|IB1wVVeMylK8J=o4sCE@Sop!x3oP7VG+ZGL2239;AHe$k
zuy}*{16D3#7z1@DEZ$-59`tkp_ZON0u=EX!KUjRi(l5*(Fn^+H$Kb-seONw($18N4
z2txy!0L*?gVVqo8yu<B<h6l{Qu=Ih;0GNJQzJ;X&n0jK}11rB_<uxqcVBrcg4;Kw9
zcVOWT*9Y|vE`2a@cshd4`{7ClF!ks(ESzBF04&~N=@MN%j1Ma>VDSwThlL}|KQM80
z8kYZH;xPMR;STc`E^}b&VCfy^URb(=#V^bpm_0BWmQG;d1yc`84{(1GhzHob4a|O6
zI)L@xVEGwlFN}uy3zlAB=D^B(T<(VHhv|cbBg`IHdj}S;Fn7T8!D*<!VD5&s7hvTL
z%zT(RaD6ZaR6nfVf`u2%epvXz%s~e*dtv1l%>A(VfcXm^&*%m~`7nE6>R|4NN;AOH
zGmH<TVdlf~A1vNs=@(W$;?fWE7p%PooBxBwE37<+rB9f>=rl|{jE30@3m<g#=zLf@
zf#pYd_z_v|!r~nkelQvqpXm0%_%Qp>)xp9a7LM@p8Kx0R!@>{Bh0pt=*EcY8;Pyc^
zz}yK_597nqAFN#mOTV!E4b=|6zXz6nVd(*u4q)L5iwBtbF#RwZrXQvbmj7YnI<WKu
zi&vOFSiXd%A6PiT#9`{tX;`?y+zF#$;;?cQT_21O^Cv95!NsBJ1z!KaB+zJ>KVkV5
z7XEN$&~gFJM`OV9E3BM`#TU$7Xvzt4;rgNOhlL|SjZg(J{jm6j#XD4*AO#B#SU!iv
z2O*8H@Q2w43rAS^!_0-HA9V9z;Rut5`2(g8<{p?jbp0@OuyBXD2bND@;Q<pz*ALH+
z(DphkpTXRL?hg$8(D;It|1fvL@)JxyEL?Euhs8H~IH0S?#fODIEIq>9flEEQI4pc{
zxf5MIj1SWX%eUzH9_D{oJi_#$)3Er1m3J_GFd7!m==xyh!paMD{jl)F(1#`f(+4Z}
z(WD7-Vc`y|C(y$IrvcD(hweXExZ>0Ym4JmCtbT#T1G;%o<row!d|>4~EFF@X4qyhu
z%z@cYtU0jof~8~h`~;JS*#o0t{)PD;7LV}!2Mu3b;RzFmnGb7sz|4iE2Uz-o*$bm#
z<pnIi!OVk|pD=y6!T}ckuyhPdKd|-;Ed9gGhuI6GVd)O$Uzq=4^0@4U$;0eLuLoiN
zhsnd$<6yx24eLk3+l@HXqYJ~@uP}RI{(-53#RI&&hDxA^7t9~9aEHYYEPX(=!6{gJ
zgXJSwIK%8i*9WUlU<SbS!T2!qVDS%gFS>u=>Y?onSh|Cm539dm@sHl_g6W6FFS>r1
zIWYY&f5G(OqG9m}i+6bX#H9f$4ht`sde}Zdn7d)}P~{jDEWTjwg~cPx9~c_Y1YqF+
ziw{`-frTSX98DXX3rl~n@jrO{LF;#zzu+3s7%=l;>R^0Wyu->fG;MG$OdU)B*8YHn
zAEEezr88K1hlL-k9EO>Ti-x6Jm^`uh7oJ|A8e#E=ZXQ$`gMzsa7Cx|egw;bB8qfq_
z;SIAFrVkc=Xv%SNVc`a=2Vm~OsR1ehi)UE6f!T{*4#U(zwIL{I{RAt=VESP0fQ2J0
zeIhhMSg`UEW<Jaxu=GnP-@(EkM#J|1!pd=2Ie@YM8|rS@cmZtRC@el;?m|x=5LYuW
zz}yS>7t}nM|6%DJ<}R2%7!4DL)qgNP%wCu~U^Gl0jE0#HD>q@{u<(M#BTO6@4J)@`
z=@#ZMSboIie{}O;`e5M=lZVA4jE{?k`5z_^vmcg@VB)xFnEPSs(c>SNK3I6e^uxyg
zVB#?IaO#JaudsN4m0NK2&~yRkqcLFh11ufD;uS{2<k7TYaAEF+rAJt~3yWV24QK+e
zb^^@5F#o{Zfu<bJh1E|mdtv^7$0yYPu=s>)gfXD<u=E47AC_)lG^{>^X@k*ld!Pca
zd<>&u<3KQTpvo9v<t$7c%p6#~0V|JT<riEZ8UyBTn0k2pL)%5T{0*}Y7S1sJu>1oH
z2e>$zy)Z5;eZb-gR*u8OiLD3V_CezVt`F)Dn0>ftSop&1h0(C`14hHb3uX>1-NMR!
zm^{oMu<(b`FnO4MSUkeQ6Q&N<kAv9@qv86X3|P8_l@~Dgz}ho9Q2)cq2dFkU1uJi1
z_QS#rmJVPvEMDOnp$wRRVCKW@h1DZ4bD_#HC|Lf6)xQ`D2nfK-323~-;t>`f1hg~2
z+zoRtEIeW6;7b26b7A2P3kR4uEMDP!91K{xh1L5oeXx2HM&r;97l!3~Si2t<@9_A8
ztHjQL<x^Pv!T7NF!4>~7(_rBQE9YS02uruH`~*{vPQ%J!nEzqs!0d&^54t`W9~Mq9
z_rmOh`5)#Vn0j;?R{p^911ulF+98C}G0eZPbO6(jZVxU#EWg9jGtA$()WgJK=?4~W
zu=s<eYgoF1tAlBT(y(xcrDIrm4)ZsxJc7wXwZSP^xWH(b|6%Td@nPu$t`W+B#TzWV
zVBrsyhOZmJr5|Q5%-^u^gYnVLf$?$CuyDh$4_c1m(g!Q=VdVyl4@-BjbcstpEdF8j
zIV`^5?Kx;W4P8Hac)`LQy*z-q16@B%JuDtz>R{;rmVaRB0bM^Vyy4=|aDtf!OUJPM
z1y=`U!0dymgSF#`Ex*z8Im}*IJm5;VQ0w9SAXqwrl{2vV2$nx!>d|SKI+%U1`~q_a
z%v^MRFg{E@tepo-2e5F#2w#{+G#Z{>p!Fau9TU<I%b&3H3iA&v|G@l#W*wXhizirl
z4bR80bO3b^u5g5<UsycB!V6|DEL>sg;1)v}F!eC~u=E3?3DrNa_C74V!@>=g|6%zS
zS2)7*1uUPy{0|RTXgI*!33WD33Kowrcf;~AE)6h$!@>t<4=mnb@-P~gIWYgC=TlgC
z;L-<E4^s#8H_U!mxWd%oG6$v}rXQ9MVdA*-!Ng(d3+{htx`W9RqG90;i*Fd8kUp3^
z%zv<OhLuY&c|tTS{9y5jE4|?|2WBrUykPoa;<)s|#9`qEb2rRCuy}yU!_=eGFm>qZ
z2$nAh>4(_|i+`B;F#o{#=rl|{th|8bKUjKzm1pSsV0>79gP8*>_t52Gd|Wil{jhQr
zt`3_2;e0d(ES_NXFnr$!R2?DzLzOYW>JM0a!`6Sn<Z;n3|HIm^uzCkZ!@?1lepq^i
z`5%_wVeWvH-!MM9Ik0etiNoRxR-d5LFm*5*7Jjh&1FKJA`Ur&|dif1YzcBM*{(;pK
zFmZGmR&K!J5vCsIK0@gqW*)5Efu%!&`k>_;EdF5n;Pyh-bHdEWMZ?koEFZ(#V=!^_
zaDkZvb016}EdF5O0HfjJFpX$5ESzES0ZV_d_#>nrrVm!$z|4dB1DE?@&VZ#CSUQFI
z8%D#-fvHEQVdW1jy~AjjI#~F?_!u;_oPe$4g0%x-@d%?a^g#t+?u6w7Si1q1o?!VD
zsvJQ<(+@0sVC5$)9l`t!GY6p&%7W^LrGJ<|VSJc-VCF-0At<PRm_CFAgar#%SUP}+
zB9T!2Fn7Xa;QFBQFn7S@;WRYdVg7@aAFy&9mJVU*9Ig+_fTcTF`h=w;n0*A}4QfBk
zd>9QI$AR&o*5Rh0=D^$wvmaJ2!15t317PumVIMU7aOs1I!{QGX&#>@-`5)G<fT_oz
zq3(y}KUjSNi&r=wLnA~0mcDWN1ELIrgw;Q=c!TMO`2!Y?7#g7hF#S+2{60pQKAh<m
zrU6RB)WQ4#%fA@$0aXvDpz#CK2b(8^=U=EiTs=AiO&(Tm!pZ?ye!!J}VDSQ@Vc`Y~
z7g)H$$|rQ|VeyNek6`HsW-g3I*AEkixfd3Hu<(S1A50uwKa3A^FH9fIA25A{(l5-P
z=>1NZyI}5w*@sTU>I+!;4|6xHK0{ZJ!H32pJl>%SVB>$Vd<7N8pkVgF;sF-luz0}G
zfF=OT=P>oKb|cI^g!~Ur2T*^&!W-6(LbDEo3oq}{%Ml{mKQR5U@)PEMn13)VL=%9y
z7d@TB(iNI=3@$AE(A)8_as)#IngC26To@V-uyzDo9E|}>7qIjPa|euurE6UNfQiHM
z3oQI#^#LsZ!0J~t>)~8j`h(H1^a9I|u=s&%fHGj}VESPGfl9;o1Hj@FCJv)v`e5lE
z-5!{EFn_}Iq0_K@4$}{7Kfvl=Lg^TmPhj~DHVzD%*Mrpu==Q?+u<(VIA24&!(<4kh
zjE3oh#Xrpbu<#({Z&<wp3ny6p0n-o57cl$KX;}J(m2<Fk2Xj9xe$n;8_%QXbbOIBH
z$2T+|z{H8Bq2&;|xy0&+<p)^$B32(<9n^j}9~T3v4whbE<tVKG4~rjM+F|hq3olqY
zh2>*dJmAz1otJ>cJ1jn6{)gp%Li%Cp1s311bP010F8%1@Fnuupz}$nb4xJCHw_*7J
z=6+cD4>J#4AB>NlPhjZ=R(`_F#g!k?=WAi*0W5xC^*Fpe2DKP&E;OCP%z^hGp!FXv
zdtvDlW*;mbVetmbzc6)BYvB|uoxtJ=R_?*v2`d+2=D{^W8R+K1!X1|G(B+}pa8fY$
z!)RFe<H`rHc!R|=EdRj#1#<^1U13-Z&41|W2gZkmAFgx=ix*ft!p8q#`5BguVC5r*
z{b&NP`W~ha<{va=7+hHThSgWFcq2CaVc`r@4+}q7eBuf}n7uIluy}(x5EhOwbue)l
z4XdwU`2-f;aDPDK2Ob_Ujc7Ei+<~QEn0{Egh0$o*7+~!jSa`wI!Q2DO7qD<a*AGh<
zdeHm}%eOFfFnJgsorc*1OQ$e<VC6r|JXn07>xah+G@M}N04%-1Xju7-OFzsVu<(ba
zYh3XLGY4iatQ`Q;2XhxJ{&D4BSop)-3G*k6hJ_0(eZlO7(Xe=j@nPu%7LKs;0T!<?
zbubzx4>KQ@k6|=SAKX1KjZhjEZZLnt+HbIY4D%PU=>Qf^F!w{PAxuHz9~NJ*cp<D2
zq5|fA2#*22J`rB7!X<Dpp!UOLVg7*SLs<C2<k4wZIKsqX`4JWlu=s$b2bek-4O0h8
z53qa-lZS;1x;-#?So(#<H>@0i`2!yR(0mTl2cu!}3o{2+-^1*K<zrmsIZQt+y}-<c
z^&en-bQ+dlVdXANKP((z{zlga<HPjB@&Qaej1LQMSbm1-gV8W`uzU`SCzv?Q9k6hK
z=|iVs`4(2+z~T?)A6U4e>x0_^Z70Fv4;CJ<d;m)y==$OILDMhHUYI%P>e2b={(`3y
zbcHzhuyBRNGt8Z^@PL&gIJCpZD`D+5n15mJhoxU!`eEr2<`0<tuyg?9<I)d{7g+j*
z`3I(-=<tB%D_DHM?1R-0Fg`9CR(`?U1M?5D<rgfxVdlfq6}o$1^5`_I-i4KSF#FNX
z#n1;$cQE(E<YDCptQ`pxN2g)o4HJijH_RQd{0Pg(Fm*5*p8ui!K)5(GKf(NgD;>eY
z8<y^1>R|4GiNo4?F!SLwG~QwP6;|%U_^@<^9zHPruy}&eu>6ajZejj_sYBNfEAL_9
z1*2j07R((medshS|H0b-uzfwScz})npt}oZ4lEvF?u6L~D;Hqu&}o=@n0sOVgM~ZH
z{V;Xt`eFG4=6+bX!}P<<gXL#*{qX#O7LTy<2c{mE{jmH2t0!RUVKmHqSUQD?!)Ta2
zFnL%yf%yxjA6Cx6^ucJDy|Da?o(^E{!xe8Zb71upJiej%8fGp`9WEM{Z(uZB9n>5+
zAB_R?FD(9X<#RM`a4sx;z|4X97p?#ruW&vZ1EwD4e^`9L!W9-TXxiXhSbl}ku=EDg
z2Qvq*fk+0-99-!Rr!i3f!qN??`eEUL(|lYK(0GRFhs7r>UT|rHi$mQFOXo0m!^#!7
zIvfmGISwm-Vf8Mt`eFG4R_?>%4dxCU*5VX~`y1vjoGKv_u=s}MA6Wi}$TGm&Nw9c@
ziNk4V_`|{-rVi$Rm^h4v>w_|2?uC`3P+<m`dtmy|>vNbsm_4xd;_&_g)Lk%jFmusq
zSbV|qA&d|62fBI~AC}Kx@eUJ*`2!{o%hxb@LNqKsU^K4!1Kk{$JurX3(hn?r(AC5F
z&~Sp;19LYl9m4o<^=J&3e_`<i>(9XQ1&l`12Is=`!P;%Gb~|hwhmd}lL9ls1SU!T~
zE0{bk8WujV`U@7HF#qGy2NQ?sgSit%!@?6*Pr%f}XjuA!<u_RS0cI~O{o`u?p^L-(
z3o{R9FU&nKd*L)R-eKts77s8Q<~~?{fa`-YVB)a$7R-K_yI|tD(hsbDhppRyjo(9!
zXMoAW_~<muK3IIi@;y2YOGoJXVetd6H=yMuEdRi0m_N|<!}zfB1?F#9xZ(0QOdrf#
zSp2}!A*>w)qha|NrVmb|xfhmhVg7;TLs)u%>w_|2?u5lZOdrhMFmYHoL$$#vSop!x
z3yg-PJ6L*#wTIyvVGL;cgO%&B@jsY<(bEG=8wL$E2Nr&~+>4<PCIHnBYyaVNFHAie
z4YL<642>5!pAZ9PKU5to-^1b$7S2#<3<}-9u=E5=moRf+@riB#th|AV!}1fXK7qw2
zEWN<g!Dv``!PLXj0n8up^n}(=hUtgd2g?ueavT~iaQ~o%D=eMB`gt(@uyz0}KH)UX
zLUbBxE-e4R${kpE!R$fT1`9WsdU*bU_Wxn>FmurL!}u_Duy}&!duVvU;t{4ECk+iR
zn0i?LgM~j%eNYKlI)at!u<(F|KU5h81uI`*`2ZFk7z%L+!2JsiH-h@1{)Ckmu=E0R
zCya)rLmbvJ!0T<Od9ZX0i!YeJ(DlQ@3D)m|@nPu%7XC2v(DlRku<(V&56nK8K3IB&
zsYj<_`e63M+zsQy<k9t^^Wpg#nh#*{NHE_(4T6O?l*<4M2N(?#$3?^HSy;Hi)WQ4#
z<D=8Cb|=ieu=)bleuUL4==xxMSbYt%2bN!8<rXYm!qj8X(C~ugUzk4hasXF2!15VP
zJ<J?f`iI3I%zx<i!`ux^hp=)K79TM4Vd)B8Ka7v=K3F<}(J*^p>d|SK`(WV-%U3XU
zFn7TC=rk-IVdXe1Kf}^L%wOpGV0`p=hPjte{)hP+rVdtr!R&?k1EvmUAB=|O8yF2w
zKWOO!SNOxsg~c;09%1PKmM(DVhq(h64Kp7`W2l3=6J|dw9%250@nPYDt{<ik?r&&0
z3JXuTIZ%7h^~3nE@m*N{hs7t%|1cV+o)8U7PdNRJ!vL6nVfh%I{-N~}x;B{kuyg{e
zztP<bi%)d@F#BNT3``$-dWVHGx_($Xht(snc!1dlqtW%j_^@&UR^GtOgZYb)ewaDv
zG%VcEX>@gj_%L_CXk6-H=?#`|Vd)7LuDJBU#9{4gSUAA)AuN7i>TuDp_=4%fr4E-k
ztbBxpKg?cOdcdU*T^trpuyPm{9_Z@O`LK9`rC*piu=*b+kFF2Khld|D|HIN1EFZ!8
z4>0xUG|ay+dtvba<HPE6bbaXJuzo)*++g8}t`EkCl{c{XgT(_(9@Y+n@zH5me8c>U
zUhcr$kFF2KhnWvk2g`3T^I+*07Ctb2=rk;TVEG?L!~72`pV0Ne;uj_lvmZVDVD3ZL
z53?5*j<ECt%a1VgVd0CeA7&5Czp(K?Si2rJ9uLdU==#z5u=WEi-NVdBSBK7r`2!XX
zu<{gFxr?qJRzJhS36?Hk^&c!hqU(e4VfMnp31%-W|G><LsfW?9aEH;b^aRUqFd7!V
zFnur@CJ%ErEWN?v50)Na=EC&BXjr=d=3iKR!Tb#iN0>P<eJ~m}E(dG3!`1=9%z^a_
zVCr$vF!#dz0W%jy!^}h14|69hf5FlX%w8A`iw|`Du<(PWA6PiU@*OPw;qpIB9n5{O
zc!v20<}P%5VSHG)!}2@Kewce;=D^gW(=d0z{0)m|n15mE3SA!tA6o9i?1QCuSo~wC
zM-zbg6PEvB@dzKsK~s;;h3SWh!@?cLCq%=-9p-OXx+bI#CJ(FsVESSC7M4C>{(-4S
zr{Vgc?MzrcgqZ{5qw9n5Vdlg94Xejs=>t|T;fzmc`y7@YVd(+JhnWuxCz!o38fFhn
z9#)>g>I0a&U^Gl0jE3bi7$4UEgZUdK4{H~|)WPZ(n7?4^VC?`{zJjaA!GMJqEWN_~
z1B*{w=D@-SR&K-iu=Ee(!}22z>*2z%aD(ZG<xlkV4cCCifTb5$K8CrAV0wn;Uzj^#
z;Q-SQD_790gL7f=1dCr74Oc*b0c*#>;ujWf1hm1&nPB>1<qzDwP=CSF2QKsA;SJ4y
zu<(PWdsw)j>xab;x_@E$86NJ?{DrO`#)tVAmVaRO!pe1+JWM^DhWZa?AKV>K`(XJP
zt{#m6vj;}Q(ml*PH079Fs6DXof`v0q_oABs)ennzSou%L-7sTd=?+#7!0HQ_{V?-j
z{)6d*(eUyaI?fJr2Q1!T=?P{&OdpJfse_phOGmJHgtaeV;R4eKqha|A7Je{sSh)iW
z2bei9c^C~-hn`>2{Q+|yOdlZ{rXE(Fz|$2Djj-|wR(`?b1scDw@)d`623R=5#(iMr
zKYDzk>xa1$W*^L*F!#X3(e=Ujuy}&eu<(Y(2h3f#!XIW2EWN?v0hVuJ=>`^#FneJ%
ztXzS`Bh3A<`UVzmxb(y9g_T1vbKx{J{Bfl}So($e4;BtE8s<M(c)-ntGT`D+dtl)O
zqhaR4>Sd@lI0f@BEWN_|Z!j7bAFzA^*9c|6;vZ%o%pO>K2Uah@)IqhuDOfoN3m=#}
zVCrGv4)X_GBN_u1kFfB9NyEYy7M?J17!8w0_a7|YVf7fyoiKec8W!)c^agW3EFHqq
z5llTg4RbdvpTPVL6NjZ=cs!#U0OLdJAz1#0wfkV{6Xp+?au^MhhxOB7<u$DT5AzR9
z9ZVmLMi+;rCzyG#b|6d~rVmD=#|tdnVCe`(!}2FgAB;wiZ<shd{Gj;)CXP<S+yzSq
zu=EB?&#?LgT_21OOD8aQ!rTv&hnWL+4@?3|!_p6w3qKDUmTqvRJD7S{zJS>Sqhaob
zwI|T+f$4{(H<-U+@ed1ELgPL#{V;oB=>b-5!1yrx&}mq>!16yVK49Sq3tx17Fg{El
zj1P-P7>z64!_>p<hq)7G9?U<maDk~ur(yXL-Muh#VfheUAB+#P7ZyLT_=2T(Sp34&
z<Dy~hD42b)_$L&8=;p!V9~KU<{Ew~<oewMTVd5}<!T5w|nEPSz1`7v5`e5=f_rk&v
z7XF0t4@^HioT23?%s;U3gt-r<9v2PsH_ZJo|G?4{F883PQ#c=5e!=xY-G^>Jdi@2n
z4_2?h(kaY6IQ2vAgSG!);R;I!aCbosfcY0D4$Hr=@*hUS)IqhuDOmXdk1weGuyPb;
zFDzc+8lenWxWL>Ai$_?#g_#dkjzPil8La+>m0PfS2^Rkt8leKP@Py@m7!4DLxeuxw
zCk3+)md;@Qfw>2lMwtI#?u5~>cz~rtT>4?{3s`!A*$Zp;6S5y>F08!>GY>|?$`hD6
zTr|wzuy}^0XIT1$#VaoTu=Il-pRn+O`3qe?Tpx7Y0Tyn!)Zr9|=1W*UfYqNc^I_=$
zLqD|N3-dS3op66c<zejsT>4@81(xn%>4{MOftd$ur@`V4CJsvnFg`9C9?#JH3ri2M
zbcIVFOdM9;!r~nkelY#8aD=Hxr(y99i*Fbomj7Thx;_{mW-lz<VBraiPgs2lQ;$x=
z@)=AY%sg1U5pq8)-eKxt{(yx8%pK_V!uinj2TM0F8dmP&O4l&`uzU{dufgII-u{Ee
zD@;8)4T~pOJi_dQ@nQZ!*9YUn@;8iz>4(J!A^os)j&2?-JYo3@CXY_T!U=9aG(2JP
z3X@0I2jj!S8x~J+^#sBVrXLp1=<PK^?uO}yg%`{mm^iH7hw*XIu<(QBTUfZk{DVs$
zEZxG=4J_Tm+yhHTxb(yF6)b*X`eEr27OuGT!^<mZIK#pn7LPD_T=@?k-mv(F(lGU~
zavSD8bN~xqxH(XF!2Cf-Kg>LseK2#-{Q={{)T7fd|HAtJu>1&fAFMn?*9YUn;vW`Y
zu>HUA{0~!zgNDTuESzBOg_(y#8(bJxZ^87z(jTn+fGfnsfQCCPzrex+ZXPauP;pqe
z!}P)G3s`v$Rfdy-sfXDQQwYlkFg`9CrXO8BdVa>G9~RHB@Q0}*6z?!~u>1xKZ<sl-
zbOBR`i-xI(jUU6}4`wbdeK2uYIRI-1!t5nf{=o7LEd9aa5f*PScfrhs*$1Ox^$RTg
zVftY5u=X^rc!24LsfVQ>m_C@jxcmW&Z&*CR@-0k1to*>GA0`g#zrfM~%ssIB3uZn{
z97e<P2dte3TL%IYCpP}j&4ZP@FmYPbuyBW^2Uz+e)?QdTfvHE&$1rhXX}CLx)Ih0y
z@c4z6gBamKz#XvohNUN1y^bC(=*GbE1uWmg%z^bM;QFEUD!P7H{(-p{mR?~rEF5u$
z57fV~em5+=!rTu}2T*kw_Cv*C`4GJvf~kjz!}z#pn7y#_1ZFPGAGq|v#i8K@%g5;c
zf~!Mgz|4oa6IPDF?T7Uv(e%N%=<x(Ae_`^tXjr`sD}Q0-2&~-z6UU_==5AOy2lFp1
z{$b&WOFv8>%)c;ouyh48AC^vG@-P~fu3$9GK3KU8%UAI5foa4+!_qM<++pDe%U3wG
z!S4Y>k3X0`SiZugALd_JxeL>WOFg<ctlo#^7nu9e)nV|V=@k~wu=s=f8!C^X4=MnQ
zA6WRq^b;HIuyBFtgT)^#JYeYvY8?gzvlkX#uyg`<7j#?*W-f*SXacZw2y+iCK49U4
zrX0?N#XHPDu<(P$2P~h#HJ~wI?uNA=U^FZoVE#kX2Is=;h4tfL=K#U%h2>|s1}Fno
z-og9_D}Q0-Ka7U;hoIWv6ih$Nov?NcEc{?JtUQEkgfd|Ip<D)-KVW=V_`%{CE)HeD
z(mBlCuy}xlA1qv<$}uQddlMF4Fg~&I4l@wue^|PQ#TUB&VCvCnm^;zk4f7YedUQT4
z9AN!_m^rZVdvx_U`Ox|lmTq9-fKxr11T6kw`Hk3e7iJ8s{Dtvx#XC$MgGMW_h}93X
z7hY~a>sw;=qlXhL9EjBqQxBtI?m#ye!@p1gSp38C3rrs@T|t#$P_X!cl^d}13QNC)
z`u{NXu>KCrJXn5$)l0DOMRx#9KTIB0-ow*9G(E!dHM)KnAC~W6<qu38*1o_M4lsSN
z@P?^}@nP<RnTJlp!X2g_W)IBYFg`3i(e=aP3s%0u!V$eZhL=ki20#R0?KYS`n7d)+
zH_V+7)tDq~z7UpfU@Bng0>;Onq4^DFFDyJ^{(*%9EPm1Tqn8t~`WHsS@(-@~hov`|
zy|DBH(+3M*SUjNH57P%z59gz`8({M2`eES;%eOH9!r~DY5AgJiW&lhd%)hYlKbU@)
zKAh<XY96e9hlK}>hL!&?d!WkU6f9g|?uMy{?IVGSqo)hF0cZ?ZeGkirF#qF<e^~s$
z^ufXtW*;ovak(F6JuIByd}z9a<!4yD!sKz$uyBLbf3Wm~OFc{+rVkc>uyl=HF2K~I
z(=dH78Wt|FaDe46SUUq<KfIiV`WqG=uyg~9N0>S2`eE|0c!1Hc{11yqbbT;BES_ND
z1uOqy?j@ujW*^MGuyg>6cUXAB>_ewv<s&S<Vc`Z(chGtST^}w!EFNI#2IekY>S6H@
zE2m)L3G)ZcJ+SnEZVpU6tlb7v2QwEY4$H6T`eE?|k6);NVd0J;4>JIoUSQ_K&XI!E
zCoubAG)x;h4Qt=S^F7plSp1=@hw)+ZFnut8!@?03pD=NlK6D!9PnbVod|3SB3OATJ
zu=EAX_b~s!(lgARF!ks(ES<pe0n9(>=^9-hj1RLPrXOZMOr8)8i+5OhBjjF~d9ZW`
zOCPXw3UfECKLJyZPQ&syM)*SGA443?0GNAW;RoZx^uh8qnl?BW7EZA84wg<~{)d%A
za1A&ZF!iwX0#^q02b_<C0UK|Er59Ly!om}WHn=d%|1kSu=>SH<72;sP{0XlIpz2`Z
z3FYFXVC5Su9AGp&UEtISk${CijE2PvtQ>`?!cD^53ClOIc!!4vv^|E~1c(gG99Ve)
z^9MwhcoJ3)z`})KJV47)Si22Ye!=v?(lb0=pg9FrUct&QSbBi@1C~!==A&tc-<JSO
z|FHHq%>6L?(c=?lFU)*cI)dqk_X{vAgb2X=12YHKj)m!m#Xn3Qq6<O7+zS(d#UISw
zu=s<;2TUD|hUtf?gM}M9ALdS&J{S$t59dSs{jhWg%fGOC2&N83!|j8nPgr>f3r|@5
z!}0}8AB=|O6PUkY@e3<YVBv|&epooc!W&**L(?Oy9EXJm%wAkHto(q53yj949wrWR
zFU<Y0bPJ<l>d<MJK3KYkg%>Pb(bc2#;qe45r(ynuwVw&vkERb+eiIvBFn7Y@5#~Oa
zyI}r-i4&sX;SG&HLh3Q(N!15c56fS$bOcMUuz10+2VDSa4op9chLwY`@IcoF3ujn5
zhlL|7KfvM-Mx*P8@nQ7_EZ$-H9Tq<@8m1ndhRMU+151~%dWcXw!r~2<?_uQ~%s;Sl
z1eZTx;Sb9{F!#a24;BwFbuf7t4U>nJ^Dy_o%6nKj4lf^I8lg1Iy)bt|g)ztDVG?LG
z%v~^hVewBW++fDQ>IoPh7XP@?4NM(O9_D_SeK0<(T?}&<Odl>9R?fo8Rakh!{D-a&
zmTqA246`3b!|a8bgRUROhm~8f^Z|=cnEznu5T+iThK>Kg(my<&q2(J~9$h1p4@<W&
zf5YMrJv~5`V^FYggXLpb{K4`Qh6XeNSi1w}epvhw@;A&FSUkYo3)2rX52g+$4K2T5
z{)gEM%YV4^!QvMdUoii|{EsW$pqmGaXP7xKdtvDcT|JzS7Qe9i8y260;vHrVEWN<|
z4~u`8|6nvsJvt3@KTIE79_l_g9|r@L{$cS*YW%_KD_DHP;tysHq5KDP7EB+^J+OKN
zW)4gpIt@1;nvP)M0V@Y!=>c6Ij1P-1Sp36in7;_61DO4=avo+LJpOUS1I%Br_=Wi!
z7LKs`111l1FFFm2cbI-yJi_WxboDSkESz98%pWlOVC5mqKQMhT8s>gjeu3EsGY=MS
zuy};&gVC^ffw>!&e_`brOdM9O!Sumsn0~lEX#B&<Ggx|pnFrH{PQ&XzXgtB%q3G&x
z@nPWuiw9Wz;ZhG1hs7_f+<@hOn7?810E-uxJ{S$N7Z$!Sf5XBBrVbYkOW!d2VCe>C
zA3WTk;eu`s%)c;m(B)z22bMn2<<a>tcf;HR3rF;J8M;20d*R^$buTRc!@?0=AB+zR
zKUg||<$qXz5?A_z>4&)+R{z1`6P7OF<p4|rlty<qEF57p%s()5q1xaSEPP@9gtfzA
z?t%FWt^u6^4JTN5qw9n5Vd(;0JIr2KK7;Ym)e{^4u=ECV56m1`e4yJ8i&t1U!Qu<1
z59S_NIKaXS-5glC1+xc6!{Y}UPU!kz=@TB$(DDaX55U|9AJ2!5Z(x`M6@aw^VESPB
z3o4D1f|a+hd;%*+VC64PjZg_#x`%}itQ>*40~QWY<!}mC{=>o_rVkd6u=YPJUf>#`
z44Anv_rvUmxd%qW!WpU!PQl_CCJ#$zuyBCI3$Ad0g%d2j!NMEn9=JXn@ej)%u=s`1
zuyBLP!ySx+0ShNsK8LvrW)2Q*aACN9s0ggQf~9|`Fir|1exT`?ko~auf~kYiF!Nyf
z2quox0Z<8ex`Bo_EIwg0R2iIt`41LPu=s}2Fom%E596cLuyhNHcbL0j{)3fo==xxM
z^zt26ufx(M%zT)7bQ+d_VDS&rhwcw_eK0;uKP;SK=@=G1Fm<?SjD8X{{&9sn%p6$w
zz}yY94^}R~(j_5t;QoZhJ1jrK;sag|z$}K+F#BNjC5#WFVc`LHCsZFg1<x<gab)!P
zhlMA)J{TWnKg=JnavYZb(bES^AB={DKdd|?HU44x;o$`hM_4$(?7>CD;s>T4R<6Rr
z50^ezxWeKarVdvA!f0a6ft7nO8s;w;4Kp7`!`urCXBZznzHsS>nFDJ#!O}6z-LUY4
zl}j-7=rnBI4$K{}`X8o_Q2vL753Jn?R}YOxm^rv;Sh|7v6Rsbs0VWUS!YNq%!SX3A
z9mC2~n0c`F5nLmb0gHDS4a-L`KFnNLdV*?$Q!sO3?tt-O`41K!xXNFcxv+c(D+gij
zhPek<{K3)*th|Bw9~O_WaD<r;cQ}**QwLk;2U8C-7bXu?j+2798)gqIJ;Cb{oH`*A
zu=s_A2h3hrc*4>#L^UP}(+5)rD-U4qfyD!+PKXFBzrx}P#)r9skbao`u<(P$8?pLf
z^#d&3!O|1VTv+~sxE7OysfVQ-Sh|IUE8HEJTEQZ)_=l;7?dO7}W0=3dim?!|aE93f
zb1%$2ShS!B!OA^24Xwvu@rj}s4+j<wFeSM14NM+J!`us_Vc|}!eptN0+zZnW3m2F@
zIB95m1tw3lKBxwmJ7M}@?trypp~`SluyO-dZ^PmZmX2_0gi65F!Qvm*UVxP&P-Pet
ztX&JsuP}F@muDCnp#m^_Vet+N2Ut3S#RF711_e_G^EWIWVd);0FJb8d-2hm=fYraS
zas%cLnEPPnqU(p*3#+eS;Rg!`SUlpgA7&mbo?-5W)&DT_VeWyc$DpC<4_02oXqY=-
z?!wRq6@b|bb0;iZVC?~@GMp4FoM7cIEZxBT1v3}j09g3I)WP(_{0pOTmfui+!}AT=
z_y#<Epj(W_ho>`Wdk2;t;qeX?N7KdtiyxRcOg*d|hKa-62jjzNn7y#^URXH7>P=WZ
z1XGWThV}bl@d&dY7LT~}!NL_*Uck(O=_eTf(C~-(8<vk?@e0d-xa^083oM>s_M(g9
z(hm!Nm_K3Z3_Tse#BrGeORuo-hLr;_cM;ML3rCo{VfMhn0cH+N9G!-TA2d8+@dz^y
zMx*P4^P&1+=YGQW|H1r$OFztCu>K;f9S93gSbBilgM$I92VnNV!VyN}(8hosF0goj
znFFJ7>4&)=UVcEs0TzC+^h8KMhCR^y4$Bv?_(eAdrVkcguzU>j2h3ft@&uQDSUkb%
zQCK+uGar`D(e=aluyBCsgO&d<cfrkpX@Jr&^)Uaz>TOs$fcqb+9-V^AL&FQEAC~S3
zg+I*SuyO;&hxr#~4!XTCJ}kUp`5hJxF#o{o510l5G%O#&^uy|P0@@g0@eB`7s0LX1
z2MZ_kbO7U{r&pM}Vdlc>1DJZK@$mEeVfhypZm{$Riw}6Xz%)W>m^duGK!xG!Nn!4S
zi=#83`3>%GXgYwUH<-WCwW0H2@eK1fTpns3oR7|c%EQ_@uy})o6D%CiwZZtX@(0#F
zg1H0cepvXx)Z?U~;Q$MNSh|6?<8W$(NWj#?+yl24W-de-P7*zyVetv`7fy{Z379?5
z@Pz3@A4h^IN2g)xVfhhO?!d|sSp9&m55|Y(Uzk3aIk0#nq#qV;u=E3qN0@$?Jj^~^
zG)z6r-LP~3i&tFwVD5wIgXMdedRRVynGX|((Xet7mR{iT4~-9)xiEjh^ucIYxdk%^
z=3ZF(hx-E-&rpppeeiZ3)IL~z!o{KL(J7d}VdWF7zK6vhA$P<4iQW!|)dMhhz{){%
z`(gTF>d@t3=>b+Qpv&V*7qEDOg(p1x&<udt3kwGr4f8k5eX#tErXAjYfSCg;Z(;2K
zSa`zyfo1^AU9kKQk1uFE!us)O+TdK6|IqU}EZxBC6}S#m224LZeM9?SFn6Pe1FCKY
z23UHAg%2zppwl?xA8H>=J*@nI*#k2dmws5d!twzu-NNz@EdF5W0cI|YhQ%9<4+{@i
zdV}#{_Q2G^XjuHg!V6Yzz|sLs9V|b<_~<k&o?+n)i%(d+NJu}-Jal_u{)fq<)3Er1
z*$Z<&x;k8Zn0lDKu<(GX$CW=}@-X!<f5P$sOr8)83wM}4SbBl$gXSYRAB_QXAIu%-
z;RO>%(}t4^GY_T!R-VB4glKg8VCfo{K3KfL+zm@du<(G1<I)dvFD&1{^uf{_Odgkh
zSa`$o5iGyJXqY}&{Q~2|XjuA#wLfA0f#r8tx*+5anEkMF1lAsb`3L4dn7uF>rXH4#
zVEG=-ho&F&c!BAM(J*(z%!7psOr8)8EBE2$AJkq#`q1TJ?tq(vt^fxgp3b1{3%LJr
zs7Duu#WyUyz~T!=qpL^f!{Qqr{!n+o(gjQ$T_21OOGmJB8kQbl<vy&Of~iNRVd`P}
zVd)<h?{N2_YlQM)=@jM;czp_;M<EoRu=*Nm90SbXu=I;_90wXsu=s=Je;5rj2WBoV
z8a=<j!T}b3xb(ro2iE_GxdRqng!~OR7n<H-`e5-5vmZvo)uS<B=?)f-Fd7yQFd9u8
zoC{08u=EPcKQMa<>4%kXu=YPJ++pUy!WV8K8Uq$?FnzG|e_`UV`CK$@7~==9c!${w
z(~qtXoe%Rr%-^thC8Qrc++pP&OdRG8bo<cxu=E477gle=(jmHf3_djd!1Tf5jadCK
zf5PGq=6+augwZf_(d~!nhlL|dKdd~4nG55i>xc89{(!Y#VdlZwSFm~lt{xWyS}qV9
z{xJ8#>?Kwo%zBu9Sp5eRC)FHS{K53Y%2ODhAPx05L3KD3pr;o?@s4f`EF56suyg_A
z6QW`94O0ip4=@@g4x?e_!^$UEJwa@^!`ut2?_l)}u5=Ew7fwUdE3CYQ*$Z<o%zZF%
zxIQ!nEMLIVIZQvS-bN1>H0|*5TUfsvRv*Lab(neR`e1zYatl@t!s;(r_`uYo(=dO+
z)j`7rT^%kyte+0E7neF*;;`_A)gQ3<fT@Sc!}#blEPP?<9p(;Lc*5#8bbYXN0<#z9
ze^_|J#9`*3>ℑx;QL8(e<O73o9>R_QKqUOFw#f1=9z!AEpnMPtfg$$;0X|bak-w
zgRT$8hv|o(*9A>Cu=ENGPnddKG%Wq$k|!jN9*(elLr5P?9z#F09EOR*;sK@}okn**
zEFNI_A67n~>x1!O=^GaQuyP#M?t+<zZVya9%)hX3hs6s_9G0$N=>nz?M#JJ6rXS{S
zSa}9ZXE1lc^ucIYxeL<|8~=fo3ov)U@+Dj!G@N1OKdk)+YcJw*FHAoyUSRnZ<_=gm
z!rTK>k50qVCoKKL>Tg&%0t**(eK0=E9$5Im^ugp|?GZSi00UO;!NM6=xrJ^FEM36D
z0p=c<IJ$atJ}jNW;v41<So%X(kIsjsPuRRa%wF{PM%M@9!{P~+4`BT#I1N*XgNEsc
zNyE&6rF)n-E*ch(uyBW|BUV35J<J{GG)$fl4YLmxf9USPr5~0LVBrPR5Azo;eK2uY
zc)@65^<n6Tj)%j{fyv{dVdWw$y};Iy!om+$F5uD+b0@64gXMpiepr6Sr5~1GVEG^B
zZdg3P!xfhSP;r=juznIuAFTX>m8VeUI4M|s!~6kL2s0PPN2g)-z{Uw-<p?|-K*w>=
z^}*Z;%l|NYVC4WT{BY%CSa`wW30CgI`u{L_Sh<dFKP<h%?1TFo+Kzyk2Qvp<KP;YL
z{)fdQEM36LC73_Z^~2%~R!+mrfvJa?0~1Hr4+~$IyJ7BurAJu$A{76ydI%POF!NyP
z0Txc^_QT>27JjgD8|H49KA1bu^~3pS^)R}5u=E614`sl@59VH2IKko{W<M_dP-EcZ
zKrlY6T)-9YFm*5*7Va=U%w9O3L<THg;o(R~KQz6<l)>T!#wSF>!V#tp!#^+y95gQb
zhz&oOKVdY?8e-cYu>1q_2h3hr`wwOwIt?%Pq3I7M4@)mFd6;=Hc{mN#2TS*`_8Lq-
zp>Tx7GpyWz<y%;I!u$ib51j#xcUZW=@(nEg!j!|@2jipDu<`*$!^$66xl70$u<`>I
z53q6ro-d*9M7I~lhqcdP`37ztOaUPp8V|7i4>ymHJ~Vl_eyDk9(zv-$^|16ps{3L3
z;OPt+UT{8co53<LcY`?$3=FV%gykPte8S|>X_!2$y^UU8!|E|~eK0;OykY4Xmi}Sp
zpt}#I4@Sf49hiS%?Ex4Ka~DiKIt{ZA7VfZq0BpP$mJZSN!T7NCVzBfJiw{`40;UcZ
z4a;A!bPvn-F#q7v2NQ?oe^@-Aha*fKE*chouy}{*Clqcl^I-a5;Rn-Ctovc<3>FVC
zb7A2C<Kv=X_QBE#tUM!B?!e50*$azrm_K0Y5~hw24c8CN&#>~3kUm(s1#7>;?1z=-
zg!IG8YnZ=b=@ph9aHT_7I)#Nl%)PMigu4goA9Q!X(k;yWu<(Z22a6AwdFc9K>S5wA
z`(QLo9u^)jbub!MKEV77QwK|TFnJga(+8tr>S5!52GDX7CJv)v>M>|&xWM8amj7Wi
z%>NkrpaL*=!txI+9m4z#Rfdy-se}0+mR@1z!uYrhfa!<DJIviMeX#Taqj8x76NiNt
z%ssGl0V{`L>d<LeI)bG`ba_}hMi+<iVc`sGzrbji|6t(?Q;$x=!V8veVftbEVB>k{
z`e1zY@PWApW-d$}u{5#e9?U#gdk@Bk`5RUqz{GLU=<bKbKQ8s?;^_9H%ah6{HQZqJ
zCd|Junpk^b@eB($Sh^)vKP>!U?EqMQf|-lU99X!)@&_y*!0K~YeuSw*HwUI3rXN<1
z!}1}l-33e6==x##4VK<u;Rq9l>BnV1Og+q<FneI-H>}*pRgb~K2VP#l90)B}Ve;^J
zfyqN@m^e%wtel6%KP)_;%8906=EA}er$(qdVdFe7buj&~@Pmn?)39)d)!(r2g1H+O
zuekC9x;QKxVfx|aCe)qi_QT>87GALaCyWoHVd)!PKg_)_ahQ4Nd~|V`JS^S7(h;oO
zgr#ek|6uV7*9T?5+y~EZP=CV8Z&*17GasrAgMx({Ed9dz{V;Q3@rj`kDga9-F#BNZ
z0AT)uD#J;k`x8C?<J1V1faO=1{V;n8xf`k-zF!Fz?u61ETpyGH3ny55fVl@2pHO8u
zDOkFL`x~Yb8lNyOE*h3T;rR$UUJQ#DT>4<*FnzFbKUjRg$`zP8bQ*R}1WZ3H9ANgs
z(jmG&7$25DVd)c=4q@R4^A}7#I*lG~Fn7c1FI?p=%p91#u=E0pH(0p@a|eu12*Ao6
zSh%65CtUhr>R|4Lr309MVD7@DAJ%?`g*PldVESS4flEIuykPMUqhaDW$N$j`gxLqv
zhbE1a3yWu1{v|r!LQRDE7s_RT@nPYDv;2i>faO!T`_aT<=0JrpC|EfRb2ltq!pdz}
zd4i!4Dgg60lnXD9VBrEY7cP&+fTbIlI(T}4mLITuiKY$Cg~`MG4{OK4@*_+ht^vw`
z)fcdI2U8F0FTm@6s5_wA;1n!eVfMntTVVcx<qKT-52g+lzc7Ep%!7q1tek;c3}wLV
zgXIsHd9e6{l?%AiGpu}o<yRODi+7m2VD5lgi$S6L6Xsr+ewh0(G(rVn>S5sxYk$Jr
z0rMABIVJ_|_ru~HMiZOwVet<$7?utQ+6&bH%dar^!onYxub|2>C|J6H`5$H<EM3CP
zg{21!1EA>=7S1qpVESO`2^OyC`eE@86NjZInEPS%3rs)Ey)gB#^aWD~b3cp^a}P`%
zTpt<(mOo+n30B^~^uyu<-Muhl;PDI%4_JJ{(j6>6!_;HYP<?Q5bUs`HI|Ds_2*o?P
zDd^z}3qN#qxcIR6hsom#Czw1TnpFL;cqe2oE_rzQ0?o&;`V5zP3~`t~C=E;h80ydj
zVC4iXy};@rSbBwtqiKV4VfMh{8<tMt;XxoB!R&*TyD)n((gO~2VC4-gykPEy>4(+7
zuzUq~IFtcPx3KaXW)3VqVC_I$=@BLl%V$ty8DRMtM#IV#m^_Szg%ivjFg`3?VCo3b
zFn_|_4GTv?`e5=fdtrQ7c><$h;^;Ii-@(c$SbV_J0jxcZt`D6Li!Ycs%v^ML!Q^2y
zdb)$f2dw_YRqw;}!)RD}4|6vxJYneprVbYkcRw_p!rYHbJ-Rroeu1T5m_N|d6}mnc
zA2xmu(+^8`#OjBc1Je%+H~2UJOaTrW7EUmK!0d(P9~|1?>rP<y!pdE^K4^Ht@&~#(
zu<{2z{lVG~FmrL`f0%n==EBMWn0{FM0Tv!Gbub#1uVFOI->`HGqhazeeJ~o9-e5G$
z-!T8c@-aML!!$x^m^j>C=(qvQ{V@Gd<pe2MeE>5T<}N}SVc`o4FIf7+<!`uo(D;SN
z6SV$<l^d{pMJWDZ>R{y$EM8&e!ove@F@yngH-yK)08<C^2P}QzvL9AX!)TcOuyhQg
zVe&A0U^IHU1M@d5{$b|fj7MmA!Q^4(7R(>8bOEyuordLG^zmO<I)tgm<$qW?2Xik>
zAB=|Shn3SXKAeWSAC_KV{)L$fR|icea6Xg)GZ$t*tlWpi8!Y@`>Y&;%C|Ejx*$Xod
z<_}!)2Qvs3kFatZW-m+}#)qj#r(yXG<_>tgLBj`KJvtv2kFa<r<X)J5SUAD*2`s<E
z?1zmb!_=eGuzUg&hs7f--=oXJ)WK+2yui{uEF54ou5<^}57P%rH!yKnJQ50bSbV|q
z4@^HS9AW-|i4&q>@dwina~G_9!lfTp@4(^#mi}P<K$tizf1{fNEAL=5OdaezK=g55
zbp5dOgC5?5;vZ%XJRD&HP#WehSbRW*F(_C*gP8+!2fSZ^p#xO_rXH4_V6w1$fG!S`
zhtaTn0J9&K{$cKisY9n>{(_}*bbk<vcbIvwbP7v1FnzG}15<}i!`c_Hd<TnfSbGKL
z9&~*$J}jNU?1hCFEIq)&1*RUIhWQ^BzOeER#)p}Ut`EkC`5RX5!16OJ|HJ$NQ;$x=
z?SYn)F!OMw2Xy`Dd|1AN#XBrLpsR!NVg5(Yr*L;e>n&J#!t|lju=E3q4_Nwx>4W8e
zn7d&5Vet<uzhLnP3s0Cjn0{FKfJ+>%4;r56>S6j|G|b(waDeHDg&V9qgsDfTVd)zd
z?=XME)WPxrjE_#k^uhcMlZVA4EPcSl(P@}G%)PL9gXt%_{D;OfEWBXp4<0{Id0051
zy8{-_==lw19xVRQ<zeQ)^uyc_vmaJ|!`gSSej-dCjE1`tDgf*E!S(~8#|u;$1I$0L
zbPJ15SUP~wu=EMj2cu!>1D38~_Q2c^iyxSJbQ-1(mhWNd1wGtg@rkY<7Qe8124+93
z{DFllu5gFhi=GZ)<sd9w;j$m*9#}ZQ;u+SCh3SL23#J~OhUtTq|FHQ#Souq+9)P6_
zm^#?F4@^J0eK0;O{$cKf`5P9lFmYHs!StciFneL~2MZ4vA6CAi>w~2a7!6Yg(+`Um
z^z@Bx4vY_vFKGD(OTRFG!PKGCu=E0R2h2aj+6xOuba$iMgOd*}Utsy4P<nxx154kq
z_<`knSp1{Q!}P&uSiVQk2XOnL?t-NQm_9fS4QH6UVf6_t9AM=-tbBm$gEC;{4LqEo
z_QA><SUkYe5mXzTg2fjseqrea#)riV%zto=PzEgAVE%)tgXx3iA9#2`^`TR+aE7H{
zSUAA^0ZW(Y`e5=faacJIEB9gX36n>sVdW$&{9)?R*At`L12YGv9~O?Vd=CphSpI>>
z4@@JJhJ_c*d{{WZ%0E~-fGWqJVC`O*eX#TZi*J}Y7#d*$(DVmOC+OzD;uWSG7Y$Pf
z3lCVl!_38{50*|~?u5lVEWTm>!=)dV{?NzmVD&bvT*9RvCJr+PmTqABVEGTG4i^o}
zpRjro7LG7~;L-;RH<<rn`2-e^u<`}wK6G<n;f<b7VCKQ<MOb{G>xZR#So(wWq2&ln
zA5QzB=?9k1Vey5Yu5pDUEWg0h3Di7Tc*6BT#WCEECIAaBm_Oj=qN#&%Vd`P&2PO`)
z7hN1C52IoE3>FWtasU>du=EU5k50qF36@S^`2prGm^tYBV0@T<Sh)?0Cm0Qj514vf
zG~7O@0+@SX@dFjcNx{l3m;#vnFg_s~=6+cC!DvGIVDhkX8&=N3;twW|i-zS_SiJ#r
zH!S}Xiho%A!qO`&ykPD}4;OTIz~T?)Za5!0j{z%3(DlLi@Nk2whviq8I|!wJn7uH6
z!_0-nKg=I6aa=S!-9qyz%pJJY!^C0c!~6+z2YPyhsmDdb)^`wVFNQhL@($JyfTbr`
zI>1nmCIE|PSUQ09r(phrr86{b@bjNx<p)e3EZ@V@7cPIn%6(Wp0*i0-atT(SpxX~K
z7v@g%dIJ`(IP()UzG3=d{(!aPVD5pXCv^K^@ecDh%pRC|Fh06I7#}7NtM6g;5G-B8
z!V#t(7Y!R9hM5P8e^|W1!U<hJEWN_w2|b))?tsM)x_(&r!u$(M_i%He;R`bdT|Z15
z7S1sL!uas=1bQwCx_+3wFn`0^A25BebOf^pT|bNu3ny6mhv|dyVdlcr<Dy~d8I}%V
z{vnhPVf{6jJ7DEGEIr_|4<-)N2Mc#t`+-pT57Q6xCoDW+;Sb9PxZ)qC9~SShaEIjw
zn0c^pfvJbl=-~|057P(Z!~6r&2cu#Bh0(Bd0OP~l4L1j-5lX|-1-d*e{$S?7>LaK&
zI0e&>9$v6;gVk@i;vc3T7XRq^6ebS05S;-n_hI^A=>S%rz{+)8;RiDg77wuW3M=nm
z{)eeUr(y1g`4d(iz}-V2|HI6KnFCLU(Dnc<Tw&^P(XjFhmhNEY!Dw9iVE%@?8|q$|
zI4*tY;;{IIr5jlIp{qmZ!}P&u*!Ul@{)U+c>nFkV!P?C*ahN)E8Wz8>aEI%Ih6619
z;0zCF`h)owR!+k53#>kYnS*XWEM3CXq07U}gXLp%c^Dt&4;UXt!{QajN2g)o4ok1F
zatc<S!O|5>9lCz>`~nMaSUP}}V=#YU=!Xix@+++U0qe)Z#9{V8m19saeXx219v;wm
z#F-DE@dtA^tlWZG04o<^{(y<2)39)cwTEEpVKlmWI3MahSUm@$Vdla80oQ<?0SjN4
zKVkX_+K*-&Odm`b#wXStuzUiu7p5OZ!_37=L*oPH4_LZ`@zM3c@)fMyhouu(dPY}|
z&WELUnEPPi2opzFkHLq=AFSMkxfhnNG1Q|8!0KyQ{Q=hpH5W}e1{Xb?VdWUCJc0R(
zQ2fEe3mX2g{13}l@bJZO0Ym^M53>(eZotF|m0z%UgqX$v-+u&)cUU;V<<S_h{0l2D
zVD2X5Z&-N4)WPx}x;UD37+jb>nEzok%v=l&Fac;h!~6#eCzyViKVbO^rVSSji+7lN
zVBrdjPh9$7@dWcXEFZzt!_payk50qvfyD!?{D+wX&ll(#p?p}rgZUTc9(el?8ctB<
z7!+I|0d?paVc`rb_hIEXj1P+sbbT;B%-^v1fz|u4aDe4UT;UJ12UgF()WOVy<s*3h
zgjo!wVd`KsEZkxF2A2L{=0ml?DVTp@?uNM+79X&51WTWAjZg-x+=8{6pu!9={V;QJ
z*$;CkES<pgWANb;I2h1!3+4_O4I2l*<$m;Z4QoHbXpC|l&3agP!NM6MzM$?xw+Chn
zEd9a43zq+2=?<0;VD`ZD!DyI1SUkaKn7d)=0%k5uAB=|C4^sz=519Qhb+B>;#)r``
zf5Xx{Ed9aq1w7md<V#q44>pbii+@=B!R*6D!^Zt!@`TD8m^zsMVeW>dXPEskb+~AF
zI6>nX)(?P(C$t}jOFw$Nz~UP{JYnXcn*)n4SpI^?6EwVG=>}JO2IgKE4Kojx{$b$&
zi%)d>VfMnp4c+}P|H0BJx_%fRR?frH6RaH%(+{iHVd~Lo*f<YNKP>;l`~#!W^}+b)
z<uEKC!Tb*kADDU!8XDfPaD%Cbg%7M8#Lx#7fQ26{zF_eW^FN%1s>ema`~fS6VD`h(
zIWB#$@PXwUSUQ4*10nar{0p-O<{y|oT;(6kJ+OF#)pPK21Ev564XZC<`5%_fVE%xm
z3tZs`%U9_3!rTuNhp9t%1}uDG{)gogn7d%^frT$F{jl^3D^FnYhprwMA7&5C9kB8r
zW*&^jr5~2=VDSJ;$FO*V(YW-(^ug?bm8UTGz-V0hVetiP$HCTr!NLWW57G6*)Wh-%
zOg$|9z~TX2ADj;@w_)WYEF970am7C@pTKFTc`*Gjf8k1hFn__^4dcVwWia=^!WCvN
zIt{Z27H=?pu=I)3eyBmPd<*j@dVJzazfkQAuyBUO1B`~50}CIRdUP6=&tUaGY#b04
zk1!ftAIyAMeF3X~VCvBMFmV_Sb2lvg!@>;~{xEmJ!Uv`gM#J>M$~jnh38P`^aD_iC
zT+wM*yur+c*#kEZ%7D2SmVTkaa0;Gp(b6Sc0kqzNxeu0~VE%@cLxl1#th|Kjht)%{
z^bB(+++H*WdU(O&1=jw9rEfHCa4swzz{-1=J7D548m<A#faNP#dWDx;(D1?)?@;aV
z^~13Efu&P)_rl`=rV&cR!WHI!Sh)?0f2cB?6wF*);R}<;X#frhbbrIj1suvTgkk9!
zW)7+Parp<`B3L}5i^I|{x;Q!?rVbWgFn7b^6J0$zAC^C1<pnIg!}1fldUQTaA1vMA
zN~h@hV0>8o!onGro?-GZJ}&>j^uh8gj1Oza!14u*hM5baVeW;6Bf5Lw@eV6zU>cz`
zOh254&U3@U1C}qeaOj8G3v(~5{(~A1zeg0tN2g)_h2=w7e8Btx3qN#yFg{El%$+cQ
zz|4i215=Mq!{pKHN0>Owf3WfpT|b-;Ew5nyhq(t9F0gVOt{%#Oxfd3{uyg{;2T*13
zat_8vr(y1cnFFI?;RB0LbbYY=0&_R4zJ~c5E{-GqVdla73DXCwH(>IFXjuA(se{#L
zu<(J2qtmeT0SjMP`hk^8uzDGm9?<o}^ufiU{)E*-=;pxHK^ZWA!_0@Nhw;&As5UqS
zE5BgnA1wZ1@disrFm-T^PzKDuFm<qc3#K0y@960Xs+|EA|M2!NT!J(PEZ$-15Ed>l
z^Ksb^i$7R71*>;pG%WqW@*}#dVBrN5hnWKlcU<<u)MJDbbi4@Wepo(2w+E&lR&Kz;
zA4bFC1?CQzc`$wGG%Oro;^_V*<PTW7gSi*Rhv|cbC(L|w8Ww-Bc05c!Y+MMIj?neN
z_^@^#%s!YnAsUvyVfMlDJt2K?d1$!{GY6L5VDSYPhigD(z|t!$ykX%0E019104{T2
z{)NReEIq(zSiEACGf-#1;ss_7%>C%%F#kZc!6}$NSbBq%->`H5YX=dEKUjGU^FM4n
z2j(7Fx`tbf#(>2i%zjwDh3SWtZ!q_wX=i|iH>~`E*#}b(b3ZJe(DlRA!NLJ%FD%|*
z`qA~l_^|R1mL6c?2h$G=N0@qa8s<NkI#@Wu;t}RAbbaW2Sa}3f4+{rE?uVHJD`#Np
zVfMr13DK~4goOto^)Pw#@PfG;79Oy40aFj7Vd)ws56k~B_rvtV@)t}WjE0HBXju6V
ztM_64htV*7FdCLmVf{XsJ7Dn#Q->?v!Ng(iMUOXF{)f>p^Dt;wIR<Ti!@>cUj$z?|
zp$~@udVIjr6%OU_b^y#CSUmts|M2{bW&kX|z`_ewU%=cAOOG&f(6l4CX#R(VE6hDG
zdk`8RELeEM(km?fVg7}g19JyN7bXddf0#KiJ}iB}+<~bRA_6OaVCewXUW2(0<_?Hz
zOcLflSUmyb!)Ta)Fm*yiVCfwe?=Tu7%K)DbhVjv9m_4v~gw^M;at-D$bbT;BOg*gp
zfaN!sxj5q=8t<@t2eS{B-e7!~IWYTRG%R1i!UtCFz}yQn2UgC&^r6$Rc!tFnEIwi8
z;&MM+9n>GNatLNVE`2b0SU!QJTUa>2+zYE0VeWwIgEC<5g_Reu^aG23n7Od{fNH~_
z;Q1CR1Dg+qg&!;)pwc)gnEPSr2<Cqnjjj>qKA1mY?uNxHEIe`LAM|vOo=#x#1EbOH
zhl#_|3oQM>)WO0R#z&`N`5dMX7G5xOak(324on`y9%w%l7Cx}>faxPd!{Q63AC{g7
z>4V8*q!(!VhRMTdSbm537bXsie;6MZ4f7Ywy|D0vr6XMWVB)ZJ4=b->?j}^;!}P=K
zgT+5QoS@+kQ%8t~rBhgXgt-Hj?qM{#IWT=N|H9%67A~;-0PBaK>xb1#=<+akz~TYM
zN7oO_FX-U~b1%#vFdCPBm^rZe6ILI<#L@M^_^|X3iwBr~xO;J=JD7f0`3Xx0F#BNP
zik>dv`p_6Kb#Qy3^04w07S2#%ObVJ_VD5*N2QdBU;R`na>OWYzgXII5zhM4=t4CwN
z;t7_1VCe-`p2Ez-6@M^$VD5vp`(WV+TlaxeKh!-i`(gUv<sfuC1*!~#g82_tj=<al
z%Lg!bVQ7R3!0dy?8>}3Fxd#^hFm+IEa0(VqFn7S>8Kxd)Kg@i%Mi>Jce=z;9d<b(t
z%s;s5Z&-SPxf_-aVaCJ6Vfh0l4x?e^7A(EN>JeCb1{QCyd<N5plZN^S<}P&ipzDLB
z7kK<Z6~OdExi~3U_`(#x`~yor1l<kIcQF01`~b^Ou=s?@qtmePf$4*lyD<GQf1$e*
zW)94~uyF^NyJ0j;9V|V<_%Iq4zOZ}>i$542mVRLFgXu%3VeW?Ie;5r*x3KaNT_21O
zOYg9Gf7tpDn7`56hcJCG8s<NEctZQJFnL@wET6#YDOflXjCZJkFn`0s2NoZ&c!!BY
zm19!S^bV7N<zHC%!DyI0Fm*5*=1!PBuyBOM8!R2c$|0CO7!Av(uyg<mKUjRh(gVyt
zFnw?un*L$xVfMk?4G&+aI=DVG1}uJI@c<kDgSiV<zMyG?b7A#8OaP`HmX2Zmf|&<X
z2czNn4C+ppewcb#eu9NJOdpJf*#k>I@Nk1FfTs&6ADx1gv*_W65wGYPp?sKqu=E29
zM_4+5DkDh2{0mDLuyDXt{=*D})ju#kEF56^aD^L8JuKbA+)Zrx4bulJH{kw-#v?2p
z!R$e&VdV?VUYNUK@d+!x(DlLiu<(M@(Dnu_zY_8XEM3C#53D@^^A{{!Vd(&7FN}tT
zD@;Gk9k6(V`42|J^r6$Rd<ruU7H=@~aOQugK`?c&bO_5&FnzG}1XYef!NMCB9<clj
z^Ea;i1B*8p4J$|B=Ao6Z7#2h2Vetja=P>^eiho$U3P!`+0ka1duCQ=`iNk4VI)$Y>
zSop)-0Sh-m@dpbxSbBq{2befc`=Jhjg)@wX3d8qd!Q2HGM`OU;2P^ksG%WmJ@qwld
z&V|+AuzU=={~P9RT=5Sx0Jd%rrXFTL%v_i{bQ+e=VD5*77c4$u=@^y|(e=ah!Qu%P
z{;={K7M`&5g{~iFE-byl$}bon=5JWNfUX}Ft}uU~%fs>`%pP=kn7d)}@bH4>3s`!F
zg$KHRm^)$V7gm13>LFNsz}$(hAI683SFrGcmD@1?<I)d{ADBI`{vymAxID}vG#aK3
zrXLo6uyBL<A59ya3rjCB_o1tY#T&dFfa`=XVD`Y$3oM_({0)m2SUQ8~!X#nw2r~!f
z4_JD_mHuG<hno+LcbIyZyJ7A^w;AR>SbW3s9gGjNALc%E{jl@{izirr4p#od(gmzM
zMAr|CH@H8b<tZ#)U^L7f==x#)gt-G|4>}EtUv&Mjd<P2$n0qk74eBm*{V;dI-3bkU
zSh)!cM_4*S*ALT&9!{|GALcH2{GuBG<ztu+buX@XhiZdUuyO+y|1cV+fKdFy;tv-8
zu>6l64>0p!G|XSH^n)IruzUc^PcVHj8fGsn-eBPdvlo`1aK%3?-N4L)#XFpa_Cs-n
zBP{>J;t}Q!bbrCpDa;-?4UH#U@egwc%sjY0GzKi4z=UD$f{CO16Rr;0-i5_CIt^0?
z_a96EMnls(EWe}2KP>)mg+I&~n0}Z)Ve3h7xf`Yr7XPq(26HDY-f_hnOg}7MVBrL7
zH^bx!(J=dA`eE+C2uEl*p_>P*mtpRJ`xlxnVD%9!T+sES=NA|smi}Ss0G5t0^g{(;
z;RP#4VD5+c3s?S!YKPx928(A{IzV?1Og%ac^B>H=@O%Uf2bj6&`e5#ZmE$mX!qNdO
z9AJEO{V;tn`(WV@i&vO>SUkb_=rpW+g6V^WBP>0@_~`mz{zA|HFn6Pyhpr#ShlL-E
z57P(Z!|Z|a(P>zH1&eoBc?=6r7>%wE7G5y(Vet-&N7%V=Fm>qqVSLzpG0ffY`U6M4
zf$4*#Ygqk-9&RxIz|_I?!Dv{#!14(!9$+-gJ?P;J(+`t}`5$HuEWBXuf#q+QJ{S#C
z4@-YAabjs$x`d@;SUkeq3kw&R`!LLf<}X;e2a9)Dx`o9XuKWWt4_02o;vHrVtUV7a
z2Vm;aX_$JLJ+S-)OBaN~2NsVo_rmOhxd&JH!`uOjCzw63^a6_?7$2q{ora|sn7d)&
z3G)vj{V?~!+yQIH!0d;mGjw}ld{}(L+z$&6m^jY(hZ+D2Cs=-g(XjA@rDv#eObVJ$
zVfh~xZ?N$knEx;}LPTKtVE%;Buyz3~eLz$rNLadu$-}}AM#KFNbss_}gaxw)J$_*A
z1{jUY|FCd{)t@kTz`_p}f4KC+!WWiqVCvy~X!{K24v4EUN!a`V%ssI514{>(I#5Mm
z_QJvg<_}mpMpcc;gQb61c*DwXc>H5(0gJ%Y!_o(=905x*Fu?C;gz?d7Sp5&9Vc`r9
zFC6g>GY3|F!u$zK2e5F2g&!<jVESM*EdF5e4>Jcgo&s|RES=&4F!#gC3v?Q%eyIOo
z@deBOu<{P(ADH>*_QS+s^#&~6!f2SkVd09d9~MrqaDe4wSp38M0n3l*`eA(7xFO6O
zm^jQFxc^`hXf&*zgM~LN{9)k$%O7ak;9R)-py3G%H(0)a`4g@IjREWDz`_$Yjs^=q
zSbj#+hTuZ&h2;Zu^I+y6G@!Cz<u}a#F#BNsKvj*&gZU2@Ua;_n#UCsjFm*yiVEG^B
z4%m1uEdQg&D?}G239C0??#EPtT?7{YuyhP7|6uWeT^9q!zIT|tuyBOwhnG`m2EoD!
z7S6D6f~7ZDdVskLO*?#F9?U+NK3KT}OAoMkK-UjTC$RDdrVeH=%s;Sl30*%dU&HjH
zt3&6b>qqCKms2qJpsT~hhlMjN9l+H=+o^CqlmW{ZuyBL5ThYTC79LP-a0-@QVdWi6
zA1pt>72;sP;tl3cSUAGV6R191=^qw9u>1onw_xcAsvUk#IgAgZVfLe|hvj3KI#~LI
z@d?qeaD}OZ*#qN~Oyh_@m^rX~1PgbJ@PoF4VCrEs%$>0O4+}pS4O0ipM{qtG0~UYi
z=^Y-fINT45SD1TY{(zMmFl%7x3dTpLVdlWp!Qu-R9)!XlrXOZ5db)z~Ve;rSEWN<O
z6IOo0$_GOB!}2XGoMGXKZVxU#EZkuB!~6|57l%ezzJ}?C*$Z<&%wIUPGr-GPX#EQ-
zPhtLm$3HFupyIIf1}pzy<qFJwaC@NYVH7NUVeW>NUoiEsc!ik*(+8tr@d8%|9j}JD
zA6Blw?1AY+r_uch%LgzTW)HeP7#|jIFn`1R1Cz%^!}2XG{$TkC7XKLa01ks-_QB!-
z)~+X%eqi>%%57LUz~T|+e;n3g2*dmj%g3;I!BB`x09ww#(l0E0VCeyuHdy@!3kR5g
zVE!Pa9~MrqdJtwWEIbIMA6PiT+VQaUe=u=)zC!Z=EZxBREim`PXk7Z>#z6BeJbysL
z52hcM9^mTH80h+7?tq0CjE1X2(+|sMu<(Z23kx?`Jpc<|bnUS80ZaEV8fG3$9u|-2
z`eEf8EdRjV4^s~dUsyb#>xcOZmTqD34=aaY`f$ZR%)c-i*8hW<L+tn;%v~_|!uowM
z|G;UOLMRP$KP<n&+zktNn0YXDP;CebntovE9Tsk|{0CErtNeqB!^#O*yusoTrXME_
zO|LNZuyDrdUYrJ@O2GV$DoQ+$V*6qB5Rq2G;v1GvVCe@|p5XEitepglKUg~l7LKrT
z5m)+w#W$?JfYp~U8kVkM<p73vpy>x@A1uGZ(gjQ$m;EsHFn^$%5A!!n9lAWse3<)S
z{)OcS7$268(e=a1FIf78sfXnwn7uglLk)z@^FX-_u=s%W|6%sP<k4x^I4{h<F#WLd
z1YJFh4^t0wH+nvTnS(3*VdAj(ht)T*d<=_!Sh|GS3!`E7!@>s^@33%#g)7V+m_BqG
zW)8YIK|ZwnfTd$tIKawt7!9kBFwBGLhtja{f|Z{zcf-N~LqAjiR?frx0do&5Kf-9J
zaySLc=P&`7J;b*AVBrm`=V9>&^Eb@>F!RxASUC@iZ&><)mw(Xwjjj*Ihs86DM$gAM
z!yRS-8V!p-n7?4saQDCjpfpSz=3kh8s5ApCAES%I<YD0hiwBtd;qHd|9~Q50eNYB0
zo?+<*md|1F2rEZnG*la$g6V^WA51^IJp)&XodL_2u>1$hZ#eynW(-UpEZxA$0a!So
zDaYW#?1Ry;a6*qi42@6$bagO$VeWz|BS^u@e^_{+$16-8mqD=jhv`F)SD3qS>4)W4
zSUkewADzafAC?YaG;AISJ>9^}LpKNRKWI4y(+4kipyIf~6()|J4q)K{GY95B4Ev$x
z!`uN2PguUhr4QX4nEzq=Vfg{(K6Lfyd|3L0iDQHVOo9LnYggb3Z(Q18?uUgVE_LYQ
zu=ECN2f+LdvmYjpt`C+j;q?_%0nC0_xWW2KP-!>?(+_huEc{^c4bulN58*l?4462q
z9)Y<N7Jo4PFn2(7AxM~hSp5SlM_}m&RzASoiO`A4f~7B5c)`LCMibHxGYwXL!O}fU
z9A*xjPk;d{hhYA|<$qYX!_0@d9~OQDtbvb<!{QAV59sLumpL%?u=D~;_waDQr4cF)
zb1%#sc(_5!1E?~hDOkH579Ozl1FPrJ4T6co>N{At!`uN2M_4?d>xY>SQwK8-mhNDD
zbbT;B%s#mJP=CY99ay^%-VcCjgwn8ZhM5ENH_SYEc>q<9PQl7sSo((LBUpNawSQpp
z==x##6_$Ts=>TR9EF56ri>@E$ewcokdYC_8=>s0GXa>Ob;h5iml@Bm`(6l4Y<$<=-
zVBrRfS6KMK+=H$kmTzF`9+nPZG%Q|V?Q3-XF!#ga7nYx4{dt&rSbBi*(P@~wVf{Ur
z{cv^AbOq-_8L)VPsfV{4q2U2l#sG6Cj1Qw>=>kT>+W)Y4gQ>$s!}2}Mf3S3e9^NqX
z(anLyFU&r8`axHS<{xxvbUubTB<jaVZzSr6<tv!GFwBEWz~c+bhvi%J^aGW~pkV16
zmVRONC$Z%}EZouKjZnD<a|ld7jE4IgM|y?nL#JW(!SWj{U&8VQEI;GY2lp>5o}e@=
zU%>JSx_(%?gq4%9bPLNbFn7Sj(e=aX8JNFe;Q-4Iuy#DG9>CBK6@ZoVFngiGL{qSI
z1Z%fpq<>r{LBk2=4_N+(g$u55hZzS;f3Wloi$|C^%v~@(OdpKK=}u_+fyu+lRhT{)
z4NLd1d;^OISh)g=H(dUJse_g8uzCuXZeZyT7OpUR(P>!u4~suoISxzDxZ)q(9$5Vg
zHy;*1Z~;^XES_NDK**h_x)~T?>S6weg&#~EEIwg;bQ+dFVE%@sBN!hR|1fu=>xc1S
z@-Y9y@((QhVDSJ`k50q#FU%ZRyu;ED%p7!mFh0!RFmqt>0+WY@FN}{)!@?0}4m`a=
z(=p6H==xypf`t=I9V|Z)@;A&pSh)j>N0`6i;Q>>KPD9-XEC1o~4NZ5jctqERix10Z
zFneL;E{u;$AIx4@K8A$@OdmWv(F}m;gT)(6J<J?f{)XjiH0^LM%-!&KfchU6Ke*x_
z7Cx~24|6AMJP4K^VCfoeAsPdg-eB<#^FPddSp1@C!{EZy!R&+Shqco%G(ZJl@-Tg{
zaE0X~n7L5p7!)kM!O|<NK8E=RSNRV!2&N7ekFfZF#UI=~Fb!xl%%3p#!pbFBIKtXR
zXxeacVfh*6Z@BrmbfBddSUrkMIZPauUSQz{b1%#vu=EI1k50qF9TtDEbPrPp3s)E)
zora}rn7y!gg6V^WBf36ZeDrXG`3q(qx;|LE!r~iN4#C_5%YQiYFErd?`3GhntQ>&b
z2UQ2>qcLFiz|4Wsuz0|geqiAZqtW9NmJVPvnze}a{ZRM8+KaG!2=foRJ{TVs|1k4m
z;Rka!u5gFxhox(neX#h4(XjLnQ%{J7#V5>uSbG2#zqs_n+zImsEdF8o2*p1vpTNw4
z*$)dJxVvyT09IbX;vGiAl;a8qm^jQ{Si28K!_oyTU%<>mr_s|5EI!cH;o`%}O_;x7
z`IeBsVet$LUs(GA79OyC1d9iB`(gDv%wCv0OdJ+3xY9AqT$npy>S5-<<k8KC$-~Mo
zSUmyr4@^BQKi~>~^z;Td2ih)z#V;%!!R&?6FnL&dg^9!B9mXd_!^$66I)dd(Li%9x
zu<(Yt2bX(c>d|Rf{J_EkT^<)77C*3b0!zoR`~x!=7Qg7`!2AnyFU&lcdYFD#_@L{D
zrE^$#!)RE1z|6&^9~REAbPsE<!SWN#J+O3!Za<6<vkz9@!Os7IhX*u0!PKMEFn_|*
zDa>8y>d^VHaDv4@%pWj+qpQc@L+c$_{K4$UP=`YRmfv9Z!T7N7hJ`20o#@8E?1iNR
zn0{Ee!@>_{9=d*5e8cp^(gQ5MVD`hzL)Q;e2dm#<_QS#x<}X;jLe~!~XJGX<Y#ax+
z{u36Dgz^>4pD^_>e__~%CIIs{tlWh81E+h@^uxj*7QX2EaK#(U81!(3)g!R{56ed|
zdtmxtG|c@lKFmB=xWmkY`46TJM#JI{7H+We8`d6%r3YB}!t}vtm^?h+K<6P~>S5^z
zW)DmsIt`0|m^f_w7G@5-e1IuHqha9>>o?*WFGAA>-#>+3U&Hb-EIp&^gYjYV@NkBf
zQ!qZPT!MuIOdpJf*$Yc2uy}*{8{Hh3K6DzE4$#vJEIwiJgsu<PPJ+>}bO2Ka3wKy}
zz{FuREM8&l1X#X<*$b10`3t5G7Y&O?m^rxA!Ng(yhSeLeaRgX+!}1YKJvt3{7qlFQ
z<zJY&u<%FM2jj!S3m!gDb718fEI-1`gXu%3Vc`#pCs_W2r3;uj==xxMm^)zZhtV)|
zVCfL19)pIu8)hF|9-5wE;RzQ<W5CLLSa`!|n7y!a6-^tQ3oD;s<vywPKP=tA`VBDi
zU>3mR2POceVftY1h3kWc2UHp2ej2y{gaIp;VKl59goQsu6>buy4wlbg`3RN{U^;Q7
zAM|_%Q;$yL(vPkV7a!(7T=E#=XyHeyeptM~%!Q?I7$2R6r85`}D?ef85b{4PeZ%4d
zR{p^Fu>6c}FDzfe{0A#fVDSP=cj)m6GY6&*CJ(DmVD5&M=eXPt(+5j8uyztWzd_>x
zW**F57!7kLEPP?*HH?Od<FX%S4$R#!_rhpcc)-IErU8wHnGehVuyy;ee1S_pEWTm&
zJ}ll~=E3tbntd=XynKg-6U;tXxe4<JtbT#%gV8X5!`uhU|1fi4@e3;-VftV+%$=}!
zfQiG>8_Zl>@dr}}ONVg%PzJ0#hs6U_7*4^`3Cw(0c);=x%zrR-aE(v~%zdzQ2rEZn
z?HX7*g5?9KHUtH&Utr+{i!WHXz}j)JctvQ0uweNXW<L5o!LW1zQH@E$!X1{*VdlW>
zM|Tgp2{8Y`XjpoL#S6?_m^;z+!{P;&ZeaRgd{{cfr5|P<%pWlIF!#XBgZT%h9!A5`
zCoI0v#bN0JW<E?FM#Id7#WyVeVKmHqSUQ2}gVC_?ftdrV_hIKhz~Tc&!}MX$(EJOt
z2i6XRr5{*0VCX{=fR&%H@Q1a_aOsETJ6Jrz`~%~|^uff@tYd)dN9#Ai!V_1y0W$}d
z?_l8r%O|k-f|&zTk50qf38P{Dhna_~+(S19R!+d;9Y({`A-V=A9~NJ*a6*rNSUf_N
zV^FYuBFx{gbcCLsF*HI2VEGDGzQNo}tbSNJfrT5)99X`AT7#2<`5)$fSbBiPA5M*E
z5-@+m(hEHP(A44L!t}xIho(nZ`G+e!VEG%S4wlYg;;?Xlr4MxbVd0NnZo=FT3s)E)
zT|dm<uy#MpKA1jOx`K(L>xc1S{zuOTu>1uJ7nph&4O0&*cVXgi|3b?pSp34|(P>yZ
zf~5yoc*5LGNIyKiK<iUjI)v%N<$erzK-~-T4=g@m?Fe-B7<{OHSbW0t!@?0mJq`g_
zz9v-u!omp_&#-g?D}Qhp4;O~T6D+)7_QA?cLjHz@6HFg0{)i24n0{Ef1B-8%e_-hg
z?qD1YSbl@K8{Hi+c^ul|`z>Ml5mxTQ!VyNJ>xY>OvlrdHu>6TD9AWms{0&Plu=XIV
z{72s(3UdccKdgL$@nPu)Hl7Sqk50qvh3SKp8!-36{EaI<VdAj#4Rbe)hJ^>redzYX
z!Uq;#F!iu}3loQ>Q*`}=_%QQeG`f29bO+0~uyBFd3ol>L41l>09&gb64+}S#|6utN
zO*;e3pRoJ`3n!R<m_Ar|qU(q8Vd`P&7Uq6fy#P~(i-wi|u>1(K7nZJJ@r<q?mi}Sk
z3oFNA{(<FVSo%WO4>uQDZllY?$|aaR=<+ae7>zCua~CXr(B)z27Um9EIKcR@{va%!
zpzDXlAIzPw@Pd`g@N|R2e=zgl<Nwg~1M9cJ@;@&3!_ya3AFg~4QwOsTM#KCIOFu9g
zmhNHdVC4af52In`!f05!ft4dLb+~9)dV{5J7!Au;xb(rqVc`plA6R^&yB8)7(?^Jg
z`3I&B-8`5$E*d>OlByrweq8YnvmaK@!_>jj4J;kv%6~9-!O9(2{K4V@o(`bt3uYfW
z4U2DByrKIaoe$Fwix*gV0V{uD=@6z4mLFk!Tr|u-F#BNRg0OswOCL-e7Jjhw1PcdP
z{)VYTr(yQO@)Jxw%zx<W(fP3WhWQ^B59sP}@nP`?b3d{Eg_#E%=Y!izpuB?VgVi@M
zcf;(3g+I(+aP?>mSUm*`KUjK$mAf!^;fjBlJ`8_D-48P#W*(aL@cmz~dH`k~tlol|
zi>?pGhv|dWd$4$brC(V5!_osxA36;ir-6kRtQ?2sCwRP|X@vO?mcL>32rM38@q?xf
z&P8`0EFZ(%L9G8_`e5lF7XPsDfm=v`0do(`{jl(axrcyu23UTF#Rn{1!omrcewaE~
zc){Eat6y-XKbUzi^I_=~7XPsDgOw*R^%yiXy~65!c)JZM4hvs&eK33B=@mL|0ZR|)
z>M{7xavhdFVc`#pKMeI~0x*5B@*AcfO&LxuOg}8VVc`!8cbpp0Bw*zRTo@W|a6S$O
zEWg9rZ7^l%@d!)TFnJgaOBb;61!f*BJ;CA!SA4+a;prRdZdiQ7>K|PBALb5NJr7Ga
zFmquvEPcS#!)a*z!~6?#C)_<yd6>W8`k)M$I#~LK<v&=uhbqHOLH!HU2TKp=8gQit
zT=u}+i%TE6`RII@zhLIW%tKd)&WGh2boDUv(AA^!Vc`!GhuKd^Kdjt<sfV@yVEGA7
z!wg2FVfhD^&S3c&W-p9J(+20l+VQY-3sVov*M#B$mVRLA6_y_f>4Q4}noeQq80H_C
zJ7DnwSC7Vkg&RyA%ssGrgpfPX;~N$Z#OjBY7clq2>U}h466C_l0a*MI)BsfgvmfSv
zSbU<VN2qc*1yc{J?_m01;;?vx`3tTQ%7B%taCvBX0IT<5?t;-!ZEy;fUSI++`(W`8
zi#PQ6fvJa=H&A<F`2^-3Sh)nt7chM=8s=YEISorUFneM8V0@T9I1RNIrXQ9LVeKCn
zAFdt;0~TH|_rvt#P>vxC(+|^!)4dovAp)@Yf#r8t_~Qz9h%UtW@i2G8>Qh)e!qOF?
zbO<*O8lJFtfSCvLA50yLhWQs(Kf=Nr7Oyb>z|u2JA36<7C$M$^%-t|PJe{Fyg!19-
zc4#>UTmJz|->~uosvJ(i!W|ZVuzmzA-ND=e*MNfo^DnI2fW<!{f5XBDrXSrsFmW8#
z!i8b!1QuSf{0H+tEFR$+&>7Hphvg$!xeH52uyO`n8#*7BZs6$xssQFbC>Mi*)jP28
zhLywU;e(+ODgbM@!DyI%SiHk%sB)YXEc`LjJ2ZUIHNxT-rXJ>gSpJ3O2Uz|<*AKG~
zrVqx4g%_+p05=EC0GK$ezJtXdOc_i+jE_#k(>c_CuzU#92QwdCAB+!Ehv7e{dFbH-
z(?^I#&p*VLU%1T075}hw0t<Io{)hPoHjaSHT$nhle1e5Pj1My(rVgEk<rkPfn7?89
z7v>Ihedv5xJi+_{%eTbphs6`jewh1V;SNjB==Q?&!O|~GKP+9r`~fS6(DlR8Im{kd
zIRJA%%slk+1a1yAox#dom^)zOxiJ63<l*{Y3}}4;Qx8)IvkxW?^9M{DjE1=%RzAY=
zAxu9^99F-;^ucIYyur$OSUAAMVKlDrhvh$5`hk^~Fn7RcT=v7<0ShNsK7qwQ%zl_X
zaP?>mnEPNftiFNqVc~(M4bFwBhs8fE9%1f+<rBCDbOtmX!1TfLEi7DM=Adgs=flDu
zJ>GG}KP>&j(ksk<SUn6&AFyylw-076ESzBKG5iblAG&^6{(#ljF#p2B8)h%A_=CyA
z?StBbt{&ztbo+7fVd`Q2gM}+D_2}X-{V;dH_~`0z@nQag(eQkLLj$~=hWZ~~4nWHX
zT<(YY4;KHhd<@eMlZUwj-FjI1fyEb0Kg{2-_=V+bbp0@MVfMkw8<>A!;fAaHg_#ed
zVftbD5f*MR_n_Mk<HO`(?tzv6u=EJy!_=eGuyBBtQ?PJ=`5R_Fx;_{m7C$h5!rTp$
zCq%>a!OVfh2O)hhd04pxvzL&6VftY418YCR^b>7AG=0L%fz|)8atM|#Ve&A0anbN{
z0IC3H9xPozg%K1qzrf-h7GJP-B#e(sKP+Bh=@(`nEIf&g57_!1Sbl@;1Av7C%zT8y
zQCYC@KUlcKXjndi$)oCGfS>;f3ujpP!O}g<f3WyP*AL^v@*k`ofbn58%v_jy7!8w0
zw-4rTSbl=l>o9%jG_1ad#TU%ouzU%NUvzyiJ}iB~;+ar*!}P&uSiHf?9auOJn~z}n
zVKl7&Psrae`(gPX7Ekbe2+dzGb+~9){KLWt79OzlgG(RGpRjO)#UqRlqhaBKZVt?R
zn7?891=jw6iNpBl`eEu}?uF$$SiHl+0hTV%^~2&BM#I#>!U5(VSo%WOkKS&9r8`*u
zgVE^v(D^X?VC4tQ-RSDj`LKA0wHIOb!O|VBd=E1R7QQh1Vd)KKKg@iXdJGy`e!{{D
zW<M<aVdi4!g9^apVdVfUKH>2VO}{YnpxSU!Fmqt_FPslG2bV@zxWnXO;Ro{%p>Tk?
z8&+<?XjuNhm40CM!NMEXZikHn!Q2I(&w**cLBre$GY1yGxcU#UaE0Yln7d*56_&4H
z@q}S5G+bf&Vd)=Me`2Ud6M)ALS~?;WUT|Ze`q1MW<}a8!n7d$nf;7}$1l8eC0J9%v
zFDx8!D8~?n#T(9WN7DgIf3W%zmVVKc;pD>V4OsaHb1y9Y;?fAS7p5N;@38a$qhadM
z&4JfD&~*#2@PgS3D+geFbp5b!g1Hw~{=>v!`3jbg(e=a1Zx{`WH(0pC$~9c=7nu1l
zbufJ}8kWA%?M3Iq)Z+>#boJ<bSiXhDKP>;l>Q{92Fg`4tVC4*YK7yMI(|}IH@(DB_
z!s=02I)H^I%w6c(VdV@geqiwr3lCU2hLvmR`eA&yJum?%4NK3s?1#lGEZkw~6=ohR
z-QkQ!sJ~(56-+-Y++gOy(h1aA7!<7i50ilH|AUp=uyBCMqtmeb2Md1~4XZbB>W9W3
z%$=}shq)J)FJR$>ZZ9nTz}yKl2bP{-;RiDpT|Z18EWN<p2@O}6Ik0qst{-MDEZxFr
zn0YYsVeUZJ4>K1QZ?JR%6Nm9(=Ai3`@nPi@EZ$+|8cZLq@I&`6tlo#21Dh9w`4eUz
zjE02|EdJ5e!{P&`9v2OZZ&<p4#Vag7;?f7pU$Ax~EF59^2bQmK=|>lb#S_e4LirvB
zVBrIccbGq5;R26Om<BW&rVi#_SiHdM6_~rww86Qs_=d$BtR8}e4~&LuKx4qd8`kfG
zr33VMLDL53!rTuFFPQt`3J5S@@dgV=n7stF!N(6^=@1rvu=pdSAC^90@dI-|dN{(;
z6S{fm<uc5ExIduf8!UX#^~2P|;|E&qz{(L={)Odlbp0?sEZxEU19Lwt-4Kco*t{Jq
z++qDWSa`tXVfMjjSh&N|53Ib0m0P&{4bu-(2h$I;9~R!QbOBe7#(<S;u=)??Us$-o
z%z?!(ns)g9Cs;iLiw9W#h4EqGfUY0Lhou8pyusW9^A9XuVCvCnnEzqouzU}r(dE(A
z!SXvS-eEK>{9x{Z>4VWQb7A#8Z2vcG{0|ntu=XWPA36<-XPCXP@IzOJiw`TmVD5pX
zXI$!G;xP3vcf!*-G#|mj52hZShNTNwI)SAhm_K0tg2|)nhm|wvG%P*9%!8SOt{<Mh
zpzej08?bZ)OW(Nk!|a8H7c4wr=>e8*Vd~KBhw)+Y3!`EAA4bE{F-$#-hS>)TFIfG7
z?p~NWm_8T{i#M2lm_9=B2Gb9xq2UJ$2UxiZlZS;fj1Qw>=>_I*SUG@hKQ8~n!xd^j
z%v=orqRGS55liDrf4KC+(h*D^mJVTjf;5_YVd)5$K3IPbCJ)R1uy7!xAC~`N<u)un
z;Nc517zYiDFIc{T<v*BySUls<&H%FyW<M<dz|6&^4<-)tFRZ@{tFK|{4i*kD^|)ww
zI6=c7S9-?K4{c|_#9`?b=5O@$08<B}VfMo6DVTnkJRusEk753V>4&!m2pIr%KP=v0
z^%bmLPe?l~o?-5XrFU37z}$t)99X`Gl^Za3!0HE>IfV4X(h)4)VBrr7A6)uj_Q28`
z%)K!G!2E|xKg@hsISQj;?uO+HLi%C#KCB#vsfVR+7$2R6xf7QDiH$dyIq-5Hdha+a
z{lMZ8rVfLKrW;uK0gFeNzhUCI%1@YkVc`xlA0`en2Nu3Cc^Hi@j$t1(ALGh@uyBH<
zcXW54rz4oXFd7ywuyhA&FTm0ZIv=JFPD9-X3vZY>%)PL52ouLe!@>(D4@>vN>W9TY
zEdRm!1+egknTt-t$}Lzrfu(a;`iGedqtW%l;tS?Zm^m;SmabuZbp5dM9Tt9Y_d>%J
z<{nr)qw9yIS6KYP!W|Y4u=t0?Bf5TgIR&*3md;`Bf|-Y|51kM5CoJ4y_M@xA#fOD2
zEIwfI4+|GU=E2H8n7h%#17;2``(fb&(+4w$SpUG>2Ma$~`hlejnEP<q4-<#^AC?Ya
z@ehk9m^xfEEWTjwf%ykkKH$;^6NlLk3x9Yxz!VUmVg82cC!m}G)<1x?n_%f2W)3X;
zahU@PUzk2vc?R<bOdgkhn0i<~f~5mo>e0nv;RA~wcs_^bCs=%->qF<m%57MA0!yzj
z{e=7h^DoSPn0i<^!rG(g_QKSm%frlp<x3bJT|X>-VD`Y|Vdld80gE4)JUR_e2hjQ$
z7B4V;==CIAKU6);{jhWnOZPDQVeWzJgE64`Ve+tefYpC6ah&NH8qctD7M9Op;Q<Rb
zSbl_A3#Xy_Vg7}uC+K`4tUQ643)hFnfVme|4#WHn_Yc%OG;J7Mn15mM3JXt|yD&7M
z3&8vX4OjTMA5<J&In2E<8rI&2rF-;t0lI#edtv^Eg(IvyArx*fb+Gh;k^gbH8|DsJ
zzK5k#Sa`$i#ToA~1E4faA8fo0<{wx*LY3j9VD$y8{D6fYq5KQ87nXlu?uLacOg~H<
z-2pIl=<x&-hr1s~IKuKZOg+qe*mxq0kBf%+8y1f+`{3?@rbAr%VetdYzc4;5TwpXV
z{jmBOM#J(uJip-ZH_SX(Jj3!i%s()Hz|2DrADDg^4T~pOzJ<jXtQ>-+E0}s14Ko)e
z4@=Lm^aBf5m^)$m;50ORVfhypez1HGn-_-h;rh@RFnut8!}23s9BL1mHu(JyF!#ao
zAuK*&<rlg>7#|i+uyBKg2P{6&!vUrbM#I#>!VMM<u<%6BS1^4r8kSCA{)M?8R^PzV
z11ufD^r6!*f56-ilZS;1EFNLvFdCM=Vfhf2USRHqg&#(_2sHo}KQMR0(jlxp0*iNe
zI70QIQ}FRWXgtHx0Zbh%Tw#248lK;w`3)AHFni(QfUXhBhxrqhZqV(A$wQT6P%!&o
z;R$mOdVFALL=%98KP((!=>ujinsN*-x<6s=g2fYt2B-i`AB={jKUld23rDDOObQxr
zFn7c3#Z-tY0`o5{{lU@=%p7>SK-C84!SV+zd|>eg3wM|~u>1|z2xY+1HAeVA(-F+y
zFn2+<!6{g}fQ1uG0v3KS_u#T07Tz#(VEG89A7(#H9n5@m8dl!G^9eLQ;N=F?Kj`{k
zd|0~y79TJgR<6Oq38o%{hS~><FWCG)EFHkZ0Zk*!z3}*gx)at8gslg`h##mq@Ny42
z?gwlC!R&?cVg7(>gHy0{1G66<UQqwQ#Nisy8Bq7a%2$|vSbBzqBf2&iAC|vi?nN(u
zVDSo5k50qV2TUBsht*3k^I`J1Xjpj<b1%#vF!$in2NQ?sgZ1NJ@dYdYVd`+v@cac0
zcUV5ar5;@z9!}8sfX6q~JaqLiJ}mvf$_bdgu=s<y3zkk``d~DyoQIhM6Nl-CnF}kY
zVDc~;7S1p}EFZ$+4@Tokhp_Ywb2nT)G(W)76|8)L*#o0t`e5o{=@=G&uyPF^o-mDQ
zG<yC<=c6gd;KIrcSa`$S1uGw5=?h&WEL>pW2D2BIZejAUa6#7(4}VxXfu$d4e;Jm4
zVBvtSAC})>_QKo&b1zIkEIiTm!^%~ddtvDa7Tz%b<ElSk=E2N|`4i?In0}ZzOg%0d
z<{y|onEPPq0+&9RIIP}+<qLQ`Lc<lN4xNV87chHa=D_)|_(9VM3olrHgT)gpy~F&C
ztNw(^!_x^g-@xpJxgQpuXx1~p;umHw%-^u`2qq2-S9JZbc!uXcs6S!;hm{}b`f%}K
z{(z+ic>2br0f#s&AHv2(aVUce!~6%MVc`d>$KeVIFktxx=6_<-9W1<I{)Op>#V<~4
zq2Ud)7Z%SjeX#rt3s-b=VCfE)4q@pGCJu8KjE}A#mak#@VD5+62TLC?d360Sf5Oub
zT0Vo7D=>BF`eFWsse`3wn0>H(1mmOY$Hj-G16=YjahN}0<v+|HFd7zagz^E*9JsmA
zd<=^hSUkYwVKmHNF#p5S8!Q}P`3T*7n0^=y3rCo{Vf8Yx<tNM?F#F-*2Th-Fb6^&t
z(=c^V8r{Ey^uyXGuzUdH!}34eedzW=`LK9`?bn039~K@^WjHCAK3IN$l|Qh00hdNt
zK858QSa}PJ4|uwPh6}nmFnzFihQ%Aq->`6lxd&Z8EPcV^4Hix?8s-j|ztHu=!U<RU
zLN^~>Kg@kFf574m7XL7J!`*>q04$xu${AQc02Xer{15XVnsx@5`(g16(+BeptpA3t
z51kLo|FCdEcQ4FbbbYXPIjr7==|j)Yu=)sHKP>&gXqb7h_=AbV!vW0zm^zq!uzUc^
zM=<kX=@Lyl13cWJ<u=SbSiHmJVd0FfAI692gV_h?L(4&!Izlu|AIv^n^#)FJp#FfR
z7g+lVmwH$_fyFN@9l>arI4m9GG6xpFuzU<l*D!Hdc%tiv`4^VoVeWycgP8|Q2QYDT
z8W#Vs{0Iv-Sb9V^7hOLrykYubG%P-0=>T0Hj1NoyF#p2RA+G!c(+8tr<pwO>!onTq
z4_J7@)T7fdb?Et+*!&M0H-Nbp7VfZlAYAc}?q8TY;54+{hn0`$?uOY1qhbDpsfUL<
zEM21+0COjdhLxYN_<)rgu=qsN4!@Tcmfm6Z5}R&d=E2f2%)K!EF!NyQaM7@I21}=~
z^nk0}LN^EIe^@-h>T_6qimo1=4+}52I5fQB=>jf+%7D2WmR@20hS>`<7v?Wi-3$z{
z@PU;Nuyg}251{2I%wOpGVd)Pp4s|yyK49qyLmgBDEWXgy!~6wHmoRy#HaG=KS1@y6
z?u7XprXQvbt`Ww7x))YX!}>e0bWAY)Ld}7X^Fi|)%wAak4_3ZGm19uw@PX=w#V1@G
zLkFq=EFNL;4ATb_M^%l<gQa(L8JKzK;^=%>_@l=YIv-shj1LQcm^m<USo(qSanjIu
zgxd$@!}29geP|Le`$^RYGX|FbVD&#toDdC5uds9i<HOPsA^os;hUHsWeukw3Li%C;
zg_U=({0E~6>4(K1%syB=z|1A2AEq9b|6u09;)9TWSbHB<@5A)L(l5+ixY{u=`(f^d
zg(rG@09Sax%3D}H28%b?I&fHi#+5E$_QK48`5%@JVE(`v4$$-tk3VR+4f6*scf-ts
z`5&eZ7GCJ)!PMiTVc`xl2UefJ%*Uk<CJs{%3lCU2hRNfiVet=hH!PfC^#RO1xb(y9
zfu%o~IWTct`e5R)_=2TBn7y!ggXL?OdUP5+p5f&Px<VX$n0}bQVc`dhe^`1%*A8<h
zjE3ohsfVS1T=fGioM7P#3ok<YVD5nV6Q0hX^A50Zgrz4~Jq*(aqhaoa=R0UVfTcHB
zc?b&^m_BqGUe7@5Ul<>*A6)|uJ}f+7{(`v=hjRG6m9X%Kr6X8-0>;Os9~R!Q@Q0~~
zg$FD@;nELtFHAoyy}<lWDBNJ_8W!HLbOJLMm%T7?boavJ8yXKVb+~AldRREa!UI=1
zkINibzK5lIob5#%2EfugOd94d7@t@go{wSvhtWhD0CNXCUZDAjpmwM|u=s<e2UxiZ
z<Kr?17Va>4c)UQ(h2>|MJUWf89~OSF`Vby2=o+DXn0>H#gT+7GT&O#s$}uQd`2x!~
zu=EQHPk4U7&<PcQmOJR}eV940@&KwDPQlv+P!V+dVc`a=N1)2!6wKc+_rc;F=6|^R
zarhtRZ<u~qIRwkkF!N!2xW#A;SpI{#6J{>VKQQyrw86RP=ELd(n1A33aWG){7Z(38
z8i#VYFwDI$f5XdrXnevI;$onMD=gjOQjSX;7SFKwf~6Z6AD2E@zJ%oum_K0g56jQE
z^uz3dse^?#tlogdBQE{$@PmdIOdmRpOFt}Kz-U<b!SgLHjZkq|`h?LieXx2A=02!$
z3<{R+VD5#58!TL5=3!_=6M*G!nEPSj2n$~{<rrL8{KLlkU<zR7;W7v2KUn<3#w}pt
zF#qAI$6@}3mGdxtu<(PWCs_Qz+y$dy{)G7-mR?}t36qD_t1x{q8WwM`aso!f#*<<4
zaC2Z9(P&ut1IzcYc!TMOiKA)5$%W+ySa|?52d4%!30Szn{DUTq!G(<%!u$z~2l#k0
zh7O1TOdTx#Vg7`r8(6srQwPz7L_*6i^zefD8>SK_597mVbaT<e3FaSM`eEr3mQG>i
zJ}m#i(+$*}Fni%NwA_KkKP)}N@)L}YEB(UEf%z9!AH(tyEIeW98m1ndhQ%{1-eKmz
z+AlDFqU(eC4>r#W4^NnXVKjzDhyW~HVeW;68_XP-{~@X|NkZyi@rS7sA_5Cn^zebF
z18Dp}RAV8b^00V^seqXaOBXP4Tr}K1r~-68R2U~ks(K8Ap#Fe~!@?EDCq%>YKP=tA
z^b@N8VdlchYnVS^<u5G%!_?uTVdXb0y~6av(ibj$FmYJ?!}1F(AHd=frVbYki)Wa9
zFn7b;g-ahy99CY#!V6Yk!f2Q{uKWPYH!$@uf560H`4na!It?ptVdW4k9AV`GY#$Iz
z9;P1_POy3(W<RXFfTaUic?Q!5qhaze8kUdX?uIJFnUA6A7UoWvyJ7JFi$7fP05cb+
z4r(2IULPh7i(i;LAsS{Mx<7E~gM}lk9RrImnEP<)gNeh!50?L6@c>KLFm<?SSp37x
zf!PboN4WIC#9{R}%-t~aVDf}$n0{Dz!_qG-y~E-Y-5i)WEc{^c57P&$S77eKr5_eg
z==l&9p0IKWLq9bA!Q2f~52IoJfaM2V`eEr8mi}StVBrXh512SQ4bunn2P~h$(jmHf
zTzptL1G5)q4lebuaD}z=VCfEKFKiqTmws3|2@5ZnK3I6b;uDvCSbV|KEzG^J@W2)B
zF!Nyc!SXjO|H0A~OdT#7W)IArFnzG}h)W+#9F}fi`3&X`Sh&K}5u#!Khs8f3f1{fR
zOJ6W^VBvzU4ksVl&V#iBVCLaek1he#53eVn`3hYbE<Squ0Tzz1bOrMlF8#20gUQ3(
z0ZX5-bPbcoprP)E*#nDLczi(RG4w$NVD`h@4GTY5IR@iHm19t_{0fVASa}L7w_y1K
zLnBl`3mV?AbO`e=%v?h00T#}%_=g$`r(od;7eHgc@*ONcqe;WpDWZ$R`B3-6%3E0Y
z!@`Y_|6%zUR_?%Pm_ArI!tFz2!0dt5w=j3W^uff@v|(^z=?4~nuyBXD149Fv04y9~
z=?La87#~eJ1{W58@O59%aK%uFCIHh1OMftFSbYNHqtmeb0ZSjS^atzD!u^9|9sr&{
zpz<(%u<{sYKF;zKnlEAMVBv&8L(Ru<KXjfNCJr+P<}O%1LJt?1dRRJz(Xe_07XL8w
zVCf5{4@SepVg81dSFrpDi+`AUTr@1b!@>!k4oQrEm_C>}aR1}77#jW<{>2gA=*Gax
zeOUPklZS;1u5gE`L)Qlvhx!|q4q)QwG%R1j@;OW%7N0OaE*e%YpvM!;TwMBK;xPBY
z^uzrPQ$QpQw-4%XSUMz9I|DrbL*o;sA1;o=AXq+urCXRkV9IgT2QYbfdV{(fmhWNl
z4htWcdUP5VZ?N%NSbl@0TUa@ct`EkCg*QwctQ>>shsnd#<Dy~d7?uxU?t<lGcsU9U
zUkr1g;;{4#%g?ZQg^9!RCAxl?I+%N5?ExKVxWelLG>tI#!Dv`}A6AaT;~(liH0=zq
zbOMVn^!SIn8(kxm4|6xnzp!wGg+DAk!2AW(2B&b<uds9glZR^{z<`y*Fn7V?lYlma
zG&H<n<p(TWVeUaFL}kJ1Nmx38`5)$fLi%CrbYbUz!qPD;Tv2Vq<iYf#=SNJHs3NfV
zhvk1*c?e7YFnL_*02V$lQ{nD~rZ-r4z|4WEgVRuTuyO*X4yF%gFDyRb`k)M0dWE?c
z7T>V=fXTz+0jdp7!P?g_b+CAXr5{*&f@?ryz}yWhe_-JcGY^-$(Tss}VftbD7cPN=
z0Shm9JV4Vg%wI4Xhjs>7yrZXASa}S~zqs_n?1#z2+z+c)VBrXpN2g)_gvAdm{9)rp
zu=q#U2jj#12TOOb_=4GwtGtEjhou`h4Q=<p;uR(iQ;&;=g+Gjj#Xlkc!_0%FTUb26
z%5hk^3{!`ThJ`z992n+rT;(phIk518g*z-g!@>bwJvtv2PcZvn=?7gME<ViPF#Rz1
z!`y{SA50wPf0#a4`2nM0=?bPEord`nW)93hu>1kzqw9n5Vd)o^UtswjM#I$MqG9P5
zR^P+SL(d<$^uxjrJ-xx}e_R@&;xK(Mdtu=P^FJ(Hp~^8RSU!Qp7c3lMG)x>rBUAvU
zA7&3MK49TVC?CMw4K<Dd#)p>&X#GT(Iv5Shw=h0TAIu#vaa=U4zJb-BF#B<-$0ZJn
zZ+Jeyr4T9(i+5Q453?5*Zm{r&D#xUt^)Jl*=;~qSVQPekpsR<q|6uV2OV<$9SV*Y-
zuyBUi2h#^Dhp^}b3!&?S@rliMFm*5**6xC(f3PL^2xxr^i!Ycy^l-st1bV)MnM168
zn7Ocg4x?e=51TiHse`327#~K%>K|Bt5AF|GzJdA^mabs>U^FcJV0>7*gy$=$ew_A0
z<zev-%YU$NfQ1Vzeqr{&X*B(?d<atyYbU_f<6yx24XX!X=HPNSx-l^S!t5or9)#s%
z*g8;L;f-!DEWBaq7uGI=@zK?z^I_#Ytel4BLzp<adUQTa9b6wYK49evx_Wdz%wAYL
z!{Z&=9z$1;&WGuPiNorBSU!M><Dy~Z49s3wJi_81mp+&{ES_NT0L$0JraM@C!_0%F
zH$wVh?uE_&!PWu6>QPv@z-XBHFdEjbfrS^$-!O4let@NCbaP<hFd7#Bu<`(=AI3-5
z4-<#wdl-$LZwQ40ES_NY!@>n-K1?3wZ(KCYy|DZbiwAUf<I)dH$FOt&a}O@{7~;_S
z5GGDYKdiim(J+6*!V5NTgv<T7%6(XT!r}wQhq((z!|a2Z4{OK4{14;9)T7g|d;_xw
z7T&OUfYIpsV0>6V3uX@7ozU_J<}R3eTr|wRu<{a?Uf|~8(g+oY>4Ui!7XDCaf)uR0
zhLsyI{}9p$3xAmV;e4q7Vc`ig7u`Hq_`uRFEWTjz0i$uHLzp<s92gC&zhUtM%ZKRp
z!`uZk2VQOwS?<H)2WAc|{L$^f#fSMD7Je{y!u)|tA50w9UV_CREZxB53DL0f7UpkQ
zd=b)zArCD#VE%xahoKIK0KDA}H3t?hIFw@u!|H#S1Uw#~=E1~q(XjA?$;0@tau}C;
zVD`Xhc)UT=Kg?eQ+aXZ(F#WLh0J?i%d>9Q&f3W%r<_`3Dg{3Q)K6DyB-UF3@mCH~r
z1EF+*OFgU{g82((9*l<7^RV!N`5RXL!16IJ{jl^8OK&j$!_>pfgM|~!UKkBi4{N8v
z(l0FDVCrzuu>1xqr(ofX%RY2-VC4oZd|~A$%suGpaq(g9g{4!NxwzD$i^J*<n7yRx
zhxHp^;SMWLVeL9}`_TEY^aG18SiHmI7hMCC5A!dKhS>{?7g#*N(iv16oI)4J@HaGH
zqN{@&fX;xbM;FG$hou*oJS-kz_QBF4EL>sgU^J}0gyl0>c?Ao1nEzq%3e$&9!}{qk
zeX#h1rAtEkVe>{X{V?-j?LVCQq2)77A1pt@;sNG<SiGXU8y4?yaj1E)^aQgPmLA~h
zU<_z{!O|Nn++q0vCJqZ{m^K&<6Ni<*uyOz<5AzR<kBf$xgC5?v;vJVcuyPQl4@Tou
zk0t@D_t1n1a$)5btlojeJB$xY$G8lF)!(rE01HQ0e86a2`Z3}gnhs#<Vd)6QhtV+q
z!qO4Uy)gH{;sfR`m_8T{(+A_j!XK7yVE(`r53qC%EB9dLz|sRQd(qXw(jQDcJUn0u
z3DB_qKdc;qrGEn2;OjnM{eM_~gVo=#bO8$&bn{^04YLpCZ&-N2Xk77!ArFmrSbBkl
zKg?VV`=R+C=3baOn7d)|gRTz-VDSbEe^`2f>Br@7n0|B`=5AOx!r~KM9XcOYPr||x
z<{p?i=<3n=u=E1Ur?C8st_~L;rVmz*!@>b(E-rmAahQI1ctPU@<}a8!Tr{lSgvrCq
zgQX9cI64h0pJCyKE>9@_Vet$r2Vm(9mY&efL+8WNHOwAZy$7qW(EWw3ALc(;zJ-Ms
zEMK9k$Hj-a7Zz@?aXgrNaOs1I!|a3Uhs8fko)8TyZ(->b79KGF!)SDKVBrq)H>})+
zg)6K*1d~VC4-<#QBTOI6Kd|}%me0}k!|aEN!~6kD_b`9J;uEG0M#IEm?uMm*n7Occ
zfvHEQVetb?cd+sc=3W?$t`D6LGY1w9u=E5=U+C&_@!|TR<uHtoOFb@eT=9)dJxm<t
ze^|bT#V1Uj5Dn80Q%9=%Vetmj4|4~MPl$$>|4@abx*rx_Fnur@Y8*ido(`b%ZZPu*
zX@seR<$GBA!BwuJn+MA$uzCxYFVMpiT_21OOXo26!}Af;AGqQHW<IRmg3+*Y0UmzP
z^bE5PM#Id7`4d)dz}y212e>&fjZhkv-eCTR>4W8Gn0{Ef2-OCsVCKU70SiBvIdFvp
z7%=z4>?NQaE)8q<!OBUPKVW>Axo{0Q80hH+7JsmE02a>Z+F{`X(+>*=m^#?F5-xYZ
z!Vebju=s<8H!NM_vLD?ZSbW0b0T!Mx_rcVo(=dBr{(;f3_8&|hT_21Oa~~|<!2AW1
z$3?@<g{E&fAD0F+ahQ5oJq2qwqNf8`e4=TG@9RMiCz$(T<u8mz*AG(%vkw;EF#BNf
z0COk0esn&p{Q?V5SiMY0Kg=EI@r+)cquWQ24~-XG>R|4KrC(S&howVU`oX0i7On<R
zdtmt%R*u5L6_<WkI|nWgZMVbX6P7Mu`3bHL%7EDi3tw1x!}P=A59S}JHaLYY4hw&n
zyJ0jee&8CR448UYIKbi`Dov1rnGX*yXgCto2vq=c7tH@KcR-~HQn2&|b2lvi6VwP*
z01JOueu0HMRGNGW8V)db5i$toZ&>*aOV=<yA^kA-!_>pV6Fq(4G6!ZKTppT^iPaCY
zA6-A3Pk;e)H(VLiemI{116JR|@;fYD5zq#ghUEhojo}}-O6&}Hy$O?s#y^HQOdgGf
zl_M~B!{%*a<r$iC3@%I^Oh2sLA=G|=#XBthp@$zVUSRSV7DEMK_QKO0bp9CKe5i6b
z1=9zMKUjW&mFIB%&~_|bBN_u%PQrv?{ZN>>gwio={0~-+!OVe$BaDxWhUtTu11s-g
z>%j?yAFLdO(HQX$O;5P&hs8529l**#nE!F5XIT8g!VBhZm_4xgf$`C4n0{D1!NLpX
zUwHY7BR|8;htV*9!14{uTy%HA+yRSMSop*85zJngdtm0n^ucMUyI}bqW-rV>^m$>p
zJ~Rd_-NM`fYrn$!2WZMMxUhT&(+5k>u=s@4a~K+-0<d%gYd^r^A09pg(ht-a_<as=
zK9qqo{Gjr%b|=ieFdCLVVBrE)jzPilJ30+h2<wmHO2@GHg2e;OURb!o(gRE!W-g3|
z=Ub=%%)PL50i$8%8&nwsEPi43!rBk;bVMNBVeW$22h$ICCjtHF=>?V!VD5wGAD93d
z4J&70_QS&$YA-DPplO41(bEOo-_U$TNI%RVSo(qKhq(t94zTb=_YbU`fsJ#*+z-ny
z=;;Y&9?V{tdRRQb#9{7$jRV8t8Kxel4wmj<{XSSaK{pSs9*qG@Pq6$B3on?zU^KeF
zVaCA4q45RtH;jh)AC@0s`d~Cn9HtItA50z=zc3mm4x?e>u=s(cKUld7OV6-yf$4+M
zuyP8<hoxs&y#+HLCXY_T+zSf_m_BrMxcD&ruy}yQCoc8q;;?u|U;haU2begzJ{TWX
z-@(c^Sa}7bVetd=A50&NhJ_DIJxm-H|LEZhlZVl;d<HWI7LPD_Sp31{Vd5|v7Je}M
zVC4xcJYnX;)Z?OI>S6U4ET7@>H_RNEzhU;GhZoF!F!ks(%wCv3Vfh`V4@RTwL+8WH
zfu(zxK3Kj&SC7tzg)giefTbT;yNpmcz|4c`gXJSwe8S=frj8JeQBD%j08<YOe^_}A
zE6-r@gv%IMK7xf8j1SWfGar|JSop)#!NMOFZZLCU`3>D1SiHf~E6hAtI)tSQSo;!P
zKP;SK?uO|jl>cGo!1TfV3o9>S;RsWQPQ&~OODC}M51x;p8gRxxwBCT(4@-wI8WwLb
zd8l!43Z@Pw01H1DA4bF63FE_QXgdLxZei|(m4`4I9v^Ux5C+VCSiXV9FD$-c=>eC!
zAf_-dpvONfo?-f6@c~nhPQ%&@Fn_}G0W2M$t4HU<)Wgy%EF55bboDSkhJ7#zC{0K|
zEWBXp7pe_DE)5G`m^eBOix*gWgQ<sw8!SDe>x1!O=@nM~!t}$$Vd@CcFneJ7VfGW!
zhb|BE53%}T=>!(fuyPlcU(mx9-9Gg639H9o=E3CA^}+bC`W=@3Vd5}<z`_M4j!wh!
zH%vXOyn&fZNI%RxSUAA^3ri0$_ruZ|dU}M(!|a8%zhL69d<>IE*AKHF7Vj`VEI-1+
z5f-25>R{q98a>`%{X*C{04$tf_P}XqISO+xPIYh%s0>(shuIH{7gSZad9ZR4=08|G
zz{GLugvh|uLwF1fu<(ZIhq((TkBf%I53Ia|xd&zrE`6}_A0`ju!_on)oP)`u)3AI4
zvll%*5-Lw%@ehk<*!o{s`hk^C==Q?m8&-b9>JeD{!_y(UMkpT^@38oW(NJl4ISk{Y
z)39(tFLz-12)$fD*AKT3S`WkA0}C&h`RMvE_)z^YeK2!i=>bDMngA@kVESR@1<V{Y
z<rrLe`v;m2Vd)NLE`|m)0oXV#EIwiSVCe}>Ih+eimoW9P_=klXdOCz_gfd|I(9<u>
z{jl^8RgOWy%4e86Vfh#qZZP-2)L|HaCIHL#u=E2<S7^#`bD`+~rU2$Y7#|l6ODDvt
zhnWM@hd%xT^EXT#E*chpuzUVs>Tu<Em^_Szr5l(zu=)bVhs6g>Jvt35S7G4{b2rRi
zFmurL!T7NJ4XbZq<rPdGRxZKpfr-OtSh|6wQy2|TFVOr%$Q`iq9hP2U;RkacEI+{P
z#h{_-238Kk`~%ZRC|$tPJ1iezm`^|e7Cx}>C!idsG)zB?hJ`;)4NwVKdkYqBuy{d_
zPpEPX3Z@@ce!|inEc`Gupb5az8!Y}|?uNSqO+7jn7XC2(F!NwEx_Wdzth^)Ez3BR3
z{)LM}(-X1g!RmY1I4?{ejE0+wivi6iu<(M#KipkVd0g6I_Q1*km^xUv!1%amn0{D1
z!0d(PKSKEjmcL=;KdfDV?jBtB!_0@Nhs8V0TwMBK<pnIg!SX#!oapoq4PW&379sm#
z<qeF6*8@=V2<eCU3zqI-@c;`Cm^>~TW<JdSF#WK2hs7f-UeV2gmAkNTg82g$4zTtN
zTpgMLFmqw?1uF+J^g;6znsx?QxdV$gm^)xJEI+{1q3eg0SLo>!<`0-SEIiQl!}zdp
zgoPJ8|KV^qOdpJf`5WeLSUC)nCq%>I4@MIk|1fi5`e5lEmJVR@glJfM85W+f@PnC0
zNI%TqFm<r@6f7OU%!kRN(=h+S)WPxtEZxD<Bf35qALd_JJi^R_(Xez1Q;$x=(izPE
zFnO5&aiu?)I#{^{OE<9afVl_Ou7jz^MZ^3J(}znPx;QM{VCewnA9QuN_%MC2d;-gl
z#JU5PZ(!jK^9PK^WiLz|R(`<34W<uPpTNwAsYj<_@egx9EFNL*Clvp%b{{O<VD5pL
z2dmG}?S=7S`e5-7ORq3@!DyIzbQ<PPn7`5G3Grd!15XD;mRm4$Vet?1Cya)f2MZ6F
zdUP754n6*1`5#_BqHBcmVdlfa0T%8s^I+i#RgOWy;u|Ib<HO8>r7M^`It|Oeuzm|H
z-NF0?OCRX^V0@T<SUki04J$`s<3cd?FdF7xnEznu2PRHzdVr;Gn7?7>!Q25e4`wfn
zhJ_Ezov`v2R(`|61EwAq4XbBi_P}UZJqU|OT>4@82c{0@9@sbnE`2a@xc{N;E?9iP
z<O$KR_=edJa}ObXFnL&b!{VP<cf<6<`Va8&0O)!}n0dJJ53GKI#WPGDEdRpHhs86D
z52IoE4wgS)d|0}Nl`FW?0Zbeg&#-WViNnmp6&^5m!O{=R9GH4oy$eg%Fn8mkVfMk|
z1D2m)_TbV76NjfGX!!|GhcE>MXju3YTi>G_2hTUqbPVI8t3&6*)WK+&J7D2~t{$Ba
zi)VE8#D*Izy}-<c<s)MC!`uzi52MlD0~1H5(bGFDU%>o@t{$Ba%V)533!{m3KTJO?
zy}<Z{${|=h!DyJfVD$y;9syW>fXTyXc>MrPFEIOH`3RP;VE%#WgVC_`4&%dUn7;|d
zBP@Tx@+qtwMfVTPTyz?y59VH&KZ%Vun0at_!q$yL%TJg*AsQBruyjMHzDG9?woU`)
z4p_LLtHZ^IwZCBQfR*PkJ}!N*avP=|7H+Wel8}CwdRV-{!XF+V&~$|>A7Y3@%S~AN
zftd%h2i+gAe2cCg<}X-$;7X@3{jm6gse^?F%p6$0M7JMiFD#$H_^|kZ>4VYe`eA%n
zy$3J%q2U0_k1%z(XjuIZOE)n4VCf2%KA1SH-3H6&uy`R9|1kZq@Pp-Bn0c^xgM}+h
zJvt5ZKa7T%2a6wAc%tir@nPW(^FJ*8!OVfV1EwCGhSwj^^a_i27!3;#bbT;BOg}6i
zz}mksJ}f?9>T%Jq@P@@F%zi@Y02b~rb7AR-SbJghFM2q_(gVy~bQ%^e=-~@@4=g;<
zHKOrh_Q1<Os6JRaLQ@Xs!rTkXf3SFmiNoCi*MWlp&2KP$FjeS$m^eBOD`#Nw4ht7p
z`a@Tb!H3!p3rBc3K*cfCqY1#&!-Qe!k=XnXOD8b*5?lVm?1SY~Sop*Egz`O1Ka7Uy
zgT))VzhLTc(XjA?>4*6XmXBcZg03GHf3R{N#)r``aa`pP%%3oMm_K0lz~Td!{V?~z
z)Wga_Sa}9(&%weMrVdU+^9#&=*!gd;@&w&{xIQ!n%)hYqC!zcY(+-O#n7?8DCzw2;
zd<2UxxcyN7z}j~(bI@s6c*61>ti1qpKg?aY?1$-x#UCvF!_0xj8@jzPKFnNLJi*L?
zm8bCZ2h)H?!`ugpFIc$2(lH_ZFymnBgJJ4n>4Z@Jhq)7GFU<ci{V@N)XqbI48kXK*
z;Q*_rVBrrdM{uPhSbW360T%8s^I`P@EIeT1a2i@Z!r~D<ox{wBt4CwN(icoWEIeT8
z5k|wp6-_$>OdK}e4GRxgc>xO-Sa_i8hxr>8|FHZD(~lmW==$M&XnujY7iJDzo&W<L
zPEd1T;Q$L40@@kS^9d~82<gKm4=YDt;fG5dOdQ>vq}q>e4>})a4=x(!UKkAzPh1+%
z#i8jKR_^0W@96rVe0V;GngdH$M5hC&K3F*h(~q8?pw?kfuyO+?0gDe<IKc7+OddwV
z(kpEJ94y_#!k<t)!pw)e6PkWu;RtIN!uT+IVKgjX!tyb!{DI{=xHwD$8Vz$F%pI_B
zAfyk?7zUX8F~T21A$9>+IKsjY#>cK2QxsM{!}O8repq<{Pd892VDXAxK0u`r6g1vo
z0x*5B^ouL~z|_Oi3oPBi;u{vfuzna!Jvt4mr(ojf{wL&qn0+uB=6;yHuzCh&J~|Cc
zZ?Juwu=*I5E@1UBx<2&qL3cMiKGDJ#-5i)cbaxW+H!OT%?n94%n7?581l?X(yur#%
z7>zEDt`8R<mj7Vpz|u7?^)PW*{zY%+pz~qs(P>yb!15nVA9}h%*9YUn`unhQ92V}d
zaD}Nur(x^G;NsA79G-sQ68IUg_6saNVeZGTi-7?azcBsqc!s)zkbd;^0<)LcaD$l-
zOXslsLaKgPyus1|Jl{g|4J<xj_Q7aac>}W_mTqDCVdmmWhZyEU!wr@nVd)A+!|WwQ
z6DltW>4$|QL3QW~py34z2Xtvde2nlQqybGHo}QrLh$c;t3roK+nxF=B1yFm*^#{!T
z#OjBY7qIpsto|W3{$ceLEWN?>6C3}qd<M(^u;76C1D2m)`2;2pqhaL-EdRjV4O34j
z|G?52%pb7w16Gg0%z@D`dto%poiG|!Z@|i37!5NIrVmEL^ugi_77j3TVC_kmI+#9m
z8s=}9edzLp_^|Q;R(`|66Sf|JkbYQs1=A1X!@`x2epo!i(hW>MEL{=O4@(E|auQmO
zz`~P|epovn7SFJB38M+=hs6&p9%1bWLi%9xu>1$pPprK#{jm6g@reyLxO!-K!O{Ud
zAHx(7pkeKISpFeZKP=v1`4MIvEIwd-Tr^A{EFHn}J&cBl6VeY0e;5sG2cd^AhB?r3
z8|DsJeuwd4{=?7*6@bMTOdrfXn0{Ekf-1+Pp!UP^Kg{1Sb7B2LOpOo`nE5dEu=ETw
z2cil$3DXC&2j*^Aek2r+F#BNX1{VIL)}t_U;4X!R3(P%maU2YoeK7wJiZ_@su=s-I
z1DHEt`5LAU7Y$Pf3vU=7W)7k7gVhtT`W)sSSiHi*6PNvPacDUJa|c|UNCwOvV&fg%
zSeQRyG|W77b-4J%#xt?{Ni`4VZd~RQ5{JbXOdYA_!SX*$KcVo0m2a?qAWS`shKa+>
zL#JWs9oGJUg%`}-uylZ~55|Y(Ggv(hEB|2f=-~kqhtaTl66SAMI{+pR%NKAy4hAgW
z!_0%_J3{V<83zjoSiXaa!|FpA9~TYN51WUAg&(Qy2v|J8+z*@g!(}flo?-1gbp5dK
zgNdV?11qP{%L|zOF!N#lLDvr}Ut#q#jE02=EM9P>16cY&4=0$rVD5+0Xbyn2vtj86
zmJZ>{p!ElwkAnd-2j*^=yD-ut4*jt3fu%QCJqj}yhc>t{ESyQT7j6Kw+=ubu@r@=9
zlSijv@dguzrF(chz$9?cF#p4(VdV&nPl(2;p2+w^w;yI7EFHu6xM+0yVSE^kOCMYu
z=1wRLa}O+D;p%WOVD5zF6PP|2jYAs)ti2Cc2eluj9yTuw<D=6s|H0BZEFZz#4@*aI
z^U(}|<y&~U3tK-4rD4j^%T<^-tbBmgW3X}vmTzGB85TY;edsjIeK7N2{Xv+2VBw0c
z4_1D`{0s9BjE02|hCY}8EPp|1n0pE8gXxFTF#o~Y&ro5aDVYCZ=>Zm>#2Nt0=dgT_
zGrVvZ2#a4>yuti~UasKK&VXL-!p0q7G%OwB(hsu-?tW-|z|sTE99TS~n*&R~Fn_@P
z56w?7^U?Le_^|MX>4W(P79X&75w3Ir(+^9puyz~F->~(3F#FJHSiXVfBUrlu7O${$
zj;;^JhqYT^>S6AI=_BNSm^>_eVdFnAJ}f=J?1R(Lc!H^er5~6-VEG9ak8piZ2D-hl
z@PowztUiS*$E2X~3u|A%+zE3B%paH<AtEq$!NLnx|HINDOdg^dHwjz+0n5j*c!0Gd
zaO;G~z|sjUz96E+ld$}O?tfUkz`_~BL}>iM!U<Mx!om}lu3+-$`eE*d`5P8*F!#Xp
zqw9n7q4vZ0FneJ7;PD8HXP81L4YLpCUReHzxd&E{;EG3>eptGL8p{AHPY9OhP<O%R
zePQ;)(i1E`VCLeYVdV@=ADo7U56nNf^uxjl=3aO?1=WvBA50vUPGR=I(l0DMz|Dhc
zK%-&)goQIK-4V>cXy(A|gXx2XCz>)0E-c-^%59i?Vftb2!O#d5fT@F(!!ZBD%z-My
zNx|F;i$_>E!qOKkd~h0oCINE~nlM2wEc{{N0CNXHjc5vB<pr!9M3ctJMYj)~k5eO5
z0$n|^;SY;nn7g6I;XIEXE|125#}hPP!^#yjWf)v^eXw}PP>4eS7T<9H6Pa&e{)Uye
zFg~n2fYoPkbJ6UF#V@SBhUtU(2bLdT=Adb3faMcdxTEWXr9+rK==x#d3$qti|H1Mz
zEMLLeU+DT_<ut7PMo%xW_6JNIx_+2?c)1Df_rTl(6GzvF&WD9BEIeTPVD3j(kIsjM
zGb|op=E263(bdEFuyBXzhw)+lhJ_<6UBL9gXjr_!(jUy-==A|i9ZVmLhS>+>!|a9e
zVfLb{gUQ3nD;Nz6cbI*!aDc@VOdpJfg)fYTg#(O+>4TXM(+8tr=@nM*z}hqD;Q>ph
zFn#DWEd9a45AGglIRdi>T_5bcAXq%Z(hsb^0BZ-J>x1!O{)FXESa`$QJur8`)ML<4
z_rb(r;ROqKm^dyP=5JVffyE!JJi?_9CJqZPm^rX;hLsyIb%bb`d(qPkA$>4;n0>JD
zC$;>B>4VX5|G+FDK*Q1<%>S@>gS96JXos(3M=vLF<vVoqVEGmnA25Bee1WbWoexXj
zFn7afSb9WPkBbk>r!aeoO-Hc!gVi4}dtvbb<KwayrXQA{VC5#RdIOg^u<<%re8SQJ
zEPQb3gQZ{CxB*N(jK-x8E)H!kz}yW>zp(raSBHZEb1zIidOE_P4K54|KbQc_9kBR-
zi4&q>@ed0RV&flH?!ffJ+z)pL0gGYj4VG?U<tZ#b63`A`M+u8>SiU7xe#6X#)i*Fc
zjE1F07#|mno=#xt9Tu;+^ufeo{)d$VuyhYA2VmtYOg%ac3xAmZVKglKh;;`n{$S}B
zrXQA%(d~utVg7~jVeW;|uyhTRN2g)t!^Rz8;SSReOHb(fV0@VW^q~0=wjLOkj$!p9
zOg%acOD`~Y!2FLMuju+<d{{n##TU#zV)euFFHAiw9$@C<qG90<^CwIl%s;Sjfr;ay
zVfMnzgOwXFb8zW{#VahlVCrDz!OVf13sZnb!}BS$Jp>C!SUx~gj=_b6H?01I*$Xol
z?j8)CXadmm1M3IC%t2FylMC}FEFIxgh(iKB-eKn9P>vytt{)bT7z&9LfW<G7%HjKP
zVCqTM57h{ZFBlCKCP=~R4_LVcrwQo9RsO^J1q8Ii%S~9jAC@2B^&d1{p_>ECx3F-6
zrFR$~CJ!qI(e=aXNmzLaD?ef4F#n^c3z#{u@P(xlSh|JT3(G&S`UR#BM#IWwc)o*5
zz|_O^!~6r4#-L#ShDpHm!Q2lM$3?@!8|EKad4Nkjx;V_gF#p5sh0Oz@t4HUfhcB!i
zhtcTj(fP3YA0`g-54yQ9d2|{UUNHX?TkgZmfyEar|G?8NOaTEJmfmosQv%xI_f^33
z6B}-DbD{YQ=6_iK9WGCZ0o4yH?@3KRu<(M#7cBl@<q9l)VK@V7FRY%0<s(@9!SXfC
z9CZD#b^|QjVCffKAFg@?7G5xS!~73350(#L{=u*xT3^H3o3Qc#rVi#GxcM*%C=Ih8
z9^cULfRz{M`2wm9Ck3mwVdlc@$Eg7-0Shl!`iJR<#T$%<Du+|Baua4RJe;8ZfSU{D
z!!<$~uzUjxcUU<HOAoN}99R6IrwbS#mL6f|!Tbfa7EZz93nmY956nJTK7fS>TqBeL
ziywIWLB(PA!pw&X<D_8a56oWl@(ia&m;_q*!t}$WanewIu>Jxp9$+*qAL7&xm4KxW
zSbBr=q2@!CVNkGeg|!=C;RaI(iw{`5!o*=TEd9acVd)Si4>KR;9+)_shPn@y&fw(<
zTE2#-Yq&-T1D3C0=?5apzyM1Z@bJKO9vsvjn7?7>!Sut-BbJ7_17<I=`q0~X=;27n
z99TTS-46{{Sb8R;A69<C^uy90A$>4;n15mR!OVe`qcC;2XqbOt{($FOs6Sxo3uZpL
zIk55ymd;@2z{)=ujmuxK{0}o1mhNHZz~piH2UbqO^uy9UtQ`P5#}QXLhPe-xeqiMX
z%-^tj6=psL4Gkw)K8CvoDvrzFFmqt>4=+cd`2yx&SbW0Nqth_=!o*?Y5itM2>T#Gn
zE*ci@FdFV292#Kh0ao9@$`P1*VetS9N0@qa<6!z><qu39EM39UCrlisA7(y`52IoB
z!QvIhhsnceSow~={u`EVU^GlUIt_Cd%zRjQ!{Q&FkI*$j`7nJ@F5<p0X#B&(anb1M
z8Rj2!b8+d1#V;)V!SWf*99;U~;!t<O%O6-d4@>uOh4>k;as%#esJZxcA!K3tVfhs1
z9)t?)ESNsHy-;PabOkdHDvUwF!V8v9(eok991M+60a*N@hd(Tx!}1ZlJr30eqhRiX
z`2&_;(B*OIhvhd|K7p0Duyg@)AIzUHbKx{Jo?!Z5{)hPo#>b@}=5AQ|1XB<5H{2c^
z3|Kmaxf4d?P>xd=7H%;8u=E4V-#9hGB%tXXmJVRTgvx!GI#@XY3rCnfm^)zVaM7^#
z5-j{+;Rz3492#Ni43<7%^%pE0VC6Cn?F=yeu<(PqA0|#H{lmfw7T>UR2&;c!{=j8F
z%zRjR3!A@#=L=YQgu?)sI(YjJ8jrB_3u`yv(9Qs>?_l*U%snvuFn8h756geB_(oR;
z3m<fG7#|j2u=E2{4|5kR9$@OxX;}J#xff;*tUM&7A7&n09W?x4d>9Q=hm(ftgT*_n
zKLGO&PJK`bSbBw}cX+r%%O|KZ3<_3$z-X8`u=s$j4}ql<bOT`Sg{g<R8&+<>;sISB
zjE^2ZFnzH61d9(?{($L&(J*-!4YLmxZm{$LQ;$x=+AA=1F!#X95nSaqx;e0X4|6B1
zJVIB8ix2ZR%sqtiJ-Rut^h&Bem^m=}VdlX650fWE!}34O|LExmp3l(?f|a|l^arbV
zVaj0U!qXW{0!qX5!O}e}9m3K9OdcMtP<=287T+*F%$>0G2lEFkeZkbhXju6HOFuAs
zVCfI$9~d8|4@Se(!^$66c);u<HvPfOgVi6f{0}RSVe;rSY`qAq{D=7$rVbviFab0g
z7S6EvfrTf`URZg6rVYVGE1zKcVc`J_KZFKU7A)Lh{)MG~Sh&GxR9y%j%)c=EVCff@
z?hq=8WI@YuT=9*|6j(Zil_R9)KUloO`~wdUTo$5<!`cn7_=f3+<pVV3a4t+eEFGZd
z2UvQBse@~TGGOwsd<(M|<_}o>!PP<a!6;aIf#oAu{K4d5d|0@`#9=fnox*6CILus_
zz3B2VbujZ`{)5FMtlbDV2Q8hW>xacRtQ>}$1Jwt!2c{35hVB1=g(FNJ7M?IZE*e(9
z!^(eHet?e)<I)Hdho*a2IK$$PQ2K#shv|cb7c3kI`5QglVetg>53GEE*@I5Q^uyG{
z${Sd^gO$VR`e1yReK2(}edzH3Q;$x=^uz3hmpjmSM^}%F4@*C=a-7(BhnWYf2Vmg<
z8~1^w16=V4i#J&Qhs75xJYeR-;uXe+(XjA`(J=GS^AF5_Fnur@W*;n`VdlWn4@@7d
ze1PeL(XjA`=|>MYcznV%;GkjUJ<NVsIfz3!PGMO5!`y>YAr1*x{)PD)hcXOdn7d)|
z4-W@uzJ`foXoL!&`yZws7Vj|gp~^8RSUkY$Z<qpDdVulKX_&dN^am@iVSJc*uyjI5
zKTI9WUU)p<$d530!~72qcc^+o?H`zam_ArL3MP*3UR-=w`46kdaiw>3{jhk2w;!PK
z4J*H3;^_Kde3*V%yBQXKu>23p$FO*U>4Vcy`(WV&FK?jX2U7<#AI68#u=EAf53?8M
z4p{z$xf7-jM#I7xW)4goM#JQBxgTaP%zRk>h1K7L;uGdR7!A`83pbcQVE%%+3!R3Q
z*D!IIdtv*6Vd)Dd52vBw4@)<&c!$Zu^ufXbCJv{e`eEfQ%wCv!SU(t+Kj8YH47hse
zz9*<Kd_MuK9RU-^pke6*O2fhnW<QLNp${qmlZUB?3NygU5m-3@6URlv^uyJog$piy
zuyBEeGb}y9)WgyPtXzkQqtod52Id}^K3w@27Vj{1FmrLKhsnceSh|OmH?a7Ir+c(`
zh3SLQF!eC^!)REzz{3-!0gZ-*7tDT`J{S!P2Q+OMTv+~xg*VJxSo*-wfF=OT@34FT
z(+3Y%XnH`?2Is=`!SVwv|HH&_m47e;VDSL+FH9fIewaK=J&cB_gU2`2oiKNxmj^I?
zFd7!luzD7z4_2PRXqb9*8a<q0`eE${m^`{ZTzpu#!~6^LA1?JUarATucLy{)2!%h)
z9GH7y=E2M%q#x#9SUkYe6D(cB%!S#5PQ&6E){cku17PI}EIiTm;o`&WCs_WX82~Fc
zVKlMrU|2Z8`WbM2(C~-32hBQ+`4^ZvSUP}(CoFu?^~3Cgr4#i01Jj2q9N_MT)<3ZJ
zBFr2Z4U0z%`=R<^@dJx5nEzq=VCJCfhsom-$K`IAdRV%Fg#&E8FKpgN2kJgp{)Xv;
z)6n>V#W&3VuyP#6$K`%lxeJ$vng=WYVD5s2Gu&P%1EwEV4#Dajn7?5C02m*t4Nk$z
zCs_Ey+F39@%w4$ZA((zx{~Z=iF!eBZ5sC+xe_`nmR$jo;1I!+{!_gS9aT-{7!R&*@
zBbssyE-atm3TF%rPyv`fVc`r*kFaoqD#J;^^uzoME9Z#SkM3Ud_=lwroDP6Wz~Tv3
z-@(EW)*e8&2da$$7B4V=!^C0gVc`O!Ve&8<R!+gv8{8dG1u#A=e?f&YC|LU+mR?}#
z9hMGY`4K}SOaPkiVCrGv5Az2sTwu!4X;{3$?1Ay&>R{yvTmY2;vkz62fdS@!T=57K
zhoxT_4NFgObD{o2Hy_4_xfkYNSiHi@FIc$1)Wc|)`(X7wOdJ-UuyO%r9!wschQ$|5
z999m&_=IR!Jq0U&;Ql705gINq`(gfv)jzOu1y--38wc}0Ec{^ZfSCs?*I??<^~2IX
zESzETuzUwMmq7Y~r309GuyBBxM~H^m2h#_056u0z(m%{zSopx=4Hn<9@Wf?5Oh1f<
zrCS&ca|g^_xXgjYH>^JgvzJi$4=d+j^$x83fa!;|BXQXaD{o=p1Je)lKP>!U=AoMd
zD>q^8hxr?pUf}wm=@eZ*ES<vah1Dl8b7AJd(j~fnn7uIluzU*(cUbx(RQ|%k0cJle
z9m2u^7Cz|q!}u`w!O|hD9D|9&%!8>%r(ynu#UpGVFf3id{D-a&#)su|Sa`tF1x!Co
z9Xbum=P-L=@d5KU%p7!mFg`q7q4^s<{9*Di8m13M!@>m~Pf&AU;;?Xmxf7-jM#J0*
zi#M1!I*m&nrXS`$Sp30gn7uIfp_>ci!{P@PUN9Qw4_x&ROh0U#50+nGG^||!i&vO>
zbQ%`VuyhA=H_RP`(l0Fi!}P(_!`y+(eptA|$_aFL!Ru9MyrRp)?17aV=<<ZZ9p)}r
zIf1SpCXX%-=R@6vD}G?<0<Ip70kapqyn&VXu<$|ChQWokyI}bT<{nu3$IyT-fL6Z3
z(gCdeL068>huH&5$8ddU=A!F^@nPi#%-yhXhoyg*I$Sg?oM7b;%ssGh#HA0GK4I;1
zn7d)=2F8b(k8Tbueqres7N4+u05cbtewaUD<p@katUUm8Kdx|qg&Qo~Vd`P&0apLR
z!voy|P(CdG!`uU-Vg83I!=Pa9g~c0;hKa-cg`p8D08<Ck2MZ^dzhM4=D#uB|+zE>Z
zSUAAq5vN9|1T1`E{)U+YqoK-hQn2ua<v*A|VDS$tr*Ihnvj?UR7A`P<;L-;Zhwa~h
zt=EL5YgqXY3tyOe3>unGVeLJbKQPqc5P;=7n7y#{2;;-z2SYp5URZqzD+gizhKa-S
z6S{u5xlnUp@c<KtxeI4HgzAIkH(0p`i#J&M!4-Zmb7A!q%wAZ2g7yF4@rmXEn7OcY
z4pR>^7v>){ZE!9uy~6SfIt`PDYrw&Pg%eCY%sns~hc@`V&9L}E_YWccu<(MV6PP_P
z^I+vGOdOqt#T!f<7Va?rz~U2E`v4Zcu=EO}Vd)=cKFpmkdtfxI+<>K9Sop*8J1iby
z=?JC|orbv&*4~2I2TKnyccSZq@nQWwSpJ2DJ8V1`rVbYk(+7_SXuAfNdYCw@yn?wK
z7XO6Y3)2q|Culx|haXe{Ec~Hd3<_pHOaj&pg}Db7|LE#q>S5^}M#I%X{SQk|Fmqx0
zU^L91FdAkKj1SBA@Nk7`gwineu<`>I9&mR<_iw<)6`<NMC|G)hnFrGk6GsnMbOT`N
z4OjfZ!U0_$oDU5zSh|Dd2berA|HI-JmXC1Rk1PMc+y(P5jE0rpF#BQQ3g^QZ&~yjO
zf3SFl(eQZ0F^>%kSD3$H`5opTm^iNZgQ<tv19KKE-^0v@#Vbr6orZ-2%%8;Odze00
zd5hj&go)#dKbX5<@d#4~tG8kC0Fy^|H!NOZ;R90-%fGO2gykc2{V+bvUYL7e;;?jx
zOFv8>EWY9Dq45pN7cleCX;}RP^B*kz!t8^kD|CG@K1>~q4+}R~dVqxwOdg$v*#{Gc
zrGNB#3?`3Gqt}zL^a@KyuyG-DeK0;Od|=@KD{o-!H(0wCrXGWamK(5mgQYi^I#~R`
z%Vn4Zl!k>9die=652hdHKBzW01ycueH#!Yd2r~!9N2g)t!2FFKuW)&EjZi);oxtJ`
zM#JJ4R!%^bV^FaCVgN1AVDScvPgpo%XoL#D?Stxv^&`;Z1F9TO!NLWWe_;6)#wQg2
zu=E4d2XikhAHwX%8UIlC!onR^j=;)ISh&H=fjb<H0n5K=!tis=aHSu3{)D;%mVaU8
z7_5ASxdYvNSiHc@f$4*VA50#W4$$?((iObChq?on|Iy2Hbp0?stQ>%a6O4w*6QW`1
z7glb-;tQ5OaOsDIJ4`)X9jsh{mcwvyTnwmsxHOax3m>>R8UvP3Vc`suhPeku!^CmX
zFn_@0Vd)x{FJa=iXjuAy(J*%tYd<W$Vd);0USQ(5?1hQL;veR2Sb0FK`(gPHW*^Mm
z#HJ%yzK7AU@&M*OLNq-7py?l$4q@R*NI%S-Fn`1BhlL|9{pk8&<sB?N!1yrtpqm5B
zA21r`UYPwbaddq!J}kUpG%WsM<36zbkINr0_ruZ~%v=}^OBXOc1`W+;u=vL*k0AjO
zfVm5nULc|j3^0Gd+QBe!Tr@1aVc`cWPht5Rmp+&{tlo#!AF%QlR-O}z2bg^@8Wx_g
z`~#~8VD`ain7J@{SbV_36XqY7dJGzx-eKt$R`0{if#oL{jiDb+0JeS*mY!hlKvRan
zg}EPA55U3`=09BNA0`e9Cs=&L!Vy*<!u*3_G1ULCatr2OSiXa$Us!yg>xa1yM#Job
z(J=F1=@eZ*Odb|qu=s+dM_4+>Wk0Okg2fN4-2==2F#Q<zq6xs<2lEGvCRRUe{0A1!
zu=s<~Xx72GFn_}G2|V7Q<pRuIa1Ce-m^)$V02Y3*@&FbOFg}`g26S;)yu<P@Og}7L
zqU(p153u-y<$qXz0w#~H56*}B6Bd6keK2>y!UIOb^+6dh^I_=&mXBcZfSw+p+At|-
zK7i#TSh&O7g{c8m1Xgaq@)0aNP*q{!LG6WwGt3{bbd5y^iV!?rpyt5hAC|6A6l3E+
z^}(cI?uWStZaz!_8Vw77m@teF3x8O-3X4yeIv5QLXBZ6=hlK;o99X)9$-`(^IKko>
z=5LrkVCKNYVfxT%SU!cv8#G*C`eE|u`rv%1dRRV$i^CW&c^Ds!hQ%Aq9`x{lm1k($
z;9OWa0CO)~0?L4eBa{oLVESP31fyZ`4$IGk!VlfOuy}#xA6UHvw;0NRxffj?R_?;`
zJuF>8wP8@O_=c&2>BmrrLjV>&Fn5!xA8sr(o?+n#YY*T|7f_8bf5P$!Og~H?q4<Y|
zKP;YL{(+?@So(rmhe<)*4Xck~;RtgNrUr-zOdeKWz~UPgj<E6+<}QdX1PM!jFn_|r
z0p=grxB<)_giZ(xmVRLFhJ_nUAIuz>e;~RLBuqa{9jqLOnS<^hgicfzES_QhhS9Ki
zKvj*&gVkFweXwwV<wHyzs3NfR3iCINhS?7fXH;!)9xR=}?1i}lrT}IhjE_#k)WgbQ
z7!4~waQOqCE@0^oO2hLrbo>IB{V@G7^{{#ZR)4|t!QvO)-EcnCURZjCwdY{+u>K&7
zk50q%!NM69AF%X<t{%pRrE6IJfwg~N<snQQW-d%0oQAp^#)s*H`4>*3se`G9nF~{g
zo=;%$3JYJDK6Dyp4on=zhq)i!T$nmoxduz8uzUauPZ%GTE@ArMG_?H)3vZY{Sic1(
zPDnp&{14_1nE9}M;jnT7CJv)v{)EjRqqiSm?t!UCr(x+A=1*8T4YLpC512g|`l0ii
zFnuuh!_pNj-e7!O`qA?%JUu}3A1?c0=E1@lR$jtrSiHmh2~&?w!^#JEenn5O(0E4I
z2jj#12@5Y+e+rg<Vf6z{Jvt2wZ&-Z6!Vy+3qN|7TVc`O&q45DT4_2?i)ML<4f5O5C
zmQG>m2o|px`k(@^aD#FQ?)QMHgVE^e0v7(T^a!g5(bd89!_o;X{lLNt=6_iJ#}y7R
z|HAac;t3X?u=s<O6EJ(xX;{3&%YA5ifu%#3x#;>}d|3Sr^EWKt!^#DiI$SiYoQJss
z7A`RRVeyHf9~%EKeXw){(+>+57$03fES<vA4~&M1!@?03AL#mF;Q~u%aCK03!NL<m
zA5;M5E|@=IG_2l-`3I^TCk3nbVC4nO{V@08)CiS;rB7JChuI4YKd3UC6fB%z@efPK
zuylY^BUA#ty#Ol@q0%@hm^)$d5A!c9K5%M;NkHQX=6_gzf=Lskq55I!VBr9dcQkpJ
zI$Si&Kd}4`D?i}kXc}SugvC27pTp7*EL>piIyCL@`=R0a3+i5&KDfEidJJ7ZEWM-0
zGps&`$-~V@GXUl<m_K3h2akVfIRrBYO*;cDy}`;!SUx~+$HDx8t{+zZ!}2|h56eHW
zbb+o9#)r8RMx&Siu>1s5k4~fKe^`El`4{FMbbYw^uyBB-A6PoTr5@&QSUAGm3yUvU
zd4x+pEdF5O1ycvx4+8TaOdJ;tOK&iD!_0-%3ot&qesuT4!VQ)VVE)1tkFfZH<y%-h
z!u$`bKVj<7?T5t=%zZF@aCOl1i%UODA51+g|H90Lg)6S~i=O{s@dwLKFn_`P0ka22
z!^%6DyJ7JFb1$qMhuH(u2cu!;!t+10{D8$fZ2k|HuVMONG<tgx<_?%V%pWj51`V|j
zmTu792@5}%Ji30E|6%C`rVc$`;O3wk0F7T*K7pA73pcoOm^<MDs0^69VfxU+0hX>&
zbuqx#Kf~e;mj7Yl21_?Ef1>M$<sWo?uzUk6M`7`dt{>(um^>^#Vet(s2Vm+j^h4_v
zSp5%E2Olqhh8uc(z|4W=Ync0C;Ry3DEI+{F6{ZhH!@>tvPQuKC`2!ZNFm*6}FdCL$
z;r2q^3)2S+2RIGWh(^QQ4|6vxJYn&JrX0?N$)o32SUQB2Be?tzGYIB?czFT!2h4m}
zxWLrIX!Q7k?E`_8+vw_Gd>9Q2Cs=sH$`M%k0c%IV)T7g|bOCcW%pb7uhNWX%?uUgl
zEWg3j!OVr*2MrH6AIgCFA69R};ty7jK$T(K%Lx~UF`(rL%-^v1gGm#LKbX5=>e0(9
zm_K3i=rk;xVC4oZUSQz@a~HZk7#|jYu=Rg1^9bdCm^xTEqM!Eyi&tF!how&#jUH~W
z{Er?!Fn7bk1(uIt{(!aLVfMq^2h#_mVdAiK2eS{BpJC-E%zT(W3>uogVC4*qhM9|@
z9x4D!r?B{j#TTqRz~yhKcKG>4Fg{E@%wMo@g~_ASFm*6-*f;<@y+G47Odb~v^9QUR
zfYqlkahSQV_(3-ZmX2WVhlK+?9-!fit`Fuvn7Od{g82(34ofF+e?bj`g)@3Of%Ugw
z<uA-!SiC~D!^;JjJ7DP!W-qK>LAMvCA0`hge_-x}<ri3c0_Go>J{S$t4-0Qt{(*^O
zlw&ZBXf(`xSUkYpOJw}R{0nn8EZ$-18qGR57p4woFN_ZhPnbX88gMXR`p~5b<v(=w
z^yb6D9ait4+Yc+pVd)cH9>#~oGd%r3C1Cn-g%4C211!E_`3@!yqhazeJ~|Dv2Uc&x
z+J7*8Fn^-!gYjYIE6jdax`*XALh%TbhlK+y9m3KP%siNVFd8NgD`#LdJUv6xKdy8L
zt1n>nE{ukiE3onm<{p^6FdF7RSUADT8<>A!`5C4TrVmEL$|bnJp%O6v!pa3$yg;Qf
zC|Lf3NuZkt6URlP=X03-F!hAeFU)^1|Dx+Blzw3G2vY|Scc=qk;!rM}g6V_B8;pj<
zKTIDieZn=OG2rsh^a6`-SUkdLG;Q$t3RpbB(gDm~SUAAVM>hb<hpC6vTd;7%Rjxp_
z!6{hz0P{aA|H2gzVnF>3^9OoA0KHs7HxA}+SpJ9SH>kNVaacM**AEkir$cCa0j3W|
z!_=YcN6+`L@Pz3j6#lUM1ycvh$1ob*K6E}To?z(&rVmD=t4HU<{0)moQsW<%-eKi9
zEdF5W58XZtJ~X^x;RYKwg3*M^6Igi(3kO(x2$r7U=>g4Rn15mA3oL$N<ryqoVEG(P
zJG}i2D=%Q`VKgjUaK!`6zcBa0{0U1pF!#Y|4Ev$;r7-<4|HH};Sh|9XqZ<I_!_qIz
z-!S*W>IYc5geu3NVC{C8zhV9$cK#0*PB3#|=?KQhun<iE=3baTVDSm#qbbMW;tF3v
z;RZ7ZmTq8t7!AuuFg{Kin*L$(aPy$$7fc)%4NL#H<Ozwx;u+>&SbW3claPM&{0=LR
zVf6ta{jm6k+XoE~^l>C~b7204#WyUy!@?cbpF-CM<HPKO`4?7Rz-X9xu=EAf2cu#6
z9;OcFZ<u+od;qftrjH;E^*<~h<1Bxm`5&eZ7Tz!#=5Cm~(CvkZ!^$C8K7^SE3rCnb
zbp5dM9p+D1IKaXWHvWUI51kK-f0#JTJ@D~ASa`q`pwX~)3oQM@@((;+;Ls0`Kd3yc
z+=Uqr^Eb}&0jdw?Z}fTwrXOw(%wQ-Db0?GwuTS9d4K*Lmhbe^8F#p2i3#uL#p0IF%
zxeuxhPQk(%7SAyCFdEjLK=&8i04M_%&*<R<6UWtlgQZhgJi*)zHI^U+FW+J1B-H=t
z?!aXbEPi0=6&6mo;tSmzSopxqfyu+d6~-q>qq!Saeh|_JOBb+kgZUd)-oV6R@rlbk
zn0lDMVetaf4>K2CKg^$S8akf?(+^XRt3QCtd{{dKrXLp#^FJ(o!f2R(aOs1I!_qx0
z{$cS6%TF+MxM*0qhL?X(g~Y}mR5^Sf5iC8!-2qcTfQF4D!0JPo`(fdWEB;~m4yGSw
z4J`a&;;?i9lZVkT^I_(|#9`?LM#I7vCXY_T>m4)+nE7bJ7+hF62&<1^@d6763=L=k
zu=s<~u=ET|PcVO=X=8xZudwtEqv7EJ&DSt>==x#mVCfa+Us(Eql`Am!pzBBXH%uPZ
zo`Z!uOdU)fM#I7hM#Jod<qP!s38oL7hPfYR4va=uht7w^JFJ}#b1y6&Ve+tWLDvsU
z@34FWtG{9HhNWW|A6-ApoiKmH`~kBU7XC0AT|X>+pt~2AUts+*T=v7n;pW5YF{ni_
zc_<er1$Q@8KP<h%#BpkbO2EodSbl-|3o1>Jg1H|yPXnt*VfhJ{0WkN$+zX2@SiZoe
z4<-)F7qIdYW)80W2Ga+lVeW>dI~X4x9x#PCXqf%5bOhtW{EtH$hA=E0!@`5u@Q3*q
zR!+mrf#(Nk`GYGwVd`P^11!D4$}w2JgQaJ5hr`T;(J=qR_%QXb@PYBsX_&uZ`e63M
z(-XP|C?Dp3SbW3mfw=?L|HI{en7y$0hNS}-4U0!y`eEWQ`(WiP%pEX3ES#avhEp(g
zFn_?}5f&e?@PN4wt`W+B*$dMT^Div?VDhkZ4b=vxVDhkh3bO}R9>U5sSUA8nLK!gk
z!^U}F=E1@bRvtr@<D_8aEzBJ-^KjXVVGuN(!^0WMhoxr>^*97z<qym~u<*d494-t?
z2XKMG!hnVYA?Lv25f<LC@`qS|!QvT~&S3EfOAom0hlMvx9#-DK(mhNZ7Y&OanEPS*
z0G6+D>4S;G@+U04!s-!Nc>+_1i-zSB4F3`t?l5y<?uF5?bPNj*7$2q{M#KCI%kS`T
zgW3-hhm{L3_2~Lx{)Ocin7`1~!T9JjJf5NL4_G>Y(XjA^r7v{-u>1ydKP)`a)#2j9
z{RbTngT*5*^)PW*c)-FPo{pgAz|`TQVc`aohs6^t++pb(mQG>nU^L8r7#}@+VBvyp
z4@^BQ{lfAqEdF5Tz}$mNKTI90oQH)2tR8~JAIu(@z34Qoe-BFsuy}-t!{Z-KBg}uW
zabB1{m@-)UhtV)`Tr{lT0t*jVdlpWkX@vO;<~~^b!@?1k4q@uhv?I9C{08$6EM8&d
zBHTR)9jGj*e)Mz#iw{)Am^_&KVDSi30do(!xiEPc4U<PN?+LmaY5*+W;O#Z2e_-x`
zr3<KX1O-(G8|Q`P7nlN=xiEjg#L;P3yusohM#KCAi%)cYFn8gKf0#M2ctqC^<HOq9
zuzZhhE-atH)T7g|{08$cOg|xgFmqt#9n8Hj^9Y3-Og}82!|a98u<`?@4i^oJH&{O&
zmTzF;ic23{9GVW${R;~ZxH=pRn0sOA4XzAkE?fWy16psv!V#tlW-g46PQ&s!ESzBB
zfJ+^^I4r%uXjr_#)WOV!rDK>ljE30{Pv0>ALj40vx48TPiy!oGgM|Z39KAgNvmcgj
z;PD2v9~S>Gb;Q!J@(vd7Fg_vuuyBHv^Dy_q!v%){F!Ryt7g)JRNI%S2n0i=vz}ydu
zS6uZkOg$_fU^FcJVd;rrK8D6OEFLi24UHF=d(mlF_`%!@3lCVi3QGs*`e1yRJ7MKN
zEWN?}1DhX)sfW=pbuf3s)Wgy(EPi0_g6V_NFn_|r3#K1dAHvfEbY23c51od&0~QXj
z{019Gg_)1855|Y(TbMgx?t-}o=0BKvbQ+d^VD5m~4|5N?dUQT4zro6XSo(vti_z7i
z^I`D^vk>Lhgo{152Oqb{sT4;)-{eK6D!14uI-Mm&e72r4yJx(9<g{eqjDXHwPvU
z^DivEVf8x9f9U#P=?i8*j1S8{u=D^k4<4>CgP{2mmR?}`Vetk_2k8ERX@|KNmd;?~
z%`knid<0Vm*9T)j!x^RyRvy9J2@{7YCq%>29V|V<%p+DGOdTvA!1#n{c=$v819Lwi
zeK2`gJj2{ew0>y(!Qu@TkFfSM%zRuldU}NU1D0-J=@OTD=-~v5cbGnyIq3Gl+ynC`
ztlWUPA7(DB{D+Cd^ucLpc*F8JtUUtL59|NI(gR!{8Uvocpyt8i0ZkcBE-c@}+zay;
zP7P2ASa`wWk63+B?eO_OSp2}!KP=o~=?bPEorZ-IOdMtoti1p;2WBoz9gK$8bI@>u
znFotcm^xT`fbn58EM8&x9F|UC_QT2zSbGYl51odUKd|_RmE$mX!^d;cHA4BYegn)t
zm_MM>I4M~C!TbkHH!y$U)CiS;`4<-dgwiQg8=QjK4;O&O1I%1lc);b+7`WUCt3P1%
z04{T2=@k}Uuz3Sme8KoI8qIq6`48}R4qABzGZ&ZrFnL)1hs7W4{C=2yIO{K%0w@j3
z$1rnY=0T+iQZW6nasXxyEFI!9045L1udwidg&Qm$(DlRO8J16B=>e9HVdCiF2a|`H
z3v)NRILv%l{zSJQ=08~b0!G8!0ZSjSd;!xBOLwsL0xbW+XqY@K9AN1QrXNPb!WkC7
z@caW!M=<kX>d|RfxerV4aC4ygVdV(AJ{TWXU&8V)%s;Sjft3sB;SbXf3m=$zSa`$a
zVdlcZ6D|&Az`_+4KQMK$@PgS3qoLa16wF>&c@6VFjE2d>(kEOalmRmr=1-V=VKhuX
zj1Sd@LBaeDOOG(~VE%xmD-4a$@($)+SbW0LGc0@vr308cn15jL3QHFl_M!>E@(qlJ
z`4{FNxHy_V7#COg!o*?jf~iNRammBd1uQ?|(g#a7FnL(K!SWxB50fWE!_`CM2bX%7
zI#~F_)WO_KDBNJ`Vg7{iVdmlr7nnL&Jj3dLm^rX^Ba9DIk3mDr30S!eD}P}Aft61f
z`k(^n;SMtg7Vq$UfMfgs7Cx|Y1Qu?vb{R}R%%4ze;S@|AEPuetFLZaq@*!L!lmW9B
zR(`<T4@>vx;Q`f#LBaAXtiFTU2XhyO1~dU!ISq?%*t%ar@dpbRSU!ij1C}0O=Av1L
z;6mdc7GJRNfTcfJIKbS6&<JHg^~2g3F#p16n0YXFLUkc1sD4;{!DyI1Sop)-htLRN
z!Q2T;M=%;z9>d}j79S8@2ojdgVdWhxy}|qqvlo`m5IP|&n0sL~JpQ5S9agTx`~%U2
zNy5~@`~%~|+J!K244u&MgVh_baEGM>m^g+$r~oYg!omxde_-OU@PI1EpkVHT`42sw
z;o%Pr4|D@y@d7KSVetp6FX8D9>Q8k2Fg`4u!2A!RVd)a)ADDV{8s>jkIzX2v#D}F5
zSh|6=TVeWO;REBN(=dO+@-6x}AS`{M>x1#(`5YQf2GDwoP`?r8UsycD@&hdV2$erD
z`(fb=^9RiTuyP3|4pR@K(Zylu78V~cb6|X!J{S#4FR*xqr6ZX8Vd`+{hlMLlJuKb9
z(lIQ3!pw)62cu#26|DY%`4{F7n0s)QA24%Z;Q)(An7d&004$%u)Wc|)J7M}^G%Va;
z=@O<Mord`jmhWNZ4s84aSGd8<K~FERaD=6ISh|3zN2g)o2NQ?6AI68d55~tyL(@Gh
z|H0f13m;tJ027CW7c3vZ>>*Ts!NLg^->`H73kO(u!r~KVE{ulz7n)yT<(M9n4=ew1
z>4&)=R&K!j3rmMEakxD&i_vI!K7xiL%v@M~2A4<E2a6wE?t|qg^mv9D14|z;ccZ6s
zSiZrfA69<B%z=eF%wAY}fZ2ym!~6xa7gpZE;tdv#==$J%XgtB(2@5ZndtmViSC7U(
z&p)vEf~7}T{G(}ub7AUX;RiDh7Ju+`3D<$jfaMcdeuae}EL>pbpz1>KVC5dH{DARc
zDsaXdQ~@m9VCe_$UTC<&`~y{vlY-?#SbW3c5tcq-@qumtOdO^U=3iL2z{Fwkimo4)
zuVDIM`5&eqZa<FoaIka-(+Be}%>C%)0J{A!J}e!;^ugMFu=)h%PMCT)4bAVcat@YW
zVeWwCFE|a?2w}j|9n2pveX#TZGY=Nd5M4+l)PA@KG~Utqu=EHQhcaO47Un;6b+B{+
zRgOWy+zU&muzUxjVg7^JgKhvUeqsKEl@~C3;rRjGUuXuw`s=WK0OP~ze>CL?E;RgL
z@eK<HSiHl;5gO1~Q2ns<3iAhyk6s_5>1KebgQZ_s`2nNh@=$xw^~2H?EF59s3=20{
zc)-m;GXSO!7VqdZEM3CF1x-5xy7{pD4x?f52=gDhewaAS-LUY6m18jTVKgrNu<(M(
z!_p<J{SNa7Odg$vts8-*R~QX*AG&&UJ}lkB;sZv*$^~@wa6Ybhg(`rDAC!+l5ztS_
z{jhusqtVTWr4Mv*I3JcTVKiI<%D^QKm4?L&EWTjwg{21=AF3RKf~8AXzJr+q<Ks*>
zXePnZ9n3sf{K5HX>e0EdaDvsxFnzH6gsvXOhpB_<L-#K%-@??v^ucIY`2wTi=@zO0
z<}Y075T+iU&Y<ZD*8YQqA1t1r#=<FB{|px1Fm<r}2%}-<!8Jk|FnzG_gT)^#JaDB;
zsCGC7^9QW{hD#91faPCUI|Sx#LfT>R0;}&}{vg&Ju=qtU@6gi$E_cA(3kxThc`$KY
z`e5R)_=53a=^mDzVD5tX6Rr-*fW;Tg->~!!OHZ(HfrT$r8=QjagSB^H{)V@Apy?5p
z{V;=I?R;3c!~G8}e+b1V+@H{PCM;ZF_QK7BS%^l%(km=K!1TkE!SWG|k50qtYgjy@
zhYL&|T_21O3wKz&!T7NJh3+1hJ{S$N7u_CM`2kahi-zfkg+I*wuyP0H9$fli;SWnE
zF!!Rn2bX?Wdkr@32TMnU>VKGhu<`?DK1>`&!^CmXFn7ZGy)ge0(ho})u=Ed0r?B({
zOMkfRMHh#whvpkt{G+Rb@nP<S#S^UE29t-^!!Q9HG<rUVnF~ulu<`(1J1pJ6)WhN(
zT^uHlt{=vSr8`)7!om^O{)frK)T7g|_=m+8j1Th{x_WdzM!5?Of0#JBdUQU_zv$vH
z|DcPb^Wo_QT7JRgVdfGFKbU@)J{XNF-eB_RG(7!5C1Cc#+yNEFpkV4??tu9l7LOPj
z&;(%V2A2O|@ePZAH079FXneu!h4}-P4lp&KiopB_D+gfyftiD<8V?UzU%^zs`~~Bq
z)39<AmQG>m7p4xz$K?;0IE;qXb1-{h{(#Xib73^Be1XLiEF93qVeJH%J{S!XhpC6z
z1B*Xc`2#Z-rVpJ)4`-OU=;{dZVc`i22bjAE>4V9`$~##72TM<|@)f2IM#KCI8y|zk
z11vmY`47EZhUtgJFH9cR&W70!lZV*@(+A_j+zTtGVC^ngdV+-mEM3F&!)RDI!{lM<
z24)US9eOyz;uF2zgZUdK5AzSKK8KkHqha>LXjpi|;t^IJz|`ZSVd)1J9x(G@{=uaW
zCJt+N!Qu~QFDxEl>d<LeIKko%=5O@yMArx7!}P;wn0{D09;S{E4buk;2UvQ9g$FDh
zqMHNrA3UEz=c!@o3zmLh{==momJVR?2lEds{9)#y>xc2t+yAii3rqj7_<*U0(J=qQ
z_^|dOEFHqq5zHKzI&>PAzG3#j!V~5XT<I644yGR#UN9Q&4zzj!-F`S9T93i}1<yZl
z1^5{-cfjHm7XSElA!K3tVD`eo4;~H(r6??ze_`rjd{}xwQH+HHiyzoHK8%LN2SIb7
z;f8KB%>S_PM3*PX$6+qIK3KS+i^KS^bOPgJ&^YWN<ZoEMf`vP}xv+8wT^z=T#Vf4b
zgNei4OCa6C)WO<)@O%Q*2h0C3bJ1y-|6utRR&K+@(bc2#Vd(}w{)=uej&Mac2Nr*D
ze?S$$>QyKggMzse=5CmJSiHc)14AdO0IYq15sy&!!{P~57lH?KH!Qwk`34p~80I3h
zf?2TgAEqDXZdks7)hA%ZSO}QEVfMi6g}DnBK3KGZg<$r;%6(Y<2P;2u^#fq`!O9Dm
zyJ7hr-8`^mNCYh1!|F$veX#TZlZUwvNh^d2O>Ynm;yyK4zCag;@nJM9Tww79^EWKt
zz}x{7hv|dSFneI`hS9M47^WYm`=JKG;tLj!u<(GTJD5FC<rox9J*=Go3xD+dfuRv9
z0IRoP?ts-Fuz5gu_(0X8Q!st7_(JESt4HU<(>FA~!R$v@hl>yMKe{|DU!jY`_^@&U
z7S1qn7)>bNVCrDz!1-w72rzMU8Xm7`=@_OCm%Cu{Fd7y=uyzA1-NE7mrVbYkEB|5f
z4a*m}(mlF4uzU`ScUZbZSBHxa3kO&_gq4G^_=Cyg(hu`DZ2TCm9u}_9bcIVlEd9Xz
z0ZWf?a|q~%#S<*NVd(+AACA+0s5@c)h4~*A9<Xv1mws4$!{Q4T4!G3g5{J1TZZ9r{
zXyUN(V_3e0r5{*1il!Wc3oEZ+>S6AOhd+i6r~oWppft>0SbBh}!b!p03sV5I9~Pf5
zaa=UI`(Wn5@&T-03yT+Yb71)dW-lz=z~U2DzrxCObp0@Ou>21*2i-nG{(;x;&~Sv^
z0|@gEtX@O6A104JZvg90!pcv0`vBblC?Doen0lB$VEGW2`(gfv#W&17@NkFbE0{dg
zIt&VyPhs}M>S0(ugxQ0k5h?(SH<-I&G|c@_WjHBV`xBPWVfMkwC7c?e60md#b3e>}
zV)HRf9W4LD+ySc(pw{4|VD_S?16aDmsSzpx%P+8SfW-%_Jj50Mu<(K9512n-G+Z2-
zuA$b!DfDy)vlo_*VC51lJ-{_W8SwCe@?rjf*$Yb-xY8di|HI-NmR?}y!u$^_*PzzG
zDOfubmOo+P4NDI&|G@ZgjZg;6Tv&cZk8hZHP~{jD%)hYw4GTAzLRh+h@d?qec!q@w
z%>B6Z!PLRzVg5u9512eU4O0(`Cm0`AeiCv&Og*}Nuyg}U2QYu3+YjTz`f0Fq3o{p1
zj=|D7Og%acOD`~cVfh!9?qKN)T_21O(+97|q2(s5T}UV$z|4WU1D3B~=EA}grVd8K
z!WW)yp!pP*-(cp!)WP(@XqY%mJxm-%!}1TTJcNnEXqbK&4HJjau=GVpKd$)2WiHHo
zSiHd82@@xphQ<>tpTNz5io?vuWgg65u=s_gKUlc{lgFhWT^ttQB<qL9J3PHZ-4DwL
z==Q+*P<^m=J1ibx;Q_Btpz7fIpbT_*SUCX8Z!mLV=^3gGPQk(#7H+Wcf`tRj99Ve3
zH9{G%{0S>RU^L7e7!5NYstr!T!UvX4VdV%c-NDR<l?QN*I2h>p5SA`*D2EHf^+82o
z^#ZPNgDQhluy}y^7v^r5IdFwI7_e}Lg#$bsq4Oz(${$#G!NLy~uCQ>2xewiXm_As#
zfw>>14rUI_eK2tt4U>n(4@?~_{lI9LJWL;qhS>*8?=bzabPIDI%pWj)FdC*GS9rn1
zaiw3FIWTv^+yT1>9L9&KL#JWxgQXvsdRV%H#V5Kx7#~(Yz`_w`4$S|sc!R|cOdpJf
z>4U{POh3$Bu=EEDSC~E+4GR}oI}GN2Sh&H=ftd@_2cu#3!_0yC7p5Lop25p?m_{@j
z=1y2V!2Au%M=<}PX@hfN<M*(0UtsQr-QS2S{lW7Uw7f=-H&{HtEka|!(jP2+!u$yf
z7Z{DE4Z(%l3o{4SeukCHuy{adz|Vr215YnF>J4;LVDSdG59%(M|6%cnt`D6L^FPcy
zSi1&BqpOF-6D;0g_QBE*EZ$+^fvz7Gf3SFm_4{Gsu=s?<4~BlI0L&e*egI5AA$?Ho
z46u3vM#It@EF56!&}mpa!O}mB4|6x6^aG0*Sa`wAhl#`d0TV~3VdlftLlwZ{0Y>90
zKVb0!izldd_`VjHzhE>>9-W4zJ6L$b(h)2iVd)xOAB+#{_rcoxFmqt#5-dDn>R~jj
z{Dt)!VB<)zc!c>MrXHP!>4$|6%pO=g!sOBQ!T9L@g5?94JS={2#UspruyPxw4&7W>
z`i7Ycqhab`<v%PQVCKR60aFLl2cu!>2G;I{*$=0o3UTR&O2f~wgY`FI;RYMO!lfUU
zzhLf#haa^3f{hEn?7>B&$1^M);Zlz-4zm}Q4`AsJT^%|f7Jjh&3rqL7;tO3rEZxDx
z(bFxgK7q-j)3Er3iNnGZW<N|ntUiXRgV8YcuyO$AeptGMnU6F7L*ogS{$TEhsfUFR
z%v_kgFdCLFVEGp2Zg_k`>pxh!hUr76VfMn(9gK#>E4q4gJ}my==^kn?%suGp(fP3O
zLl=kVe{=~bA7(x*oT0)93hEAo04jj#gZUR#mVp5lzUbmGK1?1KPcU&f4UJzoAB_R?
zAFSMj#WO5j!DuvX7+hF;9hUE4<pC@mVrW1Yz!6UH_5iwiG(OCoFneMC!WFJ)+Hr?7
z)LroM6<s5g56h>pd;^PDn7d$nsB#PnrVbXLuy!{r-@xXxFf>91VC5c^%K*#Iu=s?9
z2TUHFhPfY>575m+SC7tz<x^Pv!~B7+4xJCnx3GAE#UFZlLe~f9L*oaQ?_u>7EZj)-
z2P{9s$|G3$jw}9P;ROp{SUAGc0jxg7Rer<t!`ut=KP>)X?RJ=Z;Qod(VD`fF!Tk#j
z7nr*V#RtrMn7d)_g~cZ<T%p!tP_X!i$0xMhfz=DJ@&!X9x&YMOuy{b1Cd7xi8`f@x
zg&!;)ap{M}8;pkKH(0*Mr5_e<u=)-bpRn+TnFos}bo*iH9u`k9{V;v7aDbH$==x#t
z14}=!@)lhmF8wg|u=Il-&oFUZ?nieI++Jur5*_bQgJ9tZ%g-?XL8Wn0FneM74Ho|}
zf5XBT-2j+ASo($83v)Lt{$c4FT|X?IVESS1g@qSP9A*zpKg>Po^00aw<_}nUfcXQa
zA5KH_8O*=1bP7vXFnus{VESP0gQXjE8YU0Z57UQE!@>!c&tc;|u=GzzKg>LsI#|0O
zc5VpFU%0{_rXS`%Sop)-1@i|?9ZnjWUSaVMi&t1Y!Tf`+A0`irFIf78#VgET==#w4
zF!N#Y2R9cQzp#27T_4PUF#p2jVfh$VkHEqaCXY_T?1Ry;@(*1dPChjJVC4xc9&oCM
zO2E=7EPuelf!KB*%>S@@59V%|xv=~MwGM-V>4UYGVESR^!qNkVMyLSHy)biN^$jc>
z;pRZq<Dy{tVCe>39n3s*ahUsH=?_-_!PLXd!<m1e@dm5MVE%#8u=I#wFAf1%dPh$O
zIMV~veptH$W-P4U!Kn|X5K5zmGb}wprEyX)f5P(#R3)+L2dW%C-Uc%dRvzIDcc?~K
zyu;!RmJVV5Mh_RLHaLYIKN#f%G<;#{0j?3oKr3I+(-C^Oz_h_=SUkbv9hTl;=@K?B
z2n!dOJ{S$l=P-Z5!V5MY2vdiPhQ$*s{$c)yy91X-s5mT~Vf7!34|6}EbOZ|@SbB$*
zH!$}=tszW7;|Z1zVd;mkMu-YnI|Y`WVDSyBk6`i;)tDqqAIx4@e8IvOmOe3cLPTKU
z21^GpJ}iB}!Uv)nlZ4p^3vXCDz*LD{1XlmS;saKHV^@tS3eyjBC(PfNDzS^e;v1G9
zVgA9c8dDS&U$FRx)z`3afW;4{PKXFBeqrih`e5--C_ljB7asqxd2fjE3@~vh7lVSu
zJB)_KJFGniYae20gbKjj2h9ht{79($fNE!e`5$IKEIu*nbC?Dw4GS;y_=d$Jx;RuD
zoPwoSSoompL-!Y4Ba{IvuVDU#g&WM>uyP1aL-oNZSUQ8X_h2+k9G0$Ne3(95G^`wk
zxf|vlSbD^z4<-(q2Zp6P`1n7xo`I>uprP>&i$7Sn;_^34KP=zD${ScX!u$=(2Qc;M
zG)zA%9?-)HmM?In1DH6>zc3nR4@@4HJ7E5V)f=$%2y0Km(gCbof|(1WVd5}#FmYJC
z!NLO;t}uBRjqYDqc);w3sUt+g(hJ-?Lh%hX5LVv7(hV%WVc|lk9RLe2Sh|JjgT+70
zd|3EGt%Xytc!!l2u<(TCXP82mJup6uhUtfeGmM6r15<~~ewaKg++g}(^%zW^kband
zVd)H)dtv5b(9rw`D@S4VCoCPn+=ZbJDgd(=mJVR?2n$zOdW0&+pkVO_vj<jhz`_MX
z15^MO-!S`O?uC_uu=EU7j!8lN4VQrOVg85t2WAgk9h3nxAC|vh=>e90;N=0-T&Ok#
z1<fb0bOWPd;Rdr8SA4?khuH@UcUU~aEP|<n@zH5m_`&qS$}L#D!NL_?AB+!+Cs=-i
z#UrfVhlLMJJuVuS-(l{7nS)C`OdRH3SpNlPA1pp#;RsU?r=jrzizk>rVBrr74_G|_
z*9T?5<YDzbtR8~J1I#@zK2#f=g1H+e05b=s50<ZSg##>pVDd2c!|a2l3s`u-?19lR
z{jhWgqhaR2;vHA|fvJOq7t9?n^U?V*ada9M{_u7NG+toqis1V~&^6-V!{P-NZ@AnK
z(+;aYVd(^>4<-%^CzyH+8tPtjaaj1Fi=*>l?nO_pgvx!mJP!L|=>Xk6bUrM7z{(jI
zAC`X5)x-EOb?D&(<HPd<Oal%YUj9S<0W((%%7>LJIJCq2C$R8<<tv!Exb(rqVf6*f
zURe1Li$9n+Og%b{D}BM@6PG;99GH7y<sM8N79TJ^E*cg-u<`<?4wjx_d|WgvAH(w>
zv|b`M|H9%KrXEJa(g7}eVd60V!{Qqj-Y|1u>TuDpc!twZ_ruh~(glnUqhaMOOdl*A
zz}jE1av0`5m_8T{%eSz61`9`+c`zDR{G-P&EZxD(g_(yd9MH=rm^xTGfbn7Gz}yX^
zVetbi_hI9YFnL%w!1%amm_0BWrVgDC6Nk~T^a*SK!@?QnUi9_=Odp(vmJhIShNWLv
zdW7W*Sp2~CK^gFPfu<Ljd(hRx#i9D(6jUE9{9x{eg+E*!)SYlX8UsBZVCKQyho&B#
z3rk0^bPl88>Y(m~^U)YE|H0b3uyha0hp=)CO&gpGQwP%ztIu(@3*Z`|3|KtF!VgBn
z;}5D1or3ujrVf@~Vfg~)FIf1X>qn1gSo(*>2h3lvaspjHy1B6W0;Ucl9l#`@G`c*@
z9$5PWRv+N1hhg@?(g`fwVKm(TIN~1`udw_Avlr$sm^e%w)Y)(f7EiEoI=BSXK4SZE
zu>22;M_9UnxgSQu%|~Ov(km=JVeW;A!~BD$4bFwBhY7&SA(%c`_`>ATX?Q$9$4z1J
z21`fi=A-L}*#`?xm^@4z#>Yj&?1Q-zM#IZRTpFR`u<(Yt6PDg!`3b5FCk31Lhxr#)
z4#VOfr$(p*EdF8ch0P1;LCZ~?@eh@Ul{+wZ!|aFU16aO-S_h|K@e4B_mfvCihUHIK
zIRn=SV?fh6tla>!2Ud^6l)>Ev6M)jN@P?K5u>1x~Pf%sJ*C#?1z~URG4@N_!3F?Q1
z11$Vu;RXvw^mGX`7e>Rv50-yn@dvN}q2UBm52vB=2eTJ0j!QjU0V)ILepr0LWMSz7
z#z&`N{({9Hx<6p$A-X<vK6?DZ(gC_Sx;|KW1xqim_=n{qSiXkQ==$O112i0A=?NbH
zP;t0AGy`DngT)if9#}lW;vZ%%nsx?QxdBVRF#o{f6J{<<9RmX+0|x^m0|SExh+qKm
zHNb{4GC>4b*}+_xK@8}8W(EncenAC$1`e=N2L=;v29y8HU=9N-)O;o;h=Cjo5?~2t
zMg|iZFpGhU4?=@UA*d-J+mnrp;&>Pw7+ApWVr39v;9$s0%uNL;lw@RN5MW>cV~|<~
zBz+)xkQ{>$#AJ|Dco;mO=CCuUF>o*x7bF%Hr!p`|FoFEcz#zZ@p+V-jIr;><GB7ZN
z1cibqumr>okjXF%@du2DPBSvFgTqAvs*w@uZ$?Il04y9~G&6$$SUt!cuy6#Ki6{K*
zv_J|NG%Uarln_D+2T1r;K>f|e01A6h_!ToSFbFd;Fo2>K<{mDnei)sSnVwk^ZvdeT
zA(Rn>GKNql5XuxnnL#LX2xS4GEJ23i52uhIS8y2rM*|?8qj)p~1i?9+fuTVUQd(qy
z5-kG*0}q1%G+nbZ@PNvNq_E7?a!5W#&Oeac#=rn713;n4!_WcM$HBnEAi<EA<eZaO
zTnx_tDEgrJ6qX-gG{_7PhMCI*;xHi02bEPIAyyE<z`(!;rJ>G-=1v9%PN+B!g9X$*
zd|-d1B$gx=r<VAn=B1ZpfXX)xP>BRm45>O`_QJ{yNC^m%AH|~~KxqiT+HJ7$E?Bh>
zYyZI8;W*Aigz1Cz^I+{6n7d%@7FfR?*1m$Zb71;l?Pr)eSi29_Zi2NBVeK54IINum
zYbU|n4U>n{(Dp1$KdgNPlZUwz)?Pxl9~KTU{V;v7b|cJwnEznzg838XE_gc(lDQdR
z?PGZV2I@{&`xR~;R2&wrFdAk)EFNI-2OEEZ>4&MuMZ??+GY8gwg_#2jA6)uj;Q;Hm
zz}oe&@PdUOx_+3yVd(&7KTJJLKdfH}(+>+bm_As1!TKMtaE9rJxd)~nM#I#@+zm4i
z=5LsO^mI)~KP=o~`qAA_E&VWa(8C{=pJ4XG+yhgOK||9c%>S_T4a<+P^oy<!7Jsnt
zgQX)_c*FF=;saej%pO?0!{lM+!tx``A24|s4U0cmK7geQSh)tXA69<A^uhX>F#BQd
zhUGh$e_{Gz;R(|Z>tDnC4bum+2j*T}`3B}*n7?5(%-=A5F!NwEu6Ts$gQWwQd9ZkZ
zxd(lm5aw=Jyu;iDi!WF{K=(h)-{|(h!WCvOEc{^QBP{%2<rFObVD5&+1I%6+4cCXp
zfY}FgKdc;pxgQp8=<Y`|h5_adnEf#K!1Tk?1I%CO`eA&SIk5T#7QV1}gT(_(JuLmf
z!UI;$!1Tk!VgA7tf3SFf#UCuZVc`cWPhs|=(=d0#{0$2)nE%n$qw`_@g!u<nf57rD
zx_THNW-rYBuzU-*7h3PZ!yU?p>4(`1i+`BCu<{n>9+>}O;SUR6SUQH;3zLV1BaDWL
z!_ooFT$sJEbOU!M)PXSdF#YKAP-P6T{0lP=?k}i+Vd)2^9-W5e1DJo{e5ig{dO+6)
zb2ltJVD2SkAIuzBd5dl@%%3oS!_qHIAB={@JGy>Y{K53W+ygTgrXQAGVftb5h#udt
zbOF<cK|||5Sh&N&1*VRWKVa%$`4!zgF#YKH7v^4=K6LwG`3I&S#)sJlEAL?T!Sutz
z52g<melUILG|a!S_<*GYSowjj9>#~cA7(!+9mDKJcP~sIjE30<(+_heOh2qVgsDfT
zVd)QM4lKP8ayQHzSbl@~ALd?|J7DPsJ>9|d!|a8*A0`f?VdlX61B(}!JS@K8`l0C%
zR{p~L535IE=D_sB%1>B2fQ1Jv|HI-DR-WRrA6A~h$~zbzW)94L*tq~O`(f^f(J=SI
z(lg8)nEkNzh%o)=G)z4#Tw&o4i%(cQz|_HLn0+vJz-U-G0CPVqJYec!<p<0@xHz=j
zgz3Xoe!|=ji$8dNfa-^}<6!F0X;{3$(jhGU!R&>l19W{bKFr-Pdtu>3$X=L!nEkMH
z1G5ii4$M6;ahN_B4a+w${jl@`izirqgois!Bb0{O537G*@dfh_EIwf20do&jJG|b6
zg+I(ZnEkN)2XhBTJVNsuOh4Q|(D;MpH<)=adtvzmrVr*`SpI_XVeW;6C(M3WIKko_
zW-ct8VB#=yVD5m?F!iwX3v&l7ykPMU^EWL2q5A`-ALehEI+#0P_F&Lx;RcI;m^xTF
z0CO+g|4;?!;RlO1Sop!h2P6KV`eE{Le?!G#;R|yQ%pb7y15*a0VfhDc4m93j=^a-7
z!1Y7rVftY-tloq1Vg7@KFDzZd_v646qS3H$fX6@7KA8Qm^aRreQxA(jSp386gM~XR
zK4AJ_G@<$fW-lyV!1TdrnEznm2eSv3&R{ew9m3)R=1!PCSUC<$FR<{3`48qFSo;Y*
z{$b${(+@KrW-ct;VDd0=nEf#Sz~l+(N4FoQ9u{7(_=l;3g&!<^!|a2_8;pj97mS9b
ze;5tZ2cu!}2P;2e=D^Y|q4<ZH1EXR229_^i`e8K8T`=`98eUF8%LQ1vfW<pJT%qY1
zrVmEL?1jY#%pRCNc)EdVfQ2i}-?01w3wKyKfJ;A29W1<H?uEr8to(%OhowKL^%xYo
z|6%Te>4&8Y42>`WXt=@rkFFmUjxgouG%Ov!;tduauyBL90~W3@{V;dK!T}a;FnzFm
z2vd(v!|Z{@A1t52+zU&G==xxMn0sOFhuH_CVeWvbhttsTf`t>@Ur=$FI+#0Pd~_O?
zj$q<2f56-W3pbd1VCv9mSbBnm2P~fxig#G~28(x?Ik0d;r_sX$R_?*lFDyRc{(^=F
zEd9d#154*H`(g0_(+_h$tlWa>hl#_=PnbTKd9d^YlZS;H%w8CcEBs*L4|5mHepood
z>_^uR^FLfaw48y(KRkV)YlQM);RTC7nEfz&VeWt`$4Q}^1Ls5C1Ir&Uaaef6Xjr(x
z(lsppVc`ake`vhI@-0j~EZxGw9ae4=azD&onEzq+!u$_258dN1`(WV?a}Ug3n0sOR
z(c=N;ZghKL_Q2f*4L?}>31%NG-@w$v+zX38SU(UJelQw+{2rEmVeW(FD_FXR>4Vk7
zu=s<yALbrd{)PD$rVo}5VeUkyVc`h#56t~A{V;pc^}+Zscfi6CmQP^e0n3-@>S6H(
z3ujn3!2Az$H_RO{b71?yVD5$KgQag+dV}#{`e63M^ufXnW*;ow!1Tky5vC7D!}P;w
znEPSz2XimX{V;tn8Ww-B_=4p>SUAGM4`vQbA1)fEALbr(cfi`kF#Rz5VftX<0P_bd
z-eBPY(+8vB>d?von0lDIVeWwG!$qT;M{NFwnFmWpFn_@EJ+APF>4U{PEZkt}2<Cp6
zJ7DV3X;}Qh^+U@En0`Y3hnEk~aD?SMSbV|KAuL?c^C2w1!R&{n8<@Lc@e3<Q(8teU
z{)dGd%s;U3gSijpZ*=`IahN)odtm;6<vUn-z{*dUK3KT}i+`9qVCfT<4$=J$b0@6+
zhs8h4JXpSk#XpRO*$c~OFdEk0hRMUsgV_rU512fRhJ_z2oMHN4e2jR5rVp5YSiHe#
zm^hq<s)vOiZ2S@yKCp0uwLf6t0t<Io`hk^$u=D{pAIgA*11vmY{)V|9sthLuvmX}k
zFneL?4wpuly|DBH3s0E;VD{rO2WB749GHGs_{01GOGmJ9fx8Eq?_lnShaXe{Oh3#&
zP+>R)OK-6FgSiK0A51?iJ;F6Y8L;#M3wKyN!TgQkUZ_481=A062TUAhAFgx@3tw2c
z!SzGK16FRK=Np*$Fd7zauzU+kUod^>`3j~FM#I7zrXRyzX!?h#N2g)-!@?itZ&-eT
zg+IDJ7$25@VD`iKuyBK!19KNlAB={D8!R4R?t!@jW)80O12YGmhM5CP&oKS4@I_aT
z&WELcm^rX`z*YXj>OGi!FdF7Bn11x~AC_-n;SWpyu=)$84;BvS`+;Egz|%i8{lWYR
z^Ea$ML-!}le=z%D{)VZ8r9)Ue!txU=p3(Ke@-569Sp34w!KEK24zmYlE-atH#9``S
z?t`g=*#lDt^B2sWu<(P)!}O!qw=nm^;uDs?VC4zS9GHGse8JK`OdLkT$|IQn(enY!
zf3S1{cQ-WO!t6!&H_Ut(4KoK89x(U7?1lRqYA#G4oQ9?wn0}ahaOsEH2MZ^dyJ7lZ
z=ECen4>y?mVD`bn5vC93UtIcO>R{;~Ru04R0nA-6{V;#P^r6$RbOLh^x_)%`qw9yI
z6PSLOdtmm$+yV0!x_(%A!om;c4_NwvrArterXStiuyO-tAFTX<sfW=p`(XCK+zSg&
zSh)c+2c{35hUtTe!}P;wSo(#z3#JZ6!_>j@FU&nKd6@mM`T!=5PQ%g<Ec{^Y9YXO2
zvmaItz`_L<-nhaKW<M+)!}P<-U08Vw(+`U`m^@5Bto(<$8&+<@<YD0giw{EWSD1M)
zf57y?(jnX((EJX|w=jF*@dTBJ<pWqa!t95+51oeD3yVjXxv+Eqa|bScuyBN>E0}ti
z{pjT<x;eP)g~bE9dUQT49l^|jr6ZX8(bdDk8<r1Yd>9RLKP)|=>qk#Nu<(c32a7*K
z{)U+gOGmJHAU51!<rmC7u<(MpA7}o9rW07X2g^^ebOy@@Fh0zkFd81tP;+4ZhJ_!@
zU9fTh<{y|pVfMn(Da_q4eX#I@>4(`1qhaY2mfm3T2-6R94=(*Mb6_+~AIx2_{7$TX
zn7?4|fcY0!_@S$Z)i1F4g6V_B11@*M;sF+ZuyhX#KXm=D@P?%qn0*A(3)BIy@P_G!
z3d8rq!rTE9$DpC<1y)|b%s~%tm_IP|qY1$B3oPDX(y(~Lng5{ffX5e<4~qwwei$ES
zE{ukizcBa1?16<NA@{@T2Uz^UXjs1uW)7@ef!PbAVeW?c8<rnn`e5lF<}R2%Sp38E
z!Q2m15A!!Jd(ra=tlWi_ldyCTOIL7vp$u61f!Pn!4@<8w_v1=O==Q_H4Qf1`f`uc@
zKQMQ}(hJPruyhC$hZ_KGZ^QJX*PpQR5|)o)G|U`W{)PDmM#IgAGGOrta}UgaF!#ds
zLEQt@2B%=^Vc`l3Pgweb#S41>7j6I=1Lh8xc`*Co{a0u{fw>!IA1qzL(jhG0z`_qs
zL-oVrA7&1kGZ<j`2c{3^4s>-eJ}f`N;u9ACF#luZ1E@xr`(f^Zse{>zQGP=8!6;a~
z!_pxv9mDbyp?m;yH>~`F>4$|M%pP<a7C$im!qOwm{jl@{a|cWuW)3XeVfMq)E6f~N
ze8R#VCJ)mOqhbDrxdSE+3wN0PFmYJ^fY}Sv2TM1waEH+_eJ~oPAEpn+hlL-k{Q*lq
zuyg>^52In}9F~7z`32^0nEP?*hlL-^99TTV{0mDrFmaf@FdCNbVBrUgA9VdN_2@J#
z-NDi=%-yi~f`uQ<TwMBL_QUkU+zrzY3s-dguyBLf4@-|Q`(XNE?m^d&&WFV#%sg0p
zz|6tr4s>&1;R<sPEFHkhFQ^7sI)|qhXt@OA!@>iWk8q9u!Qugyexb%Qz~UXIAHDpA
zxeuOxpywyS@;{75uZLmj4i-*u|3d2rSh&IJPgwZF;ssqlOdJ-@F!NyH0CNY-9GJZ@
z8dfgA^uzoG3lCWSfzdF1FdAJyEIeT053?7p4jNDB`eFGMCJu`Sn7d&9ME5@|eBt2*
zH6P}0n7J_XU^J}!g4qWPcUb;~#XHP=m_8T{OYgA!2g^?|{V@N+)T7fd^)UNk;SLXH
zXnMevZ(#8QD@S4B0;_jm?tp~@y8SRdES<pI0W${{?zqAarXQV##Xl^6!r~v7KhX8V
z+yjdrSb2t?|6uxH_Q28`EFNL`8kTO*^FJ*9VBrU|50>v?=EKqf+<s{Kg2g+!e_{Gy
z=@Ax=F#WK2hJ`mQ9%1nW4<~5+!{QU>517AT`eFWs#WyUxaHU)H^*gZehPj(i`h%rg
zSUQ2(4~qv_IKuoxDE+|n!~6vcPguC))Q=VqF#Rz9!@?aNo-hwW^})gq7LTy}3bPN!
zhiQY+FneJ7VCe-G->`6ixdUbnEdF5j!f04H!pw)!=<xsx519Qh{V@Ar>S6AH`x};y
zpa!GshowiDKhWb1W*$^Kf`W!4JpDrXu=E5AN0_@28c|s=_rv@HOJA_~gVp~qU9fx$
z3pbd5VD`eo0cJmXK7i?i*#}Fnu>6BdAIyGq8s>jkx`o*XEB9dWj;;@u?qTT&77s9Y
zz~TX2AB+#P7v>L`y|8e@WiL!WESzEGA1wZ1{>K&Xu<(cJgV8W^VftYHhxr5U9%wp&
z`5P90uyg`b2a6||IBfp{%pO>I5BEQ`{RImLm^)$NiJ>2A5PCSG#{(=pVEG9>++g})
z`5o?lXugB#gIdP`^B2tBF#p5kVeW^8AI$x*dH`lWOh1edQxD58Fn7Srh11aRhv|o<
zTUh+V?1T9m<{r3yC<B(?VD`hp0hXU(@dk4bR2v2b%Lg!bz}l}c|G@Gyx<;6NFnzFi
zgV_snKYBR8(giHt!qOqkJ{S!P4|IEBe3-vs_QTR6%zl`=(d~!nhuIIK(bd89!~6kH
zM`-Z}PfyVF1`B^!IKtu+mi}S>hs8h4-LP^47O$}IMfVTP{V@G7`(XNE@c;`SnEfzw
zVD`iO4YMDXPGJ6ll_N0y=-~siA69<D<YDm!i$_>E!rTp`Vg7`f19Jx~ykX@Q%siMr
z7!6A|F#p2*2eS{CKA3u#y|8$K`5)$PSa|}o4@Sen2c{q9ZkYdJ?G2bYFnw?untovE
z7DmJ31?GNOe8STm8i3_nnEfz!!TbYr2d?--GY-y$g)6S~15*e~pD=r2;xKo?+yM($
z^!7i@9vBVtKP)}N!V?w_uycZ8_Q28)jE1=v-Cme}Sa`tn!|a98u=s|h8<;;}=?XnQ
zVd)4K{xEZ4?t%FmmX2ZmfvJOq8!R2c{10<4EFHkq!)Ta4Vd)0uewh1U`eEwPX_$R5
z`(fb?t7l;Dh2?*A{V;tn|HIM&%zrR{qlY)l9GE|0<p!)Ag4qXiKdy8S(+`Vhn0{D1
z!0d;mdsw`}?1zOLjE4CeR=>mj2QANG@ek7vYcIg;g@qq1y};ZD^9L+EVftY-%>A%%
zfQ28-Jea#-@e0#Nh=#cf<_=hS3iBT!{V?~y(izPCuyBBh!_plr{$cSAlZV*@b3b}|
zhWQ^B53u-yxf5m|Odl+KVCKTy2}_5t@Q0-vn0}afFnur@7LPE0z|sLM{9*oq#Ur}?
zuzU{F4|6|EKP=o}`eF9MXqbCp`4MIg%-yhX!{vUMzhU}e_QLFgrGJ=x=rk<cVg5k(
zAIu%FbO_TA%O5cPFmafESiHjYqq_r^E@ANp^EWI!VDSypkIVlsdtvs$(mgERVCf!K
zp2O6`X=ptR3qP2@VBrV1AFctF0kaPl4lsYh(h<x*uyg=RmvDQa_QUcI%syCr!158y
zJ+Se2^zkEDc*5+3nFq5MrXM{VVc`bzKg?cOJi_$B(;=$&;XHKzpzDYE8?FJ3f#D8l
zxeK!&W*(Y03@$7`!~72mPguQzp#drY3qM#m!1EQ<9WZmC%Hb3&eqr{)(gQ5L!Q2mv
z2e?LPyu<Xt(hscsfQ1)KKP*4M^uzSQ@(;{Dn0}bMVCf%b9!x(v4O0*EKRiC6{)g44
z==xxM^zehnCsaR%|6v-TG`!q`h6^nIVE%{YXQ*-v3T7WH{9)$7{EsXCVBrNz_b`1h
zKCIj$l>X5j1S`*A=E2GZSUkYO7v^r5J7Dnu^FJ)yVftbD0Hz-nU$Arla|g^ESh|6Q
zBhGMuYJ{ajn7d*AhLtN&Wf&C9AFy-@b2lOTU<Se54-0o#dVz&MOdUE6vlkW)F#Rxf
zFmqu3hlK|$y~6wpGY1wwuyPk>4op9chKZxoF#BQdft9x~edy-H;t@u}^uyvGT|aF8
z0%ksphUtfu1F-TA7XGksfY}doH!K`r=D_TQxtCD*!|a9WhpC6@hq(u4A36=oC$RDs
zmd;`EgsvW)4~sXL{jhul(+@Kb-QO^CVDSv|H_RQd^a%3@jE3n$r(xj$FMputO~c9`
zSbU)CgYjYZ!_p%xpA(z^VCKNW1Lh7`e8AiROHVNMxM-OFVfh)Be$dx%!NMEne^~m3
z(Xes|W<R=qm_0Cm!_>p#3zmM-%YRt9h4~X!Ucu}k=uc=p14}nB`(fdM9{#X+fSC&m
z7npun{K4D_D=%U34iks@3zp7c`eEr17S1plrXS`mbQ(SUVBr8u_b_)8%7-xb!`uT4
zUs$?_r6Y9vVeW?MgZT><Z!rC^a6{J*3wIa|%eOFh<I;z24lLck!UL8bVc`g?kI?nO
z_^|W}^9S5t(Dn-~JYnkLG&CK-+z(3^uy}*{AKiY~eRVMVVD5*R3rjyR{jl@{lSijv
z@eVT&&WE}mmhRB?!T50XQ2)Tw15`gD`{C+w=!eBC%szA)mfm3D2rE}$=>V7eVftX@
z04)At=?@lfu<%5;7nWXO`qAS7mVV&n3e*62`hfZu9)8g9g1H~6oB`ecu=ELwFPM5*
ze8bWYOdZVqFnO3hSa`wI!@>*Z514*fK7#3oxgX|lSa}EYCoEiH`2uD?%zT)?VD`hp
z9hOdD;Q=dmVEWL*0p@R5zJZwoi*J~FV0@UpFdF7hm_K0V!0dy?Kg=C4eJ~p4KUjFe
z_%MIK%z^P?`e5o|_QBi%_a8J}z|_Os0gDG%xWVdcnEznq1S~(n;t3`XOV_Y;3)2sa
zf0%w)_`~F3@dulKg1HM8Zm{@<hd0#yF#o~)k6wSn%!io+i%(d1!0HWHx`VkNW<QLE
zwF6<{0`m{9@Q3Mx(XjA>>4(K5%sv<m3xAkCm^zsIVeW>f7ifOQ<qlZ<z`_p}&aiY0
z%U7`Yhw)+d!)TbjuyBCIA50&*yV3Q*?1SZZSh)pD4>0vG8kT-xG%WmJ@dURQrU8wH
zr9W7B!|aFo9~NFPci{3j%ve}B!qOWoUSRHrxd)aHVESM*EWBXx2-6R92P|LWatF-)
zFn__q17;sATwwVCW-p9}xf>RbF!#XBf#oZBI)rJ2(lGU~`~x!&D$M}XkIQ~o_`&>-
zt`Fu9m^dt*z|_O+gQ<i07Z(07edshSy~5HFEFZw~J<Pu_b71D9n*;MVtUQ6m11!E^
z@d%?~`eES)b3e>IF!NyX4)YJpJuv++dtmVoOTVyqgP8-<59@!!^rO=-|HJYHESzEP
zhK=81*b7}Z1M@FTA1u6J_QUw-<G--<j_w{<JizqB%72(UVg7^J2Mb47IKcG5>_s;p
z#)pL;%-^tl5A!!n9R>}pXJF|EmVaRO!s-DGeJ}y2enRfYWezO8!R?3k+hOWq`3>eD
zn6)q(mQG;t3DXBlm#}b#m0!5ZarATx(+5iru>6lpKQ4d3!V%^Vn7d&#EZkt_0IVE=
z<r`T1!Tbf&2cu!(1`7w6dtvn_Og}Dn!|a2Z1LMQ|3(K!C^I-O()39^{OMkHNgvA@o
z9k|jlEdF5W7#4r9d;p6-Sa_n_kDmWv{(yxqA^kA(VCKN%1KR$8g$FErVdkRKF#WLj
zhUr5Oe^@?;g*(juuy}|06IXb_+zU&mF!NyM!0d;a2e%*UADDg^4U0cmyuthrGY94#
zxOq^GF#FNt9ai7K{EsXB!rTiB2Uxtp(i==ajE03jOdU)gEZkw?1(S#IVeW_d2i;zn
z`(Wx|{(+eTOTV!EfUX}FelYzoeK2>y^uf%5g&QoKVetpcFVNs+fT@F}Uzj;Cd02jd
zg#)Y{fQ28-J+SbD>4VWQ^I_(|Xjr`kvlpIzU>b4IFn_?(5zId@b71j@t{s+-VE%xG
z8!Wuw?ng_%=<xsxcbI!&{znftbo*fLhPew??!ffJ+z*q7>4&)!CJw6yVBreW4@-wI
zb?7ua{Xpwen7J_huyl*A55}idKg=9hIKXLWc%z3OOg)T-*$?wSEM8#c04yEA)T7g|
z^a6{2Sosh0Ka57#2jj!s3kx?`_`}jMEFHnr<Dy~t9%c?qAIuzdeX#t7Za*x%U^Kcu
zm_0E6!ov+3-Z1~-N;k0d2h#_mVeW?IGnoCb@F$dxVCKN$17<%g{loa^{(;3eOdl-X
zVftX@5lkN}eqiMdjE3b~n7eU>JFMLd3qP3suyBLvhuMct!@>`y9~N#f{jl&t*9YUH
z>xa1?7QQh3Fh0y*xb(x)53JmQse}0+<}P%9!QBllH(>f;=D_4(@qoVm6Q&-f4ptw*
z{0FP=VdXE34~suoxWm#jtQ>&l8(27?$3Lu`hS>wlN3eVki(i<2bbrI#4WnWDVeWvr
z3+8W_`(gP4rVmEL;t%G3So$TT52hdHUYLHEJw)q=8U#xRF!#g!4GVXuGB|}EUa)wC
znFI3&Ed9XT1v3DaUt#$G7O${;4s$;)`(f^c`5P7vuy})|1N8I{vmd4&mL6c`56oPc
zIWYI*G6xn8Fn_?zgXx2X1KeIT1}xpe^uy8<Og}6<(6nK2VeW>7KP=o}@ra=TO#s~;
zaDPGT1z33kizirk!NL)i?_uc~ZXQ%WJU*cI!tx8uy)gB#{0hrIuy{ZZe^@xf(h<!6
z=;aoA_`&qS!W-^hX#XGPKbU)A@qnIwVetg@4#s^>FneM8(cKTz2cu!-5zJla{Q{VI
zF!#gKJ<R{G@PnC$9uBbaA6PiS^ugj07T>UVL=S(M{V?-kG%WsL?t#S@EdRpd6Q&+c
zL+cHge_{5+!VBhpxOy}OEd9Xr!=z#99u|MN+zl(QVBrA^2Uz&R{10;vE^}b%0Ok%@
zdV={MrXM~1z}yQ<Z!rBZ`(gTE`3|NY7Y&OCSiHjG0p<@}_QTC15FfDcgsF%58y0Rb
z8Wygw_=EWaT|dm7Fn`119~REA@PoxC%>D52fw~uF4on_a-opG1a|cWw7Tz%T!tx!=
zT$uY|;Rka+u6Tf{hxr2*E-?4Q%3qlMu<(P?Fn7T8!`utA7neSmeptA{@*k``hS?7@
z52hX$4RZ&~99THQ%z^2Hg&(eb0}Dr3_{01Gi&vQYVDSda$FOn&7VfZcgO!6Y`_XBb
zeX#I@g%`|TT;(pTyhL{g%wCwkVeW$23-dobpF`6j%zrR@ak(4jP8ba{52g=h56oY%
z^bd2F2Gl;7ewhDY@eE7Puyg@47e0Rr)ennjc=$u-Az=2y;vc3TJ^#Yg!QvC<9(aC$
z*88ydgsVqmz|}+D1@jNgURZsErVY-8rCWG<gxU)WH@HGH28O**d6>Ph@PrCuP_T3Z
zqhav_OZVvM2HgNy_`}=*OK*hig~cx{ykY4O7H+U|3g#XR^Pu4j(+>+bn0lDIVd)PR
zf3W#0m_K0dfTbT;Ji*dEEM8#g2v`3PW)H0Xf|(0550>s=?H8E+FdF85SUAAq9p-MB
zI&>Q5K3F)w?1RNWEI*^`gYjYU2aA7L{K4WMrVbYk3pbeiVg7`<AD2E@c>%K@=5JU$
z!}P<_0jxYkHxCxxF#p5yF-#rI|1f`{hd(Uc!t}%HJy<$~nFn(Ry!?S#0HtB^0P_#5
z+=BT7mM&p@s5S<eeXwwZr8`(S!qnlSVg85N53>iBUUBJziKDw07EZ8q1XG7j!{QGX
zZm|3YOLyq%(fJtqq2)Hr-7tArdVt3V)F4=XgV_&Dzffs71=A067kd1|{12mH`3)8i
zu<(V&n*p?3hFbtFhhXssQx8k8F#o{f1y{Vn;tx(k?Sbis+lR)0r88JKz{7=rewaF#
zyJ7hN<~~?@hgk>n4~&n#{u35nF!#dB1N8g{HwPMSuzU}TKUjW%se`3QSa}2!hq(hr
z!`u%G2UvQ6#XF(&3=0ofIzV?f%pROH)ITu$VeK-Q{jl&s*AKH7mTqAFfY}f8H;fO{
z4^t1b50(yK{)U+YOSdq0z~o`^4$}|IZ?Jp}EBDaDA7(#{hQ%K&++gts%SY(;!t|lj
zF#o{P4J^OF+>fpv#)tVE<`496hxr$#9-W4(hsGbA56wTg(i_YiSp31l0Txd%|HIS~
zOT*#?7Jsn(2rI{MnFGr|uyBC+8y5bse2K0f7Tz#@F#o{R!}O!u4|69>A1vHp=EK4P
zrXM{VVd)K)USKpV9AW0b^ux@9iDS^v_=C9zW<Ig{;p(C0z|udl`eE*YrDK>sVCfh=
zJ;3aP#TzU<VfhbMUctg0W)3Vrz}lBE|H9l0E4N|c4dbJSAIv_O{V;oA<qga~xM-NY
z==mO|9%dh`{D7GclZW{qmfm3Qhl#_|Kg|6w|HJg-qKOTEn0}ajuyBCshuH_qf3SFg
z>4Ui&7LG9Y!om+0Pw4t!`e8II-@xpL)&J<`z|_NNn7y!gfa!zD6Ux6ZeJ~o9|6uV3
z3m2F_VDShuAEpmR!}P(z3+5k~|6uxI>R{y#Ed9XJJ4_u+9u}_X@d0xmOh3&1@Nk3X
zW0?K8@-Zx*!pwo03)2TP52hdHe;5r@2h$JpH!Quw!UL9$VftY9z|$Mly)bn!dtv1`
zOdpI!&;KxUVDd2iFn7bk7bZ_gKP;YL`eEq+rXNPb@&POzz|4io!~7332PRI4hM5N|
z7hw8f;X_D2JRYFo3JVXIdtl*@PQ%g>EM36b7chPB@dK#2uyBXvN0@oAc!QPuu=EQH
zSD5=@?u5~>b|Oq4%)hYm9HtMJKVabmb1zIDW<M<4VD`i0VeW*bTbO=W{zF#}(}zyO
z{EzN_n0{D3z*P>y(hJNVFg_voqq_r^PGIFA%w4eXhJ^!+57Q6Jzp!|R`2!Zduyg|p
z7ees>%MY;pg3Df*{pd8z{V;dH%5j)G(AC5Eu=s?9BP>0_+zoRFy8B`JVg85N5A#3F
zewaBh^|)x5IWYTS=^2)PaOs2PGgx|r#V5>OSUQ5~hnWxaFU;LA`(XZs<s+CrbQ+fK
zVdlZy53>&z?l3-j`aw4j<_@?&pbB9AfQ>)E(h1Bym^)zUAC~W7_M_X2zJ4ASe(368
z=@6D5q0V7|<quf+!RrTTzX#?Hm_GDygrzrlI)#>pFn6HmUvziD+yP5xFn7SjVetoZ
zKTI4(!|aEJGmJ)$e_Z-u=D_TQg$GO==5LrgVB+XB%)c=EVc`L@7p5QPE_D6qd{{bw
znFmXEu=E2<7wGz6?tu9R<_`3Dz%_mei*Hyyfbn7F1<W6~^uy9IEIwiWK(`+juCVxn
zweMj1VEG3o53?7R4`BXAPyaA;VD5#57p&YtuYX|q59UvpdtrQ7e8KF8m4h(*(P>yc
z0y75|e=u>FIq2aB^Ea;a2TMoj_QBkb5#LaSuyBUi3yTM+Gy^Q2VeW*<!_phfU9j|q
z9{w<UVeW>hgV_r+ALf5pJiy|IkbYQv!r}wwE|`B{?t`UsSbV|V2`!Ic`eFWq=|>L-
zm^!#Sq2tjoaacT{>xa1$77nm@fte3WH?VMlg%eC3R?or03uYe79q8pQOdrf1Sp324
zhlMw+yoI?J<_?&CSop)j59ThIKVjy=;tQ6aV0@T<n7uIlaDPI>ALdSUd1Cd$!XKs|
z<{$L<hs7@}{$b&WOFt~!Vg7{q16B{Bha=4YF#o~A5mxTO`~!0r%)KyuFg{ElIt{ZA
zmJVR<f%ykU!@?hzewaI8{(|X)g&Qngz}yKduV8$be_-x`>4VWQdtu=Uvj^s0m_Ar}
z3)2S+2bg|z8fG5M{pk8(`5#>$j1O}^Og~IL%zjwB!qlVF==Q_H8Kxf=Zs_`8d{})2
z%TF*GmVe>?hqkX^`e5}oEZ$-IVDd2gVdVi#97e;`!)TZ|%v_lLFmqt}1f~uaUobu_
z9ANPZD>vZohbe^8u=*Mn|FHZH%U`f^8D>AM{|vJS7GE&^P-7Wj`e5l7<`0Z`fR-~b
zcfisCEdF8P28&Oa`7jz5Z!rJD$_<!*Vg84y$DpD1!@?hy&S36_rDF_zXaX>I!`%T*
zCop%w%th120CO)a9AV)O3vXDsq3eV3VeW?63oGAX_7ZYGOdqTqf$1Y;FU)?JyJ7Bt
zm6I@c!1%b_0ka=w4lF&x)WhpDXn4ZRh1m}acbI-y_`&31G%OvVx8Gp)!O|ZrAH(7W
zmfv9U0pEX(mTqAFhs852{9x{fnFDhVjE0rhFn7c96)Zkr=D^|wmpL%=VESO;0n0})
z_ru}?rXHP!>4W8Gm_GDyN7o1A!@>dP4p=^d`2(g77Y*}2%p90|VdlW}!SX$<yn@9S
zOg}6<VeyXPerWu`{0XCB=?-Qe%-t|?bo*iMgryspdRTnnDz{+v!O}l0{lUsdn0{R4
z7p(sT^DitN!_pHh-NF0;b2p5J<r`Q%0}DTxJ7D1jqhb2cX_$Ukx`Fed`4#3LbbT=U
zVg85lVetj?KYF;s%z=d;%wCwgVfh1=4`KRY{($9QSo(pbQ<!<M@PqjSW*#gbpqC#o
z_rSs*<{nu3gXx3ShcNZ%G|XO@eptGO>4&8&bbT;B%>6L^Fn{CH4@<8weem#u<_lQ5
zgXxErBQX15=?*6ijenSaSUAG;qth_^VeW_72h)!$AHmFlr5~7nSh+#0epr0N@-r-b
z!152S_=Bl~<sX=SSp31#Gt3-xf1t+$%pRDzuy}--1M?5e-!OG>K6L*GEM8&p155wt
z`eFLeX>@<U+z&GcJ-lK02;Cf5x`o*bE0<vT1D5V#G|auQ@Pz4y`5#w0g5^J0c>*&Z
zR*t~J72SO>KFs~FbO^H-mJeX+aM3V*u=EGB59V)lcf;ZhrVriSF#p2B9X;LXK=Ucg
zKA5{;=^myZ#)rwnXqbLj`h&#_EF59s2XjAqI1q||n7uIlFh0zF7!3;tSbBrG2bRuY
z`eFG1rVpKl#UIQZSp31l4_!SvpHls>@PL^E3s0Ck(CvfqVdV}i9%1Po=5LreF!ks(
zEWg0?!@?P+A6-2<A68DFhdV6&psPpc!}P)Ihov8wyJ7hcS33Y^KTJQ&d{}(L^uf%5
zr3;ukcz8kc11vmY^*<~f!_on+_20032unY(bP216VDX4<KTJK${V;tnbuf3M=YN>{
zVfMn}5$0YP9~O?V_=JTAOg}n}?jD$Wn0{RG3X3<GKVaz?rVr*Hbo<cxuyPERA7K86
z`2$@&Iv?h5m^rZY3^NB^JuJRq^(`zt!R&|Whxr5D|1f)C{)UAEto(-A3yXjBav0_w
zm_C^OuyhI22XhB5{jhk4xdUbn%zl{r(e=aZg~dC}KQMJLeK2>x+zU&muy}^)gVn>Z
z^a|6D5&zKq26HDY{9*opnGe%XZ2ZIA0Z#|eb|ftPVD87Dq3(f&A50!*Kg>TcadaA%
zj$q*k4_B!D==x#qgOz`<aDe$6rXCi5u<(P$3oM*q`e5M>(+@8fp!UQ30W$~P{V@G7
z|HJuE{RG1wE&O2N0kaoY4x-x&=cDO|`2(gORt~|{qcLFN4U2zR{)L4fuJj9w2Y7fv
z-2pQmW<R?BVdlfa3ziRH^$yHl7!4DLnG2&~_QKK)EZkw~6;|HB$`hDAn7d*2!}1TT
z-h%OA^%l$@F#RwZW-lz<VBrX((e=aAAItzK4U2DBxdk&9mX2Ze6N*Pzx`z1+T|d-1
zoD|I6F#WjF5lkHBZ<xKXbP9`4n0sOFhq(vl0GNMa=D^|;T_1Y;7p5OZ!|a2(8(lwo
z{KNFYX!P_5Qx9`Dj1N-}r=jT`W-qKff#nxm`qj|%!O|_vT=aAUOP4Ttn0sOA7#40Y
zKCGOA*^evT!OVk|qi}yf%PnH{qlZ5%zKGQib2rR>n7d&5Vfh&*4x?fD1{UtHd;{|@
zOh0=0152+kf5XBPrVnO5EF57pOdO^jMx&<>Sa`tfg{23Wesmg^?_lnMg$K-DSa_o6
zADB8=xWV`^^U?DGZ2kx?kCqN$`4MIxEFHq~HB28m4YMDXj$z@B9xmwmVCrG<22&5S
z7hOFrJ}kUp?ttYNm^*N#A6PjL(+BfE%zhXRGY{Q<Sa`$q!{iCs3sVR4H%vb)oxt>?
zr$?AR7!4~oVfta|4W=I!o-p-r8ZF*o=?*53?hbTwVC6q7{lnY=3s0E4VeWv{!!YyF
zX;}V+`2&`(Vftb10GK|Q{jhQm-F{fQhv|cb3rs({eQ@)j<sK|vVfhW_4w$(xb71KX
z7Jjhw3`;LC`(fb;3m2Guu=t0$AC}K>*$1;17EUmIF!#dB3z&YGJ7D@?G%Vh5xf>Q<
zFn7cBq0=z?VfhADj-iJeE_cAf59S}3I+*)$<y&-jz|srMKd^9yr8`{hN0>RVcz~q~
zSUAFHT>4@DhPfYRFRa{v<r|p4VD`cC2`v0z@dpcc^mGIZ4_LUu;tyso%siNVF#Rw-
zOh3#&=<a~|8)hyn9%24~(dg+PW)4g}%s(*qz|ub~UBlF))3E#i^AF7ZFn43<hn8P3
z`(W`7a|bNEVet=hKP>!U_QCuMi$_?y02UuG|H9k>qtV?D<HOt!a~I72FdCLVVCvCn
zn0}c3F#Cwj2e5E})6nt^77pn4q4Ux6BP>0_+yQq#H2h)q!1Tew6_y`i;SSRe3r|=$
z!@>z>4lJL+${m=yVd)s%y|DO(nFCA5u<(Z2kFF2Khv|ofJIoxI{V;dqvLBXiVESS8
z5lkP9hS`Ts!^$0)ewaK=A1?R9!V9JkW-lz=6VeZhCs@3}{0$Q)q#x!^xO%ko0Mien
zVd~LoSop*I19Jy@_{02#t{=vSxdRrSuyl$m{9yWEG%UZt?1$wySUQ5KL#N^86x9DP
zdGvG(a}P`&M#JobrCXRjSpI<JBUm`X+y!$lEdF8U!^%lmc*61%Oh1f<*$*rKVCrGz
zHY_}0^#x2H%zl`DbQ%`^F!#a2pO8M7IWYY&cfj<+%MF-9C=E+Lu<(P08_eCf@-0+5
z11#Ub!VwmqFneL~fsy{8@eR`lvma(3EZkw~7-k;KUYI*!`eES(3ul--jE1=b7LIWB
zP<O!UHJE;wdtmxu;eks(%)K!EF!N#Q1LhC({v#~DVD`cM4@*C=bP01m%pI_BfTeGk
zJuvfN`eFGGrVnNwOh3$CSUAG`3-c%3KhSy*rVp0BVESP8!}OukFn_`H!|Z|ShowW9
zeHiHlt`U_1(+~4EEdRpN3#=cGstdt`xf^B<%wI5lF!#XBMd(Cj!Q2ls4;HSl`XA;V
zR9y`4^WkCj2&^1IPfsv&(DlRg!Q2mX2h82Dct_U<OP{cCgSi)$o?!U_=5ClduyO<z
zPO$nLW-m+}W*#iuh;<LFJcq?UEd64nA2bia?1T9W=3ZDjL{pBzh4}+!4$R#!b1*cZ
z3qZ>en0{FJ;nEMwpD=r2@d(QoFnzFc1Km1UIK#pVRt}@b7kYk2HwVUtg&QpW!Q2lk
zcVK*&dKe9JKP>&h#9{Fb3rCnZIt{ZAmTzG8;Y#l?{jl(Z*$b<8VD`e&DNH>&4GTY*
zepo($>4TYr(;d+GhUtg79~Ry)b71~~@zLD@OE0i^gynl!dWXe7PIo{JgxL@C2h1I?
z^nfcLz|sxOepoyco9|%u!Qv6>90pjt!|aFgVe&8<77uX!&~Sx?1FRf|nFI3|Oh1f<
z`2!Z7Fmqw{!u&%h9AW0c!V^wI>p%4R8>SwehUE*GzhLe_U%vq>Z(;cl?rvx}!1yrt
z>p|TCvk#W;Vg82M3oAci=?4~$xWW%+4!j(K+5^)Miw~H+=rqiqFd9~V!O}k=dtvzq
zW-cuM!SVr29G!-R8!Y|8@)<0=aM=qp2Nu7ubPP)$u=EB~hl_^!7v_Fgxdh8^Fn_@K
z=;pxU7p5PU9$?`Oi$|D$VBw0(9GH7x{(^-E%pEWqT|bNua}T<HSh&E{;i6&o!{P%L
z|1kIC(g(8-77j3Xz}yR?(e=a9J4_#}9Ds!%Odq=aF!N#Vgw?w+^I`D-(?_iTVd)5#
zk6`HtmXBcO<Dy~if%yYwKP>;^(gzbq&kwM42Qvqz4o1V$E6lyH_=A;Ku<(P`3ov~!
z8dmPW+zShLSop%i4Q4-jeuUW%GY`gx#V5>OxI179(P)@HnEzq%0P_dB{V;Vfdtm;6
z84L3VEZkxGVd)v>KNt;b|H9HSOh3%suzU<NADxDo1B(Y(z9lx@z}yeZ53q6q=6`hi
zV0>75gM|akAFyzT`4?s$OdOV9VESO;4U>oY7Z!dn{V;!_hd<mrXuShVw=j32(=dBs
zG)zCt9vBUC54wIB9~N%t=D^|&M#I$OqG9C)%p6#K3e%6_AGG=%7H_!H54wI>dWXdy
zjE1=nR_?&kEi67@^5`_oUYI%P`4(M0j1Nn%Fnut060#Ro&cO7++y~P~DBfV_cf;(5
z<rA3wu<(TCLzp<sTv+;p#Xrm(SUke=0W7`3;t_U!3e5j78fG3W{$b*<{DW>kOddwV
z!XM@ySop)!F-##28m13c4#V`p@*fUu3^0Gf?1%XSW<MeQF#BNf2y3qqibq)bg@+e3
zKfuBnW)3cQ!`une4@*BVf5Xcor~&BlfUY0Lhu2R~_rUxQa~CXJpvJ<(50>6w;Q;p^
zv^;~E1Jj32!_`CWh4Z2MVd)lKAB+!+KbXH^?u3;CFm>oOEWN|hKTJQm{pjYv>MNLk
zVd)PRez0@}(+3MbnEf#IuyBX@52g<$4+}R~{K53YX=r%C!XM@j7#|i6F#Rw-%wAaf
zf!PN$7v>L`dYFD#`htZI%pACWsC!`khN*{{i%!G*3v&l7KH%X6i+^;DP(DmOEFNI)
zhUGt4`3F^wLBae1vmX|IF#Rxp;4%k29l+8dth|Hy16Ml)=6;wtxY8fYJPZ#&1z_gD
z`~l}f!w*)DLzTlRSop*2g~cPx-!K}k0m^`>gVkd&f5XxX%-^td4b=vxVEbQT?uDgS
zSUCoBFU&j`4HJhO1Wj+Sc!SZf_6MvSg}Db~`~s>UR*u7Hn7d*AhnWMj7d_p;(jQD8
zOg$|9!|G?4ei#jNFDxIy{0HO1%z?QdrVmEL!VMPwuyh2Ihq)gXelT%34fQ`P9$@~5
z*$2}PSC7Vkr5l+4Vfh4>e_;Mc(+20l{13AimTqC;2rGAB;Q-f&#(?=B<_}mo0P_c-
z`~wSbm_J~~!~B6uKg_)_dtv^7>4)jV<qlXng1H|Sez16in-9&$F!NyMqth^d!15(b
zJuDp2)x-EOcf<S-ONTK3!}29eJ<NSDf5YMt7T<9Dpzen0gV8W|!t8_jALd?Ix`UOQ
zuyBFtN2g)#hm}jP{0}n^<{lUyrXN=R!R&{*huHK3^EWIXz}yLoH(0)d#Xrnm7!7kT
z%pEZI!Q250cbI;dyI}fZ`e1yRd9ZQ;Jse@`&}sDWgS9(g@rkY;*58Er8%D#_!_o~b
zJka$Miho$R!QvlQ9-x~8^FMk#z}yG(2h2S%b7AtZaE9d<n0c^phNT-A4RZ&s^pEad
zSbW0lg`103?!v+e7SFK!hwfh(A7($y-LQCv*$4A4A#-5<g~cB{9HIFZ7N0PCanZ2&
zgp~&{cfkCEOCK!V!Sun(Lzq5X^(M^!Fn#d+3w1Xv-NV9_ko~amgP9A<zc6!P;R%a>
zm^)$V2Nr(l@-RNkKk#$}br8%PC>Kt_`~{PT>4$|YOd(7jmY!kp0E<VMdUW^0^uyc@
zvk&HPm^>^!!|X@z|6}Nfo?ig750>t6#RDw7VfMr7d6<2;^ugQ*^Cv7F!O{yX-^0`q
zqG9n5%TF+KVDW{^9WZ-g?uX?kn0}c5Vg5pQ2Q0o|;Rq}5VdlW}!Q6qa9~S>Gcf)9y
zI4u9d!V{Jr(9MDKq2&@RKf&S$<`0baGh8DY18NQ|TwwVC{r(=9J7M;~_^@;cix*gY
zqU(o+6D%FT@*^xi!1Te=BYHT(?1hCt%$=}&4=b<He8&KbH<<rm?u4m>#TzW#Vd()T
z52IoFVD5*hgXxE<gO%gx?t$rt$2&Ct!QvejZ|M49=EA}c<{y|lVBrTd2S&s6;i6&r
z0p=c9_@jqATt76O!1TfV1B(Y(_@RdfOg~H?%s!YpxP8$20Hz)n4YL=P4q*O;r2}00
zVC_s;I)L+`_QJv+T|dk|m_As04d#AWc>@bSn0sL10vrE@>4(KHOdl*=!_o~b9AV`*
zEWTmk26GQA|H15m*$bm_l^-y3VBrS~e?s903m2Gvm^)zlVfMn*;i6&b7G^&z9}%)2
zR?fh}3uX>19AWN&#S6Oquy})&JFswt#Rsh1!Q~E^{V@N-%3YX#m_9<`080lj|D)Rr
z^AEcFVet*q4+{radchUnFmqt;h2=w7xdBVJu<(PaN2g)&4buk;519X9{($k(^~3bR
z!XM@ym_9=G!}2S7_`~uo%p6#LK(`+j->~q5@nP<Tg(EI^!0d<VhowLCb{8x>(d~!v
zVg84i1G5iiKd$(PmH#k(u=s%KgQY*1J?J#dy)gS>@dpb(m^*OgADDeG8Wzqlb70{B
ziw9V^!rXx?{9y7h`(gfpnGchP(Xe=fr2|;Hhv|o@L#NU0N6$a7atK`?EZ@Px4VG_V
z_QC9j#T%>~hnWL&CoJAz;R1^<SiHd92d$4_;Q-SIqhaR4%z=dmtlWX=Lk~Zge_`%{
z<v&<E7N!qZIKb?MnFDhV%pEZO==Q?&!DyJhu<(SXKUlhfsY9n>=?-QuES<vag{41q
zeXw!|rVo~0VeW*v9~OSF@IW^Q7H%;A!@>#XA6Wdu+ygToW)3VJ!OVftF#BQpVD5nF
z!$rgFh11Y<0}D@F`e5QPeX#Tc(+5k>Fm*5*rVi#0SbBxgu=I>89$@Jl77yt9VE)Gy
z{;+t1r2|-f4vz<@|6%a}b2rSrFneL?4ki!FuQ2;z;Q*6|g%eCaEF56!VD3UskFa<}
z*AI6O)V;9u1TzN~jxcwj>xcOd=3ZER4hs*MepvYgw-4HWhpB_<huIGkhv|dSuyBBv
zdr)&=;xO}J;Q==n>OXXKFmqruEWN<;Kg{1SeJ~o9USRndM#J0>t9N1Ufa!zLuyBOM
zAFMuw@nQO5?nlqRFm<r>2eTLEe~fU4_U~Z!!`u%`e=vP8f5XB7rXHP!l@~DkVevz#
zynxw*t{;|fVD841?_lnL*^3@;F#WLf4^xj$!_pTlAHnhmEW8Qnhs77H9D(@<=5AOx
z;wryj?uY4v*$)dJnEf#Kpr<34yJ7x-*$c~m=;;UD9WZsU_=lxmn7;_=M-NY!KA3s1
z@`F$~pqm5p7tB2{|HIr5lZVl;bPr3fxWW&nA7&p+9V{Qh(h)3Qz|s-S{pk4*7Va>8
zu>1(K2c{1ek1%^-G)z6rA22@59+<yz<tLc^==#y!4NDg=dto#z++h0A%W+)phUtfe
z6HGtM9+<mf=^3UTord`rrXQvrrXN?i3o{3ne_%8${lUTu77j4==rqjzu=s=Xq4flM
zenZy}OFuCCVetX8AC`a6^}+bCas!rMVD5+63o{2-Jizq9+zm@_uzU@(2Uae__^^Bh
z(+`Vxn0{Ei!2ARA2h1FpJS^Y9(ksl}Fnus}uzU%NSC~6s;RfeJ!yTp%W<D%G!N-rG
z^*zk}uyO_#POxx=xf^CLEPcW3gM}Nc{Drw2<_}o-!~6%+hl_@phwd&|xWe3lt{;|;
zVESP0fu(<#KG^shOh3%uFnzFe3`<Ao`eES*Gar`kVCKNW8CEX9(gn<ZSp386huH(;
z!@>_{E-c(&`eEk7)T7g|c!TMO#RE(~di<g5hoyU%ewcm4>W8H}Sopy7qx%~@|G?Cv
zr#D#mqq`H9u3_$k@nQahr56l)p$5U!!{Qs3?qKl&3pZT)p~k=|SU!Z)(0m9_|8SM4
z448c|f56fWEWBa)9#t2D2lEFk{ldZ<=6_gtAap=jaC@NT04&|X^uy8}%zjurz|Di|
zgV_s<KbU_YwlFZD$0sa2(d~!D1I%6+4YL;(jxZW#J}e!<Xqf%5aDnAZn7y!Yh3iLS
z!152AhQ=eTU5%z3!G(qgEFRJGJ*-|wXu!^br5~96F#i&?A8H&d|H9$}7XC2*LzTfP
zSopx=A7($yy|8oyiw~Ga^!P(}FWf$8_`&qyqG9fUg&#~EEWTjwfr+EjFn7cB!}2do
zKRn%{X@r$mFnusSEZkxG(ftn#Uvzyi<6-WF)u%A?VE1Rh^ucJDz3BR3?JpP&%MUPh
zFd7zrFn7V!!Qugy9%1UyY4rGn>4*CtT?0BFn*L$-!@>(@Ke}?5e_{TI#UD%^%zl`^
zVettQN1uO#g*UAHf~6<)@Q3MFfw~uFKTIFYzqry1+<j>JVfhZG9~R#*^|)wQyusW7
zYsbLMf#qLZ`eEX*@PnBHD=%Q?pvONfzG3AgOh2q1gvA>y|D)?ik3X3IVeti{VetZU
zCpr!DFRUJc^I_!%j7HZ8<-^JySp5O>H_U#hGMp4FykOx03olqY!KD$d9-0nd^$$!x
ztlq?>A3gkF;SUREm_K0Q4@<`|^U(Ff>OGjhVe&9{!Ng(l3JYggeub%r$-~?M3s+eF
z&_at3m^m<YF!Nyf52g<mE-)JA514*f{KL$F<sX>;ait@;yP@#_<HOX$?1P0T%wAkH
zEIq;e0n-Ny7hL*a;^_Ke{)g$uNLNsI!{Q%C!{QB=|6%@snS*X0EFHtr0W92L;RuUQ
zm_8T{QxEemdj7|7FH9qhMsq(b+zHtW(+;x_=5JX1!ODABx<xOK(eocH++pUz{0j>Q
zbaP<#z`_mY4w!ye`i1F-`3L3>Sh|Ikd$9Bia~Dh>jE1=fW<E?mEWN|@!|a8nJ6OD<
z=YN<zF!#gEgVnpR@PN4+W)94Ln0}bOu=ECVCoDW*=?!KMOh1f<nFDhNtUiN<H;jg*
z3z$9_4YLRC4yXhy+|d0Em4;KWbOZA*EIq^QgV_(uhj5Kh2239;9%256`2&^?(Zdg_
zodFhKF!#ga6Xp(>KVbS{=?A7C7Y)mgu<#`m59sE=+yP7HuyBC61EwBkKFlAm{0<8j
zm^?1~Vd)NLAI#lwacF%33kR6_F#BQg2Md3gI4r(k=?)ezFnJgavk#X3VD`fF!~6^5
z!}P)Y1=9!P!@>#1huI5rKP-R2!WE_;mfm3Y!O{aPeZ$0I=?SCV1q~;de_`h0(hqYV
z%>S@-1v4LJKg_={`_aPzrVbVlF#p2B5f<+-8fG4hhUF8Oewce<?tu9R=02D{Tr|u)
zSbBo#gT)KXTwMBL{(*%btQ`jPKa7Uyhvie4eK7s7`~b5TrVm&8h1mxyM`7l}^ucJj
zIJDk`=|eB)VCe&<59SV-ewaNlcf!L7YA!53VESS40N0O$0do&5JYf1U?8l)WmM>uT
z!_pfr`(gIL(lIO?VD`cE!_p<pT3Gsoxfd31FnL%yfw><&-eBrr;Rj2%F!#X3Vftb2
zfayc0VdWRhJ?QBU7C-3vVCfI04;FqfdtvQAbbaXlhlMvxA1wUP^}+b?^aV{Xu<`^J
zPcZkx$~!m>H2`KmEWBVeES_Ngg_#GVq1qYX?uFV9a}P{E%-^tb7w#UI0Z@IgdJ3i=
z7LTy}f!>aSr3;ub=rk-H!qOAW-7t5chXYJMdOX711B(xsJ7DT@(J=erG_+p8RsN!z
z1M@#DJ;A~O<_=u*AFy&4mX2XGEI-2Z!`uN&FED%1;~%CErXJ>hSh~RFZkWH(^`onY
z@nPW(k1uGtfW;S~d;s$&%pd6Cg-bs?exdfk_^@^WA^TzW!Dv{%hx-e<&IFe3VdkO7
zAI!Zl`(ZRJoMHJ0R(|11|1kSu`4Xlcmi}Sx!bQW<56pg;y9wEc9&fPtgxLeLAC~@L
z?t-}&7JjgD0A?>-9W?*I#9`qAGaqI@EZxD<D~yJfM=<s1G@<+k3x9Nd=zN&{F#o{p
zhxr>_JvtvF9Yg&M(+|r}FnzH63yUup4HJjC8>Sz<+=Hoyxfd4xFm*6}u=s=7i;ITY
z3kwIBdtu>#D;{9=5X>F$bOH50uJi|U2h82DbO7@=EWe?r7nnX64J-FxG|b)T=?^9j
z(+A79Fn7c99W4A{{(`v&7M`$n3``!TA08i2f5X(l%!S#He*ZSQ|6%%I<rpkq!{QNL
zKg>U{^al$^SiXR{9~O@=b71;l?JSr+n0sLAVKlDzhlM*V9AM_b;vGHQVD`dkX!#9`
z4_J7?_%Qvj_=M|2W5CKSm^<L%0gVrsd(hJpEPT=R!HkF53v&mIhRMU+3kyG(yJ6uD
zQxA(*m_BqGmhaHRA69RptB3QU;RkaEEZxB38CSf)^uyu{7LTxW3=1z<{R=Y}rVr*%
zn7d*6VBrh17v>Ihf56KLsQY2&!PLRx1!f+sJb;BGEdRmW4WnWHfz>y#@Q3l??uRm9
z;SGy_n7J_fVE%{s2dWKzZZ}LH%p6!a!OVgAAC`|`{(+eTb2p5Jl|L|lz|_P10W$}t
zAEpkbA4bF60rLkeK4Ip;^ux-1m_ArMz|t>FAIx1aedzIr;eTlR2^M}Z8kc@p`hm&A
z!Vl(tnE&DKho&2tK3F=0`3GhWEIq;W!~FwOfJVc@6Be&9cMz)|W*kgEJe;8A5X>By
zIv5SJ2c{n8ADBE$AB={H!}1BtJ{TXC-eKVmi%)p?K@EV}3oAe1e5iZT;~lCEp8jF_
z(91uVy)gH}(j!bi%zrR<!}1p_zrg$fvma(2Og~H?jE4COW*<yHtR95vhs8h4pD_Kf
zbPv-HlZVwKu<%50|HJgd;ti%B=1y4n!0Hk7`Wu$-VBrC)$6)ru{0}o1w*C!fKFr-P
z{jhupb1y9a!1!=|I2f?-f|&;kS6I0ND}Qll$G8s!7Oyb(!)SE<uyBTjCoKMO<zHC%
zV1ysE+<}E7x_+2{VDSZuADBB~@-T5&dVz%_y1UW&u<(cJhuIHH*XZie`RML~r5j@P
z!{QGXE--(=(m%{Rn7y#_5~d!Oo?!ll(dhXXR_?;`J<J?fxWmkc#WyS*VB#=)VESO;
z4l@@PelY#S`X82lNYxKZZ!rC^aD&;8E8WAw8x{_5KD7LVg&*ACXbf0-f!PZSXPAHC
z@dY&xO*{M?23UTD*$)d(Sh<gG9xVUB?1#~?`VZz`T=^cR9~OTweK2uY_@jp-%sg~|
z!1yq8VBrBX4;Bx&!V{(r77noZgxLoR2bet=G_*Ydvls3TXt)z9KVj(}W<SjTF#BQo
z55r!l08BqjA1pt@;tggFR5=5BK7sLJ=?A7B=5JU$!1SZjaQ{NX0VWR%cbK^_adaAH
zKP>!U{(<So$p0_{p!UJ^!Tb+P_b~l1K1>^|{D6fw%zjupfW;R~Kdk=^(~rLX3}zlo
z9%c?qAIu!s`azg|aQ8y}0W$}t4;Jq*_rvlP%pMpGvkz8|z`_q!?+^-qn7uIfz}gM4
z^aHa8PD9-b3qKeQa|g^mSiHgf0n2|d^{{w@l>;z;!u$`52beglT!Mu^%)c;mVg7}?
z69)sPA7(#HKP;YbXoCx*yC0T6VES>z6U+cuIKjdj<_}o7!_0yC8>SCN!}P=IdzgNh
zI7~mxUod?z8W!F#|HIt@Ew5nV2=f<AA1)eZ4y@dS*$<09T>8-60gGRlepq<m(hsY5
zVE%`tH<<fz>4S;G{0*m}{>Pc_VH%+{%wCxPVfhXgZ!mwr_)u+d3T7|5eptA}@&U|0
zF!NvrpzDXJhvh$5`hev>^!s06?nMtrSa|@8KbZaK{TEm{26I15A51;09)bA>=582`
zo{wPZ7gq1U{0nmjOdpJfxgXYUfyD>R976hG>R|qWxd&!0EdRjL6)auA)WQ4>>wm!g
z2~9_^c!%qQimO4(Pnf;1aD({^7Je}MVdCgCdj5gs7nr|c`3YSgj1Oxkz}yc@C$R8`
zsl!FX!VhK+EFHo83o{QME@%e9(kCn&Vg7;Xhov8wztFTZz~UXIAI67;JFFar@nJN~
z99TTV^uy8*OdrgCcsxMk4;EiA^)P?K@*T|IFn_@8gVC^fhvi>bJi*#gFg`3D;0ixj
zI)v$ise}0o<_=up2h#`h2h3eCf5Y^{%!RohM#I7z7XL7HFnze}h3SXc3-bp|KP>&h
z+yN`sVESM*Ec{^g4$MASdWMx7IP(KEAHv)ZGY=jv(D=rwALbA=8s;xp{(!|J%zbFe
zF}SesfaM2RIH0E|nE5dO!_>q453>*Ee)RMYQxEeu%p6$ygT*5}{iC@Q7XL8!!uYUo
zhv|dy(X=zb@*RvumxtL8b3ZI!!rTqh2Xi+poM7@W_rUbS<YDfG=|>MISop!pCs??{
z%z^2H(J=qP<YDCjF8%2C!@?WpKA3)3{KCwI#~(D^!t95oUzj;C8fG3W9MQuU7H+Wg
z3JW)wdKe8e4_!aZeK7lB<swWUEWXh7!TD(ZfcYC%-@@_@uKW)x=V0!K>4%vQi#M1#
zFmZGmR_?*V6BZ6IeX#Jy6%H`-VBrBX2PO{F4@+k-aa=UaKd|%*a|bLx!SzAI4;Jn)
z`(gPRRv*FaM~@GfdRTgd(J*(y^r72}9$x6-4vR0CyJ7JO3qM%CfQ1uGKg@lw`~<59
zVCf#0ez<wi@){QJ@Nhz7!2AtMx3KVr*#q-8%pYjl;apfcf%yZLpJ4i7`2el~2LnAk
zVc`HX2NoX~+M)3e^9L;6Vc`wa4+}TAeyBc}zhUu?ZZ9nS;rgHqm_0Cm!`u&*W`Nb>
zF!NyP05<=Cet!il{ldZ@7XPsH3o{3n4$=1yqWc$SKg>PoG<yFZW)Cd9VD_WygYjYU
z4huh6c)-#f%)c;mVfxT%SUUqf9AWOjm40C97hXO<;}aH+F#WjV4?X;0=E3yA!V}%S
z=zN&{u=EE@r!aRDiU(M@!Sutz52hd8K6E}T+|ctsEFGb%hw)+Q6{a5+PcZ*u<QJGm
zC=Is{E!<)Lh1E;2`WF@tFmuuM!@>a;esFW3^HH$$1RFnu#TP97!NLzc-eBbs%pEZO
zP;bEdPq6$CYxlt10gDG%zJs|Rmd;@I!}P=a4GT}0y)biN=EBs&!XK8OVDSbkcVRS4
z999mXhab#8u=s=70~5zZ!|aFEKQR61;fPB=EZ$(|z~UKZ4lEpS>4(K5EdF5O4+}3`
z?tq6EH2uNc0ka2|USQz{6Nl4KeX#h0*$;~^n0{C~gzH0N!16!L|FC=o(+{K3&4KwF
zW<M-GVet(s7h&lRRxZG-hxs37FDzZc!X0KVES<u{Vc`J_FPMH<x`L^Lr3aWjuy}yE
z111jBk8VFK9AN%}>4%vQ6Nl-C`3oNJ(D;GnCs;Vb+z$&sSiHgf12YGv59VH2yu#cG
zQwIwlm^m<aqw7PDKbU@Wcfi60R)50W3kzSEJ{S!zpP>GQ@nPn|+zksim_8T{QxD5G
zuzU{_hlK-7Kg>Na{jl^4E019QhuM!2Z_xH7Oh1f<rB|3fm^m<USiHj0GrB%FA6nkS
z;u990u=s${FneI?VE%x~!}zdpfcXO!o^bo@AuO0Vuyg=R7qD~(3qSPq19KlNzrpN-
zxdY~JT;(9lK3Mp}`~|ZIW<Sh4h-(ofOdl-2!qX`<J;CA|W)6Dz!_o~bAHw{Dt`EkC
z+YB`yrVr*0m^>_;afKU9AB=|O517AU<pazdba%tj7fe4azF_)c;Rw?Yi+`B=U^Ltw
zsDEJT1{U71_=A}X(+8tr;RTC-m^duFVg7=Nqtmc(LytdL`2n*ZmaozE!}P(@9ZVg}
zT^Qz|836MyEF59x!IZ)Lfh!(i>d<MJzhLfy#RH5+SC7tz#V;&>z~Ud*`3*34qsJ30
zU&G=bef=1^|6%z77H%+mVeK09d;m*lFn7ZFuyPVcqk9m_M^AsS@)8zKFn2?hV^FYg
zg2gv1AHd21SUQHaD`4pZW)RH1u=s+R2a|{Chq(tP4x?f93oQM^!W))uVKjQY!1Tl7
z4HkYd|D)Rr^AAiPjE3ohg(JM2g@!vU{?Ppm(+~4M%pI_BfT@S2dsupa=|iVs_QTx+
zwIAkxboDSkEc{^k4;GFvdto$8Jvt4`Uoic!^b6AmqtW%j{0*}YM#J3$bw4isuy{td
z7iJHv+=HbXSpI>9D@;GkURb(=*$*=ZmTqD0K<~f7)WPBp<{ntSf$4*(!$rgFhs8fE
z-N4+BOCP#8Oh3#Wuyg}+2QKr_%ORM5VC4-=Ka7Ui3kxsw@Pw5Exb&m@7iJ$UUBJvo
z4@X$Iz|}$PO<4H@a|bLP;<6uRK8%K$2Mae?IKt8m%-^`e50`#e{)6d<rAwH-=rqiJ
zI87iQ;L3k6b7AEX%zpIx3s!Ex!V9Jk7M?KkVf8Tj`URMJI1P;tnEPSnDa?M1@Pn#{
zxdWX>PrtDEhq(`C53JmSxdT?-!{QHSFN}t%gSii8FU%bDd=Cppm_BqGR*t~*!@>&|
z|LE#rd|3Fx`~fSUVBtl`UReHvxgQpfFmqw@F!#XAFKD>K(mBlCuzC)r50+kG?m-Vf
zSUkY|1EXQ)!u$i*2Tji~bubzh53q6o<_}o>!Rlj}c`$vr@*T_{u>246Kg=I6{V;oB
z{)d$xuyO~c50;)_`p{{ZeptB%i+5OhMOP2w!@?OJ|Im1Wxf7NSVd`Nt%-=BkVCfGQ
zA259|b71~~>4)iq(XeoUnF~u#u<(PK1LMQYfzdF3z|4b(2Q<Fn@ek90M#J0<3kO(u
z!|X+mcQow`u=I{gKP)`Z^}+Zs_ru~J7M?Kw!rTGl!}P(z3s&F3+yOHOW-q#am_C?2
zu<(NU6P9k^@dgb?n7y$41@kW~zrf^S_QJv)-5gjv!Suu24f7u?-^0pZ7#|id=<^pa
zeK3E*@*m7S7&I)MLur`*VfhA@?+BHD=<OC*IKcG7{7=X|u=s$52Q2-=`~!;*m_K0Z
zVc`agZ<zfs^)UVD{(zYei+{L&XgtI8!NMJ8FCiLc4y+u7#XF3K#UHvku=Ee}Kg?aQ
z@)zbFSa`zp!}P(@BTOGGd~oT9$)oFonG2&~{)gEQqhaY6=3ltKpy3BgPq280=|guv
z%pMpGix*fp!15zZKP;WY)T7hr`eFG8rXTK3bd69xEZ$)Lg2fxW{)4K6QLu6o7Vfb0
z2U8Er2k7Y!rVr*%n7uIf!NLs|Ua))#vlr$*n7y!Yf~kke!^%aNI6PlK^F1v7Vet%8
z2g_Hm@I+5XFn7afnEkMDhS>|#4~sXLJS_dd^uy9IOdeLQz`_$253u#Ou=ESl4+{ra
z_`$*nRu03=h3SXOqlE*^T`>Q^!UGm==;p)Bf$4*}0~TJe_=m+OOg)T-`4i?3SUP~k
z2h80t`(gTEG(7&G{(yxuEZkt`z}x}T2a6Y&y)bc@eK2>x+9fdkF!Rv;3)2r*2Xzn3
zf3WyN_XjMU!t8_P8(93p^uy9Wx_)$j!1yrt!1TfV1#>@49+vK4`e5#X$;0%+!WAYC
zqha|37Vj`~VE%=L2P{3o^ufX%M#J>M<YD0eb2l#iFneM8VE%!r$E6>p9!8_bCoG)d
z?uRKvqha>J+yV0kOdl*CplO41(aS+}cjL-$Fau!r!NL(H4$Jp2_rc-=J^#b<2~0mM
zK4I>F#Xl@QVCe^zK4IwvR^P$gi(YQP+y$dy;SIAFmi}S#F#Cv&KUjGSGY@7CvHD^D
zhQ%YypRjnuRer(30j3Xb4zwLWDE?vL57!UX2eTIzo-qHQ(=h+S%0XB@g}EP=4$#G6
z=D^|sW<RXFgXxF4A4bE>f$4{pPcV1D{11zNSUlqN2eh1o#Xl@P!NMCBf3W-rvlkZ)
z3kR5aFn7SxKe|3x{K3)>%zjw7z}ycr2bM2j=D^ZD%>U@&2h$I8KYIAV)Wht9xf`Y)
zZZFJ295gKcVDSM<Kd|`6p^X7n{=n>o#XGL>LN^C)A2c3e<q^!^u<`@F{RcA#7Tz%T
z!^C0n4>Jc>IKcG7Xjpv0XqY}24Kp7`!@?gHf3R?Xxf`Yrm-}J)7nbhe;fa=y(e=aJ
z2lqEL{9x^MSU!UBVc~$Dk6`YGr(dYOu<(HS6PAzA(;qDUVCKO54@>7T|HJHuiNnl=
zsfXDIOE+-!FokF|EWBX$!om@zALd?M=@*t?VEGT`4w!nFesuq$IfDUKKf>Gr3wL7m
z!_`CM4;DW#{V?~S+Y95v{0&P7F!#dpFDxIx)T7fdcf<6<(hJNS7#~KX#~&=-VDSM9
z512n-=D^|;RxZKZfu4?G@c?r_EFZztqtmeXgZTrNA7So=g(Ix~f$4|EKTIDi{lnY`
zix-$bVCKO5gRUQ@4(4u{KVbP8mJeVwEZkuB!}P=Kfyu+lLzp;>hM5b~4@(Cy_rv@H
zR|n14==1lmc!QY(iw{^h!Qu^OFU<cid6;@wJiusJx`l-Yx;~hG7!6A|uyBK!1FKJ9
z;Rg2~Oe2(r`5)#FSp1=v2k83I;}I5KP-7Wj?uYpw7EkE<VD5(LgV8X3Fd8Nf3pZH#
z2{R974on=T4@SfEqtkHp&~gN34on}6M%RbVhwFox15G!i>WBLWsvkYvVfLcO8?1bR
z*#}EUF!eARmOo(e4YLnc-of<4;t!?|Mx&>DSbBlUqo;dVyu;*S=>$f@`~h<Zx_+2G
zTr{lwh4~BSZkRc^^ugQ>3kR70Vftb12ADW3Kf%m{xf`Y*R&K!5!@?hC9xPm8`qAx&
znFq`7FnusSdN{)DgT)6dU%>J^x_e>v!f04{!@>_{56oT+`(PTOG|b=dbPr9huyO;c
z3?~IEr(pVF^%Wuguyg~nA3fY)^%cxNuy}+!1e!l#`eEq?rXJ>RSh)&IN9g@`n0}Z)
zVCrD;537G*;^_4U%pb7uhs6iXy|8$I>4&)sZZ6axF#p5c3#-3j;fbCOVE#o<H|Xw$
z=|@ku@Nh(9z~T`WF0goj>xZTfG;MG$Odl*fVESO;09S~E0rMBk9CZ89!vVt>Xn4Z(
z!|Mg8yV2tx7T&OY2=hP89q8pT%s(*ouyg~92Uz&S%6nM6W4IgYPFOg>%z^nA7G4<o
z&^!!FZ!rC^^a#rbF!#dBF*NOPF09;vg&VwFgN7T-U2qL(3|PAprXQBRVfGU$4`9Z@
z`~eG3Sop!pLs<TSsYj>b?uLdhOg+pUuy}#_7Z#7ObOOtVFdAk*EIwf7!NM69f3S1|
zOD`~eFn`1Jp}QBRA4bFSE4qE?`e1xmIK%YA;sK@~W<N|lE*h5JafLfBeX#I`>4$|I
zEMK9QW3c=Ovj>)5VESP8z|uX;{jhk0(XjY~#T(53Fmqt>4NEsLbJ1y-dYFEgy|D0u
zmE-98VCe?tewce;_QKKuhCZkOOg$|B!omZVu3+f}mL6a<%zfzkpvJ=Qn}x}vrw6$E
zU>eY9n7d%%0rMv;JYeY>O&gpG3r|?Mz{(9m_QDLHR6oqUF#BQtf!UAlewaKgoM8DF
z<_=i6qw9mY7Zy&iatl`O!t}w?KTJKGhL%sTc!$w2d6<3h^b0MAVe+ti4|6Xpzrp+i
z%a<^Bz|4c`N2g)>VE%#UE2zCV{Q)xw>K|CR!-QesPAEUX+z$&!Sa||-H_RWfd;v2b
zord`X<_<U?>JM1_!1TfFg~dNC-@xpLrC(V3hxr3$E=(OP++gJeEZ$)H3Ar2Q9$0+B
z!XH-t5vspo=>g_%SUCX;f1LJ1;|=D1n15j54$}|w7tEb78m1mrZo%>&Og%3BuyBUi
zi|#*||6%Tc$;12wvlr%Hn0YXF!PLX_!@?0g{$c82G<rUS#Uo6f5Djw|JbggZ9nSEF
z8VCzNnEkNugV_(`LzTlRm_4v?fR$UYaEJLDt^th!OK-4vfTdShddKB%n7uH4Fn`1H
zDU8OYALd_}yJ7hr<~*1?VE#Zaf6&uAEdF8f3u}kNXqbCo;R&+`=5Clf;Pyb{4Lu#g
z&R>A(htV+iz~T?459SY;{jl~6OdqWLLDvU!CoH|g)T7g|avtV>n0i>a!TgV|55|Xu
zJ1qQgxf7-yorbv^<_?&<;o*p`0m?^@f0%w){K53Y__)$P%-=A7z{+P>xWdW-n7d#!
z)ENvg_rvlHEPi0=8zv6RN9gPCVd`M%2xcD4d|3GdqhbDm$;0%+^ug?dxf5nDOg%ac
zi#J#}z|tF`_=njG(+5kxFnus{(Cvfq;p(CF2Q0i{`eES?Q;$xgyBik&@c4u7<AwPj
zM#IVzbo*iE!O}m>KA5{;;fcO}9#($A%!BzGmOf$i8O&ek`5)G9fSChJhcN%c%29Z_
zg8B>QJ{S#)512V{KC~Qyg$qnQIt_C_Og}9D!0g9W4#2|)8jrC002W@bbc=2uj1LPZ
zSUQ69q3$N69~NFP{V;#S^ufv_xc{N%qOV_uryrQZpfoHVVDSfwf0#RA;RY+O;e4n)
zFnzenAE-4L_u#_R!@?h?4xNU%0~T+vc!KGJxgT90ESzBaVC4r)9W4A{=D^$olZU5k
zXuQMtF#BNP0Lyo<^Z?fnW#F<O=02D{SUAG+FH}1`++g;>;t|G&xd)~nmXBci(P>zI
zgrz4~yu$RuXmour|HH}=Sa}X}FH9edhUtg77p5K-pD=k?Jius}c`$i&8kYZH=E1@r
zrXO8Bj1TiSEIwf7z~Ubk4>0xUG)z4#++gxB|H9k>6Nk~T`U<8G79Oy27^WXR-eB<y
z(+A_j%!Sb~{pkLOg)=O^VCrGwF!Nygak(F+4@Se>1Ite^{e;2~rXLoLa2i^UVAv1M
zcj)%P%z>p7m_C^Ouy8~#|6utICJ)Q+FdEkGfW-^Cepr10^DoSPn0wG^SUAJn4~qv_
zdV`hQu<{IDKa3BLCun%U@*&I}=<yEIhfc%t3(P$*|H90H*B@vaVf7y@zF_$Wz1)F?
z8%#U8yJ7aD+Y95v^kL9Yf5YsB<sVpl!{Q$!9HIJP=@e!^EFHky3-dp^J7D1ivlpfw
z7T&P%fTbT;e8Sw1?tfVL!t8^&2j(A`xiEjh+zoRFdisOuhuH(u4>KP<{lUvWXnup~
zgV8X1VdKv*f57|?a|bLvz~o{2VD`fN36qDVS6Dj+mJeY5f$4{ZFI*p#0aFii2Q0i{
z`3PnXR5_f2`3vT5SUC(!x3K&ROFu9gW&ry6eXw)`PiN5d1=9!fKP(-=!XFk-F#BQV
zz`_Mq9--@pxdWYsg$K-Euy7+*Kg=C4{jl)BsUPZ3n0sOA9!A6B0Y<~z3-c$;|FHB7
zlZT~O7!8Y8m^^y?!OVfl!)WyIhlMBX{BxK;Vdlfs!}P<_0X*KI`2#)vVe+tWg6V^q
zgHFTZA8tSN93+^2SiGU@L+8Wv!{ZI=K3G2tT|GJ<7H%+eVBrIE2dVx*Z!e;UC(M5I
z_=E9b=>!(<@cabRh(^Q8Em-*h3kR6JFmrJE1D5V!`eEf4Og+p!F!Ru?hwr<EiNo~6
z$|G31fSChRhl_@}1C~Bu^#-Bz01F3LJi^=oi${!d7tH}Mdtvz&W-l&(!?dH@3k!dk
zzhU}ed|0^R(ho}qF!kvAVftV+th|G{AC`V$?uV(vMZ@AB7GJP(02W^``*G=q<sX>8
zVdWq!-NNcGn19gY0hV82?tq0qE_V<LKUjMK7Vog|Amo0Sy)gH~{0%b)T|cb+hNU-H
zdWETjxf7-zR*%5sVKmHMm^m<WVc`#pM_72m^ucKK@PoMn7Va>Az|^DDuy}{r50i(v
z8|Ds}I64hWf3S25vme&(fSH5K{V?-j`e5k*mfvCifT_bpqo+eyxksoTfR_`{dK?!1
zFn7b!AufAi{)UGi)EroR!`y)@9^mN}rVmQP+zrzY3r}=+z}yS-KP*0A=^myZmLAdd
z!_>pv4~qwweXwwVxd)~nrVgeLR{p^9IV`?m;Q&iVuy}>J7p5O(F3dhyxWK{}mJeX^
zFnur@R<6M0VfhNCA7(#H9^K!tavWw3Odrg>FnzG_gt-?z|G?4{%>S_T3$q_)K1@H%
zURe0S%2D+ABa{wd`34qlFneL~2(u4H!_p1RKA1mX`q9H1-F{g3z{4M!Zej5a^A{}L
z!)TcO=;;Sm?!v+s<}R3eSop)z7tCH54GUM8I#~LGyA#?jgvq1FKdc;u$-~_b^%pEW
zVB^0qcfsNf<_?%Xm^>`}VdkRuA7SwTa}O-t!u$iX7nTlT=HjAZ@drx}Fm<r>0Ha~z
zFdF7=m_K0tBIJHpJi^is%pb6P087U(|HI-JrVeHw+}|(<L1~zKVfta=0*hA|4NE^T
zK8%LxgV9i9F(_RAhlM{(KdyLy`4<*$u<$_Fk6|&I0L<Mm{jl^7FF$aohlL}0I)>Sg
zrVY+TPcN{1haPXRaEEJzGGP9O<u{nSVd5}-uy{w$Kd^X5cR#GYf~7xLIzZ1q=;;WS
z4q)L0(+4Y$q0YvjVEF~+f0#b>dJkPAdb)wR8<t)O*$WFdn7y!YB4jVj9WeW0?uUuP
z+>c&v!{lK!EWN^MXgNtJ-N5w2;tiILVE%^18%!NKjUI34;YP^auyO}xKP=v1;RmaS
zVCfHLKFl4kd<N4GiwBrIjE2Pn%wMo@g_YZ|aEH;bd;_x=mcC*2ANu)uu>1<s4^t0w
zFU&lczhE@Xzc76;8kauwatr2uSUkYu4`wbbd|>GS=6{&^u<${rVdlZg0gUnsn!eEW
z!Q2Cj512VHd*S{@*9hgq@&QagEI$#eA8HH(Ed1f-K-~*72Ntg|^{{Y*xfd30uy}=q
zAFMor>4&9XSiXblgT)gp-NEt^u5gFh3v(~5yn(qFW)94~FdAkbOdopshv|pWFnw?u
z+Af0WgM|Yu9$@yu{SDWModFAXI1LSVSiZroiveT*Ff3eQ?uLaQF8%Oy0`(6}Jxo8Y
zaEF-(OXsj~gz1O58<zgz<q=FHl!o~irXQAWpwbMm@&@J}So(p<qtmc>fVm5no?!7n
z$Uo@rf%yY&Ke~lze3(CA<q=FD%pYjV;av1|3=0RCewh2=8lVhV`h(dEE5Bgj4bu<v
z2UHvU{%x2(SbGJQe_-hdW-qSt2c{2ZE_(RE^ugQ#Q-_m=rbAf#!PLRr0ZUIXarE>H
zi*J~Hgz8ar_rmfo%syDW!NLV*FFK8G4lEzQ+(9VaqL)Xo`T*t*SUm_!zc6`N{KLZw
z8h$YMp}PateuU|Vxf`w@su31%@N@$$kD$_U3YJb_=?LaNV*L%XA5KH<f!hnW2|EMk
zZkYYBaE8@8FmrIG188``@*m6{u>1!L2UtA7(ie8uFu>PC!rTL^2Vmg=i$`?*F#BNo
zVC5K0AIu%-`e1xmx`pY7r6-uXVc`IaADBKE4NDKO`WRMz!NL>f4p{nu>4U{TEFNIt
z2n%mmJiy$K9)2)=F#BNn8>SDIehKA2n0sL105ccPho&o-J7DV3X;}Qj!V%s7u<`(1
zA9^_ii$9osu<(QV52ha$UNHM$;R%z+r5_f*Fnut8!pw)c2i^TJd02eI>I+!-!t95c
z3sVnsKg>Ls`_O5azhU}e@eR|DEB(RDf%yZbAEq8%Kg|8G@(&ho@cIZXzG41=nFq5E
z7LKs+g1H|?!_pzleq8=XcQ;HwOdqa%4|4}xJ+wT6#Rp73EF59#(P{MZ1I~wr10nli
z=ApX-CJu84disa?8y1eR_<-3D3qKeQ4?n0sVfh|L!{lM*7R=qS@PN4oM#JJAmR?}x
z4$Qr<c!#Nn(J*(y^ugQ<(+^80Fn7bsahQIXdtv%v<rIvDiNo~4+ym1O%XhGH3RVul
z(=Rl9VD`iGqo)IyyJ7JL%NMZp4D%06Kd$hAxeI0<%>6KNn0{D%z{Fwk53?Uuf57|)
z(+8`6VESM*Og+p#Sh&ODAEpmi{KL$F*$0akSa`z1AC_*>-4D|b3on>EVE%#Whxr3$
z4on{`ykPMNOOG&lSp32K1B*Xceu3$OrAK)DL-Q@nURXT<GZ*GRn0wLfh2<k$`eEk4
zXqf#l^I-0Pxf|vWSp2~B!DyI&VESQvLg5F~j~;(8|G?5K%pK_A0Mmyn9AW7P77pm~
z53BEB@ehknSp37>2h)!}ehZU_(Xjjg3kO(!gy~1OA6*|T{lfAEdOV`<pMbd&W*^)i
z(DVa~H&{5p(jUxTSUkbP59WVZc*FF=)T7fd^)UO<{R=A((e=UjF!#g!4ePhU@)@r5
z2Gb9tVg7)nW0<|L^n$DWfTwe4c?(N-F#8GVhov8w|Iy1^7$4?NbQ<P=n0{FNz|spY
zcf-RO>TX#4!NLm`-!Ol`!WCveOg}8VVESS9!_0xDXP7zY?SGg#F!#gqBg`CFdW5BW
z^zmPqd(guXR=&a94GRxk`46TZ<{$L<#}yB-bOUogEIwfVhvfs9|6%Th(dg+P#)rwH
zmwzyGVe&BbF#BQY4;Fqf`(fb+(~lnRFnus{(aQ~3ISP|U*AL^v(mTBTfogz-Ke{=v
z@P^qBlZW{WM#J>uN<ZlF2TOM_f5XZlnE6m=!6{h0p{HY5_~FV&Fau!vVDSKpZ<xDr
z>4&9nm_C?0(DlRO4Q3xKpTP9N%0HMq%w8A`6Nl00>rY|%2PO}rVc`Wc2Tr4v8!&gn
z{0YlHu=EQv2NoYN^I<eB{^98XTK~Y(6HFa08s>gjJizij&iI46A7(Es{9x$~=3bb7
z7#|)FQ2j9X!u$^_Ct!S7{QwI$^!gXWKTvaF@d|SfOh0=1hv|pqKbZev{(-q4rXQvr
zordKXn7y!Yf$4*#M|6GYd|1ARnFEV&m^rX=39b+7UzmNc@PvgUOdl-#VfxVH8<vk?
z^%gwcK+_RSA1)dmPf+*3$`hFVFms934~qwwJ7DIZ`vYbkdOZT?L){P44^t1L;qHXG
z8x}4wdGz>)rC(S$!{P~MA50%SJfQZ&%z??n(i<$@z{(?7{KL{e%-yhXfa%Aeq3%a_
z53%}T?tz6nEL>pjCS*Ulepood^y7**m_K0Y7v_Fg`3G}9tUiUQhtaTf3)cq=2WYs%
z^}{qkX}J5K>S5uI9&fnxYe4nE?1jZ2)OZG1I)UY1SpJ8FBP{*EXn6X8nhVnp%QrB6
zF!Nycz-X9$m_K0Q4Xd|d>TuDp_=A;uuzCpQZWtd!Kh(W2f55^O=5CmK(e=aB!NL!g
z-eBrr<rggdz|4W!4^t1*kFFl(9=N}t=>b-L!t~(^FPQnTaD~|qOAj#pFneI(4T~q3
zJ~$1vA6-8zykYub;SI|VF#WLbgy}~QKbU%0IKcdkt`9vQ!OVyG2UcIg^uf{(tUUk=
zf0+H~<ps=sn0lBwF#o~QHM+g9@P@e$rVbYFuyBRR!`uOLH!T0b!UtU)Og%aci$7R7
z0<#C!?m}0O&WD%N(0l;17v>IFc%bV;=fl$h)ErnkfQ1{ndKe!TU$A(9^P%Yt7Je}G
za2je4OdrgCSbW3W4~qx5J~Rf*K3MvL(J*nC`(ft6XqYkR_QT43n7y#@g~bc5{wvHp
zSh|P#2c{nup19h7F#Rz1!_oyT{9yLO+yzq)qv7=iG+fcsBP>6_)T7fd{V;dK@(0Y_
zxY8p`AIv_OepvX!%1>PSVc~<Gk6`w~@-Hrb!~6jYH<<k}eK2!i>d<LezK7WlFQ1|5
z7nYyU^}*s9mVRL7!2AodA6*}e4=cZ5_QLW%EWN_w9mYrZKg^%#`eE*csfUF-EF56|
zgT*gQKP>)W`2*%Zn7d%^fu&!VJS?5U+yg6bVD%_0eZcHT??1uJfrTF|9%1@m=^hrI
zFn__q5uVPV=^kbtEFNI_8KxechPfLS4lw;N`(WXLEC0aiZ<skScf)9yzhUk{w;$I2
zhWQ&t!~BKr4p{u5n*-ycrz==`fcXQ)hpES)q2UM%519L4`eFV-*9Y@2%>A%@0Sh;n
zepr0M^+WB0sfVQ}bbYY&2iFH>z}yGZ4|4}RykYqmsvez!*$4A4%pRC~(bF4DKP;WV
z^ufX%W-lz=!SVr2AB={nhv|pe1G68l4(fh%b71;m{)VL&SpI|YVfi0cufo$0G~8kK
z!|a3k8>Sx?J~02FpWh8j|FCibW*^KvSbBhoqt~CX_=A}Pqha9-OaHKNMGqgCJun(p
zj=;=;g$K<4xY94ozp(Itxf?wlVvIjS!x5JMVc`XHKg{2-aD~|qi$9n?n0sORVE%#S
z7npuneE`!3b1y9XVftV+F8wfhn0+w&Vdlc)1)Ba~_QTT;%m6423on>^;Ngj;AF3RK
z!sQNh_rvlD%mA3bVfg^oo`9tjn7d)|2-63nVeW;|FmYJAf$4{(ADBEk4GS-rIq>=w
z>VH@|M%M@PH_Y9z@Q0-jSa`$ihu5FbbO-Y{OdqWLgO&5J@PpB?^aC>=7Vj_`-QTcq
zM0Yog4|6xn->`6og&TT&!SumsSbl-o4|6BXzc6*UXqY~j{jhQpmhWKZ!OAa~Iq>*_
zh9fNf!om#}4(R5<;saejEZkxKhJ^#nK6Dxu{xEmL`Ox$Nvlm?-Iv*DQFmqtx26G3Y
z@Q1hipy2>3KVkY|<rTWUuyhN{f3SFfr3aWkm^rxg!}P)Y0SkASK0@gaW-cuM!OVmC
z8x|h0bOBS3PNT;sEdF5bfVm4@A1vNs?uXH^{0`F(OIPUn(d~utVf7tMKZg6E@db-_
zSU!T84@*BVb71~~g$w%mC$RDwrXNPb(mTu@u<{3%PGIhZrC%5ymfv9Z!~74kA7(y`
zhNUN%e_-M;dvWQ5se{q5@Q0;KnEkNufT=^LVeW?64~uV@{jl&u*9VJ#m_C^O=<$zB
zKdjt=>4(Jw%-t{=7XPqtfW<dVA1pp#?t`gEj|W))4OU;k+zSgYn15jL4znMY4`KGh
z?SY<K1Iza?^I-a6;RkaMOdmQ8vlpfxRxZKp#}$6)_QLFe>4Uig-98u}mhNET0V_XX
z^#;rwbbrJ2!)RE%!QunvADI6MxdUB4%>S@*6rO&e^#d%vVeW>dBbdFg@Peg77!9lc
zVfta=2cu#3!u$`@2lFQe4K0UY@dnE`uyBLL2ZlbV0L&dQ_ruZ|srJJ3!|a8X`%r5z
zC|vU=FnzFigZT&D0GPXB=>=v#ES_NcVdWmWei$FU+<~d5mwxne1Qzdb`(X~nLBrBJ
zjD{(L<ri3d!qPF!d|1AN>4T*on0{C|z`_%z4`vQb9n2h<KVb0!QxBtI>d|Rfx`(*~
z79X&36J{UG9WZ~v%z@Q=uzUzJ50(#M{)eSY^l|{E4rV_r9ANH-#{)Fq!|a98F#BNo
z;e4oinEPSsVKgk<VD`cM4Ra4H{9*RP$`hD=SUG{7{$cGfSiXVjhlK-NKa>Hp50-ym
zG%TE8;egA2nEPSwfY}2JZw!4<dtvDW7H%+mVd`P-##Ii%&i{nj4-0p6f5Y4lwHA|t
zmY*<pz~TjFKP((DH9|yS@dncmQxDURo?aok7#Lvoz|t)&|G?4<tQ>@?N2k&4g@q@~
zKA8Eid;@b2EPi0=2WCHvhSe9a{09p^m^m=>;qHNkA51^YA254h_QKLLOdl-Y!om%f
zeqi$G`eES;lZWYp(J=qO<k8a+%s()BnEkMNAJ+bYg(J-0uyhR5hl_@#Lzw?z`e5eZ
z(g$+~+#k?#0~T%=;f-bxdOCoG11#UbXjph)m<NqNnEf#K!_ooF{V@N)+AlD7!}2X$
zAGAFRQwOsj7XC2%(cKLTf0#RA=>^7zr9YT^VESR<4pR?vCt5IJaAEF;`5TrFVBrpP
zKZZs$0hs?`=Af59#F_(3|FCd`<$sue(X54+AFy%{Jse>EfVl@<KP;WY?1j|>FniJS
z8@hg2_`~$W!VeZ-F#Rz9qq_rUKP=o}@ehkfSUQ230}BTj4bumsVd)FzZ&><8cQ=fW
z9{(`^z|s#a-4ZefrVr*0SUAGm0ZX?qdvVd|;elTMq3eT{?=bsd=@1qluyBIaC+PZN
z?uM1yF#BQthUr6(H*|B*`7rz8?tzwPFmut>!}u`wz`_w`FDx8k=?bPEM#I`Wu=EeN
zA8Id5KP+9r^ucJDy)gYSKFmH?dVs|TEM3CX!Q2ZAKbX0&_<@-Na|dkx0v7Ht{jl^1
zOK-4vMUO{Vet_A7PQ%QDg+I(*Lg5cH2Uh;T^uz4Or5_glF#o{90alK{XqdlX_P}UZ
zzK6LRR-U2fXI%LRJsx20f!PO(H&{Hv;tys&ES_NQhtaU`hvfs9e)Mz%i#M42Vg85N
z3k!c-@ditGu<(YZ7nuDpb71j+?rwB@VfxYA0dV)j41m(GavPR!VD5*x7p4yuA5d*@
z3Ko7acfjHUrVr+RSbV@WLK(35hUtf;2Us}6!Vlg5Q0?&hH(>f<{(#vB(+_hGOdpI!
zcRwus!}P)8AEq9ihWi)Vj)L)F=@n)UEIiTm!`utg2lF>9USQ_H@)5dzn0sOI28(}K
zx`f3W%pK_ZVfh8-4w!kc@)s6vuy{n*597n!3yTL>K7-i{GY_U7M#IuMEFZvVSa}1}
z4+|HVJ{S%2H_Tp`zhUl%>4&8Um_C>~m_C^QVd`M%4Ib{$`UK_<V)uW*)Wh5Xo4<#p
zJ6QUM*$;CUtlb2oVd)1~Jiy8iSp31l59SY;zj4yg^a~45n7eV=2Qvreepood${kqv
z!`uN2SC~GS|KafswI4m5z}yd`VftbA!0m-fz}yQf$6@|}g+GiBvmd4(mVROS;o$%^
z7p5QG9q9gt=VPcoSiHgX!^Z!i(l{x2JVL_-W-qM$0MiJ|zvwh9J;A~qW<Sh-F#Rz1
z!omTT{$T20<sZx+Fnur@7LKs+hNVN8K6w6tX+Wc4;S5U$uy}x#Td;Hl%Lj1n&~Smd
z8>SByPB4GL;u~fTOg~H>rXMB_3ny4T1dCsoKVbes*AGj#==x#mVBraqhl#`NhnIuU
zat3BEEPP=8g2g{9++pDci*H!D3zJ8;7d;+f=?0eGVD`bnALegZy#fm-bQ-1~<{p?m
zuyBX*(e=UN4HgctaDk;;nEfzw(DlRo3DXZthv@MR3qM%+pqm3rw=i>H;R3T47Vqf#
zVetgh2XjBnzcBr<c!1f9?rxYlFneJ65ate;IJ!BoaEHYkET6#4hv|p;A7&1^J7E5Y
zg&#~mEc{_K%wI75aPy)05vC7jAFgx*QwK{gu=ESd2QYuY@(a4XFn`0s9VQP8ADB4I
zoiKA??t+CEOdTw|Vetz~mvHl-`4Z+Xn7Od{hM5Pemtf|>?1R}4(+8tr{)N#neXw|d
z`3shgVD5wIN4Fmqt}t_8?uFYA^#@EpOg$|8Vc`n17nWXN<pE3|jE031%)PL1fcYDj
zyJ7la_QLeT@-2*pwSQsp0MiGfVd)1J4zP3z%TKUyg}DRfFPMH<x`+827QV1>f%zL2
z4lsAY^uz3dr6U+0rXEJa%!TQL(dhn%<tLbbam5=feqsKH#RII|gt;GPKg>Lsy)YVP
z4$S?q_(M-OFg{EloQCFmSUAFHSbYc!cU<~m{)gEQ3n!R<Sa`$Y73Lpwcf;yySa`wg
zgQW|YIWT!xe8cR8g&RyB#>b@}mR@1@!qN#Wy~4$z;R6$gxgTaOEWBavgSi_Pudr~2
zg(FNIEPi3`hlM*V{9rW9K3wq+E3aVwfw>1gykX%1a}PQVi$|EdVdXx|-7tTl>x1P_
zm_K0pVfhpmAGq9)OFzs$n0^=^-F}$=VfMq)1uWcP`eEUTt{)ceF#WLn3)2TP2VEb!
zy|C~n*55GmVBrq4AC@j)_QJv+m-}Ji2Qvp2{;>24^9L;5!R&?6uzUz}Kf3+6!W|aQ
zFn7bk1LjUxIKcdYZa-}O9IV`f^)q4aftd#rN7oO_e=zkhdtvDx=0BMKVe&9>bQ%_Z
zu<{?4e_-VTx_THNrXCi4F!gYM!xZA6Vc`gOH?)3-yBErb#RH6n8HZs%w7i1J!}2}M
zJ?Qs`!|a8}BUB?S-NEuL%p9mR1I*npf5XIK=?7N+!@?iNhpB_v3yWu%I4nP+mp?Fh
zTr|3SVCe?tZ*=#gn*+=DF#BQV!Tb*k4|u$w8-T`#`45&(VdlWX1Ew5S&%o5d+zE?U
zn0+w&VeK+>{jhuw%MY;p2h#^D4`BMxX_$Xu@d397n*Pw$!}u`!VD`iEDa>A&`(f(Q
zX;`|0l>@NwfrSIiJuq`&=^qweFneM4z}yYX&v5ra{Q)1pf#wsKepot&m8URw!pwuY
z3uZq|9+uu=?tz&DlgFhGCJ&=w`eE*Y*#iqlm^xfEEZxG=6D<7`ig#G}!0d<dVKmJD
zuzUowAC|sh@dx9h#}iCHtXzcYhv|dSu=t0mhn2rD^I-bmG&KHT{)V|1mXBdH%-yi?
zfa@o~fW-qWJ;34tmd*%hho55%^B2q=uyO#FZeZ~R%kMCE!~6qtH%vde{jhL{xff<G
zEF57pdie`;Kg>Q@eu49$`2|*vz|^DD==P(hTXgrr^+V$i=5F+G#^qj^epq^i=QpT(
zVc`T*hl@tn5Az2s{9xq|%slk;2#X(B`i1df=>%4P!O|xz9mD1iVD5ssA7(!+zrw-;
zW-crpz{U?@`r+{f4Nq7(4znNT9$37<^ucJD`7nRL+yRq^(Xey?OHXidsJ$?Ku<(PG
z+putkxgWj#0W$|qL(PM^2WAee9EH&^^)UNj_QU-TH3#NzSUBKvKg>RuIWY5K@d2|J
zmL6d8FdCMQVfh(mA51??9XgF3{xEr%eXwwciKEjn_rmfo%syCpg{5P3eK0;Oox#k3
zg&!<k!NLn>9xOk@!X0ieG`wN<!{QBQ4=mhZ@c|2On0}Zz%)c;q!0HW{J7DI)%!jFi
z(dh1m#RJUUaQ#pQtbB&WA1oYT{w1UzmcC)-H!R=5;uUHQ1I#>_|6t-U{csvuj=|go
z^CwI{ESzBaVetnmXJGz->4$|oEIq->A((!cJ7M7m^9L;4VfMoO1=9y}7c3rO`e5#Z
znG5qTEF59>!}P)Ih3SK(A9R1f^uzoC3on>D7#|iMFdC*0<`49A087U(8de^|{RfQ?
zm_OkDM`OV3gZT?44RbG{d<cs#n0{Dz!}P<<L#JWs9i|VK4q*CV@sF+#&WGk(SU!aL
z8x~%$c!8@&W5D#m?1zOTJRYIp2TM;d?XdI#(+A_j+D9-xOg$_fVdVf!A1uCL?u7dn
z>MvNjfTugCdtmy}(;v)ySUBL)4-<#!hnWx44~qwwKVbDAOdjShnEPSn4m^EA-4D|T
z^A}tmY5*+X!SVsj-LUwB=||TG)s8{I+ygTQW-rWt7#g4g=;;s^KCpBMa|cv81_kp!
zEM8#w1*RXCo-i~*1<=zWEWg6^qo+TpHuyLO%wCwkVetghkM4h1I)~W{i*Hys!0dzR
zhvjFOy%;n!zG3cwmj}@FO342(^I-7@3r|@5!SWSM9gK#lgV_r&$DsD&iZ_^gSh|CS
z2h3bpeuU*)^z;WC|AU1a%zrR{!@?0)_`&=MQx8ioF!Nyj2bg{sALc$74f8L|AL#8j
zT;T@Oj~;$7f5YrW_cu%*jE0pTF#o{Q88rN1?uNMoMx*P8#Rn|?!sKD$0&@@g`600K
z3Fd#8e_-hqrVrg-m_C@ju>20Q4@Se{A6-98J<L9sd2kx)f0+I7aDjOYMx*5$Sp37v
zKbZZnaD-`t(XjZ3$2-(Km^)zk2WAdVcR&q<r(38wa6Z&OuyO^e9KQY$W<QLEmFwu`
z2zofe)WhNdrVl+G!uT+I(P>yY21}2y@I!Ymx;|Jq!R&{HCoJAz?nl=L<HOP&jE03b
zjE03DEIq;e0dptJURb(^*#|2ZU^FcLbfED7+kXbr4+}S#yI?fT|1kf+?1$Nho{nLB
zSbBr$gOzVEbJ1y7_`}=-3xAk?boJ<bSor}9N0@$?zhU8rt`DXjW-l!MVCrGv2{Q*a
zeu^Ic@bH6%FHAp-hNWAWeptGJ*^i!YVewC>{DheUcmIBf4`Ja3E5{(BxJg*}!Q26}
zA6NZ{ZW1g!VDS%2H}Ld=rU6!8!t8~`KdgL#*$)dhH0^LMOdrhuF!eD1!_pyK0}ckv
z99Ve*OFx9tEzG~Lc!b#pOD}}NALf6UIWT|Y3OBg(q2(*g->`HG^EWKr!PMhQM=<>`
z|HIV7${qCZgvBc?{$ToH^%zVZ7LPE0!SWIM_#Z62!O{<`-3s#$tek<x2Q2@<+zC?$
z(+>+znEzq=;OQ8eKVkY|=EL;EXjpi`!V?w_aD7k)EZ$-IVDS&L9~K^P`=R<^6wLiF
zK0Lgj@e50Tu<(PagVRuTu<{2Mp0IKOmX2WI083vm_oBxi%-yhffVm&$f0#Hd{$b$<
zvlo_bVES;n8|nd=IWTuXh2iHE!{PxZj!wh;3G)xkeK7mc)uZ!a_QUkU(htmDLh%pN
z2M>Q}`hvL&79Ozhhs6g>9_DXYx`D~V%!Tuz=EB?$OD8b>F#BQdhQ${wzG3kS(+{Iz
z`e5e3)WQ4>3s0DObQ<Oln0sO6C(M3WIS6wXx_+2HVD5mmS76}*3pZGKg03GPzR-LC
zE5~5=!~6j=4_!Yj{lelM<_}oDgt;F^!~6j&?_l8ua|bNl!Tbdihq(h5A259|_rl^C
zmfvCWFmqt~VdW>xz3A#-{)4#_7Je`qJwDOn59TkJdtmnB3V)b=u>21TKUjGO3lEq&
zTr|uduyg>kmr(fu4_|2fqo-e3IN-7umfm3HILv;Sy)gH~!UNqLSp31<4@+k-8fFf9
z_`|{(rVo}5Vg82s111i$A5KI43$q_)AIu(DxWLj6EdF5T!^B}UOdj2QT=v7tO;|dF
zxfhncVd(+p4p@3XPk*rV0xLIQ{(z-RSbl)zKlJd2xew+Jn7y$457&oQe&R9*7M?Ko
zz`_$oW9WklpvNc7{V@As;f|gjVc`RFKg@nuc*6AIvL6<Iuyh3D!@?V;AH5w4cOP0h
zf|Yx)bPv-HOCM10GQjMC*$azDm^>`}VfxYI9Tsmeb7204@nPy=`eEq+W<CZD^#?2*
zVdlWnBg{WAeX#Tkvlo{CVetV|53?WL|FHfuTpzUG1&d#}`OtI+%U3Y_Vd(`%!{P^~
z4<-(C2P`~c`q61ve8S=bJ^jGUN7n}{|6uyye5ku|+6xVDm_As3frS?={h`|j<HP(1
z(+`U;SUADl3o8#`;xPB1hZ8IwVc`IihuH^nmjTpYFmaf@uyg~{53>iBe$e9|=6+bZ
zhlMxH-7s@u<v%PQ(d~t~6Q&<le!;|H`5tC2Og}me3rARbgq1rm8fHGaJ{TXC?qTr=
za|bNkVc`Hv-!Of+Xjnap9uBa0fa!zT3o8#`<qymqFnL%wz}x}T4|6BX|1fi4=?~_A
zT;T_^4`vQ5++q4*=?`WfIt_Ct%zjuoh3-#ueK31q`e5#c>w~6Km^yg;LB(O|4z3?6
z4)ZU}KQQ;f!Vh--CM><f^kdiq&EGKdVEW<yfXc(nhvf%Yyu-o|<{!BG;Rc{GVD5pL
z2a9J|c%rIC@L=wS`2)TDg5^Ji4pbH_oM7&Tr3YBLh4}*(4zTo(ZZE9-f!PPkH!%0Z
z>NA+_F!#dZ5vCr!oPeoEr(xj@3pW@a=6+cD1#>rg`iHq6R^Gw#87v*b!VzW;%pd6P
zz~DpE1uR@)<t8i~(bdD;4YL>B9k6r<3m<g-u<(P0CoFzo{)V|9XZnM>7iJDDzr*4I
zr#`4dVBrsoe^|POxf`kslY-g{b2lvBVEF~+4or;@5p;WD>5ibi5bf~wnJ|Ath2a#;
z->~q5r(b9}1Ir(<`UV!?Fn7b^0cH-&U9k0gu=EEDH<&rFc!s4{SiXd%A6Wkv=1^EV
zg_#3$2h4t0I)LeiiNo~6(j81cEIi=p5ULR7e^|PJ3Nyg$gSj6Tk8pdS@eK19%>OX`
zxM-L=VCKW@Mb{6r4{i?BKA3x8@c|15Sbl`5M^8sE^I-7<%l9z-Fn7T8!|Z{jW0*L4
zdW4ySZa>UEn0{D!1j`37b71D8(=hcgf56O#<wJNpplgKkVg7`LGt3;AyI|(P{S8h3
zQ0?&ehow^t_d??XCJyr#tlWd!2kk$=?1QCC7!6B5Fd8Nf3xAmXuyBU?3zlwS=^y59
z7!6D3u<(cFYnVH6>4WKq)z`561G5*F-wEl5rE^$#!2AXC2h1I?as*~CjE30<GY1x)
z==x##0HzN{!`u(^KP)_8@c@f=T<Hj1AI!h7_<)sTuyPb;FN}tT8_eIZbO-YfOdl*9
z;PDSL0GhsF?ttloNu!4Y%wI5h7!C6`%pEZI!{lM^z_1^t0F8#FBUt`~>4SwQOgYTG
zFn`0s4<-(aUs!sArDvFam_4w30OP~d!NLs|4lsQ%8fHGsK6rdU6~Mv^=5AOzfa!y!
zH*|fl^agVW%wI6~z|uP`-eBg!<YDO@<{xx<m_C^Ou<(Vs7as1=^Z|1R%zT)+Fn`0!
zO_)Dn`2v<6VBrU|2j+j6JD|bE0IMg^!vp3nm_K0ZVdXB|KBxrD|FCd_*$b71Q!sbH
z?1T9S<`0;?F!SIVp$u5Mhq(h5FR<`}r8`(YhH8UTF!eBd;p(8_4&%eZ9j+0|faNoo
zIWTv^+BLB7f`u!3IH3C<mL6dF36^hR;Q$L4sP#B0n7d%{0;|8!=U;Ie0QEmCTw&=8
zz5N0=AF3Xf&S36`>4*6nT|X@TVCKNm0nGg{|HHx`-T&zM1QwpK@Q1}8EFHko1xy_*
zykYjj;tQq^<{uah(+|sk@caYyKP;YL{)L%`9?t0cVg84uADBI`bOVb&baT-8u<(SH
zr?7GiW)8Y~bUvy2(Ze4WZ!mLV;SLKAm^h4v`5We5nEzq!#ib9XALd?Ic*5KROMkF<
zgV8X3u>1~-KbSt4eX#I`@nPu!rXRNc6P9jZ<s8hvFm<?S*!VNdewce<=D^ZDy1nS;
zq5B_J4#CnlEWg0a!<8P<&4rl*i%*z-So(&khvhSveX#h0<$GBD0}B^eK8ERsnGXvG
z7#|kyF#BM9m_BqG7XPqxgr0wJ`5PARF#BQdhq;qb_`}i>%zjvS6LK%i{jl@{a|bM(
zVD5*h!$rgFhtaU`hQ$ZWAFyylHwRY!!NLvZZdm$<#V<@8R(`|W3$qs%-!Of!`T`a%
zu=Im#{~W9wfW-sMAF%uY3m2F<F#o~a2TQLo8s-jIIRbMJ%>OWZU^LtuX#WsqFDxF=
z(+@2F!_q5EJ<ML1J-BF?esud`=?xZNFmuuEhnowncVOWKD@S1A4$}uqmoWdp_2Xc`
z!UGnsFn7V?AEpgvA1pn<@(;{CF#WK2L)Q<BM_4+7rAJt~1`7vV@ra&oVD5nV8>Syd
z!^C0s!qPp=UYI*!{)MT7iNnl+xfdRP&~yXKZ?N!z#RDu{VDd11u>23pU$Arz3qP2C
zSo($Ohq)K#Us$?>#V<@8PD9-XO9!xg0t-Kwe_{0`%w4efg}DnBelY*R`~gcZFn7Z8
z1<XDe4bumsVg7@~11!8@;S39RSop#8!DyI0F!#dz0}BUOc)`L4rVkdrF!#gwu>1xK
zH<*85?uYpw=5AOzfT@G&hnWjY$1r!m;u+>on0{D3frU5BJuv^n^uzoG_XmstEeBxk
zf`tRjK3M$0l;fnK=D@-e<}Y0OVfMlN3G){$USRHr(dhQV!WkC-Fn7bk8<y{3?O1gE
zuyBIuhox&+dWN|hW<M<cVCKNw39}bHoxuDD%NMYCfa!<D8%#e;9n4*@d;xO@EM3F&
z!_=X>AC`{b>Y@1;t`Ew<6@D;%u<%3AKQMp6$`M%j!qmacg}EC&TwwZO=>(Q8Vd)K)
zuF>NG>TEa#3xAk-uyBN>BbfVP;f@}Eu<(P~2cuyY!1Te=6HFYIPSEoyTpctY!0dyI
zL*oH<e;6#BVDhkVfQ1XpeXwvu4<DHQuyBC650;-`=?Uf!SiGXEgV_UfFN_Z}7iKTa
z9GHHbG}Qm_@PzprO2hQQ(h)4)VE%{6!|aFo1AY7f7SFKof~5nvIMhB^IzTrcmcL-`
zgvBc?9AM#vk^iCogV_f&2d)l!j|_S|z|4c`gXLS8e_`%~xgQ=rP=CO}A0`j87iJzT
zJ;B@$i#J&MLH9q*eK2>x!V%^Vn0>H(2y-WlhUtUFAFLdJ*$+$C==mRAKg=I6b+CAc
zxgTZ@Odp&^iw9Ud!rTFKKdd~2>qBF}?1RNW%pI_BKvNFqqK6+WUSRQvE8JiPz{-C(
z4UK15dWV?@3r|>m3Uenc-eLZQ#WT!2SbBho<4S)pcf$M)GZ!Wf3rAS`ftd?a2TM0F
z_rl^0rVqx4`5T>vg+IJJgN6f4Ke~E!KFmEZ{jhL?>4%3Onnswtuyg}+KP<h$;sH$?
zoD0(jw-=foVet>MA675IH9{G%c!KGN<pY>L7!6g9LE*9=rVmDAXoL#D;tgg$EIq^Y
z!Dy&*42oR+uyBT@A6Pj86DKzOVD%I%9AM=oEI$y6NAz?9%U7^`0P{btbO3Wdto(ze
zGnhYM_Twsl;O%v2$imzYa~I5BSiXZQgHtg5F!iu-goO*N{DRRi_rMH*`4<-dFnzFm
z53?WUewaBh{jl^3vlkZbu=s<82TVOW4NGsZbOh56E3eSiqw`_;7Zz@?cq3Haz|4V#
zA1oij`~mY9EZkw~0JeS(rXS{RSUP}(8_eG@|G~l?ef$Cz-Z1lE<rd6bm^>^z2>A<U
zAIu-H@Px$&+#V<cmQG>nVg7)H3oJZg{zq?r!R&*%7e>Rv0cJ1E-LP<hS`VjS@d!(g
zFniI{8(ae#17<JWJ<$3C=6^Kha4yVVnEzq<7^V;AZ&>`nG{VvwEZ@M)fyEyz{9yWE
zG)zA%USaxR^#d#)!@>a;@38y_*N?`4`5*2csQ+Q^hoxgQZE!BG^bd14vF5=14GT}0
z|6u6=W)93^SU!Wh8(M$D!UGnbxb(x!hv`R;KUjMQ7LG9UVD`hp8<y^2@d~pamJVS0
z(ep1%AFTd?m6x#e4$}{}7aA`xdtvT|#UFZj!}Y-&45Ojp1dBJAzhU7CvmX}TaCgA;
z<Dg;T2}^&Z=0jNg!`uZ6M_lO_mS17^!u$a<2NoVM|DZb?rVkbmuzUrJFPObBccANs
z@nQaj#RII|ftdqShl_^kgT*Jzy|D0ym4~?W!}P&un7?8Aais%T_`&qS+yP66F#WK2
zM7JLnelY#8@Q0~~>4%wzt{<izrXOY=th|BghlMA)eptA}^uzK6te$}Bhvj2*{jhWb
zOZPDSFmqw@xXKS${Ray-SpJ8(7e?dCKQMb><sHmCnEPSr50;K#;xK=}{0&RDF#p2L
zfr-Py6PB)E;xPR%f5P=c*Av3x1D5__;Rtg#%-=Bgz}yM*FDyJ@?uMBU6Ni<1u=EK_
zx3F{xQwK8-rXOw}v>b!QA1vL$^ugQ>a~CW=VESSDVEG@W4;Ef9_rSsp<_~oJuyBCs
zhpC6zi!1-a!XHM%?1i}-<{w!4gUQ3fAEpl$K5%u=aDj&-wBCTZ3+7IkyJ6uEb1y7?
