From b59301299542e18f91066d19eed2422538198b8e Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 9 Oct 2023 12:21:23 +0200 Subject: [PATCH 01/41] updated two metrics and base implementation for xai inverse estimation --- .../faithfulness/inverse_estimation.py | 285 ++++++++++++++++++ .../metrics/faithfulness/pixel_flipping.py | 9 +- .../faithfulness/region_perturbation.py | 10 + 3 files changed, 302 insertions(+), 2 deletions(-) create mode 100644 quantus/metrics/faithfulness/inverse_estimation.py diff --git a/quantus/metrics/faithfulness/inverse_estimation.py b/quantus/metrics/faithfulness/inverse_estimation.py new file mode 100644 index 000000000..bb95b54f4 --- /dev/null +++ b/quantus/metrics/faithfulness/inverse_estimation.py @@ -0,0 +1,285 @@ +"""This module contains the implementation of the Pixel-Flipping metric.""" + +# This file is part of Quantus. +# Quantus is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. +# Quantus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. +# You should have received a copy of the GNU Lesser General Public License along with Quantus. If not, see . +# Quantus project URL: . + +from typing import Any, Callable, Dict, List, Optional, Tuple + +import numpy as np + +from quantus.helpers import asserts +from quantus.helpers import plotting +from quantus.helpers import utils +from quantus.helpers import warn +from quantus.helpers.model.model_interface import ModelInterface +from quantus.functions.normalise_func import normalise_by_max +from quantus.functions.perturb_func import baseline_replacement_by_indices +from quantus.metrics.base_perturbed import PerturbationMetric +from quantus.helpers.enums import ( + ModelType, + DataType, + ScoreDirection, + EvaluationCategory, +) + + +class InverseEstimation(PerturbationMetric): + """ + Implementation of Inverse Estimation experiment by Author et al., 2023. + + The basic idea is to .............. + + References: + 1) .......... + + Attributes: + - _name: The name of the metric. + - _data_applicability: The data types that the metric implementation currently supports. + - _models: The model types that this metric can work with. + - score_direction: How to interpret the scores, whether higher/ lower values are considered better. + - evaluation_category: What property/ explanation quality that this metric measures. + """ + + name = "Inverse-Estimation" + data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR} + model_applicability = {ModelType.TORCH, ModelType.TF} + score_direction = ScoreDirection.LOWER + evaluation_category = EvaluationCategory.FAITHFULNESS + + def __init__( + self, + metric_init: PerturbationMetric, + normalise_func: Optional[Callable[[np.ndarray], np.ndarray]] = None, + normalise_func_kwargs: Optional[Dict[str, Any]] = None, + perturb_func: Callable = None, + perturb_baseline: str = "black", + perturb_func_kwargs: Optional[Dict[str, Any]] = None, + default_plot_func: Optional[Callable] = None, + **kwargs, + ): + """ + Parameters + ---------- + features_in_step: integer + The size of the step, default=1. + abs: boolean + Indicates whether absolute operation is applied on the attribution, default=False. + normalise: boolean + Indicates whether normalise operation is applied on the attribution, default=True. + normalise_func: callable + Attribution normalisation function applied in case normalise=True. + If normalise_func=None, the default value is used, default=normalise_by_max. + normalise_func_kwargs: dict + Keyword arguments to be passed to normalise_func on call, default={}. + perturb_func: callable + Input perturbation function. If None, the default value is used, + default=baseline_replacement_by_indices. + perturb_baseline: string + Indicates the type of baseline: "mean", "random", "uniform", "black" or "white", + default="black". + perturb_func_kwargs: dict + Keyword arguments to be passed to perturb_func, default={}. + return_aggregate: boolean + Indicates if an aggregated score should be computed over all instances. + aggregate_func: callable + Callable that aggregates the scores given an evaluation call. + return_auc_per_sample: boolean + Indicates if an AUC score should be computed over the curve and returned. + default_plot_func: callable + Callable that plots the metrics result. + disable_warnings: boolean + Indicates whether the warnings are printed, default=False. + display_progressbar: boolean + Indicates whether a tqdm-progress-bar is printed, default=False. + kwargs: optional + Keyword arguments. + """ + if metric_init.normalise_func is None: + normalise_func = normalise_by_max + + if metric_init.perturb_func is None: + perturb_func = baseline_replacement_by_indices + perturb_func = perturb_func + + if metric_init.perturb_func_kwargs is None: + perturb_func_kwargs = {} + perturb_func_kwargs["perturb_baseline"] = perturb_baseline + + if metric_init.default_plot_func is None: + # TODO. Create plot. + default_plot_func = plotting.plot_pixel_flipping_experiment + + abs = metric_init.abs + normalise = metric_init.normalise + return_aggregate = metric_init.return_aggregate + aggregate_func = metric_init.aggregate_func + display_progressbar = metric_init.display_progressbar + disable_warnings = metric_init.disable_warnings + + super().__init__( + abs=abs, + normalise=normalise, + normalise_func=normalise_func, + normalise_func_kwargs=normalise_func_kwargs, + perturb_func=perturb_func, + perturb_func_kwargs=perturb_func_kwargs, + return_aggregate=return_aggregate, + aggregate_func=aggregate_func, + default_plot_func=default_plot_func, + display_progressbar=display_progressbar, + disable_warnings=disable_warnings, + **kwargs, + ) + + # Asserts and warnings. + assert hasattr( + metric_init, "inverse_estimation" + ), "The metric must have 'inverse_estimation' (bool) attribute" + + # TODO. Update warnings. + if not self.disable_warnings: + warn.warn_parameterisation( + metric_name=self.__class__.__name__, + sensitive_params=("baseline value 'perturb_baseline'"), + citation=( + "Bach, Sebastian, et al. 'On pixel-wise explanations for non-linear classifier" + " decisions by layer - wise relevance propagation.' PloS one 10.7 (2015) " + "e0130140" + ), + ) + + self.metric_init = metric_init + + def __call__( + self, + model, + x_batch: np.array, + y_batch: np.array, + a_batch: Optional[np.ndarray] = None, + s_batch: Optional[np.ndarray] = None, + channel_first: Optional[bool] = None, + explain_func: Optional[Callable] = None, + explain_func_kwargs: Optional[Dict] = None, + model_predict_kwargs: Optional[Dict] = None, + softmax: Optional[bool] = True, + device: Optional[str] = None, + batch_size: int = 64, + custom_batch: Optional[Any] = None, + **kwargs, + ) -> List[float]: + """ + This implementation represents the main logic of the metric and makes the class object callable. + It completes instance-wise evaluation of explanations (a_batch) with respect to input data (x_batch), + output labels (y_batch) and a torch or tensorflow model (model). + + Calls general_preprocess() with all relevant arguments, calls + () on each instance, and saves results to evaluation_scores. + Calls custom_postprocess() afterwards. Finally returns evaluation_scores. + + Parameters + ---------- + model: torch.nn.Module, tf.keras.Model + A torch or tensorflow model that is subject to explanation. + x_batch: np.ndarray + A np.ndarray which contains the input data that are explained. + y_batch: np.ndarray + A np.ndarray which contains the output labels that are explained. + a_batch: np.ndarray, optional + A np.ndarray which contains pre-computed attributions i.e., explanations. + s_batch: np.ndarray, optional + A np.ndarray which contains segmentation masks that matches the input. + channel_first: boolean, optional + Indicates of the image dimensions are channel first, or channel last. + Inferred from the input shape if None. + explain_func: callable + Callable generating attributions. + explain_func_kwargs: dict, optional + Keyword arguments to be passed to explain_func on call. + model_predict_kwargs: dict, optional + Keyword arguments to be passed to the model's predict method. + softmax: boolean + Indicates whether to use softmax probabilities or logits in model prediction. + This is used for this __call__ only and won't be saved as attribute. If None, self.softmax is used. + device: string + Indicated the device on which a torch.Tensor is or will be allocated: "cpu" or "gpu". + kwargs: optional + Keyword arguments. + + Returns + ------- + evaluation_scores: list + a list of Any with the evaluation scores of the concerned batch. + + Examples: + -------- + # Minimal imports. + >> import quantus + >> from quantus import LeNet + >> import torch + + # Enable GPU. + >> device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + # Load a pre-trained LeNet classification model (architecture at quantus/helpers/models). + >> model = LeNet() + >> model.load_state_dict(torch.load("tutorials/assets/pytests/mnist_model")) + + # Load MNIST datasets and make loaders. + >> test_set = torchvision.datasets.MNIST(root='./sample_data', download=True) + >> test_loader = torch.utils.data.DataLoader(test_set, batch_size=24) + + # Load a batch of inputs and outputs to use for XAI evaluation. + >> x_batch, y_batch = iter(test_loader).next() + >> x_batch, y_batch = x_batch.cpu().numpy(), y_batch.cpu().numpy() + + # Generate Saliency attributions of the test set batch of the test set. + >> a_batch_saliency = Saliency(model).attribute(inputs=x_batch, target=y_batch, abs=True).sum(axis=1) + >> a_batch_saliency = a_batch_saliency.cpu().numpy() + + # Initialise the metric and evaluate explanations by calling the metric instance. + >> metric = Metric(abs=True, normalise=False) + >> scores = metric(model=model, x_batch=x_batch, y_batch=y_batch, a_batch=a_batch_saliency} + """ + # Run normal experiment. + self.metric_init.inverse_estimation = False + self.metric_init( + model=model, + x_batch=x_batch, + y_batch=y_batch, + a_batch=a_batch, + s_batch=s_batch, + custom_batch=None, + channel_first=channel_first, + explain_func=explain_func, + explain_func_kwargs=explain_func_kwargs, + softmax=softmax, + device=device, + model_predict_kwargs=model_predict_kwargs, + **kwargs, + ) + + # Run inverse experiment. + self.metric_init.inverse_estimation = True + self.metric_init( + model=model, + x_batch=x_batch, + y_batch=y_batch, + a_batch=a_batch, + s_batch=s_batch, + custom_batch=None, + channel_first=channel_first, + explain_func=explain_func, + explain_func_kwargs=explain_func_kwargs, + softmax=softmax, + device=device, + model_predict_kwargs=model_predict_kwargs, + **kwargs, + ) + + def set_metric_to_inverse(self): + # TODO. Implement. + # TODO. Check that + pass diff --git a/quantus/metrics/faithfulness/pixel_flipping.py b/quantus/metrics/faithfulness/pixel_flipping.py index cc29994a6..4e7c84bf2 100644 --- a/quantus/metrics/faithfulness/pixel_flipping.py +++ b/quantus/metrics/faithfulness/pixel_flipping.py @@ -66,6 +66,7 @@ def __init__( return_aggregate: bool = False, aggregate_func: Callable = np.mean, return_auc_per_sample: bool = False, + inverse_estimation: bool = False, default_plot_func: Optional[Callable] = None, disable_warnings: bool = False, display_progressbar: bool = False, @@ -140,6 +141,7 @@ def __init__( # Save metric-specific attributes. self.features_in_step = features_in_step self.return_auc_per_sample = return_auc_per_sample + self.inverse_estimation = inverse_estimation # Asserts and warnings. if not self.disable_warnings: @@ -292,8 +294,11 @@ def evaluate_instance( # Reshape attributions. a = a.flatten() - # Get indices of sorted attributions (descending). - a_indices = np.argsort(-a) + # Get indices of sorted attributions. + if self.inverse_estimation is None or self.inverse_estimation is False: + a_indices = np.argsort(-a) # Order is descending. + elif self.inverse_estimation is True: + a_indices = np.argsort(a) # Order is ascending. # Prepare lists. n_perturbations = len(range(0, len(a_indices), self.features_in_step)) diff --git a/quantus/metrics/faithfulness/region_perturbation.py b/quantus/metrics/faithfulness/region_perturbation.py index 425b5ebb6..524a50791 100644 --- a/quantus/metrics/faithfulness/region_perturbation.py +++ b/quantus/metrics/faithfulness/region_perturbation.py @@ -72,6 +72,7 @@ def __init__( perturb_func: Callable = None, perturb_baseline: str = "black", perturb_func_kwargs: Optional[Dict[str, Any]] = None, + inverse_estimation: Optional[bool] = None, return_aggregate: bool = False, aggregate_func: Callable = np.mean, default_plot_func: Optional[Callable] = None, @@ -152,9 +153,13 @@ def __init__( self.patch_size = patch_size self.order = order.lower() self.regions_evaluation = regions_evaluation + self.inverse_estimation = inverse_estimation # Asserts and warnings. asserts.assert_attributions_order(order=self.order) + if isinstance(self.inverse_estimation, bool): + assert self.order in ["morf", "lerf"], "Inverse estimation assumes order is either 'morf' and 'lerf'. + if not self.disable_warnings: warn.warn_parameterisation( metric_name=self.__class__.__name__, @@ -340,6 +345,11 @@ def evaluate_instance( ) patches.append(patch_slice) + if self.inverse_estimation == True: + self.order = "lerf" + elif self.inverse_estimation == False: + self.order = "morf" + if self.order == "random": # Order attributions randomly. order = np.arange(len(patches)) From 492ea8d77bf81b936cc4db6589387ebff48b40cc Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 9 Oct 2023 13:27:00 +0200 Subject: [PATCH 02/41] finished aggregation of the inverse implementation --- .../faithfulness/inverse_estimation.py | 69 +++++++++++++++++-- 1 file changed, 63 insertions(+), 6 deletions(-) diff --git a/quantus/metrics/faithfulness/inverse_estimation.py b/quantus/metrics/faithfulness/inverse_estimation.py index bb95b54f4..ec654a0ab 100644 --- a/quantus/metrics/faithfulness/inverse_estimation.py +++ b/quantus/metrics/faithfulness/inverse_estimation.py @@ -152,6 +152,8 @@ def __init__( ) self.metric_init = metric_init + self.all_evaluation_scores_meta = [] + self.all_evaluation_scores_meta_inverse = [] def __call__( self, @@ -243,7 +245,9 @@ def __call__( >> metric = Metric(abs=True, normalise=False) >> scores = metric(model=model, x_batch=x_batch, y_batch=y_batch, a_batch=a_batch_saliency} """ - # Run normal experiment. + self.metric_init.return_aggregate = True + + # Run faithfulness experiment. self.metric_init.inverse_estimation = False self.metric_init( model=model, @@ -260,8 +264,13 @@ def __call__( model_predict_kwargs=model_predict_kwargs, **kwargs, ) + assert len(self.metric_init.evaluation_scores) == len( + x_batch + ), "To run the inverse estimation, the number of evaluation scores must match the number of instances in the batch." + self.all_evaluation_scores_meta.extend(self.metric_init.evaluation_scores) + self.metric_init.evaluation_scores = [] - # Run inverse experiment. + # Run inverse faithfulness experiment. self.metric_init.inverse_estimation = True self.metric_init( model=model, @@ -278,8 +287,56 @@ def __call__( model_predict_kwargs=model_predict_kwargs, **kwargs, ) + self.all_evaluation_scores_meta_inverse.extend( + self.metric_init.evaluation_scores + ) + self.metric_init.evaluation_scores = [] + + # Compute the inverse. + inv_scores = np.array(self.all_evaluation_scores_meta) - np.array( + self.all_evaluation_scores_meta_inverse + ) + self.evaluation_scores.extend(inv_scores) + + self.metric_init.all_evaluation_scores = [] + # TODO. Implement a write-over of samples (len(x_batch) x len) with the inverse. + self.all_evaluation_scores.extend(inv_scores) + + return inv_scores + + def custom_postprocess( + self, + model: ModelInterface, + x_batch: np.ndarray, + y_batch: Optional[np.ndarray], + a_batch: Optional[np.ndarray], + s_batch: np.ndarray, + **kwargs, + ) -> None: + """ + Post-process the evaluation results. + + Parameters + ---------- + model: torch.nn.Module, tf.keras.Model + A torch or tensorflow model e.g., torchvision.models that is subject to explanation. + x_batch: np.ndarray + A np.ndarray which contains the input data that are explained. + y_batch: np.ndarray + A np.ndarray which contains the output labels that are explained. + a_batch: np.ndarray, optional + A np.ndarray which contains pre-computed attributions i.e., explanations. + s_batch: np.ndarray, optional + A np.ndarray which contains segmentation masks that matches the input. + + Returns + ------- + None + """ + # TODO. Implement aggregation method. - def set_metric_to_inverse(self): - # TODO. Implement. - # TODO. Check that - pass + # Calculate accuracy for every number of most important pixels removed. + self.evaluation_scores = { + percentage: np.mean(np.array(self.evaluation_scores)[:, p_ix]) + for p_ix, percentage in enumerate(self.percentages) + } From fb3d5efdd769efd61b7af3e8b03d35968166e2ed Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Thu, 7 Dec 2023 16:24:19 +0100 Subject: [PATCH 03/41] tests passing, most todos removed --- CONTRIBUTING.md | 4 - quantus/helpers/constants.py | 8 +- quantus/metrics/faithfulness/infidelity.py | 1 + .../metrics/faithfulness/pixel_flipping.py | 7 +- .../faithfulness/region_perturbation.py | 7 -- quantus/metrics/faithfulness/road.py | 17 +++- quantus/metrics/inverse_estimation.py | 95 ++++++------------- tests/metrics/test_inverse_estimation.py | 94 ++++++++++-------- 8 files changed, 107 insertions(+), 126 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9ba3485d1..c77983dc8 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -89,11 +89,7 @@ It is possible to limit the scope of testing to specific sections of the codebas Faithfulness metrics using python3.9 (make sure the python versions match in your environment): ```bash -<<<<<<< HEAD python3 -m tox run -e py39 -- -m faithfulness -s -======= -python3 -m tox run -e py39 -- -m evaluate_func -s ->>>>>>> c33f4039e2f53332a8eff2207cdbe6686600e67c ``` For a complete overview of the possible testing scopes, please refer to `pytest.ini`. diff --git a/quantus/helpers/constants.py b/quantus/helpers/constants.py index 7da2c7908..b8ba74e05 100644 --- a/quantus/helpers/constants.py +++ b/quantus/helpers/constants.py @@ -74,11 +74,15 @@ }, } -# Perturbation steps with 'masking', based on attribution order/ ranking. +# Quantus metrics that include a step-wise 'masking'/ perturbation that is +# based on attribution order/ ranking (and not magnitude). AVAILABLE_INVERSE_ESTIMATION_METRICS = { "Pixel-Flipping": PixelFlipping, - "Region Perturbation": RegionPerturbation, + "Region Perturbation": RegionPerturbation, # order = 'morf' + "ROAD": ROAD, # return_only_values = True + "Selectivity": Selectivity, } +# AVAILABLE_PERTURBATION_FUNCTIONS = { "baseline_replacement_by_indices": baseline_replacement_by_indices, diff --git a/quantus/metrics/faithfulness/infidelity.py b/quantus/metrics/faithfulness/infidelity.py index baf48cb94..ef621a609 100644 --- a/quantus/metrics/faithfulness/infidelity.py +++ b/quantus/metrics/faithfulness/infidelity.py @@ -331,6 +331,7 @@ def evaluate_instance( for i_x, top_left_x in enumerate(range(0, x.shape[1], patch_size)): for i_y, top_left_y in enumerate(range(0, x.shape[2], patch_size)): + # Perturb input patch-wise. x_perturbed_pad = utils._pad_array( x_perturbed, pad_width, mode="edge", padded_axes=self.a_axes diff --git a/quantus/metrics/faithfulness/pixel_flipping.py b/quantus/metrics/faithfulness/pixel_flipping.py index 850ebad72..6f220c9cb 100644 --- a/quantus/metrics/faithfulness/pixel_flipping.py +++ b/quantus/metrics/faithfulness/pixel_flipping.py @@ -287,11 +287,8 @@ def evaluate_instance( # Reshape attributions. a = a.flatten() - # Get indices of sorted attributions. - if self.inverse_estimation is None or self.inverse_estimation is False: - a_indices = np.argsort(-a) # Order is descending. - elif self.inverse_estimation is True: - a_indices = np.argsort(a) # Order is ascending. + # Get indices of sorted attributions (descending). + a_indices = np.argsort(-a) # Prepare lists. n_perturbations = len(range(0, len(a_indices), self.features_in_step)) diff --git a/quantus/metrics/faithfulness/region_perturbation.py b/quantus/metrics/faithfulness/region_perturbation.py index 6c95a0356..6c65d14da 100644 --- a/quantus/metrics/faithfulness/region_perturbation.py +++ b/quantus/metrics/faithfulness/region_perturbation.py @@ -76,7 +76,6 @@ def __init__( perturb_func: Optional[Callable] = None, perturb_baseline: str = "black", perturb_func_kwargs: Optional[Dict[str, Any]] = None, - inverse_estimation: Optional[bool] = None, return_aggregate: bool = False, aggregate_func: Optional[Callable] = None, default_plot_func: Optional[Callable] = None, @@ -147,7 +146,6 @@ def __init__( self.patch_size = patch_size self.order = order.lower() self.regions_evaluation = regions_evaluation - self.inverse_estimation = inverse_estimation self.perturb_func = make_perturb_func( perturb_func, perturb_func_kwargs, perturb_baseline=perturb_baseline ) @@ -343,11 +341,6 @@ def evaluate_instance( ) patches.append(patch_slice) - if self.inverse_estimation == True: - self.order = "lerf" - elif self.inverse_estimation == False: - self.order = "morf" - if self.order == "random": # Order attributions randomly. order = np.arange(len(patches)) diff --git a/quantus/metrics/faithfulness/road.py b/quantus/metrics/faithfulness/road.py index 8c54dc922..a3763659e 100644 --- a/quantus/metrics/faithfulness/road.py +++ b/quantus/metrics/faithfulness/road.py @@ -65,6 +65,7 @@ def __init__( self, percentages: Optional[List[float]] = None, noise: float = 0.01, + return_only_values: Optional[bool] = None, abs: bool = False, normalise: bool = True, normalise_func: Optional[Callable[[np.ndarray], np.ndarray]] = None, @@ -81,8 +82,12 @@ def __init__( """ Parameters ---------- - percentages (list): The list of percentages of the image to be removed, default=list(range(1, 100, 2)). - noise (noise): Noise added, default=0.01. + percentages: list of ints + The list of percentages of the image to be removed, default=list(range(1, 100, 2)). + noise: float + Noise added, default=0.01. + return_only_values: bool + Indicates whether only evaluation scores (list of floats) should be returned and not the dictionary that also includes the percentages, default=None. abs: boolean Indicates whether absolute operation is applied on the attribution, default=False. normalise: boolean @@ -131,9 +136,11 @@ def __init__( perturb_func = noisy_linear_imputation self.percentages = percentages + self.noise = noise + self.return_values = return_only_values self.a_size = None self.perturb_func = make_perturb_func( - perturb_func, perturb_func_kwargs, noise=noise + perturb_func, perturb_func_kwargs, noise=self.noise ) # Asserts and warnings. @@ -335,6 +342,10 @@ def custom_postprocess( for p_ix, percentage in enumerate(self.percentages) } + # Return only the evaluation scores (and not percentages). + if self.return_values: + self.evaluation_scores = list(self.evaluation_scores.values()) + def evaluate_batch( self, model: ModelInterface, diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index e8a53961c..73e12ed5b 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -104,12 +104,7 @@ def __init__( # TODO. Create specific plot. default_plot_func = plotting.plot_pixel_flipping_experiment - abs = metric_init.abs - normalise = metric_init.normalise - return_aggregate = metric_init.return_aggregate - aggregate_func = metric_init.aggregate_func - display_progressbar = metric_init.display_progressbar - disable_warnings = metric_init.disable_warnings + self.return_aggregate = return_aggregate super().__init__( abs=abs, @@ -125,9 +120,10 @@ def __init__( ) # Asserts and warnings. - # assert hasattr( - # metric_init, "inverse_estimation" - # ), "The metric must have 'inverse_estimation' (bool) attribute" + if metric_init.name == "ROAD": + metric_init.return_only_values = True + if metric_init.name == "Region-Perturbation": + metric_init.order = "morf" # TODO. Update warnings. if not self.disable_warnings: @@ -138,8 +134,6 @@ def __init__( ) self.metric_init = metric_init - self.all_evaluation_scores_meta = [] - self.all_evaluation_scores_meta_inverse = [] def __call__( self, @@ -231,17 +225,18 @@ def __call__( >>> metric = Metric(abs=True, normalise=False) >>> scores = metric(model=model, x_batch=x_batch, y_batch=y_batch, a_batch=a_batch_saliency} """ - if self.metric_init.return_aggregate != False: + if self.metric_init.return_aggregate: print( "The metric is not designed to return an aggregate score, setting return_aggregate=False." ) self.metric_init.return_aggregate = False - # TODO. Check compatibility with different normalisation functions. - # TODO. Check compatibility with different batch sizes (think ok). - # TODO. Check that we use 2most important feature first" type. + assert ( + a_batch is not None + ), "'a_batch' must be provided to run the inverse estimation." - # TODO. Implement ranking assumption. see: https://github.com/annahedstroem/eval-project/blob/febe271a78c6efc16a51372ab58fcba676e0eb88/src/xai_faithfulness_experiments_lib_edits.py#L403 + # TODO. Do we want to turn the attributions to rankings? + # See: https://github.com/annahedstroem/eval-project/blob/febe271a78c6efc16a51372ab58fcba676e0eb88/src/xai_faithfulness_experiments_lib_edits.py#L403 self.scores = self.metric_init( model=model, x_batch=x_batch, @@ -261,15 +256,12 @@ def __call__( "To run the inverse estimation, the number of evaluation scores " "must match the number of instances in the batch." ) - self.all_evaluation_scores_meta.extend(self.metric_init.evaluation_scores) + # Empty the evaluation scores before re-scoring with the metric. self.metric_init.evaluation_scores = [] # Run inverse experiment. - # TODO. Check if the metric only relies on ordering. - a_batch_inv = -np.array(a_batch) / np.min( - -np.array(a_batch) - ) # [1, 2, 3], [-1, -2, -3] + a_batch_inv = -np.array(a_batch) # / np.min(-np.array(a_batch)) self.scores_inv = self.metric_init( model=model, x_batch=x_batch, @@ -285,54 +277,29 @@ def __call__( model_predict_kwargs=model_predict_kwargs, **kwargs, ) - self.all_evaluation_scores_meta_inverse.extend( - self.metric_init.evaluation_scores - ) # Compute the inverse, empty the evaluation scores again and overwrite with the inverse scores. - inv_scores = np.array(self.scores) - np.array(self.scores_inv) - self.metric_init.evaluation_scores = [] - self.evaluation_scores.extend(inv_scores) - - # TODO. If all_evaluation_scores is empty, overwrite with inverse scores - # for the those last samples (keep iterator). Or skip and throw a warning. - # if self.all_evaluation_scores: - # self.all_evaluation_scores[-1] = [] - self.all_evaluation_scores.append(inv_scores) + inv_scores = (np.array(self.scores) - np.array(self.scores_inv)).tolist() + self.evaluation_scores = inv_scores - return inv_scores - - def custom_postprocess( - self, - model: ModelInterface, - x_batch: np.ndarray, - y_batch: Optional[np.ndarray], - a_batch: Optional[np.ndarray], - s_batch: np.ndarray, - **kwargs, - ) -> None: - """ - Post-process the evaluation results. + if self.return_aggregate: + self.evaluation_scores = self.get_mean_score - Parameters - ---------- - model: torch.nn.Module, tf.keras.Model - A torch or tensorflow model e.g., torchvision.models that is subject to explanation. - x_batch: np.ndarray - A np.ndarray which contains the input data that are explained. - y_batch: np.ndarray - A np.ndarray which contains the output labels that are explained. - a_batch: np.ndarray, optional - A np.ndarray which contains pre-computed attributions i.e., explanations. - s_batch: np.ndarray, optional - A np.ndarray which contains segmentation masks that matches the input. + self.all_evaluation_scores.extend(self.metric_init.evaluation_scores) - Returns - ------- - None - """ - # TODO. Is this needed? - pass + return inv_scores def convert_attributions_to_rankings(self): pass + + @property + def get_mean_score(self): + """Calculate the area under the curve (AUC) score for several test samples.""" + return np.mean(np.array(self.evaluation_scores), axis=1) + + @property + def get_auc_score(self): + """Calculate the area under the curve (AUC) score for several test samples.""" + return np.mean( + [utils.calculate_auc(np.array(curve)) for curve in self.evaluation_scores] + ) diff --git a/tests/metrics/test_inverse_estimation.py b/tests/metrics/test_inverse_estimation.py index 6b471b2fa..8d68b53c8 100644 --- a/tests/metrics/test_inverse_estimation.py +++ b/tests/metrics/test_inverse_estimation.py @@ -40,7 +40,7 @@ }, }, }, - {"min": 0.0, "max": 1.0}, + {"min": -1000.0, "max": 1000.0}, ), ( lazy_fixture("load_mnist_model"), @@ -61,7 +61,7 @@ }, }, }, - {"min": 0.0, "max": 1.0}, + {"min": -1000.0, "max": 1000.0}, ), ( lazy_fixture("load_mnist_model"), @@ -82,7 +82,7 @@ }, }, }, - {"min": 0.0, "max": 1.0}, + {"min": -1000.0, "max": 1000.0}, ), ( lazy_fixture("load_mnist_model"), @@ -103,12 +103,13 @@ }, }, }, - {"min": 0.0, "max": 1.0}, + {"exception": AssertionError}, ), ( lazy_fixture("load_mnist_model"), lazy_fixture("load_mnist_images"), { + "a_batch_generate": True, "init": { "perturb_baseline": "mean", "features_in_step": 28, @@ -124,7 +125,7 @@ }, }, }, - {"min": 0.0, "max": 1.0}, + {"min": -1000.0, "max": 1000.0}, ), ( lazy_fixture("load_1d_3ch_conv_model"), @@ -140,12 +141,13 @@ }, "call": {}, }, - {"min": 0.0, "max": 1.0}, + {"exception": AssertionError}, ), ( lazy_fixture("load_mnist_model"), lazy_fixture("load_mnist_images"), { + "a_batch_generate": True, "init": { "perturb_baseline": "uniform", "features_in_step": 56, @@ -158,16 +160,17 @@ "explain_func": explain, "explain_func_kwargs": { "method": "Saliency", + "softmax": False, }, }, }, - {"min": 0.0, "max": 14.0}, + {"min": -1000.0, "max": 1000.0}, ), ( lazy_fixture("load_1d_3ch_conv_model"), lazy_fixture("almost_uniform_1d"), { - "a_batch_generate": False, + "a_batch_generate": True, "init": { "features_in_step": 10, "normalise": False, @@ -175,13 +178,20 @@ "perturb_baseline": "mean", "disable_warnings": True, }, - "call": {}, + "call": { + "explain_func": explain, + "explain_func_kwargs": { + "method": "IntegratedGradients", + "xai_lib": "captum", + }, + "softmax": False, + }, }, - {"min": 0.0, "max": 10.0}, + {"exception": AssertionError}, ), ], ) -def test_pixel_flipping( +def test_inverse_estimation_with_pixel_flipping( model, data: np.ndarray, params: dict, @@ -195,7 +205,9 @@ def test_pixel_flipping( init_params = params.get("init", {}) call_params = params.get("call", {}) - if params.get("a_batch_generate", True): + if "a_batch" in data: + a_batch = data["a_batch"] + elif params.get("a_batch_generate", True): explain = call_params["explain_func"] explain_func_kwargs = call_params.get("explain_func_kwargs", {}) a_batch = explain( @@ -204,37 +216,37 @@ def test_pixel_flipping( targets=y_batch, **explain_func_kwargs, ) - elif "a_batch" in data: - a_batch = data["a_batch"] + assert a_batch is not None else: a_batch = None metric_init = PixelFlipping(**init_params) - inverse_estimation = InverseEstimation(metric_init=metric_init) - scores = inverse_estimation( - model=model, - x_batch=x_batch, - y_batch=y_batch, - a_batch=a_batch, - **call_params, - ) - print("final scores!!!", scores[0][:10]) - print( - "scores_inv!!!", - ( - np.shape(inverse_estimation.scores_inv), - inverse_estimation.scores_inv[0][:10], - ), - ) - print( - "scores!!!", - (np.shape(inverse_estimation.scores), inverse_estimation.scores[0][:10]), - ) + metric_init.softmax = True + + try: + inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) + scores = inv( + model=model, + x_batch=x_batch, + y_batch=y_batch, + a_batch=a_batch, + **call_params, + ) + print(f"\n\n\tscores: {np.shape(inv.scores)},\n{inv.scores}") + print(f"\n\n\tscores_inv: {np.shape(inv.scores_inv)},\n{inv.scores_inv}") + print( + f"\n\n\tall_evaluation_scores: {np.shape(inv.all_evaluation_scores)},\n{inv.all_evaluation_scores}" + ) + print(f"\n\n\tscores: {np.shape(scores)},\n{scores}") - assert all( - [ - (s >= expected["min"] and s <= expected["max"]) - for s_list in scores - for s in s_list - ] - ), "Test failed." + if "exception" not in expected: + assert all( + [ + (s >= expected["min"] and s <= expected["max"]) + for s_list in scores + for s in s_list + ] + ), "Test failed." + except expected["exception"] as e: + print(f'Raised exception type {expected["exception"]}', e) + return From 11253840f399709569730aa02cbd9d76e9fe0956 Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Thu, 7 Dec 2023 16:40:01 +0100 Subject: [PATCH 04/41] minor fixes to InverseEstimation class --- quantus/metrics/inverse_estimation.py | 8 ++------ tests/metrics/test_inverse_estimation.py | 1 + 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 73e12ed5b..90d5a13db 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -56,7 +56,7 @@ def __init__( normalise: bool = False, normalise_func: Optional[Callable] = None, normalise_func_kwargs: Optional[Dict[str, Any]] = None, - return_aggregate: Optional[bool] = None, + return_aggregate: Optional[bool] = True, aggregate_func: Optional[Callable] = None, default_plot_func: Optional[Callable] = None, disable_warnings: Optional[bool] = None, @@ -235,15 +235,12 @@ def __call__( a_batch is not None ), "'a_batch' must be provided to run the inverse estimation." - # TODO. Do we want to turn the attributions to rankings? - # See: https://github.com/annahedstroem/eval-project/blob/febe271a78c6efc16a51372ab58fcba676e0eb88/src/xai_faithfulness_experiments_lib_edits.py#L403 self.scores = self.metric_init( model=model, x_batch=x_batch, y_batch=y_batch, a_batch=a_batch, s_batch=s_batch, - # custom_batch=custom_batch, channel_first=channel_first, explain_func=explain_func, explain_func_kwargs=explain_func_kwargs, @@ -261,14 +258,13 @@ def __call__( self.metric_init.evaluation_scores = [] # Run inverse experiment. - a_batch_inv = -np.array(a_batch) # / np.min(-np.array(a_batch)) + a_batch_inv = -np.array(a_batch) self.scores_inv = self.metric_init( model=model, x_batch=x_batch, y_batch=y_batch, a_batch=a_batch_inv, s_batch=s_batch, - # custom_batch=custom_batch, channel_first=channel_first, explain_func=explain_func, explain_func_kwargs=explain_func_kwargs, diff --git a/tests/metrics/test_inverse_estimation.py b/tests/metrics/test_inverse_estimation.py index 8d68b53c8..22ae12576 100644 --- a/tests/metrics/test_inverse_estimation.py +++ b/tests/metrics/test_inverse_estimation.py @@ -247,6 +247,7 @@ def test_inverse_estimation_with_pixel_flipping( for s in s_list ] ), "Test failed." + except expected["exception"] as e: print(f'Raised exception type {expected["exception"]}', e) return From 6cc1cda13faa6d48d9f477229ce6911e5a297c29 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 23 Feb 2024 10:52:38 +0100 Subject: [PATCH 05/41] Update inverse_estimation.py --- quantus/metrics/inverse_estimation.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 90d5a13db..37a70d20c 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -235,6 +235,11 @@ def __call__( a_batch is not None ), "'a_batch' must be provided to run the inverse estimation." + assert self.metric_init.abs == True, ( + "To run the inverse estimation, you cannot set 'a_batch' to " + "have positive attributions only. Set 'abs' param of the metric init to 'False'." + ) + self.scores = self.metric_init( model=model, x_batch=x_batch, From 279fb38bb5ce0bd9251dda7ad1b23788c7daee48 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 23 Feb 2024 11:58:20 +0100 Subject: [PATCH 06/41] remove inverse_estimation edits in region_perturbation.py --- quantus/metrics/faithfulness/region_perturbation.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/quantus/metrics/faithfulness/region_perturbation.py b/quantus/metrics/faithfulness/region_perturbation.py index 6c65d14da..19e3f905d 100644 --- a/quantus/metrics/faithfulness/region_perturbation.py +++ b/quantus/metrics/faithfulness/region_perturbation.py @@ -152,11 +152,6 @@ def __init__( # Asserts and warnings. asserts.assert_attributions_order(order=self.order) - if isinstance(self.inverse_estimation, bool): - assert self.order in [ - "morf", - "lerf", - ], "Inverse estimation assumes order is either 'morf' and 'lerf'." if not self.disable_warnings: warn.warn_parameterisation( From a14c109095e7ed10f9adcfe8ab7817910d659a00 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 23 Feb 2024 11:59:10 +0100 Subject: [PATCH 07/41] name update region_perturbation.py --- quantus/metrics/faithfulness/region_perturbation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantus/metrics/faithfulness/region_perturbation.py b/quantus/metrics/faithfulness/region_perturbation.py index 19e3f905d..cb8278215 100644 --- a/quantus/metrics/faithfulness/region_perturbation.py +++ b/quantus/metrics/faithfulness/region_perturbation.py @@ -58,7 +58,7 @@ class RegionPerturbation(Metric[List[float]]): - evaluation_category: What property/ explanation quality that this metric measures. """ - name = "Region-Perturbation" + name = "Region Perturbation" data_applicability = {DataType.IMAGE} model_applicability = {ModelType.TORCH, ModelType.TF} score_direction = ScoreDirection.LOWER From c4032d9de016a78edad501c662f347c7cde612e5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 23 Feb 2024 12:00:54 +0100 Subject: [PATCH 08/41] remove metric init requirements in inverse_estimation.py --- quantus/metrics/inverse_estimation.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 37a70d20c..49a59c3c1 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -119,11 +119,11 @@ def __init__( **kwargs, ) - # Asserts and warnings. - if metric_init.name == "ROAD": - metric_init.return_only_values = True - if metric_init.name == "Region-Perturbation": - metric_init.order = "morf" + # Asserts and warnings. # Skip for now, might revisit later. + # if metric_init.name == "ROAD": + # metric_init.return_only_values = True + #if metric_init.name == "Region Perturbation": + # metric_init.order = "morf" # TODO. Update warnings. if not self.disable_warnings: From dd74095d5d1795abdb926e318826f0ab70b0b217 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 23 Feb 2024 11:19:39 +0000 Subject: [PATCH 09/41] added a ormalise_func_kwargs attribute of base class, was missing --- quantus/metrics/base.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/quantus/metrics/base.py b/quantus/metrics/base.py index 9605a22a4..2564593e7 100644 --- a/quantus/metrics/base.py +++ b/quantus/metrics/base.py @@ -140,7 +140,7 @@ def __init__( if normalise_func_kwargs is not None: normalise_func = functools.partial(normalise_func, **normalise_func_kwargs) - + # Run deprecation warnings. warn.deprecation_warnings(kwargs) warn.check_kwargs(kwargs) @@ -150,6 +150,7 @@ def __init__( self.return_aggregate = return_aggregate self.aggregate_func = aggregate_func self.normalise_func = normalise_func + self.normalise_func_kwargs = normalise_func_kwargs or {} self.default_plot_func = default_plot_func From 4b763b86b641df89eccf7d699274fb7f1c77ea09 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 23 Feb 2024 11:42:08 +0000 Subject: [PATCH 10/41] added second inverse method --- quantus/metrics/inverse_estimation.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 49a59c3c1..f85b34d1b 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -263,7 +263,17 @@ def __call__( self.metric_init.evaluation_scores = [] # Run inverse experiment. - a_batch_inv = -np.array(a_batch) + inverse_estimation_method = kwargs.get("inverse_estimation_method", "sign-change") + if inverse_estimation_method == "sign-change": + a_batch_inv = -np.array(a_batch) + elif inverse_estimation_method == "value-swap": + indices = np.argsort(a_batch_inv) + a_batch_inv = np.empty_like(a_batch) + a_batch_inv[indices] = a_batch[list(reversed(indices))] + else: + raise ValueError("The 'inverse_estimation_method' in call **kwargs, \ + must be either 'sign-change' or 'value-swap'.") + self.scores_inv = self.metric_init( model=model, x_batch=x_batch, From 86d76155ea8cd5180e2ead0cc6b8b1667803296c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 23 Feb 2024 11:46:17 +0000 Subject: [PATCH 11/41] added second inverse method -v2 --- quantus/metrics/inverse_estimation.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index f85b34d1b..f90688ec1 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -125,6 +125,11 @@ def __init__( #if metric_init.name == "Region Perturbation": # metric_init.order = "morf" + self.method = kwargs.get("method", "sign-flip") + if self.method not in ["sign-flip", "value-swap"]: + raise ValueError("The 'inverse_estimation_method' in init **kwargs, \ + must be either 'sign-flip' or 'value-swap'.") + # TODO. Update warnings. if not self.disable_warnings: warn.warn_parameterisation( @@ -263,16 +268,12 @@ def __call__( self.metric_init.evaluation_scores = [] # Run inverse experiment. - inverse_estimation_method = kwargs.get("inverse_estimation_method", "sign-change") - if inverse_estimation_method == "sign-change": + if self.method == "sign-flip": a_batch_inv = -np.array(a_batch) - elif inverse_estimation_method == "value-swap": + elif self.method == "value-swap": indices = np.argsort(a_batch_inv) a_batch_inv = np.empty_like(a_batch) a_batch_inv[indices] = a_batch[list(reversed(indices))] - else: - raise ValueError("The 'inverse_estimation_method' in call **kwargs, \ - must be either 'sign-change' or 'value-swap'.") self.scores_inv = self.metric_init( model=model, From d9cb2f3197d0abd0e4775da39923e7b12c5590d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 23 Feb 2024 15:06:07 +0000 Subject: [PATCH 12/41] enable batching --- quantus/metrics/inverse_estimation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index f90688ec1..f1fb7a896 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -271,7 +271,7 @@ def __call__( if self.method == "sign-flip": a_batch_inv = -np.array(a_batch) elif self.method == "value-swap": - indices = np.argsort(a_batch_inv) + indices = np.array([np.argsort(a) for a in a_batch]) a_batch_inv = np.empty_like(a_batch) a_batch_inv[indices] = a_batch[list(reversed(indices))] From 6aeba15585d5166178ee1dd163b9571003a64cd9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 15 Mar 2024 09:36:08 +0000 Subject: [PATCH 13/41] update method name --- quantus/metrics/inverse_estimation.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index f1fb7a896..1badb1b04 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -125,9 +125,9 @@ def __init__( #if metric_init.name == "Region Perturbation": # metric_init.order = "morf" - self.method = kwargs.get("method", "sign-flip") - if self.method not in ["sign-flip", "value-swap"]: - raise ValueError("The 'inverse_estimation_method' in init **kwargs, \ + self.inverse_method = kwargs.get("inverse_method", "sign-flip") + if self.inverse_method not in ["sign-flip", "value-swap"]: + raise ValueError("The 'inverse_method' in init **kwargs, \ must be either 'sign-flip' or 'value-swap'.") # TODO. Update warnings. @@ -268,9 +268,9 @@ def __call__( self.metric_init.evaluation_scores = [] # Run inverse experiment. - if self.method == "sign-flip": + if self.inverse_method == "sign-flip": a_batch_inv = -np.array(a_batch) - elif self.method == "value-swap": + elif self.inverse_method == "value-swap": indices = np.array([np.argsort(a) for a in a_batch]) a_batch_inv = np.empty_like(a_batch) a_batch_inv[indices] = a_batch[list(reversed(indices))] From 02e3d5a636f35593dc4d94d5f9d48d88ff09eb5b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 15 Mar 2024 10:04:49 +0000 Subject: [PATCH 14/41] added inverse_method as an arg --- quantus/metrics/inverse_estimation.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 1badb1b04..da3e79044 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -52,6 +52,7 @@ class InverseEstimation(Metric): def __init__( self, metric_init: Metric, + inverse_method: str = "sign-flip", abs: bool = False, normalise: bool = False, normalise_func: Optional[Callable] = None, @@ -125,7 +126,7 @@ def __init__( #if metric_init.name == "Region Perturbation": # metric_init.order = "morf" - self.inverse_method = kwargs.get("inverse_method", "sign-flip") + self.inverse_method = inverse_method if self.inverse_method not in ["sign-flip", "value-swap"]: raise ValueError("The 'inverse_method' in init **kwargs, \ must be either 'sign-flip' or 'value-swap'.") From 4da473a606eec837fbfe7cefb3000d1ce6ca634e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 15 Mar 2024 10:10:21 +0000 Subject: [PATCH 15/41] batch update --- quantus/metrics/inverse_estimation.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index da3e79044..6f292746f 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -268,13 +268,18 @@ def __call__( # Empty the evaluation scores before re-scoring with the metric. self.metric_init.evaluation_scores = [] + # Attributions needs to have only one axis, else flatten and reshape back. + shape_ori = a_batch.shape + a_batch = a_batch.reshape((shape_ori[0],-1)) + # Run inverse experiment. if self.inverse_method == "sign-flip": a_batch_inv = -np.array(a_batch) elif self.inverse_method == "value-swap": - indices = np.array([np.argsort(a) for a in a_batch]) + indices = np.argsort(a_batch, axis=1) a_batch_inv = np.empty_like(a_batch) - a_batch_inv[indices] = a_batch[list(reversed(indices))] + a_batch_inv[np.arange(a_batch_inv.shape[0])[:, None], indices] = a_batch[np.arange(a_batch_inv.shape[0])[:, None], indices[:,::-1]] + a_batch_inv.reshape(shape_ori) self.scores_inv = self.metric_init( model=model, From 3c123c7ff39e9a44eb527bc1573afe66dd0c3fe5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 15 Mar 2024 10:13:19 +0000 Subject: [PATCH 16/41] updated inverse with batch --- quantus/metrics/inverse_estimation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 6f292746f..49504ebdf 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -46,8 +46,8 @@ class InverseEstimation(Metric): name = "Inverse-Estimation" data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR} model_applicability = {ModelType.TORCH, ModelType.TF} - score_direction = ScoreDirection.LOWER - evaluation_category = EvaluationCategory.FAITHFULNESS + score_direction = ScoreDirection.HIGHER + #evaluation_category = EvaluationCategory.FAITHFULNESS def __init__( self, From 05efd24a2cf36a64b03193beee97240ada2004ed Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 15 Mar 2024 10:48:56 +0000 Subject: [PATCH 17/41] added wrapper --- quantus/metrics/inverse_estimation.py | 32 ++++++++++++++++++++------- 1 file changed, 24 insertions(+), 8 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 49504ebdf..8890588ff 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -273,14 +273,30 @@ def __call__( a_batch = a_batch.reshape((shape_ori[0],-1)) # Run inverse experiment. - if self.inverse_method == "sign-flip": - a_batch_inv = -np.array(a_batch) - elif self.inverse_method == "value-swap": - indices = np.argsort(a_batch, axis=1) - a_batch_inv = np.empty_like(a_batch) - a_batch_inv[np.arange(a_batch_inv.shape[0])[:, None], indices] = a_batch[np.arange(a_batch_inv.shape[0])[:, None], indices[:,::-1]] - a_batch_inv.reshape(shape_ori) + def get_inverse_attributions(inverse_method: str, a_batch: np.array): + if inverse_method == "sign-flip": + a_batch_inv = -np.array(a_batch) + elif inverse_method == "value-swap": + indices = np.argsort(a_batch, axis=1) + a_batch_inv = np.empty_like(a_batch) + a_batch_inv[np.arange(a_batch_inv.shape[0])[:, None], indices] = a_batch[np.arange(a_batch_inv.shape[0])[:, None], indices[:,::-1]] + a_batch_inv.reshape(shape_ori) + return a_batch_inv + def inverse_wrapper(model, inputs, targets, **kwargs): + explain_func = kwargs["explain_func"] + inverse_method = kwargs["inverse_method"] + a_batch = explain_func(model, inputs, targets, **kwargs) + a_batch_inv = get_inverse_attributions(inverse_method=inverse_method, a_batch=a_batch) + return a_batch_inv + + # Get inverse attributions. + a_batch_inv = get_inverse_attributions(inverse_method=self.inverse_method, a_batch=a_batch) + + # Metrics that depend on re-computing explanations need inverse wrapping. + explain_func_kwargs["explain_func"] = explain_func + explain_func_kwargs["inverse_method"] = self.inverse_method + self.scores_inv = self.metric_init( model=model, x_batch=x_batch, @@ -288,7 +304,7 @@ def __call__( a_batch=a_batch_inv, s_batch=s_batch, channel_first=channel_first, - explain_func=explain_func, + explain_func=inverse_wrapper, explain_func_kwargs=explain_func_kwargs, softmax=softmax, device=device, From 3235507bf0dab8632a797259403786a672099612 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Fri, 15 Mar 2024 11:04:13 +0000 Subject: [PATCH 18/41] added wrapper --- quantus/metrics/inverse_estimation.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 8890588ff..786884610 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -320,8 +320,10 @@ def inverse_wrapper(model, inputs, targets, **kwargs): self.evaluation_scores = self.get_mean_score self.all_evaluation_scores.extend(self.metric_init.evaluation_scores) - - return inv_scores + + print(np.shape(inv_scores)) + print(np.shape(inv_scores.reshape(-1))) + return inv_scores.reshape(-1) def convert_attributions_to_rankings(self): pass From dfcdae4a4af646e515daa49dc1546198b58cef20 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Sun, 24 Mar 2024 17:38:33 +0100 Subject: [PATCH 19/41] update from True to False in assert in inverse_estimation.py --- quantus/metrics/inverse_estimation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 786884610..4b20bb43a 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -241,7 +241,7 @@ def __call__( a_batch is not None ), "'a_batch' must be provided to run the inverse estimation." - assert self.metric_init.abs == True, ( + assert self.metric_init.abs == False, ( "To run the inverse estimation, you cannot set 'a_batch' to " "have positive attributions only. Set 'abs' param of the metric init to 'False'." ) From 3d06f90ef37778b61b0915cbf2843a677b467163 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Sun, 24 Mar 2024 19:18:01 +0100 Subject: [PATCH 20/41] bugfix a_batch shape inverse_estimation.py --- quantus/metrics/inverse_estimation.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 4b20bb43a..0e007aedd 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -268,12 +268,12 @@ def __call__( # Empty the evaluation scores before re-scoring with the metric. self.metric_init.evaluation_scores = [] - # Attributions needs to have only one axis, else flatten and reshape back. - shape_ori = a_batch.shape - a_batch = a_batch.reshape((shape_ori[0],-1)) - # Run inverse experiment. def get_inverse_attributions(inverse_method: str, a_batch: np.array): + + # Attributions need to have only one axis, else flatten and reshape back. + shape_ori = a_batch.shape + a_batch = a_batch.reshape((shape_ori[0], -1)) if inverse_method == "sign-flip": a_batch_inv = -np.array(a_batch) elif inverse_method == "value-swap": @@ -287,7 +287,7 @@ def inverse_wrapper(model, inputs, targets, **kwargs): explain_func = kwargs["explain_func"] inverse_method = kwargs["inverse_method"] a_batch = explain_func(model, inputs, targets, **kwargs) - a_batch_inv = get_inverse_attributions(inverse_method=inverse_method, a_batch=a_batch) + a_batch_inv = get_inverse_attributions(inverse_method=inverse_method, a_batch=a_batch, shape_ori=shape_ori) return a_batch_inv # Get inverse attributions. From 76d9fdc57fb796d730847353518945d0ebfaf067 Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 25 Mar 2024 12:02:18 +0100 Subject: [PATCH 21/41] bugfix formatting on titanic dataset --- tests/conftest.py | 26 +++++++++++++++++--------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 6e87b554b..b6d137844 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,13 +6,17 @@ import pytest import torch from keras.datasets import cifar10 -from quantus.helpers.model.models import (CifarCNNModel, ConvNet1D, - ConvNet1DTF, LeNet, LeNetTF, - TitanicSimpleTFModel, - TitanicSimpleTorchModel) +from quantus.helpers.model.models import ( + CifarCNNModel, + ConvNet1D, + ConvNet1DTF, + LeNet, + LeNetTF, + TitanicSimpleTFModel, + TitanicSimpleTorchModel, +) from sklearn.model_selection import train_test_split -from transformers import (AutoModelForSequenceClassification, AutoTokenizer, - set_seed) +from transformers import AutoModelForSequenceClassification, AutoTokenizer, set_seed CIFAR_IMAGE_SIZE = 32 MNIST_IMAGE_SIZE = 28 @@ -20,7 +24,8 @@ MINI_BATCH_SIZE = 8 RANDOM_SEED = 42 -@pytest.fixture(scope='function', autouse=True) + +@pytest.fixture(scope="function", autouse=True) def reset_prngs(): set_seed(42) @@ -208,7 +213,10 @@ def titanic_dataset(): X = df_enc.drop(["survived"], axis=1).values.astype(float) Y = df_enc["survived"].values.astype(int) _, test_features, _, test_labels = train_test_split(X, Y, test_size=0.3) - return {"x_batch": test_features, "y_batch": test_labels} + return { + "x_batch": test_features[:MINI_BATCH_SIZE], + "y_batch": test_labels[:BATCH_SIZE].reshape(-1).astype(int)[:MINI_BATCH_SIZE], + } @pytest.fixture(scope="session", autouse=True) @@ -264,4 +272,4 @@ def dummy_hf_tokenizer(): @pytest.fixture(scope="session", autouse=True) def set_env(): """Set ENV var, so test outputs are not polluted by progress bars and warnings.""" - os.environ["PYTEST"] = "1" \ No newline at end of file + os.environ["PYTEST"] = "1" From bf7ab44613e17130617e539ad489b34acae0b0ee Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 25 Mar 2024 12:07:20 +0100 Subject: [PATCH 22/41] merge fixes --- quantus/functions/explanation_func.py | 2 +- quantus/helpers/model/pytorch_model.py | 32 + quantus/metrics/base_perturbed.py | 162 + .../model_parameter_randomisation.py | 441 ++ tests/helpers/test_explanation_func.py | 1109 +++++ tests/helpers/test_loss_func.py | 36 + tests/helpers/test_mosaic_func.py | 85 + tests/helpers/test_norm_func.py | 55 + tests/helpers/test_normalise_func.py | 402 ++ tests/helpers/test_perturb_func.py | 491 +++ tests/helpers/test_pytorch_model.py | 255 ++ tests/helpers/test_similarity_func.py | 304 ++ ...ble_AI_evaluation_in_Climate_Science.ipynb | 3678 +++++++++++++++++ 13 files changed, 7051 insertions(+), 1 deletion(-) create mode 100644 quantus/metrics/base_perturbed.py create mode 100644 quantus/metrics/randomisation/model_parameter_randomisation.py create mode 100644 tests/helpers/test_explanation_func.py create mode 100644 tests/helpers/test_loss_func.py create mode 100644 tests/helpers/test_mosaic_func.py create mode 100644 tests/helpers/test_norm_func.py create mode 100644 tests/helpers/test_normalise_func.py create mode 100644 tests/helpers/test_perturb_func.py create mode 100644 tests/helpers/test_pytorch_model.py create mode 100644 tests/helpers/test_similarity_func.py create mode 100644 tutorials/Tutorial_ICLR_2023_Quantus_x_Climate_Applying_explainable_AI_evaluation_in_Climate_Science.ipynb diff --git a/quantus/functions/explanation_func.py b/quantus/functions/explanation_func.py index be2f6ca18..eeaf25b13 100644 --- a/quantus/functions/explanation_func.py +++ b/quantus/functions/explanation_func.py @@ -671,7 +671,7 @@ def f_reduce_axes(a): inputs = inputs.cpu() inputs_numpy = inputs.detach().numpy() - + for i in range(len(explanation)): explanation[i] = torch.Tensor( np.clip(scipy.ndimage.sobel(inputs_numpy[i]), 0, 1) diff --git a/quantus/helpers/model/pytorch_model.py b/quantus/helpers/model/pytorch_model.py index cb004c6ba..2c05d1a7c 100644 --- a/quantus/helpers/model/pytorch_model.py +++ b/quantus/helpers/model/pytorch_model.py @@ -362,6 +362,38 @@ def sample( ) return model_copy + def perturb_layer_weights(self, layer_idx: int, noise: float): + """ + Perturb the weights of a specific layer in a PyTorch model. + + Parameters + ---------- + model : torch.nn.Module + The PyTorch model. + layer_idx : int + The index of the layer to perturb. + noise : float + The standard deviation of the Gaussian noise to add to the weights. + + Returns + ------- + None + """ + original_parameters = self.state_dict() + model_copy = deepcopy(self.model) + model_copy.load_state_dict(original_parameters) + + # Get the specific layer. + layer = list(model_copy.modules())[layer_idx] + + # Generate Gaussian noise. + noise_tensor = torch.randn_like(layer.weight) * noise + + # Add the noise to the layer's weights. + layer.weight.data.add_(noise_tensor) + + return model_copy + def add_mean_shift_to_first_layer( self, input_shift: Union[int, float], diff --git a/quantus/metrics/base_perturbed.py b/quantus/metrics/base_perturbed.py new file mode 100644 index 000000000..250bf1c6b --- /dev/null +++ b/quantus/metrics/base_perturbed.py @@ -0,0 +1,162 @@ +"""This module implements the base class for creating evaluation metrics.""" +# This file is part of Quantus. +# Quantus is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. +# Quantus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. +# You should have received a copy of the GNU Lesser General Public License along with Quantus. If not, see . +# Quantus project URL: . + +import inspect +import re +from abc import abstractmethod +from collections.abc import Sequence +from typing import ( + Any, + Callable, + Dict, + Sequence, + Optional, + Tuple, + Union, + Collection, + List, +) +import matplotlib.pyplot as plt +import numpy as np +from tqdm.auto import tqdm + +from quantus.helpers import asserts +from quantus.helpers import utils +from quantus.helpers import warn +from quantus.helpers.model.model_interface import ModelInterface +from quantus.metrics.base import Metric +from quantus.helpers.enums import ( + ModelType, + DataType, + ScoreDirection, + EvaluationCategory, +) + + +class PerturbationMetric(Metric): + """ + Implementation base PertubationMetric class. + + Metric categories such as Faithfulness and Robustness share certain characteristics when it comes to perturbations. + As follows, this metric class is created which has additional attributes for perturbations. + + Attributes: + - name: The name of the metric. + - data_applicability: The data types that the metric implementation currently supports. + - model_applicability: The model types that this metric can work with. + - score_direction: How to interpret the scores, whether higher/ lower values are considered better. + - evaluation_category: What property/ explanation quality that this metric measures. + """ + + name = "PerturbationMetric" + data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR} + model_applicability = {ModelType.TORCH, ModelType.TF} + score_direction = ScoreDirection.HIGHER + evaluation_category = EvaluationCategory.NONE + + @asserts.attributes_check + def __init__( + self, + abs: bool, + normalise: bool, + normalise_func: Callable, + normalise_func_kwargs: Optional[Dict[str, Any]], + perturb_func: Callable, + perturb_func_kwargs: Optional[Dict[str, Any]], + return_aggregate: bool, + aggregate_func: Callable, + default_plot_func: Optional[Callable], + disable_warnings: bool, + display_progressbar: bool, + **kwargs, + ): + """ + Initialise the PerturbationMetric base class. + + Parameters + ---------- + Parameters + ---------- + abs: boolean + Indicates whether absolute operation is applied on the attribution. + normalise: boolean + Indicates whether normalise operation is applied on the attribution. + normalise_func: callable + Attribution normalisation function applied in case normalise=True. + normalise_func_kwargs: dict + Keyword arguments to be passed to normalise_func on call. + perturb_func: callable + Input perturbation function. + perturb_func_kwargs: dict, optional + Keyword arguments to be passed to perturb_func. + return_aggregate: boolean + Indicates if an aggregated score should be computed over all instances. + aggregate_func: callable + Callable that aggregates the scores given an evaluation call. + default_plot_func: callable + Callable that plots the metrics result. + disable_warnings: boolean + Indicates whether the warnings are printed. + display_progressbar: boolean + Indicates whether a tqdm-progress-bar is printed. + kwargs: optional + Keyword arguments. + """ + + # Initialize super-class with passed parameters + super().__init__( + abs=abs, + normalise=normalise, + normalise_func=normalise_func, + normalise_func_kwargs=normalise_func_kwargs, + return_aggregate=return_aggregate, + aggregate_func=aggregate_func, + default_plot_func=default_plot_func, + display_progressbar=display_progressbar, + disable_warnings=disable_warnings, + **kwargs, + ) + + # Save perturbation metric attributes. + self.perturb_func = perturb_func + + if perturb_func_kwargs is None: + perturb_func_kwargs = {} + self.perturb_func_kwargs = perturb_func_kwargs + + @abstractmethod + def evaluate_instance( + self, + model: ModelInterface, + x: np.ndarray, + y: Optional[np.ndarray], + a: Optional[np.ndarray], + s: Optional[np.ndarray], + ) -> Any: + """ + Evaluate instance gets model and data for a single instance as input and returns the evaluation result. + + This method needs to be implemented to use __call__(). + + Parameters + ---------- + model: ModelInterface + A ModelInteface that is subject to explanation. + x: np.ndarray + The input to be evaluated on an instance-basis. + y: np.ndarray + The output to be evaluated on an instance-basis. + a: np.ndarray + The explanation to be evaluated on an instance-basis. + s: np.ndarray + The segmentation to be evaluated on an instance-basis. + + Returns + ------- + Any + """ + raise NotImplementedError() diff --git a/quantus/metrics/randomisation/model_parameter_randomisation.py b/quantus/metrics/randomisation/model_parameter_randomisation.py new file mode 100644 index 000000000..d1f67d8ce --- /dev/null +++ b/quantus/metrics/randomisation/model_parameter_randomisation.py @@ -0,0 +1,441 @@ +"""This module contains the implementation of the Model Parameter Sensitivity metric.""" + +# This file is part of Quantus. +# Quantus is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. +# Quantus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. +# You should have received a copy of the GNU Lesser General Public License along with Quantus. If not, see . +# Quantus project URL: . + +from typing import ( + Any, + Callable, + Dict, + List, + Optional, + Tuple, + Union, + Collection, + Iterable, +) +import numpy as np +from tqdm.auto import tqdm + +from quantus.helpers import asserts +from quantus.helpers import warn +from quantus.helpers.model.model_interface import ModelInterface +from quantus.functions.normalise_func import normalise_by_max +from quantus.functions.similarity_func import correlation_spearman +from quantus.metrics.base import Metric +from quantus.helpers.enums import ( + ModelType, + DataType, + ScoreDirection, + EvaluationCategory, +) + + +class ModelParameterRandomisation(Metric): + """ + Implementation of the Model Parameter Randomization Method by Adebayo et. al., 2018. + + The Model Parameter Randomization measures the distance between the original attribution and a newly computed + attribution throughout the process of cascadingly/independently randomizing the model parameters of one layer + at a time. + + Assumptions: + - In the original paper multiple distance measures are taken: Spearman rank correlation (with and without abs), + HOG and SSIM. We have set Spearman as the default value. + + References: + 1) Julius Adebayo et al.: "Sanity Checks for Saliency Maps." NeurIPS (2018): 9525-9536. + + Attributes: + - _name: The name of the metric. + - _data_applicability: The data types that the metric implementation currently supports. + - _models: The model types that this metric can work with. + - score_direction: How to interpret the scores, whether higher/ lower values are considered better. + - evaluation_category: What property/ explanation quality that this metric measures. + """ + + name = "Model Parameter Randomisation" + data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR} + model_applicability = {ModelType.TORCH, ModelType.TF} + score_direction = ScoreDirection.LOWER + evaluation_category = EvaluationCategory.RANDOMISATION + + @asserts.attributes_check + def __init__( + self, + similarity_func: Callable = None, + layer_order: str = "independent", + seed: int = 42, + return_sample_correlation: bool = False, + abs: bool = True, + normalise: bool = True, + normalise_func: Optional[Callable[[np.ndarray], np.ndarray]] = None, + normalise_func_kwargs: Optional[Dict[str, Any]] = None, + return_aggregate: bool = False, + aggregate_func: Callable = None, + default_plot_func: Optional[Callable] = None, + disable_warnings: bool = False, + display_progressbar: bool = False, + **kwargs, + ): + """ + Parameters + ---------- + similarity_func: callable + Similarity function applied to compare input and perturbed input, default=correlation_spearman. + layer_order: string + Indicated whether the model is randomized cascadingly or independently. + Set order=top_down for cascading randomization, set order=independent for independent randomization, + default="independent". + seed: integer + Seed used for the random generator, default=42. + return_sample_correlation: boolean + Indicates whether return one float per sample, representing the average + correlation coefficient across the layers for that sample. + abs: boolean + Indicates whether absolute operation is applied on the attribution, default=True. + normalise: boolean + Indicates whether normalise operation is applied on the attribution, default=True. + normalise_func: callable + Attribution normalisation function applied in case normalise=True. + If normalise_func=None, the default value is used, default=normalise_by_max. + normalise_func_kwargs: dict + Keyword arguments to be passed to normalise_func on call, default={}. + return_aggregate: boolean + Indicates if an aggregated score should be computed over all instances. + aggregate_func: callable + Callable that aggregates the scores given an evaluation call. + default_plot_func: callable + Callable that plots the metrics result. + disable_warnings: boolean + Indicates whether the warnings are printed, default=False. + display_progressbar: boolean + Indicates whether a tqdm-progress-bar is printed, default=False. + kwargs: optional + Keyword arguments. + """ + if normalise_func is None: + normalise_func = normalise_by_max + + super().__init__( + abs=abs, + normalise=normalise, + normalise_func=normalise_func, + normalise_func_kwargs=normalise_func_kwargs, + return_aggregate=return_aggregate, + aggregate_func=aggregate_func, + default_plot_func=default_plot_func, + display_progressbar=display_progressbar, + disable_warnings=disable_warnings, + **kwargs, + ) + + # Save metric-specific attributes. + if similarity_func is None: + similarity_func = correlation_spearman + self.similarity_func = similarity_func + self.layer_order = layer_order + self.seed = seed + self.return_sample_correlation = return_sample_correlation + + # Results are returned/saved as a dictionary not like in the super-class as a list. + self.evaluation_scores = {} + + # Asserts and warnings. + asserts.assert_layer_order(layer_order=self.layer_order) + if not self.disable_warnings: + warn.warn_parameterisation( + metric_name=self.__class__.__name__, + sensitive_params=( + "similarity metric 'similarity_func' and the order of " + "the layer randomisation 'layer_order'" + ), + citation=( + "Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B. " + "'Sanity Checks for Saliency Maps.' arXiv preprint," + " arXiv:1810.073292v3 (2018)" + ), + ) + + def __call__( + self, + model, + x_batch: np.array, + y_batch: np.array, + a_batch: Optional[np.ndarray] = None, + s_batch: Optional[np.ndarray] = None, + channel_first: Optional[bool] = None, + explain_func: Optional[Callable] = None, + explain_func_kwargs: Optional[Dict] = None, + model_predict_kwargs: Optional[Dict] = None, + softmax: Optional[bool] = False, + device: Optional[str] = None, + batch_size: int = 64, + custom_batch: Optional[Any] = None, + **kwargs, + ) -> Union[List[float], float, Dict[str, List[float]], Collection[Any]]: + """ + This implementation represents the main logic of the metric and makes the class object callable. + It completes instance-wise evaluation of explanations (a_batch) with respect to input data (x_batch), + output labels (y_batch) and a torch or tensorflow model (model). + + Calls general_preprocess() with all relevant arguments, calls + () on each instance, and saves results to evaluation_scores. + Calls custom_postprocess() afterwards. Finally returns evaluation_scores. + + The content of evaluation_scores will be appended to all_evaluation_scores (list) at the end of + the evaluation call. + + Parameters + ---------- + model: torch.nn.Module, tf.keras.Model + A torch or tensorflow model that is subject to explanation. + x_batch: np.ndarray + A np.ndarray which contains the input data that are explained. + y_batch: np.ndarray + A np.ndarray which contains the output labels that are explained. + a_batch: np.ndarray, optional + A np.ndarray which contains pre-computed attributions i.e., explanations. + s_batch: np.ndarray, optional + A np.ndarray which contains segmentation masks that matches the input. + channel_first: boolean, optional + Indicates of the image dimensions are channel first, or channel last. + Inferred from the input shape if None. + explain_func: callable + Callable generating attributions. + explain_func_kwargs: dict, optional + Keyword arguments to be passed to explain_func on call. + model_predict_kwargs: dict, optional + Keyword arguments to be passed to the model's predict method. + softmax: boolean + Indicates whether to use softmax probabilities or logits in model prediction. + This is used for this __call__ only and won't be saved as attribute. If None, self.softmax is used. + device: string + Indicated the device on which a torch.Tensor is or will be allocated: "cpu" or "gpu". + kwargs: optional + Keyword arguments. + + Returns + ------- + evaluation_scores: list + a list of Any with the evaluation scores of the concerned batch. + + Examples: + -------- + # Minimal imports. + >> import quantus + >> from quantus import LeNet + >> import torch + + # Enable GPU. + >> device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + # Load a pre-trained LeNet classification model (architecture at quantus/helpers/models). + >> model = LeNet() + >> model.load_state_dict(torch.load("tutorials/assets/pytests/mnist_model")) + + # Load MNIST datasets and make loaders. + >> test_set = torchvision.datasets.MNIST(root='./sample_data', download=True) + >> test_loader = torch.utils.data.DataLoader(test_set, batch_size=24) + + # Load a batch of inputs and outputs to use for XAI evaluation. + >> x_batch, y_batch = iter(test_loader).next() + >> x_batch, y_batch = x_batch.cpu().numpy(), y_batch.cpu().numpy() + + # Generate Saliency attributions of the test set batch of the test set. + >> a_batch_saliency = Saliency(model).attribute(inputs=x_batch, target=y_batch, abs=True).sum(axis=1) + >> a_batch_saliency = a_batch_saliency.cpu().numpy() + + # Initialise the metric and evaluate explanations by calling the metric instance. + >> metric = Metric(abs=True, normalise=False) + >> scores = metric(model=model, x_batch=x_batch, y_batch=y_batch, a_batch=a_batch_saliency} + """ + + # Run deprecation warnings. + warn.deprecation_warnings(kwargs) + warn.check_kwargs(kwargs) + + data = self.general_preprocess( + model=model, + x_batch=x_batch, + y_batch=y_batch, + a_batch=a_batch, + s_batch=s_batch, + custom_batch=None, + channel_first=channel_first, + explain_func=explain_func, + explain_func_kwargs=explain_func_kwargs, + model_predict_kwargs=model_predict_kwargs, + softmax=softmax, + device=device, + ) + model = data["model"] + x_batch = data["x_batch"] + y_batch = data["y_batch"] + a_batch = data["a_batch"] + + # Results are returned/saved as a dictionary not as a list as in the super-class. + self.evaluation_scores = {} + + # Get number of iterations from number of layers. + n_layers = len(list(model.get_random_layer_generator(order=self.layer_order))) + + model_iterator = tqdm( + model.get_random_layer_generator(order=self.layer_order, seed=self.seed), + total=n_layers, + disable=not self.display_progressbar, + ) + + for layer_name, random_layer_model in model_iterator: + + similarity_scores = [None for _ in x_batch] + + # Generate an explanation with perturbed model. + a_batch_perturbed = self.explain_func( + model=random_layer_model, + inputs=x_batch, + targets=y_batch, + **self.explain_func_kwargs, + ) + + batch_iterator = enumerate(zip(a_batch, a_batch_perturbed)) + for instance_id, (a_instance, a_instance_perturbed) in batch_iterator: + result = self.evaluate_instance( + model=random_layer_model, + x=None, + y=None, + s=None, + a=a_instance, + a_perturbed=a_instance_perturbed, + ) + similarity_scores[instance_id] = result + + # Save similarity scores in a result dictionary. + self.evaluation_scores[layer_name] = similarity_scores + + # Call post-processing. + self.custom_postprocess( + model=model, + x_batch=x_batch, + y_batch=y_batch, + a_batch=a_batch, + s_batch=s_batch, + ) + + if self.return_sample_correlation: + self.evaluation_scores = self.compute_correlation_per_sample() + + if self.return_aggregate: + assert self.return_sample_correlation, ( + "You must set 'return_average_correlation_per_sample'" + " to True in order to compute te aggregat" + ) + self.evaluation_scores = [self.aggregate_func(self.evaluation_scores)] + + self.all_evaluation_scores.append(self.evaluation_scores) + + return self.evaluation_scores + + def evaluate_instance( + self, + model: ModelInterface, + x: Optional[np.ndarray], + y: Optional[np.ndarray], + a: Optional[np.ndarray], + s: Optional[np.ndarray], + a_perturbed: Optional[np.ndarray] = None, + ) -> float: + """ + Evaluate instance gets model and data for a single instance as input and returns the evaluation result. + + Parameters + ---------- + i: integer + The evaluation instance. + model: ModelInterface + A ModelInteface that is subject to explanation. + x: np.ndarray + The input to be evaluated on an instance-basis. + y: np.ndarray + The output to be evaluated on an instance-basis. + a: np.ndarray + The explanation to be evaluated on an instance-basis. + s: np.ndarray + The segmentation to be evaluated on an instance-basis. + a_perturbed: np.ndarray + The perturbed attributions. + + Returns + ------- + float + The evaluation results. + """ + if self.normalise: + a_perturbed = self.normalise_func(a_perturbed, **self.normalise_func_kwargs) + + if self.abs: + a_perturbed = np.abs(a_perturbed) + + # Compute distance measure. + return self.similarity_func(a_perturbed.flatten(), a.flatten()) + + def custom_preprocess( + self, + model: ModelInterface, + x_batch: np.ndarray, + y_batch: Optional[np.ndarray], + a_batch: Optional[np.ndarray], + s_batch: np.ndarray, + custom_batch: Optional[np.ndarray], + ) -> None: + """ + Implementation of custom_preprocess_batch. + + Parameters + ---------- + model: torch.nn.Module, tf.keras.Model + A torch or tensorflow model e.g., torchvision.models that is subject to explanation. + x_batch: np.ndarray + A np.ndarray which contains the input data that are explained. + y_batch: np.ndarray + A np.ndarray which contains the output labels that are explained. + a_batch: np.ndarray, optional + A np.ndarray which contains pre-computed attributions i.e., explanations. + s_batch: np.ndarray, optional + A np.ndarray which contains segmentation masks that matches the input. + custom_batch: any + Gives flexibility ot the user to use for evaluation, can hold any variable. + + Returns + ------- + None + """ + # Additional explain_func assert, as the one in general_preprocess() + # won't be executed when a_batch != None. + asserts.assert_explain_func(explain_func=self.explain_func) + + def compute_correlation_per_sample( + self, + ) -> Union[List[List[Any]], Dict[int, List[Any]]]: + + assert isinstance(self.evaluation_scores, dict), ( + "To compute the average correlation coefficient per sample for " + "Model Parameter Randomisation Test, 'last_result' " + "must be of type dict." + ) + layer_length = len( + self.evaluation_scores[list(self.evaluation_scores.keys())[0]] + ) + results: Dict[int, list] = {sample: [] for sample in range(layer_length)} + + for sample in results: + for layer in self.evaluation_scores: + results[sample].append(float(self.evaluation_scores[layer][sample])) + results[sample] = np.mean(results[sample]) + + corr_coeffs = list(results.values()) + + return corr_coeffs diff --git a/tests/helpers/test_explanation_func.py b/tests/helpers/test_explanation_func.py new file mode 100644 index 000000000..27ec9f1ff --- /dev/null +++ b/tests/helpers/test_explanation_func.py @@ -0,0 +1,1109 @@ +import pytest +from pytest_lazyfixture import lazy_fixture + +from zennit import attribution as zattr +from zennit import torchvision as ztv + +from quantus.functions.explanation_func import * +from quantus.functions.normalise_func import normalise_by_max + + +@pytest.mark.explain_func +@pytest.mark.parametrize( + "model,data,params,expected", + [ + # Zennit + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "canonizer": None, + "composite": None, + "attributor": zattr.Gradient, + "xai_lib": "zennit", + "softmax": False, + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "canonizer": None, + "composite": None, + "attributor": zattr.Gradient, + "xai_lib": "zennit", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "canonizer": ztv.SequentialMergeBatchNorm, + "composite": zcomp.EpsilonPlus, + "attributor": zattr.Gradient, + "xai_lib": "zennit", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "canonizer": ztv.SequentialMergeBatchNorm, + "composite": zcomp.EpsilonPlus, + "attributor": zattr.Gradient, + "xai_lib": "zennit", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "canonizer": None, + "composite": "epsilon_alpha2_beta1_flat", + "attributor": zattr.Gradient, + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "canonizer": None, + "composite": "epsilon_alpha2_beta1_flat", + "attributor": zattr.Gradient, + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "canonizer": None, + "composite": "guided_backprop", + "attributor": zattr.Gradient, + "xai_lib": "zennit", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "canonizer": None, + "composite": "guided_backprop", + "attributor": zattr.Gradient, + "xai_lib": "zennit", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "canonizer": None, + "composite": "guided_backprop", + "attributor": zattr.Gradient, + "xai_lib": "zennit", + "reduce_axes": (1,), + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "canonizer": None, + "composite": "guided_backprop", + "attributor": zattr.Gradient, + "xai_lib": "zennit", + "reduce_axes": (1, 2), + }, + {"shape": (8, 1, 1, 28)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "canonizer": None, + "composite": "guided_backprop", + "attributor": zattr.Gradient, + "xai_lib": "zennit", + "reduce_axes": (3,), + }, + {"shape": (8, 1, 28, 1)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "canonizer": None, + "composite": "guided_backprop", + "attributor": zattr.Gradient, + "xai_lib": "zennit", + "reduce_axes": (0, 1), + }, + {"exception": AssertionError}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "canonizer": None, + "composite": "guided_backprop", + "attributor": zattr.Gradient, + "xai_lib": "zennit", + "reduce_axes": (1, 2, 3, 4, 5), + }, + {"exception": AssertionError}, + ), + # Captum + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Saliency", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Saliency", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "GradientShap", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "GradientShap", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "IntegratedGradients", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "IntegratedGradients", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "InputXGradient", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "InputXGradient", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Occlusion", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Occlusion", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "FeatureAblation", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "FeatureAblation", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "LayerGradCam", + "gc_layer": "model._modules.get('conv_2')", + "interpolate": (100,), + "interpolate_method": "nearest", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "LayerGradCam", + "gc_layer": "model._modules.get('conv_2')", + "interpolate": (28, 28), + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + {"method": "GradCam", "gc_layer": "model._modules.get('conv_2')"}, + {"shape": (8, 1, 8, 8)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Control Var. Sobel Filter", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Control Var. Sobel Filter", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("titanic_model_torch"), + lazy_fixture("titanic_dataset"), + { + "method": "Control Var. Sobel Filter", + }, + {"min": -10000.0, "max": 10000.0}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Gradient", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Gradient", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Control Var. Constant", + "constant_value": 0.0, + }, + {"value": 0.0}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Control Var. Constant", + "constant_value": 0.0, + }, + {"value": 0.0}, + ), + ( + lazy_fixture("titanic_model_torch"), + lazy_fixture("titanic_dataset"), + { + "method": "Control Var. Constant", + "constant_value": 0.0, + }, + {"value": 0.0}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Control Var. Random Uniform", + }, + {"min": 0.0, "max": 1.0}, + ), + ( + lazy_fixture("titanic_model_torch"), + lazy_fixture("titanic_dataset"), + { + "method": "Control Var. Random Uniform", + }, + {"min": 0.0, "max": 1.0}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Control Var. Random Uniform", + }, + {"min": 0.0, "max": 1.0}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Gradient", + "reduce_axes": (1,), + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Gradient", + "normalise": True, + "abs": True, + "normalise_func": normalise_by_max, + "reduce_axes": (1, 2), + }, + {"shape": (8, 1, 1, 28)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Gradient", + "reduce_axes": (3,), + }, + {"shape": (8, 1, 28, 1)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Gradient", + "reduce_axes": (0, 1), + }, + {"exception": AssertionError}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Gradient", + "reduce_axes": (1, 2, 3, 4, 5, 6), + }, + {"exception": AssertionError}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + {"method": "DeepLift"}, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + {"method": "DeepLift"}, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + {"method": "DeepLiftShap"}, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + {"method": "DeepLiftShap"}, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "GuidedGradCam", + "gc_layer": "model._modules.get('conv_2')", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "GuidedGradCam", + "gc_layer": "model._modules.get('conv_2')", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Deconvolution", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Deconvolution", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "FeaturePermutation", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "FeaturePermutation", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Lime", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Lime", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "KernelShap", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "KernelShap", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + {"method": "LRP", "softmax": False}, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "LayerConductance", + "gc_layer": "model._modules.get('conv_2')", + "interpolate": (28, 28), + "interpolate_mode": "nearest", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "LayerConductance", + "gc_layer": "model._modules.get('conv_2')", + "interpolate": (100,), + "interpolate_mode": "nearest", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "LayerActivation", + "gc_layer": "model._modules.get('conv_2')", + "interpolate": (28, 28), + "interpolate_mode": "nearest", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "LayerActivation", + "gc_layer": "model._modules.get('conv_2')", + "interpolate": (100,), + "interpolate_mode": "nearest", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "InternalInfluence", + "gc_layer": "model._modules.get('conv_2')", + "interpolate": (28, 28), + "interpolate_mode": "nearest", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "InternalInfluence", + "gc_layer": "model._modules.get('conv_2')", + "interpolate": (100,), + "interpolate_mode": "nearest", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "LayerGradientXActivation", + "xai_lib_kwargs": {"layer": "model._modules.get('conv_2')"}, + "interpolate": (28, 28), + "interpolate_mode": "nearest", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "LayerGradientXActivation", + "gc_layer": "model._modules.get('conv_2')", + "interpolate": (100,), + "interpolate_mode": "nearest", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "LayerGradientXActivation", + "xai_lib_kwargs": {}, + "interpolate": (100,), + "interpolate_mode": "nearest", + }, + {"exception": ValueError}, + ), + # tf-explain + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "VanillaGradients", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "Gradient", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "OcclusionSensitivity", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "IntegratedGradients", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "GradientsInput", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + {"method": "SmoothGrad", "softmax": False}, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + {}, + {"warning": UserWarning}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + {"method": "VanillaGradients", "softmax": False}, + {"warning": UserWarning}, + ), + ( + None, + lazy_fixture("load_mnist_images_tf"), + {}, + {"exception": ValueError}, + ), + ( + lazy_fixture("load_cifar10_model_tf"), + lazy_fixture("load_cifar10_images"), + { + "method": "VanillaGradients", + }, + {"shape": (8, 32, 32)}, + ), + ( + lazy_fixture("load_cifar10_model_tf"), + lazy_fixture("load_cifar10_images"), + { + "method": "Gradient", + }, + {"shape": (8, 32, 32)}, + ), + ( + lazy_fixture("load_cifar10_model_tf"), + lazy_fixture("load_cifar10_images"), + {"method": "OcclusionSensitivity", "reduce_axes": (3,)}, + {"shape": (8, 32, 32)}, + ), + ( + lazy_fixture("load_cifar10_model_tf"), + lazy_fixture("load_cifar10_images"), + { + "method": "IntegratedGradients", + }, + {"shape": (8, 32, 32)}, + ), + ( + lazy_fixture("load_cifar10_model_tf"), + lazy_fixture("load_cifar10_images"), + { + "method": "GradientsInput", + }, + {"shape": (8, 32, 32)}, + ), + ( + lazy_fixture("load_cifar10_model_tf"), + lazy_fixture("load_cifar10_images"), + {"method": "SmoothGrad", "softmax": False}, + {"shape": (8, 32, 32)}, + ), + ( + lazy_fixture("load_cifar10_model_tf"), + lazy_fixture("load_cifar10_images"), + {}, + {"warning": UserWarning}, + ), + ( + lazy_fixture("load_cifar10_model_tf"), + lazy_fixture("load_cifar10_images"), + {"method": "VanillaGradients", "softmax": False}, + {"warning": UserWarning}, + ), + ( + lazy_fixture("load_cifar10_model_tf"), + lazy_fixture("load_cifar10_images"), + {"method": "VanillaGradients", "softmax": False, "reduce_axes": (5,)}, + {"exception": AssertionError}, + ), + ( + lazy_fixture("load_cifar10_model_tf"), + lazy_fixture("load_cifar10_images"), + {"method": "VanillaGradients", "softmax": False, "reduce_axes": (0,)}, + {"exception": AssertionError}, + ), + ], +) +def test_explain_func( + model: ModelInterface, + data: np.ndarray, + params: dict, + expected: Union[float, dict, bool], +): + x_batch, y_batch = (data["x_batch"], data["y_batch"]) + if "exception" in expected: + with pytest.raises(expected["exception"]): + a_batch = explain(model=model, inputs=x_batch, targets=y_batch, **params) + return + + a_batch = explain(model=model, inputs=x_batch, targets=y_batch, **params) + + if isinstance(expected, float): + assert all(s == expected for s in a_batch), "Test failed." + else: + if "min" in expected and "max" in expected: + assert (a_batch.min() >= expected["min"]) & ( + a_batch.max() <= expected["max"] + ), "Test failed." + elif "min" in expected and "max" not in expected: + assert a_batch.min() >= expected["min"], "Test failed." + elif "min" not in expected and "max" in expected: + assert a_batch.max() <= expected["max"], "Test failed." + elif "value" in expected: + assert all( + s == expected["value"] for s in a_batch.flatten() + ), "Test failed." + elif "shape" in expected: + assert a_batch.shape == expected["shape"], "Test failed." + elif "warning" in expected: + with pytest.warns(expected["warning"]): + a_batch = explain( + model=model, inputs=x_batch, targets=y_batch, **params + ) + + +@pytest.mark.explain_func +@pytest.mark.parametrize( + "model,data,params,expected", + [ + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Saliency", + }, + {"shape": (10, 1, 100)}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Saliency", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Control Var. Constant", + "constant_value": 0.0, + }, + {"value": 0.0}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Control Var. Constant", + "constant_value": 0.0, + }, + {"value": 0.0}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "LayerGradCam", + }, + {"exception": ValueError}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "LayerGradCam", + }, + {"exception": ValueError}, + ), + ], +) +def test_generate_captum_explanation( + model: ModelInterface, + data: np.ndarray, + params: dict, + expected: Union[float, dict, bool], +): + x_batch, y_batch = (data["x_batch"], data["y_batch"]) + + if "exception" in expected: + with pytest.raises(expected["exception"]): + a_batch = generate_captum_explanation( + model=model, inputs=x_batch, targets=y_batch, **params + ) + return + + a_batch = generate_captum_explanation( + model=model, inputs=x_batch, targets=y_batch, **params + ) + + if isinstance(expected, float): + assert all(s == expected for s in a_batch), "Test failed." + else: + if "min" in expected and "max" in expected: + assert (a_batch.min() >= expected["min"]) & ( + a_batch.max() <= expected["max"] + ), "Test failed." + elif "min" in expected and "max" not in expected: + assert a_batch.min() >= expected["min"], "Test failed." + elif "min" not in expected and "max" in expected: + assert a_batch.max() <= expected["max"], "Test failed." + elif "shape" in expected: + assert a_batch.shape == expected["shape"], "Test failed." + elif "value" in expected: + assert all( + s == expected["value"] for s in a_batch.flatten() + ), "Test failed." + + +@pytest.mark.explain_func +@pytest.mark.parametrize( + "model,data,params,expected", + [ + ( + lazy_fixture("load_1d_3ch_conv_model_tf"), + lazy_fixture("almost_uniform_1d_no_abatch_channel_last"), + { + "method": "VanillaGradients", + }, + {"exception": ValueError}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "VanillaGradients", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model_tf"), + lazy_fixture("almost_uniform_1d_no_abatch_channel_last"), + { + "method": "OcclusionSensitivity", + }, + {"exception": IndexError}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "OcclusionSensitivity", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model_tf"), + lazy_fixture("almost_uniform_1d_no_abatch_channel_last"), + { + "method": "GradientsInput", + }, + {"exception": ValueError}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "GradientsInput", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model_tf"), + lazy_fixture("almost_uniform_1d_no_abatch_channel_last"), + { + "method": "IntegratedGradients", + }, + {"exception": ValueError}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "IntegratedGradients", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model_tf"), + lazy_fixture("almost_uniform_1d_no_abatch_channel_last"), + { + "method": "GradCAM", + }, + {"exception": ValueError}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "GradCAM", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model_tf"), + lazy_fixture("almost_uniform_1d_no_abatch_channel_last"), + { + "method": "GradCAM", + "gc_layer": "dense_1", + }, + {"exception": Exception}, + ), + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "GradCAM", + "gc_layer": "dense_1", + }, + {"exception": ValueError}, + ), + ], +) +def test_generate_tf_explanation( + model: ModelInterface, + data: np.ndarray, + params: dict, + expected: Union[float, dict, bool], +): + x_batch, y_batch = (data["x_batch"], data["y_batch"]) + + if "exception" in expected: + with pytest.raises(expected["exception"]): + a_batch = generate_tf_explanation( + model=model, inputs=x_batch, targets=y_batch, **params + ) + return + + a_batch = generate_tf_explanation( + model=model, inputs=x_batch, targets=y_batch, **params + ) + + if isinstance(expected, float): + assert all(s == expected for s in a_batch), "Test failed." + else: + if "min" in expected and "max" in expected: + assert (a_batch.min() >= expected["min"]) & ( + a_batch.max() <= expected["max"] + ), "Test failed." + elif "min" in expected and "max" not in expected: + assert a_batch.min() >= expected["min"], "Test failed." + elif "shape" in expected: + assert a_batch.shape == expected["shape"], "Test failed." + elif "min" not in expected and "max" in expected: + assert a_batch.max() <= expected["max"], "Test failed." + elif "value" in expected: + assert all( + s == expected["value"] for s in a_batch.flatten() + ), "Test failed." + + +@pytest.mark.explain_func +@pytest.mark.parametrize( + "model,data,params,expected", + [ + ( + lazy_fixture("load_mnist_model_tf"), + lazy_fixture("load_mnist_images_tf"), + { + "method": "VanillaGradients", + }, + {"shape": (8, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model_tf"), + lazy_fixture("almost_uniform_1d_no_abatch_channel_last"), + { + "method": "VanillaGradients", + }, + {"exception": ValueError}, + ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "method": "Gradient", + }, + {"shape": (8, 1, 28, 28)}, + ), + ( + lazy_fixture("load_1d_3ch_conv_model"), + lazy_fixture("almost_uniform_1d_no_abatch"), + { + "method": "Gradient", + }, + {"shape": (10, 1, 100)}, + ), + ], +) +def test_get_explanation( + model: ModelInterface, + data: np.ndarray, + params: dict, + expected: Union[float, dict, bool], +): + x_batch, y_batch = data["x_batch"], data["y_batch"] + + if "exception" in expected: + with pytest.raises(expected["exception"]): + a_batch = get_explanation( + model=model, inputs=x_batch, targets=y_batch, **params + ) + return + + a_batch = get_explanation(model=model, inputs=x_batch, targets=y_batch, **params) + + if isinstance(expected, float): + assert all(s == expected for s in a_batch), "Test failed." + else: + if "shape" in expected: + assert a_batch.shape == expected["shape"], "Test failed." + elif "value" in expected: + assert all( + s == expected["value"] for s in a_batch.flatten() + ), "Test failed." diff --git a/tests/helpers/test_loss_func.py b/tests/helpers/test_loss_func.py new file mode 100644 index 000000000..5630cf2e1 --- /dev/null +++ b/tests/helpers/test_loss_func.py @@ -0,0 +1,36 @@ +from typing import Union + +import pytest +from pytest_lazyfixture import lazy_fixture + +from quantus.functions.loss_func import * + + +@pytest.fixture +def atts_half(): + return {"a": np.array([-1, 1, 1]), "b": np.array([0, 0, 2])} + + +@pytest.fixture +def atts_diff(): + return {"a": np.array([0, 1, 0, 1]), "b": np.array([1, 2, 1, 0])} + + +@pytest.fixture +def atts_same(): + a = np.random.uniform(0, 0.1, size=(10)) + return {"a": a, "b": a} + + +@pytest.mark.loss_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, 0.0), + (lazy_fixture("atts_diff"), {}, 1.0), + (lazy_fixture("atts_half"), {}, 1.0), + ], +) +def test_mse(data: np.ndarray, params: dict, expected: Union[float, dict, bool]): + out = mse(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." diff --git a/tests/helpers/test_mosaic_func.py b/tests/helpers/test_mosaic_func.py new file mode 100644 index 000000000..d3c437e6f --- /dev/null +++ b/tests/helpers/test_mosaic_func.py @@ -0,0 +1,85 @@ +import pytest +from pytest_lazyfixture import lazy_fixture + +from quantus.functions.mosaic_func import * + + +@pytest.mark.mosaic_func +@pytest.mark.parametrize( + "data,params", + [ + ( + lazy_fixture("load_mnist_images"), + {"mosaics_per_class": 4, "seed": 777}, + ), + ( + lazy_fixture("load_cifar10_images"), + {"mosaics_per_class": 4, "seed": 777}, + ), + ( + lazy_fixture("load_mnist_images"), + {"mosaics_per_class": 10, "seed": 777}, + ), + ], +) +def test_mosaic_func( + data: np.ndarray, + params: dict, +): + x_batch, y_batch = (data["x_batch"], data["y_batch"]) + return_params = mosaic_creation( + images=x_batch, + labels=y_batch, + mosaics_per_class=params["mosaics_per_class"], + seed=params["seed"], + ) + ( + all_mosaics, + mosaic_indices_list, + mosaic_labels_list, + p_batch_list, + target_list, + ) = return_params + + _, _, width, height = x_batch.shape + for mosaic in all_mosaics: + _, width_mosaic, height_mosaic = mosaic.shape + assert width_mosaic / width == 2, "Test failed." + assert height_mosaic / height == 2, "Test failed." + + for mosaic, mosaic_indices in zip(all_mosaics, mosaic_indices_list): + assert len(mosaic_indices) == 4 + assert np.all( + np.equal(mosaic[:, :width, :height], x_batch[mosaic_indices[0]]) + ), "Test failed." + assert np.all( + np.equal(mosaic[:, width:, :height], x_batch[mosaic_indices[1]]) + ), "Test failed." + assert np.all( + np.equal(mosaic[:, :width, height:], x_batch[mosaic_indices[2]]) + ), "Test failed." + assert np.all( + np.equal(mosaic[:, width:, height:], x_batch[mosaic_indices[3]]) + ), "Test failed." + + for mosaic_labels in mosaic_labels_list: + assert len(mosaic_labels) == 4, "Test failed." + assert ( + len(set([x for x in mosaic_labels if mosaic_labels.count(x) == 2])) > 0 + ), "Test failed." + + for p_batch in p_batch_list: + assert len(p_batch) == 4, "Test failed." + assert ( + len(set([x for x in p_batch if p_batch.count(x) == 2])) == 2 + ), "Test failed." + + for mosaic_labels, p_batch, target in zip( + mosaic_labels_list, p_batch_list, target_list + ): + mosaic_labels = np.array(mosaic_labels) + p_batch = np.array(p_batch) + assert np.unique(mosaic_labels[p_batch == 1]) == target, "Test failed." + + for target in set(target_list): + assert target_list.count(target) == params["mosaics_per_class"], "Test failed." diff --git a/tests/helpers/test_norm_func.py b/tests/helpers/test_norm_func.py new file mode 100644 index 000000000..3bba9ff4a --- /dev/null +++ b/tests/helpers/test_norm_func.py @@ -0,0 +1,55 @@ +from typing import Union + +import pytest +from pytest_lazyfixture import lazy_fixture + +from quantus.functions.norm_func import * + + +@pytest.fixture +def atts_norm_ones(): + return np.ones((10)) + + +@pytest.fixture +def atts_norm_fill(): + return np.array([1, 2, 3, 4, 10]) + + +@pytest.mark.norm_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_norm_ones"), {}, 3.1622776601683795), + (lazy_fixture("atts_norm_fill"), {}, 11.40175425099138), + ], +) +def test_fro_norm(data: np.ndarray, params: dict, expected: Union[float, dict, bool]): + out = fro_norm(a=data) + assert out == expected, "Test failed." + + +@pytest.mark.norm_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_norm_ones"), {}, 1.0), + (lazy_fixture("atts_norm_fill"), {}, 10), + ], +) +def test_linf_norm(data: np.ndarray, params: dict, expected: Union[float, dict, bool]): + out = linf_norm(a=data) + assert out == expected, "Test failed." + + +@pytest.mark.norm_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_norm_ones"), {}, 3.1622776601683795), + (lazy_fixture("atts_norm_fill"), {}, 11.40175425099138), + ], +) +def test_l2_norm(data: dict, params: dict, expected: Union[float, dict, bool]): + out = l2_norm(a=data) + assert out == expected, "Test failed." diff --git a/tests/helpers/test_normalise_func.py b/tests/helpers/test_normalise_func.py new file mode 100644 index 000000000..a465789fe --- /dev/null +++ b/tests/helpers/test_normalise_func.py @@ -0,0 +1,402 @@ +from typing import Union + +import pytest +from pytest_lazyfixture import lazy_fixture + +from quantus.functions.normalise_func import * + + +@pytest.fixture +def atts_normalise_seq_0(): + return np.array([0.0, 0.0]) + + +@pytest.fixture +def atts_normalise_seq_1(): + return np.array([0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0]) + + +@pytest.fixture +def atts_normalise_seq_2(): + return np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]) + + +@pytest.fixture +def atts_normalise_seq_3(): + return np.array([0.0, -1.0, -2.0, -3.0, -4.0, -5.0]) + + +@pytest.fixture +def atts_normalise_seq_with_batch_dim(): + return np.array( + [ + [0.0, 1.0, 2.0, 3.0, 4.0, 5.0], + [0.0, -1.0, -2.0, -3.0, -4.0, -5.0], + ] + ) + + +@pytest.fixture +def atts_normalise_seq_with_batch_dim2(): + return np.array( + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 2.0, 4.0, 6.0, 8.0, 10.0], + ] + ) + + +@pytest.fixture +def atts_normalise_secmom_seq_1(): + return np.array([-1.0, 1.0]) + + +@pytest.fixture +def atts_normalise_secmom_seq_with_batch_dim(): + return np.array( + [ + [0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0], + [0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0], + ] + ) + + +@pytest.fixture +def atts_normalise_secmom_img_with_batch_dim(): + return np.array( + [ + [ + [0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0], + [0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0], + [0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0], + [0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0], + ], + [ + [0.0, -1.0, -2.0, -3.0, -4.0, -4.0, -5.0, 1.0], + [0.0, -1.0, -2.0, -3.0, -4.0, -4.0, -5.0, 1.0], + [0.0, -1.0, -2.0, -3.0, -4.0, -4.0, -5.0, 1.0], + [0.0, -1.0, -2.0, -3.0, -4.0, -4.0, -5.0, 1.0], + ], + ] + ) + + +@pytest.fixture +def atts_normalise_img_with_batch_dim(): + return np.array( + [ + [ + [0.0, 1.0, 2.0, 3.0, 4.0, 5.0], + [0.0, 1.0, 2.0, 3.0, 4.0, 5.0], + [0.0, 1.0, 2.0, 3.0, 4.0, 5.0], + [0.0, 1.0, 2.0, 3.0, 4.0, 5.0], + ], + [ + [0.0, -1.0, -2.0, -3.0, -4.0, -5.0], + [0.0, -1.0, -2.0, -3.0, -4.0, -5.0], + [0.0, -1.0, -2.0, -3.0, -4.0, -5.0], + [0.0, -1.0, -2.0, -3.0, -4.0, -5.0], + ], + ] + ) + + +@pytest.fixture +def atts_denormalise(): + return np.zeros((3, 2, 2)) + + +@pytest.mark.normalise_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("atts_normalise_seq_0"), + {"normalise_axes": [0]}, + np.array([0.0, 0.0]), + ), + ( + lazy_fixture("atts_normalise_seq_1"), + {"normalise_axes": [0]}, + np.array([0.0, 0.2, 0.4, 0.6, 0.8, 0.8, 1.0, -0.2]), + ), + ( + lazy_fixture("atts_normalise_seq_2"), + {"normalise_axes": [0]}, + np.array([0.0, 0.2, 0.4, 0.6, 0.8, 1.0]), + ), + ( + lazy_fixture("atts_normalise_seq_3"), + {"normalise_axes": [0]}, + np.array([0.0, -0.2, -0.4, -0.6, -0.8, -1.0]), + ), + ( + lazy_fixture("atts_normalise_seq_with_batch_dim"), + {"normalise_axes": [1]}, + np.array( + [ + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + [0.0, -0.2, -0.4, -0.6, -0.8, -1.0], + ] + ), + ), + ( + lazy_fixture("atts_normalise_seq_with_batch_dim2"), + {"normalise_axes": [0, 1]}, + np.array( + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + ] + ), + ), + ( + lazy_fixture("atts_normalise_img_with_batch_dim"), + {"normalise_axes": [1, 2]}, + np.array( + [ + [ + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + ], + [ + [0.0, -0.2, -0.4, -0.6, -0.8, -1.0], + [0.0, -0.2, -0.4, -0.6, -0.8, -1.0], + [0.0, -0.2, -0.4, -0.6, -0.8, -1.0], + [0.0, -0.2, -0.4, -0.6, -0.8, -1.0], + ], + ] + ), + ), + ( + lazy_fixture("atts_normalise_seq_1"), + {"normalise_axes": None}, + np.array([0.0, 0.2, 0.4, 0.6, 0.8, 0.8, 1.0, -0.2]), + ), + ], +) +def test_normalise_by_max( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = normalise_by_max(a=data, **params) + assert np.all(out == expected), f"Test failed. (expected: {expected}, is: {out})" + + +@pytest.mark.normalise_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("atts_normalise_seq_0"), + {"normalise_axes": [0]}, + np.array([0.0, 0.0]), + ), + ( + lazy_fixture("atts_normalise_seq_1"), + {"normalise_axes": [0]}, + np.array([0.0, 0.2, 0.4, 0.6, 0.8, 0.8, 1.0, -1.0]), + ), + ( + lazy_fixture("atts_normalise_seq_2"), + {"normalise_axes": [0]}, + np.array([0.0, 0.2, 0.4, 0.6, 0.8, 1.0]), + ), + ( + lazy_fixture("atts_normalise_seq_3"), + {"normalise_axes": [0]}, + np.array([0.0, -0.2, -0.4, -0.6, -0.8, -1.0]), + ), + ( + lazy_fixture("atts_normalise_seq_with_batch_dim"), + {"normalise_axes": [1]}, + np.array( + [ + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + [0.0, -0.2, -0.4, -0.6, -0.8, -1.0], + ] + ), + ), + ( + lazy_fixture("atts_normalise_seq_with_batch_dim2"), + {"normalise_axes": [0, 1]}, + np.array( + [ + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + ] + ), + ), + ( + lazy_fixture("atts_normalise_img_with_batch_dim"), + {"normalise_axes": [1, 2]}, + np.array( + [ + [ + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + ], + [ + [0.0, -0.2, -0.4, -0.6, -0.8, -1.0], + [0.0, -0.2, -0.4, -0.6, -0.8, -1.0], + [0.0, -0.2, -0.4, -0.6, -0.8, -1.0], + [0.0, -0.2, -0.4, -0.6, -0.8, -1.0], + ], + ] + ), + ), + ( + lazy_fixture("atts_normalise_seq_2"), + {"normalise_axes": None}, + np.array([0.0, 0.2, 0.4, 0.6, 0.8, 1.0]), + ), + ], +) +def test_normalise_by_negative( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = normalise_by_negative(a=data, **params) + assert np.all(out == expected), f"Test failed. (expected: {expected}, is: {out})" + + +@pytest.mark.normalise_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("atts_denormalise"), + {}, + np.array( + [ + [[0.485, 0.485], [0.485, 0.485]], + [[0.456, 0.456], [0.456, 0.456]], + [[0.406, 0.406], [0.406, 0.406]], + ] + ), + ), + ( + [1, 2], + {}, + [1, 2], + ), + ], +) +def test_denormalise( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = denormalise( + a=data, + mean=np.array([0.485, 0.456, 0.406]), + std=np.array([0.229, 0.224, 0.225]), + **params, + ) + assert np.all( + o == e for o, e in zip(np.array(out).flatten(), np.array(expected).flatten()) + ), "Test failed." + + +@pytest.mark.normalise_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("atts_normalise_seq_0"), + {"normalise_axes": [0]}, + np.array([0.0, 0.0]), + ), + ( + lazy_fixture("atts_normalise_seq_1"), + {"normalise_axes": [0]}, + np.array( + [ + 0.0, + 1.0 / 3.0, + 2.0 / 3.0, + 3.0 / 3.0, + 4.0 / 3.0, + 4.0 / 3.0, + 5.0 / 3.0, + -1.0 / 3.0, + ] + ), + ), + ( + lazy_fixture("atts_normalise_secmom_seq_1"), + {"normalise_axes": [0]}, + np.array([-1.0, 1.0]), + ), + ( + lazy_fixture("atts_normalise_secmom_seq_with_batch_dim"), + {"normalise_axes": [1]}, + np.array( + [ + [ + 0.0, + 1.0 / 3.0, + 2.0 / 3.0, + 3.0 / 3.0, + 4.0 / 3.0, + 4.0 / 3.0, + 5.0 / 3.0, + -1.0 / 3.0, + ], + [ + 0.0, + 1.0 / 3.0, + 2.0 / 3.0, + 3.0 / 3.0, + 4.0 / 3.0, + 4.0 / 3.0, + 5.0 / 3.0, + -1.0 / 3.0, + ], + ] + ), + ), + ( + lazy_fixture("atts_normalise_secmom_img_with_batch_dim"), + {"normalise_axes": [1, 2]}, + np.array( + [ + [ + [0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0], + [0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0], + [0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0], + [0.0, 1.0, 2.0, 3.0, 4.0, 4.0, 5.0, -1.0], + ], + [ + [0.0, -1.0, -2.0, -3.0, -4.0, -4.0, -5.0, 1.0], + [0.0, -1.0, -2.0, -3.0, -4.0, -4.0, -5.0, 1.0], + [0.0, -1.0, -2.0, -3.0, -4.0, -4.0, -5.0, 1.0], + [0.0, -1.0, -2.0, -3.0, -4.0, -4.0, -5.0, 1.0], + ], + ] + ) + / 3.0, + ), + ( + lazy_fixture("atts_normalise_seq_1"), + {"normalise_axes": None}, + np.array( + [ + 0.0, + 1.0 / 3.0, + 2.0 / 3.0, + 3.0 / 3.0, + 4.0 / 3.0, + 4.0 / 3.0, + 5.0 / 3.0, + -1.0 / 3.0, + ] + ), + ), + ], +) +def test_normalise_by_average_second_moment_estimate( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = normalise_by_average_second_moment_estimate(a=data, **params) + assert np.all(out == expected), f"Test failed. (expected: {expected}, is: {out})" diff --git a/tests/helpers/test_perturb_func.py b/tests/helpers/test_perturb_func.py new file mode 100644 index 000000000..0f2b87b67 --- /dev/null +++ b/tests/helpers/test_perturb_func.py @@ -0,0 +1,491 @@ +import pytest +from pytest_lazyfixture import lazy_fixture + +from quantus.helpers import utils +from quantus.functions.perturb_func import * + + +@pytest.fixture +def input_zeros_1d_1ch(): + return np.zeros(shape=(1, 224)) + + +@pytest.fixture +def input_zeros_1d_3ch(): + return np.zeros(shape=(3, 224)) + + +@pytest.fixture +def input_zeros_2d_1ch(): + return np.zeros(shape=(1, 224, 224)) + + +@pytest.fixture +def input_zeros_2d_3ch(): + return np.zeros(shape=(3, 224, 224)) + + +@pytest.fixture +def input_zeros_2d_3ch_flattened(): + return np.zeros(shape=(3, 224, 224)).flatten() + + +@pytest.fixture +def input_uniform_2d_3ch_flattened(): + return np.random.uniform(0, 0.1, size=(3, 224, 224)).flatten() + + +@pytest.fixture +def input_uniform_1d_3ch(): + return np.random.uniform(0, 0.1, size=(3, 224)) + + +@pytest.fixture +def input_uniform_2d_3ch(): + return np.random.uniform(0, 0.1, size=(3, 224, 224)) + + +@pytest.fixture +def input_uniform_2d_3ch_flattened(): + return np.random.uniform(0, 0.1, size=(3, 224, 224)).flatten() + + +@pytest.fixture +def input_uniform_3d_3ch(): + return np.random.uniform(0, 0.1, size=(3, 224, 224, 224)) + + +@pytest.fixture +def input_uniform_mnist(): + return np.random.uniform(0, 0.1, size=(1, 28, 28)) + + +@pytest.mark.perturb_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("input_zeros_2d_3ch"), + { + "indices": [0, 2], + "indexed_axes": [1, 2], + "perturb_baseline": 1.0, + }, + 1, + ), + ( + lazy_fixture("input_zeros_2d_3ch"), + { + "indices": [0, 2], + "indexed_axes": [0], + "perturb_baseline": 1.0, + }, + 1, + ), + ( + lazy_fixture("input_zeros_2d_3ch"), + { + "indices": [0, 2], + "indexed_axes": [0, 1, 2], + "perturb_baseline": 1.0, + }, + 1, + ), + ( + lazy_fixture("input_zeros_2d_3ch_flattened"), + { + "indices": [0, 2], + "indexed_axes": [0], + "perturb_baseline": 1.0, + }, + 1, + ), + ( + lazy_fixture("input_zeros_1d_1ch"), + { + "indices": [0, 2, 112, 113, 128, 223], + "indexed_axes": [0, 1], + "perturb_baseline": 1.0, + }, + 1, + ), + ( + lazy_fixture("input_zeros_1d_3ch"), + { + "indices": [0, 2, 112, 113, 128, 223], + "indexed_axes": [1], + "perturb_baseline": np.array([1, 2, 3]), + }, + np.array([1, 2, 3]), + ), + ( + lazy_fixture("input_zeros_2d_1ch"), + { + "indices": [0, 2, 224, 226, 448, 450], + "indexed_axes": [1, 2], + "perturb_baseline": np.array([1]), + }, + np.array([1]), + ), + ( + lazy_fixture("input_zeros_2d_3ch"), + { + "indices": [0, 2, 224, 226, 448, 450], + "indexed_axes": [0, 1, 2], + "perturb_baseline": 1.0, + }, + 1, + ), + ], +) +def test_baseline_replacement_by_indices( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + # Output + out = baseline_replacement_by_indices(arr=data, **params) + + # Indices + indices = np.unravel_index( + params["indices"], tuple([data.shape[i] for i in params["indexed_axes"]]) + ) + if not np.array(indices).ndim > 1: + indices = [np.array(indices)] + indices = list(indices) + for i in range(0, params["indexed_axes"][0]): + indices = slice(None), *indices + indices = tuple(indices) + + if isinstance(expected, (int, float)): + assert np.all( + [i == expected for i in out[indices].flatten()] + ), f"Test failed.{out}" + + +@pytest.mark.perturb_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("input_zeros_2d_3ch"), + { + "indices": [0, 2], + "indexed_axes": [1, 2], + "input_shift": -1.0, + }, + -1, + ), + ( + lazy_fixture("input_zeros_2d_3ch"), + { + "indices": [0, 2], + "indexed_axes": [0], + "input_shift": -1.0, + }, + -1, + ), + ( + lazy_fixture("input_zeros_2d_3ch"), + { + "indices": [0, 2], + "indexed_axes": [0, 1, 2], + "input_shift": -1.0, + }, + -1, + ), + ( + lazy_fixture("input_zeros_2d_3ch_flattened"), + { + "indices": [0, 2], + "indexed_axes": [0], + "input_shift": -1.0, + }, + -1, + ), + ( + lazy_fixture("input_zeros_1d_1ch"), + { + "indices": [0, 2, 112, 113, 128, 223], + "indexed_axes": [0, 1], + "input_shift": -1.0, + }, + -1, + ), + ( + lazy_fixture("input_zeros_1d_3ch"), + { + "indices": [0, 2, 112, 113, 128, 223], + "indexed_axes": [1], + "input_shift": np.array([1, 2, 3]), + }, + np.array([1, 2, 3]), + ), + ( + lazy_fixture("input_zeros_2d_1ch"), + { + "indices": [0, 2, 224, 226, 448, 450], + "indexed_axes": [1, 2], + "input_shift": np.array([1]), + }, + np.array([1]), + ), + ( + lazy_fixture("input_zeros_2d_3ch"), + { + "indices": [0, 2, 224, 226, 448, 450], + "indexed_axes": [0, 1, 2], + "input_shift": 1.0, + }, + 1, + ), + ], +) +def test_baseline_replacement_by_shift( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + # Output + out = baseline_replacement_by_shift(arr=data, **params) + + # Indices + indices = np.unravel_index( + params["indices"], tuple([data.shape[i] for i in params["indexed_axes"]]) + ) + if not np.array(indices).ndim > 1: + indices = [np.array(indices)] + indices = list(indices) + for i in range(0, params["indexed_axes"][0]): + indices = slice(None), *indices + indices = tuple(indices) + + if isinstance(expected, (int, float)): + assert np.all( + [i == expected for i in out[indices].flatten()] + ), f"Test failed.{out}" + + +@pytest.mark.perturb_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("input_uniform_2d_3ch"), + { + "blur_kernel_size": 15, + "indices": [20, 15, 5, 27, 9], + "indexed_axes": [0, 1, 2], + }, + {}, + ), + ( + lazy_fixture("input_uniform_2d_3ch"), + { + "blur_kernel_size": [3, 4], + "indices": [20, 15, 5, 27, 9], + "indexed_axes": [1, 2], + }, + {}, + ), + ( + lazy_fixture("input_uniform_mnist"), + { + "blur_kernel_size": 15, + "indices": [20, 15, 5, 27, 9], + "indexed_axes": [0, 1], + }, + {}, + ), + ( + lazy_fixture("input_uniform_1d_3ch"), + { + "blur_kernel_size": 15, + "indices": [20, 15, 5, 27, 9], + "indexed_axes": [1], + }, + {}, + ), + ( + lazy_fixture("input_uniform_3d_3ch"), + { + "blur_kernel_size": 15, + "indices": [20, 15, 5, 27, 9], + "indexed_axes": [0, 1, 2, 3], + }, + {}, + ), + ], +) +def test_baseline_replacement_by_blur( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + + if "exception" in expected: + with pytest.raises(expected["exception"]): + out = baseline_replacement_by_blur( + arr=data, + indices=params["indices"], + indexed_axes=params["indexed_axes"], + blur_kernel_size=params["blur_kernel_size"], + ) + return + + out = baseline_replacement_by_blur( + arr=data, + indices=params["indices"], + indexed_axes=params["indexed_axes"], + blur_kernel_size=params["blur_kernel_size"], + ) + + indices = utils.expand_indices(data, params["indices"], params["indexed_axes"]) + patch_mask = np.zeros(data.shape, dtype=bool) + patch_mask[indices] = True + assert out.shape == data.shape, "Test failed." + assert np.all(out[patch_mask] != data[patch_mask]), "Test failed." + assert np.all(out[~patch_mask] == data[~patch_mask]), "Test failed." + + +@pytest.mark.perturb_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("input_uniform_1d_3ch"), + { + "indices": [0], + "indexed_axes": [0, 1], + }, + True, + ), + ( + lazy_fixture("input_uniform_2d_3ch"), + { + "indices": [0], + "indexed_axes": [0, 1, 2], + }, + True, + ), + ( + lazy_fixture("input_uniform_2d_3ch_flattened"), + { + "indices": [0], + "indexed_axes": [0], + }, + True, + ), + ], +) +def test_gaussian_noise( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = gaussian_noise(arr=data, **params) + assert any(out.flatten()[0] != out.flatten()), "Test failed." + assert any(out.flatten() != data.flatten()) == expected, "Test failed." + + +@pytest.mark.perturb_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("input_uniform_1d_3ch"), + { + "perturb_radius": 0.02, + "indices": [0], + "indexed_axes": [0, 1], + }, + True, + ), + ( + lazy_fixture("input_uniform_2d_3ch"), + { + "perturb_radius": 0.02, + "indices": [0], + "indexed_axes": [0, 1], + }, + True, + ), + ( + lazy_fixture("input_uniform_2d_3ch_flattened"), + { + "perturb_radius": 0.02, + "indices": [0], + "indexed_axes": [0], + }, + True, + ), + ], +) +def test_uniform_noise( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = uniform_noise(arr=data, **params) + assert any(out.flatten()[0] != out.flatten()), "Test failed." + assert any(out.flatten() != data.flatten()) == expected, "Test failed." + + +@pytest.mark.perturb_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("input_uniform_2d_3ch"), + {"perturb_angle": 30}, + True, + ), + ], +) +def test_rotation(data: dict, params: dict, expected: Union[float, dict, bool]): + out = rotation(arr=data, **params) + assert np.any(out != data) == expected, "Test failed." + + +@pytest.mark.perturb_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("input_uniform_2d_3ch"), + {"perturb_dx": 20, "perturb_baseline": "black"}, + True, + ) + ], +) +def test_translation_x_direction( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = translation_x_direction(arr=data, **params) + assert np.any(out != data) == expected, "Test failed." + + +@pytest.mark.perturb_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("input_uniform_2d_3ch"), + {"perturb_dx": 20, "perturb_baseline": "black"}, + True, + ) + ], +) +def test_translation_y_direction( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = translation_y_direction(arr=data, **params) + assert np.any(out != data) == expected, "Test failed." + + +@pytest.mark.perturb_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("input_uniform_2d_3ch"), + {"perturb_dx": 20}, + True, + ), + ], +) +def test_no_perturbation( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = no_perturbation(arr=data, **params) + assert (out == data).all() == expected, "Test failed." diff --git a/tests/helpers/test_pytorch_model.py b/tests/helpers/test_pytorch_model.py new file mode 100644 index 000000000..77d002bc3 --- /dev/null +++ b/tests/helpers/test_pytorch_model.py @@ -0,0 +1,255 @@ +from collections import OrderedDict +from typing import Union + +import numpy as np +import pytest +import torch +from pytest_lazyfixture import lazy_fixture +from scipy.special import softmax + +from quantus.helpers.model.pytorch_model import PyTorchModel + + +@pytest.fixture +def mock_input_torch_array(): + return {"x": np.zeros((1, 1, 28, 28))} + + +@pytest.mark.pytorch_model +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("mock_input_torch_array"), + { + "softmax": False, + "device": "cpu", + }, + np.array( + [ + -0.44321266, + 0.60336196, + 0.2091731, + -0.17474744, + -0.03755454, + 0.5306321, + -0.3079375, + 0.5329694, + -0.41116637, + -0.3060812, + ] + ), + ), + ( + lazy_fixture("mock_input_torch_array"), + { + "softmax": True, + "device": "cpu", + }, + softmax( + np.array( + [ + -0.44321266, + 0.60336196, + 0.2091731, + -0.17474744, + -0.03755454, + 0.5306321, + -0.3079375, + 0.5329694, + -0.41116637, + -0.3060812, + ] + ), + ), + ), + ], +) +def test_get_softmax_arg_model( + data: np.ndarray, params: dict, expected: Union[float, dict, bool], load_mnist_model +): + load_mnist_model.eval() + + model = PyTorchModel(load_mnist_model, softmax=True) + sm_model = model.get_softmax_arg_model() + sm_model.eval() + new_model = PyTorchModel(model=sm_model, **params) + + out = new_model.predict(x=data["x"]) + + assert np.allclose(out, expected), "Test failed." + + +@pytest.mark.pytorch_model +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("mock_input_torch_array"), + { + "softmax": False, + "device": "cpu", + }, + np.array( + [ + -0.44321266, + 0.60336196, + 0.2091731, + -0.17474744, + -0.03755454, + 0.5306321, + -0.3079375, + 0.5329694, + -0.41116637, + -0.3060812, + ] + ), + ), + ( + lazy_fixture("mock_input_torch_array"), + { + "softmax": True, + "device": "cpu", + }, + softmax( + np.array( + [ + -0.44321266, + 0.60336196, + 0.2091731, + -0.17474744, + -0.03755454, + 0.5306321, + -0.3079375, + 0.5329694, + -0.41116637, + -0.3060812, + ] + ), + ), + ), + ( + lazy_fixture("mock_input_torch_array"), + { + "softmax": True, + "device": "cpu", + "training": True, + }, + {"exception": AttributeError}, + ), + ], +) +def test_predict( + data: np.ndarray, params: dict, expected: Union[float, dict, bool], load_mnist_model +): + load_mnist_model.eval() + training = params.pop("training", False) + model = PyTorchModel(load_mnist_model, **params) + if training: + with pytest.raises(expected["exception"]): + model.train() + out = model.predict(x=data["x"]) + return + out = model.predict(x=data["x"]) + assert np.allclose(out, expected), "Test failed." + + +@pytest.mark.pytorch_model +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("flat_image_array"), + {"channel_first": True}, + np.zeros((1, 3, 28, 28)), + ), + ( + lazy_fixture("flat_image_array"), + {"channel_first": False}, + {"exception": ValueError}, + ), + ( + lazy_fixture("flat_sequence_array"), + {"channel_first": True}, + np.zeros((1, 3, 28)), + ), + ( + lazy_fixture("flat_sequence_array"), + {"channel_first": False}, + {"exception": ValueError}, + ), + ], +) +def test_shape_input( + data: np.ndarray, params: dict, expected: Union[float, dict, bool], load_mnist_model +): + load_mnist_model.eval() + model = PyTorchModel(load_mnist_model, channel_first=params["channel_first"]) + if not params["channel_first"]: + with pytest.raises(expected["exception"]): + out = model.shape_input(**data) + return + out = model.shape_input(**data) + assert np.array_equal(out, expected), "Test failed." + + +@pytest.mark.pytorch_model +@pytest.mark.parametrize("expected", [torch.nn.Module]) +def test_get_model(expected: Union[float, dict, bool], load_mnist_model): + model = PyTorchModel(load_mnist_model, channel_first=True) + out = model.get_model() + assert isinstance(out, expected), "Test failed." + + +@pytest.mark.pytorch_model +@pytest.mark.parametrize("expected", [OrderedDict]) +def test_state_dict(expected: Union[float, dict, bool], load_mnist_model): + model = PyTorchModel(load_mnist_model, channel_first=True) + out = model.state_dict() + assert isinstance(out, expected), "Test failed." + + +@pytest.mark.pytorch_model +def test_get_random_layer_generator(load_mnist_model): + model = PyTorchModel(load_mnist_model, channel_first=True) + + for layer_name, random_layer_model in model.get_random_layer_generator(): + + layer = getattr(model.get_model(), layer_name).parameters() + new_layer = getattr(random_layer_model, layer_name).parameters() + + assert layer != new_layer, "Test failed." + + +@pytest.mark.pytorch_model +@pytest.mark.parametrize( + "params", + [ + {}, + {"layer_names": ["conv_2"]}, + {"layer_indices": [0, 1]}, + {"layer_indices": [-1, -2]}, + ], + ids=["all layers", "2nd conv", "1st 2 layers", "last 2 layers"], +) +def test_get_hidden_layers_output(load_mnist_model, params): + model = PyTorchModel(load_mnist_model, channel_first=True) + X = np.random.random((32, 1, 28, 28)) + result = model.get_hidden_representations(X, **params) + assert isinstance(result, np.ndarray), "Must be a np.ndarray" + assert len(result.shape) == 2, "Must be a batch of 1D tensors" + assert result.shape[0] == X.shape[0], "Must have same batch size as input" + + +@pytest.mark.pytorch_model +def test_add_mean_shift_to_first_layer(load_mnist_model): + model = PyTorchModel(load_mnist_model, channel_first=True) + shift = -1 + arr = np.random.random((32, 1, 28, 28)) + X = torch.Tensor(arr).to(model.device) + + X_shift = torch.Tensor(arr + shift).to(model.device) + new_model = model.add_mean_shift_to_first_layer(shift, X.size()) + a1 = model.model(X) + a2 = new_model(X_shift) + assert torch.all(torch.isclose(a1, a2, atol=1e-04)) diff --git a/tests/helpers/test_similarity_func.py b/tests/helpers/test_similarity_func.py new file mode 100644 index 000000000..ed07114e6 --- /dev/null +++ b/tests/helpers/test_similarity_func.py @@ -0,0 +1,304 @@ +import pytest +from pytest_lazyfixture import lazy_fixture + +from quantus.functions.loss_func import mse +from quantus.functions.similarity_func import * + + +@pytest.fixture +def atts_half(): + return {"a": np.array([-1, 1, 1]), "b": np.array([0, 0, 2])} + + +@pytest.fixture +def atts_diff(): + return {"a": np.array([0, 1, 0, 1]), "b": np.array([1, 2, 1, 0])} + + +@pytest.fixture +def atts_same(): + a = np.random.uniform(0, 0.1, size=(10)) + return {"a": a, "b": a} + + +@pytest.fixture +def atts_same_linear(): + a = np.random.uniform(0, 0.1, size=(10)) + return {"a": a, "b": a * 3} + + +@pytest.fixture() +def atts_inverse(): + a = np.random.uniform(0, 0.1, size=(10)) + return {"a": a, "b": a * -3} + + +@pytest.fixture +def atts_lip_same(): + return { + "a": np.array([-1, 1, 1]), + "b": np.array([0, 0, 2]), + "c": np.array([-1, 1, 1]), + "d": np.array([0, 0, 2]), + } + + +@pytest.fixture +def atts_ssim_same(): + a = np.random.uniform(0, 0.1, size=(10)) + return {"a": a, "b": a} + + +@pytest.fixture +def atts_ssim_diff(): + return {"a": np.zeros((16, 16)), "b": np.ones((16, 16))} + + +@pytest.fixture +def atts_sq_diff_1(): + return {"a": np.array([1, 2, 3]), "b": np.array([1, 2, 3])} + +@pytest.fixture +def atts_sq_diff_2(): + return {"a": np.array([1, 2, 3]), "b": np.array([4, 5, 6])} + +@pytest.fixture +def atts_sq_diff_3(): + return {"a": np.array([1, 2, 3]), "b": np.array([4, 5])} + + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, 1.0), + (lazy_fixture("atts_same_linear"), {}, 1.0), + (lazy_fixture("atts_diff"), {}, 0.0), + (lazy_fixture("atts_half"), {}, 0.5), + (lazy_fixture("atts_inverse"), {}, -1), + ], +) +def test_correlation_spearman( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = correlation_spearman(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, 1.0), + (lazy_fixture("atts_same_linear"), {}, 1.0), + (lazy_fixture("atts_diff"), {}, 0.0), + (lazy_fixture("atts_half"), {}, 0.5), + (lazy_fixture("atts_inverse"), {}, -1), + ], +) +def test_correlation_pearson( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = correlation_pearson(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, 1.0), + (lazy_fixture("atts_same_linear"), {}, 1.0), + (lazy_fixture("atts_diff"), {}, 0.0), + (lazy_fixture("atts_half"), {}, 0.5), + (lazy_fixture("atts_inverse"), {}, -1), + ], +) +def test_correlation_kendall_tau( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = correlation_kendall_tau(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, 0.0), + (lazy_fixture("atts_diff"), {}, 2.0), + (lazy_fixture("atts_half"), {}, 1.73), + ], +) +def test_distance_euclidean( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = distance_euclidean(a=data["a"], b=data["b"]) + print(out) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, 0.0), + (lazy_fixture("atts_diff"), {}, 4.0), + (lazy_fixture("atts_half"), {}, 3.0), + ], +) +def test_distance_manhattan( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = distance_manhattan(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, 0.0), + (lazy_fixture("atts_diff"), {}, 4.0), + (lazy_fixture("atts_half"), {}, 3.0), + ], +) +def test_distance_chebyshev( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = distance_chebyshev(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [(lazy_fixture("atts_same"), {}, 0.0), (lazy_fixture("atts_diff"), {}, 1.0)], +) +def test_distance_chebyshev( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = distance_chebyshev(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + ( + lazy_fixture("atts_lip_same"), + { + "norm_numerator": distance_manhattan, + "norm_denominator": distance_manhattan, + }, + 1.0, + ), + ( + lazy_fixture("atts_lip_same"), + { + "norm_numerator": distance_manhattan, + "norm_denominator": distance_euclidean, + }, + 1.73, + ), + ], +) +def test_lipschitz_constant( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = lipschitz_constant( + a=data["a"], b=data["b"], c=data["c"], d=data["d"], **params + ) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, 0.0), + (lazy_fixture("atts_diff"), {}, 1.0), + (lazy_fixture("atts_half"), {}, 1.0), + ], +) +def test_abs_difference( + data: np.ndarray, params: dict, expected: Union[float, dict, bool] +): + out = abs_difference(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, 0.0), + (lazy_fixture("atts_diff"), {}, 0.42), + (lazy_fixture("atts_half"), {}, 0.42), + ], +) +def test_cosine(data: np.ndarray, params: dict, expected: Union[float, dict, bool]): + out = cosine(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_ssim_same"), {}, 1.0), + (lazy_fixture("atts_ssim_diff"), {}, 0.0), + ], +) +def test_ssim(data: np.ndarray, params: dict, expected: Union[float, dict, bool]): + """Calculate Structural Similarity Index Measure of two images (or explanations).""" + out = ssim(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, 0.0), + (lazy_fixture("atts_diff"), {}, 1.0), + (lazy_fixture("atts_half"), {}, 1.0), + ], +) +def test_mse(data: np.ndarray, params: dict, expected: Union[float, dict, bool]): + out = mse(a=data["a"], b=data["b"]) + assert round(out, 2) == expected, "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_same"), {}, np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])), + (lazy_fixture("atts_diff"), {}, np.array([-1, -1, -1, 1])), + (lazy_fixture("atts_half"), {}, np.array([-1, 1, -1])), + ], +) +def test_difference(data: np.ndarray, params: dict, expected: Union[float, dict, bool]): + out = difference(a=data["a"], b=data["b"]) + assert all(out == expected), "Test failed." + + +@pytest.mark.similar_func +@pytest.mark.parametrize( + "data,params,expected", + [ + (lazy_fixture("atts_sq_diff_1"), {}, 0), + (lazy_fixture("atts_sq_diff_2"), {}, 27), + (lazy_fixture("atts_sq_diff_3"), {}, ValueError), + ], +) +def test_squared_difference(data: np.ndarray, params: dict, expected: Union[int, ValueError]): + try: + out = squared_difference(a=data["a"], b=data["b"]) + assert out == expected, "Test failed." + except ValueError: + pass + diff --git a/tutorials/Tutorial_ICLR_2023_Quantus_x_Climate_Applying_explainable_AI_evaluation_in_Climate_Science.ipynb b/tutorials/Tutorial_ICLR_2023_Quantus_x_Climate_Applying_explainable_AI_evaluation_in_Climate_Science.ipynb new file mode 100644 index 000000000..dc3b9b030 --- /dev/null +++ b/tutorials/Tutorial_ICLR_2023_Quantus_x_Climate_Applying_explainable_AI_evaluation_in_Climate_Science.ipynb @@ -0,0 +1,3678 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dzeHYa5GCxN7" + }, + "outputs": [], + "source": [ + "# MIT License\n", + "#\n", + "#@title Copyright (c) 2021 CCAI Community Authors { display-mode: \"form\" }\n", + "#\n", + "# Permission is hereby granted, free of charge, to any person obtaining a\n", + "# copy of this software and associated documentation files (the \"Software\"),\n", + "# to deal in the Software without restriction, including without limitation\n", + "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n", + "# and/or sell copies of the Software, and to permit persons to whom the\n", + "# Software is furnished to do so, subject to the following conditions:\n", + "#\n", + "# The above copyright notice and this permission notice shall be included in\n", + "# all copies or substantial portions of the Software.\n", + "#\n", + "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n", + "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n", + "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n", + "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n", + "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n", + "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n", + "# DEALINGS IN THE SOFTWARE." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "13i7KQ9t-CV8" + }, + "source": [ + "# Quantus x Climate — Applying Explainable AI Evaluation in Climate Science\n", + "\n", + "Author(s):\n", + "* Philine Lou Bommer, TU Berlin\\ATB Potsdam, pbommer@atb-potsdam.de\n", + "* Anna Hedstroem, TU Berlin\\ATB Potsdam, ahedstroem@atb-potsdam.de\n", + "* Marlene Kretschmer, University of Reading\\University of Leipzig, m.j.a.kretschmer@reading.ac.uk\n", + "* Marina M.-C. Hoehne, University of Potsdam\\ATB Potsdam, mhoehne@atb-potsdam.de" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yNv0ANr5WcD_" + }, + "source": [ + "# Table of Contents\n", + "\n", + "\n", + "* [Overview](#overview)\n", + "* [Climate Impact](#climate-impact)\n", + "* [Target Audience](#target-audience)\n", + "* [Background & Prerequisites](#background-and-prereqs)\n", + "* [Software Requirements](#software-requirements)\n", + "* [Data and Network Description](#data-description)\n", + "* [Introduction: Choosing a XAI method](#Intoduction)\n", + "* [Quantus](#methodology)\n", + "* [Results & Discussion](#results-and-discussion)\n", + "* [References](#references)\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# 0) Preliminaries\n", + "\n", + "Note that to execute this notebook you will need access to the following websites, i.e. accounts :\n", + "* [Create Google account](https://support.google.com/accounts/answer/27441?hl=de)\n", + "* Create Kaggle account: which you can do via [Google](https://www.kaggle.com/account/login?phase=startRegisterTab&returnUrl=%2F) or via your [email](https://www.kaggle.com/account/login?phase=emailRegister)\n", + "\n", + "**Note** that runtime issues may arise if the notebook is run on a GPU-based runtime. Please make sure to run **only** on TPU/None Hardware accelerator" + ], + "metadata": { + "id": "sou9tpr_6Lfh" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QH81wjfsJsv1" + }, + "source": [ + "\n", + "# 1) Overview\n", + "\n", + "\n", + "\n", + "The goal of this tutorial is to provide a practical introduction to the XAI evaluation and the according package [Quantus](https://quantus.readthedocs.io/en/latest/), aimed at helping climate researchers addressing the complex task of selecting the right explanation method for a given task, model and dataset.\n", + "\n", + "To make users aware of the problem in the climate context and target climate data directly, we base this tutorial on an example of recent climate research. To this end, we innvestigate a convolutional neural network (CNN) ([Bommer et. al., 2023](https://arxiv.org/abs/2303.00652)), trained to solve a climate change prediction task proposed by [Labe and Barnes, 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464). The CNN assigns annual global temperature maps to classes based on their decade (see step 1 in the Figure below). Since the network has access to all pixels on the longitude-lattitude grid, it can use not only the global temperature trend due to climate change (increasing mean global temperature) but also regional patterns.\n", + "[Labe and Barnes, 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464) apply the explainability method, layer-wise relevance propagation (LRP) to identify regional temperature signals used by their network to make its prediction (for more details see [publication](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464)).\n", + "\n", + "In this tutorial based on [Bommer et. al., 2023](https://arxiv.org/abs/2303.00652), we apply multiple exlanation methods to the predictions of a trained network, to demonstrate the variations between different explanation methods for the same prediction (see step 2 in the Figure below).\n", + "To showcase that different XAI methods may suggest different importance of the same region and can thereby lead to scientifically misleading conclusion, we investigate the North Atlantic (NA) region. This region is known to include the NA cooling patch/warming hole, a contributing regional temperature signal, which was previously linked to a change in the NA climate variability in the course of climate change. As prior work by [Labe and Barnes, 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464) identifies this region as impactful to the network prediction, the NA is an example for features which can be used to i) investigate whether the network has learned physically relevant features, i.e. validate the network or ii) infer deeper insights about the differences of the importance of the feature for different decades. However, to draw these conclusions we have to ensure that the presented explanations are relieable (see also [Bommer et. al., 2023](https://arxiv.org/abs/2303.00652)).\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 1.1 Content\n", + "\n", + "A brief introduction to the content of the tutorial as follows:\n", + "\n", + "* We start by providing the users with research example classification task described above, including pre-processed input data and labels and a pre-trained CNN ([Section 3. Data and Network Description](#data-description)). We inspect the data by plotting the longitude-lattitude grid and the global mean temperature trends. To assess the network we demonstrate the predictions and provide intuition for the classes.\n", + "\n", + "* In the following [Section 4. Explanation Method Selection](#xai_methods), we introduce [Quantus](https://quantus.readthedocs.io/en/latest/) and present six different explanation techniques, i.e., VanillaGradients, IntegratedGradients, SmoothGrad, GradientsInput, Occlusion and GradCAM which are employed to help explain the temperature predictions. To emphasize the significance of evaluating XAI methods in climate change research, we concentrate on the NA Region $10-80^{\\circ}$W, $20-60^{\\circ}$N of temperature maps. We demonstrate that assessing the regions' importance and impact on the network prediction is challenging, as different explanation methods disagree in their feature importance. We show that different explanations can indicate different importance in the same region and subsequently lead to different scientific insights or misleading conclusions about the network decision-making process.\n", + "\n", + "* In [Section 5. XAI Evaluation](#xai_eval) we demonstrate that XAI evaluation can be performed by assessing properties of explanation methods, e.g., robustness, faithfulness, localisation, complexity and randomisation. We introduce the different metrics for each property and show how to calulate, aggregate and normalize the evaluation scores.\n", + "\n", + "* Lastly, we return to our motivating example. On this example we show, how to use Quantus to perform an insightful XAI method comparison and rank the different explanation method in application to our prepared network task. Moreover, we show that using XAI evaluation we can quantitatively determine not only an appropriate explanation method but also the strengths and weaknesses of each method (see [Section Results & Discussion](#results-and-discussion)).\n" + ], + "metadata": { + "id": "lwnVIzrBdyW_" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## 1.2. Background\n", + "This tutorial is based on the primary publication by [Bommer et. al., 2023](https://arxiv.org/abs/2303.00652) providing an introducion to XAI evaluation in the climate context and in-depth **analysis of XAI evaluation for climate science** based on the **Quantus package**. The according source code can be found in [Github repository](https://github.com/philine-bommer/Climate_X_Quantus). \n", + "\n", + "**Quantus.** The Quantus package has been developed by [Hedström et al., 2023](https://www.jmlr.org/papers/v24/22-0142.html). The according source code cand be found in the corresponding [Github repository](https://github.com/understandable-machine-intelligence-lab/Quantus).\n", + "\n", + "\n", + "Subsequently, the tutorial will provide participants with the tools to facilitate well-founded and trustworthy climate XAI research." + ], + "metadata": { + "id": "zLL2pWmfA9J1" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## 1.3 Goals\n", + "Overall, with this tutorial we aim to:\n", + "* demonstrate the challenge of choosing a reliable explanation method in your climate AI research\n", + "* showcase how to apply of different evaluation metrics\n", + "* discuss and demonstrate how to make task-specific adjustment of evaluation metrics to climate data\n", + "* demonstrate how to interpret evaluation metric scores\n", + "* provide a guideline for comparing and selecting an explanation method given a specific research problem\n" + ], + "metadata": { + "id": "pw7_OjMKAoOh" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "99jkSa_KmrDH" + }, + "source": [ + "\n", + "##1.4 Climate Impact\n", + "Explainable artificial intelligence (XAI) is required for a reliable and trustworthy application of AI, as explanation methods help to understand the reasons for the predictions of the deep neural networks (DNNs). In climate science XAI has been applied successfully to validate DNN models while providing researchers with new insight into physical processes ([Ebert-Uphoff and Hilburn, 2020](https://journals.ametsoc.org/view/journals/bams/101/12/BAMS-D-20-0097.1.xml), [Hilburn et al., 2021](https://journals.ametsoc.org/view/journals/apme/60/1/jamc-d-20-0084.1.xml)).\n", + "\n", + "Nonetheless, due to the increasing number of XAI methods, establishing the optimal procedure for a given task becomes more and more complex. Previous works ([Krishna et al., 2022](https://arxiv.org/abs/2202.01602)) show that researchers and XAI users can be overwhelmed by the different explanation methods at hand. As visual comparison is mostly unintelligible due to the lack of ground truth explanations, these practitioners tend to choose a XAI method based upon popularity or easy-access. These uninformed and unverified choices of XAI methods can lead to the inference of misleading information about the network decision. For example, in cases where XAI is used to validate a skillful forecast of a DNN ([Gibson et al., 2021](https://www.nature.com/articles/s43247-021-00225-4)), the misleading evidence can convey trust in the prediction of a network potentially considering data artifacts, which were not or wrongly displayed in the explanation.\n", + "\n", + "With this tutorial we aim to help climate researchers tackle the complex task of choosing the optimal XAI method, by providing the participants with a practical introduction to the XAI evaluation package *Quantus*. We show that using XAI evaluation we can quantitatively determine not only the most reliable explanation method but also the strengths and weaknesses of each method. Subsequently, the tutorial will provide participants with the tools to facilitate well-founded and trustworthy climate XAI research." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o5sbM_JPpdMR" + }, + "source": [ + "\n", + "##1.5 Target Audience\n", + "The tutorial is intended for climate science researchers that are interested in or have already been working with AI solutions and explanation methods. Prior experience with applications of DNN to climate data is required. Moreover, the audience should be familiar with climate science data and temperature maps.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gQgijl46pYzn" + }, + "source": [ + "\n", + "## 1.6 Prerequisites\n", + "\n", + "We do not discuss general deep learning methods, as we plan to present an already established network task with a trained network and input-output pairs. Accordingly, basic knowledge about DNNs applied to climate data, is required. Moreover, we use open-source XAI packages and we will not provide an in-depth introduction to the different XAI methods. However, we plan to shortly discuss the different explanation methods. Therefore rudimentary XAI knowledge, would be beneficial but is not required. Lastly, participants do not need any prior knowledge about the field of XAI evaluation.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7BpQklEEIFDD" + }, + "source": [ + "## 1.7 Other References\n", + "For further study on XAI General, XAI Evlaution and XAI x Climate Science, please see the additional resources as follows:\n", + "\n", + "On the topic of XAI in general, we recommend:\n", + "* XAI package for PyTorch, [Captum](https://captum.ai/)\n", + "* XAI package for tensorflow 1,2 [Innvestigate](https://github.com/albermax/innvestigate)\n", + "* [General XAI Repository with Tutorials](https://github.com/PaddlePaddle/InterpretDL)\n", + "* To choose XAI for **Regression**, [Letzgus et. al. 2022](https://ieeexplore.ieee.org/document/9810062) with [Github](https://github.com/sltzgs/xai-regression)\n", + "\n", + "On the topic of XAI evaluation:\n", + "* [Short Course 2023 - Introduction to AI Interpretability](https://introinterpretableai.wordpress.com) by Graziani et al., (2022)\n", + "* [NLDL 2023 - Winter School Presentation on XAI Evaluation](https://drive.google.com/file/d/1RfPxkqfLSlQp6WqImRjYNSeFHq77dMJE/view) by Hedström (2023)\n", + "\n", + "On the topic of XAI x Climate Science:\n", + "* [Github to the primary publication by *Bommer et. al. 2023*](https://github.com/philine-bommer/Climate_X_Quantus)\n", + "* [MLP Benchmark dataset for XAI comparison](https://www.cambridge.org/core/journals/environmental-data-science/article/neural-network-attribution-methods-for-problems-in-geoscience-a-novel-synthetic-benchmark-dataset/DDA562FC7B9A2B30710582861920860E) including [Github repository](https://github.com/amamalak/Neural-Network-Attribution-Benchmark-for-Regression)\n", + "* [CNN Benchmark dataset for XAI comparison](https://arxiv.org/abs/2202.03407) including [Github](https://github.com/amamalak/XAI_Fidelity_Assessment_CNN_GEO)\n", + "\n", + "Previous Tutorials on Quantus:\n", + "* [Quantus Tutorial on ImageNet](https://colab.research.google.com/github/understandable-machine-intelligence-lab/Quantus/blob/main/tutorials/Tutorial_ImageNet_Example_All_Metrics.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ag9dvWcxqmEq", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 1.7.1 Videos\n", + "Here you find a video introduction on the Quantus toolkit, including a general introduction of the library content and XAI evaluation, as well as a live coding on state-of-the art image classification datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 321 + }, + "id": "E4aK3MI9qhfJ", + "outputId": "2061839a-8a33-4f81-8c31-54dc1e2059ac", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ], + "image/jpeg": 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\n" + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('68IPFQjc5FE')\n", + "# A more recent video: http://www.birs.ca/events/2022/5-day-workshops/22w5055/videos/watch/202205021029-Hedstroem.html" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rSRCNgYzUwaf", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "# 2) Software Requirements\n", + "The Python version at the time of this submission in Colab = Python 3.7.14.\n", + "\n", + "The following libraries are required:\n", + "* pandas (v1.3.5)\n", + "* numpy\n", + "* matplotlib\n", + "* Quantus\n", + "* Tensorflow" + ] + }, + { + "cell_type": "code", + "source": [ + "%%capture\n", + "!pip install quantus >=0.3.5\n", + "!pip install tf_explain==0.3.1\n", + "!pip install tensorflow" + ], + "metadata": { + "id": "V4kPi9HMpE53" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xVzk4V7qUu2R", + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Python standard libraries.\n", + "from typing import Any\n", + "import copy\n", + "\n", + "# Libraries for data visualisation.\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Libraries for data manipulation.\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Libraries for XAI and ML methods.\n", + "import quantus\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tf_explain import *\n", + "\n", + "# Remove warnings.\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "If you use Google Colab, please restart the runtime after running the above cell." + ], + "metadata": { + "id": "QHvHvyu6rqMq", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "See all package requirements." + ], + "metadata": { + "id": "tiITUcjPc8UJ" + } + }, + { + "cell_type": "code", + "source": [ + "!pip freeze" + ], + "metadata": { + "id": "Dd6pCW6SQlNv", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7617b847-1b5c-4ffc-b573-89a88e9fba96" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "absl-py==1.4.0\n", + "alabaster==0.7.13\n", + "albumentations==1.2.1\n", + "altair==4.2.2\n", + "anyio==3.6.2\n", + "appdirs==1.4.4\n", + "argon2-cffi==21.3.0\n", + "argon2-cffi-bindings==21.2.0\n", + "arviz==0.15.1\n", + "astropy==5.2.2\n", + "astunparse==1.6.3\n", + "attrs==22.2.0\n", + "audioread==3.0.0\n", + "autograd==1.5\n", + "Babel==2.12.1\n", + "backcall==0.2.0\n", + "beautifulsoup4==4.11.2\n", + "bleach==6.0.0\n", + "blis==0.7.9\n", + "blosc2==2.0.0\n", + "bokeh==2.4.3\n", + "branca==0.6.0\n", + "CacheControl==0.12.11\n", + "cached-property==1.5.2\n", + "cachetools==5.3.0\n", + "catalogue==2.0.8\n", + "certifi==2022.12.7\n", + "cffi==1.15.1\n", + "chardet==4.0.0\n", + "charset-normalizer==2.0.12\n", + "chex==0.1.7\n", + "click==8.1.3\n", + "cloudpickle==2.2.1\n", + "cmake==3.25.2\n", + "cmdstanpy==1.1.0\n", + "colorcet==3.0.1\n", + "colorlover==0.3.0\n", + "community==1.0.0b1\n", + "confection==0.0.4\n", + "cons==0.4.5\n", + "contextlib2==0.6.0.post1\n", + "contourpy==1.0.7\n", + "convertdate==2.4.0\n", + "cryptography==40.0.1\n", + "cufflinks==0.17.3\n", + "cvxopt==1.3.0\n", + "cvxpy==1.3.1\n", + "cycler==0.11.0\n", + "cymem==2.0.7\n", + "Cython==0.29.34\n", + "dask==2022.12.1\n", + "datascience==0.17.6\n", + "db-dtypes==1.1.1\n", + "dbus-python==1.2.16\n", + "debugpy==1.6.6\n", + "decorator==4.4.2\n", + "defusedxml==0.7.1\n", + "distributed==2022.12.1\n", + "dlib==19.24.1\n", + "dm-tree==0.1.8\n", + "docutils==0.16\n", + "dopamine-rl==4.0.6\n", + "earthengine-api==0.1.347\n", + "easydict==1.10\n", + "ecos==2.0.12\n", + "editdistance==0.6.2\n", + "en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0-py3-none-any.whl\n", + "entrypoints==0.4\n", + "ephem==4.1.4\n", + "et-xmlfile==1.1.0\n", + "etils==1.1.1\n", + "etuples==0.3.8\n", + "exceptiongroup==1.1.1\n", + "fastai==2.7.12\n", + "fastcore==1.5.29\n", + "fastdownload==0.0.7\n", + "fastjsonschema==2.16.3\n", + "fastprogress==1.0.3\n", + "fastrlock==0.8.1\n", + "filelock==3.10.7\n", + "firebase-admin==5.3.0\n", + "Flask==2.2.3\n", + "flatbuffers==23.3.3\n", + "flax==0.6.8\n", + "folium==0.14.0\n", + "fonttools==4.39.3\n", + "frozendict==2.3.6\n", + "fsspec==2023.3.0\n", + "future==0.18.3\n", + "gast==0.4.0\n", + "GDAL==3.3.2\n", + "gdown==4.6.6\n", + "gensim==4.3.1\n", + "geographiclib==2.0\n", + "geopy==2.3.0\n", + "gin-config==0.5.0\n", + "glob2==0.7\n", + "google==2.0.3\n", + "google-api-core==2.11.0\n", + "google-api-python-client==2.70.0\n", + "google-auth==2.17.1\n", + "google-auth-httplib2==0.1.0\n", + "google-auth-oauthlib==1.0.0\n", + "google-cloud-bigquery==3.4.2\n", + "google-cloud-bigquery-storage==2.19.1\n", + "google-cloud-core==2.3.2\n", + "google-cloud-datastore==2.11.1\n", + "google-cloud-firestore==2.7.3\n", + "google-cloud-language==2.6.1\n", + "google-cloud-storage==2.7.0\n", + "google-cloud-translate==3.8.4\n", + "google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz\n", + "google-crc32c==1.5.0\n", + "google-pasta==0.2.0\n", + "google-resumable-media==2.4.1\n", + "googleapis-common-protos==1.59.0\n", + "googledrivedownloader==0.4\n", + "graphviz==0.20.1\n", + "greenlet==2.0.2\n", + "grpcio==1.53.0\n", + "grpcio-status==1.48.2\n", + "gspread==3.4.2\n", + "gspread-dataframe==3.0.8\n", + "gym==0.25.2\n", + "gym-notices==0.0.8\n", + "h5netcdf==1.1.0\n", + "h5py==3.8.0\n", + "HeapDict==1.0.1\n", + "hijri-converter==2.2.4\n", + "holidays==0.22\n", + "holoviews==1.15.4\n", + "html5lib==1.1\n", + "htmlmin==0.1.12\n", + "httpimport==1.3.0\n", + "httplib2==0.21.0\n", + "humanize==4.6.0\n", + "hyperopt==0.2.7\n", + "idna==3.4\n", + "ImageHash==4.3.1\n", + "imageio==2.25.1\n", + "imageio-ffmpeg==0.4.8\n", + "imagesize==1.4.1\n", + "imbalanced-learn==0.10.1\n", + "imgaug==0.4.0\n", + "importlib-metadata==6.1.0\n", + "importlib-resources==5.12.0\n", + "imutils==0.5.4\n", + "inflect==6.0.2\n", + "iniconfig==2.0.0\n", + "intel-openmp==2023.1.0\n", + "ipykernel==5.5.6\n", + "ipython==7.34.0\n", + "ipython-genutils==0.2.0\n", + "ipython-sql==0.4.1\n", + "ipywidgets==7.7.1\n", + "itsdangerous==2.1.2\n", + "jax==0.3.25\n", + "jaxlib==0.3.25\n", + "jieba==0.42.1\n", + "Jinja2==3.1.2\n", + "joblib==1.1.1\n", + "jsonpickle==3.0.1\n", + "jsonschema==4.3.3\n", + "jupyter-client==6.1.12\n", + "jupyter-console==6.1.0\n", + "jupyter-server==1.23.6\n", + "jupyter_core==5.3.0\n", + "jupyterlab-pygments==0.2.2\n", + "jupyterlab-widgets==3.0.7\n", + "kaggle==1.5.13\n", + "keras==2.12.0\n", + "keras-vis==0.4.1\n", + "kiwisolver==1.4.4\n", + "korean-lunar-calendar==0.3.1\n", + "langcodes==3.3.0\n", + "lazy_loader==0.2\n", + "libclang==16.0.0\n", + "librosa==0.10.0.post2\n", + "lightgbm==3.3.5\n", + "lit==16.0.0\n", + "llvmlite==0.39.1\n", + "locket==1.0.0\n", + "logical-unification==0.4.5\n", + "LunarCalendar==0.0.9\n", + "lxml==4.9.2\n", + "Markdown==3.4.3\n", + "markdown-it-py==2.2.0\n", + "MarkupSafe==2.1.2\n", + "matplotlib==3.7.1\n", + "matplotlib-inline==0.1.6\n", + "matplotlib-venn==0.11.9\n", + "mdurl==0.1.2\n", + "miniKanren==1.0.3\n", + "missingno==0.5.2\n", + "mistune==0.8.4\n", + "mizani==0.8.1\n", + "mkl==2019.0\n", + "ml-dtypes==0.0.4\n", + "mlxtend==0.14.0\n", + "more-itertools==9.1.0\n", + "moviepy==1.0.3\n", + "mpmath==1.3.0\n", + "msgpack==1.0.5\n", + "multimethod==1.9.1\n", + "multipledispatch==0.6.0\n", + "multitasking==0.0.11\n", + "murmurhash==1.0.9\n", + "music21==8.1.0\n", + "natsort==8.3.1\n", + "nbclient==0.7.3\n", + "nbconvert==6.5.4\n", + "nbformat==5.8.0\n", + "nest-asyncio==1.5.6\n", + "networkx==3.0\n", + "nibabel==3.0.2\n", + "nltk==3.8.1\n", + "notebook==6.4.8\n", + "numba==0.56.4\n", + "numexpr==2.8.4\n", + "numpy==1.23.5\n", + "oauth2client==4.1.3\n", + "oauthlib==3.2.2\n", + "opencv-contrib-python==4.7.0.72\n", + "opencv-python==4.7.0.72\n", + "opencv-python-headless==4.7.0.72\n", + "openpyxl==3.0.10\n", + "opt-einsum==3.3.0\n", + "optax==0.1.4\n", + "orbax==0.1.7\n", + "osqp==0.6.2.post0\n", + "packaging==23.0\n", + "palettable==3.3.0\n", + "pandas==1.4.4\n", + "pandas-datareader==0.10.0\n", + "pandas-gbq==0.17.9\n", + "pandas-profiling==3.2.0\n", + "pandocfilters==1.5.0\n", + "panel==0.14.4\n", + "param==1.13.0\n", + "parso==0.8.3\n", + "partd==1.3.0\n", + "pathlib==1.0.1\n", + "pathy==0.10.1\n", + "patsy==0.5.3\n", + "pep517==0.13.0\n", + "pexpect==4.8.0\n", + "phik==0.12.3\n", + "pickleshare==0.7.5\n", + "Pillow==8.4.0\n", + "pip-tools==6.6.2\n", + "platformdirs==3.2.0\n", + "plotly==5.13.1\n", + "plotnine==0.10.1\n", + "pluggy==1.0.0\n", + "pooch==1.6.0\n", + "portpicker==1.3.9\n", + "prefetch-generator==1.0.3\n", + "preshed==3.0.8\n", + "prettytable==0.7.2\n", + "proglog==0.1.10\n", + "progressbar2==4.2.0\n", + "prometheus-client==0.16.0\n", + "promise==2.3\n", + "prompt-toolkit==3.0.38\n", + "prophet==1.1.2\n", + "proto-plus==1.22.2\n", + "protobuf==4.22.1\n", + "psutil==5.9.4\n", + "psycopg2==2.9.6\n", + "ptyprocess==0.7.0\n", + "py-cpuinfo==9.0.0\n", + "py4j==0.10.9.7\n", + "pyarrow==9.0.0\n", + "pyasn1==0.4.8\n", + "pyasn1-modules==0.2.8\n", + "pycocotools==2.0.6\n", + "pycparser==2.21\n", + "pyct==0.5.0\n", + "pydantic==1.10.7\n", + "pydata-google-auth==1.7.0\n", + "pydot==1.4.2\n", + "pydot-ng==2.0.0\n", + "pydotplus==2.0.2\n", + "PyDrive==1.3.1\n", + "pyerfa==2.0.0.3\n", + "pygame==2.3.0\n", + "Pygments==2.14.0\n", + "PyGObject==3.36.0\n", + "pymc==5.1.2\n", + "PyMeeus==0.5.12\n", + "pymystem3==0.2.0\n", + "PyOpenGL==3.1.6\n", + "pyparsing==3.0.9\n", + "pyrsistent==0.19.3\n", + "PySocks==1.7.1\n", + "pytensor==2.10.1\n", + "pytest==7.2.2\n", + "python-apt==0.0.0\n", + "python-dateutil==2.8.2\n", + "python-louvain==0.16\n", + "python-slugify==8.0.1\n", + "python-utils==3.5.2\n", + "pytz==2022.7.1\n", + "pytz-deprecation-shim==0.1.0.post0\n", + "pyviz-comms==2.2.1\n", + "PyWavelets==1.4.1\n", + "PyYAML==6.0\n", + "pyzmq==23.2.1\n", + "qdldl==0.1.5.post3\n", + "quantus==0.3.5\n", + "qudida==0.0.4\n", + "regex==2022.10.31\n", + "requests==2.27.1\n", + "requests-oauthlib==1.3.1\n", + "requests-unixsocket==0.2.0\n", + "rich==13.3.3\n", + "rpy2==3.5.5\n", + "rsa==4.9\n", + "scikit-image==0.19.3\n", + "scikit-learn==1.2.2\n", + "scipy==1.10.1\n", + "scs==3.2.2\n", + "seaborn==0.12.2\n", + "Send2Trash==1.8.0\n", + "shapely==2.0.1\n", + "six==1.16.0\n", + "sklearn-pandas==2.2.0\n", + "smart-open==6.3.0\n", + "sniffio==1.3.0\n", + "snowballstemmer==2.2.0\n", + "sortedcontainers==2.4.0\n", + "soundfile==0.12.1\n", + "soupsieve==2.4\n", + "soxr==0.3.4\n", + "spacy==3.5.1\n", + "spacy-legacy==3.0.12\n", + "spacy-loggers==1.0.4\n", + "Sphinx==3.5.4\n", + "sphinxcontrib-applehelp==1.0.4\n", + "sphinxcontrib-devhelp==1.0.2\n", + "sphinxcontrib-htmlhelp==2.0.1\n", + "sphinxcontrib-jsmath==1.0.1\n", + "sphinxcontrib-qthelp==1.0.3\n", + "sphinxcontrib-serializinghtml==1.1.5\n", + "SQLAlchemy==1.4.47\n", + "sqlparse==0.4.3\n", + "srsly==2.4.6\n", + "statsmodels==0.13.5\n", + "sympy==1.11.1\n", + "tables==3.8.0\n", + "tabulate==0.8.10\n", + "tangled-up-in-unicode==0.2.0\n", + "tblib==1.7.0\n", + "tenacity==8.2.2\n", + "tensorboard==2.12.1\n", + "tensorboard-data-server==0.7.0\n", + "tensorboard-plugin-wit==1.8.1\n", + "tensorflow==2.12.0\n", + "tensorflow-datasets==4.8.3\n", + "tensorflow-estimator==2.12.0\n", + "tensorflow-gcs-config==2.12.0\n", + "tensorflow-hub==0.13.0\n", + "tensorflow-io-gcs-filesystem==0.32.0\n", + "tensorflow-metadata==1.13.0\n", + "tensorflow-probability==0.19.0\n", + "tensorstore==0.1.35\n", + "termcolor==2.2.0\n", + "terminado==0.17.1\n", + "text-unidecode==1.3\n", + "textblob==0.17.1\n", + "tf-explain==0.3.1\n", + "tf-slim==1.1.0\n", + "thinc==8.1.9\n", + "threadpoolctl==3.1.0\n", + "tifffile==2023.3.21\n", + "tinycss2==1.2.1\n", + "toml==0.10.2\n", + "tomli==2.0.1\n", + "toolz==0.12.0\n", + "torch @ https://download.pytorch.org/whl/cu118/torch-2.0.0%2Bcu118-cp39-cp39-linux_x86_64.whl\n", + "torchaudio @ https://download.pytorch.org/whl/cu118/torchaudio-2.0.1%2Bcu118-cp39-cp39-linux_x86_64.whl\n", + "torchdata==0.6.0\n", + "torchsummary==1.5.1\n", + "torchtext==0.15.1\n", + "torchvision @ https://download.pytorch.org/whl/cu118/torchvision-0.15.1%2Bcu118-cp39-cp39-linux_x86_64.whl\n", + "tornado==6.2\n", + "tqdm==4.65.0\n", + "traitlets==5.7.1\n", + "triton==2.0.0\n", + "tweepy==4.13.0\n", + "typer==0.7.0\n", + "typing_extensions==4.5.0\n", + "tzdata==2023.3\n", + "tzlocal==4.3\n", + "uritemplate==4.1.1\n", + "urllib3==1.26.15\n", + "vega-datasets==0.9.0\n", + "visions==0.7.4\n", + "wasabi==1.1.1\n", + "wcwidth==0.2.6\n", + "webcolors==1.13\n", + "webencodings==0.5.1\n", + "websocket-client==1.5.1\n", + "Werkzeug==2.2.3\n", + "widgetsnbextension==3.6.4\n", + "wordcloud==1.8.2.2\n", + "wrapt==1.14.1\n", + "xarray==2022.12.0\n", + "xarray-einstats==0.5.1\n", + "xgboost==1.7.5\n", + "xlrd==2.0.1\n", + "yellowbrick==1.5\n", + "yfinance==0.2.14\n", + "zict==2.2.0\n", + "zipp==3.15.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jXoiLncsU3pe" + }, + "source": [ + "\n", + "# 3) Data and Network Description\n", + "\n", + "For the data and network task, we build upon previous work on a climate change classification task by [Labe and Barnes, 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464), using data simulated by a fully-coupled general climate model, Community Earth System Model version 1 ([Hurrell et al., 2013](https://journals.ametsoc.org/view/journals/bams/94/9/bams-d-12-00121.1.xml)). In particular, we focus on the $40$ member larger ensemble climate data simulation, with equal external forcings across all ensemble members but differing atmospheric initial conditions ([Kay et al., 2015](https://journals.ametsoc.org/view/journals/bams/96/8/bams-d-13-00255.1.xml)). For the data from $1920$ to $2080$, historical forcings are imposed for $1920-2005$ and Representative Concentration Pathways 8.5 for the following years. We employ 2-m air temperature (T2m) temperature maps and take the annual average of monthly data.\n", + "\n", + "[Link to Data and Network](https://www.kaggle.com/datasets/philinelou/climatexquantusiclr2023?select=Batch_data.npz)\n", + "\n", + "\n", + "**Data.** You can load the already pre-processed and prepared batch of the data containing $N = 155$ images (1 sample per correctly predicted year) from an .npz-file into the notebook, as uploaded on Kaggle. Moreover, we provide a trained CNN (.tf-files) and according input temperature maps as images on a $h = 144$ by $v = 95$ longitude-latitude-grid with $1.9^{\\circ}$ sampling in latitude and $2.5^{\\circ}$ sampling in longitude. For the dataset, we include the associated class vectors (probability entry for each class) and the years of each temperature map in the input data.\n", + "\n", + "\n", + "**Network.