zz|_IyVc`$67Z!gob7A2H%SW*E19LZe`hlf;Sopx)57P&yq3(z2hlM}P9WZyo@(WBK
zEWTjo!rTGVj~@T9a7A|yM)?H|Uzq#h=?CggSUADr4`v^XhTWeB(+?Ae<wIC_!pwpB
z1EvnPegjrd!r}+!ADH`L`eFKT(Xe=gnFotUSa{;n2eS_r53qCyvlrdnFmqt(1Ll92
zIk0$w`5&eporc*5(+{&3mcL=?9%etxJXrjr+YieJu<(c32U8F84|@L%?rvy$fTbT;
zK7#oN<_@^IQ1vi>z}y3qhs7t%URZp><YD%}>Q9(@Sh|O)gXxErCopkz8r?itIAWxG
zG>x$Eg82hh@4)PZ#S5A?I2Y!AI1SA|gzSYG086j1{09pMSbW364Hl0ud6<4!xWn8J
z3kO(u!O9z0JfiD|r8Ag2VCe{!E@1fx79KEn!R&|m8<ziI=E2HeSop#8qxYX-=D=w5
z_<*Gcn0^=yQxBtI?tq0OOdl-#VEWMQN4F1_A7SYoR(`<35tgoC_QGiNc!24L*^3_T
zFnur@7XC2zquUEphfc%X4|6xn9kBSuRer<Gf#zdayu;!VW-iR%FdFU;s6Lpzuy}{L
z8x|ihdtmxu=?9ixVfta=1yhHvALbvJI#_)PGY95wSbBry7g#xlE8bxKgxLpk4@?{u
zf4KC+(m5>NVEGQ_Z&<j%`~}NTFnO50uzU-1H%uQaAHw_ra}P{EOdZVKuy};U6D-_d
z@d67!n0{D3frTT?J?Qqq!WE_u=3kgTSh)!^A4bFcjh+r+?FX2Bu<(WX7e>SM!}2lA
zepot(`5zWvF#p5shlLl657UoM!_p5dAHw1Z7H;V3VSJc7VBr7@Cz!o3K1@A24YL;(
zf3SFig%`}<uzUkcA24%Z=>}#$%snu9m^m=_pqGEJ^a~3&SbBij1M@%39GJN<8a*AL
zr+=7!nEf#Sp}QNV52g+lpD_2p!U2{ZVE%>K4+|fdJj`FPasp;A%zRk5!_ptjzwmk+
zTE5}Z4>KQTKg|6w`(f^e#RtrNuyh3rKUlcK+yiqbEM3FG9hQ$_@-Y1{b+GV&`3Dy6
zF#BNP2h)d(hNXX)zhLf$`3n|K==x##ALb62ewaR3xTCutW)93=n0~mwpzSG`IWYAw
z8m<l+U$F3m#Rtq@c=$mz!rTi}56gcr^I-WFrXN-wLbYR@8x7M3vlo^=VD_Txhna&;
z!_omP9ANPXa|fYv3>N>e@JEk7xPEAO!NMJ;9+vN5?uMlwxWAwdhV|cI?t}`%DOh@g
zxf?y+b)fdc@(HZ`hq)g;{L$@)g%hlvfQ3IyKP(^PN;fcv!t}xX0ZSJ!cf<SvQ;$x=
z(hn^BVDU`IURb#Rvlmv*z~T!Q4=@@Q5AgCE8h)^J3$q`l9u|Hu_rmm}pZ^T=KP)`a
z)uWpOlZVmh{)eSknE&DF4W<D~!~74c2VnNV(g9Q%(G<)du=D`a2#Y_M`7m)<c*E?6
z`5)#km^xUv!O{aPeqich@dxuKOdTx$!_p&6A3Xk`7Qo^UW*#j3VD5#v6PBLQ_y5Dv
zD|$S@(gDo<u=s$v1Li*T{0|d{sfU#VF#kcF%>Y}!2XjA69A+=f9(Z^_%W;@GSUAJt
z4MxNK0V^+H`qAx&nFI4b%pI`!gt;5$f0%l78fGsn9?|VbHwTwHVE%^r3zi>Y;R!Pb
zef$Nc9v1&F|G;RN`(W__n}37J!`uz?Kg@rybPp?!VCf3I{)UymF!#dJD=huN!XKs|
zW<JbcF#BQpVD`fDAuQd(%z?!#%p92iU^FZoVBrD_H<&pvedshS-eK;6#S_e4Sb9X)
z2jj!+hlK;o9kBERa|gQnVftY-EFHky0SgD1J7DI))WhtB*$49nOdJ;Oxa^086U;ss
zAEpmxA1q#A_QGhm`=Ip+tUQ3p!)Ta(^zs>IA1wai^)xiUqx%Pzj$!8FqG9I2+yje0
z^!x+U4+}4tdtmt(JsqQ`e?tC;r8`*sVYnX}Zm@D17LTxWf*x<MbP01eEWN<o3-dQD
z-=c>%OdpJf`5PAhu<(bu7u`OXK3M*R*$YeeF!NyX4KoLpo?!Z6{dZXWz`_;gA9#2`
z(*Z0U!t}$+FPM6my)gH{+y@J9Sp38E!)TZ}F#p5+0aFiiKa3C4hfc%FL0I_1X!P)e
znTM_)oexX*u=D_nNA&s|mi}P&!T2!upxckD{Q&bnEWTms7M6}+{YaQQVc`aIC#?Q}
z<sX=PVg7~1FHAoyoMAM~Jed8k_=LqfjE3olsfWcsJiej%38oH~e_-hmCJv)v_Q3oP
zOOG&r!_p6|9DwPA(J=er`k?m1;sKU!VBvu7512XVG%Vg>=EC&B>Tj5OSUkh>3oQI$
ze02L^@dDEii#M46Vg7{WSD3xH!Vl(uSUkeq0}D5p{V@B`X_z}<_QT3OSa`ws==xys
z57P%X2U=dh?1jYxEFZx957P&uVg81t7q~y5@d*nDSpNrBe!=v^+yf6Ms6H4U<{y|j
zuy}%n8?4-dnFDh_OdpJo9u6=*ES<pI1NSF1JYfEZsYj<__QTx+)d!15boDSk%)PMm
z3iB7tpM=~E(+9I3R?fikA1wS}_Q7bFy)gg6#9`q@$iL|21+3g5<X@OQF#Rz5VdV&{
zoP(u17#|iN=-~m=4-0pgJd8#+2YvrGEZkt_KP=zE!VMO_uzUo&zXs+Hn7d)+ILtg)
zJi_V$Sh%8x4=f&F`e5l07XPq##1$Vfcf<6<+zHbM^9Rhm=rl|pES!kd2Qvp&{=xhK
z%m1+S08@vHhUtfeA1uCM{)MGySUAG`2Qv>A|FCd_#XC$Mmd;@D3akHN?uDfrnEf#S
z!|X+mfAsVNi+`B?u<(T03yVisxS+caR{q2E!OVlX1Kq!He?b|rc!Px_dO1ue9$@~1
zmDe!$!u$`l1}6mzKUjK)rEgd~;?xM0fW;ro9k6(Zg##?SVBrTdAF3UGjwH+-F!k{A
z0$Pv2!UaA4VEG58ALeeDeptN1!V#8!VD`h}4;Fr~c!Ze?vlo{C(c2%ebOH+pSh|F%
zhq(u4A36;SH(30`(m5==VBv|b4{i@Md|>GxW-rVfbbT;B%)KxgR?fim!~6xKVd)7L
zKd|vDn0}Z)VdlWx155ufb7Aohi!YddSa`$y3(G&SaDc@F%>8g0>VKFyuyhQw50(z#
z>d_f!`r+XUt^Z;ELf3}Qhq)gXkFfLv(+_hGdjA6!POx+bQx6LVSU!T~YgqWf^ufXr
z7S3>WP=CSFH!K`s=EK^HF#Rw-%>OWZVeW?MNB1|(d>9Q&&#-ua#Um{JpzDY8q3(r+
zA1uCM_QJvgt{#m6%jYnEz|ub~y`m|{;KJMuOYg97goP`H2B-kc-!ON>;{}?2VD7->
zepq<J(l0DNVD%*|{BeaJESzEbVfMr9h3SXoOQ^Hq6fC@8`eA&S|6%%Zg##?z!}P<#
z52g<m|8NV@7_fMQ>4%30H2g@_4>KNSKP((z;^;Ii{$Sw_i$|C~m^rZYh^`;zURb!p
z%z@=In11x|gU$cJ?1RN4ES<o@AC~^n^&3F*3oP7V_QBM{$~~BQ=<e1-(+>*=V$&VW
zzp(g&n-4V?7G5y-!0KPPIvfm`d9ZW^iwBsy(6zzJDVTd;<tWVGu<*yF9~OVGaD?fH
zm9vE04{N`{@(nB=Vg84?A6Nc^*$1;9=5APg!_onahWQ`e-7x!M{)MT7g%8aCuyg`*
zC(K@$I&}RobufJ}cfiD9@db-tn15mA08Aas|1kY9_rc3ur~phJW-d%W%v_i}R5`r;
z2#W`pJ7DPnrVm#<!pw(-1FYVL#Xqe4ftd@l7e>Rv4VLd=@d*nrSa_n>Loj<``5)$f
zn0sOFfTbgJ_ru%^^C!%FSUmxghnWj=2aHCyA7&1$9Dva<dtu>$9uBZ{2(t$k4zP3#
zvk#V@Vc~{O!_qG--eLZMg$KHNbUrK`VeWys8{NI=`e5o|@di^5i+6PO=zN&JVeW^;
z8;lS02P_<5d|3Fy;t8f6mQP{jH!MD3;R&-JrXLpGuyhF12TOmr!W|ahF#BQdhWQ_+
z9~SR0KFod?4GTYXK0IGT>v8ma3sa9lL+cNi`(W;e`3qe=EF56r2cu!(1#>?v9MSc|
z{0)nLm^>{0ap{ApgQZtkIKs+3Sh|I!Ls<C-(+5)zqhbDl$-}}MJ>9_MVdwY3!U<*{
z%p6!a;L;CM2g~QM^aIOBuyBXzhouXc{V*D)AEpmhZ^QhJE8bxGVc`eU4^t1*4~s{b
zdUP5Vez1H1b2r?*Xz35`AL#iyFneJ%%s(*wu>1;3M=<?xcR<rCEd9Xz19Jz=AFzB4
z(+7(mn7d*52&N7eudsB3ZZAw9ET6&D!NLzF5Az4Qewh8R@PoM<rXJnjxbgukzG3FT
z@)ImQ!t8~)59WUu4f7`~J;LmVnG4GYuyBNhD@;Gk-7xiVcS7R@<{nsnMjyX{g*z;~
zU^FZpz`_$|KfD}+nh#4iFnut0!O{y%AIx7cd02R%>x0R|XqY*$_<)N;?T6Jru<(V2
z2P{7m3V)b+uyh1V$1s1w!VRVlora|&SUAAKALegd`4<-MFnzH2hv|cvgKi&;53?5*
zj<9fmg%iwPSo(p9!_yBm{9yLO;sF-#FneI_FIYIDk3Yh~85Zua@PpY8(+5jOFmquv
zEd9g$0TYL(7ijqfO9!xcfvJPJAC}Hx;RmaSVBv(GA7SAQvlrdHFnO4MSop#G0~3d(
z8(6r)+y~2#Fm<qU4W=Ka59WSY`h~e47M?KuFmqx0VeW(Jhxs3tZeZyF<}R4IFn_?p
z52g<09+<mf@eT`jnEPS+U^L8Lm^)zU8W!I$eK7yQ^ucIYx`V|7OdJ*;F#BQdhsne2
zf$4*}0~Sv3^aIs^o)2K*3UfEizc71X;R2&!`eFWpg+DBwVESR{4CWq~eptK^(hrk|
z=Obu64KIhF`2psCm^zq!Q13Fp?1kxr(J*-!4f6*q9ANrj;STc;EIwfR&}m%mf!Pa7
z2k81>e3<)T@d=9`SbB%W3rsya4T}eueptGP>4$|sx;_{m=6{%en7d*6(bE%5AG*8Y
zd}utu!V#t(orc99%wAae3$vGyepooc+z;nN?T49zZZA3?R*u8m14{?6a6m6NVESSD
zVBrtb4=aCR@dl$|;Rs7FFn`1J!SWeQ9%e7xA5ee6>J3=A1utKq@-T6jeptBxb2p5J
zg*S|b>x0&hF#qF9zc7Ep`~eF$Sa|@8KbSeNbP2N`rXN-g!om#}PO$O-=6)C-rXNPb
z;vZIi!@?VvyJ7lZG)zCt{V@ITaD^$vMMJ|2W)95%Fmni%AF%j`rC*qTVeyAcKdk(M
z#TzXB!1UvCE=(L|A1pn=?86n#Fnur@=5Cn%uy}*fu<{e#Jea+(bOTEVF#BQY1eOnA
z=D^mk!o*?a4J`e@!X4&+m_0CY^!Z<yKVas-;t!UNVC5z(|G{XOKA8D1`(ft5`~i!9
zSiGR?gZUq34lLYY@eRvAF#n_Lhv|d)50)NaG)zCtA29vsG|c@l_rUTE%)f;40WAJt
z`eFG9W<SgvSiGX!597niAy_=X`~eF`n0YYuu<`<CKa3A6m(jx!T|Z18y1QZV1#>rg
z`i0pK%QrCnF!!Q|AA0!1@(awL=<bE_VeW;cXPCV(dtl)Vi+@;pfYGpW7nUAj`q62a
zKVbP8=3kh8boDSkEFZ%B59dScAwuyD3kR5cVg4lKUYPwbdtvT|rBfIUGY3Y);uWSJ
zW<SgvSo(p<!}1-WbO2Ki3rASGf`uc@9GE&>G|U|^|G?}e6c4a+2c{oA9$@Jam%VWN
zp!FEc-7x!M=?h&y%)c;uVd)a44@RTwgYjYMAEqCc|6%C>t{;}3pc-NJz~T><U(n+Z
zrXM}tp~hfPF#o{91Lkj7_`~ueOe4%*n0gouE5~8><BES+xeN0L%v_j1VD`eyftd?a
z2MY%n9~OSFaD|mSu<{e84i^n`2Q2-<;sKZYVfhy3Zx|ou4w!yexWV!vESzES4zmyL
zKWKcz(jm+on0}c1Vc`!m2PO^+FIfD+#L;P(d*SYf+6xPJboJ<bSU!X4hxr4hA4a3=
zgYjYQ44C_2?uEIZP`ZVsADDhvIKlM8+y}D{7JlgA535ID=>!(8F!N#lfUW<AiNoxL
z`2%JTj7E14%zl_WEc{{V6~>39TbMu4^}+PRXqbCo{(_}9So(pfL#JWs6y^_@ewe+u
z(ml)^SUADr9TtCt^uhF#n%`jhVDSp`H%uJnZ&<qrCXPWv%XwJ1!}1+0{xQ^}3BckB
zrXS{iSa`ye!`uTm7n;9d?uUgttp0+f7npvSI4u5Q<M-(LVetm5=V1ParE6F^KyUxU
z<)PsLvkxW?Ye&G`5AzSqeX#h0`2$vN!_omvA1wdC!WljMVD`cE!Q26JH!MA(x2Is?
z0CNv4AHw4W8s0E>z|^7B=>C9(H!ge8^~3xF3kR5gVD_T>A6-9;53A2$=D@^Z?m$l;
zu<(ZIgM|kyJrL55?r)fR=;aSAUBS#lr(x#6!W|ZGF#p5i9bF%c534_5{)VMHn7?81
z0aK4o!^#0%`U$xo-F|fc<En>X=@e!!Ed9aa8|Hpkx`w$M7EdsJuyBLP!@>=wAC{kC
z@-P}b-NE><d<xS~DEwjWhPel(9;P1_9x!`h=>(R}VDS$t|6u-y)dT46hs77n-!OeJ
z8kWyt_QUuv`(fb+QxD5;Fmqw)2NsU#_QTA9(J*(w!U3j_P`ZKH4+}S#{jl(Y#RJS7
zSh&LMh0!o`VEGtk4$Pl0_rSvgT3*BA8>S9sK6-rw(+_hGEdF5W9^F2e`(gTE?uWS-
zM#Jodr308buyz5=y|DBE(+Be>EZpGn2Q9B*{({9D%p92iVESP0hm{Yo^ahiM`4gre
zR(`_LD@+~CJuvsd^uhcM(+>|ns6v?kVd`P#z}ycDN0_}Z_d~VADOmi&(jm-TSoout
zA8?IO1}xpe;sNGvSUC(;hC#vXg@psmUYLIg*$dMTi+7m+Vc`VxKZeCn0hoH2{V?~#
z{0(ylEFD0#!723e7Z&a?eK2$28qgT%_QS%FUix9_2IhZQx`eqK<_=u`$E6<@?=bzi
z>MvOQ!O|U!hQ%99KP-Qt1s?+}{9yWE@dr!qF#YK12&NC_Z&<j){Ru4xVBvtvewhDZ
z?t<A5Gar@?arqyn51#I!<tofv4E<1d!Sun^LG{7n4`v?B-LP<g(J=k6aD#;dx_VeV
zz|^DDFn_}I!~BVE54t`WAC`Y$;SaM9R(`<L5u#!B4a^-d{V;dnD)(Ua!}P(@BP`ru
z;eks(Ec{^Rz|srMAB5ZuvlkYRuzCuXUSTvmT|y0n#V<@h%pFi^IE9`MVE%zAfR&rD
zbOXz`uy}-(XD~i`{|%PjVeWvndtmBe@-TB@;}<adVg7){AIv?lc!0SV-5)UhuzDV5
z4=f$Q%!Szt(+_LM!Q^4}H_U!myus{;tApk{SbBo_7e>R}19LAdU%<?Rr6*WAg1Hyw
zUzq*4?1QBPm_0BW7H=?jz`_G&A1pn>?1kxrxeulfmcC##EZ$-IVc`hV50i)KhoxUw
z_`>+GaEH+_{V*C9UodlE?t<k5nEkl?k8Tbu9AWt%)*geYL#JWk4a+|;`(gIN;uBpT
zj1RL9rXLngF#p2J1(^F`=?6XDVDST!houjgI#_(d@*n#CPnbJl`46TKmJi|PLenv9
z{}U|SVeW+a3uX>XAFgnOg%>OwVD5&c1DJkTK7g5zPQ&!U+=K3Jn0YXLuy}*{6Bhm$
z;Ry9FOdpI!&v!6!SpI?84-0o#zJRHNrEi!!VC4nOJ+SzO>4)n>W5Dc#xd+aNx*O&W
zG;MG$%syDS!Q2H)hp_O+We&{!Fau%s!@?8hFIawnrE8czSiHmZ!QvT~uVL<i<!6|C
zVB#=;!)Tbfu<(P~3)2saUv&4v{0}depy7ur-NN+4XqbK&O>FqV^uy{An0{Dz!~6>i
z7npi<8WxT){jl@~(+_hGx;_{mJsqIi597ns!)RE%!R&?k1D0-K_M?Y8Oh2sO4AT$K
z$I$SH#UD&PIt{ZQW-rViu=pmVAC_KV=@n)_Odl-0qT37O!_q0t{jhQd=5JU!gsF$g
z!_qIzURb(=*$c}zFmqw?2W$VJpWg$EADB8=IKb?Ixd*oX1||<TAKLzhxf7OtVD5p@
z=<x_EzhU7I3qM%>39}dOZ<qyWG%Oro=D_TO#Uq+>I2UF=%pWj!!|a9CLvRgf3|RVs
zl^?M1f~8+V`3|NJW<Sh$m_C>}u<(G%!{QsJ4;CLV_rm-EtCwNn0gq>Be8IvEmR?}$
zVDSXA2NwRYaDmwilZV*@qha9ylSkJNi)UE519La5yoALIoQCFS^!x;iZ<u*7`(fn*
zEWBX;f~9YmI4u9d!VgBn+zShTn7J^0FdAkL%pWis7M`&1LU%VzKP((!_QKS`;~VO4
z^l|_et}y*Df56f^ES}Nz!R&?Uhs8h4ewe#q=?~_9xO-p*;Gkjo0p>0^AL<X7|8QuB
z3&X+@7XPqxflEKk-7tT_@)gV<u=t0iYnVka^I`6VwL@X*Vc`kOPcVHj8m1pT9m2vL
z7JlgI4(3jnKA8Jq;SBRXEFHqkf!T{r!}P=Q0n8sTdvT>-m^rZchlK;o9WZ-g{(-4S
zr(y97vlnI_EFHqa6FvOV&4KY@^&%{s(A@)52a|`z1FRf@<x5yO1WP}#{0!3%lZT}j
zn7d*Ag}EE1AKm@1aEHYs%pI_F151B!`_LG$@PgS7b3e?T==Py$XMouY^DoR^m^tX`
z(fP3WhQ%MT@eT7QOh3#Yu=ENmk6`fz%eS!ffUX}FkLckHGZ$tKtp5o!ALf5px`(NQ
z$;0%+!UeW|02U6g_=CketUQF}cUU~Y+y(O=Og}8#VEGkh4$OX-y)YWCAFcj{rFU3*
zftic$4w!pj;SNhTu>1`RXIQwv+=*^KEc{{WU^KeDu=Ee338hDvdtm7i=3YYS5f)D{
z{jhL=>4*6Porc99EZ$)5hq)IX-sl>kd{{ie!V?zWuzUdHLzQDtF#BQY4;GHFa+i?%
zVd);`Z<zaG=?zB1$|H0Kz}yY<2h2WLJi%ymedzHIGasH#&^4g(VeW?c7Z%R2c!Pxp
z%v@M_pyz*ByrHXy>4RAd(+|u4aB*lpf`tRjd|0@_%!Q>t^z$EK=D_kF%ssI53o{QE
zuIT4?!NLJ%KP(-?@*^xA!uT-zVDSah2eTI@53>)J4l&{p&EK%}19LA-A1vHp=E7+l
z`eE@1(+?|8;QF8pSUCrCKP;SK@dne6D?Q=zKP;YL`eE@1wHAYd*$<<M_CM4lSbV|4
z9~Pgmc!SYU<rox9J<NU>AEpoH5A<>b=6_f?!qOkCJ%Sz%Fg}I@paQUR1Lh7mAKLDQ
zg$q<U1_dh*VEWP10ebp_`4i@TSp37>4^t0|KUg|~rAPGfV|0Bm`(gDtx_+1kVD5&M
zLok2hN_Q}QFd9~G!~6qtFM583sfW|h`~&kp%pABsp!-%~=>SH<!UY!2FdF6$SUkhZ
zQJ8yRe3<<(eJ~o9zF_GMT|G=6jE0pLF#WLf2eTjMUYPwb^I`g7G%Ov%+z*QfSo(vR
z1B*wPK3p`+9kBce(+4vLT_3FeMbF2ucz}fm%pWlQFn7cJ53>(ee!=X6r6XMahnWXU
z|FCd{rE^$04pWCt!`u&Z2h9I4cf-s<*9YUn?1k%x=69HWT<(UYcbGo(@PefSSUm^}
z7npls<A<<tgNeh!3nmV;2Ns^N@k8|d4~q|&zhLf$>4TXI%Reyp!txy~AHv)Vi*Hys
zz}ycrADxD|6BZt@as;LyZZEn<C?A&oVD5nBKUld1Rfa*q@(C>6!1>U4gt-GlBUAw9
zf0+AW{)Uxbu=E90&H(cVOg+rM=;00X4=g>x;ujWwFnur@79TM4VfMn@1B*Ynf6&TL
zn0sL6z|tQq{9$~UKhSAd`2%wg%pRD2boJ<bn0{FK1=A06H@bQlAC_)m`e5-0lZV-l
z%N&?`Sbl-&gM}Zg{D7GQlZVl;^p4JlnFotESU!TO!$rgV0jHtq5SRO5=>+BvSbBkl
z3#|NxxgX{qnEfz)uy})s!@?hyeqjE9iNn;v@(awrFneMCC6sPp=?$hI*6x7mhuMRR
zhWQ_s?_u!)b1y9a!}0~p99X#xvlkZMF!#dz1v3X0jxhbO^S@y3hlLZ&J+Sl)6Gsm>
zm^{opc>agxOPDw;9MIhllZUw*mR?}~hou*Edtv%uG_3rB(J=qQ+zoRVOg)T-#WO73
z!2AOXZ&-N`cQ-U0!{Qfa4$NLy{K3qH)B7PRVd)lq{0J7_F#p5TKU5zq-NXC?F^vJf
zZV={vn0+w!qniV>ALf3TKA8Jq^$m=M<tv!~Vetsl4-YSBe8bFvr6*Xt!Q2n4Phjqc
z(XetG<{nsh!0dz3=<x{iFD(7T?1i}xW)3XeVBrF@7v_GLy)g4&`2^-4bbr9Y17;2^
zy~4r^rVo~`Vc`z*2h4pi`(f^f#Xrn^n0i<~K=(ha{RGnolZWMdSUCU-f0(^6cf<S(
zb0<tc%snvo!Sutz0j3|FMlX+G;Ry2w%zRw>VDSJ;e=vJt`4C+n+<nmW2aA80KA1V^
z`e1yRdtvs%(jhFIVE%=bCvfv%<p9)PSop)@9cDf(9$?`Na~Ev>9OizQei#k2AEq7_
zjxcj!G%WsM@ddLFCJ#&Zu<(baE0{d&{AO6V!PLXbF<7|6+<`0pVCKQ{0W3Vx^`qMl
z(+5j`u>1qFAC_-m=^v&ZokovG^mGs7qw9mkH_W}T@)A}~!Q2mXA1oeV;Q<Rbn0}c1
z;r@iC6PW$5@P+G#ItX4)LDLf~9AWVZOMkHNfTs_rdYJt%cf!g!So(#jhxrF;EdzS`
zfw>cwUSNEfdUP6=eqr$iOTV!64s!>(yV1>o@nQO4=D@-eBix|j3e$&+hUE)byu$p2
zKK=@`7Z#r|f5GxIEL?HvM^AsSc!Q-QSo($W(cKF(AMQ?Qyu#82Og}8$z~dEa5Imfq
z`e5>~bOcp~Sa*RQeo%cdb+GgT3qM$R!2A!3SGamK2F%^CbO|#Lm-}Jn!_pBfJYn$x
zD|cb~VetoxH<&%JbOUn^dVa-~o?+&|$^%$<!r}{NFU(wAG|c@l|HHxw=6+oIVB)a&
zg82iM{$TEgxdTSS^ugQ>i!WID19Lx29_9}C`~@`LVc`cW-(m3#iw9V`gyln6{~cy7
zOh3%MFn7VyHLU!Cxf>QPFm<qahS4y6aPy$)73MCO|KR%37%=z3+y_esu<$@r4(Gzs
z0W2KR!vn^LYd~Yb(lIRDVEGW{e>CL`F!#aK!~6>~2Nn)6|H8r#7Jsn*8!Y_M%N>}%
zVeWvXBUpZfrB|4KnEzq+5DG7te_-y1g&WLXSbPx*H@Lmf^a%3@%pGud!z{!>!}1Nx
zJ+N{USNOx!!Dv{zff*00w{Yo)#S<+2VBra~7v_KTdKYFsjE2QCEc{^R!t}%PElfQ+
z4O0(u2h2WL_`&$-`q0B2=6{$zSUkew0~U|4^aKkxn7y!c3UfEiJXm=O%lEMMFGl)>
z_IF|Cz{*irxdSs7W<M;vVfMq^1M??LAIv|v(h)5DVeW*P1B-u{IWTk4Y4mV}>4(`1
z^EbLaI3JqsVfi1X9~SR0cfj&3Odm`gEZxK62c`}dZ?JfTnFrI4?q8UC7!Aw+u=EUz
z7npvSzhUu=o_=BOhQ%AYewaD1au62Zu=s%0hcNf!3P)HvfSCu2FPQ&e^$N^A==m8I
z?y!6W(+_h8+<nk`2o|p}`(WV!^FQ2Pr~;U~Vc`G^SEw`tEWBa<fYC5>Vftb2hoyg*
zJS_dg{0Va(y16iWVe21Y_Q3SR!W(7}%zT(QEFHk)VfMk|AC?Yb@dyh?n0sL966S9h
z4Ra?f+~DS*g*SToh3SXc4|6B1yo2QrSa`w0AEpmR!{Q&N9_CJ1dVrN1xcmWgKP)_9
z?uVrxm_Ate!R$q+VfMk|1Lh8xeq8wn7EiGB2eTjMUl@&UAB+#v2eTJFy}{JM?1zOX
zEWTj+VCrE0gykC;4RZ%<{1aV2EZxBDf%yxT|6$<(%fB#rm_ArM!qNlG9CUp!ccRlU
zb71~~r6ZVrSbU)CgYjYM2Ns?%cf$M)Q-_O&`3L5HSool)BUt+zW)95VFneL@Vfh`F
zPht57mj7Y)!SuoT(0m7rH(0uc<!6|FSU91(6IKtw+=ZThVBra~52g;5{$b$^<HP&|
zPghX)!TgU-!^#0zK7iQ|(~qtmoezsgSUkYu5ta_o)uZ!a;Q<SGczKD#-7xpU(l0Fh
zVD`eo1164cA1wZ0@c|1jn7?7+4U0dxerUS`rVkbmF!#dBHCQ~s$^}@uf~5;sIK#>X
zSUAJt4^Bh<4>JcQ4$}|IZ!rC^aDl}G%s;UHKTJQ&{V@G7|H9;9=@{ldm^>^TVD`iO
z1G5Jff3R?e>4Sw2Og~H=%p90~F!#Xx3y)8@MhF8IUoic!_=JhW^uz3hg+I(*Sh|7v
z7naXq@du+}@eeZx=3n&igM~jVAHe(%6NjZoi0c^`VDSS>_po?_`4?s%x;~hGSh|C|
z1G?@B7Tz#(VCvCnm_K0dg@p?&zrxIc`2$@)Ed0>*!_o`9{D!7qbp5b+g83gt!@>)u
zAD8_w^I+i!i+@<W!NL!g4q){fEMLOh52Io3hUtgd3v&l7{lLNv=3baSn0>JH2#X(>
ze_-;k@Pfq~OdUL)q2&wAK3F`!_%MIN+zHD!Fn_@0VfLZxgUQ2aSa`s~58dA|K1@A2
z4GVvmzhLQyQ2K+J1J?)jH%uSQ9GE&74RbHdURXT9!Vwl9uy8{+2Nr)Y8dh$>?1hB`
zEWN<Yf!PnEVd)pfhuIHv4=mha@c>hYPQ&~Uvlmtl!O|V9JVe(A<HPKQ>4&)+rXS`%
zn0lNvH2h)d7iJDD{i3H&n7y$0fVmUqA6UA9`5zW9F#BQghn^2$>R{mxix-$a7!A`8
z^Eb?Wg!~OF?_v62=>Qg=u<{;eKg?X1y|DQoSUkhr2McGII4s@4{14L)^DoR^SUQ1~
zQ!sH@eE<`O*#pxL%l9z-Fd7#AFnPE>XgLg%hq)8(Zm2xWewcok{qXRBdIaVUSUP}(
z6D(d}?t$rp@uAw`^&iX}SiHc(4Hl2E`~cI3i-x6Jn0YXLF#BQtfa!;opRjThrXLpW
z=;;6!j<E29*#pxLGZ#j~?1R}4^FJ(HVdlW>gZUp8p1AbG%txnT?uWSx7LMrlqw9n5
zVet;r4|6Xe{jmHG(+5je==P)M1DL(Ad<U~1<_}o>!SunxAEpl$PB8mm<rPdlEM3FY
z!)REz!`ustKbU%$e_`<t(+8)a<s{5rxHy!7?hdFhP6`(PF!!Ll1E)r)1S~zl^rOcc
zR2fbR=6_iD!~6|Pf4DTl^ug?f<tLc^u<`)L$JPIa*$WE?n0+wwVDXNA{~OHTF#WLh
zD=a<2(jP26!0Hv4`7rmxXjpv(a|g`bu=D`)4@^Hg4J-Fx=@(`{y#7Sj0OiBd5iA^G
z?ts}#NIxtb&}o=^VdlX61GN@DehTM9=MiA;fQ2WzepvXz)WPh9<yUw)2CYwE<v7ee
zF#WJ_hlLl+K3MvL>4UW^VD3bxVdlZifrTF|++cikeK0;O-eBzkm_K0l!}2psJ)DM?
z1F&#_r9)UaqlX(@ACv)853?WU514;(rCV6}1xq)u@&e{hSh)zxzcBy6`~f@v3Fco|
zK7*CRuy}y!$M6T#-|%=x<HO8>g&!>c!t957A5Ov4!{ZOykA=k_E`6}@gXx1A2nz=o
zALb9(_!~?d7H@F<(DVwk4`vQ5J;KIcap{M}157=PhM5P`2g@HY`(gP2CJ&1*Sh&OZ
zFnL@w%p5ok^$*M)xb(rqVetvm4@<}B`eEr5mi}Pr5f+ay|H9%QRu01KfzdF1FmvGM
zLfs9EH<)^K8fG8Ny|8$Mxfhmy(DlLiuzUxL2UtFUg*z_$VeW+KgU2V--Gt%`=3kh8
zSa`wQ4NG^h^aLw6VeUrP5Az319!A5=gZT%R4`K2!`(fdR?q67V!15t_c*5+3nFFgg
zVdlf^gV_&DPcV60@dpbJn0>g?4=f&F<p3-mVBrQc2gXOYAKm>h{TS|p#w&XMhlLwF
z{Xx~i;tv*2uyBB_KY^JK(+3M@m_K3uf%y-XkI>x@GY3Y)%!Bz07XL7FVd~InSiXnF
zBP_ka;tj?}*9YUn>JgZ`VeUmwPcZdx8d`6_?1jY_%w8BDt{#m6OFyu9gVlSm@Px%X
z%>U@^KUn<1+z*R4n15mVVc`f%k1%Jz^uxj(mQP^rhPel3Ke~Qceu3E!D-U7j!@?0p
z!^B}U%>S@-1~UhiZeZaEqhb1BG|b)T=>_I)nE5dMFn_`H!}P)24O0&be^|PQg$qm{
zjE1R$xdRrDu<(M>==Q?&!`un;KTJO?o?-T)=Nnk~z`_fbZej5c^9L-xU^L8L7!8Ym
z7!4DL`3vTMSop!*1ycuWXTjVL3s0E)VE%{cNB0NZ95e>ZKQQxQ`H)!sF!#Xp!~74^
z50i)ITWEU?=08|Gz|_OjFSLAw`43j^z}yRqPguT#>4TYr=1m5e`(WV@HxFtL%-^th
zh3kiwI~ef~^#@EnEZkw~2&NzAUbz3E{U%s^!{pJ;fyu-4!R&{%7hwK{<p*^Cz}y4V
z53>grJ~02n#i9O$iNo~6@+&OdVBrl@2NQ?I2h9I4{jm6hxgVBpVf_|Z{G+E|So(m4
zBTOC4KQQ~??tsQ4tUQM4gVC^j3JWKgd(guj7QQfjFdCLmVfMrP4YLpCUzmGf`q2Fk
zw-=gz2<eCU8x~J68kU}6;fTwAm^)xJ%>OWdz}yGZ4@-A2_2@J#y}|Uu!vz}tu>6m%
z55`B=53kpt`eE+CRgS~d!@?iteptH06@Rew0@DWzcbGm{IKteCPQ&62=5LsOn0sOM
zKDs^_A65>++z(5SFn_}GGcI?)(gDoBuy}-pKP=o~`4eU@E*h4OVCKNmEv$UNr4Qx~
zSUAAk57Q3|7j$!A;SbXXD+kc+fyD><_#Mn%SUkY=!O96(IKb?M#S2UwtQ>>c4-<!}
z!<Ft~>R~j@9Webcf5Xgyg$GO?rXJ>Q^!SIx7fe5{_=lN?o_}EB4bzV+{loOb!U+~0
z@N@vpf3S22GZ&^0rXJ3Rs)wa(SUQ5GLzsJD=@k}!F#p5i36?Hk=ECA1T|X@RU^Fb8
zVet=(2UtA9<YDH*Xju5c%1?N{h584k4xNVOSC~Iw;RtgtEWB{VA1wS}=^mCY(BmIw
z4%~idy$lOyn7y!Yh1mxWFKGJ$rXS{BSh)wQ?_lP@!T}a<=-~mIzl50sb2rR<SUAJd
z4a^*vI7}UkhJ`=OJuv&=;RsWRgNEscr5l)jT<IPbPO$U|(+7)BSbBto3(Os`@PvgA
z%>S@>fH@E5Pni8M|KnOe1+xccK1@H%JaihCZeaGq{0}n+7Va<_CJr+PrXNPb;vMF1
zSo(qKhowiDeq1yx{$Sw=3pZFepzDL>TUdDm3x8OCB@`Yof5XB77H%+ou=XRmy)Zs3
z9l*?i(J=SI+>ep}pa#I)4YMESepq<J^uxjtstr!T^uh8!OdYKJf|&!0512++x`pY7
z`2*&ESbW0lhlK-79+qBV`eF9N%!kqF@c=7NVfMlF!{QxX9-dF2<tQv(VCLfrKbSl$
zAH(%Q&4J0o+yP6+Fn__q3#JbipD=we8Wvx$@PwsXm^jS+uyBKg2TVUqA1oYT>S6Z7
z_^^5nef$rW-eBb?%)c;y!Q^r2hvgqwe8b8aSh&K>K~GmO_rv`O6@aA!7!C6;Ed4;0
zVNfvpVd(%CUa<5BOUD=*p#m^{u<(cJ!xe8(Z45B?!@>zB4hu&@_QJ|Ln0}Z)VEG*u
z?&$7-#TP8zVd(&-4rVT_+=Qh=n0YXF!qY3X-v`qVi$5427Jo2(FdAkLEFHk|0W2TF
z^ug03Oe2(rsfX!@rB_%wg3;*V2=gy2-eBPcqhav~OE)loK&^$Jp8^YaSUN?g38e#A
zJiz<`Gar_2VdW@H9G!-RA1oeW=E33%mX6T%!T7Lvf`tPtJ))<7T;{;^!NL<3PB1>q
z{V?~y?1j;=aE7HLSiXgYBP<=j^rNRgbaP<#!pw!mJ1iZ*XqY}&dV%wy<ulA2Sopx~
zh0UMB+y_f{u=s=dA0`jWkFa!rUVg&j50;K#?uO}ur9YUvVc`jLFH9ZWozQkQEWThg
zEF56^VD5v3J1iby@ei{Hmj7V>hv|o>GpKzq^I`VE%!TDASp31#6)b#V=@wlbOh3#X
znEzqvfslS!c*4}d+y!$7%v_lLF!#gcVftV+EI-2JVc`IacUU@r#Rn`L(e=T?6{a7i
z4yGR#4}{_!rXNPb(h<xYn7d)&21|di@Q3L`PY1AYhl#_&8|H8HbO5sl7LG7J%zRw>
zVd)j-URXH7!ULusmi}P&!`u&xKUjW&(XjFZCXdU0nE5aomR?}~fQKivJV3V}rVmEL
z`~%YuD_3y2AEpmR!`urCe;6MYZ!mwr{14Ly^DoT(u<{3%-qF>=^ugQ-qha>J@*ynU
z!0d(TgVC_{Gq8Ar>4SwEtla?fKTIEthUtURu>1#e2dut_g*!|hrXQV#*$YcggzSgu
zhoy5^zJsMBSop!xJ1iVwG)zCt{jhL^xdUAtEIeWR*J1i$_QB#2<{nr%0`mtf9$@Z)
z>4&L<xgX{pn0i?H!4-Zm^{{Y*g+I(5Sop)z70g~34f8L|9We7?{({9ntQ>^t!$rfw
z5#}D4ei#i8S7^Nhvj-*)Pd89`SU!aL14hH#2@5w^IK#pVmJea^0*gP`_y^4WFm<r}
z2Qvp2F0gQi*$bm#`eES)D?ecFg5@WedtmVa(}zJr{R=Y(rXQ9LFw~<7!1TewAEqDX
zUYzzr?S<)sg#%1IEZ$&zn6<EU4vS}4Ji@{YM#I7#J^i7(7iJzT9l+8tEZxD(fw_aw
z_#Z4C!Q2mv2blRVf52#%{jhL=^P%+zEIeWAF=%Kw!qOwm9zyoQ{0XafVd)Mg4ohD!
z|HIbbz}*M67ZwjNf5Y4hYp=l45zHKz`(gft#UCuZVDSZuN0@pT4RbF{KP*0B>S5-<
zXqY%mKg|6w|H5dPxiEjj!VlJ-g6W6Ruy8`>!}2}M-?;R{+zrpS(C~zX8!Z38!V_i>
zjE3ohl_RithlMjNy~5O^)3EjuEZ@P>53C%5nFDhVx_)^12z58iJurX6^y6|r%>S@>
zgylDwzhV9%WG~GBFneJ2FU%b<|G><FsfXDMi!YcwjE2QO%ssGhfW<p3-NVuoEWN?P
z0T!MxarF5+Sh&O70ZVr<`(gfp*^j>dAEpi#jxhUR`4HxRSo%W`Pnf%5?uDg)m^dt7
z!1Tf52WBrU+|b<*OLs8;!1SZne=zsL%z=eBEWTj=g82v5|An~^W-p8n3ul;rVCfrX
zE-XA@?uYpg#)qY6Sa}2sH&}TDSBJ)c`4i?Jn15mRqL)8t+8JQ}gxL#=H$wKp%z>q6
zSUAA)9n2jtb?7uKd|>Gi=6+Z@hnWM5514+KJ+SbH>4T*Yn15mVVBr9B53KzUb2rSN
zF#BNQu=s$be^|PL#V^cWm_0CaVD5s6!}P=Khq)h?e_=E%JYeYrrVkb$F#p47So(pv
z7iK=p|1k4l{)f3ArXQAWVD`iO3)2US2beox=EL;EXjuHg{0+-5Fn#Fu!t}xHhq)h?
z{$S-NEIwi8!SumsSUkY=!_0%Z9~N$~e2MOUSh|P#8|F^9IMgCs{y;B>VdV(Set3F-
zwxeL?K#hfupTo*MnEzqs!qmaiCAxlCI)bT#`48p}SiHc>5m@;JQxA(TSUAG`4>uQD
zufoC+Hhu`x4|6XpykLBoewcYM_ruJA>4$|oEdJo>85&+Ncf-}AF<|)*<_}o>qo-R~
zI)Z73g*QweOdOW(VE%!{BP`ru?uY4vxf|wBSUy9~Pq6d{a|g_Pm^tX~h4~8>J}`4(
z_Q2c^vj>(xVDd0=baP?p17<Ev9n3v2|DvZ`7#|i+a2lFEVD5#*AI!ak;t!@C7LKrV
z57Q5e4_J8yw-?HQg&!>a!u$tI2e5PpGY_f_PQmPjrB_&Z!NsBLHqhfAW&kYQ;OQQ!
z9+v-L?t$rt(J=im|HJ$N%das1!s8Q~A7J`n?uGdiW*$r*EIeTDf`ucj{R7hvi+`9q
zVCrD;0Midk_vqyZOdZVquyhHlzhLIU^uxjzW<M;PVc`f1FIc?^OLwqvfaM>UJurJ=
z?u6M3OW!baVEGT09$@-lG|W6$K7y%-r5jkd!onHmZ&*CT?1PEJ)WOVw`2!}7i-x%a
zW-iR%xbhJ!9O33c^CK)gVc`u6f4Dj*1Kt0y@Pnl*m_ArMLbbsum_4v~gh{~S38o)r
zE=(TgZkT^z=^n<1g(uA4uyhZLKbU<m_ruaJx;?OPhq(jhE|@*Aatju}u=s|h1DJc!
zX_$VP{jhL=>4Ui!W*)kJnEznr!1(ZV4AlTjKd^X&3d1RM^{{Y-xd#@0F#p3fLK!gk
z!@?itepo#Kb3ZQquyBWkBg{RpbPLl5%ZE^F;S|gsSbB$<1M>&Wepvd0l@BljVC5XF
z9D~IpTphIBgqZ`2e^@?+`5zYkuy})|8<;)lG|XO@epvXy)WPBlW**F5m^^y=h1n0&
z2MbqNxM7$B&F3)tVfhuN4jvCsb+CAV$-~?M^9L;aVCKN$1s0DmedzvxnFEVISh|CS
zKg?cS;Ry>jm^)zh!omX<pRjz7UjM-S52Io3fzhz^4|6}v99a5->4W(nrVmz*!1TfV
z0h5R6hs6`j-LP~8OUJNu3(Lpo0HzNn4&%ez1q(-*J7DTy_Q1j$mTzJHh4}|2j&46p
zA50z0KQQ}X=>V2*VE%yF52Io64olB4aaef4+ygTgCJ&=w@eXqbEZkx4h2;mBdN>VD
z*RXJc`3EKsOK-4ngYjYVuyPlsAC}%={)EZH+ygTQ<`0;=VfMoEDa_w6ahN$UahN$U
zdtu=R%V)6gBBURe{V?~#!V?y+=-~--7c8B^!UdM!VD5ss8<q}W>R>cX9n3$laKz9D
zEr((HU^KdZboavi19KNlAI$$SdtvzyCJ$2&3vXDs!sKD$3rn}K_=C|fahN_>_`&qU
z-3d*HaQmVDgt-r<ALb5NdV-k)E2m-pfW-?;9+oa(`eEf4OddwV@+++TfSCiMVc`gC
z|HH};m_K2BnEzq^ftd@l7rp-v3rCpyVet<ucVOuTMq}h>X!ya>D=a;rryrPkF#p2B
zA7(Gi9q96~c!SZf{0mbLvlr$rSUADVg_#Ene^`9L!V9J!R*%8LA6*|T{9xj68tQ+T
zeK2>y;sIta%w2GOPzKCim_B$oLCuBnVdkLM-!OY&=@phfVg5r;zp!wETF-!<e$n;8
z;vK#Gg~cCCKg=Jv^uzoCvlo_bVdWPrzr)OhrF&SsqlZ5%ykP!@`3sibVc`OEKP>!U
z_QULf*$*=hCJr+PW-m-XEd9aEfz#0Vf~6mr`(fb&%MUPf;QDYdVD5pLgG)b59n2rF
zc!IeDmp@?E!paGlIk0$y#Rn|>Vd)p$|LFQ*<p)eXEd9aEgV_tCVetejH_+9=?1z~H
z(+9H$mX6^1pyfBrAF%ua3qQDiC<A6M%p6#J!O|-%zF_`=YJ*d-@P(Bluy}x_N0@#L
z_d`v9g*z-A!_>jdg@rdP9mDug?F=w~!qN%MUKk&y9_9|1JWM|<9AWmu_^@;YD=%T;
z0oRAdfTaUiIKs+5SpGp%j=_b68%#eeK4JQC#UCubVESSDU>3mq3kyG(ILx20^asl?
zF!iwTgQXwz_=m+COh3#WF!N#Y2h#@&f0%hN`(gIL^uzeDatP*sm^rZW9cB-VhS>*m
z4=lc5_QKTRqG9&K`~{1DT;Yyx4lMj&`eF3|Oh3##bbT;BEFZ$$4|5MJ|HJ$NOV6<I
zfvJO~8<;t;bO6%_3rCoJuyBUi2Xi+}9!A6T!)TZ|I*lIwu=EJi2cyyT!T2!$z}y4#
z4=mln^uy98OdpJfg*Qw;EI-5a!DyIzbQ<P<SUCpM4-0>oJJ9vP;vE*AFnzG{0G7^S
z=^nlP35!3NyJ7Bt`4i?ZSa|{)KY)c7ES%wdX!#G*2Mb47xe8l<1e1sP7nWaO{)CCc
z%!P#mtUQGI6XtK2`(Wx}<uc6Q@cfG&fKWb6A1-@w<$I`h_<dY3|H9mfp6+4l(P>yX
z!r}uK4utY8EF59^2gZlRBP{-5;SXPb0&OqB{0nn8EZ@T12TNZt_rTH*%pUY~0}F3h
z{J_kCrGHp?1hWrjKP>)X_QS#f7JlgQ2a7Kl4O0&bM_77*r5l*}F!#V{nEzn@fTbsx
zdtu=QQ;$xghXbs9gy|<X-N5XJxf`Y*m%Cx^fY}cVN0|Fz@dXP%n7d&5VDSU97iJDj
z9V}d6`eE^gt{;}qVESO?4lG_^{($)lrXQV#r3aXMVCf#FAI3-52jj#14f7X_57P%T
z2Uf1Z+y~1auyhBDFIawpr7M{KVc~)9UYP%3=^qv@Fmqw{!{lN1!`ulo55|YZJ1qUe
z?1$L{^EWJ?!}P)85hf0c5175^>(^lQH9Xv*<pSJ%=zJ{9{pk8(`e63J$~{<o!}u`s
zU^L9VF#WLbhRMU+3$qs%Z!mdSyusWL7l$%n=EL$Y%si+xP70<EPDAT6LjHic9~KU<
zbO+0au<$^)7^WUxZb8Ez7T+*;!_pDDei$ES4lF!j@ed0>T<(X3BTOI6A29Ve^+VkW
zD|cY&7glZ%(hm<`s5$6-Sop#0#}$4s|HINf%pdUZfW{ws{Q=VlqtW%l`B43^_<*TL
zr{U_M{(+@SSpJ34==m3J4pbj3JYnSyEd9XU4|NYrAFLdK>4SwMtenD`exUZj(jBba
zgvB4M{DGMR%SSMKVd(@O-cWbJ{0TD`W**EOSosRm2cyx`3(P&Rc!c=_M#J>M(jBaP
zfY}3!4_J7hn-6n8F8y$Q&~QT64-0pgepq=Aa|bN{!QvYhF6ik9rVbWfFdCNLVftY9
z!_0%l1I!(;_=ou$T^~$6It@!_Fn_@O4f6*sb71OW_QK*7=08~Y!qO4U9GLwud00Hc
z{0qzPFn7RcSo%Ykhna(JKTIE-hNe%Lesp)h;sNG<SpJ2D4@?}EzF_)c>R|B(D^FqZ
z0@DZcKYIMZ@(0WuSUN)Y4=j9O_QGhGdRRQb%1xMkFmqt)VKgj%!NMQLht)GM{V*D)
z51odk1DHEt{zi{4bbq6p0}D5pdtl)Li!WUMhnoY<r!f66cfj<+(lcB=8Uq&3F#WLf
z04slC<qo<zFn`1J!OAN%;~8N3;r%bD0(AYb@Q0Q6Fn7Sh13mr0(myQzVc`yoH(36I
zS_h}l^})&wnEkMHi*6sx09d@k{0j>gn15jIg@r%C_=g${(+`UeSUAAaHB=b}1&eQ3
zIKb>D6b>+hVC4kNUoiV%{)dGNOg)T-g(EEfVdAjz8fFfR4@(Cyb+CL7vmcfZVE%`N
zE6g7-_rUbS{0(ylEFZ(<Vet>M7hONheptA{(i=R!p!o{se%SaA%zT)=F!#gK4NM;9
z9+-Yu`iHq27H%*;EWg3bg@rrJ|FH55rXJ>Am^zq$VBrUI4=f&F?!us<{)Cs~(E0-A
z9t`zp0x*3r`(gTE`4LSyoC|Y5Ec{^-F#p2h4<?RI!}P=Kh2<Yu{K4WI-T$!s4zm{)
ze{gls@IX(0u<(behs7T(zrpN-xdT={z{&xbJlr2pdtmN>`2%Ji%zm8up$5VH19J~7
z-NC{UrXLnAP;CsbbOf^>W)94LSh|Ag$K`&Qy)biO`e5+`a|ecgs0O%uq2U1)W`OB~
z<zJXMjE1FiSo(w2ld$xM?q8Tb*!}4+f5ZF>QxD5GFmqt~V6+C*oiO`h>S6H*E5~5r
z21_?E{jl@{QwNiWg#%0;7GALO1EwDr4KKf;?t|%vr)yjqq3U7r2#Y_M`*Gz%SUCmL
zhwdMky|D0stsj9o0~TH|b71zu;sKT(Vet<eKZogu*$0b9Sh&IB4Q4OQJY4!<`4kop
zuyhNne_{5+^uz3d`x{zb!omyY9$319rDK>oVE%>0H!PpS?172H?172H%!k<vlZT}b
z7!3<Im^{pUn0}b~Fn7T64J^E1<rhppEF56^Vc`yQ2Q2(x_QT>8-CkJu!om^ePFQ%s
z+yzTFF#BNU!t}vtSpGxT2MaHlI$Si&oiKmE>MNMNxb(rqVeW_NhoxVbeptA|)T7hr
z_QT|1`eE*YiQ}SS?ttZgn7y$44|5kT{jhof<_|a@ny%5~4Q383eZ$fNtQ>}=8(2KR
z(hn>>!1Tl74`wecJYe!L8m1q;{RPtxOFuAw!txuseK7N2?t|G2OTV!E3DXA)KaBDZ
zT3*7!8>SCtKP*0A<q*s~nEfys7S1p}%>S_T4D&ZE9AWBk(Xj9*)_#~dF#p5C5#}yf
zIKuKHOg)T-xfd3Wu=s$<!@>iWp3%>Lfw>cAFDyO5{0-9&3x8bU04uLx<rz#JtUQC6
z19KOQMo$MY`(gUv`45&4U>cz`te$|Sf0#RQ<rk<nc)JV6huI5r4@?|Z?!nR*OdOUz
zVBrq)KTIB`9+rM#G~8X#^b6Aui#J$$h4}|Y!^Gj{KrMpV3(E&E^I_=+M#JJCz5a*!
z8|EKa`h^<L0E-7$e8S9!$-}}QrXNPb;sNGv^z;jJ7tGzTbOUn_jE1FubbrI_hoxIs
z{(+}6X!yeP!P5~;BN`2JH!MD2<u@$cV0<)fa4szVVE%#SOISXFnFA|_VCf9zZdka&
z!VTsQSUAD-!_p)A`7tnuz~o{18s=`8ewh6*^)UNk;SX~+%ssI1gP9A{2cu!}5A!!H
z9AWxk@eflEr=jr!^Cv9)VDhl^0&_PkKf&Z-;Rka!dc4EK5l6WJ%U3Y}!qPp=9k6hN
zr9W8uM|TIzJ+OQTa~CXJVd`Q2faNEcK3F`#`B3-5`~@=)rVnNgOg(zI!_qm-T=et<
zix-%FSp32K52Io3hWQ_sj$z_3{V*C9?=W}6{0|Ffm_GFM2XiMZAH&Rp*$4AK%sf~;
zz|_I)g_#4Bhs8fEd|>GVwtfT_E--nRd9d(-#V5>MSop(en0i?F!T2z9Vc`f%M>z8V
zR3ogsgXx2t15FPw|3H;vP%wAG^uxlLki9U2VBr8W2Nn)6b71N)XlOo$*$aygSp31v
zfyF<%eptN2^ufv<m^zp_uy}=u!`une4~t)TI6>V7i!WUMfW;pyox%J8GY^*EVC50a
zeheCFAFMoq<pY>Lm^tYFgZUfg4wye-;xK>0;t}Q_nEkNw6P7<<`5%^kVfMl7M-K;B
zc?a`1%s%vVi>v&H*$YcQu>1~-cbI!%?uF5?^bK=AEWBXyF#p2b4HJjc(C~)28y0`C
zd;|+em^<M5&>2v3(A@!ZH_U!mJfdrd`2*%}SUke~gPx9Xg(JGTu<(S%56mAh8m1l=
z&oKYP(m#xk9{=d}!_>jT5oRCE99a1e3s0E6=rk-Hz|4cCA9VZB^}*r`mXBfi2WB6P
zhM9-1AEq7_4lwgz;SSReqv86Y@eZ>WCJxgFi%(d1qvsn~dVz%-EWBX$!@>b(FDyO7
z!UHz`2{Q*4elT~y%1f9&T<(CWhow81c`*0D(l1OME*ci^uyO~M-eK;?r4J?!OYgAo
zgq7Q{^aBr9m<AXPjR%;!Vetvm2lGF=eptSP<r5eUD<5I{Vc`g~4o1V`2WBrU{lMZG
z7Edt$!@>imA7&p+Kg`{*{0378GY=LIF!#aAS(tuUxWm-J;u99GFmYJ>1!g|X-7s@t
z;R2I~g&QvYu=EOZKg>Ru{jhQn=5AQHz|4o)2ak7Xe8SYj;t!S{VftV+%s!a=Vd`M|
zVfJH$E7TsCK6rRS+e>hLFpJP=xO%8LuyBOg1Je(42YUGfvlm9g(i56B46tyA(J*;<
zd_(Pr@nPu=7B8@Pfa!<z-(lenD}P`#%zT(U%siNWSbBu1gP9MD2Us}3@(rxqftdr-
z2cu#3!1TlN1<W2?_QLeT$~{;(!pb37xWi~zyukFqXjr;~*$d0}Fn7b$VbIWUhuI5r
zKP-L2+z;c!XqY*$_(s<cOTVyu2TM0FcfrB~T|dm<Fm<r_hs6gh90=uqm_Ati!QvMd
z9_ZtLF!eC^!0dz7f3R?X*$<;(;Q*(h;R!28VDSaZM{spG7_jh%*$=Y^R*&G&hH?KW
z%p91#u<(Yt2i+W)dRTb?OV6<IgM}Nqepq<H;vbfdVet+NKbSvY<sd9PVftbA!}u_L
zFn7T69n2jt^U(bP<HOtoGl!7>VftY9!Sut@1x!CIUBlGFXjr)g(+5j$uy}*{8<r1X
z`e6ALR&K$<8)h#oKj88=?EFWV{jm6k*$+1#S|7m7f!PlWZ&>*OOSdrlVc`LDKP+Fu
z;tysHOdTx!!1TlTuyg^7H&}SW^y8w@(*-X5Fm*8dVC@vRf1vB<VeUsS$6)GU@c;{V
zSbV|Ui>?pd-LP<gr309Ggu)MI4lF)k`eA%@f1|qt=6;wynEPSr3FdzE_=lwrm_C>}
zF!#XnJ4`>!9dLOx2F$%M^U(7jA^k9S!}P<_4YBsa?Sq!Xuyg^-7cg_s!vW1346t~E
zxdWEoaq3624`wgSJ+O2HvlrHWf!Pboe=vJt=@%A%Fm>qVAk2K2eptN$%YQI?VeUpx
zH!yd?{0R$xSUCqPM_}a`%p6#{fTbUpy)gS=;SH0Axf7<JP&$CcJIr4&_rUTm%zjw7
zz|t)&|HJHq(Xjjn3qP1X7!C6`%)KykVESM*yk3K8gwn8Zg6W5u2h#_$7ehZZ-N5w0
z%5hly!O9a@x`ENKbODP$Sh|6wQ<!})b7A2L(+`UWn0+vFVDSfwA6UG?@-=!ofQiHG
zfyE2V9+<hX@PL^MlZX2c8V;~<gxLer4-IB`KLX}Ym^jQGaP?60VDd0~VKht}M#J0z
zOK))Vp!Fs!JYne&rVmEL{09pMm_K0o7^WY_hv|cbKP>;j(lJaPM#JI@7H%+cn0>H#
zf|(CXA24}XzK7|D$;12!QwMW5%%3p#z``5m4w!#n?trO-g&!>cz|4WU3l{z`b71;m
zG%Vf2Xjr(y+zImsdie=+KP((z=@6FxVfhYLexU1zxgVB~VESO@!16JyT!Xn6Mx*P8
z<u_QmLbo5+{yn(;&~yjWhaP^g@P~y9Ed9XTiz|F#?uF4XeJ~nk9;_UIg%>P8!rTMX
z5Az=^o?tXAJYeAna}O;3Vft~=F#WLbfQ36O9?<o{@;%J`FnO5!VD`eo4JHqx(eoiJ
zAHeK`(XjLk(+8tr`eE*crBj$bSbl@4N2g)#hs6U-JuKXCxf^B<EZkxF02Y3*{0=h*
zrXCj!3on>EVDSyp593o^KeW7t#UCsm!@?1k4sqEFb1#gBse{EAEIq^G1AYA#%wAad
zq3eg0XRvrc&%fy96)gY5(m%|8SU!TqFU%fTx`Fu{77s9UVBr9Z2UxtK>xa1$W<D%k
z!paYrewcYM{jl^7E5BjxfvJP(gP8-<2dAO&2h$I;7p5QPUUdC1f5O5Y#)rid%p6#{
zgNdWt3(G$+aaef)(+^7ruyO#V4o1V`52hZLpJ3*|`~h<ZOdpJfg%`{puyg|RH_U#R
zdKe8$cd&E|i+@;pfu(y`x`F9~(J=d9{)gKGjSoWg0W7`1X!LL(q#x!E7!50TVD5p1
zAFgnR>4Vt|^AF5Du<%1qH!yv$^a9fdOMftT!`dyd_=Cj@Ode(qEZkx4hUEvC`7r%3
z_h9%38s0GbVeyKt9#{H-*$Z<AEIeTL!|a2le^~y8g%eCaEdF8YVc`N(hwdI&x`XM5
z`4gre<{p@TVCfK69uU$G(+8)a?HZVVn7y$41B)-1KVkO6@*~WCm_K0V!rTFiH<&pv
zd6>Uo`3XJ#Vdlf!0V{W5_QApf7VfZg1EXQ~!rTE1Z<s$}`2eOKM#J0(OE)n8!r~w1
zZkRdf`2c1gjE0pPF#BNn5vC93519S1@Q3M#r5~7mu<{V*KUloL!WX6wrVi#_SU!W9
z5A!F?ewg{N^bPYr%-^thg2f9gTwwk}ufJjO221ZSf56Ox+XGF1=<^3K|H1qLGY?jO
z!~6|PcQF6M!VyNp(hV$JVESSHg}EQ59!A6539}a#9<X?W>BnU+Odrf%n19g28$I2_
z{0p-W7S1sDz|ud=epvc~xf`8^nFn_VblerDALbu)eK0;Oy~6av%1fAjSo(sgN2k&C
zqlZ5%9?|u|_^|kcr6ZVrSiBR8KUny|Xjr;~(J=R;_dj9j92O5Sdtu=Xa~CZBVEZ>=
z?uLas%p6$$gXxEbCrmwT{}Rky=<=}ehWP_m{)M>{rVds<!r~bgj<Ea)3rCnbn0sI}
zEZxENLGQDN>4W(nrXHP!xf7-z7T$3E=o--Yu<(b4BfK1fmK$iwF}Sew2eTg*{&4$o
zm;<jbpzR14AC`Y%G`j!M;}4b&;PHpUKDfi7_QB#Gmd;@L1Qw65_=D?%GGO5i(+{&B
zD$M|MFU&o#bP1D(`4greR$su%Iaqo?HwPXbP=jFlVCKNW9p-LWIKljdp8sI>z|t`+
z9l*i^77j3fpzDXF7g#wA%NH>FVC5*(xo`@@-O%`eDS+vN*$Weg(XjA?#RJTrFg`3C
zaOsDc1FJ7!{)YJjR^Gw%!{P;|51odE1I&I{et^XT%p6!cfcYP$9%eo)-@@VpW)3Vo
z(8C`V@38QInGZ7;=1y2XL{C33eK7yS(lL7Y!OVr32cu!`gwe2YgSi)1c?Yu>=6+bX
z!~72mFBl(YAB={DAB={@KP<n%^ucJDK6DzE&SCC>#Um^}(8CK|Ka3CeKQtY~{0|Ec
zm^xfEEc{{afY}Rc2f)%1dOX0w4Hk|t^I`g6;Q=epVd)1JUa;_kxgVw<M#KC8a}Tlc
z3DXaYM_72m#L;PVf57|;(~s_Nn0{FPfcYO5e=zkh{pj|?!WpIyW<Shan15mVVc`gq
zhvgHPe)RAoWIxOt7>(`^Sh)<dA7&m*Jvt4`?=bhk!U3iq7XRq_;CyJhg@pr5KP>)X
zG<rC|!V#8!Vd)B1e!%JjSU$wneuK3eVD5pXGnn}>`_b1g!NLs|A254h<vL71tUiL3
zgD`tw?n9?x;R&n%VCezoF1UUu1Lkj7IKb?Og*!Yvpz6^nSo(q43-d3`Kj`XVe3(73
zct_7SFm<?SSUkhRALd_-aKdE(nmEinSh&N?L(hL`+8AK!Vc`dphlL+5{jhuj*9T3<
zu<{QUUoiimn*%HVVBro6Zx|nz-eLI&<}O%zgM~XR{lLtJhXb@73eyiWAC_KV@eZS5
z@d=ZM<$IX>Vftb2K&N5uhSN~{3F(8G19Jz={jhWhi$|C`bQ+cqait?zJfQ1?`4bk7
z@Nk8?8<y|T^}+aX_0V`nPydA64YL<U!_0++7ka$G<)QY$^ugQ#(+5kJuzZi6j$!_W
zrCV4#1f~wghs6gh9?-{sVD`eyf%zXs!{P-Njt0<hh4};KPMA7a{K4`gy8F?~Z<som
z{V;dK`~h<}%sv<m%XhHwgQYi^JgmHig+ELjPD8^VrVkcQuyBRxC)ECe<u{l+VD5ss
z8|DvKIKb^kW5CRTxdT>@!t96p11<c~jDd4u?t%FarXN-gz!bvN!R&>VKd^X!r2}00
zVd`M!z|s>e-@yD2^9RgcTr@2FVC4@i-NEt&E`6}@gSi71elT~#XmtIs^b1oDONX%X
z2Br@dpD_LC=@zCRW**GFF#Rw-Oh1f<xf>Qw=;jd84>Jc=f57|?=R?yAF8gumhs7hz
ze)Mz$*N4V{<$qW>!r~p44q){WOdG7+fY}F2H!yXucz~&g(J+6)XjpoIrC*r4VdlW}
z!DyH}Vc`x-=P>uc?1h;F<HPi$(=hvC?tz&H(@!Y>!~74^4~suoxWi~zdjj2l7#~)C
z!TblypD_I}8m1nW?_u`C!XKWFq2(kj{iBcn!~6}i55|Y-hlM{Z{$cqUW<SiIF#BQQ
z4|4~s9EOD_Odrf1Sa`wo!Q^4#4GVwt^bfNSmQG>nVeLLxxWK{-=6{&^==mR3Zo&Kw
zqhbDrxdS~N!Quzzepq<`6NlLca|g^Em^dyP<{nr$!NMI~Kg?cOIKbQu3kO)Z!|aFA
zaC@QY17;sATw(fQ_QJytS{}jmLm9Aqh#sG?@&o2>4Ev$_Vd)R1AC?bb_QUkU(*e}o
zu<{nB9~S<wd<N48GY4uN157>4K3F)y<O%7AtAjFN;RZ7gmQG;y!`%&4k50kTD_lQx
z{2!(tmXFZ&!T9L*!_qe_ykX|R`~}koqhbDs*$)dxn0|Qpp~pWo9$@yt(g7^p!ty1$
zIWT#ceX#V0?mn1)nET=8K<$O;hv|dmS6KYQ(h*EQtbRccFPJ^BbPm%G6NlLg3ojTA
zGatr>g(J*-n0c^r6s8WHhM5D)$8bI@ykRuDMkpUWJYne#=5MGnoD?kHVDSJezv1zY
zQzt|M7XPqtgSj6TZZP*iR3k`qcf;xvn0lD~Fg{Et%syEB!}1R-pTO*em47gOFdF7h
zn0{D%!Q^4_4ogq4c!cL`wDbon?_utRm&4F}408`W9iZulg+DC5;N>q=9nAme^*7Aj
zuyg~<2QYWS@-Zx2VB^0qf5K>3Jiy`)#)pLi%v~7zq3(ml2h1NZ_rt;sW)93gSUkh*
zLk~Zg{V;#P;sK@~CJ&=w{)UAUEL>pe5FYMm`3Pn|%p6$x0CNv4AHc#7<{p?kVBrq4
z4;JsRbO#GJm^m<gaCbq&4;Egq`VwXyEdF5bhxrd?4lEtR%!8#rSoos*3+5h}eptN0
z?1T9m=6;yHF!!VTA7&0rAFTX?#W&19Fm>oO%-yhffcXdJ4_LmzWiQPAF#Rxhz|_Oi
zAIv<M{V;v7_=AbV(h)2j!}P<#0j3UCZotYNSiHde0}D?K{m}G>ets9sJuvfO=>eAR
zVCf&0K4I!$@eH>Qnx0_ph3SK<$H9Q56IlGg{DU+8p!T4*zhLU&=?v;$bZcSf_rl^E
zCJ#&Ru=v8|ewcq@;SHl<=E2e{Ed9XDN2g)-!O{UtKg|Cy`(fsx>xYFuTpu)CVB+ZE
zff0Wg20{d2?t!I4m^)zpfu$E%I)tTrm^)$d3CqW@_<)5gEWBXp7e>Rv1EvpVAIu*R
z*I|+{ccQxo-ClH^==xyk50(#c=|}fJEFHqkhs7UEKa3BPhm{vF_rmRkDuAiSr5_!@
z!VhL1tR8^rhlM-3J{TW89ANPW(+`V(n0j;?Jv?Cf16KaP%z>2)xb(x)9V|T1%O6<0
zpzDXZALb5NIwX|dVc`U`7e=FpKg>LIdtu=Oi+`9qVCrGvhVBlSJ7Mt!Hy2i3LGv*z
z{9xw6>J^y1uyh2=e=z@`y8{;fFdC*GSGfVR2bTZf@dk4r+`$kA%zl`EVD&G|K8Pwj
zB-Fn!|G?rIW<M^Su<`<y{^5M6JJG`(-5gjth1m;>7g&CQxgQoDF#Rxfu=E0#huRA>
z7Z!go8m1pT{9*oug)=N1Vd`P-fT@GI7Z!i8^Z|1>OdM_x)Lk%hVB#=yVCKT}8#F#(
z=?IpNVftb93(Ws8`(W;cr9)W!!Sut!391m5ZeaNbmhWNt52hcMo}k*`=Wf8l2^QWk
zf57S&m_C>}Fd7zrF#Rw-%wAag2WB2j97e<Z57Q3|PgwZF;t{4F?rtaprXHpr=5MGp
z1FXJ-hc`6-VDc~;mfm4}m^rZUz;F*#AuPUN`eFG577nod1ak*^`h}ez22%%%S6F<)
z%!9cfR-Z$yhtIde+ySFu@dZmSuyhU6hfc%X3s1+;bPJ1Tcs!tKgxLoRe^`9Ll)?1F
z+z%6n(XjA^(J*(wXjpjR(hoBSM#JI_<{ntQ!1Tl75vCp&4c7+^2Y5P#rXQHUVE%=<
z7nV+8@dR@REIwiGfYC62!}34O9$3DI@nP`@D_3CY1m=F2zhUOU@*PYcJioy-;Gkjt
zfTaUiI)eEZW-hvRn0sOVg}DdrPiVNI>x1!O<rd6dSUeH37ZyG+dtu=T%Qvum2TQ-O
zc!AjqQ;)767QQfl!NMKpADBEWykPp#{S8Y;uyg{8512VHcfj&L%p92cFnL(G!}P&u
zm^_SznFC7)FmYHo!txWk`7nRL@-r;G!r}p@4`v>mhUO2LyJ7la=EK4p-F}#Um_C?$
zVdWHzhKa+{FS@^B?uMy@)mN~52n!EbdVuMNr3aWhVCrD*fu$>$KVa^G`2%JSOg~&b
zbes?tUodf4x`4R@KK_RmelT~y)WOmp%wI73;rgKtfTb^3_`~84rXLo5uyl;>f2c7G
zF#BQQ0Midkx3Kg83m2F^n0+vPFg{Ek<{wzN!Q2B2A6Wds`~kBMW)I9AFnus_^!y7q
z4_e;9@;^L2U=D)PF!#b}nEkN$hPfAJ4pbY0LW@6`ez*dtJ7DI(!T~N1WuS*UEc{^h
z!t}%31=R+p;Nb&x4=f$P!V4Dfa1FQ^Q2nrS7#6;;^a4wNFmqwX!0d;`BaDWL!}P)Y
z0n4AT_(CuLVdW;we3(8M4Ht*H7d`%9@-P~v4@SepVfMq?=dkhwmS17<2}_SK{jl^0
zHwVT*a}O*YVESM*OgRRPLqE>&LpK0TKP+9r@-56C=<x?DUkJqmEZ)%d!@>(5@6dg7
zu=EZKM_9O_&p*J^8Pxr-^aisJmL6g45!m?!Fn7b^9~Pf5f5Q9^OHZ)yM|VF=A1pXw
z`41KzuzW}`A3@y@3kO*J2XhauatLNG%%8CEfW<pJJYWjZXqf+C_QLWV%wAYHqG^NQ
zp8|^?SU!N62jj!s4Kok6eh{V)7XGmG4vQz4y)Zs39l~gI{V?~z;t8f7CXP;{$3HCm
zVDSrcFN}|_AI^uy8!X&m{(#vJa}O*%!}Z}{!1TjuXgvV)H%uE$9u{w~aEIj+SUkeg
z9eO%|`5%@}VdlWihuRC%5AzR99-W4n2a8WQA8HOE{jl(Z>4&K&7=F<3hv|c*3s|~?
zg&QnB;qHd&gZUp;e#7jA@nQKFmY-nyVdqc6?1iOwm^_Szg*(h2F!#XA4X8U{@dpbB
z7!9)*!~HM|&}djZ!pbk0KAi1vH2tvffQ2J0{t4v+SbW3s5iA|T+>d4r1{daESbYEs
zH(2;%Xn+d9(mBlE@OXfxJD9t1`5%^#;QG+ag~b=l9H=w{%)RLG4@-Bj@PPRP77j2z
zOdl+~Vg7)n16cZig+GiYRF1&Rh3SW-J6O7gxgQq|(+^AkF#Wjfh4~X!@4?&;(+>+L
zn0{FK53?T@{xExC=E3Se7#|jH==C=&y}{BeESzEbVfg@NKl=K4n7uIf!}1HvJeYrA
z;g6mUVDSd?56s^%dtvQvT<H&{59Uu;c)-MA=?|8lVB+XBES%vqH2uTE4P8AtAC`a6
z!vR+RqN|6w7v@ixyJ7hi7GAjA4GVXey|DZT(+`UWSiGUz51T)Sl^3w|4Rb$CA1pnf
z>xab?EWFU;5oR8-;Rg!`m_K0g4U2zt`(b=oIKsjK<_?(uVd)sA9!^8+4OsaB%Lg!d
zSiHg90i$95g4qj;H<<fj`eErFR-VD!gPwn2>R{mrGY95xSoov+0~Y@<8fFeG-eB&7
zg+DAk!Q2P)FN}uS2UCYDJYoKamyghJgvA@od>9|*Z&*2qo}OXhhwcwpxWU~6&5v;V
zp%%gP!@>_ezr)HQSh)jp2UI%)Ed9gGfyFzFhUtfe5Bm5IESzES1`AJEK7#3k*$eA`
zq3eU`hs77nAFyzMxfe#m!V5;j;sa(MOh2qVfa!zLFn7ZI2NTC-4qP6}fVm%*-(cwv
zmY-np1WUhAZ8#}dc)`K}7XI*jhf^m+0;UfZpD=y6{10<KEF56z9VQM7cUXRcrDOE^
z7v^7>{V;dK!Uf_w2KYUYuyg<uhtaU`gSi8io?+n!%LnNB2j*UwKA3-C_QUkU?S<td
zsKqe%!|a3Q6Igt}`~&kpR2!Uv)dMhh!1TeyVetSn7p@VF0aFiiKYIE>_cu&EEd9d#
z0W%inUtIQTLCY<4eK7qneYnhl*$<01Sa`$IAB+!+H&{A?nGd63=>`^mu>1$p57P&8
z2dw;n`5Tu1G2#yz&#?G|<pY>`F#WJ{4;J3Aas%c*Tr^A{%zl`^Vg7}s7xa1yW*#j3
zVE%`>2j*{Bx*^s*u=EFW4=laF!VP9GoQB3f%pb7&3Kssb@WQDdY7or*FneMCh3SK(
zBdBr=3g&K@|6$<*R|s_{EZ)$|ADH`L_QTAB`5zXZuy}!;-vl!smfv9U2P-dN=^Ex9
zSUkXJn0{EgL$?=Z9?bu+_=M?0r(ybG?t!@*?jGp4Cb~WtA8sEsykPMMvmd4o7Y(x)
z7XL7NVfg}=K3F)x@-Hm@Vd)>2KA1R6KP((!=?PYk!`uNg7p4!EUSRPK^FOTKg2g|~
z9q8c?(+4a6VD`bxhxrGl50;)`;enp+VD5q03o{26{xE-_ryp2-0ndNXatjs@FmZHu
zz|srMUUd6l`e5=fcfkDtO((E$fQ27S9V}j8;R$m;jE30<b3ZIRVCKN$3zqL-=@C7A
zVD`Y>33V6DT$nt}9C-ePs)NNJJpDo)2D2Z^Wq|2N@BhR65A!EHd|(=J(6Dj=rXS{h
zSUCl2$HHis`7q<q^~2IVJba+(02babb71;mG|XODIKj#dm^dulVd)2^4@Se%2`s(C
z_^@;ivmcj!SUQBoKg=I6_rk&*?k||dIA~aW!u$i%j~<`s+F|y>^uzKqOh3#&==xys
z082kGcf$Myvlo^wVDhm13DXA;cWAu9+yhGwuyBOg4+}3?`hlf)m^)$S!O9C54bu;^
z9~NIQ^I-W8rVi$Abo*iQ=rqh6So(q43o94U)uZ!a=?E5ou<{ocj_B%P;SVeSVftX<
z21~!Na6#7(3xAk?SiHf^h1m}a2Uz^U{0Db0w0?!z3loRg57Q5`A7(x*{$S}2CJzfQ
zm_8UECJ&=w;SKW#EMC#=g|%a0`d~CnKP<h#!WowSVCKN$5vC7D!`u(^4=j9O@c>IV
zFmqt~U^FZoVeWwGhm{ww@I){FVESR{9v0rPc!Y%)EdF5m8Kw^wPB8bQr+b*WFmqt>
z0He|M!~6>iCs_Ew)j{($Og}80!Tb$#KTJK$-!S#C@(}KRC<B&%(ft7nKbU@8@ei{X
z=6_f?!_osR{&3k3OLs7T!2AWvZ!r5|;Rtm$oPvcj%p911VC60>JYnvFYeZwf%z=eF
z%)hX3KvRyvg{2!<K7{!f<_-)EXacbChtV+oF#p5i6HOb&_!-Rqu=EPk5AzRPKeYV<
zvkxBbPzA8`2-6QU4=T-oK7R-cKbSnsJedDs=D_>`%kQxKi%!GJ5m^3%#Xl^6!|Z|4
zF#BQQ5A!d~ewaFR8s<(|{K3)@OdZVKu=s$9!)Ta#So(*#6BZ9Jb7AJ7pI-zEe^_|H
z(i<#1VB)ZRgg$-^^EbLXVE%%+2WCGkKfv6NPQ&so%v~^ZVESS42h#^jCop|58dfgA
z@*ReLX!?VxgXKF|zK59)3wKyNqQ^hXepvYd3vZaeVfxU`g_#fYA51@thUtfeJ1+e&
z_rvtT)WO057N2l;KrM#FFD#s)!VIwV4)X^*exc<*EdF5n(d|W-hpB`42Nn-7eXw*3
zi&t1Z3$qudAC{kB`eEr3W)3V~VD`h}6Q&;~4$}|wFI*m)9$?{ys~&^76Q&<V!@>)e
z|6%17EZkw`2~0hVhWQhw59S`2e_{Gz;Rn+Pqhav}^DoREn7J@_!2Ash2Uxm;>4*6j
zmfm3T0xL&g;;`{YnEfzyFmqt)(eoWFUBUFhXjr(x^uyc%i%*!nFdAJSEd9gmgV_U1
z7qE1K?thp(tlWadJ1m@G{(`v!rXMB`a|g`7Fn7T8!Qv054_1!A^ucIYJi+2079Oy8
zgZT#|-9ghUEZku3fSC&mN0|MvbOe)!r8AiSVdVylhM5cVFU%a6JWM~#-{|(k`~{0Q
zn0{Dz!rTGV2lF?~d|0^(GY95=m^m>0Fm-S~v>yYDUzmPax<`)(Sh&OT4|@3nGZ$tr
zEIeT1F!#g45f=Zjd<cs_SUHUDe^@xe?1z~POV2R-(A^D7*Rb%1`4eU@%v_j$boayb
z!@>!s59WVZ{KMP<(+`Uum^)$ahQ}W?UBcvH{zrE|Odg(2q55F%fvJbti;@1J2Ef7}
zJ=|g9F!#dDfog+OF#BNsfaN<_c%tWHxJD=gmi}P=hlMN599Ve5!WXIyPQlc};t}R<
zSbB%i=;;Ple#7*^{15XlEL>sf4wet$7DK}u9?#JI6R`M#<qMeqVBrT#Pq6d_3qP2>
zaQmR<!}P=KN7oMvH<*4{yu<tj(+_hOEFIvoAC}Jv>4$|M%%3pxVBrjl2Uz^U{10;{
zdN{(u0p=fA{KNbWOGj{hPzKB%Sor}{2h#_04^%k@1@i|i9%1ngb1y8sFf>91VESR<
z1#=H9ykY)_D#xH;`eF9N{13Aq79SWIp#tdoVc`HvM=*0>;R4l$LBY}qEIwiGfaM<;
zjjj=v|6uV4O9!xUgoQuMJ+OFznFA{)VE%`dBQSXw4J$`s=E1~a{)f36mXBfXh1m;J
zk50qvgM}MR9u{6O{kY;0J>FsJVE%%oA6)SW3on>@SUU=qj$r2D(hqYlOdrf2uy}!m
zH!MHF)T7fd`(W_^Qx8i&=;~p7n7y$0hlMA+oPzdyVC6R~Jka&S!VyNp%!R3gg#&u~
z8Kw>v?=b(v${AQbgvBS!JQxjA4|6xn9+*5#A1ocf#9{FUb1%$)Fm<r>2y+K49$@-m
zG%TKA{(!{;%zRinMt2uXKRON1x6t|nrVnNgdj3Jz597n^hnWY9Ph9iou<(begNGZ`
z9k6gfKR+MlZ<sx>@Py?Dm^rZUL$@EM9~O_W@&KkD7B4V=q0=yb!t}%34~s{bdUSJO
z_Q3KBto($fZ<x8T`WQX`!t8_5Fn_}Q12Yd-`h~5(f$4|EE6iQ6@I%kXu>1paH;jh4
z7iJF3y|8eE#UCs@&}mq_!O{WD-LUkBt{%pRxfkXRn7?7=4lG<?>d|SKeXwwV<sVpm
z0dqer{iFK>mQP{+hs8TA9ANH)$1AivgSi9dZkRe)c*F8F%>S@-1=9zUhlLxg{Dip=
z=6;xYFn_?zf#nyN{jhWc3pbegFn7V?6J|b4KP<f9;SBX3EL>pe9v*%$4?t;nI)mnO
zSUAGm57P$=Kd3f11#<`7AJG0M%pb7ugKI!z!2Au1e;5tRcd&E_^EaAy`1m{AerP)$
zmJVR%z{3Y>04!W#`e5l0=5ClisB#Pn7C-3vVfMn}3*BCrL9lR!>4&Eur~+6zfw>39
zhf2fu&%x3k%snvsVg7-s$7K$z-h`zWSbBx!V_5ot>4%j+Fmqw?2XjBnJ+Ss8%sgEB
zVeW+KgQaI!x`XM1<!6|^FdF7=m^pBFL-PSlKd$rxO9!y<gQq`eet_wNnS;Lm8Kxc<
zk1%(^;tS?~7#~&+z|_Iq3o{4q4ygMH<sX=Sbo*iMh2?*kedsiL{)MG`n0|Ei=zLhb
z!_pBf-NVu$x_VrESUQ8H16clmr2`lZiw|^jVD5mWA6WSXqhaoWxer}GEd9XDfyDz%
zAFLcgPlvGd4|6Zf{V;dJ!x0*vFneL<!_>q44>KQ@Uf}+QX@t_S^a67)ES<sh!@>a;
zpD=l-b~pvo4~svTeptGJ>4&)ot`Us^^A9XP!ty`N{V;RTv@yWaFU)?Jdtmm%!X4dy
zn0>HtfcY1eUtr;hZVo(Np!pk?{$b|B!U>k2VDSr!N0>U8JS^S7^ug4_;uU5;Og}9D
z!^$0)Iv5SBXJO{S;sfR{n11wdMCZf&4@;M@dI;tYxO!+fz`_&eepq<I+zE?Ebo*iP
z2MZ5a{K3KrrXI$J`3G)4lmQDzSbW054W=I!E>Pug3KoB`bce1EW*^M`u=t0iPndl$
zbujy2=EA}SmVRO72P|A+?uMBQizk>qm^zrdVBrrlA0`irA6PiS#9{FO6NkAE=6+bd
zK`-ZE`eEq+<}R2&U;zkoC(ImJIKbi^W-cuJVE%yVhou*oepot&iNpL03pbcP7!9)@
zJ^o<!!PMcRVg7`rKbZSq_QL#wOFzthn0{EggXxFyap{Mpf0)1Fd}w(Oa}O^4FmYIV
zfYVU@=<x+pk50qwh303NyJ6)gx_THNmcC&2!uYWGhou`>JqSyOF#YJ^2QvpI52vC2
zLDvsUpRn+U<tJD=htcTq2y-7S-N0yAIKaXk7LVxm!q(5g;ti%AmTqC@!}P=QC(J*v
z{i85@VdXW<ewaF#epr0K#9`qEQx8kWu<(P0H?jEu<_?&Cm_9=32kuX3x`CC4aCbuE
z4dxH@@f(;wVDSg@2dq4Vg&QpYqxT<R{)f36R)4_s!_qG-eqio`*#ol|mX2Wdz|%2W
ze-vgeOdrg@u=D|QFU&o#^p75nuyBKw!?19GxgTZ@EZor3FU%h>8fG3WJ;MA0^FJ(o
z!PMiTVd)p<FPJ)9`3UA-Sa`zH9V}j8{(_0a%z>E;lZWYtg&QpVVftYHKo3WlJ+SzI
zxgQp8u=s-6hfc%X4^RKl^a_hlSh&OD1Fj#M-eLB_;}NPKRt~}9ALf3Td4$RjSa`tn
z!@>a;ez5b)(d~!Tw=nm@!V9J!#)pMF%$+d*!}19%{LsrCxc{LHSh|C`13ml+>4&)k
zmfm3Y!~6-g1}6n`H;jfUgoOhv{9ySS9<R`G9+<mf@dpbh7$4mnn0{FN!0m;`J4`<;
z9ANPcGY95ASUQLM8=9VA;R&N*;Q%X-Vd)vBAC})?_QBE_EMLIf2@3~Uyu#c8QwQfm
z%R89)F#WJ}1JjQlZ!q`5${Cn`SbW0V1q%<DdW`f34F{NgFnut8!`z8Z!`hFq_=Kf@
zxO<`TgRT$ePMAKJJWL<V9WZfp8m12xjxc?M(ml*xSp37>3rjyRdvKZq4JVlWFmvGQ
zq5g%Xdl(H1e^~m)m40CL2do^1rF&R<gt-rvKHxOeUReCX^uxjtT|X?{!r~7`!`uh6
z7nTlSe3<<(cf$0+Xt+4kJ+OFzxdWDdVg84?9~KWV{jl^4(~oXH%$+dzz|4ik2h2Wn
zf5X(n!UyIKSbl(o3oPBk>_;CzgsF$61DLyE@r_<?z~dk40GK^6_rl!|4L4YRhAPLT
z(9$7H0nEKH_rt`|X;`?!+z$(1xPEjEIQZz{0Luq3b71j^p&jaeSop)zEleFuA1wXB
z#9=hd{jhWg3s0CltlUM<*D!gQewhE!!ylGz(BloJA7(F1Kg|6wf5YP+rU6RB+C8v%
zhs6`j9$31Br6ZU>pxQCcpMtp?=1-VAVD%o%9kBR?nFA{~Vc`HP7h!yuyI}r7Z~w!>
z8|EMMc!Y%?jE0#9vmaJ}!@?1kUt#$O<`0<tFnO5!(8CXA56m5~aDk;CSa`zR3!`E7
z!2AO<9~NHd_QTu{OZPByVC5Gq9%1DZ%-t|^VESR<59dSce^_|H!T}bqFmqt)Vd)MQ
z4ls2vf574&=6{%fVESS9!`u&x4|IJnbK(Ai8US-2EWBaq7N#B+-stfT3m2F^SUkbh
z!NMO_{=vcx=6;yJVc`!GhnG9has}pIT>4@5!1Tk)8(2PoxeFHVFmqt`!~6@=4|6Xp
z9AV)D%YQI)VE%{28_b=s^b0c|mM&rT!omj{{P24$Vg7*mAEpl$4{&>78eudvoM7&S
z`5We5n0sK#(P@~yFn_@O57P%rN9g)sd|3Fw{0s9R%)c=Iz|^DDu=s)Nhbn;Shs7sU
z7=uFB56cfQ{pjT`x&g3ohS?9xM=<@ca6s3G&WD8~%p6!b0y76!IKb?K(Xey{r=jf#
zSU!ZA2eThN{9*YCCJ&1bSUN)Y4=lW3=?CTxn0>Hv2R$9Y{0qzfuyluRFHApr`h~5(
zf!PalA51^YU$AtH9*;WEbPux^<}X-&g@psmesuk?c!!s>(EcpUKQMWCcthg>=00>9
zZXeWLu<`_EKYIAV^uz3d*$c~uFn7V!!NL!H{12CYSo(v-KP(@?{R1@^rXN;c!NL<}
zA1obUxF70In0sOI1+y2X9v1Gf@PJy&02{xBr8k&;Fn`1HH7x#N?uY4z>4U{L%%8Ay
z2~!8t4=bNx;^;KY->`Uuxd-NcboJ<bn7d)-z}x}T52MlD0rMv;{9yis`3oiwiw77D
zvma(JEIeT82v&Z;@)a!JVg7>YhtaV3hq)W34?W&s>R~i2{lLmSSa|}o50)Na>d|SK
zyW!!6p3k6sba%t_!Tk$W2QwcQZm|4^?ho|z2Mc#txWW7Zvlmt`!onY>ALegZe8KF8
zrGJ=ySos9g4|5+ZykX@aEIwi32TM0Fb8*qId<ct2n0>H#MArw4H<-OJ8s=}9J7D(1
z!VzXJ%-yi?hxr%gKbU@adWXgjj1SWfvlpfwJ-xu<4P6`-|FH0fhbPP)Xn78cSD3qD
z<pexDpye2x57iGdAC?|q{(`v&t{#m6^9L+G;C!gLaQmV5!t8;mgM}l^Kd|_N#RJSb
zSp31_8x{{Raaer8%0HO7F#RwZt{&<hSbV_z3o{2-`vs;RW<M;RVESR<g{~i#-(m3w
z^FJ)z!{QC*PIMZk4`wgSd{}tF_^|MXr8}58F#RwZmL6g9F!iu>1EXQ)!PLX_!}1L*
zykPMME7xK10P`Qr9$5Ur(gCczfW;R~9Of_B`AIPSuyBK^gM|ky-NM2VW-ogC0j3V-
z512VH^I`sl=|>+wgXxFqgZUShu3$7Q{LtMGw+D>@^9QUxhJ`Eo_yx>-SbU(<uyBOA
zA6-Apy|8o)vkw-ouyBXD9~M7o&V{d|f`uc@T`+rL`2t-Zj1S8XFn`1Bf%zMz4xNUj
zJD9z&bO*B+z1%?84|6BX|L}N(mRB%;!_pDDewe#q`eFWrxeLaJxd&Z8EWBYfOg+3l
zMRNzbepvd0g&QnCp@$#LTy*^~dtl)Qa|bNkVg84uBXs?+avm1$uyBI;A6EY1iU(M@
z!O|hD+=TfHM#It}%wI5f!0dtf8y0^sf5FN@SoovoUs!%Y*9Wr?R&JsD9~SR0eK31q
z{)f2-#)p{$b2se%Lzp{Z_QCQU%v_j$c(_0ffQ38E9WeXR!vkh7EIy&y;p?|y?u5~>
z@(!jBW-iQrSbW0danjKG6qcS~=AxHBF!#g40T#co`UjRiVD3OKKVj((7G5y(VBrW0
zKUg^6D*s{m2c{nuAF%Wfb3e?#uyO+y53uwLQwQ@8EZxKGhxrR;56pkC{0UPB%a5@5
zgr#4Ye_;NF(Qt8Sc>yyQrXOZ5OdZVKF#YK5dzd^-KP((z`eFWuxf7N@VD2QO9~O?V
zaDb&hn7J@}Vd)NLKTIEthQ%{XKa7T@Q<#2O`hn?#(J=d9?tr-)W-ct>z|sRO{$ToH
z;S8f;_Q2c^Qx6Ll7!50TVD`cE!NM129xPs9{)d?d(+^9xFh0y(uyhD>KP)`p@=yjW
z|G?5O%p90KFneL?6RHhfe!#*H7XPqtgr$F2_@nEGr5l+0VfLZZuyhOa56pfT4YLOp
z?=TwXUs!m<!WX6wM#I7x<{ntQ!Q^53VCKNeA6PiS^ugQ#vls4OXnutGA105!eh21n
zSbU?ahsnd-1*<>M?T4v@#Rn|>VCeuR4~sXLe_`rj=^kb-%s!a^VeW^Cqtmc(fu(Dh
zdRRI_R}YUbXng?-N0>Wd_QLW7Oh3&1u=E3?Vc`bT2dhtD`p{`u{RDG2EIeTDfaQBw
zIKtco%QtX)q3(y<hZgVf^Z<1LtlWj!50i(bH>ffOn7?86!o*?vVetfWKP>&g;u97g
zF!#ga1J<sB<$IX<F#RxbbaP?h3Udc88m1ps9>B~+uYY0Y!@?Wp4p_Lr!UL9mVBrC?
z7iK;zy}`_Z)w{5C4+{sFJ{S#4N3e1LZVoj5VE%=N8%!gVhNWLvJizQhcQ4FeP;D6Z
zkHgXvA^TwZVg82M3yV*fe_{TIr308g7>%wU<`4AnhNT;H_rt;sW<M<4Vc`UeR~R1_
z9x(f1G|XO@dtmto79KEv!NLV557Q6JudsXwi$_>F26I15KYIFsxf7P3V0@Upu=)$8
zALbvJ{V;oB?uEraEZkx4g^9!T!R&$A3lCptx`Wvdvk%?fuyBW^SD5`Ubujy3@d*nD
zn0sLAVfhrM4wg@0?t{}%cfrB|<_}yn++WafNLV<+$~l-mSh|6wCzv=aJ;1^Zt{<iV
zO2ga_iwBr{Vg7~X2YC2G^}#5Zy|8o$qha9)GY94lbbYY!huI6$2lE#!{?OAuEPP<;
z6=puHzJd7%W*<D<q45B-7v_IhyuoOg`7n3D!VebzF#WJ{7N!qYZou*tOdgg#;Qcpf
zeFw7#7QQfhVCfeYp0IdFKfe;DAC?|r;ROp%m^)zM2cu!(0+WY@BaDXG2MaftzhL@c
zG)zA%y}{fAcRwt=p)|~WFnL%w!onFAkFaoq<wKY|U^Gl0+&-xJu=EJ?2h6`PeK2>U
z+Yd7jW*<x)Ed65Whbh28!^#(!G`fD6J7M7mvlo_5VD5qGhxr4>hlMvRox{w5xfd4y
zuyloPKg=9hIKa{wJUpT46Q&PV4#4%HF<|Kr<{nsl!rTMPm#}mN(+<-Ii!YdaVe;tp
z5G?<}<YD<2=6+Z{L=Rt>J7DDtOg~H=%-t|^VCfX54`wb*AG*6?=@k|aFn7Yj58Yp|
z^Z~OMW<Jb)Fn_`F9n3v2`(ZRp9W1@T!VBgvn7O#z0W${{U$FRxg#)@j(Bltg4=g>x
z^ux@BrCXReFmaeZnEzqv72W?Zf1|q}=3iL2!~6*|ALb5N_`%Cnr~;UNSop#G1#<^X
zKP=tDXsC90`xEAVSo(#z7ZwjN|Dd}cW)4gpEIeT40Nh>Bd<gRg%zT)BSiHgPhv|ov
zqcC}xK6w2DH2`KmEdF5O0!!a8dtvTJ4@amm80UDv#9{hi=D@^ZG%UZs!Vw-0&~^{Z
zA24%Z`d~CHoM7<<6Nk~Tbb-s=F#n_HM_73Uvlr$Ln7J?-=3bb7m^)zU1(yC{@dDEa
z3tyN%SUkbp3kw%mxWU{5(+{Iz_QB#G7SC||py>zZf0%z@`e8II-NWJ^#)rwn^uh0M
zfk{AVSU!Z68-&~q)y4qxH_TpGISF$wtUiQ^!}P&u^mGFYZ&<j%;s+)U%a5@14huK*
z_=Wi&<_=i6!rTFiH+225c!PxltlWdy4>J$uKA8Jq?tz&Pt9M{DEF56|MyJUQf0#Ki
z_rmfMEWN?fBTOASjUIonaunuYcsfMa2<5}v4GT|LJiyW$%si-a3<?&GF#WK2B3D1m
zKA3)(c^DQ$1<>6OOK&j!uyBMb$4SA`8_XZDbP6*EmM+i@fTcH>epq<G;sxd(Sh&K>
zg_#3W2eTKJ4q);y8m14HeqrLU_9HC)!t}$+H<<rn{y+~;SpI?Kf0%n=;Rv%ArXR+K
z*$-0(E5~8s0W%NgZ&-N@(+8tr>e2lH_YbsR0ZY#?_rc@|g&WMjF!#e~SbV|K4~!3s
z4_LUt?8TLjVCrGz2%HafH>~`D`4^pr#S_dQuyh7<C(OU-`e1xm_`&Rl#V0I2VD`hz
zLr*_2{jm6g#RE(~%zjw>p~pYW9dH_2-@(m;h65}d;p(6aSo(p*1I+!fau62oF!!U^
ze=v2h@PqMT{)YJ%?rx}ipw`1FSp36js5)5sfh$B~z}yeBA0`cRFDyP`;^;Iiy}-*2
zsJ$?EqpL^f!{P~+AK>8*RS1hWSa?8%;qzax@P_G!<yV-wF#lt?2O7_?c!T>BDh^B6
zu=*1o{xApNpkd(%vmX|YuylY!8(bLXf0%z@_QU)ScPCs2Dg$OO%wKRC8ZYSM|FH0b
zr8}5?uyBO=2WBt&{VA~Y11q;-=E2ehtla?%2lVwH==xyphlLlc9SDm*SUkYO2Nqs1
z^{{Y)l?yO;!`mUyaDeH9nFBKyM#DoDsvc%OOdJ+2uyPBQUSR%(`2*%2SUQH4N3e1V
z7VhZogykogI+*`q=^5q@bQ)$a%zd!<frUTJe=u=ac>&|YXjr;|xgTaf%s!Ypm^)$N
z0F#H6moR-W|H0&8G|U{Bdtvbgb1y6%!qj8X(EJM14~qwwewh0)^g#t+=@h0P?jC5o
z!_y5^Jvs#oKbU@a_(S!>(l5F`7#|jHaQ)Ezd$90>(YV47mVRLFfbn7efa%8-4(Ra*
z(+Beh%$=}sKsN`b9+r<_`56`uu>1fkS77denG3TA77j3TVCrD)81(!H(+^9hFn^${
zgXI@kI)v$ixf^CLOdJ+paB*ln!qO9Z{)O2KiyxSKVc`HvUodf)J7E5V(Xez53tyOi
zSpJ9UhxrF44l8eA_QS#%W-cs0!OVr_f0%#a`k~_`Fm-VIp$cK<qU(pnAIuzBxWU60
z8h)_y59Us&vGD#oEd9gW4GSljK6to8HNw&t%s!YtnE9}D0CP9YJg9c~zDHO+0P`0t
z-NDR-#UCvGVc`QizXhKDp#Fr#4=ns(^04@W)xWU)8!&gn%!Sdgd<+X$SpI{pe}Sbt
zm^)$ahlLlcJb<Z#xeHeR!r}++e`q-hQwP%z^FPd7^!X#WJXAl--{|2F3s;ysVetS<
zpD=ys;Q@<3n0YXLFn__)4a^;|@&=ZlVEWPR)q}bh7S6Evg}DcozhLo(p8jCr15*c+
zhq(*pPMEncJ}w&Oepoob+z+=Omqw^K%ssI528%~nc?MO6lY+S$T|dlUFdCLVVFtnS
zKP(-=%z@=YSUQ8HUs(Ew>4(X~^uzKsEWN<|0aFj7;p))h5oR9D->~pUPd~8rTkvp$
z=3iKPfVm%*?qT-AXxREOn0}Z$VetsF50?I5`d~E7ov`!<(+9I3mR`}r8>SzfhQ%99
zKg|EIa7R~<&WD9R%p91#uyBC+2j)+he)RZ<l}B*@LEAGheK2!j`eF9L?1#A@mfm3T
zhprE%A68z#(gVz1m^rX=A6GoU%z@Fc@)kzJ+zrzYcLy}x!O}ZSJ-j@C8VCzNSUQ6D
z|Izfr{140bFn`0+0n|7Kn0YWhEd9X39ajFq?1QPpMZ?lB%zl`EVfhc2KA1Qx{$S-6
zEZq@XZo$lf#UIQaFn_|`0nMK<^>7**j<E2D>4T+L7$24n;QF8pnEzq+!~759LzOY$
z(ho~-uyg>6f0#Hd{^0(A83?6e`3I&SJsx4{02Z!LZEy<aZ&>)F=XaR>xZ(pn{^0td
z;SCE1m^jQma0fscu<{F*A7L~+eL~HHwO^px7-0Dn<_{Pj7T&OMgT*IIA1s}~!V4DO
zu=s}2uzU-1KTJQ&eQ^EIatfvn<{wykfYC7hFd7zrFg`3EVESSHfaPnLI&>Ol4$NOL
z|HAx_t{$Ba^A9Y(VC57n{?XOL_^@z;g##?UVCe<s4p@1N9u6@3VC^uNJ7DDy%pO>{
zqSya$dGve??T^6150?I6@rtew=6+Z>!sKD@fR%@^a6$JsOdm`gtXzQ6FnzG}4-<#w
zTUhwO+zS)Om7ZbY1j~Q0^a1lHEFHk&1C|b8@d>jZrVeI5OdTv8!omR-pD;em|FC$2
zxf2$@FneKqbp7b&!0dzh8|F@!dtm;9>4VX*aDeH9g)=N1Vd`+vuyBC;16t0&;t8I9
zaTov#f0%xle~Hx(b2m&sES<yj<MKDmIk0jMrXQAGVfta=0aK4o!_qA*9AW7JrVo~$
z(e=Ujuy}|00~Q`Id04ta4;Pp`Oh3&182X_KVc`IC7gQL&Zxt2}u=*2Mxee0?D|cY|
z9#-DM;v42(So(wMgVC`5H!S_Y@*T`vm^rxg!~6?N_po?_<tvzfVg7*Gi;IT29~KWV
z{iNmtn15jYhtas~g{2>uy|DO$*^f@c@-@snn15mE4rUH|ISn%hJv?FM3``#^9l-2?
z<pUTWW-p9}sfWb}EZ$(@2BTr=7^V*v&oF&(KD7LRr3aXLbQ;~gFneM8VE#q77iJF3
zURb!n)WgCPW**G{=-~;AXP7xKb7AQa7Ot@L2P>Cg>R>c1{laLNI7~mR+((Z$bam+A
z0n-n2Kdy2Jmi}Pr4wj!`{($L6kAIl|Vc`dhKbSt4`(W-zFLz+$cd&93mQG;qhM5No
zKXm(H{)Lr)u=s)b1LiN7`(g15a~CceW**FZn7uIf!R*1MA0`e9ADI7O<p6s73#Ja1
zk6`YE`5TrlVBvy3{)cWKEIq=^g~bQVJaqeE<vq;5FdCK*VEGA_{$MmrAIv^DADVw*
z;SVzpmi}SmpD=gB!W+hi<yTmEz{Fwhhs6g>97e<JgSiJ*p2OlFmp+(&7!6B5Fn7cJ
z4~u`8Iv5QLCpaJ4kAb-#<_?(qVfg@-zG3c#g&!=R!t}x79cDi)9l`8_%cHg5VdAj(
zf|&~oUv&Mj^aArgJYHbtLgN)aegZWB7EZ8o0G2;s<tNM^F#qGazY-RIFd7zquyBF-
z0~QWY>*4DVVD5+I7Z@LwpJ3?#M#J>O{0oZ*n7?7^1-<-&#UD&R%)K!CVD`h(JIuYX
z@PO$<r(xj-GY95h^!P^C2TOl2_ru~H=1-Xa(e<J8Vd)W8{=@h%cfkAsi(i;KVet>6
z(anYBADDY!;R+l7hq)W34rULm+=PW2x_+4bu=E8h=V1C^@c>KrFmut}2jj!y9p)Zb
zIKuS9;vMFHm_C>~SUkYe11$Z*<YDH(!V{(+7H+U`hv!ddeuKpWEFGffKbU>6@IX&T
zF!NyHhpYa8`5)cguzU;iKg``Q8W!F#edy^8#)p{$(+6`W%>S_b0&_3SKd|%<qhb1C
z`e5M$i+@;thQ$*s++pbmrXQV##UIQc@bG}f11x=_>%+x|r9YTCu=qode^~m3xf|wx
zSa`t9f%yl${)E{N(+^8WFneM74Hp0C`v+k9Vc`jjPgs7z74I;8u<{v}Z(!*WW*;p6
z;p)*C=<bE36PWv9@dgVwSUAAk3$qtJ++gVo7Oyb(z|uc#{s^WH<}8?cSh|AwAC`{b
z;R3A}VfMq~31$vVJ<ML1KVb4O8fG6%Kb(f9J6L$Y)Z?OI;Q>p(Fn1EG9~SR0|G>f%
z=6+Z@Lbo60URXH7)WPBlSNy}m7iJ$Ud|>v$;u+mO7$2q&=5AQ|0E;h}I$Sg?ykO?Q
z${Uz_VfhtC!^#)9dC>TW`5We5SiXhD1Dp?yPq=<41Ll5M{)Lq@u<(K9Cs=xd#S2tB
zoPxO@<{w!8f$4+MFn7W=LK!goVftbIg~bOfUZKk26uP~z@&FcYuyhCuSGY!K_`&Rh
zxff<1EF9qK(HQ9Q2a|@SJD7WM`4?s{jE4CGW)94Ln0sLH2$P5T6Q&<tjzi-a7M|$g
z2ur6hb71OV=>_I)SiHm10nA^x%5RwaVeWzP(ft842WCHvhN*{@$FOn<mTqw6E0{i*
z`(fb?i+8wxp#374eK7N3?uGdm7Tz#(VCvCnn7y#_2bM2j=?+~zIv=JVW<QJ%GY1xL
zFn`0s5hf3FKP<dp=EK4ZW*$6$L(>I%{~In3%@45ff~7B5xd<!A;qovGp)@QUVD5pb
zgSiJI|3US^+ow?b;e4ojVdg-U!zq~mVdWnzKf=Nb=3ZEM!8Jk|u<(I}KP>*C((ryA
zEIpv-KbSrk4bumsVdlZo11$bv?t_g#!}UY`57Q6xAIx5uJ7D<_mM>x94om+q{jhun
zvma-AhT4nn4w!xD<ps=q7!50TVDS!1FR*yW75*^&uyBIK2h1O^@)#CwF!NyH2MZrq
z{KM44;vE(~FmYIT!2AW%2TM1waE7H*Sa`tP2h#_mVeW;Q1LMQO8J2%x{)hPkrXSt^
zuyhX#KbZSr=Hk*1GY6f9g(J)ySh&E-MVNY+|6%0-EWTm-VD3kEH%vb)9$@Z-g$K+Z
zFnzG_h3SLQFn`0+56nDRe8b`crXHP!r9W7F!THeg1D0OU^}+Zsf5P;m$2Tr_!^%fk
z_`&=Qi+7m6VetmD4@Sf6gVC^b3QIpQ|HINPOdmQ8b3ZH`Vets#!`y+Z9EItJ#UG3h
za}UfucsxME6($agFIakmhXZu}9+nPZ`eEq{rVrNsfR$sgaE0lErCV4z2D2CDPFT8u
z#TzVr!}P=A14hI2!@>y`e=zsJ!U-1dFg{EljE3ol#UIQcFneJ7VeW_VVftbA!NMKp
zUYLDwcR|}NFn_`H<Dy~a!r}pzo^YijSUAG;!NMO#!}1TzUFi10;tduKFmqt(7v_JM
z|Iqcr_%Qu2_Yg|AFn#Fu!}32Yy}`^uPd~75huI5D@38oT<sX>)(ZdfGzOZ<Lg%2#h
z!^C0c!OVfV8)iQ&9l^?3Sa`$S1yc|62aFGkADBB}@eH#EmwuQ!m^rX?3-dor9L9(F
z2i^ZLb+CK~iwBr{VEG)D4q)K`(+8tr@eR`tvlkYBF#o{97p4zp4~&MTFIYZ<nFHg)
z%z^2LwZCBMVBrpvhs6s_Ke{|D{$S-Ej1NoyuyBU?A6-Ap9$5In;uGc%n0YXD#O8mP
zdRTbE%!j3an7d%=VD`Y=4U0FJ|6%HI*$<09SUkYe6U-i%|8ePusfVRkSbV_J3oPBi
z;}2H<K-;ab@*mxuFn`0$fzdE^=<bEZGtB?!@d3-9F#Rz1!tyyR-eC5^`~ge1Fn_>k
zm_C>}u<(cFbC`Q!@dL{bFnO4ISUCl=AEpl$uCVxl<zw`Cfa!<%7Zz{u^aHJbVDSj^
zKa7U?6Xp+C`HOBJEPP@5U^GlWEFNL*h1my>cbEn!4I4j&*$dMLm1cnDH&}SV!Ve}7
zoBxODhq(_Hj<9%yxdRrDF#RwZ=6+awz}x{7hq)gX9x#3AG|WDjdtm0k+z)dfx;_{m
zt{$2%VDSjkkDh*E`e63L!XHM%(h)2?VBrTh4{87`zG3c$r8}5?Fn7Srf$^c*;S|jM
zuzU$ikFfj#i%+-)C<B&$VfMrH!Q2mXH!L1t?t*HEpTh*xkM2&GdtvT?@!|TR3|M}F
znFCV?3ol&p4+}q-zhUOU$|0D3^z;V{FIarR;t}RfSUkYO2j+j6xv+48*$Yz-b0^F`
zSUQG<Bd+-$n0sLL2FyH||6utDef}M$4(1P-yI|^J?uG_4oPyZ{b3ZKq!Qv0*UYLGZ
ze83EV<$sttu=)`eUNCcD`eFKDG^|{Pg%8YrnEPSj2QwF@4;Kw{2Rt01^&HF`T>4<~
z4huh+yJ6vnOCL-e7XGkw2aA7L_`%{2<_`3H2-62k_b~M^^I<eB9MRYR!@><_4y-)|
za|g^H=<x?L7gmqL!VTtsSUkeQA7&m*A1wZ0_QJ~rXt=}t11oo7`4FZbJ-*=Kg{B`C
z9x(l|`WL1jmR?}`VeWv*!_pTl++q0}-5qc~)P9(LnEf#QFdC*F7T&OUfcY1u9~SR0
zcf<6-X=r@|3olr>!r~344;C+QeNYB0{lelA<{nsl!Tbv=&!O7j<FhdJF!#d359S}3
zIk5Bx(+?{*VDS(4H#GcU{(+?<Sp31tPnbL`-ND3R_QUccEZxGw17<%goM8Tj>4SwA
zOg~ILx_-DgH2h)V2n#Qm|6t*cOFuk)LCt}g3-dQDT+qW0rVeH=EFNI)hNT-=xWUYU
z>4VX*^a;}si)Wa9u<(Pa$DpC^hQ$ZW-GuCgn}enwCJxgNix-$U292g4mhNEsVc~$G
z4=MmFe_`&2g(pludVYm!gHtg3VCfhZjxc?&c!#9}xJD=gmTzGCVCflVE>s!(9DG>%
zf{DYz8<vh?=Af5rFnzH33z&YGeX#Tja}UhDuyBB-2bew>4GTXQ4HJi%3(NnoaDd6<
zqG9fUnFn(>%p7!muyBI88>Sy-KP>&g{DIznf`u16{-OOXn0+uBmVeOG6D<5;_QLtl
zatvKPy8B`7gxQboUPAc~=5LrkVCfE5e4*P9<HOtmGY1x~FmvGLD@+5FhLvA1_rvlJ
ztQ>{wgN6rG8w1QfSa}6YZ?JSi$ltK^4x?e}VfMnp5oRAc4J-d)?uMl|Sowz@jxce!
zInZ(k=6;wsOdrf$uyzG39mCYYXqdgQc!Z@hSh)=I7c5+0`p{{Zf8g$cxeprd@NhxX
z2#YURJi^K`Sop#8q5B_ZA1wS}e3(9%yJ7la;R3TBc7HR>|FH0Zr8}5;u=XQ79YGC%
z<u_P3!NLs|j<E6%9&XTZfNIB}VD_S$3v(CDd<>0f0x)x6`3M%SuzZAG{=(c3b2ls>
zz`_eA4pWD&9~OV`aD>J?%wCv0Ec{^QAk5h?^I_=*rVbWvuy};2htaTbg2g|~ewe#q
z_TVc2VEsQ>IKj+?r5{+jft9~-c_;(jURe0R+zShDV&e^F9?Tq=`(gPPYAptZUXH=k
z!@>_1pBNgU0x)}F;fdjIXnex_1q)xOb_Q5Jftds2!{lK!EF56z872;MKTJQ&9+>}O
z@c~nhK||dK(~lm0xZ)3H4lJGG(hmy{n0j;?mVRLRVfhc{epo!9>x1!O`eErGR)53N
z56m1`xWM$mXjuM&*$azDSh&LMhvh4nJ{S#C2g^UO_=AbV(hp2OdU(LX8|Hpk`3-Xy
zOh3#&=<Od^ISC5~Sa|>o2Uz(F3kUT4409*UUYL0>cfsNxrXN>0!omZV9$?{!E8W7>
z!@>;~|1kY9d06^|*^lmjSh&Ib4+}S#KVkU=<_?&7aPy%ISiHgNeO&PcRSu`n(+w>A
zVD`e?0oQ=WfY}RkKg^x5_=d$lnl?BWW<M+(VD`h(Eg}11=>-;Fu<{8O?=XvC<sys^
zqhaob#Um_yVetsF9~OTwJ}jK!?uHr&i$7R<1*Q+ihbm)$&0oOmhoyH|c)--b!V{M7
zVCKQ}qo+Gq`iA)*rVnNg%zrR@VE#n+2h1M0KA3~hXqY)Lcfj%;srq5z0E<Uhe8J3t
zrDvG+FneM8VD5*7GtB?6^nmUUSh|6^8<y^1_QCYS>_ewv?u6@yDuC%nw+|`}r*Op|
z%>A(Rk1HRcyC0VBVDS%2Pq2K9%RjL2gQaJfIq3Rf?tyy%%7CeZ>4%vI%lEMGfR*D=
zZ5R}+9Ea<Nt{;N=A6Iz7!U3iaJ$=LSD=zoL+zl(o;OPw7u7H^b%SRXvhYG;L4VDgJ
z=@VuzEMB3?F({ZnVESS1fa!<DD~3j>0L*?^{)44Yn7uG_VE%z>gP(&4vmfS9SpI{R
zi!eTH{|Zb$%%8Aw1Xd5j!V{(sW<M-FpzDYE8|Dv~dYJoR>M>|&JmJcR=>8?7A7($y
zoiO)f=z|Kt!Wm{iEZ$)HVd)R59D{;|8_fSO1u*~PO7}2zu<(b)AIv;hxrZy?!}P=4
z4WnWC7G^IjJYeC79{;fX4|6{}A3`mF*$*p+VCf4g4X0rFA7&0r0u~PF;Ry3LEWN?<
zF)Y2n@(V0J(90iK`3rL|%$+cQ!om|K4hv71yI|pko<CszfT@GIALcHYI694<Z(;6-
zg&QvYu<(bO2MZUNzhL@c<tWU5F!eC^!@>_H53?U;9?btR_rUbSXqdlY=D^H_r6X84
zz|4W^gZUd4kLcwF%-!hm4^szEFVJ!T7T+*=n19goBP{-4`30sA7G5xUI3GHG0}B_J
z`7n3G)WPx(%v~^d!|aFI18Z-<)WPBl7GCK24nsdI0CCVT^I-7;3rAS~gT*V%pD_Qx
z?17~#Sp2~Bp~oN0AFyzM#Xn3REWN<Yg~bQl+0b}{g#%0+mR>N_!xTVim^)$qfcY0@
zA1wbum19uo;Q&jwF#YJ|28IDp0hs-;aEIxKg*#Lk28C`vtUf^xH(0vEFaRn53vXCB
z!uim2ixF>7eX#h0#T%^LgNeh^Gc0|>w83bY`(gfuxgQoUF#WJ}5AzpHKg=GOyJ7Bt
z*#`?pSo(wOgEC<84U0DzAC`Y$`f&OmY7UHo`5$H;Odqav1oJ=4AFyzMxdWDdVCKNW
z0j3`2Z&<j&+zksin7y$4fF6FZd<(M|rXQB>VESSHL#JWtVd)nZZ?JGDl>cD<gxL=(
z?_l<$+lPw}D?ecFfVI0|?tqz#t{)apFnzH2fQ1t*{L%9d%-t}3uyBOg2h)dcAB+!6
zr!f66^I+)>M&k-USbU@Fhs8V0y|8#kcMr^;u=E3~$6)rt`~y=5^A{`~!u$aXKbU)9
z?uF@tl_N0y==%rJ&4Kv`<{ntQ5z-G+4@*a|bO}pG@O%Ta5K6<`39}a#53q2A>4SwM
zR2zbV)<3ZLgyl<g{pjHjGXULwSUAGm4f8jwJcH?j(eUtvx&xLEVCfg8AC?|q`e5#a
z`5R^qto;C!hvk1<?uW%c%>S_Pg82((F3df!^aXbZlmQEOSbBt+3-dQD{L#lhVet$L
zH(0ub*#~n!)EW#5rXH4VVC4oZ-NC{Q7A`P8%zx<N2MZ@yJpfY=iwBswF!#djg_#fY
z4=f&G=D}!~e)Rb#n0{FK3)2r1huIICf761d7nu8D`e5l6md|0~4zm|M0Ac!I@ei{X
z7SFJFK-Y)Phs8TA++g;?+<~qh#)sJtw-@R!^mGGDmoR;>c!$LUEWN}04@-}*e2DH3
zSh&ILg{2#qIWTv@{DB^Ru=E3qZ<u>v=>e8*(d~t~9~TYN4+}?_`w4|3%siNTVCE1C
z2UtFZxgVC^VCfzv5A#2I{~s1!F!#b}n7y$21eSi$>knA@0*eoreX#h3g&&L$^Cz5!
zrWcrgSUQG<Kg?d3zu@|y3|RVs>4(KD%s!ZY7$2$)PEo2Kmi}P&!@>hrUcl^!r%$-e
z5C$x~Vfta=4s$O|Ka3C2g&=X+4~u_T`h&#-EFHj1fY}GLALbsIJWM~#9q9UD>R{yu
zEFHq~9WHmn^ufXlrXN;rz}yeZ$1r!G=Ob7?fu%cGxWoJfvj^rLSbB!J6Bhq4dtvgh
zaD}-aR-VG-Vf)Wu`eE*cr$1=AhJ_a_{9yS9rXLpWF!#dBKbSvY?#7jVVD`cC4a^*v
zdtvT}g##`c<_=hVz}$<=Ubs0>f5PGcmVRLRVCfFs9WZyo!VhL1ES_QJ!Tbkv4=g-k
z`e5-6D<5Fx2237q540Z*3m2GwVdWp(eNc7i@-X{h>R|FP^I_sJ_rc_0{(|X;@nQO4
z=?s?sVCfs?epq^dg(FNqjE2QK%pWlOVeN4kAC{hA`d~CXzM=653r|?OgV_sn2TUJ~
zhUFueJ7E5T$;0f0g*(hWFnO3hm^)zVVEGBA4i-KzK8%KiGc29J^uyf?jZZiqsuAXH
zn0{Egh2?KpJq(LiSa`trP-EcxkKlZ$eK7OkG*ldx4q)zu=|guPEM36D1s0C5^Z^Su
zSUQ2_H<<rn;xKbz?ne(-nEPPrVSJc9F!#d31?FBD4f6*q-N4)pvlo`1(Zda<4`wec
z{9)+>R_?>}!O|hjUfB3QOg}8&!{lM<5T*}iE=(Rq!`uPOKd^EK=3iL2!PMiTVfMk|
z1;&SkBP?EE=HfC3CJqZvI1Nodu>J!~9nAkQcf;(1<ri2v024=V|G~l;7XGmKgQYuI
zxWd8#W)94L7!9)@R*$3W!{u+7eXx88OP{dtf|-M^9~N&geK7lB=@l05F!!O;F#p2z
z!_9++Gps&A*9YUH>xcOR-TiQPK+T2ehov`Ic){Wcmfm6M2WB3w@Q2w8^FPepu>1#0
zKd|sb-@gSb=V1C_?t!HXSiXUUE3Ewj^9L;4VD5m?Fn7T6Kd$(L*$eY8tQ>=tL$L6G
z`5$I4ET6&bgSit%!_0-52X_b509g3}3wM}%VSJeRu=s<~Q0?&b=dk<-QxCHrrVeH<
z%>A(VgvsNgVg7>UL-g_=rVpkL=5CleSp31nVfhx_AFy-?OV2R#VD`e&4b1)M{zG>M
zEI+~0A1vL#?1!m`(XjLh*9ToU0?YR>eXwwZ>4T*gm_C?$VfMk&4NN^c4U2!6epoob
z{0mPn==m0E5PJI^JzmlMkM4e0`i9vLQxEeudisU=3q2pf)WK+2dWXe7tR8^rhlMB9
z8w{}g4~utL{)dGpEZ$-I(ftWC2WCGkAHc#77M?J5xM)~Df`tc6Kg?cO_@L{DxgX|k
z7!6D3g!IGW9cC}O`(gTF;fQWOJe;8IXIMDG@(D~oEMC#|qw`_*!~6lu&#-tv4}X~d
zVfMn(70g|*@);H$=<NrX{V@HoaDeHD$>XG<{zi{~n7d&9huM#A4on}c{DJuoW<R?B
z(e<PAVdV+TKd^9znS-t#=1y37g6<ylc!c=}W)93ASh&H`6RiA!<u`ObOg}7sVCKN$
z59VH&ewcc68s>gj{(*%XEWBavN7o1A!_>pv4@>7Tf5PGerXHP!#TzW$!pa?3IKcb?
zGY?jN!TbqJ=kRcVmbY+wp?sJ=nEfz&VKl7VBNSff`eFWtg+ELlR$jy04NFHbdAR?e
z=^qvkuy}^0Ls)ncTMoj)0hSJ7;Q%WS;r7EAX!gV66J|fm-MGR9=6@Isb1$sCf!T|z
z{DnCK7XPsLfcY1fy)b>SaD>?pvj-ObFm*70pr>b8x`(xM(8C{=ZeZ~U<HPbhEd9ak
zg{31{`h>Y3M#J0#tN+o%8RiaHdV;wZ79OznGi?7FOdZTzSp1>K11ulG(gS+?1?C@E
zxWVEN7H%-}anZ1Fg!vm*e!#*Lmp+&{Jl>({5f)xBf5X&a&`|a8_=mRtVCfF#Us!m+
z!V_i=EdF5O4)ZrGJYeAqGY{7Ph4~jo!_0+;AJkp2d<{!KFd7#AuzU(jSFrqx9*!{k
zVg7;LUkvj%EZ*V%f|h$Q`(Wu4CJ(2f?u4a7SU!ZsJ4`=ZJq`veoM7PzOK-67#G#D=
z7Vqf(fZ2;nKRkS(?uV5Fu<(P$1I!#;{)fd6Oh3&1u=s$bW4QaF;R@r!>MNN4Vetuz
z4_LZ`xdXlb3=40V|6%Tgg+Gjj`3L4MnEkMH3eyiuhcNqK>TuEUbPo+jm_4|{9o-z5
z`(gfv<yTnzqpL^f!`u%GcUXAB?1%XuT^}s}!2AshZ<sn*eu0GpEL>sou<{e`eptLh
z!vU5qVEWO+9cDkw->`5;cLz*AEFHk?htaU~3d?`6@&x8@n0}agTr@1*!Q2D$Kf!o_
z`WF_CuzUqe2e9%6=0EiO08@{yAKjfWbI`*PrVf@4VetV=7clq3`~x=!su5;C%-=9~
z!{QreF04L54^NnVuy}yk2a6AMccA+pR(`=~SUCc7CrmwhJqWW8<~~^Zhl#`72}=i1
z@4_iqI)Q~dOaf*vESzEX<4S)p`(fz^rXQC6VeJ{1IWT+BX;?VH`~iy>SiHgY!}1x_
z09d%e`~fouR*t~J0p=g5Hu(NIm_Asz!Q2n?FU(%_@Q1CRL3bz2ei#kY4@>_r`_XAw
zc){EQE5~rTA6-A(9nky;b1y9X(AC5EFneL?0o~oO`~XvrPQ%ha+}+UgM_}O%3rARd
zz-U-_!|a8{J4_sA4=g@l;ehM@C|LS~xgX|lT<(Y21G5(velUAs;RmZnVC4Z!9=-gA
z<uhF6H%uR#hQ<>t{lek_mj7Yl21`G1eK;5}|G>&ISh&H;BOKb`!Z3Hk^uzKkvHD@<
z9n3#4{jhWoa}O+Cz$}LO8x{{R8YT}52N)j~J}^GaJ{S#?hou9UI$Y@omi}Pz4=d+k
z@sI9an0YW7mhNGEn0{C|!qN>a9AWY>_rdhT)WgCHW-m-XOdo9g45lCEepvj&(+f0x
z!TbeFpRjO%xfh*=nG4TP(DD|RpU~CA$|>~thxr4h4<jC-2Ep71vk#VEVftYHhbqUQ
zVCfcSKg>K>y#zB4=5JW}12YI#e!*y1x`m}9Sh|Jjhs6U-KROKyKUloN{0-9&3m2F^
zSop#0htV+mVg85dM=$?i{)E{F^FJ)T!O{cFUoii`?1#~?`~#<<@lMFUuyhB@w=n;}
z%1xNPFniExSa`wGF)Vyw?nYOS!H1@ESo(qa9~SSh_=cGa(+^Vzb2qFUg1Hx#A7TE7
zg+ID}Sh&OD8Kxg*E-W6<^~1sq7Tz%Ru<(P~gP|W9FEDvH4GjkvALf3Td9e6E4+off
zbQ(P!!om?=E<!cH;tx(k-3PN57S6cJA6Wi{*#`>`So(+A2Ma$~x`5dS(+A7{uyBRh
z4+}qZ|HIq`3wNk@7+~sQd{{XQr=b!scf#z4#XBsVVD5k_XF$()F#Rz5Vfh%Q9_D^n
z`h&S2#)s*L*$+#<==Q_>i5`wHdvMV(^I`6S*$WGQnEznm4lAc%{=}7^Ve+u}h4~+*
z9%c@#{D;{G%U|g3g@qH$9GHGsc){&M*9hgq!VgBn;vW{DF!NyL2~-<=-4!hV!@>im
z4i+x3bPw|nOg~H?ET6*M3Cj;K`(gfvxdWCCVe9{4@dPs$7OpTpEZkt}0yh4LYy1!9
z4s>_J`~{PT`xjd7z``G<4wheG?!ZOE{0j>YnEzn<;r_;D08|{74q)jB7A~;(goQt@
z@(&jOuy}*H17<&=`~%Yu%Wp7!u<(Mp0~QZZXTvF2K7+X*7LPD@!}P<<gM~lLK3F<}
znFEUlSU!NICzypW_rmOhg$pd7!{lM%K*-%N^)Pc_@c}EBVD5mK2XhCEhN*|u<1q7J
z{w7vGOdl*9VD5(b3uYgThPe+G4zTnH6NmW+Za+*T8VyUouzCrm4@Tp%A7%{9ewaBh
zcf;}lEZ$-2VKgk<VESSHM-MkxIRMiKOFuCCVCKNm4J;gB<p7L^>4($M{11yam^jSe
zuyhPF7v>+BIv5SJAC_-n=^EV~=;PloeX#Ne&WE-`;O>A~ghs>iJIwvCd=7Ij%pEXu
z(EDGo@Pio(i)UCoz{&ww`hm&A;tS?(nE5dEFneL)4x?fEVe@}5cf<6-{0~cqFnuul
zVf6{jewh1V;Q%WqVCfH*jtJ#jm^tut1r2|gd9d)oprPpprXS`HSUN#Zx9Iv|=>z5;
zm_K0Q3G)XmUSa-#g%2$JVdlcZ2|XRc%3+whVDhkdhWQf~KQMFQ_CUh}rXQ9sVCrDy
z70mxIf5H3#i#ND_sDokU6fE9h`eES!GY1w9F!w>V<D_8zg1Hl>9~O=<d6@fQ;R*{s
zm^{pVF!#gCF<3f*`5TswVeW;6C(K@0I>vA?H2uKh9~Lh#f5PmAxer}G%>6KZFneL)
z2{Q*4|1kY9^)US~eK2(}aaj1l!v&fSVEG&7Z<zaF`eFWose}0g7XPsDg~c;0oxs8g
zCJzf=SUAGWfu&2BJ+OFz$-~+cu=s%KhlK|$9ANgKy8~uE%wI77!}u`$uyBUC17<HQ
zox$7#^B+tfIt@!tu<(SL0}D?Wjjj*Ihxr2*udsN7`4<-MF!eAR7QQfhVd)(fo-lvF
z%Tbs{C=Cl=SosHYH!Qs2_CnJQR2!Uvg&#~m%pC;vqvaczKVbD9%>S@(hlL-^U$F3o
zxfiA%=3iJi!2AbGmvH~UeE?y=)WO0ZM#It(Ed3aug#*n0Fn`0`1G5j7e$dk$#8w6d
zm_Ati!`uz?KTJQmyJ7x^g*%K73m=%d=;;R*J}~vLbPvntFmYITqU(pnE6n{c_2~Lx
z;Q|X+So(vtU(of#%z?Qd7XC0AW-o005KKSJ{V*ElURb?N$bMKj!`u(^H;fN+4=nuA
z-4AmI%p6!e!qNdOykY4F#)s*Hg%iyE@c4(NGiW}6`3vR_n0|D7Vfta=2@3~UI>JbY
zQ1fBw2o~P3{07qpGY4ib%zT)C(cKU8FU%a6J{S$t2TP~0`U}>6fW;du-eKhcOh1f<
zyBF#YxH-^uR51U-`~izkn0`17)ekcd=02EuSh&Mzm_Ar~frTF|-N4)p3kR4xVESM*
zEZkuF;qHgdW5UWqbaP<g53A>4=?&&jSbV|U2P=<Y?uWS(W-rWrFnO3hSa`tP0n2|d
zcf;ZjCJ&2mm_C@hVCfe&{sYqw3ul;qm^zrhVCfs?e^|J}%VlW1!Rmil`hkTH%ssGh
zfyE~*{9*2dhXd3+m_0Cku<(KDgVC^b3Nr_mpJDdE@;i)%=|iVs_QPnHKVbI5{EzMq
zSUkb>!NLI+|FHOkg#)_1Fn7cB!@?Ejf0%xBdtv6l?1Slty9Zi+!S%y5pwYPOhv|dS
zXxiXh^mL4FKRn#vI#3y~bOKAKF#WLl3YKqBbuqxlUt#)S=^2(@(c=x49$@Cc#!q44
z21_?Ed6@lh|3kwQCJ(a*T^}qz!u*9(KinV)0~QZ3f57q~%pVX{SV(Aof%yYg|G?~p
zm1kIVf`wrDA4bE%8<zfI=@Awl==CqmUKkBF6p4WO1LhuBx`ru%`4?8+z~Tv}52hZM
zKVabxb3d-|M-NAszhL&l^ugQ(a|bN_;?fVZ7p5NO9#}et=_geFpyy*)_`|}DkbYSD
zfyD#Ne=vP8b6_;gpD-GhUSZ`aEMLL=11rDK^~3bR)WiG%%O9|I11ub1@d~pSW<Jc_
zuy}ygM=<+f=@O<77Y&O~^zet7hf5!N_`%9snEfz+pzDW~e=vJt;SS3$@N|P#e!$WV
zOdrfXbn{{QVd)D-!`2_c!VTtMbp5b=1M>&0{)hP&M#J>O+z)dPEZ$)02xdRJJ+N{I
z<{w!4h3Q9EkIpAoKg_+bbOTF&uyh6U2fF`Z_QU)SOTRGx!Rl`q4f8)d{X)xKn7uH4
zuzUhD9~TWvH!yo)?uFS0GY@7jEFNLz!OBha_(xBtF#WJ}1Pc#XzJ<j*%v_i~Fn7V?
z1y&BDn-425VE%!HKg|6wd(rj5?1Slp<p-GmVfMk?15=NShQ$Lc9AWVQGY^+OnEf#Q
zuyO*XAC``B>4&)+77oO^8y5aBdtvs#?8RjtOdO^k-F}!kuyBCIKdk(O#RE(~EWg5N
zSa`wo!OVm4VdV$R9kBR@`2!YyFdCLlVd)Ni{SwR^SbBq{XPEn8?uMl!7!A`0t7l;O
z7#1F|asj3vmj7Y;Ve&BhVESR{4c&g2ewaUC;RjO(3ny59f`tpLynwj_?rtapmTq9_
z5T*}i4$NFwIf_321&cSBKVa%%?u5lREM3C=0kt3IeptA{!V{(*7LQQnI4PJrVESS1
zg}DQlMwtDubb?-P!2N^E0BAW3OFu9g7Tz#-z|4gk1Jwr$H&}SX(m6~Wj1MypE)LZX
z_cyeDhs8I{T$sJEaEHYQ%p90|VKgi~pzA}oAEqA`-Z1-M=D_?7qj7~JEZ@S@9n>GN
zc!A{ySbBiD3uZse|1kA1`(XJC<{y~*Vc`SQ50i(v1C|e9?uCUv%zkwJFmqt`!_0xv
zF!#d3AHDp7g&)ixuyg=R_b~Ut!U3ND(BdDa59Uvpxv+4Bxf^zVDa@a+c!TFZn1`S=
z%wAmXhuI4=4<2q%_rdZXOh1edH6DY4#TzU?!qO|O+{Dm;CIHh1^EXT%JY3M!!?>{c
zgM|mI-9gCR=>CVr7cAak=?3Nxn0>JF8|GeE`47_vi$9otSh|MkhlLX?-N5Qim^zqw
zF!#XXA4bFC3l>hWc!7lzOdjSASU!N!uyBH@gNegvSo(*#7rk8p^Eb?XSbB%4hm|8R
z_rUryFnO4}VCKNw0h5QNTUh+V_%L}G4YMCtIHJcJOdpJf#Xl@OVDSQTFU;S#?1zOD
zOg}6g!t}$^AG&^6`hn?#xgX{(n7uIlu=D|whv|ofA51@bx`(AFm_8T{(~oXHEdRsw
z!DyH`Oh1f<*$*=ZW-ct<!`u%`pD=we8W!F#{jhWoOE)n6=;;flA3Ytx(gQplL+c}4
z=>TRwEc{^lVd)N+|6%DB=5Clin7h#3kM4ii`Wsk2fw>o!-(lv!{0}czpc1ffhQ&9`
zepq^hl@l<3K(%2|F#WK2gxL?%hb!D*2EoD)t{)oSF!#d336>tv$NymdhlM{Z|G?Y@
z%QvuaK-Uj59~KX=^Z?6$FneL<z}yX|q4gZh{jhozJ^s-3quUG92QwEIpD=gB_%M0c
z_%Tc$%$+d*!_0@JKXm;tf5O5ErVkd5==x#h2`pY<?tsxSb71iS3lEq)%pDl*AD99(
z8fFg6f3Wy~wO?S$Vd)s2ZlUo3^A9ZCVc`f%moWV>b+Gt{*#`?Rn7d(om_BqG=6_iD
z!NM1&ALb5peK0=EUbs7;`2gl_m^ySCz5apcV>JEf<v*;PgxLoR2Xymc@ecDpEI*^K
z--h`gt{)l?uyPCL4_N%6>xYFGOh3#%SbV|DZ>Yau@eb1mvmX|YuyhKu7iKOl8fG3W
z-NN+4>_^uJQx6MASbBw(f3W%v7XPsH3o{26?l2mr59Uu;`2nM0;Rw?YvlpJ;py2|u
z4`x41Jvt4u7iJDDykX@&x_S&gG=5>>1*2i^hlM+gkD(tb0E;(RJi_7+mL6g0kB~XA
zdIUWlVBrp<q1M7FSbl+p2P__7@eZS5=>e`0%7D2a=59D28XvgyLAArrQG}IWu>1w{
zH%uKa8s=YEc@6U?EIx7RgQZ`1`hhAWHvPcj3tc}f-ND=iOHVNWz}*E+KQR5U^aCp=
zVg7*G0~1Fdzk|geEc{^VVEF}>zG3+YT|d;@@N-;X=D@-S=6+cH3X30@KA1X~f8qXs
z`VXcKrXN=R!Ng(d4W=KK?qTIMEM8&hUk{qDVES><Fn7T61uPxI+yT=Eb3ZJcVD5nF
zgQ<h%XZZL7R05XnVfhCZUoiK=?1hCtEMB168DRFq!U1L;EFNL{Vd`<wFn7b!11$Z(
z?Zu@LDh_i$%-=BoqQ^H>Ih;awH_RO{_ruJAg$GO{EWBay1j{!teK2!i;RVZ=u=EH^
z7wG8~R{p^3fyFo6AJFsz3wN0PuzUzp4|6xJ@(UJDFnzFahuH%&2UqyR{Er^rF#BNb
z8km34!w=nln7?7=3@rR$=E1@b7A`Ov7JsmCfcXPvA1wXB_%MCwG|YaOyJ6<R@&n9%
zT<u?&dtvbkD<@&@gOvy9`41M3u<(HCgYjYhfyD>Repvj&(ml+6xWAy~7cBl@=>ZlV
zFn_`H!{QO99v1Gf^bE5fmY(7EpfO<OAIv>4f5Y-KnsN*-Ec{{qhQ%AqT^JhB1Yr8$
z;Rnqxuyl>4oB<X;==lKVUs!m-{DH0?mVaUT(ZdJkepvc}$-~kKEd1eosC!}I5Az2s
zAHm!O(+8tr=@%Byu<{6&zF_GJef=NI-7x!L?u5kyEFNL@!qOisf57|?^C!$6T<(II
z0}Dr(I#~LHxf|vmSop)r1(-Ww`eES%GY7_p>4Vvat`DXkJs+U^7Zx6{c!TSMGT`wB
z&2KP1%wAZ0;L;ECH_ZJof5Y^_{0+4ZK|$?<>4U`wES<yj!_o~xBZLJDCzyU%Ji_A-
znhs%nh%O`&hy5^r!t95I1CmY@CM>_g+yP5>Fnus{VCJG|W?*1|l{c_(gvB2$9l_E8
zuJQ*KU$A@tOAj!8Fn7SrgV_&rFD%?(;xKiv^aj%pix-$Wn7y!a9Oge*yufH!_@nEG
znFFI?=?3N=I3F61F!#XJqtmc>fQ3J-yoKd!boDSkto(%83#%_+`eES-^B>GVF#WLd
z2^N2_@PoxGEPP=35LW)c^uyc@ix0Rs)ZZ}sVE#Z42Us}6+z)dP%pb7y2Qvp|K1?0V
zUKk%{KCHe#Pe(BQu=Q6kf5Y4d^Eb?0Fn^=l4>Jcw!@>b(9xVJ}=?|t37Y$4QF#o{(
z0V{uCG_G_Ab2qv>2*n>Pox<`1EFNHdn0>H#fYC62z|t$s-7r3^+=GQb%p90LTr@n~
zq2(Ja-NNDl#z)r=%Lg!bz{(+*{cv|f(<NL#)E<~Vn7?82u=s=Phf2WI!Qu@T&aiX>
z3kO)bg2f+HI|FR|4yGTLPhs%^qhb1C_QS^CVd)nZU$FRxnGbUhsqp|yKQRBn(lN|m
zF!OQIFnw?u8s6yoVeW&u9~KTUf57rTEZ$-D2P|K};tNK@?1SZdn0{FNq3ef*Kg@nu
zIKkoxRu1CwCv5%*rXS`%n0}Z$ap{M}H>}-&ZVt@f#O52A|6$<)vmcgjaG3+kZ?JfW
z(J*(z+yP4mFmV_Sb2r>x=)NIXIKcG7;svG;M#IuMto(+BJ1jn6>TuDpd<H9bVfh6X
z@3{2A#Nqm&<p4|{%p90HTr@2HVCfcC-r({tx;e0P4-0pgK6to5^A9{cpnRA)uyBXP
z11ufF(m%{yFmd$y8>SBy{;+t4#RIGyg^8n&pTW$B>4$|UOg}6g!1UuPzhU_SW*>}(
zmFuu@f%yaGe^@yJ3kR4xVDSfY2h5!?^)UUgaE6J))Wh-*EPi3`fR(>6{V*CM9YWI+
z%wAZ!z!iQl_ru}?<_?%XSU$oPelUN)!VTsgSh<HDZZLPlXqf+D=@Di>%w8BDrXHP!
zg%2$JVd)Q^ZqYSB`LOVT`5WebSbBl^AD6#j?uC_qFn_|#g{1>nI6$q1Q?PJ{3Bbxr
zm^)zR!1SY+qcH!%%28N4hPelp{$c)rr7M_yuyBL916B^e!X2g$SNOy78_XTBaDv4r
zEIeT48q8i84NKoJf5Pm8`3L3?SbV_32c{q9Uzj<taD(|17H%;6VeWzJhcaO47nW{e
z=>f)vxeI;%0p@O4I)J+eT28|B!@>`)ADTa5;STdB%)KykVCKW@hxs3-9%e5r9$@7Z
z)LRU&^as<2o*!WG53>gre=zf4?uMlYn7KG<sJ~$P;pRcZ1E)Ty1T4H@=?Uh3baz9Q
zV^A>tF#BQpVeyZy0p@R*dRTbC(i<$jpoc5W9GHEuc!Z@}n0lDKuzUzhmoWXXbOwu0
zm^xT`grx&?_rUbS?1kkwSUkh@!Q2BYH(>68*$4ACdOX3x9~M5a@Q0ZLGZ&^G!@tmU
z0CN}2Uod-M?uY4zxfdo5Hy@gAVD`i0VKgkhVBrAs7c9TP>_rcEn0|B`t{=_)F!NyJ
zr|9}%<t8jWz~o`>gT){E{2@#q%wCu}Sa`tXVetm5|6%H3{)VZCg)dAV7H;r#0Siy4
z|6uxI<t8kCVfh}WA7}oDngjDMEZxG)gVn?Eat)>s2Mu#K%-^v52M-q<`e4GabPK0p
z{)N&obub#1K4Il3EI-2h0Shl!_@TF7VeW;6BP_qb(jCm-u>1kD7e>SEgZUSxkC1<1
z{)5>IOW&~a4(2Xce8SuT<HP!oFn`0+Da_w68Ww-({)gpTnEkNy2Xi+p|H9k_(+^8e
zF#p5C3#K1tE{qRLcd&E-(+8)a=?~@)n0YXLu<(GZM`OUkALb93ewZ?ty)gH~Xqfw8
z`e5lDrXCg!aQmU@4;CJ<_6kftEM8#d!}1@@9q2S{`~c=}SbV|47gk=t;tyRvdisI6
zAEpkL4q@)Wm40FBVet*~4|=|V`5UGlM#I7z<`0-Tu=s<8BTPLy4RbdvJYne^R$im4
z$Ka!-ADBGMewaUC;^;Ii-@^P2i*H!C;qo`k9GE_Md_dC)EFNL%&}mpcf$4|E56oT|
zA6*}ekDmWw=?6x`%0*Z@f~5yo`i6x!%zjunz|tGc->~?F#RsnTFHAoy{$TkFR-eM+
z1HJx)nGbUZEI-5Shq)VOKP;cY!U2}<VBraiZ<sn*`h)Rd^04w1W-lxq!sKD$4|4}B
z9AWJTSopzcnE5bw63YLu_=C9{mhNET2eTiSIWTv_XqbOs?uEGnM#IEm;RZ_|FnzFi
zg7IPMVCfN-ZeZdt8kXK*;Rg#3Sh&OD9o>GIepr0N`~eFGn7y!Yg{eoUVf7wdKeRl6
z`5P8*gzSg88x}6G@Q1k{7B4XSVetk_f3SQ5ORq3-n0YY&z`_xxA7(GipD=gB@)ImR
zVBrZf2j*W`xdSs7R_?&U6{a8NE|`9}I5fOq=^hqdFn7Vy1<YMA{jhL=se{q5^Z=t_
z=E1@d=1-V@Sp31l1LiK6dtmN{`46TKorc>7^$)J_hnHVa17PJ3%>A(Ngqa6b#sG6a
zZ2b?+ov?U-rGHquf$2w&H<&+Q^(0I`EWTj&!DyKOVd0J*ez5q#MZ@9^M#Id-MZ@9&
z<{w!6p{IXb`eFWng(J-Uuylz_A50vkALb62ewaI8>R>c19AWVX3kR5eF#WLf1k;Be
ze=vRU@P(!$m^rZchv|pSpTWWj7SFJFhl#`7jUIk5_rlb{^uh82tUQ6mJ1kwn?1#~?
z@Pefym^zp~nEPSs(P@}|7!9v)pzRo#`(ge=*AI&)Sa`zJ!@?8hFIf1Ym%}js!rTuF
z4|M;dhab8-V0@T6VEG^2epvq-rXD5_^9Rh`uyBKw4={J5_a9;D9OiDAKA8D1_rTl_
zj~8hAg83U(-owHl?k;FNz`_qbeZt%gvj<kbz}jE1bWd#lhvg@jdtmnAihr0tVKiJH
z)PFF2Fmqt)aM2{|huQ-R2N(^r4;J1qKFnTNc)`LS=5JU$!T7NFfSCgeUzk2vzJZCu
zXjuA#*$0b1So(mu6Gp@08<yT+@dnce%hxdTU^GlW%snu1m_As3g!uy|52NAf78;JQ
zbPjV5EdF5O0@DYhVfMqq3uYfIAHn<sD~HkJ0j3WYjxc?&{14Ly3s;!E7&O%VuyBW&
z0}BUS?uWI{VESO?GAtZm@eXqby1g)W!)Ta3SbV|kg~dBeKg=E&4U2D>JS-o;{0$3N
z7!8YW7!6C$F#WLjhUtfe1I#}#f5Y^l)3ES|sYiDo%pMpGi)Wa7VE%{ehbhEC!}P<#
z1E!zYato#pW<M<b!u$dAKYIBM3kR4!m^m<K!qO|uAF%!(OdeLw!t}%P156$sU(j+K
z79VhVXu5^zhnWWpZ<xI>b71;lG|WD9{jl@~(+~3pOdpJf`4^@i79Pavhm{{N{V?~#
z;tdw=uzUft7e>S42|fRz>xZRhm_7^|8ji4h2n!dOe_-ap+V3#^FnO5$F#o{Z3rm-<
zekpqV!^#;r4UK1b{6XCX^C$ZF2h1I?a6_kI?F(4CLGQodia(gS=<$f2j$r<Ul{c_(
zhWQ(&A6*~Jd~_NX{xJQpaub%H(bdEG&~SqJ7Z!dn`{C+vFktq<`~h<ZEM8#oj~;%o
z@P?&dn0{Ef0W%lo4qWRmVC`6#KVk6=a|bN{p{GBXcVOud=3ZRs7rp(8?tYj)SUkeQ
zAC?bc?ts|~^DnIYhq(i0F0B3{HvVDu!}P=T!SV&nVl*0NFDxIz+z)d%jE|;`0T#|M
zf57qstX%<%cbGY_d<`=P7C$h3uy}{7gSs2$4_H2grBhh`fu&bidkGc}Fn7b^0cI}D
zzp!wEse{Eo%>A(NM~^p{`_XAw_`&=G^FNG+*^jOd#)qYMSh&FXXyE`;52w-cFU<e2
z@PgGtuyBIwgEC<1VfMoG!NLufK3KYi>4))Q;R-9aVfMrF4@?~_-^297@-IvuEc{{d
z3Udc+{stD#F!iu-hN**k4}*f~gT)stoMHN5<qa%dU>E=ufVmsy4_JPHrFU5P5i$oB
zUNCc@#xuae4VQml<sN!|fQ1K4KP+5f=E7)LzJ=Kfb3aTUEF56!(P@}^nEkMDg^9z`
z5iA~I;R`bd=6;y{@cax_04on*?m=%q!tyChKg=Ca?F=yY!}P<-5tuxB{K4V@7T&n@
z!PLR*h4~w1F1o!K<p(ss!t6)akJDb51yCA2{$S-7EWg0afu#qiHaG=K_b_+B@;|!!
zag|%Jc!ueN#UCu3VKlDvkIVf8!yoDcSh&H`0W5vN!U5(#SU!Zs1I)j$^a2xy`5P_{
zjVG8m%s()DVg81NFU%g8zhLDYdVIj#3rjz+@P+AznF~v2Fn_^lsP`FQ;SDnfmY-nq
zuyP2N4q*1c<Y6?1IneqBrVkcAF!gX68a^=lVc`l3CzyU%e8TlX8L)7MmBX-l8J1pP
z=Aio<7A`P%!~6v+XJFwE3ujpP!_0%(3zLVHGjM-G{RIzCXn4TV0X$xz>R|qZ*#k>|
zFn7Yz2dq4V>4&)krXMB`OJ6X5VfY^!elT_D;Rmx17XC1Eq1q4>)cvsZ1`7{ZI)wQb
zp#hZzQxA)On15j50J9(F9++;JdKe80XIS{d@-57Mm^m>0uyh8GXK4Ju>N!|=z|4c?
z1DJkXG%Oro=EA}O<{w=8(8B?io?z~VrDt^gu<{e;URb(;rBj%HU^FZ|VCKNm5iA^G
z;Rch3r5~6(VC^rMJS^N{`eFGA=3kgT^!S76hxr@keptA{+z$(PnEPS+VCe*AFU%jX
zaDs&&Oh0=1h3SLE7rH#mJXrjp%fs9Oqha9&GZ!Wf^FO-#Ve&8<7H+U~2`h(S;Q&*I
zPNUlkEB|2O4U<QY512VHf5O5amVRLJF#BQQ2+I#JdGz>)>4&L@>4(J&OdpJfrC*r;
zVeWvLi|%gpatvl4EFNLz!Tb#icUU~2r+;*J!1Tl10W$}tA3Ytw!y8%-!u$bC$1r)A
ze%SZ}OdMtpjE30{E6-u+4;GHF@Q3M##UIR_Fg|*|h4EqjMW<ozg}WbG9>UTgx_THN
z=3ZDm0&@pEo}lKy)T7fdf56f`%p90|Vc~`zZ|LO)EFHk&2PTgm|1f#j{4XrLVCf9z
zepvYe^FJZ`VdlWnIZQn)9AM(;{)gEED>q>3VE%@=3tb<K4+}4t|6%%J{)VL=SbBo#
zgVC^fhv|pugM|Yuy}|qg(+8tr@ehkXSUQH4KQMp5%43*57!9)rrXQA%VeKB6`(ft5
z+yj$`g&Qm!(CtBY2Q2>4^})@BrdODGu<(c34@;M@c!QNwFmYJ^frSf9AFl9+g#%0<
zEF56z4wmm>>d<Le_`&=G%TKU$2XhyUhWit05W0R?dVr-Pn0oZ`9I72o!Qvg}4qV|5
z*MP==*$XoVmJVU|!rXzT4deV6SUAGe!NMPA4on<I!|a36uyBXPA1oYT=D^(nE$?CJ
z1Qw34aEIk5Sh)a;N0>RVc!s5Sn7J@@Fmqw)3uZ1%9v08AbO<vCrXD5_QxCHjW)IB&
zFn7b^4Q383y}-f^W<N|ErXNPb;t!@Dmfm6c7d>CX^ug?hg%ga1iNnerSiGRy4^xNk
zewh6*f5XB7W-p9}g%d3PVc`x-e}weI%z@btqtVL^Sbl)1htaTf2D2YV!@>(@KP((z
z`d~CnKg?d3ewccge)M>U>Bkj*u<(ZIhq(u)4@Tn(KXm`&vLEJtSh|6^A3gkG{z11F
zrVkcxFn7Yth4EqjfVl%bykP2J{)XjGSUQ5`19bag;Rh=xVEF)+j$!EzR`0;{W6<d3
z3H1I-SiXaqkD(7L04w)k_QL!Db3arW1G+n4;RFj0Sh)oYf0#ThAEWpGVCKTYA7&0r
zKP=qg?ty85(y;Uc(+~3}vHphn7ZzVI|G~l?Y7I^brVmzb!t}w?15S--@dYcFVD84L
z4^0AAe#3>K?GIQyz~T>Hen92X{S8ZZ@bm~hhXEE3Fn6KPU&HbbEF56*1d9iBarF6D
zn0+vFU^L7>uzUkcKd^9u$-~;|u=t0C2RvOs{R>M+u<(GXgQYiEc);Qf<}R3j;QC<}
zLuptz!|a2Z12Z4yeptGIYQvyl=D^$o^A}7%F8`w&1lI>mU$AyA%pCOf3+Vb`d{{XO
zOBXQx==m2OPEh+``2a@4;tLjj=;46wZ<u|sd<fGA<HOts3vZZySUQ90N4FOik1+kP
z`8!y6!_0%FTbRFK{)V{&rXLp0u=s}QgYjYRg6V_RXE6QfG)y1NJ+O2N(+}gL>x1!O
z;SJLdE6-v6hM9w255n}rXqdlY`3dGOSpG+!|AMK9se{=I<HPGq^n3`l5FSow=?4~G
zu=D~izo7bH6s&%M#RtrOSpI{RUoiipr(c*lSbB$<3rjz+c!lLZbp7yj11&FM`e5Z4
zOdb|~xZ)A!epot&xeFG)F!#g8e_-y!74I;2z`_w`Ka7UyhnbIVKP=v0=^v&Z7Y&O?
zSpI{>7tDTK`e5R)c!Px(EZxHV0aJ%TL(>f`ykX%7(+3MTn0YY$u<;vMeu0U@{0$2a
zSbjkd2Uz^T%2!yu0ZaEV|G?5Oy8STwU^IF>!om|~FU%a6zhL@cG%O#$!XK7@Vd)oU
z4lF!i`f$-Of55^MR{o;b|1f)D;RSOyJba)Q!0d(5P+@rg8x{{ReX#TcORunW1oJ;U
zoj~Is=3iJi!r~ETA51^YUYL7f_QKo^(+87>>4TXAvma&-%pI_BfQ1*#A29b5ihr2<
z;qE~zFJa*UvlpF)rGJ=ySiXkY3v)j#J)`S~@nQafnF9+4n0^=yQxB7er59K@!f05!
zfu%om`(foS%syCo4Rbdv-NW)1%wBXF<_?&7F!eBdVfg@EA1s_;_QTA9sfVQpm^m;$
zESzBW!SuoW2MZ5$cfjmNFMnX_U^FcKz}x{#N3ixAx_(%A!_0@#uyg~9H(d1>tQ>=-
zLs&Y8>4WiM=AzRu|HJgd!U^VXboJ<bnEPS=hPfLSp6Kdfd{{id>_rbR7$2q{oragw
z&~gN3A1vMB(hsv27XPqx1WPBl>Uo&`Fn_}GEiB$)?F^VYTr|wTu<{00?!n>_Rvy8^
zA8szRT!E!eSh|PtVg853AIw}BAHDqta|gWMftnBVKf3?W*T2HT4Lu%U;xPBaX!LXl
z^Cv7kVetj?2d?mgi$mib7LPD@!{ZUI5IX~AFU<Wgdtv1lc3lWjSopxg52hcM{$b@I
zy1!v2!1Tl75f*N+aDb&_n15j9F}i-3IWT)+^&Tu=!@>o<{)f36=6{$vn0sO212Y#r
z{$cqDmX2ZR3Fbanc)<Jtqhaze`(fn=%-t~iVCrzuF#Rz1!}JpyfADkzOGjwwAD6wb
zd<u&<SbV|CTNodgeptA|^uyvE7VfZo0n0Zqf5FT{*AL^v(g!TR!{P^~A6Eas(gn<Z
zn0sOFhm{+!aD~MOdiep1H(0#G;sfRnSh|J9J4_tr4;T&eFU&re`(W`1vmd4&ordXy
z`3L5IxPM^ril!0fURXMS*$WFtnETP~hlMxHK6HP<`~$Nemakye!_JR@<y%<z!_omP
z{9*2Zr309GFn7Y-4L1kckA;;xu>1$BKVb0ya|b#N^AF4&F#BNUp{qyd!_omP-ND?A
z9zN*$VBrNzPq6re^P%yFUjD%J!Tb+X4@>W`aD;_Fx_<O{gvA4lhWP`Q4q)jYCJqZP
zn7?8AVD5s2H!K`s=@RCDm_K3Z5EkDscf-OR7LG7|xM-L@n0c^v2`oHt>4S;G!VlJ-
zfTeqwyI|^Y(J+0m^a<04?r(Jcuy}ygBe3)W%l|NQVB#?SF#p2h0T!Mxd04o^XqY&R
zhS>|#4|6xn-!Od`;RmZPpfoK0VeW&)7cAew#9{Wr!U<+C%-=A7!t90dVftV+Ed9aq
zKa7T{hv|p;1Evq1hNTmjJK*L*^BXMuVd)xOKa39xH<&+Q;;{6OEB;~nVD`h}1EwFQ
z59S|O{RNYU<$IX_Vfh>uUoaYG4on}+oiKZ0@dL95W)4g}It{x&6lO0h++pzz(+@Kj
zT|dlT7!6AgFm<r-hLt-o8r?it_`&qU%54}Qmj7Y?g1HlJ53Ig`(y(xW)6jH)%YImS
zf$k4jIKcG5$}yO|=rk-H!r~JaUodlU>4*6f77ws?8O(fG{ReXgEM8#pFn_|#fu&cN
zzhLIU^uxjr7Va?j!rTXQFDxCy%z?QBRxZH83+8W_xv=yKGZ&^GW-d%W%v~^ZVEGoN
zALcJudV$41%pEZQ!^$lf4KoK;f5H3>qhaR3^uyG_?1Q-*<{!9wq3H;w4;KxK2bejq
z^b7MpEM1`MhuIG+|6%zDrXE&~q5B`E9%e5r9$|c#KVabpw-;LO!@?gHelU5M{V;v7
z@Po;t#~&;mz~T?4A69;&yBlr}8UyBTSh|6^A7&1kattmk-NMX)*$XoVLj#%s%pWlO
zVgA8oKP<h$!Vwnl=>CU=A1pj!*23(8>4&)+md|1ChUIHmdW7X)Sh)dfN5JG^`e5#W
z*$1Ox<9BfV(DV&UXYhOi6^HYo2Ef7@=5AOzgi15O?1i}p=6{&KVCrD*hWQ6p{=>|N
z<sVqM!SVx4Ka7UuV^}(d#UspqSUP~IhtcTq2lEHay|8=>^AAiPjE30*3kO(y!qN-O
z-LP~7(+Be(Odre~m_0Ce!Q2ls2Oba5{0nn0OdpJfg#*mrF#o{pN2g)o2TKnyf57y?
z(i6Hq7$25y;rgNJ2Bsh8E|_{44U2bJIKbS2ZXb*f3lErnSh&IT!_0$)6D<DV`k?N@
zr5{$V!Q2hgkIVhA^a?A#Vc`ICFDyTyr+b(>Sa`ww0V}`Z=0e>Ii+`BAVfhyp&#?3b
z^C!$buyh0~KhWI)E3aVb2%f*7=?kVF79MbUm_wj6%pb6LfcYQhZdiVRDu+|B{14Ly
z<HO<)W)93>F!#U=fVms)4`_J7$_-dJ!_0xX2c{n$K2ZO{!V{JbVBreW2X_a|05pBD
za7Wh%a}P`#%v~^l!}P)Bq5go`3v)kA9yb003wK!hgykQYeK7s#;Rf?JjE30{qhaYD
z!@n>GKxvqMm_K0Q2D2AedVy+ZfR!^a{jhWb%QrB2n19gSgC35s^aD2^Ega$b0IC4y
zepood;vYT!aQXup|FCd_<ztvXVBrXJKh#<{1+x$4f0%n<3SjPqg(oZ>!0dyidszI!
z)Wh8YbssD|VdlW}!}1Tze=v2haD~%o;Rq9lxf^Cb%zRk=22%&ChhXwBakze{htd59
z3pbcPm^-1$8DRQhG)x@bJ+N?r%flGZ@PWlY%-t~cu=EBimtopqG_0J4g(EEe!~6?N
z2eAAJ(+8tr_QC9j>4&L@*$<;(`e5o{{)UAotel3WBbfVO{(*%rx_(%>0SjjsA7(Ck
zc*5KbOGmJHg4qMBhhh3*@dh&==02Eyn15mVVc`i&$FOvXK7R}gH<&t@IWYTS`41j0
z&~?e^;RrJaW-rV>m_C^Q;rh@Ru<(Gz2Q2(x`eEk5>NPa&3^4m)`eE|0c!!yT?hcqe
zuyO!q4on;t-Y|1u=E7*0I+#8f4NLDZ{jhj}=|iW{%Q5u$hNnApjZi+!{V@N-)WhtD
zr7x&*I0bV*%zl{rVet;r4@(ztjc5#5I)&+n<y%;|6VeZ>r(o`YrAPF71XeD<tcU4?
z`5V3dg2gw?A21rGAC`|{`3V+pFn7T6KTIDiTw(fQ^00geb2rRfLiWS-!Q2ni2MaG)
zx`%}$y8mJRh0(BhfW<2;9%1PT=5BNv=6{&KVfhSKx<}U!^Eb?XSiGT!7rH*UJE8Rm
zOdjTLSUAAM(P>!x!_pnhpRn-4r61kDu<(QV7iJE+edv5xyus1|ES<o@52ha79WeW0
z`eF9N)WO0N7T+-c!1(C;Vfi0c&cM<QEIwfA8h!pB7JhIV+D?I)4@;l0_(aza^B=6-
zhxrHQe^_|J^uzK4%zT)?VftY-Odh5mM#J>O?19lRf5O}YqhaX?7B8@Ph5HMZ?x6mL
z#Urdff`uoHhM9{_!~6kr56m2xy`<&?SbB#07wSIr`WxLHu>1te$FOt+3qSPv6PS8f
zxWn|p${|>KLr;G&dtvPan7?6sSo(v78%F&D4R=^P!0m+^3=21yeyA{<f~iNh7p4H_
zZkRc+^beDV(Xe=e#UCsm!Q2be4^xj$!}P=B3+gXec){vHbbT;BEZkxC!om%fZei-s
zX;`?y^ugQ<(+4X@(DlLiF#WLbgZUplox;Km<}O&c!pwpB8)gnHJ;CgUxd#>=u>1$p
z53?6$4lEzT?1hyZuyla#K3Mp{?1z~LD}P||2TT7jdto$8AB={H!|Z|Shw<U^(0GRF
zgM}w9f5ZF((+|sEF#WLf46_H9y>R_d224LJ{LsT2!(OO97zHcmVCKN$2WCI4{DG-Q
zr(y90vmcfYVew7KewcZ%as!s0VeW$EFIYZ8k4Kn3nEPSwfw==#kHF#umX2Wd!sKE0
zz|svY9mD(uQx9AJ0pr8;!^&BhyJ7Vy%p6#F!q=Zb(+|viSp37(!_0@pCrm#qeqjED
z*$eX@Oh3$iSbl}kFmusqm_ArI!uim82No{q`e1yReK7aH;tQ64Vd`+vuyBNh156z(
zzH!+Pb0;j_!1TlHhowVUxWUYY(Xf0AiwBrEEZxJ>3#|Nu$;09q9$wIXF)SWn{=sEG
z`uYKweK7yP;un^VVet=3e=v8$?1#Ar<~~?C0t-JFA7&3s9n77ud;|+WSa`wgg{j9y
z!_0x{hs77neq8!s;xPZh@*&K<gvt+?y|8eA`2*%|m^m<e&}mqDg~dNiJuKc}@rkYv
z#)riNEc{{Vg;0Kj>4$|EEdRj53zmLi>TuC8dtvT}`5%^!VDSL+54`+=83-+hVBrS~
zZ<s$|?t_IhuJ$La-i4`$xeJ!wVD_W8KVj~L#RJS7nDb!y4!s=FLUT9FedzH8vlm9g
z%!Q>ln0>Htg1Z}PK1?4hKfv^()39_4iw8I#T7DAJ4>J!I9x!uY;xKo>;sqv-i-!3N
zmOf$S2(JDcOg*d|g|(|-@dwLK=<yHpCrlqK9%254#UCs^!@?OBUod;%{)L7+%wCv(
zVKmHKn0}bOF#Rz1!1Tl14bu+~FQ`4RbPr3nFnL%x0MifS!{lK!Oh1f<iNo?SOh3%u
zFnO4I7!6C0Fm*6}Fmqt~U^L9VF#WJ_hlMjtKYG4_>Bkj*FnL&dgS9`<^A$`T%-t||
zz}vOZbdM44FpW?eR_?*#50(yL_T$ozo=;%<VD5ya6Il4c!WC*QoPz0tg$K-Bn0i?H
zg=;`#z{*)zI)?cN7VfZg12YdzI|F+7!T2zL!2Ayj514r{b71zv!X3tknFAAtnFFI?
z`eFWtse}0grXLpn==xy#Vc`eMhcI(s>S5sk^AAiPE*fSYuJnLjf56OvxfkYsSUkYW
z0a*CK(gRE%EWXj*4=YDt^&u=j!SuuQ!O}g<UYLFGc!1^unEkN$gM~XxAI!b5b_2|O
zm^m<eVdlf!k50qf4f7Ao{jhjMSC7tz*$b-&VfhH=E_C(id{{XIO9!y{$K`HVe8cJ~
zSUAAc!}1?I+@S4jn7uG}!r}quepq^g#TzXAVftV+Ec{{mVeWz12h$JZ!}P(@8%!U}
z{V;i0`iJR<`3EKsb0<tcEWTjj4bw*`++q4)=D_10n(kocz|sNCei)5QKTIEthN(xV
zVd)qa4{#c4KP(*3^`Y}&@eZrkVCe`J?l5<tn+LNG=6{%bVDS(0H!S|q+b^(qgoPU{
z{9)k&OAj#n;rS5i0a*TonFC7)Fg{EkrXPNO4z&FNvj^sHm^)zU1?Fy;zhU(V%pMpG
zbq)h8-@*J1vlqsPt3zYJ{0Vapti4EVxdDqWSh|Mgdsuj)SqJCB{0qy6Fn7Z8JzOCU
z2FzZVyJ7Yc>uy**0}D4;c*DXUhqdr<hUtgJ3r6}tE3aSxmOfzmVd)r_USR1LW**!>
z(0m08XIQ$2nTM_p7Cx|cA$t2C7H%-}VC4-gAHm`UmJi|S7v>RYI)<fR^z;ae512n-
zG)y}z++q4*;Q&(yqv7hI4468Y|6%TgN;AOH4=g@m=@}MJu=Rg1|HIq~(+@Wn8s4yc
z1WOmN@Pes>xd#?+u<$~+A7(y`hLw9TbKvGe?S;oDOamGXvlr$MSa`zn9W4A{@sFmR
z0j3{4{9xw5(gVyN==x#mVD`iO1JehK2Uxzw&<_=Wr5ji}hLv})aD*zupy1&JO^0xM
zpy3D0Pw3{s(l;#JVg7*G2g^4wJ}ewz4uGW}SpJ2jA6WW=hbz<_u<(GTAN2KyF!#XR
z3v&-F-eBnuR)4|53+8^9eX#Tg(+6`my8B@MfYGr02df8Q`e6A6rVbYk3kR4y&h!FP
z0HtB^hs%Cg`hhBkQ!srn|HJq&1u*}@!VM-4qhab{=@l0KF#p5+4NEsLedshSzF_W$
znFrGcOFyu5fvz8BFU%h>b71OV=D_>`GY=+@i-zTASbl<qBg`BOebD>^OGj}1(DVzB
zPbeRz9~NFP_oMqC=6+bZhv~zhq4vY<g_#R0$6)aZ&qq)LVCrD@!}LR?;q?H_-{|^b
z>S6j}=E3q0OdrgCn7OcUh3SLkN0>a!-!T2?{)fdoEWTm+2bS(&`q1r#nGdrcrVnO6
zOg&6LEc{^l&}mpX2lEH4{DA2vHod{(9X<SD{z11F#>bU^VD5&+1I!)h`3RO?Vf7D8
z9@ege>4*6TCJ!rLVg8222i#t0dW5AHSpI>z8$I4&;Rd5&?t<}Q_QS^CVdW3Zewh1V
z>S6AN)&H<~MNdaCf5Or+%v_jz;rd_>$3er=4=h|@=@;ET9NHP+`k@No`k{PW6fE3f
z;ROo^n7d)+4lG<?=D_@k9)7U;A4bE%4@SfE!|a3UhoxVbeK2(}8s;BZ`hmqi%)Kyi
zm_0Cmq2~u!dV+;B%p910SiK8#56qpg^ngD81q*kWeK2>z^+D4yOdMuD%wMql4>Jdr
zo?z~S<vUpVh4EqfVKmG>Sa`zH4NM;_U%}MFXmowBc!k9uOg}7KVftX@AIv^j`iI#I
zOaCx`!^C0wVftV+Ode(*j1N<fPNUlok1uHb2#aT!ztPh#%sg~+V0@VWVetj`Kh%7f
zei#iaM`7-OrAt`)gPDWg|AXm=g*!|iEM8#lfQ1XL^al$^SUQCHA7(Es9ANgr)j`uI
ztR9281D4KU_QLou{jl~k%wBl9g(`rBH%vb~9YEz__QUkS_%Q#&+yT=ME03VsF({Zm
zSbm1t2lGG7J?I)?_QL!D(+^93uy}`=haQeF^I-7|%LgzTCJ*xmZ2bUC9A-Z(-N4EL
zSh)ZTcbI;dJ7D@@_Q2c^GY^*kVD2Z>4uFL>%siMsVdlW>!$rf=4=mln%5hja#HA1B
zZ<v0VJ7M}^<uWe)Fn`0`4+|%l`*G=mtB0n4Sa}QcFU($;dtfx&Jg7dH{jhL@)e|sv
zu=s$*1A6?!${Sevg{4ziyu;*S?!i@lpr=2WK3KT~^A~#g0dpseMmGnR4q@>KYd^u%
z!)RE%!~6krFU<e2_(0bW3pbcO=-~(R2Q2@?{13AaJ^W$rfzdE`!_ozM{(+@Cm^m<a
z<I)fF2TUI3KUg^ii$7RChVfzP8Kw^x4GVub4Xtl5(jN{3VCfnrjqYBUI64ip4;CLV
z`(Wt?T|JBsOJA^Z6Bb{vau*hFF!dNTG#|nA!|FSjepvWn=z|Kt(gDm~SbW3s4^$aW
z3YK1B`eE*cr2`lZ4@YQxzzl?i7d-ty&vAj(A24;Wbcde)Vd(_sf0(;r?tsa|!UcW&
z7Z(07_ruaNEIwiR31&YmAHduNOTRFCVBraKKP>)X?t+B}x_+3wFn_?^1r0Bly)YW4
zALdS2_`$*xW)G~~grx^~JU|VA`5UGm#)qkc*#}D(P;Csbd;)VfEFHk?h3SLY57P&y
z(b6r<->`fG(+`VBxIQ!n%)c=AquWnNKP;SK<tMBjgXx302hCbI7hONhAF%Qa7H+Wg
z1LMOCfQ2{A{V+aE9+qxl;enq1VfMh>0rNjBK4AWW#Xoxe1=9~pw=fzOzp(TTvmbu`
zJ2d`a?uPjr=5AQI1B-uH`3*}~F!N#lg{g<7XIMPI^uzSQ%txnT`e6Qs#T!gNx_Wdz
zEFHnjf#qA6Iq2$Pe3(91IKcG7<Y6={{9)w}ESzEbVCKN$59VK3dV$ffas*vJ%pO=g
z5pp*yUSatcmQG;t1hWrjE-o4tk1&70%!RoF=3ZR-Vd60RVd)5#|6t(;Q-_O&g*)6G
z(0NOkJ7D<;T|as_!tw(w9AN1OmOjz-!}u`w!_ptj{V@G7b?7wQK4^Y|@nP<Tr2|;`
z0n-l)XIQ-h>j%K<D_Fe1(gAun!t}wy8)gqI{lUzG*$XolX1@;9y)b)V>R{ms(+|r>
zu<(GTD+8#0Sa`ww3-dqRf6)3579X(m1hXHOeqrSwtlowB7v?UQzhLf%xf^C5%-t|^
z;PD2vAEpl%4bu-Z7v@h`IR*=7Sa}CC2Nq5+f574k=1*8W!QuzK{D<j-r9YUzVeWvH
zD=`1S%72(UVEF~69%es`4+{@i_`~~O(D;MthxrfgFKD>I@*gbzVe&8<mTzJHg!7^L
z;o%6=fP;pmBba`ee_{T?p$#q!3rARbfcY1eF5wDsFks;c3kO)d!txEwJXrj|jX?`X
zm^rZW29^(C`eF44Og}7s;54+{gQZ7UxWe)wtp5ko4>KQ@PGK}mKYF@?)&DSe!r~uh
z56oN`4YL=P4q)+#t`FV6uy}-p7c3vc%*CM5!VzX3%w9tA2+Jq1c!Rkc7JnG_K?Pvy
zVd)1JE--(>;tf3=!omv{4zT<NH5N|6+>h>mSiHgPhxr#4U$FRt>4&)oZZH}HrXJ=G
zn0lD|(Uilvuylv+5174#!VhLIEdRpF30V0Dvj`S`=;b&p9APxfJ+OR??hjZw3NsgG
zKg|6weX#NaMiWXuuy}`sAIu%F^b7MBy8B`24pwf!^ucIYeFlpMSh&L653>)JUtspY
z(g93A%pEX3Oh3$C7!9)*=5Lt2u=EAf2TOmjc!RkE7T+*;qK7w3KP<k`!yOjRFn_?@
z0n-Pkq4^zVKa7UOA1wdE+=1?Im^m>2!omv{4lsYf{Er@QF#RxnFn_@EBP{*G?1%AT
z`q0xIj1RL1<}O&g!OVf_hov`|{qXpKh6_v_7JlgE2h1E84Koit9%256n-9|frD5p+
z=6_f^gt-?Mj!@+o6ngrH<yRODGanXz=;;Y&FD%|*?u6+>PY3An01GFWIk0jVrVr+S
zSos6<0L*;!aD(w-;SLK2Sh|MkM^FDSdtvf0cf$0;{120d*$+!^Fn`1R1FOez>4%vE
zqhaRYihr0oTr@2FVfMq)Ei517(g#cTaQ*1*K4^M|`5%@pVCKQ{JuEz7`3F`Gz`_w0
zkLc+KrVkeXaC4#VfaM$X_=Bl~#W&3TFg`4PVD`e|0p>n*dtu=Kb1y9Y!1(C)!}>ok
zdtvHf;SNg=Fn6NUu=z(=e8a*8=3ZEMqw9n5Vd)j79~KX=_=foxW-d%0It?r5VBrSy
z4@@7-J+N>?*AEM4nEh}X8h<eT==xxMSh&ODA7(Gi{e;2+md{|}0E<_cKA1VM@&slt
zjE3bqSiXVz2QCiv2h1FpKAbc(zrex)<_=i;K-UKgf0+HS@Q2wCEB9db!}^afb71zs
z{13}_uyP-k9%1Pi<{p@SxO%9&VetXekIUUK{V?~#+yOHWt`8c1F!ks(EFHt@Az1w5
ziZ68iFh0y3uyhPd2XOPD?!eXmg1H|S?y!6Wt50F};0k|O{K5SV)c}i6Sop)n4`A+u
zrF&So!}1v{9m4d%;vZHX!sKD$2D2X?ztH%E#Um^oK%Iq?g1HM$L&FW`4xAdH5-|6`
z!VMOWuyhPnhLeKnhovK!e)RGe7O${ygT)_A9A*K`->~q5g%7O!hJ`;&JuJLn_QKKu
zy8W>6Ggvx<nGcH}n0dHp*!c~x^a0Zcb1yD^FmbrO(0GQq59SUSAEq7`4NEt$aDe4M
zqRoNY5Az4iJeWVw^8?%+P<^m+3#K0y{xJKAO?UA01~nI!-e5G$URXH8_^@;aa|f(G
zfyFz_KQMD(<1aA%uyhAg2R9$;4p@4Gg##>HVDSgH7n;vt_QK4C#Rqyl3QNDR^ba!^
z=6;y@uyhP_7tB1EeptN2$`hEsVC4|HyJ7la_QLWj%>OWRVD`cE!_0%3k50qOU1+$$
z(g7?y(AC5FQ1vkX!f04N$CZv??uYpw7Edt$!O|0qk8Tc3A1t0==@jm7sJme43l<JA
zbujy2=D^&8-fo1O2TdO^_rUxQ3wM|~@Nk5xhq)UTo-lnd8lLZ<?uCUnto(=7tFZJ3
za}P{E%-yhXhm|`pedzv2uRr1PQ2Sx#!OVf#2kUoWq$8+4SU!T82Xikjf5Xx#EdF5W
z7^WX)A1q(N?S+ObEZkuF(CvYR2h1FpKVas-!WTxv+yhH@F#p5S4U7*9cl7iFvk&HP
z7>%BuVESR@1T5Xa^uyc>^EY~WgXu@NAC~T5{)2@F%p7$0!_0yC8<vh?@ei{X7H+uo
z!_0xj11$W|^BGJ(eEtNg5$0}Ke8S3kxIduj3+4{k{wY}a!O|U!4^s#82h4x4d<YL;
zsJXE4hvygQ`~fUoK%L0|(+_Ju!rTkf4+}q-dRRV&r2|-e!2AVM2Xi;fy)biN?u4a3
zxOyB6So(pb3s^Y7;t_{7xG+pVOaP`I#wSF>!Vl(tc)UP0!om+8e^7ClK3MvJxdT=X
z!150)9AN1O7OyaSVESP7ElfX59Ht)@f3R?e#UCs_q0WNe-vX=0VDX6Fe}Sc6SbBk_
zb67mU{0Ga2uy}>pgHFTp56nHV`~cIBt{$Baa|bLQVEGW19?{jK^I_oyb3ZH{;7UiZ
zc!24HmFF<~VBrP}7j*kz@du;P^9L;6VCKN}L-Q{zoM7P$QxEe$+<nk`1LhA{IKgOG
zJj22ZCJv{e`eEr9W-lz?!tysPeqr{*;u97gFnO4}VBrNzk1%<dxv=yP_dnDfuyBB-
zKUg}3@nQO4=?1PJrV)*X*$49%ESzBaVc~<OjRBU=VE%xm16Vl1?1iOcn0}c5Vet;r
z2U7==hlK~s9GHGsc)|P)i+`AVVd)o^o?zh**AHdD?1i}>W-q$`Vet#q2B%=*26qRv
z{(z+uT=v53f$4*VBg`IHJi@{eR<6PP19Jc@{9yWF;RH)}xY937KTIDi9AGp|9%etR
z9Ds!{Oddv~n*$3^m^jQ`==1k5d005W;tdx5uyBB-0|T`54@<8w`(WV*3tyP|F#F-{
zUugLOvlnI#%s((1=3iKR!t()CKP<dp?u4a7SpI{RN3d{&nTv~t#V0KOVD5(H3takO
z{)G7lmQG;qhPj83epoob^uzp*Q$I8uVD&eQhJ_Q%{jhj}+Yi+T^DitMVet+NM_9PQ
z%z?QJ=1-V@nE9~$1`~(58x{_*bOf^x=3n&qgP8}j7v?UQ`(fz|-5oIVVfLZxgXxFm
zOPD)h_QT|1=D_TS`2!{ovmfRU7$25SVEG8<4_LUs^uuXrJix*MW*#iu3Hbx&Zdkm-
z`~{0AnEP?r5A#2ahLv-$^aFDzOdRGenEzn*!u$_Y2WxM@%4e8+VEG^BUzmGg;RG`u
zmY-nqF!NyH0S_l=_`%{CrXN;6!SuuY1Je(47fc=&Z!rCEaj5w)aag*5`4{F6Sbl-|
z9~LgKbOS4gVBra)VdV@g9mCo!F!iwf5A#1vADo7|4;G%V^Z?Tba}QiS8UyAJnEfzw
zVD`hp3#a{1^I-a5=D^Y$Og}7qp~^8Rm_8Uyto^WXhoxT_jjkV-{?ILk`5&ea7H_b8
z12YF*ADj;jf0(;r;ScjSq438Q|1kSu@d!&t==Q?g4GVu*Ji+XTwHx62pbVIMVg7{*
zGr-&fOYbmqVDc~;mj7Yl56f?``~b_(1j7$%AbNg;<ujN&pvrJkuyBC69~ORu(j6?m
zVESS1hUv#?Ayfhu?yzu#xffO*!O9)DJD}|+Sh|D71I&J?v2Y6J9#}lU`tLCPuyz+r
zKP;WV+z*R?m^jQ}n0{D!1B*Xcc);8Zvlm@I%p4dEGY1y_u>24AH%uWK4bumUf0#a4
z{G%zy;KIsjnEzpXnEPSj0LzEC$^n?cuyBE;W0-$n;Rwr*=;~qN2cu!>0hS+O?trHY
zr~$C_14{=m^)UCsXhPu#b1$5RrZbqkq1ItgF#o~qhs852-eKX0t`QbaF!#ga4;DT!
zccbfr@nP<S*$eYGOh1f|E52aoSHsLf4<A@Q#N~fIv~nBfE*K577p5Opj={`@r8D&O
z3(G&~`4+~9nU7Ax(hDp<!@>_{FS>eII)kZ)#RII}1XB--PguCZ#@}G!4x?e_!TkqK
zKX7+L`LJ+-#WzepEIeT89OiDA{V?~S(=dH7b71nY^apbfOdJ=D?jBhA0W$}eKA1Qx
z9%1f)l|zK|!@?UD4={6J{)eR>m_2YBE&ajb58d79<po?nlmV-MVfta=35#!-KcLDn
zD40H2`3XyRuy};!Lv)R>{0|Q=n17(@1m+KPeK0;OzF_vl+zs;&%wCu~(9MI{3!`D-
z1P^zpdtmN_g$K+WnEf#QuyhKGZ<u>v?HpJ<!f14Vz~Ud4USa-#<tv!|==x#qgOy{j
z@PN4o=6_hc!15o=epvYpk3Xot(e=aZN7s*D?!e?>=^9r4!@>m?elYvdX_$Lp;R=gK
zn0}af==xxMSpI>z3zmLi{)ef9(XjFs9{y<lhxr>8?lAwM>xYFq%siMnn15mVVet#o
z52IoFVc`a|7v_JMI&>OlA1pn>%3GMd=<3n=F#BNshlM{ZAK-E?EPcb`36}m~{)WXD
z%pd6XszJjU7EdsJuyhBDH(dUJ*$WFdSUAAyaag*CnS*XWj1SWXGY{r3SbBk(12Y$<
z4^Bh<0n-n2KP=v0`5zXZu=Y1hKP<dq`46Ta79KG7!SuuC&tUpt<pfMWJUpP~C`=zb
z9--ne{V@As@c{EbOg&6L%s()FFdF7>So(pvAC^yH;fEd%FneJ7Vd`P_z|4c$52IoB
z!pc3EyJ7x^m6tGg!15i;9k6m9W)3bImY!kZ4>Jc>dPX+~7Vj`~VD_TVKf=_(+zkr{
zSiHc>Gg$bc$3ILzEWBXl04!g_@((OO!u$bqFHAoy9$@-m@ek7n<HPj9+zZnOGY3}w
zz~UF?Z<sl7K3cwk>4T|*=|}fJEdRjHe}#oFEPP=8gXxFK!`u%Gf7tqQm^>_;(DkE-
z2W<Qi=5JWI!_0@JQ<%G9?tr-u=09BF2XhB3-NEt$uJj8tALbsIKA3x8`e1x`{J|K|
zcz}fi%-t~e!u$hs2TU7`hS>)zcVPNq<t5C&F!Nyg&}o?cFn_@E0Zczm_d^YWr9ZfS
zs5neNEZsqcF({b3VftbIC00MW`(gft>4%wzVKGzymX2Wd!_on<`eEfCOh0<~!Rj-p
zbr|QLz{*jWy|8i)7LPD}FdDZ05f<+-aaer>(+^93FnJgab0<tatiFT!1Ks^Fd6<55
z8a@8d%M)DsVfMrP0n6_&_oIg!srJI^A(%dxJJ9njEc{{V9F|^T`3shwVC!em(>pBv
z!0dt93v)j#9$@hct3P4s78dUC@PM`xVEG^BZkYM-aDtW}Fnut4VeW(Jhn1@^{pd8T
zzJj>}RzAYq4U127eK3E((i6;HSiXUUC%QfuALee1aDc7@fY}cVNAz%jnFFI?^)}2O
zF!#aCfu$>$dYF4*e3<#L^au+Vm_8T{QxEeujE03j%-=BeFh0zDn0^=yOOG&j!^$g|
zyK$8RF!#aihouXcK3M$2+y%1_M#I7#t{<B2VDX2`y)b)V`e5M<OD8b*!}1NRJb~E@
zE5Bg+(cK427clq3!V#7)VftY41PecyJ7DgH>4VYe?tr-mz5IgP0}T(De_-m-X;}FI
za}O-N!tF)V0COkI-LQCv@nOo*-GSczfrSGs9mCQYEZkx70ZTuyaDlboVESR|2*o?h
zU9j>2rXN=R!TbwLe=z&t`p_7#^b7MpEZ$-Iam5!b{9yWF;SbXfFYlo7hh{DO-UnEG
z!O}CVd?wa@n7?86!_q6<esqh`_^|o~=6_f`!0ks<kIseZgVRv|64DP#FR=K7rGJ>a
zVd)nZ{^<D=quhYjL+I{<nFC8#u<(W13!`D?!`uza2k`KL`U_?bEZ$)HVc`aohl#_}
zDO5i!f563|?uOMHu>1z&!`urC7j*x@%z=eJdN{(;1FrG|=0BKwVfxX-4VHgl_QTRY
z%wAafg@q$b9V|b>%!7&JqG9n03xAlu;o*j+5tjd8>S6f+7A~-I2xbl}JYmM5$0N*N
zuy}yE8>SCN!}P<{!|aE<9~$2<|HIUy)3EXa7LKs^fTu&KIq3Ree3-p3f56fq%wF{P
zfayc0Vg7)*0~W3@cc8~VOg~H?EWBavfzhz=f`va!A1wdF?1RM{%pO?y!}O!aKP<h%
z^uzoOD=%Q~hs7UE9A-bv-7t5;!VkuW`5Ts>Vft{<F#o{Z1>?iaf%zY2eFQBBVD5m0
z2h9I4bI`*9<{p@TVESSC7?uv;;Q-SBrD5q0R^Gtug@psm-LP^5str!T)Whl_SUP}(
zKg@nuIKusp=3lrtG`+*}5iA|R+yV1Hth|BQ3(GgK@PauIR_?*{!^B}U%-^v1f~P;I
z`(XZo>4(`5(~paWrAt`6z~T=*9m33qxdSE-3s+b;!om+09vJpQ-HR*!am63ZeXwwZ
z(XeoUxf_?iVetU77v?TlxWnQVmi}P=fT@G!7g#!k$-~MmSUQHOhmHTi;tS?pn0@H}
zgr#Gcewg_%aacNog*z;sVeWycgSiK$ALehEepq<H^uy#~_M)dtm^dt*!0dsAGb~<U
z<r*&iu=E29f0(_naDe4MnEPS&!f2R3Vd)KK4lFzg#Xrm(m_K0g33E3rykP!<sYj<_
z@d(oovmd4(r~jb_!Sumsn0lCgT=@nT&M<v2{}ZbpmTzJDVd03L-eKtj>RdR5t{)bE
zuyBXbxZ)4yepvX!!V#7ZVfta=39}fMe_-y0rDK@CVft`|8_b=s@Pg%2n0sL94i^3}
zdvVb)_rT19*$XoVmp+&{%-^u^hoyg5JiyeU)9B$3E3aVgMh`cbez-rO<u1(sF!#d3
z5vCrUM(_W^;un@rVCKW@hnWYXVdVvS{K4#j`2!XXFn7Vi6;^J*!V4DvFn7T8!R&*H
z!_0@NhtY8NLeD>k@nPu*W)4h0E*h4uVD5tHBNUIY@W7RRVBvwwURe797M?KkVD5*}
zgv^2Y8<rkn=^7TEuyh3re^~m1g)c0=Vg857!}2pM9%1&t!XK9ZVdlc(8D<|W9C7&{
z=3ZDjhPfBkf5N2?=08}t!Rk?%y@bLG=6;yHFn_}85175^=D^|^=5LsPVg7{4!`y=&
zf3WdCSbBr$gVl$y_=n|3n7d&1!1TlIfjJaP!{QMZU$F3mxd#?*u=s-62XhZBy~FfF
zjm1g9!U1L;%)c=EFf>BTEm-)&(i==atlbIoH!MD3;Q?E}0gHE7dW5AH7!3<Am^)zc
z2wQ&%3qM%+5bIx<eK3E++z<0N%-tCNMiYR=1I!<=c!T*D#z)hJ!G+}?SUCdo4|+O=
zX@uDe%il2lFn_`9C!`;y4;GIwdtu=KvyTuB3qP27uy})+hpYVpODC}KgQZ`XJ{XNF
z9AWBV`eEq{=6_gvL3ckazrggt{13ASmi}P&qlY89eptN0?1#k*EF56&fu&=Zdtm7g
zW*#iuVdlZ&877XNE@9;!EFZwa8CDL#?Sb}-VeW&8!_>jT5#}D4ILuyHd4e9EFn7bk
z5ypqP1Lkj-ewe**8d{FP^uhcC^EWK~iOtur^anE!rXStkF#BQY4;KG0{jhj~*$dYX
z4M&)Mm^)$qhlL|d9+qxk;Q;G@!sKBzEdF5bhQ%|?Ui5H>>4Ui+W<N|F%syDV0Hz+D
zhUHtB|6%b2vmaeOE<ViNFn_?(4=ns(`3YS=EWTms4wmj=>R|qWr9+rF%s!Ytn0>JD
zhM5ZsKbSjU`eES(b1zIEOdb}mFn#FquyPh=KP>!U<p(U?5ei3GdV{$KW<Si|Fmqty
z=rqh;Sh|Cy2beox;fC&hm^m<i!1Tky13mx3_%MAi8kc@p{(<=eRu01S!DyJhF#p5C
z9X%bx;sK@)M#I7%W)3VJz|4W^gSi9dE|`8;_`&RliNnGVrXCg#aC4vvVfLY?TUb26
z+y!eN!q(rx#9{RWEPP=!EPcb=2TRAW^=B~u!Tb-iALdS&yI}Uf@-<99)cNr98(`{S
z=@Di=EZ$)LhRxr?^uz3d>4TLgF!#dJKg?e+eJ~oP9v1$vbP5Z9SUQ0D3l<-+b|y?8
zEFHq!36qC~FU&o#cz~sISop&1g}E0N{%{&v?!(N7>4VX*aDwTF+Y7DVVE%{chlMZ9
z{V@N+<YD&0;uYq8SbB!}7d`%A<t$7e%v@MG0COiS{9)$6;txi{!ULB+bp5dO19LYl
zUBJQ{mi}P+U^Gl0th|QRS1|X(`~wRYm_8T{i$@qA7EZ8mf%zBa512X_4NHG8b71KW
z7A`RJVD`i0Vft~=uyBNx1F-Z9a}O?kFmYJAg6W5)E0})R`cqiA!O|BjykOx9iwBth
z(cKU8C(Qpa8Ws;Qf5Y4lb03U``4eUz%pEX$VCKNW5te>o=>(SkVg7}g3!`EBVftYE
z$6)d>dtv6l?1#A<#)qlLMZ^37a|g`7uyDtv4<-)F&%~xXm_8T{3lErnSa`wW4W<r8
z!{QBQFDxCv_^^D3%l$C@Fn^=lOKkYT?1Slt#Rtrtu<(cZ16KdR?1$-txdT@2z|uJ^
z{$b$(O9!y<g2g*5ofGSBSUSh0A7&rSALum9{jl-_<_}nT3v&k{b71De^ux`A);GAy
zO_)9y4f797KP;RG>4&8oSUAAySD5=@@dvX9orc9fEdF5e2FpL_>e2Zyf5Y+}ES=y=
zx9Iv|?uNMo=5Clf(AC5EaP`po85V9Z|H8^qSop!jVfta|9A-bPzJjZRnhWE@+y(O&
z%)c=GuyP(2Z!q&<G)zBw{}C4Mu=t1B2TPwY_rmnUXqY&xorE3_u<{$`9(a60(<RJ&
zm_K0pVdlZjhpLC!3u}L(&tJgw!OAU|KhX0(Odb|)Fd7z4Fn7Yz5iCE#-Gk16#uF_3
zVCKO54U2ztZRmVhIKXMBzhUu>t{$Ba(+7)Bm_C?)(AC5Euyg|pe^|K)^FJZ?quUD$
zUzokHbO5stora|YSbl)H9~SN~cfiVZbp5b!hq)hJ9V{Kd%z>3pFn_}0AC^vF=?o?h
zOINUPfR)EEd6@k$b7AogO9wD@Fn7Vshv|cbA50z=Z?N(OW)7@8f|(C97v_Fgc)`qv
zl}9l5!_0-b9~SN~cfi#{(;ZA7tQ>=xiyoh_dJ|?2%s()Fuyg>62lV)Zr9YT`F#p2B
z6PBLP^})gi7VohB5-c8J_QApn7Cta@VBrR{4;C&k_rT1D*$)eUn0}Z%EZkuB!~6j=
z2bS(&=?A7CM#I7n<_=iAz|4oKgM}lyJ7DtYG)zCNeun8oFaP24&~gkG4lwnw@P(O=
zetsTI9OhqGc);>8EL>pjM{j?_{0mDrFd7#Bu=s<y3uYgThS>w7VdlZo0WADs=^v&K
zR^Gt;2TLEYbOVb=T=5T+hoyU%zhUOX?1Q-<<{p?lIt|kga}UfPF#WLb!<GNg^}+3f
z#w*Mm7!9)@mQG;d2lGEHK4CO0|D(qPEd9db4dxD*{V;vF(m%|6SU94)1EwEVp1|A<
zqhab{@d+zOVg7=-4`vR`|1kBiasU>tFnL&fz~dQOF2dp!W<R<)FneM0F!eC|(Zdg>
z4xNUDCoKQK%z>pxboJ<bSh&F44@*BV`(f^c`4bjjuzU~G4@(cQe1vWf%zT)8VKgi}
zVgAPDZdf{pg(FNq%ssI9z=(fXx`xuQatvlKdbq*d0dqg0`V*EOVd^pb549H-PB8bw
z!Vi}2VCfMS{xI`kG)x`LJ+S-(3s;!CVet>she1Q#19LYlUSZ)5b3ZKp(e=ap0jq~#
z?t+zzuz0|wA7(Gy{m}dZvlr$LbbDcZSUCXG4@(y?{jhWhQxBtI?u6-s#RE(qu5t&a
zAFdu6elY*R!W)*JVBvtS4_1D_(kslLuyhP_H!L1u=E26_VD5&c8yF4K4@*BV{jhow
zcK-~_epq^exew-ESiHg0A2eQI;S19TD-U4y!t|rt3o{>shK3)^KQMQ~;sHZFOaQ76
z77wuW2MY&y{6XCf(*|=tES<ybh2?u#IK$iz3kR5fSbl?rAI#q{d6@fQ`eEq-CQnE|
zth|Ehhs7hY`eEr1rXR*9RzD&8Vet(MH(36M<tIY^hq(h5-mvh4#UD%_7JsntgT)I>
z9%e5r9$@hbi!WIELDvUMFR*e4W**G_Fmqt~(Ze669;Ob)hnWlW2h82L^uzoQtAAkW
z5$0Z4{Nd7%o{nIAn0sLP0A>#Q{29#su<(QVAEq9bk74Ga(=hvB@c>I_@bUs(0~#Ne
zeqiB6D7?|M!{-lS=D^$mQwK8-M#I7tCXXKOFmYJCpobrf4^syVFBl&^JYnLn@I==S
zlZVkT_rTI6EFZ$sGfW+vhL(RY|HIM`%v~`3F#BQY5vCvJUReHvg&(Zmf!Pa7f9T-{
zGY6&*o*tm(Ei662)M3z2_rv@POK&iHVf7-qKA5{<{)PD;CJyr_JRL&a12YGv4yF%Q
zp26&crAL^4n0s*fA7(Dh9GJPVaKx4VVDSViH(=oaD?efSaHShqI)&K>OFyu1hq)UT
ze=v8#Xqf#lKCC{6l?O2Y!qN}S9WeL8!XM^dSo;O$9$32!rXS`mn0+vFVCrGvhaPS)
zb7Ag*>4*6j=3ZF5!Q^4?h1m})|6u79rVr*0nEPPq5axbZJp`LSLSH`$^FPcUT;UCK
z7tB7GzhU8so?l_^hv`G7Vetn`H*gx7-eKtnT^~9hJv?CH0P_#JdKe!T{;>QHOONRJ
z2BsdJhLt<8c!0$_%w9s_2(u67Z&>`p!Vy*uz|2LrALeeD{jm6k*$Z<A%sh1cuy}*%
zhq;GR{V@Ar_QT9Ww;z^{VD`iE4=mrq^rPn+m^tYFhn0&keT32vEZkxC!^%&Xepoty
z*@sTU;tS>uSbBoR7t9=3dPLU`^DitP!Qv0*Zdf>?>x1!e#UCvH!|cc94_x67GaqI@
z%p6#}z{43@|G~lmrVi#0SiXdnKQMV%xWi~zIRP^t7LG9eu<{e89~NJ*{09qPSbl-U
z2dqAT`5&eZ-QO_x!qO=$AHe(%(+>+57!8X*m^)zM05b=s50)Na`d~CH9AWN-(dgj;
zGY>r<!0dyC2Q0i`=ELlTg&VB?fXTzcAEqBx55Vk)se^?-Tt774VftX@ILsbc_`%!*
z3wM}2Ec{{qg}D<JUg+@;iwBr~Sa`$q!`utg50i(*D=b~W)WOmP%>6JvESzC{n7uIn
z!@>hb!_0?;BP>6{+yP6^uyO^a4@Se>33mrn0`5;}JqQ(sQ?PUg(+3MLm;#u)VdlWf
z1DHH4-NE7q<_{Pj=3ZR-VdlVS^mGUdXPADNc`)_pG%P*9!UGoGuy{jP59gz$Ls<TR
z*$d;t)uS<B@dYn`q32G)(gDmLu=ESdA24HJ?uEqzEZxA=!OC%%KVa^G#UIRGm^rX?
z1Jegn53>(O!_phfUReCY!VTtsnEPS+U^FcLVftY1fTa^c`eEk4`~h<ZEFZ)4(<<I!
z_QKo^D{o-o29Gb8L(yoMdto$8Kg_+b@PUOV%v=~BmQP^%VetpE1{RL6_=M5u@c~Qs
zFmuqu55|X?3!~xggQkC2IS3Pnl{>I>2h)d(hNS~oet?x9uzDMpKA3$leJ~mp{xE;T
z!UNqLn0mPXq5grXgSi8io?!lh$-~Mmn0{D21amjcURXGy=O<XX1q(k|zK6LRW)ChJ
zrVo}LVfxVH8JB*TI4t~O=E3{{i$7SpLw7$+A1wXB{0R#em^tX_7iKRk++gJvESzEK
z8zv5O2P|G;{)Oc~7$2q%mY-nmg~c0;53?VZeqjE9m1{6}!`uV&KTID64NW&N{jhWe
za}Uh_uyPq)KP(@j`x_RXF!$q%SD1cSc*6Vzb3e=-FneL)1q(lTyhH0Bn0sON!pbF>
zet5h<{SEUkOdLJm;PN-jJeYf6?uX?YnEPRTn0gouizk>rVDSiZKcRR-w-=T^VD{p4
zH`L!SdtvcLti3SxF#p2J0a*Hm`41NVu<`^J|LE?9#UD%`%-yi^02co+{V;Vfdtv^7
z@nPi<EdJ5m1xsHreK7aH!W(7|%pRCIFnO3bOg}6<VBrTdA7&0rAIuz>eX#I@l{c_>
zgUQ3ph4~*QkDgy(=?h&RmwuQy%zjw@fVm$QAL#Cag(JHC=;am6|1kf;+z$&k7!7j=
zEF59s1&eo>dtm7rrVmEL$}5<AVD`bn8>S8y4YL>KUzmL``*G<*AOC~-A7(!+9&ouE
z7QZle!|aFU7npt+AD25|;*^F1%pGuZp#3J8e_-~Y)3EXirVkc>u=EIv2Uz|`PY1Ai
z7nTlT<uAHD==m9zzF_)c@d#5#DEwgVh3kWshp=)7=5Lt0(cJ@cFD%|*?t$ean7`5e
zi*63A{D=7iRu94S!Dw`S=zN%e;PC;CKUh76t{%>Z`WxmSSop*2h2?ixxWM&688H9D
z^ux*vs5Aq5e8ATaqS*`cH$1&V-4C-L<`4ApA7&5C9WWXe{;+%uOOG)1a2m}WF#p5S
z56phJdNc+sykY)_sfX!@r3W-^a4yWfxb(xr1Fi$gKyx=N{$b?}%pEZQ!}xG{H2tvj
z2Maf-E%5nOn1A5$2UP$ozhUl2kAIl|Vfx|YUr_z9^bd1CEI-2Zp_j)n_rvlV%zjun
z!pa|5`i1F->4RF!0E=gIeK7aH?1z;Du<(QH$H9PwBP?CO+yP6!=-OcZfVl&f4`A^I
zqtW%j_^|MXg&WKqn0>HtN6$Afd(r(1vmX{8F!#g40cJ0(zJS>W6Nlw@m^rZY3v&l7
z9$@yv!U?7x7B8^;3iChAKQMb>G|b<y^anE^mX2Zi(ftq84~s7t4RaSPTwwl&nFG^@
zi-!3F?k{LLhRgjhdtmxt;SN)eGyOsx089Tc|HJsO@*1We7LTy-f%y}r9+r+^?tz5|
zEZku62jj!yALd?|c`$on=Hm1RwEThDgKiEi{$Sw`vlr@ZI0bVD%>S@<6)eBP+zsQy
zH9{G%b{ovSFd8Nfi+30e)rOOT#XBrt!2C~0KP>!U`e6Qo>4U`|y2UVkuzUauM_4?<
z?1zN|EIiT8f!PbAVc`X{7nYA<=?A7CW)I9>SpJ3C1Jegnk4~e97tCImzhUJtx;_{m
zR^G$nA7&m*9+qxk{ztEWVBrj-VeW;6D=c5a(xVPEKf}yNr(y1a#Rts&==l()4;Fr~
zaEIxG$-~?Y3lEq$It{ZImY-nZfbM@-e4^`z@nQakg(oaNVeUZJ4^t1b4@SfC4=mhZ
z=>a|cz|_I)gT(`^-3<#jSiHd838P{86{a7?hsndj4d#AWc);Z0<ps1ng82s)o-jVl
z9q8!?W)6&o>4Uik=3kiou<(GXhs8h4->~?Hr5l(&SU!g7L#JW-VD`f5Pndr6cth6@
za|e1lgylz=Iq2?(`4=AU&~SwDVeW?Mht&@-`(fb^b2ls;VDSyp2TKnyedsjIK3F)w
z;u#iiuy{b%2a8{r{V;Q2_M-b2T|cbcgM}L`ykPdh+=1?XSU!c>3yUup4GRaDK3F=2
zg&*wvKA3)(J+O2G^9Oo3!qP9H_1`dku<(Pq1LiMSIKa{kEFNLu5Az2s9ASKzK6D!9
zUs!m;!W~v_psPpc!~6@22UvK(!VTTsF#WLd3KovA@(!jCMx(nQmTqAFh1m;pFN}u8
z2h2V=4b3ModtvT?<x`mdVeyS_4lKN3`eF9N{0UPJGY=MzF!Nykgz1OXb1-$V_8=@=
zVeyFG|AV<377j4~!O{iHJuv;SaDl0V(Qtd9;R34{VCrG<0CzuBBTOH>9D>%*uzm<s
z8BPl3Z&-eWrE^%g;nWC~fcYC94p94H<sMWS1_ev!u<(cJhq)VOKinLsxiEuZ`2}4+
zEZ@S?CoCVp!XKWlpy3SD4|fmD9w-e9UzmGf=D^$m^FNG+#TQH-7LTxW1v3XG4~sXL
z{jhL=rC*pnSp37%6HGrWox#&T^!#3!dh~n?(+8u`?T3XQ%pd6X!Sumsn7uF>rXN-h
zz-X8_Odl-&!NLuuAKksM^Z*M7Sp31l3l@JceX#g}#T$%<g*!|h-G20Vg7IPH3`{?a
zhWP`g4(1P-I695)esp=5|6%@t<#U*O;rRevBa{!z4{#cqeqrGSRfa*q{0Fxessd&&
zEdM}-;S?-?!QugyZei($ki9T{aC@Qdg{Ko}{)ClBuyg=(07m@{)en=0<qKH2!@>`i
zo?-jPVD`hp0j3{D!~6jY5A^tltAn~1W*)jem^xfEEZ$({F-$+q|G4zQ{0Ym)uyPz$
zf5FNDn0at>pzejGJD5J0d9e6}>4W(nmY-qy3g%y!K3KX%_XkWrOdjSASh&N~!}zd#
z1&eo>ewce;`f<^){0MU=EFHkif%y;SZdiE>qha9!a~CWeVe8*v_QJvqW)CcUVD5*N
zGcf%y`(gTFG%WsL?uO+jSbl)%gSB5_`e63J+yUdm+yM_SwETgt9~O@=|H9<a-Hod}
zfa!<PF#Rxh!)TZ|jE1FCm_AszqnGC}cfj<)X=plz>4${_ES<sZ#g%Vi@dncm<HN!a
zrXR+K+Y4pD@-0k1Ed9dD8<>7rI)-Y4Q!xAB?uPmYW*^+$IN}eMZeZaCOV2R-VCE2t
zH(0uZISiH$VESO?IxPLc+zs<LEWN_=87#hF=D^H_(Xjjn3qP3sF!iu-gM}kZA1)e}
z?qT5o%QrB0;L-;Zhv|p81EwG5ADB9H8Ws-d`36>Q!@?h3AB+zRH&{5r!W-s)SU!NM
zN2g)o2-6SqKg@oZ`(ge;*AEMSn0{D!1&aq*yu-{v*AELXm^m<YuzUbZpD_Qx%!7p+
z%wCv3Vd`Mv35yq4K7f^HF#Rz5Vfh_qKP*4t(hqYl%zjwDf!Pm}hxr#K4&%dUSUUz5
z?y&fS#S1L{VeW$IgVC_^58WJ?KA62Qb7A^mG%S2!_QS#v7GAJ$fyF0GAIyEQ@PmaH
zTpn#587w`)<YDfGg&)j)F#BQV!tyW7A25Hx(g#dGEFZwkhowVw{jl_dt`3%7VDSix
zcUbts?1#~C{b&qWxWL?nt{;~EVA^2e2J;Wh|FHB4^DoTZu=XcRKTIDi{$S}2rVr*`
zjQk6&Ct&(u{)d|ntuJ8l1`9`+y@cAou=E0pS6KRmmH+7Le_-~)+>34>%w8A`^EWJ=
z!2AKLw_xVL!U3iaorc91%>S_XfcYC1?&$i^`LOtc#XHPim^;wb!}zfD2D2X)e=z%D
zG)z5=hPfMNA1pt?@+~a9Vfh!P4@Sf6f$4|wVfMlFW276H0nqdV3olss!}1+WKOz6a
zjDxu!M#KC83x8PpfT@Sk=-~h>&tUcvN=LA8hlK-7Jxo6?d(q<?=5LrjSp37>iB7}r
zKY`f`vj?URRt~_zA0`gVPq1)@>4(K1x;^OrfrTeb9nAl*aEIkvSi1*SUctm+`d~E7
zepo()`2$A7?1PyH(+7(;n7y!ggQY)M_`>Xkg)4e`hUtU316D4;?17nwOFv8<dU(RZ
zAEpl$jxhBw8kT=y{y+~0n0{D#hUtURuy}*HA6D<c<O$^?nEzq+!@>>bewcn(IKa$>
zse{=IO9wFZFd7#AFnur@7G5xWVg7)n7nr}%;|r!Aorbvs?k;Hk155Yl>S26%{R%CQ
z;C!flSbl-2N2g)w1!g}izroy(%l|NQVC5bx{9x%EJ-lJ+VKlnEFmqtx1#>s7K7)lT
zjK--SW&xCjg&)j6Fd8ZiU&jTDPgp#{?1klDSh&FTK^ZWA!^0064^U|aSa`tfh4~BJ
z{V;Q2_QA>rSbl)%hsne2gVC^ff~kYG2jTWW(*rCY!qXWJ_rk&hmcL=<!om|>KTI9W
ze=z%D@da}?EL>nTEdRj#0rNM^9GJZ@b71=5G}Qetb71iY^C!$+Sa}TB2W7zA38P{0
z3@`7X;RaQXLBYy97!8XDSos69ALbsI0kC+1xfkX>m_K3Z0T%x-{V;i$K3F`!+>IVT
zFneI)Fmqtx4bu;khttq-MVE)i2bw&({V?~z?1#A<W<Shac)o`Q3@qQl;scfrVEG8D
z3{Jt!NB0j*JuLiT@d48aOJ^|qVd)*F4yGUGZkRq8jjkUS|1fb_IKk=>m_BqGW-rYB
z=<O(&Iq3Ree3(8M4J$uk`e5M=Q;$x=+zrzYONTIb!@>cUt_kT!x1Ufw0J9fH!_0^2
zgZTrNzF_h&8kX;2`eEe|EFHt*AI69I52ha$U$Ah1r3aY(uyBC+3#Jc7!}2LCy~FBX
zSa`wghnFWXjZhlqUMQCVW)3X<!`y*OKTIFYUYI{%<`Qy0OdTxzVESR<1WW%gf8a__
z==m9@AEq9bk7434cf)9ye_`!8n15mA0Zbh_4Rb%t-Ecm%+{R@e%p6$!!~72~PoVl?
z=?11Aord`vrXOY>EZxHVjjj*Ihq)UT@38oSg*!|gI!(x4m^;w*!T2!u!f2RzFnO4M
zSiHk%SU!O1gVC_`4vTMCeBvt4(8C>OKg{2-bO3V)EIiTU1D5V#<tQxw!u$c#2Xhxp
z97e<98>Sx?59s#7+y&DIb1y9ZVE%;FA8>P_=?ETvFavPVF!#gEhn2tR{zlghvkz7t
z!pcjSy|DN}*9YUn+8Hqaz`_-l9tqhGGY1y0xXeKhM_7J^g&&NDm8<CPfW;@wKA5{<
z=@6FgVC4oZ{9xq?EM8&yVd`P}Vd)MQk1+qk%t6-=OE<81gZUrkZkRh@?uNw&%zktl
z7LPFZ!0dzRCln7bcfisCEZxKGg_#Gl7Z(1obPtO+cs_@wUtHxFEWBXug_%dtz0md}
z%s!ZVVc`yoFPQsb;Rtg#jE2P@Oh3#$uy}y^8|EIEzhL^|>Y(m{g&!=P!r~uYAG&^+
z|6%Tc<v*CcuzDP(4@Sf6N7oMvZ<v0VdtmxtG<y1n<r`S|!Sv$_KbU)A_QTADg&T}U
zPdBjg38oL04`AsNW-p8nGZ#j~(m5>tVc`yQCn5bXb7205nFDhdEWg5NboZm{hxr2*
zelU4-_n@y|gr#?we_;6@#)suASUM)O{vT#9%s;U74NHgU_QT3cn0>JH53?7RZeZbw
z9u6>jVE%{s2j&h!{)eSsn0+w!!t907F!Rvuhv|p817<(Wzp(NErXQV##TU$eSUy9y
z7hNAZA8s$S9Dv0m%pJJ$KTIDi{laKie8ch&uKE`iUNHZ|?1AMQSopx=0cIX7ox|b{
zrVi#_Sa`tF11$bwG;IAh%wCwe@Nxqh4lwt?^uhQT^H<Pyld$-Og&!;)VESSHf`vaU
z|HAwOb2ltK!`ua<Vdlg1qth_|z{(?7c);QVT|F$^VDSdCA69PQ(g!mK=5AO#z~UL^
zUs$}s)T7fddtv(F;Sa5MVd0Ig58Ymve_`qf)jzQCh1m;BCoq4*;sr*-!V%^#m_0E4
zaQ8sn4bz7np0N2(n0}bMVetmbzc6{2xiI_D%RgBB!O8(xeFn21mTutghPn@3A1oeV
z=?fMPu=s$*E6iQ!;Rmx1CJ&1@bbI0QXbf0>frTeb8m1ptIS4C%VE%@MC(Pe4f56Pc
zMZ@ABW)93fFmqt}7?*ySI7}bRA29d9`~l1Vu<(behv|ofBP>0jha*fsEd1f=3tC>m
z#9`{t?S<KcPQ$_t=5APc!}P<_0lGezKVbS`;Rg#Zn0{D$4qZRY|1kZqbO;M~n0{Eg
zMAr|?A29u}_5e&Dq4EnBUNHTz@Pz4y(dhQV_;B^m@Q24AG@rrpEqeZe>4(*SF#F-*
z3Du8Y9>Da$XmtOh>xabyOg%b{?tWN!z`_mgf2e!V^~2l`a|bN^VeWwCKSJ(+>4T+v
zn0{FJ;YvR+_rTH(EF59#VeThX55eLMW)7@8MGpsD=@8})7!7j=%wI5f!15(b9jx4e
z<pY@cFn6HmLs)qL(+_he%s!Yu(EX1q{9)x2EFNLy04)4r@rNrN!qN}S9WZ?`cfj<)
z{15XV%>A(VhJ_Q%zc6<Y(hpMy^FJ(~!DyJjVd)6newaJZX;?W16Ni;YFg`9C7LPFf
zF#n+IhvgTT`(gfvxesOz%>A%%K(`kb-mv&Xw-*-fu=IegA7(Es{9y42EAL_9hAaQT
z$_to&SbBiDn~?pmbOO^4vmZU*p{IM8KVbO>rXLorFneL{hp9&gF#BNoVc`zbj~;L6
z`qBN5Za=B|Vc`dhZ<sq^?OAmDVet=3N3i&Z#UD&REFGfjM|VFw9YM=`Sop!xCAxlC
z_`}L8n0|Eoam63ZJXkpdiwBs$2)Q4o4;F6l@&wKOu=J1a514yl_QL!Fa~G_<hPeYq
z!{lN1qw9yIUzmPe{)eT1Sop!>8CLGX?7>CD(l5;2uylhfzf#*ASbB$rJFMJ=r5i%-
zfcYC{4$R;1aKq(bs5mU#Vd);G58WJ?yP(?O^NcY4uy}x_6Ii(jOUJN$3{OAM_8#2d
z(EdNn->`HAix*gWfSH5t4p=zA?1SZ3Sh|Llr|A2K(9J>j4=g>v+yyfSrVp0SVD`gk
zn0sOR(8CX=4;JpQ@&}e5VE%yVM^FDSf5G&_{0q|$3um}~n1i7-%s!ZYn0sLEL{A4$
zZ5R|Re$c}WW-lxpVCJG50CO)a9l+up=6+Z>z|td39BvLY9l^pKE)HeD`~}kwa}QJ+
zPQl6nm_K0k7|b8AbcbO-)C5>P01Ho8K7i?iD#N6p?uXe6b1$r%gqee>5h4P!50)Na
z{)ELJEZouE4U2!cz0myzuyh9V4=lXU(>-kb4;F4Pf5OTESa}5V2h2SXS2Hlc_K(8c
z0TYMOFn`1JquUQ#KM%7XmX2WM4y@dS>4((|F!RxAxO<`T0SgD1ei$EJA1wXD(l5*$
zSUkYe4|@2+%z>p_Sa`$Y3nmW>e^_|H#BtFucfisSA^TzGz}$hJj?lv$rXHP!`5R_G
zEFNI?!~6l$2lFp1{9xr5ES_NIz`_G&4m=#8@dQ%`vkz8Y!2AI-4;GJb{V<21nFC9Y
zu=EPk2TM;dZEzZ@AC?bc{(;pSFdF7RxIQQYmTqDGfYC62!0aa!|FC=vqhaoa>4%vI
zwH8jn?1#A@W<Si`F#WjP4~s{by|8eHl?O2W7~u|e0W96X^uxjrrVkeHP~{jDTs<@%
zVCev+ALb7XjZguY`(gfuxd)~X<_}!?A8HH(EF59^2+oJLqjC8krXQB>VDSLUFL3)|
z3ejko`(gTE>4cCzSor|c2MaftK3Kj%vkuNxf`&KD-RS8H7B4V=z|tek09g4CvmfSe
zn7d%{0E>T^yI|o6vlo_bVCfEKJ}mxV>41=bVD&xBJ+Sl+vlnJAIt}wDdc4EJ16Gcp
zryrO<Vd)O$epoob;sX|LuyBBx0}CIR|6%6A{0rm5+y`?9%s(*wFneL<!peKNK4?6{
z+yOJ6kbamvoQCEDn0}bOuy};U4=laF#i91Y#9{7(rF&TT!TbvgKUg~w=5Cn3Vg7{0
zBg}oUc!z~MEd9Xjg{23WJ{S#)cep#C?I?8hFmqu25175M_=B}$VCKToIn4bq{jl(Z
z`5&eq&WDCGEd9aE!$rfw8D<X5-7tGdH3t@su<(P0JFFguxdRqouyPXSepood(gVyL
zu<(HChq)K#epvp6#UCua!}P=a1=A0+A3gqH?u6-sg*!|iIt_C-%pI_N15*cU2jVgZ
z7G7}u(D;M-1D2j)?m-U+nEfysmabso0SkXZ@d$GVEFHq!4U0!uxWN2{PQ%*$uy}#V
z!`uyvPnb9^8s-m}dtmxt{y_IXtQ>*)8<t;Se3*KeJ79dc{m}9SW*;mbVCrD{aK#_Y
z99X==(i6<xuyBN#2UCwu!|aFAF#B-%8>SyU++pzqb2oZ<2h#^j|FHOm<vUpT5z-F}
zcUXMG%z@btvlmux!TbZWA4bE<X_z^%d<)C3u=D^6M_72k^uzKAOh3$DFn7Rcm^)zg
z2V6gt0ka1d{;>3eZZCQ~!t8_TgM|Yu-@^36`~|fRPQlU}EZx8*pbS{J!OVdQ!zs9Z
z&~yw-cd+<@xfd3n=<YyIKQMhT3((U8%p90JjE02+EIwfI3gg4d5nTFV=D=u}zhLIT
z^uyP0K>Z5~H<&+Q;RUl7T|fHz5tzGS{)U+cOAoMchr1hAK0_S}tG{6J2rCC*?m!Q3
zs5UqS^Cv9-!O{&Zy~5%Xt^vk?`X83=VdXu{ojCPD^~3xN(+^V*(+^dKlY;4o`5R_G
zEIeW12a7jYzJ>V@mcL;3!_0xDH(2=r%fGPjM0X!7zF_9T;tS>;m^jQG==x#t2g|>(
zcz~5Zuy}y!hvg?&z`*pw?17~>m^{osuyg?PKTIB`9~KTU`(g4heK2>!XqY^VhPfYR
z4lEvE;Q`Y}to^WXhv|cb1I#`c4U1=(y|8ovvj?UhSGfyQ57UpHkKy*B<vW=DuyBXz
zhr0)=9+vK4>d|Rf_`}SB<zMu8gM|mWeptGN*$)eMSbBxq4|NZ^ewe+m_=E9b>7P*e
zp_>OY2bM2j_QTQvx_WdzOdl*>VEQoP4_f}f(kskfSa`zn4@^IdhNTNwIKli2D+giW
z4|5kR-@(EYmLFj9Fmqt;fW<#7-Jq+7>4VX*_=EWyW)IB0Fm>oOy1g*_Vg84yhq)g;
z9%11HOE>Ux1DZ}?=?|tK)_#YTKj`OQz~TpH4=ns)?m(wu@e8vb7H%;8FniJUq4Uwr
zK~J}^_(s<U<HN!W7Je}EVDX2`9kB3+)$_3UgQX`}x`)Ll%sv<m^DoR^n0{C|!@_}(
z`(ft6?1h;JOLs7Tz|^7BuzCun4<--OM<^a)=@%AmF#p2*0W%N1{DQd`7XL7Huyz0}
zAHu>PM#I#>)^Eey4YLns56pa6K7yrBT<I4U4lwg!{)UAE%p910m^xVe!@>b(FDx8j
zG+aFn2CV#n<wKZ%VgACQ4K56;w_xD`3kR6{Vfg~C0m>jZ{X?}g!2As>PhjB;OaB<*
z1=R>kFR=KB<tLbbVetVghhXMHwZrG{VESS40F#IL7nYCE;{ld_VD5+6hi(om{0R96
zW-rV=uy};o5AzpH9R>}p&tUpt;Q`Z+s~mvEJ4_!u9YM=+n0{C|qT35AhhXl7<vW=D
zuyhYgkLdc*`LK9}g)1!H(8C|550?I5?ttk-w;xuX!SumsSbl@~AI68Jf0+HSctm#x
z%zjw7!OCG+{)efD+Yb#_nEfysrVkc=uyDf_?l66@^agW3EWg0w4aSG7hcaN{4ATd*
z7u}z*aDZxqQ!xGTd<#u~uyh792kvjUPV5YHb71)e7XH|EF)+a58RidodWMEGq40zG
z8>SyU-^1b&!+xlLVeW>d8)DrJD?ecN!r}=QUa)Y7)n_nqSop&9!DyKM=<bB2KUjKz
znFCV?OTRGvuyhE|Ptb6L`3n|4uyBW|ht(VKau8|(EZ@WIhs7r>++gm9>4W(XW<QJ%
z(+_h$Odh5mmj7Vk1`AJEI)sG-)LC!}W-qMVfcXceA7(Gi-EfU)3|RRGa}O+C!SXT8
z9GHL5v@^iM52ha$FEDq*^uzp*t{>)qbp5b+gP8-1Ke&EqI)?clM#IV#m_A(g!~6~N
zFD$*n_^@;ZbB6{rJ;CkA!GQS(mQG;lh|vB~SbW050nUfo3-b@UwXpDp#XqbZfyE!L
zc!TMKg&WM@Fne&t7tDV2bPRJZ%)hXD9~Q1KeJ~oP9%etRT!xifF!#gEf$4+MF!iwX
z2Qv>AUoic!d<D}7i&yk;hS>{K2h)#H4nxx&ES_Qc4HllTaDjy*uJDK1152;4^a4xg
zFn_?+p)p|g!Tb-4cUZYW$X;0bhlM{ZoMG-pvkuP16@D=NF#p5+0SiZT_rT&077y@p
z87;k|#|vB?G#+67hs8hKp-^*R;Q^cfh4~j|FU&tM_rmOh#XBrL!^=@LdtvzwCJ(a@
zSGtF}4`wec-eLI<7G5xO(9<tWKa7U?8|EK4AL@VfaEIxG(Qx%pf5E~RR&L_*2h6>&
zc!Z@Vn0+w)uyBRh2cu!}26H#e9+*B@c*4}9)39^~(+_tC)ZZ|3VDX5q9~N)0^Z@fW
zEI-4-AD25|;RbUD%zl_SEFNL`0^NRC{KMQ2<HO<`W-rVg==l<s|6$<{^AAiv%wAYI
z4l@tk9k6l?W)3X9!}O!8hx4K71?GO3y|8eH<s-OyGzKiZ;50PeaJdsrJG}i2(+~3p
zEFNLzz`_ldE@9@t)WgCJ=3ZF*q5B8DJcgwkSh&IT!}0~XeptA`>_<;GFnzH402XgB
zdtv^8>BmLG+yzTdF#p5shsPIm-9OA8n0sOQ5*CiI^asnwFmqw$7OdWZxex9SXnex#
zMRzaEe3*M-=E1~a`e6Qt>4(X~^ugj07H{a`hi(qc9#}Y_#~;ieuyBK=8(6sjTR#Wa
zkLFKUI)J$sorc9b%pEX)z{(3)c*EiYT|bNu%l|NUz|4jD7gxCl(+{Iz@ePYtnEkMH
z0aFK~VfhUf&#>@^$rG|4=1!Qsu>1!LcbI-yeE>5T7Y&O)Sa`zR4NEt;!V#8^aD^Yt
z99;Io;tggmtlT7IFD(3E_QKo;(+5jGu=EC#hq(im{$Sw-vlo^gVDSP=PcVPL%+rIG
z+pzG5nG2&~_QCYS(gQ3Xz}yc@FR=K8*$azDm_C?!F!RxAn0c`9fTdelI)H@-x;~hH
zVd)PR9<clm3m2F>VD5*-J4`(+95CF8mak#%fw>b#!^%aNzhUVDW*$r*OdjT6n7y#@
zgT)W59D{{FEM1`cALb93eXw*03m2HZFmurL!}zdpfcXbzKg_)_b718mOdpJf>4VWQ
zaaj8SrXQC-(Blv0ewckQ8Wuh<^I_oz^EWJ9(e1@3N1*8+=3bb7n7QcsVd)Vj4@+OL
z_(RtZ^Cv9bp}QMq4$S>9cfiUqnEf#Q81_RAfY}T4H!PpS%!TC}Soou-f0(_nbOcie
zOAoO83=@a>3u--lA0jONVDSbEADDY!{(#vJ(+^8$Fn_@8h3SLYi>?o*A5KH#6BZvZ
z|KPG0t`Ew9*$bm#=?#`|VEGuT98ST*2Wmeoox$9VE8bz{Cd|FC@PfG)7H=?%VCKU3
zu<=(|xWV!%EZkt`!rTpW4@@1b{D=7$R(`?44dx!0|6%%J@c`TZ1uJ)8?t<xq@nQO4
z_QJ%`X;^xNrAL^1VES=|BP_ka@(nCJVE%x`Bdi{PxgTaf%>6L^Fm*8fuy!p>9A+L&
zKP>!U=>V2bVdlZif%yYQ!}P<#3!Z;r=?F^0;}cpB!T50fI2bU0!NL{hZWs*<XPCQT
z=EB?$OFuCG!_p-z9%251*#q-0%p91#=;;qV{9(?Ag(J*eu=EZKFPOP7_2@J#-eK;6
z`4dLtN<T1jVD5$GOBf9c4_LW}ZVt@fu<{R9Zo|R{7JjgN1Peb{yu<tpvmfSPn7d%^
zfcYOL4@)nwaD#;x%pRCN^!SDO0~Ss&{jhj}>4*6T=1y4phq(jhKA1T$_rT17>4WiM
z_QLeRXjuM**#~nEEdRsw!Qug?51oeD57Unx9xy&kA1u6K=?10`7Qe7`3rjz+^bgYy
zEB|2e2h#_$2NwRYaD&AgOg~H?%zpIp9u|Hu|HIN1Oh1f<#V^bpSbBu1huI5r7fc^I
z4GVvmyI|=S=5LreFn^-!huII)4@>{B@Pp|`Pd_ko(Cvqb!}P(z5hf0&q2(k@KP=o~
z?FyLvuyBOygEC;{94sBf;vE+Lu>1;h2fF)V{)L4*)L8hvB(VGhqha|0rVbX)F#BQY
z5+)DJ2Za0qR}W>t!U0Z0>kpVepvrJku>1r|U$Ah1g(J+p==lcbPndpKIKuRyhab#*
zm^rX;gp~`h^as<Ap8jCz4CYT*c*5)<RBph+8<q}W?uPjT7GCJ~!u$`@2eTLEZkW9=
z{jhk11vtzeSo(qKhsmSoUwFENDS)PXnEkN)2XiOPUYI{%+R$lO_`%!(a~I70Fn7Rc
z^z;jlU#NRv{)D*$<}O(N)q<u&m^)zM1q)AD{KLvCSh|C`2c{23!`urC2beoy=>{X*
zU>cz`tlWV6AL>3>_~Fuz9&RxEVfGTz54D~FmTqD8!|DN;epoob)Z?OI;S388Sbl+p
zA1-|`akxHcJi_$B%z>#xr(x+97LG7;VD5&+JB*K>eqich{)gp9bbDa>VC5hz{Ndpa
zbvMi&Sop#Gfo>jpxWoJbQ-^L2EM3FoVd);0ZejHeOdOW}VeWv%D_lPs1Lkj7IKb2s
z(hti&uyBC62bK;A<$v^WfcX<<FN}}oTzLHkb1y9ZVetdYhw%IZ%~!B^goP8V+=r!K
zcsQb22up|P>nC901~Ui7how81dtmW_W-J4&{(;dj`(fz;mVRL7pzDY6VetS9e^|J}
z^ufXdrXHP!*$)d(m^>^VaFu^Bbub#{ZdiE2;tv*nuyBCoADF$cc!Jpj6NkksOdK|U
z4$}{FFD%?(?uEGnrXS{in0|Ek!r~8RFU&lcKA5@i^aC>hMnm&IEFZx93CjmCK1?}`
zhQ%AqAFz0X*$1;9rXHP!)mJckVeJlBxWmkYrAOHQIaoS{#S<*NVDSW#hn0^oaoGMj
zjQ$679uH<OEPuiHF#o`4^!*F4aDb%)Sp34$KTIEtj~)&%J}ewy@d*omLgf(5y|8$I
zg$K-DT=4)4KUltl#WT!5Fnuul(bEsiK3F`#;tysHEZxKO!Ng(yh1m~Fw=nx)`e5}4
zOg%ac3ojTAb2rSrF#n_53o{26&anIfw-=fpVD5(b3#Jbj4KoLBF7!MFn0wIm!Qu%P
ze=z;9@PxSsRu01a0ZU&nf5XBNmR@1%VfhADAEMh23olqcf#pY7I)%~b`e63M{0Y+s
zvlpfhmLJgd!~BQtZdg7=SC7tzrB@gYizk@<=<3n=uyhM6Ut!?`b1$s?fTc%RIKsjo
z7G5xOVE%#m8)hCXoM7Ux^ayhg%p6$w!PLR*fw>Qs4$#YgSp31l0hS(M_QJvwM#Itv
z%zjvUgT(_ZJ;KvFTDt=-4mAMgK3F)w^h2c?VBrZX$6)ObSop&9!PLXt3G)vuU%>Rk
z;ujWfuyBW!6R_}r`3Dw%Fmqw{z-V~61I_O+^I-PE;vJTM(fto|2h1NZf5H3>(+6`u
zES<vahtaTn1al8eA1vNs>d<MJeXw|k#V^c0SUN)22jj!+gXxFy(Zdbh-RS;?nFmt`
zOBXOcEZxJx4;H^LdtvT|#Uo4}-5oIhz|uD?++gOw;veRJSiHmH1LjT`4f6-g99a5=
zxdUbn%-t}3FdAkr%ssI52{R9tzhLIT<k9uR)WPB#=6{%fap{M}KP>&h+z+z{mVRO3
z3$qtS!{Qs3Uts=#g&QpVVd`NtOdZT#Sbl-UH%uN@p27SD(+{Iz;e@UpmVRL6C@ep~
z^ucJDe_`<e(}$i8VCfGQo-qCBG|W6$x<GdqEL_m_q4Qz-;Nc2w2f)fdxO!;015*!+
zCzv~7`eEq<z5Iu{7nlEG=E2erx_+3suy}y^8y0V{aEHYM+}%(H%zdzO6Bf^~bOlQf
zF#BQtLQl6a^I`D>s}Eu7VCfK+u3`BD=5ClhF!#dJBg|c}aDbT)6NlLYlZV+uNIxvT
zVD`hx7nuF9c!AjiD^FndLxYb27Jjhsf~PNNx`4R@mXFcPAz1vu;vd~TF#F)<!Yqc;
zu>1>iCrmxMJy7Kc3hF+XeptSP>4${_LIWBLO+PFgVCfEK4lG{Lbi=uDcSFsC#S2V7
z%sjXT91NKKuyh3rFIalRp$#q!%Qvv_fQ28-9k6r{*8pX}(h1C7SUAGc6U-bKjqV?q
zKA63*^a3>=PQmge%wOo?2D2BIPhm979dLu7448eeaD$l#b05s#uzUs82B%=*1=9z!
zAEus=ez-wU1}q$4=E33(7H&{w7!<sofTkZ<c?0u5EZi_OLIq&)2MZ5aJj22PW*#gZ
zqmN&}@-58$F#BNUz{&+!e$s=s$6@|N*AMd#)OifB@(dQ9u=t0|L;VeNKTJQ&|1f{x
zN(V4^z{(*QA7&2BURb=s>_w+x;Rs7lu=)VzURZdb>x1Was6SxkAI#q{f55^OT|YV>
z<_}o7!u$<02j&l$KVk6;6Nkk!%pK_Q2lEdszG3QM^#d%u!t96n8y1c*^I`6W<s(@9
z!NL(%U%=cAGasf8orZ-YJp7^KVKDa+(hu_o%-yi`3bP;GJ{TVs-Z1yW#9{FTtDj-<
z3EMx2?p|0p!`utw!`%hVH?a9L*!nM+y|8$Og+DA_VfMhnA2xmn8~=lu4|6w+hM9*R
z4(Ra*QxCTXnvUV|3UxQEJcFsnprQI;{(*-pR6i^{VftY9!O|g&hUHtByJ0j;AB={j
ze^|K<GY=kqP<vtd7^V+K!_pZ{Kg?cOI)k|v7GALMgz1Nc4=laG;v1G<VCrD*Cm4P(
z4Nw{$uh4LXnFI45R2fbR7Cvwq8XmB81gocE{(z-7Sa`ww11ncx`3M#su<(Yt6BZ7z
zaDwH3n7?7+itb=oxWmkW>4*6jT|GJ<=1!PDVDSd4H_+9?_%Qdw(ksjzu<${TN0>es
z4GUM8yJ6u(th-_950>s>;STdZtQ>{;16KaP+y%2A=6+Z@hNTa5^I-7+E1zKT4|501
zJXpNJ@-Ivs%wKT*(13u2BP<+X=E33{mTq9~g1H~6odK5aVE%x`BYJqk+z(5CF#Rxl
z(ZdnuZkWBWc!ik@b2rRh7$0UnEIeT04+|HVy)gB#^am4%)fX^%n7y$0fT=^LVdlW{
z5llTiyrKCQJ^Wzd1@i|iJYnX;!VTsQSa`tX;qHW%Gcf(I_=c&6xd)~XM#I#@?1!m?
z*$3mp)T7fdf5YMt7H=?j!@?bwFJSs%@eNCFFn7S@VfMnp50-ym^007&>4%3aG@N1P
z!QvlXKg=9hI)TL-Odl-#VD`h!hiQb;uyP0H4wyNxaEFB>EL}pi!6{gJgV8W`Fn7Sr
zN6&{a17P6@D{o-o4bu;cADB4IJuv++d070z+yN_xVfMnp4?P@U`eFWul{>I-gM~lL
zJ{S#C4+}3?e8c<?GZ$tr%>6L^u=Dp}=?<n2rXS`Xm_N|{4GSNb{V;Q2;SI9~mJVS0
zVdlf+;WRY=!O}l0UBURU`~Witt`8RjY91_pVdWmI{R-0tt2fZ&A672F`~?dKSo;;G
zAEpnc4rU%K9ANPWi%*z8VESR<2#ZfxyusoN7Jjh!h3SLQFnzH6409(e9l`i8b71;l
z;R|y&EPP<<VBrQc2j*^A{K4#l`4?8-!Q2h=2h4vkcfibn*$>kXvmd4(7Vj{1FnM_V
zqs1>QU%||W*$bzk<r+*L77j3f!t8;?7fc^a9W4G}?tqEI+zHDEF!NyH1amJef5G)Z
z88H9D^uhd(?oOz33<?&1u>1;(KbXB38lVC&|H0f1GY4irTt772pxWRRtp5em4@)O7
zf5P;`!U3)k%7EDq(+BfEEZxA$O;~(Dwc(^-;Rp){41eIz3CriO^bE^Suy}@rC(Iu(
z8fF~Ky)b)W<v&awM#J0#AODA@Us$-q?1klXSoq=cKWzUjJp7^a8?bT*mfv9J!14#o
zJ+SpRuyhZLHy9sQuEF%9`x_Q+F#BLM%ssGtflEKk9WWXe{xJQp`X81K(A^Ky2XhBZ
zA1pp#`e6QosfW|h@Q3Mxg+I)nF#BQg0pr8+CCpw}{K3qDrDwQ3PzEd=!RrsG{V@G7
z`(ft7+z+GC+kY_iF#RxlVKmGiu<(baXQ*>9C|JD1(lNSzT;Y$d55`B=k1HL+(hV%V
z!152QJb>8?^9PK^@Bo?s%pWlGVESO=zcBk?_Q3cscf-;n%vhLy^!SIxJB)_;2bTX}
z@drzXF#o{Jh0*YMho%cy{)6d<nFmvkPQ%JSm^)zh!NMJ8FN_b<53>grUNHB=)WO05
z7A^*``T*)qm_As%!@>ua4q@Ssp8jF=2TVUqJ<MG&8fGsn{9)sN=<x>gA1wdF<YD0k
z(+|tXF!RxASiXYOQ2)Z~e{>B{KFs|v|G?4{%wCu~pvvJCEPP<@g~bOf{9*RO{0G+v
zWx({o>Qk8iVBtn6-^297;vJT5VC4<0{DWGDLBZS)^EWK~VfMoO4@(yq20+shEWBX)
zVCf0wFPMH9jbRSD0MuNV|6t~#xBp@8hq)Wpj)lc1OdJ*;@cJ8SA51?iy};B%r5RxL
zJ}e$#=D_4(_Q3KjES_Qc9u^NU_2@Lrf3Wa^g%2zppsR=RVc`m|KcMXgSU!cR!$rgG
zgN8FKd|~$E(gzcVl`AkB<{lUg4;Qrj2MZrqdV!@oxO<@LVd)lT9xVUC$`6=3VBrqa
z5BERRUoibJf5Fl-Oh3$>uy}xlCoDW*>T$&fOg}9Bz`_IOPguT$*^j<{1y=6E^uyG{
zXjnMF(i6P?gL(iKez0-}=6{&Eu=EG>2h3lv`U;l*p~k`QF@))drFWS7Vd~InSiXU|
z2gZl#hvgp(bD;4A(+7(OSU!gN8<r1Y;fiiQEFHkY0j3XT4!U|6AC^8~@eV6@Vc`f<
zhl_^AH!S{O>R|d|{)fc_OdghAVftY40aFKyf0#M2^bgYy3nv&2b2rStu<(cJgV8Yk
zF#BNP4oe3x`(gD6EF59t=l~YpFmqtx0dqG@KP(-i>xYLsG#$X)iS8blIk51A#TzW%
zVdlfq2`pS-`4g7zVCfL%4wyQa`(f%~`5k62j1SWX3qP2DVetqH514&0{V?~z^uz3h
z`5%_9Vd`M%1Lj|tc`*Gjd00M$<!cxX3l~^;!rTGV4=aCR?uOAYf5F@X3ojTAGaqIS
zES<v4gXxFG4=g-!`2%JSIt?=i7EUntpx6H}^WikK9)zV=^zeoE-=G%4;tv-8Fn2?x
z8DRMdR(_(lA7J*v!W*U^7T&OY1TznvhJ`yUK4I|>qhaY8<_?&Cm_0Ce!^C0s!qNpS
ze$e9&=5JW|!pw)IGnhCmUSZ<!d;ra_Fm<r_fvJPJ59Tje`2-6`n7J_j!}P=42{RWx
zKf%%w%)c;oFneI>9ajFq`~?dam_8T{3m;fIfzdF3!rTKh2c{23!^$aG{KMP}a|cX6
z%siMrn7d)|hs*yk_rbyq7LKs^gXxEv3rjEX_=n~Pn0|D3z}ycr2NrHH^I-9bOFv8<
zIt?=y7B8@T05b<&Jvtwz4^Bhdg>e1Q^al$En0}Z&FmYIX!2AKDVdV=fzF{;hykYqd
zR*%8L6&6o0{V;oA;SCdql|L~1VESS1gr$2J4YL;(zc71X@dXn{*AKH7CJs{%OMftb
zz~o^xEWTkhESzBKVd)*_f0(&2{V;#R%0HNYVeW?c3!cxR@rRy&Vdi1b(D;Sr6Ii{2
zUjD=U4|4}BoMGu0rXOZ6x_(&v!NLJnZo&Kki$7fX7^WX)4|=%3!VOmb!1yr#!t6y)
z=jibOiw{^jfT@R-JFswurGJ<`F!eC|U^L9VF#Rxp!{QMZ-nje?GY6J#(fKfc!NL)q
zZ(#}v(6ICai&vOC(ES68f0+Ma;RX|jg&QpV(e=aP59VH2K7geom_C>}=+1`uA7&rS
z-RSz!^}+ZseeigI<`Y8p!r}|24;IcaeeigMS%gNz{0&P7F#o~QJDPF~E-buY_QUcU
zEIeTL!^#hg_=Dzem^rZe0;Ud@{$TEem1pSr7arbF^I-WL79KEjVfg~R{Rhjhuy}x(
z2TM;d^I+i$3m;hc!@>_{56nDR{zLZ<EFfX(VD%0xox|J-^FN%1X@t_S{1108R2-J>
zVESPB0V)lrVBre0A3gqH;Rka+EF9nl5MaQ<6BbS|cfrDsfOh!#4lsYh{DWSOz|4n*
zCoF%%#9{hj@d4KlO)oJ2!@?034>0$^{14X$&7UxNm_0E4u<`?z?qU9h<p-EP7!Aum
zFnzH2hs6)fKd|%%(+BexEdQW~4=g>x`~x!=W*$r*OdTwIVCrD$A7&4_`7r;&<YD;}
zW)30!Fnur@W<E?m%v~^ZVD5s+!{Qqjf3R`~mQG>$5|;j9@dyh)7!3<|n15j54l@TH
z?r8B2(~lniFn7bk1EwFwN7o07e^@-h(hV$JVE%%+9~OTwd6@rU;RnlKu=s_U3roMS
z@PoMn=3iKTg{4zixWK{>rXOZLOh3$hF!#Xx4+|Gqy#jMTEL>swanUgIVD5ss8`l4Z
z#Xl^bVd(_sKA8Jq`eF9M^uz3hm3y#ogt;FUZZPv;{)f2(T|X@Sz}yY94;KHh_<`wz
zsfWiuw4a6^zp!)<a|cX6%-=BmuyBTzH!%Oe?1hCtOh1f<`4^@h7H%+i!1SS~UzmPa
zJiyW$%wI6~!om&a4wya|4O0(`2blX|;Rs7#uy8~Vf0(_n_=4F7OV==SVEGCr53?U;
zFU%ZRc)<J*vlkYhuzC?)KP>!U?tu9prVo~{VESO;28%x!A7(GiU9k9vnFG@g%U>{e
z!@>!cUt#)T{)UAsOh1f<nFC8tFg|*ILJv2Xeq1!nJXm@{4-Z`WVB#=+u>1>4-?01(
z3on>F%>OWdqU(pHUzmQFJ@9aXwg+JG4>KR;Zdf`%Pk(TGq55Iz8RlM?K3KfM?1hCd
zEdF8X6lNaG{jhL{nFG@gb05q;n0YYw!Qv5SFDzVO`e8K89+){W|HIV7@;@wH!Sums
zn0sOR;r@ZvmoWFk+yT=EqtWvztek+w3wk)B=RcTxVKhuVOg}8&;c^Ge{V;dK`~k~%
zuyDuaewaUC_QB#2mfm3T0aJ&IhS?8`SD3vp_rYjb_`uA8*#{3tXt=}dg_RF5^|1Lz
zxINHxi(YQQ(giGBVc`Mmf5FU!(J*(w?19m+atjvzu>1tGAC_)me3-qkd=0Y~mJVU&
z;-X>U2=fmt9C4*zm^m<i!om?|FU<Wgb?7wQ9%wki;t6IidVHblhw)+VhWQ(&AC?bc
z>TuC8{jm6emHV)8N6$Ct=D^ZDOg}8W!}P<_6S_VaAC~@M^&p%NO^?LJA1pn<+yOHm
zmpfqLj~;(8`(gPX7LMrdfa!;YAI$x*c!b%Hi-y?)b1#e!Pj68F!{P&d{Q@k#!Rkx6
zJ<#+-C>~($hUFW0xdYV?i#M44F!N#g4dz~$I4m4t^007+`v;~FYCbHz!f2Q~VBrqS
zKd^icqhaL*%-=BmFneM72Bsbse=u>FGhp!t3xAk6PIto;KxvqNVet;j_vqmTRgRN_
z#RJS8@bm+Xf0#dD?t&Wx4M$l1gZmGr4=N7}M_Bm_(+>+rSUjS~KP-G;<q0f3!1SZj
zF!NyX0#grjKe~FDKVkY{;Rwseuy}*%hoxtjIk0vLOdl*iz~o{16&@eZaDmCgXqY}Y
z4OIsVH<&tf8kXK*@dlHJ#S^Y_6Q&PlA1wZ1=^qx}FdCL0VCKX84bumUKUjFe!U^UM
zc)EZY0HtB-VfhD^j$q*qvmdG)gM!5~%>S_T0t;`LevJH&W)d!Uz``G94lF#;?SrK=
zn6WT<SUQ4*11vqj)}O+|4;tUF^bd;{xP4H0SUQB+gYJKreXw!>W-rWKm_Ati!Q2T;
zzc6{2J7M}^?tsa|@(V1!VCKQ>g@qfeJqyzZqtV?B^Divjz~T>P4to5-?1zOPj1Nnv
zu=s@e17;q~ei#kQ2QWTNKg?WMx`xF+EIeT9anUgMz`_-lK49bbu<(L~Kg=Fj_`%!(
zE8k(_u=UR{{V?-k;R#FsF#p5sg}EC>!`uxEcbGk}@P^q3Q-_O&`4i?3Sp324K~Fd6
z=Ai3`sfX!@nTM_q#)p+xF#BQV!Qu-R@38QM>4VX*aDtfw3s0ChEd3C22h2Q}{jl;3
z7XGmE45ki7!_>p#4`wecAH&i;Jl{jh0hoSRx`Fv07LPD_7!3<oSUC(!Kd^KT^Eb@B
zu<%DOH(=($_W!{2!NM1o{$T!vnFG@Yqhb1C;SRGG7H%+i;?fT@2S&r}g@pqw9O3%W
z!VRVmM#I7brXLpWF#Rw-Og%acb2m&sA^*e59ay}>;sGWO3lCU6gz;hd6P7+;;SVc^
zVc~%8K3F=2^`Bw#gwhYpURXH5XqbLj`2q6>OdRGubp5b+g{gzN9~S<wd<JtDEd9aq
zF-#wfhS?8`Z@B-V?M|3JT=ftvzrf6a*$c}rF#Rxpz}yFmCz!us;ScjS%suGo03Lr(
z55UyH(hZCcvlr$LT<H(yewh8R^a6`-s5Lk#n15mVVfhheKdjt=<!_ilF!eAR=1y4n
zz|4V#8@j(?{)OcmSUkhh2h3kEadiE#@Q0~`<sVo&g1H-355UZYxgSQu!W-rfSbBu{
z2NsSn^I-a5G%Wnk{Q)x<CJ)mOGY_U8rVkcgu=E1+2dsX8iKFK~SbBrGAC?|r?uYpw
zW<N|E7Je}Q!ov?*PQvVgg+I)_FdCL$VDSj^CoG-7(jm-#Lg^RgUl<L`$1wdc^I-Pl
zqG90xa~~|c(bEqh{jhj}>4$|sE`6|gf`tbxy}{fM<HN!qW)3X<!t}wy5#7D8a77>g
zhuI5@cUU@tr3+X%!pc8%b7Ar@{jl(dr58fs4$}vtVeW*be^|J|$`hEsVeW&aJD4~u
zykPMM^AF5DF!#g!4WnV<3oCD6{(#BD$|YF3Mh|b8K6rYE)*mqaF!N#hVd)GOUod?z
z8WyiGd005Y(i6;mu>1!LFPJ-F;R%a3n7uG}z`_e=4$MC=dtu=Y3s0CiFnur@=0BJ@
zuyBC61Evla4buk;512lfKXK`UiNo~6+z%@!VE%>iVd~LoSUQ2}howK5eptAp>x1#(
z;SP;&czQ%D2hsB_Og}mei%(d*!_qx0U7)Ln<pWqe!2AOX7g%`0(>>H(Fmqt>4f8+D
zUYI<LhJ_={JeWAFoP+6yr7u|c;c_>u{($L&xf>S#=;;q;ADo7UH%vdQ9D>Qi%z?QB
z<}R2#jE1=bM#It}EM8&yVg85dL#JWo49q>S`~uUDt{$BaiwBtfuyjW#AHej%?S+O5
zOg}6?!r}o|4#Mn*r8k(pFn`0s14hHr5Bm5$OdeL=z|s%Q9+*2|_My`-eX#h4#XHPC
zSUjTZgQa(vyJ7K5D7;|iz`_d_ez0&N<X&9*Vd(=F9<cNX^AF5?n0}c1VeWwWALegZ
zc>(i3EIeWIFmqt>2Mc#tc>%K@W)DmsdU}MZgM}l^JeWUV?uY4vnFF&QW<IRkgPR8}
zXJGna{($L&(J+6*$}yNY%zl`C=;aPf9gK#hTbTP{`4JW#g!IGQ4~q|&KA68@@d2|B
zgNEiiSa`t7MOeOr)uS+TVE%)-8x}q=8kR0$@d`_qu=EYH2j*XxIk518`5#8Z#9{t{
zxgTaeEdF8k!pwu&2Xj9xpTYFQ+yTqSF#BNshJ_=nJVe(Ab3aTRW<JbaFn`0+5nLS_
z16JR`%!7q9%zl`;XxiXhSUQBI16X>&<!`t~bOtmXz|4WU1Kk{0c*DXE7GAJ&0v4XI
zaDe51SbV_JDJ;HV`r+<|x&!7sSiHmJVd5}<!_0$)BYJv4*9X%N%eOH9!rTo@KQMJL
z|HICohKa-MM~^pH{(z~2>4*6PCJqZnSh|Cy6IlL(<tMm4Xt@N_kM2&GJ{S!PcbK^_
zeJ~p4PMG~L_rSviY9A~e!_osxKP=o};SX~UOdTx!!r~o09$@NW=D^YuEWBX$!t6t*
zVd)FzZdg8r*#~n6x;_{mW<N|n%pEXy!onSvu3`FMG%WmJ=>S%Lz`_sRJurRfG|U{B
ze_-hemM>u8fvyk6hoyU1JizRSxgVAvVCr$vF!#g410FumbbuZXFn7YtgT*(@epr5j
zm0#%d-!T0!_ru~F79Ozj0cJ03{s?9tjE30*^FPcVFn`0s50)Qb`e5M=(+A53=;~ql
z5T*}C!}P)O8%!M5o`Qux%>6KV7!7kL%%8CEK-UjTk1&1cG)y1NJ+SbD`2$9y=LcB)
z!qlV38_a#M`~>q4EWN|h33@t*`3IJtVDSnoKVkVC=3baRu=s+d2belox*~P_9v0s)
zcjJl&Sa`zx1?NN4J&ccwhUtU37v>Li^)Pq9#i8{aEdF5e11qm#=^7>u_dir0Ec{{i
z!^#U-euIT8%-^v75A6I=Sop*80o+|s1}xrS?uMlUs5Axz&p%N2!Q6o>{h=EG3qP2@
zVeW>-Bf5HYKFmKbb71a<nS-t#mi}Ss72RG~x<k+Zu>1m34~suo_`|{pmM_rlh2=At
zdtvbeb1zPPP=jFc4a;9J_2}UQD@UN(;1n!>!R&{*AEp52Uzq=4@dq!5pz#aS4+}q-
zI#_yzxf7O-(8q6K<tEI2m_K0l!_pJXU+DT_?nU<x%ssGpfVm5n9%1@m@d(RDuyg~{
zhbte$^uyvG=1-VA3F(8yKP>)W=E2+v3qP1RIt_C#%zjwBz|uJ^++hBN$-~Tn=|_)0
zSh&MzSbB!J3zj}%=D_sB;twVdi*K0wVD3P#|KZ^VO;2!lLd#uP`hfWhR)4_khs7UE
z9n4>_@P(D5F!!U!3oQS`^uy8(Odq;>n7uF>=5LrkVEKiRK3IB%>4)W0n0}b~xM-Mt
zFn7c94b0uR^ufeo`eES(EB|5X4rU%KJ;TxgOh1f<#Xl^bVfMq)4=f$R#9{RhOh3#W
zFn_?}AEqCcA7SB%o_=8Q2eThNKf%Hi<`0<pF#BONy!?fxLzw-r^b89Zn0j;?<_}nW
z!omv{j<EQJnFrGkb3ZH`Vd)m89~LgK`~+M71XBm|H;jhqgP99c2TT93^T%N8Ptfg!
zxeG?a?1i}-UM@nz2^POF`(XJ879KG5F#Rxd(P@}|Sop#GjUIpK`e1yF_=Dcx1Gg7i
zp1{<@Xqfw9=?3P0SbBlE9~OTweXwwa<sX<iuyhK`->`BI<_~oJFneL{hv|p;AEqDX
z5A<{h%eOFdVD5*9C)D3C{kYOUOg+pUF!#gEhovj@{0sLF8Ut1?!@>vVZg~3@svefV
zVeW?c7iJ$UUBmJtdif8F2UvK)%z?!R%wCxP(c2F&b+GV*#WO6w!1P0%i$TG{4VI5!
z;RUk~R&QZwL=%986U@J`bO%d^Xv*PSSUAJ%hnWLQZ!mYj`~}wtWx({q!VP9WOg+pU
zxY7;O7&rw>H?VvR(+6`8Tp^SJvmaJoz-X8`u<*kf4$yK47Jo4J!{P;M4NeNC9!A65
z1Iq_6ccZ5Vm^zrfFn7bsOPIf5G^`$gIRs`OOh3%OuzC{iUugb?>4VWQ{jhWoa}S(`
z?nj2XALb62eq1yx++gts%XctyaOp!AhovW2IHHFOEZ@TX4YLPk4$OXB=@8~mnEzqn
z2or~;8<@Lce3<z#8dm<m@(s+ru<(b)2YNXG3x8O?gM}Nc{D%1p7Vj{3z|4W^gSj8(
zKA3&5^aKkhn7J@@F!#dT4=XQW^00CZRvy9J0n5)Y`(ft8^ufztXud)>A6A~g^ugQ-
z^B2sWFneM0F!!O?A25Hw!X2g`7QV1_0n2~r=@{lun15j52}^G<_rmOl#XF3K`46Ta
z<_?&Bu=s$v1Evo~!`u%`cQAWl{)dG-OdO^UM#J>O(hp1>%pWlQu=EJi2cu!>4Ca1V
zc*EiY<_}oB!^C0wVKmI&Fn7afm^>_AVCKNYanjKA1B*vkx<YTi!`u(^KP-M>d|3Fv
z+>PFTfR!^a|D(GbW-lxqpxX~~AIuzBc>{}YSiXSKFn7SxKdjt=`2#(j!TbZ$4|flG
z_&@^^rXQvr7XMIbI0f@B%zjvS!Q2f~2#YURx`K(r+z-<S_ZL(FJlvt}XqY)L|3H=D
zq+sCzOUE#Mu=W=$zrgYzEd9a4AC`_`?u6M7>%YMC!^}qy2bh0h`34rQFbBi*!~6-;
z4+{radV%SO>4VWQ_rc-;=1!PA%pWjwVE%{6qlXVnJuDnx=@I5{n15jUanUe$!D(pw
z0hWJp>4S;G!ULusW<MeQF!iwTfTdfQ|6zQXJ+SbCg*(h%So(#fe^`9L!VkUvfW<%D
zUTD68*#k2VR*s|Vht(gjasXx?EFNI~hn1tS{0mbLb3aTy%%8CMg}EE1ALcKZJj~xP
z_rlr<xZDBL4^xj3?oj{3(i6=6Fmqwy3DXaA2h1O^bO-YX%-y)+AEpoHf0#Y6@PU;t
zFmqtx0ka<#Z?JLzt`6o8wDJet|LExmW-lzBVE%{2KTI5EKdhYza|bMZVCfdF52gV|
zL){0{2MaftG_1UVnS*XGOdTx#Vfta=1(Sz`2ci5AE6-r=fyFD#evEK~SpcKa`~wq)
z>Br@Mm^zsIVfMr9gXxFmLzsFPjjj*oZkT?Uy|DC*ULM2r!{Z(54_LZ@rAt_PMo-7E
z`~#z5;fQV@EIwiGhS9L}4@*Zd^U?hab2m&Lmfm6dVCKQ{4=f$R)WP_$@Pvgk%%2$H
z1<f}w{V@L!s~^^mf|ZXj|HIr53vXC_!SXeXhLvBi@Py|hXnh0|hxs4IhnWwHSD3qD
z;Rg#R7!7kLEFNI`VBrDtFU<WgeJ~mp?=W*<>R>d?ewca~4RbHdUYLJj@eT_ISos6v
z!}P=Kg@q$}`h<rIT0b799~Q4L{jhuj4<~3ofcXn14|5MJ{lMZE7G5xSz|$4f9GHGs
zc*5Kda|cWx+#IMrm^zp|di@J4|6utP7XPsDgoQKA-EjXw0|Mqhn15jL4-0R&d!gwL
zstr!T+z+!4=6-m3g{#EQK(`+~oN%Qdm}#*519Lwt+zGiGrXLo*F#WLjgw-1`8kR0$
z`p{`uIK#|=$-~?MD}P|(xM)~@fW-sMeq8kz%>S@-4D$~xJ;34vmM&oN3b!Ac&S3V!
z%t5b5;p)*CFneJBhPfLSkFazLOLwqz2r~v|A1uGZ?1h;ND@S1A2r~z!9~SN~J}mxW
z`2yx&bbDdyVfMlF!Q2B6S7^G2g&%r6!u$*KKP)}N%!h?P%>6JLCXa3}EI+}*4VI5!
z;ec*G%pACXq45dxFU<cibr>{MJ<R<u^I`6U@iFv41z_O^3pbeiVey13-9oi9z{(9+
zx`nC772Ys?FneJBhNU-{zhUZd(Qx~q{)dG(%zj+@VB)awf|&zLuds9j<HPbV%w6#C
zgxU);2j(AG`vq3+!Ng(l59|NK+yTosFm<qShsndj7nTmu$8TWy2Nr*@@PxS^mi}P=
zfW-^Ue3&{|`2lke%>A%*gzhewepq<H!T}}@4=1PqEc|ep19Jy@dPFbJap{NYgZUrk
zADI7P{($8tSiHl+9p+z{eK2!i;e|^-%-^u|2U7<tS76}@OP8?qo3MC;xfA9dSUQ3E
z2j)JQKhWp@VBrRHFU&re`7rZf?t+aU!Qu^OKP>&g!WWiaVBrFdMmPoYH_TpGIS9+g
zFmqtx2iFK?!2AnK-!T8e`~!<8Sh|5~!%4x)ZJ2vt{w7vG%ssGhfVmsy51bZ5C1BwQ
zi+5P~!QvgN3{Jt~2j+j6epoob@;l5QFh0y(F!#dZ0cH-|0yG9J{^9E<pz<(%F!NyP
z3}z2h8BPk8-eB&Bm47h*<J1V1fQ2{AA29#I+zZQBP~~t67Je{$VKmI$g!ID<f~7Z@
zKVazy<`0;8F!i`-Sop!xA50&i^am?%VDSK>VdW<*K49?=qhanq4?lQ5geid0&~^~I
zewZ{&A1oYU_QT>4#)qp%W5B`>PDAa3g(I4BI2RWFu=t1bq55I&g5?WX`2{lo=6@Is
z3olr@fyD=`Jb}%hz~T#LFU)_ibOVbAm^xTI!uoGI(E1;y4i>I3dtv1^j1Myx<_?&>
zFm*8hz-U;x3)4p^9AW7eW-hG11Je()7iK;>4XY1e;RUlF=5H7uT^}s{!qW|O+y$l&
zmY-qnLDvrpUpNhocUb;GSC7tz#XHO$u<{xf?&#{#`7r;&!VOlg!1TlX0ZUh~`VE#Y
zVftX{02WU${jmA~mOjwiZ?O1>g&!=OVfMkyg{4ne`bGB#%snvwz`_L<?yz(KYk$D>
zqsKce9l`8}`3L4Mm_8T{vj-M#u=)-r4$}t<N0>fbG%P$|<p?a?VD7-B4`v@MK49*E
z`5WdAT>4?+==Q_XFU)>eJiz=53olqW!_>pXVetnOhqeD<;STdBOh3#XSbB!Z!}O!a
zA50xA-eKttR$juw4_1!D@(0YnuzUxz2NrHHcfi~QQ;$x={0R$pSopxg9hMK!^}+bC
zaDeHDiNo|0TW-Ph!`%ZdM_~SdnTw0Ya0gUB%pEX);nEMUx1jX`EM8#xVex>jALd?I
zI)TL>%sg1U!^%S#4KoMkZdmyNGY=MTFmqt(0o@<4bPUrEOGmJ9hq)h?E@1Y;Xqfr1
z^aIO3FmYHp0y77u51od&17;39T|(mlJ>8<~hlLv~9l*?k*$Z<AOdl-VVD5y43ycp7
z2bejq`~x#j4I0ld_rlDFr8k)SVdWAm-eBcF%zl`AVeW^AGt_)oyus{+(J+6&+zX38
zn0sLP4yGPvKTIEthUtgtgSi`~9_Alh<tNO4uyhA=Kg@qHeK2!i_QGhG|6zPsIKs?@
z`2$z{!`usVKdc^v`3t5G77p<G8)_jed|~c~<y)BhVd(<qepo(+r3;vSu>1xyA7&0L
zU&H(XEB|5oVCf9Thq)K#e)Mz!*AI0+Odl*9VBrRHKg>Ls|6%TjX@~g(7Je{)!2Ap2
z!_?!VVd)QMFD$>n$`M@pVB#?SFn_@G!_py49WEN?epq@!_ctMbz}*eacQAWk?jY75
zu<(b)8_XTBd;v2TM#Itxx;|LE!qN?_{RE?7`eE@0(+>+jm^>_f!_p;2ctZV)9{#X=
z3!`EFKu>?Ld;`-D3rCo}F#WLf2kSq<+y`?n%p90IVc`ek!}P=WF#RxnF#BNP3Cnjd
zcf)9yJ{S#C539Fe`e5M?i+`AYSa|?b4^MB<aD<r)QwNJL7>(|Jn0i?F!Tb$V4@)<=
z(l5;YF#WLbhPfN&4wyZ-XqY*$bOZ}mn7?5(x_+2GSUAAq0cJle-{5jT%-t|^VESO`
z9^F0|AC_)l`5xwPn0>H(0c$^@hdZp?h1m}acUXAA+zZPuFn7Vshoy6vzu|mndj(d`
z!om|~9<2UH_XkWJOdrf1SbBu<(cAwpb71Ka<{uaz=6+cIf$?GbVD${l9$0w7(mzZe
z%>OWbFdAkbEF56w!{P%LkFfd#rVnmDG~d9|8_YhKyI|_k(;qB;VetlwPgwec*$Z<I
zjK&4v@dtGej1TiS%zjw<fSChx2h4tSahN|~{z7*LJf5KT!qY3157Q4T4`As7W)Dmr
z77wuagQXXky|8iwrVpKlg&WKqn7?5Dhs7tlJ{TY7519RMcS7?6EPm1RA>1CQf8l(n
zK3KYhl@IXx1F9ZoA1wdC;|HoAmd;?|4x2xLwcpYG3kwHWxT1$6+<utH(P)@HSbl){
z2Nn-7cc5v5bJ5c?EZ$)84_AoBfZGEN7nr|c_QL#uri}q+A1oYT;S9^KFni(Qie>;z
zKg{1SX_z@M_ruczOaTrWmhWKYJIvjL!VRV#<`0;Cu=I<|{jhL@>4*6T-Tg3gVCfg;
zPI!8Ou2X`Q^YHQj8XvH5gt<!vDh^A(u>1w<|G?~rxer$U!1Tj{50<ZB_QBi_b0;kR
zafKhu{V@G-J~aQ}3OAVjFdCNLVeW?63oD0V>TuC8`(g0_3ol&mMmGmmPQcs&(+6`0
zEMCy{!T7NB3v&mY4-E%cI)tgmMZ?NLSa}4CFIfDc>x26jnjT>G!r~nkez5!q^B2q<
zm^zsMVdlZ|Kb(eYz!jgcc!%X5I3MbNT>7BKGQi>;rXS{RSbB!3gM|l-52IoE03Lo&
zdtrQ-KhfJi==Q?$5iFcw=EB?qEB|5P1amJe{9yiuxeMlASUQ5yu=yWYJj22V=1y3+
z!{QyL50)=s=A+Xv^WZcz9AM_4tB3Jn_QKLXEFHn@h4~+*9!A5;F_=CWA6DMM>_yK%
zFm*6}F#BQc7g)T(%70k;h1m-Wf0#dE?t|F}6NlLY7l(!i%wAZ2h2>9JxdBTTFn7S*
z1q%<DKA1h|_QU)G(+`U`n0}ZzOg~H>=1y2S3@e{u;Q(_V%wI5n!0dymgXxF43#Jcd
z4=fyE=EKZ^nF|YV7!Au8Fnus}uy}y^2j&h~K7fTQ%wI4XW*;mZVd)K~9&R5rzF_$X
zM#J>O?1!Z<SUkaKxH&L~Lur_P7!C6e%p6$$hoximaD(Mfn15mU7bXr1f0%z@@dC9T
zHw7)nait5GMwq>@aEFB-OdRGPm_J}NOh3$>u<{EQ4={hg(hDs9Vd8N8PzFpNEF59+
z4$}t<KV0zub0;jn!O|nlKQMD(@eZ{XgMx(v%pEZMVEGM}4=^-B1z_bjOg}6g!1SYs
zA1s_<?u4m_`5zXp@NkBvW0-lc_=Ty1r4v|q!O{;*9+rO4>mQhVq26GCxf7-z7A~;(
zgw6lL+z<0N%pO=gz~URG4@RTs1DH9mbN~wvn0i9t4s$0gJYfEWl@l<3q0=yXVeW>>
z!@>_1A24xr8kX*0`eEq>rXO8BIv?g<Sop)rA(%RJ^I+*17Ot>#0n-n256s^%_rvl7
ztUN;>KZUsi7LTy@63l*>yI}bTT|X?oVCKNuJ8*Gmx`l-c%p6$w!}P(z5#~RbdRTgd
z`3Dw0Fn`1J!_o;%9V{Kf`~$NWrXP0x4$Qr<`~%YucOTUK==x#d4Rbfl99aH^*#q+j
zEZ@TX2b0G|!_0%(1B-uH{NmCF6Nl-8#VgEz=<x@$2c{2Z4=nu9!xw%05GD_c4_LZ{
znFk9unEzokOddwV%z^2L`x6@eFm>oO%%8CG1C|b8<p(T2(e=UjFnzH2hUp_1PtbZ6
zrXOYxJiS8KA;HQ!bn{^4DlFV!@dQ&3a~~|<!qO4Ue3-vs`eFGE7QQg|z|4h(3wr#+
z%1?B4u<(GTXIQ#`*$;CcOdh5mmLFjLg}Db-F2nS}XqY}&IRneD==Q+Wqth_`FneM8
zVc~_Y9-R-<4+}?FK7qL#7H;VJV0>8qf$2y0H_SaS_2@J#oMGiOEIq;Og}DP}F1mgg
zA7&0rKP(@?(lg9oF!eARmX2WI4@(cQ^aM+Pu=s${F#YK17{-Um!)a(fhLs0!acDk*
z#UHF3fcY0z55Z`3f56nk;sK@)79Oy0fTdq}`31EX7H=?hu<(NW1F8-t4l9>n=EKTK
zSbl?<14~!1bdM_?z~UX|4_Nra%!h?Hy8W>5gSitHelYW3<p|8*aQmV00Mie%2Nn*n
z^Z-kD81V(u2&G~6!R&{{6FmQ+*$34Ir(pht`5%_<U<zRI4GVV|4Rbdvy~E-G7GCgl
z038>Fg$ImA4>uSeW*;oPVDSf!U#P=i>S6wd#XHP=m_K0Vz-Xv;I0Y*|VFECFVE%%c
z3yXh3@c}ajmY-qngoO(%UeVRT+yPSui&vO_n7!!wVetn`KQMhTf574kmL6c{!0d<7
z@N@^wZ?Jekw-2Tsora|!SUCW*7nc6f)x-F(bOVb=Sh|75JIq{|dtm)1^z;V{Z&*CR
z<YDfCxdS$S2pfNgg&Qorpr;>L`3nmNSp2}|k6`YD#UCua!0d;)2S&rphxs2y!|aE-
z2Ob~L{0j>Yn0j;?mQG;$(d#c*xqz+@#)qXNSa`wggM}MR9Xbv3C(K@Scf-;lEPP<;
z0%i^@-q7uZg*z-=z|4WQpJ3q*vlo_*VCfqcp0NA?3x8Ppf!)6i(+^V*a~F(;xdRse
z@cjqSd;;@7Jl#UYVdlcp3rs&u9n5?<jiwLg9+*CuJK*Zk7_j(*g%?acEZ^g@7am_w
z`(g0`Hy)b)VD5m|-%tgxc!TMKr8}6puyhC04+{@ixWU{DHI4z6&(Z5|^mGGL56f>b
zJ}ewy@eWJBu<(GXgV8W|!{Q6(ZkRkwAI$ym_=hTh*#oODVCfL%4_H2erAwGNR67GK
zo?+nt4;N^-!@?7$9-W5edzgNhJ+OF3SC7tz>4)VfSbV|48<+jCaDs&!%-^u`4Hj-N
zb71)w-F{fO!Ng(d6c#Qp{V;dI)WK+&Iv5`o|1fbv`eFKD>S6AI*#|2(VCrzuF#p5C
z5f*MR`w8iTg(G@72@4NcxWeKWW**GFF#WLf1Cxj8hoxWG_#sRlJ^iDrN4F2A4`wec
z|HHxy=6-m1LenuUJ>b$03olsyfTb^(K3wjA<v*DJVet=Z@4@^HGaqI@ESzBR1*2i^
zhS>|tM=*E6^uyc-vlo`WVet(szhL&j$|0CMEZ@QO!_0-*2lE%q9GHL5%U_r}SpJ9k
zALbrd`i0T3bOf^xrXJ=F^!SH`157<Sjh+r*{)M?07XC1E(e=aJ4|4}BTwv~pg$J(q
zhnWXUcd+ybGY1y#uyPot9v2Ps56oS#a3d5BuzCm<jxhJY{EwdgVD`i8f$4*X12o^j
z>PwjaVd)2^AEq7_|FHB6vmfRznEPS=fu&!VK3KTH)WO^hvlnI`j7Gmd1s3nHc!PyE
zEdJ2rAJ%?`l|L|hVeW#dhq(jBhuIJF7dj2g$FT5*>4&)st{;s7a|bLOVBro+KWNI~
zT$sOM=?0cxVd0ERKg^%7c!${wHyFx*m8-CDgyny9{jhX^9$&EZ0Lzc)>(^lE4;F4P
zb7AJd-49KVP-np@nEPS+VBrQUXJF+fdi=rjC$!xOb0;j`U^L7f==x#d4YMDnA7&pc
zUBJ>IEI#1oK<$OaA50uZ!_ot~ztHm^JRr~*F#o}6X!ya>BTP9gy}<G-ES});33Vqd
zT+#Kz%!lP;SbW0Fhs7T({lMY{CJzfISUAG?u<(cF8<@Lb;Rw?Y3n!R<SUU(N4-0>o
zyJ6u63s;yvnEzq^goO(%KH=_%8Vu79^FJ(~!0d;K!~73RM^No>3T6(>9WejE%!9cH
zW)95#Fau!e4wfEa`eES*(+>*=n15jUVKhuX%ssGh#-$#n4;Ejr_=lxmSa`wsaP?>m
zn0>JP4@*BVcf<URZVpTzEZkt>0J9gCeqrecCJwV67EUnzu=s+dPndZycfisQOdjTM
zSh)!cKbU<m8W!&``(b>TJ8;o3_rPf!`4^UMVdlW?h2~pa`q9G=rVr*%m_GFQfTatV
zeX#Tn%P%k*R<6PPjS-Jf_rby+7Je{uVKgkg!`%f92blX{`eF9L!WR}UuzDRv!_pDD
zepr0N{D-a{<{lW0t{;|uVe;td23J0Ug&!<E!T7LngqZ^iN0|AzXqdlY;R!Pj<}X-y
z!t8^ogM}Z=KDd8i@dOQbSUkYe4=nw|XjnMH(i6<xFd7#A=<bK*SC~1l@(LCYu<(Yt
z8>Sy-AFTX=iNoxJ`2%J?%>OX|!t8^EH_TpG`3Vz;<$GBB2NrIy^anE^W<QLE#XBtA
zVC^56yI?fTUodyU_%Qdv;t%EynEB{5%s;U54VLd<@rte<#)rix%s()DVD82hZ!rBZ
zf55^E=5AQM4O54UhPf9OjxhJa;scjHSo(&=KP=v0@eeZ(rVds<z|4ciBg}qSIK%2+
zSa`tru=t0mhsnd@4d!2%y|DZU3rASE!1^CB{V?~z+yx77m_C@hVDhl|f~6B!{(!{;
z%)K!E=>CS81EXQ(Jls8K`5opSn0j;?W-pwE>WBFkT|JBsOE0i^faMpMe_`<nQxBuz
z_CU)gnEPPi4O0h8|FHB76Nk;ez~T{>-r)8_&4uZMxd)~n=3ZESgV_(0hxrSZ9$@hR
zGY^(tVD`h*!^B}UOg+qgxH&M3aL};$goP8V9EGJ{m^SqAf|c7aaag{BiNoeEVESS1
zhNVB4J+Sl#vmfRUbp5b=0t-i&yJ6`S=3n%5089Vy^a_nHSbBr!Cull^rvs?NVeW#3
zKg@hsI)M2H77y@p2x=ZoA1wdD^uzoK6Ni}t6NmW+=5Dw;sJSq8aB--=VB#?SF!#g4
z0hVrIG)zA%d|~?0=MP}v4)Z50oM7Pxb3ZIzVfI6VkpU(Ta}P`(tX_tte^_}8a|cX6
z%wAYJf|&zLKQQ;e(gQ4?!Tb%&hcG_6|6%r_n**~4W-l!MVE%-K3(Os`{0Fli=6+cC
zqPqj;UYNh(?t*DVqhaoWnFF&2W-lyVqG@A*#WPGlOdl-&!r}que++Y==?fMPF#p2r
zf#pY7Jfg=x%pO=cz{(3)I)J$!M#JI*SG>c@8CX7sxfdo5a~CZC!2Au1KbU)9?#AVB
zn0}bQ(c=de4>0>-{)UAw%>6L`!f2TNFn7ZI4bu<vKTIBG4lEzR^uuUa{K3i}Sh&FS
z<Dy~yhs6slUBmM~G~Qw12eS`mJ}f`M<YD&1>JON^Vdlfa3uZ4YoM7P(qtU||=5JVh
z!1Tky9asK=mA^21Vd(>=4;HVm@P?TK3on?zVEG*8E|~w(;}fnQT290K53>j69+)`H
zJurX6+y%2AM#I7#=6{&IFnzFegPt#8`eFWn#RDv!VdVl$AIv;hc);|-?1i}-=1-Wv
zVDSOd4|5MpKP>)W?uPMU{)d?d%LlM@0n-ml-{}5;g%>P7(DlLe!`ulAf4Kjk=^Iuq
z!0d<1Lk)oGhs6V|oP+rr7LKs;97e;$Vd)QQ90SZAn7?4=pt}PW?yz`+nG4Hju>1!L
zPgwedg+ELlmVVLW85W)}`(fb-b05sTuy})+2MZq<9~K@k{V;uS8XC?pb71zt?1AZr
zr3bh^C<9hcpt~35A6R(6XjnKvwZq#Vu<(S17fc@JZdka%!WkwH3xAk?nEPSrjZpf9
zr5~7nn15j52(uUF4w$_#8s=}9eptF9RzIco!|a3E4|5N(_QTQ%OdpI7izk@BVSJds
z;pGil`hlevSh&I50n-Pgan1k1;tggFEFHr1!D!h24_LVYD>q^8hviqeJ75~2G%TIL
z(hDpcVfta|3|4MJwP8@OatD@wVCe^z?$E;x-2j++Sp32A4b0uJ@IcoGb3e>pSUAA^
z4KoK8Pq6YBw*C!f9<2O<+YjCM0L$+%^I-7+8~=johlLj`{lLN#W<M<aVEG@W4_5BN
zXqbJlc!c=}mVRL20Six<|6%sS;sGWO<HOty(+9I3orbv|<{nu5!}P=4fvyjo534s|
z=D_?#syVQ74HllT@PnHJwHRg}%)hX3hlK--hUtfeBUC$l-#yH|F#BNchNT-=JmLxm
zm_0CaFv1UJAsUUI{$c4AmVaP;7!50rVEGk2{loOZ!ULABVg84;f6>byn0i<`K~Gn(
zaD(L^n15mR!_p7T99X`Exd#?+FmbeCV1Vg^nFlu?+FpgZA6*}ej}ea0b@MQH!_oyz
zJvt4mhhXtXsQiV+2h5!?eK0=EKd}4^a|f*d0*e<I4U11$x`CxLn7d)(u=EJi2eTKJ
zUSZ)3vlrc;Fg|+x!_qy>UYL8~=?$h4O2h1jm7h>y_<BrOxd}4|<`0-UbQ%_3Fmqw~
z8m1rSE_8h`J}lf}=?CV2So(v74@^CbhNW9v`e6QmsYj<_=?)$a(DVu`FL2olGY4ir
zOg}8$5vw0pco7P3SpJ6VN6RlT_u-<^^~2l`%ipl@howi1{0nV2!R&{b2lF?~UYIy6
z{loGPOdgj0VfhheA9{L%xeI;#7G^#y{$celEZ$)80W%kt|6umR^uyv2mXBfXf!Pbw
z4^Pif17P;T{Q->!n0}ajFnzG{872?27v^4Ayu->fSh&Od0ksyBLW>WWKVa^Lg+F>e
zgw@lqc!ZZDP#a+B0Oo%5_=EWa77j3bVfhy3U#M{y6s(+p>4$|kOg}7KFf>91VC4oZ
zykPz$7+z5QF#p2rhlK~azhUM=wZSR$aD&A+%w4efg!u;+9x!>BeptM~;v1G9VftbD
z3f&zr{jhj|$-~kW%%QM!0E<VMI7}Tp{X*N%F#p5!!T2zJFd7zKFn7S*3$q92UzmDy
z8WwM`bO57a@eZ>emM+ls!^&@%IWRs<KP*0AG)zA%ykPoZ@eh-S#TP6c!OVln!@>=w
zA6D+b?1#l8EF93o4`x4%hNTDec!24LxdUcDET6&D!|aEd2U7>rkE<O33vZbHuyPmX
z512W)(jQD7orbvwW-cuJVeWyehn8Eg@PyGYf57y?(gC_ZVE#w92bK?D`3i0y8Utnz
z%p6#G0P`;_9?|0;rVr*`SUP~KgSI1J@dkGf)P9(IVfMoE4cwp5aDl}S%ssID1j}!*
zasp;POdTxzVf7m<9AV)HlZTlD3ny6oz`_BRzhUO1m;Z46P=CSn!@>im9~KVi`eE*e
zsfUFFEPccL3(Jo%cR;O!w^w2L7iJ#JzcBaUY6rr@2O8e6c!lLxn0;_}!7PT-F#WJ_
zfQ2heKg@nuI)ZA0Q?T%X>4$|oy8B__0M`g*z|_O^!_0%JhuIG^52_7L!NMJ;AC~W7
z{)Xv?@!=Yw3|#gT>wj2y!R&{HC(OMt`(gfpT8p5d@dooZEMLLm5oQi7{lU^X%mA2r
zSo(qa2bPau=?<g*f!YgmH!K`s=EKqpJikH91DL%qf5PmAnGf?PEL>pe2JU{C0cbR=
z+=iJ4^9RiRxZ)3H49q@Q{(;#G3kP_)2GangVc`c02beloyus1|R5_f2m76eqF#BQQ
z2D2ZgA094f;Ry35EZxFrSbV_BPguDM+y4x62uwfR-_Ur4g%`{p=;a5@{jl(cg+JWg
zP;+4JhS?9Z7uJ7=xff<1oDbCxOTRF4VfxW&^mu@UBQE!&>xa1?mM&rS63pMQbOcMM
z==#z5F!NyH1WOmN@hiA`s6S!;MmHZ8k1%(j#~;kSFneI>2$uh0`34sLu<(T24`sm2
zfyF1xKA68@?uROeQ!sbK(lMM5O{Xybz|4o053u?TrVmzr!Qu<14<-+bPuTc1OdLkT
z%!lcN`5)$Pm_K0Q4+}3C4bu<z7&KmB=?>;DSbB!5Lu0`55iH(d<p(St(3HcuF#BQt
zfw>!IFDyRb8qgT9c!#B1SUkhx9Zfj{EPP=3AEpl$53uwL3s0CkVEad5>-XU9h4!0Z
z_QLZsG#$X=4`v?BKA3)(y|DBFvkz8(pt~QY4@Se%6HFb<ewaBhb7Ar@|HJgb!W-re
zSa|}AKbUzi{kUjY{(<R-r8`3UVetU-4=lc6?uUgNy1lUU2DcX)-Y|P%=Ai3?@nPWr
z%LlM<fQ37Z4^xj$!_qlSKP;WY^rNeX^P%|*=3kgQVE#pqFStG^17;sgKP(<$`4i@E
zSbBu<q1qW>`30sQ#)sJp^Eb>qSU!UJ3l_gH{V??~_ruCTm^)$d4=aCQ?ttY3Sh|C`
z7Zx5caag#bm;dnk9U7jn^Z-jQu=ERaH_SgU`(gTF=?A7Cmj7Yl0rMBS`(gfr(eQAF
zx(7X6VD5m0A50(29#}a6a|f*ah4};KZWs-7Cya)f2P;ou`e5xSm_8T{a|g^m7#|ic
zu=s%aALf3Tewcl*@PmoN+zpF2Sh&OD1*Q&0!}P<_4YBzb=1yGc5SM*0dtvDhrXS``
zn15mE7N#DZhUtUlKUn<1_^|W<GY4ia%p6$w!OB0FIE)XAH(36I#Rp6s%pO?y!OVlH
zgV_gj2V5T-0~QW&8d{FR>_t-!=fctpto(q*2h6{)_{S9wuyhAA5SCwH=D^Y$JX~Q4
zp)}0BF#p5sfw>!|A7(C88=QiL7fe4aAHnhsjE0#D(+De{(BmKGUs$-n!X0J~x_($Z
z!`uOjZ<sz<{KCQ$M#I#@>K~XnFn7UdSU!a1CzyV?JhYsGnF9-VSUAA^19LxmIKcda
zPQ%QDl|wLdVDW*j9-R*>2Vn6F3lCWP3#J~H?qKN%<}Vlx%g-?XquUSjFDyJ^;SEy<
ziw~H6=rqiIF#WLbfrT^799a1d*AESMn0}Z(SUAJ{1539sahQIX|6umQ)WPh7r3Y9z
z!u$a%7hwK`*@JFB%zRk*!sKE7512b(`34q_uyBUS!~6%!zv%G?b0^#%&~`M;KQMQ~
z(hqw53v(wdy}|5*+Yc?rVEWPH0p@O4IKbTl)dveNxO-p*Lur_PSUCVQ2Nn;o@)zA+
zsCM|e37CFZc*4prn0{FJ!}P&uSpI_Phs75x9$@-$xgQq3F#WK2pj1CBy~6B=#Xrp5
zFd7!#F#BQcJ(xZi4O5R!!{Q$n?=Tux?!olK$`5q?u=s-650i)aALbAA@PmaDTpu()
z!_>j-gP8;4!_0@p8!W!z;R8)~uyBB-TbO@f`r$OxUKk%%@4#qSI)s%+Fm<?Sn0sLQ
zVeTiSAEq829#DV4!XH=qhm}t-eXx8C3lCWN4f6-C`2(1KSbjwJAFTX?<tvyxEFNI#
z29|$d_QTu{iwBrKSp38E!Rkv`c>#+*n0lBwuyBOwgQb6%`(XJT?jPuS0+@bW;RrJi
zrVo~0VEWM0AIx2F8k&w^`2prGSo%lzKU^OU1}q+7=>n!7<{oryuzU<Fk6`*>G%Q@v
z%YRrn!~6|%H_X52?uVr(bo*iUz|4Wg6E63{^uy8{OdmWwL(@OZ9GH3-4f8K7ykX%5
zb1zIEOdggVVg84uKbXBRcfjHumabvquzU=wzhUFgF#WJ_gSih@j=}N^Og~2YgQgpJ
z`iJsi@dDEii$9q8=rqioF!#X12NwRY_(azS<HN%bYCkN!!NL=!4xNU@7cAey+zk&u
zwEPEKKM6~xu<{ZXPcVPN?1zUJG(EubH%vdwzp(I!m2<H8hv|pK2i*Nof5P;`@;^)-
z7GALMgS!W&0IDD6Uzqu@^aJw`%$=}!gu4r74jK(h2Qc@*;t{4FSN?;kgV_s<7g+qk
z%11Qo7+~QBvkxW@vk#X(m^zqyVc`HX2j)*$x`%}iOdpI!*9QxCnEkN$gQ-WSVetn~
z|Il;-%kSvwVSHHrhlLj`Tw&n_Q-_O&m3MIeLem2*9$?`DGY>|??17~VSo(+MKUg}0
z<pWsw!2E+geg~6>xd$F!&~Sjc52hb|{U6MKFmqtx0t-)A_`~uQEI+{X!O|H_AIu*x
zd6+&J4NDiWaDwTBl@~Dg!`uTi2WBrUTwvh`QwJ*-VCf7d4zmxIFJb1vXqY=-{)Lre
zF!NyUhM5b~2MceQ`(gTE=D_sB;uB^L%snvmFn_?p4Hi!@|DxLqvmZvo(izMhF!eC|
zVCrzuF!#dr!|W%hADZ4_=@;f7SiXhnhq(vcepvXy@-HmDVD`iE3yenB597nrH>}?a
zrD5j5(hJNzFg{E_Ed9dV0gDG%IRuLbSUke?!Qu^8e!%p>;sd5079Ozl0Mic(FPMIq
zeX#To3m;f{0ZTV9{V;i$y|8==^B*i;VBrrdhhh3*?uW%YEF59tu>1n^Kg?Zl{ZIxh
zoMHYzk3X3Ep~^8Rn7y!i2&NzAA6PiT#i1s^{14Lyb2oZ8!@?Ck{9y47iwAUbVD5&+
zA1pke)*~orK7i?mr6*W<2D2BIj?vwRFbI_eGY1yGFn_??Pf+Es@(ap^Q?Pi2g$peG
z!)Ta7nEzq%08<Z(Z<zmKe3(8M4Ra?fy}|Uu(i_fjgBk$S2MaG)yhEiKVDS%&512U2
zy|DBLb05q;m^@rPv_6FKVg82chlMXpKP-L0+y#qASop!@Vd);`Zdg3S!U3iZCJ)O`
zuy})|Ls&kA$;0%+;vc3TJ^o?g2TOmj{15XNy16iaz`_CM4wyMG|G><F#Um_yVESM*
zES<r^8x~KnbOYnV)Wd0LzJd84mX2Wg99A#E!V~5mxPB-DrXTJPXgtHh4c#1c|HHxw
z=5JVd!Suo0504k9`B3W_VCe(q4p_Q^`2(g8<`0-YbQ<P<n7y#@L^lUrAFRHCxdRp-
zu=E477gzYf{0$2SSUkh>6D+)Nm0Pg%4NE^TKFnP(dtu=K%Reyp!NLt@KP<n)XqbD@
z{SEUkOg}9AVD{kB2Qvp2PcZ%P^n%u3g@qqX9}K|OufW0&mOo+cfTbf``3L5HSUADr
z8RjlnIKsjK77sA{(P@}@F#o{f3l<(Q_o3^9g*!|i%-t}3#M%oBKbZSr`56{|F!Nyf
z38oHaFN_cKFRpNdxgY)fHCTFrl{+x~uyBCsgVC`4<FIrK6NkkEES<t=m^_?@mS3>=
zgT*`CJZL!q^EX@{0R}8QVE%#Yhnj~=JIsEVewe#p`eFV;*AI&)m_As#gXx3CAG$tt
zJ}mvg@&n91n0^=^T_4Pyu<{O;4q)*JOAqM!V0>7;2Mc#tz9$s_F#Rz5VC5#Pyuump
zFpW?e=3kiou<{$0?qKeRg#*ldsCJBXL@@WmXjuM)nS*Qo4`x5iUUdC1d(rj7+yQep
zEFNI~f|&zL59sp;u<(baADDY#=ECfS=|>M&Sh&ONgVj4Qbujy3<r2(X7!C6`%pI_F
z12YGgK3IB%xf^CbES<vK3yU|HeJ~nU{=n>qm20r{46_$z4tn_uOLwqvhsnd-0ng9S
zd=GOM%zktlR{p}m6P8|J;fSst#)pL)x<6p~2j&l$dR#QjURb!o?1hC3E`6}_24+93
zJc79&9{$j9fSCgeKUn<1!X2g_<_=i6!Q2DW4@>7T8s<(|`hl5`?jBfrhM5o353>iR
z9~Q1Kb71KaHh%z%UzmQF`_c8o@)N9If}a0i^04@axdT>zz~T=UpD_2s!U0CZ+=(8Z
zu=ERy514uw4bulpw=nx);SF;)OdO^U=02ExuyBUO11vmX?#0y(fu%bbjb2{B!X4fH
zu=s=74|6}veK2>!{0}n+CJ&=w?uMmfSiHmH7iKTaAF%QnrXLp#i%*z&Fmqt(1D8IS
zIILWO<ttdaKp+2w*#ip)n15jAz``9C4lo+#KUlcIXmoet@-Hmi!)Ta!uyBXbu=s?z
z2S&r<8)h%ef3S20^FPe}uyh6E!|a3E4+{sF`LOf}i#M3RVfta}VCKNW4`v?B{V;vF
z(htmDm_BrW!0dySYcP9Z=?!KtOdOUzVEGbV99GZ3+>IWNuzZJZFD(3E=D}!KdV!@g
zSa}4?|1kH!`~}koqha>L(m5==Vdlcp56nE6J{S%2KP<js;SF;K%s(*wF!NygVfMq~
z0WJ@1-^1JkD?efSU^FZoVc`u=kI->wnEzq%2R9#P01g_K9$@JZW)3V~(6zzJGnjv1
z{>N}XT0II2ceuNu{a=`SVd(|kJ~$s5PO$ig+Yhw|79Y6M56oU%;SVcMaD_k2UYI#B
zcfryFEWBXufQ2JW9nAl*bOZ}GSh)jJ4|4~4c)-#r%pEX)z`_q^FRpk;w-;tUEM39U
zE!=*X!=d30(+^80F#p2RBa9Do2TVJRhWQ_s-eBnn7GE%bz|4W^L#JWk4Ra67{V;oB
z;fYHh%zn6gpy>xzp2NZw=1-XUuy}|07v^4A`3s8|n7?5039}E~y)bve+yS!>mi}P<
zCs;a%nGaJBE9YS00pr8M0loc(9v-lA9cC^p9$@yv(g(UeSUCsR2TdO^eYol$m^tX_
z80K!6eq8An7Ov>_!pa?3`h|rn%sv<m^DitOV0?7@VeyLY4w!m${jhL?g*(h0FneJ%
zF8#1@hv|p;2c{23!|a98FmYIZh3QB4KP<h%^ug0N)Bsqx!}P=Q0aO}3?hFfeSU!NI
z16VqT=|>MYm^tX~fTaUic*EQQiw~GS7!6YoGY6I)VCf&`9+-Ks^;fWb1<QvpeXwwc
znG4V7(DDGi{{+(q3m=$2Vc`Kw2Qd3#?nb9!?u3OWoDa1ZW-huu7#|jYF#WLbg2e-@
z{DhkW%b!ph=3ZDng{g<pFmafDuyBFZS1|iw<sPo|4$}{#VdWM~Kg=Fj`h}^(MZ??y
z^EWI$Vd`P&50;K#;xHOkj>7x_i$_@cfw=?SKd|%$vlo`XVBrh%7cAak{({*H(+^8O
zFnzG{0Ol{4y)g4(=?P{IjE1=%mY-qv!|a8H1A6%nQwP%rvlo_bVDSlaAIv@&4Ns@g
zbOh563vXCDgsDfTVc`t3AEqAWUUc>7e3<)T@eT_=n7?7}fbn7aVet;L7v^r5e_`gJ
zy9bsZVB>GF_=A_v&~Sj|CzyG#@PvgU%s!ZV(8B>H4+{raxdc;>PQ%p0@)3-NxeJyK
zVfLfzN9V)z!@?Jq4`BX)sfW26R-VGjO<4MdrCXRjbbr9q!NLpX4_LUt(m#5-hpU5z
zH!L5*+zZRcFn`0+0nC0_yu)ai`(g15vk&Hem^e&7disHd1I#{{`(fz^7G5wKW-p9}
zr8k&7%-^v1hq(tP4@-A28kP@X{)f>p_rT&0rXLp#a|bLwVE%@ggRT!2-!Of!_=Lp|
zEZkw~5M4hkox#EZCJ)mGb2ltLVe&8<W)Dn1%pI_B!(}f_KP=v1`eE@3vlnI#EFHl0
zK^buSpy?3ChlLkRKdy9utK245Kg>Ru`(b=a^~1s+=6+n^4~u`8{c!(4+byts4-GC1
z3YKnR;SS5ku>1qFA6NLn41}dSSbBo#hs7f<_rvUm*$c}zu=)a){$Vt_eX#Tc3kO&}
zg{4cFdRTsj@zLumSos072j*XxxiI&@(lac)VE%@cOE7tu{jm6emHV*zA0`jW=dkdB
zr5l($EIeWQ(bxaL${%!fF!NyH33C^^epq<G+zE>Zn7?8AVCe|PhlM*#Kg=C4b70{J
zO9wD@Fj@<mUt#vZ(g7?UVBrlj2gZl#ht1!^^uyc*D<@$7hN(w)2Q2<!`eEk7^uyc%
zlZT}Tm_0E6!r~uY9u|Kvd070R_g`?uKP)_8=?oU1@ct9jV=(u@%z+9s!14jCJc9Wj
zef>C0JuKc}<p?bNVESO?04yEA^ufXbT_4PTm_K0U0J{CKcz~rRSop%yGt3@Xc?e4n
zu<(Vs6Ba(Oc!P<<%!9=*%sg}&W*#gYVdlZ|Ba9E%2Ti}QaD&AY%wAZ0!@>={{DH*_
zEd1d9g_hf}@PgY56^EG%a|cX6%wAYIM99A|{jl_j9{#ZSgQ>$s!~746e^@?%`5WdQ
zbp0@Q!}Ov13uZ1XzG3kQGZ$tath|Dm2TO0T_=Ks4nFG@YqhaX<<_=iA!1Tf50j3_E
zhS>*8$FO*W<rDOHN7oN?H_X4V{0xgfnEmMC12YGf?qKeK)9B;)Fb!xlOh3&1u>1~l
zKP*1cw86PB|H8r#mX2Wg29|Hp&4Hymm_Atgg_XZBf57|?GY@7lEZkuE6&60Qc!v20
zW**EPF#Wh_Sa`tv1&cqJJ8<cPxfkXinEPPq2o|5X^uxjfmY!khoshd>?uFS43x9Nf
z!`u%GCzv~6;RmB(`e5>~`~VY&`3EKs_a|C;2(u67ewcokK6w6w>Vw%2i$7R+!om$^
z4$S?q{g>$aVCe)Fe=zsK;t@u}!W*9Npyewp{lmoJ>Y(zl`WqJRFm<qU4qZRY9WZfN
z`wNynVetmj4+~$Iy)gZ-@Pz4y`3t5W7EiEz1}jHk;SMtwM#JI(rVrg5SoseNcUZW=
z!XKs|M#KCAqha>K$`P3VVd~*DH2z`s!@?CKJfPtZ*9T?5;vc3Tmabs-!rYJUZkRe)
zeuL34f5XBF7Vc2%;1tZCFneL?0Hz+M9~KU9jZg-9{K5PI3n!R<So()*!=PaH!)RFk
zf$4|wF*HI2VD%p?++qHJ#W#$GD#xH;>S6Z7(jClxSUg~8gbKj)!O|ft{leNku>6cG
z9ANH-8i&#Dh4~*w!`0znz|4cW2j+ib^~3yw9uBzD0n8b&a70fBF#p5Uq0_K(0^MF%
z{(;fx=?Z2J%)hYo3@dkG`2&_dVCfoG{=oFZ)Wh_{@)68lSUSKJe(3IpnFkAZn7y!k
z39}bQ!_>pdNtifHA1ocg#9=hde=z^U`OtC(mJVU+F=!m+7EB+^|1f{R{0}RKaOHoP
zewcl*bOQ@V7!8w$xgQ??Q1`?94NHfxauB`$2n#=0x`V|ZEZ$-6hS>|FVfMqq9d17~
zTwwl(sfV>^VESPFf0%tRf5K>3dl04%W)3X;VESS41}kS^`3ROCVc`#xhuM!#qsIp<
zTwwO%Du2+;ft4Gu@PNf9%p911(DkA7VetyfA25Hw+8;3WF#p5aeXw|g>4Uik=7045
zH?H`DnGdrU=6+aufXTzc8|GhFyus2ROg}9B!Sut_!)RD|!^$aGx`p`*=6+cD1M?qT
z9W>p;{R>Tp==Q_X56t~A8fG5MJ?Qa9DE?vYhlM9BUBmR@3O|_r=rk<-!u<pFAIuz>
zdRTnJ!U@KQr5~7oVftX<57Q5`2j*{BdV;CLr4ObbBix|t-eBc5%sf~)z|t?wUod-N
z?uY4v$)lGeu=s@e4@SfE!OVfB3z++1_QTu_a~Dh<%syCphWQ_s?qKN|T|X?n!{Qre
z9!ww1y|8$I>4*6nM#Jof`2$ve!Sut*MVNk=`(fz~=5CliV#^s={KLvWn7d)&4zmxP
zhS`s+-hk<cg+ID`VE%@M8@l=MaD}FOSor}{2Xikh++gm3@nQK9W<Sh5F#Q-bw7h`1
z3l@(s{jhQgLm!#|%v~^ZVCe>C4thACX@_&+_CwtTi+=-Xc)%3G{0p-emd;?|2NQ?2
zBVqMFEWBXh52In>1~Uhy4?SJL^uzKWOg}9D!SWl--{|27o4<$YgV_hmcQE(E@&n8q
zSUke&e|UKZ4HuX`Sh|MU3rjyRdtl)Qqha|77H{a~Kg?Y)_2@J#zF_`=nFot+nEPRT
zbp7ajSh)(zUod^>^*7AFuyBXPAIyFjALf79{1ePRnE7yjqm|z<eXw!}J^Wzi!@?Wp
zZdiJOl}9l9&}mq?1q(M=x`EjXOJ^{B@OXgc3z)lM{)dG#%v~^fSpI>n--L<7XqdlX
z;SBQ!EdRjFh2<ZZJWM|<ykYKy<$GBEf%ylPe&On1;SKc<EF94N1NS#fBN`10N0@tH
z>S6ANxd%-foC~uTrXS{Bn0~lIGzKi3Vet>k2e9}@_b-}u`1w;X`(f%~_7ie9Ed9dr
z0W7>>=?L9EbUsW!%-t~ku=Iki9-R;KH_U!mc>^myVC5H#hNUx@Iq2yKR-VDs!NV6?
zzQNKjOg}8%VeWv%J1iZ;!W9;d=;a5@d{{XSqhaX}=5APg;7SiL|HJYDEc^-Of0+HS
zbO-Yf%pRCOVE#p?$+Z_|4$Qr<dK6|K%>A%%gV8X3Fd7zKF!#g44W=IEe^~gz%!lcR
z#T(2WuzUkkhaP|E`?p}_6HGrW-NMoVEPcb=11rB^_QPnH|6%@s@nPv179TM8!Sums
zm_2YBnqFb~38oI6hQ%MuURZjA*$2-@=o+DXSop*I538qO;SN=XlY)gmEF59}gOx+L
zG{Wj7n7d*2!_>pV8|HtQd*J3m%X^r9Sh&H`9ZVg}T`+O<{0p-e77nm<1B*9U`hk@z
zu>Kd^ozVIRW<R=mm_K0Y7rp#}nG4egOaCzSFn7c3gV8X5!Sun>8!Y@`{(z~2xdSE-
z3pW@I3olr@frSgqd{{id@()ZOjE0r}uyPmXFIYIj%!RoN<`0;=VdWMqJ)?&M%zl_Y
zbQ)$3%suG-fw>D^AB+!+e>e@zf3W-jQ-@B&{0XxcULHgB!}24#J{TY7Uzoixf5Xx{
zjE2P@OdpJf>4VjS@bm)BAF%Qa7Vqf#VCfd-4p{ua@+YkPfTc&6ewcq@@dgVAm^m<g
zuy}`+r!f66^I_=>W)3W#VBrYU2MY(7Jj@(iG|XSH^bbovgyIus9xOcJ;R}sV^zt8;
z?qKR*e3<)T;SRGOS9*ZuGnhFreK7apiho$XgT)6dJYn$&3x8OCfcXzb!{Py^A6A~j
z+zZnWi%*z77>!FmEWN<=<8nVNeqi>)(hp1@%pEZM&}mpW!}P<#5f*+hb8w{_Sh|Jj
zhlM}PUYI{{`5)#Un7uG_(Cx?Nf0%w)x`FA3@zKjqn7J^0==Q?$J1pP8^uzeDa6k`t
zSpI|MGg!KVnFq5EM#J0*qhaoa$-}}A7XIkr2h#^judwt9OHZ(L05b>X519G5Xqb7h
z{15XF%p6?$VB)a&hnWKl2bei9b?7uqJuLsg+zX2@Sh%C>L(jkP^aw3)VDW~o55|X;
zQ!sO2;RnkXF!#g47p4yuUoiK=$|ab3Sop!*jUIk5{V@As`eEe`EIq;0!NLQU9$@-l
z^00IU^Dj&sPD8^3rXOBULhCu0J7MtzvmchuVfMrP2MY&S{(-53)t@l^Fg~mtfY}4n
z4|4}BykPEyg#*k!^z;vlCs=xe#RJTISa`$Cfr-QHh1mns2QwcQez5ciix-$XV0@T9
znEzq^gr!%QJS;uJ#9`*c!V9J!7G5y-!Q2ZAH<&+Q@-P}^KFl0+eT2$QbaP<w05b;`
zj>MV+OP4VJ!Q73We_-~)!WkBiuy}=q6U=^CxWn|p)WP(@{0~b{Fm<qagQa_zxv=<$
zm7_5C!qmgkJ1kwm{0|Ekn7y#@g82{bUYG-L&@lbz_QUcY%pI_NjcyDqeZj&H?r&&*
zhM5CPKj``~_)vW?_rUxK3pW@aT^}qSVBr7@S6Dc};vZcf%)K!CVCf%bAI$w2`p^Vm
z=?!KM%$+cEVDXKnjREFPm_C^OFm*6@!_0xD1DHIFhQ%YSzJR3@SbYNX2P}QS^x>jm
z=E2+tO9#08fo={gUBK*zl{>i75lla<y#Najm_K0l!`zSV4wyYKeem=MGZ;$4%t3Ge
z!_>p_FD(9G<tZ%OVfMnzfu%c`f8ps5TF=4!4Rbft83+n3y~F$qvmX|pFmqty2m>H2
zSo(v7A4HUa0cIY|ei#k&2V5O=`~w#6@Nj^N!_p7T9k6r>(+?~E(Zd0z4wim#m7g&C
zVEGYN?!o*6b2lv9VfMn(C(Qpa^I`g7=EA}Oy&QtYAI#sdbO19S7XL7N(bF$XKTIFY
zK3IB$xf|wxSa`t9hox5-4GTw@`*E2AO9!y<hS?8uFWg>e`huljm^)zM1ak+>A22@5
zUYPkXb7AohQ-_O&xd$Gu(EI=kM_l?~{znfFn7d*ALe~#-FD(3F_QU)M3wKyLMt29y
zpD=r2=>-;UFn7W74Q%}l%pI`sgP8;KH%vV&+;F)cmVROGhvj>i`LOf=PruOcgtZr7
z?uMy{g%_+ofvJP}8%D##Vd`N1h1mxahq(_X4jVs(o&O69Z}f16<xlkRfR(?n@PfG?
z7Ctcd!|a8XtFZV(AOD4g8_YbIKVabq%P+9-gSj6TUa<Iu>4(_|O9wD}akVGV(;v*=
zF!eAR<_?&>uyg}+Kg_?d{05VUr6ZU*F#Ry~Fn7bsCs=xei$lu+m^dt+VEYGP;SaM1
z7XIkz1LiK+{5ecNEWBXp1LjUxxWL^3O}DV|b6C27*$>OFF#BQg0t+9Q|6t?Su=)aK
zJ}kY$+yT=Mt4Cn|howJQIKtcsvj>)*Ve&Blz|s#aox<c{?ttk>kAGNxLyv!$ewe#q
z;Q(_F%)Kxgmwxncg!vm5jxcw_>_ewv{)O2O%QrB6F!#gq1G;`(?uXeAb3eL17$267
zVfMq)Jxo8$ewcc6no|2=@dpb>Sp324h0*Bt!uT-v!{Q$nPv|r(9if{C%RjJm3=4l4
z4O0(`4_H2dg%iwPSp2~3g{6C#y|DZXb2rT2Fm*5*7M?J9Sa}LF2fh4-`5zX4Fn7c9
zA&iFG4=o4K-2u}Nb3ZKpVSKoHGzQGwuyBOg19LB$attmk-eLZQg*PmmF*M*1fQ1_@
zK49*E#Tzbv!16oH|FCiwrVm#Cz~UF(dYHdq_QKo`(~nCZ%)c;uVeW^8JIr2m``~<N
zy@IYEW<SjT@NmQt?=W|xr+-*@!OB}$e8TD<m^<L^hPnr)52hZLpJC-9x_+2Buyh1-
z56oOxdWO}Tu=WEi|H0BfEFNI?z|_Os0S^bLg|Pkmu<{ocUa<TIvmaJ3qnD$waEI9k
z3n!R9SUQ5m2h1F(^>7NNALegZet^Y4EFGb{A7%h7d|>GbW-lyUVDSNSKP+5e`f<^)
z_<*?w7Cz|pH@be9dtl)Ta|g^n=<Roy`LOVW`2%Jj%>S@*4CWtL{KLWxM#I`EuzZBe
zzp(TU(+7(OnEPS+VCJCv6PB)F?uVrtnEPSo!S$o(Kd6B)f56nk+y(O|Oh3+Y1Zob<
zUYLJi=>-;kFlD%CSbV|!1B)+M`X!_f7Jo2*z}ycDPndbI^aJxZ%)KxgrXJlsn11y1
z1G5jNAC~`M`5xwen0wI8h1mylKg=BT_=n|hbp5b!hv|ofA1uGY;t!YoF!#gs!^$yA
z?T6V5qcPkK6@d94=5H7c%Wp9Kr1~Eg?lAqZ^a!;UK|$*cnEzqnN38#0=@ynBVdVy_
zUPm_%-hM)}7nbi}4ui!P%sg0ng82t#A1uCL=@}NEF#Ry~xcm>(2Mc$Yxv+c-3kR6H
zU^J{;h1m}a2UvW<?1RMvjE0HB!W(8Uth|SnV=x+KKe~RHJ7D@@`3~l8So(pf!$rf~
z0ka<#Ua)Y-r4L;kW<M;xVfMq_jjjR4M@t8=@PoMn7G5yruy}>#dsw=GxgVDAVftX<
z1LMQ`-!Ol}Xqde)bujZ_@c?rVto;Owf0%w)x`C;OxfhmhVKht}orc*Db1$qN0gF$9
z@qreOFn7S*O{o5Y#T%T4)|Z6h59V%IK7jL~`r+|~?qMh&7H%+q!~6;JHz9Z9ihr0n
zP-}2fF#WLbhNT~z<t>^?u=t0ScW`^5@c}ajmLFhzm@%;MhM5BkKbSc%KCJx;FF&CM
zpobq!9n2pv|H1MLEd4^YGr-J;m0z%UfcXcOj$rYD-hP1P1DHLqaD?R}So(vtA7J*v
z{0p-erXEJa%!P#m%s(*wF#BNchqXIl;R$m;EZ@P*f$2x5VfMk?1#<_?{pj(At{=vS
zxd-MxSh&O74=c}M>T%Jq@&{I~!rYG@f3W%sW)94Lc(_8-AuRl1?trO>#W&2~F#WLf
z3iChAewaQO4a@g1|H9G>tlWV47gsp|OD{0{VetSnA7(#{hKZxoF#RwZCJ%E5Og}7t
zz~o`^1JegfKQQ;g!V5;j(gjREEdRmu!}x^459V)J_`|{x7XIk&Mz<Fh?=X8|=D^$y
z%daqZ<0?mC@dh&orVf^GU^FaVVg82Eu=W-#y}@XhKVaz&<{x-If~HHDztO`DW<E?D
zmX2WM4=mnb_QBM{>_g8#u=qgFKQMhTf56foTtBoPgV_%YUzk3aI#~F@@+&OdVd)NC
zA51^YUKk(dADB4I99-cK(+3N0Sa`zuQ1_$T4@-YA_rvUkse{=ID_3FmpwlpYuzUjx
zXIOm0%tO})<HOP|ES_QNVd(^x{$S!TeJ~os{m^-Pn0lCdU^Gl0%s!aAVSHG+g83U3
ze;DN-H2uQT3yg;4AGkfx@FzArVEWMg1+yQ${|!@*i-x63Sa|_!|H9P6(g7?SV0>8m
zf%yj(59s#8)WOmLEd9aakJxksO9wFb!pb{X_`~uw%-t{=R?oxq!Q25W=VAI`e3(8M
zjY~hwKA3)3dVuMJ)6jGPvmX|Yu<(NE$CZC!<poSXEZ$+^2Gb8q&v1L83|P8_>4(K1
zOdre~sB#PnmM&oN2CEOy!yi5Wzzu@>AEqDXe^~hi(}&AFuy}{%A6PuW`~gcxF#WJ_
zfVmT9A1r^s+yx6qSiVD-ht1!@;vZ%YEd9XJJ-WRx^I_!#Og+p!F!NyH2lGG79kBR-
z`4>jR%z?!REF56^VSJc7VeWv1Gb|io?uOO>u<(MZN2g)o40Ause3(1Y)#KvB;vZ%n
zEd9gG!=(=vUa<5F^A9ZCVgAOY9~N$~d;rV0g!IA8gSi_PpD=gB;t!?{7Yz$HSop)@
z4;BwF_rSsv){cOg2eTiRUSamb(ko0JR?foPKTrc;=?50>F!N#d!`ufeztGEnn0sON
zz|4b*!@?1ko?v{a_3-;^;r@r7JBmv^OdpJf#XBtgVeW*vAC`V#<tNO4F#T}<LCYan
zxWUpDOdU)gEFNIuFneMChovJ}zJcXiSUke@K^d@cg1G}0elUAr`k~6<6uLfG{K4#n
z`4_Gq+Kzx3fNmem-!K|hE@GGiH2`Km%)KxgCJqaKnEkNugKEd1VBrR{7nV+7`Y|*>
z1z_<6O9wFj!}34O9Z==)^Zj7%gz1CDBP`v)%!P#$tQ>`<e^~hs(+^8`=;4R%9$37>
z%!TQP)#EUC!t8_j17<Ev9!A6b0SiBvxv+E((+_h8OdmQ8i$7R8hQ$}GJV95F&WFVt
z%p6#}!OTHd4|6w6A1wdF$|;!tVfLf@AASB8mR?}$VC6cj9EQa!%pI_Bht(G_{V+Z(
z9AWxl@do3=%z@Fc_=A}XOLwsFhnbHH!2AtMcd&4Q#TzUfVDXB}9GEz)K7geQSh&F4
z15<~KhWQ5;pRo9V&ELcH!Q^2yEc{^k36>6E;Ro{%Oh3%uF#p2Lft8mq8Wx@~eJ~oP
z9~SPgbOf^(?q9U>52hdHUYLHEJ7DgGr5kj2!}P<#38o)z9@IXVJ7DV3X_$R5{pj%o
zD_79<!T7Lr3eyiuXE1*gN-yZ{hlMjtKg>LsedshSAH(t+jE3cVSh|Ioi>@EWhq(i$
zAC}%=@efmnPQ&sGEZ$-H6Bchc?S<C&uyBL<1Lkj-KA5{;^&o8g2Nv!y8WwLb|H8y!
z{(+?@So;gT{(z+$SUQ98Vdla71Cxh^1FYVM<#U*OVd`M|2&Ds9euaex%)jX20kapK
zhS>|NKVb0(b3eLz7#}_UVC5DpykPkU79TKuFdF7>7!C6`%s()H!_0x{gUQ3}hv|dm
zH(0)er3<)zXugKo2a9J|x`VkJCJ&Dvs6LoEFnzG}0ZTuy^bQM8Sa`s~6J|clTv&L+
z;tLkuF!#gK5zJm#c)-E|7T>UNfVl%^KP>;l?1hCBOg~H?EdRmW1@i}thJ_DIA1qv9
z?uY4zxfd1>u=EEv2WkLp{XES5F#p5U!~6kNjzPid8JNAW^aaZ==<dZZ0ImFhg*VKd
zu<(NU16@BXoM86D!U0yF!Qv6-ADI8p&4H<hiNpK_OII-Wz-X8{7!A`8i$_?xgyj!d
zc)-#hOdpJfxf>P_FmaeZm_K0Q3zH`l|FH0fnFC9|u=EFO|G~l!mabvp1Pce4yJ6u7
zQwKZ$52g;L4;C*l_rmnS%z?QdM#I7l=6{%ZF!#dT5AzR9A36;S54e74`hwXDOHVNW
zqw9z9Vetub56nL>f56OvsfXn^m_C?0VBrc&2QdAx`VXcKmR?}$VdAiK5A#1P{h_-b
zrXFSvEIq;0!}P=A2j*@V4J#jE{zZ2$%p90{bQ)$K%>C%`21^I%`e5#c*$ay|SU3@~
z7w%4IJj49~ttVjdj&3iU57h^=9~OQvd06=ea}O@_VE%`hgC5Q>KFl0k=?4~mF!NyP
z2^O9({jmB3=1y3-3AZ0Q{{m}2!r~v6Z(;giG|XO@IWY5K=^CaFW)3X<!Qv059~OQv
z{V?~V`yVC`(+{&3W)3V~VfMi6g@qeT9OiyF4UJD&I)c@|uyBBtqj2>&7%+Fk+yiqb
z%pI`sfN6(?6O0c_@38oQ`3Dw%u=s?jhs8HcJ<L3qIWT)+G`jy`?uGdqR$jsU4NJ!`
z`_O5adYC(4{v$TLVESO?I7}a`{6e=6&WFYq%-=Bo!ty=LUodmf{S9+BOg}6g!PLY2
z0i$907Zxt?c!9bD&WDyCu=)m;u3`4W#;;)hfQ37ZhS>vi2TVVV58poqH2~&MnEPSs
zVd)NL9xVN%ryp2)frSGs-J`3AxgTmRoI(#rm_K0g2uruHd<@qJWx(u%*^gch!}LRy
zV^FyC!_qrUKP-Kq8-PncOdpI!*9YUH>qk!quyBB>N2lTbg{C8TctX<=A^ouUgT*^6
zKf&?=%>B5+AJ$HVg+DAk!O}0xKd|%%Yk$DqiRNxt`3&<HEFHq!0UN)9iNn$hJbs}5
zhWQ8P4p@4G@nPWzb1$sF2h#_$AEq9b9?<8%VfhB;e^_|J@(nEf;pHIIeK7lB;R6eI
zn0^d>a1Ce-X!ybGhlK+yyl}-E%oLdW;WX5J==mI`9-T&y2Uz&Q#9{8hFc0c(SUm@`
z9~Mq9|HIM)%wKT(q55I|Mz;?ZZ*cW!448c|{jm6lg%>RS!`uNAhZ%#b{|Zazu=)sQ
z4u=1s2Eo!Fl*<5%f0(;q{(yxeOdT#7mTqDCVeWzJhsG~VKP=o}@dl$|^%hJYEFNI`
z&}o=|VftbA!t))v1}Goqe^@$z<p)@PgP8+WjzPi7A6PjG3vXEdgM}wdBg|j0_=dR`
z7QZm}!om^eE|~ve^(QR;Vfta=2v4Wb^a)GPuyBN>16a8QONTIj!}P<-N0@(L;Q{jp
zE}Bqy;L-=PALbwQ@PN4smwuSNuyBRB8y0T3^ug4_!WkAn=>CPJ6L>oU8osb}4vS}4
zIK$ivlZWwP>!)Gi50i)Khov`|yI}Ug%!SooFnO4}VdlZq!_osR9ANrk`59Muz|4Wu
z(EJK-H{#F;b3e>KFl8`%VettQN2lTW2O6%h`~!18x_THN77j3bVd)kY-mv_H9-lD%
zuzC-ceqiASlZWX;cL&T~^z;g|7nYu3<u@!|Vdlcz3CriO@(UIYuy}&$hw)+Q6J`#~
zewhDZ?uUgpEF59xz`_HjA7(Es9ANrj>S5^#rXHP!`2&_-VD2N7USQ_H^+DqurVnNg
zOdSRdRS!%5F!#gCH(0#E{DZC^{r(S_JWM?-zG3FU<Y6={ykYK#l{2vLfUAS%9~d7-
z!{Z4$UI8m#VE%xGJ1qae^x>qT;Q=qtq4wf(Kg=AMy)b>SbPdx7OFuC6=rnrzgQb61
zIKuo1i&xnE6+C~Sxf`Y*=6_hahs6ha`i12em^)zU8C^ZhJuvsf{0R$hm^{qCuy{qc
zAEqDXFPObBahN%<bO6iuuzDC4t}ylJ=@*vnVc`Jd!}P)UF#WLjgSi9dA6PiSXqb9*
z8WvtK{jl-~rVnN>x;_{mmTq9~fcYO5zp(Ixg#%0<jE0rFFmqtx3eyKGhv4SIG(u^Z
z`_c0wOdp|g02W@bc!2o>mY-qa0E<7UwQvfiALd`U1T<a4+yOHWE{}r&^9S5t(0q(5
z{9wkw;ti%B=3YYO2P~b!{12<gVc`ag2beu@8tOloeX#TclZV+4i#J%hh4r6d>R|qa
z#XBq<VdlZ)Vd040|3&vV%s;SrgXx2pCoqelG|V2D|IzauOh2x0fa!;oC$M;e`3I&C
zR{lY)g;TKf22&3+2Uh;T^b^X5Fau%f5oQj|{e;pVESzES2@3~!`hliHm_0BW7T&P%
zgSi{#UsyQ|Q;$x=^uz3h=|@+Go{nJhF!eC~Fn_@Oi{XE$KX9#|fa!zT2a8WwJiyX1
zZ2k{sFRY$`g%?ac%$?{oEZxBL!|E}Z`7m=};fPB=EWTms2$p}*-2>yJn*-y+^uyc(
z%ZD&`z`_Bh9!A5`Ev)>8xd#^SuyO>Jo?!Z5G)z6rURb#SvlkW~F!ks(ES<yZ5tuy8
zK3MvK<$qW{f{DY@EzBHvyhF=3n0sLUfTa(ZzhUl%*#`?Jn0c^pfa!y|4@Sen9hPok
z?t#g}!U1M3OdLkT;v1$97M?Ko!1Tk)Lzq4ojb47j(g{o-EIeT9(P>yX!t8~`7cAeQ
zt4HU<;tl2wSa`w09bG*-9~RCq|HHx)7GJpXA$t75@*m86^zg$l2bw-${)g#<l}E7r
z4RZ%9JYne&rVnN>j1LP3SbBizBNTry^)MRd9$0$Em40CAU^L8rSbm1ZC(Im}`(f(Q
zY4mV~^P%M~dOm>pAC`V#=^2*pVCex?4x-x+i*H!C!_ooF99Z~aq(^A{!PLX-hxr#4
z-!K{`4)ZU}K3M)o&o{9215=Mq!}P&uSp38M3yW`D@doR^!t}$!8D=jmykO}M7M?J7
zz|_OS38oL0pJ4G1a}O;3VfxW&n0c^pgqZ^icbI$7^}*r~mS12r%wAmXM>hwSj$q*d
zi!WIGqx&DGAC}%>`e5QPeK^Y@wDbduKbU)A`e5e6!WR~=u=WSczp!)$3ujn3!r}oI
zj<ESxn0sL~%zl_Xu=s$P3-b@me3(4UeJ~mpj_CD2Odl+K&}o={n0sLP6lO23bPO{G
z=1({cEiVY!3(LQ_^x=wkn7uIlFn`0s4^~dW;tM7Y(+8tr_QJ{oSbl<qJ1jlm(hn=I
zVc`Z-2TQ*&^I-W0<{p^+Fd9~m!qO8=9Ht(YpJ3uJ8kYZH{(#XicfiseF8wg|u=EFu
zFPOVw=?E4cuyBRh4-0>oI+#D<@c|tlgSi7{J}mvh{0lP&CJsw)82*Q*E13N-{V@Ar
z=EK4TCJu8KdjAtW{$S?9(m#w3a|g^Em^h4vg(EB-z`_F-?y!7>t{;}~VE%ydVd)2E
zFM7Dc!U?7i#)qkcmESOPVD5mq7bXv*Vf7zO9+n<q=Ar9{`43jk!SWBx9WZ~w@(+4=
z!t8;?6HGtMKQMa<>4$|EJlvrM!_phfURXH-a~CYV!}USy1DHNox`BlcEdF5W2fh4<
zxfd3WuyBEi!{QMpk6!=7(jnCO46t~E*$Z<wOdb~gFdF6`SUQB|ADDia|Ka-47%>0B
z+zrd`u<$@rj=_c53o{3n4`JqDXn+d9(hE#KEIx_V4@*C=@>Qu<(P`<AmG+OYboA
z;N>6Gov`?Wr6;H`eE%veoxt?N;u98cuy91z4+}3?x`+86J^l!}ALbsI{jmH53tyN!
zVBrE&kBf%+ALcGteF7i<hiZiR6Q&Lp4p3<ZSh|DxA2$C9lZUwv7XL7J!^#_2I)K>^
z(+5lU==xy(gQ<t{VdlZ~!{*;%=^Yk6Fn7Y-0dpTr9+rP$<pwO>!16iF9(X#0mMbuK
z!2AOXFIfCzq*JIqm^{p0Sop!rhq)g;oMG_*3m=&K(Bls#52In`z`_@nUSZ`3EFZwa
z5tiOy`eFGHW)7_UhNWMac`*0G!T}b}F!N#QA7&2*4GkZdeXwwVhcC390J9I49$<W!
zc`$dt;u9thb3ZH`VCe!D4lw;N8s-le4YLoX4@Sexhv|d)8|DvKdV+-mOdrfVbbT=U
zVC4oZK49?;3xAkC7!6C`uy{icZ&?0^g(o~bU<N?LA7($y-7x!M`2}V_hJL6yFn_~n
zm^)zphM9vNZcyzEuy})&Lok2C;uXe+<x7}87!Au`uyBB>hs6sl9ANPQlZWYt*#mP2
zEL>sn1=9x$2UtFa>4($M@PvgUEWBXuhM5OfkAneA2XGpi4`AgB4sCE@n7?7}fQ38E
z9dLzE2F$;(@PmaH%)PL9MAwfVZZJM9ywJ-FsC94(rXD@qVc`q27nXkE8qpXqcf<V)
z?XSSx4~uVDIz%@Q7GALQ3(F6%bPV$kOh3$hFlWI00n-Odk1+Sc(hbZ#==xyx!tx!g
zo`J;!EF96}ALegZxe0SGEZxEC516}Q=D_TSm5Z=&gQWwQy|8eD`5)ci=;pxu4>JcA
z&M^J3bO_T2QwNKGSUAGm2@5ZnzhVA>>4*6brXMB_E8k)H2Ntfd^b2zb%)Kxg7Va?j
z!rTKZm(bk-vmfRUSopx)39}zgL-R4rd|0}J#UsqWuyBKk!{P%b4s$2Wzp(TMb1y9Z
zVfMn*!_p&6A36>52d?md`43$mj1LPZSbBzq56r(X`(fb^(+3L&n7uF>CJ&2Gm^m<U
zm_AtghuI4YFPM8_`eES*(+}(a!O{=R{V;i0`iJR<g(FNJ7Tz#@FneM73Fc3jewce;
z`q61vc){EQvk#^pW*)je7$24naOsDo3z&Kg8d`tB!VzX3EZi~T52_v(Uoe_bxWVj&
z?f-=7gNeiPD=fZX<tI!YM#K8wuyg?PA1oci+yOHOrVkeli#Iq8&9AU>1D8IS{jhL{
z^P&0)#XBsY!qPFUoPmWmA$wur2P-dN=>}FGz{(X^c);QlR{q1{8)gnH{L%Hn;sKr>
zq4g|0oj}tA%s;Sl9Ht)TE|`B{^Y<|OVetpIA8I~KKYBdC;u)6jVD5#5BTO8o9~Q1K
z`_XCi{0B3SSp6_}!|aEJCoJ4y;RmZ9(CvruVd)2E4lI0O=>ax=1yc`;cUU<D3wM}*
zVCfmAA2xph(+>+rSo(#PYq0VL7VfZggUkIe`(W{j?r)fV=rqjTF#BQQ2n%=gbOX~5
z3ny4O!@>#XPFQ+^rAJu&!{pKZ4f8K7o?zh*3lErkVESS1h0(C|2D2CDFIafN^uzoE
zb3ZJ8VE%yVgSiJ5Z!j9B9~KTU8s<-!dtmBd_QKLLOg%acOGmJ9ft8yu_oJ(a@nP<T
z>4*6T77j4`Vd)a459VK(dtu=R^C!$+m^m<gFdC*07EiGB2Gb8yhl_^Q|1fvJ%1f9z
zuyPv~j_BsV(mPB)j1LQESUQG<Bg|c}cttl4oe#@L@NybjpTOORt^vk}mcuapuyBQ?
zYgqb)DTkR4i$7RCg83ijZd~@l@(0X5nEzn*!OVf%2W7z0KP>!U=D^$s%db%77!)kL
zVfMn(3CvztJYZ;q3c%7iEWN_wAC^vG@dxt{x_L1B;pq<=?y&HN>4$|MjE1ErnEzqv
z7^WWPepq<G;sX}`=<7FO@dl4aXgYzF2QYb9IgUPl2-An|514yk`4AQ_=<x{)2UtA9
z_%Q#$^ufvjbp5dKhxr#K4~rjIc)|3;;sY9u7!*t&%-=Bg!}1|a1I&Fe`(fz_R-VDs
z!Q2D0A0B?t_=ed7GY1xKu<(Y(J1%#_)WgCLmVRL22D2aT9yA6lykYKur4w}jqG^M3
zVfx_d1(trGG+ZGX0~UU;@P@eq<{y~5Vc`JFUoc}}?LU~kuyBIKH_TjExWUYU>DPgV
zKYDn<+yRS6m_Bs-Vd);`f0%u+d=3jgn0}bO=rk-GVCe^D9!wvhc!Z@Jn0sOA6&5bA
z@PxSs7OpUN!167e4~;iidV;wRmpfqP3(Opty)b)V=^j=t!t8<3F#WJ{43>{!@d+!>
zVC6GRA1s_;`e6Qt#Xrp7u<(HUA8G*1f3R?e>4W(bCJ&2em^)zR!Q2V6AC`__^$sjO
zK&@kdg#&E<1||-3H!L1u=?`We%-=9~p_kt<bI@s+{csu@uf*zy#V5>Nu=qglKfug~
zg##>HVEF(RUa;^(j|Z4PVc`wa4@>{BbOQ?qbbrF#2g}dU^MhgWj~=hEbP0=Bn7d)>
zVetfW2TUEz9kB8d=5JU!gq06)|3TdYi%*!nF!N#d!sKD*!f04Ng2}_u6-+<OewhDZ
z_QTu>OV==e!NMOF9<XqPg*z-A!r~2<?_u!{vmd4pM#J>M%z=r+(kskeFmXaOEZxDv
z6=pv?TyYox3lCVh!r~1UUNHB-w8Q)dvj-MlFnut0!}P<_BTPSR{0L?rESzET=<yAU
zSC~A^UYLC_{V;J@JizQnk2jb(FdCL#;qHOv1DJl8e_-k{XlQzc>4&)srXSt?82X_C
zuyBN>OIW%?4?n1K2ADdSy|8eA>4U{PEIeTP(ESVJ!{pJ!AD91O`e63L(h<B~f|i@G
zatv-h8UvOO(ER}mKSJ>bvkw-2@O%ulAI&;A7gm14^uhU1eT3|V`4{GHn0k18LDK~+
zJkj$JEWg6^!|aDU5XyjsAA0=*E9YS42P_<5@eNA{uyO#_o`dzjVg81xhs86j+=SH|
zuy}yEALegZyu<Xt`~!0j%wCv2nEg<1At-1#!NLJ%Kg?cOI!0(fWx>iLn0|P^fW{-t
z9jLl6d9d^hGY95Bm^)zpfa!#VH!L1t;RO?i#V1TZuJ#Ko9$@Z<r5BjHVd)8$zF_vl
zXqY=;=D@-a7XPsP08@`n!{QUBA68$%^uzpxt`Fu<Sa_kk8|E&Uepo!h?1Sk;4<}gp
z2}@5feX#Q*(C05;<tr?F;Pn97I5sT&VftbA!|X%%A1vNrG`c)YA1wXB`~?daSbBlQ
zABKLY04$tf?t#T0%wF{PgV_i3CoJAz?t$fJn0{D1Lak$f<xiM>aPy$~1s0C5_=LqD
zOh0V>7tCIm`(XCK?1!mGr(yL!Oh3$gSUkegAB@H@4_aQp(g(~xuyh1d4-0pgyI|!g
zx_i+1=;p%mA^P||EZkuEAC`Y%@ed0>SUkYeAIv_OyW#NwZFj)j4NLd1@)f2JM#KCI
zb1#gB#UIRGSUAG;!Q73JexUIW(+7(Wm_9fS4S$%s;pHY&KTds617P}KG<rC}_)z5-
z6s-P&>4)VXSUAD_4+}?h^I-ml>4)VvSUQA-4=laF!UJY5Odb~B@O%&TKP+Bg=EBMW
z^!giS4lEzQ(gV!HF#BQY7nUAi^00OjEZ$-6f!Pn!4|6}depoob;tv*Yu=ET!AL=jk
z^b2zbjE2Pn%s+7dLG{DJ52hYQ!_>jT5#|q=dtmtw9*@xU2Xj9>+@Rtxb+Gt{`5P7<
zF#F*ChU$a)7gnCZ^uyeVi-!3Z?tiF%VdW4meK7lB{)L4PvG&5k3zm*y=E1@Z7Vj`V
z%pK_ZVD5(b8<uWi<pwPM!t}%Jf!PZahvgrbJS^T|=EBs$Xjr<1g&!>5VCfx~KA3)V
zf56nk{7oqQVeW_74@(~~`(g0`vkykY(hp1@EdF5bhov_{@d!(AFn7TD(DVay56oV4
z8WvA5{V;dI;tQr9SGyaQe_-(d(+^V*GY6MHVBrtf53Rpo<qs_UVDSM9AGklD`3^lC
zVd)W;{$ToHG+aNF0kaPl9xxgfo-p^r+K*6eI4PJuSh&K{5q$musuSi;Sh&IXF#WJ{
z2^OB{<9{%B!_>jU5!!Br)n~Buh+cod%4ziU2a9)DeuTLn>TCv>dtvs%!XKs%rVo~X
zVKhuXEPuno2iD$!xf2%euyBC!Vfg|UKd|%zGZ*G=7$2q%mTqA253?604=dkbG%P$|
z_QT>IW<Jc{Fn7b!8H|R>!{QB=4`A+sg&!>cz~T`m4+|fde_{5*<Y6>S9n3zMIWYTR
z<rz#pE*cgNu=E2<H?Vkv#V5Lcm_A(L2@7vnxWe=k>OaBU57Q3|N0@$C`wbrc&~gBl
ze_;A>g&WKqSUkYeFDyJ^=@8xBFnw?uYA?+Hu<(c34@(bl^=J&ZdZ;<D{0P%e$o(+m
zVES;yAA0z~`gySY151Z6|HIV7+yP66F#BNrH<&(H`he+!<r|p$(e=a3L8oEvhq()u
z{$S>ytB3K?!x1J9b3e>|FmZI6Tzg^ugvCF4IRh&X(d~orVfhx8{$Sw;OUE!i%pb7w
z8<uWi_QK);mfm3D1<U{F{)g#@<zHAng2g}FeK^_?u>1>)Cs_Fm^FK@+#)r8d#z(gY
z<~~?DhJ_2vTv&Mk3lEt4&}o=IVC6T=|FH5KT|GJ<7A~-Gg~cDt{e<j?nFkA3SiHc>
z5%l^WmJeb2VCKWz4+{^N`_cPPF!N#Vg}EOV&aiX<%YQI;!0dz3F#BNf2D1;QAEpkS
zhWQg_KP(<$;RUNl(DlLMAEpoHPnbJl=^y3~n0sL6z{Y=I`4%n@O|LL>Vet=h4@^HS
z{lfIa@(s*hSa`tFA51@thPfXW{xJ2h_=JT6to(wx4`v?B9WWXm?$CMw7Va>8Fm<?S
znEkN$fa${(-!S`N<qj<TVeW?61Je(S7Z@K_{=@PqEZxD<FU%ZR{KMh_mXBfK4|6xn
ze3-jo_T!?_-482=Vf8jF{BY@qiBno{z|4V#BP{-5?trCVm^v5@3olr>!}P<#4VKPu
z>4(KLOdp&NP5-d)gxN=kMo-VMbV|q^Sh&OT8_ZuY_rvlPF8g8m2Uc#w?1QBjT>9Yl
zK=TVM++p^@(jBq-VfMqq8&>Ya(h=M}(C~$s3oAch;SWo{uy}-}Z<u?~`;V}2M^^{4
z4;H_$atU2O%pGw1q2U2D7bXu2SM>S=7LG9g!{P&$E@Ae<!WDLYI?Q}nyu<P%tbBm^
zALcGtI2u614`wfnhJ`n*{D6fA%w4c>MRz|e|DvmhrAwH(F!!VPpJD!mm1D4U1ak+h
zyn=-vEPcSj6-L9{0Sgb9ILuyHc)-NbX;^r}!V#8lVBv<Y9-R+!Kg{1Scf-;hx_Vf+
z6Y@W-9D~(kuyBFJE6l&}@Q21P%s!aEVdlWn2TUHOAC{hA?uErWJRiW!h0#z0VCvA#
zfyEchUR?2p?hmN3@bzNo?uE(2XjuM)(J*^p_QKLFOg)^2=2MuvVeWwGhs8V0A8>sz
z22?++{DH+kEWBaq4rUHa8#;}yA7(!+zG2}H%SSN%uyhJ@KP>&i^uyG_{15X7Ed9XJ
z9ZWw=A1vL%{R17xLiZ0Wy}<Ot(lgB6u=EI52TeEV<p+BDf%^mM0a!f<vme&~f=a_F
zm_0E4Fmqw<f~5y^`{4#a88CBT{(^-&%zpIvM2`oUepq<I^ufX(Y7Lx%xf2%NF#WJ}
z4|6vx9N-$E=?~^km^)zVVf6;g{jhj}>4(_^O9!xW7v@fwewaASJun)k4;Ig`bOlRq
zu<{rd{;={C7A~;-2J=5m9G2c;=EK|pa~I4WnEPS+VfhYLe!<)YGZ*GAnEzq!g4qM}
zCtMyn4i2*yW<E?FM#J0zi$7R+!}1Z#Juvk!8fHJt{V@G7cfi~Wk2h%kf$4|EJKX&+
zc^C~%AF%j>(J=k6@jF;J!NMJu&S2)l!W)*3Ve+u}gOy(}cf;ZxCJ)OmF#WLbgt-Hz
z52hYwAIuzBeujkyq5KOo7nUDj=Ayd;rVeI4j1NmMu=s$v0~Vj??uY4zg%^wu3vZY{
z7$0U2EdF8YVc`f%53q26<!6|^FmagsVD`eygZUH2howJQ_`&qU`tPvx1IwQ<b+B-N
zrDvGCVKl7&2(up+k1+Kxcfi~aOE)k8%QrCp!ty!H-LQCrxd-Ndn0{O|Odrf$uyBW^
zKV14?;;?Xq#Rn|@;Q0`y5K6=R0kaS0ADDWW|Iy12bbYY&3JX72x`V|7%>A(TGfX|Y
zy)gB#@P@@VEF9qVC$xNk>4%#KEkB42Z&><;nFDM8!rV))Ik5BqGY95=Xt3j?VDSbk
z*I@AgOGhw`u<(b`Fn7bk0TvH1`(gP5rXQBxVeL+sy|8eH#XGEAh3i9Oz|uP`-NEuB
zEZxCqG;MG$dicX6aJn0&07}E$4YL;(UWDQe=5AQL!`uPW2MY&SdW5AzsI?5}`4nb9
zOdb~gF!#X16K*b!^owpk++1k7MqmF0^FPczuyPCLUYI=0UGV$^6@Y~UOdZTVSpJ2X
z19Lwt|H9H8EZ$-16=oichWQs3pD_Dj^*1d2L7l?@%O@}zmTqC;2~!6fKZL~}EWg3r
z3Cl0&`e6ED?t{fU%>OWPSh^*Y{$b|9%z?QF=5Cn3Vd`+vu<(S12Q1uR{z2CVb0^H-
zuzU|IzhVA|r3ZBV==Q<<57P&CH#FU$>xc1S{)VLknEkMDhq)h?4`AsDrXS{CnEkNy
z50i(LYcTyVcfryrOg}82!t8_j50)=r=?PswEWTjj4U>oIhox(nJS_dg(l5;2uyg`*
zCtMtw&tc-Q_8%<1VD5*tb71iWa~CWeVD5s+!~6wP2MZ^ddRYF2=|c}!m^yTGVD$pb
zy)b>~^*>BMESzBR0W%L4-mrWGiyxRim_0E6!^C0gVd00Kj$rCw`eEq~magFG7aHy`
z{jhLAcL&TKSop#66D%F0`vVppFn7T6A<W+}KFnU2xv=s87XL8)xM-ODa2lEq(e=a3
zM>hvOTwvh}OUE$vu>6Z24zTnL_b=3bm_C?4VftYHhoxg!{J`vkr6*YU!^&@%{jhWm
z3ny55g4u)a4p{z!r9YT^Vd)-S9Oe#O?uUglES<yL1EXR3&}o=HSU!ZgAC}%==@MNZ
zEPP=8gt;584jSGtf5XxfOdb~AF#BQQ3X_MW3s|_o;{~b!9<I=Q0xOST=D_k1Ed4{Z
z!6}%2Sp31#0n8t;^oA?^VDS(0Kg@lw@Q0ZXb3ZIxVGe-lhs86xJ+N{G7Vofn0j3}3
zUwHWr4F{O}Vetb?w=n(a@d<M$EF56*gl-PZeJ~p44qP-WUeV(bT|Z17W*;mZVg7^3
z!~B7+ALdV3c>&W0^Cv9*!Dv|efVmqM?=bx^|HH~rSo(p5AB+#v56d?&cf!&gEMDO1
zp!osa{V@As;SY-sSUQ5)i|%fixiEWR?trB?V)HM|epr5lrBj$YVd)QMAB=|C2lF>v
zA2c1p!V&I%m_{fKizk@9F#o~$F#RxpK()asSUADVf#nxiyu<XN#~0iHGzKjF!Tg18
zKg?V-ZE!BE9Dvi%_=cxDxJpz8Ec{^h!r~p4j$r<S<qO#Q8Cbai^Eb>KSa`$y1B+jn
zd9e9YSp5UD7iK;z{lNSI%LlOVgwg2wVd)pucMJ@$aulWy7SHJFV0@T+VfMrFJ)v-f
zyAzuJVCe{EFU(&sdtfxo-LP<gg)7WHnEklijjkV7?!oc{A^XwY4U11=^~2&B=6+cC
z5vv~_PH5o<%QrCh!}zdpgVC_`39}zP{9yWVg&!=uVEWP1F)aPS@*~V%I1P0-EWN<o
z1G5jNAD8=K`e63M>JylLnEPSr0d6mp0rM|hAJjcCf5FlnEFBRlM_};>(+|@JGY4ud
zoPvcHOg*eVfTc@xf1sBiFn`1Bg_Q#^dtv1UtXzUy3}wLVf$4{(JD7f`GMp65y|8o$
zvkzB%p&JBqKRh2m%UxXVh3SXYA2`De8jrAehou9UyI}fZ?uUf~EWBa<fw==+Kg=EI
zG)x~XJYn|X3Qt)4!`uN+|4{eC>Tg)RfZqRtg&(Zkfw>n}|HI+~mM&oagSi_P4lr}j
z;{mP@rU8wHg+HwQ1@kW~-{Q>oP;+4VVE%^bgSj8743mO}4@@5{ykYqnRxZQxD=hwC
z<CiddVc`x7H<*9n;?VhfT;UIM7|dQ+Jiz(T^bAXfF!eAR9^O#@!1Te~i>us#>4&8s
zT=55s514vfG|ay+_rvUk`5P9F==x#d40AV3KP;WW{DH0?J>9|d!QvgJ50?JX?S=7S
z=D_L!SiHi@bC^G1=E2ezOh0=1K~D!Tf55^K?jC4-!Q2hA4;Ef<buf$2XxRKA+`Z6r
z0&_1+KP)|>=OdWAVet-&KbSQz^|0`SxeFs5Lj3`AKTJQ&e3&>a++g7dQwOJ^=D^BP
zn0{Ei!}P=41JehKKUg@z@-a*u%-yhXfa$}aq4vYfftABB{TTj+8UV{bFn#cFgvJNV
zzp!!?sttZ`1T6ev_QB*~_QCYS(h*D^c77bpK6tu=`U92@VESR<2-A;F!@?cr9#}mN
z(~qtmoewL=ap@;yKe~Qc`i0pK^B=msFg`3EVD`hrVfr!R9~w@uaEGNE7$2q{rXQA`
zVCKT|AIv^jeu2ds%snu7!_pBf9ANYJFneM8VD&sq9A*wo9Oe#~{V;W~^aWD~OOLR4
zh1n0wM=*E8Xju6K^Dj(4yj+H+XP7%-<t0o%EWBXufa!<%A7(B*9Yez(rXN;Lz`_xi
zy|D0y(J=qP%z^RI{R=Y}rVmEL;v42(7$4?Nn7d*Ag6V^~7iKR^A1r)e@-X$VaEFC6
zdibH+2lGG7T$nzXdtl>-Fmqt(0%kv~T!5Jab3aTT)_;eogOwLBb+GV&>4$|UOh3%u
zF#BQdgQWwwerURZ>4&9%Sopxy!O}S_yx`?JR6oppFnJgat0!RYLDvU!FM9mL)WPx(
z%pEXsSa`zR4J(ge_QJv+rVnmD)V;89gNG+n9Ht*uZ^OzxSa}O;&%weK=6)Cr(~paW
zxd&!HF88DBhov)^ewce;`r-aV(+CSMm_E2UQ2)Zh5ltJMiynV437Gvbf57;#_BTu&
zEdJ2rAC?Yb;SZx>=@h0P7XC2(F#F);DAXUYatx*q7Jo2*!^C0jSr{LdUt#WtiNn%A
zEc{^k0%k5u9Ht*857&>zfVmsy9$0#X#UGk-3@$7jVetX;KcV=8rB_(^!3>7!hxr2*
ze(2`G${(1&Vd)H}54~JO&j&F5F#BQpVDSxe2P{3n+y|p!?uX?!SUAAq5f=V1b71;l
z_Q2c?OZPDM!r~X^Uv&NG=D_$cb71iR(+{Iz>R>d?{jhou<_}o9hWQ)j57_z@n0{Ee
z!Dv|c!`ur?Kd|_N>4VWQ`(fb*^EXUCM!JV-M5AH$!@?EjUP9>}W*;ow!@?itZZvB!
zxUhN$7H+Wg1hW_B4)pMcsfW267XC1Q!~6j=2j(6aAJ%?@#WyU!!onHmE?E4b+Yi%+
z?p|2Bg?SW~Utspa#9=h7+<^H5CJysA%v~^XbQ<PQSop)tgZUS|yhYa!^9MZrKo!8k
zk&u2^IKk|PrC*r+u=D^m7EZzJh3SLECoG&``eE*XnF})j)_#KJe^|J~!UJY6u5g5@
zgXu%}2fBWkyI}UC(=h+Q+yl#Zuy!Z9`(gTF_Tq|vSo(vhN2g)&2D29yUNC#n)x-F(
z@(pe;v_AlgZ<u>w;RF+hm0vLbqQ@UBJYn|0!T}Z!=;NQT^a^u7dOn8v2j(7_xiEb&
z_rvtT>K&LnVBrT7hv`T6KTI9Wov?6)sfW1(#)qX>7!Av(u>1}ahn3GTahN)oe_`ri
z;RI6$vmd4(W<N|n%>S_R2Uc#u<YDH*`~@=?=6)Crvj<j=!{Qqz56g$J`~VY&(dg+H
z7VfZcgt-T%9-W4(ho(Pxyg=K1=<$ZGAI68JLs)u%rGJ<^(9;i0AB=|Shs6ub-!OAv
z>d<Lec){$2g*(h%Li*AD53?VpALb5pd(rtYeX#h1*$YcI=<3n=FnzG_hv`GF_t5ph
z_%MIN(jzRr;fg<4xWVj$xgVCEVD`fN4@<`|`(X7qEZpJcGc?`6!WHHYn0YY$deHO+
z%SSMKVC5RDJb{HHEPulEqth^RVD5pXH&{HPyC0?>=1y4pgM|moJ-EUTrXLnwxa`H1
zZ(-q&ZZFKAFn7WH1#<^%{Q|mvm^m<gFneM8aK!`6AF%j`r9)WygVC_?gsF$q(0GLT
z9~N)0aEI9sSC7VkrCXSOn0lCgT=wGhKXl(5EdF5W2i+W4I)m8@<HPD%m@{GeVBrTd
z4_02o!W|Y*FneM8VdlfiVf6kxEWN?PA7&pc9%1If>_yiPQwP%za}UgZm_8T{OBXQr
z!}>|Ec!$Lwx_-F7p!pk?|6%0~%zv<Q09_mwpD=YW^U-OTdtm7THtq!re{}Wed{}tF
z!U2}=VeW^y4_zNR9~Pc4_rSsrz5Rr)55|YNA5KHV4YvLOrVdtqz}gwG_=M?$@nP<V
zxdWyjrVf_QVCKNe5tw>dIHLO>mhNHxh4EqO5N0o|9DwPEjsL;I2Ntieb`Z>6Fg~I9
zhxrqhA7J`n^#;s-n7`3!Sh|Jz0~YTv|HIskt`EkC>4)V5n15jQ!omZl9v2M@KbSeN
zcz~Hh$Q`iwgT*H-TwvjcUOvJ63s(;<|IqD)#Xp9-q2)I$zrw;DmM&r82eThuen1U^
z`4>jR+zZQxFneL<K(*nd(A@#^FD(DV;sxeTSU!iP8<_pD_=2S$SUQHA1NAp7-eK;C
zxf@mw!Qu_4gP{^Icfji<XnrR(zR~jw)Oef}ES_ND0ZaEVf8x{#m4M}6SUQ4*2h1O^
zctekWn0lDMp~f-5!VP8)te(Lz2c`jyhWQ6(E-W5l@drx}XxiXhSU!N+i|%f?LL3a3
zy)gYSd(qQ94($vuf5YMfmJe~o2h2WLc*E60(<jWou<%2-7nbhP&4JkmqhaX^t{-YX
ztek?yC#?K|g*!|?EF9qOf$E3(6PE8_;RMT1uyBOM1A6@jcMp^S(+7)3m^)zlafJg^
zJN#Y(Sh&E{!^#1eI$Si&Kd|tGm0vLb!^$6A`eE@1b3ZJ9!Q2nqe+JhN4M$iw!NMQr
z4j3P%A2$92Qx9_|EdJ2r0p@O4JmM;kVESO@z|4n*AFNz~yBm!GE4Se^wETmW2WZOS
zTv+(P{0mE8F!#dZAFct)fW<G&|FCoi^DoRlFn_@O4Kp9Megu|ZVg7{WKUlti*#oNw
zVBrtzzrx}fZXeWrFbd{>Sp31#A*`N(rFU4l2XhZB9l`tq(+4vbora|oSa`s~4W=LF
ze{_8?J}e&KG&KHT=^v&JoyM>iZM`2XUeNWy_^|MW#XroSu=t0m!$rgN!NL(1zwq=5
z^#?}z2Q?6u-eK_w3nv&2OAoO00dpTzI|IyqxIduw!O96(I)a4*tp5(v4-0QtzJY}|
z%>6KT!T7NA*I@c#G|c}n|G?4_%pI_F08583eJ~oP50-vl=^dsImY!hhVKgiq!`%rj
z&tUez(mzZ+It>dSn7uIdFneL~fUXb5huI5@2bjHt(hs`3VeWvLiym&Uc!#?O=1(Y%
z9{;fLg2fBWU+DE8Odl-#Vd(_s4p{ntr9brehm|`p_rt;wmM>xcfQ1XJ{DPSeqtW%l
z+)FSXp#F!&11#Oa^uf%5g$K+&7>#Z(EZ$)Hap{BUhxr#4PB4A2d<0X6PQ&5_=5AO#
z!0dyC8_YadxdJl>RzAb@!{QI74wg>f@dGUv(8mv8`5tCJJiMUgD9n7AIPCmHba%k)
zhtV+i!OA^Y{G*pYFmqt_BrH5(=?CV2SUCt+4`smBLDK<xJiy`;svJ(i+y`?nEF56z
z9G2f;;;`_9wVz<)4>0{O^)UUgbOkFHVC5Ms|G~zuVE%>06WoDN|G><Jg&&L$OFyu9
zgM}ZgT!DoPOdl-%;O3(-VBrCaZ#W;CZqUmGH0=y9dtu=LOTV!2Kvxgr!{Q5OFDx8j
z`e6Qrg+ELmjE3oh*$b<uVD5$a8)gnnAKZV?aE65^OdrfYu=E2<hcNvx8eZN&&4Kv`
z<_}nYgvA4lhUtgVuy};o3!`E23e$(H{DheUvlo`GVE%%+A3gu0>qGYs%siO=FdC*0
z=1y2V!2AJ=FPOcs_=Jh$(hqBg!t8;?A51+u4GV9W{V?~#^uf%5rwcR#VBrJP597n)
z9i|_aF3_|y!2Ao-kDl*f`5s*#j1Ma(VESR@Kg?d3J7DV3X>|M1^%JUx(8CWszR~jy
zy8W>92}^IV@($*1m_K0k0J?sd`(gTE=?P{ouKWXwCs??_^uyvA7XPq%111hL4_1D|
z^ug4@+>Oh<F!#gOkHhK*m_C@jFm<?Sn7uIf!~6qtA1uGX__)l0<ri2u!rTM%525gf
zhcmSNg1HM8UWD|cr(an9hUp^|9<clh^9PI%3lCU0;L1m^@Pnm4SbBxkKd^iY&j--@
z3}!w|KYG4}$3HY&VCKN$0~Ss&8Wvu#bN~xin15jMF#o{pg~`Lrg~c1pT$sCH=ED31
zb3crRxerFe^uzg3f5X!e)L$@lFneG$tQ>)r-!OCF`k@S1euafURG0y#59VH&I6949
z{=mWm7GJP*1Pd3qeyICl;RrJa7EZA6howiDdtm;6g&RyiOdTwKVC@N*IWTv?&4)S!
zmi}S+4l2w5OV6-$1k;aRf5XaKSpJ2@2P|A+{)XvCw;yI7jE2P@%wOp4hxret9!A63
zLoolq+yS!>rVkcQFnut8!{Z+sFR=K7g*&cv0E;)6dx?#ISp2~B!NMD6FD(7S@*TV!
zg60D)s5{~Qg^I(<8JK>UKhbGe{Q<KdCJ(b8Rt~}94<?UJ!_pxvykUG;IK$!{W*)kJ
zbUrK`VD3UU2R$BO=?`WOEFHr1!|a8*17;pfJuJRp>S6JTt`FT_Sh|FzA6R_C#9{hz
zr62VE6U<%kc!S0-EZxH573OZ3eK7yS;t3WHFn_@G!QvIBADxDo0}BUuxIohttbE6%
z4;BtE`(gfv*$?*@ngQtX4U>k&7cBk5#NjkFd|>$&=3bb6FnzFc0bXuG{R4|9n11x|
zgxdpUz~T`;egw527S1sJFmqw$CM@5=+y!$7EdF8P28%yfJi*FOnEPSj35y3<IKya|
zxiEQHK7fhC%0Z}i;1tXrnEPSj2vZMpH!OX^H9{G%bOX~5^A9ZD!pwmx$Dm;41<YPp
zdVqxo%zg}wPyv|#VfvuL46yhoWIwF@gt;FU&M^1G;t^&pjE2Pv%zjupf{DZ80cH-2
z57Q6xH!Q!w(haPffrSSw9AN1WCJ!?QorZ-2%v@N!!SWL>{jhk1`3Dvru<(P~k8U4~
z4@++_dtu=ZvlqsPsfW?%?uGNA`4ko(F!eAR7S1qt!f04H!|a2Z2QvpY{sl7!7XL7J
z!`dIPaD$l#ONX%dgz1N+cUXMF!V#8kVBrUI4@@7-{jhWc3qM%?hq(imf8q8*!v$s^
z%zl_USa}00mtg4%W-iP>F#RwZrXO7%RzASQVg7-o6IeQhr7Ku^g}Dpn516}P>R|qb
z*$)dJSh&IB1tty)e^~gz+yzqy3r{!=UB?4Uw=i>I`e8IIoM7n(W*>TZ!_;HY(0Tyo
z4w%1T;Q+HA=5KWUu=t1RgZUE{ZZP%e?t%Fq=5APeg{gy?2aA7LyusoT7XGk!g!vn$
z4i+x3`~yoruzUp550i)KhtV)|VetVo7oN_b;Rdq@CJqZXSa`zx4+|HVI=DF09GE?@
z{0DOvEIeT5!t}vtm^xVghnWK_w{Yo$>4(J=Oh3#&uy}|08&~@WT|dm<u>1mZ2P|A+
z_QGhmd!gX~OAo~AhnWL&Ka3_?Kh$2BK3KfN!X4&*So(z72dB~W!_pN@AIu!MdNc+s
zAHeK~l_$jNM-M-k`w8iX>4U`=EFZ(-6P8Y4=D^Yc%=s{P!|a3UgQXLgKVb1dC>&ts
z!qOkiTv)zEw;$$yn0i>cftBa5d<0AXFn_?u-=Xmavj-LqFmqw$1k8R|`h|(Z+zZQp
zu<(P08!Z38!ULuroraZjFn_?p3uZ66dUQU_URe0U?8Q}IpzDW)6U=^iI6~`bn0dI;
zKg|EI^a~3=m_Ate;|d3ueX#TbODC{$9^Jj@e3<`X=@Dih%)M}Pp#H(7AMSo=y$jP1
ztM_2;g3&N@VD5*7Ke{|DeZk@bHvb6I4|6vx{9*9{lZS;1EIq*cZ!iT=8dmPW+=E_k
z!rcW82bjGu_rYkGKA1aT^%gAuz`_Hj9u{se|HHx`mJeb6hUrJo$1r_RZ@}+mfW<2;
zzroTIEM8#h;WRWqqq_%YFU(zV^=J&3d*S+_=>Vo5maoyYVQ^vP2rOU2!Vi|OF*HEk
z4a-L`eX#TZ^EW*Hpa#I=59VH&y|8?S9&WIBgKB4h<x`kGSa`$Y1Lj_s|6%1HOg}80
z!O|Tpo?-H^bPIC_dc4Ebq0=z`!{P(x4npw<^AF4)Fmqwyhcn$k6~fXp%wCv#3E2ya
z2bg|XIKlM8;sa_81_g^hSh&O83tj&K^B2rqSUkeSVg7~DuzU-%7nc5D=EC&D90F4h
zb3e=+SbB%WKfD};X@sU*So(pb2be!#>T&6ZjsL>@4|501cvyVF^uxjvCJ!5bf`vcK
zy|DBM3on>{n0YY$=rk<d!Q2B&w=n&%@I==K<HN!MPD9ICSop)#;i6&T4$}{dFJkq>
z^ucK&?T42$(D;OfC(K@$JJ7=qrVbWv@c4t815ck&cc7;`T;Tu<f0%w)_@mnotEXV;
z7Um9^I4m8arvv!-3Dli1{jhL@#WSqFg{1?S`_bJ2vj^sGSUCuDH!gp|#9`$=EdRjV
zi(cNt)T7fdf5XZxV#`Z(bKv0(4HsB^!u$dA7rH(eA3Ypl`4yIqVCv9mSUmwt|FCd|
z`5zYEuy}##hq)J~59WWEK3KTI$^)1?VDS$-zXBF-FneI-2rOUVD#zgFLc<Fd53uwA
zi$_@a!Qutxesmh<ADDY!=D_sB%tO})=R@5A(+~3xOh1edSC7Vkg&!;)Vf7v?9?_H|
zxM=pm@*m7zSok3{pt4~0!NLz_AEEGqnF5P{SbV_T4U0z@AC|7r!vUrr7H;V3VfMhv
z0hqsF;;?)R3qM%;gP8;KA51-Z`yZD6Vg84O7tFmdcfs->`u<;-xv=yKa}O;2!^$z3
zyI}r+>4SwE%pO>|4ohz^|DwkSEWN_qjjj*o9+<mf=D_>~vmc#?xd#?6F!#dhA$0ZV
ze3(91e8BX<;saehIv=JV=6;xdSU94qN9V)rh1Dl8dtv1dx_VeV!SW4^4@);N|HHx$
zT|X?m(Blsl&M<v2bI|p}?1k9}^EXUA%-!hu24)V-y)gS>=>e8rVD`h(6HFhB#-$%-
zKTJQ&KQMhT8s=V@{jhWh(+7(`n0j;?J^Wzd0n-o5H|Y9cd|0@_?1zcN^ux+Cm^eC(
z?tgUm!@?0=AB+!I4=smb=?`W<EdF5XVKglN!qmgc4On=>!W(7|disa0zk{W7n0lD`
zaC4#UKlE^fg&VA$0do&5|G~_G*$*ohVd5|vUQa^(4-XHhz3A?S=|iVs=@w=`EWN_q
z4f7wad;l{K=5Lreu=E5=hp_esOg$|AVc`I?7d>6V{DEHnqpO4YA7(yGA1oYU{(*%z
z%)PMqfbn7Z52hYgj>F7_t-pcAKP-OWG&DZo{(;5^Og}9Bz`_TXPtfBNR(`_V57Q5G
zFU<cieJ~mp4lr|J;g24FuyBC6111kM7bXr12blk0?!ZOE!UbkNJRhQk1I(Q;b71XU
zSbl_sAIyH3Iv5R0KQQ&M{0obBm_C>}Fnur@7GALMMAr`sKlJ#A>4$|6to(zS1JehO
zH#C34^uyc_(+|ssuyPNkAD8=K>S6lf?t$6|i+7lP=rqi|F#WLjg4qiTKXiRCJ}jPL
z`eE%(n0{D1z|^DDu>1zo4~q|&yJ7hdmM+ls!@>{d4)pkgyB``Jxb(x)4=mlo;t%Ey
zSiXSiM-MkxxWn8DQxDURo^N2`jIIyHhqa4g`d~E7ei#iaH(}ujD|cbx0MiGH4;T&8
z4+}T+@Q3*yt{zAD!SXLG++gtsi#I~?kM0hb|8bQUF!eAR7XL7R!}2LC++pzmQ;&;=
zg%>P5VD5&y1BXVKe_{5+;sa(sEI;7T4&Ofu%g3;Ahvk1<`e5R)c!TMO#W$?pfbns;
z1Lkg+IWT=N^|0`SxeFGrF#BQo1{Q9x{037GGasfNz5a%!b69?Yxfe#m+y#qAbp0@M
z(anLS3z$Bbd*Jh*P>rx~L$?QJKP(-<_)u+d3f=!Ob@2QR%{REx11#Oa@)0b&Vfg^o
z?m`cDm;+$x4dxD5IKtwAQ2K@Ghs7VP9D(VB`3GhnIt>eFm_9fS^*1cs(e=UjFneKq
zxc{L7xcm*3W`OC3`5#tK!u(Ij{V;Q2;SWm(FmYJ<2a5-oIE;q*AEpn^hx#9<`=RYa
zSpI;;8!Vl`@*ymKV0?IbhU$me3kx?`yurc+?k}i&VCf$=e}JwZ7Jjh&1mnZP0T!Py
zb7Ag>*#~nktR95<2S&r(4+}pS4J$`s?t!I$n7uIn!_=eG@NkEQ3(OvvepvY6(gzEF
zSb2c1A3Yvn?u7XtW<HFDg&!=uVDW-(Ka7v=e^@xe+z%^{Vd~Loa{Ui82UcFe`~#0)
zXu2UbAHmFl=SOIK!15!EhS`gYhNUN1e89pFSN{<fA29W>bPLl5t54DMF?zVe%41l1
zg~cB%J)^r9*6xAX4+}R~yuoO6eK0;O-NE$3#9{hi_QS-{X;?VH^uy90Oh0<MMb{70
z2cu#BhUv%UZ<skSeQ+9@USRrR>TuEM;Rma4VfqQ#4@+;baDdf6uy}`=hs%DLI4r-y
z(k(2#6H2cz`(gfvnFDh_q3}nKe^@$&xgS;z!r~QX9(uUJ$_to#Ve+u>fVl@(`46)f
z=5LsPVBrtb4>KP&{sB`5OZTwwgylb2dV-k`D_3FR26G=Q++g_+rXOZ6q4E#rUs(Q!
zg%iwPn7hzvSh)#{2bjHt@*T_^SbBlk4-<#^4;Ee+=0VFvSop!rfrUTJ9kB8W?hk1G
zf`vCsKP>!U;ROq4SosFa2Qc$s?uGdqmcC%_gvrCwA50wPKA8Vt`eEq=rVf@LVfMq)
zGt7QiIKkAx`~&khJY7Kj3)2tN2cu!;!@>`ieqiARa}UgaFnuufu<(QFgUQ2an0sL1
z3afu$`d~CH|H0B7EWBa*VdlZ&1?FFvewaMW9(4D@<k4wZdWXdWy1&uYWAM?^A<SNw
zJ7DpOt`C;pVB+ZM1(xn{<y+YNGc4R;@eNBKF#BQdf!PD2VfMk=D=_!K${Sqi1*RXS
z9+v)K;Rj1kFn`0$ftd?)AFTX?>w~VBgZUTcFPMEW|G;RNIE;p+ADDTt^a)E(F#BQV
z!}P&vw0MN4FKGUOnFCjk#(;$fJUv47!QBhxqiKV4VeW_Nhxs3-AKiXf{K4WK7Tz#-
z!1TlX4T}eug)sdv`(XJ1CJ)PhF#8GhA7J4QPft+)!tBQt?lAYm+yN^WVd)>{4p_d2
zxerFe?1jZQth|EhhpEFw!|a9m9~MrqasXHUfu|2>x`FA3#RDvz!~72`_hIgU`4eV8
ztR97hH_Uuke8T(%lZTlD^FNG+*#k>YF#Rxpp|2l+se`#67Jo4JpxX;GA6Boy!W~A#
z`~{0Qn0wLF56rzV`(WV)(+6`mEF59(hxr>O57Q4zUoiEsaD~Mm%pEZQz}ycjzhLf1
zFTY^!hQ%YS{ReXgEc{^R!T7N7gvBFFKg=C4{pd7IKg|8Gc!24HyB|#>Odl*<Vg7-Y
z8!-D};RK5(m^xTE!o*?vVD`eo70p^W7iKTa-LU)v(+`VZxCR&lntx#W;N=0d{D7GQ
zQ;tr<?1i}>rXJ>Boc2Nug6V^$16X{+(;?IyuyBKFgP)fLb2rQ#=<OAlKA3yp<r382
zF#F-@8EPLaykYV%f1u}Im_C^OF#BNoVd60Tu=W?sAFy--ORq5dVCfD$|KK|R4VI2z
z^$N_Nuyh2o4@SfC1xz0-{9*2dr5p5o57Q4je-)-5CJu{#n0}1-fGUKwKVbI4@-NIj
zSbV|U1J#B>!NMPw4q)jAW<MeQ@bnH%53qP6RzEEMVDSd?2h9Dr{EzMtSUA9GXgY+2
z2fBJ#IRW!OEd9XvFn7T8!~6sDA3U8v{S7O3Vd)nZF0k|i^A9d}z|t!$y~Fgw%1amx
zGar`zVBrSyH{5)v|6%b7(+~Ft)ZH+1VE%{22Q2-=^ux_XW5B`@<_=hTf%zB4N7Dx9
z!om?2?y&HJ*VAy7*ctHr1N9Hg-LP=Nt_vXwb2lvfVC4nO-7xpS!VhLH%)PLD1B*A9
zJi0zux<S|sVZrpn(i<#(VCFzn;U;1621_rn@`F&k!AykdgM}kZA3Pjk3JK6K{jl@|
z(~ryFu<(PWdssSv<s+DWT<HKk-NE7!=6_Pd5oRyU99Vq7(lIPta0LS_{lLNj7XL7R
zz|s}Eepq<H?S<xBSp5xi2P_=n=@6RUVfx_V0o4yn7qD=K`2!X%uyhAYx3F-A(J*<K
zyJ7lZ{TSH%5zN1^as%cLm^zp~Sa`tw4|PAxeXw)@OOG&nVeTfBA7JqZb2lvi!t95+
z2W~$a17<JGKd|_Q#UGk-1Q+TLxV_MQOE7y0)n73CVEtW~eX#h)l`mlWVDS%gFD$%Z
z=E3A)=E3$)!q%U`<YD0pb1y7BU>=2;3p+my=3lseXgI*q0lGd|y2TX_u=t0W2U7>L
z7nUAi=AzRu|HASi%)hYu8(lq&53?7R4`BHQmhWKZz~U1YKQR5U_<`9E%crn>088Jn
zatXct3o{3uhWP^)4={hA+Yhq`<_?%XSh)fVN0|H3+rO}M1k(pI4;J3A@Q3AJn0YYw
z!txy~yx`_Q$HQRuqU(d{htV*7uyBO=7Z(07b?7wApD_Kf@Q3L~R}bey(<@9LEF56|
zhPfN&Z@4}v1D0N3`eFWug$GO@%p9mToD?jbz}x{VhtcC3-2hmAgX@F(3#N}y_`$*(
zt{+-|!1Tk)QFMD@=>(<^77s9eu<{7zKA3)3I)M2XR*t~b!Q2Z=_b?h(e!$lM!0dyS
z7qEDMnFC7)xY94o{jm6g`2!xW&~Sk1hq)W(4~+gBR6R@{W<M<cVSJc8Oh3#Y=>CSO
zhlM9hAB=|OcbL0j@dzu&VftX{5T*~EhJ`OI9$@k?cf;ZlCXP<S+z*Qfn0YXLuzUzh
z&*=JL=>Qh)@bm^v$1rnX?m^cN%XcvQ(anMBgM|lNKh%7fK3F)y<YDm+qv7s?DuDSL
zR$su<6RbXgsfXDQix;SNoD|GGFmqt`!|G+68qxd%E5~5^VeUcK4|6xn|M2_(P0xhv
zg}E1&9%1$oN_Q~zuyhB@U$FE7%g1o{Lfr$)7qD~#(+`VxxOyl9=6+Z_z{iiE;RP!<
z(ESgy2c{3*Kd}AVFn_?(H_RWf{0P$rGY9G{`1l*lpRo9Xr6-uXVC5bxTwwaqX;{3$
z!UJY5++Jw-q3eV3VfMk?4YLncF2U5H)3AI1O9wE2z|sMXkFF2Khq)IPj<E28g(EC|
z!pZ@7K8D60%wAah!Tb%+Z%}{3+F!8vhov7_c?I(~jE03LEc{{N2#aS}{|DxNn0lBy
zVftX<2lEfi->~u@md|1Sf~8lOI$SiY{SNa7%s!a=aOs1I!{%RM`3B}rm^m<YIB973
z!_o~b{9)k^3on?t=;pxU6&7wVb6_+qzF_8{rz4oZVfMoO0W0TV`e5l8-F_G!rVkd6
zu=)fhk8U2U{{)kVxgVwvrXI$J`3Dv+==x#iz`_Hj50(yL=@S+nu=ESlho1jn`3OB9
zz`_H){{c&XFmurL!@>`iu3_;DvmZvo^uhcK%f~SNFm)I-G#$ahAC^yG?uGF&^g#t+
z@eeCEVfhFaUQlHi6fC@9`eEe=EZkuE09KBn`x}=2VD5&6BP{>I!V?znFmqt_3VOPO
z>4U3>h6_wREIi=ufXc(%4|5O9{V@As`46Ta<}X-)z|t=){9yior9)W0gSj6TuQ2`S
zG|YZjxWLST*^jOtW<QLE`2!X{F!eC|(bEGgy}|5**$?w4EIq)(4eDT+dKe9JCwl&c
z#Rts&F!NybKTJQ&UYL7fG%Q|W_QTu{E5Bj+2IgLvKBzNsQn2`i*$*oxVD2Q<99a5?
z#RDw7Vd)2_Ltqln^ayhg%%3p#!1yrbu>1!LcUZcE#XBq>VEW<V2sHqf?qT-9%1iY8
zLTos|{0%b~&WE-iVCF)thf{EMQ2)c?0aot8?1%XWrV-|USo(#zA6*|zJvt4mzhUl$
zrBhh?gryHyxWdYBn0sLD7kIow$7^8zgyln6x`xFAZ2S*qFM9aH{0XCB{(+?@SUy1a
z4~!3se^_}0%NK;ob67mU+yk>8=5F-#2eS`m4@@5{ykP2K`eES;(+7(`m_Ar|z{*RQ
zI+*)m{)d?hlZWYp#RJS-SiXbV4~rL=J{S#)Czw0X{SAwEn0j=YQvI;>3-bred{}tF
z;vXiCPQ%;{iwBtdVet(MH*|e4J}kY!%z=e3EZkt}9i|?ghPfZ6AC`__?tu9N?ha^q
z05b>XZ<sl-_=lN;9*^khALehEepq=3i#M3PxZ)p{USaNs^P%n}<bPQG1M>$=KZd_y
z9){AebPCf4<HO1em_K0dhxrGp9b?}k+<(w=6jpx0^uy($2Egotg&TVQfu%c`eptLf
zwZkcxIWT{~;sfS?m^p9_XbhM>nEmMK7MH!~=@ynQ;O>RaBf-)kdi{sK{sR`zuzCQN
z9$@yv@&n8sn7d)>VBr9hht*>+`(gUg`ya6M0`m_n{9ySI7LG9Y!OTYsMtFLFn*&Y1
zu<{e;4s?AmKFl95_rTnX9&Rx8FdA0x!t}%Z4NGq@bKvfWX@sUzbbT=UVBrZ%4=@_0
z9Y({_4a{Cxe8Jp>UVgyb1=A1H2XhCkJcorBEIq)|6)Zkr`f<^)asf_5;{#Uz<I)Ec
zhou9UewhE!^~2P`!V4CDu=EKt4;Bxw^Z*lwg%iyGu<(GH1M@e`eptGM>4&)wR*t~R
zEtq~-c)`LErVmEL^ugQ#QwJ;mVd`M+hNUA|Ji+wC@&&xSfu=8*I+#2x9AM%w8YU03
zALb8qd6<4!c)`^{;}>QQ%w4c}fx8Q;4wrsd{)2@pEd9d#4GRyLyI}bp#)s7(u=s-6
z3ro*1{V;dI(htl&m_0E4==xy#VKgkA!TbYL538?W@don;disI6A7(!+|HA4oSbl|t
zJB$yrAC_)l?tsMy%pEZMVc~#oKP;WW;tNK@+>gs%nEkN$g83hoe_;Ay_QU)S(+8tr
z_Myig%$>0OgYIuw{K4#ng&)k{uy};!Uzj+|UYLHE{qS-WmOi00y1g*{Fd7#AF#WLb
zhxr>8?yz`;>4T|*#~V}vmj7V!0<#}ho<Nns&v}N~56gG3a7K3zEF56wqx&D`9+-Ww
zd;tqLSUQ0D3+4`(Ik0$#r3V-v<}O_E2r~yp!}2eBxWM$o(gjRCESzBaVD`e&6D%FT
z!Ud)eM#KCG3on@YF#BNoVBrAM2cu!>2bK<D{)L4TOg~H<rVmEL?1jZ6Ec{{l9~NJ*
z_=C9*rXS{hn0{D%z-U<dgOz76eJ~mpUoiK>%z@bl3kO{OhlMvxKP*0A=>-=4F#FJH
zSUkh>FU;LAf5Xf{_czQOn7y#{3v(yT9WZ~u$|IOQ7!3<Qm^rZUfQ1{(evJGJGXP4%
z%5j)^u=vLn?oe$Q?N*pQF#p2R4@Njb>vx#DVdla73yVjXILsX|b7A2OOMkF%gXxEb
z11ulG${$#Gz``3Ae=u`l=>?`A7Oyb<=<PRH{(-p%7Va>2!{wplOX%y5VCfI$UsycD
z+yjdbm^ga>17;6Q9n3zM`(fb*_cyd&fa!zLu<{3{AC?|q{(;dj^>7**kFa=vxdUb%
zEI#1&Lp8$U9Y({#51vk-=EBMoSiC^BGr;<PuyO{b4`vR`AFzCg)BP|5p)@SMVfta_
z600Ak4;G&=eXx80wFV~zOOLR00rNL3{&8xAO2EPsrXLpWxb(xq3l`rn`(f!1rXLm#
zP-_@q`3Dy7uzUvdCoKKJ;t!S%(DlRI3Cq8*@&gu*uzCj;4siWA7_e}M#Uso;81BcR
z9~KU<@)uSP!rX;J8@xRO3qM%8Bjj#a_`%{0W<D(3VD3hzVd`M{3>N;d@PxS^CXQZy
z!Ng(ih4~wn9$@hSb3Z)(pydZ_{sxxcVeW&aGnlzB_hF<XX#R%#16n@9%!AWV`(W_^
zQ;$xgha*fq%w9tAhn}8c<qFK-F#n<3i_VAn7v_IhdW3}|tp0$7Gc3Mf;SEpk(C~rf
zBba`ee_-(k^EWL1VD`h}8>Svs51_jj#)r8FW*$r&=6_gu024>2VfMpmBI6C_KUn&Q
z#S_fkgzSZ-6IgzS`4g57Vc~>sE=)blUU;}e-3^OBSU!S<11ufD=AU5Y6wIBl@Q1}a
zdOC#J1G66%-Z1qr{V;W~aDds19_}zcEdF5rfw>!|AC?|q>R~i2zF_)b`2Z#lD?ef8
zz|tYiK3F)xXjr)iiwC&9&~Sx?6U;ss4J+?p_Q2G^+<{KR!XKs|=3iL2!Qva{4wye+
z=EB?$OCPX&2(t&qhpB_bKP>;k)ZwCG=>itMuyz3Y`V*M_FneI`gM}xoT!4iOEFHkq
z!_q%29l`PwEFNI`V0>Keg_#3$H!R#>?#87LmR@1;05b<>FD(7z(ho~-F#Rxp!Sut@
z1up%tc!KGN<s(>p!_0w|2k7R&!XKs|#)riNEd9dLJ$m~aW*<yH%)RjNgO=m4ctnqX
zcs+xr4yGO!p0IF)t^bDk0~Y=;b+B{*3s;yvSo(#f19W>~@eVT=7Vj{9u>1s*hv|ov
zW3Y4wqha>L^uyc%(~paWrF&RBz``BoZghPxf5Y4lvmaI-!1Te=0gQ&FGgx@RXqfr1
z^aBeAc=~~sV=#3v`(W`86NlLYOFuAiI1P<&m_B$qf|?IYKXCPE3|RQX^uzo^&|Wli
z;PC`?56m5~_=5Q#J^o?w0}FRpI)JHzrC(Ti1v3X`J<Q!O8YT{lFIc$3_~`m!>S6H)
zD`#QiF#BQY4_A1iha;>!f$4+U3o{R9K8%K?KbSjU;R(|RGY3|l!1Tdrn7d)&2}`#y
zd6<4!IKaeVG%TE8_QTu*3wJ{40TzES8a>`|`5Tr`VetnGM_By}b04~SFmaeZn7y$4
z1xv?p|3LEt%pb6DfQ1)K9W33#%!io+%YU$NM6bVL?uLaMEd9db3zn{7<pKKm4a`1t
z8Wt}wf5O~h0QC>deX#Tf3ujpU0rL+moMHZkmH#mPu<(P01A6*FPY1C0KzA?99$0#W
zrC*qOnETN6!O}ar{V@N+;uYo&xPEAP0}CIRKVbP377s8Q<_=i;g1Hx_A7&3MoxtJ~
z77s9UVeWv%E6l$z_rmOfiNpK{6NkkEEc{{qhJ_C-ox$7(QwOWZVD5*711z3l=@6Fg
zVB#=;!OVr}gVC`19~OQv_rUxE3lEsPVfxT%Sa`w06Q&-f9~KYj`e5M;(+4vL7Tz%Z
zF!#Xp!@?J)59VLEJT#xc^rPDkTfYah4;KC~_rUTiOg~H>W<EL%OAoN{gQXK(;f}5!
z<_=gq!2A!(N9gL&`7rxo;ej6huyld055|X;BQX82@P_#xmws5fgXx2%SC~GSJ7D&p
z)39<F=5H7u7Je{u(Bltg4$MASdVuMN<rA1Wgxmo$4;Buv_<-dfSbV_L;i6&b5EdV>
zbOdt;E`2a@SUAAk0gHc_J7DTyG%WwZ?1QC0m^{qiF#Q<rKr2UK;fS8zVfMhxh0!qo
z!r~w1Zdmxk$}L#=2NQ>-8<>A#@dyhqn7J@}Vdh}CALbA!4YMB>4zP3#&xg=*0jeBM
z!SX%KA29d9(jT$$4@-YAb70{JGY94mxWzaauyBEy2TKPqbKu&b@dtB1EWBXp0cJnU
z|FHA`(+`Vpn0sOMKg_={eXwwZ=|iVs@dvB7VD`fN3*)2fgYjYdVE%>aLw7GsJ&cB_
zhs8H6ox|Ks$X-}D!{Qqj&M<wjascLkSpOZS9%e7Be1h2v3x8NThNWBB`EjuECz$;(
z^I`2bSiHdU3oPDY^04rN#T(2XSh&F41Jeid7fc>T!{Qqje=zsJ+AT2sFneM8VE%*I
z4+|%lJdB3vgP8+sKf~e;W)8Y~7$4?;I1QD6&ELWF!@?IvLzUsAVD`h@4+}q7`2#Dj
zVCe~#9?%Vhg&WK~SiHf~2h1E;dPesLx_Pkt3v(CD99a0l%z=r+;t%FNm^)zch@OvO
z_QAppW)D36pyeYh9mCuYGasfOord`vW-m-1u5<^}4+}q-JK*kux&vkoOg#n-E&pKp
z;pRd0!_psyKBxfPKBzgcb`C7Oi7kI%_QS#-7GCJ#kDfkZ>f!pK?uMy@l`pXJ0oH$m
zI-dbn-@)|5@;%Jouy}!`WAy$fEWN?vA8sEsJYeY+79Qy1kFa_N7Vj`~VCKT~!O|_v
z-LQCqm18h}!15m~TwwlznFDhV%p90K=zN$zVB)a!jIJLh4|5O9UYLJi;SSRe3s;yv
z7!9)z77ws=1WP}#d;s?cOe2(r<!4xU!0bT}H>h$93g%u|IKu3M#XE)ur~pho%wCv!
zV(o?36VUVm%TF--VdlW%5o#?1Z2lK!FD$%Z{)XirSU!N|Bba{J{y|v!hQ%wqJcO?Q
zhs7Jr9GHIe@P?&ZSUP~IhsPH*U&8dmXjr`gi+7m2VftY44^xj$!`u%`KQQ}Y;Rd5&
z=Hk*1GY1wQFneL{fQ1jbewccg{V;Q2<t|J=di=r6f#nyN|6%sP^uhQr^)MQ44>aAt
z)WQ4@iw~IpVd)8`AJ%??#UsqWF!#Xn157`RhUtg-8)gqI-NO9|br&o>67mn+Tqpx(
zF3df!^b0o!sty+g%a^e91<RMP_9ILmEWBWRSUQ1)FRXlq#V3r0sfW1_W)Dm~EI-1+
z9cDjFA36<-f0(~v`3~l9n7h#R!Qu&~9~S;F`(XJHRxiNyL*oag59WTDz3AltJpQ5i
z7?y5e`eFWs#Sbj}VKgj#!OVe$8_fN%^a~3wm^{o~F#WLjBV<2JAB={D157_G9AN1d
zW*;ovV0>8p2=f=r9Webcd3d}*!xfgUVBrb#7c87$`q61v`hk@vuyBEyLr6c&9k6(T
z`5WebT>4@Dh1m<^!_qrUKP*4P@;@y8U^FazVBrF*|6%%J{)M>@R)4|Fg{60xI4nJ*
z>xY?xK|{kI<`4An!%&YVfFAxZ_rt;;O*sY^rXS`%nEf#KV}w7{y)gB#atA%!VdkJ)
z2vZM>cbI!&>R{;^7OpUH7!AuGu=EPE2bM2j=E2+p(}#<O<#(8S2-%Nr4lEtP@;5Bs
zz|sf0dKe$(epood%1c<d!}u`uFdCLlVd({y?qL3gl~=HQgdTtB@eM0CVftZwSop)n
zKVa^K`4?tCOg$_g!Q2ny!`uxUe~0OZg)>YZW)3X;(B)zNg!vz)59SV7c*E?+6@KXc
zhou{seXx9kQU5?Kgt-$I-!SuF=@P1p0cH-&Juq>YKA1mX?uLn@hYzg(1}op-=>^)}
zg~c~a9!A5$5jOq;i$7TYgM}NcT!h6x%-yi~M^8U6f56-ia}UgXn15mAAWS_v4GVWz
zzJsM#n7!!gVSJdqu=D`47ZwhL?1klbSUAAq9TwlPc!ZgQUjO2<7Z&a?^I`shxeI0=
zEPi3>6BfQOd(rj5<YD;-7T&OQ2MZUNKVabx^AAiO7EUl4CJu8Kj1O}!%zZF%7!6CO
zu=E3qM_9bT(jCklu=EYn4+}q-KVanq%-^td2-64iKg?d3ewaUC?uF@t*@I5Q!Vl(d
zSp34$8_XTB`UG7+%s!ZZVd)m;E|@-;zv1Sg8-&J(#Um^{VE%xagQgtLh3SK(Cs_J{
zg+DA@;2K~IwE7$7A9Q`N@PTQA(XeoUxffO*!rTjUKTJJ34GS-re)MpI#W%V>7$5FW
zs6Sxo9OizQI$Sg?-eBPdOSdq4Vd)T-E@1A1nFmXMaQ8zcVCfagWq_H3K7I$Y7rmT-
z<zJZl;Nb-Azrf9f`WxmBn7uF>mY-qy7rp#~#UD&RjE02+EZxHD0a(1k>_-oOm_K0Y
z0M_n;>4TXEt7l;DgQ<tf!_0-*153X!d6+mljh+u+>5ou)fY}d=519Qh`(XZn#Ur}C
zFn#dwgYHLw#{)FpVBvsEKP;YM?uF?`R}bf-#Xl^c!omv{?{M{K448Xi@dnceOMhs}
zF}N`I!s<I%xWVE9W**ExuzUqecd&c|D~Dn61#>sd9kBEViw~GXVESP8!qmgu1q(lz
ze)RkcEAL?LK-UM0FGBGS(+>-OLi%9g1#>^lUYLHEeXw)~^Dmr+#yd<uEd9ddVc`h(
zH%tPJhPeaget5h<(<{s!XxbQH>R{mxOaHKVgT*^~e8J3t*#|4%VD5#*56oS#_=A<d
zu=E2PKZd1ScsN1b4Kp8Bj=|ys<{nsh!`uz?2TVQ8f3Wlf^AC)M`2!aXGau$Ym_LZs
z4|5NkhWZP={6@DQ=5DzEq2&<F|FH0b>4SwcEF56&hJ_Q%z3}*fX@t@+_rdIg+Yfaw
zEZ(5X;S^jQG#$h00a*Hhg%>RS!r}pD0Ib}C*$dMLO9!xg4HJj?A7(CW{1|2qtla?f
zFDzZc%z^m>)_;Q83kx?`c)`j!SiHmJVeW_Z-(dEl%cJXu*#pxLTmK1*Czw2}K85)M
z7Oyb>!@?03KCo~^k3X2ZVc`S|H(cca%pEZQ!150)JYepInFq5M7XL7Pu=)b#URb!p
z+zWFDEd9deq3H*fK4Il2EL>pr!t}%VF#BQUCoCRd=EKZ^nG1`5m^m<i;G$vS52vB|
zfmr>pc!24Lg(FNq%>A(NgM~XR{lLNp<~~?B!u$slhou{s|6%DK7JsmG0}BUqeXwwb
zxdSc_Wx(76b2obU!}P=QKP;WW(lJautX_Z`%K*#2F#RxfFneI}2UCwuqw9x-1I%8S
zyJ6vut{;}}Vc`V}Z&*5pxfkXhn7QcYpz~qzj>}*0_=Bc<nEkN&AC?Ya`5G4QuyPG%
zAI$wQdtmVolSfbQ81WBP2uq(Zb6|W}xZ(0YEd9dt!O{=R9+-ZZJ7DP%YAw9|0n-Qb
zFU+5?bOck6PQ&!U?1zOPOdpJfr7Lv(u=D~;w=nZy;xK(M|HI6~Fb66Ci#J&O!_0$)
z11ujxmBT4m{KL{6EL>sn087U(8m<w_fa!;Y1I!$ldtvT}`3I^EgM#UYn+GlLVDX2c
z0V;r=4q@R3vlr$LsB#PnW*?j;(q5QBu=s=31MvC>%^X;|gz1CPuy}&$hv!eI0+@NQ
z_=Am~L#5&8pTP1TEWg6y3l^@hctO_>3qK6|q3(zI50;M5*YCjG0ka=wA3Qxm&4J}B
zSo;%}&M^EB9fyX61I*tr{V;Kuy|8i<W<RXlgq4@@bO&_@Odq<xVd)(fez0_hs~&*a
z3-d25{lLtJr8`(Ugu4T#0F8!)7tCB(xWLSTg#(&43@*$aa2o1wn19eUz{(3)Jj2RC
zSbW3WkCA?%^#?57z``Hhy%_q?9Dtq<VD5&+11ue*$2-gzn0i<|!16yV-eBPdGY6&*
zM#J0<qha>K%5#|eVBvrskFan<*ALSNPiN40fSC`Ihq)VF9V{MT?ts}3tH;pw!~6}C
zhlLAzJfepqdi=q{1x6DJKbZSq=D@=P>R*_CbbDdu!}P<#56*|`M-Ok9J{S$lCouoQ
z@)3Ic!@>oo4@Se>4bulNZ=vZMmTq9-kM0kcy)gH~%z>E?3xAk7u>1j&hxr#){-B!=
zO9wFb!}^af{kUkDK3KYfm4~qO2h#^r2TMP&@P@@BEIeTThn24|{V;v7cz~5(Fn`0!
zZ5SV}56Xa*KQMhT_n_Mgb3arYoPxz4EFZzb52hdHUtH-BW-l!MU<SkE1)2_V`5zYF
zF#BQdft7nOeYnbBSiHmZ!}u`uF#Ry|VD5v_F#BNoVE%^Dgu)+YFDxEl=ECw1%zlh`
zhbn}{7t9=3I|HT<W<OLp1_cXeSbBq*2lGEH{$T!r830Syuy})+19K-#9+qxk=D_sB
z`fo7z!qmb14GULTdV-}Nn0{D#hq)J~59SV-e_;NAg$K+$m^m>0uyO>J|6u-w(eQEu
znjT^LanUgIVD5pr17<!<A1wXD>JwNv!1TlHhm~J2eJ~nkJ}e$!G|U{BzhUtL(+8tr
z`eES>a}Ufu7>yooF#Ry~u=EG>2P{5d@eVTwrVmEL+zX>&?u6xQm_Kl(V^}(axgQpv
zFmqt(1{VG>d*L*+o`Ja^<{y|on7y!gfa`-YVDSV?kFfF>=5CldxXKThdtvs%@&n91
znEf#Gpw=QNH21^Z15Kwe`*Fn|%ph32!NL(9u26Ge^&d<<jE2P%Odl-Yp~pKcJ;3zA
zXqf+D@ei{HmR?}t08<a6(cKFRH<*86`e5M!3x8Pu3l?868dg8T?1lLURxZQLh2<w$
zdV#5fg#*leFnzFafa!zff0+AW?t_^F^9M{FOdq`bfmwh?!_0%31JBpc_=EW$O&gpG
z^A9W@VD`eo1FjGU17<IrhT01YHyqmF!sOZy^CwI{tR9BBA7{RY+6)Ufn0{D3gz1OH
zA5=L8g>FAAJrk=RmTqA7!_ot>`e6=&<vUpT!~6{ke^`EisfW=p_rvUk#XqbZgOxup
zb718vJe;BB53Ia^g%^wuvj^rMSpJ6PJD56H{)4#_79KGBVEPG_W3c#xg$FF(z}y9M
z4>}ExH>iJL`4>GO;%fiH`~eGpnE9~qgPDWN{jhL{xdWE(aF+W}3t;I7W-pu%&40M;
zgKCH0!wCy-Sop!h4HoV&_rvtT)WO^h6NjmTr4LxV!u$hM2g`Rb_rl^GmhNEb5ms)&
z(lbmyIt?o~Vd)AMjxhJY;sNF#T>4?*0LvFJeXw*3OONRKVg7~F(C~-rL)QT1!~73(
zH!NIW_Ts8Hq1xg52Vv<Q7T$!?J4`<;oM8TjrF)n^Vc~^g9yFi8^uf|U%zjunz|t?w
z9GJZ@f5Ozk;vYR8Vd(}I9x(mrG%Ov$+ye`Dn7y!cgsu<9hq(h553q26xgS>k!SV%6
zA1)det}ydp=Af@1gQ<gsA50xA9ANnxmj2Q2&xE-f?r&(kz}y9MFDx9;;}K>*Oh3%u
zF#Rz1!1Tl70p?zqKA1aT=EKqfJpDrR1FSrPrB|4KSp2~3fu#qSJJ4xZ{}HAiCJ(b8
z=5AOzhRLJTFn_?xGZ+nXC(J#t^n|V-mX2ZKF!NyX4$}t<H<&sY4NJGMaEFBhj88~E
z%)PL1fY}cV2blW_g#*mJuyPmXURb(=xfd6W9-rvx4i=uY(htiguyBC6ALf5T=?SI}
zM#J(uA^os=0;Ufh4$$-tGY6NyVf7tMKRiC6`eFXSr60ZifQ18k_~VLyn0{C~gwe3{
z1G67J{9x%GmR?}?!qq|D4|501JeWV=;RG$O;N=ihA1uAV!WUK#!0U6UK3KYisfW22
zrVnN=EFZz<zhL&m{0lP|W)G}9gZU4pAJ%__$;12&3tyQ1Fn7V!!Ng(q!@>bw9n2pv
zeXw#2mJVU=gz1O*6Xp(>zhL<mp8lZW3eyLpVetra2h6{)aDdV9c!g<%(lC9n^b1o5
zE5~5^VeWxygP)fU(+4Z>VD5#v3r54tf%zYvPoU`l7H+U`f|&=4e^@$%g+DBuaK%4N
zA1pjz@c@f|SiXgYJIsDqJj29c?tu9V<~~?Bz|4oa2WBoTUg73J;}2#JOg}t)py2?E
z4_JDI*$0acm_8Vd9*(g30~XFO{jl(Z>4T+DSp31<0rMx!Tv+)6GY6JWVCKNo!T7Ln
zMmHCxALcHYdUP7S-30SD%w6c}(fP3WfYZ=$M^C@#`r!VD#si!W)sOCPn0}Z#SUQBo
zA1oii;t^JU!omTjAEpnM-eBni-u{Bl=fli{r6-tvSop)j8<zfH@eNatE8JoFVCey-
z55|YJ|6uM$HwTu_VDScvf0%w)dW7YFSUAAkkBf$dBg`C_KVa^_r4QzRn7?4*3D*aU
zPaFoo;scg`VD841eqiwr3kR6HVfh9Yp0IF%@nP1($_==Cq45n1cbNIGd;s?c)Bt$-
z1WmUvcR-~XVCpgQ4^%xY-NDqu+z0aqEZxJ*frTebJvt3bkFaornFk9;boDSkyu5_E
z3l{IN^a2lmwD^OiFIf1&?1z~P%YQKU!)Ta1EZxG~3kz?UdtvzyrXHP!xf>Rau<(I}
zH!R<x>x1!O?uDfTSa`ww4O54UhWQ^BUoi8~{f(Y)VfLYi8%!U}-7r4PKQMQ|?1Ry;
zbO`f5OdZT0u>1=XhtaTbhq)UTp6KZg-Tg5A==mKM9<X?Zg(qA+8Uvosq3I5m9$@JW
zW)8Z)V8+1W9i|T^4huhcxS^$6m^xTGhs6Ud9AWxkG|W7hJ{S!PH<*4{_`%XIjE0pb
zFnur@7G5xa!1yr#!r~8R4on|94bumsVeW#(8!Q~r^`Y}&?uYpY<_?&C7$039j1LPx
zc)Em!JFfHx(+8tr=@Aw$Fn`1H0Zbj7hNc@>_`&iO%$+cOFd7yfF#WLbg83Jgo?+z<
ztUQ9LgV8Ykuy97#5A!dKhPel(4`vQbAB+!kCoDa~_%L%|`qAx$nGaJ33kR5gVCfi^
zo^a`h#WSoPgQ<h*gN@(A;ul84!VBGgSpJ6ThqeD<`r!VDrW06v!{QSb{xEmJ$`e@n
zfa!;&TUfaT^Ea#<fzhyV!WE7%eK2>x{DUk1!pwoSH(>6D`5QglVd~LoSbV|!12Yfi
zZ<u?~^}+0c>4T*kn0s;QhwcA{>4TXI(+3N8n7uF>rVf@~Vfh`V4`x3seqr$r3rCoK
zTr|u-uyOzvZ|L^p(hm!Nn0{FJ6Vea!FHAqo9fb75)WO0L=6_gw6Bhok`~i12wA_N}
zgQaI!I}>IO%>OWV!1Y5JF!#dJElfQuJ;CZNm^;wRKUltj(XeoUg&!<G!Sti&AE-0n
z6s+8U>4${_q40;NcWAi4(ihBLjPQfn2y-vYzc71Y{)B}GEL>sYQ0)w`@(mUbFneM0
zFdC*0mY!kaFdCNLVKmIYuyhZL2bg*o4GVXe{qXbzEe~P(Vd~LoboayR3s|@jiZ7VG
zFdC*G=6)C-mcL>4!@>!s4;KC~^)Ua!!VT^ZsJ~$5!1Sa08zv9)A1oeW^$$!Qmfm3D
z4)ZT8UBJT^YA?Eem^m=_!`uVY56gEj_ruJG>4W84m_8UEJ>L*YH!%BQ`e63M>N}YI
zFneJ%%zrQ%RvyD>n0^=y(}zyO+zs;wEWTms2$p})^}+Zs`(XNE@ek7vO9wFZa2lH5
zVSJcBVDSmd_Za?%>Vw$_(+9H$mY!h#hPeY~4~#~4H!MBD$}N~am^)zlU^FiMuyg{`
zkKul(0WkN&;veRIn15jIfTcs2xlrx!@(Wh}!}2AJhS?7b2bex|8s=}9dtm7gW-l!N
zq1%sc4tjXN%XMh}gSiJ?A37iA4!C(x{pjHcOSdrp!`uyve^_}0^9L+{!R&*DJIp?q
zIk518xfd2MFnur@mws4y!Sti&A6Wdr>MK}%4+}4tzhUJt%>9JIA7&oRKQR3;ci?h2
ztel0(!)Ww!0JeV{=5JWKfw>>%ZkRh^;Q&j=uyBQ$52IoE9Tsn}c!ueRsY9n>_QCuM
z3ul;rVeUZJ2jj!S3+8`Vc;Rv<OdpJf`5V>_gSj6j4~sV#4Ra6d{C1eTVet-2uP}4b
z!yo1@*!d4IccX_BEIeW1j&489eJ~o9k6`YBl|L|jF!N#Vhv|dSF!#gU0}BtBJS;wN
z)xWTO3sVpCKg>O_@)x%MAEplGZdg3R!V6{|EPcTIfxiC_W)IB0u=s=3U$FE5OONQ~
z0L;HIcf#^DOdZT#SiHf)59V%|KA1aU=@;fMSbBhkAIw~s|6utK79Oy8AT|DB_QBi(
z=R?a~nEPS&;G$vvfQ2V|I>HtI=;p!9fw>1G{-O0htQ>;57ZzSH{jm6i#UuLqA(%YO
z9+)|>_=mOIVDShuAKksMaEGNsSUP~&3v)lZJ{TXCj?m)`mhNEgfvJblu<(VsAC@j)
z=>?XaVCvCnn0sO20CO+QpRjZa%U3XSVc`yoXIQ-o3n!R7EFNI~fYC7b!f05%3$q8t
zhm~J2dtvT^g)hwAFn7ZA!Qun%9%wp7_dhxx<{ntSg4qjm2fBI~9~N&g8WxT)|Kf^2
zSopx~g~caKA1vR&$_<!(FdAkbj3(Azbp0^<VdAiK4|4}Beqr+PbOz0buyg>6ADB8=
zc?AncbaP<thQ%YyKA8Eic!2o}w*CNSK1?2F4lMn^^uy8t%zl{pF#RwZrXM{V2<b!D
z4>JemUYLEj;vbfdVfMoL(DVw62bewRG%TON^uy8%%-y)s4a^*vJ{S!Xhm}__bI{X2
zEPP@5VE%^1Kg_?d@P~y1di=rk!TbSJ2XhCke1e4oto;d-hlLl+9GHDD_rdZL%pd6W
z4@^DG9#}etnGY*JVDX77JmKz#<_B1N3FaSIc)-ks(Xjdk7LM@pfXMiV)f+H(!{Q0%
zZd~@l?1zOLOh2r?fcY0@9=g9_;R|y&%p91xFdF86n0wIkKg?d3zhUZO;R4Iwu=q!h
zKbZev_QT2>SiHjO1(^Nl?LU}1VdW3ZKd^cP7LPFfu>1wn2dAO&4s!=AykP!;@!{&B
z446A%;Q&j|F#BNn5UL!5g82_-FD(DT!V4x3vmcfZVDSgD7v^7>y|D0yg$vBzu=yMG
z^bSimF#BQV!qOkiUYI$s{tJ3|!#oIcFD(3EG%WqV!U^UMSUQ0D17;7*URb>VGasFX
znFlimmX2WNpsPpc!`uOj2Uxtp+zksKbbT;;Vc`t(H_RU}|H9%0T|X>-Vfh3m56e$5
zdtu=K^A}7VtbByI8|HslyutE6tUQ3lFHAo!8fFg69k6hOg$FKuFmaeZnEznm3G)X`
z9jx4jg(EE8!}1lne)ReWrVi#_n0c`D4>J!Q{?Kw6W<N|nEWN|R5f)xBeeigNX+)!8
z=?9h{(Blo}e>81yF3et-eptN0!V6{|Tmu>d=5LsOSh)w&kIVhAas;jqS`NbW!NLK}
zIye^=Z!rC^cqdmsEZ$)HVg7(yh{k}$A1wS}@dyiVm^m;$ns)g9A(;DN`4E=RVESR{
zA6-ApURe0U?19A}j1MaZVESR{6=pBYA29o1_QTYp(>l<2g83Jw9^GD;IIKK?nFGsL
zuyhOK!^$m~dtmt;o(|CR56nCm4bum67c4$u`3RODVd`P>uyh2|4+|eyx`l-cdie!U
zZ_x07#UCuc!0d&kBba$G`(gIM!U0x}!@?2fUR?DL%syEB!{QlcF3cUUaD>?lqha9$
zE5~8(h4~AXKjG;Q8vd~G!j*nt;R=gSn15j52a9iXcf;Zt<}aB2F#Rz9!R&|WhlLL;
zykP!<r8}6vVE%^DF#Ry|VBrM|514!5_QNznX_)(9`4E<FVD5m?=<yEKj<F6OrVbVk
zu>1q_A50%CTw(5qxfiAm7JjgJLyv!0_`}={(+BfEEc{^phNXYF{ZIxh-NM`fiyx>o
z11$Vt{(*_3(=hkL#9{7(g)c1spqmd<k50qf1=A0U2Mqntd<)AjFnO4NVB)awgT)Wb
zK3KYeg%>RTVc`f1H&{Ht^ucMU{jm6g(eU_%<~Q{4gZT^QPFOs`%6pi4So%SCH_U#R
zeX#rvi*J}YFm<?SnEkN$gXxE*Cs=r5=!g0nR(`<T4^szAH?Z)7@nQ0?@PU<=uy}%{
z6PSNs>R{oIZVoIQVdWK!4~s{bKVantdN{)T4NGq@_rUxOOE<Xeg@qq1{lN6W{0$3F
zxWAzcSh&IULB(Mdy1QZN5hf2?zXgkbSh&Ib1IvG~e1q<Om^tV)%>OX`Fmnm%hlM-L
zU$FRug#*k#==Q<*==l)lUs$}s>H(N~7!6A&FneL)0h5R2ADB6?_CLCNVetr452IoJ
zfaNFj@dtEsVCe{!ZeTQex`XM1sfVWnXug8k4+}q7`iJ=o7C$h5!}zfD2U7=&e;5rD
zhuIIa7nWaO{)L4<%zl`AVESP+EZ$)5fX4@pau22tM#JI@R(`<pG0Z+#_~A-_Fn7bk
z5$1nbK7g46i+`BCFd7yPu=t0$1D4)k{znf#n0~l_q45Ue!^&@%`*EdzbpONL1B(Y(
z{=?;dm^xUv!Q2fC7nnFqKYBWX`2(GX#V1TZEF1{whlM*#Kg@kF{kY;0rVbX5u=s%a
z9~O@2`3UAdm_Atf1*<n;>S6vzA3uiW8<=}x=^2)8VBv`?|HJHuxfiA%mR?})hnWKl
zXP7#ey)bvc!Uq-}Fh0yYn0{FN!t|r(SD1TX@c~PZFn_`H!_>j_!_}dsXP7=%Jizh^
z%p6$wz|sfIKd}4&^9M{nOde)GEZ@TX2{RuSk1%~O^I`b}R(`?M!SWd_USQ_I^ufXp
zM#J0(3rARZ!O{y%AA0=3^uz3hxdWD;Vc`g)Vd)U259WWEy|8o(Z!bX0PguCZ(h*EQ
zOdl+qVCe}KPB3+_aDt0N&4uZMxfd2*uyg>64_J7h#~*rrgQX9cJ7M7pGau#;SpAD0
zZm{r$g*!|iESzBR04qme{)2@ZOdZS~nEfzyFmqx4fQiGy2b$ht;R5qN%s!a?Fn#DW
zESzBR0t-)=esp`$^~3xD(+^88F#Yg!0o4eLZ&*CQ^AXe^F#BNoVSK1|2H5@KF#BNs
zg~`L>1DAeSxWQ<cdRX|u;scj{m_0BWW)93fa6UA>qPq{K4@Se)L;Vj+UoiV&=@X_N
zoraaSF#FNnj~@Oo`(WV@%l|NYVc`L@AC~@L_QJvm9&b<ySpI{jf2jXq=0KIfDVV!q
z0<d%sGanZ3@ca)`2&G}_VeW_73o{qy9+>^G`5##NL)Qm27IWMH>JRjIgxLcNN0_;=
zavPSPVdkRKuy}yQ8%!SNURXYYiKEl7@Q3*uR$imWH@ZG_J}kY!%!7qH%sh1U=zLgw
z!Tb-4FGA@SrXC)C&~S!@AG$s0d~|oi<YDDF%pbVI5$0d?@Q0~~g(J*AF!SK{K=Uii
z9WeE<bO$RJVf8wC`wf<UVd`M^!qOEye4yb1OTRGtVd`M|VKgk=!NU=%AEpmRqx&CL
z55V-p(jm-WFnzFc0v2wt`U0j7mLAc~f$N7cpy3DO!omaAE`!A*uKEk+f0#dDG)z4#
z-eBPmvj?4q*$XQVVE%*Yhq(h5Kj`{l?ttZISbBz~3s^ov?|;MO(ZdTC53q26#UpzB
z!{Qq~9$@7+tlWmt=<bL48(lvv-eLBl`xoX<m_ArKg@-dVy}|qq7e_M)7Tz%PVEG0X
z{;+t&S&pEkA6R(8(j(0MaC6b@N9V%a1B*{sxWU2`T|JBsvk#X3VD`cM4^xLu!@><_
zFU<Y0e1lHI`~x!wrXOZ6j1O}!EFHnsL*oU;hxrp$e#6Xx*#k@e=;;QRewaUC`307a
zaK%5&99TMsr6*W93)7Fwewcprd;m)?F#WLjfT>5PVeW?64@-CS(vR+cSbl(|KUjR=
z3I|yH!Q2lEZ<zaG@d5KU%zRjSgZUp`9zfFx%zl_Y7!A`0OE0i=1WRwQ@P^qB(?^Jg
zxgVyEkbaoGFn7b<1HCs97H+WgfZl$CmD4bLVc`M`KbSsPK1O#BEF56^VdlftLBkQ|
zA9Na4ZotAH7H+u09bG>x-eK;5<yTn#L06B94+}q-d9ZQ^W*#nmu<(cLgO<lIeK2!i
z`q9%b+}%)pFn7bk0hX^}?Fv}^2a6|oxIy*9?1h;Nt5;#-u=*DkUoibJdtvT{$;0{3
zc!aqhw*LjDA6DMN{0S?EVetr~Ve+v24$}{FCoDW+?t!HvSUQC1N2g)_g_#Fa53?8M
zZghPxKFq%`cN1$bEWg9_!}-wgg1G}`4>}F=H%vb)o?-T*t4HUf=U<pSEZ$({9?V?y
zaDasu%zjur!ty^%JuLsDufK$)dzd*edtu=Yix*h9!pD!G4u$z2W)3Xf!_0xDe^|PJ
zrDLde2ADXk+=ICT77wuU2<9GG{(<R-r9+tiVeW**8!R4R^&w0jOg+p#Sh&H$8CIUa
z%t3cIEdRpX3DXaYPguCY+zrbQF#FMIm_ArIz~UViZ!q)G^}+Zs_rm-Gi+`B?uyBN_
z$3?^34Ra67-LQHHT^}sGVd5}8EM8#x(8C|59_C+Ie89pL=5Lt$Vg7;XgV8Ycu=EFW
zH!QwjG)z4@4NHHpaD&k>`(fz^mXFZ&!}u`!Vg7){A51?k_rvtV!V9J!mXBciVSJc+
zbQ<n&Xgvb6ALf5T=?4}Ma2i_v!NLLEJ{TVselT~#{Q=bva|cX4It`0ISh&OV!{Qy5
zesJl7`5UGmW**GnFd9AnVDSah2a6w=I#|Aj`5#uE;c^E|9OiyleuTLnmwuQ&SiXUU
zBg`ML_=n{~n0j;?<_}o>!R4Xx01Hp_@*AcO7LPDK%>6KPVfN!XzZ=%yh4~X!j=<^%
zSUAAiFEIbX+zYb@7Vfb4fVmqM53u-wxf5m&EZku2QCPg=(g$-dOdl-#VfhTkhm~6}
z^I-PF<YD?@;;?cS<{wyiz~T{R9?bo)^b0cw<}R53VftbIz!iTmeXwwag#*ldSiOe6
z{}QGTrXJ>gSUkYu2NwUZ@jsY7Fn7b!JuJRp^007%xgQpeF#p5i4;Ef9K1?2F9*l;E
zGgJZ09+*2|`f-IfR2!Uv`4gre=3kh7FmrH~pRjO)>4&9vm_Bs>!Yzg}(CvqXBTPRm
z+@Q)aC|JIQ*$*q%VESSHz|aU4K-Uk82bg{s4ONaop@%;#{9yiu*^eG?=mx><h1OrN
zbO^H_S3Lp?KbXBR8fHGsepood+yP6MFneL)4$J>Ab7AQo<_?&<VCf8|4<-(4FT&yj
zmXBfjVfMq^2cu#3!P?6(8kTQi;R&+`rVpOppy389w_)<=?uWSpCJ)mOD<@$3VdXWt
zdYC>K4NE^T{jl(W>BkjsF#Rxp!@>)mpP=;?%>6L+xM-NYuyBKwvxMx2nFq@^a2jeq
zJltUl(P?P7!`u%mCt%?Qi+^-&Fg`3D!t}$+W0<`#f8a_tuyBX#hZ+n^$FTYX77j4;
zpvo9v;RTC#Sa`$q!{lN1qt_oW_rvlXEd9gMEleDiZejHY%zhXRONTIjz|4iY8<vmJ
z-49a-3kR4#VD5+IdssNY?1RxT`(Wt@CJryxpy>dXo?-GZ8s<Nky|DC*t{)a2Fnur@
zmVaR75X>Ji|H9l4Q;$x=?SZC4SbV|KA0hoPf5Y4lGanXyu<(Y(KTIA*!~73Rw=i*7
zK7iSeD<8n@h4~-mADI7fg&WKsbQ%_ZFn__~4dxGY^)Nm>+@RqI%LlM<#N~dNzhUl&
znFn(}EZxJz(P>=ng@rfFA29d8+Rre3FneL~1uHjT<rqACq3IarADBEW{ln5BEdRpl
zH<-WS`k>~(<YD&1+ygTQT^}qRp!*-44>Jc=9>C%cS2}>#7tnZv#UsogFn43bKU6);
z|1f)D;SO^bJUpTPhKa-C9TtwTbPJP*l}j-F81};yLTOld!rTKDhM!*!i+`9nE*jk&
zn15j501HoC`eEX*@POrSm_GFK8<xIc;SP&`SiHc>Lzw;O<9F!ph4EqW2g^sW@PqjW
z=1y3+!@>=gUtspZ+znHYPQ%;@(+~3p%wCu|u<{IDKf3*}@Pg@snS-tm#)qX7nEf#S
z!}P<_4@^Be4Ob6M&#>@^>Bp6CVCKN$4`x4{4^8i++K(RJu=*QjA36;SKUg_}9{$9Z
z8|dK(vj-Lqu=E5IhtY8NLc<Sk4%Gj+!Vl(dSbW3s3oN~%`x};SVKl7VfQ36u9F|^S
z<sXcO#RIJT$JoCQbuX;Gfu##rzJY}UOdeK#!Q2h=2TUBM4yF&?Kd|tF(J=d9{z2Cd
z<HOXW(=h$8bO0;wVBvwT9?plx8_Zs~IneNc#Ro?IMbih14_G}4vmfSf7$0UYJe@$z
zft5Qj_rmfajE2P@EF56>3g4hwJe_<-dDm^)zlVc`YS53?VpAEpkbA7(x*{le{s
zdJHBH(+?{jVeW^eC#Z7x`Ij(zVD&z%9D(IKn7d%<9Ht-U4w!xD@c?rV%-=A5FdAkL
z%pWj!z~UWdFD!gu`d~CH|HJHu#TP8zVfxVB4bu<HpKuzQ?r@bGFnur@=3kgUVBw6b
zzXH<-qhaob`5z_@GY1y$xZDp@56kzk@)ypB`Uh9|!}P<#4dxzLx`EjbO9wFZFd97{
z!Qu}V?=XMB)T7g|aEJLH7LKs+hna&Op0M@T==leheqrvyNJmfy!16mRoM7b!%ssGh
zfcYOrL$za2uyBX@0~TH|_ruJ`&<GVk*AFZ2VD`fDEzDe~HcSdyp26w`n15j5fUErn
zi+`B;F!#X10j3{)`~YS?%-^v1gQZtkK7^$Mm_N`R4vR-vc>>G#Fmuq=qw`_*!{QSb
z?y&rit{%pRr4yJxVE%`tKbSgPG%TFa?T4jTm_K0Vq5B6`e!#*T7EZ8q409JO|Dw+y
z!1Y1n9i|QzUod%?z3A}=%daqXu<(MFt1ueoE*K394_LZ^g##?Tz`_xx9>$01gVC_?
zhv|p;7v_JMe)RBz<zHC%!SV}C9n3tKzhUVC<__5XtuX(?#Np*0G(2JE!Sutz7v^qQ
zx`o*XlZWMZSopxwJ<J@K|6w#t9gGi)519L4@rg@6OdpJf>4)VfSU!ZsFH9XQzrykj
zEWBXx0P_c|9D?bC(XjA>#RDu}VE%=re{_3c`eEq{mVaRGhUtUFKP-M>`d~CnJxo8$
zy|8eC#VbrbIt`0In15mR!Qvg4y|DO)*$Z<A%zRil!pwukA1oYT@e0!q3pZH0htaTf
z0gFHM{0q|$iw9Ud!Qvg3A7S>Qm;W$#!_qIzUU+(d-sc7jPgws2?oX&ZEdF6M%>OWb
zaR0+JLTQ*gVg7)*2c{3^AE<In3Yt%G>4&8wOpQ<xH2v^+fwr4)xf`k*<NP9cdWPzU
z<pY>{bQ%_KF!#g49p-;nIz-n8<HPbVTt8F-rXN-hL4`3WxO<`D08iIY_Y<-omOf$W
z2NwUZa3d7{@bHG(5AzR(eyDq39)PWXg!voh4p{ny@nPu)W*$sGtpAN}AI$$Scfs_-
z%5|827!9)rW)7@8fyFB<-@@#Nr3YAkhpC69dzg8!`~!<WSUCc(|DoXpcPEq&%TKUy
zhuH^nFFFl#H!K{{%TJg)(DlLiFn_?x8JK;raD%DCMZ^3Jw;$>cba%kQ5#1b^{V@Ho
z_=klTEd0>@0ShOXK3F)y@(rPM4|501-!OB~-Hj{V!1TjusJ~(90VWQMM_Bm{6URlv
z@*m7UuyDZT4p{z&sfYOoW*;m(V0?6YVfNwjKe6EmOZTw!0H>k(9+rR5?T7JU^*7A@
zF#p2RA+CBH=5JWI!{QGX?y&R(vj<K?;|u0*Sh)?;2eS|6FPOQoaD<gVF!#d5Vdlf!
z1rvwG11$d=pxF;|C(IsLyu$b}{jhL^m2)t6!}P<-16aJm@*ON4!Q6*V!~6*g4_N%6
z+l#Ib#)r8VW-rWrFnbB5BUm`X?1lLg=6+aw!0dz3uyg~n7iJI4UYI&`8Wuk=|H9;9
z{)O>j<qRwyVKglMVf7{~o?z->?t-a@nG0)w!Nxye_QS#--5gl?2h$JB7qEDT*#|2h
zVfMnq6Po{E`3UAObQ%_aF!#XX4`wecTyg1x*$1Ox=E33*W)7@=frURT-@@Dt^FK^K
zdicT81<c<tbKvfR`WI#{OdMuEJp7;%uyBX@7gis^Xjr;~>4(K1R67IAzp!wFxf_-r
zVc`y=(fczndtmt%<_?&Cn7y!YfZ2;q!}1NxJ+N?x*^90o&POYUVCf2GFM4>v^+6dh
zcf;}@Oh3$Cm^)zM2-U^_%Qxup1h)_BE?D}9r5l)j*!f>D`(gIL(htmhSiXRz8<>7r
zxWn8F(+3N8n0}bM(e=a3L8oEy4@=K5^Kj)Kbp5b+gqZ{L7t9=V^)NonK3M#thZ`&$
z(9<6*{9x%9W)93;7!3=5^z;wQf3SFjrC(S)!pdQodtm0lXjnMI?1$xRm^@5BEIq=+
zVKgk9VD`hp4;F8*bO<vC77j4|FdC*0<_?&DVfMnzgQ<tnuzmt8{lmf!rXOZ6uKWx0
zKP=o}>R{mj3r83Yvlk{0b1$qMgM~B9U9fZq6Nl-C(J=SI%z>E;YX`y7A1?o+ha1dX
zSh&IT!^B}U%)c;uVd)5F4=g{z+<~5MVeW+GV;CQ1J}h0s;sHj(<Z;ok@PL^M3qNA@
z!{QTW4y@dOnS;w-SbV|41EvlZj<E26iNpL0%kQxC56cg*@&cA_VEF)M9?TqA{K06L
zKA8Eic!7mKto;hp5A!G7-OzFy7H=?nVetyn2U8EDVdCiigry&tKVa%$<v%Q*VCrD`
z8zv6ZhaP`0|G~l?<~~@ugoOvpTo?_r9~TXa4_LUs{7)$UVf7F!JYfES#TSf^VJ|ei
zVdW3Z-LU))b2lvhU^L7eSp355h4EqXa2ndKf`unc9Oh4$eptSP$-~k=%pd6bVfMlF
z!_ps2A3Qxk?T48Qi$7SphnWLQcZ9+lHh%!q4~u`8`(XCM!V%^!nEkMHf}Y=D?t;Z9
z%pEZQqo+fdc`zDQj>F;+PDA~VzJC;^9&RtxAXvP>XjnQxufJjPu<$1}{$TdP(mgC4
z!om;cZ&<iOod>62`e63L(kCo@Vc`$UM=*^rdtv%u@duNKnG16VTt74%Vd`Mv2P-dN
z=>cXxEZ$-MKp%gHse{=EGY1ylFnzf4FU)>eIRw)Wb2rQ!n0s;2uyhDZcd+~eYfs?P
z2Mb47yusWFZ@1vk2-644cW`$@%OjY-VBrpT57a!EdRY2{`4>jR(j$8K!`%n92NrIy
zbOJS=0j3|$hsHZB|H0gk9&a%9Fn7RcSh|G8CrllzzJ-N5%zZF<So(y;1G;{gI&>PQ
z58Zz-_rvUig+DC)!`un;7tB4l;uEGHrXJ=Wm_4xc2a5-Ecf!&QEWBai3R4HO7w&Ip
zJYeX9p0@!jzhU7H^B2rK7!A9>0p@O4{KMQ0<KuES%-^tl151ana7B+VSUC=}9~TWP
z58&kp)W0x);nD{ahq(h5J}`S>;RsU)qhaX-rXIb%fQ37HIKbiw=1y3CgvB?EhJ^#n
zK3p`+JeWIR=?La-SbU=Ehm{jBcjI&ix<)7;mQP^z!qPv?UR>!1W)D2wLen3toPxO@
zmVaR8!omZV{$To{&Vf^~@PO%q#RIJVg|$Cm=?`WP%zRinfbn7aan++Ri(&ZzW-hMs
z7p5LY!~6?N4{$y-{lfeKQxBtI{)Fj++Yi+TOZVvM50+10?u5~>aDdUU{0#F4%wCv&
zSop)z4|+O-rDIrnfa!<Pu=E2n2Nv$I@&gv`uzU#92cu#652hbhuE6x6yBnqtM#I7j
zrXS{Sn0}c1Vd~Lo4E@mge3*V%_@nEC@zLWC=6;wySop)#W6;og7#9Dqc!0SZW)3VJ
z!}P=A8|H6VeuSxmg##=<qQ@Vs{|B=V7Vofh3rim`eK2!i_QU0&?uXkCRRB{D(~qkh
zLH7rGx`DY1YAl?Bg*(juFmqt~Vd(}}x`P=6i+`B=Vdlc(4Ich54QMnhykPMMa|cWx
zj7HOj!G+}mm^rZcgXu@l|1bk!?uCT|oDVHOVCey-9-W4zSD3qD<s;1hFmafBVErdp
z{KM^qmjAGP0}D?W4NK4H<u@$+VetgB7j7<EeuT-x=6_(}1&aq*eF*b6%ssI52=hP8
zT`>Q_$}L#?1!fOSKg>LsesmfZ9x(U7)WhNr7JlgZVD5$GLl_NH4@=MJ;R7=VUT;J5
zAw1nf`7nRM+yM)Jm_K3uhou{sxv=m=cLz*AOg$|AVD5&68!R2c(lsnR;Nc0g2p0`?
zKP-LX(hqYd%s;Sv0uzVD3oIPb(*ex?uzCy@&gkaB(l5GmVfMhn0cIX79AM!Na~HaP
zSUAGc4J;gC@d%?~@qn%$7Cx~20`mtfykPdD`x|BsEPcSz5iH(d=@J$WFmqu3g6W6p
zgZUp;-oxArGanXyu<=h=yujQI_Xo7xg4qw#4|6|EKg?Y){V??~`(f^ZtAm;gGY1y#
zFm<r<045HLSD3$H=ED39r=jT-<_=i=!Tbx;kE<So*$eY0%)RLTK@WGBI#{}a`2%Jz
ztiFZC1Kj=4aDcfVrVl-z!qO2eKfuBbrVmb|`5)#Vn0+uB<_@?%C<7K=uzUk^H_Sc^
ze?#@bD473Y;RVZQ=;pxG!)RFgL$?=Z4$Qr<{0GyAPQ&627XL7PuyPJo?x5>~^P%Af
z%SSNx!2As}2bQ1V`k)M$dRYE}`5%@(aK<0hURe0U+zm@VFmqw~02Uvxehkb$SUQ2Z
z59VK3dV;BgrGJ=zVCKWz2{RXF4on=TA0`gd4-<!lKTICxUs(LY)WOV$>4yd%11#KO
z=D^B7n0}bKuzU>T!}P=435$PNc*4RFrVr+SxIQ!nESzEK8I~_$`3L51SUkbB!@?h?
z59SY8`iJR*nFG@Y3m2HXVetXe2TMmV_v6Y3Fmqt#Cd@ssa3G`~<{lUga}UfvF!#gE
zfvLkq!}P=a1Je&nzqs_l@;yvH%zjw7!OX#kf2e`z@rPax!om+0f3Wa^se{=E%Reyt
zVDSL822R2B!Q2arcUby`xf{lZYlJdj>xW_S1alWG9ANrzg&WNMF!kv1PDnr08E^^~
zU$FE9vlnI#Tp=0*<_=i+!Quhte^~gz(lvVgp@$<ZKf}TUM#Id5^?zW_fR!Jxc!0SF
zW-qKfg2g|~9GHHXe_{Gz;;{H9<bRkxSa}8WC#<}I<r|nd%zT)=F#p5C1?GMj4T}d@
zIKcFy)3Er5xd&z*%pb7uK-UN3!|DrIxd%&^==C719Ea({MZ@&N!Ua|y!~6r&2TOOb
z`~!0*%s=S)5#}$Lepood{0-9wi+5P~!_0x{gVC_`3eyj>9~N#fb+~9){K08x`wkXv
zxb(r|7iKSv53?V}hoxIsdV-k;(+7($nEzn@frUHF9WZ%V_`}A3Vfta_!}P<#5f<OD
z@PPF{VdGaYcfisQES<vagT*^6K49j-{0GbLu<`@OhuKFc|G~_GxdUB4%syDS!DyI1
zSo(vdSC~939ANrk=?E5nF#WLd2WBtKpYZ$+O%Jei3kx5ZJuv&>e3*Ke01g`FUl<L`
z2QYhK;RbUzj1Myo=6;yFVg7~HCouD1>R|pxFF#@Cq0{j2gtc>_{(`#$Dh^9$F#Rxh
zpywx;{V;Vfcf<S-OUE$tVeW+SVc`PT4`smo3)2s?AC~W6@dj0nLBZk=rXQBxVft~|
z3kz46`(f^YrF-;rf^IP^d|>)v?uLaEEZxG~k8U2iepvYnvj^rLm_K0Y2xcy<e1Z8J
zW<E?kEd9XDh2;zQ_yN=+SUQ1)Bdpwjg&Qn=z`_;%{2y32!rTGRk5Kbr?uNMw=1!<I
zoPxOn7M`&34CY^0yuivom_}GQ!rTvYH!MD2>R|3b_dm?tu<(Mp6PCVV?tsNBtUQOA
z3yTLBALc)pdtmVo(+^Vzqha9(D}Q0(2aA7Le8BvV?tYm4Fn_?}8)h#oAHw1v=02GH
zFdC*0PD9HJm_C>~bQ<PPn7d)&4zm~L4s?BRKC~Qyg%`|zn7?810jnQj`e6Qsg&QoM
zVCrD$7FPbi;veQ7n0}bMVd(}IPq6TW`46TamL6g5fTcfJx`mkw3pZH2!TbZ$hfc%X
z3$quN9%1PXm;JEx1Is@!f56-gOUE#O!OVl%53?5*?lAws!V_i=&iI7JAI$&oc!Z`O
zLjH%@1JeidH_Uyo_=mY4CJu`?Sa`wGH7x#N@q!WmPy=B0z|4UPGr+<R<{nsh!TbRW
zADDVre8bek;se%>grzrFdVskHrXLp#b03^W3r}47VB#=$!Suu24f6*~9V~uf`4+~9
zg&!>a;ED&BJ+SzP>4W(bmTqBuSUkb(huI6$2a8V(_d@*-(+3M@SpJ96F!!VDhs7gI
zKP<jr_QK)`mQP{wF!#dJKa3CaFU<ci|H15qn+we!F#BQZVDSQ@VfMr1Vfg|k4@=kR
z@dxua%wMqZhUI6NepvcL*AMe2EFZx92Mb?Vc);|*{0FlSmOfzmVfMo8gQXjo`{DX<
zFkt#&{(_kU^B1}{SbW368>SByFED*D_rv@LQxA(r7#}7NGZz-`F#Rz5Vc`P{Us$;T
z6Nj4%O}FU!VdkLIuyP!xAJ*<7RzJ)iuyBB-J6JfN+lS7Fr3aY(u<(c3j}iZ92Ef7t
zrXLpX==x#t15*dH7v@eF4GUK^YZzeTcQF01{DSUZT<(R<pToinW<D(bVd)l@A7Sz^
z8Wzv!<rl0zg~c1pJurQ^Xjpu~!Vl(uT=v7wftD|@aDas?%zjus1d9h)dWN|lR^Gzw
zhxrF?K9m8AKLcobM$h*!b71zt$_<$NVKjPtz~UX|ewh6*|HEkXbO4Jtm_Ati!_>jz
z5vCtzKh!%6F#p5i3!M*h2Q0kM-3>DbMx*-!7T&OMgM|l7Jvt4u4;GFv8m13s4thHX
z77uXqpz#BXPgr>Z3kP`r4K3Zk(jCm*uyh2gmtgk8!UJX>dOX1N!NLn>FDxIx;svH3
zM#Id3g(FNohJRriU^LVpF#p2RAIx4@dVncMr(xj?w;vjQu=E1ckM3@mdUShX=EKqz
zEWBXlD9jw#_#MoCSh&N|0nB{#_=olXVC5Gq{loOZ?1zOv%zjvS!r~K_ZeZ?&r4yJq
zto(zOBQSGu(J+6&%pq1kOg+rMu=s=LM`(P&(g7^|VD5nVA7($ye=u`k`r!VDYJ}Mb
zYq!AE!@>*Q9;h}r1=9z!AJ%SxnFC8duyBC!;Re7M(DVxncNh)J2e9;qEC0caL8oEv
zf#qvh`h%GRQx8)Ivlr%XnEfz5OdonUqK5<ATxh((!V{K`Ve&8<7SFKwgV_fQH<&tH
zG|ZiF8fq^r-f`)J`4<*{Fm*70!rTE9N2g)o3iChAJeWUW`47fNHxFhnto(rKhpB_*
zXITD&m1nT@4O0)(4@)O7b+B?6mcL-`fS3PJ55dA6<{wx(h1I7pdtmVaD}SKc;q@rY
z99Ve3`~h<h%pWjun0{D08<q}X{(+TCFm*6<aNS=8(+5jWu<(F|BP<=F@4te%7ZzSH
zb+GV27e`N5FnO50F!#dDg_Q@ebOlQnFm*5*mVROB0v7Ht^)UVD<DW47Fn7T8!`uVY
z2a8vjdUP5V-mrLtr59MdqpL^f!~6?#2P_<6?nhS-<HO<y=6@Isi$9qCu=E2@|4;*9
z=^N&5ba`0%f|&yg514yl=?3ObSosE%hox6o`h%&1g)hv#Fmqt>26HdWK3M$@bv6bC
zb3e=+nE5bw!|X@*A6y-@9)Y<RmhNEr80Ie+4R-)kA1oZ<;!p<6e3*aG(?3ie=6+bX
z!Qv0*e{_9N<Kg$$z|t+uy)b{`(ho~#FnzFahxs2?p1{HnW)4gqW<M;vVeW>B!~BI_
zZ=(AHrViGgfaN2&KcEJ};t#$2gi2#jF!#gEg}DP){zo?emM&rC7|h+UaDb_Yg(F7%
zL(>H;9%1fBPd_ks!r}que?svGvmX|IFmqt;hLsDjas=jnm_8T{QxCHr7QZle!@>>b
zewaKgTww7IQxCHTW<M-l!SX#!9u_Y!|HAS&tlWg@huH(mkFfBEiNo~6!V9J!R&K%6
z!Q2Pa4~sXL|6%@uxew-Un0}c1VESR<2lEHa9GE*{@d9%fEFNIt0Mie1Crm#qKf(0D
z>?2hFz|4cCLzp?R^oAaPF!eAR79Oy40P`2j-7t5++yzq)E4N@YEZ$(|!t}vtm^f_z
z9n3y-eX#I@>4UigrXMB`(+7)Rm^)xJOh2p~gXzab!_qTMKg?cY^~2&3<}X-!gt-G2
zAGqv?r8k&<n7?59VeUa!2a9)@JfZl8^`Bws1fJfZ?SGhlnETP)3yVKkI)SBom^rX?
z4s#E>esn%8AH&=Ovlr$LboJ<bSbV|U1B)+0;SI}wu<(YZYnZ!X`5sn2z|ub~{lVfJ
z=5APeM0X!7{$TBI^!S6B1J?(Q2ble^atYS|K(`;3ZqdUHrXOxDlmW9J=5AQLL8TdB
z`e5M?k6)MqC=K&3EF59!5GD=_M_9N)wIL{IJqdF^EF56*2Mae?K0s)MuweGW;~i=q
zto;Csf0#drEk|MQfQ1t*{9*2aiNk1!YZ&1DIhcM}K7i|k#w#qmVd`N1hx-Sr4i*kD
zb71zM(=c;j`3UA-Sb9KL597nqJ1iZ*;vE)muyBXbFnzG}2Xi;fUi5f|g+F?@!`utA
z7v?`$K7+}_+z;~)jE1fMgXxFqgM}X~d|~=w;SXy+;+p?}`46TKJ>9_UfvJPhu=EEj
zM_}zHn7!!X2h#_mVg7};<Dl^mGY6&)PD9f<EdF5W5XOh;gM}Bm{jl(Zm7B15gT)g}
zKP)|<>xYFO%syB=!{QO{P8<xFewh8R^aQg97XC2z!`uNk4(flHzhUJKOg}6fVd(*u
zo?-C;HwOm;R(`<Z1!h0``fZrIVKmHsn7d%^fQ1iw{{v<ZEZxA$PgpvGr9YVaVd5}n
z!@?hy4`J?vsfW1-rXHP!`45(UVc`vPFS>ddA7(Gi-7t5-!k3V}F#FKm4YL;(p0IQY
zGaqI@OdrgCSUAJvVKl7Y4RaUFd|3Rz^uz3fse`2_SbV_p1FZgqnGbUZ%siMnSiHi@
zAy_#MD}P}6Vd`M{0A?R7{9y49ix>28fTwS0et?&U(0$M7=Ahqy0J9I~4_G}9(+3L&
znETQF53>)J9$@Z;g$GPOEIp!^4>143`~eFWSh|G8Kg@n~|HIV7!VeaoFneL;2rORF
z!yg{+(C~wq1Jeg{H!NOp*$)dxSUQBIJD5J0JJD&FJ7MmIg*(i>xa@<O0}D4;{K3Kv
zmd;_}1`9`+K6rdV{S9+BOdZT!Fn_?}6{a8NUYNgO;RVwVa|cWu?hdE{uyBWkH;jha
z3sVmZ2Uxg3wPR2)|HI6IDS){j7LPFZ!Q^4?fVmeI-mrWCOUJNygt-T%AMRdgIKtus
z=3cn@(E1#fk74>@G%WmK{y;A`VBrCCA50(2eK3E+;uThp!~6}a7hw8e<u6Ph+`Z6q
z7-0Dl7Je}Q!}O!u3kxrpIWTi!{)M>%rXNPb%z^nE7M?JF!qN}S9GJgg`f<^)_9)DJ
zm^tY2hf6;!{9*cG=>}atEPmlMH2h%k33CT59MQu8W<D%G!T7N7gsF$cKg=C4cf;(3
zrC(UQ!NLpXZ<u;?8diS8!XG9N3ujoqM=!Tw_QBE-EWTj+VB)a!0P_b-9lC#E`rtG)
zKf(M16Ni;+F!#d33+7LlI)d>BH6LaU%v~^d!t90RCs_W0g)1zaVDStK512YwyrBCZ
zJse@>1uVb7+yjeeSoovoKbZNj@)xELCJsw4FdF7Rn7d)&53?7R-eB&5g&!<D(CvqX
z3oQM>%!m0O<}O%#p@$pH9kBcXGY7_pxdWDOVEGO`{9xvw(=dO*(g!RYVCJB!N9V)h
z6Q&>Lez<;U`hm&A!W|ZlF#Rxbm_Ar|!o*?zhS4y0z|t{HJ&X?vXP7!zyu<B-mOn6g
zSa`wQ52Io63$qs%pRjxc(+^9BF#BONEPuo7g~dDEe7FJF8L;#RqhaoWnFFI?@sI8<
zSop#8!RifIzJ;YfSa`wm5iC5hy9yx+i&r=et-oRM2(uq%56pg8c*DX6rVeH=jE30+
z3qSO5fQ2*6-7r4PKQMp8${!dFa|cWw<{p?iu=qi@7p4zp53JmRg*!|fW<M-kVEGJ|
zPGI>A=099COh3%uFg`5aVfMn}A0`en4;HSl_=dS3M#I7%mLFl^4l9pf=HQ}X?t#-d
z;vbf8VD`eo4VEro;ROqSSbV_r!Tb*oN2r5f;SEb~Fg{cogM!6BEI-54!|a8*3tb~D
zoM86D+y{?GsK3$m!O|DZKA8E0?1PyDvlkXlF#WK2hovVt4bupvVetYBKUjK(O2a8w
zddC%xF#WLf1XqVvzQfWDOde(d%pGw1q2UFqzhLPC7M`$l2-6Q!haP`0_rSsvmJeX*
z38o)rKTJPN9A+<!j~)(iai~9G^$5&-SUP~^J6L*xg#*leFnut8!2Aa@2WAgC4GS+=
z_`~u8Jiejf2y-{gewaBh`(f!2=3f{MOII-UuyBOA4<?R_hNVwfdj#fhn0}c3Fn_@8
zg_Soj`(f^e*^eGBuy})o1FSqlPcJZY(cKGk2P_}L+yS!>7JumaV0@T6(d~!%7na^(
z=>et>7GJRVhl#`TFRVO)#V5KsFnzG}3)2S+FPJ!-hNcIYIk5PF$;12!(+^7*FnM%$
zz{Fwk2a|`VH)y_qse_pdvmfSPm_K0g2n#1zx`FZG`k?-R>4(J^%p90|VD`b{5oQid
zAB=|C3)2VlH%vV&9l`i8eJ~mp@96$ScR$P>uy8`x4~q|2Ji^ikOg+s1uyh5}4|6Zf
z{V;oB@-TP9;srxLOd%Q#_YW){q2Ug52byvWE-YMO?t%4xVEG@W0cH=(AFz51<{wzP
zgxd>s2TVUK++gtl3pbd3FnzFah3SLQuyPk>A1pt^(hscOfW-rP`2)+RFdE%lnEPS=
zhPel3AFSMg#S<)@z~o`!2a7+LIk518`5WeLm^)$afVm%*j?vu@QwPgGF!NyUhPf9O
zo-p+=8W!HLc!1dt6NlLcGY6&*M#J0<O9!xUfbr4Y0n-PgVfh~xUa)vT*M}beuyP;f
zZ&*CQ%!S22EWBZSm^{qCFneL*Fmqw?1JjQYe$adcvlkZ6u<(NU3l?v%_=nj8qhadc
z;RQ7p#)pL`Jp9q}56m2N8Wu0Gc!Z@R40k~7gX@Qy53?UmL&ah4fQiGxAEqA`PO$KW
zg(oZ=VKht}7T>UNgoOvp-?01yEB9dG3$qVq4$QwW8m1o>{;>1}(+8tr`32@)SUUq2
zZZLCT{)V|9rXS{in0|D5n0sLEN1s1}r!#0gz|teE9EJHG<_~oL!`uP02Ns_2auFJD
zuy{w;4~s{b`(gftxeI0v%wKSMsKqdSFdCLmVeL4WeK3E(_)zWe^&+tNhlM9B9l^pK
z-Cme}7!9)zW)3VKaoG#g2cu#3!u$aXFIc>xyBnqt7GE&^uyO_#Zm{wc<_>gyu=EEr
z9~OSF_(V?!u<(GbUqJUS%zRjQz`_lt4>tb?b1zIkEFGY`8>SCde!$E}r(yXP<{vm8
zn*Lz!K-UN3!}1$UKP=o}_QS#lrXCj!D~Dm`z`_q^4lG_V^h5I>Odl*>Vd`P}Vet#g
zAFz0W<vUoo!@?0g9mDbwZ2bq!K3I6c!U>&^Za=KtgQ<i06J{^WTsR*Z53ux%;eV(A
zdiX(w8DRFq+z%5+r(x|zxc$)aSeSkojjj*IN7oO_$1r`c_<*U0`5%^!VetU-H!R)4
z^rQP9p5LMI4>K1Qzv$@@Za-8#%wCv2m^>_h!O|_vei$F79z7mm`4HwVSbBq%|1kH!
z^uzoQ3vZbJVe+u_1hXG~{S8bVEWN|@!|F?ze_-Vjy8B__46`4W{$cjP?1$NdPQ%p0
z!V4DPuylrQAG&^+{V;dK;sa(5%pK_Q4l@T9-Z1-N?t#g}!X4%wm^dunVD`b%3(Ou^
z{K3Kzz5IvS2Mc#t_`=)`D_3CU2P_?<+YfUG%syB+!2AJAKd^8>_Xo_~Fn_?@1B*A9
zyJ7CY6(2DBVeW>71G>9m?uXH^_=DLGs|R5A!omfn9-W58AI#q{|G?~rrC(UM!t}%Z
z39}dG4w%1T;Ro|K%p910Tr{lwgoOjl{e;2+=5Dxqpyehk9MIj5Za*yiVfheNAHd=r
z<_~mz7<{yH4OYIu+<~5cVE%-sLufq%ix2epMAr|CKbX5=d{{XLb3d-|N6$yF^a+bk
zm_ArOhWQufUYNbGc!T8^m^m;$EM36#!`umrFIfD+;u)q7<`0-YI1LSFSUQBoA1u6K
z?uUgJ%>6KT!OcNq!1TewALcKZepq;-X~W>c;uWSJ<}a9gVD5#5KP<h$(>v6E808-{
zeZayWW)3VIVfhYbA1vL$%)xLdngA^PVD5rR!`zJ?k1%;yxWV+n+yl!uuyBKwD=_!N
z+AA=9u<(MJ2TOl2{jhWaOFyvqhq)VOAIw~sIq3Rf?#7^@;SNjJFnN0Ehvj#e{jl^(
zto^X~hv|o<D_r_v;RXvwn0{FLfrU5B99Ve3Xjr_#?1RNCOg$|9!DyI&VE%-q6PS8f
zI)kNaxci{#9_AjHdtvbmOP8?thovJ}I)UXYSUAAkjV=!}9~KTUd075|>4%BK+yN`6
zVCfa^E@*m!xeF!_3qP3uVdVwPA29pT-3_w`rXLnQF#YK6frUHF9GE^B4bu-R?_lu)
zb0^H*u=s)LgV8X3u<*nc|FH5CrVm!$!}P(-huI7B2h2Q}epvj#!XHM%!UZM|3qP1V
zuK0t=qw9yc4_zKT{$c49rXN<m5waf^4lw`2;tysHEM8#lhUW`tK7i?mxf2#%uyhA&
z@4&(h7LPD}uy}^0KUh4%)WQ4-OOG&pFdBXTAFP~($;09imi}Pka2lE~VDSqRhlK}B
z9Oe#KxWLrmqG9C_EL@1y4~s9jerPzu^uy98E_-3|2MbSFeuKpu%>A(P59U8u`hn?#
z#S6?HSp37>0UQ5>rAwH5VdlW>fyEmvK4JL+CJwV7W-lz<Vd(`HZ!q`5+ykRw>R~j@
z-7xpU%z@bt(+8tr`#;gsD=gk%?u3~G(+8tr?uYSV<vlDP!2AnKhcI=xXjpiXs~;Bs
zuyBCYpD=&mvKQuVSUkeQ1EwApez5q5rzfZam_ArM!r~v6|6%%J;eg9MF#p5MgYlu(
zz}L;9kAJ}Y3rh#Ed;+rv9xl-QguZ_S=6_hc!}1L*eqr$eGY@_L6wH0-G+ZCFoPqft
zMx(2TxfAApSa|@m7Z(2L{)d?Zvk#^p77s9eu=EI1k50qF5vCvAUUc>7e3<>P^aJxJ
zEdJ5eWALHj1dB&lc>>FSuyluB9>D4;m_C^OF#p5+4XYPne3*Y>^$x85fY}F24={Bw
zeK3E)#9`?k#)s*L@nPu^S9u0YXE6W5`~mY9EdF5TVuTx-04yG0=D^$y3qKeiO&fe3
z2c{pEFJSQpqhaX+mws6KftdqyKg@oZKA3-D>S27CKVjhoD@S1ZU^J}$hxrpG4pR>+
z|6nvs9!A6Lh2=X~dV%FT7$4?-m_D2|H2uQT6HGtMKRES4C1B+pEZku61WRvFWjHCA
zy|DBT(+{Iz^&%|)z|tMe|FCon_dnDoSbW360apIN;tLjzxa@_Mr!aG1_Ct-wIR6Jd
zzrgY>OdO^U=6;yFVg857!_omP9igWKSh|7HF!#aigP9An7v_JMI+#9~y)b{m;u{t|
zu<(VM3u`~Y+8?m^hUG6<eB(+Fu=)pPKFl4k_=V*&SpG$KAME@ySoopGA1s_;=>S#^
z!NL(14zO^B*@JEldbq*#!}Ouku<{q?Z&>)l!W(Wcv>u1)hq)8xPgr=v+zrzQqhb1B
z<p<2&u<{<3Z(;6%#Rs~6SoscfFM7I$xgVDQ(DlRIflkBBgSiV9uQ2_v@IcoG<D=_`
z`3I&S<{p@ObQ(QAVe&Bju=E8JN2g)w66StbxWM$oXjuA#g%d0t!@?VuUSRnc-5!|v
zF#WLjgQYu|Ik?;l(+>+Tm^)#7SUAJni*7$UAEpoH9#}ZT{DH0>oewJq;50Oy!~B7+
z9>#~IQ<#5X<uJ_su=E2fe_%8${9*Y9=5AQNfte4}4+{ra`2|b===Q?=4J!{}G|U`W
zIK#|`(J*(w`~xdTVetu5hfc%N8!X(=%TYr5Vd)O$Z&-T4mF{5n!{QsJAC}Kx;R`F5
zVB#?U!}P=S!NLz_AI$wQf5Fv5(*w+Xu<(bO1Je(ahvf%YxWL>2t3P1o!t8<3uyg~H
zhxrrcFLeE|_=2T7SUQ6FALbqy4RZ&~eK31r{($=n>R(v8Lr*ub^%pSvVDSud4=lW4
z`r-CMJqEKE7Vj{9uyhDh57UosKhzj_yBX#Vm_K0Q1=A064@@77hS>{q56pg8IKb*-
zm^tY20}E%EIWT)*@eVf^8lJHB8_a%K`h(jGRR=2{VCKTgF<AbG>4(*~Fmqt;fu%=S
zK8Dfg?uVr>nEkN$fR%eN|HI-JW-m-1EZkx4gxL$@!^}aq7iJDR4GS-L`h<oDEIpvB
zhw)+Y1q%mQdV$3oA^os$gM|ak9k6r+3x9Nf!2Ahw2P~Xm^*AgYz{+7*{)NRaEc{^V
zVdXc>|1fvK!V#t)W*^Mo@N^ID2f^G0i$9qAVg7;Xhs8I{-!K{`4vSA%IKtYmF#WLf
z2eTih9%es`hS?8WKL^tfvj^r+Sop!pGnhJz@(U(`M#J>M+yx6ySUAAa1xy=yJiz=9
zb2lu$Vg5&VKe~SO_=d$B%pJJWA<Vt7bO&=UdVIm$4WrTRhw};K8<;wny|8o$iwBsy
zVg5#UH!Qwk`3)9cu<(V&D=gk%{(-q0ord`vW**EwnEkMLK-UN3!`u(k539$C)ekcV
zR<6O!f!PZ)2j(tV_`$*pmTqC?KP-G;=@AwVu=Imr4>aGx^uzKsj1RLP7B4XSVd)j-
ze^|JqyBj?n!Sut@D@;GE+<}!>FneL)2@5BfI+#CU_QKo?^EXT%%>QuxXbf2Tfw>2k
z9$@|@lzw3PaK#%eJkYF#b7A2Dix*h9!pbvP`GqSSVFtm{KP+Ft!U5)f^!SH`A51^Y
ze3<`X?t+CMEIeT9Vetm@H!K`s@c?r-%zl{rVESP+%sw~`Ek9uHfvH2MVd)g+4)pMX
z@zM3c_%MAib71zt+z+E+`3t5G7H%;8aCNYAtDrQ@-!ON;%!iFXz~Tv}AC{kC@disb
zuyBO6-!!246&CKW`~Y(wth|TmgYjYE4@+k-8fG8N99Vk|J-%V)qtmeT3Nr_$AC~{n
z)x-F(^aaa5uyBUi3**Doqtmc>gV_%YFPQ!4>e2Zyedzv%m9yyT(fP3OhoyU1c}2+G
zuyhX74@(Cy^I`sg#UCu)z~T?)URe5r@nQKC#)q}*VESS1gV_Uf2b_lHJDB@n@d=AZ
zSos0dj~;)pd;u$uVeW^~aQDLufTm-ZKVbO;rXJ=Fm~wO)=02GFVeW+a7hOG!4|6xn
zUReCW?8R^=Oe2(r<r`SKg{2o*x`iskNx|YB7G5y>Vet+N2UtA_6NkAArVmELEQ0wT
zR*t~p0p>1v{6oVX<_>sxK;s|gKbSc%{V*D4A50$R4p=&dxgTa9tQ>^tgV8X1VeWwC
zXP7=%xdl^?PQ%;@i$|Efuy}-(tLXY*`2%Jz%pWj&Vd0LU4=Mn6Co~*k=E3|4i!Ye{
z=;;_1UNHM%=@@D}{2o$RIR^6&%v@M{hv|pqb6EVq(g{o*EI**nf5Y4XD+gie2NsU#
z`eElk!`ul=UoibJ8YT`i4;HTQ@(E@Dl!k>T%snuF;!+RQ#sCX<Sp37pVc`YKmoRY{
z4NG@0_rlT<%)PMigXu?44={Ja^uyc>GanY8FnzG_hs6(U{};?0n0}bMVD5p5!@>_%
z|HJgb{0*}YR*t~@0SgD1JK+9;ITT96!XK8N(CvjP!%4x?2`oHd{)MGqbd4~7!t}w)
zQCL2K<s+CsFx(BzcQAc0^I-PC`~wSbn0sLIFd7z)u>228_b~l1cfe?vJ{S$N7am_w
zcfs-_EF59+0839W{kUkDzhLen<PQvUpzeT~1G5)>{05ePVCe>?4(5JXc@8Uwi5>rg
z#TP98Vfh#q9_a3c*$+#{uyhSG2bTU}=D_ts{RvBVF#p2JADDWWewhDY=^RGG{0obJ
zn0sL92Bsh8epvdzr4ME<Og&6L%zn80(HO9F1k(?54@^JIJ!snCTy*^~`(XNE@d(#|
z#=y`Im51qv(NJLw3Ksq__rvNxm_As1z%;_b4`wec|HI^A@egx1jE2d>?1RNWOdMto
ztlYzuf6(>8(ksjyTr{lQf~8ZKKVb0<izi&=7cBk2{0+;8F#BNQF!M+)KhVvGnFEUl
zm_0CcFg`54VBrGGN3d`~4}X}yVc`ICH_ZPqcf-tu*#o0t`eEq>rXQvcM#JJ0rVkeW
z==xytF#BNsMRzxB{ROOigT)gpy}-<cnTKu;tp5#jFD!k*?17mJr=jg?SosO-|HJgd
z>KB-Pm^dtb!^C0bH_Uyoc!J5p;t8e?7EUnt!P*b7^ae8r77wuU0+udd;R1^XSUQ4*
z3(TD`b71uq%s(*qz~Td4A1vL#+zGQE=3baNuyO#VA69R}^ugQ(FSnuf0!%-QhUtgZ
z+pzG1rE{1(Ec{^lVDS%gA50xgA1t0>;R<svEZxGw5#}$Leq1yx9AV)L3qQC!aA|~!
z6VeZhUzk5(^$g6NuyBEg6Eyw8@-@t#Fm<r_fQ1JvJ;K}z<3pVXr(od*a|g^m7!7j|
ztlmI32WCGkoM7r<@dS$p7#~hUT>!Hm%4LAXH;j*qhNU~0yJ6`LW<M-F!on5Z9CZD#
z{0B1!mTqAA2ba5G;RG`u=3iL2z{FwZ!1%E6g8LsDzp(Iy<x80RVfi1XA4bF4Yq0ze
z^9L-Qz~Ubk9x#2d^aHa8<_=gq!`urC2e>{c19ty0dicZg3CtW=c@EVEr(o`d<pWr_
z!Q2mXFU%cqjW`&v{08$6%pJJG4c!=+|6%C@7T>UNL{|^v!{Q(2A6Wi_g*(h0F!ks(
z%wCv&n0c`9fQ3ISJYn?@EZkuFVCKNm4=ns(`eEWQb71a(*#}EUFmqu3fQ19R+=Ql6
zm_As!14{?6_=crhnEkMDgqs6(04$%t@&imBrVr*%n7Qcghq)7G4$S?qd<;_$qoLN~
zq+sa>M#J>M(h)3P&<%ivA1och+z(R+3x8NRz{FuR%>A%@2@{9e2hRu4@Q2C6XqbMO
zyJ6u4b2ltKz|_OS8<u`x@dvX9mVROJ2-63nVeKba{KLdy>S5^yM#JP`G)zA%zr)IH
zSUAGM59SUSAEqCjhNT0Te_-x_=|@+O&WGiDSUAAq36>tw)x-Gc_QK*H=5JX3htcTq
z0J9I~ZuIzv*#q;30W|($`2wa7W)3W!!Dv{03ri1haj3yCf5Xxb%$=}w0OLcI!zpz8
zVftYTVE%-;8y220d6>IlG|YW4b+B{{i+6N$VESPB9p*1sxWm#BEL>pe3s!!@)~~_x
z0nA>QIWTdUewa8+KOy^J`qAA7>wln!A1oih?17~tSUCpE$LRAvFn_|_12YE}{xE$o
z|G~-=SUkYWakx97?RJ=Y7!8XDm_8Vdo_^5NFRc88r6-s^7>%B8VCrDy3M?F8=D^$o
z3qP3uVfh^v&oFVgI%vLu<!6|?Vd`M%9Ht*;FDxEm^00IWOBXO27Tz%PVftX<0gG1{
z4f8+DpRn+U*$*pMVEGs3fAsi+se^?h%p90KFnuuh!|cUH!@>)eo?z(@=5LsJuyg|p
zCs=sG?1zcN+yT=MqhaMg%pRC}SUkeq2Xi+}AB={%6PDg#;R4eSD?ec257P&uVdkUL
zFn7S*1q)x8J{XOz55`B=4+|%lK3Mp{)T7fd|G?}=*N>k6(e=a99ZWw=9%es`hKa+>
zhov7_dW5Arn0i>cgz1CPu=Ee3VethEcXV@L`2=P!%sx0DnonWwftdqy2P{3o?1$5^
zatlhs+=FgEEPulFK^ZXnVBrRfcUZjyvmaOZ!{Qxg4$NLyd4{X}gjx^ZhXpH-;Pn_Z
zJYee3X?XsG`WxnMn0sOFK-Y)Phs6Udyy5ATfc>z12=g~A{)zQBdOn5KSA^V+p3Y(Z
zhQ%+;9WZlX<pa#$uy}^iF#WLjhJ^!6Kg?aQ`Uh6t!NUz2POxx;#UqS{=|fLHu<{a?
zUSatI7Oyb(!om|~4on=T4i^6K@PxVtmJZPKFD$>o+z0muG#$axAIx4@IKb?M`5)$P
zSo(&AGt7LLdtv^9=|`tw;SX~^%sg1Q!^}n32jj!^!NLI+4>0$`%!8>%r(y1eg$K-E
zu=D^k4?P^v&4KY@@d`_)u<(S%2TUC-9AWtg7EZA81QsqZ{cv|c4S<;s^FNFaGY1xa
zu=t0CBg|e{`iJR*sV5X}u<?6Xx`DYD7T<7pLenACxo`@mA6DPN{0XbSVfmQY@P@@Z
zEZ@T1Pe?y3-eKVYODC{+g7M)FM`OU;53?VZUf}klsfTf4_QK)~M#J(mOdYHogSiLR
ze?X5vSh|J9JB)_;A69R|!xd%$l!k>rEZxJ*gT*IYA6k6E{10<KOdrf2Fn7afsC5kR
z^ad?&VCe@I-Y|1v=^myZ?k}i5n0c^t4NH%(_=KfHn0}agTr{QnVc`Hv59s*`m%T7?
zxVxeLgXxF)1LjYddKe9hKbU@4`he+!r$@B-gQY{5dtv^E#UCO2VdWew-N4ctEFZ%7
zu<(b+AJpNn^a67~JpVxb4|4}D{ZM1z6wExByI|!WEFHlWLK!gs!_>pV0VWPh_ptCo
zcR$RZuy}`s11z3k`d~DMeyBTO;SS3euy}*zTc|P&3YKqR_QUcIEZ$(@kD(Dw0G8ii
z{)fi{T6#g#2Is=u539#u?#Ja{m_}GSh0$>TLhXfxBf2>-dtm7uW**GlF!Nx1Sop)j
z1J?h9#TTrcg~dBeKTJI={lVsMVftbA!{Pzv4w!pk_QJ|hm^dumz~UR7hNWAWy|8$N
znU7Ax(mgExVESS9qpL^f!}P)OAxs~vK1Np$<HO<smi}P=gQq{3LL4+K{9)-2mfv9U
zhC>@%7#9Ap_=5QxM#IcO4?mc_F#Rxp!2Js?e_;Ay@c?rl%mFa{u=s?jgQX*wJ+N?r
zjUU4F!{lM^houu(xd#guSowwCe}Jij#UCslVD`h}5f&dXcfjn2>4W(L7XL8&VE%#S
z512lfdtvs$+yTok=<bHO1EwEF!`um@VdXF^K4ALb<pNA2jE2S&%>6L^u<{4yZ&*13
z(*~pA{)L(Yiw{_ShQ$Xg9AJEyesmh<4_LUs@+Hh2u=X2FAIyGO`hocy<_=hTgxQat
z{$TdN{11x<SUkh@5pp-oJuvse%!9={%p6?t01GdedtmVZi$9otSo%QsCoCPp!T}av
zFnzH2gt-$oeh+siH2z@nFdC*G7XC1CSUkb(gOv}kaEG}ErXS`Hn0{O|%>OX`FnfvB
z53?WUFPJ?rcc7OaFnJh_t{*)epr;>LJi+XPr5{*$!_>p#17;7bzK83Bp0^FN50?L7
z=?<6w;qd@97-m1rT=eu0Rfa*q@)6Ac@bm?3N5JX>42@6$Sa}As7v?TlxWmE$svJ(i
z?1c+J8L)H!OJA^b01H=``(XCL)WO0F7Je{y!1Te~0~3ekS6F<&^ufXnW-rYB=<x{4
zx3KgIGY^)}VfMnx0jT#d*ZsrtA1u6~e3*Y=?ts}3b3eNOVfMn(C9J%F`5PvW?haVI
z!~6qFN3eViOMftP;QDbe(A#4$dx_N#b3ZITVD%|1JYm+t<Y6={d|>vY$0IB}VESSH
zg{3!`z3A?RwPRrVaM9@I!Q2CjKUn@o*AL4FFn7Sx6U^VRaD%xEmi}StU^Fb=VCJL8
z8_Yeh`~nX*=zdR_epq^d*$1;9W-cxo7Vfb81k;ZmZ@Bcs+yhJ3u<(M}4|hKf17P_N
zrXLpnF#WK6j6*xdxC+dESp1=<A6Pzwg+I*SF!N#d!QufH9x!>BdtvDgCJswSF#p2b
z4-ZFZ`2b5VFnut4VD5p5qq`rLeqiYi7EUmGVftbIhnb7+e^`2fsfW1-W-d%W%>A(b
zAIx1a8s>kPIWT>&@Pn0~u<(HCgV8YkFn_`F4a^*vI&>Q5PMEu4<sB@%aM_1$4lKXG
z>Oq*hVdXcvdN>~~9^vkR)&qpf9hkde`5ESJ^mu~X2W7z0Ei9kF!VRV$W-lx}pxWRR
zET6;l!~6|X0MiFE50-vl;fZcPx_X#-a2gsO=<R=4euL#Bm_0D_VCe#u@6g+SFn7T0
zftd#j4_G>arC;>;gvA#u{$c4CmY-nyaJ8Rc`e5#d<tJD<2#ZIUIk5gK%zd!%hxr4h
z4&7b2{13AiW)94LnEPS+VBrdL4=kSHG&CK<%z>v5XuQGl16(~C0~Sv(_rS^_Sh&Ib
zg{F-G=3ZDnfbn7Ohov`M_QS&s>V8=K!{QlcFQIY-mVRLI0@F`OA1vR(!VTtLSa`$u
zF#n+YAC}%=;SIA7<_=gshSwWV17QAyg&RyA%s!YrEdF8oVg7>c|Ags>8VipPSh&K>
zg@q$5{leBi!~G3yf56N^k9U~AVDc~;7T>UR0}BtBewe#q>T%I9|H9%CrVr*{T>4<?
z6qb)+@c^?Q7LVxh2U7?0Kg=9hc*F89jE|9iq3IQ-4i;~)aEIk5SiK1=$8h-rW)4gr
zto(uLBNPs>^acw@SbV_r!|DNC@rdp&xcyLn!qO3X`45XPnEf#GVg7-c1B(xI{V;v7
zcz~G)3r|=&fYCV1Kd1*_@d(Qou=EM@KSn%4^})&km_Ar~f`uO}|G?4%%-yi~f~9*{
zy$3TE79QyOVd)R1AEpmxAIv<MyU_K+%txnT{)L4nEFOr}4_6P3cbNGw{V@N)#L;P3
z_`~dn`3t5W7JlgZV0>7(!r}pz4q)+zOFt~#!@?DoUT~?0*+<B~=;a1XAB={j6IgzO
zsfW26mhNHs2VEb`pRjTQ7GAJ?0CPVqUBJ>Wto(-g8>Syt{=mWkW<M<aVc`Oc9~cc&
z4|5-^+=S_a`3oiwqhaX{mX2ZR7Uo`<eK7U7XqZ3XG_>79P(L)C!r}qu9#}ZR!UdPT
zFnut4Vd()DU$Ars<HOB?+6&VMi#M3Puy}=~16Vl1^uxj%M#IWASbW3cAC~`N<v&aw
zW-m-X%zZF<SU!Tu!}2Z69WZfNyus{;#TzUi!_>jT5vCvJ512b(=D_sB(jkn7r6-s_
zVESS5F#Ry|VDd2gVCKTaVKgk?!Qv4Xf3S1_b2rR>^z;OaCzyX>?t{fAJl#XnBP^U?
z;R4eS3vZY^VdlWp!`us}q3H+aZ<s$|@eNZ4OMftPVd8{nSo%Tt7u;P041m?!a2o1=
zSop!rhq)gXUa)iqlZW{qrVo}MVAjL@4J(IW{)fdEjE2VpOe2&=j|Z53Sa`wmBg`F8
zZ45B?!O|Pd{V;oA=?G>HEPuiDqtmeT1~U(qu3`R$nTxIurVf^lVCfj94@Se>hpr#S
zhlK;I{Drw67Je{)z|<3>VdlWn56m3&^aFD*%pb7&1{S`s_<+R&%s(*uVfMh{A7&1$
z9D}8Q7$2q&orc*53kR4x3F(97TUflo;tA$X7$4m}7$27YVfMnpnOJ*a{)UA!%wCv&
z^!x&|7Z&a?{V@N-<YDOr7H_a{gym0|I+%V~xWmd@Sh&FKfw>2L{08QKm^zsGF#BQU
z2P|CB^~3TFEc{^k0HzNX9x(UA^D$ca33CU`UoiV%G<rC~;v42}SUkhr153BC@Pqju
zW<E?GEd9d#3)2tt56t~Ab71;mG%TOO+z$(Pm^{qCFn_?(HC!GC17<&*hL+#3_{E_O
zE{yIjSoom#-(cwmrV$nnuyP$vL+yu+Kg0CF;u}W8!VeZ-u<(P?u<(G{2cu!>1!fMc
zyoRS6m_jrf7Tz#@aCbr7iyrT2+8JQ+5A!E1o?+!6x_UStntoyV4;J3AauV)Os5@Zc
z3eykE?=bza^aGQJ>4&)oW-cs$!u<(#2h4tWI77u@=EA}OJ^#Ye7c4))(h1D}Fn7Y*
zVd&?-!Q2ar2bh1*^}*6DdisOuhou{sK6rdW=k;OwVKmJD=<bK{VdlZo5lkO!{0x2l
z1}yww{)F*i;Q`YRqha>L${ASvqK5-4zF_)b@c{ETIt?qAVBrXhcbI;3_2_(R>xZQS
znEf#SpxX=M!{QI-e)RBzr%$K`SUAJ-A&iEFFRZ+Tl>@NwfNF>5Uzk2vzJZwwO9wFb
z!^$t1ewaI9`5&eZR-eP-6XtGo`(gP8=3ZF*!rTK3FPQsb_M+1;`(WuF7LKs+hv`SR
zA7&2BA29tedtv1VEF59s1JehiVeW;M7chOWdJCowgNBwfF#BQQ4hu(E{=?7*6@Z01
zjE3ol(J=qQXs9+g1q**zc)|P)vlkX0uyhF52xY+h4U1=3IKj-pr4Ondp8jC|hou8p
zIK#pb?tW-}0Mn0)hNTBsxWLLKSowuZA50wP4p?~rtB=vkf0#Y6d;;??JUpTC3rh#E
zc!k*mQwQ@uEF59(g{51VewaI8`e8K8pD^=a=^hpzFm>oOES<pYhxr5UP6FW#3m;g$
z0kaQQp2OS=OHVNSVd~-bLhC8?@PnBLO9!xY1)KkYr$4AWVg7-c4^t2G53Kxx>4(`5
zi*K0wVD5#5KP)_9{(;q7FdEkWf`<pxJurJ=;Q=!rorc99to%W@A8s$2Mwq){`e6Qt
zxf_;_(6qt1==Q?=2`jH*3URp~R(`<DLAMWP9*l;W2Mc!?4a@H^d6;=HaajDr@*iyc
z7p5QPZ<v0Vdtvs%!UN_nSU!WL6Ii~1#V0Jk!O9(6;SaM977j3TVD`Y$FH9Xe4Rbfl
zA29o2=?0b#(DlLiu>1;3cd&E-GY1xaF!NyH2h$Hrx9Il6^uhcM3kUS_6Bdpzb71)m
z#)r8B79Ozr1ZF=v4Koks9$0w*b3e>{bbYXJhv|cv0}DTxesuT4%z>o?Sop#0g{2$x
zbPdx7qhb1C?tuA|Q2fL6!`uz4PhjrFWiQNsF!#dBL3IDZ+yS!(M#IWKm_As1!_p&6
zAIu%FbO6&2^CwI_EWF_6K^5ZC50!>fFmqtx0L#C`>W7sVuzUc^_po#ew+M{^%RexC
zVd)lTFGhNUmg_KMVDS#q4~rL=JluS!K6w8R+Ae_UgOwjJf5YMfW<Sgy=;;?`J}f?9
z=>e9GVC5Jr9iyj5Sa`u`SUkdLnE9~qgw>zu{)VZCha1#?u<(SXOL#c|m52Eo7Jo4R
z!qOWo9m4zp3s)Eoa~~|+Vg84?59V)}y)biO`e8IoJ*@o>GY{q-n11x|gXxEbAIuzB
zdWPwTxdRrjFnur@BmSW21*Q+Z9DvpPu>1z|H%uHBUbytb+zCs6F!#d32bO+d{)VML
zn0sO21M@d5-NI;?`7n3j3OAT}FdCK~VESS9!_omv9XbtjKg>U{bOB3`xWXN#4i@h)
z|G?Z0Yj>dA2jj!iEzDk6xWVLM=^o||n15jL1k(qLKUny|+zSggSop*EuzD4y59WTD
zdto#zK4I>G(Xeoa*$<0PnEf#Qu=E4d2cu#8Ct>!&(mBlkFmqu3hWQ7kALf5p_`~dn
z`3GhmOh3##n0|B`9&gZc1r}bgctlqZ=R?)Q!T}bpu=ECV2aJ#I5175^`eEk7@-56C
z@bNpSy)gg6!Vy-^!f2R&n7!!n3DXaYPjq`>@dq;xCJ&=w?uV5>==Q<Nb@Xt6#UIRG
zSUkehH?%(ovj?4q*#`?ZSUrJm4?Mj?%R`tsu<`*GZm@WR$)lSG3kSG)Foig1Sh)%_
z2bLdT{)5>Mb3ZKo!ov?5zcBk?`e5+~3qM#mz~T{>e_-hs=5AR0qdOO74$Qr<_<)s%
zuy}{1KbSjU=D^|~W<N|E7N0Qp!t}%Hf0#O$|Ka5zG@fAofQ1XpUYNgN`eE*b@nQDD
z%!Q>tnEf#Kz|_NNSUAG`53?7RZ(;gz*$=Z17LKrZgt;4DjzIkfvlm9g{13Aq7Va?p
zFm>oOEdJo(4^20)c!%W&bbT;B%-^u|2oE2qJ7NAuPd_mKqT35AXJPJwxgSQu?1i2G
z0?U6e|HAac%!j!j7XC2zz|4n*8_XP7{KM>l`3DwnFmqt?xM)~-z-efB5UU>+uCV-%
zo{wSn!rTo@Z?O6v<}a8!SbW0#4-<!_H&}SV@&hb9VeW;Q3)2S+KbU?P4bu-(hhA>M
z;sX{gu=ENmKVkO3(m#w3%ZD&?VdVzg|IqM-=M$Jh95gI^Vg82kVfMnp8)hC%9Bv#m
zpTOb=R^Gt;4-0>oIk0eq>&L->>4&)s=5Jj3(d~z&0~iepf0#eet%c=3n0+vR!qN-O
z{jhw6t{=vS#Xrp7FneL?2&N8$hK3`|9WZ-f_QU*-p${qmi+`BCF!#e~Sb9S*e_-(q
z^EWJ?!|a2_CoJE?@(oNKZ2SVIA7&raSq!jvfu&zq{K5Lqu=^)q`eErGrXOY=EF59+
z2lo$5AvFA8`2prHn7h%-O_(-x8kc@p{G+Rf@nPj9dVYqbW0*T|xgQpPF#BQQ3oBn>
zG|V69?N?a&0n-Ol2TPCW`2gk~m_B&AgYLh8#XrnnF!i|7G2DD;Jix*q7JsmKhov7_
zxS`W9_rlUK%-=9`VEF-;Ik0pDiw9VE!1Tky1ta}H9RiC7Sa`s~fsnm0cf$0;;t>{p
zuy}`sBf9;tc!1F`_rmm}mj|%$goQKAKA1jOe#7MtSUCWTH}r6W*#`?}nEzqn0SzuV
z1#>6N{V;oB`3B}*n7wd~XbhM>Sh)j}hWQKT4wyKMhS?AE2Q2--^ugQ#iwBrKbQ)$q
zEFHqi9T*KuPw4t!d{}u0a|g_RnEwgshuIHHM=<{qayLvpEc{{i!tyIDy}`s`?uF$O
zn0sO670jKm^Z`q^uyBCIBd+x)F!N#N!R&{H1FZi7vlr%Xn0>JLgxLcN2UtA9+y}D<
zM#JJAW-cuK!t8~mYnXa;8m1rS9+-ZZ{kZhQ+zqoAmM>x94YMCT9%1DMOdl-$!0d&^
z56r(Xb71iSb3aTS%p90|VDhkh3)2s;KcEWW;SDXnVeW#N1Iv%F@Q0ZL)sB;bg(u8k
zu=EHkhjD3yxd)b>Vd`M|VBrB1hqW7E_P}VEKVabwvmdSx%7D2O7Je}MVet<0H?H`H
zg%7M8hPembUR?TN{)Evmbub#1?_f04*$lAwf!PZS7nr>;aacZt>4&8+n0>JH1FJV+
z@-XvZ^62>(7G5xUm_8T{3m2F?dVYZA6BrGXhlMw+Ttg2}nE9~yh38jj`h}Gn==xy(
zhlL-EhNTynIWTv_)Wh-{EPP@5U^L7fuyh2gKVabw^AF4%7$2q|7Edt$!r~p44&myc
z{X1Cxh1m;>2belo{)G7pW-ct;VESS43-dS3{jhj|xgVwvmLK5Z3$+mDPMAF~^I+ix
za}Tck2Q>zMem6`%%v_kgu<(PaN2g)o2-6R9Crm%gAL#mEd{{b!#UD&PEFHl715*#9
zVc`bT2MbqNxeF_|VDSz!2Nr*D_d?Sz%wAY{!qmgU5oQj|ewcn(e8b`o<}O$`!1SYs
zBg`Fe{ZMzn;vW_dFneL<!0g9G!}2|xhNfR)^~3xR%a5>f4dxD*y|8i&7Va?r!uT-t
z=>CT3hlLX?AHn<$OE)lmuzU&A2czNP0}VG=I)cX|)Zegpgz1CPuyhNfVeW^y1ICBB
z2WBozJuIAH<ulyBQ1`&X0p<@_dW6Lby1!xmgoQiIUYLHEyJ0k39n?OU|6%H3`eFG2
zrVpKl#Um^o!sKD~63pE&aX1aN7Zx6{aD#<6Og}7K;QF8pSp31l0hXU&`5u;Dak?LB
zKTJO?K49*J`5)#UsI_nkrXHprmLFmE!Q26(VH#oTVESO;2D1+qzi|IS(;+N9!NMD+
z9~SR0|H9%GW)3X>!Q|2P!`us#hq(urewa8cykYub;RZ7wmVROWg2}_e0aot9;veQ;
zSUP}(8%#fXJiye!?1PyH%V#kE!15Q&UUV9!AC~@M?!=YuVESS9!{PyEKg``Q`(fb&
z(+8tr;SAFcE6-r{ElfWwAHej%Xjr(x^rN?%VESS83QQlIhNgd5Ji^RFx1Uh>!Tb$#
z2fF{^_TgZ_!XIWoEc{{R5Dsl{VOYGuX=r{S6o0UM0&_nsU&7pvE8Jlg!`us_VftbA
z!u)}rZqfC__%IrlZ(#n%75*@FuzUkcKQMQ|@(r%^hi)INJcHQ>D>q>3&}mrs!Qv0*
zPnf?6*$cA|mTq9`Vfta_!Tbkv7u+4t^b0GGVBrDdqlW{m{fw?3W<M+*Vet;jKd}4&
z*9T?5+yx7F7!AwUFnus{VE%z>hf}a{gyln6xWK}JkbYRYhxr4R-(mV-e7J>Z3|Kh9
z^ux*#n0}0KN7E1E!rTF;3FJeVKVk6&3r|=)!qOis9MHoL7H+Wo3o{SyAE^DXd;oJN
zjE30<3wM}#F!hA|4Rbdv++gZq;fC&Bn7uF>W-rX$FneM3JS-ky=@DM9LDN0VUYLJj
z<qj<T;Pn$U9ANV|uyhX74~s9DyI?e|{DJugW<N|G7G5y-!)Ta$VESO?DNG+a4Ra4H
zJYe|-79Oy20bL)A57P&;AEpnMeqid*X_$Ig`33V2Ec{{ahlMLlKe~Hi{)M?4rXQ9b
zV0@T<Sa`$Cf%y*>-h|Q(`urO#o?-rm(Xe(Q%w8A`OCK=*!_qOVoPdQJ-2E_(IB1wZ
z;QoT9H}r5r*A5GBSUQBIcUZW?!xLR2ln=}IF#Rz5VESR<3RO;!f|&;^7hvfYS2)59
zgq8QObO?)YSUeDN2h9Dj_<)snuz0|k9?;4GSUkYg!|aEd2NQ?68$JAC=?Iq3Vd)yi
zhmHTk^uy{qnEPSr2NwP?|G?D4*Kb1wVCKWjfzhyZ0CNv49(17T3aTB0f~7y0dteG+
z?t!@<mi}S!4WrT3!|a8L!)Vz09hg2?`hd9?7T>V&hlLAz_`~uYto(uTVc`Ocf0+AW
z=^N$_n0lChVd)SS-!S{p?S<)w*#pxLPY=*=hSfVT_2@LrKA3)(zhUl$<pXqmFg`55
zz~UX|UPAdEmOfzmVfhuNALb62J-BF?K3F)y@(;}4u=t0?FS<FfbOiH1Ed9as!@>dG
zKd^9zxgSQu+yg6jVfxYI9p+A$KA8Jq_Q2vFW)3cQ!{QH?Zejj}>4VYe`eE*e`4{GH
z7!6Bru=I<rAI^uCU$AllmJVS4fQ3KIJh(nI1}xpe!UZM`t50G14<?RI!`uzCAC|9S
z_QJvwM#J>O?1klDSh|FT8_Zmc@)PQBm_8T{OSdo@rVkblFmut<9n8Hjcfn{__~S}P
zFmqt(03J?I_rc_0=Aio@W)F;p`4{FMSUN#>KTJJ34YLoHo?-rlg#)^JbUrLS!o<<@
z4NM$H!`z3Se_`baEd9Xp1xz1|hNW9rK7^SEEB9djhJ_zYA1s}~?1hCtES_NL3T7TW
z+@a+%EPP=4;e4n(%)KykVD`hp1Fjw#&oFyn_QT=<<}O$`z}*4$2+ZHGc!$Zu(lg8+
zSa`zvA5iV^bF^Uj4kiw>9~SR0{jhL_se`#2mfm3Y!qPF!KQMD(`d~E7-7tT^&4Gp^
z%s;U3gz1CDA6z{&9AV`sEZ$-E!}2psKP;WX;t!UNVDhl=hUrK5H!NIX`q9fhSa`wo
zW4Iq`5iEXT=D@-orVr*1sB#PnmcDT5how6VjW7XdxZ%<dORq5H=rk<;Vfta|2<Co5
z_QUkU;sZv*(hDpeVD$jH{jhk0*$*=hR&K$<50^h+?uEqztla>MUzokH_(ZoKmfm3Y
z!sKD;5|-|8>4&L@(J=qRX=pyc<$jpGF!#gqAIv;hJiz<~6Nk|-|HHx$W)4gqM#I8U
z3+f)2IL!Yr|H9HQEdRr3SbV~0bp5b+g!vnmzhVA{g&!<lVetcVH%uSQK3KTG!XFk6
zFdAk)EdRjb85SP!@(Ai*nEfzyF!!U=F!#Xx1GA4%K7g46(+BepOdl*9VCrzuuyBEu
zN3igQ<s)=`F!eBdVet<OH<-U+=Ai3`g)_`P7!7Mb!pd)$dFc9K<v&axEd9XTi!0s2
z%z?QdmhNHdVfNxGcVYTr@eK1f%)hYm9+o~}?uW$#EPi0|4NG@0cf-tu+XGEUuy91z
z4-0>odYHRm{y=vZOdU)gEc{^Zg}EP={$TdP^r6!*eK7yP(m6~&%>U^6V0>8qhxrRu
zUckZ&rVfLK);}=)Fn_?(1<Za}IKcHo^}*sDM#J0<iw77D3lErn^z%1h;RCY|7N0Qn
z=rnBqC@j2S@db+~n7?7-==x#d1WPBdc!cRA)*Z0?2Qvpf9mDb=y8SRdEZ$-IVdWys
zAB6P7)WgF8x(=RL_ru%)%lEMI7go*_vLD_5Fmqt;hs7(*9t;|qj>vUC%)PL53rl}6
zdtm7ZS2~30huIHvKP<n&{13}daC4v*!14_&{ln@Dc)El750`$JK3M$2^uh8U%zjvW
zqK79;9eR3(I*$RS50<WB;^;Ii-NM2F7A~;xgPDUqe+UaNm_C?0(9MVGH-MJIFnL(M
zgXxFm2bespK7r{+??1uPAI!Zl8fFeGJYnS*Z2S{uJ}w&O519X8;fEgo82X{{4f7W)
z9l_ECdisRvhxr4R9$?}ycjM9rQwO&Xn*U+;!R&{rL#JWk1Pgyy_`&io%zjunqU(pb
z7e>S6Vd(~@AC`_`@-Y9x(ml)_F!iu-hpC5!J1iVv@-TP9#9`?K<_}o;2#Y^h{(;3W
zOh3#Xn0}b~Fn7Yj6JC!&;~8B)ET6-|0~Y=;b7A(tXjr_#$`M$59v0rP@PPRrrVmEL
z)WgCNRu01a3yVise8BX<Xqf-d^~3Ci(J=SG^ucJD`(fb#i$|Dy;r>R;_po*&OdrhM
zFmqw<hUrJQ9~Qna{jm5(PX{pf!_0-zu=xv^epvYj3n!Sp@OX!5gwn8ZgT)&x{9x$=
zrXM}sL$$*xSa`wQ0h55~hq(tPj!whe0Sgb9KVbeQWIs$lEc{{d0*gObIzqP>#)ri}
z%zjvUgt;4+J7D<>7H+V3gXx3017<&lKcM*!=3kh8nEmMbVfh~BZgd(J4!Gh0W<I(;
z7$2iNfu?s@Ji^*jF!eAR=6;yHuyBFd2lEds9MI2SgOzWvaD=5lSpI>fFPJ)*xiI_D
z-3?O@3vZY?uzDD#50+kF?uLoO{0TD`7VfZcfVmH*59VK3ISI?huyBFd4@-|Qd(dfE
zK7^SI%eOFl(fy6CALehEepq<}(+_hux;}J1%-=9`VDSbw2VDb{4{OK4@)ImR(e<PI
z1EwBkAIu-HaEFB-jE}A#mcC&2L7l?@(+7(em^eBO%l9z*Vf7!(zp!vb*9TJ%qhaoX
z>4W7*bpOD@1GfGHW<M-_z|t+uT$ul0=E7*0ewe>u_QB!-=5CmJn7?5DfT@RtKP>&h
z+zWRHv>bqyYcTzA8d{FQ+yToEu<(cZ7p@+i0o4!lFU<WgcjL4dY7TmOh1riDAFy&9
zsttb6EX-b*Ik5Z>qhaocr9YT{^!`80oiKmH)WhNdrVmEL;tys&tbBu|SD1TY@don`
zOh3$@uzCWW56kbwh9f+kLE{6?hsHlFJYe?2XjpoIm8Y<9goPU{JkZ?_(+|@JOFyu3
z8m12xt}ykuXjr;|rDK>nSoq<J2bexsK7`Xyf5Y4XGY_4HhZBk61~Ug1Z?JHHg)^)?
zfT_bp!|EHDyJ7x;)gQ2UfTb&Rb71KQmJVTjm^zsMVd)4x9%1TX`eF4ROh3$in0^=^
zCJ&?0%OjY-VCrH1LAM{4-eC5_@)0cmz-V-RFneL?1xCZd52hZbA7(#HADo89J1iVv
z<tZ#2VESO;2rCz0`qAAFOAoO4f~8*=4ND&|{V?~!(htlZFneL{$CdtJ;SLKwnEkN$
zLU%9BUKkCt4`x3s-NI;G_M+>Dg%`}9uyO#V4i}AH{=mW?o-c9aW4J$|@dq;pW-ly0
z(d$8&J7Ds#aD&AoOde(qEF58cm^@59x_(%^!O{z^@(-pDR&K)5D@-4HI)T{>qhaoc
z(J=SG;v1H3VCvCnSop#8!O{VGcoB+!m^m<i!0IiSKVbesw;!DkOFyu52rEBe<pInc
z=;p!H!NL=spP=am<~|q=OV=>_VdXAdKlI!`m_0CiVd)xu{1N6pn0}c1Vdlfa1D1|3
z`cH6$s0^5Su=s)LCp3Nt3rARdz|t)&K2U8z@L=Hv(+3MLm_C>}2py;_n7?8AVBt(C
z9m7n4`4bkOuzCZQ9%1UxX;?V{b2rQ!n0{Ei;mU`wbPuCp?uOZe9{(_L7!8XjSh&OD
z3FZ&DI%s-C2QdAxau4QSSh|AMBd~Zt*AGj-F#BQk0jxZPxd+`}Fmqt;g!vy9f3Wz5
z*-I!MVfta=5Az2s9$@z2q@m?FEF54oEF56w!PXzZ{0XxU7XPsH0MEbB@o1R;VfhcH
z4?X<R-3K!V7LMrapJCw#D~Dm}1r{Ihd<Bhv^!WpH`(Wx}?t=57`eEe>x;_{m<}Wx6
z)ki2E;Pyb{0X_W?iho$R!`%%nM`7Ux3rCncVKgk9VDSeFPndgP=?~o;T;r!Ocf#sn
zn7`5UKl=G=u=E6rKbU<m`(f^d(XjbnSpI|QhtV+mVdmiSKg>K>zJrAy%)K!EFn7V!
zqtmeR7fwUt9hUCV)x-Gc_QJv)W-qK9fT>5PVd)H>|DpE6@+-P}7$4?Om_ArKfTbgt
zJ7Db@n0sI}uK0(Qm$2}I>4(J!EIq)&3FZ!XI6&P`DBNK242yr5KVa=H7!C6;E*e&D
zz}*40ALdU&=?11BmVaRW#g%Sh?tu9h=6;wtFn7bk3#J~OMh|~jxWn8H%ZKRtaPeXB
z0V{W4;SY;PT>4<~0P{agKg|Dx(l0FC!NMPw?qL3ag(oiiVfMmkn0>JDgX@RJE4n!_
zedzYX@;fXa!TbXYSD3$H`e5M)3pZH0f%y;S9`yDrte%CX1DHNoIKb?I*^jOtrVgeb
z<{y|iEWg3@!`uf82Y7rz>m6A64|4}hJuDp1>pz&kVfMn(AuQZsG%Q|V=EBM?Sop!p
z9b)4hJ^jPN8P;BfnFF&I=3f{Mb2p5J<v&<@hvh?D`eEk4XjnYL@--~JVd({?4xNU@
zFDx8j@dk50EIiTm!T2zL!{Q(2UYPq~>TuC8`(gTF{(!k37Vo&+0V{W5G%S6<;u#hn
zF!Rve0rNM^A25Hz(j`niEFHr5uyBUyhxr3tKg=9hK7iBE^bXSxb2m&s%snvuuyhR5
zk6}MrdkdD2VESSDU^L7hF#BQY4;D@^^I_o#i%*R7gys)edWNL~SiXnF1I#>_{csv;
z4op9chLwY`a6vDBVEG=V9%diRet0-Q%U@Xg8|Gh_|6%DK7Cx}@0haDy=ED3B3m=&M
zu=EFWFHAiw++gBx|3VqCatCG~%p6#J!~Bgi+@R*bC|Ev%rAL_k#OjBIBg{PXbPp59
zpwZ$FmY#{#4~r-C_<;Ez7BA@b!r~1U53v3sOdRHKm^rZcgP8*hZ<sz<c>^nlVEG3Y
zpXly@*$Yz-i)WZVnEzq+!@>pTPZ$kLkFfLx%ZD)c!0H8<yV27hEZ$)84RbHdJurRf
zG`Zo9ZVoK{!omX<PB3?%tB3Jn{(z-hm^rZcgXxFSu=qr;e_{CumhWNl35#!7_`~?<
z^LMatf|-w=e_-(db2rSrF#p2B5oQj|K3IIf^r6!*dtmVoGZ&T)VCJC5Ke{=vaD=5N
zm_K0pVd)NCAIyGOK7fTk%)hYoi>?pGhxr?3FM7EJ3lErjTr|x8u<(Sr7Z&d@KDvHb
zyu<8=*PGDt9+nPa`3YS=EZkuE2bOPP=?|t479QyOVc`X{A7&mb9m2vNR{z2D!@?b=
z4_1D_;u)qNW<N|ErXQBRVetlwZ<u;myuifK?T48I3wM~iVfh2bhv~<a4q)K~vk>
znE9~ukIR0TewaR3{K3K-mY!hdq0=z+uyBN>6Ii&z@)5c|7$273VD5*x7v^u6`LOta
z$-}}2ZZFh8m^>^!!Sut@F|7T8UVg#z2{hfp^ufX%rXOkz1I&Dw{jhWhlZS;jEF56&
zhsnceSpI>T2cu!>2xc$beK3t^G%WsL;RuUAm^;vvV{j?e4~s`w`2n+^RQJQe6Q&;)
zei#l$6M*Gkn0{FJ5vv~-e=z;9bPv-HOOI&Q!nrX2!}O!OpHTe4!VRV$mX2Ze6VeY$
zH!yQ>rCYdzp$u601JeiN!^%6D{jhQzstr!T@(C>bVCflFj>Gi9(g9o}4hGCTSbBuz
z8(4Va(8d5Ok74lu^9M{HEIeTGh+clc?1P&REeBxwU^FaU!0Hc}zhU7AEAL?826I2$
zTr>vEov?fhGoRFa3opl^?k1F8VBrOGKP=o}{(`w5=5I7-Gr-z!Fnut2n0}bQVdlWn
z6HFb<ei$F750?I6=?Io?VCKQ%52g=h4lMj(`2ZFku=*6{4%qxHx_vPHu=)#De!$W%
z-2Kq-h1n0&2eTKJZ(-qvD;{9>!@>{dUzmQFJ7D(UqS3<(=1*8U#H9}wZZQ2Ycfi#{
z{RuM<<{y|m%>A%*3o{Sq4_J8&3qP1SxcmjnkFfBB<qKH;#T72Fc!v1{rXQAWVD{pQ
z2bh0g@dq;p7XQTRhq)i7A7(C0KP(->#L;PV`(f!FrXQaEpc-M}53>*EZ&<v;{0|F1
zbpOM`7p5O(Kh$^zn0lCgn0YXNz~<jz`eE@7E5BgsVE%xK!}2eB`2j2MVDSv|2h2U_
zG|XNY4NF(B@PhHt^}+ej@e-JRSiJ+&4|5M(Jq`w}{Dg%cEWM-W7j*5g_=Dv;m^m>2
z!_ptRJ{TXS9_D^nxWe2|DEwgNz-U<g2XhZB-eK;6sl!FX^uyc(3kO2(frSH1AIuz>
z{jhY0(?4kO2oD!%`9;Y6uyhNvAD%Cu`U%AYTs_nsaQ{H{!@>bqxS^LD@bV0*AC`Y`
z>4&L@mA5eWz}!u2I)?clmX2ZJ4ok<d@&Mf(=<bKv53?5*{<!=BOK-693l{IN{0p-e
z<{xzXVeW?Mhov`|K3Mp`+yToc@OXm88>}9K>4(*4Fm*8fFn7V+0kapDZeigC(+5`v
zjZav)2os0f3v~yq9D=zY<{nsl!1SThF#BQQ0jn=y`eEfSx;~iwF#Rxh!~6jYcbI$7
z^~2&H7GALY1j~modtu=R3xAk7u=s)X7hv|Fn+tOX%siNWn7d)=2$p_e=?!Ksj1S9?
zF!N#hVD5ss6BaHof5G&@>_^uRE1zKD21^$(|G?r8W)I9<nEPSo!PLXT5oR9D|1f{U
zXjp!O`vbb(3KkA9cf-tu>4VX*@PVZpSiHgPgSi`)o?!Z5G|YaOKVb0*OGmJB0A>zM
z9|jFg|1fb_c?NSIOh3#&Fm<?SSUCm@S6Dd0Xk7YW;;{IG#S_eaSp37(!Q2T8H&{5s
z+zoRtOg~H?jE03fjE0HB%z>pVSbV_7uVCt6<poSX%)hX51{QC)(myPmVDSgD4_0o#
z;tysYjE3#sfrS&y9+*7Lewe@E=^m;8)_#P!7v?`09~SQL_=1+7Q0)w`^abO?!VBge
zSpI?85AzpHJq8U;f3R?bsfUFF%zO-ePytvt!qNlG-7tSam0?gY^)P#3_Q3SR>KmAQ
zVCe}KJ~02n@;@y9p<4ti4`Ayzap{Md5A!#AyrP>6GY94mSh|P#8&~>4HwTvfVCfmA
zAC`__=?6|j%Q;y20}BUu`hi&hO*b(A!218__y57%4RbH79)RT=SiHcDh0(BZhq(i$
z551g#sfW?9^b3o3I3Ma>SbW3OW6;p_3iCJ2eX#Tn<74PU6M%&`%p90MVBr8W4;BwF
z?J#vP_ruaZEWN<&hs7hz9GHGsxWV+p@+-_9SUkeq0gFFa{KMp7=?<2DVDX5aj$!7)
z?1zOn%-=8?=6;xcFmqt~U^J|}hq)gn4s##O99VjQ$-`)v{V;dK_^|v7GY6(07M?Ku
zFdF7xnEPSwg5`HuxWmkY>4VWQ`(XNE@d(ooqjBknnFC7?uy}>}2j+fU;Rw?YqtX2X
zEB^@j1EwElFDzbQ>55Q%!t}%Z0SgaU{>NoM%zRk5!)TZ|Ec{?J%pMpGvk&H87$4?u
zSUmuv(aRNB{K3)@Ec{{OFdF6$^!5YH9k6(Z<tJFah0(auAIzUH_rv@T%Wp7$<BETn
ze_`!ym^)$S!t90lALd?Iy#uosR*u2q8Rl<TdV<*xqha>K^ux`AmcKB2Vd`M+f|-w=
z&ft8gewce;G|XODet`J{W<D(bVD5(bA65^*>_ra;n0i>a!_qBGKP=tC;sIAahUtUF
zKTIDi{$cJ#r(xwVEF59(fcX>VZd~@l)Wh_{%!Anv(+`Uexc$)j52g>6?qTvU|HAac
z!VxA9QxDS*OMftT!t}xX0n>+0!^0Ws9$0+C+z*R)bbaW2Soovshow7o_2_(<e_-ht
z?rvy$L{|?}2TOM_{jhL``5!&K!OVfppTpb@ONTHT=5Cld=;05GcbI=+=HiM6bp0^(
zFdAka%pb6Dhlc}9AsP)UA7JSKBOl`mKd3n{`(XNE=?+H2(labw!NL<J4$}woH>})*
z#SbhzVd)9heuY}k0E<7EIWTcpy9s6=EIwfFfT@GguyP0%AFz4`rVpkLorZ@OH2uTe
z4U2cUf6z5T`RL&fi)WaAT<(Qx$9eB6%wCxPVc`MO2cu!-9W4A{{(<=$){cYehq()v
zewaFV_(8)3#z(JzVdV$9{jmBU7Ctb4pvOPV9q8!^7N4;652In`!t}xX56j0e`(XZt
z`4^U-VESP0h0!o^Sos07AC_)m=D^g!@+-{UFneL*uy}{*gM}YVKb(e!FU%dV^b4~W
zR)4|V1=j~<z~UR`e{}t@_=GCQNx||j+#S$z1Ll61e{dN93on>{^n3t|4|M(L`5)#V
zn7y!YK-UM$r?B{kwf|u05oRAO-Jpj9EZ$)81v3w34=g-j`3UA7SiXd*gVWG>g!u;+
z9<Xv0mj2=D(HJm&uy};UJIw!t!XKs&=5Clibbq5+3+KZ1LHz;K2Qvq*5RCyV-(l`Y
z_czQOG;Q$lFPOVw@dt}9Sh(Rz_ptba*$b=3VCe^D4$S@N;im`nCrlqKf5P;`)WhN(
zmY!h#fQ`Sv{14L)^AF4%n11wf3uX>XKP(-?^uzoIHy15G!t}$`!}1->-7tNybOht0
zy91W~Vc`HPCt=|Svj-Oqb2rQ!n0+vFaOs1I!`u(cx3Ktvl^-y5xM*0qh4~*={=xDo
zEc|h~2c{oJ!|Z{T53uwLa~Dh=7Y)+~3s0Ck2!$ssy}-f)R-eG^g_T3-_QKK+%s!ZY
zn15mZM~`=yIWT)*;RlOPSbBl!hw)+h&}o>zVdlWnAxu9m`(gIM^uhcC(?_V>gq1fi
z{V+Z({lLtDl_%)#hNoL-c>pVKVBrD_cbGZ2`~l0a=<a~&!{rZHyu<Xt$}?EJ!)Tbh
z(A^Ky4~utLI)>?k<wtaV7<{PzVeWw02Xj9xJYo7^@eM1tVd`P-fzj}E4=ul7`e8K8
zy)b)W`4{GHnEmMGFHAoyo?!Z6@dMKba~DiKIt_C_oQAp+SA4<sL&FOe?y&HHg*z<$
z!`ufe2VnUU*8haL8y>z;`(f!4CJyrtto;qk?=bzaaD=%J=5LsLU^L8KFm<qU7iJ$U
z{h@~=%>6KZFdCL$VeWys0~YTv|H9Ox)3ES?`2*d*=<Y?=597ni5m^3*r5{4&7P|eg
zbPEe_SpJ9EgHFT34d#ECe_{SdSC7tzr8`)-!Q2fCZ&*CS!UbJF%w8A`iw~GTVE%{s
z2i;vT_rt;;79OzphuI6u53uu3VD5mWdzgM${)NRG%>6KX(e=a3f%zXjKf&yUrC(UM
z!1Tdrm^)zM2~!8t4^xMWhNXKr4b6wR>J6AVF#p2*4=bl&;SCE1n0j;?7XC1I!_p6o
zhN;J84$Qr<_=n|Zm_C?0VBrrFhtaTn19Lx2KP=v1=?4}L=-~-VFEDq&>Oq(}uyh1-
z2fDvt?tsM~di=xO4YL<lIKa{m%s;Sp8!SJ;>_Mkt`32^GSbV_TOGrP=-7x*=@efP?
z==Q?+F!#b}n0{Ee!T7Lp2;Kj%aEHY|EZ@Uum_C?)U^L8rSh|Cm15*!+cUXA}a|e9>
z9clo~|LFF^!WEVtVEGqU{)dM<v|fd|A3gqI;R3ZDe*YZIepvc~>4T|5r(x#9;tl2x
zm^)$WVetnG7nnGVhNV|nIRMKyu=EIX4?NtV<qAwcy1!xWfTed_`e6EDG|WC&IKa|B
zp>Tldhv|d42c{3^ewaFR8kYZI;Ry3T%-!he6<t3p-NVu$EWg40kFFjU9~PgmaDbT$
za}Tca3#JYhk1+MH`U7q*G~Qt2m#}z(`5PAhFn7bsKUhA1*$)d(n7J@}VKmHtuyhB@
zhp==Bix-$WbQ)$KEIwiKFnz?v2h4x4_<)56%sg}&W-qK9fw>>%Z*=wOd{{byl^3w`
z20g!{>x1!O{)NQ@%)hwuEleMbhJ`<jhUtfeJIp^Y^)MP1kFazA^FK@<jK-xO7S1sJ
zu=ESdKQR3;`_O5)dT6-`3qP2CLg5c9-(mK{!VhLIjK<}DxP4IjVfh%A&SCB#lzw30
z2Gb97H!MG)`y0lGsfW2A<{p@PVdlX60dohszhUOU+zpF=SUQB|8yJm#{u@jm%)f-v
zEqebC7Vfb81~Ug{Kg_@AG)x~X-NN!A%)hYm16?0FALf5pc?b)4n0wIG!}zd#3k!dE
zet?#jgz_IO-@@Dv3wN0NVfGNBVfg?SuP}eZXjpncHwWGQF#p5!!QuhNhv|pe1M@db
zAG*6?_M(f!!W*U^UJgLR4Q3B4K4I|zOMkF>nvi~&xv+SIn*&{c2n%;uI)bT#@nP`^
zb3aT!%ze1R59V)J_`&qU^r6!*`(Wt-W-lx~z}hqD`e5M$D?ecA0On3uzJ$34T|X=y
zVBrMMZ_x4#mVRO8pzDXl1B`~b2No`{@WkbRn0lDMVetX;KTJO?9C5jWkh@^|VC@%l
z_2_)K`=Q|g3m5eBlVI+ImAf!LEIi=x3QgCz^uzKGEFNL@!`zL_|1fp1d;m8GY9CA=
ztX+rhPgr_^>4)Vnm_Atiqw9n5Vd)1JZ!q`5;u~fjOg)T-g%eC4jE2d>(l5*$T=fae
z{jhL@g*Qw+%pbV&56nIo4NHeGf5Gw}%snu5Fd7#AF!iwTN4Fon{DkR;wUc4~hnWMj
z7v_Il`4^UNVetp!!}P)2jY~hw{V@Hoavj!AfVCfB@d|S{It?=iM#IuSEZ@M~gRT#q
z53?T@9<XpDRzEBqz-eecfcXR6J{TVsZm@8G#S1L_VD5m03rrtO9ZVn0UYI<LhUtTu
z2NQ>d7fe6Q9k6@{3qKei<_?&C7!3<=m^rZg4om+q{V??~8a*Gv_^|W>%fGPn2us&6
z{V*ElZ&>`n!Ug7Dm^)zV(P?z|!^%@wyu<8=xd)~nW*^MoFg`55!|X-(Kg^%7bOwtD
zm^xTIz}$}>Z!rBZf5Y4h3pW@arVr*Gn7d&#tp0<Q8?f>R7Vj|s!_p5-ADo8PPcVPL
z$`6=*F#qGy4+}S#KA3;e;}7Nzn7uG}!t`ULH>kT{;R>tAVdV&H{Q%6}FmaeY@OXrp
z19KN_{s*QX<_?&CSUQ2}hlMLlKdk<R`4{GXSh|9R7pxwE)h{sj!|aE}Kg|8G^a4vi
zFn#FZ0&^!U9AV)M^9MXVLCY;zc);|*+zHbUqhbDm*-I$h!SuoW0SgD1J7D1eqhadN
zX_)(A`eE*exf`Y*o-fc1fW|LOKg|6weT2$wbo*iP4$}{dH*{-Zd|3K~*$?MK!ygvz
zF!ks(dibH+Pe?z^JXkt~g&)k_g!~Unr?7Z~g&%tUhm|KV`(QN8->~$99#61%grz5#
zJ{S!Pe;6O;512b(`4Q$Gm^v5@3vZbHF#RxdVdWOg9kBL0%-t{=7Vfb81`Bss_`%G9
z*$d0BFn7Yj9i|_ae$e&7^uuUay#;eW%%8CELJv=vJ{S$t2M-5my2BM;Fn#DW%>S_T
z04wic<q^7iI3F$l!SuuY4@-w|^=J%O_`%}=>VBAhLjH%vH!S_ajECuig(oZ?(c2%e
zc!Svwt54zOB-A~ybO=j7FneI}2P?;6_QBE*Jbs|&;F|w|*$*oZ;Pykq7iK@M`5##P
z!@>a;E--h%!V?y+FneI?VD5*R0}CgZK3IB$`5We57!B74RRGI>uyBW&1IrIE`(gTE
z`3dG8^mGLCKh$^(3Z@U{9$31C*$eXzhDN9W%ssGhfY}c#H(>D!RgRN_xdWE2VCf$g
z4(J+T;RLe}mR?|dnEkl?4~uV@zhVABk9SzO!|jKrE0{ZB=D^g!!VMOVaD7k)?ED^>
zewe*bX$F{karqn8zJ;lW#Xrpduyl%^?qKTCX;`?!$`e?)6B}<Zf56fs%-w{_QCPVN
zOE<9agT)&>AEP@M%7?`pjD`v`z``9Cj&OfK%Mn<3!}P)2537G*^00Idi%(el6J`&L
zrZoIu`eEq^7XC2z!|cID!}P=a0n-nQ2VDAK;;`_C>4$|svHD^9;50OxN!1Tae=vRU
zc!N0<2Mw!7;55{JSU$#~4K56`7Z#2%dtu=SR|sXm(g#dEEc~F-46yWqOFw%530`kP
z$Hig#VCKNgh0(D2A6R~a<#(9>(en{ZAB={T7chHa`4AR=F!NyX0M8fDbNXTB4@^BQ
z-NMRam_4w32=fO#|3J%6m_8T{3wKz#1+y2H58*V_0$BLNX=uE|!VjtpCk4y@Fn7b;
z3AY!gPKX37{9)k$EAL?822q8Zgq7E@^al%fSop&Hi_1Ki`(f^Y*$<0PSUBL)4@<W&
z_rqL@o_=8Q2$P4?Xz>V(2UxiUD@Wk!aWG)<4|6w6KRi5e=!4}$SUAA>&~yL`KOEW_
zVEF*19~OSFbO37y<I)ckhq)V;|6uMWRQ|xy9V{MT?uUgN%snuB&}mpZ2Nr*@c)}Im
zxb(xqA6DPO^uz4O6%Od(2lGG79+)|>@Pwr+m^`|ESa}4?e=t6}IWYb3@Pej8SU!Ns
z!)RD|!qOE?97e;!5f%?Hb71;l<q=Fhj7E1qEFZ)29ZWwg9AWxkG|c_5_=A-PFnO4J
zVg84Sqth^RVDShu2c{2Z4lI7r^~2I3EFZwa0~T+D(lN|DnEkMH0JE1^{cwHI_=M?$
znFF&2M#J(Q%)PL36BZA!c!T)^rVmD=#~;joSh)d<KbSeV!VebSFn7Sz!}24{K3p^`
z++gVs=3kioxb(rqVftb52a9)@zj5VXn0}bOxb)-lH%vb){lfIa{7<a?l)9f%{pjfc
z7XL8!<MKaDA1wS}=?*>r!_0$)7c9NN!V8}6pbB92!R&{n2dFdyx_@Em4kizaZ<u~q
zdVtBpXjs07>4(K5x_<O>8>Syd!^$6+e_-ap?1#1MVCr$vFn__q6XtJR`2c1QEc{^M
z4=V>?_QT9Uw-=@#W*@9PAryb;=D_s9+yP4$u>23R9~O?V@PdUmEIq)|2`v0!G<tr5
zsfUF(%sg1T1Lhu>d9ZMR$-~?U(+_L6!TbetH%uR{_=DAxF#FKMALehE`(fsz)39_1
za}UfvF#B=UpD=UK;}4czVE%=vL#JWw3s`u<`Ox+wEFYrlL+8WX4GTw@yJ6`FT|JBs
z_b0TzgQb61xWUZ9<qq_4gQX`}y$RC~OLs7PVKlmaSUAA!hlL|9`zdulJfA}Sj~;$7
z`(f@yr(y903x8Pn!SumsSUy76597o94KoKl{LuXmOYg9B2h$HX51M{p_QUcaOdfWA
zBg{TnJiy!u3s;zaSUke?!^#(!K3IDh7XPqtfW;$>Mt27+zrx%BD-U7z!152wei#jt
zhxs3-A7(zx-LUiz^A}7XjE2QG%-yhXht*fO^ufX(W-mM)K*OC-c>&W8b3eMfVeTWA
zhJ_={Tv&Mt3qM@uz}*c^|8PFkAL!{0-5gjtfW<c~pTpxDT>}~)=5AO#;Bq%CeW7WG
zpPvKsH%uN@FTnJ{;uC%T2bOPO@d%3tSUQ850}B`Q`5T!3Vfta|1y+B-+y(O&`uq{h
zy)bpK^Z+v#mVRO3i7P%~;Rw?YGZ&T*VD`hrVKlnEu<$~s;q@mpUBL9i?1RxTb?E5^
z7XGmG0n-nQcUbtr!W(8kEFNI`VF2cCm_Ar|!NMPwk753Y>4VWQ^|1Jd*$0b%SUCg>
zZ&-Z7^uuVFewce;{)Ux{Fn7S>5vC8FhQ%MO9D<b}aDStPJGy>Y{)2@dEWN<YL01ps
z!_>pV5he}`cS8DM?uCT|%)K!G!omR-FR=KBg%`|TSpI^=7p#2<qhaE(^*gZfYgoF2
znF|Yln0sL15AzR99n4-B4Ra67|8RBCaEF-#lZUk%VCfg;PPjN!AI#k_|HJj8F<|b1
zr309IaphZ>c33)vg#%1KEZ$(|V7MP@04&|W^r5GJnEfzwVBrhZ4nM~RmX2WQ0cIY|
z|FHB3(}zyO!Vwk@=;06Z54t`WAJ$%h>xb57Fn7c9F-$!!8WxW*f55^GmaftD!Q2h=
zFD(9H@dEQV%pK_A2TOM_cf<Iwc!24Hm8<CX!}#d&2(uR!e=v2pXqbMOzhU}u`5WCF
zSUAA^0kaq8esq7M>xbEoOFyCbhq)V<epq;-+Y95v^ucIYc*ER}OFyjKg6V_hLzq4o
z4YLOq4f8*&+=7Ka%pJJ&!Ng(q!tx<3-w_HoSbBxIA68z&+>NW=gXK3^xWnv+r5Bh!
zm^xT`fQ2J0pTX>f)yJ@U1LhC7`=I41Z2k@wUNHTz{00jTbbDd`frTqf9+qxl=E3ZR
zg&RyCtQ>%uk50qF0p=c<dYFEA`vOfPdisHf3)H``@Icds!G-CAxdRsdu=v1bKdiig
zxf>P_uy}%n7kWB`se|c**$?wC%wI4XW)7?zfUAd=(=c@~{V;dI;sd517Ot>-0<#~z
zzJR4iSo(qG2Y5XIwHFp%u=*J$57Q45hr0_JFR%ze_XjLoVE%;J3v)kA9Ht*V9m3Kh
zEd9aa59U9ZJ{S#4_ptDQg&Ryg%zhXjrVloM4+}q7_`|{%mp+(&18Dfc^uy8@ESzBO
zhN(xVVeL1Vepo!g!XFl1Fh06|7#|jXFn`0s52lZhJ7DI(;t>|_=-~iUhe1Qb0hW$n
z_QULjg(HSOr~oYf;NbujhlL|7A47#PD44%t?uPMU<t@w{^!SGv1amixhKa*ym_J}N
zOdLkT!XK9IVdW@#JpywNOdmQ8R}T$O^n8b&Z_xF__^^0{)6o11i%*z3Tr{k_frTe5
zAL5EfSp31#FN}ueN0_~^ctH0DOg}9CVeLp*`i1F(@d>#b9*<Ce!{QAVp6L2v`5xw9
zSo($e7rop^*AMeA%>6L)VD5&6ABH}t0L(sEx`Fu<mj7Yp3RF1;1=A0+9~OTwcf;HP
ztIyC4fa!<PuzUhb*D&|P!Vz6REZxJx9p-<Sepvp6g(tdx7$4?tSUAAK6&4P-@(+6Y
zf#rXg|KavR(*w*t7!C6`Ec{^h!omxtA7&m*AB={%8>Sy-9xR>1!XM@jm_BqGmjB`I
zhn54d_=mXzmY>k|qx0eUA8HOvANu$ox;_{m<_|ax4L{iU156z(zF~Y=_`&iMoDbCx
zYrn$O!_p5-J<Q!Ob7A2I3rASI!1Tk^!O|bh99X*%rVkb$Fnur@7T&P<Lw7GM-f@*5
zu=EPk4^vObUYPwb_rlD9<v&<{gQa_P`(gPDW-rVfn7J@>VD5m$KTIA*qlX(z97e<3
z0ZV@{aTtvrez5q2g*Qw;EFNLvF#WLjgQYW=KVaz|=6+bY2-Am7!|NMpdWXdWE`Ouz
zhlM{ZJYo7__M^KST|X=wVftbIhUrIFkIskrAEqDXUzmPac%bWp@nQJ}rXN=B600BG
zeptGL=_llWO2ZFkKg=9h{)MF*m_N~JSh)%FH!K~%^uy9EtR6$xkE{NG`4^_2kUL=M
z7-k;9bPP?Wu>1nk2g^?|K6*UB(lf5}W8nUSmbWnX!_qU%UodsB_7=>&FneL)2aA97
zau=o_=6+cC!|Ow+{V;Q2>d|RfIR>*I&WGy9WiQMem_AtkL-!vnAHryuJ{S#iH;jg*
z6PS9K`*GzzSbBrm4-03QewaPDXn1%*(*w-?uyDs^FS>cK`~xcoVD5&c7j*ULe3(91
z{)6cwl<r~a2Nr&?d;oJl%p7$4V0@UnVetr44@<Y`^&U(gIt>edSpI>9Kdc-@R}bUE
z(;GDWVfGPgKg|8G@PmarEFNLz!`%T**D!aYhZ`*Y!OVrlKP)_9G%TKA`eE?^^B0VU
zg#%0<tX%+W=fJ`ZmhWNZDU62cgQaVjx#%=ZKg>O_aD?fH+l#Id%7?iFmJVR{5sU|D
zyus{64=<QIpw?hexbiQ|epq^jnFEVgT;(1t{$cS8(+>*=n7QcXH_Y8I^I_=;rXHpr
zRvx0SUxAe)Fb|^F|FC)lCJ&=w_Q1j&W)3Xfz``HqE|@+T4NoV~cz}lsG@awJ7p5O(
zA1wZ0_Q3oL3m2GrbQ-1~mLFjG02Xer@(X4zTtC#kFneM8Ve&9{!`uN2PnbN6hNXL0
zeuCK#i+`AYn0sLQU^Fa!!1Tf50cI|&+<=(_qha9;QxD5OF!Nyf4d#DXxe7}^aQ8vO
z0hSM7<qymrSbB!J7oCQ=8y1c*^I`g7=Ar9@@nQO5?uUgtEd9aM;i6&T1hXF&&&1jf
z(+8tr?uF?m<bPQHfw><RFR=KAnFA|7aQOqKA0D63`~%B>xb(rqVd)oU4lLdYg(EE9
zVg84u8<_iH^%pE3!pudt7p5Pk9wrZ?VdkN)A3~2mSh|Fz1DHNo{)4#>W-d%U%zhXj
zJsrZ#f$4+MFnzFagP9L2*I@R-;t?hf(+>+jSiXbhGgv&r!ULusmY-qz(P>!vf$4|Y
zM<^Y@?1$-t<tvyzn7h#JgYn_&q2&S0-!T2Kd<9bv^B1h00<#z94!A#|=@@1ndisIs
zhs6V|Jcro>i$|C{(8r%(@dR@}%siNWn0>JH1QUnlTbR9Y{ZRM9^ufvlm_8T{+dmGo
z4`x2Byny8wn0j;?cK<lcepvYc^Cv95!o<<_!_qn2erUYG?1klfcs!sRfX0XALzsE6
z_=5QdO*xzki*Hyyfa!<1A4bD9pfO<S2$p_e@ed0Jm^;w4!S`Rl?1i}>Ru9AS4X%6#
z3wIa|%XhHwhJ_={9T?#N4R4r#VeW&uALb62{V?~y)S=U`^apbf%pEX$Vetsd$1ruU
zatoFoVEF--Z(#ai@c`2gt1n>Yz}yW>H!$_Ea6or2%s!ZYm^{qCF#BQR=rpYT3yVKk
z{(;$xt{xX37Vj{3z`_w0UNHB-_~_=q!U5(En15mE5*B{2@&H{wEdJ5cFU%a6{kZ%E
z3wM})Sh)nV7nc4Ag(ocDVg7)*3uZ66`(gfr*#}E+Fn7b;1IvGKcR&LGrXLo5Fn_?z
zf#oOk^aRxor*Op|tlWi#A1plJ8leoBzhUlx<p-!V{2Tyu|HIq|3op1nX!#HGFD%`_
z`~@=?c7Gf!-NC{Q=3ZF1!_=eG==1+D_rUCfnFHga>xc1S;RlOvSiXeibC^HS-4D|b
zqhaR2`~xemVD7-BA7&pcy}<aec!%jnZ$H57g_o1i@)VYiVESO?0$e>B1Lkj-`(fz>
zmTu9M!@00@5A!d~eXw)_GZ$9B!Ng(V0xQ2@{)V{|mJVRzuyg>62bcq3=?-Q;tlWah
z!{QGX4zT<HlZVl;bOW;w7Je}MVBrB%k50q<2XhB3Kf&xJRPMms4f78y9AN%Nw-22U
z%XhH&fTbIl|IyXs;=|$#mi}P=CX|2B!w=?PSop!x1ulDG?uXe6b3e=+Sh|GqVd)6w
zUwF7f&-H-G!~6r&4>K1we+nx<VDSzsk74eF@nPn{XqbOs@ddXZdhQ8KKg_+bbbu@U
z!PLXT5mpYv)Wht>Rer$q!}P)Y3)4p^AH&qc!U2{ZVfhf|e)RMRYrn(%kM2HL_`&qU
z!XM^d^zt8;k6_^oOMftbz`_HjA7(F%hL?lT`Ul;;Fn#DW%$>0KgoOu8KP*3@>qF<m
z!VQ+5VfxVhjjj)7Kg=JnbN~xCLirDt4`A+x*$Z<&dc2_94-0pgyJ6`F7T&OYjLRLc
zd<N48OE)lcVftX^!0m_DS1^CW)WiG@^A{|9;Nb|3Uzj{B-J-h}CJ!smVdWu=hJ`as
z9n9S@d6+mM{jhL@>4$|o%v~_|!~74c|6t(&i#M2ku=s+72P_}JXqY}24O5414lLZ^
z`k>(k(+8tr?t|G6OUJNu1k(?57fc_FhJ_C-{$cqSMx%!dOdpJf#RGac!rTSZ4+}q-
zJ{S$FH(=on%fGO2hUtU(2c{2RUO>|!%-^u`66S9VeJ}%{`e6Qs3B$^LnEzqv2`-Pu
zfV&rJ9xOay?uVy)G<`5GEdF8shs6iX-!OAv>d|SKepvj&(h)5DVd)5#uF?Gii#Hez
z3ojTAiyv6Hz{W3O{)FX!SbV_jf%yaG4wyKMhN*}78$BLi@dpbBSowg<|FHOnnFn(}
ztUN;3ht7w&6Q&;)|1kT})uZ!a<tQv%VD2Xre=u`k_QLr%@;gi(M#JJC=6_f?!o*?q
zFD%|+{(|X;`5TsQVD5*>!{QwlZ?N<PmxnT7{)gEM6^8E*BNQI6bOxhg`3hz~EFR$Y
zL-QLf+|d1v9{;d%0p>3B^apGA!|a2lH<-KO`p_6Kdtv6m!xtKVuy8}u2Is=;g_#2j
ze^|W1`~la1#(;$vEdF5bfVmrHKYIBOvj>)cVfi2Cepq<natF*lSop*I0kaRrhuI4g
zhlMZ9zp!)*vk&H9xHvSPVd60N!1TlX57Q4zAF%X;E)OdYVC5x@hQ&85pTP9P-2shH
zSpNeS4KR1W!Vl&@nEzpXn7#0DfEoZRCt><w@dEQNEFNLzK(!$#X!yX~0gD%y0$9F*
zg&Qn>Vd(^>4@Sev16Vl1+zsQy%!P$3Ed9Xjg_#el_h9zI+5v?0!^%sTy|DOz=|iVs
z_QJvsCJ(b8J-lJ^Fn_}Q4NJ$c@(ZRP7JlgNhnWLQKd^9s#UCubVE!QF4p=&a`2&{z
zVCfoW4vdDmALf6U`_a|I(lyMzFdEnT379`&_Q1jcrVmEL!VTtMSiHm11<V{+{KMp7
z`f<@P|G@GOEZkt`;nD|7&#>@=g+DI$!_0xlH#8r?&4K23T;T`Pj~@RpaaewVrDqr)
zM#I#>+zX38n7?8Eg_Q%aaDay=bi4u<{;>KNmX2WQ0G58y{Q-+V7!6Bruyh5}4@(Cy
zKFr-P8m1m*4lMp*?j)3dVeW?IADDk(_7X}rF#BQVz}x|gSC~1l@PoM<Ck;&pFmqt>
z0rL-xhUtgd4@);N^{{jSlZWYp(J=k6_=EW$7JjgF0#gs;!}Q^zVftbA!}Jk~2bg)V
z^ayhoEdRjLF-#pgjb4wz(i==ax_THN9zM`;hs7H#|H9HG%>A(a8|eCA@deX|ZVq}n
zfbD;T>4T*+xIU<RVD5+MhnWuxADBO2`4txaFmqwy4~s{bdR#Ot9AWVX3rCp$(e=Ud
zJFNVGg%?a6%wAYI0CPXgov?HV^Eb@DFm*70!P;>!b7A^n{)DNA*#}dHi-!3Rt{)oS
zF!iwTh2>jx_rvtV(km?g!NLosA7&m*KRg~`5;$m>xv=<vg$u5DfT@F(L$G*(wcl`s
z3rsz%-h+iBOdqV=MGt?Nv*Gau4L>*^T7SXPHB3D^4Ra?f9$@yt+>1*;EPP?<2NwRY
z@P(O!ZXb+KseV}e!|aEJBP_qc;v2?C4?mdwuzUkc&oFhc@&%UuVDS%&ADBPT-2oGa
z(Xey@3l}|TyujpP@dYynmhNEo!@?2f4p_Xv^uzKyEF56*3G)Xm9l^o_J^jMe!_prt
zTww7D3qP3q(d~!114hHlgNG~BzcBmJ(-X}7F#BQg0INq~{)g#@xdRqHFn`1BgSi7%
zZo&Kyi$8S#!_>plKP(<#{=}sorVk#!(EJCp7w%5zycSG7It}wL%zl`EVfMq~5nUh5
z|FCoq3wN0Puyz#8JeWDK@Q1}SEZku3fTc57x`f38OdLkT-2?SEEFNI-2+Jq1aKO+9
zHvqzb`2%JSti1u#4=dLpsu3hC{9*Y9mY!h#h1m<E5jr6(bbT;&@bHATvtjOl<p)^!
z!@>_1PB3{`I)a5iEIuK&GQh{hVDS!f2h88F^a?W%<{xzZ@OXp91I+(0eXx9k9)7TJ
zhS?7@ALcKZIWT{tyB{|H3X5-8dV{4eSh&F40W$}dE@0&gOh1f<g(J)zu>1?t4>J!I
z4saUkf0((j^aSI>!U1LuTptbwEdJp%v>Zbp|AEQF!VBg;m_K0dfR!V#@pG89uzU!M
ze^|PK`5&ea=6+cEgz1O-56%BD^)UCq)uS<B@dQf;FlktP!py;yeqiYcR_?&kEi7DM
z;^^rI=1*Aoz|4omH%uOuzhU73r=k9Z*#pxLb2ltsz-egwp~pYEzhUl#*$>kXvlo|s
zSp32A5iI^;@deWd%O9|ChQ%|?9k6_WUT(nnFn#DW%$+d(u<{$`Zdka$^B>dzSop%?
z5f&d%X$Exnqo*fW_`~$W@&PP9VE%^1E6iM2x`*k5g&#~FrXHprW*$r)=3kh8n7?82
z=<Y`MH!R#?=^sYJ!VQ*=VfMq)G0c1zjqVN@9~NIQ{qXRIX+)!8;Ry2w%s!YuVD$o;
zHU^k|uyBCM!|X+mFPJ=xhNW|ueptK^>wj1{!pb3-`(ft8@)LUa!{lK!%pb6P0CP87
zKTIJSjh=pC`HrCfq2&iG-eK;B*$bm#?uLawj7ASP7$41f46ty7m7_5IFm*6}u=Ecr
zA7JSp<_?%ROdl-VVc`aMKh$3^b+B-NxdSE+(+{&3mhWNWu=s?z8)gnnKROMI2UvK*
z;sK@~<_|*pVDd11==mNd52In}6_$SC?tqr}uyBNx%P@T~8s=YEIKa{$EWBa<hM5D?
zhfc%Hf$4|Qu<{pX4thAi?1i}>W)7^pfQ19BJVCb?#)pLiEFHko8La$(sY9n>_Th>*
zLg5dy51tO8;SRGO<}O%x!rTK>53?7R&tUl-7A~;*1eShb;Rv%A7H%;8u<(Y-!`u%u
z7v>I_J{S#i4=j9O=D_TO`3EKs3pZGLgSiuyk6`fy(~m3t!OVl@7npun`A;a_!Q2f?
zZ!kX0eK7lBG|WC24buk;Z<s$};RZ{uFdC*0RzASO56(x6f4F)y1}uJH`eEURrVPG*
z9TxvEd04uF>4&)=mJVR*VE%ylAC?Ya@-TP9>U~)HfSU&`Pht9C=@+ISrVr*GSbBz~
z3z)sQ;t>|#FnzFihuH@UcU<WQ7SAyKF#o{H4Oo1_+>1`b?1zOTdb&jqPndp~e_{Gy
z_QL!NGY6&~ora}Tn0}Z)VBrgkH<<nC{zp$guzD00e=z%C;SO^bJUn3W3Z-G`4;Jn)
z_rTg^FmurRe=vJt`3Pn|EM36j6BZw^as}o-SiXm)Hy91`Kg`{*bO6%_r=jkLg(ECn
zVDhkh12YHif2abOzhU}d@d}Giba%kafojK~VE%@=2WAdTAI$%-@P*04!U0CZ-2*L8
zVESR<3mboc*#mbsnu}oR5T+j%f3SFirGHp@gymaUyus1~%-yi`h-M7~%p6$!!O{yX
z{L$6J_%Qom`e6Qp>4UigrXEg1;~f@GFdEiwfVmr&epq^ihaXxv!{PxJpD=&J+y`?9
z%-t|^;QFBBp0IQeqtW%l(jQDej1N-}^9OqVhuIG+_h9~kxfiAn7Vj|ia2gtZFneL<
z!)RD~g1G~hj$ry>=?AVKI!*}F4|4}D{jl^8(+>+rm_0D}!{QTWFFFlNw=n<1@)Iom
z!OBzgbO1{)F#BQV!r}*RE;L=h+yhew(+_hutXzSW7clq0$}M#LFn7Y#!OVl14+|fd
zKVb0)vj<%r%sdzkGY=LYF#Yg+3^M=+4GR}o{v=jETs<^>!NL=!AKm@1^bWHRR^PzF
z4W<vqM|UnvKg_={^I`E0^FO*i7$25@VD`hrVfta|7nc8F^007*>4UigW<M<cVBrmm
zN0@$?dto$8AIx5uIIKK@iNn;v?1ia=g%4aEH2h)d7_JYRo?-gX(=V*tg~tQb99TYt
z#RtrP=<a}pH@drF`e5#Wg#)~tf!c$v4`wfV{zcah^A9Zj!NLJO++q4)G)y1NA29o2
z?uExcOamGX3qP2CSh)ww?=Tvc4`AA1;Rj29FdCN5VESP0N7oNC2NqB0_QTu{OMfu+
z=rp>1Sop#0g}DP(uA%EkuLof933D&Z9WZmy^~3TJto;B>udw)r#Um_zqw9y+4~qww
zz3BR2=@MN(EZkr;Ode(*%>6JvOddwV{0obBSop#G0SiZ1I)>@PMZ^37(+|s!xa^0S
z1G5*Fj$r<Ur(bBkhpC6rFneJBhowuHzj37>n0gouGY4ia%zjurz}x}%2Q<IK;t!S%
zVet+NFIam37Oyb#VfMko3zm;z`4^@i<`0-TF#Rxh!}P(-gQZ89J7D@@@c`2gqhaob
znFp%}VCrG|VdlZ~!Dv`~!StiM8@;@Q>4VX*@Pp+aSh&N?fzjyU2h$Hrw=j3Z+9R-Z
z4f6+#hLuk+{jl(cg&T~9g)2-R77noWYcO>%cf;ZlW-ctgVD5*f8yx8vmTqD0hnWK_
zcX0V1t`3?WVd(&-4{k3^AsP(}e^@$z<rjE)2{jiMZZPxU_CV!f=>rzdFn_?}1(ttc
z;Rw?YOE<81gxL!VS6KML^ugQ#iyxSNm_K0NgP8-<2P?;5@^BjJei$F-E?D}3<$pry
z80LQTbN~wvSh|MU1EXQ-3+8^9zhLnV3pZG}!}P&un0i=v!@>t<FH9f0{jl@~3qLp?
znm%Fi39|=-hK3tVKP;YM;SS@&?1#kzEF59s4AT$uKP=zC!V^6lVCfp>URXYa`w!}V
zSh)lXUsyQ8>__(xOdTvdVf7I#{$c(=*9UV4%>A%*1ItIa^uf%5`4gADxZDfV57P(p
zFU-9#|H9Ov)9B$3b2luWVD3lP2jj!i8!Y}{;Rmx9rXL<JFpW?erVmEL+yhe&%l9zz
zVCe#?odM==n0}c3Fm<r>2Ma%#zhHcrIWT)+=D^||<_`4qhkkw+EdF5e4+|Gqx`dTG
zuyl)_9$@yt^ufwKSh|AcV_fcs`4i@TSUC>!AIyH3eptM~<k4yL@PN4&rXO8B%s!a=
zVeWysA6AaQ^uzDZL@PgF`eES?i+@<S!QvGbA24^o!XKs|?jC44K*+zabOQ@Nn7d)_
zg_UP8`_O5ae_`c5%$=}!LsyT^hvi$Cepq^kxf|v_SU94mUzmOv4YMC+4@@7rxiEQ*
z_=d&{Ec{{NfNm~KKg|EI@Pmav%s!ZYSiHjY!Dv`MhS4zp!r~cb4lF!i`rzdi)V(l&
z!omj@9x#8v%z>E;%bzfJ!|a8rhvjEjeG1bLGar^tVD5(5hi(o`AFl9&xdRseu<(Pq
z1D=kc=^5sK7!7kj%)M~;LC0@l;RegcFnur@mTzG32v-LUSD1OQas;LiM#JI(=6{&`
zVfhl~epotyg(pluE*e%2!pcXO{V@OF(gzcV`3L46Sop){4`A^J3x8O=!Ng(t0wxcu
zS7Ggcm^zsMVc`$659WVZ`32JtPk&H{!rTc<M{x6?@eK<<bbV0m@N)!V=?4~HFnL&f
z!pwn*!|Z{jADH>Dc!K!{=1*Ao!{QGXKCtuy6NmX17H=?huyPdLd~|u3dkN`>>4Ui+
z-F}$6VCKNw1*2i{3eyiW4;BtEeXwwY<ztvUdN{)DgP8+Mr*QkA@d?uhqha>I^uyu>
z7Tz!#R=&aX!Qus$?_lW&=02D_%>6L;;L;CsH!S_Y+yTosF#p5C8D<VFoxs8orVl0$
zvmX}kF!N#Z=rk<fz|4c?OPGFG`asu*&WD8yES_QRN7oMvH&{Ht!VOmbz{+8m{pj|<
z{0qy!F!NyX1@k}59CZCKcf$P(Ew^Fu4l6fc`3I&S=1!PCSo(pfgZUF?4lEvF`eE@6
z(+~4M%%8a23)2tN2cu!(081Y*`(W;e@nQO5?uF@xg&Qn=!2Ac(2Xhxp9%c?Ky}<ae
z@)u?<Og&6LEL>pfVfta|7#1Hed6+)*`~#DRmAf!~F!N#Q4Hh3T^%yj?o`ty^7B8^y
zfQ2W_9WWYZ4$NK{4GS+A4f8+DT`==t_Q1jkW*;oR;Qob{Tj=dKm_8T{GY{r2SUCi9
zFT5OtDS*=Od<G2%SiXU|2j*^=IZ$mlDVRQ(yI|o!NI%T|FnzFc1EvoaF6b7+$|qPl
zfaL?2dRRU}4?nm$P=CYn11vql!i!M)f!Pbw4=ZnA`r-DVGobdu{0sL#G``{SkFF2O
zhpUI00}BUOc)`LQsvLuYm6tI4VfhahPO$KU<s(?Sg&BlyKdk(M>4T*M^mGU_2Ufno
z@(s*Au=s-c2WBqJU+DdRSUQ2l1I!$l`LOaA<`496g}D=!4`AsBmJeY5f#n03e_-ar
z+z$&6Sh&OV!}P(*0a*B<ufKu03+7LlI$Sg?-e5G$ewccgJ7DI)!WAYDqhbDqg)=NX
zVdlZy1xwd3edshi-$2tNEL>pu0Ok&KeK7lB`3K!zn0{Egf$4{ZKP=tC;vW{?FnzH2
zg6YGcq3(s*4^I!!d<aWFu=qsR53?T@4={JZ?1kw^cMnV*%wAafgQ<gsCoCVp!XK87
z(Cvk(hlMlDd|3Fx!UdL|VeW;|FnO4M^!gB%eqi!2cf<6<;s>S<79X&1fa!y!Cz!in
z=>+B<m_K0dhlK|$|HABn#{)F}VCKNm4=lW3{)M>%t`Ew9l@~C7!0d&E2h4sLAF2)B
z?}O=s#S_dPnEzn<Vg85dhq)7`AC~T5`eFGI7G5y@=<a~2gT)6d9ijUJrXNPb+<}XR
zxeM-2Xnr859~vJp{V;dH^ugi-R?fh}ALf6UI4t~N@eZS5;R7=lmQG>u4+{@i{KLW@
zSN?_B3kyG({V;v7{EHq=F#BQtg!uzqAFMuwyBpfSfcX!mAI3)yADF!`|DeY|%>6KP
zVBrC?4;Jn)cfrhq(XjLa(+>+jV(Sl>KA63*@(Jc2So(#<AIx6#@Pp-hSa`$Y0~UTT
zf8g>zEdF8cfSCsiH<*5y`(W;XsfYOk=6{&GVc~|Z4;J4reK2>z)WO0PW)94Ln7y!c
z26H#eURZk*<}UPjfSHeO4lJKy&}jJ<J^bM2pfO<nf~PO2Ik50RQx50C!X1}>^l*Tc
zn=k`l;Q-eUwHKxjmabvyVf6+~J<L2<K7z$Rdc6Tlf3Wa})mJd}Fn^-k2eTIz|FCol
zix-$am^)zhz{?+~e_-(sb2lv9VESSHf{DZ21+xd{4p=&axdUb{EFHn}9jso4>4VYm
z_=T3+F#WLbhUG_?dUP5+{9yS2=3bb8(DlLWf$4{Z6U@Ibb71a(hac1+n0lCg7!6Yg
z^EWIVz|4bc$Dm;84i>Ml@Pp-Fn7h!^156wi{;+t5`5zXKuyBUC2j&2nJS^X$+XwR(
zEZ^!t(+|4;VdlZ?g}Db-4#UC&7M?JB(bFH?-_Y~`OE)lmuzZBBAC?Yb?trCJm_C@l
zVDc~;7Je{3%-wMR!TbxYH(~0~X;}Qh+yV0kq4W!jFPOdP@ed0(SU8~DiyjWJ@P?&B
znEzq%0@n`>Us$;VvmfRjSUQ2}hlM*_Ka>G;FDxEm`HG<afwmv&epq<H+z$&+s5ApC
z-N3>D<}a8$ES<pg!NL({F0A~4=|>MoczC0Q2P_<6<pxYY`uRh!dKzXwtbBmE3q9Y%
z@-NK&=rl|pEZ@W20ZWJI{zumjOaHKR1xx?v=?`WP%>A(P2j*UwJj`C0IWYgB-#-Qm
zM|AhV+zSgoSUCn$2h0Dk_=m+COg*gq15=Mq!_`CM4;H^L{V?~U>x1!O_QS#r7Vfa}
z1m<3tc`zELAEpkb9~NIQaacIQ^kIx&!3=`ZaP=_%KxvqMSa?E}Gr;^0i$@qA7LVxV
z5c>QpEZkxKhM5ZsH(0uX<s(?R4pRq<cbI=+=??A>X!%Vj9%1PZmVaU44zm|#A36<-
zKbSvY;SUQhSUrHQ55~u^7aHC$_rvl(EFYlfUs!m<Xqb8!9~KX={DUk0VfMk&5v)9c
zg##?!(Ze4W?yzu!`5zW;xauvK|6%C{9)3^>Sojgr4-0>oyW!~w8t*W3aK%5&->`6k
z`vc7#xb(x^4@-Bj{0~dF#Ku3Y9)-CB7LMrk4~!3W9s?|%VE%{27mSAGBUnC!>4VWQ
zcf;b5Sa-wR2a7jYI)?cl9xu>%fTcf}dtl)X(+~4M%-t~ku=ET|PcRzhewaEqAKFfX
z`3qJ)!2E@teqs7y`2`k^F#o~QJ$ksq?1P0rjE03XOg*l21Je()9~SSh^aKk>m^m<W
zVftV+EZkw?2;;-_!_q&@A259|8m69H{V;#S^uxmu8t*XsapeP;zhU}e{)d%!F#WJ}
z0CP8thUHh7epq<I^uuUe`eEq~rXLpXFnxsVhuH^nH$4479S93|T;&)n{9x{Y*$>Mv
zu=I}0{V;dKXsERegyJ7&FH9eLK7pkRSh|CS1I&DwK3G13<y%-jgXxFGFH9chADDia
zzhUyQ@Pefym^rX~3v&ldKf3*}aE6%+3s0E2F#YK11{QxX|DpRE<}a8$jE2QG%siMr
zn0lDMaK#_2+=aUrnr~qKh3SLwVfMmknEf#Quyh0SH@uvMnhQ%8F#WJ_gt-G2KCpO$
znF9+yn15jL1FLUg{)XinSiHgX!}P&uSa`wQ4J#*M_Q3SP-2rt!Ed9XbVfMqq2__E<
z7nnMjxv=sS-T!d)P<vqdVCf0wFN}DAw%1_(fcYDy4`wecd|>v%XjuA%r9W7@f$4{-
zL#JW!2BTr&1dBhId(rj5_^|sQVfMoO3yTMsJ7D!SOdl>97JjgD1eOkQ`5R^qto($9
z11uh4_QTZSq@m#t(+^AEg!IGm1xz0-J;LfKn7d*AfyE<w`xzGRuy}xlBg`GJ^a#@r
z6Ni<*u=EQHKUloO;t?agK?4HU{SPqr!}P=A0jdp7!Q2gV2Q2@?+z-<Sb3a@o4hAg!
z!TbYDM}*QL%s80;;WRWK!rTE<hfc%d2NwQtK2$%h@PnBHi+`AYSh|446O4w%A1wZ1
z;Rn+P3rCo_u=E2<hcFu5J+N?tr9W7HgsFqYBdpwnnGd63`d~EN{ZIuk{lvy2%pWlS
z!OBaR|DnbrC}{qJr5l)jSbBo#gQaU&c*5KZ3qP2@5e7q8Fn#F$fVmGw!|a8XAFy->
zOFuAih^Yt?7G5xSz{(?-eXw{$=s;z`>OojKg2fvwzEM?U@?iR5;Q>p(Fn`1H5iDP$
zhaW7z!Tbla7iKPuhQ$NSU$F3k>4*6fW*^KyFg`3iVf6tl++g7kQx8uE(EJCp59V(4
z5P*j_G#$a*1=9zMKXiR?KAJw5dtmN{nFI45TpyGHOLs8;z}ycDH&}S$%73tQ4vRmS
zKA5{<G`jt;b^<K?;pG4{++g}(@d|Y=oPwoS7!5NIJ)UueJGwt%<t{872>BcC5GVs?
zKP)_8;RR2(P<6N{nEfz!!TgWY{ZNBo_QApdW*;oxp~^5RXu5%=TUh+T6++_ymX2WI
z3Zvoa6lxBP57Q5eZ&*0O^ufvpm^xVcgSi71-!Of!aD=Hxr(ybF{)d$xuyBKg1I&K7
zeyF=)`e5M*EAL_P3G)Xm|HIr5(+3MTm^)$h3|v3-TqT%1Og}8VVESS4hOQrG4opAH
zKd^KHa|bN_z|4V_E3o*3n+uIsSU!d6hm{+!@PU=TF#BNchRMTdm^m<i!152eeK7Ut
zG%Va<?uV5FF#BNf2J;uXewe#q?tsM~EZ$({HH?q0AC~@L;Q+HAW-rYDu=IqkA7(#H
zKP+B`x_<QZ2IHgq9~SPgaDc@pEWTjn7%Uya^`qrKSUQJ=H!L3E>d_dm^acxeSUkeY
z4VXE&;vMF0SU!a5hou)-{NeI9nsXRn=>}#V%)f;E0W$}t4;GFveK2>x)S=U`a6_+0
zVg5!p2gZk`Q&{-H!V}$mSa}S~&*=M?VfMn@4YL;(j<9q9^9Rfvm_4v^3RaH8`~!<e
zSUCh!k50qX!~6lur?B{k@zM3c_%MC2@&~35mTqC{FlZd<7v?`$IK#pLT|X?mVfMq)
zA50yr{Dajy=;opO8>8HXx(}8gVeW#_FneJBhQ%W+K49{&@PoMn7Cta}xV=#O;PD8p
zXJGnZ{(<R((J=SH^ugQ%OGhyM=>8`ZelYW4=?CU+bpONbhxr3mF2K?=%-t}3Fn7Sr
zhtaV73kyf|aD$lxQxBtI@eWG|uzU%#50(#br6ZVnSh&GxSUU(7elYW3_QGhGzhN{?
zKP-G<`4$#_=<a}pA51+g9ANn#W<M+*VD`djSa`$ih3SX82bzvx;Q$YRXnKQ%8_a%~
zxo{e)ALf4a`%B>JpbVIQVCKTgKbU@8^#RP?u=q#!Ka3Bx22P>JKP;WV?1SY4xCS%^
zEZ@M~3zLS0J1pE_{(#A&)9C(&y9*kAxY7?yA1wS~@d*njSUC#w7rMQ$c!ueR(J=L}
zaDbVIt{;~EVfhtC!~6@=4|4~Gel!7CIKs?<>4)V5H05wEEWg3@!@>ckA0z!k(=ROi
zVC6Y1AHc%}8vbzmp?m|Vewcl5hd>#yd<u(ySp31l6=okS{9x%9CJs{%w-;(IIt6nl
z%pEZQ!2Ats2cheO^P&ELg*PnS!Qu<%epr0M^}!fW{V@As=D_sB+(B&qgVld9eem)I
zW*?M>*$1Ox;Rdq@7H%+i!0H32b_4}=KTJQ&{V+Z(-N4)fix-3es4Q4~!158yJeWT8
z^)qnYP<Oz}FPJ`<yJ7aj#(!b@;PDOB4~suoeudc!<HPj9+zC?;vlrHmhS>`%$6(<J
zOV==SVCrGv1<QvpJ}lk9+z(rS1=EjC!`uUlS6F<)^rQO&=6_f@46_%OZeZybS2+&T
zN34D5?uGGT<p#_gn0>h14buk;FPJ}I=D^AWSp31_0j3W|!_>j-h2<Yud=X6lP=jFU
z0hVrI@eH#csthLuORuo_gV_(uKe#l);tQr97QZn4u=s(64=g-k=E3A)=D^|`77j4~
zz}x}Le=zm1aD&+oOZPDIVeWz13**D|!}P)Uu=s@Ohs7JrKQM808s=VDK7^SA(~s^h
zbp7b{2uwXJA7H452|&vYSUAAa0nER!`U<8TordLqxPIvRCzyU%{zcaZ<HN!aM#Iu8
zEWBXl2TVOW4YL;({xExC@r14(#)qqi`XA;GSbB%K1C|e9`d~DAzJujkm^>{1!{QH?
zF5%`u?S;7?=6{%fVetTqKbXH@e3(73d;v>0u>1$}Kg>V4+5s^AFn7Srh1o}J{K4`W
zEZkuB!u$g>7oCQ=8y23h@Iv<|x;}XO4jPUyK1@F>zvHqW=3bb;Vc`a|2NqtiaDbTy
zlZV9*x_(%=!`uUlZ<sl-bPY2H7Vj|k!0d&EJ1m{T?1zv4zzl%;9~O?VbO=j-F#BLM
zdVYqLGcfyM?j<(<VD5+0(DpIRKA1DnX;}Ec!Vl&SSU91(6J0+ny}<ROl{+whpzDM2
zVeW<Lhn1JG^b1o5iwAV`VDSRe50{7fA675H(lIPu!t8;iA6U4<+=Cu&uyhG4KhWEM
zuyBLLJIr2~`7rZf`eEr4=06yXUjM-PQ2)UF2h)H?!{QU>A6Wds(h-_+3@*(5F#p2h
z0hW$o8esl`@nQDD(myP|VBrjN56nE+{uT7}1G5+AZdmxi{0&Q&Fj@yK-@(EQ<}Mfw
z3wM}4nE5bw!|ENFeK2tt4bu;EKg?WQG%Oro=>nGS(8Cd?A3Ypk<t0oXtlWW#!}P(z
z6`uZ~^K7v2g82s)|LE?A>4TXAEB9bDEFHt*6J`!9USaJWn0{FPfw>PBA254i{(*%*
zOdZUfF!Nx1bn{^Ghn|06;S4hm7S1sD!_pNj9^w5TwDbduZ<zmKe3-c~|HIq?6Ni;U
zuy}{n=df^t*$XR&VESM*EZ@QO!@><_A1uG2$3ILzEd9db6Xsu7x`DYD<}a8&7!3<&
zn0{D1z~o`^1~UhizF_jOaE9rJ#Rn`NVE%xG13dje4S=N|n0|QtL(4swz3BR2?u6-s
znFDhlOdrgCsC5_=Odre~m;z$;!{Q&N9~S?xaD(}qQ2fE-0TzF-@PoMn7N0Qpq1y`!
zH<&t@eptN2_&D`L>u*^8f~7+kA6-9sxWU{5QwP%zOZPB;!16z=+=HcmSp34&!}0?>
zeWRxbm`BiPxO!;*hWQ_6Kbmql7v>&VJi^=q(+6`mTmzH=(+BehOdYJ;gP8+WjzOX8
zhlL+ZKg|8;8e!oLb1y9Yz|_Iq0dqerTw(I)`eEq@W-od=gr#Sgf6(oPr4v|r1<MC8
zdtl`u%snvuF#p5!!`w+oKg_?d@&{%<%p90F%)hX50A@a19GXsG>S5^?R*t~*!NLV5
z4$}`yZ!q`3^uxjd<{p@ObQ%^uu<(GDpRjO(*^90ZR{p{A39P<?*$*pkVCJBwBba)a
zzhVA@>4*6T=6_f@hVBkn{K4E0GZ&@~rVr*0Sh|GC!)RFggT*_{T`+&Z(jTGlgqa6(
zKg>O__<+$cbujy2>R{rq^iRlsm_ArMz`_HTk74eH*$WG2m_4xghUtU(8x|h0aD(ZG
z>4VWQ^)Ua!!W%uEquYy~?qKpT`(S*SJ-BFCyu<1Pbak-w1EXQ(2h6=N{jhL^r*~++
zgqa6(7tCL<@PU~FQwIwtSh&FQC(M2r4O0)JVetUd2a6B5dC>X?JzQY<1*Q*{ZeaRg
z?uDfzn0>JDhou`>{Q=9Tuylhfy~Dx{M#Id5nGZ_`uyBK=N0>Y;{9t?-4Ra5?e1ZBK
zR=&d2;i6&wK#xyc`5tBtEc{{i!@>&|4(Q<y(+77aG`+ym157_G{9*A9ix-$WSop!>
z8>SEDUzmDWx`g=ymfm4B%pEX$VCv!S#KC~2PndgP;Q+H2=6-bTu<(VsAC~@L>d?~>
ztX+s6j<9qCGY7_p`5zYkF#n+IhlLwVKipl=bOTF&Fn7UdxH@RO!O{&(9_DYjeyDjc
zd6+xl{)WoK;s=(F(8B>;KP>)W{(^-&x;We&Faw}8%pY*~L;VAD2h1O^@Puk(fTb6h
zKDd3*empE4!SXT8eK7s7{XZ~&!pb2S4YLpCZdf?L^uz3fxgRDE3s)Eoa|f<+2xcD4
zURXMS)vGZ3Vc`K&k50qV5uAp`CoKHX)x-F(`~$NWR?fk~4VIr_;xKo>{0$2~csha_
z4ATdzuVDEAKK=)dXP7>iyJ6~K=?IqZpvGcQF!#ga0~U@jf5XEa8Xo8d!2Au1FPOVw
z{)5FIjE1Enn0lCgn7Od{f~8Lw4RZ&KhUtU38zv632c{2BL){Nk2h$Iu;o?wrFnzFe
z1PfPKxWN1e(+~3(%>D5AfW{N7{DRp7^Div^VdlWXA7&rSJXn0f^ucJDJ7DPmrXQC6
zVEGEB9-W4@7hvvyrBhgXgQZt=eX#I>xf>P_Fm<qW2XjBVJ7Dn-(+6`m%zl_WEZkuJ
zfte4}2dlSW=EK4XCJu8aOdJ;eFn7bsKbSboK3Kkh#Xrn^Sp37>2Mc$YyJ7ag!WE_;
zrVl0$a~I4VFmqt}2S&rvGmM6%Ggvsl+yRe2X!#BE2aJZLCzw7s4Naf0@Pe5KE5Bjx
zh4~+@56XbKA4Wrk8DRFp;vZ%nOddwV^ucJDIE;qrhtV)`m^)$mVD5*>!~6@=2lEF^
zo>Kj=d<P46c>F=b9~NFP`(gP3W<QLExf|vWSh|3vN0@#XAEpo1eukw}SUQI3huIH{
z2VCKg?hjbFz|tFxhNVZC{V*ElZ<zaG?MIkCnETPwElfX5J<ML1d9ZQ^r+#R<g}EPA
z-oj{@e_`%`g$FEM5PN<XOdrf0u<?7?`Q>o;!rTL`KVao2Oh2yhgSj78p27SLb2luU
zaM3XT!om+$&Y-6kT>4?+Fn_?(Bdokd&o?mjFdF85Sp37{85Ul!@P?^})6npO>4Ui+
zrVr*1xOy}O%-=BmuyU4I{V;v7{0qxRuzU)OKUjRASr0#V3}y~2|H0}3n7?895AGjm
z`hb}S3vZY?uzC<C4@);Nf1$6RhN*{z2h2aPasU>8u=IqUE@1wK*$=C?VD`e)!@>jR
z4w(C3?uXe6%g-=(!NLKi58i%-833hW`e6A0W-iSAF#BQVLA5c!?1AZn#RGc!h4}}j
z4@SfE!~7372O3W>dtv1XOdmQ8b3feO(DaSresqm6K1@B7hQ%i={lMZ6mcL-yVCrDu
z4-<#!!<Fvf>Y(<){0|E!SbV|t!!$r?m_K3m!rTE1PnbTKIk@r>)HnuMeFlp^n0YXN
zz`_xx9v2Nuzp(NImY-qi7?(a+eu4Q17S6Evhw)+hVeW<52TM0Fd6;`)_QT5sm;xvb
z3wIa|6^2tV{jhL^r307(SUADTZ<sq_`e5}BEZ@N7VftbIhM5BkH<){2{(zeU*9c+2
z)Wh5l<D;h|SU5s-AxK#G!Sut*Bba+({)V{&p%cP_sfX!@nFn((Og}6<Ai5AFEFNL;
z2cu#Bhq)V;4l%+HY81?V7!4I>fY}eDVd5|vW<N|H%zk4156f>bf5T~L`31{|F#FJH
zn0{EghuIG+KVkmI<!|(I50(yL`5oqO7$4n!I3KMXf{DZQ!@>h54x?fA!_pnhewcb(
z`eE%iSh&O5F)(vsG_2f!nFFVx?uC_CF#Rz1z|tEm9AWhbu5g5fKg=9hxDyID^!S7G
zq4AH)KA8W|X;}Ed!xL%_EF93)!}##_7c^X8_QJvomY-ni(P>z^gOx+D{0=KeVCKQv
z->`fG3x8OB1k0x|`(WV;%LlOaUm8$%!sKD)1<aqYd<2V6Sh)+kf0NktUod~b!U1MJ
zEPP?*FU+5?_=oum=5Cn%=;4OWhx-TSKWO~H{D-a{#)r8FrXQAGVd)SSo-p+=8lK*u
z_QB#0mhNHk0gFGFJ{S#4Z?Jrb9$v6;fvJbluyBIugM}9?oC$>&x_<QZ1M@d59ANgs
zXqbLjc)|PuD|g`jg@!-OJun((4&2?)@PpY0i+5N$gp~uZ^b6Auqha9=%SW*M0n>*b
zuITz;{($9Um_K0g2cu!>7iK=pURXMYr4yLHVEG3YFR=UrvmZvo(h<!4u<(S17mS9f
zN2g)-!Suo03rmOS>e2Zyf5XxfOdjTLSiHc*(P^0bVc`LD2P_^5xgQpuu=Wg0KP(<#
z=@Ay6F#BQY5axfF{jhj|xgQp9Fnw?un(ko!hUEj8dP4eP_QUkU?1$xBn7d&#JYT>Z
zfWv;6zhU;n(l1OKjE1K#s5!83gV_($53?Vp4@SevJ(&Apd|14}^rMG6EWTj&!SWBx
zJuvse^uzoEvll(wVCKNw4NEUDcVmPb)F7BSuy}*<VeW^y1DAf7dtvs&X!Q6;PyaCc
z(ESZ{76Z&anEMIUKQMR0;sI8kz`_saADH<t8kS#R<p!+0gynyjepr5h>4VWQ^)P#3
z=E2+#D?ecIfu0Uv=D@-cmM&o714~b^aKI@4py?J?{=n43@-xglm_K0q*I@R-^ugi{
z<{wym!QvklpXlij=3kiqVfMk)!{QCbhv|pqbC^Duy)bvd%z@btb3e=+m_As%!Ng%S
zOdb{w==x#i!_>jjAuRvE+yQHc!}P=41Cxi-(0G8QJGg(K;;?i8GY75@2Ll!#u=vEK
zAC^vF_QBi_ix*gU!|a8nLzuNNbuj<K_^@z<sfWcQOdpJf)m!N9fQ1_@-{HzXF#BNf
z0P_b-Kg`{@?1$M4(+>-O^zg!EKP>&g^uy8x%-=BkVd)6wZ<sn*xWQ;xc%YXbu>1tm
z52IoFVD`hrVfMrFJ-Yocb+CL6(+|tfu=s<yAI692gV8Ye!r}pD9;`fqrFU37!t}vt
zn0{D(hWQ6p9>d%Za|gOVVDSxeKP>)W=ELmAr5~maM#KCM^9L;4VetV|hfc%&3(c=E
zcfryf%pK_A01G#m`(gTE<vhAR^mu^T3v(|l9AWN-rDJsWqVr+;;r@sA8({uNSC7tz
z#T!f<7H=><E*h2(VC5Ce9WZ~x+z%_4(9J>5zp#7?i*HytfUXb5hlM-LUYI^u_`%en
z)39<CrXQB>VEWPH30*&iyP@;8F#p2b4f7AWepvp1*$;C+th|PW7t9?nb76d#d9d;e
zmTq9>2236nudsBB-v5KeAIv{6^I-WJ<`0<t=;p)H8%#eeTw(5n>4W(T<}P&q!}1Z#
zd|0}LiNpA?@f(;qF#o~a4YLR4ewaI8=^2(UVfta_!{Pzvewca~4T}$$J{S$lzcBr<
zd;m-Tu=E6T7fc_FhS>*mKP)_9<s>ZpVCKQ}!Dv{x!)TblVeW<L$CV$@{SV6ruy}y!
zgSiK0FFFki2blk1=D_TSm80nTVD5$aA3Ytz>_^uJ<HP(7qhab{=^d87VeW^;Kg>Q@
zyutD*EWTm+5muhV#9{4k*!VBZ9GHDDf5Oa#`45&3V6+~zo<Ua!^B0VU*$dML^9L;5
zz}yLoM_9O{>xYF4OdOqtg%d14!THd71U>!2<YD&0!XFl1F!N#VhNT0TI4s;?{zumb
z^CwI{EIq>XquUR2H!K~(!VMNLu>63oA7($?Jy8F`^uyHSqG9m>vmaKj!SXB2-LUk9
z%N$rbfw==l!{QC*9$5N?nFG@YD>q>A081Az^|-<j-Tkm|g_ZlT@PnBHvlm9g^uz3h
zg+EL^%>6JvOdpJf>4(Jwj1LPhSop!y4>W#Y=^PeLFdF85So(qKhtcqK49$PA@(QLO
z=6+bc0Sh-6AD6pf{)fv$-AS%~Sa`wo!{PyEKFpu6c!s4vSUP~Y7p5P^hv|pY&~Sj6
z0}FpxIKk2}tXzZZLu0_)1B*vkeuLQ$<D+SVb7A=(7LKs=2eTip5S;;aKf3>6`2}VU
zx;B_UVeW<Vq45Swf9URkt)GG2KLXPaD+kf-L61jR{Q(OvSa|{SKg=C4aag>=!VzaY
zLM?`+V>k`<4?O)t)uB_cbPw|nEPui5#TEas^aj%h=R@s<xgXs=7$4?en0^=^R&K!T
zhqeD;`d~ENpU`vwD<@$2AC~TLr6X9nfwene=>``6uyBC+A0GZt`(W-y_czSFuy}>J
z2i|@~(+`Vpn15mFVfhl4j$rGbVCe^zK4Ik;OdLkT(m%{xjQD`M0~QW2^I-mkg$K;v
zFn7ViALc)pK3F)w+zU&0Fg{EljE03Dy8m&hhv|dG3(Vaxcfs5XSBHZEvlpfxW*)3Q
zg~dP2JXkow?16<ZEWM)p8|ELFI4u3a!UKo1;KHzYhm|X^{6I)Q%snuF!NL*d4_LVl
zvj`S%uzUpbH_W~0{)Xv;g%>Q}z-XAiVfMh%9W4F8^uyc<qha9=3on@eVfh(m4op8f
z4GSljzhUw)f5Xx<OdLkT+zU$&Fn_`9!^kf%jc7E?-7s_D=0eLsSp1=B!{Ea8LGvXn
zK49*F#V3YFr~oV;VD5mGLooet|3J-$#XF3KrEgexp{E0=H5e4k-LUioQvlPC%iXYe
zhs7JL9D}8E7!51OVdlbUbpOKK5A!d2x`XM1<zJY6aQmV01=9~xk50q<3kzqMIk51C
z`5PYZP;=4Ef$4|Q=<=}q2XhZBJYecz<u6P>EFZw+(eoj!{Q{SVGGP9J<vSQ37H=^B
zz|#*@A1vL&(hs^mnE%n^0haG!;SP&8SbBh&2lF?KhFZ%2^DivDp_>nvhlV@4epo(-
z*$=Z1W<M;w!Tb#?N6_toxd&z*EIp#T3vNHuJ#c;K3~0WDxgQoT==x#d3{Q7ZeK7aK
z!WWia;QG<*gQXW(IKbi)7SAyM!_qaHcKEtmm^)zh!pd!!Iq3Red{}tF^uyG{^uyHQ
zqG90#3lEq-Vd)l^KDc^lxWW7l3olqa!@?ik99VjR*$>NyFm<r-g1HBl4`A}JaDwTF
z*#ir2n0YXF!Suu0kLcwWEF59>!qNdOUBb*o&;KxWu=EN`2Qc$t`q0A#<_}mn!t}%V
zu<(cJhw)+hU^J|}frSIizv%TIuJi!27p5QPUs(LX!XL(m*$bo5;{nD;FE?TSfa!zT
z153BC^Z@He5z-F}UzodL;SMt&7G5y-!}0}89!A6T!)TZ|%)c=GFg{EkM#J0ziwBth
zVf8=UUL55XEWTm(!_pxvzF_vl;uB^sjE30<(+}el3O|^BI1SCmuyBNzYfyK?@()}+
zl!43rP-zC3yJ7x7KR*)|ZZP-5(l5+DSiJ$um#};eGap97(htntu>1y#2bekx8tQMD
z{pjw6)ngd?paL-W!)XHH1=R+paOFSrb_=>c(8C?w->~!w3ojTAvly06VE%@cW3YIF
zg*Qw+I*o2Wth|M#J6Jfv;t}Q_m^)zk2A1AnG)x~X++loJI)Ihm=<b2>VdV!bJ;3}4
zOAoOAKe~FD`(fn~EIeWEhJ_2v9GHGseuc#&%wMqZhnIWMbOF<cPQ&!U+zksSbo<ct
z!THd13=2nCJix*Yt_}wSmVROWft8=I@Q0-bboapY!R&>F7tH;z{0uA4VESS6Kd|%%
zQx6L_SiHf+Vc`!8PnbSfxWnobSUQHK1DJYPIKa%q5ey74|HEl$yur(H92#Nv!qN{c
zK4Jcbg(D8_@O^YJ{jl^0(+>+5T>4?@7Zwk&_=EWym;Eqz!om|~FD#y5_QJvqM#I7z
zmR@1$0Onp;_`~8KJ^o<lm%!|Wr7xI$u<(b8!}P=a1)Kka`4>H0VE%>agSiW)4(4uH
z`hk^aFn^)zClvoMb7Ag*g(EEd(aUd`dRREY{0obJn0sL97v>LG_`vj|)39_1^B>F{
zSbV~0bbT;BEIq*d0p~;W0fzn1at9V}uzU+k4={hj!T}bJaC4#hVfMk?57P(pKdf9q
zw;yH>%sw~`buW6n!~6-OVfMk&A<Q4J@P?&7n0j;?7JsmG1WPwCccZI^@!|0T^*>BM
z%zv=(f~9wuJ{S#)Us$-o(i>bJ>VKFyFmX5ywHI9<j1LPJSbBl^3$7oH0ap(-7Zxrs
z{jmB7O&gpGvlr$cSh&O74=;D%Iv@;K`2y1iD+gii4tV@S!vkgxES<vi!NMQr4~Xdu
z46yVHOAoMkMb{6r7Zy%1dtmN>(J*ni`Oxq}Pk%7=u<(P02TUJ4okI1)(gQ60z}ySt
z!}XytVD5(54~qww{V;Pd?1!2IQxDS*GY=N-F#WJ_fog+OFn7af^mZT29GH7x8e!!n
z%wAafg2fliJXrpLr6ZVrm^_Szl_N0wVfhml-!T2K_=Bm3l{c_-1dA7#J7D@??ts}3
z(+~3pEd9aa3+8W_eK3768om7lb1y6$VBrpPKTIEthRwgi;tS?)SbW0V0W$}t4;Kxy
zA3Z)``f>RKmfv9E2#XI`e8a*Im%Xs?hS>}A4=mph3P+fIF#WK2f!PO3w=jR8_g`W5
z!Rl?8ewaR(zhUVCz5fDBH!%I^?uNw+%suEd%>6JLW)I99Sb9L$2jj!S9~O?Vc!24{
zm40CQVKglMVd)r_-ig%@vlo^hVDS(056m8P8s>gj{K3*MOdl*BVD5p5!_0w&J4`>U
z-h#=);tv-8==(ol?uD5H(+6w6!158yeptA|)WO`3ZVt>Hu=ER~VeWzHht)%{aEHkg
zvKOWwmTqA2Pi(q@xfd30uyBWkH_UvPyD`!MwBCZnJ1if;^uzoQ3on?vVE%%ckBf%6
z3+69a{1MU*OUJN$39}zw4nf-y==Q?G9~Pc4dtmN{nFG@YD<@##2(u5?o`LCu*$azT
zbp0@UVKmIYu=X;{-{|(k%t5DN;RZ`rF#Rz9psR<)J9<3A{0GwqO9wFXVD`Xhn0@Hx
z!}2RkA3PmF(+@2CVBrocFJbCo;ec)~j1Nm^Fn7T6BTPS`aD&+oi$9ouam5?VUN{Z)
z2h9Dja6~r;UVcC|!t8~)8>SB`4X0rKfYC4sV)diP8@fB-@eK6`%pI`!g!uzzKP*0B
z?m(wu`e6QorF)ouc>JSlgz=&7fa!<j2bg{s4O0$_Z&<#E<s(@5z{+u$J`DSz_QA@3
znEzqv29^$Rr9+rKF!#dpKTIEthNWwmyJ0j;AI#mbdK2bOT<vyvxIo<x%f~Q%aQEUU
z@6h83W<Shbuy})&V=x*PF0gb1^FJ&f!t8~a2NQ>dKP)}L>_Mkt=EA}S7M?KuaCbm8
z!u$>M4>}(fo-qCB=^vJEVDS$NSC~KG=^aNnLY)C0|AYA(=5APgpxX=62cu!>4pt7q
z?1!ZvSbV_L!)TblVc`W+2h#_OFL?Tc`U71b%)KyuuyBE?gNei1zp(TFVD5$GGgx|s
zr3;w-u<(b42kiV1n7d)&4)Yg`hS>|#5Az=^oxtK7md;>&SbGGPUSQ=Q%v@YFtQ<$z
z4~q|UeX#I>`2*&Eboava!Q2ny!}P)Ig{4=RJj_2Zf1uA_quUST!{P(x4qWbsg&WKr
z@N@`GCop^A=?CU8C=E;RF#WK2gz1CXkM3`%cKA9Ln7uG@boay35lkMIK4JF4^Dk5Z
zEZ@TH$CdwJ=@e!TR67GK-NDSk<$qYdfrSGsJYne^<`0;;=-~kKH_U#RJ7Mi;Sp5b|
zzc6#*=0W$}!SW$29l`X&`~lO4Za>T%jBtR)KcVo0xesPPjE0r-F#YJ^0J9gCUSa-+
z@zL`UdcJ_^M-M+(e8B93*^evz!_pBf{$PBVdRVx@!V_jMIt`0=nE&D70u4v>^b6My
zt-sOz4|6xnez<xx2F%?sdtvUz2rsBRVCf5%PGI)J(giG?!O{aPJYnew<}O(I57Q5G
z7A&8@!UJX=Odl*92$lb^@P_GwnFF&2PD8^F=1y36!0d<VhowijJ~Rd_zF_`A*AH_y
znl|`70I=|a)f+JNFn7bk9i|^vf5Gg7mtW9$g2}_;6Xp+?Ik0>J(+^V*vk>xWAz0
zqK5-4ykO?Q%5RuEVd)5FKdd~2$-`-AJizqB%!Tn``e8I&9}WgAJ;UM^mj2M~hsne0
zNmw|*;sHG!q5B`E9+n?q@c=7-VD`e|0o}Q<@&XoqFn7T8!}2dI9AWtkW)4gr%)hX7
z1B*A9K3KTH^rPF09?md%7!6AoF!!LRADF!`dtm7YrVbYFFnO4}VKgjUVe+u>gxL>k
z55Vk)nGe$siw9V_4U0bvcR|xJOg${zU^Fb;VBrgkKbX5<;Rln4xew+JbpOKK4a;{h
zeJ~nkAFLjN`5P8qF#BQUB1|8QhNU-{{jhWcvlkW~Fn7afn0}Z(nEzq!gt-f59?btR
zcfs_-{0lP&#)qYMm_C?0VESM*ES_NcVd)<h&oKS4c!cSL(J+0maD$1%+z-<a3rCnd
zjE4CeM#IEm`eFKD=D_4(G%Ov#`~izcSh)eqH?VMp>4VWQ{jl^5E018|j4Rw=`eFWt
zg#*kySbBlEA4bFC9~MtAeX#lp=3bb8(8~o_x`MeErXS{RSiHmX1uPt3;Rnm-F!#gs
z!TbTU7v>LGIN;I`GY6f9xeI0vOg~0Cg4R2*bO#Fum^;wZKe~RH`(gDJEdF8cf!Pa7
z&oFTq4GTw@|6%b1iw9VE!QvC94<0X2_rvUmg%?aeEZt$~hZ+P+_b?hgy}|5<r5~s^
zI0f@REWBa<huI7BKg?ZljZg;6-7x!M;SWnUFniI%4?X?D;u)q67OpUHm^)zdF!#b}
zm^zq$Vd)p@OpJ4c;p(B`5BE2e57P&W2bjIE_+?;VgeqZz(#%kr1xm9*X*MX$4y8Gu
zG$)jXQG?w)n0sOVg3&Pl!T2!$z|^Dr7siK$6U^VR@PW~=@PwHQQwK92M#I7%M#J>O
z?172H)WghyiNo9vvkykY%z^P?G|b&Fedshy9_D|TeK3E(+yRq^iNoB3%iS=2F!NyU
zgV_&rAIuzdbuf3KtB0wB>4&)sCXQ|o%zZHPVESO{Vd60N!u$tQ2eTh$4~!4f53>)Z
z4`v>WhRMVH33CsO52IoFVd`P_!Ng(a!Susum^e&5%pWj$m^h3NlZUw<W<SioF!Nyc
z!u$(!7t9=(xkJS~n0sOVg3&Pl!T2!$z|^Dr7siK$6D%BI;RB;#;R!PrrVeI4jE03j
zjE3ol*#i@YsfU>Z6NkAUW*>}(nFHg)XqdZU`p{{ZJk0+v`(XZnxdSE-6NkA6m%Cy5
zVCKQx2eTjMKA1V^>R|FPaTp)w4wyK)xiI&@`~}koQx6k|xew+am^zsIVD`ZHF#Rz5
zVESO@!DyH~%wI6~!1yp4rXNPb^uyG{XqY(6Jur8}<YD44K1?3wUYLC_|G~_I*$eX@
z%-t|^VCD`L^I-0U`3pwF{0HO1`~y>u?q3)m7EZ8mgoO``hJ`20T$nnT`7jz5{xBM*
zA7&3s9Ht&-4on>8ewckQ8fFfR52Io3hUr76Ve&Bl!|a3k1Lh8xJWL$s9$fB*>4TXE
zb05rpnEPPnpsRzq3tc@-9ZWyWT`+NUdtmN^nFrGcQx6k|xfkX?m^zsKFneHpn0}aj
zFnuudU^Gk~=1-V=V0;)2(+^V*vkxW?GY_U8M#IEm>S6wX$-~59{(;HE+z+!KCJ!?Y
zW-rXYFn6JwJ5<brg#*lAFdF7R7$4>zn0j>o!uYUof`ub2d|)&zJYnX-)WOV$(XjA`
z(J=imdtl-)^)Pc_;xPBa?1RxTb6|WJ4Rbe4A36<_hxs37AIu*xcfjOf;xPB%ayLvL
z%siO;VD`h@2Qvp<9n4+m>S5|&`eE*ZiKE*Cb05q+m_C?#m^jS6F#o~S!R&|G1LMQ=
z!|a3UgP8}TVe&A4!rTMn!)Ta(n0lCfFmaf9F#RwZCJs{%^9M{GCJy7n<YDfI*$?wC
z%siOAF#p2b1v3X`?ocrg=3bb;U^L8sFh0ybF!kvEh4EqG1Pe!4_`qmbc*4wuse_ph
zqha9>qhb1C_Q1qp>S5-<#9{7-*$1Ox=D_$c8s=`8K6Dx;5A#3FKA1mX?tsa|#9{8i
z<!+chn0YYw!R&{*4`vR!I+(lA)x*@m^uyc*6GyiP=02EtFnuufFmafBVg7@ugV_(W
z2gZl#huH_y2Qv>w!{lN9gt-UChtV+oF!eC|VB#?IVESP+OdO^j<`0-WOdQ6C$-~?a
zvmfSPn0YXJVg7}=3uX??+@WF~%)Kyw!DyKOV0@T=VCvER3**DW2^Nm9@PW~=@PwHQ
zQwK92M#I7%M#J>O?172H)WghyiNo9vvkykY%z^P?G|b&Fedshy9_D|TeK3E(+yRq^
ziNoB3%iS=2F!NyUgV_&rAIuzdbuf3KtB0wB>4&)sCXQ|o%zZHPVESO{Vd60N!u$tQ
z2eTh$4~!4f53>)Z4`v>WhRMVH33CsO52IoFVd`P_!Ng(a!Susum^e&5%pWj$m^h3N
zlZUw<W<SioF!Nyc!u$(!7t9=(xkJS~n0sOVg3&Pl!T2!$z|^Dr7siK$6D%BI;RB;#
z;R!PrrVeI4jE03jjE3ol*#i@YsfU>Z6NkAUW*>}(nFHg)XqdZU`p{{ZJk0+v`(XZn
zxdSE-6NkA6m%Cy5VCKQx2eTjMKA1V^>R|3dR}WJM(+_hOOdQ=FnEPPn!Sun@!^C0k
zh4~Mr4rV{h9vB~{A7&p+AIv-$4U>oY6XqTmA4bFU!_>p<gNeh;gXxFSFmafAm_K0h
zFmV_k=5Cn1Fn_|#f!PQ1C(J!C{V?-j_6&COVD5$a3r55I2jj#115=OgUl<=2PB4GN
z!Usmf!V_jLOdTw|Vd`M!!Ng(qqpOFx6J{=qhM5E7!)TcMVEWK$m^{p%F#BNcg}DPJ
z4-<#E6PLST`e5e4+y}EC=02D?Fm*8ZpsRz)!}P)20~1F#ALcHYc`$u2^)PXmJ7NBU
zse{=Mvj@h9>4(_|(+4vTM#JP`{)4#(#)r``{V??~`(WZQ^I$Yg9_DVCxiE1UA0`iT
zFU&re|6u09?1A|Y<_?%SFms2Bc`*0F`~{<7{)6#h{(-4S_b-eO3ny4O!omke!@?70
zE=(QFd>9Q=4>K1QA24~CKA1Sny)g4(G|U_rA4bF63Dbv8!{lN9h1mylH_RO{d6+oN
zJ-FNr(+4vT=02GHF!#aCL01QJ7rJ_wI+%W#yI|tz_Q2c+GY_T@rXD5^b1%$)Fm*8d
zVfMiIF#Rz5VESO@!DyH~%%3p#!1yp4rXQvrW*<x(W*$sGjE0HB)WiG%lZT1J_%L~x
z`(gIO{0lP=W-rXYFn7Vsftfp0%!9cX<}Vlx^B;^4^AAitx_@DOSUADL5f(l$8Wx@~
zb7AUW=EG=M_`_(JewaNlahQ6TIWTdU`(gIMXqY)LK8%LB8>SDPhRMVH53>*E512b(
z@-T6jdvLiMrVnNw%zZHXVeW&OgRTzdE_C%Ubuj%fcfrKb?SZ)uW*$r*Og&5-=3bcp
zVCrD@!|Z|aVftbA!Sun*gV8W~m_K3ef$?E9Og~IL%s!Yn%siNW7!4DLsfYOkCJz&b
z@nP~X_rvUm`4?s$%wCv(VeW#N12cE1m<Mw&%wI4X=06x8<{y}PbpOKmuyBHfBP@Ji
zG%P$}=EBs$%!kpi@Q2Yb{V;oA;xP3vb70~y_rvUi(J*shd>9RLH%uQo4U>oYA7&rS
zA24^o<YD44_uz6jOdrfVnEPP%!`ufm2VEV^UFhmz>R|d|?t+P<+XHhS%siMrn0lBv
z%)K!G!PLR*huH(;!}P=KgXx2r2cu!~Fn_|_1LMPJn0}agn0+vDn0YY$Fd8NfQxEe8
zOdiIExff<1%zrTbFneJBgSi7{4!Ze+-8`6kVg7>AF#o~$u<(JYNB1v`4+|%lzhU76
zqha9*GZ&@~W<HFDg+Gjj>4(_^6NjmXnFAAtxgTa9jE0#5<HKl}yJ7m!X_!3B|1kSt
z{(!jyCJz&bxd)fKVftX^!Q2P4ALc%oIq2$O?m|}&QwP%za~Dh;-5!|xVCKQ}!PLXV
zVeW<b52g-gKg=E&AEqB>A50(2JQxj=hxrrc9vB}+!}P<{!|a2J!_0%}htV)`n0lB$
zVDd0=7#}7Nb3e>}n15mB!R&?k7v?UQIWTj{HIGvLgxmu&ALb7j4f79-4|6|EJ-Yv3
zd|0@^!VP9WjE03D%v_i{nE5ao7Tz!#rXOYxOdO^jW)4go=6;xcFdAkKj1Qw>?uO|@
zr(yCi|HJHq`2*$-m^@4z<{p^4Vd`M|VeWz12XhZhAG$i2JJ8j`)WP(_+yN6uw+H4P
zn0YXLF!eBTn0sOVfvJPp53>ixhv|pe2h#^L4@Se}Vg7{CF!eBbnE5bqn0^=ylZUwz
z<}a8yj1QBCxesO^%s(*mVD`ZL19Jz=9GJO7#XOjMVg7>AF#o~$F#o{Rqx%=ehlLX?
z9AV)Dqha9*GZ&@~W<HFDg+Gjj>4(_^6NjmXnFAAtxgTa9jE0#5<HKl}yJ7m!X_!3B
z|1kSt{(!jyCJz&bxd)fKVftX^!Q2P4ALc%oIq2$O?m|}&QwP%za~Dh;-5!|xVCKQ}
z!PLXVVeW<b52g-gKg=E&AEqB>A50(2JQxj=hxrrc9vB}+!}P<{!|a2J!_0%}htV)`
On0lB$VDd0=7#{%7!@ni~
diff --git a/multiview_platform/examples/data/doc_summit.hdf5 b/multiview_platform/examples/data/doc_summit.hdf5
deleted file mode 100644
index 8400dd06429e58d67c98d7b9b1689a534b42e0d8..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001
literal 70624
zcmeD5aB<`1lHy_j0S*oZ76t(j3y%Lo!2@ANuo#s4AIxE3U`T*6AhHY$9Z>!XCJ2L(
z0R$NsBp_S{L8$rY>Oz8CT^Se{8DQqaXs9*@0~QEN1450GBP;|wTpa^IUXB0}3=9ko
zQ2GQkok6(_K8Z=GImHYNVVSAr1`H6|5K0?CX=4Tk2H(_@#FWI6M3_EU`h}%agg7Sy
zSPo2pQYb_PBO^pW141#tXl4cpuzEoSdj<}$Ar1^C+zclFnZX<eRt65RJQEWWSSJTG
zXD~BDl`}YSLR3OXA*e7&aU6G3oB%ZcRIoziCqO8qoCp`72_x9g--VHZ4eBZe1_h|U
za9IY51CU$-JBS2@AB5&`gwP#OaagIt!3Gh>MM6TKfq}smmkNlu0)%3Su!AyiP_Xi3
zgFOxfG!o`v$Y1~!X^^m#V8~0%O)X|%U;q`fpdf*zFGw0<fQNSuD1FBpLMS5$WelNA
zAe1SDGJ{a&5Xu5k0%P|ODEWcXm<Nbp0L2p|WEdgI6e)c}^dr(cgin6DcMyb_a6lSD
zLrC;=&%@vW4KH>EH3klb;)2AY;#39(2_}$R85jgOAT%hX+#G#^T^Se{LV`j;6j%c4
zEr{U^3=BLB4p8%08AKR3z~K*)lw@QC=P*!ugy@090EEo|mSfNbsRSF#Ksa34!1*1P
zZejUX!x>_R0n}jh@(reb1t>8<l!D3Bip1Q4oYeTt6i{k`xdBlgf)$ZW2tbQd2`J40
zrD1##C?96<2WW1=B@Zi}VftX^%RtS6>4T|*(J*t-`7nJj8m10rK8z2eVfMk~VSJds
zVCrCem^zsMVD5wIgV8W|!DyI17!6kkH3()7Odcu>r(p6hf1&eX=E1~aG)z6r9q9gn
z$-~597Q)Pfxd$c><HO`(;xO~kX_$JLJ#cxbMX+#%nS)Nl<YD??{(_kcHHHDM4k`e1
zA50(2ei)4|4>K349Ztc-(bdD;2@}U<E=(L|4$Pe}K1?1a4l@_#ZkRflI7}VPf#~8e
zc^C~d2VEV^URXH7XqY;fJ{S!%52g;rhnWL&C(K<iK1?2_4;Ef9b7A5zKFofYIv5Qz
z7bXugAEpoH9+*0qy)gYSK1?2FFU&l2akzQVatOwUnFF&QW)6%GHxH^0rXFTKx;Zd$
zn0}alU^GlWy16huIt?=)=5Lt!Fm*6-m^zp|%pP<a<}R2xOg~H?%>Qunp#Fx*!}P;w
zm^v6A-CZy~Ode(*x;-#HOde(*OdMtp%-t|^Vd`M!!}u_HxHy_WVCKSTn7tVKpa#O!
z!Q25;kBbj;CsaE|H4W1Tb03UGPbV;YVD5s+!`uVY2U7==huI5r2TUHO4@Se>0aFL#
z!_0-LgP8}TVd5}zVKmHsbaP<l!R&$YVd`Ks%zT)6F!eBTm^zp|j1O}UOdU)gEZkr;
zOdX7dxff<0OdpJot_~(I<FGYb&nnSZ*+Ge`JUI8D+5wri=~lYEst2ajp4nwGP068i
zRb8IAk%Gh5aQkB#C8`JXU#U+rt5!PToc+~`M^C{a=)~!;!c&S4$rqAd{g|fgaO+;C
zN%(aYhx&fylUIvW9XNMcv$gvvI-Fg*nB#D|`T>o7mnAZPD?1qbEzMg}rsfdVF!T94
zS!IW9{ik<7{h)H-vM|5hqQ5E!{z`Jzn{lZf2n<OGPTHh$piIHyM)ETi2f_dU)s%lJ
zIGoPb%UpOv^}tg<rS?f*)eaomb#4C~X|)6IFNX02K36(WH}AsMZEKYd{PPo2*XC0_
zaPEP;SgNqvffLpr+!9=s4s1KL+?HEK)!}_ba>$1+l>>=vqRsAhDh^K_UL}3tR6g+F
z=`XE0p-K+NpI)<6n55)z`w!P-!8)}A^~Lx8m<TB!*yz0UM-G?jfu8-V>vzslbV#({
zzV_cn<pV_?@}~;@RUB;g%JARpRXtF-lyCF2Jk<lMOzd|4s8v6Zy5Xq6Di$>dMHTOR
zwGK)Lj&7P>thG+%K*fcUU*Ee`9Cj`!DBt%(>45!_H<h2?$sNe}veLdHUj0Di6+0=_
z`N|H)E37u2`k{D$yO&j-O;pXn>t+-q&nlGzj=4cSFFI8YSf9Pb`SOb5fm?kyc0FiP
zc376S;zZ9fRfk%mb^OAWDh@)b#?fDyRUPhD#{YV}LCGQaTXjqP6lI5*C$>%hb41Z0
zGtu)+fR~a(>I)^WHAzYiOIN>NY57+9z^@7a*S}q*;-F)q%{^<W{DFM;a}%pml^xbE
zRkvBypzLs%CF0bAk4g^D|FFJH-=K2f<n7NUv(!}&n6S;?BwMO<z^3ukt#enD9YmjT
zKiKtK$w6--=iEi_RSxtsIHlC*s2=!nOk|r`ovOoOxA{3@PZbU%X!F%3o=|ak*><%!
zB3;R$$N5j!nHHr3th|%ExnC$AC`wzmU87m~fa?annl0wa4qeNGPOQ&TcKD}$)OgDs
z6^99%tG7(vtaKpf<b_qdV#*E+D<%v4E>Jw+FJbx2Dn{*qO_HH~_cdjQH_mMCdiJUZ
zK6Ej5Zn&oG5W1zKA;w(AVNZxes_`?019@`AArfyC53tY5;x4mMJrL_?B7b|Kl0&Y?
z_1o*hRUKN7oR05_Q9dwR(CKyhSJeaO{qAKR*H?0w6feKfXpPc=aN84t7C9;o^UW(?
z@Kz`u*mqrS(am=%2TC7EufFhA`9M&sxD?|*RfpLuiIbdPs5n%&CQB_@t9szY?fh92
zWmFxky5ci_?N&NaS9f#G-apC?+lB6*wk}pW;QURXZL^M&LzS0owFak(L!9t))l@5`
z1J4iX=dGzybh!6u=jGPNY7Tw6$~+O*l@CmNIq%-uDM|-6el?4g?on}gwe^nM>vu{A
zoDA6AE^SqDI3x7PNhDv<p{ZZQcxsaR0k6KZ(yZT99p1C;x7)N{)nV%O1q;;wDjl$#
zSGDPPteV4PsjFMxY*jswaQ&0x{pX4YZhU-~$|tJikRW@aYPo>&0mh9Co7L7T9AF78
zci-o!bl^juyse_QszYgN)|8u5R2>v|d{o=MSoy#q3#M$onJNy9534@ipQe1^ynSDB
z^dF@I3r~oJHq2Ib=sKSF`&xpEgId1X(%%It2V9+%d;F4=4zN{z&Qao4K44+=XXdHn
zDhGBN+4#>}pzNSHabLOUais%+#zyKleU%;dUgt_)E~DzOu3h<u?N()nbGfUXR(Gi!
zST^zIE63f+4xBOx-+8%|95U^HY}eeZa-h&-{pls`Y7T}%8mpV0D?4z`&db(ct8(D&
zS-%9cUup+D4;Sq^^F#H3Z0GZ7DI1jz`031P^qs2e(DXPZI?zeRA?s6oS(=I3fnXmG
zxy2dE2hL@<Ca!5za1iRqnyO-}<iK)C`Ta#PB?mQ*HxVgs6%TAXv|Vm~pu&N99OcJ_
zUn(8=!ezfD^Q+1M!4n2;^}kge(i7e&zTKd7AoTI;Hn|ez1D?s(Elp0V97ulKlYZof
zl0&`2$`?vIlnxY!E<T;Wu5!Rz{6hS)Y3dFkPv5<`$*yqVxEsd=;RF?j!mlRjj=D+*
zgf<%=ou#7YVC}2Nk}^Z}K<V-NV?wz~4pTfBgip*@J@B2uCo=fEqQmN#DRr5qst)-Q
zPgC|rsXCOJM+i*3rF@_({E|1PmD&N%)63_{Co4GwGcGlpc0~Dr*0G?4d_{5xyw6Yh
z@w`Rtz^dQdx#oXWad`3mad6aGMThGxv2NB^R2)9t3e(}KS3dCX$mguA<?06v`R$cG
zpDQ`M<rSWDaJ|9-&$r7YHU+92a6DSG&7MWoVPcc<grKP^4jbp^+dKSLIgq;0{FZZ{
zn!^@rJ>k8xRUMpvcplhOt#V+m(3a^!%&HE5=B>Bla8fzYAw8|>-e!#hQrW9&w(in6
zaLe{mbi1AM0r8dkth=Tt9bmd8v$#}L*}>~rvhk@pWrx*4*9$j2Q$3(_+;3wWm(qdh
z&zs(T+OO)6U480P#wGa!&l)F*N#9mI(5KxbmRzIkpvtnxqGgrZfx|D>KD3uta>&@n
zH}8>{@&Vna;^DroY6m_?c)n#9S30n9w~JHzLS=__8<Nj`zpr>;M^oapt<TjQJ|F4n
zyd<b{;BM&Q12K_m4je&2M{f$MIB+r@_i~)0a)4uZbMv9yN)8-uGu%HpsXD|cb<QYx
zrR1<tjc>)n4vhoPGB*hQQdB&kwmiv9@S?iIEBQ4$`jwOpY}erbaMnTPz@)_sV|YKR
z9(c9W!|+$HvICRQP64Yqst3&XJlTItMfE^Rbk_0kT15w*13bsRzf?HTR`$iq#X|nT
zjA<`t-~6I+fT3l5&fz7h2O_#wS}1H%IIz2XU-fEs6^GkvqGPY0Q+41D*j)aGSJh!o
z&Y_EJ73v56r1|gf)KxsNLExWodyBe*@Z%`%g&ArN>PBbyWD`^zT#la+-F{E`z_I<s
z6W2<qIV7q+3pLACJRsLHCF(+(s>7nUip;OqD>%sT8AS3<R&(fgn*HnBY^4LSJH*q9
zmZ}~&{;R?vEKudZORl2sV>^@%ob!--&nT(tAbFjYA<Ia~Vd{AcMk{620|t_QQr9{a
z9TFA<re8Oab12R}^&<F`>H*d8yQzC7sW`mJeAk_ouX>>W)ZZslmZ%(1Hrf;KR<3+N
z@6uP_HA@s7W<Oi;EB>#_ffGT+w?hq84wTti2<CIDI4qp}W7}av6$d{1ShrO?8V+B7
zuG%xnRPDfu-E4*aHmV1jSOYy8det0?e>n2p-lOPHweoGXW}TA5w>-O>2Vbi=obGWp
zsdG_2us+7+;g&fn2Uc8oKSQTV#bMo{m2b-$l^kMfSA{XRC_4ll`7&wcS0x8Q*S`g)
zs#G2Pez!D9Emw9h`gX+RsFk{d)RQ?HTi+-<cppxR=US(7;939fW_tx?2gO&<GdVa^
z4}6WN+@!**>=6Do{(YK;x`SBsOp8O2N)B_?53LG|RXMQl>+FPMZpsdPC0G1|XR15g
zIdRa6xli3e=s`+wVwduP!%Mdv;pbL$IC~*(yQG+k!<-XlTY2h~5Bx1y@czRkl>^G<
zs>(-St2oU6q1<d7uX3RC)X5A+du0b_rNU1az9}E5TIqOCtVYRUy60P`e`k~(5@&pU
zX<VaxVB=B_>!J&a4jPwp?r*xKbYRhsHT#0y6b`Id*jjDiuY7=uPx=1@MpXy3rLqh6
zGpRUy^m{x1gSqm7>1o#&d#qJCAY8wfGt61_fZnP*=Dds=2Y5fud@$Wc{eZY)VV1=?