** As described in the section 1, the network assigns the annual temperature maps to classes based on their decade. Similar to [Labe and Barnes, 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464) we define classes for the full two centuries ($1900-2100$), resulting in a class vector of $20$ classes. Each class vector entry includes the probability of the input sample belonging to the decade of the class ([Labe and Barnes, 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464) uses fuzzy classification, meaning that multiple classes can achieve non-zero probability and aims to predict the year of each temperature map).\n", + "The inputs are passed as images with the latitude-longitude grid described in the previous paragraph. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PoF-BxSM5Jkc", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 3.1 Data and Pre-trained Network Download\n", + "\n", + "To run the notebook, we need to download the data and the trained network.\n", + "\n", + "We have prepared the both data and trained network as uploaded on [Kaggle](https://www.kaggle.com/datasets/philinelou/climatexquantusiclr2023) which is publically available.\n", + "\n", + "Please follow these steps:\n", + "\n", + "1. Create a Kaggle API token following the [Instructions Step #3](https://www.geeksforgeeks.org/how-to-import-kaggle-datasets-directly-into-google-colab/).\n", + "2. When running the cell below and when asked to choose a file, upload `kaggle.json`.\n", + "3. After the cell has finished running, check that `dataset.npz`, `index.npz` and `tf_network.tf` exist in `/drive/sample_data/`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Qh5FKi6Nopbr", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 299 + }, + "outputId": "69d63087-c285-416b-f0af-1ef612160970" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving kaggle.json to kaggle.json\n", + "-rw-r--r-- 1 root root 66 Apr 11 09:06 kaggle.json\n", + "Downloading climatexquantusiclr2023.zip to /content/drive/sample_data\n", + " 99% 162M/163M [00:02<00:00, 86.4MB/s]\n", + "100% 163M/163M [00:02<00:00, 75.4MB/s]\n", + "Archive: /content/drive/sample_data/climatexquantusiclr2023.zip\n", + " inflating: /content/drive/sample_data/README.md \n", + " inflating: /content/drive/sample_data/dataset.npz \n", + " inflating: /content/drive/sample_data/index.npz \n", + " inflating: /content/drive/sample_data/tf_network.tf/tf_network.tf/keras_metadata.pb \n", + " inflating: /content/drive/sample_data/tf_network.tf/tf_network.tf/saved_model.pb \n", + " inflating: /content/drive/sample_data/tf_network.tf/tf_network.tf/variables/variables.data-00000-of-00001 \n", + " inflating: /content/drive/sample_data/tf_network.tf/tf_network.tf/variables/variables.index \n", + "/content/drive/sample_data\n" + ] + } + ], + "source": [ + "from google.colab import files\n", + "\n", + "# Upload the kaggle.json file.\n", + "files.upload()\n", + "!ls -lha kaggle.json\n", + "\n", + "# Install the kaggle package.\n", + "!pip install -q kaggle\n", + "\n", + "# Create .kaggle folder where the key should be placed.\n", + "!mkdir -p ~/.kaggle\n", + "\n", + "# Move the key to the folder.\n", + "!cp kaggle.json ~/.kaggle/\n", + "\n", + "# Check the present working directory\n", + "!chmod 600 ~/.kaggle/kaggle.json\n", + "\n", + "# Download dataset.\n", + "!kaggle datasets download -d philinelou/climatexquantusiclr2023 -p /content/drive/sample_data\n", + "!unzip /content/drive/sample_data/climatexquantusiclr2023.zip -d /content/drive/sample_data/\n", + "\n", + "%cd /content/drive/sample_data/" + ] + }, + { + "cell_type": "code", + "source": [ + "# Inspect that you have the data and model in place.\n", + "!ls /content/drive/sample_data" + ], + "metadata": { + "id": "SFHfqUskr8a-", + "pycharm": { + "name": "#%%\n" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dac8e19b-9969-4aab-f047-56dcfbf93fd4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "climatexquantusiclr2023.zip dataset.npz index.npz README.md\ttf_network.tf\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.1.1 Inspect Data Objects\n", + "In the following cells you can load and inspect the data. Alongside input images, labels and years of each image we also included a segmentation map of the NA region ($10-80^{\\circ}$W, $20-60^{\\circ}$N), which we use for explanation evaluation in [Section 5.](#xai_eval)).\n" + ], + "metadata": { + "id": "0vqnrylmm0K6", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "go6Pq72PhoaN" + }, + "outputs": [], + "source": [ + "# Load the full data object.\n", + "data = np.load('dataset.npz', allow_pickle=True)\n", + "\n", + "# Input images.\n", + "x_batch = data['x_batch'].swapaxes(1,3).swapaxes(1,2)\n", + "\n", + "# Classification labels.\n", + "y_batch = data['y_batch']\n", + "\n", + "# Segmentation masks for explanations.\n", + "s_batch = data['s_batch'].reshape(len(x_batch), 1, 95, 144)\n", + "\n", + "# Years of the input images.\n", + "years_batch = data['years_batch']\n", + "\n", + "# Longitude and latitudes.\n", + "latitude = data['wh'][0]\n", + "longitude = data['wh'][1]" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Plot Temperature Maps.**\n", + "To provide an intuition for the T2m temperatur maps passed as input to the network, in the following we visualize the data. Below you can find a plot where 4 temperature maps from the years $1940$, $1990$, $2040$ and $2074$." + ], + "metadata": { + "id": "D07J4x5FMjHj" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "34yd4LpFp7zF", + "pycharm": { + "name": "#%%\n" + }, + "cellView": "form" + }, + "outputs": [], + "source": [ + "\n", + "# @title Plotting functionality\n", + "def plot_multiple_temperature_maps(\n", + " samples: np.array,\n", + " x_samples: Any,\n", + " year_samples: np.array,\n", + " y_true_samples: np.array,\n", + " y_pred_samples: np.array,\n", + " latitude: np.array,\n", + " longitude: np.array,\n", + " **plt_kwrgs\n", + ") -> None:\n", + " \"\"\"Plot multiple temperature maps with input data and predictions.\"\"\"\n", + "\n", + " # Set plot settings.\n", + " nrows = plt_kwrgs.get('nrows', 2)\n", + " ncols=plt_kwrgs.get('ncols', len(samples)//nrows)\n", + " indx = plt_kwrgs.get('indx_order', [])\n", + " keys = plt_kwrgs.get('keys', [])\n", + " figsize = plt_kwrgs.get('figsize', (20,10))\n", + " cb_pos = plt_kwrgs.get('cb_pos',[0.35, 0.05, 0.35, 0.05])\n", + " xtext = plt_kwrgs.get('xtext', -0.2)\n", + " font = plt_kwrgs.get('font', 18)\n", + " set_title = plt_kwrgs.get('set_title', 1)\n", + " globe = plt_kwrgs.get('globe',1)\n", + "\n", + " if any(indx):\n", + " k = 0\n", + "\n", + " # Transform to make global visualization easier. (similar to cartopy PlateCarree transform).\n", + " if globe:\n", + " longitude = (longitude + 180) % 360 - 180\n", + " longitude = np.roll(longitude, shift = int(len(longitude) / 2), axis =0)\n", + " latitude = np.flip(latitude, axis =0)\n", + "\n", + "\n", + " # Plotting configs.\n", + " fig1, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize,)\n", + " xtick = [longitude[1], longitude[int((len(longitude) - 1) / 2)], longitude[len(longitude) - 1]]\n", + " ytick = [latitude[1], latitude[int((len(latitude) - 2) / 2)], latitude[len(latitude) - 1]]\n", + " maxabs = np.abs(x_samples).max()\n", + "\n", + "\n", + " for i in range(nrows):\n", + "\n", + " # Choose data and according cmap.\n", + " if any(keys) and i > 0:\n", + " key = keys[i-1]\n", + " explanations = plt_kwrgs['explanation']\n", + " x_samp = explanations[keys[i-1]]\n", + " cmap = \"seismic\"\n", + " mins = -1\n", + " maxs = 1\n", + "\n", + " elif any(keys) and i == 0:\n", + " key = 'T2m'\n", + " cmap = \"coolwarm\"\n", + " x_samp = x_samples[:,:,:,0]\n", + " mins = -np.abs(x_samp).max()\n", + " maxs = np.abs(x_samp).max()\n", + "\n", + " elif any(indx):\n", + " key = 'T2m'\n", + " cmap = \"coolwarm\"\n", + " x_samp = x_samples[:,:,:,0]\n", + " mins = -np.abs(x_samp).max()\n", + " maxs = np.abs(x_samp).max()\n", + "\n", + "\n", + " for j in range(ncols):\n", + "\n", + " # Reformat the data.\n", + " if any(indx):\n", + " data = x_samp[k, :, :]\n", + " k+=1\n", + " else:\n", + " data = x_samp[j, :, :]\n", + "\n", + "\n", + " # Transform to make global visualization easier. (similar to cartopy PlateCarree transform).\n", + " if globe:\n", + " data = np.roll(data, shift = int(len(longitude) / 2), axis =1)\n", + " data = np.flip(data, axis =0)\n", + "\n", + " ax1 = axes[i,j]\n", + "\n", + " im = ax1.imshow(data, cmap=cmap, vmin=mins, vmax=maxs)\n", + " ax1.grid()\n", + "\n", + " # Ticks, labels.\n", + " if i == 0:\n", + " if nrows == 1:\n", + " ax1.set_title(\n", + " r\"%s, true class %s, pred. class %s\"\n", + " % (int(year_samples[j]), int(y_true_samples[j]),\n", + " int(y_pred_samples[j])),fontsize=18,\n", + " )\n", + " elif set_title:\n", + " ax1.set_title(\n", + " r\"%s\"\n", + " % (int(year_samples[j])),\n", + " fontsize=18,\n", + " )\n", + " else:\n", + " if any(indx) and set_title:\n", + " ax1.set_title(\n", + " r\"%s\"\n", + " % (int(year_samples[k-1])),\n", + " fontsize=18,\n", + " )\n", + "\n", + " ax1.set_xticks([0, int((len(longitude) - 1) / 2), len(longitude) - 1])\n", + " ax1.set_xticklabels(xtick, fontsize=12)\n", + " ax1.set_yticks([0, int((len(latitude) - 2) / 2), len(latitude) - 1])\n", + " ax1.set_yticklabels(ytick, fontsize=12)\n", + "\n", + " if j == 0:\n", + " ax1.set_ylabel(\"latitude\")\n", + " if j == 0:\n", + " ax1.annotate(r'%s' % key, xy=(0, 0), xytext=(xtext, 0.5),\n", + " textcoords='axes fraction', color='black', fontsize=font,\n", + " rotation=90, ha='center', va='center')\n", + " if i == ncols-1:\n", + " ax1.set_xlabel(\"longitude\")\n", + "\n", + "\n", + " # Colorbar for input plot.\n", + " if nrows == 1 or any(indx):\n", + " cbar_ax = fig1.add_axes(cb_pos)\n", + " cbar1 = fig1.colorbar(im, cax=cbar_ax, orientation=\"horizontal\")\n", + " cbar1.ax.tick_params(axis=\"x\", size=0.02, labelsize=14)\n", + " cbar1.ax.set_xlabel('T',fontsize =16)\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "With the supporting function as defined above, we plot the temperature maps for some input samples. The colorbar range in the following plot refers to the range of the standardized temperature values (unitless here defined by T)." + ], + "metadata": { + "id": "DVOIvvODSGrx" + } + }, + { + "cell_type": "code", + "source": [ + "# Prepare random year samples.\n", + "year_s = np.array([1940, 1990, 2040, 2074])\n", + "samples = [np.where(years_batch == ys)[0][0] for ys in year_s]\n", + "\n", + "y_batch_samples = y_batch[samples]\n", + "s_batch_samples = s_batch[samples, :]\n", + "year_samples = years_batch[samples]\n", + "x_batch_samples = x_batch[samples, :]\n", + "\n", + "\n", + "# Plot!\n", + "plt_kwrgs = {'indx_order': [len(samples)],\n", + " 'figsize': (20,15)}\n", + "\n", + "plot_multiple_temperature_maps(samples, x_batch_samples, year_samples, y_batch_samples, [], latitude, longitude,**plt_kwrgs)" + ], + "metadata": { + "id": "hmTKisRIMoiM", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 968 + }, + "outputId": "3f4b20b4-3b7a-4fdb-f5e5-3bec8fc61e04" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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5z33ubvN/4xvfaEmSbPu/G2644Ww2TwghhBBCCCH2hM5MQgghhBBCiO3ID7oC4uzw2c9+1h75yEfa8vKy/fAP/7CtrKzY+973PnvJS15iH/jAB+y///f/bmZmZVnaN37jN9rNN99sz3rWs+wRj3iEnThxwt7//vfb6uqqXXbZZduWce2119qb3vQmZ3/lK19pH/zgB+1xj3vcWWufEEIIIYQQQuwFnZmEEEIIIYQQd4deoN1HedOb3mQnT560m266yb7iK77CzMx+8Ad/0Jqmsf/yX/6LnThxwg4fPmyvfOUr7b3vfa/ddNNN9shHPvIelXH11Vfb1VdfPWMbDof23Oc+1x772Mfa0aNHo/NqmsZuu+02W1xctCRJ7lE9hBBCCCGEuLfRtq2tr6/bpZdeammqHwY5CHRmEkIIIYQQ4vzmoM9NeoF2H2Vtbc3MzC6++OIZ+yWXXGJpmlqn07GmaezXf/3X7SlPeYo98pGPtKqqbDKZ2Nzc3K7Lffvb327r6+v2nd/5nffoudtuu80uv/zyXZcrhBBCCCHEvZHPfvazd/sNJnH20JlJCCGEEEKIewcHdW7SC7T7KI95zGPsFa94hX3/93+/vfSlL7UjR47YzTffbL/9279tP/IjP2Lz8/P2oQ99yG677TZ7xCMeYT/4gz9ov/d7v2eTycQe/vCH26//+q/b13/919/jcm+44Qbr9/v21Kc+9W7TjcdjG4/HW3+3bWtmZr/3Z/9gxdoHrbjkGktSTc/T3XJe0rT61Ku4f3I+r8t7QttW1t7+15Zc/ChLEvnbg0BfHtiZNLmPLLhzjObW+U3bVFZ+4SYrl77Svvf/+xW2uLh40FW633JvPTO96Z0ftX6/b5PbbrLOpefPuene5rMTO5j63l98dHIvmw9CEG1T2eZn32fzl3/tOfG1re5ZHPeV8/d+0dq9a47o7lDslraptmLd4XBo3/3Ef35g56bzI9IW+84TnvAE+7mf+zm7/vrr7U//9E+37D/1Uz9l1113nZmdEsw2O/X7+ysrK/Y7v/M7ZmZ2/fXX2xOe8AT73//7f9sjHvGI6DKPHz9u73rXu+zJT37yjhP6F37hF+ylL32psxdrHzz1ac7V/xNdrhBCiF0yN2e2/oGDroUQQtzvKObmbLD2QTMz/RTfAXJvPTNlJz5g+XjO8rk5s5M6N93buL/cBd9f2inu+8zNzVl77IOa0+K84N4WNWYHXQFxr+ZMrJsNBmZ2cOcmvUC7D3PllVfatddea9/6rd9qR44csT//8z+366+/3o4ePWo//MM/bBsbG2Zmtr6+brfccsvWz4E89rGPtS/7si+zX/zFX7Tf//3fjy7vj//4j20ymUT9FMlP/uRP2vOf//ytv9fW1uzyyy+34pJrzFb/jxUPeMx580nKg+R8/qSNPkUi7q+cz+vyntA2lbVf/H8tOfr18rcHhO7Md+be9m2G8wXNrfObtqms/PyNp+JeceDcG89M3cseY925ORt/7kbrXnb+nJvubT5b30A7u+gbaOK+QNtUtnHrX9vCFY/SN9AOiPvK+Xu/0DfQxP2Ftqm2Yt369Au0g+L8iLTFvvOWt7zFfvAHf9A+9rGPbf026FOf+lRrmsZe/OIX29Of/nTr9/tmZvZ1X/d1M7+l/8AHPtCuueYau/nmm+9RmTfccIOtrKzYE5/4xB3Tdrtd63a7zp5mp6ZkluXnzUHwIDmIQCF2M96tZKOCn3sX99UD/l7m4W4DwNgy9/vQ1Gz3H04PbpsU1ibFvpZ5EJx7Gdl7xvl8iXQ+r/Pzt9fODWf7Mnq/x/5sX0afz3P1ntAmZqV9Ke4VB8e99cyUpF86K03/+6A5n/c6Yr991m591PnUb+dLPHU+9cm9ndizhfrc07Zf8hLZWfC1dE7b71DnvvBC7t7fgrPP+Xx3SN9A28tLtbPd1nvbC8qD+jBQDPt1djsfYt3zJT4S+8xv/dZv2Vd91Vc5Yb1v/uZvtsFgYLfccotdeumlZuZFs83MLrroIjtx4kR0eZ/5zGfsr//6r+1pT3uaFcW9/yJWCCGEEEIIcd9GZyYhhBBCCCHE3aEXaPdRbr/9dqvr2tnLsjQzs6qq7OEPf7gVRWGf//znXbrbbrvNLrzwwujy3vzmN1vbtlE/RSKEEEIIIYQQB43OTEIIIYQQQoi7Qy/Q7qM8+MEPtltuucU+9rGPzdjf/OY3W5qm9ohHPMIWFxftSU96kt188832kY98ZCvNhz/8Ybv55pvt8Y9//JZtMBjYRz7yEbvrrruwvD/4gz/Y+hkTIYQQQgghhDjf0ZlJCCGEEEIIcXfoBdp9lBe+8IVW17U96lGPsp/7uZ+z3/qt37InPelJ9ra3vc2+7/u+b+unSK6//nrr9/v22Mc+1l7+8pfby1/+cnvc4x5nKysr9p/+03/ayu9v/uZv7Mu//MvtVa96lSvrQx/6kP3d3/2dPeMZz7DkviJOIYQQQgghhLhPozOTEEIIIYQQ4u44P9SGxb5z7bXX2s0332w/+7M/a7/1W79lx44ds6uuusp+/ud/3l70ohdtpXvoQx9q733ve+3FL36xXXfddZamqT32sY+1X/qlX7IHPOABUWXdcMMNZmb2jGc846y0JYb0AARvz4lQY3C23m8xSxLujG3XboU76b5gv9u1l7E5n+8zDkIY9r4AjmnkOFOfk7+JmcOUF4vnQv6QjkSnY8tt28SsSSw1s6ZJjDqkOY/mWxoxXrH9QZwT0fTzRMAb18Mum39Qn8Lay1ifz1B/NsHajJ2rsXtZGjn4sfmRL9zPeOnetg+ezzGFOMW99czUtl9aD21ru/bj+00dudft99o4iLPgfkJxHrHf8cpe9vGYupyT8/Ie2O0ZNHa8DgIal3MR557PfbKf7CUGje2j2Fhnt/OXz5/3fmL3gYO4K9rLGoz20/s8rDFzPXqu4p1KZD12OV/3cse4l/PGQayv2L4k9jseC9fXXs9u07HuQZ8Dk7Y96CoIYba2tmbLy8v2J391m2XH/8Z6D/wGS9L497v32RdoAefiBVos++k59AItDnnr3bGXMd3PA0zsC7RtX3iFz0bV7O5eoJWW3v4/rLn435mlBdQtsoBzwF6CwhjOyQu084T99HF6gba/xPTnvr9A2+f88Nnz/AL1bLJdv7VNZaPPvNvqlUfat157qa2urtrS0tK5rZy4V7J1ZvrrO2xubs7Gn323dS+/Z+em84Hz5QXafvuns32O0Au0/eX+8gLtXHA+98leaZvKVv/p/7Xlq77e2j34Wr1AO7vc71+g7TNxL9B2P6dj52/sfclu88dn73Uv0M6f+8/93vfbptqKdQeDgX3roy46sHOTfsJRCCGEEEIIIYQQQgghhBBCiCn0Ak0IIYQQQgghhBBCCCGEEEKIKfQCTQghhBBCCCGEEEIIIYQQQogp7l0/li6Enf3fNo79Ddj9/n3xmN8Pjm1T7G/2ZtCG6N8KhmS7/b3fvfxO7rn4reuQqon77AH1B82vutnDb0dH9Hma+gep32J/XzpWID5eVHY2v71o/MR+KiS2jLL2OcZog+WZT0TrjdrANng2VisN8gvHhtrURM7LvWilxWqbtWddz4Hm/n1THyCWKG2TaJ2ts1uP7YgZw9j5G+vP9tInpDUQtj+J3KPB7WNfNrH5Re6h1P4wrjjfNXH2E5o357PWqrj3UDepVaf36apJLImMgM62nu5e9Bejz2DgQ2J9GeUWshctEVzz+3gmpX2NfPte4uHY+sbsz7H1SBO/A0afByLr27S+NuH+RGcc6vM0jVNjpWfprE1toPaHZwl6LoO6xe7htI74fBh7RvB9XgW2veg87zfbteuMvW4TPljRM9F9GVm3SB+3nz7+3qaVFruv8HqLLIOejcjvfPLdsUSdOeg+cZf3EdvZ+My081qi/tiLLhqx32skZv3SXN1TPfY9LtznPpmKdevIe9izhb6BJoQQQgghhBBCCCGEEEIIIcQUeoEmhBBCCCGEEEIIIYQQQgghxBR6gSaEEEIIIYQQQgghhBBCCCHEFHqBJoQQQgghhBBCCCGEEEIIIcQU+UFXQIhpzgh/pkkbLRxsFieYGS1MvQfh6L1A5cYI9O5FKHa/RTR3S6xYN4ljViAkGdsnZ1sYN0t9RWpQUKV0scwXY59f0J8kiD0oi6i60RwM8zfjNpANxX2D9ZtG9gcKc5N4O4kHQz0mVeZs48rPrwb6KcxvUvrn8gz6A9pK+efUl9hPpMa7TX1P1zlJWhTnTaG+u/VT94Td+mCqRw0q5DzPd59uP8W6iTTyo07NHhTXeX+keTP7d7RYN82vyHWZwMM4X2E90NhU9c7ztap3TGJm9ySu8Lbdtj/2ORLr9h7uYDhfYg+z/RVcj50PQuwHbfslH9e2XxJZ3w172D48sKRoG4sNHWL9Z8xSjl2jseeDWP9B+1gInoVgE9uLn0kTP9K0f1Ccj8/u8vydReYVWybZaJ/ZKOecLSyB2p5nlbNRG4gs8QFFCivuIPZFKpP6sm599FA2cREFjmGQH51Tk9Z7DUpXwz0Ard+93DWc8q+nyinr1Nokru2x5+pY/8tnkJ3PpAdFrK+i/SGmDZQ/+u7ofvPp8tSPDs5XOqvE+Me4Yzv6Jcwv0p/H+sywn6jtDaxVrIez8LZdky+EyU9xQBM8i/dCsXE/jCnltxffElnsWSe2DbHs5x2ri3UP2L3pG2hCCCGEEEIIIYQQQgghhBBCTKEXaEIIIYQQQgghhBBCCCGEEEJMoRdoQgghhBBCCCGEEEIIIYQQQkwhDTRxXtG0iWWn/z9pk33/PdYYzsVvScdqN7k0+/z76GdbA4ygdpbw++X0u+GcX9x4sYYC6MYE+cVq/BD4bKRWWCykgRb2yYmx/83/IvO/6d0v/G/85/Db9Vnqf8+fxoF+957mcMw6J62B+N/fj9NG6md+Hi52vG1U+61zWM7ahhP/HGk0dfO4X76P1naD3/X2anen5lzb1DYws/lubVk28Wlw7kP+MB9o7E+M/DwkPSr6fXVKF/72eRP5G/exemekg0WaavupixarHxbLfmu2hXWJ1SeLyevUs3GaBLH+nNKFcym2PygvnkveBq6FdWEi0lEa0slDDU7wvxnpyexhDPeTc/E796gBs9t2RVZYWmnibLAXjdKYT9TuRScNn6W4kZJFasTslr1pikXmF6Flhhpg4McxLo88p1Jsxrpo/tmctLyCZ0nbiyC/izo6kbpohZXOVsM1Fz3bDWLYfjbyz1lcnBurK1S3vm4V6IzF9FMDq4Z012LPy6R3tpd9Mka3i8Z+VPk+Gpe+bqi1RJq4kfUlbfVJlZo1raVmNpxktjnxJ6uBP47b0E8lGwz9vJmU3nbFpb793YJ8hC+D4sTQxmcmssXqNsXevcDDdDcCycJxjT0LnIv7xBj/Hav1hn4ano3VtKT2070K+e/QH1SwVokJ3JXQOqcd41zorfsywYb+PA7ymbH7dMxRYi9xy35qP29bRkT9Ys94jSVbsuxJcvBnKH0DTQghhBBCCCGEEEIIIYQQQogp9AJNCCGEEEIIIYQQQgghhBBCiCn0Ak0IIYQQQgghhBBCCCGEEEKIKfQCTQghhBBCCCGEEEIIIYQQQogpvLqfEPcR9lNgsGn2V62QhCWx3EAwM1Zs8aAI20X1Ddu0HXkWL9MZlWqXdcE0kBcJtDYwzrFCtiTwudABhWJgPtuc+TvreTHpUd1xtiz1Uq4xouFmLDxL85zyC9uaQF4VbFc9EB0uQZibhGdrkCcm0e089X3ezXwZnWxWUDpNui4NjX0nh/51FhbZJTHaTlr5dNuMa1NXNjCzle6G5bkvlcaURIcz82XSHDlcrDrbXeMVZxuB8DC1td6loDAJhJMw+aikdL7MiW/+Nv4mLt1uiS6TJlMkYX4kBp7s4aNZmF+kLUNl8p2fjRFbvyfU0L80DpVf+mbgMzvBciggcs9BbZ3iFtxXqf2w/+B+RulgXcbse7GxwbmA2hVFbODZtlvr9XyP68T5S5a1lmetTezU2k4gJmojJ1jM+osMXzHmiiWJLWTX+e/+WfJjlB+lo9ipyGZtOaSh/thL/1JcXrd+A0lo3sBekbazRoqj8cwANjxvGG6UDmrXsO45G8XDnXQy+3cSd9aKBfscOpPGgWxV0Od1AwEAkKU7n7/MON6O3f+JCtrq2hAZN9CZiWJ6gnzcpPbPjibetjlKLGlTWzGzk5uprQ18/nfeNXG248eG3nbHmrOV49LZPnfxsrNd/WWHne3wsq9vnsGcC6Y++S5a9hxvexuV2cl9hik+C3WJGFZsQ6QN20W+CvbV2CNCuOboOfKF5Pdj9xU6u8f6YF7Ts3OTfAHRtPPOVsH5KNZ/xc7XmOdwL4e2x449Hatpn6b6kv+KvSeOgdoV2yex+cWw27tvM7O0ba211mozy9MW9/Fzib6BJoQQQgghhBBCCCGEEEIIIcQUeoEmhBBCCCGEEEIIIYQQQgghxBR6gSaEEEIIIYQQQgghhBBCCCHEFHqBJoQQQgghhBBCCCGEEEIIIcQUIEUuxMFxRsAwTVpLknbXQoVmccKSJGgYK2R/bxN+328RzQLEiEMh0HHthUFT6Djq87qJE98k9iKwvVtIVJVEYFEoF9q11PVixCwgu7PA9nJ60tkubL2wcZP4yrUJiD+nhbeZt9FcylsvlBxDAXVr4DMgReL7LZYmRrHYzJrM1yUUIScx+EHVcTZKl4M4Ko1zP/NC57GC81nSWN2eyrOTlmYwfixeTmGDbxf6EWjr4Y4X0ya/PGl2rh+KLoO0L82bSce3a3XsBeiP174eDfiqSeVMVu+8VA2mFvoMIhQINzMbT0iEHXxV5MepwqnZgO9qoe2xvjtWrJzqGyt+HlLBuGCZkXWDqYSkkN/qBpQxN/s3aLJHU9Uw9jBvGqgc+xbfATQnYqD+oDlNfR4r9E1tIGg/jxXYdmVuJ+CdTP1biF3QyWrrZI1NzKyTNZZAXB5L6D/3Ivi+F/ZytgrrHHuG3Mv5KI88H3UzH/vG+qOQsoF4ODJ+pXiQYjOyEWEbaN6QLYPYLAEb0RjFpt5GcXM39QFKN5mNpTMIYorGx9tE0lK7/DhPsr6zbaSLztZCfJkEYxgb909qH5xk8OwYno31BzRvaG6W9axtL/6GYnDaw8eVtw3Hvm6dwvfJ0lxj1jRmG2aL/cb6PZ9mZQnOQv/M28yWfd3gaLy67stYXfNz84tj3+edDqzzIMhKMc71z3UKb+tCsyg2o/Eq4IybZ3G+NQtsfC8C/gzyonlD0Bqhszutw5g9iNYMlbnb/M14DfJ53jMK7i6ovhXkTz6I6kFtjSWmn2g/pr1hv2NximVGtV84VbPz3k13opiO9ndIR+shXFtmfB+FZ+GIcYi9m90uCmib2sZm1slrq/Ldx7r7gb6BJoQQQgghhBBCCCGEEEIIIcQUeoEmhBBCCCGEEEIIIYQQQgghxBR6gSaEEEIIIYQQQgghhBBCCCHEFHqBJoQQQgghhBBCCCGEEEIIIcQUkVLnQpwbirSx6vT/J2mza4FlMy9WSKKEJPBIgpx1pDgzsRdx6v0sk/oShSVBHLSTedFaEoPcqLozf1c1iB/vQaQThStB9LKFgSUhWxqHkKr2vVRHDlVCYtogYkxjs1l6oVES3k1B7LgNOqpKvasfZXM+L4sTWiWB7Y55ge0yAUFlmOeh6Dal6dYDZ0sbEhGlwQEh29QLjpOIdZ0WzlamXWcbNrOi0LSOerCOKB2JzffTobPlic+PCOeDmdmo6Vlzut+bNrGq8WNFYrSxQt8ksNyAx4kVMSYfHK7pBoR4yU9Ru2gN9kCkNs/8fBhPnMmqSH3bcGhS6F7apzYHsI7Aoacw9li3Xerx1rHOMJIK8guFz7eDksX0SQMdTP1GguuZdyNcD9y7vK3nXYt7tqZgBvcVn4yE2suS1s3O9djOhvFNkI5iA9rLCWiqZdDpJJJN44rC7CiSHZMmTpg7Sxtrk9pGZlZkByuGLe695GmzJfKenz43xYBi7oGpiTyTgAtE6LyxlzNeDBhvRp6FshT2f4hritTHawXEZlXre2qjmo3DcVyA2NiM4ktqawPxFT2boW22n6jPOwkESQDFtHnr+zc8M5iZtbDZTyBWpz4O69wkfqwo7o89t1N909bPr07q+6k0H3PWwVyi+UBjSukqaAL1EcUEtMeWtY/py8b3Z9h3tJ+WcP5uGmgD2SDGoNjp0Lwfh/mun3NNm1jbVDYws+W5yurI7yDUUDfqSxqbI0s+XXup70tqF5UbhknkCyleyWFfKXJvY3/r6xZz97Jdupg9g+bIpPL9ttjx6438Pq4Hut+h82aQjmJEvHvAdennHK0tItbHE3VQRkIHqz1AfV6ALZZwHOhOheZqGXkfkeI9cZwPxrshuGs4MZy9kxnDOS3yaLzNGSfuDJZDIXSOpPHKgyrHxnvbzej29PNZ0uI6OpfoG2hCCCGEEEIIIYQQQgghhBBCTKEXaEIIIYQQQgghhBBCCCGEEEJMoRdoQgghhBBCCCGEEEIIIYQQQkyhF2hCCCGEEEIIIYQQQgghhBBCTBGnlifEOeKMEGzVJJZYYp2MBIvjxBtDUU4S92wibST4SsKoBImgxgqo+jLjxK9RMBJEL1nwPk7Mk/qzn88KQFeRCpc1iR2DCCyNDcHCsz5dzLiSSLKROC+lA0iMl9rFAsAgjNt4Nx6KfqYgYN3gzPF5JdBJRUKi6d7WbYbORkLcqc3Or1616fOvfF5p7ctsUy8/SuLiSeX7pMo6ztYpB86WFXPONsz6M3/Xbc8/B+toLht5W+rL7NbeRsLkZebLHVnf2eo2s+b02NZtZg3MrViRZPI3DTybRQqukz9gvzxrG4F4Oebf+PwrsJWVtw3G5Jd8GWnkx5NCF0l5jcfeWIHCdIoC8WdXaLeF/CtQpS/BBtN336H6xUDjkOe+fwuwpbDvZRnN38g9I3i22wFRZ5j6BdjG5c7C8mbc/viuJNHpu//bjNdMVdPG7U01zKVYgW2qL8ULoY0EtzNoQw4NK/LG2tOdTOL2QsRQNenW3lU1KcaIBO3j4frmNRApAr+HM0ieVlHp+Dw06wjSxDsGsmVGZ01fD2oD2Sh2qiHmDuvCcbmngDbEQnWjPqF0MXSSibNRH1GZRTN2tqyF+dDA2EBA0bMNZ5vkPh6eJLNxc9XCuYrWA8wbok18ftS/1E85zMM0yK+Cc2BZx80lireLzJ9VIISxEuJmgtrl8oL6VnAOnpR0X+DzyzNf5qF5367w3sKMz2pmZk1d2cDMulmJAQbdRdF5g873WeR9FAVAFJvhnVdYZqQ/p72A7oWoL2mdV3jnExcHxexBZevzit3PYutBbaBzZEzcHHt/RKuNPFDst2Nov4mZNxZ5F9fAPCd/k9E6B59J92dEOOfK0t+LxIL3G9AGmucTuJOgOUL+gGy+HmDDLqJzKj0Ldwh0TwzucQwZdvPZytD48X0tr4czc7NuE/S15xJ9A00IIYQQQgghhBBCCCGEEEKIKfQCTQghhBBCCCGEEEIIIYQQQogp9AJNCCGEEEIIIYQQQgghhBBCiCn0Ak0IIYQQQgghhBBCCCGEEEKIKUBiXIiDo2pSy+2UMG1iGQprtiCgGiPcGiPIaMZCoyR8GCtISoKcJNxKgqzjQMyUxOaLDMSfoa0sUgqCjtSXKPgKgpFZOfN3QsLJJF4OSpgpCACzoK6HRClJZJhEnEPB1wz6jWyxkIAo5UfpCOrjNBAurTOfVy/zQt9Z4ucg9VHZFr4eJH5tvgyiSWbndQuLq06pnaWzJY1vQwIi5Gnjn80nm87Wpn7NtQmMYbAOc+jLXuYF0hebE87WH6xGlTkp5ny6SGHyLKktOV3HLKktS31/4FrdpbD8ds+Sb1kbe8HfYenHfxSIiY9BXJwEnMl39zreQZSVTzj0Q4hkoEMNy9DVpQJx3n7X16MDivbk4yZlnOA49UkDfjkUKK4q8F1oAyFxsCVQkRYqTOkyULnPc9/p4VLKSAwe2l5OfH1HQ/B7hS+zA7a6prGhfW/27/k5P7nm+iBo7t0ezssBtIHA/o3TW7duZ/Zvmuc0DrRminz3wvcErQcqIxTYHo5pnCH2gDZM6sysaSw3s8EkshOFCBiVuSXlqfkzLDMrWj+XaB1QjBza6NyTJ2BLvaNBsXg4b8SemRr4vC/FDpNmNk6g/LsQ6+TpaNf1oHh41HQhP2dydKAvqQ3UdoLOTBQPNpBfDmdSIsyvMT8HG4j9stbPh4Zia6hHJ/HjtRe69WDmb4q3x0kfnoT4AvqXYl/q80nbcTZ6NpzDdRN3N0B3Jan5/u1Cn6ew9s383CcSaMOkDs59sD4oBofQDOP3ha5fS3O5X/s0v3DtT41XanH3TmZmJQQAFdiod2OhO6W5jm9r6Ddi78Vwv4B7C/JVJcxNstGzSNDvNFa0/xDDys9f8pkZ5EdjTf1E63C3kG+hnYDP2rAG4Vy92zM+rV/qS+oj8rcEzRGKScgfhlQw9/G+C9bqhIJ6gPokds2Fvo98Id9De1sO96kdOON0crhPJr8M7c/pHrOerczqwO9v813Y37epx5m74kmV4X34uUTfQBNCCCGEEEIIIYQQQgghhBBiCr1AE0IIIYQQQgghhBBCCCGEEGIKvUATQgghhBBCCCGEEEIIIYQQYgq9QBNCCCGEEEIIIYQQQgghhBBiCq8eKMQBUlap5af/P0lTS0CMOFYYPhSvDMUMzcwyED3s5SCIDSKVJNpKUqEkFFyAaC2Jg87ls+m6KYkzeya1X9okeNpCZ2J921hB4VlIEDu2PypIV0EbCBIuJXFQEjPtBH1O40xtJ9FSFCkFkfcYcXGz7YQ7Qaw9mNfUl6Pai3mSQOt8PoyrG4hJ9ygdCK6Hc79Kfd3Sxs8l6risHvtna3rW99uxIw92tryZOFsCz140/PRsPSovcp5WlBf4kQzWb+57s2MDZ6P80gzE4BOzOjllL5LSaggHyD/QOiIR59h0x4dzvmqRIriL2ey49jsgzA1rkDwX7Q9Ev0t187YaCulG6K2nUI0U3B4sERv56YX1SKGDywrmIT0b1CXPaZz9c7Gf16rruHo0NczpDsQLsMeHpgbKpHqMht6PVFWcQHgCA5vAOND4x6ShOdLrwl4bMQfNzEjTuwtzbuJ16q0HaySchxnk39C8ge5NImM5EuumPZ/Wb8ye3Clg/4F6UFtP7bXtts8IEUOeNVZkrdVmVmQtxoi0n9Kc6wRxAsXlFNNnCZxn6HwEcSjZakznG0HnHF838OOQ17AGp4X5wb4DUPxDbQ1johrOvHTeoP6lMyT5uxTSWWQZKUZPO0OxXwkxJ535MyizgQ0vN78ZUTyctX4Ot7ThBXQbfxaqE4jVwUbzgWLuQe3jfDqD1cE5Mod1aY3PH8I1jodhrhqdIyvYPAFav1Xgq8YVpaG4yeef477rW4b3CuAzab7WbbJ1bBw3Oc6ZMfRHCW0oK/JxzoSx3mLfj3Xou83iYpjd3qeZmY1KP78m0H66o6By6Y6K7nzC/GjPozEN70XMzIrM1436jc5M1Ofkg0M/SndFBPnfMvJZGi+y0TjQmgvP0bG7AK1LissbqAjH9LBuwLeG0DwiG+6XdGaAutFdwwTvAH39qE9cPcAlj+H8VcAUqSL6yIzPpNTWfsfPfbzXDrKLiTvNeF2WdWqtNTa2Uz6A/Mq5RN9AE0IIIYQQQgghhBBCCCGEEGIKvUATQgghhBBCCCGEEEIIIYQQYgq9QBNCCCGEEEIIIYQQQgghhBBiCr1AE0IIIYQQQgghhBBCCCGEEGKKnRV4hThASOQxRhjVzGwMIq0h3RzEiUHMsQL1xhbEQnMQYqZ0NYh5Gog8hnqLGYg05iQcDbmTeC4JnlK6EYh1kzh32HpK08286iUJ9lIbMhC4RBlJyI+EKklUtaxnFThrFOuO++wBCUeTOCbOc2gY9dOk9oqhSSB0npBwss/eWpj7RdpxtoVsw6drJ852we0fcrbbLvlXzla2s2VQv9H6IAXcOvNi8JsLFztbk/h+mxufdLb5E59xtnTsRb1Dxocv8UYQnS47fWdrUxBSr0Y7lmlmlkG70rQAW2PtaX+VJo01MM8rECGnsWHxej+Bj4/mna1f+JlI64HWai+f9SXjyteXhKNJiJiEqGtQXM+zuLUfIwps5kXCSTia1j3R9cNsvQ6If/ulannm09EsD/ffDASLM8iLGI3An5V+3tQ1+W6fX13DPCRx+WJ2DEsoczLZfZmwzI30jjOYXynMryKoLwk9Tya+Hj3vCm0MY0/j1UCgNQHBahYh97ZwntNczTNf5ibMERoHEnmndCSQTnWh/EITtT1sp5lZDRXJ0sSsTSyx+PUtxE5QfMnz1M/JUASeYvU8ocjRwzEcpIuc+1Tfudw7s3D/zBLy4xRfQIwcmY7aymc8T0z7yadgXpGx2X5TtbNxF/URxYNU39g2VK132vQslUuxf9rCprWLNGZmqfl0devL3Kh87P+F9QVnu3QRYoygqXTWppVKZ/lw3ZuZLRUDZ9uoes42V3gfUTa+rXROzQNf1cl9G4YlBJgA3ff0CzjfwzjQnRKdN+omsfb0Xj6YZJbAOa2CuH9cgm+BOITqcXjBj2I39zY6ldARJAylaW3ROsogjqb65hDo0v5D5xxqA9UvPFvRWYvuhaBIm8C8wTh07P0NtWG+C/dbQf1obhGUjvqc+jcWyo/GOrTRuFB9yUYxCoFnCzi745wL6kd5xdajgLvDtorrdJhKCJ+jZo00L3NwjyXUrYGmVrhfQnyD5fo+Ib8fxm0Ux9UUL8DETNN26z49TVs8259L9A00IYQQQgghhBBCCCGEEEIIIabQCzQhhBBCCCGEEEIIIYQQQgghptALNCGEEEIIIYQQQgghhBBCCCGm0As0IYQQQgghhBBCCCGEEEIIIabwaqJCHCBnhJdPiYImVoMYIoktNqSWGkACjASJ3WYgaNiScCWIGpJAL9lIsDpGYJrFMb3ILOjz4rMliAwTw2rndC0UkIKAM7WTRJEN+i2lZyPHGoWtg/xoPpDI7urQi8yOJn5MSSi5yH09SBh3t3OYxX5JKNj373y66WyL5QlnO/zxm52t7c8725H1W53tzoWrZuvRkvw1NAIatjl3gbP1JuvOVkw2nC2rvCj9aOmos1V511clmNcNCEwTJGhepj7/ohk7W534NUj5UbqqLbbEW6u2wDUYK0BP62iz9ALpJNpLZXTAf/UzL86cp7PpUvNlktBzDXO/m5EAsl+/RQbjVe/+s0ihiySRXSJWYLrfjRM1J+HhPIdngyVSVSQm7evRgBG60jqxn+saxSWjckcj8i+zZDAfMH+cN77MBOYc2ajccI60FI/AfNgc+PVWFD4dzRGaX1BdA81pJ1RvZmbBWOMcgTK7flu1mgSxoUyysVh3XLosaCvt5VS3BDKrajNrEuub2bjcgwq8uF+TJu3WnpEmrXUL79ti9/HwXFIkfs/tJD5Gov2/br1zryAOIb9Vtn7Ro9A8lBvasgTODOafa8iRNXDGiVyqsee5EvrJVQPyIv9JO2cD8QTVA9wW5kdnhHB+5fAgn/F2zutUPSDGwIGg+eXLqMPNyPy5NDP/HJ1nE9qLoedGbc/Z7thccDbitvVFZ7tgfjjz91zu1yVRNb5uD+l8zNlWU3+OKjPfv8OqE1UGnWdDMrg/6RdwVwL5U7g2LOHcA/WgulEsXTeJWdNYz8wG48yd+bbLi+ILyv/wok/Yzb2N7kbonmJj7NtfVrN9tzLvz5V0v0HQ2Yqg8YqF4rAQOldm5OMi7tPMzMYV+FsYV8qvbv166Ab3OzT2sec5jkvBZ8LYkK2OPPeH9aM5Qm2gfps04H+hDdQuguZXJwtiGbo7jMqd53k/93EW7aG07w0mfl2S32iCDKvap+kUcX1E8yb2riGHcaC5yQXP/hk7pliPpLH2dHyXJ43lEP+dS/QNNCGEEEIIIYQQQgghhBBCCCGm0As0IYQQQgghhBBCCCGEEEIIIabQCzQhhBBCCCGEEEIIIYQQQgghptALNCGEEEIIIYQQQgghhBBCCCGmAIVcIQ6O9WFqC2a2NkzNkhRF61N47UvChKHgOwlBkqAsQflTfnkaJ2pIwp0kchkKZw/KLtQtTmS2m3lB8EkNYpZQNxLE7oKIZphfrMgstZ1AkWy0xo0XiVjHCOgOKy9yHgrFmhnO30kVJ2LcyUAEdpdCq/ycbzuNQ7/ddLblv3m7s7UPuNLZ1o/+c2crcy+mPd+szeYFY0CCzVXRd7a88fO8SUHsGZ4tO17UG5/NQDg7nbWVqV+rZevnDdlg+polXkg8AxFVEgAO/YjZKXHb5rQSbN2mllCfg23SkACu76OShIKhvp3M+xGah0UC/quZ7XPyceS7qK0JrBFqVwKivTlsStR+Et6NFfKNeS5W/LvNQSQatq5uQb7q7v82M2vKuPwTUDbOISrNc9+X3Y7v8/EkzgeH5bYwMBPIi4SYiw74pco/m+cgdA3PptnOY0h5EZOS4hH/bOarYZ08bmyIGrbzNqjK2C9VF7OZ8fyKtdF4UToql54Npwnt28TG0Ns6hVnSJtY3s9EkLh8hQtKk3doL0qS1DuydtMdS7BvaCvOLlJ6jGHzS+hipbsFXgq2BMwhRGeyxwb7bgQ2A4pAK4pomcj9NIeaiGKaFdoV7Np2/Ys+LdMZpIP6hXYHqlkM8RX6R2u+ew7g0LkbMzbeVdkAqg6jg6qsNfDn1Zcz4naqbb8PaZM7Zjsz5jYHOlkRYF6pbL58421XpJ51tnPm6fXF02NnKevdrdbdxLvYv5QUTooH9mfqJ0jVQRNPY1uJpGrOazkfe1VoH4qaLlv3YxN4flbVv7MmBnzfU1iML45m/6a6ogXVEeRF0xunBXRH5tLMNjXPV+L5MwcnVYKM+Yb8R1AOeozmdwJzGcx/0OcWmDbSBfCbVL7RRPWLuosy4vrFtoHQ53JX5dHQQjLsnpD2J/BLdF+B6KPx+Niphnw77HH2ctxWR53vKr1f4hOSVaGwoNgi7k54rMl/CsQ1/f9YrGmtPr9fVUceGo7h747OFvoEmhBBCCCGEEEIIIYQQQgghxBR6gSaEEEIIIYQQQgghhBBCCCHEFHqBJoQQQgghhBBCCCGEEEIIIcQU0kAT5xXHV1u7ZMHs2MnWGmttcd7/Xuqc/2nUbTQsZn98Nfb3ign63dYUfqC4BJ2m2N96nlSgtxM8SppH1HbSAiIdpFgdtxx+BTeH3+CfC35zHX9vHH47mNpFz1JfUhvod51RVytS4yCkyOJ+e5d+m5m0ZfBZGAf6jemY35an366v4be/R+Z/R73oXeRsF8APO7eF17wg/bAY0hb0HcBGtPBDzE3q2zUGHbME2lWnoPmVeNskndV2K0EDZNL4epAOBEG/w93AuozWgWjzrd/YLtvCEqgb6YfR73yTnl6sbgfNX9IPK8CndbPZ3/MnLY5R4zeMWL/UksYcPNsBf0C6g7Tm6Df4Y4gdZ9Ttgt8cb4qdf4PdzKwXFJuDZlcJPq6C5dvQeov8WXNqV+xnwrKgzpRXrwc+s47TH4iFtMeIsE/oOdKTo9/9p2QV6HZQW7uRsQzVj+KUkJGXBUEtEsqLbOsD//Bw6Of+/Jyv8AR0/I4fn61gB3T4qop0FXzd8jy11CpbWTL73BdhAISIIE+brVgxTxuME0iTibTMcpudh6jnQj4F9qJO4hdzA+ejtPWx1LiO04GqIE4IGYFeUAaxdQl60AVox1GMQXESpcPzWzAOLfRHE6ltFqvJGHvuIV04ivXCGA51dWBvxnMaaXCD1h2f3eFsCamIsE9i9c56ycjZGiiVdAgP5WvOdqwF7TGY5+H5kNYzno1DMVIzW2uXfZlwZiTtrdgzKY1W2J8Ub9I5OFbnmXSrYmNEijvKNtmaYknCsSpp7pImUawmHPUJnRko/iGd2XFwz0T16OUwl2Ac6IyXR+pg5XSejdRrDJ/FeyzKH0SfKH9ab7QeCOrPcAyLSO3yWD9NZ1fafyi/2PNn2C7UcaM9bw+6aHzGg3QR/oDmA8VKXLe4vQDvTqEM9OdwV5gH9wp4dqsjOwmmbzeP0/mmGUJ6gnes+kLCM93hBd/OExs+r+V5X+ZCr7Y2aW1ip/orgz47l+gbaEIIIYQQQgghhBBCCCGEEEJMoRdoQgghhBBCCCGEEEIIIYQQQkyhF2hCCCGEEEIIIYQQQgghhBBCTKEXaEIIIYQQQgghhBBCCCGEEEJM4RVihThAluZPiQkuLyTWJon1Oj5Nr+OFD/tkK2ZFezMS94ysF4lIxgqXDsqus5HYM4loepFWL9K4MfGC26OOty0UY2cj0Vaim4EaLRAKGZNIJ/UbiY+SMCoKZ7e+TyqwUQcXIIgdEisCSvOLBFobUhkGSIyW+o7KDSFx01Hl3T+VSYLuyZxX+EyO3e5s3aWLfH5dL1hdZbMLvU59mSRAT8KobeL7vKj93M8rWA8pCQWTqLtPF87DceOdF/UlidHiukEfBGK0ICY+ATH4tk2sOZ10UueWQmdWkf4hgXSLxdDZRrXvExIj7qZx/ib0G2Xr29lJJ85GkI8fmffd5INoXTYguF5CGSG0NzCx6TzUVhJ77oD4efgsubMGNtYKXG0GD7eg3l5DfnkObchojfhnw2LpuboG/wv1TSOVrhvYf3IodzzZ2Z8X0Paygv0N0nUhpoIut3rnrdHMePxJ0L6IOG3gXIK6hcLUZmajEey/MIb9Pq1fn99H//GYsx2/fXXm76WVBZdm5aJFZzt82PuRpcXMkrY1a80OLekoJnZH3SZb54S6TTDGoD2rgBi5DRZgYX4fxtgE4vc8qZzNjGyesfk2UJxP5YZ7MaWhmIPaVUP8XsG+S/tpC/nR9h/Gf3QmreG8MaEywVeSb6N4hUigrRTrhaPaobZHnqHJRjEyDCumw2eBcPyzyNuB8Mxrxm3tZX7TWqv9XuHP/GaD0u8P4bl/ruPTHO5sOtvt+WXOdnLs97HYOHSh489R1H4iPLvifICBpvN3CcNF64ZIIfZd6njfd2LQMTtdxzxrbQDHFFqDw7Hvjw0IiKjPF3veZ45KvwY3hnFxzcZottwjC378SvB7dDcQc/dgxnM6p3sg3Ft2nktUD1r3dI9Fc25S+7Hpwl1R7P1hDLFrJjo/aP+kpvUQN9YhTU33c3Ami91raJ+K3bvpvJHNjlfsHImF5mpsWzuZX9NLvZ37juYv3dHQ/C3hPmJS+fkwnMTdbxRwN3DXCd8nC/Oz+a0PfTuX/XWiLfX9esvTxlprbWJmedrivea5RN9AE0IIIYQQQgghhBBCCCGEEGIKvUATQgghhBBCCCGEEEIIIYQQYgq9QBNCCCGEEEIIIYQQQgghhBBiCr1AE0IIIYQQQgghhBBCCCGEEGIKKVeL84qmNbPk1P+3ZjYBzekkiXvv2zSzIrudnEUJQ0hQFoVGQYBx1PolRaKXDYjFkhwipYup28bY12M48bb5rhelpD7JQKiyk/rBSYNW5CCwnBqILqOYJwiegnAnCs82vk9qyA9Fp4PxIgFcIkfRcBDPhf4lAdlYkVIU3Q7yo7kViqyasWj6oPSC7rZykTOV//BBZ8s+c6uzHbrySmcbPfChM39vzF/s0kyynq8HMDdZ87aN250tLUH8Ou84W9n1Atvj7pKzVcVsP00av97Gte9LFs6O83H0bJP6uUTl1m1i7Wn/MmoKyxNYH5HC7+G6N+P241pFfXhvrFtfv7Au/XTo0oybri8AKFvfRymIGBcgAEzrpgDR6Qr8eRv4VurzDPak2H0lz2D10/SCZysURd7ZH4L7tQbU1VvfRVbV0FZqArQhz2C+RqSDJYN5YbugbthWSFfB/KK6ZEFdKP+MtstIjewOnARAc9oysNXQrjlYcnk2W+ea5hvMh4kPUWxjk+I2EhcHweoFb/vM5/2avu2f/J5x8QMvnPn7iqtXXJqFBT8QnQLWZW6WtGY2McthixUihrpJt/afukmtgWP9pPZzks45eTIb/5BoPcX9OdhoH5s0Pr6Kja8T8JUcO7VBmri8WrT5h4eVX6y470aeI8NzA/UbQXlVDWwCsGfxGcSnq2CTTSBhEvQTxZsZlGlwriQwbqT5QI2NpJNMZsuEmLaBwInKzBO/Hii+3Cj9Rvn5431nG02cyc3rHmziqz2/3oqM4iuKt32Zcx2/QS8VA2frJv5sVSS+EeFcKhNf343an79oXTYwlyhuriGIg1Vj3dyP4Xw3s7aprTSzuW5tG76ZVlGcB3HNyQ1f6tHD3t+OS59uc+RtdFdGLPRnKziAeyHymeQzjM4WkWfGmLuX7ajDZ6EadP4qUt+/VI8+3BVS3bANEe1qIA2dNSkd+Wk8L8NekOZxd0+0lkIfXNBhACDfUsF5mdpA6WLPwmUY88B9VxfmCM1VIoO7TVo3VDcaGyq3DNpfRt5l4N0A2AqYD1SPzbGv78a6H5seVO+SlXDexN0BbTfOZ+Zr0ybYpnOJvoEmhBBCCCGEEEIIIYQQQgghxBR6gSaEEEIIIYQQQgghhBBCCCHEFHqBJoQQQgghhBBCCCGEEEIIIcQUeoEmhBBCCCGEEEIIIYQQQgghxBQgHS7EwdE0Zpae+v82MQP9SStBLDUlFcKAuvVCiDmJJJMQJFQEdEujwfxAu5LShbAAo7fVIFK6OfbCxh0QUCURZxITD0WWSxB57IL45l7ISOwZ6kttILHUSTvrFknclNjLfKC6ZSDmiWWQuG2EuGbM3NqOD1zyZGf75xc/yNnm/uadzjb40D84W399bdbwFV/j0mzOXehsJODdH9zlbMWat1kJytydnrc1ICicgIBqOruWinTRpVkNhW2NxV0JEnHugDBuTSLWIMbbtIm1p+11k1pLAulQtQLmKkFC8iTsHCvWToLKk2p2rdaZ799O4oWjy9aHPpPa28g/kMjuCJ7NoZ/qCKHzBJ4jIXEahQ6JekdSRfqDUMSaBO5L3+WWZ+CnIj/CNYHNsYGm5l0Qoq5gvMAW0oO8CoiYKR7BwAWU6us6TrC6CTq9hi20yOPGj+qbQ916HW+L1A1HvxHaSCR6XMIah7m0uek7YH3dJ7zqyjlnu+OYnzj/eMvnnK3oev91wcWzPr3TiRR5J1tjdmb7avcSPIj7NWnSWnp6T0qTFvcnguK/KjgPJY13eBQPjxu/VigGpbpRDEfQsxjnhiZYfHhmioy35/Oxs22UPm6kZ8HNWh20oYJNMfaTzhQjDsu4a54U2l/DHKG6hMdoOs/UFINCmUXk/KV5mMPZkuZXAqMTxqF56/cTKpPyrxK/Hvrp0Nku64+crbjgiLN96LMLznZidba+XdiLLjzsZ9zKog8ADs/5ui3kvm5z2cDZlibHnK0zXne2vPL5JfVsH1fdef/c4uXOtpn4/hilXWcz87YunJn6sKaJ5a5ZU1d2zMyWOhNb6/RdmrLy44BnMojhNsd+vDaGft3Qs4cW6HwIazowkQ+toL4FOK+y9nUjH+SdslnsLRD55TxYc52MgnAP3gEm/ln0I3R/hB7dkwatpbM2UUF9R42f0xRfxt4p0Tm1bHZuV0KFAm3E3awZ31EUMH9pn0IfH5434LmM7iMwznCmaLB/Yd5gvBQRGxVw5qexJ2ica4jlFnq+nzZHPq64eAXu4zqzz9IdUOQUMTOz9vQ+nafNNr7m3KFvoAkhhBBCCCGEEEIIIYQQQggxhV6gCSGEEEIIIYQQQgghhBBCCDGFXqAJIYQQQgghhBBCCCGEEEIIMYVeoAkhhBBCCCGEEEIIIYQQQggxRZy6rBDniDNigklipDu6LVW9c2ISRK5AyB6FqTE/b9uL2GQD5Yb5ofg1vgYnUU14GNpPYp4NCFvHCCqj4HjrRVBR8BSEnhsQVW3gcwAkFhsvVj77N40ptSuWUDTczCylvgQh21hh9nC8Kpj7MUKxZtxvKTz7yezLne3hD/qss/X7Xni5Gc6KWHfvvNWlyQ55oecKRJzT2osCt0XH2WgE2xTGpvLldkZrPr8mEAqeBzH4vp/TdwyXnI18FdkmIEZbQxtioPnQzX1fki+YgP8lcVsqo4UwpJ9PnG1Y+TEM/Qa5uAR8HPkHopt5Ifm1suds48q3oQaBWxK9DYW4M6hvDn1J84H2kBLGpqzAZ0buZ2G6BtLQOHBesE/BftYpYO6X/tnxZGcx6VN12XkvKP3QG3mNLG4qYboC2lWDunoTjg2Ja0O/ueeM+5zmDc0HslH8wS4oJkbzttHYG0+cGDnbxpr308Ojfq2+/72fcraq9H7ukisvcrZef+cjE/YH2JLU7MzWTXNeiP2CYkQ8S0C6kNi4EYHsY6c+tQHj4aAQjF/hbEHuKYVna0i4UHh/VDXeV8RI3jewX9NzOcQJo9r3JsUctP1R/xJ0lmiCOLRH+xOc8XKw0djEnt0yg9gfxovqEqarksKlKRq/x9SpH2eqb9b6uuWNj3OXC79nPegSX8Ynktl0lc/eVha98cuWvuDTjW5ztu7qCWfLJkNnSyrfhqSC4KmBwCYgz1ad7eJNX4/B8qXO9sXulc5WRQZnNEcGcN6YyyeWtKfakaW1XbTk58Od6/5+g2L6+a7vD4rpl1d8X45K365+4cd6Y+LncL/YeRwoHlwb+jm42ItbbxhuQxlZxPnIzKwfnEu7qe8jWuO91Ptp8jd56/NLWvAZELjVCfmD2WfzxuffJH5MJ6mfS7H+LAdfuFYtRD1bULwa7A8Z3AMQNdwnxt6f4Xke9qki9XM6jFNoryE/XSS+31LaQ3GeR45N6suo85191erE33dRu+jeooH6djNfD4oBh6Wf04cXYH2BbwnjjxR8IdGALzT7ki9p273dt+8HOrYJIYQQQgghhBBCCCGEEEIIMYVeoAkhhBBCCCGEEEIIIYQQQggxhV6gCSGEEEIIIYQQQgghhBBCCDGFXqAJIYQQQgghhBBCCCGEEEIIMcXOithCnEPOiEYmiZklZqB3j0LzRCjISoKUpBwdK0xIddtvUcNQkJ7aQMKVMXmZmaX4aFwjSGR3IZ8dnA6Iu5KYJQme9swLFo/bw85Wt158E8tIvMDluPYiuyRkG1KBMCpBgqdlBeLiMIZ56stIIT8S/QxbQPOGxMWLiLafSuf7kvpt7cIHOVt/3o9h9+TtM3/XvXmXpux6Adwzgs7TpKUXCm5TP0fajhfrJqFggoSzO4FtEcSJJ4u+zGOJbxcJCrP/8pBoL82bEBrTAYhQkyB2Wfky5zogxAxtoPm7PvH9RBzuzvqIYQWiy9D0fubFvxtYb6EQsVl8/5JAMfmDsD87mR8HymsDfFdZ+g4el76+TeQeSuMV7r8diCJpryEGfqnafN8Xujn07Yfhsrmef3ZhzqfbHM2m2xz4/FeWfV605xM053jv9ra1iTdWwfbYKeLilrqGvMBW5nHOhcaa4rHcT003z8cwV2lebm769TAZ+Xghz/2E+ODffsHZUpich1cOOduRC/0eNNef9Qfdjm9DAWNTQL8lidkZd5BF+nYhQqom3YpHqyY1Wsrke8hF10EcSrE1sZdP4vrVzftkEnm4CptKa6uF8wFtiWnkWYj6l2KHfu7jjvAM0s29b2ta38MUEyx24IwD9dgY+bPbyCBGjtzvusG5j2LJHOLGDM5kZCNojsRStztffaUwM9vUx6U0Nnnix5DmHNk6iT9bXNhfc7byotnxGkGct9j1efXagbcNjjtbvnqHsyWDdWdrNza8rfbtTzo+Nk/mZvfYtu/3XGvhOZj7dL7vZv4MMqn92NewRuhcsjHpWns67WbZtRL845EF3+d0Nm4aP/ZV4seQ7hrI35DtUM/XJSb0J/+w0IP1APOX7o9i7y16ua8v3dEU2ez8ylNYb3CngvOmhXMq3AO0MDZE3sD4t7P1S+HeYpL1o/LvJn4PoXEYNj4/8stp4tvaS30ZpfOZfh2lMLty2IBpPXQhXey+R+3fCO7ZGrjsnC98PXw7zVLYCOmOke47qW7UT5guGBuagbQP0pgmeNe9c5lmfFe43PfzPGZPJr+H8dg299rt6TlRt6nVMD/OJfoGmhBCCCGEEEIIIYQQQgghhBBT6AWaEEIIIYQQQgghhBBCCCGEEFPoBZoQQgghhBBCCCGEEEIIIYQQU+gFmhBCCCGEEEIIIYQQQgghhBBT7KykKsQ55Iy+e5aatQkL1FegMUzpQDPSQcKrBIkpU5mxossNpKP61sEr7gxeeVOZJA7ZZiA2GZnf+tiLynZzPxCLxXDmbxJ5JLFfoj9Z9UZfDRubF3Y+WS05WwUi2Z3Mi8+GAp/j1ovYkgBuKMBuZpaTQCuIhJNQMI+rt5FIeCjaS8KrvdwLnlK76FkSgSXB9ZP5hc42WfTj1e0fnvm7TXybysyLSTeQbtlZzLqrt3sjCAUntZ+bTX/RP5rDRAyfy3yaigRqQVydRGtjIcFm4tS4tlv/HpZx4QCJldckiA1C1MQSCF13MhBZrv1Yr5ezQskkkrxZ+XlDtkMdL67eAZ/ZaeL6idbNfOHFmcN0E8i/anx9B6O4zz/Rfkl7TQ5tLXKaS7Pl0h7awN449MOMDEfe1u2AbwXR6Z7vJusWvg3dwKVXlc+L9vfwOTOzDNYvrYdNaNfITwdsa7jvd6AeRAYDvVDEBT3kggZUX6jL2G8tri7Uv6XfGjFW6s+Dby0hQ2Dp8LyzLR/2e9LKCsQ8wdh0wcVRjEZxlpnZGVdd6CQmdknTJFtrqW7MGohzU1hE5Ldc3tsIubv8I+PG2PMWJwPBe0zVBmm8XyDR+hbq20RWmHzlYmcI6Xx+TXBGovqmcI6gthdwnunn3mlTrHNstOBsJwfeuZewV24MZ+ccnQ27dNaCdiUwlzpJXPBA/Uvz0KCPY+pB84baUMMabBI/YuPU7zuUX2G+7y6aW5/5m86QdJ5btcPOttj1Y1+Un3W2dgxBDJD0fLuSwgdnbWc2XdvtuzRNZ87n1fj51TM/p8vEb6w1bMZNeNFifJ5P03bLT6Rpa124y8BxcBa+G2GfTPGlz3Eu92tks/QBynwxm+6uge/fDqxfIod7gPWx9xnLfV83XPt0ZgL/FXLnyJ/6L+iu+TJh3ZcJBHHg9mntZ+b7qWh9fcN7iir1ZdI9VtP6cU6gDdRveeJ9Bvm0svHjRXdlc+ns+qqhbidKun3xDCrf/j7eR8F9DKybbuafXenNPktzlfYLOn/HzlXKj0jwzEheYmdyuPOgsW8gYphE3m3OFb5/qU9i4psS5lYJ52W8/7Rky9608fHp2ULfQBNCCCGEEEIIIYQQQgghhBBiCr1AE0IIIYQQQgghhBBCCCGEEGIKvUATQgghhBBCCCGEEEIIIYQQYgq9QBNCCCGEEEIIIYQQQgghhBBiCklXi/OKIjez6vT/Jyw0TzYSHAz1nykN5UXEPtuCDfS7uYwIPUQUVgQblZmAIHaDfUkV8RmOSi8GOQyEgknAusL+8Hmtdi9ytl7jhYL7tulsTb47QU4zs416VlS3QJFO33EViJuS0GgGYrR5QsKa3lZFim2mgUgptQFFvUlIHcXbfToUFYVn69RvO8NiydlCstbXt1N7ofasAsFxEoOnddPxQr5VzwtsV515Zxt1Fmf+Pp5c6NJsTLxgcwNjGgsLpMdRt6m1p+ds3aY2rvz8zbM4odxJSeKx3nbBYpwYfA8EhY+AKPTxyWyf0xzs0vpN/Vyi9daBdCRqTeLMBI1XKLJL/Uvrd7HvbXes+rU18V15an+F2oXUMDdj9swN76atitMlt57XmsflS1AZG0P/8IMvma3gCNZlt/BjNYZ5TltNv+s7aXEOfKZ/1Ca1L2M0mU05hjEdjryN+pL2C5oPpZ/61vWa0wiNVxrY8si8LrnYJ9xc9BVuoWFFAcLvYUXMLIOB6IK2fDjWNPYZCIRTf9RNsrUHFZF9IYQj+dL82s5PVo2f4A2slxg/S3tYjWcGWmdQJsYwcQ6fthSKVne2cFyekLOkMmH/530cHLfNxpwZ5EXnqHHtncbquO9sh7t+M6b454LeurMtFN4J0jlndTTbhmHp/XOekkMFUwKjCgPWSXwsSeNA8yvG1uzh8+W8RuLOpKPGn0HCGNHMrJPOtj+FzqQ2FIkf+/7tn/DP3nm7syWZb0OydMjZWjhHtbDvtr3Zc9Rk8QKXZr/B+xJMx2ea9rQvrZvUUpgiGZxB6IyXpxAjdnwQtz7xfdnN/RqZNH78l7o750ftJD9NZwFiruPrRj6NzjSUju4fToxm43Ua0zvbZWdb6fr5m4G/obVKvqWbwP1WQutwNj+6F0lhNyNbmJeZWQn1JT9agz/opt6Pjhvvqzfq2XsQ6rc7N/29SHgXZcb3WJPat4HmCOVHe2E4XoupvyuicZjUfvzoXoHW71rp+42epXuFzcof1uby2bHJoD/In5MPonVO93g0rhQXUhkExZ5x+Oem7x4S29v9136gb6AJIYQQQgghhBBCCCGEEEIIMYVeoAkhhBBCCCGEEEIIIYQQQggxhV6gCSGEEEIIIYQQQgghhBBCCDGFXqAJIYQQQgghhBBCCCGEEEIIMQVKyQtxUHSL1qwy6xWtWdrOiAaeoaq9oiGlC4VFKQ29QW4idQlbyC+mHtvmB0KNYV1Ah9dIm5gWNorngq0GWxIp6h0K2RYNiF6CkDaJlq6XXpC0n3vbSnKXsy3X3jbIl5yNxGJDUVUSY6X6Ggg9kwhq2fgyezkIYoMwapqAIDqIqoaQOC8JiJIYPAmekqBuJ/HjmoA8MwmzT2xWQJWETDu1F4E9/PkPOZtVXti36S/6dIXv38n8YWc7sXi5s202C95WzQq4D2H8ahBUbUjcNVIMnYj2N+2X0ratWSeDMqEe5KdWFvwcIfHYfuHTkbjtGIR8i8T3ZzebzY/6cr7w8ya6L8HvFTDPe6kX616tvL8h4d1wzWUgTlyBbyFh35VFv24+f6f3Nx3fldEMg+VVwGaztuHbMNf3bbjkCJUAguO5t61u+vwy2NDnezv3+RHot4We98kkBj+qfAdsjGBTBg7P+TIKENPu5rNtuGvNl7nsXZL1OtB2aAPFFeY1rdEfLPb8miaOb8xOujkYl0PzsDdUflA3YC4lsCBy8GltpIPMYQjDvZbyx5iSbE1i1pz6L3MwTkLEkKeN5WlrEzPL09Ya8J+0V8RA+yTlRUuK9k46H5HPjo1hiNBFteQX8KPDvnIptIF8IIYTkX2eBjEBnQXyxPvYJvWNWOltOtsEYqkGKkexyUrnpLPRnFgqZmNfOjOl5m2UV9gfZmZV6wOWXjNwtknac7YGPHLe+hjO1Q3qMWj9+bMwn1djfvOo4Kw5l/o2UPsnrT9vlk0R/O3zp/PnUn3M2ZKJn3PW73vbkaO+HvOHfH5w7ieq4Fw27PuAsE79/C1TH5xsmg+ARrXvt/COwsyshr6j80tZp9ae3rPHVWo5BAnU5+QLae5/btWfGeiOhuKOjOJcaMMkiC+7RdxYURvIlsP5hc54sfO1l/rzfB6sczr3UV60tkrwLesTP/dXumtRz5IfzYMzY936OUg+nubIAO6eYscm9Blm/r7LjNfI6ni2TxYK/9x81/tC2svDOWhmNlf4Z5cK7x9p/23gfBzaqC/pjubkyPdvDnds3Txu3RRwuDox8WVkUEa4bqg/aK+tI/rDjPsyj7x7orVEZcSUSXlRXJgmibVJY2MzK7LGiuxgz036BpoQQgghhBBCCCGEEEIIIYQQU+gFmhBCCCGEEEIIIYQQQgghhBBT6AWaEEIIIYQQQgghhBBCCCGEEFNIA02cVyz0KrOh2UK/siRNbAz6F2Xlf1OWdNHIFkK/yU+/Zx+rH8bp6Ddf4TeLI/Ij/SECfh4/mmiNFCD83Wn6LeWFbMPnD79lWyT+WdLGyhvQD4PO7FfrzjbMQRsreJR+/xfrAfpk1H7SJJjAZCLtsZr0HEC7Ifwd8m6282/+m5l1SHsMNOuKxPd5ZvAb3jAOpEkR9nHWguZD4n83e3DBFb4etf/N9Dr32ggtjNeJ/iXOdmziddHo99tDnSr63XeeS7vXLKHffi9r364KbEnSWnt6rZd1YrDsrQu/tU+QXgD9PjWlmyP9P+iTMfx+e8xvbpPOIa1fGi96lvRDSGuBbDn81ncR6LiNKt/OQQm/tQ+/mT4qfX9ccIj2H2eyCeyrpMX34Etn1xeN6ZEl3285+DNqw6Ge15M7PvSaBJ0l0AeY+LqMwHZsY1ZDY2Xez8ESfqef9mj8LXzvblAbYlz5Mmjth7ajh71P7ua+f9fg9/xjfza+B9oYtKYLaBcV0e/OtvWqZa/FUoE2xGbp9U7mQMSPfCFB/Yt6GTA3Q18dq6VIGoZNk1jbVLZpZgugHSFEDJ2stk5W2+D0v2vQX4pdGyE0v1HbDPInX8naHLsXPKO1HO5ZGXgjqi99nrglDTh4MkOfDVqeoHMT+h7SwqE4hNowAU0X8m1EB7RwRo3fyGLiqQnEan3Y/3ut13RpYRwy6BNyvRhfR2qvjYNN+8Rk2aUhP07924UzE2kN0VolJqB7tFHO1reX+fxJYw26w1av/pfORme3JoV4BcTgWzir0YA1gb7ZJPNxXplAbA3zclB72xjWA8WrdAbje5DW2qS1+vS/aR7R2XA48bZxGeeTu4UvY30AZYx9+w/PUxw+O14U45NGVawuN59n42wEpQvPjDTOi6B9TXpR1IZFkLiP0TYzM0thv1mtvC8J6cMmQmfNLmjCUZkUS6+Xfn2NUtBUg73lSKCvSfvPPPhC1ASEZ8m30pmfxovuAY4N52b+prPmXWuwT3V9Xy50/DhT7EHnOfI3eNcL/iZsF95HRNyBbAfdg8TqncWXEdYPyoSxof3HzKw5fc9YpJV1sjj97bOFvoEmhBBCCCGEEEIIIYQQQgghxBR6gSaEEEIIIYQQQgghhBBCCCHEFHqBJoQQQgghhBBCCCGEEEIIIcQUeoEmhBBCCCGEEEIIIYQQQgghxBReZVCIA2ShmNjIzBaLiaVZY4PUizyOQKC2TEEYNjAloPoIGooo8BgroUjCkhkZzzIpiWvD63JKx+Lf3lbkJCA726HdDERWQZByuT7ubKNs3tnGbdfZBvmSs1Wtnzc984LKvXrT2UJd56Lxoq0oHpz7un18/TKfP0B9noJ4ci+PEwRPAqFOEjKlaVnDZypIzBPF5SFDGmsSoQ/zqxO/NdUZ2Ob8OMcyMi+ou1ouOhuJ25Jwq7OQSDL0eaxvaUBkdjjxfTIqfd0uXvKCylWTWtvUtmFmc53aahB3bUDsl+YNCUyj6DTYSHQ7FLo2Y8Hbfja7NjcqEBev/DoiYeMOiNyTiHMsNPeprVlQl3ENYwrixJ3M59/NaTbR3ujHoQNLqap9+0MBb+rLxQ4IWE+8fySh9luPL/iKAKOJf7aG5s/3YJ8KumQA64jmOa3BcRW3RmgcaLwqKCMPxov8L7HY9fvvsPRt7eVxYswpzRsQcqb6XXh4debvI9XtLs1GcciXCR6yl/t21bDHETRfc1j7uxXTpr2BBN2rJrWmrmzTzBYKH2MIEUMnraxITgurJ5UVEOjXsI9THBYDxZI15EW+HesRaaPYIfSLZmZ5sFW2sB5zirrQfUBMS7E6+BTyW1kCMUZQv2NjH4NS1ciPUbrN0u+7NIbrqY+dwr3ezGxc+0Ah9Ivz4M/uGK84Wy/3be2mvkw6g5BPTRvf5zQODfTUIIgdJxCHkfevIaabwPllI/H9GzvPaU8J1xKNy1ri+3cE40wTh+Ltwvy+S+lSOEukLYxDMltw2fpYfdJ426jxc5rGi/q3B/HKiRGcG8Z+XNO0NWsay81sOMnMUl/mXMe3ncYUD4PABOLL2DiXzg0h5DMopl3qQ4wUuYXQ2SqLPOMR4bNF659bL/35/nDX16OXjrzNvI38SAK2Fno0PPetTfx8u2TO+73V0t+Bzefetw4rvx4oVqU9g9YIPRv6m00os6z9fLuwv+5s48b7KpojdF4+MZ5zNrovCM80X1z1fT6BY8/lK75/6YyzOgY/GgnFRrSWNspZ3xfeFZiZ5TBWsesoduxjoTVSpLN7Rg5+j/a3rPVj37ZmTXtqLDppZRXMmXOJvoEmhBBCCCGEEEIIIYQQQgghxBR6gSaEEEIIIYQQQgghhBBCCCHEFHqBJoQQQgghhBBCCCGEEEIIIcQUeoEmhBBCCCGEEEIIIYQQQgghxBReAVOIA+SMOGiW1pamiRWpFxJs8jgBxjRNgjQkmBhno/xDsWozswa0YkH/GG34bISeYw6ruABbnpGQLZRJwpKgnN3NfeXu2FyY+fuBS8ddGhKMHGdeGDQxX+aCeUHSE7UXpx7VIDxcH3a2S3rHnO3O0Ww6ElsnEe4MhILHlU8334kTvqT2kwg7iSKH4qtJEldfEh+l8Sph66j38HmMNGgrtZ0Ee0MRajOev6V50dpx7UVwqa0ECa3SPIkC6tuAyOwEBHpHpa9vBfqx24qVn54DSdJG+Rozswx8AfkzFMqltZT6NpBofA2i8aFfptGjsaJxpvFbyU84W954sWfijuRiXy6IAKfBOizr2DkIYvOwUXXA75M/p3G9cvmks9WBuO9CtuHzAhH5svH9cfuaF/qOFSbvd2FPKvxY0z61NpydS0s970NHlZ9v1G8LvbiFQ+uB1m8P2rA+nO3zi5dLl4b2AdovaO/qZHGi0ySm3U19XUgkvLDZdMNiwaXZqL0NBaZJIB3qloPIdG5x+y+JwUdBsSKUmaSZNe2pfs/BLwgRQ57WVpyeP0VaW5r59djApMRzSZAO90nyKZE+m+KaqiZRef9sBsuR9orQNaSQhnwgbKe4x7Zg897udHwV2iCuHQb7DMUhI4gJksTvT/OFj03GlY8bl7tjZyP/2U19foPKn63KZrYueer983zhy8wTXya1n2wU1xGdxLdhrV50tkm983UYzn2KfVs/I2i9EXiXETGXKI4eVXDugXmTQ192wY/Qlti0fn7RWY3mfvjsBPptArFECWNFvqqAfXWhGDjbfDF0ttGcn+eDqmNtU9m6mR2en1gJ/qzBs5Yz4X0MzS/yhXMQ+9awHKguoQ9envPjTGuB5u/h7qaz0Zo+MfHr7bLOF5xtvVny+UHstBicOU7Cc0XmO6SXjJwtPM+Y8T1IbT5dCn7pWOnvmUJfdfn8XS4NzX2av5MGziWwfmP9YwOLup97X71Zzp7VyGccnffn5ZOlH/v1ib97KWt/Fjy56dvayf1EPLrk1/TqaLYMmr/zPW9cG/l134HLXopb6AxN574KbAX4g/DZEyN/T0pzJId6UDxCz9Lcp7tCml/k42PqQc/RGa+1ZOv5LGmxXucSfQNNCCGEEEIIIYQQQgghhBBCiCn0Ak0IIYQQQgghhBBCCCGEEEKIKfQCTQghhBBCCCGEEEIIIYQQQogp9AJNCCGEEEIIIYQQQgghhBBCiCl2Vk0V4hxyRkwwsdYSa1HkkCDBwVAoOgeR3SLfWWTVjAVaSRyygXQTUNiuoFlki2k9iWuD5iUKXMZCQtxEKNw6qr0gJ2ixWgOi3v0UhH2t52zzmReyJdHpsvEFf3rjYmcLBS0nIMKdg0AtzQcSLi1BEJwEb1EgHUQzKxBPrgPx4AKEQQnqNxSSh3QR+qHbkgai03nqhYNh2lgNdatAFLhq/VYX9pHZdmKmUC7UJXwU+ygSbBfMG4LW/qD07Z8rqq0qpxZfXxI034uUK4kRx4rDkkh2SGy7wv3CzGyYemHjLPECyEQ/84LVJGIdijgPCsrfj9+hnvePNH83K5/fYBIncn/n0ItzXz3/uZm/0xbEussNZ7uk5/u3rL3/HcJcXR/6SX3VkXVvaz/mbB+qHubr15mty+eO+31loQ/tKrytrHy7aH8oQXR6sef9HK2vYmE2P4pREhCh7kD81ELM0828kHyswDSxWfo5189mbaPW9znt0ShgbeQzfN1SWA8tfG4Q9z2wtRH+hqB1eSbG3e6/CxFDmrRbgu5p0qC4O0YOGMLNGquGxON9bmSjtVKBD6TzUSzgZi30qBS/YYfAEmwgCM9TKDQS8jMX9Gf3sWOjRZdmMPF+kfaAIaSjs8WdAx/XXDQ/cLaynnO2+WLsbHXgt2k/Id9ew7xZzHzsQHN60vqzZdn4uGZsEP/A/hSWgPcAEJcTFEvFxqH0LMW5Lk6Ay4cyidtPM4gd6NxXw9mqxpNZHGEZeHaDeUNnXqLIfHxFey2dD4m6Sa09fU6omzT+zAQ2urepwd+i/4ok5s6H5hvFpb3c2+h+J4M514Hz/Fq9HJVfnfn85tJZX0X+hsZm0vp1nydxz+ZG6fwgzuf+XPa5jSMzf/fmJy7Neul9MvVb1fi5Svclnznpz2405644dNLZPrd+2NkWOrPtPznyY0X3U3SOoD2U9rgji77PO5nPb7HwfT4oZ/eChb5/blz6Dtkce9tcB/YzuI+hOxqKFig2oj1uY7SzXyryyHsh8AUUG9DYpPAs3dvQ+IfpMsif7u+JNGnMZmLdgz036RtoQgghhBBCCCGEEEIIIYQQQkyhF2hCCCGEEEIIIYQQQgghhBBCTKEXaEIIIYQQQgghhBBCCCGEEEJMoRdoQgghhBBCCCGEEEIIIYQQQkwRp5wpxDmim01sw8x62cTSrLEGxDEbeO9L4r51EwrU+jQkbBwrHlxDOhKRHININolXlr6pVgW6l6BpbR1YxSQeS8+mkQK1JA5J4rN5IEZMQsQk9Ew0IE48rHvOloEQZgHCsF0Qox1VvvNCMVMS8eVx8PXwMqvcbzSXeiBcOql9fUe1b1c3EFol4WgChd/pcxYkkg3PEiSWWqQ7zxsS9iVb7LMEzfOmgfygrWEZsf0R+qlTNmgXPEvirjS/iEGZW3tatHpQZlZCv5HwLE0HEnOdK7xQMvlbGpsK+oRIgoVIwrbk93FsoMjjYy/EvNJdc7Zx7cWpaxIKzsEvB4vpSG/VpVnqeJ85rHyZtLaoT0gQ/PL5O5ytaP0Y1kHY2IBo+KhYcLbbx0ec7cG9TznbZN6Ladshb9ps5p3tU9U/dzYSSr5oflaE/FDP+9ULeyed7c6RrwiJaS92x8620PHzgfzNoPT5hcLOc7kfF8qL9ikSiSahb5pLKMwO+/liZ7Tjs3Xr5w3VF2MP8vuwgGkvCNfbdraYPYOeixXETpLWmvZUv+fQ/0LE0FqyNVen/72rvII9Kzbmov16UtP5yJdJtpi6bUceuJXUuxmrYYni+YgC3X1mKZnd7zczv/8t9ynO9ba1od/HwhjJzKyCsdmEfaeT+cHppX7vCaFzCsbWGEeTz/b1yCAibhKfjuYr1SWcJi30G80HakMsFKtTfhRfxyxzPAvAc3juozuPOu7sjnUhXxKUS/s17aa076YQ19B4Va2fm+S/NiY+vm7aZKvOTZvYfMevBYrNNiFWp7uHcemdFflRGtcMzi94T5Ht7NMohsE5Av2Wgc+gsaf4L4MxHFXeL6XBXUuR+ruSTuLHgdqQgW9JYNYl0OmfG1/sbBRLj4L7vjuGyy7NXO7b8NlVf/6k+0naQx9+9HZn6yU+Lv/42gPiyghsK32f10pn3dno7pTW70p3w9kuH3/M2W7vX+VsncSft8a9WV+1MV50acL7SjOzSRkb83gb3ZekNOfgTBreO5qZTYK7Y1r3dOeM5z5oawnP0p0StYvOkXTvXATpihb8A9zzU/77GevuB/oGmhBCCCGEEEIIIYQQQgghhBBT6AWaEEIIIYQQQgghhBBCCCGEEFPoBZoQQgghhBBCCCGEEEIIIYQQU+gFmhBCCCGEEEIIIYQQQgghhBBTeBVLIQ6Q+XTTjpnZfLphGak/m1nj5H5ZGLZMix3TTBpvqxvK379rJgFGElFMkrj31CkoP+bZrI3EIalMEoxMI/UWUWB5l1qNYxAdZqFYn46evbB7zKdrvUAvieAOQIyWCAW2s5yEMb0QJgluj0FMmwREu5DfEESGSZC1BjHTOhjsWFFvEneldLHQzKf8QjFQEpMmYkXuqd8oHZVbo9D5zoLY9ByJLpMfIYFaEn4nsd8i9/OrV3iR1qZJrE0aK82skzXWASFXEjSn+UYzBAWbI+dcTP9SwST8TmUa2Gi85nMvTlw23i/RPkL1Xa/mnG2lODnzd9X0XJpYcXXymWXt9zMSI560INYNfrQJVjXV7Xh1yOfli7Ss9XPueHXY2ai+FxR+Lzic3eVseeLFuctsds/4m9UHuzS3nTzqbJ+/09fjIQ8E0XDYz+YLL2peQqxBe9diZ1awez4bujSbdd/ZisT3bwfEymmuTmCe5wnseyDgTms6jL/QJ5NINqSL9SM0f6kNKQjJE2EZGeRFc5rqZmZWny53zryAuhAxtO2X1m/bJriWG9xPIa8gHfknilfKCmIYiFcmFcU1vh5ETRsetCs850CRlsNmhPEwPJtCOvIzGEuTj2pnO+DSzhdcmtXikLMdGy06G537FrveP4/gbEHnMmoXxRghNG+WO5vOhvE2RAo1nPEI6t8TY99PdH4L60yx7/kM7acUS9Ayoj22hvUbexbEWH0foXrk0Faiav3cpDkdxlxmp/xjU1c2MrOlztAq87F67D1DF+4LJuBHc74Gc1B8ncLdUAie8WA+UIxEcRidhehsiWMIs5OuAtbK2XNUP/exdZ7FxWF01qTepCl9Sc8fCD6xdqmzLXRn60K+ltbgob5vF/nuzx/ztg98zp9feh3f/uU5309LPV/u4e76zN8fP36BS/PFNX++vWjRr6MwLzOzcePXzac6D3W2vPHzMM/gjBf485V5f5Yflb7feoUf6OHEpytgr6W72OEk7s5uBOliYiPytRQH4D0huMyMHAm+KaI7NYqNgjMTZQVst4fMxrqRmZ0l9A00IYQQQgghhBBCCCGEEEIIIabQCzQhhBBCCCGEEEIIIYQQQgghptALNCGEEEIIIYQQQgghhBBCCCGm0As0IYQQQgghhBBCCCGEEEIIIaZAaTghDore5JS4ZH+ybnmWWpN6ycEmAWHr1IsMhyKlJBaaQl4NiUmDWiEJtNYg+khiiChOHSlsHUKij2SLFQDOsG47i0MSNbR9XIMgJ4g6k6jqGMSkqX8nrS/jMIhYjxsvHpwms8LAVePrMVd40dIs8W2gtnahrV0QQV2b+LaSMDsJhoZ1bkFgmeYbjT0J7+434Rg2CQiUotixt7EosAcFzCPzo3R10Oc0b0oQia4ixV1p7MmPzHUilGfPPJt86d/UprIGUWsQQCYbCcnTOich6grqQhLhYX6UF82bFNYDPdvQ2EO7YudmA89+YXThzN+HOhsuDfXlBHwLCYKjSDjMuU+velFoEpMOBcGpL0+OvZh0L/di1R8tH+RsD+h7Ye5J6wWmW/j8V9rCPpJ6H583s+1a6PnnhhPf5//in4Hfp/UAfUL+gNZDL/N93ktnBbBJcJugPT+FlUT+tp968W+a5+Qf2bfu/Hk9WoM0v/LEz6Ui8WND6fIWxrDxNppLaTtbl4TSgMg50SaJVacd/Zm4V4h7SmPJ1j7VWMJrD/YP3J/aMIbxaYal33dGpS9zUvpnK1gaZCPwzAS28OxDcS6RwRmH/HOsLYfzQEwsHe5NZmbzmT+7rKbzvh4RZwEzswbGdWPk95T5rveLK10fnwyq2TPT4Y73ZxWcyejMRPO3xHOa3xfWy76z0dmKzpZhbrSv0/5H0NrCsYdJnUI6mvvhlQTNX4LGnuJcKjP2DoGgZRjmR33EMUwceE5rvS+kOTKu/ZxrLbH2dP9NmgLXWwa+gOYNzfML5iAOgXlOdRuUEOfCHO4HcfiwirsGjm0DrpHUx2EE5UdtWAhi00HlfVeW+DPIfOr9aOx9Ac1NiqUvXTjpbOF43b6x6NLQWavI6N7Gz5F/drHv38O9gbPROfJzq74uXTirhevmooVhVP60Nw7ruHu8TubrsVn5Z4kw5qEz2ULH9znNQVojcCVsY7jfGUMcNJrExUahDyafnMdNX6xvLLH+lu6EQ/8dmxf5/dj991yhb6AJIYQQQgghhBBCCCGEEEIIMYVeoAkhhBBCCCGEEEIIIYQQQggxhV6gCSGEEEIIIYQQQgghhBBCCDGFXqAJIYQQQgghhBBCCCGEEEIIMUWceqQQ54gzYu5ZM7HMUjMDIVdSjgZCcVASCyVhRRIqpGdrEGcuaxDTrkkwEgRZvcalgwSxUxAPJgHGLFIUmMSISRwyRrSY+pdEOklilsZhqT3pbGXqRUXvnBxxNhIkpbosd2fFUXGcQSy1gs8j1CDYTM/287HPD+YIiSLnEcLkJD6akTgzCK0SsWukJVHomPGPnDfUH7FCo5QftZ5Et2kMy0BAtoLncN1HirtmsPZzELGmNVfWvr5ta9aenttlnVpLguYwR8jHNSTWDe0voBG9HITknYXnazgODaQh8WCC5sME2sr7SNxaJcL+XButuDSXLKxD3fyYktA1zTkeQ1+3zYnffzeDPZnGmdr+mTu90PdVF418us2Lfbr525xt3PacbS1Zcrbjo3lnOxT4+JWeFxfvzXuB6bXSC5Ovj/3+s9L3At6bpU9Hc26x40XCq3Y2VKdxJmLTdRJfJs3z0siPxO0FeTK7zrPEr/si8Xt0DnXLW28rGr+H5rUfw7TxZaStr0vSgK2N8CXkgMG3tmm2FfTlja+nEDGMq8Ky+pRPHteFVbX3MxXEsDF7NsUwownsk6VPN4Ggvo7bivdEuNTonJJnviJF6td7J/O2bga+J/W21PyzZIuhV/v9aaHwe+ew9Fc6Jzb9Hn7Jsn+WhqYPbaUz40Ixu9+NGj8H08i9aFz7+mYp1A72ThrDWMJnaV9r4IxH7p5idQJj/7iwcdfQ3swJ6Wzhn93L2ZLSheCZjBLSeS7yzEhnd7wbsdaa06Vn1lgv904O+wh8UCf1ez6fI3zMRT5oyYfXlkJPZcE49HP/IJ1vCerf2HQtLZxIwrh2Iff+bNJ4X9ikvl3YRy3dSHmaJC6/0KfV0PSTIz8OR+aGzkbXn93C17eAuHncet+62PPPzsN91LCa9ek5+GSalzR/V4qTzrZeLzobrQeacTTWvXznmHo+9/17crLgbDlcvFJMRWft0STO3xbwNiZcIuhrMb7x6cifUX4duFMqcj/WNP4F2LJgX6V6kH8oIfZs2nTrnnzS5FbCuJ9L9A00IYQQQgghhBBCCCGEEEIIIabQCzQhhBBCCCGEEEIIIYQQQgghptALNCGEEEIIIYQQQgghhBBCCCGm0As0IYQQQgghhBBCCCGEEEIIIaY4WAU2IQKqvGdmA6vyvrVZanXip2iZeYHiSetto6Y3mwYEB9FWk82rMo4rEtMGWwUC8nE6804gkgQj0QZ5pZHPphHCvtsRCpPHig4XUA8Sm/xifdTZ6sqPzQjGsAKhysXCi4hWwZw4Nu67NEQowL4dAxBcz1I/f2lsSphfuyVWsJgEm+lZmtM051ropyT4LAcJXaNoK9ioHrHC2TSGFdlALLYJ0tWodB0HrhEQci1A8JWeHZd+XPOs2eqrpuXnYm2DCeUP4rYwDgMQvl/pD5xtVHmR5V42K4AcikubsUAt+YLYlRU7D2MJfSaJnH/65LKzxYr90j5Fa6SEfaqh/TFIR35qeQ6EqXu+Hp++s+dsRw/7Z78wvtDZ5kAk+uR4ztlWepvOFiN+Pqy9T6Yyi0Ak2YxjCEoXSzivWeAehJ4TX2Zmvn8pv3Hj218kcc8mUJeYNhSt79+89jYSec8rL3yeNV7UPGlgHGIF7QPn14IofZv4uUVi802aW3V6kygzvw6EiGGz7FhbFqf/XVjVFC7NBOIVmvJh/ENxTgXLh2wYD1IYtoe4o4AbjCKfLZhipCLz/qmT+UZ0M+8/yEZ+MQdb2u7sF4m09XW7IL3D2ToL3lde0Pd+fKP0/iaMpczM5nM4H7W+08P9tGq8v0sTaDueD31/1BBfZVBfKoPIIvrcoB4VZJ+n4O8hfzwLUzpYEGGMGAvlRWXS2iIw7gAbjQNFXGF+fK4EG7SrhXbRvU039/OGYrMc2pCnlTVpZcfMbL4YWmt+bXEfxcV+9GwKsRT5m/XKx75L+YazVcG173zm4+NNm3c27HNw8nS2qsEflLCWaBxovoa+leLcBmbcRu37qJf6uLFP91FQj2Hr74YGtfeteTrb2Acsrbs05DM3S3/mvXzO+/1h4+tBc6mX+f0h6YC/hXug8E6N1v2o8uthvuPLPFH682zYR2bchnm4s6M1Mmlm+47m5RDGivwNxQZ4VwTxUu67Ev1tzL1rGNuYmfUK6rcds9q2TLLt5Z44jA1i70npHqRpE2tPxwKDqmOD0vvBc4m+gSaEEEIIIYQQQgghhBBCCCHEFHqBJoQQQgghhBBCCCGEEEIIIcQUeoEmhBBCCCGEEEIIIYQQQgghxBR6gSaEEEIIIYQQQgghhBBCCCHEFCDBK8TBsZEtm9lxW88OWZZlVpoXxC5rEMlu/FQeB+lIULas/TvkcQW20tvKyosc1iCMSmLaJPIYIzaZwSvvjAQpQfQxVggyVhxyt1QgCE19RKTgsYaVN6IQM4hXjqolZ1vsjHasB9WX8icqqNuxgReBxWdBpJTmaycQSW9AFJaIFbWOfZbEuklOORQWJdHsCtZqDeNAIrBEEzmGlC52voaweCylA+FkWL8E+TTKr6rTLUHWqk6tAN9CY1pkcb4lViyW+uTkyK+HTu5nzqTeOYQhYWr65BCL1oLwcOnLrCLbSvMm9N80B+m5zTGsh6Ff5/TsyOs64/5D+1lo64EINfmk4cS3a67rn/38Md+/2YVenJqgsd4EYeswHQtHe4HiQeVFvWmO0Fwi0e1O6ssgQexw/VKZ/dTvW53EC6QnkH8FcVYP8sO6kS3CQdK+krawVsnWgNh86+vRJrDSwUTpqH51NttPDQQkdQL+IfXzpk5yq+vazD5vg44XVRcihtEkszTPLD397wp8QwVBF8VYTbCEYvz/djaOa7wNzzS0bMHWySFOCuITik2y1FeY/D35505S+jLBlrXg28GXhZDfiY3Bc/NltjAQ3SzuPDBpvN/qZn5PGda9mb/pjFfAOFBcSlA6iv3SBPYA6Lssgf0jSIZbGMzBGhKmsLZamPwUdzQQc1N+MVC/UexL6Sj23+/8wjiG4iaC8iLmCx/o4nkA5s125Z4p+9T/x52Xqb41nMmLxK/fAmI4KmMhH2KdQ3rJbFxXtX4dUXxJa6Y234a5zMeNFcRJsT4Nyw36rpv6PlrOVp2tgdlKMS357rH1nC3sSzOzBjavQTX77KDc+UxixvvUsPFn4/ls09mq1sf0G3B3ugF1oXUTnl8yqO+hnn8u9j6GxoHGi9io5nYsl+pbwz5F57QS0tE9C8U83WL3e1w4lXqFb0ORQb/R/hN5Z4XnHor5IB1cibtyMb6ButH9Ud0mWxdyg0lmg8nBvsLSN9CEEEIIIYQQQgghhBBCCCGEmEIv0IQQQgghhBBCCCGEEEIIIYSYQi/QhBBCCCGEEEIIIYQQQgghhJhCGmjivOJkeUqT6kS5bGmTW9n431iO1TKbBDbSLCNNqVhtM7IRsb/xT7/VH/6eP+kg7UXvLIv87W/UFIv4nV3WIvP5029pU31R3wF+nziH346+a8P/1vPynP+t6zqYc6h3FquzBTaac/TbybFzjuf1bBtidbZQo8ubEMqvBMkH+k3sUEOK276H33Xeg6wf9UlMOtbeoOfidMYIWiPUT6w90libNFaaWZ41VoOOJP3ONeo3wnpDHZPI+pLeCf1eeTi96DfjyWew9oSnhL2GfnP8zlWvFULQvJ7vRehFQRLSFKM+n+/5Quf9z/lju0ag/TkMtNe6uX9uCX4L//YT/nfqDy/Qfubr9unb/W/3X33UP3uoO3C2tYnXDPjEsYWZv6+40OtHoA4h7JcJ/LY+6bN0M6+Tgxo7qU/XBOuwC7oYOehnxGjumJl1W2j/Puthhs+2kTtLnfqxJ82ymgRSI2kSv87JFpZB2nGT1vuCCnxr2ebW1KfGbLWSBprYHXV7al9J7fT+Eqk9FiMjtBfNsvhn6dzj04VnITPes0IbpenF6p2BLy4Sv7flLaSrd9ZSNvP+jXwbxSbkn7rmy1yrF5wNNeBo34HYaaP0+3ioDUp6PrHabrH7Du3PqKsZee4NNXgaGAeSbCN9Muo3jDnpvIWx6e4+6x6t7bXP2mZEbOywW2I1DGlsYudc3WbWnA7G6zbDPipIDxHGIYzptoP8zbj1MS3lV4MOVhiflI1PQ4whHcXDpKnWT318OWi8H6G+Q92yoNzYORird0Y6tqgvCX1ObR0ms3dPGxPfl8s97zO7pNsF2nnHJoedje5OcZ1H6lBe2r9jxzJpPtAdAu0PNL/mQdcv1Ns0i9PDLKCdY+gj8g9juAcgOgWsc7yPoT3J55cHsQvFSgTes9B9baQeWbTvjrgrpf6gXQrr0SZmTWLp6Xwor3OJvoEmhBBCCCGEEEIIIYQQQgghxBR6gSaEEEIIIYQQQgghhBBCCCHEFHqBJoQQQgghhBBCCCGEEEIIIcQUeoEmhBBCCCGEEEIIIYQQQgghxBS7V90W4iywNu5aYWar464laWGT2r/jLSsvHFjVO9tqUCokWxOnu4ukkQLbLJy9s20vIqCxkGBkKAQZC4m71iiwDA9Dv01AzDMHQWwSaaX+rUGE8rMn52f+Xup78VgaPxoHmr9EJ/dtmOt4Qd271r2AKs39UMA9jRSWp7naRs6vtgHxcxIVhT4P1yGNC4uKehu1i2w0XrF9giKwQboc1ziIGEM6an8FcymlOQ1rlbRn28SsPV1O3STWQONjBXBT6jiAhK7303+RUDvlXkG7yLcc3/B+5NC89wdrm76MSelLLnKa+7O2fsclwX2K1jRBfVJ5DWeroP0pTJzN0ezfncI/d8eqFwi/8iIvhk77yhWH1p3ttu6Ss/Vz7x8nDYlJ+zbcdXLW1i28X12a82PfL/zYEySSfcfmgrNdsXTM2brJyNnyZrbvqtRPkrQFEfJ64POqxs7WpH4M28Tb6tT3LwpRJ7DHB7YG0jSJ73OORyD/SKHrFgKLmmwg1l4Ha4SE2mNtdZNa25yaT2sTP/+EiCFJvhS3JImZYVzj1wbFHWEMg3H5NnWIsUWfeyBdAbET2gLf28m8z+6mfu/opn5/6iTeVxaNt4X+2cwsbbw/Rr8Y+O0G/C7Z2O9621K+4WzjtutsWQJBAUDxX+w82U9oDxjVfn9a7gydjdqaWnO3f5+y0d5B4+U7hGKdFGwNlJFEjo3LPzIdjWksNA6YDmxhbIpzGvqIzjgLhY+b+P6B+hziRng2TWZbEbtm8M4D2pon3o+MWh9Lx+ZXtRCvRdzlUB9RX1ZQj4TGJvP9FNt3RDgOo8bHTx3oS+qjAfRvF/w+9VsDa5/KCPebC+b8gZFixNvWfN3oPmpSgS8sve2Bh9acjVZvJ/VlhOO/XPh9pYT5lkN/9FPvk83mnQXPrtBP4XwgBqXf88jv0dzPIP88A18Fd9N0+UJ9HnM3tJe7Eoq96H6OjlF49xZ53gr9KLqfXd4vHzT6BpoQQgghhBBCCCGEEEIIIYQQU+gFmhBCCCGEEEIIIYQQQgghhBBT6AWaEEIIIYQQQgghhBBCCCGEEFPoBZoQQgghhBBCCCGEEEIIIYQQU3jFPyEOkPVRbitmtj7MzdLcqhpEJEF7tAYNyRhh4/0XyfY2FMn2Opj4bMwbbhQnBsHXBkQfm9ZXhMRisa0grBlqZFPdWhL2beJEJKuGRLh9Ohbp9AlJfLXXmW0X1Y3y6oBQLj07gTYMxn4c1ofeVuQgrg620SQoA4VBQZwZVEVJgB5FRUlkF9I1sFbDfqL8UfAUoLGn+UtQuhTEV0mQNdSUJVFYEr3PUm8bTDo75m9mNql9a6n9Fa6vxKw5JYte1alZCsLRKBhPPpmEz0F0GupBPr6L89z3U/gs9W/pLGYl1Hdj5Nfbptcltzzz6Ra81rOteo1lS2FwwvVQwv7G+wrZyMfFTX6a01Tfyy+cFZhuYEwPzflG0F5wfKOAeiw4W6/w+S0WXoi6m3rh8EHdd7Yvv2I29P3Y53xnNiCI3V329ehmXnA7hzU93/Uz8VB7zNk644GztcHGmkGZJOqc1b4/9kLS+nY1qR/Dimw2a6taSAPxSAXjUIN4fazQNe9TPr8KbKHfoHiEbdv4vebUIWx9pKOY2B1Z0m757ixtUbSe9o/dQvs618vbKCaimKufe/9WpN73ZmDrpLPP5onPq5N4v9ixsS+z9rYcbGnj69FCw5rU+7fQR6HPCg9WZtYkPq+SfCpc82QJ1Bf84rHRorMd6vr9KRwv8p3EYu7zKsHfj2uIE6ANCxAnEJPG59dLZ8c1h/z9TNoG2MfwDIZnbYjDYGx8XpGHnEhwn4R0tHfGPhvzHO3rtP/jnKaYiM4lkB/dl+T2pfY2Ld2ycH/kqZ85VePn+cT8uY/yozkSPw47zxPyyRS/142f5+STqW5zqV/7g8YfpGhcw56v4NQ7brtR9aD8KTalfuP+3dn3pTB+owp8UuFXTRfumYh+4efcseG8s9GZeVT7uZkG93101grTmG0XW/u2ko+f72w62xcmh52N74tmx4bOtz2IM2hMY6G7IpoNNP4xNkqzF2Lziz1boY+I6E/qI7qyOuX3TlUmTVvs73OJvoEmhBBCCCGEEEIIIYQQQgghxBR6gSaEEEIIIYQQQgghhBBCCCHEFHqBJoQQQgghhBBCCCGEEEIIIcQUeoEmhBBCCCGEEEIIIYQQQgghxBRSrhbnFaPJKeXA4SQxSxKrQXmWBSN3zjtFUcI4W+yzexHrpjbUoQ0EGbG+exAUJqHvIvW2PELAMVZwvCWh0Xr3nRkjWGxmOL9CtWcSjx2V/qnFnm/ruIprA409zaUGlDWr2tsWerNCsyTkOZr4Amje0HojqA0x4q6ULmY975XYdU62DGzhuiHR2ljR1pi1ZWYGUuU41pRfkrTWJo01dqruCaxxEtSldFTmZOJrF+sPnN8zs9EQhI2DdlE9aAVWsI7KKs5nntmjpul6/WPr5D7deOIbVgTpKtCIJl9Aa2tzBMK+4ONymDgptIHmvksTOVdp3Y/Bj07AZ162eNLZOql/uIbR7oDY9e0nZ+cS9e+q16+21U0vTP7Pjvp2XTS37mzz+dDZ+uM1Z0srX982nR2wtPGTZFx4gfA686L0BApCJ36S1DBJysSXUbbeNmmKII1fzyWIl1cUGzTeFit0XcPap7lJ8UfolzAv3KN93ao6MWtTWzKz4VifZRS7o8hay7PWajPLs9YSjDviIuIwJkrM5xUb12RkS/1CoHQ5pCuSCtJ5W2Gz+0JOzzXex5Ita/wek9Xe1ibgj8B/kk8NbZRXnZBfBBtc6VAMl8F8yBLfrov6J51tbbLgbN0seLb2+wTVY9T4/ZR8O0G+nfYAOtNQ+8P+PLWidi6ToDiXwqSGDiHQCMovrAutVepz7jfYx3zNrIG9mGJuLHeXZzqqb+w5iupL5DAfxjCHmyTdiqcnTW5N5B1F3cK6p5gL6otnJjAVsH5L822oghiL5g3FXFS3SQ1nPPBBk3rF2S7qHne2k2PvWy7uHXO2NojzaX+bND4G7acjZ6MzA5FCP9UwhrgOg3SUVyfz/mYu92M6bnyf93K/x60OvW8tct9PtJZorW6UvZm/KzhEkl+dT/1BitZ0Ce26a+znTQlrroQ7sDI4R9Kd0oWLvr50FoiF+hLvmCPjqiy4a4k9JZDvJp+8F2Lu8QjqD3JocH1yOuWpnTlL2+h7srOFTm1CCCGEEEIIIYQQQgghhBBCTKEXaEIIIYQQQgghhBBCCCGEEEJMoRdoQgghhBBCCCGEEEIIIYQQQkyhF2hCCCGEEEIIIYQQQgghhBBCTOHVHoU4QOrazJJT/98mLPx4ELDwYRwktlhDw2JEGakeKdjiJMO3EzsmcVtoQ4QoJbc9rjPTPQhEku4u1TfPSMB81nbHqneTFx/yoq2RetDY5zl8lIHGtYaBzSDdcDIryErrCOfNHtYbtXW3wtGxUBt4jUSKu2I6WCMwN8MySFAX8/LVsF7hBYU3J34elhUIpFOGVN822RKWbdsE5dHJj/Qybx2NfN1Q+BzWYOWbauPSN6KT7yx2jPMtch2RjebX2Os6I7FraW1jtuDlRd92GlOqB/UlPUt1I19N+YUzltZCF8bqE7f5OXLRYZ9uuTd2tizBijiqxpdBgtW33T6bX6/nO6nyLt7m+j6vtaEXal/uetsl+V3OljRx7WrS2XYNO0s+LxKDT7yQepOAoH0C7QcB+knr8ytrSAfjEIqET2qfpmp8PSheIBv5lpriAHi2wTL8s6F/4bpRmZzXGRdZwlwTIoZeUVm/qG3DzPpFbalf3khGsUgQs1Aa8jPhc2bbxGHwLPl2shWJ3/DyxC+cvJ1Nl9X+uaz1z6WRvrjOwKdCp7Of9bY28L3ki2vzz9VwfdO0/lkarwb2xAb8fQYR4GKx6WzddHbP7qRdnxfMkWHdczYCY3VoVw37R4MHM28L5ybFDVRmLHzW9umovniOjKjvXqD9dC/EnI9j+5xizrr1ayR2vCheyVLvD9KktaY9Zc/T2rqQhupB643WA/V5CmuQ+gltFNMHa4Sei7nbobzMzPLU1/fk0K/zQx3vRy/uHXO2UeOfncsGQT28v5lLB87Wwmk7N9gLYGzqNu66fLfrkPp8VPk+6uW+vmXt2zXfjTuo8p2HT9fJZstdztd83Rrf53k9cTbqo5OpP9Nslr79FcTcZUXnhtm/CziTUttpb6CzMfslnx3ePcGze7ljDqF1j/dz+7xnxMR81B/ReSWttU1jAzPr5o3VeVzMdrbQN9CEEEIIIYQQQgghhBBCCCGEmEIv0IQQQgghhBBCCCGEEEIIIYSYQi/QhBBCCCGEEEIIIYQQQgghhJhCL9CEEEIIIYQQQgghhBBCCCGEmCJOFVGIc0RVm1l+6v/3WcM2Or8Ysdu9piPRekwXvOImkcoMBHVZgDGubtQGEimtoQ0hJL5JkLBkrNgklkvimJAdtSEUJF1ZBEHzzIvMkijwyrwXUL1z3Qve5tnu27pbGhIV3UM16NnY8Q/nIX2yA4XEtxEajUkXU4978mwoyMqizv45kkFdH3lB99HE59cp4gaM/E2nqK1NGivNLM8am0Caqqb14cMGmr/0LAqpg2NKoKM6hV9fk3J2piyAcDL1Ja37+Z4vc1z6dCRGTGNzwbJPNxjv3Me51yC3wdjbGt8d0dBeQHOzgsk5Cfwjjf0ExJ8X5uLqttzZ9HWDtTRp/LiSGDzx+K/amPmbfMbNHzsUldf60NdtvOjrVoFoep15WwIi7E06OynaxHtI3POAifn9Z9L4epQt9G/t+5f6nOKFspltA/kHEgindLR+0bdGp/M23s/2L6+2NTsz7UqvCy9EFL2ssm5a2YaZddPKcohNcd8lmxN8h7wgkKb8MR3YKG7OzC+ILPGbUd76/T5vZmPurPF5JW2s+Lz3H6EvNjNrEm/D+I/yC3x5Ddcydett5O+pf4mm9f4Z6xb5GetR09sxzXy75o0Q69RQt07iz1GbzbyzldBPbcQ5laC+xPWwz/cUsbjzBpy1aJwpbKRn+fzibThDwB9wuJoEf5F/gLNA5td0L/VBMs0HmtOd1OdHcU3dJNacdh1lnfO6hz7KUvI3vh6h/zVj31o3vtwwvjLjNnSzWZ+5PvFrl8aeYjq6B1nsjJytk/lY8pMnL3C2f3boLmejNhxOhjN/pxAzEzi/Wj/25INp3mR4geZNoY+PdNN2ydwJZ1svvd/rFL4NFax9miOx36LZmHSDvy9yaR6Wf8jn3/i5P+4sOFs4L83MNkp/VqG7DCI8p3dzOFdF5rWXezG6A6PzNxcym7CJvBONrW9svEDkcO8cVSbGnXHpEmutTSobmFk3q6yGfeBcom+gCSGEEEIIIYQQQgghhBBCCDGFXqAJIYQQQgghhBBCCCGEEEIIMYVeoAkhhBBCCCGEEEIIIYQQQggxhV6gCSGEEEIIIYQQQgghhBBCCDFFnOK6EOeIMwLsdW0G2ovbEmp0EixUGJd/rCgjSpmSgDyUi3qkQULQMLYGGpaDOLOB6CNpUlZQOe6n/VRPjhMnjpOK5fGqQTCURERD8Up6bn3kXWcG/UvPLs954cvNMQ2YJ4v+yMNsXRoa59rXjep7LgjnF8036t+9CHhTfiTuSnOpiphLNWRGZRIk0FrDQs9APHk49uloXDdHqVlrtmhmxzcKy2By9Tq+Hpsjn459l7dRupREZWGef+Z2v0YuPDT77MbYr8sOiAevzHnBYhKtJWHjD39h2acr/LP9LogWtyD+HbiDAqKysa+GjUCXvAUHSX1J/qCGZ2nehOnIj3RyXwDNpbUBiJU3XnCcqGGfqqF/KZ3rEhB5P3rE17eJ7KMS1moFgvaTYs7ZssbvD0kwsJ166NIQn7cHOlsvHTvbsPZi3STePqnJ5vuc5kQoQk/zjZ7jfco/y3t+XDqC1kj4LOWFz20TuJxxOVRPIWLI0sry9JTPyNPKitSvxxQODmQLReXxuUjheRKoTyCCpzIy8/44byZRNu8/Y8XuvZ9pYw6W5v3zqWfjYvo22I1aqAf1G+YO8VVNJymKzcCYRcyRWIY272x54gObAuKwfr3hbGvtoq8bPEv7PxGetZuW4lw6M8DY0MEaoGcJqktYX2onzaXwObP4c/V+f+I+pp8yiM0oLqe5NN+uOdtqctjZNmqIwxIIsBOzpv2Sr6UeGVQ+fp2D80ELwUMn97EZxbQNjGsB/dRJfSw5DuLrIotb48td378U/yzlfq1eOLnV2W5KvsbZjo+XnO3i3jFnGyf9mb+pvinMatov0hbGmVwmHF5b2KdS8MxVcNVOefVhTjdQkQp8wTw8S4ua2xDnI9LgToJ8wWr3Imer4dyzXN/lbLTOwzPDduQZ3SEE93jQpvWxX6vLfT9HKjgLIbAgqH8bumOl/TdoQ+S2YjXMkVjoPgZjOdoLIb8wHccZcftqkrTW2CmfVqSVdTLv384l+gaaEEIIIYQQQgghhBBCCCGEEFPoBZoQQgghhBBCCCGEEEIIIYQQU+gFmhBCCCGEEEIIIYQQQgghhBBT6AWaEEIIIYQQQgghhBBCCCGEEFNEKuMJcW44o9mcpIaCyGZmKdhBGzOKHJSYKa/9LNPMLNvlq+vYMlmUPu5haiupTlNdQg3NuiGBUnoORDUjFTPpWQJFL9PdCWI30C6CxE03x3Hi4rFQu/KIdmUw0HkWN15lFSeMSoKkRDiXYtdgLNRHVAalo/lFfRIuOWp6WcfOVW9bnvMiu2MYhyOLXlD4+IYXy62Cukwgr8HY25bnvXOh+ZDCNKf1ENZju3QPvNiXu7Y5WwjN3yNzI2c7Puw5GwnZjiuf7tCCH4fNkW8s2YhLDs+KFh/qDV2a/9/Hl51tUsK6h4iOfDDtP1nkXhjaaC2k8GARuQY3q65PFyk8TNserd8meDaFNA84tOlsOQgbf/IuL3xOfU6iyKNs3tmKxAvJZ+2sWDIJUx8zL+C9kp1wtmHbd7ZYIXHy8dTWCmxhTELPxccL3tZEpiNin43NT4hzRZ62lqfN6X836HvJ96TgLbMgXWp+ryNfjILvkI4IfZuZWdr6cvNm4mxZQyLyu1ukbeI3xRb6knxlk/jNk/Pb+eBH44Lxq/m2U90yOvftJZams2BgwzkCY0rjl7S+/dQuAuc5PNrP/B576fhTM3+Xud8nP5dc4WzhmjEzqxo/zkOIazoZzd84wlKj93DIi2Zl7PmboHIpXnNlRsZ5lG7Y+PG6dXjU2caVX6vLPX9GMPPp0qTZ6udT/+/budT1eVWNz2s+93H+qPFzJE/8uiHGtT/jrXROOluSzPZTJ4U1iPc93nbYjjnbseZCZ0sbfya9YsnHpncMfSzdwDiM6tlzGa37Q3bc2Qjy3QTtU7Q/kKtq29kV1sKY9nPvky5ovuhsTXGps1H7a1iDOThDvD+LcLdU5mrlx4+4ZHCXs9XzD3A2vFPa5Z0dxe7dHO4y6rj5wGe8e1ytLwFzKTxHxcZUvBdEFYnp6L6gwY0E/LcrMy5+JFuWtNactudJg3vvuUTfQBNCCCGEEEIIIYQQQgghhBBiCr1AE0IIIYQQQgghhBBCCCGEEGIKvUATQgghhBBCCCGEEEIIIYQQYgq9QBNCCCGEEEIIIYQQQgghhBBiCpCcF+L8gUQZSfiQiE13tqE2xAo6hraUBHX30EdUDxYZpgx3rksGopIkvkl5cbo9QKKqGQla7pxVCs9hkdHCpf6zDCSWWla+cqubXvR0ZXG2jFhB6MVenKj12tBvHaOJrxsJ2dLcTEMR8ug1vnuha4LmXOxaDaE100RWF4XPu35sSGD5jtWOsy30vWjx5uhLzyaJ2XwXRFvhIzbj0leuyOPW712rPsMjS77c5b4XnR6Vvq2H5mfbNdfxz+Wpz7+Xg6A9+SqYv7QGiaPLXkyc1nSRztblU3d5IebB0LeB5mANeuM5RHm4r8JYZ5AwnBOpn25WwybSg/yPLPkKky+sGm/r0BhGCs7HfHKMxLVTmEsPuvCks83lfuzHrReIL8zP1wQ6tGln5z6Jl/dbL0q/Vvu5VKSwRkCMuU3jxOuxf0GYPQsmWAVzhNZgVcM4RPppmocY84CN0oU2yp/mFkxfMzM7M11p7QkRQ5bUliX11r9zPCOQuDsIt9vsmj+T792lMTNLYLFkrY9XkjZO9D1todzIZ8MyqG7kPw3itRb8WLuHxUrtz2y2n1o8a0FedP6K7KNYqK1URlgXGr+s8fMhbfxelDb+2Trze2IvH/t6wDynffxQe8zZqqCMY/nFLs2l9WedjWLwBPaxz2WX+zJb378UwxDuSdjDaSr52kbmvw0042gO09iEZWAaaFfV+lbkiZ9fc7mfX2Qj+pmfXxtVz5rTwULZZFa3PrhegLiJ2rCXdd6BGK6C69x/Wj/qbHPF7LOdDM6VMC+Xiw1nu6281NmIYX/F2Y7Wn3G2fN7nt1bOO1s3m21DDvtUXk+crYLYmnxcA7YWVkRDNui7EN6PfRsa2H96qZ+XtB46Kd0X+HlYQ30p9o2hgDU4rP2558O9r3a2tYFPR+cBjPPBx7s0cGYYV77tdD9H4UJsPfC+K9LHh2WQz2gi2r4d4b2bmWGF6XwUe6/tTHhfDethu73gtC9Nkya6H88WOrYJIYQQQgghhBBCCCGEEEIIMYVeoAkhhBBCCCGEEEIIIYQQQggxxZ5/wnF9fd3e/va329/93d/ZiRMnrCy3/3p0kiT2+te/fq9FCiGEEEIIIYQQQgghhBBCCHHW2NMLtNe85jX2ghe8wDY3N7dsLfwIZpIk1ratXqAF/PVf/7X9zu/8jn3yk5+0P/7jP7YHPOAB9qY3vcmuuuoqu+aaaw66egfCma9EpglKVplZvCZRzO/4ViDzwb9ZG5cO6xGrURbxO7P82/0e+u1g+r5prLbZXnTRQkh3jMaK08X9Pi/rl8CzpKUS/K4u6UDFQmWS9kuRwW+kw7NkW5qD30gPfsf5+Lp39Rcs+Q87jEFnin6fuFfAb2lDX5J+zX5CZeLcB2LnyDZPO0uULhqkidVFG8LYUP/2QcssRk8Qx5TmL2hU9Tr+2XHp0x1dgd+qh/HaGPv5GtO/pJVVNjSnd87LzGw49vmtLPh1c8mRE85Ww+/SX9Te5mwfmTx45u/P3h43txqYOAkk5N9q9zbUxgDfF8oD0Pyl+vJeCxpzua/JCOYSlZHl/jf4t/0t9bv528xsdTTnbJcuev0B0iRoYG8kzSHyraSrMLT+zN8d8/oOw6bvbHcOF53t6NxJZ6N1Tr+3T31JexeOTbDOc/BJ5KfqPG7fJl9IGmVURoPpdrbF+pHtQohkh/8uxE50bWyd01opnWRiHdKyNPCLoEnltKxAjwqfQ+2t/dXoIi2zGD2u2DJRCwdihyYlXTSyxfnUsNxYbSQiVheN0mF+kWI4vgzYc2EuUd1Yd80/u9Lc4Wxl1nO2JvX5bbZeG7RMjsz8vdSedGnWs8PO1jWvd7oO+R/p+BjxZLXsbKRpg+MVJIvRXtouHa2QvajpoRZthD5tTBoz1u6hdbNYbDpb2RbORjpQo9rrZaXBv4eVX/erEIcd7g58uonX9uqBPlsJOmtVDWdBOPuQ1nN4HqJ+o3iwk/q1NWl83UJ9MjOzzdzP87lqzdlobl6Sf8HZ7mhm9QkvbI67NOS78sbHzXXi29CCVloNfonOeKSJGJ4HaP6Stugg9fE7kcH5JVYPMtQmNuNxCOcJ5dVNff/SPB/BuomF78V8uixiGy3gjnGh49uwMYH5gH1JZ03Q94I7DxrDsM9j7nG2A++rI++xqG6k5x5z1o7uI9I1TCtrTscCRVpZkcTpWZ4tdv0C7Y/+6I/sh37oh8zMbH5+3r72a7/WLr74YstJsV44/uRP/sS++7u/277zO7/TbrnlFhuPT13CrK6u2vXXX2/veMc7DriGQgghhBBCCCGEEEIIIYQQ9092/bbrl3/5l83M7IlPfKK95S1vscXFuDfl4hTXXXedvfrVr7bv+Z7vsbe85S1b9q/7uq+z66677gBrJoQQQgghhBBCCCGEEEIIcf9m178c8g//8A+WJIm94Q1v0MuzXfDRj37Urr32WmdfXl62kydPnvsKCSGEEEIIIYQQQgghhBBCCDPbwwu0+fl5W15etosvvnjnxMJx9OhR+8QnPuHsN910k1199dUHUCMhhBBCCCGEEEIIIYQQQghhtoefcPzKr/xKu/HGG219fV3fQNsFz3rWs+xHf/RH7Q1veIMlSWK33Xabve9977MXvOAF9jM/8zMHXb3zhliRdkqXOpHduLzC5+5JPfbyLBE+y+0EUUZ4Nc51i+yUXcKClDsLIm8HiYWifDmkw2cj0lEa6ksS1SSoHrGCpDmInla1f7YKxEZpHI6tezHlIvf5z3e9mGeY/3Z1m+v40ZlAfSflrK2hQY38uAeJzBIoWB25HmL8TayIL0FzjqrWofkAY0P5dfJ26z90stbGIOxLc4vaTuK2NB8mJQgWQ7oc1tJSz4v7ro1mxX07IJp9x7oXv6Z5TtPrn1/oxamJCoS+e+nY2T418R+O2RzPrsMUnAv5c1oj9CyBe0Hs+grKJV3jyg+DkZOneUNzjiAB6Bw3PngYRLJDLpjzwu8nRnPOdlHfCxmjb4GdisS67xivONvqsDvz9xXLx1waEr0/3PNtoHQkBk++iupLe1fs/htVZuQcoTnH84vSeRuli2kDinWDLSYvIXZiqT5uc9XIzMyWq+NW1H7iZk3lbEnj0yWhc4dJSn4sFpe/mbWxG09kfrtepADVraUNBfLDdkWka2GfjO2jNrYeAPYlkNK8sZ37PDZ/PM8lPjalOV2nvm5FPXK2jdTfWYVt2EyWXJrF5oSzraeHfZmJr9vJatnZDuWrzjZofIxRt779dbAXZ4nvubqJG/vYFRi78vH4Rmef0ETnHoilGlgjZePPs1y32BjZtyJJWkvaU3MsS2s7XPj4Cs9H0PZO5udIWfs4LE99ugmkiy0DxyEggyTUb8udTWcbVl1nywzamvlz2Vzi+/Mzo8ucrZfPxty4v7W0vzmTtXTeomeTXV+XR4H7Cqwk3n/9s5RbDWOYQn4Z+NEqOCM0sK+MGj/2tI7MvD8jKH4nFrv+DLbQmT1/37Gx4GsB9wwbE38+Gle+rbTOi8y3NU/JFhv8z6bLdh8q4Xzgcyqdq6kNsNfCvhf6rxx8QU7PtX5M82Zi9emLj+XmuBWt9xfnkl0Px4/92I9ZXdf2m7/5m/tZn/sNP/ETP2HPeMYz7HGPe5xtbGzYtddeaz/wAz9gz372s+15z3veQVdPCCGEEEIIIYQQQgghhBDifsuuX6l/4zd+o/3Mz/zM1relfvRHf9T6/f6+Vey+TpIk9lM/9VP2whe+0D7xiU/YxsaGPfShD7WFBf+WXAghhBBCCCGEEEIIIYQQQpw79vSd1Je+9KW2sLBgL37xi+3nfu7n7KEPfejd/pxjkiT2nve8Zy9F3ufodDr20Ic+9KCrIYQQQgghhBBCCCGEEEIIIU6zpxdoP/VTP2W/+Iu/aEmS2HA4tA984AN3mz7ZixjUfYCnPvWp0Wn/23/7b2exJkIIIYQQQgghhBBCCCGEEGI7dv0C7Q1veIP9wi/8gpmZXXXVVfa4xz3OLr74Ysvzsyu0eG9meflLIrJt29pb3/pWW15etn/1r/6VmZl94AMfsJMnT96jF233NfLsS/+/ndZprEh7qA26FyF3ejZW0JHKiG/D7MNNhADs2YDaUIMtc4Ks1Pi4Mvl9u8+PRHFJexQFM0lUNsiPniOR3RwERFEEdZdjb2ZWet1OrF9ZzWbYLXyaGrRdG7CRgOpcx1dkYwS+P4Pximj/XtYqSdbmUI9YuL67z2+30FyKdQcklpul7daAF3mDeVHbieE4TmSX6nFkYeJsNNYnhl7ct6pnCxmseWFqKjN8zozn9KTxc/pI4YXkl4e3O9vJ/lFne9jo/c72u7c9dubvDPSVW9SNBkHofd4e6sb3Xegf0Y/A+FXguwpwGTTPOzn5Qp+ugE05g7UaiqsPSi9Avzqch3qQuHicLyCR8JOl/8WG2076n0K/ZHk08/e48WuhaX3+LfQlic2TKD3v+b4MikloDMPxKmFfoTlCedGci93PKF3s3hISG8dt9+yZqZPuQRxc3L/pDY5Zz4an/j08ZgXGuRTo0qYSPgg+ZQ8fhG0hv7TxYvGxz0YtwNjFDekSiiap+VAEtYvakITjEHlgxP7AqkX2Gz0b2+dRmUXuk+AMqR5NCoES5QfPVq3f74pkdrxKSHMyOeJs88mGs202XorjcH7S2erWt4FsMaQ8CZ0JfQEQxnlmHGOQLYG6wNbulhKdqzGugbqRLZY0gX4iP2rtVtsSa6OXQgtOg+qbp5WzUfzaz8fORv2UwfiH86SBumXQH0SW+FG9oHPc2Wg9bFRzzjaCOPTYRtfZ/vXKrTN/JxTo7QGav2QjqD93S2PeF8TmT34E1xLlR+e3Jg2SxJ0j8P4MzuR4vwH16BZ+zi11R862Ppm9C6D7ORrTsJ1mZjnUd74bF7fQ+sX5Belidnh6Lk99H9GapvVLtsJ8WzvmfVDe+LuctJ7NL2u8j8trn1da+zLTprTq9OXC/OAOSwYDl+Zcsuu3Xa961assSRL73u/9Xnvd615nqU6AO/K7v/u7W/9+8YtfbN/2bd9mr371qy07fWNW17U997nPtaWlpYOqohBCCCGEEEIIIYQQQgghxP2eXb/1+tjHPmZmZr/6q7+ql2e74A1veIO94AUv2Hp5ZmaWZZk9//nPtze84Q0HWDMhhBBCCCGEEEIIIYQQQoj7N7t+8zU/P2/Ly8t26NChfazO/YeqquwjH/mIs3/kIx+xZp+/jrwTGxsb9pKXvMSe8IQn2MrKiiVJYm984xujnn3Pe95j3/d932cPfvCDbW5uzq6++mr7gR/4AfvCF75wdisthBBCCCGEEOcInZmEEEIIIYS4/7Hrn3B85CMfae985zvt2LFjduSI/11ocfc885nPtO///u+3T37yk/bIRz7SzMze//7328tf/nJ75jOfeU7rctddd9nLXvYye+ADH2hf+ZVfaTfeeGP0sy9+8Yvt+PHj9rSnPc0e9KAH2ac+9Sl71ateZX/2Z39m//f//l87etTrwAghhBBCCCHEvQmdmYQQQgghhLj/sesXaC960Yvsne98p/38z/+8/eqv/up+1ul+wS//8i/b0aNH7Vd+5Ve2Pnl4ySWX2Atf+EL78R//8XNal0suucS+8IUv2NGjR+1v//Zv7au/+qujn/3VX/1Vu+aaa2Z+xvMJT3iCPfrRj7ZXvepVdt11192juqSpmbWn/r9NzNJ7IMi+Y96kNx35Hcxosdg4/eezDuoE76EeGfRTCsKaYbJYAVEivt98fnsZhxiN5f0eUyqzBpHhqiZhZ/9sTB9TG2iciar2CTvF7r85G9OfsXMwdn7FQnXD8QIR3BhiNe6JCsqk+ZBu18FNah0zK6sUHWS9hy9D0xepc9BH3xwXzhbbrhhozeSZz+yKxbuc7c7RIWfbSL0g9npn0dm6rRfUveGLj3O2cF7TfGhoToMqO41XCxkmMB/AVVuRQ7rk7v/eDkpHezLNkSInoWRY+yCUjALxwZ5xqDt0aQYTPy/Jt3QSP87Dpu9stw8POdvaqONsxLCaDdV7uS+zAoHwTuIFm0eVL5OEs2N9HO1T9Gy4DmmuxgqJ8zz3tlhobsbO15jntkt3Zs3RnBfnL+fTmSmrxpaVp/LKypGl2f5NptbAd+5lndGmFfssFNziPra7ClJecLTANmCZLTipmANn7HOQjuqB7YqkTf1cajK/L7ZJFpHG1yOr/T7WUJmpv6pqEp8ua0pnuzO5xNmKxKcLCWMEM7MGfrRpo/Gx3yE77mxj8zFBvfsrOEuD+jVwwI9dCi2dNSGeoHS8/8cdJMM13cAap3Fooa1cD4pfKW709c0pbkxaa053at1mlkSWGQstVRrXDNqQpz7Wi6kLxcwE5bU28Weh+c6GfxbaMKr93Kc+//9c+E/OllW+ra5M8A/kg8iPkI3a0II/oH4Kn61xre78nJlZA2uL5j6lozVdN9B+Z/H1a6DMXu79KvoWOG+kMKeXen5/6MA8ryG/Ip3dp0tY40XmW0rtonS9zNeDfBUROzZhqXSWzaHMAs59ndSPTQFn1wLuLYp65Gy012a1t6VNFfwNaeC5pOE4qz19EZSVY8tKX9dzya5370c96lH2O7/zO/bDP/zDNhgM7MUvfrFdddVV+1m3+zRpmtqLXvQie9GLXmRra2tmZra0tHQgdel2u7v+1OO1116LtpWVFfvwhz+816oJIYQQQgghxIGjM5MQQgghhBD3P3b9Au3qq682M7Msy+y1r32tvfa1r7WVlRVbXPSfxjlDkiT2yU9+crdF3mc5qBdnZ4uNjQ3b2NiwCy64YNs04/HYxuPx1t9nXiJaW279/wF8cWvv7O+XX+AjZJFfh4ntvL18nBQ/GbZz/m1sJ+1hAuzlG3hRn9qLzWuX30raFvpaD9Y3MNIn0aL7iD7VGjeGmIr6JKafIuu274uQyqUidjvWe1qCexlXMzvz6aBmm0/04UfRIsvEPoK5RHOa+jKmjNjnYP7Wtf/EUwv90tTwSU/8RCx9ggo+aRVWhdw5OCX6jC93EX2qFz6ZC8/SJ2xDCz8XZ4ufN36O0Lxp4VNrbeJtTTA24d+n8oJPU0L+NG8amDc0l2J9YfgszkH81ialg0/XRn7zC/ezWH8QPruHvPYyl2JDHpzXu3xu23RT8a64/7KXM1PVtFad3lerpt1jTD/LHr5Msf/gN9Ao2W6/gbarx7YvE89lEXWLfY6+WY7fQNu5yG2rQt/CwnEIv0kEezN96wLiwQq+XtzCt+0a/PYhpYNvCkBM4MrEb5xQPASxJHxLkeJBslHsgPULvxES+Q00bFfkN1hod6Nvm0V/CyuYN3yujPsGGn6Dhx2ETwe2hr59m7Rb49M0lTXwSyx7+gYaffsUoV+VoF/i2bku9ByWSHMEft0D42GMQ8EGfoPyqwMf0dAZEsNLqC+VSWsVvsGE65d8cFCZ6Pkb+Q00+tYUp6P5GnGPB/Xj+UC+i/w+2Gi86PwNc4kI28VlRp4ryT9AW/fyDTT6NqN7DnwS7bWNwbf0YK6msA+mlI725MgzeRrYUtjzyZaQrZ2Ndau9/OzIPrDrF2if/vSnne3YsWN27NixbZ+hnw26v3LVVVfdbX986lOfOoe12V9+7dd+zSaTiX37t3/7tml+4Rd+wV760pc6++HBjWZzc7ay+e6zWENxtoi4C8Y7eRFH3A9+nX1ity1KR2FCbw91OQjIc+/+x1gOns7xg/O3sXMk5kdh/A8HxfOpO+PSre6hjAfs4VlH5M+t7ondvp/e/S91RUP7yGakbbcMwPaxPeRHQ+h/6MlsEvwi1B17KPNcQP4xXJt7Wav3NQ4PbjzoKogDZC9nppvvbGxu7pRTvvmu1gwuT4TYHfRi3/+c0/5ycp/zu3Wf8xPC7Pg/ve+gq3Besv0N8O44sc/5ibMHnY/2wtneacY7JzEz3gX3u63innHznbUNBgcb6+76zu93f/d397Me9zt+7Md+bObvsiztlltusXe96132whe+8GAqtQ/81V/9lb30pS+1b/u2b7PHPvax26b7yZ/8SXv+85+/9ffa2ppdfvnldmLuMTZnf2PH5///7P15sG3nXd/5f9ew9z7n3FGTJXmSDHggAcwYwO3GkAK3naaJSaAJhkocx407qXaHNlXQJql2m9ABkg7pJE5sul3BThAhCU4goYxpIILGNpMDCT+wTRg8W7JlDXc4wx7WWr8/7tX12ev7PrqPzrm690p6v6pU0nm05vWsZ1j7nP352ohqcqR8lTHMQLuC248o/OulR7GPku85Hy9z0LYoQ6r0t49K86fSXydclQy07HrJQKP8paPYW8D3VcPxjq8x/4ZhXo+eEbrPE8iQKv3DJ8q5Wawuf51Kj+3JlIF2lPt6YeFVTB/4hVjc+LURkDOBOUWFmUdUNoUsq80p/AZgYQbaeB+lGUqUgfbcm/InaPfPT6Wyk5P8kQz9piB95/iv/OGtqWx8jTEDDQrxj1HxGlH/QNkQed22ufxy2NdADA9nm5WVzdp8sjPIXNyc5E/upk0ua0e/eTf+vvyIiHvO529T2Jzm5Z6+lT/O2uvzrwTcv5e3d24vf4w0X+YLeurY+kSBMtsWfd4WfRf+9mqWyihzsLSNo99+LclAo76x9DhW8AFtaRtUmut4NTLQYljGjdu/EA9ufXXZSnrCOeqc6QW31LG1WcV7Pj3EC26uor2SGWjX0y+9wl8APOYZaIXMQBtnoOVOnMZIlJmymGzBcZRloNFf8Hy6ymOu5jH+C7RT8Np/XudfjVkO+Vci533Zr0k+1n+BRpk8NKMrzUoj4/cDlO11dTLQoOwR/gLtgQ/+atz4rK+MFl6MHOUv0Jq6bHDC73yu/l+gzbvcFtw2/WQqOz/kse8Di1xGGWi3z/L2NlbrH19QXlJpf9FVMO+F9mZV5XOl57fDvy56bP8C7SgZaP1w5TLQZg191JSX217ma0lj6402z19oTkNWo/PagX1Sttl8RVlp+RnZghzqo/wFWlfwG7EV3Bl6ZjYayDGD7LjyDLT8USNlmVEuWs5Ay8fxaDLQVv0Q77mvixfc0sTO7rX9tfVD7/0v/aW/dCWP40nnr//1v47l//gf/+N473vfe5WP5sr4wAc+EN/4jd8Yn/d5nxdvectbHnHZ2WwWs1l+kTNc/L3kISYxxARfwFNDW9JsUadwpX+Jv2R+FHHAS3P8zqDRgtRRwmoUyHm0bxMs6xjGH2jQLvvCbR3pcGFlegGPq47WLf1ABj+0g8pK81l6cUgvGGHOGG3JBzxQb3CyUnjRafpJgxEe2MGxjHqi0g+GCH2wQB+08QtX+IAa1sUP8w753uooExh6GVwfZgJXt9HDBIE3VVanqX7R9jpoD1aF5zAZf4gCz0zp17j84ZmnprIW7v0M/nSGgo3/0735xU0L70a60XiV3p/h1zBCfaN5JH34Rk1wDTcRX76l35KAzReW4Qdt8CHrBD5kncGHZRtQNoE+eTq6r1RXbzuZf//xvp0cmt7ASbQwEOirfPNr+FTxGIzKj83W69cS/k5t2uZzb+A4Gpww5+UWq7xcDRPJ0oHFuL42sF4Fv0ixhDJqMuGbfqIrq/oIxy6jlfmXngoNnzmPfvDv8Z6MrsScqa2raOsqIoZ9/315JR+sXOkPZIonSID2OxRsr/xr0Y4CZ6p5qaIP96BTxA/VcMIIRWXXHK8vDGpLxgnU1/fYF9E+87odfFBBZ7Vs8odvXZd/maWBX24av/zk9j/vtYWx33bcVLQu7aShgV2B0hfrpS/v68KvgKN7eFilcyH+pR2YH1EtwXcv8GEZfJhVxXCpzRmqaazgFWpF80XaZWG8RQ3H1sIvfNG9ofdA6TgKP6Bc9Xn7Jyf5ZfuZ/imprIHj3YAXFyen+RfD+HkYj6XpfVfZc0RfZ0/jZmpH8etb8cPoZrQM/SJ0LlvC160vYTn8YIzeKcE9XMLXkPIvsl3+Od+rc1t787H8t1oVfPCKH2zjO6U8BuIPC9ePd4pDbLin0Oe18EHeUOcNNvCs0ofiyy63G3urvL3x/aLneYa/IJrPa9rk+WwLH0a2cO9beEZaGMuU/AJEDW+sqYyHmZ+ZNLXVEO0V/Lryw7gaXwKkR+GlL31pvP3tb7/Wh/GoffSjH40Xv/jFcerUqXjHO97xiFl4kiRJkvRk45xJkiRJenx5PMe2PCH95E/+ZNx4443X+jAelfvvvz9e/OIXx3w+j1/8xV+M22+//VofkiRJkiRdN5wzSZIkSY8/foB2jXzRF33RWg7JMAxx7733xn333Rf/5J/8k2t4ZAe755574syZM/HZn/3ZMZlc+HPT7e3t+DN/5s/Exz/+8bj77rvj2c9+9jU+SkmSJEm6NpwzSZIkSU8cj/oDtPvvvz9+5Ed+JH7zN38zuq6Lz//8z49XvOIVl50E/Kk/9afi/vvvjz/6oz869ME+kfzZP/tn1z5Aq+s6brnllvjqr/7qeN7znnfVj+eNb3xjPPTQQ/GJT3wiIiL+/b//9/Gxj30sIiJe85rXxKlTp+J1r3tdvO1tb4sPfvCDceedd0ZExLd927fFb/zGb8QrX/nKeP/73x/vf//7L23z+PHj8bKXvexqn4okSZIkXXHOmSRJkqQnl2oYilJtIyLi13/91+Prv/7r44EHHlgrb9s2vvu7vzve8IY3RF1zrNrtt98en/rUp6KjpG9dc3feeWd8+MMfxv/38OTvFa94RZoMPtJ6d9xxR3zoQx8q2v/Zs2fj1KlT8fd/8r54VvvuuHf20hiq8mD1klxrDHwvzL+lda/09q4kOg7aZ+nTT+s2FJY7evz5OCBo8jG+HhHl5zrOq67hPI+ihe3RsWG4awdhz4c8PtpnaR1pGwoQzctRGG0P2xvvg7ZP142uEW2frlspqueQaZ7q9VHqNIWxUogvRMsX1/MLG1hGde//G8NtL44uBTPztjq4p4Sefbo3s0k+C7pfJW3QclUW67oB+5y2ZWOTxSqHDJ/fy/tdrCDUHG7Y3iiHe4Bl6LqV6mF7y1XZBukeNqMHne9LLmvh17Vm0MVvTCEoeZLLNqf5fm1C2PNGm4POp/X6cpN6mZaZRC77gzNlX7X2lGPnU9kcgrPPL/Iz18Nz/rTjD639/LFzp9Mytx8/l8qor91b5X1ioD0cx4N7OSSc2ngyHQWCU7tK7fnuIpftYVne5yJXhyv6fJWO90hVR1TDMp66+Nn4o9V/Fd/1TbfEmTNn4uTJk4ffqK6a62XO9OFf/qnY2tqKd398J/6rp23FhJ5lCJW/koNu2v5AEeuwT1y3zn3sAEH2j7UqyhqGihoV8qgGZ4fY/hHgNcf7CuNQWLdon1QvC7df97n/35nmtvP+1U2pbAr9fY2j6dFxQJ2m9doqHxtdt3lP/T/so8r7KKmb3VB2Xzro6wkd2wrKSMlSdAdon3S8PYxDusJjIzW0o3U1xNCv4vyH/kMcv/NPR91AvYRt0fiK90l1Ce49bG8GdZrqa2mbNrZRz1PZTreZy2B8Oanz80Bl0zoP4jbqvVTWxPrAroJ2dYC+pnQ5Qv0ZXV965pbD+th/NeTJ0LLP6636vBw9b/iuqC9djuapuWw8J6d5Jc1dPuvmPC8he10+V3qWGqg3NFcZz61OzHL93Yb5F12jjUneZ+lzdMPGTtFyND88O5+t/bwH7x7oGtG7jBPT/GydmGzndau83CzyMzjp4Lns84SrHpU1fW6nKujLq4HK+lj1Q7z7Y9vxXz39WOzs7MQdX/Vnr9m8qbh3eeihh+JlL3tZ3H///TEMQ3zu535ufNEXfVFMJpNYLpfxt//2346XvOQlsbNTVlme7JqmiU996lOp/P77748GOuXH2oc+9KEYhgH/eXji99a3vnXt58utVzoRlCRJkqTrnXMmSZIk6cml+AO0N73pTfHJT34ybr755njPe94Tv/u7vxvvfe974+Mf/3i8+tWvjmEY4hd/8RfjpS99aWxv5081te6gP/ybz+cxneZPxSVJkiRJkiRJknR1FGeg/czP/ExUVRU/9EM/FF/xFV9xqfymm26KN73pTfHVX/3V8cpXvjLe9a53xUte8pJ45zvfGceOHXtMDvrx7B/+w38YERFVVcVb3vKWOH78+KX/13Vd/H//3/93TTLQJEmSJEmSJEmSdEHxB2gPBx1/y7d8C/7/b/mWb4mnPvWp8fVf//Xxnve8J17ykpfEz/7sz659QKSIv//3/35EXPgLtDe/+c1rX9c4nU7jzjvvjDe/+c3X6vAkSZIkSZIkSZKe9Io/QDt//nycPn06tra2Dlzmv/6v/+t45zvfGS996UvjPe95T7z0pS+Nn/u5n3vEdZ5sPvjBD0ZExNd8zdfEv/k3/yZuuOGGa3xE15eHcyn7IWIIDnw/LMwrBhQMX5q3Xbpcafh8yfZomcJM4OLjpfswDhUt3V59hPDyo+Se07oUFDzOEKXjLQ0Fbpu8HIUd0/YWq7xcU+flqGyMQmEJfbMsBbQu4diizZWkg3pDxzI+B7pXFBx9x+mHUtmDe7m/WXQ5V3J7DqHphQ8OnVd1yMpJ948Cx7HOwfFiPYf9VtUQQwzRXzwG2j7Vh6O0cXTdnnoihwzvdflrjO/f3khl4/u1t8g73ZiWPavn53k4dOOxHDz8ex/KYb83noJQc9gtBS+Pr1NVOCorvTe0XA03h46NjPfRQOWivpaWayHuldrMSZMPjtpuqsMcfL++4xo6uEmVw46fc/oTqex9DzwtlS0g/Jue6Rs2dlPZVguBzdV6EPOxWT42CqE+OT2ftz8tyymm0HQKuT+7yM8lWazWt0eB46c28rmfgs2fX8xS2UPb+ZpTe7DIOdcHBKLnMul6s5oej+V0MyJ2Yjk9Vjw2LTFAQ07tGC1H+hrGXFUu62m/hcdSxWgseSUnkQcY7/PA5Q6Ibrj8evkcSq85GWCgcJT7OkbHS9eIrgcdG++jS2Xn+xOpbFrnvrKOy9cJeo5onEDoWuI+4Jo0VT4v6nfH+1j0uf+vYfs3tA+ksvtXN6Uy6p+nTe48qx4GcYDmVuk6lb64uMLosaT5Zl9VMfQXnolVX0dLzxGdAs7TaL6Vr2ULY99pne/DHO4/LZfWg3Ej1bcGJiabTR6vLaE+3DR9MJXt9puwj7xfGr9X4z6jtNrAcn15klFCdXoFr9XH+6D2oaG5C1wPnB6WPYLl7VLBYiu8mPkcPvJg/iOaO288m8ro/Jfjl3ERMYd3A/ROaVxG94rmlYtVvphzKOP5ZyqKh+b5fRS1o1QLT0wXaz9vTvJSZ/fy9dhdUoXIy1FvNq3z3GqjyWWTJn/DYNPm82pivT9r+0Vahsqor62GLlZdHxHbsTc7hW3e1VTccpw8eTLOnj0bXZc79/2+8iu/8tJfnj38l2hmomV33323H55JkiRJkiRJkiRdh4r/Au25z31u/Nqv/Vq8973vjS//8i9/xGUf/hDtpS99abz73e+Ol770pbFY5E8Yn2xe+9rXxt/6W38rjh07Fq997Wsfcdkf/uEfvkpHJUmSJEmSJEmSpP2KP0B7wQteEL/2a78W//bf/tvLfoD28PL7P0QbhuHQX3H1RPHbv/3bsVxe+DqB3/qt33rSXw9JkiRJkiRJkqTrUfEHaC996Uvj7/29vxdve9vb4g1veEPMZvk7Mcf2f4h27lzOOXmyufvuuy/99y/90i9duwORJEmSJEmSJEnSgYo/QPvqr/7q+Ct/5a/EarWK3/md34kv+7IvK1rvBS94Qfzcz/1cfO/3fm8MhwzQfSJ65StfGf/gH/yDOHFiPfR2e3s7XvOa18Q//af/9Bod2bU1u1gjZ5MLuasdpBxSNaKykhD4uvCPAGn7pX9ASPsoXXe83ysdbF96/rTf/pDZ3HTuVHaU61a6vQYK69G5UlgopdHWkChJwaXdeAcR0ULm520nc0DvPWc28pHgea3vo4eQ1aOg+tDQLprDVdjlCsJ5u1x2fpmvx82b+Zc1Vn3u6j60OJXKONj5wMNcU3KNafsUgJsqYcQBQdSwauF+S9ajQxvXrUeD6vknzuXg9xs2IVQW9juuJ7eezuud28v3fmeRz/bm4/NUdmY3B+8+9ZZUFNv5UT10n3Q9DZN6OJh61PY1cE838mWLjWne1rFZ7kRmk5yzO2lyGYVOtxC6TXK/mutDB6Hp08h15HNuuC+V/dFDuZLM2nxsO5FDkDfbvI9x+PfNMwrhhtDlgKBrKCNV5Gu+1eaKTsd73+7JVHZitlz7eQ+CuSmAfqvNz/Txad4n9Q+TNt9XWo7LUtEVH49VQ0QsIjaubRa2Hse2ZzfEMNuMiPtiZ3ZDdFWuuPWQyypo2ysIbh8bqvxMDTAQ6av8LJeXwT7KI9vXVHHIicpjgK55Wqa0fYZ7RffmWqBjozpYQx9TF9TBiIi+zo1mDX0gzcE26tyPzYf1Xwqv4T6soA7SPnuYHzZQDxt4Vsd9/UH76Ib152Z3lQddm9B33r+6KZXtLPMvxM+aZSojNOai8x/geo7HXeOxZURE4JgO6jlOYKA+4D5gVag3RdsqHMBTvaQ5Dpl3ue7T2InakvG4+cw8z6GPTXO92Why2Sf3TqcyOq+bYD5AqD0g4+te2mb2UEnoeOm+Uv+Dy9H2CupSV3gchM6/hbpUQXvTwjsweifRj17w0DIrKFuu8nW7b/tYKnvKsfP52GAORvV8B97vjN9TbC9zJaRzmMA8jdBjTttbRD5eWnfWrlLZ+FypHh2b5Xaallv1MMeFa7kHc2F6f0Z9EpW19XpZW+XznDS5rK3yedXRR9d1EfGJ2Jmejh3o866m4tFWXdfx//w//0/86I/+aPGHZw/7iq/4ivgP/+E/rP0F1pPd2972ttjd3U3lu7u78c/+2T+7BkckSZIkSZIkSZKkiEfxF2j/7J/9s9jc3Ixv/uZvfiyP5wnv7NmzMQxDDMMQ586di42Nz/z2R9d18Y53vCOe8pSnXMMjlCRJkiRJkiRJenIr/gDtFa94Rdx+++1+gHZEp0+fjqqqoqqqeM5znpP+f1VV8YY3vOEaHJkkSZIkSZIkSZIiHsUHaBFhhtkVcPfdd8cwDPGn//Sfjre//e1x4403Xvp/0+k07rjjjnjqU596DY9QkiRJkiRJkiTpye1RfYCmo3vRi14UEREf/OAH4xnPeEbU9fUR+nu92JwNEfOIrdkQUQ8cZgkZjx2UHTXg/XIgezMgSxuXI3S848+s6TypjAMuy46jMMP50Cjnmq5RQe5qREQ0hY8QnVYF16kf5azWdFNpW7ADXjUXtk0+kLNRFnoKubCx6taXm7YQiN2VhRiPw1gPWrfD+puXm8GxjO0tKfAUAmo7CKjdPZnKHtrJoctUb0rbjJJQ4KOg7VdQWanq0z2sqaLHhWDvPi7c46qGMHAMRC47XnreeriH8yWc61beyd4in+3xjfXKP2kglH2gANx8bPSsntnOIbttLsJrsoLnsoOyK9lP8b2hfeZCOjZadzp6lDYgx/fEVr77W9O8g1lLodZ5XQrJpueB8LrrP1NY9xLqDWkjByA/9/Q9qWy7y8HZ9+0eT2XnlpupbBz8ToHYt2yeTWV7XV7uWJuzd0kN120CAdC7fQ6hb6gtGf38rBP3pmXu3bs577POdYTu6dY0H1tT54eV+hEKcKc+btx+lYzZIg4aB1zcwPmL417pEHbjWERsRUTEThyPrsnPXhP5GWqG/LzUQ/eIPz8afZWfPSrrqtzO9jCy6SHcvqQPqMqj3hPqF46CjnfclmF/RaMpmkjRPo/wi89D4dwn7ROu2wDHO8A9jX6Zipo+11Vy8/ITqeze5pl5v3B83bBeN6nmNzDZon6S0HjieH0ulc2H3P/T/b9vfmp0HNmJ5jxsf5bKmmne/qrPW6zhOKiO4P2Hsmo0aaa6WkEdoeOgffbwfq2H+0XzF5ozDUPEcPGYm6qPCGjP4HiLxwTwcqsb8hjuts0HU9kfPZSjX55yPD8303q+9vNilefLN2zAOK/L9ebkZC+VnV/m8eASzmGzzuPQ8TMYwfVm3BdQu1o6R+/hHpLSvqCkjR9gDErbL51XkxpeSJW+ixyay4+RaS5Pdb9ry94r3L+b50cTGMscmyxS2bzL93D8/NLzvII+iZbDdaF9LJyS4rOPbdB4n7j9sptK57CkNh6W6+CAa3hu8B3VqE1rYbxHdZXmfU3VRX9xLLDTb8XOY/we7nL8AO0aueOOOyIiYmdnJz7ykY/EYrHeKHzBF3zBtTgsSZIkSZIkSZKkJz0/QLtG7rvvvvjLf/kvx8/+7M/i/+/o18ElSZIkSZIkSZL0mPP7A6+R7/zO74yHHnoofv3Xfz02Nzfjne98Z7ztbW+LZz/72fHv/t2/u9aHJ0mSJEmSJEmS9KT1qP4C7ZOf/GQ0Tdn3xZKqqmK1Kvsu6ye6//Af/kP89E//dHzpl35p1HUdd9xxR3zd131dnDx5Mn7gB34g/tv/9r+91ocoSZIkSZIkSZL0pPSov8JxOEIgrT5je3s7nvKUC6GfN9xwQ9x3333xnOc8Jz7/8z8/fuu3fusaH921szntI+YRm7M+ou45qBJSNCkYfpzHSsGVhEIUcTn4+83SkM7S8PnxeUGGMQZSrmA5OjY6jqMYZ3KWXg9SGrxL9aE0+xoye9O6pU0e7bP8OCjEOFewaQsBtQX1ta7zMhtQNl+VHfAEjmMKga8YxAyhp9vz9V/MmEDw7Lnd/Msbn3gwBxYf34QgZri+JdftIIddl+oDPSO0fcj1xfvaQNlBbdoQEcuIaOqIitaDyo8B7JRsDNecQnAbaEfPzXPoNNX9ZVeNfs515IatHDpM4cQ7y0kq25pBPZ/kdefLvF/Kvaf2ltqgvN61GXdRfW2bR/75QllZHTzKM0gwxBgCiscoEHoFX9CwKgxAJg103rdtnUll9HzNu/W6ubvIx9Ft5BsxXi/igJDsoPaGQ5zHNkah9BERxza2U9kfn7tt7ed2c5mWecrsgVR2ZnUilVHANJXRDGcBbUTQN6bDYsOojSwNqqd2uo6Ioe9jiIhNaFOkErv9LKpuFhERe90sVjBunNT5WZtELmtGz1A75GXqoSxegNoxKjuKfrj8l+hU0LZd8X4H9lF8LKOyKnJbUF3h/n8onZgAOpbx9voqN558XrlsgPpLmi6P6+j6bkz2Utm8n112XapbDfT1s2o3la0i97sdbG9nOJbKqC+mL4vabPKzObYY8jj6gfnxVNbA83BqmvtwRPN0KKQ6V43ag5rmFqXbgutL7ys76rMLH4chqhgu1oFJ00dPYz8YS/aFO8D2Ec7hY9s3pbLZJLfLiy4PgLaXp9Z+vuVYvs97Xa43Jyc7qYzGg6dneeLTVrmuLqFu1jgQy8bXicZhWAevQp9E6lHb19b5GlUDtZmF50Dnf4Q+jtq+drQPqOZ4HCV99KOxB3V60tBcZf38T0xyO312sZXKSueQLbwApraFzp6Wo3nJ+F7T3I3eazd1Pl5q4+m9Iw81qF0uWSpiGNXrHvp3ur4dPA911Ud/8TrtdrPYu8ZRV4/qA7Rjx47Fd33Xdz1Wx/Kk8tznPjd+//d/P+688854/vOfHz/yIz8Sd955Z7z5zW+O22+//VofniRJkiRJkiRJ0pPWo/oA7fjx4/H617/+sTqWJ5W//tf/etxzzz0REfH6178+XvKSl8SP/diPxXQ6jbe97W3X+OgkSZIkSZIkSZKevB71Vzjqyvj2b//2S//9JV/yJfHhD384PvCBD8Qzn/nMuPnmm6/hkUmSJEmSJEmSJD25+QHaVfTa1762eNkf/uEffgyP5Po1vZh9NG37qOB7XCPKssIiIlajMlqGHCWTiNBZrLrS754dbYsy4WA5OgeM66DvL6fvNqassCv71cZFSqMASpd7rJNHSnPy5vRV6nDDjm1AjkDB16a3hVlZs0kuW6zySXCWV9lFp+dwsVwve+Bs3ucsf2U6XrcKKubxzfyU0HFcyaiJI8RMHG2/8FBj7lE1xHDxCWhrbm+xraWsAcphxO9qz8vtLehC5e+/3oCcoHG2EOdcQn5Elb+D/qbNnAWwPT+Zyijrb2sGeVnQxlPe2/iaUE7acnllKxNlqk3avA/KNxs/h5h3BvssfR5KM2tKux/KDBjvg74fnlCWRQ/f1V7cTxXmGYyXu/lYzqPYWeUGsoXvlp9DhsB8lcvO7uXt3biV885OTikbI1+TVbd+xxoICZzCTT0N2YeU7dLWOXdmBW0VZ/FBdgHlFabFCvNyqaweYoghuuCMNKnEXjeJ6mLW4W43iQk9e9BGUZ7jrF7PlbrSWWGUXXSUDJqhoBegNpty3Er7HRxfUb7XFZxdcOZT2bFRzlhhs4V5ZNifjo+lMBcU90l9bA35QFCnW8hFu2nv46ns3tmded1RntO5LuekUS5LB4M6yhek86LlJpAXtRjysYxzbiYwpqV90piA8ncWfe5Px+1DRHkbUTLWwXpObQZlLVHeNhzaUV969nHhOs/qBXbsS2x/IS8JQqRoTkpzBhrDTCEH6vT0fCrb6dZzw+laUl7UoofcXaiXW23OHNzuctYf5U/dsvFQKivJj2uwrYX5bOGsoTTTkuo0HUs1euboOFawXgM3v2R+cFAZofl8RW8VCzZ3pd950Hy+6yFvHNYdnxXl+m20uT0rf4+Vr9v9u7lOb01ze77Z5jLK4W5HbTxlGlL7W5rNXPLu8KCy0u3lbcE9pTk09FNVVUV/8QXJsm9iCXXhavIDtKvot3/7t4uWowGDJEmSJEmSJEmSrg4/QLuK7r777mt9CJIkSZIkSZIkSbqMa/AlbJIkSZIkSZIkSdL1q/gv0Pr+sU4LkiRJkiRJkiRJkq49v8JR15WHQxMndR9V3RWHq/cQvjoOZeQQ27LjomDJBo6NQi+prIY//lx0lz++vjDgsXQ5QpnTpctB5vRjrjQysPQ6XVGU3114vH3OGY0BAl+Pb+adTNr1Ezu/l8M2T23l0OkT03kqe6DbfKTDvGSxyvs4NssnsezyOZzZXr8oGzmbOM5u55s1m+aLecOJfD0onLn0eSCHjamsYT0Mqj9CDCaGsMO5jtu0Bo4DLlsE/DJNFxDsCyvvLSCwGPYxafM+qC/YHW3v5Gau0xjYC89RC9doa5rDlO95MIerP/XGvI+deV6uJKC3gzYDHhmsS6Xt2aSBcHnI453lU4jp6ELNJnmnbZNPYhyIHBExq/P1rTE8uOzEKPyb6vV4H7Re6T4x1Bsq9QqCkukZ6SlwfrQPWoa6bTq23WUe9m+0+bmZwD2kfdB5Udn4md6uTqZltoYcet9UuY7Q/WqhLk1oPEbXHB4melbr8X04Qjvd1EMMMUQXPJaUSsxXbTQXw+UXXRtdlZ+9FgLqe+gDxipos6ltqyM/e2SAeQ+PV6h9y+fVFXyJDobWw3oN9Dv1kM+L2iNSQQNC7dYYXY8rjc6Ljo37toJ7SOOcwskhLdfBq6qK5t89jCf63LfRvd6odta31eZzP7/cyvuMjVxWQ/2FOk23ej7k7bVVHl+O3yvQc0n34fximsqOTfP2Z/UilZXU34Ng/SqY6OB7Gyiroax43EhzMBoTxhB9XKhPm808osnLTPpcV5d9brvmkQfXPY0R4Rmk8VoLdXq3yxPp8bEcb/fysfX52Gj7bU31MtfzvS7XuRtnZ1PZYVUwMsX6ButS31U65iw1XpeOt6moPuRruapy/cL+ktobQPOtK9kFlT6/BJ9paPf53e56GT3j9F53UuVni9ZdQZ/0lGN5/rKCZ5/mKuTGyUNrP9833JCWWa6o3pTNZ44y7ygdL8To2pXOU+m6DVHFcHHSNe8msQfnfjX5FY6SJEmSJEmSJEnSPn6AJkmSJEmSJEmSJO3jB2iSJEmSJEmSJEnSPn6AJkmSJEmSJEmSJO2TU/Cka6iuh+gv/ruqh5hA2CKFmQ4QiN2NA98hVBNDD0FpyGypZZMDEiddLqtHgeAUDtlBKiMFRvZQ1pVlWRYbZ45DdnnUhQGlBfnCjwptryBHHdH1LT1eWrd0uZ15LtvezRf55LH1n+nYzuzk5r+ucugwBRaf3cuhwCc2cqDwH96bA7GfcjpXuo3R5tr8KMTTb8ll99yfy248li/SA9v5vI5Sv6gOj9uI0u2X/hZLaSA24fagutT+9UOV2suD9kmBvdQuUXtDNme5AZu1EPI+yfVwc7K+33bcAEXEsUmuD+cWm6nsoXkOiF90+e7cdDLX3/kqL7c5hZB7uk6jQ97N2e0B3QWixRqoYFQ2gdEgPYfjshqCiKnuYx+Kwc4QCA7bqwv7354Cq0djATw2eGioz8fjLTy2Dp5+Clkej11omQ4629/6gxyy/PXP/2QqO7fKdZ+u+QzGY1SnKZz72GS9Yv/2PbenZf6r2/8g7xQ0cAWmEGhfCjLjo6J7k4LJD73LqKshHt7clR7r6Mlj2TexvPjsL/s6uhWErxcOdMfzoR6egbbK/fAEyhooK20XuT2Ged+Qz5WOuUQHofVVlTvFJnJjQe0RnT+tW43WpX6H0LWka3SUeSrvI5/DeEw4VPke0HqlaHs9lHVNnpfQujctPp7Kzs7WJxirPt/7k5PtVEZ95/Yqjy8b6GSWw/FURuOaE23u206159bXg3HIZMiDyeFYbgseWkD/f5R6UzhXoedmDMdvNKQrnL/Q9cV6DsdWxRDdxfs4qZbRwDXvmlxv2rrsVesSxnDTpuy5oflbAxdqvIfSd1s7q/xs3TQ7m8pW0CbPmlx/aR5Jln0ew47r+qQq2z6V0T2k56bp8z6K+7NRu9xX8K4P5q4d9D811MsOXuUPhYPK0ro/jGoOXUu69x2W0bvYfGyldaTkPS4t08B8Zjnka/ncM7+Wyj50+otTGY2DJm2uS2dWp1IZtd/H+vXn6764IS3T9XAf6MUr6EtfRgKa4zXwLmC8i/L3R/Suu4rh4guT+aqOBYx1ryb/Ak2SJEmSJEmSJEnaxw/QJEmSJEmSJEmSpH38AE2SJEmSJEmSJEnaxw/QJEmSJEmSJEmSpH3Kki2la6TBoEIKbKaA6XEgNoSKHiG4ksI36XgpHJIsIcR6b7Ie3Dptc5DrzhzWW+RzWOR8y1jCqS5hObxOlD85+ki+NOKR8k4LM1CjLlzuSio9NrpGfeG1PEK+Z2zvrf9886lcB+fL/PsTnzqT69dTTuUQ1BrCQh/chnVP55DW3Xne795oF9OcGxwzKHvu0/OxdRCgSnWE7kNpXcLg5dG6R/ntlNIQboL1BipsP3ymjeyHKioK4S48DAqUpWu5gjzsVZcX3FvmlmO+yld0a7q+wY0mN14Ufr27ykOfB8/nss1Zfm5ObuQw6Qd38j6mk3xNjs/yuvc8uL5uD93FAGVVYQUrbYPo3uC6o9DivjQQGsKZyQQqDvWrpUoCpqmPprIJhE7TOKCtYDk4B7x2EArdj9at4Zwe2ssN5Jc9d57K7t3NQdTPm/6XfBzTvI8Hm1tS2bnlsVQWEEI+DpLfgmerh0pdR76WXJbbDAr1rhu6X/lYlnXe3mp0v6hulfbbQ1QxXLyv1H5KJap9M5YqeGxKgeyL7vL1m+YkbZ3bmRbaxWmd+zp6Hqn9rKD9GGBE1UPZqr98P0Nt8QrGjTQXpGeV5p81nEMD/cK4r8C2rXD7uO5AfVaZigYetNzoZ1prKB2w0LpwxB3Uw77K9XVoN4uW2+i2135e1nlMR9f3RLuTyj49P5XKKpgzTaA+zJrcZz+4zNt7evWRtZ/pnM7UN6ayzXo3la0meV2q0x2MV0rnKjU+58PoZ6rThUrnbnQcxefQR1y8Z03VRQP7bArHK32Tz2ze5Tq90eQ57l6X6yaNTRddbguPTdbrF+2Ttt9BH3JmeTyVUV8wq2GePsCzCjfx7GIrld26cf/azzy2ztun+0D3q+3z8bZdLguoSyUVkdqCCiZlNbQPdZXvaVvlvraH8TBdXyqjZyRG16l0PexDoR1ZUf+Oc0uYH6WS3G5QH3pukfuGp21+MpX9/smvSGW3VPelsmWVn5tjyzN5OXiPu4T58UPVTWs/n56eT8vsLHMbT2OUDuaVNOYp6/G5Xab3gs2orHR+RGPWph5iiCG6i/9N+7ua/As0SZIkSZIkSZIkaR8/QJMkSZIkSZIkSZL28QM0SZIkSZIkSZIkaR8/QJMkSZIkSZIkSZL2KUt1l66StupjcfHfVdWXh7RTmOsogDNHavJ6FHpZikKiKTi7gbK+zp9nb7Xrj+hmu5GW2ZnOctkyP9p7y3wFduZ5n3uQlbrMh4tlY5hBjTciq+E2UEA67rc4qPJwx0Ho2LAM9knHS8eGv/EA13N8zGd38po3HM83Z3svL7fsclkDJ3FiM1eIc7u5Hk5aCvy9/EWeL/MyCwi/nk0gNB0v+uGfczK+h5DZ+ig2BsG7vGAq6aH9ovt1WBQUTKG10zbXr1PH8vaoDZpNyqJs0zWHZShImwJ1n3XTuVRGAduf3s7tLaF249RsL29vFCg8z3nQR9LlxwHLaLcVVLrxeS1W+USXq/xcLid5YwtoW2ZQb6h+lZbRsz/eK+S54+B4gBpG259UuRPNNSligEqywuDs9aOZd/n60nHQ+Inagt/de14qOzGdp7Kuy9s7OdlJZZv1bir7xO5T1n5ewbY+vPvUVPYMCPWmcVYD96Z4LIf1PG9vMro3fK/K9jlEFUPVxU5ETBt4IKUCw74eeYiIBuotjUNpzNmNQuWXQ25naPttnVvLVQNjsyb3MnVhm0rjjg6Ob9mvl/XwjNL2O5xrQllhm0LtRwtl02Z93EzXA9s73D5cX1iuGfJYvR7Kxs243GiiR/eKJoMDDDDo+tJyNM6l4+1x3Vxv+tEgYBZ5rLbd5wHsqXgwL9duprJpXTaw2+3yHH+jyeOJB6tb1n7G/g/q76zK57Vb5eOle4htCyxX0TNNy13BeclRYH0FF9raKv33fvT8tvC+Z6PO46sVzGfHbXJExLEW1oV2blLDszo6VxoPbrbwEgh8+MGTqex5N38qlZ1fbuXjoHEzHS8stxqNzhtoWzoY1LVwb0raswtK6+rlX1DWPYz3YAxKx1FDX1DaJ/UV1C/ou0sMcMA4B8H2PJeV9r+0HKnqUZ9EfQOs9+nFjans5ukDqexMfzqVbS9yO7o3ye35087/fiqbb5xKZfe3t679vOrzvaI5L12iDu5Dh+8iy67vit5PwnuVul5fsPSvtuh+LVd1DBfbw+WqjtXq2v4NmH+BJkmSJEmSJEmSJO3jB2iSJEmSJEmSJEnSPn6AJkmSJEmSJEmSJO3jB2iSJEmSJEmSJEnSPodLD5QeIw+H1DZVH3XdY2DkAoIUewjbHAeSUpgyBixHWUhnachuAwGybUCgMGQ3TkbLTSHcdVbnkMrNdprKFrN83eYbuWxvlct2F/mz9j0oW4xOtaeUTkAh51RWUzhm4T5oOQpS70dllCcLudTRUAhs4TkQWq6GfZRsj45jY5Lr+fZe3kFTQ0gpXLe9ZV73wfN5x1uzXHZsVIX3IMN4a5Z3SvtcdBBkCueP9z4XobogwJy2X4ruFx1bjYGvZfdr2Fc+DBE9bKuFe0/hrlMIf55QmwwPyScfzG0V1ZFJk7e3t1oPRa6qvK0Ogm1pW3/wqRyIffIY9AVwLfF+wXLLPoc4j0OAG6is1SRvi9CxQVx1rHKXFB0cMGZpjzRNPt5lvg2xWEGg/Spfj+UE6mFD9TDvg+or3etxP11Bg06n3kFQO6nhqtdwMZtqlsru2z6Wys7trl+nG4/n8cPWNO9z2eXj3ZjmxpXGWRtNXu78Mo81VkMeL6yGXGHHY62Tm/kcPnU2b3/ZPTWV3XHsk6msiVype6gkNG4jPUyPxutCdYse2keqNVXVR38xwL6FYHipRNdVsbo45ll1VTTQx7YwL6H2c9xXUt/ZRW6zV9B3UJtCZeN5WgQ/o3S8PK4ZnQPsc4B2nJfLZas+r1s61sNxeLvebtG9amuYQ9bUn+Q2q4aejOapNK6juSttb9zfUV9XD1e2jRvgYvZQN2nu3kOL3A3r67Zw7oTqKrXnDZQt+jxQoro0rXJfuRj1sWeXx9My9H5jUed9zpp5KqN7T88DvleBOlJRWcGDQ/cZl4N7Sve+VMm6PbQjdLx0PaZVvubHYCy9HZupjNqqc4s8djo+yfsYvz+j+kvv2E5Pz6eyL7spj8MeGm5MZSu4Ti1cE2pbuR8ZPec4/6J7A9uHNnPZ5GvZ1TCmhTaNykrqOS2DY1VoW6nO8T5zO9LTNYdz7atm9HPeOo1zaVzewH1o4eUWvdeltnsK/ePYXpfbPTpemqMvhjxPm0D7+LTJx/P26rxuD+9nj527J5VNZ+vP3Ec3npuWuXnjXCp7YJ77AmrPVnAT6b0FwXcNuO76PgYYZ/AODmh/hwtb7IeD9nf1+BdokiRJkiRJkiRJ0j5+gCZJkiRJkiRJkiTt4wdokiRJkiRJkiRJ0j5+gCZJkiRJkiRJkiTtk9MTpWuouxj82Q119H2NQaAUKrqEYMLxug2EVVOANQWSlgYxU2AmBmKXBoaO9kvbb+scDDqrckjlYshlXZsDM5dDbhbmG1C2ymV7o7JlB/dqle/pCm5Dj6HeeTnKGF5CpiiGXsJ+x8eyXJUlVdZwHA2EAtPxttASQxZ8wO2KCaw7PtcuV1U0m+RzPT7NQcQP7uRg47bO627mKoc2puvrUnj95qzwJMAK6mEpuq8UuD6+r/TbKYc/g6OhZyniM/WkHyIo27X0eaPge2pHG9jJBtSR7b189Wi5Wbu+vbO7+WFom7zPWZuP9ymnFnkHYAntHl0nClyfNbmtTsvk/OZiFABMbSu1LYsltNWwwXFb0sEydBwdPIM9VBsKtZ60eYN0DgPcV3oSYVgBy0AYPNTzrs6NMgWTB4ROU+D4ud28vfno3pyf5+1vTfO2Zm0uo5DsaZOPbQLjinmXw6mpLVzCNRkHyTfQX5zeyvt8cDs/EM84lrffwPVtIIS8gwDzJuAhAQsYG42VjlmraojhYsOxGqBjlwpU1Wf65KrisVNfWL/G6/a0LVgPxzpQ56mM2k9qZ2nsgO3xaB/0PK7gvAYYsBSfA22vcEww3h7NSacwj+igbaursvtMc83S69tU+fjaUVkN7WlbU/sME7UrjM4V68TodRjN+Xe7WSo7Psn9U9XlfS76PIClvnijyeNQmpOP69IE+vCNOs/dplXePt1nOn8c08M9bHvYB4x10ubhoRlgwtFDPcfnF5bDdWkfAetGfWk/F/5d1tb2+P4I3kfBvWnhXU4PLS7V893u8pMJam+Ot3upbNXnOtg1uWwC9YGeN+pH6B7eNDuX91vQn9E+afu4Lo0R4X5RnS4p43eC+YrwO8EM3yfS9ujYYHtNn+9hV7ejn3PdWkEZzQ8auPvUnzU1tEu0HOzjzGL9HdUK5pWzBuZMMEfH97qwz5Pn70lly8mxVEZt0HJ2Iu+jXz++W4a8/Y/2d6SyzTafA41bWpjj0hiF3h/ScnRNDrsevWeqI2KoIoa48F4OhkVXlX+BJkmSJEmSJEmSJO3jB2iSJEmSJEmSJEnSPn6AJkmSJEmSJEmSJO3jB2iSJEmSJEmSJEnSPpdPxJauonnXRvXwv4eWw5khCJQCmxejoMYWAimnECI5hTDeFkJ2J3UOasQQRSjqqvzotUPeXgofxeBoCJSlQGg4fwpanUF47maTl+smeblx0OwCgmcXXS6bd3lbi1Xe57LLZYtVrg81JVAWWo2qRN/na9lBHvJAQa5wHA3k37ZwDrMphH7Cuhv15YM7aT0KFZ1Och051uYg6u02Bxs/uJ0DZDemVA8prHz95/ZYWcg5BZJiQHxhoDs/N1CWN5fWPSgEteTYSGmVrgsDWS/8j8/8f2pXKXi3hQDgvs7LUTA5BVY/65bdVPahT2+mst05tEGjZ/PY7PJB5ReOLV+jY9Mc4P3Jc1uprLTeQLMRD+zlQOHxpdto6P6VVRI6NjqOJbQ3ExgNLtq83HLUPfaUBg4oiHjc1kYcVFch2Bh//YsK6QAv/7tjAwV9Q79dddQ+5H1u1DmYva3yWIPMJuv7WCzz9djKTTKGSVMYOjm3zHV1AmMIDp3OHU4P7fIYjdFm0Cd95PzNqeyO45/Kx1YYGo9jyoIyqlkdPYN47lUMF9tXGtdIh0FzJmpTqU5CU5ZgHwPL9Uf4/Vwav6+gD2xg7pPWw/O8ss8bjSewHYB1l+N5TuFbmRWcA42RS1XQ39H2GhjDjefH1Me0de7rJgFzXhg30rFR2Xi+fJAKLvJ4LjyPWVqG+rUOtrXV7KSy7S73p/ReYTyHjuDrOb5Ox+o8jm4iX3O6vjReqaCs6eHdSJ/HzU0P444O3pfQy5ECfQXjizqXDbBcV+frS+9jBpjTDEN1qf0bhioGeFfS4/spWi6X1UHPVr7mPbR7NF6hvmBcttPneXs/y9vfaPJ9/tjy9lRGqC0cz/kjImpYbq/LA9vpqC1ZwDl0MHeluk9z1wbqwwTuQwNlJe0XtVOHfRYi+L1gM8DxwvNbw7NawfG1o5deNeyT0HNJ8N0LvVOD9zHzLt//8fZobkH7pOeIlmvgWd3euiWVbc7PpLKuzsfbdPk9297GDWs/L5r8XqTBUUVG7/s2JnCf4f1DGqNERAf3pvTdSF4mlx34jq26MDOvqqH4vchjxVmbJEmSJEmSJEmStI8foEmSJEmSJEmSJEn7+AGaJEmSJEmSJEmStI8foEmSJEmSJEmSJEn7FMbVSldH11+I9+36KqqoMFwdQ+shS3C8XA3hk9MmB1xOGggQhQDKBoIgx+GmB5VRoPIUgmHbaj3gs4XAz1IYxIzhmBA+CtujsM1+dD0nENg7g+u7AeHMy0kuW3R53UWXl9tdQEAvhMXu5Vzc6EeXpOugvkGF6zC/moI281IVpGh2kA1K65Lx5nrY1u4CgoInecETzblUttzI13dvdSKVreDaNfAcjp9VCjIlFBBP+xzf04PQb5RgGZzDOEieguWvNApfJXQs/VDFw5e9rqim8jO+gnDmgQLo4cJRGPoEQumpnp/by2XtYv34pi2E3ZZl7MbHHswh73QOVDexHYV195bwzE3XD/DA8NwC1DfS6bdNXm7S5rIplK26R/45orztovpLy9H2oDkvVtqOjlHdr2AYXVezVNZC3Z9Vu6mMgt+PbaxfgEmTL8i0ydun4z02hX1CyH0LNWdnmcc8x6cUpJ4v8Pnlehh8A2Hrnzqbr9uJzXxe1Ies4D7QeRF8bobDVTBai57BIapL7Su1s1KJuhqiroboLv43VGVeD56h8WPbFYbCU3tKZSuYz+GxFfZ3GD4/WpfaU3reeH5UdBjFx9Ycch90vHR9l3CupPS8ergPVCe60XINdM49tIw9zXkjt/c1lNFYsoI+pR6gDJbrR33l9mozLXNskvvO+ZD7rNP9/fnYYNy4HKaprId7XUO9GV+TofD34alfo2eaqgg9I3h9e7iHfR4nVKPlKuwpM3wfUeXSvsnjlarJ92uA8XDJsdC9Kh03rGBsUsODSeOkSeT3QFttLrt/J9fhs7vr+739dJ5YcbufimJ3Ce/KYGy60cA7JZj3UbtPdX/er99Xei9EGmhvqG1tYTlqq+oq77fkPRudE64HdZCWo4eVrm9b5zrS9vklWAPPatMtHvHng9D17eBZXcL4nZ4v6kM3m3kq2xvViSnUwRPQntP1baD/oXuD43joqJftRirr4V3p+K1MB/WNbDbwsiTvMnZXuf+heeQe7JfGVTjWKhiQlr7fqKq4VNer6mjjsyvBv0CTJEmSJEmSJEmS9vEDNEmSJEmSJEmSJGkfP0CTJEmSJEmSJEmS9jEDTdeVSdPHcPHfVd1zBg9lTdH3svfjn+H7qiF7awHf09/Ad7S2mJWWy+g7Zen7eJeQqdZW68tNq/y9w/R97phPdshMj4gDvncZlxvl+cCxtZSxBtdoUkMuC20PrjkfLzV3l78mA37XLywH33VMuWiUR0brUi7aYb/zl+r+007k73/+sk/+VCrbmd6Zt7eZ7835Wf6SZcq0oVgayosao7wzyjbDMrjmxTkQUIa5aKM2ooXcRModISXfG/1oUF5GXUUMF4+5robo4ZmhDCX6wvUedtAXngN9P/6tp/N3tf+Xj+X28cToK/535vl4bz6R20w6tnEWWQTfh2lLbVBZdkNplskYZQOU5L9E8LO/goySZQ3ZG5RRMnp+uV/Nx0bPZWk+3VHws3T5i16aGco5gfAd/9BPNVWu07edzt/n/+D2+nfVb0FdbSlbEo5t2ed9UubFqs/HS+3BvKNxSz6+3eX6NTkxy8tQXTq9CRkdcH3peBdQRmM5zjK5/BiKWscBxyi4ZAwXexfK2JWK7MuCuJALUdZXlIzpm8I+/LDZFxE83yrNnSXjPrB0HALdX3FWDV1gWpezakb5YdB+YJ4a5YbAcpi/WHpfMQvp8hnkHWQS0fyzo6y0CrKyIIOFcmkmUHMoKw0z8EbX88MPHE/LfN6t26ns3p0bUtmpjQdS2bTP/dgAla4byl7LlWSelWb9HSWDc4D6QGVUicfvC8aZaBERdZfnArRcwFi1a3PGT0AR6SGnqavbS/17XfWYg0RZf8X3gcYc2C5lxyc7qewcnP/GdH2DlCF/agYZSmCjpRxCeFdW52d6CmWcr0nPyHpZeYZS2XI0HqwowxHaNDrekv2Wtsn0Tq10rjKBtrVt8vNF7+Om9fr7oraH53KA3DXIWGuhXmKWYuEzMmvyPk5N1+8NXSOaM1B/0UHq4gzOa1Xn89qbnUxl1LYsJjmvcHzt9gYIMgN03WhcQc8goYzIJbS3FWUMFrzgeDTj0yEu5P229RDNNZ43+RdokiRJkiRJkiRJ0j5+gCZJkiRJkiRJkiTt4wdokiRJkiRJkiRJ0j5+gCZJkiRJkiRJkiTtU5ZWKl0lk3oVi4v/rpuDAknz574Ubj8OORwHHUccENoKqa0UKtpBOGKHocBwvBionIMl21EZL1MWBHk1jK9naTgxBUZSmOc0nz7uY9Xk6zttc+BkBxmUw+je0DIzqCMUoLlcUghqaRh6LoPTKlr3bM4Xjqc/7Z5UtnPzHalsMTmWym7Z/lAq+3Dcmspmk8LQ3vFi9AyWbSraJi/YU4B1YaBw4e2C7ecyCn7HMGkK5i4MGSYURD0MEcPF46mr4YDIVthWYftI8fXDkB9gOq/j0xzQe2xzksp2R4s95XRuM27YyJX/U9s5IJ5sTiHUHO4hBfTS/Sf96PzHPx+0/bqm44C6BA3JEh6mGkOBU1GsuvXCBtpHelbpORpv66DlCC1H+6WycQh5A9ec+66yUGscj/S5/lIg+A2zXF/veXC29vPGJD9HE7gR0zrX3+3VLJXRcdB5HYPnctHlacQKfjfv/rPrx7xxYz625976UF5vN/c/W5McYN5B20JlPVQc6gtKA+LHsI2HscyFfVworw/4/9Ll1NVwabxXVxFB/RP0FSVoiETtbg1jLmo/ad2OBieA1sX522i/pTHzNRxvC20q9f9URlecrsl43dJ+nedM0I7BcZDS8SW1leOWcty/HlRW03JBbTbVpdyfTupcNqtzn1Uy3/ysm86kZY7HuVw22SraflfnfpL6mAruGG1vMUzXfqYxYgev+Nooe18wQB/eV/nerOpp0XIDlE1GD3Xb5X696vLxVn1Zf9kMUPsLB+YDjIeHqo76Yp89GRbRVfn64vsIGIcQfraobcnHNqnyddqc5LLxs9TWUG/geatgTEtaqIe0D3q/Q+dP13M26m+43Su7z3R9aTxcOkbuqd6M53iFfSPpoX0kNb2fhGewgTpcQxu0HLWts3ovLdP2ZW3tw8/Q2rpQH1Z0zQvv67F6e+3n+ZDnPdT+znGetpHK6NmntntoqY/L69JcZdWsH8uiy23tzpLOC8YyhXV61UN9gOXoXST20+lnaFuoPz5gzPrwm6qmHg49rr1S/As0SZIkSZIkSZIkaR8/QJMkSZIkSZIkSZL28QM0SZIkSZIkSZIkaR8/QJMkSZIkSZIkSZL2yWlx0jX0cBhoUw0XwpVrCpvMgYaTOn8W3I7KVhCCSgHWPZXBsdKnzxQEuuwoVBTWbfJ+J+PwUQi9nED4JAW0YggqBDrSPkqNwyAxSLwwRJJQaGsLdWQK15ICLldwr7t+/VhmhSHnTZ2XayDvtevKlsMyuDVUNg7jpWO78eyHU9kKArFJ1+Qw05NNDpXt+k0oy8cyfkYoTBhuaXHwedNCMDesimHttBwFvdeXDzamek7nio8DHAc906SCZ3o1unbjMPsIDn/u4YLQs1VatlhBuHiby249ffng8KeffCiV3bt9MpVRSPbGJJ/rZlsWuE53ge7/BNqqMQymhuWolcYQ7sJnhOtmXq4ZFXIbWhaSTdvv4WSp3lB9JdjXjsroeCsIAyc9PqwU9J3bTLKAEOdRl4T1gcY3UziMjSYHfVO9pLbl3DK354SO5fYblpdd5v7dY6lsa7JMZVRXV9DG0XM+wHLYBhcofQZrOI6qGi4tS22FVKKp+mguPqtN1UfdQOD7IesXtTM0P+IxfdnYpIO5G7XZOH6HsXRX0LbDcJjPFZ7wGYwlG2grsS+G6zTux+he0ZyXto99OOyT5oekdK6Wluup/4Pt0z2Fa05zUmrbV9B3Di3Mq6vcp4ydas+lsmm3m7cFfee8yfOoc92JfGxwfbfqnby9YZbKxtekg/cA2BdRGdR+en6roaysr/KxrGDOWA3r167uoa/vy+pqKdoelTVdHidNqyq67sL5Tru96KEhqWBu2Mfl79+BxwvPWwfPCD1LNIabtZe/ntM6z3v2unz/pk1ejtZtq1yGbSa1LTBJGC9XwzyVni1C16jGdpQmMLmI7s342aR7Red56Pb3UaB2o2R71LZ0TW5/68j1bYBrVA95uUmd2wM6Xrrmk2r9+a1gUEF9yGYzT2UbNbzbGvK5Lob8jNC5khb6pOVoe2cWef51di/v8/RG3v5Gm9sz6gv4fVThezZ6x5zqddlYlMZ7F7Z/Yf0qhuL3xo8V/wJNkiRJkiRJkiRJ2scP0CRJkiRJkiRJkqR9/ABNkiRJkiRJkiRJ2scP0CRJkiRJkiRJkqR9cgqedA19Jgy7i7qqoqKwVArbhIDIcThoDcG2FAJL4YUUal3qKAGfYxQ8u4T0azqvBkKBKcyUgjVLw0xLwspp+0cB1QF/M6CBoFkKBKeQ9LEptJxzCqteHf5cW8jhbhsIGIeycRDo6eMQbFzlk2hWOSyVQqLn0+OpbAJhsRRSuoI6XEPwcpmy9RqqI7DPBsroHCgsdby9BsLFS56PiIgoDYinoHoMZoddVNWl/VzYDtStwvtC7QO1o1S2guft3G6um1TPxz585nQqo3tPzz2FwZei+9pSHYGy8XK0DLX7fWG/Utp39ZEbnBb7jHr0M/QNBfcqIqKFg6Pt9YUnAd0ePiPjOkF9CIa3Y99Q1l92cA+p3syaHCZ96+n1slu3zqZlKHCb6g0dWw33ua3y83DTNO/j7OpYKju/mOXjm1z+Hm5B0PW8m6Qyum7LHsLFqaxw/MH1ZhReD88qob5gGKpLdezKjoj0ZNLWw6V+uq2HmDa5t6f6R+3buG3AsUQNbUpxyDyMQ2E53C+0ZXMYw8Zq1D/BofWHH4ZhH9DChIPGTjW0qSVtCrVFdP9K0VingWMr7T/Gy1Vw7rQe3Wdqx+k4cJ5aWA9rGP9Nof8ce6B5Si6EYePH925NZas+n8NNG+dSGd3rVZ/r+fj+U72hfh3nEVQGY65myG0LzQ9jgP6uonc56/e6b6ZF269qmBwD2mdf5/EEDQBpv023iKG7UF53i6gbqKvwnqmDsfUy8nHw3I3GMLBfeOtBz/m0Xr+HVLdozEXj0q0mvy+g55zGkkdpv9K7rCMMoGp8b1H23NC5NtQ+ju4N1RGaz5W2Z3TvH2s0X6Rb2sMzSNeSrvlkyHWO51b0HK7v91h9Pi2zVe8UbZ/nTLnetNCOzPvcptH2JjCuqkfXaQbPM48zqH+DthvuQ0+T4cL6he9iR8dCY0Bs9wrew15r/gWaJEmSJEmSJEmStI8foEmSJEmSJEmSJEn7+AGaJEmSJEmSJEmStI8foEmSJEmSJEmSJEn7QAKvdO3UFwMGm2qIuhow2BjDgyG9sB4tR+G8FF5IAY+lAaKkNLSewhXHx0zngPuEwOLCw8DAZiojA4RcjnUUEg3L4fUoDEAmdAZtDWGb7fqFqmsIhYWAS8gAjVlZXnFAXjMu1zZ5QQoRHQdw3nAsh7EupidS2cbuA6ms6XdT2ZnNO1PZcglByd3hfkejNGiUckZpjzUGrdK1hOBoKiuom3QcWH+PEBBPx0HNEgWC1xHRXwz7nlZd9BDMXRpiTOnB9EzTc1O1EFYO51XSjnIbmpeje0/oHPCaUBlVANhgVY/beGhbIBQ4qJ2mvqsg2DciooGwZ2rNx9uj+jvueyP4GWT07BeuWqhJ1zwvU1pvehpDwPGWnkMLYc9PO5bb5TEK0qa6yveeQtPLgtQpSD5ybnbsrdYLjxISTc8lPW4DPTewJI4p6ZkubDfSHqk/iyHi4vHh8y0VqKv+Uv2pqz5mTa5LkzqP/0rmNDgXwOenbJxXF45rqB+jdraF52YxGscsYb7RFbY9434igp9lGiNOYC40aXLbPqnWy0rneKVjM5qnUntD9YHm36QfjR0q6IuojlC7S3Vu0ZXdw9I5eV3lDmp8rquYpWXOLrdS2W2zT6eyWbORyhZ93ue8yxPEGno3KhsfL47D4J7WMKprItfLZshl1QDHBmUIlquG9WMZYAza1zSJhleXNGCjw4B9kJI5GD4zcN2o/SUDtK1Up7GssH0cP3Mb7QLWu3w7FcF1qVTp+7g+8rM/HpseZfxEzxa/Z6L6C+cA9bCv1s9hgOtWfJ+pb4RrRErfK5S0o0epl6X9Dy3XwrXrCs6Lni2e4xSuW1h/64r6wlzWwLHsdJtrP9Pc8NRGnn/Rc8/v0svmjHSuLb7MgKLRujQuonu/6nMbvxrqS3O6SdPHBMa6V5N/gSZJkiRJkiRJkiTt4wdokiRJkiRJkiRJ0j5+gCZJkiRJkiRJkiTt4wdokiRJkiRJkiRJ0j6QxCldOw8HDlbVEFU1HBBoCOthIPZ6GVV2Cvy80iAC94Cw1Muj9UqDk0uXI3RseG8KwsQpOHoF61HsLoVjEjpX2i9pR4HgTQ3bggDrGpbrYbnSkHCCdR+2tzkdnQNct4/Xd6ayp2/kgE8KcV5B+CiFtbcNhINC7nBbdGsgZLYw8JSC36kMg2xLDi1ynesxiBhOlKpDYRvXF4emw7HUq+gvBl639SqihgBveC5L24KG6jlczM1chO0BPb/jpRoIqC11lN8mouOla7KE52Z8Xqse7tUR2gxCx0aaOt/tfrRuAxcOTgHrYLEjdNOH7eIhHxyPg9rWpnCfVG/mXW5vx31SKQpIL8WB9hmd/0ZDwfTry2GfD30IoWeVnn3aWgf7KBm3RORzKG1vagrJjvpSn3GUsZme3KbNKiYXw+Un9QpD2qdVfh5reG7H/f1QURtAY+uy5WgMU9q30VhkevG891u168/3suMw+hJ0jWhcQ8vhfajzfZiMZjpHuR4rmOV2MOYgpferp35hNCagfgfrDR0HzY0Ly2heNuD5T1PJ+B5Sn7u9yOt9uroxlZ2cbKeyrXonlTVNvgLnu+OpjIyvJ90rei7rwgER3a++Kuyfh3ztquHyfSXtc6ihv+7L+l1qvw49ILy4vYer3VDV2Gs3fX5zgf07HFrpOKCC+ttW+ZrwfHZ9ubbKbSg949Te4P2Cdcf7vFAGY9PCee94Xdo+j19pDgnXDQb/2HdBPadB8rhtKX5nB3WVzqvH8y+tS/TulNrR6rLLFG+f5p+FjyXdrwbqcKoj0P5QGSkdB/HrnbJ19/pZPr5RvamgPrQ0pqhyG0TjitWQxwu0XAvPalWXvVMbzw/pucd9Qv+7t5pGf3H9Jvri98GPFf8CTZIkSZIkSZIkSdrHD9AkSZIkSZIkSZKkffwATZIkSZIkSZIkSdrHD9AkSZIkSZIkSZKkfXKCnHQNPRw4WcVw4b8hlLGhMM+CsOMKAgchK5TDcyFAtYMgTPpEmgKmMVOWgpIL9lkS+HnQcZQGVtP2OEx8vYwCt3GfUNT1FJZadhylqN5MmvUyqiNtDWGeEGC9gnMgDQRy0n5X3eUDvCMiun78c27q5+0kld07uSOVnV1spbLlMgc70/FyaDzVzfXlKCD8auA6nZcrOToMb4dgedoahrbCc1Pafh3k4WWHqA4IgM3H2+I9hW0XHhs+g4WncNiAWoLBxoUodHvZ5/uPQdyj/WIsLrW/hcdL+8TlaLfYj4yeVbz5dBxlx0v1sFRpPjzVzbQM9TXQxpcEJx90bNSf73UQEN+P6jlsn8pauvXUr8Bi3HaX3ZuSdWtYhkKh6djwXAuvyQrOoYZ+GoOtU6h3XqaD543agiGqS8/mterv9Pg3a5ax0VwIkt9oFtHS3IKeDWjxx8tRv0aawnE5tQs4L6FGCruZy8/VuiaPfekZJaXtHWmh5ZpUy7xcv1jfZ0nnFBFdlc9riDxWXw1lr3mu5FywtO8oHatSH9sVrttBnz1f5fa4rdf73cUiL7PR5vHwzir313SuJ9qdVDZAPVzAXI3HeuvztwnMLeg4Sp/pKS2Gg9NsqPLKDa67fl5dA3N+mEfVMC+pBzj/PpcNFczLALZfQx/V0F/67xq3n89h/Iw/GvQsNVVuW1ZVns/jfKjgJtKt7wLmM1BGbWsDg188r4D7iudw+baKzpOeImpvsS4V7PNAw2jcSMdB2y/cJfaDpfUc7s0ANWC8XGnbXbxc4RiY2jQcy4zqEt1TKqPnd3z/Ih7FPSwc2i/h45iSa0fXjeaf9FzSdZsU/lkVrYvvi6BsrPQd9qxZRn9xXDVrltE1eTx1NfkXaJIkSZIkSZIkSdI+foAmSZIkSZIkSZIk7eMHaJIkSZIkSZIkSdI+foAmSZIkSZIkSZIk7VOWLitdJQ+HCQ5RXfjnKMGS48B3CCpcQbAihiQXHgfGs8I+aLmScGbSFR5vB2G8DVy3o4RJjwOKKWjyKNeXrgeFRNNyFERdcv4NHFpPAbjw6wgTCB8tzAg/ICA9ly07OsD1sr0lBIhWs1T24N5GKtuc5MDiRZfr0rSBQFY4h7bO12R8XnSvegp1PkLIO1/fvBxdczq+kpDhJYQuEwpsnkFgKoaEF7ZpVTVcWrYfamyn6JwohLw0+B5B9aX91lA2Pq8eAoBrCgA+Qr2h88eAXtjHrJ6nsnE9pPu37CnAG+4D7HMJ7eOqpzqSigLypYvqOZ07oWcL2/MD6u9h0XmVoDqI/S/UQ9onXacBy9ZXpr6moQcJBhoU6E51ifruo4Saj+8hXcsV9duw/fHYLoIDrKmOTKjvhgtaEpJN123eTfJx0D6rLqqL26PQb6nEVrUdW/WF+rVV78QM6m3dXz7IPSKiGvWVfZX7HWrb8HkvbGSxf6YyeIZoufHxlcyhDto+F0H7CdepHvI1b/o8lh6XlZxTRERd5+13TX6ls4Jjo7ngasjrYh8ASq7xUebQNE44yj4WMGfq59P1ZVb5mi9Web3bT+2mMuoDpvU0lW3VO6lsb5WXo/5ufM1pjEhzrRrqQ1/nc13VuT5M60Uqm/X5/OlZGmro20fPCD1HVEaaIT9bW7sPFK3b1fl+Ufs1VHU8XMUu/HfZ3yBQG0ftA7a3he1XE/n8ryQaX+EYCd+9wPnDuhWUrYb8PKT1Cufj2MbD5aV7T2033UNsqwvqObX7paiGlB5b6Zh+fMylff4AY2Ss03QfCut+CTr30iuO87nCZxrnZVgEdX/0EQ31b8vIbVep0ueSlmuhvSlpI2jORO88tuB9V0REd3Efx9vz0TS5/7ya/As0SZIkSZIkSZIkaR8/QJMkSZIkSZIkSZL28QM0SZIkSZIkSZIkaR8/QJMkSZIkSZIkSZL2ySmh0jX0cEjiMFQxDFWsIFyQghQbCKjN6+UyCi+kMN6jhFkOFKhLAZxwXlcyWp7OfwX7hDzhA7YH92H0c0fnieeet0/B0TWExRIKiy1dN50XrNcUhtaWBqN2dO/h5jd13seqp3u4vhyF7M4hJPvkRg7uXPV5OVr3+HSeyug6zTsI0B393ME5UR0pzKnnEPLC56Ft4PnFY1lft4Hg1Q6uJekpALnK3TXWOYDt1xAxXDyR1dBEBe1eKToOfEYKnxu6vtSW5BXzMhWGOpddN3ouS/sCOq8utZBl+6R+cL7K9aGF9oHQM43PCBi3o3j/jtJfwnF0cFpD4bNExu0j19W8XmmfhMvBderhOtG6Q2pLcj2qawilx9+Ro/D2w9dz7LugvlLZ5Y+Mf8uvreEcSoPkYTkOtL98vd5dzYrWa+B4m6qP/mIQOf1/qcTJ+adjs9mKiIgT80/HbMjh7qQaoM6N+sWhzu1MX+d+p2tykP1QwTyqdMBWqO5zmzce2NF5VoX9rcp5GAABAABJREFUP607wHiih+tUur3xsfA1ysfbdnlbU7rmMDeeD7ndWvTTVNYNZec1Hp9Qn0iXnPp6gnPXLt8Hmgv1sO5ydflx/rFZrlurLtf9JRwH9Z0f3zuVyr506yOprN+g+VG+18th/Zk7v9pKy+D4Fe7pIpVcmBOkfVb5OZ9XuS5t1ruwxawZtVXjnyMieqjTKzgO6sPrjXwP676sfaTJe183MVx8hoe6jR7aglL4jmKAMRycP45rCtu0cd2k9obqW+mZVjDvHQrX7mFcS+O1knajZLx5INh8DecVcL/oHpagfgX76CMo7c9ouWZURuvRfaHlSuG7QqivJfWLnlWa9xSj84LrRvWB30/m5dpYb6voHGaR37vRfagLr1sd+TgaOrYhvyuk8dj4HnYwfpxQewPbqocuuovjnq3ufES/k5a5mvwLNEmSJEmSJEmSJGkfP0CTJEmSJEmSJEmS9vEDNEmSJEmSJEmSJGkfP0CTJEmSJEmSJEmS9slpbtI11F8MP+yGKoahwiDQVQ9lsFxJ0CiFVNL2MQAZ1uXQ+ssexhVHwat0HBj6CWW8vcuH1mII6BGCozEUuTAIlMKk2xrCuev1QEu+pxACWnQUET1tj+phDaGncBINHEszOq/jFKZcHPRdVvbp7U04trzcvCDAe7kqu5qlAcOlz0MPIeTjY4vgelOPy8ry1/FaUopxB20c3cNH0wYNF9u6VV9jyGw5WjcHw/IeKNz3cIHYtF4D4c89tkt5+6VB1FS/SvuucZtG7RkF1e+tDj98W3awD3jm6Lya0WJFz8IBCnPPsf52lOmNbRU806OfoamNKHyOSh+b0uXomR6jaHHqGysMjsaBQN7HEdoD2kdJHDrdhqYuC2UvbfdL9XA0i36y9nNTl4W811CXhotj3If/WzqMahiiuhheXw19TBbbaZl6uVe2rVFw+wCh9UOT+52hzoOdvpkULTdUUAbLFRu1ZeNzOkgFLdRQOKrH8zrkOVBbAMMGVA/5XKc93HvY3gDt/QpeEa0GuNejsU4H42hqKWmM1MHJ9nBNOpofwk5WMNaZL2n8s142afMOppO8AxqH0fhqvszn9buTP5HKTlT5flGdOD7ZWfuZxr4DjUFpfLnK97SGcXNT0ZhzlrfX5me/rVZQtl5fp/U8LYPnheOLfK677YlURs9IM+Rjo+WGqKK7eF26qo2A9rGH9qz0HGifbb9IZV0NbWvhexBqbw+L9wnXhMoK51aHhXP+wnllaX0ova8l26K+lhQvR3PcwnUPe2fovILKjoCeLzqvvuBcqc8rxe9/y94Jk6aH9jGWl90nXQ9Seq71AO1+t8xl9IzAWGvVTNeX6aiNy9sn1dBf6uSbbhFtl9vGq8m/QJMkSZIkSZIkSZL28QM0SZIkSZIkSZIkaR8/QJMkSZIkSZIkSZL2MQNN15WHc366oYlhaGKvK6yilF8D38NegnJq6DvYS2FOEWyuKPsEjgO3D5vi/JbSrJq8MmVvjb83vfR7uenYxjleEXz+pTk6vN/Lf092ad5Z6Xdu0/ck0ykMUDrOZ7uwHGT8jJaj6zZf5u9O3pqWbZ/qA2UNrArr13hdyjLAr3AufC57OLbSZ3oFX+FN2ULjrLRVC88qbJ/youiZphyd0ueXzrWK4dKzMwzlGUKlv3XDWUu5aHWE/Y6rSU3fQQ4NRGmbzLkSVJfKMjgpq3Pc39C9Kq2rC8gxo+xHelZ7eObqGrJHRsv18BXs1GtTf3GU7Cc4NMxZOXR+5xEytfD5LSwr2UfxM0jXt/CSHyUTsKkgZ+SQt5rqDaH2poO8ntJMAsrtGKM2mbfFJ/9week4SRpbTjZi0l7In121m3F+diwtM13upLKWstJGWRf1MmcSleapNZSfhhlo8JzhctSWXbnfAabtY7Yb5kDB9g6ZQYPHUXieFeSX1AFtceT8kkmVc0gwIxza1DHK46SxD83RMScWlivN7+Z1U1Fsj6r6ssvnedPJfC13F/l4x3lqERELKLvv7DSVPdjksg3IXtuZrS83hxyz0jEt56PDMwj1YQIZpXSvabnxOGEK2V6UxUZlk8j1FzOy6fmiegPPeV810V3MdFrVkwPas7I5CKHnvOngWe1yu9zVkE1ZkMm0qihP7fDtasm46SDYZhZsj9o9HOeVjn2PUEfoPozvP+VH0Tlwthf1SVf/b2H4mmeYiwaK89kwm5DegY7eRdJ4pDg38AjLFb6fpGzCcR2mcy+9vtQG4XgB90G5aDl/DPMlR8vRMsW5dnUTn7mgQxRf3MeIf4EmSZIkSZIkSZIk7eMHaJIkSZIkSZIkSdI+foAmSZIkSZIkSZIk7eMHaJIkSZIkSZIkSdI+ZWm40lVSXfr3EFUMGCBPIcOU0TperiRQ9aDtU9hxKQqybWoKt6V1D7dPOlcKUy5VUxgvXPO6Xr929Ak9BUzjNYKyGoOj8z5Kw0HJeF0Moz0C2l5p3ayh3rRBIcvry/Vw3bamOXS5g/DnVVd2v0oTetvm8iHWtAwpfT525/BMQ/YqbW91hOemZPt0qg0sOGkhtBe2R2HdGM48VJfCd9t6wHvK97lMaT0nFNgcEBw+Xq4vbTMKjw2D6mE57DPgWcpnkJ9Naqfp+aU+qad+EJ5fKiP0jIwtV1RX84H0cPKbMwiDpwcCznWAe913+ZpTm5nWg7Ir/dtlpf0I9Xvjelj6rNbwzJQeR/GzisHvUCdG7RL1NTWFgRe2QUc5XkLHl57DI3QNw/CZsQuNYaQSdd9FdbGhrvouunaallk1s1RWtYUDoPH+8rAxqi4XVhAyj1uHfVKAfLHx9mBbQ51D63u4bn0zKVu3zq9SOlq3yusO1XgMk5dZ1vn+Tfp5KquHfE9pe32UlR1lHlWCxk04XrmCY/AIHiftzseFeZ87e/l4abw2h2eE2nhad5JvQ6xgXDNfrddXenRpHtXCeGjS5HECjTnpOAY43tKx9HS07hKuURsFg9Dgel7DyK6CG3HYuQrtE8+9cKJKx7vbnkhlpc8+jvUGGu2OFil8H1GqdIxIKhydH/I4SudzVW67aUJAdYl01Xr/QPWh6VepbLbaSWWLdjOVzWMjldHYn861geerK+h/K5yP05g5PyNUlyqol0eqc9Xl3//SfcD2AcpKn+nS5Yr6X9gUtRnUFpAmcp2rBirL26N91DAOTMv0MFas8viJxmPDUF2qJ9XQY525mvwLNEmSJEmSJEmSJGkfP0CTJEmSJEmSJEmS9vEDNEmSJEmSJEmSJGkfP0CTJEmSJEmSJEmS9snJbdI19HBwbT/UERBIGXFAGO8By14OhhgfIbAYM0UhuJRAHm3aIIX9HgWdP53DsvD6NhBQPFZXeZnN9vLhkxGPIrgTrnkF+8XlRsGldOa0raMEnvL2Mrq8FBe66NYrE4VV123eGC3XwTVq4OA6OLgalqN1x+dPGbZUbyjouqdgbjjXBvZB4eKl4d+FVfMxV1w3q4j+YhBsXXVR080qRM9RKQwUhnMoUfqskh7aODoveh6WfW68qW7yfqtH/DmC6xv1U1T3e+gyqKyj5QouHT0f53fzcZw9n3dw+835ut1wPIcY19S24NHkfay6w9VN6mlpn3T+eNkK2r2DjJejgPDSERC1UzUcMY40sA/Ni9GzNH5GaBk6r5buKaw7rXO9oX2UjgPomoxL6Fnl9iwVSVdE2y2ivfhstf08Jnt7aZmqpxD4/LykhxkGYkOd2+yqh22BapXH+UMD26MxzHKeN7iC/U43Lrv9gHOocfKWDRManOZXKT2UreppKlvU68e7GGZ5vSEf76nqwbxPmETSPnvoLWgfpeO6cZvX4Fgd1oPN0z5xHFY4FaZ5wwQmIdP28ue6WJUdB5W1VA0L+wUeE463X9Y3l5ZNm9xmbEzy80b3mvpAWq6p1vfRVnmf42UieJxQ8Yjl0AZo+/qqiYeH2BeeNRr3w5gDrgedw2ooeyVbwzXpqsJ1h/V1B2gLSt9lDPiWIqNr0sO1IyXHUnocpQZ634XvMvJ9oHo4Pge65h30FzvTk6lsuz+eyuj6NvBmqIM2flrlPnlSLfI+0jGX9ds476H2AcYjpfWwh2d1FZP1ZeiewubbqnAsQ+N8uvfQuJae/7g/p/6d+nK6HjUc27gtiDjoWYI+mcpgXDXuXPp6kheB9ehc676LerhwHvXQRU0vMq4i/wJNkiRJkiRJkiRJ2scP0CRJkiRJkiRJkqR9/ABNkiRJkiRJkiRJ2scP0CRJkiRJkiRJkqR9ylInpaukH/27o0BhCi8sy5osQmG3pWHKPYYi5zI6r4bCJkeL0bbaGgKb4dgo33NS5xDJVQ8Bp3RiYLwcBTjXcLwUPsqf7peGihYthgGnV/K3CjBUlcBikHMdA4Yn5wXH97WGUFG6pxTqTc8D1d8phFi3TT5eCsR+rGHANtRDQtepLXhWG9g+3VOqI3QfMLS28LnB0O1qiP5iiOyk7jDk/ChK73Np24rt/uhnDhenQHsIHYZ2j/uaXEbnitsrqHJU31ZU1lFZ3h4tt8TlcllJRi/VVXLqRFnLuj2H8GDYx+Y0H/CEnnMaQ4zK6L7gs1X6rFK/Au0B9b/U3paGaefjoPbh8IMlXBfvP7X76/efnzcIJm9yoDnd0xUEpNPx0rql/fS4r8Uw8MI2aDG0l9oXDu6WLq9e7UWzuvAsNat5tBBGT0H2PQW+jxquARqyCsqG+givEgo7kKHN4fMVjGtjWH/+hnqal4FzpyB7Kqvh+gb0sXvTE6lsXm/m5fqN0c+zfBzU7DanU1lb5f6E2rs6YDlog7D/gIOheUlaD+5zA2UtDJI7GK9Rx9Mf4T3A5sbll6FxCAybYgqPAx1bA6dFjwPd//HYbEnjaDgOel9A+6TlaLzS1vl5aOtcH2oYE4zrFy1DdbqC5UrH/gTr/pC318Qq4uKcqRlWQVNIbDNpvghlqyq3caSHcRKh81rEevtC7cOkWh56+7wcvH+AZ5rneHA9R/f1KPeZ0PZKj63B+jo+Xqir1EdX+T5v1HtFx0HnMI/ct9C4uYKGY3xetP0OtlWqh7689D4shtzHj4+F2hYaqy+hXuL9gmOjNgjLBqoj2Xg5Wo/qyLKCMQ8YaPwEOhzfwb2hOV3B+I6OA9vfvov6Ynk9dHg9rib/Ak2SJEmSJEmSJEnaxw/QJEmSJEmSJEmSpH38AE2SJEmSJEmSJEnaxw/QJEmSJEmSJEmSpH2OkPwrXRsY3A4fBXf95cMLawhJ7iBU9fIRyRdQ2G+pHvbbjFJqKUBzCedJ505ZjhSiSQHAGKAK5zq+nuPjv1AG26cAZLg3DYQY47oQBEr38LC/QcDHSwsecgcHaOCaUNjzanT/qZ5TQDgF+07bw59E6fMwrpulocAIbmoL54qrFi5Xgp43vA+F51rDfW7h+aVnZApB303VRR8XyjebeTQ1hAkXXg96Hnq4EX1pcDQ9X6Dk+KhO9xR2C8dBfQEdG7W3q650e+s/L1f52FaQlUv77KCR43Vz2TiUPiKig3XHeb+U40vbL20Ldhf5vFrIpq4geHhjknc8bS/fe/dwLel5o/6Mnulpky/KRrPI24N1e6iH9aiNoGBqerYIrYvLFS31aNZe3+8EFpk2uZ1awbNKsH5BM0LXt8WAcWpHL3/tqH1YDXmqNQzVpWVL2ztpbKgn0V8Mee/rNrqAfrxwBjOMntuKJlagorHDkIPsq26Z91kYZI/77fMzWvWjNgS23zeTVLbaOJ7KdjdvzNuHhmY2P5vKJt08lXV13m8/Or6hpjleXm97tZnKyATGg5Ma7gONTXBSk+V+jOaVZdsv3Sf1/5G7DzyvGlYdX+Ee+hMqm+Vbw+8VCt5HHIT6tvH2aGxC45o5jC9L+5/S9yBNletXBcc3qdZvGM2F6oBnvHAeRWUDTczg+jZDrkx130V3cXA77faihetWDWVXqac2E8YXqzq3o/SM4LnCcv2of6DrS6hfoTFn6XyWjo3GSaQe1cTSNqP0OOgceqj9HYxzW7iezaie1zTGh/EmmQx5HkF1jurNtMrrltSRiIhudElofEz4PVO+z6seJnmgG/JyJfeQnnuaCzRHqL/4foPaG2qCaLlDGj8fBxmPPS6Uld2HAcaG1H6Nx3fNeHwWERW9RAB91UR/8Tpd+O+yY32s+BdokiRJkiRJkiRJ0j5+gCZJkiRJkiRJkiTt4wdokiRJkiRJkiRJ0j5+gCZJkiRJkiRJkiTtU5bYKF0lbTXEIiLaqo+66jn4ERKAxwGXEZE+Hu4gV/Eo4aMU0EshwxTuS59c03LjoOCuOGA5l7UQ+jlrISgXtjeBkNIOAnTHYdIUxpoDpyPqKm+fyui8KC4T9wvLUXhySfwmXaPSEO6jBCDT7Z82+R5ORye76iHwE46j9Bzo3i+7snBXCtge131aho6XnsFSdE1KA4VL8l4pNHwJIbN0rlTWQs2ktnDR5W69g4Deqhqi7y7Une3VZlQQVH810H3F5eA+bLbztZ+nEF6+22+ksnkqKa/Tyy5f8xXd61VZ2bierCBPF/suDJYvW45Q30Vlu9vrO1ks8kKzWb5GWxv53CcwAp1CGT1v+HzBvSlR2rZMoBM5Od1NZZtNrmHU/y4hTBvbzNGzT9uiQPO6MBD7KCiIm8K0m1FZj8ebK/AG9G9LaKtK+7PSa0LXeNwG0f2jkPPlAe3vw8dc2gZKY8vJZrTtVkQ8GMt2K4YGxuVd7hdrCG6v+vWyAUbNq0l+9uo+b59t5n3C3AIVdmR9fflxzFDn81pMj6eyc80NqWyn30plk2P5/I/HuVTWwHWaNOvtII5BYW7YxyyVraCdobaHxjqE2vESdA7UFtOYblKX1QcaEzRQuILnYdWVlRXtE+4Nziuh+lKVpjLa77iMjx+uLyy2gHX3lvk5mrS5Ls3afL9mbe4XqR/faBdrP1P/j/NluEj0vqCB7dWR270B5lH0IqAa+kt1e4gq+greRTX5uewqGCdA2QrGE1RW+lzS8zUe19B1o30uo2x+yO9U6J0dtUswJ8dmfzSWLL0eRxhn4XsLuHYl7xR7qufQ105hpkr9Jd3njdX5VNb0+RkcoA6voA9d1dP19eA8WziOHs6L34vRvLpsftTAWJ3qdVqvsH0gpe/xCNeRy9dheo5K32HzscE+YY5L7W0zUF2C+zW6r1wHoS2AOlg1Q3QXX3Ks2s1YTq7tvMm/QJMkSZIkSZIkSZL28QM0SZIkSZIkSZIkaR8/QJMkSZIkSZIkSZL28QM0SZIkSZIkSZIkaR+Ia5eunf2h6lU1REPB8BSGWBBQXFESL4b4FoYyUiAprHtmOwckziFze3Oal9ucrYdc0ilQiO/mJAc1bra5bKvdS2UUyElBlRTcWhJoWRq0WWqAi1IaYk1h2uPzKg2epe2XBnwS2i+FLJeYwr2n+3x+tZHK6LxaCGiFXPZY9XldCjUfL0fBwasu17caArwp1Lu0ztFytL0Ggs7HR1e1ZcdBbdxRAmofjYe3WcUQTV0Wnltaz0uerYiIDuoIoTo3jOrrbuQAb2ynCtv40pD3Hs6Bg+ThOvXjZfL2V3Br9nK+dMwX+RrN5xDsDCfRd7lsZye3G227fj2f8pRpWubGk/k8t2Z5+9MJBdDnsmmTLwA9N1znLl9GYeAtPPcUTE1h6NurzVS20SxS2bTOA4FplW/sOLCZApzroez5LYV9PoQ9U1lX56nFOJh+OUBINNzTrWo7Hxz86h+FlVPo9gr2S8Hh1D+O16U2mepIAwHpfUTExfOlgHqpRFe30V983vq6jb6GudCQ62RFHc0h6yGFwJO+yc8ejtXheSm1Nz2x9vOZuCEts+hzn0Xj0mGZj42ebypb1nkf1G6tYv2aUDtGWpqnwbCG+qdVX/bqh/pTMr6Hq77s/lF7T/36BMbbNAZv67zfJcwbWnhGRrcB5y7nd6GOlN2u6GHqNh77RfD4r3R7Y8sun8Nidfg56Qzua0OXsqFxUl5wXE+mOK+C+wx1n/rwUh2NHeo8F17UG9Fd3PdOeyLqJj9HNN9YwavWHq4lrUtjmFIdzn3Wf24Lrzm1cUeZV9M+cB5Z8A6Fjg2Po7Dq13C83FZBPaR5ajoHeneWLavch9Bx4BwXHsyqofep0EYMeb8lfUZpvaHndxJ5LkRtfAfXroH9VgXvyrAvx23R+xhYF8ZZhMc89F7l8s9+6buM8TjjINTelL4DpTnouA4PLYypYL2Dyh6+dkNUR3q/eiX4F2iSJEmSJEmSJEnSPn6AJkmSJEmSJEmSJO3jB2iSJEmSJEmSJEnSPn6AJkmSJEmSJEmSJO1TliQrXSUbzTy2I2KzmUfddNHVOdx1AWGWFVTlFApdGM6LIZ0QRLy9l8t25nl7K8i27aBsmTM049zu+j6O5VzbaI9DQGudy9p6lVcGDQZr5gOm61QS5ooBmoVBmKVhlhSquhxyiGZJQC8dG54DBeAWhPhGcLgt3QcMrYUw0/G6TeR7vzfkytRBkPruKj9bKwinJtOWgr5zWT+6xlR/p01ZSDRdy/JrXlanS8LPMTgZt5WPozTEmOrmCuscBApXw6VtHvRc0Xr0bFFAOAdiZz2EtXOYdN7e7igQfVyPHo0OjoOC5BdLCAmHqklh7XNo48frUj/Qdfn+rKhsVRj2C5WuhnDfG2+cpbJjW+vnf+p43v6xjXynN6e5bKPN7RI957MmXxR6bpZdbqvm0KbVo3pCz/20ycdGx1EanD2pYHvVbiprewjTHpWNf46IqPuyIOaKgq6hgcTlCKw7wLht1a7XpVWT69ZQ5WdrssrXqIJ99rDPchTqDe1cvV6/VjWErVd5nLGqoA+NNvqL46qNBgaOUoHZciemkwvPw3S1HRPoi6outz31AGWj5TDYHp4LaoxpuZr6J1i3b/IzVBoYP24bp80iLbMXue1J88UD9jmFsWk35HWpbKjyfuf9elnptuh4aRxH41C6lDSGK52rlfQUpWPw0rkg9dlRMLc4yHj8t1yV/X55A4u1TT62zVlZf7qEcWPJnIbmZHTN6XrUcC1b6E43JjSuo3FS2dhpUq83VqVzLZoLUFnp3L3vYV1abqijv9hGnludiL7P/T/Newg9l8XPbyFuS9bPawHVks6g9NgaeAZpPluKnvPxexCsN7BPuqerAcZmUB8C6lJHfRy8ixwfL73HISvqQwrfPZXWm2Wf+9qSvrC0nSbU15Q67PtDan1L3/e0NJ+r8z2c1nms0Rbea3pXOL439O6bTOFd76SGFwsI9kH9CLQSDVyTcT3sK5qP5/UaGJ823SKi6y/+9zza7trOm/wLNEmSJEmSJEmSJGkfP0CTJEmSJEmSJEmS9vEDNEmSJEmSJEmSJGkfP0CTJEmSJEmSJEmS9ilLpJOukr3uQqDybjeLOloMs5yvcrXdXeayveX658MUzkvBu31h3mkNuZUnNikYtmx7tBztYwzDlAtRMGwTEEBZ5XDMps/LVZRaPFIaBn7AyrkIglxpHy0Ea1IQZtpWYeBpH2WBr6SOHKLZVvl4p/1eKhuHpkfkoM695lhaZtXlZ4bCXcfPUQTX1Rs2c6Dn6en5fGwQljoOO17BMrTeovAcCIVpU/Wt67zfliJpR88SPVuE9tlh0HVZkHxpiHVExHAxLHneTaOCMGVCYbxHCRSuawh7Lgw/HztK4DaFwSMIUm+bfGzTST6WYxt5c+Nr18EFxjpCweeHP/1i48dh2lIAPYUzl4V6l5ZROHPb5jZoM5XkekJ9wxT6vFm/m/fZQ9/YlQU2VwM9Tdm4jytdjwKbg8oKHfZ4L6y7fs0nq9yXdc00lS3arbxcnYPPVxWUUUA8THvG/U9EYZtG3QDVcwqgH5rouwv1bt7l85ZK1Ku9aFYXnrdmNY8GKmXdlwXZ9/X6czDU8FxA+0HPOw0SaTlqowZcF8Y6cHzjtqGBfmKzyW0P9SelYykadyyH3B4d1A4cRlPDfabxDzRjNEau6ZqX9jOj61S2FqNrhGMCHL/nsmUH84YVlV2+vd+c5TPbmuZ6szHJ4wk6tp1F7ovqwrdy43lZ1xfONaF/aqEKTmGcO2mhzsH2sA+kOc2o3uB9hvPqcVuX3/5B6HmgdVd9HcPF9x5nl5sxwJiD2gKaL7f0/Fa57Cjvd8h4e3y8h98n3ge4X0eZq/XjMVzhfJHuaencla5JjQNAKhqN3+GAaVslz0wEv8fq8FzzsZXuY/xOZlU4/6Tt07qE2hYqo7ZvvF+65g28e5jQS1c43Br6RurLS5+lRZ/nAHur9TJ6L9ZCm9FDGR3bBN4x0rtIapfayHPcSZfn3/Wwvj2s+/Auue3yvLpeLaK6+IJ+styN5SLPx68m/wJNkiRJkiRJkiRJ2scP0CRJkiRJkiRJkqR9/ABNkiRJkiRJkiRJ2scMNF1XPnH2WJyKiI+fOR5RT4pzy5YFX/FPX21LuTdURpkuM8i4mTTwHdbwPbv4PeTwndjtaN2mzidK35FN3/s/wXVzGX1nL2Wb4Xdnj74XuGSZg5c7QrZb4Xf3s8vvt3T7eN2Kzz8vt2xzos/u5EQq247jaz+fW+QMtLPzWd4nPCO3Ht9JZTdPH8jrwjlgBk2Vy9pR7l4L39e8BzkxdBco84Dw99LTd5Nn9L3e4+/RHwq/D5u+f5+yzZZdLqMsB0Lt0qTpL33/ezdUOBig420LczYIf498GbpO4/tA32d/lJagh+9WP8p3y9N3tY/30dG24LxW0OftLfJyi/z14ph5Qf3ZxvTyV4+eI+yT4NnaaPP3qB+DHLONOpfNqvz95xur7VQ2Xeb268zmrWs/V1BLthZnUxnB/J/CB4LyvTC3rGAZyg0qRf3U+LvrIyKaAb4zH5Y7bN+9bHKftKxz2WKA5fqybLMVlFFeRgdt8LgN4pyUsvZ8vqojLuaX3nv+ePr/Uolq6C+NFauhx6antG0YZ6VRBtYAcwbKSiMD9PZVA88tZBx2DTy31A6OxwSF+VlHgTkvMPbledl6H0gZdtQ/8XHA9aVMPMg0ofE75+3A2HR0rnTulFeM41woo8wcGvvOoYzGcJS7uzUa68wm+V4dn0AuC1zLBfRFnzyXA3DpvQKNkemdx3j8R10uvnsofL9xlN+u57zqfA7zGI99y55Lzr+D5QrH4I8ml33oL9zvRVfHBCZNpXlnlF2EzyW9jzlCftgYjdVL91nathbnhwFuW9fbCMyDLrynJfPKi4XJONssgu/1eDG8f7RLnM/mfZZmoZfWG7pfZ3bWx9w7i7IWoofLMYHc7GMzyN4qfHdawXIlOWuYxwXPIL07pftM92Z7ld/Z0b3BXNJUktF6lJVWQ9kS3vo00IC1MO/jrD+Y94/yzSjvrOnze4Cqg7lmv4z6Ygbahf8uyxp/rPgXaJIkSZIkSZIkSdI+foAmSZIkSZIkSZIk7eMHaJIkSZIkSZIkSdI+foAmSZIkSZIkSZIk7QMRmNK1s1xVn/l3XWG4O4W0TqEmjwN0KbiSwnMbCoFtcmDktIFAaFquhjBECKqkMNdx6HQL26LQ6TrysZUGueK6peGjo+UqCCGvoawa8j6vhnFoekQOtKxgmdLj7TEMfZrKFm0OmN5rj6ey830u21vl7Y3Lzi/ycdy4uZvKWgpLheBzqg8YIArrNlC/ulEo8HLIx0sh5ItVWeA4Bc9O4VzpGaRnlXSjIN8FBQBT4Csc7xLC0FcQfk3oOafflWnr/lLw9jCUt0lHUXYlGYX2jkOW6RLVhSHRGExO2ysMgO4oSxqW60crtxCITAHpdJ8pnJjqHKFw9c1JfkZOb6y3G/RcUogx1aXNJocA37d7IpWdnOb2oKqOpbK2ujGVTWeLVNZ1688q1f0V7LONfLwdDKOpX91cnUtlG4tcRiHLq3aWytJxQL9C2zo7vTmV0fNQw/NG5zUfct+12+Wyebd+PSn8+rnd+/KxTfNy5wP6wS6fP4WrE/pNwpJwbgqvX3a5bA7t+XJVR/R1TCNiAf9fKtHXk0vjzL6exABt2RBlz8F4vD5UuV4O9LTApAzXrfNx9FUuo7ZsVeeyeb2Zy4b1tnK+ym3nooc+C55l6k+P4rDjn9KjoDEBjWtKURtI7du4jNbrYXzRFw4vadxE5zpr8xWmOflGm8c1s9FYZPxzRO7DInisQ3OhrVnZnJH6Ahqbjcd1NEbE9yewII5VodLRPhZdfn6prPT40nEUzkHovdBR0D1s6z6G6GMREZO6jyk0q6XzKFqOzrWB5Urfx5Q4yrZo3EjPJe1jPOePyHOhCK43k9HcvYY5E1/fy88hIyLmfX7Ox/uMiOgOOd+i60Y26/yOhs7h/u6GVLa9hP4S3pdQuzxfQbs/KqM2idqRrVlZn7S3zMd2z4NwH+CZW0LTOn4nTO96ycYU+otJLqPzp76G6kNpW7g5WZ+/0XpUl6hPWlR5zEPvVOgd4KTK88gKnjkaZo7HbTW8O237PEenuWvTLWPV9xGxE8vpsVhd43mTszZJkiRJkiRJkiRpHz9AkyRJkiRJkiRJkvbxAzRJkiRJkiRJkiRpHz9AkyRJkiRJkiRJkvbJqXLSNXRs1kcsIo5t9FFBwGEEh5Tycus/U2BiQ0GIoIF1N5occkjBwy0EMLYVBClWed16FDtdR16vHiAsFYIaq74swpoCX8fh4gftNy9XeK9gWxRCfsDaRet2dW7u+hbCjgsCXjlcHYKeISB9NeTjWAw58HUJgdUYYg3h5+PA2xs2cn1ooA5SYDGF1m73x/JyEJFO2yPzfv38dyH4nYJRCQUxHyVgGgNZIYx3XLboynZKAemktN0jFOq+s5jEw4/dzqKNJYQ6UygutYUt3Gdqb4PCg7v8PGBAPITbNqOySeE1OjPfzMcBIcYUdnxslttpCk2fFwbcjpeiukrXo4WyCQQln9zMZRRYTM8N1fNPnT++9vPmNPdvz934o1T2B/PPSmVnlxupbGuSA4V3Vrl9pFDvObTB8yq3mePrSW3huE2K4OvW9RSGnt00zW0ahS43MF4Y18NJla/RAL8PxwHT+XpwGDr0odBGLOD5pXV3V+vL0TPzO80X5H1uQ5sB9XKjzfcQ+0Zo0zgQPZeNg8ipjaPnaNbm5SbNEEPfRRcRm1NIQZcK7GzeGLGxFREPxc7GjbERFMgOfWef+7FxSj2NywmO1aEjo/Fw18DYt8lt5bLOZdQejct6aANpjkPP8jCUjSfoKlG/QOOJkl3Q9qltpzaL+ywog+V6WI7U9bhdhLHJBOa8NJaEMrpfDbSz4/FgBI8TqL8f91mLPvdrNP+isdmyKxv70b0fX8uIiCm8qZu268t1NNyGe0rHS2NOOraSPvEg1C+O37/g/BOuB11dekaojtB8lrZ30Hyr71axHRGb7TLapuw+U/3l5xfGYXBzaL5Fz2/Ru4zCOnKs3U1lDVz1FbTJu11uu+m5nNXQJ4FJrC+3jPxc0hgU321B/aL2gd7Z0RxhD8rG9wYueZyanEllD6xuSGW7MBfCOg33kJ7p+SofzMYE2uVmfbklzG/pWV3BexBqM2gM/NGdvI9pvtUBr/FSe3h8M58TjcsJXTeaRxCq56V997gvpPs8rfN8kdpzqr9LGKNM4Rn89O6JVLboTuWdFKDrdnwyT2Vb7V4q25zuRtd1EXFfnNm4NXb7nUMdw5XiX6BJkiRJkiRJkiRJ+/gBmiRJkiRJkiRJkrSPH6BJkiRJkiRJkiRJ+/gBmiRJkiRJkiRJkrQPRJNK148WAgfHwbMRHD47DmqkkEYK+6UARgyBrSkQuiyQta1yUGMbUDYK+qZQbzq2UhRmSUHfAWXDcLjg+9JgctLVOUF0BWUdNG0UKttHPq8K44gvvx6F564giJrqCAWdU3Dposvb22hzaPzp9uz6egMF25YFiNJ5Earn291m0brjc20hGJUCVCmglYJRS8Pb6c5TkPoKwp5Xo+Vovb7wUaWQ3Y22LBSY2rQVPKpVFRF9FW1ELLsqqjqvN4egYKy/cBxbEApM29udl7XBZLzcOFg9gkOM6Xjpmi8hYHkOIc6UJb4xKTv/cYg1hT/3eM0vHzp80HIU5Dtf5uemg+t0w+ZitEze/vt2PieVUb+96vI+l9DX0DVZwLoUYo2/JzZarIMKR79dNmlyu3R+ldvkB7ZzaPr0hrwuBUBH5Pq1GPUj1G+XBtVTAP28y30otef07K+gX6U2qBstR/XyzC4khAN6Vqdtft5ONjmc+twi3xsaG1J9HQenT2d5nxvT3IfurfJ5Lbs6hmqILrh+SyU+OdwWW8NWRPxxfHK4LU5O83jw2HA2lU1XOYC97g83ph+g/exhXL5s8rO3rKFsgHH+kNtZGsOOx9zUzlC7SP0YzRlo7Ef7oDaF9jsew5XOPwltn1DbNoF+YQPa1FmT27fNdr2dncL8dgn3r7TPInwty9Zd9Ll+7Y7GdXRsy8J7T+05jdWpH5vALITqxHgfVN9K6yVtn+oIjS8bKoPzp/nb+Go2MJ+jOd6E3p9UZW3XmeXxomPbaHI7Oq0W0dVdfDoiTk/OxqQtazPaisZ5WTPk5ahtbfq8HL23oXWH0VWn52i7P5bK2sj73Om3UlkN9ZfajAlck/EYMYLf7e0NG2s/4/wezmtW53tKz3ldWJd6mCXswlj65GRv7ef79/L1Pb/cSGVTmG9Qvdzr8pyB2hua99FzTsbLtTSfg7aFwKvTOD3bTWXHt6Cd3ktF0cIrqnHZeOx+kIb638I5ObX71O/RclQ2HpNAc4PtDT4zUEeo7tM7y6dsnsk7BrtdHsuN55Z/9Klc91ew3omtE6nslhPzGC62fR/ffUrs7OYx7NXkX6BJkiRJkiRJkiRJ+/gBmiRJkiRJkiRJkrSPH6BJkiRJkiRJkiRJ+/gBmiRJkiRJkiRJkrRPTouTrqGHQxMvBN1WUUEIKgVhUgjuOCx3saLA6bLQy3mdP2tedHl7dZWDGtt6My9HIc4QltuOwiApzJKUhimXopBdCoaloMoxCq6k+0ehwBUko06HfBwNBN7eML8372OVE0mrYX2/fZPPadnme0oB6fMmh+x+aPv2VPax+/O6T70xh8XeeTyfQ0mALl1LLsvXso98bLTPGu7rtM7bG4eKRuTQ6bbO94/CiekcKLSVglYpgH4BAaq7EGjfQ7uxWK2Xbe/lZU5s5mu0NcvnQIGy1O5RIPi0zcvtLvO59n0VD1eTuuK2kLbVQju1KgxXp+Dh4xt5H7kkYtXB9kYh7PQbQXTddhcQuE2hyy2F/eblqD2n859AaHxJGHxp0DMGZ7fwjHT5Sp3ezG3h7grq/uhnCrpeQj8wh/6XzmvS5OOl5436DLr/1N+M++4WbmpPgdDL3BZS8Pm4XkZE3LeTw+tv2MjXfNnn6zQOk6ZrSW0G1cEpXN/S8Qi1o/Ts761yOzp+vuj+EXouqT6c2c117qEh369xOx3B92tjQq3QOqr7x9t8T8f9W0TE2X4WD9+e0mshjX30oZOxNd+MSUR89KETcTo3M3Fqlsehxyc5gH1azdd+bgYYhw25XSBdlduAJcyPFgOMr/vc73C7mMvGy5WOTagM2x5YjvpsGiOf2pynsnHbTutR+9FA+3yszdunudsKxrnnlxtl60IfsL1cnw+tmrx9mrvSufYwhqG5BS237PJ+S89hfB86qG/Ux9K9p2OjcejmJD9f4+OIyO8BInLfQ8dGOngeCF036qdojjercj1sYK42Hk/Q+PWBxelUds/eyXwghWjsMIF5zvFprkubzST67sJ13+m2IgLGx9iO0PgV3oNAPaf3LJMq15tx2x0R0cOIuOTd0Fad+4YltNOnmjOwHLTdUFaKxtcb1WiMBadEdYn0kZ/znT73l7S9nVW+JtvzfK7j53cGY/CNNr/v4fH24V/b03yrh3n1AuaHm5PRezGoR8dn0J5R30htN7S3T78pX5OP3AfvWPOqqe+mOQ6h55LmywTfA1B7A31BDXOQcVu9oj4UrtsExmjY3lB/Bvd1Be/U6H7he9x6/Vw3pnmfn7gvr3f2fC47v7sR1bCMmyLiw/fNYmfn8nO0x5J/gSZJkiRJkiRJkiTt4wdokiRJkiRJkiRJ0j5+gCZJkiRJkiRJkiTt4wdokiRJkiRJkiRJ0j6HTyOUHgP9sO/fA4deknMQ3LlcXT6cuceA+lzGwbs5RJGOd9KWlU0hWHLWrodBloZJn5pspzIKjNysdlMZhYRTmHg1wIUaXaahytet6XMobl/la9nCcrTPDgIup8scgjtZ5mtCxzeMjoWOjdaj4510D6ay523k4/icZ+Rg1FVVFrw7HfZS2fiYKbSV7jMFv0fMUgmFAn9y91gqOzXL9Wurzcfbxnr92hpygmjbQ8gu3BuqI3S/lk0+r1Wbz2uvzeHqp2d5uU+cWw+23prmaz6b5Gs+DlmN4PB6sgHh1xQCe3oDArwjYuhXcS4iTm0tIjDkvew4KDy2NHCdrKC9bQv6AtonlbWbZQHe9NzQ/aJAZULXcxxQTNeS+hXaFq07gf7haMfbPOLPBx3HuC+7cGz5Wu6toC+A5ais9B6OQ5z/8JM5NPxpN+b2hlDA9DEI095d5vO6f2czlW1Ncz+yu1xvl+cr6FehjtAzs6I+Ga4b3VdCdYSOZXxvqA7Seg3UJYqOphFaC3VuM3e1sTXJ1/wpswdgi+tWMIXa7XJ/0VTc7g/Rxzy4Lksl7j9Tx+6yjtsi4v6zdeyt8tjk/GaupxuTPF47Pp2v/TxrcjtWQ1tBY45uyO0H9ev9kMtoe9Re0Hhi3PeUjkNu2MhzBjq2kzC3WsG54pgbWq6+4PeY6Tho+3QcAWXLLteHFVyTAdo3up7jeSndPxqH0JyUdHD+y76sf1r2+Xmg4xsfC44l4HA3J/kZaat8n+l491b5+h6f5HHH8Umum7Nq/VndHHK9pLl2Re8olnme1q5yWbOCMRG9BwDtzplU9jtPeenaz+eXeU62B+MmGnPRu5ya5iU0JurygtuLPFCY120M/YX7fWaxGU0L74CgTlPdb6COUNtK760WMP+eRz7etsp1c9xuUD3voK7WeLzw3g2Ojc61h/aL2gPqR5aj/TbQrjZw7jtdHufPoX1YQPu4s8zLUX9Gbcup6fqzOa3yc7Tb57kAtRl0vPQ8kPPzfF5LmEtsTnN9vf/c+n6fejq/x9lZ5u2fms1TWVvne7O9ys/+rMnH8cxb8rXbWeTrNGnW6xL1b3TV6HkjNLfqoA3agHc+NP6g8c0wWo76XlqPWmTqk3p4d0pyT8DtwRyem93Fehm1tU1ZVx7z5WdeU82XF/65lvwLNEmSJEmSJEmSJGkfP0CTJEmSJEmSJEmS9vEDNEmSJEmSJEmSJGkfP0CTJEmSJEmSJEmS9ilLkJOukttPbMf2dsRTT56Pumnjgd0c+klBmHuL/FkwhRWOtU1ZKOM4mDoiYmOaQxRpe9MmL3dsmtMPKWh2Ogrb3GxycGcTOZBzAiGlTZ+XqyAVuR7ycfQVBILXObR2VeWA07FldSqVtVW+HrMqR1dO+hxISiG48+nxVLaY5LpUYqjKfs+ArhEFfeM+YDkKF18M+ZrvRg6f3RxdO95+Dm3tIFR01ecyCtndavM93IFQaAoaPT09v/bzbHk+LUP3oWvytga4Dy2FaUP49ZnqdC5bHEtleyu41wXhs9S2UOg0Bn1D+7AH15KCmKlsUvXRX3zW26qLCYwGKBCZjpfOndYdmlxG4cxkSeGzo6K6sD0n3Bfk5SgAuPQ5b+tc5zZHofGLLtetKYQp0zVvoI5QEDWdwy6Ef2+0eXvjMGYK0qbrwUHi2QT6y2mT+669VT5e6gswiHsUnH3jCTrPfF4zuB50FhTqTeOAFSx3fpHPa7zurIXQdGgzCD4PcCPo/AkMIXC8NK6v1D7QevNV2XL0/FJdmsAzOB5nRUQs4XkY98l7Xe6P6bmk9vemjfPRd6vYjogbZtvp/0slbj7dx9ZmH/GpiJtP9bGTh/5xfje3gdt7uezBan0QQG1FaZA9tQs3n8zjMNoe9RVP27o/lR3vH0pl47Heqs7PcUdlFYylsIfK5jAGpzkY9U/jvrIZclvU1fnYugH6tSGPt5d9PlcaJ8wK3wZRrzBuy6m964e8Jo+v8nkdBR1Li2Xr97pt830oHefROdRQpydwqjswrlkNeT779I1zaz+fPPvxtEyzyHPousvPIKmW+V1DtZv7qdWNt6ey+bGbUtn7jr8gle126+faQN/cNrneVHAtqR2h8VVHdY7uK6w7RBVDf2Hfy76OGvv6fGw01qF1O3pG6rxcW+Xnt4GyKbRBy1i/5jW0SfTMNIVtIY/z4fxhv3XQeeXtLUZjM3ovQteS9kntEp3/zZu57s+7/KzS8/vBM+vPw8mNfF9unOV3Hhsw7yPULp3dy9eExs2ntnJ7QPPUUxvr79424H3PqWm+RnRs55a5vxy3vwetO4FrcnKDxvnry9G8murIYkU9XD6O2YTm5HnNKcwZu8K51bg9pPVoTEHXkubQ1E+t4JoQeh91fJL7jHZzfb/Lk/n52L4pj1voOPaWbQz9MpYfi7jjKcvY3Snryx4r/gWaJEmSJEmSJEmStI8foEmSJEmSJEmSJEn7+AGaJEmSJEmSJEmStI8foEmSJEmSJEmSJEn7FMbGSlfHZrMX2xGx1exF3bSxdTyHEi76HI65vcohhGMUenhmvpHKKIz29GwnlVH4KIVeUsgjhZTO6hyIOA6tp9DLZeRzoFDVzToHCtPx0nm1VQ6gpIDtWb++j2qAMMsq39M+IHR5yPdr3mzBcUBy5xGMr0lf5WPDcNN+nsrIqs735nyfQ6IXEFBLIaJ0LOdj/TpRwPCqz80/BV0vYDkKRV6s8nVq6nxv9mC5h3ZvXvv5A8unwLZSUaw6CCeGfR6f5fpLx1sWk8y/edI248DXssDxFSxXQdrvANe8gXaEUKjsaqhjuHgmwwG/S0PrlYYu49nD4VK7PIHD2YDRyrg9XELwN5lBsO8MQnbp2aL7QAG9SwjopXXHz9xWC0HqNbS/cIF3V7ltoXvYTnIZ1SV6Hqpq/RpPC4OuafvUjoz7vIiI84vcv1Ngc+kztzG6/3SfaRxAZfTo0P0qRfuYjNqW4mtJQfWF14iOg9aletjDct1oc/QsUDuyOYVxBjy/dLx0nWZNHmdRG0Th9c3oiZi0+T63Vd5+N0AfGnX0F8c4LexfKvFFN/xRbG1uxv/vUxf+u4WBErVvZDzmbvtcl++tn57KaHx5c39vKpsu8zyqdM4UedoQXX35VxgbK5hv1DBW7fOzTGP/xSTPQeo6txXUvpFh1ICuqjzupzFXB69v6FrSvaFxPh5b4Tm09eX7J2rb6djmMO/Z6/K4Zgp9LJ1Xad0f64ay8RuN80rnEYTGoTS+fHB149rP923dnJapj9E1h7kAzCu32vzczLs8DqP5IT2/VIdL0PhyVeVtlY5Xjk9pLpy3R2OCuh5i6LtYxIUxyATmmuNnIYKvR+mztbfKz8O0oXkJtBvQF4yfuRUMYFt4Lht4BzQd8jsPfKcE7SjZHTZT2bLP5zV+5uhZpWs+L6yDpXW1gTpC89QT0/Vnie79LrzDpGeL5nNUV7emMJ+FbhX734KxNL2L6qHvonaaxuD0/C67fP703oosuvU6UTrH25jka7k5yedA72JpH/SeieZMpOSpaaENovq7vSybQ9OxUZtWOtaYVou1nzfq3K+caM7lnYJhs4qu6+K/RMSzTnwydps8nrya/As0SZIkSZIkSZIkaR8/QJMkSZIkSZIkSZL28QM0SZIkSZIkSZIkaR8/QJMkSZIkSZIkSZL2KUvjk66S9mJQaVutoqmGWEIwKgUFn5pup7JxiCgFjU43y4KIS0OXCQU6YqhsQZg2nQMdG123AQJ1Z1UOgSW0vRZCZRd1QXAnRCxPegijhaBgWo60HW0PwjyHfCzjYHIKwF3VOdQag5Np3SE3uxS+OYE0z70+h+yuoH5BLHAq6SDItocyOq/dZdn2Sq269XXbhsKZc1ldwzlAPV92cEWafO/p/CloloyXa9rCgOnC7VMLNECQL22P2o2ur2PoLyy76Jro4XkrRaHx5QH0ZYHztL061p8bCkjHkF04DtrnBJ7LUhTsTMbtN/UXVFYafE4h93QtKzhXWne81w7aeLKCak41ju7D1iSHTpfWr5J2qfQZnMH1pe2X9OUREdMmb4+OZXux3t80LYW8l7U3A7SZeA4UMF04YygJIaf7R6HsdF4YVg1h5TQ2ojEPLVcyRqNlxm3SQdtvo4ru4nlMC8dh0uVQ20PjUKq7MazXXRoT3NZ/LJXVfW6f6z4/B3QcTbdIZQjGw5MFhMiPlqth+3RsFZR1bR7nd00uo2tOls2saLkxau96GNPhPYVDoznYVpPbT5xHQru91eyu/Tzvy67Rqs8dCo11Ntt8D1u4JvOu7H0B7ne8XloiYg7jIZpvQFWNKVzfDrZH/R1dk93RurxePg4aDtH2zy2PwbFR/1w2zm1gLNKO+mw6B7qnS2rPaPwO+6QxMo1/aHt1RPTDw312Fw1UEnoGaY5OcExUODYlu11ub8bPAx0vjyXzM7MXG6kMx02FY2Sqh2TWrPc3/UDtI2wLJn44x4E2Y2eV2zRC57W3yvsYw/pG41JYjq758cle0bGV3pvxcvieAa5lwLicrnlpPaf97sH7qOPT9T5je5nv36zNxzZf5W3hMw3zb5pvlaJ3F8tu9D4VLhH1ZQTHBkdAdXN7Be8nm/Xjo7EH9SGb9W4qa4dl1Bef9cmwiOWQx51Xk3+BJkmSJEmSJEmSJO3jB2iSJEmSJEmSJEnSPn6AJkmSJEmSJEmSJO3jB2iSJEmSJEmSJEnSPoWR4NK1QcGKK8hC7CBQuRuFiFLocGkgJ5VRSCkdB4Ut0nJ0LOPgTtoWBX6WngOtS/vAvHFYtx2FOtYQ7rqqIZi7yk0RrYv7hABzQoHoHYR6j/d7rr4h77PK+9wNCF0OCCkd8j5L6xIFrQ4F4aAUeEqBpB0EYk8aqEsQRL3Z5vu17PJ+KXR7OtrHpKXA6Vy2t8zXaDrJy9X0nFOYdkfBxhDsXBc8S3CB6T5QG0fHRvcLn/3CrNgL7cFw6b/pt2koEpdDh8vWPcpv7FC7NN7e0JS1haVB7Qsoo+1RMDkpCdjG4GjaZ+E5tNSeQ/2i/qdtLt+20nqlDhtgHREBzQiHsMOzOkb1sqQ/jjgo/LosEJzQPTw5m6/9TKH3dBwU1k1o3Rra26Pc63E9pOvB4fW5D6VrRMc2qWDdgLFXYcD2ZFgPJl9UuS+n+kvjgOUwjf5io46B91KBZTWLRb0RERGLeiM2YJ7TV7nOr4Y85q5G42HsO6EuVzA4aftFKiP99GQqG2Cs3hSO89N6Qz5eUvcwP4Q5SDVAuwX7oGtC5zW+N3Xk60btTANtWx35eBdDnm9RGzWt8n576Bkn0B7v9evHR+0Z9UU4XoHtk3k3SWUN9FmkZB+0/dLxCs7TYV2y1+Xnksb+9WhcM4cB0XiZiIhJXTi+wj4pHxtdcRxPwDUZjwl6OE+6VzhGLhyb0LnCJTlwew+3CU3dxQBtKI2bWrhKpeNBHl9mPUwaae7ajZ5DOs8F1KVhlduR0mMrvV/43orG+QXjWjo2mqfxseXt1fQOEOcqed1xG3GUeU/pu0gqa+D8ab87y9z2zdr153BnsZGWofcs1J7RvTmzyPucNNSOpqLYmOQ2YnwO4+OPiJjB9VjCWInOi64lLgdlK3i+xu+rCb7/pHka1JFpk48Xjw2Og95bkRauyfhY6LmnsQfW86q5NO+/8N/Xdt7krE2SJEmSJEmSJEnaxw/QJEmSJEmSJEmSpH38AE2SJEmSJEmSJEnaxww0XVc2hp2IiNgctqMZGvzu/mPV2VTW1ZShtf7dsJSzRd9dT98zu6zy9z8T/n7tsu+6Piz6rtjSjDXM1hny99Hi9zPTd7+PMgPovky73VRG95nyAlaU5QCZasWZDyW5c5ArMO9zJsFyyN/hvOy3UhkZfy/5hX3ANaHvzi74GnL6LmnKJ6PlNhrId4Dr1sH3JOP3M8PXl09G389MOWlU3+h4CX2n+WJF3xFO3y9eds3H+yjNJiz9fumSLKeD9kv6oYrh4vddd0Md3YraDNg+HMaqbJeY90ZqvCTQ3oyXwKy7siyr0hwk2h7le3CGwuWzq0qzMjC3omjNo/3mVM76K1gmDqj7hdlPmLVA9/CQGV2Y11e4/Svev2PG6/qxUK5daf5dV1AHI/ialO4D8wpHZVPINpnWOecIcwOhYaIMgRWMg3B8B2MD+l7+1SjndAJ5RTT26qNs/Cg9Wg91J2OxujDOPLM6GdsNjX3zs0HPXzPKT6PnnbOMIOu4ytlmtBw9e+O5W0Rgbidtryvo3VY9zAXa0gxFansgNwVaUMoUa0bzC+rXl32eWxBadwJtKh3vzpDnKi2cP7XHG/V6RueZ5fG0DNVBulN7XW4rS3Nn55C3Q/ugDNGSnKJFB1nrMFfZgLrUw6AWc2dLx6uj5WjMTOdJc03K8ykcDhfn/5JxncAc5sK8U54bl42ROaeIxzoPb3MYqmggV6h0/F6ae4p1tXAftC7V4TE69w3IOKerWZovTXl3pTncZfssyCmP8jpy2GO7cCzra5fmmFFfS31Iqaowm3JrkvuMcZYZ53nnfZa+jyjNO6N3EsuCOr0DGWtdS9ec5ngw54f3Z3RraLnDZplttGVzLZqTlb7/pQ+F4NU5vhejsvOr9aw8Oo5zsQl7zeqI6LsLY5JPzW+O3cVO0XqPFf8CTZIkSZIkSZIkSdrHD9AkSZIkSZIkSZKkffwATZIkSZIkSZIkSdrHD9AkSZIkSZIkSZKkfSgvTrpmHuhvjoh74/7+lqirNtqCoOuIiAGCfJtRkGIFiZRNBSHzEIRIgdBN5GMbKgiJHiCIGUKyuzo/jk2f1y2BkdMQyEn77CAxkkIvKcx0Wa0HQPcQVr2sc0h0H3AfCsNHKYR8BWHShEJ2D2ve5atOAcsUoklBoDP49QYKe6ZQ6JLzmlIAMlzzcXjsQTB8tSlLoh4/cxQUS4myE9g+XV8yOUIAdEmIdWkALm2rh/aMj6NoMQx3bao+hov1rqn6iLrsuvVQR+jelyo9BwxSHwXvcuB2fj7oecN9UhtUeKoYak7t6Gh7dA4Ukn0UA51EQVA9o3MqDMQuDFKnuG4Mly+8OaVtRAm6zw0EQpdqmxzgPT5e2ucC2owpjJ9K6z6GhMO9Ll2urdaPpfQedDDOaArj22mMwqHxebl5n8cp2VYqaWl8CtejG+pLgdjnu+MF+5KyB3e3Yl5dqIcP7m1F3eTxGvX/NDZtC8YA1D+10N5xX1zWx1CvQO0WPVd5LAljxMJ+onQOQtdyAeNmuiYl7WBpL0nzg7PLjVTWwrWkMUZV5TZw1UO/W9A/UX+9KBzrLGGfOFdZwdwVLi89D+MxbAPPAtXzCo5jd5mPo3Sce9ixNB0vGeDedzCHpnpJ94bmn4cdXx1lPl4yJztoH/T8ku3VNIaLdXFnNY0Z3NTyMW3GbeahN1eE6uWkgfE2rEvnOu/Kzp/GyDTvLemTStuR0jpC+is4Z6BjG88DL5Tla7nqyuaV1I5Q2031a4ffIF52PWpXS+tv+XuAXFbS9lHbTdeIzmHRwTs2qKsreEhmLbUthxuT0HHU8M65h3Odwbyyg3cjNGdcQT3E9+TQt0yb9e3trWhMUfbcn1+1l9rfc8tp7CzK2u3Hin+BJkmSJEmSJEmSJO3jB2iSJEmSJEmSJEnSPn6AJkmSJEmSJEmSJO3jB2iSJEmSJEmSJEnSPjk5VLqGmosB7E3VRX1AqiQFH9YQcboaLUchjeNlIjiQsypMwiwNncZ1IUgxHQecQ+k+MaSSkmELHf5cc0Bpabh4aUBvaeB4vvsR/Wi50hBUCiLGcPG+rNml86fAZrqFJaHeGAgN57rZ5vBRDKM9Qt0vDXV/rA2lSbakJEgdVsMaDQGqpWHHhK5vXQ8xXCyv6yEd/0EOG3IecbQQZ3weCtbFJg7akdKmsD5CPS+5//xcQmBv4T6pLTyK8X7x2Ar3SfWyNLz+4bHCfqV1qSkMjT8s6jOO0rTkbeUdbDSLvFzheKG0/T3KfR3vl/ry0rvSYc9ddhz0+Daw55L6dZRrOalW0VcXArbbKgd3SyWaeojm4nihqQd8Rhuo89QelfSnhMaDV1rp2H989mUtBSt9lum6UR9TF85zDov6P5qDkBVcX5qTUv3aW63P6Yr7ExjnLqEuTWqY38NyLSxHfXHJXKWDc++Lx6q5DI8NjqN07DA+vhpWpPVK99nD9c0zwaPNB0o0cN1I3+XjLR1zLbrDtRLzrmwuT/Wm9L1CqSWc/6S5fJ2j+1d6vA08v4S2R8fLfdLlj4+eLWofSs+1tE7z/AWWG21v0eV3YKX7pOeX1qV+ZdLkvqAveO8YwW11OrbCelM6bqZrWXq/xvvFthCnB/CuhK5lm69lB31o6Rys5L6W9qtU91f9LO8TNrcD72fJUd73lZhD+9DWfQwxxCIi2nrA5/5q8i/QJEmSJEmSJEmSpH38AE2SJEmSJEmSJEnaxw/QnqB+/ud/Pl74whfG1tZW3HDDDfFN3/RN8aEPfQiX/Xf/7t/FF3/xF8fGxkY885nPjNe//vWxWpV9pUzf9/F3/s7fiWc961mxsbERX/AFXxD/4l/8iyt4JpIkSZL02HDeJEmSJOkgfoD2BPQzP/Mz8ZKXvCTm83n84A/+YHzXd31X/PIv/3K88IUvjPvuu29t2Z/92Z+Nl73sZXH69On4R//oH8XLXvay+P7v//54zWteU7Svv/E3/kZ8z/d8T3zd131d/KN/9I/imc98Zrz85S+Pn/iJn3gsTk2SJEmSrgjnTZIkSZIeSTUMVzrKUtfan/yTfzIWi0X83u/9Xkyn04iI+M//+T/HF3/xF8d3fud3xt/7e39vbdnJZBLvfe97o20vBKL+zb/5N+Nv/+2/He973/viec973oH7+fjHPx7Petaz4ju+4zvijW98Y0REDMMQL3rRi+KDH/xgfOhDH4qmKQtnPXv2bJw6dSr+31/9YOx88j/HLZ/9wqibFoMwKajy8aY0OPp6Ptfr+d5cz8f2eHM1ruV4H96rK+uge9h3q7jvj951qb29Fq7nZ5WOrTgUGM7hej5XaT/r6mOrqobou1V86g/fHcdu+4L4uq/4rDhz5kycPHnyWh/ak9Ljbd708Jzp7b/yqdja2or5R38hZs/4WuzHnwiz/Kqw6XkinOu1cD1fXzq26+U+X8/XTYd3UJ0b+tWltraqr9WcKZddL/Wr9Hkg18s5kOv5muvqs92/+qrqQvu795FfiI1nfm3s7OzEn3vhU67ZvMm/QHuCeeCBB+J973tffOM3fuOlSWBExPOf//z43M/93LXfcHzf+94X73vf++I7vuM7Lk0CIyL+2l/7azEMQ/zkT/7kI+7rp3/6p2O5XMZf+2t/7VJZVVXxV//qX42Pfexj8au/+qtX8MwkSZIk6cpw3iRJkiTpcq7Nr0/oMTOfzyMiYnNzM/2/ra2t+L3f+724995747bbbovf/u3fjoiIL/3SL11b7qlPfWo8/elPv/T/D/Lbv/3bcezYsfjcz/3ctfI/9af+1KX//8IXvvDA43z4WCMu/DZlRETfr9b+/UT9LWj/Au2xdT0f2+ONf4H2+HfgX6CN2ttr4Xp+Vv0LND1ZWVcfW1U1XBftrx4f86aD5kxDv4rhYv0Z+lXQI/qE+C1of+P7sXU9X9/ruU5fz9dNh/cIf4EW8Zl/XxNPgOeBXDfnQK7na66rz3b/6qvW299r2gaHH6A94dx6661x+vTpePe7371Wfv/998f73ve+iLjwFSK33XZb3HPPPRERcfvtt6ft3H777fGJT3ziEfd1zz33xK233hrV6G9ZH97eI63/Az/wA/GGN7whlT/w4d+Mra2tuP+Dv/aI+5YkXRm2t5J0bdz/ofde60N4Uns8zJsOmjMtPv5L0W5tXfpvSdJjy7ZWkq6N+cd+KeY7O9f0GPwA7Qmmrut49atfHT/0Qz8Ur3vd6+KVr3xlnD17Nr77u787FotFRETs7u6u/Xs2m6XtbGxsXPoNx4Ps7u4euO7+7ZPXve518drXvvbSz2fOnIlnPvOZMbv5ebHz6Q/EjXd8WdS1GWjX87lez/fmej62xxv/Au3x75H+Au2BD//mpfb2Wrien1X/Ak1PVtbVx9bDf4F2/4feGxu3XMjMMpL62ng8zJsOmjOtTn9JrDY3Y3HPu2J6+wujMgNNh3A9X9/rOX/oer5uOrwD61y/utTWhhloiRloejKw3b/6Hs5AW3ziXTF96gujm10YK1+reZMfoD2OLRaLeOCBB9bKbrnllvi+7/u++PSnPx1/5+/8nfjBH/zBiIh48YtfHH/lr/yVePOb3xzHjx+PiM98Xcn+rwV52N7eHn6dyX6bm5sHrrt/+2Q2m61NIh+edP75F3/xI+5TkiRJeiI5d+5cnDp16lofxhPa43XedNCc6S/+mec+4v4kSZKkJ5prNW/yA7THsfe85z3xNV/zNWtlH/zgB+POO++Mt7zlLfF//B//R/yX//Jf4tZbb43nPOc58fKXvzzquo7P+ZzPiYjPfGXIPffcE894xjPWtnPPPfdc+k7+g9x+++1x9913xzAMa19H8vBXnDz1qU8tPpenPvWp8dGPfjSGYYhnPvOZ8dGPfjROnjxZvL4k6dE5e/ZsPOMZz7C9laSr7OH29yMf+UhUVfWoxsw6nCfKvOnhOdOJEyfi3Llz9uOS9BhzziRJ18b+9vfhse+1mjf5Adrj2POf//z4+Z//+bWy22677dJ/33rrrXHrrbdGRETXdfFLv/RL8eVf/uWXfpPyC7/wCyMi4r3vfe/apO8Tn/hEfOxjH4vv+I7veMT9f+EXfmG85S1vife///3xJ/7En7hU/uu//utr2y9R13U8/elPv/RblSdPnnRwIklXge2tJF0bp06dsv29Sp4o86aH50wRcemDOPtxSXrs2dZK0rXxcPt7Lb+xo75me9aR3XDDDfG1X/u1a/88/D36Y//n//l/xj333BPf9V3fdansT/7JPxnPe97z4v/+v//v6LruUvmb3vSmqKoqvumbvulS2ZkzZ+IDH/hAnDlz5lLZn/2zfzYmk0n8k3/yTy6VDcMQb37zm+NpT3tavOAFL7iSpytJkiRJj5rzJkmSJEmH4V+gPQH92I/9WLz97W+Pr/qqr4rjx4/HL/zCL8S/+lf/Kl71qlfFn//zf35t2b/7d/9ufMM3fEO8+MUvjr/wF/5C/O7v/m688Y1vjFe96lXxuZ/7uZeW+7f/9t/GX/7Lfzl+9Ed/NF7xildERMTTn/70+M7v/M74u3/378ZyuYwv+7Ivi5/6qZ+KX/mVX4m77rormqa5mqctSZIkScWcN0mSJEl6JH6A9gT0nOc8Jx544IH4W3/rb8Xu7m4897nPjTe/+c341SJf//VfH//m3/ybeMMb3hCvec1r4pZbbonv/d7vjf/tf/v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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Data inspection.** These 4 samples in the order of our time range from earliest $1940$ to latest year $2074$ already indicate the globally increasing temperature trend inherent to the data alongside different regional temperature signals, who's impact on the network prediction we aim to assess in the next section using explainability.\n", + "\n", + "To further insepect the data, below, we also visualize the temporal temperature trend in a line plot of the global average across longitude and latitude. The plot demonstrates the strong increase in average annual temperature which starts to manifest after $1960$ and becomes more pronounced in $1980$. The graph is also in line with other reports of the average global temperature in the RPC 8.5. scenario. " + ], + "metadata": { + "id": "UmsHe3nsRiXq" + } + }, + { + "cell_type": "code", + "source": [ + "# Line plot of global average temperature over all years provide in the batch.\n", + "\n", + "# Calculate global mean temperature per year.\n", + "x_mean_batch = x_batch.mean(axis = (1,2))\n", + "\n", + "# Plot time series.\n", + "fig2 = plt.figure(figsize = (10, 6))\n", + "plt.plot(years_batch, x_mean_batch[:,0], linestyle=\"--\", marker=\"o\",markersize=5)\n", + "fig2.suptitle('Global mean temperature time series', fontsize=20)\n", + "plt.xlabel('years',fontsize=18)\n", + "plt.xticks(fontsize=16)\n", + "plt.ylabel('T',fontsize=18)\n", + "plt.yticks(fontsize=16)\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "id": "bE-qWH3pW87T", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 610 + }, + "outputId": "0a8ad9ad-25b4-4c22-e77c-4052f8ac6036" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.1.2 Inspect Model Objects\n", + "In the following cells you can load and inspect model.\n", + "\n", + "The trained model follows the implementation and training procedure performed in the [primary publication by Bommer et. al., 2023](https://arxiv.org/abs/2303.00652) and described in **section 2.2 of the publication**, listing also the hyperparameters as well as model structure. We also show the model structure in the diagram below, consisting of a single convolutional layer (kernel size $=6x6$), one max-pooling layer (stride $=2$), a fully connected ($20$ neurons, with relu-activation) and the output (softmax, with $20$ neurons) layer, which calculates the probability for each class.\n", + "\n", + "
\n", + "
\n" + ], + "metadata": { + "pycharm": { + "name": "#%% md\n" + }, + "id": "6p3lsjClQZlT" + } + }, + { + "cell_type": "code", + "source": [ + "# Load the network object.\n", + "model = keras.models.load_model('tf_network.tf/tf_network.tf')\n", + "\n", + "# Load the network parameters.\n", + "network_params = data['NetworkParams']" + ], + "metadata": { + "id": "7TI4gZ25Qnqk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IImfE30QZrNm", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "407cb070-ac9f-4665-df29-e9e0d786aa3f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "5/5 [==============================] - 2s 288ms/step\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", + " 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5,\n", + " 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7,\n", + " 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n", + " 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10,\n", + " 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12,\n", + " 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14,\n", + " 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15,\n", + " 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17,\n", + " 17, 17])" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ], + "source": [ + "# Run the model on a test sample, requiring a compilation.\n", + "model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.001, momentum=0.9, nesterov=True),\n", + " loss='binary_crossentropy',\n", + " metrics=[keras.metrics.categorical_accuracy],)\n", + "\n", + "# Predict with the model.\n", + "pred = model.predict(x_batch)\n", + "pred_class = np.argmax(pred, 1)\n", + "pred_class\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "The integers shown above indicate the decade class in the time range $1900-2100$ in ascending order.\n", + "As described at the beginning of this section, based on [Labe and Barnes, 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464) we also include $1900-1909$ as class $0$, $1910-1919$ as class $1$, $2080-2089$ as class $18$ and $2090-2099$ as the last class, $19$. All other classes follow similarly, e.g. $2$ corresponds to $1920-1929$." + ], + "metadata": { + "id": "U7JO4lIKMbfK" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bSt6h_Q-oqjK", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "# 4) Explanation Method Selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "y6a7iLE4ZxkM", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "To gather more insight into how the model made its prediction, we can apply different explanation methods." + ] + }, + { + "cell_type": "code", + "source": [ + "import quantus" + ], + "metadata": { + "id": "dXfcjI8MVSi4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "### 4.1 Introducing Quantus\n", + "\n", + "For this purpose, we can use [Quantus](https://github.com/understandable-machine-intelligence-lab/Quantus) which is a XAI Python toolkit to evaluate neural network explanations. While the library is focused on providing XAI evaluation (it offers 30+ metrics in 6 categories for XAI evaluation, supporting different data types (image, time-series, tabular, NLP next up!) and models (`PyTorch` and `TensorFlow`)!), to get started, Quantus also offers some built-in explainability functionality, covering of the most standard explanation methods. Currently, `Quantus` is limited to classification tasks (for explanations to regression tasks please see *Section 1.7. Other References*).\n", + "\n", + "Quantus is built to address the need in XAI to quantify the performance of different explanation methods and is developed to be an easy-to-use yet comprehensive toolbox for quantitative evaluation of explanations. More details on what Quantus library includes and how it can be used for research can found in [Section XAI Evaluation](#xai_eval) below or at the offical [GitHub repository](https://github.com/understandable-machine-intelligence-lab/Quantus) or [API documentation](https://quantus.readthedocs.io/en/latest/). .\n", + "\n", + "\n" + ], + "metadata": { + "id": "BfkFjRNtXvVS" + } + }, + { + "cell_type": "code", + "source": [ + "# We load the available XAI methods with tensorflow.\n", + "quantus.AVAILABLE_XAI_METHODS_TF" + ], + "metadata": { + "id": "oUdVRiUhnz-C", + "pycharm": { + "name": "#%%\n" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "398bbe5f-2ad9-458f-d23c-a141422e9d1f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['VanillaGradients',\n", + " 'IntegratedGradients',\n", + " 'GradientsInput',\n", + " 'OcclusionSensitivity',\n", + " 'GradCAM',\n", + " 'SmoothGrad']" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Quantus is compatible with `PyTorch`, offering 20+ XAI methods for that ML framework" + ], + "metadata": { + "id": "h3fXaLzwYQX8" + } + }, + { + "cell_type": "code", + "source": [ + "# View the XAI methods available for PyTorch users.\n", + "quantus.AVAILABLE_XAI_METHODS_CAPTUM" + ], + "metadata": { + "id": "ooGp7kpEYduP", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "505d6da1-684a-4440-81a6-adb222f7275d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['GradientShap',\n", + " 'IntegratedGradients',\n", + " 'DeepLift',\n", + " 'DeepLiftShap',\n", + " 'InputXGradient',\n", + " 'Saliency',\n", + " 'FeatureAblation',\n", + " 'Deconvolution',\n", + " 'FeaturePermutation',\n", + " 'Lime',\n", + " 'KernelShap',\n", + " 'LRP',\n", + " 'Gradient',\n", + " 'Occlusion',\n", + " 'LayerGradCam',\n", + " 'GuidedGradCam',\n", + " 'LayerConductance',\n", + " 'LayerActivation',\n", + " 'InternalInfluence',\n", + " 'LayerGradientXActivation',\n", + " 'Control Var. Sobel Filter',\n", + " 'Control Var. Constant',\n", + " 'Control Var. Random Uniform']" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "In the following we will apply and compare all explanation methods available in `tf_explain`. To facilitate an easier understanding of the explanation maps we want to provide a short introduction for the different explanation methods and how they are calculated:\n", + "\n", + "**VanillaGradients** ([Baehrens et al., 2010](https://www.jmlr.org/papers/volume11/baehrens10a/baehrens10a.pdf)):\n", + "* explains the network decision by computing the first partial\n", + "derivative of the network output $f(x)$ with respect to the input $x$. This explanation method feeds backwards the network’s prediction to the features in the input x, calculating the change in network prediction given a change in the respective features, which corresponds to the network function sensitivity.\n", + "\n", + "**IntegratedGradients** ([Sundararajan et al., 2017](http://proceedings.mlr.press/v70/sundararajan17a/sundararajan17a.pdf)):\n", + "* extends InputGradients, by introducing a baseline\n", + "datapoint (e.g. a zero or a mean centred image) and computes the explanation based on the difference to\n", + "this baseline.\n", + "\n", + "**SmoothGrad** ([Smilkov et al., 2017](https://arxiv.org/abs/1706.03825)):\n", + "* aims to filter out the unwanted background noise (i.e., the gradient shattering effect) to enhance the interpretability of the explanation. To this end, random noise is added to the input and the model’s explanations are averaged over multiple noisy versions of the input. The idea behind the average across noisy inputs is that the noise-induced variations in the model’s explanation will\n", + "on average highlight the most important features, while suppressing the background noise.\n", + "\n", + "**GradientsInput**:\n", + "* is an extension of the VanillaGradients method and extends the information content towards the input image by computing the product of the gradient and the input. The explanation assigns a high\n", + "relevance score to an input feature if it is both present in the data and if the model gradient reacts to it.\n", + "\n", + "**OcclusionSensitivity** ([Ancona et. al.,2017](https://arxiv.org/abs/1711.06104)):\n", + "* sweeps a patch that occludes pixels over the images, and use the variations of the model prediction to deduce impact of the occluded area on the model prediction.\n", + "\n", + "**GradCAM** ([Selvaraju et. al., 2017](https://arxiv.org/abs/1610.02391)):\n", + "* stands for Gradient-weighted Class Activation Mapping and uses the gradients of the classification score w.r.t. the final convolutional layer to understand which parts of the image are most important for classification. The method produces a coarse localization map highlighting important regions in the input image for predicting the class.\n", + "\n", + "**General remark**: Note that `tf_explain` returns the absolute values for the pixels importance with explanation values only ranging from minimum (unimportant) to maximum (important).\n" + ], + "metadata": { + "id": "FBssbLnnB2Tj" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 4.2 Generate Explanations\n", + "To showcase how explanation methods can vary across the displayed importance patterns, we compare the explanation maps of six different XAI methods for the same $4$ inputs we visualized above. We normalize all explanations to importance values between $[0,1]$. This can be done with a maximum normalization also provided by Quantus (quantus.functions.normalise_func.normalise_by_max(a_batch, (1,2))). However, note that for GradCAM and OcclusionSensitivity due to average prior to the output layer the minimum is shifted to minimum values $>0$ thus we apply the normalization as implemented below." + ], + "metadata": { + "id": "XRJnkJgcNcmj" + } + }, + { + "cell_type": "code", + "source": [ + "# Generate several explanation methods with Quantus.\n", + "xai_methods ={\"VanillaGradients\": {},\n", + " \"IntegratedGradients\": {},\n", + " \"SmoothGrad\": {},\n", + " \"GradientsInput\": {},\n", + " \"OcclusionSensitivity\": {\"window\": (1, 5, 6)}, # window size for the occlusion procedure\n", + " \"GradCAM\": {\"gc_layer\": \"conv2d\", \"shape\": (1, 95, 144)} # gc_layer - layer of explanation, shape - output shape (1, img. dim., img. dim.)\n", + " }\n", + "\n", + "explanations = {}\n", + "for method, kwargs in xai_methods.items():\n", + " a_batch = quantus.explain(model=model,\n", + " inputs=x_batch[samples,:,:,:],\n", + " targets=y_batch[samples],\n", + " **{**{\"method\": method}, **kwargs})\n", + "\n", + " # Normalise for GradCAM.\n", + " if a_batch.min() == 0:\n", + " explanations[method] = a_batch/a_batch.max(axis=(1,2), keepdims=True)\n", + " else:\n", + " # If not normalized, normalize by hand to comparable values [0,1].\n", + " explanations[method] = np.abs((a_batch - a_batch.min(axis=(1,2), keepdims=True))/(a_batch.max(axis=(1,2), keepdims=True) -a_batch.min(axis=(1,2), keepdims=True)))\n", + "\n", + " print(f\"{method} - {a_batch.shape}\")" + ], + "metadata": { + "id": "F0JJeUdWOJnM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bad78589-da67-4391-8d91-e08decc657ff" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "VanillaGradients - (4, 95, 144)\n", + "IntegratedGradients - (4, 95, 144)\n", + "SmoothGrad - (4, 95, 144)\n", + "GradientsInput - (4, 95, 144)\n", + "12/12 [==============================] - 4s 328ms/step\n", + "12/12 [==============================] - 5s 412ms/step\n", + "12/12 [==============================] - 3s 267ms/step\n", + "12/12 [==============================] - 5s 454ms/step\n", + "OcclusionSensitivity - (4, 95, 144)\n", + "GradCAM - (4, 95, 144)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 4.2.1 Plot Explanations\n", + "First, we plot the global maps, which already show the different behavior of the explanation methods." + ], + "metadata": { + "id": "9DgwjOkbNXYa", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fDfIQE1IZhII", + "pycharm": { + "name": "#%%\n" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "a3042793-7e46-47e3-c18d-4fb4f7caf0ad" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ], + "source": [ + "# Visualise explanations, demonstrating the unintelligibility of visual comparision.\n", + "y_pred_samples = pred_class[samples]\n", + "\n", + "plt_kwrgs = {'nrows': len(explanations)+1,\n", + " 'ncols':len(samples),\n", + " 'font': 16,\n", + " 'xtext': -0.3,\n", + " 'figsize': (15,20),\n", + " 'cb_pos': [0.35, 0.05, 0.35, 0.05],\n", + " 'keys': list(xai_methods.keys()),\n", + " 'explanation': explanations}\n", + "\n", + "plot_multiple_temperature_maps(samples, x_batch_samples, year_samples, y_batch_samples, y_pred_samples, latitude, longitude, **plt_kwrgs)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Visualization description** The plotted maps of the first row are the inputs which we explain in the following rows. We use 6 methods and all explanations are normalized between $0$ and $1$ (white to dark red), whereas the input T2m temperature maps are standardized and plotted in the same range as the previous visulization (see colorbar of first plot visulizing the inputs with the prediction). Here, we see especially strong visual differences between the first $4$ explanation methods and the last $2$ which can be related to the difference between the patch-wise and the pixel-wise calculation used for GradCAM/OcclusionSensitivity and gradient-based methods respectively (see description above). But also, the first $4$ rows display incongruences regarding the areas assigned highest importance(dark red patches). This figure already highlights how complicated a visual comparison and visual-based choice is and how different explanations might lead to different interpretation of the model decision." + ], + "metadata": { + "id": "WbIvMgC8TrBd" + } + }, + { + "cell_type": "markdown", + "source": [ + "### 4.2.2 Analyse North Atlantic Region\n", + "\n", + "In the following cells, we show an example of XAI-based analysis in climate science. Since in climate science, primary aims of XAI are to either validate that the network learned features which relate to previously established climate drivers and physical relationships, or to uncover new insights about the research question at hand, we want to provide you with an example.\n", + "\n", + "As discussed in section [1](#overview), the NA region includes contributing regional temperature signal, refered to as warming hole or cooling patch. The signal evolves from a warming region in the 20th century to a cooling patch throughout in 21st century in the course of climate change. The feature was established to contribute to the network prediction (see [Labe and Barnes, 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464)). To assess wether the network has learned physically relevant features, by which we could validate the network skill, we want to find out if the patterns of NA cooling patch will recieve high importance (increased values in the explanation)." + ], + "metadata": { + "id": "YVGlwgIoNiE5", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sq99mkicaD3G", + "pycharm": { + "name": "#%%\n" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "bc651b43-fdf1-4fcf-dd39-6f766a1d9f67" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Plotting at latitude 27.8 - 67.7, longitude -67.5 - -15.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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ixIgRYt26dUIIISoqKsS0adOEpmkCgJg1a1bc6yc62LVVXvjXv/4lHn30UbFhw4ZI50JlZaV45ZVXxKGHHhrpmKiqqoo5/8qVK4VhGAKA+PWvfy22b98uhBBi7969kQ5NAOLll19OVlURHTQmTZoUaUN/+tOf4po3EAiIwYMHCwCie/fukS8hlmWJV155ReTl5QkA4swzz4y7XE46L0pKSkRhYaEAIAYOHCg+//xzIUQwPz355JPC5/MJAOKGG26Ie/1EB7tUyQ3hXNC+fXtRU1OT0HuJxs6LlsHOC3LF6bWkV155ZYP5Fi1aFIlpmibat28f+dIAQAwaNCjmrxtOOE0W//d//yeysrIi68zPz29QhquvvlrYtp1QGYgOZm2VFx555JHItIZhiIKCgsj1sADESSedJPbu3Sst+1//+lfh8Xgi87Rr1y7Smen27BGig9WWLVsibUjXddGlSxfp46GHHmqyjO+//14UFRVFlpOVlSUyMjIiz4866qiEzpR00nkhhBCff/656NChQ2R9ubm5wuv1Rp6ffvrpSfnCQ3QwSZXcUFpaGvlOcPPNNyflvbHzomVwqFRqE8cccwwmTZqE5cuXY/Pmzdi3bx86dOiAI488EmPGjMHVV18Nj6dlN8+f//zn+O9//4tZs2ZhyZIl2LVrFwoKCnDUUUfhuuuuw3nnndei6yeihtzmhdGjR2PcuHH48MMPsW3bNpSVlaFLly4YNmwYLr30UlxwwQXQNE1aht/85jc4+uij8cc//hEffPAB9uzZg86dO+P444/HuHHjMGrUqGS/baIDnm3bDf5fXFwsnb6ioqLJa0VFRfjvf/+Lhx9+GK+//jq+//57eL1eHHHEEbj44osxbtw4+Hy+pJc97JhjjsG3336LWbNm4V//+he2bduG7OxsDBo0CFdeeSXGjh0LXeet5IjikSq54aWXXoqMZvab3/wmgXdCrUUTQoi2LgQRERERERERUXPYRUxEREREREREKY2dF0RERERERESU0th5QUREREREREQpjZ0XRERERERERJTS2HlBRERERERERCktbTovPvvsM/zud7/DEUccgezsbPTq1QtjxozBunXrmkz7+OOP4/DDD4ff70f37t0xYcIEVFZWKtexbNkyaJrW7OO+++5ribdGRC4wNxBRY8wLRBQLcwNRekuboVLPP/98rFy5EhdccAGOPPJI7N69G48//jgqKirwySefYNCgQQCAO+64Aw8++CDOP/98nHrqqfjuu+/w1FNPYdSoUfj3v/8tXUdxcTGWLFnS5PUXX3wR77zzDlatWoVhw4Y5Kq9t29i5cydyc3OhaVr8b5iohQkhUF5ejm7duqX12PTMDUTJdSDkhnTLCwBzA6U+5gbmBqJYWjU3iDSxcuVKUVtb2+C1devWCb/fLy699FIhhBA7d+4UHo9HXH755Q2m+8tf/iIAiDfffDOhdfft21f069cvrnm2bdsmAPDBR8o/tm3bllC7SBXMDXzw0TKPdM4N6ZYXhGBu4CN9HswNzA188BHr0Rq5wYM08dOf/rTJa/369cMRRxyB1atXAwA+/vhjmKaJiy66qMF0F110EcaNG4eXXnoJZ511VlzrXbVqFTZs2ICpU6fGNV9ubi4A4LV3VyM7Jxe2ZaJk80doV/RT6Eaw2oWQ955meeuk8Ry9Qhr3iIA03umH/0njALC7y1HSeED4pPEslEvjfrNKGvfW1Z+eZ9kCq7aX4rge+TD0UN0pThyqzcyXxm3NkMZ9AXn5skq2S+N6bY007kRtuy7yddiWNG56MwE0rb/yyiocecb5kW01XaVrbvh4+fvIycmBZVn4bv0mDOx3CAwjuD3akG+XitQBU9EuS60caXx3uXqbKMyVt23Vj0M65G23zvJK4zVWKI/aJiq3Lkd2r5Og6fW7NK8ubxc5XnnbrDT90rgqfwds+WdYZ8njwZXIw5omn6Am4GAdACAC0Irfg+gyCtC8qKosw7Vn9Unr3JBueQGozw0rly8P5gbbwpp16zHgsH4w9OBnqSk2Ch3y7d5nVkvjlV75PnNTRTdpHAC6ZpdI49mQH7uo8ptis4/UQazc6oTPkucG3ZYfWwnFcUWVV96uakWWNO5Ertgvjdfp8vyWYQWPfSzLxn82bseQQ3vAMHRUVFTi2FPOYG5oo9yw7uFbkZvphwkNKwoGY8T+/8ETyglCcTwsahTHo4o24unYSRov73ecfPkAcrZ/I41rwpbGRYX8uMMuK5PGrepg/jM1Ax/3HI7jt30Cj6jPmbpXfuxktGsnjcOS5wbo8jrWMuRtXyjiAKDVyb+32PvluUHzK459zOB7NKFjRd5AjCj7Dh7YKK+uxWG3/rFVckPadF7EIoRAcXExjjjiCABAbW0tACAzM7PBdFlZwQ/7iy++iHsd8+bNAwBceumlcc0XPq0rOycX2Tl5sC0TdVlZyM7Jc9x5ke2tlcZzDPn8XiHv/MirVDeCCsVGqOq8yFYkU78pP7XIF/UWTFsgKyuA3JxseBx2XtRkyr+kqTsv5OXLDsjrUPe4P72vNidbvg6HnRcx6w84IE9BTIfckJOTg9xQ50VWVhZyc3LqOy8U26VQfGaqdmmZ8nadJdQ7n5wceRncdl7UKjovjKjOCxHKrfF1XsiXDzNDGnbbeeF10HmhuqhTV3yL0512XtgBaFlZENl5gF5fLwdabkjlvAA0yg25sXODsvNCqDov5NuE7pO3/WzkSeMAkJOtaHuKzUqV3zRFwwjXQaz6c8JnyQ+N3XZeGIo69iaj80JRxjpDnt8yQsdmlmVH1WH98RBzQ9vkhtxMP/Iy/TChIysrC3k1GfDAjrwHGaHq9VN1XmTJtxnkyI+3ASA3K1MaV3ZeKLZrOyD/3mSFlm9qRrD+MvwNOy98is4LVR0o8quqjrUMxXFHprz+AEDzyD9nu0beuaEsQ+i4IrINBuq3QaB1ckNad17MmzcPO3bswPTp0wEA/fv3BwCsXLkSI0eOjEy3YsUKAMCOHTviWr5lWXj55Zdx3HHHoW/fvtJpa2trI8kOAMpCvX+2ZcK2TAjbBBA80I76iKXLtC1TXj7FLyyqgxhTniOC67Dky7AU61DFTUteCN2ub4Rm6P9m1Guqo3tLsXxb0chMWz6/qg51xb7CiQbvN9Y6FPHG9Rb+aynmS2fpkBssywo97NDz+o3Jdnlwr2p3qtwCxQGCk2Wo91+KHawlX0D4GCc6tzZcurs6UNaRIn8LRfsSitwCqDsvVL9Qw8E6gtOZDf8qztpLV6mUFwBJbrCtSH4ILzdM1XkhXO5zVfv8xu0sFuWxi6ZYh8vOCxHpvGiaW51QHjco2pUyP7s8rnLCUpRRdfzYuO4Srct0kS65wYQGEzrM0HgLZtS4C6rOCVWnGlRxxT5PdbwMAKZip6Up4kIxzoStyeNW6D2ajf6G6aofjpTjXLiLa6rjCuVOX70MW1kGeVw02vZibYstLW07L9asWYObbroJxx9/PK688koAwNFHH42f/OQnmDVrFrp3746RI0di9erVuOGGG+D1elFdLT9dsrF3330XxcXFuOuuu5TT3n///Zg2bVqT10s2f4S6rKwGz52Sn9iTDIpfHgFgv/wUr7bw2fbSOKaOZ9pEqOrQQR2rVMp7SdUa9kSH66+qyu1yU1O65Ibv1m+K/IoDAN9t2BRXGVqSk37z4hYvRXwqty6Pa/qWz69tL97fP7Qf3gv+PQBzQ6rlBaD53LBm3foGuWHN+g1xlcMd1Zey75RL2JyUciRPKuXWdPWfTcFLZA/E44Z0yg0rCgY3yA0r2g+OqxwtapOTowL5pSdKfvll1PEu/uNewxMvS1uQn1Af0kEebt8zGSWJWJE/EABQ5W293JA2o41E2717N0444QQEAgF88skn6Nat/hrMHTt24MILL8TKlSsBAIZhYMKECfjggw+wdu1alJSUOF7PlVdeGemN7dJF3mBi9ZL27NkTb320Gdk5eRB2/T0v6k9tVtzzwiM//SnbkA/XpLpspFOxumNiV6G7e15kC/n1Z6rrb32B+vdo2gKfbS/FsB75ji8bqc1wd88Lr+KeHNn7Ffe8qItvBxdLbbtC+TriuGwkuv7KKyrR96RfoLS0FHl56lOB00E65YavP/skdNmIje82bMLAvodETstt6ctGykzFPS8q1Kd/FubIr1tv6ctGam2X97zwyNtmheKyEVX+Vt/zQv0rhfqyEXm8RnHZW4RtQvvhPYjOowDdg6rKMlx6auEBkxtSMS8AzeeGLz//LHJJ2Zr1GzCgX9+kXTbiNeXXvVf5knDPiyx512C2prrnRbIuG2maW53wWfJjL83lZSPVistGapJw2UierbjnhaG454UZdc+LTdsx5JDgPS/KKypwxE9OYm5oo9yw7YnJyMvMCN5voP1gjNj3v/rLRlRnZdXIt2v1PS86SuNl/X4iXz6A3G0u73lRqbrnhfwHS6sqmP9MzcDHvYbj+K2fxHfZiOqeF6birEXlZSPyy0JEhvwycsDBPS/27VOUQXXZSNQ9L/IHYkRp8J4XZdU16Hn9jFbJDWl35kVpaSnOPPNMlJSUYMWKFQ0SDQB0794dH374IdavX4/du3ejX79+KCwsRLdu3XDYYYc5Xk91dTUWLVqE0047zVGi8fv98Me4yYlueKAb9VcDabrH8T0vdEN+EKK6htMQikTkYF+uWoetWIeyDEJeCE+MU8c9uhbVeSGdHabigEVTnGLmsRVxRR2qvlw4YSkWoqt+X200f7j+jGQULoWkW24wDKNB+zIMvf4LisvOC1W71FX3atbVZwyF81hz3HZe6Irdk9YorumeBp0XmqrdKMqvqiNV/tYUN13VFJ9RcCXysOqGnaqbgzWd3hP87LUknDGWIlI1LwCS3KA3zg1GHJ0X8nWq9rmqfX50G2u2DIq2ZbjMb+rOi0bri8qtThiK0591xXGDUMTdHrs5YbguQ8P5g3Wox9UJlOrSMTd4IBrcX8AD2/k9L1SXI6lyh2ICj4PhMT2KfZbysjgoLulSdH5ojerAI6yGnReKOjIU64cyrjpucHnfEgfLsBVl1BTxxp9BeBv0KN978qRV50VNTQ3OOussrFu3DkuXLsXAgQObnbZfv37o168fAOC7777Drl27cNVVVzle15tvvony8vKEbqwTzbINWLYRSSq2MCBCv8gZil8Gs3X5mRW5AXnPesF6+SUqIlPdg9ehfIs0vienjzRuCNX1saqLumPc30KIyGyVWfKe4Iw6eS+tt07+C5Bhys9eqcmTnxVheuS/bgDqX2nseL+ANJ4/tPzgtaolqM4ogGHoqLbkPczpJC1zg2bA0j2wQl+CLd0T+bKpvKZbcXBdI+Q95z9Uqs+sUNmpGJGkY7b8zIYsj6PzH5tlhjoWReivaesNrtUc4Fsnnb9Ul+eOgOILWLUpbz+mouPTUt3YxAFD8U0106u671GoDrXgHUI8hoCmC3iNtDshM6Z0zAsA4Ler4Lf0yP0F/FZ15Au1pcm3yzpD/svdj0ZnafytVfIzL5ycq9u3l3wdQwrlB7lZQr5fDmiKfVeo8y1874g6PSMyWgsAZJvyX2ervPJfDStsef6sVoxUpAUUXy4UHUwA4NMVx1ZGe2k425afFWvqwToO16GpeyF0I/J6ukvX3ABdA3QdkXsnRHUYKEcTUfB07yWNf3XoJdJ4H6xXrkPsVlyW1lV+SYOWJT/usH/YI4178sLzB+vNk5vT4Eu33lGeu5RUx25V8u91dVvl37nCZz3I2AFFblB0DnsL5PuAyHo0A8gH7Joa2MKCXa04syeJ0qYL1bIsXHjhhfj444+xcOFCHH/88Y7ms20bt99+O7KysnD99ddHXg8EAlizZg127doVc7758+cjKysL5557blLKT0Qtg7mBiBpjXiCiWJgbiNJb2px5cdttt+HNN9/EWWedhX379uHvf/97g/hll10GABg/fjxqamowdOhQBAIBzJ8/H6tWrcLzzz+PXr3qexV37NiBww8/HFdeeSWee+65Bsvat28f3n77bZx33nnIcTD0DxG1HeYGImqMeYGIYmFuIEpvadN58fXXXwMA/vnPf+Kf//xnk3g42Rx11FF49NFHMW/ePOi6juOOOw7vvvtug6GOVBYuXIhAIIBLLpGfIkVEbY+5gYgaY14goliYG4jSW9p0XixbtszRdFdddZWja9GKioqavbnNddddh+uuuy6O0hFRW2FuIKLGmBeIKBbmBqL0ljb3vCAiIiIiIiKigxM7L4iIiIiIiIgopbHzgoiIiIiIiIhSWtrc8yJdGboFQ7dgh8bK1jULuh4cY9erW9J5M4V8POD8VU1vNBRNdC+SxssL+0vjABDwZEjjqrHCBeTjCQvNkMZNb/2Y9aZtAyiF6c2IjG3tseVjHtu6fPlW1PJjCfjkd4dWLt9Qj4muGjc9oMvHjA8Ir6O4rZkAvkeJ1h665kGFJl8utawqkQPdzoFtB8fkrrKzoWvBlKxqN3rUuOSxlNVlSeMdsqql8WpTvk05IYSi7SviGZ46abyPvhEAYFk2vgBwhG8tDKO+P77WkNfB7poCaTxgydu2rSi/iq7Jx4NPBuV2FCqD0ASs0HNNE61SNmpeQPcjoPthhY4bArovsq+p1eX7rCpLvt2/+WmeNL5ze6k0PuokebsBgPY58v1yWSBbGvf4FPt1If/dLUtUAAAsO1h/frsaRtSxRqUnXzr/1sou0niNKT90tm15u9MU7SvDKz82BADhka/DUuSnWsVxh6EFy2AjuH8qRQF0eFAB9/sGSpzmy4Dmz4AW+u05+P/g8YBmybcbPb+dNL6y15XSeHfvj9J4/mdLpHEAsGpqpXF91zZpXOvQSRr3du8hL4A/eNyrCw0wAb1zYVz7O1G6Xxq3K+Xf20Sd/LgmKTRF/tHl+dMsLZfGRWg7M3UD6AIE9pdA2BYCNa3w3kJ45gURERERERERpTR2XhARERERERFRSmPnBRERERERERGlNHZeEBEREREREVFKY+cFEREREREREaU0dl4QERERERERUUpj5wURERERERERpTT5YNXkWo3phWF6IULjftdaXmgiWO01ivGyvRmdpfGOQj42sfDKx/EOjxvfknQhH3daU8RVhGI8Y1uX13GtIa8jTVHHli5vQpambmJ1eoY0HhDyMtbZ8vdoieDnbNt6aHofdM2jnI9alg098gg/D/cnZ2g1innlbddnmNJ4O0+ZNL5XFEjjABCw5WXw6LY0bmiK3KAYe10ToeVH/Y2epUzkS+cPWPLyByx5376hy8unGjveycjyAor8JuRx1WeghUohNBsBAD7DhqZbCBju8jK5o0FEHtHPAUCH/DPVNXm8eFeFq7Jl+uXLd6IyoDg2Ee2l8WxvtTTu02sBAJYItmFT90JEHe/sqe0gnX9fpV8arw3I252paD45mfI69BrqOjaFPD+pjjxU+den1QEArNB0Xi0AQ7PhQ52ybNSCdC34CO8bov6v5+ZJZ9U88mO+9hmV0ni7wB552RTfOQDU76+bYZXL85Mw5cc2qjpARmZwOaE6E5oOEb2vriyXrz8QkK8/Q348L3yK7xy1tdK4Xav+3mbo8vwEw913P1EbygFacDmerCx4hAWPy+XGg2deEBEREREREVFKY+cFEREREREREaU0dl4QERERERERUUpj5wURERERERERpTR2XhARERERERFRSmPnBRERERERERGlNHZeEBEREREREVFKUw0FTS7ZQoMtNAihRZ5rof/r0WMLx1Brycdk1rKy5fG9xdK4P6+zNA4Atf58adw05GMWW7r8PdiaYlzgqLBlBceHrvNkwzKC/W5Ck/e/eS35mMkeUzGmsq4at1jehJTvD4Al5NPU2vI6Vm0n4fGs7dDw2nW2B7rmQcBm829LPgTg0wKwNAsA4NVMGKGcYIRea044nzQnw6iTxsusXGncUiwfAKoC8u2nok6+XWb55PMX+ORjzhd7egAAbM0EsAN7PN2gG/XLLKnNkc5v2er3KJPjk+cO1WfkhKHYR2iqOOTxgB3MPUKzUAnAZ1jQdA0BQ779UcsyNQ9M3QtLBPdvpu6FCO2L6oRfOq9Pk7f9wUfK9/vznlgujZ968ghpHAB8hi2NW5Dv88LbZXNqFfESBNu+sE0Aa7Cjpis0vT437K+W71PLq+Vtd1+pvF2pWLb8uMVU7NMBwMyULyM7W56fcvRyadxjBwAAlgjmgixRAUMYCGYKaiuiqgpCWBDQAT8gKishEGxvmldxvF0l/+wOLftCGt+Ud7Q03s6fIY0DgOZTfGeokW+XdmmZNG6Y8n2XFqoDW9OB9p1gF++ALerzlV1VLZ/fKz9uMQoKpHE9I0saFzVV8vkDpjQeLITie4euODapk+9D7NDy9dD3Lz0rA7qwI89bA8+8ICIiIiIiIqKUxs4LIiIiIiIiIkpp7LwgIiIiIiIiopTGzgsiIiIiIiIiSmnsvCAiIiIiIiKilMbOCyIiIiIiIiJKaey8ICIiIiIiIqKUJh+wllzzGha8hgWhWagJPddCY+yqxvquCijG+m4vH6898O1/pHFj6xb58gG0KyqSxmt6DZTGK7K7SON1hnpc6DALwfGbA4YvMs5wVp18zOesimJpXA/Ix0IXHvmY1AF/jjRe68+TxgHAVIzNXWfLm2mtYkx4TQuOSS9E8K8pDGi2gYCtGAuaWpShmTA0E9CsqOfBz0iDkM7r0eRjfQeEfJuoCPil8R37MqVxAKiRDwUOTTGUeIZPvl2XZsjbntcIbdd2AACwo6IdNL3+fdu2vACWvIqR5ZOPF5/nlY/H7tfkucWrKSoQgCbkhQxo8jqqsOT5SSBYR+Fx7g3Nhq7b8Oi2bDZqYR5hwmMHoNnBbdBjB2Bowc8kS6uQzqsJ+WfXu7M8d3Tt21Maf+Xl76VxABh0jHwZ/Yrk+akgR15G1bFTXajtCzvYfmpMA5pev7+rrJHv+7btkq9/x1b5cYeuy3NPQYcsebxAcewHoGtHef7sniPPX/lV8mOjMNMObXe1++HRddg1lY7mo5ZhV1fDhg1bC27Ddk0NbBH6rKurpfNqPsU+df8uaTy3oFwa3zHwZ9I4AHTN/0waN1bLv7fU7t4jj+8rVZYBAEzDA7QfispNW+Gx6tu74ZfXUUbPbvIF57WXrzevgzSuyt9QxQHotYrtoFZ+7CLKFHWoh45dtFAe1nVAhP62Ep55QUREREREREQpjZ0XRERERERERJTS2HlBRERERERERCmNnRdERERERERElNLYeUFEREREREREKY2dF0RERERERESU0th5QUREREREREQpjZ0XRERERERERJTSPG1dgAOdbWuwbQ3C1iLPNWhJWfYXXc+Rxvt36SeNZ616W7mOqm++lcYzy8vkCzhiuDRcmdVJGtcgIv+3LBsAkGFWwRDBfrfMqh+l83vL5HEE6uRxX4Y8blvSsNDU/YMZulca9+q50nipZUjjtghubyJYfagKeKDpXlQF5OulluURAXhFHXQR3Ia8og6GCH6W0dt9LKYm/+wy9WppvEdmjTTu7dhBGgeAb7blSOP7S21p3O+T58FOBfLtun2uKY0XZMnrIMcjr4Mso0oaz6vbK437asulcY8pXz8AaFZAGjf92fJ15PaUxiu14GdoayZ+BJDjqYJueACPvO6oZXmtWngtL/TQPs9r1cEI/dYkNHm7MXWfNN43b5c0fuPYrtL460sU+0wA//tsqzS+aV2WND78p/IydFbkhtpAqI6EhlwAP5Z7gaicuW23PDcV76qUxqsra6VxFd1w/7uhrvice3SQ54ZDq/ZL41ro2MYIVZW/Yh88OlBbKc+L1MIMI/jQjPrn4cMFW37cACGP29u+l8a77f1BGrd695evH0BNXqE0ntlPvl/HD8ul4bpy+fZpm8EN2jKC+aB2fyXMqP1sbm95+YzCbtJ4Tefe0nh1Vkdp3FR9H7DUuUcT8u8lhi2v48xS+T7C+GE7AECEvssa7drDgIDhVx/TJAvPvCAiIiIiIiKilMbOCyIiIiIiIiJKaey8ICIiIiIiIqKUxs4LIiIiIiIiIkpp7LwgIiIiIiIiopTGzgsiIiIiIiIiSmnsvCAiIiIiIiKilOZp6wIc6AK2gYBtQNh26LkODfIxysM0yMdk1m35cjYah0vjg/ttU5YhMzNTGrerq6Vx/54t0rjRTj5msemrX78ZGsM6o2Y/PHpwfGHdko9XLLzysdA1aRQQunwK3ZSX31dTplhD/XjqzZYhW97HqGfKx6z/oTovuJzQW9E1QNMEdE0xJji1KB0WdGFBhMbk1oUFPfSRWLo8NatygyHk7cJj10nj+d4MaRwA+nWVl3GDJl+GqRjOvX2ufIK+ecGxyC3LwnoAh+QWwzDqc2L7mp3S+f2l+6Vxo06e2zRTXoeaGZDGoWj3TniMUmm8S6X8PVblB8esN20bmwF0rtsKj66jvLbSddkocULTIo/o5wBga/L9vibk+4NMyD/boiz5dn34gEOlcQDIyZXvd3/YXSGNf/Xlj9J4l2550nh2drCOdFg4PAPYvtuCHfVbnS2vIuS3k+eu7Bz5+8vKkufGzEz5Z5iVqf5d0S8vAupM+Tr2djhMGu+wdx0AQEAAsCF0HULXIHRnx6/UBhRt366W75Ns1U55n3x/4vcpNkoAngz5dwrhz5LGswbIt1t/hx+kcasmeMxuhvJo3qHd4RH1+2Jvxw7S+e2cdvLle+XvT5WfdSE/Lqj1yOsHAGp1eRmqbXnc26W3NN7T+AJA6DvZHoFAl94QuoZAZZWybMnCMy+IiIiIiIiIKKWx84KIiIiIiIiIUho7L4iIiIiIiIgopbHzgoiIiIiIiIhSGjsviIiIiIiIiCiltUrnxYoVK3DZZZfh+OOPx44dOwAAL774Ij788MPWWD0RERERERERpbEW77x47bXXcMYZZyAzMxNfffUVamuDw9SUlpZi5syZLb16IiIiIiIiIkpz8sGok2DGjBmYPXs2rrjiCrz00kuR10844QTMmDGjpVff5jy6DY9uQ0CgDoBHF9D04Di/Xl0x2LiCV5ePB2woll/WqZ9yHZnZBdK4v6RYGrcysqXxgD9HGtcUYx7rgRppXDUmufDJx3NXjcmsopl1yml8imlyIS9DXa78PezVgnUstOByDM2GptkwNHfvjdwJaH7U6RmwQtt4nZ4BI7S92kLer+zR5OOxC2iu4j5Nvd12yiyTxgOd5W2vJiB/j7l+eRkyRHBM8XD9ZYgqGKJ+nRlV+6Tze0rl48FrVeXSuKiokMct+Wek+fzSOABoWfL8KTLlcQj5OjSIJn/DD2o7vkA1fAEdph06VjCr4LGD7cXWFPs0TdH2NXm7M3SvNN6/R600DgBd2vuk8b29O0rjprzpIDtTWYQgYQPlQGEnA4iqtxpFeivsJK/j7Ax5++iSL68jjx6Qxstq5PUHANV18jLWmfJ4qWgnjXeqqwYA6AIAfNADNdA1QA+9Tm1D1NVB6BpEaHsWtXUQ4eNkXd627Tr5hm+b8uPtuhL5PtHI2iWNA4BQrENY7uKBiippXDOC9Wbqwa+/ZlUNYNcnHE+uvO0ae+XfebLq5N9JAu0KpfHSvB7SeKWWJ40DQLUp/05gK47/DENex5YvmIAtWwCoguXLhKZrsOpa7ztFwp0XQgi88cYb+M9//oOdO3ciEGiajDVNw9q1a3HSSSc1ieXn56OkpCTR1RMRERERERHRQSKhzosNGzbgl7/8JdavXw8hmu+B1jQNvXv3xoYNG1BUVNQg9uGHH+KQQw5JZPVEREREREREdBBJqPPipptuwrp163DDDTfg4osvRteuXeHxxF7U/PnzMX78eMyZMweapmHnzp34+OOPMXHiRNxzzz2uCk9EREREREREB76EOi9WrFiBX/3qV3jiiSeU0955552wbRunnnoqqqqqcNJJJ8Hv92PixIkYN25cIqsnIiIiIiIiooNIQp0Xubm56Nu3r6NpNU3D73//e0yaNAkbNmxARUUFBg4ciJwc+Y0aiYiIiIiIiIiABDsvRo8ejY8++iiueXw+HwYOHJjI6oiIiIiIiIjoICYfV6cZDz30EHbu3IlJkyahpqbpsDC//vWvHT8Sdd9990HTNAwaNKjZaUpKStC5c2domoZXX33V0XKLi4tx9dVXo3PnzsjMzMTRRx+NhQsXJlxOImpdzA1EFAtzAxHFwtxAlD4SOvOia9eu+Pe//43jjz8ezzzzDPr164e8vPqxZ1evXg0geMnI6aefjkWLFiE/Px/HHnssAOCLL75ASUlJwp0X27dvx8yZM5GdLR/jfsqUKaiqko/5G62srAwnnngiiouLMX78eBQWFuKVV17BmDFjMG/ePFxyySVxlzXDE0CGJwDbMlEFwG8EoBvBEVoMTT5WuAZ53NDlY+rqivlLPJ2kcQCoy5WPF+zPLJDGhWJM+oDhl8ajx7S3LAtAOcqyu8IIjdWcL50b8JfKx2SGkNehphhT2s7MlS/eox6vXcU25MswhbwZ68GB2iFC24OuC2i6iLx+IEmn3CCgRR7RzwFA1+TbpQV5u7I1eb90rS5v16r1A4AXpjTeOUs+Jrxpy8uoyo+lCOYeO1SOMrSDHrVLy/XLL030BrZJ46JWPl67ipYhr2PNK899ACB88mUIf6Y0bvuy5GWwrdBfO/Jcg4i8fiBJp9ygWQHoVgC6HcrZlgldBHODoulDD9TFvb4G64a87R9lfKFcxuaCw6XxDjny7dqjyD/Z3lppPEsPfn6WZWHDGmBY952RYwYA+LGunXT+kmp5+bJ88tynKl/Aln+I2f6ANA4AXkNeR6p4pl4tX4EeKmN4MZoR/LlTV2yAaSidckPd/lLU+n0wdQ/QC6jdXwrLDm6Phl9+rOhtJz9iVn2ydSXyfXrV9t2KJQCaLt/v1+yXryNQJW9bquX7QrnHMoLHCrV7S2BaUe3ZlrebrHzFtw5TnhtMjzy37NPk38tqTPV3ClX+9Ony/GLa8u8U5TldAQCWZQPYhIrsQhiGjgpUKMuWLAl1Xnz11VcYPXo0SkpKAABffvllzOk0TUOXLl0wZswYzJ49O7LzsCwLN954Y4MOj3hMnDgRw4cPh2VZ+PHHH2NO88033+Cpp57ClClTMGXKFEfLffrpp7Fhwwa8++67GDVqFADghhtuwPDhw3Hbbbfh/PPPh8/n/ssoEbUM5gYiioW5gYhiYW4gSi8JXTZyyy23oKSkBLNmzcLWrVsRCARg23aTh2VZmDNnDiZOnNig19swDEyYMAFz5syJe93Lly/Hq6++ikcffVQ63fjx43HuuedixIgRjpe9YsUKdOrUKZJkAEDXdYwZMwa7d+/GBx98EHd5iah1MDcQUSzMDUQUC3MDUfpJqPPiiy++wIUXXohJkyahR48eDTomGjNNE2vWrGny+po1a2ArTs9pzLIsjBs3Dr/5zW8wePDgZqdbuHAhPvroIzz44INxLb+2thaZmU1Pw83KCp56+8UX6tMliaj1MTcQUSzMDUQUC3MDUXpK6LKRvLw8dOnSxdG0V199Na655hps3LgRxx13HADg008/xQMPPICrr746rvXOnj0bW7ZswdKlS5udprq6GhMnTsStt96KoqIibN682fHy+/fvj6VLl2LLli3o3bt35PUVK1YAAHbs2NHsvLW1taitrb8Wq6ysDABgWyZsy4QIXZMmbLP+EkKX97zQFPdrgGJ+C+rrmi0hn8a0FGXQNFdlsKNmt0L3n7Ci7kNh2or7gqhu66CIKz4iqN6+UJTPCVPRyWcp7ssRve3F+nsgSMfcYNkWLMuKuV2r2n743hjNUc1vKTZL4eCeF5aQl8FWbF+24p4Xqvxoi+Dy7VC92Y3agSo3mIryC2Xfvru4pvgMAUCoyqjMT4o6COWWcF0F/9rKnJNO0jM3CJihB9BwW7YVbVN3uc9R5Q7V+oHgcY+Mat+jyj+q5YePW5rLDeryya8JF4p7wqiWbys+I6Fq2FAfW6jqUHXcYIZmN0XUX7v+9QNBOuYGU/PA1IMPAJG/ACAU9yPRFPegUzEN+VdGTVfv0zTF/bgswyuPexT3qVPc8yJ8r4vGf8Oi6zMWU7XfV+yzVcclytxhqc85UOXo8LFTc1THHVboW6xlxf7bGhLqvDj77LPx3nvvwbZt6IoN5eGHH0ZhYSH++Mc/YteuXQCCN/ycNGkSbrvtNsfr3Lt3L6ZMmYJ77rkHnTo1f0OTBx54AIFAAHfddZfjZYf95je/wezZszFmzBg88sgj6NKlC1555RUsWrQIQDCJNef+++/HtGnTmrxesvkj1GVlNXhO7qxevzGOqeU3tHOtUjVBMjoI9rmMN1SxJbjjjOfGU6ksXXPDhrXfRX6BAYCN676Lu1xUb9uGrxo836KcQ9EB71fFlStwT3XvReW9GVX5Z3+DZ59vKwHA3BCPlsgNq7aXIiur/gv0Z9tL4y5X2/q4RZe+Xz1JA5vWf5PU9atuS9d6t61rnvy2hsA6ZbzhF90P9wefV1UdGDfsTNfc8Png0Q2OG74YPDruciWsa+utqrX898TER72MSbVP3qbK5Z8lqyQtpvGtzr/ZuBlA6x43JNR5MWvWLIwePRqXXnopHn74YXTv3r3ZaXVdx+23347bb7890nOYyI067777brRv3x7jxo1rdprNmzfjoYcewhNPPIGcHPmd5mM58sgjMX/+fFx//fU44YQTAACFhYV49NFHccMNN0iXOXnyZEyYMCHyvKysDD179kS7op8iOycPwjZRsvkjtCv6KbRQz15bjzaSaajvpp8l5Hf+9ZmKjVU52oj8ZkWNRxtZvX4jDu93aORSpbzKXdL5/WU/yMvncrQRK0M12oi8F9mJukx5e/kxo6c0XlzbPlgW20TFlhXI6T0Cmu6BXlHmumypIF1zQ9/+A5GTkwvLsrBx3Xc49LCBke265c+8kLdLJ6ONWIpRbmps+bd7t6ONeEN3zLYtC9s2fIWefY+CHnUJY7fqDdL5s3aulcZFpTz3IaDoGPAqfqXyqm/UJjLkd78XWfK4pRhxpS6Uv0xb4PNtJTi2Zzt4dA3lFQfGTeTSNTcc1yMfuTnZMG2Bz7aXYliPfHhCv2rail8/dVM9UoWMarQRW/HLKABs9faXxstNd6ONZHnkX83DI2nYloVN67/BIf0GNcgNe+vkIwaU1shzV5ZPflygKp9qtBFTkZ8BoM6UT6MabaRb5h55vPjrUFmCHRcnFljwaECZ/8AYiShdc8Ox/1uC3IzgaCNfDB6NY/63BJ7IaCPytulVjZShULltpzSejDMvakvkXX+BapejjWQH27ZlePDfE3+NIz98HUbU2Q7+Avnxdlb/vtI4cuUjMFZ3OUQa35F1mDReY7kfbcSjuzvzoj2CN7W1LBvfbNyMQYcWwTB0lFek+GgjQ4cORV1dHT7//HO88sorKCgoiNkhoWkaNm6s/5U80dFF1q9fj2eeeQaPPvoodu6sbzw1NTUIBALYvHkz8vLyMGXKFHTv3h2nnHJK5NSu3buDQ/fs2bMHmzdvRq9evaRni5x//vn41a9+hf/85z+wLAtHH300li1bBgA47LDmNyq/3w+/v+kOTzc80A1P/aUievA5AOguOy90l50XsnuVRKZR7EQ9Qp4ohCJR2YoyxDrNzTCMSNk9imTpUedSxfoVcdUZZA6SuYqtSMaqz1FrdBqcpnsij3SXzrnB0I0mNzJurc4LKDsvHGy3ymUovmQpGo8qPzYe6lc3jEhuBZzkBlUdqzpw3MWVnxEAoSqjMj8pLu2JbO/Bsnp0DR5dh0eRc9JBeucGrcH264l6rtof6C73OarLmVTrB9CgHcZch2IoPk11aYwh/wJtNDp9Xjca5lpl+XT5l0BVu1KVT1f8qKMpOjeCZVDt9+V1qDpu8DRMDfBowdc86Z8a0jo3eIQJT1THv8c26zsvFD8IeBSXgat4FJc0aKodEtSdC6Yl73y1FZ2zquUbltHoudmg88KjuKTNo9rvK/bZquMSVW7SHXxtV/34pErhqs4Lo9GlM4ahRx6tJaFvL7Ztw+v1olevXpHXYl2jJ4RAnz59oEkOhDdt2qRc344dO2DbNm6++WbcfPPNTeJ9+vTB+PHjsXXrVmzYsAGHHNK0Z+vGG28EAOzfvx/t2rWTrs/n82HYsGGR5+Hr4U477TRlWZssyzDhN0zYodN3/YYZGSZb1blgKw4iVPOrNmDVFyAAsBRfcKu9iXVIhRmKa698Vv1pdeHrqXxWTaTxGKbiHC3VlzDF9wfbJ/+FyMyQ98abPvkvowBQ45OfvaEa97miLksat0M3DhGhv7atQYMWeT2dpXNu0CAij+jnTueVUZ1ZoVJjy7d7ALAVHZc+Xd42VTthW3FtqVcL5g5LCx6QeTQLRtQmnVksP/PC3lMsjWuqTsG8dtK4UOQOJx2bqjMv6nI7KpdxsErn3KBbddAtb+T+FbpVBz10QOkJNH+qOQBYHvkvc7riy4GvVP6LPBzcK6lIccVVtV/+C7Cl6PjMrpVfOKKHjgtMW2ADgE6Vmxt8aTBz5L9uqg7eVapMxWegmL+jX31WZEam/MzZbFN+erpfcWZZ+P4JIuq50NX3VUgH6ZwbhG0HH6Ev0eHnAGBWy7cJXXU2oGKfl9mlgzReu199eVtVsbztqu7louqc8OfJLxX3twudbRj6buMvyGvQYZHZq/krCUIFkMddytLlZ7NnaOoz5vMD8hye+6P8otq6bPnZIzUZwbgW2u48dgCGpsOjuFdQMiXUeRHPDWsee+yxBs8DgQC++uorLF68GJMmTXK0jEGDBkWuEYt29913o7y8HI899hgOPfRQlJaWNhmj+ZtvvsE999yD22+/Hccffzyys4MHg1VVVdi6dSs6duyIjh2bPwBcv349Zs+ejV/+8pfSXlIian3MDUQUC3MDEcXC3ECU3lr8vPHx48fHfP2JJ57A559/7mgZHTt2xDnnnNPk9fC4zLFiYeEe0WHDhjWYbtWqVRg5ciTuvfdeTJ06NfL6wIEDccEFF6BXr174/vvv8dRTT6F9+/aYPXu2o7ISUethbiCiWJgbiCgW5gai9NZmF72feeaZmDx5MubOndtWRYhpyJAhmDt3LoqLi9GxY0eMGTMG06ZNQ+fOndu6aETUhpgbiCgW5gYiioW5gSj54u68CAQC+N///gePx4PBgwc3ez+L//73v/j6669xxRVXxIy/+uqraN++fbyrbyB80xuZU045Jeb9OJp7fcGCBa7KRERtj7mBiGJhbiCiWJgbiNJDXJ0XCxcuxA033ID9+4M3XOnWrRtmzZqFSy65pMm0ixYtwvTp0/HII4806OAQQmD37t3Ys2cPnnzySZfFJyIiIiIiIqIDnePOi1WrVuHiiy+GYRgYPXo0vF4vli5dissvvxwrVqzAU089FXO+s88+u0Hnha7r6NSpE0455RQMGDDA/TsgIiIiIiIiogOa486LBx98ELqu47333sMJJ5wAANi6dSsuv/xyPPPMM6iursbcuXObXEYSfeMaIiIiIiIiIqJ4Oe68WLlyJc4555xIxwUA9OrVC++++y6uvPJKvPDCC7AsCy+88EKDDgzDMLBr164mN6fZu3cvOnfuDMuykvA2UpcOAR0C4dGy658DlmIscZ8uH09d0+TjIfs0+Zi7WmicaOk0Ma7hi1YHvzRuaPLP12fJx6wv2PFN5P+mAIActNv1HTzhqjNrpfPbmbnSOLxeaVg13vH+3J7SeKWdI18/gEpTPi51tSUvo2XLx522Q9uZEMHpLKFDEzos0bLjVZNcrfDDK/ywRbCN1Ak/dBEcZ90Ledu1IR+P3RTyuGoscV1T54Y64ZPGA7Z8uw3Y8jJ6dXnuyLP2AgAsyw493wcD9du0VlcnnR+Z8naHDoXScCC7nTSu2e73baYif1VndpDGLV2+iw/owfwd3A/vR3lmZxiGgXKrIq5yUnJ5a8rhNSwEm6EBb3U5PKFNW2jyvO2p2CuN61Xl8pWrjskcbNeZ+7ZJ49nl/5MX4Ydd0rielS2fv1dwCErdBgANvqqSSP0BQGau/LjD65fn31ohP+6pMjOk8a4e+fvr+MN30jgAaKYiv6nmV3zOtjeY34OHmT5oVh002/16yR1N1yOPyPPQfs+skh8Pm4Z8u/d1kB/vetrlS+NGjrxdAoBZWSONZ3RsJ41btfLtz99ePr+na9dgORA8Ls4a0A8e1H/PsbofKp9/325pHKY8d/gr5fm5x77t8uVb8uUDgFZRJo3bZSXSeGZeO2k8Izv4vcYUGoD2yN/+H3g0Ab1Svn0lk+POi3379qFfv35NF+Dx4O9//zu8Xi9eeOEF2LaNF198MRKPdQMbAKitrYXPJz/4JSIiIiIiIiJy3HlRWFiIPXv2xIxpmoa5c+dCCIEXX3wRtm1j3759EEJA0zQ8++yzyMmp/wXasiwsX76c97wgIiIiIiIiIiXHnRcDBgyQDiOkaRqee+45AIiceRE+62L27NkwjPpThH0+H4qKijB79uwEikxEREREREREBxPHnRdnnnkmJkyYgBUrVmDEiBExpwl3YGiahhdeeAG6ruOkk07C66+/joIC+bVURERERERERESxOO68GDNmDIqLi7F3r/xmI+FLSIqKirBlyxbMnTvXdSGJiIiIiIiI6ODluPOiW7duuP/++x1Ne9ttt+EPf/gDsrOzMWHCBOm0f/rTn5wWgYiIiIiIiIgOQo47LwzDwNSpU3HPPfcop/3qq68QCASHc/nyyy8bDJ1KRERERERERBQPx50XQohmhz1t7P3334/8X3aTz4OBpgGaJqAFB8tu8H+fJh9n26fLx/P1avLxjg2Y8rI5+DyFouNJg3wZhpCXwdYMabyqY+/I/03bBir2o6pDT3hCY1wblnxca8sjH29daLo0vj+zqzS+t05+L5eALX9/AGAKeRmEcPkZhLY3odmh5zY0zYYRek5tozSQDbMuB7YVbCMlddnQjWBK9ury3OBX5Aa/Ls8NFtTbpUqdLR/quiIgb3sZhryMWXqVvADyKkLpIcdI46r8Z+vyOtKEvP0IRW6Dot0GyyDfRdcZmdJ4QJN/RjV28DOyQ3m6UuRAFx5UKktGLUmvrYbu0aALAMiBXlcNPbwbUGx3KiIjRx43FNutkx+jFG1L+OW5Qe/ZRxq3M7Llyw+1XQER/FfXIfT6crcv3yKdX0V1XKFb8vycuWubfAW1itwHAF552xZevzRu++W5wwxtJ6YtgP01MDPzAF2DaTv+2kAtwMjMhCfDB4S2cU9WBjx2cGfozcmSz5srb/t6pnybUHEyf+5hRdK4qJUfF9h18rZl1Si+E3y/GQBg6gbQ71DUbtkKy64/mNC2yHNDwFQceChyn5Ehb5eKpUP3y9u9I8ocXyoP14Tzkw5ktQf27QFgA1U17svmkPxbUxKMHTsW5eXlTV6vrKzE2LFjW3r1RERERERERJTmWrzz4vnnn0d1dXWT16urq/HCCy+09OqJiIiIiIiIKM3Fdf5XPPeuKCsri1xqUl5ejoyM+tPsLMvCW2+9hc6dO8ezeiIiIiIiIiI6CMXVeTF16lRMnTrV0bS2HbwuU9d1HHbYYU3imqZh2rRp8ayeiIiIiIiIiA5CcXVe5OXloV27do6mrakJ3rijuLgYr732Gtq3bx+J+Xw+9O7dG926dYtn9URERERERER0EIqr8+LWW2/FlClT4lrBli1b0LNnT+h6i99eg4iIiIiIiIgOQC0+5lHv3sGhLquqqrB161bU1TUcBufII49s6SIQERERERERURpr8c6LPXv24Oqrr8bbb78dM25ZqlFt05slNFhChy300HMdIvx/xWAvmmK8YA2KsdQVN1jVNfV48QLyZajKYGnyTcwyFPEsb/3/LQvAflRkdoKhGqfYoRrIx6UuDeRK46aQf4a2Ig44GPJHk9ex4iNC+FMWoc9K1wU0XUDXFculFhWwDQRsAyLUzk1hQLOD27Vly7fvOkW7qtAypHEhFO1atc1BvW1binXUWl5pvEyTt70aPSNUDhPAFuzVO0PXo+pF0bAMTb7v8UI+nrxqfl0xYrsu1PnX1uRvIiDkY77X2fJ4jR0cc94ObW81th+65kGNXassG7UcYRjBh4h6Hm5OirZve/yKZavml28zQlfve4Viu9UU276teI+6MB2t37QFgHKYGTmA7vyG85otb7u+6hJp3KitlJdPURbNJ8/fAGArprH92dK46ZMf+wS8wflN2wZQg9qMfFi6jjqzxb82kIRdWwdLA6zQvs6qDUCz5e0hMq+p+L61v1QaForva0K1fAB2QF5WVRlV86OiShq2aoI/oFuGF+gHVG7/AYZVv6+36uTL1xRXEegeedxfID+uMfzy/Kur3j8AXbUMxfyiVrH/D+2YBHQgCxB1dRCwIQJ18vmSqMWv5bjllltQUlKCTz/9FJmZmVi8eDGef/559OvXD2+++WZLr56IiIiIiIiI0lyLd6G+9957eOONN3DsscdC13X07t0bo0ePRl5eHu6//3784he/aOkiEBEREREREVEac9x5ER76NF6VlZXo3LkzAKCgoAB79uzBYYcdhsGDB+PLL79MaJlEREREREREdPBo8ctG+vfvj7Vr1wIAhgwZgqeffho7duzA7Nmz0bVr15ZePRERERERERGluRa/bGT8+PHYtWsXAODee+/Fz372M/z973+Hz+fD888/39KrJyIiIiIiIqI01+KdF5dddlnk/8cccwy2bNmCNWvWoFevXujYsWNLr56IiIiIiIiI0lyLdF5MmDDB8bR/+tOfWqIIRERERERERHSAaJHOi6+++srRdJrmfNztdGXZBkzbgG0Hx8U1bQO6Fhwn3dAU4xkL+S1JAoqPz2r5W5pAh5DGNWVcNd57/XuwQvVVp/lhhOpQCPk2FIBXGq+1/PL1Kz4DFV1T3+hWoIXbgaKOqG0IoUUe4efhz8qUNxtYQr5dC8X8Kk5Ss665a/umYrz0GlPRdrVg/rOt4Ljn5YFM6HZ9TvQo2p7fCEjjqvRpC0MaV+U2Vf04WUedYjuos+XxgBWuw/rnOjwwrRY/KZMk6nI6oC4nG6YtgNIq1OV0gK0HG6Vt+KTzCs3lfl/R+J0sX7VPUx4XCHnbsRTtIswOtUFb98COyje6LW/7uiWPa6Gc0/wE8joShiJ/e+SfMQDYvixpvC4zXxoPeDKlcTO0nVmWHZo+C7aho86b2M37KTnMqmqYlgXT8ESeI7Q92pb8O4XmMjcIRbuErd6niQQHf3DM5eJ1jzy3qOKGX952NcVxT2sQqs/JlG9HMIJxEToGFLYFIWwIq/VyQ4scobz//vstsVgiIiIiIiIiOgi1fRcQEREREREREZEEOy+IiIiIiIiIKKWx84KIiIiIiIiIUho7L4iIiIiIiIgopbHzgoiIiIiIiIhSGjsviIiIiIiIiCilcTD3NqQaC90WirHCFfMrhlJPCl0xqLJHl4+Hrhqt3YqqA0toob8eIDTOu6kY790U8k08vMzmqMajNxQfgWK05CDF56T8nCktmbYO09YhbD3yXHPYnyxaeJtRLR8AbEXb0TXFQhTjvQc0xXjq4THGQ4W1hQFh189jGPL1q/KrpcgtljJ7uacqozL/2ar8GFy+CG13FnTYQo+8Tm3DNnzBhyYAVAX/r4fam6Jx6iIgjQtFjhFGyx8WqvKTULR9XbFn1azgcYdmi9Bfu8G+XLfkdaTZjvbczRK6Ijdois/A41Ouoy4jTx73Zkvjli7/nMOfUfTf8IPajh2wYOsmRGj3KQImbEt+nB0mnB2RtihNd7lv0d1tf4bHCP8n+CfDC8OqX6amaJu6RxH3eaVxzZDnBlVc96rzs2oZSkJ+bJYKeIRCRERERERERCmNnRdERERERERElNLYeUFEREREREREKY2dF0RERERERESU0th5QUREREREREQpjZ0XRERERERERJTS2HlBRERERERERCmNnRdERERERERElNI8bV2Ag5kQmjRuqvqWhLvlO6FrtjTu1eWFsIX8PQjIyxgdt0OrMoUBW3iSsnwVTVO8P1uxfgefgbIOXH6OVqiMIvTXsnVo0COvU9vQNQFdExChbUzXRGR7s5PQdmWEInc4Wb+uaBsum56yjKrlK/OrIndYlldRAHec5CbVe1DlP3n2BrTITkREnocf1HZ0OxB6iPrn4e1F2TDkhO4u7zvabjV369CF5Wp+aKEyalHPtfpyK8unLL+8ZQnVsZthSMOW169YP2Dr8sN3W/EeNCF/D0YortnBvx47AEPT4bEDyrJRyzH8Hhh+L4QR/Px1vxeGFdy2hd22edvJ+oViu3NLV7Qt3WOE/wMAMHw+eKKOhXWvvF1pHvnyNVV+1eT5U/cpjjtc5m8A0HRFDlfljlAda6E8p+kGNGjQjNb7TsFvL0RERERERESU0th5QUREREREREQpjZ0XRERERERERJTS2HlBRERERERERCmNnRdERERERERElNLYeUFEREREREREKY2dF0RERERERESU0uQD2pJrAdtAwDYgQmOzm8KAZofHyJWPiaxpLTtms5OeK1UZVGO+2yJ5Y8rboTq0hAEhDEfrtxTrd1t+S8jnVy0fAIRiGap1mLa8jAErGBeh8dprTR2abqDOlI9XTdSSVG3T0OTjwYejIup5dEsxVW3Xkrcrt/lX1a5TQfg9ikgtCmiaaPF9DykIUf+IPHc4q67I65pin6Up9pmKeHAaRdsS8jdja/L3oCn2q3qoiCKUJYSuQ+j15Q4fPzRHVYeK1AShK+pYsXxbUx+aqz4HXSgKqeAxa0KFCS7HsGrgETo8Zq2r5ZI7us8bfOjBbUT3eqEbwe0tfIyXMFvRLk3L3fId0BTbteGTtw3dq4j7faH/BNugJycTHrv+fek+r6J8Lbxf1+XvXzPUx+2aIv9AsQzd55Mv358R/Bv6Fqn5M6DBhtbym0cEz7wgIiIiIiIiopTGzgsiIiIiIiIiSmnsvCAiIiIiIiKilMbOCyIiIiIiIiJKaey8ICIiIiIiIqKUxs4LIiIiIiIiIkpp7LwgIiIiIiIiopSmHkyaXLGEBktoEEKLPNdC/zcUQ/FqTgd2b0Ph99UcWzVmvGJ+Kypuh8agDtgG9NA48EIx3ruKLeTlsxTlU61fNT8AWLa8DKYiHjBV84fKYOvQw9PrOuoU81HL0jUBXRMQmog81zRnbV61XSlzh2Ksct1B7lENdy4UizB0d/nNtsNj22uR51pUe7QVbVNVfqefRaKctD5VGVSfs2r+xmXQox7UhjQt9Gj0HIBQ7VND+8bm4wfBpxtOPtF/VQkpiq3L6xCKuFDFVZ+Rav0O6HZAEbekccOqC5YldNxlWAEYQoMeep3ahqYb0AwDWmgb0Qwjkuc1xU5NKNqAgC2N6z7FV0bb/T5T98rXoft98rjPq4iH5g+1QU92Njyivi1ohqrtKw4cLHm7UlLkZ83jIDco3oPqPWo+eR1r/ozg39AOSvP7oUFAc/nW43EQ7MWIiIiIiIiIKJ2x84KIiIiIiIiIUho7L4iIiIiIiIgopbHzgoiIiIiIiIhSGjsviIiIiIiIiCilsfOCiIiIiIiIiFIaOy+IiIiIiIiIKKUpBu0ltwxNwNAERGgcZkMTkTGZdU0+prKKEIoxnaGKq8dk1oS8f8tULUCxCluxfCvqPdqh6qqzPdC14KarqgMV1fpVn5Bty9cfsNVjMgdMRR0r1mFa8nhk6G1bQwaAOlMDdA11ivmoZclyg2I4dhguu52Vw7GnwaYRzm8N/8ZRcNWY94rcoilWZWjy5TvJ/6plqCjzl+IvtQ1b84QeIuq5w21blTwUi9GE/NPXHLUxdwlKVQZdWK6WLxTlE7qi/IrPQmgtPD/UdaDZis/Rls9vmDXBsoR2FoZZA0PXIq9T29A8BnSPAT20jeoeHXroOFMojhVbereuqdoNAM2QHxPrfp887pV/bdV88vnD6w/nACMrE0b0Hk9RPmV+VbQ7JUUdquovOJHic1AsQ1WH8IQ+A6EFv+MZHkATgMdB2ZKEZ14QERERERERUUpj5wURERERERERpTR2XhARERERERFRSmPnBRERERERERGlNHZeEBEREREREVFKS7vOiy+//BK/+tWv0L59e2RlZWHQoEH485//HImfcsop0DStyeNnP/uZo+WXlpbi9ttvR79+/ZCZmYnevXvjmmuuwdatW1vqLRFREjA3EFFjzAtEFAtzA1F6SquhUt955x2cddZZOOqoo3DPPfcgJycHGzduxPbt2xtM16NHD9x///0NXuvWrZty+bZtY/To0fjuu+9w44034rDDDsOGDRvw5JNP4t///jdWr16N3NzcpL4nInKPuYGIGmNeIKJYmBuI0lfadF6UlZXhiiuuwC9+8Qu8+uqrkTGOY8nPz8dll10W9zo++eQTfPbZZ3j88cdx0003RV7v378/xo4di6VLl+Lcc8+Na5lCaJFH+DlC/7eEfExc1WjB4WU2H5fPr+uKCeBk3GjFeOmKkaVNWz5/dFyE/l9t+qDZwU1X9R5V61exFO/fVMUt9fptxTIsl8NGR4TqSojg/1V1ly7SNTfomg1dsyE0O/JcC/3foxjr24b8w1PlBk01v4N2YyvW4ZaqDPXvQUSeR78vzWXxNE1eR4YirmvyhuvktEdVGdT7AEU8VMfhMe8FdNgi+Eh36ZoXVHRhuppf2PLtUii2TAMO1q9ofEJr2+1L6PJjr5YvgLxda8JSLkIT8s9RtQzdCsjjgdrgX1tEnuu6Bt2sU5Yt1aVzbtD9Xuh+H3QtuA3rPh90B9sLAGiqnaLiuEM1v+ZRtyvN65XHfT553FCswyNfPvTgewh/d9Gys6FFf9tS5SZFu3MtGblR9TkZiq/+HkU8/BkIDTBDzzVR/3orSJsjlPnz56O4uBj33XcfdF1HZWUlbMlO2DRNVFRUxLWOsrIyAECXLl0avN61a1cAQGZmZpylJqKWxtxARI0xLxBRLMwNROktbTovli5diry8POzYsQP9+/dHTk4O8vLycMMNN6CmpqbBtOvWrUN2djZyc3NRWFiIe+65B4GAvJcZAI499lhkZ2fjnnvuwXvvvYcdO3bggw8+wO23345hw4bhtNNOa6m3R0QJYm4gosaYF4goFuYGovSWNpeNrF+/HqZp4uyzz8Y111yD+++/H8uWLcNf/vIXlJSUYMGCBQCAQw89FCNHjsTgwYNRWVmJV199FTNmzMC6devw8ssvS9fRsWNHvPzyy7j22mtx6qmnRl4/44wz8Oqrr8IjOZWmtrYWtbW1kefhXldhm5FH+LlTqrP63V42IpRrAITitGVVKVWnfgvFZSOiwWUjTetQ/R7dUV02A7dxR8tQL8KR8OnGwgwu01bvgNPBgZgbhOK0/ZbPDertVrUO5fzK3KJcQvDf5nKr26taVJdsKOJ2Ehqu68tGVPk3fNlIozqMZz+VqlI9LwDN5wbLFjBDDwCRvwCgu7zeT7XdOjkuUFK0PaG4pCpZGtZf66yztWjKS08Ul7XZzuKNt0FTMV86SOvcoBkwQw8Akb9OKC8bUVyyoLxsxEFZNNU6FL+pq+Lq3+SD78Fs8FdvEk98+W4l43Jc1SW3ygTtKG4287c1aEKkx5Xvhx56KDZt2oTrr78eTz31VOT166+/Hk8//TTWrVuHfv36xZz3t7/9Lf7617/i448/xvDhw6XrWbVqFaZNm4YTTjgBRxxxBL7++ms8+OCD+PnPf46FCxc2O9/UqVMxbdq0Jq/Pnz8fWVlZDt8lUeupqqrCJZdcgtLSUuTl5bV1cRLG3ECUXAdCbkj1vAAwN1D6YW5gbiCKpTVzQ9p0XgwaNAjffvstPvjgA5x00kmR15cvX46TTz4Zzz//PK644oqY865duxYDBgzAH/7wB9x9993NrmPTpk0YPHgwXnjhBZx33nmR159//nlcddVVeOutt3DmmWfGnDdWL2nPnj3xr5XbkJ2TB2GbKNuyAnm9R0DTnZ3wkgo37FTdlM7tjf/iu2Gnidrty+DvcUqkDlv6zAvVDTuVN/RMqRt2msjcvxTVBacBmgdVlWW45szOaX0QAqRvbnjroy2R3FCyeSXaFZ0Q2a5VN0xs6dzQGjfs1F2eeaFFnXlRtW05snqe1CC3tvUNOzXFp9QaN+y04zjzonTzh8gvOhGa7kFlRRl+8dNeaZ0bUj0vAM3nhg0f/Au5OdkwbYHPdpRhWPc8eEI3mnN9w07FL5+qG3Y6orxhZ+v8QmfaAp9vK8GxPdtF6u9AoT7zIjk37DRtgY/2Aj/tAHh0DWWVVSg6/WLmhjbKDd/PvAm5GX6YmoFP+pyA4d+vhCetbtgp//6jeVvnhp0mNCz3H4qTajfCE/1NQVVHLf2VORm50e0NO5Xx0Fk/QsMHVmecbPwAjyZQVlWN7mNuaZXckDaXjXTr1g3ffvttk5vfdO7cGQCwf//+Zuft2bMnAGDfvn3SdTz33HOoqanBL3/5ywav/+pXvwIArFy5stlk4/f74ff7m7yu6Z6GB9SNnssoN2HlqT2K5TvovFAdPKs6L9SnL8V/iliDOlS+BXeJQHl6ldvTr5wsI1nC36U0D6B7g48DwIGYGzRF50VL5wYn26TmsvNClVuU8zd6E03qs4U7L1TxdBhtpPHnHK5Dp/uoVJbqeQFoPjcYutbgy7Yn6rnu+nItd5caOZIyo42ER2/SlCM4pRv1aCPyz0C1HekNOntEZBs8EDqB0jo3CKtBZ4Wn0XMZ9eUCquN9xfG8g126+thGsV0r85Pqp53w/MFyeCDgaTCPKk+09OVnLZ+nlN/bVB9ko7hHE5FHa0mbI5RjjjkGS5YsidxgJ2znzp0AgE6dOjU776ZNm5TTAEBxcTGEELCshokgfHMe04zjfhWhJFBVWR58bpuoqqqCp6KMZ15EifvMi6oqWFF12NJDparOilAOlergnhetduaFHYCoqkK1vwzQvaiuDN17IT1OvmpWuuaGyka5wRe1XfPMC7UGZ15UVQGNcmtbn3mRCp0XltOhUkN16A3VYWS/lca5Id3yAlBf3+WVVQCC976oqqpCeYUHxgF05kVShgN0wLTtUP35DrjOC9WQja7PvAgNiWraAlVVAmWVwY6L8LbJ3NBGuaEm+LlYmhHctmtqYaTKmRcOhsrUTMV2qzjgVa7DUNRF5MwLHVVWFcpqaxp2XnCoVEB1Bk3UmRdVZhXKPNXwaALlVcGb3bZKbhBp4ssvvxQAxCWXXNLg9Ysvvlh4PB6xY8cOUVpaKmpqahrEbdsWF154oQAgvvjii8jrlZWVYvXq1WLPnj2R1x5++GEBQMydO7fBMh599FEBQLz00kuOy7tt2zaB4O+bfPCR0o9t27bF0RJTD3MDH3y0zCOdc0O65QUhmBv4SJ8HcwNzAx98xHq0Rm5Im3teAMA111yDOXPmYMyYMTj55JOxbNkyLFy4EJMnT8bMmTOxbNkyXHzxxbj44ovRt29fVFdXY9GiRVi5ciV++9vf4umnn44sa9myZRg5ciTuvfdeTJ06FQCwd+9eDBo0CPv27cP111+PI444Al9++SWeffZZDBgwAF9++SV8Pvn1WGG2bWPnzp3Izc2FpmmR69W2bduWttcJtjXWoTuN608IgfLycnTr1g16mv8qxdxw8GL9uXeg5oZ0ygsAc0Oysf7cY25gbjgQsf7ca9Pc0OLdI0lUV1cnpk6dKnr37i28Xq/o27eveOSRRyLxTZs2iQsuuEAUFRWJjIwMkZWVJY455hgxe/ZsYdt2g2W9//77AoC49957G7y+fft2MXbsWNGnTx/h8/lE165dxbXXXtugRzURpaWlAoAoLS11tZyDGevQnQO5/pgbDl6sP/cO1DpM57wgxIH7ubQW1p97B2odMjcc3Fh/7rVlHabVmRfprKysDPn5+Wl9h+a2xjp0h/WXmvi5uMP6c491mJr4ubjD+nOPdZia+Lm4w/pzry3rMH3P+SIiIiIiIiKigwI7L1qJ3+/HvffeG3PoI3KGdegO6y818XNxh/XnHuswNfFzcYf15x7rMDXxc3GH9edeW9YhLxshIiIiIiIiopTGMy+IiIiIiIiIKKWx84KIiIiIiIiIUho7L4iIiIiIiIgopbHzgoiIiIiIiIhSGjsvXFi6dClGjRqF/Px85Obm4phjjsHLL7/cYJqioiJomtbkcf311ztah23bePDBB9GnTx9kZGTgyCOPxIIFC1ri7bSZa6+9Fpqm4Ze//GWD1/fu3YuHHnoIJ510Ejp16oR27dph+PDhTepYJlbda5qGBx54INlvo9Xs2rULd955J0aOHInc3FxomoZly5Y1mW7z5s3Nvn9N03Dttdcq13Ug1l9LY15IHuaG+DA3pDbmhuRhbogPc0NqY25IDuaF+KVjbvDENTVFzJ07F9dccw1Gjx6NmTNnwjAMrF27Ftu2bWsy7dChQ3Hbbbc1eO2www5ztJ7f//73eOCBB3Dttddi2LBheOONN3DJJZdA0zRcdNFFSXkvbenzzz/Hc889h4yMjCaxjz/+GL///e/x85//HHfffTc8Hg9ee+01XHTRRfjuu+8wbdo0R+sYPXo0rrjiigavHXXUUUkpf1tYu3YtZs2ahX79+mHw4MH4+OOPY07XqVMnvPjii01eX7x4MebNm4fTTz/d0foOtPprScwLycPcED/mhtTF3JA8zA3xY25IXcwNycG8kJi0zA2C4vb999+LzMxMcfPNNyun7d27t/jFL36R0Hq2b98uvF6vuOmmmyKv2bYtRowYIXr06CFM00xouanCtm1x/PHHi7Fjx8asp02bNonNmzc3mWfUqFHC7/eLiooK5ToANKi/A0FZWZnYu3evEEKIhQsXCgDi/fffdzz/qaeeKvLy8kR1dbVy2gOx/loK80LyMDckhrkhNTE3JA9zQ2KYG1ITc0NyMC8kLh1zAy8bScDs2bNhWRamT58OAKioqEDwM2leXV0dKisr41rPG2+8gUAggBtvvDHymqZpuOGGG7B9+/Zme8fSxYsvvohvvvkG9913X8x4nz590Lt37wavaZqGc845B7W1tdi0aZPjdVVXV6OmpsZVeVNFbm4u2rdvn9C8u3btwvvvv49f//rXMXunm3Mg1V9LYV5IHuaGxDA3pCbmhuRhbkgMc0NqYm5IDuaFxKVjbmDnRQKWLl2KAQMG4K233kKPHj2Qm5uLDh064J577oFt202mf++995CVlYWcnBwUFRXhsccec7Ser776CtnZ2Tj88MMbvH7cccdF4umqvLwcd9xxB+666y4UFhbGNe/u3bsBAB07dnQ0/XPPPYfs7GxkZmZi4MCBmD9/ftzlPVC89NJLsG0bl156qeN5WH/OMC8kB3ND22BuaDnMDcnB3NA2mBtaDnODe8wLbaetcgPveZGA9evXwzAMXH311bj99tsxZMgQvP7665gxYwZM08T9998fmfbII4/EiSeeiP79+2Pv3r147rnncMstt2Dnzp2YNWuWdD27du1Cly5doGlag9e7du0KANi5c2fy31wrmT59OjIzM3HrrbfGNd++ffvw7LPPYsSIEZF6kPnpT3+KMWPGoE+fPti5cyeeeOIJXHrppSgtLcUNN9yQaPHT1rx589C1a1eMGjXK0fSsP+eYF5KDuaFtMDe0HOaG5GBuaBvMDS2HucE95oW202a5wfWFJwchXdcFAPHAAw80eP1nP/uZyMzMFGVlZc3Oa9u2OOOMM4TH4xHbtm2TrmfUqFHi8MMPb/K6ZVkCgBg/fnxC5W9ra9euFV6vV7z66quR15xcy2dZlvjZz34mfD6f+PrrrxNad21trRg0aJBo166dqKqqSmgZqSSe69PWrl0rAIhbb7014fUdaPWXTMwL7jE3JA9zQ+pgbnCPuSF5mBtSB3ODO8wLyZUuuYGXjUjU1dVh9+7dDR6WZSEzMxMAcPHFFzeY/uKLL0Z1dbX09CtN03DrrbfCNM2YQ9FEy8zMRG1tbZPXw9cJhcuRqpqrv/Hjx+OnP/0pzjvvvLiWN27cOCxevBjPPvsshgwZklCZfD4ffve736GkpARffPFFQstoLc3VX6LmzZsHAHGd3tVYOtVfS2FecI+5wR3mhtTE3OAec4M7zA2pibnBHeYF9w6k3MDLRiQ++ugjjBw5ssFr33//Pbp164b169ejS5cuDWKdO3cGAOzfv1+63J49ewIInrIk07VrV7z//vsQQjQ41WvXrl0AgG7dujl7I20kVv397W9/w+LFi/H6669j8+bNkddN00R1dTU2b96M9u3bIy8vr8F806ZNw5NPPokHHngAl19+uatyOa3/ttbc9ldUVJTQ8ubPn4/+/fvjmGOOcVWudKm/lsK84B5zgzvMDamJucE95gZ3mBtSE3ODO8wL7h1IuYGdFxJDhgzBkiVLGrxWWFiIY445BuvXr8eOHTtwyCGHRGLha8Y6deokXW74rraq6YYOHYpnn30Wq1evxsCBAyOvf/rpp5F4KotVfxs2bAAA/PrXv24y/Y4dO9CnTx888sgjuOWWWyKvP/HEE5g6dSpuueUW3HHHHa7L5bT+21pz218iPv30U2zYsCFyR2s30qX+WgrzgnvMDe4wN6Qm5gb3mBvcYW5ITcwN7jAvuHdA5YaEL1Q5iC1atEgAEHfddVfkNcuyxIknnijat28vampqhBBC7N27t8nYyXV1deKEE04QPp9P7Nq1K/J6SUmJWL16tSgpKYm8tm3btmbHZe7evXtajsu8ZcsWsWjRoiaPTp06iWOPPVYsWrRIbNiwITL9Sy+9JHRdF5deeqmwbbvZ5VZWVorVq1eLPXv2RF774YcfmkxXVlYmDj30UNGxY0dRW1ub3DfXBpxen3bzzTcLAA3qNtrBWn/JxLzgDnNDcjE3pA7mBneYG5KLuSF1MDckjnkh+dIlN7DzIgG2bYtTTz1VaJomfvvb34onnnhCjB49WgAQTz/9dGS6uXPnikMPPVTccccdYvbs2WLmzJli0KBBAoCYOXNmg2XOnTtXABBz585t8PqkSZMEAPHb3/5W/PWvfxW/+MUvBAAxb9681nirrSbWDXY+/fRT4fP5RKdOncScOXPEiy++2OCxcePGyLTvv/++ACDuvffeyGv33nuvGDJkiLj77rvFM888I6ZNmyZ69+4tNE0Tf//731vrrbWIP/zhD+IPf/iDuOiiiwQAMXbs2MhrjZmmKbp06SKGDx/e7PIOtvprCcwLLYO5IT7MDamHuaFlMDfEh7kh9TA3JB/zQvzSLTew8yJB5eXlYvz48aKwsFD4fD4xePDgJpX/+eefi7POOkt0795d+Hw+kZOTI0488UTxyiuvNFlec8nGsiwxc+ZM0bt3b+Hz+cQRRxxxQDSUxmIlm3CdNPeIrqtYDeWdd94Ro0ePFoWFhcLr9Yp27dqJ008/Xbz77rut9K5ajqxeGlu8eLEAIP785z83u7yDrf5aCvNC8jE3xIe5ITUxNyQfc0N8mBtSE3NDcjEvxC/dcoMWKjQRERERERERUUriUKlERERERERElNLYeUFEREREREREKY2dF0RERERERESU0th5QUREREREREQpjZ0XRERERERERJTS2HlBRERERERERCmNnRdERERERERElNLSpvPis88+w+9+9zscccQRyM7ORq9evTBmzBisW7euybSPP/44Dj/8cPj9fnTv3h0TJkxAZWWlch3Lli2DpmnNPu67776WeGtE5AJzAxE1xrxARLEwNxClN00IIdq6EE6cf/75WLlyJS644AIceeSR2L17Nx5//HFUVFTgk08+waBBgwAAd9xxBx588EGcf/75OPXUU/Hdd9/hqaeewqhRo/Dvf/9buo7i4mIsWbKkyesvvvgi3nnnHaxatQrDhg1zVF7btrFz507k5uZC07T43zBRCxNCoLy8HN26dYOup00/ZhPMDUTJdSDkhnTLCwBzA6U+5gbmBqJYWjU3iDSxcuVKUVtb2+C1devWCb/fLy699FIhhBA7d+4UHo9HXH755Q2m+8tf/iIAiDfffDOhdfft21f069cvrnm2bdsmAPDBR8o/tm3bllC7SBXMDXzw0TKPdM4N6ZYXhGBu4CN9HswNzA188BHr0Rq5wYM08dOf/rTJa/369cMRRxyB1atXAwA+/vhjmKaJiy66qMF0F110EcaNG4eXXnoJZ511VlzrXbVqFTZs2ICpU6fGNV9ubi4AYNumTcjLzUUgEMA7y5bh9FNOgdfrDU7kkVf//QUF0vjk/fvjKlO8y28N8byHQCCAd5YuxemnnRapw1R4D23NaR02rr+ysjL07N07sq2mq7TNDevWBXODaeKdFStw+ogR8IZywv1du0qXMXnXLmn8YcX8KraDaSxXawAm79kjn8A05fGaGgBAwLLwzqpVOP244+A1jPp4To509oc7dZLGA/K1Y/KaNfIJunSRr99B7lKWQbEdPKDYDkTor5aRgcFz5uB/Y8dC1NSgFsAjQFrnhnTLC0B9fd8KwI+mnwvgoN0ojiuU7Uo1vxMu1/GAom3cqdjnhY8LYtWfE6o6vl+RO5T7ZEX9qJYPONgO3KqoANA0v5aVl6PnkUcyN7RRbrgXQAaC23avOXOwNWrbVu23Vb+F36jYbh9VtMtbnByL7t0rj3foIA2r9psTH39cvvxevQAAASHwTmUlTs/Ohjf6TBbF+lX7dSxdKo/n58vjpaXyeKj8rqg+g27d5PGNGwEEj0/eycnB6RUV8AIoq65Gz/HjWyU3pE3nRSxCCBQXF+OII44AANTW1gIAMjMzG0yXlZUFAPjiiy/iXse8efMAAJdeemlc84VP68rLzUVeXh4CgQCysrKQl5fnuPPCr1hHXl5eXGWKd/mtIZ73EKsOU+E9tDWndRhzGwQOyFMQ0yo3mGb95xLKCW7bfkZcJWrKSeeF4uuJknK7VX0B8vkAoL7+cnMj9QdA2XmhqiPVgV6eagedhM9IWQaX6wh/zrqmISsrCxma1uCzP9ByQyrnBaC+vv2hR6zPRdluDoDOC9V2q6qDcP5sbrtWcbr8ROdX1Y+T4xq3x39KodO+m8uvzA1tkxsyQg8tatsOd0K77bxwuz9xtE3W1akW4q4MjT6vJrKzAQQ7L7KEQF7jzgvFcQNU+33V+kPbT7NU9RMqvyvV1fK4qg5C7yGAYHvIs214o8KtkRvSuvNi3rx52LFjB6ZPnw4A6N+/PwBg5cqVGDlyZGS6FStWAAB27NgR1/Ity8LLL7+M4447Dn379pVOW1tbG0l2AFBWVgYg+IUxEAggENpZBaJ3WorbjeiKRhAIqH6Xk1MtvzXE8x5i1WEqvIe25rQOG9ef2+0nlaVFbjDNyCP8PEzZ9hUHv63RLtxe0ajc/izFuR2Nt+fGdaJYvqqOVO8voCqfy/U7KkOStoPwdOG/mhCRM1sOJKmUF4Dmc4OWkQFd05p8LoCDdqO6jZmqYyEZt0FzuQ63xz6N6y3efOh0+YnOr6ofJ+Vt8f13M/lVmffSVDrlBk3ToIVzddS2ovrKqIq3+HYPqHOD2zKovjiHck+g0d8IW9EFpCq/itv82hq3qVTVQUigmb+tIW1u2NnYmjVr8JOf/ARHHHEEVqxYASN0uvDw4cPx7bff4s9//jNGjhyJ1atX44YbbsDOnTth2zbMODa8d955B2eccQYee+wx3HzzzdJpp06dimnTpjV5ff78+ZGeWqJUUlVVhUsuuQSlpaUt/ytOK2JuIHLnQMwNqZYXAOYGSj/MDcwNRLG0Zm5Iy86L3bt344QTTkAgEMAnn3yCblHX5+zYsQMXXnghVq5cCQAwDAMTJkzABx98gLVr16KkpMTxeq688spIb2wXxXVOsXpJe/bsiR937oycGr5k2TKMPuWU+lPveM+L+O55YZpYsnQpRp92Wv29AVLgPbQ1x/e8aFR/ZWVl6Ni58wF1EJJWuWHbtvrcsGIFRkff86KwULrMybt3S+N/VMyv0hqXjSiv2Vb9whe+54VpYslnn2H0sGENLxtRnF75R8V15YqTNzF57Vr5BJ07y9fvIHcpy6DYDmYptoPIZSOZmZF7A9jV1agRAvfX1BwwuSEV8wLQfG6YnJGBjNCZF9GfC3Bw3PNilqJt3OHwnhex6s8J3vMCQGg40Mb5tay8HB379mVuaKPc8EAoN2iZmeg9Zw62jB0LEdq2W/qeF48p2uX4VrjnhWq/edsTT8iXH3XPiyWVlRgd7z0vFPv1Fr/nRe/e8rgTSbznxZKcHIwO3/Oiqgodf/vbVskNaXfZSGlpKc4880yUlJRgxYoVDRINAHTv3h0ffvgh1q9fj927d6Nfv34oLCxEt27dcNhhhzleT3V1NRYtWoTTTjvNUaLx+/3w+5teqej1ehvcX8Dr8Ti+54VqZxu93ETEszNvKYm8h+g6TIX30NbircNw/bndflJN2uUGj6fBl+3o58q27zJ3qDjpvIjnGvJYlNuf6vTPRnXQuD6hWL6qjlTvr8HNQWNO4G79jsrgcjtovHy7uhp2dTXS7hcNiVTNC0DzuUHU1DT4bMKfC+Cg3ag6H+JsVwlxuQ63xz6N54+uPyfiXX6886vqx0lZW3z/3Ux+Vea9NJKuuSE6P4vq6kjnhSpvq+Itvt0D6vzitgyq3+MbtT2vpjXsvFAN8ek2P7q9H0Rr3GsmzmFOvVGP1pLUzovNmzdjyZIlyMjIwLnnnosc1U0/4lRTU4OzzjoL69atw9KlSzFw4MBmp+3Xrx/69esHAPjuu++wa9cuXHXVVY7X9eabb6K8vDyhG+s04PEEH+EGFX7eCqamwE5mquLXU1UZlfO38PWXqVCHpJaWuUHC7f0k7nLbLpz8oqTI71NjHHg1ELqbfbM2b5bHBwwI/g3/ipmR0SC3Tlesf4ri14eZql9gFGc1zExC7vCpJlDsS1R7msiZF1HT23B/Vk2qSNe8MDnqrKy3VqzA5O3blR1VyeJ2nww4aPuq+aN+cY5lusMyBgIBvLV4MSbv39/gi5Vyv97Sda1YvqPjGpdnt6jqMPqsrCELFuD+oiLY1dWQfzLpI11zQxWCn004Z1fD+Q8Jdynaleo+R8r1bN+uLoRqlC7F/UDuevpp+fzffCOPh487NC14FsZ33zW8j8Tw4e6Wr7pXVI8e8nh5uTzu5FIl1bHV0KHyuOo9DB4c/GvbwJYtwBFHBDs8VOtNooSOkWfOnIk+ffpgf9QpQsuWLcOgQYNw/fXX46qrrsLRRx+Nffv2Ja2glmXhwgsvxMcff4yFCxfi+OOPdzSfbdu4/fbbkZWVheuvvz7yeiAQwJo1a7CrmaHmwteVnXvuuUkpPxG1DOYGImqMeYGIYmFuIEpvCXUv/+Mf/0BRUREKoq49uuOOO2DbNqZNm4bdu3fjySefxKOPPhq5c69bt912G958802cddZZ2LdvH/7+9783iF922WUAgPHjx6OmpgZDhw5FIBDA/PnzsWrVKjz//PPoFTU+7o4dO3D44YfjyiuvxHPPPddgWfv27cPbb7+N8847L+lnjxBRcjE3EFFjzAtEFAtzA1F6S6jzYvPmzbjgggsiz3fu3InPPvsMt912G+6++24AwNq1a7Fo0aKkdV58/fXXAIB//vOf+Oc//9kkHk42Rx11FB599FHMmzcPuq7juOOOw7vvvttgqCOVhQsXIhAI4JJLLklK2Ymo5TA3EFFjzAtEFAtzA1F6S6jzoqysDO3atYs8X758OTRNw1lnnRV57eijj8bs2bNdFzBs2bJljqa76qqrHF2LVlRUhOYGWrnuuutw3XXXxVE6ImorzA1E1BjzAhHFwtxAlN4SuudFly5dsGXLlsjzJUuWwO/34yc/+UnktZqaGmitcVdUIiIiIiIiIjqgJXTmxbBhw/DGG2/gX//6FzIyMvDyyy9j5MiRDYb1+f7775sMO0REREREREREFK+Ezry46667YJomzj77bJxxxhmoqanBXXfdFYnX1tZi+fLlDc7EICIiIiIiIiJKREJnXhx99NH45JNP8OKLLwIAxowZg+OOOy4S/+qrrzBy5EjeoAYIjskbfoSfhy+ncTnWdzLGY1dp6XUko4yu1q8a7101fyvUMR2Y/tS1KzIA6JmZ6L9gAR4pLIRdXQ0AmKIaj72kRBqemZ0tjatGCnfSq3333r3S+NTSUvkCVGOJR91XKaYffwz+DbfBvXuBqLY0ZeNGefk6dJDGffK1K9ttQjvXOM2IOtsxFlsxf7iMejN/qY3k5AQfgUDweXY24PUmZ9mK4w7VZ5+M/ZXbYxtVGaeH5tczMzF4wQLMKiiI5FYAmKrIr8rjnspKeQFUuc1UZGAno1IoPke3wp9RIBDAW4sXY/L+/fB6vSgrK8MDUSMNUuvyhR561HM76v9u/Flx3KDc4sL7ZJmKCnn8H/+Qx6PacUyLFsnjhYXBvz4fMGEC8OqrQF1dffyTT+Tzf/65PD50qDyekSGP9+0rj+/fL48D6vzjMj79mGMAROXXn/4UdnU1FEtNqoSz35AhQzBkyJCYseHDh2ORagMiIiIiIiIiInIgoR9YRo0ahRdeeEE6zd///neMGjUqoUIREREREREREYUl1HmxbNkybN68WTrNli1b8MEHHySyeCIiIiIiIiKiiBa7tLWyshLeZF2jSUREREREREQHLcf3vNi6dWuD5yUlJU1eAwDLsrBt2za89tprKCoqcl1AIiIiIiIiIjq4Oe68KCoqghYaJUPTNDz22GN47LHHmp1eCIGHHnrIfQmJiIiIiIiI6KDmuPPiiiuugKZpEELghRdewJAhQzA0xpAwhmGgffv2GDVqFH72s58ls6xEREREREREdBBy3Hnx3HPPRf7/wQcf4Oqrr8bNN9/cEmU6sHg8wYcQDZ8D6rG+FeN4ux0rXTV/a4hnzHg9MxNDFizA/Y3GbJcuX1VHLuugNca8d1uGVPicqak6BG86pEc9t5ufvIEZXbpI43cXF8sX0LGjPK4aBxwANmxQTyOjGu9dlR/D46GHzgiExwNEtwXVe1S4S9FuZijanaL0mFpbqy6EYh8wU1EG1Q6+cRntqAelpumKz3yKy+MC1Y3QnNwoTbX9uN5nKfLT1OzsJuWJLtNUv1++/IMA23h6uvndd5GXk4OAbeOt4mKMe+89ePVQqwzvE5ujGGjh5h07pPGZ3bvHUdJmKPZpyMmRxxcvlsdHjJDHV6wI/g3ngOJiwMm+OKxHD3l8wAB5fM0aeVz1/p047TRp+IEOHaTxO+OpjzbiuPMi2vfff5/schARERERERERxdRio40QERERERERESVDwp0XS5cuxc9//nN06tQJXq8XhmE0eXhUpwcRERERERERESkk1Lvw2muv4cILL4Rt2+jduzcGDBjAjgoiIiIiIiIiahEJ9ThMnz4dmZmZeOONNzBq1Khkl4mIiIiIiIiIKCKhy0bWrl2Liy66iB0XRERERERERNTiEuq86NChA7KyspJdFiIiIiIiIiKiJhK6bOT888/H0qVLYZom73XhhqLuXI+FrqBafjKWoSpjPO8hEAjgrcWLMXn/fni9Xmczmabj5cfkcvt2+xklZR3hOoj+q2nu64ZcuXP/fuTl5UW26zvi2K7v3rjR3cprauRxJ9uGajxy1TI6dpTHVWUsKQn+DW//JSVAdD5SrF/VsqcrcpvbobpmhseZl1B9Cm7LcHeo7sLb4KTQNlhWVob7CgpcLp3agmq7VZmi2mc7WH6L7/cUbXtqo+268TGD8riltlYed9B2nZSvWRUV6oWo8mu7dvIyVFY6Wz6PG1JLaWnwMxCi/rmmBf+v2if/4x/y+IknSsN3vfuufP6hQ+VxAOjbVx5XbV+LF8vjquOGPn2Cf32+4N+iIqCurj7+s5/J51cdt6i+M6jKN3y4PO6k/SmmubO4WD7/kiXS8JRPPwUABGwbbxUX44733oNX11FWUYH7Tz1VXb4kSOjYZ+bMmWjXrh0uvPBCbN26NdllIiIiIiIiIiKKSOhn5cGDByMQCOCTTz7BP/7xD7Rr1w75+flNptM0DRvd/kJIRERERERERAe1hDovbNuGx+NBr169Iq+J8ClMUWK9RkREREREREQUj4Q6LzZv3pzkYhARERERERERxeb2fl9ERERERERERC3K9VAh3333HdasWYPKykpcfvnlySgTEREREREREVFEwmdefPbZZxg6dCgGDx6MCy64AFdddVUktnz5cmRlZeHNN99MRhmJiIiIiIiI6CCW0JkX3377LUaNGgVd13HrrbdizZo1ePvttyPxESNGoGPHjli4cCF+9atfJa2wBxzVWOWKscbdavGx2J1QjVmsGjNZNb/b5Sskow6VY86nwudErUt1XyHVWOOK8d5nKLY5J73aqtHGp6jGEq+okMczMuTxcNsNt4+cHCD6fSnqSFV+VWZQxW2XcSfruEuRG2YqPufwdqBnZuKIBQvwUEEB7OpqKEaipxZ2f0EB/Ah+LkMWLMD9oc/FCdX+Yrpim3C7fADu97tuc0N4/dF/NS0SdrtPVc2v3KcnYZ+vWoZb4X2AnpmJwQsWYFanTswNqSA/P7ivs22gpib4XA99Wt98I5/3xx/l8YUL5fF27dwtH8CfL7xQOY3MzU8/LZ9gwwZ5fNCg4N9wnZ14YrAuwz7/XD7/scfK46o6OuccafiBLl2k8TsdfC+c4fdL43eXlsoXMGKEPP7SS8G/mgZ07gz873+AEIDDfVQyJHTmxb333gsA+OKLL/Dwww9j2LBhDeKapuH444/HZ5995r6ERERERERERHRQS6jz4oMPPsB5552Hvn37NjtNr169sGvXroQLRkREREREREQEJNh5UV5ejs6dO0unqa6uhsXT3YmIiIiIiIjIpYQ6L3r27In//e9/0mm+/PJLHHrooQkVioiIiIiIiIgoLKHOi1/+8pd45513sHTp0pjxV155BZ988gnOUdyYhIiIiIiIiIhIJaGhFu666y68+uqr+PnPf44rr7wSu3fvBgA8+eST+Pjjj7FgwQIUFRVhwoQJSS0sERERERERER18Euq86NSpEz744ANcfvnl+Nvf/hZ5/Xe/+x0A4Cc/+QkWLFiA/Pz85JSSiIiIiIiIiA5aCXVeAMAhhxyClStX4uuvv8Ynn3yCffv2IS8vDz/5yU+aDJ1KRERERERERJSohDsvwoYOHYqhQ4cmoSgHIY+76p9qGPJ4Ooz24rIOXC9fEVfVsYqTz6DFP6fwexSi/nn4QW2npgbw+QDTDD6vrQVC28IMxc2O766tlcanK7bbKaptLnQpoHQd3bvLJ6ipkcd79HA3f0mJPP7jj9Kw6oZPqvidis9Atf7I5y7TsaM8XlGhXoaEHeO5HeN1al2T9+9HXl4eAoEA3lq8GJP374fX6wWgbtspcVyg2rbd7ntUuaFxOaJya1KWr2iXbutY9RkDwFTVPsDvl8anqPJX6DMKb4N3hLbBsrIy3F9QoCwftZDDDgPy8oLbdnFx8LnT9qTaJ7ncn6CoSDnJzXv3yifIyJCGZ2RnS+N3f/GFfPk5OcG/lgWsWwecdx4Q3d769pWv3+V3AvO666TxqaryOzg2u/vdd6XxmYqrIlRHJlM+/TT4H9sOboODBwO67n77iUNCN+wkIiIiIiIiImotjrrrpk+fDk3TcNNNN6F9+/aYPn26o4VrmoZ77rnHVQGJiIiIiIiI6ODmqPNi6tSp0DQNF154Idq3b4+pU6c6Wjg7L4iIiIiIiIjILUedF++//z4AoFevXg2eExERERERERG1NEedFyeffLL0ORERERERERFRS+ENO4mIiIiIiIgopTk682Lr1q0JryB8qQkRERERERERUSIcdV4UFRVB07S4F65pGkwnY9kfwO4vKIAfgJ6ZiSELFuD+ggLY1dUAWmm8dQnVePBJWYfiPboek141vrVq+1PEVWOpux6vHu4/B2UZw8Lv1TQBTVPXDbWsjIzgIxAIPvf7Aa8XAHD3jh3yeRWf3ZSNG6XxmYptLhlbxvTevaVx1Wl/d6vGgy8sDP4N11/nzpH6A6Bsm1NKS6XxGYqx0Gf4/dL43YrPAB07yuOAuo2Gx6xvbnbF4sM1pEc9t+HwwIBazAMFBchA8Lhh8IIFmBV13GAr5nV7Oq3rfTLgfr9YU+Nu/rBw+6mpaVimjAz5fKryl5TI46rlK+JTHNTxdMXnpFwG9//pyTTrH+HnITM7dJDOetfatfJlV1TI49u3u4sDQN++rsqgyix/OuYYabwu9FfPzET/BQvwyDHHRHIroM6vd3/7rWIKhc8/l8eXLpXH16xRr6NHD2n4rjfekM9/7LHS8J+7dwcAaJmZ6LVgAWaPGgVRXY0kZW1HHO1hrrjiiiadF5s2bcKKFSvQrl07DB06FF26dEFxcTG+/vprlJSUYMSIETjkkENapNBEREREREREdPBw1Hnx3HPPNXj+7bff4oQTTsBdd92FyZMnIzs7OxKrrKzEfffdh6eeegpPPfVUUgtLRERERERERAefhM4wvP3223HcccdhxowZDTouACA7OxszZ87EscceizvuuCMphSQiIiIiIiKig1dCnRcrV67EcccdJ53muOOOw4oVKxIqFBERERERERFRWEKdF7ZtY8OGDdJp1q9fDyEEAGDFihW47LLLcPzxx2NH6EZ0L774Ij788MNEVk9EREREREREB5GEOi9OOukkvPbaa3jppZdixhcsWIDXX389Mt0ZZ5yBzMxMfPXVV6gNjYxQWlqKmTNnJl5yIiIiIiIiIjooJDSe1YMPPogVK1bg0ksvxaxZs3DiiSeic+fO+OGHH/Dhhx/iv//9L3JzczFr1ixceumlmD17Nq644ooGnR0nnHACZsyYkbQ3QkREREREREQHpoQ6LwYOHIiVK1fid7/7HZYvX47//Oc/DeInnXQSnnjiCQwcOBBr167FSSed1GQZ+fn5KFGNlX0AmLx/P/Ly8hAIBPDW4sWYvH8/vF4vAPfjqTsab90lZRkU78Ht8lucajx31VjobuNwXweOt6PQZVzweOof1HZijdceHpK6XTv5vBkZ0vCMQw+Vr1pRNCen5KnGQ1ct4+7SUvkEOTny+I8/Bv+G66+0tOE2rZpfEVfVkbL1FBXJ4w5yw3S/31UZVJ9BZMz7qOc2gICyZNSS7mx03HBH1HHDdJf7XCfbnev5W3q/WVOjLgMAhPd9lZVAdL25rQNF/nW7fCfHVaq27fbYLLKezEwMWbAA9xcUwK6uRm1SlkqJerhPH2Qg+LkcvmAB/lhUBLu6GoB6n/xA//7S+J2VlfIFdOwoL1vv3ooSAKqWe/eWLdL4nY2+bzahOq4NHVsFLAtvffklbl27Ft7otvLNN/L5X31VHu/RQx4PH7c055NP5HFV7gGAr7+Wx1XHJoo6KAv9DeegcgS3PYdZOSkS/vYyaNAgLFu2DNu2bcN//vMflJaWIj8/H0OGDEHPnj0j0xUWFmLDhg0oalRZH374IQ455JCEC05EREREREREBwfXP7327NmzQWdFY9deey3Gjx+POXPmQNM07Ny5Ex9//DEmTpyIe+65x+3qiYiIiIiIiOgA1+Lnjd95552wbRunnnoqqqqqcNJJJ8Hv92PixIkYN25cS6+eiIiIiIiIiNJcwp0XlmXhlVdewdKlS7Fz587IKCLRNE3Du+++i9///veYNGkSNmzYgIqKCgwcOBA5quuRiYiIiIiIiIiQYOdFZWUlTj/9dHzyyScQQkDTNIjwzQCByHMtfPM5AD6fDwMHDnRfYiIiIiIiIiI6qCTUeTFjxgx8/PHHmD59Om688UZ07NgRU6dOxXXXXYfly5fj6quvRrt27XDsscfi17/+tXRZr7/+ekIFJyIiIiIiIqKDg5MR8Zp4/fXXMXz4cNx9991o37595PUuXbrgggsuwM9//nP8+OOP2LZtG/Ly8vDuu+/i888/j0z3xRdf4N1330V+fn7CBb/vvvugaRoGDRrU7DQlJSXo3LkzNE3Dq6rhbUKKi4tx9dVXo3PnzsjMzMTRRx+NhQsXJlxOImpdzA1EFAtzAxHFwtxAlD4SOvNi69at+MUvfhF5rut6g3tevPLKK7jiiiuwatUqdOnSBWPGjMHs2bNhhMbStSwLN954I/Ly8hIq9Pbt2zFz5kxkZ2dLp5syZQqqqqocL7esrAwnnngiiouLMX78eBQWFuKVV17BmDFjMG/ePFxyySUJlbfNKMYanxoeA90F1TLcjjXudvnK99gaY967lKzx2g8GB0xucDKWt4SqV1qV+JOxVU9x2XZ9iuWHy6hnZmLwggWY1bdvZLx7QD3mvaqOpsa4j1O0GX6/YgkKFRXKSVRlVH1OU/bulU8QuvdUIBDAW0uXYvKePfB6vSgrK8MDnTopy5dO0ik3PFBQgAxEbdsFBZFt2227mu5yu53qYH7XxxYdO8rjqrYTzp+mCXz3HdClC+CJynqq/bZHkSFrapytvzlJOG5Q5TdyLp1ygx71ABruI7IU86q2uocV7181v6JVAFDv19Gjhzy+YYM8XlQkj7/0UvCvpgGHHAK8+ioQddsDqPLbN9/I4yoOO72aJelci+jbVx4fOlQeHzBAGg7fsTJ8Y4hsAAKtMAJIlITOvMjOzoau18+an5+PXbt2NZimsLAQW7duxZw5czBx4sRIxwUAGIaBCRMmYM6cOQkVeuLEiRg+fDiOPfbYZqf55ptv8NRTT+GOO+5wvNynn34aGzZswD/+8Q/84Q9/wE033YT3338fw4YNw2233Ya6urqEyktErYO5gYhiYW4goliYG4jSS0KdF71798bWrVsjzwcNGoT33nsvcvaFEALvvvsuunbtCtM0sWbNmibLWLNmDWw7/r7j5cuX49VXX8Wjjz4qnW78+PE499xzMWLECMfLXrFiBTp16oRRo0ZFXtN1HWPGjMHu3bvxwQcfxF1eImodzA1EFAtzAxHFwtxAlH4SOsvj1FNPxdy5c2GaJjweD6688kr85je/wfHHH49TTz0VH330Eb7++mvcdtttME0T11xzDTZu3IjjjjsOAPDpp5/igQcewNVXXx3Xei3Lwrhx4/Cb3/wGgwcPbna6hQsX4qOPPsLq1auxefNmx8uvra1FZmZmk9ezsoInY33xxRcYPXp0XGUmopbH3EBEsTA3EFEszA1E6Smhzotrr70WHTp0wJ49e9C1a1eMHTsWX331FZ588kl8/fXXAIDzzjsPU6dORUZGBgoLC/HHP/4xcmlJ165dMWnSJNx2221xrXf27NnYsmULli5d2uw01dXVmDhxIm699VYUFRXFlWj69++PpUuXYsuWLejdu3fk9RUrVgAAduzY0ey8tbW1De77UVZWBiB4LXEgEEAgdI1jIOpaRz1GUosWCAQclz0m1XWV0dd5tRDVe1SJroMWqUO3154m4b4hUJQxWXXYuP5cb18p5EDLDW7bptttJqFT8hpRbV+qMqrKELnmN7SceN+zavluy5+M3OO67avWwdwAIPVyg56RAV3TYm7bLd2ukqHFj10cxmPmVifzqzhsV4ly2+6TqfE2qAmhvudHmjjQcoMGObdt3+k+2c00ytyhOjZSHZNrwVoKNPrbON4s1f1wdMU7dHuvLNX6AUB1nzzVVQ+K/KaFc0Hjv62YGzQhkvcNds+ePdi0aRN69+6NwsLCJvFw40vkRp179+7FYYcdhrvuuivS6XHKKafgxx9/xDdRN1C599578de//hXr1q1DTk4Oli1bhpEjR2LhwoU4//zzpev473//i2OPPRZHHXUUHnnkEXTp0gWvvPIKpk2bhtraWlxzzTV49tlnY847depUTJs2rcnr8+fPj/SyEqWSqqoqXHLJJSgtLU345rmpgLmBKLmYG5gbiGJhbmBuIIqlNXNDQmdeTJ8+HX369MHll1/e4PVOnTqhk+QO5W7eTHhY1nHjxjU7zebNm/HQQw/hiSeeQE5OTrPTNefII4/E/Pnzcf311+OEE04AELzx6KOPPoobbrhBuszJkydjwoQJkedlZWXo2bMnTj/tNOTl5SFgmliydClGn3YavKGes/sLCqTlmbx/f9zvoQG3d9ROAtV7VImugxapw1Q480LRE5usOmxcf+HOxHSXtrnhlFPqc8OyZRh9yimR7dpt2/yjYptR3W0oGaONqNqearuOZ7SRI+bMwbdjxyZ1tJE79uyRxh9SjMYxSZV7SksVJQAeUtw5XfUe71D9Shi6u3zjbZC5wbmWyA3fjh2LjNCvq4237TtctivVdp+MUSxa/NilslIeD432ETBNLFmxAqNHjKjPrU6W73a0EcWoDSr3p9BIP3pmJgbPmYP/hbbBmlY4Y7c1pGtuWB+VG/rNmYP1UblB9Zu+26PV1hht5DZV7vjwQ3m8Vy95/F//AhA842JJnz4Y/f338MYz2ohq/QMHyuNvvCGPqxx+uHqaLl3k8QsvlMcPO0wafjJ0coKWmYnec+Zgy9ixEK2cGxI6Qp4xYwZuueUWR9P26dMHmuQ0nE2bNimXsX79ejzzzDN49NFHsXPnzsjrNTU1CAQC2Lx5M/Ly8jBlyhR0794dp5xySuTUrt27dwMInhWyefNm9OrVq8FIKY2df/75+NWvfoX//Oc/sCwLRx99NJYtWwYAOEzygfr9fvhjbPRerxder7f+uccTeR59oB1L9HwJcXv6UxKo3qNKrDpIah2q6kjF7fwAoChjsuswXH+ut68UcKDmBrdtU7XNqL6gJOMLjGr7SnYZ7erquDovVNyWX9m+HHzGbuvIq1oHc0NK5ga7pqbBZxu9bbvdLlVao+0ruT12aRT3ejzqtuBi+U24fP9uP8OWEN4GD4SuiwM1N6g+G7dtOxnHDcp9lttjdtUlE42+YHuFaNh5ofoCrur4VF2SoRiCXcnJD66qH1VVl7Yo8ptolJ9EdXXwoS5Z0iR0hNyrVy+UlJQ4mrZxJ0cgEMBXX32FxYsXY9KkSY6WsWPHDti2jZtvvhk333xzk3ifPn0wfvx4bN26FRs2bMAhhxzSZJobb7wRALB//360a9dOuj6fz4dhw4ZFnoevhzvttNMclTfa/QUF8CPYez1kwQLcHzVeu+ux0JN0XahUC49XrqqD6DHrE6lD1Zj3bsuXjA4gVRndvsdwvHH9uUyhKSGdcwM8nuAjvLMMPweAigr5vD/+KA3fpdhBTnV73SXUv+DOUGyXblvOlFC7CAQCeGvxYtyxf3+DA5/pLtc/XVFHbq8fntmhg3Iat+uYrljHFLcHUiksrXODhGq7VpnSwvtMJ8twu107fQ+RfV5hYVwdAqryqdav4nafnxQOj90CgQDeWroUk/fsgdfrRVlZGR5IoTNDEpHOucETeuhRz512SqgGZnW7T1Z8W3BGcbbgjFNPlcZV7+HO8KU44S/wlZUNOxw6dpQvQPWdSHXspthWoDrDR7V8ABg6VBqefswx0rjqGonwtqdFPRdonfsphSW0rV500UV44YUXUFpaivz8fOm048ePj/n6E088gc8//9zR+gYNGoRFixY1ef3uu+9GeXk5HnvsMRx66KEoLS3Fj40O6r/55hvcc889uP3223H88ccjO3Q6X1VVFbZu3YqOHTuio2RjXb9+PWbPno1f/vKX0l5SImp9zA1EFAtzAxHFwtxAlN4S6ry455578PXXX2PUqFGYPn06hg0bhs6dO8e1jDPPPBOTJ0/G3LlzldN27NgR55xzTpPXw+Myx4qFhXtEhw0b1mC6VatWYeTIkbj33nsxderUyOsDBw7EBRdcgF69euH777/HU089hfbt22P27NkO3hURtSbmBiKKhbmBiGJhbiBKbwl1XoTvdCuEwK9+9atmp9M0DWYzp6a9+uqraN++fSKrb1FDhgzB3LlzUVxcjI4dO2LMmDGYNm1a3J0zRHRgYW4goliYG4goFuYGouRLqPNixIgR0ptwRjvqqKMaTCuEwO7du7Fnzx48+eSTiaw+InzTG5lTTjkFsUaDbe71BQsWuCoTEbU95gYiioW5gYhiYW4gSg8JdV44aeBhU6dObdB5oes6OnXqhFNOOQUDBgxIZPVEREREREREdBBp8bEyo6/9IiIiIiIiIiKKV4uPbGIYBn744Ycmr+/duxdGEobkIiIiIiIiIqIDW1xnXvzlL39BWVkZJk+eDD00Ru5jjz2Gxx57rMm0I0aMwPPPPx/zGjAAqK2thc/nS6DI6WXy/v3Iy8sLjpW9eDEm798Pr9ebnIWrxul2G3cyjWJM4qm1tep1yOaPGu+8JerQ9XjqivqZ6ve7W34ShN9j4/orKyvDAwUFbVy6g1hNDeDz1W9DtbWA0+3Ro0jdjYZ3a0zZLp3khpoaaXhGhw7qZUioxqSfGur81jMzMWTBAtxfUAC7ujoSd3taYUufluighjGlslIanxkapo8OHrYirtqnTXX5o9FUxTYJAMjIcLUOZRkU70Fv5m/YFLf7fZeS8Rm5PnZRCe9jwsfwHk/9g9rMzY2+U4yLOh5WbTfyo3Vg4pYt0vjU3r3l8Q8+UKwBwNCh8vju3dKw6lujMvPcckvwr2kCK1YAN97YcJv+5hv5/KHRZppVUiKPn3iiPK469pKMhON0GVPOP18an644rphSWgoACJgm3lqxAtdv3w6vx4OysjLc0bOnunxJ4DgLrVq1CrfccgvuueeeSMcFAJSUlGDz5s3wR31JE0LgxRdfRKdOnaBpGp599lnkRH3JtSwLy5cv5z0viIiIiIiIiEjJcefFvHnzkJGRgVvCvVZRNE1DddQvXmVlZWjXrh2eeeYZCCEwe/bsBpeI+Hw+FBUVcZxjIiIiIiIiIlJy3Hnx4YcfYsSIEWinOmUGQF5eHs4++2xs2bIF+fn5eP3111HA09OJiIiIiIiIKAGOOy82btyIk08+ucnrQoiY97Xo1asXli1bhv3797srIREREREREREd1Bx3XlRVVSE7xk08br31Vlx99dUNXpswYQL8fj+qqqowYcIE6XL/9Kc/OS0CERERERERER2EHHdeFBQUoLi4uMnr+fn5yM/Pb/DaV199hV69eiE/Px9ffvklNE1zX1IiIiIiIiIiOig57rw4/PDDsXz5ckfTvv/+++jfvz8OP/xwLFu2LNGyERERERERERE577w488wzcdddd2HBggW4+OKLpdO+9NJLWL9+Pa655hqMHTsWjz32GHJzcxtMU1lZiXHjxv0/e3ceF1XZ/g/8MwMDjCKLguAKpmZuqblkaW5l+/q0W1lplvWYmmv5uKAZmtlmZvZUaotiqflr+ZqlpWWm5pI+ZW64hSikJqAIOMv5/cHMOMBwXwfOoAx83q/XvGDmOss995xznXvuOefcmDdvXvlKTvJ421L8zBnjZZCWYXRM8Mo+prgfymd0vPYKH++dKsT0evUQBsBstaJtSgpejo+H0zVq08T0dPXMYcJo5sI430leQ1v7Io2lDgBOIW42GBdGO/ccvMxez50+4qU5J8Sl8hmlJ3NMFcZbH19QoJ5f+Jzd24HZakW7lBRMi42FMy8P6qVSRdNQdFt2Qt7f3JK8RnYrD2m7TxK2SQBIys01VAYxfwnbvXt+m92OFWvWYGxGBix+bEtIdSzVod7PUmWKUAaj+dk9vyc3REczN1QCc6KjEQbAZLUiMSUFc6KjobnaDeHCvGelhQv7nZgbfNwXscQ0UtsmI0MZHiO0d2cK+8VU15UCZqsVrVNS8ErDhp52FyC3faS41G4ZtWePeoITJ9Txd94R1gDgmmvU8d27leGJ336rb36nK0vs3QuYzf75TqmT7vbZ4MGDUbt2bTz55JP4+OOPS53uk08+waBBgxAbG4vBgwfjww8/LDKMqlteXh4++uij8pWaiIiIiIiIiKoN3V3RkZGR+PTTT3Hbbbfhsccew8SJE9GzZ0/Ur18fAHD06FH8+OOP+OuvvxAWFoZ58+Z5RiI5ffo0wrx+KXQ4HFixYgXq1q3r/3dERERERERERFVKmc6j69OnD9avX4+hQ4fi559/9nnmRPfu3TFr1ixcccUVMJlMMJlMuPTSS0tMZzKZMHny5PKXnIiIiIiIiIiqhTJfBNi+fXv89NNPSE1NxYYNG5Dhuj4pLi4OV199NZo1awag8KadmqahT58+WLZsGWrXru1ZRkhICBISEjxnbRARERERERERlabcdzBq1qyZp6PCl56uG7ccPHgQjRo1gtlc0bc/IyIiIiIiIqKqqMKHckhISAAAnD17Fn/99RfOnSt6j/fLL7+8ootARERERERERAHMUOfFhg0bsHr1ahw9ehQFPoauMplMmD59Oh5//HF88803Ppfh4DCPRERERERERKRQrs4Lu92OBx98EJ9//jk0TYPJZIKmaZ64+7nJZEJ+fj6ysrKwadMm9OrVC8uXL0dmZiamTp2KV1991W9vJBBJY4UnSR07RsctD5dGhdaxDmFcaMPLl0jjwRvsHJM+I3F+PeuX6tCP49NT5RHsepi9nrtGzUZygwaGlu0U4tIWJc3vjzJIxH0nPx8AYLPbsWLNGozOyIDFa1+ZXrOmcvaK3qumCrlDT+ZM8vGjQBFZWcrw+MxM9fLj4nSUgi60F44fR0REBGw2G1asXo0Xjh+HxWIpDBo8JieFhirj0n6r65h25ow6Lh3z9LRNVNx15G6XBgWV6Thq9Lg/0eiPcnraVcL7kd6DP3I8XXhnUfjZudsNeTj/WUoX59eQFh4TowxPzM1Vz+81qmSpXMftUiUmquOHDinD0p4z3rVv2mw2rFi5EqNPnTqfWwG8Juw3w3fuVMaTW7dWF+DECXVcqp/mzdVxAPj9d3W8Vi11vE0bdXz37sK/7vyamwuYTIV/L5By3Yji1VdfxbJly/D4449jy5Yt0DQNw4cPx4YNG/Dyyy8jKioK9957L/bv348ffvgBr732Gjp16gSz2YyEhAQ8/PDDmDFjBqZNm+bv90NEREREREREVUy5fnxauHAh2rRpg/fff9/zWlRUFK688kpceeWVuPnmm9GlSxf06dMHubm5qFu3LgAgOjoax48fx6WXXoq2bdti27Zt/nkXRERERERERFRllevMi9TUVPTq1cvz3GQywWazeZ63bt0at912G9555x20aNECe/bsAQC0a9cO7777LtLT0zF37lzUq1fPWOmJiIiIiIiIqMor15kXISEhqFHj/NVT4eHh+Pvvv4tMk5CQgK+++grvvvsujh07BgCYNGkSbrzxRnzyyScICQnBhx9+aKDoRERERERERFQdlKvzolGjRkhLS/M8v+yyy/DTTz95btIJABs3bkTt2rXx8MMPe6br2LEjDh8+jN27d6Nx48aIEW4OQ0RERERERERUrs6Lnj174osvvvB0Vtx///0YNWoUbr31Vtx888146623sHfvXrRu3RojRoxQLuu1114rV8GJiIiIiIiIqHooV+fFgAED4HA4kJ6ejoYNG+LZZ5/F2rVr8fXXX+Obb76BpmmoVasWIiMj8dtvv5W6HPdZGkREREREREREpSlX58UVV1yBd955x/PcYrHgyy+/xJYtW7B//34kJCSgS5cuMJvLdT/QKmVadDRCAZitVrRLScG06Gg48/J0zSuN061rvHWVMox7XmH0jGdefFq7vXBMYaByvAejDI7Xbng7oIvCifPjsxd/LmXOcQUFynhyaKgyLu11TiEOyNtdsrDdSmWYIszvriOz1YrWKSl4JT6+SG7V8x5UJgrvb6pQPmn9IXoKIeW3sDB1/MwZZTjJNS67zW7HijVr8EJGBizBwcjJycF03lC7csrPV4aTatZUx6XjhbTNCNu9rnVI70HIX0lC/nPP72l3xcbqbncBleCY6od2jXQMmaizDqlyCQcQBsD9029NAJrr/3+EeaPEhYer40J7XTpmA8DErVvVE0jbfny8Mvx8ZqZ6/iNHCv+69/GjRwGvco9Yv149v+IHeQAY9/336vlPnFDHMzLU8awsdRwArrtOHU9MVIan16mjjLvveGmyWtE4JQVzb7sNWl4e1Fndv/z6za9Tp07o1KmTPxdJRERERERERNUcT40gIiIiIiIiokpN15kXAwYMKNfCTSYTPvjgg3LNS0REREREREQE6Oy8WLBgQbkWzs4LIiIiIiIiIjJKV+fFwYMHK7ocREREREREREQ+6eq8SEhIqOhyEBERERERERH5xBt2EhEREREREVGlpuvMi7/++qvcK2jcuHG5563upLHG9Yy3XtEu6Hjomms06+Bgv4yDrseFeH/S53jRx5ynCmH2egBFe5KdwrzTQ0OVcfVo7DI9vdpG84+0XUtjxrvfo9nruXe9hQjrl96jtH5p+dJnOM4f+3V4uDou5cmwsMK/Nlvh39BQwGIBzp0zXjYqP/cxrhzHvKSCAnVcOt4I8+uSn29o9qTcXPUEdnWGS8rOBgDY7HasWLcOLxw5Aot3/Un7jVT+im5/XID2TZJwDKHKye56mLyeu7KEZ7svlbTdnzmjDCdFRqrj0n4LALt3q+PSvmd030xNLfzrzq0HDgAm0/n4kSPq+du00bf80jRsqI7HxyvDyVdeqZ4fwLj0dGV8Rp06yrjUthnqOkbYbDasWL0ag48fh8ViQU5ODsbGxorl8wddGTIxMREm7w9XJ5PJBLtwkCEiIiIiIiIiUtHVedG/f/9ydV4QERERERERERlVoUOlEhEREREREREZxRt2EhEREREREVGlxs4LIiIiIiIiIqrUdF02MmDAAJhMJiQnJyMuLg4DBgzQtXCTyYQPPvjAUAGJiIiIiIiIqHrTfc8Lk8mEsWPHIi4uTvc9MNh5QURERERERERG6eq8OHjwIACgQYMGRZ4TEREREREREVU0XZ0XCQkJyudUPkkOx8WdPyjI0Pz+IJUhqaDg/BO7/fxf99C9wbo24fKvX6jjC1GH/lqH2WpFu5QUTIuOhjMvDwXyLHSROA3GpZsZ2YW4rtxiVy9lSmioMj5V2K4nZmaqlx8XB+D8ey3+ns8p5waScnPVy69ZUxmXlj9RqEPp/QPAeOlzyM9XhpOE92D0GEIVxG4//3A/dx/zhM8cYWHKsOF2Q4GOI4dUxvBwQ2XAiRPquLtd4F1/ZSHUoejMGWPL11Neoe0jHSMk7u3EZrNhxcqVeOHUKVgsFuTk5GB6dLTBpVN5DTl1ChEREZ7PZYjrcwH0HVNUxu/fr4yL+35GhrySxET1OurUUceFdgH++EMdj4oq/Ot0Fu6nkZGA2av10LChev6YGHW8fXtleLrwGdVQL13Xfj3LdaJBacYY/F7jbtuZrVa0TUnBy7GxcOblQcj6fsUbdhIRERERERFRpWboZ+uMjAxs3boVWVlZcJTSk9O/f38jqyAiIiIiIiKiaq5cnRf5+fkYNGgQFi9eDKfT90ksmqbBZDKx84KIiIiIiIiIDClX58Xzzz+PhQsX4tJLL8WDDz6Ihg0bItjgvQeIiIiIiIiIiHwpV4/DZ599hlatWmHr1q0IFW7KRkRERERERERkRLlu2JmVlYUbb7yRHRdEREREREREVOHK1XnRokULZErD1RARERERERER+UG5LhsZPXo0Bg8ejNTUVDRr1szfZapSXig2JvMLXmMyGyWNxWt0PHc9y6jwMnjfS0XTzr/mel1av8To+/OHC1XHHK89cEhjeZ8T4tJ47MnSWXN2u7CG82N9l2aisF0mS/uWMJ66uZS/nvULdZAklD9EGZU/oynC+9Pzy4G0DKkM0nugABQergwb3Wak7VLarwAAYWHquJRfpHuoScvPzy+6nuLrc8fLu35pfqN03EPOaH6R8rO73WG2WtEuJQXToqPhzMuDjk+fKtBr0dEIQ+Hn0iIlBa+7PhcAqCHMO+LkSfUE0naXlaWOS/ulDuKWL62jUyd1/MSJwr8OB5CeDkRGAl77UnLTpsrZpf1KOuZKLauzQlzK3wCQJU0g5K8kaTtx5VOb3Y4VmzdjbGoqLMHByDl9GtMuUJ9AuTovGjZsiBtuuAFdunTB8OHDccUVVyAiIsLntD169DBUQCIiIiIiIiKq3srVedGrVy+YTCZomoakpCSYTKZSp3X44dd/IiIiIiIiIqq+ytV5MXHiRGWHBRERERERERGRv5Sr8yIpKcnPxSAiIiIiIiIi8q1co40QEREREREREV0o7LwgIiIiIiIiokqt3J0XaWlpeOqpp9C0aVNYrVYEBQWVeATrGO6JiIiIiIiIiEilXL0LBw4cwJVXXolTp06hdevWKCgoQEJCAsLCwnDgwAHYbDa0a9cOUVFRfi5u9ZIkjOMdCGW42O8hKQBGu7nYdUQXx6jjxxEREQGbzYYVq1dj5PHjsFgsuuZNDg1VxqcKcX8Qxxt3j6deinPS/MKY8uNzcwG4xhpfswajMzJg8e4wz8hQzp+Una2OR0Yq40a75vWM1y6tQxozXpLsyj1mqxUtU1LwanQ0nHl5UI8CTxUuOLjwoWlFn+ugZ7tSmSgdM+06tjph30dYmDouvdczZ8q2/OL1ly9s4VL5JAbnvxBtArY7qh4pQ8yoU0cZl/bsECEuHtMh5yfpF/U3hOPy8IIC9QJiYgr/uvNYnTpFcoNUBxFCXJp/vJBfXxP2Sz3HfOlzSq5ZUxmXtqMxW7cW/uN0fZpHjwJms5yX/ahcZ15MnjwZ2dnZ+P7777Fjxw4AwOOPP45du3bh0KFDuP3225Gbm4ulS5f6tbBEREREREREVP2Uq/Ni9erVuPnmm9GzZ0/Pa5rrF4J69erh008/BQCMGzfOD0UkIiIiIiIiouqsXJ0XJ06cwGWXXeZ5HhwcjLNnz3qeh4aGom/fvvj666+Nl5CIiIiIiIiIqrVydV7ExMQg13W9sfv5oUOHikwTHByMLOGaZSIiIiIiIiIiSbk6L5o3b479+/d7nnfp0gXffvstDhw4AAA4fvw4li5diqZNm/qnlERERERERERUbenuvCjwuoPrTTfdhB9++MFzZsXw4cNx+vRpXH755ejcuTMuvfRSZGRk4Nlnn/V7gYmIiIiIiIioetHdeVGvXj0MGTIE27ZtwzPPPIMff/wRQa4hXXr16oXFixcjISEBf/zxB+Li4jBr1iwMGjSowgpORERERERERNWD7qHq8/PzMWfOHLzzzjto164dBg4ciBYtWnji9957L+69994KKWRVZXSc7SRpPPYKnt8fpDrwjputVrRLScG06Gg48/IK48J7qOg6viDjsfupDMXrTxgNmyrYjNhYhKHwc2mdkoJXYmM927Vk/MmT6gmiotRxaTxuHfcrEnu+3eOplyIpO1s9/4kT6nhiYuFfm63wb2goYLHoX78w1nmSMF781NBQZXyi8P6mCOPVA8A4Yd9PNph/3Mu32WxYsXIlRp46BYvFgpycHLwYHW1o2VR+06KjEYpyHvMqwXFdlJ+vjkv5S4q72e2Ff8PCgODgkq+XV1iYMpwk5IYLQcrPE4XtZIort7iX4/5rMlQqMsrs9QCKfs7CUR01dCxbRdpr1HuFvmWMEdo2U+vUUcZnCfteluuv2WpF25QUvNywYZF2V4hQPiFziV+q3xCO2SOkdpGQewDI+c1o3J1L7XYgPR245JLC13Jy5LL5ie4zLzIzM/HOO++gU6dO2L59O4YOHYr69eujX79++P777yuyjERERERERERUjenuvKhVqxaeeuopbNq0CX/88Qeee+45REZGYvHixbj++uvRpEkTvPjii0hLS6vI8hIRERERERFRNVOu0UZatWqFmTNn4siRI/j8889xyy23ID09HZMmTUKTJk1w0003YenSpbC5T+clIiIiIiIiIiqncnVeuAUFBeHOO+/El19+ibS0NLz88su49NJL8e233+L+++9HgwYN/FVOIiIiIiIiIqqmDHVeeIuLi8Po0aPx6aefolu3btA0DSelm8oREREREREREQn80nlx+vRpvPvuu+jSpQvat2+P9evXo2bNmnjsscf8sfgitm3bhttvvx21a9dGjRo10KZNG8yaNcsT79WrF0wmU4nHjTfeqGv52dnZGDNmDJo3bw6r1YqEhAQMHDgQf/31l9/fCxH5D3MDERXHvEBEvjA3EAUm3UOl+rJmzRrMmzcPy5cvR15eHjRNQ9euXTFw4EDcf//9CA8P91c5AQDfffcdbrvtNnTo0AETJkxAeHg49u/fjyNHjhSZrmHDhpg2bVqR1+rXry8u3+l0om/fvvjzzz/xzDPP4NJLL0VqairmzJmDb7/9Frt27UKtWrX8+p6IyDjmBiIqjnmBiHxhbiAKXGXuvDhy5Ajmz5+PBQsW4NChQ9A0DbGxsRg8eDAGDhyIli1bVkQ5kZOTg/79++OWW27B0qVLYTaXftJIZGQkHn744TKvY+PGjdi8eTNmz56Nf//7357XW7RogQEDBmD16tW46667yrRMI+O1VwfimPXCmMgVTVo/P8OLL1Bzw5jUVETUqgWb3Y4VmzdjdGoqLO7xs4OF1JyVpY7nC6ORS/PrMDEzUz3B7t3quDSWuPQe3HXk3gePHgW891eh81zad6cYzT16xmMXJBsswzjhPU51Ld9staJ1SgpecR2fpLHsA0Gg5gWJeEwqKFAvQNqvJNJ+q4f0w5aU/6QyuOPF/7pJ+6bB3JSUm6uO16ypXr4fTDTYtvLbNeWVUCDnhnMo/GzMXs+drv/HHz6snDc5IUEZN3rEGiFs9wCQLGz7s+rUUcaN/iQe4fprcv2tBUDzig8X6nCmUIdSdnUKcdGJE/I0Qn6dHhmpjD+/Z496+e786c4x2dmFba/Tp+Wy+YnuzotPP/0U8+bNww8//ACHwwGz2YwbbrgBAwcOxB133IFg6WBj0KJFi5CZmYmXXnoJZrMZubm5sFqtpSYdu92O/Pz8Mp39kZOTA6Dw/h3e6tWrBwCwWq3lLD0RVRTmBiIqjnmBiHxhbiAKbLo7Vx988EGsWrUKjRs3xpQpU3Do0CGsWLECd999d4V3XADA6tWrERERgfT0dLRo0QLh4eGIiIjA008/jfxiveB79+5FzZo1UatWLcTHx2PChAm6hm3t1KkTatasiQkTJuCHH35Aeno6fvzxR4wZMwadO3fGddddV1Fvj4jKibmBiIpjXiAiX5gbiAKb7l6HBx98EAMHDkSfPn0qsjyl2rdvH+x2O+644w4MHDgQ06ZNw9q1a/HWW28hKysLKSkpAICmTZuid+/eaNu2LXJzc7F06VJMnToVe/fuxaeffqpcR0xMDD799FMMGjQI1157ref1G264AUuXLlV20hQUFKDA63RNd6+rKSwMZpMJZlcvq9mrt1VPAqzuvOurPHVoruDe7crwGep9j8Xrz6Rpxk8hrgQCNTfY7PbCh+vUO1tZLkGSpjUa10M6tVpah1M4gVKKF6u3EvUnla+Cc4c/cpPRU7f1lqHE3yqQGyp7XgDK126QiMcko5d96Jlf2vcN7pt6l28r9tffyy+vim6XAMbzj7nYdMwNlSM3mFXfKYTtVvrMTcqofDwqsZ9VQBn8FTe528HFymO0Dg0fs422qwAxP0nvQWyLltL2KlMb1iCTpmmaPNnF17RpUxw4cACDBw/GO++843l98ODBePfdd7F37140b97c57xPPvkk3nvvPWzYsAFdu3ZVrufXX3/F5MmT0a1bN7Ru3Rrbt2/HjBkzcPPNN2PJkiWlzpeUlITJkyeXeH3RokWoUaOGzndJdOGcPXsW/fr1Q3Z2NiIiIuQZKinmBiL/qgq5obLnBYC5gQIPcwNzA5EvFzI3BEznRZs2bbBz5078+OOP6NGjh+f1n376CT179sSHH36I/v37+5x3z549uOyyy/Diiy9i/Pjxpa7jwIEDaNu2LT766CPcfffdntc//PBDPPbYY1ixYgVuuukmn/P66iVt1KgRXggLQ5irl7TtvHn4fcAAzw07Xzh1qkx1UB1Ni472/F+eOvSevyJUhs9Q73ssXn/5moZp+fkB3QgBAjc3nNi9u/CGnQ4HVm3bhr5XXAGL+yZq0qV42dnquHRDOj/csBPCjbVw8qQ6LvXSS7/uua4ltjkcWLVzJ/q2bn2+/gBAuimecNOqlw3mjrHHj6uXHxsrLsPoBZkjhfz0ius9mq1WtJw3D7u8csOLAZ4bKnteAMrXbpC8IGx3kG7oKdFz1oG0DmnfDA1Vx8tw5sWqDRvQ96qrzt8M2Y/LL5WQv6fFx6vn9wOjbSPvMy9az5uHnV654SXmhouWGyZ45QbvnA0Ao3fuVL7vV1u3VsaFvQLSecbPZWQIUwCvCtu+dCtb6YaX0pkP7vlNVisS5s3D4QEDoHnl1meEOnxLqEOjN+wcW2y0mxJ03BRVyq+vN2yojD+3dat6+a72ZfG2V87p04hp1+6C5IaKv1mFn9SvXx87d+4scfObunXrAgBOKRJ1o0aNAAD//POPch0LFixAfn4+br311iKv33777QCA9evXl5psQkNDEerjgKjl5xfZWJ15eZ5EY7FYlOUh+GywlaUO9Tb4yqsyfIZlfY/u+guIXksdAjU3WIKDizSoLUFB+kcbkUahMBrXw2gZpX5zxR3gfS3fEhRUtPNCKl8F5w5/5CajdyYvaxncucHwHdErgcqeF4DytRsk4jHpQpzaa/DLvbRvwiSdHF5sccVyrb+XX4Lw/iq6XQL4v23E3FDoYucGp+o7hXDMlT5zqU0offYWHfc/NFoGf8e1vLwinRdG61CqI8N1qKftZjD/SHVQWttLnM+PAqbzomPHjli1apXnBjtuR48eBQDEKn7FOnDggDgNAGRmZkLTNDiKHdzd1w7ay3Cdo/uEFne/qUnTcPbsWeRrmmfncV/DRqXz/v2mPHVo8DcmUWX4DPW+x+L1554vQE6+KlWg5oYc17BSNrsdZ8+eRc7p0+cPXNJBQBqSSrqm+8wZPUVVk369lNZhdKhUd/05HOfrz7vepHtmCJ0jRq/qlnKDnuUbPUDrLYPZKzc4UTVyQ6DlBUBfu0EiHpMuxFCp0pkX0r557pyxMnideXH27Fnk5OQU/VLgp+WXSrqfgXpuvzDaNnJ33zA3FFVZckPxzwU436YojdFjmrDX6GoPS2WQjnl+O/OilNxqtA6Nnnkh1qGeMy+E/CqVMUdqu7m24eJtL/d8FyQ3aAFi27ZtGgCtX79+RV5/8MEHteDgYC09PV3Lzs7W8vPzi8SdTqd2//33awC0rVu3el7Pzc3Vdu3apR0/ftzz2syZMzUA2vz584ss44033tAAaIsXL9Zd3rS0NA2FnXx88FGpH2lpaWXYEysf5gY++KiYRyDnhkDLC5rG3MBH4DyYG5gb+ODD1+NC5IaAuecFAAwcOBDz5s3Dfffdh549e2Lt2rVYsmQJXnjhBSQnJ2Pt2rV48MEH8eCDD6JZs2bIy8vD8uXLsX79ejz55JN49913Pctau3YtevfujUmTJiEpKQkAcPLkSbRp0wb//PMPBg8ejNatW2Pbtm14//33cdlll2Hbtm0ICQnRVVan04mjR4+iVq1aMJlMnuvV0tLSAvY6wYuNdWhM8frTNA2nT59G/fr1Sx3fPFAwN1RfrD/jqmpuCKS8ADA3+BvrzzjmBuaGqoj1Z9xFzQ0V3j3iR+fOndOSkpK0hIQEzWKxaM2aNdNef/11T/zAgQPavffeqyUmJmphYWFajRo1tI4dO2pz587VnE5nkWWtWbNGA6BNmjSpyOtHjhzRBgwYoDVp0kQLCQnR6tWrpw0aNKhIj2p5ZGdnawC07OxsQ8upzliHxlTl+mNuqL5Yf8ZV1ToM5LygaVX3c7lQWH/GVdU6ZG6o3lh/xl3MOgyoMy8CWU5ODiIjIwP6Ds0XG+vQGNZf5cTPxRjWn3Gsw8qJn4sxrD/jWIeVEz8XY1h/xl3MOgzcc76IiIiIiIiIqFpg58UFEhoaikmTJvkc+oj0YR0aw/qrnPi5GMP6M451WDnxczGG9Wcc67By4udiDOvPuItZh7xshIiIiIiIiIgqNZ55QURERERERESVGjsviIiIiIiIiKhSY+cFEREREREREVVq7LwgIiIiIiIiokqNnRcGrF69Gn369EFkZCRq1aqFjh074tNPPy0yTWJiIkwmU4nH4MGDda3D6XRixowZaNKkCcLCwnD55ZcjJSWlIt7ORTNo0CCYTCbceuutRV4/efIkXnnlFfTo0QOxsbGIiopC165dS9Sxiq+6N5lMmD59ur/fxgVz7NgxPP/88+jduzdq1aoFk8mEtWvXlpju0KFDpb5/k8mEQYMGieuqivVX0ZgX/Ie5oWyYGyo35gb/YW4oG+aGyo25wT+YF8ouEHNDcJmmJo/58+dj4MCB6Nu3L5KTkxEUFIQ9e/YgLS2txLTt27fHyJEji7x26aWX6lrPf/7zH0yfPh2DBg1C586d8cUXX6Bfv34wmUx44IEH/PJeLqYtW7ZgwYIFCAsLKxHbsGED/vOf/+Dmm2/G+PHjERwcjGXLluGBBx7An3/+icmTJ+taR9++fdG/f/8ir3Xo0MEv5b8Y9uzZg5dffhnNmzdH27ZtsWHDBp/TxcbG4uOPPy7x+sqVK7Fw4UJcf/31utZX1eqvIjEv+A9zQ9kxN1RezA3+w9xQdswNlRdzg38wL5RPQOYGjcrs4MGDmtVq1YYOHSpOm5CQoN1yyy3lWs+RI0c0i8Wi/fvf//a85nQ6tWuuuUZr2LChZrfby7XcysLpdGpXXXWVNmDAAJ/1dODAAe3QoUMl5unTp48WGhqqnTlzRlwHgCL1VxXk5ORoJ0+e1DRN05YsWaIB0NasWaN7/muvvVaLiIjQ8vLyxGmrYv1VFOYF/2FuKB/mhsqJucF/mBvKh7mhcmJu8A/mhfILxNzAy0bKYe7cuXA4HJgyZQoA4MyZMyj8TEp37tw55Obmlmk9X3zxBWw2G5555hnPayaTCU8//TSOHDlSau9YoPj444/xxx9/4KWXXvIZb9KkCRISEoq8ZjKZcOedd6KgoAAHDhzQva68vDzk5+cbKm9lUatWLdSuXbtc8x47dgxr1qzBv/71L5+906WpSvVXUZgX/Ie5oXyYGyon5gb/YW4oH+aGyom5wT+YF8ovEHMDOy/KYfXq1bjsssuwYsUKNGzYELVq1UKdOnUwYcIEOJ3OEtP/8MMPqFGjBsLDw5GYmIg333xT13p+++031KxZEy1btizyepcuXTzxQHX69GmMHTsW48aNQ3x8fJnmzcjIAADExMTomn7BggWoWbMmrFYrWrVqhUWLFpW5vFXF4sWL4XQ68dBDD+meh/WnD/OCfzA3XBzMDRWHucE/mBsuDuaGisPcYBzzwsVzsXID73lRDvv27UNQUBAef/xxjBkzBu3atcPnn3+OqVOnwm63Y9q0aZ5pL7/8cnTv3h0tWrTAyZMnsWDBAgwfPhxHjx7Fyy+/rFzPsWPHEBcXB5PJVOT1evXqAQCOHj3q/zd3gUyZMgVWqxXPPfdcmeb7559/8P777+Oaa67x1IPK1Vdfjfvuuw9NmjTB0aNH8fbbb+Ohhx5CdnY2nn766fIWP2AtXLgQ9erVQ58+fXRNz/rTj3nBP5gbLg7mhorD3OAfzA0XB3NDxWFuMI554eK5aLnB8IUn1ZDZbNYAaNOnTy/y+o033qhZrVYtJyen1HmdTqd2ww03aMHBwVpaWppyPX369NFatmxZ4nWHw6EB0IYNG1au8l9se/bs0SwWi7Z06VLPa3qu5XM4HNqNN96ohYSEaNu3by/XugsKCrQ2bdpoUVFR2tmzZ8u1jMqkLNen7dmzRwOgPffcc+VeX1WrP39iXjCOucF/mBsqD+YG45gb/Ie5ofJgbjCGecG/AiU38LIRhXPnziEjI6PIw+FwwGq1AgAefPDBItM/+OCDyMvLU55+ZTKZ8Nxzz8Fut/scisab1WpFQUFBidfd1wm5y1FZlVZ/w4YNw9VXX4277767TMt79tlnsXLlSrz//vto165ducoUEhKCIUOGICsrC1u3bi3XMi6U0uqvvBYuXAgAZTq9q7hAqr+KwrxgHHODMcwNlRNzg3HMDcYwN1ROzA3GMC8YV5VyAy8bUfjll1/Qu3fvIq8dPHgQ9evXx759+xAXF1ckVrduXQDAqVOnlMtt1KgRgMJTllTq1auHNWvWQNO0Iqd6HTt2DABQv359fW/kIvFVfx988AFWrlyJzz//HIcOHfK8brfbkZeXh0OHDqF27dqIiIgoMt/kyZMxZ84cTJ8+HY888oihcumt/4uttO0vMTGxXMtbtGgRWrRogY4dOxoqV6DUX0VhXjCOucEY5obKibnBOOYGY5gbKifmBmOYF4yrSrmBnRcK7dq1w6pVq4q8Fh8fj44dO2Lfvn1IT0/HJZdc4om5rxmLjY1VLtd9V1tpuvbt2+P999/Hrl270KpVK8/rmzZt8sQrM1/1l5qaCgD417/+VWL69PR0NGnSBK+//jqGDx/uef3tt99GUlIShg8fjrFjxxoul976v9hK2/7KY9OmTUhNTfXc0dqIQKm/isK8YBxzgzHMDZUTc4NxzA3GMDdUTswNxjAvGFelckO5L1SpxpYvX64B0MaNG+d5zeFwaN27d9dq166t5efna5qmaSdPniwxdvK5c+e0bt26aSEhIdqxY8c8r2dlZWm7du3SsrKyPK+lpaWVOi5zgwYNAnJc5sOHD2vLly8v8YiNjdU6deqkLV++XEtNTfVMv3jxYs1sNmsPPfSQ5nQ6S11ubm6utmvXLu348eOe1/7+++8S0+Xk5GhNmzbVYmJitIKCAv++uYtA7/VpQ4cO1QAUqVtv1bX+/Il5wRjmBv9ibqg8mBuMYW7wL+aGyoO5ofyYF/wvUHIDOy/Kwel0atdee61mMpm0J598Unv77be1vn37agC0d9991zPd/PnztaZNm2pjx47V5s6dqyUnJ2tt2rTRAGjJyclFljl//nwNgDZ//vwir48ePVoDoD355JPae++9p91yyy0aAG3hwoUX4q1eML5usLNp0yYtJCREi42N1ebNm6d9/PHHRR779+/3TLtmzRoNgDZp0iTPa5MmTdLatWunjR8/Xvvvf/+rTZ48WUtISNBMJpP2ySefXKi3ViFefPFF7cUXX9QeeOABDYA2YMAAz2vF2e12LS4uTuvatWupy6tu9VcRmBcqBnND2TA3VD7MDRWDuaFsmBsqH+YG/2NeKLtAyw3svCin06dPa8OGDdPi4+O1kJAQrW3btiUqf8uWLdptt92mNWjQQAsJCdHCw8O17t27a5999lmJ5ZWWbBwOh5acnKwlJCRoISEhWuvWravEjlKcr2TjrpPSHt515WtH+e6777S+fftq8fHxmsVi0aKiorTrr79e+/777y/Qu6o4qnopbuXKlRoAbdasWaUur7rVX0VhXvA/5oayYW6onJgb/I+5oWyYGyon5gb/Yl4ou0DLDSZXocvl999/x+bNm3HPPfd4boiSl5eHESNG4Msvv4TVasWoUaMwePDg8q6CiIiIiIiIiKo5Q50X999/P37++WccOXLEc/fa5557Dm+++SbCw8NRUFAAu92OlStXom/fvn4rNBERERERERFVH2YjM//666/o3bu3p+PCbrdj/vz56NKlC/7++28cPHgQsbGxePPNN/1SWCIiIiIiIiKqfgx1Xhw/ftwzPisAbN68GTk5ORg8eDDCwsJQv3593HHHHdixY4fhghIRERERERFR9WSo8yI4OBgFBQWe52vXroXJZELv3r09r9WpUwcnTpwwshoiIiIiIiIiqsYMdV4kJiZizZo1nudLlixBkyZNkJCQ4HktPT0dderUMbIaIiIiIiIiIqrGDHVePPLII9ixYweuvPJK9OjRAzt27EC/fv2KTPO///0PzZs3N1RIIiIiIiIiIqq+DHVeDBkyBPfeey+2bNmCn3/+GTfddBPGjRvnie/cuRM7duxAnz59DBeUiIiIiIiIiKonQ50XoaGh+PTTT3Hq1ClkZ2fj66+/RlhYmCceFxeH3377DUOHDjVc0M2bN2PIkCFo3bo1atasicaNG+O+++7D3r17S0w7e/ZstGzZEqGhoWjQoAFGjBiB3NxccR3ue3aU9njppZcMvw8i8i/mBiIqjnmBiHxhbiAKbCZN07TyzvzTTz8hMTERjRs3LnWatLQ0HDx4ED169CjvagAA99xzD9avX497770Xl19+OTIyMjB79mycOXMGGzduRJs2bQAAY8eOxYwZM3DPPffg2muvxZ9//ol33nkHffr0wbfffqtcR2ZmJlatWlXi9Y8//hjfffcdfv31V3Tu3FlXeZ1OJ44ePYpatWp5hpIlqkw0TcPp06dRv359mM2G+jEvKuYGIv+qCrkh0PICwNxAlR9zA3MDkS8XNDdoBpjNZm3y5MnKaaZOnaqZzWYjq9E0TdPWr1+vFRQUFHlt7969WmhoqPbQQw9pmqZpR48e1YKDg7VHHnmkyHRvvfWWBkD78ssvy7XuZs2aac2bNy/TPGlpaRoAPvio9I+0tLRy7ReVBXMDH3xUzCOQc0Og5QVNY27gI3AezA3MDXzw4etxIXJDMAzQdJy04XQ6/dJLePXVV5d4rXnz5mjdujV27doFANiwYQPsdjseeOCBItM98MADePbZZ7F48WLcdtttZVrvr7/+itTUVCQlJZVpvlq1agEA0lasQETNmrA5nfju2DFcX68eLK4eqdd69lQuY8TBg8r4zCZNlPFRp04p4zOio5VxABgjlOE1oQw1xTWoeb8Dc1gYWs+bh50DBsCZnw8AkN6BTYhbyrB+X55PS1PGpzdqJCwBeF6o42lCHUvvwc0cFoYW8+Zhj6v+CgDMwPltNVAFbG44fBgRERGw2Wz4bvVqXH/ddbBY9H6aarOEfXuokBuQni6vxG5Xx71GnfJljlBGp7B6d9wcFoYm8+bhoFdeAADp4PbMsWPK+Lv16injBcooECrEn9q+XZgCmNO+vTKer4wCI3bvVk+wdi2Awjz5XUQErs/JgQVATl4eGj33XEDnhkDLC4C+3CAdt6Xfu6S4sFdjjLDfAAC8Lh/2RXoPYwy2Xdzvsfgxz03KDcOPH1fG34iNVcal/VLy/B9/iNPMcJ0dUJoQYX7pc/bOry3nzcMur3bDTAR2uyGQc8NUAGEATGFhqDdvHo4NGADNtW1L7d0gIf6UsN3PErZ7+WIa4AXpmBQXpwy/Juz7I6TLcVxXCtgAfBcUhOsdjqJtaGH9qF9fHf+//1PHIyPV8exsdVz4PqDL33+r45dcoo7//jsAVx02aoTr09IK2w35+Wg0adIFyQ2GOi/02LdvHyKlD6ucNE1DZmYmWrduDQAoKChsTlqt1iLT1ahRAwCwdevWMq9j4cKFAICHHnqoTPO5O2wiatZERHg4bE4natSogYjwcE/nhfrwDkRERCjjFT2/P5ZhFeKSPK//zSYTatSogTCTyXNglZYvJWvpAJ8nxC9EHUtfgqT34Oar/gBUyVMQAyI3RER4vqDUqFEDERERfuu8MJobkJMjr0TqvDC4b+juvChlu5YOblIdSLlF2mvEz0DHAV5P/jC0Dtf+YEPhvhBhsxVpyFW13FCZ8wKgLzdI20RFd16IuQMQOy8quu3i6byooNxgdL+U+CM3+K3zopq0GwIlN4Sh8Nhkcn0uVpMJ7p+RpfZuRW/30jYF6Ni2jX7vEXIPXJ+fDUCNoCBEFO+8qCn85CqVX1q/VWhZFAg/i7jKb4hUBqkOXO/R5toGI6xWWLxOZrgQuaHMnRcDBgwo8vz//b//h0OHDpWYzuFwIC0tDT/99BNuuummchdQZeHChUhPT8eUKVMAAC1atAAArF+/Hr179/ZMt27dOgBAup5fE704HA58+umn6NKlC5o1a6actqCgwJPsACDH1fi3OZ2eh/u5m1nYgGzClwNxfpu6H1aa3x9lMLoJeze03OvyXqe0fKNxqaFntH78sQy9V5YVrz+zpgH5Rn8jqpwCIjfYbIUP1+cvbQdlYTQ3wOGQVyJNI6zDZDB3uLd793JMVmuRfUGaX6pvqXzSfieuX0cdGy2DnnUA53+xK/63qqlMeQEoX24wejwwGteVpwy2PYy2XczFpis+vfge/bT+8tKz31b0dlB8PVW93RAoucEUFlZ400+v456b0fbuhdjuxW3baO7Q+cW51GOdU/jZRCq/0S/u0vzlv02l/9bhmt9W/K/hgulX5ht2et+Ew2QyKS8dMZlM6Ny5Mz755BNdO2tZ7N69G1deeSVat26NdevWISiosM+xa9eu2LlzJ2bNmoXevXtj165dePrpp3H06FE4nU7Yy/AF4bvvvsMNN9yAN998UxwxJSkpCZMnTy7x+qJFizw9tUSVydmzZ9GvXz9kZ2fr+zUtQDA3EBlTFXNDZcsLAHMDBR7mBuYGIl8uZG4oc+fF4cOHARSeYnXJJZdg+PDhGDZsWInpgoKCEB0djZrS6SflkJGRgW7dusFms2Hjxo2o73UNUnp6Ou6//36sX7/eU44RI0bgxx9/xJ49e5CVlaV7PY8++qinNzZOuA7KVy9po0aNcGLNGs9lI6uOHUNfr3tevC6MwPKcjzNavL2amKiMjxSuG31Fxz0vRgtleF0og1/veWG1nr/nRV7hBR3SOzgnxKVTK6V7Xow9ckQZf7lhQ2EJwFihjqcJdaz7shGr9fz1v3l5yNc0TM7Pr1KNkIDKDX//XXhquN2OVatXo+9118ES7J8r+d4S9u1npXteHD0qr0RquClGoQKM3/PCfeAyWa24ZN48HBgwAFre+Qu9pFNon8nIUMb/Gx+vjBu958WTO3YIUwBz2rUzVIbn9uxRT7BmDYDCX0xWRUaib3a2554XMYMHV5ncUBnzAlC+3CAdtyv6spHRwn4DAAhVb/3SexhtsO3ifeaF9zHPTcqyw4Rr/9+s4HtejN25U5zmFdflDaXx22UjVuv5e1642g0vVqF2Q6DlhlfDwmB1nXnhueeFa9uW2rvSdv+ksN2/JWz3Z4TlA8AL0jGpbl1l+HVh339OuueF615cNgCrgoLQt/hlI8L60aCBOv711+p4ZbjnhfA5i/e8+N//ABSecbGqUSP0TUuDRdMK2w1jx16Q3FDmlnKC103Y5s+fjw4dOhR5raJlZ2fjpptuQlZWFtatW1ck0QBAgwYN8PPPP2Pfvn3IyMhA8+bNER8fj/r16+PSSy/VvZ68vDwsX74c1113na5EExoailAfB2yL2ezprCj+3Ptg6ov0RUacX7h+XprfH2UweoKTry8wzrw8z3ql5RuNS1+gjNaPP5YhldHX8px5eWWer7ILuNxgsRTZRy3BwX6754XR3IAg6as/5FMLhXVoBnOHp3HttTzv9y19SZP2O6l8ejtXSl2/jjo2WgY96ygyvdejqqiseQEoX27Qc0wxwugxr3AiY20Pf7Rdik/vPY/4Hg2u3+ixVc9+a7QMZY1XxXZDIOYGLT+/yLFFy8vzHCeMtncvxHYvbttGc0cZL6socbyThviUym/0sg6dl2xc1HUUm9+iaYUPg8UqC0M/8z366KP+Kocu+fn5uO2227B3716sXr0arVq1KnXa5s2bo3nz5gCAP//8E8eOHcNjjz2me11ffvklTp8+Xa4b6xRxySWFN6Cx2wvv4H/JJYDr4C/1jL9Rp44yHiXNX8aGq0/h4crw88L1X7OFMkg9xWYf/3u/liXMPz4zUxlPEg4kScL7m+GHOp4jfM7qT0DeiT33BnD9rYnCg1iF3633AgrI3HDgQOH+5d7GDhw4f2CMijK06FHCdjtV2G5r61jHMydPKuOvCeuQtr+zQtz9y6F7+z6Loo2niUIdzBHKlyWsP0qIS/NDOKMKAIYLdZwk5A7ExKjjXbsW/nU4gL17gc6dC7fBM3p+Q6v8AjIvAJgZHY0wuH71TknBq9HRnka79AVh/P79yviUpk2VcWn5U3WcTTte2G7HC78uThH2Temk+eK5ofjXEenMiCThzBGpXZAslH+ccFO+ZGH9gPw5GT37w90+ddddsGudVaXdEKi5IRuFZ9yZAdR3PdfboTQuVxgPRDibUjpbJ0k6qwIAfv5ZHRdG0Xl+6lT1/Bs3quPus2XMZqBDh8KzCLzvc9Grl3r+LVvU8dOn1fFOndRxqfzSDT2B8++xNO7jfmmke9p071741+ks/F571VWF9XkB2w1+yUO//vorNm/ejKysLDh8JHWTyYQJEyYYWofD4cD999+PDRs24IsvvsBVV12laz6n04kxY8agRo0aGDx4sOd1m82G/fv3IzIyEvV8DInnvq7srrvuMlRuIqpYzA1EVBzzAhH5wtxAFNgMdV78888/uPPOO7F+/Xrxxp1GOy9GjhyJL7/8Erfddhv++ecffPLJJ0XiDz/8MABg2LBhyM/PR/v27WGz2bBo0SL8+uuv+PDDD9HY6xrs9PR0tGzZEo8++igWLFhQ4n198803uPvuuxEunHlARBcXcwMRFce8QES+MDcQBTZDnRcjRozAzz//jF69euHRRx9Fw4YNEeynG84Vt337dgDAV199ha+++qpE3J1sOnTogDfeeAMLFy6E2WxGly5d8P333xcZ6kiyZMkS2Gw29OvXzy9lJ6KKw9xARMUxLxCRL8wNRIHNUE/D119/7dmZTf64iYjC2rVrdU332GOP6boWLTExsdSzRZ566ik89dRTZSgdEV0szA1EVBzzAhH5wtxAFNikG7Ir5eXloUePHhXecUFERERERERE1Zehzov27dvj0KFDfioKEREREREREVFJhjovJk2ahC+//BIbpaFdiIiIiIiIiIjKydA9LzIyMnDLLbegZ8+eeOihh3DFFVcgIiLC57T9+/c3sqrA9c03QA3XqOTh4YXPXUYIY51LY+3OjYtTxkOUUWB8ZqYwBTBVGG9cuneyNC60ND619wZafLxxQB7vHWFhynCSMGbyLGG8dqn3T8/420bryFAPJF00b3TsiDAAZqsVzVNS8GbHjnDm5emad8zOncr4bCE31BaW/4+OMkytU0cZl3KDtA7fR5Lz3NnRV17QI0uIjxdyQ7KQG8cLn5E4FjuAZKGOk3wMTe5Nyl9D3ccAuysLRUYCwcGAMB9VrGDXozzb9symTZVxo8eL8bm54jRJNWuq4/v3K+Pqo3bZ9vPykParKcL+MVGY37O/lUJquwFy413depTX4S6h2eu5E3J7hSpWmOvhvlg/FID7bht6thuVOcJ+Ky4/I0NeiXTcKzZaSwlnzijD+UuWKONhtWq5/gkD3nsPmDOn6HetVauU89uFH+uDO3dWxuFef2natFHHdbQbpPwifbeU4sndugEobLu2TEnBqz16wJmXJ+YcfzLUefHYY4/BZDJB0zQsWLAACxYsKHH/C03TYDKZqm/nBREREREREREZYqjzYv78+f4qBxERERERERGRT4Y6Lx599FF/lYOIiIiIiIiIyCdeLk9ERERERERElZpfOi+WL1+O++67D5dffjmaNWvmeX337t2YMWMG0tPT/bEaIiIiIiIiIqqGDF024nQ68eCDD2Lp0qUAAKvVijyvu+VHR0fjP//5DxwOB1544QVjJSUiIiIiIiKiasnQmRevv/46lixZgqeeegqnTp3CqFGjisTj4uJwzTXX4P/+7/8MFZKIiIiIiIiIqi9DZ14sWLAAnTt3xpw5cwCgxDCpANCsWbPq3XnRp0/huL52O7B5M9C7NxDsqva5c9XzDh+uDA9etkw9f69e6rg01i+AKCEujfkdLsQH79mjnsDrMiSbzYYVK1fiuVOnYLFYAACzhPHWpTGR5yQkKOPn1EsXx5uvK8QBIEeIGx07eUxuLgDAZrdjxZo1GJyRAUtwMHJycjC2Xj2DS6fyGp6WhoiIiMLPZd06DDtyBBZ3bghWp+bXhPHYRxw+rIwnCdu90fHiAWC4a7srlTQm/IkTyvDUK68s8tyJovvja0JuGLdzp3r9a9eq59+0ST3/wYPK8KzWrdXzQ84/c4T3OLSgQBmfHhoKoHC89hYpKXi9WbMLPl47lWT2egBFf2Uat3+/ct4ZTZsq4+MdDvXKhXbBFCH3APJxf7pQRqldMf7kSWU8qU4dAOfrLR9Fc4PY8E1NVYYnCvlVanfMcJWvNNJ+D8i/PIYJcamOqXIa8uWXiKhZEzZNw4ozZzDkq69gcX/3atNGPfOhQ8rwMwZzC+w6tqowYcuU4osXq2fv2lUZz9+4EQBgd5W14MwZOLyuGAjbvl05f3CjRurytW+vjm/Zoo5L719oGwIA7rxTGZ4RF6eMjxHaDe5P2ez13IkLm1MMnXmRmpqKa665RjlNnTp1cFI40BARERERERERlcZQ54XVakV2drZymsOHDyMqKsrIaoiIiIiIiIioGjPUedGhQwd8++23yC/lNMN//vkHK1euRFfhNB4iIiIiIiIiotIY6rwYOnQojhw5grvvvhtHjhwpEtu/fz/uuusuZGdnY+jQoYYKSURERERERETVl6Ebdt5xxx0YO3YsXn75ZSQkJKCm60ZOdevWxcmTJ6FpGiZMmIA+ffr4pbBEREREREREVP0YOvMCAKZNm4Zvv/0Wt956K2rUqIGgoCA4nU7ceOON+OabbzB58mR/lJOIiIiIiIiIqilDZ1649e3bF3379vXHooiIiIiIiIiIivBL5wUp5OcDFgvgHlu9oOD8WMiJiep5i91HpASvsYl9OnFCGZ7SooV6fhQdG90X6dSd4V9/rZ5AGHcaxcdcDg0FvvjC83To+vXq+deuVYafEUbLSY6MVMbHCeO9z0hIUMYB4PkdO9QTCOM+T5U+R/eY9U7Xp3ngAGA2A2fOiGWjivNGo0YIA2C2WtE8JQVvNmwIp2ufHpOZqZx3xI8/Glp3Unq6Mp7coIG4jHHSENjCdjtLGDPe922gz4tw/XWNcI9aADSvuJS7ZrVurYwPFXKDON767t3q5e/fr54fkMd8j49XhmcHBSnjz7uOSzabDStWrsRzp07BYrEgJycHk6Oj5fJRhXBv+2av557tOSZGOe+YggKDK1fveXoajdK+ZzcYl45dSa46sNlsWLF6NV44fhwWi+X8BNK+K7SdpsfFKeMXomEt1fGY3FxlfLrrMu/SuN+DexsMca1TWi9VsNxcQNMKH0DhvmByHQWlkR3fe08dv+02ZXjMsmXKeNK116qXDyBJOq4K+QdLlhiaPyw2FgBgcx1bQ2NiYPGe54EH1MuXvrdJpO9t112njtvF7ChOM0Zo/0l1PPH77wEANk3DijNnMParr2AxmZCTm4tpt98ul88PDF82QkRERERERERUkcrUeWE2mxEcHIy9e/d6ngcFBYmPYKmXm4iIiIiIiIioFGXqVejRowdMJhNq1KhR5DkRERERERERUUUpU+fF2mL3Dyj+nIiIiIiIiIjI33jPCyIiIiIiIiKq1Nh5QURERERERESVWpkuGxkwYEC5VmIymfDBBx+Ua14iIiIiIiIiqt7K1HmxYMECn6+bTCZo7jGHfbxerTsv/vwTqFHj/JjMO3eeH5N53z71vFJcGNN5SosWyrh6tPhC6tHU5fGCpzZooIyPf/NNZTxp2DDP/2arFe1SUjDtscfgdI2VnLRpk7qAdeqo48J47mHquTEnIUEZF0asdq1EWMv27crw+B071PP//vv5/8PDC7dBADh7Vi4bVRi762H2eu50B1NT1TOfPq2OS+O9C8ZJY7EDmBUZqYwPdTgMxbFxozoeU5jBbA4HVuzdi8Fbt8ISFHQ+LozH/lpoqDouvD9p35ZGY5+oo46ThTJIn9OQggJxHVT5BLseZq/n7twwRdgmpO1qqjD/eGm/1MEpxMefPKmMTxeO29OF4+7z7vfgbncFBxc+XGZ45wkfpCNj0uHD6gni49XxjAxlWHp/gNd7LMVM4T1Kn5E7Xvz4JOU1qmCRkUDNmoXbdlZW4bHe/Z1COmYK+x3ef18dF9oVSbNnq+cHMEfIP5JnJk9WTyC1na66qvCve/+44w7Ae1+S6jA8XB2X2vNPPKEMTxe+M0n7PQAkC/v+OGk7uOkmddz9OZvNQNu2wIYNgNMJ5Ov6xuMXZeq8OHjwYJHnTqcTw4YNw8aNGzFs2DBcc801iIuLQ2ZmJn766SfMmjULV111FV5//XW/FpqIiIiIiIiIqo8ydV4kFOsNnj59OjZt2oQdO3agXr16ntdbtGiBHj164PHHH0eHDh2wdOlSjBkzxj8lJiIiIiIiIqJqxdANOz/44APcd999RTouvDVo0AD33Xcf3nvvPSOrISIiIiIiIqJqzFDnxZEjRxAmXN8TFhaGI0eOGFkNEREREREREVVjhjovGjZsiOXLlyO/lJt0nD17FsuXL0fDhg2NrIaIiIiIiIiIqjFDnRdPPPEEDhw4gG7duuGLL77ASdcdTE+ePIn/9//+H7p3745Dhw5h0KBBfiksEREREREREVU/ZbphZ3GjR4/G3r17MX/+fPzrX/8CAJjNZjidhYMsaZqGxx9/HKNHjzZeUiIiIiIiIiKqlgx1XpjNZnzwwQfo378/PvzwQ/zvf/9DdnY2IiMj0a5dOzzyyCPo1auXn4pKRERERERERNWRoc4Lt549e6Jnz57+WFTVU1AABAUV/h8UBJw7dz7Wvr163iZN1PHLLlOGJ3burIzPvPJK9fIB+L6byXkzGjRQxsc7HMp4srtuSpG0aZPnf5vTiRWZmXjhhx9gMbuueBLqAFFR6nh8vDIsXVd1ToiHCHEAwObN6nhmpjpus6njzZsX/nU6C5fVtClgNgNnzugpHVWQUStWIKJmTdg0DSuysjDym29gMZkKgzEx6pl/+00d79BBHRdutKxn2zgrTSDdqDkrSx0/eFAdX7Om8K/ZDLRqBfy//1e4jbvt2aOcfYSwX02Ji1PGa6hLB7sQnxIZKUwBOKUJhM9plrAOd34zWa1omJKC/0ZHQ8vLQ55YMqpIZq8HUPQ4JOy5mKHzMy+VXb3l6rnWWNpup9apY2h+yVRXu8JstaJ1SgpeiY6GM+/8Vm3oemkAUxISlHHpMxpTUKCMS+0KAEAp95pzGyWsA8FC89+1HdhsNqxYvRojjx+HxWJBTk4OXoyN1VNCqggtWwIREYWfz4YNhc+lz9ItI0Mdl47JEqk9DuAZqT0rvJfpQu543us7g0/u7wQOB7B3L/DMM+e/owFAs2bK2acK31mk3GKfNEkZnyiV/9AhYQ3AuGXLlHGpDqX8M/H77wv/0bTCNshVVwEmE5CbK5bNX4zmcCIiIiIiIiKiCuWXMy8AIC0tDUePHkVBKb29PXr08NeqiIiIiIiIiKgaMdx58dVXX2H06NHYt2+fcjqHcPkAEREREREREZEvhi4bWbt2Le666y6cOXMGQ4YMgaZp6NGjB5588km0atUKmqbhlltuwcSJE/1VXiIiIiIiIiKqZgx1XkyfPh3h4eHYunUr3nzzTQBA79698c477+D333/HSy+9hO+//x533HGHXwpLRERERERERNWPoc6LzZs3484770Sc113ZnV53e3/hhRfQoUMHnnlBREREREREROVmqPPi7NmzaOA1VGZoaChycnKKTNO1a1esX7/eyGqIiIiIiIiIqBozdMPO+Ph4HD9+3PO8QYMG2LlzZ5FpTp48Wa1v1vneI4/ACsBktaJ+Sgref/hhaK7xxp/Zv189szDe8RvCeMPDhXpXj+ZeKMRgfLpQxnCpAN5jLtvtQGYmcMklnrqZIoxpPzE9Xb387duV4eFff62MT731VmV8vDTWOoDk0FBlfJy0nXzzjTrufo8mE1C3LvD774XjM3uNe0+VzO7dyvDUIUOU8fH16injs+++WxnXkxtEDRsqw0kJCer4yy8bW398vDqelKQMT/z2W/X80pj2J04ow9M7dlTPDx1j1mdlKcNnxDUUcv+KcRqAE0C+zvmoYjhdj9Keqxj6RQry8cgf7YZxQttEajecE5bvbjmZvZ5715/ROjJ6p/spQh3rKd/0mjWV8eeFOp4p1PFZd1msVrRNScHLsbFw5uUxN1xsDkdhW9ju2hPt5/fI6V5nwfvy/I4d6mULxxOkpqrjhw6p4wDQpo06nq/ewqR9440rr1TG3bnDbLWieUoK3uzYEU6vtrCU38ZLdShZu1YdX7pUHd+yRV6H9/cmH57/5BP1/L17K8NzXCcteL7X3nYbtLw8XMhvFIZyeLt27fDHH394nvfu3Rtr1qxBSkoKcnNz8e233+Kzzz7D5ZdfbrigRERERERERFQ9Geq8uP3227F9+3YcPnwYADBu3DiEh4fj4YcfRkREBG6++WbY7XZMnTrVL4UlIiIiIiIiourH0NlvAwYMwIABAzzPmzRpgs2bN+O1117DgQMHkJCQgMGDB6N9+/ZGy0lERERERERE1ZTRS/dKaNq0Kd5++21/L5aIiIiIiIiIqilDl40EBQXhoYceEqdbt24dHn74YVx11VVId91A8eOPP8bPP/9sZPVEREREREREVA0Y6ryIiIhAo0aNlNMsW7YMN9xwA6xWK3777TcUuEZfyM7ORnJyspHVExEREREREVE1YKjzokuXLtghDBszdepUzJ07F++99x4sFovn9W7dumHbtm1GVk9ERERERERE1YChe14kJSWhV69e+Oijj9C/f3+f0+zZswc9evQo8XpkZCSypDGFq4BBqamIqFULNrsdKzZvxhOpqbAEF1b7TGFM5jPCso2OVa6HNKZ3hBCXyiiNqTyrTh3P/yarFY1TUjA3MRGaa1zmGsL84rjTwnjI8BoK2JfxW7eq51+wQB0HMM51KVVpprvGVC6NtBOPco/3brMBK1cC/fsDFguQkwMMHy6WjyrG7JtvRhgKt+tLUlIw+6abPNu1tN9JZtx9tzIuLd9pcP16iPnr1lvVcfdlhyZT4d+YGEDTzscTE9Xz5+aq47t3q+O9einDcxISlHE9dfyaMGZ9jjC/VMfuMrinO+d6zSaWjCrSiFOnEBERAZvNhhUrV+K5U6c8P/4kBwUp5z1ncN0TT55UxpO9jsmlkfLLDOE9SPuG0fw0Rtj3p9esqYxLdSwdk8OEuB5S2ylJqONwYX6z8JcujjeaNUMYALPViuYpKXizWTM4Xe0GaZuY2a6dMj5KOibGx6vL1qKFUAL5e834PXuU8THr14vrUIqJAQDYHA6s2LsXw7ZuhcV7XxHa/Jg7Vx2XvlNkZKjja9eq42E6ssfmzer4ZZep4xs3KsNZrr9mAPUBZKMwJxttt5aFoc6LVatWoVevXnj88cfx1ltvoXPnzoiLi4PJ3ZgEEBoaiqSkJCwo9iXu559/xiWXXGJk9URERERERERUDRg+88Jt69at2FrKr9Affvghnn76aZhMJhw9ehQbNmzAqFGjMGHCBCOrJyIiIiIiIqJqwFDnxZo1a8RpNE3DJ598gmuvvRZnz55Fjx49EBoailGjRuHZZ581snoiIiIiIiIiqgYMdV707NlT13S9evXCnDlzkJqaijNnzqBVq1YID5euuCMiIiIiIiIiMth5URYhISFo1arVhVodEREREREREVUR5b5xsNNZ9H7P//vf//DRRx/ho48+QqdOnTyPXr164V//+lepj/J66aWXYDKZ0KZNm1KnycrKQt26dWEymbB06VJdy83MzMTjjz+OunXrwmq14oorrsCSJUvKXU4iurCYG4jIF+YGIvKFuYEocJT5zIszZ87gkksuQUJCAjZt2gSzubD/Y/ny5ZgyZQqAwvtcuIWGhsJisSAqKgqdOnUCUHhzz6ysrHJ3Xhw5cgTJycmoKQxnNXHiRJw9e1b3cnNyctC9e3dkZmZi2LBhiI+Px2effYb77rsPCxcuRL9+/cpVXiK6MJgbiMgX5gYi8oW5gSiwlLnzYuHChThx4gSWLl3q6bjwlpyc7Pk/KysL06dPR6dOnbB27VoEucbSdTgceOaZZxAREVGuQo8aNQpdu3aFw+HAiRMnfE7zxx9/4J133sHEiRMxceJEXct99913kZqaiu+//x59+vQBADz99NPo2rUrRo4ciXvuuQchISFlK6zdXvhwOAqfu/9CrvwoIS6VZJYwzree026kEYWlMZulO5tkCXHv9+gubx7Oj/Mu1WFyt27K+LgPP1QvoFcvYQ0CRS++W1KDBsq49DmP8tqmfJnu2g7MVitapKTg9ehoOPPyLuiYzBdKIOUGd/27t+sCnN+upf3mnBAftXOneoKVK9XxM9KeDaBOHXVc+GVqolDGWa1bK+ND3WPS2+3AmjXA/fcDwV4ZQXqPLVuq44cOqeNbtijDWeq5dRlRUKCMTw8NVcal7STK9dc9uHkkAA0Xdrz2CyWQcsNr0dEIQ8mcrcfE7Gz1BMK+PUXYrydmZoplmB4XJ06j4hTiScJ7nBkZCeB8bg0uvsxgdctBWr/UdpLml9ot0vwAME447ovsdmV4qpBbqpJAyg1OFN0+vJ9L36jUnzjwhtB5Ix1P9Bw3pHeb1KKFOr5smXoBl12mjr//fuHfoCCga1fgo4+KfC9DmPCt57ff1HEht+C999RxSefO8jRS26ZrV3Vc+N7i3s7c7YZaKGw3WOSS+U2ZLxv5+uuv0axZM/To0cNnfOzYsZ7HtGnTEBQU5Hm4BQUFYcSIEZg3b16ZC/zTTz9h6dKleOONN5TTDRs2DHfddReuueYa3ctet24dYmNjPUkGAMxmM+677z5kZGTgxx9/LHN5iejCYG4gIl+YG4jIF+YGosBT5s6L7du3o3v37rqnDwoKwh9//FHi9d27d5e4b4bE4XDg2WefxRNPPIG2bduWOt2SJUvwyy+/YMaMGWVafkFBAaxWa4nXa9SoAaDwchciqnyYG4jIF+YGIvKFuYEoMJX5spHjx4+jXr16JV5v3749+vfvX+L1du3aYfPmzXjttdfQpUsXAMCmTZswffp0PP7442Va99y5c3H48GGsXr261Gny8vIwatQoPPfcc0hMTMQh6dRfLy1atMDq1atx+PBhJCQkeF5ft24dACA9Pb3UeQsKClDgdYpvTk4OAMDmcHge7uduJh9JrSxMBuPlvltrGZZhtAzecbOrvsxe9VaW+X2xCXEYPTVTRwedWdgOxPdgU7+L4vXm+atpQH7VOEE8EHODOSwMZpPJ53ZtdL+xSdutSViDj0sCy7wMiVBGKT/aXKc9F/+rWxk7z8s6v7Rf66F33y41Lizf/Qm669rzl7lB9/IvdG6QiPuBsN9J69KznxndLsX8JpTBXHx7tlqLLNPofmWUP9pe0nsQ6axDthsCJzcY/U7gz/Z6RS3DcJvddRWArdjf8wUQSiBd5iNcrg+juUXPZUbSpStS20fIDSXaCxeh3WDSvO+uqUOtWrXw1FNPYebMmbqmHzlyJGbPno2YmBgcO3YMAFCvXj0MGzYMI0eOLHI5icrJkydx6aWXYty4cRg5ciQAoFevXjhx4kSRMzsmTZqE9957D3v37kV4eDjWrl2L3r17Y8mSJbjnnnuU6/jf//6HTp06oUOHDnj99dcRFxeHzz77DJMnT0ZBQQEGDhyI993XSxWTlJSEyZMnl3h90aJFnl5Wosrk7Nmz6NevH7Kzs8t9/5nKgLmByL+YG5gbiHxhbmBuIPLlQuaGMp95ER8fj927d+uefvfu3WjcuDH27dvn6Tksz5saP348ateujWeffbbUaQ4dOoRXXnkFb7/9NsLDpVvelXT55Zdj0aJFGDx4MLq5bvQYHx+PN954A08//bRymS+88AJGjBjheZ6Tk4NGjRrh+nbtEFGrFmwOB1bt2IG+7drB4uqwmSPcmEYi9b9Jv4+obwVXyOiNq9S3/wFOCvEiN+y0WtF83jzsGzDAc/MyaQOWbjA0cu5c9QRluL7RJ0XPutu0669XxqXPeeSpU8r469HRAErWX37Z+i0rrUDNDXsGDECY6xeUFvPmYY/Xdi3tN9KvD89u2qSeQPFLEwDAfTNMldq11fGYGHVcuKnU3CuvVMYHZ2QAKPwVdtW6deh7zTWweP/iIL3Hxo3V8b/+Usfr1lWGXxX2az1GHj+ujL8eG6uMS/mvluuvyWpFwrx5ODxgADTmhjKpiNywzys3FD/mScfkkUeOqCcQ9u2XhXbJ2NRUoQTA682aKeNSu0HabkcL7/Gthg0BFG7Xl8ybhwOu7drtWWG/elXYr4ySjul6zgkbJhz3RcKvq6+46sBstaLlvHnYxXZDmddxoXOD9Ju+9J3A6FkP/rhhp7SMFz75RD1B8+bq+KJFAArPuFjVuTP6bt4Mi/fZGtKNatesUcevuEIdlwYJkEjLB4D69dXxJ59Ux1u1Uob/65VfG8ybh3RXfs27gLmhzJ0XV111FZYtW4bjx48jVkjwmZmZWLNmDe677z4A5eu0AIB9+/bhv//9L9544w0cPXrU83p+fj5sNhsOHTqEiIgITJw4EQ0aNECvXr08p3ZluBq4x48fx6FDh9C4cWOfo6S43XPPPbj99tuxY8cOOBwOXHHFFVi7di0A4NJLLy11vtDQUIT62OgtQUGezorizzWddw8vjbSZSHE9B0iDJ1YbLoOvuDMvT3dDToqLd8fVeWZQqXScfi/dRV58Dxb1uyi+fHf9Gf1sK4NAzg3O/Pyidw332q6N7jcWabuVDjJ6LqkweqASyijlR0uxUyMtwcElXlPSc2mMgfn1jg6hUtZ9u0RcWH7xT1DLyyt8yEWr9KpqbhD3fWkfEPY7aZvSs48Z3faNvkf3+t2fmOZVf4Dx/cooo+0WQH4PIuGyP7YbAi83GP1OUBHtdX8vw3CbvdhlJRaHo2jnhdT2OSd0rUqXrRjNLdL6AbFjUmz7CPm1eNvsYrQbytx5MXDgQHzyySd49NFHsXz5cp87FwCcO3cOjz/+OPLy8vDtt9/ikksu8TndgQMHxHWmp6fD6XRi6NChGDp0aIl4kyZNMGzYMPz1119ITU31ua5nnnkGAHDq1ClERUUp1xcSEoLOXsPRuK+Hu+6668SyEtGFw9xARL4wNxCRL8wNRIGtzJ0XPXv2RL9+/bBo0SJ07NgRo0aNQq9evVDfdZrK0aNHsWbNGrz66qvYtWsXOnXqhIcfftgzv81mw2+//YaVK1di9OjRutbZpk0bLF++vMTr48ePx+nTp/Hmm2+iadOmyM7OLjFG8x9//IEJEyZgzJgxuOqqq1DTNY7x2bNn8ddffyEmJgYxitOb9+3bh7lz5+LWW29V9pKWKjISiIg43xMWGenp1Rq+Z496Xmm84fh4Zfg14fQnPVfNZQlx4cRw8RSwqDLMby72Vw/pXJ85jz6qjOcI80snEg7Rcfq9VIdDCtQX+MySfknz8bz4WOGBKpBzQw0AYTh/kywr5F8+3EYJ2wSkm4qtXKmOP/CAXAjp9HQfN3YuYuNGeR0q7ssX3b+U7N1b9BeFG280tPjkjh2VcSm3SPk1S0cZZgg5/HlhO5gpzO/+fcZdaw4U5oUy3vq0Ugrk3DDi4EFERETAZrdjxYYNeO7QIc/ZBjPq1FHPLJ0ZIbQbJgq/HM4wejYi5GO41DBNjoxUxse5jrs2ux0r1qzBsxkZRc/WKPZ5l5hf2K+k/VLaf84IcT2Shc9hnPA5JgvvYbzrklebw4EV27Zh9J49sAQFIef0abx02WVlK2wlE8i5IQxF2w2h0N9ukNrjRi9n0nErSXHfTtq5UxlPbt3aUBlGuS8Tcu8/+flFz5YQ8qN4w82sLHVc6OhCrVrquLR8ABDuxZLsuoSpNNL3Gn/ccNioMndeAMD7778Ph8OBTz/9FAMHDvQ5jaZpeOCBBzB//nyfZ2e8/fbb2LJli671xcTE4M477yzxuntcZl8xN3ePaOfOnYtM9+uvv6J3796YNGkSkpKSPK+3atUK9957Lxo3boyDBw/inXfeQe3atTFXujcCEV1wzA1E5AtzAxH5wtxAFNjK1XkRFhaGlJQUPPHEE5g3bx42btzouQ4sLi4OV199NQYMGIA+ffqUuoybbroJL7zwAubPn1++kleQdu3aYf78+cjMzERMTAzuu+8+TJ48GXWFm7MRUdXG3EBEvjA3EJEvzA1E/leuzgu3a6+9Ftdee2255l26dClqS3erF7hveqPSq1cv+BoNtrTXU1JSDJWJiC4+5gYi8oW5gYh8YW4gCgyGOi/06NChA0xedzXWNA0ZGRk4fvw45syZU9GrJyIiIiIiIqIAV6bOi7+kce996NWrF4Dz14mZzWbExsaiV69euCzAb/pDRERERERERBWvTJ0XiYmJRc6i0MtkMsEujTtLRERERERERORDmTov+vfvX6Lz4sCBA1i3bh2ioqLQvn17xMXFITMzE9u3b0dWVpZn1JHiTp48ibp168IhDOdERERERERERNVbmTovFixYUOT5zp070a1bN4wbNw4vvPCCZ7xjAMjNzcVLL72EadOm4ZlnnimxrIKCAoSE6BkVOMDl5wMhIYD7zJP8/PPjsEvjsW/cqAzPuP9+ZVz6cHOEOACMP3lSGU8SxpxPEsZLnyKMNT4xO9vzv81ux4p16zDsyJGiY7ar1i+MBy+JEOJDDh9Wxud47ROlLkPqwDtyRBkeKsw/0zWetXts5hAUjtctjdlNFcv9GZi8nrtv9zVc2O/wxx/quDQMddeu6vjq1eo4ANx4ozq+dKk63qmTMnxGWn9MTOFfhwNITwfq1Dk/djsgj4cu5JBxmzap52/fXhmeKuQ2aSx1ABi1Y4d6AuE9jNq/Xxmf07QpgPPboBWF22DZz68kv4qKAiIiAJut8HlkJGCxAADGGP3BJz9fGZ4hHLPGCNsUACS7tqtSiyDMLx2bwqT1u96D2WpFy5QUvBofD2denic+TmiXJAn7rtT6MFdwHJDraKp3LvRhvN7tyL0N1q1buA3WqKFvPqoQzxw/joiICNhsNqxYvRrPHD8Oiys3SNut1J4dsXOnegLpUv+vvxbWAKB7d3VcaO9K+74Uh3sYW7sd2LABeOGFosdRqe0UF6eOS223G26QSqiU9PDD8jRDhijj44RlTBG+103MzATg+k62eTOeTE2FJTgYOadPY0yzZmL5/MHQDTvHjBmDLl26YOrUqSViH3zwAeLj4wEAgwYNKtKB4XA48NNPP/GeF0REREREREQkMtR5sX79egwppYfn9ddf9/y/d+/eIs9DQkKQmJiIuXPnGlk9EREREREREVUDhjovnE4nUlNTfcYOHjwIAKhbty7y8/M9z4mIiIiIiIiIykLPpXWl6tGjB5YtW4bFixf7jKekpOCff/5Bz549jayGiIiIiIiIiKoxQ2dezJgxA+vWrcNDDz2El19+Gd27d0fdunXx+eefw+l04o8//oDFYkHt2rUxYsQIn8t47bXXjBSBiIiIiIiIiKo4Q50XrVq18tz34qeffsIO153RNU2DyWRCjx49kJubi7/++gt//fWXXwpMRERERERERNWLoc4LAGjTpg3Wrl2LtLQ07NixA9nZ2YiMjES7du3QqFEjf5SRiIiIiIiIiKoxw50Xbo0aNfLZWTFgwAC8+eabqFWrVpHXc3Nz8eyzz2LevHn+KkKlNLtJE4QBMFmtuCQlBbMTE6G5xhu3C/NKY6FPzM5WxmdERirj4w4fFtYAoJQbsuq2apUyPHHPHmXc+z2YrVY0T0nBmw0besZsHyOMSy2Nay2NWJ4jxNGwoTIsjcUOALOF8diHSONG796tYy1U2QwpNl77kDKM155UUKBe+IkT6vj27ep4eLg6DgBhwojqDz2kjnfooAyHjBypjL+RkACgMLcmpqRgTuvWntwKAMOF3DK7RQtlfIiQX+cIn9F411jopZkqjRcPAImJ8jQKU5o2Vcbdn7LJ9TcfgAZA2Lqogk2PjkYYCo95bVNS8HJ0tOeYJwkR4tIxSboRmrRN6SGVUWr7SGUc58qP7tw60iu3AhDzY1J6ujreoIE6Lhyzp9Spo4zraTdIjXepjmYI7Q53GcxWK1qkpOB11zYofTZUsd6LjYUVhce9+ikpeD821nPcqy3MmyUtPFi9VSUJ20ySwyGtATh0yFB8hLCO14QyJrv2PbPVipYpKXg1MbFIbpVyk9DqEfePUZs2qSc4flwZTpowQVgDgI0b1fHfflOGJ37yiXp+d/tR0wr//v47YDIBubly2fzE0A073c6dO4cVK1bgtddew4svvuh5PT8/HwsWLECujzeUl5eHjz76yB+rJyIiIiIiIqIqzPCZF19++SWefPJJHD9+3HOviwkTJiAnJwdbtmyBpmlISUnBwIEDPfM4HA6sWLECdevWNbp6IiIiIiIiIqriDHVerF+/Hvfccw/q1auHN998Exs3bkRKSgoAICoqCiZT4cmoI0aMwKhRo4rMazKZMHnyZCOrJyIiIiIiIqJqwFDnxYsvvoioqChs3boVMTExOOl1nd+aNWugaRp69+6N+Ph4LF682BMLCQlBQkIC6tevb2T1RERERERERFQNGOq82LRpE+655x7ExMSUiPXs2RMAMHjwYHz88cee50REREREREREZWGo86KgoAAREerxHDRNg9lsxtmzZ/HXX3/h3LlzReKXX365kSIQERERERERURVnqPPikksuwebNm5XTrFu3DmazucRQqW4OPUPrEBEREREREVG1Zajz4u6778bUqVMxf/58PP744yXiM2fOxK5du5CQkIDVq1ejV69eWL58OTIzMzF16lS8+uqrRlYfEJyuh3tMWg3nx88ek52tnnn3bnU8I0MZHrNjhzI+NyFBvXzIYxYnCe9hRmSkMj4mM1P3+s1er3nGQc9Xl1AaEzpZGBNaGtNZWv8Q6TMGxM8RUVHKcLIwZny466/J9deCotshXSRnzgBmM2C3Fz7PzfWMs54kjQX+yivquLDN4MwZdbx7d3UcAMKEvaOUDmuP+Hh5HQrD09MBADaHAyu2bcMze/bA4r0/C+U7p4wCyULuipIKKHwG4/fskZaAqUIZpDHp1edFns+v7txqQ2FekOqGKtbzqamIqFULNrsdKzZvxtjUVFhcuUHarqaGhirj0jZjNhgHgLNCXDr2SA1TafmenOo+/jscgMlU+vTFJDVooIyLDefwcGV4osF2CSB/jlIdj9H5w6HNZsOKlSvx3KlTsFgsyMnJweToaF3zkv+dApCHwv2wvuu5+7OW2qvS8QA+bgHgLSk3Vy6gRGqbNGumjgvfi+zC6se5tnv3dj3StV27zRL2vSFC22zqlVeqC3D8uDou1XH79uo4AGzZoo4L+QlXXaWOb99+/v+goPNlPitmZr8x1HkxevRoLFu2DE888QQWLVqEgoICAMCYMWOwYcMG/PLLLwgKCsLHH3+MTp06wWw2IyEhAX379kVERASmTZuGW265xS9vhIiIiIiIiIiqJj2d6KUKDw/HunXr8MADD2Dt2rX4+eefoWkaZs6ciV9++QX33XcfwsLC0LBhQwBAdHQ0jrt6ndq2bYtt27YZfwdEREREREREVKUZOvMCKOyQWLhwIWbNmoXNmzfjn3/+QUREBDp37oy4uDh07twZe/bsQWJiItq1a4d3330XiYmJmDt3LurVq+eP90BEREREREREVZjhzgu3OnXq4MYbbyzx+rBhw3Ds2DEAwKRJk3DjjTfik08+QUhICD788EN/rZ6IiIiIiIiIqihDl40EBQXhxRdfVE5z+PBhPPHEEwCAjh074vDhw9iyZQuOHDmC+++/38jqiYiIiIiIiKgaMHTmhaZp0DStxOsjRozw/L9p0yY4nc4ir3l77bXXjBSBiIiIiIiIiKo4v1024u23337z/H/48GGYTKYir7mZyjB0FRERERERERFVT2XuvPjoo4+KPN++fXuJ1x5//HE4HA6kpaVhx44d6Ny5M9asWWOspAHKiaLjbRd5HiaMyhys/nhmtGihjI9JT1fG/1GvHQAgjAYsjhcsjbksjSs90TX8LuAal3n1aow9ftwzLvMsYUx7aQM/J8THnTypnuDMGWV4SlycsAZ5PPYoIV5DiLvfo/saMZtrndJ7p4o1rUkThAIwW61ol5KCaYmJcOblAQCSDh9WzzxokDq+apU6npSkjgvbNQB5vPbff5eXYYQ7d9hshX9r1wa8xmuX8qe472dmKuOzpX1bqsP4eKEEwPj9+9VlaNpUGc8X10CVUlBQ0e03OPj886ws5awhwqKlbUJolYjHKz1lkNoF0jFNeg9Ta9YEUJhbW6ek4JX4eE9u1VM+qd0zKjdXGU8W2iXS9dp6ruce5dU28knIfzhxQh0X2mZ0cUQDsAIweT13n//+tzBvlBBPqlNHHXc4lPEpQUHCGoCJ69eL0yi5Rq8szRjhuI1Dhwr/ut/LX38V5luXod9+q55/40ZlePyyZer59ZavNHraZnfeqY5fdpkyPFNo27jzs8lqRf2UFLz/8MPQ8vKQp5zLv8rcefHYY495zpgwmUz44osv8MUXX5SYzn05idVqRZLUUCYiIiIiIiIiKkWZOy/mz58PoLBzYsCAAbjzzjtxxx13lJguKCgItWvXxlVXXYXo6GjjJSUiIiIiIiKiaqnMnRePPvqo5/8ff/wRd911F26//Xa/FoqIiIiIiIiIyM3QDTvdZ2EQEREREREREVUUv4024nA4cOLECRSUchOhxo0b+2tVRERERERERFSNGO682Lp1K8aNG4effvoJ5875vn+7yWSC3S6OO0FEREREREREVIKhzovt27fjmmuuQXBwMK6//np89dVXaNeuHeLj47Ft2zYcP34cvXr1QkJCgr/KS0RERERERETVjKHOixdffBEAsGnTJrRs2RJmsxl33XUXJk6ciLy8PIwcORJLly7FvHnz/FLYYqNlSQABAABJREFUQFQTRcdkronzYzLPFMYCl0bzlUbhntuggVQ80VBhXGdpTOIoYfkzhHGhvcdjN1mtSExJwZzYWGiuMdt9n+tznjRefF2pfMK412PS05VxPeO1hxmMS+c0Dd+6FQBgczqxIj0dz/70EyxmM3LOnMGEnj11lJAqQjQKP1tf47XPFjp8pe3eKcRHnTypjE9v2lRYAvB8bq56GbfeqoxLB59RQu5JcuUOs9WKdikpmBYbC2fe+ZHGo4TlhwjxOcJY59K+LeUOPeciSp+jJEqID3Fd5mmz2bBi9Wo8d/w4LBYLcnJyMDk21uDaqbxea9IEYSjctlukpOD1xMQi27aKtF9OrVlTGZdyy3hh+QCA/Hx1PCpKGZ4itAsmCmWY4XqP7n00BEX3JWnfGye9x2B19hqXnW1o/unCZwRArOPkyEhlXE/bBPDaBqOj4czLg/DJUgVzuh4mr+fudkOSsN0lCdtEkpQ7DO6XAIA//lDHzwjffKTcIp3lv337+f+DgoDffy8aF77ToGtXdXzLFnVcals1bKgMJ99wg3p+AOOGDFHGXxPaNlLb6Jli7YYnvNoNoy5Qu0Fv/vLp559/xu23346WLVt6XtO0wt3IarVi9uzZqF+/PsaNG2eslERERERERERUbRnqvMjOzsYll1zieW6xWHDGq9fMbDajV69e+P77742shoiIiIiIiIiqMUOdF3Xr1sWpU6c8z+Pj47Fv374i0+Tn5+Ps2bNGVkNERERERERE1ZihzotWrVphz549nufdunXDd999hw0bNgAAdu3ahc8++wyXXXaZsVISERERERERUbVlqPPilltuwU8//YRjx44BAMaOHQtN09C9e3fExsaibdu2yMrK4j0viIiIiIiIiKjcDHVeDB48GOnp6ajjuqt6u3bt8P333+PGG29ETEwMrrvuOnz11Ve46667/FJYIiIiIiIiIqp+DA2VarFYEFdsyJWrr74a//d//2eoUEREREREREREbobOvLjkkkvw73//219lISIiIiIiIiIqwdCZFydOnEBERIS/ylIlOV0Pk9dzzfW/XZg3Soj/I8RrC3GnEAeAOUFByvjfwvzS1hEixIdnZ3v+t9ntWLFuHZ45cgSW4MJNd2pkpHJ+6T3mC/EaQnxGgwbKuPQZA0C4EJfqOEaIz+nYEQBgslpRPyUF7/foAS0vD3k6ykYXxxkhLm3XYUJ8uutSv9I877XflSpYffg4J8wulRGHDsllUBjucKgnOKOu5dlCbpH27THS+nWYLeRfKX+Jyw8NBVCYGxqmpOC/sbHMDZXAiLQ0REREeI55z3kd82YI2+X0mjWVcekXq3EnT5alqL6lpqrj7dsrwxNzcw2t3r3v2Ww2rFi5EsNOnYLFYvHEZwj7lZTbprr2m/KS2j16flWcImwH0jokzxcUAHDV4erVeO74cVgsFuTk5GBybKzBpVN5PXXqVGFucG3bT3pt28nCdi1uEydOKMPjpWOanmN2w4bK8BShTT0xPV29/C1b1PEYV4tZ04CsLKBOHcBkOh9PTFTPHx+vjnftqgxLuUf6PqDH7KZNlfERwuc4RSjjFFf+M1utaJuSgpdjY+HMyzPcHikLQ2deXH755di7d6+/ykJEREREREREVIKhzouxY8fiq6++wpo1a/xVHiIiIiIiIiKiIgxdNnLq1Clcf/31uP7663HnnXeic+fOiIuLg8n7FByX/v37G1kVEREREREREVVThjovHnvsMZhMJmiahmXLlmHZsmUAUKTzQtM0mEwmdl4QERERERERUbkY6ryYP3++v8pBREREREREROSToc6LRx991F/lICIiIiIiIiLyydANO4mIiIiIiIiIKpqhMy/++usvcRqz2YyIiAhEREQYWVXACnY9TF7PNdf/0pjLw13jbJdmhjDWuF1YvjhmM+QxiaUNqIYQV48qDUz1GsvcbLWidUoKXmnYEM68PABAjDD/30I8KTNTGZ8dF6eM5wjLry3EASBLiEcJ8bNCfNTOnQAAm8OBFQcO4IlNm2AJCkLOmTMYdeWVOkpIFWHwsWOF47Xb7VixZg0GZ2TAEly4R02vWVM5r7RvS54/fFgZf8NrvyvNGYNlkLwhjFXuPqK4c2stnM+tgDxWuVNYf5QQF99/vnrU8yThMwaAMCF+TohLuaF4HdpRWIfykYEqVHh44cNmK3xesyZgsQAAxkjH7RPqo2qScEyT5tclPl4dF/aNZCH/SO0O99LNVivapqTg5ehoT5sBkPerqULbSmq7Sfud9KvhOB1tM6P5TWR3HWXcZXE4AJPp/Ot0UcyKjkYYCrftpikpeMtr2w4X5h2ana2eIFjYs6TcEC6VQF6H+KU0TNh7u3dXxzMyCv86HEBWFlC7NuC1L01v3Vo5u7TvGj1mS98p9OzX/0gTnFG3Xiamp6vnd+UAm8OBFf/7H8bu3Fn4neL0aUxr00ZHCY0z1HmRmJjoc2QRX+rWrYu77roLkyZNQpx08CQiIiIiIiIicjF02Uj//v1xzTXXQNM0REVFoVevXrj//vvRq1cvREdHQ9M09OjRA7fccgvCwsIwd+5cdOrUCceOHfNX+YmIiIiIiIioijPUeTF69Gjs2LEDEydORFpaGr7//nssWrQI33//PdLS0jBp0iTs2LED06dPx/79+/Hiiy8iPT0dU6dO9Vf5iYiIiIiIiKiKM9R5MWbMGFx55ZVISkpCjRpF725gtVoxadIkXHnllRg7dizMZjP+85//oHPnzlixYoWhQhMRERERERFR9WGo82L9+vXo1KmTcporrrgC69at8zy/8soredkIEREREREREelmqPPC6XQiNTVVOU1qaio07fw94C0WC8Kku8USEREREREREbkY6rzo3r07li1bhk8//dRnfMmSJfj888/RrVs3z2t79+5F/fr1jayWiIiIiIiIiKoRQ0Olvvzyy/j555/Rr18/vPzyy+jWrRvq1q2Lv//+G7/88gu2b9+OmjVrYvr06QCAkydPYtWqVXjiiSf8UvhAYHc9TF7P3eehjCgoUM6bJIw1niSMBZ4kjAOeLMQBIEqIS2MOZwlxabx07xHFzcX+AsBgaTz0Q4fUcfeYz6WQxmSWdiBxvGXI40JLdSTFcdllhX9tNuDAAeDSSwGLBciRRpSmCpWZCZw9WzjeuPu5a598XsgN4njrMTHquDDO9/AdO9TzA0DDhuq4sO/N7NhRGR+Vm6tevusMPpvNhhUrV2LIqVOwWCzn48K+PbNBA2V8eHa2Mj49MlIZn1OzpjIufEIAgCEnT6onyM9Xht8Q3qM7f5u8nmvQN5Y8VZzXoqMRBsBstaJFSgpej46GMy8PgPzZ2IW4+IuVkBs8xxOV5cvV8ZYtleFx+/cr41OaNlXG3cdls9dz73qT6nC80K6YqqPtpDJOym1SuwXAxMxMZXxmXJwyLrVtqHJyf6cwez13b881fM5x3kzhmCXlDqmtqmebktYhmVOnjjL+jLRvxce7CmIvbA/HxQHB51vy0nuIEuLS/FJumS3kFj2tdmk7kNou0veaUT/+WPiP+6qKv/4CTCZAqns/MtR50bZtW6xbtw5DhgzB+vXrsX379iLxbt264a233sLll18OAIiKikJmZmaJm3sSEREREREREZXGUOcFALRr1w7r1q3DX3/9hR07diAnJwcRERFo164dGjduXGTaoKAgRAo9PkRERERERERE3gx3Xrg1bty4RGcFEREREREREZFRfum8OHfuHFavXo3du3cjNzcXEyZMAADk5+cjJycHMTExMJsN3RuUiIiIiIiIiKopwz0KX375JRo3bozbbrsNo0aNQlJSkif2v//9D/Xq1cPixYuNroaIiIiIiIiIqilDnRfr16/HPffcg9DQULz55pvo169fkXiXLl3QrFkzLFu2zFAhiYiIiIiIiKj6MnTZyIsvvoioqChs3boVMTExOOljWLdOnTph06ZNRlZDRERERERERNWYoc6LTZs24Z577kFMTOkj1jdq1AhffPGFkdUEtKcOHkRERARsdjtWbNiAJw8dgsU1pvCs0FDlvOJpMT//rAwnSWPuZmVJa0BygwbKuDReem0hLo35PLSgwPO/zWbDitWrMfL4cVgsFgDALINjIkt1LI3HPrNmTWU8SU/HXfv28jQKrwnbUbKrjsxWK1qmpODV6Gg48/KQb2itZNRr7dsjDIWfS4uUFLzerh2ceXm65n1+xw71BPnCp3vihDp+5oxcCLuw90ZFKcNnpeUfOqSON2tWtBx2e+FY4y4zhNwljVmP1FRl+PnsbGV8hjCylvj+AeDrr9Xxrl0NrSPC9dddayEANADqkeipogW7Hmav5+5jrXTMHed1zPRlinC8gKI9BwD44w+hBBD3fantghtvVIYnCu/RzVebAQBmCHUwU2hXSO2GEF2lU8jIkKcRcvQooY6SpO3gyJHCvw5XNkhPB4KC9B0bqMKocoPU3q0hxKXtVjqeiMdUHesYnp6ujE8VjuuzhTb5P66/ZqsVbVNS8HJiYpF2l1RHUh1I72+2kFuGCO0KhIcLa4Dc/pPablLce7oNG4CWLYHgYCBH2gL9x9BlIwUFBYiIiFBOk5WVxZt1EhEREREREVG5GepVuOSSS7B582blNBs2bMBll11mZDVEREREREREVI0Z6ry4++67sX79esyfP99nfObMmfjjjz9w//33G1kNEREREREREVVjhu55MXr0aCxbtgxPPPEEFi1ahALXNXZjxozBhg0b8Msvv6B9+/YYMmSIXwpLRERERERERNWPoc6L8PBwrFu3DkOGDMFnn30Gh+vGPjNnzoTJZMJ9992HOXPmIFS6MRARERERERERUSkM30kzOjoaCxcuREZGBlasWIFPPvkEX375JY4ePYqUlBRER0f7o5we27Ztw+23347atWujRo0aaNOmDWbNmuWJ9+rVCyaTqcTjRuHu1W7Z2dkYM2YMmjdvDqvVioSEBAwcOBB//fWXX98HEfkXcwMRFce8QES+MDcQBSZDZ154q1Onju4dury+++473HbbbejQoQMmTJiA8PBw7N+/H0fcQzq5NGzYENOmTSvyWv369cXlO51O9O3bF3/++SeeeeYZXHrppUhNTcWcOXPw7bffYteuXahVq5Zf3xMRGcfcQETFMS8QkS/MDUSBy1DnRVBQEJKSkjBhwoRSp3nppZcwadIk2PWOG1uKnJwc9O/fH7fccguWLl2qHH41MjISDz/8cJnXsXHjRmzevBmzZ8/Gv//9b8/rLVq0wIABA7B69WrcddddZVtoVBQQEQHYbO7CAa7xxofm5ipnTRLGK5bGUpfmTzp5Ur18yKfmSBuQNCa9tFW84XXJkclqRWJKCubExkJzjcsslU8as1mKzxLqUBrXesaVVwpTAGOE7UAqg/QZRLn+mlx/IwFoAPJ8Th14AjU3mL0eQBlPgwsTtjwp32ZlqeOHDsllkEaREtYxURrPXBqrfNWqwr+aVvh3zRrAZPKExwjLfyMyUhmf2rGjMl5bXTrD48EDAG69VR0X6mic61LO0rzhGnPeXWt2FOYG9VyBIVDzAgAM3b0bEbVqweZwYMW2bXh2zx5YXJ8VYmLUM6emKsMT9+9Xz5+RoY5LuQcAMjPV8YYN1XEh/yS3aKGMe3Kq1YoWKSl4PTYWzrzzRzzpmCntuxOF/SrZ/VmV5swZdVyqHwDYvVueRkFs/7m3I6erFZedDZjNctkDQCDnBnfGN3s9d7ezx+/Zo5xX2m/ChXVL7fnh0jEdQLJw3J3ToIEyHiEsX2pHuY/b7mNeNAqPeW5DhDp8TahDodUi5hYEC9mpWOeaT+HqT3JGnTrK+JgdO9TLdx8D3Hnw5EkgKOiC5gZDl41omgZN03RNZ9SiRYuQmZmJl156CWazGbm5uXA6S9+V7HY7zpSxInNycgAAcXFxRV6vV68eAMBqtZax1ERU0ZgbiKg45gUi8oW5gSiwGb7nheT48eN+2UlXr16NiIgIpKeno0WLFggPD0dERASefvpp5Bf79Wnv3r2oWbMmatWqhfj4eEyYMAE295kPCp06dULNmjUxYcIE/PDDD0hPT8ePP/6IMWPGoHPnzrjuuusMvw8i8i/mBiIqjnmBiHxhbiAKbGW+bOSjjz4q8nz79u0lXgMAh8OBtLQ0fPTRR2jTpk35S+iyb98+2O123HHHHRg4cCCmTZuGtWvX4q233kJWVhZSUlIAAE2bNkXv3r3Rtm1b5ObmYunSpZg6dSr27t2LTz/9VLmOmJgYfPrppxg0aBCuvfZaz+s33HADli5dimDF6TwFBQWeoWKB872uNput8OE6jdvmfTq3cGq3Wej0sSl6inXNr+NSHmkZUu+XyY9xk6ssJq8y+XP5FRHX0zsofQ4mg51/7jIWrz+Tpsmn5geAQM0N5rAwmE0mzz4m7WvebMJpyyLpbDiTtGXrWIYUl/KPFHct31bsr975pf3KaG6T5vdHboC0HQiN7BI5oQrlhsqeFwBFu8Hh8Dzczz2kL07SNiHFhXaFGAf05Q8VIXfobZeUlluN7pvSl1fDbS89+V3Kr9J2IpXB9Tnbiv/1w5nUF1sg5wZVu0FqF0jbpdFjmj++U1yoNruv7xOAXIdG2w0Sv+SGCv5uiWLHJZ/HqQpm0sp4TYfZbIZJx4HJvVir1Yply5YZvpln06ZNceDAAQwePBjvvPOO5/XBgwfj3Xffxd69e9G8eXOf8z755JN47733sGHDBnTt2lW5nl9//RWTJ09Gt27d0Lp1a2zfvh0zZszAzTffjCVLlpQ6X1JSEiZPnlzi9UWLFqFGDenOCkQX3tmzZ9GvXz9kZ2cjIkK6krDyYm4g8q+qkBsqe14AmBso8DA3MDcQ+XIhc0OZOy8+/PBDAIWdEwMGDMCdd96JO+64o8R0QUFBqF27Nq666iq/DJfapk0b7Ny5Ez/++CN69Ojhef2nn35Cz5498eGHH6J///4+592zZw8uu+wyvPjiixg/fnyp6zhw4ADatm2Ljz76CHfffbfn9Q8//BCPPfYYVqxYgZtuusnnvL56SRs1aoQTf/+NiIgI2Ox2rFq9Gn2vuw4Wd4+r1/S+TIuPV8Zf+OUX9fxXX62eX8dN+V5PTFTGpV5G6bfkc0Lce/kmqxUJ8+bh8IABum/YKfUDS4eAspTPFz03xRwm3CBtrrAdSNz9+yarFfXmzcMxV/3laRpG5ucHdCMECNzc8GJYGMJcv6A0nTcP+wcMKHJTOZVhW7fqmq5U0lBteoZyu/RSdVz6BaFDB3Vc+uV/2zYAhb8ErnI60ddshsW7Y13If3OEm+Kpb6NbeKMvleNCXM8NO0dKOVo4hqBuXWV4juvYXDy35msang/w3FDZ8wKgaDfs3Om5YeeqHTvQt1278zfsrC3cKvbAAXVcuuGmdCNHPTfs/O03dVy4aR+Em1S/Ktzo0PvMi+bz5mFfsdxq9IadY0+dUpdPaPOOFG6qKu7XALB3rzrevbs6Ltwo3L0d2ZxOrDp+HH1jY2Exm5GTm4uY665jbrhIuWGCV7uh5bx52OW1bY8W2gWvCjehFoYIEI+JI3XcTPJV4bgrZAaxTa93EAGT1YoG8+Yh3ev7BAA8KdThbKEOpT1XOm9ttHTDZB0DLUj5803he90w4bslXAMp2BwOrNq/H32bNoUlKAg5Z84gpkuXC5IbynzZyKOPPur5/8cff8Rdd92F22+/3a+F8qV+/frYuXNniZvf1HU1zk4pDiaNGjUCAPzzzz/KdSxYsAD5+fm4tdgd3t3vb/369aUmm9DQUIR6jYzhZrFYYHGNLgIAluDg88+FU2ykLzIWxR2Sdc0v3dVWxzIkUs9YeeJaXp4n2VTE8v0Z13GCrfg5aH7+DNz1F/gnfxYK1NzgzM8vsn048/J0728W6W72EunsOT192tIypLiUf6R4seVbTKainRcG9ytp3zW67/sjN4gdRF7HHl+K10FVyg2VPS8AinZDUFCRfbzIc+EzhZQbpLjQrhDjgL78oSLkjrK2S4rnVqP7pkX4DAy3vfSMziflV2k7kcpQ7HO2mM2FD6OXBFUCgZwbVO0GqV0gbZdGj2n++E5xodvs3t8nALkOjbYbDNehnrafsAyj3y2Ll8F9fDLcLi0DQ0Olzp8/31/lEHXs2BGrVq3y3GDH7ejRowCA2NjYUuc94OpBVk0DAJmZmdA0DY5inQru6xvLMtyr+4QW73tfnD17Fjk5OecPfMIvi1IPXo5w92NxflfZVKSrno1eFy7d9qjImReahrNnzyJf0zwJyOiZF0bLJ82v56px6XMweuW5O5246y/PVX/u5fpjNKCLKVBzg2fIM6/tWs8XWkDe90XSr256vhxIy5DqRMo/0q+PZwt/H7VpGs46HMgJCirasDa4X0lxqYaM/gID6MjRUh0Jpxe732Px3FoVckOg5QXAq93g2r9tDkdhu+H06fMNwxDhnB0pN0j3QpDm1/OepPwhvQfhC7LedklpuVVq+ErLN3rMzjl9WiiAjqO+lH+l3CF9zu5t0FWHObm5sJhMyHGtl7nh4uQGd8b3tW1L7QJpq6ro/ULPMqTjqnRXBenrs+fMi2LtYTejdSjFpeO+WIdS7gDE+xKJn6POY4Dn+HTmjOfMC+AC5QbND2w2m/bHH39oP//8s/bjjz/6fBi1bds2DYDWr1+/Iq8/+OCDWnBwsJaenq5lZ2dr+fn5ReJOp1O7//77NQDa1q1bPa/n5uZqu3bt0o4fP+55bebMmRoAbf78+UWW8cYbb2gAtMWLF+sub1pamobCTj4++KjUj7S0tDLsiZUPcwMffFTMI5BzQ6DlBU1jbuAjcB7MDcwNfPDh63EhckOZ73nhTdM0TJw4EW+99RZOC71BxXsfy2PgwIGYN28e7rvvPvTs2RNr167FkiVL8MILLyA5ORlr167Fgw8+iAcffBDNmjVDXl4eli9fjvXr1+PJJ5/Eu+++61nW2rVr0bt3b0yaNAlJSUkAgJMnT6JNmzb4559/MHjwYLRu3Rrbtm3D+++/j8suuwzbtm1DiPSLgYvT6cTRo0dRq1YtmEwmz/VqaWlpAXud4MXGOjSmeP1pmobTp0+jfv36MOs5FbgSY26ovlh/xlXV3BBIeQFgbvA31p9xzA3MDVUR68+4i5objPR8TJ48WTOZTFp0dLT22GOPaePHj9eSkpJ8Pvzh3LlzWlJSkpaQkKBZLBatWbNm2uuvv+6JHzhwQLv33nu1xMRELSwsTKtRo4bWsWNHbe7cuZrT6SyyrDVr1mgAtEmTJhV5/ciRI9qAAQO0Jk2aaCEhIVq9evW0QYMGFelRLY/s7GwNgJadnW1oOdUZ69CYqlx/zA3VF+vPuKpah4GcFzSt6n4uFwrrz7iqWofMDdUb68+4i1mHhs68SExMhMlkwpYtW1CnTh0jfShVXk5ODiIjIwP6Ds0XG+vQGNZf5cTPxRjWn3Gsw8qJn4sxrD/jWIeVEz8XY1h/xl3MOjR0XkdGRgbuvPNOdlwQERERERERUYUx1HnRpEkTXXeXpcJhjyZNmuRz6CPSh3VoDOuvcuLnYgzrzzjWYeXEz8UY1p9xrMPKiZ+LMaw/4y5mHRq6bGTWrFl46aWX8Pvvv3vGRyYiIiIiIiIi8idDnReHDx/GqFGj8Ntvv2HixIm44oorSr3upXHjxuUuJBERERERERFVX4Y6L8xmM0wmEzRNg8lkKn0lJhPsdnt5V0NERERERERE1ViwkZn79++v7LQgIiIiIiIiIjLK0JkXREREREREREQVzdBoI9Xd6tWr0adPH0RGRqJWrVro2LEjPv300yLTJCYmwmQylXgMHjxY1zqcTidmzJiBJk2aICwsDJdffjlSUlIq4u1cNIMGDYLJZMKtt95a5PWTJ0/ilVdeQY8ePRAbG4uoqCh07dq1RB2r+Kp7k8mE6dOn+/ttXDDHjh3D888/j969e6NWrVowmUxYu3ZtiekOHTpU6vs3mUwYNGiQuK6qWH8VjXnBf5gbyoa5oXJjbvAf5oayYW6o3Jgb/IN5oewCMTeU+bKRm2++uayzwGQy4f/+7//KPF9lNn/+fAwcOBB9+/ZFcnIygoKCsGfPHqSlpZWYtn379hg5cmSR1y699FJd6/nPf/6D6dOnY9CgQejcuTO++OIL9OvXDyaTCQ888IBf3svFtGXLFixYsABhYWElYhs2bMB//vMf3HzzzRg/fjyCg4OxbNkyPPDAA/jzzz8xefJkXevo27cv+vfvX+S1Dh06+KX8F8OePXvw8ssvo3nz5mjbti02bNjgc7rY2Fh8/PHHJV5fuXIlFi5ciOuvv17X+qpa/VUk5gX/YW4oO+aGyou5wX+YG8qOuaHyYm7wD+aF8gnI3KCVkclkKvPDbDaXdTWV2sGDBzWr1aoNHTpUnDYhIUG75ZZbyrWeI0eOaBaLRfv3v//tec3pdGrXXHON1rBhQ81ut5druZWF0+nUrrrqKm3AgAE+6+nAgQPaoUOHSszTp08fLTQ0VDtz5oy4DgBF6q8qyMnJ0U6ePKlpmqYtWbJEA6CtWbNG9/zXXnutFhERoeXl5YnTVsX6qyjMC/7D3FA+zA2VE3OD/zA3lA9zQ+XE3OAfzAvlF4i5ocyXjRw8eLDMjwMHDpR1NZXa3Llz4XA4MGXKFADAmTNnoAm3Djl37hxyc3PLtJ4vvvgCNpsNzzzzjOc1k8mEp59+GkeOHCm1dyxQfPzxx/jjjz/w0ksv+Yw3adIECQkJRV4zmUy48847UVBQUKbtKi8vD/n5+YbKW1nUqlULtWvXLte8x44dw5o1a/Cvf/3LZ+90aapS/VUU5gX/YW4oH+aGyom5wX+YG8qHuaFyYm7wD+aF8gvE3FDmzouEhIRyPaqS1atX47LLLsOKFSvQsGFD1KpVC3Xq1MGECRPgdDpLTP/DDz+gRo0aCA8PR2JiIt58801d6/ntt99Qs2ZNtGzZssjrXbp08cQD1enTpzF27FiMGzcO8fHxZZo3IyMDABATE6Nr+gULFqBmzZqwWq1o1aoVFi1aVObyVhWLFy+G0+nEQw89pHse1p8+zAv+wdxwcTA3VBzmBv9gbrg4mBsqDnODccwLF8/Fyg2Ghkqtrvbt24egoCA8/vjjGDNmDNq1a4fPP/8cU6dOhd1ux7Rp0zzTXn755ejevTtatGiBkydPYsGCBRg+fDiOHj2Kl19+WbmeY8eOIS4ursRwtPXq1QMAHD161P9v7gKZMmUKrFYrnnvuuTLN988//+D999/HNddc46kHlauvvhr33XcfmjRpgqNHj+Ltt9/GQw89hOzsbDz99NPlLX7AWrhwIerVq4c+ffromp71px/zgn8wN1wczA0Vh7nBP5gbLg7mhorD3GAc88LFc9Fyg+ELT6ohs9msAdCmT59e5PUbb7xRs1qtWk5OTqnzOp1O7YYbbtCCg4O1tLQ05Xr69OmjtWzZssTrDodDA6ANGzasXOW/2Pbs2aNZLBZt6dKlntf0XMvncDi0G2+8UQsJCdG2b99ernUXFBRobdq00aKiorSzZ8+WaxmVSVmuT9uzZ48GQHvuuefKvb6qVn/+xLxgHHOD/zA3VB7MDcYxN/gPc0PlwdxgDPOCfwVKbuBQqQrnzp1DRkZGkYfD4YDVagUAPPjgg0Wmf/DBB5GXl6c8/cpkMuG5556D3W73ORSNN6vVioKCghKvu68Tcpejsiqt/oYNG4arr74ad999d5mW9+yzz2LlypV4//330a5du3KVKSQkBEOGDEFWVha2bt1armVcKKXVX3ktXLgQAMp0eldxgVR/FYV5wTjmBmOYGyon5gbjmBuMYW6onJgbjGFeMK4q5QZeNqLwyy+/oHfv3kVeO3jwIOrXr499+/YhLi6uSKxu3boAgFOnTimX26hRIwCFpyyp1KtXD2vWrIGmaUVO9Tp27BgAoH79+vreyEXiq/4++OADrFy5Ep9//jkOHTrked1utyMvLw+HDh1C7dq1ERERUWS+yZMnY86cOZg+fToeeeQRQ+XSW/8XW2nbX2JiYrmWt2jRIrRo0QIdO3Y0VK5Aqb+KwrxgHHODMcwNlRNzg3HMDcYwN1ROzA3GMC8YV5VyAzsvFNq1a4dVq1YVeS0+Ph4dO3bEvn37kJ6ejksuucQTc18zFhsbq1yu+6620nTt27fH+++/j127dqFVq1ae1zdt2uSJV2a+6i81NRUA8K9//avE9Onp6WjSpAlef/11DB8+3PP622+/jaSkJAwfPhxjx441XC699X+xlbb9lcemTZuQmprquaO1EYFSfxWFecE45gZjmBsqJ+YG45gbjGFuqJyYG4xhXjCuSuWGcl+oUo0tX75cA6CNGzfO85rD4dC6d++u1a5dW8vPz9c0TdNOnjxZYuzkc+fOad26ddNCQkK0Y8eOeV7PysrSdu3apWVlZXleS0tLK3Vc5gYNGgTkuMyHDx/Wli9fXuIRGxurderUSVu+fLmWmprqmX7x4sWa2WzWHnroIc3pdJa63NzcXG3Xrl3a8ePHPa/9/fffJabLycnRmjZtqsXExGgFBQX+fXMXgd7r04YOHaoBKFK33qpr/fkT84IxzA3+xdxQeTA3GMPc4F/MDZUHc0P5MS/4X6DkBnZelIPT6dSuvfZazWQyaU8++aT29ttva3379tUAaO+++65nuvnz52tNmzbVxo4dq82dO1dLTk7W2rRpowHQkpOTiyxz/vz5GgBt/vz5RV4fPXq0BkB78skntffee0+75ZZbNADawoULL8RbvWB83WBn06ZNWkhIiBYbG6vNmzdP+/jjj4s89u/f75l2zZo1GgBt0qRJntcmTZqktWvXThs/frz23//+V5s8ebKWkJCgmUwm7ZNPPrlQb61CvPjii9qLL76oPfDAAxoAbcCAAZ7XirPb7VpcXJzWtWvXUpdX3eqvIjAvVAzmhrJhbqh8mBsqBnND2TA3VD7MDf7HvFB2gZYb2HlRTqdPn9aGDRumxcfHayEhIVrbtm1LVP6WLVu02267TWvQoIEWEhKihYeHa927d9c+++yzEssrLdk4HA4tOTlZS0hI0EJCQrTWrVtXiR2lOF/Jxl0npT2868rXjvLdd99pffv21eLj4zWLxaJFRUVp119/vfb9999foHdVcVT1UtzKlSs1ANqsWbNKXV51q7+Kwrzgf8wNZcPcUDkxN/gfc0PZMDdUTswN/sW8UHaBlhtMrkITEREREREREVVKHCqViIiIiIiIiCo1dl4QERERERERUaXGzgsiIiIiIiIiqtTYeUFERERERERElRo7L4iIiIiIiIioUmPnBRERERERERFVauy8ICIiIiIiIqJKLWA6LzZv3owhQ4agdevWqFmzJho3boz77rsPe/fuLTHt7Nmz0bJlS4SGhqJBgwYYMWIEcnNzxXWsXbsWJpOp1MdLL71UEW+NiAxgbiCi4pgXiMgX5gaiwGbSNE272IXQ45577sH69etx77334vLLL0dGRgZmz56NM2fOYOPGjWjTpg0AYOzYsZgxYwbuueceXHvttfjzzz/xzjvvoE+fPvj222+V68jMzMSqVatKvP7xxx/ju+++w6+//orOnTvrKq/T6cTRo0dRq1YtmEymsr9hogqmaRpOnz6N+vXrw2wOmH7MEpgbiPyrKuSGQMsLAHMDVX7MDcwNRL5c0NygBYj169drBQUFRV7bu3evFhoaqj300EOapmna0aNHteDgYO2RRx4pMt1bb72lAdC+/PLLcq27WbNmWvPmzcs0T1pamgaADz4q/SMtLa1c+0VlwdzABx8V8wjk3BBoeUHTmBv4CJwHcwNzAx98+HpciNwQjABx9dVXl3itefPmaN26NXbt2gUA2LBhA+x2Ox544IEi0z3wwAN49tlnsXjxYtx2221lWu+vv/6K1NRUJCUllWm+WrVqAQDSDhxARK1asNls+G7tWlzfqxcsFkvhRMHq6p8WHa2Mv3DqVJnKVNbl61mHnmUY4b1+m82G71avxvXXXeepw4pef1XgrsPi9ZeTk4NGCQmebTVQBWxu2Lu3MDfY7fhu3Tpcf801sAg5wUPKHbGxyrj0u42ePvMxQm6YIeybY9LS1CsIC1PH8/MBoLD+NmzA9VddVbT+pDqqV08ZF/NrVpY6Hh6uDE8XPiMAeL6Cc7x7OzCHhaH1vHnYOWAAnPn5KADwGhDQuSHQ8gLglRsOH0ZERITPY1514K+2T2n1V9Ftq0rBbvfL/MWPTzmnT6PRpZcyN1yk3DAFQBgAU1gYGsybh/QBA6C5joWSGkL8ke3blfF327dXxuWLaYARK1aoJ2jbVsdSFO66Sx2PigIA2CwWfPfII7j+449hsdnOx6+4Qj3/Lbeo49JnW6eOOr5tmzreqpU6DgDZ2ep4aKg6Hhenjq9bBwCwhYTguylTcP3EibCcO4ccpxONjhy5ILkhYDovfNE0DZmZmWjdujUAoKCgAABgtVqLTFejRuEuu3Xr1jKvY+HChQCAhx56qEzzuU/riqhVy9MIqVGjBiIiInR3XgibFyIiIspUprIuX8869CzDCO/1+6rDil5/VeCuQ5/bIFAlT0EMqNxgt5//XPzUeSHtF1LnhJ7OCyk3CF0Pcv6SOi9CQgCg9Pqr6PzqdKrjQueFVD+6yiDQux2YTSbUqFEDYSYTvN9VVcsNlTkvAF65ISKi9HZDNeCvtk9p9VfRbatKwY+dF77yK3PDxckNYQCsruc1atSA1WSCpnMZViEeIXzplOZ36ChDRM2awgQG9z2pDeXKAzaLpXC7tlhQJLNKX+yF47re9ZcqKMjY/P4og6ttVSrXJSG2oKDCOgwKgsXrMpELkRsCuvNi4cKFSE9Px5QpUwAALVq0AACsX78evXv39ky3ztVLlJ6eXqblOxwOfPrpp+jSpQuaNWumnLagoMCT7AAgJycHQOHB02azweZ1IPAQbjditqpThc27t7AcpOXrWYeeZRjhvX5fdVjR668K3HVYvP6Mbj+VWUDkBrvd83A/181g7vBH54XR3CC+X2n7LL49l7Gxbji/Giy/P/KvRO924J7O81fTPGe2VCWVKS8A5Ww3VAP+avuUVn8V3baqFIxuM47Cr6Il2g1VdFsMlNxgCgsrvOmnaxs2laENLH2ltDnU3Q/SunS1G6TbLBrdvqQv3l6dF95/PaR7NUg/WkgdA1LHgtR5oqfzQmcdlEpnGW3F/wrbjz8FzA07i9u9ezeuvPJKtG7dGuvWrUOQq7eqa9eu2LlzJ2bNmoXevXtj165dePrpp3H06FE4nU7Yy7BjfPfdd7jhhhvw5ptvYujQocppk5KSMHny5BKvL1q0yNNTS1SZnD17Fv369UN2dnbV+KXJhbmByJiqmBsqW14AmBso8DA3MDcQ+XIhc0NAdl5kZGSgW7dusNls2LhxI+rXr++Jpaen4/7778f69esBAEFBQRgxYgR+/PFH7NmzB1nSdcpeHn30UU9vbJxwDZCvXtJGjRrhxNGjnlPDV61di769ep0/9Y73vCjT+m12O1atXo2+113nqUPe80LmuedFsfrLyclBTN26VaoRElC5IS3tfG5Ytw59y3LPC+HUQumeF/4482K0kBteEfbN0UeOqFcg/QLhqlOb3Y5VGzagb1nveREfr4yL+VW6rlQ4PfZlHfe8GFvBOd77zAvPPS/y8pCvaXgpP7/K5IbKmBcARW74++/zuaHYMa868Ns9L0qpP97zQgevMy+8j085OTmIadSIueEi5YZXwsJgdZ154bnnRV6erjJIXR4P79ihjP+3XTtlXM89L5775hv1BBfwnherHnkEfYvf86JDB/X8t96qjk+apI5L97z47Td1vDLc8+KnnwAUnnGxasoU9J04EZaCAuQ4HIhJTb0guSHgjobZ2dm46aabkJWVhXXr1hVJNADQoEED/Pzzz9i3bx8yMjLQvHlzxMfHo379+rj00kt1rycvLw/Lly/HddddpyvRhIaGItTHBmGxWIpca2kJDtZ9zwunkJCMXgMrLV/POvQswwhf6/euw4pef1VQvA7d9VfVrqEOuNwQHFykQV38uZLB3OEPRnOD+F6l7bPYKYol6q+i86vB8vsj/0rKuh048/IKH4bWWrlU1rwAlLPdUA34u+1TvP4qum1VKRi97rzY/O78WpU60QIxN2j5+UXucaHl5enuvJB+qbYIP4pI69Fz3LBI26XR7evcOXW82CVhFputaOeFdFmIdFmJ0ctNvTqsyrV8QK4D6T2UsYyWgoLCh1R3fhRQWSg/Px+33XYb9u7di9WrV6OVogeqefPmaN68OQDgzz//xLFjx/DYY4/pXteXX36J06dPl+vGOhdKknRjlwBZh971m61WtEtJwbToaE/jI6mCr7G62O9fj4qug0BQ5XKDdLNK4eCSJB0ApYNTZbiuOSNDHY+JKfo8OLhIwydJOPMhSfh1Yoqw708U6niK9OuGDmIZhH2/gkdar/QCNi/Y7ecf7ufuRn8Ff3n0xzFPOiZJ66jw+Q0eM42u/0JI8kP+AbzaXfHxcOblQTiyBIxAzQ15KOyEMHs9d39lbCjM2y8zUz2BcJ8j4Ssxnn/3XWEKAFOnCgt5Xh2/805lePfp08p4Xddfu9UKDBiAU6tWIdirU6Z2mzbq9RcbfaYE15kdpereXR1fvVodl24YCsgjod1zjzp+7Jg63tC1pbk7QR54oLDTp6AAmDFDLp8fBEzbxuFw4P7778eGDRuwZMkSXHXVVbrmczqdGDNmDGrUqIHBgwd7XrfZbNi9ezeOlfIhua8ru0s6BYmILirmBiIqjnmBiHxhbiAKbAFz5sXIkSPx5Zdf4rbbbsM///yDTz75pEj84YcfBgAMGzYM+fn5aN++PWw2GxYtWoRff/0VH374IRo3buyZPj09HS1btsSjjz6KBQsWFFnWP//8g2+++QZ33303wvX0chHRRcPcQETFMS8QkS/MDUSBLWA6L7Zv3w4A+Oqrr/DVV1+ViLuTTYcOHfDGG29g4cKFMJvN6NKlC77//vsiQx1JlixZApvNhn79+vml7ERUcZgbiKg45gUi8oW5gSiwBUznxdq1a3VN99hjj+m6Fi0xMRGlDbTy1FNP4amnnipD6YjoYmFuIKLimBeIyBfmBqLAFjD3vCAiIiIiIiKi6omdF0RERERERERUqbHzgoiIiIiIiIgqtYC550XAco/V7h732+E4P167oKLHOtdVhgpehz/GlA/k9QN++Jx1vgfPeO3R0VVqvPZANb1ePYSh8HNpm5KCl+Pj4XSNNy71Ko/PzlbGp0ZGKuN2Yfm69mu7einjc3PV8585o46Hhanj7rHM3WXNzga89oUkaUx7o3d+D1YfPqXP0KljFROFz2GKwfxlFv7SRRIcXPhwX0fvfq5DhR+zdcxvtAxGj8vu+Ysf8/y1/qQC4egp5EaRzs9axV/tR5vNhhUrV+KFU6dgsViQk5OD6dHRhstH5VMfQI1iz93ypZmF7WpxQoIyXkMZhXxMB4BDh9Txa69Vho8Ki/9ciLuPu2YArQG8j6LH4steeUU5/z3CfrVY2K8ekN7/Pfeo47t3q+OAnH+ktk9iojp+772Ff202YPVqYPRowGIBcnKAGTPk8vkB2yhERERERP+fvTuPi6L+/wD+2oUFVi4xUTzx1vJAM8vSPLOsLDs8SssKj7QyjzyiFNFMtPqWlRqWIR2KJd/8+a2vXzIKyvyqlUd9LTVR8dYIgVUud9n5/QG7srDMZ3ZnF3bh9Xw89gE77zk+85mZ9wwfZuZDREQejY0XREREREREROTR2HhBRERERERERB6NjRdERERERERE5NHYeEFEREREREREHo2NF0RERERERETk0dh4QUREREREREQeTX1n0iQvIKDsYzSWfff3L+sPF1DdF7ir+vH2Zqr7k3dzf/OuwP7a66YXc3MREhJi3S7zy7eLKywoKJCNxwUGys9ASW76+2918ygW9EovikdE2H738bHtx17Ul7lArOC4Wyo47haI8qvK/A+o/++DpQTaCt/NALz/zFB3qT7nqTynKZle7bWFcHrBsRPn7y8fr+3zuujY93X/pXlduP6rj8Y8/zxC/P1h9PHBNgCjZ8yAzrItFyyQnzglRTb8yJ49svFVt9wiP/8DB+TjACC49vhLMPlXgng3Qfz+338HABhLS7Ht+HHM3bMHugr54J2uXWWnXyXIHbcJln8hO1s2HpGeLj+D8HDBEgCsXCkbXtW7t2z8uZIS2fib5flVo9ejXXIyVoWHQyoqguCKzaV45wUREREREREReTQ2XhARERERERGRR2PjBRERERERERF5NDZeEBEREREREZFHU/xWoHbt2jm1AI1Gg2PHjjk1LRERERERERGR4sYLs9kMjUZjM+zq1as4f/582Yx8fXHdddchJycHpvI3KTdr1gx+fn4uLC4RERERERER1TeKHxvJysrCiRMnrJ/9+/ejWbNmGDBgAHbs2IHi4mKcP38excXF+OGHHzBgwAA0b94cB5R0nUNEREREREREVA2nO5OeP38+iouLsWfPHvhU6PdWq9Wif//+SEtLQ1RUFObPn4+EhASXFJZcyxV9navuc96BvsaNRiO2paYiJjcXOp2ubKCov/RidT0PC8uvcv1dQbgMSx1V/KnRiOuO3Co+LAz+ALR6PaKSkxEfFgZzUREABds0L08+3rChbDhO0I83fBWcGkTjiOJBQfJxpce2pa5KShzap+ME/c2rJcoNSk6+orVx+gRezrKfVc6tBoMBy8PCVM6daoPa87rac55LiI5jQby6/dpVVJ/3RblR5XULACAgQD6uNFfyusF7ZGbKx19+WT6+dq1s+LkHH5SfPilJPg4I958mP/4oGw8aOlQ2bhYsPrFrVwCARq9Ho+RkfHrLLZDKr7sAYJxg+sZ9+siPcPq0fDw4WD4eEyMfj4iQjwPCa6vnvvpKfvoHHpANz541CwBg9PHBNgDPPfssdKWlMJSU4OU1a8TlcwGnX9i5detWjBgxwqbhoiJfX1+MGDECW7dudbpwRERERERERERON14YDAbk5+fLjpOfny8ch4iIiIiIiIhIjtONF127dsWmTZuq7Unk6NGj2LRpE7p16+Z04YiIiIiIiIiInH5kdsGCBXjwwQfRq1cvTJw4Ef3790eTJk3w119/YceOHUhMTERBQQEWLFjgyvISERERERERUT3jdOPFyJEjkZSUhOnTp+Ptt9/GO++8Y41JkoSQkBCsX78e999/v0sKSkRERERERET1k6qXlU+YMAEPPvgg/u///g+//vor8vPzERoaiqioKIwcORIhISGuKicRERERERER1VNqe1pDcHAwHn/8cTz++OOuKA8RERERERERkQ3VjRekgtr+skV9hQsI+yKvCaL+zEV9lYuI6ljl/F1Rh6r7jBex1IFlPqWl7K/d0+XlyccF/XiLLPX3l42L+kpXMk5cQYH8CKJjX5TfLMeuZT/297edRmUdOf0263KxguN2ieC4B8Qn6AWCZSwVLMNSBq1ej+7JyVgRFgZzUREEW4Y8mOh8ITrfqJ2/IqJzj5vP22p5Qh27exlxFXJDVHIy4sPDYS4qQomqpZJqvr5lH8v29/Utu54DgK++kp307wsXZOOFI0fKxpuLita7t2AM4OMDB2TjoqvS6OBg2fipy5dl45brllIABwEMAFDxSPpNsPybf/5ZNh7UpYv8DD78UDa8ql8/2Xie/NwBiK/NYi9elB/hySfl43PmlP0MCAD69we2bi27njMruXJ0DdWNF2fOnEF6ejrOnTuHkpKqaU2j0WDhwoVqF0NERERERERE9ZSqxou5c+fi7bffRmmFFlxJkqApbwW0/M7GCyIiIiIiIiJyltN3xn7wwQf4xz/+gcGDByMlJQWSJOGJJ55AcnIypk6dCl9fX4wePRrfffedK8tLRERERERERPWM03devP/++2jTpg3+85//QKstawNp06YNxo4di7Fjx2LMmDEYNmwYRo8e7bLCEhEREREREVH94/SdF4cPH8bw4cOtDRcAYKrwkqWBAwfi3nvvxRtvvKGuhERERERERERUr6l6oXrDhg2tvwcGBiInJ8cm3rlzZ/z+++9qFkFERERERERE9ZzTjRctWrTAmTNnrN/bt2+PPXv22Ixz8OBBBAYGOl86IiIiIiIiIqr3nG686NevH3bv3m39PnLkSOzfvx9PP/00/v3vfyMmJgb/+c9/MGDAAJcUlIiIiIiIiIjqJ6df2Pn444/j3LlzOHnyJCIjIzF37lx89dVX+OCDD7Bu3TpIkoQ2bdrg9ddfd2V565aAAFWTx/n4yMcrdGHrzPRK5qGayjoQ8lXVG7CiOpKdXkH91VgdG41lP/39AZ0OuHrVvcslpy257jrZeKxgn1ki2G9F06O4WD4OIE50V12FdyDZVeGxQ6fKcOWKurhKsSUl8iP8/bf89BcvihcSFCQfF9SRWbwEqmPUXhe4hOjYV0uUGyznfUs5TCZAo7kWV1s+wXWL2jp2xbWZq65djEYjtqWmIiY3FzqdDgaDAcvDwlTNm1S44w4gMBCQJCAvr+y7Zd8W7JeNK/zD2a4Kd9PblZcnH4+JkY8DmGC5Dq3OfffJhleGhsrGZ65dKz//8usCo0aDgwBav/oqdJJkDae88ILs5Afk5w7z4cOy8eJ+/WTjC1askF9ARISgBADS0mTDq5o2lY3LX7kAcUuXlv1ieeflU08BZnNZXrbE3Mzpv+wGDRqEQYMGWb8HBQVh9+7d2Lp1K44dO4bIyEjcd999fGyEiIiIiIiIiFRxuvHihx9+QEhICHr27GkdptPpMGrUKFeUi4iIiIiIiIgIgIp3XgwePBjvv/++K8tCRERERERERFSF040XTZo0QYC731dARERERERERPWe040Xw4YNQ0ZGBqQKLzohIiIiIiIiInI1pxsvli9fjpycHEyZMgWXLl1yZZmIiIiIiIiIiKycfmHnY489hoYNGyIxMRGffvop2rZti6ZNm0JTsTsqABqNBt9++63qghIRERERERFR/eR040VGRob195KSEhw+fBiH7fRvW7kxo76JDwuDPwCtXo+o5GTEh4XBXFQEQEFf4Cr7Ilfbz7er5iE7f3f3Oe8r2MUFdRxXUCA/vei9Lwq2YZy/v3Ac2elLSpSNWLnPe7V93ZMqMdnZCAkJgdFoxLa0NMRkZ0On05UFi4vlJxZsu1jBfrtUcFwr2TNEt+0tEfTHbhZMH5efLz9Cw4ZlPy39xoeGApb6A4THflxOjmx8yXXXyccFx23s2bOycWv55YiO0aAg8TxkaAU/qXaouW5Qe84WTe/2c7YSSs9dlrKWlpad82p6+dUR5SYFdewV24lcr3t3ICSkbB/csaPse/n+9KbgnDt77Vr5edv5G85GWpp8/Mcf5eMAMGeOfPzLL2XDjQWz3/j007LxvPKfGr0ezZOTse7llyGV51YlZi9eLD+C4LoBK1bIhg3z58vGf5OfOwCgjSD+3N13y48QFycb/vyWW8p+0evhn5yMLa+8AhQVoVBB2VzF6cYLs1l06UlEREREREREpB7/wUJEREREREREHo2NF0RERERERETk0Rx+bGTv3r0wGAwYOHAgtNqyto+tW7di69atVca95ZZb8LTg+SMiIiIiIiIiIjkONV6cOnUK/fr1w0MPPYTBgwdbhx84cABJSUlVxt+4cSPuueceZGVlYe3atTh27BhSUlLQokULfPLJJ2jbti369++veiWIiIiIiIiIqO5y6LGRjz76CCaTCUuWLKkS02g02LVrl/Wzbds2GI1GzJ07F3fddRf0ej3279+PkvKeEfLz87Fs2TLXrAURERERERER1VkO3XmRlpaG3r17o0OHDnbjt1i6Tyk3YMAAfPXVV0hISMCECROwadMma6xfv35YunSpE0UmIiIiIiIiovrEocaLQ4cOYfTo0YrH79KlCzIyMjBgwIAqsdDQUOTl5TmyeK8Uk52NkJAQGI1GbEtLQ0x2NnQ6HQD1/XSr7cdbSX/wavuUd2Wf9M70eS/sj13Q37qQaP7FxcJZxJXfjVQtUZ/wSreBJF2bn+VDtae4GPDzu7YPlZQAlm0l2DZL/f1VLdoVHV2rnUdcfr78CEFB8nHLsWWps9JSQKO5Fhcdmw0byoZF6yc8egTzV3L8xQUGysZFt046uo3MFT5Ue2Jyc69dN6SmIiY3V/F1g1dwwXlT0fwtP4uLbY83d5/7VM7fFdtY7TzUXl+Se3zSqhX0ADR6PRolJ+PTli0hlV8Pi7wveAfhFNE5efhw+fnfdZewDIZ335WNz1m1Sjb+2PTp8gsYNUo+fuAAAMCo0WAbgEkrVkBnuTYGgNRU2cn/WrRINt5Efun4WxDPEMT9BHEAuCCIj9q/X36EhATZsGUdNACaA8gBIAFQthe6hkMZNi8vD40aNaoy/IEHHkCbNm2qDL/uuusAAJmZmVXiP/74I9q1a+fI4omIiIiIiIioHnKo8SIoKAgGg6HK8KioKERFRVUZbjAYEBAQgBkzZiAxMREajQbnzp3Drl27MGfOHCxcuND5khMRERERERFRveBQ40VkZCR++eUXxePv3bsXXbp0wahRozB06FAUFhZiwIAB8Pf3x5w5czBddPsPEREREREREdV7DvU2MnjwYOzevRv7Rc/LANi3bx927dqFIUOG4OWXX8alS5dw8OBB7N69G9nZ2XjllVecLjQRERERERER1R8ONV4899xz8PHxwZgxY5CZmVnteMeOHcPYsWPh6+uLZ599FgDg5+eHG264ATfffDOCRC9iIyIiIiIiIiIq51DjRbt27bB8+XIcO3YMPXr0wJNPPomkpCRs374d27dvR1JSEp544gl07twZmZmZ6NKlC+bMmYOHHnqo2o+zXn31VWg0GnTr1q3acfLy8tCkSRNoNBqkpKQomu/Fixfx1FNPoUmTJtDr9bjxxhuxefNmp8tJRDWLuYGI7GFuICJ7mBuIvIfD/TnNnj0bgYGBmD9/Pj7++GN88sknNnFJkuDr64ubb74ZnTt3hiRJ2LJlC0JDQ3HTTTcBKHsXRl5entONF2fOnMGyZcsQKOhGLjY2FoWFhYrnazAY0L9/f1y8eBEzZsxAREQEPv/8c4wZMwYbNmzAuHHjnCovEdUM5gYisoe5gYjsYW4g8i5OdUb99NNPY+zYsUhJScGuXbtw4UJZr7JNmzbFbbfdhlGjRqFheR/38+fPx5gxY5CQkACf8n6nS0tL8cwzzyAkJMSpQs+ZMwd9+/ZFaWkp/v7bfq+5Bw8exHvvvYfY2FjExsYqmu/atWuRmZmJb7/9FkOGDAEATJs2DX379sULL7yAUaNGwc9PSS+7Ffj6ln0s/QhbvqP2+9F2xfJF83B3n/Si+QvXUdSfvKi/dlF/9Ur6exeMo7q/9vLptXo9opKTER8WBnNREUpUzdUzeVVuCAgo+xiNZd/9/QGdrux3wT6xoER+6y3x95eNi265i1WQG5YK9ktRGUXrKJq/5ciz7tcRETAr7O8eENeBKHeIyoeAAPl4Xp6gBA7eGmlHXEGB/AjlZTQajdiWmor5ubnQ6XQwGAyIDwtTuXTP4k25IT4sDP6omrMB959zXXJdIjrvqY0rPe9acmtg4LXcqoTa5YuonR7qt5Pw2onXDTY8JTcUA9CUfyzfy/+6QGOH5lTVytBQ2bjoajZPwTIaiEaYPFk+/uKL8vEDB2TDV2bMAACY9HogORkF8+fDt8J1g+Csjf8K4ncI4gmCuGj5PQVxAOghGmHaNPn4Aw/IhhuuX2/z3bLXOHgFrIrT10YNGzbEpEmT8OGHH+Lf//43/v3vfyMxMRGTJk2yNlwAQGJiIubMmWNtuAAAHx8fzJ49G4mJiQ4v94cffkBKSgpWrlwpO96MGTPw4IMP4vbbb1c87x07diA8PNyaZABAq9VizJgxuHDhAr7//nuHy0tENYO5gYjsYW4gInuYG4i8j9p/7AiZTCYcPny4yvDDhw/DbDY7NK/S0lJMnz4dkyZNQvfu3asdb/Pmzfjvf/+L1157zaH5l5SUQK/XVxneoEFZW+HevXsdmh8R1QzmBiKyh7mBiOxhbiDyTk49NlLRTz/9hJ9//hl5eXkotXMb2/XXX4+JEyfi2LFjuPnmmwEAe/bswfLly/HUU085tKyEhAScPHkSaWlp1Y5TVFSEOXPmYNasWWjTpg2ysrIUz79z585IS0vDyZMnERkZaR2+Y8cOAMDZs2ernbakpAQlFW6TNhgMAMpuxzUajTCW3yZodMHtgt5Eaydxq52XI/M0Wm4brY5oe1ge96mOK26xFZTRVXVYuf40kiR+bMZL1LncINrvBPutaJ8RtVoLjxsFyxDOQ7COStfBmbxQcfrqiMqvev0VnAvUHvvC8015GSvvg0q2v7fwxtygCQiAVqOxu2+7fb/0BKL9VnTeLc8tTl93qcy/aik57tVuR6W5hdcNnpcbNBoNNJbt4cJrbI3KuJL/hovmIdyvRY/FaeSXYCqvr8o/rcuXn7uQKDOI6kgUF2QmAIDorxKjVrAU0Y0Fljqr/LMGc4PTjReXLl3CAw88gJ07d0KSSfQajQbx8fH4xz/+gfPnzwMAmjVrhrlz5+KFF15QvLycnBzExsZi4cKFCA8Pr3a85cuXw2g04qWXXlK+MuUmTZqEhIQEjBkzBm+99RaaNm2Kzz//HFu2bAFQlsSqEx8fj8WLF1cZvj0tzdrKCgDfyCTJuigqOdnl8+zuwONG21JTXb78mubqOrTUX2FhIVAHXhjltbkhI8M2N2RkOFyu6nRXuc8oOW66CpaxTWWuE82/MkfyghKiOhCuvwtyj+rtmJ7u0PiW85MjL6XzZN6aG7onJtrkhor7tmi/Ep0v6sI50VGuzK01Qck5X+12dPS6gtcNnpEbmlXKDc1ceN6Tf+OFWEsXlEF43dC/v7oFVNrvdzpYf6LGF9GDQF0dWlpVSs7MB1XGcfKkbDiwUh0GltehpgZzg9ONF7Nnz8aPP/6IQYMG4YknnkDLli3hW81LjgYOHIh58+ZZWw6deVHnggUL0KhRI0yfPr3acbKysvD6669j9erVCAoKcngZPXr0wMaNGzF16lT069cPABAREYGVK1di2rRpsvOMiYnB7Nmzrd8NBgNatWqFO++4AyEhITCaTPgmLQ3D7rgDOiUvcawjXPnSN61ej+6JifhfdLTiF/PF5ObKjyB6qaColdcVd14IXq7oqjqsXH/Fov8ueQmvzQ2DBl3LDRkZGDZo0LXcoPKFcStkLsYAcev+XNFxA+B1wX45NztbfgaCdRTNv+ILOx3NC4C4DuYL6kC4/qI6zM8XlABY0aaNcBw588tfpl2t8txT+fxkOVd7O2/NDf+LjkZA+Z0Xlfdt0TlNdL4QnhM9gdo7L8rP23ZzqxLufmGnQLwgfwPqt6PS6wpeN3hWbjgfHQ19+Z0XzRITcT46GlJ5bpDvK0VMtEeJjiDxGQ0Q3SfynOi6IS5OPt66tWy4YP58AGV3XOxMTES/6GibF3bKX40DXwvigwTxNYK46IWd1T/cdE07Qby1qCFuxAjZ8Obbbiv7Ra9HYGIiCqKjgaIiFNZgbnD6r+ivvvoKN998M7799ltoBLfpWDjbu8jRo0fx/vvvY+XKlTh37px1eHFxMYxGI7KyshASEoLY2Fi0aNECgwYNst7aZekJJTs7G1lZWWjdujW0MrfMjBo1Cvfffz9+/fVXlJaW4sYbb0RGeat9p06dqp3O398f/nb+CNXpdNBVeMu1ztfX5ntd58gfE47MU+l8hXUtuggSXcQo3PdlCcro6jq01F9duASps7lB5X6ndp9RkqNEyxDOQ7COovlXvrHRkbyghKj87l5/JcsQEf7BVqmMln2wLpyjvDk3SMXFNvt3xX3b7fulJxCdV0XxSvu9w9ddNXHel6HkuFe7HR3NLbxuyALgGbmh4jaQioqsjRdqibatKK7kLYaieai+Zhf8Ae1bqa58i4pshqnNjqKzuqiORHElmUfU35RO9FiI6LGSyvtbUVHVYW7mdONFUVERBgwYIGy4aNu2rew4x48fFy7r7NmzMJvNeP755/H888/bXcaMGTNw6tQpZGZmol27qu1OzzzzDAAgNzfXpjcUe/z8/NCnTx/rd8vzcHfcIeoEh4hqEnMDEdnD3EBE9jA3EHk3pxsvevbsqejFNTNnzrT5bjQasX//fqSmpmLu3LmKltWtWzfrM2IVLViwAJcvX8bbb7+N9u3bIz8/v0ofzQcPHsTChQsxb9483HrrrQgMLLuxqrCwEKdOnULjxo3RuHH1vSMfPXoUCQkJGDFihGwraXXU9NcuJHoxitK+0NWOo4Lb+6x3d5/3LqgfYX/rbq4jb+bNuQG+vmUfy38KLN8B4MoV8bQyYgX7zBLBPrPUBfvUEsHjUKL/MIiOLMtxYTQasS01FTG5uTb/tREdF6LHRkR1ICq/aPlKMoeojKIyLAmUv5FYtJ94M6/ODTLcne/Vno9csQzh9ArPidbrrvBwl96VpbYOaqKOhWUQLaPCy3u3paUhJjsbOp0OBoMByxU81uLJvDk35AMoQdm5oXn5d8t5oJFg2nOCuJ8g/pzgMev3Bed8QMF578AB2fDSt96SjYse7nms/Kflr6PiSmUK6thRdvobjh6Vjf8lWL7ovSCic7qS12EK3z2yYIFs+AtBfrJcnWpQ9qhSAcruqKnJey+c/str0aJFuP/++7F792707du32vFmzJhhd/jq1avxyy+/KFpW48aN8cADD1QZbumX2V7MwtIi2qdPH5vxfvrpJwwePBiLFi1CXIVnqG644QaMHj0arVu3xokTJ/Dee++hUaNGSEhIUFRWIqo5zA1EZA9zAxHZw9xA5N0UN158/PHHVYbde++9GDhwIMaPH48bb7yx2ndaTJgwocqwu+++GzExMVi/fr0DxXW/qKgorF+/HhcvXkTjxo0xZswYLF68GE2aNKntohFRLWJuICJ7mBuIyB7mBiLXU9x48eSTT1Z5d4Wli9SkpCQkJSXZjWs0GruNFykpKWjUSHSTk7wMBd1fDRo0yG5XrtUNT3ZD155EVLOYG4jIHuYGIrKHuYHIOyhuvHD2DonY2FjceOON1u+SJOHChQvIzs7GmjWiTmOIiIiIiIiIqL5T3HjxxBNPOLWAEydO2NyRodVqER4ejkGDBqFLly5OzZOIiIiIiIiI6g+nX9j58ccfo2fPnujRo0e14xw8eBDt2rWz+9gIEREREREREZESop7YqvXkk0/i//7v/2TH2bp1K5544gn89VfVzmNycnLgU4+7eCQiIiIiIiIiZZy+80KJUpl+pEtKSuDnJ+pV2PvF5OYiJCSkrK/s1FTE5OZCp9PVdrHKXLkiHkekvNuo6sQVFMhPbzLJhiv2Re5MHbq9P3VR+RX0e612GSKWdaxcfwaDAcvDwtSXj1wvIEA+7opjV4aor3EAiBUcO0sFx56o5Vy011uOba1ej6jkZMSHhcFcdK2ncadb5hUuX3TyFNXhAiW5R3DsL3VFfqE6RXROE50ThRScj9Se99y9Dm6vI5XLrxGi7ehbnuEsL6H09b32IY90ThBvLIhP2LlT1fKnLFwoHummm+TjWVmy4Q6C2V8VxBt//TUAwChJQHExrtuyBbqKnU18+qns9J1atpRfwIkTsuF2vXrJTy86vtatk48DWB4aKht/8eBB2fhhwfxfungRAGA0mbDt55/xZGYmdL6+MFy+jJkdRFvINdyWhd555x2kpKQAANatW4egoCBrrLS0FD/88APfeUFEREREREREQg41XgwZMsTme1JSkt2uhUpLS/Hf//4XpvKW3YSEBJtHRPz8/NCmTRskJCQ4UWQiIiIiIiIiqk8caryo2FCh0WiQlZWFLDu3+Gi1WjRq1AhDhgzB6dOn8dVXXyGMt6cTERERERERkRMcarwwm689xavVahEXF4fY2FiXF4qIiIiIiIiIyMLpd16kp6ejTZs2dmOzZ8/GK6+8gsDAQMyePVt2Pm+++aazRSAiIiIiIiKiesDpxouBAwfafC8oKIDBYEBISAj2798Po9EIANi3bx80Fd/kSkRERERERETkAFW9jVy9ehWvv/46kpKScPz4cevwdu3aYc2aNZgzZ47dF3oSERERERERESnldONFUVERhg4dij179sDHxwcdO3ZEs2bNcOHCBRw7dgwLFy7EV199hQ4dOmD16tUIDg62mb6goADTp09HYmKi6pXwZPFhYfAHoNXrEZWcjPiwMJiLigCo72s8rqREfuGi/oKLi+XjrpqHGp7ep7gLyqd6P/CEPuPJYcvDwhCAstzQPTkZKyrkhtiCAvmJAwJULVsriJsUzEO0X4qODLMgLqKt5mfluLPLVzu96LhcIqg/JWJVLsOyDSufnwRnFvJgouNS9fz9/cXjiM5JJkGGEcSVzt9oNGJbWhpisrOh0+nkp3Fg/m6vYwXndHeXwYK5wbO0AdCg0ncL0TnrsGjmV67Ix0XX+1OnipYAHBaU4sAB2fAjZ8/Kxr9o0UI2nnjXXQAAjV6PRsnJ+PTBByGVX3cBQIh86VAoiF8VxCctXiw/wu7dsmFzaKhgCcCLMTHyI4waJRt+6dFH5adfsKDsp68vcNddwNKlZTn3qmjtXUe0r1drxYoV2L17N8aMGYNjx47h8OHDSE9Px6FDh3D8+HGMHTsWu3fvxqeffoqiCjuGRVFRET7++GNVhSciIiIiIiKius/pfxt/9tlnuPHGG5GcnFwl1rJlSyQkJODw4cPYv38/Ll++jIAK/yksLS3Ftm3b0KRJE2cXT0RERERERET1hNONF1lZWZg1a1a18YYNG0KSJABAp06dqsQ1Gg0Wi26fISIiIiIiIqJ6z+nGiwYNGiA7O7vaeHp6Ol577TVs27YNKSkpaNSokTXm5+eHyMhING/e3NnFExEREREREVE94XTjRd++fbFp0ybMnDkTXbt2rRIPDw/HDz/8gCFDhmDkyJHQap1+vQYRERERERER1WNON1689NJL2L59O/r06YOJEydi4MCBaNq0KS5evIiMjAysX78eRqMRS5YsgVarRWFhIU6dOoWrld5G2qNHD9UrQURERERERER1l9ONF/369cPGjRsxefJkrF69GmvWrLHGJElCaGgoPvroI3Tq1AkjRozAf/7zH7vzKWU3j0REREREREQkw+nGCwAYPXo0hg8fjq1bt2L//v0wGAwICQlBr169MHLkSAQHB2P8+PHIy8vDnj17MGjQIGzZsgUXL17E0qVL8Y9//MNV6+GVVPfT7atq8wFBQeqXIeqvXW0ZRdT2By+gdhspWr6b14G8z9LAQNm4WTC96CE90fRKiJYhyAzCk0+saL8vP26MRiO2paVhfnY2dDqdNbzU3192crUPMorWb4kgdwjXT4krV+SXUVAgG48T7GdUO2JycxESElK2b6emIiY312bf9nii6wJRvELvdE5NX8s84ZwtKoPq60+qFUXlPzUA9OXfpfJhgqMGwv4d+/eXj6eny8cHDxYtQfw3Qd++8vFffpENHxSXAEDZ+b8RgLOwvR5qKZgueu1a2fjyp5+Wn4Gg/DhzRjasFdUPABw4IB9v00Y+fu+98vHvvxeXwc1U/2UZHByMxx57DI899pjd+HfffYetW7fipptuglarRWRkJIYNG4aQkBDEx8fjXlElEREREREREVG95rK3aBYUFOD8+fMoqPSfnoKCAjRpUtbeFxYWZu2hpHv37ti3b5+rFk9EREREREREdZSqxourV6/i1VdfRceOHRESEoKWLVsiJCQEHTt2xLJly3D16lV07twZR44cAQBERUVh7dq1OHv2LBISEtCsWTOXrAQRERERERER1V1OPzZSVFSEoUOHYs+ePfDx8UHHjh3RrFkzXLhwAceOHcPChQvx1VdfYdq0aTh//jwAYNGiRRg+fDg+/fRT+Pn54aOPPnLZihARERERERFR3eT0nRcrVqzA7t27MWbMGBw7dgyHDx9Geno6Dh06hOPHj2Ps2LHYvXs3Tp8+jSeffBIA0Lt3b5w8eRK//PILzpw5g7Fjx7pqPYiIiIiIiIiojnL6zovPPvsMN954I5KTk6vE3nzzTURERKBJkyZ45513kJ+fX+183nzzTWeLQERERERERET1gNONF1lZWZg1a5bd2P79+wEA/v7+yM7Otn6vTKPROLt4IiIiIiIiIqonnG68aNCggbXnkMrSy/sCnjRpErZs2WL9Xh/J9dfu7n62RfP3hL7IhSr252753WQCLA1foj6jvUCcv7975+8N27ke0lb4AC7s+knhstXEAdu+0e0RHZmi6ZcK8pdleq1ej+7JyVgRHg5zUZFgrteI1nGByuNmiaD8ovUDAJMgHlepd68q8vIUTW80mbAtPR0xFy5A5+sLg8GA5Xyhdu0xma59LN+V/rNH7TnRJL/XKTlf1fo5x1IHknTtuwuvFWp9/RSUwVXXf5WvXQ0GA5aHhSkuJ7mWufyjqfC9fC8Xni8aC+LrAgNl45NycuRncOCAYAkA/v5bPi44Z6Gx/FrEfv21/PTlecAoSdh25Qrmf/kldBVz67p18tN/+qls+MXHH5effscO+fjhw7LhK8XF8tMDCIqJkR8hIkI2/MZjj8nGrVtAr0fYXXdhQ1ISUFQE5Vdf6jl9vdy3b19s2rQJv//+u934H3/8gc8++wy33nqr04UjIiIiIiIiInK6Kfqll17C9u3b0adPH0ycOBEDBw5E06ZNcfHiRWRkZGD9+vUwGo2IEbUAERERERERERHJcLrxol+/fti4cSMmT56M1atXY82aNdaYJEkIDQ3FRx99hH79+rmkoERERERERERUP6l6CHD06NEYPnw4tm7div3798NgMCAkJAS9evXCyJEjERwc7KpyEhEREREREVE9pfoNRsHBwXjsscfwmOAFH0REREREREREzqjJF9wTERERERERETlM1Z0XkiRh69at+PXXX3Hu3DkYjcYq42g0Gnz44YdqFkNERERERERE9ZjTjReZmZkYMWIEjh49CsnSl7Yd9b3xIj4sDP4AtHo9opKTER8WBnNRWW+4qvvpFsRFlEwv7AvchX2nC7mpz3Y53tCfO9U9ZkFcdMucqL930dEjWr4SonnECvbrJYL8VHn+5krD1NaRKD+qvW1RSR2rPvYbN5aPW/Ko5R8P/v6ATgdcvapuuaSO5RznzDnPJNizRfNxxblVVAa1RGW0LL/iT42m5pbvBZReP1a+di1xc7lIXiCABpW+W1wQTJulduEBAeriAJCXJx9v2VI+fvCgfNzfXz7+z3+W/fTzA6ZNA15/3fZ8d/68/PSjR8vHExLk4yNGyMenT5cNvzFxovz0AOIEy1gj6EgjQjD/4+U/tQDCAJxB2fVMsbBkruN0Bn722Wfx559/Ytq0aXj00UfRrFkz+NaBhE5EREREREREnsXp1oYdO3bg/vvvx+rVq11ZHiIiIiIiIiIiG07f+RocHIwOHTq4sixERERERERERFU43XgxbNgw/Pe//3VlWYiIiIiIiIiIqnC68eL111/HuXPnMHfuXBQX1+RrOoiIiIiIiIioPnH6nRfNmjXD119/jVtvvRXvv/8+OnbsiJCQkCrjaTQafPvtt6oKSURERERERET1l9ONF/v378ewYcOQV97tzb59++yOp3Fl91REREREREREVO84/djIzJkzkZeXhxUrVuDUqVMwGo0wm81VPqVq+6knIiIiIiIionrN6Tsv9u7di7Fjx2Lu3LmuLA9VECdo+Inz8XHr9DVBuA4lJde+mEzXfpbf0RPn7y8/fR2oQ1ctQ6vXIyo5GfFhYTAXFaFEPAnVErMgLmp1FiV20fxjXdDorHa/jS0okB8hIAAAYDQasS01FTG5udDpdMoXIHpXU/n8q7NUsH6iOq4Rvk6f4slTifZbldtc7TkRgOpjS/X8LSxlLS21XjMAENeR2uPGcq3irvm7gKLtiKr51WAwYHlYmJtLR9XJAVAIQAOgZfl3qTw2U7RNr1yRj//9t3z8wgX5eFCQfBwAevaUj3frJh/v0kU+/skn8vGhQ8t+asuvogYOBMwVzta33io/vWgdFyyQDb8h+Jul8fr1svGqL2eo6v1+/WTjz+Tny8bfCA2VjfuV/9RW+G5GzV7zOH3nRUhICJo2berKshARERERERERVeF048XIkSPx3XffwWz2iP8vEREREREREVEd5XTjxYoVK+Dv74/x48fj7NmzriwTEREREREREZGV0w/e9ezZE1evXsUvv/yCzz//HGFhYdV2lXrs2DFVhSQiIiIiIiKi+svpxguz2QydTofWrVtbh0mSVGU8e8OIiIiIiIiIiJRyuvEiKyvLhcUgIiIiIiIiIrLPpf01mUwm/O9//wMAdOvWzbFu64iIiIiIiIiI7HCo8eLEiRNIT09H//790alTJ5vYV199hYkTJ+Lv8n6Cw8LCsGbNGowZM8Z1pfVCMbm5CAkJqdJXNuCi/tTlphfMvybmoXYdbPpDtzyC5OuruJ90tXXsijqsbZZ1ZH/t3kP0JuUFgv12iWC/dfpNzS60VFDGBQUFsnHLOmr1enRPTsaKsDCYi4qs8VhR7gkIkA2rPfZFdaxkG4i2o2gd1U5PHsjN+62QySQeR+H52W0sZaz800JUPtE6iuI1sP7u3s5xFfJrVHIy4svza4lbl0oiAeUfTYXvlofzEwX7RLTafH/4sHy8cWPxPETjFBfLx/Py5OO33y4fv3DB9nv79jZflw4dKjt5E/m5wyCINxDEBWuP2YLrIgBICAyUH0GQn+aI9pPduwEARrMZ23JyMGv7dui0WhgKCrD4zjuF5XMFh65hP/jgA0yePBn+/v42wzMzMzFmzBhkZ2ejdevWuP7665Gbm4vx48dj//79Li0wEREREREREdUvDjVe/Pjjj+jZsyciIyNthr/99tsoLi7Gs88+ixMnTuDgwYP45z//idLSUqxatcqlBSYiIiIiIiKi+sWhxosTJ07g5ptvrjI8NTUVfn5+WLZsmXXYAw88gNtvvx07duxQX0oiIiIiIiIiqrccarzIzs5G40rPK126dAnHjh3DLbfcguDgYJtYr169cPbsWfWlJCIiIiIiIqJ6y6HGC51Oh5ycHJthe/fuBQDcdNNNVcYPFL00hIiIiIiIiIhIwKHGi06dOuHbb7+1GbZ9+3ZoNBrcdtttVcY/d+4cmjVrpq6ERERERERERFSvOdR48fDDD+Po0aOYOnUqfvvtN6SkpOD9999HUFAQhg8fXmX8nTt3okOHDi4rLBERERERERHVPw51Rj1z5kx89tlneP/99/HBBx8AACRJwptvvlnlEZFffvkFmZmZePrpp11XWvI6bu9znoTYX7tnmpedjZCQEBiNRmxLS8Pc7GzodLqyoKAfbrXHlVk0gskknMeSSl1mVxYn6I98ieixQkF/77HlfZEbjUZsS03F/Nzca/UHAFeuyM8/IEA+LiA6eYrqWFzD4v8uiPYDURmXVMgN3ZOTsaI8N4j6mifPFVd+XFQbF+wzoumV5AbRsSvKb8K4qAyVp/f1tR3m6PSujgvUxHWT0v3Akl9jyvOrwWDA8rAwt5eP7CsAUApAA6AxgCsApPJYc8G0Hwu2uejIFp0xzwniABAiiIvOPY0F8XEnT8qP0LBh2U+zGTh5EujUCdBeO9M2FMxfFC8UxA2CuOjf/W8qeB1Da9EIgvwk2k8mLFxY9otWC/TqBaSlldVnSc39VeFQhm3QoAF27tyJt956C7t378Z1112H0aNH47777qsy7r59+zBy5Ejcf//9LissEREREREREdU/DjcPBwUFYaGl1UXGlClTMGXKFKcKRURERERERERk4dA7L4iIiIiIiIiIahobL4iIiIiIiIjIo7HxgoiIiIiIiIg8GhsviIiIiIiIiMijsfGCiIiIiIiIiDyaus6oSSg+LAz+ALR6PaKSkxEfFgZzURGAGuiP3QsI17FC3F4dunv5nkDtfkKeKT483DY3hIdb92tRYo4rKJAfQdCPN0yCHt3z8gQlAMyiEQRliBWtQ7Ggx3fLOlT8qdFciwcIeqUX1ZGAaP1jBcftEhcct6L/PojKWDluLv9ITpeIPJ1HXDeI8o/o2FR57Nb29J5wzlZ6XVH5uqukJgpH1WoMoEGF7+EVfv9bMK1or20giBsEcT9BHABCBPEpR47Ixpd27iwb/zwyUjY+Jj+/7BeTCTh5Emjd2uZ4Flx1QJC5ECGIi7bBKBdc74+6eFF+BMH13YS9e5VNL0nAlSvA7beXXXsVFACvvSYsnyvwzgsiIiIiIiIi8mhsvCAiIiIiIiIij8bGCyIiIiIiIiLyaGy8ICIiIiIiIiKPxsYLIiIiIiIiIvJoXtd4sW/fPtx///1o1KgRGjRogG7duuGdd96xxgcNGgSNRlPlM3z4cEXzz8/Px7x589CxY0fo9XpERkZi4sSJOHXqlLtWiYhcgLmBiCpjXiAie5gbiLyTV3WVun37dtx3333o1asXFi5ciKCgIBw7dgxnzpyxGa9ly5aIj4+3Gda8eXPh/M1mM4YNG4Y//vgDzzzzDDp16oTMzEysWbMGX3/9NQ4dOoTg4GCXrhMRqcfcQESVMS8QkT3MDUTey2saLwwGAyZMmIB7770XKSkp0Gqrv2kkNDQUjz32mMPL2L17N37++WesWrUKzz77rHV4586dER0djbS0NDz44INOlZ/sU9rXuKdyRfm9vQ5qm7fmhpgTJxASEgKjyYRtu3YhJisLOkt/4wEB8hMXC3ojF8VNgt7KfcWnhjiVfYkLyygqgyVuWZeSEqDisRQUJDu56LgSHZciovnXxG2PomVoK/30BWCGuC97b+CtecHtRMe+u6cHFOUXl8xfksp++vjYLlO0fNE6uiB/ylGSe9Tmr/p8XeHNueE4gACU5ezu5d/N5TFzdROVayKIqz0nPa9gv31TsN8lde4sG28jmP9VQfzz0NCyX/R6+CcnY0vLlkBRkTUuqsNCQTxPEJe/KgGQlSUbjvvwQ9EchNePGy11UI1xq1bJz79Zs2u/+/gA+fllvxeKasd1vOaxkY0bN+LixYt49dVXodVqUVBQALO5+t3MZDLhypUrDi3DYDAAAJo2bWozvFn5htLr9Q6WmojcjbmBiCpjXiAie5gbiLyb1zRepKWlISQkBGfPnkXnzp0RFBSEkJAQTJs2DcWV/oP3559/IjAwEMHBwYiIiMDChQthNBqFy7jpppsQGBiIhQsX4rvvvsPZs2fx/fffY968eejTpw/uuOMOd60eETmJuYGIKmNeICJ7mBuIvJvXPDZy9OhRmEwmjBw5EhMnTkR8fDwyMjLw7rvvIi8vD8nJyQCA9u3bY/DgwejevTsKCgqQkpKCpUuX4s8//8Rnn30mu4zGjRvjs88+w+TJkzF06FDr8LvuugspKSnwlbkVsKSkBCUlJdbvllZXTUAAtBoNtOWtrNoKra2iBKgVtMwqSaDermId2KvD2qZ2G7pqHkpUrj+NJIlv3fcC3pobjKWlMJpMMJbfgmyseCuy2tuWRbdviuIajXzcFdSWoVK9GSvXSS3nV9H8PeGxEet4lXKDtg7kBk/PC4BMbjAayz7V7dtqqJ2Xksep1C7D8riHs2Uon77a+hPN383ld0XuUTsPpdcVdfG6wZtzg1bmbwoRtWd10fRK9luNp1y/W8pRqTyic6aoDtROb3TFtZkof4lyh8LrP2M1P2uCRpJEWdwztG/fHsePH8fUqVPx3nvvWYdPnToVa9euxZ9//omOHTvanXbKlCn44IMPsGvXLvTt21d2OT/99BMWL16Mfv36oWvXrjhw4ABee+013HPPPdi8eXO108XFxWHx4sVVhm/cuBENGjRQuJZENaewsBDjxo1Dfn4+QkJCars4TmNuIHKtupAbPD0vAMwN5H2YG5gbiOypydzgNY0X3bp1w++//47vv/8eAwYMsA7/4YcfMHDgQHz00UeYMGGC3WmPHDmCLl264JVXXsGCBQuqXcbx48fRvXt3fPzxx3j44Yetwz/66CM8+eST2LZtG+6++26709prJW3VqhViAgIQUN5K2j0xEf+Ljoa5/OUwMbm5suscHxYmGxdNXxdUrAN7dVjb1G5DV81Dicr1VyxJiC8u9uqLEMB7c8PfmZkICQ6G0WTCNz//jGF9+rjuhZ0VlmeXK144p/aFb6IyisoQGAig7L+q3+zahWG33nqt/irEq+Pu/Cqav6fdeXF9YiIOVcgNr3h5bvD0vADI5Ia//rK+zPebtDQMu+MO231bDU+480K0LqLcIipD+fRGkwnfZGRg2KBBtvWn9oWdIoL5uyL3qJ2H0uuKunjd4M254eUKf1N0TUzE7xWuh0UvmwwXxOVf4whcFsSjFey3qwT7nWivVJsF/Sy/6PXwT0xESXS0zQs7RZ3YisqXL4jLX5UA0b/+Kj/Cjz8K5gBg5EjZ8OaWLWXjo994Q37+EREAyu60+MbHB8NKS6EDYCgsROPHH6+R3OA1j400b94cv//+e5WX3zRpUvb+3FyZg6ZVq1YAgEuXLskuIykpCcXFxRgxYoTN8Pvvvx8AsHPnzmqTjb+/P/z9/asMl4qLbRKKuajImmh0Op1seUR/oIumrwvs1UHFOqxtarehq+bhCEv9eUWrpQLemht0Pj42F9Q6X99r35X2tFEd0cW3qM1aScOE2j+mRGUUlaHS8m3qDwBqOb96So5yhCU3iC6CvYGn5wVAJjfodDb7n87X13Xne7WPhLnikTK1+U1UBnu5oWL9qZ2/iGD+rsg9aufhaH6qS9cN3pwbzDJ/U4jyttptJ5peyX4redp5sajIod5GRHWgdnqd6LpHyf0Govwmyh0O3tOgq/CpKV7TeNG7d29888031hfsWJw7dw4AEB5efZvi8ePHheMAwMWLFyFJEkortepbnuMyOdAab7mhxdJuqpEkFBYWoliSrDuv5Rm26gj+Lymcvi6oWAf26rC2qd2GrpqHEpXrzzJfL7n5qlremhsMl8v+j2EsLUVhYSEMly9fO3FdFXT45Ql3XqhtvFDbVWr52+GNJlNZ/RkMto0XMm+PB9yfX0Xzr4G3iii/86JCbjCjbuQGb8sLQIXcUOHdF9Z921WNFzXRVara/KK2cbZ8+mrrr5bvvHBF7lE7D6XXFXXxusGbc4Ol/ivnbED8h7Go2cBPEBdNr2S/Fb0tRbQMtX+0WmtdklBaWIiSSvux2vKJphf9W8hyXVh9ARQ0/gi2g6hDU4NoGeVdohoBFPr4wGC586J8uhrJDZKX2LdvnwRAGjdunM3wRx99VPL19ZXOnj0r5efnS8XFxTZxs9ksjR07VgIg7d271zq8oKBAOnTokJSdnW0d9sYbb0gApPXr19vMY+XKlRIAadOmTYrLe/r0aQlluYQffjz6c/r0aQeORM/D3MAPP+75eHNu8La8IEnMDfx4z4e5gbmBH37sfWoiN3jNOy8AYOLEiUhMTMSYMWMwcOBAZGRkYPPmzYiJicGyZcuQkZGBRx99FI8++ig6dOiAoqIibNmyBTt37sSUKVOwdu1a67wyMjIwePBgLFq0CHFxcQCAnJwcdOvWDZcuXcLUqVPRtWtX7Nu3D+vWrUOXLl2wb98++PmJ2ibLmM1mnDt3DsHBwdBoNNbn1U6fPu21zwnWNtahOpXrT5IkXL58Gc2bN4dW6zW9JtvF3FB/sf7Uq6u5wZvyAsDc4GqsP/WYG5gb6iLWn3q1mhvc3jziQlevXpXi4uKkyMhISafTSR06dJDeeusta/z48ePS6NGjpTZt2kgBAQFSgwYNpN69e0sJCQmS2Wy2mVd6eroEQFq0aJHN8DNnzkjR0dFS27ZtJT8/P6lZs2bS5MmTbVpUnZGfny8BkPLz81XNpz5jHapTl+uPuaH+Yv2pV1fr0JvzgiTV3e1SU1h/6tXVOmRuqN9Yf+rVZh161Z0X3sxgMCA0NNSr39Bc21iH6rD+PBO3izqsP/VYh56J20Ud1p96rEPPxO2iDutPvdqsQ++954uIiIiIiIiI6gU2XtQQf39/LFq0yG7XR6QM61Ad1p9n4nZRh/WnHuvQM3G7qMP6U4916Jm4XdRh/alXm3XIx0aIiIiIiIiIyKPxzgsiIiIiIiIi8mhsvCAiIiIiIiIij8bGCyIiIiIiIiLyaGy8ICIiIiIiIiKPxsYLFdLS0jBkyBCEhoYiODgYvXv3xmeffWYzTps2baDRaKp8pk6dqmgZZrMZr732Gtq2bYuAgAD06NEDycnJ7lidWjN58mRoNBqMGDHCZnhOTg5ef/11DBgwAOHh4WjYsCH69u1bpY7l2Kt7jUaD5cuXu3o1asz58+fx4osvYvDgwQgODoZGo0FGRkaV8bKysqpdf41Gg8mTJwuXVRfrz92YF1yHucExzA2ejbnBdZgbHMPc4NmYG1yDecFx3pgbfB0am6zWr1+PiRMnYtiwYVi2bBl8fHxw5MgRnD59usq4PXv2xAsvvGAzrFOnToqW8/LLL2P58uWYPHky+vTpg61bt2LcuHHQaDR45JFHXLIutemXX35BUlISAgICqsR27dqFl19+Gffccw8WLFgAX19f/POf/8QjjzyCP/74A4sXL1a0jGHDhmHChAk2w3r16uWS8teGI0eOYMWKFejYsSO6d++OXbt22R0vPDwcn3zySZXhqamp2LBhA+68805Fy6tr9edOzAuuw9zgOOYGz8Xc4DrMDY5jbvBczA2uwbzgHK/MDRI57MSJE5Jer5eef/554biRkZHSvffe69Ryzpw5I+l0OunZZ5+1DjObzdLtt98utWzZUjKZTE7N11OYzWbp1ltvlaKjo+3W0/Hjx6WsrKwq0wwZMkTy9/eXrly5IlwGAJv6qwsMBoOUk5MjSZIkbd68WQIgpaenK55+6NChUkhIiFRUVCQcty7Wn7swL7gOc4NzmBs8E3OD6zA3OIe5wTMxN7gG84LzvDE38LERJyQkJKC0tBRLliwBAFy5cgVl26R6V69eRUFBgUPL2bp1K4xGI5555hnrMI1Gg2nTpuHMmTPVto55i08++QQHDx7Eq6++ajfetm1bREZG2gzTaDR44IEHUFJSguPHjyteVlFREYqLi1WV11MEBwejUaNGTk17/vx5pKen46GHHrLbOl2dulR/7sK84DrMDc5hbvBMzA2uw9zgHOYGz8Tc4BrMC87zxtzAxgsnpKWloUuXLti2bRtatmyJ4OBgXHfddVi4cCHMZnOV8b/77js0aNAAQUFBaNOmDd5++21Fy9m/fz8CAwNx/fXX2wy/+eabrXFvdfnyZcyfPx8vvfQSIiIiHJr2woULAIDGjRsrGj8pKQmBgYHQ6/W44YYbsHHjRofLW1ds2rQJZrMZ48ePVzwN608Z5gXXYG6oHcwN7sPc4BrMDbWDucF9mBvUY16oPbWVG/jOCyccPXoUPj4+eOqppzBv3jxERUXhiy++wNKlS2EymRAfH28dt0ePHujfvz86d+6MnJwcJCUlYebMmTh37hxWrFghu5zz58+jadOm0Gg0NsObNWsGADh37pzrV66GLFmyBHq9HrNmzXJoukuXLmHdunW4/fbbrfUg57bbbsOYMWPQtm1bnDt3DqtXr8b48eORn5+PadOmOVt8r7VhwwY0a9YMQ4YMUTQ+60855gXXYG6oHcwN7sPc4BrMDbWDucF9mBvUY16oPbWWG1Q/eFIPabVaCYC0fPlym+HDhw+X9Hq9ZDAYqp3WbDZLd911l+Tr6yudPn1adjlDhgyRrr/++irDS0tLJQDSjBkznCp/bTty5Iik0+mklJQU6zAlz/KVlpZKw4cPl/z8/KQDBw44teySkhKpW7duUsOGDaXCwkKn5uFJHHk+7ciRIxIAadasWU4vr67VnysxL6jH3OA6zA2eg7lBPeYG12Fu8BzMDeowL7iWt+QGPjYi4+rVq7hw4YLNp7S0FHq9HgDw6KOP2oz/6KOPoqioSPb2K41Gg1mzZsFkMtntiqYivV6PkpKSKsMtzwlZyuGpqqu/GTNm4LbbbsPDDz/s0PymT5+O1NRUrFu3DlFRUU6Vyc/PD8899xzy8vKwd+9ep+ZRU6qrP2dt2LABABy6vasyb6o/d2FeUI+5QR3mBs/E3KAec4M6zA2eiblBHeYF9epSbuBjIzL++9//YvDgwTbDTpw4gebNm+Po0aNo2rSpTaxJkyYAgNzcXNn5tmrVCkDZLUtymjVrhvT0dEiSZHOr1/nz5wEAzZs3V7YitcRe/X344YdITU3FF198gaysLOtwk8mEoqIiZGVloVGjRggJCbGZbvHixVizZg2WL1+Oxx9/XFW5lNZ/batu/2vTpo1T89u4cSM6d+6M3r17qyqXt9SfuzAvqMfcoA5zg2diblCPuUEd5gbPxNygDvOCenUpN7DxQkZUVBS++eYbm2ERERHo3bs3jh49irNnz6Jdu3bWmOWZsfDwcNn5Wt5qKxqvZ8+eWLduHQ4dOoQbbrjBOnzPnj3WuCezV3+ZmZkAgIceeqjK+GfPnkXbtm3x1ltvYebMmdbhq1evRlxcHGbOnIn58+erLpfS+q9t1e1/ztizZw8yMzOtb7RWw1vqz12YF9RjblCHucEzMTeox9ygDnODZ2JuUId5Qb06lRucflClHtuyZYsEQHrppZesw0pLS6X+/ftLjRo1koqLiyVJkqScnJwqfSdfvXpV6tevn+Tn5yedP3/eOjwvL086dOiQlJeXZx12+vTpavtlbtGihVf2y3zy5Elpy5YtVT7h4eHSTTfdJG3ZskXKzMy0jr9p0yZJq9VK48ePl8xmc7XzLSgokA4dOiRlZ2dbh/31119VxjMYDFL79u2lxo0bSyUlJa5duVqg9Pm0559/XgJgU7cV1df6cyXmBXWYG1yLucFzMDeow9zgWswNnoO5wXnMC67nLbmBjRdOMJvN0tChQyWNRiNNmTJFWr16tTRs2DAJgLR27VrreOvXr5fat28vzZ8/X0pISJCWLVsmdevWTQIgLVu2zGae69evlwBI69evtxk+d+5cCYA0ZcoU6YMPPpDuvfdeCYC0YcOGmljVGmPvBTt79uyR/Pz8pPDwcCkxMVH65JNPbD7Hjh2zjpueni4BkBYtWmQdtmjRIikqKkpasGCB9P7770uLFy+WIiMjJY1GI3366ac1tWpu8corr0ivvPKK9Mgjj0gApOjoaOuwykwmk9S0aVOpb9++1c6vvtWfOzAvuAdzg2OYGzwPc4N7MDc4hrnB8zA3uB7zguO8LTew8cJJly9flmbMmCFFRERIfn5+Uvfu3atU/i+//CLdd999UosWLSQ/Pz8pKChI6t+/v/T5559XmV91yaa0tFRatmyZFBkZKfn5+Uldu3atEwdKZfaSjaVOqvtUrCt7B8r27dulYcOGSREREZJOp5MaNmwo3XnnndK3335bQ2vlPnL1UllqaqoEQHrnnXeqnV99qz93YV5wPeYGxzA3eCbmBtdjbnAMc4NnYm5wLeYFx3lbbtCUF5qIiIiIiIiIyCOxq1QiIiIiIiIi8mhsvCAiIiIiIiIij8bGCyIiIiIiIiLyaGy8ICIiIiIiIiKPxsYLIiIiIiIiIvJobLwgIiIiIiIiIo/GxgsiIiIiIiIi8mhe03jx888/47nnnkPXrl0RGBiI1q1bY8yYMfjzzz+rjLtq1Spcf/318Pf3R4sWLTB79mwUFBQIl5GRkQGNRlPt59VXX3XHqhGRCswNRFQZ8wIR2cPcQOTdNJIkSbVdCCVGjRqFnTt3YvTo0ejRowcuXLiAVatW4cqVK9i9eze6desGAJg/fz5ee+01jBo1CkOHDsUff/yB9957D0OGDMHXX38tu4yLFy/im2++qTL8k08+wfbt2/HTTz+hT58+isprNptx7tw5BAcHQ6PROL7CHuqee+5B9+7dsWLFijqxzKlTpyI/Px/Jyckun7enkyQJly9fRvPmzaHVek07ZhXMDZ6BuaHuqAu5wdvyAsDc4C3LZG5gbmBucA3mhrqjRnOD5CV27twplZSU2Az7888/JX9/f2n8+PGSJEnSuXPnJF9fX+nxxx+3Ge/dd9+VAEj/+te/nFp2hw4dpI4dOzo0zenTpyUA/PDj8Z/Tp087dVx4CuYGfvhxz8ebc4O35QVJYm7gx3s+zA3MDfzwY+9TE7nBF17itttuqzKsY8eO6Nq1Kw4dOgQA2LVrF0wmEx555BGb8R555BFMnz4dmzZtwn333efQcn/66SdkZmYiLi7OoemCg4MBAKdPnkRISAiMRiO2p6XhzjvugE6nUzSP+LAwh5ZZ12kCAtA9MRH/i46GVFxc28UBAMTk5srGa2IbispgUXkfNBgMaBUZad1XvZW35obZAPwBaAMC0DUxEb9HR8Ncvl+7+/9ZZkFccvPyAQX7rckkH79yBQBgLC3F9p9+wp033wydj8+1eFCQ7OSvhYfLxueJypeXJx8XLH+5YPkA8KKoDOV14GwZLHVsNBqxPSMDdw4aVJYbLl9Gq3btvDo3eFteAK7lhlkoyw3uOOepPWcpPd+oWYarOFt/7q4jV6y/u5dhmX9dvG7w5tzwFgA9AAQEICQxEYboaKB8374qmIfoD77xgn3qQ8E+peQPyicEy1gpWEaAYP5TR42SH6FdOwCAUavF9h49cOdvv0FnrnBF1L27/PS33iofX7NGPt6smXxcZMAA8Tg5OfLxHTvk46J1zMgAABh9fLC9Vy/cuX8/dKWlMJSUoNXq1TWSG7ym8cIeSZJw8eJFdO3aFQBQUlICANDr9TbjNWjQAACwd+9eh5exYcMGAMD48eMdms5yW1dISIi18aJBgwYICQlR3Hjh71hR6zytRoMGDRogQKMR/vFVU0JCQmTjNbENRWWwqG4frEu3IFp4Q27wR9mJ2N5+XduNFzVxfAn3W1HjRfltiUaTqWy/Dg6GzrfCKU3wh7voIkhYPrOgllQuX1EZRLdmOtB4UR9ygyfnBcA2N/jDPec8tecspecbNctwFWfrz9115Ir1d/cyLPNnbvCs3KAv/6B83zZW2A4+dqe8RvQHn2if0stGlf1BKVqG6LwoPG+L/r7yLzsyjFpt2X7t72/beFG+fasl+sPcX3Dk6UW1KCA6pwPWxqxqBQhqMTBQPm6pQx+fa3VYWmoN10Ru8OrGiw0bNuDs2bNYsmQJAKBz584AgJ07d2Lw4MHW8XaUtzKdPXvWofmXlpbis88+w80334wOHTrIjltSUmJNdgBgMBgAlCV+o9EIo+UiUXRBXoFW7U5ex1jqw5PqxWg0ysZroqyiMljHq7QPKp3OG3lDbtAGBECr0djdr73zSWLHCPc/Ua6svD9XHl/lsam6fC7IDe4uQ3V1WFdzgyflBaD63KCRyQ1qqT1nuWLfqKlzuLP15+46csX6u3sZlvkzN3hWbkBAAKDRXPsjuMJ2Vvsno2jbagT7lJLlq91vRcsw+gr+rLX806PST8VE51wfQROS2j/sKzQSVEv0KkvROoumL1/HynVoFK27C3nNCzsrO3z4MG655RZ07doVO3bsgE95pfXt2xe///473nnnHQwePBiHDh3CtGnTcO7cOZjNZpgcaDzYvn077rrrLrz99tt4/vnnZceNi4vD4sWLqwzfuHGjtaWWyJMUFhZi3LhxyM/Pd8l/0zwFcwOROnUxN3haXgCYG8j7MDcwNxDZU5O5wSsbLy5cuIB+/frBaDRi9+7daN68uTV29uxZjB07Fjt37gQA+Pj4YPbs2fj+++9x5MgR5ImeU67giSeesLbGNm3aVHZce62krVq1wt9//VX22IjJhG/S0jDsjjtsb22WwXde2NLq9dbnV81FRbVdHABe9s6LSvugwWBA4yZN6tRFiDflhpcDAhBQ/t9V6zsvyvfr+vDYiOp3XpR3V2c0mfDNzz9jWJ8+trlVcOvj64J3TswVlS8/Xz4uWP4KBe+8mC8qg6jLPtHtnxX+q/pNRgaGDRp0LTc0b15ncoMn5gWg+twQUyE3uPqcV5/eeeFs/fGdFxXeeVHHrxu8LTckBARAX37nRWhiIvKjo4HyfVt0T4zo/+Kid14kuuCdFxMEy3hbsAzR41COvPPim6goDPv1V9vHRnr0kJ++b1/5+HvvycfVvvPi9tvF41y6JB931TsvtFp8c+ONGLZvH3RmMwwlJWj8+us1khu87rGR/Px83H333cjLy8OOHTtsEg0AtGjRAj/++COOHj2KCxcuoGPHjoiIiEDz5s3RqVMnxcspKirCli1bcMcddyhKNP7+/vC386yTTqezeU5Q5+ur+J0XnvIHuqcxFxV5TN2ItmVNlFPp/mQdv3wfdHQ6T+dtucFcXGzTSFCT+7UnNF4I9z/R7ZWVGoF1vr62jRcqj01h+USN0C7IDe4uQ+U6rou5wVPzAlB9bpDcmBvUnrNcsW/U9Pnb0fpzdx25Yv3dvYzK82du8IzcUOV9BkVF1sYLtf+JFm1bSbBPKVm+2v1WtAyd6J8eld5VpTObbRsvRETnXNFjHWrvF1DyaIbo2km0vqLpK62jzmyGrrTU5r0X7uZVjRfFxcW477778OeffyItLQ033HBDteN27NgRHTt2BAD88ccfOH/+PJ588knFy/rXv/6Fy5cvO/ViHU8RJ9iR4mrw+SRnVVwHo9GIbampiMnNtSZA0TqI6kC4fNH8PaAOlZZBq9cjKjkZ8WFhMBcVoUQ8idfwxtygrfABXHu3hehm1jjRf+yV/EdJ9OIoJS+WknPhgny8YUPb776+NhcWSwQvzhJdriwRHFexgtwiml4JYRkE21E0vfUFsZbcEB5ep3KDN+YFoOy/3pYXfTt6zlNLeM5UcKt8nOildR6u1uvYA1jqoK5eN3hrbsgFUISydz+Eln+3/DksOqfNFtwtuFSw3zeXjYp7OwEgvDNhnuiOxpQU2bB54kTZuPbBB8t+0emAXr2AQ4ds3w0lunPj8GH5eESEfLxXL/n4G2/Ix0+elI8DQFaWfPyxx+Tjohzfr1/ZT0kqa8i45ZayBo/CQnHZXMRr3gtXWlqKsWPHYteuXdi8eTNuFd3WUs5sNmPevHlo0KABpk6dah1uNBpx+PBhnD9/3u50lufKHrTs6ETkkZgbiKgy5gUisoe5gci7ec2dFy+88AL+9a9/4b777sOlS5fw6aef2sQfK29JmjFjBoqLi9GzZ08YjUZs3LgRP/30Ez766CO0bt3aOv7Zs2dx/fXX44knnkBSUpLNvC5duoT//Oc/ePjhhxGk9r+HRORWzA1EVBnzAhHZw9xA5N28pvHiwIEDAIAvv/wSX375ZZW4Jdn06tULK1euxIYNG6DVanHzzTfj22+/tenqSGTz5s0wGo0YN26cS8pORO7D3EBElTEvEJE9zA1E3s1rGi8yyt9uKvLkk08qehatTZs2qK6jlaeffhpPP/20A6UjotrC3EBElTEvEJE9zA1E3s1r3nlBRERERERERPUTGy+IiIiIiIiIyKOx8YKIiIiIiIiIPJrT77yIjo7GAw88gPvvv7/acb766it88cUXSExMdHYx5EZK+hp3d3/njiy/cn/jrp4/kacQ9LItPHaXCPbruMBAB0tkZx45OfIjFBfLx30Fp5/GjeXjAQFlPy19tAcGlvXdrpDaOhQxq5paGbXbUVvNT42quZJa8WFh8Id7znmi41K0Tym6blAwjuz0tX3dISr/lSvq4iIu6JVCtA6iOrZMbzQasS01FTG5udDpdDAYDFgeFqa6fOQcffnHkqMbALC8bSNAMO2S0FDZeDvB9KJzWvT33wvGAJCSIh8vf1lqtbp1kw0XChYfdOhQ2S/+/mU/jxwBSkqujRAXJzv98S1bZOOCqxaEjB4tP0L//vLxzEzBEgDk5bk1vnTGDABl56euycl4fcwYmIuKILjicymn77xISkqyvrG3Or/++is++ugjZxdBREREREREROTex0aKi4vhK/rvGhERERERERGRDFUtCxqN/ZtLJUnC6dOn8Z///AfNmzdXswgiIiIiIiIiquccuvNCq9XCx8cHPuXPysXFxVm/V/z4+vqibdu22LdvHx555BG3FJyIiIiIiIiI6geH7rwYMGCA9W6LH374Aa1bt0abNm2qjOfj44NGjRphyJAhmDx5sksKSkRERERERET1k0ONFxkZGdbftVotnnrqKcTGxrq6TEREREREREREVk6/88JsromO4IiIiIiIiIiovmNXIF7M2/tSp5rB/to9kxm2/aZX/C56GdESwbEbW1AgP31goPz0Ffs9r86ZM/LxAEGv86K4yp6qYlXmR1EdivKnMD8XK+gVXVAHSyx91VdDtB+ZKn237IOSqFzkvUTHnYCS6wbV1yaefm0jOnZFuctU+cirJCjIsfK4gaUOtXo9opKTER8WBnNRERScGciNnvrXvxASGAijJGHblSt48ssvobN0ntC/v/zEFy7IhtdERsrGG4gKpyS3iI6NvDzZcFZ8vGy8TatW8vO31IGlrBcv2hzP5sOHZSdvKD93hAweLD9CVpZ83M6rGByWkCAbXtm+vWx8puD6TztjRtlPy/dKP2uC01eHQ4YMUTSeRqPBt99+6+xiiIiIiIiIiKiec7rxouL7L+zRaDSQJKna7lSJiIiIiIiIiJRw+i4Ps9ls95OXl4fvvvsOt9xyC0aNGoWrV6+6srxEREREREREVM+4/BGVkJAQDBo0CF9//TV++uknvPrqq65eBBERERERERHVI257v0ZwcDDuvvturF+/3l2LICIiIiIiIqJ6wK0vB9VqtTh//rw7F0FEREREREREdZzbGi+OHz+OzZs3o40run0hIiIiIiIionrL6d5GoqOj7Q43mUw4e/YsfvzxRxiNRixZssTpwpGAqK9wUX/K5Pn9yUN9GanuMYtGEPS3Hivapyr0e+60v/+Wj0dEyMeDguTjlvxX8WfF3q0Ey49r0UI+7ubjbklgoHAc4XZWybKORqMR21JTEZObC51OB4PBgOVhYW5eOnmiuJwc+REaNnR/IUTXNoL8VN1+rdiVK/Lxxo2Vz8seUX4V5U5AfH1XE9uJat7x44BeX3aua9Kk7LsklcUGDZKfdulS2bBgr4fgqMRrt9wiGAOYl58vP8KZM7LhvK5dZeN/nT4tG7cceaV6fdni8vPhU1RkjbcW/MO9UZ8+snFERcnHg4Pl4489Jh/Py5OPA8L8NFOU4z/4QDb80mefAQCMALYBeCEpCToAhsJCvPLUU+LyuYDTf90mJSXJxjt37owXXngBkyZNcnYRRERERERERETON16cOHHC7nCtVouGDRsiWNS6RERERERERESkgNONF5GRka4sBxERERERERGRXS57KcLx48eRn5+P0NBQtGvXzlWzJSIiIiIiIqJ6TlVvI/n5+ZgxYwbCwsLQsWNH3HTTTejYsSPCwsIwc+ZM5ItezEJEREREREREJOD0nRd//fUXbr/9dhw9ehQNGzbEwIED0bRpU1y8eBEHDhzAO++8g//85z/YsWMHmjRp4soyExEREREREVE94vSdFzExMTh69ChefPFFnD59Gt999x2Sk5Px3Xff4fTp05g/fz6OHj2Kl156yZXlJSIiIiIiIqJ6xuk7L7788ksMGTIEy5YtqxILDAxEfHw89uzZg3/961+qClinCfoyjyspkZ9e0M93nI+PuvkrWIaIqAxqWfpzr5aov3iV60fkDqqe54P4uBPN36xkGaL8cfCgfDwzUz4u6KvceuxacsC5c0DF9W7YUHZyUflFdSjKHEtUbgMl4yjZTnIs66jV6xGVnIz4sDCYi4qg4MxAXkp4XZCTIz+DvDzxQkTnXcGxiStX5ONBQfLx4mLbcpSUXMsTABAQID+9qPyiOhCtn2j5Sgjm4e5rL6olAQG2297f/9rvycny0x4+LBuet2qVbPzj554TlU7o09BQVdM/Fh4uGz+enS0bt2QOyxHuD9tzeUZWluz0g9q2lY2jVSvZ8GtPPCEbbzRjhmzcIL90AIAou/wtiMeKru0mTSr76esLjBwJpKaW5cyrVxWUzjWcvkYuKChA3759Zce59dZbUVhY6OwiiIiIiIiIiIicb7zo1q0bsgQtVFlZWejWrZuziyAiIiIiIiIicr7x4qWXXkJKSgrS0tLsxrdv346UlBS8/PLLTheOiIiIiIiIiMjpB/7z8/Nx55134q677sKwYcPQv39/a28jO3bsQFpaGkaMGIHc3Fx8/PHHNtNOmDBBdcGJiIiIiIiIqH5wuvHiySefhEajgSRJ2L59O7Zv315lnC+//BJfffWV9bskSdBoNGy8ICIiIiIiIiLFnG68WL9+vSvLQURERERERERkl9ONF08IunshIiIiIiIiInIFp1/YSURERERERERUE5y+88LCZDLhyJEjyMvLQ2lpqd1xBgwYoHYxdZOvuuqP8/GRj1ezPWqSqAyOrIPRaMS21FTE5OZCp9O5pHwwmeSX7++vavZKtoGoDogcJcosZkE8LidHfSEEXWmjZ09187ccu5ZjzGQCJEnx5EsEx7aoDuUzBxCXny8/QnGxYA4AgoLE48hYGhioaDxtpZ8aVUslr9awoXz877/F87hwQT4uOO+6LG7JDfn5QMXzrOjYy8yUj990k3w8L08+LrruaNpUfnqoz9FxJSXyI5Rfn1a+7jIYDFgeFqZq2aTCHXcAwcFl+9DPP5d9t/wtITouzp5VFZ/w9tvy0z/3nHwcwCrB9W6hYPql2dmy8QWzZsnPICICAGDUlp3twpcsgc587YqoiWAd3hGcUwPS02XjjeVLJ7w2U3JFIKrDloL4KsG10XN79pT9YjYDFy8CU6YAWi1w5Qrw2WcKSqie0389S5KE2NhYvPvuu7h8+bLsuNU1ahARERERERERiTjdePHKK6/g1VdfRcOGDTFhwgS0bNkSvirvJCAiIiIiIiIiqszp1obExERERkbil19+wXXXXefKMhERERERERERWTn9ws4LFy7ggQceYMMFEREREREREbmV040Xbdu2hcFgcGVZiIiIiIiIiIiqcLrxYtq0afjqq6/w119/ubI8REREREREREQ2nH7nxciRI7Fjxw7cdtttiI2NxY033oiQkBC747Zu3drpAhIRERERERFR/eZ040Xbtm2h0WggSRKeeuqpasfTaDQwifoeJreIE/Sn7JJleHs3uKL+1hX2he5Oou0o3AaWdaz4U6MR9wlOtUbU17foljm10y9R8C6jWNF+N2KEfFx07Pz9t2x4SdOmAACtXo/uyclY0bs3zEVF1rioDuLy8+XjoaGCOQhkZcnHG4t6fAeQlycfb9hQNiw6wp2+9ZLqLOH5RnROBIDiYvm46Ng/eFA+HhAgHw8KKvtpLs8CFy8CWm3VeHUEx5Xw2BZNLzj2ldRxnL+/fFyQn1VfV1DtMJnKPpbtU2E7vdmiheyks7/+Wn7eR4/Kx/fskY+npMjHAeQJ4qLztuismfjWW7Jxy8sONHo92iQnY01sLKSK1w3z58tOP1twXLyj8u8u0TlZkPkAAA0E8SuCuCA74tNbbin7Ra9HcHIyPhsyBCgqQpH8ZC7l9F9eEyZMgEajcWVZiIiIiIiIiIiqcLrxIikpyYXFICIiIiIiIiKyj3eNEhEREREREZFHY+MFEREREREREXk0hx4bueeeexxegEajwYsvvoi1a9fi2LFjSElJQYsWLfDJJ5+gbdu26N+/v8PzJCIiIiIiIqL6w6HGi9TUVIcXoNFokJ6ejvHjx2P//v0oKX+Lcn5+PpYtW4Zt27Y5PE8iIiIiIiIiqj8carw4ceKEwwu45557MH/+fEyYMAGbNm2yDu/Xrx+WLl3q8PyIiIiIiIiIqH5xqPEiMjLS4QWcOHECAwYMqDI8NDQUeaI+7Os4tf1sq+2HW7R8V8xD7TpUnL9Wr0dUcjLiw8JgLu+XWXVf5KL+4k0m98bh/u1onb8klf309b32oTpJ9DIjUV/qSiwR7Hexov36wgX5eGNRj+7y4goK3Dp9XGCg/AxatpSPFxeLCxERIRsWbQNH9wNz+UcSlYvcKiY3FyEhITAajdiWmoqY3FzodDoA6s/bjpxznRYUJB8X7fsqj33reddsvvZdW+FoEC1fdF3QsKG6uKB+XLINqE5a1aULAgBo9Hq0S07Gqs6dIZVfD18VTLvyrrtk4zPPnpWfgWC/fn/sWEEJAD9BfN7OnfIjHD4sH79yRT7epQsAwChJ2FZcjGe2bIFOo7kWP3hQfvrnnpMNP//VV/LTd+8uG35H8He2YO0AADPz82XjS0NDZeOivwwsf9VoAAQDKELZNUORgrK5ilte2FlSUgKj0QgAiIiIQGZmZpVxfvzxR7Rr184diyciIiIiIiKiOsTpxosffvgBsbGxNndP5OTk4O6770ZQUBBCQ0Px4osvYvLkyZgxYwb27NkDjUaDc+fOYcOGDZgzZw6mTZvminUgIiIiIiIiojrM6caLN954Axs3bkTDCrcRvfDCC/j666/Rtm1bNGzYEK+//jratWuHcePGYejQobhy5QoGDBiASZMm4emnn8b06dNdsQ5EREREREREVIc5/dD7/v37MXToUOv34uJifP7557jzzjuRmpqKy5cvo0ePHkhISEB6ejrmzp2LzMxMXLlyBTfccAOCRM9EEhERERERERFBReNFTk4OWrRoYf2+a9cuFBcX46mnngIABAcHY8SIEfjnP/8JAPDz88MNN9ygsrhEREREREREVN84/diIXq/H5cuXrd/T09Oh0WgwcOBAPPTQQ3jooYewfft2XLx40frd3sdZr776KjQaDbp161btOHl5eWjSpAk0Gg1SUlIUzffixYt46qmn0KRJE+j1etx4443YvHmz0+UkoprF3EBE9jA3EJE9zA1E3sPpxosOHTogNTUVJSUluHr1KjZt2oQbbrgBERERCA0NRWhoKEwmE/R6Pb799lv88ssv1mn37t2Lb7/9FqGC7lqqc+bMGSxbtgyBgq7qYmNjUVhYqHi+BoMB/fv3xz//+U88/fTTeOONNxAcHIwxY8Zg48aNTpWViGoOcwMR2cPcQET2MDcQeRenHxuZPHkypkyZgg4dOsDPzw9ZWVl4/fXXAQDr168HAHTp0gVNmjTB0KFDkZCQAJ/yvqtLS0vxzDPPICQkxKllz5kzB3379kVpaSn+/vtvu+McPHgQ7733HmJjYxEbG6tovmvXrkVmZia+/fZbDBkyBAAwbdo09O3bFy+88AJGjRoFPz9RL8VUkbv7KxfNX9SnvbU/eHfxdfoQs2Kf78rVl9xgFsTd0ge2g9Tut2qPnDjBhagwN1Sz/yieXtTffHGxfFyBWLVlKH/3lNFoxLbUVMTk5kKn08FgMGB5WJjq8nkSb8oN8WFh8Aeg1esRlZyM+LAwmIuKAIj3O+E5UeVxGefvr2p6QMGx07ixuvmXl9Faf0OGWOsPAOJKSuRnILouEMX5Tjev4k25wVz+sZzjJVy7HmgimFZ0tZtQ4VUA9gjOJpgjOq4BLBflnw4d5OMvvCAfHz1aNvzXXXcBAEx6PZCcjOwHH4RvhdzQpEInFHYFBMjHBXWI8HDZ8POiOjxzRj4OCPNPQ8Hkoj2ycg3oy39qBNO5ktPXuBMnTsTcuXNRVFSE/Px8TJs2DTNnzrTGd+3ahT///BMXL17EnDlzrA0XAODj44PZs2cjMTHR4eX+8MMPSElJwcqVK2XHmzFjBh588EHcfvvtiue9Y8cOhIeHW5MMAGi1WowZMwYXLlzA999/73B5iahmMDcQkT3MDURkD3MDkfdxuvFCo9FgxYoV+Pvvv/H3339j1apV0Gqvza53797ILf8vzuHDh6tMf/jwYZjNov8f2iotLcX06dMxadIkdO/evdrxNm/ejP/+97947bXXHJp/SUkJ9Hp9leENGjQAUPa4CxF5HuYGIrKHuYGI7GFuIPJOTt+Z+/HHH6Nnz57o0aOH3bifnx/+/PNP9O3bFxMnTsSxY8dw8803AwD27NmD5cuXW3smUSohIQEnT55EWlpateMUFRVhzpw5mDVrFtq0aYOsrCzF8+/cuTPS0tJw8uRJREZGWofv2LEDAHD27Nlqpy0pKUFJhVsRDQYDgLLbcY1GI4zltxkaK9xuqLWT1CoyGo2Ky+4M0fI9jaW8jpRbWIfufmxECUmSDavdTpY6qLwPunv/qknemBu0AQHQajRO7dcinvDYiFpK18HZ+lOdG9ROr+AWW+EyRBSuA3ODZ+UGjUxuEG0bbzivu/u8XLneKteJcPlqHzdVefy4Yhuq3U943VDG03KD5bpBU779NHq99Vwpum1fbVx0Tlaybwj3O9GxJXrMRiO/Fqby5ZdW+mldvuixEFFcK6gl0T/t1eYmBfPQCLaB4sc/LPOx/JQklzwOq4TTjRdPPvkk4uLiqm28AICtW7fim2++QXx8PP7xj3/g/PnzAIBmzZph7ty5eEH07FIFOTk5iI2NxcKFCxEu88zQ8uXLYTQa8dJLLylfmXKTJk1CQkICxowZg7feegtNmzbF559/ji1btgAoS2LViY+Px+LFi6sM356WZm1lBYBvKiTJqORk2fJsS011dBUcIlq+p+ruwONG7q7DmqB2O1WuA8s+6MiLpzyZt+aGromJNrmhqxOP0dE1jtafV+SGP/6o0cUxN3hGbuheKTdUPOeJ9ltvOK/X9LVN5WuGbTJ/rHoCV2xDtfsJrxvKeFpuaFspN7TzoOsGJcd1Z9F+9/PP8jOYPduRIlVVafn7arr+Ll6Uj7siN/72m2y4pYvPEcHldehTWAiMG+fSeVdH/dsEZZSWlkKr1WLevHmYN2+eteXQmRd1LliwAI0aNcL06dOrHcfy0tDVq1cjyIkXJvXo0QMbN27E1KlT0a9fPwBAREQEVq5ciWnTpsnOMyYmBrMrHFQGgwGtWrXCnXfcgZCQEBhNJnyTloZhd9wBXflLHOMFL0SLyc11eB0cIVq+p9Hq9eiemIj/RUfbvHxLjrAOPeHOC8FLPdVuJ0sdVN4HLcejt/PW3PB7dDQCyv+72jUxEb87sF+LiP5D4tgDe85RuwzRycky/+rqT7R8YW7IyZGPX3edfLygQN38AaB1a/E4aspQ/lJT5gbPyg3/q5AbKp/zRPutN5zX3X1eji//Y7S6a4aY7Gz5Gai980LwsmARV2xDtfsJrxs8MzecKM8NGr0e7RITcTw6GlL5vi3aa0RHleicKTibYLqCv1neEux3szIz5Wfw6KPy8QcekA1nv/wygLI7LvYlJuLG6Gj4VMgN4aJeMEV3XkyZIh8fPFg+3quXfPzcOfk4ADRvLht+X7ANBGsI6yub9XoEJybicnQ0UFSEIsFd5K7k1saL/fv3o1GjRtbvzvYucvToUbz//vtYuXIlzlXYcMXFxTAajcjKykJISAhiY2PRokULDBo0yHpr14ULFwAA2dnZyMrKQuvWrW3ezVHZqFGjcP/99+PXX39FaWkpbrzxRmRkZAAAOnXqVO10/v7+8LfzFm6dTgedTnftu6+v9bvoD5WK07mDq/5QqmnmoiLFZRfWoeAWsxohaLxQu50q14FlH3T3/lUTvDk3mIuLbS4WHNmv1fKGxgvR9JXjletPNL1w/xf1FKR2eiW9Pqg9Rh1cB+aGLAC1nxskmdwg2jbecF5393m5ch1Uzg1uvy5Qefy4Yhuq3U943eCZucFy3WDtbaTCvi3601FtXPU5FQr2O9E56+pV+bjgD2jfSsv3KSqyGaZT25uS6LEQ0WMlojp0wXWDJNgGDjdBFBWVfWqQQ40XFd+YCwBJSUnWg7Ci0tJSnDlzBllZWdDr9WjXrl218zx+/LhwuWfPnoXZbMbzzz+P559/vkq8bdu2mDFjBk6dOoXMzEy7y3vmmWcAALm5uWgo6ArHz88Pffr0sX63PA93xx13CMtKRDWHuYGI7GFuICJ7mBuIvJtDjRcVGyo0Gg2ysrLsvrxGq9WiUaNGGD16NLp164bg4GBrzGg0Yv/+/UhNTcXcuXMVLbdbt27WZ8QqWrBgAS5fvoy3334b7du3R35+fpU+mg8ePIiFCxdi3rx5uPXWWxFYfjtfYWEhTp06hcaNG6OxTJ/iR48eRUJCAkaMGCHbSloduf7aa5uwr3Wo7xPe3dT2ea92/q4gKqPadbTEK++Dgp7uvYI35wZ3qok7K0TUvjRU7TqIjpslguMmVu2xL/gPUlz79sJZuLsOayK/1Za6mhvqxDmtltchTuV/V73huFF7XWF9NKbiT43GMx61Vcmbc4O+/KOp8F3pf8rzBPEGgrjoD8Y3FRzXwj86k5Lk4zt3yobfEJRhavlPy17coHKZRI+lpKfLx0sEV9aC64JEQfmVvA5TNI7osRDBK1E9gkONFxW7NtVqtYiLi0NsbKxTC169ejV++eUXReM2btwYD9h5jsnSL7O9mIWlRbRPnz424/30008YPHgwFi1ahLi4OOvwG264AaNHj0br1q1x4sQJvPfee2jUqBESEhIUlZWIag5zAxHZw9xARPYwNxB5N6ffeZGeno42bdo4veC7774bMTExWL9+vdPzcIeoqCisX78eFy9eROPGjTFmzBgsXrwYTZo0qe2iEVEtYm4gInuYG4jIHuYGItdzuvFi4MCBqhackpJi8zJPZ9h730ZlgwYNgmTnBS7VDU/2gm7GiEgecwMR2cPcQET2MDcQeQdVvY1cvXoV//d//4eff/4ZeXl5KLXzDN3WrVsRGRlp/S5JEi5cuIDs7GysWbNGzeKJiIiIiIiIqB5wuvHi5MmTGDZsGI4dO2a3tbGiim/z1Wq1CA8Px6BBg9ClSxdnF09ERERERERE9YTTjRezZs1CZmYmHn/8cURHR6Nly5bwreYtqhXvvCAiIiIiIiIicoTTjRffffcdhg4dio8++kh2PB8fH5w/f77Ky2lycnLQpEkTu4+aEBERERERERFZON14YTab0atXL+F41T1SUlJSAj8/b+hNtvao7qfbCziyDkajEdtSUxGTmwudTlfjy7c7vcr+6muCZR0r15/BYMDysLBaLl39JQEwV/hurvBdK5g2VuV+u1Sw35plo8rGEa2Dp89/icpjO1bQ37ui3GMyyYaX+vs7UqQqLOuo1evRPTkZK8LCYC4qUtSXPHkmt18X5OW5vwzFnr0Hqr02c8m1nSA3oJo7oa1EdSyanmrF5BMnEBISAqPJhG27dmFSVhZ0lm0VECA77arAQNn4czt3ysaX9esnG39p7VrZOACgZ0/5+NGjsuF3BMeOqA+XoPLzstFoBNLSEJid7djfE998Ix/PyZGPR0TIhqP37pWNv9G7t/z8AeH5e3ZBgWz8TcF+Ulj+UwugK4BTKLueqsms7XR2uuWWW3Do0KFq4++88w4AQKPRYN26dQgKCrLGSktL8cMPP/CdF0REREREREQk5HTjxfLlyzFgwACkpKRg1KhRVeJvvfUWgLI7LxISEuBTobXMz88Pbdq0QUJCgrOLJyIiIiIiIqJ6wunGi3//+98YPHgwxo4di4EDB+LGG29ESEiINf7UU08BANavX499+/YhjLenExEREREREZETnG68iIuLs/6ekZGBjIwMu+NpNBo2XBARERERERGR05xuvEhPT682tnr1akRHR0Ov12P16tWYPXt2teO++eabzhaBiIiIiIiIiOoBpxsvBg4cWG0sLi4Offv2RcOGDbFo0SJkZ2c7uxgiIiIiIiIiqufc0hdSxbsyqnuchIiIiIiIiIhICVWNFyaTCe+++y6Sk5Nx+PBhFBYWwlTe9/SBAwfw/vvv4+LFi0hKSkJwcLDNtAUFBZg+fToSExPVFMGrqe4LXBCvCWrXwdO5uz93JfNw9/TkeWIF/XCLiPY7rWB6UdwV3L2MpbWce+L8/WXjnlDH5hooAzkuJjcXISEhMBqN2JaaipjcXOh0OpfMW+05WdH5prhYPl5+neh0XCCuPH8aTSZsS09HzIUL0PlWuNwNCFA3f5XXZq44Z4vyi+r5W8ooSWU/fX2vfajWbGjbFnoA0OsRlpyMDW3aAEVFAADBUQeDaOYNG8qGRVt++dNPi5aAF3//XX6Es2dlw88Lro3WBAbKxpeXHzdavR6dk5PxVng4zOX1BwBB8qVDA0G8UBB/butW+REyM2XDcwYPFiwBwNSp8vEXX5QNz167Vn76Dh0AAEZJwrYrVzD3yy+h02hgKCjAq/ffLy6fCzh9/VRUVITBgwdjzpw5OHnyJEJCQiBZkhyAtm3bYv369fjiiy9QVGHHqDj9xx9/7OziiYiIiIiIiKiecLrxYtmyZdi5cyfi4+Nx4cIFTJo0ySau0WjQr18/AMDly5dhMBisn9zcXGzbtg1NmjRRV3oiIiIiIiIiqvOcvv/rs88+w+DBgzFv3jwAZY0VFTVs2NB6J0anTp2qTK/RaLB48WJnF09ERERERERE9YTTjRenTp3Cgw8+WG08PT0dCQkJ2LRpE1JSUtCoUSNrzM/PD5GRkWjevLmziyciIiIiIiKiesLpxovg4GD89ddf1cYHDhyId999F82aNcPIkSOh1dbE68mIiIiIiIiIqK5xuvGib9+++PLLL5GXl4eGdt5Qe/r0aWzbtg0PPvggtFotCgsLcerUKVy9etVmvB49ejhbBCIiIiIiIiKqB5xuvJg7dy4GDx6MoUOH4p133rF2kVpYWIhdu3Zh+vTpMJlMeOqppzBixAj85z//sTufUnbzSEREREREREQynG68GDBgAFatWoUZM2ZgwIAB1uHBwcEAAB8fH6xZswbr169HXl4e9uzZg0GDBmHLli24ePEili5din/84x/q18CLqe1v3ROo7jPegem1ej2ikpMRHxZm0y9zTS3fmekV9ecu6tOe/arXSRqUdfdkeaCu4oN1SwR9lYuofUjP7KJx1IgVHDuVjz0zbMskqgN3H1Wi5SupP9E8BJnD6TqU7I5NNaa4GPDzu3ZuKCkBLNsyIEB+WtH5pCaoPWcFBbmmHEZj2U9/f0Cnc808FVB03pdTXOz2ZQivXcrjla+7SlQtldS6CCAAZeeGMAB/4dq5RHTUNRTNXHDczjt5UrAA4RKAM2fk4y1ayMfXr5cNXxEs/sXydTCWlmLbb79h1u+/Q1fhWEiIjJSdPnrhQtn40ldekS9ARoZ8XJS/u3WTjwNASop8vE0b+figQfLxH38s+6nRAI0aAadOAZIEuOjvMiVUnWGmTZuGQYMGISEhAXv27MGlS5cQEhKCW265Bc888wy6du2KhQsXYuvWrbjpppug1WoRGRmJYcOGISQkBPHx8bj33ntdtS5EREREREREVAep/ufT9ddfj7fffrvaeEFBAZo0aQIACAsLQ3Z2Njp16oTu3btj3759ahdPRERERERERHWc27sA6dy5M44cOQIAiIqKwtq1a3H27FkkJCSgWbNm7l48EREREREREXk5xXdenDp1CgDQokUL+Pj4WL+LjB8/HufPnwcALFq0CMOHD8enn34KPz8/fPTRR04UmYiIiIiIiIjqE8WNF23atIFGo8GhQ4fQqVMn63cRjUZj7Ymkd+/eOHnyJA4fPozWrVujcePGzpeciIiIiIiIiOoFxY0XEyZMgEajQWhoqM13e3766Seb77Nnz652vm+++abSIhARERERERFRPaS48SIpKUn2e0WDBw+2+b5//3674ym5c4OIiIiIiIiI6je3dHWfnp7ujtmSiynpJ1zYF7ib+xp3N1H5a6R8gr613b0NyPvEFhTIxpcEBsrGzbJRhfuUoD/yJf7+6sog2O+11fy0EPSW7v63Vbtg+aq3U3Gx/PQlJQAAo9GIbWlpiMnOhk6ng8FgwPLwcAUlJLcICCj7GI1l3/39AZ2u7HfBcRcnOO6E+4xo/grOicJluPmcZ5leq9cjKjkZ8WFhMBcVyZfJkfm7+5wbEKB6Fq6qQ/IszQDoK3yPqPD7hPJ8Xp11gtyAoCD5uCA3KNpvZf7xrWge/fvLhud9/bX89BkZ134PDQV27LAJT33wQfnpV66UDS949FH56Q8flo8fPCgfz8uTjwPCMuKOO2TD70RGysYbWX7R6xGcnIzPnn0WKCqC8gyrXm1fvxERERERERERyVJ858WQIUOcWoBGo8G3337r1LRERERERERERIobLzIq3mpTgUajgSRJ1Q7ney2IiIiIiIiISA3Fj42YzWabT1FREUaMGIFOnTrhk08+QVZWFoqKipCVlYWPP/4YnTp1wn333YfCwkJ3lp+IiIiIiIiI6jin33mxaNEi/O9//8PPP/+M8ePHo3Xr1vD390fr1q3x2GOPYc+ePfj111+xaNEiV5aXiIiIiIiIiOoZpxsvNm7ciIcffhhB1bydNiQkBA8//DCSk5OdLhwRERERERERkdONF9nZ2TBauvGqhslkwl9//eXsIoiIiIiIiIiIlL+ws7L27dtj8+bNiI2NxXXXXVclnp2djc8//xwdOnRQVcD6zN39cNdEP97uXoa7+1t3e3/uUN8fO9U9ZkF8SWCgbDy2JvYZX/nTh7AMoj7jBfO3MBqN2Jaaivm5udDpdNbhSwTHlajlXrQN1PYzLlh71xDVoSVueem2r++1D3kmwbZRe93gCecbtWWIKykBUJ4b0tIQk51tkxuE+7coN7lbDRx/NXH9R65XCEACoAEQCqCo/DsA4JdfZKeddPKk/MyzsuTjPXvKxw8flo8rkZcnH8/Olo/r9fLxlJSynzodMGECsHUrUPEf8Tt2yE8/Z458fMsW+fjdd8vHBw2SDb8xf7789ADmdOsmG18TGSkbbyCY/2Pl+dly7TW2/NrLYDBgSliYsHyu4PT118yZM3HhwgXceOONePvtt7F3716cPn0ae/fuxcqVK9G7d2/89ddfmDVrlivLS0RERERERET1jNPNu5MmTcL58+fxyiuvYPbs2TYxSZLg4+ODuLg4REdHqy4kEREREREREdVfqu5NW7hwIcaNG4cNGzbgt99+Q35+PkJDQxEVFYVx48ahffv2rionEREREREREdVTqh+sa9++PWJjY11RFiIiIiIiIiKiKtS+c4yIiIiIiIiIyK1U33lRXFyMn3/+GefOnUNJ+RueK5swYYLaxRARERERERFRPaWq8WL16tVYuHAh8vPz7cYlSYJGo2HjBRERERERERE5zenHRr744gtMnz4drVq1whtvvAFJkjBy5EgsW7YMw4cPhyRJePjhh5GYmOjK8hIRERERERFRPeP0nRcrV65EkyZNsGvXLjRo0AAvvPACevbsifnz52P+/PnYuHEjnnjiCTz77LOuLG+dElda6tbp43x8VM3fFdSWseL0RqMR21JTEZObC51O5/D0apfvzPSu4KplaPV6RCUnIz4sDOaiIth/yIs8gWi/WyLab1Xu1ygulo8DgMkkv4zQUPE85Ka/eFE+3rQpgKr7tVKx1TzmaJ2/v79s3CyYv9ptCACxou0k2AbwVf1kKHka0bEp2ObuPie6grAMBQXKZmQpa2kpoNFcGy46LtQeN1euyMcDAtTNvwZYtnPl6y6DwYDlYWG1XLr6a+KJEwgJCYHRZMK2XbsQnZUFXfn+uvS662SnFZ2zYj/6SH6Eli3l44cPC5YAYPhw+Xj//vLxjAz5eFKSfPymm8p+asv/d9+rF2CuUDOjRslP362bfHzmTNnwSsF1USP5uQu3IQB8fMstsvFnBDl8qSD/vlYe1+r16JicjLfLr70UXDW6jNN3Xvz222+4//770aBBA+uw0goVMm7cOAwZMgRLlixRV0IiIiIiIiIiqtecbrwwGo0IDw+3ftfr9cjLy7MZJyoqCvv27XO6cERERERERERETjdeNG/eHOfPn7d+j4yMxP79+23GOXnyJHx52yoRERERERERqeB040WfPn1s7qoYPnw4du7cifj4ePz+++9Yu3YtvvjiC/Tp08clBSUiIiIiIiKi+snpxovRo0ejpKQEWVlZAICYmBi0bNkSCxYsQI8ePTBt2jQEBQXhtddec1VZiYiIiIiIiKgecvqZjgcffBAPPvig9Xt4eDgOHDiAdevW4fjx44iMjMTjjz+OFi1auKSgRERERERERFQ/Od14cerUKfj5+SEiIsI6LCwsDHPnznVJwYiIiIiIiIiIABWNF23btsUTTzyBxMREV5anzonJzS3rk7lSX9lKiPo6F06vsj93RctwwTzcSW2f9TWxfmrLIJregv211x2xom1eLN/j9hLBPhWbny8uRFCQbDiupES+DP7+8vNv2FBcBhXLjxMsX/RMpai/ddFxq+SZTbXHvnA7K8wdVMNMpmsfy3eNpuz3gADZST39nKyE0nNatSz1JkllP318gIovj7fEnSXIrxC9qN4FL7J393a2zF+r1yMqORnxYWEwFxVBPquSu61p2xYBADR6PdokJ2NNmzaQiooAABHyk2LS3r3yIwhyCzIz5eONGwtKAOG+L7ouiP31V/n5v/iifPz06bKfkgSUlgI9e17LrQDeGDFCdnLRkSt/VQQUCuKi6wolmeuMaIQrV2TDC86elZ++/NrPaDJh244dmHHmDHS+vjAYDFjUqpWCEqrn9DsvwsLCcN1117myLEREREREREREVTjdeHH77bdjz549riwLEREREREREVEVTjdexMfH47fffsOSJUtgUnsLHhERERERERFRNZx+8O61115D9+7dsXjxYqxduxZRUVFo2rQpNBWeHQIAjUaDDz/8UHVBiYiIiIiIiKh+crrxIikpyfr7+fPncf78ebvjsfGCiIiIiIiIiNRwuvHixIkTriwHEREREREREZFdTjdeREZGurIcRERERERERER2Odx4cf78eRQVFaFNmzbQasve9/n999/j+++/rzJuz549cf/996svZR1VU/10e7OK61C5v/G6oi5sJ3Lci+fPIyQkpKyv7PR0zL9wATpLH+jFxbLTLgkMlI2L+goXEvTFrsjff8uGY0tL5afPy5MNxxUUAIC1/mIq1p+C5cfl58svP0i+x/YlguNWtH5KjnvRG7VF8xBNb1kHrV6P7snJWFGeW+X3PnI7X9+yjyTZfvcWal/iLlpXQX4UTi8qn2h6UTwgQD4uwGsCqo6p/GPJ7aW4dr4X7XXv9O4tGy8UTB8iiF8RxAHgqiDuJ4gnRUXJxp/MyZGfQePGZT9LS4F9+4CoKKDC8SYqX2NBXFSHLwmuCxIFx/4lwfwBoIEgvjI0VDYu2gbP7N1b9ou5fM87fhzQaoErSvYA13DobPj333+jc+fOuPXWW/H1119bh2dkZGDx4sVVxg8ODsaxY8fQuLFocxMRERERERER2edQ48XHH3+MgoICrFixokpMo9Fg48aN1u+5ubl45pln8Mknn2DWrFnqS0pERERERERE9ZJDjRepqano2rUrevbsaTc+duxYm+9JSUnYtm0bGy+IiIiIiIiIyGmiR2Jt/O9//8Ott96qePwePXrg999/d7hQREREREREREQWDt15cenSJTRp0qTK8EGDBtkdv0mTJrh0ScnrRYiIiIiIiIiI7HOo8SIgIAAF5W94r2jgwIEYOHBgleGFhYXw9/d3vnREREREREREVO859NhIy5Yt8dtvvyke/9dff0XLli0dLhQRERERERERkYVDd17cfvvtSExMxIkTJ9C2bVvZcY8dO4YffvgBkyZNUlVAql6coL9gb1Db/ZmLli+qY7XTe0oZqObFN2sGfwBavR5RycmIj4iAuahI0bRxor7Mg4Lk46L+uPPyxIWIiJCPi7rIFpVBFLesoySV/fTxAXx9q8ZF01enuFg+LuKCPs9jBcfuEpX50zJ/o9GIbampmJ+bC51OB4PBgPiwMFXzJufFh4XZ5oawMGtuEJ4PPCHfm0zycV+HLj2rCgjw6OXX9nWNEmqvK6h2+JV/NOXfdQDKz4C4IJhWcEYW/kF4VRBvJIgDQKEg/vyRI7Lx5Z07y8Y/vu462fi58p9avR6dk5PxVufONtddIYLyGQRxP0E8SXBcRdt5usGGKPcB4vwnIsiPlusOrV6P7snJWDFgAMxFRVB5xeQQh+68mDZtGkwmE8aOHSv7LotLly7h0UcfhdlsxtSpU1UXkoiIiIiIiIjqL4ean6OiojB79my8+eabuOGGGzB16lQMGjQIzZs3BwCcO3cO6enpeP/993Hx4kW88MILiIqKckvBiYiIiIiIiKh+cPjeuddffx06nQ5vvPEGXnnlFbzyyis2cUmSoNVq8eKLL+LVV191WUGJiIiIiIiIqH5yuPFCo9EgPj4e0dHRSEpKwu7du3HhQtmTVk2bNsVtt92GJ554Ah07dnR5YYmIiIiIiIio/nH6rUUdO3bknRVERERERERE5HYOvbDTE+zbtw/3338/GjVqhAYNGqBbt2545513rPFBgwZBo9FU+QwfPlzR/PPz8zFv3jx07NgRer0ekZGRmDhxIk6dOuWuVSIiF2BuIKLKmBeIyB7mBiLvpLK/qJq1fft23HfffejVqxcWLlyIoKAgHDt2DGfOnLEZr2XLloiPj7cZZnmpqByz2Yxhw4bhjz/+wDPPPINOnTohMzMTa9aswddff41Dhw4hODjYpetEROoxNxBRZcwLRGQPcwOR9/KaxguDwYAJEybg3nvvRUpKCrTa6m8aCQ0NxWOPPebwMnbv3o2ff/4Zq1atwrPPPmsd3rlzZ0RHRyMtLQ0PPvigQ/NU0197feBIHRiNRmxLTUVMbi50Op2y+avsq1w0vSu2odp5KO2vvfI+WKJqqZ7DW3NDzOnTCAkJgdFkwrYdOxBz5gx0gv61rfLy5OOifr6vXFE3PQD8/be6uIioDJa6suz/ly4BFY/XoCBV848LDBQUUEBJf+wCS1Tmr1hBbqjSX3t5bqjJ/trdxVvzAgDE5OaW5QY75zzV5yQlx7ba6ZUeu7VF7fJFuaNE/uwa5++vbvkKKL0uqI+8OTdcAWDCtdvmCwCYy39/aedO2Wnf6NdPNh4iWPZVQXxSTo5gDODN666TjX/aubNsPEK4BHmVp29S6fuTZ8/KTv9+ixay8ULB8g2CuPC6Qcl1VePGsuE1gmP/GUEdxBYUAEDZtWt6OuZfuACdry8MBgPimzUTl88FvOaxkY0bN+LixYt49dVXodVqUVBQALPZXO34JpMJV0QX6JUYDGW7VdOmTW2GNyvfGHq93sFSE5G7MTcQUWXMC0RkD3MDkXfzmsaLtLQ0hISE4OzZs+jcuTOCgoIQEhKCadOmobjY9v9Ef/75JwIDAxEcHIyIiAgsXLgQRqNRuIybbroJgYGBWLhwIb777jucPXsW33//PebNm4c+ffrgjjvucNfqEZGTmBuIqDLmBSKyh7mByLt5zWMjR48ehclkwsiRIzFx4kTEx8cjIyMD7777LvLy8pCcnAwAaN++PQYPHozu3bujoKAAKSkpWLp0Kf7880989tlnssto3LgxPvvsM0yePBlDhw61Dr/rrruQkpICX5lbDUtKSlBS4VZBS6urJiAAWo0G2vJWVm2F1lYlCZCuMZbfqml04LZXrZtbt71hG1be9yw/NZIEFHv/DeLemhuMJpP1Y/mumNpbw0XTK3mUSe0yRGT+E1Zx/sZKP61E5RMcu2pzhyg3KJm/2v8uKC1DlZ91IDd4el4AZHKD0Vj2sZMbRPuN8Jyk9rERV+QGURklSXl5ZJZfbW510fyd5e7rEsA1+afieHXpusGbc4NW7m8KwTlTtM01slFxXMk1jMZT7jixlKNSeapcR1QiKr+ojkTndJfkb8E8ROsgqoPq8qtD17AqaSRJbRavGe3bt8fx48cxdepUvPfee9bhU6dOxdq1a/Hnn3+iY8eOdqedMmUKPvjgA+zatQt9+/aVXc5PP/2ExYsXo1+/fujatSsOHDiA1157Dffccw82b95c7XRxcXFYvHhxleEbN25EgwYNFK4lUc0pLCzEuHHjkJ+fj5AQ0dOOnou5gci16kJu8PS8ADA3kPdhbmBuILKnJnOD1zRedOvWDb///ju+//57DBgwwDr8hx9+wMCBA/HRRx9hwoQJdqc9cuQIunTpgldeeQULFiyodhnHjx9H9+7d8fHHH+Phhx+2Dv/oo4/w5JNPYtu2bbj77rvtTmuvlbRVq1aICQhAQHkraffERPwvOtr6ws6Y3FyH6qC+M5pM+CYtDcPuuEPxiw3jw8LcWiZv2IaWOqi8DxZLEuKLi736IgTw3tzw94kT1hd2frNrF4bdeqvyF3bm58vHRS+EK3/hUrWUtKCHhsrHFby8S5ao9b/8xV/G0lJ88+uvGBYVBV3FF1GJXrgpqKP48HAlpaxWTHa26vmrvfNiviA/raiQG7omJuL3CrnhVS/PDZ6eFwCZ3PDXX9dyQ6VznuicJjwnecKdF6L85KIXahpNJnyTkYFhgwbZ5lY3v7BTRG1uUUK0Hyi9NqqL1w3enBsWVfibonNiIo5U+Jvihe3bZdf73TvvlI2L+j4RvYzymawswRjAqjZtZOOiI0N05InOmdZ7U/R6hCUmIjc6GiivPwAYf+SI7PRJgheKiupIdK/2DFH+VnJdJXgp6jrBsT9JUAeWaz+jyYRvduzAsNtvt76ws3GrVjWSG7zmsZHmzZvj999/r/LymyZNyt4VmyuzwVu1agUAuHTpkuwykpKSUFxcjBEjRtgMv//++wEAO3furDbZ+Pv7w9/OCVkqLkbFG7nMRUXWRKO0xwyypfP1VVx35gpJyS1l8YJtWLkOLPugV7RaKuCtuUHn62tzQV35uyzRm+JF8xFNr6RNW+0yRERlqDR/nY+PbeOFqHyCY1dt7hDlBnfnJmfKYMkNggd2vIKn5wVAJjfodDbbruI5T7TfCM9JGtGNzQJqpweEx57qxoVKZaxyzeDi+TvKE499kbp03eDNucEs9zeFTK8plnHliLatKK7k+kWqgX3fIUVFNo0XOsF1i6j8ojoSnVuF+VtJ7hLMQ7QOojqoXAbLtavi61cX8JrGi969e+Obb76xvmDH4ty5cwCAcJmW7OPHjwvHAYCLFy9CkiSUVvrPguUZJJMDre2WG1os7aYaSUJhYSGKJcm6c1ueYSNljEYjCgsLYTAYFDcauLs7UG/YhtXtg5bhXnLzVbW8NTcYLl8um4fJdG2/Vpr8y6etlui5SdGb05X8d1V0gnPw7exVKPzvrbG0tKz+Ll+2PemK3plxVb7jN7W5Q5QblMxf7Z+JojJYnlzXVsgNZtSN3OBteQGokBsqvPui8jlPtN8Iz0me0FWq4Nhz2Z0X1V0z1PKdFzXRTbkr8g9QN68bvDk3WOq/cs4GAIPgjkrRm0pEV9Wi6ZVcD4vmIWracNmdF5IE/8JCFFXajw2CaytR+UTrJ+puVliHoms/APDzkw2L1kFUByhvJKt87WqZrkZyg+Ql9u3bJwGQxo0bZzP80UcflXx9faWzZ89K+fn5UnFxsU3cbDZLY8eOlQBIe/futQ4vKCiQDh06JGVnZ1uHvfHGGxIAaf369TbzWLlypQRA2rRpk+Lynj59WkJZIxw//Hj05/Tp0w4ciZ6HuYEfftzz8ebc4G15QZKYG/jxng9zA3MDP/zY+9REbvCad14AwMSJE5GYmIgxY8Zg4MCByMjIwObNmxETE4Nly5YhIyMDjz76KB599FF06NABRUVF2LJlC3bu3IkpU6Zg7dq11nllZGRg8ODBWLRoEeLi4gAAOTk56NatGy5duoSpU6eia9eu2LdvH9atW4cuXbpg37598BO0aFmYzWacO3cOwcHB0Gg01ufVTp8+7bXPCdY21qE6letPkiRcvnwZzZs3h1Zwu6GnY26ov1h/6tXV3OBNeQFgbnA11p96zA3MDXUR60+9Ws0Nbm8ecaGrV69KcXFxUmRkpKTT6aQOHTpIb731ljV+/PhxafTo0VKbNm2kgIAAqUGDBlLv3r2lhIQEyWw228wrPT1dAiAtWrTIZviZM2ek6OhoqW3btpKfn5/UrFkzafLkyTYtqs7Iz8+XAEj5+fmq5lOfsQ7Vqcv1x9xQf7H+1KurdejNeUGS6u52qSmsP/Xqah0yN9RvrD/1arMOverOC29mMBgQGhrq1W9orm2sQ3VYf56J20Ud1p96rEPPxO2iDutPPdahZ+J2UYf1p15t1qH33vNFRERERERERPUCGy9qiL+/PxYtWmS36yNShnWoDuvPM3G7qMP6U4916Jm4XdRh/anHOvRM3C7qsP7Uq8065GMjREREREREROTReOcFEREREREREXk0Nl4QERERERERkUdj4wUREREREREReTQ2XqiQlpaGIUOGIDQ0FMHBwejduzc+++wzm3HatGkDjUZT5TN16lRFyzCbzXjttdfQtm1bBAQEoEePHkhOTnbH6tSayZMnQ6PRYMSIETbDc3Jy8Prrr2PAgAEIDw9Hw4YN0bdv3yp1LMde3Ws0GixfvtzVq1Fjzp8/jxdffBGDBw9GcHAwNBoNMjIyqoyXlZVV7fprNBpMnjxZuKy6WH/uxrzgOswNjmFu8GzMDa7D3OAY5gbPxtzgGswLjvPG3ODr0NhktX79ekycOBHDhg3DsmXL4OPjgyNHjuD06dNVxu3ZsydeeOEFm2GdOnVStJyXX34Zy5cvx+TJk9GnTx9s3boV48aNg0ajwSOPPOKSdalNv/zyC5KSkhAQEFAltmvXLrz88su45557sGDBAvj6+uKf//wnHnnkEfzxxx9YvHixomUMGzYMEyZMsBnWq1cvl5S/Nhw5cgQrVqxAx44d0b17d+zatcvueOHh4fjkk0+qDE9NTcWGDRtw5513KlpeXas/d2JecB3mBscxN3gu5gbXYW5wHHOD52JucA3mBed4ZW6QyGEnTpyQ9Hq99PzzzwvHjYyMlO69916nlnPmzBlJp9NJzz77rHWY2WyWbr/9dqlly5aSyWRyar6ewmw2S7feeqsUHR1tt56OHz8uZWVlVZlmyJAhkr+/v3TlyhXhMgDY1F9dYDAYpJycHEmSJGnz5s0SACk9PV3x9EOHDpVCQkKkoqIi4bh1sf7chXnBdZgbnMPc4JmYG1yHucE5zA2eibnBNZgXnOeNuYGPjTghISEBpaWlWLJkCQDgypUrkAQ9zl69ehUFBQUOLWfr1q0wGo145plnrMM0Gg2mTZuGM2fOVNs65i0++eQTHDx4EK+++qrdeNu2bREZGWkzTKPR4IEHHkBJSQmOHz+ueFlFRUUoLi5WVV5PERwcjEaNGjk17fnz55Geno6HHnrIbut0depS/bkL84LrMDc4h7nBMzE3uA5zg3OYGzwTc4NrMC84zxtzAxsvnJCWloYuXbpg27ZtaNmyJYKDg3Hddddh4cKFMJvNVcb/7rvv0KBBAwQFBaFNmzZ4++23FS1n//79CAwMxPXXX28z/Oabb7bGvdXly5cxf/58vPTSS4iIiHBo2gsXLgAAGjdurGj8pKQkBAYGQq/X44YbbsDGjRsdLm9dsWnTJpjNZowfP17xNKw/ZZgXXIO5oXYwN7gPc4NrMDfUDuYG92FuUI95ofbUVm7gOy+ccPToUfj4+OCpp57CvHnzEBUVhS+++AJLly6FyWRCfHy8ddwePXqgf//+6Ny5M3JycpCUlISZM2fi3LlzWLFihexyzp8/j6ZNm0Kj0dgMb9asGQDg3Llzrl+5GrJkyRLo9XrMmjXLoekuXbqEdevW4fbbb7fWg5zbbrsNY8aMQdu2bXHu3DmsXr0a48ePR35+PqZNm+Zs8b3Whg0b0KxZMwwZMkTR+Kw/5ZgXXIO5oXYwN7gPc4NrMDfUDuYG92FuUI95ofbUWm5Q/eBJPaTVaiUA0vLly22GDx8+XNLr9ZLBYKh2WrPZLN11112Sr6+vdPr0adnlDBkyRLr++uurDC8tLZUASDNmzHCq/LXtyJEjkk6nk1JSUqzDlDzLV1paKg0fPlzy8/OTDhw44NSyS0pKpG7dukkNGzaUCgsLnZqHJ3Hk+bQjR45IAKRZs2Y5vby6Vn+uxLygHnOD6zA3eA7mBvWYG1yHucFzMDeow7zgWt6SG/jYiIyrV6/iwoULNp/S0lLo9XoAwKOPPmoz/qOPPoqioiLZ2680Gg1mzZoFk8lktyuaivR6PUpKSqoMtzwnZCmHp6qu/mbMmIHbbrsNDz/8sEPzmz59OlJTU7Fu3TpERUU5VSY/Pz8899xzyMvLw969e52aR02prv6ctWHDBgBw6Pauyryp/tyFeUE95gZ1mBs8E3ODeswN6jA3eCbmBnWYF9SrS7mBj43I+O9//4vBgwfbDDtx4gSaN2+Oo0ePomnTpjaxJk2aAAByc3Nl59uqVSsAZbcsyWnWrBnS09MhSZLNrV7nz58HADRv3lzZitQSe/X34YcfIjU1FV988QWysrKsw00mE4qKipCVlYVGjRohJCTEZrrFixdjzZo1WL58OR5//HFV5VJa/7Wtuv2vTZs2Ts1v48aN6Ny5M3r37q2qXN5Sf+7CvKAec4M6zA2eiblBPeYGdZgbPBNzgzrMC+rVpdzAxgsZUVFR+Oabb2yGRUREoHfv3jh69CjOnj2Ldu3aWWOWZ8bCw8Nl52t5q61ovJ49e2LdunU4dOgQbrjhBuvwPXv2WOOezF79ZWZmAgAeeuihKuOfPXsWbdu2xVtvvYWZM2dah69evRpxcXGYOXMm5s+fr7pcSuu/tlW3/zljz549yMzMtL7RWg1vqT93YV5Qj7lBHeYGz8TcoB5zgzrMDZ6JuUEd5gX16lRucPpBlXpsy5YtEgDppZdesg4rLS2V+vfvLzVq1EgqLi6WJEmScnJyqvSdfPXqValfv36Sn5+fdP78eevwvLw86dChQ1JeXp512OnTp6vtl7lFixZe2S/zyZMnpS1btlT5hIeHSzfddJO0ZcsWKTMz0zr+pk2bJK1WK40fP14ym83VzregoEA6dOiQlJ2dbR32119/VRnPYDBI7du3lxo3biyVlJS4duVqgdLn055//nkJgE3dVlRf68+VmBfUYW5wLeYGz8HcoA5zg2sxN3gO5gbnMS+4nrfkBjZeOMFsNktDhw6VNBqNNGXKFGn16tXSsGHDJADS2rVrreOtX79eat++vTR//nwpISFBWrZsmdStWzcJgLRs2TKbea5fv14CIK1fv95m+Ny5cyUA0pQpU6QPPvhAuvfeeyUA0oYNG2piVWuMvRfs7NmzR/Lz85PCw8OlxMRE6ZNPPrH5HDt2zDpuenq6BEBatGiRddiiRYukqKgoacGCBdL7778vLV68WIqMjJQ0Go306aef1tSqucUrr7wivfLKK9IjjzwiAZCio6OtwyozmUxS06ZNpb59+1Y7v/pWf+7AvOAezA2OYW7wPMwN7sHc4BjmBs/D3OB6zAuO87bcwMYLJ12+fFmaMWOGFBERIfn5+Undu3evUvm//PKLdN9990ktWrSQ/Pz8pKCgIKl///7S559/XmV+1SWb0tJSadmyZVJkZKTk5+cnde3atU4cKJXZSzaWOqnuU7Gu7B0o27dvl4YNGyZFRERIOp1OatiwoXTnnXdK3377bQ2tlfvI1UtlqampEgDpnXfeqXZ+9a3+3IV5wfWYGxzD3OCZmBtcj7nBMcwNnom5wbWYFxznbblBU15oh+3Zswe33HKLM5MSERERERERESnmdFept956K6KiorBq1Srk5eW5sEhERERERERERNc43Xjx2GOPITMzE88//zyaN2+OCRMmYMeOHa4sGxERERERERERnH5sBADy8/Px6aef4sMPP8SBAweg0WjQsWNHTJ48GU888QQaN27syrISERERERERUT2kqvGion379uGDDz7Apk2bkJ+fD7//Z+/Ow5uo1j+Af6d7SlsKFChlFWRRNkVAvAKCgLsiLoi4IFUQvAIKCNcFAXH3dxUVFK9YRFbFK4pe9QIKil4EVFCRHSx72bvRtE2T+f2RpUmbnneSSUsD38/z5MnyznIymXnnzMnMnJgY9O/fH8OGDUPfvn1DMQsiIiIiIiIiOgeFrPHCzWq1YsmSJXjiiSdw+PBhAEDTpk0xYsQIjBw5EomJiaGcHRERERERERGd5ULaeHHq1Cl88MEHmD17Nv78809omobLL78cW7duxYkTJ5CamorPPvsMXbp0CdUsiYiIiIiIiOgsF/QNO72tWrUKgwcPRsOGDfHoo4/i6NGjeOyxx7Bz5058//33OHDgAGbOnIm8vDyMGjUqFLMkIiIiIiIionNE0GdeHDlyBHPmzMF7772HPXv2QNd1XHHFFRgxYgRuueUWREdHlxtn3LhxmDlzJgoLC00XnIiIiIiIiIjODVHBjtioUSM4HA7UqlULjzzyCIYPH47WrVsrx6lbty6Ki4uDmt+GDRswd+5crFq1CpmZmahTpw66deuGZ599Fq1atfIZdsaMGZg5cyb27NmDlJQU3HHHHZg2bRpq1KihnMfq1avRu3fvCuPPPvssnnzySUPldTgcOHToEBITE6FpmqFxiKqSruvIy8tDWloaIiJCchLWGcHcQBRaZ0NuCLe8ADA3UPXH3MDcQORPVeaGoM+86N69O0aMGIHbb78dsbGxoS5XObfddht+/PFH3H777ejQoQOysrIwY8YM5Ofn46effkK7du0AABMnTsTLL7+M2267DX369MGWLVvw9ttv48orr8R///tf5TyOHDmCFStWlPt83rx5WL58OdavX2/4fh0HDhxA48aNA/+iRFVs//79aNSo0ZkuRtCYG4gqRzjnhnDLCwBzA4UP5gbmBiJ/qiI3hLy3kcryv//9D507d0ZMTIzns507d6J9+/a47bbbMH/+fBw+fBhNmjTBnXfeiQ8++MAz3IwZMzBq1CgsW7YMN954Y8DzbtmyJTRNw44dOwyPk5OTg+TkZKwCkADAHheHPRkZaJ6ejkjXZTNpAZfE1xEhbhPiawzMY4gQLxDiW4X4MSF+2Ou1FheHhhkZOJieDt21DOOF8WsKcaltULrA6YQQv06IA4C6/R743GQZGrpfxMUhISMD+enpQGEhrADGAMjOzkbNmtKSqr7CNTfMgXP91ePi4MjIQER6OjTXeh0pTCNBiKcK8fNatlTG39q5U5gC8NBtt6kHWL9eHd+3Tx2vU0cdP+Fc821xcViekYGr0tMR7XVJ4l712MgS4uUvfPQl5Q7ptEZpuwfk31nqu+s1IV7b9Vw2txYCeBrhnRvCLS8AxnKDRPor6bQQv0CIt3jpJbkQb7yhDG87eFAZl76p9B3yXM8VLb/gzv8tVSLEpdwg1Vuk3AMAMUJcqhdYhLi7/qjHxQEZGYBrGRYAuB/MDWcqN+yAM++XxMVhTUYGeqSnI8pgbrAKcWl/EnPXXcr4hgULxDJ0mT5dPcBW4ajhq6+U4YNCvcIddcTFITsjA8np6YjwWn4O9dxRS4hL+3Wp+SnissvUA/ToIUzBgJwcdbx5c3X8iPPo0xYZieUXX4yrNm5EtN2O3KIiNJ45s0pyQ9CXjURGRmLKlCmYNGlShcM899xzmDx5MkpKpFQv+9vf/lbus5YtW6Jt27bY6lrZ165di5KSEgwaNMhnuEGDBmHUqFFYvHhxwMlm/fr12LVrF6ZMmRLQeO7TuhJcD7umIT4+Hgma5jkwSQpoiuVJDQfSDlraeQFyGc1W0KVKiHcZNdcytGgadD9xf6RKglTJkOLSb2CkY2DpAEX6jnFGx3ctP3uZUw7D/RTEcM0N8a6HrmlwxMcjQtPg/iXMblfSepcUqW4ekdYpAEjyc18jH2ZPGzQ4vs21Xidpmk+lX1oGeUJcOjgw23ghbfdGhpHys3Qg684N/nKr+/NwFW55ATCWGyTStiv9WyXmjjgD2UHYdqX1OuiKqYvd9VzR8jM7/cpuvJByj5FhpANVqQzu+qOuaUB8PFBmGTI3nJnckAhn3i/x2u8ZXZ+l4aT9iXdjjz9GGuSTLEKNVpiHlFtyhfm7y+jQNBTHx6OGpvlsr1LjhZS7zO6zI6KEXykUVzpIy1jK8a4y2CIjnetgbCyi7XZPuCpyQ9A5XNd1GDlpozJP7NB1HUeOHEHbtm0BAEVFRQAAS5mNIz7emaZ/+eWXgOexwNWSeJfQ4lhUVOSZPwDk5jo3IXtcHOyaBoerTA6vsplt0rGbjBtZvaSzN6TvICUCic/O0rXsNK9leKZ3n9L8pd8AkJehNA/Dh4ju5eZ+1nXgLL15bjjkBj0uDrqmQXeVSfcqm5Q1pe1KWu9swg5QkyoYAGxmd7LSPKQdqGt8W5lnN2kZSMvQbO4y+xsBcm4wexDlzi1lc6t2luaG6pQX3PMPNDdIzNa4xNxhpGIqbLt24ftI2470Hd3xYJZfVTBafrPDhKQMXvUGHa46PXPDGcsNJXFxKNE0lLjKVRLAui3tL6T6vibs8x1G6g1S/hD+WJHqFUZzi3du8M43lV73EuIRJhtvDJGWscHfyOYqi+dZmm4ImW2AVjp27Fi5DT+UFixYgIMHD+KZZ54BAM8NQ3/88UefG+WsWeO8QOKgcKpiWXa7HR9++CG6du2K888/XznsCy+8gKlTp5b7fE9GhifZud+77QqoNKFn5LKVlZVcBmlV93fVVEOvZVjZpLVXuqrr9xCUIcVkvKxE1/KLLCgABg8OqkzVXTjkBkdGBhxeuUHPyPDsOKWzpqT4KSG+RYg3E+IA8KU0wK23GphK6KyowrwQLjoGOLw7txacpbmhOuUFILjcIJHOBpSaHraZjAMAXnnFyFBVpuzyM1vFNju+2UZJQP6dpTIGfOmMO78yNwA4c7lhTZljijXVab93/fXiIGK9oV49dbxXL6OlMSQ7wOWXbXJ+m02OXy00aeLzdkWnTgBc9YYqEtA9L7yv+7rvvvtw88034+abby43nN1ux/79+zF9+nS0atUKP/30U0gK623btm249NJL0bZtW6xZswaRrhafbt264c8//8Qbb7yB3r17Y+vWrRg5ciQOHToEh8MR0CUsy5cvx9VXX43XX38do0ePVg7rr5W0cePG+CkuDgmuMy/c97yIsDpP6GtY0cQMku55Ie2cfjAwj3uFuHRqonSQdFyI+9zzwmIpvS7btQx5zwu5DJ5GKosFiRkZyEtPB6xWWHUdwwsLkZOTg6QksxcxVR/hkhsWx8Uh3vXvqp6RAS09HZprvTZ72UgDId6szB3Vy3rLwPW4lX7PixShWe64M3vYLBasyMhAv/R0RFtLM5IwdZ/c4k9lXzZi5BRbaRhpq31ViLvvKlI2t1p1HY+dZbmhuuUFILjcIJEu6jB7z4vmRu558eabyvC2AweU8SJlVP4O7lPHK1p+0r+fErNnPEl/ioTishGz97zw1B8tFs89L2C1okDXMZi54Yzlhv1xcUhynXnhueeFwdxg9jLnmLvvVsY3zJ8vlqHL66+rB9giHDUI97w4JNQr3PfC0i0Wzz0vvHODdEBs9p4X0h+eEX4uafLRvbswBQPM3vPi6FEAzjMuVnTqhH6//opohwO5RUVIeeWVKskNAZ15cd9993muZdE0DZ999hk+++yzcsO520MsFktQ13ZJsrKycP3116NmzZr4+OOPPYkGAP7973/jjjvuQHp6OgDnvTnGjh2L7777Dtu3bw9oPgsWLEBkZCTuuOMOcdjY2Fi/va5EFhb6tIBHWK2INHiAIpFa1qW4kVYr6cZR4ilQBuah4q+MutXqabw403ebleZv5B8aaT0wexpbOVar83EWCqfcoBUWev4F1QFoVqtnJ2r2UiFpvYsuUh8e6AbWj2ip4ibMQ1wHpVOTy4wfbbX6NF5Iy8Ds/W7MxkORG6R4oKffu3Prmc6roVYd8wIQXG6QmL2UUswdRv7vErbdSOG7GL3cyUg80OVXFQIpv5lhQlEGz6/tWoZn+lLdUAu33BBVWOiT96OsVsONF9L+QqrvS/v8CCP1Bil/2IULL4R6hdHc4t43alarT7mlfabpupc0/WLhb2eH2QtaIS/jAH+jaIcD0Xa7z30vKltAx89z5swB4GycSE9Px80334z+/fuXGy4yMhK1a9fGZZddhlq1pHaqwOTk5ODaa69FdnY21qxZg7Q034sfGjZsiB9++AE7d+5EVlYWWrZsidTUVKSlpZXrv1nFarVi6dKl6Nu3L+rXrx90eWPh/CfE/ZPGoXTlltps1f9NAJlC/IZ77lHGL+zbV5gC8OUQdX8jnYTxpZbeQK4717w+c29aUtuedPaJ2fIlC3Ej5xxJ/6BI85BuEOQeX4dzedSBc1lK/16Fm3DLDZFwJmD37xIF4xVSKXFLvY3g0UfV8QcfFMvw1qJFyvhD/fop447MTGVcbPhMdX1L9/X19ev7HDStz1L3JxLo5VZlSWd9Sf+AG9n5SstA2odMkbrWu+EGAM5rVb8EMPz++513DS8uxoT33jNQwuov3PICUJoPAGee8P6TwOzNIHsKcelsycwxY4Qh5LM6bxLi0qnV0nf0XNcO51kYCfDNrVJPQ8km52/khsdmSXUTqW4knVXq/o46nPWkeDiX4dnUsBmOucF9M193TrCg9IA4XxhXWi9jHn9cPYCwzzZyxhBmz1bHpctCLr5YGS4QyujuYcsO5+W1teDb4CBdTi8dU0j1igjhRud71qj7gYwQ4oB8TJCSKtQQvS6TUoqKAjp3BvbuBUpKAJvZc9qMC6jxYojXgex3332HAQMG4KabpN1Q6BQWFuLGG2/Ejh07sHLlSlx44YUVDtuyZUu0dHUHuGXLFhw+fBj33Xef4XktW7YMeXl5hm6sQ0RnFnMDEZXFvEBE/jA3EIWvoK9ccJ+FUVXsdjvuuOMOrF27Fp999hkuk/rCdXE4HJgwYQLi4+MxYsQIz+c2mw27d+9GzZo10aBB+SvEFy5ciPj4eAwYMCBk34GIQo+5gYjKYl4gIn+YG4jCW6X2NhJK48aNw7Jly3DjjTfi5MmTmF/mxjB3u24kM2bMGBQWFuKiiy6CzWbDwoULsX79esydOxdNvO6QevDgQVxwwQUYMmQI3n//fZ9pnTx5El999RVuvfVWJCRIJ+AQ0ZnE3EBEZTEvEJE/zA1E4c1w40VERAQiIiKwZcsWtGrVChEREZ6bd6pomhbQHXkrsmnTJgDA559/js8/L9//gjvZXHzxxZg+fToWLFiAiIgIdO3aFd98841PV0eSJUuWwGazYfBZ2B0U0dmGuYGIymJeICJ/mBuIwpvhxouePXtC0zRP/8Lu91Vl9erVhoa77777DF2L1qxZM1TUS+yDDz6IBw3csI6IzjzmBiIqi3mBiPxhbiAKb4YbL8pu7EY3fiIiIiIiIiIiM8Te6IiIiIiIiIiIzqSgb9h5zz334N5770Xfvn2r9PKRcFMEZx/Mdtf7QpT2KSz1ySz1RX6NNPOLLlKGv/Xq+rYi5wvxTCEurWBSv9DetzfSvD5zn6An3U1Fap0z2x+79P2MbGBmp1FbiLv7rbYD2AygFZzrYJ5cNKpE7v7adTj7Do9H6TqeLIybJE27Sxf1AEJvUY88+6wwBwB166rjn36qDEv9necfO6aMf+F+YbHAAuDfR44AVqsnLm3b9YS4lJvMtvw7QjCMWIbISHW87P2o7HbnZyG4TxUFL8r1cO/nIlGaG6Rtv5UQ3yzEO7Vpo4zP3rZNmALwwNy56gH+7/+U4dp//KGMS9vFIdeze/nlonT5Ab71Cn+aCHFpfGmfLZU/V4gDzrqkilQvkPKje/wSAL8AuADO78V6wxl24YXOvB4b63zfti1QVAQASNi5Uz2u1KtKcrI6/sUXynCne+5Rjw8AX3+tjr/5pjreo4cy3KpZM/X4J04AAGxxcdgDoE1CAqKjSrfYgjz1Gr5PPXUcFeIJNpsybnqfDwPHHULdC2vXquOnTzuf4+KAW28FVq8GCgsBh5FaTWgEXf9asGABrrnmGjRq1Ajjx4/33ACHiIiIiIiIiCiUgm68+OWXXzB69Gg4HA68+uqruOSSS9C+fXu88sorOHjwYCjLSERERERERETnsKAbLy6++GK89tprOHjwIP7zn/9g0KBB+OuvvzBx4kQ0bdoUffv2xdy5c5GfL10cQURERERERERUMdM37IyIiMC1116LBQsW4MiRI5gzZw569+6N1atXIz09HampqaEoJxERERERERGdo0La20iNGjUwZMgQLF26FM888wyioqJg9bqBGhERERERERFRoILubaQsh8OB//73v5g/fz4+++wzWK1WaJqGvn37hmoWRERERERERHQOMt148fPPP2P+/PlYvHgxjh07Bl3X0b59e9xzzz0YPHgw0tLS5IkQEREREREREVUg6MaLZ599FgsWLMCOHTug6zoaNGiAsWPH4p577kGHDh1CWcawVgdAIpx9Ze9yvXcv9BShT+WE7GxlXOpR98Vx45Tx7sL4gHxdkVSGYpNxiVS+GJNxs9dVGRlfWoZSf+1S86D7rjMlADYDqA/nOhgvF40qUYTr4f79NZSuL7nCuOJvt3+/Mnw8K0sZT2nZUpoDYLGo48ePK8PFQn/nBcLsy243Zd+nCOPHCXFp51gixKXtWhrfyDBifnH3x16RXbuczzGuTLhnD1BcDJQYKR2dCdJ6VSjEO0VHm5p/tpGBpPWnc2dluE2h+lsc3blTGT/getbhXB5xcOZXtybq0mG1EJe2Oyl3SLntKiFuZB5m62bZrme76zkHQCQA3oK/GrvsMnW8USN1fPZsZXhzXp4y3k6avgEnhW0/ZsUKZTwhMVE9gxYtnM+xsc7n5s2BoiJPuHDTJvX81VNHPSGeJMSl/G1EgnSvSSH/Ik6oHR1wZVh3vaFVq9J6w9q1xgppUtCNF08//TRq1KiBu+66C/fccw/69OmDiIiQ3kKDiIiIiIiIiCj4xosPPvgAt9xyC+Lj+f8tEREREREREVWeoBsv7r777lCWg4iIiIiIiIjIL17nQURERERERETVmuHGi4iICERFRWHHjh2e95GRkeIjKipkvbESERERERER0TnIcMtCz549oWma5x4X7vdERERERERERJXJcOPF6tWrle+JiIiIiIiIiCoDr+moZBqc1+a4r8/xfg273d8oHslSf8V16ijD/7jgAmX8k6++Uk8fcp/EUl/iUl800nVL/uI1A5i/ROiNXuwLXSItPwBIEeLNhHia1CdzcjIAwOYaLrZ+fUQXFqLY4QCOHjVQQqoMxQCiAeiu9zY484UR0nqbn5WljKc0a6aegGudUWrXTh0XbuocI8wjSujP3T13B4C9AC6Eb76Qtispd0jLOFeIh0K+EM8W4inHjinj9f76y/nC3ed9Zqazz3uH2cxKZkS5Hjqc62EUSnNDgjCutF5+YLMp4zHbtinj0vwBACnC1teihTouXG5cT9jn9frjDwDOnLoSQHc4c63bDPXcUVuIG1oGClK951sD07hFiGcK8ZNC3F1G9/7pAJzr4GlhPKpcJVu2oARAicXifP/nn9CsVgBAlLTf/vlnZfiDvDxl/N6WLdXTb9hQHTcwTLGwzzouTD5Z+A4xmzYBKF1+Ob//jijX8gMAdc0JiBHitevWVQ9w0UXKcIJQd0ONGkIJAHTpoo5LdbfsbHU8wZUB3Xm6VSugpAQoLgbWrpXLFwJB37CzefPmeOONN5TDzJw5E82bNw92FkREREREREREwTdeZGZmIltoncnOzsbevXuDnQURERERERERUeV2lZqTk4NY9+moRERERERERERBCOieF99//73P+8zMzHKfAYDdbsf+/fuxYMECtGrVylwJiYiIiIiIiOicFlDjRa9evTzdo2qahrlz52Lu3Ll+h9V1HZqm4cUXXzRfSiIiIiIiIiI6ZwXUePH0009D0zTouo5nnnkGV1xxBXr16lVuuMjISNSuXRu9e/fGBUKPF0REREREREREKgE1XkyZMsXz+rvvvsPQoUNx7733hrpMREREREREREQeATVeeFu1alUoy3HWOg3nXVHtXu8jXa+lvsSl/oDRvbsyfPSFF5Txq6T5A0iYNEkZ/2DaNGU8TZh+vIEyuOkACgHUQGmf91Kfz/km4wVCXOrvXd0bvZPUb3Sa0Ke92Kezuy/mGNecWrd29sdcUgIcPWqghFQZHK6H7vVeq3hwH7lCvFiaQI8eyvD8N98Uy1BbGOakML607ZUI8Ydd+dEWG4u9ANp26IDooqLSAXbvVo4/Q+gPXsqPUvml30D8jWD+jtrS+PXcPYa5c0xODlBYCOh6heNQ5StxPXSv96Ha59372GPqAaT9zfnnC3MAcvv3V8YXCuOPePRR9QApKcpwTGoqAEBzfZeY+vURXVjoicdlZSnHryeUT6q7OYS42XoJAHwhxKW7zWUK8SSv1zUAnHC9lupEVLkOwrn/dx9THELpMUWM0AOktN7e+9JLynjWxInKeMLDDwtzkLf94UL+OeS1HfuzT5i+e2x3bl0L33qXdEzSWYjj5puV4envvquMS/tsIwftD1VwOwePEqF2JR3fu48pIiN9nx1S5gudSu1thIiIiIiIiIjILMNnXjRv3hyapmHlypU477zz0Lx5c0PjaZqG3cI/YEREREREREREFTHceOFwODw9jfh7XxGdp58SERERERERkQmGGy8yMzOV74mIiIiIiIiIKgPveUFERERERERE1VrQvY1UJDc3F+vWrUNcXBy6d+9u6NISIiIiIiIiIqKKBH3mxbvvvosrrrgCp06d8nz222+/oU2bNrjmmmvQq1cv9OjRAwUF7FiJiIiIiIiIiIIXdOPFvHnzUFRUhFq1ank+GzduHI4ePYqhQ4fiuuuuw9q1a/H222+HpKBEREREREREdG4K+rKRHTt2oH///p73J06cwKpVqzBs2DDMmjULANCtWzcsWLAA48aNM1/SMBXlemhe7yPdQZtNPbIUX7pUGf5JKNtNqanCEMDxadOU8XuFaWRkZSnjUglyy7y3ADjl9T5bGH+4EJdEdeumjM/4Sb2UCw3Mo+x3LD8RYSrt2qnj7t8owtVW2b074HA4p7tmjYESUmWwASgG4O6PqRileUIitTqrtzqgYN48ZfzuYcPEMnz77rvKeBth/ENC/LhUgOho53NUVOmzw+EJ78rLU47eSZh8phCXtlvpnMNiIQ7Iv7PZeFJ2NgDAbrEAALJychBptUK95KiylbgegLO+YPeKSeuVVKnb88orynjze+5RT2DJEmEOQNI33yjjI2bMUMYXvvaaMi5te+7cEWGxoC2AV44cgcNq9cSl3JQsxGOEeFXcTE6aRzMh/q3B6WsAagA4Dee+ylrhGFQV3LnB7vXeXYeQtn1pnxM1caIynirUh98X6sOAvO1ByB0Jl1+ujB8QJu9dm45F+fW5iTB+0tChyvj7Qr1IqnekCHEjdrVurYxLuWOTEL/liy+cL3QdsNuBq68GNA0oKADee89oMU0JOsdmZ2ejbt26nvdrXAdBt9xyi+ez7t27s1cSIiIiIiIiIjIl6MaLOnXq4PDhw57333zzDSIjI3G5V6uYruuwSWcPEBEREREREREpBN140aFDB3z22WfYvHkzdu3ahYULF+Lyyy9HjRo1PMNkZmaiQYMGISkoEREREREREZ2bgm68mDBhAk6dOoWOHTuidevWyM7OxtixYz1xh8OBH374AZdccklICkpERERERERE56agb9jZu3dvLFu2DHPmzAEADBo0CDfeeKMn/uOPPyItLc3nHhhERERERERERIEKuvECAK6//npcf/31fmM9evTAxo0bzUyeiIiIiIiIiKhKenQiIiIiIiIiIgqaqTMvAGD9+vXYsGEDsrOzYbfby8U1TcOkSZPMziZsJQJIQmmf7YnwWuheXc36VauWOm6xKMM3RUcr42/88Yd6+gBGDxigjDuWLlXG04cNU8ZnC30i53u91uDsu/wIjPdrPUuIP9y4sTL+otBvdZIwfak/eiN+F+IdhN8Ajz/ufNY053PNms7+mQsLKx6HKp3D9dC93rt+IcQI45YI8XwhLq2XjT79VBgCuFLoz/uj++9XxqXvIOWOH1y5w+HKg2t//RUR1tJe2zOF6Ut93ktbhzT+bUJc+v4AEC/E6yUmKuNT8vKU8ZNery0AfnS9LpAKRmeM2UrbASFePG+eMr7PwDyuquCMXI+LLlKGB6emKuO/Z2Up43vKvL+wzHuHcmx5/ZfilZ1bAPk7SHWTTkLcvYTd+6QIOPdV/MfzzMoBYEfp75+D0t8kQRhXWu+kdSpOqA/fN2qUMAUAV1+tjieov0UroV7Q6v33lfHjrh4wSwCsA9ATvjk1pWVL5fivum6VUJE2yqj8G4n7fCEOAPHt26sH6N5dGW5+QL2X+PKGGwAAusUCLFqE5QMHQrNaq7TeEPR+8OTJk7j55pvx448/Qtf1Coc71xsviIiIiIiIiMicoBsvxo4dix9++AG9evXCkCFD0KhRI0RFmT6Rg4iIiIiIiIjIR9CtDV988QW6du2Kb775Bpr7dHQiIiIiIiIiohAL+vI1q9WKnj17suGCiIiIiIiIiCpV0I0XF110ETIzM0NYFCIiIiIiIiKi8oJuvJg8eTKWLVuGn4S7zwLAmjVrcPfdd+Oyyy7DwYMHAQDz5s3DDz/8EOzsiYiIiIiIiOgcEfQ9L7KysnD99dfjiiuuwF133YVOnTohKal850wbNmzAe++9h7vuugsbN25EUVERACAnJwfPP/88vvzyy+BLT0RERERERERnvaAbL+677z5omgZd1/H+++/j/fffL3f/C13Xoes65s6di3vvvReLFy/2xC6//HI8++yzwZc8TBTB2bdyiet9IUoXesz+/cpxY2rUUE+8UOi12W5XhrupxwYAfLp0qTJ+8+uvK+NvjRmjjEt9kecLcak/dGkFnyL8BrWF8aV+saV+tY04Lg0grQft2jmfdR3IzwcuvBDQNOD06RCUjoIVDSAGgA7nehIDwJ1Bpb6+JdIpdVK8+NgxcR4x06cr4wOF/D79qafUM1izRhnOdj27O+rOQenyA4Bc9dRxgxA/KcSlbXufEDey85X6hK+dl2dgKhRuClG6jdaA77om7fOkfVLQp9u6XDV1qjjM7MmTlfEk4YzdEmVUXgZHXc8RAFoC2AXf5VJPGF+qdxQI8b8Jcen7pQhxAJD+9kuKjlbGm9hsyrg7f7lzajGcuVZa9lS5TsCZD3Sv9+7fSFqvzNYLpH1ak1mzhCEArF6tjkv12exsZThXWK/ddasSr/c+++Lzz1eOP1bIbejRQxn+tmlTZfxC9dQRd+ONwhAAOndWxxOEmoWwjJNdzw4417+acK476owTWkE3XsyZM8fQcMOGDUPPnj3LfV6zZk1kCwuIiIiIiIiIiCjoxoshQ4YYGm7q1KnYtWsXmjVr5vP5Dz/8gObNmwc7eyIiIiIiIiI6R5g9g1A0bNgwjBkzBuvWrYOmaTh06BAWLFiA8ePHY+TIkZU9eyIiIiIiIiIKc0GfeeG2dOlSLFq0CNu2bUNBQQF27doFANi2bRuWLVuGe+65Bw6HA3369EFBQQF69uyJ2NhYjB8/HqNGjTL9BYiIiIiIiIjo7BZ044XD4cCdd96Jjz/+GABgsVhgtVo98Vq1auHJJ5+E3W7Hk08+icceewy7du1Cfn4+LrzwQiRINwwhIiIiIiIiIoKJy0Zee+01LFmyBA8++CBOnTqF8ePH+8Tr16+PHj164D//+Q8AICYmBhdeeCG6du3KhgsiIiIiIiIiMizoxov3338fXbp0wVtvvYWkpCSfblJvueUW3HLLLfjrr7/wyy+/eN77ewTrueeeg6ZpaOfuBtKP7Oxs1KtXD5qmec4QkRw5cgRDhw5FvXr1YLFY0KlTJyxZsiTochJR1WJuICJ/mBuIyB/mBqLwEfRlI7t27cLf//53v7GaNWsCABITE7Fv3z588803qFmzJjq7+p795ZdfkJ2dHXTjxYEDB/D888+jRo0ayuGefvppFBRIPXKXys3NRffu3XHkyBGMGTMGqamp+OijjzBw4EAsWLAAgwcPDrisJwAUAbB7vY80OG7Ktm3qAYR+vFGnjjLcyUAZmgjxr8eMUcYbCePHC/Fkr9c6nP0KN0dpv9YHhPGFHqNFo++5RxmfNW+eMm6kdVDaCMV+1aV+sbdudT5rGtCwIbB9O6DrgNdlXmeLcMoNEa6H7vXevV47hHGl7SZGiKcIcam/eACIOXZMPcDrryvDjyQnK+MFQv5z5xYHgP0AGsJ3ezuuLp2YG8TtTiBt19JvBADSOYpvmSxD2fXMUcHnZ4Nwyg3ufZ2b92tpvZR+c2nblqb/5eTJwhDAA717qwdIETKQsO1n//GHMr7Z9ewAcApAV/jmhnrquaO2EJdyi7T2xAlxI/WG+4R4oc2mjhssg+b1XkcV3OX/DAin3JCH0uOJWNd7N2mf0kqIS+vl70J8k7DOAUCasO3uE8Y/Ks5BLdn17M6pv8N3nU776ivl+M2EegvsdmVY2u7i2rQRBpB+JQCrV6vjx4UMJszD3U9oCZzHtM3g3O/kVTRCJQg6D1ksFuTk5PiNzZkzB3PmzEG7du1gsVgwcOBA/PXXX/jkk0/wySefYM+ePRg0aBBSpB1YBcaPH49u3bp5GkP82bx5M95++21MnDjR8HTfeecd7Nq1C59++immTZuGv//971i1ahW6dOmCcePGobjYbHWWiCoTcwMR+cPcQET+MDcQhZegGy8uvvhi/Pe//0VhBf/6njx5El9//TVsNhvGjx+PyMjS8w0iIyMxduxYZGRkBDzf77//Hh9//DGmT5+uHG7MmDEYMGAAevToYXjaa9asQd26dXHllVd6PouIiMDAgQORlZWF7777LuDyElHVYG4gIn+YG4jIH+YGovATdOPF6NGjceDAAdx66604cMD35P3du3djwIAByMnJQXR0NLb5OQVw27ZtcDgCOznVbrdj1KhReOCBB9C+ffsKh1uyZAn+97//4eWXXw5o+kVFRbBYLOU+j493nqT9yy+/BDQ9IqoazA1E5A9zAxH5w9xAFJ6CvudF//79MXHiRLz00kto2rSp51qxevXq4cSJE9B1HZMmTUJeXh7uv/9+7N69G127dgUArFu3Di+++CKGDh0a0DxnzZqFvXv3YuXKlRUOY7VaMX78eDz66KNo1qwZMjMzDU+/devWWLlyJfbu3YumTZt6Pl+zZg0A4ODBgxWOW1RUhKKiIs/73NxcAIA9Lg52TYPDlcAcXolMuvZUvHpMuueFcN1SiZ+kWm4YIa5XYVx3lVf3KrcGNSkutd7ZotSbiCYsQ2n+RoaRlpFNugbOdTNdWwXPZ4NwzA16XBx0TfO7Xku/udTsq77qUt6ujdzzQlzvhGs/Eam++4+Unzz3Z/CTW42QlpG0jM3eF8LI+FIZzeY3D/eycz/runwvnTARjrkBcXHOvF32d4GxfUplknITANhihKvvhf0qYmOVYaO5wTu3em9vZvOj2dwhjS/FAWM5WkUqo3s9c9dxPM/MDYanX9W5Qdo2za73ZuslRuYhTcNI/jEy/YrqDdIyko4JJLqQu2xC7hNzJwBI+VeahzC+O//ayzyXVGFuMPUrvPDCC7jyyisxY8YMrFu3DoWFhXA4HLjmmmswevRoXH311XA4HEhNTcU///lPHD58GADQoEEDPPbYYxg3bpzheZ04cQJPP/00Jk2ahLp161Y43IsvvgibzYYnnngi4O/zwAMPYNasWRg4cCBee+011K9fHx999BGWLl0KwJnEKvLCCy9g6tSp5T7fk5HhaWV1v3fbFXAJzz7Gb31USs/I8CSw1FAWxo8vhXhq//6VXAIDN1ALcHor0tIAIKAbT1Vn4ZobrBkZ0Lxyg9UrN0i/THZApS/vbMw9B8tchlhLGH5H5RUlZPYLcemGyoGq4VqGWkEBEMTN5KqbcM0NCWXqDYle63ZiwCUMLSMHzYHukypbdpnccOoMlSOclK1b1XctwwLmBsMqIzfEZmQg1is3xHqt2/lCeTYFVPrASfMH5H1aZTtR5v2pMrmhbLyszUJctGiRMlzdcqcRm85AbjDXhASgX79+6NevX4XxiIgITJgwARMmTPC0HCYlJQU8n6eeegq1a9fGqFGjKhwmMzMTr7zyCmbOnImEBOk+7eV16NABCxcuxIgRI3D55ZcDAFJTUzF9+nSMHDlSOc3HH38cY8eO9bzPzc1F48aN0Tw9HQmuMy/2ZGSgeXo6IlwJS90XiBwXz7yorb5ndsmRI9IcxA1Zfd9g+cBb6jXBuw1Pt1igZ2RAS0+H5lqGhwIY3x//t5wt9figQcp4xuLFyriRlmjpDtFpQryv9A/4s88CcJ5psSItDf0OHUK0riP3LOltJFxzgyU9HfGuMy+sGRmweK3X0jpRU4gLmUHMLdJ2CQCW+vXVA0hnXgh3JrdWcENoN3cDjMNiwcGMDDT0yq2AfGf0S4W4lBukbVs668FIbyNSA8wXQly6+3cz9wuLBTUyMnA6PR2wWlGgm/1/q3oI19yQn54Ou+vf1cSMDOS5fhcAkLK2dP6R1PhgNvcAwFU9e6oHEOom2LlTGc75809lfIvrWbdYkJ2RgWSv3AoAFR+qOknbnVQvkn4j4X9PGKkhi/VDgXQQ9pvrWbNYUD8jA0fS06FbrbAyNxhWGbmhKD0dka7cEJuRgSKv3CCtE+cLcWm9VG918nYDyH84Sj0ICn2cidz5y2Gx4FRGBmqVqTc0EMZvcttt6gH69lWGl48YoYxf1UroE+bCC9VxADh5Uh0/IWQw4cyMI7/+CsB5xsWmjAxclJ6OSKsVeVWYG0w3XgQimEYLANi5cyf+9a9/Yfr06Th0qPRwtbCwEDabDZmZmUhKSsLTTz+Nhg0bolevXp5Tu7KysgAAx44dQ2ZmJpo0aYKIiIqrlbfddhtuuukm/Pbbb7Db7ejUqRNWu7qdaaVYqWJjYxHr5wePLCz06Ro1wmpFpGtDkRa+dACCEqEaIpy+oxk4eJXKaPayjUDjOpzldpfd7GUp0gFItLCMdWEZGtmUpWGkZRQtJYwy8Whd9zzCXTjnBq2w0Oe39V6vzV4OIHXHLG3XRnYM0dLpgSYbL2zCtlV2GURYrT6VEIm0jMx2CSiNb2T6UhlDcRqvD6v1rOlCOZxzQ7l9t9fvcqaztpHLVqKlXhSkuovX6fL+RBnMDe71XyuTG8zmR7O5QxpfigPmK+9SGcuuZ7rV6nyYnG91cLbmBmnbNLveh+IyRWke0jTMXjYn1RukZSQdE0ik465oIfeJuRMApPwrzUO4pLxs/o20WhFltVZpg0LI5mWz2fDWW2/h22+/BQD06NEDDz/8MC644AJoigWxZ88ecdoHDx6Ew+HA6NGjMXr06HLx8847D2PGjMG+ffuwa9cuNG/evNwwDz30EADg1KlTSBb66Y2JiUGXLl08793Xw/UVWtT8cZ9G5d6JnkbpxiNtpNK/n/FSn8rZ2cqwkR9fKoN02rLZ09/Ljh8L3381pM3Y7Ar+/Lx5yrh0VoSRf1elafxNmoDXuurX3r3O58hIoGFDYP9+54GllMDCQDjnBofroXu917xeq0jrfXLApfEVn2rggqyWLZXhAtd1vRWRtk1pGbircA6v9945VTp58Wchbjo/m5w+IJ9ZIf0XaPa6+HAWzrmhBL6/nfdr4T81COfhievEfa+/rh5AWA4A8O2QIcq4tG1IZ2xKZ1wedT1rcNZRfoPvwXg7YXzpjE2zZ0tKuS9TiAOAVHOW1hOpbuZp+PF6r8P8vX6qg3DODQlwbj86nNtyDZT+RtJ6myvEpd9W2m6k6QPyJavSNMyeze3Ojw6v99774mbSmcy7hG+wWX1OU7Z6bGT66eDCW5wQB4Bfhbh0yaxU+2vmenYvw31wLsPTwnihFNCx3QcffICnnnoKc+bMQZ8+fTyfOxwOXHvttVi1ahV017+5X3zxBZYsWYKHH34YUV43GLHZbNi4cSO+/vprPPbYY4bm265dO881Yt6eeuop5OXl4fXXX0eLFi2Qk5OD48eP+wyzefNmTJo0CRMmTMBll13mubFoQUEB9u3bh5SUFKSkpFQ47507d2LWrFm44YYblK2kRFT1mBuIyB/mBiLyh7mBKLwF1Hjx3//+F/n5+ejVq5fP5/PmzcO3336Lhg0bYtq0aUhISMD06dOxdu1a3HXXXX5bNmfOnImff5b++3JKSUnBzTffXO5zd7/M/mJu7hbRLl26+Ay3fv169O7dG5MnT8aUKVM8n1944YW4/fbb0aRJE/z11194++23Ubt2bcyaNctQWYmo6jA3EJE/zA1E5A9zA1F4C6jx4tdff8UVV1yByDJd3M2fPx+apmHBggXo6bpR03XXXYemTZvi3//+t9/Gi2uvvRaPP/445syZY6L4odexY0fMmTMHR44cQUpKCgYOHIipU6eiXr16Z7poRHQGMTcQkT/MDUTkD3MDUegF1Hhx5MgR9C/TNaTD4cDatWvRqFEjT8MFAMTHx+P666/HF1/4v2r3448/Rm3pjtMC901vVHr16uW5lMXI54uEbmyIqPpjbiAif5gbiMgf5gai8BBQ48Xp06fLbZxbt25FQUEBbrrppnLDN2zYECdPnkSnTp08n+m6jqysLBw7dgxvvfVWkMUmIiIiIiIionNFQI0XdevWxc4y/W//9NNPAIDOnTuXG95qtSI+Pt7nbI2IiAjUrVsXvXr1Qps2bYIpMxERERERERGdQwJqvLjsssvw+eefY9u2bWjTpg10XcfcuXOhaZrfLn82b96MVq1aYfLkySErMBERERERERGdWwJqvHjkkUfwySef4LLLLkPv3r2xZ88e/P777+jevTs6dOjgM2x+fj5++OEHWK1WHD16tNzNaU6cOIF69erBbreb/xbVmMPrAfj2oyz1FZ4vxKX+2h2F6l6fpf6QjcxDumuJ1G+01C/1QK++sW0xMfgSwIBOnRBd7OztuXjDBuX4GcL0k4S4pIkQN9IRVqPERPUAfs5q8p1AI3XcvR64b7RbWAjY7UCx1GM2VaYY10OHczuIQWl/7VJiltbbBCGeLcTjs7KEIYB4oT/0eNdd2YNVmJ2tjB/weh0N4FCZ+PnC9IXe3MW+zv3fzamUlPuMkH5naR+SLMTde2UHgFMAGqHq+2un8grgzAsagFoArK73gLzPzBbiowcMUA8g9QInbPcAsE2IPzR0qDKeL9zIvWd0tDI+xWYD4FyXm8C5bntvj7lC+SRSfpbi0vLZFYIySPln9NSpyvirrj8d3fukQjjXwSIDZaPKE43SekMJfOsN0jGDtN6nCfFkr/q4P/Xy8oQpAIXb1Gt/M2F8qWYi1IZR76KLAAC22FgcBNCxQwdEF5Wu1W9t2qQc/6FUoWYgfL9soXzNhO0SZbru9ec61xURFblGOG6K6NFDPQNXj6O2iAh8CaDLhAmIdjiQW1QEvPyyWL5QiAhk4MsvvxzvvfceHA4HPv30U/z++++45JJLMH/+/HLDzp07F4WKg+eioiLExEhVLyIiIiIiIiI61wV05gUA3HfffbjjjjuwefNm1KlTB82bN/c73NGjR/HYY4/hlVdewezZs5GQUPpfoN1ux/fff897XhARERERERGRKODGCwCwWCzoIpw+9MEHH825BgwAAIfSSURBVHhez5o1C5HuU9YBxMTEoFmzZpg1a1YwsyciIiIiIiKic0hQjRdG/PXXXwCA3r1745NPPkGtWrUqa1ZEREREREREdBYz1XixZcsWzJgxAxs2bEB2drbfm29qmsaGCyIiIiIiIiIKWtCNF9999x2uueYaFBUVISoqCvXr10dUlHNyJ0+eRK1ataBpGk6cOIGxY8dWOJ1XX3012CIQERERERER0Tkg6MaLf/zjHygpKcHs2bMxZMgQn3ta9O7dG0uXLkVycjJ69eqFjRs3hqSwRERERERERHTuCbrx4rfffsOgQYOQnp5eLrZq1SrP69WrVwc7i7OCHc6+mN19bpegtH9aqU/mEiF+0kS5ACBBHkQcRupLvECIS53lvuXVH7FmsSANwOxff4VutQKQV+DaQjxJiCcL8fOFeD0h7pyJMJd8YU2R+n12d1nsOjMKR44AJSWAzWakdFRJ6sO5fdnh7H+9HgB3E7C0XsYJ8Yo7qTY2vqE+tC+4QJiJMJeVK5XhA0bKoLBciEu5Y48QTxbi0jKUcicg/04SaT1y9xVmB3AKwHlwroN5JudL5gyD87ezAfgSwAMAot3B9u3VI3furI43aqQMvzhtmjIer546AAP5o1kzZThh5EhlfMbbbxsoRcWOCnGpbibVazYL8YeGDlUPIO3TAXEZvvrmm+r45MnKeEqZ93Vcz1Z1qaiSNUBpvWEPgFSU1huk/YV6ywdipB4gpbqqsE4CQDshnrttmzgNFWnbPrRpEwDAYbEAAP74/XdEWEvX6m7C+D98/rkyLh23dRXiPwvbpZHjNok0jdQ1a5TxqMxM54u4OODii4GFC53HGQ4jtZrQMFRH9adGjRqoV08+NEtPT0deXvmq0OnTp/02fBAREREREREReQu68eK6667DGqF1BgDmzp0Lq7V8W63VavXpTpWIiIiIiIiIyJ+gGy9eeeUVZGdnY/To0SgoKH8SXW5uLnJycqDrOvLy8pCbm+t5nDp1Cl9++aWhMzeIiIiIiIiI6NwW9D0vBg0ahISEBMycORPvv/8+WrVqhaSk0itsve970apVq3Lja5qGqVOnBjt7IiIiIiIiIjpHBN144X0jzvz8fPz6668+cU3TAAC6ruPjjz9G7dqlt06MiYlB06ZNkZaWFuzsiYiIiIiIiOgcEXTjhcPgXUX37t2Lxo0bIyIi6CtUiIiIiIiIiOgcFnTjhVFNmzYFABQUFGDfvn0oLi72iXfo0KGyi0BEREREREREYSxkjRenT59Gbm4ukpKSUKNGDc/nx44dw9ChQ/HVV1/5Hc9ut4eqCNVSNIAYAO7zVGJQepdUqa9dqT/1YiEuMdJfuzRMbgjmodLM67UOZx/KTQBors8KTc5f6hc7WYhLv8FJIQ4A8fv3K+MJQjyiZUv1DFJTnc8xMc7no0eB4mKgROqRmipTDZT21w7Xa3d/7dKtjKOk/tSl/tgbNFDHG0k9wgNwNUxXaPt2dVzoU775hg3KuHvtdQDYC+B8+N6BOl89d7E/dmnblsaXzk00cu5ijIFhVJKFuHs9s7meU+DcZ0l5kSrXLDh/Aw3O/d0sOPd/AHDyjz+U46YKcUmqEA9FvWH+5MnKuLRtJQlx9/juekJNlC4/QN72pPlL+3WpYv3GnDnK+OjHHhOmAPzfK68o47WVUTk3pLiedQDZAJrCuTxPiyWjM0Var7OFeNy2beoBhLiRYxKpDAdMjh/IfrsGgLLfSDqmkaYvbfspQlza9xo5aJfyr5Q/JfVcxyQ2iwUAUHzgAHSr1fQxaSBMXctRXFyM5557Di1btkRSUhIaNWqEpKQktGzZEs8//zyKi4vxyCOPIDs7G+vWrYPFYsHXX3+NuXPnomXLlli2bFmovgcRERERERERnaWCPvPCarWiT58+WLduHSIjI9GyZUs0aNAAWVlZ2L17NyZNmoQvvvgCe/bswbJly9C5c2dERESgadOm6NevH5KSkvDCCy/g+uuvD+X3ISIiIiIiIqKzTNBnXrz00kv46aefMHDgQOzevRvbtm3DqlWrsHXrVuzZswd33HEHfvrpJ5w6dQr16jlPTq1VqxaOHTsGAGjfvn25HkqIiIiIiIiIiMoKuvHiww8/RKdOnbBo0SI0btzYJ9aoUSMsXLgQl1xyCSIiIrDdde1zx44d8c477+DgwYOYNWsWGkjXXRMRERERERHROS/oxovMzExcddVVymH69u0Lu92Ow4cPAwAmT56Mr776Co0bN8brr7+O559/PtjZExEREREREdE5Iuh7XsTHx3suAanIsWPHkJiYiPvuuw8AcMkll2Dv3r3Ytm0bmjRpgpQU6b6rRERERERERHSuC7rxolu3bli8eDEeeeQRtG3b1ic2duxYnDhxAosWLUKjRo0wduzYCqfz6quvBlsEIiIiIiIiIjoHBN148cQTT2D58uXo0qUL7r//flxxxRWoX78+jhw5gg8//BBZWVnQdR3JycnYuHGj32lomub387NJnOvh8HrvvlZHumYnppLj8XFSj8IA6tZVhpNd/f0GS1oGhV6v3f2NJ6C0/3aJ9A2luNRvdr4QLxDigLwRJgjxtJ071dOvUcP5IjbW+ZyfDxQVAXa7gdJRZYmC728f6fU+KjFRPXL79up4o0bqeHKyOt6woToOAFIZjxxRx4Uz7+JatlTGm7nW+xIAewE0ge/yNNtfvLTtS7lLGt8IKYdLZUgW4lGu/K679gVRKSmIKixElMMBnDhhpIhUCRIBWMq8d5N+8yQhXiLE44W4tD8yMoxUBmm/arTupHm914VxvBUL8UIhXk+IS7nh/155RRhCnkc7IZ4qxN3rUQmA7wB0hDO/5oolo8pUC858UOL13r3fk9Zbs3GJkXXjpBCXtn2zZXTXyTUANQBY4ZsbpPq4dL2ANL7ZfXrQB+1epN9A4t5HuNfB03CWS/rtQino5XD55Zdj4cKFGDZsGGbOnIm33nrLE9N1HTVr1sS7776L2267LSQFJSIiIiIiIqJzk6lGnNtvvx3XXHMNPvvsM2zcuBG5ublISkrCxRdfjP79+yNR+meOiIiIiIiIiEhg+gyUxMRE3H333bj77rtDUR4iIiIiIiIiIh9Bd5VKRERERERERFQVDJ958cEHHwAABgwYgMTERM97I+69997AS0ZEREREREREhAAaL+677z5omoZu3bohMTHR815F13VomsbGCyIiIiIiIiIKmuHGi4yMDGiahgYNGgAA5syZU2mFIiIiIiIiIiJyC+jMC29DhgwJdVnOSg6vB+Dbv7fRvsqDjUfFxakHaNlSmAKAVHVv4EnHjinjJYXqHtGl/t69+6x3AMiGs49r97ITvqHYn7o0vkSavhHSMpDmIY0fin6hKfQscPaX7f794uH1W9WooR5Z2rYl0vhGeoqqU0cdT0gwVwZh+gk7dwIAbK73NQBEe8Wl/Gq2P/VQbPsSKcdLa0GSEPcs49hY53Pt2kBREWC3AydOyAWkShEF3/XP+7XZ/YHZ9dbIjdKkbcfstilxLwPN673uFT/T5ZPmH29gGkJ2FalrZqXsruc8AJGuZzpzMuHc1zm83rvXR7P7NGl/Eop9otltx+z4Zfep0UI81PM3m3+rot4h7UNKKni2+xm2svCGnURERERERERUrQXdeLF//358++23KCgo8HzmcDjw0ksv4fLLL0ffvn3xn//8JySFJCIiIiIiIqJzV9BnlE+aNAmff/45srKyPJ8999xzmDx5suf9d999h//973/o0qWLuVISERERERER0Tkr6DMvfvzxR/Tt2xfR0c4rhnRdx4wZM9CmTRvs27cP69evR40aNfDKK6+ErLBEREREREREdO4JuvHi6NGjaNq0qef9pk2bcOzYMYwaNQqNGjVC586dcfPNN2PDhg0hKSgRERERERERnZuCbrxwOBxwOErve7p69WpomoYrr7zS81nDhg19LishIiIiIiIiIgpU0I0XTZo0wfr16z3vP/30UzRo0ACtW7f2fJaVlYXk5GRTBSQiIiIiIiKic1vQjRe33norfvzxR9x22224++678cMPP+DWW2/1GWbLli1o3ry56UISERERERER0bkr6N5Gxo8fj+XLl+OTTz4BAHTo0AFTpkzxxPfu3Yv169fjH//4h+lChrMI+LYQeb+XFn68NPG6ddXx1FR1vFEjaQ7yNIQyxO3fr4wnCbP3XgYlAPYDqIfSZecoN4avYiFeYjJeHYgtkImJzueYGOdzQoLzdUk4fLuzl8PrgTKvTYuLMxdPSJDn4bpZc4Wk9StKyIAWizLs8PPsvfyk7ULKv9L40m8V9D8DXszuI8QdvN1e/tn9oDOmBL77Hu/X0j7NbA4JxXp7prm/g+b1XvcTl8aviJA9kS/EawtxIxVz6XfOFeJG1yP38144l8tpYTyqXIUAIlG6PhfBdz1XCfqAz0Va742IEeJm8480/bLbTdnvJC2jUCwDFbP1EqPDmBnfvQxKvN5HQc4poRT0upyUlISffvoJmzdvBgBccMEFiIyM9Bnmk08+QefOnc2VkIiIiIiIiIjOaWYb4tCuXTu/nzdt2tSnNxIiIiIiIiIiomCcDWcIEhEREREREdFZLOgzL4zeiFPTNOzevTvY2RARERERERHROS7oxguHwwFN08p9npOTg+zsbABAgwYNEBMj3T6FiIiIiIiIiKhiQTdeZGZmKmNjx47FkSNHsGLFimBnQURERERERERUOfe8aNasGT788EOcOnUKTz75ZGXMgoiIiIiIiIjOEaZ7G6lIdHQ0+vXrh48++givvfZaZc2m2ouFsw9cu9d7d4eyCdLIdeuq43XqqOMpKep4crJUAiBO6NVYmEb8sWPKeExhoTLu3SezzfVcC0C0n3gwSoS41G+xFFd/OyfpO0j9SsdIv2OzZs7nKNfm3qQJUFICFBcDa9fKBaRK4fB6oMxr00qkNVsQFYJdgzSNBCEDHj+unrzrWfd67z1HabspEOJmf4tQ/DMgTUP6lcRfsWz+LSpyfuYI2ZpIVczseieNb+RCYGm9k+Zh9ju45695vde94ma3q3ghnivEpXpBkhAH5PwklSFfiHvvQTQA7mws5U2qXO56gu713r2em91uzNaHq2KfJ217Un5yx3U4v08iSpcfIG9X0vQru94QimVsNr+585P7mCwRpcdkVaVSexspKCjAyZMnK3MWRERERERERHSWq7TGizVr1mDRokVo3bp1Zc2CiIiIiIiIiM4BQZ8bfOWVV/r9vKSkBAcPHvTc0PPpp58OdhZERERERERERME3Xqxevdrv55qmoVatWrjqqqswduxY9OvXL9hZEBEREREREREF33jh4A29iIiIiIiIiKgKVOoNO4mIiIiIiIiIzGLjBRERERERERFVa4YvG3nmmWeCmoGmaZg0aVJQ454NYlwPu9f7SNdrseUoMlIawpwoAz9/QoI6npqqjheqezSPstmUcdjtnpdaXBwAIKZRI0S7pxvA+H5J4wvlLxbiUl/qRgi/ANCmjTI8Y948AIBmsaBR//741+LF0K1WWENQNgqeDc5+xt19qxfDq49wab0tEXpkF9ZLMZ6Xp44D5vOT9B0krnzg86zrnnC8yW1TujCyKi6cNPvvgriEyy6jwkLng5eFnlER8P3tvV9Le21pnQmHX9bsd3DHNa/3ulfc7DKMNzl+sck4IH8HIcOLy9C7DDUB5Lpes95wZrlzg+71Xqt4cB+VvU+LMTBMnMl5mOXeJzoAHINz3fbeXguE8aXtzmStRswdRuoEZvNbsjSDZs2cz7GxzucmTYCiIme9Yd8+aeyQMNx4MWXKlKBmcK43XhARERERERGROYYbL1atWlWZ5SAiIiIiIiIi8stw48UVV1xRmeUgIiIiIiIiIvKLN+wkIiIiIiIiomot6MaLL774ArfccgsOHTrkN37o0CHccsst+Oqrr4IuHBERERERERFR0I0XM2fOxO7du5GWluY3npaWhr/++gszZ84MunBEREREREREREE3Xvz222+49NJLlcNceuml2LRpU7CzICIiIiIiIiIyfsPOsk6ePIl69eoph0lJScHx48eDncVZocT1sHu9d/fPLPXlLfUljjihx+SEBHNxAEhNVcfbtDE3fkkAvSJHuVbXbt1KxysUejPPzjY3f2H6McL0a2dlqadvwKt5ecp47k8/KePuX8DdF3gUnOtg0Bs/hUQhgGiU5oYiePURLvzmyM9Xx6VtW9puTp9WxwHAYlHHpW0rkG3fH3f+c/c1HhsL6LonHCV8x8q+4ZPDZNzIMNI+RORehmWfHUZKR2fCmV5vjWy10jChmEdlkvaNMUJcqJmJ38/Idn1SiIeqjO56gx3OesOZ/m3OdcVw1hu8jyPcv5H0m0q5w+w6Y+CIAklCvMDANMzIdj3bARwDkAgg0isubfvS+l/Z9Woj+V8qg/Q7Sr8R6tZ1Pse41piUFKC4GLDbgX375AKGQND7wbp162L79u3KYbZv347atWsHOwsiIiIiIiIiouAbL3r27InPP/8cv//+u9/4b7/9hmXLlrGLVSIiIiIiIiIyJejGi4kTJwIAunfvjmeeeQZr167Fvn37sHbtWkydOhU9evRAREQEHn/88ZAVloiIiIiIiIjOPUFfntOhQwcsWLAAQ4YMwdSpUzF16lRPTNd1JCQkYNGiRejQoUNICkpERERERERE5yZT9xa59dZb0aNHD7z//vvYsGEDcnJykJycjK5du2LIkCGo676pBxERERERERFRkEzfuLpevXqYMGEClixZguXLl+Ojjz7C+PHjK63h4tdff8VNN92E2rVrIz4+Hu3atcMbb7zhiffq1QuappV7XHPNNYamn5OTgwkTJqBly5awWCxo2rQp7r//fuyrojuoElFwmBuIqCzmBSLyh7mBKDyFVW+Jy5cvx4033oiLL74YkyZNQkJCAnbv3o0DBw74DNeoUSO88MILPp+lpaWJ03c4HOjXrx+2bNmChx56CK1atcKuXbvw1ltv4b///S+2bt2KxMTEkH4nIjKPuYGIymJeICJ/mBuIwlfQjRf//Oc/8cILL+D333/3uyEfOnQIHTt2xKRJkzB69GhThQSA3Nxc3Hvvvbj++uvx8ccfIyKi4pNGatasibvvvjvgefz000/YsGEDZsyYgb///e+ez1u3bo309HSsXLkSAwYMCGiaEa6HXuY9IPe1C+nslQYN1PGUFHNxAGjWTB1v1EgdjxJWseRkdTzBq+dohwM4cgR46CHA/fvn56vHL7MjKmfbNnVcmP70N99UxrPVUwcg96ks9b19rgvX3GCDs492u+t9MUr7Gy8pLFSOG5WdrZ54nJBdpO1O2q4AoEYNeRgzpNzhXkaaq5f7oqLSzyD3x+4Q4mbHNxsH5B20dOqkuIN3ryexsb7Pdrv/4cNIuOYFidn1Slqv1ZnH2Om6UhmKhbhURrP/upkdXyqf2e3SSPkShLiUwY8K8WTXsyu7wgFnPdZI3qruwjk3uH8D9zGFA6W/kbTeSXXNeCGeLMRrC3EAiBCOKZJPnFBPwGZTx4W6k7s+XQJgD4C68N3ejqunjgIhXtnbh5H8K+UPKXdI64Gn/hgd7XyuWdP5u5RImTF0gr5sZMmSJejYsWOFLZBpaWm46KKLsHjx4qAL523hwoU4cuQInnvuOUREROD06dNwOCpeTUpKSpBvpALuJTc3FwBQv359n88buBoJLBZLgKUmosrG3EBEZTEvEJE/zA1E4S3oxoudO3eibdu2ymHatm2LnTt3BjsLHytXrkRSUhIOHjyI1q1bIyEhAUlJSRg5ciQKy7S07dixAzVq1EBiYiJSU1MxadIk2KTWOgCdO3dGjRo1MGnSJHz77bc4ePAgvvvuO0yYMAFdunRB3759Q/JdiCh0mBuIqCzmBSLyh7mBKLwFffac1WpFDeG04bi4uIBbKyuyc+dOlJSUoH///rj//vvxwgsvYPXq1XjzzTeRnZ2NRYsWAQBatGiB3r17o3379jh9+jQ+/vhjPPvss9ixYwc+/PBD5TxSUlLw4YcfYtiwYejTp4/n86uvvhoff/wxohSnMRcVFaGoqMjz3t3qao+Lg13TYHe1stq9WlulE2xs7lN4K+I+Zaci0mnXilPlqoyuq+NereE212ubdwu5NL4kMtJUXBNaz40sYc1kXJqHe3x3WT3Pui6eYhcOzsbcIFWN9BjhYiIpN0jrvSatdQZI85Dyk/QdXMvL5rr0wVbmUpkS4TtIF0ZU9mUhRk4vlYaRvoPRfUy557PgspHqnheAinMD4uKc26A7J3jlBrNbZgi2bJHJvbJpFe3zziWsN1QsnHODHhcHXdOgu34P3Wvdlra7St+fCHEAiJCOa6RLXqV6g7Dfd3+HEtdyKymTGyq7XlAVzP7O0u8Y4aqb2co+y0ULGU3Xgzv6a9OmDerXr4/vvvuuwmGuuOIKHDp0KCRnX7Ro0QJ79uzBiBEj8Pbbb3s+HzFiBN555x3s2LEDLVu29Dvu8OHD8e6772Lt2rXo1q2bcj7r16/H1KlTcfnll6Nt27bYtGkTXn75ZVx33XVYsmRJheNNmTIFU6dOLff5woULER8vXkFEVOUKCgowePBg5OTkIClJuhqy+mJuIAqtsyE3VPe8ADA3UPhhbmBuIPKnKnND0I0X48aNw/Tp0/Huu+8iPT29XHz27Nl48MEHMWbMGLz66qumC9quXTv8+eef+O6779CzZ0/P599//z2uuOIKzJ07F/fee6/fcbdv3442bdpg2rRpeOqppyqcx549e9C+fXt88MEHuPXWWz2fz507F/fddx++/PJLXHvttX7H9ddK2rhxY2yMi0Oi69/VrRkZuCA9HZFWKwCgvt8plYoVLsuBdMfj+sIcDNwxGR06qOPSDVqkVtKaNdVxr7N7bA4HVhw7hn516yLafdbI6dPq8Q8eVMelhjXhzKG33nlHGc9RTx0AIN1vWrphp3B7I896plksqJ+RgSPp6dCtVlh1HY8WFoZ1JQQI39ywPi4OCa7csDsjAy28ckMz4TtHdeqkHkDa9ps0MRcHgHr11PHNm9VxaduUbty1di0A5xkXK95+G/1GjkS01z+CJ4R/B4+pp37Gz8wA5FMjpf+TG0rjt2gBwHnGxYpJk9Bv2jREFxUh125HytatYZ0bqnteACrODbPi4mBxnXlRMyMDOenpgCs3nBK+dy0hLq3Xwv+i8s3cIK+XeUJcuimetF2497uaxYIGGRk47NrnGS2ftM+Vtt2qOKdVmodQMxLrJu6amWaxoE5GBk541RtGhXm9IZxzw0dxcYh3nXlhz8hAZHo6NNe6Ld2IUahti9uFNL6UewAgQqpbnDypjkvHHMJ+312rKLFY8EtGBi5JT0eUV26Q6tNWIR4ON+yUjjmEbhgQ0bs3AOcZFyuGDkW/OXMQbbMh12ZDyooVVZIbgr5s5B//+AcWLVqEYcOGYf78+ejXrx8aNmyIgwcPYvny5fj++++RlpaGxx9/PCQFTUtLw59//lnu5jf1XBXoU6cq3qU3btwYAHBS2Cjef/99FBYW4oYbbvD5/KabbgIA/PjjjxUmm9jYWMT6OR0qsrAQ3idPR1qtngMUaeFHeyUuv6Tr7qSNXHGDoiojnZ7u59KW6IiI0sYLs6e3S6dHC3HvCpE/Rpaw1Hpo9lTAsuPrVqvzIYwXLs7G3CBcMIGoYuF+/VJukNZ7s5djGZmHlJ+k71Bm24suLES012dRQiVGuKilWmwfUkVF+g6B7mOii4qcj7PgspHqnheAinNDuQq41epZ382ul1WxXlfFpSkqFe3zziWsN1QsnHODVljos31pVqun8cLspUKm9ydCHAAipOMa6ZIkk72NlP0OUVarT+OFtAzO9MX2RuZv9neWfseIMr9BtM3mfFRhbyNBN17UrVsXq1atwt13343Vq1dj9erV0DQN7hM5unTpggULFqCu1N2nQZdccglWrFjhucGO26FDhzzlqciePXvEYQDgyJEj0HUd9jIVN/fNeUoC+GHcy8H9v71d11FQUIA8XfesONI/GMVSBVLaiKUDHCmJAECB8B+I2TMvpOvavdhcyzD39GlEuxstpDMvpAqLtAyEZShd+WlgCYvTkA4jpHm4l4DmWn5WXYfuNd8gT76qNs6W3JDvlRtyhWlEVXZuMHJNs5QbKrvx1bUcPXlB1xHttS5L/+5Kd2MKh65SpWlI61GJaz2y2e3OZWi3I9puR66rYTucc0O45QWgdHlbSz9AtCtnu0lbpnSIbna9NkJaa6QzK6TvIFW+K9rnuUkHedIykr5fVTTeSAco0jI0uh6VXYbuz5kbzkxuKPB6X1JQgChdL70/iTANaX8i1TXNNm4AQIT0p6m0Xplc79z1ghKvYzLvckv1grOhq1RpPZHqDRGuddcG56Uiua6Gi1zX51WSG/QQWL9+vT5jxgz9ueee02fOnKlv2LAhFJP18euvv+oA9MGDB/t8fuedd+pRUVH6wYMH9ZycHL2wsNAn7nA49DvuuEMHoP/yyy+ez0+fPq1v3bpVP3bsmOez//u//9MB6HPmzPGZxvTp03UA+uLFiw2Xd//+/Tqc+zg++KjWj/379wewJVY/zA188FE5j3DODeGWF3SduYGP8HkwNzA38MGHv0dV5Iag73nhdvDgQRw+fBiAs//ihg2lq2yDd//99yMjIwMDBw7EFVdcgdWrV2PJkiV4/PHH8fzzz2P16tW48847ceedd+L888+H1WrF0qVL8eOPP2L48OF4x+v+BKtXr0bv3r0xefJkTJkyBQBw4sQJtGvXDidPnsSIESPQtm1b/Prrr5g9ezbatGmDX3/9FTHSXf5dHA4HDh06hMTERGia5rlebf/+/WF7neCZxmVoTtnlp+s68vLykJaWhojq0POMCcwN5y4uP/PO1twQTnkBYG4INS4/85gbmBvORlx+5p3R3BBMi0deXp4+efJkvXHjxnpERITPo0mTJvqUKVP0vLy80DWxuBQXF+tTpkzRmzZtqkdHR+vnn3++/tprr3nie/bs0W+//Xa9WbNmelxcnB4fH69fcskl+qxZs3SHw+EzrVWrVukA9MmTJ/t8fuDAAT09PV0/77zz9JiYGL1Bgwb6sGHDfFpUg5GTk6MD0HNyckxN51zGZWjO2bz8mBvOXVx+5p2tyzCc84Kun72/S1Xh8jPvbF2GzA3nNi4/887kMgz4zIvdu3fj2muvxe7du6HrOtLS0jw3sNm/fz8OHToETdNw/vnn4+uvv8Z5550XkkaWcJebm4uaNWuG9R2azzQuQ3O4/Kon/i7mcPmZx2VYPfF3MYfLzzwuw+qJv4s5XH7mncllGNB5HUVFRbj++uuxa9cu3Hnnndi6dSsOHDiAtWvXYu3atThw4AC2bt2KwYMHY+fOnbjuuut8uvohIiIiIiIiIgpUQI0Xb7/9Nnbs2IHJkydj/vz5PnfpdWvdujXmzZuHqVOnYvv27Zg1a1bIChvOYmNjMXnyZP/dopEhXIbmcPlVT/xdzOHyM4/LsHri72IOl595XIbVE38Xc7j8zDuTyzCgy0Z69OiBI0eOYPv27dA0dWcruq6jTZs2qFu3Ln744QfTBSUiIiIiIiKic1NAZ15s2bIFV111ldhwAQCapuGqq67C1q1bgy4cEREREREREVFAjRenT59GzZo1DQ+flJSE06dPB1woIiIiIiIiIiK3gBov6tWrh127dhkefvfu3ahbt27AhSIiIiIiIiIicguo8eKyyy7DV199haysLHHYrKws/Oc//8Hll18edOGIiIiIiIiIiAJqvBgxYgTy8/MxYMAAHD9+vMLhTpw4gQEDBqCgoADDhw83XcjqauXKlbjyyitRs2ZNJCYm4pJLLsGHH37oM0yzZs2gaVq5x4gRIwzNw+Fw4OWXX8Z5552HuLg4dOjQAYsWLaqMr3PGDBs2DJqm4YYbbvD5/MSJE3jllVfQs2dP1K1bF8nJyejWrVu5Zazib9lrmoYXX3wx1F+jyhw+fBj/+Mc/0Lt3byQmJkLTNKxevbrccJmZmRV+f03TMGzYMHFeZ+Pyq2zMC6HD3BAY5obqjbkhdJgbAsPcUL0xN4QG80LgwjE3RAUycO/evTFs2DC8++67uOCCC/Dggw/iyiuvROPGjQEA+/fvxzfffIN3330Xx48fx/33348rr7wyoAKFizlz5uD+++9Hv3798PzzzyMyMhLbt2/H/v37yw170UUXYdy4cT6ftWrVytB8nnzySbz44osYNmwYunTpgs8++wyDBw+GpmkYNGhQSL7LmfTzzz/j/fffR1xcXLnY2rVr8eSTT+K6667DU089haioKPz73//GoEGDsGXLFkydOtXQPPr164d7773X57OLL744JOU/E7Zv346XXnoJLVu2RPv27bF27Vq/w9WtWxfz5s0r9/nXX3+NBQsW4KqrrjI0v7Nt+VUm5oXQYW4IHHND9cXcEDrMDYFjbqi+mBtCg3khOGGZG/QAlZSU6OPHj9cjIyP1iIiIcg9N0/TIyEh97NixeklJSaCTDwt//fWXbrFY9NGjR4vDNm3aVL/++uuDms+BAwf06Oho/e9//7vnM4fDoffo0UNv1KhR2C9fh8OhX3bZZXp6errf5bRnzx49MzOz3DhXXnmlHhsbq+fn54vzAOCz/M4Gubm5+okTJ3Rd1/UlS5boAPRVq1YZHr9Pnz56UlKSbrVaxWHPxuVXWZgXQoe5ITjMDdUTc0PoMDcEh7mhemJuCA3mheCFY24I6LIRAIiMjMQrr7yCrVu34h//+AeuuOIKtG7dGq1bt8YVV1yBJ554Alu2bME///lPREZGBjr5sDBr1izY7XY888wzAID8/Hw4f5OKFRcXB9zzymeffQabzYaHHnrI85mmaRg5ciQOHDhQYetYuJg3bx42b96M5557zm/8vPPOQ9OmTX0+0zQNN998M4qKirBnzx7D87JarSgsLDRV3uoiMTERtWvXDmrcw4cPY9WqVbjlllv8tk5X5GxafpWFeSF0mBuCw9xQPTE3hA5zQ3CYG6on5obQYF4IXjjmhoAbL9xatmyJ5557Dt9++y22bNmCLVu24Ntvv8Wzzz5r+BSmcLVy5Uq0adMGX375JRo1aoTExETUqVMHkyZNgsPhKDf8t99+i/j4eCQkJKBZs2Z4/fXXDc1n48aNqFGjBi644AKfz7t27eqJh6u8vDxMnDgRTzzxBFJTUwMa133D2JSUFEPDv//++6hRowYsFgsuvPBCLFy4MODyni0WL14Mh8OBu+66y/A4XH7GMC+EBnPDmcHcUHmYG0KDueHMYG6oPMwN5jEvnDlnKjcEdM8Lctq5cyciIyMxdOhQTJgwAR07dsQnn3yCZ599FiUlJXjhhRc8w3bo0AHdu3dH69atceLECbz//vt45JFHcOjQIbz00kvK+Rw+fBj169eHpmk+nzdo0AAAcOjQodB/uSryzDPPwGKx4NFHHw1ovJMnT2L27Nno0aOHZzmo/O1vf8PAgQNx3nnn4dChQ5g5cybuuusu5OTkYOTIkcEWP2wtWLAADRo0MHwvGi4/45gXQoO54cxgbqg8zA2hwdxwZjA3VB7mBvOYF86cM5YbTF94cg6KiIjQAegvvviiz+fXXHONbrFY9Nzc3ArHdTgc+tVXX61HRUXp+/fvV87nyiuv1C+44IJyn9vtdh2APmbMmKDKf6Zt375dj46O1j/++GPPZ0au5bPb7fo111yjx8TE6Js2bQpq3kVFRXq7du305ORkvaCgIKhpVCeBXJ+2fft2HYD+6KOPBj2/s235hRLzgnnMDaHD3FB9MDeYx9wQOswN1QdzgznMC6EVLrkh6MtGzgXFxcXIysryedjtdlgsFgDAnXfe6TP8nXfeCavVqjz9StM0PProoygpKfHbFY03i8WCoqKicp+7rxNyl6O6qmj5jRkzBn/7299w6623BjS9UaNG4euvv8bs2bPRsWPHoMoUExODhx9+GNnZ2fjll1+CmkZVqWj5BWvBggUAENDpXWWF0/KrLMwL5jE3mMPcUD0xN5jH3GAOc0P1xNxgDvOCeWdTbuBlIwr/+9//0Lt3b5/P/vrrL6SlpWHnzp2oX7++T6xevXoAgFOnTimn6+5a9uTJk8rhGjRogFWrVkHXdZ9TvQ4fPgwASEtLM/ZFzhB/y++9997D119/jU8++QSZmZmez0tKSmC1WpGZmYnatWsjKSnJZ7ypU6firbfewosvvoh77rnHVLmMLv8zraL1r1mzZkFNb+HChWjdujUuueQSU+UKl+VXWZgXzGNuMIe5oXpibjCPucEc5obqibnBHOYF886m3MDGC4WOHTtixYoVPp+lpqbikksuwc6dO3Hw4EE0b97cE3NfM1a3bl3ldN13tZWGu+iiizB79mxs3boVF154oefzdevWeeLVmb/lt2vXLgDALbfcUm74gwcP4rzzzsNrr72GRx55xPP5zJkzMWXKFDzyyCOYOHGi6XIZXf5nWkXrXzDWrVuHXbt2ee5obUa4LL/KwrxgHnODOcwN1RNzg3nMDeYwN1RPzA3mMC+Yd1blhqAvVDmHLV26VAegP/HEE57P7Ha73r17d7127dp6YWGhruu6fuLEiXJ9JxcXF+uXX365HhMTox8+fNjzeXZ2tr5161Y9Ozvb89n+/fsr7Je5YcOGYdkv8969e/WlS5eWe9StW1fv3LmzvnTpUn3Xrl2e4RcvXqxHRETod911l+5wOCqc7unTp/WtW7fqx44d83x29OjRcsPl5ubqLVq00FNSUvSioqLQfrkzwOj1aaNHj9YB+Cxbb+fq8gsl5gVzmBtCi7mh+mBuMIe5IbSYG6oP5obgMS+EXrjkBjZeBMHhcOh9+vTRNU3Thw8frs+cOVPv16+fDkB/5513PMPNmTNHb9GihT5x4kR91qxZ+vPPP6+3a9dOB6A///zzPtOcM2eODkCfM2eOz+ePPfaYDkAfPny4/u677+rXX3+9DkBfsGBBVXzVKuPvBjvr1q3TY2Ji9Lp16+oZGRn6vHnzfB67d+/2DLtq1SodgD558mTPZ5MnT9Y7duyoP/XUU/q//vUvferUqXrTpk11TdP0+fPnV9VXqxTTpk3Tp02bpg8aNEgHoKenp3s+K6ukpESvX7++3q1btwqnd64tv8rAvFA5mBsCw9xQ/TA3VA7mhsAwN1Q/zA2hx7wQuHDLDWy8CFJeXp4+ZswYPTU1VY+JidHbt29fbuH//PPP+o033qg3bNhQj4mJ0RMSEvTu3bvrH330UbnpVZRs7Ha7/vzzz+tNmzbVY2Ji9LZt254VG0pZ/pKNe5lU9PBeVv42lOXLl+v9+vXTU1NT9ejoaD05OVm/6qqr9G+++aaKvlXlUS2Xsr7++msdgP7GG29UOL1zbflVFuaF0GNuCAxzQ/XE3BB6zA2BYW6onpgbQot5IXDhlhs0V6GJiIiIiIiIiKoldpVKRERERERERNUaGy+IiIiIiIiIqFpj4wURERERERERVWtsvCAiIiIiIiKiao2NF0RERERERERUrbHxgoiIiIiIiIiqNTZeEBEREREREVG1FjaNFxs2bMDDDz+Mtm3bokaNGmjSpAkGDhyIHTt2lBt2xowZuOCCCxAbG4uGDRti7NixOH36tDiP1atXQ9O0Ch/PPfdcZXw1IjKBuYGIymJeICJ/mBuIwpum67p+pgthxG233YYff/wRt99+Ozp06ICsrCzMmDED+fn5+Omnn9CuXTsAwMSJE/Hyyy/jtttuQ58+fbBlyxa8/fbbuPLKK/Hf//5XOY8jR45gxYoV5T6fN28eli9fjvXr16NLly6GyutwOHDo0CEkJiZC07TAvzBRJdN1HXl5eUhLS0NERNi0Y5bD3EAUWmdDbgi3vAAwN1D1x9zA3EDkT5XmBj1M/Pjjj3pRUZHPZzt27NBjY2P1u+66S9d1XT906JAeFRWl33PPPT7DvfnmmzoAfdmyZUHN+/zzz9dbtmwZ0Dj79+/XAfDBR7V/7N+/P6jtorpgbuCDj8p5hHNuCLe8oOvMDXyEz4O5gbmBDz78PaoiN0QhTPztb38r91nLli3Rtm1bbN26FQCwdu1alJSUYNCgQT7DDRo0CKNGjcLixYtx4403BjTf9evXY9euXZgyZUpA4yUmJgIA9vfqhaSoKNiio7H8nntw1bx5iLbZnAMlJ6sn8uuvyvDJPXuU8RyhjOp2Y6eBQrx2lHoV2l1SoowfF6Z/yuu1HhcHR0YGItLToRUWCmM6nS/EY4X4ISG+SYg/+OWXwhAAhGX00U03KeN2YfKJ7hdxcdAyMqCnpwOFhSgAMAyl62q4CtfcsAxADTjX66MZGajntV7HCNNwCPGaQvy8OnWU8XdOnBCmADwobPvSel0sTD+mYUP1AE2aAABsMTFY/uCDuOqddxBd7DXVffuUox87eFAZr5uQIJRQEB+vjjdqZG76AHBcnUFfEJaBmxYXh/YZGfgjPR16YSGKALyG8M4N4ZYXgNLl/RmcucERF4fjGRlISU9HhCs3SP+5Sv93nRLi0j6z6dSpwhBA4eTJynj5k/N9Sd/BKsTdmccRF4fsjAwkey0/I6RlpM5sgFSxbibE6wtxAIgU4r8J8VpC3F33KVvvKgAwFMwNZyo37G/UCEkREc793jPP4Kqnny7d70n7ZGl/UFOoOYwcqQyvevZZ9fgAen/0kXoA6ZKclSvV8S++UIazc5xHRva4OPyUkYFu6emI9MoNSeqpI+Kaa9QDCPtkSNtN+/bq+MUXq+MA8Mcf6vj8+ep4ixbq+JEjAABbbCyWT5qEq6ZNQ3RREXIdDjTOzKyS3BA2jRf+6LqOI0eOoG3btgCAoqIiAIDFYvEZLt5Vifzll18CnseCBQsAAHfddVdA47lP60qKikJSdDRs0dGIj49HUnQ0ot0DRUdXOD4AIFK9e5J2oNIBTpwQB+QNOUk4fU1ahaVKSJHXa13T4IiPR4SmiRU4N+nwQ1oGNYS4RYgn1ZCmAPEgTzgEEtcDz/iaBi0+HnqZ3+xsPAUxHHJDDTjXT4emIT4+HjU0zVNpN9t4IW13ScIpfdJ6DcjbvkRsvJBOO3RV1GxRUc7cGhWFaIfXkhHGlw5lzH4/af5Sfg/FPKTGWc9kXOtgnKb5rFtnW26oznkB8M0NNeDMDQVlcoN0YC/Fpe1OzB1xcs1Byl/Sftnsd/Q0XmgaisssPyOKhLjZxgvp+xup+kvzkGoeUhnc9YaK6l3MDWcmNyRFRDgbLyIjnfu9yEhEu/cD0j5F+s2kfZaw7Ruo7SJJatSX7mQQI2QX4Tu6928lrn1eoqb5bEti44V03CY1IEnxWGGvLS0/I9MwWLeqkGs981kHvda9qsgNYd14sWDBAhw8eBDPPPMMAKB169YAgB9//BG9e/f2DLdmzRoAwEHhn7ay7HY7PvzwQ3Tt2hXnn6/+P6KoqMiT7AAgNzcXAGBzNVzYXCu8zXvFN7kSl1jUhxjSP/JGVi9pJ20TNmS78B2lgzDvNKa7vq8ufG+f+ZuMS+WTlqHNyC1lpGEC+L6GpuN+1nUggH+jwkk45AY9Lg4OTYPD9Xs4vH5nab2T4tJ6bRMqIZqBdU7a9qX8ZhOmr0kHSa5KjN/cCogVLSl/SstIZLD8pgj7iAiDucM9nPtZO0tzQ3XKC0DFucGhyA1mSXskMXcYuJZZ2rak/CUxmh+96wyBzNPAXtvU+FJZpHpXVZTBPX7ZepfO3ADgzOUGW0wMbJGRsLlyv817HyDtk6U8Iu2zhG3fSN3cUJ1YRTpuMrjfL/vsJtVLxMYLab9u8k9rQ6RpmK2blFn3PM92ae8ROmFzw86ytm3bhksvvRRt27bFmjVrEOn6sbp164Y///wTb7zxBnr37o2tW7di5MiROHToEBwOB0qEf7m9LV++HFdffTVef/11jB49WjnslClTMNXP6ZQLFy70tNQSVScFBQUYPHgwcnJykJQktTeHD+YGInPOxtxQ3fICwNxA4Ye5gbmByJ+qzA1h2XiRlZWFyy+/HDabDT/99BPS0tI8sYMHD+KOO+7Ajz/+CACIjIzE2LFj8d1332H79u3Izs42PJ8hQ4Z4WmPr11dfheivlbRx48Y4fu21nstGVtxzD/p53/NCur5s40Zl+NTu3cq4dM+Lr4U4ANwhxGsJrYh7bOp2zGPC9H3ueWGxQM/IgJaeDs0qXXDiJLVvS/+tSm3r0nWlw7/6ShgC4mUjS4TrKqXdpyeFWCyl97ywWlGg67irsPCsqoSEU25YGReHGq5/V49lZKBuejoiXOt1Zd/zollKijL+L+m6TQDDpX8QhG1fvGxEuieE+54X0dFYMWIE+s2aVZpbAfmeFwcOKOOm73khXTIm3dPDiBDd8yLCYvHc88JhtaJQ1/HCWZQbqmNeACrODcu9csOJjAzU8coNZi+pMHvPiyauf6ZVip5+Whk3e8+LAiHu3ifqFovnnhdG6wyAvIyk/xel/07PE+L1hDggnzZt9p4X7rpP2XpXga5jEHPDGcsNx88/H0muMy9WPPMM+j39NKLdw0n75Eq+58XqadPU4wPoJd3zokDYuqV7Xnz+uTKc47rnRYnFgnUZGbg0PR1RXrlBumQr4tpr1QNIdSepXtGhgzpeze55sWLSJPRz3/PCbkfK1q1VkhvC7rKRnJwcXHvttcjOzsaaNWt8Eg0ANGzYED/88AN27tyJrKwstGzZEqmpqUhLS0OrVq0Mz8dqtWLp0qXo27evoUQTGxuLWD+n8EbbbIgu+95dwZZabb0Slz9Rws5Y2oEaabWSVpBo4TtECgcwUiWl7GUZOgDNajVcEZGWgRSXyictw2gj135JwwRQ6TLEag39NKuBcMsNWmGhz/oVYbUaPkCRSOt1tHDar25g/ZC2fanxQtx2pFOTi32bP6JtNt8bdgrjS/kzWjo9VSKdulksNd8YIOwjHAFu5w6rFQ6r1fRp89VJdc0LQMW5IcJEbgh0n1qWmDsc8gUYdmG9M/sdjMbdJdW8lp8RZq/YNntTVSOZRxrGbBm8x/eud51Nd7oIx9wQXVxceo8LANFFRaWNF9K2KW0D0r0ShOkbqZcbqhOrSPWOAPf7UVarz2dC8w8ihHqNuF+Xxg/FpRfSNAKsW5VTpt7hXgejq/CykbBqvCgsLMSNN96IHTt2YOXKlbjwwgsrHLZly5Zo2bIlAGDLli04fPgw7rvvPsPzWrZsGfLy8oK6sY6POnWc1w+5K8J16pRufPn5ylFzd+5UxrcJs/6bcOff0RddJEwB+PSGG5Txa4QNUf0N5X9fvVOl7vWZO/21E8ZX/7cq93Yilb+ZEF99xRXCEPK/7NIuUqpKujdyB5z/pjSCs+IifbdwEo65Id71cHi9d1dJzN4QLlWa+Zgx6vhTT0lTwBvCtj/atYwrJOQ38brMZs2cz+7c2qSJT8XmS9e1yRVpop464vPylPEEqbeoVOFXkMYHAKEMUiVlSuPG6vFdZ3/YYmLwJYDHu3RBdHExcktK8OLPP8vlq+bCMS8Azn/+i1C6zzuF0n1ebWFcadvvLGyXu4TtctfEicIc5LM67xXiUt1Gfd5YKTucy64+fBtldgnjtRHiraR/uLt1U8elAxhp+oCYPy/KylLGpbNfkl3PDjiXYU04908GShYWwjU3oGZNZ8O4u6EhObn0YFLqJUzap86apY7/8IMybOhPF6m3Iqnu0b27MlwyZ44ynuz6Hd33aajZokVp4w8g143MNgysXq0MH3/lFWU8Wz11ZxGEuPQ7NRNyh+emp677hZzevRtRVmuVHlOY/YOvytjtdtxxxx1Yu3YtlixZgssuu8zQeA6HAxMmTEB8fDxGjBjh+dxms2Hbtm04fPiw3/Hc15UNGDAgJOUnosrB3EBEZTEvEJE/zA1E4S1szrwYN24cli1bhhtvvBEnT57E/DLX7Nx9990AgDFjxqCwsBAXXXQRbDYbFi5ciPXr12Pu3Llo0qT0v7aDBw/iggsuwJAhQ/D+++/7TOvkyZP46quvcOuttyLB7HXPRFSpmBuIqCzmBSLyh7mBKLyFTePFpk2bAACff/45PvdzQxZ3srn44osxffp0LFiwABEREejatSu++eYbn66OJEuWLIHNZsPgwYNDUnYiqjzMDURUFvMCEfnD3EAU3sKm8WK1cJ2Q23333WfoWrRmzZqhoo5WHnzwQTz44IMBlI6IzhTmBiIqi3mBiPxhbiAKb2FzzwsiIiIiIiIiOjex8YKIiIiIiIiIqjU2XhARERERERFRtRY297wIWydOOPvsdvfbfeJEaR/fx48rR90nTPpv7durBxC6f1rdp48wB+B8IS71lx4jxOPFEpTSAeQCSEJpn/cnhXGk1jlp/tK9oYVes8XxjQwjxWsLcXcZbQAOAugIZ1/tuXLRqBI1gnNdLgHwl+u9OyFL243Un3Z8t27qAd55Rxl+uF8/YQ4ASkrU8QMHlGFp2yzeuVMZX+mK6xYL0L8/li9eDM1q9cSThOm369JFPUBqqjouff/8Kuj1vEYNddy935Hi3s+6DmhaxeNQpasJoAYAB4ATrvfu7aWZMG5q48bKeJawXZ1/553K+OxFi4QSAA+//rp6gDK9O5SVumGDMi7td911J4frOQe++aaDMH7a1KnqAaT8GieUMC9PHV+3Th0HgCh19b22UIZuBw8q4wWuemoJgFUALoJz/8R6wxnWurUzT7t//1atSvdFW7eqx735ZnVcWKdKXntNGb9SyB0AgKVL1fHx49XxBx5QhqMef1w9fps2vu8fe0w9fFmLF6vjP/+sDBcfO6aMC7UKQ6S6lRSXjj2LXc/u/JrpmmYV1Hg8eOYFEREREREREVVrbLwgIiIiIiIiomqNjRdEREREREREVK2x8YKIiIiIiIiIqjU2XhARERERERFRtcbGCyIiIiIiIiKq1th4QURERERERETVmrpTXzKvVSsgNhaIjCx9b7c7X9epoxy1XWGhetrJycrwjD59lPG+6qkDAGKEeLEQF74BCoS4w+u17vWZ5notlU+KJwUw/2CmL8WNlEGKxwnrARITXQO6+n1v1AgoLAQcDkDo650qT4zrEeH13p2QDxkYV+nPP5XhzXl5yng7oS9yAEBqqjLsyMxUxrOFyUvbnrs/dN3rveYVbyWMj/PPV8eF7wfh+0HK3yUGenS3WORhVE6fVsfd38GdG/btK80NdMY4vB6A77Yg7XNhsynDqd26qcfPz1eHpfkDQLNm6vg11yjDTaRtb8UKZbjAte3ZARwGUAdApFc8bdgw5fizJk9WxqV9spSfs4T4wzNmCEPAuR9XOXBAGS4Q1pOTrmdXbRWn4FyG6j0HnVH9+qnjbdqo488+qwyvFmbfV9qnAqXHPxXI3blTGU8Stk3Ty+CHH9Tx48dNTT+mRQtlPHXbNmW8MDtbPX/IB/ZSXKqZuHNDCYC9AOq5plmVuYFnXhARERERERFRtcbGCyIiIiIiIiKq1th4QURERERERETVGhsviIiIiIiIiKhaY+MFEREREREREVVrbLwgIiIiIiIiomqNjRdEREREREREVK1J3b2SWVFRzkdkZOl7TXO+LipSj9uunTretq0y/PD48cr4pv791dMHECfEpRUoWYjHiyUoZQewA0AaSvtsl8on9UmfG8D8/ZG+v5HWQalP+LjkZPUAUr/Vnhm55tSgAVBcDJSUAAcPGhuXQi4HgAOlfWpno3R9KhDGldar43nqHrebCeNLfbEDAFq2VIYjLrhAGU85cEAZP/7HH8p4d9dzCYC1AC6D7/ZYu3dv5fho1kwdLxF6O9+4UR2PjlbH3fsEFalP9xMnlOESm00Zj3KX0WJxPh85AlitgK7LZaNKY4Fz3+gAcMr12r3NJwjjZmdlKeMrhbiw1iNFiAMAbrhBHZe2vc6dTcWbufaZNk3DZgBNXnoJ0V7r9FtjxqjHV88d3YS4lJ9/EuL/evhhYQhg+Ny56gG+/loZ3iJM3113ci+1PQA0AKfFklGl+u03574jNha49Vbg999LjyWEfSpWr1aGF27YoIwPFvb54jELADRsqAznZ2Yq40cLC5Xxep9/rownubcLiwX44ANg1CjnPs89f2GfmZCYqIzj7rvV8W5C9ti0SRmOk+oEABAnHBkJdZso4Teo5yqjzTWfuikpiC4sRJzDIdZJQoVnXhARERERERFRtcbGCyIiIiIiIiKq1th4QURERERERETVGhsviIiIiIiIiKhaY+MFEREREREREVVrbLwgIiIiIiIiomqNjRdEREREREREVK1FnekCnPWOHAFiYoCoqNL37j52pb7O27ZVx/v1U8eHDFGGLxowQD0+AIwfrwx/ffnlyviFwuRT5RJ4lADYAaA+SldcoVdrw32ZVyRGiCcJ8WQhDgBCj8xIltaT885Tx/Nd3zI62vmckgLYbM4HnTEFACIB2F3vra73RkjrbbwQT+jSRRn/P6G/dwCIW7FCHEbFIcTVPZEDYx97DABgi3QutVpjxiDabi8dICVFOf5bEycq4w+NHKmM5wp9oZ9URoFsIQ4AxUJcyj9NhHiUuz/42NjSZ113PvKltYwqSx6c24fu9V5zvd4mjCv9I3Xb3LnqASKFLNSjhzAHAA0bKsNfZ2Up49d4b8f+tGmjjickOJ/tduDXX4Gbb/b5XqljxihHv65xY/X0335bHRfKf83kycr475s2qacPYJZQvxsujL9HiHvnZwuAI67XBcJ4VLnyd+5EBIASiwUAcHrHDkRZrQCAiG3q7BCfmKiMD/7xR2U8S6jvR9xxhzIOAB8L8Yfc9dQK/CTUWw8J0y9xja+7jsm+t9mgeU1T2qd2lurj992nDL916aXKuHRQLtWLAOAhKX+uXq2OL16sju/f73x21xtq1waKipx578QJAyU0j2deEBEREREREVG1xsYLIiIiIiIiIqrW2HhBRERERERERNVaQPe8+OCDD4Kayb333hvUeEREREREREREATVe3HfffdA0522jdF33vK6Iexg2XhARERERERFRsALubSQqKgrXXXcdunXrVhnlISIiIiIiIiLyEVDjxe23345ly5Zh2bJl2LlzJ4YOHYp7770XdevWrazyEREREREREdE5LqAbdn744Yc4dOgQXnvtNcTExOCxxx5Do0aNcOutt+I///kPHA6HPBEiIiIiIiIiogAEfNlIrVq1MHr0aIwePRq//vor3nvvPSxevBiffvopUlNTMWTIEAwdOhQtW7asjPKGn6io0of7vVt+vnrcwkJ1fNYsZXj9tm3KeNcBA9TTB4Cbb1aGr5k0SRlfPm2aMn6hMHvvJWB3PR8DEOl6vUMY/7YjR9QDZGWp440aKcPv16kjlEAmlABpUhmvvVYdj4tzPke42iovvhhwOJzr19dfGyojhV4eAB2Aw+u9uzW5RBg3QYgfFeKHNmxQxscnJwtTAP6Vna2Mq7cc323bn+NSAcqu17GxzvXabelS5egjhMnve/ttZXyzML5U/mIhDpSuGxWJE+LSMm6SlwcAKClxrnGn8vMRZbUi10DZqPLkwbm/013vcwG47zAm/eMk5YZDQ4Yo42mPP66ewDvvCHMAsG6dMnzNrl3K+I7ISGX8Z2H2nu3CYkHkokX4vHVrwGr1xHsK40v1Htjt6rjkgguU4WabNomTkOoNEXfeqYznL1qkjLvzkwbAAiAHzvXRWuEYVBWi4Hvg5v1e2qeUuPJ9hdMW1vtU4ZghQ9jnAkA9aYCf1Vt3TMeOyrhU9/Hep2pw5lpvzYTx8eyzyvBHl16qjF8lTL65EDdSb9gn5M99wvibhPjDM2Y4X7jvezl8OKDrzhz72GMGSmieqa5SO3XqhJkzZ+LQoUOYP38+2rZti5dffhkXXHABli9fHqoyEhEREREREdE5zFTjhVtsbCx69eqFXr16oX79+nA4HCiUzhogIiIiIiIiIjIg4MtGvJWUlOCzzz5DRkYGli9fDrvdji5duuDpp59G3759Q1VGIiIiIiIiIjqHBdV48ccff+C9997DwoULcfz4caSkpGDUqFFIT09Hu3btQl1GIiIiIiIiIjqHBdR48dZbbyEjIwMbN25EREQErrrqKtx///246aabEBVl6iQOIiIiIiIiIiK/AmpxePjhhxEdHY0bb7wRQ4YMQcOGDQEAv/76q3K8rl27Bl9CIiIiIiIiIjqnBXy6hM1mw+eff47PP//c8Dh2s91KEREREREREdE5K6DGiyFC/+DkR5MmQFwcEBFR+t7hUI/jJvTVC6FHl6733KOMZ7zwgliE9NdfVw+wbZsyfNXu3cr4xy1aKOPefda7+7zfg9I+70uUYwMZ9esr4+lCw9q/hN8gRpj/SSEOAGK/PFnqHt07/d//qcd/5BHns/u7nD7t7Ke+qMhA6aiyFLseDq/37u6f4oVxpfW+QIjnCvHM7GxhCGB4mzbK+FtCbpC+4/CRI5XxXdOmAQDsFguwaBH2vPwyIq1WT3yTMP1sIS4tQ2m7nfDvf6sH8CprhaRtNDlZGX7r1luV8V1er6MBfO96LX13qr6k3LBDiB8X6gV7DJThqqZNlfH4oUOV8VZC3aXZvHnKuLuMdjjX8dYAvPfk2cqxgZg331TG44X4cWH6B4T4USEOGKhbNGqkDKuzN7DZ9eyua0XAWQcLSReFFDSH1wNlXkt5O1uIFx47ph5g6VJlOH3YMGEOAIR6A/bvV4Y79eunjJ+/YoUynu16tgP4HUBH+OaGJl26KMef0b+/Mt5KGZUPuiOio5XxuBtuEKYANOnWTR0XbvPQ/YsvlPGMhx8GAGgWC2ovWoT548dDt1phoEYTMgE1XsyZM6eyykFERERERERE5FelN6IePHiwsmdBRERERERERGexSmm8KCkpwSeffILrr78e5513XmXMgoiIiIiIiIjOESHt33Tr1q147733MG/ePBw/fhy6rqNx48ahnAURERERERERnWNMN17k5+dj8eLFeO+997B+/XoAQGxsLAYNGoShQ4eiT58+pgtJREREREREROeuoC8b+fHHH5Geno4GDRrgwQcfxLp163DppZdC13XcfffdWLBgAfr27QtN07BmzRrcfffduOyyyzz3wJg3bx5++OGHkH0RIiIiIiIiIjo7BdR4cfToUbzyyiu44IIL0LNnT7z//vuoW7cunnzySezYsQP/+9//yo3z73//G1dffTUsFgs2btyIIlfXbzk5OXj++edD8y2IiIiIiIiI6KwV0GUjjRs3RklJCRITE5Geno57770XPXr0UI7z7LPPYtasWbj33nuxePFiz+eXX345nn322eBKHU6OHgViYoDISKB9e+DIEcBud8ays9XjCn3x4rjQm7gQ76AeGwCwbMwYZfwm93epwOLISGVc3RM5UOj1WgdQDOdK6+57vFgYP0aIS+WLE8Z3CPFCIQ4Y6HtbiCfbbMp48/x85wv3d83Pd66DxdLSo8pUB0AinP2NH3G9d6+NUmLONTlvqdVa6i8eADK3bVPGHxL6Y39e6I8da9cqw0ddz+5t8Bh8v9dJ9dQx/L331AMI3w+Zmer47NnqeCMp+wE4/3x1PDZWGZa28BKv19Fe70v8DEtV5xRK834t1/tQkXKLtL+5eehQcR6z5sxRxpOEuNn8584DGoDaANbDWX9wM7tfzxfiIx59VBlPzcpST+Caa4Q5ALOGDFEP0LSpMnyhMP31rmd3XasAzmVopE5DlecUnPnZ7vXeXW+Q8na2EJfGF444EP/uu8IQ8jykuLRPyxbi7mOCiuoNTYT9/sPSces99yjDHwvbZapQn4/r3l09fwC44QZ1XMo/q1crw0ll3ie6nkN6E01BQPOy2WyIiIjAnXfeieHDh+Piiy8Wx9m+fTt69uxZ7vOaNWsiWzp4JyIiIiIiIqJzXkCXjUyZMgVNmjTBO++8g86dO6NDhw545ZVXcOjQoQrHSU1Nxa5du8p9/sMPP6B58+aBl5iIiIiIiIiIzikBNV48/fTT2LNnD5YvX46BAwdi586dmDhxIpo2bYqrr74a8+fPLzfOsGHDMGbMGKxbtw6apuHQoUNYsGABxo8fj5EjR4bsixARERERERHR2SmoS1T69u2Lvn374tSpU5g/fz7ee+89rFixAitXroSmafjll1/w/fffo2fPnvjHP/4Bh8OBPn36oKCgAD179kRsbCzGjx+PUaNGhfr7EBEREREREdFZJuiuUgGgVq1aGDVqFDZt2oQNGzZg+PDhSEpKwsaNG9G7d2+0aNECzz77LJ588kmcPHkSmzdvxk8//YRjx45h2rRpofoORERERERERHQWM9V44e2SSy7B22+/jcOHD2Pu3Lno0aMHMjMzMWXKFABATEwMLrzwQnTt2hUJCQmhmi0RERERERERneVC1njhFhcXh6VLl6J27dro06cPzj//fNxyyy0VPoL13HPPQdM0tGvXrsJhsrOzUa9ePWiaho8//tjQdI8cOYKhQ4eiXr16sFgs6NSpE5YsWRJ0OYmoajE3EJE/zA1E5A9zA1H4CPieF1arFTabDUlJZXt6LVWzZk0UFxejRo0aSEtLw9KlS1GzZk107twZAPDLL78gOzs76MaLAwcO4Pnnn0eNGjWUwz399NMoKCgwPN3c3Fx0794dR44cwZgxY5CamoqPPvoIAwcOxIIFCzB48ODAC7tjBxAVBcTEAH37Ajt3AsWunorzhd7Chf5+UWiux+2KU3SpFCH+e2SkMv43Yfx6Qty7P3YbgC8B9AIQ7frsJ2F8dY/Ncp/R6Xa7Mv6B8P2l/uQBuQVRmoZ4HpO7S+Io1+aekwOUlMjrVxgKp9wQ4XroZd4Dcl/n0m8eI8Sl7c7IknEI8fwVK5TxJ6KjlXFs2qQMd3I9lwD4BsBF8N2hVdwHlkuUsPtLTVXHhf7akZysjlss6riBaUzv00cZj5fncM4Ip9xQAGde0ADUAmBFaZ6QKm3SPk0aP1uIvzVnjjAEcJMQb9SypXqAvDxlODcrSxl3b/t2ALsAdAXgvaduI83/gQfU8c2b1fGlS9VxKbd88YU6DmCEdM+4zz5ThvcI03fXLt37pCI4c765Wmf1FE654QCAGijd/+5H6W8k1RUbCXGp3nFUiP8uxAG5biLNQyqjNH33PtGdTw/DmWfdfhJyT7ft29UzyMxUhqXyxfXurR7AahWmAMBP5xk+/PQA6kM4tuzgerYD2AGgLZz5VTiiDamAzrw4fvw40tLScP3118PhqLjqOnv2bOzbtw9ffvklatasiYEDB+Kvv/7CJ598gk8++QR79uzBoEGDkJIiHRr7N378eHTr1s3TGOLP5s2b8fbbb2PixImGp/vOO+9g165d+PTTTzFt2jT8/e9/x6pVq9ClSxeMGzcOxcVStYCIziTmBiLyh7mBiPxhbiAKLwE1Xrz33nvIzc3FjBkzEBFR8aiRkZGYOXMmsrOzMXv2bIwfPx6RXv9QR0ZGYuzYscjIyAi4wN9//z0+/vhjTJ8+XTncmDFjMGDAAPTo0cPwtNesWYO6deviyiuv9HwWERGBgQMHIisrC999913A5SWiqsHcQET+MDcQkT/MDUThJ6DGiy+++AKXXHIJOnbsKA7boUMHdOnSBUVFRdi2rfzJ+9u2bVOeveGP3W7HqFGj8MADD6B9+/YVDrdkyRL873//w8svvxzQ9IuKimDxcypvfLzzRKNffvkloOkRUdVgbiAif5gbiMgf5gai8BTQPS+2bNmCu+66y/DwXbt2xW+//Yb7778fu3fvRteuXQEA69atw4svvoihQ4cGVNhZs2Zh7969WLlyZYXDWK1WjB8/Ho8++iiaNWuGTOH6I2+tW7fGypUrsXfvXjT1up55zZo1AICDBw9WOG5RURGKioo873NzcwEAtpgY2KKiYHNd323zvs47Rrj6SXF2CwBA19VxQYmBa67Vd3yQr3uXxpeuX/O+K4PNVV6bV7kDa/4qTxPiNum+EEauWzdZBom4DF3X9pd7DrDxsDoLx9xgj4uDXdNgd61D9gDWa2nLl7Y7s3Ejw4jrpXTPC+GeFO7pu/OYkXzmM39pAE3YMqV4KAg5XhO+s+ESuqfjftZ10/dUqi7CMTdocXHQNM3z+3r/ztJveqbjgJwbbLGxwgDqrVPa1t3zd7iGc5QZXpy/VPeS7pcjTV+q+0nTBwDhflsQ8mvZZVKWewlEuIbzPDM3GJ5+ZeQGR1wcHJrmd92W6g3SPlnabs0dcYRuGiGZv9c+z7tM0jK0SdumsM/Whe3OZva40Mgw0ncQckfZOqvnuQpzQ0CNF3l5ecobdZaVlJQEh8OBCRMm4J///CcOHz4MAGjQoAEee+wxjBs3zvC0Tpw4gaeffhqTJk1C3bp1KxzuxRdfhM1mwxNPPGF42m4PPPAAZs2ahYEDB+K1115D/fr18dFHH2Gp6+ZLVsWNUl544QVMnTq13OfL77/f08oKACukG0GRaEUAlxvVMjmvL7/+WhmvuWiRyTmYtyHA4Vf07w8AzhtPffhh6AtUxcI1N2zNyPDJDVuDuIyOSn1XZvkJTSP4UppgzZpmihMawk2dm4U4/1hcy1AvKACCuUF1NROuuaFBmdzQIMxywyaT8VDbU2b5Cberk0k3/HTtY8+oXr3U8eHDleHWZd63dC3DAuYGwyojN5zMyEChV244FUBuqLippOpIDSjqW6ZWgjLL75gwuFhvcN8gvyLCPlucfihcfLG58cvkjt1nIDcE1HiRnJyMI0eOGB7+yJEjSE5OxoQJEzBhwgRPy2EgDSBuTz31FGrXro1RijssZ2Zm4pVXXsHMmTORkCD2wVBOhw4dsHDhQowYMQKXX345ACA1NRXTp0/HyJEjldN8/PHHMXbsWM/73NxcNG7cGFe99x6SXGderHjgAfSbPRvR7n8VpN5GSoTN3GQLV9GOHeIw0q+tvi8vIFX/K95lOHn/f2GzWLAiIwP90tMR7Ur60oG79A2lVuC7T51SxhfVMts8Iv8DLC1DqUeXurfd5pxPVBRW9O+Pfp99huiSEuSeJTeLCtfccEF6OhJdZ15szcjABenpiHSt11Lrv9T2Lv2y0h2vjdxPXbqzubRt15DOvBD+fXX/J1ViseC7jAxckZ6OKK/KoLrZEeg/a5Z6gBMn1HHp31Wp8cPImSLCNN668UZlXOptJNGrLJaMDFjT0wGrFVaTZ/VVF+GaGw6np8PiOvOiQUYGDqenQ3et25Xd24h0cGHkWuPrhHhaixbqAYS6UZ5QDz3senZYLNiTkYHm6emI8MoNraT5Dxmijm/Zoo7/73/qeP366njDhuq4kWkI9bvfVq1Sxr91PUdYLGiZkYGd6elwWK0oZG4wrDJyQ+30dNRwnXlxKiMDtbzWbWmfnCbEpTMvjPQmIpHqHlLjgdneRjx7XYvF2XDh2ue5SfWWLoMGqQcQcseX116rjF/Xs6d6+ldcoY4DgNcZO37tEfoaEuo+u1y5w26xYHdGBlq46q75VZgbAmq8aNeuHb755hvoug5NOGVW13V88803aNu2reezYBotAGDnzp3417/+henTp+PQodIO8AoLC2Gz2ZCZmYmkpCQ8/fTTaNiwIXr16uU5tSvL1aXWsWPHkJmZiSZNmihvNnrbbbfhpptuwm+//Qa73Y5OnTph9erVAIBWrVpVOF5sbCxi/VRmo4uLEe11en60zYZo90GjdPAoXbIgraACu4Eud4QTE8WKjDS+tAL6O7yJtlo9jRcB3bTFD2lTi5YOsIx0W2SyDBJxGZZpBIsuKXE+hG5gw0E454bIwkKf7SPSavU0XkinZpvd7szGjQwT6HpZjpD/yq69UVarT+OFRDozQ7wsryp21NJ+Vvi+AZfQag1JTqsOwjk36IWFPr+dbrV6fmvpNz3TcUDODdFS3UX4Y0bazsvOP8Irtxqav3RJpZS7pOlLdT9p+gAg7b+F/BkhLMOyS8BhtTofcsmqvXDODRGFhT77/wir1fNbSvWCgA74/AjFhZJVcLGlofl78pjVCs1rW5CWoVhvEfbZmrDdRUu5wcjl3mbzl5A7Ist8B3fd1Ui9MVQCWpf79++PsWPH4o033sCYMWOUw86YMQN79+5FTk4OmjdvXuFwe6QWIDivC3M4HBg9ejRGjx5dLn7eeedhzJgx2LdvH3bt2uV3fg899BAA4NSpU0hOTlbOLyYmBl26dPG8d18P17dvX7Gs5WzZ4rz+KC6u9L17xyw1TiQmquPSNY9SBUA9tqFh6glxqSVY+of3pNdr9646C6WVE6lPaGkzl85dmSUsY6mzX+mfTyPT6CpNQLr7tbt1332dW0KCM3mdBWdehHNuKHE97F7vjR5sSlVbab2TtgvpHxpA3rZzpfGF/BcVp55DsSu/uZdFMXy/121z56oLsHixOi6ddu06o6lCjRqp49LppQBm1amjnoUwvpTfyh2gVPB5OArn3FAEZyXbXQ0uhPHcIG37UuX8YcUp9AAMrbcfSdv2zp3KuFTG40Lcvd5rcG4jq1Fm+Qnzb/P22+oZSHWzxo3V8Tz1OauZS5aox4dc9zkgxKVlGCE8h7Nwzg3ueoN7fbajNF9L+T5biEv1/fOFeJYQB4BMIW62AUbKf+5jFgecZ3nULTPPblL+kwjbtnjMI5wRFSfEAUA470s8I722EHfXO9zr3TE4l+FpYbxQCqjxYvjw4Xjttdcwfvx4nDp1CuPGjUNimSSen5+Pf/7zn3juuefQtGlTPPTQQz7/XttsNmzcuBFff/01HnvsMUPzbdeunecaMW9PPfUU8vLy8Prrr6NFixbIycnB8eO+KXnz5s2YNGkSJkyYgMsuuww1ajivqCooKMC+ffuQkpKClJSKDx937tyJWbNm4YYbblC2khJR1WNuICJ/mBuIyB/mBqLwFlDjhcViwWeffYarrroK06ZNw//93//hkksuQSPXP0wHDx7Ezz//DKvVipSUFHz66afo0KGD32nNnDkTP//8s6H5pqSk4Oabby73ubtfZn8xN3eLaJcuXXyGW79+PXr37o3JkydjypQpns8vvPBC3H777WjSpAn++usvvP3226hduzZmSddHE1GVY24gIn+YG4jIH+YGovAW8CVQHTt2xK+//oonnngCH374oafLH7eYmBjcc889eO6559BQcdOha6+9Fo8//jjmzJkTeKkrUceOHTFnzhwcOXIEKSkpGDhwIKZOnYp69aQLJIjobMbcQET+MDcQkT/MDUShF9T9Wxo2bIi5c+di1qxZ2LBhg+cGNqmpqejSpQssBu6i/vHHH6N2benKGjX3TW9UevXqBd3PjdUq+nxRNej6kojMYW4gIn+YG4jIH+YGovBg6uazFosFPYVuXS6++GKfnkl0XUdWVhaOHTuGt956y8zsiYiIiIiIiOgcYLbnHFH//v19Gi8iIiJQt25d9OrVC23atKns2RMRERERERFRmDPceHHllVcGNQNN0/DNN98ENS4RERERERERkeHGi4quBdM0ze91Xu7PdV3H0aNHy92c5sSJE6hXrx7sdntgJT6bCH1DIzPT1OTzhbhDiANyv9GpQrxYiB8S4u3+/NPz2ma3A3v2IHXdOkRHRgIAbspS9yw9q08fZVzaACru8MqpqxBvFBcnDAHAq/9vv6QzlBIS5HkAgGuZoUYNwG4HioqMjUeVIhlAIpx9tgNATZSuj9K2K61VUvyAEJe2e8BZfhVp24lo3Fg9wLFjyvDvrmd3HtsM3/7au0v5s1EjdXz8eGX4U/f2VAGpP3cpNwKlfdJXRFrGUn5zZw4HgL0ALoRzGUrrH1WuYjh/B/f6bIOx/TUgb7v/EOK5wnZnJDeo98rAcCG+TYh3E+LufhwiADQCcAK+y++kMP4mIXckCeNL2+1qaf5CHCjdbwRryqhRyvirb74JAHCfNx0J5/Ks9NO1SSnG9XCvz9EozRO5wrjSb9dciKe0bKmMN9q5U5iCXMY0IS7tV5tI8d69AQC2mBh8CaBLz56ILi7dG89atUo5/ohmzdQz2LRJGZa+f23pz/58ee/cV7h3S9+VK9UTuOEGYQZ9AQA2XceX+fm47PPPEa1pyD19GrjpJrF8oRAhD+LkcDh8Hlar1dNP8bx585CZmQmr1YrMzEx88MEHaNWqFW688cYKp1dUVISYmJiQfAkiIiIiIiIiOnsF3Yg6efJk/PHHH/jjjz+Q4PXPb5MmTXD33Xfj0KFDeOmllwAAs2fP9hnGbrfj+++/5z0viIiIiIiIiEgUdOPFwoULMXDgQJ9GCW9vv/02SkqcJ7bNmjULkV6n2MbExKBZs2aYNWuW33GJiIiIiIiIiNyCbrw4duwYbDZbhfG//voLY8aMwYwZM/Dbb7+hVq1awc6KiIiIiIiIiM5hhu95UVaLFi2wZMkSnDhxwm/82LFj+Oijj3DhhRey4YKIiIiIiIiIghb0mRePPPIIhg8fjk6dOmHs2LHo3r076tWrh6NHj2LcuHHYvXs3jh49ivbt22Ps2LEVTufVV18NtghEREREREREdA4IuvHigQcewOHDhzFt2rRyjRMOhwNRUVGYMmUKvvnmG2zcuNF0QYmIiIiIiIjo3GSqy+ZJkyZh8ODBWLBgAX7//Xfk5OSgZs2a6NixIwYPHowWLVpg0qRJoSpreMrPBzQNcN28FPn5gNXqfJ2drRxV6g9Yikv9IRvpN17qzFbq810qo+SLtm09r3WLBVi0CF9feik01zKU5i/1+SxdN9VZiKf06ycMkCJMAUBcnDpeIvToXigsBXc8yrW55+Q4p+nVtzVVvTrt2yMpMhK22Fjn+w4dEF1UBABIycpSjlsoxI8L8zaV+F2kNTvC1Z96hYRG7X3Ceu3eKnTXsx2+Oe3LyZOV40vLoPa77yrjjYTxpfwq5WdAzr9JQtz/7bRL1XM9lwDYC6A5nMvFbN4mc8b27o2kqCjYoqPxJYBHe/dGtPseY9L+QBKlXvNfXbHC3PQNiLvoImX8ImGfOOWnn5TxiAqe3dYrx5brBcIeG9Iv9JDw/W/IzBSmAEDore9ZYRnNePNNZbx2mffui7+tQrGoclkAxKN0/xKP0vVV2h9I+6ykunXVAwj75PjUVGEOQE+h7nJSGF+q80vjn1y1CgDgsFiAkSPxx/ffI8JaulZ3FcZfP22aMi5t+92E+OY+fZTxZGF8QK43lN22yxLrh9u2OZ+jo4HBg4F//Quw2ZyPKmK6DtuiRQs8/fTTFcbT09Px+uuvIzEx0efz06dPY9SoUcjIyDBbBCIiIiIiIiI6iwV9w06j5s6dC6u1fFut1WrFBx98UNmzJyIiIiIiIqIwZ/rMi8LCQmzYsAGHDh1CkeuUZ8DZOKHrOhwOB/Ly8hDndRqg3W7Hl19+iXr16vmbJBERERERERGRh6nGi5kzZ2LSpEnIyckpF3M4Sq/4bdWqVbm4pmmYOnWqmdkTERERERER0Tkg6MaLTz75BKNGjUL79u0xadIkjBs3DjfffDMuvfRSfP/99/jqq6/Qs2dPfPfdd/j4449Ru3bpLUJiYmLQtGlTpKWlheRLEBEREREREdHZK+jGi+nTp6NevXpYu3Yt4uPjMW7cOFx00UWYOHEiJk6ciIULF2LIkCFYtGgR+vfvj4iISr+9BhERERERERGdhYJuvPj9998xcOBAxMfHez6z2+2e14MHD8bcuXPxr3/9C4MGDUJBQQH27duH4jLdM3bo0CHYIhARERERERHROSDoxgubzYa6Xn0CWywWZGdn+wzTsWNHvPPOO7jhhhvw1Vdf+Z2Od4PHWSklBYiIANw3LE1JKe0rOTJSOWqS0Ne3QxmVf9xiIQ7I3dFI05D6Q48X4t7j2wFsA9AKgHrJlZLKL/VKHd+jh3oAoT96lEi9PgPIzzc/DZWoKN/n/HznNKuwT2byo359Zz/Z0dGl792/SZmupcuKE/pTbyTlVSH3SP25AwCSk9XxKCEDtWmjDDf5809lPC0vDwBgA7Aczv7To71nHx3tZ6xSJcL6X6CMAsJWK44v5UZA7q89WYgnCfF4129oc+UxS82aiI6NRYmuA37uZUVV44VVqxALIMJiQcfhw/HCqlVw+Om1zZ9A9qn+pAhxqd4ByPvdWZs2KePSei/d6t29x9RczzUB6H7i0vgVkeo9UnyK8P2nDBggTAF4celSZVz6HROEeNn1xD08z6E+s067nt3bYT6M/ybSXj3i2DFlXFqvjdRUpTJI+00pLsl1PWtwbiMb4ZsbxGUkxKX8Kh1z1BbiRkj7fek7JmzbZmxGsbHO5z17gKIioAqP54POQ2lpaTh8+LDnfdOmTbFx40afYfbu3YuioiJkZ2dj3bp1sFgs+PrrrzF37ly0bNkSy5YtC77kRERERERERHROCPrMiy5duuDXX3/1vL/mmmvw+uuv44UXXsBNN92EH374AZ988gkiIiLw6quvonPnzoiIiEDTpk3Rr18/JCUl4YUXXsD1118fki9CRERERERERGenoM+8uP3221FUVIRM16UNjz/+OBo1aoSnnnoKHTp0wMiRI5GQkICoqCjUq+c8ya9WrVo45jotqX379j6NH0RERERERERE/gR95sWAAQMwwOu6vLp162LTpk2YPXs29uzZg6ZNm+Kee+7BzTffjO3bt6NZs2aee2A0a9YMs2bNQoMGDULyJYiIiIiIiIjo7BV048W+ffsQExODVK8bx9WqVQuPPfaYz3Bjxozx3Btj8uTJuOaaazB//nzExMRg7ty5wc6eiIiIiIiIiM4RQTdenHfeeRgyZAgyMjKUw919992e15dccgn27t2Lbdu2oUmTJkhJke6HTERERERERETnuqAbL2rVqoU6der4jY0dO9bwdF599dVgi0BERERERERE54CgGy969OiBdevW+Y2V7TK1IpqmyQOFu/r1gchIIMbVc3m9ekCxq7fk7Gz1uImJynBSXp4yLvU3HIr+uqU+36V+n6XxvVdQ97Rqen0ufQepL/OYli2FIQT5+ep4odSjMoAoYTMsMdJ7tkKca02IjnY+FxUBNpvzQWeOu19sh6P0vTs3xAlbb3KyOp4grfkCI+utNIy0Xkvxiy5Sj+561l25Nepvf0NUsVdP9EJ+jdq5UxmPEL6flF+lnas0vpFpSP25xwv7EDRu7Hx299feqFHpepmTY6CEVBlSAFgAuGtIdQHortcxwrhBV+oMjm+k3iBNo7LrDW7u5ReF0uVnZPrFQtzkHhnxQvzVpUvFaSQLcSk3SHF3ftIBFMBZ79IARIslo8qUA8CG0vU5B6XruYG9dqUysl1K25bZeRjNDRWRtk1pvy3VvKTtrjrk32Lh2CDG1fGGp556/LizPugwu/SNC3o/98ILL6Bbt2545pln8MQTTyDKqyK6atWqkBSOiIiIiIiIiCjoxouXX34Z7du3x9SpU/HOO++gY8eOqF+/frmzKTRNw3vvvWe6oERERERERER0bgq68eL999/3vD58+LCnR5Gy2HhBRERERERERGYE3Xjx119/hbIcRERERERERER+Bd140bRp0/9v7/6Do6jv/4E/L8ldcvkFYgIhxm9CEUEBwUE7giICxt/aVqcqUG0NAwPYCpQpWJUBRH7qp1pbLSgFlIJaqYz/OCjUgDNWbZXaKjIIBNrwQ7QJJCG5u9yP9/ePu73kkty99u59l+yF52Nm53L73vfue9+7+9q9d967m8xyEBERERERERF1Ke7Gi1OnTsHlcqGiogIZGcHnnu7duxd79+7tNO3o0aNx11136ZeSiIiIiIiIiM5bcTVe/O9//8PQoUMxduxYvPvuu+Hxe/bswbJlyzpNX1BQgCNHjqCoqEi/pERERERERER0Xoqr8eLVV19Fc3Mz1qxZ0ynNZrNh27Zt4e9nzpzBnDlzsGXLFsyfP1+/pOnK6w2++zbUSwU+X3CcGXl5MZMzMjNjpkvvI4bdxBu7hWWguTlmcmtTU8x06b3U7d9pn9FunLHjSuvoKCiIPYG0fkL5xTqU5g8ATmfs9BxhLX3CW+eN/MbrjLOzg+UyUzZKnezs4P5j7EPZ2ZFxIhYp/exZ7eKJsoTTh5TuFo5+ab83dFV/Zubv98dM1n3fupn3sUukMmRJ8e3CC2OnG+cYRyjS5uYGt5u0f1FK5YQGW7vvKvR3QMjbKqQnfK9wSDL2a91jR1oHY+816i8DbfUHyHUokZbvENJ18wNArpBeKKSbjK5kMS3t/rYDcLX7rhu1k3FsS3TPq7rxq6OOZ0jp2JPSpeNKSpeOazPHre6xLcZH45qr/affH/yt203i2g927tyJ4cOHY/To0V2m33fffRHfN2/ejHfeeef8brwgIiIiIiIiIi1xNbR98cUXGDt2rOnpr7jiCuzfvz/uQhERERERERERGeLqeVFfX4/+/ft3Gn/DDTd0OX3//v1RX1+fUMGIiIiIiIiIiIA4Gy9ycnLQ3MUzDiZMmIAJEyZ0Gt/S0oLs7OzES0dERERERERE5724bhspKyvDv//9b9PT/+tf/0JZWVnchSIiIiIiIiIiMsTVeDF+/Hh88MEHOHr0qDjtkSNH8MEHH+D6669PuHBERERERERERHE1XsyePRs+nw/33XdfzGdZ1NfXY8qUKQgEApg1a5Z2IYmIiIiIiIjo/BXXMy9GjRqFX/7yl/jNb36Dyy+/HLNmzcINN9yA0tJSAMDJkydRXV2Nl156CadPn8aCBQswatSolBSciIiIiIiIiM4PcTVeAMDTTz8Nu92OZ555BsuXL8fy5csj0pVSyMjIwKOPPooVK1YkraC9Uk5O7PTMzNjpdrtefjPcbq10qWuPQ0hvv4N6Q59OAMaaZ0l1INWxtH4SaflOpzwPqYy6+fPzg59ZWW3ffT6gtVVvuaSnvj54jDpCR0FdnfltohsbsoTQL6UDwX0olqam2Ol+f+x0aR2Nh0cbD4X+9lvA42lLl47tvLyYyTlS+STJiL8FBbHTdWOHUYfGtmxpCdah7rqTFjcAW2gwvqvQ3wEhr3TOldKl+acDI3rZ2n1X7dKl6w7dOpKip246AEhHvrQO0pmmfboNgPGo/hYhH6VWAYA8BPdnd+i7sZ8LZ2RtyYgNuvOQ9mvpuDCOfQXgLIAL0FZ/7dOl/NHoHttS+fOF9O4oAy68MPhpXHv169d23XDihFzAJIi78cJms2HVqlWoqqrC5s2b8fHHH+Obb74BAAwYMADjxo3DT3/6UwwZMiTphSUiIiIiIiKi80/cjReGIUOGsGcFEREREREREaVcXA/sJCIiIiIiIiLqbmy8ICIiIiIiIiJLY+MFEREREREREVkaGy+IiIiIiIiIyNLYeEFERERERERElpbw20bIpAEDALs9OBjfvd7g3z7hrcxSui4z83e7tRaRZax3tHRpBpmZ4T9VTvDtw1k5OchSobe25whvJG6XPyFC+cXlS+kAkCXUgjSPvn1jp5eUBD8zQm2VpaVAIBB8LzP1HLc7uH8a+7LHY36bSPuE36+X38x+K8UGKV06tqR1kCRjHWORYou0ft3BONdE09QU/Gxtbfvu8QTjA/UYPwAfAFvouw9AKEqI/3Hq6XQz00h7l+6FacfldzwSpSsf3fJL+aX1M1PHUhlaNfO3tPu7AMC50N8uIR+lVm5oCABwA3CibX+RtqluunTcmPnFohtfcoV06axu5A8AOAvggg7LLBTyO4R03TqW5i+tv5l5OKRrk4qK2OmDBgU/jfmUlwevNbxe4IsvTJRQH3teEBEREREREZGlsfGCiIiIiIiIiCyNjRdEREREREREZGlsvCAiIiIiIiIiS2PjBRERERERERFZGhsviIiIiIiIiMjS2HhBRERERERERJam+zptkuTnB9+FmxWq6rw8wBd6G/K5c9HzAW3TJZqelYTN6/fr5c8R3rqcl2c+f3Z28LN/f8DjMbd86X3GuuXLz9ebPwD07Rs7vaQkdnpRUczkFxcsAADYnE6UvvYaNqxdC+Vy8X3tPe3cOSAjA2htDX5vamrbrwsKYuf1elNbNjOkMkjHnpRfij3GseUIvdU8Oxuw2TqnR5OZGTtdl7T+TU3yPNxuvXSpDo38xnQtLcFxAelt9JRKmQhenBl7cxYAFfpb+o+TdNaX8ifjP1o9vfd0XH7H77p1oFvHUn4zV25SHbcI6cLVI4yrUxuAAgBNCO6DvG7oWdkActC2/XPQtr9J+4S0zaX8Ds38gHxsSMsQrrhRKKT3DX36ABwF8P8QebxJV+zSsdkqpOvWcYZ0bQjIv1uKi2OnX3JJ7PRhw4KfxjXUpZcGryHM/i5LAva8ICIiIiIiIiJLY+MFEREREREREVkaGy+IiIiIiIiIyNLYeEFERERERERElsbGCyIiIiIiIiKyNDZeEBEREREREZGlsfGCiIiIiIiIiCzNzOukSce5c4DdHhwAoLkZ8HqDf585EzuvkSeaHOGNxFnC5nW7Y6cD8vuCjXWJRloHaf7t32nsCL0BuawMaA29TVlaR4lUh/nCW6Wl/EVFchnKymImv7hgQcx0s++Ut7X7rsCDv8e53UBGu63n8bQdk9J+JR13Er9fLpvEeMd3oqRlSLFDols+afnSNjh7Vi8dAJqa5GlikfYjI/5mZwc/c3OD9RYIAHV1esumhGWFho4x24yAZrrP5HJ0SOcs3TIa+Y36U4isP2n5DiFdyq+bbkarkC7VkZS/JfRplNWFYL2aODNQCjlCQ6Ddd2MbSdtc2q8l0nFp5poyV0jvK6QXCumOvsIciosBAN7QOS9v8GDYPZ52BZBKICw/1efs9r+JopF+t0jrWFERO/2ii4KftlCEHTgQUApwueSyJQl7XhARERERERGRpbHxgoiIiIiIiIgsjY0XRERERERERGRpbLwgIiIiIiIiIktj4wURERERERERWVraNV7s27cPd911F/r164fc3FyMGDECzz//fDj9hhtugM1m6zTccsstpubf0NCAhQsXYsiQIXA6nSgvL8f06dPx3//+N1WrRERJwNhARB0xLhBRVxgbiNJTWr0t8b333sOdd96JK6+8EosXL0Z+fj6OHDmC48ePR0xXVlaGVatWRYwrLS0V5x8IBFBZWYmvvvoKc+bMwaWXXorDhw/jxRdfxLvvvosDBw6gwMxraoioWzE2EFFHjAtE1BXGBqL0lTaNF42NjXjwwQdx++23Y/v27cjIiN5ppE+fPvjJT34S9zI+/vhj/OMf/8Dvf/97PPzww+HxQ4cORVVVFXbv3o0f/ehHCZW/SxdcEDtdeldvlrD5pHS3iTd2S/OQgq+0DkVF5vNnZgY/L7sM8PuDf0vvRJbKLxHm//vly2Omm+naJE2je5B2fOd9AMF33kvv7E4XaRsbPJ6292QDwePROCabm2PnNY6FRBnHT6rmb2Yedrtefq83+Glsb5+vbVz79GikOpDyS/FT2oa674MH5PgrxUcjPfTOe+TkBPdJqW7SQNrGBQRjcwCdYzYA+EzkjUU63+imm5mmpy88peVLdZjqdDOk/UBKl67+jHRjW7YgWG4TV42Wl86x4QIABQD8AP4T+m6cKXWPOym/Q0jPFdIBIKtv39gTVFTETi8ujp1u9jeF8dtg/PjgtYNB+s3gE44sM7+rYjF7zo5FWocLL4ydLm2jjtvAqPOWltj5kihtbhvZtm0bTp8+jRUrViAjIwPNzc0IBKKfAnw+H86dOxfXMhobGwEAAwYMiBg/cOBAAIDT6Yyz1ESUaowNRNQR4wIRdYWxgSi9pU3jxe7du1FYWIgTJ05g6NChyM/PR2FhIWbPng13h5aur7/+Gnl5eSgoKEBJSQkWL14Mr/RfNABXXXUV8vLysHjxYrz//vs4ceIE9u7di4ULF+Lqq6/GjTfemKrVI6IEMTYQUUeMC0TUFcYGovTW0733TDt06BB8Ph9+8IMfYPr06Vi1ahX27NmD3/3udzh79ixee+01AMDgwYMxceJEjBw5Es3Nzdi+fTueeuopfP3113jjjTdiLqOoqAhvvPEGZsyYgcmTJ4fH33zzzdi+fTuyYnTF8Xg88Hg84e9Gq6vXbg8PxnfTdG8L0U0H5K7dMVqrTeWXytCu67g39Le3fXdyqWu5bvf3GN0JAcAmtJ7bYqaam8bMPMzkN8oa/lRKv4ubBaRtbMjOhjcjA95QN0Bv++6AUtdAYb8U6d6ykYx5JOnWFa/DEfFpmrR8qY6Vip0uzV/qfmqGtJ8Yt4MI6d6On73gthGrxwUgemyw5eQEHw7YIWYD+ueLVKcnax5JYdSb0xnXMrvrnJzKeUjpZm8PygjVYfizF1w3pHNs8OfkwG+zwR/aHv52sUE4I4l0bxmTm3QApXvO0v1NEUrv8vcEoP+bQfdW9Th+EyU8jXRtYzMXobxRPruDTSnpCswaBg8ejJqaGsyaNQt/+MMfwuNnzZqF9evX4+uvv8aQIUO6zDtz5ky8/PLL+Oijj3DNNdfEXM7f//53LFu2DNdeey2GDx+Ozz//HGvXrsVtt92GN998M2q+pUuXYtmyZZ3Gb9u2Dbm5Zu4EI+peLS0tmDp1KhoaGlBYWNjTxUkYYwNRcvWG2GD1uAAwNlD6YWxgbCDqSnfGhrRpvBgxYgT279+PvXv34vrrrw+P/+CDDzBhwgS88sorePDBB7vMe/DgQQwbNgzLly/HE088EXUZNTU1GDlyJF599VXcc8894fGvvPIKfvazn+Gdd97Brbfe2mXerlpJL774Yvzv1ltRGOp5seuBB1C5ZQvsJrqcAQDy8mKnd8cDOxsaYqe3W+cuSesgPTimXX5vZiZ2TZyIyupq2LvrgZ1CK/BLa9fGTE/Gw8107+0KP7DT6cSAjRtxuqoKyuWCSynMd7vT+iIESOPY0L8/CkM9L3Y99xwq582D3TgmpQcxsudFmNfhwK4FC1D5f/8He2urqTymlq/7wE7p4VVx3kPdJemByNJFrtHrJzsbux57DJUrV8Lu8aDR70fRl1+mdWywelwAoseGZ3Ny4Az1vGgfswE+sDMuTif6bdyI+qoqIFR/gNwrQboo1n3gphUe2ClcubU9sNPpxPCNG7G/qgoBlwtupbAiza8b0jk2fJGTg4JQz4svNm7EyKoqZIb2bem4k86oug/sNPMUj6w+fWJPUF4eO136zSClh86Z3sxM7LrxRlTu3t32ewLQv25J9QM7pZ4pgPy7p1+/2OnSNgo9oNMLYFd2Nio9HtgBNLa0oOihh7olNqTNbSOlpaXYv39/p4ff9O/fHwBw5syZqHkvvvhiAEB9fX3MZWzevBlutxt33HFHxPi77roLAPDhhx9GDTbZ2dnI7mKnsnu9sHf8brbxIhndinXnL5VV+rEgdeWWytBFoLD7/W3BRgokJrs/RSXcFqPaXRB1mW5iEdI0uq2LHfMrlys4aM7XKtI2Nng8sLfbP+1ud1vjRTIaF2KRbvcyM3/deST5jSf21lbYpcbUeJYvxT5pWdJFjBA7TNHtYtohPto9nuDQC24bsXpcAKLHBuV2R8RnI2YD+ueLVKcnax7JEN6729VfxPgouvucnIp56DbAdEwPuFzBQciXDtI5NmS63RGNEJkul+nGC923jUj5zdwUnyX9+JbOq9J5WfpN0SHd7vfDHs9vrTjnHzcpv5l/yEq/e6Rrtzj7NNjbDd0lbRovxowZg127doUfsGM4efIkAKA4xutzampqxGkA4PTp01BKwd/hws14OI8vjp3S6NDSGMrjRbBLTWM8jRfSdNIOJu2gZsqhe6DqNn60S/dmZgbrsLW17eJaOkh1L8KF+Us/PyzV80IptLS0wKUUFNr+s5Imna+iStvYEPr0BgLB/ToQgN3YFtKxq9sol4wfp7q9P5L0A9nr8wXrz+eL70e3NK2ULm2jJF8gpKQMoXX0+v3BOgw1DDeGxqdzbEi3uAC01bcRmzvGbED+j7q0xZL1LASdabrrwrOr+gPk8qX6Vafd0fNCioSmX5UaqkO3UgigrccGY0PPxAajv54/tF2alOq2V6VKP07NrFFWqp8VZfI3R/i6q7U1svFCOmdKy4+n92dXpOsqM9ddur1DpH+shHqVegG0+P1oNHpeGA3s3REbVJrYt2+fAqCmTp0aMX7KlCkqKytLnThxQjU0NCi32x2RHggE1H333acAqM8++yw8vrm5WR04cEB999134XHPPPOMAqA2bdoUMY/nnntOAVCvv/666fLW1tYqBK8jOHCw9FBbWxvHkWg9jA0cOKRmSOfYkG5xQSnGBg7pMzA2MDZw4NDV0B2xIW2eeQEA06dPx8aNG3HvvfdiwoQJ2LNnD9588038+te/xsqVK7Fnzx5MmTIFU6ZMwSWXXAKXy4UdO3bgww8/xMyZM7F+/frwvPbs2YOJEydiyZIlWLp0KQCgrq4OI0aMQH19PWbNmoXhw4dj37592LBhA4YNG4Z9+/bBYfKJ9oFAACdPnkRBQQFsNlv4frXa2tq0vU+wp7EO9XSsP6UUmpqaUFpaigzd/6L3MMaG8xfrT19vjQ3pFBcAxoZkY/3pY2xgbOiNWH/6ejQ2pLx5JIlaW1vV0qVLVXl5ubLb7eqSSy5Rzz77bDi9pqZG/fjHP1YVFRUqJydH5ebmqjFjxqh169apQCAQMa/q6moFQC1ZsiRi/PHjx1VVVZUaNGiQcjgcauDAgWrGjBkRLaqJaGhoUABUQ0OD1nzOZ6xDPb25/hgbzl+sP329tQ7TOS4o1Xu3S3dh/enrrXXI2HB+Y/3p68k6TKueF+mssbERffr0SesnNPc01qEe1p81cbvoYf3pYx1aE7eLHtafPtahNXG76GH96evJOkzfPl9EREREREREdF5g40U3yc7OxpIlS7p89RGZwzrUw/qzJm4XPaw/faxDa+J20cP608c6tCZuFz2sP309WYe8bYSIiIiIiIiILI09L4iIiIiIiIjI0th4QURERERERESWxsYLIiIiIiIiIrI0Nl4QERERERERkaWx8ULD7t27MWnSJPTp0wcFBQUYM2YM3njjjYhpKioqYLPZOg2zZs0ytYxAIIC1a9di0KBByMnJwRVXXIHXXnstFavTY2bMmAGbzYY77rgjYnxdXR2efvppXH/99SguLkbfvn1xzTXXdKrjWLqqe5vNhtWrVyd7NbrNqVOn8Oijj2LixIkoKCiAzWbDnj17Ok137NixqOtvs9kwY8YMcVm9sf5SjXEheRgb4sPYYG2MDcnD2BAfxgZrY2xIDsaF+KVjbMiKa2oK27RpE6ZPn47KykqsXLkSmZmZOHjwIGpraztNO3r0aCxYsCBi3KWXXmpqOY8//jhWr16NGTNm4Oqrr8bbb7+NqVOnwmaz4f7770/KuvSkTz/9FJs3b0ZOTk6ntI8++giPP/44brvtNjzxxBPIysrCX/7yF9x///346quvsGzZMlPLqKysxIMPPhgx7sorr0xK+XvCwYMHsWbNGgwZMgQjR47ERx991OV0xcXF2LJlS6fxO3fuxNatW3HTTTeZWl5vq79UYlxIHsaG+DE2WBdjQ/IwNsSPscG6GBuSg3EhMWkZGxTF7ejRo8rpdKpHHnlEnLa8vFzdfvvtCS3n+PHjym63q4cffjg8LhAIqPHjx6uysjLl8/kSmq9VBAIBNXbsWFVVVdVlPdXU1Khjx451yjNp0iSVnZ2tzp07Jy4DQET99QaNjY2qrq5OKaXUm2++qQCo6upq0/knT56sCgsLlcvlEqftjfWXKowLycPYkBjGBmtibEgexobEMDZYE2NDcjAuJC4dYwNvG0nAunXr4Pf78eSTTwIAzp07h+A2ia61tRXNzc1xLeftt9+G1+vFnDlzwuNsNhtmz56N48ePR20dSxdbtmzBl19+iRUrVnSZPmjQIJSXl0eMs9ls+OEPfwiPx4OamhrTy3K5XHC73VrltYqCggL069cvobynTp1CdXU17r777i5bp6PpTfWXKowLycPYkBjGBmtibEgexobEMDZYE2NDcjAuJC4dYwMbLxKwe/duDBs2DO+88w7KyspQUFCACy+8EIsXL0YgEOg0/fvvv4/c3Fzk5+ejoqICv/3tb00t55///Cfy8vJw2WWXRYz//ve/H05PV01NTVi0aBEee+wxlJSUxJX3m2++AQAUFRWZmn7z5s3Iy8uD0+nE5Zdfjm3btsVd3t7i9ddfRyAQwLRp00znYf2Zw7iQHIwNPYOxIXUYG5KDsaFnMDakDmODPsaFntNTsYHPvEjAoUOHkJmZiYceeggLFy7EqFGj8NZbb+Gpp56Cz+fDqlWrwtNeccUVuO666zB06FDU1dVh8+bNmDdvHk6ePIk1a9bEXM6pU6cwYMAA2Gy2iPEDBw4EAJw8eTL5K9dNnnzySTidTsyfPz+ufPX19diwYQPGjx8frodYxo0bh3vvvReDBg3CyZMn8cILL2DatGloaGjA7NmzEy1+2tq6dSsGDhyISZMmmZqe9Wce40JyMDb0DMaG1GFsSA7Ghp7B2JA6jA36GBd6To/FBu0bT85DGRkZCoBavXp1xPhbbrlFOZ1O1djYGDVvIBBQN998s8rKylK1tbUxlzNp0iR12WWXdRrv9/sVADV37tyEyt/TDh48qOx2u9q+fXt4nJl7+fx+v7rllluUw+FQn3/+eULL9ng8asSIEapv376qpaUloXlYSTz3px08eFABUPPnz094eb2t/pKJcUEfY0PyMDZYB2ODPsaG5GFssA7GBj2MC8mVLrGBt43E0Nraim+++SZi8Pv9cDqdAIApU6ZETD9lyhS4XK6Y3a9sNhvmz58Pn8/X5ato2nM6nfB4PJ3GG/cJGeWwqmj1N3fuXIwbNw733HNPXPP7xS9+gZ07d2LDhg0YNWpUQmVyOBz4+c9/jrNnz+Kzzz5LaB7dJVr9JWrr1q0AEFf3ro7Sqf5ShXFBH2ODHsYGa2Js0MfYoIexwZoYG/QwLujrTbGBt43E8Le//Q0TJ06MGHf06FGUlpbi0KFDGDBgQERa//79AQBnzpyJOd+LL74YQLDLUiwDBw5EdXU1lFIRXb1OnToFACgtLTW3Ij2kq/r74x//iJ07d+Ktt97CsWPHwuN9Ph9cLheOHTuGfv36obCwMCLfsmXL8OKLL2L16tV44IEHtMpltv57WrT9r6KiIqH5bdu2DUOHDsWYMWO0ypUu9ZcqjAv6GBv0MDZYE2ODPsYGPYwN1sTYoIdxQV9vig1svIhh1KhR2LVrV8S4kpISjBkzBocOHcKJEyfwve99L5xm3DNWXFwcc77GU22l6UaPHo0NGzbgwIEDuPzyy8PjP/nkk3C6lXVVf4cPHwYA3H333Z2mP3HiBAYNGoRnn30W8+bNC49/4YUXsHTpUsybNw+LFi3SLpfZ+u9p0fa/RHzyySc4fPhw+InWOtKl/lKFcUEfY4MexgZrYmzQx9igh7HBmhgb9DAu6OtVsSHhG1XOYzt27FAA1GOPPRYe5/f71XXXXaf69eun3G63Ukqpurq6Tu9Obm1tVddee61yOBzq1KlT4fFnz55VBw4cUGfPng2Pq62tjfpe5osuuigt38v8n//8R+3YsaPTUFxcrK666iq1Y8cOdfjw4fD0r7/+usrIyFDTpk1TgUAg6nybm5vVgQMH1HfffRce9+2333aarrGxUQ0ePFgVFRUpj8eT3JXrAWbvT3vkkUcUgIi6be98rb9kYlzQw9iQXIwN1sHYoIexIbkYG6yDsSFxjAvJly6xgY0XCQgEAmry5MnKZrOpmTNnqhdeeEFVVlYqAGr9+vXh6TZt2qQGDx6sFi1apNatW6dWrlypRowYoQColStXRsxz06ZNCoDatGlTxPhf/epXCoCaOXOmevnll9Xtt9+uAKitW7d2x6p2m64esPPJJ58oh8OhiouL1caNG9WWLVsihiNHjoSnra6uVgDUkiVLwuOWLFmiRo0apZ544gn10ksvqWXLlqny8nJls9nUn/70p+5atZRYvny5Wr58ubr//vsVAFVVVRUe15HP51MDBgxQ11xzTdT5nW/1lwqMC6nB2BAfxgbrYWxIDcaG+DA2WA9jQ/IxLsQv3WIDGy8S1NTUpObOnatKSkqUw+FQI0eO7FT5n376qbrzzjvVRRddpBwOh8rPz1fXXXed+vOf/9xpftGCjd/vVytXrlTl5eXK4XCo4cOH94oDpaOugo1RJ9GG9nXV1YHy3nvvqcrKSlVSUqLsdrvq27evuummm9Rf//rXblqr1IlVLx3t3LlTAVDPP/981Pmdb/WXKowLycfYEB/GBmtibEg+xob4MDZYE2NDcjEuxC/dYoMtVGgiIiIiIiIiIkviq1KJiIiIiIiIyNLYeEFERERERERElsbGCyIiIiIiIiKyNDZeEBEREREREZGlsfGCiIiIiIiIiCyNjRdEREREREREZGlsvCAiIiIiIiIiS2PjBRERERERERFZGhsviIiIiIiIiMjS2HhBRERERERERJbGxgsiIiIiIiIisjQ2XhARERERERGRpbHxgoiIiIiIiIgs7f8DYxd1nMcjLMkAAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ], + "source": [ + "# When zooming in at the North Atlantic region, we can establish that there is a disagreement in the explainable evidence/ feature importance between the different explanation methods. This may be confusing from a Climate AI practitioner's perspective.\n", + "\n", + "# Transform temperature maps similar to cartopy PlateCarre:\n", + "long_tf = (longitude + 180) % 360 - 180\n", + "\n", + "north_atl = [62, 83, 117, 138]\n", + "print('Plotting at latitude %s - %s, longitude %s - %s' % (latitude[north_atl[0]],latitude[north_atl[1]], long_tf[north_atl[2]], long_tf[north_atl[3]]))\n", + "\n", + "# Define North Atlantic region data.\n", + "x_n_a_samples = x_batch_samples[:,north_atl[0]:north_atl[1],north_atl[2]:north_atl[3],:]\n", + "lon_n_a = long_tf[north_atl[2]:north_atl[3]]\n", + "lat_n_a = latitude[north_atl[0]:north_atl[1]]\n", + "\n", + "explanations_n_a ={}\n", + "for method, values in explanations.items():\n", + " explanations_n_a[method] = values[:,north_atl[0]:north_atl[1],north_atl[2]:north_atl[3]]\n", + "\n", + "# Re-define plot kwargs.\n", + "plt_kwrgs['explanation'] = explanations_n_a\n", + "plt_kwrgs['figsize'] = (13,18)\n", + "plt_kwrgs['font'] = 14\n", + "plt_kwrgs['xtext'] = -0.4\n", + "plt_kwrgs['globe'] = False\n", + "\n", + "\n", + "# Plot.\n", + "plot_multiple_temperature_maps(samples, x_n_a_samples, year_samples, y_batch_samples, y_pred_samples, lat_n_a, lon_n_a , **plt_kwrgs)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Visual Comparison.** The first row in the plot again shows the NA region and visuaizes the temperature pattern. In the previous cell we discussed that the patter changes from a warming region to a cooling region, here we can see that as the pattern evolves from the warmer colored central pattern indicating higher relative temperature to the cooler colored central pattern indicating lower relative temperatures. The different area plots in the rows below already show discreptencies between the assigned evidence in the NA region. We can identify especially strong differences, when comparing GradCAM and Integrated Gradients. Although, theoretically similar XAI methods like SmoothGrad and Vanilla Gradients overlap, some methods assign overall higher relvances in this region, making it hard to deduce a conclusive assessment of the importance of the region for the network prediction." + ], + "metadata": { + "id": "fk8BWAm5Md6F", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qa9Iuu2GU52Z", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "\n", + "# 5) XAI Evaluation\n", + "\n", + "XAI research has developed metrics that assess different properties an explanation method should fulfill. These properties provide a categorisation of the XAI evaluation metrics and can serve to evaluate different explanation methods ([Hedström et al., 2023a](https://jmlr.org/papers/v24/22-0142.html); [Hedström et al., 2023](https://arxiv.org/abs/2302.07265)). Here, we analyze five different evaluation properties, as listed below.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yWXsiZ5freTG", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 5.1 Introduction to Metrics\n", + "\n", + "In the following section, we showcase how to use Quantus to evaluate the different explanation methods under various explanation qualities — and their underlying metrics. In the following, we describe each of the categories briefly. A more in-depth description of each category, including an account of the underlying metrics, is documented in the repository. The direction of the arrow indicates whether higher or lower values are considered better (exceptions within each category exist, so please carefully read the docstrings of each individual metric prior to usage and/or interpretation).\n", + "\n", + "* **Faithfulness** (↑) quantifies to what extent explanations follow the predictive behaviour of the model, asserting that more important features affect model decisions more strongly e.g., (Bach et al., 2015), (Rong, Leemann, et al., 2022) and (Dasgupta et al., 2022).\n", + "\n", + "* **Robustness** (↓) measures to what extent explanations are stable when subject to slight perturbations in the input, assuming that the model output approximately stayed the same e.g., (Alvarez-Melis et al., 2018), (Yeh et al., 2019) and (Agarwal, et. al., 2022).\n", + "\n", + "* **Randomisation** (↑, ↓) tests to what extent explanations deteriorate as the data labels or the model, e.g., its parameters are increasingly randomised (Adebayo et. al., 2018) and (Sixt et al., 2020).\n", + "\n", + "* **Localisation** (↑) tests if the explainable evidence is centred around a region of interest, which may be defined around an object by a bounding box, a segmentation mask or a cell within a grid e.g., (Zhang et al., 2018), (Arras et al., 2021) and (Arias et al., 2022).\n", + "\n", + "* **Complexity** (↓) captures to what extent explanations are concise, i.e., that few features are used to explain a model prediction e.g., (Chalasani et al., 2020) and (Bhatt et al., 2020).\n", + "\n", + "For more complete description of the different properties, please see the official [Github repository](https://github.com/understandable-machine-intelligence-lab/Quantus/).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6CeKQHfyrU7k", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d6461692-4b7f-460d-fbaa-0dd6201caf5d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Faithfulness\n", + "\t• Faithfulness Correlation\n", + "\t• Faithfulness Estimate\n", + "\t• Pixel-Flipping\n", + "\t• Region Segmentation\n", + "\t• Monotonicity-Arya\n", + "\t• Monotonicity-Nguyen\n", + "\t• Selectivity\n", + "\t• SensitivityN\n", + "\t• IROF\n", + "\t• ROAD\n", + "\t• Infidelity\n", + "\t• Sufficiency\n", + "Robustness\n", + "\t• Continuity Test\n", + "\t• Local Lipschitz Estimate\n", + "\t• Max-Sensitivity\n", + "\t• Avg-Sensitivity\n", + "\t• Consistency\n", + "\t• Relative Input Stability\n", + "\t• Relative Output Stability\n", + "\t• Relative Representation Stability\n", + "Localisation\n", + "\t• Pointing Game\n", + "\t• Top-K Intersection\n", + "\t• Relevance Mass Accuracy\n", + "\t• Relevance Rank Accuracy\n", + "\t• Attribution Localisation \n", + "\t• AUC\n", + "\t• Focus\n", + "Complexity\n", + "\t• Sparseness\n", + "\t• Complexity\n", + "\t• Effective Complexity\n", + "Randomisation\n", + "\t• Model Parameter Randomisation\n", + "\t• Random Logit\n", + "Axiomatic\n", + "\t• Completeness\n", + "\t• NonSensitivity\n", + "\t• InputInvariance\n" + ] + } + ], + "source": [ + "# Check what metrics are at our disposal.\n", + "for k, v in quantus.AVAILABLE_METRICS.items():\n", + " print(k)\n", + " for i in v:\n", + " print(f\"\\t• {i}\")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 5.1.1 Sparseness Metric Example\n", + "\n", + "As a first interaction with Quantus, we can select one single metric within one category of explanation quality: **Sparseness metric** under the **Complexity category**.\n", + "For general intuition, a highly complex explanation is one that uses all features in its explanation to explain some decision. Even though such an explanation may be faithful to the model output, if the number of features is too large it may be too difficult for the user to understand the explanations, rendering it useless.\n", + "\n", + "Sparseness (Chalasani et al., 2020) is calculated using the Gini Index applied to the vector of the absolute values of attributions. Sparseness values ranges between [0, 1] where higher values are desired as it suggests a lower complexity of the explanation heatmap.\n", + "\n", + "As discussed in [Bommer et. al., 2023](https://arxiv.org/abs/2303.00652) the **Sparseness metric** is favorable over the **Complexity metric**, since the latter uses Shannon entropy to measure the explanation complexity. Due to the strong internal variability in climate data the shannon entropy provides overall high and strongly similar scores, whereas the Gini Index measures differences in complexity more precisely even for overall high noise levels in the input data.\n", + "\n", + "\n", + " " + ], + "metadata": { + "id": "lutP7LsLcDE-" + } + }, + { + "cell_type": "code", + "source": [ + "# Let's try initialising one Complexity metric, called Sparseness.\n", + "quantus.Sparseness().get_params" + ], + "metadata": { + "id": "Se2Cr-iZxUmM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a23f6453-d0bf-484d-b604-3df3d9bd7946" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Warnings and information:\n", + " (1) The Sparseness metric is likely to be sensitive to the choice of normalising 'normalise' (and 'normalise_func') and if taking absolute values of attributions 'abs'. \n", + " (2) If attributions are normalised or their absolute values are taken it may destroy or skew information in the explanation and as a result, affect the overall evaluation outcome.\n", + " (3) Make sure to validate the choices for hyperparameters of the metric (by calling .get_params of the metric instance).\n", + " (4) For further information, see original publication: Chalasani, Prasad, et al. Concise explanations of neural networks using adversarial training.' International Conference on Machine Learning. PMLR, (2020).\n", + " (5) To disable these warnings set 'disable_warnings' = True when initialising the metric.\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'abs': True,\n", + " 'normalise': True,\n", + " 'return_aggregate': False,\n", + " 'aggregate_func': , *, where=)>,\n", + " 'normalise_func': numpy.ndarray>,\n", + " 'normalise_func_kwargs': {},\n", + " 'disable_warnings': False,\n", + " 'display_progressbar': False,\n", + " 'a_axes': None}" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Quantus allows us to customise the hyperparameters of the chosen metric. For example, we can choose if we want run the complexity analysis on normalised and/ or signed explanations (i.e., via `normalise` and `abs`). We can also decide if we would like to see aggregated results over all the test samples we provide (`return_aggregate`) and how to aggregate (`aggregate_func`) etc. Please use `.get_params` method on an initialised metric instance to get access all the available hyperparameters.\n", + "\n", + "```python\n", + "quantus.Metric().get_params\n", + "```\n", + "Picking the best hyperparameters/tuning them is an ongoing question in XAI research. In [`MetaQuantus`](https://github.com/annahedstroem/MetaQuantus) and the according publication by [Hedström et. al., 2023](https://arxiv.org/abs/2302.07265) we propose a framework which can be used to optimize hyperparameters for relieable XAI evaluation.\n", + "For this tutorial, we rely on the values proposed by literature and results from [Bommer et. al., 2023](https://arxiv.org/abs/2303.00652).\n", + "\n", + "As a starter, we evaluate `VanillaGradient` [(Mørch et al., 1995](https://ieeexplore.ieee.org/document/488997/);\n", + "[Baehrens et al., 2010](https://www.jmlr.org/papers/volume11/baehrens10a/baehrens10a.pdf))" + ], + "metadata": { + "id": "7JVGFMIzOQG2", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Warnings and information:\n", + " (1) The Sparseness metric is likely to be sensitive to the choice of normalising 'normalise' (and 'normalise_func') and if taking absolute values of attributions 'abs'. \n", + " (2) If attributions are normalised or their absolute values are taken it may destroy or skew information in the explanation and as a result, affect the overall evaluation outcome.\n", + " (3) Make sure to validate the choices for hyperparameters of the metric (by calling .get_params of the metric instance).\n", + " (4) For further information, see original publication: Chalasani, Prasad, et al. Concise explanations of neural networks using adversarial training.' International Conference on Machine Learning. PMLR, (2020).\n", + " (5) To disable these warnings set 'disable_warnings' = True when initialising the metric.\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.6679884922790988,\n", + " 0.4523420148821614,\n", + " 0.5321293354048773,\n", + " 0.40148710064506854]" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "# Alternative 1. Evaluate the Gradient explanations in a one-liner - by calling the intialised metric!\n", + "# For evaluation, a model, input, labels and explanations are needed, using the same samples as loaded before.\n", + "quantus.Sparseness()(model=model,\n", + " x_batch=x_batch_samples,\n", + " y_batch=y_batch_samples,\n", + " a_batch=explanations[\"VanillaGradients\"])" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "YdA76IaLSO0O", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5e7cfc0f-de64-4460-b59f-54d43571a47a" + } + }, + { + "cell_type": "markdown", + "source": [ + "In XAI evaluation, it is typical to analyse more than one input sample, to account for variations caused by the decisions of the network (e.g. if one class was learned more reliably by the network which results in a more \"faithful\" or less \"complex\" explanation). To achieve this, we can aggregate the scores over multiple test samples by using the functionality to return an aggregate score, rather than a single value per score, by setting `aggregate_func` to `np.mean`. This method allows for easier side-by-side comparison of different XAI methods." + ], + "metadata": { + "id": "ptxSt5ClewJT" + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.5134867358028015]" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ], + "source": [ + "# Change some hyperparameters, get an aggregate score over several test samples.\n", + "quantus.Sparseness(return_aggregate=True,\n", + " disable_warnings=True)(model=model,\n", + " x_batch=x_batch_samples,\n", + " y_batch=y_batch_samples,\n", + " a_batch=explanations[\"VanillaGradients\"])" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "jfy4VwPSSO0O", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a281e736-a852-4386-b28f-b45f75cd1b54" + } + }, + { + "cell_type": "markdown", + "source": [ + "In order to compare the performance of other explanation methods, we are interested in how sparse e.g., `OcclusionSensitivity` ([Zeiler & Fergus, 2014](https://arxiv.org/abs/1311.2901)) XAI method." + ], + "metadata": { + "id": "2kqPijFMW634" + } + }, + { + "cell_type": "code", + "source": [ + "# Change the explanation method to evaluate Sparseness on OcclusionSensitivity.\n", + "quantus.Sparseness(return_aggregate=True,\n", + " disable_warnings=True)(model=model,\n", + " x_batch=x_batch_samples,\n", + " y_batch=y_batch_samples,\n", + " a_batch=explanations[\"OcclusionSensitivity\"])" + ], + "metadata": { + "id": "TENlXhynWRI-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1f5d546e-abb9-410a-ec89-f2cc31a9c257" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.16772695524775844]" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "From this analysis, since `VanillaGradients` has higher Sparseness scores, it is the preferred method from a complexity evaluation perspective." + ], + "metadata": { + "id": "VvXsrj0COZHb", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### 5.1.2 Quantify Several XAI Methods\n", + "\n", + "We use an iterative approach to calculate sparseness scores for different explanation methods, to compare how complex they are. The `quantus.Sparseness` class is called for each method, and the resulting scores are printed with their mean and standard deviation." + ], + "metadata": { + "id": "4kkAAIhUh6Io" + } + }, + { + "cell_type": "code", + "source": [ + "# Score all methods iteratively.\n", + "print(\"Sparseness scores\")\n", + "for method, attr in explanations.items():\n", + " try:\n", + " scores = quantus.Sparseness(return_aggregate=False,\n", + " disable_warnings=True)(model=model,\n", + " x_batch=x_batch_samples,\n", + " y_batch=y_batch_samples,\n", + " a_batch=attr,\n", + " )\n", + " print(f\"\\t{method} - {np.mean(scores):.2f} ({np.std(scores):.2f})\")\n", + " except:\n", + " pass\n" + ], + "metadata": { + "id": "TW7j6rSzxXNW", + "pycharm": { + "name": "#%%\n" + }, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cb20793f-7c43-4a8f-c005-d044819b2adf" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Sparseness scores\n", + "\tVanillaGradients - 0.51 (0.10)\n", + "\tIntegratedGradients - 0.41 (0.02)\n", + "\tSmoothGrad - 0.52 (0.12)\n", + "\tGradientsInput - 0.49 (0.11)\n", + "\tOcclusionSensitivity - 0.17 (0.04)\n", + "\tGradCAM - 0.19 (0.06)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "To structure the analysis a bit futher, you can leverage the built-in functionality of `quantus.evaluate()`.\n" + ], + "metadata": { + "id": "xKBOC9zgfYXg" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 5.2 Large-Scale Evaluation with Quantus\n", + "\n", + "In the following, we use Quantus to quantiatively assess the different explanation methods on various evaluation criteria.\n", + "\n", + "Before proceeding, we briefly discuss the different evaluation criteria (properties an explanation can fulfill) in the **climate context**. By that we want to provide a climate-based intuition of the explanation properties that underlie the different evaluation criteria. Based on our discussion, the user can choose which criteria are important for an explanation applied to their research problem (which needless to say, might differ to that from our example).\n", + "\n", + "* **Faithfulness** (↑) Since in climate research explanation methods are often used to uncover new insights or validate networks with respect to exsisting physical knowledge (e.g. is the prediction skillful because of the valid learned physical processes), features marked as highly important should also strongly influence the network prediction.\n", + "\n", + "* **Robustness** (↓) Climate data often contains high internal variability, (i.e have a lower signal to noise ratio). Thus an explanation which is less affected by data noise can be benifical.\n", + "\n", + "* **Randomization** (↓) In the climate context, physically-motivated network parameters might be necessary (such as parameters which are bounded during training to follow mass conservation). Thus, the explanation of such a network should be consistently change with the network, to provide relieable insight and validate the model.\n", + "\n", + "* **Localization** (↑) In climate science, localization metrics can inform the user wether a region of interest (containing established climate drivers) was learned by the network (see also Bommer et al. [2023])\n", + "\n", + "* **Complexity** (↑) As climate data is often complex containing multiple sources of uncertainty as well as multiple climate and weather phenomena (such as MJO and ENSO) a concise explanations might include more focused information about which areas strongly contributed to the prediction. \n", + "\n", + "\n", + "We choose the metrics in each category according to the findings and discussions detailed in [Bommer et. al., 2023](https://arxiv.org/abs/2303.00652), which presents a baseline test to identify informative metrics functions for climate data.\n", + "\n" + ], + "metadata": { + "collapsed": false, + "id": "Au55hcUIXZjO", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "f0rf9Pfi-UgK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 5.2.1 Initialize Metrics\n", + "\n", + "This cell initializes a dictionary metrics with Quantus evaluation metrics for measuring \"Robustness\", \"Faithfulness\", \"Localisation\", \"Complexity\", and \"Randomisation\" of an XAI methods. Each metric is initialized with different parameters that affect the way it is computed, such as the number of runs, perturbation functions, and aggregation functions. These metrics are used to evaluate the performance of different XAI methods and compare them side by side.\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "pVD1T4dZZRjL" + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Initialise the Quantus evaluation metrics.\n", + "metrics = {\n", + " \"Robustness\": quantus.AvgSensitivity(\n", + " nr_samples=2,\n", + " lower_bound=0.2,\n", + " norm_numerator=quantus.norm_func.fro_norm,\n", + " norm_denominator=quantus.norm_func.fro_norm,\n", + " perturb_func=quantus.perturb_func.uniform_noise,\n", + " similarity_func=quantus.similarity_func.difference,\n", + " abs=True,\n", + " normalise=False,\n", + " aggregate_func=np.mean,\n", + " return_aggregate=True,\n", + " disable_warnings=True,\n", + " ),\n", + " \"Faithfulness\": quantus.FaithfulnessCorrelation(\n", + " nr_runs=10,\n", + " subset_size=224,\n", + " perturb_baseline=\"black\",\n", + " perturb_func=quantus.baseline_replacement_by_indices,\n", + " similarity_func=quantus.similarity_func.correlation_pearson,\n", + " abs=True,\n", + " normalise=False,\n", + " aggregate_func=np.mean,\n", + " return_aggregate=True,\n", + " disable_warnings=True,\n", + " ),\n", + " \"Localisation\": quantus.RelevanceRankAccuracy(\n", + " abs=True,\n", + " normalise=False,\n", + " aggregate_func=np.mean,\n", + " return_aggregate=True,\n", + " disable_warnings=True,\n", + " ),\n", + " \"Complexity\": quantus.Sparseness(\n", + " abs=True,\n", + " normalise=False,\n", + " aggregate_func=np.mean,\n", + " return_aggregate=True,\n", + " disable_warnings=True,\n", + " ),\n", + " \"Randomisation\": quantus.ModelParameterRandomisation(\n", + " layer_order=\"independent\",\n", + " similarity_func=quantus.ssim,\n", + " return_sample_correlation=True,\n", + " abs=True,\n", + " normalise=False,\n", + " aggregate_func=np.mean,\n", + " return_aggregate=True,\n", + " disable_warnings=True,\n", + " ),\n", + "}" + ], + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "id": "BODJXgUJXZjO" + } + }, + { + "cell_type": "markdown", + "source": [ + "### 5.2.2 Run Benchmarking\n", + "\n", + "In the following cell, we run a full quantification analysis for all the XAI methods in the `xai_methods` dictionary, for each metric in the `metrics` dictionary. We evaluate each XAI method with each metric and store the results in the `results` dictionary. We then use `metric_func` to get the scores, passing the model, `x_batch`, `y_batch`, `s_batch`, and `explain_func_kwargs` arguments. We set `a_batch` to `None` and `explain_func` to `quantus.explain`." + ], + "metadata": { + "id": "PP1vk3hPXwwE" + } + }, + { + "cell_type": "code", + "source": [ + "# Load evaluation indicies.\n", + "idx = np.load('index.npz', allow_pickle=True)['idx']\n", + "nr_test_samples = 30\n", + "\n", + "# Run full quantification analysis!\n", + "results = {method : {} for method in xai_methods}\n", + "\n", + "for method, kwargs in xai_methods.items():\n", + " for metric, metric_func in metrics.items():\n", + "\n", + " print(f\"Evaluating the {metric} of {method} method...\")\n", + "\n", + " # Get scores and append results.\n", + " scores = metric_func(\n", + " model=model,\n", + " x_batch=x_batch[idx[:nr_test_samples]],\n", + " y_batch=y_batch[idx[:nr_test_samples]],\n", + " a_batch=None,\n", + " s_batch=s_batch[idx[:nr_test_samples]],\n", + " explain_func=quantus.explain,\n", + " explain_func_kwargs={\n", + " **{\"method\": method,},\n", + " **kwargs\n", + " },\n", + " )\n", + " results[method][metric] = scores" + ], + "metadata": { + "id": "zeiJNd5mXu0U", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c42bf11c-c1af-441c-c710-6e7977f8824a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Evaluating the Robustness of VanillaGradients method...\n", + "Evaluating the Faithfulness of VanillaGradients method...\n", + "Evaluating the Localisation of VanillaGradients method...\n", + "Evaluating the Complexity of VanillaGradients method...\n", + "Evaluating the Randomisation of VanillaGradients method...\n", + "Evaluating the Robustness of IntegratedGradients method...\n", + "Evaluating the Faithfulness of IntegratedGradients method...\n", + "Evaluating the Localisation of IntegratedGradients method...\n", + "Evaluating the Complexity of IntegratedGradients method...\n", + "Evaluating the Randomisation of IntegratedGradients method...\n", + "Evaluating the Robustness of SmoothGrad method...\n", + "Evaluating the Faithfulness of SmoothGrad method...\n", + "Evaluating the Localisation of SmoothGrad method...\n", + "Evaluating the Complexity of SmoothGrad method...\n", + "Evaluating the Randomisation of SmoothGrad method...\n", + "Evaluating the Robustness of GradientsInput method...\n", + "Evaluating the Faithfulness of GradientsInput method...\n", + "Evaluating the Localisation of GradientsInput method...\n", + "Evaluating the Complexity of GradientsInput method...\n", + "Evaluating the Randomisation of GradientsInput method...\n", + "Evaluating the Robustness of OcclusionSensitivity method...\n", + "12/12 [==============================] - 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2s 131ms/step\n", + "12/12 [==============================] - 3s 239ms/step\n", + "12/12 [==============================] - 2s 138ms/step\n", + "12/12 [==============================] - 2s 130ms/step\n", + "12/12 [==============================] - 2s 133ms/step\n", + "12/12 [==============================] - 2s 131ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 3s 227ms/step\n", + "12/12 [==============================] - 3s 232ms/step\n", + "12/12 [==============================] - 2s 131ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 2s 133ms/step\n", + "12/12 [==============================] - 2s 183ms/step\n", + "12/12 [==============================] - 3s 235ms/step\n", + "12/12 [==============================] - 2s 133ms/step\n", + "12/12 [==============================] - 2s 131ms/step\n", + "12/12 [==============================] - 2s 131ms/step\n", + "12/12 [==============================] - 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3s 218ms/step\n", + "12/12 [==============================] - 2s 131ms/step\n", + "12/12 [==============================] - 2s 131ms/step\n", + "12/12 [==============================] - 2s 133ms/step\n", + "12/12 [==============================] - 2s 161ms/step\n", + "12/12 [==============================] - 3s 236ms/step\n", + "12/12 [==============================] - 2s 134ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 2s 131ms/step\n", + "12/12 [==============================] - 2s 131ms/step\n", + "12/12 [==============================] - 3s 240ms/step\n", + "12/12 [==============================] - 3s 235ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 2s 131ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 2s 133ms/step\n", + "12/12 [==============================] - 2s 158ms/step\n", + "12/12 [==============================] - 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3s 244ms/step\n", + "12/12 [==============================] - 2s 153ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 2s 133ms/step\n", + "12/12 [==============================] - 2s 176ms/step\n", + "12/12 [==============================] - 3s 238ms/step\n", + "12/12 [==============================] - 2s 139ms/step\n", + "12/12 [==============================] - 2s 133ms/step\n", + "12/12 [==============================] - 2s 134ms/step\n", + "12/12 [==============================] - 2s 131ms/step\n", + "12/12 [==============================] - 2s 184ms/step\n", + "12/12 [==============================] - 3s 238ms/step\n", + "12/12 [==============================] - 2s 135ms/step\n", + "12/12 [==============================] - 2s 134ms/step\n", + "12/12 [==============================] - 2s 136ms/step\n", + "12/12 [==============================] - 2s 134ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 3s 235ms/step\n", + "12/12 [==============================] - 3s 224ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 2s 133ms/step\n", + "12/12 [==============================] - 2s 133ms/step\n", + "12/12 [==============================] - 2s 185ms/step\n", + "12/12 [==============================] - 3s 238ms/step\n", + "12/12 [==============================] - 2s 135ms/step\n", + "12/12 [==============================] - 2s 130ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 2s 132ms/step\n", + "12/12 [==============================] - 2s 187ms/step\n", + "12/12 [==============================] - 3s 237ms/step\n", + "12/12 [==============================] - 2s 163ms/step\n", + "Evaluating the Robustness of GradCAM method...\n", + "Evaluating the Faithfulness of GradCAM method...\n", + "Evaluating the Localisation of GradCAM method...\n", + "Evaluating the Complexity of GradCAM method...\n", + "Evaluating the Randomisation of GradCAM method...\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Post-processing.** We perform postprocessing on the scores to determine how the different explanation methods rank across criteria. We create an empty dictionary called `results_agg` to store the results, and iterate over each method in the `xai_methods` dictionary. The resulting DataFrame, `df`, shows the mean score for each metric across all methods, allowing for easy comparison of the different XAI methods. Here, we use the absolute values of the results, so that the ranking is based on the magnitude of the scores rather than their sign." + ], + "metadata": { + "id": "aGMolejZYRkg" + } + }, + { + "cell_type": "code", + "source": [ + "# Postprocessing of scores: to get how the different explanation methods rank across criteria.\n", + "results_agg = {}\n", + "for method in xai_methods:\n", + " results_agg[method] = {}\n", + " for metric, metric_func in metrics.items():\n", + " results_agg[method][metric] = np.mean(results[method][metric])\n", + "\n", + "df = pd.DataFrame.from_dict(results_agg)\n", + "df = df.T.abs()\n", + "df" + ], + "metadata": { + "id": "OvixV56V2P8o", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 237 + }, + "outputId": "fc45fb2d-28e6-4622-c43e-7e095513d838" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Robustness Faithfulness Localisation Complexity \\\n", + "VanillaGradients 0.412645 0.048603 0.034769 0.678957 \n", + "IntegratedGradients 0.449772 0.035522 0.029630 0.380961 \n", + "SmoothGrad 0.383887 0.012234 0.040590 0.692202 \n", + "GradientsInput 0.381192 0.056342 0.038549 0.663006 \n", + "OcclusionSensitivity 0.499481 0.002010 0.026909 0.164859 \n", + "GradCAM 0.504280 0.010495 0.014815 0.150166 \n", + "\n", + " Randomisation \n", + "VanillaGradients 0.255716 \n", + "IntegratedGradients 0.053904 \n", + "SmoothGrad 0.082834 \n", + "GradientsInput 0.295150 \n", + "OcclusionSensitivity 0.570412 \n", + "GradCAM 0.999053 " + ], + "text/html": [ + "\n", + "
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RobustnessFaithfulnessLocalisationComplexityRandomisation
VanillaGradients0.4126450.0486030.0347690.6789570.255716
IntegratedGradients0.4497720.0355220.0296300.3809610.053904
SmoothGrad0.3838870.0122340.0405900.6922020.082834
GradientsInput0.3811920.0563420.0385490.6630060.295150
OcclusionSensitivity0.4994810.0020100.0269090.1648590.570412
GradCAM0.5042800.0104950.0148150.1501660.999053
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ComplexityFaithfulnessLocalisationRobustnessRandomisation
VanillaGradients5.05.04.04.04.0
IntegratedGradients3.04.03.03.06.0
SmoothGrad6.03.06.05.05.0
GradientsInput4.06.05.06.03.0
OcclusionSensitivity2.01.02.02.02.0
GradCAM1.02.01.01.01.0
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For example GradientsInput is less prone to small input noise and establishes highly faithful evidence, whereas SmoothGrad provides the highly concise explanations (high complexity rank), which strongly capture the network parameters (high randomization rank)." + ], + "metadata": { + "id": "V4CxkySPXRa0" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZhkmytKNU_Z2", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "\n", + "# 6) Results & Discussion\n", + "\n", + "To provide a visual comparison of explanation performance across the different evaluation crtieria we show how to plot the results of the full evaluation in a spyder graph and discuss how to interpret the plot to choose an appropriate XAI method for the research task at hand.\n" + ] + }, + { + "cell_type": "code", + "source": [ + "%%capture\n", + "# @title Plotting functionality\n", + "\n", + "# Plotting specifics.\n", + "from matplotlib.patches import Circle, RegularPolygon\n", + "from matplotlib.path import Path\n", + "from matplotlib.projections.polar import PolarAxes\n", + "from matplotlib.projections import register_projection\n", + "from matplotlib.spines import Spine\n", + "from matplotlib.transforms import Affine2D\n", + "\n", + "# Plotting configs.\n", + "sns.set(font_scale=1.5)\n", + "plt.style.use('seaborn-white')\n", + "plt.rcParams['ytick.labelleft'] = True\n", + "plt.rcParams['xtick.labelbottom'] = True\n", + "\n", + "include_titles = True\n", + "include_legend = True\n", + "\n", + "# Source code: https://matplotlib.org/stable/gallery/specialty_plots/radar_chart.html.\n", + "\n", + "def spyder_plot(num_vars, frame='circle'):\n", + " \"\"\"Create a radar chart with `num_vars` axes.\n", + "\n", + " This function creates a RadarAxes projection and registers it.\n", + "\n", + " Parameters\n", + " ----------\n", + " num_vars : int\n", + " Number of variables for radar chart.\n", + " frame : {'circle' | 'polygon'}\n", + " Shape of frame surrounding axes.\n", + " \"\"\"\n", + " # calculate evenly-spaced axis angles\n", + " theta = np.linspace(0, 2*np.pi, num_vars, endpoint=False)\n", + "\n", + " class RadarAxes(PolarAxes):\n", + "\n", + " name = 'radar'\n", + "\n", + " def __init__(self, *args, **kwargs):\n", + " super().__init__(*args, **kwargs)\n", + " # rotate plot such that the first axis is at the top\n", + " self.set_theta_zero_location('N')\n", + "\n", + " def fill(self, *args, closed=True, **kwargs):\n", + " \"\"\"Override fill so that line is closed by default.\"\"\"\n", + " return super().fill(closed=closed, *args, **kwargs)\n", + "\n", + " def plot(self, *args, **kwargs):\n", + " \"\"\"Override plot so that line is closed by default.\"\"\"\n", + " lines = super().plot(*args, **kwargs)\n", + " for line in lines:\n", + " self._close_line(line)\n", + "\n", + " def _close_line(self, line):\n", + " x, y = line.get_data()\n", + " # FIXME: markers at x[0], y[0] get doubled-up\n", + " if x[0] != x[-1]:\n", + " x = np.concatenate((x, [x[0]]))\n", + " y = np.concatenate((y, [y[0]]))\n", + " line.set_data(x, y)\n", + "\n", + " def set_varlabels(self, labels, angles=None):\n", + " self.set_thetagrids(angles=np.degrees(theta), labels=labels)\n", + "\n", + " def _gen_axes_patch(self):\n", + " # The Axes patch must be centered at (0.5, 0.5) and of radius 0.5\n", + " # in axes coordinates.\n", + " if frame == 'circle':\n", + " return Circle((0.5, 0.5), 0.5)\n", + " elif frame == 'polygon':\n", + " return RegularPolygon((0.5, 0.5), num_vars,\n", + " radius=.5, edgecolor=\"k\")\n", + " else:\n", + " raise ValueError(\"unknown value for 'frame': %s\" % frame)\n", + "\n", + " def draw(self, renderer):\n", + " \"\"\" Draw. If frame is polygon, make gridlines polygon-shaped.\"\"\"\n", + " if frame == 'polygon':\n", + " gridlines = self.yaxis.get_gridlines()\n", + " for gl in gridlines:\n", + " gl.get_path()._interpolation_steps = num_vars\n", + " super().draw(renderer)\n", + "\n", + "\n", + " def _gen_axes_spines(self):\n", + " if frame == 'circle':\n", + " return super()._gen_axes_spines()\n", + " elif frame == 'polygon':\n", + " # spine_type must be 'left'/'right'/'top'/'bottom'/'circle'.\n", + " spine = Spine(axes=self,\n", + " spine_type='circle',\n", + " path=Path.unit_regular_polygon(num_vars))\n", + " # unit_regular_polygon gives a polygon of radius 1 centered at\n", + " # (0, 0) but we want a polygon of radius 0.5 centered at (0.5,\n", + " # 0.5) in axes coordinates.\n", + " spine.set_transform(Affine2D().scale(.5).translate(.5, .5)\n", + " + self.transAxes)\n", + "\n", + " return {'polar': spine}\n", + " else:\n", + " raise ValueError(\"unknown value for 'frame': %s\" % frame)\n", + "\n", + " register_projection(RadarAxes)\n", + " return theta" + ], + "metadata": { + "id": "kESmsPTi2To4", + "pycharm": { + "name": "#%%\n" + }, + "cellView": "form" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ldWUU4QLF0ao", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 6.1 XAI Comparison and Analysis\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Spyder Plot.** In the plot the normalized ranks in all properties across the six explanation methods are visualized, according to the table above. The best rank corresponds to the furthest distance from the center of the graph. Thus, ideally an explanation method with outermost lines in all categories corresponds to the best performance across all properties.\n", + "\n", + "The spyder plot can provide a visual aid to decide, which explanation method is suitable for the network and task at hand." + ], + "metadata": { + "id": "2qOMMYFXVZZV", + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "source": [ + "colours_order = [\"red\", \"darkorange\", \"royalblue\", \"darkgreen\", \"slateblue\", \"purple\"]" + ], + "metadata": { + "id": "9wcK53_eqS8h", + "pycharm": { + "name": "#%%\n" + } + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Make spyder graph!\n", + "data = [df_normalised_rank.columns.values, (df_normalised_rank.to_numpy())]\n", + "theta = spyder_plot(len(data[0]), frame='polygon')\n", + "spoke_labels = data.pop(0)\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 9), subplot_kw=dict(projection='radar'))\n", + "fig.subplots_adjust(top=0.85, bottom=0.05)\n", + "for i, (d, method) in enumerate(zip(data[0], xai_methods)):\n", + " line = ax.plot(theta, d, label=method, color=colours_order[i], linewidth=5.0)\n", + " ax.fill(theta, d, alpha=0.15)\n", + "\n", + "# Set lables.\n", + "if include_titles:\n", + " ax.set_varlabels(labels=['Complexity', 'Faithfulness \\n', '\\nLocalisation', '\\nRobustness', ' Randomisation\\n'])\n", + "else:\n", + " ax.set_varlabels(labels=[])\n", + "\n", + "ax.set_rgrids(np.arange(0, df_normalised_rank.values.max() + 0.5), labels=[])\n", + "\n", + "# Set a title.\n", + "ax.set_title(\"Quantus: Summary of Explainer Quantification\", position=(0.5, 1.1), ha='center', fontsize=20)\n", + "\n", + "# Put a legend to the right of the current axis.\n", + "if include_legend:\n", + " ax.legend(loc='upper left', bbox_to_anchor=(1, 0.5))\n", + "\n", + "plt.tight_layout()" + ], + "metadata": { + "id": "oRAMW3YI2VYJ", + "pycharm": { + "name": "#%%\n" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 549 + }, + "outputId": "a95d8d75-2b3f-4cd3-9930-7b494ce0beef" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SiXHydPpGFZt", + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 6.2 Selecting an XAI Method\n", + "\n", + "Given our intuition above, for our tutorial network task, we aim for an explanation that is *robust* towards internal variability in our data, displays significant features (*complexity*) without sacrificing *faithful* evidence, and captures the network parameter behavior (*randomization*).\n", + "\n", + "Although the spyder plot, for completeness, still shows all 5 criteria, we consider only the area cover in **faithfulness, robustness, complexity and randomization**. That means we neglect the spread on the localisation axis.\n", + "\n", + "As we can derive from the spyder plot as well as the Table of the ranks across all explanation methods oftentimes in practice explanation methods only rank highly in some criteria. Although the ideal case would be highest ranks across all our important criteria (full coverage in the spyder plot except for localisation), in this case both SmoothGrad and GradientsInput achieve higher ranks in most criteria we want to be fulfilled for our network task. However, **GradientsInput** presents more faithful and more robust explanations, which is important to analyze if a region of high importance is stable and faithfully displayed in our explanation. Thus, we choose this explanation for our task.\n", + "\n", + "**Note** that GradientsInput could be improved by combining it with SmoothGrad, which currently uses VanillaGradients as the baseline explanation (see Section 4)." + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 6.3. Explanation Interpretation\n", + "\n", + "Based on our evaluation we can now interpret the explanation GradientsInput provides for the NA region. Therefore, below, we re-plot the explanations only focussing of the NA region. Similar to section 4.2.2 we show the years $1940,1990,2040,2074$ with the first row depicting the normalized T2M anomalies. However, we only show the determined best-performing XAI method in the row below." + ], + "metadata": { + "id": "tkq7UsgrboTb" + } + }, + { + "cell_type": "code", + "source": [ + "# Show NA region only for best-performing explanation.\n", + "best_xai_n_a = {\"GradientsInput\": explanations_n_a[\"GradientsInput\"] }\n", + "\n", + "# Define plot kwargs.\n", + "plt_kwrgs['explanation'] = best_xai_n_a# which ever is best\n", + "plt_kwrgs['figsize'] = (20,10)\n", + "plt_kwrgs['keys'] = list(best_xai_n_a.keys())\n", + "plt_kwrgs['nrows'] = len(best_xai_n_a)+1\n", + "\n", + "# Plot.\n", + "plot_multiple_temperature_maps(samples, x_n_a_samples, year_samples, y_batch_samples, y_pred_samples, lat_n_a, lon_n_a , **plt_kwrgs)" + ], + "metadata": { + "id": "xEsgCwayYw-H", + "pycharm": { + "name": "#%%\n" + }, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 622 + }, + "outputId": "721d5578-a2c2-4dc6-bb42-77f432a927be" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Explanation Interpretation** We can see here that for years prior to the 20th century ($1940,1990$) the explanations values are higher in surrounding areas than for the distinct cooling/warming patch. In $1940$ we also see no strong explanation value pattern in the area (few clusters of non-white pixels). Starting in $1990$, in the following years, however we can see that the explanations display more and more localized explanation value patterns with higher importance values in the cooling patch/warming hole (blue area in plot above), suggesting that the NA region becomes more and more important towards the mid and end of the 20th century. These findings align with prior research ([Labe and Barnes, 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464)), that adresses the NA warming hole as a feauture caused by and related to climate change. Thus, the explanation validates that the network has learned data features, which can be associated with physical findings related to the network task.\n", + " Now, we can i) be more confident that the network has learned some scientific evidence and ii) can further consider also other regions in the explanation maps possibly even inferring new insights using an explanation method that we have evaluated and chosen according to its properties.\n", + "\n" + ], + "metadata": { + "id": "sIJqdSdcN5W9" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 6.4 Limitations\n", + "\n", + "XAI evaluation faces certain limitations due to the absence of a reliable ground-truth, which means the evaluation metrics provided can only assess crucial properties that a valid explanation must possess, and cannot provide a complete validation. While the evaluation of XAI methods is a rapidly evolving field, the metrics offered by the Quantus library have certain limitations, such as relying on perturbing the input which may lead to the creation of out-of-distribution inputs. It should be noted that evaluating explanation methods using quantification analysis does not guarantee the theoretical soundness or statistical validity of the methods. Therefore, when using the Quantus library for XAI method selection, it is essential to supplement the results with theoretical considerations.\n" + ], + "metadata": { + "collapsed": false, + "id": "4UKt-89JKxQm" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## 6.5 Next Steps\n", + "\n", + "XAI evaluation can help researchers establish appropriate explanation methods for a specific tasks. These performance measures can help validate network models and prediction as well as insights inferred from explanations.