zr32G4|0w+CRCiFFd1nv1r}_ao4bN}hQ&bOJc;xn4WTVOf*CkR8e|!`je#@AM?sQT)
zaB;<vUnkEfA23O~AiR0Kl7l5n{M<!1l^xdeetq5ZM(u#W|0#QQ(p4RnZvVE4TTAJ{
z6iu-$zmKXM2s^Uq%<dlw4pRM3IM<w3a=7&C_kZgK<pZIWf#r%j6&+S_-@22!Makh#
z%Ar%zo|*?l72ma$x~Lrxh?7WrbYJ1XMiJdSp*hM2j(Hr4zTU2S;HQMbqoxE!2aDGp
z{g2+LIz-nz{rQMN;ehkaSz3j@Dh~b~;mmG3l@DxO_1yK?2W5v}eI2q6qACtwIiB4}
z-Ku=x_0pbKmJgI2YW7N`ehyPU(EK~WWvZmofth=`ru+|6ckonypnC3;%7OkZLR~^n
zln<m_U-kCK6h#NkM1xhg)+#w<J^pFBmtW1{w!ks|3lkL%tP0+C{mpzehnuq&xYo+5
z9hfasZ+`r$ibK`rLvt<IRUJy_ezBW#SJ{E#kY?i3M+y$HVLDqhb}Bo3W{3=O3{*N$
zVZ2aV+EC45u8i#_QzO*_wSwvE54})6aKYfgnHFc213z|ZSaSQT9QglbrnyhE%7Nnb
z)q(-unht!K)8;qYC>@wlzQH8ETghRUYhi8fcjW_ZM(6lK&nP;q=uG*2>bkOnyL4vc
zP7mb+Gu;<1;Mt<&5bx{}m%l~%K$wn*gx?mW1ID+w9lB1aI22zjGOOrPJs{k>##}O6
z(Lv{~(yn)DN(b2f`8*a>Rdjgs^v$|ArRoPFUx>L^nJGKS7VfGROIJRy{NlyGK?O<<
zmJLhGJx;3}SXC3A8*ZX_V9uI-4sXNM4kWAD)fYvmIvk(TE~>sz`M~q2@4u6ol^w#u
zRGZ%#D?7L{`)2Apt2+F+snqk3N6}#$?~G-s4^<CL7Z71AP*geav(=pcL$>k(%NI=M
zTxrS&cDY*@mbWN6)UGnW`1zQcgHNB~oX47~4xc&<p9%d@b>R9_TpKr0!{Mvcn#&&@
zl@4S~{JGpIP4hs)ffwo%p2;03k)9+RZmI4dCy>GE)2!s6sHOOM-C|XT=<WQn<tx=3
zxbNIq+4MsBKyQdrHKVQafi<GMZ9m$S53Jr9&}R8V*}*~NoKE*%Wrz2xh20n@C_AhP
zcsKoRuabkSry2L9M@k3IuGo7`TS)DIhh)gN&3!5k>bt^Mr?jd%G`VlzVpXH!AfB!&
zV<x6?AV75D*(qC956p7);c2Q@JmCB6-@Nky$_^)LFMPYZP3ge8Gqda`uTgOLET7K3
zW0J}NuJi9i6?_yAT)FV=!izcT4%64)s^9cn^}t>EKn}y#DhHOosuRzCt#sh|s}&oA
zR8<bl=_y#_d_wua8*YU|g*%iFT>rDC^g^NX0VR`ZJT+M=4s~}slkK-CJ1`uP@Ty&>
zeBk!=xk7m})g0DqzA$K;r+na7w@l1Bd8GqFJL0zZ>&iM9Y%TcK@=(EHx#N~!e;iaD
z0vWEPGgm1eSoy$R_3B?G2fsTh2^V@*9AaOeyv}B;bYO<znZ1>Jl^qnM1X5qDQai9s
z#_7t;I;8_KiyKO+Y}Fk2i!PSDs8Vv6cCvH+jJ=8up*t4TCq<|nU|M)&?Y}0a1CjbY
zF}3qm4ls+o_`3awl0%{jztYMhN(Vl$wp}`$r0Bq|RKuIVrh0&1Do@QiR>dJq#i3w(
zwxYxFEAvjWuU2-D^X_o;e5`)JWX5}z&0=Z?VwZ}F_Hij4xcOW3`UgX$1D5N|ZYZay
zI<%{4{Cn|M*&$`=*$K+^$_}gE3QJ3JDm&csy(XIfP|@LxnN{Ut7PSLY|7*^XV^cm*
zw*K@X#Yn{iQ<tdi*e;-Qfai2dv(pRZ1Iu;$HPmh^ACTSU!?m?Z#ep%=LrbPi@xb<F
zGuO8lC^@8VHCymyhQfh<(*@IAG}Rqm9i2NVK~MES)~<(p>R&54+`QuZ`=YF>1KVul
ze#!lc4hJ0*@_GAI9m+1O{n@Zh>A)m~hjS(~s5wMO7d<c!RdKLr+*|rQRP8|dYk3F7
zeANRNb9|n4dMG;_SK^Z1x>xZ4@5jayF}G9>)R;<W-|tsG@bE~2h=!i(0q0lxk8ZRn
zJIF*!T(wG4K5(smuX{?j%7M^LaRH0psT|n;cthat29*PS`L`yq{Zn%AyLEim{MU*H
z%$7`+I+dq!;P#!S9nb324m7NIboAs2#RJ82IX_wzC>;30aP(%#ZKVTqybeDLs#H6m
zy><4)o2yk0m|4G{DBq)efOCPG^g=0Bhl@8qEAPlsIq*|zmC~IBDhHGk9l8YWD>yi6
zNHK6dQ$FB%w&?XgdqoE~zg-`AzA8C%Gya^A5U=cTP0}y-Xts*OZmHtOYuS_?j<1(g
z^th<(Q2(mySoHxFhxavl;VTqX95x(UHak64+2PW{KHkPu6^C;{&8*pZ$__Tc73L55
zR2;&h84t*BRd(<xI=%Q&wz9(}9qaGO45|k#4RX$_KTtjpx#I`d>TgO1?#|$w#BfCA
zfF0YSlT3$I9b_*$OuR3q>fpkYS^W2_s)J+PlNyGdN(X$dB&W`)Pv*K4Wmb%g_S
zjx1fdPekRw#Ka>Tma?iJIB{;(`6(TW4s!R!W|lH2IvgyP-kjB-dSITc-HPu23J2UJ
z!^<o8C>>a~uIuOI^Qs4qu~hKZ&QLyZt8-0^<qahV>lYlRGF)m0<~%vKeDOS`13qlw
z_l<?r9U@g{mY)Bma^Pq^zrm4Ar2}#`)~c^HR2`(0?>@dAq;f#vv%cD|Im!qAbZI?^
zD^he&6p3UuxvS)0`(1tRWhd1Gmwv@wKK@e4;b*su#VSq}ho!&g$%lSZc6gD$<@<R_
z<pWY5#W>A4)ebxee^v7RznX(y?`Ni~If@4|Vpg8HxJ%`LZBS>c_atS9IDggSHC)OE
zxcFa)Pp(%z5WCe_FSuRlz`S24KJ`CSJrFPE`nH)z#o_ju)degclpSg|I(kZ%C^-b5
zY47WLpx|KoNvOy3vWkOG_+IOO`;-nG+caHLomKIGT+_n(|J|ww7NwLeOq`<XuqIS#
zFW(QP1A6?{mtSsDbXds|ti{)?=CG?Fy|l+x>A>DQa<M!LY6rIUKY!`8Ncn(RPnXSw
z&58$%!{#dn-BUfFCD)Vp^t{pm?MtBndXb6;K3GfsUvf|7z=t0V9)<sv4}_dQUKVMl
ze85RQ*hXJn)q(e9nbx^Hl><Mgn+tt@sN!(;k=)Cl#cB?kjBc}g|0p_$-&6axs8sd9
zbd8B?BW5c)T(K7Jnf*`MAx6<KI#^5Dp+9t=+3y@BhkmaWi}#qRIEYQWB|K-7vO|a5
zrHNIal@FN8GL*k)R6W4qYp%c7MfJeB`^GQVFH(2VIWbQ+G*HE1zTP6gbUl>=EbRs@
zt<#hbs4gjUvJO^uP~tE+I@Md{!0v<3ln%^RIN<y3*}moeY6lYbh`nR+Q*&tSFm(R%
zTG`=7@xB<H|Edl)Pn$K*tXDeF^<bfjk*2!CP2Z`GQwx+FR>+ijayF|TxXkRal54K=
zfv^*m@*CAv4%|q%w@A8K<-kqWg-+K_t2jJ7ojfC6TJ?b4%RsKzr<4vz-}isKNLbBb
zZS7alM<OZ*X3nxPirTC0V9F<8WDur&AmXQOUTvwWL;IiO8{SE&9?(=eK0&`z*};-A
zYteyR)dLkaX$_%8st)rs_iZl>Qa+&Zt15f#O=XAAu2(J#Br7|d{Th|9@r{bZ_hpP;
zYp$p{2vmLK{%fRs;PmmQF#=Y~4sM&v)+_0&IQ(C|+$4FvvV$9AuuP(dlEa*=D_`pp
z)eZz2%$syXUDcug#gw}}Pn8_zu46J+-KFmE>tw{cl2@t@Q)Ck+WtuBG%;}5mT2!lW
zU{-4ouh(h$0~&`D=9sytI4GC5tkF?ZJJ5GrqJ}|O`M@u$7mW_Fst1-&I`s0FqN;;L
z>2#NDK2?WX-}bznCaLUjyDoDkle?<J@0-pic&8{k{1cUHdB>!Dpz7OaQ*BR0ho;t&
z14kw+Ikc?cSn{w^`M{*jubFKwDm%=oS^Z!|z1o4Fnub-M^i&Sybf5iX^-uZ0AG29?
zwP%$NtWW0Jv{F{t!DbUjQ>vkg!-5G_-*Xrh9rCXnDS2e1<ly`A?g36|C5JHf{a4hi
zR1VZ#lw5J_in>EX*o_mLZz?-1*xK)&^;6}*m;US1e_AU$e3bgN^v5e@hfABEmOo}x
zc8Fqq85<#|e!x5|v*iH0(t%0#YnBTqDLW)c95OXqr0THK?!K{!o$7&!W~O)Rs+14N
z?%j9v%~6E|O#ctcW^PkEQ2xZ+^X_Y9hv^Kl@lOks98`EYKP}s=d|>j8E2cMAsT?@@
z(br6Nma@aFZK40ASE(OpOsf##h*EWE;cDh$tyXlH`m66_uc)F!H=q6q=KCrKluCP#
zym3=<_&)8A31gz#fraNi{-iBcIl#L*?N;RuxdSuxqi5x8QFSnxxci9y9Q6aSg-=>~
zHmf?^Z&BT$nyKW#cFRNdqq>sAt>4bcX9|@aTm+?-e^yj=SiN>_o5dkz2i=kv@kSLY
z4t#6h<?LOla-eF9_TJc^$_E~qDePsGR&^+xeER#RoyrGxmoN()6jyaP``+b=Gmo6Z
zKTEx6u4)AbiMs!+d#0!!_`_3>8=|Llz^rXzP?edA!&jA;4GBHU4qk;uVt1KT96rpu
z;;?LiibLx}i5Gej$_|bq8!|-Fln?k@-z(bwRN0|U>O_^$0%eB-Q~MOJUs5^nU{SD3
z=XIq6rM*diqvk3*DE}$qxWcA<z&q_pH{U8nhtADvb43eO4-`f2Q+VjFdSKR-kFJ}Q
zl^il}@QR&qS3Yn_WV4*@er1Q&<$R3U%he9tf7$nL#wvA(3F340j@PO<eD~c^J117<
z0RNVMSN`2nIuL9>X&=Kg6^D|$t5VGFsyf(SvpceRld{8;|5D=2a*7UCRpQ%XmM9&F
zQ@Z<i$xQhJt1iBl{i>+ykZ7niZ_x_n12G3*t()~#>A)^dhq((&RS!%GurvO7T+P8?
zb;4O*Q8kBGp`te)v8g+p^`04=^-0CSj4fQ_g}%xG=b8O=CO=gitPSt*a^FyO;12r0
zZ1hU$fOBrHriZbz!=(`Re1+Re4pIk2Ik#G?9GK#=^~Ua>N(Un5b^nOoqH^GrVA|$2
zTxt$Y-7(6GLRAkq-pT(E#;E8Z`1*mG|8nI6$BpFKB*T>tMARF+`_ZH9u){ZI|NbUr
z2USIj`F@WS9k!}`b9VGrI>5(&t%D&@<p59o{KD!(@(vuePhz)PsvY=p<M>fyR>cGH
zfy>0qTh$!4p0b`j)l$_#l;zNhC)%nG1^TivXJnKQoZRw2`|w-k17e%SFC-mQIWY0U
z?&4_<$_JR8!gyBwQF7R9sV(_sg0h3iKmFTHD^wgLx@)AxZz~@7Jp1S(4p!v@op;u6
ziddw2pzdT|gJ`;@gZZt+MlzD>4)%ddMSg5kc34-x;c8T?@&Pq3>*Ph+$__VOjs87e
zrE=iO1xE1?Yg8QE9$38)3srG2f6;$qOTVgv;Esml*&h@gUTXb6cePN(L3SVS$@Au*
zd58O<s;iV7PW-bv>b6YT;adHTxMMGs4t!vAJH1p=^?>qj$wemL6diP%%-1!ZS9G|R
zY+qtysd1oIf9=KBpOhW^nR|K`o=|motehrsUPJLfZR4l#Tau~{$Ikc_b3`i~_;Gud
z`PU3BhlT1kepB^S4@9iwliw7q;@}r`m32e6$^nzTac*0lD>*dXPpV$OUeUqEpEEOG
zT+QLdc59oB?^PT$kIT;UDOEiX^JMDcBk@WOo8FbP-kzfD5Un0Ln_<6_!-HJ4qh~AB
z4g{q=n0P5(#o@I0r1MLXl@Fx$Fgb-TRdir@Zq3zItK<;8V6{7oy2^n)>sbTJd{rFM
z%-K{K%asot>}iSPZBjijZFh#K+jK>Tl2_*YH`}NjaCi3i0WZM<E%*YjdxOj!Lb(j-
zsd=eIi6yBi@nxB*<?)8F<)%2~jbO_mamX8k*8G8Nz#(q}k^|uaeh@i|M?+vV1V%$(
zGz3ONU^E0qLtr!nMnhmU1V%$(Gz3ONU^E1%5dw@1ko}nS-}lA<*_*~N^!B|$#z0^@
z)Ue0Gz=i7p=$7=M7p{=8qrn=k4K<LM9(v&l8FRxQu7u7X2$%`+J!Cg1ggr`*oDhJW
zTY<iBorl4I0hGQN8Q2+kK>LW3Kqn7C_9H{m3S`R>c#N5W0jw4jYCH@LU^b|n6k(8H
z$V+n0Nh~gAz;$i_uKnO3vk(}zzXi6R1-35+eIE;K-;Im|4}%5N9eiN-gU&=KPA&0C
z%}X!IV8HDU*#2+mj%b7vh8QcvO|XqiNzFl#&uZb-jmi#QiCU}XtyDU|yNA1`=aizu
zGzMXHy|3C1w-yDM{Cq3ruuGx)^r2R@1DV^qH6}KzI4o6qZF=L0vO~m^^D?snR2+Vl
zt1NySs_1Z7U`I)ii@L)^A6}Lf`;{I39$2@D-%91c!L#1YiQAPO)bA-yzWPDwK<s?&
zJ!iHkA2`Uw(D>%J(t+urU$&`zP&%L@`{tNfpYnnH1(O_7eUuJ}YnR{6cT{os-zdW5
zuu$EhEOupWiG|{U-rhvEn>K0(atqT+m;+S~B(YS<zIvc?AnCLd&!%ZA4p)q#C#R&V
z9!M0H|8g&0`M^1!dCIHbt2oTA`W?o?r*hy)qw2D%T2+VWvir_K*~$(U+wU#Be?s}d
zk^05i6SgZmTwgwQewD6@1DF4<n`=KQ9GIi_kVhjz^?>ir%iqpyRXtERfq|pWOU2>P
zvaa?$Z&eQ%|2els#Zt-P$-50V<nJjtT#{yb)A3gAfbsdDW8I&W55%?_oE1-0bFf?6
zpq8~j^+4SdyPbQjR2=k}H*_9crg9)4+w`)(qpCxg{;Cu^LA3+Bvsh2Bc2qlXRmw5?
zgo%m+^TPuxgtL_o9G`ZvmUW`4L(-QQi$6809O%!QYa6gt)j?w6@9ES1H4fx8D}R5t
zN%4UH>xBtQyVMRuZIA1%&{K10eB^LxlZ>)MzS)M9+4of(vTrA@`qQlFaDKkoG0V-$
z2bM<j-1x4p<nZ6V@N@ejHHX&kTla8ksvaoGYFM|bQvHAg$JUDn{wg@!y*s<Zrd`Ql
zPwM_F&KHyq*c{$+MkrSKK(m0uJXK2-2OF!{Z|(d_2bzo|e*QnMaG*Eut4mR?iUZTL
za5g<HB?r#<Z@a&qlRvP<{vhu!DfI&$7q7F%7%4kcm6h`pzE(Og>CUN|P8Vf|x51NY
zwIfs<67KY8E)G>Y@N@Q$$$aOP4t$F>INxwx?m$LD_wn2mRfkhNKf>p~QE|APyGdHt
zLis?6=Sj8f-%1D6?&h!G#;)QZz}ZnK@<QgoYUb-P-0zeRSWYpxkaSb|0N48MIt+hR
z9ct=pVmA0I9gqyVlIbX=?9dp$^6H&)DhDnF1s7~(Q8`dkmXWW%R^`CiVmH5Y+G-A2
z#ecuN>QHl7(Q~dlZL9o&hmVEMEap)<FoR|3tg9S~2e_WZu3~Uhap>)9Kd>-a#es$M
z{+jK13I}c#mrcDYsCwYN)r-v^S1BF%zlJ?#YMRo4{M6NZ*1cDDNIWumxA`~K1AX<Y
z{!Ot^bqL`(dHKR`wF45I#e&)uDh_EypOm`pDIf5AbgG<Xp|V5s*S{fOc~l*KK90Wb
zpRRJ?Q?Jtdv`5MglK(E`25(e#*fuFlwZ}yHz?RCDZhy}xA23^Eb1}eE(Lt8)*Et?G
zl>=+eh8uOxQFchTTlhqIxr&1U|EFhS!m0;W2G{J~dRNuKU}pVdS4|~{818AtSH7tn
zkW~nE_S&xEpnvt(w-wf^2a2vb1bZx0J0PNFs=1{|*}>^nKt#`d#RFEXjw=~GRSv9j
zh~b)5uX2D#{sPNmB^3va>&GO6KB_q=W?V>_)T(sg_qG`#l0wQ3XO$xKS+dm}uG})a
zAt9{nkZEvxBgYhF2lLJLzG;$b4r)cezV2SFbYM>E0dY?s`2%fRYQM)VP&u%0rK8uq
ziz)~5%}mcZ7brSxRCT?Y)U149=LzFaDw9<Ym~uy5D*vJ8ps>*D`ilK(4pD20!)FG{
z9oQ<>{P~4|vcsB)!!H+xC^;PZwt2^~?WzZ^H7&h4@0GGc*t!#U=e4Lhgxb!FRi3Qi
zpmge`#~)Rt1M080&$ZQ3Igo66;^nnAl>;7@DYMkdl@7SNDthdCpl~2lEx|@;sfxoB
zx5F%d7?ltFY%lq;FICNfcSm8yiV776jgGs~KX_FgOoD!$G+(al@S)qY-E)evgT|BJ
z2|lVy2hQEvJ?)c&!hxkd>%QzdsN&H3q-y@0E6NU4$KU-}P_F90vocn|z)bl7%Of*a
z0X0>J{cba~YqlsIV1DeV+AVAE%|y}#I%9RyzpXHIWXJ<yl?m?3AX@`0d2$;VS;
zl@IuFh49AwQF6Gk@b9M2%*qaiQQ^k|)~PrwSGkn9OHuj2HTeYPIALW6ADM+Kti_ca
zHa$J?@pqe=!_l9;od!)R2PR!RuQc6N`9NB!Bf~az6$de%dBGPol@BZ`eVBdSNBO{>
z-|Dq}Z<G&Y@&;~MvP=2EZ=Tl@KbEK*Xq#O9mbp{KLFQAs>E&-K2bg5V&5Qz690D67
zZ?B!A>~LZB1%2hq$_MUd?mzNhMaAI<lfxz#E0qHc6&I)d)>U?B)Y<0ptw+T{aQ!32
z_eT{S*iM-J*)U(-VZYQerI(S)4u@w6aO~Nqeqha!E`<X(l@3gPzdLuCp2h*$32D>!
zpI1KMcC(_9wMO}X?z;?)`c`F!>+L#g;<l(cth1iK`pFa}2j$op=k6kf0|vz=7Kh%+
zAGq%N{fzWZWrq^?xsm1kDhGB%M4fx0sd6B;`&%0G7i9-8fA>DEPjUxVPW`R*<&3Js
z`ZZ$pJ2F)rY;J7SJ6)!B;N!7F#jLMY4qWq^F`M66)!{7rGIQ^Xst)g$KKSO{qIBTP
zudB{;os|##GrN26<y$3(cgC-RVj8s^ESsAA4IU{TnAiL+I>1@QA^c+8j$MaU95%jO
zx2iQ;*&$@|sb^}TN(ZEpr}`BnD;>BZHcR~EI%S7`#{J7wcvTKCA1{A;`IE|lYr0on
z@k^*5`2F%k&F5*V2Wo;(?Qj2~av;v|@T|R8)DEmy+Qb~hpycrOkbL)3J*5NgpUf(=
zW-B^8-lYAI&rtcmhKIhsO3#%JTozw<`uI_W1J}gAv|QV->~N)sU;o^8WrvgZSuGRp
zDmyH+b$*-Xu5_Rz<)(J3g3N(Wr#YtSh^jdpIsHog&My^*jj4JwWb)Myq|1t4IpU{u
zpnRcM<^M}62bPLCv3{~saX26KkZYBV>H(qJ1cs%TlpOfK*dIQmpzN?EHN@&<gz5pE
zePx#{l9e59<*4#F`KumKR@VCRd%vo~w&lrLtzpUzr?qxJQ=O#hpxJ-J*dkBK!CX)4
z?%z0N2a{d*58jnkKHyOP&FlIiWrzG-_L|m`$_|l5Y*uax$_H5gUEkj>q2zFDox<r=
z_mmtCxL>t%7gTa+cp?zIw@l69Sk#6oXVNqr<^+6?Uvo_5z|S&vuO0G=2c9ompRj+I
zvP0RV?HVQy$_@|SY*bsoq2w@iirWvLpGpoZR=dtFO;I_}c~0uUk1Z+(+Su=XeRxdW
zq5Va2*Qbjr2TUrAIB(BVJn&pVh0!xe!C}taqwBBFQ8^(0PTKSVqlyF5lwXs-?Nf4a
z`q21nr;LU})0B7y!HbFxE991#zmHUK_&ZrP%HfTI!;avGobMDh4=^l!$Zx5xc;K7t
zh4l1|Y7TOm;m=()R2}B46`nSjEbCxe`cJ(vPuby<cGHgSo>~VMeOW3eJYCsA>R!u(
zl2BELSZUYVXBJ8h2IhTt#krLaFnp5ciCLiR;9{1m$MIg-A?co)6QjM#fhA6r4LeV(
z9QdS^d1k>eWryA7>iYV{$_MHT)n8X|sXC~nNbdW^p>kl-iI!Qt%T*ojEP3>_Q%TLi
zZtqNq!Yri&VVe9)+T4^KTr54G9onMoP&h;Kb@WG-1LoD2yIr_d4yeR$*>P1`-NCcx
z?4l`h$_`~(pN*OAR1XMy{@KiKqH<te&Y8Vl_9_Q_9)H%0*`#v7vy?l@y;;TK&%{e}
zWU^EbZ2GHUFT6ncfTqvYki%wb4s*1ZbM{HAILx@f{kX<O?Lc4FT>d%N)DN7xfA`tX
z8fAxtla|;>uU9{?;Mu7K5ymPGthwHL&5M;BbPu#nw~SFfFk#{U3(Q}Y9o}pxOl9#<
zKCmkIdBxd$wFBa_uG@V3qIRIAccNsAl!`-Jgn$vNouWg|L(3o5RcZ%PiuUaLGDYRU
z8S9<G*^O!k)-C9&Nq($y;L5)sR=opi2b#9d%lYB0a^O81&o1SuN)7?8y)5tRl@G9f
z4>&3<tLiZCz4yfPnkom3z4v%5?No8lTe`mbf`ZC{3GbaQKmM(9U_#u5)Zbmo4sOjw
z54uv+4$S>i^en_x)nV#HId`$!$_|_#-w50mQ#~NXIRDYN2dWMy|5du~t5!RpA{P4i
z9hc$(X6I+2^E*@=*h72xb*8I0oH=vADY9D8VL`@=${CT$2Z|=%d$aeHf`iW))xHNG
zR1dJ+-)OV-k+Q=(b<epe+ms#l-e9u0ucqR#wQ54B;Sv=GJF~sP*`3M`iF-<S|5>kc
zK;?Pbht`WK2Xw?Y&(Gjjap*W&Gx=76(t%e(>PsdXs2phgq;XVJS>-@SLTYN_apeQw
zcOLzy*sJXD>gU&H^-|>n9VgB9g*;Gk2(q1%%pjxUa687kvz}YYp|PUusrG9nhxhC4
zo-wtn9&o(NVQ?u{)gfl%EE(@sl>>YAWBnDjDjm?gw0+MFAyo&tW#8}qi&JsXO1^k-
zO{AK`VVz%_<9n4IRA(OYZgWs}=xy96o>8N6z$#|7;4*O)hm|{DD{bviba=dUa>`n3
zWrx;3o6Bp<)erb`o$*aDS32-#h1oWdw@L?my17>#^ig)`+ukuJrAOJpg2SPIj=Jgr
zXOm|dKj$kSxR#~U`&m@Y;le(DjxP)v2LiJ^zbbeu9=PXqEzrY8=|G!p;DpEjRSsx{
zoqydOq<&znSMK!BwJHY;A1^()&r9Wi-7ABAj^-*3SDH4R+GL^Tu*@R;gX%Q}hyGZ<
z%)8x62MltbG)ZeI9k`?yE+(I>c;Lg=`)ec>RUE=|TQ2!)s5;n5{%*YFqu_9QC9}XD
zCryWcmK7>|pOg<we0y`}5j&LwiMKh0?6g!5bc;IbJ(#HCplx|_<^&H_hr`9cwz4f!
zcIe#6X&h-Gd*G<Lhgi9ss>2Np_KhXdY7P(In#ROPC_5Z{%~){znX1Flkkz;E-B)tx
z5IC}U(t0(AvYPYeLL4d%(p80pacdL~xSTVbC4NW6!B=D1ua=$42lVHB{Pdqg)uF|1
zb?<WtWrqt9)9;>(Qa&(SL(1ttzmfw_g#6-(9_j}krgi`3^i?_VdZ9>3ma^&r4#)Lo
zevK*zQtr9d&1g|MkS6!#?XhOn13M%)?st*ZJn*BZ*g#^nszbGa$hWQP$_`sho6KcT
zC>^kntv%{xuj){wQ`hj>R{20ifQ)3*6J>{>^AdH78B`t0boN=Q$SWV<Stk&drK#*7
z@xd|SPL-NNin6Q4`GZOhQxXqFOxvX5aAVnGkA?Tt95g-%zB{a_c%Yf<!U_R5)dT9X
z-48xoRy*)OeEa2-&dLtmv-;i|u2XVQYYch%S6$`6>bLjw8?;mp%+j%!eYZ#PK(*MF
z52t064zRMB_)X7OcCbBCCLk-L;&A!K68B1T6^HAWcWz2mRypuZK-glNf~v#Hl5nZo
zCu#>SKIBV(=%RF>+jD|v{{fW)Uj1%|%kotYq}}h_wfVB@0eMyLO<_gK4uKUvKl1%h
zcKG2apnY<iii1GB#Y{g|We1i&RjvG18V)aixhcB)DmX0j-}&NIm&}1jnoMU(eyJSD
zUGk}tK|%e1dZYI#6H(;@)ndo?+q<Y7Xn)D2np&@N;9+v-Z#@Q;12<e^7iveUIHa%R
zbA9fra-d@Q>b4XSl>>?kO+T74sX5rhzW%@MnCgKD>5Mb}dMXaPL_ONFx2rl#wppzC
z%2?T<s%e2^>t%%l372?pMPF1nz$Rq;ZF;@3LnY6;n&=5i2fp2!YbN(X)nQ}*xhrqa
zs5qF+DG{BzL-_#XjIXmZ?kgXVd#PA;{;{&d=9rDo%hXjJaypX&1eeG;ToP^kE4M|%
zVei%hnyDuh4|LSuzZ7hueBj2vHxKJiDISo~(%!>mseIswT#l_~gVF&O;bMh_jw%kn
zG83e-o+=*@n|bHz!E4F~lr7)8``=SO@M(FUp0T-tgZbP?Kjww09hf-N>Q=v)>VY^5
z7y0E5$_G9?dcP&AU+F;E5^=^`&MFRCr%#*cKTtZrYqNIxxoye^?%#1Z8U0l009%@I
z&_)|2ha0;N1d2~qIq*P0;b&xwvIASd^Y`2_iU(|szI>ZIPx-*c{U7q9)+rrWvG>XL
zUp&eVcle)~PC2OTu*lXZqfAH1A@1hdxY@sy9X1<mUjFU9lEd_iA=~?ZDLd@=AFug$
zjfz7A`&PByw+ap$%nTFn{8V@NeepogLuG{nHywR0D{fUj@FCi=;OaxA1Kh73UfOU|
z(ShZD&I%a~HHXgM<vx2)sW_M<EN^jhRCTz=<TLGJjk3elhOSq#E0rB;d@ElrV^um3
zXz_0em%qvZ=WDGA*|Dkz7}<n5dstN*lD^Mn`IRN>uxGOKfqFN!1DaQQzHeEoc;M{2
z#sioBDIfTeXjWFaNBMx1j8ty=CS`|H7Z#eVexTw||B>e->uM#3Jze%nP8=!+yjL#y
z5&A>b;rPVui!W_fJ@8}AT^qX~l>_O|7YiC+QFfTRw&>12ca;N~qME$f&Poo>#h=e^
z`=spP&UotWqD`s?n%rNoT{F=-up+oVex8V&gY+Y#*Kv+Y4&u%)CoVBnaS*t9Eo)Y+
zl7s9nh1DBQsT>HH?eKDvy2^nMrkA!HXi_<FRrNyA)T_!4!B?v9{E<>~;Pk%zP^(b&
zz$EobmZPr}59~936)eV}>L8%_wz;!G`M~#2EUtUksW?ciImh<vg0h3y`CV=LUdj$l
zwUfgWWK<74T$6WJQ9$KDctNCP;a)|DU1Dw<6l;_n-aeXP{3%5Fz*=jLcoqg#ha>an
zRcVT=99ZAq^yap=s)ODW<9+_US`Lpi&flMWN$!BZ<eb|#(-aRlzVbg4Sgmki+oM8<
zBPuEm%OmG?r%9<C*l1dG*L}6hf%BrzbgWxc5AcSjNO3wV9JuvJ)Q(qJ<-k${v6hko
zl>_%hIyEmQDm(o131<1UU-`g>Ou-$$qLdxp88Xf5wNg58#%Z0`U3*1`)61<E^X^e`
z2st{ZYNoidgNgiv%Cm=69n|j4Z~pRF!Qr~Y3}=~s6$fqhx1T!tRU96E2ye9KQgCSM
zy3~AbtC9nc_JitU(JBs14$<u|zAHJHUuV3_A+F-EW7lnyY({N|`PGL@XR;|Ch-f%`
z=JPA%12Z+^72PkZIQ+e7{$}l7l><&?N-~d`6%Q!*7^(jJqHsWg$zkKpNy-kqSN>kB
z|EcV7WVPv&+ONtE(sy1M3nnQa@F{2d!78qL;7IE`=>=Pr9ZLV2D4u<wav<WzoYv4r
z^#i>hO@5~eC_A{^o+bD;P1&I&=*A@deia99zS##IJXH?Zs4Y!fw_C+QrKxLs>OEx#
zy(ig+ZXQ-XARUw&YAvPWFhf*vXWD$F10}&>3kvzv93J^=JPH+5IZ$8mty^@t%7LF=
zCth<Ks~mWIq^$XBxQav23GT~ot5qEKU%EX1|9Lfs1<pQm^Ia7WEICxSZOKKo1NWk`
zn&X_54;Y9|@pRvzcpx#RH+5dGl0!%KU57g{st5F<lcs!5QFVB-bH23i9VLh5ey5K4
zeU^1NA;lGbP)6fGq4BEge+v{HCWoAP(IcyI;9o(@!5Vkv1MNMY?*w*h9I*KxRdYX1
z#o@x!dwFeo$_^ciOPd7GsX4eC&8RSYp?F})<?RPoDk&de&biG|?W=siX0uyB<OLN6
zdHoZWrmIvOG?kA%zF4H<F!S@BZA&>-4n+R?`|JJzWrrm}RpuRzDhI3{O<S~yUCBY>
zUJ`%IX{7_-!gT*#HB~uaRd!*qN4Jv0r<Ox=;<S_x9BDoOt58h!zzeP^USXS+50pL9
zJzY6p`9OPnmdYG1)dN?yraNporR-3;$Ln%qfwIHTqOi5EN|hac=DwHM-L3B6A|}Zz
z_D|_R+|Sn=u7)TaSmktLgZ>9)hZA90Ij$`#4$p#Y{?(?c9thOmx@B&wvV+^+r3c=J
zsUDbdtLfUL9Q6YhGZddsJf?o2>L`b7bcBk-p>ulo)NU#{l>J?@>sY?(ff-Ns34JVA
zIl#g{@AgJv<pbPt4QBSsln!`jOuXMKrQ)!+yxTgvNA*D6g0MXH3KfTD?R^!$FR32b
zr&BfW=N=V@ZF78uR&7vrIJ-YAW0AM=fj92WP8{#$9k#D{zKOq7;Xu@mxQ8vN>Ic@e
zmWC`{tK{(VMV|7`M=A&QI!~W2<E?TaIOyH-^&)BqRBd)`E4rfU@c4;cfJmkKfytiL
z`?8NH9#E}JS<LiR`2bgaNiv^+$^nD#D>gdLR&iLKcE{a@N%_Db7Qv@;6qFryuXeQS
zd8y`5C-%>{n?vcqXW#hO`%RP&JPej$%GXeGFq?T-WSO7Jfr_9jnjU;A4(-{&uG*2x
z4jb+p_$eu9I&h?<&D?xX!C@w!K&7{p%7OapUf*TiRUM?B-pWugS3U5qZ+12dn~H<L
z!LUiME7TqSrPO$6?ox4>6LKvp@w@VYs73X%6Z=#gl<JfB#_FgzTt2d(;CzMRfrtFF
zmuXt799ZI^`OMf)<v@LUjo=4gjRQB1#4^=gRB~9ht!cuw7fKE$>tnPxtyDf>$SJ!k
zN=W%Y!dl1b33e(DC)`yJC+VslIO}`j#)ejv16tedULC!xdcd~pTisSM6^G?juFs}1
zsUF}dn6r1)M-_*O+nz=|ey!r5p`Te$(4=%=uActP54<V|Hl6y$)6b*o5FNnhAM{ZD
zfQq>8<XtBe4+L3EI<mS~^*|gm=Y+ezRU9@cUXQjmQ9F>6(IUD3j<SPt(dNk1Gs*{^
zh#x(s&Z^>|^uqgT?O8>KORhg}yvvbwsJPr>=&GZ6K<eb`?5Ow32cCb*Gh0)x=y26>
zOW9Hd6^FWM|Fjp0sX6R3db3=8y1K)@(iu}{os>UdG3mwae|hQ$D)!ymaNwBwfh89?
zrvHgoao~w8K5omVa^UixEZ5r&Dh_*YM=R~`Q#o*MC$moCZOsGsS4@33PhZI)?3z^<
zYnYNlM8sN;^SR0g+SUKfPTZ+{AU&%i{C=qN0se>*Yspor4(*3j&K*uuJrJ@gc4yvX
zr33%Fe?RIKRCcg?J4ycMQAG#CC97Bjrm7uS{5+Ub@Rs_4TubrIdY@Ge{H@<4)39It
zfJ58j#rc|Q4sUlwul(~$<$!Za&ZWgsN(aug*!d;wQg%3Zd(z#mnMwyv#C%rfn5lAL
z%kM|(PWMzDI=655EBRjez^6UR%eH%{I81eCUenO1a^RgFyTQSYY7YLQJAR(|sqFCX
za!+#&m;8Ypx2E}~uTyo{7k0hceWj|yq$xk98vj;wn8v?TEID4?Va=-UM$H&ShlMS>
zRgOd}JM3Ju<H15!Rfm}dMi(r<Dm(NqUF<!vMe)FeiSs@^cT_xZtZ}j9&wa`cA6F$P
zrtejDC|DW6m!GYCKroiyYRLy>hc|jJ{%H0pIqY{%Dmc!jeBk+dHIFh*C5J^x#;4+b
zD<3#q`djJd31tVJ49CBT1<D7exOv`Ka!S=fC}hQ)V+jfe9vMIIuV15bU`BR#xzz?`
z2lXXafAU1BIHcT}^Recpio?cU->#F3ln*S}zTxm62~`J+qSgs-gH;abz50={(@N1{
z{j)Q?PtuhRFmm6Ema11hU_Uj+GI)W?0p?Q^-T3CI9*CRs|H+;%Wru?&v^EBKs2(Uf
zabwPzhl&m<E033@s;D?56a_aw>`*x%FLn8XS+TOiQy)G*#SE1L+-)l&dpgt{{(ffA
z;j2|Ta7HTec=~Il1GT?D{J$ij=AgQ3ejmG#lEa)YJENnNB@Zy?ynS?Wnwmq3K!I(~
zcSVO;Clt@F{w8xEo!Ldt@0hB?HHPDkIXjgcydKWnv#?s`z|N}br1CaZ2Z_>u-?lzh
zJfQXE!QFS6Y7TG8bNfCCsvLNgyW!z|PSpb@^1mlZTB#gJyIDBzwuIV&%dcc!{Mf2?
zz+k>h>5s=M2VShYQQ}*p;!wUp^F`qaWrytewDSxJ$_Lh&z4;_OOXWb2fZCSIFUkiV
z*VN{SSgRcHdb;&v%QS@pd{@p#lpj<)z-I0J%&Jl8fKESqCEG(qhixaO=<NaR7d@8M
zu}V+H;c8lEWRj$YgQL3JZIuRv1Ge5hKQ}ci9!T?e(OofF)nWS2=C1F&N(T~^9<Ma!
zRynYB+Yy^{OBD|=bH2D}y;9A=IVa}(=C#TP0>gN-!%9>f)?I#hC}E<?fg)kgFzus?
z4ocr2%n7=vcp!!0#nI&H$_@e_?5%i(RUG)@&6M9&DjndNXJ#teuHvAy>cQQ4bCeEL
z@lErxn6Kc_JNdKa{)K7>Y(52lbktLIm=oTSu{lG<VRD$xLzjn24mon(^>-SS4{U6G
zbZu6H!h!p5MYUJ2Q#p`1sd?)4S4s{Jxz|?xex&NK?9A5V`B^Fstqr?Yt(~Xhu){#V
zP*X?Uq4)I6C#^G79E`SZuv)95>agz8@~7->$_IjG2)ttvRXH$km0ay90VM~i!nQJF
z233dOyH+{<j8;D2#QT4@=@Vs#1*(T$G2PcZ5L#JSZEB`^AmDF%@|y@XhijYfRR3>K
zJdm?$Yo_o!<pURNQ(r9dR&>a(+g58{uIXTqTg~^GPv*cjNyAN{o8=D7`FQH0-D(8~
z6K9?uS~t}W_~rzpShuM-yijGUJaApvA?J0@?(}@s1DuaT|0S=LcX+nVUvTRtr2~#n
z!fx#FS9X}E`%dH9F%^ebKX#VcF-si?KU;NX?NOBjhx2b7d1k8M5T$y{j#XIcz};0A
z5i!N82d<?E>oXN8A1HelwvN$8#i7_jNhgC(<$$`)#%VJYl^yh#$z2t-RXo5Mxc0xE
zy7GZXI^UmYKT$fcl56hNvyYS=e%*O<OUOjUK{M%9@10l52a=cYb2((H9<c0D(Ksln
z;_#_zYgeVX>VYk3J>pp*st$e|ZT8MOqkJIb!R2$}-xUws7gpu5*`#=2nr_0^$VbWt
zBK`|y=C4$C2xq+fqN73C;geNDgK)pvf$J|<yZ_8mK5#j`{cB!>qC@o(w_*ziwFA9t
z^UU%NsW=FG&rt36P;zj*(raU0rFy{gh+nt;a%G3dyI8zFEK@!pq_8rJ|An$cL2L2i
zxAtldb2jyS(%7naz->)0^X^Xy2jbRTw(*~@bin0D)3k%9RUAT^o?hYjqvWuDi^-gq
z&lC>qx@LROQ(eV@=`|b6I!+Y_w&+~vB|0hxg3Lu!5+A4@X#b@5&itS90RxS)c;Q$T
zhn@VTvn^DW9gZ)yW;({M>cCmV=<=yg*}*n^`M&#)lpS1sSM^@YRdJA8_wK_YW0eDD
zb{(pGkCYthMZ14ol~r|E8+<r$N}RHTdR$)Y3OyAEi>sm2r4A`O+_?Pe^S!6a2Qt@|
zp3<*VKagyDPUoe*`hhQ6U!O=ds~)g$u53%WqU0c4s9CYgQ0+ji|N98-o5}}1Z{HC2
zKuFVJgWt{-6L!lS@Go{3nUt%1V8)iy50@>HchD$WeQ-j$l7s6(RqbahlpG8U)ap!K
zRUDpvXWY8*t%}2hv$LkW|D)_M?VTz^+zYh>f4?PaZOu_Vz!}`LX8BW<1Bu7=ranHa
z?9kTQQ~LL@vcn^_YKE&PR1OHaaFi_GpzQFVI6BDni`oI(&w3R-dddfUc06xca!|=(
zd(P=s7kQN(7N_R+D=boWIJx@6q$ht=54f0HesuIwKH&6ss&9&)io^S+Bggi<Q9dyF
z$q^Uzc+~^_HEq`U>M92;Ch44ViIa7><@TfDbeQ&m<*EzIwyse>U?Y3BMVvv=fz4`j
zEeD6%f$4j7&z-xi=1_ZiK9|fxr30K7Y#Pq4S90jPvDx@&jDo}b;DcLQ-YYv?TlvJ3
zFHGq`?-kz*SyfsNw-Of_y!xx+AQU=H;lUQA166LxKW_P}ILs~je?oG;%7HW9DZllX
zsX2VVHcQcSm->PHs*kf~_$nSa@?&+JuB`k4wx8|SZB8j2&~bMRvfiM0fKfSox9%Br
zhkF}5m--f{9=I{>T=drh^#cK?RBzo0R(3daKy;NusM-N3_x(OArz$(_|9!e^xtz*@
zg-`y4l-yHuxXWVp<kDmnhq4g9j=nM#hyN_c_>0X{9O@?v)icN_A7Is(c(UZQvcvDZ
zBMLWzl@6?Rom3w6P36G(H)=ClFRC8Udb;zGfQQO~W6w5CIX7G7K!;nI#J@YL4)!O?
zrq#Ysa)?s)60xgLbNIyKwC3&!HHX8-Wt;7`syLWCJ~Y=kt9;;2$P5GdE6N8pTk39i
zxToUq#BL+|fm*c#+xM1i0G;3GV)Sli)D|^|`>)DYsjgQ!(3+O`c~XM%ftPyA+uPSG
zI~Yp{|2qFf^}svR6PtRnRUOW8iOE)7Ry(k#kzL1PtFnX8a`{;&DpVY<+b(_=nx%YT
z)#__;f1_0#TI?1z|1nlN@Plv1@!$>(hgrJIURTakJy7nJ+VbIu@_|3E`y}kUR2^<#
zT_@UmU-f{&B%_qhK(zxN3wPSS?^ZdW68=M9G+6n7nE&cKXMd_3h+sB-{q4K*fu=N%
z*-J%~9i+QmPi>s6a$wT!jb7Uhs~qsEno@lrL-jy$jb3QLMzsU;ip7LgSEw8)h(4BL
z<*MeuG~2M7uUO6Dn&E~Mf2ONA96NuF|3jF@fiMoXxYF|~2bk{33!HeW>R`$#E3c%a
z?4aOw!A&$o^?;^WvGkE&N(W@3wsW67sB&Ph)YP<(qN)x-%}b3HQq>O3sM)dSO0Kd)
zMO2qe#~)>f!fNI-f7hxWkns09BhaXLz=hvnN>;kkfg1<71=2q#9ng3<?K%Htr2`(a
zJDaaPR6HPdZj$H8OyvXY+WRiuPg8NQ+k1!c=vie4M@@6Haz^C?#yk0ympo8(U|Lxf
zljfs-AZ2r=qK|^gft}9U6L!y2a=2dQ7O?c3qJupjpMubSWrqt@;=!@c6b|fjdAE%H
zosz@v<fY!B3Q7+5CjYZ`Q`2yeO!F>XbxOs-w*Ke4q)Dm{cd8G+t8~zCxbSXK{XWpS
z8;_ig{=8Oo*d-R(R6kY5;p8lhrd8*Z96HTvc4+NVbeQUTNFgFw*<tnb<|+41DIa)J
zchMuNOV#0_<IR(X=1LA5v?u-Mf3E6qv0nbN%x@(J?zituRV-B;D*BE*sd=sBu&DKW
z<YRpmho2Wz>W*wsIk5NaZwrknDh}zTaqN>XsyL|qV&i>Uq2j=0U~-##pVEOdOMd!J
z->-Z?M4VC1r&YzFBuVhVlI7|S7dC!P`TSYg;qzMo`&nIz2hRQJzv^&H*+GbTPL7d-
zl7oQZ^ctCc8VB}TX(owesvM{ZpUY{$rsi<ubuiC-4z&a8?|fX%X{+RLa^w9~pLZ)c
zT(&B%o7bq~;B#=&evc5919{K0i)^x$99miHj8CVjIJ_ud;~W&Ee4y$k_tT(#$__6t
zwLDA;Qa<qKy!qxEZ7L30+b=Q%W~n*sUHEd32$!Nm+Eu>nXOGkl{Bm_Dar~#`Fh%F4
z@Vdjw2i&+9x{79MILu@IQRgJAae#MiIM3H5DhFC!J1>9#q~wq)zkIc#pR&Vw!MW$9
z{;3}LAm8P?yF~dwlGU6pPa!pj)33wYPwS~1$oAW|;%kG7Lxy?IjXZf32W5q&H9Ov_
zI>cNqJ^9H}`G9|o(94&_N(aQwX$3^K$Q&rmTI_!8r}Ba3GU;gye##CV+Y%gW859pN
z{%Nh(`lRfza$B0lf?g$ugWCSvk8vv=(3!M*qENNcfmsW-tiCc|=|E{tT#2uv(t+X)
zxy;`hRULj`SKEAiqRIguk(!U4zf>GVFFdWiP^NU?NlQxFiyuk{I1XFvc;~NjK%k&y
zTfrxV120vc_|;!kaVXH4x}lp%<v`23`!Zd}RU9m}{3^mERStM&nz%I7D;+rViU0kx
zE)9of@qBsXCu#>64|E^m^HDz#<+oVm$qZ$Okbk1zJT9p@`1ts!trk#nu;gfcBCf4^
zplYc(|FKjxhpdV}X@Ozt4rkW{XBK2B9#G`2+4}6Tl7mp~+MI@oDh^%}dP%S1)f@sU
zBB#fFQg*n$l(!+ZQsuy{b%z!NpH*|Xw?Rn!RfO7sEB|Hs?e8i&9NXB#TNkN%AmnV5
z#9be?1L6BxCmo-laDYvH=c=n7>JD>llUX-hP(Bdip!(u;n2N(ufe(5KzmyMrVmtFv
zI#R`<S@+|w+S@7)&wp=@_AOC9@Q8!;%FhaA2f-x<&6Dc24loqdON1CGAK-E>pS>wd
z-9d2ShCfpnl^v`d^3LTos5t!E@t#wuLfIk5Io{sqva&<+#JBqQPO2R6YJ2-iZoZ<!
z?)@vmxA7<+=v=%!QjSH{p}Vr%{n%IK1A0~dh2Os`A5hHmFZY#IJMcju{nImZC5P#j
zaRo_P$_|46%-R^IC?2?W*eI*zkMe<!2JaR=`K#!lXqBhWw^#i@)kYq9aW}OC!LRuR
zM3*W%v~JITaZO3pVdcd7w*Fnp4zG3Is_^A2J2;mHyi5O~;*h)U+fsHt#RH$5e#iyL
zC_5~U;ji}NS9Oq>v^Yazg8G4d%w`)@56B;gUVLx1+;PPNax%O(tam6n7#6>nrZi3I
zfUZTKDVv3|L&o0d{`CQh4(8YX@*4RnJ49r%uWTq#aWGOBSXKF3^}zG9|1X)`RyuI6
zr*(^|v#NvLL;2Ipzf~Qg4Ek%2x+^#wS*EId&Q$%tUx9oRS9kRTpDXsVmT4(Es6<-N
za1v5-*b*VGohYU3(EOTdbLIq<14WXq&JrTZ2biMw1b&oIIk2g4-og(HWgI-aHf+7I
zPtoDEZ=L;u|B4QJ7xGfhN+=#sTeNP@;y~2{HKOx{a;=pddOzGxm}jBv;N+NITcoRU
zpz_EeTZ1)f4mO;+yOyyjA6VMTP{3oPa^NX{)6)yvl@BEGO2sXouHfKr?mg|}M`ed-
zk?9>Ri<KNsUc3Ezo2RnF#~1<o&g04lVlo>3&AqCAVB3_Vi`U2~ABbg`=_k8E%|WTV
zw0@Sjs)LCQYs9Cu$`0S+?%2L)RdMhaFZo*YO4(sX{QZihY$^__Cd>=h$EY|Qy~-}W
z;HKJvoyoVRI|(TsusAWHh|yH}fTQw;Ys@>999k#5*fLXD#o=;6;QN$%Wrv>D7uvDL
z$_J+Du47YhRd5JdC7~hVu5rLj?|pMkf$9PM1uK}198?|pmh<c?|EA<{f%D(}{*Q_W
zT$l21`_QcHu<<R+hoYrQ2TpQyy_zbcdLZz(@vlj4$_FkrD66brr+na{r0w6{PUQnD
zzw}R^u~W@~vvDPpk(;W+>CZ}!L;t8ate?8%(uE8~2gACH6>CeC9a?8<JL~*Ya@bTN
zyYqg9$^rFu*>6j%RS!7jp1-tpfyx1kjx+8tUsMlVQaI<dCrRZ%o4&)Ft9w-rT%COJ
zkG+-3f&L{Tu1kFs9UjhPnyU9g@jy~M`^xF%DhK8sTK03@Q3Z#oO9daWFe@G4PM<dK
zX{f40l7Mwf`8(wU2lx+2+n-T(xERNl8Sp~+z!%{Hy;^(a1IyR7KIaNkcKFoYQPNYd
za^UKnnjd$KRU8DfCmhdOs^Z}H_>j?&e$@l+j8P0FyHyVy_xyFDr$gD{+@DC3Rbfg8
zPV5!?cH*_lf$VKd_JPhtYMo|svcf|7fa=|3Mxi;12lg&V;`eS=K2Y~#?rM)P*#iP$
zPoDnP1C19xTE9YH(cyrfSJcl&l>-OAN6!slR6UTEyt1~ML*+oMQTG9--%1B2MSeUH
zr>1g%b*G&3xr53F{G8`aGWf3KuuDwNy!(RM0W~jq!>!#)2lmJAIrOtt{s8j?_r|Xu
zRS#@_(0JSbhk^s|feX@2r<5I-AE?ds^He$T|5-`d=fx@xCu}|SW!aPu*aQRwpYT;W
zFvWOj)Z-ZC1C8~$8ocr<2e|9f>#`bD9a==c25PTRc9_+%ZP%AT6^HJF!v1gNlpXH7
zI?0H*s5n@926)~JQF16vUUSW;O4Z@$gP;rFGc^tv|39N2GE?P1$dgi`YEXNA{jL3s
zca#sj+gRFh>$9rE!iiaeuhLZ=obIML*3D3Hcr|IJwE966hm{uJzMeHxK2SE#n7Lr0
z!hx(<Nnc5O1&3I3hf*F7Rfm|6&pA=?N)F4Ks*eA(Rd%@J>lf|0R^`BVod<kEaf%L$
z?o|rj*HJofj?L5Im7eke2NzL;8?MR+?(;kBx4x$2@Z^>EhSyIN9m*cwa89|P?BKX5
z@XVy=ss}>Sqh_wERd%SGbz{5HTxAFIlF6&%3=|GD?RfJ@Ax*_WKJjAw5)CDX?HLu1
zqiR(gx{ZRw-_KQcaHyO3D158p0jc2WI~)$GAFwP}`Q80c#i2cEP0_LEY7V*6IhniT
zRUBqsUv)5LuCl|gHno`+>dFV6UKjn@Wv6x^I7?H?^@H*OzjE(``7=};_N@*-v;2;#
z!<$cDtJJS49>_2WWBk2G=>U&-Hfwi*!hx&dvElDdsvNkTcPKc<T*)Eg-Ffrd)=CcT
zn#Ug8JEr9Di6^T2hQG4I=AbtIhsKHy6_eT$?!MJH;BrQP`a&Vq0}tMJb^W(cJn(Is
z=TgHj8V(G08fTqERSyVyDSn##R>`6DnDv<mB~^zhTOUbG+^c%vZ{h3Q)?~E<8_a%%
zO~_C_aAb$x|Eb^99KP?|KWANo$^j>_#oYWXst1~-eEzg<S2?iu+cz<WT9pG6{=B+#
z2bAx&C)=)gtbCw7{+O`fJEa3L2m3t#wy8SgefS$E@K(hkJmJKbN=aphz<c&LuH>s6
zFlopN-pr)xz*iTy=V`5`gUvO$Mu!t>4q?p<DtGRwI`Abf`2BX7nnTFc)~z$+RUNLf
zv#RbntK#6iWXf`LE|mjDq3v@yY?KcK9d~+j{Il`_srBq0k(IIsCO<aZTX0s%!OS^u
zar7M}hy8r7owF7x9hj!W$2RSTqQm<g<tZE)3I{YN6m7lZu703>Z^XYnjmig(|1?-G
zdsE3_hfLH2P9{}{>h}M4%*~Y@QtqqISv6DPfSL2Iut_(R4j9M1*}C<dy2I5^!CxF@
z)DL)0dY`CpSNVXnAbXy@k@5knyoine{wg2%a>!%tJT?`F&1Wp-J8vo<NS^g*mX(-_
z!>tEtqIH{89J<?A1lWF5a;S6LSi7cG;lNK$%U=f9l@BBs%vo;esNztmU@`64BIN_~
zo@#GDpQn6a#`48CTzixbFwFn;ZeN3n!wG3|ySu^44y(Tg`>8xuad^4n>j$kTst0a7
z+;g%tLG{2%53TTYXXOK32iz1Y%2W<4dYE6G5U%JjYi{?aP<xF7ew#P7{`xHAa4^1J
zugyU1z=R%q*-JlF9BPDnOR|zx9qbb0>*KB{Iq>9a%uO&<J|KT?l_0|n)dOc-SLVJr
zq;%kW(dUexvdRv}lHwRNO_dxfde5Ev(XQ^0VOnW3JyrgI){!Tjr(UQW=#W2reOa8!
zf$L1aetH$EI*9(=zbD;Z^+12)P6780WrzC1JCvtPR(6=f^t^A|6J>{;dOL31_E$Tw
zV{%)MQ-iXDptfG<+5L(Riy7)ZRWPa?_$V5*tk*`}LF(N5BZ?|&2aZH^<nHWMbvU=B
z<-_*dN(VZnszrAMs2teZX}o!bw6eo@);ob8wyQW?KhdrfprLx;OM#*@%Wq|eh4T{5
zIGd?B=!PtC4Y5@|FpJ;E;MN@#hudcJ|Ju4KJFKYJX!Vy<IdDib^RTI)$^iy`N45tG
z6dWEY<~K|~q;Np{u%O2$EwuxjEUStihAJJ%kKzn8S*POQws*fV&mj#5<A2BMk25PA
zm@~yGVM3qcfphmg=RH2DaUf79lz~T3=>S_*x~bT8bq6*1!+)RMQ9aOdHeF=aA~lCs
zvSOSYpQ#^+J2Bhg-c==sG|%93p`dj{*MywIqg5PioSw*fo>p?$pw7!@!>j7ReJk;|
zMZU^`rx84+2S2Mga8-zW(<@Xtu&eE5=yN~i1GgF17ldRg9e8%?&6QWzR2=>_AC^4%
zQt1GT!{@ZFkIDz!??1R?!J>SiyL7>oNk%FM4k#9Pt`AgksF?MURbrB=1IHBZ`=O7N
z9m@G0HAQJDI`F3Cay2!p9k4mWFv&7q#lhiapsh!dvcu#<TWqtxt2h)oI~v8Ms2y1N
zdluhUcNGW5g9q+g2B;h`kDuRnmRrT)X#LN+eW6MR^g=u53vW_#STS{XWKyY;L(!9c
zJ02fXahSt#eYKXXvcs!`>Be@46c42L1n)AsspPPGzVqv?aY_#Fp4e~WJgw|d>3KrJ
z&qDRUwby-ZH3w7<$OI`ba+X(dm>Il!%FQgb1CHu!Tvtq09U3M*X;TYSb1;6!qFJJ$
zbU<>dVC4LNY7Y0*7koV$t8(DPi~hqbrD_LkPRnSgwWu8sDe?|Y{HSt3Ft^jOaHg_D
zUQ^cO3r#8qrkTkV-(**I=)7i`8$VIS!S;{F?!1S}4(IDR1RwRO9N2eq3Xg@jvV+Cr
za}zJcDj$#yRer&AU*$l(xkT-~2Z{%Z*<6+u{#JEZ_;;R4#3|(iw@d`y@oiN+uq4D^
z-+Y^@!|aa6llKm)IxJCRKeIMc+2O*;?V;D!sW=>)eC(~chRT8FFE@SnHY+<6z7bXs
zN>y=qS?KsnV29cPyW6`sxn?OkJmkN4`n#c;!>Skud%IIg4i~Zn%VTHD9<ZqXpp+4-
zen4V%^W=mSIfvdIJ?eiIR1a*}_(IyiN6A6v&;FI76O|7L+~nDQWU-2ae}&ki8=I96
z2)o^lU$jr@fbq&&v)leE4s|CcWwRtHJ4~|LKe;Yh@c_>{b<r{fWrq?q`K;?RRU9@q
zEk1iTT;;&Lv-5Xf?@)FKnRWNlO?l9M64`qX4=Ot>{4zgL@0QYm86M4Ug33w<j;(Ub
zeqy71;Ma}9&1n}@4me1~9Xy+(;vi$u(Q=Sa#lg3rT|#QPio-`Y=Cfu8ln>lI#G`yQ
zM&&@b$87D5F{%f?83x_2;Z$|tkhW4e!=vh;owkcRvqROP*`d&&TweLWsdk&s4<0K!
zM67Fx?cJ*4&^x>CCf8i017|9Yo=ELdIiMA(>ZGfu=x`uZl1(63$$@Ww$KoG6$__nA
zk<a+!ln%Hb)RfFTsC*#H#Ep|{ld{8sxuSNPEtL-ZP2K*&+EL{I`_WBrCRZvt*e|MM
z-5sXtP_M&#L2`xi0UoyGiy;RU9U>%m7-+9ncG&R0OFQDak^`^&$Lw`%Dh}1+zNtC4
zlpRDkS95OgR&iMMuOePATJ^x4x?0`;T*?j;_gK0-Jgsuzwcu5@$#%*I!c#Ku1Tm>N
zM42A^b#K4&0lpoPOtXrW4^01`G=1Var2~JyPq8joqv9a$c=d*ovZ}+{v^%rSLe(9*
z{gW0eO;9-S<MpLEA4^pogpU<%HJ>4MpoB+io=2kUf#3NKRc8+=IIK3m>FE}!dLZUd
z+~pgEstzyr8RUn4Ry)A3$M1ahAEg5&UNX&o`sxms1Gy$C{!=}Wv*g##<(WzjzgFeQ
z-{4hts5ezux=l&Rq44*@gWK;aIeg1?+VFU@lEd=bZUTb-N)Fl=7&Iaoln(g6-KF|J
zSk)mZy6x4EKsATuH&!%1{;%lJq+;TCK1<QT^Q=~JKda(_8#le{_S{r;2uTaMWpAi-
zpx8k1uhb0{hn@CMZv75YK47*~=EH($wF9<O)^;zlRy}Z@g*|dhkK%!VmGQ;eMoI@3
zwm)Fza#B6eDDdR$*G^RjhxcYTW7jA->^W>IIm1%r03(0S_MA5516~P5Rl%0Z2N)J|
z*6t}+KCrwwVb04|<pa#Td$-+7Q$BF}mBNt^cB%(B7E3W&aHu%kj(%tqe^v2-&(2RD
zrkqqg5Kz0OdQ-KMgZsA25-+x^90<6cx%}%?Wrtg{*yE%5lpGRXd41?PsN}G_OU?Yx
zBgF&l)lU+$R;nC0(<@Vx6{zAMdSI=8RjkT^gJ!);HPh7`)@)zbCHO_vVa}I^=OrO3
z2jaGc+*`n;df>t0^AiJdl^sMCFQ5BRs&c@oEhpY^n~H;@aGY6fpUMG-_6_NazN$Dh
zo;;yi@Iv{3J*(iE?>tHeV&vEVovx$o@RMQsj%g>A95$BlWT&iAb~yiQRgULA<pZf3
z&!lx<P(83A_QC37!HNeoI-Uex3Q}>{Bi61e{!+!ksISdhJW|<VR{6D%z-kqTi|3j-
zv=%5i++lTpG~<hsgW&d!JFJ|P9CVlUOqjAm+2Q~11#I*3l^k-i^1GPCln#7+_Ofzn
zj*`Q(3$|KC-YN&?%iGq3?^1Sf{9(R*+d|a?3tvyVSs<=;;N#5e3EJsu4xv@W@A%YI
z9JJ@It$Hh{a=@rx?BoSq6$fGA>$TdV$_L(v1sgHAs5&?rHl+S2Rdx6zIX%5PNY+7S
z^>_cY4{8T2R0DTic%|r&Rj;(_-#tZ#=Rz!^5A9VBF!8C@U64{f;A1qg^1PDL0lwM2
z8wK~MIxIPm^Y{HRRfqR+S&#J_RSqn@u}}WjVx<F~AFoYVa$m(^pJAYnv9#iW3xW5i
z+Vm?Oka@G=km^Kb2iDj7>rS|-IApF~^-0o3>A>0-*OgAbQg&Ep`zXsbMcH8~tBIrk
zT;&71)l)j_vy>fv*WX<DwpY<1+(+`Uda#m1|D4^7xnC6zSUeBj@RCjSK$NtFujV=h
zhoc`?dl(q09>{#S;$J?4vcs8P+0s4d)f{G<o?&B`RdJZGv*W#Cqsjr5uSqBR-INaS
ztx|c%ZldB~=aQ^&i&x`-L8qKn=w}rNr-zSf_jaisXkGMGH*vnQLtD&V*2^a92c}$3
zIvNq9?7)4)<Wgsls)Ki$)9d_gst&(R=YPMrT<yS~fX@@k9x6F-Z|mDyuCH`};X~re
zB6hU{Sz1x`4c%%E8}hF0Fu$pMz^7GkK`@i5L+gXArEGml4r>+83+*XWcBr{!I`N*j
z@`3Kl;f%r3Dh`$cXT7}Jl^q0^UwQxTpz?vK`7aoETd5qlU=cngY@YG~mXL`I)s9LI
zs{EHtn-;4%G_GP1+}N+~aE9q^1ph7d16`}cw^>bBbddhYbIG?{$>GCeVb>ye<pZS`
zrv%MZS8*s%-Nb!bOxfY~ArszzJSq<M`<7ozKd9!gsPXpo;w35vrXD$Vo5fi5K*hUl
z8fKf64+t~brJOsibU>GJ?U&Z+$_GTBoDz*!RdkrU>e#-_sVWDYs$adkRjqR1PJqd}
z>M~`A!@n8DJANuTbWBm)*Lhdrz?{qG$5!uBc3AZEV!dFriUZru+}@}L#RH*}lQcv7
z6dZiHo6f~wRCV}ls=Qo(qM}3F+_+mDe98wzf@OHW%BUS!aXD7atXlPeaBHoO!xUwQ
zA0ZWTSrW<)>+a_=OBpIVZ0Fu~=|{ctfgN@FnZA1!9rWIt6ne_4ItVuU`CL4z<Y1<E
z_`)PdWe4X>r{;<JN)9V31p^L0RXh-0Hl@IQj?#g>{woXiYN$ATIvwOS@3+c<mNst3
zR%_J*sgo0)wck`Zu*FeaNJCK3p)u~_C(BF92MYBUPg!29df;q($ajf+6$hi$uk|~*
z)DLVGn$x=Yt+K=74N1l>_R0qie=O|a>rinxC@+2Wc$$hsooqRa>J(Lny}H+<<c_Kw
zQ1`d|J@19`foU!K)=vmmb}&fsl#w=4Iq-7tEk)kHN(ZW|?&K%jR6g)1)AVV(mx6=H
z`#&`kr>h=#qsaMY7L(F}c@^jVznxM$aMsCrrsf9K1162<@|$-mAE;12v2Jy&lEZC{
z$_2>*st3F$Ih(XuC^`IMYN%_Rq;i1w%(ZzZY?K}Lq|NgEabMHnnbc#xH8&Lw{8=?~
zn^Ux!!y)m%byJyD9YpU|6+0>^JJ>9J#5rM~ibMX3&gT8Q)f{Y_8hvM*DIZw6<Q~^6
zPL%`prP-<L7b!aIv@Oof&Qv+DR%N=^w)aX0w0@s?Zn96=!DmT~?Cx2L4tIV9266B!
zJ0vVv(XIST>3~W7!=6>26%TY33jN%eq3odABd)A7Rq;ULoBWp{w^R@8Soh3KeS)gP
zk@P1F&x@4~tee8A7CcAQf&WIwT{i_42lm!tAyy0Z1E!hFxBAyB9^lKIck=CPWe1m<
zh+_M2r2{VI{8lfuLF<jChlspYb=a}>XQ6D3x`Us_`nORllpQkuuHwzKS3a=g#mV#O
zRq75amd1U$o~3%=l7YA2?^0EVg1cY&7l|t#xNv%N*A-d01BQCH3mUqW9L#xwe_U-)
zIWT3CzW(I^wFAQUd^gB!RXMPr<7lvAtI`3lz>AzF6O<1uU|OP;BdBs9eh=%FHzkS=
z=61|W%Xcatc>3>>ZL7D^fil&A`_@a94@5AoWO;L3<-q4tLMN>RR1eIr-IDM(K;=M~
zd)3^9ehLmVbysCBzNX~x=I6RN%^0NvTl`pOzEoE}(9<g9nEX-M;lF+Ic}7=dhtoT3
zf9s2>9=Kd{_+e{<%7LCLo;<hJY6q;3D6Qw4rRuQady}krvC4ta5RZ;<1r>+m>f4(B
z*{VAfY~2^Nh*|xB<Ayb>^wy|2nArNQSqj=e!FBNVqa8{Ien=#^oZh2y!2IqVOUZQA
z13L<@Y<j|^dZ7I5!Skw8Dh^!_?rg~~RCd_@CMtr1N9DjbjVZ2SiRunNwzJ%`a#cC7
zNnoe<MLD$tS8vX4D4D11;Bl|<=Vx10hu061`Woz29F`W$$(WX>>R_0#mF=U7%7Nlt
z5w2^us2*77Q}FtBiM&Hm$>Y|o3zQFVvN45S*H=1FW%DQC`W`h0p2piuYn@dbUK=ba
zTOO(A@Z-XkZb=svhxJj#Ez+(^2c~Q*U!YT=bf8RF>u+|DqQjye<_}tVRS%ppb5mkc
zS3I!U!#h6VtdaxALWQ?gy-E&BPV;?a9F-hwU6>mDHz+zhRXp!<Qe4%+H*DK<k27iq
za#IwFl6zGSTz@TT@@<lm!_P@F{^5TW9gL2!IoH=KIcWX9I=iz|$w6e**}F|mN(W~B
zTidbhyRyTU8N2VFI3w?nkr~|OQ>)}K<MeUorU=CYLcuAno~>#Q4NJ1`dWNbxnCq_>
zGKy7m5QsWC(Wg!6z?q-lnH5&5IK1HVw_Rwhc0k}|VIohWg2S7G@hlAA)ean(l(n^W
zpRz;iRc4!~Nh%ICd4Y!)X{#PM^-jih<^{zA>w<$;tUe@rAWOe(uYbI<L-UQtiL*Z`
zI@~#ZWS7@|l>>imrBYWVs2rHbSihb{N9BOccY!2{2`UFV9A}AaNmM@Y!uIPiRU4%P
zGAut6zwc6Ucw9VXM`)|^0iPLvlqa86Iq-FY$2@CRRfi7A>)Nx_l^x84{{F2KS3Mwb
z^4T>;FJ%Yg6)Or?NvR%q8-KmCF;2}vK73*|dz!)l;osBWyZljc*yqFg`|>@-14dh!
zo74F;95mKQoa&sa;xKE$?cEQJRS!tFotSg2LFGVPUfk)P^~w<1f+*7@&N>Q{%{f
zRz(em<R;zXUVT*uk-XsPi>;InXjw!s#oSjuaP?ZNl2M1sfxL%V|L$9;9w<G-ePGgW
zr2}%`KTr5+q3rN!rue<BBB~B8YO_7G?kXSf{UWe7-(1z<pwNUvS5B)R_|+#{p|MTv
zfK8&0$n6^{4hn+2Q^cmL9SAH<baH7>a9FkVowj+k+JV@q0<sDdR2-z!v{#x>QE|BS
z=~}A1qRN3~eN06nmsAe$XY872ud3!y{Qs5mlNU-3?~1uB55y`T(3q8TF561YA+19D
z%)t*T4$IdwO4LnLb|_l;Wai_3B?rG5+H+)!6%QQwuT}q-RoS6cPT%;?DkX>eZBq+3
z>{N2F%5j)yrlxYhrljq<+cz19C4vTL*IKI|2$E+sOWLk<VDqXR@!Q|z53D}4YzvR8
z%7LPriSh=%ss~i=G6YwIX*le$b}84bQ#ugzC}`0OPZbBJof{W_T%+WmHzDNyrGLr}
z0`u?Ht(vatu=vd3r7R4p2VQEPxt$gyc_3ltc8h}fY6t97Oa;2`s2ng9$~~>isOGTu
zqL}OqeKm*QjW=g;e^GW=$F3{9Lrl%V@{ZBpYZH|Y#7;B4mMX95;H<r)=h1pqhjiP_
zl5%YohrTDVS57ym9Z1+^>C>jF?7;8E7P>fJ(c$0b=><z>svY>aJSqRkcO{2?jx&D$
z6IFFM9J$1e@4oVZC5rX272DJfObmXy)%Urg!-F50-?#ZHAIN^STJcSg%7Ok#8tY%@
zsU0x7TYleJOX)xz+rGX3z9=4u`OG^{gjwOh+l^vJ67DM;IQIVZ+#Q@M4(+`L+R>$o
z4$scpZ_G>9I8Z*{!G8KAC5Ib4q7L(Nln<=#XyrT0rf@*4B~pZcxq`#Yt4lsb3#%RQ
z*AD5pv|Ys^=H{cX*P0X@HYYahS|6!;;Mr08E&bdo2QuHLJUn+-`G5;=_Ew1vN)G(K
zU5-bBlpP#R5*5M<lpG4CEn4E3rs^<H?`OZq36%p(ya(M>7O5U^St0i28mEfG_o=66
zee6~_(A;@*y+)Sm0T(B?&Z*j}4pIMBX<gM(cG#%v*vD0&;_&&X><f*_iVpRQO-|f;
zt#n|^-RwP=oRuBs$Yn;a*{*V6&F|g!UKuMKsJmV5%>7u&A^M{J>aBjt2NYc2CaajM
z9B{a>;bG_*)dMnCccpV1RS#6VPrJ-?Qsuy^Mh5;3p~?r$X6C*%H&Ao1jF-%PqNd`&
zzSq5?@wKYMR2Ny1rV6D4S~qj=EM*sU=$RrGB-yNbK>UZ{=U<sB2mVcX!oEU4#Ua8x
z&DmdF#i4mcQTmyF6^Hk#pVc>%C_2o4@L-=$ld{839kx<CQ8kA%FG`G`il{i4Z^)=x
z^;h-4jF-A)b~0)Q+)ACRziw7NU|-Rg=5t8J;n#^6^)vno4n^-Ry(VkQJN%QLd2z=(
z$pg)Mr^p2_Q$C=(Vz(;;x55GUg%Rq8!b%PkI`=eJc&R$<`PCG7W`VLpzeD_y#&X32
z$qy9dCa+R(Sk@`xXzi|YVA&2$#Yiuu1B;llX4jrlI-r+!N%fSB$^j*h{^BQRR1Wm=
z$413Ds~nK+Ym#)FrRq@gj{8>aWMzl<7i+pEJXCVv-*Nc(IUSV)uNJ798igo3EbC&i
z`Epv#A@~Z1dz_5Qf$P$<r@VGlI`Alw;pzH%wF3tyHoa|)R6MY0!y{RjYNZ1g168DM
zr70g^?OS-m@`$=aoJW-M2?2!z0X!1^|7Iy2*l|}fwd=o%L)dJ)5Hlr}14@A#IIf>p
zbhy6JhxuN!@_|n`%Uo*wlpT%-?tP#-UHL$d>CMjRzN!ZdgbkD|)~PxaJ3J6RxmoGJ
ziUr3m#~xI3@Y2(6RoSiTaQx=^%}W<5AFw!bJ0es;{lFCS4$jv;stzToh7Omus~nK<
zRsXq2L+Jp|zo@_b&y*Y<d$YeTH&8lY7#kYNeMsrRj_LUf!fRCzEVdIl=Bur8;MLQG
zH(A@29r%AH_bzZ#K5()<WqnD4(g7Z$Rlz$?sUA3$Hg}rKZIuJ9D_SLaUaB5gSMaoS
z^={Pz=E+;{awjMsm^&qD_f7%j1GQG?g{$Yt9kBE{8g})kqQk{2=a&b$DIG{sT5@m2
z6XgSkuDyA`yGg}i){fhC%Bm_39fda*h3-%}(9c}j4?4FYCqX#lg@ek0_HC!x^Yl~>
ztn)|^GGb6UAQAWIPRU;thaC6#o42GD9M*q6GG}FmvV*<Ohr6fLlpSvD>)nv!t?clJ
z_v=IVZAu3wp42-is;T7AC!ptc`kku7pU&TDPxX`?QpJBAO7B%ZkSe@(Z9t;pf$FfI
z6VDVYA7K0&RQi6Ol7mxn^p@<UDh@Bx&6IdNl^xEkwD>2%sOF%=)On>wUCn`I>X)-A
zqG}E?);3ES-zy!cSQ0;D;y$$lnRPG3^!F+sSoD442elR@2ZJTnFB^qa4x}hwt}BUD
zc33n+Y(iJK(t+n&EKK_aRS)D<2u$$^R6Vf9=r-HB_o@yBPp<E}e?iSb`}u3nv?*#1
z{BmDhzr0aC;J;j$=_kLc!`i!C!k?p*9M1m@n5lbQ>A<BdX6Cwu$_^?!xSh`>syJjw
zE4|6BRCM^_xxq43Q00Kdll#@bn^g{ks^ljM{!l*fw@6^U!Z!tn+k#WllWbHRTwnjQ
zx5!p<5E7r$6ZA;=09VEW$AcVd2hLg-yom}{I*@U;W9!^Y3I{~JZw3CdR(4pPeOTLZ
zo07xXJ83*OIh7BzO#9?_W2(jh@!6Y8?<6T6_-XM@VOzQCfj_df3s-WfIlS2Az>&5?
z$zlC8_lP;al^s(5Jeqjynvz4l*WyX_Z<QTZ&YRuRTds28+Ue^jRbMF{xZiguY*D)M
z0q$2%gycmO9cEpBZGWv(>A>UG?!5x)DhIk&&bP7sui~I^x;y2`4kZW98;V?htCbG4
zXIyocI-+!-r_z7E^aPayF4v}<oNJ|WU}{>fMx%$yf%J}Vv!`&Y9*EYDkSlzoe4u&e
z0l^Lll>^K&g7MkLDhGc2VJh`3QE><{b(52oQ#uf~-*qdmgNlQ=NX*&<4b=m;Ze_a2
zIjK3^@0-Q=g;mkvMZk?m6YP`@uurU?>pMx+LA&g^msE_Z!^C~ZR_t4<a^U~Hr8-U5
z)DO&6z8-zDPSqh^%W>U`4wVCwr$2JtZLEAC!;Wi-L51>x$Cj(ZcgCqY^pqX*cr`)A
z!Tg}5@zWx;1L>YYj}Ho{9N5FvF#X9QHHQgjR_N}zrhH&RsIFC_soDYi_bXc$omM??
z&f4zN^I8=Lj)mzCW?d={*AlxK{#Yv?*s|5|QlY<!!-Vb&v7dRA4jh*KczF9i6^C`*
zMrS&=Djmq}4G0QVRz7fcg_+(fBb5V3jeC~7eV}~6$n&w8r?`eg*_qfWn-(h`ShL~{
zoA+K-hk%VDE;Vjy4#!-7#yZ6)98gJJajEL7io>#*{pYH7DID0l^RB=Wb47<lS%H+4
z^-2fymlVpTW~d%eSRAU*IYHroX<(M0Y`KcV(G#-A>&ulL1nyhqCwizj^fYpA(2P|(
z;I+w0$3RNeVdjr*n`WL>cUZUcKvKP;n#0r9IK|f0Y7Xw%ir-sB6c0?dzsR!4Le=5#
zx2r3rGpihk_;R8wrAWm=g~NRPZcAkczKkFvVMTR^$GoClK2MYn7*xb2wNFz&uxN#-
zu)<Z<1M>oQc`b8MacEU}vO(**qQi0pXS3-Q$__g&f4I>4Rq?>>o`w&mQYr`J=e8X`
z^jzh@jo<rpr%0+gD13Uc&u6}pgVe^yx6U0?cF3>h>&R<Ya}bv@v$C0_;xLO}ee$UU
zl>=9@YR+E1r|O`n%DuCAhLS_rBPEZgl}ZP03N^89@l|mUy2-zL?mEQ-%wG$SrDm!e
z$d*}`ZuDIF0K+!6{yWji2U?f}Cr$dO<gi@gp%mA0MTadL|D2QUQaJE?`XeJtPUQnH
zL(~2zt7<re*(yo=kW+Q&FHUXR{9oCDGwO(K*EeMc|0}iK^;c95C{L?4=g(6<P}I%z
zLgj?AgS3u_#XWcB1Bu7$dopX44_w}-GH0Ke@`0Q^hYtP}QgdjYYY;y5xq?H(x$Hwi
zOBD|!dnEGB_@#QlX=^~yQ+Z{FoQt#8UtFf(@Ndf3+X6ju2Q<2qtWI^S9k|R>$S^fj
z-9gdFmif&R<pbYj?SH@gr0me;o~-^%LG^%YRCmX&waN}Mxo4ksGpjnBH9u+ca=V(t
z9%ua@dhb;a+zPtiY9OcNFhyNc*fUf4fQn!Le!X&~1IvBOnEO^J9%$msoB4`I%0aZi
z%5ln8wF7@2ME?EGsCr<V$%<D>Uz85?#r3TS7FIs+_!8Tv?3+ps2XohJZeFA6uz?}%
z#inh_4rg4nlpckvI82S0|H?8+*<p<??~%S23J1F0OLMHAr|iI0I6?c?DWwCuUi8eU
z{j2QYFlSG|lOrk)%hIOz#9UW%IH*+CVt!E7f$71@y>YkH54=9OC&SZI+~Jhg|AJ||
z6&<QhT={!?nu^0`u_wH**C;vEoDH87uc6|=8vaz@uvz(lyI4KDx3r3bq+eOvBNjD>
z6T6a{^QXukaI{{ZZzZE}Am-4Wh}+Cc4iZmQH&!fAIUpJMCTLTYvV)s#_SC}Dst1G&
zWo~ybR5>758Y26xO5MTl;OV;C{fY;&^vl2TI;kG;yU%ndE>i74f6t^%a}8AvWJIxS
z`_3oh(4Y{o-@HxpK=`WiOBI_H9o(Cq8rUT%JMdNgeX7HtdSLZ$dyV2$r32^hckC5+
zmT~A=*=u|IiQ0j`YhC`nJ)-Df#BnR)S+UXqtr*FNKmIB@Oyi4~&QPp;z}V}&UGX=C
z1Iw-`o>f?;<*@tgzj<jFG#or<t1YunQgPVqwwt@SUD09cu9q>a{z?bR+Oh?<OjUKr
z-MUToXN#J{y(7jaDl*j%@T}5sDLkz1aIy3BRhL)|hkHH~Or0t;4%i=$d|KwMbRcH7
zq{iomDhCdm|7&1lQ*oI3u)L}+U&-OfZrT1Ly~+o^7_SlPx}<bq<9+wozctDS3YGWm
znJ=gApuA0O*Q`^D4!o7dd_P204}3bk^}x2Z8V7{xvUg6|sdC`W-}k>$N>m+uHZO~q
z<g9dH?W@N3!v1OpPCwgvXw^PdhhwWxp0|0U=1^!k<-X`nHHXJD&$+L8sN%5ZoT^Ax
zl<EPuvmPcUo+=L8LwD^xDW!Hmdy(IT<_W3}%8Jk8=C>$1{LnG`)%#l6VYO-XLx;C2
z2j2Guemml!;?NXa^X0pvio+UX<%=^HD?7NdGFhCOqIlrfrE{C4P1GE0PI_eXPgOi1
zaroX3!RIOtH#ubwDH|y}2v0HFwH&lxZ{o6_x*@6urYzlDwXR>q;ltV7Z{ED>4i)<I
zXICFrb;#?ypuPT~lEd^JpZeFhC_Bu0n6z8_zmkJ_2J^%<rAiL}-9#ST_^ool?`zZL
zdmJhUE*nhx|N5PhLu+L2!_rLU12eMPRJ5E_9OmlFEXoy8abP#_|8+Z4!$G5?<FV~r
zRR@>H7R{<_6%VNM&$Mv)so-Gtv^?__hw_0JBFB@M{;D`gKXB<znXhyp@ph$h=w|f;
zhYDLOw5wDd&PSWDwzR7`%(+}=RPCVVa6q!B{lg;V150w9_HUV_?vSu(evcxnvcrY_
zk;*msiVl067Hl?%Ry%N{B;o!{Ic0~MYwnEB&y@}Y9K2rF@lNr8hLD+#LywAssZrbA
zpo>ZemfzT}uX0(*p(#64_0uh-1AVWjs5Y9Y9_Ti`apwLXC5Kd5nFWu!l@Bm7urYwo
zHU$$542%qrb9Q0pmBP*mh0)B=^R|T<85mL+7#LWgd?qF)un8Pc{mhIG3m`{{E+~f3
z5K@Q%a;hpwaU6G390NlKL}HMT!G8WOj0~VtT_Fx~m;s?j$<YuP4S~@R7!85Z5ExD&
zkXn(LTac3)Z$Lv&!R#=kgB?b6u)~-RcEB$ZrGZ_r_%Nk`*)Su0OG`=<bK+f#it>x%
z4dZDN^+BmArFkidc_s0N@n$fKsZ68z!yuj}LFSwf%7VG6c_oQOmGMS2$&0W!H>6=k
zhucAu*oH(b+zuLMbd)$ZjW>tbOkEQ04;p54)O2MKPm3Uf`-4^)kb!{#)gOj5DNW%1
zu%K={Bb$cm4<nisil}xN(<Jf2!peva`P_&Wi540us9|MDlfoSyR&>kfW^_nb2Jy7;
z4x&D%QANwZzyOaA8s>9kf6%I}2=j+A4eN81deA7ICSF0!gXT2JgQ#|x(WHHa9OtyE
z3Q^+2geF-Ko<EFf<p~A`1~h-rq=ZM0b6Qw}h;!Q19iVComd{OSn9ote%FvJ&DGTlo
zT0{gWppeRT6PlD%5KG|xphX1;u>v)J7}BJ;g6DG+8s>9&Ska^h1qm$Fv};L|_AaU&
z<}?{=Kq(UqXyOgDev}z4vN?twv`AjCbY)71d~Qm=d`^>+5gt~whzLj)M2mA%nluzq
z^Pm|`G6-^gPOGjhEI!O=Sf3;OL7RRWDB4lu!;mHoAe1uEgeKiuw6tqUll})h&S^4w
z42li3HnlNr!U`omjA$~Ph!!6fG_eF8AC{1?qXmhYKTK(ou2AiuO``!8R^~LRogg8P
z>JJl|G<f0upkaLuw}XcH9Jy?#RUL(r&rRu2mKf8d$pG);nA0$y!{Xe6hV?nz4q8Px
z`k0IXO|l?rzt(`Zac)7we2!XPS<<8*g^~wpGj)en*P7F$O^xCYBbr!&<_|-fG<s3v
z+?=NE5=$E9b68noNyGXaZU+tf=cw^vNRtI1sCHP;B%i~>iiZ7jlsrhA=|Om1Ye~a-
zHPrYpp-Eu^iw{Es+EwTXJ7|~D5$!}86}m{{?S=-l%jl?aZfHo0Rs*UXrZgGfMZ||8
zP0}r>CxvVWtzrZ{tZ0#fQSC6KNnML-hb2wo9G=e&X%R}G7)M_lMVm%7YFJs&q;r5$
zN6}_!6k0oxHVYb1{9!_i(h5D!4QUeTh_p+)3LRkw?K<eFY1fP<5rPuuG%DUv>@cCp
za3Wg%pp7M{@nJxd!6j5X3~5q<z|*b~?HcZg`kXdp6{rS54J#v>BrHT&(Jr6E?V#a;
zIJ7)SlZZ!+4`bTbwX|vO!ux}U#<UxVL&Q1l2Ha8N+?Xaq07zj)yM;2S>B@|@`P`Ux
z`5e_Bmh`AUXcFo0v};1UfjER6wCW8a)>9jr&>^3j&@G?SBvKIZL9cvnNt5~mH4j?Q
zWIO}a4pW-sK~y^oX)=Khk8@L+Bq>l64pD#5a6ueuSeetI+%>03+C{0)X|iewsZ2Db
zS3alBVjM);rB{7!NR!?(+#hDN8;3(lSH?7HUZM5REod@kiJH%8vegn%UYXIZK1akk
zO}c5|au>OOPOI4>gg@vo4rfNUd`|na-GC<911%4l(qwJ|IbG4J)c{Xd=5%Pco6|0z
zqm;X5G>HyGd1X$!`Wz7-w9DrxVMUvvSJX9LwAl)PY6neL`(T-4p+NzUTG!HMfjdf^
z8_}d2g~%Tkw9Du4{9!@6`W!VrXtQnt;Sbv7bA%nVtItu=6-`!Oq31!`9K-?-D;h3{
zL)bxwaX3rb)#qq&P7}`{!iskJ9K|2B*=~SZN14&4Es6*$+U0YY9Y%Cm4`*aRyZ$-C
zAGB&wGcX|9?KIg!5AJiK)=?HT8HYoJ747PCRDaN>VS<v+X|n|j-u5vvpj~~A;t$$P
z(W91EG)lgR{6V7)CKzXh(dHZ>c>XY?Nfn4CpVMSm6p?lfX_wCtVMV+89JMSlp-E+n
zk_TzCECZ?jpwR+Iq&3?{Ml|Tqf>SPfyWNr|gEFXbPMh)s)eb|N6ejTeVMM2PJ8h;?
z5oHMt*TZ4N2Mvl8M10V$K1bL=hju#+*Tcd6VNAPrJ8GQMW<fQgT|%37D5zLO^#^S>
zJR{<RcJ(<*J!ndkX&j__kVYF{G1`f=@dzS5XqV4X)0GiT+CHdu&}L!{)eb|NL_A8`
zrOnZ}h<cEQ>)|lMiU!3msvRaYX~V(uhY9WG)lmImOq2Y9@CWVkIiilD$#w=%6@^H<
zrZh-UU>gu?qm1aV9?pn{>*3&WPQwG@5O&b6K1a<TG}&H@l&)x(&k<ooyLLND{Xv_m
z7PXF|%`$ydJ7{y=1R~DOXgA&tk8?BHjl&`Apk4nQH4mE8WRf1$4r7|s=csmA&}6SA
zq8_AC2E^za&}MQQp03PkHx7qVUeTuMgIbo*W_vB#nmbFHq$@;x&@P`N?4VtJj<ACU
zgJaM(9BMu{q{#`@sCJmsB+gOopv{5$@c5wN0dc7Qpv~fQl(K|2S16#2`_QVTh=>o`
z<#Y7-pha&AxgMldX@UqVTEzxZf6#(<?RNC|phe8X!^)CQ^*L>hZ%3<ZEoicl1*I&Z
U%@TU_b_q=qFp58Da~v510CsMp#sB~S
diff --git a/multiview_platform/execute.py b/multiview_platform/execute.py
deleted file mode 100644
index c43c4362..00000000
--- a/multiview_platform/execute.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""This is the execution module, used to execute the code"""
-
-import os
-
-
-def execute(config_path=None): # pragma: no cover
- import sys
-
- from multiview_platform.mono_multi_view_classifiers import exec_classif
- if config_path is None:
- exec_classif.exec_classif(sys.argv[1:])
- else:
- if config_path == "example 0":
- config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_0.yml")
- elif config_path == "example 1":
- config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_1.yml")
- elif config_path == "example 2.1.1":
- config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_2_1_1.yml")
- elif config_path == "example 2.1.2":
- config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_2_1_2.yml")
- elif config_path == "example 2.2":
- config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_2_2.yml")
- elif config_path == "example 2.3":
- config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_2_3.yml")
- elif config_path == "example 3":
- config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_3.yml")
- exec_classif.exec_classif(["--config_path", config_path])
-
-
-if __name__ == "__main__":
- execute()
diff --git a/multiview_platform/mono_multi_view_classifiers/__init__.py b/multiview_platform/mono_multi_view_classifiers/__init__.py
deleted file mode 100644
index 9e2c30f3..00000000
--- a/multiview_platform/mono_multi_view_classifiers/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from . import exec_classif, result_analysis, metrics, monoview_classifiers, \
- monoview, multiview, utils, multiview_classifiers
-
-__all__ = ['metrics', 'monoview', 'monoview_classifiers', 'multiview', 'utils']
diff --git a/multiview_platform/mono_multi_view_classifiers/exec_classif.py b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
deleted file mode 100644
index 91d931be..00000000
--- a/multiview_platform/mono_multi_view_classifiers/exec_classif.py
+++ /dev/null
@@ -1,814 +0,0 @@
-import itertools
-import logging
-import os
-import pkgutil
-import time
-import traceback
-
-import matplotlib
-import numpy as np
-from sklearn.tree import DecisionTreeClassifier
-
-# Import own modules
-from . import monoview_classifiers
-from . import multiview_classifiers
-from .monoview.exec_classif_mono_view import exec_monoview
-from .multiview.exec_multiview import exec_multiview
-from .result_analysis.execution import analyze_iterations, analyze
-from .utils import execution, dataset, configuration
-from .utils.organization import secure_file_path
-from .utils.dataset import delete_HDF5
-
-matplotlib.use(
- 'Agg') # Anti-Grain Geometry C++ library to make a raster (pixel) image of the figure
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def init_benchmark(cl_type, monoview_algos, multiview_algos):
- r"""Used to create a list of all the algorithm packages names used for the benchmark.
-
- First this function will check if the benchmark need mono- or/and multiview
- algorithms and adds to the right
- dictionary the asked algorithms. If none is asked by the user, all will be added.
-
- If the keyword `"Benchmark"` is used, all mono- and multiview algorithms will be added.
-
- Parameters
- ----------
- cl_type : List of string
- List of types of needed benchmark
- multiview_algos : List of strings
- List of multiview algorithms needed for the benchmark
- monoview_algos : Listof strings
- List of monoview algorithms needed for the benchmark
- args : ParsedArgumentParser args
- All the input args (used to tune the algorithms)
-
- Returns
- -------
- benchmark : Dictionary of dictionaries
- Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.
- """
- benchmark = {"monoview": {}, "multiview": {}}
-
- if "monoview" in cl_type:
- if monoview_algos == ['all']: # pragma: no cover
- benchmark["monoview"] = [name for _, name, isPackage in
- pkgutil.iter_modules(
- monoview_classifiers.__path__)
- if not isPackage]
-
- else:
- benchmark["monoview"] = monoview_algos
-
- if "multiview" in cl_type:
- if multiview_algos == ["all"]: # pragma: no cover
- benchmark["multiview"] = [name for _, name, isPackage in
- pkgutil.iter_modules(
- multiview_classifiers.__path__)
- if not isPackage]
- else:
- benchmark["multiview"] = multiview_algos
- return benchmark
-
-
-def init_argument_dictionaries(benchmark, views_dictionary,
- nb_class, init_kwargs, hps_method,
- hps_kwargs): # pragma: no cover
- argument_dictionaries = {"monoview": [], "multiview": []}
- if benchmark["monoview"]:
- argument_dictionaries["monoview"] = init_monoview_exps(
- benchmark["monoview"],
- views_dictionary,
- nb_class,
- init_kwargs["monoview"], hps_method, hps_kwargs)
- if benchmark["multiview"]:
- argument_dictionaries["multiview"] = init_multiview_exps(
- benchmark["multiview"],
- views_dictionary,
- nb_class,
- init_kwargs["multiview"], hps_method, hps_kwargs)
- return argument_dictionaries
-
-
-def init_multiview_exps(classifier_names, views_dictionary, nb_class,
- kwargs_init, hps_method, hps_kwargs): # pragma: no cover
- multiview_arguments = []
- for classifier_name in classifier_names:
- arguments = get_path_dict(kwargs_init[classifier_name])
- if hps_method == "Grid":
- multiview_arguments += [
- gen_single_multiview_arg_dictionary(classifier_name,
- arguments,
- nb_class,
- {"param_grid":hps_kwargs[classifier_name]},
- views_dictionary=views_dictionary)]
- elif hps_method == "Random":
- hps_kwargs = dict((key, value)
- for key, value in hps_kwargs.items()
- if key in ["n_iter", "equivalent_draws"])
- multiview_arguments += [
- gen_single_multiview_arg_dictionary(classifier_name,
- arguments,
- nb_class,
- hps_kwargs,
- views_dictionary=views_dictionary)]
- elif hps_method == "None":
- multiview_arguments += [
- gen_single_multiview_arg_dictionary(classifier_name,
- arguments,
- nb_class,
- hps_kwargs,
- views_dictionary=views_dictionary)]
- else:
- raise ValueError('At the moment only "None", "Random" or "Grid" '
- 'are available as hyper-parameter search '
- 'methods, sadly "{}" is not'.format(hps_method)
- )
-
- return multiview_arguments
-
-
-def init_monoview_exps(classifier_names,
- views_dictionary, nb_class, kwargs_init, hps_method,
- hps_kwargs): # pragma: no cover
- r"""Used to add each monoview exeperience args to the list of monoview experiences args.
-
- First this function will check if the benchmark need mono- or/and multiview algorithms and adds to the right
- dictionary the asked algorithms. If none is asked by the user, all will be added.
-
- If the keyword `"Benchmark"` is used, all mono- and multiview algorithms will be added.
-
- Parameters
- ----------
- classifier_names : dictionary
- All types of monoview and multiview experiments that have to be benchmarked
- argument_dictionaries : dictionary
- Maps monoview and multiview experiments arguments.
- views_dictionary : dictionary
- Maps the view names to their index in the HDF5 dataset
- nb_class : integer
- Number of different labels in the classification
-
- Returns
- -------
- benchmark : Dictionary of dictionaries
- Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.
- """
- monoview_arguments = []
- for view_name, view_index in views_dictionary.items():
- for classifier_name in classifier_names:
- if hps_method == "Grid":
- arguments = gen_single_monoview_arg_dictionary(classifier_name,
- kwargs_init,
- nb_class,
- view_index,
- view_name,
- {"param_grid":
- hps_kwargs[classifier_name]})
- elif hps_method == "Random":
- hps_kwargs = dict((key, value)
- for key, value in hps_kwargs.items()
- if key in ["n_iter", "equivalent_draws"])
- arguments = gen_single_monoview_arg_dictionary(classifier_name,
- kwargs_init,
- nb_class,
- view_index,
- view_name,
- hps_kwargs)
- elif hps_method == "None":
- arguments = gen_single_monoview_arg_dictionary(classifier_name,
- kwargs_init,
- nb_class,
- view_index,
- view_name,
- hps_kwargs)
-
- else:
- raise ValueError('At the moment only "None", "Random" or "Grid" '
- 'are available as hyper-parameter search '
- 'methods, sadly "{}" is not'.format(hps_method)
- )
- monoview_arguments.append(arguments)
- return monoview_arguments
-
-
-def gen_single_monoview_arg_dictionary(classifier_name, arguments, nb_class,
- view_index, view_name, hps_kwargs):
- if classifier_name in arguments:
- classifier_config = dict((key, value) for key, value in arguments[
- classifier_name].items())
- else:
- classifier_config = {}
- return {classifier_name: classifier_config,
- "view_name": view_name,
- "view_index": view_index,
- "classifier_name": classifier_name,
- "nb_class": nb_class,
- "hps_kwargs":hps_kwargs }
-
-
-def gen_single_multiview_arg_dictionary(classifier_name, arguments, nb_class,
- hps_kwargs, views_dictionary=None):
- return {"classifier_name": classifier_name,
- "view_names": list(views_dictionary.keys()),
- 'view_indices': list(views_dictionary.values()),
- "nb_class": nb_class,
- "labels_names": None,
- "hps_kwargs": hps_kwargs,
- classifier_name: extract_dict(arguments)
- }
-
-
-def extract_dict(classifier_config):
- """Reverse function of get_path_dict"""
- extracted_dict = {}
- for key, value in classifier_config.items():
- extracted_dict = set_element(extracted_dict, key, value)
- return extracted_dict
-
-
-def set_element(dictionary, path, value):
- """Set value in dictionary at the location indicated by path"""
- existing_keys = path.split(".")[:-1]
- dict_state = dictionary
- for existing_key in existing_keys:
- if existing_key in dict_state:
- dict_state = dict_state[existing_key]
- else:
- dict_state[existing_key] = {}
- dict_state = dict_state[existing_key]
- dict_state[path.split(".")[-1]] = value
- return dictionary
-
-
-def get_path_dict(multiview_classifier_args):
- """This function is used to generate a dictionary with each key being
- the path to the value.
- If given {"key1":{"key1_1":value1}, "key2":value2}, it will return
- {"key1.key1_1":value1, "key2":value2}"""
- path_dict = dict(
- (key, value) for key, value in multiview_classifier_args.items())
- paths = is_dict_in(path_dict)
- while paths:
- for path in paths:
- for key, value in path_dict[path].items():
- path_dict[".".join([path, key])] = value
- path_dict.pop(path)
- paths = is_dict_in(path_dict)
- return path_dict
-
-
-def is_dict_in(dictionary):
- """
- Returns True if any of the dictionary value is a dictionary itself.
-
- Parameters
- ----------
- dictionary
-
- Returns
- -------
-
- """
- paths = []
- for key, value in dictionary.items():
- if isinstance(value, dict):
- paths.append(key)
- return paths
-
-def init_kwargs(args, classifiers_names, framework="monoview"):
- r"""Used to init kwargs thanks to a function in each monoview classifier package.
-
- Parameters
- ----------
- args : parsed args objects
- All the args passed by the user.
- classifiers_names : list of strings
- List of the benchmarks's monoview classifiers names.
-
- Returns
- -------
- kwargs : Dictionary
- Dictionary resuming all the specific arguments for the benchmark, one dictionary for each classifier.
-
- For example, for Adaboost, the KWARGS will be `{"n_estimators":<value>, "base_estimator":<value>}`"""
-
- logging.debug("Start:\t Initializing monoview classifiers arguments")
- kwargs = {}
- for classifiers_name in classifiers_names:
- try:
- if framework == "monoview":
- getattr(monoview_classifiers, classifiers_name)
- else:
- getattr(multiview_classifiers, classifiers_name)
- except AttributeError:
- raise AttributeError(
- classifiers_name + " is not implemented in monoview_classifiers, "
- "please specify the name of the file in monoview_classifiers")
- if classifiers_name in args:
- kwargs[classifiers_name] = args[classifiers_name]
- else:
- kwargs[classifiers_name] = {}
- logging.debug("Done:\t Initializing monoview classifiers arguments")
-
- return kwargs
-
-
-def init_kwargs_func(args, benchmark):
- """
- Dispached the kwargs initialization to monoview and multiview and creates
- the kwargs variable
-
- Parameters
- ----------
- args : parsed args objects
- All the args passed by the user.
-
- benchmark : dict
- The name of the mono- and mutli-view classifiers to run in the benchmark
-
- Returns
- -------
-
- kwargs : dict
- The arguments for each mono- and multiview algorithms
- """
- monoview_kwargs = init_kwargs(args, benchmark["monoview"],
- framework="monoview")
- multiview_kwargs = init_kwargs(args, benchmark["multiview"],
- framework="multiview")
- kwargs = {"monoview": monoview_kwargs, "multiview": multiview_kwargs}
- return kwargs
-
-
-def arange_metrics(metrics, metric_princ):
- """Used to get the metrics list in the right order so that
- the first one is the principal metric specified in args
-
- Parameters
- ----------
- metrics : dict
- The metrics that will be used in the benchmark
-
- metric_princ : str
- The name of the metric that need to be used for the hyper-parameter
- optimization process
-
- Returns
- -------
- metrics : list of lists
- The metrics list, but arranged so the first one is the principal one."""
- if metric_princ in metrics:
- metrics = dict((key, value) if not key == metric_princ else (key+"*", value) for key, value in metrics.items())
- else:
- raise ValueError("{} not in metric pool ({})".format(metric_princ,
- metrics))
- return metrics
-
-
-def benchmark_init(directory, classification_indices, labels, labels_dictionary,
- k_folds, dataset_var):
- """
- Initializes the benchmark, by saving the indices of the train
- examples and the cross validation folds.
-
- Parameters
- ----------
- directory : str
- The benchmark's result directory
-
- classification_indices : numpy array
- The indices of the examples, splitted for the train/test split
-
- labels : numpy array
- The labels of the dataset
-
- labels_dictionary : dict
- The dictionary with labels as keys and their names as values
-
- k_folds : sklearn.model_selection.Folds object
- The folds for the cross validation process
-
- Returns
- -------
-
- """
- logging.debug("Start:\t Benchmark initialization")
- secure_file_path(os.path.join(directory, "train_labels.csv"))
- train_indices = classification_indices[0]
- train_labels = dataset_var.get_labels(example_indices=train_indices)
- np.savetxt(os.path.join(directory, "train_labels.csv"), train_labels,
- delimiter=",")
- np.savetxt(os.path.join(directory, "train_indices.csv"),
- classification_indices[0],
- delimiter=",")
- results_monoview = []
- folds = k_folds.split(np.arange(len(train_labels)), train_labels)
- min_fold_len = int(len(train_labels) / k_folds.n_splits)
- for fold_index, (train_cv_indices, test_cv_indices) in enumerate(folds):
- file_name = os.path.join(directory, "folds", "test_labels_fold_" + str(
- fold_index) + ".csv")
- secure_file_path(file_name)
- np.savetxt(file_name, train_labels[test_cv_indices[:min_fold_len]],
- delimiter=",")
- labels_names = list(labels_dictionary.values())
- logging.debug("Done:\t Benchmark initialization")
- return results_monoview, labels_names
-
-
-# def exec_one_benchmark(core_index=-1, labels_dictionary=None, directory=None,
-# classification_indices=None, args=None,
-# k_folds=None, random_state=None, hyper_param_search=None,
-# metrics=None, argument_dictionaries=None,
-# benchmark=None, views=None, views_indices=None, flag=None,
-# labels=None,
-# exec_monoview_multicore=exec_monoview_multicore,
-# exec_multiview_multicore=exec_multiview_multicore,):
-# """Used to run a benchmark using one core. ExecMonoview_multicore, initMultiviewArguments and
-# exec_multiview_multicore args are only used for tests"""
-#
-# results_monoview, labels_names = benchmark_init(directory,
-# classification_indices, labels,
-# labels_dictionary, k_folds)
-#
-# logging.debug("Start:\t monoview benchmark")
-# results_monoview += [
-# exec_monoview_multicore(directory, args["name"], labels_names,
-# classification_indices, k_folds,
-# core_index, args["file_type"], args["pathf"], random_state,
-# labels,
-# hyper_param_search=hyper_param_search,
-# metrics=metrics,
-# n_iter=args["hps_iter"], **argument)
-# for argument in argument_dictionaries["Monoview"]]
-# logging.debug("Done:\t monoview benchmark")
-#
-#
-# logging.debug("Start:\t multiview benchmark")
-# results_multiview = [
-# exec_multiview_multicore(directory, core_index, args["name"],
-# classification_indices, k_folds, args["file_type"],
-# args["pathf"], labels_dictionary, random_state,
-# labels, hyper_param_search=hyper_param_search,
-# metrics=metrics, n_iter=args["hps_iter"],
-# **arguments)
-# for arguments in argument_dictionaries["multiview"]]
-# logging.debug("Done:\t multiview benchmark")
-#
-# return [flag, results_monoview + results_multiview]
-#
-#
-# def exec_one_benchmark_multicore(nb_cores=-1, labels_dictionary=None,
-# directory=None, classification_indices=None,
-# args=None,
-# k_folds=None, random_state=None,
-# hyper_param_search=None, metrics=None,
-# argument_dictionaries=None,
-# benchmark=None, views=None, views_indices=None,
-# flag=None, labels=None,
-# exec_monoview_multicore=exec_monoview_multicore,
-# exec_multiview_multicore=exec_multiview_multicore,):
-# """Used to run a benchmark using multiple cores. ExecMonoview_multicore, initMultiviewArguments and
-# exec_multiview_multicore args are only used for tests"""
-#
-# results_monoview, labels_names = benchmark_init(directory,
-# classification_indices, labels,
-# labels_dictionary, k_folds)
-#
-# logging.debug("Start:\t monoview benchmark")
-# nb_experiments = len(argument_dictionaries["monoview"])
-# nb_multicore_to_do = int(math.ceil(float(nb_experiments) / nb_cores))
-# for step_index in range(nb_multicore_to_do):
-# results_monoview += (Parallel(n_jobs=nb_cores)(
-# delayed(exec_monoview_multicore)(directory, args["name"], labels_names,
-# classification_indices, k_folds,
-# core_index, args["file_type"], args["pathf"],
-# random_state, labels,
-# hyper_param_search=hyper_param_search,
-# metrics=metrics,
-# n_iter=args["hps_iter"],
-# **argument_dictionaries["monoview"][
-# core_index + step_index * nb_cores])
-# for core_index in
-# range(min(nb_cores, nb_experiments - step_index * nb_cores))))
-# logging.debug("Done:\t monoview benchmark")
-#
-# logging.debug("Start:\t multiview arguments initialization")
-# # argument_dictionaries = initMultiviewArguments(args, benchmark, views,
-# # views_indices,
-# # argument_dictionaries,
-# # random_state, directory,
-# # resultsMonoview,
-# # classification_indices)
-# logging.debug("Done:\t multiview arguments initialization")
-#
-# logging.debug("Start:\t multiview benchmark")
-# results_multiview = []
-# nb_experiments = len(argument_dictionaries["multiview"])
-# nb_multicore_to_do = int(math.ceil(float(nb_experiments) / nb_cores))
-# for step_index in range(nb_multicore_to_do):
-# results_multiview += Parallel(n_jobs=nb_cores)(
-# delayed(exec_multiview_multicore)(directory, core_index, args["name"],
-# classification_indices, k_folds,
-# args["file_type"], args["Base"]["pathf"],
-# labels_dictionary, random_state,
-# labels,
-# hyper_param_search=hyper_param_search,
-# metrics=metrics,
-# n_iter=args["hps_iter"],
-# **
-# argument_dictionaries["multiview"][
-# step_index * nb_cores + core_index])
-# for core_index in
-# range(min(nb_cores, nb_experiments - step_index * nb_cores)))
-# logging.debug("Done:\t multiview benchmark")
-#
-# return [flag, results_monoview + results_multiview]
-
-
-def exec_one_benchmark_mono_core(dataset_var=None, labels_dictionary=None,
- directory=None, classification_indices=None,
- args=None,
- k_folds=None, random_state=None,
- hyper_param_search=None, metrics=None,
- argument_dictionaries=None,
- benchmark=None, views=None, views_indices=None,
- flag=None, labels=None,
- track_tracebacks=False): # pragma: no cover
- results_monoview, labels_names = benchmark_init(directory,
- classification_indices,
- labels,
- labels_dictionary, k_folds,
- dataset_var)
- logging.getLogger('matplotlib.font_manager').disabled = True
- logging.debug("Start:\t monoview benchmark")
- traceback_outputs = {}
- for arguments in argument_dictionaries["monoview"]:
- try:
- X = dataset_var.get_v(arguments["view_index"])
- Y = dataset_var.get_labels()
- results_monoview += [
- exec_monoview(directory, X, Y, args["name"], labels_names,
- classification_indices, k_folds,
- 1, args["file_type"], args["pathf"], random_state,
- hyper_param_search=hyper_param_search,
- metrics=metrics,
- **arguments)]
- except:
- if track_tracebacks:
- traceback_outputs[
- arguments["classifier_name"] + "-" + arguments[
- "view_name"]] = traceback.format_exc()
- else:
- raise
-
- logging.debug("Done:\t monoview benchmark")
-
- logging.debug("Start:\t multiview arguments initialization")
-
- # argument_dictionaries = initMultiviewArguments(args, benchmark, views,
- # views_indices,
- # argument_dictionaries,
- # random_state, directory,
- # resultsMonoview,
- # classification_indices)
- logging.debug("Done:\t multiview arguments initialization")
-
- logging.debug("Start:\t multiview benchmark")
- results_multiview = []
- for arguments in argument_dictionaries["multiview"]:
- try:
- results_multiview += [
- exec_multiview(directory, dataset_var, args["name"],
- classification_indices,
- k_folds, 1, args["file_type"],
- args["pathf"], labels_dictionary, random_state,
- labels,
- hps_method=hyper_param_search,
- metrics=metrics, n_iter=args["hps_iter"],
- **arguments)]
- except:
- if track_tracebacks:
- traceback_outputs[
- arguments["classifier_name"]] = traceback.format_exc()
- else:
- raise
- logging.debug("Done:\t multiview benchmark")
-
- return [flag, results_monoview + results_multiview, traceback_outputs]
-
-
-def exec_benchmark(nb_cores, stats_iter,
- benchmark_arguments_dictionaries,
- directory, metrics, dataset_var, track_tracebacks,
- exec_one_benchmark_mono_core=exec_one_benchmark_mono_core,
- analyze=analyze, delete=delete_HDF5,
- analyze_iterations=analyze_iterations): # pragma: no cover
- r"""Used to execute the needed benchmark(s) on multicore or mono-core functions.
-
- Parameters
- ----------
- nb_cores : int
- Number of threads that the benchmarks can use.
- stats_iter : int
- Number of statistical iterations that have to be done.
- benchmark_arguments_dictionaries : list of dictionaries
- All the needed arguments for the benchmarks.
- classification_indices : list of lists of numpy.ndarray
- For each statistical iteration a couple of numpy.ndarrays is stored with the indices for the training set and
- the ones of the testing set.
- directories : list of strings
- List of the paths to the result directories for each statistical iteration.
- directory : string
- Path to the main results directory.
- multi_class_labels : ist of lists of numpy.ndarray
- For each label couple, for each statistical iteration a triplet of numpy.ndarrays is stored with the
- indices for the biclass training set, the ones for the biclass testing set and the ones for the
- multiclass testing set.
- metrics : list of lists
- metrics that will be used to evaluate the algorithms performance.
- labels_dictionary : dictionary
- Dictionary mapping labels indices to labels names.
- nb_labels : int
- Total number of different labels in the dataset.
- dataset_var : HDF5 dataset file
- The full dataset that wil be used by the benchmark.
- classifiers_names : list of strings
- List of the benchmarks's monoview classifiers names.
- rest_of_the_args :
- Just used for testing purposes
-
-
- Returns
- -------
- results : list of lists
- The results of the benchmark.
- """
- logging.debug("Start:\t Executing all the needed benchmarks")
- results = []
- # if nb_cores > 1:
- # if stats_iter > 1 or nb_multiclass > 1:
- # nb_exps_to_do = len(benchmark_arguments_dictionaries)
- # nb_multicore_to_do = range(int(math.ceil(float(nb_exps_to_do) / nb_cores)))
- # for step_index in nb_multicore_to_do:
- # results += (Parallel(n_jobs=nb_cores)(delayed(exec_one_benchmark)
- # (core_index=core_index,
- # **
- # benchmark_arguments_dictionaries[
- # core_index + step_index * nb_cores])
- # for core_index in range(
- # min(nb_cores, nb_exps_to_do - step_index * nb_cores))))
- # else:
- # results += [exec_one_benchmark_multicore(nb_cores=nb_cores, **
- # benchmark_arguments_dictionaries[0])]
- # else:
- for arguments in benchmark_arguments_dictionaries:
- benchmark_results = exec_one_benchmark_mono_core(
- dataset_var=dataset_var,
- track_tracebacks=track_tracebacks,
- **arguments)
- analyze_iterations([benchmark_results],
- benchmark_arguments_dictionaries, stats_iter,
- metrics, example_ids=dataset_var.example_ids,
- labels=dataset_var.get_labels())
- results += [benchmark_results]
- logging.debug("Done:\t Executing all the needed benchmarks")
-
- # Do everything with flagging
- logging.debug("Start:\t Analyzing predictions")
- results_mean_stds = analyze(results, stats_iter,
- benchmark_arguments_dictionaries,
- metrics,
- directory,
- dataset_var.example_ids,
- dataset_var.get_labels())
- logging.debug("Done:\t Analyzing predictions")
- delete(benchmark_arguments_dictionaries, nb_cores, dataset_var)
- return results_mean_stds
-
-
-def exec_classif(arguments): # pragma: no cover
- """
- Runs the benchmark with the given arguments
-
- Parameters
- ----------
- arguments :
-
- Returns
- -------
-
-
- >>> exec_classif([--config_path, /path/to/config/files/])
- >>>
- """
- start = time.time()
- args = execution.parse_the_args(arguments)
- args = configuration.get_the_args(args.config_path)
- os.nice(args["nice"])
- nb_cores = args["nb_cores"]
- if nb_cores == 1:
- os.environ['OPENBLAS_NUM_THREADS'] = '1'
- stats_iter = args["stats_iter"]
- hps_method = args["hps_type"]
- hps_kwargs = args["hps_args"]
- cl_type = args["type"]
- monoview_algos = args["algos_monoview"]
- multiview_algos = args["algos_multiview"]
- path, dataset_list = execution.find_dataset_names(args["pathf"],
- args["file_type"],
- args["name"])
- args["pathf"] = path
- for dataset_name in dataset_list:
- # noise_results = []
- # for noise_std in args["noise_std"]:
-
- directory = execution.init_log_file(dataset_name, args["views"],
- args["file_type"],
- args["log"], args["debug"],
- args["label"],
- args["res_dir"],
- args)
-
- random_state = execution.init_random_state(args["random_state"],
- directory)
- stats_iter_random_states = execution.init_stats_iter_random_states(
- stats_iter,
- random_state)
-
- get_database = execution.get_database_function(dataset_name,
- args["file_type"])
-
- dataset_var, labels_dictionary, datasetname = get_database(
- args["views"],
- args["pathf"], dataset_name,
- args["nb_class"],
- args["classes"],
- random_state,
- args["full"],
- )
- args["name"] = datasetname
- splits = execution.gen_splits(dataset_var.get_labels(),
- args["split"],
- stats_iter_random_states)
-
- # multiclass_labels, labels_combinations, indices_multiclass = multiclass.gen_multiclass_labels(
- # dataset_var.get_labels(), multiclass_method, splits)
-
- k_folds = execution.gen_k_folds(stats_iter, args["nb_folds"],
- stats_iter_random_states)
-
- dataset_files = dataset.init_multiple_datasets(args["pathf"],
- args["name"],
- nb_cores)
-
- views, views_indices, all_views = execution.init_views(dataset_var,
- args[
- "views"])
- views_dictionary = dataset_var.get_view_dict()
- nb_views = len(views)
- nb_class = dataset_var.get_nb_class()
-
- metrics = args["metrics"]
- if metrics == "all":
- metrics_names = [name for _, name, isPackage
- in pkgutil.iter_modules(
- [os.path.join(os.path.dirname(
- os.path.dirname(os.path.realpath(__file__))),
- 'metrics')]) if
- not isPackage and name not in ["framework",
- "log_loss",
- "matthews_corrcoef",
- "roc_auc_score"]]
- metrics = dict((metric_name, {})
- for metric_name in metrics_names)
- metrics = arange_metrics(metrics, args["metric_princ"])
-
- benchmark = init_benchmark(cl_type, monoview_algos, multiview_algos,)
- init_kwargs = init_kwargs_func(args, benchmark)
- data_base_time = time.time() - start
- argument_dictionaries = init_argument_dictionaries(
- benchmark, views_dictionary,
- nb_class, init_kwargs, hps_method, hps_kwargs)
- # argument_dictionaries = initMonoviewExps(benchmark, viewsDictionary,
- # NB_CLASS, initKWARGS)
- directories = execution.gen_direcorties_names(directory, stats_iter)
- benchmark_argument_dictionaries = execution.gen_argument_dictionaries(
- labels_dictionary, directories,
- splits,
- hps_method, args, k_folds,
- stats_iter_random_states, metrics,
- argument_dictionaries, benchmark,
- views, views_indices)
- results_mean_stds = exec_benchmark(
- nb_cores, stats_iter,
- benchmark_argument_dictionaries, directory, metrics,
- dataset_var,
- args["track_tracebacks"])
- # noise_results.append([noise_std, results_mean_stds])
- # plot_results_noise(directory, noise_results, metrics[0][0],
- # dataset_name)
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/__init__.py b/multiview_platform/mono_multi_view_classifiers/metrics/__init__.py
deleted file mode 100644
index 4a7ca0b0..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/__init__.py
+++ /dev/null
@@ -1,33 +0,0 @@
-__version__ = "0.0.0.0"
-"""
-To be able to add another metric to the benchmark you must :
-
-Create a .py file named after the metric
-Define a score function
- Input :
- y_true : np array with the real labels
- y_pred : np array with the predicted labels
- kwargs : every argument that is specific to the metric
- Returns:
- score : the metric's score (float)
-Define a get_scorer function
- Input :
- kwargs : every argument that is specific to the metric
- Returns :
- scorer : an object similar to an sk-learn scorer
-Define a getConfig function
- Input :
- kwargs : every argument that is specific to the metric
- Output :
- config_string : A string that gives the name of the metric and explains how it is configured. Must end by
- (lower is better) or (higher is better) to be able to analyze the preds
-"""
-
-import os
-
-for module in os.listdir(os.path.dirname(os.path.realpath(__file__))):
- if module in ['__init__.py'] or module[-3:] != '.py':
- continue
- __import__(module[:-3], locals(), globals(), [], 1)
- pass
-del os
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/accuracy_score.py b/multiview_platform/mono_multi_view_classifiers/metrics/accuracy_score.py
deleted file mode 100644
index e9faae69..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/accuracy_score.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Functions :
- score: to get the accuracy score
- get_scorer: returns a sklearn scorer for grid search
-"""
-
-import warnings
-
-from sklearn.metrics import accuracy_score as metric
-from sklearn.metrics import make_scorer
-
-warnings.warn("the accuracy_score module is deprecated", DeprecationWarning,
- stacklevel=2)
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, multiclass=False, **kwargs):
- """Arguments:
- y_true: real labels
- y_pred: predicted labels
-
- Keyword Arguments:
- "0": weights to compute accuracy
-
- Returns:
- Weighted accuracy score for y_true, y_pred"""
- score = metric(y_true, y_pred, **kwargs)
- return score
-
-
-def get_scorer(**kwargs):
- """Keyword Arguments:
- "0": weights to compute accuracy
-
- Returns:
- A weighted sklearn scorer for accuracy"""
- return make_scorer(metric, greater_is_better=True,
- **kwargs)
-
-
-def get_config(**kwargs):
- config_string = "Accuracy score using {}, (higher is better)".format(kwargs)
- return config_string
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/f1_score.py b/multiview_platform/mono_multi_view_classifiers/metrics/f1_score.py
deleted file mode 100644
index 6b9b89df..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/f1_score.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""Functions :
- score: to get the f1 score
- get_scorer: returns a sklearn scorer for grid search
-"""
-
-import warnings
-
-from sklearn.metrics import f1_score as metric
-from sklearn.metrics import make_scorer
-
-warnings.warn("the f1_score module is deprecated", DeprecationWarning,
- stacklevel=2)
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, multiclass=True, average='micro', **kwargs):
- score = metric(y_true, y_pred, average=average, **kwargs)
- return score
-
-
-def get_scorer(average="micro", **kwargs):
- return make_scorer(metric, greater_is_better=True, average=average,
- **kwargs)
-
-
-def get_config(average="micro", **kwargs, ):
- config_string = "F1 score using average: {}, {} (higher is better)".format(
- average, kwargs)
- return config_string
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/fbeta_score.py b/multiview_platform/mono_multi_view_classifiers/metrics/fbeta_score.py
deleted file mode 100644
index 60a5141a..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/fbeta_score.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import warnings
-
-from sklearn.metrics import fbeta_score as metric
-from sklearn.metrics import make_scorer
-
-warnings.warn("the fbeta_score module is deprecated", DeprecationWarning,
- stacklevel=2)
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, beta=2.0, average="micro", **kwargs):
- score = metric(y_true, y_pred, beta=beta, average=average, **kwargs)
- return score
-
-
-def get_scorer(beta=2.0, average="micro", **kwargs):
- return make_scorer(metric, greater_is_better=True, beta=beta,
- average=average, **kwargs)
-
-
-def get_config(beta=2.0, average="micro", **kwargs):
- config_string = "F-beta score using beta: {}, average: {}, {} (higher is better)".format(
- beta, average, kwargs)
- return config_string
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/hamming_loss.py b/multiview_platform/mono_multi_view_classifiers/metrics/hamming_loss.py
deleted file mode 100644
index 665dd243..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/hamming_loss.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import warnings
-
-from sklearn.metrics import hamming_loss as metric
-from sklearn.metrics import make_scorer
-
-warnings.warn("the hamming_loss module is deprecated", DeprecationWarning,
- stacklevel=2)
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, multiclass=False, **kwargs):
- score = metric(y_true, y_pred, **kwargs)
- return score
-
-
-def get_scorer(**kwargs):
- return make_scorer(metric, greater_is_better=False, **kwargs)
-
-
-def get_config(**kwargs):
- config_string = "Hamming loss using {} (lower is better)".format(kwargs)
- return config_string
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/jaccard_score.py b/multiview_platform/mono_multi_view_classifiers/metrics/jaccard_score.py
deleted file mode 100644
index 248ec66d..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/jaccard_score.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import warnings
-
-from sklearn.metrics import jaccard_score as metric
-from sklearn.metrics import make_scorer
-
-warnings.warn("the jaccard_similarity_score module is deprecated",
- DeprecationWarning,
- stacklevel=2)
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, multiclass=False, **kwargs):
- score = metric(y_true, y_pred, **kwargs)
- return score
-
-
-def get_scorer(**kwargs):
- return make_scorer(metric, greater_is_better=True,
- **kwargs)
-
-
-def get_config(**kwargs):
- config_string = "Jaccard_similarity score using {} (higher is better)".format(
- kwargs)
- return config_string
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/log_loss.py b/multiview_platform/mono_multi_view_classifiers/metrics/log_loss.py
deleted file mode 100644
index 2b5ab917..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/log_loss.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import warnings
-
-from sklearn.metrics import log_loss as metric
-from sklearn.metrics import make_scorer
-
-warnings.warn("the log_loss module is deprecated", DeprecationWarning,
- stacklevel=2)
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, multiclass=False, **kwargs):
- score = metric(y_true, y_pred, **kwargs)
- return score
-
-
-def get_scorer(**kwargs):
- return make_scorer(metric, greater_is_better=False,
- **kwargs)
-
-
-def get_config(**kwargs):
- config_string = "Log loss using {} (lower is better)".format(kwargs)
- return config_string
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/matthews_corrcoef.py b/multiview_platform/mono_multi_view_classifiers/metrics/matthews_corrcoef.py
deleted file mode 100644
index b3b8ec6c..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/matthews_corrcoef.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import warnings
-
-from sklearn.metrics import make_scorer
-from sklearn.metrics import matthews_corrcoef as metric
-
-warnings.warn("the matthews_corrcoef module is deprecated", DeprecationWarning,
- stacklevel=2)
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, multiclass=False, **kwargs):
- score = metric(y_true, y_pred)
- return score
-
-
-def get_scorer(**kwargs):
- return make_scorer(metric, greater_is_better=True)
-
-
-def get_config(**kwargs):
- config_string = "Matthews correlation coefficient (higher is better)"
- return config_string
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/precision_score.py b/multiview_platform/mono_multi_view_classifiers/metrics/precision_score.py
deleted file mode 100644
index d1c861f9..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/precision_score.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import warnings
-
-from sklearn.metrics import make_scorer
-from sklearn.metrics import precision_score as metric
-
-warnings.warn("the precision_score module is deprecated", DeprecationWarning,
- stacklevel=2)
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, average='micro', multiclass=False, **kwargs):
- score = metric(y_true, y_pred, average=average, **kwargs)
- return score
-
-
-def get_scorer(average='micro', **kwargs):
- return make_scorer(metric, greater_is_better=True,
- average=average, **kwargs)
-
-
-def get_config(average='micro', **kwargs):
- config_string = "Precision score using average: {}, {} (higher is better)".format(
- average, kwargs)
- return config_string
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/recall_score.py b/multiview_platform/mono_multi_view_classifiers/metrics/recall_score.py
deleted file mode 100644
index 26126199..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/recall_score.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import warnings
-
-from sklearn.metrics import make_scorer
-from sklearn.metrics import recall_score as metric
-
-warnings.warn("the recall_score module is deprecated", DeprecationWarning,
- stacklevel=2)
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, average='micro', **kwargs):
- score = metric(y_true, y_pred, average=average, **kwargs)
- return score
-
-
-def get_scorer(average='micro', **kwargs):
- return make_scorer(metric, greater_is_better=True,
- average=average, **kwargs)
-
-
-def get_config(average="micro", **kwargs):
- configString = "Recall score using average: {}, {} (higher is better)".format(
- average, kwargs)
- return configString
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/roc_auc_score.py b/multiview_platform/mono_multi_view_classifiers/metrics/roc_auc_score.py
deleted file mode 100644
index ae21428b..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/roc_auc_score.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import warnings
-
-from sklearn.metrics import make_scorer
-from sklearn.metrics import roc_auc_score as metric
-from sklearn.preprocessing import MultiLabelBinarizer
-
-warnings.warn("the roc_auc_score module is deprecated", DeprecationWarning,
- stacklevel=2)
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, multiclass=False, **kwargs):
- score = metric(y_true, y_pred, **kwargs)
- return score
-
-
-def get_scorer(**kwargs):
- return make_scorer(metric, greater_is_better=True,
- **kwargs)
-
-
-def get_config(**kwargs):
- configString = "ROC_AUC score using {}".format(kwargs)
- return configString
diff --git a/multiview_platform/mono_multi_view_classifiers/metrics/zero_one_loss.py b/multiview_platform/mono_multi_view_classifiers/metrics/zero_one_loss.py
deleted file mode 100644
index e3a34492..00000000
--- a/multiview_platform/mono_multi_view_classifiers/metrics/zero_one_loss.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import warnings
-
-from sklearn.metrics import make_scorer
-from sklearn.metrics import zero_one_loss as metric
-
-warnings.warn("the zero_one_loss module is deprecated", DeprecationWarning,
- stacklevel=2)
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def score(y_true, y_pred, multiclass=False, **kwargs):
- score = metric(y_true, y_pred, **kwargs)
- return score
-
-
-def get_scorer(**kwargs):
- return make_scorer(metric, greater_is_better=False,
- **kwargs)
-
-
-def get_config(**kwargs):
- configString = "Zero_one loss using {} (lower is better)".format(kwargs)
- return configString
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/__init__.py b/multiview_platform/mono_multi_view_classifiers/monoview/__init__.py
deleted file mode 100644
index e94c1495..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# from . import ExecClassifMonoView, MonoviewUtils, analyzeResult
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py b/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
deleted file mode 100644
index eed46949..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
+++ /dev/null
@@ -1,255 +0,0 @@
-#!/usr/bin/env python
-
-""" Execution: Script to perform a MonoView classification """
-
-import logging # To create Log-Files
-# Import built-in modules
-import os # to geth path of the running script
-import time # for time calculations
-
-import h5py
-# Import 3rd party modules
-import numpy as np # for reading CSV-files and Series
-
-from .monoview_utils import MonoviewResult, MonoviewResultAnalyzer
-# Import own modules
-from .. import monoview_classifiers
-from ..utils import hyper_parameter_search
-from ..utils.dataset import extract_subset, HDF5Dataset
-from ..utils.multiclass import get_mc_estim
-from ..utils.organization import secure_file_path
-
-# Author-Info
-__author__ = "Nikolas Huelsmann, Baptiste BAUVIN"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-# __date__ = 2016 - 03 - 25
-
-
-def exec_monoview_multicore(directory, name, labels_names,
- classification_indices,
- k_folds, dataset_file_index, database_type,
- path, random_state, labels,
- hyper_param_search="randomized_search",
- metrics=[["accuracy_score", None]], n_iter=30,
- **args): # pragma: no cover
- dataset_var = HDF5Dataset(
- hdf5_file=h5py.File(path + name + str(dataset_file_index) + ".hdf5",
- "r"))
- neededViewIndex = args["view_index"]
- X = dataset_var.get_v(neededViewIndex)
- Y = labels
- return exec_monoview(directory, X, Y, name, labels_names,
- classification_indices, k_folds, 1, database_type,
- path,
- random_state, hyper_param_search=hyper_param_search,
- metrics=metrics, n_iter=n_iter,
- view_name=dataset_var.get_view_name(
- args["view_index"]),
- **args)
-
-
-def exec_monoview(directory, X, Y, database_name, labels_names, classification_indices,
- k_folds, nb_cores, databaseType, path,
- random_state, hyper_param_search="Random",
- metrics={"accuracy_score*":{}}, n_iter=30, view_name="",
- hps_kwargs={}, **args):
- logging.debug("Start:\t Loading data")
- kwargs, \
- t_start, \
- view_name, \
- classifier_name, \
- X, \
- learningRate, \
- labelsString, \
- output_file_name,\
- directory,\
- base_file_name = init_constants(args, X, classification_indices,
- labels_names,
- database_name, directory, view_name, )
- logging.debug("Done:\t Loading data")
-
- logging.debug(
- "Info:\t Classification - Database:" + str(database_name) + " View:" + str(
- view_name) + " train ratio:"
- + str(learningRate) + ", CrossValidation k-folds: " + str(
- k_folds.n_splits) + ", cores:"
- + str(nb_cores) + ", algorithm : " + classifier_name)
-
- logging.debug("Start:\t Determine Train/Test split")
- X_train, y_train, X_test, y_test = init_train_test(X, Y,
- classification_indices)
-
- logging.debug("Info:\t Shape X_train:" + str(
- X_train.shape) + ", Length of y_train:" + str(len(y_train)))
- logging.debug("Info:\t Shape X_test:" + str(
- X_test.shape) + ", Length of y_test:" + str(len(y_test)))
- logging.debug("Done:\t Determine Train/Test split")
-
- logging.debug("Start:\t Generate classifier args")
- classifier_module = getattr(monoview_classifiers, classifier_name)
- classifier_class_name = classifier_module.classifier_class_name
- hyper_param_beg = time.monotonic()
- cl_kwargs = get_hyper_params(classifier_module, hyper_param_search,
- classifier_name,
- classifier_class_name,
- X_train, y_train,
- random_state, output_file_name,
- k_folds, nb_cores, metrics, kwargs,
- **hps_kwargs)
- hyper_param_duration = time.monotonic() - hyper_param_beg
- logging.debug("Done:\t Generate classifier args")
-
- logging.debug("Start:\t Training")
-
- classifier = get_mc_estim(getattr(classifier_module,
- classifier_class_name)
- (random_state, **cl_kwargs),
- random_state,
- y=Y)
- fit_beg = time.monotonic()
- classifier.fit(X_train, y_train) # NB_CORES=nbCores,
- fit_duration = time.monotonic() - fit_beg
- logging.debug("Done:\t Training")
-
- logging.debug("Start:\t Predicting")
- train_pred = classifier.predict(X_train)
- pred_beg = time.monotonic()
- test_pred = classifier.predict(X_test)
- pred_duration = time.monotonic() - pred_beg
-
- # Filling the full prediction in the right order
- full_pred = np.zeros(Y.shape, dtype=int) - 100
- for trainIndex, index in enumerate(classification_indices[0]):
- full_pred[index] = train_pred[trainIndex]
- for testIndex, index in enumerate(classification_indices[1]):
- full_pred[index] = test_pred[testIndex]
-
- logging.debug("Done:\t Predicting")
-
- whole_duration = time.monotonic() - t_start
- logging.debug(
- "Info:\t Duration for training and predicting: " + str(whole_duration) + "[s]")
-
- logging.debug("Start:\t Getting results")
- result_analyzer = MonoviewResultAnalyzer(view_name=view_name,
- classifier_name=classifier_name,
- shape=X.shape,
- classifier=classifier,
- classification_indices=classification_indices,
- k_folds=k_folds,
- hps_method=hyper_param_search,
- metrics_dict=metrics,
- n_iter=n_iter,
- class_label_names=labels_names,
- pred=full_pred,
- directory=directory,
- base_file_name=base_file_name,
- labels=Y,
- database_name=database_name,
- nb_cores=nb_cores,
- duration=whole_duration)
- string_analysis, images_analysis, metrics_scores, class_metrics_scores, \
- confusion_matrix = result_analyzer.analyze()
- logging.debug("Done:\t Getting results")
-
- logging.debug("Start:\t Saving preds")
- save_results(string_analysis, output_file_name, full_pred, train_pred,
- y_train, images_analysis, y_test, confusion_matrix)
- logging.info("Done:\t Saving results")
-
- view_index = args["view_index"]
- return MonoviewResult(view_index, classifier_name, view_name,
- metrics_scores, full_pred, cl_kwargs,
- classifier, X_train.shape[1],
- hyper_param_duration, fit_duration, pred_duration, class_metrics_scores)
-
-
-def init_constants(args, X, classification_indices, labels_names,
- name, directory, view_name):
- try:
- kwargs = args["args"]
- except KeyError:
- kwargs = args
- t_start = time.monotonic()
- cl_type = kwargs["classifier_name"]
- learning_rate = float(len(classification_indices[0])) / (
- len(classification_indices[0]) + len(classification_indices[1]))
- labels_string = "-".join(labels_names)
- cl_type_string = cl_type
- directory = os.path.join(directory, cl_type_string, view_name,)
- base_file_name = cl_type_string + '-' + name + "-" + view_name + "-"
- output_file_name = os.path.join(directory, base_file_name)
- secure_file_path(output_file_name)
- return kwargs, t_start, view_name, cl_type, X, learning_rate, labels_string, output_file_name, directory, base_file_name
-
-
-def init_train_test(X, Y, classification_indices):
- train_indices, test_indices = classification_indices
- X_train = extract_subset(X, train_indices)
- X_test = extract_subset(X, test_indices)
- y_train = Y[train_indices]
- y_test = Y[test_indices]
- return X_train, y_train, X_test, y_test
-
-
-def get_hyper_params(classifier_module, search_method, classifier_module_name,
- classifier_class_name, X_train, y_train,
- random_state,
- output_file_name, k_folds, nb_cores, metrics, kwargs,
- **hps_kwargs):
- if search_method != "None":
- logging.debug(
- "Start:\t " + search_method + " best settings for " + classifier_module_name)
- classifier_hp_search = getattr(hyper_parameter_search, search_method)
- estimator = getattr(classifier_module, classifier_class_name)(
- random_state=random_state,
- **kwargs[classifier_module_name])
- estimator = get_mc_estim(estimator, random_state,
- multiview=False, y=y_train)
- hps = classifier_hp_search(estimator, scoring=metrics, cv=k_folds,
- random_state=random_state,
- framework="monoview", n_jobs=nb_cores,
- **hps_kwargs)
- hps.fit(X_train, y_train, **kwargs[classifier_module_name])
- cl_kwargs = hps.get_best_params()
- hps.gen_report(output_file_name)
- logging.debug("Done:\t " + search_method + " best settings")
- else:
- cl_kwargs = kwargs[classifier_module_name]
- return cl_kwargs
-
-
-def save_results(string_analysis, output_file_name, full_labels_pred,
- y_train_pred,
- y_train, images_analysis, y_test, confusion_matrix): # pragma: no cover
- logging.info(string_analysis)
- output_text_file = open(output_file_name + 'summary.txt', 'w', encoding="utf-8")
- output_text_file.write(string_analysis)
- output_text_file.close()
- np.savetxt(output_file_name+"confusion_matrix.csv", confusion_matrix,
- delimiter=', ')
- np.savetxt(output_file_name + "full_pred.csv",
- full_labels_pred.astype(np.int16), delimiter=",")
- np.savetxt(output_file_name + "train_pred.csv",
- y_train_pred.astype(np.int16),
- delimiter=",")
- np.savetxt(output_file_name + "train_labels.csv", y_train.astype(np.int16),
- delimiter=",")
- np.savetxt(output_file_name + "test_labels.csv", y_test.astype(np.int16),
- delimiter=",")
-
- if images_analysis is not None:
- for image_name in images_analysis:
- if os.path.isfile(output_file_name + image_name + ".png"):
- for i in range(1, 20):
- test_file_name = output_file_name + image_name + "-" + str(
- i) + ".png"
- if not os.path.isfile(test_file_name):
- images_analysis[image_name].savefig(test_file_name,
- transparent=True)
- break
-
- images_analysis[image_name].savefig(
- output_file_name + image_name + '.png', transparent=True)
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py b/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
deleted file mode 100644
index dcecfa6f..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
+++ /dev/null
@@ -1,231 +0,0 @@
-import pickle
-import os
-import matplotlib.pyplot as plt
-import numpy as np
-from matplotlib.ticker import FuncFormatter
-from scipy.stats import uniform, randint
-
-from ..utils.base import BaseClassifier, ResultAnalyser
-from ..utils.hyper_parameter_search import CustomRandint, CustomUniform
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-# __date__ = 2016 - 03 - 25
-
-def change_label_to_minus(y):
- """
- Change the label 0 to minus one
-
- Parameters
- ----------
- y :
-
- Returns
- -------
- label y with -1 instead of 0
-
- """
- minus_y = np.copy(y)
- minus_y[np.where(y == 0)] = -1
- return minus_y
-
-
-def change_label_to_zero(y):
- """
- Change the label -1 to 0
-
- Parameters
- ----------
- y
-
- Returns
- -------
-
- """
- zeroed_y = np.copy(y)
- zeroed_y[np.where(y == -1)] = 0
- return zeroed_y
-
-
-def compute_possible_combinations(params_dict):
- n_possibs = np.ones(len(params_dict)) * np.inf
- for value_index, value in enumerate(params_dict.values()):
- if type(value) == list:
- n_possibs[value_index] = len(value)
- elif isinstance(value, CustomRandint):
- n_possibs[value_index] = value.get_nb_possibilities()
- return n_possibs
-
-
-def gen_test_folds_preds(X_train, y_train, KFolds, estimator):
- test_folds_preds = []
- train_index = np.arange(len(y_train))
- folds = KFolds.split(train_index, y_train)
- fold_lengths = np.zeros(KFolds.n_splits, dtype=int)
- for fold_index, (train_indices, test_indices) in enumerate(folds):
- fold_lengths[fold_index] = len(test_indices)
- estimator.fit(X_train[train_indices], y_train[train_indices])
- test_folds_preds.append(estimator.predict(X_train[train_indices]))
- min_fold_length = fold_lengths.min()
- test_folds_preds = np.array(
- [test_fold_preds[:min_fold_length] for test_fold_preds in
- test_folds_preds])
- return test_folds_preds
-
-
-# class CustomRandint:
-# """Used as a distribution returning a integer between low and high-1.
-# It can be used with a multiplier agrument to be able to perform more complex generation
-# for example 10 e -(randint)"""
-#
-# def __init__(self, low=0, high=0, multiplier=""):
-# self.randint = randint(low, high)
-# self.multiplier = multiplier
-#
-# def rvs(self, random_state=None):
-# randinteger = self.randint.rvs(random_state=random_state)
-# if self.multiplier == "e-":
-# return 10 ** -randinteger
-# else:
-# return randinteger
-#
-# def get_nb_possibilities(self):
-# return self.randint.b - self.randint.a
-#
-#
-# class CustomUniform:
-# """Used as a distribution returning a float between loc and loc + scale..
-# It can be used with a multiplier agrument to be able to perform more complex generation
-# for example 10 e -(float)"""
-#
-# def __init__(self, loc=0, state=1, multiplier=""):
-# self.uniform = uniform(loc, state)
-# self.multiplier = multiplier
-#
-# def rvs(self, random_state=None):
-# unif = self.uniform.rvs(random_state=random_state)
-# if self.multiplier == 'e-':
-# return 10 ** -unif
-# else:
-# return unif
-
-
-class BaseMonoviewClassifier(BaseClassifier):
-
- def get_feature_importance(self, directory, base_file_name, nb_considered_feats=50):
- """Used to generate a graph and a pickle dictionary representing
- feature importances"""
- feature_importances = self.feature_importances_
- sorted_args = np.argsort(-feature_importances)
- feature_importances_sorted = feature_importances[sorted_args][
- :nb_considered_feats]
- feature_indices_sorted = sorted_args[:nb_considered_feats]
- fig, ax = plt.subplots()
- x = np.arange(len(feature_indices_sorted))
- formatter = FuncFormatter(percent)
- ax.yaxis.set_major_formatter(formatter)
- plt.bar(x, feature_importances_sorted)
- plt.title("Importance depending on feature")
- fig.savefig(os.path.join(directory, base_file_name + "feature_importances.png")
- , transparent=True)
- plt.close()
- features_importances_dict = dict((featureIndex, featureImportance)
- for featureIndex, featureImportance in
- enumerate(feature_importances)
- if featureImportance != 0)
- with open(directory + 'feature_importances.pickle', 'wb') as handle:
- pickle.dump(features_importances_dict, handle)
- interpret_string = "Feature importances : \n"
- for featureIndex, featureImportance in zip(feature_indices_sorted,
- feature_importances_sorted):
- if featureImportance > 0:
- interpret_string += "- Feature index : " + str(featureIndex) + \
- ", feature importance : " + str(
- featureImportance) + "\n"
- return interpret_string
-
- def get_name_for_fusion(self):
- return self.__class__.__name__[:4]
-
-
-def percent(x, pos):
- """Used to print percentage of importance on the y axis"""
- return '%1.1f %%' % (x * 100)
-
-
-class MonoviewResult(object):
- def __init__(self, view_index, classifier_name, view_name, metrics_scores,
- full_labels_pred, classifier_config,
- classifier, n_features, hps_duration, fit_duration,
- pred_duration, class_metric_scores):
- self.view_index = view_index
- self.classifier_name = classifier_name
- self.view_name = view_name
- self.metrics_scores = metrics_scores
- self.full_labels_pred = full_labels_pred
- self.classifier_config = classifier_config
- self.clf = classifier
- self.n_features = n_features
- self.hps_duration = hps_duration
- self.fit_duration = fit_duration
- self.pred_duration = pred_duration
- self.class_metric_scores = class_metric_scores
-
- def get_classifier_name(self):
- return self.classifier_name + "-" + self.view_name
-
-
-def get_accuracy_graph(plotted_data, classifier_name, file_name,
- name="Accuracies", bounds=None, bound_name=None,
- boosting_bound=None, set="train", zero_to_one=True): # pragma: no cover
- if type(name) is not str:
- name = " ".join(name.getConfig().strip().split(" ")[:2])
- f, ax = plt.subplots(nrows=1, ncols=1)
- if zero_to_one:
- ax.set_ylim(bottom=0.0, top=1.0)
- ax.set_title(name + " during " + set + " for " + classifier_name)
- x = np.arange(len(plotted_data))
- scat = ax.scatter(x, np.array(plotted_data), marker=".")
- if bounds:
- if boosting_bound:
- scat2 = ax.scatter(x, boosting_bound, marker=".")
- scat3 = ax.scatter(x, np.array(bounds), marker=".", )
- ax.legend((scat, scat2, scat3),
- (name, "Boosting bound", bound_name))
- else:
- scat2 = ax.scatter(x, np.array(bounds), marker=".", )
- ax.legend((scat, scat2),
- (name, bound_name))
- # plt.tight_layout()
- else:
- ax.legend((scat,), (name,))
- f.savefig(file_name, transparent=True)
- plt.close()
-
-
-class MonoviewResultAnalyzer(ResultAnalyser):
-
- def __init__(self, view_name, classifier_name, shape, classifier,
- classification_indices, k_folds, hps_method, metrics_dict,
- n_iter, class_label_names, pred,
- directory, base_file_name, labels, database_name, nb_cores, duration):
- ResultAnalyser.__init__(self, classifier, classification_indices,
- k_folds, hps_method, metrics_dict, n_iter,
- class_label_names, pred,
- directory, base_file_name, labels,
- database_name, nb_cores, duration)
- self.view_name = view_name
- self.classifier_name = classifier_name
- self.shape = shape
-
- def get_base_string(self):
- return "Classification on {} for {} with {}.\n\n".format(
- self.database_name, self.view_name, self.classifier_name
- )
-
- def get_view_specific_info(self):
- return "\t- View name : {}\t View shape : {}\n".format(self.view_name,
- self.shape)
\ No newline at end of file
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/__init__.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/__init__.py
deleted file mode 100644
index db257abe..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/__init__.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import os
-
-for module in os.listdir(os.path.dirname(os.path.realpath(__file__))):
- if module == '__init__.py' or module[-3:] != '.py':
- continue
- __import__(module[:-3], locals(), globals(), [], 1)
-del module
-del os
-
-"""
-To be able to add a monoview Classifier to the benchmark, one has to :
-Create a .py file named after the classifier
-Define a canProbas function returning True or False whether the classifier is able to predict class probabilities
-Define a fit function
- Input :
- DATASET : The data matrix used to fit the classifier
- CLASS_LABELS : The labels' array of the training set
- NB_CORES : The number of cores the classifier can use to train
- kwargs : Any argument specific to the classifier
- Output :
- classifier : A classifier object, similar to the sk-learn classifier object
-Define a ***Search that search hyper parameters for the algorithm. Check HP optimization methods to get all the
-different functions to provide (returning the parameters in the order of the kwargs dict for the fit function)
-Define a getKWARGS function
- Input :
- KWARGSList : The list of all the arguments as written in the argument parser
- Output :
- KWARGSDict : a dictionnary of arguments matching the kwargs needed in train
-Define a getConfig function that returns a string explaining the algorithm's config using a config dict or list
-Add the arguments to configure the classifier in the parser in exec_classif.py
-"""
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
deleted file mode 100644
index 88a042ec..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import time
-import os
-
-import numpy as np
-from sklearn.ensemble import AdaBoostClassifier
-from sklearn.tree import DecisionTreeClassifier
-
-from .. import metrics
-from ..monoview.monoview_utils import CustomRandint, BaseMonoviewClassifier, \
- get_accuracy_graph
-from ..utils.base import base_boosting_estimators
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-classifier_class_name = "Adaboost"
-
-
-class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
- """
- This class implement a Classifier with adaboost algorithm inherit from sklearn
- AdaBoostClassifier
-
- Parameters
- ----------
-
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
- n_estimators : int number of estimators
-
- base_estimator :
-
- kwargs : others arguments
-
-
- Attributes
- ----------
- param_name :
-
- classed_params :
-
- distribs :
-
- weird_strings :
-
- plotted_metric : selection of metric to plot
-
- plotted_metric_name : name of the metric to plot
-
- step_predictions :
-
- """
-
- def __init__(self, random_state=None, n_estimators=50,
- base_estimator=None, base_estimator_config=None, **kwargs):
-
- base_estimator = BaseMonoviewClassifier.get_base_estimator(self,
- base_estimator,
- base_estimator_config)
- AdaBoostClassifier.__init__(self,
- random_state=random_state,
- n_estimators=n_estimators,
- base_estimator=base_estimator,
- algorithm="SAMME"
- )
- self.param_names = ["n_estimators", "base_estimator"]
- self.classed_params = ["base_estimator"]
- self.distribs = [CustomRandint(low=1, high=500),
- base_boosting_estimators]
- self.weird_strings = {"base_estimator": "class_name"}
- self.plotted_metric = metrics.zero_one_loss
- self.plotted_metric_name = "zero_one_loss"
- self.step_predictions = None
-
- def fit(self, X, y, sample_weight=None):
- """
- Fit adaboost model
-
- Parameters
- ----------
- X : {array-like, sparse matrix}, shape (n_samples, n_features)
-
- y : { array-like, shape (n_samples,)
- Target values class labels in classification
-
- sample_weight :
-
- Returns
- -------
- self : object
- Returns self.
- """
- begin = time.time()
- AdaBoostClassifier.fit(self, X, y, sample_weight=sample_weight)
- end = time.time()
- self.train_time = end - begin
- self.train_shape = X.shape
- self.base_predictions = np.array(
- [estim.predict(X) for estim in self.estimators_])
- self.metrics = np.array([self.plotted_metric.score(pred, y) for pred in
- self.staged_predict(X)])
- return self
-
- def predict(self, X):
- """
-
- Parameters
- ----------
- X : {array-like, sparse matrix}, shape (n_samples, n_features)
- Training vectors, where n_samples is the number of samples
- and n_features is the number of features.
- For kernel="precomputed", the expected shape of X is
- (n_samples, n_samples).
-
- Returns
- -------
- predictions : ndarray of shape (n_samples, )
- The estimated labels.
- """
- begin = time.time()
- pred = AdaBoostClassifier.predict(self, X)
- end = time.time()
- self.pred_time = end - begin
- self.step_predictions = np.array(
- [step_pred for step_pred in self.staged_predict(X)])
- return pred
-
- def get_interpretation(self, directory, base_file_name, y_test, multi_class=False): # pragma: no cover
- interpretString = ""
- interpretString += self.get_feature_importance(directory, base_file_name)
- interpretString += "\n\n Estimator error | Estimator weight\n"
- interpretString += "\n".join(
- [str(error) + " | " + str(weight / sum(self.estimator_weights_)) for
- error, weight in
- zip(self.estimator_errors_, self.estimator_weights_)])
- step_test_metrics = np.array(
- [self.plotted_metric.score(y_test, step_pred) for step_pred in
- self.step_predictions])
- get_accuracy_graph(step_test_metrics, "Adaboost",
- os.path.join(directory, base_file_name +"test_metrics.png"),
- self.plotted_metric_name, set="test")
- np.savetxt(os.path.join(directory, base_file_name + "test_metrics.csv"),
- step_test_metrics,
- delimiter=',')
- np.savetxt(os.path.join(directory, base_file_name + "train_metrics.csv"),
- self.metrics, delimiter=',')
- np.savetxt(os.path.join(directory, base_file_name + "times.csv"),
- np.array([self.train_time, self.pred_time]), delimiter=',')
- return interpretString
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/additions/SVCClassifier.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/additions/SVCClassifier.py
deleted file mode 100644
index 06d6da20..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/additions/SVCClassifier.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from sklearn.svm import SVC
-
-
-class SVCClassifier(SVC):
-
- def __init__(self, random_state=None, kernel='rbf', C=1.0, degree=3,
- **kwargs):
- super(SVCClassifier, self).__init__(
- C=C,
- kernel=kernel,
- degree=degree,
- probability=True,
- max_iter=1000,
- random_state=random_state
- )
- self.classed_params = []
- self.weird_strings = {}
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/additions/__init__.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/additions/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py
deleted file mode 100644
index be9f5e24..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from sklearn.tree import DecisionTreeClassifier
-
-from ..monoview.monoview_utils import CustomRandint, BaseMonoviewClassifier
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-classifier_class_name = "DecisionTree"
-
-
-class DecisionTree(DecisionTreeClassifier, BaseMonoviewClassifier):
-
- def __init__(self, random_state=None, max_depth=None,
- criterion='gini', splitter='best', **kwargs):
- DecisionTreeClassifier.__init__(self,
- max_depth=max_depth,
- criterion=criterion,
- splitter=splitter,
- random_state=random_state
- )
- self.param_names = ["max_depth", "criterion", "splitter",
- 'random_state']
- self.classed_params = []
- self.distribs = [CustomRandint(low=1, high=300),
- ["gini", "entropy"],
- ["best", "random"], [random_state]]
- self.weird_strings = {}
-
- def get_interpretation(self, directory, base_file_name, y_test,
- multiclass=False):
- interpretString = "First featrue : \n\t{} <= {}\n".format(
- self.tree_.feature[0],
- self.tree_.threshold[0])
- interpretString += self.get_feature_importance(directory, base_file_name)
- return interpretString
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
deleted file mode 100644
index 7136370f..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import time
-import os
-
-import numpy as np
-from sklearn.ensemble import GradientBoostingClassifier
-from sklearn.tree import DecisionTreeClassifier
-
-from .. import metrics
-from ..monoview.monoview_utils import CustomRandint, BaseMonoviewClassifier, \
- get_accuracy_graph
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-classifier_class_name = "GradientBoosting"
-
-
-class CustomDecisionTreeGB(DecisionTreeClassifier):
- def predict(self, X, check_input=True):
- y_pred = DecisionTreeClassifier.predict(self, X,
- check_input=check_input)
- return y_pred.reshape((y_pred.shape[0], 1)).astype(float)
-
-
-class GradientBoosting(GradientBoostingClassifier, BaseMonoviewClassifier):
-
- def __init__(self, random_state=None, loss="exponential", max_depth=1.0,
- n_estimators=100,
- init=CustomDecisionTreeGB(max_depth=1),
- **kwargs):
- GradientBoostingClassifier.__init__(self,
- loss=loss,
- max_depth=max_depth,
- n_estimators=n_estimators,
- init=init,
- random_state=random_state
- )
- self.param_names = ["n_estimators", "max_depth"]
- self.classed_params = []
- self.distribs = [CustomRandint(low=50, high=500),
- CustomRandint(low=1, high=10),]
- self.weird_strings = {}
- self.plotted_metric = metrics.zero_one_loss
- self.plotted_metric_name = "zero_one_loss"
- self.step_predictions = None
-
- def fit(self, X, y, sample_weight=None, monitor=None):
- begin = time.time()
- GradientBoostingClassifier.fit(self, X, y, sample_weight=sample_weight)
- end = time.time()
- self.train_time = end - begin
- self.train_shape = X.shape
- self.base_predictions = np.array(
- [estim[0].predict(X) for estim in self.estimators_])
- self.metrics = np.array(
- [self.plotted_metric.score(pred, y) for pred in
- self.staged_predict(X)])
- # self.bounds = np.array([np.prod(
- # np.sqrt(1 - 4 * np.square(0.5 - self.estimator_errors_[:i + 1]))) for i
- # in range(self.estimator_errors_.shape[0])])
- return self
-
- def predict(self, X):
- begin = time.time()
- pred = GradientBoostingClassifier.predict(self, X)
- end = time.time()
- self.pred_time = end - begin
- if X.shape != self.train_shape:
- self.step_predictions = np.array(
- [step_pred for step_pred in self.staged_predict(X)])
- return pred
-
- def get_interpretation(self, directory, base_file_name, y_test, multi_class=False):
- interpretString = ""
- if multi_class:
- return interpretString
- else:
- interpretString += self.get_feature_importance(directory, base_file_name)
- step_test_metrics = np.array(
- [self.plotted_metric.score(y_test, step_pred) for step_pred in
- self.step_predictions])
- get_accuracy_graph(step_test_metrics, "AdaboostClassic",
- directory + "test_metrics.png",
- self.plotted_metric_name, set="test")
- get_accuracy_graph(self.metrics, "AdaboostClassic",
- directory + "metrics.png",
- self.plotted_metric_name)
- np.savetxt(os.path.join(directory, base_file_name + "test_metrics.csv"), step_test_metrics,
- delimiter=',')
- np.savetxt(os.path.join(directory, base_file_name + "train_metrics.csv"), self.metrics,
- delimiter=',')
- np.savetxt(os.path.join(directory, base_file_name + "times.csv"),
- np.array([self.train_time, self.pred_time]),
- delimiter=',')
- return interpretString
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py
deleted file mode 100644
index f3631bf6..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from sklearn.neighbors import KNeighborsClassifier
-
-from ..monoview.monoview_utils import CustomRandint, BaseMonoviewClassifier
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-classifier_class_name = "KNN"
-
-
-class KNN(KNeighborsClassifier, BaseMonoviewClassifier):
- """
- Implement extention of KNeighborsClassifier of sklearn
- for the usage of the multiview_platform.
-
- Parameters
- ----------
- random_state
- n_neighbors
- weights
- algorithm
- p
- kwargs
- """
-
- def __init__(self, random_state=None, n_neighbors=5,
- weights='uniform', algorithm='auto', p=2, **kwargs):
- KNeighborsClassifier.__init__(self,
- n_neighbors=n_neighbors,
- weights=weights,
- algorithm=algorithm,
- p=p
- )
- self.param_names = ["n_neighbors", "weights", "algorithm", "p",
- "random_state", ]
- self.classed_params = []
- self.distribs = [CustomRandint(low=1, high=10), ["uniform", "distance"],
- ["auto", "ball_tree", "kd_tree", "brute"], [1, 2],
- [random_state]]
- self.weird_strings = {}
- self.random_state = random_state
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py
deleted file mode 100644
index c91d2355..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import numpy as np
-from sklearn.linear_model import Lasso as LassoSK
-
-from ..monoview.monoview_utils import CustomRandint, CustomUniform, \
- BaseMonoviewClassifier
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-classifier_class_name = "Lasso"
-
-
-class Lasso(LassoSK, BaseMonoviewClassifier):
- """
-
- Parameters
- ----------
- random_state :
-
- alpha : float, optional
- Constant that multiplies the L1 term. Defaults to 1.0.
- ``alpha = 0`` is equivalent to an ordinary least square, solved
- by the :class:`LinearRegression` object. For numerical
- reasons, using ``alpha = 0`` is with the Lasso object is
- not advised
- and you should prefer the LinearRegression object. (default( : 10)
-
- max_iter : int The maximum number of iterations (default : 10)
-
- warm_start : bool, optional
- When set to True, reuse the solution of the previous call to fit as
- initialization, otherwise, just erase the previous solution.
-
- kwargs : others arguments
-
- Attributes
- ----------
- param_name :
-
- classed_params :
-
- distribs :
-
- weird_strings :
-
- """
-
- def __init__(self, random_state=None, alpha=1.0,
- max_iter=10, warm_start=False, **kwargs):
- LassoSK.__init__(self,
- alpha=alpha,
- max_iter=max_iter,
- warm_start=warm_start,
- random_state=random_state
- )
- self.param_names = ["max_iter", "alpha", "random_state"]
- self.classed_params = []
- self.distribs = [CustomRandint(low=1, high=300),
- CustomUniform(), [random_state]]
- self.weird_strings = {}
-
- def fit(self, X, y, check_input=True):
- neg_y = np.copy(y)
- neg_y[np.where(neg_y == 0)] = -1
- LassoSK.fit(self, X, neg_y)
- # self.feature_importances_ = self.coef_/np.sum(self.coef_)
- return self
-
- def predict(self, X):
- prediction = LassoSK.predict(self, X)
- signed = np.sign(prediction)
- signed[np.where(signed == -1)] = 0
- return signed
\ No newline at end of file
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
deleted file mode 100644
index ece278a5..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
+++ /dev/null
@@ -1,82 +0,0 @@
-from sklearn.ensemble import RandomForestClassifier
-
-from ..monoview.monoview_utils import CustomRandint, BaseMonoviewClassifier
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-classifier_class_name = "RandomForest"
-
-
-class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
- """RandomForest Classifier Class
-
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
- n_estimators : int (default : 10) number of estimators
-
- max_depth : int , optional (default : None) maximum of depth
-
- criterion : criteria (default : 'gini')
-
- kwargs : others arguments
-
-
- Attributes
- ----------
- param_names :
-
- distribs :
-
- classed_params :
-
- weird_strings :
-
- """
-
- def __init__(self, random_state=None, n_estimators=10,
- max_depth=None, criterion='gini', **kwargs):
- """
-
- Parameters
- ----------
- random_state
- n_estimators
- max_depth
- criterion
- kwargs
- """
- RandomForestClassifier.__init__(self,
- n_estimators=n_estimators,
- max_depth=max_depth,
- criterion=criterion,
- random_state=random_state
- )
- self.param_names = ["n_estimators", "max_depth", "criterion",
- "random_state"]
- self.classed_params = []
- self.distribs = [CustomRandint(low=1, high=300),
- CustomRandint(low=1, high=10),
- ["gini", "entropy"], [random_state]]
- self.weird_strings = {}
-
- def get_interpretation(self, directory, base_file_name, y_test, multiclass=False):
- """
-
- Parameters
- ----------
- directory
- y_test
-
- Returns
- -------
- string for interpretation interpret_string
- """
- interpret_string = ""
- interpret_string += self.get_feature_importance(directory, base_file_name)
- return interpret_string
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py
deleted file mode 100644
index 09c345ba..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from sklearn.linear_model import SGDClassifier
-
-from ..monoview.monoview_utils import CustomUniform, BaseMonoviewClassifier
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-classifier_class_name = "SGD"
-
-
-class SGD(SGDClassifier, BaseMonoviewClassifier):
- """
-
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
- loss : str , (default = "hinge")
- penalty : str, (default = "l2")
-
- alpha : float, (default = 0.0001)
-
- kwargs : other arguments
-
-
- Attributes
- ----------
- param_names :
-
- distribs :
-
- classed_params :
-
- weird_strings :
-
- """
-
- def __init__(self, random_state=None, loss='hinge',
- penalty='l2', alpha=0.0001, max_iter=5, tol=None, **kwargs):
- SGDClassifier.__init__(self,
- loss=loss,
- penalty=penalty,
- alpha=alpha,
- max_iter=5,
- tol=None,
- random_state=random_state
- )
- self.param_names = ["loss", "penalty", "alpha", "random_state"]
- self.classed_params = []
- self.distribs = [['log', 'modified_huber'],
- ["l1", "l2", "elasticnet"],
- CustomUniform(loc=0, state=1), [random_state]]
- self.weird_strings = {}
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py
deleted file mode 100644
index e5d293a6..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from multiview_platform.mono_multi_view_classifiers.monoview_classifiers.additions.SVCClassifier import \
- SVCClassifier
-from ..monoview.monoview_utils import CustomUniform, BaseMonoviewClassifier
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-classifier_class_name = "SVMLinear"
-
-
-class SVMLinear(SVCClassifier, BaseMonoviewClassifier):
- """SVMLinear
-
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
-
- C : float, optional (default=1.0)
- Penalty parameter C of the error term.
-
- kwargs : others arguments
-
- """
-
- def __init__(self, random_state=None, C=1.0, **kwargs):
- SVCClassifier.__init__(self,
- C=C,
- kernel='linear',
- random_state=random_state
- )
- self.param_names = ["C", "random_state"]
- self.distribs = [CustomUniform(loc=0, state=1), [random_state]]
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_poly.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_poly.py
deleted file mode 100644
index d93bdcc3..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_poly.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from multiview_platform.mono_multi_view_classifiers.monoview_classifiers.additions.SVCClassifier import \
- SVCClassifier
-from ..monoview.monoview_utils import CustomUniform, CustomRandint, \
- BaseMonoviewClassifier
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-classifier_class_name = "SVMPoly"
-
-
-class SVMPoly(SVCClassifier, BaseMonoviewClassifier):
- """
- Class of SVMPoly for SVC Classifier
-
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
-
- C : float, optional (default=1.0)
- Penalty parameter C of the error term.
-
-
- degree :
-
- kwargs : others arguments
-
-
- Attributes
- ----------
-
- param_names : list of parameters names
-
- distribs : list of random_state distribution
- """
-
- def __init__(self, random_state=None, C=1.0, degree=3, **kwargs):
- SVCClassifier.__init__(self,
- C=C,
- kernel='poly',
- degree=degree,
- random_state=random_state
- )
- self.param_names = ["C", "degree", "random_state"]
- self.distribs = [CustomUniform(loc=0, state=1),
- CustomRandint(low=2, high=30), [random_state]]
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_rbf.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_rbf.py
deleted file mode 100644
index 1af02e4d..00000000
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_rbf.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from multiview_platform.mono_multi_view_classifiers.monoview_classifiers.additions.SVCClassifier import \
- SVCClassifier
-from ..monoview.monoview_utils import CustomUniform, BaseMonoviewClassifier
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-classifier_class_name = "SVMRBF"
-
-
-class SVMRBF(SVCClassifier, BaseMonoviewClassifier):
- """
- class SVMRBF for classifier SVCC
-
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
- C :
-
- kwargs : others arguments
-
- Attributes
- ----------
-
- param_names : list of parameters names
-
- distribs : list of random_state distribution
- """
-
- def __init__(self, random_state=None, C=1.0, **kwargs):
- SVCClassifier.__init__(self,
- C=C,
- kernel='rbf',
- random_state=random_state
- )
- self.param_names = ["C", "random_state"]
- self.distribs = [CustomUniform(loc=0, state=1), [random_state]]
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/__init__.py b/multiview_platform/mono_multi_view_classifiers/multiview/__init__.py
deleted file mode 100644
index a3ab07f2..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# # from Code.mono_multi_view_classifiers.multiview_classifiers import fusion, Mumbo
-# from . import ExecMultiview
-# # from . import Mumbo
-#
-# __all__ = ['fusion', 'Mumbo']
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py b/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
deleted file mode 100644
index c89034cf..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
+++ /dev/null
@@ -1,356 +0,0 @@
-import logging
-import os
-import os.path
-import time
-
-import h5py
-import numpy as np
-
-from .multiview_utils import MultiviewResult, MultiviewResultAnalyzer
-from .. import multiview_classifiers
-from ..utils import hyper_parameter_search
-from ..utils.multiclass import get_mc_estim
-from ..utils.organization import secure_file_path
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def init_constants(kwargs, classification_indices, metrics,
- name, nb_cores, k_folds,
- dataset_var, directory):
- """
- Used to init the constants
- Parameters
- ----------
- kwargs :
-
- classification_indices :
-
- metrics :
-
- name :
-
- nb_cores : nint number of cares to execute
-
- k_folds :
-
- dataset_var : {array-like} shape (n_samples, n_features)
- dataset variable
-
- Returns
- -------
- tuple of (classifier_name, t_start, views_indices,
- classifier_config, views, learning_rate)
- """
- views = kwargs["view_names"]
- views_indices = kwargs["view_indices"]
- if metrics is None:
- metrics = {"f1_score*":{}}
- classifier_name = kwargs["classifier_name"]
- classifier_config = kwargs[classifier_name]
- learning_rate = len(classification_indices[0]) / float(
- (len(classification_indices[0]) + len(classification_indices[1])))
- t_start = time.time()
- logging.info("Info\t: Classification - Database : " + str(
- name) + " ; Views : " + ", ".join(views) +
- " ; Algorithm : " + classifier_name + " ; Cores : " + str(
- nb_cores) + ", Train ratio : " + str(learning_rate) +
- ", CV on " + str(k_folds.n_splits) + " folds")
-
- for view_index, view_name in zip(views_indices, views):
- logging.info("Info:\t Shape of " + str(view_name) + " :" + str(
- dataset_var.get_shape()))
- labels = dataset_var.get_labels()
- directory = os.path.join(directory, classifier_name)
- base_file_name = classifier_name+"-"+dataset_var.get_name()+"-"
- output_file_name = os.path.join(directory, base_file_name)
- return classifier_name, t_start, views_indices, \
- classifier_config, views, learning_rate, labels, output_file_name,\
- directory, base_file_name, metrics
-
-
-def save_results(string_analysis, images_analysis, output_file_name,
- confusion_matrix): # pragma: no cover
- """
- Save results in derectory
-
- Parameters
- ----------
-
- classifier : classifier class
-
- labels_dictionary : dict dictionary of labels
-
- string_analysis : str
-
- views :
-
- classifier_module : module of the classifier
-
- classification_kargs :
-
- directory : str directory
-
- learning_rate :
-
- name :
-
- images_analysis :
-
- """
- logging.info(string_analysis)
- secure_file_path(output_file_name)
- output_text_file = open(output_file_name + 'summary.txt', 'w', encoding="utf-8")
- output_text_file.write(string_analysis)
- output_text_file.close()
- np.savetxt(output_file_name+"confusion_matrix.csv", confusion_matrix,
- delimiter=',')
-
- if images_analysis is not None:
- for image_name in images_analysis.keys():
- if os.path.isfile(output_file_name + image_name + ".png"):
- for i in range(1, 20):
- test_file_name = output_file_name + image_name + "-" + str(
- i) + ".png"
- if not os.path.isfile(test_file_name):
- images_analysis[image_name].savefig(test_file_name,
- transparent=True)
- break
-
- images_analysis[image_name].savefig(
- output_file_name + image_name + '.png', transparent=True)
-
-
-def exec_multiview_multicore(directory, core_index, name, learning_rate,
- nb_folds,
- database_type, path, labels_dictionary,
- random_state, labels,
- hyper_param_search=False, nb_cores=1, metrics=None,
- n_iter=30, **arguments): # pragma: no cover
- """
- execute multiview process on
-
- Parameters
- ----------
-
- directory : indicate the directory
-
- core_index :
-
- name : name of the data file to perform
-
- learning_rate :
-
- nb_folds :
-
- database_type :
-
- path : path to the data name
-
- labels_dictionary
-
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
- labels :
-
- hyper_param_search :
-
- nb_cores : in number of cores
-
- metrics : metric to use
-
- n_iter : int number of iterations
-
- arguments : others arguments
-
- Returns
- -------
- exec_multiview on directory, dataset_var, name, learning_rate, nb_folds, 1,
- database_type, path, labels_dictionary,
- random_state, labels,
- hyper_param_search=hyper_param_search, metrics=metrics,
- n_iter=n_iter, **arguments
- """
- """Used to load an HDF5 dataset_var for each parallel job and execute multiview classification"""
- dataset_var = h5py.File(path + name + str(core_index) + ".hdf5", "r")
- return exec_multiview(directory, dataset_var, name, learning_rate, nb_folds,
- 1,
- database_type, path, labels_dictionary,
- random_state, labels,
- hps_method=hyper_param_search,
- metrics=metrics,
- n_iter=n_iter, **arguments)
-
-
-def exec_multiview(directory, dataset_var, name, classification_indices,
- k_folds,
- nb_cores, database_type, path,
- labels_dictionary, random_state, labels,
- hps_method="None", hps_kwargs={}, metrics=None,
- n_iter=30, **kwargs):
- """Used to execute multiview classification and result analysis
-
- Parameters
- ----------
-
- directory : indicate the directory
-
-
- dataset_var :
-
- name
-
- classification_indices
-
- k_folds
-
- nb_cores
-
- database_type
-
- path
-
- labels_dictionary : dict dictionary of labels
-
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
-
- labels
-
- hps_method
-
- metrics
-
- n_iter : int number of iterations
-
- kwargs
-
- Returns
- -------
-
- ``MultiviewResult``
- """
-
- logging.debug("Start:\t Initialize constants")
- cl_type, \
- t_start, \
- views_indices, \
- classifier_config, \
- views, \
- learning_rate, \
- labels, \
- output_file_name,\
- directory,\
- base_file_name, \
- metrics = init_constants(kwargs, classification_indices, metrics, name,
- nb_cores, k_folds, dataset_var, directory)
- logging.debug("Done:\t Initialize constants")
-
- extraction_time = time.time() - t_start
- logging.info("Info:\t Extraction duration " + str(extraction_time) + "s")
-
- logging.debug("Start:\t Getting train/test split")
- learning_indices, validation_indices = classification_indices
- logging.debug("Done:\t Getting train/test split")
-
- logging.debug("Start:\t Getting classifiers modules")
- # classifierPackage = getattr(multiview_classifiers,
- # CL_type) # Permet d'appeler un module avec une string
- classifier_module = getattr(multiview_classifiers, cl_type)
- classifier_name = classifier_module.classifier_class_name
- # classifierClass = getattr(classifierModule, CL_type + "Class")
- logging.debug("Done:\t Getting classifiers modules")
-
- logging.debug("Start:\t Optimizing hyperparameters")
- hps_beg = time.monotonic()
- if hps_method != "None":
- hps_method_class = getattr(hyper_parameter_search, hps_method)
- estimator = getattr(classifier_module, classifier_name)(
- random_state=random_state,
- **classifier_config)
- estimator = get_mc_estim(estimator, random_state,
- multiview=True,
- y=dataset_var.get_labels()[learning_indices])
- hps = hps_method_class(estimator, scoring=metrics, cv=k_folds,
- random_state=random_state, framework="multiview",
- n_jobs=nb_cores,
- learning_indices=learning_indices,
- view_indices=views_indices, **hps_kwargs)
- hps.fit(dataset_var, dataset_var.get_labels(), )
- classifier_config = hps.get_best_params()
- hps.gen_report(output_file_name)
- # classifier_config = hyper_parameter_search.search_best_settings(
- # dataset_var, dataset_var.get_labels(), classifier_module,
- # classifier_name,
- # metrics[0], learning_indices, k_folds, random_state,
- # output_file_name, nb_cores=nb_cores, views_indices=views_indices,
- # searching_tool=hps_method, n_iter=n_iter,
- # classifier_config=classifier_config)
- hps_duration = time.monotonic() - hps_beg
- classifier = get_mc_estim(
- getattr(classifier_module, classifier_name)(random_state=random_state,
- **classifier_config),
- random_state, multiview=True,
- y=dataset_var.get_labels())
- logging.debug("Done:\t Optimizing hyperparameters")
- logging.debug("Start:\t Fitting classifier")
- fit_beg = time.monotonic()
- classifier.fit(dataset_var, dataset_var.get_labels(),
- train_indices=learning_indices,
- view_indices=views_indices)
- fit_duration = time.monotonic() - fit_beg
- logging.debug("Done:\t Fitting classifier")
-
- logging.debug("Start:\t Predicting")
- train_pred = classifier.predict(dataset_var,
- example_indices=learning_indices,
- view_indices=views_indices)
- pred_beg = time.monotonic()
- test_pred = classifier.predict(dataset_var,
- example_indices=validation_indices,
- view_indices=views_indices)
- pred_duration = time.monotonic() - pred_beg
- full_pred = np.zeros(dataset_var.get_labels().shape, dtype=int) - 100
- full_pred[learning_indices] = train_pred
- full_pred[validation_indices] = test_pred
- logging.info("Done:\t Pertidcting")
-
- whole_duration = time.time() - t_start
- logging.info(
- "Info:\t Classification duration " + str(extraction_time) + "s")
-
- # TODO: get better cltype
-
- logging.info("Start:\t Result Analysis for " + cl_type)
- times = (extraction_time, whole_duration)
- result_analyzer = MultiviewResultAnalyzer(view_names=views,
- classifier=classifier,
- classification_indices=classification_indices,
- k_folds=k_folds,
- hps_method=hps_method,
- metrics_dict=metrics,
- n_iter=n_iter,
- class_label_names=list(labels_dictionary.values()),
- pred=full_pred,
- directory=directory,
- base_file_name=base_file_name,
- labels=labels,
- database_name=dataset_var.get_name(),
- nb_cores=nb_cores,
- duration=whole_duration)
- string_analysis, images_analysis, metrics_scores, class_metrics_scores, \
- confusion_matrix = result_analyzer.analyze()
- logging.info("Done:\t Result Analysis for " + cl_type)
-
- logging.debug("Start:\t Saving preds")
- save_results(string_analysis, images_analysis, output_file_name, confusion_matrix)
- logging.debug("Start:\t Saving preds")
-
- return MultiviewResult(cl_type, classifier_config, metrics_scores,
- full_pred, hps_duration, fit_duration,
- pred_duration, class_metrics_scores)
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
deleted file mode 100644
index 9ad93b6c..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
+++ /dev/null
@@ -1,197 +0,0 @@
-from abc import abstractmethod
-
-import numpy as np
-
-from .. import monoview_classifiers
-from ..utils.base import BaseClassifier, ResultAnalyser
-from ..utils.dataset import RAMDataset, get_examples_views_indices
-
-
-# class FakeEstimator():
-#
-# def predict(self, X, example_indices=None, view_indices=None):
-# return np.zeros(example_indices.shape[0])
-
-
-class BaseMultiviewClassifier(BaseClassifier):
- """
- BaseMultiviewClassifier base of Multiview classifiers
-
- Parameters
- ----------
- random_state : int seed, RandomState instance, or None (default=None)
- The seed of the pseudo random number multiview_generator to use when
- shuffling the data.
- """
-
- def __init__(self, random_state):
-
- self.random_state = random_state
- self.short_name = self.__module__.split(".")[-1]
- self.weird_strings = {}
- self.used_views = None
-
- @abstractmethod
- def fit(self, X, y, train_indices=None, view_indices=None): # pragma: no cover
- pass
-
- @abstractmethod
- def predict(self, X, example_indices=None, view_indices=None): # pragma: no cover
- pass
-
- def _check_views(self, view_indices): # pragma: no cover
- if self.used_views is not None and not np.array_equal(np.sort(self.used_views), np.sort(view_indices)):
- raise ValueError('Used {} views to fit, and trying to predict on {}'.format(self.used_views, view_indices))
-
- # def to_str(self, param_name):
- # if param_name in self.weird_strings:
- # string = ""
- # if "class_name" in self.weird_strings[param_name]:
- # string += self.get_params()[param_name].__class__.__name__
- # if "config" in self.weird_strings[param_name]:
- # string += "( with " + self.get_params()[
- # param_name].params_to_string() + ")"
- # else:
- # string += self.weird_strings[param_name](
- # self.get_params()[param_name])
- # return string
- # else:
- # return str(self.get_params()[param_name])
-
- def accepts_multi_class(self, random_state, n_samples=10, dim=2,
- n_classes=3, n_views=2):
- if int(n_samples / n_classes) < 1:
- raise ValueError(
- "n_samples ({}) / n_classes ({}) must be over 1".format(
- n_samples,
- n_classes))
- fake_mc_X = RAMDataset(
- views=[random_state.randint(low=0, high=101,
- size=(n_samples, dim))
- for i in range(n_views)],
- labels=[class_index
- for _ in range(int(n_samples / n_classes))
- for class_index in range(n_classes)],
- are_sparse=False,
- name="mc_dset",
- labels_names=[str(class_index) for class_index in range(n_classes)],
- view_names=["V0", "V1"],
- )
-
- fake_mc_y = [class_index
- for _ in range(int(n_samples / n_classes))
- for class_index in range(n_classes)]
- fake_mc_y += [0 for _ in range(n_samples % n_classes)]
- fake_mc_y = np.asarray(fake_mc_y)
- try:
- self.fit(fake_mc_X, fake_mc_y)
- return True
- except ValueError:
- return False
-
-
-class ConfigGenerator():
-
- def __init__(self, classifier_names):
- self.distribs = {}
- for classifier_name in classifier_names:
- classifier_class = get_monoview_classifier(classifier_name)
- self.distribs[classifier_name] = dict((param_name, param_distrib)
- for param_name, param_distrib
- in
- zip(
- classifier_class().param_names,
- classifier_class().distribs)
- if
- param_name != "random_state")
-
- def rvs(self, random_state=None):
- config_sample = {}
- for classifier_name, classifier_config in self.distribs.items():
- config_sample[classifier_name] = {}
- for param_name, param_distrib in classifier_config.items():
- if hasattr(param_distrib, "rvs"):
- config_sample[classifier_name][
- param_name] = param_distrib.rvs(
- random_state=random_state)
- else:
- config_sample[classifier_name][
- param_name] = param_distrib[
- random_state.randint(len(param_distrib))]
- return config_sample
-
-
-def get_available_monoview_classifiers(need_probas=False):
- available_classifiers = [module_name
- for module_name in dir(monoview_classifiers)
- if not (
- module_name.startswith("__") or module_name == "additions")]
- if need_probas:
- proba_classifiers = []
- for module_name in available_classifiers:
- module = getattr(monoview_classifiers, module_name)
- classifier_class = getattr(module, module.classifier_class_name)()
- proba_prediction = getattr(classifier_class, "predict_proba", None)
- if callable(proba_prediction):
- proba_classifiers.append(module_name)
- available_classifiers = proba_classifiers
- return available_classifiers
-
-
-def get_monoview_classifier(classifier_name, multiclass=False):
- classifier_module = getattr(monoview_classifiers, classifier_name)
- classifier_class = getattr(classifier_module,
- classifier_module.classifier_class_name)
- return classifier_class
-
-
-from .. import multiview_classifiers
-
-
-class MultiviewResult(object):
- def __init__(self, classifier_name, classifier_config,
- metrics_scores, full_labels, hps_duration, fit_duration,
- pred_duration, class_metric_scores):
- self.classifier_name = classifier_name
- self.classifier_config = classifier_config
- self.metrics_scores = metrics_scores
- self.full_labels_pred = full_labels
- self.hps_duration = hps_duration
- self.fit_duration = fit_duration
- self.pred_duration = pred_duration
- self.class_metric_scores = class_metric_scores
-
- def get_classifier_name(self):
- try:
- multiview_classifier_module = getattr(multiview_classifiers,
- self.classifier_name)
- multiview_classifier = getattr(multiview_classifier_module,
- multiview_classifier_module.classifier_class_name)(
- 42, **self.classifier_config)
- return multiview_classifier.short_name
- except:
- return self.classifier_name
-
-
-class MultiviewResultAnalyzer(ResultAnalyser):
-
- def __init__(self, view_names, classifier, classification_indices, k_folds,
- hps_method, metrics_dict, n_iter, class_label_names,
- pred, directory, base_file_name, labels,
- database_name, nb_cores, duration):
- if hps_method.endswith("equiv"):
- n_iter = n_iter*len(view_names)
- ResultAnalyser.__init__(self, classifier, classification_indices, k_folds,
- hps_method, metrics_dict, n_iter, class_label_names,
- pred, directory,
- base_file_name, labels, database_name,
- nb_cores, duration)
- self.classifier_name = classifier.short_name
- self.view_names = view_names
-
- def get_base_string(self, ):
- return "Multiview classification on {} with {}\n\n".format(self.database_name,
- self.classifier_name)
-
- def get_view_specific_info(self):
- return "\t- Views : " + ', '.join(self.view_names) + "\n"
\ No newline at end of file
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/profile b/multiview_platform/mono_multi_view_classifiers/multiview/profile
deleted file mode 100644
index 40a016510edec99a8c0e78e9ba4bf248d41b8c62..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001
literal 1288343
zcmb={U}j)oDAs3SV9?LV&rQ`&$<I&HPs~e9&d*CpG}6z>Owuo?EXl~vGtx8HFUcrM
zO-#wmOV=x?%#>qdU?^c_U|?`9OU*0M0I@Qo85kJAM0)?7StZd*=Jx4r|4W!z?=7^a
zz@~PPO&kmi4Ds=qd6^~g@fs{JdvI800P&U!ao#G<EJ@WZNKDR7OiwM=&rK{T$jL7O
z`MfweF((z|S0M%lh7wK&28Ljeh*M&5D#X8xAa{U>^xn5y7kodq!4A%V`j=`>0NGLk
z3Iv<n{FKt1R69r@fP^za1l(dc0~!dP6b3>`QDR<kT7FS(F(@P^vp{1#CAFX=0}={6
zpilr2P^&M%Lg9iPG!&@ohWi{03_+kI00{z+&6ywq(|TwS@G&qj_?G4-<?Dk<kUvsE
zF$&U@oS$1zT9WFRQ;?Ae@lFNEJ0K#x%{=?VJ}r0q^mSXmuhcRLfF)uq=G_39#|0va
z5;OC_)<9ebGAI*7V44PXod<;xl#-uZTv7zhmFf%(3_+mu0PzbblrljCG;AK&rGLnH
z+_Ck?6Ij%^Q)qi~eolT-QeqJ(P(?tNg9;Rg*O@>82_m4DBc%&S{-mnUL7ommSPtSM
zXN&`O=^;-93X8Q)+CeOLrO@MrrI}@^U~hLaGBA{|F)%O$f`uTS$74S<-nEHyKtW|n
zeljR@XE35fExH985FUi)I#Mk-0P+Vr0|SFch_8=Jelm8CAlU=;h%<2>!CB1a<|HP8
zJZQtiz)-@^z`)>^UzD3z5|WW$TAY}dQVdDT2>&ubK{}LXfclqO?%05ujPY0u4FFBz
z0wB4#4CGCaGr^TfYDIEtPJBsX61oR*RVJj_1IqSzG6Ygx1@(q4ao#|U$AZd&#G+zo
z>9d!afuV#KR8xZlQ~mOt6O%KbbtS0K%+zCKU`TJi<2qweOq6}PyR2-u^6ny7u}Zo9
zc3jYU8f?91Zb1$t4TB1%Oi&I@U!?Y{W{RM=eY%D_<D!IPA@-2MfO4yCKoui50|Nut
z>R@nz46z*SaS)My^7uO@RaP;C$EjucI!*?LAW&@vvC#rl@X9bSFr-&ZQ&3)T$k0AL
zHDB|2_ug=-dDjfp#%UlMLDr`0A6&_=^4`Zj{o4h*un)HjsAl6zE)+k4yq5_g(%<-%
z&#O2mW}hC<eoiv*mN(UG{D*3z6v&St0$PQt*r%)g6x^T0OTAD%gK8tFxt9qd((PBD
znR4wulYM&IF=59!?ZNiYEK6jGP+Xh?DtSCW1w5#c5*+N~oS#>cS^;ghBZ@d!9SN^2
zp=B#+7DeDHK{1Sn`oo)?q6ev^o|d0i5}%uxmzWN%l0gL#DCCM$OXAZqbD(7qqJJXJ
zb%W3C4YPeZi~;p0wH?yW1go?`#WA?M0w&=0!WqzzptQq~l30=oZmA1{3M5dO;G3UU
zlHrq|oLG`yL~Kn%HCN>0N(mTs9Mu286sClv#N_PMyp&>nC?h^G9o${z1f_j&)s&uE
z0uqb|r5Q+gfZD*BAR;~NquaBI_8#`>Hs9s+EZ>#DswP^wY73~x0P0w#6eX6&gEJ&J
zod|=<8V~`^@XGe-yE%fVct3WBmTt6i&;^i#WI<I&Y6;jq@#UE*B^mJ<shQ~+CGjb#
z#mS)FKG=~aAV-3T^ez7xudRx8wog}?Hf8tukA<+}2^6M8j4PnFDDp~k3o7+<GV>C1
z(xDWn@Tvj13=~xHMX8B7A(aKtJbj*pfguw_q~B>;x~pM&q<#ANYX=YBJCXp4JZie4
z8{`U5H6NdxpPQSX2X+TIG(b+k;SOlhrZiI}C*|j7gMxySm4TrI?1=c{)RN4S)ZF-Z
zNJ^FfMG1&V*U;R+u#88>KHY8FnT>l^%fSMJRtX1`zk&oA7#JW;5Ku=f6GT8O%O`f}
ztc%oBgPdPOyI-JIFv&>+sl*566;N2ngYq>vEI|1>C$TcWv;^V=QBYWbh;*(!ll}X1
zCGDY3kcK5sHww#|qU7S#lF|Z@Gwy)W1*pP_k55j_$$`ZOsP~%*A`o3*NELXKTK#X>
zm>9?r!ORQ{L1OSY0NIPfa%hS)CN2O<^D;|HQj1IUa}zW3Ko)`83y`h_c-Rlbh4i&&
zeo|Z^wO7hM-D5G6`t1X@uvBMGqD9EQ(#2JUU|0tA6-WaSNh>8YEiES#-1F1{#U7~V
z8Jt>JnwpoK>YG@SoRNy7x;y|I-#P$`MAB`P#^otUy@+H5)KefQ65*-Rq8zB7Kx0ne
zZf0nZPkupa9;R=$$J<33#@w|_hcTdSAd;;T$K@HA!EhU(o}un|;9+(Kh7wQ*3qDjC
zUyukJoJ6Ee*k}RzumPo|Kmj<dfZJS~K!p*gHUL%UkWvVb{m{S&CoXTGR+7cZnFW>l
z#o*2+w35_MDbCL+O9k=33{XY|RRtxWQrS1Lq$sl@C^0WPJh3P*GcO%dUm#ouNq0zt
z0?@=jOV@#HhZG~AFawt$U;<iLA~^;UR3#t{b(x@uEC%_^CaE+tr$je1Pa!w8BqKjX
zp|k+h{I!Gl4N?5Tid<-?24413y)G;)F$IOXJ17}|>ZGF7;>@a4$OH)?FG9uw!pKPs
zNYx^=)r)BU7M7SoTfjY_Tng$jqbE$ri0~EI(C`&INN5*RJ}9z-13ID?kXTfl>RM4y
zlv-SznV$!VAW&>V;=5(%-P5&2BKGMp1~l<e?gA^2r$OxybWg)<f?EmoG+9Lxq{#}J
zN=PixkI&32E=Wx-0i^`c5G;6TII}o8KLy%iRR%RoKty_)&q1m5%O7DQw?AR^33VL-
zYHF2$#&y$EOA?FHiy=V)azG}Cz;p#PD5%+P&H~l2prMEO+{EnE#G<0aN=PC=G@Ez+
zmXX&zmtmjY?r$|QVO1rp?LbXuoC4)cP;$u3D}w|B9{ZueK<Pw5T4s7_5qKJr1=k4v
zby4F(x0@C0(?!FyR;>7y3iCP*N&^#UX#koiDgn)F=B1X$L!1a3K!F4dB6niA4jM3l
z6qZ#bMVa~~MVXm-C8<RP`8kOt;6MV!4x~~*Yg?9O&*VA&S<*h;xpSJ$ylMKdK%#WW
z2ILIn_9nssXhk-L1EApmYWCYerr_+r<r%oy<ye$nnwy$ef-!x8(I|ozb{NJ$LIKSY
z7`ma3FeEZEic*MdyQ#P&u|egKb(fC{h7oYeTtytI5s&Wr&SQy?O}x|Q`ivz&*0
zI<M3(^&Js~u#hLuP7Tm>J}7SE!9^n2e;{izK?J5<Q2&wKDE$B$0tA(Mi76=vPa#q@
zt_Bau7U69DV%Pxn3R$H)QXLO(=7QUtrXYWU8XIXv`ML2)`K9m~vuR8W44EJz{o9l|
z!qpk^_USWxM0>nmErCTKr4z9EX=%l&B}w@ept|l4sE7x(H9;Nm^31%H{POtJiW2y6
z5tg8bG|YZNhXg1sXme82Q}a?lPFVm-)}a1ed>Y6dU{}WHz-BZN4MTX#=TENxgd6Yp
zXgs(U3Tljif&smRg`|`RcIg{R>}<Rz2twUK#hytbDAR#bYCNQk4C)~zXT-yk62wI{
zpcDfl(%BWZc252sYoBgbDku8Cd<oP=ltvMxq5(w_qAmm%0H9C-mz~hI{{_2rWqud2
z%tyRXn?a2jBKp6my(HvJlUf08k%7jGf<OTQ@iAz8C=*1a%c!{gY!VZ)Pv`QsS&(ze
z6*fp@MqI&~l383*T9TPlte;v`lwSnSn%1Bk0UD2Sfk}FT>bktd99NJUNHL4>IjCO)
z!ss1T3S0mhFU4Aofb0QbxLwe)$$>cE=9cD^WEK?VCxhDadFlGe`FVM%$t9Wj;NhXm
zpzH@Kk|J_*d@_qmunj&yk~~ru4_XUS#Q~sZEmm)1*b4PF<sFUbpv4QIS}i{>)jthA
zY9X^spY75EuiyF|B5DOIfGM{ZX9owvN~l-0h>PK()B^D07Er6C1e8UCQVWP44<peI
zP!_-wU6_Mml-8idB}J);xu9i!prR-elw3g-muFsCVoqj?XI@Hb1xY!Sy6ypoHmDT}
zDWX8311TCo85V@mGp!eKQHrx{OwP~Af#hCru~iS6UJzhlU~tYXO3q1jhRdR-YDo2c
z5;kz~1=f9~vU@<?My$yL!+vNI4<IKzinDW46N~cnA%%EuYDr>haY<$_Xx0=QDxkI!
zWC3PQVs27OB6O4ql;tx)1hnFM02@x_h2?Rwx=AqCq@^a7loqAN7pFpFNWUlz+)V;i
zVI`nM66EFzNhcsj;BW~vT!@^+PfP-@Hv_ewz{9GENyQ~aiOD69K{iMt0M$vEAObq4
z@*6h4^Ba~3d?<;8<f8oI;&^awOoZ1epe76`S%TKz24|F}rRAgs7vyA?K)jF0CK!c0
zWN3}ZLHfMX+@w@U{jCJ5zd>CH=ltA)oYV@4MIa|+f(ZDiHk<*mh^$5uEW%5QQd9Lo
z1gK;Jr#X;SATPV5CTD^M&q9h)Qz0%vco|eGfH1sdfVhNoFRO#fLQt6xYN@0bB|;Za
zfx3v9AOb#w3THrU@*^j<A;V?``8kz&`MH^iIr{M+c2Z(7IE90zzrnMdj!w=2`8k!K
zFo5_Qw5kEeieHFp$SQzfF_4>DQk0ootY4g*Uj$iAq5?_>p!Q5W$PK|@afnM0F#s=%
zU<`;$sGbWRf!qQr5y5WpcFWI6L3au$A%HN&iD4zkE^uv>3QGu}qybry1j%I>YkGso
zNeHl@$1Lq&i2*cH1zuSg3@!-si+nRHGV{=V4sjDwqY_&1QQDD0n&_<rg$XDDLJKF*
zhzWER6=>N+CWwIMjh`6fx>R&WBgh?~Mhn~>p!F;zpk)!D7y~c*1{2VF2_vG&8dgQ>
zP$3VQp-ntXf<}%&1a$KM3!;SpjS$d6ERuV3*yg-aGt*P^^b?Cf+c%&TsNe(@+K?s#
zxK>1D)9c?KDkz?Ew@;6sR+gn_?g6W$W64UkSSI069E2Q3pyCYTByjxTaS}9)C`~N6
ziACAaiVVCu0bG2imc(b}XXX``RKnK8fo6mt3n?LMW1rhW7s)<{)#Q|}Tgl1HgDgp#
z3R(;YvJaAPK`b0LLxTWR|B;+bAhimpkk(I6Ey>Bu%`5>o;HH2^UqH*1AT1bhEPx1T
zBE4b<U1j+R=5Mm<aU|Pw6O%I_8zV|UnH5xY#;4?i_b@>M0AxEPkHQC{APYS|!KUEI
z8rz0A0knZBtuznZDUXL0qv@cnGoYjg34^(e3=EkdB7H7T-=y&85%%di&N8nbnMJ|!
z1!%~M<QRZv)RM%KV*Pl~z(^iwSh*;%xTL5w8PuEwWzj&;T34`Bz`+2Dm`o6nKKp;T
ztmP$5`*i<POWMpBw4nn)l%_(^%rH0|pe;!RwV@#^l^{dwZ($Aix6olp+66%)E6U=~
z6i{q{h;+%5atqTuLhaM9S<LQZ$P0r8fiES6RBAG~g_W9<lUWd-kyw-qiUd&jBNa;M
zBco3cWq|>tJws_Mz}K*Of?D&SnD<P9l|&%NKsve@^*D5Jh-$VY>IaY$z?OmuXl04n
zf*>m+BGs0l${9Al1`7DEAn${kap1vn@Zw5HmRta;J3$1r5y5E>XFvmi+D_RES||)E
zoIqpcAa_CvCk>E8Km@dh_Zrs2gRS5rt6z&2B+2=CX_@Ju!RV6IBJkMZI#5vwS{;=R
z8Z}HTNd>RdMO6oJAR@71Bo|0prFuYt;tUi}@$ngn#Tl@DsAZsl0uj)ObVhqP1Da{b
z8Xkg_;^1lwJY8N;l$i$}!R`Y&1JpV9$<Ir7%gIkHf$W!pq#weWh`O!;wXcv$5IojH
z1B3D*&sCfZ48@?4Z5vP;sVFFd4#j|4!<irgQKQ4^RY=wek^@B*sB2UVa<)xkUL|xJ
zEhr~uf(T5lkhDa(tCxdz%z%Oil6pZUP$r0g_tM}DXh2ar`Q8Ri6oP8I^i*hv1(Ac`
zRo^m)wDgPCqOj_p+HL@qH9??;EhG@|H~<<5w5xnUDFBjiK`e0T4<^#5q{s2?e(zzQ
zF63jnc^Xp_EG#IgeZfs3P)n#NGpQ6bpapJ;fXWhx6VPfQ3@1QB11bH2x|tZu--1%}
zQc{ajiyYI_p;-cy-XOj*&f4c?S7vUX4r4&FL^0LZz(LoSfFcgE5gE;IF#F*Sf%vVM
ztfYoiNamI1rWR!;gL=T=zy%FNKmr#neml4R5Mg+1X%F2Tmk5hrN;@0z;Ds!CiQuhP
zpv{I5+tIv_VLR0Opm`i38rtX+Z=lg?P<tW(G;Rm#V!GuQWjmH97Nv%O4{PwrPtHcK
z7(fLe2*V2;Xi}ii2{<R+Kz4yJ+&-w6K}m!NFQZOd5Y@L>0SXOJ4N+7UpH`G#m5Q<A
z5K=K9wQr$eKsz^qQX$q9f#DQr@Q_tcBeky*D^iQ~;XS{vpwb^y!-00hC4v_yqNfYU
z$mLDgs1<yfEOi~?310+<mNPJ{r%5=dfLc$Wi~?$SS0q+s79%Z!0L4)zC<$a91Z{yb
zjP1B8pQvG<en7tNU-gG7SV=+YS_F`-piDajluSVj*W*EsDalOEHUf>6K<Y-&qJ&Ja
zLm@M{%=YQ24;rSeOUQ=y2B{k@;h=B<kJ+V`#6!a+wW7obDRjW`1$GvQNWaHmeE*>T
zAG`GDCfr-Bmq<XJMXkVD0ty_^m=!p1(u*=v(831n2uRca0c=Oc18AR}x?y9BJ!}kd
zht1D9yJPiz84){<AgxHUMwF1Uba4S}D6JI~I^fiiS`rTyMt2No4{j!ifDY%e+rt?U
z$51Pxz)nF)#fE4x1$HN7WQ)l@eO0ZMoN+)VY<&%-Q3YSZ1g`Z}LB$*>p@Q}-K?*of
zI)N<lfy9cSeY(fN;1g4;++g{c(r8I6D#<Jc_stAIDFu{hL8WXmXru+a69wd;Oz<HD
zAR=ASZo}jK4g&V+j+#+B#b>xea}K2;fm-0d0;LdGfe%@C1u0k%Z9+)qxnKv+%2X<G
zQKMuR8v_G);yg32EVZa4H3bqa-aHHpnIIzF#{?7~t@i2d9kYW^G8}<L3#H+K>I@-J
z{|l5KAfW)V8He@IP#~*8ij-MFLn+8ZJ`A9w37SPr2W8okqSTUP=mAQg;hszokzN$2
ztJHOp+aAt<HcP1O7PNjCXqYDxM8Jo@;S5MwLA$Y@AkaJ%BuWsyK~QTEgyAhlh%2Zb
z3ZVH2P_rLmKWGdm6GWt6`hD#ByhtW{I0Kp_$ePVXN)piBIp9VaxVYlugl=8SEyw|l
zltb6iL2?Kpqe7a9S6~@c29`s}>XIV4C%&LECow5LBQ>W0I#UE%(hc4#l9F0nQj}i_
z2^l;tfrg9|IYZNs-dkE)i9Tq7HmF6k12jhi8lylD0eDdbV?Z+oXe@!`J_N#E<Ow>E
zC$TorVOBy1hcCb;XFw^JWV<2#m;8c~%-qbXRQ<fX9B@KFSPpK3g1C@=JEZZa3_GYo
z85Z?q<png$k$wLJ9`fMI3XcPzF;32zfuO~m;4=f`^YTleyP-CNW<5XzG_x7Oa-9*(
ztK`@;88nOx8eU6GE-cL~f-aarlrWGuMOs-1DPhR5$`n-5fP5w;d-HfWp?L_mvC
zC3`r7e0$D<+7_VHQCyN(1YPtL1+oZ4q)*uAd3iy`KRY;se2Yp!9s&(u<mH!Sra_AU
z(2PCg<Pykk5Dt6jRt*kl>xG=4+YO7QqSTyJ(5Z@`U<7Yj2NTc?N0nG=1J%AD<3ZEk
zkWwuk6nG#4zSJDffTnS(%{RRV1t};wq7?9;?p7v<fVKz??9=l<ewfF6+7XuYsb1lL
z&Q1g`ssdFwFvoy`1w0lFCeo+e*t+-bBpLhk-Ic4H^V(n&u#`5(P@AV{!vKh!390M8
z*rjL4hWZMW%0rVErBMOul7LHE@VTX+kylU?9polRVgRkOgRJV?<a#OZi=n)Iy6@gR
zL5bJeu#O5@RSi;QTb`Mco(frt04@hXIV>D3hP|>wnuMgJvcv8IP{#mIWrt)pG%Jv`
zzz<U2fW}4BixNvx^$SZAQ;NU~T0xaPcuf~*BFiy3xwI%Txe~|LbWlqWgyF40XmHTL
zNuX*EYp{Uq2VuAypus{`H4JHgfT#A*N3CT+1qCQ_;uBL+;=zO2=&1veFZk`@2cJQM
zgW66(44{L{2T;&Jnj{zwfQAKm?IDz9*J+s*kflPPdJYm1AQsqOFp<8U$HMz$k&}IT
z+}DQ66H}H`R8qp$9fC&Di}j%^>7bifK>mfe0L}XtE`WNU(((amk>O%cDgc#8P$#9v
zXJqD;z)x*J6qXpx7-(7`D=Lsm3gng{+TuPumO}%=k^H_uZlXRYTYyT3BstjP7;p*z
zmDtD&Pd~sm%PGQA0ClVdjTRwAJ05GP>f2gaOUTy|>&_EML?ac;(1@mqr$I>{lG`w>
zg?gHt#vZt615L`r$ESj}!NJZ*LbNOeADga!wMNZ8UG#pyj~rPWSj>`Rn**q|4C+Z1
zr$XDx=)S@@5ttlnLP0|aaquITKuz&X5Ro3mz4^kqOf^_5!W!0!AS-7=Dh!Mx(Ljfh
z1%VPQq>4ZsWl(jv_Q|y+)%NMe2A?*miFLpNlA4x-j#3B$t&E4XpYT`?O*3TGPmmS_
z^qd>e9(B+OBB|g_zTm@VLFaMCgJ%db5{u)L6Z7)(GLsW?;z0+NCze3M1--<A&lWJi
z!iCyS1244z4T%?*=EkR#7UX0mgN|i{gcKg<Ktl?&uZD<$g`E8K^vt|;edNO04wURc
zg_c`pP6=o&03-;|(>`WFLV=y2qj->tOgwf${D-uRL5+cd!3MIgNI|_Aw3!FoPFGjR
zPg77YE-6x1$WO{jO)e>hu4jmsVqnMw5$SQC?0@bqZ?T6npamjmIFE?1!#1fL4_Y#t
zlA4@f1lsfks)J5~N@Y+xJ-H|~u>?AZ09yPH*?j_8HG_137NpNj)^q|Sq3326C+nx=
zCnrNTo`9?ZA10BKpA1dW;KoQWC>TIQ`lM4L#R4TQ_HYIyh>NLHFX)3h&7gD~?~)HX
z`^_C33>e!$K@DRNhBuC(aX}qNfC@OQ$qQs92*a&~`km5=tz77N<m{j=y`cUHWPKfk
z3@*X&K1L-8^**)Sf%80a42z)wU`Jd741FyLaygO<O8KD5I=P@UJ~_V>cEblEb3^*+
zS70-*@UoP=y;rcE6eZwA%HTEf@$sN62s(cZYwTm#5A{4*bsUbSuQO;TEGYeF7DHC|
zBqo<+mO%$D!8s8`K)3(BMU1^c696@x0=lpQYdC;%00_ee%OHgi)z@`F&o;{gB@EDn
zF6fB6_{=nvZ~=t|xZwyU;4Xr$V20hiK=nZ_@X2PNS_o3XBGM>iNeO&|@)hXn09u93
z5nM}KAamc3?4XA|f3}0BRZth3^vVj_V=e$~(TAVFfSh?j(Ev#+;I0!QJks_`oa!ud
zwNL-Yduw%nJA9@UbQl!)&Jum+oW$bd%(TqZqGEmD{1gx?#TRtgLo!GLlp#Uml@P;0
z)_@}#Or&EN4)r9Fn@}@KN(!JySB8MH9cUKaDKRCuB(bEl*e5d&n(Gjr0VR46h8O?P
zTt~Wnpn@8!mq5mYFx(2Lm&mK(q45UV+za2~+W~5?fxKE!l$w&6ToPYUl%E8>h(@M|
zhanS0q#ryY?>aY9%^^LZ$3rCQgTDjRtE9vms9Xl$t`QHjDk(EBu?RZ653&Y_T~I%h
z)v!V;S5eLi0`J8FMJ1$p3L5Z&EWd^v%g17${?ZY&I6@uPLZ_<jGeDg-u<hU^4C*C9
zb^t*}w%*&NUp*7W<?)*zI`&Le%f(R(32+Vt5zx}|gB|>u2uMLrRm&|<eU7Co0x7DP
zVBJ+3o<Il6M35E2An$|A5HJBPB$@5gjm=6n+Xrldr2!hc#RycZ$ulr8V4j8-pPpHk
zng=~1550nc%+4Y?6`EP7S@WlZiU?361l-Mq?1V|lgtkJ_WAK1o`Xh@ESv!q`VWCFp
zv`sN+t0;U_4wP%~7nP7skuEgrP%|K|fC2(EDvBso;?wdHbD{k*kkcRu2+}S-V3&UV
z=7jn*0e@IxrbRbD2-F~glwydufVVqf3rV2mH7LoGUf;vo#zn=c`YDMeiJ)4z9+Y4~
zVG*C3nHQg56rY<|5f55dik?CtVX*<Rkq=U;kTsnK>7&92WAZ@`(FF}vgEDYQesXaU
zcH7}2Z9icRA0iLgE67aF&H*0=@foy94YYVF-Zi(Nq!QdrfrL6D+Ceon2*Yb@XsDBH
z8O|mS$aD~f+W^h(WVL7^gL<HI)j@+edHKbuAU34qzY4S(1LQM^A3;r;Oc0R{IrCi0
zKD}+lq4^Kb!Vi+7AZf$bq98XR(UyatC+(MZ(2c^spiOGBLI9GU5YvOm4ghTh##+$8
z`+X3`6Q~2osvIB=fF@|@@G4{jKeAIm!)ZuPL4*aQu6zWWs(uf33RxvCic^qnKMP6<
zpe70=Zy{`l6f!r_w<}ZmB!is%q=IDBF-Fj^0M_t;6#aJ*8|fis4X6uGS*Zt&8t4J%
z$f1LlOc0?1NgY?vLx-$&YLK`=wia~N75*TAH6<ZIK;DT|$OS(%2#_6-gDYh~{Qd+s
zH1h$PF3HNH_)4k&pt2v-d5H(Biieb6kaP+fB7kgPsGnc>(7{8@9zG=k2@+6$g#1)N
zXq*U<5a5{<(pLKn4GfB`hwi0E8m7tzHLMZ26jXF0w?*KmuTZo>0g?`}q=Xd&Qv#~z
z$!e8BYzGgLBd0=8rHqsn(0%>@ww>rDG~mfvN(`|ZT2Ln!rxzs_WT4D@w4#Op!WHmR
z5_Y-?B=u1c0^l)v<PZQItA;fM;C4e8un?eHd6)^l=K&=gKpsbO3nCjpoNyDpt|F^<
z35^Z#rdH&D@CS8mv1I~y9)y*O&~_7969kCXzkV|K5DV~XDrg5OFBP=VC_Xtiu>jI^
zngCkl2O^+-dVTx!j7|0P*m#m)9VF^F0yIm66!0J$GeHEVwNSs4Gux~NFL0p?CgY0}
zVZ+g&<))b+BE5QX*o=MG4PbX%n!r3tj*XzjtwD(SZIJmmEP;B9f^rq!5Y5fZ1D)pt
z>RuqyGpLUNE>gin`lQqK9{X!`?b9864z=1eMZ-KvL4t<cA77N858loLTCs@~sUXMT
zupR1qvYLKK2^w|FN*HK&0O%|X(8PB>Xvz|?$tylBF}Wnas1lOOV_6v(GC@Q-PehSu
z_x%$4bj=svCLPOI3kw(Ox-19O9Rr0<Jb0ZN^hS1&18}$k8a`AwMgi3E1+5$eUnv4Q
zkI%ItIkf<^rydd%h)9BTeUVlVL;Hi2I|8&*8f&`1uoCKTs?Rt;ciC5iLIX5Q44#dF
zp7;d`2gK=Gkc-!(?bA!w1XV4Y9sx@dWQ_qJC06iMJt&jnJl6}uW@vDbwQmL7#?%L$
z5D7ai5~=+Knx=)s2wFMe=6q~%$f02S^x~^F?oW27z&uY@M*!jk<SGwzuL;Cn@K7=$
z@nP5t^*Ys40(4&PA!u+6)D{meO#&Tk9Fm`(69`=ZgK!l_`h;c)TDb~jGuGh2a0oPb
z$QmI-N|WI0=)vo}mw;O2Ap0TN0aO=5DrAV&2KMP%A?D)sx1C`rf_hfJ1H}cXmkhp=
zAt$pKx&jR`%>iqUqwS%fPE!Qb9tr}T4+-gswSihdAOboZst?;>;{+QQ1vOxa>53JD
zkG9wjDsw<XkItz@C7Eg9rA=7ZEP_VLKo~w+22E_FSqIv(i8Tp<j0R!2@z5lsN8IX%
z<otrl_@Y$s<*=Z}Blwyo$bcgx#emWdq_&5&osp)Ip&lWt69FkhK&xEwHQe!74)rFD
zhZN9?(L|$A1Fe<L_USSX``_Q+L)nT#)MeLL)_kLtr5Kh&gTR!?m<BJSM+xn(poTQa
zmgK}@XdwyqFo=MzR=_;Hm8@C|+E*%o4jJG*eh*|j<OFX>ibHY&)W`P3`54qx)CD!6
z6Vp?R^#eS8^y49uy`b6}l-MB|3Tz)Dr9pbg-(gb)-(lxqlGR=US?Zaam<}Gj2en}#
zR)WGDoT<PB^h5$AE1~`*Yp4pT8o=0SdjfPdDrk%tlD9#H3gmEvlgHmNsj{-!LuXyt
zs8t&v&IFhastrJGG|*+e@#)F&pmPBrMKc~}Km&r7X$~#=A@{A=+Nb|Hc%b<GLwNs~
zf*u{{m>Kj1T{uU|Fzkm00j14U_`#q4piT>DxXL5Hv<SzA=#Y$e5;kac5|#(3<_NUY
zM-jOX!)B=WY1K5F1ggeC*)cdHzZ{Y)5RL)Ws~`-oTcI@`tsDcg8EXLnas&v&9Rdv%
zvdW}_O3;0&nR(zkUma8;fb50%y$RGx0ukwl_RV!^sdu!8GoXH_+)5TU*p?PhxPz}x
z029z1mpb-v24s+uavOKS&-(@22x@s{f(XR5@@>b29p|(wL#OL0w^1MUSYObgV31oT
z-$kz#p8i<XJ{`t@#3a&UaL}X*d@*=IeohW#8F)O}I#oSq_+>-r%fk`Fo$v-Wi~)^A
zP|ux+rY@S5`UUy<;I{7_P`d#%`T#yNA-=ezG%XEu$5>`@2BbvC;{u3(i^(d{k*YSx
zEobo=iFqkGkjY}up$XuNIv}gm;~`ZK^xi>)gWyZ6xEf;~onVHadq;f-fi}Q^59^1z
z72+WD0K#w&G=Rw3)`=8A1x5LwEd!bPdBytaso+Hlpll8)UeM}1$iTD`Y`K{^toEn0
zoI`d4@^AsdA>jN7;zAli7!H9323eIaq>BOC&5#MY$|xRmpG7vfQ~U`uO#-qNQs{xk
z>LDxNAhi$X*>6;~e?Mr30Bk>+{~?`nq_H|k2}ot@uaRgyXkjQ3r3Gqp8xs1tiTbG(
zB}IwgU=Kt+=o=IOkZ8y7HZ<DF8l3_)bU-^<^-HSCGr@-`gEBwXbPuuj17e04T0D}~
zzd*7V)V}}?d_XM48plYMLOo0Azzd|50b32)uL80kw70(`vjo@4;ul~SQapjR@2KYn
z#L`x*-bb<;>V2}pJS#s5l)qv1QUj=70_Em{QfPM;eAF?BNQa+dc=grl)l6G6U}YSI
zRpC>Rm7rnQ_>$D3+{`@CQQaWtK#F;g&6ywq(|V}4DVV51S}~NL0`6e?ftvQ9vOT9b
zKLxsc8cQyNlydM!Jyo3mT8oV**~8bsorJA{qjbMG(s>e~U69}-)<DO-x)dd1wC7<S
zhmLK)Lx75I-~v@Gpd<)M0Ep1Xup5#9kQ#ofpk09AVp9(^E~W==eSr@>*8`n`kdmJa
zy<HhnYk^i!XM%|IBWs%^UfdG5hclqX7+I^~zz)y{<tmJ8Gh+D}7)n4M1UV-!wLArO
zl`O~=IGh6UWih1%3#9D?D%@B>84fhU4>~^%K2Zn>lGC7zTR}v+U`THqZ$OlNdi<8-
z+%G>Qz;ZiTRT)yNssws}sw-$%9W=KDiDtwyA4oL+gjFdF&>jV~?EMY0R~x?38Z?NN
z2_g{7=3x$iv>Yg%?*yF;gE&3+IcP@;$gi;Lb3t=o@sPV^AOQiYY9WUfLELc?77!Pp
z>s}}wK>*nZZcKm%P#_yQ;E4cqfdaGx0=ly<6GWu1j=B`cm&S}ZOArz;lqN_-ZCe9M
zkf8JS!2?^6gbLcfQcw&D5>R=JJe+wGePJM_rB6X(Npc3{ypWxs(q{%dc{GCZ1BgIe
zY%@#gz{}V@@vu1$N}~gQkx4Kp6hK}r$<M^xB?%h40Acv}1+;{sn)RSbc&yb9$W{=B
z+YBv`h`gJ%xFiw01i~CNOa!tdINmeZ$Ily5-Y9}bOF%?A{In$ffS?3MA5B<!L*!zU
zg8bsl3eXv;pc1YHWEW^5O=fXUW?nWV6@hHY1QD24L48E|pv5UJ1_tmxYyZ4d|03V~
zB8<&ukOnj6wt33!1~tR5Mj(cjQ1AMaGY0^hazWiRn3I^43Le);0EIlL@=nWywja^U
zvzM@ywD5aPDcZaUyJQi(tq?Ma2t9X41=NNIZN(`lf?jwTUzD1hn3EHq2ELIozPPlY
zARl%B9v){xgNv+5I>@XIp0$pk3=i(YfeGlXA@Jkm;ZrAM_2ZzHBdu-#jf`PUBk+~J
zu(=zkr-|J1P@DnkFM_fjD8RudGh&^|#Yl?KQjBD4K*5I9BN#S7Jwn!uIHV<qbe0f1
zXdxAN*v>bxI6E~Z0Cbm*Kh{M<@Y);3fO?fW4#9br0n9dd10Cvrvg!#)0)nlU%?A&G
zfoujhVUzRo<8u>BiZUx8MLBvy9ditbYHk2og*EmezDKed;(Men0;s8H6JK1Ess~yN
zmzk3aJrE=YR1txQ^aYYf*vtN?+rt^qhy$HyOUxKfQEEKYXi$DI2Tj_6sy(O}q%Hzk
zlnEl>mcSVpE(W#KZNSx=UQupJChU?TP@fpmF@7vH|Kz_)EBka91L|OsGu0(f;SH)b
zi*iAGiy=<MV-JQ?K_ezMP>({x6*ON7IppL!!`<n|w_WYiVGO7<Dd;?d%T<(vRzM5b
zOF-i#xry1(?R<D#0P#RES<OdC*#PQh>O;@o14Xw#I|D-rsC^F_ID&LG<6*r(NH#%K
zG?1bSsXq@55GpzcEu-ME8yXa#y<tQo^2|KYj4ZfaXADX-pq)*b#ku(@rLd7Q(7Cyp
zAR_&1lYj9iC1Lw?$f^18W7bHw>ISHV3`%3^sU=XmKv500225bu1@#tLL5kFAg)WE$
z#qfW42LV(Efv+6}6VU!6gMIpQv0Xxquk2wBSh7kzB)j4LHJqy>Aqfe|W~i@0WgX-+
z1~3V7eI_WjKz9cu=H!HBrj{3jH$Y~lRfebL<UkT=O*aQaCWuJ?-rxE{V8?s=^sA>@
z;$J=g1oIIVbBjRcf>J?g0caH01=@218H2+ts2hnqVg}pDcuFbciX_n1E$}Q+QEGZ-
zaY<?sILARe11dZr_l-jW3BD`-8*IRqteF8=#Ry%DgVe$Tb*iyeO%VH$ayz6KP1f)N
z#CpQ}cMwG%WJldwM8_E#{A5{=lsF-y8z2&ty1-=tC`&q4q!vRW08|1&nzr!mTWOyy
zc%H3?4=7RB2_XBhhChb&&=4T2S&tL~$g^@Ds7nDr`2lj&<Wu#lcC!7J_UTLZPe0V0
zRu0Qa)U;e4G;|HBdWw?c3lfVGbBjS&R^`U0W#&L@LQvR1;)3Vo$s=o<H0;yYeb5vC
zI5z`2#zsx&+yQmJz)fX@don@CAHZA#YG`DF2*kahusL=})}W?qaGrWJ7kpo_hJAYb
zy+;pi+0$UdI@BKdeGD4-17(er%(OH})<D$8@Z1S0BtF2(CbEXuAcX_8(T=>$KL~U-
zD5Oz`ND1&3GsF#dp@WQMc^}1QP%{v5#v=HZUQl5SI%XihD848)r8F5MSwg)309Hyp
zg3WLc*;FVk%E`%0G6I!EcVL;pBP1liIX^!;Gc}+He8y2e^uP;55I}6j9Fig3R?uog
ztc4GT70|+mtO!Obd=RVD=Yskf;tUK7pc9S~i-J?rb3uorra0&4<QKs=v7vh!X`UR~
z_@thT3_uMf&`=P1l7j>WlGV__pk{vG3<?fVR~3BL2`CqUhqmLB6O%Jw7YBjd23fQK
zanS?Vp2q*M{6|d}xquq4ppIa9QDQ+sD)fX4JZ^x73grWXFF}hHK#e5yVh7@Fq=<l~
z0m`j>2J$SZ`iqax1Wk28_jMskJPfO$zNOYIPdh07p<RV|a4vy(9#lU<c9TO~06)U@
z0W8j`<pNjGJQKG0EewmHUZ*tYL9XTk6@K0D&<D-%r4(dB{0?fJWr7IkjR@~y>HR&-
z@06}qg07Y>)`v3Up}Wg&f*b^DjDp-#1if4qQY@e+38Z2e(h8t7K0s&LfCA+osAn$5
zz`y{Xc>|4E$Abn|z<d5cbu+ke1Sa5<9dHK3HPnlw22ctGEqefu6l7MV#%Jb%CZQm~
zf?iNlJy>AVuy;VtfJIF~QGP*uNofJZ4WLQVOb~$>m4*!yLjr^9$pyLv%^g%=fd-Nx
z?m!O<qyh`#4r(QlOQ2=7psQri?FCJLfiPNPpq9N-91IL4prQhnG$BXa#e?DqX99&3
zBUDcpu<Y^{6c+p7^Pq@00EGhx!@~kve33Vc138%lasWVbKIm$K()`k5&<$oKdEiNE
zJ5bR98gOvSED6rfDN8LPX<~s!t^y7Hkkrs4tCI_9Ps4YU=jG?+K$hdFf-)_r?C=EL
zgjk&Fm6)8Ll$nTeIWZ)SAmtZm1y5bq;M}^3VLdddP?~Q*`2tj=vw<4<pt38o7&H@+
z0nRXxx(;*>UnYo1zwmBCQiF@Kefnyf#tp%1;0Ik%%Mm#1d9Mu?bADdsu}@#=JLk^(
z02OE-n_BkDg9=GdgC9~@f!ex|%SqFG4oan87PL=~_*WCXAw(T&FImIvNL>v`nUs`Y
z0Sb9eQ0E@BjtNwur&g4t=9NI40TP28zzOMC{<MQG9}$Litcl!fmztNHpOTrEUaVhQ
zk``|PvJ{ly!J8{VBmODSnRU<r0%Q~clG+|1&c=l#cBHu<kO9z{l=Re+<nokKJBX_h
zr5nVM3$QtAXm=R98KC{R;Q1JE(*aZ@Ko+UM&42{KOIR<KMrBbXHv>ZnC>MZ^@lVXn
zO^i>+yrvxDCd`t6Ms5NH2A=W}$sN!lfyl)qpuM)Bq+kT96+nIlP3h<76_*s1CPUX7
zfaX^-K}33Ce%)c0W)u7L(^pg~oTXf0gKi|+1)6#XPbq;chol#f@i^>&`iIC{5K=OW
zOF%Q{#rmm5MfpYG8V@u|0^Wt;0+S@E#-qSyP=5(etRb~EpuQw>hjdAPc4}T`6?lOy
zXgLpfeL!wvWfC+GW9b7xvJQNCDH#@libf>Q;IRYh8L~S2kXgX^%v{h)lK7JRe8?@F
zp`i2$8vG1MOwLYBPxZ-9Oi3+rNlhwEhtyAq5(m_l0%3T23Q}wpQ`0#h+mONn#KqCe
zfrbTWAd84;K-5_oq{;%;|4T|NhAgY&2lc){TPBlp@{3dBQ_Da#2B?$(y8}!>Z?^md
zD|`P!d!<N)uO(=GFYK0&)ZEMx$i}XK%D@s6y}%OZ{31{qD=cx!FU?B{&P>ls1a19-
zUHkzqrV)OJ?36*W4(fNR&DTu=Rg$3G4C>*5X7S21Q%ayWHG@{DWr7I!c40UJ8vj&t
z#!t{V9LU>{vI}G>4x6C?K=sIf1PTaH8y*x8Y57H|=|!Ljg5(6yr4*SUBE78h)!N3j
zM)v7Ki)tLXIpN34P^-m+v!w>992xA>uZtQVy4|b*Z81?gX^gb0bQ7qt2I}EK;s7)Z
z2)QB?Vm16!KdP_sMVm7Q&9-KO2<U9JfPFe-UQ7+@XR62jJW$+&Di%=OC*~AnBtn7!
zv12{#quaBI_8Ru-m7(cd56A_>8j4g5{%Fv$EzoczsJRMSBnnA-piBWNdLYpNpLh_0
zH5I7n2DBuA$8Kmg1T`{9E`y=ld2&;8^NT7$EhS!1_=6hBzF;Bj9!F}AK}#R%SPm-S
zvDQ0~@J6y0>Sxf2GbH;Nwj>o)*?_z(395WSZGGt4hRoC=-^_|kjNUP*)&*honwKh$
z0L3p>uY+s^VYsbOuM^pBE6vL+0UeL3Ukt9^z%53EU7!F5r)V&dUKFUS)OC{E9(tEE
zA1qlD*$0E$RaBan2iYQwunMg<#jpzMEn9LH8Kdm4Pl9hf2m21ukb_j<k1%e_q1;Ne
zrPO$=g!+>DTc6uNH3lfOapqXqge^43QaUn?*p2}j&jI(deNxj?^HLz?HOMKDL<P^g
zFb6=xfUF_|(tbu-VJi(PufTgjAUA~tC#I#kWR_*Z`uhlXfHDyX!!r^zAgJRG(0B?-
z`ImzBI&>iqgaVadph0)=<=Q@(d5$^hSo?n<uY)kU_o?X)&}a@xfk4fy2hQz?=<d&U
zK59`PY@fdF?b~Tj8hT-68Cm{C>Z%~G-@&;`1jBNwrg_l$+a;hyd7$PA<etKKaPeMJ
z3fn-U0a~66A`lI?)u%NX4LRLm;Xs{apDxG10M7gAsU`5uiJ+DfWE~S^Z1E%PWG8WG
ztC2c(?~`C)2)YWNasYMUGeHFOQhNsbbpP^s73ahppth3LFMt;~h)wmcK^-K7y`aWw
zCWwFr^#?ns0~Bb`GEE0<7y>oV9P`q1QjM_}_DBOS&@zsS?b6MlwlTQ11V0TW7<5w$
z_F^B&DbOrHd6qxV$-qzy3UHhB)V$P+0@w<7^tBNO&^Oy7jSMItj|^nxXXZgh22gMQ
z1UFgmm<sg~S<{-ZMkr`7yeKa*r&u4_6bFsULfTHC{rTX$<&&SDo{D1`5>lHO8rQUP
z4yb&^TDZe2co+j3E}(%>k{jpXu^E&j^FT#9#9ojKz-<aJ0nKhmDGV9_WF7R5RKi0C
zql)zlOTarbAo~$PjXH=kz{vvSDjd#$dLLB!65)OD5L71U@P-mlQY$V^f@Es2Eg%9h
zo1PH~T48MfE5V5!<V>w70j(hdH5AG~T^&&Ik&>DM8c_q8p9v>0ZGw7*tR#gkM4>?&
z51qK*1#%9kEOE<BFD**-P0UM7Pb~@zfrJ1eh#{l9@NK-0pd)?7v~m~7W~_0I;Sgv5
zQC_GqfbM(+Rh+@41qGmWkmz9mYP*3jdi#xXtI>KdAkRZmHpn^<hFeKB-xh&ZFo1@4
zgG-7s^U{M$5{sbIF?ei;X79o4Ves)01(0Q<psSUjOi;!HT@wM`=nFry1QNfX6RRNi
zw)0*4Z+T|Ax_!F*hXV|U+Jj)Z927f56eAdRLk|uEot6>=DqSG;Gamb(kxN!934Z7%
z<o0inkC{Mu8yv;H{{A5z@xJ~+t`I*Xq8L(PV3y$2asy~a1#3RWuo&ujvKBfZ6?E`}
z5y0at44|eG=uR)VFl13_UVdJDVp<yfYEeW13?HI^I1s+Dn~Lo>v~fjHwFWsp0%GL_
zJLpc(o6wcr)D04IQ2P~>D&mV$%Mx=kL7RUdDHGuk$fB_;h}Ih<a*$eYpu_!az%6r(
zb6bj1i%WA#;z1|xLU$B`PK?e35$X3BjPD=x|6>PdKuZ*|7O#V?$9`&yA81ShG-jVz
zmRgjU4qdhms&<iA9$*aOlQotOaRzu>A#C4ZJZxYS)J6uM&XWV$f0qbbKM$&9Al)-a
zNPmHa^cQH9la<<#9D=mJ(H)cpKtYjN44%nDk84PUigfHLB(94ooy`IbMS}AnXj3bs
z2taE+LiUa@+e426W`~uxl-dkR(O{c#9=M8OGt|!%BtvlgLxUZZ^uZ2T2TF#Z)>V9R
zVqSc5eri#2YJ4JW+a7vy#9Y}-ZTB1jxd)UH(=zi?KrQRs{5)_*g(M0*4uS>{S=9n^
z=?pze=nZH&94Hrn#!o<t!yxq%Xf!7iM8Kz3;S6X&K`jTM%`AcjEg&NokRj5yuqo}g
z&<sf}dvTs+1j&X-`{1dwP!<%nptb;L(QwdS_(%<CxDv8Z7(PA(V^G&#oNL}71s}YM
zxdK~q1v+$wh{hJ`I)>s*@EW(ZpkX0U+Xj-FKrBcz8d9-hwsI)33}iZ_SOYg6KwKP_
zLGlh#<q2w#*u-Zf7H5ENJ<l&l%_}a>i7(DbG%_`VR+$LnK+S&;hIav=If2N%PB2>v
zDl=0+JAOfS$Ah=n!0b(it;WiS?r_0lH^eK&)Gn9UK;;s+O#*URW=U#pd_49zynwL-
zf~?VG$U0rf@V!20abj)(D3^eWYCLg@WH~e-$SM}FG}S>9{NN)xf)ewxAuV`x|AW#1
z2&1P1YB>RCOC4k>2*WLg`kk!hsgQujJOu)D&lu8;;Gl(qkTWPEWX}D`;xn*M_c$1Q
zVrrEetdT%f+fRZDjske;h{txQ-zlBCE6GgGh72)AfOeUJl73lcYB}s!IdDS>T&#cy
z=x*EJcJMR8p+x|t9RtuNaKu4(wV(xGpfx{<r6u{r$%#3s@gO%rng$?ufQP9t9Rm##
zvhpEP(;zpoBm-7kb%1)Vpg?fT&nv;$mJTV+F^65L=>pKMGdv{$lI2kE6ImQ1ZFuDY
z6?&i&2ejceAhEc(JijQ#Hyy_)GiK#Qx{aXj4IWP+SwWho;uDjS^K-zbiAjL!7*N9w
zRJ|pZlt6uh-nIM!TV&1*%g;pm2Fa=<kX4|@Y<zriX%hG}v5fru?0D$TMMN!z)};Fj
zZPJl$BWOAx=mWf%K-d6pyun5fpy5YpiUj2c(Bdah-J}94qd-<dDhBirWwD2@#)Pkg
zqNe4badA8;=K*^E6_nCQZp9%L2Cz|TP(K>nlL8%d1gRguB@rkbkXu^}_Rw;P8#d!d
zY1s%LFaleD7F0HZ=GH;|736^<ghN18BnZQ+NoXNWR#zS=C~&Vy#A7#=(qDQ}W(w%o
zAW-@P?d}6_T5vDQObISXO$J@S2uX_whd_#E%#JLjX%X2Wpdu50T7<a*8VY131xP~$
z%TBJ#pb8u0hIq*Cu6WSQ4rq5csJ;R(5d{<Je{%gN+;}HvpUxnja{5zgB&?vLu4_Om
ze~<zLJkA3mFs+9M2Bpa{r7W{3H8BO0BEYvBfiguvaT=(e_Jv)%i=G~kYD+3*3M5y6
zR$(B803N%cAwbsb1-#7;@;K-kl@jpA<jmZ}^vt~U_`Lj*_{_ZGlEj=G*jWYW4neA2
zp@BfTLvUWM1<3(ORzm$w)~YwOtN>aUU!0m)l$-$y3-I~lpw(ZXQ!IkP!r_TUd6{`c
zZ=0aLn?Ni0u_g&n`3%DFG8!5z)N9T^Wn*9fZw2ztNpUPK$;WZm6eNBy^9A)B(hq7b
zfU0ds`yD+sB3TU$1FBcqa-hAQpt1<Gj=Z!8dR7@Iy<~z2_#_9M0nHX<b!d?)Oi-f<
zlrqtV_&_as$T<=iLr;()FG@>B+!Zw_5FnKTB;GOeC6R4Wq~60uQ0o^oy#VSxU|ntq
zX_O)j>_fwsbh|)F18aW5umYNvK&^7uiqvG^(wve^P$v=;AE3n&;C*D^jdPHegAHhr
z9EeEoJ)iR5hS$PAeW74Z%>Q*)VAUm7qd=({$w43kGC>5UDNqMdGSCOQIuJZI6YrB)
zRq2vioDA*MfLwvx2t|rkXi!sPEy#2vKZ3Y8EQ9)ytcC@0z9~w~D^ANV$_2N0?Loa$
z&_D?IB3sZJDR@tO7U+a^5Rv{oC?@n<Y@vO6@%GnCHJO&cB8|FEF$JB`2HFA&DaxjS
ztOpV4Hf_i2#Jmgb(?6tFY-W4E7`n=knvKl`ppghrFn}8VxK18{bTIG2mbLRi-9g<@
zI18FG0kttp^D^`EAVC2t-y!EAK@Nd_j#%aiNfbzf&Y+PUo8*AZ<m{YOy`22S6pWGP
z`*-4P-$l>0PyfkR_lV)wTv%+9HQI}mut1H&_*}@!B2YoF8<ey_Ltc<8cjG~hf%pfJ
z$T8dk@eg?izaqH>sgPa{TJHmz@^UUMDob^Q6i5gcfZ7Ql3~wty^E4H!iK*PsyG0y}
zit@{`uHgi^0EFSLfQAEE4FyO;4tmZUsA~<14baqA35W;=-H@2-nwFNCoSB-JTuEXx
zj(RQv#R1lm4-^m}j2;-2P8h>4bWjE5c2FKG$uEWtU4dMKqu_<62&!2RYA0azIId;`
zSv?4ptvc&C85n{<<r^gVgIJlMF}(B*C3ZI66BO;!H-x>NHQ^e3YbWJa?gbU+pzIIH
z^B~(GL;V<5Len}~L;Xm39_1X2)1Y<}sM886&>=DquY*bg$l?&zMe3<R&ads#<5lH%
zb5AjWm4j4u#~)Dp95f9J2?azdgS4q`+NBGB$X4MzqzbJ}sA@S2XsZ%pb1NvtLk_}#
z*JO|zWRzi}X;e$%6G3OJfjkQdbPx+UbHTeM7hnraDJ^cv7%8{_n~Nr^NJC0^i1Xd{
zfKnYOg_VL2TZVWZF@y^_XPXgr9=sGR#Zj{*!vHGVK$Co+gkM~mRFIQj0zGvWJqf}_
zvEi$<ARQ#?1;;#4;s?b&y3LTpPxcWs$>5bYC~MctLEZ+HHsDEb(2NA;)H1}@n;5Io
zKrMeNjAA1W*DEYBg`TemYPn%8>M`5`Ed;~K*_DY>;lpOTi$P~{>4O%eff%4B04OFP
zjscehATA_RLT(6lwok8Be6r_%zcVadQrj`eJGwx}UP2s$Hqwvb7)Zz<HE}@~AK+M&
z2s#O-Bp=%31^W_2q*u4HerJ~ZVFzbGt8}tT9qjAxKr4U2htcHcr9#hG2U!g%Z!zqL
z_^Fr%DbEBW<$;}ni0qf~lTL{g8`!6Z$gs!hmT!VaHd6S4S_d}qu%lDrF^*!wFahct
zN;^WR^VBavWhrRnBOY{P1ZXr8y*P!hGKH+@hHto|t~)@LC}dm!T$Cmjr9-0^QLbY+
z1R5Zu55XjXW*tDy7PpW956Jz`5MLwW57aUSVR-8pnwCko4Ci<c$aoNjTLJYCXo)8g
z1!Z!6N@_BAbOCgcN(rdB8y}yST8>#FLmIsB3sbMaS_7bUGDKK}bQD=$Cdk(K)Z*mC
z0?<)2nV@-8@J`+I)RH35cIlF$O7v<7!+NMcK}R7GVSPykXo>`S*}Mj*-wvw!<6W^G
zp#!lBbLfa1t3YW1Yte~e3DiGi4YfjwInZU9c?Ff3;2VYz7NeCZa)woT3$-2X(=EO>
zKAG$62`y8|>&7E2My@XqcB9o77<NNF4O%xqL?oiEFwD+LO)SdOPfJY%ZSjpSPR&V8
z2Ax8yUz(R$mROVtx&s$32&z<7;0+JZL@D+)T^R8K&9ZcIBF>=0a2GV_DBafw8VO3y
z&r8cp2W`VBNi70jRlN{ipo31cO)V;^gcNPy_yiHqU2&L;Mkw!$a)3Ic+u%z&K(;}a
zIYA}~kgS9TK>#`JFr+>K^7Zfq`8kP2ptORB2(<hI>4B=)r~jLAZP~`XX0ZH2>9#v$
zR}ht%FdPC64odq3h=p)FL48utU;rfYLBo}hTV5f(5hi=+JQ4hwNwNwrq?mx6o7)H)
zjs#V$nV^ekKn{Y|<9M6^^*w0b6MRt*N|ys?Y+__9Xm%%Njss*Uq*0Dx3e+)v<fKg4
zCTP^lAwj3_gVN<bP#FdahTxLKyp+VElwi=ZK#b`Rc%LOhHq=+36n;244O|3j_hGH#
zFx&tQ74ojk1Rrt?U;GhYQdC+DYAitSsRV@xc*qn~vImwXrW7TXWF`lfCKVLrC*`E(
zLQ*gy#vo-0(pq<Dywk#MAX~8p5QZb50R(F7kz5@jWh0EGTJ@lFs6ozv1OuWq?0;%W
zn;C<&J#^bRd>1vPg&EWZ$lYucQ0fJpae;Ns7-Vou*dE&J(|``K(Xi76DkiZyWx_ts
z%L_98!R}8Hf(^LQ=#<w;SXq~xU!0kjiu>GI%myiqTm-7{u%=gB#T@7uI+D{XteC@W
zzJaGVbMsRmm*IjMIN)<#;(b8Py^>&XGcO<eX=Lym1=&va8P-XmsS80Z6s+Ne;TmZE
zCTk24GH{feS)7bgvw;>qK`aMF8@M_G6X|QyIgaa88`-C)PtUK7$S;AlQ^;y9K`e((
zQ$a_qauZ8Hn=8ScN1S747_NW@0a^Wih%4ZAKyrRkWlkcfSOE<tVhw~?iI=W#>s0O2
zVGL+7PgV(wW<BT<rp#REiM*io)eskeQUf>!zy#c4I0G60M0O^Ni&8-&!o}cz@?B7?
z9Mnb6EY3^;om2si@&?d?4G;lcZ~NO0&Vc%s9J?Na>;m<XGmG;QbD_I+LAGRq2)GS!
z2Gm<bj%=i)CTHeCE*^XUvIx}Na?S^z!=IU-7hIWFl2`$$t3b6DBqTwj93TuI>45r<
zbSq0hW8I)6i$2HzG9HBCRzQ74L2}8=D@jc+N-Rl5$+d_PRd5s{Jo({!_svP6j`r!B
zCm#&_^eP6HYbjWf2U;zhnV6#=pPG}CSrDI*Sd<IOGax&$rdSLoKm&ouaLy<xDFE%E
zE2zvg2aS+{&VUQe%gimvNrhad<(8R~>YGpeQFNr+3+jeo^%^K{K^Q%L$-2H0G4_#{
zm<K8eL1Pc#(h#(+4Prl9>_S?^FYTcHrkAkliGs0h(4Y@d-9-$$q0vfSeE=PMfgHP@
zn_7~XT3nKun^*!pejSmxu!aDqefojPVtT7LaKh3zsFtT<6QU>$TwsFAN=QVYmCBI7
zKyn5&FhD7XWM@Fnr~+LVnUe!)v}A*N&Y(bm6k?!R5Xd#tv;T+7T3)iWPtSMxz9xH%
z4b1<b{tKkr3MN4r6x3D*--29Hl$e<po|>2qJ&HB82-+LQV=UA^phhmq{z2+VfV$q0
zO>t;PX@UxD$oUbFP0EV)>Bb%WZ+_^*yAYJlPlLC+fx-dghZ4}?7NA9|DXD3Rprb5u
z@{_@dEj}?XB_6awF0rHp9xmX>1`+8H$1&PN*WSaHfsqx>NWlUdr??6lNCs6T$vMyy
znL+b0kfjqdKPj$|+AC$B{@jFni}ex>ST};KrCW%y23F9(&$9rfGDsRn%WAO23hDkY
zzN&xRX$(th6c%=wc`2zC@g+r>$=Tq-4q-ni#KBcHn1C;qfZGpElQbBD=mS+Wpm7MN
z%)G>+%qm>x!eMryXy6!-J0KB)79hB~Pecxxq910ImS2<&ozMVX*dBBbG-L`+1BmmC
zK9@@VyUgoipT7GQ<L3#Q{;)Jarj=-CH$kif`x1|p&}b*}Xl87ed4SgJmVnktATIN$
zNKMAN!WvY&gD|{?hc*%^upMW7g6sidxLr`6Qk14Z%X%S4AA)jvab`L=ll@}Ee!3&L
zfes>|132)**WstZ5!re|+Qg{>S`Wm^$iM(;9fM4VEW(1MUnDCa{zadS1}(|0&;uQ1
z0G-<hClC;kzRV#l{i3z1J)8lJ9nd;-BC4s9)Z7BlMa-aD0d(FdcvnY!Zgw$9B0dvz
zyf(zmAj=?g1{gL%++0jniyo;4fDUkhOJQ&fwXiZUK!%gRSuhQ>A{s=bw{DI;onp~v
zpDwoW2G=aHC$KC?br*buy8!HckmZ>m0@DT1Fd(ZBjuZx{`!OGZmMwxR14xqx#DdfV
z@KF%h$_i+tQ))fx5`;gXadFU^*?3ShAty5z+%iBo5t1<QxC$C5WOcNV0tIpcczgzA
zK>#SFf>(KgdO^@d0-#-4&iOge$x8Ilf@Ctxii-N~0`2%Ash*<IaASf1149XDe^^OT
zX<l+(eo-#&F;Mto!~?Ld6O9}N%EWjA2gx1KbVF%&E-A{?FDc3_N(G&O2TC@e9szhQ
zOGr^>5Lgsrk_S}zfH1rQg61r$y9Lz5#TptQ+d&xa0%&M}X5rjZOMFX9a=@F7T#`Uu
z0<EnDdnrA&#IZclC9xzCJ^7ze4ZZCfVd#)<Z4@^9|8ElqsFy(f4UFwepxlhrSs123
zokdpD7j$+RsGqA}l$w)Rl3A9TlA2Qj9(DvJTBI@`)Z4_drwr-~D%%g5$3gNx9{ZvG
z2MrbzQO_n9mw}=SR6v8H%Ox|pBq$Yh`4)O~K_-G9!S=X3hviDrECSV6Nd5ptE2Qec
zFdpg;(5VkZ_ycvl2s|E3szAP51nRqi=9uG4sxp%kbIMAp(0vCfi{U3`!<Rf!!+M;p
zN(@V(ekSs|5iIL%|AR(YK?Bsu@n!j$De(p9{siS<5QgVsXmTarN}MqYvId0VRzZCS
zN^2y?C{neHsOUj)TL(Hx7ZkUkh1I!<B}JJP5I=(s3CIKy(DpKeJ#<Hb3anX1U56|M
z?L7wB2X|k5L4GFeo=;Hzg&Z>qYZ|3@Kd9Hfxls{1{YhQN>;laKgJ#0e+d}Xp2V+1M
zms8hvE>QdMBdCuGN*v(30l)+_CXiY@w6NV16cM1VOniK5A?!RFMEJwosE`up6ZAY9
z>W08PkQ+c186q)2&n*Fm2%^}7L<3TYKr;!Ey>`@M4`pxBO-=>|@XBY`#AN6tdg#_7
zHEp290Oi(#j%&tRz(ahBWF^$6pfNv6ii5<WlFZ@~P*I=-8X5w%=CaE{S8;$?nV{)H
zFp;jz?;@7@NY*}`Y5TipEBR)^N&&JeYNX-_boLTtF(SBg2r7z@{EwdQzSyPf<t=91
z!&(H*_mp}cvC9QC9*JZ#BC_GzipsKQ@*MvhNhO;R0S~eYtIhCzvAuD;0a95ZP@5?o
zF9xkT2M0DNGvT+`{_t$miuhD2SzMBslmnh^0QK>(S`3+Ocxjhjb!=bC<AY&Pizyw2
zhMlT_);dR|E*)o?*N@D8*`@o<Vsrh>PzJS^tl1`{#ErCJ7tLx=oI_e}@JPPMaF2<z
zD;{b!rM*~4pC7ta3LM)YE0F>n;d#i=|3|y@nY>0PPefM0#@|5|Ajw4wWa=Mv{X-^X
z*A8fjBDh-sDs2$10T(nNE+jlAy}a<U(%j8HU8dZN{q_GGSV2RzZ97oc5H%Hu;TUMC
zOKEini5=K3<|Uv8Ht1|qP$R4oG-U$5THYugUj0GZWT0#VxpbXt&t(7pTuJ+M)&mu6
zynlmXfkbIW0oe_ToqM1$BGBPPpd(E&90Mvh!0rGO=@^cI1`_qk<}%RAFHl{Sn34iI
zFAub51rj8PbOTww$#0*&qHE<B;dAR?K>}(<5Z@ohFa_!t%4>E#P)i#$R1qJLUz`b=
z0S^J)Q;qHoNIj3#qM=vQ0?A6KpUFy0komXVM191CSAp>1R<x`R@hj4m0Z_kE%i`&9
zi@_-tkybD)hWeP&Yz@z^-~u-T)QbSO<H7!(!o$Fj2_n)@uAG~g<?U+^XF&Z;UF&1H
z7#M=U)`NYIDDNS@M~ZJ+bR*C@F%v+Z2NCI){LVgQbN01|GiYJE1?Un_Y<>szs6iOL
zS8Yh#`g63_DroTnwyVTI;|Q4`BK@iQRXf>!O?zmw)d^ZEA<YC4f0Y=>Kpdt*(=U-*
z*&s)kfXaDLr2}abfRiX9hsdb7{A?04vQNKo!T#XUE-$FNi^;l_3%cwBbecDG8Wxly
zA@+h;;EEqiq+{3%^%>RLyvm?n4roXzvp7B_u{Z;E;++y`Z6Jt%_OcmZE4>+DUZ<K9
z&@RaaS(*tVFl~l<o~-f^sTm5Z)qOE)bwk`u49GzezwM}fJvz)WNg&5?F)%Q|n;3?W
z(H2B22g3=_@E~hdFj9D6KadkG2SB<alJ@D09lNVUC1qjBfKtno^K<fxk|0~BKrJk+
z*#N_GsLv^xqXzpAoZUbKbo;;$#8Dm4N{GCYPz|(D6jXdb;t*s5WLg<MUI$xb1N8}L
zl+~sfe0`T4IO~^y8ft#|MY)MNpbMviz!&kLw~ip;f^^XY)X^BG;R#hF-B7nsD}Ski
z$`;TVCa4{so0*qi6c1V!20a5DQ31op&|nOxFR0}X(5A2;LHN1^JQhO(fShH|w?Jd%
zpm;7$P0dD+Cr}j&!tiPq>O*oY>IM}Q-~mbS8BdS|21;t+WCOPW&Vcv{sfIN`zN|C7
zD8IBIB{R7Ma${$FaZ##XaFA=fZ(>PuhPndeI^1IDdJItAoCzY}hl#)$P!CbwFEC?=
z9byCuOt5WW0&WwW0r6NdStA3`Zh1*!NwI!BczqOf-!G^Y&;v@hpzc+9W^qPhQ3<wL
zWmr;$ZfAwh@KE11pkTn7eKDK>4G_?xC(_3$Ah)i74qhw)C8h{slQ1Q=fh@x6HBepw
zVYEbCOlcPyRQy0zr8a=Z7(ip3KKbcxpi&mJ3KZSXkm42d)J^I+1Lw)E7*<0gm+JfL
zpeKodX6(RK4ydfo$jmDNorIj0lL$Te7gWn<f{1j;I%G}z^onT;$_ow|!rB3}a@ZD7
zO$u7V2Px=4E94<3zd~9=-1g~ToBWGEDG5Ud3Tfqz3m|uZY=^i5v=1W_M5IfJ^R=Wf
zD%+<+#=|vX!-ZtkUht`AaM=#tPtFD^??DxMd@^{S3-o4B#GE~(k04~9ZW!BfRX$My
z79-Shz;}?(L0J{zb37J9(<mq{5m5kPnQ&GH`4!X~Nscd1&PXg8lrzqtY(RwfP$$!n
zn%l^iqx=HhMF6VI0}_jhF&7`7dl>aLQD4M99Wv=cwMI9x6F_MiYaxtbIW&4Huj83O
z^O>Ob_h29LBl6PeeDL@dXs1C5X#Enn2bWq=kY7|%9G{a}49&5iECgwzDcWs#yx)Pr
zJ{@wVkvJ^J5;<N`oS&SX3ZA0`^?$I&9fl3ixFgaA$l27Ow$LxoP&24onwXN3nU|Ii
zKl>Zu8;tQYXj_nEyFiODvHAwX2B>eynva5v$bxr2W#*UU=jVVU4>2DFN|)eh0u$-Z
zM=k1wxfJc84L$gB53)*Egxy$<+W|H7z;=QOOpBqO22};9Jw;F#D>ySfH!(RUvjBTX
z5fXx!cX?nKh9~@ybVHruLEI=I&XN#vWM@)7_`*L;(CIDUb1cCzfas+`nnE}2(#><C
zr`%LXhUFHj+73DZEC_5nI2!QS4)r_L+ZoVH+>fAc07GQ6Nl_Xz=iKtNPv5RubWrk7
zFDw{nc+!CpsOGPNue`zI7HFVQI%1DH4=BzBJyIFeCanN(>%!SWhjoUaWe;^7QVF{K
z0MuDXEGWpSj86n@j4gq*h7e%_Nv0=Zw<p0b!K8GY6n>y_918<O5NMbI5(qm$BeWnQ
zUASJP^qHKKeY#&vU${b55iDhpRYV~-RB{r*t8X`ek^!iIgm@Xmf*e#1nH~IZm)>X{
zu-s;sBeV%lsgJ=i4XS)Vfs3_3f-JUrWS0&*tpPG=LRNi%<YA=s2@Ify8K`u0%CEqB
zm^Y+Ey@1#<Pjx432CaL$jwScOt%e;*2(g-K!OjWl8-u21LHQ0G8SyFk$;Ifg4{^o=
zyYx%Hk6oV^$pnpkDn<h6O!Xi}_`n?~IY8=0NM5^X2cIH<gaD;A0?3!(Ay`o21K(l^
z&TiluVI3&Dfrxb1OL<=m<sIzP_XZp&Gf2vZb@+%J%meRof?WjH0?JyT6LcYYS`lOk
zh)C}~q}L_)!@@p&TFI2=n=Br%JWXVysyG9*@Eqg~@MUYu;He0)^Oxu3$s=o<EbP<W
zw#WvTr@BL<j~rjrfVyp<HSO_P`I&h|sfjrt|DmTBNFU}dBGW=D4WyAtP(a&c!Y(r?
zk!N7Y1QF@8ln%U%-4kyQXFyANvX)arXWt6)^TC%KGJ`g)fl?O4xrp$A)F>FYxr0_D
z64Cnuk10ZTJ!a;m>%*c0d_Eqi%T<(`o>>e!q%1KFM1uBYLR<i<Z!<vz^t$YCc5ntH
zm4j~2Ai@QpQ*uEGzzEc!I0{bypv~c#AOhN_q}lD*IB%(jrvn%R;w?(6Y}CaD;BW?2
z*`Q5b$r+hBDMhK!i#B}Z7#K1^L^_i@6R%lmoIRWYaS4%m3^8rY3rhc>#9N$L20icu
zk&Ym~#0X}xc5@@uRiNV+kuT!74(h0ZdY_<!bmNPXL5B+D7RP6&LJvd*4Kg6_#>8AY
zOKEcfwyF?uR};7)59-xHu4CUP+R2&q!_7YZ@0_MTIZwJ^Wg4Z!sfeYcmY_)(P^_mE
zC6>c4^8>{U*acuB9m5%ra;TWn1PD4+E-@Fh77LW^K&unL3#LI=EJ8|mP$>hczaUj7
z(vlNsfks^?fc%ZM`GjFTGz2Ig**OJjqk<|mh>t<j_?aLAx^Gw1KAm~$$M?p1KCrl_
z+)4w`)i0pRCnYr}GdHs&HN^)s<O1?Mc#8y>fcD<6zzzrJfYx@DTMw!akOCPrZ3I~%
z4r`pHm(*;rNS<K~wUMmzO3?B>=CP}wW#^C$Wnh1Uf+G_|KnwZ5cIhoU@1CwLGJ(2+
za(@@`GBA{YhHAklcZVdx4$KC%nKMBIyw*zJ*Y;pX&ulGNJ1K;mQ3KeaZ-`y)(0Ux}
zDjIOXgUD`>(}NZ5(^KrbmwN0|f+aq(`tUeilAD^FUsMUo`ylI)(gYrdKr<eh+t`ET
zL2YA@ZQ#WPU;>)bS?trfZaw+nsU8ILEE#sZ1l4PxWE7H`TaaIrSX2o*f(g=00jDz%
zfoUPsQ)GoMQc8#Jlt(@a(i9ZB?w|$@=muLz8G{I2$kK6k`}Dl_wxcVucS3!R)W8JY
z^J$Y*nwe9go0+GOn_7~QpQ2ElkzbB+{5^(QP*0Jy;0rSBhrGfXbdXpH=zv_`{FKt1
zRF~9(qSWL>&?;CGZ)&2Fd(f_)MT9A678ZojXJW~!sbE<L)EF+-Ppv2c%|n2q6f~p&
z$uQu=jIiD~YoC{0nYn#>hV`;{r}QkKQCdvVNg3GB#|4#T5I2BQHMpJz6X_UkfF=d9
z+<=t6k<V>TPX(X7J`0xNeDia1%2P8_QXMN&q3dN3fdVPYS?tqI%fIZ8>1LxSQ1Ckl
zWGU7_!EggKP>hKy+)MK^OF+w-^pg`IClrEKUY3BWjo|Fef{;|qITKJK1YvkWgf^MT
zwGgMzK$d_o+#;yY$eQ+pt}`h}O$HsLmy}qXnG8zrpi~YCRuBuE%)mtY=Vt|Lzin{0
zPrp#v8@s4#HY{pE^*a%LKGczRRLhZzpL|fK7nJEBWjQD;aM%y^JXy<HkdhW;U>E!L
z8?+!e^}WX>gC*KN{pzWf_*c*2Hy2WRHUY#5@tJv<CGqi~NC5d75(#J-9>WFDKmaxM
zh=>IAu0L#~K@t?`AWI?j7UJL|hGjf57cRNlr;C5$$Xk+=2#a(I?NbC5CZP5xY}C3k
zFBuZwc&vtcmB>{+h%=YUQj3bACu;M6>;*+Ccnt%@vxwd&<a9GG*s={}m}e=mksZ_`
z0Ea9%Ch=GX^&XLfTqt*cfCh>n)4NXjrFki-DZ#0^i3J(?MX3-kg6xN6B8=`8w5TG-
zR*<P!6DfuzP_NOd)m07aTE{0BrGhTVD0D2qI1hkJFYHQ!)F%7%?nUf&Y}&AEhl*+7
zDEy~_V>kpFI+T`Un1}R$R?I<qUZ5nO$psq01rh0x79{)@SNI7R)OUgzykG$*e^8Ra
zkq{x{Thw<!8)z6CWG|$AK`ar1q*A1rV_Lc3HmVB{^S2NeV2*ZyR>2Zcn1iaUqWt94
zV(=<~W>7%`t~bGv4e~K0q#>gkI`+_g;kvL=jm#n()IbJRQJ^IySaUyyeUS8x)YXv&
zt@6d_>XamwlomTB7C{&Erh^U}0}<)Fv;w*h9-d+kXF$s=8suDXC+;Vxk^prr<3Zi8
z_}tXu;>2|5*eU4jF~|XA5@+HSi&Goy(_suq@}ZTZ5OeQn87a2Y;VZvVr+qq%0SWG6
zvf6}@-XQcWbO;5C>r_wy3mPm1&ALH^<H5J9$EQ}5<R@i8YMJva3=EkdA|0|9H_aZ-
zfW{a#-E<0+-a(gaLGm@oUL2NF5+F$JL{NtiHjWIMC4nS<w9Ix{cv^+S%@q4|@7@dN
zue?r!Wj3;Wj$}E=V$j4DC^kTu9AY_IQoyhr>T^m5u@Os?w}TP_s0s<mOwRU5%_+cK
z7l=`)LF1l!j=;Gv8N+I*|H+zrg=K{#{o;(of>iLKh@jAqV8(uGC3>-rF@la%0D=;Y
zO=3<C#?TI^a|gok4j!aXp{fT2L9;QSlv9$K11;4Ml?5aSk=zaqLP{s_;Rjh*f+p}m
zl`v>fDLDh$Km~Q6GC@ST4gb4yw{pYn({(FtbnOdDVHt>O4oCs5<vItSa|BJOWrB$G
zdV!M@l3#n;r!VQ*!C>N*4jqoBn#E{)2|;^RAiGC?@(S|?eQ>c)-<EnH(ra2WY%rRv
zDP;I6ZODOOk3hi<>U2QP1A`RQc%mDcS*c+)C#cy2Dytv`G#*Q#X^gD4AL>9taY0&X
zUVJfV?+hrff~I#MB_$}wfty2M0y_0@*AC8r`kP*38IXxrB>SO3K-N4bqND<~&A^Sp
zSD-u&9<oYIOLZ+v%)uV<NKH>@08q;TpxB0lJgB4r2RfL5u8Kjj80vG%XMIXReg-WK
zDo!l{FIjg4Uz`Z>IjA`YnRSBSfekwg1L||i9RO-jBl#JW^dW1}Acg7!SZezVJ&21g
z(`}%l7IJ<gWMSGTJNPXHkh+C(e}ksmf&xI(J>a-T_#2+eVe3_(aZSm13i!H9kf-9~
zlXDATH@YJ1gfG8>SpF7vcq63^FytZ1GEgfBv<EghKer&UC^a5B!i2F54Z|_e03d6I
z0=YF<l$cixx*!0Y*=<2v7C@zKe0*Y2QDP-5P(XP(6GTAov*Cu_7$gd<RFPUFSi5B4
zl?tF%C$yac8nA;*Aj1a`U<VR`(-M+-pfx5KvsqyCaubWPQ;UkBMp}TzBtQi8Dj`;T
z=;CrgXf23jCTK^24Rm5m9lSZTn3$cR;G!QyV3s}5tWRll6qi)yr0OT<7lBXADuksD
z&>>3kV7YkE0pHNn0dfiCQf^4GfLRn$>KxS7Mu@XTz&0bA9gt0GzwOfB_?6GAIHv|p
z6cn#DLABlqv``wnY%sM1ae_f=3gpPhV(@|1$Oi_3*12bb2<Y7H7uYeZ?9lcYbwkG&
z<UG)XEvRP;cR9#;i6t2rjsur(AOd>V%}qOKYE^`$)?%u)7TZDVx<R!Wc*jmrdQxJ1
zT4FM^xeUrMnIIw^QkyB-r!$yt%~`7tJG6v)?m%m`f-J>hGqh}@Gz8#l%7s8%ok3|f
zxFj(dW7~TD{KAJ09%A<CFa|U)Q_Xr%9fMSxgBmoD77WZnxXn<HI}kU$h&B%fy>ke5
zz8EOROF&BjoHG*h(o<8MQZo|EGV_Z_oO-8<TR>jM8UmmmDhR`Ssn8Ih(Xr)SpsWB|
zIS1OemRFEpj&0LBMqvs~4>WQUC|zO=8w__q!-lN(2c%^Wnv906(*;-f-k=H}lv1Fn
z!Z$H5F&)=P3{qbn8YI+o3aBu|6Ann0L&JfRwjZdG0<jDf{NUUOCek;Q*x7hb5VTJ>
zWt%Ht^#Z=Gh%BEXwX={LeV{Q=h~*$ZgY5+qn3h9*3TmzpF^Z3LwOlf&!~^Xo2i>ON
zn3n=t(G*^kSb$}e?XlGSlm9BE?9*WkXxx%+DX4*n)n71^;Z~65FUUSkBTzT|0xSc2
zgoFeHLvM_L^vghH3?vvK83!rBkme&K>p<g3Sbc<H1=L4W+!j&>ib7Ct#IYzDyALne
zrNb`ch5C?+)`M)u<8>svp<bt2b-5BW&;%-zGK=FOX9dOQWM)I#D~p*K7&1Xb`WY_Q
zUGswz?4hek(qWBGO5Fh;UqoxKfLfA}<|L#q_R}sMwlfUeWuaa}Nd`3h0Gby7wIm^(
zoYa)`RPY&6kP$G1TObMXgB|?57>HXaZQ3SQq!#PL2dcr_M!DeKa>V#FWFX*?9kdbq
z5;`0~=>R?0r{MbO9ca?G13ttGE-yg@H0a;M_91?uy3L%RsXOp+XMTK9dJ-huGeLb!
z5CPqiCu|ROh&;4QLAAsOYP1KX!-E{rScdF*7l7RnB?`5e(jbRS`GbnsDWJhtP+<!3
zGKd8!RzdAF5QcVsH$a0NRO%B^IiWUIk!nWJd=$vu1*rDMf`S@Eq^nw;)^XZsYM;)x
zW8DnFdv;XvF(Q%qfub7JNG}0LHT0@N5k>}vOc0SC@j!dm*I9P<>9hM7^2GGrpbnt+
z-j;q)S_cI;B;cb#0RSS>4=wOneDbJ;J@obiYHdJ;caOM1<qc?iJ*>Db$&XLU&(DFl
z1LPt|0)Uh?NGG>K${I?OUU5NYUMe`Z3xgs8)MbfJPRvP;hisfjboC+b0gXjNmfpiE
zKj<P9Sht>Pc~Bb^E}+x;z#V5uK@93SgHsN~F^GH#Ir5IfKK;6=@uAzz0yIh_`XD!f
zrcFURzS0tNGjpIjpSFRL3y6TWA|371A?KTW!3GpSX^G^F3Y!!G9o(W1nXlJR%}h_t
z(@!iaNKDR#QlJdH2;?+SHu5Y<E%MAO!`^7YY?jg5T_7jnX~`ft2-=dNwDN=x{(uwp
za?q)?piBe_k5*m=hD;EVF2Q>6@$KEy?9<oP<xG<QxF6Plp|15|p#9a&@ESCjm4P7>
zM5O;dvDS0DYLk6>N;KP?r70_+36{FH3xRA09VrjVE=C*-44EJz-PG|)EkjVZefpn(
z%p+&)Hp3Drnfo__0zp+JxGx849zgbpK(gcyyYwG^XQ~XRz|TvkW?$|OY=|~KvpBxI
zC=-0!L3~PP5yZoI90N^kv|8l@S}F#<0Macpy|gIRIWey+u{baUk_bV`5mKhZhu>ig
zXitJx&H~wtH8aBOhZg3~?V#a9*3=fH)PfdQpmQuT6LaFh7ab)-Y8r$yz-b1=g@gxe
zt_M1u3!n5MD_fvABR8=mtu!wgTqb}l#Tp72)<Z*q$VIg%yY4GMNfT7~rDPW6CFVjq
zP@olonIHmMQ7PD`hkbN=Hql-KR{nstml9D=AXY^eR~AF|>VawjP~C*pYmi<O(#au^
z)&n&&V-Kw72(GYG5=#=}L32)!tcZwXcsm9%nRo@-kd<X%U;y7Pt`EIPJTtYZSRZ-d
z1{9cB{RS}+KL2t7Y9eU(fQXPpZ5pGF)aB(Dr-DZ9Am=%*LTw|1hG;TDM7rCwGaL7;
zma|Wf;@*7WT&5bVI0Z=(;ewRZ<jmYe@JRtL`LHia0*#nuf(UqD56*xV8Km2FoDX&>
zEV!HmnU2E>Xf~j<X$Noe9s&)nfTFWFF)g(!6S~A3H0A-BXNR4*0-rj9`jOJME~1-d
z07|Q%nP13W4Twa1Qho&_wc>FKG%UzUTuA*c=*B~ECm&oW{RFuPH0<DBlvt3FnOy9g
zp9kukIVBcD%ML^qg3S26hMf!Z8a6jhbw`0lI<RIr3>QFyg{&k3+p>q8*+C;v5Su}z
zHMn*F6X{cKY~6czl8$}4_rx022|}x3<)%8;^20YjB{MBEHO03yrzA5uClRv58nh7%
zVmLVEBLWt~aEP~%=B`1mu_-Rig)VLet<28^5$Uf(e$Jc7ao8TtfTmecBEz@~1=P+8
z`UP(mf(*c63d8|ap37weEu91T8qyv)0$QOBBGQXBoI`z<*4w9l+7OUEt9%YDaLKBF
zky1Nqo7^5`{U5mRXMwB-5$PTEH!dw+S!bVqSzj=JZvAYUG>SKXG8(9!1Uo7xu_QGG
zlIcN7E)zt+PkT+DZ!WshV4DF=+@TBVuz}KFUTF?SXCGEKLPxqjL#s6E#={CwNPq^}
z3KCONV4;8*UWB)XR!3b5<V$0wNhqj-(n2UaEg&u;fOO;)?9;#Zx4sataH5Ira-a|Z
z4QV9iC4+WZK~n-E4B$hov<w5#FjomE9@2{Pt6(R{Alv{iYfm13$E3>225VhVS{ou(
zgM;fH&`=BVLDjyAB^jU!2vSCXN()E<3~6m4_1mEZ9rc_;U|l$p)zH$BMsvfjL3e+H
z=Bvx{^KvtbQ;V_9SHm0DFa|U@XyhhP{e!jYf>{r37oLRm;3zFJAafBQ64bxg1M1a)
z3Qy1gSY=LPQYyxZRJcPRW!Mu~;{<C(2ufD?y#q4^64gjGF{l!^i3c6ck&~IEmtT;Y
zS6rMEU!0L>YG?$lok6DKPqaj~nqjsSRA#1tIsq@>1r{DFATBPZ^#EHDeAqU)GB-E1
zq$o4l5j;2;kXV$Mi?OjEG=~7f@HHRM^h#@If|~nSV;bZb5QaMl8gA4{xUls&Gq@QT
zN<euTwAwPUB)$^VKZO)+>p@cuAR;{~#rnQ~Z@PUt=r+4N-C}Are?YkhF`u^-%_+(G
z`9;u*2jq%O5P|6wXsFOSnsh*^3AD&M9$zBDRR_@8bvP3dhJ&ENMcqVH3%WPt4Jhw}
zied1fPhcXw?U=CRoOWgV^f}ox7JhUIpl-qeB_B}XdkeJu0@Pvzo&N=Cvm#mv7*2qO
z0I?-pNl5{y94)BKQ~~wQK}i6#%EPrHIkf<EW(*{+Ac{LsmIYyWwuP1>q}vCY!lZr4
z02$Z;FZ3?akI&2ljZ1>hH%Fu?aHJx92uV{&`--7HBr8p!S`Qg@0G|*6s!Xx^9>aQQ
zgo1*Lh(RT6!&;Er5TMZvI@APWF`B>OE$@7n?`yKR=)e*)t&ai1uovoKiq7%^@1y}u
zouwug#U~aQXQt=nrskD^gC68>tRVoO+Jr4JfyO+jpF(ob!$vnCRdca^a!x7eOiulz
zOehPKwLz&4;uKK+2De$j1bm4Rbfv?8ShJ4Ofm`HFu%|)G5JC33W#(Ws=OD=tsm%=y
z3u-z5H0*`d_ZXH#lLV!fBabw#2Gw|=erIw<YI1f;YC(KKaXe^vJu@c<b1f#Q$OB>Y
zVvm|G!nr~bWHAWCEr$k209h-RlQRn{^-*t)MxG_P0cz@l)+GcK<)@S;m-rW?7A2PC
z7ok@KkT|*kJ16-9ti4YYM}Y=r@uU<aS3rY?tN}@+kt@(4QsD8~Qc!S!h6;kfhw2tp
zVh;z*j6yXRfHt?{@jH^uP`^`}CXves@J2*Xqae5>vm!V#uh=a=uLPrW0Z9u;E*T7k
z1zDrwuu?m@D8INkzAP~(GX;Ef9;mSX2TBT{%EQ|&KPQFQbU<}yfJ$tV(gRtWfuToW
z!VZ}71TB-g2X6v{wufbci1ba#$^z?Oo7<=Re(=4Ve*7P-)j(Er1!ietQDRO`DtKm0
z1!OVkG($+k7i1?6i=nBX3e7a6R$>w;&OvRp^wg5XqI6K}5fbsB>w7XmL^}K~BS=%+
z7#0GcgRn_%H9*>nxry-IXgQ#9hby3f2gN(sMlgYCE!6*@U93b{i|ZsYP~lMmTEp&}
zSecaSnwOlPl9`w8T2zEt1HeaoU<_!kr_cpB52k@xgFe(nUPBSN8cZ%u2hG0eqh)wR
z>kO3Z!3{Go0X=b86}B8y6&B?bESrE<$Z(e+bro7cNdQy>Lb3qJRXCghO#(C;gWSXc
z9ZN|s%}q+piO<0{E()m%;FIzPV9f#=xd{|8cnU)#cR<62$nDY?*#mJ<lP)7{{v2dE
zI3~aZG+Exnn4YEFN&;B|$x5g%DczC)S#}9-0PP0Nm4I3R@u0C^(Bwf%et90G5HtfV
ze*_WG<&TE;>1DqhW0wTL&&{EtLqN+c!N+*U!;T&Smxv(iGeHEV8=!$fR*itv76eZi
zg2oTQ;lTyU1)%9d@N$>T;&|xpS8!qgO`JovD=723h-E(FwNGE^JLk^(0QlWr)OE}^
zkYhkIIB++{=jG?YoCFRZJWhg!4_Pe~r0~I5)8Gvnumar|0NP4XoSj;doDmPXGXRoo
zKovwLh)9RbvwgCIGayw6(r`a$c?$N8=%7VL&|T<=jqQ*+0_omq@OmdC^FR#<?B+q!
zJk&@;ut7>Rq%H=!k)Ykt*o_3uqs3?DCZ?xCO$GIMAy<q-Ohp=Hf<^|Bg$3#`PeCQ<
zJj~3zbWk&OGOSx44jNwuCrd<?0SO<>YiB9978IXYOJWQwp*{uWSt1%LIr-`7pxu+m
zefL7xP^C|PdU|S+V@hH{31*)i!(vF{DyG0<P(zd?PtvH`I}R$8LCw<S#M0!9N^BJ|
zF5lD0MWA#>QpkWBnnXlDYC?y05WubEc*G?s@t{^Zs7&^AaRi;9fn(DpB+pT)n~K9J
zpmrM8aKNw}nhhu&CxGvq+XouZ0#($YQa3F#CkK3V1*F79lxDD@O=yYB1Z%od%^9Hm
z8$qR@h2dz`4~EUq03fSIg!R)AcQzD5ZW#w{UMd0A2fq1bsVR^n@F8J<C<H-cX&?+A
zPlFZ=)O88SKCCGLWHkuGt%n8#k>wI-XbQAARUb5{od~&N6m)tF<N#sMypq(SqSAsA
z9NTn37K1R{a;V=au$*L%5;<2?o|>3ll$r*r)AB)S8#Ij@54x$kBr!9u7<NhyXgxqC
zh=87w^BdOw{tfGTP@1&i>sQ}{TK>uKzBtHI95zE^l+xBO(xriEpi2Wmi-m#<OB0Dd
z7n+JL09{)c<Oa{!pje0WdvKL(Wc7xT+La|mnffI~nVFyzU67xXSOPwtClJ)Q1hu2$
zUHskRQ;SP7b3wz?@#(4gx#$@k!)4IKP8*kzT=dhZDvkwp3P719JwLY~wWy>DTk`-C
zG)VnpXwcBeO*oqe818^(9r7=F{|ssjfJW?-<FViK4zUum89=_3pqdVAQIBC2)OXZd
zGzZ?j32OSdWmcr7xaAk+CYE4n3u8D08r{@$2+6DWC_EIkq_QA29z1sksvr<~50uit
z<r0`k-vqf*(>`6|?5S^x`3qs~9kL2WaJy3<e7`r+_#CJt#2Wh;c2g<#5tq^}1T7{8
zt*`^N`M|Rkpdrjs*u5?@K}(!KM7r8f!Tm|R;r8jjG?euidg1r@QX2mVr|^KvWl%ya
zC@9LzD*=rbm82Fy4^0EPArnMkIs_UfR3C%@&8C)sf(CvYCUl$vQb~ZaEM&tXq@#p1
zWB|<?RCiGjsACRt5%T6h@IDW?V{o_#8b)MA79`Dr4!6_KO#~H)pzH#=BPs|qMFR;8
z#B!o*QeJOex76CFD;;I%R125_%P!Qj8Z@H<xzqscYdls%eGPIZ$sI+AuX7XiQ!7e}
z5+MV3@L_bcB0})7>H1e|)a=t072=&(+s&!q=iEe;Eo_=_tHFK-6&R4x3BzirpULWp
zpfsJ3513>IB|MPLpiBvgaS#uZ*kCK%(;-Je3c`F(T_-$Xg$<g6ZAQd8q+<GDmkv2%
zgMlXN{YBXr7=l1EX^<Hdgzb=}_22B$VW9vGf69l$o`PB-ppjYdQMSRAc_oPzBrmF?
z+!;7W<v@Kv5Qg^yspfMI(AEynU_B(Ifp)z@w!T6Jdxh-NAq&iGU@47qU+x823F`Di
z;unvVP+yW&`y=Hqq>dS)TZC4uK+=|)eY#hyQQyQjjxg_1(QZVm2b`-A<qC$~P=A98
zB4WzYV({F%HmKJP3f6eXU_V3uq^#8B5=f*XY=Y!p%*HfnHh~T+z}keuFrGAzBo~)~
zsyoo;>Jm`&q*f$j?p(mI1KI!}(hE>KKruj)7s#sDkkVjcMJBjjy8?<Okmn*nmpWjN
zAIyFpwQR?k1R<e{WHB`6$g&uIDn4kQ1F|a{)R2ONH^}ec>J&_*^Q^8={(QpTK3zt|
z<!6%^{4haEhk-zD00($JuG$>KW~i6RYDvOIII*onN(5Cdpp5C0npl?VpO@-iijgrv
zg*ynt3wUU%rn+NrCVY_XAPjc_G&sn*+!7WXhyh>lxel4>;3xr|N(sI(7(6CgQVJU&
zL)7$;lP<p7rQchtb1^avUK*1%4FPiuWR-DRW(DMY3s6FYBuP-1fVT*M3Fzq>NR~rG
zf~;0GQW21pT9paO4WQZ963{5WUw$5V2)H;kxg;|$Js`0p181`Xl0`AgZt6LUK<R*F
zH6%okCThX6s<6|-lk*EIA!miBWG0uWE98UgqGITNFVF!EnIHmjXcz4CE@+}BE0%H0
z@GyePTTn;WuQa!yGBgh|4Fx@V22n9Wx|K*Xt>9_n;z+XA>_Sf&MkIb5ZrcYc7(hdR
zV7CQiCTD}zGJ{hi!etoI4sjWItK1>Ws6ZEX=z~{J=f!8{r4=O>mlTyIgYqG$`bEnZ
z=q^F3BOopzZ$THXFamAjgHDZu_CA1j+<GJyXQX0886;pZoI_;$6n)81T7FSB)Lzg=
zf5@>w5HZpshPFvY0W_(x1<SBLBmpChi$EfW(taUmSCf7`hy*2#YoG)R9?b`hU_sC0
z$Vkjf$w`F{9igWdq$Lv&2T@~rs}d*>KsG@-5THdOkX5E@(>adoR4du1!x#`d$r`(a
z9844s8a_-(P0k0est1L^15g-%2CSi?&`X^_b9$K|0y?pUbnXYl5wy#&`$5YKKt&vA
zwhfY%<5TjXH@v2S7JY#T_{9To2E<)t6}CvV8uCgH&?p{Kl?sX?NGlJ1N&$=kv7632
z5y2%Zh=4Bs!zk{lno!<@5(+3qKod$nv;+gyvXB#WAiJiozz%4I9kN8$7A|NNgH(Ki
zmOBS+!%|d03QnZj91<DSDJo!Fd0&E>-Jk<vA$b%O>W~pE9JW(6=;J_^gHj*Fa!@2d
z@+PE|M2dF<h~KH$%l`)&<OkKEjzvZJ<>(ze$f(LAJLr)i@Jo-W=m1dq!kP&&?1p-t
zI!oWv<ro-(u7al9L7oSnJ_;tFKK*P5KY$C`%A=08Ng!(xQ4K1XaaarWEv3mEyiNzc
z;2%_iftPaR<R_+p&xr!n7)6PgJCEQ=@95FMot;rxumL9Ox(5^zSR)_9dT4M^+IvU?
zADsxQ7~4QCZBSMNFSP|V86b&219Wl_h)CZj+R2&qBg{VC<6!WKsa0jLAfTQb-hhS{
zK^-ed#s`H4qymDJIu~F??GxxxJ=C-L6e!z+vR-md8g|PMz$}NA^VGB47c`p(O8TH?
z893y@<t^g++?y;C)9mku*{AOm({;F=RR%kqhOAMR<g~O*{j{{qf}F$>&_Zia<by0P
z0ksUnQj0RvGLymEuzWL%i!<}mA;|y{7obrF5QdL5Knnz_IR<1E)=U7h6@=k7Lo)#d
zyC9(lXo6181P^h<7Z(<l<br|(wEPF+3UGl4aug&jLaKchd+5P=EU;`qR!IP9rb1nT
zyfYMJDa3kkTEJsHBoJ_Q=1Ve5a#A6^`Qnly^v*nD$Oe+bkXjYcSS7MQhPrYTWr;M}
zjdysgg!rVGtW(EP0x}-dX3H&zFRTK^Bq;14K?w>}a3q2W=qc|XV7qERz}B!)9+Swe
z>J^~Y45(<qUalZn5A{D;Z7HNCJ>*dFr2Gny=bJ!V`oKjh*z<_chws1lI@f8tr9Tvw
zjHzln_*w<9?ch|5$9AaS$qITTzr(j?fJZ9la56BIfVNjVCFNIy!0rJI$wx2JAc-Go
zECQPNsp}e0M;DR~!GQrvmdNQ4$$DsD5Lu$4kA<X^=H^zSOo@XI#|BTygeB&bVw`jd
zDbSGmF;L%A&3e$q<5)e8VKda@lovV%yx7lp1r6qBf(Yo*@xNdf%<w_eKT<ggy8RD+
zg<@8IW*)X;6daWx196xN^$~Sb66jdNpaxLi5S*zIDGAa)zYFu!KZ;5;q-+IhlpqdQ
zdIcK52?zBRz`n<0J=FK)4W8f{V?-`o!6$`*3s<y|fSinOVV|xr-Qtr&N+C3Qi^;pR
z80r+r6a=#UAgi$^Z4CRNK|%8+pJ?H5*!{>9k45SB>B^o0>l&PP!oq=)`=h}F(4f;h
ziZYW*LFX2M3tLbd5^GRkH~|_IM3yh8O$$gp58c3<lAjH#<3I@zVl$fOA-yI}d+60c
zJTT9bl^aoPMot4~aaHgb)<Zo`mi0)vu_U!37jlao$oHTT>k=*o28NK-3LKZ)!FPlF
zj%NDczY;!6OI?TH+&GJ2J+;CCR-=Q{ALyLXpeA^sh-hm-uKi@hxc-gO4jg0v9TWu-
zpxz^BA3vn;3_a&GJ_&x-H6F)6!-7WTLK3KJ3EHOwz7DcHH8W`_u1_a&zYM5If)vrI
zpd%E(sRA@I2c8XbcFxZ&$jnJC!g{;{s9Oob@UA7a4Mc&>ps^H4sf1Qbg6sidxLwdB
zK-OXnq+$WKMrAo@_X@~%aB2i?B8RkhLDSZmphK7t$5}8tHt@(=FMySc)N%nIXv-+5
z-3=Ph$W6@3FNy~_13JkBE~G&Od>LZ;+xI#B_X_Hv>k+Btj2}!44B+i)MX9ju^iU^&
z*0W@Srf$<A^)!clI+vSjDbuVOu%Q)dxgiY{6d;>Q3qWgH;z1kAphx<EMi??d1hjhk
zV+Xz08-BzErR6hfvuzq^Wh%IoDu5iY0Co&$3OOIrHAR$CkecfxtR2Y*jTq{>=O}1~
z1+=gxzM?WdttdY?z5;sF6F9?w2<S}DPrLM@KwYJ-lhUy0A!{BI6ak>}LB9k%E&$3R
zkPHe+jNq;qn1HSzF}F|GGJ95fMA4d}>;bnF+iEJ1AHjBl2~3M2B?D5QM4W+v0b~1n
zYDH>tJal;ms4WCBtX`z_ncO>nhjiP^-aV-%0S?e2jaK*GOa?_Ks1cZ(n3<Q8pI-n;
zRCpW&@m(?Hy}jk2JD5NgK?*w1X_J{C0#U$tZLpa0^D6w9Mar$T1m$Z`=8ccfFN%+c
z_!VR)4vV3FrD#DIDD4*OgS-3iYZfvXuy=Gpo98n@1Y(p6(mDJK>l0GlDWILEC7`xO
zX<lYwX=*(9<Z5Vv3vvk#=RiURDUE}cQDda>`1rik^7wd#{4@p7W+Vm3q9ldH5(R?_
z^E7h<bBn}Ovos?EXf_bgWMIey5$WMmYr|RoSKGrG&};xY>5Yihxykt{sgN6uVnEG&
z9#H!dE!RRukC28=A(N714WvP~Qh+y*q^FkTWaeg;fSWPkDN%&wh>{wI<)D0M1G!q(
z4m_b2gwVWzje#K(M5Iso#uh)r$JHLrfVdoVa|TgCQ&1USlnOov1EdAq%mSSklb;7!
z#sHf#2VGi}2_n)>%fIZ8=~lCcGawccx%f9RDH+t-4nvIyYtRx25Rv};@MacC#!!1W
z17bQTT@v9Pq#e+rpaBj<fE|-#V8{d!>3i&ouPv7;wudtyHh_FjgbkpDb+7;fr6zE%
z4V<v^ld@oC2xwk46GXu8=YlgJmeF8EC+Ng^qyz%44nYL8>_8fsggAl75JkFki3v0e
zjR;Xt!3k;lfEvOe3~vlWY@ob=7DA0f(4DWDAR_&_3HKK3B^>r}2BZKgrcn><38-%f
zYHEOcVAy(J@WvL50WG0u<RDPQLYh5j<rvI*Xrqg2w;Q02$<GCCm<H|U2ldDDL4!N)
zsd=eInaS|+c}OJ=s?H$|8A!c#*AAKw?!qcCN?W6-?!q}bk6}GDy-=eGDi3Nif%;`7
zRiOLp;xqH&3o1)8^7D)!fq=*-kjOz=AP7w))NlnTAi;-yl~h6Ijv%cWL=1p3C<vov
zQfk-@+CBuHnkcC%ErBk2Ls*QXcB6*Hi$Eok8kXuD64FSEj36ZvHSGKiT4MofB$iYa
zCFZ3=*B;WvyVpS}4V0Wps?suZ=xH;kg<1mfbWVP9BE~gLpo9g&@WchpUo_~an1GrO
zpx}m-f1r{OT*QD0=)y`dduTUI9M<gvwKs^U-9bf}F6hGG#Prl+eWY<2=vL@reJCTo
zu*4)W9o%ffIeqZPuY6v`IV8701BJZR+Q{q1i$SCJ@wpHxIX^cyKM!xHK(fP6J7}%&
z6IS|?*L^^83DgNmiN%@VW*w+Dg2W4m1&$Ih0k;^=fQALA*djSzAfx#C1tpoenN_L!
zdHH!cncx7p1<D|xW;1BtE_eYhIC+3Xzzyhh_%Jez0WE>4>x?W=;}(=G(92**JRns7
zkYb6twqFMA90r}~0kIvFYa#vFdkn_+5BmSHgAY|f;{Y_gN^%?^<&C1`;?$DT0#FLP
z0cx;;`iYRL4>V8-8LfmYd}OjmKShh0OP_;5E6+gpvO_$M$8xC0iEN;PJL}NlHqgO(
zP$tN~Q(*}%vp7C6C$kJXFCPW!&VYz?i8pPz-A2~->E1Gx3Y$FOmpg;{$3%oVhSmA`
z;EkqFLE#K)2}8UJvMLiqVA==uDh&!^Q&0fpfs%R|Xc`%u(?I1iB*UZC`tZ~4DV?w>
zs03Z=0<zj2R1t$}L&*9^NJ!vu05l}Xnz2Mm?eN2`xIxLS7}WT{>UDUB>jA8^B`cdF
z*^D|8Tn=i3gG$--qD<(mNa&tNast%zlsW-*<!vD-@>Ad;0E!DphY+6I?|lA~x!6h<
zRtr<t`f5<-6JcOrC`wICi3d9kbftM}a!Gy>bPyVklb|6&)(8($q(B<m(1ltbK$8`q
zX<X1$1w;l?5F*BU;8g%5g9yNahN|wsxnv8&c4#P2nknFCDu#jPD#7ysDMg9p@x}Q$
znb3p(K}i*|{Q%;Kmv-qHr~K?!bi2TUfNGA=M|A|)aggExk0YQVK~_9K8YIO9i6xnd
zIr`ZtB}J*=qt`ZoQYa`5db@-ar9vwQL@vF0CW_1BH@`h}h?}ysfXfY_b_muc8HVl9
z5CDx^5m7avHkBb`o=C^=pA%<b03VAVAD;+1VKhE1GcPeGvnmyK3?U*IKph7VhIbwy
zjWVRsWG&E)32bX?S!QZEWEB_KQoXzsumwoVV!^SD$9$+qi5>Av%t-~=0jhKAKoJUR
zPC7#cA=w*Y7shHhh+m4yunUxKutpq)9Z>&JFwYGtn8AxI1Bz0i&6`0x#ZFeI1*yb_
z_259;q`>3y;XDisL9Fm<3=x@-YU~lL+w>Avk<+R%^%>Nn0Zs1a<(FipRf6|?f{x_F
zGRlDAG-w*2W_$WP$T^@D0N_L8Ko>rh7N?e^R-mVOP~HV$wESC4O}F6O)(El~gyEJ$
z1A?Lv_5ye=0JZl)H(-J?|3OgZ2Q35e&CK&ntO!m{1nm_^Yy810H^`79e5Q^D?g0%*
zV=W*s+yD&|YORL=Ck)VtCunI1s5p)XA30i<2)z^y(dC33k@XR_D1sGM)l<tQp!;Qm
z5Np%%SPTsUed10NNGnQAhA)Z)RVR>eMvH6s5gFI+3pLbxrNVqi*6vaG8iS&u(j0K>
z0puHqy&xBWyH#KUI=1!QE*)ks)N@2mhZbjmCZRzo8sr~{4ImcSa4?bnD?w6v*G+x<
zbjV?l1~8wIm8HSev_9wrW>9Vd*$pmsL7swG4q}1r1rwN-L%l~)7DAi^0p247JqBeD
zsE7mQqu|W+yhPCP3+RO#Mr8}F&}rZpk}G_&`qHqYSU_!5eGmak2+5!cF3{dFSJ3Hy
zpth=W4(Jldv`oxf0w5h1q^WXfh)~Zpp!9$>WnfqhO<E<e)ZLQ8qaBgL>1TgaEn&
zBDf^62;+Dl47;J;rrd7OSP)6xrL?02AHrw^ttJCy{p6g~MCcVYpzdoXh=2~&J%Zge
z@(9-ap_=taLDqvRR8T!qoSBr9nU@Y-YXWLxLxyZ2l^J!$Op#iFsDmknps^eoP!kZ8
z_P}QlfeGkvHPS>9G{u1$%w%?wK*w2tdo0DNCHV#5qR<dj41sOVGlX`R(MJq8?V-E4
zg<)keb)B#i<OEO#C{8U&$xKT#gpMiUaR)Rg$SVoKlL(+@LwZqSNveKfX<|wdxBvzf
zN1!G+Bu9V}C%9Gs6Y%MNI0Kpq$?6k8ddvtnAnyk-0S#?|y3UYs1CWDoxBwaoWW@%~
z^axs38FUtu9>D>Chy+M=^u;c{<Fe<rU2~KuN(?xy2ag|dfvg87N<7v>eNR>zfV2w=
z@^dQl@^e80Cm<%gc>roeLAs>*Ih8K?paqE}bxCR9B+!x>NU{V63nEc~Iv^m7-U%VH
zwOL%03cBv87~JXuA8rF``ehcUWTt0A>mfuG;HrnnvFkA?m5XDEXADcA@eFG8yQh};
zmX_pXf*SrVNucZsvbq>lW!QiY8v|V(4qd^A2qB1p@BwM~EqB-r)CRdr1@109#z5T#
z8XzK~I8Lw11W!|eq7M?0;OvEN{s%i~yH^30y{J~@fg%=SGsq3#A_Yu9S7{;H4D}gV
zB^y%4#u)8j166e(A46KYpo2s+K}5P?Y{ymkL=Ag5gOaipp9>CvM#({qUC?UKbm)2!
zkP~pY0~!)U4sT(*{^}v9=MJjeeDc$SKqpB-D_Kx5K!P8XdqEhUgQ1?Mz*5jODAuS3
z*#p9GyPzH<Yv>s%pi}abA*)3}d9o7ZWzax8c)?;yYD#Jzv?%chO-_Oc=-~%nV25IS
zfi>{RYRMxx1TjYkI`St7RF^`kQBY_?t{#MRouAu5`?AlebQlQ2W>CR`6#AgWYLKgE
z;3H=+1~l}^8j^riVW1XWUO{DMJ}BV9S8IW?xnDt*8)%gd)*=S8B&3%8pfru-aS#`h
z#4(Csh{utJ*_Duo*^5$h^UGkTPNZTSAsNiVz>o<dpfP9;TXF+G4c?Hrp_X{)n%&~m
z5>W6-fzmCg^&cOfT9H`-TTu(L35RtMZxoZ&07JxINqK%sW-j>nAdoXaO+QE|qD9~C
z%2m#JZPNDXkPVUYuo{A_11}KPg9ar)15R)UfZ`3}0JMq%!vRqLlQlGhlxaa%FM@7}
z#OwPjydo9q^W*H(H{Q9F<#A^*%=c8Ur9fkPSiO(o0;u<??t%rN9eAL@_*Bq!O7YMk
z`P4FaISR_-kcBlZJMW&ZEmE;hf54j=@*qVU7AmxwC-?=*BcS#c*lDg{CxKH59w$MA
ziPE(`Nawg0f~s+01_lOD?;R{24_}Q7X<O8Qj-3J#=^ws#-<%X0XrCU^ck<Cc<+HF5
zqM~zlg4&ETKr2zeK>@Nh6GULzP5q#l4N5DZ7L5<q6Xzf$FXo6D6@8C5R3GenQ0akO
z>Lb|=^*v~{5D_hA)FCt2*dbyt6?*1jZe={^r1%0*5e6R70IhK>0j<D_&&`g9tWZQ!
z0ZASRN6!4DxI${Llzn==o|KW?Quy9>8aonE$fE@shI^oa2I>G35omd!>%NP?;lKwP
zRsmIKLEz)KU^65LuR}WYm<P9zZWSo~VJ$K-tbqE4tm7jfRaS0dNl|76sNIv2ng-#3
z>J5;$AWlH5H8(YuY|LD0U=Q7@YXQsuWR=?JPCy>F!5O6(E`SCCdB>W7N3X!kZ*d>u
z>;qby4hj)SYEJ_-yFo-c*PhA#{kfL*>60odt-_}y!2*MJP6^~fxl9JMzb_L+q#u|p
zrnh<nC+xIdBj_Gs+PS0=v^W(UDBvsyI`<+IM5Ir8dEsTHxtV?Xoa`A3Ke{BqHVKne
zMnX~ucn4k)xVs0=i-;qP&{8MlE*oL{bgPL8t7dK$fkg;eO)4b2krO7!&ycJE_Be<O
zDWEYdhbBx)dxOdOd7x`QA*bDg`o7@f{+(f>kVJ`a2BeNcy0jbG0j8=mK-OUm1Pt4u
zfk0NL2&qvEJ5Henw9^-KU_g8+WD7q?A~P=ydWslm4gzwN0LE+sG#IGmkkufEfMNr*
zLJ)F}060D1aRDSKkh*E04M!MzYr%W(GfPs75|eULp~G)rlR*S@bP2Q5Nmls_YXyLZ
zYSFebgQ~5d3-FOUkmZm?e~=hPask9!#h@dSiKtQ`eQS_&!8=r)O4HI(i&9fO{lVA4
zq2*_YT}W*nXe5$k7pOCgC#@ox4)qCWz?ul3ptY#LH3{gzT7Bq!%%Cw6@K&4n_?*-{
za#qWMLc=B}HO~$*=mN@gkfH`O=mf%O15Zdv3*>3|acSj6nI+Hx0MK?Lqy=^$8^F;D
zCekncK6ZUxB$GXy0S#}m%1@-+i(G!*0X6R{up}%D%c0Q$N{K{72XyoS)Otbe7zX<f
zv{@LkV>o(~)*)ZFR{QjwfhlVjUg?9?52Ra>3GxMEivk`ipuV7VC=PMH(FagV6m;|i
zsJoVunwD6aQxXrkIG`v#H?aUxAt2@%ASEHv%qKKbspleAPzMqe7$6rx9E#!~aN&U`
z^-{2T1}Tw2CW1jEC=GzFR|whxPi6?)F@ggcK}1gfV{GJroZbSm5`1YGsQi!jP0Y*-
zfJi`M17STV+k!AW>q29La_e!P_yw{KgyB{~eN5I|2xP7mdgNPDaS_Plpr{9LpHC_V
zr9e=R0^Qq?MRb_khN<QTPzYl!<uPoA`kvC{Ux2cICFm|Fsey|dkV_!Nv^dudKDRf*
z_UVgH8N6e=xD8g^fZ8vv6{*R-r8y<w**8!ig0x}v0EQ`050JG77TV1(fNnpU1KNHB
zvKbOzpl#QX+dsr-HpDA?I@qVHRya$E?sy1uIyEe1hWDqy1p^*Sp+2Sh@Edf)Cupf~
z39KYdPRuJyERHYFOex8T&q&Qo&wx&|fJVkLK?HQ9_`MyR0nNLVwkbg)wxA^73JMs|
zfGMQD2ib|kVo1Os)%T!IpiNqGUP+D~n1nVQK?4?$)i<zxlJK3AP`4Agp1mYBw*b_3
z067!9OB`Izf!e;wIr*R?k04IQV;{uH<c;n_3LWUd+{ldy#Bt7OSx@jcOF)lKrhWSE
z3o*+lPMZZwzo3RE5se9KZM))v)MU`&hWw(EjQsTcyhQK@KG4V()@+O60%!n`)elCh
z=D-8AkXzUKxEL6~+w~pu(sM92rQm8jQ_%sNK}S7-HnT%wA5@S)N<Iv`p`ItJX$>v2
zkgj6^l^c*iM~n8}^C|yrcrEPH)jD?ZJ7mFkX_B=S25N6^YHof}B`D#8EQ44Jasb$;
zU?LsETBvu)Di{Jhee^wZ6Vp@OGIL5Gz5eH*c>>U|psTNwe{e9Qu*MVG(7a78>p_zz
z*!}%>>w@pcFnds-Nog{38GJK%d`VGhaS7-Oa{_zfVb>=^mpL-QvN~BO1wcv~td59J
z$xqLL9IylmdaMD0;TC8@AhLCbTI`^#YyAtFnE)lg@Qln79C;n7Qw~i5l-o;SO)HX>
zP_Kfzy+p)4w&OuT^*nfR%O^80o1`^j6j%xx!@(Mtpe8d2qc@u=Z8#Q|W|l#+wLfUF
zHYiS=5{r_c=TL%@7Nk&tH(Xg4siy`x|EAWW4ut)nEkRhlj$t)4Zi(zCgO;7<WP(?e
zwSiU!gIWb1CAm2+nQ3X*%Pyp31Wmc5TO|$Zl7prVuvbP%RzQ72!vX>{Kve>2=DUE)
zjNsCwf};GSoYY+G-bX4yq28yV+dwG{Ypmm{w8@)9fS%HhF~eApSd^HPlL~G;m4oUv
zP)jm6F)h`tG!ML0hu9#ZowGoRh@=prv`7G*#|tSEj6pk|L5+Ez%)HdZBA@)^#FG3X
z>_LK*qM@mVdhX~aH8T;JU*i*#lJj%Gkq_!h27Lg{^Mi^&aG3`t5M$i1wPjG>k#;a4
z=ui#tZDR59#idE$BRav`A7KlTK-mC?ebDj&v>KL(nyetdII{w_;3f?;I0st2k(dP9
zMV<kPInXlLOb`LTw=KQ<kY1PE4-Z&l1r!)W*oE3afHqKJoq3%7YQAg#Eze9Bw@-f_
z6chR_))6{6NJiK1H7HrggN8c5EnqkSZF|ADm<U0wFd}a0SWae=Zb`PjTV_QF+8qL5
z8_=U_1EL2G89D^ra!*9)A=(_pmBq>Vd1;y8bDuy9aPVaJ19s5OBQK$A8;NXG;Tb8!
zIaCKJf)CiG!}eQ3{6|hoECHoNP|aJEnwSGhp4h9!3wF?{{2x%?k+u8|secMSz5{-z
zAL3*Ya7==8I*33d7|3d635uqPk*r5L!zc+90-&WUWr;bNpk0~q1;yZ-nlp3Y`+Y%0
zJ@N&jPY?@Pp`{Kr=RVL5Ho{{$v<RZ$GBxNBEp*TX`8KmHpjsC+xa137{1K2}o?7Hz
zkP5z)7gAm!G91Q|6KJT=#9^TDz?u;;TmcOpA}bxFqfdW=(gdh{c1can%*9;A1DPGb
zJYbD<n@T`MCCEqU`3}PhsAni0mq{th1f8D)8mItOH6<XsUCJ`iV-()DhcTcrN=55I
zNBLllN0^oH!$YB7r?kw0FINOF4-o+kYBz%_DNsoR-u?$BpvCx2yL89_<doe&gH+~#
z=9~~0GI@fm2jw92h=&yJ7ZAs`K?-+DJ&$VpNzfSbUr?(GT;PJ-f!4}_M8aP?XxNiA
zGzM84gLF#F5zr|ypnJ1@6N|G`Q=CDkPA9@PDj*yIN&6>Zm;Av`)S`|fKxIAFbcbOr
z)bC`C1|vm0#-1EI_^K#y(!=6$h*NIDI`dR_!EaE98`Po!AKL_3<&2gRAWnc!2;YRJ
z1f<ynQ2PYFdY}w+O<ghMpdQe+F!bdE2v0&1IA&*-to4aV!5m*unUk0lpOKnVkP2Ci
z30i(v0xD)eGtP-QIq_+k>G9BOn4lYv@i+?_`$V>?N-{vjYGxj|t=JCAULd0(#VY8^
z;!F^cz6^Y2NtS*3<(nrTXjCqRHSEc;#F7PNl^@7DNFIjmbb)U8VS%=?L1T18G=DJ~
zq|iyg4p2=6vIJ6Kfo3!yXAu6j-RL*<eWrc-&jsn5UllK<RvQgz3gaLzbPB^MvB=R0
z5~v`jK>Uj<K~Y*)Avs_wHz=$@l?7TZ!LS>eYRD?CVJoc=qan~mLZD;CgFqgJcpY>+
zZzhO<UOvNW4`)EDY3f=J+T#SV9_)Kn(9%{A0quh$U583RU4_&0;4>$|o(J0wny$zM
z5s2#`b)02hKQj9TEuo5u9OlKgb1)jz@B@wYy5$$;CYF@oIt~MKP=x|Zaqb+1BrYVo
zpoxpp0UX4l1S`-v7@#I#K_YaIBxo;qCWuJ)Sj?n;`+%-}df->>x%}siU}Y8cY}W_b
zF3Z5c06M?7v>+bhF7WL}pwoOIyBnbeHONUg+yx00q>4!md1G5@ML}X-N=af8v~t37
zrE;hg14AZ=NN3Fa&%7qI+aAt<<|nfJ44Ea$%`8sVPsvYCh78?-#xa9H{ar{g8Vs@;
zM5Hf@ZJjbdq}v|OfaE12hY?VRd{E|uY(Vt{Xs{4sBVx%HuH*&ETtrk9s8%9X#Gu8t
zp#Fs$C^LatI1wS4$=Q&i3zUzMOKqgY4Gn5)*$zt8SetpcyiHadBY7J!Ji8Fobq2Mr
z;$1-Z`Z`u*Vock>1}&l6DWAhyden0S&aqhxt0DQPn5;%7tkg~}$}cXChYq1+Lgr#k
zLBRl;6^;)`EGo$aZ5Hwbtyso6RE^mtq`I3xRS2F`f#d>csE}2YBBct{ewQq0E)O*0
z1G-cMa^PQaL1tbmM*4uv=DoH{zyAH9g5oK6Sm8@u=iuywVptCi4YIZeK*~YzqG!-0
z@8A(6Q278#ACP7RsE`03LIftD3#b0urI%&T<T?IX6XtoU+O7y{&w>gGNU?}0nBc=m
zkS)Lep(CDDwH$mm6v#@5<)9V^Xzx4&qD^!1_&X+5Ry9~-nygY0%Nmh0pgaj$BjVy3
z;^^svy--HV1(X!ZSgZ$?1|(%avO*mx`$0}+hMw0v5mX_8hV?uG5|guWwe2u{PgO_Y
zoKA<-OGvgu^Bh@2%Sd@HzAUw<7+NGxhedyUd^(n+c_B_XfN^aPS+x?x3E<`rWJf3{
z&l4zrk*ub~_ecpKH?brGwsJBSH0l9%K`G`!IM5;v5QZ=0fF^yif**$iK*Q-+lRwB}
z5QbY0^*wp@A9#}q;-D+gVOyX}PC(&*4zvg#G~W~NUr+*?OGhsOK&}8`xJ#hHKy{aZ
zie8e!!koBSLeyp{v;z$)Y9W?^s~Qj&(h5DkYez5J2RHllJz-Aeo^wKBRSl)Fj=cOd
z6<1AzVL3F`$vVpl))K>hnC%Wwyo354zKIo%NyXR?vxR4W7y}yjG;j~7#)Cu%C_jSZ
z0!%=M6p*?F&@cf_RuIuCDX2`z2VYI|7}U}NCG?Qgijttz0-{$(kZKWPn<<e#0S)F5
z;S;oU49?TgEy$p~o|$>2`K6GF$~^FVMit1zpiW0hdSY&FBB+6hp10A4bWg%M9W-(g
zD63(OV+?md<Cw@%P?Yo2Kn)|LMkMHt#!L_aoxPE<hclpwnH)<FfyQ&g;KP3)^Kn>0
zflqot#l%a{oD3-7GAD8|Fo214iH-gYM^7Z#r*j<FsTR7C11qD*@rf;{2?R<dkQ3-Y
zi(Vj2(21ZyJrDss4@|;7{ldEmNewQ_(BWfpZ0iP1a#_H`O_!O0ArnNTAN(J-DOouH
zc8E|o)D9xg5CSCt(6MuwIp8Do96*g4P*w+>JOR2d9^#|@tPBj9AR^sLf%A8nP@sMK
z=kOCTk|EI)*|rhnCv{LXf%7|P$~O~4AbKs3d#|}EvSke@o<L32_~Ojm0@&qmpcUSk
zAR;}*zI&<1J|+9~2$^$#viJ-r>L-9!J%D!i>Lc#E-UJF$(0O9<MXAN5IVGUe^U;eF
zNG*ZXV1?!|P?jXQfWzVjP(K-K`GH|EG}n>UMnP(-VNBF9fridbgW?*TB|**LOb`K`
znf?oFD8r9^r{a{uCQv#<Z1Vx71<3J37^Np<ppdLRa!8&BUz-AIB2EPrb_k2@Km`Mc
zK=e(~#@neL?1w;AnGoof?9`I@<eba`&<({UiHPkh=wq0eX%G}kB&R{7&;Sis6@yPG
zn+^(&9#9$p=XX3-LsB8qI5eoMXH#5RtQVi2T9Q_jm<!vf4>B$jL_k+?3EIOM&^$!e
zI1j7|h%tZk2~?VZk}zlw7WCqCL>m|*J3$f@CGG8vpg=QXVqkz2#RTmlYsU|yg9#p2
zf;RInf~rt#)`EI8APn!-K)eV_)+DDA$f|~vQpnCO&}oN3YvIWQG|mn=YXv@x1Y<y~
zBx@`I$)_kIXrS&q<SG+z+61-lahz)bv7E^I6}0v?6}-wC+_F>!r5;e50d(JON=Yfa
zIsz5+kf}Jxk!URT=`aSwKC(&%Nch6;56H_0T|!a}-Qu(g6v3d>1W8w*dKXgpLeenO
zOe7>T7E`hU4Rq@!_`ILwqSTU9@R~qKQ(GC-nF7UhQhrWJRc>Nl9=6st#7Rg!8EDp~
ziIYH?8&6${<O*5_Oc*F&K&^(N#JrTuypkaoEJ%ensJ#nc-BOaDUz7s6(F(f2C9x#6
zBr`Wv57cTwU+IFUhJdC3B9~NyE}VhgL<JhM#S_0s6(A(%k+o7B<Qs51u%rsq5Wv|-
z2bH8C3@=TgX^PT86XdbF^PsUhP^xyz%*laXGy}@`$fE>EzJ`V@HC=#nunv;3kt~OL
zA5<C<QJZ9xloUYsJv{{N;{df1JVHVOf}Qj8@*u@7_LeqMCWd;KbQ?iMHlB2ZWChey
zlr}5jmrwA6{0gc?f->{cAqV=PdlObu!k1t|y-77UfEv+QeUD)?)c2s^A|gsr$7+z8
zk+6|x#9${VNrTfcm`I;;W9#0#lXUFU`<Pq~_GnhX`u3D|BtSz%`K91<H$Y7Tte(cO
z8|rE5wVlD^CTBs7Be1tYLu-%?E|3!!x$M*B46E`MYAaI9+ek~cOhEl7u-%{l0Eaf1
zfQ~S3fbG{f37y}gp08hm8o!`ZBOr|w&|&qNAOhNf;<AT2K>^ljC$d?Fc62g$A1o+g
zC8j{v?tlhDGC>4l49EY}k~T92X;}D@zBwvL7~YBp)rgrOB3(ws<!6(a5UjZ;0v+lg
z&2-S=NF|_Q8_@1;Py-JVT%eP@GeHEj6!>We4LA7uF0z&ZBW23uTu|)>>fwU75yuy$
zf@a8I(}AFQ;Y<*b4xa>vF`&f}b?gV7y#+q_t0=WB8FU~MIGKVh#$h`&capbj9=gvG
zb#fplF$uE6at&y-2edBR2PEQ@nU`3USp^Fa!nH0foP@LSfvfhVR#6SU;~Nwgpos(Y
z8mhXL^*giN57^3d23WqLmgS)AjJ2%Buo#*eC|@2H4mutLR4;>$QAo@M4Ff@Z4XP|5
z2g^Vf6f@bU-+XrLON*)=%-57#{)K~qA;=zH!ho7WkSnEO+Yi#&*T>!}h|q$TFqGQ}
zS_O?1!idH?d{_-Ky2V8`8$kh#<VS>!@J0tDa^aiXh-}qm=7G+d0gqAL0M&J%zA+@`
zCWD6TKm>FJxQczcqC&hAYdid~eo8Al$VyF6G5QtMP6rp)sU`8CyV^m6@VSY3DbRJB
zAZKKP2u!y?lQ5`8A*PLmd}|J3VgcN+04?;-1l8{8I#*fuoY~N5pI%!Ux<IUXIxMM@
zV+m-kBuE#YI6>xTf^5RH1mY8<&LilwDvUEb6H`)xQ$ZWsOY)1LCodrq1LRy-X8Uv)
z16tXDYI$NpC$l6~7c|MAn4Vg!k5t(~$Fz&}p^W>rsTxr4gupfQa2%)HE!_>%nm
zoTS7eNEM97RS=&RleJd^GKvMA!vt?LC`&BLOe_IU;(!mi2c5EoED@gvT85p2Z5RQb
zd0-4^cv0VFpt=C5#6VPsFq@$%`wDFRIay=3NKpnUh(UP?6j6skbu%Xe0|VqzEMM5s
zjR<!@YDA>28#E}W>kgd3fMGo}jZj*~q-Ca;7J<`71E@j02i}c9_#D<SOD|CDf8cg1
z43^tzmT5rcEa*I5NIt>i5NH?>S(GDfJzdJjzyQ7nEgp1sb81D2Z(?3zI`M5W(rv`K
z1r-vpnByFvhN5q2Zc@HJm;@zKQ2POVwj-!8$pl~g4ylR|K~x)&e}(JvcKh_mg&yCF
zxsJi&3X5qsK%=~%5ij&$#xMgK%w&x-LyIE<=e2?B7lbb$7vw6~r}J-Nat_-LKQWH1
zfhSZKBqbJSf|ss<ti)R2U|0_Iz85(wqaefV;6u|vhe_v^=B5@w><<TR%K)`R3raAK
ziG`PWuyc5!Weug79kF=c22?SD#*jgqLgLdv6+j6j9U!VD3|Bw{f_koy2aQ&NT;W%m
zlY{qs6{P$@^{~LXh96hf1Z}1PXG(p<g}%l5$cxcHt$Ik}0wpt0n}z{Qzz=gyH#fad
zbMEdrJoyup9Ux|aDh051Fo9_XBwmmjMxa?;8^~TsJ<zVq%#_r;5@=({n2~`Y6GWs#
zjv`UFhclodOJpk!Vm@>)CJ$&b8B{)j(-YV}NOA)C0^)4YST+d5$Fm`0=;YXjb5kbB
zd=RFB9iY~9P%NlA1P4DNTVSLL^8HZ`O8=ndVRC6vQEFaE2zYxdB;a&G@dP5$YbROf
zC>g8U!x`jTxEoaFC*t%JQtJkiriv*j^gtJxWag!$R>YSSWhQ5XCpn`*#SW<d=?Ri>
zttco;Ee5TcMz6-;-9#7znl!2G98luKn#Ew&LPy6=!Wx~R<5r32CxFI<Q!7fK^A8AH
z&<X=c24shgR0_Zf1G1_)NKFHq&;?B}fkPaWc(HmB!*XbhgL;{mw~^xX17rvR^Dt@9
zvLfQoRmLy{>X;;Q>Ri|YjkHwo-N*68sX34dAN|t2OlW5aAqZ+%d4Q57sP!KVR_a!c
z<4{M;T8K^#L<B!5KEdTFn1GIPBe@G2a5}^VTz)}vehRqt4@#PlbOK_54F?nHkUM@9
z?4el!zD<b8Vd#`ha5BW{0Sx1z9srFX5s}lt{n!G?@$Q*<>H45_4~|h#e-7l6l+>is
zbV%`H1-dy6M5Jq(Ju5w;Xl)N?K#LbLErlKW^%T@`0iAUQDZzO_B{+zHj_Aq3hW6y3
zqh1tRcL!u0sPO`^4m279IWQcur3&e6Y)FMcp>@YW3sgWsnw*ngoQiHIBnKm{)uW1?
zpxe5G?!oITgcl)|Fw#mCs#tdklxjdFSA0=wL4HALPGU)FJScV{zD1nI04Y3?nobbw
ziJY@QPqf9AdC3_?`FWXD;H(I$<RFW!!I`}ZG|mVjple_GVb^5x!)CU~nz@B^i@@u;
z;$gW4kxN1G49@>x0y_4>f^h-@Sv3{JX6Rv9If<b0G*Fg}0wrfqhX@kbAh+PK99l#W
z*$FQ#%E`%0G6Grg4%FTPb%jIovh(uG^E?vsQsBprAuI&7AVC=3ii8#qq+1B;GDAvL
za8%;40_rD_1|o_*)TwY-g$IhpVtsJx0-v!6@-oQHkh_DxQ3`ejh)5UxyKtuZbrt*c
z?!fD}euu#K<dC;b0Xp-Qn+j^{73+ho&rHiq24Bzza)%1&hHTK5ZAk1Qn$y=sjSt;!
z7O+o0v~R9UOFisBL>f6m26R;v=xicX7lQM95k?Of(hQV@)qS3@3`b<`3R<6)51s}U
z1eKkjv6keF{LEx%>jHElMJ9+yhfmaRD6zBgo*)Qo2okx33Hd^Rc2E%pir~D|3aEcT
zR^YG)n(D|4Y=WC*mV*Kt)V)b7N=?p4Er~C}HpUDo%8|Mr(A-8NM}e{d)+~wP4rt&|
zx=189u_zmQRMI(6<bVPMbXh}MW=;<HfJR8`!~oP<01@f+^9vt3c$nFz8!tbdl=OKb
zEHzNkA)rPIQWXQ5na%_e=?B1f7i!w4H<rrShe$O-TPkGLFi3;#p!rU4JMajokPn5=
z_OyWNHV^?lgHp*p{mG50wt_QFu&f4J%|b+h2;Y(Ri4Amy5U9lfifC{J0wyq9%Ftv+
zss#jgqG2}0X_SsZ8&tr9!JrlUnRpg~BDo0aWg58%6ySJr7Lq%lIg6~;15&C=FUm{-
z&CY`A7tqKCc%6%TQD#bTL25E2S0QR#jO+~!3~D+8G_rvw1dz%&Xb4biMZ*Em{4i+v
z%`q=8zXV+AqWd2*n)?K{AWITfs!+=bpq3j}zhhVo^*iWb3L<hg=sri#sD?gdixeoX
zK`l6NT&E>w=A<PSLoWvcxd3v0EMzIgBRlv|DKxIhw^9mJa)SamB(=CC2)tPf(vZPp
zDb$~o4wpkNaRd1q<b3d~l}k}#dOGM%N}ts9)I8|AG(=K|G+W?@3c`1wQqLuzDP*jX
zjbSx35U9|uMLqT=8x$d+tr+onrMdCNiJ<L!(DPk-`1lzzK}5P^{X+Midw=ZH-<{Hv
zi22Ox08Ix}cMUhFeFvJzfE;U{SX7i)2}vI^Jv<DVAR>Kx^-ljjryus|lUIn>%rfS5
zfb<!u?v_QMkO4&y#4XABdBvH<C8>EOpsO#_OEMtt0yPmK7fr%53+g%7pv{+<lN`SJ
zDVb@RsVU&=(!gzKP{QN_c^@?AQBaf$I+hY*MNShFLneqw?|bq|{!;!W`}EhreJ3CN
zdjzZ8iCky|?$IC(@}%V#Wkcs3z|*Rrln*KH5IyvNo1Yzzk1DZGcb~YmEvWi7thgi7
zN~rB1&w{N49o7f&B_1oGX`ifY2j3-tJgo%kH9@i+sICRKYrq7&qY7t06FQ}p8?xm%
z4_E+ol|dM8In>{zq@ewvL<`yr;GdToRveUCT!NTdgG4PTk|CajlrcA9H5vT;R#NPf
z1Wkm1%5F#`;xQcR8Bp0vUTp<38*8N9`TQqyv6ZfU`np|wSEr=-!jdSFTLR$~BXT5x
zyZhMug<%=gUu5kGgeT6TqEg82aDC9o3V4|@NTfIwS|x-04v93#d1&8Zk@gdIDgu@4
z2Ngb$Kn53Vc<hDv8)>rC0{I-nqSTz!#A4_Lz42%Tk6v(Uu1|h)Hu_#9#IPhJX}*N{
z@+HidWNj2eyHcV7)O7;QN<xw>9!nu!EC#JnCnCwBjwOMPlR{2VptcqyV9|;kNP8No
zQ4cM0$m*p+h6KT<7!_pZfX1;vB`wZK#jqRdV^88{Jy3m&)S*B<yZkI@P8;NZ$eeaw
zNorA9Von95I06;LnIHn%v3y{c{@jFni}ex?ST?5AF{o$S?dD`)0JnKTLkAT(nYoZu
z0CE8iXV4@}jNl!Ow6x5;RBRW8L-GJ-@l0uWphgZT`9snrT87849vTj0rAwr=fpnxl
z=&);u-Dr6N($!J1PdCom=Ve!>4oeD@R>PnTPM~W;K$!wmxI-*Q^Eig(P>)k;IdW+L
zTCfwe9zMDP8qtKTB!^foVxMk$*4ppl#2RY(9NF^qp!^7$r7cPZovV_V3tjw-DC{8@
zs|wnuH`H*7%ET2wN3SSNdEikx_%$lkpr*rp_;D-XBmg3y$5p(wOV3)EQk0dR0c+(_
z8Ue@~Hn~Az0P6OH=4FDWZr$?pN}$~yL@+=i<BA=0$`;;0qNYPYZ8bc}5Xo|AGNeWo
z2%1bS0om+aT3nK!n^^_w0|#U#XJeVf!Kfpk^%FJR0qW^t^*n~9P|p+D>p-oTP|g<)
z;bvd}Z)9@IFDg$g!szouY`y^7Gjjpf@}S&i(6lig&mvg~^(<LETBMQ|wuT;b8V-2S
z_#x=NP0+L~Xf0bg#--5Uau7tM|H<{AaN`}HJ@jfX_!b(fyC@KTw*<&ua0?eqK+Ax;
zcIl|c6VYI)v<av(08Ka}jpL&i0blH(K>}NGKm&Jx_c?%0?SiyUGOJQOGV@BXw@w)B
z(_zzn(6mBnt&Fr4)d5sAf|`s0`8k#8`FW6b8X`#GIRvux=oc)9P|*oE>t+nQq24EJ
zUIo%vhTbWak`KP64YVW`JR6XbpB<kIzDo+-{}>G;Xn{yIH-Lf~Ye9iwGsO2ueK62H
z2k>Jc(o;*I2S8*dmlPD`S5%^RxIh&k<Omk{;1!Gk^%Rjar^)#RmGMQ8i-bW#I^ZCT
zFV4v?0iD7EZC~KA4dRhvYF4a*pu!F`kq}&(6kJ-6TI8Hl3f{L1YGY)A!Wc}z2V39_
zsL!eC7Eo1-HAX?hJs=FX92yWr?y*KY`UZ590HhQH`w?8Ef(U5WiorhpW&EU5BE<%<
zRwYG8Wg^aPN-NDvF3wB`*D;=;C4is-e$dvj+|-iPB53R*dRUNT{L(IcnL}FoMQc%L
z>yieJxdrO#gQ6O}7=^U#zSu$Mh~WoUQ?U$F6o77xK^&A3kXVuoy*(4;C`kH)7iqA?
z3()jOMW+xhPGNRKlV1cm{eRf%8N}KZaDLM-%_|0Fzp~8Y)D%!%3-UT7Gk{Ymhzkjc
zLkoNspFFBzpI$Ap&i!GP3oMm}P}4QY9U)L92XP4~mw+7sCekrn0u2|kIzX^+LF5<c
z`Cq3%W3`|P$2UKvG$%E_II|)?H@_^kI3BXp3e-6(hOTizgc2mZV2&42*KHvCum%l=
z_0XUp@0=*;awpJ0Rk40<YDr>haY<$_=z3sqVFFrHP$C9e+mIHYlA4p64q8W;kysp`
zn_7~Q4{f`HQxS-OcGEED>jEiBK$xqai_*Z$3qeyuL7?GmNZ|r<K_-a6bOtntKnJms
zJdTLjo-fufEQwD|$t=k)(g$rg%1NxuF9mO`js;C0fvP;veL&9nxk>rqnJMX*W5|#=
zM4Cm17CAI`Am|hcJgExFMbIDvE#V|N$RI2Dv2B|Guk`@s`uL*Mk}}v$nTVPR5-><T
zF=)V0*%`k<BOl<ibHU{f9{ZueK*hQM)b|A+Bmy1=%q=a!QNTQ~OP{dM^YVfWK3FkK
zMMt=>GcW{!meWD94ydq!6ugiKK(ZSe0Aww$fQ|bjUCs_VE(58BgqU-LpDqMrKx3X-
z7WaU%CTLzKB@=Xqb~1GK7>@&>KBxW;@%QjPH>k7%4|;<MXe;r7UHTGZgHM~(%wQ2u
zeJ6mDJ)Uylf?fK%=(WPrAFD!}hSYb#DbP_Kpmc{l1zxaAkK*2Z;an!XWeA!OB%&lj
z+8eC}igHk46O><?my!yttw44{(mJT!55n-aKQzgaZWDMW9n{D}_YBB*5QbX;O;nWj
zZIByLKS8Y+kT=6pi;_Sa8o;Rv;Y~<W`-vTN?fVnhZ~-;lfOAS6!*XZ<P(ML=!@C-2
z{)cSnxNDcbK=KHC*&p~^2KAkQGgo0a0U8QKrZh-v6uj_X8q~W3?TQ5*0q9y(lwTA8
z*)x=nabp1}-GMMX?U9zekt_v`I$|v?K*obG+zP0_$XX}@X*Ga%nCGVE<|Y<^`YNCq
zsuIxb3`7WhC<szrVYbDo=KxSX!s>SntD$};Z%rz+9ggU_B^DRs*meL;-iRiP&Ae&y
zJ5DLvr%#>!$N%5ESXiY&BX@xI9Rvl!*FEEL2P8a@#>PMnx5-T`fbQQ0tpI==^|Q%C
z_&tOFG5d61!3SUT?j47D0W?yMvGo!(AQl8V{smIj+JHJIAR_&&WrdoF=P`RY1F|fQ
zypbHxA;v+*aBqTIA&{esA!mq4+e43glZIB1MB0LO6#)aNh63$Vjn6MA$;{2HN{vs>
zNGt-aIsug>;2B9U0Y4}iddHb0)LKwr5K$?X<Y%YCHf4c(P1x!x$f(dwyL8sBh$9;g
zcR;P6eBPxLv|}a#KF<i+HUK&E8d5C&g-!Jcz~&jrYUo0W4^XEA`35Ucy$H#^pwtJh
z_rV1GB1LFCzJ}F`l-dnBA1NMGcHt~KAWMjz+rjOIWEJwRRK)C`#>3~EK$mBL`Wp;I
zsl_GvMbMrGXmBYLL?8kI<|1en0ZQ&fBrDWeJ|u^Lruji3;0ij42Xt~GB(H)-VIfDv
zK?+Z#ejX&Rk`?$!_99K9;Jgn3Vl`5g0kN8_$pa*-;TuD6-iH9sUoZwV*2!vzLAzGq
zL?2%Oo;U^tI3uVLv;~ya(2_gEPWZ?SWi#nGEC&@Tkh}&iyg<nxlGiXShx*)zxM883
z%p~2CY<;)PiV*M=5y)VOJ!qa3=eoh?_U5x)x}x2N$NL>%#}FV@L7*8BoA~(5+=Bd~
zlK6N#NV)_W2yrQf2~d|(njv6!+%5sd4`?(D(vSh|-^&CMP!}86r_Wj3*SC6NEvyQp
zmc<}*ApLG|+<@%FVKJ3_3@ZZefQo><@P#&@0Y%7}zK}$rX`gNnJ{}f+7#GN;)F^x8
zK|u|w>>zQB$6~0DDX-`n;eA?g!bG%<AZ5-4yL8CWjXz+O4dqr6Skth<4qnDXiaAi)
zqa<|G^7BfP^YhX&)8h-k7oO*ViXG7K98&5+<R?fZ!;77lu;WL_y4C`zv`xuRhFtdz
zvK@5DBDj`I$&UxkgF^BaXgoX<L_oI!a@xZg&{~eHP6Co63MyfT?tz+ckP-=;yAb||
zL_9`iLsjoLfCe)`_liIgI_PxWOb`Jb2Iqur`sIW*uPF6*Vo^yZ`eirZ1P^LcKpGa1
zgB#@S)0@^T4~vggrB-nUD^+kdEMOT7I+n%&ZN$>AVetVr&hiC%P&n-y7O+{|bjFU|
zRicuzuz05W8kxlObWnZ+-9!W)!A>bkEC&?>&?!~WVzNvS0l%Fnz1z6W(s6bSEc)pY
z4UncRyM20D=c~1iYlEOEfNIeY2D;?yA*hK3&g*ajn%#cdrE7(li`U<FhD8IV*$uI6
zE(?^?L6;UmN;pI@16eN0XrF#$ZIi@{TjH?rrk>THiWF-Jhha6eHYKZ7i&VHGuNAEX
zt<?sVFkzrQ8R(wJC}W|;J2hPZ>bYR`K8EE`?^Bx6k@ng@1&!~57Ic6X%tOQ>L0|;h
zXb&RNV@~}{dVe(BK0U!2bo=8}So~Ab6*xEA$FrZ447}xSpMJzfJM*Y{3$^px8PMnf
z=tx&c_0|R&?*S3%5xuHwdN(@Tr~kHO{dx05D=fc(8Y3h(aljoZ@XBa?wBs#pVXN^H
zi_(kpi!k;Bp*3;fJu9;E9&|+<_$n#z$;Y7bwoe4QW1=WAFFPf%BoPu4i0qHy1ZX0p
zw3&f&8#-uO8<GV;IT74l0u#`gB2(CPnbxqxM^*qJl>mrx4`<;6nWRFp8tQRSvybE?
zfE@Oq+q=-O@#=)-!@Q)-y!>3;Cx9R|_n<`)P22;D21tZ}>mWpOz*Q!JMoCEymE^Rv
zO#Q@yOi)4uSpl&TWH&hW!9@C>l^-wpCMnpbD>Csd^g0V)t_)h$OP-C$6I=*u(L9P_
zEj0Qmt?P?HS83t(@T8X)URIi$*{9p++80b!cYvjNN;|KxaRQt}DHs+*y-eO{Kf!}<
z{_ry}fR~b%6lEsnrQ=TXNGnI6X`V(d0;N1i8e{?01faA5NrRA7kK_($$aoVs$b@q&
z6V&64&xM?-4N4Q>)!CrUJD}<qyrBkC8%Kkj10vEBjY18yRw~=4Lk?huFZQIiOZq`W
zE1&>?)CVBDGeHEV1E4`cX^RLsQyvBFlLfU(ijw1@r-VYn0puVYF#=5))N}^UiDz8N
zf#yrBK%2_ISq8MD$p^eN412&}RueRL8-e9tNG^h=7$VQcEH2IgWsOGA9lW44VFNll
z0d(aybgB=LCLl|i{@JCUJpPVJm6Z)vaFS*bXrKXWA%I~#)E{KEgdhz9@WFy8*Qi>9
z8UmoDMDg*NC8<U6@sRihP3A*x7lpLPFgH(A)fI7|&LAjYA-Nxq?a(lwR^9XnbQl_F
z#0yja<|bz47scn~Cxe!ZLc#$wYnuro(jjMXaoVFVWTRUB3~FKqNn`1YLk0nmX4oL3
zpVSJ1tDurs25v8CRsph$12TWW3_FRO9oi>Bnt=fIBy9@vK`W5MT_Ze0f<qibLW80C
z#28eEfC%Ubng8wJ3~1R&<cKlaet*z1RoFoz&7jl^syhqvb8;YQ2V`R=h=AJ$XF$^q
zS!<h-+VZGdz?X6}Fa&jif)7-iXRZhJV?jjv<~N=R#e8}8>19iPozwT404p6p3s6XI
zM<Q8|u@Ub!>=vus{L<o7@S<->P=K5Pb`YjpAVGl?m7pE<(5OsH$uCIFgT^8#a3Lqs
zK$62}yL1=>8jHkE+o$Fhq-8?RrFaRN&IkmB8Qgdrc0k-o{mJADppG!8bO+`8l*HnU
zROo(P(2yTw+z&FLk9meR)#Hx?)cOD|BmlVuJoQ>qnFBq_7mstGK|@wU3~7V_dih03
zWkD)<m3%!YOMzUInwOrFS)37{lV1+If(2COXM%|Idkn_+5BmRs&B(CB5;wJ7BMV9?
z8K4<Aa7qCk^Op%CpaYaIVRi2}=m0gf?FSw8Ujix;;}cU-;uCW+6N^)!TaZA`fVc%R
zLGT_iBSuNoAmw!Q*19w((Sqt~(2iSBUV_w7pi_u5K?F2sGTXx$&@4(;sNiwPUeK^H
zXgm&5&LHLnLF?y07`}!MQdQArqHZ0i(Ew_H6X^=7hXSbL1my;Z{biu~4n)91AI^ZL
z2T%=9Ny(4670L{hNkRQK$BNYA;F84TY)Dc7HKCCED@cn{ph<yxZUE&9tnF4vZw1L}
zsQ1a*l82NE3ySiyAXf$y>!+vYgBs};pcSN`rT?CJWvN9asVP2*xk;%-B_TzLdBvdZ
zEZBnusbdWd78<ziA0q=p38?&rx(O6jkd%TROGwUv1{GPc1lt-`Qk0si4<bMz1WKPl
zmY@O@+&n?I`T=ac+5^}$KJ}~ymDqR^2$I!MUz4{r4>Imk09t9N4?Vs-vp7GeEERn1
z0Ju&A`5NL9v_OCzt_Pju`v?mJP@jzCoC@h;L3e<ox&+j<N-W7N18?<0xCRtL;Oq(}
z;EsSZpy5LK;_j5p;*uiJ4#31*aDX6&<j?{H(&%|>2R$<3Ei6Dn$r*Tp1_*cp05k|)
zk{6$umsXTmTvAk;Tmrq|4&(qx+@OU9hEoh6l?jno6M<Hzf)fQO7l3x^fLC5878D?k
z=44@D$Yf+;U`S^_yRKSW$k{%f=j6#FYnwb^tr>ExLR`-Y_Rk4s28K)!0lhfd33g$S
zJ9KV|96Qi%=LBW%Ob`L@=|Hzfut2+P#h_Avvh-YB0Nz#!3QW*VMc^s(lKl8QaH@us
zvY?gikOjW5ZU4}Lk#De6L{<$9O-10i)CY~jfNm#(Z2qkSRfC{<2y&<<sK8Hz9&!N=
zd=LR$JaGYb?#@eSOOdR8JDOX-b<j<aBS3zS2fG4N#3PzSkWC|3?4W09KZ30qqE`1k
z4paq#tPjad&W1JAK}8ItOaZmVKp5U4gI0LdvK~>AfC3wACzycS2WLRDFQ~yra#BQU
z1t5>xg3g`_N(C)!2bCgVd%*-E&Wo?wxIful0?W|Uw4A_b?FGAZnB`EPQ{#*yUr>Jt
z6uXct2bu-T1QF1Z%fdeWqD_Nq-QNb7kEvnl3Q$W1)FlKRxmQ{cUs#%23hM=dEXUyj
zsJDqcI1_cw0yLylP?;G9O5C7!E~o;{EJ@9U_R>??85lA_MEWh0ZP%ObMcb#Jsh*R0
z=vy+(-*k*=q~;r>h9awIK}zDuN%@fTV+29o2d(;t9&`&SThc%`CV_}_Wqud2%tyZV
z>1lf<PIZ=rLaP+&4sdM-m2aTfhYe2@6y+B{`@o1|4$|ny2nw=VlSn~<Sf2M9<P1>f
zw;(wqF)uwe1riu5LG>$$NYDTHVIK2oNBi_UO-pw*O!tHa23ga#NG?bQUljtLYye%)
z39%JadP26)wCucly0(bLKHY1B#hjm4d7!pZ&)+XWy-Zg4SlMR=28K)!kuLbybp5L}
zYWDEu){w+UR$~##zo2u9GC)fc<3X!4K=nP&A|4Xw@QIcKl+>(9E`TS022k<`4br7#
z7ANQD<)tP=D|b+J2We?Q8i{uiwJRhLC=LDs_-a_t9q&O7pehEfe#7B;YL+_|u<@Rt
z<bcE?EGH|1Ds~Wt)^{7A5kO5hcz{+cfh!!a|3Mir6GXt9GjIkZ<S8xtGIJBt!M!A9
zP<{uEO2>mb&YmD)@VTa7XMh}pT+?HeJJbvU#POO~?S`j+S_FSNDEJY%AK`CMw1Y5O
z)Kj<UHw4*^m>NRZj+Xdo;qyBnyElLaF2JFV$np@oFTj>2Ux6liO0#@&eolT7xN{7;
zP84g86k_uMSWn_4m25@~>#>6}JgB)7pPQJS8lRmCy{-T~;E|jFZDdjEd&C{omqCp^
z(2OXgHwCT*;)^rjiv{CA6)uQKe|$eZ#zLUT9?pPvEvTCYyFo{6g8Fz+_f&wYB}grd
znBRgd6;6k(8BT}R!qj!i4A4wp2fQXm*v_mn%R#U^!ag0wfJO>gO%LdPwA|F({Gv)w
z^~(v05YQB~Uw(-%NHnu5HN~wokLbH}sN)P!4-0>;fMpM;@5$=yBQ*oiXFm|b?I5RQ
zf=1fG1hgUKW}p7z=ViB2s|08l0BLd)RCw5ABo=2t_sA828c`r3{pq&a{$H<M?bEB%
zw}g78#6uDel2!%ebLdgdKtwwJ5L`kb+QN`7{}b5IDs0~dwFBf4sD#XcmyihiA=|oN
z+oi+SvqNKq^7(d9Z!!pUSu3Rb3HCRLfKJE3_eQ}lx1rohP&)uA-GOX_%oRfxrXX1f
z^(8f{enj;PPIHKI7!t)-U`ra_!$!EMX*tez2ZrTPpA)$SHLEy3Pd_yebWn8>sDJ?v
zWPrQ|iBu2^N81Woz=Kv-5HW-Uwg-8*CK)tg2kK2gif=p?K|MrPXhP<;q2uom3gk=B
zw0#L^@pXI(=p4!1%)He2RM43t@u0Qou<3t<n;?Pw#4dex)TKbaG-hb2Lf)yOINSv4
z+aRTFJeETO1gTuoLN1qzOA?Dpipw)gG9X8^mO_ppM=GJf$=8jGfguw_q<;=S5hEGm
z4O`~!4f7mzDooG<8BmaeyX>GlT|o2IkQMOp8Q{xZi{o<=lTveVj3QvPs44AKfEQJO
zTr>+*WcI>)bMrtWDIfxVoDQ4;>CF&1I1gR|1-l9WROAJLZXk!Ga?oPHOb`K`)nT-U
zGaz<Q+H^w<k=zG0A3>e7%wot=1&9k`Ss55IK}0(9)Q|6t^=$0n4C=T5(R2p2Re}oP
zO(f7vK_-YukCk}o`nHZ0aaA$IW}?>)Rg{z$K@QO=1C4;*g4cSW;skQN97cTrv4Wf^
z^aM4zKmnPTUz7`qS4cpDjw;Fo5zy)m={60BU7#fqL==puL!C%Xo}%R9)RI#0SwV9_
z-Ct0v4|>5`YCLq^7sM%eszHcT$l9fW<P@lF#rntvILPndDeZ#FoWvx^XfVV*h@uP9
z3dG35)Jx?HL3tQ7Bn`275@^94h=8t8SAZ>5SAewTipfa;pfeT0>z-393KH{DN{dsA
zAhv-@>`V{=?RO)sa)5+8IktiJJC}e<=;FjAXqgD=1!jT>=v3YnM1L1j7Zg((qKI63
z4V2bEBfOc#@nF&ToXl+K`6{4AHklwIy`*M~Me+<|`}BH&lM|9(N5bk1Dkl23Acug)
zeBg;5>>NnYgB*dwCD0H7jmlDz{XnChpju)9sGR~z(U1laXmN5Th)Dlcm$j#4vWI>8
zzZut-ZQPp%3j#_lhV7L32rAo5;Ke86U<=sJ^YrU&U4{SDT%bvtTK1NJ$~#c1jtAA$
zpkOe9ggL?mpGzhGUFKD>PfxM$Uh1*WiOMc$0PTu}y8zrcfVcn@4UpC-WT^Kq;y@Hg
zhND&(B;g8s$oZ9O_UR9_cYU2DNA0j@1dZ>nhc`(OAr8L^{`P`z(bj>m5GQLYAE{tR
z*#|iTWH~5^A%TskOd(4e|Jy<56*Z_F-RnR{wr+rX8sq{<BtvSC2e9=gf1yKc)QaTE
zpkxNBRlx}z5)|>E^bYYq!X5Ax46scY)J<%MLH^%}#s82*e%CG?b{z)9Uh1^VK}TtU
z$~UxOT|}%ynovJs2ic3kmXCn;V-QiWrevn2<zyy-tdIs3p&)}HMJOU*;SIri{kQe!
zuYoVMA+k>izg{spu^2o}05TbC7Yf57XeB|`{d=&bb+8aT2P!Z@ts&pkl0;Y7DdWDG
z6`43LI6-O;K|9e@aR*M{L-G-ltq|WMbs#})3!C)RlEjjdBIsKGen&}$Oc0T7T6rpK
z*Stse=~q-AOPrkd2v*UNHIxYH{N^Ttccg<7$68Pe4&-!5u0t%OhGjPB9-DvA+z%Rc
zA|m;rHmjlIs0I1?;O$(X24m1nc#Z>IRF?@Npv@~0dpLs@ISw?39yAATJ7_!-GJgrl
zaY%z$5ZlQbDnwhHlv{vUD76<9;<@ln6Jn?kZa-wW12&mLX@?t>O~G?1wV)OqsA&mm
zSS05|R_Q|9(xBn0Ob~%swg#(qAfZ5M3m4HdZUyy>L2GeA&Olkke*v_T1Vp4m&K`BP
zhcl?{h}od90JUzR;SgT|4kkz@m;vgfgNStR-V5iiybiO6Ga&9DYlSkb+(O(Ifqc6P
zczzueIiOP_;z2ScnfZAT_n;?HjJ_^e?GQ*3&CDxFO)p9;N!5?fPXU+WexMBnphcPx
z%RyxWa(e)&9D@V~Xgr085+N}u8C2eYkNXgY=RVM&LnerTX8A9$t`+Q*BkH9)w8a}8
zpm_%n0WIL<V7m!nH-S;lX0%@NOi(WwM5NE<>6;Y(T*4mCfP^SjqjMW*;Ra}kqAWiJ
zx{(PHrJz}B5QZ-dggAgwuOntrPk{=%{qSBC!e)432V+2NrnCbKImQND+<}g_2|}Fj
z0y@to6GWuvnjbXxov&*TXHZ}@q&@|oEe+kg1xn<(pe6~Z2L;OOd7!;Y&<&BGRv2W2
z45Lv1aRewU5RsWmkT&qufQmAZ!9{uLkSq&|ZOFU~#1@Q!5z?}3Bxv_Fw26|JT2W90
zow@rDO2;4qI#6qF4`)C;0~&oL!ZWCC1<>`%;1wpIfJ8K1!G1y{A5fwNVYGxxR@)r2
zV1}-E1Ra0}zOygBC>6Ym0ekRbl!%n9vYZcUtnY%CkKmCg5CJV8k%pciVN7IHLO0Tb
z=2=TX2kyX5DlM+eP0ELj{ezaRL)NZCx?G=NOSwKl8-G-3Cxfo;0&nBXECyXH2WlAQ
zLt7y-Jv<DVAR;|X)Aq7=&lP((1L9jENA$qeXMTPTxEuhjpC|!U^`!-%TiN45gVzPH
zF~oGxtOJNhhYYHi*uxnR8>v?m?g7OtV)+ng83kl-H>975w9f-#vpYH4JRs!}WTp+e
z0|%6kzz4j6W^W-W_&jL01&B!B^yzd$kB5sroB^?$$WA6`8XwxpOab}s1-w;@-ekdO
zRa3Q&I|gb}f~-u*FDS}1g?N%+VL_b^RFWJ61JV(tpqUEDqC801i%~dG$5wApBMsz3
zh+h$j31aI9*m{Q#(40$2kp!{^YquU^7lse1emrDh3HS<CchCTtBfMw?t;Nm+5zsEn
zd&B|&NFoMZ!9#NOgIO6sJFmHw@u?NbsRf{BIk;Q_?T`!l3vb$h22CJm9pETD$;!`2
zgN5lunJL8uso+*L=zRYmHn`oOq>tQ(#pobVuM~R>YHfg$TRgNlEy>6%#-0d~HWfl5
zgseOdDbc_!tn}29oXp(J5^(T?_r`;2M)1M-5Ep<(fgqEDkfe#x@S!vu5WU!?pl|@y
zDzFY@CDsn4!_VF2S7rp*r^6VKkf2T>5D#ixgK{Ut_n?i)nIHmwwKALmv6ZZmTS)ll
zChDhFloTa`gC4Y&Am}Q*tVa)ZjCj`}vL?$<&Q1kicL%C|kV+iTATi|N7ud)Myfp;z
z7+DDmSO2#TRGEMhRuu_7ChA0JI;bu|1T3O1f%qHgswIfOLF;J9F5$p?2=$?}dzty*
z^R~G`6ECswA{NvXgS5xe`8;)}_*}40hcO@yAghIsa%(%<Ipd&lA;^&*kbWUXNd{VL
zLxh(>$901mK2D%!$uW3RZ31=rK?M9jTQ~#aA-bH704g>zK?HQ|0@8s_5LXb{sD}=z
zfV(N6RxW6HKygW8UUDjU_hWoKq+Hkp@-&D@ub8Hwyx@?bJ)A*~bzz_&<$>2dXF<I{
z5RvZOIn8F?G<|zG17bR*<CpLSLueOZpyv*Zt`S*<J)~5CZXAbAF@W!L1=*JfKF9#J
zuN74ELWagbT~QE5>yDB&bPOqQLHEsoyD^#hdEk3jK&d&FpMfE0Iy^PM00ljWfG)dL
zvxhSvK~J4hX`ciG15$Ah8lr#<*+DukNT>2ZY^5OFb2E#R^;7bblT*Q~Awk#Qm4JHw
z@hSPqki-lc=zxr%Kne(qjs<AP01*X5acL5CBOfSWn?dCs$Y_Wqh=_%>BQTn{WQ{W*
zwdKH9bb=PqgX>dIHU@?u(4~S9+d&BpxeM_Cai0$)fXS-Ik!%O8q5}E)9;l`R-J%np
zl9~ougPD?AP?VaSSdy9oAAyFp?LfmnkR>pX5W%S9X|~2GXcfFV0!0uc?jerAh!zUk
z0hp)pf$~`pqQ4F@8*<(f{3-w#0}?AB1;ix0%#u{yg2d$P#Prl+eO#RjP|1O{a{*}y
z&~TU>G(A-UI>rS&Enl2k0=nouJ~1yPz9g|IJ+%ayAg+PR9uSf4xPyu7>la0PI0F(K
z)G2#5a56A}2aDnpi;5B}p|?YWiUe?K1QYPaAe;el0a+0ODgQBNMWjIogMs>4DftB!
zrpAz*iYO;Qg+B<RRR9z?0W(2@mOUYjG=OGeGeHD&4eLicI0G`lTujzrI->dkhj>9E
z_-c1hdl=Fp2eH7F9+-eyiL{UwI;Bpb-JqsSW=TB$>&_r2#vs`Z^*31+5K{Sw=;eWY
z0Y0n55qvcX`T`2b6e-fc1Jv`>a{wrXLBbuJ=0RLYxMNri^*dR01ANTgEi<PCQuTrk
znE_`!-_(@M#5{-uy8khXVW|J9<p@yE607eqEQb0X)Nv%D3@%Si%q~hz1J!|2pqd{P
ztPsB)lVf1W1QF@~W;)g+9PhV>GoXGW-HJ?5gBf(!J*3b8nU2E>s4vJ0KBV*uT0{l%
zn<uD#2W_(eofeRhUzAyupI4HYlbDm4o|l`NN9e`@_{JgXC1<obSWvh_k~2nQ4jL5X
z4)_OwcBnz3v>Md?0TJmkY@odsp7!Yqi4q&0Xi;`E7_{atK)Qn*G|CRSFDf-Bu@ZJF
z0%#aA6GWu*rLO2&`Ni2jeWzsJf#>ak(1m<tHJxDNl#pJ0ZfZ$lDs1T!xJ?PF!I3Hp
zaH$O<px1VNN9?76RF;(X(%=ULfUaZ%pGX96OhS@9C@(|C<{)t`X%9V-NDUecro@dB
zfri;iQj1IUi&BeAbHKwzpssXKB$j3(#HN?9lQF+T2g8Z%B0x_chfZ*S2G+pSNKj$u
z$?Xu&frg<WW6_s>AG<y;lF1%=XcsSR*&bOVCEz;}a`Kb2LAe=}Ss;ZWC?3Jt224Ot
zHpDy(7IYj85rrY@^efm(jB6RtDj(RXvv8}SxfN7tkZdEQ#Rb~<RjiNmWU@dm*ena!
z^Pq(?kgHzhgf-7TE3~jr&%VgCWo=q7Y;_!YnF?}#9sH0q^qV?Bc0-~793XgH0SypJ
zOLEv^^e3RvW>6vnt(nTnhi*3prF+OY5~Myv>Xbn%D{9#fn%D~h^%Nn=9goEjk0Y&Z
zlm%^$12-!5K!f;t;1i_5O-sGh+=7xy=z7NwEldoVAR>Ly+ZnsGcVDxIGoXF}wZ%wI
zTu8Md($bJ^pc;8Pd<ZKEGzknM(huo>{k%-c)gI2Epm2nyYuH(7;Pc5qN7x2|PD+D>
zBcek~{h<^{h60nI(gSqYPf!!Qj06=XkkSJq=}^tf#o#fVIiREq$~YzY$;B9hI1t-0
z=4r@^Yj7e1FKpFET8jf-aK-_TYmiGaK?Jn3h%u(F&cMLnTbi4cukV}#xhp-js94`O
zKP59QGd0DxG^Zpp86*J;R4!2CF&-YMIZaFqnIIzlMBMv-73;6r!x@l31@-DlPQsYp
zf}al!x(WrUJVNYOfJ{L$*+UP6VS-k%lokiDfg?~a7pZZ9o|urL6H*)$lXVahGy_3O
zV{oximROXTSOQ+9xDGx64vI{0QU()<y<m_Yl02+JpuQ78bpTT6V+jSA6DSD<q!O&8
zC{w?rC==8G$u9+GF3?1538)$hDav%t&nw9<EecM>XxBqB8Pd5t&=QrbAi?V%P#E9|
z5+o-;gM`wq1>&rflb`__(DV`blBM_}(79xg4ghG>1~NMiIgEkZKK<OosJDswBCxbU
zJy+;~?yvw=k4c$%@k#lmc`1oSnXvQu@Hhk-9F(@g;d3PtpwIvf+Jd@%mEdDq!1)BU
z2q_apq{Fu~!5Gj)K{Y36fEJ#D#<D?90J{K`VKQ4l-7ydWJrP6E9?pOil9U!wur`xD
z$bQgTH|RnI(8h4k;tfdEYXNcwh(H{l4?9O68W&`Z-60j$D619tKr2E))qQSa1te91
zaxJ6@i_w~f#spaofEJpNRtM5SvEar3pwIxdAT#q)QY+$9K=lYDG(h<VdD<0YMv|-v
z4@h7XCFZ5%=c4Rg1|8{)R7@bYKwvb3AaQ}zoCKv}n;iI+7!Gg!7&4c<^J7Tgx~%fJ
zrqvVs^dsl%`Qxmg!16W)w>E<2;2=Q-Ds{8L{c6x|<e-=EDL91FL8HGQ45}MJGy^0x
zlNE_bWh}~t)S$KrQoRf65I}mJ7$rQ!a?qr(duoYqX-Q6IS!Qav3wS>m=*IFO6?lsX
zQ7Yjug@U*T9TA*Zq931`2U@uV?yUEL+Jm6P51Rf;1kEXcq#(%<R1Rl?2x#LBV@j8*
zg;UTqc*_k`9)U|H=#2bNJ2(Rpl9XBu8npnYA5cCF+6=cCG!&5uBB1RSq%Iu9VzR~!
zP*zxgCUk;885|PEYd{4bh=BVL&VblRX^MsH<V?!10JV@gK}(bl!Lth&$Z`+?wG(6Z
zmRg1XThNs1KDfQ0b_sIx5M$<$@?gFSvJuoCg9I~(1zFyUBUh2N91v2XVl3|kEpRCT
zT>urISX`W1R1%b!m+e^MlbTpu0^K|gTI`z%BGMs;q?*{n8IV|~G=am%|2SD07=jR^
z-Jo&EOb`K$af}5$WOenB3P_}LI_`kF^&q<-QGh6$AyI%4>7bH^<n9=f)rf5_WuRnk
z054KO6+PaGY9c2Qkcv%TkPWxs<pyFj72-FfmNg_kDfL@%L1rH05~5g8(*=~ALG3uy
zMii)Z2U)2AuJITc7|?1zBK;Ykn3SBK1Kt~|0$SwD3J*zelMzI~4Tduy{-jocp$ba2
zW^j8EX$ObTh@6-Jg;xcv+gt%kpH*->K*c_!?1zj~V`L3#4;QQhr6JI^C`kT5RQx#n
zNLCRAofiR@RPhCw$=Q%=#z0vDsmX^Zka5^ed4W8ghk+po6vhzWqBoW>N&~V=K(IIU
zK@CMna|TrBf%Z<4;#EqsDxwd(3N+ydS}j|Ym|2_}pP2?;vYwg(U9&+jQBx~3*@80D
zJ$OPVn9!)5(0M`O04gFuH^@S6IEl~6&(AJ}os}*HYKMRbTn$aK+Pp|r1L)W_efYXP
z&>2=p?RG@shZNozNs+7+fztB>^|wK35wuD^2-N0;WCDcEG|P63><o~bos%;Xi;5kK
zgG->ByFkr8<l~w!d<_~lfsDL@Ns#kJKy?qOvj84)%}lEV%Ru~sNX<Axk-Bks6m(1o
zVhV&{NK%@YV3YW+pb8OTFQ}E52_m5FDU230weu3%7&AeCla+Rnat@+V^#)X3A$*Ro
z8Ap6mUS-S&wc$bbpr<gJ<(8G8c}-B`3SuLur32Zyi^E1D_i=&ln1ZYfC`&CWhA!?0
zt=SF=!%`~KEIglq=0~=}dtrzii^F$hbv+SPaB)Ur0qAT#$Z0f!pyU$+_bw<}AzeTm
zmQ$KxVdIYfKn2BaxTk4uFRXQW95k>3DnnsAv&s^4;8(*if=&tt5$V@OjSt;!R<MUN
zAnj=C7M5{53=Acph6t$7kX%|+oL>}=bX5t#FaZ@hB=^i<OJZ_ULBn;$`p`NfGdU47
z><g++R6t2$Av|h8frUJOj!`2~uPg(dLl6WScZcM2L;&E(d(=vde4v)tNq8new8U`O
zOJujEB%>%bF(orE9Tch)K$SFTt|2eKBr~nj5qA4C!jm|199b=Hlx9wHK6qMv2Po--
z{Fa_tl9yKn31h@;C}hnv#$X~@H6@z$L>=sd!~0~VE)@G=`%^#@dEhlypzH=N1tCEI
z3KGc77mnyAE7!yF8R&3}qP)bMVtwd&37`na+VO$(Z!wxGhQzJ-0^J@68c2p-SPkC$
zS`8lwQ3920AR^uW)RHzc25EaZ15&bqwt$gdmVj3Rf!AOY@*E^+k><Q1)>3g?3skZP
zVH?-Nk+7(+Ef7524lY1JIUF>M6LbMyiXeu4Ab!Ufd?IqgCuC*_WPu>4p%4U*Q;=UF
zgN8V=E@;;v5pfFk1$3KFW?s5J>;SS$pn({FxOJej#~=rgVaz{4;*!Wx5oNFkJW;e7
zZU?A*gzQ$p5ozRQhK-<r15FD-GJGXyR|bfH?uqAztpE{%t^gs@C!j0`o<amg4yfG-
zUe+3)TAZ9%06jzq6vvq$0y=Ynkpajm-jF&1;Nxb&b0&TuPZq(GUoWWh4kDmyEHO^b
zAgfeEvKe)ERurfT)r8w_1v)zeM5N0ZR^=_!HnfK`Ac=*nS`Eo|_(=L8ki|7{iwPFy
zWYuSg)-%R#Lk(C@zN9EKH@+&hC?7hGM6j1aR_g^iaEQF18gvFP_yC?FP?IjT44U*n
z**6nJ;5xdC@}A%s)P5PF=K?A5Fj|UKD00gZb23xlP1S5r5fuY3K6?2088SgcdV9<J
zzr440+rt@<R7Yu=M;)-L1@&Rxz#GcwNe`pIr?j#wNGwUtfJ`?ufsTa*4f(_u6clCV
zmBg1MCgngQehVn)g9vDugK^9WImH{uo8UE6@!<LnrH}*NIFkt?aE-Q5TF8}vFNy_~
zvP_`mDxjJb)P~E+Plo0-f+-Kw_9LPyL|x^LRNSE41p!`E25L<QO@K!}!UZ^LE^-3>
zAgsk6AD@?60liA_gCYY%CWwIUAoquDCHIHcUX<r+@CZ35lnaVd13=f5L+VY?Ie?iU
z0vgX4>t85M;7E&)kAl1onvai<&&$sP6|1m~`k;#~GeHD&lg4w{#)aq5jSJK&SHY8P
zphgzN_n?gekb7ex3l@<!v_T3N%EMg*RG@(R2M`-ULj#Z#Js{;NM&p5Mm1rSoK_005
zD#$NLg!N28IU16!akMXKH2xq1s?h`Cg*w5UMOKZD(nAFGn?NV5LGI`l0j&xIt)MC>
zD$RqIsi46}<b?_tRhuV~y)=}4&5&jVXu-cexIYS?3IQc0@NEu>B{`t=3GqhI%VVpT
zs4`@Ni1g}RyOKBhUbcrbAc>IDq83unfr?u2l5j*~Bp3}8_BNh^ny(-`A!#49=r9vR
zVD6oTcoY;wB$wzYOMX;Q$FV^<9dCDvoD#en<SPq!>$DQIhXO>TLw3b$z^<1ug_hk^
zEI~n2*hnXPf;#w_AcCsJCQ=m%n~ISKc^R~y5)zT11Hdvt1g=U0RLZ$lq$Yzm4S?3)
zff64m-60K<fab-Kmx^FCt;ouLNRCHs*lB>q>Ojjt<I{^0lfZRvd}2{Lw6Q_3VNceI
z03^pCZ|g?8R5St9wgVB+JqwRuhfh9&R(>=plR;w};C+4ZkVA7oD-z+S91tuTsNU}9
z1vLXfWpZ+UPHAple11{9A+&@9)na%@^QeCCUSc|UfEU!_4gyW5L()BH_7O4#iKF%;
z@r)+Wp}aw$RuRN_(7_RzAOcrHpl*&R=U`w6LabE)Wly}*M3lC-A^8z>{7g<h<Rm*#
ziGs8`fMA-Vv?4@YsI3q3y8@y;0vbJHVq{=|7HAl&U@0xbk-B*^KnHt(mIQ#hJK&8H
z&@u=S0g$F4Mtg*M-nRmIUmos#4p0>aA`pXTkoGV?v<jnc!ear|CZHW;kYrCVcT?IV
zhVKor1g$6lt?GkX4DvP`69Yp!-?jgiXQs2;!x@nF2<ZH0lG`JglLXN59dOH#n1x|D
zqMy8RQqal8L3;40UdP73kO?B7CnRdy!x<1iQ@vaVAJPWy^+DRhMX3eQbuktak_?$3
zB7IGMLeb(Ar|jVjh+C+W>_FuXsQvLD)cph{Hb{FIk_Hf&4o4rKtez)Q#SfbtZv@T2
zf=2rx_7ZGxP%Y7;9Uh110YZ`)MhBF_r3eN2IhA?&xtWRJMXjRn&5B?TgQ|DDT`tOd
z0Ii^+4P*}_oDn?$h<7nsndIfF*`U@Ws1T3O&5lpXEXhqQfY=8*mI!h-5yU=>Z4G1<
z;*g##+8J>mPX-mjN2@@kY9@$Chi~?SF(A2#S}FMwsE<X$G7s9FjgSv&GKw%TFd(gV
zOe{(V&)-1Hm^4u18bm<bR2V0_lhXsV1yx|W@G{i_)Or9B>5wxpgzTXgXGuY;PfGJL
zd}8D_$YM~D1xaj(_9$eY8Dkwak)A`Dz613(!1q8F7vyA?KrBMc6E0HwRWn5pV|<Rt
zTm)L{o|+6=lmedKQUeY3BfJAHB0vOur8}GfNx>S#4agLM_eq0xzC%a1^K%O_b5i4r
zQVXEBQ4nltQkqv_OOrqzKx*O2gHj`ifVML+B8;rk0jah@jH=s!+B%?v!yz#U3RcMa
zI!L)hoyH~BatU1FgEruSb`a*3B<GjrmBfSFU=XKh1v4;Yf{1jBU0&taZzkBo8IWkk
zI(h&)d^6}5ytG4%+)|eGQRaK^fyxum+9+@d5)Zm-Bqy;1I$TIF&5$)j00}|xa*KGR
z6^o$Nj!5YS)M|lrfN``^C`~trDd%^fbOV|YfJ7E3E+FI8kZ{IW%0h+qB6zU^cnS*C
zOxp-*rhy^>v`IHUKMi{NBE&hMIuE%4fl-}P8W4yK1Rjrov|Ce4;vswcAdWz!1RQyZ
ztad9TCFB>BWaeg8rRwM9=jCLAkEXs18aw|CPx<KEIWU5NtQbKmL!g%~g9om`#q@2^
zlyEn^x?2yb?mz_eQhH^3I0KS@X>>_g5a>cD^cbK~et>ilK*`Ynlpkv0qrC*nQOaAe
zpgWX;nBl=q&_+;R0q+jb2a_O2D}tJ*pw<C6SHX4<BvpErz^;EGXewE07~I7LZwdmR
zb5xvKl3xH$LWZD*%LjNUBFZxyr3y(k{WDNA0@1~d0adaf0@qwQsD(pzw;uNz+Xz+$
zhGNiaC!6$C<lCY^-2}YbKR^SqM7Rg(>_$+#4yk&0tj55Q2_n+x&Nh2&;*x3)XFzf!
z<%u#6l%hb>$MNw=`T04piCfUPSSE;oHm@<puBknd2kOru`5cjqaTElgW&;sn8(N%N
z<dT^T-lq>T6UmBWzMz9KeHj?ir%igQGG$+ZJ)8jvTu@z2gcYdk5D2ZU#4*53R&XMf
z?P>XWCGpS${6GbT8)#k>RH+t&W-Q~=5_2<iDj{VoNEB~XOPd=5K#SCa1mGnFC^8`J
zR<x4hCUkOytO_4e*Mg4-(+88Fu)ujx7>=X`8mT8a-jU)WITvIr==Piv&=f;_Q7Whb
zm>Qp)n^+K!I2r;pYYjQK3)0%asPD-N0VH=Inmju}r8FpqK~eyyVnN;|K%Jo?NZ5m0
zyeX;4`9+B(`QXJjil9;)G${zN9MmnuI~@Tkbtnmbq#=1wZx1QQBU)H!Y3?L6&5_l@
zf{hD9cPF8&d0PbXdmOyVM>H^TBt5c<0Yv4DvEcDBsEG+`ctgUTp#7BBCtpCrbfANZ
zAT}Zf7;qGPpc;>u*e*(q2Y22;S*sHis)&Y^0_bvE5COgDP6c-3oeFfAhSH3LI5|KV
zl#xo{g&3&bgq#+DBMXx^e4+}n#TcGyK&dzrL_lY8FcveB)ucl%D2fvEz}J9)n{;PE
z4dZ|CZV7r}fib|~Pu$=QS~Z!SlbTqRr(Xb?HONm+EiMMFX3t4XN(B!ugD$iIZ~uTV
zT!thkM1bSSK$J!`VkXfY6bX0XQH?0JAW@AG3}i(DB<q5zXtYH|pynpZq9WLoIr=a^
z4Qq|Zpb!8JJVU~sVBtZv(NJ^H3O7&#p`f%l1Kn~Q!B1Avjg$v4I>#-bq7xMB5C?!J
zJTgH<diR5R?VB47?coeaYmuy?8&YRN@8&^W*#<gOHE0^V5I~IAKmvg3@s7A60DQdC
zak$N(D={)b1ave&7&a^*Or_!#u|K*EG(-gQX>w^%QEFaEe0FMOJZPE^5(juvAjYX`
zpnV)Rkjpg`)Qd|Ji%QfL^3xR5<BN+@^@4+3<9$I3wAB^zld@8iONyaq_JWSl%>)t9
zg;(EU>s7zoL65MbS|X1F&7j?a7qcKsao7y;TQOP9a#-U8v9k?2X`PZ;3_gP!)G&iA
z-U7ErK?xqxI)ltH{f6Dt^BWcbWG%_T>5QEG^mNGe3?S<v4guwSup7Vxbk+;WA<*!k
zysdHyv|<C)D2$IUE-8wSha6*w2m*L35pwwKCs+_rZgVaup@V!JUs?=1O#+X-P#=>w
zz5(9%2ks2%gQn9;GV@CFON$}rl7lP!R&E9c@OrVF{Pdi}f}F(U)cEw&JoM1OD0iSa
zf<~?ag$~yAfZ+~k=up~kNGZz%Z2<-~Fr`4Jt$^AZu6fD%piRNVh6NSf0Gj?J$^W!y
zhk@3;L9R}MEF}cDSK>?aGL!RDQsa|K(;)$a81}<x<wFw*EgT1GBnGiCGcZ7E2XL_g
zBA~;A7&Rm<+yGkM8MFXiiX&<$c-aACKvOAM%|xVj3cNuK&bn7Y8`?mt?VNH_^HNe%
z+*9*Xi!zf#U=4am^q`k4NVAjB=%KEwK%G9UNf#0pNY+CF1?ezU(Ac(3QfX#RiEd_|
z0<`|FfY#1wpmkj!B3<if-?4>t;r4I_)E{J3PDr5(Z3PzVLmBbVi<-qiqokmoP;P!{
zajI{AS*mLp=(-tDa?1p{2u#3T17~3P4>TkUZ<eIxB$kw<=0O@J;H;$wI$gOau@b#q
z0%`|lf{64&%eclDo|*P=2Gr+d#UR*DY{UAXWDUM0sx&XNuoOB`ssb8+2NBSID8D_N
z0cir0<p!kCM@`${Lm0sGbf5!%krz}JfzknpNdNsr!$IUshCQ4C3GiadYiI}1rae&G
zz&o|FJijOfJttw*LeO}i+-A_;46L~a!%C`o)(<pj11crZeF-nlsony@=}QbNp}r(*
z79J^a<tCP7z_vZ)fg%^w!0;$4%}GV~G$fpnI{472rKSr&<s6<!MzS30eb61X#Eg@n
z>{)^}yAg9+rNt$wMf%011*t`4nZ=MjRG@%>xCO)lH@d+@I`d|yiSM49*uxpnfB@|f
zp_*HeH&B4`8^k5xf)$h^akvB;DwLKupuQ#IP`eADk*_<TjxSn3!0WA_b9Tq-`!d2#
z;-;?km7pG>E0z%_4C|r3r%}s04piBJTE9uDC5hO20!4wkN?j+VVK+j_!h(ZF?g2#x
z)>MJv4rsVg+D8YkX#({FK$k#+mzsf=^n*9NmZcU|Ldpxo0o#!6VI20*ePSH2;Gmi_
zeuBokL74&4(8XgjGyp(1dl8XA5ofBU78T_efm@W}pq?pc2qrWyJ1@T+bWLKaD@YdN
zYmh4-SrS(%NrBy<K~Jm^jbRtmpX3dRKsv<W*#v#)!PB7IQH#<NlferNL3foyoB>LC
zVBdm?^wU>VDx9TM?b9Rv)kJRyQHMn|=<t6cf*WlF9Gf$W^K;5l^)pLSixNvR%fPMy
zg*e1DXk{XXYoLKbR<Z+Ml9ra1S&)-h0y_5<l==}iqlE+Pt`X>g+we<A$eK6>+l*WZ
zAgo36Fov~I4^ukkj~IVE4Jv_WgL-plK8CN3h2-@gu#q6@Sq&OzN1Cbx1p*||VOS0I
zHCeYnz#0UQxk}_v2QB6X7j^N_$=g)Wh1rly4{C%%?$UsixZL*XiAJFYS}TQNIe^NJ
z0WAVWIvxs-{m{T5uXTha2gavX6y(N(7cLg$RDyDW45&5)4aWE+R#o~JfO2MjkzamM
zu4i6aJ|uh)k%duEL0e2TbRTHQ0c-hy;TC9Ek=1B{B^<;!GIXH}=vKlY(8*BfL4h$r
z2Mr49S`P|(tiH#v9_oA0JSxd$HBvVM)`hqY8X^Rh5}>95v`K)Nn}c*>kZxjwdY)Rg
zp8{<<0=HDbWh9({4t*hYA0Rz*O6`Shb^=Z82VDoXkik}hj<U@J5tysQputVnv>aqu
z0(2!RXi^wj9zokDpq<{32nPi|xQGN3&=SemK7Ccy&$(*Gbrcm%s4hS*CvkRBASEJ_
z{m>8~tJeszA3QINY&jyeqj{diQX_HM+kX4>TQbkh9>y(%d7i>OU*MJ6;E`WYNr;##
z0!Kc`F_56gupR1k3YvnT0vCM$hf8L03AnZb-(85cC5n;Np_z>W>v6V3G3<hR6;yqa
zT&zH6e<9Zaf*Rv_@tJvPMTwB}-Af_cUG{@IrMp1ALU5dewmfBmh;-SIM^?u~!|l_<
z_;feTeZLtN=d^PQ;>ucZ0stLp1-b9*jbHh^igRlA>B8JKuG{09p%cTjb4erW<UD9U
zZzhOHcgPY}|85;(pZ>Y0_T;hVjj+p#sn<}G1nngOE&T-*{K<)VWr@X*st;5VXMzZ5
zA&j)D4w@jzDh!e89@wBX=#W%!XRRKzV-J)MAss`I<v3gbO{SF2Pa$1R1G;Xj1XLPB
zPkBp?PXw)PgkIH%?i8dN2^tt=m5WG$fm|*IgQ`Qs4Lx`)hlT@L^<jXgkG^MaVtT4u
zW=;uY()t6aw+u=QZk~QF@vbiJuECJR0E!ey;fPUUK>bfGCxDiiLMlXXro>}0)bB*D
zNsEUrOD|3ZFP;XC`$PIC;9}?+Xf6Omq~~9r|JOq>(jLx$`VZ8LCZa@4OiBjTEuaQ+
z5a?J=NJ<7RS<D0x=?~@zF5P=D+#b$=G-yaO9duMtkTAU2q6%_6h=AUeAz}|_K*NZv
zMk-R`L>*V0$OXMX0#syFf@d-ysRWTw;k`V@j@?zFlDe?MoVpG{I|LDr_0Vu2a+Q5~
zVo_dZUOKo;3IVMe1EoTU7r}`VL|`@%p$Ubof&kVZ0XGfw(=sa{M=gTVCf3XV=?q~^
z2@|>cG&eIhwIn}32kb$R-|*OhRMJ6`Dp^qoX#hfw6UKMZB*-z4at1O;jIoUmG~q}@
zHq0m~DagqLU#18un6UZ}!v<&~A#1=1DYL+LK?i`={ejjJB$XzCPRInsCfIXe0-8s!
zz%DX;N&P{XWY9pX9DGFqB5)!7IV7v0UI#7mCn9vSiu3bOVi9Z+s67qIBnXRUeo|Z^
zwO7kN-N;IT^LJSitQ;U~Xbrm5D>pSazX&{*0SZv8QHx<M)PI!EW(t9d18~6(vOE*C
zt{6<D`|iyXlz6RepZ?O(Q8Q|%In1Y&TPY2)5)`D6x&%?m;V1+sx6urA79?n24a7#o
zH~{2oLKge<4JCFq-V+3&4P(k}1h3Hqjf+8S1l<#u2_n)>%fIZ8=~lB(e`N6?Yp1a>
zv;mB?!~&$trX({rRS!f!msTtXRf!-X{lH`~z115m?coe)F->GqiE@uZ8>n5D1R75T
z)c~3CAWJ|5^nPSP*hR^(a3QkC23j--8W})t0!o0gE;vVltpY_qq||^!I!37sD&>gC
zxu|3I&<urqpfk>cN8?rHcXLlMu}_CFAhiKG!DtFf&~fl!1VtcZI2PV&fH9!imeSr1
z(k@xhFc+wa30{MdlwT2Fk{_R#mz<Gb1Sz@@xeKFufF>O3Ic5PU96$?uA&DN3)zEOD
zw7Y|}l}MSFfdPDQcf3<kVsbXFE9sHa8Pxk!bi-6`28JL;&`<!OG!FGY)t5s-kIy>=
ziU`oSA0!PRG8<%igu_04zLfr56}FwQLLJn+CpotvH4&gaUzBxOP`C7eGCtTXXn}&^
z7HFW5)gD7~3+gJR2k?;yaQ%izlaO-ffgSWne_mKnP-;DD*WwQ-CKPZci35oB>yYAy
zy0*Up*$$d9hGa-YI)Ib~H|^jD!a~EI(y{=tN#Q=IYFQ5I(t*Q$GiVPOh)9QAnG#^1
z&dt?q)T<T_3wP>S-3qdL3&?74ihBXNt_4J-&lD*3yjtdLpDy3hP{S$e1Fh|;XEV6g
zN9@cv4LYy^M5KQ&G;t5Bv$RkD-rxE{z`_Yy>ld34w>K*pG|*eDAD^0+l9>oTn*uaQ
z0N%aol$q`d77Z>bDorjaEh2ik6xnuzLKSN(6_TZpidX|kq9&^@Pf09E1@#{F!3zPv
zJ5|4fiV{#FMlaSNdm&!ir7QEhh-E&4FL<Gr#fUyL$nW5G3z&fJeL%7p;$x)N9H@s5
zUr?HuoLrh)nv+<PioB+@6tpcAeMKqaE-6TZPZHMPlZ0g$vijvn<z#wMW(sIT5H!34
zT6zsmPM~H4NFp9|G!^)~C`i$PC^sQpEu^V4NF0+jlY!(K$T=_2JKRBMRfA_2;z8~y
z0M+Ly(34=GXLf?t=Rr<{g*2_i?V&q@#9>*BiuL+=(A{#Ni6!UKqOw#+C-mfpk?sdU
z(}JuSf5@l?=x$-q5mFWLpj+*-!GkLIK?A9vWk-$`nZ?fedC7?-sd=Cs<k-UkX&?t0
z7F2c*s1(3c1|iuG$&tlmb<iQ<k(;Own&|`OleM6#1!OnG+n|PbCWuIXZo<9AdI^Vp
zx;WPjKDRgUg%{MZ7E~A@`4(g&4r`&lrMv@sm4kr+JbjRpnpl<^o{?FST3nEr46R+j
z#UN-2BEsX4CI9ZQ(uq1Q2vUO&_JI11kYmdsN94Y@OSfpzGOb>q3a!Q|x6uW3-V(w_
zaKi=UQ|N>d18h5kJFJW$YfmXsp@)7d$Rkkl$^msG!FD4GUHC0wkh?SmV4+NH2kZqc
zumY8w=%qe}10V?vsiFf-@7Tm=Bo=3Y#wqg)QuB(7bK-MTOrawfpj-o~G;tM|#14f*
ztthC>OaV0rUVy?26ir~$!2~qIFk2!-T2We*larZb1j_L?po3aK`OhOHB)}yzJ+-*R
zv9u(^BQY-}Cl%VqrkdA~>;=t5B4v3zRzPBc*ew;1rBg-lE&b5D>OfuvjbI>6Aw_{k
zgFr<3_sG;`TmF^Vr>in%W!;RbhowHy9sweHjTlzu=YtOs0Ntk*gt!wAWK|}Jz_btQ
zMNq;d!ame7A*AevG+b~3w8H>2lpP;jQi*Ge0a7gjt;MM50#JE|CxVgehWZ|K-vr6N
zhmMHFgZ8c`f+q?Q_JYe8JP8ZxXR_LvP<ufw+xW~pP%{(Faw0P<SuJ>^D2FYK3I?qb
z1TBb2EGWpSj0YXPladL|zM$Nl2_n*ER9t>Gi3!0L5R1bSI<;H@G6#~wz}^ShiNj)O
zoP(-Zo8pq9%)E3vaLHBz(&(F-h_U+@UO>Yb(AdK;0c)CrX@U-h+_i(+4{|WcX%5Rg
z{9#b@5!{0I_Y3h44T3bE2&X-2*$-+lVf8phT?F+wSu59((jBN~PtMOv%S?|i0Bs^H
z0{7ZMS4cwcwS*q{1<rz?i3#KbzZmSH{Yq_E`lGyQ!vShpfX*<0RP1=Hga!bmmrg*2
zsN*vr^(3fz0iT)!igHNggUTUDdk&uXP9A^9q{{k@TJwewCxEQZ0<{w^!rKXWtcLoY
z(jg(liA11V4}!oeLBVki%8rl|i6C2*1ntu|?pZBoz~2drYwB6u2g-Dy&ByVndBvrm
z1Df)SGeLd&_{`$?;?xpIQo!RPXkbvD`c{L&9})GSv4Ttx0pFhrXFy9W%B{2jExiII
zbBHfNEJz;1F@8b0jaN}^1g((F1QF22D8|i^l-r0lt%-<9NU4Ujzzs4+L%EIM0Z!2T
z79>;=6RD6s*?&7|ME-|WAXIEHxqud0A`%gzbEM-e^ZJq5FFR;J8^CfBb;r_qK{IWj
z^>m;eci=;BKqY%J^!y``yTBDRVj0b2sre`WRl<&<r!-q5Ho@TBi2{lB4|eG*edpYH
zAD{y3rGkbaiKxVp7YTv95|j-pFu|o3sL=sA3=%ThhI9-)G{chBNF;TU5M<T@V+jqB
zQ?{dvgcukYpm_wTXn+>-L<S<t@`2@`Mm1=4WqfdIZel@3KCa0Jq|OX9y^v!aD39T3
z#35M%^^+-Svn%nLd6^~g@t_97WKh)!D(9eAv=wJoK}WtohfF}u=7el05Jn6<L%l}6
zt)Og%<Uu@EK|M%T^B6v;nOad&ln5T(bp<W#0^J<oOWg1-(!dJT)6{YaC^aDk0Vv8r
zWf3ghB3TR#0!k|wq!rVkD`ZMQ4F$xC={)GpAw*>ZDP)n{0Sy4M3P7X|1M*rKl9x+^
zHhB?I!s8qmDJja-FDc4Q%u5GNPG#nUJ1d~DheQRq4ghf>Q2|*im1v(nSL?p9(85Gm
zeM{+JB*+!W!2oKeLmU9g7+|}>1az?$k^`V=fykU!nwMDu+PtJ+lv-Sx10I)<0yR*h
zKvTwOeuYH$13T!tEjE~6$+rp=j#zz#VHMO@)R{t9C&9o_0xr?git=+I2lqfaVju@W
z!WH5I%(P7%7l1}P@OT`_TBye<UAJ1CoR|aNqCF2(nuDzO%qvSRDoIW8$xk=Jc?J`%
zP@tM~KmkrtKo}5NkLBlqc0YoK-wP@;L8lOa#~PAz@<E5U5*yiMSw^y#C=E>bn&btb
z?LOe14`@?9d{y#I&=Eu+0=nWN$UZ%B@tXTSK2%$8g7fAUP@fOd@q;f*fSt|*>4s6-
z6+nz@;9PJANy86dU7`oj^Rp?n8L_;l7&M*)igidw5LB>0mOR2ILMQC=yu2WT4>~zV
zX?%mqY;aSh7F1Gi29?y{N&(RWg)AqNuus4F?AVtURXHkI3_oiG?T`=9EmvS7y>^my
zj*_uD?656;sKu0qH6nfafmU%9!Pk&QgXU#GMEchz|Kd+d!uIKgu^m_C6D6o*Goq^r
zK4%d$Y@3!@k&{^r9Wesc%aD;f_*e=or9%=prEN0sNiLA%PS7si23_2n2_n)3ADga!
zwZ_dpJ#GGRjq`hapq*W$2}xxJ1_t=b8_>S^ycEc)8<0=Xm)m>+1uBS0-`RCEpG(`+
z9?pO^@W`5efwZ-e8#UmeNKgnuG7h-80U{8)Cm?}(1)61vyyOCuKfrAoWV=AKoy1i%
zka#0&zy#t&Qg*^%?6{-AcBB?G>cY#Tpe6r^Wv85=_yrNT!kMfz3-LQ>afp6iX)fqw
zM^NVxR4kGb>}0i@F|7u#5d_&^4eFMIN2);k*-8uI6LT^Xi&LR1r$Da5(cFW?184y^
zF`bEg&?;+?eWWHcvige92}L|h`$4B#W`c-xIm4>Fh1!Ppa0bL<ln;OEgBFN@0uYjU
zl|YFKL_kMXkVYUNHd2tGAQQGIo08D_#)v*CjtoUsl19o<NYmv_pt0?j@LCr%aSpi_
z57N*;YP&-`PI*wLfet+e`8Ym4r#K@stt37k66m1dhooN|-Ucm=B_{nArGjV0Ga-G1
zC!jt8Xn`9{Fg`gWu?Rj(0va2DTrUfmt$GVvH~tn{PLmbbkW36(Zx^4L58uFe6PAC2
z!4tpE`H)@<^kP+n|8aypS=kFI+F?`XKS1N{pq&Jea7VPXAogO+XHdHs1D#ffwHT#p
zyl(_GuGB#z-Qe~lXopiKh=5)c`Vz655t6crEFB9nld~ZSOA^#zMO65Bq7T~b1x<_*
zlYUW8k_4ZXgn7BodywtBL2XA+BLm!O0~7Ft|LK9(Z~YDtwSo<7P}O#vR|~<X5tN||
zwjqH#FbB0krWJ15LDw?En$e&YSY*aSaY159W@3(hd_hh=<a88JJqe0>tO+{J=b%*j
z<&Sphb9wqEg+JGTwq=XSsy>i%a&bvfYGN+P4It|^K*b8EL=6TD`z97=V;>Vhs>Yyg
zU+Ovpl+mz;1EgR<vL2cvC@s-)a}vQjr(c8W6i_43Ex#x?vBW<q3wr<{RcFuupq2|Z
zfU+Ygg#o4gX81S>=s1NEP<z-nu>_PNax#-#QVWVwlM_KJ0>U8&1fT~8Mo9||4yw5d
z)VIW%6fkUtCIw1UBI@R_)1YPzC|g22{z`y>ArnNT_jrfidL-3vpT6?Per2tY?Xc33
zy4HhRRn4Hn8I&b5L8t10iFCJVXF#h$?9(mMZcaY%47O2;tTqNxd4SZfUJDvF15Gf2
z7J`76!xd$wXFxmnp!yF|2|^acDcPsLdli~e;t&KKVW6Tzwt^f28a{$Mq_hCi;6S(n
zGSjAPpDyM-u|{<Q%oWs)3=7a1+aKW#0YvK>QazlsgD&m=3~lg{RZ${E2I__vP+JR}
z5Wy({P9Rnj)Xy(`=-?p+jR5L;or{x!Aqe4h(E6lI5Rv}hhF3zRw#q&|-u?(1xB5b;
z?UY78QpX|@<Z)035)$V%pkZ4Ok*@H{j$x&5j(vKh?{(o=@p)9T8Yv6x23b7~p8F8V
z4YEg1$v#~#Z!zN@R#<YQbU+U=tcG(12V@n^dpqb!#jutLRg>Bbkhc*@4bhc@TfRE#
zQXpTNGBm`go78rJN?cGg4-((!Sr`~HK}7oP1>d5r1B>j_<qq~v(Va96YCEM#4Jp1s
zn^7SrsDg$?Kr4WAQqwR>JjWeOTwlK^+NXmyY)bY7Lft`W0{}EJ18RiHgSs1_HF*&K
zgC?aQD}XyLdv4n`N60>%!E|fRT7CEqQL=i{pcT0Bpv{vYD?xD$sbj%i01y|_e}>FT
zFu|@1V}@07WLpV7d<$fyG_Gb2hLzB2oqBm|8p!HJpj?gSTX^FVHogjN%TUj1@X)R$
ze2@eY&=^)jeNE)Ttdjif)V$0p@Kh!!>0pga*tt~clY^}fUzyegi%d}2N<=RPX^lS4
z4l9NgP;Y=b>O@$9y7~a+Y{lgKJj`paw(~PElz<kc6s0C-7G&m?r6-ma7lT&cL0W5A
z!WS0M(5R)6!$1iO5(nUV8jm}m0Yt&xNATD(XvQ9Cd^sJoS_kBaqQtV)97te*dNr9K
z0=k0#uU$H1HMJNlAyB$N1+*&|w2uRnzd_rDgOuP~^AK$%NdA6gm(F+XzvY?f?9kGa
ztUQLCLcrIffo#TkS2QeFL$?7xhqltl>%>DZKLwSb#rn|IK+sh&yrAmy3p|q}s!z0W
z!N0H-uw)I3A+-}hI|-0)4(|s!0F>q+5nl=#g9Z`leQW+0Nhk-|r(3Vo_qH}IhebT4
zRTpeL@FS?|G69vW;AD@ex?C^ieKC|buuqpfDYr1qBNQ6x)Up?Joo~>3c!(qH{a4k>
z`kh(NKHX;CH2EE;f~jn81E{I80d6lS$RYJ6B&Pq`L8p>nw-{49q8Eau*pI<GB%mcq
znIHl>*&tw#)=H#ScqieCXGOaWkM}zm*rz8vN%htFK<#*D1SM>2@qFj=pUlNpy7uXJ
z<89wXucdZ8uLI2#VvA>ZVdk~LV$RR2Jkaz-R$+#ep%KeBz{g&K(iNmJ2x1|pu(x*U
zmwq3+J}*)TYBQxV3~Pjf?!!achK8^g?qf(rDnKQBVI|09P>$M&B}YN5`(OuM*#|pr
zj8gx?>^%&MVQluo{fipIly-SgXQB;3`51Id0eUP$1{PkzW|DZJ!@fvM7{Gg~(Qn%;
zN=+_-?x_YB#2^AuCP0D`zCoOZ2Rng6348^1L1JcJ3DSLgAO}Hq3&0!$cOJAs1ky@!
zixDXcK>`>=g4zUgKvQ?1fo)K~t^#~VIizkxBy5O7HrT<(Q=lzG(1f3BMQXBdX--LI
zS!QZE$UKlXJncIqQ=s0VbT|~gP;m{&C!nKE{1Qt_ixP8u^3w?{Q$!jphkA)>jv){X
zNH#-*fzrM@(y-8S(DrQb@MJ+jQD$CAd@*R3HuNBGM4QX7`exZJDM$NslUXr6jioWL
zCJz-|0vfDGN(>+_WCCq><tpdAHf{U#lNN59+*f!)%StL*jq^A;NCQOHKK+B&r02iv
zY^ZPbX3&V>b$HtYloTK}6J)Oymwmc%);=%0GIi=(%>XK_-Qdkkgw^m-V8{`gX3*l2
z*n<yHyPpM>pkt*n^U^^X-jI`lp%~P8vPn<PORXp<f)=l!&O;`MfDiq`8Ia6|R8)id
zV>XF7Id+gz9<+)!6GT9_^1g=M)BPISF(GS^1;y3+pf~~r55z{$+z#Zf4@kX&af@p)
zrNf9R`N@#2=%6qI&4WX(00Q|u6Lg3um`Jy2aIO0*;B60QK!U26(*7rSJ!DcM_#iOQ
z5?iE9iRgbqat6lY466FP5L8Hm%BGaml+?VEc<|k11u2kN0G*+c2_n+ponbk=v(3{U
z&VWpDQ`&kgEX^!~oNez9YFF@KX-z?nAo`3xVL?5cJ3&1J(9|8o&!992DIG9^A6hd|
z8u!R|gMMU&9!wNmk_ftuHzlChEkCaWS~nov0&m+vHd@2)grufhKvgwTI>ci+G$2Uc
zHd30G2Z?Qv*$}J1`3=N{ggK;(U}T^EZ^pG{8~2*Q>OdlozJ@Ox%}vZi+x7!)$KkOE
z;wPjs5L5(VTn=5FS`w0<pA(W<04-I)i}gVSwEv2EF(cKFMTOoH?GEZ;I>K`d$PJ)r
zE(T18K)hB=-bJe5r8vc)`XyCAxj4Nju^>Yqys9ieFEy_OyowzZs#s$awvZZLZ9q#;
zvd*J`j-a8r1ZkUND`>DB)TBYr$QaIm1_-TU<`>8zzd)5fICX={mP`<l4ms~i!9M+t
zpdWYK7gK2JMoM&`5pj$}ms*jU91pFELFV8v4C*UNo6IGNNszT(Q$bY&$Oj=H5%f+7
zWQg(!?8w*WuyZb`W<6+V43etQvMz?rP>)mI2x$ggodIf)`{kE7!gfo7Qa7Y<g7>>%
z3}|wu++xt#Q&{~9vkBVg+yEPg1zkTxa-S2nLmjkzpeQdfr&u4l4jH_1Gaj+ZZyzWD
zZ-pmd^bj}zTSj;i7U{Hg4k%CK2^}QoKvM#!IY&h3AUtdbO6s5kl>$I39W(RNv0nX!
z5gE|<C)qklQ22s;g<eu&*Z}nqjplwpgOuP&WYFEq1x1;;skkSkFqe_h$WfpY2TyDx
zxdR$Fq_z!`6N|yKkDz1?u?S=(IE#Y`=tZ+G_UURr1@|ZM!Z+E`WcLc>(gq~UpuPgt
z=ctEefi^(|7o;W@<tFA81%Y?_q8G{-oh4`r#V`!wZ?u9HLpRhZly*2!76<i%vKpv+
z>YtWYoLb_PUxD2dNC#s;Jwa7xfC3XwARyTe4FpinnTRq4wPTJn$_LsHUQ(2q20Dut
z-0cIkFH1m`Tu4!3L26ZgW{PiOK}b=i8(0N;pg>9nq+u#(pitdeh+Kh|Ixt)S4Hy~~
z1BYPgBr&HT1M9``7!HC435^^CDkQL`7z}qn0|r#76Om#FA1Dd38N3$=Or%e&PZN;8
z(P*FkX77(hwsCi%t;k{u?dt$lN}$+>R7#+|M>y<*`j65Hje^9I4A9^U_*y&AnRdmQ
zxdl0yX_fJyb6JWr@^exkxd7DH&jb<BIZCACx*<`H)PM%948~|cmllA|Uk9CVlo1bV
z{N$AsLz~lx!4`})y5JEGB$GkQfG|u>DM~B{n+r7$wDuFSbr@2ZB9$ZP=79njyLr%M
zUQi=J$A@Qv2<T#nyRa!1__>CZj?yBX8j=pmb)exO=sJPa__Un-MCdh(pt2?tL_l4_
zYY%5Y;(_8}G35Q8Xy=B2?8RX@G(A!}7J|Gc1XTJ!CKbV%50ot+Z39SV|6rGXkHPr<
zLH|Fn7BHnJ41%VA^y7<5lR(Sa!M$owR}kU^tOYTW6QKU5yc@Eg9eQ=3GibM%Uw(-v
z#>t!z|06ZRpfOLm<)GpVPx*>uCDr^J02+M*rS{^~5=eup*e@{`dNvZGrw!lHa&qO|
z#4K-HSb<A*KZCjikog$M*yCNhbosB>wRLvLLuV!^x3Lm*gAC~UDo81hNZ9c53evr1
zp_+{%pppGh`0^?&HbOkBOEnvdco`Ui0zlnYaLz%L@sPxO!7d%L_(K3T=|X7%iZs`E
z98`<`2H6cRIN$`L>VnPALo*JjUL&HFfU{kRIGhu7MoJLaYOuHA1hkO83tL3T1`TK`
z`g$kG*E6upu)$LmYzz*Xcgb2rh}4=+&d<q7O$KfD0hg6;L5m1MP5OYulH?3$xFn?g
zfJkzX1c2F!rm}ND)Ax9aG9>$<!9m5SxXa1FPy$MJ@lJ`w(4q_x0PwAfFa|UgQqci8
zXIo)b!WT6{Jdf1G0u95!54k8#%}a?-sw_z@h8%PekJeDubI#ApgQNlU^DjV6D@fZF
za<GP)J@kYPHCWLN+5t^+u7i%26+lmX2OVMpKEWE~Qb=)%$6|<Qizy9g(Bk6!Qg8<n
zbSp7<$%-?0l}Jcx1+<-ph;B%FN2)rh6x~R!02MQM(mIme&=8=!4qF7e%>z_Dhvyfi
zxaL87350!3xy>ZEQ7A3lK^r?DQ)b?vA`aBO013sHCFYc-LT3pP^&6~B3Z2+jhNX7u
zxxyW_`GCi2XlxTXM+mzU3~4&Y57ZR_4HP-Mh59*%L<YD*N^{Uy7G$E!`KU#`Fqfiz
zdiR5R?VB4FVcsR*PEOFSV$h+9@kObi^;gM>$r-7Ttc^%skmkx?SU2o1bPWXgwxVSr
zM4<l3^`CI#9iM$Vi~;UL7L(Q2fK8vJCWH5Kr{?5j7Q|;H7UhDv;2_T-WnxhEkqIK;
z2L(b85a5Khm_UtY-_qQqe0?wpG6U2>!?y0E0<@+ZM5NpBzdLs;H{3q`!Ug+-N4u(E
z@kQ1^0<17jEK1DDNd;fQfp$A;1#XL>iIX}j_mZIF$<sg!LeIdPeu!v<B$&H)@QY}n
zr7(4^-wkSKC&TB1@mLS_Jy~@iXdnf&lR&?u3KZ!XpoZTqxSv7k2+~i6^dc}uYKp00
zDX36DO09S-h5D4zN*8H>1e8`wKr43O10<kftoYQ5%wpK84MfO8s^0^!g_@sWg9Vh1
zRNxsV!DBfzFhD*ax#@${^oFfNmI7@t2F(;gie*GBz?%B-Q#qi?omv(Lfv$A|jh>?y
z%or9!eN5?;H2l~BP@*dV%?}6X7nR_;FA1r25A`_J><6`ZKyF|__c=VF!5C1VYZG_Y
zW<h02eljRKuLcbOfous*P6VyhL62I9@knhts1HfCM+H=DfJQs8dj!b>s7I*Rf>8o(
z(FaYtfVb%97p0^Y`KS3Nrsrjrl%_!YONdMaS$FjbwmaqvEC*B1JxriwJg>17+Za|u
zgM$2a#!FC(3Y2`4!Tl=q$i)}|hej^>7NRw55Jd!rRZzbXd60TB==u-Pc~+n_`v#Pj
zUV#D-)MCj5o%0VS(o^iamwN0|vQIC|p2>6kvnH&$L5?Nephbcfpn4AEC$RZo0@D(x
zPsl3l;FT+QkrTL+w*%B71?5Oki?lR{=pG)m><5iNgNr<flZef)WLb};l4%81GN6l}
zA&C`KWIzT;A?=GRutxY>SjME5#alr`!=M!*@zBX5P`3-mp@Rot#RKe^OX`&lX#Fij
z4uY4&un{&$*MNF9qm@hOHp3U)+=MMjqBP`lq30U?0iA0E>VrYfHG&))2Z?@!BQOdO
zXjw%qN8mi#2*YA%ey3U;F9@m+X2XlKS)emSKt#IXi<S$H(;Mv5%XxoYx;_6QtT>}?
zFJcpD2^^@44eI_@=49qVay+6&gd{wqMh+z5fqH+Gv?`N8*CK-k{6J@Wf{OJ@P*Vi7
zBsn)9G|U^HmtO*!8cr=s%`1VpDGd}fAR>Kh``&;9WexV}CE)eL7oh{(RCg0A$W5T6
zS(FO8Ln9u^F^HrI2_U2<42=S4J!n7(6hN>NDmAYpwJ08Z!!0C$Kvg~DU`t3LfwYN&
zMsE5I>H<rH5-+%#Bp4{vO}jXI!1Rie1)$wNKjEzeP_+$NtBYe31Jx7gBv1kcEu{db
z0xWq3;Uq`}iqt`eBne6@5X8AaeV~>Ls3ZY5v{MQ)A&C-^CLk`jf?j!2Jy7^Tv2qH_
z<P;=XU=(GP)*-MC@@mjnDQJl$#N(hz*Gv!ry;bZz?0T{Huo{%o+<<x<+!D}u5Gb@E
zb!Q!@v;-09x2!B~%$6v!Pd}GGBV$6-blOxTe?To}1$bTnIRr8?gjN>)gbtrlFX7=l
zkP3&*l#UJ|&Hmy%04!ru{X90FBK!2{=lAt~mrjR`qf**z$jnVl2QOv?^?e{i10WVS
zq`^e`?Em4imX|c`(;=hfhOpSCUU-8lM2OXBg+7MW(6$oj#4M8A3b65OP`6J%Co?ZG
zCtW|YI6tQ>RX-DSSw=}_88{$7u?e{;BtJJPGY{IM2L}p>fKNU`C*<KhdYU>4l=_i^
z2pmoz0=|PAy1xGdG&6(}H*SNvC;(3g<!0s;rGjV5(Od%JLedFZVR!*nC{bD(f`&&y
z?YVoP4SvT!O#@Igfb9em@KM5au050e`*ST}36xqEgF10YVUO@JWLXcRJ=|hw@+7MQ
z0&k(v2VGi_nU}7glA4@flvt7vx%L5cY(WXACIq#qK}sMcGM+Xyv^PUmg#b+kC5a`)
z`nmZ<sqoW=K;e!Q5O}PI1_X^3(tZXtQb2`9dTL&JVs7q`T-*yfV3CM2EGIub9h4xD
zN9@mnhT%Z|k55c4EX^!R^~q1hIJ_9%ID$+b!N)f!u=giuNCPx?lAc=Plb@cRig5-z
zhNaMi04h_7@GNR^gfyCiG=Fjs)G7q6)Pc{R6lbTFBxhjH5*O^C6OT7x<2!VmIzh4=
z8V+RTN2G8-ZJ>FAmN9^W!YL;;FC{g_F)b}KFV)Be5)_DX3aw;501FE0x(2j#1*vqv
zV?8u5Km{7=1{F}p!zDE>u{5V7EVU>ztui37Bm=rg0bw2_ks)<FpoJZVd01V8p&RNN
zkli-$VplgkHBZ;j5OT{r#2irR2d;3y1hl?I>ZL<nLDnV{NRf+qUi}Kt(DZVgJr5+?
zp}rt;s2#q|xd^_a8Z;kP0-BWvEdvTkErzaVB{nUQZ!suhAt4NE(0~IMOh7{zsdokS
zBju}bY(Nu$plTiBJCLt4K}342#7oz=b*%R3xdlqUwtg*!H6<vwQl6KA0Xz&|T$GrX
z4qXz0UV}ccOSjx|qHN#%DChtbS^J!k$_vnGdCB>CX_@JuJq9JIMc_T{RiIQA2Jcyc
zY7)q0;IOj+()YDJ*wHgP3)aaXYrP1PGazSt#b+eurQ|>sBZ0PBgHxLaM9i-sKNHKT
z1&|IG(jEwCCZoQCKu5`8O@$avfTlu9>k;Hb9CTqzE`v)FOQ0PNP_}^N0}K~H1A&?@
z_yro`u!koJJeEVfPhQgwdXi*rYDrOMa<P7LPGWI!W?ClX)O}DRA9Oxz&}VofACW;I
z$9I0SOHWa>V(DXnU(i7#cc_3m8w=qDFCKS5!-L36LD80=>K9k$C1(`n!_Kn>Eh7d^
zm@z;KCQwGr1QF?+_kT-o3D37r&rkC3Q_Cxb6-?BzHV;%FfZ`l7(1h^qlp9<3-kl_4
z5A9|uL5n5o*g6T-R>W#67Xv;$+2TC=bkV;HXR2Q>ffYkU&bxv(e<h|s4>Pg?&DMjf
z?&9E7=;WstBLhPwh)BQ6@z-Z>o}xXR0nJ+E*z_ILItGoHrzEE4=I23zG77YW3`9WB
zE@ZNYGa$pI<k-{(N}Ql0hS2SRcSvCj^6j_<@(p;fx7aZ!vkY2cA?{>=>^f$#hwgQT
z?e-wYJ7+-?R`Kw>1`4fA5Rnc!UF(+}oB_>i)Gz%(8}Px`0Yj@JY%AY!wW6u-8k}_z
zh7+IxLV5G#C;Tcma9#mNJcxj9bYZZEA3XxiA(UH*wiFsMGY?<p4_S>2J7|<PZ6QR7
z3vPWPiaz*SFi4;?K#M-g{f%>T5u~Gj7dD;20xR}Fbs`a+XQVUB-9VWaR1_7L6cwiy
zLMjtW1_p*q5RneK(SRA_q<hjW@&%Pdw?Nf3IP^fK<FEpnQYjsigPqy>4>kmno>~%L
zlwShAwg=)*4Uj)U1boqRdefTaVezr5uu79!j&KDPT%djvq#OoKrDlSNbjW4D|Lovb
z(n88%YT1kP)&$6%75`!9jf=xl5Tz|e<c+O}!_L7a1*p{kX{JIl47Yvyw5P9){w&vq
zg*G)U2jB4g7hYB1u^gJ-D9^6_JPZsapp$3gQ;U-m3sQqp(^D&gKz%sy9#=@31LZZy
zL^-_9f*tuzweoU4XlfkfV@PVlV<pt@WDW1Yj`%4lN=-$%ZWENjAsr;Nc%Q~>du3aq
ziG8}>kyrf&JQc8b2i<lDUfZwl3~lD878UEeq$Xz;XXfXH6s3Y^TR<Ly7z?rhoRYvq
zI)<@OUy*h3Fm$bdMq+UWv|R`~K?mFcjd#w^E6yw~NzE&PHxNONg17=>wGK2ospJZf
zpRxKL!(OQGwTPV8DM>5=b*DhZAb2h-INmeZ#WM(!T0uh{kns+9RSRh_euRw-5_xGT
z=zP$k%w*UM4d?<7@Xo=^;*?D2*f1Wupk5+twgi?;K|2Z1?<4?~$l#kY{POdhd>n&a
z@=I`B=mx3Ukw(#=u}gI)ff5wfc*bx6G*BoQrB`QRU;v-V6rYw^QCbj?SOhv|4-)er
zcR&(0ys-gs!2?*#Q(!S@dLXEfnE_*QIfh+OPdX5pm{G6xgpMJ=HqC(cjs)!iH9^4T
zl1vW|Lneqwm)h4_`uOex`}CthD)F2uuVLj9=yp@ga>+M8B{MBEH3fY1HF#VClz+gj
z!%BF412Pwf;ZQFT*@P-B%E`%0G6Gq_0qUTFw&j8P9MJ9udSqdg2GDd$x^1A8jWr=+
zSON7AkzIY%nH$JlW^STB=)M+^C;x(4N}$9C@fo764@rGUW4utGQO8=)Dt#p1;;|O$
zThNw4BAVK$zJ--V1x2X^Mfu68#l@fryOhL|M3C2mLD>;Bxe$<_Q<;~ao0*v7mI^x0
z9Jc!s6fBTD37MKeTF(Rx3>vr!G>eB6GI-no2^pk4)}Wh@;d`tL@(UoFt5Y(QOVD>w
zgIa!>AR_(1WHG(f8#wLZ45-h@YN8+wEJF@GffWUFK?6#l1yYa}5~y-TK6oFgoQJf>
zDOn@|o*j=*$t*6(i_gqUD@rUbDJo4aDJ=rGYCuI1Qt%>rm5}s~;S#duM<KZkl*ROs
zu0xv4&A<Q|LInFCbdo?Oh=9(6B8{a(0)f&qObd$gvml%3i&5H(ATO23GcYhf^F%yC
zVSG_4c-lG_Trq*2i3lN#f(YVFBIlQIYy%F8fmchQI}S2I1ayAsf*qUzu?f^>BBBh)
z2d{?t4w{Vyl`94L(0e*T)1Z)}1Hem(BQZw&K#MtuFdwyyK}z$`h0ewLP)2-ViAiEQ
z_~dp_)fNOgzzUK95V;y-3IXC(>SRAqCIppqw?MOMpnWb7+rjY)BB0@mk?5&w`+rd8
z{|;&gfb7gfBq)q5X8`d$Sr-&R@*L=jdeGVLDXH<GiHvM;QUDE?LGnB(_k&AxFabTg
z_&3IhPoTyH$q51B0MIFa6-f4jVjE&VH~>Ig9QH#4fUIf-DIp><bg_PVYCb3l=z+2W
z=-esKyt34ylGGH(%#!@HG@Q3~V5CcEy+8w}{RWK~feJ#1lN>8jF*f61xCR<fL^iWB
zN=gcHGQn4CYl4>6gXUHJ@=KtnjHF_mGJ#RjK|N2pb)bPItWl3)1=L4mrG3aEh5Uk&
z%-qbXRQ>q8(%h8Hv@}q>gAyCW0cdG|iLt?_O=@QL=~3L9FPzJSAIDBX)r)XIPH~}r
zPHJLtNqli(X(FNZi5O0Sh6QNspNMRTIO}&cC?$b%JNT?$%w4>YeV315o3<apx>h7x
z1RA4&geqE&#;^hE6|zPp;Clv(ib^5pc7tlD5>N{fECOm>L7MHLScViS7&!%+bgAS3
zP~c+qIEKAYj|(y|FrY2oF~_rZ2P0cRoll>&I~cY@Jb~221`VXZo7nO3pu=(!GxLh$
z;~{PBw4(f~)V$(Uj0QKT4$K4*=~vH0ae4gaw}&&JJ|uEr2kmq$RnSs=V|d*PvH^!R
z5U&)IGYx17YCeFH7<yuZ4<A4pR6?+hJUNzt%*UFtFf4)kggT8BP#yx6lUG6Y0%)CD
zVoFMU4t#MrqOk(s=kxsVW)?}tP?%q-;|v@vH9XcsLjhF55K%^=wpkHP74Rq>$p4_-
z3XskTxSn_bnyLX2>3>#!yyTmtU=L?N(<`+s23-jnbPk?^L3U<>2)KQ42Gqx(?hDC2
zMrs^Ek6;CP8gZ{7*wdg}%`!m*bkTvKeY)G(@RP^iO@c)>D3mC%8<eoXO;S+r7d)*E
z-GU6dITzA62X%5XK?Guw?6{jk+;#m5=sYLY-0&0BE(c{hh{d2Y1~WlKx|Z3q(j$u6
z_UW4C^B*rh+6kMiiXmrI5GmxL-J)Xs!jkyZl+2R+BK@@dqTHOs%KTFBpg1UM!J*=u
zpOatYR9aG!k8w&VWHReD?CSX+usWRPj#B~!7pOxC2`*5;L8@E~7eNy$Sye8iDF<F}
zTAWz{N~EBS2Hs*24=S2c@{=K@Fu36XA}~8~&>Tu7`#~N>Dwsi5f>JL7roB*qleO#u
z;&1SV27SnJfMtnAnTaLfK^s3%;S3rXNXab9OwPzjEs2NJw2+_xxeyW+kRB!Gpe6O4
z1PTVEP{HE_XsCdqk&>bVHov|dw1NOMcLUmS0$N2{o|>7S0bM_Z#TjVxN0jajODrnM
zECy%Rm!OejRxAq>;gJBd8X5w$Stc?W)aC~j-|>0y^L;_}IOH5T_%s@X@f}uLP(J(2
z0a{?O4L$*Z$4aR8LBp+-Bmm@Psp6pCJZR|JH?brGlro$kd+Y;J(y(U<q#+(?=|D~A
zfR<L^$qY!AL&Jis%m8V%K*kZFz4?ux$Op~0$LFOKloVn2J7#e}E$czoli=|;lEqMe
zleZ=oa!(gzTpQHm$jmFvFD-@)af4eEO`z>2ph1oF(%hufocJQ#cfBCF37QaS<R(y-
zBq?l&Y}cT#(@iPO&8<Wk-v({>1b331^Yc<NK_^ULPZvnZ5*id#a{|eSV3Cy~k(ySp
zjp>&_OHp=%N+nSEgDXWa0bM5i33gMr5v)Z^EsJrU+y)sDL9!T{704P$gcJb<@cBCZ
zjMSps%#zf2a5F&!wEGZL5PLvGp{I%>Dn-a3#ZB1BLU&=c0S(+@jjOeY;Ra}UP%!QW
zZ8hPV@;VRN!vLDcE-C|+8OcSdm^&HZoopBbnj>iEDp2~wT0X#B06*It8bCy5PSl<*
zQWk+9^#|In4BqgQo1Kzbgt3VY5<w?11_>$c=$E8c<R<1NrbD*bfUZ>wI)bJ3gke3@
z`=CX+L^L1Ji$~ar*507S44{!i-^2pQeF(%BiWFL`j4LBRnu$n-Fw~<os*g4cLa$@Z
zNX$t~PtD8AFUlQ?)novLop9v2p@RGz$UVi&K<y|{ry?XHwImU8Iv37UFp;7RT1wEw
zO`u{MYXN~P;gB_JhZti|%}E3uG?9{;16dvdK7$>!6|o3e20honvnz}N4H7Cl1~d?e
zH85b-!WR)ha}lU~A)>fIEiGZ|Wk44x;k`u?w1O18dNCulBEA?;!yC461{x}~brooE
z25TTeQVh~revm*y8rR2q15|2JVsUCvYH|tmLSRszA2Rf@a>1fk;a2hX=`aS=f1vpp
zl0zM-j)ISnK^gIdC8p371fc3C$OXPw0*_lDo+}0|C?nY|uoM6u7uE+?Ch#S9pw3p%
zKhQ`msO|>uO9d0@wUexKl#JEw(;w_ga;vBgfVC~D?gCIbgLV!%I2=H2K1hFmQ)9`-
z%%uwU>AN|Cr+7bhhmM(&SEoSNGZM2W66_2(0o@kGWuMM<>&XXCbtBjaDp})DNa-UU
zHV6<8Tf~M)^WaDU6`GI+|M5BLZ@kUy?9(sAzYtvf+zl2fWQ__UIUz9(e5^dq(NGL~
zp~(?6b4EmREKe-T%gjp$cWV<swGyaX3%)%rH7zp-UMGP)3nHMc#J8~K8f6D@qbC2f
z%nHcKIv|U%7A)}67gEApfS&O~*4PSk1hfEhihojKaVGfOW>W1YYo-*^uL3QeLG-Oa
zW6z)zjn(5Y|3Z&if}M^^g&Yq(JR~z8ydfTxo{<~?ss_Oeh@gYd7hqemu2AbtBiK=;
zVW7<=pcWCNG31h1l3J1pyFU!%Do8HFm@$S{1eA8GVa_1=uu`(Rvan>2sQ1%K^OB1*
zA=!`tG|;;ZUhX5}0KU_7x7rd@wz&?lB8CQT0Nu|Uv<co_!s7;LaYI2C1W&%im!%eg
zcEf;jK@q4N0@X3`$@#ejiAB(J06;}A<iuh~F>uo^eYWh%eV@DGgDcc@1Zb`j+-r@G
zPcMm&N1ud(WXKJ6>5v-G09x@;(*>YgM4>Lo!Qui)Ai!6#!VdqVrVFNn`jYCPln5%A
zz&Q|1KuZIR1|E5BAf(C;)(iu!5&&08mq0E6Wq<U30AzmQxgB&jt^h1AP`Zd0>H@@?
zQfPA!G%y31uSW|C42M871m!axOHp?sgNEH9JL@1re2-vbi|}P2lv`=Q!@v;a56||X
zfJct-4R-J|{~#+zX*0zH>dPU8H8{XQ1hmg}6E+vk0nK!j`x|X1GP=JH*g==BzJ^tM
zB(Gq*11i%%o=7RpEr29u#QJES=Td1IHiq`;kEQ0H{8#A(OUz{5@B-c82OEe2U9A=r
z0q@7)u@stcsW)Sp3z|9woyz41p2YObD=tV)F3HE3HiDF7H|^4IvPev`zYlLZQ_m%!
z2t{hvfVhx!jbSyF0wEc+paNWi9R>vgXc?bRe!5?NQ7$Cz5iWpNBrpcFMMJ6Q5iT$Q
zm7$=@6K5R;vl{AovZ@fIQXDiv3fl=eA2f3cs>?x#X$2&fKu@Pb_#YkvFb35B)N};M
z^Z275W;rwjsIn>;=>oPV@F)OvFThC;Or+QANg2s44YE()uI`aj&ffsb`Jn2Ch~698
za1!#wC9c^CkOQ!W1%^{74~xvalGOB~#FA9~{FKy!#N=#H>khQ?9K0*j-^DdEkJJsR
z6vPZx*MM5KSc3zWC!lSe4X~1*(tHUy0Spunt+1>CKGi!1w$KPYYn()E%YkMM>Nx?_
z$VIAY@mLK_44@TbB&SPAZx{FWEKrgJ@5}-d&|3Z{#x4=Eyp3dcZenr<WcVF)Vi1zI
zL1_T3mkWt~%ElL<!_J;Gxf5G&z>{Rg098<h5JVKddBTUdfpnPGd95q^dheb8{f
z3AL9-LwGok6T_H>gtRcJnBbZ~^Rl4Qr#!PHBQr0)BtHjIBO<~A5(n@LSx!R3f~>j-
zlJcO(YeOhdt_Ka-1TDqcr@|az0S!cxoaK;f<D$g8V$ixzaC0OVROo=lS>i$G#ej;$
zqO!!C3P`I2bQxbJh=8AEm5z4BHl>aMt-IEb2X*g2VNnY@YY%kk6nZ>B(&S0RXcHtc
zP%AH-2d&}<Uxfr}k%RA*029!%{(~JfFTncL)Ur1O<X?onpk@hVKm#^y107wx3r%;_
z4ehO<^bcx^fnvS_ejg2%ekE*$EhN;*DtD0*AmUne(B0-GpmnA3i78p7#U=4M`H%w-
zvBy8sB`c8X2WbKcbWI+7V@OecX<iCsM+i9A>E)#)78NB{qHn`ScolxWCyW8DT0vb;
zk~16FPVf$YebBmcq%Fv|Kvw{PYz4azI*0_~Lpt&>%b~ZKGQy@U$Z`czh@)*<DPm(_
z0Iz5cEKN-DO)bV~gF}ivq(ho12@Txt0F}LX+FD5VL!z7d!%5dcy>O5-@(WV);?s-r
zOQC&n&{<BIAOd=Z{zp4F0~!{T<^n{!Zv&|G0UgZ(N%e?E7slW=B>z#m5C^^qVk&50
z8DTMKN+}aWK#M(${om9revX0)KZNZBy-jJM3+jb}DjA$-lR+vOq(TM~+>}N%;@X}!
zpe4(6^f9cd_X5=0MA!?e?IDL8;qWn8Z7ig0j&y4aXv!c6VKr#n1G#2}w{AB;YdA_H
z8RliQ<&cOL9Y&sl7A=&<GUyIN$jUNJ&;So;*3>mG9Wr?ZpEX6KaY)F+2kTG5RzgzG
z8U3J02el~BOBD>Op&>x6pzi~%0tI#9A{{F-ajfn`%4E>ulv=ife2leDfp{CqVyL%4
z?QfE6c%;M*oge_WVZkkGb5I=*O6bm|MaB7;>l@*fE3A<R4FRe<1eEQt1_g!-pg}?8
z_Ijk_G!gr-K@kof!U7ZNa)woT3$+dH)9d%o(2W$G2ph)&HPeU)ZTPLpI2UqbSOE0{
zS(PJf>K}XoQc5QHbRy8k=@L-WASE*qwCtlOvjWm7LS$liIKmjvn53E;Kvv<8L72@@
z-&4B21=1)%K92qysBr>v2TspJGS*9Km0%DjfGX-NPziPsw3Z&+D+4(KlDJ@=hW5%{
z!iM6=%0aNX_vE7d;^KJJ)eoSJ*WmN=d=isVbAmHU)6#NMg9~ypacuWOs?MNEf$B~I
z1qGfYf#d>ck|3+^fO}OYC`W)-9+s!3W~U@pVow&ByIiR33{ZuRCm4|IhXw=XbAq5V
zw7>-d_*~4AL@bvwKoTZW96(b6<(7jMf#dNnl9f>Z5;-B6Uyz)i0^Ym<Iw1)hu~~^p
z#qs&jeh{K>3h~<ojJ+EqTLh{pki3G&2B=raT2Tcl(m~@{kO_x)s5k3CZSX^&;Wm&v
zz~u>;fcANi5)L%(sA@YXkdge3UZf$}4)r@(RS8n-1itA)6m-}gD6He--AZ$EuxDGO
zWCZm+)$Hd7onc`C3U^Qm1`cvC0S$K~o1s1@YvBg0Fh?w;g3f+}E(?P+&q2`+i2@J{
z>;^Caozd5}Pw(9*9M=@A4{Neecg-|tVOKm-3kqj@6*A_A<Q!<YP}(^|I(}g*sQ>Q(
zngRd^1?Yg&Oc0U&JSZmgTdbjd`lX}GraCF4!-9f}c7t~A22F>rECE@I!)~a*sdMh-
zP6^l<fM73!4&=%N5zyHGZI^!TVbt41eHWOQsbekLaho6;aaarWEofL4^#(9d#p|1&
zl8Sxv5PS&`i~-HR80J8dK3X1xX@c%jd<1Jy5!oR~NlnhoP0Rtc+0sGN37~CGp?N8(
zMQJ(t<>)01Mh6_~Qqt`z0ksc7`3Jp+i(v&MP?1K9L5+Kxf_%^k58<9}t`VLgt}f6`
z2B1B~nIHmjRz2*{dZ<f@tj)oT_n}8Tf{r~+%P-1?9<kjFa(NfLFa+6#!%B$Diz)3L
z!^XZ|gT^L6wE*bwg0e(N+<<}*GVK6q%Adrj<0&1LNCdC<0i_SLLksX&O^MGTl|N)#
zW;UdG1Kv#p8kI>;EG;h1Ow5bVPJyl?MbyG@|HBy27$+<Eu{Z)Wm4-C~!t8_gXima1
zAZP@di0(4lvJdD)GwP|kIiM~KC<>ZDiH`+d>Vt~{5CNZbNWXBw{@~FrI$tCOasp&?
z4}2B`<_>65Agd(?t^ac?<5Me=Qwu=5(ZQoMpi@)9t4`u`vk_8|3;|jol?fu!|Ebr1
z?~@9)hcloFf~rmdEnx}z3$F@67UQrT5)eq`80bV(o21groD$v4JcZoUl8pQm1?Z7R
zpcPl~poUOhNino+1NE993mf1|$6@#SK|Mz45I^cQoW-Ex1~k71brNVc40PRI2BgHs
z;~a=ji$O!xkQ2$lBq;gb03}~g5eA7XMCJ{C9{91|y~IBKdwBovu;zFNsFy(YyH=zo
zgU>H7%S<f?nF7*=H78=20(A)}l1R>pNDXn&Nip$A`;W^&?QziV#$wPqn0WAEJ9*Gy
z1<>hJkRzxtDrTsksO_4)pw>C4bq9$UJPv>c2X)hl1qTB|&_~cn54cr^xJwIiACIto
z`UCA<UuVggz*=P>r&1CdxMwI3#gL-ihR6FI80?|tq9Jr~F?D?&3tH0+_Bq&M(2#8=
zh=5iEKkeWQ$Yd0CZRg^Ig*%$<@Wb|C3~02|ZW*FGXn#DYjayU(+7+H!lnFg&3zSnK
z9W+QK`4X}A8k!ks=O|EW#9B&VI0YI$plvcFr%O;Pw4gFG6I8H)d<!YqKqKXuAOdmq
zD&!I)epn`;#4?aQNInDEgTpeY&&V3TgDivqZEM4QBr_<C!SM?w(ly)}7bP5Xu}^3F
zAm$P9Cj#bKvT{7ae&kES(fkhL;;<X)cd{}ar1=Lwk~b$QExj0g0y3y~1POXHudB*b
zb+>=lu}^30*j*(msSEQud1J`nwZCxd<8w3fz=soqDnC%C5aI%Gwnqd2h6|tpK<Ow@
zadIN${M<6odJ9mc?vtNxXn^zZYsgq5(wGo5r&G-xATMJL0Sud=AwWr9s{wf$bpA`c
zPhwT2Z+=Q?4o1%d!(OP5DY2JiPlD0~>YxLt!{l9Dg7eO6j76GIXJeRzHLx*sLj#-g
z4mPL-5wsoDq(;kQkb)a?2{q+b;yh;s!%C<xi5&1i8Y={yX#^gk&nyNX3IM4n@bsaf
zu|ZaE1yX*2_hQ2Deg_pE??KhnZcxyI3lBWDL;Xos@rBfK#+YCa1D!?*YAQJA<QJ#9
zmZiel&7cGiDamnVC#t&z)K<fqwlQ1)4GqwqdeU16&>npn<a(U=;?x|-#UlEUenWC<
zJm_lQ;*9*96i^(2vMqStG7NNvXI^q@NJddASQe6G2nQ5hoC!)mBn2LMYtX?pA!INJ
zd|)`n@qI0P3=AcpFe}I}Pc6zxEC;nfv4<4W3=K4M(a24patlw`Ah`pYuqdf1_JV3C
zPyob(SApl!t%^DfvKUk~=Hw@)#6#@`B@1vi1`{|Zh{$RYB9$ve$;GK9r3Ik;vI67`
z&^Q5f`ad<kBtJhVDG|Ce5iw2yud%9IS-&&O!4E$sYeN8%TacTl!JrvR(6yS7zBnGs
zp)pUzfM^62?w~0TNa_d21c-o745Y(4gwWJaLyv>(g$xM7M?qkgLp@GprHwi|T2NV#
zSOmI<3>5K3ybKIz2ikx#9{4O9Fab?{5A4$Azh2kY*&z>Ylp~G7q1^hFm7kdhS$JMt
zQiQbJ9PAlTs|bgwP#=L*ptit3jb-oRc%$OPTpZ`d!P6v+0gW0Av#=H?Fir5C22huf
zbA>=CXg)CxK8yppsT^`$`AU6nYt!l8_UXST|G2~XZ9X&*$+`>y=bh*nmOy<%WIL-o
zH8Hy=6?|ulC1?^0R2+cLo5;;9i7!gc%`Z#Lfi7kQ$0dk>j?G}M;w17eiFl};#i`%}
zoxzuz|A4owKsG?qKfDDAV?cdIP8{t64atIxMx54+*a8k2Zo|C(+L@d)@1P^^1&Kw8
zIXS7|*+CUh0zq6uhsR>556L<#32HIYmA0Vq%%E+ckOk*%MDqs{pqK}eQpsLWIAhIq
z81_Q_i;-u+&3(tB^x%S2jPWo?6Y~LVJ1G33QVc_|IsrpB)Cm+F9)mjN1Un;34%T1?
z?RQVi$*jV>83^JU%pN}tT!R>Y0YwkEjR7X0MLm)mpdmt5_X(N^G3K2>{Uu0zpe054
zMUVxG{SVwug~7^5YUMqI#c1Bfuo&uX@<zR&Jr&Tr6XXO#=;$V>c^YJeB@1Fq)<df=
z>RJ!V@{nLh^F4<3P~TItVPgSm(ai@<_oKx*<VtidduZh=3yX7VTAm7O(t#o$(wqbZ
z1SGj*SPu0$kqi0~OHwN`!SxnsG7b{9Xx@V@aEE#ie%v!?u@4b_V9=T^(83Ai#xbb!
zfY^l=j~I4AeM46EL28-9Hk2fT<|jaQV%yXJIi^L*9y(O73X4Z-Sq_RmtiHyu80u^C
zG7q?;3?DMi%g@Wn1Rnx71LSv5L)1Mr4>U33RFq$tl8SLN46ZVqx{d*_3I_!UdV0pN
z9vT)D%v^w`X3@8B@`5IAL4D=o)Wo9XjN*LIWsZ>U5-1};HgZDT@&I-yBK$-q@&*f_
zWh(A-FmZ08hqwdjTrS8gB5jwIfN}}A;R+_8jpUnl=^IMyY`iB3!Zx*1Z7}}@sJRY0
zRtU6@89dbhzKR6eT1Sr*2K)5qCfr-BmvF#xDb*ap32NTDgX%bN5Fm<LNOKQk`#RMu
z2HAvEJ|Kn+Af@6H*n+ON&|P+9<w%r5F|`=6Yu^*TMFh>;aN8jhm+xs}yB#R@L2bHt
z(8Z;otKuOA10oRM!v~NhweT~$s8u?@0X1(yD+)o2>T)yl@{1t**kK1-fqIgVdzIn+
zE7S5X`(wJ<sGb5r3-yAe;VBRtA0PrTeglgWNI^iYAh-%j`7&^OLB%8FXky6l!e2Y+
zA`1A9c2IMWh$aB)&<(UH2|5lkXcxRW1*!`m%NHSjyJD9POK;GoBqf$bgN6n{yZInP
zqtGq;c<hAMI-r;!-Gks7I5)F6SwAH|IT^BG9n=^s0gVS{=4GT7WtPOJ<R^o!Sk6la
zUBU%DKMdg@NRf}!i-86Lc~uYCLHNebb3ow(>T$x)^9Q9ja0?Smq+>L&At{eW&WQt+
zMZ%!PjRWcoOVA!zP@$Bbn46mjo=!k>4@NXW(+w!L$xJukxjN)?Q^BV>Edd1vTEiS-
zIeZSm09Fo>)zgI}+G5bCYN~#50r+?$(AX-d07j~mKy?%(MPS$t^*ULDE{L6KuseDu
zf*NR`u#X2{(u?JEd`O{m7gj66`z2Iz0I0OU>TwKvp&qAIb&MA48k!pzmhs5gr>8tV
zD7L$H9W2%<-82e0!>713sURo61iZKww7&uAJS{}jV>kgC0z{7Fqc5q4uC52AHt^y2
zRq(Wq7<;ywn6PT*RuTJj7z0`!f_Bsqk<l=$%+Ci8Wr7c)^#@f#;EavOKByN#WhCm*
zKB&p+ky%`lpI(%hi+x)MB&$$+h|i9=;aqgDBlp8W#T0nTG@vLyIkmXhwWtWz+(E=P
zM$r!qdMelt8l=FQ$1v=K20v)$9e53~zH?4uadBo^W@=HfzHfdCh?N3f8w_4k0ZL0;
zph_ej6u00Ul+(n-kO?Bv-}v&JJQ<_tkiPKhNy)C@5ZDr9P~RA0IG6;PAp$bv0muw+
z`Y>~3WXJ>&>2nTmy>*yR&>{U%(H^FYf1Mp5^&u7`6hTHXfRZ$t5jHoHwx3@m=#ZXZ
zaoxaqrHcd9k7RZ0AZz3h$Ij~`m>_?E+D?#w28RVAay<?PpO{)@W}hDMuO@m!h&x54
z35Fw((;6tBK%4*$2Ru%IhJyon={q?;FAuV;0_1VTdLA^73qCeo|7wkzefk2)BkW~=
z)L|Z{LaI&!HKstG49F}<g>+*OzQwQ?>RU={57Z_5pFm5owLvX;u(xek7#K1^M0!ul
zwmIH~rS|C?v#WQjT5f@Po4VHj0xd)bZ%zRP1K4ITfoVO|_he0eLy{YKbO-5huIbzi
z3_;oO=}XWGBgg@iu>D`q5e!9`*U4(*BiT*L0hF)>2awVNRQwWAd7*WmpyNp37Mecc
z11RC677)fuX!o63eMZ6uP(tpnWrQxgC$Gl<^)ne`XYgSISg#!t^i)i6paUp_*1!vR
zP=gt=I|owjUqEzZAy$(W>yY#YId4)QLV=1m&`LP0$4){b`y^tOJv5_H-b4fM*Nlhf
zFGQ1RZ92zso$4n0bUnFy#R-;oVEKzyO*9Pqpk5>^KO+r`Ax`=N6^h`g+r;AdoXleA
zz8+BIW`c-xp<UjO)^udqr+=LrKV`xG0+^4f=YnaVX`e-)avWTcBB~Kc+QK-}lzKL|
zftoG*;5KJ4F)(C;i1g__$tu+?S@!8St3SF{?<s%{1cNpo5K)-H&jbSX@k>Ah9xj+i
zBtv|MIr>4W9XQV&!mt3Eb|@Xog&)cXiY`cN2V4OoBJN%ETH)!BP3_Z#FG)4~apc0%
z3|R#dQmQS_Oi53L^xHsH9>i{tE5N;VFp-X7H`LdlbWTLnq18i3gG#yiMXBK7B~Y0J
z@-W1D5DRQGm`FFy+UI3gW^SLpC8Fzzc4G<5>y(UFK(2E}I+GoA%>~4EG|yw$4)r`}
zogB%YhZM)TiQwZwL7v_RI+5Tiyi^2rogqg9Kx%vm`}Dm_45QpbonfA)j<tw`5Wv30
zV=ctDNDJOT>osjaTRA;*6Vp?nTT8*FfQWPk@s!h_N;B=@3}~-!AnxYFa0|pUWGy@c
zU34E53y&y7ZGH0iJ0?}uZy4v3Q!vea7F2o{z*`20LI6I51Z$l@S_WhtmV#6UK-bt~
z+;VdYRF;F*xTAMQAnriA^$i-ZWKFihPHllT(!g6{i$IwJw7(fLkOVFfK(|C@f(ZCw
ztZ)W2kx|+HpP&vID81(<W~atO+yw~=kSlOF1sW6-EnPyap8*{h1G@=qJIFbp3C;M_
z+=3E}v$rtDKA?d@1DAjtfwi#2a04_nFo(ZDBOY$?`FW}3iIvz#zaULfq$3-l{=qN{
zt4lC+LtO%Dfs$No!;&cK0=IZj<baM8h=+HgA#*mM3=44-INgG{keUoK1^FAJte~iK
zgXSotIt^4HL)-ykft>&*5XarzV=%sd(EktYzzHqlhECF}GQkVyLCqxaNz(3BnFVf{
znETpb#T0x35*mdh+5>VuR)1ia4~=9K;{1_ZQdt0+%1+Ho$xH;Vbpb8Hg`5jfT%K4^
zkeU*on_2=rC<PIX7^xiUEwZh}=|K#epdKW0RdZ=kPEKZ$5vVK$m7^t~Oy^$!TE^p&
zS%mfSB}fAo>Fj5y*GRVz+{Fa7(;+1$BJ?n<fclBZE+%Sg5@kJqD`@lzH2Vx$kON{t
z=Aq%YvOzN7YuIU(lv}wMw73scML>Ls$4aO#$!ar0nvLM=4@>ls&kvgbYAJw{9mL0=
zxhKd0%aGy^b8R|xt?vU_e+xd$jmLV3?~$63peb#ec+l~K$;Eo5C28>%@t}={DX<nL
zsEUECrh-Qvi~&uOpfg;ED9KavlJiqQciQTMZ2{#4@Q^ZS^ARM{@K^<Lb}?CVWYC#W
zNKXSin*!dhR{}n7@eXJqKIlN8+{BWi%!+u>{CQ#yXiC3|_@#B!cNWfq2$E@$de+d8
zA+lV^C@Cq($pmkJZv;)yg6#1K2?>BCTlBI4J~e#+mQhK!1~g8Iwa$R}1j!1hPe2-o
zNKUB54^kr<wp8>V=wSQZ@Qwn)Z;<sVUofsIrIy8Lho0lH80upR1{A@A=b*YVBR>T^
z=L>4+K!O{@0=F{21hnS=1X~LUzaW~draG(~EJ{URmkFw9Aohcj7T9VqfoVU~^F;Qe
z!TA<C(F|IRTwIx#oKcjYmsthQ#-J7u#9B0OLoQ`Ew@;6Lo2W0c!4}qir@}PR0Z=yp
zw89}cH8-&!Bfkh^t_Z_&sGmVqGRa9C*7k+W8$h>6LhpxO1S<dIK;=K!`-o04q=R@D
zHiQj7w1lFDF06wHo~g}D%Y<AO11iu##Y50%kUPM!kH;O*kRanabWo)MZtQ~YNH3@~
z&W4m-po9lme+DU7kcvoXJq4PqCL+{}(vmHVz)dw!zJw$yux}6<8KX4_>HdQbw<E%W
zJjmHspqRpHI)?iprW3i%EH^VZ6|`*@TpEC!jMWYdA3)=SymWyyl8Ak4cRc7YeqII!
zhOGRIyyE=4;!^NpEKm@DO9wE4x$_4aaWrxi$abuOgAoDHz@fOB2VMSHl$lft>Z^j|
z2h@dzI057sa43L@^fzg4_ELVC_USq)>v&f29)J~FWX%sCH-O-yyvspF1}M`&+FqcP
zg2QU4?<uhwvMLRBzZfVSgPwv)39zpb%|1xLf55ophpcnfpuUD!4QjF%r4|>bf)8c@
zZ(jlV7~*$CF$HNFTtUC!jI3D&H2d>G<s*3R4X?$J{sQc_KWclt7*=J0@3t&X%}GNK
z0hs;p{Q}U~Cu@2GshNN@!9NeQR2<YrNh}6m2L>s>FA6X)WP*rv*O|OVCr?z`r!R_;
z+W*085=ChrsbPq?8^#8-P8xL78M^Nw2@t8*4JmqQ75|`=0LgR^i;)_`5Q{13C4!Fo
z0tat#YDs<pxU?|@4I_VmPYHr6H4p)fYoz0NAhuJI<H5Zxcwy5g!oU#J1j_Ls`@miX
z69}7O0|F46sn9?IHyFV!6i~(kuS@&~cK{Cq0|R740@6Tw3u_=zv^fHj+_41wPEf#q
zga<sLyulIeWTiKxln$T8v;-yeSh&SAK;8xs>G7)aySb;R*r&^=xcqDqbAm4JAj@KG
z1>H?h?J^x6-r!gV5r_zf6tz#Ft63<SLWGp?NK2k;L6+ZyTaMWH0UNl0?u3Nx!=ZX=
z1I<d52r)1)#Fyq}7M7;QBN8K|!~xa&kRvD{=?`fUG$aF%6%p7{0BGt2G~~ztwV08C
zA(MlVfdSgsGO$mV`nl)PN_Q`43IMfviD<*3_P${WKQA#S9cgh3c#j}x0UyNf^Poj+
zAR>K=j{3qctqJz&E0(FeE+|Qb+D%q%2eUg#A3h5JYiIa@#snXMMgTxb51ik@L^>?(
zK~p1aNflZ4qow}b0-VhZ^wNF<dNYHp(jF<&;j<s$(mslnfgwl}p7*Ulp${U`3z+Tf
zdj#U_)16*FJnbUbL`mr5@qHaA7fyrcLPU~>g#H!8(k4ifr)m+92(lb>u_7etp*Jxv
zz=lB{LCYVi7XhH%QQ!%T%wkCH2Os<maSfvOfwZ<>!nQ!b3M5KLqCjU(gK9m{t{<e#
z&&|xhkO?BvZTR1vyOry1pDvXpGU?2#SZETUZUSTjWj|1&hxqykXay38NIznuoq5ze
z#6DeJ@L@blZzgmF64IbFXkH3_jcjs$0qDxuGzE2VUdMHdEEC9gAR@h2<1Oz#gG_rk
z1KN+IuJ@YQ7#M<@;rR=6)?p@yfbVdCGa%`ky0+sCT%MCBkF0IVuuq3EXkj~eSv|t<
z=nWdA1~jz$K=}mEPS8L-Xtb>$zqlj-bVVGb-vAo=$^;RJ$y7*N@;7YEnsU3*CYM0A
z;jj|wU9x&hNF_Pa?J%JF3w+-dXj?Tz9O7}%*?^fKA{}ys#4kHI1Df8c)`$S@AP?$?
zuY&=d;{!Pu9TL*7(Tj4bSqv({k?t)8ErCHkmm6tK0wk4F&0-l)*;N3~+;}X8#yDAv
zJCI_$peR2Jvg@H(KRp#ZSl|WfCxg}kdL-uNrWS$rRYPJPRHj0@PmncFZ1(A}(_^49
zPXpJ0EQJhJfg4jGE)F+9LximI4qU?PgSt@hpks@Q^Gl18!2|rD1G&K2ASe}dVN6MW
z5yrY3jLsA^FsSPgP<s|@IAB;04F^gqbJ(RZ??DA1=msOu{`RueqLR$y#GJ&O%=Elm
z_|dE2r~whs<~`<?HEOvAbcIb2=oB+Z<qjH1L>_%dssSK5ldQ1>r2K*N?ixfwghT+6
z&5#H{YF5ZIFfd@;(@~sS5}uitl3(tboSz3>Mu{*K(n3QjXrPIT>P!6!OH4scwj-dS
z0+98Pa0Rg-*$l%W5Z_UJClPGAR1P#94C*R_&mIMhmBl9|<yWLa&+kA~@sJ)XuYG#&
zrKR<rTFtQGIZ9I&%pqvgr+6%eh5%JN4xlB#;BqFjxHz@2G&L_7<B)Lp+zBMX!RN-Q
z>JHGwY$c%PPG)gtUP(@7Hpb*4hBKhSK~`4=Qtm?5K!8hMq}6MnybUSwz{B;3B-wUM
z*l|v~vVD4^b-;3)U5>DnK*5wU$Zp)16o5)LaBct-=@|A?5(1E(EM#YnemrQVxu_I$
z<T@x*g6zc_0+76bG<pLq9muM3P+fo&0ibhnA=ZN&0`@zYfDU6JSq}{WPy>yKL7XCR
z&8h@yW`H`#X_-0jE7B3U4pQ#G56yW3ZB-+cs-SsQcxSD+GzoI>YJ71~s$OuAYkWXr
zNl9uE=s4CSPz$Hn4pOq=u>tA}A}2hHi&EpkLx7-|;sF((&p?9?;Iz6EG~fv$(hu#M
z>(WwhXrC@8ta<iXp#?0hl4D6X$Px>9;}B$iCWyea1nLtaS4frSWtM;r`PT;>!wgx$
zss@^{0kr``Qj1G`@)J{Vo!N*qbpTC+<XZ~L;aJlftiXe|E)Kw!tCBTv1}Snt86IQc
zP6M=b=Q%tU!KofZK<)hi8@+)qVj-*OMK}O;uQlk7R-{oAkj0=55Nya4$#!V;l9k($
z+cp)T#jmhYOVIdy322c6sGXObpOaq{pIn&?ThoikHjrU7q(h~k@lIXWbb|)cKnVgp
z*J4-?4Ggln9+0R&L<4yDJ9wcBC{u%1m4Ip?^zIL2&$~Em^Sd}KQ`2?_J~%;uf&@|s
zV>kjD5Fl3*Q3xX~4W0)&!3q@f&WXtxscs<w9*KD=Ik<8><^mVeZ3MLy@Z@wPE1;et
zYkB~-a;c;!HB~>gq9DHrd|)LgMj@7i%W6c3!l!9q3`$Zqisi`X8Gxb@Vm*ijj!Q6+
z4zm@`fcl-t;6++u83>9{P@6g^wYVTZuQ=5&za%I%F$JUR3UAB77*HRRZY8J{j@4H%
zlR;y`U^c{8NcA|V)qtqSQ%mAOJx@qw?iOO?mY-LGbdowa%0Q_h6GWto*A>{6t|_vI
zGoT)zdN(OCJsngeYylNZpcytuu_O$t=RriewELQ)%NZi<;S5OklUDAy0CES&Vu(9H
zE5I{BMEb%TT(hP{WZJ_Skl-e3%Uda^y#X5OgOq=uv(Pd@M0(=lHTQjd-0jnYwiM|b
zXotYcKO(o;29~Clf}4P#(-wk28|xsxKx_$zEMRoEPtRM-xQA6J1UgSZWTOdm8A>8(
z$u}s*CV)Kg7*waDWjA;O6T-L&YgB;J6%pkp>P#e3t(u!yk^xG=Aj=oCF))DF47%r(
zrs6)t8>zg5reA700+dX!W=%-53dwS4TA_3l8R=?`bD%~(s3ZzY%*g~D*5#R3l3G-j
zh;aiwxM*NtV1O4B(14(#TR=4&)^NbE8yXJOz4{&8)`&02OwNW}hBgz_#0I%3%Fo5I
zs3@^Aya=@82zu!VqE^Ci9W-QU>pDawiI#0JoC6IkA|ndCA_3IPO)Uo9!l9p1nwwjx
zk9zhFXfz!>7UB#)k_S=(Ap!*>=|TgAimo6q;EI`TK+dM5;7Cr&&(8*x8Z4k0bdX;m
zz6OuRfCy+egux!pfcl!6rA0637#y(W;D`sM1IPethHR*>K<Rt*6-%_L;Xv1VBK3wq
zE`l_ZAW@Ih?}XH}l!gJQ$cD{dgID!~Y=qQHh`tXFyJ-~$pgjsDpv`4Lsd*`>MX5!O
z>FE$BfgA@34oK~Vw9yL^9H90XC5aIf9H6u~71YqM!%}@h`e7JLRw=a?76qUo^Ab=B
z3`i`2-WQGVGlt#JGDw%WZJ$V8pCzC|7_>wrG{^_*A$E`$N6PHbEJwOkB=>nJpFg$)
z6(r|D14iIxGidEAWOpDW0KeF!yI#usVki$k0hDqpL5KDrRkC1vK?HP`{59-GV@+t2
zE|9n=!`a#bU96p)pO==I4!T(!G~fqHwV;t;tc3?Ww?Jl)e?ynyQ10&{UIvDs08mha
z5-m7uf(d9*eF3)A<}a+7N!{U+4o(IJ@b2CU&~!$8W*%swE;R*GfuNUP@TsZ~&_UH=
z>N;i?$T6V7L(oW4MN)ogUJ9ht!s8G~4o2z~f{rAy0i6z$UzF-!mReMlnUd;UT2!20
z1f45Gq-MxLVIMJ;?^C^4hR&7!1r-_~|K+46mO*cz1jjlaCqevIOnE|+16|sE479iq
zl&vyB?Sf2DD>A((P*<tzB)5Hf*yXP-*IWHzoifU;yb9V@t;Wm%UCW7R62M0+O5&2I
zuehfL&C`_Ii0~vhTtTx0kfBg`!Sz)As-0}VCDm*U04=--h5Hdv*T72**zL%W`jv7&
zx`6I}M}#UkHbDgZjy>pkgO1Q;YQ>aJWfvD@=B0v{Xl@1d%0R0DK?}AMi{imDkg@_C
z-XH>gWP7@Qr0;d%SojH_)GaGyLE#R%7z|QEfNX}u^v^lFWA%L*?cvr#a}cN+rKDj4
zJ?kbOH0%uyj8~x23Uq$ITV{G`QL1lZUSfJ`5%?wnP;`JxaWDa0zKhi4fwl#x?j+E%
zAEZhYkr*M#AISyKKrtY$7ER921GVd*8?iwN08&YU{Rz%wAOgBN8L3GL^(R@jfHu*U
zfQ-#Z%_&GNf<!Zj582!g&t5PF)JFlt`3SXL0GY`I@BYw_PsuD!E=ny)1u;tUz)`&y
zbdwFp+bKDT1v!bysif=%q=}0_A&)htG1{HbaG|si04=b9Oz}NnhF*K?nU|7U;gg@7
zSb}+mGsGE4oh@impq?}OK^+WGl>$i_h_sGjH8cb$FL^6L&0^4AJV=p;Zsi6$=tWMf
z)T=@XG!K!ig!+<t0lWmXzy{Pp0H1PQkdulxsxf;y)N=`es7A6H5(r4GLC_ec4ftM_
zoK(Hy)RLV1<is4<Q6Zqz0~w>h)e;0P6(FM0fS3yEK$w6^3Q*e$ZWknZ;;{<i=3;wt
z3NGk%fd$ah<-1_@a{y?xFf%VbAU~hz5mRb706gXZ@+-vGh#Z1z^pC7Y0i?Lba^u)p
zP?80e(>|Gbsfk6tnH8CN={Pehq@dV9QC|k9Q^0$Pu!RK7R%i!r1FT;M+Co4?QcKUt
z2cL!l3R*}70ICYW?F=x1SYsn4&exK{s0=HMs5Z(7>a0L)1~<Au4#8nFG-VT+i6EmZ
zpiHzMRKtL*NlZxrHMg+GAZF!4vPGc2B37?JA_>U`s8>LZ5+WiAPwN8Il)|`49(1@b
zc)BO4w5SMo+XBfw(CU;%?g1q!lEQ_^V=+KwZ(d3w_>9IbP-hray%eXGq-7Qrmz1Zb
zW~U@#Of^HI0;!CLdYud_TR=?)P<ll#oiOZxMmGgVaq585Fz5t{;F6-$#9R_rMo?fe
zsC|Mb?vMf!>PfPSV5E6V$UQC~5|npAC!GdOf)6u*LK{*#K-%dqVQnN)Sh)n6<a4b^
zO$N6z$}%BmjexXa^#q0~P)|_3EA<T2y8|Upzx+J+(oENi<kSMtF&OA&5k|!ZO`cSD
z5YE0Gh6|uULY;U8mA{~Pbp}<fps_2^*j!>>at3CbA3mxBizH}JP}d<iuMNbo9vTj0
zt+hi+-}z~2#i=Dp`4ymGFa~97P_l3=tq3Vf%qvdIFUobwuRu>0kP-#6z^1BWK<>br
zOE7GQ1_fw%o8s!LxFj(Ta_%>%{)gBPsxZLW985q5JigdLM+APtnpmK@Im+w@ok)`x
zpP83dlvrF+RGM5;3Rx5kasb3BAV+~+0VXh=VgMP3qcl+#msIAY!cXI_2GuP0L8JH}
zhk$Jc6YwQj>F2T<ZPkLZV5J^)tp_cx3EB&?9?kdgJOQ&F>U&y^aD#5E22VVKN{QUm
z;^M?~=yrrO&~91~k*>QwCV%$I0{e7BPVrA1dFNn3LiG~!HfW%<8$JaIvOE(+V7dSr
zDl|B1+yuJ*3v`cE5a?KH^nwDiBkr4Bdd>t-w{wTK!9s)5-g7}E=vp;Ukp;S0Hs~&v
z#DQToG#E&qqBH>YdO=-n(9w)RsVU$yV(~V4FiS?#Z3QJ5ta%<+@kiu1WPE&DDX3hD
zj|T-GNE5^sw5$!Q<)A&Tx3EHvtm^>5i+uEvmwV#uKw{VnjW!~eu%Px$km@<eUS05b
zClhQO0knrVF{cDHMVbLgxu9kUWJ@c&Y=JSL)jg#hP0-u|*cCW8)50u-m#)wdAggkP
z^x%sN5=$}@bM&)QK#P7s@%{nSKLHh|dHE^OG7Quzg%s<M6o%B6hWeeVF4zF7R32l=
zxEQuW{ZCez0r!7VajHIO4=c#?;EplKyO2mnA9jC<QL~e^qXNZhyeFQ4;sG+fgLZQF
zC+I{xS&0tKe()w{oF|?^vK@SK1HSB&(rj0dSdyFpnO<`S^*KO`ghAOZx3nY?)ZvH+
zUri2)2T+XQPk>Z(O*Cl2_#CJw2M&Eac0-aM(x@w_qJyu@&MZkS%1*5;hOEp^$xOyr
zmJJ%2&jb<a@G1$$faWXEb%LZf95ca9_5e`j1X`*Ca|opO53&!3r5OGJjm^RRlUh-b
zn3qzLm<07vX<jDiG-~v<=AdCn$mu;8#TC?3AQzDADU@@|K{xCLfd&~MUczG~#7k7$
zeESfzI9d}l>kY0HLA$&&K}5RqQHy$EE<yYB`uT+q9X!Nf%^6CUcY#)?fC?s%Igq(W
zaH0g+iNj*3k13dagAXu+=6o~rGV%)wGV{Qz`N0c0Kp8bCF)t-Q*Cn$UJS&LU*N(_6
z7`=07<kQ4mpz&r%TLm0Ccw7Mu9kPl|q&5S5>1hwBwge3iq@)%Ur6wnqz}66fLManO
zU>>Lp4GB<`lAH~Y+yI)H1|^MSpv^0wE^)kLUS2+=@C47sgTn$&K>MWdg8U@32BFj$
zxjBiDfq4!F2Jj+eH%~Wzr_!`E*xWTDHZbaMNS73}YLk+HNX`WX!!l4zfbMGu1|7Fh
z<eOMfkeHN%u^StQ8z>D0*fPv}pye6I;C(O9j2L*y5i}J%g6)Weoo7X<y|D5j8FYZ)
zWl+Hcj&MYnqgCIqSSPE=gp~Ne`yIipRYX4lY%}_jHKev8H1Cn+Wk^2(e2RKzUK+T?
z0_w{{GCjB~MtB)x(FZick+tF($ztSQ3dlBy<zQcfxR3@4hUHLSQ&N+IZe9pF1BzTU
zpF$Gz13P$!0P0h!6>p%)D6HPZuo>!2s@V+cy@RiWEXhyH$xnn{35f`2jC4&muVdH@
z@j6m75Y*_hf$Y+UuDwKbBoys7Jl^lXV4n`TuZ{!OP#`jWfW`sA`)&2hQj3bAr^<sj
z(}%%3On5AVIF;h&5UlE10U8wswZ1^ZUZ9o5#hF#O`;3sR0Xvw0;^lcnpER=pc7Ed%
z*qk$2-BqNLAU(AtCo?w_G8!BQ>aK#K0Mb_hRY92`BHe3)#hjm4dF<1Z<J@I7HBNy=
zHCeVp`fTu~S#eHrA^1uukWF}UD17Pi1!#3o)&MNTa_E^Gxrrs9g8;yVKd3xHvK_tf
zzW@vQD^S}h=q|&(9-mj5o06H92CDQy<pGlYATFdBhLlejz6YIXPI6NN#rL2)cXA8j
zi<2`-!PB`QE0J7)2mwg%=eu3HS*e`p|MDr&mA9Z;i%b_3gHO+Zp4+OQS)89!ma3mq
zTnKiD5hxXc+QE=65y)A1LxN^gBH;QE<PgXTBODPy-ex@T{RN1G07?a^MX7n95v<g*
z{2cIk{h(w4asyJNpa%xVZN6lkg#bww2-oBk7wRYH=jP^vFGd5|iQfs3kU2oboB?wS
zC<s6cbg|kGslIN)_E+46+E0x_sVcQ7A6zWp^gP6Jqyx;UR3X8d!JxJ!q?-&XnKG3?
z+s#3P80qm_j&r~K5MZDFiRF;r#;gsnvKXX+<m`yl4u)C09CQ-O33wF@?w^CA0BWCu
zeY#2V#ga0k1<+!ETJ~0g4mZVS@9E^TCltyZ?bA0d+obVk(E`|_c(N>pRDGZ$VDbto
zGr_HI&>;liWy(&8>HbMskh%{P@5rSNQsW=mJfoKNAe*ozZ%D2}vKX4e$Sd!%b5awF
z^7LUB=|FBY1!XU5SZ^~vpePkIwigWQn-IMhnaZxg*$BWGg@J|!rNgD5)m-_I3h)<b
zm=QDr;+&sXl3!XBoQly_hc*15lhW`OAQfGKv&6@+8yW&+byOflemv;z9MA?j@H`o)
zs)M9NaFGK_n2;=h>&!8-=E-0-XXa&=K<4Kxa8>vi_CkG3R?82is6#&eXfDWl(3&eq
z&PTL+AVvI3#8I=*oKGF=Vau&<u`n<MfzJ1WG<Wb=5A{7+El(uhgL*gdxsbgspaLKR
z)b#-^`vrA<ic5-8OOl~8g`h<VkYhi@XEwwudpg*sLzbk%uOz3oTlztRKcIvM2@E_A
zfCdJoqd$mE5$d3Lh=vbMAmRa|9EK(X>RGJ<vKpKML75-4l#~HXz!w<98Bkx7Rreqz
z0a#r>8C2(P1dZ{b<#_mNdPv(}o!X6zLezK$#W<v>!LS(WW3uubq^N<lCH3?2^Kvr5
zP1>)ZrEs9C#<e0jvm`Sw9p5^0%!LlrbqvmB0+57<WIZ%2$VzyShEG~riGEsINkL+A
zHYn>WfQmj)u@5Qw5YaGUpXcQT8FKdNFa|WulU2_ltR`yQ5oRUaZm743JOU`KC@~o{
z>k4kZOMsR^fR?qy=NF_FfwnotrzPg(fOY~xPniWLd=LR&1OjJ3{Y;L%pmprv<=}2m
z*FefMkUg0o0&WMK0rezVGYF6X&P{|bW(xvceg`VjeG`kbQ&Yfa%wUgVKKt~Rop(>y
z7Ky;JICWeA>NY^y65t{YJ!K(T3-vfzvuQ|08uDr81^GFU73w=cZClXnYEWWIW?~MB
z+n;FSBv43T4H!sn$87G?XoJ8cUg#uOL1IyANj|nM3y}5?(li@1L(s@YpnQQfWH8(T
zO)^9-u}2Ivrxq3E7lF$K&`zlm&^^>HnZ+gGQRtA=+=86Ml2oimfnan#pdmql3pn9L
zFIon{unX#2vgUk{G6-_nD+($WK`sC_`EpXrQlVSS5RD#;Fo*h>ny!!sHG9?*vpfYd
zyGl*F1wqRnKzR{TFoRf-1_&e@VpO_Fb0U(UBi-XO5{olHyQ%XFQuB(7bK;9L5{-;Z
zppzu%#v$cuXgCtNRuX1QL1ktND8alSCbXalhP<WP&~bv)3ebTni8;mkunmfdMTt2%
zsX3r#!xK=k3Yr=UfC{45Ef|3a^)>CB1Zu=!Ej%!s0tp?Y_ynEy1z#Ej+B%+Clmc1l
zRGf;j&<Ql!n+YP)J1%=}+cihX9?pRJk;pYc=%=AU4@Lt8FKAUPxbYC5oRgZES6To`
zqo8w<GC@Q-yparJK=KFWHiM7eO@gN}(1|{oAR_%9gYo@?{yg?@1|$@V$r^h=DwN=>
zPb5Jbvq9Mgl9WKv2^n>V6htp!s}11iFM|S!lHpEJ6EHO|1+*{-vUINowALK#0&szh
zC<@`@E|7zRUPFhO$!dioc|8q$<^(9dK_)?ldcih>+<;aVL7Z>}I_Za$c0o;BjO!&T
zp`{RLr95Q0JY-FmqdjzS7yQ_KvZip5`~VHaVtpth9(qiU7^rmR1NEiBKEmS=YNw;U
zpi=T2yhjUKwgg$bbma8qor|Uz*r%6Yzd4~kEdbWsrL-#zn?MGwT?$fUft{2C_BV)t
zo_@t`51mAnq0+E9@?xbRCAhuliRXh|y5M8e^{>{bLG2~0d5;w3h$Tuv%2;fL<dNTY
z>3rAzTb`LN4z-oiP$sx22{KeDYoETow*S{_Q8Oyp3mYiGxhM%zy?n5Pc2@bI_JXoB
zC8ZM)i<02WFS50|59xL3Q8X=r6u~er>w|`9Kxec;N@P%n4zf=3=Cfm8T2%Gy(~mpf
zzk5okmRdOpW-%zOAQgvrEQS^ypw2cCfsHtS)e$t}32Od^=0OB8W;-Bj)IVX=IwV^K
zYAj-Hmtfcc^$b}fEJ)FZv>3ez)XImgX-Q5j0B?AM#3pELAQMC&+7ghFzVEO-FjRC0
zT7-gDVnNO#gk;*ch|}93?HEck5BN+ga5oQhDm7A40+q**JqVCi1^kK;Sj|fDaw1q7
zJ`9>f#Wt`C_j009sDai>_}LDiFd`xi<J|fKvl!>VDu%_-^hRW(5!GUpCG2+Kg`%KF
zBd7<T394Yg1hhz$vrj)9KiTw^Vic^PpxjEFXX?P?6;?VzQWWJjq8-_W-afr)m;T1D
zd|t&lF=z!qxs9N9H&V%g#l~2Pm#%N?M5$&Y+F^g7akESi0bP^%-7fv9tJ;I>9!6BN
z5$&))&=$)~5CLtc3fiaJ|GTuv?xZi(Y(zWk53#@yo<FqAo|PU^)P_~3WR3MBmB*+z
zBwqs6JfMk(c+kj8CiFyf=!qGKE(gRhcVP!E^TTourH%p3IqHMEJfMEZCQy$j9o|aE
zV?Cs-K<amZY84yMt%wzRY57Ij&<+RU6n~3P4k;7WZri8B7?gF6L3ThlZlLY%!eb@G
z(ZpVVfO4l3Xq*DP10Xpkzc@9$D8IA-Qg7yhmf?U1XuT<DpFZWr*1dNp$-ttB$k_rE
z>p-)=L5SO(K;~zH2uw?$exkItfOT5*K=WXrxC}|HC<)Ij$#6+5hR*#WIufwXCv?rj
z6<7;_T8;pfY)Azi9*d#Ar*zN(zCE=Slp8@M94IrU6eX4;wUF9CF%2T1JsEa;I0KrN
zspgEIpgt?e+mLad7Ld&#BHgt7%l?>dHhVY&Qk#*r^b@H_%1KQJjeKN4uKEF$1fY46
zpb4NV9Na`glmw9O4aR6Fl}`=c3<`ib@J>6zdU38Bd~R<(+ra|>8uZjH2Va7$=Y+3#
z!(%-(0fLq#kla{<Oh%)wd`-<vPtDU$ECS8PLMc#mNP!yeprIW{u#8{6b3W))=X7jW
zLqcK*bAXxFZUnXBvF2I~2SEdktYw*ynN84gJ;Y62pb%OE%DJG*8zgK9J?t8kg&+fG
zxQZ;Qx&f3`u!aDJ?a&aQVwZ{sv{Mcg1+F<cnFYl-b}AgOgYM1v0V}hq=mea_HY5Qc
z*$wqRS=~C6yR={%`_o|gBfboJw@iFQd;w(jBBY*{>EU6>1QF>!?v|Y9EBtGpUiL?v
zgGtE60a}Dm&ov#OfszV%;R&)8ht<%4AS)FjmES1G89e}n1!ycIATcE+HD$;gWJFf$
zA;8l|-!nHcJ=G0-aVV%H099S!+ajENLW3ZYk0=KqNfEPfq?YBN;bp8v0EWfTs3)uH
zh7^LJk}fYl7j(o0qymQ=o5aixy=Te?T&ovh6a|njG14k|Xa=CZQ$Vu`SVIHD3DD4>
z(L#b9+zbrhBaCwL)ARC+a<Q##fJ6{d!v-2EG;$JXWCUx_V7LPsG(^r1r=%ul<|cwC
zWq*MtWkCtjC9^COw4cs1w;;c`I1{vU7-T)TW&{)Pi?-klsOL$y6x<sF)dY~HD<bFN
zTEzg`Nniuoc?4dJrJJ6br)z8nb`>Oe&|C@$9?T3(RuKwmfyIMb6L1%RiV*N+$IIab
zIKmScwnHO_Mv3Gu=<pCwZ!9yfEIqNbxOgZgT(VjikP;5G?*OztI<+V>85A!9pjB+3
zMqY7hNoo;vj9vuPI|32Vle$Ii;S*fYR7^z|%;9EW2>Jo<8G!Z%WP*tFOTUj@pBKqw
z5AUHvdS+C#dOFBz&@G9OoCUHKV)b(q?k(0!IPBARSFUo-Ym<fzRFP#jQmX>#@Te1@
zq6E~Y$SjUe%go7vodkh6ISNvu!w(U932R?ctxqfi8dNTThddsep?Q+X!(@X?iZb)k
zJ^exHOAgfU0~cOcOC8M3=%n6nU6fi}nghOg7F4D{3OG=&0bJgJ3FyLZB&(p_p)?@j
z3oBlM4zmDN3-Nxb#U-gJ&N+$2#qOz~3(|>gEm6%apbiV3P(Ui?p`iepOCYnOjkKv8
z6vYs0vBooIBY@WXS|IfblD$yxlGP_c%EFL#At-5sPyYh>8$FJ5%@3OU&iA!XfB$C@
z%iq>G*svvaMpQuK%OLA<CU*?$AwEYMSylwCu1hM-%qh{$%u~osEy>7FQ2>q2f$s*6
z&jhvpp~KCf5t2+0kuG#&PIJ0bvVFSCU3P`7oyjm?fm);_ryryMg)VDBtZ#vy1&Q-6
zU<`*q{72rrB9@hRJ)ng(W$=+Q#OQZ-;PqR-LqhG-rTk{j&Ue`i3jwlvI#?_Q-P03<
z*rI~RVyKU)lb~Q{7xROf^q@L0J+&mh63=;RNc-fW%>n8<q!P4d)fHYZff71$L5^fS
zG#toUrv@AK0Iy8ehYoT;kH}~M?NS6Cl9Zg0nw%XEuA)oehcy*6F)?I<i1ag)v$;~r
zZri6z-JCjYV%0NPiXiKzdYFR>ic+Bq6*KeF^>Y%FAPYj9Ks6}~d?pE8$AJjwl))3k
zItoZT2{gy&Tbi4cuMe({GSf0si;DGq^HV^q6!1CX$)I`)RF-gol6*WU$%9PH%mH~2
zM5MDdF-Tg0JlMGY#kK6JXRwA4Ss{*;=}U?-^-GE}i&E1v^T2mi8-todppHvuUMA?U
z=#Zk!Ah0B~Ns68bkP3Ea8l<|DKt(&GN&^Q99v489A&t5$VuH}ohw{Xnl%d#Nq4hAi
z4=A^S%FX!DyyD7S$o6Z<us&$<bS`uk5_$w8HHDx_h}I4zxot*PxeDomW7&~64>S`A
zT9D+DnV9ZWlwX;Wnup$Y!Du!?gNC{;A$dC<Sye1jc>`O?na&3twsHsc{WBBuyj@(0
z4FYPp0eoOBsBnSwI?<CQQppA_S3oPQh-hI2mZp}1%Tkbukb(=XEe@IM6tjn}mz9Q9
zSD<AuL|A~aoF00g5@>Nh#6Glw62m^IhbV1>BbIe@f##?{yCl*wD>6$`ab<O+FomXP
z>N$ZO)QesSuRB43kK9^8vYP6?2dx|p0v!$kNy3OpRmkkv6WIB~@Sy|h`MM0`YXdB8
zG>Fw#U{>Fx`bvIq*<pz#yfJKshBsOJ=OKkCbS4?~(B<EtGzS_&j0Z0;D$Xy0w+unK
z8nUGVQjTI)G1PVp=n%&eDFy}xh+{Gmi{q0M^YZgDlM{2|K@F+I5=b$FNE{f$#*p?R
zStAgr!Bh-BTWSudp?VQZ1Yv{;G=e~fQ4vx6WEJP<>8Iv_P7wy%1M(51*g-4i{^a^k
zxbcq9K7G;FMQ021;0Gyy+L*-JgS?Y38RQ{Q)IvfMk;5@8f_jLQrgsFW@d6ra^)D?c
zC@pc$Pf10z*CF{DVId?0kvc%o(u@=fajureFdXU|vbN|#6Glm5Nio*DLc>ArebBO?
zq|~Ck{L-A9Ox!J7%v?khCxKECBt4;3V~~JBas@PC$U6rNWAk5HX<l-1W;%E=^=?qG
zfY&a9TdtrndGE|TjI|dSE`p{58n_5FH$Y0LAPqCgf`*ymlS?WKQj0;iN`el;fesnQ
z=jNxt?w3SQ-beur^%jw1!VsHKu7?1{Bq^>Zb|qs;Mp0^F3Us-oIjmhB@0*yJ7Xp^T
zo^vt1M&wdP6#GB{Mv|vO$hkBVHXMPmOBJ+V7V7f_pyUebQTUbS7F2p97H6bl^w%+}
z8)&4`%uzVw9K$8hd`#9XJ*0qx_p;(MO7qeai!u|zXP<yZ(I7DdDpbJDK`;RwtNCIF
zKf)gx7!))M5Kf3k9QOp;Jq&RGC^W!!g9%IrK*NCYwe-xOW)UbY#FrFh=Ef%$WtKqK
zT!V^v$Rc;hh8!k)==s)6&<+SvnE?tfo7}_#J4juNM=R9fWQ81LE)+CL4Jv#=Cz-$(
zY{&95Fa%8pO;m%Nk}1i^zyK!F<ENEn>6v@jr_VPRU1_i_0qS=04yJ`TATcE+J}<RA
zB|jNl_#;vcIMP7d=QBY>x^dP%FS{~#`}Bzkt7dK$Nr0}@r`m8u3gmFmcr^E*l{27j
zRwjsmFL+Bo_b}>hqP_@hRFHxJFhnW_9gJC0lok(KK@2%z1ZP9fFt+2We4>VZI*b9$
zO;mG7PH`cUJJ7-d#D!!knDuaXK+`5!gC<C$7NDsf#8!g;pq3)&9!SvmJV*+BVKdHK
z#WCv|>N@8rX!{jt&KQzY5ut(6IDv)+rQ;{C?N<Sy!D@X_*AtvW5qlRiWJ7%gO8?uX
z>nd!K+vgJk%c0b=xCCVJdU(eMkHwVu7*aGq+A^TSFA6F%y+G>^K}9QQY9PKiGq)fI
zx&#f>4TW5v0@;iuV4n_SK=T4wp2y+_Q0FNK(Fww1G1T*9r8cO=`9-PviA6<;l?6qa
zdEm)?P*g*#2eH7}4osxiPO{EXGB&qQU&OKU_RVlRSfrD+R0GNSywY6Q8W~X1hS&`*
zmqA<{c0;`m8j>NRqlvoT4`Mf{PRh(nNv#0YqRH9ddf^%<-GOYy-l&9+FkOJH5TUaD
zAgi(ZACl9N?1%cFtf)t8P$3SI(+91?2K9-&iz;*S%e)ek^OG_YF-EQ-%VjW6QlhS7
zz@txyYyb)xNLs+K9vT*)lW&Pg3y_7T;M<--2|O2c-4!IdLGyl@AR>K}>!rLehVu65
zw}RL!ZD+vCHyZT%-hzfaK%RpX?%;F}BB0m8+_X#oIcImQzONy)DMIO<?NZQ+GZ2wJ
zm#1%1_;U&Sbmz`#HuI+0LoFcdTp~!40xw34hju(cvz?&J6OeW+gBM|e2xzPHlU@3>
zNl#U#>~ny%YbdZBdI~;hEo3hE1b)!0G9)vB;u)NFzyx$m{x`;cM6wDph|i&xCnXj`
z7W?6xe}bPV0jWayVIHTbj|Qr&it>|Fi;F??*qONn(5u=&Q2<IySVIAF2#$_@dh@OP
zw=SMXV8uCkO*-gU3#v<yhxQQe0I|Rk0Vbel@F2MZnw`lSc|vN^!B@K=y8^UyENB&|
zVS$z=AVb<;?9!#c2MBJ0g$AYL_=syyK~wIKh(HSf46C65KvsVXk{q#)rh{5h;BBDs
z$;I)xi6upu6_9cdRQf>T0<u&0E$j?>_!Xkmb_&QwtYLxS0BBek5;v}roS&1Eia0M*
z6;z!Y!($uNK+6OX>53D#eN@+)ZJ)k;<6f~Z_ZGln8#K*BL=}SCoPY*7WB@iPGcPYS
zCB7JR-B)o5q%c79EvQa|OkHGMZ`pEeL9l)L)mN)mGi{B4mcSI$qab^W^>g!!Qo$@x
z0RT$qNMQ}?`a+i8mYG(b%G#A@pY9RxN22(85!80FQao}Pz-<OCLPxS0v~D01M5NCU
zPmrIxtI0lHCsp!Mgm^pDW>CYIh|n+1%Pc8LEiTaqttSHc5G{N`Tu5?;)Zz~I=@GrE
zYkD_2L+zpJa(mDUKO|2gEQcTTzq9LTK9{yD)N)PYQgU%IcpF3nsC58JFp$;(sDBPQ
zOZk=ZhxYmZ{p{1-nRv}g<wBvR6S?OhGq1QHH5uI8N1LPo4MJyvi1byPqZ0Z%Q|#00
zR&ntj*^~*jfykA~rA0a5TTwvq0~(!13L=CjOx(ljBzpqw)2AP5PP@Jr<_WUa7a^y>
zTyPQu4KzVAAgI7aasa4jp9vz;@5X>8mMZMiU6bFw&*`s&`jN;`1YL6i?KeuJ#uFkH
zNd4UNXr;TAefo2;T|$koV2vzdZ9r^@!MQgDW^npS-#K^Q2e`mm6lBdZBGsQMi6xMu
z<R*YdLqNUIprF#6R2-+yVl>L3onorFA|F=>W7rI>GRSI5BKbePC^H3=OF$`jGH6%;
z6a=6FA(#Yu<q7GdVGagS(;*8$H5%x)Pe^%yNM{(9LqmbAx&+ceE=mO5C5kf1gD8$b
zwF<b91rzD!60bHK(v7!IH@~~d`Oo1rScOW~+H;8Y$jvsKeL)O+q24C15evS?2wZmP
zgJ#f5GV@CFON-+{NABi<#|Pel7Vd(Ehte~1l8O>bu+5}mq(^8@pplb65rI^;AyNc}
zJD@>BX^sSq7J~yr1GH248oYG{I^!V|M5Jd#e(!I6VPKzL*7<5}<60+JVxXGMIFFdX
zuo>!S&<Q9+v@Gzn>%eIsJ~J<^D6zPts5H5x6f%zRn1_J@eDpG?WB@5D%1i=ZcNL$P
zn41b+4~eMpd~WaGbZLo+eLCd8Eck*6dMA#T@sm!86dTy5!>+=BtV|)R&xKs`g7Qjo
zQEEvlh*1K`G&oZzq|6eqPxn8yq|J;0zW0mr8L}+USwI}172F`p!9^69fNoI2ETbv6
z5_G~%5GTk=P-_Eh8<@bf(f|_HWVN%A>L<j~JSor$F>{dBXugFUg7@4GI+pYkHfl*d
zt8p&L!>}6aYYNU72Q?#0K)q=ADd&zw=~xdqha@b_Sf;>o(5MyGViv<Ls7J|~>PCuQ
z=(>HR3!OkkJH!cS(F`9zfCM`H&_l9%?PyLwu7yGU5QqcNk~oF~pkY8{uN`!H6lmdJ
zW)65!Ed!{HQw^_II6(8lAR@h?hEr4~F2O$idWPi%=iF>qy+WQf;E8)HP_6;{24q7f
zh`_W4;v1x?b5+oC8|Y>(^#ag+r=W>#=qcMJMd}LqNm;4MCB@L4U7&D-boen63$$%O
z<k}SQ5oNH$V{1Uc<N=zT1G^uOB@p))gD$2bq7nqJ`^(HrFGgwkff}(Tpb^#L)Wo9X
zjCj~=K6)b<GQRoR4tkEb5-g6$DsmtVK~U02EYUBhGzQ=D0(J?=%f*=$rRdFI4C|qO
zC#xBZk`)s3ib0D!!7ZN+pq5WPDCogC5j0~0xmgx+!jrOn`f8iT4Z&-yV7><pIuQ}{
zsO=AA>r*Sho%P=!i$ROjA^yf=G1T8=1v`?(h&`C!LA^jwDF|xlq!lIRLgz(6r4po~
z!&vkKP2ALT!xT^-5M&|5`ydt$tD)YfG}=LP|A=J)te|>!HhjSfIPO6Nbldz#yL8Bg
zP+3^CQ`dS!ko7X4R0m4^-~t6qKvx<)f!*o*1-cNNy0%{h9ckYOFD?;HTF8*?T|4NK
z3<=oy1CeDKsPKo66m^0km>FL5E3h&!WP*rv2GgxMYxSM%p_9<=un;E4614LPb(t9$
zGC@T85@Ul;o7Bwg(|6{SobxZVfsO`|SG{Zm70;m6!jQBI+MS;XBH&jf!BY*mIfzud
zfRdC=PHG->VO9dD{sR%v+v=1Ni*Ud?iCsvE6416F*Xe=+8l09u^MRQl0(#0VAL1Se
zN^%mU{02?5gO?Z-r<UXwfD6zBP(1U45);U?nJS<;Ne}_;78%1%q6mb=GhMfNLMB`p
z?bF+i2|LbdSB9novhowoAh-ov&<Q>^wKz4o#4rVtR6q;AAgjM1U3gAdgiAmjLf-f&
zbcPNzXkM%j-e3gZl)4cV9sl5|0W{DBS?U6rq?ET$=UH8${P_fI^pXk=D1#hz2Hgd`
z2;_h`xC77&m$$GTw@kDO0r2?k2T+0p=LbX-K#oQcvQICm*<z7A!x-uSTBJvCrU0$;
zg(L$+AV6Z_J?!Sr-*(VEN!ENdBtfDdP6Mu4L9Jg%dk&l{K?HQJ{59<8Jq}ogBx|M&
zsbK-_-hdN7=z_BnaMM2t)W`sxfe;^*no^pan&ROc67QOmnwy$e0tppFoeybCUx3Zx
zzo(?ug9Hkeg31t7PJV#bxrjJ{ba}4866PnU?UeTVAnmPq;tw;u0816GVX1<wwk9F~
zz~uvI%o|kPLC#13C4|fepfVanq+2={9-sXu%s##1rJ5nfniyD;pxPo?$T^}p`AG%I
z>G}`~<RH+vE7tBOyafVD5)Ytp0rCLJjc&+b7IZmBZej_jp98M4K!Jc{J0c}O3h9?r
zNrw>IAtTWECUFtD0J6^eksb6@^XE{%gE~nh`yG-9KvS!rWhC%~04foYe2&F#NM?8f
zwVS-!0cq@=!1Nu+J&=13;1xA^%|;Omv~Z?Y4gfWXg21=$fUN|bBb5mvpfhbD_UXcj
zhc9cVMndf+tD}P)<=}`1C3Uo!b5OrK6GXt<*y(Q5&TQPXS`OCOCUR&UT<s$rd680@
zn_G#}&j+=_AdwA9@ZjDRm_Rg+At%aP!*V@^JDM+nN_^0o1juHdfc*R%^nxDVEQi?)
zt^TNBb5I)S(jPRR!uL}DS^4pjZ;}I*d`fulHq1VV|B!kFpsO7*?!_w3tb*<9LKH@j
zoOlJ+6@;H|PW7XM3QJ5u3Dq4`KZ0&?gG3r0hd?}4OytCOd1_*IQED2<3Q!jhvW5^G
zPmk3Y7&1Xb`jc;`)wSDJ+o#Kh?RcuJxfK>qWR?3UO%vp?9?;Yg#CmWca35qnh)AF4
z#_G4NakYKAl9=He1GTNtLV(CI=A8WWbWkaaJiZEA8w1IEV5<-XEo5BxIbvBBq~k|s
z1^^Z8pn%1hAmND!wLT@QSVam~*uW(yxdgF*G9K8Qh%^JK%Ra%{nm=GI8uB+Ko&<HW
zK#lq2_>}xoEN6#8e26(yK)#hAKVgkT46C3y!hpyHL8VF1f*TZf6F>_RK#Lij6F~<W
z20&JSk~A4Ymc7lO{0<swhU9ld62;{^vN9v)hREW~3Xr$IgW?mEeth#&N^?@<i!&?Y
zbMwnmi{nd+a&j`0;)_xXOH+$WAiWqou7eIuQrC4L`$&p<8qNCN;b&kd0j+N;PR-3s
z&MZnU%}Ff6wp0NUY4BYI2VnDqG;$ZH{KgYLNbZ1U4Jz-hXa|j+gCYr%JP-vBBpfhH
z0V>-MvKq<%c<hJzpU74n&S?YCN=b0%1vC>_0_s;ou3iA2rwqESCoM4<V;CP27)X%;
z%_~%M3}^=fQZRr*1=7UCuo)T*pcViTof(v!+nS&P5|jtx6O#)|GmBE=K}%xuVF!wX
zM)5O2L^{JV9+?Z5OzqSA*8DM&P!58%BFM4S6jVCKfhGt*wE)<BFo9_a)MsR!2ns#V
z5Y&Q51s~4=Iv%qGw0}3gxUe)ewJJ5fBoj700~&IMj5`~5@W1(?Z(yG;`gh?>_3I`u
zk5b1CppkCy<a}{Peo+bZJQRpKz+r*M9ni1<rBNbc88xv(x~#~fS&%aj3Mw;oL6Ol9
z8dC(7JKz)sCem%@O_SeoO4&ZWZN;Ja56{9U>8R}hP$`Nvontrv8UkeXACVeZkijER
zJqey01|@GuNe2!95CLBZ0-dSSg(U*&k6VGNR`9X)r3Ijsr11qs`Our6!RY}+Aa()m
zYkRPxXSNo!2~E9l01s<|3K;a}3S`jcCPq_}tfk>dQGjxp1?b*j@P><k#N_PMBBw-L
zH=AJAJXCcDXygEELWC5!NVY>0B9Uvn%M*+8Knn#xDGt;*C;`n!r-ARXfJs5pA80)}
zWKnrhpsrHaNpAb}gkrvVoWAhV7L;1Z%=~zlse+OjIM;&-X!3t)mwsSl`uWr>*wufu
zS)~eUCPOA@Pac2Aq{{luF8%bj%hx&*U=xdE)u^zxF>=Qd6z*6f7~XLd{*bN0c}NwS
z!N{rGK)DF39gtqzBfE6)P1FMVupuduwqt^(wM#%HRD5x1QUxT{fP4j6RsmVn@f&@9
z9chFF)TV&ndS6_c1X+s{UtE-`7aZgoACL$-Av_OhVGcN9;IRRc6Oe4cJ_eJKUk+X5
z0}3BVB2&M8Kz2oNoqf8p_p^w5p}S#aEUm_1WI;n{ApaC+<d>rtbQq4o@EE9cfL?xB
z3du`QA0g6jihcJ|k9|h==`aSgNl4_XN%T=iq<$>8k_H7Gy1U_H`Y;A$$O9D5M3fT{
ze}KxR6i|;595|_|(D_wFyBl@^DEuZ+NZ=GxFoqKkI#C(yAy8@t@6`blh(#K(**s_%
zQef9l(1MUc_@pJsR!9njg&DMheFPntr@*RA(CQD61?cevsYu~_!EeIGY(P7IiHIoF
z)*Q5#KOZz3ybIoP0|z9CKosAQlNT6ZIg1j@K(kgsH{pQ@vIpc@229H!i36!f0Ii0y
zDNZd(EGa31<{3X028K)!0bf`RXF%hdteOs1=p#;uf(8{RN{~wB4A3MUh=87Y#AXj?
zK=KczO)JEr<Hw*#+7F5(Q0@XR7XTB88DmJP{TiD1D2XF9U)F(q2_m3&al?*T<%ZU8
zWVN1<x)SNBB{`Y7nI+)9b3MqT2jCtB4Z3B52<XCYq$2_$K}=*3Rh*xkoeEym2O1Jb
zibF)D1kc7W2E+og;t)E`1YT*SpORRT2r3-WcBzAW1@RiZPKPlluo<a!jOd`CHOxU%
zyO0aqA>|3uRV)yjDM>wOIUF<`3|U?SALoWKAa;>I_FE3>a<ao$3xJX>q(H`48VRup
zG`B!{A&E3o3fX0WWm`%<XsGKaJkc6~){udS^s5|yefH)l+QS(Tmr%D<0{IK6Oa&JZ
zAOf0Ykj9oEwiDSN1<l2m<mcyrvn6OFK+s;e4+}v)1QGE2li&=99b}aWkX!>k)&g{{
z1o)OOP{sj`@gPkKf%Yb5f(ZDfx^M=h(kLdfM^%!VTaX4_1`k?aTmss&A0LvMTaaIr
zSXAkjnUfk`lvq%JdG;TqD~D7VL8}`wECv+=NaYbC6=T=|^&AC33q7cyxBxUM1&-Ha
zpavVLV9G2m&PXhP4o@Q5qL2dOCTxcD32e-otkD}t{6ZZ8T4!FAT3no(0*YerWE{xv
zkQNW3(FiLnpe?#9(E5$M&Kx8!LrUf3{Ctqb;Elcri$N?%Ap!~h4KSZyfi<;2U2l?0
z5u{QXcG}kqP(=qmW(?d2LexW$N<hgz{deEPjq{IAf;GFys)&#+E-J|^23LWWAgg`h
z1t%V>p-GO&$*cJIv{KMcz4&-g$byFROF+ecyjyA_NEp)3#1g`=poV55vTOrs!jp|K
zM_tHT&W9A7MajjfC8gk_Z{~u+b0w&71V<&P?1ikDf#)8yUI<z1g^=t<9*qbFbxT0q
zWk}A!V>vW3K@A`x5^zRINda`by%p@BD$hJnuFXj;NexO(B>M1P(k%q#F;XHEsh0q<
z3_dhblnO4?Aj1>j7N=fb3V3le(%1wz&I;HW7&1Xbx<C|vyjoX*J)8kem1Nb~NO4wD
zguKqd9u#YT;ByzVm>3u`K}7mH+nAy|nz8n91_kvg*vI;yb(Y{@0G-ePUiT>ovOET!
z{}F{PBqL%p7|1GYk-QGy!wfpzJ}4G$@eEMU0z{;LZSpVvq@-*QXFz;S-7x<J8Z1Cu
zG-LznJc5Yyy8O%Y|9T|a!x^OI6r_7pw}7tU09B{X`FSO&6&N$)kOBdzy$wxVpj1ml
zo<y<<TyKHuX-Kw5l&Tn3Ks`fgc~_p9lAa3bqgk>sFo0*kd=iU`^Mg}!Qj>9>)`^su
zpnj#ILqIFp@RSosc0&V!cI&8vQb9{>z-0*{E@CBKy1uPrwTGW@2Mq$+I0KX#u!aVP
zGoYbC<bWdTs8K;BsQm;zC;JWPmQ_$J#QT-z<b>pB<GOeUss4qA0p*s17Gz=dFQgbl
zvJ&dwSR&W{WR|4r79=KTC#I(s>!UPUp(~XkOQsWxK#EY-bAl%QAx=U|3Dt|kX6(Ce
zV4u$SCE#{jU;wO97e-wtAvX*_O)rRBz-<CVs>E;$G<Zlagd9QB{vdn&%TkNda`MZG
zZQ_w`70&VwM`R;42|znI;Y|YQ`UFUm0J4TbFDE}a8@)j=4OBaTi1fzF6I1xhqa5H2
zXaWWqOGKFszr6@_A8*iPc$JU11#xp#`|X8^8|~9!3`pfiWH%k+2WWmoTM}Ud>ZXH;
zbT3xX(Ea&Q4sZs<4p0^%!Vefz3eXFZz-N+xdgqWz2;n8jh_M3f&|U@TNFh01Y6CS^
zlCT8Z8^7{-73ak4(_svVm&oXXw}aa6!Jzg#IH$k~=l}{*H=k@P?t=Q+pxaR5K^uCL
z6Z29s!SnQxV@tp`A~GmOl7?7IPUwLa<^<({swS`<phXIhH8L`6pTkeYc-p7K806al
zO4C80vK5jF5Y0(QQv^O+aT3~h2es~rNOY*x4s;n#L1GbT2`2coHpG3VV80<!-=PIQ
zi%%Z4uuq3EAbz8ky&pm2jxXTJ1!1o;zl&JrBU$@&7z1K2Iq{|gDp^4@9-uoq^UL#K
zvuWQzO=b|0-etR`U-Zv1dpHAHiqqs!8Bo1e0&-4LW?p_S?!}ErSp`~3(!@ES$`xzX
z2FW={u7Cy#d7FyOLBqOBK}8S9!ps;@*n^1la|O(H_B{^v>ANq_RiC$cDXcapX%hY!
zsMOJeCnJ!-U@I_9hkAmHk}U^R?0_OKJ_vlgm|tRUYA|TgZXV`JM2y@DjX^T3#W|*l
zVF%P}WQ}R!>fVA1Qb_6mM;nL>8PbD{tbIc4B82*otR4x%cH~AE$TEoC;4%rs#bGzp
z=VT3}LhQ!64v!a9UETwwDm2gYUHfl&W;(lly4MDaIX|!Rz|t(W9e{Hk9)<&;AwX8K
zj5N9cy8_b@bT$}hEYmH&C^xahH!~+EGd~Y{CKw_jAVmaH=Kz`rsO1a-w_qY!4D~;G
zWm76BXM>_REwiGu0MhOT&7Ed~h;$uinb(iZxa^@5BjT{gCeJR!5kcTY4O#%32_m2i
z9zMY~WB!L!@JLN>kOPr!y#dXYXMzZ5%ZkB19WpWsKbRYodPr^?K)Yy=BY{CjIANS5
zxC?ZX6=>;AT4G6NdTDA-W?CxlS`2C20Gc>x<SbC|U@a{mi4@5lkhFpnvY>--;hmc7
z)XHK=k0vEExdgo{18R0=f(ZB-7;pyEdlVFa$b}GS)IS&0INAX!f52%4PridRjwmSk
z!B)qE_q69Ff?I^3m5)f_iD-%9m=Pvx3?jhON8d9yF&#XV3O>mIH1Gmx`y#xH!$vBG
zG{`O_&w{v+ss@sHkO~?|NYl8W2L%FT{UyYHq{IucpVGnH;*!doRDDpSgVGtOCj(vq
zTa=oUpO^xgGzQJtqDKS7Ef`@z>0BD(oOvg91_p4;I5|HDRPPiNqdNh%oCJCl&0R<#
zSxoil0QDF^xg31k6)0DvrxwL0=42)or$XHXDsCXFtsudI5k=H?(;ASQKv^U{IX@3_
zkY{{yMq*Js=&sZFoc#RkQs^pmx~E=QP&oyviqNAAN5-Xg(14d>g7-^+%Sk-B2U7GG
zQ-9CEVo(zTG;`>Yn3s~1TI5%dpP5%u90^%fPjthH`VIqK`%7HO1`QiZ*ZLL|<%3QT
z1T_~?hI2uuwU>bM6ll-}p(q}Fln<oMg-Ey<D;1!XE3F&{T3;H}2_M?Q;}B>lk#$l8
zQbQYh#RqsA5xjWh8z%z;WL*xpWI?z9(x`iHm%cjcQXpR%GpzYZX;462P^=GS#6$PR
z9|2`%koD-98N(saprB-dTL!351D%eFYgcS)QBgk8w}n#T5|EFv<`axE4C-x?#wRX-
ziU&}ODz!KnzA^~JhwLqZbTNLxcFF&RtqdZoO@%V4bdHCCp%^sSWRsqnms(L!1U;G!
zoX9{#I^^KT&xqSbAuSBD=BZ(eeUW?dpk5@_fQ5`-+=O}hE_6{4Qo9_a6}rB^ptK}4
zuME0WSphV71R~Pyd#gW7t6AB@8PIZq$jHeDXT2Ms6b~x9At@eYa3+X=8wzJYqn@lO
zM`*_iG~t)351so0wJeH2J6S=kFwkOq@Oj+vjwxB7+QS!o(T#sWDtPf8dJ&1y?0|Zm
zHjV?`{ed+WF`NMnBT8F^C7Ga&XP|Id3YsAY?T2+NEy?%I%*!vraghVWDM;;8XoyhH
zDgB^fMo{wtl2Slr299n5G!%$Thne8zb0Bw?fC>dqf7~1HklLA0?~!DO5`G^bnGW><
zk+X|P*RoWAHuZp(MLU7!0^L9t6=Ia&xT1=5+d$0<JP8viH9$Q?R^t<rc`@#v1trgT
zc!+|EQ*d$s6X{KBmWRd1s@kW|{G_--YOfZo;sz}-CUaN@)d4xFiNz(Ljnj!mkn2uB
z=@DyKU^oRD7NBu*lAVItiYeAFEQwD|$t=k)(g&S1oRe6YUkY9w2|9HRypI$#ju;MJ
z^@6<|dte70#(Ds&`)KYq0$BmcMUc=!s+d5T)TS)42zE?1;@C+@!bZ{y&7@=v%|KEe
zs5`EYvW9yFD9M2i+JY2Acx;EbyqM~()oRfAFla8MIJG1`BeS?9zo;@kskEd7K1hrh
zFM<RKQbvRZ3Dq4{1G+i^lm{Te0%AdOB1RJq(!`;<1HyS27=l>gOY#s7zz70pyinf0
zy9{c(fcI^HOCR)5hnMH@Y(=@1^1KWTL1pl5XLziH`jVonM{`q4iZYXn^@~!|K`Y#$
zr>=t5$v{dvu(#1e`U9*q`2Z_T$ck%Na~9?X<Yp~sqygdr5DT2nzy!1yLvjH$6eu0C
zLp{*D5wutg)IP2Rt$~lv%qvSRDoKSdBgPUXkbyJ!t%%fh%tTPelf{=JVAey!0@Tu>
zq~1a7<(mg83PI72Uai1;7m%R@5m@a@J*!zjSr2SAS_;6h8tQAZdhD<?h}b^~UA>W;
znwwu#32K3GgB-F1G);gtGhmjyv~mc@dOUdo$sy1Xq5g0eXaPykMEEiT^qla)E`5dR
zS!=(GiPTCDh>d{;pcM|a@X{ZT)llD)HGhHB`YHh3{|ssQf$wqxwF8_>i^@{nGC?P+
zIihC?j9Lg<5mV6}Q@KH-Snx0a<qqUh5i<pV`gN`qsmZ>jIVG88nW^QV1P#&#Nk^a*
z1vUUoq^Ef{%!=C9W}klWkmoG>X+;jz(4Zvi7!0K56?9MXMNnyC1kXu%pm7EeksfNq
z5K{O!%|5;GpPGo$g9)(ILnV6=9!4ulAge5x?9-<`eQoq-xi)m|9|c_q@IkHm#O}UK
z1BC#HNMGiVmVVJX)jmDS?Un7+%Y9InkX4aD0v_I(LEPeg8MIgbGbs0h!vVDTKNCcx
z!wvySpKN~MO!<dk*hw9rTm49`NMJ>38e}U~d@*>@eP({1ep*p}ZhUTjN-B5;6yyf1
z$q>E<9JV$Mnvuv`fDAp|9@90*%?(gSfVc(ZDsXN96X_Ukf#wKM3(2Oqq$o2l-43i0
zJhA3jQUbo^4Z1fGl<vTi&>eyBi#XtW6ETdz>Ig_3i=-Rk2&5hp$o)3Ci51WhyeXh9
zLLefY?Sq&{z@G^FbkK>ZYa}CKIg8SI8Zmxk11gu@L75sHbck{pQcq(nsU#~&BNfZ2
zo0FnJBix_?Sny)i%G|^X^xg@iSVmfB2kD)VHvmh@u1p-=B1-*_+E4|zlDObC0wVMw
z-iCKj4nU`JDYYFn^b<kNWYAbtUVa|vhI!C&l92ERwVfeH^k5`jDu+LKb`o^v1H@uP
zp2HFTL@sefxfEqOXnp~dSJP8V$}>|+G9Y$=CiWpqlp$%I(;j*@2q(1oCT}ecw8VmS
zi@-B@nQ58HiJ-g0Koz(Qs2mT3XE|`b0};?gPLB}XGb)GmO;80s9d0ptYC~#&P|f0E
z@Ii)iKo(zwTTC#L$r=wrDr6w@%1Qa)vt>bd%z~#@!DUQhPG&mjB6o-q=yC<n$q<<!
z0=jbIEo|E5Ei~Uzu@TtB#=sEt5*`+y1+0*>Ibg@CKo38Gov%XH4qnKzP3SxdgaV~J
zPzw%xTxfh^adB!<iC;=^VQFGfYF=tFG$hW0Hlu)u^mS_fk-pc1?coeanxJabfUeaH
zIs|XgfR@ccj>Ctv>>k0^&%zEmqN?TFL3Ix(_(4<dpblhSNjyrb04)y41QGB%7~l*@
zSWsFEBi8olb1*OjDZmp26KFRLh)9=FarxOKCS(t1Kx`&!2`RK|jJ&Zy1yuWkPDFr|
zh={{H(|is}rC%0=-Pa@tEfJ|(|C|Nc{tq7gTA=1Mh)Az)W&O@9_XBZ?BL%)jDi%Qd
zWI@?L9yBBXT2>12bqL5_5CL5x`X8~%6r-gEn)$Q==Po_)`Y`Z*CGb9Pz2wBa_~gXm
z5@;h1RIef*eRI<eTD#nYjk=MQnjyUd=*bpH$7_JnDR_FUv;gD?h|lo20phb_vLY1X
z26zu8Cn+txI0+Q6pmSQl<%<tU$TP3ZBel3VpeR43G#O)qGNgrrROdju3Dk8FsBMY0
zQI26fG(aeb6wIjz&;(D=PEhj>)WXjMIR;EXSH&{gr#Ihmov|n;3f8U%9e!g2pUu!s
zPtDV{fOb$o;}`K6sW~a}knuf841=ayA!{MwM?&6wcI-=wsvR`PVlmMWY9i=l;k^93
z_@dO*JW#_BdJ_|9`UbM_0-g=eD2YTnUJioxE73bHi8(pYG{3u<i6Ik2q?^`ozdp8m
zv3+{(>d5q(_$4q8Qa-@G3sh8sS`d%`08PDTf(U5t`e}z&R1&!mJvl!Iw7DF#FbkBD
zRY66BF}y(j4=N%+MEXj7Z)?-(*7oUNQ~q5v&y0cwGg-+7T#gVvH38&V<bEj9Eg+C`
zk@7&*hp(>!HAFK(lYd|WQ4&LHWG1M0DVi_<9|@$7dQn^hs4DP)FEJ}<Vq(Yy5$SJd
z|6YB(cCkI20nMjGb^yWUGxS_a(6t<x7vq2euLP92TtIi`KrdXtJ~)Wf9fKBDRC5L5
zdLD2DAfg;nP9fP0^*>n&4yi7Lv~-|N<QbsQ2iXbnH=<C1T*M@3pI-8o`$elY{CZAG
zTRu3)h4EMo^*C886p%cgmY-J=pPQJMm<~B)eIaNY6X<X%h~Gi=D5TK`Ioy}cK7B)p
zosIVdMOYC=R@)270kBqxFsO*>htCUvT49+W0=njm**;zV>ve6N9rDmTM^?p%WH0K-
z1KOpt=zT$q>*tUPeNgED-w={nlA4QWN60x)OBO_=3l`n`f1K0V9?pPfAyED!d5{ui
z72h*ZKyHUOBN1M~F@!+H&fIcPXU-iShlr{ZGHilTQWIGjfkrPt=N~~&j6>`W1s4vW
zwI7iEn~-q}q(fdH^#PIR1(bje(EwF;pq!7Vk%Te$M&wDDNr}a&W+osDK)D*p0z?c!
zJb*FCNLC())TW?TIp}`c)S}E}_>}>mfgB|JLH>nQsF2bXsSyeZKu~*vh%}j)lnnA6
zXka^t2h_>~M~^kAtpFn6`4`TBm~KzbNgS}w8}vK`B~Y3N`2*4lLUi093s#x!;iU+~
zPHKlJV)G~1&mfoJ4N>}T&c>0fh>X_aV(<h9X!ZnaqJfx>)SrU{F_EkD;uDiVZB0<Z
z0r$QUD`LPQ2_m4YB#<h7hz+1*OhmFoJ<b5qS%Xe&VcoI_>YHHosQ;-YZDtJ8_USMN
z#2G}E*yv3!Xe$ilahx}lL4p>eJRqyN0r7ZlW^uB9N`7)Oq_zh&^nwcEbp>erJrhL0
zPYQ=Kpv?^`Ru<Xppy^+b-JlQ$XBRL5?YW$Ujr(4J4G>V#?sK4u3Q<NO@+&;VAw}T@
z%ys6VlOr)MDNHF!EDy-6$W1JOcKtx<7IGA!(~AY(7GJ{b(_su~MGq<_Nlv{;?HA}y
z43sSyiRtN}^aN@e1-ZbhVLWbu#ww9X7&RxMG&)v-R<oJFBNKEoRVIi)%oV~q<WTQZ
zZsl~4m7vonA!R<uHXK$$eM!5C@J7%S1}G6h8qJZQDi1`YS6mFAkfxJmpU!sa=(4F!
zJ7A4w`V?ItCuD*MOm{$n1gRAZYQ(@>u|=r`sfnQN%xMbh;C2MA(;Gp4M$5FY90ZML
zvN~p9J4qP{fLRJJJ0SimHYToZ0lGj8bRV#Oa$+%fckwS~=+$y=rFqGqiK>v);u2>P
zF18`pVo-o$t$ZNm5mM6(>O-=^7pWLQ>G)=Wno+Yr!w%pg8C-CH8u96n>*1B`(<ApD
zetTg<1}q&@)ppRSoI#+*IwV7bf&!AZAPWNj+NGx`TCw!8P&CMd%j;i3b8?8e7f=%&
zGLQ&4Aye2sUEb4^-+6U1wEIa_pM$F0AW+2u@j0SH0K4!x{owzwP07ju&>=_i#;K9K
z4jSJq2Ccpa?`c{HJEQ^B9!kv2D~?YpO3X{nfRttk*FfUotsV4MW<6Lc0M$2?RAs2H
z*$Q$EXxstKHSxu%1)$wcsX3MC!Gz&3XyzbmdlO19W$Kp{WrBBJ73Ak6mVgU~r631_
zHmAnB_`AiY7MEn^CYIzE#ph<`#k)en43Ty*N+xKzNE@etk}cNa1H&265CSa&KwbF*
znj{HGEGkY7N-ZwU!I-Cj^yHCN??C;7VGvelVCaTAgVI4H*ikf$pwVz8cmalp21xPw
zz%KpB+9rt?x71-tms%DRID7`lVyKUCgcrEnMRzt*sZUa9Lp+V78)`RI%dUx_9b6#a
zgrruKI2NTQV$T6c{X?i9sOk#PTshVh2TAxywnM{!ycQS<i&?-ii6}dbv-Ww}m8sjO
zpR{n><h~*kmH-K?X$4oocuax%hN@u+S_u$@Xa#`QRAz!E$I><285boSGqF!UDtGHR
zS6w#D4^*`sts(|D>%nah*GqX{4CM{%(|tep1(&F#)1e{;6{nC^K*wdzZM)_O*{3h!
zSb6(qxF4({rnFCrbeMf0XaNT3A`IwFlEsN-scD(%&^tyUxeAdIF!DAuSCO?o2C2$J
zUPp>{!aW|#p~;ZQ6YG)_b5cRa!Grp$pgtqgQ3eP*Fh;AP9wfsKQ1U@49Proy^#@s7
z4IrJCg8ZDyy!_nE#2o#c#PrhCq9Smc0W<&#aRRtpK$P%kyZL2dd5SttK#onEMI?q3
zprJt4ASqH(LZ4a+0o4?srIc>@c_jfw`30#(C7G!h$DLr5*wBclwsSyP4H6~bLKYDv
z7!H7j2N@|(0_0`TqAJiX^qf?W5MLi>XyS)tXoRh}GBg>sqNR5{c0hebX#*a0E)6^w
zt_K=^LQCtAfPIO6za_=J8C2`hrt%QJ$FLsidrA{lNfEf00Kc*cRBJ#I7MlMR?KV8#
z@4#S>emV;^9DtnD5e`60*%%Ihh5%Xd4=L}!6HMS0Uj;>(dEkBAYe0z}w9v*UKQG-m
zKer$!wF0!II}xKZfvb$Cu6sbS0ErK@;J~mR8XQC}V?Z4aMJo8<*KX8+LIPCIfY;i>
zySb?;@db$`(C#&8`WUjf1kyQTu}|+*3pk?G<_oI|sOFrhpmhz1VFo-lLj!`Wct8pW
zxR1fZ4EmscGpIcWT44$~@(NN2mV+)~0TJmJ*Ixg!U_pp|diNo{F1a5OuppqWLqOXs
zf~JCciD(G|qYQ@3@KD$G6`)vXgwK!SX^qgr_6eXNJ^@hS4E8&mKn&DF7P&A&o7dF!
zdlRUQ3mRbnXNZi{%=C;B$Y4$uC=Gy!^jgIyd;a$a+rt^O2nGpIFeGD%1V|EiV25_C
z9(9AE8WaqmoB$36q&*mj=zuIX<gibNbbAzF9S+cfU?RralJg6|+kLA+dp*E5fkfgn
z^NKMFVMw)j6Sm6tJ#4y-M4Ldn8-u_LhrpgduXd5lhZZiN83!VKQjnRPoda$|gDy@3
z-+oh&T2!1_T#}ks5}yg(#g3Tqhpfy)S}PB2K9X!5FDQw4!;=Ue8=xK{Yas*ZG6K*F
zK>d;`P<QA$Xc+n?$Wl<P1s?te6Nou*NOYZq#S%3v1uewIT0ufG!vWY*?UT^i&|)HY
z9iiNiu^Z&GMEDFLqS%8}drx4~z3@{QK*#J6kv?+r)6+pq8Ic=6*FdQrG`AgJlnOfd
zCe<fDIU7>WAkrn=a}Wo>E;uA>zdKT$hcPb;os>NVN=}H%7u_8X?BKg8pvj3^Lz60?
z<5xgY3~7UT=3$fokdQzcCxm7hYPo>G;3$&CP_L7>o))~F5xxvRzPKPYxughkJ~3$c
z3Vdj7ykEX^KIr_5^i(V-@nbXyp#ed4_uy=yW4HhsBD9$<03FIx0vZ<xDa!QBD@iQ^
zPjX<Mp$JJJNDJSf;X)f%;S3xMXFvmo(lM{x#F7lyGSo28vBIDlEI1<*db&G$Rz+Hf
z1PupjIsg<W_zPo5FdTpt#*_|0BApCz6LgCnC_T6(<|S7;78T`}2Y}9vfHcz);Q%W!
zp=*H!Va;?ZIt6DUz_1&d2#8#7R$7#klbK`$s%t^pQA$9g+d-g<IdC<MkXBzoW1DoF
zK;;M4IE7W_XzLb`)@6Ve>A}}!6qFP|PMZh!y>TtdKvb=eQ&^EUa6x?lx*?W`sui_^
z3$_z{3^@4cgS^sQ(4taMK!$^6-Q3{2L-5!SjVlW52QAY;ukOKd2ud*M>2#&<oICFW
zRP58IRyRg2^k|2rQ?l|alK+w08lYT(HEuC1hkBm8nI~}NhHu$oEk6ST_}1T|%;ceX
z#X>O!?Y;M){uF4K)u}WsEfsW(1*EN0g6jftq$US6OHg1tXqN)k6aXoxk?ew|01A>n
zc(@I8@(5D)vjDYrK~+{sVm9=w4iFEr{18&Y+(ew%3-vEqYe<orxrie(B0!7ZUV|F;
za9eR$4fQ@*N4!CDAJXg!hzaUAfrpm-K|uhTgbv8hsl;|3Dn`2s8VodW3#fX)njA3P
z01XeSjqibiJqWyo2HbfBo%NatBGQkXzPxkM6a)M8SkV<nA0$@6%0aRwnUE3zWZzPJ
z2ILrfP*nhG0YFX{NJ&jgEX^s2ht6ge6y-y29Dt`1=;;D~?V#>+fDSoP+U)=xatCtD
zE6{i&Xns8&JoW)fvGE{hK=<~8_LyaYi1bOP>pk|@>e{D2nVp;G<6i+AYoXL3uvIA*
zpdJ{g3j!&X0#b{TQ-?q=jI1#)*m+5y69kI#5_5|6p~q!4fcpQtK#c@&;zXnZNGW*%
zamEBRYf{&G(Bj{qn^?v@F|3E?N>H1P<Tet__sK>1#l`WcdpaXQl@w?w&L=S`HOJd6
zKL<K?1!_Sc5Aq;Y7|@`gx?4b@kF|_~ga(oeprJwTdREZ=p^);x5q!i(P-13rYB7#W
zx-kk!sQ1aW92BrveTiWa)R$CU$YKE+=Lb0f(kex?iXmbA!7g1xa|6RN9vxU|Olg%2
zDUCrTAK}vlVOG<^<A@bJ;G7O7K&2ma43oh={jmFyDISZ`VIHSyup?G4ff6}*^$wVT
zPH<eYOTWB}H*5b^nk?i+ET9Hk4$AG2vjgri7~enW|Hm#}BmRODlUq8hicx1^VDK%?
zP0H7I&PgmT&P>ZpEh^Uc%})WbQositf=^5W#VX?Ta&VA>k`|=c$gp1a?v$Q~eR}Ag
ziIq?I&q2c#sTT<v3$=-l&&(~zFDi+Thu-Ue#{_7)AZMT#bc1S89cXq9>`c&|shJ=m
zeSzc=_Od_b_UU_%9<JuoSq^n3S)<9NpwZL>c%w5KwBrIqr2kp@@se+nf_=KzxlY?H
z{ie`11vwr;yVDtQK9+OmG@E(T^zG9XHhBoYXIKu4Oi<&8<SGNwu+7O#(vQ!~hp!=+
z1!~ZOV$CNrFEzLzC$j`+Uj}nHii++4^{TNJp^!oh$!=(hA**YR)TBfni^~Dkn4pE$
zzR&?Wr^Mv!)Vvhu#LS|6NU4fQ!Vm``)m+fZgPIN^FgAx|IW#mVEz&`4R>)30cF@HZ
zprr)CmAR0!)Dm-i^3y@<ib`+|*doOaG%%>=9s;*pAXyC!3bLAWNC~63q$o8pw;(4U
zyvAfFD1U$|m0-AVa4OLg8`O0RsHn%2M=(+adbwPX4{9QV#&)2a2WNsxGZ2w}@bH%W
zckAQq;S6Xz61j06bd66@W-@5838(@AE%*Q*ij-NLmYI_ZnGgfH9@KGUfL@->3%f;|
z7g}_ZXBX%^DX?9s6`92)&@LC~zV}QJkuJbCubO{)j6IwIndB$WF3=LWpeA_R9<;;|
za#|yN)h>(yO^cvTF%boICitcmP^dwo7hInqN*w?4c@^ix9PHCKU8&lCvCkV;pMji1
zgy|)zxdotT0L2Vw><VHBI6Cmy0rde{MLbe&%1tcFPAvkDo!kdyB~VoZT9%WXSOC86
z5mLH>rYkc+1bl81&VVLFs*PQstxW=1io<4T2vBNsa#DVNHYn^XL3@EgArA2~qMZ&I
zI>Q|Qr?m7yU9M;cYRO!OCtf_(Q_17Spyk&fkADC~JZR9&CqF$HBnSxrP*6bHm5?eG
zsVJvXq{CeRvI=Xm$FLddcS@5z(sKKJkncekd6gyRWP(;<#HZ!tCzizL7sdPK=RwC&
z5wU^MWrT(WrOgZEsRW$s?J+Ee1_z}n5p{hY3+QGBP?SK@0cgY|6GR~PeK<>i?of+{
z6?BwZkD3u|LH%XW<Orl*0=0EAK?L+PaZ&qp=BXdw8|&FX>m};iPGHsWNjvBXBA=m0
zpi<X%(1i>^dZ1-L;PM~jZHV7t6<4~%o3`9;BWs%Yy$iHV71X-~)c}w?LO|2uhVk(9
zfgUAq{J?b|ZKEU{lpsJSrJ&oMXcTIowNe;%5QjCa|3y}uz=rHF1}xh^1q67lqHlg#
zYKkZ5WG6_@0Ob)#35k(0p``+KT>`QXYcYvoJ+xdPa;;l_0q8nlP*ngrfdo7io>i2Z
z6Q2)V_yF2R02#i3m&cHuws&Ex&q%fi)PzFv3LYDvULh|e!Lz0Cr9g-~1H?d60-!3-
zF)t;xs5n0lJwo9gg)u0KP+ZOcoxzM03ZVFfq$ilI(4jK;8V{;9cJe?|^#?)YCZL$e
zGy?TBKm>FyM%F(4UG!Sv>5o-mWjv*GAfSQ-l)~6p7#M=U7K2MTkey)LF)fBfJW?kK
zlm~4<;{tlcg+;LQWYNP6sh0(fG9uGvQDR<7elEy~pk4u<SV6J@;>2P~mkJi(xcwM3
z7Y(^!7#v9muflR4bh*e&ST>}ULqN@~pmuoM6puroAwpJaft}@*Sd^HP1G!CO4`}2E
zR5-+gca=bLDPjdKq-BnDKs_|dsbxL*Br4FfKBVTsV=>g<WR+4#-7(ab5TY%D7XOg-
zJo5JG8;x7fWxrVj%cRt`-V@X#0mU`M_aGLebi%M6;(Mg5s>;B?U;{pBN)K`-vtB%e
zh74>b=jX?l<R=#wK?gbzM&nwwO;*7Nc7Q&_V*TWNa4#O`#R?cULp)Us+L%g2FCKn$
zOmS%@<ah|sk^=DkDWEfBf<dB?<cshuMkx<XzGPboG8#|Gh17$CdXB0yE>A$sN6-ic
zsQH+inU|ScnwyeZjF{sFPlJF6Xtjp9kd&(KK|4ktWHIDmC`h;?*$xd2N8(Ni!nPbP
zHxaxd4pe=B4%{MXCo^@d1@#q>;vP?r73y11i<*dpjp|#ZvI@D@0<9MYUugk4uLZoo
zIv_tMu_&Y{F%Nva4EB_d6a&zdPZKv0$Q4MgfQAfN)hK5F0a~9S?%V^7x`Qw4_R3H4
z$xL$2&&kOw&dkpXPb|vI%uB}}NJz~MXduzbd7w%LDFuOo4N~4i@(Yqfpy5R6m`8e1
zW(sI^J}B2L097`B;SExR<?#DIbP{uW=6*1O)peAvS%Orx#idE0sRZx<DCp8Q@V+A0
zQl-SA#9ZizH6Hgs0|V5XKs`YRG^iAiU!GbNpORUYS_Ix{fc-EXczX`UfW|b2nOLI-
zrU}}Fz6)CrPS$D>qzn$-!4eNTuofIZ>p?60vOvv1P*TZE18vm<5zyn%Bka@Tr<G;t
znZvhhQr!hDAQyn*0@5S_Sq^pvrVF64LggivDWF{ept;b*yh=!R{S3O43`C^oKG~)D
zbZe4*`sdyk^GbW$U<nnJ>q#zWA+y2YRi3$t$r*{@!nFjnbPCkAh4}sj$Z8Ogo^<n!
z;>y+}`*agQGn>6ZZB!Z^f)D$EWB)JcMDQF?4h8qzK(PQR9w4polXlSS8$Lou#>k3%
zSSbo#(WwtUD;2)(VI#=<|3E9pzzzU)3m~Hs5C^=pOTT(1ip%3SKh*o6F+`FB9_9eZ
z+-k8t*mjhAs(yf)IiS91d_hqfC~Ie?U`(MwO3VkaAYp`!Op#S|BV|eCIv07w7&O&|
z)aF3M4aUe2v}B=`LqIlTEu=6U0?nY*?Xo=NW?(1*9XSBHVmvRgEHgc^BsCs%IU^*u
zf}96Qw2&A=Y7s*NhPv*-*@eQeo=U-inq{RygDoIGLwpaKi_HWP>2rDdCWSwjuuq><
zQE3%E#R*ncQP=l>Ky|tTd^8*65ahtW0NYRc6Ix<W*Y;N++rx2|SMa6qY|!F@ta2JD
zZ$j>$0kvho$2fv~3n`~TEQr4$dzPNqr9U^}-eSFkgGx3h7L{ZcgO@{q7yN-v6o%Lg
zS~HUgBGTbQ<sOTf)NdcKg%wDkMe0P9kr^c=1v#1E%Z!*<85l}H^}k0*NI)<oVIh17
zN!LhCMQFAn-5yXqhqWZZumX~GkVYRsXNSV~N);uRrRET^TWSYr2oyx5Lv9L^vxhUF
zejqD(LMn35vM%u9D8;EI`32zW6m<Vl&_{UYz|&uYEQug%1OzF0!Ve>{1Wl*J!Y!Tw
z@;8WpwxJ~K;SB0n3>p~$`?wZVe{Y7@-=G!wnIHmj!6R%824XRl+ivNgwxcts^$Ko2
zfObMdZk>YXN*DuTJ!sIAh^&FyKZZm%xZQ@dGe;S8j4-Ilkd~8Jl9!)Xm0FY!$)R|1
z4#XAIO>~B!GVudE?!f^8BA^4wNNXG*nW~u5E*@yO9yav<2DEey6zpNnsh~~9sYI`P
zr=}}NUb{!tF{x>w2mnPoq~HW~B_OMkA#K>3u+t9&U^OzOwGz(7;CQTt#ywd_N<ara
z3ZNHqYyu77gVyJzq~@fSq{gS^=cK?k;~|FAV>=zb@+*1Trx&PPNC<vj2=hEOT;K^R
z?(V^x0eCEhdK=W1hAhPflb{M6R6Le|Mm8ZM1J3!yCAo<upbeJLQ|l0>=A3!m;cWKR
zK3%)zYQ&*i|6yLiV(JZ0c?jyoK#Br9W<XqqRKpP(UoS2KkFO_VjITqI8B#un)@@|f
zE~w+{1n#uOuo>d1VzS~Gv`z=qMTDM~0y_Oa=sKua12s&*Z7nc?SnCh59e&3oH7o^{
zWLO(0kZR-#taW=6HXcK1RxU2c%u5B=q%EM7^BeA8PzerMx(T~nA-yP2SE=hHH!Ss$
zRhJ<(9+1uk0Ij$U0%ZtDs>NeBG=f15Ig;zq08bx%&)mfHRJY8W63EJD(DfwX`4r#8
z3WzAA3<Z^@kl4m(8A1~`wOjxy0kFn6hQ&~?QySxlhW$EFISSg}4w_p@hn;q018Td0
z2<YX=zwO`*O1zE~@vyqv6;z6X7GpwU9aM>Cf(ZDSBb)(gwooy_fvOOsL-9d@kO?9X
znH!ejAXbxAlp%Q;-cUwciV9l)n+YPIUPZbM8yek|4oO4mYUFLSpktuGw=jA@w$@@j
zg##n?LF1nK&H>HtkdgtAYAlrVD{>Pni0F}lra?171hjR^X%8J?G=vpvl;$tQwh9|i
z`gVu+O+d9WWNZ<ZwxCwSj>97>k)hU?kojn|%jXfrDXcG)4m+$9lDf$%%aBqxEJLGR
z_6$1YEfYk*{h6+1_N?@XqBd-#fV|;c@X9C991rLq<ou$NjQsTcJjhv0b+C1HU}4ZU
zenT7`0?bi5syikMRHK5L#E|k0y-q=L0W`0J*5(jVt6@8p3^a~i0_qLMCugK4XZxg<
zrRG4YGQ=PNq`iXKPNTruc=%u-IFaG83+hE8_sW58Wh?+)6ai`}gZk{?N;<zFH7~s=
zzZANJFc(x)fr#`?kb?~E)7PeR9M`E<g5_m$Z0iQ~rYu11Ah4G}=4XNkOiLhMLMo3y
zbs6%F<)xsWs$P0(NorA1eo_{+;S9<V$l2l}Y()PfY{?Z_6&?0b0s+vvYZuUlNw9A~
z<93k2J9w)O#()$BRJ6K;i-7^WO9v7FkW7GZ0HlUNnka`jfUIsg#P2zod5Jmc`Vb0~
za6maaXeqn^Mx;)N-Iy7W$Pq=Pdj&yz(1Spm9Uz$vv|s|VxEfN%e}OgFIblf>RA&*9
z$D#LKP5~7m!f+qpF&>f_i^;0JVDnQYMX9O!AOaM4k3g+M&~&p)YBG35K}b<*Dq<rs
z#J_mTG-!i?dJY05U_1>4%-AGyktSlLvNWjn09Df|scG<q%H^O^21KO8Zz+Z`pk5@|
z9?**DpwsY739=E=Fu+KckUT-M1@a(|fZDTpiMgpDpMb(H6I4Bb30$pQlI;QQFb~=V
zAO1vm1f!}U(<7j+H&UE~Hf3jm2<T!j275RIn*PaZLm@R~K!X6_?(kJm@&|R!Jo8dg
zD_jywQXvPdVA(2-;aO<9pqg7i+li1u0g>r2Y=(vc<rg5PaWgOkS%P8~6b0a}FqnXb
z_XRt68iIP3aw|d27Z7HMkI&30&d5wFiI0a=8VC<VG6<6GP!E$eqKy>okcB&-MJb@7
z?-OWf9kjF$)G<y3wKVCr`xn(Am7wO8E4(d&9u7!TV$g6PE6G7>&$P4>{fyL{0?5D!
zsK9_EIkfnP)N7b`bWzZ70NISZ@BrjZh}|GpfRi1VfF?O4yP>|OJneCUPEi7_jPlRR
zsq`n|^j6BPwgOf0*m?=LRyWuYx1toh&INiAF#4f}pk6$92p}XgH#NVs#I>jhbDuwk
z{m`6F1^Yo24b~XOuoD`?WaV#2*BHxA9mEzHw8Wj}b5JV%vY~x?ZON-s>6LM?MjKhv
zHE7ny7v)2a7Xa1ESUrznJJj>!tvQ19l1maxiuL0`w;g8YmFAZg$ERc#m*jzKxND#T
zSwYh%1)%kWdHF@T@#(2VZ(5>}qd*aYHE<v$KV~D(l(^ggxu9MjxtN&`Y9xUM+zTq>
z^OLetlQ9Z;jMNCt0p!~WD$uZc55p>G+>_NELaNw6Lo=`=8bIAsP^XLmbempCegOQM
z5Kw^&DPbX@`~Ws%_yE=;rKU4LWgXTqz_1(=21p%jP`LqLgb^Q~T9H{29}gMzgPaGi
zmy@5It*!ux&SGeX8+3M0CWuIf?|6kVpb<*sI03|t%slW#*q}8?K|kPiGst`#mO#8w
zOjZqv6qQKxB~wAIGe(fzp!@=kIxvy$b9?`$OG`}b({qnyc4+<agcV>^v>RviVb~4z
zH)!~Vh#oJ_H4kVfl1>FR&Okkl#G>@v#0p41l+%ZiArnNTN1slyn0J1QeR}8t+w=3y
zPQXF{%UtOyPyqmH<|Sw3=NG4jWu}%x+M9@3)5lWtPyVZvvQK}ie$`I4UlY2x3X5SP
zpkbp2@SgfsE(V575RtyK>u5fgcB*|ko9Fzni}F>tjZg#`!NAPG0C9zxBO^m5h)B0L
zH%VPCzr{ZNkbq^aZTxYHhEE}_J<!l7^eVZG)S_JI_9C1&$w3y(|Aw8TI1o3<VK@Pr
zyeJ*KhMnkn4zvbMn1O)-w9GR%F)P0adOIS##|K(h1X)}Jxw4wcKHaqZ%l?>dHdrDf
ztND$T#o>onfJRM%q(K=RRAYeWVZj7+n&>X9`}G96%8^=ua1}IVAp^G;baG23h=AJr
z*Df7)gAX+H&55giQ8PE_NTPzuOmG()v~<)rKczG$HNH5rB0e|2EVVcua*#SCYa=Qr
zNY~>EM&(3_8$hOGt&A`%gLoLJG6D@2!YiZ1qV(eYBI;agPvrvB7PQ8`5neKZd<>~9
zaE%_2m5L#)U0nMj(W)uP?vlTD=?{1_Lms4P!}1R(*^^vVpx6xS!k+^5s*YgsG=|lX
zc%^v1HFRT3u|AX$Usz%S8cP6e#KXFZUe2&8Z=trLefsMM3HeDLn_yu8T4_si7$B7)
zQ2S9F0=*^%6uc0(fJ+rbO2BXnG*HO8JP=aBATL)esVso3F#%Z~)CL;(L`x6{CX4B<
z-oR;}KJ$~}3aPzZu)v_Y13<|F)PjLjv4{}BZ~!y}Xw=QQ3Yr@MH9c}t%PQk@hGNGH
zG`T`@I)%+Nz;?nQ_s2kK5>gbSB@W1NyPJKwu-oQMwI`}!i32qH3|>x0()p$!Yry3n
zn1BxIBN+~fc%*V3bc{E=oKMTlOM!GaO7k*7C!C;nHbASGGC>4%#WM$N=`#n+*+h0W
zz$f>?x^BNg>2@`|I0N-sk=Getfwc!<7fpe3I?0iPREQ+!=j209a04%cL*y9H(TtfO
zB0W~(rR&=|R(m)D5^%*to=^uqHoYhn+?9#}`R)>^)d7xqP?4DlBGMtZ2Xnzj`QZy#
zK~tzicn@R;s0=m&HLQ=qmurHI#$i6x50PZGYM>XmBM$q4u3H5ci1CQK=6pa?3!n`}
zQJDpPrMU%_p?MI=BJ>syq(_W2%nR+W($;;T*oI_8w7i1h97u2><rR>xKpR%`3-odm
zvs1x4hoHT1^x0QTUx9k}M8qnniZ=t-t!NI#V*|vY#k9Fo>LaKh37VP(ou>=hppN&x
zD~whbG)`&bHnbo@RCpN9fCdrhP&OhW0j)ugE9n;?ox6f?3OJV|oD#-AquuAHgMGS;
zip$R?u_RbQNnNKPS2Zc10te(C^kx8tQ=p*&PS+`^$@xWzCHY0V>8W|TCU)Qo1d>tE
ze1tKY2aRJ2#*yPe+kN0J0F`PhK%Q6*FVzs?iD5g`6BP6-K$d5MCPL#uE7Y^W<Htup
z#TaNH#u>aSfY`E&%B}!;8xjY^6;|Y}kU**$5$k97f;v{mL6tH%`jMvBcfXt_`zYT&
zy=WQdZU18(umnwMt&Fgm2Q)Fb5>#V?eT~OzXxx)Efr9Moiqv9#@UDGO(mM;<5(^#|
zNG*wnZHa{>fc1<F44EJz{ekwbue0P#?BNV(CLpWfh~yM#2M6pJa6-HdTI19WiU)9D
zfGo%10!UyW)mNyy`XKjtLYjAwv0kj>$xWa$3P40U^Jb@s@1C33!x>O7f{Z1i5DhF%
zEd}2`n+?j!li^tzv~VyJL?9MULgt2GD@8$bhD4Z;J_QKdK%)T~<%z>$9c)=B+WJr;
zTda^EgAR|Q9iopIQiUJ%1Y<y!h7j3oL_XLUbl7uHFg)JDkq;u$F$zGkt+)${m|9SW
z6=X2Dzy}k!tRN?(5SQ12?LcqEAWeBeyg}AjAG9foTp#y<TE%7X-~**MNTP!bksvKa
zhuBMw-`YS$BxsEsq@qRG0f~I1+XKnBLk;AQCU|N_T%`c9gWo<K#(>yCt?&cwvkiKI
zGyML-2Fw4#_Ue#jF;c+?>kNXDJor2$&@R~cqSWM)#Juzzj8lc-(a6}byGm3NK4wqF
zxu9rJL2?cr^oXR3VK+v}3mW`@9K4v5pAPLJ)PR=Sfr#`+79X;98XMcg8PI~2(jbK0
znuT_2Fv8*RVP6;nl4h_>_@si022jNhN!k@4w}S}i8e}8bVq_!egb$Iu8=OZ*BHRdR
zy5F>eZef7$1*2pfyc^Vq0s9hk9<5_ZNl|7}X$dq<f!bJ*Vo#ahMJ)4?EbJJ6c$%Wb
zYLMwzTW}bbK@t(Mce<dgA-4q;61t$w3W~B!(1=4OGa~~-`VO0GJv>F>_USA4eeM?e
zG9A`dBeJwZu>@`DIH+x&32N=6Ti^HZot_tDpU!`I-_HG2J<udUjva?USuPCT@&kJW
zM5KQ%mHc;^*974aNN6E7cEDLKIWZ?EF)1e%n&o6c=ly_)^nGm)cJ$2FvWGLES&oXu
zg)Aon19;gHXo@W<zXEa_0M=R$X><UR13@Pr64B%#)Gtjd%}XxMOb53cL5Vg9v=a=H
zgAo}FW1Iux1WFe{AooD6K%FO$&&!JvG43wJVLxcxisT?bsy~v!C((j-n+1WkCP93C
z5>#%1h;;ZhQZNR@*JSlsk!;ORODj$-Ny@JPbsSEE3Qf>aCJ@U(y+`Dg&ls^zy&h95
zs3o!mQ~-cNJ@W-K0|S^yhm8D7+QS(TZ&T0am!KTW3eT|}pa2IExB{GdHX}NyU_W02
zb<IEo{HPc>15#E{bBW4A(CRW!SsR|3l8#Z2V5D&B7dD7BGGNbxY8gl!hG93<^OR<9
z<dX+Md+9-wT?~*;F~V|4!|1LZ`duf~wEPe#lYy*+WHJy7;&Tkkp*|;a0~l!OAZUYp
zu|8-&6J&Zo5!7w~Et7Z2EG_{{x@G31lC++b0^33JAXu9Y7(GC!PbnP+0S{=W<rn3G
zE09O*3=BxIj9!5}u!EK<@XbHewZ5N&fuRI+wL(%(YF<ie3gSwI__W02lKi4dNCLp)
zE@-e2*$_eL;5&ehy9W(C#e-_|l*H6rTqoWmmFm!JPL6%;pkfr1mLZ-3u^_1$SAHgI
zKpi$gi7|--8#}QCRotLJcP=e1$<NKK0yU-rGLy4&GV{{G13Ktg9>aamK%kBLKrw<f
zOJFzy8uw(SN$`}GesX?pL1Kx1d}bbaj|8ZKZv~Y-wV*X@pt3&`baytGfUeyav4=CD
zp+H^h`#{#;g4gnRtcUuZ$dS5&%9Q+MkpDnu?UaBv3V4M0`UIsG5Ito_szsn#XOiNP
ztkV}!*3LjSPU^?!=cR&HgQr4{e*^7(h1{~61X^61ni8LylbV~FS5lr?46WDE3mD7{
zPb<fPY(@$t5H}M<K({F&IRp~lNWCG@Mojp$etA)5Nh)OEH$F38&#AH`wb;`ieQqBy
zd<E%(OWH%b;F7RjBaxka$aF6#J>3B%*kz#n0&b;%&fUla5$We1M!ikc7qN#kAk9*e
zE!Y4muR!%=abj6&d|rMY^juNIfUG#z4L-LwpAnZ3L4vB7taGzr<tMl)pr4ysl9*at
zl9>ydH3L_fp#26Vpt;g`&$Kj;#A47$gG*|0a#3ah&Z{PoVi8(s(8Ot=8U-mWfw=f9
z7$<U82|x#8GZKq4pd;nrCKPB>NW5>QM`CdXdi{aXmVpKWm8=J)JFMQuuos#xsMkNK
z1)X{eN)e!acF8%J1@WLEPKf_OxfnSsBMpN?{ZCfSfz&ZY-D-Cgbdp~mJR{<<9uf>l
zB?%}3;U!5i=ssOYNm5)=gkFLaW~^q&1QF>hAr?&`r??#845+Ut-%vK4hk+pov}}i9
zLkUu(kk$1-&F5*E6_5pfvY_q&Xc#dbe0oECS!xmJFnWkf(8CldUqf6%)=&ea3L`i@
z4w|h*UdntIHave9x`dj@HfaIqLRC;Q1(n|5qj*=qn;W1yI}=18jz)!@kqYr3Xc>Wf
zYKd=YNls>2W@@<$`1Dgy(;!F%K0uI_x|$&qM5O1gIbx%IlFI?kfS5v7gdrt-<Z-Aw
zpkWcvXgDN_+CWpAAR>KdPRTj{LK}NH17bJj$+L);fgvaW?pyRkft1gwWg}>|6sd$m
zw-MtA3Jc=O_0+uN{FKbR^kS4<WuRMT!N>Z6nox;3nN_LrFolpHMz2>eJWSPG02<i~
zDuB<>fY!VqpACt07z`w|LCz;)j4?SsCnq(zBr_kfl~5Hlx^4{52cQA~a!D>^+8XJE
z6NoL?^8qO5fQN_EQ%fAn6MYhsQgfhJB_OW!!f35POe8B$AQc_3k_wco!Q;Z9Xo5r-
z`XD7zgh8r;Vo-kowCXK0FCDTd2;5$VT>}@NU7VO(K+j7W$XYcG2@LoG{@jA1)Z*gQ
z6i|Q^gBq#tKy@RyQ->u3!48puR$|n40O-0b$UqCIwE}JyfC*?b{{mt-4H_dfSVV6M
zT|^IRb_9X$<AJnOzzzZt>F4#mF2uhGv`=5IBzjD&sR!C8Kx!y}_PW?4m1gFY=w{|A
z<ffKn<fka4R-`7!+d+~V$Q;N%9}L4F9z!w=G{gvRbtLDP=0P`~;8>RoTJn|&BGSD!
zSj_o(mB${=fJPcw+dh!075Jsch<gCR{sCEv!)A!5Q1cHcbwNsNaAE`HLmbU;sJk(8
zA4E6UZali7c2i!VT?O6o3~F9L5(MIeqY3*wFE7a8vriZPkgdXbNEDhNiYd1eJg^TM
z>4790JXS(|NmksVln6<XqgX)!3JG7d*nyos51rNEfq52`U5Kbq(B|zRo7cf@NR-<3
zET|*`Rbmj2gIohi92nL^Jx<n~8Dw+|>G~~2P%8;!86=9)YXqc*EHq70$68Q784}pw
z#Ei#UsBb|n9U=l7*Sav!BAODARRQ_=(9`2Uo`!@lWXJ<))dAG26xs<s3kh3{V%P`u
zBU#NOq+$wXKGF#^Is)o@hXo`iXTuJWMtB-y&=l%vsyc!nSE9zS9U26bPRS$PRK5cg
z0H7{7{HF4xr2Gm<G6$8!$kprtJLquNNopMnfZP+oc|kcO6p$>3h5}h73sOP=uReg^
zum@T;7SsY->4#RZLDJz#SQFzr%<t5-9+c#evO0*1ob8aThx(p^5*^%Ejt7m<ff{$2
zpwtJl4?WUhE1;l_EXvM;fYc?>5$2r4g2d!(kmXUJjsqh1BT^iO<xp>vmGmLyIe156
z5ois63V0||3zYOhjfbMtWbi#2kN^M$1tjR<2@l4AW_~KV0o3Ke8tpJEp@9#-teLEW
z2g(2GsU<m?xtS&45C8={#CEha4{6S`+e1%&h9CJ&Rxb<5cI5mH3TKGrpc)4p;$Q+g
zGmd0AH0sIPIDk4%g={}g&qMmGj`rzmR_bu=)>s8=$v6-<EP&RMf$od}ZCU~)c$~h*
zuodcSN{boP8EYj@lo@Mqw1Ws}!T!@OT}qs<C52HL=4t9$9|p3%89o{TvN;n(U|J9L
zJ&}WQX+?>M!3fY4Nstbfk{mvC06W6~nxDzB1e9j6rd|w7pgtio^`JM+lJoQOAOkI+
z)Uy{<WPt`xoZ-UZMTrFk#5Y{1;tZT^Hb|+3)EtI-p3*4;#F^SHpwnMK4K|<rWYDA%
zdYKKcH((5C98=E)pv|vXV;*KBbcXv1Y}q%}<~}ov^K;5l^>c~~!8bR8#>5~F0c9a@
z*$F0~1?Y1-=(_j!uoesG-ZCP32WSNta#svme?uLDT$<u6*C161l4GDrB7~CC6k~Np
zJY*>XD39PQ*CB}$X|xxbG^pzmq=`M8Wdw#xpkbm*T$rS!CTHeC&O2WKnr{J}O9MKj
zH#afIwIVsS05mL%o-Z&a^Prw5-O3VBdl3}K=v{COE1<rjU@#DVb{~uZieS)uO9{x&
z@u2;-!KsNw$r&UqIHR_MKm{@+BZ6WaoCm=K<|ZLXh#(E(fwbBbR~CZ~2`@@b0}tat
zS7w7+rkNlD+62Trl#MQfoM113oPonF5HC@4<)|8HRb4oIv>8;gK!OvJZIEggXc{J~
zJ&x27L^@cF0c5uud~ynp-B5p%)gp$j0RT6Qk<JAGWhzKILMzZA8zFS<p-0f`!y0E~
zjWR(kN8BI|vJ7G^IIa<X#;_LZXBwS}Vh&mj02)V4DoRXA%P&g7eJToO8laJzaE1*=
zT7-rTrR@yFg`bZ><?MdYoEpdx;HhgcfoLm3*6G7XTB&FC9+1_D73-il!C^JT*GL5#
z%36q=tkPoWtOS;|5TJFxnIHn;Bgk0GPgsM6iW$2JG)NCxi4Uo@K-S{08{(y6(DE=M
z8cWH=WuQ7~8K@oxHA{o@GSkwCZ;q2@3n+hL&8fIjBd9AxgeQtKV1q%IKrs*U45a;l
zSR__0vd;Zsm5Y7)+byO_671=)B8aR@&!9yRY)LI>3r^52P=^iV!Av}sLgR+gU4XD7
z{~ADvVgo#jAxb{Ti9>9#^Zm?VzNMDMg}Bx-V^|FJF=#mt5fKSG=Qp*Y1bVA3cw8B@
zwHy+e=+g%e?9%J!7d~|G5QBM-tjY|jB1P^Hpe-!HV>#4|lonrzKB7BlDHmwU7~*fl
zpdw@+@O!)TH-6>wD$c3F{7tQBZ3Imd3Bp?scr1qc8FUUe5z&g;BSQ*s%*(ynKp7a6
zkRg5t6-me!n?13EUX1$*R>o4_30vV#0B2`Vs|K><1X4;}Kwo%8eHSc2bphBJAOc#^
z+=PwgKZkYEDXnPAGgH!2A!Dj@Ko`=2YHXjx;^KTrEP&#JwoO<h2Y_~Yl2l2MHN*#Q
zyMiZA^ouhR3sS+Sae!*VEufkZl-tXSOA?c_Aprmie8{>6$cbLRU}tzLz_K7&jzG!@
zsKcCf91ILW8dx$Sq=E6$F8#Ww@uAzz0?>`P)V19LWIO0kC`elaQ4BziuzhWp?tIju
zUYJV|c8)Jub2&(!hp)T<Z%zRPen~#)A`?j96Ha|pvmaF4kd*c)&HM2Ml{tw?@sN8f
zz;haDpu8`GrA~*JKCp2*XzvlUL7S2?7~?pa9*_&b)ekt?qX+*5#94jNT7bNBu5gdb
z<)r5pL6+l!8~}+AP>BRCg1`i{Abbg%(0vK34F+V05S}Sv3~0E3MjcR>)qxgJIc4VM
zrKSXzB$i|rmt-azrC?uK2Z?UXK@$u^A>jiGDR9t$3CtlEh?9^yIw;Gpic5+z3m}WW
zaV)>u#KORk2_m3dZY}KL3}`x}wEa_1S&##{U_u{M{wRR50N7WJpbf_$A{{a}!E6s_
zK-NxDTKgd_C*T83P=L0B!j}_(Pm;{ZEG~f*hM);g$P#Ic2!c3<(zY1r!UIq}gm$?s
zDCJ~=2+XL3*h|B<uN!EuIfzL2KeeRIj6vES&VblWweoi_sOUKdk9p92W|<%Y+UR=(
zYyCZfu6d_cVE+d7XF$u?A(4z&{|m{{NQ;K3?BO`jIcT@w6N;b{p)x@P^c-O=*nz@Y
z(5e<G1EY*ol;&mT=Rq=XaVluY0(>6`>;efy5f1SW(yD1_<|T3%9W*%r?VN!YnPV+_
zFsy(SJ;a`Rg0epW)c*(XRs=0ZPfmpG*8^7uAOczsVETuer*UC;2$BXsLoh_tyVx!}
z1y!zC!w%OJ1bI_%(5ZWj$tJ|m=|fOs7gXH&<(K%T=H?e=R;A*ok1#?3nsR96G*I+n
z4I>PPK+_jl4nb;!Lfb*e=Z>3#3S!W)c0uPrZE=v3z|||5fEL6b?9wY<|GAxN65s$0
z5qdYskX!%_3QEgk_-)_)pgr=S^brr*;Z~HMlo+3r7Z0uyA#HQeVJw*-0>08F{lLcb
z^Ql=zu#OSc+=BDwaY(O(+a7K+G!!UUY6Lni6t!EJ#LU1@4BCcjlb)KFT2W90o#jGw
zjv(y;%#nSnIztdNLK6c@jNqmb9^0XTK;53<5zvVZpw-&(nR)OQQf6LBYEfPybipmC
z7R`)hU|@jXjgX$P=I`9M7o%Y*g1S!X1T`6CSYemWfNahLxdGF9Xo!%NAd}P5GWFBa
zG7EANOF+kGfC>gs43vN-K*LgtGSf1X6G8LVSSNCEB@L=M1ysRfEi5r?h6Vzq4JX(L
zGzX}?3>vWkouQEf+VErqDHsq<D9D}dpX}ggazcwqYB^ymC`+V*rdvS~0^YU-CZKIb
zB#R+_M`}WWT0QVBD0%sLRjEb!kVX`Ect|fV1-z~jeG@EVL=aLJVRl2wTIvV36EY-)
zc4gEh(A7Smu?eUfA+1zUq$AfvNc*}VEflhn7i6I;d>d6hD5Zm!On|IIw-^$)7|n9B
zI%G)R&nwMMEy@I+SD_4w-5c<^6_6v4{ft!HLSna=ybXY$)7gSLKphWoB@Y^cLO$0E
zbI}cX))a!;TVNYXQgfkO1M%1e^%AXGHY}hCYtY*Hc*yW;a!!76Y6>L(A_5wo#?#~Y
zcE6W^-6@HbjVu`$7~t6`H8-;al7|8+14~Tw0!y6pi$I427nV5Xm*%AeXQt;R<{)J$
zaIVE;9W)TAF;Qm-TCHsbk7dv~6_CR!R!3b5<V#bA&6mL^R;XdA639}J{~(zMkEKwb
zf;v4!bSxluOo6f|XsQal?=&$b1ssPN`T5X+YmmK={DRT7fEL3f+Xfnhz*^T}*Z}np
zt&%V35Xqn{cx)otLhxZC*zI=^uOg*b(6#Uw8ySl;^2?$77!kn+nR5FNJ6uE<I^a$#
zKY`AqN7^Wga0jF!e_#i_QsftG=7;>NJV9eEC7`k>IX*KF<LXd|eMrSCBn*qmw-1!h
zv8EdgtDxzIyhBp33@hg*mVl4V1mzXbhK&-?#B#iAML}*rVoIt9_|9nbQV+iD6UKo0
zpC*n1H7KwK4a{=*l2~ZaP`3%a6ts*$6V#~yB?s_2IWPe|z5OF>lwA|nf~U0UhGUEq
zltLi0Y>+9ACw9>L3Duyvj=J9F2OYbQm}CP*0%V>SV*3@u`eR6Qidw#w2UQ55QWR1l
zpy%-e81pNjW-1XC0@C0dXv=cY3(&A4I7Sgo3`k%3HLN)M44XG0-3qkvKRi}I(=4U)
z1BlBbKvyM{fOZdqIvwC+cVH*?fqEjDAOfCY;P*X3^DkMwaimcql--w8Kz&b8{6S(8
zWHAogp<#eAc>_L53T;M+j`Kmq)Xh<npoyEA@PY)<)S1iEH!1wN1mZ|>Xlzi|dUH^Q
z1`TOKq62hYbS8*^mLhES&{=Z@niMCspjA5$KzR;ajwyg<tw01~WUOW9-P5&2D$t?)
zVzRntNJ$YsN(VZwumm(V1<ntko6jJ%8R(GSOc0R{IU|_U9?qar_@f>{wHcHh=71Vx
z;1EDG$RIU5b^52+0$@65m=aXdfdc>%`v}`H+NF@<oNB@U6Xb0~DbNCncn|@dAmxC~
zlybl#p2)%$(!e(YwOSs6dTO9$tN}&&6_p-|c_}%VYcw(Z35{gZtpkl<V{MOMSOG29
z$XW&g9cd^@EGgEH2k#pzODxJvECF>EGeHM!gWG9fiTI+j__U(@s?<D4$_9ltq+0_|
zT99rkd}lZHT?9(gSOW#a3D7_x?|f7&?N{&sYg%G5IA9b(Nda`QLVR#?VoqubXb(wZ
zk$*ud_+~Hc!Gqb1r;Xb{tAX(Z5t1{YK}5AYcm^~~)Bq}aK*c~NXcZxtK-^M$Kxuzn
z2X7m!2}?DbapwL_jU^j1mnzt&*IfQ`lks{hw9+9fVImiLsp+YCDe)PQG5}OK>;RQN
z6W|pOqTquh%nNqV8b}&e{!q8pwP$BwC;>Hnit<YmOH$)gQqv)+a3bg|HV~1%%87Yn
z&a(vj^vC(tO-IV$cT`Z<A;BPrfX3qy4v9}J0*yIAYJ?h)dq6~b)1%`vc27&NPtUc`
z;r+8>8Z>HX)W8R2)=UrqU$&CI{oS*bd^<g15kmQaI?kYdKOCSzaB%tug+eBXNbk7p
zxoy`RA^Y^Q?3p~rKf@1trQAx;xw=7|AS+owH6F+|99BXzJL&aM6sTSYU1{%_R9phu
zBJB|p5`d%jgCusOB8K|4ABGiBkAbR7BKl{jtDsRg_JeD=<ebD}@ESnSl9!+lpr8fE
zCaBi{>5xN~*MGBvp2%bhE6u3x0?_3^Xw#kGSjOWNXkbt`PlNVSKx!0lNFeHc$Zatx
z_UY4ml2xi(vS1-Wy<R5BE{N4&pM$uNCI^PqP@hv;e8BE@65?TC0C#WQ@{4j4OWbnu
z6LAdBA+@NXshwJG0G+6b)$@=%fMhY$^F(gt1E=$XqWt94;^NG_bp7JWyyT3c{JhL6
zaN{NiRF%DkCwxShL#)Nj-PExbw5Sh{Z;`Bp`j%>~2Yyfq2kW|3q$HLkLh4S?4z^4X
zk<PVevVVWBrahcNrI?1z;BEnpMqLNBoWRK(G^~>eA`q1oY&f0D-6rrQj@RH8gL+Jv
zAR-;sO@=d|flg`G1}|Fym#?2e({P{}YG+Vq**QP2EVZZvvqJ^zQovW4L4%%p4gqb8
z!kX|ftcHdG<<+(>7Xw2Hs7?(?EGkZQg&c+NnwOE7mkd1(A3fJ$PJUAE3ecfzSUnEO
z=tx#VJx;-DyMv&<0mxI3tOa603UY{DNVyvtzZBS|4qDF%I^zS<<iuka)L-P!r@VzN
zy$FbO3-xmji41Uscn~p=Z8b4r)y%D!cUF*ZDX5G<iZ?t~LH$Q$QbDg5pj8B@djo3Q
z1Q~-?0DzkSh&v1*r{DzIr$c<10BZtJZY6lVLp;1kg~v*$FUcwqkjFoYL8Blb-}Z?x
zFa&|_=7q#1A^|}rBKhspv$eVp>2>MBd`s54Z`8&o+;Wg@NU;mzLUIa*<xqc<)tN>1
zH^#Wnf6z*7kj3c!hfm5F#&%qlPt<_<pZZQfOXDENKtch-3D8g=vSX7`Qc?h2Jbe{3
zhy$vCKph&ySxD%9h7?ekdq7CH5Y#Tj8nYNyK>b8kvVtt(DFNM>k(aIy+LH~gZVf;k
zbI_ctXL??KQ7Xx2yin6EI3pL=&?cq*8Pp;2gP`4&pp{&idGR0_NQ|d}MkYW+dVQmU
zLt9_0efpC*H=KXpcmPY~phA%3E+*2b5p;1gc$XtMPFg^-6`-UIsi;7fXMzY!7eEsP
zS!=qGT!3^)$q!Hm2Q-@PTv}9?>Xw<4lbYfL2?>ykAejx4Ffl7<D!PN@8}_JZw+`qA
zdQkQ0l!$Q!6Gr0?+Ag7@^_xKl*<FX%aEM^Xu$%hPe+^U|fa>xf@Yo!P1&(qs0bNy3
z(|9NOkQ}mZs(}=S$VUn^fVv%^tJlIZi!+mQQe9FDKu33imJNp|7UgB;K?hC|v4K%2
zK~n;i9Fzs>Zi5mbq!7SkFEkYp*)qm<(ITh>120l@&d)6v%vUT@eQ6i;+<iMxdmXg%
z2Xq!#eqMZLZelv<LMzb8Z~36HLG%(3SLBn`*hDIc5l8gA0riYPGvvg^Kar;|f_$S3
zIwChQJ+)XL*4Ts{A({XR2ypSAQ(9b-TI8Eqk%?no53?AciVHvq9Zzcr$yR8(C%wVt
z4O+4d8WVR;%*)F!2~N#R2}&(2O)bV4%EJ}Sq}z$Jk%wUg)LX{Ho!3{ImstX8)amCY
zX6Au9DWGMs;1SA@)Z&tWqWtut#9Z{$3vVpL7|^1NTsv{b8O(h2V=F<SK}nMdHbAx+
z)FYVzsw%-X6{rCTS)>NPjTUky1Q)EWM%@<UK2TQyyalVY0CXk-<dRhI+%F{8;&Bi(
znu#p6vF%ip1lbN++vS#-L&69d1vY|8NUV7iSIdN~c!r(elvtFQlarbQO4Rc~wKJ$s
z=98bCotolVLfVWvwVVN}j<Nb5qd^RfT_T4&P|M_kO3>XDnR)3TpM%<gB_N+eh6HgP
z!HMAjXpW}b0R%<^G0S7J0vo9cgyrpfpuul__<}XiBrRm77LwS0+NDopw!N|~F$9*k
zsbw)Es8CrCAN<B+F*KIRvKUlefO_HjpvX=IAG!-#TppByWrhWE*~>rJqSybhm7&zK
z7<Aw%)^v?wG1SLo6{E1W24c^1ab^W5je~Y%fzx<=W^O@#QAvDmeoAQ$bi^A`gu*Km
z*v=+s8mF#fKplagUGUh)V?8u1$U5;A(g6YA8UWo;RZ>|1IaehbRIG#6Y~&|p85Jjk
z#3A)JsPus>f`$zFy@wq&1-~|etOc}4PC+@qKMvFZ;Q{48a2Oz#uE6V7*a~>a<P){S
zfeq9EehI4E!S;jN7|6ZQ2Qc4LcF-1*@1YR^N|8@M&0tU*Bo-&;Rf0DfLgL`4iX=lO
zh)6%A*CqF3##j4vF8*1u-@|^xN+D2RisYsZENg%YgkpW@?%m9MaDxui)&yVr7oS*M
zoLW>8pO}*qpO%@ISpvOnSEh%DArnNTFBH0NxNgT2`}C~a23$?YUqYt^$;u!|0fV~R
z8dRkOeS~KaO_1#%B0c}}G0()fXZGoj`WKzMbNCI^c2MpnIY5wXN84|?6*NKt$_j}&
zIgmycsCI<xDuvX7AMDbrTUoy|%l&|!L_%d3q=44*TmembfD`6tb_Rw_5RpFpP;=V#
zy`lE$JXVS&7gS@Q0YFypBUOl~$B+F2&0?{DDhaUdHlVYAKt%c#UXcp*`EmBpTNaWj
zu^lP+Q6nET9UjCAw;fb*WP%9jjPz%!9DW9|9kkCMob!rPOY#fA1EUF`B8eYvJ7{SD
z<h&n6yA6-`J22QoBc225cM6ImknNz2NuU8B@W?EvSa=K?nB533k`P5AB;g^gynqym
zR8Dy5AnTps-Ur1+CWwIcuAaa)r?5c1Pr=wcxMqrn+#(1LdeF2act=`%Zfas4B;$b-
zAc#nZR|zl%v~DD;fr6#|4>~#w)H-5-l){KfKgcoCvi9jP2DD8;EsJ|VjnFc9VTZ?J
zsE^4i{*f{}#$>=c(4`ch2^!G)`NX`E%*33`MEF)<(5l`{5P{fH4{062&)}ruz%=+Q
zUBr+Sc&Y?MK*xnKl0Vf};@~`91d{JQ*g;+J0lM=NX*VfoEXxLbZjoMgW=;<DWDrC=
zPI`IaWu>{9eL9Q*O+cVpj)+<deQrE0zbG5pb6Wu_tnR@J3p`drlMY#9hmf)oG)|dV
zqF+#H48HjalqVtWdGN3ZqUi)_Lc<G+C$NqLSydE@)yQM%poSB~dXPiFjW#fWUQRuM
zMi+TIonY1%rJ|h@3W_>N0|o4RP}?LEM5MDTb(?(DV{u4#e;lx%dpAGqP#}tYk6}IN
zkkTMg_@O7D1PD3c9MW87w}+k(ECwy5$;u_rd3tc)Q9n5!|49(ALK&J%9zdH#WEEBT
zTB_Wj!ZH?KSc00Qkap?Q%7XH1pn%ujxA4QwUHlHv6i=4dkqS%rE-M*OU!efhR{=*k
zsEmT_E?en4=g#{86?^E`Y%N$TooY6t6-{_-hWZ&)SQAmOq-27tT~OAAWF@f2KwL;x
z($L(%u#88>KE0%7i$(GbW0=Q4y#*qS&&(?>NKGyQHEKYUUJxtLaw~=vP#=Isn2E3g
zbx;ddspsc|2lU`qL;9xX<|Y<o=A~o178FvvBMmV@3ukJ$0JKLHtG6*Mg?gK;#vE#P
zK`tag8!&@F1FDcF4ahl}AOf0Ff7zigDvcm+LI5?`VfLq`CYF>IrN$ShLa$cW2X(7J
z11*pNR?wcKAW={TLQ4dY(17n#c?~T))EO8Ud`oka^7WlT3*$4>GE<9+^?mbGK&%ws
z(wve^_=<V71O{p`K#mQETub-SE<IjVemD0N6=;D7I`4(#^ab;1K~XAneHv&9GicE+
zD2?v}l}ti7y$4TWu%eL$PB;i^4!ndr0nvAW3}>E1Z^IOmck2lBT*3JG<c!qh?0C?H
z;3+7#9D};fkaUk$qCoo060km$1gu0MtEhxU2XcxB_0u6Xqopwno1r<5tOgNs^#I<E
z1&V*fW^go5Lq^i%?b8G9#{9|kFMxTPtR?}H)u8o5`ruVWAnX5w;{H3lVnmeU7}i65
zPR*X1259aC6yl(PBS>K*3_3IxM5H%b2Q0VQ<!GP2`xWEo37Yv(|06Y^LG=~<0HMSj
z(CI*F3hLm-7nU=)K=x#UOa>E}#zXx_QT~Dql7Wkt`0~`u^bBw{28vin>H@LAl_Z#e
zE)b{EIbg7K4RHf<>PF-%a2O!s7?Qt{?1zQ{C}4>w<`R>@XKjH~urMeIfxQRXg_LOm
zI>~RJXeVdZ4>$XC?Ut(%hi*k+#vrH#YLi)9T$%(9Gw3loTA+<#prSc_r)1uN=k1R6
z=`4&!C2tl4L5pGXPSH{WC3TSJ(T4~i6S2SS(u)FhmAX!HLv0}~6u^V?he4qL4rI{e
zC*&xwwdowkb*h!@)1NO*N{(~)f$iQSYaAfJ(?{PEv;qV&u?ot;pne?qJV93%ch`7V
zKMzMgXV-XN{~+kPZjg^5$qiCr!fTWbur3z0TmqVIhNN?F5a6*Gn$E~7zmbYE_yqt>
zppghrkVoWTyB!l_Ap^9rMm76E4F{|~$FLddbFzXQ$>*@6a1e*Wfb$;c1WU*<mdA2V
zuHrC{v`;^>Q7>;XV-Ga%6;sP%SI|r(s7nFKOrV`1nIIxP-{t$7>@7C->3Lq2Yg^tG
zK{FGz?8SNH_BoTp-zO#f?bB^*S^j<aTo1LEta&G-fJQmE1+;B7=n#C|1T>fcIk*L~
z*Gbtvy)eJ-uuF3YERT~l%8S;4&MhcPEiO(?0p)ejNKMcaP$2`3Zcu<jLLD};1|MsK
zrf_OIKm;_z{{iFxa25xRuR{jeA<c#NuzBG3(8f4fISe%biop}mb3ldnMYzSFDh)E>
z2&u!6j`D`&Fr;PzsN9D)6N*w3Q^0+5$YFzNnK`LQhYEs26;W<M%topop`i*|pyi%g
z;#*phlUbITTJDkriX70Ge-H<JbOnzw5GNKBxeF1s+XU^cA`asQ<*cBKpgA8j*JG4D
zP}ft(T99QV6?v47_99)ka0HYXK;a8%GlE$7Yd9*}4I1yp>TitN6&lN+_#vV(g&NCP
z>Umz!#4o6#3HERd3UKu!*6-A^pXAz|W)}>D&Ylcn0cA8$A_T`gn83Vv7MkK|<`PgL
zh&3@_<UMF&AZuU~+@sM4Eu{nv9OUH}r-Im!g*l)`55xs%VF6zs1Dk+^h6M%vH;}C$
z7l3;hpb`(K_c1JodY_`s754Fyv!E5Tp#HmeYEfQlj&myLSaHlvxfr<<8XPom5oq2M
z5+$JU0jEqb0UhH;YJWpQ1*xh6^?Z=3s+7`XQ0)Zo)Ruy#ebK9{6i!u!Oc0U&=dN{@
zkAt!UoB{PC1yPPM*$Qg%K_*)hb2Fint)St*Oc0TNlSN{h{e3fgI0G`-Oic%X`h_qD
zBvwFLpL&c844EJzU9ZSLyj*9UJ)A*(2Y?1Gf_y-w4>)=eBMP@~>z-uXon@a6V^H62
z&{TJjC){qZ13*MN{Dcn}gZg&M!v|}MOLHONe(sM7LneqwzkL0(>1kPI2RMWJ_JeMo
zDgk*mJ~6KnV*gLj`W6t8&K$L?DfdXRJ)8l_TE%3Qw@A%@j5%*m`iEq0wDQ)l`exZJ
zsX+U5uXqm2_y1SJI$UIp%_F%WIX?$FE)Je`0rfc`_Je`~T&{qLbPW5UAwX7Z4^rdg
zCW4QZ2bJ`8pbj>uyAkh_4_eOeUX+?xl3L_iSPHwq0nrG<sQaMB5OrJvTJwrE05GhD
z1_0HQz$s9T1BwUV{L<o7*RoVx#~>rEI)DZN)!l+fj%djNQr{uD02&%(^@kzJ0a6!$
zPnF9{1W%RS1{DdQ2623DCiq~Mc<`FgVq%-7RCNrZC4@C7Fsj{RBjRSQvWoNb^ixuk
z^HU%#E>Jy#wBQGk?jgNJq)We`kxt~+6|g<X!?wwwUJj_E3n}dISOkq*N+%@{R|(Gr
z^)*08{DW5ZloqG@r==CALbo6x`uvb38(&~+yS~826RGEpHqhi4$m5V8z+*Ke2#}gm
zpp%kq;z9S67p10Wf;NoBr(`CAic;u88^kmhd=UbS0gX5!2Y^9#6jXvY_aGHac#Ma*
zwHP#5M?}Xkur#$4e9F;LP<l838Ug`VJ)kqLz?X)n-+XrLON*+UJ)8k`KdBZ-fh<6T
zHXaM0KA?PbCnYlxRK0*Kg@hg0YalM9R)ZYY!DycjxoUwKR;yBbTnl6)!~tl92ZjTn
zUMH(p4Jk)+6ZKOoN{SM}_4#vnn1g)|>N}t<oLpjT@M)78{B8+qS<D5p7*V?5u^8%O
zvLYL)Ap_pH3W{vd{uS^Ut$z7=p?R60dy&9X8rW+Mq$&)Wtf}S@P>5rVbX=7uSp^$p
zG&3hNFEJ-wKPd+i?fvl32RQ<q*1<&j?#flpd2QDAa0b-Vlv|yfSqz@8!<p1EtcFHB
zSt$-F;tLW>GC=Xp460N>t%c%@{L-A1_~Ojmf}G5>N=UqeJeUb0pvR8>hMhn98(OU(
zmF=Le0LDEW(0z^|+u}j{c=Ae$q4SA|q8JjfNKFB7HwDRL(BvhC$tgvN<?$eMq2__s
z!ax?pK+Horb_(4*P#KEdyxhd1?9?Kd6G2C(LC#Nub>QG#I%q7AmFgjB4|XdP`bauj
z(u1sZ_yN17TM<^)f+B^8dJ1&_6UAoOT9<lIs;`C5pMX*#B-LYB4fV7gaZR+;yyX0p
z%)InsedsmB@u|hhi3Q+V6VyTmA0QB1Qk0sQ8(x%&WkV`P-vOHIs9-<n=u51H42GRh
zKa+Jx10=ZPL0bc$JJ-NN1y-O%J!sJkNwY|K0P0sN*$awPtp3HY7vf)}%m;3=ftCf8
z=w{|AKvwuG#DkjN7)>_Fd>7IUVo*=S5SL(3(<)L~1?}$_>lc>9r>10<<QM6K>ZhE<
z%KTFBS`N@mLeK>GMhB2@AxRk4c7$&@02c+tR6iD?umpU)=q^yp7}WoQw2Tq%fCMvA
ztqBQc>RlTEzH2BH-V6nuy$CsgQU2?7ZJizR_UY$3F5P>;-3%)~$SS#z;siWh2dY4D
zo|%ndGsMqGNekuH<D#_8ycEd%UvWth`gkhns-8>`k$&!B)Z0XT5qmfTn#d^FrjB;d
zAEIW$C=DRVN1wR#4BkZms-Zx^Q~@ewt3Wv)+?E9$7MTem(jjxS|6y~q|DkiV<k$gP
z0T}cPlnB6ffT}r2U*x$7_ZI6Vn3orjH3W>52yzokGGL1yz+3J?(^0-qL8rvz?9{vz
z$8?O7a4>QMGzzKd9MG^Bo)Ql;E0VQF7``dHqzV+t(?MHtLDL@bB~_Wpi8*B@RiK>~
zkhT<hX@FVSQ^N_ME(0EaBUuW`j^yp7fGukSHR(Xt#TM&BD>ye$3HTAqyf<VV25ChA
zG|tIdIEmAGP#9zNJ%;sA-;>phhR*gQ4-0|L*oIh)R<WPncKKRI0%+xp@Vlv%tLMSW
zUr;K6EQkh^pwxrY@fc=69FJ6zf`-{_Ky4^!o%|eh2px!kE|oU1hclqz4%+lWa{CeG
zqzTaWi6HPbo8Zz0<RRo;wdn0{it9S~y0QhJRZeE`+WscUuOK2FvU$VA9?pP-Q!(X!
zUCMy{#%R#FqM0B9zL6BpfP^zr9D(i+f=?r6<!9z$tL(r*2{I6esZbw<5my7Dwx}S@
zQE0yd+VxG%Oi#^2S?`T#8l$CTw`pfK?pZBopMJyjBC~vRC@dv|YGsmB5;nIW=O^&t
z;fR3{P}YF7=rCLX4H4?~hd{>zfe%EA&&|w>&o7G4O{~CPf?u#phn&i50t*Z3xnd2d
zy>J6wmm>lIQh6d-4GjRYhV7B+@|67K;*uihUPREmG16uebI{sf5Rsl}6l$QgQrJFy
zO6&Udr7ImN@;H+11(gLk`QWxZXrL_!v{D(;h6Xk3GC@T8*CzksPfEh}>G#&^T#QU}
zgsvqdE6B0h3|c@2DZ|0J5IxA@O@;?{&>+_%E*F+V&I1IGELMW*#~+|(AUJn{Edmkn
z4hozBDL;zIY7@cgNAQ{u{j|&q$Vnj}D~a*o0n*x_xtY1CCHeU|U=M;qjTAe`s{bKF
zwnd3~Dfzi5g%>#8Vk<TvxdtAEFdq`R`wi)|Ur>VqkN+^+SD^Edh$tS>HwJ^E4%8?B
zU)}%;R7eqoXw%m!KH2lXKiEFq&H32kkVC<+oJgj91<;LlpeyDe6KSAi3GP0CiF6G6
zpqT))@`4EeWrDYy`h(^sL4({OsTC!j{!WR-&{KOrK7<tTkeEUWM`%VM$sQ$8>4`fX
zAej#J1!x3;2w$L9sE}j`o`FR=3r2v0fuRIci)I!VCnx5>j)VmVCy0RVYQAEJp4F-7
zh9=OFuxsE=B#^b>sW42tq5h}(9C$3~><Lh;LeedW1&LosOZ+Bm``QEOKr;0gh48a8
zFa({#GOmf?0%#bJHGPGd20#Zefs4YYpu`SZ8=90^l9!kZP3fTdyi5=QzrZq`eSPe$
zf(ZCtV`{rY7gXo;!<!NyyKy)G8W5nSJh)e)4<<py&?-=ddJpf{A&Mc@lf||2d`|Z1
zN6v{@t>3sERt#Y=;|9nKP)`L?v*R%X>JB2??dU}((wPdNwp<YCU@Ax|1eJ}MAR-+$
zf0yn&p;;*H)@7Kp$+Qx>g%lK0-5_5k!IK@xHXK$$eMw#o558IuJhU8NmReK{J^Kk<
zdM^d#dr(~u&TwD?y2|kY`t(gPCF9HSpfjc7<3XMV&6MD&6_M<PdYaPVM^MQMo=Lb5
z%48x83=E!-i(gWF^3y|#67z~dBgBwcM=yIY2O+5D8jw|3!vRv}AlVEF2c+&H%3x?t
zet9Zr3^Ywa9m`-SXs2B!h`==~S4>tBg48HZ%g-x`&rQrrOouG5aRcozVFINqa8&}T
zi!(t4v_3&vZ2}n~C95+6=@Wv}G-!GOd<P~dalHrSsNJA8GuU!a7$YZYq{<v(IcOz1
zF?BiUXi#vM1Jnw{8f!Sle<|&9ATBTB1QpGN#Cwf;Hv55WE`r+(DwZKTBO&cCq|qBl
zN-d`Hl1cF9K=87`)b!Gv#G-)woJwc-s!H^X3t7~3#V&ml^de4Zqmasu0<}!BCIk%o
zp@9NA27#EQmRXXDbcQy#>_#1j0WaW#T?=~{G$#)Vn|SBa;*$JaVy=Zn>e@iVhBj^k
zEx*DVM38iY<P1m<A(e2TrEAcmm2yDW)Ii57K?PqXh)5UxyKtuZbrpL!1L_lU=0ZR-
zU_tZX)iB6>9F{=bNpX=`Qk03(6$SM^AOVF|75izfpL!uS$Ua@xY~oDrm^@h1gRGt~
zQkji<gaD|NgjkPOXku6o^*?p2hn)-G1sbD032!fg5;Np5UdW8BtbIC1&sScbJgOhY
zdmB`)8N<spJQhR!4I1^q+%^c>oKyljZ3Vn2EV(2IJk}6Wl$Z&++94RW>=G2pkh<vK
zj=!Hyc5Spz&lJDx^WADEELK6QBV8*}lfg%vfVL%qYBZ2GtdWCZ3e=HAR%wtC5s(d_
z${At<TIzeSE6J^*+QB|u(L(R>GB)^Kn?#Ofm89ktfVO9X>;O&nLF_<FVi<NneL+@B
z7b%4!4-lI%F)%<*IZaP3iLXdWk55ia&Pa_<$%J0Pf|$jHFH4xq(>E#nIsBj!YB~pW
zU3CelfQtt?2kaD(OTYnx$0g8!p|t%8pBniGx`Gna<;+d2NXblwE)E7IH%Kal6w*j7
zCTIyuH3xv~#+oKDY=(Fpsd@m7*<h^7N=!@3%u9uwlMiaqL+gwp&?%-MB0X*XagFnP
za_r#@sE;Tu&QK3gUCPG55Tpq&RluV=AObqu^8@_^S?bz84`jPG-1aPx=Rrhz^pu+l
zlY4UP;S5Nil2?~On%|%cO>#4<QuSdBP)=VAy6Fm3kT?}#*;jF7ZIi@{TjKWVFa|UV
z$m)dPaRsQP#+tTZ_QD+i4F*bE%ZWuLnZ@89!b{Lj8_>BZ(8Dz25#u>CLEGFw1bi+C
z>I`#O6+k^_@PN8uE8z(NWNRjfz_c0~0u;>6!Qx*Zbee5uUPgWa=+re(YQWjDdBB?)
z@*qXqKAm~$$M?p1I<NqsMnXU?`=5Y1gdkTy29glP8-@d*!9cwj$OTn9prxjuV!jM=
z^%JDCG8Z&Q1|rfg{XTYmUZjvcoB_=bl)54ZemvC%P$LJlh7vshFiKfSkC@Wp9lqEL
z?es!Wk(~)5(jjM6K1ZCf4zZZhauCv{g*7UVf<|;n@HS|!hUDHEQhNiow8<6Jm_^tN
zS`nEEBA}ycNOw9yd`+pnup2GpK-v8}eAERrq>8-4g6j6d{EPEA7mQIX>iYKuC|@JO
z8R1_@r{OMQ#sp$7r5TEd%d;RE3aM8Mv6-x?8l;qkbb%tNha{6Vw1#9g%Go*3V56r<
z#aVoEMrKY*Q7UvFIjHNA2_m2eqy4mlGaz9>-8^mxI+;}lUd18W&XAZuYPv#fr&eAI
z0_8PC+C#S&DFs056e4E|P-hHL7Enp@GB7|^I)Iv1;4^>0L^^zX6XcW#0a!gsxs@EC
z(A@?f6#=zqa99cTB^4983#jx4b*UlsC8E}bmnv7!L~(ii=7)KgigrhHGB5<4gZmqg
z-B5p1eT6vm;y`84&>m<^pdddpucSD>BtO0&vm!MIx;qzilvE~&NdJ@TKjFqZIeTbg
zFoXpPrDYz`jfexVe0~zT8iaZ_M}Sh{Q7m~Jo)TaMJ|vG*waCi?wVh^xMy9}>bHu<m
ze2f>eA{%yC7F8_=T?QV6t(2jM&x=9L7;FcnK`g%sYrK7jMmVinjiAXw$hN9w4r%EZ
ztwrtA>*p6fbnsAvF6p9bkZ(tgZ_pS`CWwF^$_AaoVu2kGN73Okhy~H0Q(=lT)4?kN
zZh_nYDs&)4AEI)Bq`4<{>3rAzTb`M&4$I{<a03G<_P4=T0pM{1v<Rd$zax6jpjAJR
zHAUcxP6^aw1`+8qx4*Iq2~M(4@0hDu5|^9~^FFmI^+J%J!Ql?h>j*!?4;O{xb=am0
zswO<}39JaqL32xxa|t1dUCcgx%8jji?@p3|rh4kOqdehB4(xT1J0L@Vke=FiJNUWb
zkTRCi*hbVVX#Pgn3@K(_!dmffsa?$A+=;~4vAarC($_wHMPI1b`7f{oYpIpWK;}S_
zH#n$4$3<m=i1gf0p_}ZHX7=ejb4t$n7urB08l;{Y-O>f1?h+_?A)^4GWiFW@BAwOn
z!dCqS?y#c={Gj$y8pVk25(}=x1v#Oe(LTLp=iSq_MX-y7sMwMN1s+mhgT|X7hr`1%
z6MTvoT8xopH)IqNG)Y;kp9`iy17D!Pg$zJ~%P~;UL-G`S;u5lDRs&X!kyV}`+l@Tx
z<qryUY@=QnwnKc5G_(#1C7Ytey!2G)_<3xV1Vbi>NM||ri%o6&Q+qfAI#@;Id{A;?
zPHJ9CBKR~=P*VyLJm7qQ2p$~ED9D<;hpe{)ZK=)7OG&MWFDc4Q&IVVqzd_jnasMBp
zu!r;<k$P~D5|7A}kI+|_6<6jZXB6e<WmbXLAA!#o#TF|NTWQu`<pZs+JPB_@gF_rd
zKwC3cVCTPFftIqM!4&XnKE#^7Oi+NWhIfi;x;Yp!K}7mFF@Y%l_!IVU1|-0+7y+u(
zf<VJ#kRoVaHv>Z^h)C}`cI*C4xn6rX17ZXjsR}gtUjphe#pmQFC+1{UrKZH^=I51U
zK&(WhG8_Sp#nqsg50Zk19bzHkJqF|Z2mN{M(_svVtH~-jAqf?{I5;;kIRlbKN<cvl
zD#9R+2d!Jn1QF0eE5IJkfY=OL<V8$XQIr}F+Gv=W15T3+ppv2*-qZsv1jz&u=@|P!
zA+}%%B1Mo94Dg{x(3bm55Rv}WRqerb4<~y#17ZZFbE)Y?nJJ(fW<bR(xVfkVw;PnI
zA&ZwGF^rLs$%-DN5ivy51!u<;hs~g2F(RTTzW{Vj707F#dIM__327T4tq+6*E0Gp}
z2FT4!Ko)>XBCHnR@Bt`e5x+bEVhBc=h9&(Jff71s4-+Jz->G3_$OIATJJ#yj=!-74
zhch4^#bQJ&XlxQR$qG3`#H|=&)P+|Z44EJz-DP3wU3;-h_HYKoDA0;qA|eI7T!(Fd
zxCC;!KRojw3J25jFZ*M<+3eF{42X50SxM~cM}kt}XF?JzrN~-`3*UWQR8$Jd`Jjp(
zDf&U7mkA<p)e2N!_HWL`z!0<*o_7iQ8%w++mYRceJ|Y4j=eY~pL+=h1hBj+Kt2c>=
zZ=@TnUVyrVplK^eit-2DZUQ3GSr1gO@%}Zohch5iOh&B*iciQD)reXvB@uc;CL&aD
zw2a8=3c@O!+=9ds{dgqr_JLCLEqIDnW?*2*1QF2H!>?hB!eMtc`4YFS4RvH2Vta04
zNl|76sCAZ-ng-#3N+pnok&-2d3u!pxDE7!|86t8+YEEKFW?5=VYEB9GATv-?5Uce#
z$}_SmGe`h{>wDyTP(jDNm4FT-h)>Qh%`1s7N(G%C9G_W|nhPz^z^NQWK<8C3DuGaP
z;sd1@k&;<lk{6$umsXTmTvAk;47%D26fgTh83A-?2_z$=fl>yDfKF@**uxo+j6lla
zjNs#cAgjko4Sy_!!X{8<06L}$QW?0X=A{-T=D_NNXFr%2GC@RoS?1xy%m>@;;S7kw
zshgod4Y(jg`2osHkPHP$&PZ1iKx`*#{R2{Zf^IH@Z!7|z^8u>qA<;%KJCWCloDFIM
z{(u)7pu;ILK?J;S4`)Dpi6z#++iz~e$J#*+07xs~M_1n7W$a7s(_svpMjQu4JZQ2V
zCE}g)i%WdLWep?<Uez%$WP*tF3z{d+yRGZChcj@RjJ6#Kbdqu=h)DN8wWQ6ALE0Y9
zz-a_%yD509IH>VmT9WFTk`BFNJ%f>fArnNTi@bWN{GmPH9?rmNng}SsAHV|~lxs3U
zL^}N7b{GR<1ZYDx5fvXO^MiJi7V9Hz6ygEd6$bADfx7OH<^PZoX{7F>0mL%08rhIS
z2z17seoB6FGJ0tMY7K+y0WbkA{xFYfC960_vKo011?Td0NY+KN9O`eXPXt1birNaA
z2n2PXlXLQMoL3A#bZ%Oh^CLO$c<87S(zp&NUtlbmOsz;wj)xwKhbS5_41@X)6d*(-
zBIt3dptIsiK(2PlOfJEGY8#|ZMe3wM2b4&&2vi^9sj`ubhx&uaRv^+&ZqOxr;LPh0
z5)$B>T3no%4&9=O2sMajkUBh2&ya2#xX!><W@A_Z^$=N=22!sUeG69@XbB@I>OiLl
zL$1Mt%tnDuXUhZ;=`#h2J+GEI+e1$|bb}^1@|Kh0aSCWJD$*EkG^p7DBGTE<uB+A-
za<)&mXwfpQUf>FC;e*1Ch%}1YAVsntwyp`ZAv&l6OQ#Hy`H|{vXdIB$JwiQvp)4^c
zGbOPEx<3<i1`N1c;Fn+GmRVAgn&Mhiln-68j!1SG-2!N`qq=)QR$|R&7%qT@2w9`)
zNKpYjU=!5%JPqoW%m-CL;CcWw+nfm^FuNsC--9G6Ne1v$jNsc{!8U`68Sur=U;?uS
zg4BIfvlujt71R&!9fLM$K(<lB);qwD1b_xNXrz*4FGE_6NcRL(O2GC?<R(^R=9cEh
z=NF_FffFU92mmDmNF9t3@6fQPjzd7ZaY#yTprtS*`yZA85p(g-1G{o7<5Me=Qwu=3
z5j<r8Dvu%V0VhF3FhJ(96z$WWAKuI&$p}B|h`g>C^p0H&_Y@=+CFbNnE>uQ11)L`F
zI0c$C$m*pd<wVHR+TzkA(7`+4&{zm+YJ;K(Di{yG{xd!;F}Wna2znqes67g4ltSt)
z23Qx70ajU%)lfuo7h;yN475zh08~Jr1qr0M#^@(gKSV&W07)NcE`T+};Z1W$q#!lT
zKsBum=zK)I)QSS=tlPmi><pP8BE8?(_eP!3E_*lwn$58Ep+Fs=pkjEXW$>GkArnNT
zpKqu#&e}K29?pOiR#=Qc>xNDE$jXojBGRvLUH&fBwAmicfEYpKs57?RW@zCAi7CvA
zmC_m=wn7?_YtS+<>|CyNR)59tSCIj*)JfJr08;9N<QmX;JE#i7nLaSAhop~UN;5}s
zL1rH0EGO`wo-U{=0s9<u@I&Sf1_lP`nf$``=>=@{`)BBO!hB9Wt6M>RM^L{A()R|P
zP?QO>AHLrKZZ#x1QO{;!kj*9V5&<-_4H@2s6h@ev*2!8&g%sTdMfq8&$>91zKRp$E
zgb{d}xeFffpd<jPI%6eXy1uPrwNF1VSxj&B22NPO)4&ZNOR<(q7;b>3JF=Q>kah%k
zL=!p!1xoYJ;l~@Lq~?`mmOv|pZqOh)h)Cb~bB9?-PqTe`+e+Qi3D+mX!hzDPhq@zg
zIXeRb_=06ms520UexQ{>@U|6XUk($rQ$bzlsDYdV%8AK2sfk69j3EZ<*MJCUijcHV
z-x9>}M95u_CQdjF+9e1|84%k`KoJ5W()TVgjB*ccwogyq`m_G(#L2LQj?@i-v!Jo$
zf1uR_pkM&6$pI7T!XL6#I1j1Xr}v&u`ESGP0ZR|msssLk)|KxDS&SC(tc%oBgPghT
z)Bmjec*!@(fJ&i`S_RaCY<~l{9qa@UfvYb;EnmL{#r8h9y`Xr96hn|Im(xD|UsWsX
zcV<{xqn5pwK=xt_ZOBSWe*1L3F9EmP0u5lPjMCB{v5XY7niG6!KzeFPd}3Z^ZemGl
z3Zw%ATF?tw)r&EH32j+X>I_7)^bM$4ide&q2zf{)gfv(Lss1R<W~k{6Z4Vq`2M5G<
z%qo!5&MSOxIjHh20Ua-$oDVr_31ip-;sm4sfQCKQoPajHjVI_K^&V1t95h`6KYbTG
z<_bA}7j&2b`eE~+{0r);Go(WnsH@t;8BiaQH6)GHu0S4=z5^Nusem_X@K_G<R56i*
zk146inYoZF;RHZ~&7i>Z%uB6EB>q?c(rp4Sa00c;A(arp7$Vj)rA0Y8nMp>V2FM1`
zKqlzIQOBg>lA^@q5|_;M)Z!Ay(vl31#JrRoT-VGZWesR766rl8yL~`$h1+{bRzTy5
z(qb1jba@Q4+)oiSlLoF`5#b6c@4ncj!-g!NexsJfAkSiLYGPOn^)Xp<_pl+G+*Htd
z&SL#yP^%NXs5BNdC;=+v;sc8ElM;i$5|Bb45eT>n9xA&9)V0PM78v$Z5*A3!?zE!(
zT%@M=Ezsf`Q1XZPAJk2U?97DMy^uxF@QwGBR(goL2W>#DJ9qfpBx1f0;`Il1>9DQ`
zq|yU*4@n-lLn<KQSH3_Q@zDKqTR<5QbnpzMd{F~6u|WiM(3smky>^myj*_uDwVEG@
zAkYJ4!7HF30B1o&5Pa`%eIa0>WS<@(bM8+Tp8>3aOTEyK09pMS-q=G_5s*L^wNIB4
z=W9t}RE9Qjsb{k{$mVEJZx?JSV)##)-$g9*5wAUT8;KMwyooGRk>=t2KsUF7)*(74
z=H=y=1gGYuc%&w#U>4)J#->TP5>z;1&Bz#5Kr%8?I|8(^1Ky4(Nz6vM3LLZ&z9_L0
zcY6YoQQyNZdwCB#{FbbO3U@&P+F=*86I5G(V+~F~FPlNKAL2hUPZ<ZFV+TI35%~;K
z(0B)A!8T}BL}F1YOcGLRfhv?t5CQEWAr0d~t1hw{SCILB499@ip)Q7}Uy!{xEQf{!
zSy=}uCUR4Aa}x_d?G#Y;1W9Dz@&d$#^w3~?W1x#NSzzS_S^h?{8o5ykvJGN6IPyVU
z9F{}<P1XnzB+$X@I|?#$K#NR39tSlvA$FsA9CA)ixPAKEZEh@&9~4pKaU{EuJq}uC
z0I?j+;~17hJx<m<JH&G65ssi_UX)r~oSFjieI2NVng*{Xz^(uh=^IMyY`iB3+NW1k
zoNs)}p$YRnwH*N3VvjVt4ze54nZa-XBm|HOH&8bkdO~Avc1mUublw871UJRLd#T4h
zBl~n11Da()GnqtG_xa#?CeT<h_<;1>#O&0R%p&MINIbSc99m3P8bxYU6eSm@mXv}s
zrzNOI1PUqW{CsLWc)dhH5ol(#Br_GdA0O24gdC6n8CH2|mu`Gp^jCspHZ1nY%Ctys
zLQcEEppga8fFY!DgU51cfPfYP5)tgE3nS3m+TdBf%(Tp8@PsfZ34_MfA$bTb1J8vX
zpqG9*ZBE;YL;0{k0CkHfb_a4ufD$dl1t1o<<N_1v7%qT>1X3CXE&qpC(2yyrc+lyp
z>8VA<kS>2NXe2pB4|1eqa7khbs18p8jq?{n6Fj20glq(of$a#Af%%xkRsR^qK%7n9
zd=N_E1DlE}2la|Ula86i#RZU+*O2-)a)~NKCWuIH?zHc+D*kMrE)Lq>b&S~ont92p
z@?fc`q$o92A4Gr>P8w)_#unc0km=!J$OIATr<0OC7v|owPj9KU?&PR{18t|$t>}Z#
z0l*lLUJ6+QQz*fooDZH90uOtDQVk^Mf>@CB3h5ppEmwnBOi_jb)w9L=(1HuPu!tKJ
z^0A;=2b^I*fes1zw$MEjE1%r5Pd}in6=FX9B_!mL@(XAcDLlUv6s3ZWjfCVAXcX&#
zV>cd5h7_eDXBITO6kLp++^D)?pYFw_)1b-x64oaGSwVX7kXZprQsC+o6rPaOjOcYi
zLiPjf9*GaIrZgp%f$TvlcJNq6N&ZDD{Sm!O70~%Zpb81%Peexx;?D<m(DN!D!Tbs8
z!;l=PNLIt9&>KOOjv##a5s$?XKO-d_P{#|NbRb17B;~|IIM5;%y*mW*WhRKgJT(ql
zB7sIWi0~WKmV(Mmko$vz;K?6kI1U>io+t)gFhzt7$>25j`thlGDVd3Rp!5hz@{lBg
zRyV?1SCD}?L0C-zy7HF<n~*M5Kv)H0fy)yxfoMs>tb%%q$VFP{JKxZDG_M4WxPd$u
z<ddJA4M~omi~+G5qlXNQGV0ikb2l}HwGb~OB}PzYwaG6?&4bqA>Iw`DnIIzl^^vC)
ztc#Z0!x>N~lU4g5<sfj25$TeI-JqNVT2+i*3BV5^hA|+O0QNS7HmLug0?&`~Aa8((
z^xTJ)D{stLZVzWbOrd52^c<)W0df{-fnZ)dXl?{_0bXfQ4#Z2KFotXsz_F7Uq!)8D
zBsf+%;7gbgi3w60zeSt~0*O`fh9i(-2DvF&1`5w4P<VpUG`RB*Cg8hQ(r12BTp_g=
ze*F$L0~B#BC^!ovh9BT#05Hp;KBw|PP%!A=UQlx$)<=b8XY@k;04xUX!fF@t+JxW%
zA!x4{$My1ftcC^w$hkI<s|fADWljlbK*6yn-7_yOAL4#Q3WDV*^x6@_5J;H~js-lr
zp-!OH==B9qS_Ewdgm?o~31xx^_yIBLVf-`NeSS8;Di2C4dD!%kJg9jH+ARXf$Dlz>
z$PqP=bEnMh)0wLto@<?02WcJ_lXY+jQU*jhWReq9E@C@(Y<1M7K)y6#`*b(eQl?oP
z-mvx}r8DNB{vG)KAMkL4H|Xp?4R`?nP68kTI%D<`)`EnuV5P40O`ru6%^>SRmV(#L
zf(dwvh3>KZ06nOKy0)VoT#lam;77=QfZb_HR*Hjfy)8&ADozE@+kFOAC7{&;@g-Fd
zQAnCcWIp&oRFFe+I4DZ<SX=;_Qox$)F)W7WdeFc%5iK?BqbUVNsnAQnGV{{)VTE5g
zsQd+us`=-odgPb5=7A1^z_D%usgnq8pwPfUpcug#D3ERjk{h6bLhRo6{DR8(qSSOy
z6B(o>=prb^gKOL#(EZFHBK?Ecr02ivY+zSbIzwX|sSyX7VYNvP$V|@8N!82APfUUC
zV*_c=1QD2KK;22B5)14D@URMqNQd7(JnQ_<+g~rGKz%^n@)GUDLXgFfgX1u4hx(n;
zanbV3l=RdRaA66u43a*;K1bx%Ld&?u7oM5+=`q0yn>WAch54MUkqo4o0ye)38pi^=
z0JO3S;sS7aiN^&{--AxJAwAn7H&vi_VS`2qi(q#!Re%n92NfZ(^`-vcEi;&VwIBn-
zn6p^4aTd<$4Gd>MLkDzd4-ugQ-mRaRmtL%2nwJSGq~cSHlM@TT#|eQBC<0%#;+dCR
zl$x8GSCW{6qa?%Vra?<GD!2mFqQjalFzkf-oUED*DP_V2NRmKhI4CznI96mr=LHa{
z5>jp=E&PG{m|C`jx(ryojbSm=+eCK6QERaPPal2H+{E-$-~1GCoP*km;EjqP0hiR`
z<f6=i63oPp5%y5uQ^ygY-XvDPV^|CIJB_!Cfv43EfF_ec$syAKwD<%>q`O|q`(h{`
zU=L?NgMr5G0bNoR^cPgof{RL!Q^1abI|I&uh7V}Kj)=k_uQWF)wFq1qgW4`7pl-h(
zSPZ=l2T6pOD?UlL2xJG=xW=#o>K9N+NQ_@n%Ry}tP=rB@2eH82N-zO$e8YPK(5NGC
zryrzNDM>6T){h6ByMXP`6_5iUP65XndQtKLwv_K9MNM)n1sP}+XwYoXDk6{*GC>Dj
zfr<2zxa8?8?rGVlzdOTncxRg@tZt`P8TJv>`!s>4Q_vPe$YpSlO6#Rv`qw7^;!jGj
z6P>7KFKDMw(0cf&Fv4EQsKF;Y_+}}{P#U%D)dww>1J(bK-ZsKsc+UfJ8iN?Lw@od3
zmw>_<o4t^6;*YRJdZJXeml5P&Y~c(!vFo#4dZJONf!0cD$FBm&UeIZQ=-~{RZvA5i
zy%$M@%Kin7CI;b7&p%;jZ&KS{@Wwk(p^xrgc>JDw81*($pW60L0i`Q!;SA}y|DaX;
z3V`Aln|~oYdwwIr8CoY$+7Zf4EXsyfe2Ji05YR<WzF@K7lFA&6(?Q^a;AWAF<rZDE
zg_WCBbOvY$0BdD|VK+1g$ePrE&h+ZTmR}bo=4EH*fjjevW;eLfK-5-&*Khp}5!JF!
zhuqp>5A!@(9YS;mAP?Y!YDug?fZ+mY5P+PFI)DrsRtV3`NzSjx&kIT|F3rKX7!fiP
zfpo(Vv>AY57FL&F=!Uw4tU(FL`T|gAG`%RXBvn5i+9U_}NI^dF%qvL+N#z$gB^F~O
zN=SPNX&?vc6RNsJ9W;;)>Y6}GB1EddupJr_pi_K_Xss0&rN)Ct3qfr*(3v!lg>2xY
zw-Gds1R|iv*jm`b8Bi~hV@WrtY_ov3O+bypOc0Si?dfZyKg+f4;S5L{fgC$}L7{~>
znVKJTHW-LV&xi!A9apf2Gsw5Y7UU0Icw5I3)HeYU&|zjKdpLu%7%9jv&a5a%EXe@H
zNDasm4^Uu%V+0ZH@P-_uVZ#P142TRZ6idLZHc_}w@K^%%30ZShNbO_9B8*j_nToq0
ztHJ(5A1%l1<x|gUoD1zCISR>YsIMu%3@Q&4n~d=8EU4DX1QF0nO*!n-y*60P`FWKG
z7Mqk?X$G2$0j+C<lpCN^QZqpW^u#GPd+1~=7qrDjxs7K)du9+ef+`cpYFJ$1N>Tj<
zS(*i2U;$oM3Cc8}IpAW@xVueyYF=tZ0c=$*$Wf5BrI6;#YrAx1-KpD^=a|95o9a#h
zb<2Y;fbuUm9PqdRn#e$l5r`<Z3o29clR<tx4B7?{8Z+?CPbtkw4NgwX%fsBr0SS7{
zQ3FzK1FtK<T?-*u0QC-e>t&HU^~w2pC7F4p`K85>{ylh*#27R}2=aA$YF<HNQEEwk
ze0pjg_OTMAj0~-{XyhnR35K<?0ttO2cR&M&tg;Mg`vKyVd(eD6#A=WuK(#3Yn1Js1
z_=&iC1{wrpExACl8o9X$sxKgxgVQ*Oi^FoLzlltBfu*UX;Gxkopq>H9?D&FGXgd-#
z&Ih@+4YIDo#2$K~6Z{TfQZ2~_H5n#@#%e$|fz1XJ&{Zo)7C?PL!7?+@Qh3ntTXK9-
zetr&mvc(u{g~k~L7J~eSE!$#P1@#+QO;@A}J|#aHvYrG~M1c|nBz>SI&g-Jahi*3u
zQ2p9o!~q5ftHF^9;zE)phSgAyQ)^%dF>nC(GiU`0WB?AnzrFa&kG;P2ZLpe$S{8%M
zfi&U4J_e73fozBGr%#7j4Dm72R4`~X8@{*{bmBvKW=cs$d`4<!dPWIknW<Zdkz0Nq
zh=a0vAGD$dvaAL+>;xZwf~H;CukQhS7mq_A-Yv%HUV;x;2q{WTP6bVjqUS|O(ft6n
zV&wsBwHAgkkOU5P1R|AT=!QCik`>dSiM=3Zc#9cf8Ae`)1U*s&gOZ|64rsk5nmaMH
zLfuK$YJKElAh85;b{6Os)DqBP;*O;y`7VhisXqD1iCAX0AOVKim8P0Q@^O`f7&b$L
zfUG(iGN1t7rI(zao12e+9W!Js<1b=t4H9wWt;s~P8o7=J&D~&4q8OG#{Y~kjC|qZ#
zfYKni2nG}BZ~V&VRh$#EPY?Y$Zz4zU4p?zWR<I+bJVX@$>XHZf!du{o{DNUM)aRf9
zAGCX$5O?!{osa0HmT<^$n%sD6pFaD4xUA(YSJ(z0&@u)>m%QLH1?mz?;{bJ4UJhu#
zLkp;Z1NKBLD+5C&h)AEenYHEG;dJ}-t!+Wog1PHqaX?+`Z-P!S1MN5j-NaB7pH!5Z
zm>mx~ZXrG=HLV16EpHL1o`G~z5n%%v=Qs)5zs(Qrrc&xQ)Z`fs8uktV1r4}h1Xr3M
z0(!n46YP2tXQ=IzHjhviYx;mJ2OSuR)9aAisRf~yg9Z$U=pmvmH$ci7NH?J;f~*F0
zQ6W~>fX4ekM7r|4H)(G6QTFMdS6ez5xX*`LP2Etp1=$Yji$QD$1vsRQ1i6Hp!9Lx?
zXm$AxSudK{{s%M`qW~`#5Gf9F%*qwJbkwOG>iWG4<oA>CeR~MML*kmzKK+6AuCKG?
zT%dlZYKofyvK&+pLINJtF@P-4hi5j(FcfX>NnHZ6T@N1c2(QDdq9ZoinMciGUZ-xr
zquq9o@H;%(Ap@$gnPMvDww<812O^_4f^r*(NZ)k>bV^~6eY%uhk$-sk9N4OCP|hSG
zrz0IZwH?$`1`P%{=OpG9z|Lv|MKz?lg|u>zhH0Qp4bp7_@4p0v9wZInv4SR**<%g{
zhM*asL<FwPKC&?|WO}hPFr?R1<(zKVoMNB8uKs%ZxnHpLqDZY^=)hn}Vo6C6bg?VC
z>oIO(r!)<s*2D~;G1&+3>IPirfC%W)7^HzB$m|4lZRY~n{t<3_J}4Q2i1d>dZkya!
zc-q4mw6OgMDB@(`JCi`(hKvqFazE01AT4Z1Bpa~Bk)Syn5COm749=j1?OY(ggA*;-
zcF?+8$SL@c30;2JLAH|6LZ7<vE(x-ICMe5*ZMOmW9Ymzxe0J<hi>jP`I?u_IN7goJ
z(8Ts{sJ4R+R?7qt>9-EYX(U)j*uxoADlbr5c(Xu>o*h)*g3Aj;Z4X%>^c=RJ!kAi(
zUsUVS&Md`aJ=FJPE$2jP<R#~VM&3YUjF4U**w=_+j%Rg+^5+x&_USLaO*)n#TMF|v
zS)1XIt<BHLfu7zBvLCUw9h~Iw*bntKS!*ef>_^`qstDQ>13KRWG@J#JOwP|s%S?|i
zNGvHyErL!MAYucuO6?JBv>d)ck=o7zpWF)?6M<BocpLx?7qYs;NJ9aLyP&}J_EGro
z5vT@-wEf^20a6gZg^hH98t6ncLQuOK5Uau0c<aYwox#ZgHHL13rb5w@2D}{xX^h^4
zZqlNWGvYwK0O0|5h9&4s<2zWKf!1I<09&EzPR<R5&`UYe@{4j4OX7=55|gt*xfC=h
ziM5n~%#w23!;kudwiZES%p@lXsO3c6z=2^gBpV<#;$#^Z7;M1vQF@^BDfPe;z==gg
ziIsX)sYUs)V`V}nGBIR=i1hQj+)g*jUABiapnkzJ+5;}NTtQg`oHSKI6(xvBFV}i7
z@40`pJ)8k)Bw#TD?WQ|K!3T*?qybKd5unvQkTECcoW$bd%(TqZqGEmD{1gx?#kVx4
zBojWn%>_!b@o?wmG%+z`f{1jJPnAc5`!CtU84$C{YDIv9Rv&b&Xi0f~N@i|6Xtxk3
zu^a&PFhHe7Y9;jIy>8I4=O7}T>(-MGp6W^Va0bKy6ck?gcQ&I3D^kA(Vl$SQLA&yA
zI~M~(CWuJ?xb0H*=8Z-6a0X5z5Tk+M^x*(XA0PtSkkYe<GayEgm0gf3H5^-tK{rD}
zu7~`S>p$VfJ3jk#7z2`Bipg5b0pFMcitW3gnMzRG0lZZOeR2fX*@~3gD-Eh0K&cE;
zK_Vg=!%C=EX;eM1g4P*;<}K1w^K$ajGmA?ylX0&!K+4w8(uG#e!WBFi#S1ieKu2^D
zQDTEjQKU_^up7GXfQp=cP?r&u7BWGjxL^WtecTP60>^DRKVj`i3hjFevTq8=K5!1h
zV;|Iipe4&h_z$g-0=*>)vV;h9S6O0FJgD?d&jW3_iw9)@P&*tvaTV`TnwOqfl$n?Z
zKEDh-D?kDssQ`i|0Gc@r6cLb08yrN49D?BzXb>fl(_(<F5>87^1l?d9Uz`eU-h(zR
zmVtt!Bo!eD3a~2BE>6&1Hh0j4!I{aSd6`gk!C<}E0}eCi(8;-=aKjUfNbZ6LBUJ}t
z1waS!f+{IT&^f%|(*T{4@+;882+|~eVwYYNsH@a<k{g!GsOlC_DTg&AFl>j01d%IE
zOEQX56H}mT+b4iB_G3`n1(bflW0GJ3I`Rb%ZumSqIhHtp2ADzp3-rnZk~NSlf%=53
zaD;Yoz-y=ViwnSo2q**8g39Kba4&*3G-QH^bmyZM^}<|&_UWJ%$e`1!p+y8)bK5Ak
zLyyGM1qCkX^fq*V!+KKCc0BCXd8%0q?o)zt5_XGE+CiiD0kkhiHH&Y8!g@L=tigpK
zq8|<41_e3rgc&-dMFoqC!DnsE0a<(zZZXL3kaP?WYFKv~(gY-H00AwIa|;kh-hz%>
zLt5p7@HoVN`1Nl;p&?Ff`$4HJ2z=x&IN;#~bolhM9ehPIG>e6kRr^5JfkLtv^vHme
z;{2SlRDI;N4WLCokdh0nP_daeO@7BIW&8AQ<2Fmj**>sx6ExFHdTRlTV~|||DsUjK
z0EY%5Nnp4F8YE=(%aFSTMTvQcGk2bYM!G;f`FPM!S8irrL4GmxqA*12Kq~+bz{Yqe
z?V-Rn?#~0w@q@b02sc2A1UycFga%UA9yDla13KFUdaPVKsDA|_;QK=03`$B+q>wC5
zPRs!>GzCQ(QXGOhAdtZ^jDUk=Yf7UKb&%K-RDOe;00}d6+mRZV5ZlS>W))O|vJ<pA
zb_NYQalnV2K&w_VK?LS8kq{fn@-Jj40dyQ%Vu^lyW?peYYH|svrkn&SL_wJs;$P4a
zl#t6cAfb&h4Q5DO8Ip`T2CWJTRbzNj4NB^nAR_(H0-wbvk80S%84y2`Ra7D+CeSEz
zJklA3dqK-?K(ojYU!vy(Bws>uLNQ4vd*1;04m7|GX)}UuyUqj=>9BiA(qWV7(7Zy{
zz#%LOi%<(*P+CRGE?B$<tBxQ+3kr6U%UvwvhAN;29;gb01Sz6>sG+%mVHuB(eY)|_
zwAs>^onaH{WNqs}ic`?a(A2yX@E{m?|3MJwh_Th6xiqvY2j1||ah7@g$m|!agr&L*
zK$C|^VGoK5NDBnR1<+(gyW<s(Gchn=+irt6?f|}T6vlugMx;U?lxE>;kF)YK^C0Vu
zi%W`-&awqptawa?`jk#x$2p*}F;L4M(y{e+N-c(NPC!Hkq$EQsxgj1erjrvv4#Ap8
zFx&+VItp4w`2{7JxtUd|`tdo%g(&Sage%Z83w*=pxu8vRS}ujd+Gk|VLPA>0a94m@
zK+yROg#F+I1mZ%n3WojAP#~*}L@F+jn}whQUW2+o>m9(J2er^3ThL&4)}}Ld?5+}(
zl!bX7v<jbyLJ~BomReDglbHmnN<oXLf_{L?Bd`}iO(@8zv5<2L8112ltuR76LS$J2
z9w7j2lYtbw2ut9Dval6%&~PMc;2$X*5jQ~20_8PO%cMB9B)$Z+PBcCzu`(aF8we4l
zkc54~4!ZH_CanCWo>N#rH7>|PNGO0<kko`>H8d269Kt}^h`kroGy)k7sn+8_O<)j_
z4w;;lwNIbMY<p!}q6sY3kYmX`P&N7&-ZH{ceWP|_$+6=Q$c`|0!USzF&jb<akOM<x
z?4cV)jA2zZsHH?i@<$!XMM^VBH;#bsaR)aOz*!IBKgfvI6^uke&3un`vj`r`p~-}-
zwl!>gHnjq@oFOr%Sicx@h`%Z*!+<JP$hOe<;>?Qp-2AfC;&{m2it$CM#Rd6!n1>l)
zYz2YVSk!eMC~0G@m@uq|h7P6qH#t8iA9B7H4`|F4RCf90`{ftqLNXZ0J&;_C;Q*-r
zspkMt*-Mh&X*7v+AJka~4L)Zi=A<PjmL_LZ4#lZ0YGzu{a&Yi|Fi<rEI*T3S^lL~V
z4<AB?kDyS~74o1S<9hh)7b0z9SPsn`WOXiJMF)7`S|2>~2VY_UY6fC!(IZ+fkQExQ
z?b4l>KffCh0Ka0K>P`WzOvb-~9_9jQV31W%Aypu#OLc=m%N9W~04XOx<p!i-1*t<=
z?9(ATN#R#eQrG%^koA8-%NEhvbr{w|e2>(l0y)(tFSVisI*bO&>6st`dI{bY*nYDs
zur`t>apNmEt6tR6EYRsIL7ecKA5j5fq+-b600omU;5!IYQj_zGKzj;7?co)m_S15B
z7DA+3$bbaW*|CstE2fU$K_LLL{uU?)gU-2uq#A_ZA<ehHcInwWjfHuhXTh3ppr$SH
z`^7L!fqH_hAVeP90Ud!<ln1%N`VnZM4`?evJmg9ih*v;a0a8&yLh>d?BZ{hy03~9q
zbtQ)F&>*1tKI6g?Q&5b$fTp!U&B*x7<b3G5QP3XXOc0UYaoKa*t~pBf=?f%}u$TQ&
zhea!`Tm!lmILHy+atFBqheM#jLTU0YE-6Y)%q_^t2M^rFf*O~gP30k}MY)-Ii6yBa
zMTwx>-13W{Qx1q^f~!46T^E5$N37`t!+L0d_)=Kb<)o&A1_U!8*Pnw@2xv4L+;#xX
zeN=#sqle@eMCON-*-z}CeINKCKh$^24p8wo0bcy!aRM|rC{3sZMfsrk&&<y&Mj846
z_2z=wK@$w1yaO&czyx$c7=D+S04&E)+RR1_8I*xemNJ0XKZq0wseh2HhK2!=2Zq2;
z-r)k}0?>iUu0=)pMJ}a9nR)4+xdr(}M2`ZJY9q;~?~pf;23-~fJDL!2Gd!sE0Lf)&
zv6*Xr(A;;vtbO{|lz$h^Gp%5;Nl|MMW<T<18qR!<;R0x+f^J42VmP5BHMaoN-Unr5
z&{ShkFsN_^`x0a&WQWp(eV&&WWboOi@BA$zuX_&O<|V@p(9PmOpete^gGhMnfcOKc
zT_*`z`5d2-SeyYG`vYCSRa~4CU!0L>Y+(Xz>LJoPM)3#j&4OChL<9@WmV(O66i|`!
z0$!xxu>#`aVj`_5Pfg4&N=*X=Jm^#m@X6Ojsi2_>Nbz}6mVqG?M5MD=?Ct*kxXm8U
zfck~fngq4MzZKMUTLf=$BVq<pY#>e7L7J^(&1oTZH(*oeTS3KDDyXCcTM23qLZ)IN
z_QH!R*rhd;7L<qs668QO_rq-t1C2F;2<Tal60k!bC7@%+pfa6^w1cyofP4SB5jz7z
zCWuI%s<w2&ZSO35I0F*cpo85=wi?#aMU0U_rxfGConFLp*Z7RooC4@!w4kgHDeAz{
zfk^I<=s+3(fVhl`fl>-;YJ!?{pdtRm%sl92%_)2g44EJz{qz-;3TLTOdpHB)1WNM(
zsPm!^p5g>$MzrnmhyZ}JypeiP5ZlRGT7{Gkl9TfD!MjlkSs56D-h+x?aL9vNF_|C&
z+G;|&7#LzPS#=DO#TawDA3+WP`4>_)?*VNs1`+9N(>adoR43WP84w3hFDIfMRRZ=u
zh=7jz(|B(zGiXkG4ZOjGXf)!8akAQslxIDp$y9LoBUO!{ZoW-=vL18=2y}BdXmw&H
zh)BOD*V0hKX=)E=K&vXyh%6DsN_>1;DR>n$cx5<fod{$J9;mFy1of1`1hi?3<V<Ki
zMV2ifO-SVs9$TP(A#za=TG6MUmR|%rf4T_N@dx=S0MxjE)WV?bfZT9LD*vFqqugF)
zPzwXJ=n0bV@K_1;D%I8$W@4@>1lbM=cTh-xy$&YQK`oUvl9u-Arti+?&&Vi%HFZHl
zrbMJ6)LIcTXARxt1YO{W+}y>vrVzsw&>$gl?NAYTwHs(d2e`deoSImaoB@exL<)m!
zhW%s*E$)n9J|!#PV-~o@`mmB0I>b^9ivI|B`2&gpNRUGu^2jb7GG(p_ZHJIG<PHgO
z@PJ`_S!z)+^fGTy(-V@JLFFxYgcnRek8plumu_1AWq(Y!7;KsnbTJJP(G9wh1Tw&m
z9Ic=eD6l3j_~9?GWy{daMV42Qf*jd$P#X$jIoPX+V8*Z<nx?2;!A=A%RRuM0KqDo^
zd5Hy(ln2@~lnElBYZX2su4RD)15!y3+8<_<RGOJnqMMngkegbPk)NWFQj}N@+S#1}
zE%-reb0ACaA&oZlwmXt}pe1D3&C3N}Mo<hj613_Ha%2NMQNkGLm60N-pN7?mp#6OD
znV`F$pr(R`(lS8=wC+H1C^Qbp8X|(Eb?7E+aDflqi34iifg&H04A2U=<8#vAc$?YT
zr+4-6c^ul83(NLojq9Q~0qLS`f6yWdY-K%$?a%-qtC0n@9kk#zRUfv2^*_jJc2F?`
z_BhB9$ZLF&!X6{lf%2_QdMbgFV<3s`0A%GoQW^v6&PXgqI(G)q#)4^rrU3XpZ_v4Y
zM3gg$N#MhvLEW|>9=PAEL0vfzk*<GmCBMphAN%yV+uT?lKPZAlDp`3OGGkDXpHrEa
zpPQMOqn`m91}*{DDInV*$qJOfz~wiXK;&uEi-*Wc*ccATNlY(IErQf)gk1nBTwzz^
zM36HQ4C_;3?u!GTXqTIxlA42>%g}-YK7R^fa6{`fBJ1zWe30Qvpb8U|ejyDeuniyr
zzV{+MUR8cK_Y@VV;h>vMh)7GQO%#l<jt50NEHi;hajek|8SDOH2fYtX0qO*r4M>9g
z4IZTf6Ntm@1s|KPf3-#pYCY&i9m+!h6abKv4~hkF(*Y3+i24*B1h6x4!8JrNc_kTi
zOtz#bHB}!(fNTc&87c6=#Tkf5cih3m_4SLQeR}OA>l`IxbExl$9Far1iw<r24P<yG
zh=A^8Q?yT?HtDI#lzk3R8>o}Qpv6f%kp)V`znbU`A>vRc5V=S#ttc_M1T<ZNB_I(|
z2wB+p-wt{PICV2paz<)WWpPGo8ORBsz6?@aBAkFD6@l&?BRM7GF13o%z<~{F{3E#n
zQ9Z%0or2uE2+Pi74LQMFff$d2u30ky4Sa#-7|JtCGU9VnOA^5gQXr)pC@Dgk<gns9
zy=l$zu=v<eXvCAZN(!2<Fw1dR2?q)Tq#!{=3~X&AbeNO_>WqO|*$s*~$mqj{5<463
z34-?Na>ANtpA}j_os~e{S`OTh%u7y<2kq`H&dAS60VQIz0tpdTkP)PpcInFeE@GLF
zc%hCWtMvg(KFLM-#l`Vui8+}m;EmH*8~{p7kR>mWG^1<}-wgvPi?xUgmGr920xZTO
z{0GmZkRGZe)PGcJ+<>Q=A&nbQ-y3Ucgzx%*T!$b6>!*T7sYos-Aay?I@E6b_{wb;P
zB}JLZ+2D;Ypf%tnpdEfLrMbD4j-Uf~AiXz4U_hF(nB!m6b_yuXV+{)o2SED}lup&;
z<|Kj_+jH<RFhB+iKy_gzsDuC$(6&3KgxFx+BLP6OKsCNM3A`k2x|dHBXiP@^SC
z8s4!0TMr_j&3Z=r^v6>3PyVZfpO{9emtoUl2S9ZKD80plmSlq61E~){wnN%_@GTB7
zCqO+<WOE1U^wh(kkx$Ufn`a)lT!CCbo(Ws;gGk~OPOBnW2^yEinusy1fclEaaaPnu
zJj$j|@ReI{K@%+C<l6!2B!URU4H1x&(bZs`M9Qt)3z}jC4aY#@4rE&<h`_WG>Ps`?
z3g(>r^z_WUbd)70??HBfqKepZi2}Pon+veU9<CyYte`|Wk!cO+z9-PC6Yz;l0r{DE
zB_5DPNa+5>NI{VC3@V%o1sZ+B>TL`wq0vdL(qJKI?LO$*r{w&+vecrI_|m-066gi-
zpt#5c5zzImBKGMIcr!yDq*%k!F{ltDxh_N+MMX@RgRXQA+6u}-;9>|g?*!S@1-~Qo
zUsWsXcV<0kLx6fVF9r4a4`Q(yK6-lyx~&-+^q^hUM0h$UAGQY`)Cv#c2Q7dA7Z0GG
zEo6KQ(ug|=TQ>X!wrrSW8xlZex(EXULwagSK~ZWNXyhg(H7_|IbV>}Qgur7f)L&%f
zB$RRxRGlGJ<9|Tq;6IS7AcvpZ*rz|(mE=}Y?Ep(Z)U*<uZt%1mAnjAiZ8QVz@CT(&
zNVtM_1VQ$P!{#&K)9Tc+5v{Dh0}56Uk=}o2R!OvyxqW)))*m7ak1b(MR7#glAno`P
z2QA414Wxss??lkqR3)Hgc{!P|t+}B2)JzZoy+l{uKK*m4<iE?jDzI3l=3EQfCNPk_
zI4p;zYD&YRJToOdwFKN!2d!W$0mXb!VoGMdQ)x*_ejc=_1l5j^QCUbmg*lr`MR$N2
zZ&)h}47;IWKvrb|T~~m7x@Z-wHJ6f_lUSLW5}#I@2Ojf*RFsGt<RAqbr#*DfC?_l>
zfVz*6Lpi}Ds0Rq@v7&8^0q0~qhCzKrR<eS1#PSkz()A(p!ys2&1XThv;YIX$76yh)
z5Ru;KkbY^|t324iUpdSt)U^B*X#NDW5*1RSfb7L#In?KrR({0=nR$>~q(JEwl9a$z
z6Nn2*#*pDD4*T?2iI=W#>qKGxCTn~OxyG+ZEk-Pf0@(+#9vtT&E)MIVJ_j8hNJQO>
z+E$0ed2XUU_*~n9O7N8^5NpwV-8E@~$L$}%_UZa<MT=rvVOJfHH^L0J7J051H2Vax
z8qLQTRzrPE)+8ZPfTLWDGaEEd3~K5+<|Sw37p127r==CAmS8y`50ZnC)}%q3x>R)z
zXy^lLU|`q|4GgjhGNizO^??gPd#FLF4N`C+S`9NlDXx&(%VnQ_YDt?JgLDIJlKlfv
zr3xy=A@jE&S3$Bq9*00e0BQ0QG)n?M-y=RAd{T3KK~XB`^49ox$YCGgB&e5{0+vKN
z_5+;k5#<cz3=u2)bQl8~*>ox7V0OYShxoLZyzzJ}M~Z>BV5KD{gU93Ffr4I$fq@}D
zxHQ)%GcPr<2r?#-Uj#`Dpi}`VgCMmj=GYT$9EP)Cz;FgMe2DBMKsKj?>bRpU*bkKf
z9SxfaBGR?Yo|PU^w8m(rlWY~}v{Iyu4Kf^u4N%We+B!m70j<u$zyKakbxX`k292lY
zmlKFt23RhqqANh%Eu;{@nW`9IZFbNlltk2Quufeu=(yPA{FK!A)Z*mC0&v;}H6em{
z;X{h(c3!Z9uf2liZz7jmA?!r1$v}leP$H<wgjSP55-gH!&^RXlvfX!}b|+}MDmgwm
zBM~E`VvLbMy+^)<ARl3kJq)X$egm~)iHJSab_r6&1Y5WVnj-`EHFko=+)P2C3offc
zExt?;0d0@5!Ww3*G)TLfK<!b`x-3Yg1ez6q%!EOzWuy(NkRC3jW8$E5qQRb?3aXY7
z7K5@xCWt^>8hGjVvFr09nP8cQ$f;?RgJ7<Jn);ws6OdBj1!xlhh)B<f{NCUCBEdf0
z4}67v4J>5Iu>|cP7;qed2<S-K6+8HOfRG?0#}3eL=pYMFb_XTb%xaKFKt%e>_(`Wk
ziWBV9Z_JjMG`X`HI%GzU9dAH^^$N@27(A21rpd_n2hIa+;6s0I)6Q(%vsw-o>!5r<
zNoGTgI<5nG543y}k`_SSQ^@QlWcm?vLqDZfLpHgBYGcq@?2xfCaO@!pYm6cW60%gw
zWOATP2KF=9V$gac$WCW?l)@Mgi>c@38c?|c8t8?TD+Ikv-M%K;S)g>bofTA`Bm53(
z9ze!`A%Tt5%7oM?NDY0^Dt>rFKQpg5wFq=uc$$JbXbcX$Q4boPf()EPjx<2p;R3A{
ziQHRRo>-KZnU@YOmqAM%f)>K}vw;dS$f6~P<B=A4LXr%TR)EIGL46ZYb^sMlL3yA<
z-N3055jzkokiv%~E0BEw>O2M&f~r)o6`=M6Wcdrk3Zx-BNE8&4wYCkiLKZTz1RY>P
zUKS7X7^GzZ4n+_b(%XP7dQOKFJx^eB0c2$Y6h|O8+Q6rHfD#{i7R0a~8U{whO&ewv
z=jZ8z+li^*)4V|~=@QWNq*t)NpDSo0A{C?Ah7s)01WKlrpuRICWWmvk2w4n!pk5=Z
zU<3E4^>Y(ZCP)!|H?&ASzhGf~Q+KF+`sGD-CuPkFVND7r;zAX*xd^cuIX1IE)fcwd
z#IP6YWl)=)lA1F&u_Obu)*h5&13*1#(9D1@RM06gIXg8kB_Oc`y72`O1(1d#=4cW%
z-2-w8*7(P;92ywpWpC*6HqbyzQGRl2aWQDNJSQ;;vN{=b)pH5RCEyFDd_clZnR$st
znN`H6e_FT;v{D{x;NZ#}WEDF|9U9cFuXdoC|2i`R1EigWNEq<(wta06cJ$1KFLt4>
z^&tDO`X0l2Xwo2S)h%op9by$Ga$W_M3=lVfSl}`rOr&G9_n^ff=>98`^D4{@pnfDM
zlj8I_hOJPalecys+o&642ViDNYEfcIW*K;Z0#xFbfEF8oRvx9~Cxh0)<)uT?1UOVc
z1a!^_v#_A4)6fD6T!nxL=t3~0#sQ@LLCM+Akli!TCIWP)WNv0&5&D!2$bN7d0TWP{
zAhm=bQHIn~1g+14w-n>!Q}aqvi(o^+pk<|rDSA2i$=T>_#%R!lGKhd$VPFquK+__q
zbw)&5gxCQ+311eJ=MiVyg3QkZ5pc8N42VC9LGuze@H5AB(^K<w4eY>P$C_^-B^Od~
zK;25#3<adY1m465cL6B>fZI!mQ_n!D1neTH+mURCdIB^dO+-k64kAFDH4Iv49mEZ4
znSwF^xKRow;J3>_t@{9L6jEAmA{R`cJ2Hbdz>gt8G>akoXkNpvn)v{`D3gL!r=STq
z{k+oL)S^tt5on+=J_70*Jp(xa?0Zmq2GTWxEJOSVyE_(s@;e1F4BZ%vbh(fes0}q6
z6!vJ@4c=^kG{~<&cApi4MvjPxU~Ek{(DL8Ryp+@mq}9K_LDqu~_JcIYL5*0*0#r!%
z?JZ($5Hx|2RWm}Gjv)KvLC0tn=a&{irb9q&ZSazYc(=@)RR4n1ywoD1mo!k<DWGmH
zB%`B61SF#)mB7%D0QKi6hzal}Vo>@s28}L*r-VS&5^PCBd|qO1Dx_)x-7B97BA|;a
zMeX4XXqiJTr+`8<2(;o3k{UpE;;<MR2B4CI<S>8~cF<j{ppqyvuQb23I3BdMIuG38
zHvk=E3u?xur{;mqvK`77HIfxKNG;id#F7k9G6A1`{SY)13NEG?L3gx*h;()}^R6|f
zCHCoB|D)n|-fM$(@~CAosD+3;Qi$7PXrch^_$DGzfcGyW&g#*JT}_F|h!Bs0S_hzt
z4}NMvdZ${z5v4X?m`6cdD2T8RwMc|6FCy#2Ajq*XM)q)rKs^r1J|sH?QZ|BXCh#<Q
zK~ZKN_)JFdy^EmMs%KtFYI<ss8~BoENRa>v4;&2vXpul&w}4^-PvMB!4gn3okQ@}K
zsXjRa(jfH(6$YST;^4&clKgzk-aVv%Msfo*2&m`=2GF8QH~3r!p5TWj1+vm4vj2;)
zAHN7%wNV19ECS#Pf>J@Z*kYd32?-XYdJ7sXv~n6KQ1GM?B!@u52-IC5B8@;+5Ey~V
zz!RXkb<nV$M@UEjiHqb(w+%F@iq%87I<I8y4nvA`$m~o!(h;OvLCrHzz(TS;qHPLk
z9p1EqH_@Sy3+mz$5vHhRA%+t`VGSPK1FcJhv;`0?LrA)U54yvyfTX_X+dw@yu;+2L
z$Y4!lXo3Po7|EVTZjI!E8zrC<(=x%QFM#ZYqySI>2rh8J1oWIXRr~bs$E&ZNYN>}+
zMPvm(vJ1ct00lp&(+9B{<Or~>U;@)>sOQNl@R4F3GGYcILDpXbCA=M=0uStS^n~}*
z4t_H^G~rR#dhltQ%Rr4Cu<!9$PgUQ;hONGV_A`LCje%O^pdg6{?WQOpevKA&LIdU;
zoKuI8tcX<eK|_PmrXOsK9W;>^R0kh71)XJ)2_m4Sj<h|T0Zj$evKX{$17a~a{y}!)
zuo&uNvIYep84o%T1{!zCEr>5J18;l=6+4jZ2TuPWE+qRw4iI>6mkwh<eNI+m1H}PJ
ziN%mo2ejY?VmH|Hc<hFHo}A$^(3x;SouCO%um=&vJFHBFp1AQ1Hs(WVvjMuqs2F}o
zSYmoQsH6ccsSaYsG5`We+!qj49AxB^4x9TPz=oRC85kJAI|cNeb3oU~r)8!V73=%v
zr(~vOrlx@BSilDwfcya(m<g(c4<pI+@GxY8i1fJ6tIlOD6m>}Fu{(RRyCcv6nqo*X
zoC{PH#>2})khwSvhb9TCE!UV1Iz1QE`T$`D@YoQTz&T7#-J)+Zs2-XF_c$V%LK;9w
zqx4XZ6M3a5Xh|060@KVK@XP>cvLi?jWDPhsfbO%(+{nbhkS^7cYTUl0%s$;G`|^!A
z?k-qGPpLKFoE^%<z>o<#voF11gSnxKda-@Fq1~CdZFAe8OQy)PMFX_L22^B1T5e#^
zfQWS8y?KHXue}kTfrc41hjT$!)`1Vr2j5zpkq;Zh0?hzsf(YmW0~Y)ANvG>Q_Sfpd
zf|Q!BZ~^6)ZJ^8u4rq|QI4p+-0p<Jh{6X7zLCaqPOY=)oQ-Z<U)F7D#QG!FtG^DN&
zv@D?9deEQ?)=C4zN~n)1t*Sx020-QBD^LNk2ULE8vkoW(APazCCugLKwVinR`ezj^
zo~dOqALvxylb|LlIF9jH4D~UnrXiwxfIco&T$z`gQIwyTSp_aCLO{c{pkbN#+{Elu
z(4jUN`6<vbYtRO@Oc0UY{h(g^=0-((I0G8d)Nup2t^uVgNDc?th{IZl*OBJvK%;B$
zIl9Dxg4Dbe$X*R>tGTpa@iJtBi1bgYAq5qV7wq8-sDG$a+Jl<dptOY6><7myh=9&N
zAvJU%Z2__xT}XW%q-FcdL1!hqgPOV^3o}8j3@`yLu8@{uLaYWw0TGD_wWAFg>jTY$
z5SWJn4fAAz2wXlVr^#muDilF?PC`-*Xf<Fah)B-`U-n>Y4`)EUNLFVSDRdDx^t}Vs
zNTA~(AU2->4e5f2biQl<EzeA6w}&$zHiM>O-BU|^OG|Px%Q92TUBIWbfG*bvQh{gq
z>sky9nIIzlRK}CfUCZO_;S7i=&g86eLUKQ>ghM;XAF)aoqYVPFm&mvQH9^2r@t~9s
zN=`u!L8T}-`Q?H_2t=gA);Gf$5Ie}qcaY=<YWASr+ujFiW!(Zbf55hbrpY05<q#jf
zgk1^s5;_S<t&l}4!a)fhlIkJ$BDL-yzNTc=LrG#uu|8yuDLykVtthd$q^L9*bow_a
z{2zl3;)#cc|4C5zgNSrRyA6-`J22S884#CHnl~UreIT0+KzXAUo~jW|6-dR6QRY!;
zZBSZiUUG3JWPL3I$O+rv(GN}oAObY=10L&yGa#PFo;^VoV^AunM+wrK>E^(|04CBe
zI8HBkp&MrpXFyB=<s(Sv3QU5+0#rK&Nx{Qn3n&&q1az2z8#dkmJ6w~<G2M)kk^<1x
zDg~99pp=WXnFRJH=&T4B1L8<Z;~8;#krZf|fjPW_K@4fZEJO=ra!L`jEg4flQ*|IB
z{m;semwb~H?BNWE?}*%3mtO$d=>>`)c~C*|6rN2$L-F{|V=g9ZSO%#GPRq|LiO)^U
zOH7BX>jmG64GKC)MS|!K;>axIb)XQvL2yd;5@2A+1QGE15zc@FBUuYVV1;o}s(yNE
zNls>NCghX{Pz4P+@fU14K`)ZkZGx@S1200*Ps^-;Otr~^qL&Sxg%N9UaHLU^(+{YN
zfRz0}vwFyzKae^`kdP%Z6BOhZXI8)_%E6<hE8wNs5>O!mBA^3WZ1!*l#12rAKyphC
zDUHIK338y0(06zS2NiFS%{-8_iQzXQ3$M();)2v<@Qko8s0cw+)1WRN<al-X>UkIg
z;xkGIfr>#pBtY4o6I4OC!^>qvR}11bj5-SBG9rQ$y{83R0dNVF4E^CTh&YO4XHLmE
z|3V%6bQlBTPf81E#D**$P#ms=dvqov14AZ=NQWQ731dKPCaZiy%K7MP(RPCtwS$f&
zh)+yOf$pg<sVqo^6n7wrOb~%N_y%zfr4bIlvWFAYw-$qspMaJGK~Ca=L^yo%?*b(a
zLg*3<<P%<cKwXqFEHMvR42aPHAg_Gi2(kq<{0a$HUeGWch=3jnEMgC5K!TOh^o3{y
zM1zhy1C<Qm=&i_4Do!mbOD%%91Jt31Ofo=186#Vf)#yhlsX&LHz&hKAD{YCZXviuj
zvBdF5Q0TwF6337whqCt2%aLTEgP>%EK4fMkH?ufdKP5jo8Pa+Hm0LlD@KjxLkbxl+
zL_iOWDuJCGRRXn{$m%VzB()+FTtKUW3N9<S*FeX)XMzZ5e#PiVk>xd{2ra0DUErz@
zx>i~NZZjLG-~tiQX{!IQ`KtfW3`<s0LMph5GZG6@K@;WRF)`5aWza{s?V6yu4-k=F
z6CbnclGu5BI0KTQ$XY7^u{}4jq$sljG=!3png-#3YA8_NL26jM2UYqY0y=(zaoHYO
z{znRVP$oj0Bg6tq(x5pSNJb{uW+Uqe4<y?`&En+zytK@8(CsKCsYQ99FjxpmeFE^x
z3!JM#1T+jV8X%w(T!^TpQHQXgCwrrXH;9Y3=pw6$49Ucx^Hb7`5=&C`<MUI%$qiI=
zBSki-QHi`C6r<cEG9M$lIbaKR!JBdf+rdOOQ&C2Irh;N?3Ovs+f{y$G5$VDovQ;<_
zsoKLCkUT?PRrdsBixu1_A3!}q5CI)<6Ninw!4BJ};)rS!XhiiT+=DkjSrSA*o8RB;
z;0#JU$N<WE)o@!bfa-D(k*=Y+fngbsj6IwIv4yOn2Px~t7gXjXCLv!eSqqvF01aJ%
z7mFl=HXA{T0fLzVRBV!*0Z~r81U0mgast8rIBg0Z@G%CUekjD>plTK}1%zW5kGh5L
zRZv^C51zykg)fdyD><3f7L>|#;i(K%)MbJQ=*b{L_HYIyl~LD+M?tGk5$BSC%5=y<
z**JViUQvN_+8BN>0*nFiBgv&IXzD5GG<@&~QL5rdCS;XGkX8bCIW@F%1*#fBSrI9p
zfViOOV}Osj!5I+0QM+FU+9!czKd31U9*Trk%^3YDN*jiV9>*Kdm;oZ~5cD}Yx#J#a
zc;GKQcOcRyj(!ZN0|XxP)d!QH2m)0ML7)@?DMCQEw`GC|cpU*}Kq82&42G08P}{Gd
zLH8ig5g8EMK`Z|tS2aUYCe=?Ig7>q{L1h;kma+>Hau`iEvYJsywHL~1jNr}Upo$CJ
z87(RS9jObCJ4gWoaRx?%igxob`Jklq6P}c&gBlGW0@{Q637d)f3GK&Fv5d$CmGL{^
zR(}Rr4I-dNC4GmTne-iMHPr@N0zioXbZ0jt_0c4YJ_W641uZPh0q=;;A<`;J%OS+b
z8u$?D<M7Z04XQy#)*we}Vbq|Y<^;)YBcu$Dyu}n0qDZAL!4?82|GQSCCW9CFf##n<
zB@~(=HlXAIBA}xcZ|&d=0}6^Lq`ZoHi~WC228I&Q#9J_EP&Or`C^4@%2YgKqv1^RT
zDl&1p2UIvCg()a>AT=ta$UrK&puqvE0!fZgNDmOaC|*Av>sG<@ybKKB^=3Kwxrr6=
z>8W|dh6jxt1nP~F6fl&=OL0kMPAdFtVbBm1_>6<h;&`x3d|GA>bR#LqX^@UGe336~
zk295)^MGxHy9G4V7L)=XkicU-G>w4fe@RXwkW7zsyz*&r1_sCs8rViGNfR>Cco(*`
znL5^@%}wI57V2Aja@ME722}I&!Pik1fhw&CPy+(&S5U5mw3`wS{%v4qbFfeUeI{~#
zkxv?I^&K@VZ2(z189t7R$5N<IiR>yA=O<^Uf(KGpgZk9qMRv(K`NgRz5E)`~Hp$k3
zrs7D-v!H%A5eXI71Q+PKj}lON@$^kB2+hkcN=YqBP4NVuRYLqG6AB#x?v5em($Ui^
zQlk$Vi)1C8l*E!$&@8ilNfoFT^#-*=KuNWvs<;Hlkz7bNLyIM9*bHvtlj2>nS|CXE
zC2|V{bUShhsNIncEs8-?oS7g3+OXAx-Bq9o%c#_}ULQ2A2<lxGr55BDl|UMQAlq>`
z0UF_CIRR1}gFE1`#j&7>2h~eKA)uuNpxDp+$<Dw4CekAwXz%(u%MUgX3A^C{X~`<+
zcr(P3)x1i`dR4GJSk4v)_h3GQj0X|vwI#1krC0jdr*~f9Fk4q&4w>2^()4uDc@&w+
zNC(z{O$UV}<bV`NW9Nb$^n%_Wu#F?2Q)9^}ACQv_XhmQNsHf~ynwFMY6q27GoB@hE
zaPT2AE5;BIG|N!I1)yRSYpIA~Cp5i)hBb)rHR^-{QjUSG-~bKAKu#QhcosBh4L<7N
z?u%qQg$tSX=|@Wx!|Gcm!tx4Py+9<3p;y9#(kW=uK+tq}n<WV}o(v+=<I-+uyf#m^
zPyc+Rcw57TE=WQurnL2j+RCp5bp=7kZa}gq;&LBILQ}9$?>(RL--g!%YCEMKM_K^=
z4>VA;2kvo13WHe9V4uDTa#bwUYHCF`=vJGc@9<GUguU>!!CZSL`}gO<uHdA!+YLYM
z*%CC=5DWJ(X!CF;h)DOjy?@iCB_{UiH`E)FQclJ|Jxpm>BPFs#(5%c9P+<rzPY?s<
zj2*kHL?vbI(;+9rSwXF)ZXz@1Vqge*2yeb25}Ewh>)JXy<n7aE_c7#&>AOK~r_|#}
ziL3-Pl3fZP1wceLWZ}{$*jY<lP^&2&e?Z(cvlY~4<%B0QaJYjA=)gUbeLCd+EH&6E
z&!D70Nnr-v?+BhF2OnzF25QrSR=fpg<d-`o7DHPCh%kU;G|biy)m;J_4#rxrW4HiX
zu!Cv>BEq5|GdUZwpAOVC1)skf02XpBD#C2mV$>qgnu}!1Btf|eTgwi^2B>$)ngay4
zH}nxV#p^?saHHSi3#qX{gF~Q*h2F*X2ew^a4pw7P#|g;o1<<-MtdV+;!TA0`e<UYB
zLxIu)9JcNeu{{FpcTg)AvO`q#@4}ht*H!G(ExD_2JzT#M=67mY90Xdd2TIG3$`X&o
zP#=@k&qrz|B<F%!2%rT$K{lW=4D4T!Zy<9y^#Ug+B)^WdPxt)zk@IF{G|a!$u@-bp
zSP(O)rw=N&z&3)3bPQ{uz6BLEHjpDOA;%bkhlQL=bJL1a3xiUNOLI!Fcc>r0j@)|y
z8z8|j3Tr}xlpaXBp$-WkXPg5m*bCrC-4uiJUn2N~XV4S`q=d=ONi1?qOUul|QJ^9X
zMM6s$nm7oQ-SGqqk}IIWLZk6VDbNx_P|8djf`g$*{W4Ia$Od%avtC+GetwZ1B&~u{
zB90Z~(8#5<$xxD+oDEqn2+HF@?x2hg&T)wH1*2Yp4ER&eW+hP5G!dTH>1Z=(8Envh
zxSzou2NBSTYRptjR^tRxmx9L#K>LtNGV}Am?Icj~ipWQxL=2u&025FbAQdmr2&Z_I
z0&)vlJgVg&PlN3R6PT7mGcjo7kcgHM(hejYQ2qqXx<aoc4-N8xByEt#A(mmJFQ`vR
zw+vJ{VJ(y}tbqE5@+C>nKn?Bfpxy;I{y=kS$aCqO_Rw3PgkaSp<yM+;f(HCSR)S*<
zRB}MpA3>%$SnSiUiy9xg-7Em@RZwo@8BiArWDlh20k#%IAkqn>L-PnWAVk*0J!JL^
z^Hg9^td@XE!t~S<aN<QzK#-akvnN4SSAdq+27xv#K*9i#d@*c?rY+F6DkAa<>hKV3
zrxNTeq(z`!6li7u)Pr&@F3HSI#BweZWZeeR{!wTSqKXqhKE;};Fl>c-oV+cY;6u&x
z3raF`Gpkbd<8z7&^^@~+bMwJ#eGzT|=Q0o%Qg*cLynDK~hy~+*F0xiMK@uRU8%m1O
zKqX~7u1*(*1E7IGK`{+Ip$S5PEJxf}3-&$YCdtxMi=`6-H^P?V?Shpt)Ez+C1zOn(
zIu0I^^6^*?^*iMSP6Fr<Owjrb(4CR4c`4{Q9wX^N6FlWMgSzr0rFKdO=-`W^zJNv~
zK`9&Evls;o)U#Bx85Gc1{fuEVG=f1p=1DKwz&RXxBse5->!(1jEdX!-%1bQ=T~Yu_
z@SsqDI0&5JL0m|7hj;E-7pbQPIZMM5d^kCME=YFA?jUeI6Ax}Anc&L$7><F44ye8&
zIdmXx#N0&v)QXa#MDPgObkOKH$nQ841lq(twJhes<zskyfLRRnF<FHjQkNIh?|~h{
zcb|=cp#(JL6kP0{Q;N~a#c0VvGXpi906K~rlKeo89B|456QGG2u>Eib#Q#X$IMC>_
zO;TxQPKj=2o<eSFNk)E(LUC$IcxGNoetAe{Nlq%XYX^#0h^dga(<j({p`T#G5L7>c
zuCT-ul)H|Anyet}A>}!Ug~K5b?-i3ZA`Z!2(0q>Nus^h%ee>C|FD<He_UZNW3m-ao
zxWRHZSydT|-N>Vc;0q?Ojh$jx5Aixu%z{oKwn@s&ODs;#%+$+Bt;oqttAxfSqQ{DH
zehD;r5;@9Sl$e*24<2?)0p-CG_y9g08!%i7TIpZ|E>HA8eNH{_vJCKH+j`0QdC7?-
zsd<Sduw+;R8oLD%@C%{g45-sVGqYqCXP|v+1djeecnngwAa!^l!wh640I=`%LDqx)
zU7T8yUjQysZ-Ig}1s<&6{0$<Y>k_EG^qKJV8{e0J+iigc_USMNExZlBQji}$!UDRw
zI1@yqL(;vRJ)A+Y?Vu#5kGkF*bf!s=D18186yC_|t*NtH5fbHCY<~gjLV+rjjN+2S
z<ZMWxho0<^wj@CE6P0JWmxE>kLDSvnbvdLZ`_e8QcKs?e*O8U%k*ZYGiFVMMHLNun
zhV@Y2gPOraWO{JXg5^?!3edVI(0~DG#TTxHUJ$!6d#x1O4J!Wd_!Y@Ms9z~<N5jvJ
zcn4Z{1nR68gBC5vrzIwr<iifCM<jZ96>;+TJ0?|D_=&S*wce0o95z9905qDq3rlXp
zuo&up^7c=y0ktCZK|`nDnw%fB1sg=9EAzXEWj?aCPq*)_{w%HL1*^%)vj#MT0XeD^
z9D*PlGC>5UHBjG>6^yWDrin#~IXRHK1wk3G1T@PSo?n!mS_Hb4qNu2}0Amsf+#p0g
zNeP;PspSq(YXxg^#;_O~03e4Gk#*6=oxq(l_|QjQeqK%{c;sasC{2qnFffFq7UgE<
zftTPr=jWwlf=02i7iLH~3>p^Hby60n4hIDbq$o$UUoi?4NQfXc;z03f13o@Sueh=p
zw5J@S1qV6k8tDW9s2`}X!VGl6T~G;p;1=O<cux|>fYic7-fmW0nglI^L9P2{P`)G~
zbc(4~{7nQ^w4mb8CqLZ?ykjFRzbF?yjX;`tn1i)ca|S3_v1S+yn<2@A>Zf)!g0AfV
zg+no@H4<N3Qj}Vf485ckbWU+5h)7>zZ18E5nwfpNVQj}$`9up?F-g|QB~mp2IxGSC
z46(hSWdood93;1Z?8e~$XmF6VRs+cakcA@1*LswJ$`Ek|1_tOICeE2f$vLUc`8heM
z$=He!jEI6}LF&5<d>tLO%!1(rXyAZWsDgKKBKC43(i^xU1aTqV=|rPY1Fe<D_UTQ>
zx0mz&x&n(~&>Rt={Z@EPfw}|~r6d>gNYPZ3T%1}`S^z3CK&2ui$DsLuRk&WH^qHT1
zy7C@d4#uBFFdtAl*n-?8PsLT5V^~g!$05A~P=_!+GruH1KL^|!1dW7%uNQL8&nrtU
zDoHK!$xqHEY55je(SY4GpmYd{25?gckpytn^yIau5NBk7W19oia03zPxyLd)wElS7
zryuOz*JP3mzdoBhYtR~Qe?j96AR_(H0-wbvk9ydrzl@)BN~E{|I!j5OEof(3Ax?x?
z9d#*?FOAtg{a;lp>vv{7*g8khx*{U7O=(_cNl9vPiGFfoG58RcW>Dn@nuH8VEiQ4!
zvYZiiS^)G2qW7>-JaR1rML*WmfMF3dUyxNg!sZ)6dsd3|A!Fg7H45PD0qQhEoB&QK
zAQwSOHpuAvYs6g@&;pvgnLn&|e1k5|1eHxV_pxBO0U8XT<MW7!V6^5h)D4Ksq@g{6
z+{*aWisaM+P&EY(6HuoX+%53RPs+(ma)n6}+svV{^Kh0SxGFTVnq83bRY+>l&&dRD
z%>xaZmw;vtax!z`AtzO#hY4)f0J?P;z9ErnZUBwyKoTrk35sDeG_jD?F+r+CQP0I{
z<6>X{2LWiILS<5ZDQw~r<Sxh}QFs>xHs}w{In;H?M9_K-S@>`t9_yjuK-RJ*q;SAk
z{jG{RcU1t|R{|o^r<F`;zRBWY4`)CFfa)%2fx7^l7Scd201?nTPTcI_3`l2->JA7*
zbpWFDgr@-*gGLUp8)yf_;&K3_FhW{gN23r}02+1u3E%Sqn&-|05$TY9;EeWg1|%OK
zjTC_T-tb*`DVfEINja&Iop|v%#RWzAX?g)g`Jlb#MM&G_(5gv@H<7!#7}kIWW^v5b
zBo-y+rNgFaK#Tq}K}5RM#DrBdx2o8~8PE&_IxK<UB?6!t9kP`#R^p}W+d5Xn!Wl>u
zP;XJE9w_p!fW`;G`43?;Y}ga+h#TzgM>}O5RJ}oND~F6S^4dek0ePXVNV41yt<Jzl
z=7SGk05v@NL>L%?n&9q7WM9ZCJB%ALu)7~L$r8i?AB;tq0&m5^7?AKMs}zECCqRP@
z`k=NOsI3BDUkYwNJ%*1tfm*PT1;y2^tlydCeqik7q;T{dbnOMwW=2r@hV0Pa_EB9+
z`dW#7`dhUh%U*7N2<uCdwb>ckI)j~{JO?!91qxfp7zZ9pAsLY3$sN>s|0HO{9n`l$
zZ}7khc=&A!(7a0B+P)Gr!s7}r<v`&8X{KXX5A{7+eO9E}9%+Z$9neB8(4dH0VqS8k
zQ&N6KKq7pw3*99*VLL4ZV3AKnmw<`}tZhF?C?MGl4Fwu)NZ7*1z)%7j;wi{4Pc6#H
zPtVIQ%Ei4m0l9X76ah4H7pQK+8a^1c6f}G&9pph?)KkIDzyRLe?VDJV5t3R_5|CI_
zoQmT>1f;oGXnvulQ$YO`JmG+3IW!!|Dw2_M3r5?x8Z<r(Dwjhtb5n~z3wzPa4M>zA
zEro!F1J#`Z+D?WiFpyjT4Gbz~*jJz<o<IXO!I`-QIjOE?sd+?Cu2In$cR{P^!{L1;
zM45$a)QwilvOw+Bph!?L4$7*TpuP&2NY`abu4SH_ZJ+*g&!d&@QfHurCsJe8l7WE%
z-dIh|%`AbmR0AplOHA|vOPuq;+cpbJoIqXD;LP;A#2lnnE4Y1z$2w@#leM-sz|%)R
z9yGWFYEpqt*aw#aDXD3xMMbG8@u}E$%0f~<X2D3g13;0BC%Ge83H38slei#1d*&vl
zr@Ce4ltA`Yfv3hmLr3n3rNzaWiFr;rr5L3jMuLOpdTKcWw1N$*?=dWf`kt!$CqTy<
z2C>4EHz+V5yH?;Q;(2YbnDg_h3^aKcQ`L6x!avX@<B%329^0XQ2Q`#2x08Aol@@0w
z=3w7WiZRFpjXw;dutoxgZm2_OFgOM-UqE>Q(oDSMclIfpvoGw1QeRjEkkv|s_NBl#
zz3C&*^PI&s&x2t-)Z=8uKcp)Jn&&}mT?Z9CpfU~;>}Z~cO!v#%r^^{u<t@|>gn6E<
z8U|rIvd=*w4Y3>K3UHW%3Fu4#lHE|B)2IcwkB5N)bRq>qUh)v%EDPF`4zKUP3rryu
z9=1z8K||8Wx9lM;@r342YR|a4f%;=i@D>GP<OALlfiWNrezNR`BsFlKAA0aJ=;9pk
z6vYwHMv`anE<ZTYfe84jbvOf>-iSN|xg?_~H8CYK4}6a~=<>i4P%#?sQkt7v83L9=
zZ^vR(EYJ`q$3mRB2*VPnugDq-f&?q*gkYrYD4-++$-roZ2lLdA?~V0z?9(6TF1?<X
zycAY=IFYj*1!6UF!GUw36NbG|FB3V3nhalg0ZuQ7kVW&}64CnsKXPpC(^C|!So&Dt
zEBPoW8<7(%s9Z+a4N9TlVi8QFW7rM#B1UBjZcsYrrKA=W=jYM0Vy4Ex)e6wS)lASJ
zBRJ?016Pnqat-@*H8c6^ZC#D9lts1CIdE{}u@4&Dl=e7FiZb;}iZatcg*5me3q*v2
zeGKA4A{=rA6qh~p3>hAnkI5<>k-Fa?7a(UUoa00o4uA#$rGpR1mV*vJ2Or}BnjwS^
z3_uD^#2g*k`0^9jLIO&U@qk)ajM6Ftg+HWd11AU&7ZM%tP8g)FIte`moUB3>DK-#S
zIDzih3%U#H4`8+V0_=e4yRbdwWHm=2g(|qz0q+DUD9X$OAGs<3TA>aaeQ?XkPb_iE
zFUn2C7=3_;0gM5yzNqUGP(HvP1u*NO36jzrfxbeJi<yA|zQ4XWvkJQBP7AbR2Sh+8
z$v)b_8PIT`wnK0(RfIIs-@|V8dJj#Ebh+LIkuf3BfHZ3fi4UZ47|<>+c)K*QC_Oi^
z0@5%ACqpc!cY@+RlhcQh0ZgR(Z52yfICr-_oB<7J3btT@lYUxJelE0?2�af*OeL
z;G3ZE*bng`c}s**dV<Nt$sn5#gH}<2JRM(Lo>%}W`9K9SWKItwfT7+eE1jWQpPyS$
zlv-SzngVh~9jGfh4OHubiyk~NOhE%3kNqN`4#Eex{fI&rS5p>r6+97ZnhGjY^1;E(
z3>skuO>~2X+FkOKApwoB2NKXoMJzO+Nwo(&y9tUv$fyb)3!n*!Mm^gd?9hcbsYN-7
zdFexOwuP)Tj#ROt7wTt0bCMjOPADk5ffpZv3Ft=0lXmI*+8*ranXN^wb{4t=&<b-z
zCWAF7;0}PM4YJlRB833tFn;9L7AQSJYF2O<fVhyx4P>lM);@iy?z)dN-{ix>fO@SP
z&}smP)!-rqkJV7$Q*t}uVbJ<|(6%?<{FKt1)cE4eium08vee@E(xRN4%%u3D{G|L6
z@Q@tQgU*z=2jp9<p2t<bQEP;IKB#&DHR?k1iZc?63Vaibv$2<bNJIV5vX5GBz&Xke
zDRhx6hQvHl^8qw93EiC(?&%in>F(#~<LUz4p#)kunh7G(-$kz#p8i<X9?pRJfXMj~
z^gWwt`9;~#K^@Q{=b$cl#RReqhm{bwQ+JI1HUk4gF=$@Y26Pa4MFH#(Jj8X(kgm}a
zyL1=>;&Y@R1fRbEJ$6AoD?c+2(*7zgL0+Z>&XRacg?f&xRu@u%gf`5=4r*qDmP!O9
zmSo`G`~oi?A%0|ml}%K34Ps0S92%f33n?2h?1zR1S)CrF&_G=>QxD3ip!vXf=+?D(
z(AB>2Wr;=ciACwf*fT9s(;E`;R9?pe3Iwd>79{H;Sq}{pJ>tgsK)ZqSQXmI;z5tcg
zpm9^s<iBfPaz1E472Uh=IDj#r-X+66(C9N(Kf#QKHwd79qIARuI?MoGfCe63>;bj5
zLAedxmyjjUO!nz}bC)^USO+*jeM!AqxC>-;F}$M#ieE?~$FLged5ShLAT|$xR^b(A
zrh~m74T^A3wg*qXI~Jvv=BDPAV5D$JZbw>G2n`GxxCm#Gz;FXJR49!Q#2KTTK!E`&
z;?qD)l+3(RXgGk@OF@oJgs)45oKG$WEB>hGhR2{WSI`yKkWLGtVG7CR4`2gNZ=s_M
z)U$af$YyX(0GGL-M3D(1(tpm`9joulh&U?}8t_Ec=%{;HP;SiK1-d95l<py!4|KOk
zCWwF@@}h+}{2rR#D7R7{w4erTCD@msxw=dck-p3!E&Zaks(rfn%!YVnPY39dUCM1-
zj%p*Q;|p2l4c`O?xm-aVwh4@^wjN4dmI=<=Y@owUL9I5>aBgm5R(?@DXv=eANeOIi
zTO8;z4-k=lA^wHn;^%Jma0WEMsavmhb2BgmErz$?)`K?pfr#{{>R0V#`z`I^49L(1
zb#0#kvb_U7rVFw-6GWu%p8orZhJ%ScoB>VXlrL^bE(T94fNqxpZ@oz_F2b?<2&2M-
zMgY~^07~<uRJBOWR?t8Sj%@&-YqmjCyDpi@CD5(_$RbERhS8yd`iZPL3-BN%_%=J@
zrU)=BheRrpw?H+GO-5pI2Grr8Yi~2X7#SGSr#*db^k=!Yefo*%0S6zlCc=t&Y7c|#
z1r39Ez~{otKx5h<0(#>J7wqa0*bN+%mJF~{>?}Y-tm^P}|8XFDK?Ho$1DpZLht#t7
z4#>ZI;r;~;=w*Tk_%;dX{qR1}!WR@YM2sw=)-WhTo5cm7<$d7d5WJgyHoUn78W_Ye
zunX}tr8O(!+Gp@gIp~HPNS%%+gFtMi`Wnu}bnvPOPEZXG8vB8`1C$LjK?JnJDq^4h
ze&w8RQ|1K2Y9dMpy)X}3SORMEf&u_i<Adv95COf{_c`KTUuaoDeJ7}bLILaqkj3C6
z04Cu3KA{X)3yb<L_=f5NkXs-HCZvrAU*!D&Hn~XVygDf9gJu*!bqM%c*5cBn_>|P7
z(sW4ZBPQ4}cAP;&pF)cdfMOfbq{L$%q;RIV$BcTe*c;F$FD+1o4<2Dfw58#d?OdL|
zN#V~WVC6fd?G04xFN0dQVC%sN4UhFu-&0!RB!aIE2gUeOP{4yOwuD6V3|0n)Oc0U2
zeB)lRFZUu~2U)^zBBfRd?+Pm6LF@J*MGUC*nF%7`c?x<cI|H<Yr!>S-$89!&b{3~I
zGcZ7G2M0TdfQ};Ev`arQSxj&B22QB$)bjN;kgvg8tU$J9g8B?#A|2k7hwO_Kf!Yho
zkR;b@NG*KA=QjSy^`CI#9UpAftS;1Iverf+S&X!|DiP#o&?N|vu>Qfqz>o<d(k-0}
zkI()SW}j}J6FudoLNc^6L27t`s(koFbP;IPKV%#f+{DuZ4X+lV-17*|y=tIF3W!Mm
zv-0C5-y{WlI0IT(ku?ShwiD8<M4N}c1xidC;E4%jbtZ^_TMK7Evo93`7t}EZcNl^|
zp;MBdUjjX$3B9gB8nB0k0u^1s0NSAJ1}Z<n$r_K{&=8<BKfw+Ov;`I1_dphdqaIOm
zLdJkFhS({tH&Ba_a!|nywjS(nJk~?}jg+)OYe(TpJ3hX+G$}bJvA8%RKR-J@9+JxQ
zld@8iL3?1pbTPE^0opwc*+LDO{<{ks=z*U@0U8(~qR>c80#C9}0o4G)@UAu<;~{=1
zrqSeg06%nZT~TUsCTKK0F$dcOIJ`!NF`%iLMy>)SD^fbc<ekEXRJK6Zw}86J;LgY_
z&<q3U6t>j7^qkD%jQG-m0@$bw;t(`Q_3_CreZoG^%L_98!4}(*)eptvnynz$fTjjv
zu1QWT$Sg_B0j;(IX8}G?;R_<5)%OECXqEaBIt)o|7d-_f-$Zy171Vdj1QF1#$`izD
zFWM&Gf1pECKqrvpq?T327ngtzTELcmAuV^LIhX-TzZmX-1`nu7LPX(#+LJ~Zs|BsW
zL)tozUKt#K_1vDr5-H_Yf_fiVb1B@Hu;U4!z9ehB0I7MCpO#jfT9TAs0ct~AfRdmx
zsQw3)7T{Jam`ImVarxOKrevR<Yktt&cfKyH_dr$Kw}bkKv*4o@prRrZM5LEx&*VA&
zS<*heY0dJm_*hkF%Z{p+XQ5gS8YqLD%K^Vn26l%NG~g+1bRZq~2VR5+>N1oi=465{
ztdGwtPA!a211*Gw^kxuA4`YlEn(L_O98liJng%iKh6V-oI&q*)ui$PwX#74grywH{
z;(t)!KvE*4vc%joL}@z=Hcp7MH-}*{)bpUhCCp=9Koi5opecQul+5Ik<ow(MXrl)a
zlaL$@ANjZdD@3px2<kutsesyt;Mxg~F;I6=?Mj%`%=FYe{lubz#N=!!1u82hgH}&~
ziV2@m&%827G@-i+v#CyNSAnLQu%=x|Xd^iY8cZ?7tr$S9C6LNb=<Is2eql*`YD#8F
zevv+C#wjPUGQSkOu6-(Kh82_&Kug+PG81$1p+iyVfrVL<(%fyJSwK9Yh2$b=Xqgcg
zS}B>uC7_YGVtw#_a&YqxG|3FH3zTTUgMVNm9df<8rG5JB|KYNhmn>m*3uq5L5%o91
zF6aT$nc#t6P>4gUL(4B1)<L~S>97pyK-gB$isD6}x(w`FM1Kl?N+RU2O#_&3DYYK8
zJNF#ao16)sb--gi)b~^#NC3?=KtdnveMC}+v|hyR(~AOimAX#C?~5YK`$)}u)V(Ai
zOCi>yMLvf0Q16p<MKZ+t+(htD3aCh&2`Uo7Yp6i$98*)AGV>CPD*X#mi@>L}V=p6-
zG8wcgr;cMl6R=pr08$bnSqlvVvYJznnJ3IEA`#pBL2&^-R{%_;@2*_soY$sppT1Kv
z@4)kR`1Wq9+I|w$jxB&Ef6(eK9JWLJjx?G98ghV-W)zp^LIyCvQH^W6h>L_ILneqw
zUw8e>f(2K;*r)SE6p41HE5jC0k+p;i^(0tm(=Zcq{UT@(GRPg?SCr}DVaNm#=?TX~
z))bmOvQPh{B>W+}MAQK?y+&3G4d#H7qSRD<5CKZ#9iTDL3V6_hEQQ$2nB&m0qv*MP
zdcm8U#WSM>p*B;{O9mYp09v?(a(tQ}Xdd?w+<rvVLbCRA#EDjrs4XU|VF+r3gDNn6
z$Qo^s$A5t`Hz+a3Czg~Dy=t3UwtoeAI~mk7Lo3m6w8@cDG$@E{l5_HlAt(A^v_l~c
zTg*Ems6HhNz3pr(sHas5PrHb^0mC7X7)0`lD)O;9#RWOAb9GAdGL!RDK*vF%3^0O5
zoH9WK^bRvM*c^}=tk$JsuZa)jEeW`{Kq(~?L_kN4ka{eTED0JmC8Bu)z8j+;u_ObO
z20cM`tbmVjf|^vBAOgM*5YB+u0h;M1!Va8`TjUeNet|~D?cp5|P{$n7JBQRK4EE4*
zGT1a1HSL}S+Z*bXn4O=S=#~#!uZflsAkILl)*wMZX$&Jx|C|F|yUGI(0Ysw;Vl`6V
z0%A3#Q(oXpi$KxN2`Y0z+jAlFPN2pWWcnVGV=$5yrG7>ln!q_00P!<Ma8onZxj|(f
zXhZ<wZA3u<u^S`22W0mtQvV$i>y(B7@_wm1pb*#quk1jV#$|#C=uxvGurp_2ldNRb
zHb|XW*dT~JsGI@a4+05!P*XA!L_mk4zrgl;e1WajAglR~Y;jRyUNNZO4DKpwg1XG0
zo_suL7^u<!(r^Y9rjQa9GDq+T<_h?Re6m_ONUnga-2vse4WIx(%;}>iJ&a^eX~4r~
z0pEg(n0@dFN4FQA<PSjSAgJf#2vAZxinDOTNaob?@g<OtcjNRiQb<Eu1LSReMv7#3
zsR}w125EF3T%CXjXgvlG>kY7hIjWWC??9>R1H7pKvK0CHMI@V{ekQ9-LF$^tm!%dJ
zL%R;3;YIK{L4KvV1(jhC2}lk@Pi{zo5AB*#(;=V?PErTehPc5e)YeRLT3V)lVnHS-
zSA!N~m4IqbH#bjoA0JuUB=O>wxP3Z|0rfG(mVyEmt2bd5!EJ;_Hdz}?kP>)7Q9fw4
zJu^SASU){AALQ{{paK;%m*@$(RVT$aKPRU=H8UlZz^!1IDS!s9n+3`a!SJF76kw2%
zK}bE1<OWCxA$17Ay9=@0?(CMITv{BEpOcvkyAc?VsnD3FReuL`rxB!_0cUhP4uN=&
zydEo30)SN}QJ^9{3{<p&t4{Rx>Ib{@&iIXO-FJ#%y&+0#anvzLLy+|{@My(jJ=EWn
zR%XQonR%(;1_Wr^aS+&Qu+PEa4kDmS^6uKDe`3{DNhp~~7oUS{hLrE{buKXLp*}Yy
zu4GFFucbiQ3jjLA5nP%l$LHkdrKjYVCgnhni$Nqbj4=XeiYMP%ke9F)Y8X~QeF#d1
zB=<yN15jzHi6x~)sqw|B(EI67f&di75VwF>;F1GOKv&q?+NZxh8sN8Obpb50$x3H1
zw}8gM;Dcc}g8_0G0+Q9xV4!3ZKX`q}DfpBEB6=akBvPvZ8oiWQ1~MHIs31Rr!xKzk
zHjW@ZLn@O&Q=f<tRKy@nd~s2#UT~0Wd;oYm7HA_l%4iK@${n)Pkj*|F#vsi@h#4WI
zW)G;}9ux*Ijqq3o@lY{YsRTSute=yamza~TpP82iu6+@+Y-kA-(tXDqc_XVFMzI>X
z6A2zG2DN%1ojFj<LZTAGdZ^DS9X`lS1l`XIE{M574G+)&WISlsR9=2bMrIy#`m+nP
z6AnbALpB8}+rt^q=%%7Owt*%Dro&?&WNjvhfLjS?K*NB_^I_-N7#P4U&*D<({i|J1
zxEV4*MEXL(oS6UXKH8^;T|NEYBvSx3X#iS#hIX^%43IZ&gNgufu1u+6WXJ>&=@ZrN
zG0xj^$Ufc8Zhgfo!H3WlGFXf_4l<$`-uF0D$H0&YBGRAdJ1H{pt+Y?SCcX90m%Ybu
z8_^0fA|GzVmt1y+Oc0S?8CCZ{>-<yu^gg-$+h6*#K&Jr6ThIu-04_JRq$o4FSRcHm
zG&3zT89bE+YShYr<~Rf4gQ1A_1!TqS74*Z&umnZ~$R{OmpJafNB#1~4Fnh}Nz9`T>
zJxGH)KK^wP?!edtGNK4>#GgtghD;EVp4PIRT~Xnzefm^)^Fxc2UPIS}7n3y?gw(o&
zE;vAJ=Vt{C%!0-P(YrM8sb)xkh`{PUS~YY*Ba}h%p!Oy>bs`EH42MvX$&i8q?L3c*
zpcxuaEc;~U5xmS6X+Q&7+>n*!ak~cR0t85PjbuMGG$<WjfbWnf1vR|AKuHo@-?V@h
zvVsWchBJ11I0Nc)s@eP#lo>$r4ykHDmS%zoxP@>A)Xy|XiYB140dy2>P$j6m2L}Pj
zNsu}Z68M;HC0aQIWHTx4sA3A{OTi}#qOZD+0VM_#P^Lr+33y*boa+Xk+ndj@Q7#IG
zc%bc`lvL1qH&8U-oISv>9GV`;I#Lxf&{Yh+Ei67IvpBgZwImh9D9HmiLjHl4dV}Vx
zGAavFi_%l`@!bA{G}r*m95itfC@%0M3nW)S1BS@18vK^rqSCxP$W0gJAm4*ZMbOcM
zpu2K|z*6Xy1teo2Sq$|)`4$sM`bbtm{YZm6XbMV&Ye69Z8f^rf@KY2Rg5B##1v1p@
zv~mxDfI)HyG+-z#pFyh`z&YVNC?r75@#2!A0+6Ut9;`hIauog|l4`EV2h|eTIwX)Z
zh*UU3{EyVPR7YL_kXe#iRF;@inpzB5qyt)Mi*$<)D34{Tf%fQuh;)fJZMoe>*7k4)
zG?pppB?p7%r9l-v!YPoN4{SGxfX@CS_3<FHgrH6t5#u4{sfpP|scE28yd4y3dTg+b
zo{AukfrxbfQ%l;+7_9B#3`nRIgH8%2!U|Bi2{IpaR6Y1Iir{$9;2>9bNG`$Bs)lqM
zZ^G&ekUk=;fo^extz-cAiI2d0GI*?k`i9D_Gtl`S;N6C}&wYcWMg~~8!j~#i*(IRl
zgQqG$vL6}}<aMW#GYcy9L2+HIAD^6`SCW}mnqOLsR7>iEZiEAs4LSMgnZ+fU$=LS%
zK;j-LHA6EIjobvvL0H2E!yVADA#2bEsSS@fz~U6B-LxM|3lh?Fd25%h<1F*~k=ZX;
zKA@h}prf#`P8eWV4fQoybFq-|qTEFN)QXa#L~!-+8dStwg|`(!D~lk@i(pN}bkV;H
zXR2RUf%PP*W$|;6#h@ApQhMUCm=Yf&#WQS*<PzwdV$if8c%?x>W<_caI7=kvC8x$G
zf=1NiK}ylfLik$4wVvBmiw?q<tdkWUSe&&Ll=xENtNjqABZkG$@F21}$tWo)$jM9s
z7565fld?cdJVNub^YY8{AQOS9MXoqbK*Si>hvt0JZ3YDr)=Z6I1!<nk%quQPO$P6|
z09gUryvLAP3^{rf5`q4pv%f$@`fS;i`#yI&*{4fX>+j3xcZVftB7K8ol?o`TCWFce
z@Sq3C^h^+eX$90bRBJ|k0FAAJX7e3OOY&V3OHx4_Ou$3y5Z{9G6Qo6l5wOs>qncB2
zw&*Zyh6Vy?l{FEKhT`HJP+ENh@+qiL2oClM&QH!x#kd#(z9I;gL7+Y)%^vV@Ahw1n
zhVf8uP`=;u91r#*b`kRvkiokvcIl8&wBOMB2B}dDo&<-aa|QfI?BX#M>LVgEKuT(I
zW^N)lazHl-mVlC>OJ-RnXpu>1UP@|OW?pIvdNPDGsF0fX&`2WP&Js|4fi0pitblrp
z(#jceU}hJn+~i_lVDQOL_sLIAEXl`M3l4DvQvVj}PwF|MAHV;RtcLm@R3H+Q-!e;5
zbwT_564O(Q^^qzj*up?z&{B<lP{jmlnq`8{IRX>#?L6ti^&+Lu<eXpyDy0^KYF|(h
z@Cvk_Y7e}>fX8B}k0~wNGC}u+f@~HC_3=Q3k7Gq@G58iy?CBb*+YXIysyP7^)>vyy
zNJ>Mp8R~he&oTT4t<sYQ&9#8s0<KlS1avz10b;HN>UT=hI-()|22{b~UW$oiHN^8s
z1tX|8W0P5&nO9r_YS2M<KExLLGGxXT`!b}T{^ETk+MUHAU17%J<L`Z0U^8!IIUYI+
z3EnWGpOjdf3Q9cXAjfxrhT_4kgUBVS44EJzef6}GDb12!?BNW^umD+oOGrx|v|9#r
zsaHijXu>QT+@SvrDkolo8uVc6L8E|>^KCH_4#ax0CZ3Ut36y2L4?q)ipjHT^RgY-J
zLTpDWr69J461NHlb$uT+*x`e}C3*3gd1*z7#U(|h$)HsuplEo^!@v*(x*!qa5&_UW
zABaeY978K^4`)DJLRJDm3J1hWB=ELvL>Pdh0YpGE4n`PII_HhFbUz7HR*Ql1EXYF8
z1R(>MfLe`I4?{dnd1GcTD7Axbdw>KpDEmRS-ND;1Fb2d%Dth(~D5ZmH1&Gz?X$`}(
z7R05sRA_Gq^^P{s6iHAbyfOksFLG&!VHu^#2~il9fkM~--rGR;CPs3i)Mmu-dZ1=r
z5NQ1YB!EvbF)(C;i1g^UiTWZNZ0+F;NZeAlDA5EROa=;Pi0z=!!%PqX9bU!Q0Yk~4
zF6cClpfjL04mjU{Y(<V;j4-A=-++!jLmKd@22I$4i1e>b{>7h^gzeKKWX}D`;xm9Q
zLm;xjjMPiD24#=qa6f^X&d6l}QVRkSdqnyP{pJ$TVOOce;Hn3F9ODJJjfk5|Aj4)-
z_R#S&DX5=_v=O!NM>(x$I;fC0fLC^ic*E#~Li|Wp(-bsmg0?Y~4OGtB!t*3(`CBH4
zfR?ilU^`MDK=*7=?o(-yjUfL)as&7fXAl9c#n@rh89Ov=sXeF63o3=~!D|-KSb8Ri
zfch9CHBnj?psp(hZ-PYl9>jv=PaN?~d3fi7at|jwErSe%oOP*ax8d=A2L}6e7z2{B
zK=lL3y)8&94m^h+59!Q<^AWfKQ3!8Af{P>&0S#-6x`x_utqV$P{aE4}G6auN@{-l{
zfGw^C<*Z_T)UEEIC4@n-a0ggONHS!Ch;&gC-Hk?4&+Xw1NZ=E>ueP`-H6Gkn2el7-
zK@p5-Cr{*JV8{d!>6;yPJ9jY#*~1wSJBTcRkw!_FKwCILYfvFs7Sx}F46Wm63sVq{
zppF2>w)u3>`HrBh0<nKCs09onpga0R?BNWE4=L@tf)*lx({C*(Xg9;7&jD1ofe5I5
zEHH~%XjE2#W~Pw(&Y+PS$mR1m0-nebL+Fu{nIPX;!F>lB2FnByP~Rcl9s>zhP|-nh
z$$(PJffj%Vf!0((A`%o&nIHl_JP2n%Y^1d8MLpIg2Q&<Wuss%Z$TEmX_sPm&-oO}W
z4`)DZCo-cTwNx&GLR1l6i-U&JA>-*@8!YDhyeea#4r4%UprFSLE^E<Fz)c7BS)JiU
zJtCjdtGH_b6%-!uzC}S36GJA5fF3P;&>qf!_@D9=^$b+PgH{(nQWT<3fula9Zi+%X
zF&wl^2Xa*mB!6PmZ&Z$N(8BB>4fv`G(1L8ps%*34JAHSA-?C4KF{l;XhdD4t{6Y0O
z_!1y60X;$clO3D^v6`%%cd(ij`&C$=Zc-5F3Mfd_gWUlla5XL{IHac-T-oX)T}}b6
z8E(SMPSChPCWydQc7hyBMA?aT_Xp_Uyr4vQ8y(UAfn;l>1r3m}CbHZ@s^E|FF)$$Y
z;jqlKppJcz)kZ`bVoEQ{OaaYzf|@VwpfVHDd_m-3NFKuIu@bqa56gD5I#5}GunE+l
zfV91F*hK0017hR@yh(H=yd8{gGe$W<WDFwJJfQwykO{mfK-4^+OC|qZ=2fvzhcO^&
z1XSx1kseWdtVl7KoRpuR4T`>%pdvpXo;VkSW~o6$`oF4H*6+-A_HYKoVk!=sfp>$D
zFl<I-;~M4Anlw<F5rOA(M9kr+U#MEhfL5~v{eai!1hWR^)u|oGyNE0TT6&!cBA{g|
z#&7^x)hVpAm|Kuoq92cxh{4@OMDq!B2XQ8dK<s>lEF!uK9gHBWpMjK!iN0hSM`V-b
zdnEf&o2Ox*d*?uR=0fT-Apr)4Oc0TN*6Q)^M?tCfa0VnNQM$qxd8<A0mOD@m4Qhv{
zIz*C(6q6W*6{W=_VwkoVG&BRU4-x{P`V>;BLTttunxQ<sTY_55=i#LnVvY$?wPFkq
zkd@+K>AfgbAAN&EG^q4+g;xyV`AHA~&2Jc$A6fPzr8x8yuMZ0GGPwUi2i#_Yi1g<s
z+*_=dNZ6+{PyP7bSkDI9!KKdWj{2ZkX;330Ei)%4H3bseAU@>KY8<(Z^4JFN@d6!j
z39*p_)SU+rxI!CLsghhtX5}Y=1~Cc}ixP8kQo*ZCR6wzesHi|o_A)_4dYgInhkaV^
z_UTExs~g^(&V+_C>D7N9Xl*=rc%T?IdIIsI5@@A0h)7?X&T(9)TFD;HfRrMXhpGr@
z3m+oaA}(Kr71r?L8)74+<wbISPJR)1cm}lc5`5rxMrwr-SQz4IP(1><rw-zTx3If8
zU{{2Jia3%Z7AYGcO;n15YRvuc91I%5&jb<hQB^nt;(M|h2+%Z@TN$5Pk(^opI#UuH
z0HER^=r26`8iNuvh=A@?gWt~f99m&gD?w+2JdKEQP+Ep0XvhjSq)mDdPg68u30k8c
z#10Q(khRFoc8vN6q@I|_ElQ0CZS~5`0WWq?0+mUi!XUG_xTFZQ(Hqp4%zOe0QV@||
z4QkHHaoEEd5U)`&DS<AoD*<iwf{r1l#wX?H=RlG)VhZZg?_<~JMKal^!x#{EkX~kh
z5=ziY_+Zs!kWWEGI{Ypw7z1JhS%Vj_E<9v#9J!eRI-4j6Q79rRdwA6YV?eA2%}Nmw
zt>uYDd6{{T%d$bqJ*W^q90jrrvfvBWn}zq*Ay!aYG9iv&1K;KYN~w_81F;}!7DwGi
z+Q2Q?bi`dj1WhNa%t0#dQu31_c>>hoD+Kk|J>bnl&^4r)AR@i(n6TrVc4d1w0}`l|
zS1|gh^XH%*0i;_1iAJOY{UA0{nkA8rfB+v`0vgqX#A6C*;Q)w8e;2)0c=}^gdpHAP
zHK;%)B9WqYtMRlCWkCgP20V{~<|!eQl{ng`L~ff(&M&A0#ibF*_@i*+L5l}6K?L;5
z6d`*!1L8eOLl&vt1WnC>SKh)4J5bxS7=D8hq8Wk1tJLj$^@FCE{=$n2(B9un5CLzy
zz!{K`AhNiPPfP;s^9O}BxDg0ihyqDU;MxU5KpUSI?9%Ts7~enWF9U4^f@%YjlM+&<
zh0Hq_mnIeD<d=Y33!n`XNTYm+Vgu5>!l+28otW2vQd>Jbj0q-YN{eGe$I<}Q00*7o
z1POakd6)?z;B7NF0}{Jrr7c)C#u(NBt^W+NhL6o5t_y>NJ4UybEZdP{9(7nD50tX8
z-D(Q49ixj$#XP<P6yZGZmK*x`6-I)iuE*s-k=_Jv9HZNg;c-xcAUVT9A|2e}%g?FI
z%g@b(Y%CB3%{78@7$n7kGCJfGHAtvq#5omK9)Q;~flrzN6*r(tqZl*;Xam~3T2WAB
z2XToAsF?>MpsUYbqTdWh*2pk)AuD8>Bsm{^auj%<10o0z;g6#YM3%)!$sRr=4Z5&A
z=p1~^7IaTuCWwHRv>1ndQ#aX3qsBa_(9Hx9xWb#PWCsav%$qB4-W>sHDq&3NQ(D9#
zrZ`BwGo6aXOeARV1Jt;OHzh#*ruf{%%B0lzy!^aWNXkbfKu8|MD1NA(2RDM6%KzXA
z5#&Tj{{vFf|FKIC`{?#;qP+&R$|s|X13FMUXd%2c4jMaz95an0lhI%vmH|}GZiD+0
z5zIK8K;$({Ir-`7nR)3brzL^|^Z~pDLC`9)<|!dXI(U5r@>({~ykn3jyfQ<S=s4P^
zplY6o>ZAxf`tS^tQuN?q2)a8f6GWtMD6zBgo*-xsXV9X70&3_YO&)?=o(Upw6<O3R
zFz11`=V-(0=`7HQC5T8@%JWlYb_lhHGa!DayhEAB&A<?33C}d3IttP|$5AklGjRxB
z=!v-P0@Po~1QF1c!(6Z>#9Yw4PgY|aQVl>?Cx8aCic*V<Q&T|oKd7)lYV0Ex4dBR?
zl!s~&F9SnR06ZQEhANRuB2fFv#hKu{K0$^fEsV1RHEKXadWLMMuR!U0#Fj2dngI=;
z5s?|u?1G-+hgg1sYaBKNlzp*{!$J}i#<VF}k&V>U0d<w*a})Ct(;>$sxq;doOz;E^
z%Dc!J7-JfX(#jX<UJ+T)tmRKEMKZ+e7_Dqd%MM7l7Pdw00;uk}11~p0$pVs-X_m^(
zK>h`74uUi)K&>}OR}xYUaN0u)O-@SkHKYmx^(tVqIp7l$LHQc(3Ms^_8IBen<@x#x
zr~_#a4`a|+ekO>3j#vDF4P5+zPBKtXzyAbHM;5}<F`|CQk(ek=$4KK*Xg46CFXh8X
z$7E$;Jn<UK&%h8g9X=HI9JIz6M5Ir-v32j=Niz0u2BbuzqSp_Cy6Hv4hdNoUDo9lY
z?y(?W=uiYIIYH}%AlZpva;DA%bdNX#19+w-vlw#Op<_`|VkN{0oNNpXnIIzFs+gbi
z{%>!4I0F*$M9wdP#{E*0;Zt?0pg=Z;=OWP5L?(!Uc1i_cH&6*c%MHr&tpTV$1})x!
z_zMxVI5HFEHVT3Kh}b6pI=V0uM5NFBq_{$AuarHU0SQU6(l8_sft%;h)fAv&Q2<m(
zC17ctV|dm8VmFbo2pT*o%1nlx`~~Wv1vP=P3b?0*STFK(&hA)!Uq<_M`LEZtb#}<Z
zju0R#uOQX*$TMa=pvtbAa03grSBsi<{{ThpWqA0429_XOKp@rLPrLMpe>Kq?Ld2oT
zmzs8ix5rk%{f+2(LE`oSYy|5k>_R6{ED_P#D=o?a4_JZHk1VJh0yUD+&qvtgdMWRV
zp}u{3X5ldd^^9a##Oe^Y2e-5+2YO}^XbcFc$%<&IVAudnp=3<}KypWJVo6bE1*rXy
zl9~qLfIN8tG*AL62OvV|UWFVKgS2!TdK?1v9Rl(^*08{E0yHcrZ8f3}iARA}eSz-j
zf`ot-sK*H+(iPVn6S{V+!am(0E&Zak=pI;Pg9>UQ5)^9p0Vx;54gvw4*cD_1p9w&u
zWVpY1o=c@=*cd`{5m^N!lD$an^iojau?3!iK{wN7f{1hpmM}iuO}X~zzo#T`+VOt_
z)M`q-3_ElM?MiykPy=LQ4N@m?*{5H+TWa=Xz9*IK{SERjXdw`!_Cu7^@Fgm8!kTBF
z6?#y~URV?_0*z*1i(*Lo{EJ<>&+Yx2E-f*E+Dq1;BT^J&oT%OcO5C6f2J!fMP(u<#
zq|a?mcba@8$v)k=@c8UMVoPCXx|5a2Amt2tEez_?mw<xWIX|}`C$%CRvKRxB%|LE~
zbj~2<Hq!Vjw4kA)OF))kEnzV1hL$j(jvUEZ9H~(YIybrqbf6NbpaqQwfZLaDrFq~c
zpl@P!DvqO;kfws5!9Z1~fR<C^2?!+Hp#eeG2r*KEL|#@5y}_#-bOaix{)64%<&l`1
zn_2`pwJ*OY7km0Z>T*E?h6Yaa0<~+tfEsk*!WfinkkcrV8=%2M>5K{DFfPP7Sm3rK
z!s@A8eLwD45oVvBrmg>T{-0V{EksuKK#CdA$*K9JMW6%<@+{U=fnhh)+n|z^i0n~N
znUbFjvH&!>53vBvbNMdc*JN+8u}|OFWp32}XClmVWOeLd<8i6E`9<IbEg;Jv)`ENp
zj!7_)j$tj-UzD~}kuMnrt?&Tn`jDd1RM2f#kgI+{ZUQ?3Ou(0%!5PrRO-+{w!&|dx
z!2qd*kfs5l!9c;7jWejC1tnfcF2*xv1NA5cc7aYoLK@V@V;9t4WR21wWf}B=)>IY-
zh7!<H#bQv^7hhab3A?ZiG3E+y%fT2>e^c8ndqIP~pfUkc(c^IdG%zSFx)947L31Z1
zpec3G#Yjm>`4#ac`SFQ)$r-rL6U40RsplB*YDjPbL`&?rs(R3_dLqg))Yd$ts>gQ*
zH5V5H1Gw>%nO9bxnweCZlY{LPYKW_lDi&z+ppmOUr4QB;5yKtOR02v-B!>=CmO<)s
zJmzI!C;|01gEK+RK}Z1siYZ7*2yp>s(x9RXrh;br7~w+)c<hGyp7KF3G0<&Upy6Y`
zeBZ>9<P3CQLk3KcHtj;=pK_b6aFzNPRzf|SMBEq#T1J4*iecVsSelnvmROXTSb}uf
zA*d~IAJqH<jTi-kuRwGwz|j)MObK*yAShYj$s<Vaf(9Is%WOfV1t<VObFq+o5bx>c
z3%$YvGys_iBGQ*ZMjPzm3}}iV(H4+dLHh8)1@y`h$rq3jP!f#?)h|Jf@Rl;Dmj^kH
z5t6zv<`_vd9@KRVnhT%*0Cih3K?L+}J7L%bd&018D_NsbNaa*Ktmzh?l30=mil$d=
z7>65xO3zGN&_N*}0=hFo&mPWz#uWA40vd`$N|GSk!R~<D3}---B-O_e?t@0uQ$P^{
z4g^q%207aTc3f1tbLTXhdDHY^EqJQCpayl_KPZ<XR~!cr<GzptO?3x+1I_h<tcA3&
zKrCbjT(C<&dHfxdD(g4s%qOL7nu1EuT5V8L1n*S5i?iN(V3(feb5JV%vLLNSk?KLi
zSRfBWybf+zfe5I*A7SUhDZsKLbw_Bog7y?@fcy_me4ss>nIHl>xsJ4G6`BL7YrQSV
z_sJmZL4F3uIhcS>)?pN-)U~}1l=Hxz2ip!R3Xsn`L~1%h(mi!;Zv(YoK*vCVZ<WX_
zjxSCH?PUes%nWu8c#j;2fbWQaGic!)Pf%1Kmal{NxPl01R3HTgtqQ_K(4py|ZF%4m
z(=+q)T+34PFb?sD)Pk7JI;s~+;N1kEGy%z(pgammn;0&D1_%Xrz~vW|Waeg8rRsy0
zpJe7`<QEiV=B0xQC{Xnexp1i@zbG*s<JN9mZF`zH1{4}dl_erbFkAr%5~NeMK?&U^
z9&%WWUVy)+Ux;fE^r&q_0}8Sck;5LkE1m;Z<AIih646zHn2xfLl@m1V7z=Mq;js$h
z;$pJ$IAo9>G!2*tTHjEdT9RJ?-U%cH?<s(bSM&fwTCWBzUdif`A*@H9#Rgf1C$f<2
zhI*aS#ua4PB0eKAFC_=ETn-eNLHVFHYhdq#i!~4dEmJ<&rPm9boRIw56Bg0bcft-(
z3`_vEu0R%pw}yZTOea7>0V&CXTE&Pf%JYj#AQ#BxWEO*1D7TV>b}nXui1dBm%`?{9
z`Psu6(1@jU(?xM|B4q#c3s8!_22atTUO(hC2S~Am(LJEFt%TTl0FKRvaGNthdo)2r
zdOZ6%$-rCQ_HYJu>L=6{h(|#!6;PRsUK~Tp9;Epdi0wqK8vreEGy~rTi?eWp1T0dG
z1F?X50gLnU0vg$zSX7c(488)12Q;*~65jp-4Y)!Mf`KoyfibA#XGFvME2z2Q0AHpB
zDy$&2D`eXEyB+jsh3`~v@?Qpxl7M`go>~&0mYJScnp=!LO<^=YX>}41=sN5Y@LsXh
zl6b_tUVL6+ZYp87K?<&7YAq!A1G*p^)b0e0IfGUgKnB)8r@Ci?i1dnz^NmkAH0|LG
zXc0%L17I`z|3Rx~Kuu4O19B3RQgaL;@dIiLL54yg(<>Mo(m^E@5$y!jaW<r(3P_0q
zB0){BSWwk530`#|k|@M>q{$uXWDZFE39=b<igXY*n;~T#Qd=BiGqpng5U2$X3T1f6
z$Ac39xatInLqZ?YCH`m!@1s)36(FlXzFz<ecTi|UVjXnm0p#EVw`pfK?pZBopAKVC
z$6i>v3j!5}h#CgbR)idGa~F2F4eV4jYK1xns79BAuNXpvGvqF>*RV^yUPFsQYT0`g
zG@&R1FZ@Ak?U66ycwz^gIeh|K?gu)NndIyRJD3Di(Sl+Zv^l*5bm#zlRYGc!Z)Qbi
z9;BE7#XF=TfTVS#G8kISP{kD>Yw$FVk!*#Ou1L*7PztdDH@WpdbCY_dpz%$;;?$DV
zqN4nyENG`j6tq(YL_m+R`C$iVK>a~MQy*k6?1(c^m@Wd{$_g4dj4w_tNl8tEWE_wm
zk%JPW*r1{d1VBTGE})h^xU2xxWtkuXK5z$TK*E!}eike(AvOv@w<+a-ii{s1>%koj
zbe|(dCp7TMswr?<53-Mx)KpB%hD1;hXlep29N<+JBv(Cv-VaDKhky^S0Xqbo9zX#E
zNs1T_fu;uv#w(!P@^Uis5_8f)_tAm&@_@VFh$cN+aO`V)u%l<TmVG)~{r(xck)^Qy
zC)Hem+z|&SPLNa3vn7TrAYp-C)+Ci?=9K7W<|#lfd{9VCNeRi%&v8mDf|fPtp^MZm
zf+lxL(;Ia0W3fJz5noth3MzPxfT}K#_2^!ML^F~@AYLn`V98x(UP)?tQDRA|etdok
zcr$Mn=qNGJ5Q`^ND!<4%KQFnos3<irxze?$2y>ML#&$Jm08!Olp!?^s76%x%LqmnE
zWQWvlEh);>FDc4Q%u6rLNd)bL0mTidivwLHg(QPs%R;)CNTUYO0HKMKKq&=lz+kun
z8Zcz_v0=q@Nl|L5K8OH?#uZRn0oBJYsmb6ANJENJQ-e~|K^u87lL@4h#H==`=N?e8
zh&3oMtcC^!Xx5pC#uwUdDDag|pa~sFyo0)RnIIw^wq^>>fW|v$>ogJ8;685&WI7Hj
zpgtihuYns}`rw97dTL2dW^N|rtfgt7njgIR3S7h^8nKWh_0ldKHi7~5AX!m}WP5I6
zaz-L}Z72BL6;NXwQuyJq8sc%JavfAcAchvfZMMYH9MG_0nu2;tW-^F{GQbGBC@T|0
zK&PYmU{m9Ku&OJNoHZs$We8{mUUGh3T4s8D0r-CCJW#e-2nt@%C5R9|f>JH=@F_-<
zlC020as^^&sui?Fbqjoe$RE_61`*JKTa3eHC>>yhpK9|SRHr4wyZuHWi$O#>{C;~F
z1JeBj$q|ubvWoNb^uad;LdM2H-Akkb0CYWbCWwH}ydssz7^w%eMi{vV0zQxkJo5nR
zhQvdT5R3=4f<Scvw2=U^4>VfK01ZIQ!i|dE0e{e7?Fx7p;;|dzxnd$mYl{-|Qu1>_
z2@`bLR0$}{g=Z#b<R<3jd1U6~KngifEdyz6LdsI4L<%hfNVW~UCI?#+4#Nhhe?VuT
z5s@fRN1`B09}#nbB}JvhB}J*}nfZC~Df#I+so)twkoO=72VCNTxDel#WzXa}{#nu<
zIxM3ID;da|8AW$XZe|{Mh7uI^pdudP0&sbN#|6;PptNj)wr@Z!z@mcuoWv4v^AEJK
z3_NP;T$+@b98#1CY6e2&F>7>4gA{4Z4w@8b<1|oG!5Tss&VYmvQq`c!z`y{n8j^ET
z6N@0zPHrJapk?mr3i&7%186mSCWwG84v@8nGoU`CdfPuS9h?rqQ>&n9T}agcasv*B
zK>S!tUOI%Xf=n*TFD{NROU%hkfwyp(K#Rmd8{GnmQlYoW2N&dIVk{YeWKzrmkLnHr
zWe2R$j^P4mkWjuB6?Eiy323mqD784X#5plJ1G+>2)Gx{e5%7tW^a=YsFE7aW2kZM&
zn)I=^7^*-mhA@1iim;X$r23>h2y8&(wxD$*kc5aBKZRKYEf7D##xX%{Hj)cxEO&Yy
zflX9^cQ+Sh=B4GM*FYHg9hxbr<pj_`D%KQ-VKFqtkySAvHPD~~04O5?&;kKb=%H0i
zJ9A3T`4{@wr~l*B3%ioA305)Dz%3>iZUN;<aQz4-(lOiu4Hv46DuegmfzqE-W^ztx
zaS4u*ZAiLAnox!&3aYpPR2ySW5g4{YeNI-h4=F_;?L&AAT2Kda0D3ln^fn*aK^GY-
z!%_gH-37#y8P3fq7*<2QO;O<wIb|KSHsS?Mr+fi*Sis32u`mPHU4WlR290j=Hljf5
z!`#%8qRixCeegM?nQ58H@LCbngAe))atAo+!wG1+;H@3nf%c$I70IO`Y>cI#C>460
zGw29G@MJBhg$O<^5j2knDUuQGe|WJ3J3kB>8}{V%Ug1S@NfpS_-=L1$4N%PkjsQ?P
zgyeZh?F(PVd=gdxQo~Zvb_=Z84Z~8XPbuxE!tYG21XZ*kPoifwxXmyIG`&&HW>EJD
zzn@_?Q_0WpzPUGO&wU+esuUc>peZlNT6sviLYlIqlAqx=2Y_srgD-vpg*$TMLTX$>
znl)q<FG!^-w4Md62TcXfWrTw+E=L?|j>uB5u!i>Q4?t&$DJ@zN>%LBbMq>7Z@)fu&
zA?S5V(-}O>B|%{hvJO&;fZ7+3_1TcB3}c9-7*zBTQIsKF$fzp@yO0r_pg@-4h*oHV
zBHaq`Fd@k85MSW20-9SuJ9LQf1=`pF<h;hMpe8A3<R84KJU2h36t-^`>^~3z9bm$2
zeo$iXRZ!Xc5i|h{P9q?DAjjiEq6*0}s3$>V2sV(BDm!q9mw-fkGmCSv?d5}4;xGm@
zHb8n%Ou<?#!8Ad~x*x&%0pwM{$(aR}`k)<W#rpA}o&K45rTL}B@t_+;^T0WE4QPKV
z$YnXHWtF&hrXm#?P~XtVJ)p?I8ZMBM1IZoGxFKtD9I5OAEuaFgWCst^uz->V7pV9E
zIRrce2_~RzL1z2(7M>?JpGNz`8imxg9<;wB=o7p-geW&xM_mf!OGC0A>U%0*zjF_?
z+)$E%fx#uQB+(@^H#M&~Ge0jeC$ppy$EhtCvxLy(L1l-5iX^Pb0>gf2zz|vVK~CZ_
z0=38{gO(D4Cfx#x@+&Hx5{omF9ZO3xJRmE?vA0{0k|WgLq}vOse6e~BSBj@rcl;};
zs!Rs862NI4QPx9>!JmkobWp!h%i_-<i+e#0BXIeP$6{z?f`;ozu4It%IPwAqeNYa6
z1hO3L>r0>+6A+PJJIOjn$ynV!J;Qq0yHk1=upCaSA<0u9hk({+VXta0AXc<N8fmn0
zM<~2$4vq~tfv9?6O9~<Gpk`#afWlxKeCh=>8Ub0g2}%1O?9i@Yp;Z`c0fhnR=n6<&
zfKE!u1QF0m_!GU{p$rO-Tu|Z!M+4}9>P!#;Kj=OEYm<NRCnaU*4o_;vMJgx^K;<kX
z48XYqM8KQs&;)T4+GeM8(FL^AP>iw#3KR!HtKbzNqG*652zYsW0qO{927o+hXm&k3
zF(N8oh~4m+IhfrvKDq^AKcqZ92`dn;K>bdu%z$&Q1a|XZI^=Lld1xY}b~u3QU_2Ef
zd}!n*tZ7W-*+HO2FJvqXwD=WrS*RMQ3j$jE>5^Go0+#d#U!+OWdMpa;$2lT{t6f9)
z!!JNZ0JzTsCejm)LJhQ5D%+<A7w<e6(C!MWCCM5dhYieOo*IH&V}o3PH9cUs1=?J2
zB4>OKvT6p|-fmFZ0CkPx{qn;Sb4sCeRXEF}4X~j)D%lUdJO@!A<FOa&Zz4~)1#QmD
zOMxs40_8REg?pYkIjQN1IllRMB^e|oeljctjW%PAP+W<g(%~V*@p@-Kl^o~}FzoGl
zco}~H7MIks8ga58xNb*}P9&?L(MeX_21&-?rDw?Z)PVYQK|!E~1t>RV>VeMx1QF@~
zW?Wmgaj%(udV>7iU7sS?LGv@x96#t5QurKyZej&wh97K?9w-?VB~~KM^Mm~cTH=-o
zBGOxS-aTDg!~&b=*M(LYWc3cgRzg<Qr4{ApBAxTy!ok2$0<t+VH?acJeE9`x*Mo@k
za}T55ChD8mr!zcs?wn?`4t6#LrQ3Jo3o3IGlj1W{a|%+CDihEYKXkJm>@p5|r8UHc
zMFJ;<V5T)tCzRwK7OeQjw%lPpsPF{UZa%4rWvK!Fr8pKkAZ>+(_A;sN6i~vzTHHX|
zbx1CNW<2WU`5Mro50L#%iN!do52Uz&1_1S}C%MQ0Egd5w8RUb94!?ssD;@Ch1H^sb
zkhNW!_UZnomb95MSVPMc@-}@!PxAzwHj<N*ngbfC0UfD{bV)QGi=h!p`Ld0BpiN7l
zh;+$J#*vcYeG_;)fO2bb9^M8?x=2<+eM)KCDJ;z_gDm-c49mcQU?K0+O7vn3GL3Zw
zHcWJrT2&^(4WP0VYtF^68tQ#Y%k-q2{N(J+y!80Yyn<5jrgPAUYzb&hx)W5&IX|x?
zzqH5)wAB~a>CKpVn)(g{ot%s{Y#>Pk$qCS~p>(DzCo>N+xpx;dPzo9y0d2g6T#KEW
zlAfBJp95RT*aE72K?MA46gUG~#!<~NKS2#TP+JO8?Sm}M1QBox;S5MHAT?}2SBAqI
zHbtogIf==skj71M31}T7%F;ze&;b%4BE7ve>hhk)DfVy%)Jve-kBBHc!K<o4chRGa
zdw|+{L5c7wDUgRC{Vj~xgbc!g2JMKj3~kyCc{T{t8_tW*%u6duEG{W3O)e>gTsjD9
zA0nkI(0~l&o>bT(2>5Mk5SLJ1$n}FJi|ax42&jMs-((IZplbo2!%kOt4juQP+(tk6
z(jTymh|q?Qw7?kD@?#RJjfk-wy2tN+(8`}(pcyW(-$CVDCWuIXeKf#t%jyJsI0NE$
zP!&W(N&_$JM_OG|ke?5(t3gBSLH_UoJ<wn#WJI%b>kkoz$Cmc#Fb2dr+9m8j(5_!l
zR6|M%&>GB45P@r}L<Bjb2#}TNsGB@9i}Q2JQuQ-SQi~ExGRwg8jG%RFLAmfW56X1l
zsZZ!&D^i;j5-j9xs6xA53bckc6GT8q)dlR~42Uh1=Pb1A9`U4e1BjJmEo*|c3885W
zw1fasWP!sG6vvRl6W;ZMbVE(3-4F#0&Oj^%=OsKALw!qWwFf^~3A7F_2z0;+q)`U8
z9z;M-LAYX<Zd(3je@r(UMP5dlLW7+<u^Ci+fGq}D2i{%<CNM3AdYP;v{2^Wa+(dA1
zAJpVGl3-vc0o6tEsTCzfskw>q`32zYluz_g4AgN5Xuu3>ltT(~%oaI~)=9{MdX}K#
zE488^KQAx8C>QtQ4Wzapw5>uTM}e{*p1?tJ2PAPIHO4?we>MgApd%8}Q%edmQ=mJ+
zR)9vTKt%dl&+V#32QBU445(j-EVM8xsG|Jj)M9W)7QE;w9zOAdsG}G=c2|i?%G#&H
z7}P7Xm_hjnRA4}ABSaq-l8!#Wj#B*qZS)X1%vzF~TacES1FmCgKuzPF@TwP{U!ipj
zQfxrHMR`DiPQO9Ai2)Ii_45lKI(Vqrr^6T!8$qpJBH9+HtyoCeP?B0)0$Rljp0fd!
z5Fqa%r9JeT2PtDh?55neQ$gW-2i}rG_!hE;n%N%OvS5Y|dQxs9XhtFkGy?=lG@zzg
zCWwH>C`O_I$q^C0=vf04rl26*0P-NVGzw`QBb~Pk2~rBJI{>l{bSgT;cZe);kHPr<
zL4TwxWg(?UF_EKHnfV~kfEH?%fCeOk63adPv5m)J%sxO17?P|}0(k<|E`WFf6nc=h
z2!`oUPf&aI)Cbf%2epVmy>sx%`><naqd~oL5CLuVDcGk!;LQwqkfIH1Bv9Kepnb1M
z<pId<Ob~(T0BB&4HFE}ESzlCC3Mu(OU2%xLpc)Ch=o(BwcNHKVjs=ZkD%lHK;*C@W
zfw(yAh5DDQv2LVR8FUQ=V&U~|P$#DwROW+y4!X+^a-pBR_K)0-k5%^RA9vLq%)4;`
z7Uh(-Na5?ByFsJDh}?_EW~krED*TcB4m!ySb_$y&=)ern#zOF(!W7WnQ%J)YPyT~O
zJvE&H9+-9mooEEAjllUHOr+z={8W#B&|TT!b5uaNKPx{o59u-@(AaP$h(OHBLHb23
zu*3lBl@d{3pw`DoQG&F)Y7;0qAXc{_vI3;;Kw4uBsXHi5hw#zKsi1}x!eTp6p$8(M
zOSzcr;S6XxB&%N#;OV39nVXoN>I*&I2eh{slJ&tE50UO*mjS@f6oaNaB3lEfVGXwy
zRHI4bs%kK-g!+`M;vLf50#7JIXIw#-n1S2Pe?hqpRHep)gg_^Opl3QrV-~6H1@%0&
zT>|RQKtdla<6$@e8Wf;xKype)8QgeSM$@OFUW0_+#)LRL?{g&oNFmZE7AtWE$W
zHmttKuo~)nvJSOD%IBb+1c{k>kclGD9%^ur6I_y-3z{7P%b=$Oj1mx<5~%7FoB@Gh
zJ2W83Kdb#9XnG8^kR>_3G!xds0@;tFz=wL5d<#Jd8*3!v@EcO!8dOl&pdZWz+JIkL
zRGg1_E*oU$J)b>v3qJhN9;&Z5gr3W`1XPTGDsNDol?-+hdgB?xNznKs%So_h3y6am
zq2r0U@EarfK`YeHgGL0<N^^|b3mUJqatO$Jtg(*a5NL>ix;Z2lY)Dxf^-k=0po6cq
zL0w#sLo%~KcVdHx^erbJPWDsDuup&8-Lh?tcO$F?L0#+pK-ROsN6A4pgWZ5>J=FI^
zuEfmAPfrI89w4vw69?H<2(k<8RYX>X&oz5(u$c4nDi6%7pn{*|kS4AI$FLIWJ+iL4
z#Z2y?!<LFO)4_H4TTmkc<XK384Ju_I<GGNU^8;)Hwh_$RG;jk0sL8es)M<fRkHZbn
za3HJA4r$0h58i}OpjK2qs2~Roq-GXF@23u_EP$@nna9b%kO?Bv8>igZy7z99eR`}=
zy6Y8}d{}5u(<R^yA)qUAA*J7WP-X)W&^7<T_UYyluQnXgjfajWQPXbl$P#F58{KZ+
zWr;sJW(2|Zy~V>;&rw=$r{pI?HdKP*zyOp8!P|wwc~2kIW(N`JkVB=pVIyh6uwW-^
z{UA~)hj#EFsPaK7WkDx(K@OKj+l=`MIv7h<>k-L%#CBznRY*1?MlIpxJFG0APB#fY
zfdtC(NEU;*kd_PFVi*HrF{NHcoRtT%3aibK5(i^gnrb~mP~n2dVx(RQ#LJXgT#}ds
z8IA(wD?Aos_?WDHc1X!9HzzR(l&!$yw}`Pw5DR%r`bpRv18hSJrS`%`V?Y)m`4<%K
z$mKcGWj>IArZjC4v0x0Iwop?w6$d=fJPn$wgshu^cfuf6|AdwyWR>1XkqlmLl>}ME
z58A+j6yTs72Fc8DZ^Ian0H<^{8f4FOacL6hW<qc?0%RqU3$T=95Eop4?%6@=oq&pb
zo5ZAI=!D-T&_XK^k*>VQmV@!9lRcaPZEI7SOkq>mmq6z}8o@{F5N?Jz6(d`cHE4kp
zW1u6*U`xV5*>f^z3KvuXK&}lhPt8ovD1p?!pect;5CPqDhcR46X_i6kh+qOWzFxx{
zUj##ex-I-f(0KJU__{aHxLqcQfDY0<vV$`qDVKWwJ_*|GcNy+)P&`2Dd>pY)R#5?I
zb0QC(fg-*Mbc5&`P*($9G(yTpSX~caOabvUwG#XyP#1@U1W#6ABY7F;>BGLDJPsnD
zhut~CjsbOqHf@RAMUQkg3OE__fO@LnFa?#ykh%_Icmxur)XvO}pyc!gw7~{!KPc})
zGB6}gkq-NT_F+KtU?g`RQHK8@eHi`J%=FYe{lubz#N=!!1xjQGK=Xj0!CB9e)FS@^
z&%8274-ZsQBUcVcOU<C=E3I7ynhQXxEATi78dO9sev2<pP0TJzO)E|XuiOUBMhA6(
zGB~J)$;<|IDnLZ~k+n?{FK(&Zr$3gOfAU|Y6|7SM+UP_?j3g!{gTfblhMO?RcyNS+
zOa|M5X*|>y)L7}30-9C13QA>QKiPo%1R|h2Ewo`<F5ok|)UebSWGN_?AaxVSzDy8-
zX(`mFWc3YEl6G+cXbl3mWdW*?AgKlHWso}{$rqBbSnSiSCMK+!xfQ;+kE~J)#dg@x
zKFBVJ)!--wadB7;^*Cs=D9Q;xC7@+ZL5X?U#ihB$L8-;1IT)KqAk9O}DQOI|u(||8
zH^e1K%`5Qv+tAB6)Z^oeOOwE-@TC^V$3qsogXV6KHt>N%8FXk`CWwF@1Nh4h&VUvY
z6lE5W$vBLMxWAaJ!L@=)(AlQYyJ(ZS2_2RNNkC6v^D&e*$dLyV(auspq#Q`&Vgqa|
zKm4{T3bG9R2=3yX;=%$@(F9I(pc)Xp#DC*gKCj}Ontgi2znbU`A?mP_kF05ENS1-y
z9#7Pv+ZZmOT6}}*e()LV0f|M$sgS%@j2-~6D2Fz%;BilRas$^k;4sHpR$$csNI4X=
z&<X4GOh#&QHfRh1+A{)`gpk9iAh{lMN|CHt2&5hgbU6vidAXnj<qqm2IKt~XJPv_I
zCs}jqkd`CTqTf(C28I&QQYO&RWo4;FB}Tc4=rIb(P)L0>X!%4P7Z5*k1NAtO%QTU<
z;DdH6AuU$IQdYqH2=y8nc7P@-vDP0Dqv1ne2Qa(mpqd`OODZ`(FE15*;YXT+dO&4h
ziHTldiE}=9=TKpZQ+{b)N^oX+USbY<g#ykLAR;|h;-%}`I#GK#1L`YUrQK@K*m4BC
zV}J-=$i<M1u&xIqwCh1Dcc9%q1u7~a9Tdp05T`w~tIr7=VW)nt4_vh)GB0FO^`Bk(
z{B`dlawgA$)dtkEcq?cUH5J~?#bYrv^H4B**A8!^f<1|dWwZni-<wN;U7$s7#AK!o
z(9A@I#*7(g!~irz0V)1K6?`UWA`aRFx3*8eohv@;wMh{y4k@qD_JU#$RQN(#{UF;Q
zM{~h8b;GTM#2!+G25R2Hi;SYwl+xtX#1cqj6I{mYm4ap<(TfN~oIz@7%<C`6>a#<d
zDbO8R$@#ejpi8t;Q$W?4C#YK61e)asl}zBKJ(z&D4v-uG@nSJXn;hKW@JTI8&4ac$
zAn}9n3M6wQ9sUX}?J!KjnxHUrL)}4E83kSB5RbOnJ3a$(fCVV;KpX;Mfx`k!q`$L`
zDY~O+YoG3$@{DQv@hn&}Bde%@Is|z~Kd3Z>Sd11E7#2f904cG77F@#<TMlSWu>g|P
zux#RGvS47y1QF?n_RV!^sZX?rGobN8LFX|yvp88lB|kYCvMj0rRPX$Ox0$}0GcaU=
zi1e!*e|`4mCECLokO3*mbF3sU149t#)D=jM1r-;N5lu*Q5vhv_v613IHbgSVC}knh
zR1E5T5;3GwP??es9#mmqV_*RHndAL)Qi4k=b5fmi5{ogK@fg-YV~<qpK$GoQlO2Wy
zQ2$VO@H-k*X-x$UexqeKv;q*`O(840A+_&eGapi*-N^e07ku#UDOr7Ea9I!D!KaUW
z6cH$rf(qedSl|o~BA|=&-@=CcKT*^%$6+~W{auhJ$a0XC;8AxlfoVCFVjMD61{+Z-
z1TA#~9i0F=A*d1*9r5r5SCFa$wEsC1w74+cqD9NJdV#8aI^;N&N{Y%{EQvrLRQrK;
z14D`nP**1tL?D6!Qf|D3PFRvvG$PqsTmU;jq7{_xL3J3!X3$3KOb`Lx7Qz7AmdpTc
zl2e-NP~)Hr<ZsaI55#tmmm&Vvah7@g$n2LLbOcu%YCE+uodU>S(7s(Hd%;OT(Qd=z
z{SFNF>5mVJ?XC^WgxX78UxOA|27yleLb4s?b!cq=rIoJ*K)w!v7d0R&kxy1-vQIyL
zsa<bHekQEjLuoR@w$KEvc>TETQugMJLH6knw>)r-cdUZtEV4>Dq!donlstyz(1M2Y
z`d=M1pA1^bmYN3|H3~>f&Mr#C7;lEuElA^0(7KaS&m(Wg0VQj!^(uztP|uT<)-e}0
zf_4uVRAxQ{HNCp9)T<cPI@IsfwI0+g!s>er>!H3UZ}=5@w+?usL?7I!2M_FnIyOb1
z0Qd;%d4p3vq9q6~;vhW>PFTvPx(hafT<{Ox`o-e{Xc&O110q_AsM}#MQvkGY0b1Y-
zX@<vumUn`tK?901^GZT8bD>K+L6HRM+dzT>X@vzeQP9dopeVtbJTM#r4H~jiCsMHl
zZ()Gv)4qcO1l0X>O3JUmwhb0z_YgEZsOt>Spf}b~z_6Z@P(W%07H1?Dq=Gj}f^rBb
zMFg?Ja|q~Wx=auOKP?J+b+H^QhmhraoYubpWsi4QnxT+1@X`*tb^J55?M0UDNJ#*-
z1#}c-`&syqD7+;M-9`>yp9ed8fUL<Wh~INElk^Mnb1L)lb2Agc2Lg$L7D|9}AEY6I
zupHLfgr8gj>7`QF>xlb3;1)yL|B#i&uVE8&EU@MX<<)X0s80(z$<8^kI5j7=xY#?j
zvOK>i1=8LJg#@G=!l)gf`HpfI;GCPnuo9ZrK!bf$D2zd)e6W26=Rw5|DAxrf7A5AU
zmS7&82TA&vLuE8@49@8g3^zam1ZlVlv{c*%w5>KVFF6%sVh%E=@X-#suIVGJ3rkjA
zf|P%8P0Zn(+`wpPK{6Aq+yU;}fYuE`GBqMmLozke&=Vv;iz#j8BUaG2feJHrcu9_!
zWq`ybX1z{5t0#l32F(UQe2vFysIQ5<GZWmHLpn<hI_s9155DDoJIK}ta1Vp#=OD{*
zAjf3b*r%Th+BB!-k~^&G0_}b#A~&PkioBj74Q?m6*u-Ne)Td-s8PLXCZe@IGMRF?m
z9DZ<<1vCp0^aE5|g4#~t(h^KS>nr%|C46#+s<z`Co5odof_APD5#A*kMX8A?(A6pm
zpdpbTpeh;cMNn3PoRqO8h~tTnyPkcz&Ae&yJ5CwHB9$CV96){X9C%+GkxL<^1=2~b
zkkW!2J3>KrfX)Ge<O#%~IJxEr&3)(V+Cy*BGKN*Kq>n<s0#zlTDKMAJvP{tZR8Eyq
zsYMvK8DlK`gx05|TL?ZM3X}{WenNyBh82+1k5pZN?w5eiP81={PL$?lCg-Ps&KpHJ
zW)xvIM(Tn(pVD0^gm(a8*bMPiF*(V>6qK&P$pM_MXEQJ`WP%9jK~QP->HHieYPrQx
z4p8rb+A2h39`wQrcHE=}YVt!AR3`3Wb&@@S_UZmM>D(NJQ4Tan;;H$X&%5`A+o#K#
zO`ORcGY>kDM^3o4fo5M3Cq01!4@5wh;c3~Y-?dwPX3DjCST-WZ5=&4<0(AxB!%~Zq
z@^PI|j#<8vV;LxAW9{(aN^X=Uboj|MEucYnP~bV0<|SvKH@h+NFf<`j&3c^6oiS{N
z<}*r1;?j#UQ;G{x!4qnrgN;f+9b3@e`tr<_l8kuJA*A^@rqnRkQBl(|s-RW`C>@}e
zqmU$kWH}@xkjha||H&pfATv2TCsi+{G`9e@T?kRZOnds;=+E+K`*aus8j)m;-$R=s
z$aB=->sZdhhqUlm3~_xiS*;MHMt^=9XeoPAeg$a2A`(=vJ%Be55N%C(3wuL}osIVd
zL0AKUs<wl>&7gfbkeI_`JJj!#<{tRP(Y2s713GjRl5au7Mwy`P!s*UOE$W524DHj8
z_8XS#D%!*RO;&Uxg*s?b4iw-ELHGIY1vNUsO<qtlKNCbicVjEtr!O%!__RsQ3|h+5
zz{g;V5gQ31J{CkjM3Gt^P6K)PG$@6E?FIFbGeJc9fyrWet2c1kr#tRo;`;hU5mx0;
zx{EL;H9a*iB|Zak!6qooL9O}{&=e8$(#?Rxk__kkoSf9;l1$8lUNOo~Xojc0yQDyg
zkc0+cG3YcXo0Qb#{G!B?{36}-)I427$j!k)^WgImpumNMFr=$`(=L6ZaqGG4H$kwp
z44M=rqA`RvC=NO09kdiaRUf)X2-^|Vpn@2wLdN47Xr3l=-DzlXY7yv`3{ZrE4n7N7
z1WHq&`ZqHY)cgYx&;{t?u$AcIFdtJ|-5_^buYih7&>^UhLJQ<yPy%DX>^4H$g4DFT
z2V{3O$m8J5M9^*`TU6i!atjhmGC+a*8<Z+n!z(1v;94e#fcg(<V;97KpynPCsR?v3
z0Ay0BxUx7oKQApa9b_A5;0r0a5cCmQp$Qo@2Je?dj!BSZNOmKtAB=_r#HVDWR8WsQ
z2z0CjB(Z>4nIHnXyGR7K%}4}V+>^By5viI%-wX^Y^^m*>@-dFY3eAn6BhQG4O{`@@
zNn#1E`@m&*plj}P6D!J7b8?2@snW%y?M(ueN5!CgW0Rhmms(K(I}ob^R9JzC^dqM)
z?_4y+z&?G`r_%{N9xkxLiZpvbHzWpucBw#87|7&I5P@ktB!-b%zo7ePY#`%BdWktX
z`N@eTsqvuuXrKeo??H!TgNXE!xa8?8?s?h68PGbB$nphZ2Xsuz5LAYN5+cOGpd11j
zllojL`R_8XihVka0m*gb*n!ym04^udb2d`flYBc6V-R3FK*JA^F^FGvS$j$*TiB<=
z7!W&%EIp8q+6KiVWDf_}3Q(XyaxX?31!4s`LDh~cI_C29O$vW5VV@3TK<pr^U5KUM
z13uIg)bfQS18@r*L_qI>`ELhjK<p*QZ=e7puJj|xj@-=L)RO%C9PlVIsdj+YDH2h@
z29~Clf{$AVHxfaXU=OGFuyA@0^#d8n4Ad9E8caBn8Kr#^_z;RbXb1&VMg}Kmq^6YS
zq!vLu3Gy!9gihgbo+N6TNA$T@M_mf!OH+o8Cd2N-CadWW+aZveoSB%TAD^0&lUWd-
zkyw-qYW9G9h&9L|O%J333Q}qplXXr6Qp2Dqxj40?6ughKhLM4x1hl@rEHNh&bjo#n
zUTQk{3YmEDakrrL(ea>bTazmxy<_y^^8jo*`wDC#lB`x4lIxILWx=2^N>J4VNeg%^
zhXxI$GjE8ceTtxwOVBDaP;FQq54yrCwJ5VH5z$QqU3i)aBGMsSPQ~ou3}}@|Y39fT
z9sdhTB66U0cc9DxT2TW&6c`d9prixI9FVkvG`<BX#X#*#B1#z4CLB^^pq*mY1d5Mj
z_=p9%^+-vTCf4hK);WL%J41>xeN&4wum=K$D=1AAkWmCsJb(x2E@GM9f&@QCe~40>
z5p&R>LljFu9cZ`A^wOeK>^`S;^n*7u7{HG+LPS4gQ3A$BM@rK_{CG3)Js4ih3=G))
zjS=HivsfKe3V<41&Z#*$*e%BhbV`e7q$AGDKm%rZIQ#e*R#Q2<7lBUm*@!dPX>I*v
zRt5&}&Ec+~`m#6`X`BueCE$V&OhAVtFx*2{`xL44gALRsgW3p}i4O-#?S-9kcK{Ur
z5%A#`M1F%5HyHk<a`ayZHOW8+zr`a$K0LD|BRI1vwV24vKxqkxIC!rUG|vkv{IT1N
zQ3g<IGh#CdXyBv-G`biJ+Fyd*dW^WBv<QON{i{KzEP>XiKuUg6YJW=8AuRoaPWJ|1
zejbvUoQ>{($jCC%A}C1JPc{GR!2BNo-uys{|0#6?xCsoZ_xFIR0Z{GhlbM%l<U(W+
zP<LIa9Vq=@C%zD(Vl@QX(gw}|0f{BaI9gp)Z~Gu}KIoq4pgb(i4oESGk@G2yc6inU
zUEdqD0B8A&;bltm8?4+1H+Srau@~CyAuFySol@xZO;TcUCU`pkGpNu9P3QX*mlPNJ
zB!bR-b1f=@-MfsagD?gJpq(Bnx&>6)W9=DZ*bNN_vdVl&I6$}WrzDmnf^z&C(9FjT
zP!xa)esC)TOdy&XqJJ08RKE^C6O62GFKl-SWHKZzvjTGcHONY={)Qy@19s^!%c1_J
zd;zyRsB{7Mv_Y1G7p#K`=;l4lEwhwc`3z(wXsIcrI7M{s;cEpU-hBWa&7#~!X^@Se
z6pU^o{19%4)$mQpWTj=K1|(8b-kKA7y(nmzOm1ljcpxDjyi^V{vI24!auP!-)}T3y
ztkjL<8bm*K11KO6i!Jb24NYKVt?GuQZalYOfD#)drGr>t?}LeSxkVRckDPO{Pk*^-
z#*LboXxM}ZX!A130~$CTf;`rg0?P5&X5cZL0u2+&(<3A3c;Nf+LIyPdnF%7Gh0Fuk
zKJ`biu{FxA`~m7xaKKv;Alq<Q3H2pei+tckY6)n(GdrcEC>1<_4H`U!+>n@>T#}fV
zo`bPf4wB`OYF234r>ZkRu?$IdSo@JkwnGDf(v}?R{y5P7q!Q3_vyh_1yyCR{qTJLJ
z(7+z{asa7k0Syc4x&}1Sf+sMLtcL~$ji!Zv@-Z-g(?$_!c>wN7Va#%oMh*g%NO%GU
z$sN#up&*TB=9Q$T7bTXY>c>NGdpiaiYTXAf8xTb_Mt=*MDyV8ZXxs~{-!W{5`kkya
z30sSiTaZ|yAD@{AT2cx=SQk_^1zCey{GdnxpK=T)(yLopzcb7IuuF&RMEL_N7^!PL
zXjOF3WcaWf;-qB^>nZU)W~~I>Ty74koq6ECM?^hFvlklmWcePa^&tDO#yy7hP~Q`I
zcrw~tLLegp1GYsG+FT3_nIIzl)RHzc2I&C%bncnj%~{RC&^7?lss&K$uu06xv4b?|
zK)P^fg*uh2RytA>3i${Ed3NYYLXM>+`5xe-^_?>ki?EmCNTmfd(x~Ya&^#5^D1%g|
zNS4ze9KL{NWk40FcWO}{y6-W{3wrt<!*ZzaiQJ!)QBqQnlL@}(!~--GEzZEe;F()c
zlwXiq<P15cGBZCfxFoRzM>7T~+@ank-Dc330!dLzX)ggW?#Tx#hd@O!BvL_9jMgWN
zniZ|M<`{f2DWz5;W?w-I34=hVf<rPk9;=~IN>&kwv;F}!&Vo$fB`~4_fKPNmY61>u
zkyK1w>v7gU7}itbd!%BmxTGjGF&A{z3TW&D6xxs^2P$8|{V_0+E@xPkw@_OVu}mA9
zs>$*_lJ$_Y13)CmdYttShV@Y2lhyJ;vL5B4Fz}&z;3Mw9o(G+#lnElz|E&CY$u~*C
z9(sssBrNJFP4oqouy6-0twlOr4|INLCWuI1r1q<3ilDfC`tdpGZ@kU?pbJE)XESIq
zIV8qGaSvY904AV2DVgolA87CTI!n$4wlE*G>4)TQIB0zdXrxOYevA4y(6~A%oWUZH
zVg?lbkmLrbypTE|(BwuX%W;k_V%Q5!XH+=C2D;1!y3d*$RKUi<+xRj)JPes2BE6KE
z_1;N!IfwKK>C^LTw>ZJJE|L}Fu!bI}6Rr;;K$$EJWVJ15qX0O@LAK(s8tQpa*PY}T
zN6KXJ1(i98N%2TWfr9tl+yE^m0i^(Nl!FQAV!!ux&|#1dutq*vzDIHbBBy~zJMY39
z_=qHzAsgx|P>N(V#P>)|P6Y-A2KaW{f_&&npgEbvpyOvzHrlQREtv%o(BnqT?BNV(
zLMCgX0y4G|4?6ZF9+aCvu?b2@C7`8y>8T}&Md_e^RD523d~s?{8l(cX1i1x7q&KZu
z9u^;~Y7b{Xrt3h>43eW4%Tmsdpmg;Dl%&Dw3bC9nihJ{gbD3)P=`aSwUb2D!DNf<*
z2SLX@2EB({3|dVJ*-Ql+(uE$m3m@ABEy^a@$B@8B+(?j;nU<DXl$r+`=SnTh&nX2h
z4+F&r$oG&E6)kZ?3Ks@@=w<C}uxf;?)&A(tiO<Z-EQyZ?Ip7Jd;Y<t%K$3VdXp)bJ
z@JI$-=25JVJaW1PREmR2)8zQlocz3W^l}`d-v~|q<lBe{T@VXgYJdsQW+1R_a0b+K
zR2<0v09uO&Is?x+vnV+yH6TByG97d07$lT8*g;zhKVfAU6&*rg6cfpAXdqB6uZ4no
ze4q$-FUl`1z&vLM?s*sk8sSuP0LX4g?m~-yn1#@)1Aaa_rNN$?Sd^Vw1YYF59JCn7
z5j27h&e@;_8l;s5Id$34K0V<{s;|z6R9McYqTSm-nR+_B-oj%yG|DM$nU|zi<R<1N
zrbAA;1J8$pk{x;uZ`pbGbZrrfeY#V*=N!>M*hme<E0vS;^Gfndi$LWWD59}OIfmU(
zuY-<=C!#znC;*+>2eJWlglGvUV+RzamXuV2PxHf-wlN!}BwL3FOpw>W#TS@>CS4>O
zpdO;;fYDb_AqKM7BPqWEJvPB5C<6lnyby(EUurr4=ZF=C<&^jy+CTxF5{5jy3`)X~
zv;@w=h~{6SQK*5|N@e?WRjboFP8&mES(vP=UZ9rersn1sRe};2$TEnvppXYgHke4q
zuomiPvfA8`HbF6D10MJgld{C3%)}D#1RN(9bV+4qURil&aYkZMNqkySepM>Qz#XI{
zMM{OxL_mEvfdT_-uwXa=8Z2bR24t)U>FOU-&;$!8>LJM=lqMjzrC?YK^*ME{1uZ5-
z@+}@~p}wWG`aoKP5C~d>0Lpowv%%qO5E4P%1&l*{A)$adf=NZ^M1!szIfrvKJ(Atf
zprB~-KR30cC^NZOKRKtgxFoemKPeN+0%c85I)KCqD3gM7C74KO?ATo;Dk*E9zBZlX
zxK6batV94+7DUu8s2eL_J#loWAUCFQHf=Cm0u2-@MhK`u9yAX=><J1KNGQP9$H308
zgk}jU+6~GGkQe}!2ViT#1pId1beP>xe^Y4`C<nB>l#_vhAwIJtwJ0+$4dawANKU{k
z38~}&oDq&;FVy1{4MQat<rf#nqYgvO1x;>%mi)P8mbfKm=A@>CCl=*p65o-dx{E+b
z0Bd5PYU==L7y&s4B5qbhD+~GqjF)shDz#5v>$zRE=-^6NSpW(;*NW6+-_o3t%(BeX
za?}z9<QZ^M1{3KRra+<<eTWHMU+IC)DbNE~;)z8?iIsXunR&_irFkXLUD==|$&m9s
ze$Lq)tMAKbpAKU{b0~RhBcKO=Bg(17;^NG-%w+JQHIVOAK$Yi0c+ri=9T49YlQp~n
z4g<*b9(kp?sYRKP!3U57KnGt0S-^W?h@uK(1P>Yz)OG+UV6i4q3<p3%fUL3-5&~&y
zB`5<fAm2jlMoXcl<zM#4beq|y^L+`p-4^HoOQB>HRS>(8a|U?(8>pa$3@U=00ZG#s
z_CozkQNaKj+$=^rb{<qvWA!;K*`ud_(9{jt^>jf|D$*ILIf+TB;Pa&sjzG)W7><Aj
z1I4{?*g7%r0K_rS=nLpfJ;$^($aVy@Q~;?skXErlvjBCSL15h&lJ(F~0Oc66qarOe
zv81#p6*Qy`IjTt?cHs?Z*gFVx2{oi0k4Th|U^rk09fCUv8-k;eJ3#A~AxRT0RbaRS
z8Xn}FVz37^`1B7nZ3B*T#PB-gq%b%8^!HL<mTtEXhvj^7EI~U40*@t7pHRO~2$}&0
z?>LGN2zJ34v9OSZrf=#y0~C|^;}qruXh=}Imbn2MS_hRk@u_8rIeCe&yCp!qNXV5!
z6ZUytUXa0OpAKU{1B2Qwc?ueOOa#r=fdU0=H<*Ci3ui!sg1V*6Wzc<MTA-0naKZ-@
zpq4XYvJlokg8HAj)-MEIDF7lMH6JLiL+(k2_l$}Hb(OkKN<*jSscSpJ^Wd;Y*bYm5
z@G(Qkq#C8RgH~eb!!NRU4%&n~6W-QC*p9KhiaNHVE|#_cMf^v&?dZije2@TkuW&J0
zlMK*7GsuZ{$@%%9CML)#NahEpHV_xm+JW33!UkIj0>9#htYAm68M(1_7L?(zHMTIU
zhh{h;*Ouj#<|aX>GSoqnwV?A;u$Qn8?BKh9pqYwvD?mL1NFaifE+P;iNf*fqs4vKh
zKBS%;d_p)8w2=+8Z?-ryCpE7m9yCRd-JkF@1V7!KYK{OEQFy$MWHZ$Jl+<AbiJ)m{
z{diEDwForG1#T`PVgM}}!;3B$0~*O>?IMRKV~7)wCZfY}rDK@2@DUzp5Rhd*ateSS
z=Lz;dXcv3X3@n3M@Zt-yLI8e%4VCQ&ZLz_c1TgG}`k%;gX7H#0D5_J7i!<}m^@}U>
zk~50(^D?WzBmI*=!4Ar9kTefEJ`8deoRm0UOA4d0eR})7M-Oe;%U~@<D%|0)8q`_=
z<#V5W*gkPQ_Cmc&%~_=@plK~ondyW%s|6`=Fo*G|X+LOe9cx@;SPu0(1%m?M1wfhk
z@WaRUfR1tk-C5wApI4SzRFYbRb=xk)B}fGiG}lwnC7=kv6ADOnLqY**=nHh-A$)aJ
zZe||jo@MYzgkD|>cnl2vQg6^9XPF=(edZ^{6;gY-?9+7;b9?4~FoLCQvPyG!fmT$U
zst;`u+yv#R>F_m1pp*_-5DhQNAj^V(KzFQ?wWtcIB!le-mj~6;pw;J))~6Qe*hmlo
zUqb}F=-CjqJQEZgM0B;26LV5Qp$cm4fI5mnd7$<^)@tGt?3}MJux>ONb|9)EaL|ED
zCrFD55`IW_KoUkV4K8K^9pwNyBNOaTI00QAb_LevxeN0r4cveh4tU%E4F}M~3^DPE
zxi+f~lyqK#n$KWc5J3)EO8yaX4IH!_Am6HTkX7uUE*e_wVps+B6<OH>zK9syRtGN)
z0Uy-{8W4*w$;?g7Nv(iXc?iEk$~?@XpIQz8O#t92@Q^Hq`kcHWDDbJ<h_#0~#fADg
z#f9L5`Vzqb0rD}#9atk<(LP=HL$(U%AyZh%7{I{5Kt#Gio5jT94&;st!WCH40g@}A
zp+V`o-jdAZY{&v7(5wJ>XKiqDVoqX_TYgb)VhNUu;2;a?pW8uK)<1{kb?UjKA5>L<
zDndw+4GI%TQHNnQG!Q7?+PxUGwgc4K1YK3+U*wx#MD(^K$}I==A|Xi+Ez%)*52Ma6
zCTn~Oscu9+-C7^i^aEu?(AtKS)U?FXoRawB%qnOYfUcs?1QF?XKL5#FY^7_TUR(0&
zRC=WyEH6;oK|-L3WzgU>$Uz{7#AoKk7vyK=l|awV0=Wi<lb~q=6sSaGPxPJu?53jQ
zpaR(s)La1<2cQA_Ob`LB>QwBZSCMMKf`F{@5W36>`5qIT=lMc<T1XZ{JxYG16ASVq
zD0iWk`LGSb&;i2#uq;l#RiK28HHl+b1@#r>xe9#d6QcGu0X6(UL^@=&k=q_R@FxI^
zLCUSX3<^+0EshxNgTx)i4Q_$tR3T{j7;*(bd;z$u2H!vkzV~nnXnGeE>Y1R*9ZbLr
zY&Zil=194}ah?qW@1{c-H=%8N+W5N{RBeDxJcCplh)b;@mcN9xPT$hh6`+azAOUzw
z4iWnBn}A>pYK6WeF9QS8);EN|;ngK<^$)ddG(fcx;YUb?bpaOAckQ6LnbNjFQfgHu
zq?H0{7C;ITaJdXBy&!D^h}8o2(5sn+VY!;DzBy7JLs|leup6AQ@YoIYHl=o#XQrg5
zmVig%WI*eTz*`Mrr@sWJf-c^{JX;Ks+L6u_gmzA-=onB#2@(lt0fAvRG$1J5Wr}jw
z7I*_1DDWZG7NV4e+YVV$0pA`(Y3f6q{bT@|Lav2pZah{)Jx*4u082M51XQyi{0w3t
zC;J0-&>2Mdh3M3>7}P4n8r6{ehGa3+$CL&(_@GK~lJ^7oxd;^4;H)kQ+J*-r(m!sy
zl)ZUlkbSx*zw_$J#wj$&>cXG_LC}5%(1~MdiOH#8XF)0!(5bGOAR;|h;-%}`I#&Di
za}T55ChEg3pP@9TAr(2Go87?sDnRZ?O-V;~2grGuAOhMH<*<i3NEAAeO1-Ff2TJ!H
zppg)8hBpGWr9ecwqC&hAYrDC9`kkhwyBel@LTx5%HV>)LMV&;A<6&S3dIV~VgKbA7
zet3q5FksCKvc@ctY=<9R+7Ft!mIjUMfh`7|^`8kM;8%I1|EmHWs-p+>Hq|U{2U(mB
zO8Q`nLFY(hf(YmeEkXPADL1z6y*o(;YB5=B#gKdqy}%qiZ4J(Qk3ee_&%h(R9Tedp
zBK>(#Oz5{*NBi_G{~52XiVcR1nvzw*ql^<57l0S7fyProhnALrDjCnbl++5}%!*7L
zTP%>e#?V@hs_p=l?|2G7B-^2dAGQ0zdqL>{RGgq!b=8Z*X6(D}V4v>x%697Ic}cJ$
zklGID2CZKJI{=&zK>-3;yMf^VXb4bR<{*wSc@1jL&jsaraPhyAnSmh_M5MQiFssaR
z2((ZCdNs5$iYE@{cj{UF3p5u8Dp^6LE__J^r1bv^$_gMNo$J<<51#5q_UTv8L~(ii
zwuk0L>Nx{6`&t5?g-<Ps2i>Kel!N2)0(eS10PWjQ&k-*{ML#RNC~5_HA4EWJ_*Aq{
z_gKuNe*1tf)x9qVDuepr{a6Q(%^(8WeHFJ)zwmBCQiBWZb{XpV`4Gs@pfx0rC<k3N
zlnElBXArR2Lq~yN6+ZQB1~)K4SCK(%M(o6g%mXmmr~k?IpK#+HAJs!!9kk>F)OQ4>
z`DD-?JD}Z-;9L({h>-~*px%a$W4xq#YU>7tyehoL1=RtWAOgOa1{(R_scy3fXvh&{
z70CZp`9&$POOy~xI3Z(IGWO}lS^K=~%G9YI2TUM0fL1Jm+yGuO4M_%|jwkZ|I;0LZ
zq)Sh|1SkuN0MN1%h|RX3MmLB^zsVvo&Hlcbefl3M&!z%a_-QtjT8-#jv4X4y5s=Cb
zXEP7J7@2xjgR>o?Ux&wPXf;P^0~cPYfLmL|ppxz&sH6i|b%<IPGJ^BnE*)~ouOzIh
zqttrT_BGB$iWt^IeNVv@9CU9jXmfgIUTJ=5aXje6(mZf5fO-_*&Q5l5NqK%!W_sRG
zJjs;OREcN-fs+L&EkZH`s3njIBA^Wpe*5$}%g*!dSXTi{7Syv^3)Jq@h1dEZTX9$o
zO%k9sA`#OqsI#F+JrJZmZxg8BUj|=v2C8_FYo8~u(Z#p0X$dOY4H`B<x+xNm-B5p1
z(BHs3w-#}7EVvv1MLlGc2a*xp?bEM+f2g2%${iNrWcB+Xy)Go%kro_+CP=XMcraW*
ziT{z}9c5&%7*qos0`)q<{s+~@klJ6I>jt0Oo6mO80S;D}|H%q{T()lqrMp?6{UP91
z4<6g0ekbzSilW54lzi}Qg{q+X9n{7Ot}HG|%?$!eKr%NXuHge~Fb1?|OR{aClNYhZ
zCCp&x1Q>kvH)t6X5qTKp7FtQr)G=sHPJBpaF0K<l;N27WB4Ki@0tFEs{~%ccjUNl*
zrq@#QlJiqC^U_fdW&$0|8^jK3iGtH2$Z|+tgp4(vgq^VV09Fc7WE*H9Wl$nK?cuQv
z>Ng@6x}ui1;I)AINar}{gEE{Iyr>1mBV<b~B)fmNgCAlDO@x#-JW$&`pmhg9tKq)J
zV?ET@lvZ%yQCx6agA0^yL0LSwGS??R-7mi=m)ML*Jx3UThB!ctdq`s&5$m{eD5W(W
zsM!TQ6Q}|-p#)m;8d#c`;tV+}C^!|zQ5Hz6u%HQniY@__ws`V2lHJf$02%}!qMSls
z+K~)Db{I6q_#HIH1M+`xesXqdiBD#6NopSP1sGM_K;Rf7%o2^#q=#7TB?yXsQ2GaT
zH9_l7a*HA9A9O}zCWwGu$f^drnN<x|=~B-TFG0hbpi~b@{~%j&SPhMNB1=8=m`7XQ
zKLM2Ow!uq1kOLq^JH`nK(8Nw1Yr!{wV$1Cq)<S&?YK#(*&QZ&Fq}B~=2{ibO3Q%18
zr==CAmbew=6J6F(%LyQFla$BFvKa4jDsVLpTEYcc#|7#*%@C}!PmfDss_S))h8+_N
zDiVB4bCdG*!6c}t07ZY06udKp#|&u1k<~1PG|P*j8<7zAzOaF&B0+;fnR$7sMS1xt
z&{cValPy)9u>q869>W6xkL}PvAacJO(#;ipAS)0zS17SCFl2&=bitx~|BrJz+o%8O
zUSK$R1-$!1fd|27z^0@o=NBcG<bxLgzX#dA8}32GAPh!jOr0^e0{Ge0UqSZ@Ie=z5
zKviMp3D7_{h)6e#?YJtRC}E%8{h(g^=0-*6fFtQ43qEoLaVdvCXf6drKo|Jg+ozkd
z%@weE;RLmTssS4Z3RqBjft2##qzodUXA9g!w4@;^2Wd+SXtLENsWdaEL^m@}Avd)o
zBR@r<IJLw%HLs*7F((|nJ_dT83&;jYF%CIJ<i8!%cktzlWL?CCRGva-NsINNjQGM5
zQ&9PE1k`Z^Sr73bh=s!;5D!vtTr6l#xCFG`D&DO$FFBxu=o>T0U%H7{B??ZR2;V{S
z1!g0K^7Vw*xfvKBqcGq?8&r9q4S&P-@j@$T%B=(~7e=a}5V;@T-GeNm`wg9VpxnkH
z&?343czJ^}TtC1T(UCXU13iui+u2pspeY|vedm*!Se6>#13II{zZ6Fo04biKflhVz
zfYKLI9N-KQ_{p@On=XiGAc01h!D|cjiz|yE>t#XN7StkwSO-pJ2<x1WTGR`3DcYx>
z`rhM`!2-WO57bE~!aAJoG31RNh=Z~~z6KW;U;;6%3bPziW*`-cpcu0$$Om0`l%86W
zT%H0g01;^xk~5J`k%7h-k%<<)`3&7C3kov=Jr<<C8l;jWa-0Y3n}WoW43Hh*<Ml=0
z)gdBYAl^c<0}?dFM0yJ~UIILQ^uhNbgRI;Lx{e5RE(llzezlxOVqQuPW}gtFy-r&2
z;&BJ)vS>(>L`xMIRzkfF8l@s8NfxEXC+FuDWafbPH!FdLeQe<!pdFwF2Z%_27rj<^
z`eRl5bl<&sf)cN_VO>7*tT6{!vlKL(3-(P6$QlrlE~Db|vq?<IKArE{f6Ft|#i6YZ
z(10Zoo+$$R;Tfn^p$AVTAagT8L^^D$EZy}|-WNl81=tEDB3n)IiAkW#wm_*uiUIpA
zTj1gXL_p`mpTPEaK7oz}B4t*PGMmJ_O6V<H;D7)Tm|7tjhIChg)(8ZF8n5WB4;*O*
z-Ek?IMbH8SWOpWrNcT+g-1%E3-X6|?rf{;B7eP8Uh`o16(+xNe+J_JL!x)gzA#x17
zATv1|lF}tXsRY!1Ko1=F04IzAv4Ff&Qo)=0pfj7G$|f&9GcT<uvACqDG#PaA7AP@+
z5(3t&4f7#%;reH21B1NU73>lcr*<GMoSSy=_BbRc$m#<?d*Hd1@u?NbsRf`L9l*n_
zpoA0j7fWLYvU~Xv;<i?Z<wS0fF3HbMg&o=e+T)EifFb^S3G*Lp(F&2JO+MKD;Cjyy
zi~C{AanRP~kQI24i~>zZNV&ux)W5)%h?V(W#4;c8+NZ-9kia8rkREC|cxN!mfr+4X
zP*}qil5>zYf<kNutzIIc6i2NEQEZ1^tDc&fo|=c^6i~+pt5YCRjI`$g;uO${2*fz0
z7<@Eg5vZ)#4X;r^88#C{AZip?)dDdcv`U5;(=$s_bwM}5C#I(s>tpk1N^yQpSt^Q8
zan8fy@F}HZvWP_ot)R6iTi~I9nE8LIe$`I4-_t%F#(;zZ1>2yZ*TzGm8@Y9y1Bwc4
zDIU@Ud|(Hix`bVCPS&wA@TegE*d6#jJ{SWM1{7JJnhd(<8QJ&wpmq~B-$Qnwi^8^|
z!`Aszkl(R{{R&)R4_W^92XTKa#PhMl&9V}k-*fVl3X;=NG6QI^2y3>7qz0r80mMbr
z%M5!#nc+B=%)sSsvmob`t9?3*0kN5CMM5TKkpSukVGRWwMFMEuBI(5wbhrTX+IjRE
z9dw90)*2lWDM(q=01`H2jlRM2DCi^^SYpl3OU(ma{9O#b?-<nWg@g;Jum*Rc!34B9
z^U@A}_&Kx@OWzU;5<5t)fCdNk66t5qOeScbR%UTLe11K?7&ey+$~~al5gDLmCg!jJ
zS&bE>aZ!{tJT;)g>n3R31gM(>KH>*VKy61FMuH>@vI;LG%TbpyvVt<~Y<QOwoFYI3
zG%+Bx5TMaO<NyWAnioV)1Q`uZY+wR<h?16l`jpo7>q}QU!n)67`4O^M1G0Dsr8tbh
zRX{_|a6+;k>PJe)t&lHno&}0tMflJGC@3I)hm6R6vx8qV4$W=Uv>fLVVHlQE$>+$m
zvJ0pc4!T4NGEWW~J<J3R#id^tH9mB^Spas5tRF1aDfKzB<r<)&T9EhSK__BVq@>3~
z#xdhlGK(NxTTn3w*_s2}U!JaLx8d=A2Lb5l3Z>3LP6Z!9*~}BPU>Tgr5vc%P_(Mj^
zUqd^yWMu%POkP}4nFBgW3^McsvJO(>quCBAYi}ZsrhybOl=>VwBCdeae+|5V0XYSd
z{vmdsgl)wA2lY8M?REhr0?=>|I1xY-Kz>nta$<5uDk3F-0|i7N#+V>oHP|>CHQi(e
z3Xe2+agRua5G$YCK`&?$hNePFQv&LxYM^^4gFw@7=y?DV8!zpkMY0vtcF-^c$!#K}
zbclX%4rs_2e@cYRSewHJAt`BlKyIZ<$t+GTN-Y7!cX0`1-TV<y0|3-FPEXCx#l2)6
zshtchn`z=2P@RZ1Odw?gk}IHvG+9eMkRm0qA`?7DvkjCzK&>565m^j6pAS+}XMo0?
zK}5Qav&`#9W}rh3p$up^P?{)F+aTgx3=Bb_+5(a&5k&+>06>Z-YWaT)sDc9R8HCsi
zT3nb3BA{)bpRfV2pU|R-TK3w5iU5SYh>H~=nGnNX>V`HyXs*~DzJ?SL+Bk;4i^=jf
zv>yV_g4rqH2?)>$0FZPJO7Y-I08Bs^YZ%(62QwuJ={+%owFk&*W+PjSTo2(K6ND@k
zL9!j1%gC}FDVL!%@<GQE1c5fBLvk26&_SV){<X=!_>+>beY&rYc+yvQs_jfc>=N4m
zO66dyK~)?$#K8n~W8iZ;xYdx{MI(QMEe8`I&%>Kq>Gp?bn^we^!peKH(mJH&51Ow_
zEYU~4dvPKI?4%!1V1oxI!34B#g;casQn2B)9=ty57JM`W?gmWjDM@pX6pu8Akq^ml
zw?XURK*N!sfXPX%h{v)L4x@yHh67oN0GC4$yL9034@rR-wnKw~n(Zx6M<D15y#Ix0
zZ$U;RpTM@rh{8%8YFeHSD(S1?jbA*LLw!zGxFZ!h#U+W!*#$ZI;DH`c3kPd~i!p};
zjdijzACm2mY8^y^Ch$P{9AZ6Mv}0Hg^*m@hBN2_wtm6DU{o>?A(CSW*J)ls9*aOaB
zAZsDX3(}%iw}&ok^M}PSDfWO?Jf=WS(n8pS<{u1up#C8%B|!?4lGNf7&^hbiRX89|
zMt~MvfVwitIjM<7@rijU@yUrL$r<tGklXX1i_sCDhqNV-#&4i;Oif3D!T}QUAQm{#
z!34Acg=9G-M383EL1hB`?Cqk|f}F(URLJ?;rFogj`6(byQdVkmNilS;osEfsArnNT
z^E{VI%dj!Dhclo)qjZ3<0KUFdA9M|f0=(RA1ht1j1blN3oB^pa$!Z}X<r~<#b2iZO
z{)6!10+dcOK?JlN{>u)|fY?jvo)V-zq~}0OYI)%EwV<>P863eVqajvP8rX=96`Mfq
z6CqFm4NgShaaj<7XrDmx*K=qqoKl++Gj|I?gEA}NezpQ_-3JlS5jhQeID<NVMr@`6
z^)iq$4r13VB(#x6oFN&f7*v$o6qgib=B3+#vseje_TH^1Gc_eQF)ukIv$(J{u_!et
zwYW3~V?83IC_q}T0L?HMh9Wr$y&yr-4RsRed=DZ@>%h{~QgHJhbUaK6sDyUSEhwo(
zY6pX?g0_f|2D_ndB-Iv>VOYHZ30Wiypxz*>&V#gc;45_U^7C>s!AFVq!Zxfr73Ei^
zq~^J078PT}2gE5zkqq@Kb)ACq{zMGxp&>!mSPxoAz=8x+Wr0>ugAb+hP0h{EtV;Dt
zOeQwgsqGZ-Igr>Uh#_HtnJQ>9(DN2F-U4cn7iXvD<Rs!5c|vj!G;PqtL7?17Qf8sF
z!YN8FPAw@d0Huz%psv|EP#p_$MP?nSjs+3vicEY9z0T^{r^}j6oXH&nKdp+2cJqO%
z90~XsEU1cw)U&zf2hDxw%i5>ExhwxZR67_t3QAU+38{WZ9)}19RXSquIv%uYEfYjQ
z&!~{MPfywKC?q@44ca3{YBPa~e4BvEz!DR^z!K+t@YsG~NpMa+?1o<SS{t*70WJ6>
zqR>WNdw}E<=thoW{lb#?)RfGU{389d{G!~P#LE0qaGwJdOF<K`OmsjhA*3cDq)sJk
zk{g?Ype}=LG1&!*;@j|G22~5-dK0svftG&c%xji|GBjutc1T81YGMj{=?0t9f!g^2
zy4bat9IHScz?xMtEP?n3DcOM5NZWwB6&N=>r@)iWHvtBQOc0Ttq-)1|pkkUmoB=I?
z$f})?VhTRf4jy~<=44<9(tx+Uz)e380qr_qEIy>J?XjTV6lmld?5Lc?64)_utPBj9
zjI0a{>2iiuc?-20?coeaa;2^_^gzx4rJthw61W>Cf;u`NBE2_`H$W;Y#U9R}g&Vj)
z5rMe!5OnZ)CWuIXCSN;wnPa0poIwlQb3h>gssJF<51<r~3EF55CenMiPmP=1n`#ed
zV5DtOj~HXWTSjVTdIqda1RDt=pt%rfoeeZ$)2avpAG<7%r3k_pXorL>t=s{c?t^R&
z0|f(kUl^Fc3=l~07SpD>1I2RY7f>GyM5M1=u;^8|mA`$u+KPwK5$1KU!5L~=eg{<7
zg7)D;CU_D+Z43~RKJDpiqd&{F?bBZ$4e;BtI)MiDJlcLGELAgX9XllUsp;=>kiQW}
zP$KMxRKhpW7mriZZV!;%pxO!&<`X~>4kFSQNFHG?`{Qn(evvJKS?LIT#GkCqNClOk
zb=R4B>7b^h0qD46(EJX11q>f(gseDGfTc0Ytwh`X2=Xtad4XXiG*3}FL<F0&{SRt3
zhk!;)!1)QZo(8h02GW!H0E=2Cm|v-7@m5g0rotO`cr1qc7}OG^q^XHCI5h)QM}39&
z6%e5fNzd@3g)YEaSd@+t5ixs)VKvm-lv-U_npp-JZ|(#&VL?Mf=*b#Ba{*bn^p{$`
zMp(@cN@dvk%NSNeeNAa(Bb~DYIu@z~l)Rv4?0|RKV(uw~_+8c>deEsXEWK0FC7|jO
zYnvOxZfGc&6SuM(wBrdpu&-a1T2!2w4<0!J-GT$DrNJ2+)GCCWECmVjD|YEN^QOt~
zIHe5pC^ZZ1GEjUc!CT{aEQk7((ojYk2mswJiR5w6n!QXAk?wzLNt+piw0-*LQptap
zc~xK@r=s29rTb^$g&Lxwgcswm?iHkxrlQqoSqwBnl?fuCcN0k1r(gPg?E1V&CCqUE
zP{+k4Be6KKqy)ATajlCZLneqwKeB12a}YO+L;86g&VXQU7T7T@v}#>~W;cT(;7JEd
z*$gWwAwfu1-HWpK7}TFd%9e;ogH(Y?lll-F?TO2ksAC?`eR;6k%?v?hu@$HW0u>6G
zHlUUyh=6uiX}n}-Gbnq`f!mHHd%_9{h>vMh=OaQI7Nt<{pM*}$fX1;%uJVhaJ18I}
zB6ur(a!z6~c$8ox$m9Ru9tZXIGC>5cxgo0ECt(SixCb5N0cpj8JO&x1gIs8>Y@ZHe
zKti0XRE^Sz1+|+?K(&2wYKdQBE_6TwR3bo%1^9|>7z1K4k^4TA^9w5Di&DYO<#<q_
z=E4INbi8IJh=BG7Kfv~ieSpR)r7aqq6K*p>eFG4Y{y=-z*I9BV_HYKobD*XG5$TE0
zd6eKRizt|IbiYAOdLk?XCs61C51<)(SPAhIR6<OF_x3@B1Gt!g-kv9I4`)F9M!^&Z
zbQ>UOSx-@FadB!2s1pR<d6f&dA3d)k9U26&pVC-GG?CHH0>@%Aq_}|HHba)rkp>cp
zUQ-OI50D1IAwH*c3=g!vMIU_g3n<(TLH!~bEcGA6c8uDH<|FW+BcPC~M?|`ZI0$KK
z2@)KjvW<vzk6Jx~hm-YTt5ovxi&H^tNWbMjC?_<)QzED>2Wim3k^!{VfTag&)mCWB
zYC(lZCWwHp>SuvnLcszZ?Ey7BiSRh6=mbSHxS_KSzHSy=sDKE#Z{Q3H!WCa-2yXX$
zfHzh_v-hBZS_Y{9uD}k}xB@M=$?70LA~rd{C{;fma(o!LA_mRn26@6~%s~D|-lT?+
z$H=IbK;8pi-jbZ32RR5LAG%Wqk%Vy+-IUk9r$8eN9PqG3*oY(3fDZj2BJCiZjRL-=
z7BMD=2tjJ*3RtT(u_!SoCl$O&|05_jzJS;ETR<&W5CN}_;0&rI+p@%*%#=jXQ5K+l
z(*SBpdBF3{0~S_>Oc0T7d)d1u_18{&ID<wG$Og4{V&EaLL|d036GWs3%iZPZ_WElN
zXFwc4)>J!E{RFC)6HD}qOOv3@1W+h|1~iaLWke;3BY{z#q<KK={1HhS-A0UxiO3{f
zT9lKMnPdd=6R74u@)N;KL#=`yZQll{jE5BT7~54K5lW2?pb{t>fqV$*5Fm~;h1iL)
zG>@!>ypY8-m?v#M0Hxra@HPgh{}0)q3>%$*@7RWT7!)x?luoEsI%?H}nuobTzK?}x
zA(<W?hD;EVemmRoeXrt0dpHB)0MP1A@M*C6&Y-SbW?E)yQL(;nehP?{;#-<ik_qqQ
zae>T^hnt<##Ke#ZBGNO8=iF|ozHAR?K+FamE=#iKAk`YygWSO-F``QiIScbW>@3Xp
z(8`p~$5%tVO5Wj0(Ec>Wi3FfSt6{YQXl$31tV?9!kdm65nVSf%cS=BM7Gw=1!+^35
zWQGJ%-(f5SA*00rE>pYU4NZg<ILcJ&HvIR2>NN*=lL%41!p36Y?J`JYlGxld0%eP%
zaL<8KeI|&2o_8o{4`)D(2Q4WjCR?D?O3ffkutgG%5G3+AvBZ+picIiwAW*)-lg=<I
zA+p-6NOdprN-MM(SVZ!`kphWK<4ET(qaBt83P{MzI*xWKdHET9L>csuE6~&dw!_lE
zC*b^q7ousPRvw6ejwWl^!x@m&Le@Ywq<YHDEKb%h$jkwa*MV9ApfM|~QH&!HK-(RO
zNT@ma>FJ>H733HN4gHZ~73nd01C$|F!E@bYP&k4J=-3#8J)8jvM=I2`-~lCgZ3sS7
z3v^llq}T-c7&77spOJtuAPyk1*eprSEl7hNW+{YPGJ~g^Km^ol7=1aaHf7Mxwnq35
zGN*PIc30J1*de~OnzsP$!!H4CN%c!C%S;FNRzmXgbCME^i~>W@XIvpA2GSaM8ZR9J
zl~q_1I)+1_^KoRQL!@#Owt6xtu{agn;Wq|dcL8c>x)ddryOyQq;aJ6vG@Jwt6RJA}
zw09M2U|>{I(7*r<RuECbB$recq@vzov<7xCKyrL(aYlX-hNm&gG^nS^w-VI+#OgZ?
ztDwFks}}{IKy%B?DS-^r)r0&BI+VpXKP43+jGpW<0vPIBYB>R|Bm$*+NE;EuVyNGZ
ziJQtR&C4tSjYsOk>L`%OkU$0%bYMHc1pElm^p(DI?z|6hfz2@!8J}>AiohFhK-D3_
zDp1UVZ2=RQRzdwlX{!{xgcY<18=P<RKwH<eKwU+!PZ6zBNRU2&9XBKa^C@+$e*?M(
z5o|r!_js&_`ksQy1#`!9Iw(nlT47jrIzviMq;3Z^EmPG2pnfkToq@d%N(+$4$FLph
zeaeS<`gs``N<h<hxry1S9wELyA&Kc2n?WIwzrikD-qVxcd37=@@+r3-G;4;{#~4;Z
zeN0x%6RD;`S~-H)#sN<2poS>q-pdL5JTEWE;ImJk#%z0KTVe>zyVUGmpzY(pV>#60
zWSx15<Z;j;+o{Q*QB`n82bJ8AfCqaXk<@OPY`flcFWNrc?6HZ9=)Vor@;rF=Vp1ab
z>K9PE31T@~I>)da;(4Uq2%vQ@D2MTvLO1v#d;rNTH!;p3q-OLHIF<^@a)=MeTNDi$
zc7h(m2%$iA#4%6{{5Gh!29CS)pnH2jM7o(=&c@s6srKpVSD$BTe@mw*?r>Os3e?2`
zU9AP_VSwz-1QD2)Lw!!^Ofh2q|1QYtS@6mUblPwxh`?OV0WF*<Ei91Mkb<`vgPM{M
zZzJ+6B()(`43Lf$)s~V>1&tFSECx-_W`YQ4cMEAh0yMmd-0})O${UoLKp6*;8NsOu
z#D!EkKj-X@)%RtzhwoO0hAOdrMq<ti$FKwH2}*-6IVnFs8{|7v&>kyLY=N#z%_sh{
z6{<M^WH%&u(aJh_4F_XDy-sP13Uz$}Vwe#eyNDJQB;CHWOV>ZRl3(S$53H=9uJsQ<
zS@tk|loOG-;QjTm%U@ltx7tG|)v0Sc&czk*F;@2Vv9}5$te}e|$vdkGOXnyV)RF{s
ze|<CahDP5Asfq=aE*P7lN=uT{it=+yGILXlOA>PnaM!w!gmn|Ps+_FZvy{Y=R8Z$j
zzoZJ3i9sQWH4nk;g0G!`<{@9=I(n!xSV)bf_=3uu#H9F))SQCUB5->NbR$bpG-zZG
zoR1I*8FISBcf0iYHab^kJ&T3qBU%kSv4H9WMJzcA!y%BcK#EwTwo`dxW=TON>?R`*
z&`LTGk>0@f`6=s-%l2>vw9F^6r4JsAgic$7MqktNi?X4+Y??uhmM&1E1zZk-Y|8`@
zaGT%^h`WoaJ`lvh#=uYlN=gBl$=O3}Oe%?-1Pz;#O-oG#-HH-loSFma0qB?JWtJrt
zWhR!SA_PGhEgW>_7wGhqV6f7F)S~3nypqfuj4Lo<br!T00>5XRPVNL{Kdc1;hP$AN
ziPA=BT4s7Fc*A^VAE-vl0WI}GD+u6|=}}ER4P5RCu!4YkR)bFg1bH3O1_iMoEj0|Q
zp}wZH5tEad2N}-24Z6J>G~5Qdy*MSYI3qqQKQj+}N-$*5uLU&I0V2{jHI{75T&iH7
zUOUM;N6A<n77SE#%TG{20`fZ~6hM|{f(T5TA)$a&0Aj7#ic?E`^Gl0ULy8iUL4_4Y
z%?6t>Nry3@Wg-nqcoR@21WnUIcC>&?N|09}(G0U6?huFvi-|0pQENMt)#IR|5n?4c
zQG@2#GC>5?CL!2*bz@j;Qf{Rx=)@?H-yqS5n8``=IVhEWnGtsEnmn{+NV$z7?66xd
z!Ql!j<}yJ9)RUj=(qZ>~K)P_0+sKdVN04tJF$pQA;M3QzYgH(>aXzYzpw0(mObadT
zFi|a3!H1oJd<Ka}(2PA~;~CsW)ABF-W4hU3ZER`|!w7;3W3c7mbPHl(4Pf}rPHI|?
zbAbR_>|TJ)MUu609X1S;q@Pxr2OhtJuO0saI{g6DpeajDF3B&-tRlLarnXB!0Z&qW
zOlfV9nVXmnJ{C+4bmrY;_=aYXLm(*t(lt1VSk(+IwW($^D8*s*GltF3Vw$XJFQiTh
z?7mLWNoWv@K~4bs7EC~=h=lFa8?6JD+w7w3It(O>QKyJNYZoEbgNso_h(it}K(Zd<
zZ>0JcG*|~ewGnh`I5-+1=Qg^97`f%=m8dJ^qnz7_SpE+$Phbpagi^g4PD}^ya{{e$
z4g$4#AO#y9hd}&UOjf3XEQ&$QMdhcYg6=K@wF5w7HIRTti|2i94|ep-*0P5lwyy=N
zl*#Hbp*sM1!3@q@(J)*94T3;&x+zF04YW50de1^ZVo6DA5qNJiXj%+%U}1h<2}C*o
zA{&rcRE)W|8$P-NV?a|1ZCnScV<FLmmLFiY!x!>F1Bu9GMdhi9*+r>opp;Mts^&m9
z!9W_@V0%CWqUF-E^X}=|A{JOmptO*K51%rCMjPhAd+i`gAsaU!nedZcI?QIM=ZG8}
z2Hh5rmjam$0u64LfU=ioPEKliVorE!YIaIurE5`9KGD~$lVL5WrGhoyAk{b0$`q*A
zs5qM-#=*c)0(L-tX<mv6Bw7(ADTWK637Cp5U;s6S-9U3G;5fx&H`MoJO>`p~F9rEI
zm3jHOnUK}Jpabl{n{|Q`^RmMsM-USm2vl_l$nzv60#N6Sh{ZwZ17XFLdC3_?`LMfF
z^Wc40aM2Db=^@AELfQ^DVYj%zPiwFvu3$yC7P&A#0J8HnXy5|mX|Q!*0zP&My*Wl1
z)=(#_I)TJEBDVE2Qj2mkOTcY4aZq~|R9t#M#IVQs1=zhA@F8TXJH#3k6rhD3kg6R$
z@gunan)s<do($SW4L;p8Be6I>Ei*l@G#7htz&nWtV5I={9fKI$1}6>l@IZ0`G(0G6
z%EFE#ngXiW{NbbOph_SUbjMG6ti(&#w{@!a>Fk^4Mb(zJ!YVduSqw4<GT;IBG00BH
zCQ}THp+2T`VhXhleG|0IYA$FHADsWeZU7O`rot1u^tn8Jlfs|VXqnYQP^Sq*K+-?R
zW=O3LX|f<$5A{8f3->|GKh3~nSfI=WDObTBM5L%GH@5D*J4wbq{qz-;3TG)*Si}-(
z17!Ocs7(Yq%@kq-*!g&Dfck;R!4>o(6gry$vJzBmlz=8tl1qz<Qu9jUp>mLn3+fkT
zf(U4Vigc(4wE6}mIwI;0)Nwj2dosa$K|up%kmv*1iNj*3*QqxI{RcD<42s<1<V1+K
z5djZRJVk-JN?j+pVNpvx+si<k<qfbjS}?4JdOV1@Y7;fYVbdnLsU=03$;JBM#r2tK
znaSW)-k^jK!OFl;3>tW`Nl(p7ttfyUqXSw;2RRD^UXR1(xgkjasV@x57dE+x6?TwF
z#G@7J7jlk$eGUq+Do~vdvMv*JMJ<>}@3`!_ZPy$ldpHB)KC;f1-2>_){Dbe%#bXK7
zCuHRVq+TMd_Lv7MMVG@%QA8^W(tbv&#-YBWmc=JQi%LLsMowmKW=VWeVqSV`d|G}{
zJkl|wcw7Sw1|kb2)WnH0Y+uU7zyNMUxaH^M<d>(WI1zm?KIN7ZUb+d1h7A-Ae<DRY
zXmB7tH!&|U9kK#_Hz;v~4m3nBh#*JYu-T`7mcNuA&pr=U5Ro-NgU11&6o9n~!f*gI
z#>wisAUObZ%w1w;9>n{gf)`>tIB$cvkYT_Blg0E_Z;-TypMVBU{gjr&h$CBY*1Z^3
zLp@I9kVi&INkL9#5~zsP2W=n$)wQ8{*?IZpc>zWFCHcwuIp}c>OJnF0(WKi5DjXo;
ziPoUNumb8SO2ZSeuu&FNf`aZchxpDGv>XFOq-%wki`U;awoey4`h?fuvo9<qQ<{)a
zE|=Q`>J5Qy2YVcGW)!?ug*1v;p>sx{fFijWgOrM4#di%T|03>=1`Pvcf(Ynt^xv?t
zB@w8-l!iAV{nmiG9SEDj`3FQmTW&Y)phM}Op}V)KXEW%8C!~G{BGbYX8KmU^I}(l3
zZU^dOK%BRALt^?0Z1>z-sK2Qd(*mgBeF?N!07RremYRR^U!|6Pdf->>x%}sys1(!1
z1+aw+pvACA;SEmUAOhOax`Mt>pSt1w3gmA@&O*dCERx~QfXp~kE4)GGAo&`!bvzS9
zK)cnfuqD09(C`MSr$&x01eIeTUqafn=uMylh>;zLe<{sZh*deKK|NYTjt02_QW?VY
z6@&qsX{T;H<6LJ7v2+8>-zQ;BA+oH846T9BRw~X&EJ%gV1A}}EskuOf7}(!n0$L{`
zEfI&d1jw2NN3tGySP@}4C|`l?1rwN-Q>ofST>bS8c3YBLW<_d>PkwS@39cCfq#-P5
z7*Lw_5YFfa)t{g~F(lQ40t7h(kgSG=0BB_m5!nsf7WP*lFN20qeIe~&NP_|0N~F09
zsBbB-5;V{NNnKbY7|AZE_lRtIAszOiDaOE10-Dy2_sLH;G=N_Cgzh(t9j#Emk!~N(
z!$mNxfO?5q1tj9=A#hFwwRRv)o@}k|Lwa3$_UYHZKU7dWWe#iTQOjbq<JUnc2(m5+
zvH?TTJ{>kN2Pv7UWiQAaq?!oSV}cCuzKdQfJpHk%J#^Kc0d&#<X@CT@G|MI)vddbp
zII}7>KMlGx4{_lVWES!f?9}>4upx3H_uoPc*GDeSK&LeX8GuGiz^NT{MhxVDe0Y@&
zV?Y9me49Y#BjqblHiI110J8wv8ih3lK|x7Gsew9N04=n^Th*aGE6^rrBzw^Zo{)Aj
zQ91G70Cnj=L;Rpcq`8@S`9+{HlU(pjJ|wS$q8zd$57LAF25X-Ehea%<6Wibc8c_cd
zRQv}?!-o<<k%2q}eg#%#Q~Q+3tDq(l;=E5p=tG(cA7J+Wgf$gFEm$I=9ez*Z70}ib
zP+1yKl$l?YSyBm>frKxjo`9q=q-&C(Whtq)flNe7Odu|#?!&MEl9-V4Hz>hlY%?iJ
z1?6;TK1Z}4A)}~o5&aXWbE&@er?3P(eF2)~MJl$?y@hm=2c#6Bfjdk=sTA$*9fUg|
zsRgNJ2MJ#CHZVa7iJZ(N@PP;5?JlLDHqANEx({$g1v)`46GWs7Cmz16o$6_yp0}8B
z535iLtfB&KjUghLqLxmOwMWIEyI4~7A^ThPQ!~?3^YnA_lM0g4p%f@2K=&TvNoYuS
zctB!=thGKU?ny3AFG?)PK&c&CLBWE%sRutE;Rmz>O}i7D8bK!|fYJk`(Fq#E$pjJU
zhxEUGUZ&(~pB}&EIQPpBu)B%K>Ty8Bqa?AUSU)$v2(<I71e`oTi2`eEK++|~89)@+
z4ow!hi6x-*uh?uy6ccdUVdEK)v_RIlFCtTb>;>(c$I{;dR~w*MKrZx7B8n-9{S<V!
zK*x}PHd28$!-4${vJp?n!@Jn9Rs&gfL*gA8@;QmH1&L9h;S5CT2RQ{&XQCC)ywG6<
z+NJ(LE|jJGpz1CYL_m)z<FHSE<5xbf;+z`PC1kB2frJ9&<g{Ec1xf*#$)Ms0<aeZK
z0C6Ef0jc+H!j>IBfffWbYP=zm9z5y8wgEzt9_^ywGH50k6y`-`@t}E$%)HY4(qc$y
zgzl^d=mV-0q{`xg)a1;>9Q~xkVn|frEQ=ti@~&MvWJk$IXjFh&e&p6v;F1uW6hMh!
zIcjYHnyttL5ztY+FLvpQb{ih=cVK{aZi>k&29ZY2Q5TU^gR<jV(7Y=s2*5Uj38<|C
z_URQB=Nq4LXu|4fvT^~E^%$poJp?Tx1T7+iL^~oEK$`Dt_URTaTBg+tRH50Bte{77
z0Ad7x1IX{7?lZ(@^aO`AC;`a_RGl#41WjBV0<BsC`xtbTN+yVa&l;q|PW7dvRfgp6
zlGF<DahQCdd1uh%TYPB&Xp3}wa!zIeXcbC9YEemMD)ihQPzen=M-pBb!5GkHGg+k(
z7WYViIx|N=n=iltfyZKKK#-O1AvJM)W^O@#QAvDBetr(PCIrPfqzDEle-IZ^3*Tcf
zzJJi4$3Fc}uK$D^@AzQJ--(>XAW+NWGxIV_;^RTN9_PjA81_=)X{6B3O)SZPrGVR@
zJ<On^`~6bW-7<4>h@KB6E97xF0@R0xggiLf5g`w$gpe{HGz2J3Z@D>%;7Q?1kncgM
zEWp_jy+;eN8)>kWN@*QtH|QQvJf23f7~*N9o-U}9Z<ASET$%)4N(AlQ_VY6^WM1ZH
zU`T)UVu#etscH7<@fE5ki)-^?2^O?nhKQOfF$vrx$pqC#h&!f0dtEa@L^|X+tABRs
zKDYO8y0pXvS{ReHm>iNFz;h6JrMam^kSZ9I0+7N6y+MwYASft}k*rQnEdj5*0ha^d
zBSCqvjO0L41cv3bYZ5&MT_hS0s*u6?0(1ySCWuIf918v)F`5Ml1|l~tltb3XgFOq%
zNkN5hD?nQaAiFIgB|OsL9mEQ<vOOfEVa@-{yu=*vhNyf{0;_?S_VYLy7&1Xb`p(}n
z^1A0D?BNXR+uaSa8#H<hsqaAtPC-tcny}Mog_%X5eL9Q*v73S}A9CUbkDG!D{b*24
z=L%YC298=#83d_fVLeS~n-6w*3TasvyedE#G};0-Jq$EI4%+mT4j-L|1hEG+>r&fy
zpk<Os`4==Y2N{ZnG@+5k-5{Y$L8=0WE>Ty*;7HMAwdA0SXmcy$Q!A2F3qZYjaCQO(
zWYAxD*#SBPJrhJgPZaxZ2WLP$4qDqtM2U<x%7yDheGKP>fKoX&=RoE;KEdWWKEVcu
ziM%ls%{`eFpd=jtO46Y1vyhq;v>-4OL?HUmkbaXOtU99F*!v4m<NX?Zjs?9Cy9x8=
zBT9N~NNpFyLaUdcmU|XF8-vzFKo&+oPSE%b>$b!0c%rn!hPwR58MH`29A0>XYE4Ls
z6-P})z3vjIY$*ZlWd&`zh4iB0GZOPsa#EppB!SBv5CNUG#~8sQE6XFr0rDIhfsOO<
z8v6h={wYm(;9bU`B(Dt0Ukl*nEWro>O#_ge-H|+<oC~rQl)jNlS48UwUcy1*{{qyz
zltz0&WkF6pxW9NAl+gm=H8#3`F$N1MoyI6A%FlwFFob$8wmc}@gYdZn5(3nUaQL}3
zdq5V?g<FgWdWd~UDGkzSCvSuX(q01BY>7ohiIoLKnR(#w-wx`pgT~05^K%PwQY+l@
zi*gf*J`|j+!Uv~YK%HcyRES6mxYj&SnhX((vwT72-T_c83vxte8WRHpm`Gn-@-lwX
zsSx}0N5$$l>gQI&Y8t8!RJEYamf&##G#!xD%76?QL)*=15E@hffP4)JdJqd-<bw(L
zC9rS?w9iUb*9^sW_~<BTr3b`rQ0We~7EHjcgfpN%2d#L3+`R-QLGBj;6(bKo#U|MO
zh+L#IGr2O{_JMtRiuHZ}-c}dbWznE{eAkN9WbjQ*WtpkvAX7jo4#_2WOo6(D(tVDo
z^&H3vp!<Nq>x?`gb)QdWUaFA`r2PYO9wcNiG6XbMsP7&rQ1JxnOF()xc$@$Y5+e5i
z6&Iz(gC|u%p1uKE5CDq&l2q7m5vWFjbQ>Y%AkyFk)UV`N)D21|7NBenvJGrLn1D7Z
zkSu}vhSCxS62TxpB8G#(DHIWqkPZ~1eR>7g&Y<&64X_f1QmYY7N=r~2`oevU$7-mr
z$zL(#13H}#6t>PTK0fHF7FSG?Zym@cqzsGj8HQC*pOJM$d0uI5L8U%u@j+%@N@_(s
zXwO|XxO4U!6tXWt!|dQlMHCpY{e{qt0@4&kDx}y1&*|ucNl<#~6JcNoY66vbpg;i4
za4>)g=w+AO_He7AUMF%5BGTngDxj{&WKg;R`_7-4fguw_q(?6O;-hbvW}m+BN}S7W
zL-^Kb3aTW`D*-{N0%?H@X!Zg!g#qblGTEm?rf^^rsASbhNG)NcleCwEjwo}7_x?aD
zQz2)~iT+(UQ~kP%eL92z8>ggRRt4X2eH<RE*Ff8BKt%ecWMzT%ufy%r55Kc+J6=}?
z9cCe`-vLQa;1G`o&Dw*K6Zrna74T-LCCFkBk<RS&`r&DpK>PIhEN0Ii%q)OS6q02#
zQgDMNhroHr57Yt$jXxI^mFA?D7bO-HV4jx)$<Oe{F#Ir2s<{F@a)_;-!>}2eoye+c
zAt4XW!}{^yZQRf^PC-#{3ba=gRD_|IoA3d=DDKS{&Sjdx3KQx(0aTP=El)9=01XAu
zXbj2SPm~R!shkW9C7^=SpXifxD7Td4jiE%2gNGKU7P(|5gL_uso;;#%0Id$o1QF2j
zy63QgyXUZlqeM1n3i69HD_{q$fh(ZZ@bZEaG%O1u(jmj5NSBR4MhD682WZ(`5UAdS
zv;aX-1*vEtIRj}J2AU?Q+bKK&Djq;BewW0ORM6SpL#l^LWTlI=tF#c*f&vwX9w8wC
zL8-+B`FX`S4oyUAu0q?yq+17`t-)RHAz1;9U9v{mpo81shE#k(W^#58__Q7HT17_C
zXd}3R0V@0O_iw0aImpvk%Q}qC71ZZst=B}V@k)v^^-GE}K?ms-73Ak6LQWiF0WDGj
zrSEtbf4BJ5;*!i<QuYYZ#!)!K29oD7Qw8NkGUDi4a9RNM&mrsVubzqG^7t)ppUzaP
z<UeU$C9L+N+)A`s08}_bN@&Q+JTd!pcUjqR<=sWl#unu^P6tiDf@W_ZWho-T!;5Q%
zWjrz$E}24Yq%?Lx=Mg|oBaBBntPoU~BekqSl|E!;AjB1a?b35Yg>JG(hQZo;<kU<B
zpguil83^daoxH?cXc-2MTo3_081NBnH^EC-HAq(Vft1a_M}F&rhB`rwszOi?Ed|tv
z0ecf<b0&zuv>uwvK@*K6w`fq>b%>>g@!-pB!L8gGplS>h9BG-w+41SAdFYKijGP56
z(rM%#P#9n>{V?1C4Hr-|fZT8a5A+bddLHC7a5VrXpj(W7!e-o<VEI3moV8NWh{Ek4
z<Vhe<LkepcL1smeoCFOc$_oe|(5Z_cdqLxV!6k{9_j5wx2eT(cx!q{RA);`?uoCKB
zB8!ERjH1-U6zClrhM*~3kkOFz4@y;;AR;~Mddrq$3!LrK&z|h|Ik0{qESkx&L=I#L
zs2Yb9vmo<vSOWD4vF)bB;tc2(H49L=0;(n<{s8$1l3b_gs4x7|nqZ&)NI~(G_!jsb
z3q+nCR+N~Rk`JDf(}3FmF1hg70QCe_M_<H1%}`LY#5q3?w6YJ!W?oPrgD~h`N)XKe
zEw!lX7DZek0k)BWfdOtiG$cU#2Z<=F;CGuQCl-U-qoB2DC7_l^ylX{4YH~?xid$l4
zPH7Re?m|QXMvj1bn_P=QCSxsWFf4-lkkSz+#MbbIps_BH{g8wV@;#*81!)ZM+NXam
zmHc;^*94X!DIIblyuT5$Kj67t`la8;uFs1Uf;NUgT@@l?6?G*EQo{gts0+^hjeOVs
zTb`ND4qKJ02W$RNUbHS^!+sjuZcu+6M8MA|hBF{33TcU^0s{jB{A6;_-6oJj$%{*h
zkPajV7Zo4_GeHF0FgOF6d_XNgl7kX<=p1Os5OwnW7O1cQt=(|W&nrtUN>9y8PQ`I#
zE=HDx__LVGE&`RzSThuc{m>8zAip&MnMKu4NlnhrEr?If$j?bF&aMPCK0(<E;tI4j
z4LpUz7|>84tL+PE(?DH;+<sYstK5Xy3LlDs27(_sfdKB*>mwbf8xLZGTK%9P058M_
zok|HQUO*uNsV8Ck58!Kmp+P_cH-MVmkZ1sB4?J#wgacBhlLTctNCQT%7}Ok#&r2<j
zhX_CyNrM*LK~60Ev0qs$#5~<T9marqiJT_PSx^%ObOJA=34_NHh?|RP)K4%3Wo^)K
zlv`$rOJYeP)+5g`G8NS0G;$LtGhvNS40k}ohOEUJu;MPcD8INkzAP~(GX;LSCiu!u
zP*v%Znpl?VpO@;Bn3S61kL&JEq%l%x$WYxypp=a@Odu@?Bo{!#gwhrS__Abh*Om`-
z;0vg!=#yDolA7Y2p95M$RDho0Ar8UpxKPg_pn(-UVSr>cGz_TPN-YMJwxH`MARb37
z+=O@>DF&cvfXGe<(uAxpXhQZDe6$~r6;Ka?PR%Bwo(CNfTa=j$n^XX8-wHYkAC_$g
zHBvxCy4A#lRWrA`*r$i7r)#{cfM2o>>Z%Z7O+Glebb!LC4Zgq?aWpVwI+x8p-TA0R
zy)c&|wAn}2?VQj7=YstF98fI;z8d)!$VZ@j0zQctOu$zbrRN^Y?9lq-3AGb+d<zjC
z%E?U9Ey>n*%d7|i-{cFbS&$MSVyXl(W&H?!k11KzJyMGnb*Kw;u_5@9ip-MKqWIF{
z)FRLnZ&6|qXxkmw-yruvYF~Jogf#^q(?ev9uOm4Kx%Gi||2U{c2HB4Qu^WCc1Oqh8
zscE+hsP?=HpUnn)9z;Ob`8<FPI{t*NXQg!QKv8mWYDsAUD5-&ZDM4}YwF8JW0P!}w
z82A9Sni|QQ8RT71<U$$?HlPd(BGNBhus?XT%gR1oX6oz}OLvAsgPPJV9Q^K;d!Pbo
zHr%ryCm^RU_^EQRBWkD_$Lm3Mr@{+EEOx^tts!Zf(#%$pT7h)3ZzQM`1D$;W337z(
zFiW93s@}u)L68;ZkhV2w<xv`_P6f{^PX*Zw%1w}Z24OQ~FT_X0W^!mf14@xZ6lSRX
zW`xao1(likpp*@=38{1gaUqe7VKKD!p}gPB4jP<z0?NmrHXr!haxekyxO}&RuXcj^
zm2xZ5MnOSMUmR9KeMwf?fe7CCvecsD%zW@IQlKt$kTTr6h!BR99Z1t|Q14RFZk(%d
zG3<u=n;Hd|5@<ap$eWO~jA#qMkBB*9qn&xwJOnoQL=8)eK$b?p3oSgBLVZeBC?nO1
zNGr?Ef=&g=15HPP6Ew){kURtlWuzu5B{7WM?#-MG3_;gHixojtA=p|l0j(5~?54iI
zr}8o|lz{r-PM|AnQ%i8{jYe_<_5DAU8+0NIDC~*!KgBghQDR;(sNe>-dqHWv1eD9-
zK|7o(3?NA!>=Y0I-<=6xx&sXXvWjn{6aX9c{0BM-12ig#USmO$9#Y`~sV^z*mtkA_
z4Du*A&4CH@ga_MOLp_^8xjg77Jj&4<V@SJ;ph+GyRYY=K2^oOHc1BoY5$IqdeFz1r
zLqP|MgA+k;flsMtUKu12Ac_Hu5e#S|ptZX|=@M(*j^Q9^KoQw_g<OVX1oFBcXrT+}
zR<(el{EAAK%=Faa635b#4CqD&^pJq0O3VU*bc;b_Em-}AVFlE0WHr^0avXey12pal
zzC#W)o10h^47yT~SbtK@5un6HlK06vgancYKpjr~V#wxQguQ50UE48X$2slF_UXOn
zQ~ulVTEOadvN{Jy_9C~WaHeMr%b{^jX_<p<w*|uApaa&^t6N#WGs{`nrz;tY@36VH
z1Lki^ttPsly<nGKQnSS(d4@5xA4sXqh(ql8Ky50-VMpL{9z;MlA-uLrpR0A>SZHA&
z)MiSnR@7a~M?qB^!gkPhM#x!fvEVz}MD5d~UwE;ss^0;%ozhLD&{L+0^`VS-=zYAW
zP(uNf10WS0q*M6B4j%Q8DweGAN2G!V<&F%{UXUO}Ab>(2vZ@Pmw8wk9bitx~|BrJ9
zLj!@*$VY6Rd;{u<BWy<0vXJYRKij1*)yggGJr4_hYPD!yfO^6RdlAE-f{#ttzgnYa
zpKfHO!1=pu64b|34Q@?bo`%#a5A4#tw(XiDB#;cXoLZg^0_{{qcpB7Y%mfk8E-|}(
z`u`Y{6dSLpu-R+ctRY+n3VTqA4XMu%aSri5=E-iLb1X=1Ss|r$&{9U^tCbW%;XfZf
zjEu(t&=Q`=G2N`<{5<enZVKcMBhbYl;5ytZ*x%13H90>8*X|Ug7BsZgMy8b@zu>8{
zk?etbjjZwvQgh}Plw{^+R;B94mlTy2mlUO@XXfX{=Ou%W(*dnnDiL8|U~qO1E=kSI
zNGwecE=?*Z%1_Ek#c>xXW`m7ZjsumhctQ!uA<$5wQo9B;e*x~wdX}UXVOy$=(PW0E
z1}fQ))882OLj6tMy0#Kj{<wm=REXvc$Vt#{>s#2i7I9efhPu|HRrMg7Ayq77xe}7~
zP~VevKn7%ZE;kXrj5u3@fdPCvNqlZ*US@7-ZhU?LDE2e+F&6S-L<2PXspAmP0aI90
zJ%+VV|ATrzl$5)$CJtyp2Y4LgK4@V#Xsvv5PJVG}e0ouSDRizEbW3g~h=8uy#XP&6
z(l!SXO<jy|fHbuz?M}hZ>zE3fYeCe%pe2KlGwdK0AAEiXc2o+Tl0Ob_Q`+hPE&T*_
z-d$0B4O-O$xr`WxuPKdaoQ+FFhNqE_VZ&u;jZ4t_2E4ayP;rnLJX?)OVxYb(<R%nI
zU?bIRkhD!{B*VOn*0cmQLm)f-FoGIdwve}IFFCWIQXe{6mYkngTvAjD*)a*S5K^|G
zRV|Q7YNVsiphYTKc?-pU<O%Dupe8MNVh!APLZmbd>!IGKdeK@5>c?h)W+uP^fH(uj
zLL20U7W;IuJNy+14R2v}D_LzTq((Kga7T1x3roP4I)GMx2Z7f0K*oykI0PCLWTgZo
zha~6c<Uo&Q0u?vGpe041hNn|ees*e}Q(}>aGh79v$AO3*NUk_w2R#qq8*C#HmE8n7
zfB=#@(UJ#-{m?KWYc4Xt(?{PkH!&T&9$ghw)q;*X1ob;|3yM;UK{JI;`MF6XuCArr
zDblzy2d=V#I?Wv}P(lEOe7sA3ZenI$NaA2`Z;@3dA>{<rk*!|P?s$;@!E;7Q`K5U&
zkmL_4D<Oq5WHZu9#M&ZC3P+p{nFX3WWQVVy!ec!&^%J?%yabfLGpoSKeG(|SgFG7o
z5+rG*DH*nbSGi#;?s27PvQ{P|1t{{-bLpwzB^BUYH4k3kqnBw&H9a&^Y2XG>IDn_5
z+*9)%D^hXXkAUP9Xatirnub#O78iiG5`YSP@G(=fLCFhT^C6NKeB>08%zwfPce2{^
zD7Gi(gQGwfG-g|eWmX)%W)D>LP7(YLU9Uw2i;IguUY-LgQ7__KzXYpTC<$<+^oKH9
z4C=<0fEKqn<|Sw37p10zq*h=Y`v-9VQq)6JAz4w6%O#-eDDc#)NVd}=9BM!vo136v
zL~z0bSM4AII-GGAw(S&tZy#BQt|0jx(uIwO9&HLr2%xq7K@;IA5M(=K;2tuhf#d{e
z08qZHzN9oewK69$Hz_4C9()=N$Q_{42NDtBpa5|pLo1Mlx!>&4-KL${xM#H-to2E`
zJFsq*2H6gA2RJD3xC0s<q}~VzE^I)?LaI{40-#*;gXX^Tb?wu?ZF;-KR4D@HQP7$&
zBF0d_2l^Bg<tKyMX?f}Tu+_()5ksW?_IT`rdWftt1}Tlf&sItT&AEf7!+bOIN=s7l
z-am_!(V*F!YR(|}a3vxq`O8xivx`#GK#5lqRE#6yR2H-Z7DS{kb4W|SXf0|FXFxp%
zI@F7ZIEC*KEzV5OOGLlc11*6<Dyu87Bbs3+t&`PDgKa-attd$?0@<t&UDKYMnwwt)
z-d=_?qd@9PjFa|>j8F7roKl*bTZwYVoC>Jw!q#-c5ddWM1&H@9q?$sS7J&?l5b0gi
zb+1Ud2)@esJt%E~Zb^XjhCzG6GC>65?0d)=j)Jf$H%dpp5PNG98L@911&vi?f{66%
zqQ-}AH!Ik~8Ibgiw1*9pOl%VKD(xW72kFA06<Yj&ruB%(8aM+Ec~Hd)6n4Mh4Lfj#
z0TIyUZckv-1@Kd_sA)OcDc&G^AvG%`og!Hd^*LE{r6{dR=r&C7AwOw}$>4sCCFtxA
z&}c?{Kz?~@5o9rtKY0EC*Ja?Cg)wcM2D$_SYh4S;Q%KH$h7egJ2`C4?d4MKv5o@{y
zK_xYafKFh4w@WuI|FS=(n+;a`Qf{RI=-3^9(3}-G34p2zNXrJ|OQdChkdlLP8$m4w
zq;N&_XW%wM7_eCa%55y-Wnc&jfcp_)BfK(zBzjn-M7fQK#rfb+1$8DME9Kz}PSEa_
zqv8;`38+{Bm1K}693n<xo`k0+NO4$9X^{+Rwu4$4;G0lD7D9?s5DSu+Fsz1V86vm;
z=4R%m!fy@(l@{Pmxo>K5abh}-11OLto1oq#$1ac$kkTrM3-J&vi9*-uUVv?7i6Cd2
zIIKOBmYP^nT9g`JoC+QA)lVzR&yCN`Pe}!D)dLR#f%=5OU<GbP`MJIzIrNBzI1Z`O
zhlT)69S2J2c!CPaHPE;RZ4)B7Duo3VM$->;&1*c;eo#;o4B{M69S@E)Fp(awD!-e1
zii&-@#n;9sbDce5MJe4|q8JW=h6|;&d@j<3bC*F^u7S=Ahg>*^o=`A`^PmZZnoa=C
zhe9F(ltRF904CrE)4&-}|5JYy0o<ttB?xr?L+-aUwNLjr7<^)C75wyA>XnF~5k#!M
z$FLgedrE^Ixq;aO8o&YFGm7qS_~;32{EkZTj%@j6kmXz9LpO+`6T@<-|c_6*bw
z%YlzwfhLL|cVa;XY}oAK9Tce7scQRnP!&82)UpSsdC({WWYI089{d8^hsX$B07+HL
zvrsJu<$lOP{*czrOT=slq(w(n%UM8{Bj)G9WiN<8^l2b>HNA#am_!a|q@*Ti<|cw`
z!Q*_Oy-J{j1u_`CZyHQM7m;(=r>~B>6v&q*3@h15x1t1;dcXw+IQ~E^99BTnE~Nzq
zVu?Hxs0(lmzRwY1HGIq+QoDbEjrLP_Qf)KH=X2mA?}(-{B;{T~jQK(m4|Q$#1f?EO
zNrB#eg!uUa>}vMAu+&3o+D*w%E-op8p1%hgiUF@)bSx-H&BM8t6{+J#rLqIX6(H|p
ztuY{BfMh!~?U6N?4_U<r+C7baZ^M3g9DuVI$T^Vu8d7|mM2z4;eNSckK^9{5KZgBK
z{}b7+ha5}<jcOiHF7pH>b2Lw41Txf<<XB<}S`G^G4))ZH8Iv@cG|&=YU?>4C(kdw`
z%}Y+LD9Fznil?lC210B=Cy9Yq7VD;`=II*Rfs-aAxuC@)uC^^%MFwOX3%rF3?gCJU
z7krEu=puRaD8{fInpi2VC=kP+uRy0yfwm3fKU4^*-vv#|)N_IXsImanQ0Tsg<RT=i
zp}r^b%$<1X<VA5R_*4gQ^Zf^C_y!!s!7L06nIIzlhr}9-t5F&D=}nK0&)7Y!09N6X
zXAO8<ssmIefGd5_vWZL(0c}@E*h7!4kcT$fh-_9s{Q^oY+d<)_2ddk_HYkET0wU7y
znFTA#b*9^={}c%6jpHpK+lD$&;XMoP2e36D0y<3b05PNtjS*^ZJ-G}jSixl;IIa-w
z2YBfXNgE8XxT3ZL&=!H?aR4L)kR}7vKnL38rj}&nrzohWW#*+oPLWKiEJ+1l42X1=
zB-nGaK$rf2i1d~E-qxnmt?l6qXjUUKA%ZUa0v(W;2|4<o0ThYV@JIyNkO?B-ro$N!
zFBFqCcnE0$fX)KfFG@|$FG>NgU<aLnPy*^QK*Z2Hj*!Shs=1&ZrJ@UPPM~4f4fQ=)
z>wYlDkw61T#hK~gP~ZZ^zy<i)BTx>4Opai<0qTDmxZxM(^cLJ5I2;2F5Kudo<bgFv
zazNVFQ7eJc%>w0VaAgZEN{|Y7Xc$n(TD1I&$6BawDIG3?EGjH6O#)rJ3Xc6W&{{jt
zFe_+bAxxqmH5olKVAS!@fS|sM@<0{GCHQy(B2X}#01XsM>v+VF{xn!K1AH_`PChgg
z;7Jjh959On>N#NnC^v%k+d-Ncc&vu{9<<Mf%nSfJpEWN(H#0E@>DX3K-Tju8fuRJn
z05c#zrxKz9Jw3p?$gsV=(DXnH=YXybhctV^!GTB-7><C33AO9lD$wd<Q1i1mwIsec
zvkE#569byE1QF1CGR!c>R;lffe$Y50$p4Vk0Xhu?a-cnAq|eMgeTlKbr%h^R&_)%d
zJ3?UFen3l8gUs0(7$Ek7TmUH_;V}SXKx2UNS;fN~*jGG&`i+?&0=mqf4c1iUhqkhi
z8aberi%nuq4s^u>Xp%b<M5NziFus4#|Bqcd?5J<Z01xG^-UYwA4V+X#mO*TU&xOJm
zkbpvREa)Zy__^MR$%UnvMX8WR8RUjI#DX%t;M827{N!x(HX7)tzf2I3t~<T=+|SIP
z_HYI?cY>PzL=+b2D}P`M*zSM|r+(0AB-nR|1#B-b?N^x!vJb|9geNsl>1qJkIT?$c
zkVdwoJ#>?yBy7<QIinZ1KrQ)dcpC^5hLC_X&f4c?SEg>CKIO*Ny>}<+K+{+;tpd{Z
zQr;Isc?H<8t}-+qQ)t~&P#A*dt0CDJ5pR&`30`~n3Hy*#SxjlU0ozIT5o9rFjvi8d
zfZ98mAOgCyL(M*Y_a&pC_bYQ?;YuxwLFOPWwFKFT!(yn9DGXr)P{9lu9E5ljw3HWe
z7Px(H^=D}{EBkc4uqz2tg;B73r$G)RA}6A*t%5FvD@ZI#%*laFlY>L}1-$=+$6}~Q
zL5?KZVptC%Hx+c}ZLxkas4E3tz5Wx_T6+x7IiQUUnIHmwoI7+xfddx9RJI>9)Q8kI
z09lR0eyIP+jA+mf1yIvy4XC{gY6`ez<|XF9n*xX$5m&E?LVLltNa7xTLTZvi{YkY|
zk4HggZ-SOSLXtMf@0lP1TJHX|gSLv{qa4(oCaDGOg?s}q|G^axh=4Bac@DE4c8)G}
zZQl*r?vxDAUg#q%H(|CvfbI&RuI-yZr$nZMG8f3l;B8)D0<kFplG+|Xr-+bR6QHBG
z;jIZ+vlY^ua0@YV%g-x8Z#{q}<1;}-`g0TRE!ImU?BNV(>w&CQBS;HrF?N|kk4xeN
z)e@i)Z%Fk7asv*BK+`r^BQQt~K{|-@0%%PpxDZKAFU?6T0`+*(F%NTqG&XM9!HYF$
zkWkSjpv;X_k|9bI47;JBK(!nm2kQ2M!av>#w6V|^JSqr2oez@b5l(?5H>AP_8Vpo(
z3TTfz)<D3pnM#2GKhFVNk%4>)@pB8PZwDgMC0N4vbT{SNr`NuGd*0P~8LT2Bs|H6(
zgs?W!6j161b(<lnAJJ6lxa_%Y*BmGN^xswm=Q=LcLED0qj<`UEJCHXX?Eux`6R;Gq
z@GCbBt8bRwl7b!DMlGM~gBnes^~~r#cih3m_4SLReR{!Eb%C<3QmD_VW$zMDh-0(&
zbn@8~3gwRW>5$tuVfIqDPzPNF9Rw=!AihRaDX?3v(sy<p&F9i~rLwOTK%tE-odtf?
zp38sU$v*vFVOQUiPxVx`w-prHA@J0NNZ%UnjEfSEx!9*q?wQkS`?i6~_6mUfi_Koh
z;u0tO^kA!q|IZETVI4<My+d-#7%4*|uSL8LT8juO%phwKb0CEZsCa>ln_!G(LJLxA
zx`E`yh@epwk^=xTTMXVjs2`7YAD@*VbV|OwD6t?pu{1fOawty8(_q>Iv;e6DG)NU+
zmY)gT&`&sx(7+*}NieL{E=D?qR=d<HiV>L<6b#^_gdqKN`1!1S`wV7kNwiYC)&+S0
zeSiRzVbU=yhWePSr4&e|1In)J98mE9I<3*AC^0=fF)1h2KP{~|wZtjE0==ZbX!t-&
z3aYvXGy#M){xNKah6bfoVhPHT=%Jtn2q@u0Jbzz}fguw_r0=%Bw>nI1j(xh|GL7lS
z^!LD00d=i^zzUoFL+fV22DPD!A>LD|lZWd0<)}l(t-K5jnIIzlx-L_4EpxMd`tfGn
zvfpbKLF-5A`rQ)Ly9Wn7*zdt&p!FtV3=HYr_f|Gk&+E5O|EMwbLrdIxn%EwU>UU5v
z1X;cdTR58zxgL;<CbmBU4J{!2j%bO&dt8vF!Y7)<yC$e}kFXtafE>h9A^UW$TTecC
zsvFV7@6xF2^*~GaGeHEj&(8tdC#^~o+u1<l?FhdgVPas&1QF?wgPK$A)4xAGQ`TzN
z0c)F4I>Cxqb9@fe*#(VXfd@tM5}{`nf*b>Bnqd@q&;plwPIw7wnX-b`c!BdA9;>0H
zEm>VYXu}NUiu3rq{37tB(ulzZaJ`Fg0;I@$0$cd{1h(>;f_qV+N4!CI#3FB-$N`o6
zAg4gOLU>#O4G7RoFzGE+$T{@Epil=dNCSIW4YVK)M5L#`-Nt>kG|E2xW@)}_Qd1PH
zlA&~L2kA1fMvzBAYepa*1xGuGfR3TUcj&|J$D*{1hph7e9n}dMv&MN=Dn?R=Oxsav
zJ8J#b3G%xPXm<+8$C;otfM6oM%3Mr@Ej7v>&VWWT<+HH!;guRVszLoQ$iay%JMW&Z
zEmE;hU$IQ(bwNogEUGED5;VMz)Pe%r3nHKun3R3`*U!t8M303*%Tvm2#JSNIKB*TS
zxoGnlr4U#d3YuUhBJ*VC7UUO|fL6;RXH`(%g!m8SD{x5(CZH+!rybOzzhDIh)el7k
zZM1>70Gy6M4#MFAXgZ>9GCl!X0|H7(@t}&Mz!1_@MYsaCyDWW|Cl8Z$dl)QfQCe*v
zPC#t{6<~Yd11Wf{h6Vsx9Yv%r3zmb*d_lD*sJGyen46ng6as6;qlX5h8bLY%1zL&H
zz)4=9@ue^DNfJb`V7LJqEYwXGj-aD;I2jliaE1k>J@*K)n+O^f)O81FQXgyfz_1<~
z3>4Jpu&O*au>`!5G`_g7s07@u0Nv{YUO5&F5+Uj6KAN}()T6)}D!2wVK#SmsC?rdZ
za=;BMQ1w~~ni2*bgdJRvlUWj!T3nh#^a*NYSO+@I2&<PcYIA5rQ#w)r8q`b7%!6Em
zkO}fE=!gfW#3JYXyb`bkdhW+?2{gr1)g_>ePEt5fH^03D%?xXR8u8!?5wuz|6GR|x
zx<Na>gu2%60yS|!XB<FUl%T+X)P|6-M_RE=i#8n>$nziJRS~H8%LEaKNqI;|juqDS
zAgeV1>A@gwrpPHS)K4lyKYI%t{0RR;N`1@@3<ZPRs4mDUE{q4w8YY6*zkn>pnhG)8
z0nLr%t$zWZl?^R+L7TF23*rmW>_-a+jA9>}2gyo?keGnlkK8N-?+L@!gTt^M>V2}-
z5J0R)L<5#9N)YjY77mcH94fU>(H#Q51Oj?$Eoe*uYf8Xy0W>HmNRjxaTP(O37)n69
z@U!wW@{05Gij#)oYzvVUBj_f*yc9@Q{0eHDf(F$=#~bFPf(|luPRvOKNfTQ^kzp&&
z76zmsMp`QYjdrq%LZntXbQMiJsCN(E!f^o9FZcqli9nS?CWuJ)KeeRIj6vEyT_-WO
zXYL0hSeFDe_d!G(2X*Zsk_%v~!f{>(0Z;LeL+J#deO^ijRFIbK;Jg|E(hYlVmkv8~
z3R3-1x<Ca~;DZAj?b;7J&@vGakq){1TF^fIsrpqr*?vt}%}1%t@H0GbgF2v~W@cn+
zVi72bmk@tw9Mv2_@(G@xZL>tAHBcKfwE{Hi0BTW#&g2X_4_b8xF3dr_ZpiozY_kb;
z?Y|f-MUz#jA*JZzlFFP^_$5Dxx&&>c0aB-5fi3!jEjgrg(jK`&MLVhx<P_v~9sE>?
zx6nBWvh0TJe=A8XF3HSG*9R?a1*dJ0Z;@&+5Eruf4l<&2*A9Bv2s1Q4lNGlhA0b%+
z;zB$KDKt*Pj*IySUDH@hY1)Nn-F#3vISbTi1*a;+giBfWOrGPPCGFFHy+4>e-OLA;
zs;Fi&_;Be}@GWh4Y=+hm)EhK<3mSc$3fhJZ_B0~6C+zdQyddMBUHY`AuZ{jJ*M@nT
zdRBvaiCAMA!)mCnDIXKnhtH{jeG6JNoe3fkv-*E>{U_XbX9x2w<yIzxZxaWlDsX;5
z1TdujKY$qafz(8l+t>;BB-lp8v?Zi?f_HtsK#K^<ZCr-xN6=EyOc0R{DUXHi(+};N
z>(Wwh2rU>Ww{a<|A3=>Q$iy&w=N4pbB0tq^L>s_Igetu7g9I=2LiH!A9}%@2+>dj4
z`X+@xmw+`TD9xG$m7s0Epb9nxbe#yOsT`kJT9Tihnimg0mIu;Q2GzisAR-+yWg=n^
z9oLYC<s9lc29%PJ$_bFIIIM=26J!k|LFzovz7_p=@Tt6ckQ27bK?}e^MO8?CzDHtF
z3Q1#@RCNg`kt2lzq9nmJR!LT08mZL-?MYzV`UF~(1-=skybT(37HLvq5hPt890gyY
zv1sd}vxRxVuyjEyM;!qT^MkBMAMnC(2sD)td1MLF&GccQ7B=YgJjbNslA^@q5~sxC
z%w&Rh(;u))f9dF`8MV_L76znS4eAtPjeB@40<!|@JJ4h@5$$T!J{)xa7`Bt+!Sy95
zcp;6klDFJ1TCKzF(=Rf5-q=2Q2Fzz<bseGB64lJbuo&uJ3Wm*!3sRFod)z_yT%;D2
zl!FRDkWV4ujplK9X%AyShs~&9KeGS9ZBcCFW-x2vTYRAY2PF`a;~P@u!P@zG`NgRq
zHe?|GKPW#nfZ8qK5CE-x1TVjYmh;^9>Hn%g7uD;*qMoci7Q_WbiFqmcxhPi$fCeZa
z5rDN3bHxrmu?%T8ku`Pzu{|xV1jTAZ+@o0y4|_;g<R`TKMAqsXh}B3ZEFi}|XaW_h
z=ix4Z6l%YrE+8xML0kZea8M@?*?Le*1Ic<&ctCPJ#NQ{;`?nOtJeDD2P~nJVJEEZl
zvHd1`%#(FpGQ{)9p3VYwgb+Cn-CFojf3Wr)1sN_kv7{)o0(}Jq$Zk^nO;!U7;_uu<
zlmrhtg#fF~kQVI~JLn$WAJF7R!I%Q1eHEXXmst`Yk6O^8Wj1)l25|%|*eS^8=m8E&
z*I2y`vHAh*fX%<Kkp`+aU7-6Lw}6r!sI?7gLm`S<NUzD!KHWr<^={my3RqD~RxOLv
zup>A?i(xUemZA1}CEcKv)u1Sa_#Bbx;00Y-=c~1iYm;C;r&Sby_FDykIvSAHBOZr9
zLIP<gEvT@zi7zfn)k{w;$pKy5VF&Te575{zh=88=D`F34KpPF@9f{@uN-lb!(NA!e
z0WB}c1QAfvSzy+%K>NPr*#aJDY=VzT)q^|(A~0=%gh(;9(+22Dn-We21_sb(WY@fs
zqDn}xg0d~7I)da=q!|=wuu|J4pe<=g1qB`lK!bvUr7F;wyOhl0<f7D))cBOl;*vb@
z=qu>#a`4iboc#3S)FRxIc9;P|6Ze3cg(QUvS?wgGQVVlqY7=Ni50w8r67y1WQj5TS
zb1Z#PTnUBxE&?CV3988<<p|-VLfwlWz}0(vL1uC`<n%-ZP$PZ|eCiL~Iq+2=4`8b@
zY3m$Nz~D(TNX~&~B(hqTXp``H`FS~+d7!q&Lr`)7O~ZR7Cg&$*VqOspNhfe;9Ds!i
zb)A9M(!?1l2Vm_EY8;ca6tpf2)W&cuDoU*MP0h_sEC@<XDNRnr7`cIj0%j>f4QGJn
zC-I~JBuk+wfUIUV>^K_m%$0swVoqjCVo7E`I1$Wdz<zF6$P!hCOc0TtYZjI??<=DN
zoB^p;kg9wI<ik<3@-y=w2WJ$Q6d@g&0j``u2I4Rk>Os)BG7%kjTt{vxf^v8$JclFB
z_gT4M(W`JPfBW>9lYPAodp5yZP-L|op(8ZN2MhIpEG~n$Lhx7&^(dudp`g}PW^QH)
zxH1H_M1nvI8z5~0M0*W(U|V`*o5i)Gxl>@?rmF36pcWVpDA2)?jK_9}-;t6IsQ9r-
z$t=swPk}ZQL033sf(Yp1xbLuYiNC{&AF_IS7!@a|1Oy*LRSS1GdeMWF{UD<%l-gX9
znVbz7PC5zNPj(reTtH*lkO><|Y4ZR!rT{xAhpav(lAqIyGE+bUdmvwf&&>P_8p;Ak
z7sA(&t|ihcbZYxr57fW80{1m&8Ub?k8HTSR38ffxVj~fy0qU?Vq{2hpMwObGo|>nh
zSX7XhoDHQwK_LjL0zrkaBUlF9V1i^JM5sXAhLjc{<sZ%6hI7~n!$FY1LdsRD$OSTZ
zdr?ke3G{SC)KVGLYR!BE>db%$=)i`AJ)8mcE0KF)QuC7YQ!?|?i&4f?Kpn0icKD7a
zJRu87Z=imsduoYqX-Q6IS!QavOA;umz-K>mz)dj%xfw)2CsjYAUtI&Tm6)Vclp2q8
zzV>WTG5!OdLP2$WCWwF*M(p-*2E?so^{v3oQt<K_{k+oL)S}GflFEWqL_yvMw;xnY
zXMzamdZ~|y<sP)LAGF>hNCLF}0bDkKj>XCZ5m0YGhZV-pVU01e+JZ<06F7CI<rn3G
zz262Z7UzN@8JsY|ZU7O`Gt(|$Y@4F4^*2HLtHIWTECny-0TY<kL$X{kjn<?x@-Z-g
zXA}!k6N`%T^Ng|eZXx5p@V$^HVbvpz+ysgctW8x6cR<62(kcsiG}@6Fdn@%Ms1yJZ
z>9F+;a0ayGqNXb>K-;kA!!swy-b@eyw-e5Q1_9{MBx1^-%#u`HP|cK>o?5IAYprA6
z_*j$%z84o!F@WL(JoW=7(s@=_D1Sa-Z=bHLJ9WGAoHSTtFO0euPa;PQ$OTwa1%_Lo
zK|<XOD+JnD0h$|wRN9iDP0B_b3=HY3+}5*c@V45g7pPjBF?~D-3ktIOPe{cTbPWJv
zHE|25Ndv0VAR`>>85tNdK}5Rp9$OB^pGo%V&sIN4E^mY#M?qG91IYofW6&0XHnbSQ
z3u;808@`i3=(^#$9Tu~oqn@BqWs;L3lD$ZaiAzBX1Gj(*X>ci-20D!ZM5KE*%!=A}
zE!{qyvF+*k?X1V4R?{lV!M1~=97LpFTzmb?f(0e^=@DxlDt#|G3ALSC0e%T|`USQC
zhcw3?*rmHoJF{`mYB}igBc$dSD3d^L5(lv|Q$U3lh)9=FarxOKrevSqdp_mA4X*_(
zm4fE{h={EC#H8f>9Pq_=pk)|A@8NBFL~?-796%Oae}v9iQNHACIVkHP5<aL<f-E|Q
zgvSN+g?yCuoe;Mh`GMMKMetr5sE+_S)qPWA$;Qm33ij~x<)LK`)iZq{Jkx_C6ch@O
z5*X5hdSZuugCx~mzzRx@pnC@)#SR`9K*NBXW4?1hYrQ}{^!U8g^1Q@c=sEs~v5iF>
zD{tQn_p?ujF`$($IhM79N)?blAU?ul3DieaTW$>6BOWvtRFtB{CZs53vWGUl1!38f
zYBqyPFs!vFhRskvgSrAl6bz{SVMuoaG~J6>Ujgb-iGmv2;1CD<8{`;BYZr3n{(soY
z0!f&^DQ{VplxC+^f)*B~B*ufMc0ld`IRPmY@VEmS3X~Qlh+!CWQ2!q^4D6O)l$%%*
zTv7yKln_4*Og+bdZVEsO2T-s;N)<?niZlWN4F~GZPJlXLi6x+DNzMjE3utr(;v8`D
zKsX0JrU+v|b3N!-GLjP~thI}AX*crVD$a=#m<!++Q$Rz8tZq3{YbPx~uOvP<F)uM4
zGF<lxwA2waP6V2K^@WH-nw*GW!RQA;gN52|!8!ek;Q(l0kX2lv45B6H=YvuN`087b
zhap}^G%_GZu`=1GZz!>|@t$A^D=^5)8z?rXB$gzCtOgHo*TUO3AV)y*27Ch>>=bSa
z#zjzU1`P@or4|>brhx1Rx3569LTV=v3t~Sc(0|&czws-dS8+}a+WIRd%j-xf5V82&
z2~^d$f>s`&<wHoZaKR3GJqr&-p^nw+ub@tb1H8kF@HK|j(4<CIhZo6eX!ojEzpw;6
zDwhbp<Pnt6u$DxEM=C8EUv98Z-=+8GLCvcTun+)UGDLDogu@xBDVZhtMf#x9F(<Jy
zzZC2u(3T{Ki@>!NB499F1PvG}cE1uq4JA+y0g?+ro24>A1awu@4EywyGO@kEjAvn?
zK}EX@L3aO!H@!jD;;<X)Z%VTPqN;w0+FB6+b)rB7bo^J?KK;+ikC%Lt3}D`-p4AIM
z*#H#BkjxLVH4{W&S`GEJ7I6bEMX3egLkU3VU_cI61?O;3Z3k-FGa!Z?VfhVOrI2Vo
z$mv*9E5ul&h6SYX)F#gP1(hlJ$)Lh<2B<>@vIG)7AQq%>gqV-CfPg&npMu8SK&E0h
zAIbfY@JH%;gC^E&Qp*x^ptFmhIy@6Zq&pwAs2Ao^gw<J!uw+BlXa%I^z_NN5?Z5|+
z%{Z)wIK7yx@k&SwAGUTGJk1Y^7j4k7xuDA$!V`<~GV{`XQ;Ule)1kdMP<SH85>hBa
zQyDd#0xBknOM=jFAS?Gmq5(Sd2WvWj)&hbLc!<x<%*#!zh|kTe$jpNjo`eH|dhP&?
zkdPDvWTix;S`2k+#S*mQ1$52@{NOFW{1l9|02#nQngWKV1?sv5=iyrz)<aVRS%Y|x
zTmkC$!gB;@9uvH1Fy1A#peQvtu>>OoAY~S2ZlIPEKuM0IWI*ly!%<LQ2DFegBsI6d
zIWakd=pdlBOK|pNFya833@A;7kaIpkB&c|N4O+Ic4Ai;>SG%ByK`sy<*g?l<g(#{T
zkTN95V*Pj!39|k_$olUf>p_Jh*k&*Rt@V+thx*=_xN5mHFS7(x73e1?7K7)DKyAem
z&>%;AXkKbXL27bIYD#cva&l^MF|nl}xmJV1l%&K?`4AK6XpGFf()`k5q{YIZOPfnT
zm2YBEa&cx}Dz^DlNTNe(tU!xC8aWA+=I{g!k~<*LkJP9ERp~Yb`Nf$PdZnPnoY~OE
z4LBWv2>5UaoB{O<kq7paq~;c+L2n#?32NL0!khdc;~}GjxW-;6*nI`w4wF`tp9@_U
z0m>$7phgTRu!=Jh3!p_6s8xr&TnZ_$pdKY_z#TRg1+Fg8)=Bb$dOly^xfPEKAkkF}
z+Luj4A2cUFJso|+*)>qfumMYtA2Qeg-*5(Brc8ldKS6ElLM+FDV%P=s7o}w|YO911
zR2hNNlv5JsT@H}39n1xQ)O7;R#t4S>&=4SNJPoNDfS<6E0t)+cpg}EA`pyJhV+|(K
zAy-)m*{3_)&blUfKb^W25a=W<P|kV|n#Gz4vK|xx;G6Qm1hfJ=V3&UL*|9Gzs(MtO
zNyT}O6@1oX^G2c1YnFsk*<PIYSpA%{J67M9(LVj7x|Z~{kTBR79%$hc5m~qhT+2QK
zWm@nyByeOt0gX?9i1Zz&>+Zfh7i^#YI8R`$XfFIhG?Gm30+}8KDiXkP)dVsfL?CX4
zz4dVYNvW>^&@dy(aL`q^K{w$E0c-<^fbMPjf}RjaGW-C@<)G#Ekj{HJ$mJj+{lo5;
z(_|m{+o$WYSZX9LTS|`0!BZyN;7bueT`b5XG$ik`+o!*bpL9y3*Z>x#6ilFjD<_n*
zFiwEFRG`WjbnruFUP(bbcp)G5M%4v7Xui4tTc|}rj{<B@aZV<9WhAI^hNmTgWHGb=
zBXaT|V~u@LKB&zIDmt=p>>2=V-OL0L>D40Z+#goC*uxo+iW#XDL;S7*kbyW%h5CrB
zUOG~djJWUO4`|LC)H_N~Es4)bOiG2`MTV!shxn|R(pm}BFF^DT5S<fHP6U_BU?Lsk
zbQ8#!8g*?4w?KBl8-1Vwc!=$gMlw?Gn3}f3DyFNTim4CYCc$GnH2NtRse+E#K`2nt
z1`o!9hG5aN@e*T$Pn*=t?9+eMW$h`M3}386R>;G~Ch`(<(oq&mgF0(iGc$(uP`?wI
znNgPn6jXwa<Ic=W2l*IrTot(H0xiFW99IQDSn0yM2}unu#;|4x<yIQ-Ffau9!_yb2
zn~2=^-GEr<3rSy;+xQsuOfD=oLe7wQ4P78b=}Z=A201xDFD)}Yz96xrB((^<+X~b-
zf{gT|<!X3q8&Yw7hq{7tfA2!|H`rPb0Y5MWdJzTe$|%ZhypC!kC@Vo`GvI@JkYmtb
z$0Aa0BWTYUQl7zLBcx?11Z`qaZX?=dB3Nu(9d#*?FHIQM?x3{K06*4r4yermiop2L
z5NGrv3Zry}R!vm1o#gXOsaG)zgDPh5tXpacC`)C;r>3Q)CYM0Ruxvo>N)Ul)v_jI`
z7mAu&NNogyr$j*VF#JFk*yTi2oPNc5j4wvSLyApG;~m~pMLWh9H2s|kBA}-qy|#lh
zptTaEHYX?L=Yv-vWrK#FK&3S#txJNU8APN*4%$<(hcl>@*5Nk42icqq?-hY8%>)r}
z3*ii?pDB%KqzkY>N6?gjD&jE6X5CcJyx@`|(9SAIFn|ICQei?89p)|&Dmn&qq5x9y
zipOqfKu|Col?iJ4fowqcCnSYmz_@6Q0=vL#dO$@tq-P4U6%wWxc0v6`<g!-q$ZSDk
zNd~BK<q1kfD?sCh;KmiGd6x+ypzDRq?9=&DS9GoX;tXqCf#&Im7<2#~gaH|+EUqku
zG&DfgAzH7XIs)8g2NTfh{;eJKDClp{1_n7^N&|UG1mq>K9iY_$nIIzlUG!Sv>5onA
z(;@9eXISM&)-6Vmxm?602B6z4Qu5Q2@+(081g(I8q)BkFBH|9xwm{kq2rU-KTbu~K
zM*`IiIjM=oCGo|DrHMt5EqfrlAx;4Y2Z#$vMi@?kCL^*s46w0j#5zgP@P9#NrU__Z
zdIx9-1RN5Gekx=Y?|ZxSJz-Aeo^wKB2@KQ*B_e^Lb{JsR=cbkvWhNKv7lTGaz>8Nv
z;~!YP56?xAZUhgs@kLgP0A_tjQEI9_hybMlP;g_l8D1t<gHGg=<AB;s(VReXQGRi8
zJnGpAXbu1sC6Jab!~r+$(m??)8W;?90C_V>**U3+MS1#|+rePnWl;YZYdAn+-~g=5
zWP#;JO6!L5%#`$0$iV}k&D`LLwcyeu&}q3L`T5WR6GT3MI|IgmW=SeK1FhJ@V>dJp
zk~NhLDUzVeOK`0Z1?NIglt5<5iUM_&x=wQ2rysG=&OB=F0?UPD4Ozgl0i@th%dCKu
zKp-ozCV-R2-!ZAOezQxTt99R4XrUuC9g@{JL-Ra%L9i~UCa8lqkr2TS*>&{RF8#>b
zCW#lf#Gw|`F4RF;5nSqm3Ft`c1H1I^{jDzqEMRv6Q^DiK;6e}73k$jkuL2Pshx9p~
z*ri)dOjtE@s|YO1k+lUAsq+C}7z<^@7nYbnkIMt~e!*=;x6Jg?qEzR^yt2gNz!1mu
zbnI;?q`^CAMx>$RKxHD<qzNetklX^zn4m^4zRmxjv<{AAFaZx{_&f;IOW2J8wNipq
zunarFVhe5z)HT%IVF4cWLpqMC5HvM159Ay)Utt6e)K|214ykd4G?ou?zfDzU0dyh4
zX)XqaOc0Si#mDb0PjI(AoB{O-s3Y%Mk(%sVno|Nk<N;KngR&9U!~$>1z!;G3Eoj1&
zh?WfG$ZC)Upc}ifS^yjSgLh*g7Emx-1X^|mUa<h)Dh#q*5H#Bx1Bz{MVFj)tKm@d~
zLR!NBv7D@CB2tk7nNNT<5?DY*HE0<Oq_YFE7}7C-q%{oN$(o!(u{{%fqC+Fd*Pvro
zAoha24kDnw#yIGPEMG(FCU`p?am3kWP<Qe(yn;f^4?-%ayRb7&??Ssnl#dL8N@J`^
z3`aPV)d&I?B>JEv7N414lAjN`<L@RYiml<(CZOB|DI{@NO;$@BDT*Nn5Q366splwx
z`V79Mxk>r@U=n162*~jd;5F)2PzeGe(sw@K`B!JyZ4YNaat9V8z-NCk!2OR%;*jR}
zOW1zKm$0)-D35Iy(47=&AS*#7Ah^Ys3ED854lAhALl3T4;^zQA?w@iiuY%4f0b2>m
zM&SMiB$gq*ylI!d%pooPqO~cs%|*G5gr^W-o`ggV<u)RM73@bWDcNH&lltugwy@?E
zXhemGnjkX|bOsi92M_3|Ht<Sd*Sr+}G^d>W<ZNP_OQc%{8aBjJC}6gNC=Wtq(6TvD
zdFPW^l3J9Aqf>@tGqhr++-3qLAd;2PMiyCJA*6CBH?brGHaOo4pL_ymNKlgooJF8p
zxIZH9<bwK_RwpQf#+#64`VdJNl1)#d*F2O40w@4rV~*gXvOqRM>Rd!Y2&whn+Cht_
z|FD9PtV#$e29OR1kp&$K@e{OA2^;}f?1mhA0bf*2y;=yoj0xdw5DQW=!s|cOGcc&=
z>$jjn8Dt?O#6c{G)sWbJ37a3`h80*;KcycW+6a3m>@-?oW|3&0F0om4wbGq6uwe%*
z)h0+2*1`$H3}}8eC2k!$WQ`Ka;)V^N$xF}(WpaE@ejY~u6k}-%G>?*RA*lMm>NgCl
zpnjvY*$c{spymue=wJa*Cls`ds3^Y#eBNq&E_}xgXzN2Jh=2~vBeg`Ko~D*d<UtK9
zP{#+7G(mRauoxNyvBX6$+W0Vdq#Qal23bs_4>@x&CqJnm8Fh^dXn7SRR?t$d{MYN+
zIy>a;(~YzCdD)ez!%{6-0~C->0X8QgA7BM)twG!ZN-N+@3ntPr+yV_AvThQBG}v<!
z!H52V(nl*OCxF7iH?cSy>#>s<DHNJ6sAE4U8DRA`hP4oHBlYz`=R(*NXBL3Y_fIUy
zgm(Esr3fV8AVnVLj3lKiYM>)uIQNF5#UF+<ARZ|u>yUG3S}I8_Db|k%uZt{8EXqtQ
z0k8bk1+7c~opg>Y5uXP-Q6wimtth`L6~}Hvq)s<9a;fh)P^e&yXn3UvV?e`)inSuB
z^#!pTEuWuutx-?g>tUb%W$AW%ao$Q;KBv@fq=^ZfGXfZPL;X!;!xtRK1(21npnV%q
zCMZuY1P$k1fOiHFCm7BAq_{$AFPD8fi~)^pP)nJJUJHhm`T5{8zb=97^M`kt@z@9T
zB3UzkkfIAT-KUSXGv^H#149WYedVUY&W;E1z`bB-3-i5Qy4MDaIX|z;z@nJa<{sie
z0Pv2S|8Vc)u^Qri^dc-iJ~Ou<zo;ZW9$JLSg3j*KVPs%PFR9sLkvt>VKHd1X=&uCH
zD=^1{wvQ4KVc3ts1-0;l5<x9vuu~BQCggrsKKt}3H@5D*J4puGaVKlC1QJ`Ik)gEG
zykyWJ@9^=*SbheEpy}|jFiDVaK}7n!{@eQV*Lc{&85DHm13Z27J#!P&!3Co<D1m|s
z0Z7vW;bX|mxUfBRgQXJG#}rJ@#DlyX51JPP<-|;o#USe-79%<#7?lFVtL9`ic{0H(
z5kQxT1i66b-@(xeYEM9hfZ$U>Fb2dfka{B05Nh)ZsjmgE29aAapxvE8tKfM7<aJ18
z3^|!c$Ugn#x-C6dsy9JHT9$!<0X+1k@0^oZT%4JfnOcN;XftT&08(~?y@d!u#*W=p
zqLRAy>Hf!l7*yXp2Q`traa-uzHpZd`P)UUps9-062<WIW=9UJ~o$^G4Dtb|h9ERYH
zDzD+iH^Rq|u{?SE^v}-<)_&UnzqS%|B@hvIqQ)d_3K!vJ=w$+sW(Bl5$;?ZS&q&QF
zNG$>nqJyeWq{u)-FQh|%7ct!kNn>OUYEa^iT=RqGzVl`6(@$Sfsc@EZg>`euN)<?n
z9d)zdU(j4a4yXhJ1qe8SgNby=^4>?VlaT(vmV8obJ?a$71<*>HJ6JLqhV|Ghob+Tp
z&~crq$tBPVN1lO!ArnNT|1PmfTVV9eKE1y6%v49yXRr!~to9&M_{A4g<|HN|CneCa
z=s}<(Ga*GIA}Kle9BQ>`indROF(65atZpbYDIp)>rUD8)(26^Vy`W8(nIIzl>g1p9
z_f?+Rr$;X7EN@VM21`1Wl*vm#y(`ci67j*A6~3t@i2;cv8J^(U9@oKsm~C-N8~_Sd
ztQi*8*oGEl@H=hDTN(uEbVGV2SoghK@-i@#fQs_G<PvOq-ZAP!Xv>sF&H-gxtigie
z4vZWG(rS~DS^>>Epa6#CeT)!=dI8kjBDr*iCR}hU3TdJq)P}+8ZVbyI?k*-{pZ#4>
zo23@iJOp_a+=vDf(Dm$3U_*$nVTBtRc7QjPfd+&inGjT?;;;ki53<^z$em?Ks!UJK
zORfYp%s`bYBp|__M8qOw+Y8e50?=3_s{}`OKyH3fD*U8Tkfjjo!CuE>Jr(>9+F_Nd
zkGf0@WFy1@Ag6#6FqlXWyngF<h^Ur*I^-Bb_~tSSn#9nNuVl!IC4|jrUdON*>UFa6
z4^q{GeB(?VXfzab{04NDU~oZdGWOXvq?R2t1yIu+I9IYm@-dR-kU&7{GJ<wH*yN^`
zWaOtPs23HN6lE4*yX6WTtr4Jl9YjD6856XJGoYR#XW4ZHsDP+~uOBf0Spp)UceVVm
zgEJsCH#v6v2HCM1-oyb7P-cP%=<U)Ndt1n{15~#J{es&8Y6L)90+41DlRdOK#{}(J
zkktYKm*D!jiTbG(B}IwgMl=`5Z=jMKQuu)~1SId_81gJ8a(NNtwr)`A20GI{2s9!B
zN!y^p4zk7uQoV87!v_|i$$(@VKn*vfSw%cHKoW2<S@TRtxi~Q`7v!^GPzDBtouivC
z#B+$?!^k^O&r!*0P{xB4nP>$UhP_ZP6FHEF+ATxb5L?f|zyQ8#6|(2XH7^CbpOL1r
zp?;>^ZqS$@9`7Pq3H2_e<s)(z*ag%`zY0GI9UR~w0y;^>YoESS-`m=BdNizmLQTul
zL6%p8dUqfz!5IfkK+B$=u&a*(q2&TK?QQ_soem$wLF6k)B4e;mZ#yRJIHw)HrG+}P
zFg@Z73_*_Y@aAM=V8{d!=@l>43^~^L*r!M8NzeTp90Ln)vYG=(*%`jMZ6(OV`S8l*
z252x4M5IHmx;C^=w@AA=`M@(9Xkb%nF?=VXCCK7fxWxt_i$O$s)0*XB@v*A*>30PE
zxZ}RSmM@W2`Xl)mwk85}ZUR!t2|9Nu6GWuHvyCabqiJiOz7l#vBs5=9o|R9679ZM!
zDmrkh0Cea{CWwIU8)SiPU1Wj9F6CB!0j)y-TM5oXpi!|*5COfu3+X5&NE?B28^b{>
z;1D)~Rysj;Ny64zz*pWt!<DSfV8{_#l%JIf9@#F|PfrDJLjcWg2Whf1FrXLH@PmCI
zj$woaG!5JUst>W2+Zb+uh67o<k&waxIt~mT-UqjDK<ll+TXEuxQ%mBLON)y0p?BGW
zF6YSv5$V_4x(ffPrP!xyxHB$FIF<qn5UM-oHYizigVH`YA%ZN&;R0xIkk!#c3J%nf
zLSK;epnZyv6ajVvh=4XNAK9g|pIuk2Ed)Q(f@<X(+D>VZrI0Q*q;-pAGt}dt2?HBY
ze<Cm44%{9EU;O5hnw*)Nm=jW3kOMvQ4wS&a^3c4D*;mFe32S=9=xIaULFvRC;s~AV
zpd$V-yjDXv98#IWN7^pHMlvY18nF<W6Es3R3EqalV>Q&*lvXdW>$t9h{44{i*1%;w
zXpIEqX478@lG3|w>f5LL+u3eQJ+Km%<0wt<s6*3+Ab*3c2PZg0`vr0g1+#s+?yJdn
z5``<F)j6dhUR;ow2ibczAJnTt*bKHFM5KRh@-P0RBy69~QzV|)RWy_8HiMS?fzNd=
zN=?r!E=etl2So;?a|dz(B-=r5q!6@EfB0_A-Li$9P-jpp-oXdrgOVA<UPQn{ihV)*
z^suCPQ@1>-h7D^|8uEy2wg<GZYdfgT3QlsMBLgx)1bnOkdK;lUEXh&2x&mpHC(eT?
zAQkOL*y`55(0vuODqcaIL&!lC@GdrF08IiG-efHuL#kJx)oigol!3JI4s`o$P!*`K
z0Y?BR6+jAT9cP)>kIa7ArLWVu7?~!x4Hf}3bc;GD8-lL=g``6~Zh;njl=hyGhxb6+
zq`*56K@E?L)XemZlK3Lfibv?#EC|=2)c^-zH2^hT0}2J?mOf~l4CDg%lna~z4GOY~
zV#oj!XmmHRL_Z#BV?iG%CT@XRKHyZL46+_XKu2~p5VzAo#|)^`l>|+T23-LqMsV2&
zvN01xU|I|HHCZJf#MdapYM`UyN<agvprvlPc@?mACy<sGDBvNP0uuE|;}=lxQ_CGV
zhxafnh6Vsx(GHnfz_Qb96KD}2=y-jf{PLj0ylmgZlA_EC^gw`=QkZKasq2<3P!a@<
z)}a>$7}i6A0%@2VRDr;UxfAm$A!FR&XxB?m%>&(4t*(%t1nPkmL#N_E>$xF^F~ggd
zkXiuV-6Cri32Y~Lj~gsSfx9}O(MUf~YX@{+ImCZ>902hjdBKZtKs@LwiQ@dyqGa&0
zCeR((;H^V0i6x1_U}2iZGfszq@;BBtK8|=UCUUqwH#0XCzN-+FgCUlH%QFxclA)c!
z7pNG*hQ{DsDI&K=Wt5Z@Ko2kl6#)<{z-1R6D=3LioLLuqxf>|!Iu+$trljTtrzYoP
z4*6ot5<tr@I%Qo9>!Cpb8r>mcnj7E3C(w$t5@7}g2FKEpjMTi6%w$mAok!BDbSgLl
zG#P?572xtZS&d3aD!?^2ik8G{%MO$t?P;)2H+nezS7mS`tRY81oI`C!9(6y9tGLFn
z9vbIFj@qHeIdp9qs02ln9B4k?<a#OZi=n)Ix<!kYY4rkCn2%{0zZiBxy-C&t66l~r
z&@xc{qN36qaB*$U!oW}h8XEyEfXOUL%|$P)Aq@ohumrqON+tXCK^K>SE^UL<R*2FK
z!(OPrDeWSrfzJT}Me}4($^tLC0!K5VcZya)+=R8m$;whly(Rekf;njHn~j-)0pe*q
zHbXs4R_7E_GlJKqBi$I03+fo}fVaRw4I#)L&_B8U6K=edvxgp322b2nv>Oz=NQD?4
zyP^Ijt0+MV@e*)vD+zim8pu9~_238xxdu{M!`2|fR}(>NFRBmx%7Bs_$Qh7`$KwKM
z0MMXQYzjRY2XyLuP#Ttb0Z7in?2FLQEubKQ#0**+2g5DUaG^9S!e`u`gRa;BRqvqQ
zd2V7+c4|>-N_uK}W=cs0q$q3w^-)1Yx`yTkhGjf57{x!;9P<;DJV1#8k~~0`W`YPz
zo1wu#Y5piKsmuYb!h&4V0J0ep3uyTRvIpazUHU8K5AE~+=fUbdN*CZltVgcx5l%o$
z2pCR)gaA_K3^dPWlT@0SQ=*%hrvP1y1dc~&R}4|iVx%f)ekW^XI8t!~9R))k4l@PS
zH141>7qpZa60aanLJA%Xhd?GCY2}V1Aa{UlM|TIVMkJ-_3w0d+56F52_%r~>A&_8z
z%>FRhr^61efW`-Pt$ziw9yG;_?t2XDA!(}^)GR`sd;+bS4#>~UD@n>MPA$qz%qa>=
zEiTO|fs_;o55adHz!*>uVVDTXZfI#8rU|;R<Sy(W0<wYwb4vyEXiV@K3^zfI5O8h>
zy8%QXHm5;G>EQzvWX%i1tS<mv=L)_d1GFy?G-LqE7@*a2NI?Lq1|W?Qc#RAhHG!S@
zPEiPe8otH)sTC!lBQZd>gRDcc9T9yH+ppNcCljFwg3^K5<oulcqNGIdA$g#r1hE_(
z{0Pfm7v9gByCcRv-MeI3WJ}U^SYb<P!xwR9HfZNP#A;A{gR>x*NXM`m>Tj}Q9;sX`
zz+9EU2pSOtopa`sn46SZR06+u6Fn<Iib%}<2Myfx4^%)*1~rY*!Ue+(&~PCuIU<D%
zr2Yf7&_T%&v@Z`l(ok9e>Ziq*7N-`0cJ37=7FA*ol?`^#Df>^bE(&#BG#k_&X#uUe
z#~L6=)<Xk?^1k;fkd;;NQVFsC6;=>H^W=A!k3qv@L=5quE@*`AWFTq_4u-`Lzaq6f
zKw~qAVd3JE%7Rqnk>R|Q#G;}^&|opj$nXmm28K)!kq&9w<k`a+(At86kzr7p(uel?
zK<)vrfr$Z)44c4bK@dd_q{EA`ewnOxE$R?bK6nXI2`ExQ)}hzDkc@@Y)qu2rKpPxM
zj#Ef?7qrtgGcP5zA|7-Ub2ixD;C0(C;r>Rn1L5U7i~+G8G-F4m_26(%Pc6yG%*}+H
z0X!AtcSN8Qw4AIu23E^LsyM<&-$KG2sZfP@oUHyXs@>pWwPet?Sx}L-5#;%Q@Dde$
zE&^l49Tid>WJ3qq5wwU<$Ki9bLLHXkz}FN(j&%Wf`V^=d0i7}hN$uxB`5Z)~-)P_{
zeEKHW9?pP-Iu$$($!2H=4ioe=S<wygG&r4OOg({{bC2N}4b<9&?DmGVXy3vXKD>ps
zXvv!0g%;kRyFpXIO)2p7ejccr0ym`)bp~X(*1<mg!Ug+-N4vaWO(~k!85kBr>WpHt
z#?p`)43Gf|aG?fj|3DgXU{8ZeS4blcex~-O#*&ShOC4aICM%gC*$p2u0ks7nHiKAT
zOTk1shRsl46WJ<2t)fx(99-pQU?>6k)4eFav;e&^gwcD2)}NHyi*vsLhLupSQX0WX
zXSAFF?J5B+m2=KWP0n^IEh)**BYMdQ72UxFD#JltB}j3GNN5;EDKrd-oZ>`U74ibK
zJ`l882@<pZ%nS^fAR@g^I%&_d&|Ld;wj1ut*VUB4(lY5*sDRo&lR;w-;6w{D9fuWA
zUy#)X0kzvfb+CR>YEEKFW?5=VYEDTas8RqG#*j<{O2**)0w$mhcp-cEVcpQgOKElm
zIR#u$;JlRzGVFn5Gt}plXPZ)11_tmN0MO80St{rh8A!~6LIJXE1ugL~z+#qi%hB>I
z9xI{#C3359a$-&@s3{MMO;B?IvWf~Eo9K2RHE5x+NroMu#u6mOfKx0UJD~m`E5$;p
zb8M|<&<awL{77Z{K?Myd{zvMNfkGSJA<IoHNzQ=u$Ks2NQuTs^T;l^Emky#Y*3n^N
zV8{d!=^Mh{&YEz|!ye9n#v}CxVe~<pQ54`!<3`Xd1Bgg}EH(e+ze+88I0Mp3B(lg$
zfh>&&B~m6(smcQ{@<2s6WN;0mH3qSPx?>06g@$)Q3vj{77o1N(1a!k1(s|Pm+sW#5
zK{}hDK@$Cx)a3l4#FBh)i*N;~&RPy{h=OWS$bxf7vBO{wUFFCCUGNOb&m{NwklIF|
zaxSwZJ{}b8he0z+pv;?D9A8?L1F1E^d=LR2(}go2-Y4nSXq@+5`=46UX2u|GpAKU{
z45u{Jg2yevmC18Zz}kRzFMwkebY%qOb}Ps+K>YU5(PMsSz*1hEo3S%61c4$D5|E&9
z$^;S6xmpo>I0NEWO6yJdBz+ucWx_3Z8rlxZpdbQz_NBf(oB^?zy4eSO`$Y;o^MY~?
z==L`TX!b!GtAUpOlpM&i19Tz`=mdha%)AuPIjp6yeKj*cz6KG9DM;8XD6~W*t1pF=
zb#ikO!S{57PQOJe?m?wLq=5v<vPe}bB)DnU!bKF65UVkUNkHR$M3fddZ`J^39S{MH
zOr#aB5WkVtF-Hnh=sYBNNhmnmqOChXPfZvOAhNO{W|bl&3^AgQtmQ*UUW9JF2Xz`q
zNi~#~gg7TrK+y@=Qv>lZ(rFoxIHg*-1Uj$?$;*h;3K_-4h)YV#CCD5uhy>McpnQpB
zJ7S0jhquWZ!$ykdlA=tML$X2MCB=Rs52b`0b_r?*BUj~Uo&|C7mPvG64*>~ojBMpV
z+*ys_QCjGVN6>t1a(-SOWZN((juGo0!KoU(=YdfkQX0pg+rZ;<6Y~<&A>(;y$1kJX
zkKt(=EKD+iE|Nk^ZUmhIn$aZw<|araVuUBHmVbcKFH+tC`3=$`fVcyr$fCMCpn-}O
zzaVEpvK7Xt8l>o?w5kUkvz1>8?ihd)G*S>CDni%{5VSRU1-dnl21yIH{tguUNKQdd
zS{SJ!hPbSaI*N)^l0q8=;1j}AQ!-2Pi}cg-i*j=kEAvaim%fl%my*@;0}V@oN65ej
zfPv!#lxL6v0@O=_?3lq3CzRIVhz-%8C1Xf7gJJ|ZLtsP!rQJl({RglkH$ax*u^S^Z
z64~ekElf%VM<*y-Az1+8;>~4bwaDPn313i-);vKp5FpJHq>c+Db5k0o;Jzuwni^0(
z2RFyS1hlu05uem)8o@4+1BEbBfFm+Aj$)0h@iL?g4LvS3Gd~a9#zz~cM_3ODc#L)-
zrP&B^)_6Z?Pym#kAzgG(I6zVwq+UU<uaO4oK>d0f<fF#H@kYpiVj{aIkPes;s7V9r
zQDKc6h!ZhV258q05nVi-g)OMrT2hpmn3rA(nh40x133cZFH#&qWR@?^PtHySXL(S#
zkYWLmV+M%Z)XP$fiZdaeK|8)26#d9$Cwin8Q}yO{g0@j%M@||jRX+f&L?SLxQrb#E
zIzRyAJ*0R9r5k)1s+h=54k8|rJ2~J<dytL9<=$els(qwl3v><zC^vwtB*l~QG-&^T
z_X2_TD?`grP;G_PO*o2hO3P5hW+YHah-5Q3$$<!H)rFCr?8ylYcx_!$1&RT*m<L&h
zx4NXXszy$ApyT0@{0ka<%mfkeVR1ME(oCRq_=NE41xQVR5zv%&f{@p~A_o8{QzHcc
zqPm8-1EWTwG^rtuMj>D`MhH;WFf6Gm&ji<gARi)m7~~2_p@t)GQJUI7tGq!CZcs`G
zZSX;|8{`zM)6fT?-9FGpe<G^?<l-`r>7a~*WI92g(dDiukfR{E8Atmsk(?3)sT{}A
zpDZp-D#*z%0qu|ng$-JFfMD2Awh15XX@Yi8+U!R?O%1f%3@P@}(-uZ{qO_e*lw6!z
zQd$5CT+qQHq*zVXayz6bPX-@%3+kXF*-9|DL2Yj$G84|WG-&7<WegZ(BPkA`wC(^^
zo8T@M+EN;DY6B6t=5Z(;4~F<0nxnuj!0B~Jt$~!-pwoh&^Q1`*e8{vq_+WVbc+lus
zW?pH2X>mNL0SW1Bf|j>}4*||D&d*B)?Hz|K@Bz6GN2v~-FQk!!KqGm?r3q-jP+G6U
zi$5qMzOV$m*G~+z`j-#XZ2&n5e5@mwNRQ}xqTM*9!#=&+<Rjk)m3Pn;Pe{FC&~OyS
z4#w1q)Z}<ONZTB7P$Y(7P+yUi*C1<v2rcOadk54&hAin-wA=7_zk`B(`b$Sg&8VI5
z+bAh*r+~+Ez-j#*X#GkDd;%U+>_El^x%N!<@6VOAPfxM$Uh1(Ac0m@UxeYQd7@v`t
zmy!dShyb0H7_<?-h6Pcy-(xVof6)JrT{`5hE@s$yYM>4{5xK2AH8Hy=H4RjRgH{v-
z>46qhfWvn^Xt6kmNWWKdn(rfjwSBrqwYO#bn_g&gA@8&vq%s_}^6CV2a%JE?1lgPk
zA~3CoCNQ#EWk?Au9<nhP9KqmO=|GU>V84Uz1jqyt>5$;%u}}Y+^6#Q~rWMrhNLdBc
zKtrtX%FKhT?*iMS2U?_uy59y=MrVQ+g@K85*uKVe$k`hD(6uULjmLql1YcZ=egWWp
z&^hTKn-g<0^C0<G3$&U7M5LcQ{*FnN^_w01BoS!vQabJgEsRiaD+3kEkg^dR&LA$N
zjE1xfU&3y@eh=GIMQI+!IZKYmYG?pZT2~i?mT7`A)=tox7SJG-XC7z;YHEsKVo7OH
zVvbLKI>~ztspcq<{~<8}N*Lfs029z&K2oO-8XRQB1ho1F&#>sjw%US@Ju3lSa2=mq
zl$uzQ3eAX+Y8!MzUM7fu7HFSgCEI6;k_1+Vp!Enqw&JiF8U|!_mteb+Fm947fZh+v
z0jj4#r|LsO02DLGQGwKwf;L=e;|$Q42c8sx<P2zNP`C3^2g)U&nS;!{vc#NBP)ipQ
z9xFH)7&1Xbde8i0dw=p4+o!L+dCL2r`g~|%Oy!D5l#PKQ2y{#%q@+YtwUEWCckR+=
z_c7#&>AOJ-Yw89=F=%(dK~SR&oG%f!!>1r&hxS26qo`}U8fb}Q9jG1z+b#yGB|rpp
zZvc~h`hkt<=TozcXyW&?AipQU7a}A44(U=Hu!En-PYb`F0NL&UI)w)8cSJUXhrB|f
z#D*tYVKni3EGXn3fLdP=+gTYHG8tJJ7}D#1{Cs~fJKa8guT0r}Lz`(dv0V>j`$M?x
z6G5xbK}7mZ7Kv&0_rvVdPwxr6zHn9&O>EBr+5QM_`*%>P0}<)<^9vt3c$nFzmtVg*
zp+2pECbnM!#k(13&mK7Bv7|cKZZb%@ht&21O^d<Xe(9+tWr;bZ&<)q|nfZE7l_jag
zp8n`-`$5Ar$Tul{!q_`c){r`MGa{^3DFW3h5%7WpkEIZA6_eHAgVw8%s|`~!i<65|
zOHz?~=b&aDq*4K~z||U<NWc1O^=hWA7WV1ag=59*3i@DWIaysNWLJPj28uG1N<nuj
zgWL9?jv~Yf;P^%4YYZnq1A?rcE3B3Wttcthht^=w2};l^qY@ASI*8OcAJhs2&0U5k
z7UgB;r9)~tgtIUPp`o=t)tv>h5^KO<xBwb3MD{6DGSkwaCx-q3ovi`tF1uu=rQy2L
z6RBkZ^(yHWftuG?{Q{{vkgS0Eh0=yJd~rSlXl!5}sM119;jmHzz5@d4IkMsrsoM<g
zSRs~F^KmjT1c7dqhcs9~(F)097%qVL9jO2V4cpoj<b%%P$;m7(Ny#jNo=^oE{LTas
z=`UxUG{1eP!ahA(xv8+=Wd$swg33N31{jJnKsf@`4Y~yC;4gy@q96(+$W=yx_Ru?x
zVE0CW&W3SME%7Za$;m9sOf7c-FU|+;^9WLb??wQ5AQMESPrVQ;l<rz$4`)Ey2qfR2
zjR?N#R@U##av1mZQQG1{J!%DQcLl<B$TpV$cF+zw8*E^QtbQ0$@<GhLfi5KkZ)HwQ
zD}m)r(4kb2%a7#0Uf0&yA#a}^#l88$xlA=!yi?D9&^nADUwBmFu^N(YiCt`iw4L`N
zD9tH?(i}K*BKF!wzfIH^*<fp*zMaRy`(%+5%y&eVJV;g`b_|277d%!#eE~Wn0P{|F
z-~1HN?d~bQr8y;;@KY;5yT?jE{nB{n{Ji3l#JrM#qSTbk<PzAGL5TDMDd>>;?a<<e
z6zf2@Lg6Vzkqn3U1*s$f<ui;C<Km3`a^KYA;>7e+Xt{zIH`X_~p1EY{Df@I71L|H{
zjfVdOwTB|%sSS@qAbu+*vW7rkz@Ac?n_G!8Nd(H`;28?P#N5>4g2ZHun}Q&5hg3;H
z!-8r~08NtMiC83?p`NESmm`+s1%M6@03~7noRr|?#GJ$;xBQ}9@UB<%mJhCApq^uh
zUmpk!2O`UZyi(B7Vc_N_sI>zLYH*Q*$e@r-?o9USN7gn;ytw5KD{{!%vw$?%i+;8e
zXj%*60C0hX#{p13lC|Fz;sDS#mEw%Vf>dy81XRllf_hUiplpWbefVW7knS}6GCfKo
z7cy4@vKp~X9L?hxRzp0FRC<A0I`EEMacW`_vHP@Vg6>5G5$P`k7e5!<m1GZRK=UxA
z{zKH)3qe7<0u;1h|4jl}4I<LrUfE8)JTJ)}&VV$1Kzmb(XrVz)Tm|QhZSavF(9mcm
zh)8E$q@Eh&{2FnsIVAANxjMZ9R2zfRBle7sRJ}mM4m43hgbzwma|_ZkA*YBVu4e}a
z638}4V;EAnAlU)+2U)2fsXdjFpIls0l$n<fN`jy<RPeoU!KF#XC7C7QUMRY6A#)u_
ztDvF2rK(Fn9>-b=VAu`~2Z}r1@!*=UEU_pvu>^dedpc;^2{g{1nO9bxS)7qrRDx}K
z9wTC)!9jfofl>t4Fu`yFG)#!>;}m2jXG2oo22dk|36zV#r5~b?1DT^1uuos+kd}VY
zS`=3JlWYSo$OdnCA%MpQs2|8mRj|>F+*Hunb)Zv(i@{Nf2tly_K#2^JyG+Z!?2qYY
zvrmUHppi&c<%Vv1QDR;;G=t$B%z@bpcL3D;lor3(CJ@p5u9WAe%IpwopZ=<NtLX33
zr(kgl+D%0K4m=D~pdO&&A}upe`HQ$_095Hg`uMOZg>=4a|1Hl<XNUQKigtr~GC^nI
z!;x4<Sz!eer1GMo)it0jGZ8+#Oi!!lf<hd0hcjeo29$lUu7tb^UF}KMng*oG2zgm=
zFsQizn!JJ74eAGGf(YoEhCg=j)d$d&M(Mx?>Us{)qJtoihauG;hy_`$0~y$Ok1?-H
zUF#Qu&ddN25Z{BX2NBSjtVamzA?b~}wj(?bvJ)KTU;?^4?<Q=-{}Jr?L9!}4NE^H$
zKc_M;KQ}WmM?WVqy)?B5(x7|F2E9(j2Pz1O2Skw#X@_5dorHK7Hos1Nr&xn>0cev2
zBrfnc0h$siKda>jXcCtLRNR0IYta6JOb`LD9^j|LKr1TBtt{eYU<d;Bb|Jn*6f>}b
z4Z5`A0yM8vZeuPeTtQdqLTm&z*CB0oNYt{}Lw7Fl!p07XY;EKhB<H7q>wC~m0YOhe
zb7SCO1T}9Vdm|t=z!T3~SmGhs29UW(Z8AJIK+^_jSl9-B#FB1$YM!nU<U(eMF(3oM
zZ4xj6x=tQEodIV+<D0x@QF3NMr9Qz!o)8HfY&8P|1Kf6~CnzoFk#`h;juQsYF8L;w
zWPr9AI3*@$r{<+NWtQY75`97?HC=?Z$ANHg&}z(27Bn|g3{SoYhd@r!er}ik#i;A)
zw6NW<1`pNeW_mzb6x`$jR|I%m08JTGcY!nLS}jm7EIqX(J_B?DT|8*xH>5TIoluww
zBH(A4!x@l#f;2_~nqtEk;Y}$@EC-thT^EeVzwlBI#sJr|NalgMF4)b3o-GSC60|lm
z6GT8uv%82I3*AW2NI!NXLHQy+GZ#D%4UP#=8qEX|&?*_}6gX&1kX1ehc>3sjf~*0J
z27p2gYoYY@YG`8=Pn>;vU|O?}QBfK!`H|J=g-m|uW)>&w7i8wZX4^oO0mN>wcM%B>
z!)~Z|$y#%Y-cu_EE!_qmr2rmrwt>&QAnb?twP95VB%u{k*?v%p$LfC!`=R~^^(;}Z
zSOHBd2Bj8&mMEq`E?2=g^cKD(490+Z2E#C{PJwBHCv2!wh^$15(vmHVz<bKsK?nVT
zvYul~N>OTYu}@}k2_%D~hda_pKh&utTL!8ruzCj)vPd>Syn~eGL3Y81S>xl2Qd3Hk
zQ{z)B;^QH!rXd&BqOX?$?G?%d5$S?QpYR%dwzW@h@o1BYJ`X=o8nlp^h;hQ?{DMkQ
zz65O}3_1o%o8Y_wG8%{Z5Fb!{ST@pG%Aj@!_&{36iqzup%#`$095ID7QVESIvPuV}
z&LNU3K<g~=#37R1R0;v4E#-(i>A_7kP(cN0wW`Wgb+>=lu}`n4IN$h`BNJ9CQaXf#
z7(qvyamHgc)Z3KqFbCDNpcHo=w73;iD1a7#XC|jY3z9pam2Myc-W!24AaRbAcR-a1
z_PPyZ3AC;Rl>v|jIwUh<oZUj?j3Uk*aPZB`Q$bBDP^}E<dnkZ>0wSQ*DaPR{)a_s6
zTqO(fHc}Y`ZX!^5g>WtC(#!|&@&n}UOb`Jt$KedH?MRUbI*Jw}5}}vufTA%Tv`9R!
zq!=2Vh(06^e^FW_KwDa%BzhFoQUz^vf<!3j2&POBfjI#L_7s(a)SQcfA?P8NAceFN
zp4dT8!+HYkJX4ySAp03%=jA*HMd(bp?ciDgL_mA&NW~%8??@3!Y$FzQE=DGZz!jWS
z?*c(jI0JV;>OjlWKpp~@4PXMAPmyM!z+Nk+w6HBM$jnOxPlSWEy#%cX4Z(nIh7<6<
zFq{D`Y$;95DP@^Osfj6|#2gP=cL*AB3Q5CpjWALX0V!%JuH=!dN8G3e_BEbH7}V>O
zhB{)oMI^}Qpz6J#peQr1B)%*$2htJ*HHb4o1T-fh9ajtu0O~nnCuqbS(dz}-io<HC
z|3Rzzh-knCmZp}1$E84nKqa8Cbt}z*`VUVaL;XjpB_I>=G?I`~5Y!i-<}DGvz;;R!
zxG4m(5He~d8n9w{|ABb>^mh}_hJKbwgf)eTv;lJ56euHrHu7Ti1BMMyKM=X#1?g~i
zZBghzb9{JyQFc%&#*h<6-35&g(yapD-UZ6mkU9zxUKmzDeM46J4YrUzu_!SoCp8BY
zj1yt)H|NBh9MJe0Bv0XS0Mw7vasbHFSmP0w&q1qzh)Ii?C8@dviOJcC>8Zu~kf8*8
zyX}hw85l}Ht<3b)yuAFPTx=8iumK-<6$1?m8aWA+b+85vhC85%f=2Gh=3!s}ul>kN
z%uY;C%+16WHjwB-YETSN*kHH=8a7lPe)Iv&LbAeFM1aN`Ap?$(*!gJ(O(j2}GYCj+
zE3$e#NTo8kL4#x-v28U_uLd&U$cn!93EfDt1{^V(w9v4pv`vdT{~82p(`tYgihwc*
zxLN@dh&C;3$pkbBQrCJ<&;*<o$a+vU4YnCfU|J9LJy|IaG6PzYT3iA;I~qPK1a5zV
zk{Wo~ZE<QzVlhY!q|nCGsiGt#kju{8{GwEFDGI7PK*5hxKH;$*5)w!$R*Hdv0i(NE
zoLQCXk(pNlT~UoN4|WCx{4ffrcc|XAfSxacc850}hd?}5OrwJL52)Y;g)-LVWsq_P
zsh<N46&g7P<PN0RMzjzx+yM;~vRVj8qk6DI3_%z81o?yduplRZr#3P{d(NQy=#}l$
z%>%uQwOd+YZAofb3^E6DI}<pkgX{#`j%hK}$CQpifv0gmIZFprx6A=8>;ZcjOISmS
z+AGkyh1eziI7h$WHiPWLAImVCp?)T7G7074K~+$J3i2YP6a)3HGC@ST&Ae&yJ5DLv
zr=NQm^)^x81Qx$!of3eQus~<wC+Fv-Wu}8B_)1cXz-Q-yrfHC38?@RTaug0^w-kpx
zbia}qbgwSu9zO+I$<qQ#*x>X9Y6(M*cZDpd|74fGI_gp&Um7#iM$r5O$t_<be<QD<
zLOUB06#SVW0@{auVh63xK0t@1DEBR>)<Q~IU@wCRXm0v!m;T&@dyDlF4ybP_w-IgK
z3dnoNe!K!}p}nM<AN4_leIR=v-8j&?9LVAv_=ZKu%$O{6-3n5T2%1)e&mg7dr4*NE
zmO!smD=sNQpE>dc?PCWK=~Ci+Eh&u3_HYKY7647hkepbMt9s<r;tv}30Hsfezd*y8
znIHn1T3BEgM6p2Q4zvK12+P6yGeM)npfV2>-$6<6+yH8{Wr7H3wqt|kKQ^cx6nK!}
zIv3DMv5?EmAe9@&pnoVi2RovKE@(G*UVLU=T2W$gNl|HXNhxG?+hZOEhM;)R$_`NG
z$P@tWg#Z!hUK=dt{JhFz4`+az?8Ts9BO<6#OMaB{Hvm-Ggu;h25pzelx>BSLH-RoR
z1GiUwGSl+GBVCY^9+Xia84_X>QV$23h)J~xWFk@-1LESazyKV2Ncj&mhh<ZmmzkUa
z+bIk>%_kE?AePplbu1_kdr8pJI>gQ8h;#?30v;ijQ-a-FOk~C?E=r9DA58?xSoc5^
zT7TgK<#+-GVh82EGT>!kC<YaNHtDH(sTBo9&{>~Y&|DgbfG^O6Ga&In-dYKyDh}<u
zetFRH0ni|AKz>f8bAC<^<N_Y-$?pJS-7qxyftmy)=Plf>0S#W`Nu5acLqmhoc>|=m
zX=BhyB%r8=_#M%uffR6<y$CAWoej4eT%>_qfg@K!5+D_=zK!2%q~R)Pkdrl40O_n3
zCFZ5%=jvzXrR9UN9q3vcNNj`J)L;*T324I#X#yLX+CY^Z$*B#+YUC;#bjTmXdaQwt
zWIZK5M@{VcMd0y?Z=lu!D3&2<Z2~CQgNXFwyLR-leK50!GoU^vE6}l8y#!R&fvpDn
z8f0rGh=AJ&XFz>TR-hwUjXE^-7*yVX3h{W*n#A19yn_7V3P>m*avY?xLrQwkPylr|
zNsfOcr@;Elxu7(^95gTq4g)+EL&Jbt79Ro)%!R^xt)OxjGC9_=^X}=|A{P5}NLNY%
z){&z$@F8>EAQF_}=78q;UE%E*P%40AICv2U3Hb-m=00_8-vhF}4a=|~+;&LQ|1Q*a
zY6bdLP>Pp<+Y3s3kd`a_1WrirSp?eUAn$Go`07u$%$yR)!L6VrM<u)r3=B@CnK>ny
zc@P;$Vn>8M#yW9I5<3=GfcD>GE#@#Rh9q{R79V(s8*)^tf_i3NN@@jUtsa){7Q#R9
z*n}~l{sFCkAR_aETK%Aftf+^Ff|^QLJpr=>I+bt|wyB2F#DjSuELt6PXo1h-lSeJ=
z)0O#M#4;bn!g4I7RWmUw^)T#)#2m%T7?2Ki2kit3vV)gYpd*GdK}7l+zw&t%=iKbm
z7f2pqFZ%;O%7B_fFKFA>K=$IW9O`jOt`HIg)y3cr1~`U6gD{X;D)?qK7z2`!k@5$)
zN0bK6@6h%;9^Fu<lGQ0ks>AZr(uz|{lJYA+1^Oz`0)NnytUp*3y!srye1R0{NUa%&
zH;Sq15b%0w#PA2AG{CSO8Vp2s({PM`fMY5T)M^3|>A&i-_LNMvuuuQq-}*wp!U>ia
zshK7<K()vfP=-KjGr&fBp(PM(J14DXUR*%#SPyTzfRZ5c-pc=W>9G<oUEkKRLI;7U
zah@PE$WBn4LI$@$OZziHMEa!D^&b0cb?wua7#n=rq-F-S6IAh$RMZz&7DI-cL5bQ3
zwBQ0%ZN?W=mSp7T#V3_!=A=NZMl{vn!44Y-fb@*WNmt-a!XEGj0w@_nMwwyl5om`5
z)-0klSt0M>`Uo0Y^2E|4gWC<;uLSWQrA;!_6Uabuid3y4LKjl4-b5c<0YwcF5u1~l
zq+61$@0M8+k`0PcQmr8;64OA%xCqWM9Hh<!Bp@k`MC6|26;S@Dfe*|g5)h>Hdw@7V
z1!6ZDXPdkRHJ;_+X%t)rfe2`+gLHa2#0s(wUqGs6Kr=viZbL?R5t33~!WQX%fi~W$
z8NA>k?<&0LMHG3EJn|OZZdw(+?x1LP#F@k{*g+$M)>Q!7<U1&LA$Q|m+NDDdb@)hw
zxG)2SK^naJL6jJfq<#TygC6Be#ZxPg?&OXH<pPlR(Q5-pO1KM43DjCDj#^rR4s#1i
zf#*SRBLqYs&J}>Q6rqhAP)ddGD+6(YctE;AdNL7*1<saTx$kqglYP2XF+b=1-`=p+
z3z2Iik<KGj0aYfGK~{i@27Fdvb08=kfxD}2IhpBssYQ@|Z|KcQcu2z-(B>q_dvL#k
zj6$kcKwL-)g=vECl7Kpd$PyoA)c!FjF^GeF3l31wa@R}{k^Z&Gzxb1quzfn@@GmJ?
zfRbZLH>g^%09gVKQjqyLEP?ujtO^=3Mwy(STaZ|Sa(*CUKpgBxL_ossyGakce(QIL
zs20qRWVPyH))%Gf$7kk&ru!4YOJ6}V{8*zA!*;0Wi45VA4A5eI=&?R5pb!Rm43g5a
zK+}03B7Ksr)7c4Hi|o_wB`V(8#w>z)ksM1tfzrwhcry!RekO>(v;^uCB72mmlZK$P
zbPFmoL8oa4xq!+@aHN592Be7&N&RnO^#p9)V=*O`6@h%V3tlN9DrR^C8Nz@SiImti
zAJs0f??42i0EGDv8j6$-h9FJP9Aal+C;=x($D*SAa!6tUB^yY=0;zs5=SQjN0tV2i
zu^YT7z+*Sm_hc0XNNq6Gk@Lfx3=AcpGku(r@+;t%$AskjCRX_8<x~<IAk=jfXk9JV
z5&|PWpkYF3yAZkWbR2Zd4Jg68Wnydu!5G|xR#VipA9REOR?lNt4)r`)g)?ML1m=Pa
zV^B94G=q?yT9Q_plapAKUR;oxOl&%!s#`z<#aKfES2Cb<cow!K27E{j3ur9}s2l<p
z1z-ZPAOUhWnlP;Lpq9m;@d>OxhAn`AF4_`?O)3!C=PNDBfiFt{WmWKo3rI`MH?tx$
z4}0`tj_#3R9q1TmtX_gdB9a}@<V>U;$VYU7&K(cp1l3mH_yV`7Km>Gz<%3=NfyrWe
zt2c1M;)@JBaCVg;{y?$=;t!+&T~Ky{o{68BoSa%*3|(jqaw75}UKm>!iYYBukcSgM
zyTXGi;1vxX%OM^hcFhY)v+FFVBL}+89Fn3CW9)|K?zcFx#Mr0n2ktf95DIU0frhq;
z82bUA1X@s(4+=fd!hG1BB%mu4krt2Qu@CAgN|y@5?_lc#ok9&NtAi_ZA%|Lm?_UEQ
zNKNh2xKW#fNL4x9F`!lho=k?+fPn@Br8EDC%iWVftv5MP8yKATKrVrlm5?Evo3QH!
zzQLAEQ_t!@pmn@I;UhSBtcLp9kF3>$**U3+MS1$5jr2u%i8;mkX{C9|#hK~gH5cHv
zBdE0iN$?0abX@k_wrh@(eY)_6Y!%K!s<5JmtSk*X6$dhfnU+}r*$@p1ajemfVL2rM
z0O`je9SWG7k(yLloRJE?ARKhb0>lj<*MOrQOr%fP=XrTS#y{8vjPkGm0L=jsk>gPl
zJ%$^QOE#R-L>Nwh1_R|Q?RIf9FhI&3u&)t?DZDmNscxC27#afeHRV=<N=isTgKYz)
zImmnhd{0>m&y$-^qy3@NC6wEUa~(G%YoD}BhomzjSZjpH&BRHW#i_|9s6+VRU_|7e
zu#axfCfaM*r^6V~hBlF-SCH+>;FF9%yTFiw4UZMj!~tp_k(@q}3T4#(RX+y<L(pG%
zQxef@hfKhHhuxBD3QH)IS`S*4qYpnke;=qmb^r|;fD;XtfmOrUj;r#C63|3LX+sva
z7ODZXw`2pTMFzGP5$f=y16km}OJ#f4fqK(;?KOp-D-D^@pl)dY0S!7Sz?)cz(1r|_
zzz?^iwy!6HLVF{=&_?w&wPN`&D73NJ3n>LJz>dU)-yu!b4kAcb6}<aVKRzilFE2GE
z9>mKmF3C*BTZ+IYEYjINh<OD3iGbxX3VNv^i%}O5q3zK?By|kip#=<C6(CXp1IgjV
zrAY-j`6b{+2x$BmsWw750lomR@MPigk`3!%0YJSrSQ%(?zyO})@mLM@Jr#$}{8^!c
zcg~qb$vGIUZg^1xD{7&22o+rbs)VstE*N%0eNSn?7gT~G9hB(xK@|&Viza%*6LJZm
zhkbf2^JHn)YgyEabA;8Pvr@478pCR+ugPljLJ!RVxBTJ}7b56_%Gl5F3KfyyFowsV
z(M?UuamF`><xrnfF|C0zcu+5>kO#Mw5Zi^|X${7JdYp=OgDMP={S1&9QapA;{Y`1T
zirR=m9FGR}I_P9H$nj`$hE;hBwH58t<F_2=e)%B)me#50b;RL<;Fb)WfR4m{wo7;1
z!Nm3Tiz2ioLtUSvonnSKNCGk?B5R+%ZWrIxDJj0NCODDPSSVATptER#Kyv_)qJ$qb
zR|X=WCyPnj!x_+&MUEwCQ=TC6GeHF0Y&ZjwgpkJ1K%=5IMX8`;3S7%l;pY?}vOX-~
zLsv?igbg*&Y{>?w3<3=jL$U)Nhd}&7-r8@JDnAok(U*WMUJow=z&qnX1azgGntl3Y
z$$ag-X4$Ze0Lr+ObY@`fd5}3s1pvrS$XNi8C3;8}LwyW75tR~)<CAkiwH#<)D)_oW
z(7ul3{G9xvc+ltz=qg@FVg;4RkZv%f&51PB1x>6}atY|l0q|zZ<kF<njMSXW3P|MR
zaR4+ZC@qYTj#T6T4LX9B-h+apI4`jv9yIz|oROcC0tpU80RdYy0zLopJuFX9Isl66
zyiPneL&Jg6wh!Xe)lHxy=|H3EpkPQ$PmhPJmWBkuI?w`M5CJVvxM9T?H!M?7&mo$i
z*)q@&U{Y#Yeo-nUMSw=cAUB>uMrMA)7V7<muAHKt{osj@Uic03cx<Lp93T#3Yy}lX
zpaK>W>Y#!6%nhK62`tiXPCoF=#y&mwSZ0UTpH6CpI>PE$kkxlUr69OAMvSRIy4hds
z;A4-p=;RlIhE!VMqs}3q<Oa%A=~`ybN{=XN+ox~s&``^7sH9SZ0Mgxq9W1H>vK=&z
z4M}SV+ch*dFf8NIu}@cJS^es1c@?Zd08&eGK@4tm>mv>Mf|%gJXHcPR2TJvzy)&TQ
zy{<(?m{-SQG>4&OF%2988WF_W8NqM^Gz)-wFKE|&SPr@CK8S3Ap=@*jHy%Vm*#_K-
z#?oqmj93W5ax7@-7V}<V<dJt!M*wT!!&_u9BcXv0iY+2iBhq1zh!YyoTz$kwJM*Zy
zi+%d$X-t>?vBR$sr?hlMoO}wt!4g!zL-HCTz%Z<a`i`vRjnvF5D9X=DO$Ie?iuKb|
z!MmCNf$|z?g2OShBtI<;zCs65>LQ#3Df+I!ma4)JD58OreuHNGx<E62;NlmL8=%2L
zX_p960)j|T>GL0C{dX+M7*gB5wo5OVkujlZc>ydLlXhz!hAC9?1Zt1g33OmhDrndY
zoT?FxC-{LiKNnu=m{}VK^8~1dAtK|T&YB@5EyB$<44a`Ir?mNswEgcAsNx2t7f9*_
zRUwdLdLV6gq_rT>`htpfgYpnkQH95Dh`*8gpP<zu@FRji%V|M3VnFt+f-6eByp+VE
zqQpw{!-BAk|HHB=)PH0hbOE+gA9OE@ep*p}F7&o$P}T?r%>#5}neBmD3q3dJ3T!_s
zwfA#O2F=LVg4#ylWDUA6I1@yqD>Csd^g63&pZ;g%$4kCR2C!sJZ3pZHIRKPuAVoIF
zZX6DP#Bwo_7kw0^7Jx6I0VQ5YDgt{D#D&auLqhlotf~JR*4-lVB%OlFlzi}92Ix2{
zhy_>+S0oFd9w4i_L!MFr&uD;B&vH<~3a;+Z{MIhQtTM|X&_2B`IAZ03MTcOKN1tgF
z4BMd|r?gf<Es$S><}p^ohn25`&iMin>8h)h?xcix+QS*ptV><%b3rBJT6mobI_^Ca
zL?BkML5fJ&v2fJ2eFDgK0l4jXAkTw{^wp;|84Wqz?cogSbf{2+UK3=y5~$DsCwXw<
z0})WaBaP_L!ghU7i2;syu<fAEJmmO6NRCEoQPIM7C6M2x;T?Fe6F>yi?-=_FsT=R$
z)q0>7CZx1PjOsyV%%0mp?{0ez9k`&b?M)!RuK{H_kdMK4NPr2b-;r7av<P_#knPE!
z78uxeH~}9+hBIhk`(IFTlLOz|iKs^)*__5{9@G;o0ga!5kIMn?%_&RFsf45gP_Y9k
zdmwwDKf$)^exgY#s0PKuS}dsm(uKr00g$?BPy}RqGdvX_T5J&8k&fqr)H+D5LN(A)
z%CKWhic1oUO7I^Yc~6dkArnNThufQ-?EjH!4`)ED6k67&A3?)ZAOf+59d%6zd8Zdc
z*G57Xz!vK#=Oh*vXQpK)CxSY$phg9F^WZ{wLPrEJB%xyzaO4bZfL6L8onQ%C@s$Z8
zpl23{+QS);tW38<Btbj;GC@ST)x?BVGq;M^!x<2lfCg+y?$tpXZ{P*}`Z<|-i8<-|
zDaH9YWvM8e{6L3~AhjexOO+v~C__RXqj)8&A&Jz(L=4Zjf(o}SpdLE7RITD+V8{d!
z=`4&!C2tl4*~1x-P@vRi#L9%Ppbn4&+~yCUL=Pg;bIlK$`_7lOhch5HgHjI3;g52N
zC)%n^f(1D_=^GrAYv8_82Kfp^z-wnX1L7;Pib&WJW5hCN=)OacmypsChzsfRU<|NA
zY$vNr2T9ZU1tpoenN_Ii_zApQ0!~fQAm4)s==9EOM3)O<KRF@n391HGz(W{R5NCo2
z#Lf2i7>w^9^#22`0?C?Fg!mD><%&?yLb51ED*$w~A<4-VDT^jS?r|>Ghce;|OH81*
zdV%sF(gZIemLU}(Mm0>M>H;)A9P}9;5s2yn;tZtv7!ui_2{>Xxq!_&O7`&@>H{A5&
zpoI<~0y->$bb%E)rZ+M$FcfovS_LVY$t9q1BIxl=pp#cKK?F6EEO-=lHQX<tEt8ob
z0=jD!qt#|g+}?-elFEWq)CG>^pv=JzZzCa+1|*eYRN-U|YC$qbaY159W?~LXJqD^H
zk;)&$P$`ZM8fdhRh$K;xpPibQSp~lQ3%q_3Jn{i9Vi4E2LR!EWrvZbUM1&QngRyWQ
z78R$$s_jh7Y8w>1NZti4%gF>0@a_nl0ZF5v!7h^Bf;5DPG<*migxCR3QstnM7DPZ#
zJose?XF#l`WYagAH$hxTt%4&_5xKRXI6pZ%6}+_oT#_K-6x{-hvK5p+Ne)MBahjT$
zo|>nhlb=+OoQ{%^K|{_+F$*dwGC>5izl)KNK*^9~7eQA|=H#cRLyn6Cr|n#L{|Zz@
zK*|Zo-VGsp=q3&!=yWycm;sV)#TE|8^Ms(DE|S+ln~XC-1iaw_XFyUGSuGf(imn9Z
zd>2q#3CVVXNrCcK;Y!e9@}OoLq)QEoW#p*FNPM*Gk$?u$ko=wk%F`eMS2Y3ZH<20r
zpmBusqQnx^<{Ky!kQ@NI02Xo!ERM33tTqm|^asg@$f*#tPz1>-pn1Ve5CJby;S35=
zA&OIwZ9j|Jjs=}`2RZHzQsO;;-TXnV^Wc!}Mmy^YT+4w7Xv2p(gPahbLp!9%wy!|-
zc{L~vf(U4){|*~w{0?nhQ`CS+O-64(<b#&XAZmgTP^%9_K+jm^g&npE+m&NTWJ?Kt
ztUY*XrYfkFWDKu7LB|qjf{661XQH?~e)HSI8IZ&UYH*XB(U6KbM0a~HsJnd}ZnFkE
z14AZ=NS{_RrTHd{hdrDD8N;Id^sJ)9yyCR{qFk`Y5%;oz${O&+>yUG4U^~3hXa5hE
zwY;PWn>M1ja7VE|53~;gaV;DuRYUgFL(aVZ0Xw-;6k5Mg*Y-M)?ch7{z!rm28Dupo
z<dCReu-&ixG_l<RWIJe34^qt_DmHl8injcQEYBk~7Qxfp;A?<EV|#Z&%P>Ln>LHoA
zsVS*BC5dkNMY)M3klB5NYcK{1pyQp?bqy%>A(ifUtcPX;vNnbw<-qi!%oNbsD4?JK
z9bpN+suQ##H9a*SG$0B+JO`XGKm@d%hcxg84F_sE1ax0pkP>_<4-pEGS_Y%#K~1|s
z<LFq&5a?j{0#Nb$7e3BRSAVyI>bIA~*bPZ}NRtSlZVh5-Noj6zX)X~9OAx+0dHfxd
zD(g4u9I^nZz`^SQ^uZ)3W%r3NFa$NhYZp9LLp(>`eeY1KA!FJpi6x03tLs7GS_=wS
zP~HZ255WX{d2c#oe<S>YI<o3itX9teSse#oNrUh;{73<q)lgrPGwKK$Lks!^3R<xL
z5M8rF`{uf|)EnBT|LI;}IC+IRtmXtQXCb1^kPohxK`XL@+TdQmV>;9eWHtSf%0tis
zJ(LbFDAXV>470E+qcCAJl8#+^A@uKJC<PK|k(!F#nMie@HGsoJs<^jsapdsN@CP
z3MSGqtcH4?thxxvYSbkSKS1*k;AR2X=dZ*W7&1Xb`qlL%b~fGx_HYKY3ZSm_O(5$*
zr7@&k0GgwK96qmTx8d=A2L}6e7z5H4rLOJsK(=ec#|tx97#K1^M7j<CyK}d4-R<EF
zTG-CT$-oeVXcJ8oU|`4u5$U<1LO0nX)9m34TG)=bBN;8-;iv+s8}i;Dzk`z<*mh8V
z7t$2RF?vp2+n0jk9kiGSlIp-t01?o34@T32y0)XWg%L9cbP0J|klzujNkH>MnIHmD
zRlzC_TBN#NAlpF~PeDRH4YYm>M5G)0<gRSqV`mR%KubGHR}-fsmOxG*QUhHT0a{5C
zo|>ASl33}JpPX2NxgP{04xq&z)!dN}s;@!4FGvU=su~QNp&>wNRfE_iU;`Rn03G5D
z>GmKJ9%K=fIBffwHnnOwgw;$St6zf}5TJaPiN|V6d<|(J<z^Nq>!;)=Cqssn8$bd6
z2V^z4h65e9n+YP)jpPMh_}pr-PuJ?vWwF$lNRh9xS`Dt-L8oRz;u>Tt4y&QQCV%P+
zG;&n}+T@ZPU!0L&gweCbNL|o$O}>qwh{W1(!LSPIIqD_ko1pGIX!Vy*embNhUy@qn
zT2YXnmzqbk&#C7eoV#yug#_ip_7$L`UqJ&J&WXjTIjP0PKA9z{MTr>4z+$)n8ts(3
z0Mt~YYxE=5&UArtbTMe62%Mu4MJS~4@X{`Qe#q|<8|wfEsBb|-=dKm0$-bpIC7ETJ
zspX(P8%P^g4`7%AO}~`30#Far2kjRRk_4@%Me_tC$}S)-F@So4y4Gic!mt5kJt!Z8
zdmLZ_+5|$f9_o8g|DT8+T~2;-Vh(t!IssJV?*sMfaCs0m@(A@H)ixl&Ll9;GX<mRF
z$p%Uypj8+ppmN4JKd&S;uOuWtKNwWFV%<p!u@$N92=x|`0f(>^JP%4r3xU$MNpezt
zKKPy*Q2hbP5@=C2v+>lA?~?BJ>Dnz<BM#kyA5TT;m>lxl3(ouIF)W8hCt2$WkUGLh
zhpYbpomK!^P#>I;n1Xq@I;2E6X$QTX@&|0o3>94fif>5Jqvb~oyP>`}Aa3plbodtJ
zFj`Q8ZU^PbtyuCT<dBxTh}%n`iI*%}K>c~Fe!;K>>K97S8OupcPt8k-&q&Nm$$>Qb
zdO({|L8q~RM!7-L0{NMFCB^9NUP#y?tr&y)n)>dU1S)Pff&v~Ky@>WNh7%w`f;8|7
z%8E9*nR(EonA=Pk7&1Xb`jWi#_{s@+_HYK&^<*_{u(n?jO=qyHr)n}VWP*tFTE!=O
z{`V)_!x@miB9TLjpp!rnQ=r#hn1Y7G<3K~FU^_sW5VETUG9mO4Q4f-22g<QQ(?Ov>
z9n@d|+wqi@fguw_q*sfqbAMRnVh?9P>>#UH1EsP`*m5bz+%hQ3T?E-Z6K?l;&=?bl
zNbhi2n0nXV%^uEx*iBaKA(d*7ya2M<0F>El;iFri_6~9tk5p4ZY^J<T2)d&x2-N9;
z^mjlk$W5~t86RRJrA->tp-i+rLZJPUnIHms*Unp5;r14~rxYoVft0}~H$V+UlBPB=
zmiIw3B3a9#Ajv2<5xi{{6pg3F85kh<CV>+#A{sHQg%lmco`#Nc;sSW>3wYr+sJO`l
zwOPOfbl!r+KHY1B#hjm4d0?GnBC|S*C202yB76dAI$<m;BgYQ3Yw$pA%1jWEzB=ks
zAYU4@eL8ISI5f0C{TL$JwdlvXgN_&h#m)gx_Lcw@U*Ld4%v}V2)t<|L-pM}w#yhzM
zzc>AZ1ss|7!H$tX3o0(}f$RgP20Zpb{YPmV1?fKCYEY#LDxjT8OG@%Fu3~^xWk?I3
zq1k|nE&wf0!`iaIup8=oGvZoFh)b7KL5IYEXQe=^b4x&bQCu>MOF+?G9GqHGT0m0U
zp91UA)?lJ%7NpV}>Q%CO0g!4RywX1&<VR4F<_0CP6`)<N;3RDcT7?86(xbRHUpSZP
zW}lw7m~juQPzfwa({4?m52y+Qc^y)rq=D9Bfrxb1OL<=m<qho9uSt2mb=?vRU9>_w
zmw=aM{RE97g7e)OkV`;By8o#qZDtJC_UR$r9Z8?qGN3LYt7?P927GTxUVdIqW*#U?
zF0(Q)1bv3*dQd+NynG3|O#ioCI^+s>73f(?NL3r?2ugU>mX)8G2dUbyTwz|ioQEN^
zZaEJ_dZqQD%!Axa4(TS>GnXu75`vARkmV^zg$CMW0J_@^>9)Q;P|<k{9;BePubChM
zT3;h|Mkr`45F4bRnSo3YffxXR)JhkiOZ_Ns9f3-Itc4fEq7Qb^xlRFS5+bV=iqsNB
z?p*hPG7@M5D<mU<vJ^Gig6Op(^xhLtk_9#Ck(SsX?mOv?;|-9?3b9W=mGR_r*YY^1
zZ;4znPy`-Zx&TT#-S9>=s7``(8ZhbsNYWvvC4sin3Uu5>CWuJC^!wQLd67)^a0bK<
z(7}^LG$c^BT0k;KPG*umsPqNbJD^M!1a47)OBztPKn@0hocDuqMjzz~^Au<@mILQX
zEcl(KZ=qQMbS{~DYKd=YNe*~4)g=iO;h?Yy;(&K?LF>#iK?HRDHpbQzvN}4D?2I<U
z1L|i4A*NaKBp4VnK}0%uHK$~TJ)8lFDau1^I%w5AXlw+Moe<r5c$-`ix_$v-BL!(A
zH&H*eqNFGhoHoE$J6(m>1E4kn^07o1E3?TO$bh8D+{BWi%nDG-Oi4|Ha6qvPDmjn}
zF?63lfgS4wyKWKGiXtLcAm5P%UZfTbuNT0%07T#_nTgEYC?f)9%nS@Ap!t*3ih{(v
zlz5Q+If*6Eb1@OV!dMUriBH<KGxmcD>Rs?oJE*af2_m3%gMoee$(3^xv%GDg#U|xx
zS)P}HAqaFhK6+Y)R7(%+(la8z_qV=KfZAw5T$4IAFF7AH#)G;;542)Eh#g)?fKn6Q
zdWhKMgS15sw4JI1)N)NP$}cT|cnZ{v$GfGRtQBRjjmY4lTfZzZCo=_pngDp{>>E50
z%k=OtWP*tFx%0}C9T}M%(pz6WdzAlM&;gR7D33?9J8(b)%8&)#klFq3cIiLo?2gs<
zWrW5f?b0wP@R7<0MC}Y;><wc;{7%+BY)B~uK3fdBF%Y~T6jVlmS1W@24~cw4Zic65
z7z1KI<zYV`w5$b`n9*&741E2yOWy!mO*cUh8up+a+eDO#*xCwUALF~F7~x$Ug%@b~
z4at^6D&AtqvRBCHrap8}D0t?%B(n_MG6Qw?gL2_*2~bkV1QEFQ@6aw4qV?G3fqHBp
z0$PnQ*uxo+Q~*jfqz4PZV?|^@UAI7ZL4b%INE$@y$3vV!R?>jP4!9eilA4@f1X|ey
z$_Xby?c@S@djZr?f(*z)&fNYFtF2j}=@K;VM@$%$6hTMt%|K%Xps8#~!bdbna5Th0
z<v$S?B;(z&)CP)55Ru+}NUuxohlPE*`O(LVqFh3t-k>}~8-OZB(4Z*9U!dj(WUmhF
zbcOW#`GpT1Jj9?KZt~WWBX!mg3xa2XM%h49SHX~ba{>~J5_6$bSD+#WxxPdiGKCJd
zk=3fk>JCt1K`O=ZSWU?Q6q42OJ0g8Sv$3FAaMTMcah@m($w`=DLDq;EHV1+HPEt^i
zRih!<oR*(g5}%uxmk8Y&y%e-~kqdNc1E~0q2i-3Lnaf1<4k3Yn)B}Jf2x>b8=ed;_
z4uB*Jq@gI#V6{zBX=YA|Zf2eW^gybVqQvre=*0ohDNe+}2||XdlU}5T*r$gYF@zL8
z4ug$2f=+WMq8LG~Kas*0da4NGcoFE?BA^lOAW+Ey$@X|00`XiiStU(?r;om8ZelvP
z!t4Ou>kf+Ac(9OjQGNj=YC(w(Ij<wt6ws)p+;VA9{eWmG;jt3xU(nzLB>|5*V^;{e
z(j8RPp<L;X-TRo?o4RfRB~Fs^ICW<Cyg`RLg2El*YeZWaa&Px%JNPIQG}5VKtte{5
z<FOVR@noenl=G(${S#1o5S+5XMEajx{|PtV@!6**8ig8YtxSexa>}jj2i4-QK{FZP
z3LF&7IIM*FlF0cQ(3usWyPc6oSUW&#Qb7ar9*|WikoZJc2`L^iw^UMKC1{WkPm)El
z3+g?xDnv-z658@lE(V|d3-T7kX0*73>@{Ptho9XCO|@j@RTP_%!y4^?0+4GU@rq$R
z)Z^r@a()MDi-3wKXSdQk@PYRsl?BiX{6GaKq>BxST+9-he0$%58rH_}ff77cLA^-i
zNC{|lCg>1wQ11Y=?IWlFWCvO-LcD|7mLS6pP{{&GDQH;`!w#rF$ZEi%q!ieuOwd_G
z;GNF#Adz^;7$$nQg!EG|mw-{v8K5!&YY1Ri4GjU%W+<Cta2E@5+eZngE$jroPBgeA
zu>^dZXi7>@YH?`}#%g!SfCJLp88ic87>d<N7`mZO0`=-CsrHeUTAl+9mGFQDIKhby
z(I1Aa_hN)?LV@3bL`A!0K@%N6;nPNV?1uWAtUD=S!-55%quBG4Q;Um1bp<F5f-)Vb
ziwp^Su>TQd*L-u)l?K}!?9-XqJm-g9^oLc06pc%ux&gTi1!YHwJ3uUO@d+l<G28(S
z3u<>HE`TabP{S9rL^CZhH!}x%48a0W_Z~z*8wA3zmVq!VNT}@;oP8Qdn#5RxNY>@R
zNZk)m_bxd<FD)}Yz96xrB((@U$q%{-EC@8|3+a3yQYWOSL}~y-MvAB&3ZSV6(C`z)
z{xVQBfC%V(Jg+^RK}m{0DXKxM0zg+(q^5wfh9}4Yn?R#gXjua`TnwEAVSuFr>J`$6
z4g6@i0mEvj-$AnsM3jh-RsG=W-St3s0CO`iFt}yrq~d5KVvN{AOK;L`15IdP&E6PR
zKs`i$QzZk`mpB93N&=2HZ~%e`==dr%TScJqI4A}_x(u=fGAswlB1l$2eMQ#9D^gB{
z-70w!lr(>VdJ<@UgjC`$VQWkO!nOvG)!;?37<9HJ>@Kz4po%LQ%OEnm!iEF@wJu$N
z9FPFBeG}*kpmcbsBMK(?Y&R?vprKA(&v$b(Fa#|Ic^(}0>p|<$Kty`k-=@18atrO#
zuPxiSSL{n2tXQC~^-Dq4cY+!$;N%9fITJ)+S`YC((tsDrvf|?Wq7um3EAZ46uB&(u
zBYL?{c4<D{>SUh|V?YZ7vb+bjQy(;)4?Y>GIJG3d0KB9i0hD|B;gJhk#Q|C7GO41{
zDtwBQeL9Q*sdcDp`z?^|Dex8<ID|n2wCup_!cws{7!AtP=it>1B3Z*T6=Y;W4wkN{
zXgAtj&3NpFB&uQ}Cr;waQxmg`QqzhdM~i}HFoMp5A{U%>K>fE&5CPxF1ZP0g7J1f4
zfnpJ~hYHei0NH@U8mMom7?Pk()j<c~B{Qh2o(UqLNA|JXL#MqJX%LH`+68Hi8>puT
zS(XWDH!|8okD--<&P$QiUO}o95EqMrmb4?;49YT)iV&W5VTa05kZX}_My(JGLFf3%
zz>7pg9|lqjZLmW-o|1~uZ2}q>0geAak{Y_z2Vhp;#N6ZoI?xlo5;iTTv^WD&9H(XG
zq@rJlwguGq1rg~pKPj$|+N)&`XFzibk)xC7D<sqMi?X3}r{Mb{K^}m31!P+$h=AJ!
zXFzieSw%0bH;Qpu{wGjK8o{eokV7CHK*&j5Z1(AI{L1H5oD+j(9O_yRvJYzm6~lU{
z?<vhZh>cOgpn{(bOM?}@!V)%)2#ss%S&g<Q3sLSv7U?`k-|a?Lm}BHyQ0@hFu&#m1
z=^gNVi?AKC9N<0TcvEPYgL;ER^m0(=+2B1q@B!)BDJ4az;F&Ja7$zjf!Icpx0YDNQ
zB-pv_p)&#S`j(>8RS6!?j>mqe?=i;Az&8;&B^H(Bq?VLmAAf_igfYhxG0cL51i0Wv
zxCBEtPM3fNPJHuIN^?>@^V0IsM>QekFy;U*ajwA74Rr-*Y=Y#}fRq?>6H78mQY*lR
zo`VklDgpW4J*N~poQ?<vhy##%bI^Q9O$UIM1Y(T?49lUuCo44~n%$XsDXA6lB}JLZ
z*^qWOXkr08I_?a*iU>UhF}mr{AfU1<Kw*wG6fo?Eh61Gp66yr-ZO{ZTXgN8`1h89C
zelB`=Kw<@H3=A3`)O8QZlfY!ns3B#EOxQ92(8iab1z2ih*lAqQW%Nw2+L&rKg9>A;
zNdUuUXc7SJl7U>d4JJVq3usAU5NOdYB-%i&7)VnlKq^aQ(wSrS=`u=reyYspV6Mkv
z#wt+d^d8<7L~IyYwWF~6=gy7x>5kQ3KVB%`4sA?hG2#Zu2vDAaw5Sk9e5+2Ht95_3
zeR_3D@l_l5L$Fm_WThUQEh^B;=pbe+afp$bAc+@gqyQ8NHaV$z(0f=w*%DGWVrYd%
z0u`Io46u@@IJG1`F%c4lSX=<ff*W8XT$DD75G&rmT{w`1kZ1$3&^!;b8tQpUtxhZg
zO`?GkE+1$g0H`hnO+r-V7p0^Yq5B_ZH*`pX2L68o?oNRQB0v=)*y~^dZX=ul^*@n)
z57cHE%3=(}`bJR71Mi&z6X}qi>qk5I^<$8vO5Q>z6f2E*85oK|vtKspsd=dt1x3)b
z4$7}M?1p-mtceatD-^z$GCn6Wub?PD9Te%H<DeiV9@z7U^k%>M%#>^Qk#;IUJx>(}
z#AoJZmc++{9DsIF7#;^eLx4Jqen4xjOF)Z8d=rbaQ&Sv4H-WpAmXsEyh9?%~W#*-$
zCw+{<2O1vKaSqxVBt$4+SPKmW%9A}}uM0Tl5gi~%!~3OOI%CJ~DpAQ;SUp9#m1w&@
z@mLA<r9E-`Yfv*c^fDdTDFL>izWsktT?+OmB4}|HX4J40G`9uGR&*=IN<d@t&vE+m
z0Bl<3F09g`v<HDagO2mSDo7wBSq_a^&~PCo{U=!8=`yI&0!1vS!mfa1TF^G{Ob`Lx
zXz>NMwEqjt>(sLS6sWek18U`fqZv*>D=DNVI%MdPQhQ-ja9coW8g$V)q?LeJn+qwm
zk*<-01~(;XIwi9hd?FgoF$>7l7iK7vRWw3#FKAgI<oGI3<YLWA7}i4l3MyrYNLuKl
zk+AXtajFQ&X7H_BU?SaZ+L?`eR_ocPf0q4ogmDY}6cb9q7G^1Ux*yw7CK#4NJ&7@T
zQ39&Ty;Jj25_57&5=*g<$iPR$VGL*`hG81k0EcOUu6?)*9ab(TcBc&dmbdM&Z5{ED
zo8R)2vQm>VmTy6tUySz9b{He99wON~(24=99>TBz8n{H(bm%DvX*?U;g#~38NQs1~
zbc8=-t8g9?wTDhNNx(csWL`nHvbZuYIin~ab|oTc#uBSfF{~xcr>GSyN>jEGvndPC
zH=r{QGC>4%@(Jl$dPt%}>hXcfYWQpfu5&rSg)7KF9Hv5jMCp2@lA=uglA=t|wP-~J
z`JjE!pjN5}XwwO3m@MAK-z`41xFj<dw683_w3zr#5^Y=tN@Q5098&Wl4IDxPh^(vH
zkcMe6_KOsjm_oOVScCF4s444~nO<6y>YSKYmRKAZ;+UT9lvs>0vI!}_k!~@Aq>Ex2
zIuK`IVYmevSVYbNgL-jAnaKr-B^jX90-9Y5Is$Ksfm1GsNQcblz%PD*PbZPpsX~eq
z&>}kIJyK|k_z(?k$n?T%*sXQ2<5et)YiA>lhAOTshAi3xc^7oDMbHY+91_TrNC%oj
zlHdbG^9vfPRNt~xSYirt2<SZbAV*Mh3|s^uN?nMzrR~!vov!!TU+W7ibwMk`h-fOH
z7Jo?L4$TYkpw&U(k?b1K@*t4CkcJtE1sT4;Z~-(ZsMR7|4;t<R<s3+b1FFy=mq<V+
z)4toKZ+UKH%6YE<=5=aW90h6ugDnO}J|2sqKBjcE4!S=M6yIw>lb81JB^{vcBbguq
z+F~%YPZxY_y8hLgG?=HUX!kZyfKP|dAA+pKVK>y@)XHOMNgJNWV2AoaOHs<K0}TV>
zsZ}1>rNgX)`i}f1lWRajuAo&aKAA~HiA9w@`H5IY9x=v2pxz|kUQlwy>O~Bzpk5@h
zRft+xptO}b;iUsO5)s8RWXR<v?B2ouuzo7#R+fUc+&5z>n=!0}`qGZLssM4MNoihY
za(+r`d}?uWVgYz^8#Js|BE-PJ;F*_Pl$x8GSCW|Hk_u9UF`$eQ*3f9Bf-68J5lQh&
zX{}UHln?60Waj6gp7R2l(+je~QY%5SF4DR_sOM?r5KwJ|HAFBR0!<5)wpc;U5=b)v
zd2K&vX&A&A;9LN56>=T<+b*4Z<1b|%W~!CiMajjW(<(tV2I!b0h~1#<54ILeAPx_N
z*$oW>P`OS-3js7-0ovJuJSGOp1`xZ@{3$-OAzs<j!9IO$I>&LHYA0CjL}VWjwSXah
zyadBqs23@nJ<Z9?gIp{05i}MD>Lh{2;vm&@T4Hi)a()i%ZlM;?QZf*c4mmr5#~#jr
zrhTe8<|n9r2Bj@XOAKUbCWwGr2xmZpf!b~G>!5~_52%3vj()_{4x~lKZVx{;1L}2Z
zJK#3R0iZP!kPtxBHjwQ80sRmpYTFMQnnhZdiLf8O!U{I44jCM!w*8Ml{s-M*kM4g+
zTl_WR3Ij?~0Ay7Q=qL((^s|=^gIYGAVYzrvh5)ZMj|U&j1Zks#5+|hafsaE$TqX@G
z5XcG<JPrbFpM#W0Xr&*91E3*7Nr{x3nwwu#2`Y5;LE!*u;rW7uGOJQkK>LT$0|8@n
z5t=Qi;|5S3#OienYoT7Jv><|CB(fFcdr&HHPRz*xbr^$E(=&@pil96E5UBu?Es+Ln
zpaDQNmmn%$wCKmM85#!E>Lcc|GcW`#hi|S%lmzg=hs;|%g0*|8Wieu_64=LhEQb1+
ztVNzkIW9f5BquXBvjjXCFcnk<fwmn$k~+e6m~WxeL?2+@CTlGRlI^*P$r*`7pezSo
zPYnu1^s*1bYDzqgG!l}Z2HIzllwSd|{WfSjE2skiD)(|T^D=Wwb5l}_lj(RP8DfPq
z+A?E2`3({jNaNC=z2-LY8HvRiIhjd%`30$Y#l<=C#Tki)(9?MkhT-bSfaV^EXg|X2
zD5%U#0i~K3SXRJbSOIZwu_kdnw&db6P~3nr0;C`W6^Y>b2TY_hEaQ>6aLLp@-EE6(
zaCs{H`eINjBEko#tx}|vk(-kUJ`;BX$X}oVSV)pY3OaO)q5dMjYu^GYazWk;sC4o1
zf#g0U`=9}t4y75O<t+L3A;J{I0tY9UfZG6PK)q&8T$G`PX>levb47!~@g{uG3)Ibp
zjPyYUm)Y#o%d%(k9RDl{YempJ0^y}Nq#5xMdNeX6o&?1fB$c9tD14wE(u#f#wTo7D
zB%;iQ)R7MmceX<stz@m5M9LkY?FjnuAQF^0(Y%eY9p+nTr{xdS+n`fRNY0y}YhXdg
zwCWcXl|rtk!`U!^4-!Fw9d?j0S?y9JPlLt<kf$p^Q%3k>8`3a-4V!T$vQ0=}wiI;R
zHK<9<fL=mC;u^ChMjdN$PSRpn3oR|kie;qWj!(`7B`(nHT+k-aP#~yi1Q$?X0@@0>
z0Gp<{0&9*?$zD*;7i;W7{EK8S)W2k%)D2nV2tADzbns_xL40v?Mk%;CgveKDUWX*`
zpLXf_JAZBTteXNWGs()<C{92g2?AY6i!}r=tcQjGS@Tk`wb-D%U5s`FGpHF7<PL9v
zgHB(D9Kn3;-ttu!61W}GpH2&Nex#=iyWWE8E&$)85(^py1()b}TmTINvYO(MC@3y~
z?07=#w_*gf2tZY3PHJLWUVaM3WHhWT0I#W`jRLB=0^9_83>p#whXE*6LUJI6?a(kF
zt3!a4^AYRHL9GVx5~29iypp2I__U(@TyV96UiLwfB<9UU)N@KQr~xAfYQUg{0*2Ml
zP>3g|wS*K3piXycUP`fkVM%;yN@huZkv=3~Kr5p2^T2&IP}+cGQM3{wTMKlsuAY5*
z{i02YzNs5wB?P5|^l(Q(8S&6LY*3<rI0P+pFdPC69Z=RLq74Au3=gUZ!1dl{cwrz3
z8k7YQh(T~jb^9Au5s+0HB1HhGd7}@WBnMd!zDxw<O-M%o(a3?+2N;Vc$tsGFY=;Ff
z;YoTN4xn1&k^wYdJr7<6g2Dij|8ZDMb)VOR4jcnn3kiB~3Ih?)J?cnXH6g8AsypBt
z=<pVV13)a~5I|~jK^#D7(8K2@<Um0WTBQu}IcPjC6GTAM{A)Wn1DdUfoO(rXVj`^p
zYzCEcU7*+o7b)qWvsyqz`u=Tozs}E!w@<&nX!6o)zc0gz6f&(ux@ZU#UqQy8g-4)R
z&8!6FY!H$D$l^oRPGe{L^mn!~MRzoPq5H<jv=PaZGeDjMt;&OhD`@R)CWuJC{{5kX
z;wg9gbQ>>Kn}ydeQ)VNQAHkz)ci@#MVq@FsD=HPvQl|FlFC85<qjtJe&Bi03Wo@7e
zBDEs31T>8e@higSy-N(E+(V7+)7y>-JI-l$hHa^%w9Z8A%>q@ILH(dQ6P%VDKoJWf
zpyv{p*{2`(OuYBl#~GHEsFra+M`=K81}7=Rkvvut6IRXKs$!qc^IR$|!^RLA+?0+s
zz!vrkgO<31eGIl2G?|(SBGTvb^i2wXu3?|vv}So&e5@%fuqj>JgLJtgXzs8C)NKjQ
z$;7f21*4q;Z7WjI1vuB=VAu^Qu8;;TLBlTa#k2)QsVSMs(6g4H9Rxk_Aq4SYGNdRK
z<Gc?<(_E?})wunLtV8<7maT?mMGCOa0a@u9YP&wjUN8Y_3xG}v4yu3;cH*%b!=IcC
z3=B3YsX2)yB}LHTSkN*3kUJe96)NVYe^B5M(O4od@CHtipaKCh@CJz!jLMU&Rue3g
zFpekzZCDEe5Bq^_hZE4=J5s9&5>muoeT#Ax6ykDda50kus<O5)Gccrg|L{9gWtd{0
zZhi3I>pNRAVZ{tN_e~)z0eixOiGd*#bpK&`u|Ua|RjRS}>9>E@W$h_RhL$KquE7O)
z1ayXCCS*(tR5}Lffofl{Em&F_kZ3yz9ZDn5mW`lNN*$hlV?gCHh)BQiZbDLni?e;Y
z(*31ZR3F2x!X(cY&=R>IeYjuvLD2#t(z*6b_V3S?w1+bwagS6QfM#KA;)_d)N|Q_U
zk`r^1LH8v<vkOQr6GT9pe=qIe3}}f){*d@&P`khYRAGUGAGARoa-}3}BoVqd3pS)k
zzD*$WkvdEuE~w68z?m_KZ1kX(0VwPEL_n?BP>|=q*#t}4f~+&vg=G`Utwfv{5B4A)
zD<Mgsn5@bOGJFE+7UyPGrRtaDfloMB1jQwI<_68X@?WoO>+F!XPlqv}$p+L6CB2kQ
z&Mc_Zhua+wo;S>_0<S0pm9<E5jPO0oVz>*SzNf<aL3vQC4^*QBmlUNY<_4u=Tb2r`
zXOOxr&=8=4{W$%MVJFnjpgS0e2=(OrJWv}8G^1Hi3BEBAe5SU0YF;Y%t|VuOyf5U&
z74+dD49lS&Cd+bAJYtPj3|pXHBr9Pd4Fy0q_Y{Daq=DKW2p6CwZO8}+QnwK5PfDjg
zKrTQY8U|%6tU-X`0B8^dP&gQdyh;_czX?1(UJgp@pfln<^U6|-N>Wn-@^cc4NWNr<
zCT>GS5!R9qqv3;8`GFE8yeXWQTAox{k_tZ80&>_Lc;$p%3TR0d`k8m2#y{jB23RWr
zS~Z@8jfqh<FkDoes$X0H9;pW<!$eRCI~88R<FOr@3dn0m%m%eqet@h2r*_cB)l3ip
zJ>bIDK0W{Ahk4AW9bxqrdDegqQ3<LBSp$wSkPSGjf%*p2sU)K0$_J0segF;SgZ9_Q
z$ET#`WR|4n#>YcSDa1%3d`23Ui=eretS#^0WTFpV;fI`a(b6Rz%b~smEyX0lm#FOy
zSbqSVRP>SUmj$I61<+UzI5I)bK^{s&$`?@2Q`vr;B^RXdMzSC3f3k)oAT1KujVt;2
zpxkm3<n!sE^ab`gBIK{0iQ@A3&2OL1ckRFBnd$1VWK1=i!ON;ZAq^P;!ecYU&q!qv
zXg?0TEK1GIg!ah;Dg#SQ^a4wq^TD@y7nV5Xm*%AeXQt;R=Aai!>7bqxh)DN-EFS0i
zsmDJ3uHsRKPBr*ZQnZ?s0IgvR0&OLM_!|@gkhBI_bMnnDomIGAr1Tl=0&`lqV?JmU
zR}z%w!6i!?C_q3&`t$5*r`?|S*r#t-o+GyB#aY;(5T(^M`bvd2pp|W)!UuFlQdweA
zW?~+6-#X}$hD;CvJ*kunc1kK2EEQ7QEohrOL4_#fa&*`-9OyPU*m@k=?B*bFQv*`d
z7&4+y^-usM2~cGWNt&Qb8Ztoy^r&M-*qO(Sup~j&;1yCE5-}*t1G=wlC1@lXTn>Sf
zMJ9+yH)WeEVD%!%KK<&H6xqIYlVB-;dRA+Jwsq-(tOjQUkgYhZh7?4_WEK8Mz6P~h
zONufJa`M4PR)R_ZNV65}br2U)AV3O#%z-ybqa9>Fa_vW;_Caz0Gyp&caT8Jdf-XM?
z9fAz1XFx+ESUvgNgnNti5)1our`Hcpy9gG+;+KNvMG5FkfxL8ma6J#Q92Bn*%Rww~
z;DU*C49lS&B&*DYH7^jujnMr>h}HWOKz#$yzA5<Xee7q8Kw=)L+J`nfXyq<Y!owOs
z7!H935cxw)oS=4dESB*e*GqX{4CNJIXPaxm5(4>Ffg&5LuQ05F`iiWK4QbMXZa_dk
ziZvV*+o1J;c`5PfMVTp(mNk0*#*AeuI|F1P)?mODxs;9!U@Q`t2iiRZK3pA?&@w@%
zdV&e)Jh8NW`mFOiZ-2cM0*iBMO?<h6Dm2iEPLK)>w4W{$L?8~2fHZUEp~K^34fG+E
z>`3>Kq20s)N(PWK{NcL>BL3AxZwOI`T1{5F8p&$->^!LJ8-(yPX!I--L_l|$3nMN9
zgQj|7&j88*m8YQM6m%p9c>PmwL27blVh-ryA=jcJ*v3RqdV>_NkOYR*Y=IVIB-;o|
zHh7XSk_`|)A@vnhK~r1MJAu`U(lYZ>AOjZAt=8!M0+3CRi{)_*RuDN&Q;=VrSphr5
z1awt;kN{}8D5y*Vn-3=710iq*#Qo%rI3uM7q=X2b{M`?~$U+1(tpXyTR&v^>J0G>E
z7v`etMHY}PtAFgEb_+r?0i}&PA}+FkMC(o1k@T=d?_^CdA_X|=vMd4@Ss>je0cprn
zvD`@j4e5Z!yFvSf(lT>$AVnXd{6U*EVu1!d6`g?dE)7W7pR|KcCp~~IK_@HPQARUh
zw;+OUj41(4_{W1fmEhq>NUaNM<Kn+lm3ofAdFdO*(o{(7lU4Mict063m;>s>1l7T3
zP!R<&?JO<^k9p4ljUrtHjUs`|H-yC)H2}1HqkIxijGKW0vaTdMvmgY=O$A651GMU)
z++xs*EK=&CVzLqzQqfY9nVbzdzR?+!w?RFr;F2QW)RK(+6u11MT<}egkW`K*;GzDf
zo@4q!aSqB>kOBse)zCP%A!oKFIV~+yKd~Sa)b0TJyaZIVxw&~_ZEs*CbEu~&wi)C(
ztZ|NEBh;^Cb<~jJ9NL5~)`v3Up{K%uSFV6ugC5^r8!YDhyvk#r&VF`XwYCs^?Ha8d
zaud{z+l8g5!EgvPM94~bD1{to%`qq$fJ}m%g#d1>p-=E&uDk$gAY!-zZL$D7g$^Ao
zhgC12O{@^B!Q~tttD(LoazANtaSq652B1L2c84*f;=E~xeo-lD#)JA@NU0bx;*McF
z)b*g#c5FcVf54O3y6LHTx)zYDqantC!yl9y@i+HDBZ_2(e>|vP4R--3jKKSUmSahH
zxT+dTN6XNMt@S~j*)q^@A=vw%%^;Z|B3*fpEeGRIC;N2BnH=%3&MdVZ&=2a=f<hfq
z@Ph2d;Q(kfQreq_os<lk?F>2v_c>@i2V~th>^{Tv;yp|kCmXGV`JAkbhg1q8n)9)s
zoPq6dXxOnQ>AETjC6&b;(AF%qynGcD)`%PFz+MIs>5$I5xINs<kg!JTc!DY_8_?yP
zsVUGkVjzu>C6&t@($X(li`u8d7|@D}ijDB)pzR6npm8>=Mb%H(`m~?WHG5<=i~>A;
z^gUr$%}C>F5I~9-q-{`;W-q0wqo5Ktg6;%56QdPAl?BQhkP^wX{LB8BZZ`XL7z1K6
zSp!_K;a$)uNU=V6uR=2Dh9yv;1ll(gbODqzz<~`XFwg6SB$HyAtnL8K<(7b!@`6{K
zI98+<qqh<uCt%BAOzP6aIiP5O)Dz(F0C6F)g5e5ipn&!dlUYv`mn4=H>&JuEV`b)*
z=9d=7gU(llY)6|bz`#%f8gwry%}%Y%Nz6@3NsLcV%|j0yjARPUlr(Y{sP@1bJQ(hP
z1`nnA1u@ws0;<A}!iR(qn=dppH-L_=uuo61?_TP$&xra3InE14(R}?9S}aj_T;?dK
zpg@d`B3eL@#=`~lQ6cID`Abkm!U``TLO`Q%AOe1qD}3t*G`7ha6hNvw;p4%eWDdSy
zFbuRBts*!zCpEc*__125xdT)PU`_9kut(~uQxXCwJ%(h+=)@IJ#l#6u?w}G9yjBys
zY#Q^_Y_eKxs5a;478Iox7pJCxst3@JOwcs=@CbUh0i$N5w*4ZY$@LF#`$3&{<gqZM
z87OGbgO-_*ToOZPJ7L!_fOdL8`bwaZE)&!T2NTe9gw<dN3ah~i8ERMxx+M)_DJZ#t
z?E@2-mO_0>X^RVKr;`onTzgO(FSsBv4_7urE9T%!AgSmM(C{bLjD}%1Gz`dU`k^f1
zX95jOfjY<W0f|M$sgOzS;_%Fp41e%hR-iJ70bKcm3F!DQQkI8SwUoPr<k2}A)$Yci
z8z@1EAtNy-Eib<)cPO?vh+OB9oR|Z;D+N3fTmec6Ab)#=`1&|QCD5ZEBOyYgpA7pz
zWhqIC9n=6JqG$^&O)Uj)mk<R_;)6;sun4g}A=MU8<Cr9G5V@K-IX?$f-Gk0i2316b
zpqfwtep&&d{R4@So3LYRp1@XS`;jwd3+pK%7Q&>J<|P+rri0fUgRkewhgU{;+yE_}
zJjihaWK{-ap)vUWk-S9k5E~-BfZ`Nf8i5JuHK<1R=~vg6*x7i)&-SNa9TLQLr2F}B
z#<D&5f<Pq8p<bu7{R!Ti0S@)&pqRITw}C+EGZRFl7X|7nb)D3vdXpbvHF(8OA-w4e
zvK5EbP+wDC3oHVazMy*0H?br+!!5I@7`n0)6a$d>hO{~{4-BW=aspKll9f>ZlC_u=
zGn1jUzgB``JP@?z04=3Me2!W9(#9E}<&;=UHVkJ#LjyF$K|}$Kb+jHb3IOi<$7klH
z6(vG8YnFo7gJ)g_HG`S3gb1Ycdx<f;PCKW7q6BNGU^oRDDrBAGh1?xXO;62BiO)#P
zOUZ#uCW2a=LAybNW+2Caw|RmI=(>=TcIhkcxagi;>i}z?Q{M@oeZ?i9#zK5)0q7*X
z_=2MR0?4Q~xcLquFr5Pp5lTk}P;U(X#=*c4G!s-vfkVTNg@GXxM5Mnv!*Y0MTc&+_
zn0mU#yNY61Xi(RBbI@%T58<;KSSm$Gc6mUhVG&f%{{m$U7Wh~(s3VgJBGPMId^%OC
zGVRk7&P-d=RZt9D_l;#@$aKg}41pH;6H(AWc1m@CO4BxY%L#O$JLD!q$h`wf_UUg7
z)c)=j@q?ug8uVuISPIP(6x^^dk&S@?bnPjFUujN`2c(!lZ|y?TAZE=$fwiDxNU#=!