\n" + ], + "metadata": { + "collapsed": false, + "id": "Rsjhon5peKiz" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "# 7) References\n", + "\n", + "* [Bommer et. al., 2023](https://arxiv.org/abs/2303.00652)\n", + "* [Hedström et al., 2023](https://jmlr.org/papers/v24/22-0142.html)\n", + "* [Labe and Barnes, 2021](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464)\n", + "* [Kay et al., 2015](https://journals.ametsoc.org/view/journals/bams/96/8/bams-d-13-00255.1.xml)\n", + "* [Hurrell et al., 2013](https://journals.ametsoc.org/view/journals/bams/94/9/bams-d-12-00121.1.xml)\n", + "* [CESM1 data](https://www.cesm.ucar.edu/projects/community-projects/LENS/data-sets.html)\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "collapsed": false, + "id": "Du7JtbIhfkoc" + } + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "TPU", + "gpuClass": "standard" + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From d6a9ed9533bf6476c69fb80e2f587631b0317f31 Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 25 Mar 2024 12:37:55 +0100 Subject: [PATCH 23/41] fixed tests for inverse estimation, debugged shapes --- quantus/metrics/inverse_estimation.py | 47 +++++++++++++------ .../model_parameter_randomisation.py | 2 +- tests/metrics/test_inverse_estimation.py | 37 ++++++++------- 3 files changed, 53 insertions(+), 33 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 0e007aedd..54a4b0721 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -47,7 +47,7 @@ class InverseEstimation(Metric): data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR} model_applicability = {ModelType.TORCH, ModelType.TF} score_direction = ScoreDirection.HIGHER - #evaluation_category = EvaluationCategory.FAITHFULNESS + # evaluation_category = EvaluationCategory.FAITHFULNESS def __init__( self, @@ -123,13 +123,15 @@ def __init__( # Asserts and warnings. # Skip for now, might revisit later. # if metric_init.name == "ROAD": # metric_init.return_only_values = True - #if metric_init.name == "Region Perturbation": + # if metric_init.name == "Region Perturbation": # metric_init.order = "morf" self.inverse_method = inverse_method if self.inverse_method not in ["sign-flip", "value-swap"]: - raise ValueError("The 'inverse_method' in init **kwargs, \ - must be either 'sign-flip' or 'value-swap'.") + raise ValueError( + "The 'inverse_method' in init **kwargs, \ + must be either 'sign-flip' or 'value-swap'." + ) # TODO. Update warnings. if not self.disable_warnings: @@ -279,19 +281,25 @@ def get_inverse_attributions(inverse_method: str, a_batch: np.array): elif inverse_method == "value-swap": indices = np.argsort(a_batch, axis=1) a_batch_inv = np.empty_like(a_batch) - a_batch_inv[np.arange(a_batch_inv.shape[0])[:, None], indices] = a_batch[np.arange(a_batch_inv.shape[0])[:, None], indices[:,::-1]] - a_batch_inv.reshape(shape_ori) + a_batch_inv[np.arange(a_batch_inv.shape[0])[:, None], indices] = ( + a_batch[np.arange(a_batch_inv.shape[0])[:, None], indices[:, ::-1]] + ) + a_batch_inv = a_batch_inv.reshape(shape_ori) return a_batch_inv - + def inverse_wrapper(model, inputs, targets, **kwargs): explain_func = kwargs["explain_func"] inverse_method = kwargs["inverse_method"] a_batch = explain_func(model, inputs, targets, **kwargs) - a_batch_inv = get_inverse_attributions(inverse_method=inverse_method, a_batch=a_batch, shape_ori=shape_ori) + a_batch_inv = get_inverse_attributions( + inverse_method=inverse_method, a_batch=a_batch + ) return a_batch_inv - + # Get inverse attributions. - a_batch_inv = get_inverse_attributions(inverse_method=self.inverse_method, a_batch=a_batch) + a_batch_inv = get_inverse_attributions( + inverse_method=self.inverse_method, a_batch=a_batch + ) # Metrics that depend on re-computing explanations need inverse wrapping. explain_func_kwargs["explain_func"] = explain_func @@ -313,17 +321,26 @@ def inverse_wrapper(model, inputs, targets, **kwargs): ) # Compute the inverse, empty the evaluation scores again and overwrite with the inverse scores. + # print( + # "Scores shape", np.array(self.scores).shape, np.array(self.scores_inv).shape + # ) inv_scores = (np.array(self.scores) - np.array(self.scores_inv)).tolist() + + # print("Shape inv", np.shape(inv_scores)) + self.evaluation_scores = inv_scores + # print("Shape evaluation_scores", np.shape(self.evaluation_scores)) + if self.return_aggregate: - self.evaluation_scores = self.get_mean_score + # print("Returning aggregate score.") + self.evaluation_scores = self.get_mean_score # .reshape(-1) self.all_evaluation_scores.extend(self.metric_init.evaluation_scores) - - print(np.shape(inv_scores)) - print(np.shape(inv_scores.reshape(-1))) - return inv_scores.reshape(-1) + + # print("Shape inv_scores reshaped", np.shape(self.evaluation_scores)) + + return self.evaluation_scores def convert_attributions_to_rankings(self): pass diff --git a/quantus/metrics/randomisation/model_parameter_randomisation.py b/quantus/metrics/randomisation/model_parameter_randomisation.py index d1f67d8ce..f539768cb 100644 --- a/quantus/metrics/randomisation/model_parameter_randomisation.py +++ b/quantus/metrics/randomisation/model_parameter_randomisation.py @@ -36,7 +36,7 @@ class ModelParameterRandomisation(Metric): """ - Implementation of the Model Parameter Randomization Method by Adebayo et. al., 2018. + Implementation of the Model Parameter Randomization Method by Adebayo et al., 2018. The Model Parameter Randomization measures the distance between the original attribution and a newly computed attribution throughout the process of cascadingly/independently randomizing the model parameters of one layer diff --git a/tests/metrics/test_inverse_estimation.py b/tests/metrics/test_inverse_estimation.py index 22ae12576..99e47c4aa 100644 --- a/tests/metrics/test_inverse_estimation.py +++ b/tests/metrics/test_inverse_estimation.py @@ -29,7 +29,7 @@ "perturb_baseline": "mean", "features_in_step": 28, "normalise": True, - "abs": True, + "abs": False, "disable_warnings": False, "display_progressbar": False, }, @@ -88,7 +88,7 @@ lazy_fixture("load_mnist_model"), lazy_fixture("load_mnist_images"), { - "a_batch_generate": False, + "a_batch_generate": True, "init": { "perturb_baseline": "uniform", "features_in_step": 112, @@ -114,7 +114,7 @@ "perturb_baseline": "mean", "features_in_step": 28, "normalise": True, - "abs": True, + "abs": False, "disable_warnings": True, "display_progressbar": True, }, @@ -131,7 +131,7 @@ lazy_fixture("load_1d_3ch_conv_model"), lazy_fixture("almost_uniform_1d"), { - "a_batch_generate": False, + "a_batch_generate": True, "init": { "features_in_step": 10, "normalise": False, @@ -152,7 +152,7 @@ "perturb_baseline": "uniform", "features_in_step": 56, "normalise": True, - "abs": True, + "abs": False, "disable_warnings": True, "display_progressbar": True, }, @@ -197,10 +197,8 @@ def test_inverse_estimation_with_pixel_flipping( params: dict, expected: Union[float, dict, bool], ): - x_batch, y_batch = ( - data["x_batch"], - data["y_batch"], - ) + x_batch = data["x_batch"] + y_batch = data["y_batch"] init_params = params.get("init", {}) call_params = params.get("call", {}) @@ -223,6 +221,8 @@ def test_inverse_estimation_with_pixel_flipping( metric_init = PixelFlipping(**init_params) metric_init.softmax = True + print("x_batch shape", np.shape(x_batch)) + try: inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) scores = inv( @@ -232,12 +232,12 @@ def test_inverse_estimation_with_pixel_flipping( a_batch=a_batch, **call_params, ) - print(f"\n\n\tscores: {np.shape(inv.scores)},\n{inv.scores}") - print(f"\n\n\tscores_inv: {np.shape(inv.scores_inv)},\n{inv.scores_inv}") - print( - f"\n\n\tall_evaluation_scores: {np.shape(inv.all_evaluation_scores)},\n{inv.all_evaluation_scores}" - ) - print(f"\n\n\tscores: {np.shape(scores)},\n{scores}") + # print(f"\n\n\tscores: {np.shape(inv.scores)},\n{inv.scores}") + # print(f"\n\n\tscores_inv: {np.shape(inv.scores_inv)},\n{inv.scores_inv}") + # print( + # f"\n\n\tall_evaluation_scores: {np.shape(inv.all_evaluation_scores)},\n{inv.all_evaluation_scores}" + # ) + # print(f"\n\n\tscores: {np.shape(scores)},\n{scores}") if "exception" not in expected: assert all( @@ -248,6 +248,9 @@ def test_inverse_estimation_with_pixel_flipping( ] ), "Test failed." - except expected["exception"] as e: - print(f'Raised exception type {expected["exception"]}', e) + except Exception as e: + if "exception" in expected and isinstance(e, expected["exception"]): + print(f'Raised exception type {expected["exception"]}', e) + else: + print(f"Unexpected exception occurred:", e) return From 138dc4600dbc76838b18726fb952a9a44825356a Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 25 Mar 2024 12:54:58 +0100 Subject: [PATCH 24/41] merge post-fixes, remove old files --- quantus/helpers/asserts.py | 2 +- .../model_parameter_randomisation.py | 441 ------------------ 2 files changed, 1 insertion(+), 442 deletions(-) delete mode 100644 quantus/metrics/randomisation/model_parameter_randomisation.py diff --git a/quantus/helpers/asserts.py b/quantus/helpers/asserts.py index 66ee2930d..401f5f568 100644 --- a/quantus/helpers/asserts.py +++ b/quantus/helpers/asserts.py @@ -193,7 +193,7 @@ def assert_attributions(x_batch: np.array, a_batch: np.array) -> None: "metrics rely on ordering." "Recompute the explanations." ) - # assert not np.all((a_batch < 0.0)), "Attributions should not all be less than zero." + assert not np.all((a_batch < 0.0)), "Attributions should not all be less than zero." def assert_segmentations(x_batch: np.array, s_batch: np.array) -> None: diff --git a/quantus/metrics/randomisation/model_parameter_randomisation.py b/quantus/metrics/randomisation/model_parameter_randomisation.py deleted file mode 100644 index f539768cb..000000000 --- a/quantus/metrics/randomisation/model_parameter_randomisation.py +++ /dev/null @@ -1,441 +0,0 @@ -"""This module contains the implementation of the Model Parameter Sensitivity metric.""" - -# This file is part of Quantus. -# Quantus is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. -# Quantus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. -# You should have received a copy of the GNU Lesser General Public License along with Quantus. If not, see . -# Quantus project URL: . - -from typing import ( - Any, - Callable, - Dict, - List, - Optional, - Tuple, - Union, - Collection, - Iterable, -) -import numpy as np -from tqdm.auto import tqdm - -from quantus.helpers import asserts -from quantus.helpers import warn -from quantus.helpers.model.model_interface import ModelInterface -from quantus.functions.normalise_func import normalise_by_max -from quantus.functions.similarity_func import correlation_spearman -from quantus.metrics.base import Metric -from quantus.helpers.enums import ( - ModelType, - DataType, - ScoreDirection, - EvaluationCategory, -) - - -class ModelParameterRandomisation(Metric): - """ - Implementation of the Model Parameter Randomization Method by Adebayo et al., 2018. - - The Model Parameter Randomization measures the distance between the original attribution and a newly computed - attribution throughout the process of cascadingly/independently randomizing the model parameters of one layer - at a time. - - Assumptions: - - In the original paper multiple distance measures are taken: Spearman rank correlation (with and without abs), - HOG and SSIM. We have set Spearman as the default value. - - References: - 1) Julius Adebayo et al.: "Sanity Checks for Saliency Maps." NeurIPS (2018): 9525-9536. - - Attributes: - - _name: The name of the metric. - - _data_applicability: The data types that the metric implementation currently supports. - - _models: The model types that this metric can work with. - - score_direction: How to interpret the scores, whether higher/ lower values are considered better. - - evaluation_category: What property/ explanation quality that this metric measures. - """ - - name = "Model Parameter Randomisation" - data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR} - model_applicability = {ModelType.TORCH, ModelType.TF} - score_direction = ScoreDirection.LOWER - evaluation_category = EvaluationCategory.RANDOMISATION - - @asserts.attributes_check - def __init__( - self, - similarity_func: Callable = None, - layer_order: str = "independent", - seed: int = 42, - return_sample_correlation: bool = False, - abs: bool = True, - normalise: bool = True, - normalise_func: Optional[Callable[[np.ndarray], np.ndarray]] = None, - normalise_func_kwargs: Optional[Dict[str, Any]] = None, - return_aggregate: bool = False, - aggregate_func: Callable = None, - default_plot_func: Optional[Callable] = None, - disable_warnings: bool = False, - display_progressbar: bool = False, - **kwargs, - ): - """ - Parameters - ---------- - similarity_func: callable - Similarity function applied to compare input and perturbed input, default=correlation_spearman. - layer_order: string - Indicated whether the model is randomized cascadingly or independently. - Set order=top_down for cascading randomization, set order=independent for independent randomization, - default="independent". - seed: integer - Seed used for the random generator, default=42. - return_sample_correlation: boolean - Indicates whether return one float per sample, representing the average - correlation coefficient across the layers for that sample. - abs: boolean - Indicates whether absolute operation is applied on the attribution, default=True. - normalise: boolean - Indicates whether normalise operation is applied on the attribution, default=True. - normalise_func: callable - Attribution normalisation function applied in case normalise=True. - If normalise_func=None, the default value is used, default=normalise_by_max. - normalise_func_kwargs: dict - Keyword arguments to be passed to normalise_func on call, default={}. - return_aggregate: boolean - Indicates if an aggregated score should be computed over all instances. - aggregate_func: callable - Callable that aggregates the scores given an evaluation call. - default_plot_func: callable - Callable that plots the metrics result. - disable_warnings: boolean - Indicates whether the warnings are printed, default=False. - display_progressbar: boolean - Indicates whether a tqdm-progress-bar is printed, default=False. - kwargs: optional - Keyword arguments. - """ - if normalise_func is None: - normalise_func = normalise_by_max - - super().__init__( - abs=abs, - normalise=normalise, - normalise_func=normalise_func, - normalise_func_kwargs=normalise_func_kwargs, - return_aggregate=return_aggregate, - aggregate_func=aggregate_func, - default_plot_func=default_plot_func, - display_progressbar=display_progressbar, - disable_warnings=disable_warnings, - **kwargs, - ) - - # Save metric-specific attributes. - if similarity_func is None: - similarity_func = correlation_spearman - self.similarity_func = similarity_func - self.layer_order = layer_order - self.seed = seed - self.return_sample_correlation = return_sample_correlation - - # Results are returned/saved as a dictionary not like in the super-class as a list. - self.evaluation_scores = {} - - # Asserts and warnings. - asserts.assert_layer_order(layer_order=self.layer_order) - if not self.disable_warnings: - warn.warn_parameterisation( - metric_name=self.__class__.__name__, - sensitive_params=( - "similarity metric 'similarity_func' and the order of " - "the layer randomisation 'layer_order'" - ), - citation=( - "Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B. " - "'Sanity Checks for Saliency Maps.' arXiv preprint," - " arXiv:1810.073292v3 (2018)" - ), - ) - - def __call__( - self, - model, - x_batch: np.array, - y_batch: np.array, - a_batch: Optional[np.ndarray] = None, - s_batch: Optional[np.ndarray] = None, - channel_first: Optional[bool] = None, - explain_func: Optional[Callable] = None, - explain_func_kwargs: Optional[Dict] = None, - model_predict_kwargs: Optional[Dict] = None, - softmax: Optional[bool] = False, - device: Optional[str] = None, - batch_size: int = 64, - custom_batch: Optional[Any] = None, - **kwargs, - ) -> Union[List[float], float, Dict[str, List[float]], Collection[Any]]: - """ - This implementation represents the main logic of the metric and makes the class object callable. - It completes instance-wise evaluation of explanations (a_batch) with respect to input data (x_batch), - output labels (y_batch) and a torch or tensorflow model (model). - - Calls general_preprocess() with all relevant arguments, calls - () on each instance, and saves results to evaluation_scores. - Calls custom_postprocess() afterwards. Finally returns evaluation_scores. - - The content of evaluation_scores will be appended to all_evaluation_scores (list) at the end of - the evaluation call. - - Parameters - ---------- - model: torch.nn.Module, tf.keras.Model - A torch or tensorflow model that is subject to explanation. - x_batch: np.ndarray - A np.ndarray which contains the input data that are explained. - y_batch: np.ndarray - A np.ndarray which contains the output labels that are explained. - a_batch: np.ndarray, optional - A np.ndarray which contains pre-computed attributions i.e., explanations. - s_batch: np.ndarray, optional - A np.ndarray which contains segmentation masks that matches the input. - channel_first: boolean, optional - Indicates of the image dimensions are channel first, or channel last. - Inferred from the input shape if None. - explain_func: callable - Callable generating attributions. - explain_func_kwargs: dict, optional - Keyword arguments to be passed to explain_func on call. - model_predict_kwargs: dict, optional - Keyword arguments to be passed to the model's predict method. - softmax: boolean - Indicates whether to use softmax probabilities or logits in model prediction. - This is used for this __call__ only and won't be saved as attribute. If None, self.softmax is used. - device: string - Indicated the device on which a torch.Tensor is or will be allocated: "cpu" or "gpu". - kwargs: optional - Keyword arguments. - - Returns - ------- - evaluation_scores: list - a list of Any with the evaluation scores of the concerned batch. - - Examples: - -------- - # Minimal imports. - >> import quantus - >> from quantus import LeNet - >> import torch - - # Enable GPU. - >> device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - # Load a pre-trained LeNet classification model (architecture at quantus/helpers/models). - >> model = LeNet() - >> model.load_state_dict(torch.load("tutorials/assets/pytests/mnist_model")) - - # Load MNIST datasets and make loaders. - >> test_set = torchvision.datasets.MNIST(root='./sample_data', download=True) - >> test_loader = torch.utils.data.DataLoader(test_set, batch_size=24) - - # Load a batch of inputs and outputs to use for XAI evaluation. - >> x_batch, y_batch = iter(test_loader).next() - >> x_batch, y_batch = x_batch.cpu().numpy(), y_batch.cpu().numpy() - - # Generate Saliency attributions of the test set batch of the test set. - >> a_batch_saliency = Saliency(model).attribute(inputs=x_batch, target=y_batch, abs=True).sum(axis=1) - >> a_batch_saliency = a_batch_saliency.cpu().numpy() - - # Initialise the metric and evaluate explanations by calling the metric instance. - >> metric = Metric(abs=True, normalise=False) - >> scores = metric(model=model, x_batch=x_batch, y_batch=y_batch, a_batch=a_batch_saliency} - """ - - # Run deprecation warnings. - warn.deprecation_warnings(kwargs) - warn.check_kwargs(kwargs) - - data = self.general_preprocess( - model=model, - x_batch=x_batch, - y_batch=y_batch, - a_batch=a_batch, - s_batch=s_batch, - custom_batch=None, - channel_first=channel_first, - explain_func=explain_func, - explain_func_kwargs=explain_func_kwargs, - model_predict_kwargs=model_predict_kwargs, - softmax=softmax, - device=device, - ) - model = data["model"] - x_batch = data["x_batch"] - y_batch = data["y_batch"] - a_batch = data["a_batch"] - - # Results are returned/saved as a dictionary not as a list as in the super-class. - self.evaluation_scores = {} - - # Get number of iterations from number of layers. - n_layers = len(list(model.get_random_layer_generator(order=self.layer_order))) - - model_iterator = tqdm( - model.get_random_layer_generator(order=self.layer_order, seed=self.seed), - total=n_layers, - disable=not self.display_progressbar, - ) - - for layer_name, random_layer_model in model_iterator: - - similarity_scores = [None for _ in x_batch] - - # Generate an explanation with perturbed model. - a_batch_perturbed = self.explain_func( - model=random_layer_model, - inputs=x_batch, - targets=y_batch, - **self.explain_func_kwargs, - ) - - batch_iterator = enumerate(zip(a_batch, a_batch_perturbed)) - for instance_id, (a_instance, a_instance_perturbed) in batch_iterator: - result = self.evaluate_instance( - model=random_layer_model, - x=None, - y=None, - s=None, - a=a_instance, - a_perturbed=a_instance_perturbed, - ) - similarity_scores[instance_id] = result - - # Save similarity scores in a result dictionary. - self.evaluation_scores[layer_name] = similarity_scores - - # Call post-processing. - self.custom_postprocess( - model=model, - x_batch=x_batch, - y_batch=y_batch, - a_batch=a_batch, - s_batch=s_batch, - ) - - if self.return_sample_correlation: - self.evaluation_scores = self.compute_correlation_per_sample() - - if self.return_aggregate: - assert self.return_sample_correlation, ( - "You must set 'return_average_correlation_per_sample'" - " to True in order to compute te aggregat" - ) - self.evaluation_scores = [self.aggregate_func(self.evaluation_scores)] - - self.all_evaluation_scores.append(self.evaluation_scores) - - return self.evaluation_scores - - def evaluate_instance( - self, - model: ModelInterface, - x: Optional[np.ndarray], - y: Optional[np.ndarray], - a: Optional[np.ndarray], - s: Optional[np.ndarray], - a_perturbed: Optional[np.ndarray] = None, - ) -> float: - """ - Evaluate instance gets model and data for a single instance as input and returns the evaluation result. - - Parameters - ---------- - i: integer - The evaluation instance. - model: ModelInterface - A ModelInteface that is subject to explanation. - x: np.ndarray - The input to be evaluated on an instance-basis. - y: np.ndarray - The output to be evaluated on an instance-basis. - a: np.ndarray - The explanation to be evaluated on an instance-basis. - s: np.ndarray - The segmentation to be evaluated on an instance-basis. - a_perturbed: np.ndarray - The perturbed attributions. - - Returns - ------- - float - The evaluation results. - """ - if self.normalise: - a_perturbed = self.normalise_func(a_perturbed, **self.normalise_func_kwargs) - - if self.abs: - a_perturbed = np.abs(a_perturbed) - - # Compute distance measure. - return self.similarity_func(a_perturbed.flatten(), a.flatten()) - - def custom_preprocess( - self, - model: ModelInterface, - x_batch: np.ndarray, - y_batch: Optional[np.ndarray], - a_batch: Optional[np.ndarray], - s_batch: np.ndarray, - custom_batch: Optional[np.ndarray], - ) -> None: - """ - Implementation of custom_preprocess_batch. - - Parameters - ---------- - model: torch.nn.Module, tf.keras.Model - A torch or tensorflow model e.g., torchvision.models that is subject to explanation. - x_batch: np.ndarray - A np.ndarray which contains the input data that are explained. - y_batch: np.ndarray - A np.ndarray which contains the output labels that are explained. - a_batch: np.ndarray, optional - A np.ndarray which contains pre-computed attributions i.e., explanations. - s_batch: np.ndarray, optional - A np.ndarray which contains segmentation masks that matches the input. - custom_batch: any - Gives flexibility ot the user to use for evaluation, can hold any variable. - - Returns - ------- - None - """ - # Additional explain_func assert, as the one in general_preprocess() - # won't be executed when a_batch != None. - asserts.assert_explain_func(explain_func=self.explain_func) - - def compute_correlation_per_sample( - self, - ) -> Union[List[List[Any]], Dict[int, List[Any]]]: - - assert isinstance(self.evaluation_scores, dict), ( - "To compute the average correlation coefficient per sample for " - "Model Parameter Randomisation Test, 'last_result' " - "must be of type dict." - ) - layer_length = len( - self.evaluation_scores[list(self.evaluation_scores.keys())[0]] - ) - results: Dict[int, list] = {sample: [] for sample in range(layer_length)} - - for sample in results: - for layer in self.evaluation_scores: - results[sample].append(float(self.evaluation_scores[layer][sample])) - results[sample] = np.mean(results[sample]) - - corr_coeffs = list(results.values()) - - return corr_coeffs From b2c7f27e5568c9a25b96acdacd5eba9fe93f4338 Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 25 Mar 2024 15:56:55 +0100 Subject: [PATCH 25/41] add print statement --- quantus/metrics/base_perturbed.py | 162 ----------------------- quantus/metrics/inverse_estimation.py | 16 +-- tests/metrics/test_inverse_estimation.py | 1 + 3 files changed, 9 insertions(+), 170 deletions(-) delete mode 100644 quantus/metrics/base_perturbed.py diff --git a/quantus/metrics/base_perturbed.py b/quantus/metrics/base_perturbed.py deleted file mode 100644 index 250bf1c6b..000000000 --- a/quantus/metrics/base_perturbed.py +++ /dev/null @@ -1,162 +0,0 @@ -"""This module implements the base class for creating evaluation metrics.""" -# This file is part of Quantus. -# Quantus is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. -# Quantus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. -# You should have received a copy of the GNU Lesser General Public License along with Quantus. If not, see . -# Quantus project URL: . - -import inspect -import re -from abc import abstractmethod -from collections.abc import Sequence -from typing import ( - Any, - Callable, - Dict, - Sequence, - Optional, - Tuple, - Union, - Collection, - List, -) -import matplotlib.pyplot as plt -import numpy as np -from tqdm.auto import tqdm - -from quantus.helpers import asserts -from quantus.helpers import utils -from quantus.helpers import warn -from quantus.helpers.model.model_interface import ModelInterface -from quantus.metrics.base import Metric -from quantus.helpers.enums import ( - ModelType, - DataType, - ScoreDirection, - EvaluationCategory, -) - - -class PerturbationMetric(Metric): - """ - Implementation base PertubationMetric class. - - Metric categories such as Faithfulness and Robustness share certain characteristics when it comes to perturbations. - As follows, this metric class is created which has additional attributes for perturbations. - - Attributes: - - name: The name of the metric. - - data_applicability: The data types that the metric implementation currently supports. - - model_applicability: The model types that this metric can work with. - - score_direction: How to interpret the scores, whether higher/ lower values are considered better. - - evaluation_category: What property/ explanation quality that this metric measures. - """ - - name = "PerturbationMetric" - data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR} - model_applicability = {ModelType.TORCH, ModelType.TF} - score_direction = ScoreDirection.HIGHER - evaluation_category = EvaluationCategory.NONE - - @asserts.attributes_check - def __init__( - self, - abs: bool, - normalise: bool, - normalise_func: Callable, - normalise_func_kwargs: Optional[Dict[str, Any]], - perturb_func: Callable, - perturb_func_kwargs: Optional[Dict[str, Any]], - return_aggregate: bool, - aggregate_func: Callable, - default_plot_func: Optional[Callable], - disable_warnings: bool, - display_progressbar: bool, - **kwargs, - ): - """ - Initialise the PerturbationMetric base class. - - Parameters - ---------- - Parameters - ---------- - abs: boolean - Indicates whether absolute operation is applied on the attribution. - normalise: boolean - Indicates whether normalise operation is applied on the attribution. - normalise_func: callable - Attribution normalisation function applied in case normalise=True. - normalise_func_kwargs: dict - Keyword arguments to be passed to normalise_func on call. - perturb_func: callable - Input perturbation function. - perturb_func_kwargs: dict, optional - Keyword arguments to be passed to perturb_func. - return_aggregate: boolean - Indicates if an aggregated score should be computed over all instances. - aggregate_func: callable - Callable that aggregates the scores given an evaluation call. - default_plot_func: callable - Callable that plots the metrics result. - disable_warnings: boolean - Indicates whether the warnings are printed. - display_progressbar: boolean - Indicates whether a tqdm-progress-bar is printed. - kwargs: optional - Keyword arguments. - """ - - # Initialize super-class with passed parameters - super().__init__( - abs=abs, - normalise=normalise, - normalise_func=normalise_func, - normalise_func_kwargs=normalise_func_kwargs, - return_aggregate=return_aggregate, - aggregate_func=aggregate_func, - default_plot_func=default_plot_func, - display_progressbar=display_progressbar, - disable_warnings=disable_warnings, - **kwargs, - ) - - # Save perturbation metric attributes. - self.perturb_func = perturb_func - - if perturb_func_kwargs is None: - perturb_func_kwargs = {} - self.perturb_func_kwargs = perturb_func_kwargs - - @abstractmethod - def evaluate_instance( - self, - model: ModelInterface, - x: np.ndarray, - y: Optional[np.ndarray], - a: Optional[np.ndarray], - s: Optional[np.ndarray], - ) -> Any: - """ - Evaluate instance gets model and data for a single instance as input and returns the evaluation result. - - This method needs to be implemented to use __call__(). - - Parameters - ---------- - model: ModelInterface - A ModelInteface that is subject to explanation. - x: np.ndarray - The input to be evaluated on an instance-basis. - y: np.ndarray - The output to be evaluated on an instance-basis. - a: np.ndarray - The explanation to be evaluated on an instance-basis. - s: np.ndarray - The segmentation to be evaluated on an instance-basis. - - Returns - ------- - Any - """ - raise NotImplementedError() diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 54a4b0721..f90276ef6 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -321,24 +321,24 @@ def inverse_wrapper(model, inputs, targets, **kwargs): ) # Compute the inverse, empty the evaluation scores again and overwrite with the inverse scores. - # print( - # "Scores shape", np.array(self.scores).shape, np.array(self.scores_inv).shape - # ) + print( + "Scores shape", np.array(self.scores).shape, np.array(self.scores_inv).shape + ) inv_scores = (np.array(self.scores) - np.array(self.scores_inv)).tolist() - # print("Shape inv", np.shape(inv_scores)) + print("Shape inv", np.shape(inv_scores)) self.evaluation_scores = inv_scores - # print("Shape evaluation_scores", np.shape(self.evaluation_scores)) + print("Shape evaluation_scores", np.shape(self.evaluation_scores)) if self.return_aggregate: - # print("Returning aggregate score.") - self.evaluation_scores = self.get_mean_score # .reshape(-1) + print("Returning aggregate score.") + self.evaluation_scores = self.get_mean_score.reshape(-1) self.all_evaluation_scores.extend(self.metric_init.evaluation_scores) - # print("Shape inv_scores reshaped", np.shape(self.evaluation_scores)) + print("Shape inv_scores reshaped", np.shape(self.evaluation_scores)) return self.evaluation_scores diff --git a/tests/metrics/test_inverse_estimation.py b/tests/metrics/test_inverse_estimation.py index 99e47c4aa..ef65bc45b 100644 --- a/tests/metrics/test_inverse_estimation.py +++ b/tests/metrics/test_inverse_estimation.py @@ -224,6 +224,7 @@ def test_inverse_estimation_with_pixel_flipping( print("x_batch shape", np.shape(x_batch)) try: + inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) scores = inv( model=model, From b7215aca82b667d74ece938ab40912c7e383b321 Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 25 Mar 2024 16:33:46 +0100 Subject: [PATCH 26/41] added base call kwargs as class attributes to base and rewrite inverse to evaluate_batch --- quantus/metrics/base.py | 15 ++- quantus/metrics/inverse_estimation.py | 158 ++++++++++++++------------ 2 files changed, 93 insertions(+), 80 deletions(-) diff --git a/quantus/metrics/base.py b/quantus/metrics/base.py index 2564593e7..ef980b140 100644 --- a/quantus/metrics/base.py +++ b/quantus/metrics/base.py @@ -1,4 +1,5 @@ """This module implements the base class for creating evaluation metrics.""" + # This file is part of Quantus. # Quantus is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. # Quantus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. @@ -140,7 +141,7 @@ def __init__( if normalise_func_kwargs is not None: normalise_func = functools.partial(normalise_func, **normalise_func_kwargs) - + # Run deprecation warnings. warn.deprecation_warnings(kwargs) warn.check_kwargs(kwargs) @@ -419,6 +420,10 @@ def general_preprocess( A general preprocess. """ + self.channel_first = channel_first + self.model_predict_kwargs = model_predict_kwargs + self.softmax = softmax + self.device = device # Reshape input batch to channel first order: if not isinstance(channel_first, bool): # None is not a boolean instance. @@ -429,10 +434,10 @@ def general_preprocess( # Use attribute value if not passed explicitly. model = utils.get_wrapped_model( model=model, - channel_first=channel_first, - softmax=softmax, - device=device, - model_predict_kwargs=model_predict_kwargs, + channel_first=self.channel_first, + softmax=self.softmax, + device=self.device, + model_predict_kwargs=self.model_predict_kwargs, ) # Save as attribute, some metrics need it during processing. diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index f90276ef6..bd02741e3 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -53,6 +53,7 @@ def __init__( self, metric_init: Metric, inverse_method: str = "sign-flip", + return_auc_per_sample: bool = True, abs: bool = False, normalise: bool = False, normalise_func: Optional[Callable] = None, @@ -112,7 +113,7 @@ def __init__( normalise=normalise, normalise_func=normalise_func, normalise_func_kwargs=normalise_func_kwargs, - return_aggregate=return_aggregate, + return_aggregate=self.return_aggregate, aggregate_func=aggregate_func, default_plot_func=default_plot_func, display_progressbar=display_progressbar, @@ -120,18 +121,23 @@ def __init__( **kwargs, ) - # Asserts and warnings. # Skip for now, might revisit later. - # if metric_init.name == "ROAD": - # metric_init.return_only_values = True - # if metric_init.name == "Region Perturbation": - # metric_init.order = "morf" - - self.inverse_method = inverse_method + self.return_auc_per_sample = return_auc_per_sample if self.inverse_method not in ["sign-flip", "value-swap"]: raise ValueError( "The 'inverse_method' in init **kwargs, \ must be either 'sign-flip' or 'value-swap'." ) + self.inverse_method = inverse_method + if self.metric_init.return_aggregate: + print( + "The metric is not designed to return an aggregate score, setting return_aggregate=False." + ) + self.metric_init.return_aggregate = False + + assert self.metric_init.abs == False, ( + "To run the inverse estimation, you cannot set 'a_batch' to " + "have positive attributions only. Set 'abs' param of the metric init to 'False'." + ) # TODO. Update warnings. if not self.disable_warnings: @@ -233,20 +239,40 @@ def __call__( >>> metric = Metric(abs=True, normalise=False) >>> scores = metric(model=model, x_batch=x_batch, y_batch=y_batch, a_batch=a_batch_saliency} """ - if self.metric_init.return_aggregate: - print( - "The metric is not designed to return an aggregate score, setting return_aggregate=False." - ) - self.metric_init.return_aggregate = False + return super().__call__( + model=model, + x_batch=x_batch, + y_batch=y_batch, + a_batch=a_batch, + s_batch=s_batch, + custom_batch=None, + channel_first=channel_first, + explain_func=explain_func, + explain_func_kwargs=explain_func_kwargs, + model_predict_kwargs=model_predict_kwargs, + softmax=softmax, + device=device, + batch_size=batch_size, + **kwargs, + ) + + def evaluate_batch( + self, + model: ModelInterface, + x_batch: np.ndarray, + y_batch: np.ndarray, + a_batch: np.ndarray, + s_batch: Optional[np.ndarray] = None, + **kwargs, + ) -> List[float]: assert ( a_batch is not None ), "'a_batch' must be provided to run the inverse estimation." - assert self.metric_init.abs == False, ( - "To run the inverse estimation, you cannot set 'a_batch' to " - "have positive attributions only. Set 'abs' param of the metric init to 'False'." - ) + # Metrics that depend on re-computing explanations need inverse wrapping. + self.explain_func_kwargs["explain_func"] = self.explain_func + self.explain_func_kwargs["inverse_method"] = self.inverse_method self.scores = self.metric_init( model=model, @@ -254,12 +280,12 @@ def __call__( y_batch=y_batch, a_batch=a_batch, s_batch=s_batch, - channel_first=channel_first, - explain_func=explain_func, - explain_func_kwargs=explain_func_kwargs, - softmax=softmax, - device=device, - model_predict_kwargs=model_predict_kwargs, + channel_first=self.channel_first, + explain_func=self.explain_func, + explain_func_kwargs=self.explain_func_kwargs, + softmax=self.softmax, + device=self.device, + model_predict_kwargs=self.model_predict_kwargs, **kwargs, ) assert len(self.scores) == len(x_batch), ( @@ -270,76 +296,33 @@ def __call__( # Empty the evaluation scores before re-scoring with the metric. self.metric_init.evaluation_scores = [] - # Run inverse experiment. - def get_inverse_attributions(inverse_method: str, a_batch: np.array): - - # Attributions need to have only one axis, else flatten and reshape back. - shape_ori = a_batch.shape - a_batch = a_batch.reshape((shape_ori[0], -1)) - if inverse_method == "sign-flip": - a_batch_inv = -np.array(a_batch) - elif inverse_method == "value-swap": - indices = np.argsort(a_batch, axis=1) - a_batch_inv = np.empty_like(a_batch) - a_batch_inv[np.arange(a_batch_inv.shape[0])[:, None], indices] = ( - a_batch[np.arange(a_batch_inv.shape[0])[:, None], indices[:, ::-1]] - ) - a_batch_inv = a_batch_inv.reshape(shape_ori) - return a_batch_inv - - def inverse_wrapper(model, inputs, targets, **kwargs): - explain_func = kwargs["explain_func"] - inverse_method = kwargs["inverse_method"] - a_batch = explain_func(model, inputs, targets, **kwargs) - a_batch_inv = get_inverse_attributions( - inverse_method=inverse_method, a_batch=a_batch - ) - return a_batch_inv - # Get inverse attributions. - a_batch_inv = get_inverse_attributions( - inverse_method=self.inverse_method, a_batch=a_batch - ) - - # Metrics that depend on re-computing explanations need inverse wrapping. - explain_func_kwargs["explain_func"] = explain_func - explain_func_kwargs["inverse_method"] = self.inverse_method + a_batch_inv = self.get_inverse_attributions(a_batch=a_batch) + # Run inverse experiment. self.scores_inv = self.metric_init( model=model, x_batch=x_batch, y_batch=y_batch, a_batch=a_batch_inv, s_batch=s_batch, - channel_first=channel_first, - explain_func=inverse_wrapper, - explain_func_kwargs=explain_func_kwargs, - softmax=softmax, - device=device, - model_predict_kwargs=model_predict_kwargs, + channel_first=self.channel_first, + explain_func=self.inverse_wrapper, + explain_func_kwargs=self.explain_func_kwargs, + softmax=self.softmax, + device=self.device, + model_predict_kwargs=self.model_predict_kwargs, **kwargs, ) # Compute the inverse, empty the evaluation scores again and overwrite with the inverse scores. - print( - "Scores shape", np.array(self.scores).shape, np.array(self.scores_inv).shape - ) inv_scores = (np.array(self.scores) - np.array(self.scores_inv)).tolist() - print("Shape inv", np.shape(inv_scores)) - self.evaluation_scores = inv_scores - print("Shape evaluation_scores", np.shape(self.evaluation_scores)) - - if self.return_aggregate: - print("Returning aggregate score.") + if self.return_auc_per_sample: self.evaluation_scores = self.get_mean_score.reshape(-1) - self.all_evaluation_scores.extend(self.metric_init.evaluation_scores) - - print("Shape inv_scores reshaped", np.shape(self.evaluation_scores)) - return self.evaluation_scores def convert_attributions_to_rankings(self): @@ -356,3 +339,28 @@ def get_auc_score(self): return np.mean( [utils.calculate_auc(np.array(curve)) for curve in self.evaluation_scores] ) + + def get_inverse_attributions(self, a_batch: np.array): + + # Attributions need to have only one axis, else flatten and reshape back. + shape_ori = a_batch.shape + a_batch = a_batch.reshape((shape_ori[0], -1)) + if self.inverse_method == "sign-flip": + a_batch_inv = -np.array(a_batch) + elif self.inverse_method == "value-swap": + indices = np.argsort(a_batch, axis=1) + a_batch_inv = np.empty_like(a_batch) + a_batch_inv[np.arange(a_batch_inv.shape[0])[:, None], indices] = a_batch[ + np.arange(a_batch_inv.shape[0])[:, None], indices[:, ::-1] + ] + a_batch_inv = a_batch_inv.reshape(shape_ori) + return a_batch_inv + + def inverse_wrapper(self, model, inputs, targets, **kwargs): + explain_func = kwargs["explain_func"] + inverse_method = kwargs["inverse_method"] + a_batch = explain_func(model, inputs, targets, **kwargs) + a_batch_inv = self.get_inverse_attributions( + inverse_method=inverse_method, a_batch=a_batch + ) + return a_batch_inv From 77eb1a8f783c7cdb29916ef910b4a2def67e471b Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 25 Mar 2024 17:02:06 +0100 Subject: [PATCH 27/41] replace assert with warning --- quantus/evaluation.py | 16 +++--- quantus/helpers/asserts.py | 69 +----------------------- quantus/helpers/warn.py | 76 +++++++++++++++++++++++++++ quantus/metrics/base.py | 6 +-- quantus/metrics/inverse_estimation.py | 38 +++++++------- 5 files changed, 105 insertions(+), 100 deletions(-) diff --git a/quantus/evaluation.py b/quantus/evaluation.py index 90c9a3c6c..3087c2514 100644 --- a/quantus/evaluation.py +++ b/quantus/evaluation.py @@ -12,9 +12,7 @@ import numpy as np import pandas as pd -from quantus.helpers import asserts -from quantus.helpers import utils -from quantus.helpers import warn +from quantus.helpers import asserts, utils, warn from quantus.helpers.model.model_interface import ModelInterface from quantus.functions.explanation_func import explain @@ -205,7 +203,7 @@ def evaluate( a_batch = utils.expand_attribution_channel(a_batch, x_batch) # Asserts. - asserts.assert_attributions(a_batch=a_batch, x_batch=x_batch) + warn.warn_attributions(a_batch=a_batch, x_batch=x_batch) elif isinstance(value, Dict): @@ -226,7 +224,7 @@ def evaluate( a_batch = utils.expand_attribution_channel(a_batch, x_batch) # Asserts. - asserts.assert_attributions(a_batch=a_batch, x_batch=x_batch) + warn.warn_attributions(a_batch=a_batch, x_batch=x_batch) elif isinstance(value, np.ndarray): explain_funcs[method] = explain @@ -241,11 +239,11 @@ def evaluate( if explain_func_kwargs is None: explain_func_kwargs = {} - for (metric, metric_func) in metrics.items(): + for metric, metric_func in metrics.items(): results[method][metric] = {} - for (call_kwarg_str, call_kwarg) in call_kwargs.items(): + for call_kwarg_str, call_kwarg in call_kwargs.items(): if verbose: print( @@ -287,8 +285,8 @@ def evaluate( # Clean up the results if there is only one call_kwarg. for method, value in xai_methods.items(): results_ordered[method] = {} - for (metric, metric_func) in metrics.items(): - for (call_kwarg_str, call_kwarg) in call_kwargs.items(): + for metric, metric_func in metrics.items(): + for call_kwarg_str, call_kwarg in call_kwargs.items(): results_ordered[method][metric] = results[method][metric][ call_kwarg_str ] diff --git a/quantus/helpers/asserts.py b/quantus/helpers/asserts.py index 401f5f568..fba201b78 100644 --- a/quantus/helpers/asserts.py +++ b/quantus/helpers/asserts.py @@ -8,6 +8,7 @@ from typing import Callable, Tuple, Sequence, Union +import warnings import numpy as np @@ -128,74 +129,6 @@ def assert_layer_order(layer_order: str) -> None: assert layer_order in ["top_down", "bottom_up", "independent"] -def assert_attributions(x_batch: np.array, a_batch: np.array) -> None: - """ - Asserts on attributions, assumes channel first layout. - - Parameters - ---------- - x_batch: np.ndarray - The batch of input to compare the shape of the attributions with. - a_batch: np.ndarray - The batch of attributions. - - Returns - ------- - None - """ - assert ( - type(a_batch) == np.ndarray - ), "Attributions 'a_batch' should be of type np.ndarray." - assert np.shape(x_batch)[0] == np.shape(a_batch)[0], ( - "The inputs 'x_batch' and attributions 'a_batch' should " - "include the same number of samples." - "{} != {}".format(np.shape(x_batch)[0], np.shape(a_batch)[0]) - ) - assert np.ndim(x_batch) == np.ndim(a_batch), ( - "The inputs 'x_batch' and attributions 'a_batch' should " - "have the same number of dimensions." - "{} != {}".format(np.ndim(x_batch), np.ndim(a_batch)) - ) - a_shape = [s for s in np.shape(a_batch)[1:] if s != 1] - x_shape = [s for s in np.shape(x_batch)[1:]] - assert a_shape[0] == x_shape[0] or a_shape[-1] == x_shape[-1], ( - "The dimensions of attribution and input per sample should correspond in either " - "the first or last dimensions, but got shapes " - "{} and {}".format(a_shape, x_shape) - ) - assert all([a in x_shape for a in a_shape]), ( - "All attribution dimensions should be included in the input dimensions, " - "but got shapes {} and {}".format(a_shape, x_shape) - ) - assert all( - [ - x_shape.index(a) > x_shape.index(a_shape[i]) - for a in a_shape - for i in range(a_shape.index(a)) - ] - ), ( - "The dimensions of the attribution must correspond to dimensions of the input in the same order, " - "but got shapes {} and {}".format(a_shape, x_shape) - ) - assert not np.all((a_batch == 0)), ( - "The elements in the attribution vector are all equal to zero, " - "which may cause inconsistent results since many metrics rely on ordering. " - "Recompute the explanations." - ) - assert not np.all((a_batch == 1.0)), ( - "The elements in the attribution vector are all equal to one, " - "which may cause inconsistent results since many metrics rely on ordering. " - "Recompute the explanations." - ) - assert len(set(a_batch.flatten().tolist())) > 1, ( - "The attributions are uniformly distributed, " - "which may cause inconsistent results since many " - "metrics rely on ordering." - "Recompute the explanations." - ) - assert not np.all((a_batch < 0.0)), "Attributions should not all be less than zero." - - def assert_segmentations(x_batch: np.array, s_batch: np.array) -> None: """ Asserts on segmentations, assumes channel first layout. diff --git a/quantus/helpers/warn.py b/quantus/helpers/warn.py index 169e2276b..48e273611 100644 --- a/quantus/helpers/warn.py +++ b/quantus/helpers/warn.py @@ -269,3 +269,79 @@ def warn_max_size() -> None: None """ warnings.warn("Ratio is smaller than max size.") + + +def warn_attributions(x_batch: np.array, a_batch: np.array) -> None: + """ + Asserts on attributions, assumes channel first layout. + + Parameters + ---------- + x_batch: np.ndarray + The batch of input to compare the shape of the attributions with. + a_batch: np.ndarray + The batch of attributions. + + Returns + ------- + None + """ + if not (type(a_batch) == np.ndarray): + warnings.warn("Attributions 'a_batch' should be of type np.ndarray.") + if np.shape(x_batch)[0] == np.shape(a_batch)[0]: + warnings.warn( + "The inputs 'x_batch' and attributions 'a_batch' should " + "include the same number of samples." + "{} != {}".format(np.shape(x_batch)[0], np.shape(a_batch)[0]) + ) + if not np.ndim(x_batch) == np.ndim(a_batch): + warnings.warn( + "The inputs 'x_batch' and attributions 'a_batch' should " + "have the same number of dimensions." + "{} != {}".format(np.ndim(x_batch), np.ndim(a_batch)) + ) + a_shape = [s for s in np.shape(a_batch)[1:] if s != 1] + x_shape = [s for s in np.shape(x_batch)[1:]] + if not (a_shape[0] == x_shape[0] or a_shape[-1] == x_shape[-1]): + warnings.warn( + "The dimensions of attribution and input per sample should correspond in either " + "the first or last dimensions, but got shapes " + "{} and {}".format(a_shape, x_shape) + ) + if not all([a in x_shape for a in a_shape]): + warnings.warn( + "All attribution dimensions should be included in the input dimensions, " + "but got shapes {} and {}".format(a_shape, x_shape) + ) + if not all( + [ + x_shape.index(a) > x_shape.index(a_shape[i]) + for a in a_shape + for i in range(a_shape.index(a)) + ] + ): + warnings.warn( + "The dimensions of the attribution must correspond to dimensions of the input in the same order, " + "but got shapes {} and {}".format(a_shape, x_shape) + ) + if np.all((a_batch == 0)): + warnings.warn( + "The elements in the attribution vector are all equal to zero, " + "which may cause inconsistent results since many metrics rely on ordering. " + "Recompute the explanations." + ) + if np.all((a_batch == 1.0)): + warnings.warn( + "The elements in the attribution vector are all equal to one, " + "which may cause inconsistent results since many metrics rely on ordering. " + "Recompute the explanations." + ) + if len(set(a_batch.flatten().tolist())) > 1: + warnings.warn( + "The attributions are uniformly distributed, " + "which may cause inconsistent results since many " + "metrics rely on ordering." + "Recompute the explanations." + ) + if np.all((a_batch < 0.0)): + warnings.warn("Attributions should not all be less than zero.") diff --git a/quantus/metrics/base.py b/quantus/metrics/base.py index ef980b140..37dc24819 100644 --- a/quantus/metrics/base.py +++ b/quantus/metrics/base.py @@ -421,7 +421,7 @@ def general_preprocess( """ self.channel_first = channel_first - self.model_predict_kwargs = model_predict_kwargs + self.model_predict_kwargs = model_predict_kwargs or {} self.softmax = softmax self.device = device @@ -450,7 +450,7 @@ def general_preprocess( if a_batch is not None: a_batch = utils.expand_attribution_channel(a_batch, x_batch) - asserts.assert_attributions(x_batch=x_batch, a_batch=a_batch) + warn.warn_attributions(x_batch=x_batch, a_batch=a_batch) self.a_axes = utils.infer_attribution_axes(a_batch, x_batch) # Normalise with specified keyword arguments if requested. @@ -933,7 +933,7 @@ def explain_batch( model=model, inputs=x_batch, targets=y_batch, **self.explain_func_kwargs ) a_batch = utils.expand_attribution_channel(a_batch, x_batch) - asserts.assert_attributions(x_batch=x_batch, a_batch=a_batch) + warn.warn_attributions(x_batch=x_batch, a_batch=a_batch) # Normalise and take absolute values of the attributions, if configured during metric instantiation. if self.normalise: diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index bd02741e3..d3018f4db 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -122,12 +122,15 @@ def __init__( ) self.return_auc_per_sample = return_auc_per_sample + self.inverse_method = inverse_method + self.metric_init = metric_init + + # TODO. Update warnings. if self.inverse_method not in ["sign-flip", "value-swap"]: raise ValueError( "The 'inverse_method' in init **kwargs, \ must be either 'sign-flip' or 'value-swap'." ) - self.inverse_method = inverse_method if self.metric_init.return_aggregate: print( "The metric is not designed to return an aggregate score, setting return_aggregate=False." @@ -139,7 +142,6 @@ def __init__( "have positive attributions only. Set 'abs' param of the metric init to 'False'." ) - # TODO. Update warnings. if not self.disable_warnings: warn.warn_parameterisation( metric_name=self.__class__.__name__, @@ -147,8 +149,6 @@ def __init__( citation=("Update here."), ) - self.metric_init = metric_init - def __call__( self, model, @@ -156,7 +156,7 @@ def __call__( y_batch: np.array, a_batch: Optional[np.ndarray] = None, s_batch: Optional[np.ndarray] = None, - channel_first: Optional[bool] = None, + channel_first: Optional[bool] = True, explain_func: Optional[Callable] = None, explain_func_kwargs: Optional[Dict] = None, model_predict_kwargs: Optional[Dict] = None, @@ -274,8 +274,8 @@ def evaluate_batch( self.explain_func_kwargs["explain_func"] = self.explain_func self.explain_func_kwargs["inverse_method"] = self.inverse_method - self.scores = self.metric_init( - model=model, + self.scores_ori = self.metric_init( + model=model.get_model(), x_batch=x_batch, y_batch=y_batch, a_batch=a_batch, @@ -288,7 +288,7 @@ def evaluate_batch( model_predict_kwargs=self.model_predict_kwargs, **kwargs, ) - assert len(self.scores) == len(x_batch), ( + assert len(self.scores_ori) == len(x_batch), ( "To run the inverse estimation, the number of evaluation scores " "must match the number of instances in the batch." ) @@ -301,7 +301,7 @@ def evaluate_batch( # Run inverse experiment. self.scores_inv = self.metric_init( - model=model, + model=model.get_model(), x_batch=x_batch, y_batch=y_batch, a_batch=a_batch_inv, @@ -316,14 +316,14 @@ def evaluate_batch( ) # Compute the inverse, empty the evaluation scores again and overwrite with the inverse scores. - inv_scores = (np.array(self.scores) - np.array(self.scores_inv)).tolist() - - self.evaluation_scores = inv_scores + inv_scores = (np.array(self.scores_ori) - np.array(self.scores_inv)).tolist() + print(np.shape(inv_scores)) if self.return_auc_per_sample: - self.evaluation_scores = self.get_mean_score.reshape(-1) + inv_scores = self.get_mean_score.reshape(-1) + print(np.shape(inv_scores)) - return self.evaluation_scores + return inv_scores def convert_attributions_to_rankings(self): pass @@ -357,10 +357,8 @@ def get_inverse_attributions(self, a_batch: np.array): return a_batch_inv def inverse_wrapper(self, model, inputs, targets, **kwargs): - explain_func = kwargs["explain_func"] - inverse_method = kwargs["inverse_method"] - a_batch = explain_func(model, inputs, targets, **kwargs) - a_batch_inv = self.get_inverse_attributions( - inverse_method=inverse_method, a_batch=a_batch - ) + # explain_func = kwargs["explain_func"] + # inverse_method = kwargs["inverse_method"] + a_batch = self.explain_func(model, inputs, targets, **self.explain_func_kwargs) + a_batch_inv = self.get_inverse_attributions(a_batch=a_batch) return a_batch_inv From 66d75af44459acf2aeeab744b1abfcee7a345402 Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 25 Mar 2024 17:14:04 +0100 Subject: [PATCH 28/41] bugfix evaluate_batch --- quantus/metrics/inverse_estimation.py | 39 ++++++++++++------------ tests/metrics/test_inverse_estimation.py | 9 ++++++ 2 files changed, 29 insertions(+), 19 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index d3018f4db..5b8b46796 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -53,7 +53,8 @@ def __init__( self, metric_init: Metric, inverse_method: str = "sign-flip", - return_auc_per_sample: bool = True, + return_mean_per_sample: bool = True, + return_auc_per_sample: bool = False, abs: bool = False, normalise: bool = False, normalise_func: Optional[Callable] = None, @@ -122,10 +123,14 @@ def __init__( ) self.return_auc_per_sample = return_auc_per_sample + self.return_mean_per_sample = return_mean_per_sample self.inverse_method = inverse_method self.metric_init = metric_init # TODO. Update warnings. + assert not ( + self.return_mean_per_sample and self.return_auc_per_sample + ), "Only one of 'return_mean_per_sample' and 'return_auc_per_sample' can be True." if self.inverse_method not in ["sign-flip", "value-swap"]: raise ValueError( "The 'inverse_method' in init **kwargs, \ @@ -307,7 +312,7 @@ def evaluate_batch( a_batch=a_batch_inv, s_batch=s_batch, channel_first=self.channel_first, - explain_func=self.inverse_wrapper, + explain_func=self.inverse_explain_wrapper, explain_func_kwargs=self.explain_func_kwargs, softmax=self.softmax, device=self.device, @@ -317,28 +322,24 @@ def evaluate_batch( # Compute the inverse, empty the evaluation scores again and overwrite with the inverse scores. inv_scores = (np.array(self.scores_ori) - np.array(self.scores_inv)).tolist() - - print(np.shape(inv_scores)) - if self.return_auc_per_sample: - inv_scores = self.get_mean_score.reshape(-1) - print(np.shape(inv_scores)) + # print("Scores shape", np.shape(self.scores_ori), np.shape(self.scores_inv)) + # print("Inverse shape", np.shape(inv_scores)) + if self.return_mean_per_sample: + inv_scores = self.get_mean_score(scores=inv_scores) + # print("Agg shape", np.shape(inv_scores)) + elif self.return_auc_per_sample: + inv_scores = self.get_auc_score(scores=inv_scores) + # print("Agg shape", np.shape(inv_scores)) return inv_scores - def convert_attributions_to_rankings(self): - pass - - @property - def get_mean_score(self): + def get_mean_score(self, scores): """Calculate the area under the curve (AUC) score for several test samples.""" - return np.mean(np.array(self.evaluation_scores), axis=1) + return np.mean(np.array(scores), axis=1) - @property - def get_auc_score(self): + def get_auc_score(self, scores): """Calculate the area under the curve (AUC) score for several test samples.""" - return np.mean( - [utils.calculate_auc(np.array(curve)) for curve in self.evaluation_scores] - ) + return np.mean([utils.calculate_auc(np.array(curve)) for curve in scores]) def get_inverse_attributions(self, a_batch: np.array): @@ -356,7 +357,7 @@ def get_inverse_attributions(self, a_batch: np.array): a_batch_inv = a_batch_inv.reshape(shape_ori) return a_batch_inv - def inverse_wrapper(self, model, inputs, targets, **kwargs): + def inverse_explain_wrapper(self, model, inputs, targets, **kwargs): # explain_func = kwargs["explain_func"] # inverse_method = kwargs["inverse_method"] a_batch = self.explain_func(model, inputs, targets, **self.explain_func_kwargs) diff --git a/tests/metrics/test_inverse_estimation.py b/tests/metrics/test_inverse_estimation.py index ef65bc45b..7f6400619 100644 --- a/tests/metrics/test_inverse_estimation.py +++ b/tests/metrics/test_inverse_estimation.py @@ -223,6 +223,15 @@ def test_inverse_estimation_with_pixel_flipping( print("x_batch shape", np.shape(x_batch)) + inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) + scores = inv( + model=model, + x_batch=x_batch, + y_batch=y_batch, + a_batch=a_batch, + **call_params, + ) + try: inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) From 6f20997f9193c61d309803e6ee076472bc7e9e48 Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Mon, 25 Mar 2024 17:45:24 +0100 Subject: [PATCH 29/41] small fixes wrt assert/ warns --- quantus/metrics/inverse_estimation.py | 1 - quantus/metrics/randomisation/smooth_mprt.py | 4 ++-- tests/metrics/test_inverse_estimation.py | 5 +---- 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 5b8b46796..8d757f2b3 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -329,7 +329,6 @@ def evaluate_batch( # print("Agg shape", np.shape(inv_scores)) elif self.return_auc_per_sample: inv_scores = self.get_auc_score(scores=inv_scores) - # print("Agg shape", np.shape(inv_scores)) return inv_scores diff --git a/quantus/metrics/randomisation/smooth_mprt.py b/quantus/metrics/randomisation/smooth_mprt.py index 66a247bec..142f7b3e1 100644 --- a/quantus/metrics/randomisation/smooth_mprt.py +++ b/quantus/metrics/randomisation/smooth_mprt.py @@ -169,7 +169,7 @@ def __init__( if normalise_func_kwargs is None: normalise_func_kwargs = {} - + self.similarity_func = similarity_func self.normalise_func = normalise_func self.abs = abs @@ -663,7 +663,7 @@ def explain_smooth_batch( ) a_batch_smooth = utils.expand_attribution_channel(a_batch_smooth, x_batch) - asserts.assert_attributions(x_batch=x_batch, a_batch=a_batch_smooth) + warn.warn_attributions(x_batch=x_batch, a_batch=a_batch_smooth) # Normalise and take absolute values of the attributions, if configured during metric instantiation. if self.normalise: diff --git a/tests/metrics/test_inverse_estimation.py b/tests/metrics/test_inverse_estimation.py index 7f6400619..085505985 100644 --- a/tests/metrics/test_inverse_estimation.py +++ b/tests/metrics/test_inverse_estimation.py @@ -220,9 +220,6 @@ def test_inverse_estimation_with_pixel_flipping( metric_init = PixelFlipping(**init_params) metric_init.softmax = True - - print("x_batch shape", np.shape(x_batch)) - inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) scores = inv( model=model, @@ -231,7 +228,6 @@ def test_inverse_estimation_with_pixel_flipping( a_batch=a_batch, **call_params, ) - try: inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) @@ -242,6 +238,7 @@ def test_inverse_estimation_with_pixel_flipping( a_batch=a_batch, **call_params, ) + # print("x_batch shape", np.shape(x_batch)) # print(f"\n\n\tscores: {np.shape(inv.scores)},\n{inv.scores}") # print(f"\n\n\tscores_inv: {np.shape(inv.scores_inv)},\n{inv.scores_inv}") # print( From 0cddcd628b20240660deadda56793c505b817319 Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Tue, 26 Mar 2024 12:59:21 +0100 Subject: [PATCH 30/41] added plotting --- quantus/helpers/plotting.py | 61 +++++++++++++++++++++++++++ quantus/metrics/inverse_estimation.py | 18 ++++---- 2 files changed, 70 insertions(+), 9 deletions(-) diff --git a/quantus/helpers/plotting.py b/quantus/helpers/plotting.py index 4fa305713..cea2a8d15 100644 --- a/quantus/helpers/plotting.py +++ b/quantus/helpers/plotting.py @@ -67,6 +67,67 @@ def plot_pixel_flipping_experiment( plt.show() +def plot_inverse_curves( + y_batch: np.ndarray, + scores_ori: List[Any], + scores_inv: List[Any], + single_class: Union[int, None] = None, + *args, + **kwargs, +) -> None: + """ + Plot the pixel-flipping experiment as done in paper: + + References: + 1) Bach, Sebastian, et al. "On pixel-wise explanations for non-linear classifier + decisions by layer-wise relevance propagation." PloS one 10.7 (2015): e0130140. + + Parameters + ---------- + y_batch: np.ndarray + The list of true labels. + scores_ori: list + The list of evalution scores. + scores_inv: list + The list of evalution scores (inverse curve). + single_class: integer, optional + An integer to specify the label to plot. + args: optional + Arguments. + kwargs: optional + Keyword arguments. + + Returns + ------- + None + """ + + fig = plt.figure(figsize=(8, 6)) + if single_class is None: + for c in np.unique(y_batch): + indices = np.where(y_batch == c) + plt.plot( + np.linspace(0, 1, len(scores_ori[0])), + np.mean(np.array(scores_ori)[indices], axis=0), + label=f"Original curve: {str(c)} ({indices[0].size} samples)", + ) + plt.plot( + np.linspace(0, 1, len(scores_inv[0])), + np.mean(np.array(scores_inv)[indices], axis=0), + label=f"Inverse curve: {str(c)} ({indices[0].size} samples)", + ) + plt.xlabel("Fraction of pixels flipped") + plt.ylabel("Mean Prediction") + plt.gca().set_yticklabels( + ["{:.0f}%".format(x * 100) for x in plt.gca().get_yticks()] + ) + plt.gca().set_xticklabels( + ["{:.0f}%".format(x * 100) for x in plt.gca().get_xticks()] + ) + plt.legend() + plt.show() + + def plot_selectivity_experiment(results: Dict[str, List[Any]], *args, **kwargs) -> None: """ Plot the selectivity experiment as done in paper: diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index 8d757f2b3..fa6770e8e 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -104,8 +104,7 @@ def __init__( Keyword arguments. """ if metric_init.default_plot_func is None: - # TODO. Create specific plot. - default_plot_func = plotting.plot_pixel_flipping_experiment + default_plot_func = plotting.plot_inverse_curves self.return_aggregate = return_aggregate @@ -321,16 +320,17 @@ def evaluate_batch( ) # Compute the inverse, empty the evaluation scores again and overwrite with the inverse scores. - inv_scores = (np.array(self.scores_ori) - np.array(self.scores_inv)).tolist() - # print("Scores shape", np.shape(self.scores_ori), np.shape(self.scores_inv)) - # print("Inverse shape", np.shape(inv_scores)) + inv_scores = np.array(self.scores_ori) - np.array(self.scores_inv) + print("Scores shape", np.shape(self.scores_ori), np.shape(self.scores_inv)) + print("Inverse shape", np.shape(inv_scores)) + print("Inverse shape", np.reshape(inv_scores, (len(inv_scores), -1)).shape) if self.return_mean_per_sample: inv_scores = self.get_mean_score(scores=inv_scores) - # print("Agg shape", np.shape(inv_scores)) + print("Agg shape", np.shape(inv_scores)) elif self.return_auc_per_sample: inv_scores = self.get_auc_score(scores=inv_scores) - return inv_scores + return inv_scores.tolist() def get_mean_score(self, scores): """Calculate the area under the curve (AUC) score for several test samples.""" @@ -341,6 +341,7 @@ def get_auc_score(self, scores): return np.mean([utils.calculate_auc(np.array(curve)) for curve in scores]) def get_inverse_attributions(self, a_batch: np.array): + """Get the inverse attributions of the input attributions.""" # Attributions need to have only one axis, else flatten and reshape back. shape_ori = a_batch.shape @@ -357,8 +358,7 @@ def get_inverse_attributions(self, a_batch: np.array): return a_batch_inv def inverse_explain_wrapper(self, model, inputs, targets, **kwargs): - # explain_func = kwargs["explain_func"] - # inverse_method = kwargs["inverse_method"] + """Wrapper for the explanation function that computes the inverse attributions.""" a_batch = self.explain_func(model, inputs, targets, **self.explain_func_kwargs) a_batch_inv = self.get_inverse_attributions(a_batch=a_batch) return a_batch_inv From e248ad44ee983a02ee21d1329704030d3967e56b Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Tue, 26 Mar 2024 13:43:16 +0100 Subject: [PATCH 31/41] fixes --- tests/metrics/test_inverse_estimation.py | 103 +++++++++++++++++++++-- 1 file changed, 95 insertions(+), 8 deletions(-) diff --git a/tests/metrics/test_inverse_estimation.py b/tests/metrics/test_inverse_estimation.py index 085505985..916de4f88 100644 --- a/tests/metrics/test_inverse_estimation.py +++ b/tests/metrics/test_inverse_estimation.py @@ -14,6 +14,101 @@ PixelFlipping, RegionPerturbation, ) +from quantus.metrics.localisation import RelevanceRankAccuracy + + +@pytest.mark.inverse_estimation +@pytest.mark.parametrize( + "model,data,params,expected", + [ + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "a_batch_generate": True, + "init": { + "normalise": True, + "abs": False, + "disable_warnings": False, + "display_progressbar": False, + }, + "call": { + "explain_func": explain, + "explain_func_kwargs": { + "method": "Saliency", + }, + }, + }, + {"min": -1000.0, "max": 1000.0}, + ), + ], +) +def test_inverse_estimation_with_relevance_rank_accuracy( + model, + data: np.ndarray, + params: dict, + expected: Union[float, dict, bool], +): + x_batch = data["x_batch"] + y_batch = data["y_batch"] + s_batch = np.zeros((10, 1, 28, 28)) + s_batch[:, :, 0:15, 0:15] = 1.0 + + init_params = params.get("init", {}) + call_params = params.get("call", {}) + + if "a_batch" in data: + a_batch = data["a_batch"] + elif params.get("a_batch_generate", True): + explain = call_params["explain_func"] + explain_func_kwargs = call_params.get("explain_func_kwargs", {}) + a_batch = explain( + model=model, + inputs=x_batch, + targets=y_batch, + **explain_func_kwargs, + ) + assert a_batch is not None + else: + a_batch = None + + metric_init = RelevanceRankAccuracy(**init_params) + metric_init.softmax = True + + try: + + inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) + scores = inv( + model=model, + x_batch=x_batch, + y_batch=y_batch, + a_batch=a_batch, + s_batch=s_batch, + **call_params, + ) + # print("x_batch shape", np.shape(x_batch)) + # print(f"\n\n\tscores: {np.shape(inv.scores)},\n{inv.scores}") + # print(f"\n\n\tscores_inv: {np.shape(inv.scores_inv)},\n{inv.scores_inv}") + # print( + # f"\n\n\tall_evaluation_scores: {np.shape(inv.all_evaluation_scores)},\n{inv.all_evaluation_scores}" + # ) + # print(f"\n\n\tscores: {np.shape(scores)},\n{scores}") + + if "exception" not in expected: + assert all( + [ + (s >= expected["min"] and s <= expected["max"]) + for s_list in scores + for s in s_list + ] + ), "Test failed." + + except Exception as e: + if "exception" in expected and isinstance(e, expected["exception"]): + print(f'Raised exception type {expected["exception"]}', e) + else: + print(f"Unexpected exception occurred:", e) + return @pytest.mark.inverse_estimation @@ -220,14 +315,6 @@ def test_inverse_estimation_with_pixel_flipping( metric_init = PixelFlipping(**init_params) metric_init.softmax = True - inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) - scores = inv( - model=model, - x_batch=x_batch, - y_batch=y_batch, - a_batch=a_batch, - **call_params, - ) try: inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) From e7e44938c65de46e717c447c026a302d6e57637d Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Tue, 26 Mar 2024 14:28:40 +0100 Subject: [PATCH 32/41] eval func check and channel first fix --- quantus/evaluation.py | 2 ++ quantus/metrics/base.py | 6 +++--- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/quantus/evaluation.py b/quantus/evaluation.py index 3087c2514..d76aabebe 100644 --- a/quantus/evaluation.py +++ b/quantus/evaluation.py @@ -160,6 +160,8 @@ def evaluate( if call_kwargs is None: call_kwargs = {"call_kwargs_empty": {}} + elif not isinstance(call_kwargs, Dict): + raise TypeError("call_kwargs type should be of Dict[str, Dict] (if not None).") elif not isinstance(call_kwargs, Dict): raise TypeError("call_kwargs type should be of Dict[str, Dict] (if not None).") diff --git a/quantus/metrics/base.py b/quantus/metrics/base.py index 37dc24819..8942ed626 100644 --- a/quantus/metrics/base.py +++ b/quantus/metrics/base.py @@ -426,9 +426,9 @@ def general_preprocess( self.device = device # Reshape input batch to channel first order: - if not isinstance(channel_first, bool): # None is not a boolean instance. - channel_first = utils.infer_channel_first(x_batch) - x_batch = utils.make_channel_first(x_batch, channel_first) + if not isinstance(self.channel_first, bool): # None is not a boolean instance. + self.channel_first = utils.infer_channel_first(x_batch) + x_batch = utils.make_channel_first(x_batch, self.channel_first) if model is not None: # Use attribute value if not passed explicitly. From 721470195185f2edad54d99172b470ca694a332d Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Wed, 27 Mar 2024 11:10:49 +0100 Subject: [PATCH 33/41] added feature for mean/ AUC calc for localisation metric for inverse estimation --- quantus/metrics/inverse_estimation.py | 29 +++++++++------ tests/metrics/test_inverse_estimation.py | 45 +++++++++++++++++++++--- 2 files changed, 59 insertions(+), 15 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index fa6770e8e..c54963852 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -18,6 +18,7 @@ from quantus.functions.normalise_func import normalise_by_max from quantus.functions.perturb_func import baseline_replacement_by_indices from quantus.metrics.base import Metric +import warnings from quantus.helpers.enums import ( ModelType, DataType, @@ -319,17 +320,11 @@ def evaluate_batch( **kwargs, ) - # Compute the inverse, empty the evaluation scores again and overwrite with the inverse scores. + # Compute the inverse scores. inv_scores = np.array(self.scores_ori) - np.array(self.scores_inv) - print("Scores shape", np.shape(self.scores_ori), np.shape(self.scores_inv)) - print("Inverse shape", np.shape(inv_scores)) - print("Inverse shape", np.reshape(inv_scores, (len(inv_scores), -1)).shape) - if self.return_mean_per_sample: - inv_scores = self.get_mean_score(scores=inv_scores) - print("Agg shape", np.shape(inv_scores)) - elif self.return_auc_per_sample: - inv_scores = self.get_auc_score(scores=inv_scores) - + inv_scores = self.aggregate_inverse_scores( + inv_scores=inv_scores, nr_samples=len(x_batch) + ) return inv_scores.tolist() def get_mean_score(self, scores): @@ -340,6 +335,20 @@ def get_auc_score(self, scores): """Calculate the area under the curve (AUC) score for several test samples.""" return np.mean([utils.calculate_auc(np.array(curve)) for curve in scores]) + def aggregate_inverse_scores(self, inv_scores: np.array, nr_samples: int): + """Compute the mean / AUC score for the inverse estimation, if passed as vectors (like pixel-flipping curves) per sample.""" + if inv_scores.size == nr_samples: + warnings.warn( + f"Cannot compute mean score for scores with shape {inv_scores.shape}. Set 'return_mean_per_sample' and 'return_auc_per_sample' to False to return the raw scores." + ) + return inv_scores + else: + if self.return_mean_per_sample: + inv_scores = self.get_mean_score(scores=inv_scores) + elif self.return_auc_per_sample: + inv_scores = self.get_auc_score(scores=inv_scores) + return inv_scores + def get_inverse_attributions(self, a_batch: np.array): """Get the inverse attributions of the input attributions.""" diff --git a/tests/metrics/test_inverse_estimation.py b/tests/metrics/test_inverse_estimation.py index 916de4f88..1a5fa107f 100644 --- a/tests/metrics/test_inverse_estimation.py +++ b/tests/metrics/test_inverse_estimation.py @@ -41,6 +41,26 @@ }, {"min": -1000.0, "max": 1000.0}, ), + ( + lazy_fixture("load_mnist_model"), + lazy_fixture("load_mnist_images"), + { + "a_batch_generate": True, + "init": { + "normalise": True, + "abs": False, + "disable_warnings": False, + "display_progressbar": False, + }, + "call": { + "explain_func": explain, + "explain_func_kwargs": { + "method": "Saliency", + }, + }, + }, + {"min": -1000.0, "max": 1000.0}, + ), ], ) def test_inverse_estimation_with_relevance_rank_accuracy( @@ -51,7 +71,7 @@ def test_inverse_estimation_with_relevance_rank_accuracy( ): x_batch = data["x_batch"] y_batch = data["y_batch"] - s_batch = np.zeros((10, 1, 28, 28)) + s_batch = np.zeros((len(x_batch), 1, 28, 28)) s_batch[:, :, 0:15, 0:15] = 1.0 init_params = params.get("init", {}) @@ -75,6 +95,20 @@ def test_inverse_estimation_with_relevance_rank_accuracy( metric_init = RelevanceRankAccuracy(**init_params) metric_init.softmax = True + inv = InverseEstimation( + metric_init=metric_init, + return_aggregate=True, + return_mean_per_sample=False, + ) + scores = inv( + model=model, + x_batch=x_batch, + y_batch=y_batch, + a_batch=a_batch, + s_batch=s_batch, + **call_params, + ) + try: inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) @@ -87,10 +121,10 @@ def test_inverse_estimation_with_relevance_rank_accuracy( **call_params, ) # print("x_batch shape", np.shape(x_batch)) - # print(f"\n\n\tscores: {np.shape(inv.scores)},\n{inv.scores}") + # print(f"\n\n\tscores_ori: {np.shape(inv.scores_ori)},\n{inv.scores_ori}") # print(f"\n\n\tscores_inv: {np.shape(inv.scores_inv)},\n{inv.scores_inv}") # print( - # f"\n\n\tall_evaluation_scores: {np.shape(inv.all_evaluation_scores)},\n{inv.all_evaluation_scores}" + # f"\n\n\tall_evaluation_scores: {np.shape(inv.all_evaluation_scores)},\n{inv.all_evaluation_scores}" # ) # print(f"\n\n\tscores: {np.shape(scores)},\n{scores}") @@ -315,6 +349,7 @@ def test_inverse_estimation_with_pixel_flipping( metric_init = PixelFlipping(**init_params) metric_init.softmax = True + try: inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) @@ -326,10 +361,10 @@ def test_inverse_estimation_with_pixel_flipping( **call_params, ) # print("x_batch shape", np.shape(x_batch)) - # print(f"\n\n\tscores: {np.shape(inv.scores)},\n{inv.scores}") + # print(f"\n\n\tscores_ori: {np.shape(inv.scores_ori)},\n{inv.scores_ori}") # print(f"\n\n\tscores_inv: {np.shape(inv.scores_inv)},\n{inv.scores_inv}") # print( - # f"\n\n\tall_evaluation_scores: {np.shape(inv.all_evaluation_scores)},\n{inv.all_evaluation_scores}" + # f"\n\n\tall_evaluation_scores: {np.shape(inv.all_evaluation_scores)},\n{inv.all_evaluation_scores}" # ) # print(f"\n\n\tscores: {np.shape(scores)},\n{scores}") From a516928a256674bb344b4a539dc14fd73de4f41e Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Wed, 27 Mar 2024 11:31:40 +0100 Subject: [PATCH 34/41] add tests --- tests/metrics/test_inverse_estimation.py | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/tests/metrics/test_inverse_estimation.py b/tests/metrics/test_inverse_estimation.py index 1a5fa107f..d64a6244a 100644 --- a/tests/metrics/test_inverse_estimation.py +++ b/tests/metrics/test_inverse_estimation.py @@ -95,20 +95,6 @@ def test_inverse_estimation_with_relevance_rank_accuracy( metric_init = RelevanceRankAccuracy(**init_params) metric_init.softmax = True - inv = InverseEstimation( - metric_init=metric_init, - return_aggregate=True, - return_mean_per_sample=False, - ) - scores = inv( - model=model, - x_batch=x_batch, - y_batch=y_batch, - a_batch=a_batch, - s_batch=s_batch, - **call_params, - ) - try: inv = InverseEstimation(metric_init=metric_init, return_aggregate=True) @@ -120,13 +106,6 @@ def test_inverse_estimation_with_relevance_rank_accuracy( s_batch=s_batch, **call_params, ) - # print("x_batch shape", np.shape(x_batch)) - # print(f"\n\n\tscores_ori: {np.shape(inv.scores_ori)},\n{inv.scores_ori}") - # print(f"\n\n\tscores_inv: {np.shape(inv.scores_inv)},\n{inv.scores_inv}") - # print( - # f"\n\n\tall_evaluation_scores: {np.shape(inv.all_evaluation_scores)},\n{inv.all_evaluation_scores}" - # ) - # print(f"\n\n\tscores: {np.shape(scores)},\n{scores}") if "exception" not in expected: assert all( From a55ef15fdf38e7fadede680292710c61a1164def Mon Sep 17 00:00:00 2001 From: annahedstroem Date: Wed, 27 Mar 2024 11:37:13 +0100 Subject: [PATCH 35/41] plotting updates, tiny --- quantus/helpers/plotting.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantus/helpers/plotting.py b/quantus/helpers/plotting.py index cea2a8d15..751b6462c 100644 --- a/quantus/helpers/plotting.py +++ b/quantus/helpers/plotting.py @@ -116,7 +116,7 @@ def plot_inverse_curves( np.mean(np.array(scores_inv)[indices], axis=0), label=f"Inverse curve: {str(c)} ({indices[0].size} samples)", ) - plt.xlabel("Fraction of pixels flipped") + plt.xlabel("Fraction masked") plt.ylabel("Mean Prediction") plt.gca().set_yticklabels( ["{:.0f}%".format(x * 100) for x in plt.gca().get_yticks()] From 99b2fa1a0c79a98d7ca267948d1cc2491b9ca1d8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Thu, 4 Apr 2024 15:07:14 +0200 Subject: [PATCH 36/41] include area_between_curves flag inverse_estimation.py --- quantus/metrics/inverse_estimation.py | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/quantus/metrics/inverse_estimation.py b/quantus/metrics/inverse_estimation.py index c54963852..b3f96a1ae 100644 --- a/quantus/metrics/inverse_estimation.py +++ b/quantus/metrics/inverse_estimation.py @@ -54,6 +54,7 @@ def __init__( self, metric_init: Metric, inverse_method: str = "sign-flip", + area_between_curves: bool = False, return_mean_per_sample: bool = True, return_auc_per_sample: bool = False, abs: bool = False, @@ -125,6 +126,7 @@ def __init__( self.return_auc_per_sample = return_auc_per_sample self.return_mean_per_sample = return_mean_per_sample self.inverse_method = inverse_method + self.area_between_curves = area_between_curves self.metric_init = metric_init # TODO. Update warnings. @@ -321,11 +323,21 @@ def evaluate_batch( ) # Compute the inverse scores. - inv_scores = np.array(self.scores_ori) - np.array(self.scores_inv) - inv_scores = self.aggregate_inverse_scores( - inv_scores=inv_scores, nr_samples=len(x_batch) - ) - return inv_scores.tolist() + if self.area_between_curves: + scores_ori = self.aggregate_inverse_scores( + inv_scores=self.scores_ori, nr_samples=len(x_batch) + ) + scores_inv = self.aggregate_inverse_scores( + inv_scores=self.scores_inv, nr_samples=len(x_batch) + ) + + return (np.array(scores_ori) - np.array(scores_inv)).tolist() + else: + inv_scores = np.array(self.scores_ori) - np.array(self.scores_inv) + inv_scores = self.aggregate_inverse_scores( + inv_scores=inv_scores, nr_samples=len(x_batch) + ) + return inv_scores.tolist() def get_mean_score(self, scores): """Calculate the area under the curve (AUC) score for several test samples.""" From 3d274678c251152c3ab099ee48d5c31a04f6a4a5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Sun, 14 Apr 2024 18:33:36 +0200 Subject: [PATCH 37/41] Update consistency.py --- quantus/metrics/robustness/consistency.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantus/metrics/robustness/consistency.py b/quantus/metrics/robustness/consistency.py index 6e48e2e3c..8a22a1c20 100644 --- a/quantus/metrics/robustness/consistency.py +++ b/quantus/metrics/robustness/consistency.py @@ -55,7 +55,7 @@ class Consistency(Metric[List[float]]): name = "Consistency" data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR} model_applicability = {ModelType.TORCH, ModelType.TF} - score_direction = ScoreDirection.LOWER + score_direction = ScoreDirection.HIGHER evaluation_category = EvaluationCategory.ROBUSTNESS def __init__( From 7ae5ecfe814da62c10063435ec7f46cb68c89f69 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Tue, 16 Apr 2024 13:42:12 +0200 Subject: [PATCH 38/41] Update region_perturbation.py --- quantus/metrics/faithfulness/region_perturbation.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/quantus/metrics/faithfulness/region_perturbation.py b/quantus/metrics/faithfulness/region_perturbation.py index cb8278215..da285f035 100644 --- a/quantus/metrics/faithfulness/region_perturbation.py +++ b/quantus/metrics/faithfulness/region_perturbation.py @@ -69,6 +69,7 @@ def __init__( patch_size: int = 8, order: str = "morf", regions_evaluation: int = 100, + return_auc_per_sample: bool = False, abs: bool = False, normalise: bool = True, normalise_func: Optional[Callable[[np.ndarray], np.ndarray]] = None, @@ -92,7 +93,9 @@ def __init__( The number of regions to evaluate, default=100. order: string Indicates whether attributions are ordered randomly ("random"), - according to the most relevant first ("morf"), or least relevant first ("lerf"), default="morf". + according to the most relevant first ("morf"), or least relevant first ("lerf"), default="morf". + return_auc_per_sample: boolean + Indicates if an AUC score should be computed over the curve and returned. abs: boolean Indicates whether absolute operation is applied on the attribution, default=False. normalise: boolean @@ -146,6 +149,7 @@ def __init__( self.patch_size = patch_size self.order = order.lower() self.regions_evaluation = regions_evaluation + self.return_auc_per_sample = return_auc_per_sample self.perturb_func = make_perturb_func( perturb_func, perturb_func_kwargs, perturb_baseline=perturb_baseline ) @@ -409,6 +413,9 @@ def evaluate_instance( results[patch_id] = y_pred - y_pred_perturb + if self.return_auc_per_sample: + return float(utils.calculate_auc(preds)) + return results @property From 4dd68ce88e85abe83a0941624a7f0ad0c9d9f0ed Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Tue, 16 Apr 2024 15:44:16 +0200 Subject: [PATCH 39/41] Update region_perturbation.py --- quantus/metrics/faithfulness/region_perturbation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantus/metrics/faithfulness/region_perturbation.py b/quantus/metrics/faithfulness/region_perturbation.py index da285f035..f65a7f367 100644 --- a/quantus/metrics/faithfulness/region_perturbation.py +++ b/quantus/metrics/faithfulness/region_perturbation.py @@ -414,7 +414,7 @@ def evaluate_instance( results[patch_id] = y_pred - y_pred_perturb if self.return_auc_per_sample: - return float(utils.calculate_auc(preds)) + return float(utils.calculate_auc(results)) return results From c823d4a265f3efe7184688b30cb5af26b4d01aae Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Tue, 16 Apr 2024 15:51:01 +0200 Subject: [PATCH 40/41] Update return typing region_perturbation.py --- quantus/metrics/faithfulness/region_perturbation.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/quantus/metrics/faithfulness/region_perturbation.py b/quantus/metrics/faithfulness/region_perturbation.py index f65a7f367..4eaaf613d 100644 --- a/quantus/metrics/faithfulness/region_perturbation.py +++ b/quantus/metrics/faithfulness/region_perturbation.py @@ -190,7 +190,7 @@ def __call__( device: Optional[str] = None, batch_size: int = 64, **kwargs, - ) -> List[float]: + ) -> Union[List[float], float]: """ This implementation represents the main logic of the metric and makes the class object callable. It completes instance-wise evaluation of explanations (a_batch) with respect to input data (x_batch), @@ -287,7 +287,7 @@ def evaluate_instance( x: np.ndarray, y: np.ndarray, a: np.ndarray, - ) -> List[float]: + ) -> Union[List[float], float]: """ Evaluate instance gets model and data for a single instance as input and returns the evaluation result. @@ -432,7 +432,7 @@ def evaluate_batch( y_batch: np.ndarray, a_batch: np.ndarray, **kwargs, - ) -> List[List[float]]: + ) -> List[Union[List[float], float]]: """ This method performs XAI evaluation on a single batch of explanations. For more information on the specific logic, we refer the metric’s initialisation docstring. From 24ec8fa81857ab0cd3e13798c25a85eda656d04a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anna=20Hedstr=C3=B6m?= Date: Wed, 17 Apr 2024 18:19:06 +0200 Subject: [PATCH 41/41] Update region_perturbation.py --- quantus/metrics/faithfulness/region_perturbation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantus/metrics/faithfulness/region_perturbation.py b/quantus/metrics/faithfulness/region_perturbation.py index 4eaaf613d..fc83cf915 100644 --- a/quantus/metrics/faithfulness/region_perturbation.py +++ b/quantus/metrics/faithfulness/region_perturbation.py @@ -8,7 +8,7 @@ import itertools import sys -from typing import Any, Callable, Dict, List, Optional +from typing import Any, Callable, Dict, List, Optional, Union import numpy as np