z7<NIuNaXUnl8mC%#FWfD@Wsray9ywq0^rPz$X)QGKq0ol5056tk|IzF=>(+^P_cz{
za~FmsP@hn@1d#?cc|iHnIX@S)_OK+?F)b}KFV)BeJr*HV4AP(zwD(0__ka>B)`-Qh
z9vU2EwRKSj-Czp@K#e?z)!^s`aUpF!|5Hoa%ow!o(>H*c_!Hn;Gs*Hfiq**5DL@OI
zA=ZNv0v_w3J}1k1NCyWrJsuBQolu-#T9gd#u!2^PBPDi(^^i1<G}uQ;yyLVUaa$*v
z?=h^0`kt(KM{3z1rZrkY(;8bqJxg%BgIb=DjXY})Ot`vsUb1~Ur-4rWKHUmf8BIN_
zi$PX{f*R6#!DBVl*JO1ok$jz-SdsxcdH~d`w_#^sC;{CG;aFOdAD)>~lHr_@h;cm>
zB>XW8Z)&;(w6-2=(#NnI8Vtm)1czUD`yMnL0}9@F(9R<4m)$~o(~s<+eR}v!2qaqu
zp4J78mP29{6u*#y1j7cXcc_=3)Isw_N8tT+EX#Kx)yj9+NDcK{=b&OKC?C{80Ow>l
zftWUd?Ffg=s#Ch=0+yU5;9dt?2?~G6Yz1WH@)bn9LPMRb0V1Tll#-uZTv7yWD}aZ{
zJV0$>a3KOZjt_E4?!LAMJ9=ho*{6fHLyITm!wL~fy^Z5cf6(#~$dOo(#P-Q9oq4m<
z#COk4pi5jp{Wl_-uc-ZGBwr(K5%>oxz4m}QKj08Yq;s^o8n#x1(f~)f;6ebj{c<V1
z-Gs0l9^DWI4Hl}agZe#3u~-fFx1!yK$NL=^pg|6*S}6%~w9(imP_QRsu^v)dUa*6o
z!~@A-RE+T-p!HM-KtmyDF%Gf%0Bj)m3zhntknIs55>#OsgPO{q&CZZ=RSPs34k8d+
zlnm|7O;VS~L&KdW%TqwBWrDuLg8<PffFF2qUDWu{?Pl1$zLb{jkYkcT-hT`7{!~!e
z4i0>9IRhdPtA$`^E<oa)%E^u$l<fH6)?>+Vu=_|M)>ARrfo^ULT8PDJNV402zWxGK
z1yPdWKnV_USh5``(Sxp%gCsjpkVAH1L8{W{cIj6+{`&0AQ-mfvDtf#FG&c;Y#3LOY
zi;D8gAud1!J-onyB)-4UY)9popA0H$r(+3zxb={_h6x$~lvZu98vOuhEn)<yVGd64
zSiBu8@zV8eohZ~^Dh9ki$jckxxg23NJjID~-QaV3^O-gwUJtS!RNSFQINW;J0TYnC
zMrk2~STPHlw+xzzFRmex4y)6t80Hs14LD1jVSWH{Ss=vIRJ0n@Sj5u-xnP&>v6xBy
z_5oe$r!*c=RD(uM(W4r^M+A1j3)OvnlaGNRXe+#>j!5K?C_V|B*q~NbhFEzE8gN1i
zZ$u>uw;AGTHtL7>2T<c}6}|*s-OBo%S?&ikXHzkitp?Sgzp;cjJgQL(6{@YF+XHGj
z%*E$tNSEw4_5JJs@^dzpG8|Hp9YDX#iON-%JjnVb_{tR2raBd4y9|`E^RV=eAU@xK
zUYt=awn3F8QszQrYly}0)vDjAAKQ08(_z2iT@&<tebX)-vhjisny;xG+rgmNR>2qB
zs2Pk}4HcZ#2c!#n1#zi7)yr^20S1Pk0(|)ka##%qG{mWxz*RtZO@mg&1|*hb1b|jd
zpjTn=(i|4-(2*BPlR9Y97rZ>B6x2Qj4PgeSCg<m+_~a)i(s7M4+!^_x@)eW_&;tOH
z<B$eVp#ebEk&q716dY&>)ukvgJv}igC)F`8FTW(QBr_jlvjT>Lpo95Tbr5LN1fBv2
zGdPG`JOkc51HFa<bkjSO3F;fmg4Pd$j;C-eEy+mDE6Gd-dp{UFK8HS&1+Q*l3}_%w
z?hKr>7%-dQcUwUd0@ZQ?xTgr(>Fk@CT$Eo-I6GiY=upiq1hNB?&CpN)UC>KJf4wv>
zvjlWVmws_+W(j!G3UrMUWF?48YEo%BWC1a;(M`6kpc)lx`o<N@<jf0$uL=a&1o4g%
z=$u6mk<Qq$yGm5j*B;J*re1O^X$N(=z?Og-4dCsdU;=tsjj=tP0japivBMCT9q=WD
zFb4T{AZF^p{y?-lAmts>+Bx#=U;zySA;KyPbOQj0NZ%0lcGiSz7WQxkG*y!|{0NyI
zMjXMOlvtdZmyusk0G($5-Qfp5M9CL)r*mdiYEgJmVnIPF(erjRaTTcU!<r)?;g4D3
zQ#yNyJaK-9nSmh)bgv&Ir+}8~Le7oe)L61Hb7_Em`ZhuTGULVYsTXQmE)VKXu7|I{
z!eco!RZu!qgBTB<0;==C7iguH#Fu2IXOzU}Bv$5^;#jka6baB0gL+PR1BwE~202hJ
zfy{W~h<~L0&Y&~?ZIVheb4qkG^Aw;bZk6OiX4c}9(u)$I+n~WY8$_h9tC{xv;N41l
zI0NdRFmhIWBTa2V56wWFo>5q03OzsrG@=~j!py(`Y2ksK0a^8k;TC8#Q?)^r1!`Z;
z01X^~2cJQ0J;-@<kWoNE`*heqC^RIfYWsFj!8Z$JJJ{Qx=5{8CNQVrRaM-8A4)1^z
zu2i)gRKf-!EJx%o$l&{TJNR|X(154a$t9qRhl7|wp#Y8pL=uM_+wdMXSjGUG;H8yA
zz$3Jv0vD1L@i+t;BA~QOMC%IYj4$eJ2*^H2QI8f5vA?D-F*@pFpMFFo=7jQ&L|8ab
z$9j+>Kn;C__29$^;zCLq4C|r32Th3EKuQzHQd97r?hsHrGCnge1$&bbGM#eQE*-{z
z<|_=tAR&R~6qqKsZm3g0TNsFlh=R(LeDI|&2S6DQWF@3xL--X^sy;wWdqJH?ss#kD
z!A7zG>I3S;C2Yan1Caj?gGP(ck`g>`PbyC0f15m&x=9IUJ=!Ixh~UMr9_o8a$4-ip
zi&INV3qZ*j)Q~O#B^%HdCrI>yu3*aq5r|{6b)02hKQe=#UQR{(LDS}t)qWsHfUN}+
zn07<`PS#Kv{G1QB%$yR)_5si(x8U>Fyi<$vQga|)M+7}2Wn(r~sAWB<hm1APVpt6I
zH+g4%fDeHJ?H@}oN-Rm$FDy+=DM(Dt2F3hA(DC)4Jx1|CiFxU%Zl!t2=z)OIjf7@z
zs=EbLi(m~63>QE{gRC9qNG&Y%Ly?3)?Z&;Ji3xBCun)AN14KZRVW55b_IJ-#^6iX-
zl>n4BS#!YqSwQhH1=OYjB|Pla=zqKPOTUj@pBD+6)TUK+4&LSlnh%4x1JpOk1QF@~
zW?Wmgaj%&@bajCvbO4vyQLq=(JqI<}AohdK?S~xR-{G<_^{%~}eR`hJDxSxA0kBOR
z)Hqlh9NVD4ht%Dcmn0=BBZKYJ>ki#qn`#sTi)|-z8uXAQ#>gE+aPt|I)*)WRV=pwB
zQ?P*x(R0r!F4WISO)M^nFD@)iEJ_6rScB|_<T$h-5PWR9{?!^Y`*fWx#j{v%`ND#L
zD&2LEQ;-7$;R+T|jstNa5rN?fXn>HF=pYdR-p!g<nwwgbnGCw-2^<XI+Z5ZeG^Qal
z20t+x)6{kVX!|}S9?(Jn!vW9`ptRsYEH^g*WjIh*6;kYjcCtWj)!hU>UtZrny+iQO
z;$G!4SQ4P)5?%QD%Ba)tWYxY%#XjsLIM6sr5ZLSBPzQ}&Ku&^#+#9WG4?W?b09w{j
zx4C&0G$Gpu@53Vo2_gO1A9m?7DlR{p#GIhEQ_I&;ph^zmYlOY<(6-@!ckWiMJFJAI
z?jA63K?B-W1KD`?kw3c1<Z7~gdg8LT@sTed!crMozJ=`_PAp2y$w>w8lve>IZ&0F!
z6t;LQhUP5FXI++ps`3k<hz0u<aW2(zCDCJIO*Z!FJ0<fDJa30@+yQO1CZZ#TG2fJ*
z5BB0EkbVB}IafUPLA{tjPCW+OshyUZSW;S)8ea@PfhjXTPaksWQF3ZLXt!E%Mt%<X
zv>;HHf)r`sP(T#wkY2<uyL90X*(#ieL}4)wN|Ho`0_wN`ZdV~!Fd!E|+yZhHxNZRx
z&||BR+yV_DB9HJW$<I#BgI-7qYQTXHs0uF0F9?B1WagzqN>zmKF`BZ_#xxl=f=;Tz
z>MIO8puQq9X`_ZP$_Y9DKpT8PTb&`^0-aNy2_n*Ai;UA-v`niPh(^F7m2xZhf{H89
zRvAbJ2HA$gN~kX>?Uo|#C_t=`0;M2uk^vKl-Q2LgIW!ijXg8>Vh&6U0O@foK(U$M9
z<LAlhjv>{Gp!<|ciZVfWOF;U@pk5*PAjXiQOgGT2Fho!4P~9n@{DU<xaP^ZxJqXeZ
z6L3En-ej&yErRTt22JFHHx&ov=a)c6=rGQt#K?2d6hT#o;0y)~+o5@ZthO3t&JTJP
zCAiW7m++7&?#G~AdZ4?z%QK5J5{pXW(~9z|Fi)Yxh!<#ZP~SnI5}l-^K~~EGIiI9f
zfG<1)Uk79kTHS?KI6(Y<0kOdg>UU~cyaH4rs)BkeAiskPBrpMOts+?rO$9{OEs)hI
zp8lZW2+)8W_>g*-Af&(tB|wN>@KOlIfO?V)yFhI+tT6~P8eZH&{X^->kP^_MB#<u=
za~Eh)c<y1;+eCd4`}F-+&d=@&fmdhL=>ZU&JixFX;&G&DO3;8U{O(ZjF&G8;#i@`x
zfxsPIy}XpfqN2n~^qYYY&3#BR#T?rtYY`;aPRNb8X+`<D&<-{zjDLfsg+a^m;I4#J
z<aitc@oh2rBRgwBBl)1T3Q4u-Rw1qUfu>sWtpa61q{NHIDu}O;;t=I7YEZSCm;<`t
zHBCW1zPKn=FF43GJ^*|xMjm=3f`*MVK?L*$FgANQ1L`3nvmt0}OL<Wucn$z`7<|wz
z_|gH8@i^>&_@tO3JHQzZVF%>wbv}FOL0;Cd_9LZPJvk{qA3V;z7t~2Q2k#`|u^H++
z3My{sbqlE#B}IwJ;JN~oEFh@_>~B!ggcPHYol2OSyvZtPpcQuk=<rIEi=lCz<$z&5
z<(|j9>)<3P=s|afL2@Ig`v5sVzJ7k;LkABr`}838O4}JC@T&$W2zpH0AAoG%319aP
zDv2P+=0b)$c<j?>7~dBCl@JLXW}&L(pnd*9yWy6D+Vqf3;EK>4x#@5G%I8&_GlPw_
zkTtG{67R6-M9`{N@No*k;PeolSd>RXca(bW0Ht+E(gzil;8YJL;7d>83~1T|Rc*vH
z-HTG=!9D|3GEYIHJh!k+-9jok%mSYrOYVUz`3o;XKpunm1j7=DPmqdwP-NH?gRk(`
zgHX^_L<mDL8avREoaz%jg(aq-Ae#)z7w6zf6OTh6z9|N+I3XrpGD}i*L9>gA>8Zu~
zkaj_CBDiS)vi1S!fFDr*!8fruJ2l0#Bo%xs3#4d3I06!+n2i(aI0Dh`Lraqw)<XSm
zMBIp3R&jowKIop86i5#mG>-;e-w~gdlb=|UoS#=*l9*S5(RqZ&J#484H15f?7E#-v
zc@M)LsP`xzrTN0ezyR(q_!ofI`6m)RlTNwSpwUyT1q`mpq_iIfJL*E1hk*gIXaQWQ
zgF+pAUKX^wauZ{@4z(->EiOg!F&>Md@k?20hvqwY)uqhuB9{4x7uHlG@*q0MA(kAV
z7W!{^S&HxihUri*kX0=s_1aMm>ktRo4k~OQ#SYkmAOgBV>aAV+fyrWet2ao({6<yV
zr@?IpXHP_<0Fq!Ipzlhfs^#vemLmclQZ~TPPG*6%NGQ*sOF(xVf{J|bsJL@}UK!{L
z2cicPDR%(LB?MWmAEd;VoS&0l1fE%b2Wm5bPF;kgG|-T0CWuJ)I2e3lYL%INx_9q|
z^H*Mn!BQGo6&$kFxu9AWbR=m|BfKvMvJr>1(CkI2j}f;!@`8N)AC$8}ISO1Lf{Ap<
zraNZ)bgPL8t7dLhf%%wvRx^RDehso3oUssT8^da-uR#l2$ZXPpj;t#Mt(MEl%m(lM
z0r?hED1%twh9Q_hoHGU6eF?4H$;#G{_7uo+(Bg&o<cw6vGA>Xd0<j(Jbx_%c!*+<*
zk=mx}$X81jr<RnY7D2|5K*!jo7L{NOA%R*?nIHnXd-fl!!1_ngDPGWC4CL@*^kL`I
zpw_AusDY2>KS-I0)KG*h`lht11r3aVGSV^7B)uYhtQC=@AjKup@skjHiM+}%xws5u
zI;fW)q>7~t4)GOIw}m9rq4zMp0TuOTpw(<x!wkc8vJxax(SuUOqMe6DFyJUy;{=_l
zz;#~s3Q#bBW~3nFj)<%Q31p0<Lu6wKG}#S5nzbBM^4y09^#J<T9psY3pv4DRBOSxH
z)Sr+6o!AcE5(du0@kM2zJ3~Ort+0nbMlz&U)<rw62N5Tbc*n4p$aV(USJ17#XuEzv
zD;P>Z$0G$4<tL{W7ehP@Dw-jKJ`jsBcK=c8;ey1H3{YtT+PW0P0xzq;@ed*}YamD+
zP)ycQ`A8Waw0t%>KQApaJ-z@kPz`cM6{x5V0~KoEiV!i}2N`T+vQLK`V4DqV08lz<
zj<k;55|r25;nf5lyP-iqK??x1WdoD|z&9y^Jgy2l)Du)MdgtYr=eZ?j=9Cttx>h8o
zg1f|!1|XvJ!I-jzHVdffB2esOZ53cx4h;^<^8on1tQYWEQN&^-$aRT&_URID+H$*%
zykW5qYMc|%2E)j9(8W(rK`{<mdjrXAc<h6Ek<#WSV#zjm<zy{9p(COea_*hDeY$*0
zLk*{B4$Q06vl`@K$j}2Qioq>CFp-X7HPqLjqKSx5MqT=Yl-2R<MeG6PckpIQaDGPw
z17tVgN4s>VS7qO(vhRci1Fc3h!ChNWAqYtdcpL%^6j1Yv<Um2Rz|l^uMjW<)77nm8
zW}xSF3&O&Ita2Y=eO^IjW<IFg0eKW_F2}GK>Tl2<F4R3}ph_#aBqcs2wG6$Sf(=PR
z$42hL+V~iTV08k9Zm1K;KJ4HRsE`Gf>+!BdMfpV@`8g@LHk%{W@X)fAY+G>#F{DaI
zvI*)xO6M)%OB6u8v!H39mL*!Mg4lckb^-EDSiDfpW>8IrHBK>XhWZ)Q#wIyC!4`0W
zP6;WFFH6kHOaUL20!rARvJK(_G`~-K`r7Eva&7x`^N^m;<_i;GIf|?X4a^0Y3tNlQ
zz&(3V=72Z@<REZ43ntProB<68S`~0hKyCYKENKDKvif8P9b*xOg$G$Nf?4XvgXR>$
z!&#?6>pzP@wL4l+V7LGp6lA4F$VdQqDmNa~x&ozza8SMnt<ZN#EhtJ&PAo}H@yspA
zFCzMM0jjx#Kv{s)!-9qZS?ya$Igh+s1r!P;pzwbLYT|=)1ZZJmCWwG<c~6Heq=1%q
zRJ2<FWVZ{*ZgAX#ti@qB)Zb*)Z%C;RwoStxl<cpAhV{Un1~oM@K}5Qi*|X9kirV(+
z543lEoh4@iOZL>Vcq?e3T`IgU4JrpRK?HPyhtnRqLqiC<bbwm+g3Li`-GK^X$atIR
z--R>PudCRn!}jJtLz=8uMha=9CC{LPn}S$57#Pso0q_AY*bEINDGa;apq?_;f)>MW
zsK3b?v4WH?&^0>61)u^3T(*HmLP|hqS)}Bb=Ou$S%4O!26o5A6r&gc`2BZo^s`jCQ
zLDs%76bB`vT)7OsWpWdI93NElK&l=L2S7uFtR^i|Y=9T(rR5jpf`g<9)VW#%%JSfx
zR|-1x2}Go?^qq6(eL#tQdUNyMPDRT~Sk9w*h4T<})j4SX0Me!hmpmW>-Wr7tZaje=
z9!b3})&`K@LB$NjW;{U;8Jne5r7s3@2Oqo;N(03Kh)ADb+B=Qew!}XDVRVG~J=scV
zAw*W5KuQ59qqv-)67Uc_{UPR&AafZ{U{l<0VVe-h89e<CvZM;+LvSiXbm7DJXSDnL
zG_X&<@NPm<gNrdNl~Fik0Gf_NDqiu}2hCKJCaQwUf}DKtI>5`I$PC2Nf`f$T7dvRx
z@)OpYr=Hc|-tk?~Xay+$fJ+fD0nI>2RzrPFWK<R-ufqbj(m<&MJTaAClwVqaz34=0
zB|(c$a;)P41?MwR%K*(!km3=^5~!a*H7m(o6r{`nYm%%6O$#xCECwf8P=6G%q!B*T
zh}I6Hmc>^=Z6O(WEaI^k>SNI5KScPrv?wPhGsy_l#?%3w2m#t#<`EJS;F6k_SejD;
z>1&gAogV2ngK7_~^*@g2M4H3|wMK1<D~t7tQ%h2E@(WV)(sB~hi=nIR(8vCf5(YH&
z(PnEu9(zfPo{W-`f}G4GQ1nEzLT@<q$;?X)$<O!6$J}Iy5nfPV5*Z~(wt><uDN#~P
z-3G`CP;zesC3kQ>Lxd;1yhK}sL}_`67~PEk)k;S}R)fnHJXS-a(ww+8f~Z5R#hDeL
zNYw`UQxVj!2Ky7y*uWU5hsGo&mVr#i8mAbRL48K#tN^OdQ1*@Ta5FFjalos1L|{Tv
z>H&;;k8&$>LBrCZ_6wx_hQ~^%FF{G2h>{Z3msskCMo_{L1SK4BT!O|dAg2nz$L2xT
z_)Wey9oC$omc?k@RXi3$eGDpZN%k?Mw}SmJAy6q?0@_;PnOBkuI%_C1KM%*kb4bKv
z7Q|F{6V6U0h6|vf5=mA*M%_9ZpOTrDmRbZl`8hEswJblU6tpT9l+!PQ#<8Rs7#LiT
zRJg)a1n1|JVGd_w45C8|0ouA0RP13bCNP`>4LC~6Uc{=o8qhiFpq&~4`Q@oae)&bY
z*jtg99dPQo0kk^{tM?)42FYru_d#d4keQpHD_D~A^GY)FO7lyL<H3trz<qGgF(Kf6
z9BD<V$r-67@j0ON6xhQ9Gdt19QJ|6yPv9WA0~$D_9tG&g#=rnRm(3$J2g_-~SI<Oo
zdHm+LPw%+wxoy`RAy`pLsx>%I9K^5y>JPHYYAnZ+fMzO!1mPVBP?ZenjX_cuQUF5p
zI<+h&xj3Y}CSCzrCl8u>gCtr|_Ykre72;Q<oCNhN<yKAySqaL)kjfvAmC$%4tJ*=T
z{Gs#Xh?$Z}pn<Dwc#8)#Yz!GYhMevrVV^$pli~`gy;88GORMptzo2#y2WSirRG@?1
z04AWv<svx*8Wcos>kKVUEpo|(Y;Oda3>i@Y6%3h=)fgDSMEVq?l?!<fOtVk7oc?Il
zruVa8o+Yv)Ql6TaU6h&zsy9Ky0}v}fmVr$N6PQ*&y#X4|rKmX01((Y4;9Z>Hk{Dz$
zq|gKV5|L-&C)&Um&~!}Juso!F1-4rs;slWQK^M3}>;}04TvUJw#0fSqyP@7E%Wk9u
zjWi|D0!qr@%jeMi4avWlWfc|eW&qjk25L;B`5VJ-sK3eTe?r1MCo?ZGCtW``u>`cP
z8yw{z4?}_-!~%ynm`E?np2>6kv!y-sZc}?$!lvAM<a3KwfbuS=#D)}Oh!lrmJ=FI}
zWVKzhb5awF^7Jt$z)SNop+|opj(Y;7Mn2FG5vY9@3_b+PEd^sU2S!&CnkDGuKu}0Q
z;teg?V7Ln!aAeI6BW0K3<is5CVgb+%TP2`HNS=^wE-As4xw)w&MVZNoIX?O6Aw`LK
z#i02WNL>hWCnP~Z(lcgVNHvFntil>37&b$Lgz{Q=A_D_MF({eXq^IVkRumLLhvPs_
zf%qCDI-tpfa=Sru1X#U`VI|bNWKG>6r2y#hNhSIDIpCrLbR<3a(4df_Ovk*$oXRT9
zjvZuP^CoQc1HOib+HL_AAy@+g!vWC1pz;|YEueeYK$l=ZQvELm28K)!k>0lA(ENvI
zz3kH$y*|W$ZuvS`F$&r(=3APZl&=paL1oTyP+3?EU)pN}G6O_FCx?{n(>eWLd{zJ0
z37rSSVni#*h<vyapet=MK}5REylL_~P8r*$Pm63x($1KL+X&EHY)}yvBOoVKs@bRi
zKT-1H%bsee5oFZ|D5(lsUVzioS<o=yKlpSU=#ZXF5CLsVa@nUpmYRR^U!@k*b}V5q
z0~7|g;mtwN**lPf7!!>`4YXD&+oxxo-YF73wHLPkkJ27BVnp>4=p-1>1bcj9K|xV|
zMLcMeO?*Zw=&BG%!ayIS!)#hm&ozkEyWlDUkJZpTL1{^xlbHuOboUSFqB=29*_c`q
zpPN{eom!NZn4Ah8{z%M$E@%Q>A(ROsp!0Jq_HYKYjHH@-eu9z&s7!{mGC-E%uo)T>
z)J@dppmjwLL5UjN#!>)fY!H!d7~64GK2gIyy*hnMsAmfNco6DZ?*y_w72d)G&G<uB
z7r__XSft&YeBfCOG*402c3+U~X>i-YNfAWAuMvSR=~ISfM<RPpDXGbsxryN8K_Bs9
zuZ2N_keMJN9dd}_bJ&T3&ta3#q}z2IbcHV>DS=GKVFffPQL(E48X<x75W(pP(c^~f
zJdm?bha9Wr0;^;wElUu$RD*_{AXbCo4tx_2m`I0gBj>bFk5`r7%{|2gnue(8YZp+J
zfCzC!F$U=ma@nUdPyP7bSWk!gR=)++>IkbrEJ&*kz8Dm8xQ!Sr?NW7V%0JLND5#MI
zzh=@Yu{ae{k06|a(YS>co>X-T=yVsX%_|Jsp{X7;`bX~A1o+Gw{p8~GqQrs>lvAF-
z%^Oft4?PfO#U<%n)(^B#-@4Bu<MhrDSOy@gD1-Esq08rxJF7SknZmFh8VZzFVDPiO
z<Ux600+v-Z7~v0%eX7|EDr88Cd9t?mL)HQ#9iju8_yM1`l9HbspO;?(P4@7@2fB*r
zC9Grd5>}#9$9m9$R3u*`${dWDA;{7=P{~Yka))~wF#rb|#>HwcWHrDQJ7^Q>3T!nR
zrSS~EQ8ykm-~l>>*d?(f74Nma)bGK=9YOLvrW9-?jL!rwu8)UzXF=U~@HA9FVsdt3
zda6%;B9@Iekp3jnu6SsMqozwhg*#Fz0C6GJ4u<8>at3p!(mGIs3*1UT=!TRP5A2|)
zs6K#Y8BE>aA{Lz2@aTrxP1dMBN)Zb$>2nebauSm>6Z1g5VUQajfq>=>j4TL^0a{l%
zXyJk33}|?eRqsN=1F_^FK0gIK)+P<Q?D8beNi@u?NLAaxJF`IX0O>ZNrv)V2p?;_O
z9McrgBq=CkW){a6CzfG3-Vc(fk(>fe4P@2)NW&7K6KwUtlU<<1coZ~959&A(8xmv{
z2T0bZ7iFdt7o>u3rLzN#Du9+Nx))`p1Q(<xyA|c<2A3v*mfV84=#3#rkYJ81P}5BW
z8iYueQ<5}b8`rRHo!buz6_7K6ONtUpGSf0sQ@q{sb5e*64YJZIJ_muy2S|YhVu1^6
zFaa%YF@uD<NpusapPde$jzu(CAWeracIn1h`@HPR)G2BkBPCJP95NAPy)4Lju<!9$
z4^0rX=$nFT3b6ek0(zgAkbOGjUSbEBniR?zZnPWV_O$W}<V7;=>vlGRXO`;jw!
z1!*W2<mXi8<>zK5=ICdn7UgD^q{f5m2+;U0B;A3%k0_<#5&vi9$4kCR3NY`JHE)dJ
z2IP(rC>uhY01g2>PJjjjrAZ%k6HhLvmH;gjcT7vm%u6+L!PZZPgvJwC3;GGH^+R2!
zfJU3~ganfH(2yW+`4Z0gOwc}7P=y7`e-L+|C5qKimje0Hm|=MWo+t)-+7iQE(C{Iv
zm_{n4FqW-2gGN<A9i5=m;>@a4*Rs?+=zugL+2X1}sqPq1lL8V=Xc-5?1<>FiYZX7J
ze+AkEs9#i63Yo(NodzGY15{e0B@c+bNC)^pn^RP>m%wNOlD$y>l9xTeOI0BYOyaRF
zIn)GAP=NY9$r+hO*j5{2^t7PiKqL2nVgYM9z;FjNTqs!`UjwSNK-Cm_Qwd|@80u+C
zECXdKtUklA4C*r?hvbnKPGy2-cR;gE5dR#LV_?Vx5$Rv+`942oonfE;V%LY}&ns8M
zDnRNM`3phTdx84f;5_UDvK~aFzdrJ`f_2e!`}C!|U+0{#Ukx3f2F=?OF;;|r91ti2
zgGzR=b)cJ+AzcyBSyY)IBE9>NUYFbt3;Xm77wiun?ec<+NmJLiTR0gQf;2$B1*O@{
z2GC~BMkWS^beRpSPX>n-+o$Kx5qV~NZVGJzdmbADLy$JedT?NaQWvC=jgg!n19Q~%
zJQv9GA3+m&VB10a03a7gKn4T;+NFbUl$2M1Mm2S9_XPP}3+{JN#~k_8HKYy(E&N^w
znhOC(JlOA`1_o%?G6Vc@dpLs@wp)N~M}$1Wc1VRqW54eLg?t1&-qS&e9z>-7kBZxQ
z&#c%!U3zDpSLNC%uslar6C0`30_j^7L;CpOg%R69yDAhJ7#QLMGLy4&K==DVhX9;&
z5{rwSK?koFl_r<uV=Pp|2q9>xPJK6mawFEV9>WRHvL3X4nTVngRE4Kjlt8x!g2(26
zfU*I&1Y!ftrh^E?gfeVtJJh3;j(Z^Ybnbw*K7mdrhm^h`dqJyy7%(k|dXdWOjP8T-
zK|6dK9cXC`WPJ=Ihz0D^|5dfJerMLBP4SC!c@Bp4Q16p9KLHsHMLit{RK<ZeUV-;r
z#}_5$rR3+vgW7n|siAC8Isg&rkgkNVJ)8ke2V^C9h+9DR6zj)>>tyJ9QBYkBzFrrU
z>LIBCoE||0;_L=U#c&sTb_4B>fb{`Q5F%WX1_}%i0Y8xq&VWV?S?Lm~gi6lO$w^HH
z?fC*1PoVQyOF%g@xFoSCAU~%vJwMMGt_0FB133>;Swm6_QlSlvAu2lxZ8i;${m?)m
zvhA6f2O63MA9vsjDi3bK2k5~=)gS_TN|Bs>`b$Sg&8VI5Q8x+(=)oBRboU1MJ`qs*
z1Fx9}c?**MKo&!8{(<bxN3tF2L9%Rz4E=x)YD!Nn$;r&kECI(kXlX(Ts4)<qR#XLv
zV$e0jkh`_vD<ohHXcSY`1=B%IZ}7ewkTbwrZomYt{6|&Gr=eO78h?VEkO1*9(v4S;
z1`$;)pNeWZdV2_IJ_H)`WOd-7+qyCmi!(Cw(m^S%7G!TIXuuOx&Vh3?n7|z5hQ>FQ
z>;=UyNlgpN*H>GB8hxNxg(PhYP>~8Epl8DK*{4H}|Ae1*Nx7A1Hvxle%LEaaRzhQy
zq#H>NfZ`5xbvMLApp#58K}34QznbU`A?o(&I*n6qY~8yQ+IB!Xd;%2NHo1ufc94Pw
z6dRxc1P1tWg!C8buS&ZQbU-^kI>ZfgL(b#?r7O_%7M}Qpr{@dMnv~L>RdGpWPAdG$
zCQuQI)pq#s3#7&}fO?O}4P@nz8_B?hC1{06P$4MefCCSdt{|laBoTeIOJ{a^{qVF)
zAk+%78cxXdGwfPG0z+;G?9!jAU$v9%w}e_uWF3h61Pw%41P?ukA044KfCe9lX!V!o
zWtM<Wpw`a;FT?<)5}fe_iJk*?(1W+Np*B%E(UVwIl35I{=W{{z{BqEMDLDE-4uO<=
za6bw@HeLT}jT_WvQtq`XPEO3rgIuQt3OlR`5#qrOuxoofq1F&NGMAEBTml+DD%LMc
zEh^5;2QRR&2lXgHbvWn(+~krlhzulyBm4)k_<~)!oUrEEXN4Y6izzSb_(9h!gUXel
z)b!K}bZg<k3OV6d4r(o>xdJp24$h8spxI-P=RwPz-13WZ!Kb^Uy8`0*19s`kx>L6+
z&oP6#g2*faDc!&)4uye|j}NHZhL(KbHvH*cU^sb&JJg04;>u6dlR99N)2S6DsYQ8-
zImP<W`a3>1H8;PgGCl*m(;V!7Py}O5Xz+l)Bk0E+_r(<IAR<e=(xM#j>P=8)l?7Ed
zpg}6^p?bkCJ(wv;NbiXu)Cx+{@)J;629>Ek`T51Ec$4x0yL5(SJTez9!MsLkAyEK7
z`%E9yIadI62C&BR1-o>}Sw6N<ACsPrKod~-qYcvdRD#+-X*F4rT9KQWmzWMIoj{8+
zgN}ekUeNppPc+{d?oKzp?FzLWROb;<(4(eeNN)pl{|~st0;MFviM4i;b&is;In-XV
zR)Qhbo!D=EK&wc>K>#A4P2UH0={viQ=5uMod{0(;A2Qs4+0z4!{9%oFcmeXpK<)2d
z5j$uQfCh$$2>SG#eDIN5ghN<H#pP#{7%W|pQoW_-CFetLi$ts5K!pQj1RFL?lI{<>
zD^|<_>I+JXd-(e4IM6ye#LdqLiy_(NkzIPM#7oz=b+EW2GG`*~K`B8k?LY|wxrO}1
zF5ON(^$6>NLa5hB2|-YwAA1Ob0u(X|4_V%D#V)=7&a9GXC0JIVv~UA&*#g%Xps`x~
zDG(Bgx=;@i8-$r@Y0!aP&|VrmHf*qio&gQp=s|fKWIm{i2TJ>pDhd&4kX-e^E<IDi
zPHCA-64XziBN~XvRcWA0u`~1Yz~Kfm6sxb`g=VedlRf|Ylc9EyRct^CP4Ey+JZKdu
zsQL$$oS^Y(B+C)$9^%0ZcIjT{I&HV~he9o<JV-@AD<u&@ia4SRcBpdth1dR%iq$Ki
zHd1qNJ{2_&f?R>tNDsUG)#ZAtJ=AU@8#N{Q*{OM%Rp7-|X#G?~{(}c7XeXVFjWg5=
z(7q`mQcOW*N<Mh4C(h$~Am(qdgKqJH?X$8ZuAzwN02EgiLoPG|B@eU#Qbh8AmnL$C
zRe1}wVVzP6(k7@vN3382S&lPfz`Y8)eE>SxL1c6#=NDAQ7o~!StH4FdQ=I!|Fh`)t
zumiLOIj9RhIfzJgaDTuUP=C;94asNF4h~QwB{?G#+maTDE8rt32Ve`}XyhDl*AA4T
zAZ06hupqeu5-dogO`wH2HaYppi8-+2O%Ws85MO+NZDRNU^95N+6WTw@t&C5FU19|;
zvq8>ADmKyGj+AU5L%U=(bdegIu=~wG9a2*4rL=ZO>|Dor-4Z0ckv5G$d<;5Lnuu}@
zwP}yDgGzA(1Q1sYqB;jD0-&n|i&1tPf|hp#t-{%zLrTGrFd=K}8ImKC!57=2RoMj7
zo<BKWhjcTb#{og8c%-2rkfo%gLr_;Ae2j%YcrgNKF@6zhSc1wWBooo=O{5%!Q5q;A
z-`)y3SSGOqa(ioW3FsQvq^#8B5?DC_nu*H<5$O|WKV++^i?D|?pz{%AWf@5GDlsJ`
zJ}<RAB|jNlNT5}o2rt3=6fg!Pd4PJ))XOqpC&d?l)`EZ=Cg2X@0(=9Mu=XazHI$Cf
z?*~nXfQWR{@-O>iy2b3_42T6}&E>*M2*`Ot#rnmdCT$U@wE(VVLFF=}IsuLKK*rB8
zw)R7;HzY1wpiN_EGh&=10BWXW?g9B0M5JeX+(~}vY;F%{K&&IBJBPbN3_27d6GT9d
zuKQsJXF#kWudar!7Ka6DGHAh2W?E)4{2D45&|&X^@RAPH)yo7C(2Wxch}Gf{XOI`T
zplg7GjN!I`D$Ps~k^W$g;L^PZ-R<EFh%IFG{$VKydC?A9+5werkbVKAa>FRd$@3n_
zNVKz{(9#a%bOsgJF%2rzS}g{t#^W;}r`LmW3upukeA7!}N>*ucNqkCCVtHm>dVFG0
zY9eIz4^dpf$_40_>XWb*6<Nz=kg79GSAiN0L7*eEAbA*%6Cjm9F$IM+sJ~GRIw1sd
zbF3h|YY1wWf})B6Oh6|vG0&bM>i`RIp{x(G8aera(i>9U2jb$e8|rPc=FE{oJ|4X#
zf#zq(6%NAo>B}6_(l1(zLg#ynY21LpuomiFvVt1PyXBcF>8X$<DWK6r@S(cSrA5WW
zFNLI{3(#5xh{%SNH5d&Y@~#trB{cAHj>u^ZH0lOPYgpsl*d97Q=nji>vieOh>yz|}
z?9xM`9?5oS)RR}A5!~Lfl#_v>1l&Z(D@#wz%}pGF{o!KJTo1|ZMWk#2J+7}P6|^S~
zR4IU3Pgv6i>mv2kAZKoS=pt4gSlXb*A$`b+6X5`G#fZoo7!H6Y4^ZoXk|2OxW&^s%
zCTKmV<pItWpmmCn>*iW^-aTDgq+*}W`*238KNI}IZfaQ!G6(6{Bs>;FeN1UdgmkFw
zX3%DZ>!5Kdu%{8Bee>C|FD<Ha_UV&eUU*q)ZU&2PD%y=RrZMb>_#0{P2b67XazPiq
z=j5jw8bA-!M2sV6$cFj~l>WC%hcPJW*db?^%;apyG^Y({Ev-8!wSbc;qL~6I!ZDhQ
zWVKST+N=b!IT7B8qm9jw!W-1F($7sy&PW7zufWq+pwx=qG=umVX=5IwZB9Ku^Md^R
zA0FD^AO{h+)^JkK=98dF!OL))K?5(4wfvBj^&XbQ-a`-DK}w{W$YT<TIXRF432?~j
z<)tJR6(v@xE99dLO$akHFl2&=^eFDl7tUq6*{7cjn;y)RR0PX^lup(|HjTh`0M~*h
zV%~tVKPWW8bCF;II$e!)Aq^x(L6J{MSqmG(1fOF15FV<GAa8?+bp89kHr!j`YM;I`
zW6j^WZ($RmWQ`MmLmIq%Pd}|FKR2&5H?=4eJb|(m<ZTc5@ui@yUnYo1ha6w}%`P47
z$TrHSyhK5pNkK!m0f|M$sYR|iskvD86k{YRXt_nX{h$qnka`^)(};o|!%9eDTTIrG
zJxB!>e6$qGh%YQL1vM}>ffiMP8o<7ZnR(%vc`5njkj^H;Ns!_MsR)IJ0<D|`vKedG
zU^oOCHe~gIAjuKw_%dEl-3ziC-TxTd6ruj7j<uln1y<i;SPS(nB^y$}3%&FqgU9-r
z#h`nA^m8-wiXcb8f;<m#3R>#}67V1F;29Dc0-%-1B$q#sact;OeW*@BZg?UbgO)=u
z90Ls(N{eFD^C>hzXQP1J07(s?0qsl>k^Xbe?pS?aMf>!Ux7;sUt>K+AvPQm;GAR5Y
z-h5EyItyM2fGovfGt}dh)&j5=cMzy<1)XjH@iL+%1L+A0+o!wBe`ahvw*^-DQCh7d
zmg<70#Djd{tJLsV4fQjn16`1M7kRU09%!`b5@@kETH=Q+k$G#Et`%Y~UVj^Y-z)W<
z0A8p7PIG8U9>WRHP#|jx4VwQ-5=)BpK{q-j7p0b@f*2)`#V^xAbJC#c+^WoiG*UX@
zG;s|mG9YCVT9`nJ0i;?98YYz2&CH;)7QoQ}vK-t10~63z7}9VMv>2q^%4Z-eLED9~
zH|3G6gf#ppw^15oBiNJRa7E-eT-6Sdt6~C6Q%k{<TcB;7L6bqn7&sI`<H(sHB7N@?
z!zlMqXZ!Rn8_!*7pEklu6Ht?gh{^=KpR=GSKRLCyI5RI@zaT#!e4w-<$U~v<A_Qa=
z4*Q@9C4{)yCe-<7*i;3^dTpfrR>q)`WE;pOV4s5m88UVZ8MVI&TL%F@%#mg;0Yxp=
z%!A<)Xqb>UF%6xoDkw@tikF<kq*QRkfHp&c2ljkGBCe2?rI0EH5j2q8jFiryS&J49
z0xe9z8Y&o$fP@NC9}={8%O*FqBqKjXK|M7urMNt^Bm>fsEX~UVt(isdLW1UOGeHD&
zs^%x`^jr94+C(l;EXXg;tbldcK>0ribo>*fya46ROb`K|dW16|<prfRHDbLyXut`)
zr#l|jkthb=wv(Bk2XPCYIE4gfF{mR>L`F|c0(TC<C&gHS`ljIA1ez4e1QF1a;+X8y
zzkXh(BznvZR*ry%(}^%XuQWF)wFtZ_05pn#lpsL42D!k0V3!VAQl|)AAP7nYMA(p-
z59(ln+HXjPgDTcc5RncUKz(hOerSQu;*&=;poSCaeB8^J(Ieo3T{>*Y6p|P~iH8X1
zqZWK97w3YCY9v2lu@T}qZm6Hg8kB}~(7<C}(6NG&$^uA71+?d~1ayEMXw6_|aY<@X
zJXj8r0zm_rph+bLXqLNam;Nq#t?=~6s<6ph^14vaVh*F|FGws(%*lZib`G#jEl!Cg
z$r-6B&WSlW#l#kNv~w0HNbp1$Qn?Gwqtxw8wSm@b`@xrVf|5TZ4?ubrpY5QT;Wum^
zo4VEqgZB7<5-B8o;ISU+dr%=qat=Z2%OQ>Emx6k8TR>x5;NmL{bPNrMNZ-6AxOnHm
z3HIqrdUi0FoO%E&z9{uNq-O&gQ9K9=c{7mp;8G1_b0&zuv>xJhq`D3?FkzEanwe9g
zo0+Eot@Kj!QlO*zVDmr(=4F4-x;u=V1{P8%LMxbJeJBGoWR;i>ULy<&q#zggpgYJJ
zkP;hmzr-&)xT7H6DyCwL?&g7B0_$8_RF>*QZ04q-3#NkZEoTHZFu@)NrEEw86GsDv
zy!mRRm_QvGNdcv)2k@3Xo~{ElA%J3wlI(<PJ>q5_u<!9$4~=_J>x&rcONyWeCYXV`
z65!SqIM0IGi;yEaApP*W80V1^S!ZKCg9PjeJXSzG0U8g7EY}2+pq2&V)-SL#LC2Uv
zF3oa^{P8O){D*ydX~70_!zFAE(0IUN#tl$k1=Rk56peVyfVzV^gPt2eW34CPjXH$)
z;ltmM-3(t~6#{jv4FV17>4L_1&_W%<TBvU+9pk}VPh|$G4?sDVK)C^5>Q8CY9pVH~
zddULCHRz^yNL+&)fwkB;fH`*tiXa=%#zVyNejQLv1|kp#T|oAI^TBE|N?nfVbf9h0
zZ3M0C1QF2r#<zBG2Bd{cWPSwo*`fQ`K%G>i1`;SoK;{)OhC?7WfI3@5lqukCNT9F>
z1vZlTATA{AahOj*iy5?(4Rlp3=$t3`VfeB93=Bb_qjw;2FUiQjkO?B7V_FRMa0bMG
z6xffo#SU6D6a;F8LhJ_(Swq&5LaHpJVLXWaL~blc?V}WDR^Sb5h+RmHU5H&|RZoyn
z)ck^y%-qbXRQ<gCyqrw%5e=6?>vBJXYDaJx16o86x!D>=OcU7~E-nTSrWAql@NQ7S
z2{s)xZk`Dupc&vUEGNKDF9U@;5$O~y90@Mg0#!Ij(Md2l55&+gMm&=i(x9UwY3@a&
zHWsvzi@F5^=XMqxiHUke?pIJF&jFs$2*xn|wv!}-?uCYoxus+#gZ7o6tl0ofU_q`*
zgl*+WhcTedAR^DQNd^xopzJQ<1T_p};oS~I(-Ts%V3cB%kF+R(=H%i)J!f!!mjcy%
zAObVLLt8AwR(&NU1<<MwR2D-LIw)6zYb7v|&e*ZLN>tL;J{@vWLpZEnC(;U}qf|i4
zA|Y0QeSt_!7*;@iLDmXaq|B6`mR6ivl9Ugw4-7#QTA<V8T_7v!9pMY=5e~tKKxkT_
zszX2{R#<}p!**yeP_WJh)C$PVOG&MW2i@|W4Ibyb4O;5~3W&UvRPc~mVhQvhPV|64
zDm$S8L1m|aEW{HONcKa6f@;(2T%fxIK;>$2YDs)?eokged~#v|_-rvqlN@o=EaU=a
zWqasgf)*?TP|Ynk@3_UV85#;?#R5{kK-)TX3bYDJl7WH25j2LHS)4s&PXQ&XqXuci
z;W#HYorQq`e6?IkW>IExMowx8DRbe}cMquK#FJx?>K<rXp>DIb4K%O~S}^CA@0_1k
zmRgjanwN}GdSEn5prr?O-2(2&B03YGvIJ6NVOS3h3Ys)OSHnuvg8a<l;`}^Pk_Sy3
z1ggGCN+Fb<;{iTsCJC|_8FWw&c$U{WKPSH^DX|FKeLj$S4)Y)YYC1#&)I-9SI4~@y
zBypgWsksG-CHkPXM37Yppgt&Qs0|VqpjZK?PA~!8yY>X*R%=Rw0D0*g&NEdYgK9{Y
zLxX_Q3JrclpAD$c1g$1@Nvy<iAt^>4g%%N1a|6x`Nil4O`ku(Gqw&z|ONvv$r%Qr%
zH$b+DfMYnAg@GXxM5OO|`s%pa&p>-P1L{HYtN~B8cEI~#ptDXgK?HOiBBMQ=0U697
zvT+Xe3n;0AmK_D@!MhlWAdi5E^rQwCVU46fdpLt!8$bs)2F-%|0aTMgW@{kJP>?QW
zg~kY|zd-V+1yYuT_NBq2U*MLnHmK1D8pQNW$-#bo6h`8J#v0X~0_vYaN(OK^5U#wa
zmNCH5;K;$i0BPqUY=%bzWSKpDa}m{S#+fBCY=$NP$~(@ypz({_pi~Xco(RuEssW@9
z1~j56w-TIHao0RZRziJgM%)BxPJVhiXr&u+5x*PMatC$g0&)_QQ$6x?uv~kL%cm4r
z3py(vYvRSQ3+hD*8h+5F;h;Vm=$I2wQ3Z-ci1lcZ3K`+~WCtDU`2?FbqJs7DnR%Hd
z@$n$*SKw+|U|0|JJyk~!K>LEgEv)p^lKA9|%$$^>)I3O51|@_{5CL8Bf>feHQ#Yjp
zgNRuq1JDFkEtbh=j1m-5T~IX)z*C-}T?de91W-!@a(Mu}Ab~NUF+f&_8QL!_NGt(e
z+=-m+KvrW-fL*KNU)EHl*{A1yV?UMqI}bKgK;HN}bfppQ!#_YvSRn2|t3)u|0SyPT
zsxjDNS8P}Lz5)%Zf%bg{XOyO;<)j7|<YZ#p$%v68psA7St^tJt*6_e^0W>_QR<&t?
zS{9&wG3ZcgNYA(=6~}!}NPTo@Kv2yYIBQr)!Hi@xGyp(tNFth8p~b01E}6;T7H>4D
z_yc7qNTCB_WrB!w&;<l{C+XOy`@i_A{&6RKhXghIG#a2{=L)Dk2gNn`{8TUjowvl;
z>`JT6Y1N=Wh`>_(LHbr#5FI;6@drAworrKooq&U$aR*!4&J6N9sP2Q5dWc0Dkit$9
z{iq+%h(6hNA}wZA0oe<x^3m-*Fj-7*^#)1%^s5|yefH+T*2RKOup!xA%w~JBeql*`
zaz3b64XSlPvn*KiHY91on^gy(*GzzVza+Z?R@7lE?u6VQg;>{)d>}ih5kiW~sF@#J
zKyh;wo*%%O0z@G410-?WgoX-Pc0-on#%Jah<QJ91m*nT?fO}J*q=?7gNQDg~50GUy
zQo2B{fx+u%(%=gkL5Tyo9Dv&myL5!CasZnDs2TTUEvP^W1JL>A@kr;ogH~tai350f
z_yD??r<n5o%^A?@DNvOUDgO~c4=MV-*rkKdrVtB(HGU|!6131e2-L8Jlt6f_gjQ>I
z#C56AHxVJHFwk;ANXUZnH+a+zOh9v%gMB*lW~Yhop2NL4KuHYAAyCg#wW<9FRMCN!
zIYD9@l;@Fy;elQHCBL&z*_>lxu}!@$#s<(dJ7}B)66<)ZhI*X5RyM&^E)Ad~i9jQ{
z>4~Mq#Tk_asYRp=^3%vwpi~J-pP=#{94BA`nm*wrAZ!_5F=$T$*%cyeAvq`#Ahv?+
z2U`dx(la8z_qV=quuuOw-_-u!rB$%xKvvThRt+QPM^FSnY)11hhRx8_K-Fvj+MQJb
zDuI%c@+;yYJC-3if)lhB14Q8LbW_zKO`r-Nktq;GAzCwN1GK`Ys^y@&*Mo3dj+Evg
zH9l1>XF)9v!08V}z%MU`GiZ?jz_)bH0IlZ(*$HlIfC*@JyI=?1h$aL}_+(A}BGvSe
zp%&zmAwhdtv7W&QiFza_Km&lRVJ{>npf0#q1r4Rn0Cjo5ApmM$XM%`yzH9$2&rD~x
zhclqPTIyQQ09q>!-fjTS|A>SSY2YG_b%E0!(he3-=CnyqElDgXDT3aDhtLfPJB*Y|
zUAI35?K}bp9oX%l<AgIo1oRq0M%aCXjL?m8)U~}1RE2lJZ?K}X-@k$UJ`<E|z<x(8
zsfL6+yoGuJ)<UIKeF>_?L9=xki8*OG`RSR(C7H=Xu^y+ifmK|PnFrZF3GO`11a%(4
zIT}>ALn?DfOa2LL9GwYPrcuvokX=}7a}2AYWf>LsO*MfEvX`KFNU*mNDH3+59=tIF
zEy$>7_i~Wk?(n6fc<hGy8&v3%-D<-a#=tnOAGC+LM2vxfA*3iVuNc(72VGC^3cFGP
z(!>Ep79?Rn@+^F;Wdm%2g~qM~&8A^ZEg0^Bgc?%e4~k-&<lF-2c14g$kWImmgmwkC
ziR%i?D@1NNKwQfLnw5gy%cufsq9nr0Iy{y^+*xcxTt5|cSTH#)EmJ?SAQLny2Fk)E
zpbE~}B{j{ps0j1mT#TV_XriXrYEY`c8YvhyLOn~?_zz_43A~vk9<uoqJoY3Hs?&l&
z<3FGX&OG-=g#k>YGyDl&<#O|zeR^=X-RaLiRH2*RkWvI_{XhKhhT_s($Qp96Jy;F^
z1Xo%v5|Rv=AR@iXs+ga1{s;SXvAw~J-_IyGfEyddpz&{#b1B$LtS8}}2bB^Upn@4}
zeb*CihD;EVev`F1``wEh_UT_TR;hnp&*A_LTJmO6z&&61-j(>=%slWe7EroE%q)S!
z7Zlf!62Q2F|IH752m5r$C6l4Bs)4M5EyxTT!Uaf^te|Tlv8FBz2S9^>()M;zPJVJW
z=%Dq?yn<5jL<eY$AAH7z6I9AKzqB~j6C{mZE<)O%H(`f*+=LY-)OQs4a41m0f<7pM
z;RI;7kkto+j(lQN7vMXt60octffU3@&1h&TLsi>B-o~0HFl>kVor)EA0;p*J1ZqTs
zGX!Ek7vzYG*LLYefx1dvC%Iu6f{J#7CUPKa%)rF~9=oaJZ=_?{!7&cX{*WY(=sZBu
z8&bCm8slUYMM#}aj73}Zpu!OB0<iD#xB%*VP;il47$dm=b`ahH&``@RcpU?_9YjDE
zZ+@{$|CJyqz3ZkvEXpbEwxOQcU<kT+SO(shK==1eyYyW1gXX^TWucu$YI(a5<ZW!>
z4cYeo)-K)os71Xnmm=&K6|!O+Qu%={2?wod%F8cK1+gKgI5&V6nS&N}_~jR;hNOZv
zB|uUi$W@SP2qVW+lKSwu1=K<(smviOG$0ir==#U>qQsI^{le13l!C<MY*1Bv9kj#(
z<cxT~z|zDN>>+{KB&WJN2$a4^E`a7kN=v|$vdp5?#1v2%d<8AC2jzgUG-CZvMcWCi
zCCBtRXtbD^5~wIO9()ul$cqO-E!sEmUTKRU14AZ=NOyBSwm9Tav3+`;NMyFg)^b>B
zLyjfqK$b+oPd=~%ZQchF>B1kfRX7j1+NYniaNFd*0(MC~Id)tD*#T-0Ls}>=_!t;6
zK}5P@VgFM8*aG|XS!IT2OV$@d%Pn&3*bA})R2o3+hy#tmgNSsC7A@221*-Pxf!A;S
z4iVLYl~JI6E)g+O1U|En160@lhDQZx<^ggLb#*K2cV@XCh=Zu1r4C7^gNpQ^B6u9&
zF`Y{JC^xYv8+ydqQBb(OgZm70C>i8XS;!sHLiXu~u^m_C6D44YjI8M!q`DM-ob6Ol
zt%8_3z+*Gi*F;WNp-<r^=jY|6CW8(=0A;FDP)!2r%lLqf2g}2?A`58{5*njav0WJy
z+Mr?+l1f486}k38vK8uQ(3~?7sT8uM7UaJSP~8AZL2ik8$(5l&KKTWySbT|Vtb=6x
zaP~ehY=HU*6!td7;3IYHz<w_Qxz;7MIJqb@DK#jyxHJc2xgf->C$N#3C$P>GhFMta
zWenXAmmm!QfT9IH0FYUnl9`?f+CHA9ppNBQ!vCP61w^F7Z$XAJph<<u4IOBEF41mO
z1KE%XA~3CixWAavoCv>Ay%2OV#d~-e0J^gda&g@&<qz%i|J&K8w;dC9oYU?MD+8$2
zu>1ik0#3mjw0JCrdYRHH9O?YEIiS<JKs`6`X<hVcLm)YT)Wf=v(kp271w6fiR(+?W
z7AL3XLDQ^Th>=@9n1gcu0cfx-6GWtMD6zBgo*-xsXF$^}S?g7higsv64RJ0T^iZ}f
zpi&RCvl>$B34<~ah)9Q@@&{u;#?ENvjtd}nfGmc%!vy3G5CNU1=d_12pveu?W+9^V
zMIDTVwA_meQj^gaiGvSt2bcb!bO$c{z(o4iWtGP@t&;51zxS@U-K??zmd`-3MY{da
zMdGC;nK{M!X_*y}sU47&Sc^vt%b}j9VqQ}LbzIr-4HCgF^nj*BD%uSSZ>;`?cNk%I
zL;Q`D%|O?%!m}Bu$|``23}dMj7J<%90uj*jbv5ka45*htN7K5emiU&I<Ybm*rk1-T
zfvRipU<wClL;*C8kO{hZ6ih((t2}~DxILm|@dhMC#)D3NNJ&l3FG?)Q2j3F}-nOzF
zw1NR_IcQa0CWwHpU_jcA3(1#cjp#zU(jdDDojoNE3PTW)o?_p<)MKBLJ)8ltpRA!n
zi2dN{g7nmqoXp%z$nAplpuzw&paUrkK!XdA9U~Z%6%fm*R@;JmD(678EjZLcQ#Fu7
z1Toqv5R1w3HLfT>3G#ITJjy|t12Xvq$qh&ySBT|A?ixipbeIWLr}Mz;baWe#281Ct
zkTpaG@g97d0CC6`+6e#%+c5?wA-TPntfowWr;om8Zen_>TV_rPWQ6J}s9ynE1MBMI
z?i%mv=i%t*?26v1z(`uqvVmHT0M)Tr%Q_5;p}r?8N23KkEEYh;8K{thH~>`LfGamJ
zk?!0%&1T*-eb}aCM_3I<4F@2v3B)-`gy8_nLjZbH4QN9`QEG8<DtKH<1avOb2T<+?
zhX6Q>g9zxkOn+gQKyy$b1h6^)ytZc=$N?a0!P}|91f~O^AwX6JL~8Y+j!|FchThf*
zIz=Zhu`Dwk%UMDgnGu=~sOuC@(4Y;diw!B^K`cmO#IPP35@fYHkm?uk(Mgb-%jSSi
z;{5?C1;NPxQM*72`7f~DK;p1uK-NMUB)bu}|AEe@MVcD|jgdmuCBnT8JJb|X0Mn(B
z39nutJ7QtY8L|pKl(d%#p8VemYG9_q7ttX644I+0YX?1x^dGbtMc#@sXu}%!?n`hx
zcOkrXMK}Wz>sRdHEh|XSQ!&9cffhrq!4d+HkcZz8_!1fdWW_opy+QY1Lnu(G51LpA
zS_&_65E%__H!K>U8I6J_4Xn1&k1xv42T#g_CO@&(IfeOkhh3Uo?9)r8ABhw+Erew?
zy45)t4uE=}(ksCe({e$<4jM7V>TyU$_k?vpA}I1WQf?!9s}Y6+pdJT}TM*HoEvQV%
z2Ve2=6qKz&$qBt71y5KIo8Z^nlWGAosJ7;UFRKAr1*sx1EP(oetO5(Vb^`f22+&z_
z;DJxi+}zZZ%*2vZ$AW^KO6>kbYL!DPN@_U+)INqJA#h^>-Tz1yL;X+w5t)xb4P;PW
zO^z>3s>D2B5Tl6-^(y%`g1m&)a~M`ZJxAmou~g7aMVWc&#rn{zed1G#lM@TT6Gfm6
z-zA_b-#I@eHJRAxq=Lnup>V98g`{N6m;@EtMC3)(u`Q&+06N%*I7(+9D1t#fprF*e
zl+>bBtOtE9Qu|diMesSS`STn$l23KlfF_o(h6jcVppj2xPZ#NSpp~G$G$`uOqZv{t
z-Ncx#Al(YkBtKSPU|0e51t=My&aQwKxch)^@=OPHQn1gjz#3)nQ735L!!Ql2TQGD(
z-9qVz34EsTFX-?vP}dUFr$|XG&cJyv=Str>ciso6*wg=DP7Ir&fk0&ORhpMs0$T2(
zUsRfx2N_WSWu+33uj9juGD}i(@?2nJcj)O2-nxV_pwUOZ-Jps9Yh1%@fUbDDLgOtO
z-k?buQ0FN(Gc_kW75C;0r1A~wdGeb0;03VIgFln=^GY)FO7lyLkuJdmmF-wV23I*t
zR>uyhoQ3TA0=4%dK&_#p@IED?cL7;&_#1Y&4g5G->RG)2)RhL!4MV!pc&vsdd(bd4
z$+Zj0RbszD)d}bb+Mv|r(xT$bvQ+n?#N6D(B9gZvQ0^9z5AdS2=mQl8;EpZmNUjo4
zG3;28S?rmYmXDtIF_IlL`BTjWpjam<!4nzW=*u*qCux8ZI_Q3=AUQ14MX+vUI;;|h
zdX{o4K`Q{T`VzxRXaWcA86YNsqu<T|DnByeL^@=ZjgozOZm7^r_DJ{%d!!kUb|nEG
z<Dsr6GQdz9e<<1WHfVtB6KK2vY#XSd0=X9jqlO?Yj__ECcIE>fE1|xm+4ZTQgZ6_O
z;GPH7mXK~EM$ZE3d78O|<US5rJx8SK2(}bD2~;DPz$+uruA@v4k#1W4Wq(Y!nti(b
z9Dmu>Q{V@lQOjbGIY`5sAUknb3{3@O4YDEm7-`9d45&E*N_X)?Wzk46Swn3|zE8<d
z2F-lufp6CXEvO6v9q0_Hh1P)<P=JW^Y_0A?dR=<3^QH=5BTiJcy%5yj@&M&JP^!-a
zIR;EXm$4|?r++w@J>5(u4Z4h!I<pC&=?ze&1iri(<XK1)2$AO@HShsgZ|@6qfR(CV
z2d#e(GJy{)gDyYL1QF?6dnWt$=SteAyL`QNAdE8%wz`$bM2}txr<CUAR_dd!m;{Zf
z1*KuBjUjzd%>5ozv>Ru&jA1u4vr`)CDEEUP4wVGgao{Kibw;4wWF~uPFGvKI+^N^#
z#(9_~TJ<UdP3tuBH#nn#S15p*{D_r^9hW_~?V2M5OKqSwA`y)t#41(9s!>G9f`T{Q
z(z)>X>_1`l>3z>moYHS@goP|w(-la?81fu3=!goeZ9NRjp@|CA{3pVD=$Q$$01H%d
zM1i&{fEKyB<rif;mM0daf=)osfs~;jU*m5(P-r{OEQHZkf%+Cy!4lzH)P@>TP{UdX
zm7rZ(C*Umv&`1WPqCWR9>TROFh<*C&qXB+fR>N1jQOjaJ&~nz3AdA7t7o5UC1S0uL
z{<nL4sIdW>cc^7A+G<wNz*{D05C^&vjKe<NlZQ#WU2G;S+faR-V_}IYD8X5Rmf(OA
z7J3q!{XbmR@{*=~I_UaO74=|P?xK}L1V9e)25pf9#{|epkhG5B5NO&XE1Sb+C6n~i
zGV>DiK+7&cp#UnWA=w-h`Cxm&1auDe0c<k%0c;GK$a)Jk;Ug?ZE@D7Fh1iYeaY$nU
z$!@5}iM+-Xy@)BU%uCKF%FoNJ0?$5x*0hy?(w$R&X<kZdN^oj!VnIfJ5yo{a5NBWx
zno-9Y;6;s~sU_@9B_wO1z9*~RgoHe3^-W@lenF)%cxxOe0R<(2w$h+QI%JIOwOu-F
zpcdLQBhts%B0V<|yj35R)<C@wtnrOuE!4+E)|f?U$reUNARE9Q1BWS^$6!mR;VT@V
z9wTxSRc>Z(D&g}xF)V?4gRFuBc9t9FStO~s`9+l=-x`8?mybXN2byounm_O}I%wq(
zko8z27Q-RX5TSH`KV-NU`H(Nr#-ktxc>5b%pnwQOITpX=IQPpB4zSXk`c44#q=NE6
zxe8=4csUuEz;psM6ew+uf`;A`Goce9pne~CBE%JRnn_Y(k#l}t30M}re#2<eK=T4s
zorAM%$FLn57@(|6NrNgku_U7;wF11@B@;9%4!SPT7b@tKn4F!Ohhq;g(qda^NKn%)
zpq&(0^9H1LL9!eg5M)jA!b%HJYr0s!EHNiDB@won4-plhd<w4J!9@C`)Ab(vYjy3@
zH~dZM;orO*mNm%R&;ecd1#tm#H4iF}v4#MK{m>8~tBHZswk}F8PAw@d02Ps-3);cC
z0vaT#@p;9mh4Gnr7`x{n(ScO!Lel~j9Ro^{SOWsXZfHPIIz0iO8*T+PF%N-O@PLy*
z2WUtNM8J=nPiHl}uvLG7J1hxMtF=D`R7HWtU?7bgPzwOky@AY4u-ijVzn6jbGN@lF
z?*NU5O@I#y!HZ@@sSK&4U?Wo0^0_`J$U#TCqx<~13HKK3B^>tYUK=dt{JaYDIkoIv
z0t#_#_QH>wgxog(vlmorQBv-LM*j4{>svtijTJQeIvXC^pz#pMvRX(WW1R0#Enh2u
zLK|B;gA9Rj+NUe?yNG2zf*txnR$CgWh=DXfKt8Skg*U>-bW2~YAbUYGCg|x)$64m}
zBQq}hbhl||Httz12hCa33Tbd}6`Q?~P5vDA(2I{{Vf}Xs`VL4(#DU5!NZAQuf$J|Y
z0nNsk<u+N%M<Kl><jM<es|=_v#bGa`%tqQ62P*7r;&bwo6LV7a@^a#H67$kC^V0I6
zN9y4*3R?9L+5LgqP*9nf0LlgX;QQq8*Z^@Xd7GmlJ6qt}D)K?O09?vG0WEF@M-ONP
zV<w1z?iGD$2R*X&BWxIftf~#0&AOm`0NTz0$p>KTK?F2mJU}d>gRCZ}n#DIk70Yyx
zm%(`+QPe?d^9zVW&7grzY5N*w)cr8X_6MNhJCJ9Q=E@<qBQ-0biHUk89=POL4WD7c
zlgc0=PE~)Son{RVdJqAPZlrPm8tP=lH&RIt-~SI<CsrcMz`y`nUzG|vZYVw_wV)_9
z8Qjp0&rK~ZPE3c6A%V7-Wr7H3vKNG%R4fQ<U{TFkXd5L#mg2A(8X%Nb1h7sP=%AM%
z(6kq%e+e48hn)7Jp}B!!8IO*AdT{hBh4t*sFfUVTF>J_O4zwKoJ3Rg2u^8%ON{%K4
zZ<TX`k1HS&JEUQ8(k>mcF&e%mg%Zo8L75uVU4oQlcr1hZjL2)KN{e#f=Os0Q>{tRC
zr2rQk2s<DJ2T}%t793>Q0m>zi${Lh4!SyAWz?n%YZG@tpxZcObzyR*_fer>MDof0X
zhmMpKLy{_J)pI6@fVxM(9?pRJnOaE|ZO0WNsbZ`Kh7^_5viAijl0iO%BqW5rkUj?w
ztn<MGYot*$=z#5lJJ3dkAkYFTNRXo^GtByl>Mj5!Jv@mJ$pz5NL}?;K?Fl^x&9BV_
z^@PCr4ADHe$6$Q_p#LAc^y_V1h5yuCV8tVKt-lJgz7JkV;;|m;`%rR@RE9RlN)k(o
z^&!iD<1_QpiV}-Uib|79N+HLBfe-Zf32G>TLm~|{_y8i(Kh9`8_2YY{eR_}AD;5*M
zX|Rx>ol^oqcQAp{A|zjc_Mv2gh;;3is}YB8McAi9c75hUJ5986Nh2tJ5H11TY5=*~
zV8>cr8-3Ae`}F(E?0-*Gngq+Cl#W9oEznxc%fJ9$F6&eT8W$~bLeHZZW3<p>k&14>
zd9DwJ-3E}N5wymah;a(U`QoXdad7bRV9-Lu63}6UE}6w8;PFZK)RO#y67Wblv3{q(
ze$dhotR)dHzmnBrhU7)~&d;jUqI|ILL5U1vJ2?A+xRA=Y`$4_-&5ero>7W~k1T2(b
z*^h2JKQSBtjp`V3iXx;YJ9N_(_#(vAl+2R+BK@@dqTHOs%6!NP)#af10nn{P&iOg{
zMLuA0^mG7e4k6VN&^$nM$00VXf@&~uRDlU-8wJTl(BLAg(Eu6g$W7Ewttcr<1Sgbs
z&|R#cRrI+<@yR)f#n9FgBEdj{1ha)mEeCLcS}oX`zZe!neNJhg7IGgIX#8R!>_9-!
zIbopsE43)IDiu0m1X|CS2_m4UL-5+e8PG&QJ%@mnv<Gbk&FO+;A9Q1KCWwH>J_D?&
z$^fmQsAsbQDD-==*bM3E{Dh5X{DkdhB647lz-kOoSyuuYDhf``^~q1p#-0x`6CHJI
zCwUPDS#x8M%mzB$DY2+1v9bg-dH^|G3REkBZzXp`l0XjvNCaRmA*Hfga8^?o_CvED
zsCiFBm5jP71Z95&6AuGJ5MqA>ILU(u_?2XE1~j8lZYA3O2#{@%S$bTRF{O<jq}fPN
zD=)|aJ`IOxltT(Q`0*3)o%)ncL?e%62ZN?&Kv&j4+P-)!hej`1$qP0S4Lkm$7<8r>
z_#Cw+P^Al6b`>9zQIuMok)M+i43<Ps?idjPje9D)2h=Hsw2Q!L08!0h*bfa4N+Tl~
z)YwQ$1P8`h&}Lmw&jZ{7NlGjVNGwUtKo1E>vcN3+sOb_=34}EmFf4}#1En=iNoI03
zWbf`SP>KLGI6ym=L8E&R|AUtDWP%9HIuDu>$ZBvPH4s5|folD8pibBa&{!Ba34ju3
zCWwGqjI;m|(g&fIy*EJiGQg`^kd+X7X*?|S24wFI_@Ds7UPuX#bPo%4{rd=HZzpI3
z04<y`{0mKEM3%YeO$%%rGY~7g(YinIp|C%>{u6Gz<AZg7h#Z;6ZZ~p)i?eNkVKda%
zL=GLJ)~Cgp6`-Wt3u?WA$}mVC0<HAR1QCe6FR<PgG!Ic?8OR=_G6Q4}4$GiEqqNLG
zG}J*e?Lk{XNeLXCGguiIGC@T8v@qvKa^CUw=?guRJa_)ifkh{!c@{R^;R>2tMGUEe
zmUU%<2<QyYN4xZvop(>y7O_AZ=F}a|+YLInDjDQ$a1MeK(2mGmJLqZ>9;ofq^7S>4
zum6HZF~L@XnqQE^Pa(@Gcwqy{u+=0)W**cej8rniS1f^7=f{HPH^CNzMu#&&M7lHh
z5*0yv=w*WjP!AJXV&JqGX?QFV<Y&++B#?&E4;BW7Oc0T7k#=+PfoC!H>0b5^-&`}w
zgbmG+mAWB)0#E};AKR&P;M9$1qd^MFPj=}W^3??&#!rEzZcy)u<R&<h)%Xs3#bY}(
zTa(oeL9!kBW;)RHYLF*r?E={A;8+I{h;3wZdHN=WKZjqjKvr`Q$?~+K{9L572;YE)
zhI8R{GRSI3zW@^SO!nz8`=OpEYpELAAYFV$X<m9_QD$Nus0`18wTI$8z`{fybVYrq
zkUSJaX~r)GuaYUK%*+4<0;px;lb;U0ToW{O=ag6sommG}JvfHLpcNa{TmrHRsg?tA
zao7w=5lBmaKuOppzbFN|)JFnTOM!^=CB_DyHmRxE!x>OVgQ`%H^D$Dn2_N!=GUBlx
zDv!w07@ZtQi;u|t5XHzh@N|P#;8?(m>MNk~5=5j!wy>$#!x`k;@dnhqc?GZkKz%pJ
zVgrm`3i)<`j$;mb2DbyW`YIDdAl5KJn#r)kAwiWP$#I4<{9(Y$z<{)28PpZZ1QF2w
z2j);OmCL+Kpmw=Dyv#+UaEM1w!gg!JH;|B3(?QBS$Z<hn5>(dpi7+q(HNgWLkJXUO
zRZMAn3U!HqA86Yp3#caq%Bq=Jpfn32(!Vp@oo;+P)IOa{r$LjMy#-d$QrCKY76yi(
z)$oodqOyS`=LdG_Uz_}kKPd@ATQ}6T{S)YFacy{pwE=k^M5JpA*R7my9%`TNyh<Z3
zHMbdRJGB~<@}PnOw5b`=u+joe1cL~~mQzU2NC(!iqE)E~TA2wc*}<`oNPH4++H$*%
zqV3baDZ8db9F2y>KIp6+lCwI#;<>QI6k0-q<~ty60jB~yZh>Y5vhpC3TM*+fk3sPP
zT2Ke+Ga@Pn$YSj$h!KBiEk$WUfQ`EZfs#L>FNMcqsGrGd-6OU2klJ~OBY?qikFXnZ
zhS6WU^h>{wU7r^z1dDqr+U*YN#er6oK{5ayyP^IjtAPzYNEPh;_=3#j>>Tj6*Hxe@
zbSo&=gFTK&Xpl08N~4T8EXSG3F)WAroNBE&PEc>o9jCV$?4ieG!Os|{n$4gP#+tJs
zenzsHN`8hfRsGD&zyQ91**&!+C^a#sAh9IFF)t;!B$4QCPn3oN+%2G~Jv^a+l<c6P
zK*_)q_=qo1xI;=b(6aDM5CPr&!3*04!VAmXlvsusas!v#pvnp|3I^E+%xn+6xrrHC
zjZ$J4Xh$Q`c}}2Zx0xUUYQ<+eI0G7rWGz#GP2OPaIgZZ-Z#s_8NX;pLUWo__Wk|fD
z6(^9&gT+4mS6$YglF1gZpaxBTkz9x3bP?#9ocPQ<&;?T9EuT0G7!2n?1Bmk0`vXu<
z6*L4GkXTfl>XulNn1l1wMU3tiv__@e6`+GpAn^cBg`jAG^inabgnFE;J_J(x0qsJ*
z$)G;OboiVm!hU!kB39z1>)Se3SURAx{WwP|Fzko=pU5t5Ne1W=-Au@lUge+$ix;T3
z0#59pJtvtUBK_&M+Wud!UG39bX7^|v-r)`NBsrGs0o7Fh;GKdfQ1ci>q}NWe&QUTp
zw@-g#p!RpKh#j;%A+m_3V4?yZgfIrAb4^w=5ZW+6KHgmg<Y5W;;IbUZ!yp2B-lLFx
z`WwIUc@^ixpdJP#bs|a-bPpHg=jVXBoKHc`-6?SEKtY=cBGOk!T?*t&Q?^fsY>`xl
zT1QSCwt*(uL3fKlT6iGiA$Gt958=D=ps5iwz(9nb5Jy~quXF~@96P3@6y;<VmpJ2I
zs)aOw04;Awwhq(+#ag^T8Zk&VKs^*s&f+~-F<(%W3O(Tzbh~*xsE`NkVvmP+-XRmm
zprv~yppw_EG!J}dEqtI5*G;uZ%?xNr(8Hae!Vpj3Avp>fctl1|Vo7R6Cb-&D1vToe
z-~|(C?O-N|fS$R=VxJB<TZJ1|FcG<oH77qk9UKMmnR%Hd@$sPi1Inqy_~rogY(IN)
zQUa`)faY0dP!RzNMo4}Fl@6I8BK_x_-Ld+<jIh=_J2cOdH4=_gxy2V$<|HN|w{vhd
zEHE+vq;Mp&kOD1B1_d%=x*DxI$6+04r#BJh2hMpf=y)-Btt{k}Ado9SJySeUjhXAo
z>LeqDdwFI`dMf0~!Q-IL3n+*CmgbaXI+qp|=NCbm^PqG9Y1_c+Bk0g9e7zJEok6%=
z0doX2?-IFB3N+7=mjYP@#0)y~4s`fgNNPoib7D>^NP<|ul3^d{P*$wb3iAp)JRyEU
zn$ZA_)4^}~OwCJy-0>M-T$HL89ON4B3tGOUu8@y%m+pN~g$^R%g%F$pErf||!r)B3
z@LpydsDTDPa}}Izwt;L05$UkTC!7JvrIfe3&Da?jf<SQxDVY$rGDGGIG43P<Riq?m
zRLIad_)gMzq|NZ4o+MIlkQ3xv5P@^v05qFTgyoQ<C&4Faf#%vFNd?^EMP$8~@sm!8
z6g$|bZ=V`ByLV~^tYRU{e@G<&^2!;UXHQ~S4oRoQlv<8_cu*Or`~ih#a7ktb=!$3@
z>)?^Dp@*hsYPtf{x`Bj0I6))AA5sq>Sq=>X8r|gZ6SPnfG~Je#pOccDSel$s3EKaR
zUPwV)g;Xy<gM>z|0;Nejp@ZZOXy{NgYP>-EBtS)}V>0MEaqK~X6hY9Spr#W@P931t
z6+{#O$t9Hqsm1!pc~K2iM}Y?MlH<#hGZHbXVo1y*HD#gRCErF+g2xlNNLE23mqv-q
z5>&$9fzJ^lvLIwg34Tu@{Af%XxdUfn$8ZNUJSc4wf^M)&&d*EBOph-}EGbDX0<TU1
z?PV<im5cFdiMg3MnW@ln5mX04PH=^sJoC#A+M#BKRSlF^wj7{tHK;y^RJM4mga!eP
zqGA;r0|WR%y7bgM(8j6MT&%b4U^J(od4fi+!kIuY+yM<8N~aJDOEb$LJ#oa5W*|p^
z8|PpGzNaw#vDEyN|0?0P?Rb*YDMV@}KsH%}NKiiolqazk72o??Ux2nW!mWq;oUEZM
z6zd^Zse(^xn2lxB6Ecf<7q)WZ3GBQZYFUi4JBnd3B|gS-bvJm<+!4!K5nL4jSw6;M
zG3Z1StZ|HCG1SMDrtac`%)C_aj!0q9h+zqQ?hv#z2eLkA<|oA!QhT{z1C(m8l8$;-
zgO<Jq-2n~kfT}~VtzaS@!)mCn{m98!;5lObg8ZDyy!_nE#2o#25IZTc7(8PP@-HOh
z(K0ur;`w3+zs3YwzJU&4Bchdo+MUC21IP&w=YX;}xUU0B^^l?;l;a^K42C110YSm`
zW%!v^xdlb=yUH(v>Yf15usk>vKtm&tBkdriE7GP3XedzI0pJ~Lx$tpoJPv?{0O`HG
z4p5&5v=hi9BqYEou{bl?v9u(^BQY-}2XjOfV`2&FZ_=$rbTYw-91*t|RzQ4)G#(+(
zz`y_>k4Q{`jz<)ymY@$pB(pIvWP*tFNfnh=;ZuU_;S8v|iEN%=Oe+-SC#M#J%ZU=u
zC@g3|8Ind3qyF&zDvSXceE@CpB_f|BCV}tw1uZ@b0xeF2m~IK`?SlyDV9IMdI0It3
zA#o)^e0geOc2R0taVmHu85H|L+3-{r3|bThBGT*iq>SX2I@!Y+5L+lM>Iy0gav)bz
ze+8|@bAT6hpaY6BK?L+hfFE{n2E=Ca{0Ewz4+8BffP^Awe;H(x8KfL}f~c`cvIT86
zR25WX7{mRt2NYKz0<lXZ&F7$0`sI(XP1Izyhag2JWVluzc@`X0=LUhs{~?`H&|q>V
zh=6Vrk+p|5&6Qyl7Im!$O?(AS2Gy6~{0y=ghxL&BTufw<fpncL$a6s!pkxXzjS)o#
zWS-+SEE>PS8eXJZ0qWXAsy(nT@K^!$1(5{^YPE;55V#*SiLM6kYJ#TsAm`G+TXir7
zX=N53D?#%VSbd4ntb*hR$~}1%wCoh@NpQG=2H3%)?9jQYE3l}$0-Zmi+(sABo(_bK
zh?*0_k5r3Ee$b$S1HSX(Ho0EP`(h{$%Yl^p5@$?88Yn02(qUFYeMwfPN9qK@XJf&Y
zA3rFNH9(mjoFhSJzh#05Xi>v#pMG7`_|WZU0ho`eYdsfeP31>WfdEcbAe(Vm5A{7+
zTc%(W)Ua*suqHBSHFF7QQ+;S&N@`kWUTTVOYDrOMa(H4<US?i8q!>n&AQ<f$Xm+Nm
zn?T`!HD_bk4h<0^XPpsCN!NnfJ0M>{{0Ewlh0MzO?#&aFc&%-p4r4(5M~$gqB~aB0
zwiM(+uzg?xZWo*Z^%m%mVj@~usAVK9qES~OWabu>mVnRs1a(P3Z3pm?C7vK*%my-~
zbio_}rkR640fIHgF<b%(8l-kKXx<9mj!w&jP3#qy6rneyK|v4cc|lA?8f1sYGN|=I
za)3kHGAIWTfocXk-a)b$;+<kpKZ0b7kqR{EBslmKb8wmV5mX<8JPb+TX-o_ZnIIy4
zR>TACU0<v0)Ah5yuV#pU0xQ#~?t)5CJ(2-x5r85WY&n>~bOAIBh|R<WMVWcv20y5E
z1_@<wxef9yBzoZuVo3e*64sIe-R($3D1&Zg24B*i3>`)QnGCTA>=h6fhec4Ykd=;*
zqBA!qF$t7U-9Q(Sf)bNkeo<~>iA!P$j!lF}Qwo$MWh_qE04gc5jb1?V5R%1I@;hvc
zMF=SUyn@%tASYyk2<Ud&M|RM;;t#Ocr8LlC7NZ?`iimAUYC_sm25DDP+K)tRXK@E*
zTF@dTNDmK-&5%I90Bv7V&t@jjDy7%(ULO{lVa<1FEK|1$Bns+4=YX0(;GB$T=E9dE
ze12B2_S**d?H-g`4;p?)-1)E%<a-Cu$S^p6<FOv<d$LvlLzY4m7bKQ|#wkI^JSOHP
zgNG?WjSEQm0`@*4^dtV&L~jUDw@-&LpqZNr_9M@bf{tf{*bnwU9{ZvGCud>lf6!uc
zP+1o5oS&DH30f5ishbeiK~gtT?FjWQIo5$p#p)*vOQ3!VAa2kJt+ADySx~7DKe7^3
z|7PZ8<QEh`mtBJo2m<W|cT7nvD9J2K4K7J7D9)@Rx)`U4t3bslp0L3z(?QEwh^RQx
z!UlSu5|+IfpmlMGWwOYN6+syQ;xrHoT+4z9=yFLx`}DSB!j5y=;p0E#9dL`wX~>Ja
zafT9P<tCC-pecx~T!~cWqK(AqfC|MaSZ3%TDfj|nv;mq;sBAwduR@{%tyI9UAL@T{
zvMFM@IXJ!%1J4ZNDW^Y`dfKO-?ccv;g?|&QoeCN&AR;MZjCDho<KkRGk6|CwQ)JaN
zkS+n1;mt{))|3gnq60N~Av^TpXKE|jZFs!jfdS@S>RR6ivi=sln#W^3)c0g9#DG_H
zMa8N5IhlEhIqCY~gY451A@yA(=&VxEaJ_G0Nl|8nPi9_fVv&CV<WNcMl>$<k1WoO<
zaT;i115ctyat1Vn$m%!2LkQdyhzHFhfR;~#dv1t|3!EcBTu2EBxu)TpUHSo~{dFC@
zZm=XmR<=iT0n#FQoQ(kt`=KE~`S_ncXao%8QApf_M#v$10IVh^teUx1#Xj9QYoC{0
z8NB&Nxs|6t%WYdg84+B#;jt3xOG?|?X_@KJ)8yGf)3KoAdLh0A`5O|!kp9I@*#2!M
zSOkM6euyaYP-|bLdZnN!KPwfy%LH{{9B8e1P&>Sbj>jR;Fd*{8T2zNX_d|g8V?b8i
zfRZ&Ni-A~RuY-y7H+SXVhiW_9r^lt;(0FYQzqXpJb%#)EK^t(8=a+C+>KK+o{Y-gM
zGv#1lC;?Tb?)f44&iOf|xq0X@52;a+3KwV@N4e#o>Jk#z;B=0NV+<>y{w1q4fz@|K
zsrqP#QvC*vd5Xhl6A)c1$QaEP*a;0cVZC@zW7)MLH5t5jqAWAD95fCBayF7L@R$Pi
z1t@8goMn)FfppwL05<~z_<-a1Qt)Qo%(P6{vDzSKL6R=Uz%(=hspt-nWk_KF;^MFy
z8U~=nFE+&`MVWc&c3_p@8xz97hjk@mbU@&R4~zlz0fs49-2l@B-F|TsHaSezCT^sn
z3p%a|-rR%rAbe244RH}#VX{%wM0cZ6lzsZEm_RR%u#K?76tu{J<di|cMc|?x)CtVb
z16SdoFoQS>Ez@B*3K~`94ot>_8d9L51%8niG+sf=ogvrPLGn4~P(Ha<HiMdw-{E5=
zcr1eYjl2dDw1Wn273o8-zRk=BFXL?hx!@b95etrTnI0a7Oc0TN<k{m}0UQ6?r*C;~
zWXfqL<p52>RCfV4s8EXqxd0r$Aj@&M01^gBvlT|jtA2`86N{2Fit~#~QlZO#z(tTA
zXcJXYVkP?g1!!776GT8qJ~3~7qH^KQ3u+`?g*OmDR^zZA;!pB!V?@ea@WV5;L2aZ`
z_~0*SxkM(2NQbP@ma<RJclo|1dy5V%TT{9Z3vP2e$j|8@o567lvJ{8SP(PD(O=n7C
zNh&C9>lYQ3=77flK^X~BW`I*QqIg%H_a@EF-pfAyRK}CfUCZ;R<ztW&z@=3_C_jPP
zMd+i>7&b$FOyq(T<h@v+)p?)=Z=ltA0g1)M<@rS^zUf8bnI#!6scDI&Ip9-Xa*1sh
zk>LVdo`jT-NKG%OC&^lkij<U*8{O%t;44Pwf;OUn76bd{=j4>9W~QXVcYR~`IOa$w
z4O}$~RG0<B6Fef}K|%(}4bYIGppe0Kfdptn2G)cRnQ{1Q2R~d5TAWkRrUKcIJn#+j
zEW~bbW(RS>EhXq`GbFpAekW_01GrGv2OpG+a#}6u2GkO8ZU<eei#-WoR@_u|0w~*K
z4FE{wBiRlO07|>Th{>++p!opMzC6#ol++5y1ZN4Zr3gs*6PgOB=a7C<iv}Y1EtjO`
z7NliDO7|L&9XmlS25@|XQyYkY-q!ZsE`7m@oQU8^_+5Hr*wGHMBN$`{$Yk)*zhDB>
z4rp8^k~8ChRK($EoEMiSfd;fnpd+rJC@ul52Mo;&hRB5E=jUKg?U>O_A2%W@UvSVN
z$^l3=Kynr|=*SwDMG8991!%#bscv0Rn;M)tYCwxMKt%e1$zpn|H)PtUt13?1_ECKk
ztnMeP2t=|T_gUc}YcoLvrrl7lQ_*f0P}Or9GzbUwH)2y9d~g89fM$9s+6`JQfV2Z0
zlmZ|fCYY7*MV-`{<wxwv0iAk=WHUI$fe83bL2w4dW=hi<>Rh)WsAVmKrMkx$!hpm#
zQr|@lx$lx$l3J8mlA2o#IY%KSGZ|y!LK-`0(GfcXLwezZt3~N*9`<ksG(JH=K}2V~
zpfV*Nypg~WG$p1D8chNP7`Uni6VMJ3Qa*(QojExr9P~blFwmlaS@7aC5#$*V0bR@Q
zVGn0O>>{#F3f;2+8r7-;C4YnsU@w6Pc>Kc|kf<Pbu{qMAasi;&M$9+iDcGPfLZmN{
ztndX{ftXXmV+GU~po2Jwi1g(Af=W;*f%;cL$KZtoh?@x_(&4vs=dde(<6IB{D;&uh
zE=DRG(~B}wK&SG7EN1{k5gV4=0%@Gyg$-Z+gs#Y;e8AQY6r5o1f|5ceXviH*q-&Wy
zD?Os9ZJ&PYaGXYhbp-5)VIpHJB{exSHxYb{7<jP{csmZb*hO^fA)fkc2km8Z!9tXD
zE5Q4SK#faCpAwH1&_n_9ClQGP>(m0|Xh%?VfsSekl7d&H=xGtDF9S{P)Ug&6jCfkn
zNY+AqOV$QEq;3Rscp7}x0m$Fr4R$`DauMVdaFGZmpylEPyYwjT%@@vP!nb)++X0|`
z;dnv-$pO$1AZttxDU(9mo}dLcke#l!pexfsoom0uvdnbwN@q|zGbypi2)(TdUsMKT
zKobMi-2^%y8f&P)EQYUTgoX-vbK20cQjB#ruww(;K!un9eE%nUu0X0ap#eb~XMj$`
z!WtU58rhU~@eot4Z$O1AC^pcO0;GY>X`jB(b=&8^S9ZWkJW8!bT=xXp=NRORr3S&U
z8kz<uwHo1TkV%k+CR(WgPkxXB|1E6p4WxnOLKrDiBCH0T$qum^T#F%+9ER0Y@-<>y
z0dy?@xW5THT5%{1D^Rg>!wb3}9n|M`E-flcb<51jNlkIYxFj7T-$AoK6&-@}l5`Ba
zq3M9${e85Y0a<+|fU)C?(j^$s>Z({D%7`y4F$GoJpp1dFOvG>qG<>Mr8Qcn5c(V>P
z!2=ElL^Ex|KF`YwGXB}6Ll*t8!OBJIT90-o9Qw5W6FcZQ>|1E-n!2|4f;_Jdzb^}6
z`y#bpHB$tiBQ_X7I(XEzy#r)B8>mtSmyihCAvxm#tY`5OHkD3RQx(!?%uNK3K7$fm
zz61jUq+kZeI4J!gw-k|@s?frKI@W^EUP1H+@K_5?=ahG(R)ThQf`*hJbv|fYMJ9+y
zU*?dOe$iUgKE34pF1OQ-ez3Gnxs~8%IH;Ke>GgnY!(k=Vmy}kxh-L<O-6Htbb#T0b
z7V$xDU56AT3ijzo);3AJxTOyBE%mHMYvJIrni5~bwl$U%rKakG2vBOC44PL2HTqpE
zN{SLgic(X9Qqw^PpI{!i3dvTOZC$d$0jpy``50?ij$t)496-x&iD_<SfM$w8-USW7
zfe$bPEs%08Dk9#OBwGbaMI?EKtVRQ*c)~p^2XY0tcmfmYtp1AOuObud)2li+`s{aE
zLQz_V*qxY?5}%h^o|2yoo(l)9rNQcL46C7WNS3#en#@Qe@z#6{4B+WNx5T_;qHj*5
zq60{7tbx`!p{_6!WME(j0qvcPH!M!fEeJ|2F3rKXu?5oWxC^V@@4~tt7{+0Z2@KuP
zm>_5O!6(p^*$jC5CySARArnNT-;--;sNoE?PdBryxzx$B7?#q=v7{X|kPNEvAhkj?
z=tLC|k$&||6qm<ueZ+A-kah++b{OKaBgMXZsmDGi`}7t0GcTtaE`**kPUOIQU}<V8
zcy)>tD4-C2Ky1^1#D=bYdO|VZJWk&rsQI8}r$iKOxOQnsqIw5m9X!ZxxL#zIZw`eH
zf+9^)fYy)N6y$>j@k{fHQ%h3w%Aj{xf;Nh0f{666k8aN<+H2Uu8PK+l2658``QW1B
z187v`EtcjZ##%^7w31}_caY(rGp!(L25~+CWFfpdY(=~}Gysa#85kIROLLR*^})9(
zfEKA173=%vr+`=~;Kj7bplcRD)yNXqIu!rB)Uaazyi|l1NLvXM(U1}s;+K=K#wh%l
za#F051f?BpZ9fddp*fkz6aGOvC_y!2v3@G3Dg;LgXn7s@Gz*u^;u5fAKu%%_=mJ)(
z)g{K97c?PI-~z;Q7EpTt9ExB9K9>V$Kz&P-9e&cFC<aw#pu;@!3-WU^p=X^Uq86i7
z0`)sh+yjb!tfe`IE1;o5)(|98y;@R~sb5l*3Ep;8ke`!S0xrhxgU*5mwHM=q3qV&M
zflhrd%B+Y_&d*0r`4ER8Er5Xr4{aPqAbgOV0SzC}^feI$Eb4p-Qux3&yYqpzoSy^@
zPlDT8h;C?xY^bk5DZhQXq2jYitKLq7)qvEpxDhlYD+sa}oKNsr4D~Tt4IM}$54`jW
zI%fhZ07F1a`a!LY)S@ELeXh_&(}eQ|72N=`jHDbvo#6-2VgOJOAgl++JBSM@rk37X
z{@9Q?)js_#_ls6*ad^B_dM^pcV*PmN+8}UQ4YCt!2w*q?ngWO%ia>8v6j$aYXB6e<
zWmbX90MM$tAVixF<O+z-AvvDx5fB2_s^F>xF|399maJu#kZpa?J(nq|pdG03Ii+|b
z9DeK^i~%j^$VzW0_9L&fT!G8`Fk9hAKtjDwR=*s%fGaA=ECwGF^AgmUWCfKp;H3T;
z)cyt$>1p$iYn<QXW1s$K<;P3DNeQqbj;vgcWHnNs4pdq~hTFiU93rtn+CDe!pm%pZ
zhmLSkZ5jJdP<Vr^gESyPI~_AYM0$Rbho4%Wt9?5A`q*0q5mC?vJIJBbs80@o>XT5=
zrggA~L5u$&*PKHJRlnOor#zXU_L7y#kir^Ltbj;RB98_68nnk6;%h{u0z0!3+UMej
z+D@sjOHwPqNo)bgUPNfefsz=AfS=ou-f`J;+palA(B?W)e+4u`3GJ^GWEP}C`zkgd
z6F@}z-X(@n?xDu^a0WDcf{ySaBEry1rR4m)Jjh;9P<U+zx%&aA{|k;SkcF8b0&W?c
z0nL>}o?MD{l0i49H40i`=bT@fhh>c~Br{<4GRU(H6c1SI6%1>jo}zS7J*bpTEJ3+*
z0^tU5nFHb?S2CExkd!W3LN04Tr*&Wr07zAXWH~edC@pp1ml*8>jmLtLl3!^KjwQaB
z^()nE2c<AP{zkGH>Tk+N{8xcmlAvj`&;rn5Vc0tnNC6Jbyp&rE8eqZWS0pPTenl#A
zKtmw#1M_l=@*xX2L2HT7H=KbstYm_SbjY>#Hui7^)FafZrNMJ2$KlH@XM%>}Kt%cn
zuSw5;+1c2`8Ia)=N^Q<bO;62B0TqJ6p!NY9d>{vud?D-R;cFFO42+-zU7}!<RGOJn
zqMMngkegbPk)NWFQj}N@x^^T3T9$(*gfc-y`qEEjEti|Z?b9XRwB>diMZ+2#RG(c=
zOiu^-tOr!Q*TKUS<OUoL!SESqzz@S`#i=E3`N^flP>-RPYDm3QX!4-C$Dq^ZM?k}4
zAnPIZJcxx{ydpUS;;~|~nsnGZk8GR_4B(yAi8(pRIr+uV&Lg6&0T~j#3v1fJXWYmt
z;$bsQ;IS<Iw9E>~YBx}=f|UL7SPl&U3P!X*cOrm2?30;<brcPwT7kws1$N>bMZ>TQ
z>OV?WdOLtpQz0mufPxjAkiZ0dqz=x2dW;gwKxq{z=0IE=mO*@mRB(e_XOo;!1kHZz
zpcUyLBE56#4-tmPmiBN4)SX1m`hkuDHZuVg7-+*#pw-WrAOd<#2Ae&c0V#xOyo?lN
zHN<{M(~Z*}dcFWBwEa%jpadja=Yo$O0!=-pq^3bQpb!AX2U7GPT(F_U&c=I!pnW=w
z0SN(m*5nWyiQE}hT9gAG;sklhhmnDy7?h=Kz_&gX6xl(_%_pEr7DS|%WzXa}{#nu<
z&Vbkj8crvolaW=NpQoRimz<vhIpGCV93cfFsKEi5c!6{RpTmkqSg)7Jbc#5>`3|UJ
zTL$m9Af`tk7hyBo!;kKU_=89bAUl>pA$F97P>4ZJWE6)T%_t7-*%gC^6;bCzLF@2>
zQghOZQVX%qheFD<2e9=<@Y~ohj6w=iP&$Q_8yLDF<wh~J`nD3F=Ft&Qj~-P1fRA7W
z6VR-96ShX|8*CbvI$e5Dp#+KvwEaq;5(<a)P=6CSeF~l;EJ!TL09o=Il%7_@YXHzx
zT_%Wt?lF<EPq&#jO@7BI_^E?r)#XUT7RcSTAE4^zGHBBRxakSXr<ouk{nGDa*XKnF
z!3sS|XrTu>EtH5%i8}NTJq`)BBpm0h)DSzF?9+<^b(OkKazpK;G){|4Dsxidi_pNg
zY)Hae+~{c;v!h0}E+S|oB1i(hnFw?uHsnz1ht8eTZ04oer&pd`ZMB;9BCKgnHJb%N
z*=RN>iGgDokIm3@OsUQA6D_8KY(^}82c4J$IbH2={AANtic$9IZw%D_?iJ~S`I*wL
z6=GEy+R`vQRzp2aR-pw;+lfVqIXS7|ElwXndFllyw84cIqJl8a)>L>~zrsFUEl~f5
zNl6s!j2LQJtO5#bZ~_BIGaic}K1OQZftK*W`{jvwpyOZC6x6}36<jC6d<HcEK}7mQ
zt*-FM+adPpy3wz@Tej7};*zXc1hAdp+=_mjR|;sS@)h{1Rgl%0AOh2Vi1&)A-UEbo
zZEu1i7<9TW`jCq_*9|_mH_Z0wJG+kNb7{N60)kc!IR$bEICp^~98s2GI0PCZWVKn5
zst(A&MSKS2YBNynWdu3}3N(%kz3wcev>*rCD@8a5*HQ=SI|np$fVHK9;RI-iP+FNH
z-+jFqG{X;y3BUXlj4*&Sm5{0zXg`UX4j^!`F_Ps_-&3pP$pW=u&ccg4#G0u~erKPu
zImg<kTWEvY-zBgd0h*hEEEximpp*pK0f()lfX6VXFUVRJ0UO*%OHC{(ElQ0qPR)VL
zP3RY;f#+)ZKobX`5<JMw)j143t3#p_X{Zw#owRWafzf3oXF!7l6qqEp(;yS5pk*uR
zMTsS;`h}&5DFuni*`U|~wS6HO3lt>a)<2j?hwRqSvrl)Mc4p(A)q1cPriu%Yo2sCl
z<PaBtvKZKMFoEd;Xc$o5J>Ci$VF!i1bAE18W*&|_jTHLOL_oRCqz<tc)BLc*Do}O+
zbtggF+CmbO!ZTCSQ!$p;Lb3%?K?4mDnmZ5Fyu+G0FkA#p9b`2$Avpwk(J8E#m<&2h
z2;6B(Er~DANGyP+6HvvR2_m4=&W~X8&yQfeRkE7dC~ip32cPbx3u*||fw~spya5^m
zf!wl+;d{uK1l24C@3jY2^pHUv5DStiAr>QL4JuoF6IA0&2h})WAA_dBkdFsMN`X|f
zxVQ*Z5Y7Qvd=YLjxHJS2(CzLRJ|=6J5-pCAFVqFK?~zt@Afg(hg#+<8we2qk`Tre!
z_zPh_ju@vrpQR+0B!WV|9%OSZ@rj(;J_qHmplR@!2d6s_0Ux1<GiVd?BB0YDKM-R-
zw1~qP!vh~B?3h=aSd@u<G!Qb>jd>|EhDlh9S`6Jd-2uAz9<o)VC^ZMWQ{XdMFb1?H
zA<ii<P0&r8cVQbDDeZB@7gXjXCLv!Mg0o@tC)a<%jdybP&?5vDC~6oX^}%4fctO<<
zcr7ESioqHm7%qUu2U!^dsb8LwnwFfASd>^)ln6d0r<IL?0lYN~E{fh-hV111hA~`A
zZI^(y0AURZ3<p4if~*b@Qe?oEGS`4s6wd~gO`y5}T<e1g==pd5?b0EaY{<Y;0kte{
z0G&#;0bVHJtbk$Hc2UdTb)dnR0kHQl$li@O{R^-6sqNp%pm4@(FYN3#Na0VdaJ~u}
z(?cAY2CDrb+j=2|?N2-C-dgw^5a=`<GJAfY<><xw@yYplC7F4p`K872plvRYW5qi_
zN8ErGapWWx<Rm7i#;2#^xJdvW1qWcwTpBqEl+&<gc1TME$sN$lPF7O{DYGNa`2e3{
z1-`Nl<Opz68%)5b{?cQAO<!Vk)P~yG9p}yk_~awZVyK^~l@8~zLNAiYNGwZ@2i;N$
z-PD7p0SK+JDV@IoEwDx0mp2WR5zl~DRD;@H;7|t>nC)+92vBo??GJ2(&p9Kp2)&ZS
zNOn;FQ_}$?kMPkb?}5(k0JkPn%2P9wN^^3CVtGK#h<OZa19>>7f|eO$Uef_d9!UK|
z`X>)0%b}Tr$kX+Z4vzE(4K;x7?1m(N&<4v)5CLsz|Ap<W{tGMp$r@`wDs|xteBXm?
z1`W1A#u`AD;;<R&InYUsWLAa91CO9%_#yQSxUfewvEtd!Ne14swolh&^Dt>t3WF8)
zRN0`4VK~%NM2;<@uf2!fcnIo8cZ2%TplNVO41$IlGeJc9rpA(unM)n)(?1{cOuYB_
zGR!|9Ng~1#!%FBO%xKr%flR|;AJmIv4ILw;7>pj07O3ZJ51R7_#~`R3kO?B7jq*RR
z*7+Y;nk8$=CXx$~cHwLSrCLxI4U%d>qsfr7ej()nQY!`0aHKT5V3~RcSCn?3T^%3-
zdcfu{*h!neV0~k<vJ0eJ2wKVpI!7Lqyk>!t7pPYZNnVKT0`V}?`dDZINLFAY>_yJ1
zAj=?G73^ma7kM}m$!=&;1Em5Yk~Zpk7Kq*8jh(rPpgu3i+n}yz31}G7H!(TG5l7<y
zsQ`w0o_Y=dr7S#tN3t5~cS=)QW^Q6Sc&2PMsPj7uzDf%b>aexz@Qr3rPgBiiP<F=Z
zXAGO6ekSVz1Lzu$+{*aWisaM+(CxwCffvx|MbKYRQxxP5a6$tU>ANdeIp?)W+o#uD
z{&JJ?dMK<QrK;_qEQZzZ7`8+GPHCtYCnrKK6fgkg@L%xw24^)8f!PFy#y7P(Atynd
zVX(y@>%bc&zyzkn5HAOTm%xAq#*0C-hc@70bv?-Rjb1#223-|Ute0F|6rYh;9G{$+
zm!FrJoR||2I=3OQ1iIWA(aC}J^5G}6L%j$}3nUjLVE5=l+@TNJ$ptz-Diypj1iU3B
z7c`&*_9`9+K)hN^WV-;hBrB){t<#0x7d;)+#RDBsP@Gy4o{?FST3nEroC@mcK)ZUN
z<eLd1;3xCJ8PHIm+!1~puruPo{zjA~klF*O6oXWkl-rob&A<?337^nF*a%8DAdEJw
zPq~drsD4DmEyy+yrn-%YOHRR|3Ysj<1QF?wg+ib0;0$Vosy^rhJWyCdnmr)PAU1-Q
zflLwn4qJx(9oG6FYh)gpfl3lfiuL2c?awmM6@!oiH$V#>OF-3fE_k_pd|rN0ZX(u>
zDr77Ta}Ox>-2@7Ltc5m)6QJ3eto8v?OB69?bq_Rl0UE*cO|0<q$xlu!!92nZW7-6o
zuc_w>oEP3;SPcyTO6v~THfak`BSjsw(ivP?gLX(~f(Ym~X>r*0LaL3>!z?DSKO4zn
zsE^5N4<ki6Qs<)xbQUjYX@XNxVsdtB3FbMxpyCFE;YAKK#;NECoJYHX>;+-C-OvEQ
znBpq|)k@x_MJ4&gnZ=+J-mp&@Lh=x`XD^A|zk%2w0?I5Upc&54Jg{J9UOLvTYap+K
zFx>mlpeNZv&`1c@LIq?x2*YiF1~6HT6{KDd`UD`TkcBt^oVP&5KBOTGKgb7m?kUuZ
zWR1KbIe@4>ABN>n-&0<J9|j#X!2xPAfc+~A>RN*c%&s-mzm!{<1*%WMR)VS!aOi>w
zc+|of)T+UMfCkzTHiAkK$kq@@OAKkt5VdS{2F>juYy_pMOc0R{KS2Y=pq7oG26hl9
zC?dh3il-$8O*>R97QcadQC{#02~_B1f(U451}S`@iHM4JgDO>|MMR*X1IVBhMlB2p
zX)0O`+SM3z4Kx-94st|U0ZCR!4SZ;jgE|}}_pFerL(uW6;ESk1qckC)KnK|g8Jq%b
zrp*KqI7b3Og*hd*7gU1oqXSu;0y=ct6Yg=)2IWi;0X=-^w;h}T33KY%49Y{0;+e=G
zr}_Xa2WSAc7c^@RE-*o@P{?5b)vc`GndN@i!PiMpKdloOfc=12lmZENN{30{1F&Hr
zPd@|=4}pUnPC#c$?jn|hLQ^_uWSoe!2JYuT$MZAu()D5c>Ja1X;6MhCeSnDc#U(G}
zC!I>MPhVm(`*`Apf3V>wGVKE$2LMXY2S9lVv>*Y}#RFND2_i7<gZhuGS|2GV=O&h9
zz*^NCK)o4IBg;3jBqJoXq9i=AC@(WF9g?p>IUG_wVw5V-1V&A_5SWz3EYd*3@MJ_Z
za;s-GDDbzz$KWbJO%M=~u7Cg6hI=bq?bCCun07?&senZ`HFj=-u988jHbC}ef(T4Y
zp)pQYLlY^+)AI95;&T)864N1@yAenEgOfNSn&ax0pX}M5YM;)y-))jY%tDy2DUD`O
za)WKu#W~=LVL8<IWbJ>0?MngWvSO5Tozg(-%t1wGd}#sb0`2&e{A5US2MI#TQ;g9S
zXmY2rQ$j(hPYm8}!ec))C_r^A5h)&ZbP{xqazSP0M!3I02d3xdr<B5Wn1PN6$OIAa
zaY#4=>TgQy1*I;eY7=A+4$GjPq`Z~C0JPx*R9(lr<rkGF7Nz**2c;&L7QqgB0XYd$
zu7SFBAPjHzLp@Bn8$h)JlDF|#3H3ISVGLSJ21(RqsYS(^`FWr^1XL;o*<-mp4$_xD
zX_wBdGRr}*+XI%NDX|Q6ViH!LVOR$78PfCt=>B;4{@8;20?4^l#i=Fe$5w%603jO@
z;D*8&&=?}CafMncfLj3YBeg+mID#%<X=gy{K8zV=@|FZaFM7*OEh)-OF4hOzo|%@J
z48H9S6qR6Sd<M1gz%3m_Ukc(3%vuoC7AL(-z;H)taY<?sN<j(g&OqWBt(K7gdR<#*
zhrWIK^h3>Q*Z0Eb<tU00Sco9k&Y%#0xB$cg=U6b2j^P4GN-HMvU=`54r13?m-~+be
zL1l0*D7?YG1nt4i1QF2n<_z}Gb|x=0>ma3O&<GPeHOI#nrKXf7r^csN#K%L@b$(J-
zD$13*U|)a?$6*817nIhv$U%4oG}i~Zx*Sr)A&MdRQtr}Ii=`6-1EJn4rl#fVL4(Dq
zps)q|9FOHtpOe)=MjFsUIv`pZWcLlwHg|BmBJ76bqYJR}@NdEvtB}>mg3g?PrvE@^
zV<7J^@&twaCiv7N9tTk3d8AfeQF3u=NofJN5CN^0v4=NnK^H1yf(S(ayk+Oz)3rq`
zw21X!P{@Oqwt-_EWG@cOp&kcy--#%}a`MyDK^-;Z<edzfYy};XAC#Y8;*+1Ao{F(#
z1XR6)FnSeFfvuq48rE_NWDf|#?SlF+jJR7V(AsUunFW=wX>I6v(DA7i1-XzZx`G_=
zt`SfL0dWh~bpF8(KI{jrI4QUs06N7Eb4z??US>&rJSf5CfJ#wN%O28+MNjNVu7HLJ
zsKr7=oTG*asJRYm`{<Wcfh+~Bhz`02s<E+p9ch^o)a%r+loP*Ckt~J!l%iTOxhTK5
zIKC_~Co=`!LbwbnSU?Rp^dcQnvcX#j@QnjhcL6A0L(&|G1+J381a!<5$pz3bAggRe
zD%#LHu#Z5;Wq@KL9kf>%bTAPlEI^ZlnIIxvncqb$^AWE-oB>S&)ON{U(C8GX+JYo}
z(1}x!W2YdI@d0)s*azt8;A9<afEE(T`FUxX>7esTOHzx#?f-S4L<y?7A+7+G3COGY
zG1{(F4+T(?1dT95>@NciqkxEXSTzG@KobOL<ecQ}j}#2B4P*a6<5!@b9w;H?CT8Uq
z#pfW;$%E4;h=3k-hPgNaRESXKlGI|vwu)NN+{+tyr4O<hva|};dx5)yN&x{HSpk)W
zYe1E51$-I=Q2@g&hRox=hc$O7jRBa&+d&qCF7bmzJsyjpJ_emdN^*e1I&K9;sYpY%
zIf+S-v!Xyn0i-}es|lt(eQoq-xi)O~r~$0KLs91q)e*?e5m0<X+yP>N%S14dj^Pey
zV33viAPrv7Vqx&!(c;vS`~vVw%AKHQAL!gHNNI>D1|ZArp1@WMiBlyQU_Ca>KCB8T
zO)P}x0zB@31_P1Tf}pkrpzBFN%|ghqCg^mGpf-4`7m*YpWih;3+5pP~)Ug(n<gi9M
zhP6=Nf~F@?&zA(9;}e{nT2Ajp8Du3tSYAY2Ru3IvoDE8TilC`EP>_HVKA3>FhT#lo
zWlUY`LGg_>9zZQ}5QbY1jRzuc(91|oEGPt(v7pf_hy`f=i<Nlk`nJy0J{`t@`j5(&
zAHYn9TLbk6=+rr4O4MTTxq1<xwgR}Rj^>NnN!B?^#_IOz4tpg{<5lBfg#>7rg9zgh
zC$EabZ9pppF>HW(fUIG4q}Bl1T@}rsMgeG2%e5>uuf#b&2YMtGq*MeY6G*az<WHm)
zJ2cv;>>P0S6dd4a!GU2vG&so07LZB-yqf}icuqX%U;|L9eF2J%ckq@0qA-R`ay*B1
z5E)_dO;O!QMx}t^4oZRnDOHpdCFT`_)}MpxMhQ^ilnn1IA_kZtvGBky9Wsfo0SgAQ
zVgbo|)cv*u8iqF!v)JJ2bEHv5&~Q7(G5$yw6o3;IB32>ZIbfH5eAkX%whwBsLygJu
z4$h(;JZcKcNsv6H3Tp6!2xt$G0e0XmCq<!&(|VB2kdheedyvhUAOh2RXj&q#3k)8B
z$uB6$%+0Jy)sHVONKGy&N(7gpp!^JR0a_^v*)qdq4?V4l2^IjL-VqU{C~6Z2GQ5az
zK~a7___89z&^*}xpx}TMX&AOc{ZC#^1TqutEJ3hu|AV3%L_pWOE8C}kE|vUunb!pB
zTcqR*IyoFZNS>9SnFkpgFD@xUn)3ns0%Tw&h`=-z>I+KO@+9Zy<QIWU13%C$H=tgF
zA9yu;W)<|XF+@m0n#@=1(08a%&kf-HKcIAk-gCmRno8bB9P0ZER3{vQx0Jtv;uA!q
zS1%5mvG2NpeY(buh`{Dd_<SMNR`axiMhd_-gX6pdWHX3>-max&58d@;4L!A*(rzkj
zpa9e>3Nir||6qGTD<CpK1T=cT*irdfF_^t2plWA5d<q0)CB)v7$KNrjvVMat*`n5U
zW-xmP>Oh+$kbkj-Go*I?L91|P1f?r%;S9N)@V6awK2HFeuE;8mk!tt+w6x;XlBE0!
zP!nqg$Z}B80m;drJdJ$7{Yh9M@&q~vNUcOo`1Byy=nuG2QA}ysRa}sn2f4fk)OA8y
z!UWD!AOb$T1>dVmZJT333G5C$LxK9zkoCThQu7Jo-~{T}3^}VA6w5Uro56=ufqV+8
z8yUa^w9&BvHluiw%CSt~%p{uGOW=$s40|a}U1^!=rA6SK(5FFz>UcvM(#nOMPDNJR
z7AZ5MHr3Its{ltfh(M%mNUnNm2d$UMYFr{&k8#if=o*U>P)jQ<C$pdu(!>U>j?4rR
zP-kH7&!MzbfsN?8f=V;QQ41hDaaauXKWI}h5lvgrX-A*|F8$)tOvu(k(A01V=rCNT
z#NyPD)Z&t0kPO7XpyCvgy&$2DIdV_7wIHJ*ZFf+#f-M0P(2zy43F<*2r-~6vvk<r0
zgS{xiz`y{%&E6Nh@{XjfOcdA;YU5({D`-FrgwY4ZDA=qC@27y*Zl@*|C4+Bf0Yxyx
z37{km4rwrv9`@1g*+hE{dpH9+{zBI56k1ma<`z)F4VoW<H~=laF&qGmZ}LV!z`aF$
zhrgW_U|=W#E%GeQ%LI)}48_Bqh`gGoxF|IqyeI|~{)a#XN*Ji^4Nd@{VfsuEkscv)
z?oSq<gMIp(&X-PW{>^~)9*c?Wt%588ZTHWFoTp$8vSulK)&XP#4r`#%tjNH?06sTg
z-#I6-xHvN{GqtE#-z7CU6SS@}q$m|!86ipwu<JowNb-f83(9Ao&XvsV$ma!HB97Ge
z1ugZmiI30BEyyn_iI0a~*@edhsKdybmq4mel5;`9z6{jKQ^v9a3R0lHv`hCtwWQ6A
zK^hk9)Ug&cvW7LcVOR_GEok1Fi0T7%WCY2#$om=IgBCf1@;hih1E@lC0&UpMO9_CU
zy?_XNNDBgUPKcVW0acw?g95{HXi$I_5>OHpNPCvUL3dn&5+G=1JSo2de0>vo)r#Q`
zXbDV3cSM8sYn_AVN<0w&4Fe)en39a5)Wj6%nHvtEtPfg)gziB|r{D@~;`2Lf=8qgp
zEJ3YOkozIMJP->~5MWpW^$Dd7GT4rZyP!l3Dtth<^Fb4GDrn#YL?D**L5{|S9lnKB
z41q>QF-DM~n;$@S#Diw?@=A)K<r88o8Pq-iVR#z>+@(S?nW(X8P>l!KRsl(FNLw<|
z%>(Uw!f+{gtUEWcC_A+X=0wn0a+x3ky4mkFY}emwXy1#{9s%myK`m&b@&kAp1`TOK
zE<=PAX-G|RXv|YOTn0a^?>4B<2})^@gbgZCAZ>B@!WzD7|1Hl<XNQ&Hl%_e@_z~zX
zRiq9+B2B;(JB$I2!(vJU9B&s46abK{21!BiQu_cjx~bRoWPt}bT3Zq3UFd+}6{yWr
z@B7t*_KSl&3@K$00T1!|1K23TOQ-{==k-&dRU7+3%``NxLu|%aL`JQohITeHsLq0{
zkB3ZVd_tV61I=Az)lNwBqe!d77lBSK3WpDbgTft>&>%S*K5};jR&h|#ZjfbITL&0+
zL;X$UrX=vT7Nk{DP$sBJcotTZWTvF%L2sP_ZB&9BB&p*p^ZJpQls)wP1u0mW0#ZRl
zs|3SdLI+o3*a!6|HJf0|;n@jPfr3X0!31>B4yi7JHhrjRIjFcsiebc9B_u~7SxzON
zrx#_WfMzK{aor3r$-q7br8Y>?hFE?9HYg2G+LZbn+46Q!-{U2`#>8Ve)aOJFaex+k
zq3;1G29;YEuq-=*cobe)z?XedU>DAn{}^^b{YC7Kq{N)myp%-n)i{U_6k5dH`TQqy
zv6ZepbVa`*EPs-9;wXGAeNj;<r2mX_QW?WusNX0}FyQIfB#5WOKohc{4B`x$qfNp*
z793tE!WdM_ugH!7jng6JS466X*$E#ZgoXfl14S5n?;wK@h&_NmK;4<E@SZ!kItCGl
zw*J*KQCuFs`C%!5M(#jc#|d%*WHmFas{x-hg@%V8`TJa8n@YeNT+)hClQUAmOC3Ow
z0ErW{+yH4EVBQ8sR!bBzdI)txZemGVX<jmTp#aEIi1lFKgIohCMKP?01_4>EBIr`q
z0;KD2_kl`KBX}A>SPUs?FjuWn%VN;%B~~9}SPb<swTEj#=9Pd}#QH;5-rzf?3~8_)
zS|EV56VX~h9ZrJ8KWxxgAI1RHqoC0wtl@#HP#`NJkjAq>!&V@RnLt+sf^08NEr~D6
zF9B}=iqC~#iV8Y*4|4XN*9MC@Kd<t@&fepJ6${jI31~+RQfCm4#n2=Hiap54DwqWM
z1~j-BBn9eSfU*?$gcC54{#EmMO5y)R`}AoJUT<!>M#HXi#$v_|P@Vu)kC46*XtV%_
z8Bli+*<wab3n-_#YJ!H*Kr1-o!810&CHV!At~h#z#B63zZn-kZa!`JB%u9iI8IP?H
zFCz`kfTo^oN-}d(^*~!<azHHTIlSnmAT>mxnTV{E18-7*NB+TWneU(&16haN`A8N+
zoL@{<{|YJdAdP@Jf$rJ?mD-L)$;QM+8Wru|3>x{oj<XGnlwhEKC$Hv$wkAMhctv@M
zImP<W)%c)+fuM<?!$rUu4ipfO${VBHg2p*@tp`OqQn880dZ_QI81h!2_R~Tvy?Xc{
z4x}#oKvAzAd#VFvS0sOff&tQCz_1(YZ%_$ulaiX8Uj#ZzRyRF0PuIc@tR-kJy!r>3
z3RzMFarpte^esUgPlVj<U}Zg715uD_ueczwBr`EbKOS_(8fbqlxa9+KIg&^4xB%)A
zveFMyAfk@Bf(B)RX2R<W^l>nx(I`riFHY;tK?g-XglAqn)<b<y^~>oCOH4ua=4Vhh
z{y97xK(PW&9MEw{_?|BK)CMTulidD63J1h89MCmQK|ev+2%LNISPcyWvTBV0Pal0x
z&>aYFnK>npja)xL!&IPpKG@gaKg5IBvYT4ggO15XO8E$XL-HPG!A;hh38Wy$ybcpo
zD3pM*oO^N_cJE_$0I2T@kOS}p1d<b=34p8-0aziFnhYw0k`jwElR*g|bp1~W=opFM
z0<er*X<jnu&Io#(P=~`wpo$bvcwi<BO7kM(*h^6D803xRgeUk>qmYHJuVGah^{fV^
zMXbKYuo{{QKt~u6Q39o;CTHeCc2k&vx;mhxO2MT`kn`oy6Fq1e5`^JXk<e0~bi2TJ
zZG%!adXp4nJP5<BfO>~&lX>?*hn0X<L;0rW<`-4^=I3Q%IXoUx$Y9oQRC5N-aXt*2
zp#eZvxr${-0~F|xgapb+;93k!r1Pw<Q2u<v-aeg;TRrpNj51i8h7yaBw`GIo$srb_
zRb&_zLw!u%cpAY4V3T<m7)n5coO#I=MniG+7v){r0#I|N1GIb_oCZLh{7evmXwE=N
zmA9~RjB+dGc^Mdj%0L|<aO|Tu<=~|@Y%-ZPM|8{t^@OK@3UIJ1&}TUxA?9_VX^_(9
zH0q2x__jJwO9oP~A_4*4ErH!(0xejnYdzYUYCP6MeNWa%GGvaoAU~%vFFzO5m`Kh@
zO{y%;NG$_3D1Nd-@7H#Qi9*5x6e^H%2+}jY2|Kgp0W1yDz%k*V<NzuV(Q6?LH$Ve~
ztfm!G8xwhz+&j<)X;3l@OUkc64+KyZ2*U7c5Sk#U=>W8eQA7-YECyk?<xt;)Mh}T-
zQlJjxL6SpmqCRL>G$;kcgJudrmO=asTD_DBBGQFFWUFu<5{1pIDZ(ls>R1cf^o3OY
zfNaEJEmeJM28w0Sgk`)-er{rBUTTU{W?o`ZrGG(c5qK;a5(=R3fFyrN{SPk>;q#?5
z2?adXQW6SCDIT&4))<`8K&3yp!U663f?U1@$!L=H>8(+h_dFJfr6|rZtOfO6k>VVW
zwNT%J8u%oqa3pJy20m|t4j}@CFZKw20Gs)L0xRFBX!kkLdJ7)Va0RG*0jDc4fmm+=
zsc@daHqTJnYfZ|@PtMNFOOMaYD<}oe7=m_)m4Nb`6I9AOwbD1gv^dohqyl@GU?zI%
zy9?*B7#R5unk2}oc3^wvQ4c9fNi0bOm4KjT5ctCBp!}rL;*wy{U4$4XCPLhU*#n`0
zdq90Yq-+98w8+T?$qmpjp|n|oI>`77w4|2>UO3pWFfe3-h;;S29tKy>RN1H3<z<}m
zv)=(L9H?u(KFIpjpg~@6W<j)y;FHUVMxh2;E0v*}R;g=y6lhheCOoTJfjkc)(l43)
zb4fj1WuN}kIl#2abO%L8NI)uK&|y_2<@qU@x$tvI!1o?3fS*g!&CI}%2_n)rxn9cq
zVpwmV9v!)8^BJWUSlE+QN<pj#H30OHmi;{kT^(2hvKt)mAZu~h4NZ2G_d>path9%B
ze8BdCCNR@Y%fIZ8=~lB(ub8Hwyx<UgRSz|-1ltB0$AQcMK@JiUwNHnw?18M|rQF7F
z(5N}slVBUcE5AWCA@oECVf*xbZ4Y+z%+`V?Xv%E_<<=n3m<psFjmSc<Ze;quD$wO9
zday)H)|diPJqw$i2d|a|??VQ+E5QB*5zr+<FJT+|sdnHVY-RR8Q0=}OJ~M$RG9eW!
z(uoz2Sf$j%Fngsy2^h301rpK-d*Rguq(w#T?ayyPEehQB!fS3=_nlh)y#%rsTR21f
zi?n_cT1HSh;tF1g2A+CDoD2d^TZo`OeMP0hS<2Kt9dfO`H$_DwQldsWIvR9t7S<XW
z!){8v4H;mEt+&c729NlI{0Xrd&D)MUn7F=vQM6ANd~CY@)tWGvx5=sxQLIKDC0qgS
zDPYN17}i65PQg0TpP-&-A-v%M@;Rj41F!iZi=(+<UZub;aZo}A+XX70z}A2X=m|xS
zU^m!v!HP&qN1DOge?hHHX3#Ocpu!on2Q3%0gQqOD2)dsgw81<RL_k-weuSMv_7T=A
zp_((Mf|dh-tb&wWAQldrp%Dw}tx?h!L%Az01(d9gfsz$CRfCS-&IA$A;Qs_W`RfzR
z-;|akh)E+*jStCCXfY2OVgX_JFbkxrpsK$cK>h~p(T9XO=-Agx5P|7$XsFX@&>gfb
zFz7axtOl<yVF%Db2fAqF4p8i4jSLKTK*NKq(haFlNA2oN01dngz=NTPiGd*#M5Gs7
zh^w{vQfr@n?x$uS^ZGVei9_`T4T<UC3pZ|p9I^|PAJ7sdYyuOy{p1&PgA;Xw;T>pE
zmIl1jgD7|*?ZvyW!jLw$%YjOwCb;e3000rt`<A}hrBB%Bd3iy`KbrV`3##99KuG{Z
zq}%_yw8-vct$q3$i>pyHTH9b10a@1;Af-U)!6wD}P)2-Vi77M{oB=KI0F8+FC6;BT
zgNGwS^7C_&5{rxiLmbnIzJQK~P6Un4W6iLT1|YoQxdAqmMR{)~7nC<ZwI*bg3Y0V;
zJvNAy2kfA|i<8j00%^4;sCco-NzH?9;>DvCTD*|eokogWqyfi$;9Yd^vIUW;;Le6I
zphYYd?Z$a6Hq1)s$m0Q6EKzEA5#pMao1iKV<XcE|fmo216{v0nVR-#YC4VE?jkBE&
zvKNHmc0=QvtZ7Y1V;S|{kIcN1)S|S+Wbo-2?4TwdXq8TUKv8~5X>y5AW?pJy5oC@b
zAIH!lQr3Y63~d|-IvAOx98A?YE;a@R@F7lNIhn=Ky_Sd+0_!WIWoU}8UqYVd!#T@_
zVL3F3P?|NsO?=1!n}0xA1AOcQs9*v&x4{JThT8WS!y~kB?ZYlWfI9)|Ys!mleozk!
z6u0Q*06d`4nwXSZ3EGp3HECm53H2o?FA&kENlXHF#lemD!|>5c&^1<>AR_(d<O9!S
zY=Z661w(q{cmuMatwp4Y2(+@nCbKv*uec;JFF6%jAAvOEFahd7s<$_x3uqNUZLVlg
zdlQ@zLD2*$q~R?-2m`+2gwmNG%+;Kr^NfPPP5@WSc$@$Y3u;_cv>w#L0<HCmFDc2)
zOUp-3n~?euX$>c|D5i$p1g3S6EQNZQMt!b$&;}aN07OP*L2+twVqVD*Ty=$%x3m}-
z7~qE~r)B2kKn_y|Pjcvi_74{&Rw5m%3{LPlO-u}#AR=9rW%aA4<-6?R3}}plR@#F1
zX`!5h<(r=ZIuR?yw=}0D6MV-9Xh??()IyC16%62{3^Eso;n0{Ra)D-fYGQU#Y8uE2
zP)!hY9^RT%1kFo<i1Z8bF9a7qce95xpza4{FCvPzc&Hu4kd38Mpc?82ye|f_ArnNv
zO@}j}J|S`|cS(MBYF=g)xOD{be9%$&)^t#X3^~3f&F7$0`sI&^OVgoVA;S((Ckm-1
z0PXsMT(<}BPs1#O4Dyg+1xQQK75F?QqWprCAxN{dWLp6`kSXW{sFVh$E5tkwh857*
zAo5m<qO@cSBO_3<0$;YQ3y%wgCm_ZDUD&~~ccFc&Vv=nDwH}dT0*?(4KOkjn&`co4
z!AHfZC5|N}MVU#ZC8;=e?je<=&=f?24zUTe&kh=xMA`<5@E;_OklX_CUvU&U+rg2#
zn9yN^V*SFB_|z28Vri5MYC#Twgg3aL192fWJG>19V?e`(R!t0$%@Bux6EBF1!y(Xs
zA#0!&T*`yjvgxO%mgHpSW|n|wi>8ALGVnkvI2b^iRWm^Z{LVG#u>tUH!Jv2~qP)bm
zm<~LVo|~AQ0Xek+yp|p`;RYGw1=)(jYKX^?Qat!jvfR{?jQkV@^_=|j)FMb-R+^Uy
zI_3<$7W>b}z>o<d(&M)r=YILY!5+?lMlPk}gpl>JAQDts#DW5K5;5fwq{ySR>x9&^
z2em(g?BLt3Ky9f^5CI*?K<Ye0tOnJWBu6JwE`v|kaDf(t1j3gnw}6hA0}<&5CX4B<
z-oR-OXFx2bqMt$2TS1_;ijaf^TD=81CLh*uN{2BZR#VN-e?d!d3bFVZcCK1Fj6r3K
zpMh3qf*J&%-TlS+IhiT(pxZGZ$25YA5m3-)f(Ymg86#|YDkF6D1SsQC67jH^eULdw
z<vOU5nF%7&AvNP$Sk3qrIx0<R;1`2doq;mhJ5Xct1AG|{*w-KeS4yX1g3|?A4Za~6
zWFff01SX*4F-S-HLE@dPG7~A2Atv|zKsFb_+nqZ=xeP==?;ql@hch5HQySXv?5z!&
zb1sEj3_3m$a)u&g0_qEFS?CvNG?R6Z29lT2FKu-LWi=*HyB-|mI~W-lGC@T8%uk9d
zr1nbL!x<2dQ!%!|txJA*E(cY=kVU|-rZF^&eS~@)lp{#aSy+xgU<1XqEj-9U7mH<r
z2xtPwxE&VMf+X2qq##Eg{p$q{%7dCBkRlZ{n*rHD4vB7>trM~Z)gt%cenzz9L4(pD
z3?G=LPBbIULx5)1g391ECur~^6GT9R8>xXxb*s_VJ=5IRNbQBUpr*<?c$d5mRG@$e
z=tMixRa6jPQ>{S3xvm0|!k)ki6xg*Vl$N0dm7oI(Koy5OC|})$r!i0}hcvh$JrRr!
zDWx_eN^ee(&4qBAGeCh2BH)wOa0ZnFyB$<|ro$~pl-@WhY0wfAN{SMA-wSz(6nJO+
zDtJ;ulqopEozfy4DQ|;n%pg<v7zB7}DTsh>9K<NjsFuoag982oyifr($RMpU*a|IZ
zjKh~tkyUvh72>dF8DilhxL=1z<gli2`pfu9r$mb3ml{*c;vkTZL93)7H3uGxA-%d{
zN-c)1sl5gos{ae>bb);gnkC5u5$V2r^8_VcYul%@uaCV|5Mc$Kb)eW{SWx3!QwvYm
zkdcI&l(Y|!q8R33(E8z^y&w;Rn?#^nl`=s@y3M?4@;go`+oykR@-P0RWK5+NKg{0W
zpnwKh1c_nL23*K4T!=S0?bGY$7d~|G5Tla4uy7{4rWWD=YK1e*UIJ^EFTi>@cc~P=
zFnbYM7#zQdoCHZ%7hvl-Zo(EPP&$Z#y5<Xf`P>fpkSFMP%S;fFe(CqI>+>R+?9+<^
zb(OkKa>McyrPhN+PxRqCJkahuMYM?^zK7S+w7JG<4e0nU&}Mtk;?v5U%v?xX2W>~l
z1QF2Y(IfPu<0&o7U~wM_%44tK6*j0H3TceOD{M$P{vDd;C~c!4E@#^bY7nl6ceK#M
z9ckq{q>Vx?AGd;b2q1inXdS>ZH9W|n^#-Ng9pt9T9gwFhK*0^JJHYu3M8L;y()V9E
zKf5a=0+!aPX}Jq1uCKz|2q1eQ>$YHnM{vubJ|`=q!M65c9QBq5@;j(-fz0uO;xiLO
zK-)$l_Rvc;;Wxcd*Lsl6NW<SCn{ikV^*!y!;lXJD<St0JfOlEqkB%8Z_UR18x={_=
zHp0>X4JJ!Wp_3(`)Q=P>c-#UF6teO^%K9wWjo+YA#S#z^3_gg_4RpnA365ivKpk=r
zhIh)Lqv_Oh4anzM0|I0%2*a(01O(C?0qBAz8_-d_pmU!Tic?cl6cS4m3@Xgi%ni&9
z(o&4gQw*RJV~9cywlxNQj|{QXd5L)``QZDLzzg?F;5`mJHb5L+O!Z_9O-hZRl^me;
ze(|LRph2d1$g~OcCKyooLQ*;Wid7f`8ke+k7kEY$WId#21+j2A1R6jf4J7wTF-tux
zM+@bHO2Chxkz}--4{2&3&6z;MgmzBBnej240u2?ivKmrn0x>KCnkEWD%mjiaRWd;Y
z^q6jW`}EvnnH^exB4Onp^{htQY67wqht*JDlT`&F`5LvQs|MN>SqG1Q(1O%V5CL!C
zrY|{k!)ZdZ6U^tJAz+e|B9iqe_y2wXRsRn_Z4YqGk0>c1QGNin*zrHKsYg}In?RN$
zdg}<w;jOOcCfr-Bmq<YSa#XdP6EvrA2;PB3OaMbH|7n+=@A7?3_7)vlSPpJsV+(f3
z_~R8=ukJ4`g53fX?4TkKQutvBc39sE(#WQ2u;aXN8t!w*u5wuaozi@cI1{uFH2#?b
z?^}QZ08-||ZH6624$bF8R=cG|IXRh0pt0Q;&`l7agp^iNP+pW+P>_ma6*;{Bc!0ze
zI|#eb)=nXO1JAfH2Glp?Ej0rTg9a@El@H+BNe(pg1tQWfdv4n`M<~D^&VbfV<Sy0)
z7eDtvW6~fZJ=gr8x$k^mdpHA<6iG9E3aDxohL8C=fJ_Gw>E|9sy-m~?v4=CDVMNwR
z#7IRebmSR4i~wHXQUf|w02DWn?hS|qNpnBv?2gs<WwcNKl^`j->!v@faHo2M2fCEl
z5;f1`aR@XhK<odAsAbafauZ7+m*s+nv>~l!a8f~70ZE8RQ&`ZXVoKZ^EBNKhIr)hx
zkW+R+W<#t3CmRqK608_jL48AH;!n=c$w>tt)e4S1&^k--t+)O~DXB%NDK44G7`q`L
zHX@w{5A_^*HiCSD$74v=Ks^RpaYIBjp)QC*O0keT;^Q+C^HOpkVXFu_&=gcDdqBj3
zQqxOw5{m-zb1I$T!GoThA)$a-tWn>2pg6%3LP$=4h7hfC6lkzNhy&Ee0w;IGAlVzg
z@_7~K#O$GW)M`VsHd5yhRCZ%5nM|!nO@`gGi^njiugF^AfV2z`dZJ88WkD)9sqY3=
zj@RI=P|%`I$o&)>O6+XBCkWa@2VRAszA2`*0}g{6@DMcM3(nGb8~_ag>bAmjK&|jc
zpkXj@Y=SO-$OIATEj&+dK8^ObPd}Y}_Jl%tBh3F)o20`z6%E<+BX6JnO73Qi>G?ot
zI}}tSQ<CG6b~>#ERSNd-77?Q713T&_U8*D1xP3_&)M~0W1aUV0x5wK>8phnUgDxie
z0c`?P(cim4{+<SE)PU0(!rzcvrM2zTC%wGzveF#pZz@{76=byomLP{)eOWtI@=-)M
zG|1`J{C#2vP3rMbo2lsO<seTxV)1lIT=Mi4_q6QO!|lyZ_WuZl?cS!;ZluX~@QlI?
zP&W^pu^T}<$3aB8j_17BzY7ZN(|P~DWaHX%0hY0;X!j;inFgv#AssD5sSY`?mf1d?
z^*{w1?_XzVkW(7jiA5!u#o&Vk4L~6d8X|_+44OHEOrJq)7Pe1cDsV!z&!zxsGZj5u
z3bJ|&s3rktt~5}9gNXEdg<XA5KIPk|XDvGKE?Ia17TKUFT*w|CFbV2N9S1esi$P|9
zBMng*B&@w<s<`%^eR{vK?~S@m&tQ>;#SEM`E@7Aftx(K~YmTBerJy&~fNDde8*4yw
zcgS~jVzzxLu?($|h{rOh&p-<iNUkza?vi>8Uy%on5ReOy8{F_=BKVPElv`N@I{P{R
zUP$Ay66#Aza}T5`3`%|rK=~Hb`-e1h5d$Q!BQDY*hZ`Ehk{?wo#?zq054y}5l6Udg
zPKn21W78!?sj2!P0_1T}j)gR?(V`jB_zAR6e^izc(X$JF%sp9+FchniCqfa{gIM4M
z1}30wFC^=sKBvy{zCxgpKv4REL^p^9u@=&+!5o37j<ukj0+7H)^DTz8P~X}SS3sd(
zW{e!dX`r<A08|r$BiM!kbZRIA14BA!`{Ei&Py2Mwj~_X2Rz|~8Hd$M0VY3|2Ta<l4
z*$EV?kOYm#Qm991w57HIbgThrk9T@%UTQ@_e%=t=QCkdZtP)X{W4moQ0JJy`R4rmp
z=a`ig1$Kcp#NbKXNOnQv+=#fuomHHlrw_Vv3_217DjgyAfD<$*l|l0ErpA(unM)Py
z(^K;`pLg#Khb3sxq1r@*AJ`t`oRbX7IiLwLNFv5#5!6FO*6{GrEo93;gKt=U1zFqX
zW}ohTg57p@Xg<tWpq2SV_zJb{0vQe{Ni8lxzn2=+Qia%$Ry1MQ5AiJ0*oHFlKJU!p
z#GJg+T*&sg;u6q4xumSr<dS0O)VB^N14AZ=NQdnB)wYK-C>ll!WMp6{25mgEfn@a}
z=ur+Jt8mx{@kp^Jamf%hAd$w?Q0MlVK&NYfZZn7EI7G<@nQ~BpEs_j|#Urgo6%;@P
zJ&1s=DH69&_mA|wE*$Glr49hnsKS3xP67A0z=;Rc|ACyJ0$I8zWS?#pxma$|MO#=0
zfYOE=Vu}Y;xI@w{T4@k|?w(|BXp((;<;nj~-rrjY3vWuTMoeI%%_1Yp2MnvBDUE_G
z3*O3_R+OI$9mNAR-L`@Tusq;hCQye6vMv_hX8qpZ`a-}$3FdXOx_po#18hH%-E5%K
z*|x&>D1pl4Ob~(CyaMTVex_t72fNkoAgiB%y7%D31}C6aKd^)DzGR1{bL!@APf-5W
zg7;AnJu&#^EQrU2VM!e{`$Td%4QnsM7D-uwM(9Bi9F&?~lv-SzU*wxvk(mc6V-N`s
zqZtV;QmNtwkTsB;1`cu%7gFzH*a}VVWNqqzt`RFqEGgEHPsuD!E=ny)1u;tUz@yfn
z%Oy)d6Rrh0iFwJ{q_pQ~;vP_`gC|rlCnw0dmjJ1{DJaSZ)u);HdBytasrjIS;2)^9
zJsC9V333fMQosaaPd#LUoefqH(7+9#<bX9CT6W$&U0cLr4?Q}b4VEv+asw=1AQqK^
z7TgtQri0UlCukfhA07^%@(5BWLDqM?w1dviz*m-%wW=282Jqdp`e~UJkm(dq&?5x^
z9?PNGgSzEXET}Sg0H4iZWMyE;1QF>+|81)Nw4uR1y>VSF<KBYhurxr`0SugrTi_=|
zLkbB7Xqik^%W*Dff-G5oj=r*nx&a>y3V2;mEetLee}E1E0ukvviqHSn&uOqvzrJVH
z=BR|_wD3C4r8V$yhuogPN(;+zE-HdFb)UfYi~O{Mrg%_gX;WNMl$n<fxicDk;-D{R
zH351}2A>gvF`zXWh7m|v4pam|$~~AS=$Qa_VGf|Qtqoew3NC5DD>%VjLr}H{TM8!9
zuZtQVy4{>$pPn)CZH>KY9W1v|n&u#TRX`-Dzz6vhtH&{{hlV_8D<s;r-G&B`IDnXg
z<_*wFXAp+3c7_H&S@{-HrN@JARRMM5!8eD4d;vbfemT4$2l5mo5J5JBFx+;CFOa&N
zpmU>a;*%kB*(o{s1)!s3(fj}@agZuts2_-2g90%e<p>|pT}?<wPaqtOVHL!|#h^|-
z5pBQh)XMVwq7+b=;9OAyslt#>dVu<d%8O1gtbzK3teO(3)skM6nNnPk3O=rp0n}Up
z9X5cT=^^DcyM6j&sre`WRZ77!4>c_Z9qJyW1R4$n7uSe5#IPLdbFvnQ!P*GPMft_W
z@nwlQnJM5?qd`$z2U;=+TJ9TM0=n5QEi*MGIHNQzEhja&ASV;YY9pl4Gibz9-CdyY
zfD{HG7C0w@3246x$pz4m0d1WjB6Xt<oIv_?pt1nmegXvuC=MVNqvhax4952l`t#VQ
zCyKE2=f8(<q$MjoKrDt0eZegU`4wV0hz0gAn1CNc1-BgPYm6GG1XQ^CCg$apXJnS-
zrB;>(r52awV8kOvodk_X4D+zM217U0HPoqBVBO3(9tMV>NALzTC}JRi0jbfiz`EAY
zVQW~aYrQ9^U;tT)KH!RBJ=FJPr9h-o33K-&I0b^nLeX|VLfTVIuo8({7QX-uoPmbR
zAU?)pG1SMP!wreZbI8X!g0w)+Lj-#c6uyv1h16ObV9hkxG3~`54J6tDTHb~=-D21Q
z^#`S+a3z_^*^tGHk3m8B8lGkm{Vs^rnDsxk;;s=ihbRai3BqGB)X!uM|G{b!aDj!=
z8Uan8Kw2Z<cm=f_GC>4pc?~UTBFGu&hS{H%npjd=lp0^03SBO%4;qb(&&^Ls%>fnS
zpfk~fMB$-<C6z(qV*_+Rkd$LwB|%XPZf%3hXhbf77>N|ckR{_34J}~5igF*QXc8jc
zdo*yuK~N~XggXI|-at)d5Jqb*Q#`Q&>TMwQ$L|IWyC!4FXNq<k9`AQxuuq4~zrhdq
zpl;nT3uHYzXzeXH4Iq4vVLddr5jh!`l382=$y;TqMa7x<d7#7$nl>o`C9^Py7<xk$
zlI}292~c7u=%Qu(MJUWxs0Ya^P9UuyQ1dG@FD11izN9EKIUC%i<ptf10P=HQ3Rnue
zpONw!v{a$8Ge89go?t+-9~unQt4S7s77Kt%P{-1ee4qT}#1hOaAwc;IgyDG&8Vb~N
zML#H;BMLiEctEOakhLHTw;CD%WUY3G^vTh#$|%myDNEJQDb6hdWdg+X7Px*vI0Vub
zMLK8^8U~<S;)!TCp!NswI0Sim9ArDzWP#xrXsA#+)B`_=5Onlq&^ORb7dRLYCo26}
z`SFr(l7fA@=--7i)vv3-+J;oK`6nnPfIJRq9OAJV>Swb0MM%wI<W71!$nuw<!5Of(
zLC%0ALr6>b5yl00l<xb0)HcPXNuXoXz$Fs6Q3<jb;(rhehZCUwCu@-wbmdZRYHof}
zB`DqZfqecRv@HS6=aBLjsRV+id+JyVvJ7hq$FP<Pz6Grnf;68&tG$A-tq_8Y1f9g_
z9Z=$1gta(*i(xI)w`2`>Af;`LeR?9GlOI9lDfWsEvvQ}p3qbuoJTZ>s0%#aeTCx<w
zj<N+Uwgz9U?vtM$Qk0lioR(jdi#;4LlRMR10m{&L{EuWa)c=&WC{b7H=z%6~9>QnL
zCW2P2gNStQ3)^Ou9&5Kx?`4mE^DS~JtYD|E_4Xj^b>Q<xAe%Em1g7;+-;<T`Anm=}
zMDR)MpwjINsF?@a91!o4pPQJO=bVw6ob8!cl3G-jn1ekDASFafN<0jwkh~g}(neNN
zYE>qr)SC%9qy%);iW68UI5j;tH4kIR4rAB}>V4|D1JusLnguYdh9&|c7X+u~CFiGP
z=A{?wLr<uWPc2SPEC65g0UB_G*a<3x!8IzFNQWPs4msi&cAGEKv<qmll}&tnW^O@#
zQAs>(>mn#0<1hi@Y9tfD6E_9<pgk1fA+ABb!Jh8Wg@K>}flLsQ4)4*y7|>uRa)SZ-
zV(7H|qU_APbWq}N2374{pgt_P6A7{nhm{bA7gMlgB)^~}GdHs;72Jc)%*)6xD1f>`
z1(f(egT_vY#hG~?5Q(7DygcI14xov<a3)4vEf8vrq^$!Lg&RQ8f|ep7ZOu=1>5y4$
zRai+(EsG053r0Z67kdj6$zo`FptK5rG;(3X?Vv;dg1*DM7NB$fAcy@yCO-bajwEJ*
z`J1}dw}DP7_yw<l5JM`Esj%m;gJ9l6hwP|p`zDa>>F|j-gzb<>$2jSby0$lhY@dW>
zI0@39ybEjgzJx6u0F|03YZroCuyhgNAqQhXi!Myvpv;Dz>R_6n3mBin>?U$RzO*O@
ze1saPr1QaC4h_y*PeG@7fC%U*wbu6OqCH-(SWFyYX^afZWI@F$!b2eAGeHEV9gu8<
zR3Ct}+JIXIdY~nydf>u4v8X7qQV&c*D~CyQTp2P!L^}KV7PZb)1qV0-8p33CKfxC3
z7v$$u=H=&RCg$kpB&L_978QZ(No!PJfolv9f$P!*N)sq*>lEkJ&+wK3i~$+*CTnE|
z#Q&htz~YR=f>iJ!CZN<K2%6f6fe$r<N=Qfr52*=|8iWwbK`T?iJ2~~iB*=(WpwN2{
z&tQoCoLA38ae4gaw}&3{#SdMX1R8B}Pc88+Ey>9&%S<hI0UxgeI`ub51?~_~8iQO5
zuq~<nrfuap`*aus;t&c#0hIjoQLZYQ2J!;<fHF|DXM!3(U;>%|ULwi}h~<<v3lX~!
z!Kc`OLLI$?z~~)AY$j{A0TTI0i9aK?C^xetH6DNJhZoN<2E+-VbrmEJ{$V%);TljH
zdJF2Jn}Ld1a0DQ#2uLA~Gy(~61C`2v)b!N66i6As3~HG;z*8~81;l3rP`52e3Z8@!
z8$#}dfKGW!uuq3EAfBLV5=Pv%1r9;bx@pLjR}l9j9WDd0oJt{xREj<Yt(TvSB@Q7j
zK<eZ}TtL=z0@64S+Ic>pwsw#KsFM#408pNSlw`2f3qR!-Vm(=dJQ&Fsnv+0jSR9ms
zzQG-Uo`R_3e~1G><v4ii25DC3H>eB)595OUkFXw+g^=zMg!rG*mNo3mc6rczDQEx}
zQi6fH`j9KKj;w8xcyUYIJ{`t@*h}5Ya|>v)Py?RlazH~nAR^r(;EzP{^&)#XgBG@f
zj#mi+T`3Lm`z(;{AR_(kOjX9LtV(-0gBG?UhPA*k4jNK|j4Ks?@X44IQDmPEV?b=j
zQg?%{KMpE_cMSeiGBIR=i1Z&16%<d2U$=)dAVyGH@u7~(1aWXOFhGJ1QQt!<KBVQ3
z5Sz)$uCOHykZE6VixYlhENI1M&;@wP0S6(7fEN>R2E+lN(w)p|4{1>Dh&Tg7kQAr`
z46+NnX8}w=kHp5fE`&PueR4)>Qe|;QY8j}U0!`jQMuosmfD_QwBp5XXmSockDr@rL
z$>vKgJ3}UjNSBBycK^@)(H_o#gdO%KUkJ#QRJbXKDr=K2^SPi+bL`V$42UUM90EGe
zEQkT#wOn+VnIRKIq(3^Q`|Nq!3wt;NVg%?Y50WDhW#Te9Keqt1FDn(ih!?yQ8dTLl
zQZb@m4@t#Hb1D$~sayCV&b0zZBIrmf$hlT-)6Q(%v)a!-9maszPMyRAt@0p^M-fn^
zHxnKKh*}3y>0|UCs2c!mpxz52_>X|fUl5T#`-sAZqKrg)I0F&@WECQiDi_gS)rSTE
zC@<mcIpU~vsS^Vz+4(0Z23~`PmcWSsJq9qE9b{zzNEm=SPSCBPkOlb#m6@P(Q6Yyr
zgY5^!2Bh5u$pRP!IhI@u%K1UA@IrB3Hv>Z^h)CZhu-g8R!3KLc0}_o?+}%14)OvXj
zPrnxh7#K1^MEasPx=(91w%fxQ5UXj?`2npZ!df6h5)nq{hpfqe)T$8IUN%JR;mE&O
zVh^;~FbK522T}%RfX1CbM0%U_ho6ZpE9~J6NDz{h2~nMoy3C#%6o;|!<YOTr$&d*m
z();hSdU4qZIlvhZ2Y@C6z@rzG+$(=!`n`WKCWq|PVGM}bRPED&2L}+t65#v=BB19e
zyhgtw2213KfWqbhJZy5Bm>4oaM7sRd4flm#Kd^^0AnvD9w-Bjg4O+B>HFe`CAE=nQ
zpMX5?25(>-Rgq-K1QF@-jrM>3e7|N7XFxnp-O5Q9R5>-k8<>c!1@SmW+klE5N831$
z-Ymfw&mzm?&}I>M|8{PEQED-W1*#N5!_Qb_9I{{jvt2rj0SR`pmKi{82M>emC+CBA
zM}x2Z28A=EBtrBlU<0V|RyM?9vb>En0)aY?%MY4pKy*bBwqp$T)53Pdu3%6J0p1V{
zCei~J?%Mg|cAY((K?~beL1`UycLgNm5rY7bYDB;u+PM&bjsb(#kdxdFM+x~%{gR^0
zOwekEg8ZDs67Wc75@?by6+VUsYF0qTz#y9?1?-`lHU(fi)5)qyk(`0NZm|Jmc{-@D
z0i}O%rw&ZOcc!M_WRaL=f8Px@{X$L4T|gz>HqaVAaO@)*fsiJ|T|2ntka?M6vho~~
z&y$n#^Rq#j{XS@w2{@#S<MT>$b2772A!|zzix_*)r~J3!wXjczF`yGUl)3@Fa1-b9
zNtlIjo1wl3-MdUo@+(S>2Ny6PkDdedR--_P3mm@@pwo>&M0$1lmQc?WfBST$fWOTi
zZ85O;CC8F(P&`_|>kJQe28K)!k!~2<aaBIi!#;hNCl8Z$JM8dUa_j)z`y8YTZxRTA
zI!z!VT|;vN!!jNn`}75pN7&2$n8R`jS#1Jn`3$NP^y9%JFJ*~Enb3^79u$_~f*Kr_
za01b=ft1)^V7t$#?}Y!L><V@QILClkkZud4#5w?*@;M2e5~sck-h<{mK=wk)OhlfA
z_#J*t2<-Al>bqba=*&2T3qUM7hk-XJy@Tw9gaL?!>;j|<Frhh@(g8?pw-JJqA2=0&
z2<RbN?Do)tn-`X!sb@7G$k(7Xp%7n#Y(=}44^nLN!g3&`B_(`0C}>$!31~;MXI@!q
zQAuiwPky?g!4O&)N>(O7s<)DI@{>Vp#Nspa3QEDJDJy~2Xn_|9LZv)GGLSk9ksdMH
z?9e)l`tAWur69GQ@i+mRCCKVGAO#81dD@dehfjfy)+<geiO)cs)&bfq1lcAFDIJk^
zh(p7HijDwHOhI-#f-?plyP*L<R-YSEKY<#M=|zbpsrvExDd0BbHc-8=9aKAliv>jR
z<MKIGZ3hiaV2ykX+aZ2OT3-X&RfcizL}E&cBlyY_=;|8K>Lke7)i%2J1yj`>?9*Wk
zXhed#>%=sTGD}i*3lfvF6Vp?R^^sB&boG6)K9ms;y`#n*6t0e-a0SOI9*1D~40Mc#
z4diY?1@)Ao#Pay`oXUcX;`oyM_@vB|+{6ON8I*1zMsE3eCF%<KDCbauZk)^n5$VQ1
z(`HLwcD9EzXys?<1&Lcg%^cA3Pe?frasv*BK>S=xR<{T?G=?}S3%b#z0D464c2ITW
z4eyztCuyWHdT4mi#u=dfc6ib@k~5&8L1g2$B%>%b5wrvv)Jm@fmFA%Fj`$#-{N!x(
zVjPl<kjBKJJ|@R1P&nf850WKN|A5AMNzPiJ_0t8FnHxdb#|V^t(26DaZC8+G4)7Bo
zDX|Qv&oC^5`i#={3A`r<+V)!l8pZ@wK+t1HGpjIql#uZwW_##B5;LrcMm2Zf>``La
z3=IKLrA2bw26+1Dd*&vlr@Ce4lt8vCfJzHUqC$)36h$kRJ{Bw3#Y5IGFOyaALMp%9
z%;IGIg3KJy>^LazfNENZ-QXe`lnNk?aSXemo~G{jSt4lsY#O|S4N3)&Lr!7$L_)8N
zlZAPmQtMHdXrtW<4-Ntlkq$p&6>`Ka6LbiR((VA_T#E=$NANX#@CIZlq&on+FBjS^
z{tfMlQrF*z-Tq*U5#3NoALOkabm;B_)OPCm8&o9)MZ@Rb5H>?f=?`}4Cy&2lQf2)H
zwVAs9#<?mT($s&6SQ8IT?PQJDL2?@SM6`IMV+Rmf4K15@Kd9Hfxls`|Qt1Fo>rP~y
z=#iXRP>FJqhy|!c0vefyv`7$H9K&9yr$KEmBFZ%^N2nbI)x;owCda4c<R@ZmxdLs-
z1Y!6lcW6~izKx*ti?#d$Sp&jwtDv5vG(n+`|I2VOFqD9<!-jXKQ&R#GOES>gAQAs+
zqBn$y+o!`AP>)mBHK1kKSOWuQE8Kc$V33!*vvX1ti}LhARY)=7gjCR4{~$JaixZKv
zA=jS0N0cd0zf;q4P>w?KIUdWQKBu%qOU}>9F9Ppl-@(ej0KQYwCo?^xBsjmcC>h#z
zLF6<@;fB=Og$4ojTw#DKlVey74FHPT`<TZC6{UfD(%^v%P;N%g0FdhdUfV$z*uifd
zq>VE`MG#VQKtu$FGoYbCy@)u$!oUFDeC?E9nwOGTROyFh^EITpL7EMwUwXi>8X5u=
z3~7LCI+U|N72&B7lrg~#N-&ZBwaLHulajK1`te;mdf7gN!KymYdJ-b49HI{w$t+0)
zoxWHGPL!bN$C?^2TmuaYvPveT@sQlak_^~VX=Bjv8mNWu3l(%qOwLZtO9?JZN6#Y|
zSrwWpsOcQgcm&q4z_1(|7NBLEXlKis;W=6s!x*R^sG7WR9xaPuJJb`@S;xyL&cFa#
z^Td!_mY<n|J@zn1NvUHuXm%Y>ydhZ&^)gv43&@f`&`tyJ_PpZMlKcX2C3g#yp;O@f
zG*GJx(p`aHdkwiDK@par$yzf98`VNRq%9{g333Qs6UY%P%&_I-cpL!@1L~FPoS@;f
zLeLy7xIjw>t#<<v>8sb?h%fW^vrk_?eHlkV75r8r>REja<ZIByu8`*YI?%;0AR=AI
zS?2X4GcWt}q>r-?d9j94sgH_i+4zAxT?FzpIH*AvZ)SpsbokjwkX!SVp;J~!1JgX9
zOM^->b5r$-b5c_apd-<sF*?W=Xj!v~Gr42x?9*WkXfZ*~n4BrdnQ`#0G#*Q!$%3r%
z6qYt2tzP)LCq&%>P8c9Aq^5(nG+Zy`eKC}WpPNV4Qe2qjxv8KN-HP>#67xV4TA%?O
z(4acjf&s$;P~X$&E;a>zXuGE<u`(w$Gd%;_-5nT>4`{U0$W5Rq!5TIg?tq33k;xBq
zD@93ueh#>31Wn(7YlZTn#DZc-txyKaq#y!%YN7+|_(XW$o~*G$*cg9eQDROGq)f~L
zl@4b?W5nP*8U?Z#M5O<#YGwV-Y-bN=K$d`k`Uyldw374lKn<9jOz=Kk(6w<v=i!!s
zTGx=7LCE;?BUr2c5p>Xo(ji&+LZiJP-<^Y73~EMYf{66hQI`Vw(wObx42bVQJr5#$
z2fw1N2z-_^C>w!B9)m#lTtG@$&~OQ4D>9@>fmy$j6@kcY#GFKM*y{5zFo18oamz2t
zO)PQB&nNnv9%?yZ11OY1K8E-mQHDYMj?`_1W)tdFZrL~vGB^QB#~=cFSc14coB=5^
zkm?#xF1Ja{tAt(&2hx=ZBH%jU45;(TD*qu(BRmI;gPZ{_`oTo{%;d^&+k`m#^cM9m
zzb5~vfi;ZCS_zM8IlQX{E`LCyPgn~%3>QGXPuA=Ka>`9C%1(uheSlUU22BUG5Wv0%
zt;Ng)5$TXhufRTih3Q#qzl-qe*{EpuQ&6e}Ex^<OmwZN`CK-rGf3Pdbt)e=?K7CGC
zh(t8UBxt*cidKV{1%|?lB?FMvAR;~N@>iGZt?~BhTOYW_JI<O2Z5S0(eS#f2*}MbP
zxdbHux6Jg?qEzR^yt2e%$8;P$OQb>!l8I>ME>e3dNVyqwI4^wSKRp#Rv!AA*o|2gi
zVxi3OPvK!;$ehN*z>uD2w2J3(UVwdi{FdX~FFypp@)}v4PDow@Z+k*HF9OuU4B`Q$
zb#QtERYQ=v2r_4avDcc?LK1NaA}Emu?SVIN@s!VyT9DG}7gRlhix$wtdeCM>ItLZX
zpaukVZVF>B3TQDn5!El&Q5?vkQP6qKdGVQfX+?>}B}JvlphL+))pH<do)F}5NM;8u
z-O2<J(0MAPlnn_2A{%nW$cueJXA=ZH1od&j5eyp2%mfk813{2R>mYW}DpP~|nxJ79
zh+jb+`AiT2t;W8?u4(uVZEaDZM#;-BP6btMkojov;Vy{41~n%$K?HQ{_7kkR_X+9%
z&;%0^(T+H^4tWVZs4@;pgoiq)83Ji|fhKQ27(Rgm33bq!F+^BaP??eszK+HbbV7wT
ze5<4-C=G)MsAn+Ru0-Cr14=odZNZs2;2}oP6^uc8@SH->7i7g5tQQFBf`Zpq6W-Yl
zX~Q8cOo0R=IT-|0_ys+Kdr}-!?STmRtP-37u>%yfL_`&8aR4cPb29T1bJF!86sQ2G
z0oC@PgDoMM;ymacHV~1{v${h0^9g%<I0IrgD8rC!H&P=9b)5j{h>ajnzJ%BgE+0Sy
zwD>`4Awz5@tCNppds=>8NqlZ%USc{VM>c{MZGQpHt$}RK1f35ECZI*rPuRlspU@mh
zX$=7HfE@+pp7-!d4>Z^SSxSf_Sy4CGao!jMv7P3@E(F@)v=@H5%RW%q0U{7*X~GWG
zghV^_s(jG2SP-Z}f+RS^d?v)_7};5exTOLGnaSCZ0*e<kXa-t|53vAoZV{w4^B4VA
zMM_f?e6`n9(4scPI(Riun1TrCvM!8`f;3sv2C7#;7_DxhbYVB7&_F)qd?BdVs)lD=
zL_C6Q2Vpc9Xb~5W$@vA~!^}Y|M1qdNTZW*O{+S>G+Ccja+bj%QMMP;V!pns?P=N%R
z6o4cj&~fsaAOf*)3buj@k~64NE`Y)pR2hITB>xVth(JvhNT(Z;J21)xaw@wwpqP6F
zx1$G?PC*2;$Y+4<ZiL-=O62jisFhtoC1|S|^x#-`P?iMk(}092xTFUWP#ZDwJhdyC
z+n~}8Z<zy`<6(q$sA<>QZv-ul1H~#NBqBj!4<g`e!{7`^>Y{c?Oa`UyT6npy0kR)N
zK&xAft<;p38}S8|If+T}8L2r1(18_Di5QdxcL69YASoPFW`HnSxdCz=5zQUQv3DGx
zg!UVrv_V6kkVTh}vY+ZbS;UMIs0kGWo<;*D?95Z3fCUlhuw|BT1|(w1>cB&m-$5@w
zDJ}rFmO-V46evf}hW9iOWigIYg96JjS44o1>SKjBq(Jjy$fr$WbS}v{GzQi|K<o+t
z%~Zhd&t?GCyr8WvkX(hBCxiGOqkJK<Cxmt+pDHME8N(A7XpA}&L_kZi4~S)8kSGQn
z$wfrU&CCPcFACngbpvDrsE-Qq$7E0<0uk`E17{dOtRSzTg06Byge8{Q7mzDTK!aVL
zxw)w+pq;MH`MCw9C8<t`eN>3NixJMyQ8?PU57ZCEIyQ{q6lhS9mFys0Hl)LnKumbH
zvjSziub`p~JVuNN4p1=$!tkOD8XPom11OPU4F{0@APjc{G#tpPQ6S*}9yvrmFlj3&
z5G3J&fL`RjMKsi)fk15s=z=m$KRjFFaR4L)kVa%dYHi|+i*xi!DoTv<ld_<*`G^7(
zwqzS#Ekiw_NnC3dWOPAgCTIj5X@C`v@en5#Q{Lpt1w{)dXhjJ)ZGw_&CdkzEKe_%B
zZoHGTPiNljH1XYY6WE9{NRo)s3QsG~0CWkfKfEFU<xt3Y7i3QQCaip<Uc0g%WMe(Z
zli*4hQSQU5d)PECq`0Kqk38HA3_%=l8xdI^UhF`&yMKWWS5R(aC+hexBJDsz^&~7*
zFHp_K`Jj;zkk25s5MoXovMlG9U3&fe!iNqXV$jwy<$eSoyN9q5bk{~Eh(L@+#H-5h
z=ANQLH5*k?{RlevFcU<i!#AlwB9wY*^*kp7LosMGvQ2twUTQ@_5p+@)XM{e0ouUuw
zACR0gF&j+KN*-L4$0LSIK-m;hfq)ANP%J>&<B+0H47No}8dgw{H93IOIf(1aK-NPX
z0?y2M90DyQDIJqSY*_*wxmE(2bnz?A$$_j^D8Y572~rmvS{G2y9XPL0gd|2Jt06&v
zRFx@%mZiaV6&I!E=9i^Hwh`xK7GumWf_nRzAOboxB@EkoC=82a(C`cCX^!{}I*?NG
z3GAYfC(y!+oZ$>m8#Blp-uVGle3>8udiM&eJ)8ll1c<Z)Wgl)SsEv^TAB_U-M#%&b
z(3Oh6?BEPY#1w<-TOz^_JkwE7l%JehT%4Jgt`FPeEDkz`2(-i4v7jKQ68pXE7-<5U
z94WLKl#sAyLk#<%UZpho7gT~SP6TyhK~3o(2T)56R2G0sAuy2+*=J*KpFUHd*z;;x
zFf5f)&uV=R28JL7kkx2~4~ErHUsJl~0<;V*zZ865Dab}hh@*M?W&EU5BE<>z>HKe#
zS@-i!gn65+*(anq81Se?QX<%L(8LqOa*#8?!3`$TF)W9AoUGy>$?}4t{H#>)Ar8eT
zd-)1M4b~udKLSzw!`nHqgXf^>oYKfgtU>?{SzrwV46C7GK-TyPxZ(w0XNP)Q2k4S;
z@G8r+%)GRG$SO;O?;*JkX%zs}_f&NPq78tS2rz7i1^`*3?T`S7k5A6eOUq1;j|XLW
zgw1Ga9}@IP$L&J>O;*JOvpF*_vjk!<cyTGna!4B&5#AW~LVZl+G*nSyUP^v0$Y;@@
z^b9f=5~GM_9z0ATOU&Vg8IkuFm89ktq(SHA!BfXOv6Na6I}X^TL+o&dwwp=z1ZeOT
zYtF#102)k`_XWTuF(TX$eL6_YytGUA+F&u~=T#n9X-K)1exSl1Y$ev@e*m^B1hxp6
zavMwNZet^=jfmt3X^CAyKgE=CKPKU_5fYInVWIj7*3Y4^;i3T=1O_c-gJd|+vL49V
z9?)$y6Plgu)BPiTuL~DOIY5gN3hgrh*@u`^!ebvaZ%|roBk#Ri0@|7jDl$Dl?R$ui
zK`9tgcSCX~Qg;FBV`{p9<n4Ae+6j>d3JFjRo|d1Jl9ykUi*0uXBtVen0iXdwBR7E}
z27j>$a}YFXP-hYuc0D8L)ZU;^plSi+5b$m`Fo9SYW@vA2lDga<R)kW=de|KHEzlez
z=;%*K-3l&KK?J7tP~Q_-Eg{A%K+y(nGQ|g%<QEW|`boA1)H1^pjY#<m>JiXv5D~pT
z)G<_~wsB%bCU|w~ZBTa;w7nMMHPAE+<PsuCvSF}KFSY)Cs`*O@ECQ)z@kfxwCh#F#
zJQhQJOjbh>$;a@;Gy0%J^9W=!TAGE|PgWBXR?Xb%0`oG}Y(`tQ14^lo+=5{<)X!vf
zJW!@?QxZ!OL2X3P&?+Q>fjx~Vz+rbxK~H#AfO(p%xf2wtK`T2E7ruaOgg5}~Z4ehy
zgJ3uS>UT<O5aji-he4|{T0l3ffqjo?=|NKY1H_Rv(9)WkmY)Mz-UVO2gvWBIJ
z!is2HP~!nqjG^anNaI$<KD~?i#rMCn;k$0BmBW2OIUHQHfMXny!!ay|`Wv(&ljJfD
zHrxxID%H<TElEr*F3HSIEP<XZ0vc+E9Eu5!dN=`H2>u+l>XQ?e@M+=-P#+rUt`|J6
zfQAKWvwjYsEi9lc9#C3TkY9{(bP8y87KGt5w9sTunk}I7C9q~;kkKFvHy-K<vKD9~
z71^+pFhNNfTwCd~F))BSGz{LUm9Ax2uA>LJ0)*i%fqIncE&(mRBqc16#?L{G1Ni2g
z;?g9@;CXy;QL0{WkZXKEVo6DAQ6BnXoU1`AG(bf9`{J4{7Red*>B5PJFKee}!V)f}
z)fC}%iBcV@#_dbe?9*Wk$S^l(oh`}v7AcxR3$>vmH3bmQgK8|$_WU4FISgsrA-a)}
zi_Tf?(_zQkL0kbEKp?^ukkwn@3u11<N7=yz6o`N}95Hrh5Itg2Q3Be*403-NsQ5#y
z?nG>LQncIfc)x>yeL9Q*aX)QBu@^KpqywKt2K8PbOQs;Rj}rFj%N)|uFIua@cG*$T
z<NzOwn3A8I3|VgvvMUHQ)(WY3SU@`+Kt%fQ)9+3mcA9ISp1C5>!)WzpSOO+%1t7G^
z0onNkp5#C}E*})7NV(Jql!rh>y3EwsE0*qbwNF2^Z>~#AeIj(!iux|Vc~U%N-N0Kr
z_}Pq*R6u<foI)KOK{UJ|jm|H2>B1kfRX7i+(ku*c-WUK$gFoz`TVB+mE&xrl5s{No
z`!*=)0M`z<{h+qcE>P1Slrl2UfYJkqNH1^BSesEi$3C6&hv_7ihnt`-p?+-Oyt@E0
z2hL`nzO(CSK9{yB&0GN5uK;Nffx`fi9$<^j)0Y?<eA=X@1}zbgYA(=(9lYi$El5c$
zfzJG5+gTa{x*h{Wq;GgS^>wbXt38|nt(0kUs1MjvAOgD17-?J|lEbK0O@i86NQDa`
zAba0#UGV)F(hv~DVtXPNa-kd$4qZwCo2_LAC18+EkV+9WF$|d<hKzJ_+CzQK2~9pk
z&On1!xWdok0PUI#+6x-f0R>}bH7JvS2<XJKls%jQ@fWCVOLBIF`YX3GKD8n_wV)(3
zKM&l(15K1*4OB>Jg>lU*H3D@GsHVmps2D5NN!(uo+D{(@I@A*qXP{J{2_m4g3o`a_
z1|&SG)BS@Dk%3P>0&Q`J*bee0q~8rmrAQ-75ZlS>{Xu5EKu2DIyJ4W_56Blmpw1V>
z-Upym1R|g_?E3a_2E<;_pf<^=2-f+-SYJ$Wx&k?$bmxFqrQ{csWaeg8rRwM9=jCMP
zfdb<)XfMWRcykj}azM_0fv<CeF(83K>6Aax%@pTA%g;cYP9gDtXh!3Re^AOK+2cr!
z`DE~vBxsNWYc_@~a(IszaDjN3YK1cBE^n;08^pU9g)&)%7m{~V@{@~8ilCd1K->C*
z5c?BAg>NQ^fELOaJ50&49MaGsVRHn;=NPwBlQqDH<a1E34S9PlXk9c?$b&YDWP%9j
zZ9o{CAt|5sdj=}Ex5IN4Xc;GDEhnU|z*zD{R-Q)kadAmyPAYg=1t?F0XWjS0+mZwe
zRuXFsP>mY|>ODa6Ey8$6O^>nHh0+`ZA07vtkr6Z(v^oY{{o^SxAfr4;?L$xu*u+E5
zoX{)H1Ko0hvCJKk8ZlRyfVyQwlsj3)`FZ-FvwKn?olJ0JMGAg00lI6E?18wpn7qyf
zG5c6{gU;;+opk};w*uP3l9LK**ElEU<bcjAg>-`v5eW$iqzV|?ET)|caW<|noB|Cj
zvdRcZ0R^uGVGK~70UbUGKIz*ZT1SQ_7UgB;VH~vs8o~o%^kF<|y9b<lK%s)3$v~Ea
zFx&yq00E6h5s^w!yQWA*2=b&vI%r-1bSMHOVN?k*Fl2&=bOx!V-v1|+*uxpnghAHO
z43g!EMJ1WV;N4$)z$GVqN&++toCzY*mHA!7G9U5U!x@n3nbMLI)EL!|2a%vu5DOZU
znglPm(eo@u9Zgn~6v^KOi6t2zdyj+ceF*Q^FtIT(WP*tFtI^ESsu3~va0bNJl-diM
zV_gCYZP4%@B$b7Ln!+FgI`k$A8-o*tcG{@e`V$6Oy&qnfBjz0-RU}4timVKV6x8UA
z0v}KsFM-<+sxvb|1bklyoB;`PN|QL^mO{`GH%QG9#GxAypJOx*sF}0PLCt+OxUUfj
z9AY;{s8i}|M8?(v*$mpI21#n5b={dD0=}CQ&VYnBrAus~Gc<@x%b;hffmZwn)xonG
z$O#a4;AsET${pan(x6fv5*~OeUr2aR8WV^W=AcX*<PC3_fGQct7Bw7cfST#g8I%f2
z;RP(X9S0(yU2crt7p1;NtZ$wLDsNW8D;9#`P4#$(UYZBGmN&>7G~o~OH+aAUOh7kK
zBQ*;l@lLIx{vpW!n(*ls#GE%I*1y1Zp};onQOn-o0kT&gG#-hqYJ>P!#2$Kaya=>v
zqjvoQUdyuwUebcj70Lt=>GEH%YwPULw}&&R8_(<585n{#z{@v8?uN9aG13>+dS}l;
z6)Nb)El7a|8ga-35zsSZ`0U{fh=-}3!Jq{^&Knvb^I90aeM*ZM#16VmprkGYk8;pa
zmzf|U9d@t^oB;^|YGpByIY<o(9nf?ph=A6pM)q(9#9m5$j3`t=lcq>EqZg`FZ!g2z
ztqq`|Lu`p0lDTQvi~(JIfaGHr&{PtLNZ&pE_Y(~V7kfAZ64q2t<<M#b?e0~AAy4h%
ztqPRe&2d&^7%@+6``tiGC7IyW8EA<`CWwG8z2JbY!Qg-v@6=24k3oGk&~z=NoCA#~
zLoPjlZL^22)PQZ2pn3{`mUE!P(1UE?qkN!NS|*5qo|!HTJ3L(&8UoagfecUz(1dR}
z1@)aE9Vp18y{J8O_8!)Eq_+KGAp0xe4LZ;guuKq<-m>%V>DnR@dpHA<6G5dUB?Ba|
zvS%`=>;c`~3W<M27YfJlCv`?BKnu-4BLy=+sqYUw-6L$rQN2^wcC-^42{tZhJY|C;
z#HrS#LAx?mkC}lX6GWuHi(V@{{jsY(oB>H<)K231pd=1jtp!PHh%Cm~vAarCQr12l
z#(>yQ^`Zt^*&G2iY(P~E#2p|Oq|XA00E~qUpkj!UtcE-mDGw^>*TZ{^bRQ?W4vGUG
zcrlAu*8nMISYX8r3$&P}ZiX`i*)9XO9Z}gp8naYC%MCF~06wnpF5G5BxI-GVAMK!x
z*^kgu3qYd^l*BpG#4xy3c>vyyMp%s_xlujYLu=Rzpt2UU#S4<`O+cfgAOhZzfioc4
zkJ1_jbR`R@jCTcfT|vuuA@+h?0J&ox(z?ays#DswLG)nSP%}Dc!8zn0V)#}F7z5&C
zN^Q<bO;62B0r?rc>=jh7K>UnYlL}vn2V+2N2KDSINpA4r!NQVw&~}hS@azMqIz?I>
zK(KzJc7g8z%I>Z3&O4&OhopQ-*di)PXkkOmf;JVD+Ce^s!~l473PeDc2w)WQ)Jp9O
zK=vYH9#O<WQaeUpj*0~h$O}lFInd!ZkZT8Uj0J<#Q<CUlO~}KbF&Yw@kd)dBD_Gb-
zb?HHPT?(4m&IA$AqFm4(&VXbxO6`UD_bRwRgV!aXwO5dXrXa~0Y4r(oGLy)&DdWpi
z6SIp_(~48UxfgtlM>fcN;3^{+bcj5NNY}K<D7zb-Xb(NWE)BM<n>=ekTc(3L;F%P(
z06!B%q~BvOzJJi4$39&~#pP#{m=H9Rf=(wSBGo|s0xAqaD@TI#;1!@E$Ri*keamwr
zQ_gz{_URSu6_*aBrjl(#9jKt61@{9epF{ThL1N@4?0oRMuzl%d<vPUTyZFp}_$tnu
zAiJ$WT^MjQfwDfNH3@H7Kyv;UJLv2(73~I<Z%73^9=o9_k<#WI>V4v!pt4Q|)Mo{I
zeFX;tL#7!g14FvB*IU;uvd#AC(mHdN{7jw&OQF=Y{s?HnJ=l71go9?gGeNVF=^Mh{
z&YEz|!an_Q{AANtic!#XMP1two(J0w%6yri%Z<`IE_-g<HOI(4eU{RJm$7@|X<~aF
zDCoQ316JUm2NCHfCnUd?Jk*E?dT7j3+O|QAl176jy{^M!9g*lDef9&ejeaL#37+Z=
zT0;Fe42M9&fP$Qdd`>E;OmpXCU<i5wFPuRwtxOOBEt9{&j%iVbC4DN|%?7f2D}2Ti
zWGxQ6q5dYTT?JWP1i8~hKPM5i#th`~C{S?+Dt91@X%Mmhr+b0n<Q4Ar>BhH3e<etI
z!#qw_!b7n<H?tUgIS$A!tfd`>)lh#^n(%O)YlUzKxZDGAAw~YZwK^9g(;Q))X!u@!
zvcdp)wFmgZG*Iw^_j-YYA6)9-u^Ji%pk=W{RM(&zf<UMHgYO42V`g9oIsz(O!2Uxd
zFpTOHTBU)8s)?`!vL#Rnl!AVNDon5!@R$zu0$JrYBn3gwRYx9yISVQ|!669t93ljb
ztQ0tZmj&6U_w07u61JrimViN5ruvrVCgtlp=Oh*vXQpMQ78UFJ=BI#IDZZsSC7JMx
zjc^_>fnhi$enUzbpru~=@v!04a!`$U57aV6^Bbf@e1LIqGg(24(|WYw9YoM#SP%6*
zCD)~ZRtyF$!0A`a<(`yS2J#Blq6gwLB+DQ^Lt3i~nly&*<w`6lNX<)uY(z#riasT^
zuoSe#3+2i{&`u4=aYvARhqUPkn(siZ9U}5wU}<V8IAeph;04VDRo&pMhsOelH;V1Z
z*-8azC#RGq=Af2XXlYWM>jt0O8>B0Apq?TtO+vCew6;Rd?knKg9qeULL_*4H4C|p@
zCbEB%lA4^E3-KXnK(hpN?zm@OSz=CRiZkRoV)P0UBdI|BOS*+6xcr1+1;kHCsZj;_
zOxdjbOwjiCGzIn2yv*eM6pR$u1e$vX5$R9WuiDA>d)mVpP{)(itU)S3kj6=ugJz=K
z;q?M&XfhK-K!+v&*})l*?mUq-TS0ztW(90zDtN|c1-zrk2^s?h5%5V)I0Iq_rE@RE
z$%#4OoCivGNCgO@(t|`9#so8^Ee6C&#h0MUJPV%ZLAxU|K?HP#7RGiJva%mijH1>^
zcA&~0H0b~thC}x^Qo#uca4M#!CQy2M36E>g9-vGRk^Xbe?pS?aL3=m@Vl{O=4o+#H
zSciBV#DbJNkg^<OTM5WzL=@#X7rdkFj{;2`Bkc(U)qI&C0-A&DV2hXRpnX%SC38;D
z&=_d01>$RPkqaW=*$U2pggNz+InD*SGe0S=klM>-pAKU{Y^HR?9kc+O;8_|VucHk_
zL8AXAbef5*o*7bRL$2x2rWO$?4z#}lgh4GP5X}Gy0ZPLjF}Vgl-xG1tGU!e>$i;4u
zoTdoNX^POD?!}a5HPm6K3!viyL0h6iiW2jR)AEaQ!;3%-ts?Xq7%~utGz1T=#Hi~W
z(3~n#QICiR4C^7ab1`V?3lUi#bkQ;B+FSjc{6y%cERfj{tI*n3W!W=%j(?W4PoKtY
zdu3Z9{6-K^AwY^%$mt8zPJvhkPSzm1Atf4yWe|TMwK^1$TOFkZ1*t`lCU9{Hh=J1R
z1)B{bp!E|{%?OPxvSz6uZBo$2Y48^O%=|ofr%3}eeeoP#Pk`)(<XMcW8RD;E(C9c3
zfmo8Cotl?f1+M-#gSM=J7UzfN!GtkVC&W6W8WHMWGOPn_j>8&N7<NFt1Uf2-2rnUy
zLIzb=pzS6MLEzJo(Q_K4ltbFk5A_Sl_JBqfu=)hU2B=R!4kN-RsP!nM_fQNu+a?}y
zq%`Q}8;I>_`D>HwrMxeO@%HKAXE^ftosPi93P8J!NVgrj>9L?F73to9OwiR_iJ*Or
zpk5RxRYF_>VuAA=m`KNP2{ari-}S2u8fJV98fHZEJfs}^Vh11VghnpqR)XiEz+*FL
zzQnK+>Pt%NFvO@f`1Ei~P$_`sTgcLsw|3Blh|I94rk>ShpcXplJ}F3}2~n_OSPk_x
zS*-?08V7G@k1tCtD$dLYpO^ru5g?PsV1Fa#t6{T#=?9ed*LCo^QRHuk<)DFF{p9@I
zT!_`6I7Uj-h`s^jR)Ftz>3rAzTb`ND4sE59bxu6QX5@MdbOAh)wOE21au<>r)LKeP
z6!4I$KGL<0AWveo9KPHZHZB2aq){`fi$KE$i{Tw?kW(P@29UD!r(OD7t^3A83k_k-
zG_uw^LTcLFMEL%FUuFgd@U?^SzKO-zsVSj(iA9zE1*t{g!VSIVz^I|1g$s4u0@^2p
zHTW^Cg=RIfx(PT>OM#s(1ZoLDoB_^sh$IL(okYt%y(BJq`igs6uuL}qTh%d~0u2#L
z%RR)-QSc(EQus6js5pQ$yddS?UAy$PVmFeNmcmEkDQ%~t4oy4&4NV+|4~*ck9_n*S
ztw-&(aDl4VkMJ2pXV9t$&~5eUhi9Eyw4r;Fefk39%;3`g4X`voE9=1?R|8oOBGOe$
znPzcF*V(6szkFvU{b@e56r`^0L7+Kg(3$4gtGv6g?R(T(hKd^W7NBVoaL|MOjwlFO
z7pbQPIls0`Kld=|ZK6JGGaVHNrNEo25Q`xZRzu4FPj=~ok4@LVTBAmjV2=e|Jc!NX
zkQ(5nT{^g3E3ZHk+dqMVT^lrG0}gf@P?7@?=^O8@5MB1Z)jnO}-K)@)k`1u>2Xs;b
z5v}Ru;xbSZ8FU6t3FyQpm(0YR)Z`LkYZlUM0XZE~bc0ynHaM7oc2|+QZ_rYM(y=x8
zX|bS?E8zllB=bQB0+wXvVjMq=QHwz<PpY{A6ucz)p3-6iwa9)2Dzd}jWhSEDgdeHs
zb9?`$OH1G<%2U^RoMXBe)<ff-tR7K-r;om8Zeluk-~e=nW(la2h<7D6JyUL}G^nA7
zts91szM%dj>k?#0%N#ak2Rdpfw;;YCKL>J&87OWcDGjZO1aSk>v;{PclNHsdZb0h!
z<D53cupSx&WQBS`C8&b{J=Dw&R5*jycR(T<+!F(>0fi2kGufx#lWS?H;S7Z}2q?F5
zFR14UYK%gvVUTUeoBxokg!+=wf&_l}l^kfI0JKIM;#tJ0A+S;U^aC5y&!=V?!8}W8
zvlFp4zXnu@%mx)A;1V28q(f@|-*(Uyd`eWR1W}7HR#1>5YzJj+<WAU2yYvkub~fG<
z1fjN5>TjfpKv0k)4$VW@4Dt7C*yT+^u<DnrW)GzM2O4NaUL^YqRBeG4r28f2riOtg
z$wBu9K;j;h(;-zTq!og+G#{F^spt?;sRk)l!10d9ZfJ@p>ttf285&5si_b{ROUZ$p
zr1lAPhX5$kc|gR1OHy;uLjqFDAT=hSAwhlDfLcvh0|dhf&;X(8aL6`LP4g2pB!RV*
zhY$W=fUPB^s_n->wwJ;8{2~$q#P3M9L;X%^@dt0q{o`O@C=p{|V2Jn3D=taQOHTDo
zEy>7F3CK*&&LRGqVyd}^<aQlsq?U-LIryA-q(N=askNZC`a)31gPK#2gkJ?(hXEqe
z=koMT3V&{4pT15FbhR7&CPgBLf6(nKuFOl$D9VRT!+@@KgA8_pb3e$&Ob~%-EhNH`
zrm#RW7w~0V=|%aa1&}E$_;nJ#i6zMy=$A?S1TCNe5zyIZE!Z?Pd}N2x@*U?aY$GU4
zK?Hmh70!SREmDw`z@x~Z7F!Xh7zaftxV^L-Ua5kbF_7yIAS3rkbFL7}iCnycV_7OV
z_7D{qB)cM&mJmBAZy%X~rUpTNfwYf6r=LKMK!J}?!59!5DUD1-WxWv8OjrT$D_eom
z5r}}EeWC<A1O?Ve0Zq;lkspy(tucX`1BmmAK^@CX5CPpFhp|9~nj_JONe6IDf}6S^
z0@H3tJ}l;AU|@i(!3C3`6bBy7UIR*T;Hm>O>kc`geaeFU7nIq)*r(^b5xN?Em>G6A
zBo;GnfQliI#gO>LV+Pb6pj{qB1T!evg0^8o?{o!UV+oqcgp_tMpqvRJpeOd~+NbM9
ztm3YiIT6;_CC?gjP>e1Gg$OuQK{jN92uy3BzM+0=;x;HiLG>uar|4M}sYZo1XeiAw
zkmeOAJ%A_hF2a3}$7(A1J}D<ZIXg2iJw7w9pcK4g4>9cyPLiN-fHZ7(SFUo-Ym>H5
zcbj%*<DS)Wuy`h`VT;tb#Cqd6IP&q>4fQ^j?9d8|y?oG!IN1G&?6B-S-;Q-1LJsMM
zTusNfiz||or7+BZx`VtK7VrV^(AE9WA%x7llGLKK#ANWe3203XculTPW?pJy5oEe4
zA7jl0M(qSmmb7scXrvKqTN}d}(6FI2eWa9S7NsVFccg>PMum)ymSwu-=iwL~g=f$M
zum(33od6nM!0LTS{f%Td)ca(u^g+rVpf!5PC#NG$SpuhTP>BpVW$A0mzl-LXR`%(S
zEIwrIG<JsNPii}0FKEXyC`2Lk4IT$TLx8+NE$Dg@#AOY|nH8WS5>zNaA^_}v5EoKD
z^==f7YYMivPgh;7bSEXm6Xt&ha+d$YW<Oz@Qb5)~Y(*=XFl>c-n%D(TNVlf5F)=Wd
zfYMhmXo@v8B_uVsAipTFsM0Mn2V;~R*DwPa_JR&}!0I;)JD`4}w8sV?K$`_>Aojx7
ztF(dI6d)pf+SAuYf0kR@ryKuFn=O4Ao|dU*GukmiEg+jgM7m9bYu#S~Yy0&6Z0DmE
z^{^Z2so%=j0jj4aV5#aMlW31%vxiS$+r265t%9y&g2XafzJaVyF}F|8y56$o*aCQ1
z6S-{$y|;?IN(I#4gxHCeYcTADrYy3G3Z%jkb(k_7)RqJtm;!0Yf!0kxt}8Z-?YJtR
zC}E$z<8<BKm*?OebZQ;05C9sT)rWU{@K_A>H(4!6$hbeW{QxVdPJxou5qOpYm0OVF
z5t5lt+NBo->MC`e)P@z0WK|C+R)abTh^u=+CrKa;PvCI?)bEtWIqI}Hcu>X*6!hR^
zzF2^PArnNTM|SWvDmPBEPiO6X`;KuJ{2oE-TK@>NAOhrPNaYA(p#?u`XPdgVF9%Id
zgY1OZ-pb3skO?Bv1v{>P<u03SpWa%XdT-V14bX9U>e|i>+N-h#6!hTS4kw_q$`4=%
zSp0?V8=$W3ETEYugx^7njWR(*I^>Wd4f}N1v?^qngSxgaL5+9NVdj}2BE9Z`mcPQA
z6#I1b-7_!tw>8rw<R5{Cco2R^?>1hrg9gG6nuL5XYN`VbML;ItAW8kN9ongn)D3wh
z)Q|_~dJq9$*o$`b3w3SRL``){pmqd^NH;D2vOlI<%s&0Xy9r4RF2*#8cWF>l91-uJ
zu6rhkfcjm`KE3Uju;ZL|W7upMs6r-U=%)ZYf)8qK^MYzJ&?S(NDiqP1E&*LDJj2*N
z{e#z}=fCW1VATo9HrxSKSj#}OC*T4Dj}6fJf|?_upo9YMJ%I^mXeryL*EcFSwDsA-
zJVm*cXp_{4P7@@8kgSCIlB{YOF>i^nLBtQ#)P4kNZi4-c$fS^NyNG>y;PqR-Lqy?Y
z`k+hTiAXAFdk~<#w%pW`qRixCeejtAnQ58H;AOI)1O+<kCMXcpJ^@EJ9(O<kf~?E9
zVcj>FI|>pri^?;L!J{gmp>Rl}6dV$u>;tKyr`*`O_wFPc`*ausI*tO`h)9KyC@n5Y
zEz&Q|D=sZaEh@__P6ZEXfUJkO1{@wBE)Lf~!UJhGM-_QPQchNBaS3EL2gg2W&}loF
zAOf)#1Jc!Cf+cfMr=DbwBDKDe_CcfVTLoE*!)}PTicN^?rNj4+6_;j~fTI%>Vvqnt
zi%!VVRf6{EiAJFYS}TQNz66ci6X8p^O~@S@&<q8{GPKyjung)g%1aK=Vuv8a#ZRCu
zf{=UbAPsuVg$9&c3EuAvwi4`1IDt8{0clE6ZX;;7Z4hWG8Pae?*a+V0$-uyXw(XO0
z8_})<M6BI`1S`_a5i};r${dIS8F?Ye8PMVh&^<HG`FYS?EI6(<gA6JnO$tJbBq}-v
z=VB5JyP*L=R$mpVlt8SV?*>)<=iqe`BGw^RBaNj};%y|WkqRiZHOzSIhI*T<(R3uc
zLF2fXd#w?Z`Dmrrby4F(x0?m*(^Gq^I^4<=V5JvroPl%c1co!9p+P|c1fN1yl$xAh
zlmfoQ8Fau^320drXw!3INjzvZA+FUUR7w(%Y>Dm=(6J&|3wMm<NToo?E6q(U%0xRB
z3@sf*^s27u-56}2ZqcG;TD>3@mJZ08$wu-wa;=7_Lc!Sp#D%m$Ff509oVtA|OVGFh
z$WlljBUp@qArnNTm#zt_S~k7IKArv56nFDOn_zyYbQBnVpb7Zq5YX}c=+zD+#vj<F
zLpHK8Lwj#zrFx_?AUClnJGBU$>cNY~ro#tIl0cW1fQa;?{f6bbivITL*;kxRb~RT(
zt)^~};~Xr3v||+Q)0c1DEB57{2h?`zmHD94o{)wI!3hpTK-(5K?VyW8n4mUO>TlE$
zVZ_>dke%Q}4kn;OR0j})?vR!ZsCXu#t%%wQLW*$Y<FN`VGr=W{0sIhIP@0EW59x$m
zK_4NbG}sg2cX)zZk%*Lzuo;rl55U&V!fr^R)<`(o)mH@VrEYZp0gYZLz%v;Z+Yc@9
zS$y)S29**U>QFS!aWVK>WyqEbKB(=~3UjpEP|$t70X?oME!B`(RiJB^ka9O-AQIlH
zhNQO-(1A!QS`EIh^*21O!PO{;fG!-mi@p(?TA_`0?-hDzV-zWrrf}3W)&?2@oC{j*
z0xr-H0S*c01BmktAO#wwB?_YP4!$h51Rmaq4iseL`*XYWuTB2NpOj#C9Z}ccI7fXU
zCFv8`A(7nByhUBxagO%Cbad2=+8J)2&dy#f>|E4D6WdS2+e_d$e+8Q20ukvSJ=KML
zOq=b~_a1(GVS~h4sO?nDWS~rgl)pjUhD;Cvtv^4&P7H=cI!HYw^(XAKdbHE|(K8vm
zgZuzmu246+aZc*NGZ`$+L)ynkV|1Y995%(3#d_(fB`G=i1*v&yIf?1T(2MSI`eOrZ
z<q}!PML?SQ$Sr*Ex<XJ-AJWjpV=u%9#gq@QOau*bfYL?==)i=^Vo2lO3^YLkBH$;H
zz!}g!KIJxpPHztajq*Y~3$iT}M8Iu=GoYR&t3QD{1r9mf8FVcScz0`ZadLcaVo6bE
z1;pFv0giO#C)C?ia|6gKJiS;Xo1wlZYY9ChiGh32>8T|-nYo!I;BL)V(2x$u2@roH
zMk^r)$_v>;_oWHJ`f_C1juQ4r2b0u-Y`+PgTmbD)f?OmGnT=z!hfd3}K}$xmiV29<
zL7T%$%JWk)bKz%jf$|qp$b-0$OPq0BT|$BFkTb0Gk+V2xmIBFkL`wkP+JrHn@lIBX
zL+;xo<>zOErgR_|n1g2UK<zE?MnEtDoo9FiYj1vn6>3zoIRJD5gB-|aa7_qGcaZ)R
zytxTsz)mWpwDp7Sq$R92!$;*`!d7pA3LqkC!j#P7643slV*S*jqI}3u0jLfr0UgTi
z0+V#AEJ=0F&n?KzNp%G&gJd>@_aXHXyjFzQCKNaVbigc9^1@>mG<gv@&4Su>hOY5k
z2kN)20<~(vDGHBe(9)9<%Rr`+<TE0-aiRJwIV~*{CBGu9Mf2lKfnv|AWybdDtOqLC
zc>luJ<B?S{qU^)Rxg{RMN@yHXy6O=!_)=V&RFIPosUJWULJ6o`4u%LiB^KjY+KMy-
z1C3<ry9N;lSaUIw6DSD~Xl(^Km=C;MvMjMEGZ8X<xe`=m$znO64w8p2*ue+JC<z6;
zP5}8GPX<780yGrJ8hb{nIdT(AGGO;#rGw5r1-ZZ{vlz#Ee#~-=nhpT9+3@%t$#Me&
E04ohAI{*Lx
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/__init__.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/__init__.py
deleted file mode 100644
index 6e242133..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import os
-
-for module in os.listdir(os.path.dirname(os.path.realpath(__file__))):
- if module == '__init__.py' or module[
- -4:] == '.pyc' or module == '__pycache__' or module[
- -3:] != '.py':
- continue
- __import__(module[:-3], locals(), globals(), [], 1)
-del module
-del os
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/__init__.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py
deleted file mode 100644
index a4984519..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/diversity_utils.py
+++ /dev/null
@@ -1,200 +0,0 @@
-import itertools
-import math
-
-import numpy as np
-
-from .fusion_utils import BaseFusionClassifier
-from ...multiview.multiview_utils import ConfigGenerator, \
- get_available_monoview_classifiers, \
- BaseMultiviewClassifier
-from ...utils.dataset import get_examples_views_indices
-
-
-class DiversityFusionClassifier(BaseMultiviewClassifier,
- BaseFusionClassifier):
- """This is the base class for all the diversity fusion based classifiers."""
-
- def __init__(self, random_state=None, classifier_names=None,
- monoview_estimators=None, classifier_configs=None):
- """Used to init the instances"""
- BaseMultiviewClassifier.__init__(self, random_state)
- if classifier_names is None:
- classifier_names = get_available_monoview_classifiers()
- self.classifier_names = classifier_names
- self.param_names = ["classifier_configs"]
- self.distribs = [ConfigGenerator(get_available_monoview_classifiers())]
- self.monoview_estimators = monoview_estimators
- self.classifier_configs = classifier_configs
-
- def fit(self, X, y, train_indices=None, view_indices=None):
- train_indices, view_indices = get_examples_views_indices(X,
- train_indices,
- view_indices)
- self.used_views = view_indices
- # TODO : Finer analysis, may support a bit of mutliclass
- if np.unique(y[train_indices]).shape[0] > 2:
- raise ValueError(
- "Multiclass not supported, classes used : {}".format(
- np.unique(y[train_indices])))
- if self.monoview_estimators is None:
- self.monoview_estimators = []
- for classifier_idx, classifier_name in enumerate(
- self.classifier_names):
- self.monoview_estimators.append([])
- for idx, view_idx in enumerate(view_indices):
- estimator = self.init_monoview_estimator(classifier_name,
- self.classifier_configs)
- estimator.fit(X.get_v(view_idx, train_indices),
- y[train_indices])
- self.monoview_estimators[classifier_idx].append(estimator)
- else:
- pass # TODO
- self.choose_combination(X, y, train_indices, view_indices)
- return self
-
- def predict(self, X, example_indices=None, view_indices=None):
- """Just a weighted majority vote"""
- example_indices, view_indices = get_examples_views_indices(X,
- example_indices,
- view_indices)
- self._check_views(view_indices)
- nb_class = X.get_nb_class()
- if nb_class > 2:
- nb_class = 3
- votes = np.zeros((len(example_indices), nb_class), dtype=float)
- monoview_predictions = [
- monoview_estimator.predict(X.get_v(view_idx, example_indices))
- for view_idx, monoview_estimator
- in zip(view_indices, self.monoview_estimators)]
- for idx, example_index in enumerate(example_indices):
- for monoview_estimator_index, monoview_prediciton in enumerate(
- monoview_predictions):
- if int(monoview_prediciton[idx]) == -100:
- votes[idx, 2] += 1
- else:
- votes[idx, int(monoview_prediciton[idx])] += 1
- predicted_labels = np.argmax(votes, axis=1)
- return predicted_labels
-
- def get_classifiers_decisions(self, X, view_indices, examples_indices):
- classifiers_decisions = np.zeros((len(self.monoview_estimators),
- len(view_indices),
- len(examples_indices)))
- for estimator_idx, estimator in enumerate(self.monoview_estimators):
- for idx, view_index in enumerate(view_indices):
- classifiers_decisions[estimator_idx, idx, :] = estimator[
- idx].predict(X.get_v(view_index, examples_indices))
- return classifiers_decisions
-
- def init_combinations(self, X, example_indices, view_indices):
- classifiers_decisions = self.get_classifiers_decisions(X, view_indices,
- example_indices)
- nb_classifiers, nb_views, n_examples = classifiers_decisions.shape
- combinations = itertools.combinations_with_replacement(
- range(nb_classifiers),
- nb_views)
- nb_combinations = int(
- math.factorial(nb_classifiers + nb_views - 1) / math.factorial(
- nb_views) / math.factorial(
- nb_classifiers - 1))
- div_measure = np.zeros(nb_combinations)
- combis = np.zeros((nb_combinations, nb_views), dtype=int)
- return combinations, combis, div_measure, classifiers_decisions, nb_views
-
-
-class GlobalDiversityFusionClassifier(DiversityFusionClassifier):
-
- def choose_combination(self, X, y, examples_indices, view_indices):
- combinations, combis, div_measure, classifiers_decisions, nb_views = self.init_combinations(
- X, examples_indices, view_indices)
- for combinationsIndex, combination in enumerate(combinations):
- combis[combinationsIndex] = combination
- div_measure[combinationsIndex] = self.diversity_measure(
- classifiers_decisions,
- combination,
- y[examples_indices])
- best_combi_index = np.argmax(div_measure)
- best_combination = combis[best_combi_index]
- self.monoview_estimators = [
- self.monoview_estimators[classifier_index][view_index]
- for view_index, classifier_index
- in enumerate(best_combination)]
-
-
-class CoupleDiversityFusionClassifier(DiversityFusionClassifier):
-
- def choose_combination(self, X, y, examples_indices, view_indices):
- combinations, combis, div_measure, classifiers_decisions, nb_views = self.init_combinations(
- X, examples_indices, view_indices)
- for combinations_index, combination in enumerate(combinations):
- combis[combinations_index] = combination
- combi_with_view = [(viewIndex, combiIndex) for viewIndex, combiIndex
- in
- enumerate(combination)]
- binomes = itertools.combinations(combi_with_view, 2)
- nb_binomes = int(
- math.factorial(nb_views) / 2 / math.factorial(nb_views - 2))
- couple_diversities = np.zeros(nb_binomes)
- for binome_index, binome in enumerate(binomes):
- (view_index_1, classifier_index_1), (
- view_index_2, classifier_index_2) = binome
- couple_diversity = np.mean(
- self.diversity_measure(
- classifiers_decisions[classifier_index_1, view_index_1],
- classifiers_decisions[classifier_index_2, view_index_2],
- y[examples_indices])
- )
- couple_diversities[binome_index] = couple_diversity
- div_measure[combinations_index] = np.mean(couple_diversities)
- best_combi_index = np.argmax(div_measure)
- best_combination = combis[best_combi_index]
- self.monoview_estimators = [
- self.monoview_estimators[classifier_index][view_index]
- for view_index, classifier_index
- in enumerate(best_combination)]
-
-#
-# def CQ_div_measure(classifiersNames, classifiersDecisions, measurement,
-# foldsGroudTruth):
-# """
-# This function is used to measure a pseudo-CQ measurement based on the minCq algorithm.
-# It's a mix between couple_div_measure and global_div_measure that uses multiple measurements.
-# """
-# nbViews, nbClassifiers, nbFolds, foldsLen = classifiersDecisions.shape
-# combinations = itertools.combinations_with_replacement(range(nbClassifiers),
-# nbViews)
-# nbCombinations = int(
-# math.factorial(nbClassifiers + nbViews - 1) / math.factorial(
-# nbViews) / math.factorial(nbClassifiers - 1))
-# div_measure = np.zeros(nbCombinations)
-# combis = np.zeros((nbCombinations, nbViews), dtype=int)
-#
-# for combinationsIndex, combination in enumerate(combinations):
-# combis[combinationsIndex] = combination
-# combiWithView = [(viewIndex, combiIndex) for viewIndex, combiIndex in
-# enumerate(combination)]
-# binomes = itertools.combinations(combiWithView, 2)
-# nbBinomes = int(
-# math.factorial(nbViews) / 2 / math.factorial(nbViews - 2))
-# disagreement = np.zeros(nbBinomes)
-# div_measure[combinationsIndex] = measurement[1](classifiersDecisions,
-# combination,
-# foldsGroudTruth,
-# foldsLen)
-# for binomeIndex, binome in enumerate(binomes):
-# (viewIndex1, classifierIndex1), (
-# viewIndex2, classifierIndex2) = binome
-# nbDisagree = np.sum(measurement[0](
-# classifiersDecisions[viewIndex1, classifierIndex1],
-# classifiersDecisions[viewIndex2, classifierIndex2],
-# foldsGroudTruth)
-# , axis=1) / float(foldsLen)
-# disagreement[binomeIndex] = np.mean(nbDisagree)
-# div_measure[combinationsIndex] /= float(np.mean(disagreement))
-# bestCombiIndex = np.argmin(div_measure)
-# bestCombination = combis[bestCombiIndex]
-#
-# return [classifiersNames[viewIndex][index] for viewIndex, index in
-# enumerate(bestCombination)], div_measure[
-# bestCombiIndex]
-#
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py
deleted file mode 100644
index 29447d15..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/fusion_utils.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import inspect
-
-from ...multiview.multiview_utils import get_monoview_classifier
-from ...utils.multiclass import get_mc_estim
-
-
-class BaseFusionClassifier():
-
- def init_monoview_estimator(self, classifier_name, classifier_config,
- classifier_index=None, multiclass=False):
- if classifier_index is not None:
- if classifier_config is not None :
- classifier_configs = classifier_config
- else:
- classifier_configs = None
- else:
- classifier_configs = classifier_config
- if classifier_configs is not None and classifier_name in classifier_configs:
- if 'random_state' in inspect.getfullargspec(
- get_monoview_classifier(classifier_name).__init__).args:
- estimator = get_monoview_classifier(classifier_name)(
- random_state=self.random_state,
- **classifier_configs[classifier_name])
- else:
- estimator = get_monoview_classifier(classifier_name)(
- **classifier_configs[classifier_name])
- else:
- if 'random_state' in inspect.getfullargspec(
- get_monoview_classifier(classifier_name).__init__).args:
- estimator = get_monoview_classifier(classifier_name)(
- random_state=self.random_state)
- else:
- estimator = get_monoview_classifier(classifier_name)()
-
- return get_mc_estim(estimator, random_state=self.random_state,
- multiview=False, multiclass=multiclass)
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py
deleted file mode 100644
index e9cbac4c..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/jumbo_fusion_utils.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import numpy as np
-
-from .late_fusion_utils import LateFusionClassifier
-from ...monoview.monoview_utils import CustomRandint
-from ...utils.dataset import get_examples_views_indices
-
-
-class BaseJumboFusion(LateFusionClassifier):
-
- def __init__(self, random_state, classifiers_names=None,
- classifier_configs=None,
- nb_cores=1, weights=None, nb_monoview_per_view=1, rs=None):
- LateFusionClassifier.__init__(self, random_state,
- classifiers_names=classifiers_names,
- classifier_configs=classifier_configs,
- nb_cores=nb_cores, weights=weights,
- rs=rs)
- self.param_names += ["nb_monoview_per_view", ]
- self.distribs += [CustomRandint(1, 10)]
- self.nb_monoview_per_view = nb_monoview_per_view
-
- def set_params(self, nb_monoview_per_view=1, **params):
- self.nb_monoview_per_view = nb_monoview_per_view
- LateFusionClassifier.set_params(self, **params)
-
- def predict(self, X, example_indices=None, view_indices=None):
- example_indices, view_indices = get_examples_views_indices(X,
- example_indices,
- view_indices)
- self._check_views(view_indices)
- monoview_decisions = self.predict_monoview(X,
- example_indices=example_indices,
- view_indices=view_indices)
- return self.aggregation_estimator.predict(monoview_decisions)
-
- def fit(self, X, y, train_indices=None, view_indices=None):
- train_indices, view_indices = get_examples_views_indices(X,
- train_indices,
- view_indices)
- self.used_views = view_indices
- self.init_classifiers(len(view_indices),
- nb_monoview_per_view=self.nb_monoview_per_view)
- self.fit_monoview_estimators(X, y, train_indices=train_indices,
- view_indices=view_indices)
- monoview_decisions = self.predict_monoview(X,
- example_indices=train_indices,
- view_indices=view_indices)
- self.aggregation_estimator.fit(monoview_decisions, y[train_indices])
- return self
-
- def fit_monoview_estimators(self, X, y, train_indices=None,
- view_indices=None):
- if np.unique(y).shape[0] > 2:
- multiclass = True
- else:
- multiclass = False
- self.monoview_estimators = [
- [self.init_monoview_estimator(classifier_name,
- self.classifier_configs[
- classifier_index],
- multiclass=multiclass)
- for classifier_index, classifier_name
- in enumerate(self.classifiers_names)]
- for _ in view_indices]
-
- self.monoview_estimators = [[estimator.fit(
- X.get_v(view_indices[idx], train_indices), y[train_indices])
- for estimator in view_estimators]
- for idx, view_estimators in
- enumerate(self.monoview_estimators)]
- return self
-
- def predict_monoview(self, X, example_indices=None, view_indices=None):
- monoview_decisions = np.zeros((len(example_indices),
- len(view_indices) * len(
- self.classifiers_names)))
- for idx, view_estimators in enumerate(self.monoview_estimators):
- for estimator_index, estimator in enumerate(view_estimators):
- monoview_decisions[:, len(
- self.classifiers_names) * idx + estimator_index] = estimator.predict(
- X.get_v(view_indices[idx],
- example_indices))
- return monoview_decisions
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py
deleted file mode 100644
index 0916f76f..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/late_fusion_utils.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import numpy as np
-
-from .fusion_utils import BaseFusionClassifier
-from ...multiview.multiview_utils import BaseMultiviewClassifier, \
- get_available_monoview_classifiers, ConfigGenerator
-from ...utils.dataset import get_examples_views_indices
-
-
-class ClassifierDistribution:
-
- def __init__(self, seed=42, available_classifiers=None):
- self.random_state = np.random.RandomState(seed)
- self.available_classifiers = available_classifiers
-
- def draw(self, nb_view, rs=None):
- if rs is not None:
- self.random_state.seed(rs)
- return self.random_state.choice(self.available_classifiers,
- size=nb_view, replace=True)
-
-
-class ClassifierCombinator:
-
- def __init__(self, need_probas=False):
- self.available_classifiers = get_available_monoview_classifiers(
- need_probas)
-
- def rvs(self, random_state=None):
- return ClassifierDistribution(seed=random_state.randint(1),
- available_classifiers=self.available_classifiers)
-
-
-class ConfigDistribution:
-
- def __init__(self, seed=42, available_classifiers=None):
- self.random_state = np.random.RandomState(seed)
- self.config_generator = ConfigGenerator(available_classifiers)
-
- def draw(self, nb_view, rs=None):
- if rs is not None:
- self.random_state.seed(rs)
- config_samples = [self.config_generator.rvs(self.random_state)
- for _ in range(nb_view)]
- return config_samples
-
-
-class MultipleConfigGenerator:
-
- def __init__(self, ):
- self.available_classifiers = get_available_monoview_classifiers()
-
- def rvs(self, random_state=None):
- return ConfigDistribution(seed=random_state.randint(1),
- available_classifiers=self.available_classifiers)
-
-
-class WeightDistribution:
-
- def __init__(self, seed=42, distribution_type="uniform"):
- self.random_state = np.random.RandomState(seed)
- self.distribution_type = distribution_type
-
- def draw(self, nb_view):
- if self.distribution_type == "uniform":
- return self.random_state.random_sample(nb_view)
-
-
-class WeightsGenerator:
-
- def __init__(self, distibution_type="uniform"):
- self.distribution_type = distibution_type
-
- def rvs(self, random_state=None):
- return WeightDistribution(seed=random_state.randint(1),
- distribution_type=self.distribution_type)
-
-
-class LateFusionClassifier(BaseMultiviewClassifier, BaseFusionClassifier):
-
- def __init__(self, random_state=None, classifiers_names=None,
- classifier_configs=None, nb_cores=1, weights=None,
- rs=None):
- BaseMultiviewClassifier.__init__(self, random_state)
- self.classifiers_names = classifiers_names
- self.classifier_configs = classifier_configs
- self.nb_cores = nb_cores
- self.weights = weights
- self.rs = rs
- self.param_names = ["classifiers_names", "classifier_configs",
- "weights", "rs"]
- self.distribs = [ClassifierCombinator(need_probas=self.need_probas),
- MultipleConfigGenerator(),
- WeightsGenerator(),
- np.arange(1000)]
-
- def fit(self, X, y, train_indices=None, view_indices=None):
- train_indices, view_indices = get_examples_views_indices(X,
- train_indices,
- view_indices)
- self.used_views = view_indices
- if np.unique(y).shape[0] > 2:
- multiclass = True
- else:
- multiclass = False
- self.init_params(len(view_indices), multiclass)
- if np.unique(y[train_indices]).shape[0] > 2:
- raise ValueError("Multiclass not supported")
- self.monoview_estimators = [
- monoview_estimator.fit(X.get_v(view_index, train_indices),
- y[train_indices])
- for view_index, monoview_estimator
- in zip(view_indices,
- self.monoview_estimators)]
- return self
-
- def init_params(self, nb_view, mutliclass=False):
- if self.weights is None:
- self.weights = np.ones(nb_view) / nb_view
- elif isinstance(self.weights, WeightDistribution):
- self.weights = self.weights.draw(nb_view)
- else:
- self.weights = self.weights / np.sum(self.weights)
-
- self.init_classifiers(nb_view)
-
- self.monoview_estimators = [
- self.init_monoview_estimator(classifier_name,
- self.classifier_configs[
- classifier_index],
- classifier_index=classifier_index,
- multiclass=mutliclass)
- for classifier_index, classifier_name
- in enumerate(self.classifiers_names)]
-
- def init_classifiers(self, nb_view, nb_monoview_per_view=None):
- if nb_monoview_per_view is not None:
- nb_clfs = nb_monoview_per_view
- else:
- nb_clfs = nb_view
-
- if isinstance(self.classifiers_names, ClassifierDistribution):
- self.classifiers_names = self.classifiers_names.draw(nb_clfs,
- self.rs)
- elif self.classifiers_names is None:
- self.classifiers_names = ["decision_tree" for _ in range(nb_clfs)]
- elif isinstance(self.classifiers_names, str):
- self.classifiers_names = [self.classifiers_names
- for _ in range(nb_clfs)]
-
- if isinstance(self.classifier_configs, ConfigDistribution):
- self.classifier_configs = [{classifier_name : config[classifier_name]} for config, classifier_name in zip(self.classifier_configs.draw(nb_clfs,
- self.rs), self.classifiers_names)]
- elif isinstance(self.classifier_configs, dict):
- self.classifier_configs = [
- {classifier_name: self.classifier_configs[classifier_name]} for
- classifier_name in self.classifiers_names]
- elif self.classifier_configs is None:
- self.classifier_configs = [None for _ in range(nb_clfs)]
-
- # def verif_clf_views(self, classifier_names, nb_view):
- # if classifier_names is None:
- # if nb_view is None:
- # raise AttributeError(self.__class__.__name__+" must have either classifier_names or nb_views provided.")
- # else:
- # self.classifiers_names = self.get_classifiers(get_available_monoview_classifiers(), nb_view)
- # else:
- # if nb_view is None:
- # self.classifiers_names = classifier_names
- # else:
- # if len(classifier_names)==nb_view:
- # self.classifiers_names = classifier_names
- # else:
- # warnings.warn("nb_view and classifier_names not matching, choosing nb_view random classifiers in classifier_names.", UserWarning)
- # self.classifiers_names = self.get_classifiers(classifier_names, nb_view)
-
- def get_classifiers(self, classifiers_names, nb_choices):
- return self.random_state.choice(classifiers_names, size=nb_choices,
- replace=True)
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/utils.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/utils.py
deleted file mode 100644
index 5fbd4d56..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/additions/utils.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin
-
-
-def get_names(classed_list):
- return np.array([object_.__class__.__name__ for object_ in classed_list])
-
-
-# class BaseMultiviewClassifier(BaseEstimator, ClassifierMixin):
-#
-# def __init__(self, random_state):
-# self.random_state = random_state
-#
-# def genBestParams(self, detector):
-# return dict((param_name, detector.best_params_[param_name])
-# for param_name in self.param_names)
-#
-# def genParamsFromDetector(self, detector):
-# if self.classed_params:
-# classed_dict = dict((classed_param, get_names(
-# detector.cv_results_["param_" + classed_param]))
-# for classed_param in self.classed_params)
-# if self.param_names:
-# return [(param_name,
-# np.array(detector.cv_results_["param_" + param_name]))
-# if param_name not in self.classed_params else (
-# param_name, classed_dict[param_name])
-# for param_name in self.param_names]
-# else:
-# return [()]
-#
-# def genDistribs(self):
-# return dict((param_name, distrib) for param_name, distrib in
-# zip(self.param_names, self.distribs))
-#
-# def getConfig(self):
-# if self.param_names:
-# return "\n\t\t- " + self.__class__.__name__ + "with " + ", ".join(
-# [param_name + " : " + self.to_str(param_name) for param_name in
-# self.param_names])
-# else:
-# return "\n\t\t- " + self.__class__.__name__ + "with no config."
-#
-# def to_str(self, param_name):
-# if param_name in self.weird_strings:
-# if self.weird_strings[param_name] == "class_name":
-# return self.get_params()[param_name].__class__.__name__
-# else:
-# return self.weird_strings[param_name](
-# self.get_params()[param_name])
-# else:
-# return str(self.get_params()[param_name])
-#
-# def get_interpretation(self):
-# return "No detailed interpretation function"
-
-#
-# def get_train_views_indices(dataset, train_indices, view_indices, ):
-# """This function is used to get all the examples indices and view indices if needed"""
-# if view_indices is None:
-# view_indices = np.arange(dataset.nb_view)
-# if train_indices is None:
-# train_indices = range(dataset.get_nb_examples())
-# return train_indices, view_indices
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/bayesian_inference_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/bayesian_inference_fusion.py
deleted file mode 100644
index b1cd5f9e..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/bayesian_inference_fusion.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import numpy as np
-
-from ..multiview_classifiers.additions.late_fusion_utils import \
- LateFusionClassifier
-from ..utils.dataset import get_examples_views_indices
-
-classifier_class_name = "BayesianInferenceClassifier"
-
-
-class BayesianInferenceClassifier(LateFusionClassifier):
- def __init__(self, random_state, classifiers_names=None,
- classifier_configs=None, nb_cores=1, weights=None,
- rs=None):
- self.need_probas = True
- LateFusionClassifier.__init__(self, random_state=random_state,
- classifiers_names=classifiers_names,
- classifier_configs=classifier_configs,
- nb_cores=nb_cores,
- weights=weights,
- rs=rs)
-
- def predict(self, X, example_indices=None, view_indices=None):
- example_indices, view_indices = get_examples_views_indices(X,
- example_indices,
- view_indices)
- self._check_views(view_indices)
- if sum(self.weights) != 1.0:
- self.weights = self.weights / sum(self.weights)
-
- view_scores = []
- for index, view_index in enumerate(view_indices):
- view_scores.append(np.power(
- self.monoview_estimators[index].predict_proba(
- X.get_v(view_index,
- example_indices)),
- self.weights[index]))
- view_scores = np.array(view_scores)
- predicted_labels = np.argmax(np.prod(view_scores, axis=0), axis=1)
- return predicted_labels
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/difficulty_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/difficulty_fusion.py
deleted file mode 100644
index 0c66e561..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/difficulty_fusion.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import numpy as np
-
-from multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils import \
- GlobalDiversityFusionClassifier
-
-classifier_class_name = "DifficultyFusion"
-
-
-class DifficultyFusion(GlobalDiversityFusionClassifier):
-
- def diversity_measure(self, classifiers_decisions, combination, y):
- _, nb_view, nb_examples = classifiers_decisions.shape
- scores = np.zeros((nb_view, nb_examples), dtype=int)
- for view_index, classifier_index in enumerate(combination):
- scores[view_index, :] = np.logical_not(
- np.logical_xor(classifiers_decisions[classifier_index,
- view_index],
- y)
- )
- # Table of the nuber of views that succeeded for each example :
- difficulty_scores = np.sum(scores, axis=0)
-
- difficulty_score = np.var(
- np.array([
- np.sum((difficulty_scores == view_index))
- for view_index in range(len(combination) + 1)])
- )
- return difficulty_score
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/disagree_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/disagree_fusion.py
deleted file mode 100644
index cee032a8..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/disagree_fusion.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import numpy as np
-
-from multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils import \
- CoupleDiversityFusionClassifier
-
-classifier_class_name = "DisagreeFusion"
-
-
-class DisagreeFusion(CoupleDiversityFusionClassifier):
-
- def diversity_measure(self, first_classifier_decision,
- second_classifier_decision, _):
- return np.logical_xor(first_classifier_decision,
- second_classifier_decision)
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/double_fault_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/double_fault_fusion.py
deleted file mode 100644
index 12eb6b64..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/double_fault_fusion.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import numpy as np
-
-from multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils import \
- CoupleDiversityFusionClassifier
-
-classifier_class_name = "DoubleFaultFusion"
-
-
-class DoubleFaultFusion(CoupleDiversityFusionClassifier):
-
- def diversity_measure(self, first_classifier_decision,
- second_classifier_decision, y):
- return np.logical_and(np.logical_xor(first_classifier_decision, y),
- np.logical_xor(second_classifier_decision, y))
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/entropy_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/entropy_fusion.py
deleted file mode 100644
index 3c3d5aef..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/entropy_fusion.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import numpy as np
-
-from multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils import \
- GlobalDiversityFusionClassifier
-
-classifier_class_name = "EntropyFusion"
-
-
-class EntropyFusion(GlobalDiversityFusionClassifier):
-
- def diversity_measure(self, classifiers_decisions, combination, y):
- _, nb_view, nb_examples = classifiers_decisions.shape
- scores = np.zeros((nb_view, nb_examples), dtype=int)
- for view_index, classifier_index in enumerate(combination):
- scores[view_index] = np.logical_not(
- np.logical_xor(
- classifiers_decisions[classifier_index, view_index],
- y)
- )
- entropy_scores = np.sum(scores, axis=0)
- nb_view_matrix = np.zeros((nb_examples),
- dtype=int) + nb_view - entropy_scores
- entropy_score = np.mean(
- np.minimum(entropy_scores, nb_view_matrix).astype(float) / (
- nb_view - int(nb_view / 2)))
- return entropy_score
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/majority_voting_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/majority_voting_fusion.py
deleted file mode 100644
index 53a255c7..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/majority_voting_fusion.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import numpy as np
-
-from ..multiview_classifiers.additions.late_fusion_utils import \
- LateFusionClassifier
-from ..utils.dataset import get_examples_views_indices
-
-classifier_class_name = "MajorityVoting"
-
-
-class VotingIndecision(Exception):
- pass
-
-
-class MajorityVoting(LateFusionClassifier):
- def __init__(self, random_state, classifiers_names=None,
- classifier_configs=None, weights=None, nb_cores=1, rs=None):
- self.need_probas = False
- LateFusionClassifier.__init__(self, random_state=random_state,
- classifiers_names=classifiers_names,
- classifier_configs=classifier_configs,
- nb_cores=nb_cores,
- weights=weights,
- rs=rs)
-
- def predict(self, X, example_indices=None, view_indices=None):
- examples_indices, view_indices = get_examples_views_indices(X,
- example_indices,
- view_indices)
- self._check_views(view_indices)
- n_examples = len(examples_indices)
- votes = np.zeros((n_examples, X.get_nb_class(example_indices)),
- dtype=float)
- monoview_decisions = np.zeros((len(examples_indices), X.nb_view),
- dtype=int)
- for index, view_index in enumerate(view_indices):
- monoview_decisions[:, index] = self.monoview_estimators[
- index].predict(
- X.get_v(view_index, examples_indices))
- for example_index in range(n_examples):
- for view_index, feature_classification in enumerate(
- monoview_decisions[example_index, :]):
- votes[example_index, feature_classification] += self.weights[
- view_index]
- nb_maximum = len(
- np.where(votes[example_index] == max(votes[example_index]))[0])
- if nb_maximum == X.nb_view:
- raise VotingIndecision(
- "Majority voting can't decide, each classifier has voted for a different class")
-
- predicted_labels = np.argmax(votes, axis=1)
- # Can be upgraded by restarting a new classification process if
- # there are multiple maximums ?:
- # while nbMaximum>1:
- # relearn with only the classes that have a maximum number of vote
- return predicted_labels
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/svm_jumbo_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/svm_jumbo_fusion.py
deleted file mode 100644
index d9a2e38d..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/svm_jumbo_fusion.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from sklearn.svm import SVC
-
-from .additions.jumbo_fusion_utils import BaseJumboFusion
-from ..monoview.monoview_utils import CustomUniform, CustomRandint
-
-classifier_class_name = "SVMJumboFusion"
-
-
-class SVMJumboFusion(BaseJumboFusion):
-
- def __init__(self, random_state=None, classifiers_names=None,
- classifier_configs=None, nb_cores=1, weights=None,
- nb_monoview_per_view=1, C=1.0, kernel="rbf", degree=2,
- rs=None):
- self.need_probas = False
- BaseJumboFusion.__init__(self, random_state,
- classifiers_names=classifiers_names,
- classifier_configs=classifier_configs,
- nb_cores=nb_cores, weights=weights,
- nb_monoview_per_view=nb_monoview_per_view,
- rs=rs)
- self.param_names += ["C", "kernel", "degree"]
- self.distribs += [CustomUniform(), ["rbf", "poly", "linear"],
- CustomRandint(2, 5)]
- self.aggregation_estimator = SVC(C=C, kernel=kernel, degree=degree)
- self.C = C
- self.kernel = kernel
- self.degree = degree
-
- def set_params(self, C=1.0, kernel="rbf", degree=1, **params):
- super(SVMJumboFusion, self).set_params(**params)
- self.C = C
- self.degree = degree
- self.kernel = kernel
- self.aggregation_estimator.set_params(C=C, kernel=kernel, degree=degree)
- return self
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
deleted file mode 100644
index 6635119f..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import numpy as np
-
-from multiview_platform.mono_multi_view_classifiers import monoview_classifiers
-from .additions.fusion_utils import BaseFusionClassifier
-from ..multiview.multiview_utils import get_available_monoview_classifiers, \
- BaseMultiviewClassifier, ConfigGenerator
-from ..utils.dataset import get_examples_views_indices
-from ..utils.multiclass import get_mc_estim, MultiClassWrapper
-
-# from ..utils.dataset import get_v
-
-classifier_class_name = "WeightedLinearEarlyFusion"
-
-
-class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
- """
- WeightedLinearEarlyFusion
-
- Parameters
- ----------
- random_state
- view_weights
- monoview_classifier_name
- monoview_classifier_config
-
- Attributes
- ----------
- """
-
- def __init__(self, random_state=None, view_weights=None,
- monoview_classifier_name="decision_tree",
- monoview_classifier_config={}):
- BaseMultiviewClassifier.__init__(self, random_state=random_state)
- self.view_weights = view_weights
- self.monoview_classifier_name = monoview_classifier_name
- self.short_name = "early_fusion"
- if monoview_classifier_name in monoview_classifier_config:
- self.monoview_classifier_config = monoview_classifier_config[
- monoview_classifier_name]
- self.monoview_classifier_config = monoview_classifier_config
- # monoview_classifier_module = getattr(monoview_classifiers,
- # self.monoview_classifier_name)
- # monoview_classifier_class = getattr(monoview_classifier_module,
- # monoview_classifier_module.classifier_class_name)
- self.monoview_classifier = self.init_monoview_estimator(monoview_classifier_name, monoview_classifier_config)
- self.param_names = ["monoview_classifier_name",
- "monoview_classifier_config"]
- self.distribs = [get_available_monoview_classifiers(),
- ConfigGenerator(get_available_monoview_classifiers())]
- self.classed_params = []
- self.weird_strings = {}
-
- def set_params(self, monoview_classifier_name="decision_tree",
- monoview_classifier_config={}, **params):
- self.monoview_classifier_name = monoview_classifier_name
- self.monoview_classifier = self.init_monoview_estimator(
- monoview_classifier_name,
- monoview_classifier_config)
- self.monoview_classifier_config = self.monoview_classifier.get_params()
- self.short_name = "early_fusion"
- return self
-
- def get_params(self, deep=True):
- return {"random_state": self.random_state,
- "view_weights": self.view_weights,
- "monoview_classifier_name": self.monoview_classifier_name,
- "monoview_classifier_config": self.monoview_classifier_config}
-
- def fit(self, X, y, train_indices=None, view_indices=None):
- train_indices, X = self.transform_data_to_monoview(X, train_indices,
- view_indices)
- self.used_views = view_indices
- if np.unique(y[train_indices]).shape[0] > 2 and \
- not (isinstance(self.monoview_classifier, MultiClassWrapper)):
- self.monoview_classifier = get_mc_estim(self.monoview_classifier,
- self.random_state,
- multiview=False,
- y=y[train_indices])
- self.monoview_classifier.fit(X, y[train_indices])
- self.monoview_classifier_config = self.monoview_classifier.get_params()
- return self
-
- def predict(self, X, example_indices=None, view_indices=None):
- _, X = self.transform_data_to_monoview(X, example_indices, view_indices)
- self._check_views(self.view_indices)
- predicted_labels = self.monoview_classifier.predict(X)
- return predicted_labels
-
- def transform_data_to_monoview(self, dataset, example_indices,
- view_indices):
- """Here, we extract the data from the HDF5 dataset file and store all
- the concatenated views in one variable"""
- example_indices, self.view_indices = get_examples_views_indices(dataset,
- example_indices,
- view_indices)
- if self.view_weights is None:
- self.view_weights = np.ones(len(self.view_indices), dtype=float)
- else:
- self.view_weights = np.array(self.view_weights)
- self.view_weights /= float(np.sum(self.view_weights))
-
- X = self.hdf5_to_monoview(dataset, example_indices)
- return example_indices, X
-
- def hdf5_to_monoview(self, dataset, examples):
- """Here, we concatenate the views for the asked examples """
- monoview_data = np.concatenate(
- [dataset.get_v(view_idx, examples)
- for view_weight, (index, view_idx)
- in zip(self.view_weights, enumerate(self.view_indices))]
- , axis=1)
- return monoview_data
-
- # def set_monoview_classifier_config(self, monoview_classifier_name, monoview_classifier_config):
- # if monoview_classifier_name in monoview_classifier_config:
- # self.monoview_classifier.set_params(**monoview_classifier_config[monoview_classifier_name])
- # else:
- # self.monoview_classifier.set_params(**monoview_classifier_config)
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_late_fusion.py b/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_late_fusion.py
deleted file mode 100644
index 403791ce..00000000
--- a/multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_late_fusion.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import numpy as np
-
-from ..multiview_classifiers.additions.late_fusion_utils import \
- LateFusionClassifier
-from ..utils.dataset import get_examples_views_indices
-
-classifier_class_name = "WeightedLinearLateFusion"
-
-
-class WeightedLinearLateFusion(LateFusionClassifier):
- def __init__(self, random_state, classifiers_names=None,
- classifier_configs=None, weights=None, nb_cores=1, rs=None):
- self.need_probas = True
- LateFusionClassifier.__init__(self, random_state=random_state,
- classifiers_names=classifiers_names,
- classifier_configs=classifier_configs,
- nb_cores=nb_cores, weights=weights, rs=rs)
-
- def predict(self, X, example_indices=None, view_indices=None):
- example_indices, view_indices = get_examples_views_indices(X,
- example_indices,
- view_indices)
- self._check_views(view_indices)
- view_scores = []
- for index, viewIndex in enumerate(view_indices):
- view_scores.append(
- np.array(self.monoview_estimators[index].predict_proba(
- X.get_v(viewIndex, example_indices))) * self.weights[index])
- view_scores = np.array(view_scores)
- predicted_labels = np.argmax(np.sum(view_scores, axis=0), axis=1)
- return predicted_labels
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/__init__.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/duration_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/duration_analysis.py
deleted file mode 100644
index fb3a539c..00000000
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/duration_analysis.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import os
-import plotly
-import pandas as pd
-
-
-def get_duration(results):
- df = pd.DataFrame(columns=["hps", "fit", "pred"], )
- for classifier_result in results:
- df.at[classifier_result.get_classifier_name(),
- "hps"] = classifier_result.hps_duration
- df.at[classifier_result.get_classifier_name(),
- "fit"] = classifier_result.fit_duration
- df.at[classifier_result.get_classifier_name(),
- "pred"] = classifier_result.pred_duration
- return df
-
-def plot_durations(durations, directory, database_name, durations_stds=None): # pragma: no cover
- file_name = os.path.join(directory, database_name + "-durations")
- durations.to_csv(file_name+"_dataframe.csv")
- fig = plotly.graph_objs.Figure()
- if durations_stds is None:
- durations_stds = pd.DataFrame(0, durations.index, durations.columns)
- else:
- durations_stds.to_csv(file_name+"_stds_dataframe.csv")
- fig.add_trace(plotly.graph_objs.Bar(name='Hyper-parameter Optimization',
- x=durations.index,
- y=durations['hps'],
- error_y=dict(type='data',
- array=durations_stds["hps"]),
- marker_color="grey"))
- fig.add_trace(plotly.graph_objs.Bar(name='Fit (on train set)',
- x=durations.index,
- y=durations['fit'],
- error_y=dict(type='data',
- array=durations_stds["fit"]),
- marker_color="black"))
- fig.add_trace(plotly.graph_objs.Bar(name='Prediction (on test set)',
- x=durations.index,
- y=durations['pred'],
- error_y=dict(type='data',
- array=durations_stds["pred"]),
- marker_color="lightgrey"))
- fig.update_layout(title="Durations for each classfier",
- yaxis_title="Duration (s)")
- fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
- plot_bgcolor='rgba(0,0,0,0)')
- plotly.offline.plot(fig, filename=file_name + ".html", auto_open=False)
\ No newline at end of file
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
deleted file mode 100644
index 97aa6baa..00000000
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
+++ /dev/null
@@ -1,289 +0,0 @@
-# Import built-in modules
-import logging
-import os
-
-import matplotlib as mpl
-# Import third party modules
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import plotly
-from matplotlib.patches import Patch
-
-# Import own Modules
-
-
-def get_example_errors(groud_truth, results):
- r"""Used to get for each classifier and each example whether the classifier
- has misclassified the example or not.
-
- Parameters
- ----------
- ground_truth : numpy array of 0, 1 and -100 (if multiclass)
- The array with the real labels of the examples
- results : list of MonoviewResult and MultiviewResults objects
- A list containing all the resluts for all the mono- & multi-view
- experimentations.
-
- Returns
- -------
- example_errors : dict of np.array
- For each classifier, has an entry with a `np.array` over the examples,
- with a 1 if the examples was
- well-classified, a 0 if not and if it's multiclass classification, a
- -100 if the examples was not seen during
- the one versus one classification.
- """
- example_errors = {}
-
- for classifier_result in results:
- error_on_examples = np.equal(classifier_result.full_labels_pred,
- groud_truth).astype(int)
- unseen_examples = np.where(groud_truth == -100)[0]
- error_on_examples[unseen_examples] = -100
- example_errors[
- classifier_result.get_classifier_name()] = error_on_examples
- return example_errors
-
-
-def publish_example_errors(example_errors, directory, databaseName,
- labels_names, example_ids, labels): # pragma: no cover
- logging.debug("Start:\t Label analysis figure generation")
-
- base_file_name = os.path.join(directory, databaseName + "-" )
-
- nb_classifiers, nb_examples, classifiers_names, \
- data_2d, error_on_examples = gen_error_data(example_errors)
-
- np.savetxt(base_file_name + "2D_plot_data.csv", data_2d, delimiter=",")
- np.savetxt(base_file_name + "bar_plot_data.csv", error_on_examples,
- delimiter=",")
-
- plot_2d(data_2d, classifiers_names, nb_classifiers, base_file_name,
- example_ids=example_ids, labels=labels)
-
- plot_errors_bar(error_on_examples, nb_examples,
- base_file_name, example_ids=example_ids)
-
- logging.debug("Done:\t Label analysis figures generation")
-
-
-def publish_all_example_errors(iter_results, directory,
- stats_iter,
- example_ids, labels): # pragma: no cover
- logging.debug(
- "Start:\t Global label analysis figure generation")
-
- nb_examples, nb_classifiers, data, \
- error_on_examples, classifier_names = gen_error_data_glob(iter_results,
- stats_iter)
-
- np.savetxt(os.path.join(directory, "clf_errors.csv"), data, delimiter=",")
- np.savetxt(os.path.join(directory, "example_errors.csv"), error_on_examples,
- delimiter=",")
-
- plot_2d(data, classifier_names, nb_classifiers,
- os.path.join(directory, ""), stats_iter=stats_iter,
- example_ids=example_ids, labels=labels)
- plot_errors_bar(error_on_examples, nb_examples, os.path.join(directory, ""),
- example_ids=example_ids)
-
- logging.debug(
- "Done:\t Global label analysis figures generation")
-
-
-def gen_error_data(example_errors):
- r"""Used to format the error data in order to plot it efficiently. The
- data is saves in a `.csv` file.
-
- Parameters
- ----------
- example_errors : dict of dicts of np.arrays
- A dictionary conatining all the useful data. Organized as :
- `example_errors[<classifier_name>]["error_on_examples"]` is a np.array
- of ints with a
- - 1 if the classifier `<classifier_name>` classifier well the example,
- - 0 if it fail to classify the example,
- - -100 if it did not classify the example (multiclass one versus one).
-
- Returns
- -------
- nbClassifiers : int
- Number of different classifiers.
- nbExamples : int
- NUmber of examples.
- nbCopies : int
- The number of times the data is copied (classifier wise) in order for
- the figure to be more readable.
- classifiers_names : list of strs
- The names fo the classifiers.
- data : np.array of shape `(nbClassifiers, nbExamples)`
- A matrix with zeros where the classifier failed to classifiy the
- example, ones where it classified it well
- and -100 if the example was not classified.
- error_on_examples : np.array of shape `(nbExamples,)`
- An array counting how many classifiers failed to classifiy each
- examples.
- """
- nb_classifiers = len(example_errors)
- nb_examples = len(list(example_errors.values())[0])
- classifiers_names = list(example_errors.keys())
-
- data_2d = np.zeros((nb_examples, nb_classifiers))
- for classifierIndex, (classifier_name, error_on_examples) in enumerate(
- example_errors.items()):
- data_2d[:, classifierIndex] = error_on_examples
- error_on_examples = np.sum(data_2d, axis=1) / nb_classifiers
- return nb_classifiers, nb_examples, classifiers_names, data_2d, error_on_examples
-
-
-def gen_error_data_glob(iter_results, stats_iter):
- nb_examples = next(iter(iter_results.values())).shape[0]
- nb_classifiers = len(iter_results)
- data = np.zeros((nb_examples, nb_classifiers), dtype=int)
- classifier_names = []
- for clf_index, (classifier_name, error_data) in enumerate(
- iter_results.items()):
- data[:, clf_index] = error_data
- classifier_names.append(classifier_name)
- error_on_examples = np.sum(data, axis=1) / (
- nb_classifiers * stats_iter)
- return nb_examples, nb_classifiers, data, error_on_examples, \
- classifier_names
-
-
-def plot_2d(data, classifiers_names, nb_classifiers, file_name, labels=None,
- stats_iter=1, use_plotly=True, example_ids=None): # pragma: no cover
- r"""Used to generate a 2D plot of the errors.
-
- Parameters
- ----------
- data : np.array of shape `(nbClassifiers, nbExamples)`
- A matrix with zeros where the classifier failed to classifiy the example, ones where it classified it well
- and -100 if the example was not classified.
- classifiers_names : list of str
- The names of the classifiers.
- nb_classifiers : int
- The number of classifiers.
- file_name : str
- The name of the file in which the figure will be saved ("error_analysis_2D.png" will be added at the end)
- minSize : int, optinal, default: 10
- The minimum width and height of the figure.
- width_denominator : float, optional, default: 1.0
- To obtain the image width, the number of classifiers will be divided by this number.
- height_denominator : float, optional, default: 1.0
- To obtain the image width, the number of examples will be divided by this number.
- stats_iter : int, optional, default: 1
- The number of statistical iterations realized.
-
- Returns
- -------
- """
- fig, ax = plt.subplots(nrows=1, ncols=1, )
- label_index_list = np.concatenate([np.where(labels == i)[0] for i in
- np.unique(
- labels)])
- cmap, norm = iter_cmap(stats_iter)
- cax = plt.imshow(data[np.flip(label_index_list), :], cmap=cmap, norm=norm,
- aspect='auto')
- plt.title('Errors depending on the classifier')
- ticks = np.arange(0, nb_classifiers, 1)
- tick_labels = classifiers_names
- plt.xticks(ticks, tick_labels, rotation="vertical")
- plt.yticks([], [])
- plt.ylabel("Examples")
- cbar = fig.colorbar(cax, ticks=[-100 * stats_iter / 2, 0, stats_iter])
- cbar.ax.set_yticklabels(['Unseen', 'Always Wrong', 'Always Right'])
-
- fig.savefig(file_name + "error_analysis_2D.png", bbox_inches="tight",
- transparent=True)
- plt.close()
- ### The following part is used to generate an interactive graph.
- if use_plotly:
- # [np.where(labels==i)[0] for i in np.unique(labels)]
- hover_text = [[example_ids[example_index] + " failed " + str(
- stats_iter - data[
- example_index, classifier_index]) + " time(s), labelled " + str(
- labels[example_index])
- for classifier_index in range(data.shape[1])]
- for example_index in range(data.shape[0])]
- fig = plotly.graph_objs.Figure()
- fig.add_trace(plotly.graph_objs.Heatmap(
- x=list(classifiers_names),
- y=[example_ids[label_ind] for label_ind in label_index_list],
- z=data[label_index_list, :],
- text=[hover_text[label_ind] for label_ind in label_index_list],
- hoverinfo=["y", "x", "text"],
- colorscale="Greys",
- colorbar=dict(tickvals=[0, stats_iter],
- ticktext=["Always Wrong", "Always Right"]),
- reversescale=True), )
- fig.update_yaxes(title_text="Examples", showticklabels=True)
- fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
- plot_bgcolor='rgba(0,0,0,0)')
- fig.update_xaxes(showticklabels=True, )
- plotly.offline.plot(fig, filename=file_name + "error_analysis_2D.html",
- auto_open=False)
- del fig
-
-
-def plot_errors_bar(error_on_examples, nb_examples, file_name,
- use_plotly=True, example_ids=None): # pragma: no cover
- r"""Used to generate a barplot of the muber of classifiers that failed to classify each examples
-
- Parameters
- ----------
- error_on_examples : np.array of shape `(nbExamples,)`
- An array counting how many classifiers failed to classifiy each examples.
- classifiers_names : list of str
- The names of the classifiers.
- nb_classifiers : int
- The number of classifiers.
- nb_examples : int
- The number of examples.
- file_name : str
- The name of the file in which the figure will be saved ("error_analysis_2D.png" will be added at the end)
-
- Returns
- -------
- """
- fig, ax = plt.subplots()
- x = np.arange(nb_examples)
- plt.bar(x, 1-error_on_examples)
- plt.title("Number of classifiers that failed to classify each example")
- fig.savefig(file_name + "error_analysis_bar.png", transparent=True)
- plt.close()
- if use_plotly:
- fig = plotly.graph_objs.Figure([plotly.graph_objs.Bar(x=example_ids, y=1-error_on_examples)])
- fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
- plot_bgcolor='rgba(0,0,0,0)')
- plotly.offline.plot(fig, filename=file_name + "error_analysis_bar.html",
- auto_open=False)
-
-
-
-
-def iter_cmap(statsIter): # pragma: no cover
- r"""Used to generate a colormap that will have a tick for each iteration : the whiter the better.
-
- Parameters
- ----------
- statsIter : int
- The number of statistical iterations.
-
- Returns
- -------
- cmap : matplotlib.colors.ListedColorMap object
- The colormap.
- norm : matplotlib.colors.BoundaryNorm object
- The bounds for the colormap.
- """
- cmapList = ["red", "0.0"] + [str(float((i + 1)) / statsIter) for i in
- range(statsIter)]
- cmap = mpl.colors.ListedColormap(cmapList)
- bounds = [-100 * statsIter - 0.5, -0.5]
- for i in range(statsIter):
- bounds.append(i + 0.5)
- bounds.append(statsIter + 0.5)
- norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
- return cmap, norm
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/execution.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/execution.py
deleted file mode 100644
index e620a934..00000000
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/execution.py
+++ /dev/null
@@ -1,247 +0,0 @@
-import logging
-import pandas as pd
-
-from .tracebacks_analysis import save_failed, publish_tracebacks
-from .duration_analysis import plot_durations, get_duration
-from .metric_analysis import get_metrics_scores, publish_metrics_graphs, publish_all_metrics_scores
-from .error_analysis import get_example_errors, publish_example_errors, publish_all_example_errors
-from .feature_importances import get_feature_importances, publish_feature_importances
-
-def analyze(results, stats_iter, benchmark_argument_dictionaries,
- metrics, directory, example_ids, labels): # pragma: no cover
- """Used to analyze the results of the previous benchmarks"""
- data_base_name = benchmark_argument_dictionaries[0]["args"]["name"]
-
- results_means_std, iter_results, flagged_failed, label_names = analyze_iterations(
- results, benchmark_argument_dictionaries,
- stats_iter, metrics, example_ids, labels)
- if flagged_failed:
- save_failed(flagged_failed, directory)
-
- if stats_iter > 1:
- results_means_std = analyze_all(
- iter_results, stats_iter, directory,
- data_base_name, example_ids, label_names)
- return results_means_std
-
-
-def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
- metrics, example_ids, labels):
- r"""Used to extract and format the results of the different
- experimentations performed.
-
- Parameters
- ----------
- results : list
- The result list returned by the benchmark execution function. For each
- executed benchmark, contains
- a flag & a result element.
- The flag is a way to identify to which benchmark the results belong,
- formatted this way :
- `flag = iter_index, [classifierPositive, classifierNegative]` with
- - `iter_index` the index of the statistical iteration
- - `[classifierPositive, classifierNegative]` the indices of the labels
- considered positive and negative
- by the classifier (mainly useful for one versus one multiclass
- classification).
- benchmark_argument_dictionaries : list of dicts
- The list of all the arguments passed to the benchmark executing
- functions.
- statsIter : int
- The number of statistical iterations.
- metrics : list of lists
- THe list containing the metrics and their configuration.
-
- Returns
- -------
- results : list of dicts of dicts
- The list contains a dictionary for each statistical iteration. This
- dictionary contains a dictionary for each
- label combination, regrouping the scores for each metrics and the
- information useful to plot errors on examples.
- """
- logging.debug("Start:\t Analyzing all results")
- iter_results = {"metrics_scores": [i for i in range(stats_iter)],
- "class_metrics_scores": [i for i in range(stats_iter)],
- "example_errors": [i for i in range(stats_iter)],
- "feature_importances": [i for i in range(stats_iter)],
- "durations":[i for i in range(stats_iter)]}
- flagged_tracebacks_list = []
- fig_errors = []
- for iter_index, result, tracebacks in results:
- arguments = get_arguments(benchmark_argument_dictionaries, iter_index)
- labels_names = list(arguments["labels_dictionary"].values())
-
- metrics_scores, class_metric_scores = get_metrics_scores(metrics, result, labels_names)
- example_errors = get_example_errors(labels, result)
- feature_importances = get_feature_importances(result)
- durations = get_duration(result)
- directory = arguments["directory"]
-
- database_name = arguments["args"]["name"]
-
-
- flagged_tracebacks_list += publish_tracebacks(directory, database_name,
- labels_names, tracebacks,
- iter_index)
- res = publish_metrics_graphs(metrics_scores, directory, database_name,
- labels_names, class_metric_scores)
- publish_example_errors(example_errors, directory, database_name,
- labels_names, example_ids, labels)
- publish_feature_importances(feature_importances, directory,
- database_name)
- plot_durations(durations, directory, database_name)
-
- iter_results["metrics_scores"][iter_index] = metrics_scores
- iter_results["class_metrics_scores"][iter_index] = class_metric_scores
- iter_results["example_errors"][iter_index] = example_errors
- iter_results["feature_importances"][iter_index] = feature_importances
- iter_results["labels"] = labels
- iter_results["durations"][iter_index] = durations
-
- logging.debug("Done:\t Analyzing all results")
-
- return res, iter_results, flagged_tracebacks_list, labels_names
-
-
-def analyze_all(iter_results, stats_iter, directory, data_base_name,
- example_ids, label_names): # pragma: no cover
- """Used to format the results in order to plot the mean results on
- the iterations"""
- metrics_analysis, class_metrics_analysis, error_analysis, feature_importances, \
- feature_importances_stds, labels, duration_means, \
- duration_stds = format_previous_results(iter_results)
-
- results = publish_all_metrics_scores(metrics_analysis, class_metrics_analysis,
- directory,
- data_base_name, stats_iter, label_names)
- publish_all_example_errors(error_analysis, directory, stats_iter,
- example_ids, labels)
- publish_feature_importances(feature_importances, directory,
- data_base_name, feature_importances_stds)
- plot_durations(duration_means, directory, data_base_name, duration_stds)
- return results
-
-def get_arguments(benchmark_argument_dictionaries, iter_index):
- r"""Used to get the arguments passed to the benchmark executing function
- corresponding to the flag of an
- experimentation.
-
- Parameters
- ----------
- flag : list
- The needed experimentation's flag.
- benchmark_argument_dictionaries : list of dicts
- The list of all the arguments passed to the benchmark executing
- functions.
-
- Returns
- -------
- benchmark_argument_dictionary : dict
- All the arguments passed to the benchmark executing function for the
- needed experimentation.
- """
- for benchmark_argument_dictionary in benchmark_argument_dictionaries:
- if benchmark_argument_dictionary["flag"] == iter_index:
- return benchmark_argument_dictionary
-
-
-def format_previous_results(iter_results_lists):
- """
- Formats each statistical iteration's result into a mean/std analysis for
- the metrics and adds the errors of each statistical iteration.
-
- Parameters
- ----------
- iter_results_lists : The raw results, for each statistical iteration i
- contains
- - biclass_results[i]["metrics_scores"] is a dictionary with a
- pd.dataframe for each metrics
- - biclass_results[i]["example_errors"], a dicaitonary with a np.array
- for each classifier.
-
- Returns
- -------
- metrics_analysis : The mean and std dataframes for each metrics
-
- error_analysis : A dictionary containing the added errors
- arrays for each classifier
-
- """
- metrics_analysis = {}
- class_metrics_analysis = {}
- feature_importances_analysis = {}
- feature_importances_stds = {}
-
- metric_concat_dict = {}
- for iter_index, metrics_score in enumerate(
- iter_results_lists["metrics_scores"]):
- for metric_name, dataframe in metrics_score.items():
- if metric_name not in metric_concat_dict:
- metric_concat_dict[metric_name] = dataframe
- else:
- metric_concat_dict[metric_name] = pd.concat(
- [metric_concat_dict[metric_name], dataframe])
-
- for metric_name, dataframe in metric_concat_dict.items():
- metrics_analysis[metric_name] = {}
- metrics_analysis[metric_name][
- "mean"] = dataframe.groupby(dataframe.index).mean()
- metrics_analysis[metric_name][
- "std"] = dataframe.groupby(dataframe.index).std(ddof=0)
-
- class_metric_concat_dict = {}
- for iter_index, class_metrics_score in enumerate(
- iter_results_lists["class_metrics_scores"]):
- for metric_name, dataframe in class_metrics_score.items():
- if metric_name not in class_metric_concat_dict:
- class_metric_concat_dict[metric_name] = dataframe
- else:
- class_metric_concat_dict[metric_name] = pd.concat(
- [class_metric_concat_dict[metric_name], dataframe])
-
- for metric_name, dataframe in class_metric_concat_dict.items():
- class_metrics_analysis[metric_name] = {}
- class_metrics_analysis[metric_name][
- "mean"] = dataframe.groupby(dataframe.index).mean()
- class_metrics_analysis[metric_name][
- "std"] = dataframe.groupby(dataframe.index).std(ddof=0)
-
- durations_df_concat = pd.DataFrame(dtype=float)
- for iter_index, durations_df in enumerate(iter_results_lists["durations"]):
- durations_df_concat = pd.concat((durations_df_concat, durations_df),
- axis=1)
- durations_df_concat = durations_df_concat.astype(float)
- grouped_df = durations_df_concat.groupby(durations_df_concat.columns, axis=1)
- duration_means = grouped_df.mean()
- duration_stds = grouped_df.std()
-
- importance_concat_dict = {}
- for iter_index, view_feature_importances in enumerate(
- iter_results_lists["feature_importances"]):
- for view_name, feature_importances in view_feature_importances.items():
- if view_name not in importance_concat_dict:
- importance_concat_dict[view_name] = feature_importances
- else:
- importance_concat_dict[view_name] = pd.concat(
- [importance_concat_dict[view_name], feature_importances])
-
- for view_name, dataframe in importance_concat_dict.items():
- feature_importances_analysis[view_name] = dataframe.groupby(
- dataframe.index).mean()
-
- feature_importances_stds[view_name] = dataframe.groupby(
- dataframe.index).std(ddof=0)
-
- added_example_errors = {}
- for example_errors in iter_results_lists["example_errors"]:
- for classifier_name, errors in example_errors.items():
- if classifier_name not in added_example_errors:
- added_example_errors[classifier_name] = errors
- else:
- added_example_errors[classifier_name] += errors
- error_analysis = added_example_errors
- return metrics_analysis, class_metrics_analysis ,error_analysis, \
- feature_importances_analysis, \
- feature_importances_stds, iter_results_lists["labels"], \
- duration_means, duration_stds
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/feature_importances.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/feature_importances.py
deleted file mode 100644
index 459f664f..00000000
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/feature_importances.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-import plotly
-import pandas as pd
-import numpy as np
-
-from ..monoview.monoview_utils import MonoviewResult
-
-
-def get_feature_importances(result, feature_names=None):
- r"""Extracts the feature importance from the monoview results and stores
- them in a dictionnary :
- feature_importance[view_name] is a pandas.DataFrame of size n_feature*n_clf
- containing a score of importance for each feature.
-
- Parameters
- ----------
- result : list of results
-
- Returns
- -------
- feature_importances : dict of pd.DataFrame
- The dictionary containing all the feature importance for each view as
- pandas DataFrames
- """
- feature_importances = {}
- for classifier_result in result:
- if isinstance(classifier_result, MonoviewResult):
- if classifier_result.view_name not in feature_importances:
- feature_importances[classifier_result.view_name] = pd.DataFrame(
- index=feature_names)
- if hasattr(classifier_result.clf, 'feature_importances_'):
- feature_importances[classifier_result.view_name][
- classifier_result.classifier_name] = classifier_result.clf.feature_importances_
- else:
- feature_importances[classifier_result.view_name][
- classifier_result.classifier_name] = np.zeros(
- classifier_result.n_features)
- return feature_importances
-
-def publish_feature_importances(feature_importances, directory, database_name,
- feature_stds=None): # pragma: no cover
- for view_name, feature_importance in feature_importances.items():
- if not os.path.exists(os.path.join(directory, "feature_importances")):
- os.mkdir(os.path.join(directory, "feature_importances"))
- file_name = os.path.join(directory, "feature_importances",
- database_name + "-" + view_name
- + "-feature_importances")
- if feature_stds is not None:
- feature_std = feature_stds[view_name]
- feature_std.to_csv(file_name + "_dataframe_stds.csv")
- else:
- feature_std = pd.DataFrame(data=np.zeros(feature_importance.shape),
- index=feature_importance.index,
- columns=feature_importance.columns)
- plot_feature_importances(file_name, feature_importance, feature_std)
-
-
-def plot_feature_importances(file_name, feature_importance, feature_std): # pragma: no cover
- feature_importance.to_csv(file_name + "_dataframe.csv")
- hover_text = [["-Feature :" + str(feature_name) +
- "<br>-Classifier : " + classifier_name +
- "<br>-Importance : " + str(
- feature_importance.loc[feature_name][classifier_name]) +
- "<br>-STD : " + str(
- feature_std.loc[feature_name][classifier_name])
- for classifier_name in list(feature_importance.columns)]
- for feature_name in list(feature_importance.index)]
- fig = plotly.graph_objs.Figure(data=plotly.graph_objs.Heatmap(
- x=list(feature_importance.columns),
- y=list(feature_importance.index),
- z=feature_importance.values,
- text=hover_text,
- hoverinfo=["text"],
- colorscale="Greys",
- reversescale=False))
- fig.update_layout(
- xaxis={"showgrid": False, "showticklabels": False, "ticks": ''},
- yaxis={"showgrid": False, "showticklabels": False, "ticks": ''})
- fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
- plot_bgcolor='rgba(0,0,0,0)')
- plotly.offline.plot(fig, filename=file_name + ".html", auto_open=False)
-
- del fig
-
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py
deleted file mode 100644
index fff1e365..00000000
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py
+++ /dev/null
@@ -1,393 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import pandas as pd
-import plotly
-import logging
-
-from ..utils.organization import secure_file_path
-
-
-def get_metrics_scores(metrics, results, label_names):
- r"""Used to extract metrics scores in case of classification
-
- Parameters
- ----------
- metrics : dict
- The metrics names with configuration metrics[i][0] = name of metric i
- results : list of MonoviewResult and MultiviewResults objects
- A list containing all the results for all the monoview experimentations.
-
- Returns
- -------
- metricsScores : dict of dict of list
- Regroups all the scores for each metrics for each classifier and for
- the train and test sets.
- organized as :
- -`metricScores[metric_name]["classifiers_names"]` is a list of all the
- classifiers available for this metric,
- -`metricScores[metric_name]["train_scores"]` is a list of all the
- available classifiers scores on the train set,
- -`metricScores[metric_name]["test_scores"]` is a list of all the
- available classifiers scores on the test set.
- """
- classifier_names = []
- classifier_names = [classifier_result.get_classifier_name()
- for classifier_result in results
- if classifier_result.get_classifier_name()
- not in classifier_names]
- metrics_scores = dict((metric, pd.DataFrame(data=np.zeros((2,
- len(
- classifier_names))),
- index=["train", "test"],
- columns=classifier_names))
- for metric in metrics.keys())
- class_metric_scores = dict((metric, pd.DataFrame(
- index=pd.MultiIndex.from_product([["train", "test"], label_names]),
- columns=classifier_names, dtype=float))
- for metric in metrics)
-
- for metric in metrics.keys():
- for classifier_result in results:
- metrics_scores[metric].loc[
- "train", classifier_result.get_classifier_name()] = \
- classifier_result.metrics_scores[metric][0]
- metrics_scores[metric].loc[
- "test", classifier_result.get_classifier_name()] = \
- classifier_result.metrics_scores[metric][1]
- for label_index, label_name in enumerate(label_names):
- class_metric_scores[metric].loc[(
- "train", label_name),classifier_result.get_classifier_name()] = \
- classifier_result.class_metric_scores[metric][0][label_index]
- class_metric_scores[metric].loc[(
- "test", label_name), classifier_result.get_classifier_name()] = \
- classifier_result.class_metric_scores[metric][1][label_index]
-
- return metrics_scores, class_metric_scores
-
-
-def publish_metrics_graphs(metrics_scores, directory, database_name,
- labels_names, class_metric_scores): # pragma: no cover
- r"""Used to sort the results (names and both scores) in descending test
- score order.
-
- Parameters
- ----------
- metrics_scores : dict of dicts of lists or np.arrays
- Keys : The names of the metrics.
- Values : The scores and names of each classifier .
- directory : str
- The path to the directory where the figures will be saved.
- database_name : str
- The name of the database on which the experiments where conducted.
- labels_names : list of strs
- The name corresponding to each numerical label.
-
- Returns
- -------
- results
- """
- results = []
- for metric_name in metrics_scores.keys():
- logging.debug(
- "Start:\t Score graph generation for " + metric_name)
- train_scores, test_scores, classifier_names, \
- file_name, nb_results, results,\
- class_test_scores = init_plot(results, metric_name,
- metrics_scores[metric_name],
- directory,
- database_name,
- class_metric_scores[metric_name])
-
- plot_metric_scores(train_scores, test_scores, classifier_names,
- nb_results, metric_name, file_name,
- tag=" " + " vs ".join(labels_names))
-
- class_file_name = os.path.join(directory, database_name + "-"
- + metric_name+"-class")
- plot_class_metric_scores(class_test_scores, class_file_name,
- labels_names, classifier_names, metric_name)
- logging.debug(
- "Done:\t Score graph generation for " + metric_name)
- return results
-
-
-def publish_all_metrics_scores(iter_results, class_iter_results, directory,
- data_base_name, stats_iter, label_names,
- min_size=10): # pragma: no cover
- results = []
- secure_file_path(os.path.join(directory, "a"))
-
- for metric_name, scores in iter_results.items():
- train = np.array(scores["mean"].loc["train"])
- test = np.array(scores["mean"].loc["test"])
- classifier_names = np.array(scores["mean"].columns)
- train_std = np.array(scores["std"].loc["train"])
- test_std = np.array(scores["std"].loc["test"])
-
- file_name = os.path.join(directory, data_base_name + "-mean_on_" + str(
- stats_iter) + "_iter-" + metric_name)
- nb_results = classifier_names.shape[0]
-
- plot_metric_scores(train, test, classifier_names, nb_results,
- metric_name, file_name, tag=" averaged",
- train_STDs=train_std, test_STDs=test_std)
- results += [[classifier_name, metric_name, test_mean, test_std]
- for classifier_name, test_mean, test_std
- in zip(classifier_names, test, test_std)]
-
- for metric_name, scores in class_iter_results.items():
- test = np.array([np.array(scores["mean"].iloc[i, :]) for i in range(scores["mean"].shape[0]) if scores["mean"].iloc[i, :].name[0]=='test'])
- classifier_names = np.array(scores["mean"].columns)
- test_std = np.array([np.array(scores["std"].iloc[i, :]) for i in range(scores["std"].shape[0]) if scores["std"].iloc[i, :].name[0]=='test'])
-
- file_name = os.path.join(directory, data_base_name + "-mean_on_" + str(
- stats_iter) + "_iter-" + metric_name+"-class")
-
- plot_class_metric_scores(test, file_name, label_names, classifier_names, metric_name, stds=test_std, tag="averaged")
- return results
-
-def init_plot(results, metric_name, metric_dataframe,
- directory, database_name, class_metric_scores):
- train = np.array(metric_dataframe.loc["train"])
- test = np.array(metric_dataframe.loc["test"])
- class_test = np.array(class_metric_scores.loc["test"])
- classifier_names = np.array(metric_dataframe.columns)
-
- nb_results = metric_dataframe.shape[1]
-
- file_name = os.path.join(directory, database_name + "-" + metric_name)
-
- results += [[classifiers_name, metric_name, test_mean, test_std, class_mean]
- for classifiers_name, test_mean, class_mean, test_std in
- zip(classifier_names, test, np.transpose(class_test),
- np.zeros(len(test)))]
- return train, test, classifier_names, file_name, nb_results, results, \
- class_test
-
-
-def plot_metric_scores(train_scores, test_scores, names, nb_results,
- metric_name,
- file_name,
- tag="", train_STDs=None, test_STDs=None,
- use_plotly=True): # pragma: no cover
- r"""Used to plot and save the score barplot for a specific metric.
-
- Parameters
- ----------
- train_scores : list or np.array of floats
- The scores of each classifier on the training set.
- test_scores : list or np.array of floats
- The scores of each classifier on the testing set.
- names : list or np.array of strs
- The names of all the classifiers.
- nb_results: int
- The number of classifiers to plot.
- metric_name : str
- The plotted metric's name
- file_name : str
- The name of the file where the figure will be saved.
- tag : str
- Some text to personalize the title, must start with a whitespace.
- train_STDs : np.array of floats or None
- The array containing the standard deviations for the averaged scores on the training set.
- test_STDs : np.array of floats or None
- The array containing the standard deviations for the averaged scores on the testing set.
-
- Returns
- -------
- """
-
- figKW, barWidth = get_fig_size(nb_results)
-
- names, train_scores, test_scores, train_STDs, test_STDs = sort_by_test_score(
- train_scores, test_scores, names,
- train_STDs, test_STDs)
-
- f, ax = plt.subplots(nrows=1, ncols=1, **figKW)
- ax.set_title(metric_name + "\n" + tag + " scores for each classifier")
-
- rects = ax.bar(range(nb_results), test_scores, barWidth, color="0.1",
- yerr=test_STDs)
- rect2 = ax.bar(np.arange(nb_results) + barWidth, train_scores, barWidth,
- color="0.8", yerr=train_STDs)
- autolabel(rects, ax, set=1, std=test_STDs)
- autolabel(rect2, ax, set=2, std=train_STDs)
- ax.legend((rects[0], rect2[0]), ('Test', 'Train'))
- ax.set_ylim(-0.1, 1.1)
- ax.set_xticks(np.arange(nb_results) + barWidth / 2)
- ax.set_xticklabels(names, rotation="vertical")
-
- try:
- plt.tight_layout()
- except:
- pass
- f.savefig(file_name + '.png', transparent=True)
- plt.close()
- import pandas as pd
- if train_STDs is None:
- dataframe = pd.DataFrame(np.transpose(np.concatenate((
- train_scores.reshape((train_scores.shape[0], 1)),
- test_scores.reshape((train_scores.shape[0], 1))), axis=1)),
- columns=names, index=["Train", "Test"])
- else:
- dataframe = pd.DataFrame(np.transpose(np.concatenate((
- train_scores.reshape((train_scores.shape[0], 1)),
- train_STDs.reshape((train_scores.shape[0], 1)),
- test_scores.reshape((train_scores.shape[0], 1)),
- test_STDs.reshape((train_scores.shape[0], 1))), axis=1)),
- columns=names, index=["Train", "Train STD", "Test", "Test STD"])
- dataframe.to_csv(file_name + ".csv")
- if use_plotly:
- fig = plotly.graph_objs.Figure()
- fig.add_trace(plotly.graph_objs.Bar(
- name='Train',
- x=names, y=train_scores,
- error_y=dict(type='data', array=train_STDs),
- marker_color="lightgrey",
- ))
- fig.add_trace(plotly.graph_objs.Bar(
- name='Test',
- x=names, y=test_scores,
- error_y=dict(type='data', array=test_STDs),
- marker_color="black",
- ))
-
- fig.update_layout(
- title=metric_name + "<br>" + tag + " scores for each classifier")
- fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
- plot_bgcolor='rgba(0,0,0,0)')
- plotly.offline.plot(fig, filename=file_name + ".html", auto_open=False)
- del fig
-
-
-def plot_class_metric_scores(class_test_scores, class_file_name,
- labels_names, classifier_names, metric_name,
- stds=None, tag=""): # pragma: no cover
- fig = plotly.graph_objs.Figure()
- for lab_index, scores in enumerate(class_test_scores):
- if stds is None:
- std = None
- else:
- std = stds[lab_index]
- fig.add_trace(plotly.graph_objs.Bar(
- name=labels_names[lab_index],
- x=classifier_names, y=scores,
- error_y=dict(type='data', array=std),
- ))
- fig.update_layout(
- title=metric_name + "<br>" + tag + " scores for each classifier")
- fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
- plot_bgcolor='rgba(0,0,0,0)')
- plotly.offline.plot(fig, filename=class_file_name + ".html", auto_open=False)
- del fig
-
-
-def get_fig_size(nb_results, min_size=15, multiplier=1.0, bar_width=0.35):
- r"""Used to get the image size to save the figure and the bar width, depending on the number of scores to plot.
-
- Parameters
- ----------
- nb_results : int
- The number of couple of bar to plot.
- min_size : int
- The minimum size of the image, if there are few classifiers to plot.
- multiplier : float
- The ratio between the image size and the number of classifiers.
- bar_width : float
- The width of the bars in the figure. Mainly here to centralize bar_width.
-
- Returns
- -------
- fig_kwargs : dict of arguments
- The argument restraining the size of the figure, usable directly in the `subplots` function of
- `matplotlib.pyplot`.
- bar_width : float
- The width of the bars in the figure. Mainly here to centralize bar_width.
- """
- size = nb_results * multiplier
- if size < min_size:
- size = min_size
- fig_kwargs = {"figsize": (size, size / 3)}
- return fig_kwargs, bar_width
-
-
-def autolabel(rects, ax, set=1, std=None): # pragma: no cover
- r"""Used to print the score below the bars.
-
- Parameters
- ----------
- rects : pyplot bar object
- THe bars.
- ax : pyplot ax object
- The ax.
- set : integer
- 1 means the test scores, anything else means the train score
- std: None or array
- The standard deviations in the case of statsIter results.
-
- Returns
- -------
- """
- if set == 1:
- text_height = -0.05
- weight = "bold"
- else:
- text_height = -0.07
- weight = "normal"
- for rectIndex, rect in enumerate(rects):
- height = rect.get_height()
- if std is not None:
- ax.text(rect.get_x() + rect.get_width() / 2., text_height,
- "%.2f" % height + u'\u00B1' + "%.2f" % std[rectIndex],
- weight=weight,
- ha='center', va='bottom', size="x-small")
- else:
- ax.text(rect.get_x() + rect.get_width() / 2., text_height,
- "%.2f" % height, weight=weight,
- ha='center', va='bottom', size="small")
-
-
-def sort_by_test_score(train_scores, test_scores, names, train_STDs=None,
- test_STDs=None):
- r"""Used to sort the results (names and both scores) in descending test score order.
-
- Parameters
- ----------
- train_scores : np.array of floats
- The scores of each classifier on the training set.
- test_scores : np.array of floats
- The scores of each classifier on the testing set.
- names : np.array of strs
- The names of all the classifiers.
- train_STDs : np.array of floats or None
- The array containing the standard deviations for the averaged scores on the training set.
- test_STDs : np.array of floats or None
- The array containing the standard deviations for the averaged scores on the testing set.
-
- Returns
- -------
- sorted_names : np.array of strs
- The names of all the classifiers, sorted in descending test score order.
- sorted_train_scores : np.array of floats
- The scores of each classifier on the training set, sorted in descending test score order.
- sorted_test_scores : np.array of floats
- The scores of each classifier on the testing set, sorted in descending test score order.
- sorted_train_STDs : np.array of floats or None
- The array containing the standard deviations for the averaged scores on the training set,
- sorted in descending test score order.
- sorted_test_STDs : np.array of floats or None
- The array containing the standard deviations for the averaged scores on the testing set,
- sorted in descending test score order.
- """
- sorted_indices = np.argsort(test_scores)
- sorted_test_scores = test_scores[sorted_indices]
- sorted_train_scores = train_scores[sorted_indices]
- sorted_names = names[sorted_indices]
- if train_STDs is not None and test_STDs is not None:
- sorted_train_STDs = train_STDs[sorted_indices]
- sorted_test_STDs = test_STDs[sorted_indices]
- else:
- sorted_train_STDs = None
- sorted_test_STDs = None
- return sorted_names, sorted_train_scores, sorted_test_scores, sorted_train_STDs, sorted_test_STDs
\ No newline at end of file
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/noise_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/noise_analysis.py
deleted file mode 100644
index b4fc8121..00000000
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/noise_analysis.py
+++ /dev/null
@@ -1,56 +0,0 @@
-#
-# import numpy as np
-# import pandas as pd
-# import matplotlib.pyplot as plt
-# import os
-# from matplotlib.patches import Patch
-#
-#
-# def plot_results_noise(directory, noise_results, metric_to_plot, name,
-# width=0.1):
-# avail_colors = ["tab:blue", "tab:orange", "tab:brown", "tab:gray",
-# "tab:olive", "tab:red", ]
-# colors = {}
-# lengend_patches = []
-# noise_levels = np.array([noise_level for noise_level, _ in noise_results])
-# df = pd.DataFrame(
-# columns=['noise_level', 'classifier_name', 'mean_score', 'score_std'], )
-# if len(noise_results) > 1:
-# width = np.min(np.diff(noise_levels))
-# for noise_level, noise_result in noise_results:
-# classifiers_names, meaned_metrics, metric_stds = [], [], []
-# for noise_result in noise_result:
-# classifier_name = noise_result[0].split("-")[0]
-# if noise_result[1] is metric_to_plot:
-# classifiers_names.append(classifier_name)
-# meaned_metrics.append(noise_result[2])
-# metric_stds.append(noise_result[3])
-# if classifier_name not in colors:
-# try:
-# colors[classifier_name] = avail_colors.pop(0)
-# except IndexError:
-# colors[classifier_name] = "k"
-# classifiers_names, meaned_metrics, metric_stds = np.array(
-# classifiers_names), np.array(meaned_metrics), np.array(metric_stds)
-# sorted_indices = np.argsort(-meaned_metrics)
-# for index in sorted_indices:
-# row = pd.DataFrame(
-# {'noise_level': noise_level,
-# 'classifier_name': classifiers_names[index],
-# 'mean_score': meaned_metrics[index],
-# 'score_std': metric_stds[index]}, index=[0])
-# df = pd.concat([df, row])
-# plt.bar(noise_level, meaned_metrics[index], yerr=metric_stds[index],
-# width=0.5 * width, label=classifiers_names[index],
-# color=colors[classifiers_names[index]])
-# for classifier_name, color in colors.items():
-# lengend_patches.append(Patch(facecolor=color, label=classifier_name))
-# plt.legend(handles=lengend_patches, loc='lower center',
-# bbox_to_anchor=(0.5, 1.05), ncol=2)
-# plt.ylabel(metric_to_plot)
-# plt.title(name)
-# plt.xticks(noise_levels)
-# plt.xlabel("Noise level")
-# plt.savefig(os.path.join(directory, name + "_noise_analysis.png"))
-# plt.close()
-# df.to_csv(os.path.join(directory, name + "_noise_analysis.csv"))
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/tracebacks_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/tracebacks_analysis.py
deleted file mode 100644
index 329a27f6..00000000
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/tracebacks_analysis.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-
-
-def publish_tracebacks(directory, database_name, labels_names, tracebacks,
- iter_index):
- if tracebacks:
- with open(os.path.join(directory, database_name +
- "-iter" + str(iter_index) +
- "-tacebacks.txt"),
- "w") as traceback_file:
- failed_list = save_dict_to_text(tracebacks, traceback_file)
- flagged_list = [_ + "-iter" + str(iter_index) for _ in failed_list]
- else:
- flagged_list = {}
- return flagged_list
-
-
-def save_dict_to_text(dictionnary, output_file):
- # TODO : smarter way must exist
- output_file.write("Failed algorithms : \n\t" + ",\n\t".join(
- dictionnary.keys()) + ".\n\n\n")
- for key, value in dictionnary.items():
- output_file.write(key)
- output_file.write("\n\n")
- output_file.write(value)
- output_file.write("\n\n\n")
- return dictionnary.keys()
-
-
-def save_failed(failed_list, directory):
- with open(os.path.join(directory, "failed_algorithms.txt"),
- "w") as failed_file:
- failed_file.write(
- "The following algorithms sent an error, the tracebacks are stored "
- "in the coressponding directory :\n")
- failed_file.write(", \n".join(failed_list) + ".")
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/__init__.py b/multiview_platform/mono_multi_view_classifiers/utils/__init__.py
deleted file mode 100644
index e0473b52..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from . import dataset, execution, hyper_parameter_search, transformations
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/base.py b/multiview_platform/mono_multi_view_classifiers/utils/base.py
deleted file mode 100644
index 34894b5a..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/base.py
+++ /dev/null
@@ -1,377 +0,0 @@
-import numpy as np
-from sklearn.base import BaseEstimator
-from abc import abstractmethod
-from datetime import timedelta as hms
-from tabulate import tabulate
-from sklearn.metrics import confusion_matrix as confusion
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
-
-from multiview_platform.mono_multi_view_classifiers import metrics
-
-
-class BaseClassifier(BaseEstimator, ):
-
- def gen_best_params(self, detector):
- """
- return best parameters of detector
- Parameters
- ----------
- detector :
-
- Returns
- -------
- best param : dictionary with param name as key and best parameters
- value
- """
- return dict(
- (param_name, detector.best_params_[param_name]) for param_name in
- self.param_names)
-
- def gen_params_from_detector(self, detector):
- if self.classed_params:
- classed_dict = dict((classed_param, get_names(
- detector.cv_results_["param_" + classed_param]))
- for classed_param in self.classed_params)
- if self.param_names:
- return [(param_name,
- np.array(detector.cv_results_["param_" + param_name]))
- if param_name not in self.classed_params else (
- param_name, classed_dict[param_name])
- for param_name in self.param_names]
- else:
- return [()]
-
- def gen_distribs(self):
- return dict((param_name, distrib) for param_name, distrib in
- zip(self.param_names, self.distribs))
-
- def params_to_string(self):
- """
- Formats the parameters of the classifier as a string
- """
- return ", ".join(
- [param_name + " : " + self.to_str(param_name) for param_name in
- self.param_names])
-
- def get_config(self):
- """
- Generates a string to containing all the information about the
- classifier's configuration
- """
- if self.param_names:
- return self.__class__.__name__ + " with " + self.params_to_string()
- else:
- return self.__class__.__name__ + " with no config."
-
- def get_base_estimator(self, base_estimator, estimator_config):
- if estimator_config is None:
- estimator_config = {}
- if base_estimator is None:
- return DecisionTreeClassifier(**estimator_config)
- if isinstance(base_estimator, str): # pragma: no cover
- if base_estimator == "DecisionTreeClassifier":
- return DecisionTreeClassifier(**estimator_config)
- elif base_estimator == "AdaboostClassifier":
- return AdaBoostClassifier(**estimator_config)
- elif base_estimator == "RandomForestClassifier":
- return RandomForestClassifier(**estimator_config)
- else:
- raise ValueError('Base estimator string {} does not match an available classifier.'.format(base_estimator))
- elif isinstance(base_estimator, BaseEstimator):
- return base_estimator.set_params(**estimator_config)
- else:
- raise ValueError('base_estimator must be either a string or a BaseEstimator child class, it is {}'.format(type(base_estimator)))
-
-
- def to_str(self, param_name):
- """
- Formats a parameter into a string
- """
- if param_name in self.weird_strings:
- string = ""
- if "class_name" in self.weird_strings[param_name]:
- string += self.get_params()[param_name].__class__.__name__
- if "config" in self.weird_strings[param_name]:
- string += "( with " + self.get_params()[
- param_name].params_to_string() + ")"
- else:
- return str(self.get_params()[param_name])
-
- def get_interpretation(self, directory, base_file_name, y_test,
- multi_class=False):
- """
- Base method that returns an empty string if there is not interpretation
- method in the classifier's module
- """
- return ""
-
- def accepts_multi_class(self, random_state, n_samples=10, dim=2,
- n_classes=3):
- """
- Base function to test if the classifier accepts a multiclass task.
- It is highly recommended to overwrite it with a simple method that
- returns True or False in the classifier's module, as it will speed up
- the benchmark
- """
- if int(n_samples / n_classes) < 1:
- raise ValueError(
- "n_samples ({}) / n_class ({}) must be over 1".format(
- n_samples,
- n_classes))
- # if hasattr(self, "accepts_mutli_class"):
- # return self.accepts_multi_class
- fake_mc_X = random_state.randint(low=0, high=101,
- size=(n_samples, dim))
- fake_mc_y = [class_index
- for _ in range(int(n_samples / n_classes))
- for class_index in range(n_classes)]
- fake_mc_y += [0 for _ in range(n_samples % n_classes)]
- fake_mc_y = np.asarray(fake_mc_y)
- try:
- self.fit(fake_mc_X, fake_mc_y)
- # self.predict(fake_mc_X)
- return True
- except ValueError:
- return False
-
-
-def get_names(classed_list):
- return np.array([object_.__class__.__name__ for object_ in classed_list])
-
-
-def get_metric(metrics_dict):
- """
- Fetches the metric module in the metrics package
- """
- for metric_name, metric_kwargs in metrics_dict.items():
- if metric_name.endswith("*"):
- princ_metric_name = metric_name[:-1]
- princ_metric_kwargs = metric_kwargs
- metric_module = getattr(metrics, princ_metric_name)
- return metric_module, princ_metric_kwargs
-
-
-class ResultAnalyser():
- """
- A shared result analysis tool for mono and multiview classifiers.
- The main utility of this class is to generate a txt file summarizing
- the results and possible interpretation for the classifier.
- """
-
- def __init__(self, classifier, classification_indices, k_folds,
- hps_method, metrics_dict, n_iter, class_label_names,
- pred, directory, base_file_name, labels,
- database_name, nb_cores, duration):
- """
-
- Parameters
- ----------
- classifier: estimator used for classification
-
- classification_indices: list of indices for train test sets
-
- k_folds: the sklearn StratifiedkFolds object
-
- hps_method: string naming the hyper-parameter search method
-
- metrics_dict: list of the metrics to compute on the results
-
- n_iter: number of HPS iterations
-
- class_label_names: list of the names of the labels
-
- train_pred: classifier's prediction on the training set
-
- test_pred: classifier's prediction on the testing set
-
- directory: directory where to save the result analysis
-
- labels: the full labels array (Y in sklearn)
-
- database_name: the name of the database
-
- nb_cores: number of cores/threads use for the classification
-
- duration: duration of the classification
- """
- self.classifier = classifier
- self.train_indices, self.test_indices = classification_indices
- self.k_folds = k_folds
- self.hps_method = hps_method
- self.metrics_dict = metrics_dict
- self.n_iter = n_iter
- self.class_label_names = class_label_names
- self.pred = pred
- self.directory = directory
- self.base_file_name = base_file_name
- self.labels = labels
- self.string_analysis = ""
- self.database_name = database_name
- self.nb_cores = nb_cores
- self.duration = duration
- self.metric_scores = {}
- self.class_metric_scores = {}
-
- def get_all_metrics_scores(self, ):
- """
- Get the scores for all the metrics in the list
- Returns
- -------
- """
- for metric, metric_args in self.metrics_dict.items():
- class_train_scores, class_test_scores, train_score, test_score\
- = self.get_metric_score(metric, metric_args)
- self.class_metric_scores[metric] = (class_train_scores,
- class_test_scores)
- self.metric_scores[metric] = (train_score, test_score)
-
- def get_metric_score(self, metric, metric_kwargs):
- """
- Get the train and test scores for a specific metric and its arguments
-
- Parameters
- ----------
-
- metric : name of the metric, must be implemented in metrics
-
- metric_kwargs : the dictionary containing the arguments for the metric.
-
- Returns
- -------
- train_score, test_score
- """
- if not metric.endswith("*"):
- metric_module = getattr(metrics, metric)
- else:
- metric_module = getattr(metrics, metric[:-1])
- class_train_scores = []
- class_test_scores = []
- for label_value in np.unique(self.labels):
- train_example_indices = self.train_indices[np.where(self.labels[self.train_indices]==label_value)[0]]
- test_example_indices = self.test_indices[np.where(self.labels[self.test_indices] == label_value)[0]]
- class_train_scores.append(metric_module.score(y_true=self.labels[train_example_indices],
- y_pred=self.pred[train_example_indices],
- **metric_kwargs))
- class_test_scores.append(metric_module.score(y_true=self.labels[test_example_indices],
- y_pred=self.pred[test_example_indices],
- **metric_kwargs))
- train_score = metric_module.score(y_true=self.labels[self.train_indices],
- y_pred=self.pred[self.train_indices],
- **metric_kwargs)
- test_score = metric_module.score(y_true=self.labels[self.test_indices],
- y_pred=self.pred[self.test_indices],
- **metric_kwargs)
- return class_train_scores, class_test_scores, train_score, test_score
-
- def print_metric_score(self,):
- """
- Generates a string, formatting the metrics configuration and scores
-
- Parameters
- ----------
- metric_scores : dictionary of train_score, test_score for each metric
-
- metric_list : list of metrics
-
- Returns
- -------
- metric_score_string string formatting all metric results
- """
- metric_score_string = "\n\n"
- for metric, metric_kwargs in self.metrics_dict.items():
- if metric.endswith("*"):
- metric_module = getattr(metrics, metric[:-1])
- else:
- metric_module = getattr(metrics, metric)
- metric_score_string += "\tFor {} : ".format(metric_module.get_config(
- **metric_kwargs))
- metric_score_string += "\n\t\t- Score on train : {}".format(self.metric_scores[metric][0])
- metric_score_string += "\n\t\t- Score on test : {}".format(self.metric_scores[metric][1])
- metric_score_string += "\n\n"
- metric_score_string += "Test set confusion matrix : \n\n"
- self.confusion_matrix = confusion(y_true=self.labels[self.test_indices], y_pred=self.pred[self.test_indices])
- formatted_conf = [[label_name]+list(row) for label_name, row in zip(self.class_label_names, self.confusion_matrix)]
- metric_score_string+=tabulate(formatted_conf, headers= ['']+self.class_label_names, tablefmt='fancy_grid')
- metric_score_string += "\n\n"
- return metric_score_string
-
- @abstractmethod
- def get_view_specific_info(self): # pragma: no cover
- pass
-
- @abstractmethod
- def get_base_string(self): # pragma: no cover
- pass
-
- def get_db_config_string(self,):
- """
- Generates a string, formatting all the information on the database
-
- Parameters
- ----------
-
- Returns
- -------
- db_config_string string, formatting all the information on the database
- """
- learning_ratio = len(self.train_indices) / (
- len(self.train_indices) + len(self.test_indices))
- db_config_string = "Database configuration : \n"
- db_config_string += "\t- Database name : {}\n".format(self.database_name)
- db_config_string += self.get_view_specific_info()
- db_config_string += "\t- Learning Rate : {}\n".format(learning_ratio)
- db_config_string += "\t- Labels used : " + ", ".join(
- self.class_label_names) + "\n"
- db_config_string += "\t- Number of cross validation folds : {}\n\n".format(self.k_folds.n_splits)
- return db_config_string
-
- def get_classifier_config_string(self, ):
- """
- Formats the information about the classifier and its configuration
-
- Returns
- -------
- A string explaining the classifier's configuration
- """
- classifier_config_string = "Classifier configuration : \n"
- classifier_config_string += "\t- " + self.classifier.get_config()+ "\n"
- classifier_config_string += "\t- Executed on {} core(s) \n".format(
- self.nb_cores)
-
- if self.hps_method.startswith('randomized_search'):
- classifier_config_string += "\t- Got configuration using randomized search with {} iterations \n" .format(self.n_iter)
- return classifier_config_string
-
- def analyze(self, ):
- """
- Main function used in the monoview and multiview classification scripts
-
- Returns
- -------
- string_analysis : a string that will be stored in the log and in a txt
- file
- image_analysis : a list of images to save
- metric_scores : a dictionary of {metric: (train_score, test_score)}
- used in later analysis.
- """
- string_analysis = self.get_base_string()
- string_analysis += self.get_db_config_string()
- string_analysis += self.get_classifier_config_string()
- self.get_all_metrics_scores()
- string_analysis += self.print_metric_score()
- string_analysis += "\n\n Classification took {}".format(hms(seconds=int(self.duration)))
- string_analysis += "\n\n Classifier Interpretation : \n"
- string_analysis += self.classifier.get_interpretation(
- self.directory, self.base_file_name,
- self.labels[self.test_indices])
- image_analysis = {}
- return string_analysis, image_analysis, self.metric_scores, \
- self.class_metric_scores, self.confusion_matrix
-
-
-base_boosting_estimators = [DecisionTreeClassifier(max_depth=1),
- DecisionTreeClassifier(max_depth=2),
- DecisionTreeClassifier(max_depth=3),
- DecisionTreeClassifier(max_depth=4),
- DecisionTreeClassifier(max_depth=5), ]
\ No newline at end of file
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/configuration.py b/multiview_platform/mono_multi_view_classifiers/utils/configuration.py
deleted file mode 100644
index fcd62c6d..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/configuration.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-
-import yaml
-
-
-def get_the_args(path_to_config_file="../config_files/config.yml"):
- """
- The function for extracting the args for a '.yml' file.
-
- Parameters
- ----------
- path_to_config_file : str, path to the yml file containing the configuration
-
- Returns
- -------
- yaml_config : dict, the dictionary conaining the configuration for the
- benchmark
-
- """
- with open(path_to_config_file, 'r') as stream:
- yaml_config = yaml.safe_load(stream)
- return pass_default_config(**yaml_config)
-
-
-def pass_default_config(log=True,
- name=["plausible", ],
- label="_",
- file_type=".hdf5",
- views=None,
- pathf="../data/",
- nice=0,
- random_state=42,
- nb_cores=1,
- full=True,
- debug=False,
- add_noise=False,
- noise_std=0.0,
- res_dir="../results/",
- track_tracebacks=True,
- split=0.49,
- nb_folds=5,
- nb_class=None,
- classes=None,
- type=["multiview", ],
- algos_monoview=["all"],
- algos_multiview=["svm_jumbo_fusion", ],
- stats_iter=2,
- metrics={"accuracy_score":{}, "f1_score":{}},
- metric_princ="accuracy_score",
- hps_type="Random",
- hps_iter=1,
- hps_kwargs={'n_iter':10, "equivalent_draws":True},
- **kwargs):
- """
-
- :param log:
- :param name:
- :param label:
- :param file_type:
- :param views:
- :param pathf:
- :param nice:
- :param random_state:
- :param nb_cores:
- :param full:
- :param debug:
- :param add_noise:
- :param noise_std:
- :param res_dir:
- :param track_tracebacks:
- :param split:
- :param nb_folds:
- :param nb_class:
- :param classes:
- :param type:
- :param algos_monoview:
- :param algos_multiview:
- :param stats_iter:
- :param metrics:
- :param metric_princ:
- :param hps_type:
- :param hps_iter:
- :return:
- """
- args = dict(
- (key, value) for key, value in locals().items() if key != "kwargs")
- args = dict(args, **kwargs)
- return args
-
-
-def save_config(directory, arguments):
- """
- Saves the config file in the result directory.
- """
- with open(os.path.join(directory, "config_file.yml"), "w") as stream:
- yaml.dump(arguments, stream)
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/dataset.py b/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
deleted file mode 100644
index 00ea3aad..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
+++ /dev/null
@@ -1,769 +0,0 @@
-import logging
-import os
-import select
-import sys
-from abc import abstractmethod
-
-import h5py
-import numpy as np
-from scipy import sparse
-
-from .organization import secure_file_path
-
-class Dataset():
-
- @abstractmethod
- def get_nb_examples(self): # pragma: no cover
- pass
-
- @abstractmethod
- def get_v(self, view_index, example_indices=None): # pragma: no cover
- pass
-
- @abstractmethod
- def get_label_names(self, example_indices=None): # pragma: no cover
- pass
-
- @abstractmethod
- def get_labels(self, example_indices=None): # pragma: no cover
- pass
-
- @abstractmethod
- def filter(self, labels, label_names, example_indices, view_names,
- path=None): # pragma: no cover
- pass
-
- def init_example_indices(self, example_indices=None):
- """If no example indices are provided, selects all the examples."""
- if example_indices is None:
- return range(self.get_nb_examples())
- else:
- return example_indices
-
- def get_shape(self, view_index=0, example_indices=None):
- """
- Gets the shape of the needed view on the asked examples
-
- Parameters
- ----------
- view_index : int
- The index of the view to extract
- example_indices : numpy.ndarray
- The array containing the indices of the examples to extract.
-
- Returns
- -------
- Tuple containing the shape
-
- """
- return self.get_v(view_index, example_indices=example_indices).shape
-
- def to_numpy_array(self, example_indices=None, view_indices=None):
- """
- To concatenate the needed views in one big numpy array while saving the
- limits of each view in a list, to be able to retrieve them later.
-
- Parameters
- ----------
- example_indices : array like,
- The indices of the examples to extract from the dataset
-
- view_indices : array like,
- The indices of the view to concatenate in the numpy array
-
- Returns
- -------
- concat_views : numpy array,
- The numpy array containing all the needed views.
-
- view_limits : list of int
- The limits of each slice used to extract the views.
-
- """
- view_limits = [0]
- for view_index in view_indices:
- view_data = self.get_v(view_index, example_indices=example_indices)
- nb_features = view_data.shape[1]
- view_limits.append(view_limits[-1] + nb_features)
- concat_views = np.concatenate([self.get_v(view_index,
- example_indices=example_indices)
- for view_index in view_indices], axis=1)
- return concat_views, view_limits
-
- def select_labels(self, selected_label_names):
- selected_labels = [self.get_label_names().index(label_name.decode())
- if isinstance(label_name, bytes)
- else self.get_label_names().index(label_name)
- for label_name in selected_label_names]
- selected_indices = np.array([index
- for index, label in
- enumerate(self.get_labels())
- if label in selected_labels])
- labels = np.array([selected_labels.index(self.get_labels()[idx])
- for idx in selected_indices])
- return labels, selected_label_names, selected_indices
-
- def select_views_and_labels(self, nb_labels=None,
- selected_label_names=None, random_state=None,
- view_names=None, path_for_new="../data/"):
- if view_names is None and selected_label_names is None and nb_labels is None: # pragma: no cover
- pass
- else:
- selected_label_names = self.check_selected_label_names(nb_labels,
- selected_label_names,
- random_state)
- labels, label_names, example_indices = self.select_labels(
- selected_label_names)
- self.filter(labels, label_names, example_indices, view_names,
- path_for_new)
- labels_dictionary = dict(
- (labelIndex, labelName) for labelIndex, labelName in
- enumerate(self.get_label_names()))
- return labels_dictionary
-
- def check_selected_label_names(self, nb_labels=None,
- selected_label_names=None,
- random_state=np.random.RandomState(42)):
- if selected_label_names is None or nb_labels is None or len(
- selected_label_names) < nb_labels:
- if selected_label_names is None:
- nb_labels_to_add = nb_labels
- selected_label_names = []
- elif nb_labels is not None:
- nb_labels_to_add = nb_labels - len(selected_label_names)
- else:
- nb_labels_to_add = 0
- labels_names_to_choose = [available_label_name
- for available_label_name
- in self.get_label_names()
- if available_label_name
- not in selected_label_names]
- added_labels_names = random_state.choice(labels_names_to_choose,
- nb_labels_to_add,
- replace=False)
- selected_label_names = list(selected_label_names) + list(
- added_labels_names)
- elif len(selected_label_names) > nb_labels:
- selected_label_names = list(
- random_state.choice(selected_label_names, nb_labels,
- replace=False))
-
- return selected_label_names
-
-
-class RAMDataset(Dataset):
-
- def __init__(self, views=None, labels=None, are_sparse=False,
- view_names=None, labels_names=None, example_ids=None,
- name=None):
- self.saved_on_disk = False
- self.views = views
- self.labels = np.asarray(labels)
- if isinstance(are_sparse, bool): # pragma: no cover
- self.are_sparse = [are_sparse for _ in range(len(views))]
- else:
- self.are_sparse = are_sparse
- self.view_names = view_names
- self.labels_names = labels_names
- self.example_ids = example_ids
- self.view_dict = dict((view_name, view_ind)
- for view_name, view_ind
- in zip(view_names, range(len(views))))
- self.name = name
- self.nb_view = len(self.views)
- self.is_temp = False
-
- def get_view_name(self, view_idx):
- return self.view_names[view_idx]
-
- def init_attrs(self):
- """
- Used to init the two attributes that are modified when self.dataset
- changes
-
- Returns
- -------
-
- """
-
- self.nb_view = len(self.views)
- self.view_dict = dict((view_ind, self.view_names[view_ind])
- for view_ind in range(self.nb_view))
-
- def get_nb_examples(self):
- return self.views[0].shape[0]
-
- def get_label_names(self, example_indices=None, decode=True):
- selected_labels = self.get_labels(example_indices)
- if decode:
- return [label_name.encode("utf-8")
- for label, label_name in enumerate(self.labels_names)
- if label in selected_labels]
- else:
- return [label_name.encode("utf-8")
- for label, label_name in enumerate(self.labels_names)
- if label in selected_labels]
-
- def get_labels(self, example_indices=None):
- example_indices = self.init_example_indices(example_indices)
- return self.labels[example_indices]
-
- def get_v(self, view_index, example_indices=None):
- example_indices = self.init_example_indices(example_indices)
- if type(example_indices) is int:
- return self.views[view_index][example_indices, :]
- else:
- example_indices = np.asarray(example_indices)
- # sorted_indices = np.argsort(example_indices)
- # example_indices = example_indices[sorted_indices]
- if not self.are_sparse[view_index]:
- return self.views[view_index][
- example_indices, :]
- else: # pragma: no cover
- # TODO Sparse support
- pass
-
- def get_nb_class(self, example_indices=None):
- """Gets the number of class of the dataset"""
- example_indices = self.init_example_indices(example_indices)
- return len(np.unique(self.labels[example_indices]))
-
- def filter(self, labels, label_names, example_indices, view_names,
- path=None):
- if self.example_ids is not None:
- self.example_ids = self.example_ids[example_indices]
- self.labels = self.labels[example_indices]
- self.labels_names = [name for lab_index, name
- in enumerate(self.labels_names)
- if lab_index in np.unique(self.labels)]
- self.labels = np.array(
- [np.where(label == np.unique(self.labels))[0] for label in
- self.labels])
- self.view_names = view_names
- new_views = []
- for new_view_ind, view_name in enumerate(self.view_names):
- new_views.append(
- self.views[self.view_dict[view_name]][example_indices, :])
- self.views = new_views
- self.view_dict = dict((view_name, view_ind)
- for view_ind, view_name
- in enumerate(self.view_names))
- self.nb_view = len(self.views)
-
- def get_view_dict(self):
- return self.view_dict
-
- def get_name(self):
- return self.name
-
-
-class HDF5Dataset(Dataset):
- """
- Class of Dataset
-
- This class is used to encapsulate the multiview dataset while keeping it stored on the disk instead of in RAM.
-
-
- Parameters
- ----------
- views : list of numpy arrays or None
- The list containing each view of the dataset as a numpy array of shape
- (nb examples, nb features).
-
- labels : numpy array or None
- The labels for the multiview dataset, of shape (nb examples, ).
-
- are_sparse : list of bool, or None
- The list of boolean telling if each view is sparse or not.
-
- file_name : str, or None
- The name of the hdf5 file that will be created to store the multiview
- dataset.
-
- view_names : list of str, or None
- The name of each view.
-
- path : str, or None
- The path where the hdf5 dataset file will be stored
-
- hdf5_file : h5py.File object, or None
- If not None, the dataset will be imported directly from this file.
-
- labels_names : list of str, or None
- The name for each unique value of the labels given in labels.
-
- is_temp : bool
- Used if a temporary dataset has to be used by the benchmark.
-
- Attributes
- ----------
- dataset : h5py.File object
- The h5py file pbject that points to the hdf5 dataset on the disk.
-
- nb_view : int
- The number of views in the dataset.
-
- view_dict : dict
- The dictionnary with the name of each view as the keys and their indices
- as values
- """
-
- # The following methods use hdf5
-
- def __init__(self, views=None, labels=None, are_sparse=False,
- file_name="dataset.hdf5", view_names=None, path="",
- hdf5_file=None, labels_names=None, is_temp=False,
- example_ids=None, ):
- self.is_temp = False
- if hdf5_file is not None:
- self.dataset = hdf5_file
- self.init_attrs()
- else:
- secure_file_path(os.path.join(path, file_name))
- dataset_file = h5py.File(os.path.join(path, file_name), "w")
- if view_names is None:
- view_names = ["View" + str(index) for index in
- range(len(views))]
- if isinstance(are_sparse, bool): # pragma: no cover
- are_sparse = [are_sparse for _ in views]
- for view_index, (view_name, view, is_sparse) in enumerate(
- zip(view_names, views, are_sparse)):
- view_dataset = dataset_file.create_dataset(
- "View" + str(view_index),
- view.shape,
- data=view)
- view_dataset.attrs["name"] = view_name
- view_dataset.attrs["sparse"] = is_sparse
- labels_dataset = dataset_file.create_dataset("Labels",
- shape=labels.shape,
- data=labels)
- if labels_names is None:
- labels_names = [str(index) for index in np.unique(labels)]
- labels_dataset.attrs["names"] = [label_name.encode()
- if not isinstance(label_name,
- bytes)
- else label_name
- for label_name in labels_names]
- meta_data_grp = dataset_file.create_group("Metadata")
- meta_data_grp.attrs["nbView"] = len(views)
- meta_data_grp.attrs["nbClass"] = len(np.unique(labels))
- meta_data_grp.attrs["datasetLength"] = len(labels)
- dataset_file.close()
- self.update_hdf5_dataset(os.path.join(path, file_name))
- if example_ids is not None:
- example_ids = [example_id if not is_just_number(example_id)
- else "ID_" + example_id for example_id in
- example_ids]
- self.example_ids = example_ids
- else:
- self.example_ids = ["ID_" + str(i)
- for i in range(labels.shape[0])]
-
- def get_v(self, view_index, example_indices=None):
- r""" Extract the view and returns a numpy.ndarray containing the description
- of the examples specified in example_indices
-
- Parameters
- ----------
- view_index : int
- The index of the view to extract
- example_indices : numpy.ndarray
- The array containing the indices of the examples to extract.
-
- Returns
- -------
- A numpy.ndarray containing the view data for the needed examples
- """
- example_indices = self.init_example_indices(example_indices)
- if type(example_indices) is int:
- return self.dataset["View" + str(view_index)][example_indices, :]
- else:
- example_indices = np.array(example_indices)
- # sorted_indices = np.argsort(example_indices)
- # example_indices = example_indices[sorted_indices]
-
- if not self.dataset["View" + str(view_index)].attrs["sparse"]:
- return self.dataset["View" + str(view_index)][()][
- example_indices, :] # [np.argsort(sorted_indices), :]
- else: # pragma: no cover
- # Work in progress
- pass
-
- def get_view_name(self, view_idx):
- """
- Method to get a view's name from its index.
-
- Parameters
- ----------
- view_idx : int
- The index of the view in the dataset
-
- Returns
- -------
- The view's name.
-
- """
- return self.dataset["View" + str(view_idx)].attrs["name"]
-
- def init_attrs(self):
- """
- Used to init the attributes that are modified when self.dataset
- changes
-
- Returns
- -------
-
- """
- self.nb_view = self.dataset["Metadata"].attrs["nbView"]
- self.view_dict = self.get_view_dict()
- if "example_ids" in self.dataset["Metadata"].keys():
- self.example_ids = [example_id.decode()
- if not is_just_number(example_id.decode())
- else "ID_" + example_id.decode()
- for example_id in
- self.dataset["Metadata"]["example_ids"]]
- else:
- self.example_ids = ["ID_"+str(i) for i in
- range(self.dataset["Labels"].shape[0])]
-
- def get_nb_examples(self):
- """
- Used to get the number of examples available in hte dataset
-
- Returns
- -------
-
- """
- return self.dataset["Metadata"].attrs["datasetLength"]
-
- def get_view_dict(self):
- """
- Returns the dictionary with view indices as keys and their corresponding
- names as values
- """
- view_dict = {}
- for view_index in range(self.nb_view):
- view_dict[self.dataset["View" + str(view_index)].attrs[
- "name"]] = view_index
- return view_dict
-
- def get_label_names(self, decode=True, example_indices=None):
- """
- Used to get the list of the label names for the given set of examples
-
- Parameters
- ----------
- decode : bool
- If True, will decode the label names before listing them
-
- example_indices : numpy.ndarray
- The array containing the indices of the needed examples
-
- Returns
- -------
-
- """
- selected_labels = self.get_labels(example_indices)
- if decode:
- return [label_name.decode("utf-8")
- for label, label_name in
- enumerate(self.dataset["Labels"].attrs["names"])
- if label in selected_labels]
- else:
- return [label_name
- for label, label_name in
- enumerate(self.dataset["Labels"].attrs["names"])
- if label in selected_labels]
-
- def get_nb_class(self, example_indices=None):
- """
- Gets the number of classes of the dataset for the asked examples
-
- Parameters
- ----------
- example_indices : numpy.ndarray
- The array containing the indices of the examples to extract.
-
- Returns
- -------
- int : The number of classes
-
- """
- example_indices = self.init_example_indices(example_indices)
- return len(np.unique(self.dataset["Labels"][()][example_indices]))
-
- def get_labels(self, example_indices=None):
- """Gets the label array for the asked examples
-
- Parameters
- ----------
- example_indices : numpy.ndarray
- The array containing the indices of the examples to extract.
-
- Returns
- -------
- numpy.ndarray containing the labels of the asked examples"""
- example_indices = self.init_example_indices(example_indices)
- return self.dataset["Labels"][()][example_indices]
-
- def rm(self): # pragma: no cover
- """
- Method used to delete the dataset file on the disk if the dataset is
- temporary.
-
- Returns
- -------
-
- """
- filename = self.dataset.filename
- self.dataset.close()
- if self.is_temp:
- os.remove(filename)
-
-
- def copy_view(self, target_dataset=None, source_view_name=None,
- target_view_index=None, example_indices=None):
- example_indices = self.init_example_indices(example_indices)
- new_d_set = target_dataset.create_dataset(
- "View" + str(target_view_index),
- data=self.get_v(self.view_dict[source_view_name],
- example_indices=example_indices))
- for key, value in self.dataset[
- "View" + str(self.view_dict[source_view_name])].attrs.items():
- new_d_set.attrs[key] = value
-
- def init_view_names(self, view_names=None):
- if view_names is None:
- return [key for key in self.get_view_dict().keys()]
- else:
- return view_names
-
- def update_hdf5_dataset(self, path):
- if hasattr(self, 'dataset'):
- self.dataset.close()
- self.dataset = h5py.File(path, 'r')
- self.is_temp = True
- self.init_attrs()
-
- def filter(self, labels, label_names, example_indices, view_names,
- path=None):
- dataset_file_path = os.path.join(path,
- self.get_name() + "_temp_filter.hdf5")
- new_dataset_file = h5py.File(dataset_file_path, "w")
- self.dataset.copy("Metadata", new_dataset_file)
- if "example_ids" in self.dataset["Metadata"].keys():
- del new_dataset_file["Metadata"]["example_ids"]
- ex_ids = new_dataset_file["Metadata"].create_dataset("example_ids",
- data=np.array(
- self.example_ids)[
- example_indices].astype(
- np.dtype(
- "S100")))
- else:
- new_dataset_file["Metadata"].create_dataset("example_ids",
- (
- len(self.example_ids),),
- data=np.array(
- self.example_ids).astype(
- np.dtype("S100")),
- dtype=np.dtype("S100"))
- new_dataset_file["Metadata"].attrs["datasetLength"] = len(
- example_indices)
- new_dataset_file["Metadata"].attrs["nbClass"] = np.unique(labels)
- new_dataset_file.create_dataset("Labels", data=labels)
- new_dataset_file["Labels"].attrs["names"] = [label_name.encode()
- if not isinstance(
- label_name, bytes)
- else label_name
- for label_name in
- label_names]
- view_names = self.init_view_names(view_names)
- new_dataset_file["Metadata"].attrs["nbView"] = len(view_names)
- for new_index, view_name in enumerate(view_names):
- self.copy_view(target_dataset=new_dataset_file,
- source_view_name=view_name,
- target_view_index=new_index,
- example_indices=example_indices)
- new_dataset_file.close()
- self.update_hdf5_dataset(dataset_file_path)
-
- def add_gaussian_noise(self, random_state, path,
- noise_std=0.15):
- """In this function, we add a guaussian noise centered in 0 with specified
- std to each view, according to it's range (the noise will be
- mutliplied by this range) and we crop the noisy signal according to the
- view's attributes limits.
- This is done by creating a new dataset, to keep clean data."""
- noisy_dataset = h5py.File(path + self.get_name() + "_noised.hdf5", "w")
- self.dataset.copy("Metadata", noisy_dataset)
- self.dataset.copy("Labels", noisy_dataset)
- for view_index in range(self.nb_view):
- self.copy_view(target_dataset=noisy_dataset,
- source_view_name=self.get_view_name(view_index),
- target_view_index=view_index)
- for view_index in range(noisy_dataset["Metadata"].attrs["nbView"]):
- view_key = "View" + str(view_index)
- view_dset = noisy_dataset[view_key]
- view_limits = self.dataset[
- "Metadata/View" + str(view_index) + "_limits"][()]
- view_ranges = view_limits[:, 1] - view_limits[:, 0]
- normal_dist = random_state.normal(0, noise_std, view_dset[()].shape)
- noise = normal_dist * view_ranges
- noised_data = view_dset[()] + noise
- noised_data = np.where(noised_data < view_limits[:, 0],
- view_limits[:, 0], noised_data)
- noised_data = np.where(noised_data > view_limits[:, 1],
- view_limits[:, 1], noised_data)
- noisy_dataset[view_key][...] = noised_data
- noisy_dataset_path = noisy_dataset.filename
- noisy_dataset.close()
- self.update_hdf5_dataset(noisy_dataset_path)
-
- # The following methods are hdf5 free
-
- def get_name(self):
- """Ony works if there are not multiple dots in the files name"""
- return self.dataset.filename.split('/')[-1].split('.')[0]
-
-
-def is_just_number(string):
- try:
- float(string)
- return True
- except ValueError:
- return False
-
-
-def datasets_already_exist(pathF, name, nbCores):
- """Used to check if it's necessary to copy datasets"""
- allDatasetExist = True
- for coreIndex in range(nbCores):
- allDatasetExist *= os.path.isfile(os.path.join(
- pathF, name + str(coreIndex) + ".hdf5"))
- return allDatasetExist
-
-
-def extract_subset(matrix, used_indices):
- """Used to extract a subset of a matrix even if it's sparse WIP"""
- # if sparse.issparse(matrix):
- # new_indptr = np.zeros(len(used_indices) + 1, dtype=int)
- # oldindptr = matrix.indptr
- # for exampleIndexIndex, exampleIndex in enumerate(used_indices):
- # new_indptr[exampleIndexIndex + 1] = new_indptr[
- # exampleIndexIndex] + (
- # oldindptr[
- # exampleIndex + 1] -
- # oldindptr[exampleIndex])
- # new_data = np.ones(new_indptr[-1], dtype=bool)
- # new_indices = np.zeros(new_indptr[-1], dtype=int)
- # old_indices = matrix.indices
- # for exampleIndexIndex, exampleIndex in enumerate(used_indices):
- # new_indices[new_indptr[exampleIndexIndex]:new_indptr[
- # exampleIndexIndex + 1]] = old_indices[
- # oldindptr[exampleIndex]:
- # oldindptr[exampleIndex + 1]]
- # return sparse.csr_matrix((new_data, new_indices, new_indptr),
- # shape=(len(used_indices), matrix.shape[1]))
- # else:
- return matrix[used_indices]
-
-
-def init_multiple_datasets(path_f, name, nb_cores): # pragma: no cover
- r"""Used to create copies of the dataset if multicore computation is used.
-
- This is a temporary solution to fix the sharing memory issue with HDF5 datasets.
-
- Parameters
- ----------
- path_f : string
- Path to the original dataset directory
- name : string
- Name of the dataset
- nb_cores : int
- The number of threads that the benchmark can use
-
- Returns
- -------
- datasetFiles : None
- Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.
- """
- if nb_cores > 1:
- if datasets_already_exist(path_f, name, nb_cores):
- logging.debug(
- "Info:\t Enough copies of the dataset are already available")
- pass
- else:
- if os.path.getsize(os.path.join(path_f, name + ".hdf5")) * nb_cores / float(1024) / 1000 / 1000 > 0.1:
- logging.debug("Start:\t Creating " + str(
- nb_cores) + " temporary datasets for multiprocessing")
- logging.warning(
- " WARNING : /!\ This may use a lot of HDD storage space : " +
- str(os.path.getsize(os.path.join(path_f, name + ".hdf5")) * nb_cores / float(
- 1024) / 1000 / 1000) + " Gbytes /!\ ")
- confirmation = confirm()
- if not confirmation:
- sys.exit(0)
- else:
- pass
- else:
- pass
- dataset_files = copy_hdf5(path_f, name, nb_cores)
- logging.debug("Start:\t Creating datasets for multiprocessing")
- return dataset_files
-
-
-def copy_hdf5(pathF, name, nbCores):
- """Used to copy a HDF5 database in case of multicore computing"""
- datasetFile = h5py.File(pathF + name + ".hdf5", "r")
- for coreIndex in range(nbCores):
- newDataSet = h5py.File(pathF + name + str(coreIndex) + ".hdf5", "w")
- for dataset in datasetFile:
- datasetFile.copy("/" + dataset, newDataSet["/"])
- newDataSet.close()
-
-
-def delete_HDF5(benchmarkArgumentsDictionaries, nbCores, dataset):
- """Used to delete temporary copies at the end of the benchmark"""
- if nbCores > 1:
- logging.debug("Start:\t Deleting " + str(
- nbCores) + " temporary datasets for multiprocessing")
- args = benchmarkArgumentsDictionaries[0]["args"]
- logging.debug("Start:\t Deleting datasets for multiprocessing")
-
- for coreIndex in range(nbCores):
- os.remove(args["pathf"] + args["name"] + str(coreIndex) + ".hdf5")
- if dataset.is_temp:
- dataset.rm()
-
-
-def confirm(resp=True, timeout=15): # pragma: no cover
- """Used to process answer"""
- ans = input_(timeout)
- if not ans:
- return resp
- if ans not in ['y', 'Y', 'n', 'N']:
- print('please enter y or n.')
- if ans == 'y' or ans == 'Y':
- return True
- if ans == 'n' or ans == 'N':
- return False
-
-
-def input_(timeout=15): # pragma: no cover
- """used as a UI to stop if too much HDD space will be used"""
- logging.warning("You have " + str(
- timeout) + " seconds to stop the dataset copy by typing n")
- i, o, e = select.select([sys.stdin], [], [], timeout)
- if i:
- return sys.stdin.readline().strip()
- else:
- return "y"
-
-
-def get_examples_views_indices(dataset, examples_indices, view_indices, ):
- """This function is used to get all the examples indices and view indices if needed"""
- if view_indices is None:
- view_indices = np.arange(dataset.nb_view)
- if examples_indices is None:
- examples_indices = np.arange(dataset.get_nb_examples())
- return examples_indices, view_indices
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/execution.py b/multiview_platform/mono_multi_view_classifiers/utils/execution.py
deleted file mode 100644
index 3570bb2b..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/execution.py
+++ /dev/null
@@ -1,426 +0,0 @@
-import argparse
-import logging
-import os
-import pickle
-import time
-
-import numpy as np
-import sklearn
-
-from . import get_multiview_db as DB
-from ..utils.configuration import save_config
-
-
-def parse_the_args(arguments):
- """Used to parse the args entered by the user"""
-
- parser = argparse.ArgumentParser(
- description='This file is used to benchmark the scores fo multiple '
- 'classification algorithm on multiview data.',
- formatter_class=argparse.ArgumentDefaultsHelpFormatter,
- fromfile_prefix_chars='@')
-
- groupStandard = parser.add_argument_group('Standard arguments')
- groupStandard.add_argument('--config_path', metavar='STRING',
- action='store',
- help='Path to the hdf5 dataset or database '
- 'folder (default: %(default)s)',
- default='../config_files/config.yml')
- args = parser.parse_args(arguments)
- return args
-
-
-def init_random_state(random_state_arg, directory):
- r"""
- Used to init a random state.
- If no random state is specified, it will generate a 'random' seed.
- If the `randomSateArg` is a string containing only numbers, it will be converted in
- an int to generate a seed.
- If the `randomSateArg` is a string with letters, it must be a path to a pickled random
- state file that will be loaded.
- The function will also pickle the new random state in a file tobe able to retrieve it later.
- Tested
-
-
- Parameters
- ----------
- random_state_arg : None or string
- See function description.
- directory : string
- Path to the results directory.
-
- Returns
- -------
- random_state : numpy.random.RandomState object
- This random state will be used all along the benchmark .
- """
-
- if random_state_arg is None:
- random_state = np.random.RandomState(random_state_arg)
- else:
- try:
- seed = int(random_state_arg)
- random_state = np.random.RandomState(seed)
- except ValueError:
- file_name = random_state_arg
- with open(file_name, 'rb') as handle:
- random_state = pickle.load(handle)
- with open(os.path.join(directory, "random_state.pickle"), "wb") as handle:
- pickle.dump(random_state, handle)
- return random_state
-
-
-def init_stats_iter_random_states(stats_iter, random_state):
- r"""
- Used to initialize multiple random states if needed because of multiple statistical iteration of the same benchmark
-
- Parameters
- ----------
- stats_iter : int
- Number of statistical iterations of the same benchmark done (with a different random state).
- random_state : numpy.random.RandomState object
- The random state of the whole experimentation, that will be used to generate the ones for each
- statistical iteration.
-
- Returns
- -------
- stats_iter_random_states : list of numpy.random.RandomState objects
- Multiple random states, one for each sattistical iteration of the same benchmark.
- """
- if stats_iter > 1:
- stats_iter_random_states = [
- np.random.RandomState(random_state.randint(5000)) for _ in
- range(stats_iter)]
- else:
- stats_iter_random_states = [random_state]
- return stats_iter_random_states
-
-
-def get_database_function(name, type_var):
- r"""Used to get the right database extraction function according to the type of database and it's name
-
- Parameters
- ----------
- name : string
- Name of the database.
- type_var : string
- type of dataset hdf5 or csv
-
- Returns
- -------
- getDatabase : function
- The function that will be used to extract the database
- """
- if name not in ["fake", "plausible"]:
- get_database = getattr(DB, "get_classic_db_" + type_var[1:])
- else:
- get_database = getattr(DB, "get_" + name + "_db_" + type_var[1:])
- return get_database
-
-
-def init_log_file(name, views, cl_type, log, debug, label,
- result_directory, args):
- r"""Used to init the directory where the preds will be stored and the log file.
-
- First this function will check if the result directory already exists (only one per minute is allowed).
-
- If the the result directory name is available, it is created, and the logfile is initiated.
-
- Parameters
- ----------
- name : string
- Name of the database.
- views : list of strings
- List of the view names that will be used in the benchmark.
- cl_type : list of strings
- Type of benchmark that will be made .
- log : bool
- Whether to show the log file in console or hide it.
- debug : bool
- for debug option
- label : str for label
-
- result_directory : str name of the result directory
-
- add_noise : bool for add noise
-
- noise_std : level of std noise
-
- Returns
- -------
- results_directory : string
- Reference to the main results directory for the benchmark.
- """
- if views is None:
- views = []
- # result_directory = os.path.join(os.path.dirname(
- # os.path.dirname(os.path.dirname(os.path.realpath(__file__)))),
- # result_directory)
- if debug:
- result_directory = os.path.join(result_directory, name,
- "debug_started_" + time.strftime(
- "%Y_%m_%d-%H_%M_%S") + "_" + label)
- else:
- result_directory = os.path.join(result_directory, name,
- "started_" + time.strftime(
- "%Y_%m_%d-%H_%M") + "_" + label)
- log_file_name = time.strftime("%Y_%m_%d-%H_%M") + "-" + ''.join(
- cl_type) + "-" + "_".join(views) + "-" + name + "-LOG.log"
- if os.path.exists(result_directory): # pragma: no cover
- raise NameError("The result dir already exists, wait 1 min and retry")
- log_file_path = os.path.join(result_directory, log_file_name)
- os.makedirs(os.path.dirname(log_file_path))
- logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s',
- filename=log_file_path, level=logging.DEBUG,
- filemode='w')
- if log:
- logging.getLogger().addHandler(logging.StreamHandler())
- save_config(result_directory, args)
- return result_directory
-
-
-def gen_splits(labels, split_ratio, stats_iter_random_states):
- r"""Used to _gen the train/test splits using one or multiple random states.
-
- Parameters
- ----------
- labels : numpy.ndarray
- Name of the database.
- split_ratio : float
- The ratio of examples between train and test set.
- stats_iter_random_states : list of numpy.random.RandomState
- The random states for each statistical iteration.
-
- Returns
- -------
- splits : list of lists of numpy.ndarray
- For each statistical iteration a couple of numpy.ndarrays is stored with the indices for the training set and
- the ones of the testing set.
- """
- indices = np.arange(len(labels))
- splits = []
- for random_state in stats_iter_random_states:
- folds_obj = sklearn.model_selection.StratifiedShuffleSplit(n_splits=1,
- random_state=random_state,
- test_size=split_ratio)
- folds = folds_obj.split(indices, labels)
- for fold in folds:
- train_fold, test_fold = fold
- train_indices = indices[train_fold]
- test_indices = indices[test_fold]
- splits.append([train_indices, test_indices])
-
- return splits
-
-
-def gen_k_folds(stats_iter, nb_folds, stats_iter_random_states):
- r"""Used to generate folds indices for cross validation for each statistical iteration.
-
- Parameters
- ----------
- stats_iter : integer
- Number of statistical iterations of the benchmark.
- nb_folds : integer
- The number of cross-validation folds for the benchmark.
- stats_iter_random_states : list of numpy.random.RandomState
- The random states for each statistical iteration.
-
- Returns
- -------
- folds_list : list of list of sklearn.model_selection.StratifiedKFold
- For each statistical iteration a Kfold stratified (keeping the ratio between classes in each fold).
- """
- if stats_iter > 1:
- folds_list = []
- for random_state in stats_iter_random_states:
- folds_list.append(
- sklearn.model_selection.StratifiedKFold(n_splits=nb_folds,
- random_state=random_state,
- shuffle=True))
- else:
- if isinstance(stats_iter_random_states, list):
- stats_iter_random_states = stats_iter_random_states[0]
- folds_list = [sklearn.model_selection.StratifiedKFold(n_splits=nb_folds,
- random_state=stats_iter_random_states,
- shuffle=True)]
- return folds_list
-
-
-def init_views(dataset_var, arg_views):
- r"""Used to return the views names that will be used by the
- benchmark, their indices and all the views names.
-
- Parameters
- ----------
- dataset_var : HDF5 dataset file
- The full dataset that wil be used by the benchmark.
- arg_views : list of strings
- The views that will be used by the benchmark (arg).
-
- Returns
- -------
- views : list of strings
- Names of the views that will be used by the benchmark.
- view_indices : list of ints
- The list of the indices of the view that will be used in the benchmark (according to the dataset).
- all_views : list of strings
- Names of all the available views in the dataset.
- """
- nb_view = dataset_var.nb_view
- if arg_views is not None:
- allowed_views = arg_views
- all_views = [str(dataset_var.get_view_name(view_index))
- if type(dataset_var.get_view_name(view_index)) != bytes
- else dataset_var.get_view_name(view_index).decode("utf-8")
- for view_index in range(nb_view)]
- views = []
- views_indices = []
- for view_index in range(nb_view):
- view_name = dataset_var.get_view_name(view_index)
- if type(view_name) == bytes:
- view_name = view_name.decode("utf-8")
- if view_name in allowed_views:
- views.append(view_name)
- views_indices.append(view_index)
- else:
- views = [str(dataset_var.get_view_name(view_index))
- if type(dataset_var.get_view_name(view_index)) != bytes
- else dataset_var.get_view_name(view_index).decode("utf-8")
- for view_index in range(nb_view)]
- views_indices = range(nb_view)
- all_views = views
- return views, views_indices, all_views
-
-
-def gen_direcorties_names(directory, stats_iter):
- r"""Used to generate the different directories of each iteration if needed.
-
- Parameters
- ----------
- directory : string
- Path to the results directory.
- statsIter : int
- The number of statistical iterations.
-
- Returns
- -------
- directories : list of strings
- Paths to each statistical iterations result directory.
- """
- if stats_iter > 1:
- directories = []
- for i in range(stats_iter):
- directories.append(os.path.join(directory, "iter_" + str(i + 1)))
- else:
- directories = [directory]
- return directories
-
-
-def find_dataset_names(path, type, names):
- """This function goal is to browse the dataset directory and extrats all
- the needed dataset names."""
- package_path = os.path.dirname(
- os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
- if os.path.isdir(path):
- pass
- elif os.path.isdir(os.path.join(package_path, path)):
- path = os.path.join(package_path, path)
- else:
- raise ValueError("The provided pathf does not exist ({}) SuMMIT checks "
- "the prefix from where you are running your script ({}) "
- "and the multiview_platform package prefix ({}). "
- "You may want to try with an absolute path in the "
- "config file".format(path, os.getcwd(), package_path))
- available_file_names = [file_name.strip().split(".")[0]
- for file_name in
- os.listdir(path)
- if file_name.endswith(type)]
- if names == ["all"]:
- return path, available_file_names
- elif isinstance(names, str):
- return path, [used_name for used_name in available_file_names if names == used_name]
- elif len(names) > 1:
- selected_names = [used_name for used_name in available_file_names if
- used_name in names]
- if not selected_names:
- raise ValueError(
- "None of the provided dataset names are available. Available datasets are {}".format(
- available_file_names))
- return path, [used_name for used_name in available_file_names if
- used_name in names]
- elif names[0] in available_file_names:
- return path, names
- else:
- raise ValueError("The asked dataset ({}) is not available in {}. \n The available ones are {}".format(names[0], path, available_file_names))
-
-
-def gen_argument_dictionaries(labels_dictionary, directories,
- splits,
- hyper_param_search, args, k_folds,
- stats_iter_random_states, metrics,
- argument_dictionaries,
- benchmark, views, views_indices,): # pragma: no cover
- r"""Used to generate a dictionary for each benchmark.
-
- One for each label combination (if multiclass), for each statistical iteration, generates an dictionary with
- all necessary information to perform the benchmark
-
- Parameters
- ----------
- labels_dictionary : dictionary
- Dictionary mapping labels indices to labels names.
- directories : list of strings
- List of the paths to the result directories for each statistical iteration.
- multiclass_labels : list of lists of numpy.ndarray
- For each label couple, for each statistical iteration a triplet of numpy.ndarrays is stored with the
- indices for the biclass training set, the ones for the biclass testing set and the ones for the
- multiclass testing set.
- labels_combinations : list of lists of numpy.ndarray
- Each original couple of different labels.
- indices_multiclass : list of lists of numpy.ndarray
- For each combination, contains a biclass labels numpy.ndarray with the 0/1 labels of combination.
- hyper_param_search : string
- Type of hyper parameter optimization method
- args : parsed args objects
- All the args passed by the user.
- k_folds : list of list of sklearn.model_selection.StratifiedKFold
- For each statistical iteration a Kfold stratified (keeping the ratio between classes in each fold).
- stats_iter_random_states : list of numpy.random.RandomState objects
- Multiple random states, one for each sattistical iteration of the same benchmark.
- metrics : list of lists
- metrics that will be used to evaluate the algorithms performance.
- argument_dictionaries : dictionary
- Dictionary resuming all the specific arguments for the benchmark, oe dictionary for each classifier.
- benchmark : dictionary
- Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.
- nb_views : int
- THe number of views used by the benchmark.
- views : list of strings
- List of the names of the used views.
- views_indices : list of ints
- List of indices (according to the dataset) of the used views.
-
- Returns
- -------
- benchmarkArgumentDictionaries : list of dicts
- All the needed arguments for the benchmarks.
-
- """
- benchmark_argument_dictionaries = []
- for iter_index, iterRandomState in enumerate(stats_iter_random_states):
- benchmark_argument_dictionary = {
- "labels_dictionary": labels_dictionary,
- "directory": directories[iter_index],
- "classification_indices": splits[iter_index],
- "args": args,
- "k_folds": k_folds[iter_index],
- "random_state": iterRandomState,
- "hyper_param_search": hyper_param_search,
- "metrics": metrics,
- "argument_dictionaries": argument_dictionaries,
- "benchmark": benchmark,
- "views": views,
- "views_indices": views_indices,
- "flag": iter_index}
- benchmark_argument_dictionaries.append(benchmark_argument_dictionary)
- return benchmark_argument_dictionaries
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/get_multiview_db.py b/multiview_platform/mono_multi_view_classifiers/utils/get_multiview_db.py
deleted file mode 100644
index b3d2a24c..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/get_multiview_db.py
+++ /dev/null
@@ -1,1311 +0,0 @@
-import os
-
-import h5py
-import numpy as np
-
-from .dataset import RAMDataset, HDF5Dataset
-from .organization import secure_file_path
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype" # Production, Development, Prototype
-
-
-def make_me_noisy(view_data, random_state, percentage=5):
- """used to introduce some noise in the generated data"""
- view_data = view_data.astype(bool)
- nb_noisy_coord = int(
- percentage / 100.0 * view_data.shape[0] * view_data.shape[1])
- rows = range(view_data.shape[0])
- cols = range(view_data.shape[1])
- for _ in range(nb_noisy_coord):
- row_idx = random_state.choice(rows)
- col_idx = random_state.choice(cols)
- view_data[row_idx, col_idx] = 0
- noisy_view_data = view_data.astype(np.uint8)
- return noisy_view_data
-
-
-def get_plausible_db_hdf5(features, path, file_name, nb_class=3,
- label_names=["No".encode(), "Yes".encode(),
- "Maybe".encode()],
- random_state=None, full=True, add_noise=False,
- noise_std=0.15, nb_view=3, nb_examples=100,
- nb_features=10):
- """Used to generate a plausible dataset to test the algorithms"""
- secure_file_path(os.path.join(path, "plausible.hdf5"))
- example_ids = ["exmaple_id_" + str(i) for i in range(nb_examples)]
- views = []
- view_names = []
- are_sparse = []
- if nb_class == 2:
- labels = np.array(
- [0 for _ in range(int(nb_examples / 2))] + [1 for _ in range(
- nb_examples - int(nb_examples / 2))])
- label_names = ["No".encode(), "Yes".encode()]
- for view_index in range(nb_view):
- view_data = np.array(
- [np.zeros(nb_features) for _ in range(int(nb_examples / 2))] +
- [np.ones(nb_features) for _ in
- range(nb_examples - int(nb_examples / 2))])
- fake_one_indices = random_state.randint(0, int(nb_examples / 2),
- int(nb_examples / 12))
- fake_zero_indices = random_state.randint(int(nb_examples / 2),
- nb_examples,
- int(nb_examples / 12))
- for index in np.concatenate((fake_one_indices, fake_zero_indices)):
- example_ids[index] += "noised"
-
- view_data[fake_one_indices] = np.ones(
- (len(fake_one_indices), nb_features))
- view_data[fake_zero_indices] = np.zeros(
- (len(fake_zero_indices), nb_features))
- view_data = make_me_noisy(view_data, random_state)
- views.append(view_data)
- view_names.append("ViewNumber" + str(view_index))
- are_sparse.append(False)
-
- dataset = RAMDataset(views=views, labels=labels,
- labels_names=label_names, view_names=view_names,
- are_sparse=are_sparse, example_ids=example_ids,
- name='plausible')
- labels_dictionary = {0: "No", 1: "Yes"}
- return dataset, labels_dictionary, "plausible"
- elif nb_class >= 3:
- firstBound = int(nb_examples / 3)
- rest = nb_examples - 2 * int(nb_examples / 3)
- scndBound = 2 * int(nb_examples / 3)
- thrdBound = nb_examples
- labels = np.array(
- [0 for _ in range(firstBound)] +
- [1 for _ in range(firstBound)] +
- [2 for _ in range(rest)]
- )
- for view_index in range(nb_view):
- view_data = np.array(
- [np.zeros(nb_features) for _ in range(firstBound)] +
- [np.ones(nb_features) for _ in range(firstBound)] +
- [np.ones(nb_features) + 1 for _ in range(rest)])
- fake_one_indices = random_state.randint(0, firstBound,
- int(nb_examples / 12))
- fakeTwoIndices = random_state.randint(firstBound, scndBound,
- int(nb_examples / 12))
- fake_zero_indices = random_state.randint(scndBound, thrdBound,
- int(nb_examples / 12))
-
- view_data[fake_one_indices] = np.ones(
- (len(fake_one_indices), nb_features))
- view_data[fake_zero_indices] = np.zeros(
- (len(fake_zero_indices), nb_features))
- view_data[fakeTwoIndices] = np.ones(
- (len(fakeTwoIndices), nb_features)) + 1
- view_data = make_me_noisy(view_data, random_state)
- views.append(view_data)
- view_names.append("ViewNumber" + str(view_index))
- are_sparse.append(False)
- dataset = RAMDataset(views=views, labels=labels,
- labels_names=label_names, view_names=view_names,
- are_sparse=are_sparse,
- name="plausible",
- example_ids=example_ids)
- labels_dictionary = {0: "No", 1: "Yes", 2: "Maybe"}
- return dataset, labels_dictionary, "plausible"
-
-
-class DatasetError(Exception):
- def __init__(self, *args, **kwargs):
- Exception.__init__(self, *args, **kwargs)
-
-
-def get_classic_db_hdf5(views, path_f, name_DB, nb_class, asked_labels_names,
- random_state, full=False, add_noise=False,
- noise_std=0.15,
- path_for_new="../data/"):
- """Used to load a hdf5 database"""
- if full:
- dataset_file = h5py.File(os.path.join(path_f, name_DB + ".hdf5"), "r")
- dataset = HDF5Dataset(hdf5_file=dataset_file)
- dataset_name = name_DB
- labels_dictionary = dict((label_index, label_name)
- for label_index, label_name
- in enumerate(dataset.get_label_names()))
- else:
- dataset_file = h5py.File(os.path.join(path_f, name_DB + ".hdf5"), "r")
- dataset = HDF5Dataset(hdf5_file=dataset_file)
- labels_dictionary = dataset.select_views_and_labels(nb_labels=nb_class,
- selected_label_names=asked_labels_names,
- view_names=views,
- random_state=random_state,
- path_for_new=path_for_new)
- dataset_name = dataset.get_name()
-
- if add_noise:
- dataset.add_gaussian_noise(random_state, path_for_new, noise_std)
- dataset_name = dataset.get_name()
- else:
- pass
- return dataset, labels_dictionary, dataset_name
-
-
-def get_classic_db_csv(views, pathF, nameDB, NB_CLASS, askedLabelsNames,
- random_state, full=False, add_noise=False,
- noise_std=0.15,
- delimiter=",", path_for_new="../data/"):
- # TODO : Update this one
- labels_names = np.genfromtxt(pathF + nameDB + "-labels-names.csv",
- dtype='str', delimiter=delimiter)
- datasetFile = h5py.File(pathF + nameDB + ".hdf5", "w")
- labels = np.genfromtxt(pathF + nameDB + "-labels.csv", delimiter=delimiter)
- labelsDset = datasetFile.create_dataset("Labels", labels.shape, data=labels)
- labelsDset.attrs["names"] = [labelName.encode() for labelName in
- labels_names]
- viewFileNames = [viewFileName for viewFileName in
- os.listdir(pathF + "Views/")]
- for viewIndex, viewFileName in enumerate(os.listdir(pathF + "Views/")):
- viewFile = pathF + "Views/" + viewFileName
- if viewFileName[-6:] != "-s.csv":
- viewMatrix = np.genfromtxt(viewFile, delimiter=delimiter)
- viewDset = datasetFile.create_dataset("View" + str(viewIndex),
- viewMatrix.shape,
- data=viewMatrix)
- del viewMatrix
- viewDset.attrs["name"] = viewFileName[:-4]
- viewDset.attrs["sparse"] = False
- else:
- pass
- metaDataGrp = datasetFile.create_group("Metadata")
- metaDataGrp.attrs["nbView"] = len(viewFileNames)
- metaDataGrp.attrs["nbClass"] = len(labels_names)
- metaDataGrp.attrs["datasetLength"] = len(labels)
- datasetFile.close()
- datasetFile, labelsDictionary, dataset_name = get_classic_db_hdf5(views,
- pathF,
- nameDB,
- NB_CLASS,
- askedLabelsNames,
- random_state,
- full,
- path_for_new=path_for_new)
-
- return datasetFile, labelsDictionary, dataset_name
-
-#
-# def get_classes(labels):
-# labels_set = set(list(labels))
-# nb_labels = len(labels_set)
-# if nb_labels >= 2:
-# return labels_set
-# else:
-# raise DatasetError("Dataset must have at least two different labels")
-#
-#
-# def all_asked_labels_are_available(asked_labels_names_set,
-# available_labels_names):
-# for askedLabelName in asked_labels_names_set:
-# if askedLabelName in available_labels_names:
-# pass
-# else:
-# return False
-# return True
-#
-#
-# def fill_label_names(nb_labels, selected_label_names, random_state,
-# available_labels_names):
-# if len(selected_label_names) < nb_labels:
-# nb_labels_to_add = nb_labels - len(selected_label_names)
-# labels_names_to_choose = [available_label_name
-# for available_label_name
-# in available_labels_names
-# if available_label_name
-# not in selected_label_names]
-# added_labels_names = random_state.choice(labels_names_to_choose,
-# nb_labels_to_add, replace=False)
-# selected_label_names = list(selected_label_names) + list(added_labels_names)
-# asked_labels_names_set = set(selected_label_names)
-#
-# elif len(selected_label_names) > nb_labels:
-# selected_label_names = list(
-# random_state.choice(selected_label_names, nb_labels, replace=False))
-# asked_labels_names_set = set(selected_label_names)
-#
-# else:
-# asked_labels_names_set = set(selected_label_names)
-#
-# return selected_label_names, asked_labels_names_set
-#
-#
-# def get_all_labels(full_labels, available_labels_names):
-# new_labels = full_labels
-# new_labels_names = available_labels_names
-# used_indices = np.arange(len(full_labels))
-# return new_labels, new_labels_names, used_indices
-#
-#
-# def select_asked_labels(asked_labels_names_set, available_labels_names,
-# asked_labels_names, full_labels):
-# if all_asked_labels_are_available(asked_labels_names_set, available_labels_names):
-# used_labels = [available_labels_names.index(asked_label_name) for
-# asked_label_name in asked_labels_names]
-# used_indices = np.array(
-# [labelIndex for labelIndex, label in enumerate(full_labels) if
-# label in used_labels])
-# new_labels = np.array([used_labels.index(label) for label in full_labels if
-# label in used_labels])
-# new_labels_names = [available_labels_names[usedLabel] for usedLabel in
-# used_labels]
-# return new_labels, new_labels_names, used_indices
-# else:
-# raise DatasetError("Asked labels are not all available in the dataset")
-#
-#
-# def filter_labels(labels_set, asked_labels_names_set, full_labels,
-# available_labels_names, asked_labels_names):
-# if len(labels_set) > 2:
-# if asked_labels_names == available_labels_names:
-# new_labels, new_labels_names, used_indices = \
-# get_all_labels(full_labels, available_labels_names)
-# elif len(asked_labels_names_set) <= len(labels_set):
-# new_labels, new_labels_names, used_indices = select_asked_labels(
-# asked_labels_names_set, available_labels_names,
-# asked_labels_names, full_labels)
-# else:
-# raise DatasetError(
-# "Asked more labels than available in the dataset. Available labels are : " +
-# ", ".join(available_labels_names))
-#
-# else:
-# new_labels, new_labels_names, used_indices = get_all_labels(full_labels,
-# available_labels_names)
-# return new_labels, new_labels_names, used_indices
-#
-#
-# def filter_views(dataset_file, temp_dataset, views, used_indices):
-# new_view_index = 0
-# if views == [""]:
-# for view_index in range(dataset_file.get("Metadata").attrs["nbView"]):
-# copyhdf5_dataset(dataset_file, temp_dataset, "View" + str(view_index),
-# "View" + str(view_index), used_indices)
-# else:
-# for asked_view_name in views:
-# for view_index in range(dataset_file.get("Metadata").attrs["nbView"]):
-# view_name = dataset_file.get("View" + str(view_index)).attrs["name"]
-# if type(view_name) == bytes:
-# view_name = view_name.decode("utf-8")
-# if view_name == asked_view_name:
-# copyhdf5_dataset(dataset_file, temp_dataset,
-# "View" + str(view_index),
-# "View" + str(new_view_index), used_indices)
-# new_view_name = \
-# temp_dataset.get("View" + str(new_view_index)).attrs["name"]
-# if type(new_view_name) == bytes:
-# temp_dataset.get("View" + str(new_view_index)).attrs[
-# "name"] = new_view_name.decode("utf-8")
-#
-# new_view_index += 1
-# else:
-# pass
-# temp_dataset.get("Metadata").attrs["nbView"] = len(views)
-#
-#
-# def copyhdf5_dataset(source_data_file, destination_data_file, source_dataset_name,
-# destination_dataset_name, used_indices):
-# """Used to copy a view in a new dataset file using only the examples of
-# usedIndices, and copying the args"""
-# new_d_set = destination_data_file.create_dataset(destination_dataset_name,
-# data=source_data_file.get(
-# source_dataset_name).value[
-# used_indices, :])
-# if "sparse" in source_data_file.get(source_dataset_name).attrs.keys() and \
-# source_data_file.get(source_dataset_name).attrs["sparse"]:
-# # TODO : Support sparse
-# pass
-# else:
-# for key, value in source_data_file.get(source_dataset_name).attrs.items():
-# new_d_set.attrs[key] = value
-
-
-#
-# def add_gaussian_noise(dataset_file, random_state, path_f, dataset_name,
-# noise_std=0.15):
-# """In this function, we add a guaussian noise centered in 0 with specified
-# std to each view, according to it's range (the noise will be
-# mutliplied by this range) and we crop the noisy signal according to the
-# view's attributes limits.
-# This is done by creating a new dataset, to keep clean data."""
-# noisy_dataset = h5py.File(path_f + dataset_name + "_noised.hdf5", "w")
-# dataset_file.copy("Metadata", noisy_dataset)
-# dataset_file.copy("Labels", noisy_dataset)
-# for view_index in range(dataset_file.get("Metadata").attrs["nbView"]):
-# dataset_file.copy("View" + str(view_index), noisy_dataset)
-# for view_index in range(noisy_dataset.get("Metadata").attrs["nbView"]):
-# view_name = "View" + str(view_index)
-# view_dset = noisy_dataset.get(view_name)
-# view_limits = dataset_file[
-# "Metadata/View" + str(view_index) + "_limits"].value
-# view_ranges = view_limits[:, 1] - view_limits[:, 0]
-# normal_dist = random_state.normal(0, noise_std, view_dset.value.shape)
-# noise = normal_dist * view_ranges
-# noised_data = view_dset.value + noise
-# noised_data = np.where(noised_data < view_limits[:, 0],
-# view_limits[:, 0], noised_data)
-# noised_data = np.where(noised_data > view_limits[:, 1],
-# view_limits[:, 1], noised_data)
-# noisy_dataset[view_name][...] = noised_data
-# original_dataset_filename = dataset_file.filename
-# dataset_file.close()
-# noisy_dataset.close()
-# noisy_dataset = h5py.File(path_f + dataset_name + "_noised.hdf5", "r")
-# if "_temp_" in original_dataset_filename:
-# os.remove(original_dataset_filename)
-# return noisy_dataset, dataset_name + "_noised"
-
-
-# def getLabelSupports(CLASS_LABELS):
-# """Used to get the number of example for each label"""
-# labels = set(CLASS_LABELS)
-# supports = [CLASS_LABELS.tolist().count(label) for label in labels]
-# return supports, dict((label, index) for label, index in zip(labels, range(len(labels))))
-
-
-# def isUseful(labelSupports, index, CLASS_LABELS, labelDict):
-# if labelSupports[labelDict[CLASS_LABELS[index]]] != 0:
-# labelSupports[labelDict[CLASS_LABELS[index]]] -= 1
-# return True, labelSupports
-# else:
-# return False, labelSupports
-
-
-# def splitDataset(DATASET, LEARNING_RATE, DATASET_LENGTH, random_state):
-# LABELS = DATASET.get("Labels")[...]
-# NB_CLASS = int(DATASET["Metadata"].attrs["nbClass"])
-# validationIndices = extractRandomTrainingSet(LABELS, 1 - LEARNING_RATE, DATASET_LENGTH, NB_CLASS, random_state)
-# validationIndices.sort()
-# return validationIndices
-
-
-# def extractRandomTrainingSet(CLASS_LABELS, LEARNING_RATE, DATASET_LENGTH, NB_CLASS, random_state):
-# labelSupports, labelDict = getLabelSupports(np.array(CLASS_LABELS))
-# nbTrainingExamples = [int(support * LEARNING_RATE) for support in labelSupports]
-# trainingExamplesIndices = []
-# usedIndices = []
-# while nbTrainingExamples != [0 for i in range(NB_CLASS)]:
-# isUseFull = False
-# index = int(random_state.randint(0, DATASET_LENGTH - 1))
-# if index not in usedIndices:
-# isUseFull, nbTrainingExamples = isUseful(nbTrainingExamples, index, CLASS_LABELS, labelDict)
-# if isUseFull:
-# trainingExamplesIndices.append(index)
-# usedIndices.append(index)
-# return trainingExamplesIndices
-
-
-# def getKFoldIndices(nbFolds, CLASS_LABELS, NB_CLASS, learningIndices, random_state):
-# labelSupports, labelDict = getLabelSupports(np.array(CLASS_LABELS[learningIndices]))
-# nbTrainingExamples = [[int(support / nbFolds) for support in labelSupports] for fold in range(nbFolds)]
-# trainingExamplesIndices = []
-# usedIndices = []
-# for foldIndex, fold in enumerate(nbTrainingExamples):
-# trainingExamplesIndices.append([])
-# while fold != [0 for i in range(NB_CLASS)]:
-# index = random_state.randint(0, len(learningIndices))
-# if learningIndices[index] not in usedIndices:
-# isUseFull, fold = isUseful(fold, learningIndices[index], CLASS_LABELS, labelDict)
-# if isUseFull:
-# trainingExamplesIndices[foldIndex].append(learningIndices[index])
-# usedIndices.append(learningIndices[index])
-# return trainingExamplesIndices
-#
-#
-# def getPositions(labelsUsed, fullLabels):
-# usedIndices = []
-# for labelIndex, label in enumerate(fullLabels):
-# if label in labelsUsed:
-# usedIndices.append(labelIndex)
-# return usedIndices
-
-
-# def getCaltechDBcsv(views, pathF, nameDB, NB_CLASS, LABELS_NAMES, random_state):
-# datasetFile = h5py.File(pathF + nameDB + ".hdf5", "w")
-# labelsNamesFile = open(pathF + nameDB + '-ClassLabels-Description.csv')
-# if len(LABELS_NAMES) != NB_CLASS:
-# nbLabelsAvailable = 0
-# for l in labelsNamesFile:
-# nbLabelsAvailable += 1
-# LABELS_NAMES = [line.strip().split(";")[1] for lineIdx, line in enumerate(labelsNamesFile) if
-# lineIdx in random_state.randint(nbLabelsAvailable, size=NB_CLASS)]
-# fullLabels = np.genfromtxt(pathF + nameDB + '-ClassLabels.csv', delimiter=';').astype(int)
-# labelsDictionary = dict((classIndice, labelName) for (classIndice, labelName) in
-# [(int(line.strip().split(";")[0]), line.strip().split(";")[1]) for lineIndex, line in
-# labelsNamesFile if line.strip().split(";")[0] in LABELS_NAMES])
-# if len(set(fullLabels)) > NB_CLASS:
-# usedIndices = getPositions(labelsDictionary.keys(), fullLabels)
-# else:
-# usedIndices = range(len(fullLabels))
-# for viewIndex, view in enumerate(views):
-# viewFile = pathF + nameDB + "-" + view + '.csv'
-# viewMatrix = np.array(np.genfromtxt(viewFile, delimiter=';'))[usedIndices, :]
-# viewDset = datasetFile.create_dataset("View" + str(viewIndex), viewMatrix.shape, data=viewMatrix)
-# viewDset.attrs["name"] = view
-#
-# labelsDset = datasetFile.create_dataset("Labels", fullLabels[usedIndices].shape, data=fullLabels[usedIndices])
-#
-# metaDataGrp = datasetFile.create_group("Metadata")
-# metaDataGrp.attrs["nbView"] = len(views)
-# metaDataGrp.attrs["nbClass"] = NB_CLASS
-# metaDataGrp.attrs["datasetLength"] = len(fullLabels[usedIndices])
-# datasetFile.close()
-# datasetFile = h5py.File(pathF + nameDB + ".hdf5", "r")
-# return datasetFile, labelsDictionary
-
-# --------------------------------------------#
-# All the functions below are not useful #
-# anymore but the binarization methods in #
-# it must be kept #
-# --------------------------------------------#
-
-
-# def getMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES, random_state):
-# datasetFile = h5py.File(path + "MultiOmic.hdf5", "w")
-#
-# logging.debug("Start:\t Getting Methylation data")
-# methylData = np.genfromtxt(path + "matching_methyl.csv", delimiter=',')
-# methylDset = datasetFile.create_dataset("View0", methylData.shape)
-# methylDset[...] = methylData
-# methylDset.attrs["name"] = "Methyl"
-# methylDset.attrs["sparse"] = False
-# methylDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting Methylation data")
-#
-# logging.debug("Start:\t Getting MiRNA data")
-# mirnaData = np.genfromtxt(path + "matching_mirna.csv", delimiter=',')
-# mirnaDset = datasetFile.create_dataset("View1", mirnaData.shape)
-# mirnaDset[...] = mirnaData
-# mirnaDset.attrs["name"] = "MiRNA_"
-# mirnaDset.attrs["sparse"] = False
-# mirnaDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting MiRNA data")
-#
-# logging.debug("Start:\t Getting RNASeq data")
-# rnaseqData = np.genfromtxt(path + "matching_rnaseq.csv", delimiter=',')
-# uselessRows = []
-# for rowIndex, row in enumerate(np.transpose(rnaseqData)):
-# if not row.any():
-# uselessRows.append(rowIndex)
-# usefulRows = [usefulRowIndex for usefulRowIndex in range(rnaseqData.shape[1]) if usefulRowIndex not in uselessRows]
-# rnaseqDset = datasetFile.create_dataset("View2", (rnaseqData.shape[0], len(usefulRows)))
-# rnaseqDset[...] = rnaseqData[:, usefulRows]
-# rnaseqDset.attrs["name"] = "RNASeq_"
-# rnaseqDset.attrs["sparse"] = False
-# rnaseqDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting RNASeq data")
-#
-# logging.debug("Start:\t Getting Clinical data")
-# clinical = np.genfromtxt(path + "clinicalMatrix.csv", delimiter=',')
-# clinicalDset = datasetFile.create_dataset("View3", clinical.shape)
-# clinicalDset[...] = clinical
-# clinicalDset.attrs["name"] = "Clinic"
-# clinicalDset.attrs["sparse"] = False
-# clinicalDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting Clinical data")
-#
-# labelFile = open(path + 'brca_labels_triple-negatif.csv')
-# labels = np.array([int(line.strip().split(',')[1]) for line in labelFile])
-# labelsDset = datasetFile.create_dataset("Labels", labels.shape)
-# labelsDset[...] = labels
-# labelsDset.attrs["name"] = "Labels"
-#
-# metaDataGrp = datasetFile.create_group("Metadata")
-# metaDataGrp.attrs["nbView"] = 4
-# metaDataGrp.attrs["nbClass"] = 2
-# metaDataGrp.attrs["datasetLength"] = len(labels)
-# labelDictionary = {0: "No", 1: "Yes"}
-# datasetFile.close()
-# datasetFile = h5py.File(path + "MultiOmic.hdf5", "r")
-# # datasetFile = getPseudoRNASeq(datasetFile)
-# return datasetFile, labelDictionary
-#
-#
-# def getVector(nbGenes):
-# argmax = [0, 0]
-# maxi = 0
-# for i in range(nbGenes):
-# for j in range(nbGenes):
-# if j == i + 1:
-# value = (i + 1) * (nbGenes - j)
-# if value > maxi:
-# maxi = value
-# argmax = [i, j]
-# i, j = argmax
-# vectorLeft = np.zeros(nbGenes, dtype=bool)
-# vectorLeft[:i + 1] = np.ones(i + 1, dtype=bool)
-# vectorSup = np.zeros(nbGenes, dtype=bool)
-# vectorSup[j:] = np.ones(nbGenes - j, dtype=bool)
-# matrixSup = j
-# matrixInf = nbGenes - j
-# return vectorLeft, matrixSup, matrixInf
-#
-#
-# def findClosestPowerOfTwo(factorizationParam):
-# power = 1
-# while factorizationParam - power > 0:
-# power *= 2
-# if abs(factorizationParam - power) < abs(factorizationParam - power / 2):
-# return power
-# else:
-# return power / 2
-#
-#
-# def easyFactorize(nbGenes, factorizationParam, t=0):
-# if math.log(factorizationParam + 1, 2) % 1 == 0.0:
-# pass
-# else:
-# factorizationParam = findClosestPowerOfTwo(factorizationParam) - 1
-#
-# if nbGenes == 2:
-# return 1, np.array([True, False])
-#
-# if nbGenes == 3:
-# return 1, np.array([True, True, False])
-#
-# if factorizationParam == 1:
-# t = 1
-# return t, getVector(nbGenes)[0]
-#
-# vectorLeft, matrixSup, matrixInf = getVector(nbGenes)
-#
-# t_, vectorLeftSup = easyFactorize(matrixSup, (factorizationParam - 1) / 2, t=t)
-# t__, vectorLeftInf = easyFactorize(matrixInf, (factorizationParam - 1) / 2, t=t)
-#
-# factorLeft = np.zeros((nbGenes, t_ + t__ + 1), dtype=bool)
-#
-# factorLeft[:matrixSup, :t_] = vectorLeftSup.reshape(factorLeft[:matrixSup, :t_].shape)
-# if nbGenes % 2 == 1:
-# factorLeft[matrixInf - 1:, t_:t__ + t_] = vectorLeftInf.reshape(factorLeft[matrixInf - 1:, t_:t__ + t_].shape)
-# else:
-# factorLeft[matrixInf:, t_:t__ + t_] = vectorLeftInf.reshape(factorLeft[matrixInf:, t_:t__ + t_].shape)
-# factorLeft[:, t__ + t_] = vectorLeft
-#
-# # factorSup = np.zeros((t_+t__+1, nbGenes), dtype=bool)
-# #
-# # factorSup[:t_, :matrixSup] = vectorSupLeft.reshape(factorSup[:t_, :matrixSup].shape)
-# # if nbGenes%2==1:
-# # factorSup[t_:t__+t_, matrixInf-1:] = vectorSupRight.reshape(factorSup[t_:t__+t_, matrixInf-1:].shape)
-# # else:
-# # factorSup[t_:t__+t_, matrixInf:] = vectorSupRight.reshape(factorSup[t_:t__+t_, matrixInf:].shape)
-# # factorSup[t__+t_, :] = vectorSup
-# return t__ + t_ + 1, factorLeft # , factorSup
-#
-#
-# def getBaseMatrices(nbGenes, factorizationParam, path):
-# t, factorLeft = easyFactorize(nbGenes, factorizationParam)
-# np.savetxt(path + "factorLeft--n-" + str(nbGenes) + "--k-" + str(factorizationParam) + ".csv", factorLeft,
-# delimiter=",")
-# return factorLeft
-#
-#
-# def findParams(arrayLen, nbPatients, random_state, maxNbBins=2000, minNbBins=10, maxLenBin=70000, minOverlapping=1,
-# minNbBinsOverlapped=0, maxNbSolutions=30):
-# results = []
-# if arrayLen * arrayLen * 10 / 100 > minNbBinsOverlapped * nbPatients:
-# for lenBin in range(arrayLen - 1):
-# lenBin += 1
-# if lenBin < maxLenBin and minNbBins * lenBin < arrayLen:
-# for overlapping in sorted(range(lenBin - 1), reverse=True):
-# overlapping += 1
-# if overlapping > minOverlapping and lenBin % (lenBin - overlapping) == 0:
-# for nbBins in sorted(range(arrayLen - 1), reverse=True):
-# nbBins += 1
-# if nbBins < maxNbBins:
-# if arrayLen == (nbBins - 1) * (lenBin - overlapping) + lenBin:
-# results.append({"nbBins": nbBins, "overlapping": overlapping, "lenBin": lenBin})
-# if len(results) == maxNbSolutions:
-# params = preds[random_state.randrange(len(preds))]
-# return params
-#
-#
-# def findBins(nbBins=142, overlapping=493, lenBin=986):
-# bins = []
-# for binIndex in range(nbBins):
-# bins.append([i + binIndex * (lenBin - overlapping) for i in range(lenBin)])
-# return bins
-#
-#
-# def getBins(array, bins, lenBin, overlapping):
-# binnedcoord = []
-# for coordIndex, coord in enumerate(array):
-# nbBinsFull = 0
-# for binIndex, bin_ in enumerate(bins):
-# if coordIndex in bin_:
-# binnedcoord.append(binIndex + (coord * len(bins)))
-#
-# return np.array(binnedcoord)
-#
-#
-# def makeSortedBinsMatrix(nbBins, lenBins, overlapping, arrayLen, path):
-# sortedBinsMatrix = np.zeros((arrayLen, nbBins), dtype=np.uint8)
-# step = lenBins - overlapping
-# for binIndex in range(nbBins):
-# sortedBinsMatrix[step * binIndex:lenBins + (step * binIndex), binIndex] = np.ones(lenBins, dtype=np.uint8)
-# np.savetxt(path + "sortedBinsMatrix--t-" + str(lenBins) + "--n-" + str(nbBins) + "--c-" + str(overlapping) + ".csv",
-# sortedBinsMatrix, delimiter=",")
-# return sortedBinsMatrix
-#
-#
-# def makeSparseTotalMatrix(sortedRNASeq, random_state):
-# nbPatients, nbGenes = sortedRNASeq.shape
-# params = findParams(nbGenes, nbPatients, random_state)
-# nbBins = params["nbBins"]
-# overlapping = params["overlapping"]
-# lenBin = params["lenBin"]
-# bins = findBins(nbBins, overlapping, lenBin)
-# sparseFull = sparse.csc_matrix((nbPatients, nbGenes * nbBins))
-# for patientIndex, patient in enumerate(sortedRNASeq):
-# columnIndices = getBins(patient, bins, lenBin, overlapping)
-# rowIndices = np.zeros(len(columnIndices), dtype=int) + patientIndex
-# data = np.ones(len(columnIndices), dtype=bool)
-# sparseFull = sparseFull + sparse.csc_matrix((data, (rowIndices, columnIndices)),
-# shape=(nbPatients, nbGenes * nbBins))
-# return sparseFull
-#
-#
-# def getAdjacenceMatrix(RNASeqRanking, sotredRNASeq, k=2):
-# k = int(k) / 2 * 2
-# indices = np.zeros((RNASeqRanking.shape[0] * k * RNASeqRanking.shape[1]), dtype=int)
-# data = np.ones((RNASeqRanking.shape[0] * k * RNASeqRanking.shape[1]), dtype=bool)
-# indptr = np.zeros(RNASeqRanking.shape[0] + 1, dtype=int)
-# nbGenes = RNASeqRanking.shape[1]
-# pointer = 0
-# for patientIndex in range(RNASeqRanking.shape[0]):
-# for i in range(nbGenes):
-# for j in range(k / 2):
-# try:
-# indices[pointer] = RNASeqRanking[
-# patientIndex, (sotredRNASeq[patientIndex, i] - (j + 1))] + i * nbGenes
-# pointer += 1
-# except:
-# pass
-# try:
-# indices[pointer] = RNASeqRanking[
-# patientIndex, (sotredRNASeq[patientIndex, i] + (j + 1))] + i * nbGenes
-# pointer += 1
-# except:
-# pass
-# # elif i<=k:
-# # indices.append(patient[1]+patient[i]*nbGenes)
-# # data.append(True)
-# # elif i==nbGenes-1:
-# # indices.append(patient[i-1]+patient[i]*nbGenes)
-# # data.append(True)
-# indptr[patientIndex + 1] = pointer
-#
-# mat = sparse.csr_matrix((data, indices, indptr),
-# shape=(RNASeqRanking.shape[0], RNASeqRanking.shape[1] * RNASeqRanking.shape[1]), dtype=bool)
-# return mat
-#
-#
-# def getKMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
-# datasetFile = h5py.File(path + "KMultiOmic.hdf5", "w")
-#
-# # logging.debug("Start:\t Getting Methylation data")
-# methylData = np.genfromtxt(path + "matching_methyl.csv", delimiter=',')
-# logging.debug("Done:\t Getting Methylation data")
-#
-# logging.debug("Start:\t Getting Sorted Methyl data")
-# Methyl = methylData
-# sortedMethylGeneIndices = np.zeros(methylData.shape, dtype=int)
-# MethylRanking = np.zeros(methylData.shape, dtype=int)
-# for exampleIndex, exampleArray in enumerate(Methyl):
-# sortedMethylDictionary = dict((index, value) for index, value in enumerate(exampleArray))
-# sortedMethylIndicesDict = sorted(sortedMethylDictionary.items(), key=operator.itemgetter(1))
-# sortedMethylIndicesArray = np.array([index for (index, value) in sortedMethylIndicesDict], dtype=int)
-# sortedMethylGeneIndices[exampleIndex] = sortedMethylIndicesArray
-# for geneIndex in range(Methyl.shape[1]):
-# MethylRanking[exampleIndex, sortedMethylIndicesArray[geneIndex]] = geneIndex
-# logging.debug("Done:\t Getting Sorted Methyl data")
-#
-# logging.debug("Start:\t Getting Binarized Methyl data")
-# k = findClosestPowerOfTwo(9) - 1
-# try:
-# factorizedLeftBaseMatrix = np.genfromtxt(
-# path + "factorLeft--n-" + str(methylData.shape[1]) + "--k-" + str(k) + ".csv", delimiter=',')
-# except:
-# factorizedLeftBaseMatrix = getBaseMatrices(methylData.shape[1], k, path)
-# bMethylDset = datasetFile.create_dataset("View0",
-# (sortedMethylGeneIndices.shape[0], sortedMethylGeneIndices.shape[1] * k),
-# dtype=np.uint8)
-# for patientIndex, patientSortedArray in enumerate(sortedMethylGeneIndices):
-# patientMatrix = np.zeros((sortedMethylGeneIndices.shape[1], k), dtype=np.uint8)
-# for lineIndex, geneIndex in enumerate(patientSortedArray):
-# patientMatrix[geneIndex] = factorizedLeftBaseMatrix[lineIndex, :]
-# bMethylDset[patientIndex] = patientMatrix.flatten()
-# bMethylDset.attrs["name"] = "BMethyl" + str(k)
-# bMethylDset.attrs["sparse"] = False
-# bMethylDset.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binarized Methyl data")
-#
-# logging.debug("Start:\t Getting Binned Methyl data")
-# lenBins = 3298
-# nbBins = 9
-# overlapping = 463
-# try:
-# sortedBinsMatrix = np.genfromtxt(
-# path + "sortedBinsMatrix--t-" + str(lenBins) + "--n-" + str(nbBins) + "--c-" + str(overlapping) + ".csv",
-# delimiter=",")
-# except:
-# sortedBinsMatrix = makeSortedBinsMatrix(nbBins, lenBins, overlapping, methylData.shape[1], path)
-# binnedMethyl = datasetFile.create_dataset("View1", (
-# sortedMethylGeneIndices.shape[0], sortedMethylGeneIndices.shape[1] * nbBins), dtype=np.uint8)
-# for patientIndex, patientSortedArray in enumerate(sortedMethylGeneIndices):
-# patientMatrix = np.zeros((sortedMethylGeneIndices.shape[1], nbBins), dtype=np.uint8)
-# for lineIndex, geneIndex in enumerate(patientSortedArray):
-# patientMatrix[geneIndex] = sortedBinsMatrix[lineIndex, :]
-# binnedMethyl[patientIndex] = patientMatrix.flatten()
-# binnedMethyl.attrs["name"] = "bMethyl" + str(nbBins)
-# binnedMethyl.attrs["sparse"] = False
-# binnedMethyl.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binned Methyl data")
-#
-# logging.debug("Start:\t Getting Binarized Methyl data")
-# k = findClosestPowerOfTwo(17) - 1
-# try:
-# factorizedLeftBaseMatrix = np.genfromtxt(
-# path + "factorLeft--n-" + str(methylData.shape[1]) + "--k-" + str(k) + ".csv", delimiter=',')
-# except:
-# factorizedLeftBaseMatrix = getBaseMatrices(methylData.shape[1], k, path)
-# bMethylDset = datasetFile.create_dataset("View2",
-# (sortedMethylGeneIndices.shape[0], sortedMethylGeneIndices.shape[1] * k),
-# dtype=np.uint8)
-# for patientIndex, patientSortedArray in enumerate(sortedMethylGeneIndices):
-# patientMatrix = np.zeros((sortedMethylGeneIndices.shape[1], k), dtype=np.uint8)
-# for lineIndex, geneIndex in enumerate(patientSortedArray):
-# patientMatrix[geneIndex] = factorizedLeftBaseMatrix[lineIndex, :]
-# bMethylDset[patientIndex] = patientMatrix.flatten()
-# bMethylDset.attrs["name"] = "BMethyl" + str(k)
-# bMethylDset.attrs["sparse"] = False
-# bMethylDset.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binarized Methyl data")
-#
-# logging.debug("Start:\t Getting Binned Methyl data")
-# lenBins = 2038
-# nbBins = 16
-# overlapping = 442
-# try:
-# sortedBinsMatrix = np.genfromtxt(
-# path + "sortedBinsMatrix--t-" + str(lenBins) + "--n-" + str(nbBins) + "--c-" + str(overlapping) + ".csv",
-# delimiter=",")
-# except:
-# sortedBinsMatrix = makeSortedBinsMatrix(nbBins, lenBins, overlapping, methylData.shape[1], path)
-# binnedMethyl = datasetFile.create_dataset("View3", (
-# sortedMethylGeneIndices.shape[0], sortedMethylGeneIndices.shape[1] * nbBins), dtype=np.uint8)
-# for patientIndex, patientSortedArray in enumerate(sortedMethylGeneIndices):
-# patientMatrix = np.zeros((sortedMethylGeneIndices.shape[1], nbBins), dtype=np.uint8)
-# for lineIndex, geneIndex in enumerate(patientSortedArray):
-# patientMatrix[geneIndex] = sortedBinsMatrix[lineIndex, :]
-# binnedMethyl[patientIndex] = patientMatrix.flatten()
-# binnedMethyl.attrs["name"] = "bMethyl" + str(nbBins)
-# binnedMethyl.attrs["sparse"] = False
-# binnedMethyl.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binned Methyl data")
-#
-# labelFile = open(path + 'brca_labels_triple-negatif.csv')
-# labels = np.array([int(line.strip().split(',')[1]) for line in labelFile])
-# labelsDset = datasetFile.create_dataset("Labels", labels.shape)
-# labelsDset[...] = labels
-# labelsDset.attrs["name"] = "Labels"
-#
-# metaDataGrp = datasetFile.create_group("Metadata")
-# metaDataGrp.attrs["nbView"] = 4
-# metaDataGrp.attrs["nbClass"] = 2
-# metaDataGrp.attrs["datasetLength"] = len(labels)
-# labelDictionary = {0: "No", 1: "Yes"}
-#
-# datasetFile.close()
-# datasetFile = h5py.File(path + "KMultiOmic.hdf5", "r")
-#
-# return datasetFile, labelDictionary
-#
-#
-# def getKMultiOmicDBhdf5(features, path, name, NB_CLASS, LABELS_NAMES):
-# datasetFile = h5py.File(path + "KMultiOmic.hdf5", "r")
-# labelDictionary = {0: "No", 1: "Yes"}
-# return datasetFile, labelDictionary
-#
-#
-# def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
-# datasetFile = h5py.File(path + "ModifiedMultiOmic.hdf5", "w")
-#
-# logging.debug("Start:\t Getting Methylation data")
-# methylData = np.genfromtxt(path + "matching_methyl.csv", delimiter=',')
-# methylDset = datasetFile.create_dataset("View0", methylData.shape)
-# methylDset[...] = methylData
-# methylDset.attrs["name"] = "Methyl_"
-# methylDset.attrs["sparse"] = False
-# methylDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting Methylation data")
-#
-# logging.debug("Start:\t Getting Sorted Methyl data")
-# Methyl = datasetFile["View0"][...]
-# sortedMethylGeneIndices = np.zeros(datasetFile.get("View0").shape, dtype=int)
-# MethylRanking = np.zeros(datasetFile.get("View0").shape, dtype=int)
-# for exampleIndex, exampleArray in enumerate(Methyl):
-# sortedMethylDictionary = dict((index, value) for index, value in enumerate(exampleArray))
-# sortedMethylIndicesDict = sorted(sortedMethylDictionary.items(), key=operator.itemgetter(1))
-# sortedMethylIndicesArray = np.array([index for (index, value) in sortedMethylIndicesDict], dtype=int)
-# sortedMethylGeneIndices[exampleIndex] = sortedMethylIndicesArray
-# for geneIndex in range(Methyl.shape[1]):
-# MethylRanking[exampleIndex, sortedMethylIndicesArray[geneIndex]] = geneIndex
-# mMethylDset = datasetFile.create_dataset("View10", sortedMethylGeneIndices.shape, data=sortedMethylGeneIndices)
-# mMethylDset.attrs["name"] = "SMethyl"
-# mMethylDset.attrs["sparse"] = False
-# mMethylDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting Sorted Methyl data")
-#
-# logging.debug("Start:\t Getting Binarized Methyl data")
-# k = findClosestPowerOfTwo(58) - 1
-# try:
-# factorizedLeftBaseMatrix = np.genfromtxt(
-# path + "factorLeft--n-" + str(datasetFile.get("View0").shape[1]) + "--k-" + str(k) + ".csv", delimiter=',')
-# except:
-# factorizedLeftBaseMatrix = getBaseMatrices(methylData.shape[1], k, path)
-# bMethylDset = datasetFile.create_dataset("View11",
-# (sortedMethylGeneIndices.shape[0], sortedMethylGeneIndices.shape[1] * k),
-# dtype=np.uint8)
-# for patientIndex, patientSortedArray in enumerate(sortedMethylGeneIndices):
-# patientMatrix = np.zeros((sortedMethylGeneIndices.shape[1], k), dtype=np.uint8)
-# for lineIndex, geneIndex in enumerate(patientSortedArray):
-# patientMatrix[geneIndex] = factorizedLeftBaseMatrix[lineIndex, :]
-# bMethylDset[patientIndex] = patientMatrix.flatten()
-# bMethylDset.attrs["name"] = "BMethyl"
-# bMethylDset.attrs["sparse"] = False
-# bMethylDset.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binarized Methyl data")
-#
-# logging.debug("Start:\t Getting Binned Methyl data")
-# lenBins = 2095
-# nbBins = 58
-# overlapping = 1676
-# try:
-# sortedBinsMatrix = np.genfromtxt(
-# path + "sortedBinsMatrix--t-" + str(lenBins) + "--n-" + str(nbBins) + "--c-" + str(overlapping) + ".csv",
-# delimiter=",")
-# except:
-# sortedBinsMatrix = makeSortedBinsMatrix(nbBins, lenBins, overlapping, datasetFile.get("View0").shape[1], path)
-# binnedMethyl = datasetFile.create_dataset("View12", (
-# sortedMethylGeneIndices.shape[0], sortedMethylGeneIndices.shape[1] * nbBins), dtype=np.uint8)
-# for patientIndex, patientSortedArray in enumerate(sortedMethylGeneIndices):
-# patientMatrix = np.zeros((sortedMethylGeneIndices.shape[1], nbBins), dtype=np.uint8)
-# for lineIndex, geneIndex in enumerate(patientSortedArray):
-# patientMatrix[geneIndex] = sortedBinsMatrix[lineIndex, :]
-# binnedMethyl[patientIndex] = patientMatrix.flatten()
-# binnedMethyl.attrs["name"] = "bMethyl"
-# binnedMethyl.attrs["sparse"] = False
-# binnedMethyl.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binned Methyl data")
-#
-# logging.debug("Start:\t Getting MiRNA data")
-# mirnaData = np.genfromtxt(path + "matching_mirna.csv", delimiter=',')
-# mirnaDset = datasetFile.create_dataset("View1", mirnaData.shape)
-# mirnaDset[...] = mirnaData
-# mirnaDset.attrs["name"] = "MiRNA__"
-# mirnaDset.attrs["sparse"] = False
-# mirnaDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting MiRNA data")
-#
-# logging.debug("Start:\t Getting Sorted MiRNA data")
-# MiRNA = datasetFile["View1"][...]
-# sortedMiRNAGeneIndices = np.zeros(datasetFile.get("View1").shape, dtype=int)
-# MiRNARanking = np.zeros(datasetFile.get("View1").shape, dtype=int)
-# for exampleIndex, exampleArray in enumerate(MiRNA):
-# sortedMiRNADictionary = dict((index, value) for index, value in enumerate(exampleArray))
-# sortedMiRNAIndicesDict = sorted(sortedMiRNADictionary.items(), key=operator.itemgetter(1))
-# sortedMiRNAIndicesArray = np.array([index for (index, value) in sortedMiRNAIndicesDict], dtype=int)
-# sortedMiRNAGeneIndices[exampleIndex] = sortedMiRNAIndicesArray
-# for geneIndex in range(MiRNA.shape[1]):
-# MiRNARanking[exampleIndex, sortedMiRNAIndicesArray[geneIndex]] = geneIndex
-# mmirnaDset = datasetFile.create_dataset("View7", sortedMiRNAGeneIndices.shape, data=sortedMiRNAGeneIndices)
-# mmirnaDset.attrs["name"] = "SMiRNA_"
-# mmirnaDset.attrs["sparse"] = False
-# mmirnaDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting Sorted MiRNA data")
-#
-# logging.debug("Start:\t Getting Binarized MiRNA data")
-# k = findClosestPowerOfTwo(517) - 1
-# try:
-# factorizedLeftBaseMatrix = np.genfromtxt(
-# path + "factorLeft--n-" + str(datasetFile.get("View1").shape[1]) + "--k-" + str(k) + ".csv", delimiter=',')
-# except:
-# factorizedLeftBaseMatrix = getBaseMatrices(mirnaData.shape[1], k, path)
-# bmirnaDset = datasetFile.create_dataset("View8",
-# (sortedMiRNAGeneIndices.shape[0], sortedMiRNAGeneIndices.shape[1] * k),
-# dtype=np.uint8)
-# for patientIndex, patientSortedArray in enumerate(sortedMiRNAGeneIndices):
-# patientMatrix = np.zeros((sortedMiRNAGeneIndices.shape[1], k), dtype=np.uint8)
-# for lineIndex, geneIndex in enumerate(patientSortedArray):
-# patientMatrix[geneIndex] = factorizedLeftBaseMatrix[lineIndex, :]
-# bmirnaDset[patientIndex] = patientMatrix.flatten()
-# bmirnaDset.attrs["name"] = "BMiRNA_"
-# bmirnaDset.attrs["sparse"] = False
-# bmirnaDset.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binarized MiRNA data")
-#
-# logging.debug("Start:\t Getting Binned MiRNA data")
-# lenBins = 14
-# nbBins = 517
-# overlapping = 12
-# try:
-# sortedBinsMatrix = np.genfromtxt(
-# path + "sortedBinsMatrix--t-" + str(lenBins) + "--n-" + str(nbBins) + "--c-" + str(overlapping) + ".csv",
-# delimiter=",")
-# except:
-# sortedBinsMatrix = makeSortedBinsMatrix(nbBins, lenBins, overlapping, datasetFile.get("View1").shape[1], path)
-# binnedMiRNA = datasetFile.create_dataset("View9", (
-# sortedMiRNAGeneIndices.shape[0], sortedMiRNAGeneIndices.shape[1] * nbBins), dtype=np.uint8)
-# for patientIndex, patientSortedArray in enumerate(sortedMiRNAGeneIndices):
-# patientMatrix = np.zeros((sortedMiRNAGeneIndices.shape[1], nbBins), dtype=np.uint8)
-# for lineIndex, geneIndex in enumerate(patientSortedArray):
-# patientMatrix[geneIndex] = sortedBinsMatrix[lineIndex, :]
-# binnedMiRNA[patientIndex] = patientMatrix.flatten()
-# binnedMiRNA.attrs["name"] = "bMiRNA_"
-# binnedMiRNA.attrs["sparse"] = False
-# binnedMiRNA.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binned MiRNA data")
-#
-# logging.debug("Start:\t Getting RNASeq data")
-# rnaseqData = np.genfromtxt(path + "matching_rnaseq.csv", delimiter=',')
-# uselessRows = []
-# for rowIndex, row in enumerate(np.transpose(rnaseqData)):
-# if not row.any():
-# uselessRows.append(rowIndex)
-# usefulRows = [usefulRowIndex for usefulRowIndex in range(rnaseqData.shape[1]) if usefulRowIndex not in uselessRows]
-# rnaseqDset = datasetFile.create_dataset("View2", (rnaseqData.shape[0], len(usefulRows)))
-# rnaseqDset[...] = rnaseqData[:, usefulRows]
-# rnaseqDset.attrs["name"] = "RNASeq_"
-# rnaseqDset.attrs["sparse"] = False
-# rnaseqDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting RNASeq data")
-#
-# logging.debug("Start:\t Getting Sorted RNASeq data")
-# RNASeq = datasetFile["View2"][...]
-# sortedRNASeqGeneIndices = np.zeros(datasetFile.get("View2").shape, dtype=int)
-# RNASeqRanking = np.zeros(datasetFile.get("View2").shape, dtype=int)
-# for exampleIndex, exampleArray in enumerate(RNASeq):
-# sortedRNASeqDictionary = dict((index, value) for index, value in enumerate(exampleArray))
-# sortedRNASeqIndicesDict = sorted(sortedRNASeqDictionary.items(), key=operator.itemgetter(1))
-# sortedRNASeqIndicesArray = np.array([index for (index, value) in sortedRNASeqIndicesDict], dtype=int)
-# sortedRNASeqGeneIndices[exampleIndex] = sortedRNASeqIndicesArray
-# for geneIndex in range(RNASeq.shape[1]):
-# RNASeqRanking[exampleIndex, sortedRNASeqIndicesArray[geneIndex]] = geneIndex
-# mrnaseqDset = datasetFile.create_dataset("View4", sortedRNASeqGeneIndices.shape, data=sortedRNASeqGeneIndices)
-# mrnaseqDset.attrs["name"] = "SRNASeq"
-# mrnaseqDset.attrs["sparse"] = False
-# mrnaseqDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting Sorted RNASeq data")
-#
-# logging.debug("Start:\t Getting Binarized RNASeq data")
-# k = findClosestPowerOfTwo(100) - 1
-# try:
-# factorizedLeftBaseMatrix = np.genfromtxt(
-# path + "factorLeft--n-" + str(datasetFile.get("View2").shape[1]) + "--k-" + str(100) + ".csv",
-# delimiter=',')
-# except:
-# factorizedLeftBaseMatrix = getBaseMatrices(rnaseqData.shape[1], k, path)
-# brnaseqDset = datasetFile.create_dataset("View5",
-# (sortedRNASeqGeneIndices.shape[0], sortedRNASeqGeneIndices.shape[1] * k),
-# dtype=np.uint8)
-# for patientIndex, patientSortedArray in enumerate(sortedRNASeqGeneIndices):
-# patientMatrix = np.zeros((sortedRNASeqGeneIndices.shape[1], k), dtype=np.uint8)
-# for lineIndex, geneIndex in enumerate(patientSortedArray):
-# patientMatrix[geneIndex] = factorizedLeftBaseMatrix[lineIndex, :]
-# brnaseqDset[patientIndex] = patientMatrix.flatten()
-# brnaseqDset.attrs["name"] = "BRNASeq"
-# brnaseqDset.attrs["sparse"] = False
-# brnaseqDset.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binarized RNASeq data")
-#
-# logging.debug("Start:\t Getting Binned RNASeq data")
-# lenBins = 986
-# nbBins = 142
-# overlapping = 493
-# try:
-# sortedBinsMatrix = np.genfromtxt(
-# path + "sortedBinsMatrix--t-" + str(lenBins) + "--n-" + str(nbBins) + "--c-" + str(overlapping) + ".csv",
-# delimiter=",")
-# except:
-# sortedBinsMatrix = makeSortedBinsMatrix(nbBins, lenBins, overlapping, datasetFile.get("View2").shape[1], path)
-# binnedRNASeq = datasetFile.create_dataset("View6", (
-# sortedRNASeqGeneIndices.shape[0], sortedRNASeqGeneIndices.shape[1] * nbBins), dtype=np.uint8)
-# for patientIndex, patientSortedArray in enumerate(sortedRNASeqGeneIndices):
-# patientMatrix = np.zeros((sortedRNASeqGeneIndices.shape[1], nbBins), dtype=np.uint8)
-# for lineIndex, geneIndex in enumerate(patientSortedArray):
-# patientMatrix[geneIndex] = sortedBinsMatrix[lineIndex, :]
-# binnedRNASeq[patientIndex] = patientMatrix.flatten()
-# binnedRNASeq.attrs["name"] = "bRNASeq"
-# binnedRNASeq.attrs["sparse"] = False
-# binnedRNASeq.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binned RNASeq data")
-#
-# logging.debug("Start:\t Getting Clinical data")
-# clinical = np.genfromtxt(path + "clinicalMatrix.csv", delimiter=',')
-# clinicalDset = datasetFile.create_dataset("View3", clinical.shape)
-# clinicalDset[...] = clinical
-# clinicalDset.attrs["name"] = "Clinic_"
-# clinicalDset.attrs["sparse"] = False
-# clinicalDset.attrs["binary"] = False
-# logging.debug("Done:\t Getting Clinical data")
-#
-# logging.debug("Start:\t Getting Binarized Clinical data")
-# binarized_clinical = np.zeros((347, 1951), dtype=np.uint8)
-# nb_already_done = 0
-# for feqtureIndex, feature in enumerate(np.transpose(clinical)):
-# featureSet = set(feature)
-# featureDict = dict((val, valIndex) for valIndex, val in enumerate(list(featureSet)))
-# for valueIndex, value in enumerate(feature):
-# binarized_clinical[valueIndex, featureDict[value] + nb_already_done] = 1
-# nb_already_done += len(featureSet)
-# bClinicalDset = datasetFile.create_dataset("View13", binarized_clinical.shape, dtype=np.uint8,
-# data=binarized_clinical)
-# bClinicalDset.attrs["name"] = "bClinic"
-# bClinicalDset.attrs["sparse"] = False
-# bClinicalDset.attrs["binary"] = True
-# logging.debug("Done:\t Getting Binarized Clinical data")
-#
-# # logging.debug("Start:\t Getting Adjacence RNASeq data")
-# # sparseAdjRNASeq = getAdjacenceMatrix(RNASeqRanking, sortedRNASeqGeneIndices, k=findClosestPowerOfTwo(10)-1)
-# # sparseAdjRNASeqGrp = datasetFile.create_group("View6")
-# # dataDset = sparseAdjRNASeqGrp.create_dataset("data", sparseAdjRNASeq.data.shape, data=sparseAdjRNASeq.data)
-# # indicesDset = sparseAdjRNASeqGrp.create_dataset("indices",
-# # sparseAdjRNASeq.indices.shape, data=sparseAdjRNASeq.indices)
-# # indptrDset = sparseAdjRNASeqGrp.create_dataset("indptr",
-# # sparseAdjRNASeq.indptr.shape, data=sparseAdjRNASeq.indptr)
-# # sparseAdjRNASeqGrp.attrs["name"]="ARNASeq"
-# # sparseAdjRNASeqGrp.attrs["sparse"]=True
-# # sparseAdjRNASeqGrp.attrs["shape"]=sparseAdjRNASeq.shape
-# # logging.debug("Done:\t Getting Adjacence RNASeq data")
-#
-# labelFile = open(path + 'brca_labels_triple-negatif.csv')
-# labels = np.array([int(line.strip().split(',')[1]) for line in labelFile])
-# labelsDset = datasetFile.create_dataset("Labels", labels.shape)
-# labelsDset[...] = labels
-# labelsDset.attrs["name"] = "Labels"
-#
-# metaDataGrp = datasetFile.create_group("Metadata")
-# metaDataGrp.attrs["nbView"] = 14
-# metaDataGrp.attrs["nbClass"] = 2
-# metaDataGrp.attrs["datasetLength"] = len(labels)
-# labelDictionary = {0: "No", 1: "Yes"}
-#
-# datasetFile.close()
-# datasetFile = h5py.File(path + "ModifiedMultiOmic.hdf5", "r")
-#
-# return datasetFile, labelDictionary
-#
-#
-# def getModifiedMultiOmicDBhdf5(features, path, name, NB_CLASS, LABELS_NAMES):
-# datasetFile = h5py.File(path + "ModifiedMultiOmic.hdf5", "r")
-# labelDictionary = {0: "No", 1: "Yes"}
-# return datasetFile, labelDictionary
-#
-#
-# def getMultiOmicDBhdf5(features, path, name, NB_CLASS, LABELS_NAMES):
-# datasetFile = h5py.File(path + "MultiOmic.hdf5", "r")
-# labelDictionary = {0: "No", 1: "Yes"}
-# return datasetFile, labelDictionary
-#
-#
-#
-# # def getOneViewFromDB(viewName, pathToDB, DBName):
-# # view = np.genfromtxt(pathToDB + DBName +"-" + viewName, delimiter=';')
-# # return view
-#
-#
-# # def getClassLabels(pathToDB, DBName):
-# # labels = np.genfromtxt(pathToDB + DBName + "-" + "ClassLabels.csv", delimiter=';')
-# # return labels
-#
-#
-# # def getDataset(pathToDB, viewNames, DBName):
-# # dataset = []
-# # for viewName in viewNames:
-# # dataset.append(getOneViewFromDB(viewName, pathToDB, DBName))
-# # return np.array(dataset)
-#
-#
-# # def getAwaLabels(nbLabels, pathToAwa):
-# # labelsFile = open(pathToAwa + 'Animals_with_Attributes/classes.txt', 'U')
-# # linesFile = [''.join(line.strip().split()).translate(None, digits) for line in labelsFile.readlines()]
-# # return linesFile
-#
-#
-# # def getAwaDBcsv(views, pathToAwa, nameDB, nbLabels, LABELS_NAMES):
-# # awaLabels = getAwaLabels(nbLabels, pathToAwa)
-# # nbView = len(views)
-# # nbMaxLabels = len(awaLabels)
-# # if nbLabels == -1:
-# # nbLabels = nbMaxLabels
-# # nbNamesGiven = len(LABELS_NAMES)
-# # if nbNamesGiven > nbLabels:
-# # labelDictionary = {i:LABELS_NAMES[i] for i in np.arange(nbLabels)}
-# # elif nbNamesGiven < nbLabels and nbLabels <= nbMaxLabels:
-# # if LABELS_NAMES != ['']:
-# # labelDictionary = {i:LABELS_NAMES[i] for i in np.arange(nbNamesGiven)}
-# # else:
-# # labelDictionary = {}
-# # nbNamesGiven = 0
-# # nbLabelsToAdd = nbLabels-nbNamesGiven
-# # while nbLabelsToAdd > 0:
-# # currentLabel = random.choice(awaLabels)
-# # if currentLabel not in labelDictionary.values():
-# # labelDictionary[nbLabels-nbLabelsToAdd]=currentLabel
-# # nbLabelsToAdd -= 1
-# # else:
-# # pass
-# # else:
-# # labelDictionary = {i: LABELS_NAMES[i] for i in np.arange(nbNamesGiven)}
-# # viewDictionary = {i: views[i] for i in np.arange(nbView)}
-# # rawData = []
-# # labels = []
-# # nbExample = 0
-# # for view in np.arange(nbView):
-# # viewData = []
-# # for labelIndex in np.arange(nbLabels):
-# # pathToExamples = pathToAwa + 'Animals_with_Attributes/Features/' + viewDictionary[view] + '/' + \
-# # labelDictionary[labelIndex] + '/'
-# # examples = os.listdir(pathToExamples)
-# # if view == 0:
-# # nbExample += len(examples)
-# # for example in examples:
-# # if viewDictionary[view]=='decaf':
-# # exampleFile = open(pathToExamples + example)
-# # viewData.append([float(line.strip()) for line in exampleFile])
-# # else:
-# # exampleFile = open(pathToExamples + example)
-# # viewData.append([[float(coordinate) for coordinate in raw.split()] for raw in exampleFile][0])
-# # if view == 0:
-# # labels.append(labelIndex)
-# #
-# # rawData.append(np.array(viewData))
-# # data = rawData
-# # DATASET_LENGTH = len(labels)
-# # return data, labels, labelDictionary, DATASET_LENGTH
-# #
-# #
-# # def getDbfromCSV(path):
-# # files = os.listdir(path)
-# # DATA = np.zeros((3,40,2))
-# # for file in files:
-# # if file[-9:]=='moins.csv' and file[:7]=='sample1':
-# # X = open(path+file)
-# # for x, i in zip(X, range(20)):
-# # DATA[0, i] = np.array([float(coord) for coord in x.strip().split('\t')])
-# # if file[-9:]=='moins.csv' and file[:7]=='sample2':
-# # X = open(path+file)
-# # for x, i in zip(X, range(20)):
-# # DATA[1, i] = np.array([float(coord) for coord in x.strip().split('\t')])
-# # if file[-9:]=='moins.csv' and file[:7]=='sample3':
-# # X = open(path+file)
-# # for x, i in zip(X, range(20)):
-# # DATA[2, i] = np.array([float(coord) for coord in x.strip().split('\t')])
-# #
-# # for file in files:
-# # if file[-8:]=='plus.csv' and file[:7]=='sample1':
-# # X = open(path+file)
-# # for x, i in zip(X, range(20)):
-# # DATA[0, i+20] = np.array([float(coord) for coord in x.strip().split('\t')])
-# # if file[-8:]=='plus.csv' and file[:7]=='sample2':
-# # X = open(path+file)
-# # for x, i in zip(X, range(20)):
-# # DATA[1, i+20] = np.array([float(coord) for coord in x.strip().split('\t')])
-# # if file[-8:]=='plus.csv' and file[:7]=='sample3':
-# # X = open(path+file)
-# # for x, i in zip(X, range(20)):
-# # DATA[2, i+20] = np.array([float(coord) for coord in x.strip().split('\t')])
-# # LABELS = np.zeros(40)
-# # LABELS[:20]=LABELS[:20]+1
-# # return DATA, LABELS
-#
-# # def makeArrayFromTriangular(pseudoRNASeqMatrix):
-# # matrixShape = len(pseudoRNASeqMatrix[0,:])
-# # exampleArray = np.array(((matrixShape-1)*matrixShape)/2)
-# # arrayIndex = 0
-# # for i in range(matrixShape-1):
-# # for j in range(i+1, matrixShape):
-# # exampleArray[arrayIndex]=pseudoRNASeqMatrix[i,j]
-# # arrayIndex += 1
-# # return exampleArray
-#
-#
-# # def getPseudoRNASeq(dataset):
-# # nbGenes = len(dataset["/View2/matrix"][0, :])
-# # pseudoRNASeq = np.zeros((dataset["/datasetlength"][...], ((nbGenes - 1) * nbGenes) / 2), dtype=bool_)
-# # for exampleIndex in xrange(dataset["/datasetlength"][...]):
-# # arrayIndex = 0
-# # for i in xrange(nbGenes):
-# # for j in xrange(nbGenes):
-# # if i > j:
-# # pseudoRNASeq[exampleIndex, arrayIndex] =
-# # dataset["/View2/matrix"][exampleIndex, j] < dataset["/View2/matrix"][exampleIndex, i]
-# # arrayIndex += 1
-# # dataset["/View4/matrix"] = pseudoRNASeq
-# # dataset["/View4/name"] = "pseudoRNASeq"
-# # return dataset
-#
-#
-# # def allSame(array):
-# # value = array[0]
-# # areAllSame = True
-# # for i in array:
-# # if i != value:
-# # areAllSame = False
-# # return areAllSame
-
-
-# def getFakeDBhdf5(features, pathF, name, NB_CLASS, LABELS_NAME, random_state):
-# """Was used to generateafake dataset to run tests"""
-# NB_VIEW = 4
-# DATASET_LENGTH = 30
-# NB_CLASS = 2
-# VIEW_DIMENSIONS = random_state.random_integers(5, 20, NB_VIEW)
-#
-# DATA = dict((indx,
-# np.array([
-# random_state.normal(0.0, 2, viewDimension)
-# for i in np.arange(DATASET_LENGTH)]))
-# for indx, viewDimension in enumerate(VIEW_DIMENSIONS))
-#
-# CLASS_LABELS = random_state.random_integers(0, NB_CLASS - 1, DATASET_LENGTH)
-# datasetFile = h5py.File(pathF + "Fake.hdf5", "w")
-# for index, viewData in enumerate(DATA.values()):
-# if index == 0:
-# viewData = random_state.randint(0, 1, (DATASET_LENGTH, 300)).astype(
-# np.uint8)
-# # np.zeros(viewData.shape, dtype=bool)+np.ones((viewData.shape[0], viewData.shape[1]/2), dtype=bool)
-# viewDset = datasetFile.create_dataset("View" + str(index), viewData.shape)
-# viewDset[...] = viewData
-# viewDset.attrs["name"] = "View" + str(index)
-# viewDset.attrs["sparse"] = False
-# elif index == 1:
-# viewData = sparse.csr_matrix(viewData)
-# viewGrp = datasetFile.create_group("View" + str(index))
-# dataDset = viewGrp.create_dataset("data", viewData.data.shape, data=viewData.data)
-# indicesDset = viewGrp.create_dataset("indices", viewData.indices.shape, data=viewData.indices)
-# indptrDset = viewGrp.create_dataset("indptr", viewData.indptr.shape, data=viewData.indptr)
-# viewGrp.attrs["name"] = "View" + str(index)
-# viewGrp.attrs["sparse"] = True
-# viewGrp.attrs["shape"] = viewData.shape
-# else:
-# viewDset = datasetFile.create_dataset("View" + str(index), viewData.shape)
-# viewDset[...] = viewData
-# viewDset.attrs["name"] = "View" + str(index)
-# viewDset.attrs["sparse"] = False
-# labelsDset = datasetFile.create_dataset("Labels", CLASS_LABELS.shape)
-# labelsDset[...] = CLASS_LABELS
-# labelsDset.attrs["name"] = "Labels"
-#
-# metaDataGrp = datasetFile.create_group("Metadata")
-# metaDataGrp.attrs["nbView"] = NB_VIEW
-# metaDataGrp.attrs["nbClass"] = NB_CLASS
-# metaDataGrp.attrs["datasetLength"] = len(CLASS_LABELS)
-# labels_dictionary = {0: "No", 1: "Yes"}
-# datasetFile.close()
-# datasetFile = h5py.File(pathF + "Fake.hdf5", "r")
-# return datasetFile, labels_dictionary
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py b/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
deleted file mode 100644
index a13f6cab..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py
+++ /dev/null
@@ -1,653 +0,0 @@
-import itertools
-import sys
-import traceback
-import yaml
-from abc import abstractmethod
-
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy.stats import randint, uniform
-from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, \
- ParameterGrid, ParameterSampler
-from sklearn.base import clone, BaseEstimator
-
-from .multiclass import MultiClassWrapper
-from .organization import secure_file_path
-from .base import get_metric
-from .. import metrics
-
-
-class HPSearch:
-
- def get_scoring(self, metric):
- if isinstance(metric, dict):
- metric_module, metric_kwargs = get_metric(metric)
- return metric_module.get_scorer(**metric_kwargs)
- else:
- return metric
-
- def fit_multiview(self, X, y, groups=None, **fit_params):
- n_splits = self.cv.get_n_splits(self.available_indices,
- y[self.available_indices])
- folds = list(
- self.cv.split(self.available_indices, y[self.available_indices]))
- self.get_candidate_params(X)
- base_estimator = clone(self.estimator)
- results = {}
- self.cv_results_ = dict(("param_" + param_name, []) for param_name in
- self.candidate_params[0].keys())
- self.cv_results_["mean_test_score"] = []
- self.cv_results_["params"] = []
- n_failed = 0
- self.tracebacks_params = []
- for candidate_param_idx, candidate_param in enumerate(self.candidate_params):
- test_scores = np.zeros(n_splits) + 1000
- try:
- for fold_idx, (train_indices, test_indices) in enumerate(folds):
- current_estimator = clone(base_estimator)
- current_estimator.set_params(**candidate_param)
- current_estimator.fit(X, y,
- train_indices=self.available_indices[
- train_indices],
- view_indices=self.view_indices)
- test_prediction = current_estimator.predict(
- X,
- self.available_indices[test_indices],
- view_indices=self.view_indices)
- test_score = self.scoring._score_func(
- y[self.available_indices[test_indices]],
- test_prediction,
- **self.scoring._kwargs)
- test_scores[fold_idx] = test_score
- self.cv_results_['params'].append(
- current_estimator.get_params())
- cross_validation_score = np.mean(test_scores)
- self.cv_results_["mean_test_score"].append(
- cross_validation_score)
- results[candidate_param_idx] = cross_validation_score
- if cross_validation_score >= max(results.values()):
- self.best_params_ = self.candidate_params[candidate_param_idx]
- self.best_score_ = cross_validation_score
- except:
- if self.track_tracebacks:
- n_failed += 1
- self.tracebacks.append(traceback.format_exc())
- self.tracebacks_params.append(candidate_param)
- else:
- raise
- if n_failed == self.n_iter:
- raise ValueError(
- 'No fits were performed. All HP combination returned errors \n\n' + '\n'.join(
- self.tracebacks))
- self.cv_results_["mean_test_score"] = np.array(
- self.cv_results_["mean_test_score"])
- if self.refit:
- self.best_estimator_ = clone(base_estimator).set_params(
- **self.best_params_)
- self.best_estimator_.fit(X, y, **fit_params)
- self.n_splits_ = n_splits
- return self
-
- @abstractmethod
- def get_candidate_params(self, X): # pragma: no cover
- raise NotImplementedError
-
- def get_best_params(self):
- best_params = self.best_params_
- if "random_state" in best_params:
- best_params.pop("random_state")
- return best_params
-
- def gen_report(self, output_file_name):
- scores_array = self.cv_results_['mean_test_score']
- sorted_indices = np.argsort(-scores_array)
- tested_params = [self.cv_results_["params"][score_index]
- for score_index in sorted_indices]
- scores_array = scores_array[sorted_indices]
- output_string = ""
- for parameters, score in zip(tested_params, scores_array):
- formatted_params = format_params(parameters)
- output_string += "\n{}\n\t\t{}".format(yaml.dump(formatted_params), score)
- if self.tracebacks:
- output_string += "Failed : \n\n\n"
- for traceback, params in zip(self.tracebacks, self.tracebacks_params):
- output_string+= '{}\n\n{}\n'.format(params, traceback)
- secure_file_path(output_file_name + "hps_report.txt")
- with open(output_file_name + "hps_report.txt", "w") as output_file:
- output_file.write(output_string)
-
-
-class Random(RandomizedSearchCV, HPSearch):
-
- def __init__(self, estimator, param_distributions=None, n_iter=10,
- refit=False, n_jobs=1, scoring=None, cv=None,
- random_state=None, learning_indices=None, view_indices=None,
- framework="monoview",
- equivalent_draws=True, track_tracebacks=True):
- if param_distributions is None:
- param_distributions = self.get_param_distribs(estimator)
- scoring = HPSearch.get_scoring(self, scoring)
- RandomizedSearchCV.__init__(self, estimator, n_iter=n_iter,
- param_distributions=param_distributions,
- refit=refit, n_jobs=n_jobs, scoring=scoring,
- cv=cv, random_state=random_state)
- self.framework = framework
- self.available_indices = learning_indices
- self.view_indices = view_indices
- self.equivalent_draws = equivalent_draws
- self.track_tracebacks = track_tracebacks
- self.tracebacks=[]
-
- def get_param_distribs(self, estimator):
- if isinstance(estimator, MultiClassWrapper):
- return estimator.estimator.gen_distribs()
- else:
- return estimator.gen_distribs()
-
- def fit(self, X, y=None, groups=None, **fit_params): # pragma: no cover
- if self.framework == "monoview":
- return RandomizedSearchCV.fit(self, X, y=y, groups=groups,
- **fit_params)
-
- elif self.framework == "multiview":
- return HPSearch.fit_multiview(self, X, y=y, groups=groups,
- **fit_params)
-
- def get_candidate_params(self, X):
- if self.equivalent_draws:
- self.n_iter = self.n_iter * X.nb_view
- self.candidate_params = list(
- ParameterSampler(self.param_distributions, self.n_iter,
- random_state=self.random_state))
-
- # def fit_multiview(self, X, y=None, groups=None, track_tracebacks=True,
- # **fit_params):
- # n_splits = self.cv.get_n_splits(self.available_indices,
- # y[self.available_indices])
-
-
-
-
-class Grid(GridSearchCV, HPSearch):
-
- def __init__(self, estimator, param_grid={}, refit=False, n_jobs=1, scoring=None, cv=None,
- learning_indices=None, view_indices=None, framework="monoview",
- random_state=None, track_tracebacks=True):
- scoring = HPSearch.get_scoring(self, scoring)
- GridSearchCV.__init__(self, estimator, param_grid, scoring=scoring,
- n_jobs=n_jobs, iid='deprecated', refit=refit,
- cv=cv)
- self.framework = framework
- self.available_indices = learning_indices
- self.view_indices = view_indices
- self.track_tracebacks = track_tracebacks
- self.tracebacks = []
-
- def fit(self, X, y=None, groups=None, **fit_params):
- if self.framework == "monoview":
- return GridSearchCV.fit(self, X, y=y, groups=groups,
- **fit_params)
- elif self.framework == "multiview":
- return HPSearch.fit_multiview(self, X, y=y, groups=groups,
- **fit_params)
-
- def get_candidate_params(self, X):
- self.candidate_params = list(ParameterGrid(self.param_grid))
- self.n_iter = len(self.candidate_params)
-
-
-# class ParameterSamplerGrid:
-#
-# def __init__(self, param_distributions, n_iter):
-# from math import floor
-# n_points_per_param = int(n_iter **(1/len(param_distributions)))
-# selected_params = dict((param_name, [])
-# for param_name in param_distributions.keys())
-# for param_name, distribution in param_distributions.items():
-# if isinstance(distribution, list):
-# if len(distribution)<n_points_per_param:
-# selected_params[param_name] = distribution
-# else:
-# index_step = floor(len(distribution)/n_points_per_param-2)
-# selected_params[param_name] = distribution[0]+[distribution[index*index_step+1]
-# for index
-# in range(n_points_per_param)]
-
-
-
-
-#
-# def hps_search():
-# pass
-#
-# def grid_search(X, y, framework, random_state, output_file_name,
-# classifier_module,
-# classifier_name, folds=4, nb_cores=1,
-# metric=["accuracy_score", None],
-# n_iter=30, classifier_kwargs={}, learning_indices=None,
-# view_indices=None,
-# equivalent_draws=True, grid_search_config=None):
-# """Used to perfom gridsearch on the classifiers"""
-# pass
-
-
-
-# class RS(HPSSearch):
-#
-# def __init__(self, X, y, framework, random_state, output_file_name,
-# classifier_module,
-# classifier_name, folds=4, nb_cores=1,
-# metric=["accuracy_score", None],
-# n_iter=30, classifier_kwargs={}, learning_indices=None,
-# view_indices=None,
-# equivalent_draws=True):
-# HPSSearch.__init__()
-
-
-
-# def randomized_search(X, y, framework, random_state, output_file_name,
-# classifier_module,
-# classifier_name, folds=4, nb_cores=1,
-# metric=["accuracy_score", None],
-# n_iter=30, classifier_kwargs={}, learning_indices=None,
-# view_indices=None,
-# equivalent_draws=True):
-# estimator = getattr(classifier_module, classifier_name)(
-# random_state=random_state,
-# **classifier_kwargs)
-# params_dict = estimator.gen_distribs()
-# estimator = get_mc_estim(estimator, random_state,
-# multiview=(framework == "multiview"),
-# y=y)
-# if params_dict:
-# metric_module, metric_kwargs = get_metric(metric)
-# scorer = metric_module.get_scorer(**metric_kwargs)
-# # nb_possible_combinations = compute_possible_combinations(params_dict)
-# # n_iter_real = min(n_iter, nb_possible_combinations)
-#
-# random_search = MultiviewCompatibleRandomizedSearchCV(estimator,
-# n_iter=n_iter,
-# param_distributions=params_dict,
-# refit=True,
-# n_jobs=nb_cores,
-# scoring=scorer,
-# cv=folds,
-# random_state=random_state,
-# learning_indices=learning_indices,
-# view_indices=view_indices,
-# framework=framework,
-# equivalent_draws=equivalent_draws)
-# random_search.fit(X, y)
-# return random_search.transform_results()
-# else:
-# best_estimator = estimator
-# best_params = {}
-# scores_array = {}
-# params = {}
-# test_folds_preds = np.zeros(10)#get_test_folds_preds(X, y, folds, best_estimator,
-# # framework, learning_indices)
-# return best_params, scores_array, params
-
-
-
-
-
-
-
-
-
-#
-# def spear_mint(dataset, classifier_name, views_indices=None, k_folds=None,
-# n_iter=1,
-# **kwargs):
-# """Used to perform spearmint on the classifiers to optimize hyper parameters,
-# longer than randomsearch (can't be parallelized)"""
-# pass
-#
-#
-# def gen_heat_maps(params, scores_array, output_file_name):
-# """Used to generate a heat map for each doublet of hyperparms
-# optimized on the previous function"""
-# nb_params = len(params)
-# if nb_params > 2:
-# combinations = itertools.combinations(range(nb_params), 2)
-# elif nb_params == 2:
-# combinations = [(0, 1)]
-# else:
-# combinations = [()]
-# for combination in combinations:
-# if combination:
-# param_name1, param_array1 = params[combination[0]]
-# param_name2, param_array2 = params[combination[1]]
-# else:
-# param_name1, param_array1 = params[0]
-# param_name2, param_array2 = ("Control", np.array([0]))
-#
-# param_array1_set = np.sort(np.array(list(set(param_array1))))
-# param_array2_set = np.sort(np.array(list(set(param_array2))))
-#
-# scores_matrix = np.zeros(
-# (len(param_array2_set), len(param_array1_set))) - 0.1
-# for param1, param2, score in zip(param_array1, param_array2,
-# scores_array):
-# param1_index, = np.where(param_array1_set == param1)
-# param2_index, = np.where(param_array2_set == param2)
-# scores_matrix[int(param2_index), int(param1_index)] = score
-#
-# plt.figure(figsize=(8, 6))
-# plt.subplots_adjust(left=.2, right=0.95, bottom=0.15, top=0.95)
-# plt.imshow(scores_matrix, interpolation='nearest', cmap=plt.cm.hot,
-# )
-# plt.xlabel(param_name1)
-# plt.ylabel(param_name2)
-# plt.colorbar()
-# plt.xticks(np.arange(len(param_array1_set)), param_array1_set)
-# plt.yticks(np.arange(len(param_array2_set)), param_array2_set,
-# rotation=45)
-# plt.title('Validation metric')
-# plt.savefig(
-# output_file_name + "heat_map-" + param_name1 + "-" + param_name2 + ".png",
-# transparent=True)
-# plt.close()
-#
-
-
-
-class CustomRandint:
- """Used as a distribution returning a integer between low and high-1.
- It can be used with a multiplier agrument to be able to perform more complex generation
- for example 10 e -(randint)"""
-
- def __init__(self, low=0, high=0, multiplier=""):
- self.randint = randint(low, high)
- self.low=low
- self.high=high
- self.multiplier = multiplier
-
- def rvs(self, random_state=None):
- randinteger = self.randint.rvs(random_state=random_state)
- if self.multiplier == "e-":
- return 10 ** -randinteger
- else:
- return randinteger
-
- def get_nb_possibilities(self):
- if self.multiplier == "e-":
- return abs(10 ** -self.low - 10 ** -self.high)
- else:
- return self.high - self.low
-
-
-class CustomUniform:
- """Used as a distribution returning a float between loc and loc + scale..
- It can be used with a multiplier agrument to be able to perform more complex generation
- for example 10 e -(float)"""
-
- def __init__(self, loc=0, state=1, multiplier=""):
- self.uniform = uniform(loc, state)
- self.multiplier = multiplier
-
- def rvs(self, random_state=None):
- unif = self.uniform.rvs(random_state=random_state)
- if self.multiplier == 'e-':
- return 10 ** -unif
- else:
- return unif
-
-
-def format_params(params, pref=""):
- if isinstance(params, dict):
- dictionary = {}
- for key, value in params.items():
- if isinstance(value, np.random.RandomState):
- pass
- elif isinstance(value, BaseEstimator):
- dictionary[key] = value.__class__.__name__
- for second_key, second_value in format_params(value.get_params()).items():
- dictionary[str(key)+"__"+second_key] = second_value
- else:
- dictionary[str(key)] = format_params(value)
- return dictionary
- elif isinstance(params, np.ndarray):
- return [format_params(param) for param in params]
- elif isinstance(params, np.float64):
- return float(params)
- elif isinstance(params, np.int64):
- return int(params)
- elif isinstance(params, list):
- return [format_params(param) for param in params]
- elif isinstance(params, np.str_):
- return str(params)
- else:
- return params
-
-
-# def randomized_search_(dataset_var, labels, classifier_package, classifier_name,
-# metrics_list, learning_indices, k_folds, random_state,
-# views_indices=None, n_iter=1,
-# nb_cores=1, **classification_kargs):
-# """Used to perform a random search on the classifiers to optimize hyper parameters"""
-# if views_indices is None:
-# views_indices = range(dataset_var.get("Metadata").attrs["nbView"])
-# metric = metrics_list[0]
-# metric_module = getattr(metrics, metric[0])
-# if metric[1] is not None:
-# metric_kargs = dict((index, metricConfig) for index, metricConfig in
-# enumerate(metric[1]))
-# else:
-# metric_kargs = {}
-# classifier_module = getattr(classifier_package, classifier_name + "Module")
-# classifier_class = getattr(classifier_module, classifier_name + "Class")
-# if classifier_name != "Mumbo":
-# params_sets = classifier_module.gen_params_sets(classification_kargs,
-# random_state, n_iter=n_iter)
-# if metric_module.getConfig()[-14] == "h":
-# base_score = -1000.0
-# is_better = "higher"
-# else:
-# base_score = 1000.0
-# is_better = "lower"
-# best_settings = None
-# kk_folds = k_folds.split(learning_indices, labels[learning_indices])
-# for params_set in params_sets:
-# scores = []
-# for trainIndices, testIndices in kk_folds:
-# classifier = classifier_class(random_state, nb_scores=nb_cores,
-# **classification_kargs)
-# classifier.setParams(params_set)
-# classifier.fit_hdf5(dataset_var, labels,
-# train_indices=learning_indices[trainIndices],
-# views_indices=views_indices)
-# test_labels = classifier.predict_hdf5(dataset_var,
-# used_indices=learning_indices[testIndices],
-# views_indices=views_indices)
-# test_score = metric_module.score(
-# labels[learning_indices[testIndices]], test_labels)
-# scores.append(test_score)
-# cross_val_score = np.mean(np.array(scores))
-#
-# if is_better == "higher" and cross_val_score > base_score:
-# base_score = cross_val_score
-# best_settings = params_set
-# elif is_better == "lower" and cross_val_score < base_score:
-# base_score = cross_val_score
-# best_settings = params_set
-# classifier = classifier_class(random_state, nb_cores=nb_cores,
-# **classification_kargs)
-# classifier.setParams(best_settings)
-#
-# # TODO : This must be corrected
-# else:
-# best_configs, _ = classifier_module.grid_search_hdf5(dataset_var, labels,
-# views_indices,
-# classification_kargs,
-# learning_indices,
-# random_state,
-# metric=metric,
-# nI_iter=n_iter)
-# classification_kargs["classifiersConfigs"] = best_configs
-# classifier = classifier_class(random_state, nb_cores=nb_cores,
-# **classification_kargs)
-#
-# return classifier
-
-#
-# def compute_possible_combinations(params_dict):
-# n_possibs = np.ones(len(params_dict)) * np.inf
-# for value_index, value in enumerate(params_dict.values()):
-# if type(value) == list:
-# n_possibs[value_index] = len(value)
-# elif isinstance(value, CustomRandint):
-# n_possibs[value_index] = value.get_nb_possibilities()
-# return np.prod(n_possibs)
-
-
-# def get_test_folds_preds(X, y, cv, estimator, framework,
-# available_indices=None):
-# test_folds_prediction = []
-# if framework == "monoview":
-# folds = cv.split(np.arange(len(y)), y)
-# if framework == "multiview":
-# folds = cv.split(available_indices, y[available_indices])
-# fold_lengths = np.zeros(cv.n_splits, dtype=int)
-# for fold_idx, (train_indices, test_indices) in enumerate(folds):
-# fold_lengths[fold_idx] = len(test_indices)
-# if framework == "monoview":
-# estimator.fit(X[train_indices], y[train_indices])
-# test_folds_prediction.append(estimator.predict(X[train_indices]))
-# if framework == "multiview":
-# estimator.fit(X, y, available_indices[train_indices])
-# test_folds_prediction.append(
-# estimator.predict(X, available_indices[test_indices]))
-# min_fold_length = fold_lengths.min()
-# test_folds_prediction = np.array(
-# [test_fold_prediction[:min_fold_length] for test_fold_prediction in
-# test_folds_prediction])
-# return test_folds_prediction
-
-
-# nohup python ~/dev/git/spearmint/spearmint/main.py . &
-
-# import json
-# import numpy as np
-# import math
-#
-# from os import system
-# from os.path import join
-#
-#
-# def run_kover(dataset, split, model_type, p, max_rules, output_dir):
-# outdir = join(output_dir, "%s_%f" % (model_type, p))
-# kover_command = "kover learn " \
-# "--dataset '%s' " \
-# "--split %s " \
-# "--model-type %s " \
-# "--p %f " \
-# "--max-rules %d " \
-# "--max-equiv-rules 10000 " \
-# "--hp-choice cv " \
-# "--random-seed 0 " \
-# "--output-dir '%s' " \
-# "--n-cpu 1 " \
-# "-v" % (dataset,
-# split,
-# model_type,
-# p,
-# max_rules,
-# outdir)
-#
-# system(kover_command)
-#
-# return json.load(open(join(outdir, "results.json")))["cv"]["best_hp"]["score"]
-#
-#
-# def main(job_id, params):
-# print params
-#
-# max_rules = params["MAX_RULES"][0]
-#
-# species = params["SPECIES"][0]
-# antibiotic = params["ANTIBIOTIC"][0]
-# split = params["SPLIT"][0]
-#
-# model_type = params["model_type"][0]
-#
-# # LS31
-# if species == "saureus":
-# dataset_path = "/home/droale01/droale01-ls31/projects/genome_scm/data/earle_2016/saureus/kover_datasets/%s.kover" % antibiotic
-# else:
-# dataset_path = "/home/droale01/droale01-ls31/projects/genome_scm/genome_scm_paper/data/%s/%s.kover" % (species, antibiotic)
-#
-# output_path = "/home/droale01/droale01-ls31/projects/genome_scm/manifold_scm/spearmint/vanilla_scm/%s/%s" % (species, antibiotic)
-#
-# # MacBook
-# #dataset_path = "/Volumes/Einstein 1/kover_phylo/datasets/%s/%s.kover" % (species, antibiotic)
-# #output_path = "/Volumes/Einstein 1/manifold_scm/version2/%s_spearmint" % antibiotic
-#
-# return run_kover(dataset=dataset_path,
-# split=split,
-# model_type=model_type,
-# p=params["p"][0],
-# max_rules=max_rules,
-# output_dir=output_path)
-# killall mongod && sleep 1 && rm -r database/* && rm mongo.log*
-# mongod --fork --logpath mongo.log --dbpath database
-#
-# {
-# "language" : "PYTHON",
-# "experiment-name" : "vanilla_scm_cdiff_azithromycin",
-# "polling-time" : 1,
-# "resources" : {
-# "my-machine" : {
-# "scheduler" : "local",
-# "max-concurrent" : 5,
-# "max-finished-jobs" : 100
-# }
-# },
-# "tasks": {
-# "resistance" : {
-# "type" : "OBJECTIVE",
-# "likelihood" : "NOISELESS",
-# "main-file" : "spearmint_wrapper",
-# "resources" : ["my-machine"]
-# }
-# },
-# "variables": {
-#
-# "MAX_RULES" : {
-# "type" : "ENUM",
-# "size" : 1,
-# "options": [10]
-# },
-#
-#
-# "SPECIES" : {
-# "type" : "ENUM",
-# "size" : 1,
-# "options": ["cdiff"]
-# },
-# "ANTIBIOTIC" : {
-# "type" : "ENUM",
-# "size" : 1,
-# "options": ["azithromycin"]
-# },
-# "SPLIT" : {
-# "type" : "ENUM",
-# "size" : 1,
-# "options": ["split_seed_2"]
-# },
-#
-#
-# "model_type" : {
-# "type" : "ENUM",
-# "size" : 1,
-# "options": ["conjunction", "disjunction"]
-# },
-# "p" : {
-# "type" : "FLOAT",
-# "size" : 1,
-# "min" : 0.01,
-# "max" : 100
-# }
-# }
-# }
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/make_file_config.py b/multiview_platform/mono_multi_view_classifiers/utils/make_file_config.py
deleted file mode 100644
index 5810e37b..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/make_file_config.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import importlib
-import inspect
-
-class ConfigurationMaker():
- """
- Find the name of the classifier from the dict classier to report
-
-
-
- """
- _path_classifier_mono = 'multiview_platform/mono_multi_view_classifier/monoview_classifiers'
- _path_classifier_multi = 'multiview_platform/mono_multi_view_classifier/multiview_classifier'
-
- def __init__(self, classifier_dict=None):
- if classifier_dict is None:
- classifier_dict = {"0": ['mono', 'Adaboost',
- 'multiview_platform.mono_multi_view_classifiers.monoview_classifiers.adaboost']}
- names = []
- for key, val in classifier_dict.items():
- mymodule = importlib.import_module(val[2])
- names.append(self._get_module_name(mymodule))
- monInstance = getattr(mymodule, val[1])
-
- def _get_module_name(self, mymodule):
- for name in dir(mymodule):
- att = getattr(mymodule, name)
- try:
- getattr(att, "__module__")
- if att.__module__.startswith(mymodule.__name__):
- if inspect.isclass(att):
- if att == val[1]:
- return name
- except Exception:
- return None
- return None
-
-
-if __name__ == '__main__':
- ConfigurationMaker()
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py b/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py
deleted file mode 100644
index 0b7210a7..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py
+++ /dev/null
@@ -1,323 +0,0 @@
-import array
-
-import numpy as np
-import scipy.sparse as sp
-from sklearn.base import clone, is_classifier, is_regressor
-from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier
-from sklearn.multiclass import _ovr_decision_function
-from sklearn.preprocessing import LabelBinarizer
-
-from .dataset import get_examples_views_indices
-
-
-def get_mc_estim(estimator, random_state, y=None, multiview=False,
- multiclass=False):
- r"""Used to get a multiclass-compatible estimator if the one in param does not natively support multiclass.
- If perdict_proba is available in the asked estimator, a One Versus Rest wrapper is returned,
- else, a One Versus One wrapper is returned.
-
- To be able to deal with multiview algorithm, multiview wrappers are implemented separately.
-
- Parameters
- ----------
- estimator : sklearn-like estimator
- Asked estimator
- y : numpy.array
- The labels of the problem
- random_state : numpy.random.RandomState object
- The random state, used to generate a fake multiclass problem
- multiview : bool
- If True, mutliview-compatible wrappers are returned.
-
- Returns
- -------
- estimator : sklearn-like estimator
- Either the aksed estimator, or a multiclass-compatible wrapper over the asked estimator
- """
- if (y is not None and np.unique(y).shape[0] > 2) or multiclass:
- if not clone(estimator).accepts_multi_class(random_state):
- if hasattr(estimator, "predict_proba"):
- if multiview:
- estimator = MultiviewOVRWrapper(estimator)
- else:
- estimator = OVRWrapper(estimator)
- else:
- if multiview:
- estimator = MultiviewOVOWrapper(estimator)
- else:
- estimator = OVOWrapper(estimator)
- return estimator
-
-
-class MultiClassWrapper:
-
- # TODO : Has an effect on the init of the sub-classes.
- # @abstractmethod
- # def __init__(self, estimator, **params):
- # self.estimator = estimator
-
- def set_params(self, **params):
- r"""
- This function is useful in order for the OV_Wrappers to be transparent
- in terms of parameters.
- If we remove it the parameters have to be specified as estimator__param.
- Witch is not relevant for the platform
-
- """
- self.estimator.set_params(**params)
- return self
-
- def get_config(self):
- return "multiclass_adaptation : "+self.__class__.__name__+ ", " +self.estimator.get_config()
-
- def format_params(self, params, deep=True):
- if hasattr(self, 'estimators_'):
- estim_params = self.estimators_[0].get_params(deep=deep)
- for key, value in params.items():
- if key.startswith("estimator__"):
- estim_param_key = '__'.join(key.split('__')[1:])
- params[key] = estim_params[estim_param_key]
- params.pop("estimator")
- return params
-
-
-
- def get_interpretation(self, directory, base_file_name, y_test=None):
- # TODO : Multiclass interpretation
- return "Multiclass wrapper is not interpretable yet"
-
-
-class MonoviewWrapper(MultiClassWrapper):
- pass
-
-
-class OVRWrapper(MonoviewWrapper, OneVsRestClassifier):
-
- def get_params(self, deep=True):
- return self.format_params(
- OneVsRestClassifier.get_params(self, deep=deep), deep=deep)
-
-
-class OVOWrapper(MonoviewWrapper, OneVsOneClassifier):
- def decision_function(self, X):
- # check_is_fitted(self)
-
- indices = self.pairwise_indices_
- if indices is None:
- Xs = [X] * len(self.estimators_)
- else:
- Xs = [X[:, idx] for idx in indices]
-
- predictions = np.vstack([est.predict(Xi)
- for est, Xi in zip(self.estimators_, Xs)]).T
- confidences = np.ones(predictions.shape)
- Y = _ovr_decision_function(predictions,
- confidences, len(self.classes_))
- if self.n_classes_ == 2:
- return Y[:, 1]
- return Y
-
- def get_params(self, deep=True):
- return self.format_params(
- OneVsOneClassifier.get_params(self, deep=deep), deep=deep)
-
-
-# The following code is a mutliview adaptation of sklearns multiclass package
-
-def _multiview_fit_binary(estimator, X, y, train_indices,
- view_indices, classes=None, ):
- # TODO : Verifications des sklearn
- estimator = clone(estimator)
- estimator.fit(X, y, train_indices=train_indices,
- view_indices=view_indices)
- return estimator
-
-
-def _multiview_predict_binary(estimator, X, example_indices, view_indices):
- if is_regressor(estimator):
- return estimator.predict(X, example_indices=example_indices,
- view_indices=view_indices)
- try:
- score = np.ravel(estimator.decision_function(X))
- except (AttributeError, NotImplementedError):
- # probabilities of the positive class
- score = estimator.predict_proba(X, example_indices=example_indices,
- view_indices=view_indices)[:, 1]
- return score
-
-
-class MultiviewWrapper(MultiClassWrapper):
-
- def __init__(self, estimator=None, **args):
- super(MultiviewWrapper, self).__init__(estimator=estimator, **args)
- self.short_name = estimator.short_name
-
-
-class MultiviewOVRWrapper(MultiviewWrapper, OneVsRestClassifier):
-
- def fit(self, X, y, train_indices=None, view_indices=None):
- self.label_binarizer_ = LabelBinarizer(sparse_output=True)
- Y = self.label_binarizer_.fit_transform(y)
- Y = Y.tocsc()
- self.classes_ = self.label_binarizer_.classes_
- columns = (col.toarray().ravel() for col in Y.T)
- # In cases where individual estimators are very fast to train setting
- # n_jobs > 1 in can results in slower performance due to the overhead
- # of spawning threads. See joblib issue #112.
- self.estimators_ = [_multiview_fit_binary(
- self.estimator, X, column, classes=[
- "not %s" % self.label_binarizer_.classes_[i],
- self.label_binarizer_.classes_[i]], train_indices=train_indices,
- view_indices=view_indices)
- for i, column in
- enumerate(columns)]
- return self
-
- def predict(self, X, example_indices=None, view_indices=None):
- example_indices, view_indices = get_examples_views_indices(X,
- example_indices,
- view_indices)
- n_samples = len(example_indices)
- if self.label_binarizer_.y_type_ == "multiclass":
- maxima = np.empty(n_samples, dtype=float)
- maxima.fill(-np.inf)
- argmaxima = np.zeros(n_samples, dtype=int)
- for i, e in enumerate(self.estimators_):
- pred = _multiview_predict_binary(e, X, example_indices,
- view_indices)
- np.maximum(maxima, pred, out=maxima)
- argmaxima[maxima == pred] = i
- return self.classes_[argmaxima]
- else: # pragma: no cover
- if (hasattr(self.estimators_[0], "decision_function") and
- is_classifier(self.estimators_[0])):
- thresh = 0
- else:
- thresh = .5
- indices = array.array('i')
- indptr = array.array('i', [0])
- for e in self.estimators_:
- indices.extend(
- np.where(_multiview_predict_binary(e, X,
- example_indices,
- view_indices) > thresh)[
- 0])
- indptr.append(len(indices))
-
- data = np.ones(len(indices), dtype=int)
- indicator = sp.csc_matrix((data, indices, indptr),
- shape=(n_samples, len(self.estimators_)))
- return self.label_binarizer_.inverse_transform(indicator)
-
- def get_params(self, deep=True):
- return self.format_params(
- OneVsRestClassifier.get_params(self, deep=deep), deep=deep)
-
-
-def _multiview_fit_ovo_binary(estimator, X, y, i, j, train_indices,
- view_indices):
- cond = np.logical_or(y == i, y == j)
- # y = y[cond]
- y_binary = np.empty(y.shape, np.int)
- y_binary[y == i] = 0
- y_binary[y == j] = 1
- indcond = np.arange(X.get_nb_examples())[cond]
- train_indices = np.intersect1d(train_indices, indcond)
- return _multiview_fit_binary(estimator,
- X,
- y_binary, train_indices, view_indices,
- classes=[i, j]), train_indices
-
-
-class MultiviewOVOWrapper(MultiviewWrapper, OneVsOneClassifier):
-
- def fit(self, X, y, train_indices=None, view_indices=None):
- """Fit underlying estimators.
-
- Parameters
- ----------
- X : (sparse) array-like of shape (n_samples, n_features)
- Data.
-
- y : array-like of shape (n_samples,)
- Multi-class targets.
-
- Returns
- -------
- self
- """
- # X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
- # check_classification_targets(y)
- train_indices, view_indices = get_examples_views_indices(X,
- train_indices,
- view_indices)
- self.classes_ = np.unique(y)
- if len(self.classes_) == 1:
- raise ValueError("OneVsOneClassifier can not be fit when only one"
- " class is present.")
- n_classes = self.classes_.shape[0]
- estimators_indices = list(zip(*([_multiview_fit_ovo_binary(
- self.estimator, X, y, self.classes_[i], self.classes_[j],
- train_indices,
- view_indices
- )
- for i in range(n_classes) for j in range(i + 1, n_classes)
- ])))
-
- self.estimators_ = estimators_indices[0]
- self.pairwise_indices_ = (
- estimators_indices[1] if self._pairwise else None)
-
- return self
-
- def predict(self, X, example_indices=None, view_indices=None):
- """Estimate the best class label for each sample in X.
-
- This is implemented as ``argmax(decision_function(X), axis=1)`` which
- will return the label of the class with most votes by estimators
- predicting the outcome of a decision for each possible class pair.
-
- Parameters
- ----------
- X : (sparse) array-like of shape (n_samples, n_features)
- Data.
-
- Returns
- -------
- y : numpy array of shape [n_samples]
- Predicted multi-class targets.
- """
- example_indices, view_indices = get_examples_views_indices(X,
- example_indices,
- view_indices)
- Y = self.multiview_decision_function(X, example_indices=example_indices,
- view_indices=view_indices)
- if self.n_classes_ == 2:
- return self.classes_[(Y > 0).astype(np.int)]
- return self.classes_[Y.argmax(axis=1)]
-
- def multiview_decision_function(self, X, example_indices, view_indices): # pragma: no cover
- # check_is_fitted(self)
-
- indices = self.pairwise_indices_
- if indices is None:
- Xs = [X] * len(self.estimators_)
- else:
- # TODO Gram matrix compatibility
- Xs = [X[:, idx] for idx in indices]
- predictions = np.vstack(
- [est.predict(Xi, example_indices=example_indices,
- view_indices=view_indices)
- for est, Xi in zip(self.estimators_, Xs)]).T
- confidences = np.ones(predictions.shape)
- # confidences = np.vstack([_predict_binary(est, Xi)
- # for est, Xi in zip(self.estimators_, Xs)]).T
- Y = _ovr_decision_function(predictions,
- confidences, len(self.classes_))
- if self.n_classes_ == 2:
- return Y[:, 1]
- return Y
-
- def get_params(self, deep=True):
- return self.format_params(
- OneVsOneClassifier.get_params(self, deep=deep), deep=deep)
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/multiview_result_analysis.py b/multiview_platform/mono_multi_view_classifiers/utils/multiview_result_analysis.py
deleted file mode 100644
index a980b3be..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/multiview_result_analysis.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# from .. import metrics
-#
-# # Author-Info
-# __author__ = "Baptiste Bauvin"
-# __status__ = "Prototype" # Production, Development, Prototype
-#
-#
-# def print_metric_score(metric_scores, metrics):
-# metric_score_string = "\n\n"
-# for metric in metrics:
-# metric_module = getattr(metrics, metric[0])
-# if metric[1] is not None:
-# metric_kwargs = dict(
-# (index, metricConfig) for index, metricConfig in
-# enumerate(metric[1]))
-# else:
-# metric_kwargs = {}
-# metric_score_string += "\tFor " + metric_module.get_config(
-# **metric_kwargs) + " : "
-# metric_score_string += "\n\t\t- Score on train : " + str(
-# metric_scores[metric[0]][0])
-# metric_score_string += "\n\t\t- Score on test : " + str(
-# metric_scores[metric[0]][1])
-# metric_score_string += "\n\n"
-# return metric_score_string
-#
-#
-# def get_total_metric_scores(metric, train_labels, test_labels,
-# validation_indices,
-# learning_indices, labels):
-# metric_module = getattr(metrics, metric[0])
-# if metric[1] is not None:
-# metric_kwargs = dict((index, metricConfig) for index, metricConfig in
-# enumerate(metric[1]))
-# else:
-# metric_kwargs = {}
-# train_score = metric_module.score(labels[learning_indices], train_labels,
-# **metric_kwargs)
-# test_score = metric_module.score(labels[validation_indices], test_labels,
-# **metric_kwargs)
-# return [train_score, test_score]
-#
-#
-# def get_metrics_scores(metrics_var, train_labels, test_labels,
-# validation_indices, learning_indices, labels):
-# metrics_scores = {}
-# for metric in metrics_var:
-# metrics_scores[metric[0]] = get_total_metric_scores(metric,
-# train_labels,
-# test_labels,
-# validation_indices,
-# learning_indices,
-# labels)
-# return metrics_scores
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/organization.py b/multiview_platform/mono_multi_view_classifiers/utils/organization.py
deleted file mode 100644
index 1fdc0ecf..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/organization.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import os
-import errno
-
-
-def secure_file_path(file_name): # pragma: no cover
- if not os.path.exists(os.path.dirname(file_name)):
- try:
- os.makedirs(os.path.dirname(file_name))
- except OSError as exc:
- if exc.errno != errno.EEXIST:
- raise
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/transformations.py b/multiview_platform/mono_multi_view_classifiers/utils/transformations.py
deleted file mode 100644
index 17e7b90d..00000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/transformations.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import numpy as np
-
-
-def sign_labels(labels):
- """
- Returns a label array with (-1,1) as labels.
- If labels was already made of (-1,1), returns labels.
- If labels is made of (0,1), returns labels with all
- zeros transformed in -1.
-
- Parameters
- ----------
- labels
-
- The original label numpy array
-
- Returns
- -------
- A np.array with labels made of (-1,1)
- """
- if 0 in labels:
- return np.array([label if label != 0 else -1 for label in labels])
- else:
- return labels
-
-
-def unsign_labels(labels):
- """
- The inverse function
-
- Parameters
- ----------
- labels
-
- Returns
- -------
-
- """
- if len(labels.shape) == 2:
- labels = labels.reshape((labels.shape[0],))
- if -1 in labels:
- return np.array([label if label != -1 else 0 for label in labels])
- else:
- return labels
diff --git a/multiview_platform/tests/__init__.py b/multiview_platform/tests/__init__.py
deleted file mode 100644
index 194018ae..00000000
--- a/multiview_platform/tests/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from . import test_exec_classif
-from .utils import rm_tmp, gen_test_dataset, tmp_path
\ No newline at end of file
diff --git a/multiview_platform/tests/test_config_hps.yml b/multiview_platform/tests/test_config_hps.yml
deleted file mode 100644
index bce80ba6..00000000
--- a/multiview_platform/tests/test_config_hps.yml
+++ /dev/null
@@ -1,80 +0,0 @@
-# The base configuration of the benchmark
-
-# Enable logging
-log: False
-# The name of each dataset in the directory on which the benchmark should be run
-name: "digits_doc"
-# A label for the resul directory
-label: "example_0"
-# The type of dataset, currently supported ".hdf5", and ".csv"
-file_type: ".hdf5"
-# The views to use in the banchmark, an empty value will result in using all the views
-views:
-# The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "../examples/data/"
-# The niceness of the processes, useful to lower their priority
-nice: 0
-# The random state of the benchmark, useful for reproducibility
-random_state: 42
-# The number of parallel computing threads
-nb_cores: 1
-# Used to run the benchmark on the full dataset
-full: True
-# Used to be able to run more than one benchmark per minute
-debug: False
-# The directory in which the results will be stored, an absolute path is advised
-res_dir: "tmp_tests/"
-# If an error occurs in a classifier, if track_tracebacks is set to True, the
-# benchmark saves the traceback and continues, if it is set to False, it will
-# stop the benchmark and raise the error
-track_tracebacks: True
-
-# All the classification-realted configuration options
-
-# The ratio of test examples/number of train examples
-split: 0.25
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# The number of classes to select in the dataset
-nb_class:
-# The name of the classes to select in the dataset
-classes:
-# The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
-# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
-algos_monoview: ["decision_tree"]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_early_fusion",]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# The metrics that will be use din the result analysis
-metrics:
- accuracy_score: {}
- f1_score:
- average: "micro"
-# The metric that will be used in the hyper-parameter optimization process
-metric_princ: "accuracy_score"
-# The type of hyper-parameter optimization method
-hps_type: "Random"
-# The number of iteration in the hyper-parameter optimization process
-hps_args:
- n_iter: 2
- equivalent_draws: False
-
-### Configuring the hyper-parameters for the classifiers
-
-decision_tree:
- max_depth: 3
-
-weighted_linear_early_fusion:
- monoview_classifier_name: "decision_tree"
- monoview_classifier_config:
- decision_tree:
- max_depth: 6
-
-weighted_linear_late_fusion:
- classifiers_names: "decision_tree"
- classifier_configs:
- decision_tree:
- max_depth: 3
diff --git a/multiview_platform/tests/test_config_iter.yml b/multiview_platform/tests/test_config_iter.yml
deleted file mode 100644
index f44b34fe..00000000
--- a/multiview_platform/tests/test_config_iter.yml
+++ /dev/null
@@ -1,78 +0,0 @@
-# The base configuration of the benchmark
-
-# Enable logging
-log: False
-# The name of each dataset in the directory on which the benchmark should be run
-name: "digits_doc"
-# A label for the resul directory
-label: "example_0"
-# The type of dataset, currently supported ".hdf5", and ".csv"
-file_type: ".hdf5"
-# The views to use in the banchmark, an empty value will result in using all the views
-views:
-# The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "../examples/data/"
-# The niceness of the processes, useful to lower their priority
-nice: 0
-# The random state of the benchmark, useful for reproducibility
-random_state: 42
-# The number of parallel computing threads
-nb_cores: 1
-# Used to run the benchmark on the full dataset
-full: True
-# Used to be able to run more than one benchmark per minute
-debug: False
-# The directory in which the results will be stored, an absolute path is advised
-res_dir: "tmp_tests/"
-# If an error occurs in a classifier, if track_tracebacks is set to True, the
-# benchmark saves the traceback and continues, if it is set to False, it will
-# stop the benchmark and raise the error
-track_tracebacks: True
-
-# All the classification-realted configuration options
-
-# The ratio of test examples/number of train examples
-split: 0.25
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# The number of classes to select in the dataset
-nb_class:
-# The name of the classes to select in the dataset
-classes:
-# The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
-# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
-algos_monoview: ["decision_tree"]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_early_fusion",]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 2
-# The metrics that will be use din the result analysis
-metrics:
- accuracy_score: {}
- f1_score:
- average: "micro"
-# The metric that will be used in the hyper-parameter optimization process
-metric_princ: "accuracy_score"
-# The type of hyper-parameter optimization method
-hps_type: "None"
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-### Configuring the hyper-parameters for the classifiers
-
-decision_tree:
- max_depth: 3
-
-weighted_linear_early_fusion:
- monoview_classifier_name: "decision_tree"
- monoview_classifier_config:
- decision_tree:
- max_depth: 6
-
-weighted_linear_late_fusion:
- classifiers_names: "decision_tree"
- classifier_configs:
- decision_tree:
- max_depth: 3
diff --git a/multiview_platform/tests/test_config_simple.yml b/multiview_platform/tests/test_config_simple.yml
deleted file mode 100644
index 02b85b58..00000000
--- a/multiview_platform/tests/test_config_simple.yml
+++ /dev/null
@@ -1,78 +0,0 @@
-# The base configuration of the benchmark
-
-# Enable logging
-log: False
-# The name of each dataset in the directory on which the benchmark should be run
-name: "digits_doc"
-# A label for the resul directory
-label: "example_0"
-# The type of dataset, currently supported ".hdf5", and ".csv"
-file_type: ".hdf5"
-# The views to use in the banchmark, an empty value will result in using all the views
-views:
-# The path to the directory where the datasets are stored, an absolute path is advised
-pathf: "../examples/data/"
-# The niceness of the processes, useful to lower their priority
-nice: 0
-# The random state of the benchmark, useful for reproducibility
-random_state: 42
-# The number of parallel computing threads
-nb_cores: 1
-# Used to run the benchmark on the full dataset
-full: True
-# Used to be able to run more than one benchmark per minute
-debug: False
-# The directory in which the results will be stored, an absolute path is advised
-res_dir: "tmp_tests/"
-# If an error occurs in a classifier, if track_tracebacks is set to True, the
-# benchmark saves the traceback and continues, if it is set to False, it will
-# stop the benchmark and raise the error
-track_tracebacks: True
-
-# All the classification-realted configuration options
-
-# The ratio of test examples/number of train examples
-split: 0.25
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# The number of classes to select in the dataset
-nb_class:
-# The name of the classes to select in the dataset
-classes:
-# The type of algorithms to run during the benchmark (monoview and/or multiview)
-type: ["monoview","multiview"]
-# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
-algos_monoview: ["decision_tree"]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_early_fusion", "weighted_linear_late_fusion",]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# The metrics that will be use din the result analysis
-metrics:
- accuracy_score: {}
- f1_score:
- average: "micro"
-# The metric that will be used in the hyper-parameter optimization process
-metric_princ: "accuracy_score"
-# The type of hyper-parameter optimization method
-hps_type: "None"
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-### Configuring the hyper-parameters for the classifiers
-
-decision_tree:
- max_depth: 3
-
-weighted_linear_early_fusion:
- monoview_classifier_name: "decision_tree"
- monoview_classifier_config:
- decision_tree:
- max_depth: 6
-
-weighted_linear_late_fusion:
- classifiers_names: "decision_tree"
- classifier_configs:
- decision_tree:
- max_depth: 3
diff --git a/multiview_platform/tests/test_database.hdf5 b/multiview_platform/tests/test_database.hdf5
deleted file mode 100644
index 63206a1219daf9e09d9a91d5a2440ef19a88af82..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001
literal 8952
zcmeD5aB<`1lHy_j0S*oZ76t(j3y%Lo!4D;f5S05L!ed}afHD}NbO)4P!31G2GJqfh
zg9L=jAP6-dU0q0!t1ANoBLmEQ7!B3NV88-l!OVg1M#+&90v@i80U*CdfCvT#1`8;C
z0-DaCT!ygB)N%s`1~6?1rHvRE7<>|wQgezK7<^Mp5>paO5@G6L=~e+IfJQSia4~?@
zfC*3v)c~ttWP%8=f_V%KY)~4V&&(hI)-S-pz`y}k;{YaE892b=OiU1iIG{O%nGvdt
z;R9G5j1YnfgKSk@I472e!GQskB^Vi48AKR381fQxQ$cDa85zMj43vvOYGJlQ*bsMq
zfH(wfzW`V@149MW;RPT=z!+o#n1MnB`}w<o?1Y7vfi#LV1_u&a3=9_R5Gj~D9AqH;
z1gJRFNel|i5OG{2%vCR-?!~1JR!%&S#HC_j#d#P!pz+7fpvJ(#P+X8$RGiAdAi)Gm
zV+;(S^a2Sh4hA<zpI}!828NKJP!I)H0FguX1I)+BVhA=US%LI8fCvVV5s;XnT{`{%
zbr9{-F{lWje|WNi^AFUM46uBZAO{Ka2M~$@z1)JO^9E?ThbjY`1!98>IS_+R*cfV0
z43y@B((X{&21<)S=^!W_2Bp2w=x``M8A^*oX;2)2n4%zpfq}sUN<)()15BSRR9qEG
z!{qIud}}BTa}TT>@`j3ALTQ*iu=)a2CxE2fo&9~lW<m=RFq;8VE`sDi^@pDU0|U&x
zuyP$NZpgp@E1poqK@|*0GXn#|3VtYKlo}0z(GVC7fzc2c4S~@R7!85Z5Eu=C(GVC7
zfzc2c4S~@R7_bnCg4%2ar8S{+G?a#oFZe_Gu<;OEC?7`aq48lu0A^5e*mwnO904W|
z8#jQBUwA;(t3hc&C=DBDfQ?_k+{Fu(hm9jeLiy<9DZx;2`j7iPP=r`b`*A-|7}0;+
zj~y~r2OT(KgpS)YGC~Ak!_qLC^zr`-(3v*K_$(wW85o2x#{Vs#<Hu|apt%Fk_<u12
z1A{OlWQ+>t9$5N=(UqyidHG;NA@dMmCItj!-i`t1yd4Ebk7^wTApo22MW65CVK9KE
zUv>r_(EL>rXoM9q?*S>>An6m5s~~9_6dVYB98i7EIf=!^;Q114`e5mvhXIyf_`v!=
zbAZLEB|fQn=_MHqxb21IV+QCv8ORNzcr*ltN(i90Yuum?*M`!tX((8G23q+r_(J7j
z^HIuBK4?xI#01s7AR3lGVfn-qB*=isuP{Dre#s6h4{PVZ+AlEoKpPJXo=|nL_7-eD
d3D*9JgUaheX;?cA<{lUgm4?*WFb)F)0|1<Eszd+)
diff --git a/multiview_platform/tests/test_exec_classif.py b/multiview_platform/tests/test_exec_classif.py
deleted file mode 100644
index 71cdc9e8..00000000
--- a/multiview_platform/tests/test_exec_classif.py
+++ /dev/null
@@ -1,415 +0,0 @@
-import os
-import unittest
-
-import h5py
-import numpy as np
-
-from multiview_platform.tests.utils import rm_tmp, tmp_path, test_dataset
-
-from multiview_platform.mono_multi_view_classifiers import exec_classif
-
-
-class Test_execute(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- os.mkdir(tmp_path)
-
- def test_exec_simple(self):
- exec_classif.exec_classif(["--config_path", os.path.join(os.path.dirname(os.path.abspath(__file__)), "test_config_simple.yml")])
-
- def test_exec_iter(self):
- exec_classif.exec_classif(["--config_path", os.path.join(os.path.dirname(os.path.abspath(__file__)), "test_config_iter.yml")])
-
- def test_exec_hps(self):
- exec_classif.exec_classif(["--config_path", os.path.join(os.path.dirname(os.path.abspath(__file__)), "test_config_hps.yml")])
-
- @classmethod
- def tearDown(self):
- rm_tmp()
-
-class Test_gen_single_monoview_arg_dictionary(unittest.TestCase):
-
- def test_no_config(self):
- conf = exec_classif.gen_single_monoview_arg_dictionary("classifier_name1",
- {}, "nb_class",
- "view_index",
- "view_name",
- "hps_kwargs")
- self.assertEqual(conf, {"classifier_name1": {},
- "view_name": "view_name",
- "view_index": "view_index",
- "classifier_name": "classifier_name1",
- "nb_class": "nb_class",
- "hps_kwargs":"hps_kwargs" } )
-
-class Test_initBenchmark(unittest.TestCase):
-
- def test_benchmark_wanted(self):
- benchmark_output = exec_classif.init_benchmark(cl_type=["monoview", "multiview"], monoview_algos=["decision_tree"], multiview_algos=["weighted_linear_late_fusion"])
- self.assertEqual(benchmark_output , {'monoview': ['decision_tree'], 'multiview': ['weighted_linear_late_fusion']})
- benchmark_output = exec_classif.init_benchmark(
- cl_type=["monoview", "multiview"], monoview_algos=["all"],
- multiview_algos=["all"])
- self.assertEqual(benchmark_output, {'monoview': ['adaboost',
- 'decision_tree',
- 'gradient_boosting',
- 'knn',
- 'lasso',
- 'random_forest',
- 'sgd',
- 'svm_linear',
- 'svm_poly',
- 'svm_rbf'],
- 'multiview': ['bayesian_inference_fusion',
- 'difficulty_fusion',
- 'disagree_fusion',
- 'double_fault_fusion',
- 'entropy_fusion',
- 'majority_voting_fusion',
- 'svm_jumbo_fusion',
- 'weighted_linear_early_fusion',
- 'weighted_linear_late_fusion']})
-
-
-class Test_Functs(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- os.mkdir(tmp_path)
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
- def test_initKWARGSFunc_no_monoview(self):
- benchmark = {"monoview": {}, "multiview": {}}
- args = exec_classif.init_kwargs_func({}, benchmark)
- self.assertEqual(args, {"monoview": {}, "multiview": {}})
-
- def test_init_kwargs(self):
- kwargs = exec_classif.init_kwargs({"decision_tree":""},["decision_tree"])
- self.assertEqual(kwargs, {"decision_tree":""})
- kwargs = exec_classif.init_kwargs({"weighted_linear_late_fusion": ""},
- ["weighted_linear_late_fusion"], framework="multiview")
- self.assertEqual(kwargs, {"weighted_linear_late_fusion": ""})
- kwargs = exec_classif.init_kwargs({}, ["decision_tree"],)
- self.assertEqual(kwargs, {"decision_tree":{}})
- self.assertRaises(AttributeError, exec_classif.init_kwargs, {}, ["test"])
-
- def test_arange_metrics(self):
- metrics = exec_classif.arange_metrics({"accuracy_score":{}}, "accuracy_score")
- self.assertEqual(metrics, {"accuracy_score*":{}})
- self.assertRaises(ValueError, exec_classif.arange_metrics, {"test1":{}}, "test")
-
- def test_banchmark_init(self):
- from sklearn.model_selection import StratifiedKFold
- folds = StratifiedKFold(n_splits=2)
- res, lab_names = exec_classif.benchmark_init(directory=tmp_path,
- classification_indices=[np.array([0,1,2,3]), np.array([4])],
- labels=test_dataset.get_labels(),
- labels_dictionary={"yes":0, "no":1},
- k_folds=folds,
- dataset_var=test_dataset)
- self.assertEqual(res, [])
- self.assertEqual(lab_names, [0, 1])
-
-
-
-
-class Test_InitArgumentDictionaries(unittest.TestCase):
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- cls.benchmark = {"monoview": ["fake_monoview_classifier"], "multiview": {}}
- cls.views_dictionnary = {'test_view_0': 0, 'test_view': 1}
- cls.nb_class = 2
- cls.monoview_classifier_name = "fake_monoview_classifier"
- cls.monoview_classifier_arg_name = "fake_arg"
- cls.monoview_classifier_arg_value = "fake_value_1"
- cls.multiview_classifier_name = "fake_multiview_classifier"
- cls.multiview_classifier_arg_name = "fake_arg_mv"
- cls.multiview_classifier_arg_value = "fake_value_2"
- cls.init_kwargs = {
- 'monoview':{
- cls.monoview_classifier_name:
- {cls.monoview_classifier_arg_name:cls.monoview_classifier_arg_value}
- },
- "multiview":{
- cls.multiview_classifier_name:{
- cls.multiview_classifier_arg_name:cls.multiview_classifier_arg_value}
- }
- }
-
- def test_init_argument_dictionaries_monoview(self):
- arguments = exec_classif.init_argument_dictionaries(self.benchmark,
- self.views_dictionnary,
- self.nb_class,
- self.init_kwargs,
- "None", {})
- expected_output = [{
- self.monoview_classifier_name: {
- self.monoview_classifier_arg_name:self.monoview_classifier_arg_value},
- "view_name": "test_view_0",
- 'hps_kwargs': {},
- "classifier_name": self.monoview_classifier_name,
- "nb_class": self.nb_class,
- "view_index": 0},
- {self.monoview_classifier_name: {
- self.monoview_classifier_arg_name: self.monoview_classifier_arg_value},
- "view_name": "test_view",
- 'hps_kwargs': {},
- "classifier_name": self.monoview_classifier_name,
- "nb_class": self.nb_class,
- "view_index": 1},
- ]
- self.assertEqual(arguments["monoview"], expected_output)
-
- def test_init_argument_dictionaries_multiview(self):
- self.benchmark["multiview"] = ["fake_multiview_classifier"]
- self.benchmark["monoview"] = {}
- arguments = exec_classif.init_argument_dictionaries(self.benchmark,
- self.views_dictionnary,
- self.nb_class,
- self.init_kwargs,
- "None", {})
- expected_output = [{
- "classifier_name": self.multiview_classifier_name,
- "view_indices": [0,1],
- "view_names": ["test_view_0", "test_view"],
- "nb_class": self.nb_class,
- 'hps_kwargs': {},
- "labels_names":None,
- self.multiview_classifier_name: {
- self.multiview_classifier_arg_name:
- self.multiview_classifier_arg_value},
- },]
- self.assertEqual(arguments["multiview"][0], expected_output[0])
-
-
- def test_init_argument_dictionaries_multiview_complex(self):
- self.multiview_classifier_arg_value = {"fake_value_2":"plif", "plaf":"plouf"}
- self.init_kwargs = {
- 'monoview': {
- self.monoview_classifier_name:
- {
- self.monoview_classifier_arg_name: self.monoview_classifier_arg_value}
- },
- "multiview": {
- self.multiview_classifier_name: {
- self.multiview_classifier_arg_name: self.multiview_classifier_arg_value}
- }
- }
- self.benchmark["multiview"] = ["fake_multiview_classifier"]
- self.benchmark["monoview"] = {}
- arguments = exec_classif.init_argument_dictionaries(self.benchmark,
- self.views_dictionnary,
- self.nb_class,
- self.init_kwargs,
- "None", {})
- expected_output = [{
- "classifier_name": self.multiview_classifier_name,
- "view_indices": [0,1],
- 'hps_kwargs': {},
- "view_names": ["test_view_0", "test_view"],
- "nb_class": self.nb_class,
- "labels_names":None,
- self.multiview_classifier_name: {
- self.multiview_classifier_arg_name:
- self.multiview_classifier_arg_value},
- }]
- self.assertEqual(arguments["multiview"][0], expected_output[0])
-
-
-def fakeBenchmarkExec(core_index=-1, a=7, args=1):
- return [core_index, a]
-
-
-def fakeBenchmarkExec_mutlicore(nb_cores=-1, a=6, args=1):
- return [nb_cores, a]
-
-
-def fakeBenchmarkExec_monocore(dataset_var=1, a=4, args=1, track_tracebacks=False):
- return [a]
-
-
-def fakegetResults(results, stats_iter,
- benchmark_arguments_dictionaries, metrics, directory,
- example_ids, labels):
- return 3
-
-
-def fakeDelete(a, b, c):
- return 9
-
-def fake_analyze(a, b, c, d, example_ids=None, labels=None):
- pass
-
-class Test_execBenchmark(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
-
- os.mkdir(tmp_path)
- cls.Dataset = test_dataset
- cls.argument_dictionaries = [{"a": 4, "args": {}}]
- cls.args = {
- "Base":{"name": "chicken_is_heaven", "type": "type", "pathf": "pathF"},
- "Classification":{"hps_iter": 1}}
-
- def test_simple(cls):
- res = exec_classif.exec_benchmark(nb_cores=1,
- stats_iter=2,
- benchmark_arguments_dictionaries=cls.argument_dictionaries,
- directory="",
- metrics=[[[1, 2], [3, 4, 5]]],
- dataset_var=cls.Dataset,
- track_tracebacks=6,
- # exec_one_benchmark=fakeBenchmarkExec,
- # exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
- exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
- analyze=fakegetResults,
- delete=fakeDelete,
- analyze_iterations=fake_analyze)
- cls.assertEqual(res, 3)
-
- def test_multiclass_no_iter(cls):
- cls.argument_dictionaries = [{"a": 10, "args": cls.args},
- {"a": 4, "args": cls.args}]
- res = exec_classif.exec_benchmark(nb_cores=1,
- stats_iter=1,
- benchmark_arguments_dictionaries=cls.argument_dictionaries,
- directory="",
- metrics=[[[1, 2], [3, 4, 5]]],
- dataset_var=cls.Dataset,
- track_tracebacks=6,
- # exec_one_benchmark=fakeBenchmarkExec,
- # exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
- exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
- analyze=fakegetResults,
- delete=fakeDelete,
- analyze_iterations=fake_analyze)
- cls.assertEqual(res, 3)
-
- def test_multiclass_and_iter(cls):
- cls.argument_dictionaries = [{"a": 10, "args": cls.args},
- {"a": 4, "args": cls.args},
- {"a": 55, "args": cls.args},
- {"a": 24, "args": cls.args}]
- res = exec_classif.exec_benchmark(nb_cores=1,
- stats_iter=2,
- benchmark_arguments_dictionaries=cls.argument_dictionaries,
- directory="",
- metrics=[[[1, 2], [3, 4, 5]]],
- dataset_var=cls.Dataset,
- track_tracebacks=6,
- # exec_one_benchmark=fakeBenchmarkExec,
- # exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
- exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
- analyze=fakegetResults,
- delete=fakeDelete,
- analyze_iterations=fake_analyze)
- cls.assertEqual(res, 3)
-
- def test_no_iter_biclass_multicore(cls):
- res = exec_classif.exec_benchmark(nb_cores=1,
- stats_iter=1,
- benchmark_arguments_dictionaries=cls.argument_dictionaries,
- directory="",
- metrics=[[[1, 2], [3, 4, 5]]],
- dataset_var=cls.Dataset,
- track_tracebacks=6,
- # exec_one_benchmark=fakeBenchmarkExec,
- # exec_one_benchmark_multicore=fakeBenchmarkExec_mutlicore,
- exec_one_benchmark_mono_core=fakeBenchmarkExec_monocore,
- analyze=fakegetResults,
- delete=fakeDelete,
- analyze_iterations=fake_analyze)
- cls.assertEqual(res, 3)
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
-def fakeExecMono(directory, name, labels_names, classification_indices, k_folds,
- coreIndex, type, pathF, random_state, labels,
- hyper_param_search="try", metrics="try", n_iter=1, **arguments):
- return ["Mono", arguments]
-
-
-def fakeExecMulti(directory, coreIndex, name, classification_indices, k_folds,
- type, pathF, labels_dictionary,
- random_state, labels, hyper_param_search="", metrics=None,
- n_iter=1, **arguments):
- return ["Multi", arguments]
-
-
-def fakeInitMulti(args, benchmark, views, views_indices, argument_dictionaries,
- random_state, directory, resultsMonoview,
- classification_indices):
- return {"monoview": [{"try": 0}, {"try2": 100}],
- "multiview": [{"try3": 5}, {"try4": 10}]}
-
-
-class FakeKfold():
- def __init__(self):
- self.n_splits = 2
- pass
-
- def split(self, X, Y):
- return [([X[0], X[1]], [X[2], X[3]]), (([X[2], X[3]], [X[0], X[1]]))]
-
-
-class Test_set_element(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.dictionary = {"a":
- {"b":{
- "c":{
- "d":{
- "e":1,
- "f":[1]
- }
- }
- }}}
- cls.elements = {"a.b.c.d.e":1, "a.b.c.d.f":[1]}
-
- @classmethod
- def tearDownClass(cls):
- pass
-
- def test_simple(self):
- simplified_dict = {}
- for path, value in self.elements.items():
- simplified_dict = exec_classif.set_element(simplified_dict, path, value)
- self.assertEqual(simplified_dict, self.dictionary)
-
-
-class Test_get_path_dict(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.dictionary = {"a":
- {"b":{
- "c":{
- "d":{
- "e":1,
- "f":[1]
- }
- }
- }}}
-
- @classmethod
- def tearDownClass(cls):
- pass
-
- def test_simple(self):
- path_dict = exec_classif.get_path_dict(self.dictionary)
- self.assertEqual(path_dict, {"a.b.c.d.e":1, "a.b.c.d.f":[1]})
-
-
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
diff --git a/multiview_platform/tests/test_metrics/__init__.py b/multiview_platform/tests/test_metrics/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/tests/test_metrics/test_metrics.py b/multiview_platform/tests/test_metrics/test_metrics.py
deleted file mode 100644
index 301a42d4..00000000
--- a/multiview_platform/tests/test_metrics/test_metrics.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import unittest
-import multiview_platform.mono_multi_view_classifiers.metrics as metrics
-import pkgutil
-import os
-from sklearn.metrics._scorer import _BaseScorer
-
-# Tester que chaque metrique a bien les bonnes fonctions qui renvoient bien les bons types d'outputs avec les bons types d'inputs
-# Faire de meme pour les differents classifeurs monovues et les differents classifeurs multivues
-
-
-class Test_metric(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.test="a"
-
-
- def test_simple(self):
- pkgpath = os.path.dirname(metrics.__file__)
- for _, metric, _ in pkgutil.iter_modules([pkgpath]):
- module = getattr(metrics, metric)
- self.assertTrue(hasattr(module, "score"))
- self.assertTrue(isinstance(module.score([1,0],[1,0]), float))
- self.assertTrue(hasattr(module, "get_scorer"))
- self.assertTrue(isinstance(module.get_scorer(), _BaseScorer))
- self.assertTrue(hasattr(module, "get_config"))
- self.assertTrue(isinstance(module.get_config(), str))
-
-
diff --git a/multiview_platform/tests/test_mono_view/__init__.py b/multiview_platform/tests/test_mono_view/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/tests/test_mono_view/test_exec_classif_mono_view.py b/multiview_platform/tests/test_mono_view/test_exec_classif_mono_view.py
deleted file mode 100644
index 784bac2a..00000000
--- a/multiview_platform/tests/test_mono_view/test_exec_classif_mono_view.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import os
-import unittest
-
-import h5py
-import numpy as np
-from sklearn.model_selection import StratifiedKFold
-
-from multiview_platform.tests.utils import rm_tmp, tmp_path, test_dataset
-
-from multiview_platform.mono_multi_view_classifiers.monoview import exec_classif_mono_view
-from multiview_platform.mono_multi_view_classifiers.monoview_classifiers import decision_tree
-
-
-class Test_initConstants(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- os.mkdir(tmp_path)
- cls.view_name="test_dataset"
- cls.datasetFile = h5py.File(
- tmp_path+"test.hdf5", "w")
- cls.random_state = np.random.RandomState(42)
- cls.args = {"classifier_name": "test_clf"}
- cls.X_value = cls.random_state.randint(0, 500, (10, 20))
- cls.X = cls.datasetFile.create_dataset("View0", data=cls.X_value)
- cls.X.attrs["name"] = "test_dataset"
- cls.X.attrs["sparse"] = False
- cls.classification_indices = [np.array([0, 2, 4, 6, 8]),
- np.array([1, 3, 5, 7, 9]),
- np.array([1, 3, 5, 7, 9])]
- cls.labels_names = ["test_true", "test_false"]
- cls.name = "test"
- cls.directory = os.path.join(tmp_path, "test_dir/")
-
- def test_simple(cls):
- kwargs, \
- t_start, \
- feat, \
- CL_type, \
- X, \
- learningRate, \
- labelsString, \
- output_file_name,\
- directory,\
- base_file_name = exec_classif_mono_view.init_constants(cls.args,
- cls.X,
- cls.classification_indices,
- cls.labels_names,
- cls.name,
- cls.directory,
- cls.view_name)
- cls.assertEqual(kwargs, cls.args)
- cls.assertEqual(feat, "test_dataset")
- cls.assertEqual(CL_type, "test_clf")
- np.testing.assert_array_equal(X, cls.X_value)
- cls.assertEqual(learningRate, 0.5)
- cls.assertEqual(labelsString, "test_true-test_false")
- # cls.assertEqual(output_file_name, "Code/tests/temp_tests/test_dir/test_clf/test_dataset/results-test_clf-test_true-test_false-learnRate0.5-test-test_dataset-")
-
- @classmethod
- def tearDownClass(cls):
- os.remove(tmp_path+"test.hdf5")
- os.rmdir(
- tmp_path+"test_dir/test_clf/test_dataset")
- os.rmdir(tmp_path+"test_dir/test_clf")
- os.rmdir(tmp_path+"test_dir")
- os.rmdir(tmp_path)
-
-
-class Test_initTrainTest(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- cls.random_state = np.random.RandomState(42)
- cls.X = cls.random_state.randint(0, 500, (10, 5))
- cls.Y = cls.random_state.randint(0, 2, 10)
- cls.classification_indices = [np.array([0, 2, 4, 6, 8]),
- np.array([1, 3, 5, 7, 9]),
- ]
-
- def test_simple(cls):
- X_train, y_train, X_test, y_test = exec_classif_mono_view.init_train_test(
- cls.X, cls.Y, cls.classification_indices)
-
- np.testing.assert_array_equal(X_train, np.array(
- [np.array([102, 435, 348, 270, 106]),
- np.array([466, 214, 330, 458, 87]),
- np.array([149, 308, 257, 343, 491]),
- np.array([276, 160, 459, 313, 21]),
- np.array([58, 169, 475, 187, 463])]))
- np.testing.assert_array_equal(X_test, np.array(
- [np.array([71, 188, 20, 102, 121]),
- np.array([372, 99, 359, 151, 130]),
- np.array([413, 293, 385, 191, 443]),
- np.array([252, 235, 344, 48, 474]),
- np.array([270, 189, 445, 174, 445])]))
- np.testing.assert_array_equal(y_train, np.array([0, 0, 1, 0, 0]))
- np.testing.assert_array_equal(y_test, np.array([1, 1, 0, 0, 0]))
-
-
-class Test_getHPs(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- os.mkdir(tmp_path)
- cls.classifierModule = decision_tree
- cls.hyper_param_search = "Random"
- cls.classifier_name = "decision_tree"
- cls.random_state = np.random.RandomState(42)
- cls.X = cls.random_state.randint(0,10,size=(10,5))
- cls.y = cls.random_state.randint(0,2,size=10)
- cls.output_file_name = tmp_path
- cls.cv = StratifiedKFold(n_splits=2, random_state=cls.random_state, shuffle=True)
- cls.nb_cores = 1
- cls.metrics = {"accuracy_score*": {}}
- cls.kwargs = {"decision_tree" : {"max_depth": 1,
- "criterion": "gini",
- "splitter": "best"}}
- cls.classifier_class_name = "DecisionTree"
- cls.hps_kwargs = {"n_iter": 2}
-
- @classmethod
- def tearDownClass(cls):
- for file_name in os.listdir(tmp_path):
- os.remove(
- os.path.join(tmp_path, file_name))
- os.rmdir(tmp_path)
-
- def test_simple(self):
- kwargs = exec_classif_mono_view.get_hyper_params(self.classifierModule,
- self.hyper_param_search,
- self.classifier_name,
- self.classifier_class_name,
- self.X,
- self.y,
- self.random_state,
- self.output_file_name,
- self.cv,
- self.nb_cores,
- self.metrics,
- self.kwargs,
- **self.hps_kwargs)
- def test_simple_config(self):
- kwargs = exec_classif_mono_view.get_hyper_params(self.classifierModule,
- "None",
- self.classifier_name,
- self.classifier_class_name,
- self.X,
- self.y,
- self.random_state,
- self.output_file_name,
- self.cv,
- self.nb_cores,
- self.metrics,
- self.kwargs,
- **self.hps_kwargs)
-
-
-class Test_exec_monoview(unittest.TestCase):
-
- def test_simple(self):
- os.mkdir(tmp_path)
- out = exec_classif_mono_view.exec_monoview(tmp_path,
- test_dataset.get_v(0),
- test_dataset.get_labels(),
- "test dataset",
- ["yes", "no"],
- [np.array([0,1,2,4]), np.array([4])],
- StratifiedKFold(n_splits=2),
- 1,
- "",
- "",
- np.random.RandomState(42),
- "Random",
- n_iter=2,
- **{"classifier_name":"decision_tree",
- "view_index":0,
- "decision_tree":{}})
- rm_tmp()
-
-# class Test_getKWARGS(unittest.TestCase):
-#
-# @classmethod
-# def setUpClass(cls):
-# cls.classifierModule = None
-# cls.hyper_param_search = "None"
-# cls.nIter = 2
-# cls.CL_type = "string"
-# cls.X_train = np.zeros((10,20))
-# cls.y_train = np.zeros((10))
-# cls.random_state = np.random.RandomState(42)
-# cls.outputFileName = "test_file"
-# cls.KFolds = None
-# cls.nbCores = 1
-# cls.metrics = {"accuracy_score":""}
-# cls.kwargs = {}
-#
-# def test_simple(cls):
-# clKWARGS = ExecClassifMonoView.getHPs(cls.classifierModule,
-# cls.hyper_param_search,
-# cls.nIter,
-# cls.CL_type,
-# cls.X_train,
-# cls.y_train,
-# cls.random_state,
-# cls.outputFileName,
-# cls.KFolds,
-# cls.nbCores,
-# cls.metrics,
-# cls.kwargs)
-# pass
-#
-# class Test_saveResults(unittest.TestCase):
-#
-# @classmethod
-# def setUpClass(cls):
-# cls.stringAnalysis = "string analysis"
-# cls.outputFileName = "test_file"
-# cls.full_labels_pred = np.zeros(10)
-# cls.y_train_pred = np.ones(5)
-# cls.y_train = np.zeros(5)
-# cls.imagesAnalysis = {}
-#
-# def test_simple(cls):
-# ExecClassifMonoView.saveResults(cls.stringAnalysis,
-# cls.outputFileName,
-# cls.full_labels_pred,
-# cls.y_train_pred,
-# cls.y_train,
-# cls.imagesAnalysis)
-# # Test if the files are created with the right content
-#
-# def test_with_image_analysis(cls):
-# cls.imagesAnalysis = {"test_image":"image.png"} # Image to gen
-# ExecClassifMonoView.saveResults(cls.stringAnalysis,
-# cls.outputFileName,
-# cls.full_labels_pred,
-# cls.y_train_pred,
-# cls.y_train,
-# cls.imagesAnalysis)
-# # Test if the files are created with the right content
-#
diff --git a/multiview_platform/tests/test_mono_view/test_monoview_utils.py b/multiview_platform/tests/test_mono_view/test_monoview_utils.py
deleted file mode 100644
index b0f414ba..00000000
--- a/multiview_platform/tests/test_mono_view/test_monoview_utils.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import unittest
-
-import numpy as np
-from sklearn.model_selection import StratifiedKFold
-from sklearn.tree import DecisionTreeClassifier
-
-from multiview_platform.mono_multi_view_classifiers.monoview import monoview_utils
-from multiview_platform.mono_multi_view_classifiers.utils.hyper_parameter_search import CustomRandint
-
-class TestFunctions(unittest.TestCase):
-
- def test_gen_test_folds_preds(self):
- self.random_state = np.random.RandomState(42)
- self.X_train = self.random_state.random_sample((31, 10))
- self.y_train = np.ones(31, dtype=int)
- self.KFolds = StratifiedKFold(n_splits=3, )
-
- self.estimator = DecisionTreeClassifier(max_depth=1)
-
- self.y_train[15:] = -1
- testFoldsPreds = monoview_utils.gen_test_folds_preds(self.X_train,
- self.y_train,
- self.KFolds,
- self.estimator)
- self.assertEqual(testFoldsPreds.shape, (3, 10))
- np.testing.assert_array_equal(testFoldsPreds[0], np.array(
- [ 1, 1, -1, -1, 1, 1, -1, 1, -1, 1]))
-
- def test_change_label_to_minus(self):
- lab = monoview_utils.change_label_to_minus(np.array([0,1,0]))
- np.testing.assert_array_equal(lab, np.array([-1,1,-1]))
-
- def test_change_label_to_zero(self):
- lab = monoview_utils.change_label_to_zero(np.array([-1,1,-1]))
- np.testing.assert_array_equal(lab, np.array([0,1,0]))
-
- def test_compute_possible_combinations(self):
- n_possib = monoview_utils.compute_possible_combinations({"a":[1, 2], "b":{"c":[2,3]}, "d":CustomRandint(0,10)})
- np.testing.assert_array_equal(n_possib, np.array([2, np.inf, 10]))
-
-class FakeClf(monoview_utils.BaseMonoviewClassifier):
-
- def __init__(self):
- pass
-
-
-class TestBaseMonoviewClassifier(unittest.TestCase):
-
- def test_simple(self):
- name = FakeClf().get_name_for_fusion()
- self.assertEqual(name, 'Fake')
diff --git a/multiview_platform/tests/test_monoview_classifiers/__init__.py b/multiview_platform/tests/test_monoview_classifiers/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/tests/test_monoview_classifiers/test_adaboost.py b/multiview_platform/tests/test_monoview_classifiers/test_adaboost.py
deleted file mode 100644
index 94f5f835..00000000
--- a/multiview_platform/tests/test_monoview_classifiers/test_adaboost.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# import unittest
-# import numpy as np
-# from sklearn.tree import DecisionTreeClassifier
-#
-# from ...mono_multi_view_classifiers.monoview_classifiers import Adaboost
-#
-#
-# class Test_canProbas(unittest.TestCase):
-#
-# def test_simple(cls):
-# cls.assertTrue(Adaboost.canProbas())
-#
-#
-# class Test_paramsToSet(unittest.TestCase):
-#
-# @classmethod
-# def setUpClass(cls):
-# cls.n_iter = 4
-# cls.random_state = np.random.RandomState(42)
-#
-# def test_simple(cls):
-# res = Adaboost.paramsToSet(cls.n_iter, cls.random_state)
-# cls.assertEqual(len(res), cls.n_iter)
-# cls.assertEqual(type(res[0][0]), int)
-# cls.assertEqual(type(res[0][1]), type(DecisionTreeClassifier()))
-# cls.assertEqual([7,4,13,11], [resIter[0] for resIter in res])
-#
-#
-# class Test_getKWARGS(unittest.TestCase):
-#
-# @classmethod
-# def setUpClass(cls):
-# cls.kwargs_list = [("CL_Adaboost_n_est", 10),
-# ("CL_Adaboost_b_est", DecisionTreeClassifier())]
-#
-# def test_simple(cls):
-# res = Adaboost.getKWARGS(cls.kwargs_list)
-# cls.assertIn("0", res)
-# cls.assertIn("1", res)
-# cls.assertEqual(type(res), dict)
-# cls.assertEqual(res["0"], 10)
-# # Can't test decision tree
-#
-# def test_wrong(cls):
-# cls.kwargs_list[0] = ("chicken_is_heaven",42)
-# with cls.assertRaises(ValueError) as catcher:
-# Adaboost.getKWARGS(cls.kwargs_list)
-# exception = catcher.exception
-# # cls.assertEqual(exception, "Wrong arguments served to Adaboost")
-#
-#
-# class Test_randomizedSearch(unittest.TestCase):
-#
-# def test_simple(cls):
-# pass # Test with simple params
-#
-#
-# class Test_fit(unittest.TestCase):
-#
-# def setUp(self):
-# self.random_state = np.random.RandomState(42)
-# self.dataset = self.random_state.randint(0, 100, (10, 5))
-# self.labels = self.random_state.randint(0, 2, 10)
-# self.kwargs = {"0": 5}
-# self.classifier = Adaboost.fit(self.dataset, self.labels, 42, NB_CORES=1, **self.kwargs)
-#
-# def test_fit_kwargs_string(self):
-# self.kwargs = {"0": "5"}
-# classifier = Adaboost.fit(self.dataset, self.labels, 42, NB_CORES=1, **self.kwargs)
-# self.assertEqual(classifier.n_estimators, 5)
-#
-# def test_fit_kwargs_int(self):
-# self.kwargs = {"0": 5}
-# classifier = Adaboost.fit(self.dataset, self.labels, 42, NB_CORES=1, **self.kwargs)
-# self.assertEqual(classifier.n_estimators, 5)
-#
-# def test_fit_labels(self):
-# predicted_labels = self.classifier.predict(self.dataset)
-# np.testing.assert_array_equal(predicted_labels, self.labels)
-#
diff --git a/multiview_platform/tests/test_monoview_classifiers/test_compatibility.py b/multiview_platform/tests/test_monoview_classifiers/test_compatibility.py
deleted file mode 100644
index 91c566df..00000000
--- a/multiview_platform/tests/test_monoview_classifiers/test_compatibility.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# import os
-# import unittest
-#
-
-# Actuellement problématique a cause de la pep8isation du code. A voir plus tard
-
-
-# import numpy as np
-#
-# from ...mono_multi_view_classifiers import monoview_classifiers
-#
-#
-# class Test_methods(unittest.TestCase):
-#
-# def test_simple(self):
-# for fileName in os.listdir(
-# "multiview_platform/mono_multi_view_classifiers/monoview_classifiers"):
-# if fileName[-3:] == ".py" and fileName != "__init__.py":
-# monoview_classifier_module = getattr(monoview_classifiers,
-# fileName[:-3])
-# self.assertIn("formatCmdArgs", dir(monoview_classifier_module),
-# fileName[
-# :-3] + " must have getKWARGS method implemented")
-# self.assertIn("paramsToSet", dir(monoview_classifier_module),
-# fileName[
-# :-3] + " must have randomizedSearch method implemented")
-# #test to be changed find name of class not same name of module
-# # self.assertIn(fileName[:-3], dir(monoview_classifier_module),
-# # fileName[
-# # :-3] + " must have it's own class implemented")
-#
-# monoview_classifier_class = getattr(monoview_classifier_module,
-# fileName[:-3])
-# self.assertTrue(
-# hasattr(monoview_classifier_class, "getInterpret"),
-# fileName[:-3] + " class must have getInterpret implemented")
-# self.assertTrue(
-# hasattr(monoview_classifier_class, "canProbas", ),
-# fileName[:-3] + " class must have canProbas implemented")
-# monoview_classifier_instance = monoview_classifier_class()
-# self.assertTrue(
-# hasattr(monoview_classifier_instance, "param_names", ),
-# fileName[:-3] + " class must have param_names attribute")
-# self.assertTrue(
-# hasattr(monoview_classifier_instance, "classed_params", ),
-# fileName[:-3] + " class must have classed_params attribute")
-# self.assertTrue(
-# hasattr(monoview_classifier_instance, "distribs", ),
-# fileName[:-3] + " class must have distribs attribute")
-# self.assertTrue(
-# hasattr(monoview_classifier_instance, "weird_strings", ),
-# fileName[:-3] + " class must have weird_strings attribute")
-# # check_estimator(monoview_classifier_instance)
-#
-#
-# class Test_canProbas(unittest.TestCase):
-#
-# def test_outputs(self):
-# for fileName in os.listdir(
-# "multiview_platform/mono_multi_view_classifiers/monoview_classifiers"):
-# if fileName[-3:] == ".py" and fileName != "__init__.py":
-# monoview_classifier_module = getattr(monoview_classifiers,
-# fileName[:-3])
-# monoview_classifier_class = getattr(monoview_classifier_module,
-# fileName[:-3])()
-# res = monoview_classifier_class.canProbas()
-# self.assertEqual(type(res), bool,
-# "canProbas must return a boolean")
-#
-# def test_inputs(self):
-# for fileName in os.listdir(
-# "multiview_platform/mono_multi_view_classifiers/monoview_classifiers"):
-# if fileName[-3:] == ".py" and fileName != "__init__.py":
-# monoview_classifier_module = getattr(monoview_classifiers,
-# fileName[:-3])
-# monoview_classifier_class = getattr(monoview_classifier_module,
-# fileName[:-3])()
-# with self.assertRaises(TypeError,
-# msg="canProbas must have 0 args") as catcher:
-# monoview_classifier_class.canProbas(35)
-#
-#
-# class Test_fit(unittest.TestCase):
-#
-# @classmethod
-# def setUpClass(cls):
-# cls.random_state = np.random.RandomState(42)
-# cls.dataset = cls.random_state.random_sample((10, 20))
-# cls.labels = cls.random_state.randint(0, 2, 10)
-#
-# # def test_inputs(cls):
-# # # DATASET, CLASS_LABELS, random_state, NB_CORES=1, **kwargs
-# # for fileName in os.listdir("Code/mono_multi_view_classifiers/monoview_classifiers"):
-# # if fileName[-3:] == ".py" and fileName != "__init__.py":
-# # monoview_classifier_module = getattr(monoview_classifiers, fileName[:-3])
-# # cls.args = dict((str(index), value) for index, value in
-# # enumerate(monoview_classifier_module.paramsToSet(1, cls.random_state)[0]))
-# # res = monoview_classifier_module.fit(cls.dataset, cls.labels, cls.random_state, **cls.args)
-# # with cls.assertRaises(TypeError, msg="fit must have 3 positional args, one kwarg") as catcher:
-# # monoview_classifier_module.fit()
-# # monoview_classifier_module.fit(cls.dataset)
-# # monoview_classifier_module.fit(cls.dataset,cls.labels)
-# # monoview_classifier_module.fit(cls.dataset,cls.labels, cls.random_state, 1, 10)
-#
-# # def test_outputs(cls):
-# # for fileName in os.listdir("Code/mono_multi_view_classifiers/monoview_classifiers"):
-# # if fileName[-3:] == ".py" and fileName != "__init__.py":
-# # monoview_classifier_module = getattr(monoview_classifiers, fileName[:-3])
-# # cls.args = dict((str(index), value) for index, value in
-# # enumerate(monoview_classifier_module.paramsToSet(1, cls.random_state)[0]))
-# # res = monoview_classifier_module.fit(cls.dataset, cls.labels, cls.random_state, **cls.args)
-# # cls.assertIn("predict", dir(res), "fit must return an object able to predict")
-#
-#
-# class Test_paramsToSet(unittest.TestCase):
-#
-# def test_inputs(self):
-# for fileName in os.listdir(
-# "multiview_platform/mono_multi_view_classifiers/monoview_classifiers"):
-# if fileName[-3:] == ".py" and fileName != "__init__.py":
-# monoview_classifier_module = getattr(monoview_classifiers,
-# fileName[:-3])
-# with self.assertRaises(TypeError,
-# msg="paramsToSet must have 2 positional args") as catcher:
-# monoview_classifier_module.paramsToSet(2,
-# np.random.RandomState(
-# 42), 10)
-# monoview_classifier_module.paramsToSet(2)
-# monoview_classifier_module.paramsToSet()
-# res = monoview_classifier_module.paramsToSet(2,
-# np.random.RandomState(
-# 42))
-#
-# def test_outputs(self):
-# for fileName in os.listdir(
-# "multiview_platform/mono_multi_view_classifiers/monoview_classifiers"):
-# if fileName[-3:] == ".py" and fileName != "__init__.py":
-# monoview_classifier_module = getattr(monoview_classifiers,
-# fileName[:-3])
-# res = monoview_classifier_module.paramsToSet(2,
-# np.random.RandomState(
-# 42))
-# self.assertEqual(type(res), list)
-# self.assertEqual(len(res), 2)
-# self.assertEqual(type(res[0]), dict)
-#
-# # class Test_getKWARGS(unittest.TestCase):
-# #
-# # # TODO : Find a way to enter the right args
-# #
-# # def test_inputs(self):
-# # for fileName in os.listdir("Code/mono_multi_view_classifiers/monoview_classifiers"):
-# # if fileName[-3:] == ".py" and fileName != "__init__.py":
-# # monoview_classifier_module = getattr(monoview_classifiers, fileName[:-3])
-# # with self.assertRaises(TypeError, msg="getKWARGS must have 1 positional args") as catcher:
-# # monoview_classifier_module.getKWARGS()
-# # monoview_classifier_module.getKWARGS([1],2)
diff --git a/multiview_platform/tests/test_multi_view/__init__.py b/multiview_platform/tests/test_multi_view/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/tests/test_multi_view/test_exec_multiview.py b/multiview_platform/tests/test_multi_view/test_exec_multiview.py
deleted file mode 100644
index e0e6d872..00000000
--- a/multiview_platform/tests/test_multi_view/test_exec_multiview.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import os
-import unittest
-
-import h5py
-import numpy as np
-from sklearn.model_selection import StratifiedKFold
-
-from multiview_platform.tests.utils import rm_tmp, tmp_path, test_dataset
-
-from multiview_platform.mono_multi_view_classifiers.multiview import exec_multiview
-
-
-class Test_init_constants(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- os.mkdir(tmp_path)
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
- def test_simple(self):
- classifier_name, t_start, views_indices, \
- classifier_config, views, learning_rate, labels, output_file_name, \
- directory, base_file_name, metrics = exec_multiview.init_constants(
- kwargs={"view_names":["ViewN0", "ViewN2", "ViewN1", ],
- "view_indices": [0,2,1],
- "classifier_name":"test_clf",
- "test_clf":{}},
- classification_indices=[np.array([0,1,4,2]), np.array([3])],
- metrics={"accuracy_score*":{}},
- name="test_dataset",
- nb_cores=1,
- k_folds=StratifiedKFold(n_splits=2),
- dataset_var=test_dataset,
- directory=tmp_path
- )
- self.assertEqual(classifier_name, "test_clf")
- self.assertEqual(views_indices, [0,2,1])
- self.assertEqual(classifier_config, {})
- self.assertEqual(views, ["ViewN0", "ViewN2", "ViewN1", ])
- self.assertEqual(learning_rate, 4/5)
-
- def test_exec_multiview_no_hps(self):
- res = exec_multiview.exec_multiview(
- directory=tmp_path,
- dataset_var=test_dataset,
- name="test_dataset",
- classification_indices=[np.array([0,1,4,2]), np.array([3])],
- k_folds=StratifiedKFold(n_splits=2),
- nb_cores=1,
- database_type="", path="",
- labels_dictionary={0:"yes", 1:"no"},
- random_state=np.random.RandomState(42),
- labels=test_dataset.get_labels(),
- hps_method="None",
- hps_kwargs={},
- metrics=None,
- n_iter=30,
- **{"view_names":["ViewN0", "ViewN2", "ViewN1", ],
- "view_indices": [0,2,1],
- "classifier_name":"weighted_linear_early_fusion",
- "weighted_linear_early_fusion":{}}
- )
-
- def test_exec_multiview(self):
- res = exec_multiview.exec_multiview(
- directory=tmp_path,
- dataset_var=test_dataset,
- name="test_dataset",
- classification_indices=[np.array([0,1,4,2]), np.array([3])],
- k_folds=StratifiedKFold(n_splits=2),
- nb_cores=1,
- database_type="", path="",
- labels_dictionary={0:"yes", 1:"no"},
- random_state=np.random.RandomState(42),
- labels=test_dataset.get_labels(),
- hps_method="Grid",
- hps_kwargs={"param_grid":
- {"monoview_classifier_config":[{"max_depth":3}, {"max_depth":1}]},
- },
- metrics=None,
- n_iter=30,
- **{"view_names":["ViewN0", "ViewN2", "ViewN1", ],
- "view_indices": [0,2,1],
- "classifier_name":"weighted_linear_early_fusion",
- "weighted_linear_early_fusion":{}}
- )
\ No newline at end of file
diff --git a/multiview_platform/tests/test_multi_view/test_multiview_utils.py b/multiview_platform/tests/test_multi_view/test_multiview_utils.py
deleted file mode 100644
index 6cb88063..00000000
--- a/multiview_platform/tests/test_multi_view/test_multiview_utils.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import os
-import unittest
-
-import h5py
-import numpy as np
-from sklearn.model_selection import StratifiedKFold
-
-from multiview_platform.tests.utils import rm_tmp, tmp_path, test_dataset
-
-from multiview_platform.mono_multi_view_classifiers.multiview import multiview_utils
-
-
-class FakeMVClassif(multiview_utils.BaseMultiviewClassifier):
-
- def __init__(self, mc=True):
- self.mc=mc
- pass
-
- def fit(self, X, y):
- if not self.mc:
- raise ValueError
- else:
- pass
-
-
-
-class TestBaseMultiviewClassifier(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- os.mkdir(tmp_path)
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
- def test_accepts_multiclass(self):
- rs = np.random.RandomState(42)
- accepts = FakeMVClassif().accepts_multi_class(rs)
- self.assertEqual(accepts, True)
- accepts = FakeMVClassif(mc=False).accepts_multi_class(rs)
- self.assertEqual(accepts, False)
- self.assertRaises(ValueError, FakeMVClassif(mc=False).accepts_multi_class, rs,**{"n_samples":2, "n_classes":3})
-
-class TestConfigGenerator(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.rs = np.random.RandomState(42)
-
- def test_simple(self):
- cfg_gen = multiview_utils.ConfigGenerator(["decision_tree", "decision_tree"])
- sample = cfg_gen.rvs(self.rs)
- self.assertEqual(sample, {'decision_tree': {'criterion': 'entropy',
- 'max_depth': 103,
- 'splitter': 'best'}})
-
-class TestFunctions(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- os.mkdir(tmp_path)
- cls.rs = np.random.RandomState(42)
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
- def test_get_available_monoview_classifiers(self):
- avail = multiview_utils.get_available_monoview_classifiers()
- self.assertEqual(avail, ['adaboost',
- 'decision_tree',
- 'gradient_boosting',
- 'knn',
- 'lasso',
- 'random_forest',
- 'sgd',
- 'svm_linear',
- 'svm_poly',
- 'svm_rbf'])
- avail = multiview_utils.get_available_monoview_classifiers(need_probas=True)
- self.assertEqual(avail, ['adaboost',
- 'decision_tree',
- 'gradient_boosting',
- 'knn',
- 'random_forest',
- 'svm_linear',
- 'svm_poly',
- 'svm_rbf'])
diff --git a/multiview_platform/tests/test_multiview_classifiers/Test_PseudoCQMeasure/__init__.py b/multiview_platform/tests/test_multiview_classifiers/Test_PseudoCQMeasure/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/tests/test_multiview_classifiers/Test_PseudoCQMeasure/test_PseudoCQFusionModule.py b/multiview_platform/tests/test_multiview_classifiers/Test_PseudoCQMeasure/test_PseudoCQFusionModule.py
deleted file mode 100644
index 65e22eb8..00000000
--- a/multiview_platform/tests/test_multiview_classifiers/Test_PseudoCQMeasure/test_PseudoCQFusionModule.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# import unittest
-#
-# import numpy as np
-#
-# from ....mono_multi_view_classifiers.multiview_classifiers.entropy_fusion_old import EntropyFusionModule
-#
-# class Test_entropy(unittest.TestCase):
-#
-# @classmethod
-# def setUpClass(cls):
-# cls.classifiersDecisions = np.array([
-# [np.random.randint(0,2,(2,5)), [[0,0,1,0,1], [0,1,0,1,0]], np.random.randint(0,2,(2,5)), np.random.randint(0,2,(2,5)), np.random.randint(0,2,(2,5))],
-# [np.random.randint(0,2, (2, 5)), np.random.randint(0,2, (2, 5)), np.random.randint(0,2, (2, 5)), [[0, 0, 1, 1, 0], [0, 1, 0, 1, 0]], np.random.randint(0,2, (2, 5))],
-# [np.random.randint(0,2, (2, 5)), np.random.randint(0,2, (2, 5)), np.random.randint(0,2, (2, 5)), np.random.randint(0,2, (2, 5)), [[0, 1, 1, 1, 1], [0, 1, 0, 1, 0]]],
-# ])
-# cls.combination = [1,3,4]
-# cls.foldsGroudTruth = np.array([[1,1,0,0,1], [0,1,0,1,0]])
-# cls.foldsLen = ""
-#
-# def test_simple(cls):
-# entropy_score = EntropyFusionModule.entropy(cls.classifiersDecisions, cls.combination, cls.foldsGroudTruth,cls.foldsLen)
-# cls.assertEqual(entropy_score, 0.15, 'Wrong values for entropy measure')
diff --git a/multiview_platform/tests/test_multiview_classifiers/__init__.py b/multiview_platform/tests/test_multiview_classifiers/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_additions/__init__.py b/multiview_platform/tests/test_multiview_classifiers/test_additions/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_additions/test_diversity_utils.py b/multiview_platform/tests/test_multiview_classifiers/test_additions/test_diversity_utils.py
deleted file mode 100644
index debdc51b..00000000
--- a/multiview_platform/tests/test_multiview_classifiers/test_additions/test_diversity_utils.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import unittest
-import numpy as np
-
-import multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils as du
-
-
-class FakeDataset():
-
- def __init__(self, views, labels):
- self.nb_views = views.shape[0]
- self.dataset_length = views.shape[2]
- self.views = views
- self.labels = labels
-
- def get_v(self, view_index, example_indices):
- return self.views[view_index, example_indices]
-
- def get_nb_class(self, example_indices):
- return np.unique(self.labels[example_indices])
-
-
-class FakeDivCoupleClf(du.CoupleDiversityFusionClassifier):
-
- def __init__(self, rs, classifier_names=None,
- classifiers_config=None, monoview_estimators=None):
- super(FakeDivCoupleClf, self).__init__(random_state=rs,
- classifier_names=classifier_names,
- classifier_configs=classifiers_config,
- monoview_estimators=monoview_estimators)
- self.rs = rs
-
- def diversity_measure(self, a, b, c):
- return self.rs.randint(0,100)
-
-
-class FakeDivGlobalClf(du.GlobalDiversityFusionClassifier):
-
- def __init__(self, rs, classifier_names=None,
- classifiers_config=None, monoview_estimators=None):
- super(FakeDivGlobalClf, self).__init__(random_state=rs,
- classifier_names=classifier_names,
- classifier_configs=classifiers_config,
- monoview_estimators=monoview_estimators)
- self.rs = rs
-
- def diversity_measure(self, a, b, c):
- return self.rs.randint(0,100)
-
-class Test_DiversityFusion(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.classifier_names = ["adaboost", "decision_tree"]
- cls.classifiers_config = {"adaboost":{"n_estimators":5,}}
- cls.random_state = np.random.RandomState(42)
- cls.y = cls.random_state.randint(0,2,6)
- cls.X = FakeDataset(cls.random_state.randint(0,100,(2,5,6)), cls.y)
- cls.train_indices = [0,1,2,4]
- cls.views_indices = [0,1]
-
- def test_simple_couple(self):
- clf = FakeDivCoupleClf(self.random_state, classifier_names=self.classifier_names,
- classifiers_config=self.classifiers_config)
- clf.fit(self.X, self.y, self.train_indices, self.views_indices)
-
- def test_simple_global(self):
- clf = FakeDivGlobalClf(self.random_state,
- classifier_names=self.classifier_names,
- classifiers_config=self.classifiers_config)
- clf.fit(self.X, self.y, self.train_indices, self.views_indices)
-
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_additions/test_jumbo_fusion_utils.py b/multiview_platform/tests/test_multiview_classifiers/test_additions/test_jumbo_fusion_utils.py
deleted file mode 100644
index 9e242ed8..00000000
--- a/multiview_platform/tests/test_multiview_classifiers/test_additions/test_jumbo_fusion_utils.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import unittest
-import numpy as np
-
-import multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.jumbo_fusion_utils as ju
-
-
-class FakeDataset():
-
- def __init__(self, views, labels):
- self.nb_views = views.shape[0]
- self.dataset_length = views.shape[2]
- self.views = views
- self.labels = labels
-
- def get_v(self, view_index, example_indices):
- return self.views[view_index, example_indices]
-
- def get_nb_class(self, example_indices):
- return np.unique(self.labels[example_indices])
-
-
-#TODO
\ No newline at end of file
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_difficulty_fusion.py b/multiview_platform/tests/test_multiview_classifiers/test_difficulty_fusion.py
deleted file mode 100644
index ff298b8c..00000000
--- a/multiview_platform/tests/test_multiview_classifiers/test_difficulty_fusion.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import unittest
-
-import numpy as np
-
-from multiview_platform.mono_multi_view_classifiers.multiview_classifiers import difficulty_fusion
-
-
-class Test_difficulty_fusion(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state=np.random.RandomState(42)
- cls.classifiers_decisions = cls.random_state.randint(0, 2, size=(5, 3, 5))
- cls.combination = [1, 3, 4]
- cls.y = np.array([1, 1, 0, 0, 1])
- cls.difficulty_fusion_clf = difficulty_fusion.DifficultyFusion()
-
- def test_simple(cls):
- difficulty_measure = cls.difficulty_fusion_clf.diversity_measure(
- cls.classifiers_decisions,
- cls.combination,
- cls.y)
- cls.assertAlmostEqual(difficulty_measure, 0.1875)
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_disagree_fusion.py b/multiview_platform/tests/test_multiview_classifiers/test_disagree_fusion.py
deleted file mode 100644
index bb08e016..00000000
--- a/multiview_platform/tests/test_multiview_classifiers/test_disagree_fusion.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# # import unittest
-#
-import numpy as np
-import unittest
-#
-from multiview_platform.mono_multi_view_classifiers.multiview_classifiers import disagree_fusion
-
-
-class Test_disagree(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.monoview_decision_1 = np.array([0, 0, 1, 1])
- cls.monoview_decision_2 = np.array([0, 1, 0, 1])
- cls.ground_truth = None
- cls.clf = disagree_fusion.DisagreeFusion()
-
- def test_simple(cls):
- disagreement = cls.clf.diversity_measure(cls.monoview_decision_1,
- cls.monoview_decision_2,
- cls.ground_truth)
- np.testing.assert_array_equal(disagreement,
- np.array([False, True, True, False]))
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_diversity_utils.py b/multiview_platform/tests/test_multiview_classifiers/test_diversity_utils.py
deleted file mode 100644
index 46c9e596..00000000
--- a/multiview_platform/tests/test_multiview_classifiers/test_diversity_utils.py
+++ /dev/null
@@ -1,42 +0,0 @@
-#
-# import numpy as np
-# import unittest
-#
-# from multiview_platform.mono_multi_view_classifiers.multiview.additions import \
-# diversity_utils
-#
-#
-# def fake_measure(a, b, c, d, e):
-# return 42
-#
-#
-# class Test_global_div_measure(unittest.TestCase):
-#
-# @classmethod
-# def setUpClass(cls):
-# cls.random_state = np.random.RandomState(42)
-# cls.allClassifiersNames = [["SCM", "SVM", "DT"], ["SCM", "SVM", "DT"]]
-# cls.views_indices = np.array([0, 1])
-# cls.classifiersDecisions = np.zeros(
-# (cls.views_indices.shape[0], len(cls.allClassifiersNames), 3, 6),
-# dtype=int)
-# for classifer_index, classifier in enumerate(cls.allClassifiersNames):
-# for view_index, view in enumerate(cls.views_indices):
-# cls.classifiersDecisions[
-# view_index, classifer_index] = np.array([
-# cls.random_state.randint(0, 2, 6),
-# cls.random_state.randint(0, 2, 6),
-# cls.random_state.randint(0, 2, 6)])
-# cls.folds_ground_truth = np.array(
-# [np.array([1, 1, 1, 0, 0, 0]) for _ in range(3)])
-# cls.classification_indices = np.array([])
-# cls.measurement = fake_measure
-#
-# def test_simple(cls):
-# clf_names, diversity_measure = diversity_utils.global_div_measure(
-# cls.allClassifiersNames,
-# cls.classifiersDecisions,
-# cls.measurement,
-# cls.folds_ground_truth)
-# cls.assertEqual(len(clf_names), 2)
-# cls.assertEqual(diversity_measure, 42)
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_double_fault_fusion.py b/multiview_platform/tests/test_multiview_classifiers/test_double_fault_fusion.py
deleted file mode 100644
index 7e6fd3d7..00000000
--- a/multiview_platform/tests/test_multiview_classifiers/test_double_fault_fusion.py
+++ /dev/null
@@ -1,22 +0,0 @@
-
-import numpy as np
-import unittest
-
-from multiview_platform.mono_multi_view_classifiers.multiview_classifiers import double_fault_fusion
-
-
-class Test_disagree(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.monoview_decision_1 = np.array([0, 0, 0, 0, 1, 1, 1, 1])
- cls.monoview_decision_2 = np.array([0, 0, 1, 1, 0, 0, 1, 1])
- cls.ground_truth = np.array([0, 1, 0, 1, 0, 1, 0, 1])
- cls.clf = double_fault_fusion.DoubleFaultFusion()
-
- def test_simple(cls):
- double_fault = cls.clf.diversity_measure(cls.monoview_decision_1,
- cls.monoview_decision_2,
- cls.ground_truth)
- np.testing.assert_array_equal(double_fault,
- np.array([False, True, False, False, False, False, True, False]))
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_entropy_fusion.py b/multiview_platform/tests/test_multiview_classifiers/test_entropy_fusion.py
deleted file mode 100644
index dc88bfcc..00000000
--- a/multiview_platform/tests/test_multiview_classifiers/test_entropy_fusion.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import unittest
-
-import numpy as np
-
-from multiview_platform.mono_multi_view_classifiers.multiview_classifiers import entropy_fusion
-
-
-class Test_difficulty_fusion(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state=np.random.RandomState(42)
- cls.classifiers_decisions = cls.random_state.randint(0, 2, size=(5, 3, 5))
- cls.combination = [1, 3, 4]
- cls.y = np.array([1, 1, 0, 0, 1])
- cls.clf = entropy_fusion.EntropyFusion()
-
- def test_simple(cls):
- entropy = cls.clf.diversity_measure(
- cls.classifiers_decisions,
- cls.combination,
- cls.y)
- cls.assertAlmostEqual(entropy, 0.2)
diff --git a/multiview_platform/tests/test_multiview_classifiers/test_weighted_linear_early_fusion.py b/multiview_platform/tests/test_multiview_classifiers/test_weighted_linear_early_fusion.py
deleted file mode 100644
index c8660752..00000000
--- a/multiview_platform/tests/test_multiview_classifiers/test_weighted_linear_early_fusion.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import unittest
-
-import numpy as np
-import os
-
-from multiview_platform.tests.utils import rm_tmp, tmp_path, test_dataset
-
-from multiview_platform.mono_multi_view_classifiers.multiview_classifiers import \
- weighted_linear_early_fusion
-
-class Test_WeightedLinearEarlyFusion(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- cls.random_state = np.random.RandomState(42)
- cls.view_weights = [0.5, 0.5]
- cls.monoview_classifier_name = "decision_tree"
- cls.monoview_classifier_config = {"max_depth":1, "criterion": "gini", "splitter": "best"}
- cls.classifier = weighted_linear_early_fusion.WeightedLinearEarlyFusion(
- random_state=cls.random_state, view_weights=cls.view_weights,
- monoview_classifier_name=cls.monoview_classifier_name,
- monoview_classifier_config=cls.monoview_classifier_config)
- cls.dataset = test_dataset
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
- def test_simple(self):
- np.testing.assert_array_equal(self.view_weights, self.classifier.view_weights)
-
- def test_fit(self):
- self.assertRaises(AttributeError, getattr,
- self.classifier.monoview_classifier, "classes_")
- self.classifier.fit(self.dataset, test_dataset.get_labels(), None, None)
- np.testing.assert_array_equal(self.classifier.monoview_classifier.classes_,
- np.array([0,1]))
-
- def test_predict(self):
- self.classifier.fit(self.dataset, test_dataset.get_labels(), None, None)
- predicted_labels = self.classifier.predict(self.dataset, None, None)
- np.testing.assert_array_equal(predicted_labels, test_dataset.get_labels())
-
- def test_transform_data_to_monoview_simple(self):
- example_indices, X = self.classifier.transform_data_to_monoview(self.dataset,
- None, None)
- self.assertEqual(X.shape, (5,12))
- np.testing.assert_array_equal(X, np.concatenate((self.dataset.get_v(0), self.dataset.get_v(1)), axis=1))
- np.testing.assert_array_equal(example_indices, np.arange(5))
-
- def test_transform_data_to_monoview_view_select(self):
- example_indices, X = self.classifier.transform_data_to_monoview(
- self.dataset,
- None, np.array([0]))
- self.assertEqual(X.shape, (5, 6))
- np.testing.assert_array_equal(X, self.dataset.get_v(0))
- np.testing.assert_array_equal(example_indices, np.arange(5))
-
- def test_transform_data_to_monoview_example_view_select(self):
- example_indices, X = self.classifier.transform_data_to_monoview(
- self.dataset,
- np.array([1,2,3]), np.array([0]))
- self.assertEqual(X.shape, (3, 6))
- np.testing.assert_array_equal(X, self.dataset.get_v(0)[np.array([1,2,3]), :])
- np.testing.assert_array_equal(example_indices, np.array([1,2,3]))
-
diff --git a/multiview_platform/tests/test_result_analysis/__init__.py b/multiview_platform/tests/test_result_analysis/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/tests/test_result_analysis/test_duration_analysis.py b/multiview_platform/tests/test_result_analysis/test_duration_analysis.py
deleted file mode 100644
index efe6c68d..00000000
--- a/multiview_platform/tests/test_result_analysis/test_duration_analysis.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import unittest
-import numpy as np
-import pandas as pd
-
-from multiview_platform.mono_multi_view_classifiers.result_analysis import duration_analysis
-
-class FakeClassifierResult:
-
- def __init__(self, i=0):
- self.i=i
- if i == 0:
- self.hps_duration = 10
- self.fit_duration = 12
- self.pred_duration = 15
- else:
- self.hps_duration = 1
- self.fit_duration = 2
- self.pred_duration = 5
-
-
- def get_classifier_name(self):
- if self.i == 0:
- return 'test1'
- else:
- return 'test2'
-
-
-
-class Test_get_duration(unittest.TestCase):
-
- def test_simple(self):
- results = [FakeClassifierResult(), FakeClassifierResult(i=1)]
- durs = duration_analysis.get_duration(results)
- pd.testing.assert_frame_equal(durs,
- pd.DataFrame(index=['test1', 'test2'],
- columns=['hps', 'fit', 'pred'],
- data=np.array([np.array([10,12,15]),
- np.array([1,2,5])]),
- dtype=object))
-
-
diff --git a/multiview_platform/tests/test_result_analysis/test_error_analysis.py b/multiview_platform/tests/test_result_analysis/test_error_analysis.py
deleted file mode 100644
index 07ec87c2..00000000
--- a/multiview_platform/tests/test_result_analysis/test_error_analysis.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import unittest
-import numpy as np
-
-from multiview_platform.mono_multi_view_classifiers.monoview.monoview_utils import MonoviewResult
-from multiview_platform.mono_multi_view_classifiers.multiview.multiview_utils import MultiviewResult
-
-from multiview_platform.mono_multi_view_classifiers.result_analysis.error_analysis import get_example_errors, gen_error_data, gen_error_data_glob
-
-
-class Test_get_example_errors(unittest.TestCase):
-
- def test_simple(self):
- ground_truth = np.array([0,1,0,1,0,1,0,1, -100])
- results = [MultiviewResult("mv", "", {"accuracy_score": [0.7, 0.75],
- "f1_score": [0.71, 0.76]},
- np.array([0,0,0,0,1,1,1,1,1]),
- 0,0,0, {}),
- MonoviewResult(0,
- "dt",
- "1",
- {"accuracy_score": [0.8, 0.85],
- "f1_score": [0.81, 0.86]}
- , np.array([0,0,1,1,0,0,1,1,0]), "", "",
- "", "",0,0, {})
- ]
- example_errors = get_example_errors(ground_truth,
- results)
- self.assertIsInstance(example_errors, dict)
- np.testing.assert_array_equal(example_errors["mv"],
- np.array([1,0,1,0,0,1,0,1,-100]))
- np.testing.assert_array_equal(example_errors["dt-1"],
- np.array([1, 0, 0, 1, 1, 0, 0, 1,-100]))
-
-class Test_gen_error_data(unittest.TestCase):
-
- def test_simple(self):
- random_state = np.random.RandomState(42)
- ada_data = random_state.randint(0,2,size=7)
- mv_data = random_state.randint(0, 2, size=7)
- example_errors = {"ada-1": ada_data,
- "mv": mv_data}
- nb_classifiers, nb_examples, classifiers_names, \
- data_2d, error_on_examples = gen_error_data(example_errors)
- self.assertEqual(nb_classifiers, 2)
- self.assertEqual(nb_examples, 7)
- self.assertEqual(classifiers_names, ["ada-1", "mv"])
- np.testing.assert_array_equal(data_2d, np.array([ada_data, mv_data]).transpose())
- np.testing.assert_array_equal(error_on_examples, (ada_data+mv_data)/nb_classifiers)
-
-
-
-class Test_gen_error_data_glob(unittest.TestCase):
-
- def test_simple(self):
- random_state = np.random.RandomState(42)
-
- ada_error_data_1 = random_state.randint(0,2,7)
- ada_error_data_2 = random_state.randint(0, 2, 7)
- ada_sum = ada_error_data_1+ada_error_data_2
- mv_error_data_1 = random_state.randint(0, 2, 7)
- mv_error_data_2 = random_state.randint(0, 2, 7)
- mv_sum = mv_error_data_1+mv_error_data_2
-
- combi_results = {"ada-1":ada_sum, "mv": mv_sum}
-
- stats_iter = 2
-
- nb_examples, nb_classifiers, \
- data, error_on_examples, \
- classifier_names = gen_error_data_glob(combi_results,
- stats_iter)
- self.assertEqual(nb_examples, 7)
- self.assertEqual(nb_classifiers, 2)
- np.testing.assert_array_equal(data, np.array([ada_sum, mv_sum]).transpose())
- np.testing.assert_array_equal(error_on_examples, np.sum(np.array([ada_sum, mv_sum]), axis=0)/(nb_classifiers*stats_iter))
- self.assertEqual(classifier_names, ["ada-1", "mv"])
\ No newline at end of file
diff --git a/multiview_platform/tests/test_result_analysis/test_execution.py b/multiview_platform/tests/test_result_analysis/test_execution.py
deleted file mode 100644
index f42f818c..00000000
--- a/multiview_platform/tests/test_result_analysis/test_execution.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import unittest
-import numpy as np
-import pandas as pd
-import os
-
-from multiview_platform.mono_multi_view_classifiers.monoview.monoview_utils import MonoviewResult
-from multiview_platform.mono_multi_view_classifiers.multiview.multiview_utils import MultiviewResult
-
-from multiview_platform.mono_multi_view_classifiers.result_analysis.execution import format_previous_results, get_arguments, analyze_iterations
-from multiview_platform.tests.utils import rm_tmp, tmp_path, test_dataset
-
-
-class FakeClassifierResult:
-
- def __init__(self, i=1):
- self.classifier_name='test'+str(i)
- self.full_labels_pred = np.array([0,1,1,2,1])
- self.hps_duration=i
- self.fit_duration=i
- self.pred_duration=i
-
- def get_classifier_name(self):
- return self.classifier_name
-
-class Test_format_previous_results(unittest.TestCase):
-
- def test_simple(self):
- iter_results = {"metrics_scores":[], "example_errors":[], "feature_importances":[], "labels":[], "durations":[], "class_metrics_scores":[]}
- random_state = np.random.RandomState(42)
-
- # Gen metrics data
- metrics_1_data = random_state.uniform(size=(2,2))
- metrics_2_data = random_state.uniform(size=(2,2))
- metric_1_df = pd.DataFrame(data=metrics_1_data, index=["train", "test"],
- columns=["ada-1", "mv"])
- metric_2_df = pd.DataFrame(data=metrics_2_data, index=["train", "test"],
- columns=["ada-1", "mv"])
- iter_results["metrics_scores"].append({"acc": metric_1_df})
- iter_results["metrics_scores"].append({"acc": metric_2_df})
-
- # Gen error data
- ada_error_data_1 = random_state.randint(0,2,7)
- ada_error_data_2 = random_state.randint(0, 2, 7)
- ada_sum = ada_error_data_1+ada_error_data_2
- mv_error_data_1 = random_state.randint(0, 2, 7)
- mv_error_data_2 = random_state.randint(0, 2, 7)
- mv_sum = mv_error_data_1+mv_error_data_2
- iter_results["example_errors"].append({})
- iter_results["example_errors"].append({})
- iter_results["example_errors"][0]["ada-1"] = ada_error_data_1
- iter_results["example_errors"][0]["mv"] = mv_error_data_1
- iter_results["example_errors"][1]["ada-1"] = ada_error_data_2
- iter_results["example_errors"][1]["mv"] = mv_error_data_2
-
- iter_results["durations"].append(pd.DataFrame(index=["ada-1", "mv"],
- columns=["plif", "plaf"],
- data=np.zeros((2,2))))
- iter_results["durations"].append(pd.DataFrame(index=["ada-1", "mv"],
- columns=["plif",
- "plaf"],
- data=np.ones((2, 2))))
-
- # Running the function
- metric_analysis, class_met, error_analysis, \
- feature_importances, feature_stds, \
- labels, durations_mean, duration_std = format_previous_results(iter_results)
- mean_df = pd.DataFrame(data=np.mean(np.array([metrics_1_data,
- metrics_2_data]),
- axis=0),
- index=["train", "test"],
- columns=["ada-1", "mvm"])
- std_df = pd.DataFrame(data=np.std(np.array([metrics_1_data,
- metrics_2_data]),
- axis=0),
- index=["train", "test"],
- columns=["ada-1", "mvm"])
-
- # Testing
- np.testing.assert_array_equal(metric_analysis["acc"]["mean"].loc["train"],
- mean_df.loc["train"])
- np.testing.assert_array_equal(metric_analysis["acc"]["mean"].loc["test"],
- mean_df.loc["test"])
- np.testing.assert_array_equal(metric_analysis["acc"]["std"].loc["train"],
- std_df.loc["train"])
- np.testing.assert_array_equal(metric_analysis["acc"]["std"].loc["test"],
- std_df.loc["test"])
- np.testing.assert_array_equal(ada_sum, error_analysis["ada-1"])
- np.testing.assert_array_equal(mv_sum, error_analysis["mv"])
- self.assertEqual(durations_mean.at["ada-1", 'plif'], 0.5)
-
-class Test_get_arguments(unittest.TestCase):
-
- def setUp(self):
- self.benchamrk_argument_dictionaries = [{"flag":"good_flag", "valid":True},
- {"flag":"bad_flag", "valid":False}]
-
- def test_benchmark_wanted(self):
- argument_dict = get_arguments(self.benchamrk_argument_dictionaries, "good_flag")
- self.assertTrue(argument_dict["valid"])
-
-
-class Test_analyze_iterations(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- os.mkdir(tmp_path)
- cls.results = [[0, [FakeClassifierResult(), FakeClassifierResult(i=2)], []], [1, [FakeClassifierResult(), FakeClassifierResult(i=2)], []]]
- cls.benchmark_argument_dictionaries = [{"labels_dictionary":{0:"zero",1:"one",2:"two"}, "flag":0, "directory":tmp_path, "args":{"name":"test_dataset"}},{"labels_dictionary":{0:"zero",1:"one",2:"two"}, "flag":1, "directory":tmp_path, "args":{"name":"test_dataset"}} ]
- cls.stats_iter = 2
- cls.metrics = {}
- cls.example_ids = ['ex1', 'ex5','ex4','ex3','ex2',]
- cls.labels = np.array([0,1,2,1,1])
-
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
- def test_simple(self):
- analysis = analyze_iterations(self.results,
- self.benchmark_argument_dictionaries,
- self.stats_iter,
- self.metrics,
- self.example_ids,
- self.labels)
- res, iter_res, tracebacks, labels_names = analysis
- self.assertEqual(labels_names, ['zero', 'one', 'two'])
-
- self.assertEqual(iter_res['class_metrics_scores'], [{}, {}])
-
- pd.testing.assert_frame_equal(iter_res['durations'][0], pd.DataFrame(index=['test1','test2'],
- columns=['hps', 'fit', 'pred'],
- data=np.array([1,1,1,2,2,2]).reshape((2,3)), dtype=object))
- np.testing.assert_array_equal(iter_res['example_errors'][0]['test1'], np.array([1, 1, 0, 0, 1]))
- self.assertEqual(iter_res["feature_importances"], [{},{}])
- np.testing.assert_array_equal(iter_res['labels'], np.array([0, 1, 2, 1, 1]))
- self.assertEqual(iter_res['metrics_scores'], [{},{}])
-
-
diff --git a/multiview_platform/tests/test_result_analysis/test_feature_importances.py b/multiview_platform/tests/test_result_analysis/test_feature_importances.py
deleted file mode 100644
index 2a69e88c..00000000
--- a/multiview_platform/tests/test_result_analysis/test_feature_importances.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import unittest
-import numpy as np
-import pandas as pd
-
-from multiview_platform.mono_multi_view_classifiers.result_analysis import feature_importances
-from multiview_platform.mono_multi_view_classifiers.monoview.monoview_utils import MonoviewResult
-
-class FakeClassifier:
- def __init__(self, i=0):
- self.feature_importances_ = [i, i+1]
-
-class FakeClassifierResult(MonoviewResult):
-
- def __init__(self, i=0):
- self.i=i
- self.hps_duration = i*10
- self.fit_duration = (i+2)*10
- self.pred_duration = (i+5)*10
- self.clf = FakeClassifier(i)
- self.view_name = 'testview'+str(i)
- self.classifier_name = "test"+str(i)
-
- def get_classifier_name(self):
- return self.classifier_name
-
-
-
-class Test_get_duration(unittest.TestCase):
-
- def test_simple(self):
- results = [FakeClassifierResult(), FakeClassifierResult(i=1)]
- feat_importance = feature_importances.get_feature_importances(results)
- pd.testing.assert_frame_equal(feat_importance["testview1"],
- pd.DataFrame(index=None,columns=['test1'],
- data=np.array([1,2]).reshape((2,1)),
- ))
\ No newline at end of file
diff --git a/multiview_platform/tests/test_result_analysis/test_metric_analysis.py b/multiview_platform/tests/test_result_analysis/test_metric_analysis.py
deleted file mode 100644
index a34f06a4..00000000
--- a/multiview_platform/tests/test_result_analysis/test_metric_analysis.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import unittest
-import numpy as np
-import pandas as pd
-import os
-
-from multiview_platform.mono_multi_view_classifiers.monoview.monoview_utils import MonoviewResult
-from multiview_platform.mono_multi_view_classifiers.multiview.multiview_utils import MultiviewResult
-
-from multiview_platform.mono_multi_view_classifiers.result_analysis.metric_analysis import get_metrics_scores, init_plot, get_fig_size, sort_by_test_score
-
-class Test_get_metrics_scores(unittest.TestCase):
-
-
- def test_simple(self):
- metrics = {"accuracy_score*":{},"f1_score":{}}
- results = [MonoviewResult(0,
- "ada",
- "0",
- {"accuracy_score*":[0.9, 0.95],
- "f1_score":[0.91, 0.96]}
- , "", "", "", "", "",0,0,{})]
- metrics_scores, class_met = get_metrics_scores(metrics,
- results, [])
- self.assertIsInstance(metrics_scores, dict)
- self.assertIsInstance(metrics_scores["accuracy_score*"], pd.DataFrame)
- np.testing.assert_array_equal(np.array(metrics_scores["accuracy_score*"].loc["train"]), np.array([0.9]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["accuracy_score*"].loc["test"]),
- np.array([0.95]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["f1_score"].loc["train"]),
- np.array([0.91]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["f1_score"].loc["test"]),
- np.array([0.96]))
- np.testing.assert_array_equal(np.array(metrics_scores["f1_score"].columns),
- np.array(["ada-0"]))
-
- def test_multiple_monoview_classifiers(self):
- metrics = {"accuracy_score*":{},"f1_score":{}}
- results = [MonoviewResult(view_index=0,
- classifier_name="ada",
- view_name="0",
- metrics_scores={"accuracy_score*": [0.9, 0.95],
- "f1_score": [0.91, 0.96]},
- full_labels_pred="",
- classifier_config="",
- classifier="",
- n_features="",
- hps_duration=0,
- fit_duration=0,
- pred_duration=0,
- class_metric_scores={}),
- MonoviewResult(view_index=0,
- classifier_name="dt",
- view_name="1",
- metrics_scores={"accuracy_score*": [0.8, 0.85],
- "f1_score": [0.81, 0.86]},
- full_labels_pred="",
- classifier_config="",
- classifier="",
- n_features="",
- hps_duration=0,
- fit_duration=0,
- pred_duration=0,
- class_metric_scores={})
- ]
- metrics_scores, class_met = get_metrics_scores(metrics,
- results, [])
- self.assertIsInstance(metrics_scores, dict)
- self.assertIsInstance(metrics_scores["accuracy_score*"], pd.DataFrame)
- np.testing.assert_array_equal(
- np.array(metrics_scores["accuracy_score*"].loc["train"]),
- np.array([0.9, 0.8]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["accuracy_score*"].loc["test"]),
- np.array([0.95, 0.85]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["f1_score"].loc["train"]),
- np.array([0.91, 0.81]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["f1_score"].loc["test"]),
- np.array([0.96, 0.86]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["f1_score"].columns),
- np.array(["ada-0", "dt-1"]))
-
- def test_mutiview_result(self):
- metrics = {"accuracy_score*":{},"f1_score":{}}
- results = [MultiviewResult("mv", "", {"accuracy_score*": [0.7, 0.75],
- "f1_score": [0.71, 0.76]}, "",0,0,0, {}),
- MonoviewResult(view_index=0,
- classifier_name="dt",
- view_name="1",
- metrics_scores={"accuracy_score*": [0.8, 0.85],
- "f1_score": [0.81, 0.86]},
- full_labels_pred="",
- classifier_config="",
- classifier="",
- n_features="",
- hps_duration=0,
- fit_duration=0,
- pred_duration=0,
- class_metric_scores={})
- ]
- metrics_scores, class_met = get_metrics_scores(metrics,
- results, [])
- self.assertIsInstance(metrics_scores, dict)
- self.assertIsInstance(metrics_scores["accuracy_score*"], pd.DataFrame)
- np.testing.assert_array_equal(
- np.array(metrics_scores["accuracy_score*"].loc["train"]),
- np.array([0.7, 0.8]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["accuracy_score*"].loc["test"]),
- np.array([0.75, 0.85]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["f1_score"].loc["train"]),
- np.array([0.71, 0.81]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["f1_score"].loc["test"]),
- np.array([0.76, 0.86]))
- np.testing.assert_array_equal(
- np.array(metrics_scores["f1_score"].columns),
- np.array(["mv", "dt-1"]))
-
-
-class Test_init_plot(unittest.TestCase):
-
- def test_simple(self):
- results = []
- metric_name = "acc"
- data = np.random.RandomState(42).uniform(0,1,(2,2))
- metric_dataframe = pd.DataFrame(index=["train", "test"],
- columns=["dt-1", "mv"], data=data)
- directory = "dir"
- database_name = 'db'
- labels_names = ['lb1', "lb2"]
- class_met = metric_dataframe = pd.DataFrame(index=["train", "test"],
- columns=["dt-1", "mv"], data=data)
- train, test, classifier_names, \
- file_name, nb_results, results, class_test = init_plot(results,
- metric_name,
- metric_dataframe,
- directory,
- database_name,
- class_met)
- self.assertEqual(file_name, os.path.join("dir", "db-acc"))
- np.testing.assert_array_equal(train, data[0,:])
- np.testing.assert_array_equal(test, data[1, :])
- np.testing.assert_array_equal(classifier_names, np.array(["dt-1", "mv"]))
- self.assertEqual(nb_results, 2)
- self.assertEqual(results, [["dt-1", "acc", data[1,0], 0.0, data[1,0]],
- ["mv", "acc", data[1,1], 0.0, data[1,1]]])
-
-
-class Test_small_func(unittest.TestCase):
-
- def test_fig_size(self):
- kw, width = get_fig_size(5)
- self.assertEqual(kw, {"figsize":(15,5)})
- self.assertEqual(width, 0.35)
- kw, width = get_fig_size(100)
- self.assertEqual(kw, {"figsize": (100, 100/3)})
- self.assertEqual(width, 0.35)
-
- def test_sort_by_test_scores(self):
- train_scores = np.array([1,2,3,4])
- test_scores = np.array([4, 3, 2, 1])
- train_STDs = np.array([1, 2, 3, 4])
- test_STDs = np.array([1, 2, 3, 4])
- names = np.array(['1', '2', '3', '4'])
- sorted_names, sorted_train_scores, \
- sorted_test_scores, sorted_train_STDs, \
- sorted_test_STDs = sort_by_test_score(train_scores, test_scores,
- names, train_STDs, test_STDs)
- np.testing.assert_array_equal(sorted_names, np.array(['4', '3', '2', '1']))
- np.testing.assert_array_equal(sorted_test_scores, [1, 2, 3, 4])
- np.testing.assert_array_equal(sorted_test_STDs, [4, 3, 2, 1])
- np.testing.assert_array_equal(sorted_train_scores, [4, 3, 2, 1])
- np.testing.assert_array_equal(sorted_train_STDs, [4, 3, 2, 1])
\ No newline at end of file
diff --git a/multiview_platform/tests/test_result_analysis/test_noise_analysis.py b/multiview_platform/tests/test_result_analysis/test_noise_analysis.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/tests/test_result_analysis/test_tracebacks_analysis.py b/multiview_platform/tests/test_result_analysis/test_tracebacks_analysis.py
deleted file mode 100644
index 61296f85..00000000
--- a/multiview_platform/tests/test_result_analysis/test_tracebacks_analysis.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import unittest
-import numpy as np
-import pandas as pd
-import os
-
-from multiview_platform.mono_multi_view_classifiers.result_analysis import tracebacks_analysis
-from multiview_platform.tests.utils import tmp_path, rm_tmp
-
-class FakeClassifierResult:
-
- def __init__(self, i=0):
- self.i=i
- if i == 0:
- self.hps_duration = 10
- self.fit_duration = 12
- self.pred_duration = 15
- else:
- self.hps_duration = 1
- self.fit_duration = 2
- self.pred_duration = 5
-
-
- def get_classifier_name(self):
- if self.i == 0:
- return 'test1'
- else:
- return 'test2'
-
-
-
-class Test_funcs(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- os.mkdir(tmp_path)
- cls.res_file = open(os.path.join(tmp_path,"tmp.txt"), "w")
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
- def test_save_dict_to_text(self):
- keys = tracebacks_analysis.save_dict_to_text({"a":"i", "b":"j"}, self.res_file)
- self.res_file.close()
- self.assertEqual(list(keys),["a", "b"])
- with open(os.path.join(tmp_path,"tmp.txt"), 'r') as res_file:
- self.assertEqual(res_file.read(), 'Failed algorithms : \n\ta,\n\tb.\n\n\na\n\ni\n\n\nb\n\nj\n\n\n')
diff --git a/multiview_platform/tests/test_utils/__init__.py b/multiview_platform/tests/test_utils/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/multiview_platform/tests/test_utils/test_GetMultiviewDB.py b/multiview_platform/tests/test_utils/test_GetMultiviewDB.py
deleted file mode 100644
index c1068e7b..00000000
--- a/multiview_platform/tests/test_utils/test_GetMultiviewDB.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import os
-import unittest
-
-import h5py
-import numpy as np
-
-from multiview_platform.mono_multi_view_classifiers.utils import get_multiview_db
-from multiview_platform.tests.utils import rm_tmp, tmp_path
-
-
-class Test_get_classic_db_hdf5(unittest.TestCase):
-
- def setUp(self):
- rm_tmp()
- os.mkdir(tmp_path)
- self.rs = np.random.RandomState(42)
- self.nb_view = 3
- self.file_name = "test.hdf5"
- self.nb_examples = 5
- self.nb_class = 3
- self.views = [self.rs.randint(0, 10, size=(self.nb_examples, 7))
- for _ in range(self.nb_view)]
- self.labels = self.rs.randint(0, self.nb_class, self.nb_examples)
- self.dataset_file = h5py.File(os.path.join(tmp_path, self.file_name), 'w')
- self.view_names = ["ViewN" + str(index) for index in
- range(len(self.views))]
- self.are_sparse = [False for _ in self.views]
- for view_index, (view_name, view, is_sparse) in enumerate(
- zip(self.view_names, self.views, self.are_sparse)):
- view_dataset = self.dataset_file.create_dataset(
- "View" + str(view_index),
- view.shape,
- data=view)
- view_dataset.attrs["name"] = view_name
- view_dataset.attrs["sparse"] = is_sparse
- labels_dataset = self.dataset_file.create_dataset("Labels",
- shape=self.labels.shape,
- data=self.labels)
- self.labels_names = [str(index) for index in np.unique(self.labels)]
- labels_dataset.attrs["names"] = [label_name.encode()
- for label_name in self.labels_names]
- meta_data_grp = self.dataset_file.create_group("Metadata")
- meta_data_grp.attrs["nbView"] = len(self.views)
- meta_data_grp.attrs["nbClass"] = len(np.unique(self.labels))
- meta_data_grp.attrs["datasetLength"] = len(self.labels)
-
- def test_simple(self):
- dataset , labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(
- ["ViewN2"], tmp_path, self.file_name.split(".")[0],
- self.nb_class, ["0", "2"],
- self.rs, path_for_new=tmp_path)
- self.assertEqual(dataset.nb_view, 1)
- self.assertEqual(labels_dictionary,
- {0: "0", 1: "2", 2:"1"})
- self.assertEqual(dataset.get_nb_examples(), 5)
- self.assertEqual(len(np.unique(dataset.get_labels())), 3)
-
-
- def test_all_views_asked(self):
- dataset, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(
- None, tmp_path, self.file_name.split(".")[0],
- self.nb_class, ["0", "2"],
- self.rs, path_for_new=tmp_path)
- self.assertEqual(dataset.nb_view, 3)
- self.assertEqual(dataset.get_view_dict(), {'ViewN0': 0, 'ViewN1': 1, 'ViewN2': 2})
-
- def test_asked_the_whole_dataset(self):
- dataset, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(
- ["ViewN2"], tmp_path, self.file_name.split(".")[0],
- self.nb_class, ["0", "2"],
- self.rs, path_for_new=tmp_path, full=True)
- self.assertEqual(dataset.dataset, self.dataset_file)
-
- def tearDown(self):
- rm_tmp()
-
-
-class Test_get_classic_db_csv(unittest.TestCase):
-
- def setUp(self):
- rm_tmp()
- os.mkdir(tmp_path)
- self.pathF = tmp_path
- self.NB_CLASS = 2
- self.nameDB = "test_dataset"
- self.askedLabelsNames = ["test_label_1", "test_label_3"]
- self.random_state = np.random.RandomState(42)
- self.views = ["test_view_1", "test_view_3"]
- np.savetxt(self.pathF + self.nameDB + "-labels-names.csv",
- np.array(["test_label_0", "test_label_1",
- "test_label_2", "test_label_3"]), fmt="%s",
- delimiter=",")
- np.savetxt(self.pathF + self.nameDB + "-labels.csv",
- self.random_state.randint(0, 4, 10), delimiter=",")
- os.mkdir(self.pathF + "Views")
- self.datas = []
- for i in range(4):
- data = self.random_state.randint(0, 100, (10, 20))
- np.savetxt(self.pathF + "Views/test_view_" + str(i) + ".csv",
- data, delimiter=",")
- self.datas.append(data)
-
-
- def test_simple(self):
- dataset, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(
- self.views, self.pathF, self.nameDB,
- self.NB_CLASS, self.askedLabelsNames,
- self.random_state, delimiter=",", path_for_new=tmp_path)
- self.assertEqual(dataset.nb_view, 2)
- self.assertEqual(dataset.get_view_dict(), {'test_view_1': 0, 'test_view_3': 1})
- self.assertEqual(labels_dictionary,
- {0: "test_label_1", 1: "test_label_3"})
- self.assertEqual(dataset.get_nb_examples(), 3)
- self.assertEqual(dataset.get_nb_class(), 2)
-
-
- @classmethod
- def tearDown(self):
- for i in range(4):
- os.remove(
- tmp_path+"Views/test_view_" + str(
- i) + ".csv")
- os.rmdir(tmp_path+"Views")
- os.remove(
- tmp_path+"test_dataset-labels-names.csv")
- os.remove(tmp_path+"test_dataset-labels.csv")
- os.remove(tmp_path+"test_dataset.hdf5")
- os.remove(
- tmp_path+"test_dataset_temp_filter.hdf5")
- os.rmdir(tmp_path)
-
-class Test_get_plausible_db_hdf5(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- cls.path = tmp_path
- cls.nb_class=3
- cls.rs = np.random.RandomState(42)
- cls.nb_view=3
- cls.nb_examples = 5
- cls.nb_features = 4
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
- def test_simple(self):
- dataset, labels_dict, name = get_multiview_db.get_plausible_db_hdf5(
- "", self.path, "", nb_class=self.nb_class, random_state=self.rs,
- nb_view=3, nb_examples=self.nb_examples,
- nb_features=self.nb_features)
- self.assertEqual(dataset.init_example_indices(), range(5))
- self.assertEqual(dataset.get_nb_class(), self.nb_class)
-
- def test_two_class(self):
- dataset, labels_dict, name = get_multiview_db.get_plausible_db_hdf5(
- "", self.path, "", nb_class=2, random_state=self.rs,
- nb_view=3, nb_examples=self.nb_examples,
- nb_features=self.nb_features)
- self.assertEqual(dataset.init_example_indices(), range(5))
- self.assertEqual(dataset.get_nb_class(), 2)
-
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
diff --git a/multiview_platform/tests/test_utils/test_base.py b/multiview_platform/tests/test_utils/test_base.py
deleted file mode 100644
index 027da26f..00000000
--- a/multiview_platform/tests/test_utils/test_base.py
+++ /dev/null
@@ -1,261 +0,0 @@
-import os
-import unittest
-import yaml
-import numpy as np
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.model_selection import StratifiedKFold
-from sklearn.metrics import accuracy_score, f1_score
-
-from multiview_platform.tests.utils import rm_tmp, tmp_path
-from multiview_platform.mono_multi_view_classifiers.utils import base
-
-
-class FakeClassifier(base.BaseClassifier):
- def __init__(self, no_params=False, accepts_mc=True):
- if no_params:
- self.param_names = []
- self.classed_params = []
- else:
- self.param_names = ["test1", "test2"]
- self.classed_params = ["test2"]
- self.weird_strings = []
- self.accepts_mc = accepts_mc
-
- def get_params(self, deep=True):
- return {"test1": 10,
- "test2": "test"}
-
- def fit(self, X, y):
- if np.unique(y).shape[0]>2 and not self.accepts_mc:
- raise ValueError('Does not accept MC')
- else:
- return self
-
-
-class FakeDetector:
- def __init__(self):
- self.best_params_ = {"test1": 10,
- "test2": "test"}
- self.cv_results_ = {"param_test1": [10],
- "param_test2": ["test"]}
-
-class FakeResultAnalyzer(base.ResultAnalyser):
-
- def get_view_specific_info(self):
- return "test"
-
- def get_base_string(self):
- return 'test2'
-
-class Test_ResultAnalyzer(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.rs = np.random.RandomState(42)
- cls.classifier = FakeClassifier()
- cls.n_examples = 50
- cls.n_classes = 3
- cls.train_length = 24
- cls.train_indices = cls.rs.choice(np.arange(cls.n_examples),
- size=cls.train_length,
- replace=False)
- cls.test_indices = np.array([i for i in range(cls.n_examples)
- if i not in cls.train_indices])
- cls.test_length = cls.test_indices.shape[0]
- cls.classification_indices = [cls.train_indices, cls.test_indices]
- cls.n_splits = 5
- cls.k_folds = StratifiedKFold(n_splits=cls.n_splits, )
- cls.hps_method = "randomized_search"
- cls.metrics_list = {"accuracy_score": {}, "f1_score*":{}}
- cls.n_iter = 6
- cls.class_label_names = ["class{}".format(ind+1)
- for ind in range(cls.n_classes)]
- cls.pred = cls.rs.randint(0, cls.n_classes,
- size=cls.n_examples)
- cls.directory = "fake_directory"
- cls.base_file_name = "fake_file"
- cls.labels = cls.rs.randint(0, cls.n_classes,
- size=cls.n_examples)
- cls.database_name = "test_database"
- cls.nb_cores = 0.5
- cls.duration = -4
- cls.train_accuracy = accuracy_score(cls.labels[cls.train_indices],
- cls.pred[cls.train_indices])
- cls.test_accuracy = accuracy_score(cls.labels[cls.test_indices],
- cls.pred[cls.test_indices])
- cls.train_f1 = f1_score(cls.labels[cls.train_indices],
- cls.pred[cls.train_indices], average='micro')
- cls.test_f1 = f1_score(cls.labels[cls.test_indices],
- cls.pred[cls.test_indices], average='micro')
-
- def test_simple(self):
- RA = base.ResultAnalyser(self.classifier, self.classification_indices,
- self.k_folds, self.hps_method, self.metrics_list,
- self.n_iter, self.class_label_names,
- self.pred, self.directory,
- self.base_file_name, self.labels,
- self.database_name, self.nb_cores,
- self.duration)
-
- def test_get_metric_scores(self):
- RA = base.ResultAnalyser(self.classifier, self.classification_indices,
- self.k_folds, self.hps_method,
- self.metrics_list,
- self.n_iter, self.class_label_names,
- self.pred,
- self.directory, self.base_file_name,
- self.labels, self.database_name,
- self.nb_cores, self.duration)
- cl_train, cl_test,train_score, test_score = RA.get_metric_score("accuracy_score", {})
- np.testing.assert_array_equal(train_score, self.train_accuracy)
- np.testing.assert_array_equal(test_score, self.test_accuracy)
-
- def test_get_all_metrics_scores(self):
- RA = base.ResultAnalyser(self.classifier, self.classification_indices,
- self.k_folds, self.hps_method,
- self.metrics_list,
- self.n_iter, self.class_label_names,
- self.pred,
- self.directory, self.base_file_name,
- self.labels, self.database_name,
- self.nb_cores, self.duration)
- RA.get_all_metrics_scores()
- self.assertEqual(RA.metric_scores["accuracy_score"][0],
- self.train_accuracy)
- self.assertEqual(RA.metric_scores["accuracy_score"][1],
- self.test_accuracy)
- self.assertEqual(RA.metric_scores["f1_score*"][0],
- self.train_f1)
- self.assertEqual(RA.metric_scores["f1_score*"][1],
- self.test_f1)
-
- def test_print_metrics_scores(self):
- RA = base.ResultAnalyser(self.classifier, self.classification_indices,
- self.k_folds, self.hps_method,
- self.metrics_list,
- self.n_iter, self.class_label_names,
- self.pred,
- self.directory, self.base_file_name,
- self.labels, self.database_name,
- self.nb_cores, self.duration)
- RA.get_all_metrics_scores()
- string = RA.print_metric_score()
- print(repr(string))
- self.assertEqual(string, '\n\n\tFor Accuracy score using {}, (higher is better) : \n\t\t- Score on train : 0.25\n\t\t- Score on test : 0.2692307692307692\n\n\tFor F1 score using average: micro, {} (higher is better) : \n\t\t- Score on train : 0.25\n\t\t- Score on test : 0.2692307692307692\n\nTest set confusion matrix : \n\n╒════════╤══════════╤══════════╤══════════╕\n│ │ class1 │ class2 │ class3 │\n╞════════╪══════════╪══════════╪══════════╡\n│ class1 │ 3 │ 1 │ 2 │\n├────────┼──────────┼──────────┼──────────┤\n│ class2 │ 3 │ 2 │ 2 │\n├────────┼──────────┼──────────┼──────────┤\n│ class3 │ 3 │ 8 │ 2 │\n╘════════╧══════════╧══════════╧══════════╛\n\n')
-
- def test_get_db_config_string(self):
- RA = FakeResultAnalyzer(self.classifier, self.classification_indices,
- self.k_folds, self.hps_method,
- self.metrics_list,
- self.n_iter, self.class_label_names,
- self.pred,
- self.directory, self.base_file_name,
- self.labels, self.database_name,
- self.nb_cores, self.duration)
- self.assertEqual(RA.get_db_config_string(), 'Database configuration : \n\t- Database name : test_database\ntest\t- Learning Rate : 0.48\n\t- Labels used : class1, class2, class3\n\t- Number of cross validation folds : 5\n\n')
-
- def test_get_classifier_config_string(self):
- RA = base.ResultAnalyser(self.classifier, self.classification_indices,
- self.k_folds, self.hps_method,
- self.metrics_list,
- self.n_iter, self.class_label_names,
- self.pred,
- self.directory, self.base_file_name,
- self.labels, self.database_name,
- self.nb_cores, self.duration)
- self.assertEqual(RA.get_classifier_config_string(), 'Classifier configuration : \n\t- FakeClassifier with test1 : 10, test2 : test\n\t- Executed on 0.5 core(s) \n\t- Got configuration using randomized search with 6 iterations \n')
-
- def test_analyze(self):
- RA = FakeResultAnalyzer(self.classifier, self.classification_indices,
- self.k_folds, self.hps_method,
- self.metrics_list,
- self.n_iter, self.class_label_names,
- self.pred,
- self.directory, self.base_file_name,
- self.labels, self.database_name,
- self.nb_cores, self.duration)
- str_analysis, img_analysis, metric_scores, class_metric_scores, conf_mat = RA.analyze()
- print(repr(str_analysis))
- self.assertEqual(str_analysis, 'test2Database configuration : \n\t- Database name : test_database\ntest\t- Learning Rate : 0.48\n\t- Labels used : class1, class2, class3\n\t- Number of cross validation folds : 5\n\nClassifier configuration : \n\t- FakeClassifier with test1 : 10, test2 : test\n\t- Executed on 0.5 core(s) \n\t- Got configuration using randomized search with 6 iterations \n\n\n\tFor Accuracy score using {}, (higher is better) : \n\t\t- Score on train : 0.25\n\t\t- Score on test : 0.2692307692307692\n\n\tFor F1 score using average: micro, {} (higher is better) : \n\t\t- Score on train : 0.25\n\t\t- Score on test : 0.2692307692307692\n\nTest set confusion matrix : \n\n╒════════╤══════════╤══════════╤══════════╕\n│ │ class1 │ class2 │ class3 │\n╞════════╪══════════╪══════════╪══════════╡\n│ class1 │ 3 │ 1 │ 2 │\n├────────┼──────────┼──────────┼──────────┤\n│ class2 │ 3 │ 2 │ 2 │\n├────────┼──────────┼──────────┼──────────┤\n│ class3 │ 3 │ 8 │ 2 │\n╘════════╧══════════╧══════════╧══════════╛\n\n\n\n Classification took -1 day, 23:59:56\n\n Classifier Interpretation : \n')
-
-
-
-class Test_BaseClassifier(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.base_estimator = "DecisionTreeClassifier"
- cls.base_estimator_config = {"max_depth":10,
- "splitter": "best"}
- cls.est = base.BaseClassifier()
- cls.rs = np.random.RandomState(42)
-
- def test_simple(self):
- base_estim = self.est.get_base_estimator(self.base_estimator,
- self.base_estimator_config)
- self.assertTrue(isinstance(base_estim, DecisionTreeClassifier))
- self.assertEqual(base_estim.max_depth, 10)
- self.assertEqual(base_estim.splitter, "best")
-
- def test_gen_best_params(self):
- fake_class = FakeClassifier()
- best_params = fake_class.gen_best_params(FakeDetector())
- self.assertEqual(best_params, {"test1":10, "test2":"test"})
-
- def test_gen_params_from_detector(self):
- fake_class = FakeClassifier()
- params = fake_class.gen_params_from_detector(FakeDetector())
- self.assertEqual(params, [("test1",np.array([10])),
- ("test2",np.array(["str"], dtype='<U3'))])
- params = FakeClassifier(no_params=True).gen_params_from_detector(FakeDetector())
- self.assertEqual(params, [()])
-
- def test_params_to_string(self):
- fake_class = FakeClassifier()
- string = fake_class.params_to_string()
- self.assertEqual(string, "test1 : 10, test2 : test")
-
- def test_get_iterpret(self):
- fake_class = FakeClassifier()
- self.assertEqual("", fake_class.get_interpretation("", "", "",))
-
- def test_accepts_mutliclass(self):
- accepts = FakeClassifier().accepts_multi_class(self.rs)
- self.assertEqual(accepts, True)
- accepts = FakeClassifier(accepts_mc=False).accepts_multi_class(self.rs)
- self.assertEqual(accepts, False)
- self.assertRaises(ValueError, FakeClassifier().accepts_multi_class, self.rs, **{"n_samples":2})
-
-
- def test_class(self):
- base_estimator = DecisionTreeClassifier(max_depth=15, splitter="random")
- base_estim = self.est.get_base_estimator(base_estimator,
- self.base_estimator_config)
- self.assertTrue(isinstance(base_estim, DecisionTreeClassifier))
- self.assertEqual(base_estim.max_depth, 10)
- self.assertEqual(base_estim.splitter, "best")
-
- def test_wrong_args(self):
- base_estimator_config = {"n_estimators": 10,
- "splitter": "best"}
- with self.assertRaises(TypeError):
- base_estim = self.est.get_base_estimator(self.base_estimator,
- base_estimator_config)
-
- def test_get_config(self):
- conf = FakeClassifier(no_params=True).get_config()
- self.assertEqual(conf, 'FakeClassifier with no config.')
-
-class Test_Functions(unittest.TestCase):
-
- def test_get_name(self):
- classed_list = ["test", 42]
- np.testing.assert_array_equal(base.get_names(classed_list),
- np.array(["str", "int"], dtype="<U3"))
-
-
- def test_get_metric(self):
- from multiview_platform.mono_multi_view_classifiers.metrics import accuracy_score
- metrics_dict = {"accuracy_score*":{}}
- self.assertEqual(base.get_metric(metrics_dict), (accuracy_score, {}))
-
diff --git a/multiview_platform/tests/test_utils/test_configuration.py b/multiview_platform/tests/test_utils/test_configuration.py
deleted file mode 100644
index dc1fed6c..00000000
--- a/multiview_platform/tests/test_utils/test_configuration.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import os
-import unittest
-import yaml
-import numpy as np
-
-from multiview_platform.tests.utils import rm_tmp, tmp_path
-from multiview_platform.mono_multi_view_classifiers.utils import configuration
-
-
-class Test_get_the_args(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- cls.path_to_config_file = tmp_path+"config_temp.yml"
- path_file = os.path.dirname(os.path.abspath(__file__))
- make_tmp_dir = os.path.join(path_file, "../tmp_tests")
- os.mkdir(make_tmp_dir)
- data = {"log": 10, "name":[12.5, 1e-06], "type":True}
- with open(cls.path_to_config_file, "w") as config_file:
- yaml.dump(data, config_file)
-
- @classmethod
- def tearDownClass(cls):
- os.remove(tmp_path+"config_temp.yml")
- os.rmdir(tmp_path)
-
- def test_file_loading(self):
- config_dict = configuration.get_the_args(self.path_to_config_file)
- self.assertEqual(type(config_dict), dict)
-
- def test_dict_format(self):
- config_dict = configuration.get_the_args(self.path_to_config_file)
- self.assertIn("log", config_dict)
- self.assertIn("name", config_dict)
-
- def test_arguments(self):
- config_dict = configuration.get_the_args(self.path_to_config_file)
- self.assertEqual(config_dict["log"], 10)
- self.assertEqual(config_dict["name"], [12.5, 1e-06])
- self.assertEqual(config_dict["type"], True)
-
-class Test_save_config(unittest.TestCase):
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- path_file = os.path.dirname(os.path.abspath(__file__))
- make_tmp_dir = os.path.join(path_file, "../tmp_tests")
- os.mkdir(make_tmp_dir)
-
- def test_simple(self):
- configuration.save_config(tmp_path, {"test":10})
- with open(os.path.join(tmp_path,"config_file.yml" ), 'r') as stream:
- yaml_config = yaml.safe_load(stream)
- self.assertEqual(yaml_config,{"test":10} )
-
- @classmethod
- def tearDownClass(cls):
- os.remove(os.path.join(tmp_path, "config_file.yml"))
-
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
diff --git a/multiview_platform/tests/test_utils/test_dataset.py b/multiview_platform/tests/test_utils/test_dataset.py
deleted file mode 100644
index 76644bcb..00000000
--- a/multiview_platform/tests/test_utils/test_dataset.py
+++ /dev/null
@@ -1,423 +0,0 @@
-import unittest
-import h5py
-import numpy as np
-import os
-
-from multiview_platform.tests.utils import rm_tmp, tmp_path
-from multiview_platform.mono_multi_view_classifiers.utils import dataset
-
-
-class Test_Dataset(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- os.mkdir(tmp_path)
- cls.rs = np.random.RandomState(42)
- cls.nb_view = 3
- cls.file_name = "test.hdf5"
- cls.nb_examples = 5
- cls.nb_attr = 7
- cls.nb_class = 3
- cls.views = [cls.rs.randint(0, 10, size=(cls.nb_examples, cls.nb_attr))
- for _ in range(cls.nb_view)]
- cls.labels = cls.rs.randint(0, cls.nb_class, cls.nb_examples)
- cls.dataset_file = h5py.File(os.path.join(tmp_path, cls.file_name), "w")
- cls.view_names = ["ViewN" + str(index) for index in range(len(cls.views))]
- cls.are_sparse = [False for _ in cls.views]
- for view_index, (view_name, view, is_sparse) in enumerate(
- zip(cls.view_names, cls.views, cls.are_sparse)):
- view_dataset = cls.dataset_file.create_dataset("View" + str(view_index),
- view.shape,
- data=view)
- view_dataset.attrs["name"] = view_name
- view_dataset.attrs["sparse"] = is_sparse
- labels_dataset = cls.dataset_file.create_dataset("Labels",
- shape=cls.labels.shape,
- data=cls.labels)
- cls.labels_names = [str(index) for index in np.unique(cls.labels)]
- labels_dataset.attrs["names"] = [label_name.encode()
- for label_name in cls.labels_names]
- meta_data_grp = cls.dataset_file.create_group("Metadata")
- meta_data_grp.attrs["nbView"] = len(cls.views)
- meta_data_grp.attrs["nbClass"] = len(np.unique(cls.labels))
- meta_data_grp.attrs["datasetLength"] = len(cls.labels)
-
- @classmethod
- def tearDownClass(cls):
- cls.dataset_file.close()
-
- def test_get_shape(self):
- dataset_object = dataset.HDF5Dataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- file_name="from_scratch" + self.file_name,
- view_names=self.view_names,
- path=tmp_path,
- labels_names=self.labels_names)
- shape = dataset_object.get_shape(0)
- self.assertEqual(shape, (5,7))
-
- def test_to_numpy_array(self):
- dataset_object = dataset.HDF5Dataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- file_name="from_scratch" + self.file_name,
- view_names=self.view_names,
- path=tmp_path,
- labels_names=self.labels_names)
- array, limits = dataset_object.to_numpy_array(view_indices=[0,1,2])
-
- self.assertEqual(array.shape, (5, 21))
-
- def test_filter(self):
- """Had to create a new dataset to aviod playing with the class one"""
- file_name = "test_filter.hdf5"
- dataset_file_filter = h5py.File(os.path.join(tmp_path, file_name), "w")
- for view_index, (view_name, view, is_sparse) in enumerate(
- zip(self.view_names, self.views, self.are_sparse)):
- view_dataset = dataset_file_filter.create_dataset(
- "View" + str(view_index),
- view.shape,
- data=view)
- view_dataset.attrs["name"] = view_name
- view_dataset.attrs["sparse"] = is_sparse
- labels_dataset = dataset_file_filter.create_dataset("Labels",
- shape=self.labels.shape,
- data=self.labels)
- labels_dataset.attrs["names"] = [label_name.encode()
- for label_name in self.labels_names]
- meta_data_grp = dataset_file_filter.create_group("Metadata")
- meta_data_grp.attrs["nbView"] = len(self.views)
- meta_data_grp.attrs["nbClass"] = len(np.unique(self.labels))
- meta_data_grp.attrs["datasetLength"] = len(self.labels)
- dataset_object = dataset.HDF5Dataset(hdf5_file=dataset_file_filter)
- dataset_object.filter(np.array([0, 1, 0]), ["0", "1"], [1, 2, 3],
- ["ViewN0"], tmp_path)
- self.assertEqual(dataset_object.nb_view, 1)
- np.testing.assert_array_equal(dataset_object.get_labels(), [0, 1, 0])
- dataset_object.dataset.close()
- os.remove(os.path.join(tmp_path, "test_filter_temp_filter.hdf5"))
- os.remove(os.path.join(tmp_path, "test_filter.hdf5"))
-
- def test_for_hdf5_file(self):
- dataset_object = dataset.HDF5Dataset(hdf5_file=self.dataset_file)
-
- def test_from_scratch(self):
- dataset_object = dataset.HDF5Dataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- file_name="from_scratch"+self.file_name,
- view_names=self.view_names,
- path=tmp_path,
- labels_names=self.labels_names)
- nb_class = dataset_object.get_nb_class()
- self.assertEqual(nb_class, self.nb_class)
- example_indices = dataset_object.init_example_indices()
- self.assertEqual(example_indices, range(self.nb_examples))
- view = dataset_object.get_v(0)
- np.testing.assert_array_equal(view, self.views[0])
-
- def test_init_example_indices(self):
- example_indices = dataset.HDF5Dataset(
- hdf5_file=self.dataset_file).init_example_indices()
- self.assertEqual(example_indices, range(self.nb_examples))
- example_indices = dataset.HDF5Dataset(
- hdf5_file=self.dataset_file).init_example_indices([0, 1, 2])
- self.assertEqual(example_indices, [0,1,2])
-
- def test_get_v(self):
- view = dataset.HDF5Dataset(hdf5_file=self.dataset_file).get_v(0)
- np.testing.assert_array_equal(view, self.views[0])
- view = dataset.HDF5Dataset(hdf5_file=self.dataset_file).get_v(1, [0,1,2])
- np.testing.assert_array_equal(view, self.views[1][[0,1,2,], :])
-
- def test_get_nb_class(self):
- nb_class = dataset.HDF5Dataset(hdf5_file=self.dataset_file).get_nb_class()
- self.assertEqual(nb_class, self.nb_class)
- nb_class = dataset.HDF5Dataset(hdf5_file=self.dataset_file).get_nb_class([0])
- self.assertEqual(nb_class, 1)
-
-
-
- def test_get_view_dict(self):
- dataset_object = dataset.HDF5Dataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- file_name="from_scratch" + self.file_name,
- view_names=self.view_names,
- path=tmp_path,
- labels_names=self.labels_names)
- self.assertEqual(dataset_object.get_view_dict(), {"ViewN0":0,
- "ViewN1": 1,
- "ViewN2": 2,})
-
- def test_get_label_names(self):
- dataset_object = dataset.HDF5Dataset(hdf5_file=self.dataset_file)
- raw_label_names = dataset_object.get_label_names(decode=False)
- decoded_label_names = dataset_object.get_label_names()
- restricted_label_names = dataset_object.get_label_names(example_indices=[3,4])
- self.assertEqual(raw_label_names, [b'0', b'1', b'2'])
- self.assertEqual(decoded_label_names, ['0', '1', '2'])
- self.assertEqual(restricted_label_names, ['2'])
-
- def test_get_nb_exmaples(self):
- dataset_object = dataset.HDF5Dataset(hdf5_file=self.dataset_file)
- nb_examples = dataset_object.get_nb_examples()
- self.assertEqual(nb_examples, self.nb_examples)
-
- def test_get_labels(self):
- dataset_object = dataset.HDF5Dataset(hdf5_file=self.dataset_file)
- labels = dataset_object.get_labels()
- np.testing.assert_array_equal(labels, self.labels)
- labels = dataset_object.get_labels([1,2,0])
- np.testing.assert_array_equal(labels, self.labels[[1,2,0]])
-
- def test_copy_view(self):
- dataset_object = dataset.HDF5Dataset(hdf5_file=self.dataset_file)
- new_dataset = h5py.File(os.path.join(tmp_path, "test_copy.hdf5"), "w")
- dataset_object.copy_view(target_dataset=new_dataset,
- source_view_name="ViewN0",
- target_view_index=1)
- self.assertIn("View1", list(new_dataset.keys()))
- np.testing.assert_array_equal(dataset_object.get_v(0), new_dataset["View1"][()])
- self.assertEqual(new_dataset["View1"].attrs["name"], "ViewN0")
- new_dataset.close()
- os.remove(os.path.join(tmp_path, "test_copy.hdf5"))
-
- def test_get_name(self):
- dataset_object = dataset.HDF5Dataset(hdf5_file=self.dataset_file)
- self.assertEqual("test", dataset_object.get_name())
-
- def test_select_labels(self):
- dataset_object = dataset.HDF5Dataset(hdf5_file=self.dataset_file)
- labels, label_names, indices = dataset_object.select_labels(["0", "2"])
- np.testing.assert_array_equal(np.unique(labels), np.array([0,1]))
- self.assertEqual(label_names, ["0","2"])
-
- def test_check_selected_label_names(self):
- dataset_object = dataset.HDF5Dataset(hdf5_file=self.dataset_file)
- names = dataset_object.check_selected_label_names(nb_labels=2, random_state=self.rs)
- self.assertEqual(names, ["1", "0"])
- names = dataset_object.check_selected_label_names(selected_label_names=['0', '2'],
- random_state=self.rs)
- self.assertEqual(names, ["0", "2"])
-
- def test_select_views_and_labels(self):
- file_name = "test_filter.hdf5"
- dataset_file_select = h5py.File(os.path.join(tmp_path, file_name), "w")
- for view_index, (view_name, view, is_sparse) in enumerate(
- zip(self.view_names, self.views, self.are_sparse)):
- view_dataset = dataset_file_select.create_dataset(
- "View" + str(view_index),
- view.shape,
- data=view)
- view_dataset.attrs["name"] = view_name
- view_dataset.attrs["sparse"] = is_sparse
- labels_dataset = dataset_file_select.create_dataset("Labels",
- shape=self.labels.shape,
- data=self.labels)
- labels_dataset.attrs["names"] = [label_name.encode()
- for label_name in self.labels_names]
- meta_data_grp = dataset_file_select.create_group("Metadata")
- meta_data_grp.attrs["nbView"] = len(self.views)
- meta_data_grp.attrs["nbClass"] = len(np.unique(self.labels))
- meta_data_grp.attrs["datasetLength"] = len(self.labels)
- dataset_object = dataset.HDF5Dataset(hdf5_file=dataset_file_select)
- names = dataset_object.select_views_and_labels(nb_labels=2, view_names=["ViewN0"], random_state=self.rs, path_for_new=tmp_path)
- self.assertEqual(names, {0: '2', 1: '1'})
- self.assertEqual(dataset_object.nb_view, 1)
- dataset_object.dataset.close()
- os.remove(os.path.join(tmp_path, "test_filter_temp_filter.hdf5"))
- os.remove(os.path.join(tmp_path, "test_filter.hdf5"))
-
- def test_add_gaussian_noise(self):
- file_name = "test_noise.hdf5"
- dataset_file_select = h5py.File(os.path.join(tmp_path, file_name), "w")
- limits = np.zeros((self.nb_attr, 2))
- limits[:, 1] += 100
- meta_data_grp = dataset_file_select.create_group("Metadata")
- for view_index, (view_name, view, is_sparse) in enumerate(
- zip(self.view_names, self.views, self.are_sparse)):
- view_dataset = dataset_file_select.create_dataset(
- "View" + str(view_index),
- view.shape,
- data=view)
- view_dataset.attrs["name"] = view_name
- view_dataset.attrs["sparse"] = is_sparse
- meta_data_grp.create_dataset("View"+str(view_index)+"_limits", data= limits)
- labels_dataset = dataset_file_select.create_dataset("Labels",
- shape=self.labels.shape,
- data=self.labels)
- labels_dataset.attrs["names"] = [label_name.encode()
- for label_name in self.labels_names]
- meta_data_grp.attrs["nbView"] = len(self.views)
- meta_data_grp.attrs["nbClass"] = len(np.unique(self.labels))
- meta_data_grp.attrs["datasetLength"] = len(self.labels)
- dataset_object = dataset.HDF5Dataset(hdf5_file=dataset_file_select)
- dataset_object.add_gaussian_noise(self.rs, tmp_path)
- dataset_object.dataset.close()
- os.remove(os.path.join(tmp_path, "test_noise_noised.hdf5"))
- os.remove(os.path.join(tmp_path, "test_noise.hdf5"))
-
-class TestRAMDataset(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.rs = np.random.RandomState(42)
- cls.nb_view = 3
- cls.file_name = "test.hdf5"
- cls.nb_examples = 5
- cls.nb_attr = 7
- cls.nb_class = 3
- cls.views = [cls.rs.randint(0, 10, size=(cls.nb_examples, cls.nb_attr))
- for _ in range(cls.nb_view)]
- cls.labels = cls.rs.randint(0, cls.nb_class, cls.nb_examples)
- cls.view_names = ["ViewN" + str(index) for index in
- range(len(cls.views))]
- cls.are_sparse = [False for _ in cls.views]
- cls.labels_names = [str(index) for index in np.unique(cls.labels)]
-
- def test_get_view_name(self):
- dataset_object = dataset.RAMDataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- view_names=self.view_names,
- labels_names=self.labels_names)
- self.assertEqual(dataset_object.get_view_name(0),
- "ViewN0")
-
- def test_init_attrs(self):
- dataset_object = dataset.RAMDataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- view_names=self.view_names,
- labels_names=self.labels_names)
-
-
- dataset_object.init_attrs()
- self.assertEqual(dataset_object.nb_view, 3)
-
- def test_get_label_names(self):
- dataset_object = dataset.RAMDataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- view_names=self.view_names,
- labels_names=self.labels_names)
- shape = dataset_object.get_label_names()
- self.assertEqual(shape, ['0'.encode('utf-8'),
- '1'.encode('utf-8'),
- '2'.encode('utf-8')])
- shape = dataset_object.get_label_names(decode=False)
- self.assertEqual(shape, ['0'.encode('utf-8'),
- '1'.encode('utf-8'),
- '2'.encode('utf-8')])
-
- def test_get_v(self):
- dataset_object = dataset.RAMDataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- view_names=self.view_names,
- labels_names=self.labels_names)
- data = dataset_object.get_v(0, 1)
- np.testing.assert_array_equal(data, np.array([6, 7, 4, 3, 7, 7, 2]))
- data = dataset_object.get_v(0, None)
- np.testing.assert_array_equal(data, np.array([[6, 3, 7, 4, 6, 9, 2],
- [6, 7, 4, 3, 7, 7, 2],
- [5, 4, 1, 7, 5, 1, 4],
- [0, 9, 5, 8, 0, 9, 2],
- [6, 3, 8, 2, 4, 2, 6]]))
-
- def test_filter(self):
- dataset_object = dataset.RAMDataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- view_names=self.view_names,
- labels_names=self.labels_names)
- dataset_object.filter("", "", np.array([1,2]), ["ViewN0", "ViewN1"],
- path=None)
- self.assertEqual(dataset_object.nb_view, 2)
- self.assertEqual(dataset_object.labels.shape, (2,1))
-
- def test_get_view_dict(self):
- dataset_object = dataset.RAMDataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- view_names=self.view_names,
- labels_names=self.labels_names)
- d = dataset_object.get_view_dict()
- self.assertEqual(d, {'ViewN0': 0, 'ViewN1': 1, 'ViewN2': 2})
-
- def test_get_name(self):
- dataset_object = dataset.RAMDataset(views=self.views,
- labels=self.labels,
- are_sparse=self.are_sparse,
- view_names=self.view_names,
- labels_names=self.labels_names)
- n = dataset_object.get_name()
- self.assertEqual(n, None)
-
-class Test_Functions(unittest.TestCase):
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- os.mkdir(tmp_path)
- cls.rs = np.random.RandomState(42)
- cls.nb_view = 3
- cls.file_name = "test0.hdf5"
- cls.nb_examples = 5
- cls.nb_attr = 7
- cls.nb_class = 3
- cls.views = [cls.rs.randint(0, 10, size=(cls.nb_examples, cls.nb_attr))
- for _ in range(cls.nb_view)]
- cls.labels = cls.rs.randint(0, cls.nb_class, cls.nb_examples)
- cls.dataset_file = h5py.File(os.path.join(tmp_path, cls.file_name), "w")
- cls.view_names = ["ViewN" + str(index) for index in
- range(len(cls.views))]
- cls.are_sparse = [False for _ in cls.views]
- for view_index, (view_name, view, is_sparse) in enumerate(
- zip(cls.view_names, cls.views, cls.are_sparse)):
- view_dataset = cls.dataset_file.create_dataset(
- "View" + str(view_index),
- view.shape,
- data=view)
- view_dataset.attrs["name"] = view_name
- view_dataset.attrs["sparse"] = is_sparse
- labels_dataset = cls.dataset_file.create_dataset("Labels",
- shape=cls.labels.shape,
- data=cls.labels)
- cls.labels_names = [str(index) for index in np.unique(cls.labels)]
- labels_dataset.attrs["names"] = [label_name.encode()
- for label_name in cls.labels_names]
- meta_data_grp = cls.dataset_file.create_group("Metadata")
- meta_data_grp.attrs["nbView"] = len(cls.views)
- meta_data_grp.attrs["nbClass"] = len(np.unique(cls.labels))
- meta_data_grp.attrs["datasetLength"] = len(cls.labels)
-
- @classmethod
- def tearDownClass(cls):
- cls.dataset_file.close()
- rm_tmp()
-
- def test_datasets_already_exist(self):
- self.assertEqual(True, dataset.datasets_already_exist(tmp_path, "test", 1))
-
- def test_init_multiple_datasets(self):
- dataset.init_multiple_datasets(tmp_path, "test0", 2)
- self.assertTrue(os.path.isfile(os.path.join(tmp_path,'test00.hdf5')))
- dataset.delete_HDF5([{"args":{"pathf":tmp_path, "name":"test0"}}],
- 2, dataset.HDF5Dataset(hdf5_file=self.dataset_file))
- self.assertFalse(os.path.isfile(os.path.join(tmp_path,'test00.hdf5')))
-
-
-
-
-
-
-
-
-
-
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
diff --git a/multiview_platform/tests/test_utils/test_execution.py b/multiview_platform/tests/test_utils/test_execution.py
deleted file mode 100644
index 1e97963e..00000000
--- a/multiview_platform/tests/test_utils/test_execution.py
+++ /dev/null
@@ -1,361 +0,0 @@
-import os
-import unittest
-
-import numpy as np
-
-from multiview_platform.tests.utils import rm_tmp, tmp_path, test_dataset
-
-from multiview_platform.mono_multi_view_classifiers.utils import execution
-
-
-class Test_parseTheArgs(unittest.TestCase):
-
- def setUp(self):
- self.args = []
-
- def test_empty_args(self):
- args = execution.parse_the_args([])
-
-class Test_init_log_file(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- os.mkdir(tmp_path)
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
- def test_simple(self):
- res_dir = execution.init_log_file(name="test_dataset",
- views=["V1", "V2", "V3"],
- cl_type="",
- log=True,
- debug=False,
- label="No",
- result_directory=tmp_path,
- args={})
- self.assertTrue(res_dir.startswith(os.path.join(os.path.dirname(os.path.dirname(os.path.realpath(__file__))),"tmp_tests", "test_dataset", "started" )))
-
- def test_no_log(self):
- res_dir = execution.init_log_file(name="test_dataset",
- views=["V1", "V2", "V3"],
- cl_type="",
- log=False,
- debug=False,
- label="No1",
- result_directory=tmp_path,
- args={})
- self.assertTrue(res_dir.startswith(os.path.join(
- os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
- "tmp_tests", "test_dataset", "started")))
-
- def test_debug(self):
- res_dir = execution.init_log_file(name="test_dataset",
- views=["V1", "V2", "V3"],
- cl_type="",
- log=True,
- debug=True,
- label="No",
- result_directory=tmp_path,
- args={})
- self.assertTrue(res_dir.startswith(os.path.join(
- os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
- "tmp_tests", "test_dataset", "debug_started")))
-
-class Test_gen_k_folds(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state = np.random.RandomState(42)
- cls.statsIter = 1
-
- @classmethod
- def tearDownClass(cls):
- pass
-
- def test_simple(self):
- folds_list = execution.gen_k_folds(stats_iter=1,
- nb_folds=4,
- stats_iter_random_states=np.random.RandomState(42))
- self.assertEqual(folds_list[0].n_splits, 4)
- self.assertEqual(len(folds_list), 1)
-
- def test_multple_iters(self):
- folds_list = execution.gen_k_folds(stats_iter=2,
- nb_folds=4,
- stats_iter_random_states=[np.random.RandomState(42), np.random.RandomState(43)])
- self.assertEqual(folds_list[0].n_splits, 4)
- self.assertEqual(len(folds_list), 2)
-
- def test_list_rs(self):
- folds_list = execution.gen_k_folds(stats_iter=1,
- nb_folds=4,
- stats_iter_random_states=[np.random.RandomState(42)])
- self.assertEqual(folds_list[0].n_splits, 4)
- self.assertEqual(len(folds_list), 1)
-
-
-class Test_init_views(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state = np.random.RandomState(42)
- cls.statsIter = 1
-
- @classmethod
- def tearDownClass(cls):
- pass
-
- def test_simple(self):
- views, views_indices, all_views = execution.init_views(test_dataset, ["ViewN1", "ViewN2"])
- self.assertEqual(views, ["ViewN1", "ViewN2"])
- self.assertEqual(views_indices, [1,2])
- self.assertEqual(all_views, ["ViewN0", "ViewN1", "ViewN2"])
-
- views, views_indices, all_views = execution.init_views(test_dataset,None)
- self.assertEqual(views, ["ViewN0", "ViewN1", "ViewN2"])
- self.assertEqual(views_indices, range(3))
- self.assertEqual(all_views, ["ViewN0", "ViewN1", "ViewN2"])
-
-
-class Test_find_dataset_names(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- os.mkdir(tmp_path)
- with open(os.path.join(tmp_path, "test.txt"), "w") as file_stream:
- file_stream.write("test")
- with open(os.path.join(tmp_path, "test1.txt"), "w") as file_stream:
- file_stream.write("test")
-
-
-
- @classmethod
- def tearDownClass(cls):
- rm_tmp()
-
- def test_simple(self):
- path, names = execution.find_dataset_names(tmp_path, ".txt", ["test"])
- self.assertEqual(path, tmp_path)
- self.assertEqual(names, ["test"])
- path, names = execution.find_dataset_names(tmp_path, ".txt", ["test", 'test1'])
- self.assertEqual(path, tmp_path)
- self.assertIn("test1", names)
- path, names = execution.find_dataset_names("examples/data", ".hdf5", ["all"])
- self.assertIn("doc_summit", names)
- self.assertRaises(ValueError, execution.find_dataset_names, tmp_path+"test", ".txt",
- ["test"])
- self.assertRaises(ValueError, execution.find_dataset_names, tmp_path, ".txt", ["ah"])
-
-
-class Test_initStatsIterRandomStates(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state = np.random.RandomState(42)
- cls.statsIter = 1
-
- def test_one_statiter(cls):
- cls.state = cls.random_state.get_state()[1]
- statsIterRandomStates = execution.init_stats_iter_random_states(
-
- cls.statsIter, cls.random_state)
- np.testing.assert_array_equal(statsIterRandomStates[0].get_state()[1],
- cls.state)
-
- def test_multiple_iter(cls):
- cls.statsIter = 3
- statsIterRandomStates = execution.init_stats_iter_random_states(
-
- cls.statsIter, cls.random_state)
- cls.assertAlmostEqual(len(statsIterRandomStates), 3)
- cls.assertNotEqual(statsIterRandomStates[0].randint(5000),
- statsIterRandomStates[1].randint(5000))
- cls.assertNotEqual(statsIterRandomStates[0].randint(5000),
- statsIterRandomStates[2].randint(5000))
- cls.assertNotEqual(statsIterRandomStates[2].randint(5000),
- statsIterRandomStates[1].randint(5000))
-
-
-class Test_getDatabaseFunction(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.name = "zrtTap"
- cls.type = ".csv"
-
- def test_simple(cls):
- getDB = execution.get_database_function(cls.name, cls.type)
- from multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db import \
- get_classic_db_csv
- cls.assertEqual(getDB, get_classic_db_csv)
-
- def test_hdf5(cls):
- cls.type = ".hdf5"
- getDB = execution.get_database_function(cls.name, cls.type)
- from multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db import \
- get_classic_db_hdf5
- cls.assertEqual(getDB, get_classic_db_hdf5)
-
- def test_plausible_hdf5(cls):
- cls.name = "plausible"
- cls.type = ".hdf5"
- getDB = execution.get_database_function(cls.name, cls.type)
- from multiview_platform.mono_multi_view_classifiers.utils.get_multiview_db import \
- get_plausible_db_hdf5
- cls.assertEqual(getDB, get_plausible_db_hdf5)
-
-
-class Test_initRandomState(unittest.TestCase):
-
- def setUp(self):
- rm_tmp()
- os.mkdir(tmp_path)
-
- def tearDown(self):
- os.rmdir(tmp_path)
-
- def test_random_state_42(self):
- randomState_42 = np.random.RandomState(42)
- randomState = execution.init_random_state("42",
- tmp_path)
- os.remove(tmp_path+"random_state.pickle")
- np.testing.assert_array_equal(randomState.beta(1, 100, 100),
- randomState_42.beta(1, 100, 100))
-
- def test_random_state_pickle(self):
- randomState_to_pickle = execution.init_random_state(None,
- tmp_path)
- pickled_randomState = execution.init_random_state(
- tmp_path+"random_state.pickle",
- tmp_path)
- os.remove(tmp_path+"random_state.pickle")
-
- np.testing.assert_array_equal(randomState_to_pickle.beta(1, 100, 100),
- pickled_randomState.beta(1, 100, 100))
-
-
-class FakeArg():
-
- def __init__(self):
- self.name = "zrtTap"
- self.CL_type = ["fromage", "jambon"]
- self.views = ["view1", "view2"]
- self.log = True
-
-
-# Impossible to test as the main directory is notthe same for the exec and the test
-# class Test_initLogFile(unittest.TestCase):
-#
-# @classmethod
-# def setUpClass(cls):
-# cls.fakeArgs = FakeArg()
-# cls.timestr = time.strftime("%Y_%m_%d-%H_%M")
-#
-# def test_initLogFile(cls):
-# cls.timestr = time.strftime("%Y_%m_%d-%H_%M")
-# execution.initLogFile(cls.fakeArgs)
-# cls.assertIn("zrtTap", os.listdir("mutliview_platform/results"), "Database directory not created")
-# cls.assertIn("started_"+cls.timestr, os.listdir("mutliview_platform/results/zrtTap"),"experimentation dir not created")
-# cls.assertIn(cls.timestr + "-" + ''.join(cls.fakeArgs.CL_type) + "-" + "_".join(
-# cls.fakeArgs.views) + "-" + cls.fakeArgs.name + "-LOG.log", os.listdir("mutliview_platform/results/zrtTap/"+"started_"+cls.timestr), "logfile was not created")
-#
-# @classmethod
-# def tearDownClass(cls):
-# shutil.rmtree("multiview_platform/results/zrtTap")
-# pass
-
-
-class Test_genSplits(unittest.TestCase):
-
- def setUp(self):
- self.stastIter = 3
- self.statsIterRandomStates = [np.random.RandomState(42 + i + 1) for i in
- range(self.stastIter)]
- self.random_state = np.random.RandomState(42)
- self.X_indices = self.random_state.randint(0, 500, 50)
- self.labels = np.zeros(500)
- self.labels[self.X_indices[:10]] = 1
- self.labels[self.X_indices[11:30]] = 2 # To test multiclass
- self.splitRatio = 0.2
-
- def test_simple(self):
- splits = execution.gen_splits(self.labels, self.splitRatio,
- self.statsIterRandomStates)
- self.assertEqual(len(splits), 3)
- self.assertEqual(len(splits[1]), 2)
- self.assertEqual(type(splits[1][0]), np.ndarray)
- self.assertAlmostEqual(len(splits[1][0]), 0.8 * 500)
- self.assertAlmostEqual(len(splits[1][1]), 0.2 * 500)
- self.assertGreater(len(np.where(self.labels[splits[1][0]] == 0)[0]), 0)
- self.assertGreater(len(np.where(self.labels[splits[1][0]] == 1)[0]), 0)
- self.assertGreater(len(np.where(self.labels[splits[1][0]] == 2)[0]), 0)
- self.assertGreater(len(np.where(self.labels[splits[1][1]] == 0)[0]), 0)
- self.assertGreater(len(np.where(self.labels[splits[1][1]] == 1)[0]), 0)
- self.assertGreater(len(np.where(self.labels[splits[1][1]] == 2)[0]), 0)
-
- def test_genSplits_no_iter(self):
- splits = execution.gen_splits(self.labels, self.splitRatio,
- self.statsIterRandomStates)
- self.assertEqual(len(splits), 3)
- self.assertEqual(len(splits[0]), 2)
- self.assertEqual(type(splits[0][0]), np.ndarray)
- self.assertAlmostEqual(len(splits[0][0]), 0.8 * 500)
- self.assertAlmostEqual(len(splits[0][1]), 0.2 * 500)
- self.assertGreater(len(np.where(self.labels[splits[0][0]] == 0)[0]), 0)
- self.assertGreater(len(np.where(self.labels[splits[0][0]] == 1)[0]), 0)
- self.assertGreater(len(np.where(self.labels[splits[0][0]] == 2)[0]), 0)
- self.assertGreater(len(np.where(self.labels[splits[0][1]] == 0)[0]), 0)
- self.assertGreater(len(np.where(self.labels[splits[0][1]] == 1)[0]), 0)
- self.assertGreater(len(np.where(self.labels[splits[0][1]] == 2)[0]), 0)
-
-
-class Test_genKFolds(unittest.TestCase):
-
- def setUp(self):
- self.statsIter = 2
- self.nbFolds = 5
- self.statsIterRandomStates = [np.random.RandomState(42),
- np.random.RandomState(94)]
-
- def test_genKFolds_iter(self):
- pass
-
-
-class Test_genDirecortiesNames(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.directory = tmp_path
- cls.stats_iter = 5
-
- def test_simple_ovo(cls):
- directories = execution.gen_direcorties_names(cls.directory,
- cls.stats_iter)
- cls.assertEqual(len(directories), 5)
- cls.assertEqual(directories[0], os.path.join(tmp_path, "iter_1"))
- cls.assertEqual(directories[-1], os.path.join(tmp_path, "iter_5"))
-
- def test_ovo_no_iter(cls):
- cls.stats_iter = 1
- directories = execution.gen_direcorties_names(cls.directory,
- cls.stats_iter)
- cls.assertEqual(len(directories), 1)
- cls.assertEqual(directories[0], tmp_path)
-
-
-class Test_genArgumentDictionaries(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.labelsDictionary = {0: "yes", 1: "No", 2: "Maybe"}
- cls.direcories = ["Res/iter_1", "Res/iter_2"]
- cls.multiclassLabels = [np.array([0, 1, -100, 1, 0]),
- np.array([1, 0, -100, 1, 0]),
- np.array([0, 1, -100, 0, 1])]
- cls.labelsCombinations = [[0, 1], [0, 2], [1, 2]]
- cls.indicesMulticlass = [[[[], []], [[], []], [[], []]], [[], [], []]]
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
diff --git a/multiview_platform/tests/test_utils/test_hyper_parameter_search.py b/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
deleted file mode 100644
index 41287784..00000000
--- a/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import os
-import unittest
-
-import h5py
-import numpy as np
-from sklearn.model_selection import StratifiedKFold
-from sklearn.metrics import accuracy_score, make_scorer
-from multiview_platform.tests.utils import rm_tmp, tmp_path, test_dataset
-from sklearn.base import BaseEstimator
-import sys
-
-
-from multiview_platform.mono_multi_view_classifiers.utils.dataset import HDF5Dataset
-from multiview_platform.mono_multi_view_classifiers.utils import hyper_parameter_search
-from multiview_platform.mono_multi_view_classifiers.multiview_classifiers import weighted_linear_early_fusion
-
-
-
-
-
-class FakeEstim(BaseEstimator):
- def __init__(self, param1=None, param2=None, random_state=None):
- self.param1 = param1
- self.param2 = param2
-
- def fit(self, X, y,):
- return self
-
- def accepts_multi_class(self, rs):
- return True
-
- def predict(self, X):
- return np.zeros(X.shape[0])
-
-class FakeEstimMV(BaseEstimator):
- def __init__(self, param1=None, param2=None):
- self.param1 = param1
- self.param2 = param2
-
- def fit(self, X, y,train_indices=None, view_indices=None):
- self.y = y
- return self
-
- def predict(self, X, example_indices=None, view_indices=None):
- if self.param1=="return exact":
- return self.y[example_indices]
- else:
- return np.zeros(example_indices.shape[0])
-
-
-
-class Test_Random(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- n_splits=2
- cls.estimator = FakeEstim()
- cls.param_distributions = {"param1":[10,100], "param2":[11, 101]}
- cls.n_iter = 4
- cls.refit = True
- cls.n_jobs = 1
- cls.scoring = make_scorer(accuracy_score, )
- cls.cv = StratifiedKFold(n_splits=n_splits, )
- cls.random_state = np.random.RandomState(42)
- cls.learning_indices = np.array([0,1,2, 3, 4,])
- cls.view_indices = None
- cls.framework = "monoview"
- cls.equivalent_draws = False
- cls.X = cls.random_state.randint(0,100, (10,11))
- cls.y = cls.random_state.randint(0,2, 10)
-
- def test_simple(self):
- hyper_parameter_search.Random(
- self.estimator, self.param_distributions, n_iter=self.n_iter,
- refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring, cv=self.cv,
- random_state=self.random_state,
- learning_indices=self.learning_indices, view_indices=self.view_indices,
- framework=self.framework,
- equivalent_draws=self.equivalent_draws
- )
-
- def test_fit(self):
- RSCV = hyper_parameter_search.Random(
- self.estimator, self.param_distributions, n_iter=self.n_iter,
- refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
- cv=self.cv,
- random_state=self.random_state,
- learning_indices=self.learning_indices,
- view_indices=self.view_indices,
- framework=self.framework,
- equivalent_draws=self.equivalent_draws
- )
- RSCV.fit(self.X, self.y, )
- tested_param1 = np.ma.masked_array(data=[10,10,100,100],
- mask=[False, False, False, False])
- np.testing.assert_array_equal(RSCV.cv_results_['param_param1'],
- tested_param1)
-
- def test_fit_multiview(self):
- RSCV = hyper_parameter_search.Random(
- FakeEstimMV(), self.param_distributions, n_iter=self.n_iter,
- refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
- cv=self.cv,
- random_state=self.random_state,
- learning_indices=self.learning_indices,
- view_indices=self.view_indices,
- framework="multiview",
- equivalent_draws=self.equivalent_draws
- )
- RSCV.fit(test_dataset, self.y, )
- self.assertEqual(RSCV.n_iter, self.n_iter)
-
- def test_fit_multiview_equiv(self):
- self.n_iter=1
- RSCV = hyper_parameter_search.Random(
- FakeEstimMV(), self.param_distributions, n_iter=self.n_iter,
- refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
- cv=self.cv,
- random_state=self.random_state,
- learning_indices=self.learning_indices,
- view_indices=self.view_indices,
- framework="multiview",
- equivalent_draws=True
- )
- RSCV.fit(test_dataset, self.y, )
- self.assertEqual(RSCV.n_iter, self.n_iter*test_dataset.nb_view)
-
- def test_gets_good_params(self):
- self.param_distributions["param1"].append('return exact')
- self.n_iter=6
- RSCV = hyper_parameter_search.Random(
- FakeEstimMV(), self.param_distributions, n_iter=self.n_iter,
- refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
- cv=self.cv,
- random_state=self.random_state,
- learning_indices=self.learning_indices,
- view_indices=self.view_indices,
- framework="multiview",
- equivalent_draws=False
- )
- RSCV.fit(test_dataset, self.y, )
- self.assertEqual(RSCV.best_params_["param1"], "return exact")
-
-
-class Test_Grid(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.parameter_grid = {"param1":[5,6], "param2":[7,8]}
- cls.estimator = FakeEstim()
-
- def test_simple(self):
- grid = hyper_parameter_search.Grid(self.estimator,
- param_grid=self.parameter_grid)
-
- def test_get_candidate_params(self):
- grid = hyper_parameter_search.Grid(self.estimator,
- param_grid=self.parameter_grid)
- grid.get_candidate_params(None)
- self.assertEqual(grid.candidate_params, [{"param1": 5, "param2": 7},
- {"param1": 5, "param2": 8},
- {"param1": 6, "param2": 7},
- {"param1": 6, "param2": 8}])
-
-
-# if __name__ == '__main__':
-# # unittest.main()
-# suite = unittest.TestLoader().loadTestsFromTestCase(Test_randomized_search)
-# unittest.TextTestRunner(verbosity=2).run(suite)
-# class Test_randomized_search(unittest.TestCase):
-#
-# @classmethod
-# def setUpClass(cls):
-# rm_tmp()
-# cls.random_state = np.random.RandomState(42)
-# cls.view_weights = [0.5, 0.5]
-# os.mkdir(tmp_path)
-# cls.dataset_file = h5py.File(
-# tmp_path+"test_file.hdf5", "w")
-# cls.labels = cls.dataset_file.create_dataset("Labels",
-# data=np.array(
-# [0, 1, 0, 0, 1, 0, 1, 0, 0, 1, ]))
-# cls.view0_data = cls.random_state.randint(1, 10, size=(10, 4))
-# view0 = cls.dataset_file.create_dataset("View0",
-# data=cls.view0_data)
-# view0.attrs["sparse"] = False
-# view0.attrs["name"] = "ViewN0"
-# cls.view1_data = cls.random_state.randint(1, 10, size=(10, 4))
-# view1 = cls.dataset_file.create_dataset("View1",
-# data=cls.view1_data)
-# view1.attrs["sparse"] = False
-# view1.attrs["name"] = "ViewN1"
-# metaDataGrp = cls.dataset_file.create_group("Metadata")
-# metaDataGrp.attrs["nbView"] = 2
-# metaDataGrp.attrs["nbClass"] = 2
-# metaDataGrp.attrs["datasetLength"] = 10
-# cls.monoview_classifier_name = "decision_tree"
-# cls.monoview_classifier_config = {"max_depth": 1,
-# "criterion": "gini",
-# "splitter": "best"}
-# cls.k_folds = StratifiedKFold(n_splits=3, random_state=cls.random_state,
-# shuffle=True)
-# cls.learning_indices = np.array([1,2,3,4, 5,6,7,8,9])
-# cls.dataset = HDF5Dataset(hdf5_file=cls.dataset_file)
-#
-# @classmethod
-# def tearDownClass(cls):
-# cls.dataset_file.close()
-# rm_tmp()
-#
-#
-# def test_simple(self):
-# best_params, _, params, scores = hyper_parameter_search.randomized_search(
-# self.dataset, self.labels[()], "multiview", self.random_state, tmp_path,
-# weighted_linear_early_fusion, "WeightedLinearEarlyFusion", self.k_folds,
-# 1, ["accuracy_score", None], 2, {}, learning_indices=self.learning_indices)
-# self.assertIsInstance(best_params, dict)
diff --git a/multiview_platform/tests/test_utils/test_multiclass.py b/multiview_platform/tests/test_utils/test_multiclass.py
deleted file mode 100644
index 178308ad..00000000
--- a/multiview_platform/tests/test_utils/test_multiclass.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import unittest
-
-import numpy as np
-from sklearn.base import BaseEstimator
-
-from multiview_platform.mono_multi_view_classifiers.utils.multiclass import get_mc_estim, \
-OVRWrapper, OVOWrapper, MultiviewOVOWrapper, MultiviewOVRWrapper
-
-class FakeMCEstim(BaseEstimator):
-
- def __init__(self):
- self.short_name="short_name"
-
- def accepts_multi_class(self, random_state):
- return False
-
-class FakeEstimNative(FakeMCEstim):
-
- def accepts_multi_class(self, random_state):
- return True
-
-
-class FakeNonProbaEstim(FakeMCEstim):
- pass
-
-
-class FakeProbaEstim(FakeMCEstim):
-
- def predict_proba(self):
- pass
-
-
-class Test_get_mc_estim(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state = np.random.RandomState(42)
- cls.y = cls.random_state.randint(0, 3, 10)
-
- def test_biclass(self):
- y = self.random_state.randint(0,2,10)
- estimator="Test"
- returned_estimator = get_mc_estim(estimator, self.random_state, y=y)
- self.assertEqual(returned_estimator, estimator)
-
- def test_multiclass_native(self):
- estimator = FakeEstimNative()
- returned_estimator = get_mc_estim(estimator, self.random_state, y=self.y)
- self.assertIsInstance(returned_estimator, FakeEstimNative)
-
- def test_multiclass_ovo(self):
- estimator = FakeNonProbaEstim()
- returned_estimator = get_mc_estim(estimator, self.random_state, y=self.y)
- self.assertIsInstance(returned_estimator, OVOWrapper)
-
- def test_multiclass_ovr(self):
- estimator = FakeProbaEstim()
- returned_estimator = get_mc_estim(estimator, self.random_state, y=self.y)
- self.assertIsInstance(returned_estimator, OVRWrapper)
-
- def test_multiclass_ovo_multiview(self):
- estimator = FakeNonProbaEstim()
- returned_estimator = get_mc_estim(estimator, self.random_state,
- multiview=True, y=self.y, )
- self.assertIsInstance(returned_estimator, MultiviewOVOWrapper)
-
- def test_multiclass_ovr_multiview(self):
- estimator = FakeProbaEstim()
- returned_estimator = get_mc_estim(estimator, self.random_state,
- multiview=True, y=self.y,)
- self.assertIsInstance(returned_estimator, MultiviewOVRWrapper)
-
-class FakeMVClassifier(BaseEstimator):
-
- def __init__(self, short_name="None"):
- self.short_name = short_name
-
- def fit(self, X, y, train_indices=None, view_indices=None):
- self.n_classes = np.unique(y[train_indices]).shape[0]
- self.views_indices = view_indices
-
- def predict(self, X, example_indices=None, view_indices=None):
- self.example_indices = example_indices
- self.views_indices = view_indices
- return np.zeros((example_indices.shape[0]))
-
-class FakeMVClassifierProb(FakeMVClassifier):
-
- def predict_proba(self, X, example_indices=None, view_indices=None):
- self.example_indices = example_indices
- self.views_indices = view_indices
- return np.zeros((example_indices.shape[0], 2))
-
-class Test_MultiviewOVRWrapper_fit(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state = np.random.RandomState(42)
- cls.X = "dataset"
- cls.n_classes=3
- cls.y = cls.random_state.randint(0,cls.n_classes,50)
- cls.train_indices = np.arange(25)
- cls.example_indices = np.arange(25)+25
- cls.view_indices="None"
- cls.wrapper = MultiviewOVRWrapper(FakeMVClassifierProb(), )
-
- def test_fit(self):
- fitted = self.wrapper.fit(self.X, self.y, train_indices=self.train_indices,
- view_indices=self.view_indices)
- for estimator in fitted.estimators_:
- self.assertEqual(estimator.n_classes,2)
- self.assertEqual(estimator.views_indices, "None")
-
- def test_predict(self):
- fitted = self.wrapper.fit(self.X, self.y, train_indices=self.train_indices,
- view_indices=self.view_indices)
- pred = fitted.predict(self.X, example_indices=self.example_indices,
- view_indices=self.view_indices)
- for estimator in fitted.estimators_:
- np.testing.assert_array_equal(estimator.example_indices,
- self.example_indices)
-
-
-class FakeDset:
-
- def __init__(self, n_examples):
- self.n_examples = n_examples
-
- def get_nb_examples(self):
- return self.n_examples
-
-class Test_MultiviewOVOWrapper_fit(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state = np.random.RandomState(42)
- cls.n_examples=50
- cls.X = FakeDset(n_examples=cls.n_examples)
- cls.n_classes=3
- cls.y = cls.random_state.randint(0,cls.n_classes,cls.n_examples)
- cls.train_indices = np.arange(int(cls.n_examples/2))
- cls.example_indices = np.arange(int(cls.n_examples/2))+int(cls.n_examples/2)
- cls.view_indices="None"
- cls.wrapper = MultiviewOVOWrapper(FakeMVClassifier(), )
-
- def test_fit(self):
- fitted = self.wrapper.fit(self.X, self.y, train_indices=self.train_indices,
- view_indices=self.view_indices)
- for estimator in fitted.estimators_:
- self.assertEqual(estimator.n_classes,2)
- self.assertEqual(estimator.views_indices, "None")
-
- def test_predict(self):
- fitted = self.wrapper.fit(self.X, self.y, train_indices=self.train_indices,
- view_indices=self.view_indices)
- pred = fitted.predict(self.X, example_indices=self.example_indices,
- view_indices=self.view_indices)
- for estimator in fitted.estimators_:
- np.testing.assert_array_equal(estimator.example_indices,
- self.example_indices)
-
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
diff --git a/multiview_platform/tests/utils.py b/multiview_platform/tests/utils.py
deleted file mode 100644
index 9a3f04cb..00000000
--- a/multiview_platform/tests/utils.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import os
-import numpy as np
-import h5py
-
-from ..mono_multi_view_classifiers.utils.dataset import HDF5Dataset
-
-
-tmp_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "tmp_tests/")
-# TODO Convert to ram dataset
-test_dataset = HDF5Dataset(hdf5_file=h5py.File(os.path.join(os.path.dirname(os.path.abspath(__file__)), "test_database.hdf5"), "r"))
-
-def rm_tmp(path=tmp_path):
- try:
- for file_name in os.listdir(path):
- if os.path.isdir(os.path.join(path, file_name)):
- rm_tmp(os.path.join(path, file_name))
- else:
- os.remove(os.path.join(path, file_name))
- os.rmdir(path)
- except:
- pass
-
-
-def gen_test_dataset(random_state=np.random.RandomState(42)):
- dataset_file = h5py.File("test_database.hdf5", "w")
- view_names = ["ViewN0", "ViewN1", "ViewN2"]
- views = [random_state.randint(0,100,(5,6))
- for _ in range(len(view_names))]
- labels = random_state.randint(0,2, 5)
- label_names = ["yes", "no"]
- for view_index, (view_name, view) in enumerate(
- zip(view_names, views)):
- view_dataset = dataset_file.create_dataset("View" + str(view_index),
- view.shape,
- data=view)
- view_dataset.attrs["name"] = view_name
- view_dataset.attrs["sparse"] = False
- labels_dataset = dataset_file.create_dataset("Labels",
- shape=labels.shape,
- data=labels)
- labels_dataset.attrs["names"] = [label_name.encode()
- if not isinstance(label_name, bytes)
- else label_name
- for label_name in label_names]
- meta_data_grp = dataset_file.create_group("Metadata")
- meta_data_grp.attrs["nbView"] = len(views)
- meta_data_grp.attrs["nbClass"] = len(np.unique(labels))
- meta_data_grp.attrs["datasetLength"] = len(labels)
- dataset_file.close()
-
-
-if __name__ == "__main__":
- gen_test_dataset()
diff --git a/setup.cfg b/setup.cfg
index 43c4b4ed..5241fde3 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,14 +1,14 @@
[tool:pytest]
-testpaths = multiview_platform
+testpaths = summit
addopts = --cov-report=html
--verbose
- --cov=multiview_platform
+ --cov=summit
--cov-report=term-missing
; --cov-config setup.cfg
--cache-clear
[coverage:run]
-source = multiview_platform
+source = summit
include = */mono_multi_view_classifiers/*
omit = */tests/*
*/examples/*
diff --git a/setup.py b/setup.py
index 8c9bfe2f..d477504e 100644
--- a/setup.py
+++ b/setup.py
@@ -85,7 +85,7 @@ def setup_package():
# La syntaxe est "nom-de-commande-a-creer = package.module:fonction".
entry_points={
'console_scripts': [
- 'exec_multiview = multiview_platform.execute:exec',
+ 'exec_multiview = summit.execute:exec',
],
},
@@ -96,7 +96,7 @@ def setup_package():
# Il y a encore une chiée de paramètres possibles, mais avec ça vous
# couvrez 90% des besoins
# ext_modules=cythonize(
- # "multiview_platform/mono_multi_view_classifiers/monoview/additions/_custom_criterion.pyx"),
+ # "summit/mono_multi_view_classifiers/monoview/additions/_custom_criterion.pyx"),
)
if __name__ == "__main__":
--
GitLab