diff --git a/.gitignore b/.gitignore index efd163e..159b1eb 100644 --- a/.gitignore +++ b/.gitignore @@ -9,8 +9,8 @@ __pycache__/ *.pdf *.npz -advertorch/__pycache__/ -advertorch.egg-info +deepcp/__pycache__/ +deepcp.egg-info data/ .pytest_cache/ .cache/ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5dff569..822ccd1 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,6 +1,6 @@ -# Contributing to AdverTorch +# Contributing to deepcp -Thank you considering contributing to AdverTorch! +Thank you considering contributing to deepcp! This document provide brief guidelines for potential contributors. @@ -10,19 +10,7 @@ We ask that you follow the `PEP8` coding style in your pull requests, [`flake8`] --- ### Detailed guidelines for contributing new attacks -- *(mandatory)* The implementation file should be added to the folder `advertorch/attacks`, and the class should be imported in `advertorch/attacks/__init__.py`. -- *(mandatory)* The attack should be included in different unit tests, this can be done by adding the attack class to different lists in `advertorch/test_utils.py` - + add to `general_input_attacks` if it can perturb input tensor of any shape (not limited to images), - + add to `image_only_attacks` if it only works on images, - + add to `label_attacks` if the attack manipulates labels, - + add to `feature_attacks` if the attack manipulates features, - + add to `batch_consistent_attacks` if the attack's behavior should be the same when it is applied to a single example or a batch, - + add to `targeted_only_attacks` if the attack is a label attack and does not work for the untargeted case, - + add entry to `attack_kwargs` in `advertorch/tests/test_attacks_running.py`, for setting the hyperparameters used for test. -- *(mandatory)* Benchmark the attack with at least one performance measure, by adding a script to `advertorch_examples/attack_benchmarks`. -- *(mandatory)* If the contributor has a GPU computer, run `pytest` locally to make sure all the tests pass. (This is because travis-ci currently do not provide GPU machines for continuous integration.) If the contributor does not have a GPU computer, please let us know in the pull request. -- *(optional)* When an attack can be compared against other implementations, a comparison test could be added to `advertorch/external_tests`. -- *(optional)* Add an ipython notebook example. + --- ### Copyright notice at the beginning of files diff --git a/MANIFEST.in b/MANIFEST.in index d313488..810b2dc 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,8 +1,7 @@ include tests/*.py include external_tests/*.py -include advertorch/VERSION +include deepcp/VERSION include pytest.ini include LICENSE include LICENSE.GPL -include advertorch_examples/trained_models/*.pt -include advertorch_examples/*.ipynb +include deepcp_examples/*.ipynb diff --git a/README.md b/README.md index 4dad1fe..5efe94c 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,13 @@ - - -DeepCP is a Python toolbox for conformal prediction research. The primary functionalities are implemented in PyTorch. Specifically, DeepCP contains modules of post-hoc methods and training methods for classification problems and regression problems. - - -#### Latest version (v0.1) +DeepCP is a Python toolbox for conformal prediction research on deep learning models. The primary functionalities are implemented in PyTorch. Specifically, DeepCP contains modules of post-hoc methods and training methods for classification problems and regression problems. ## Installation -### Installing AdverTorch itself +### Installing DeepCP itself -We developed DeepCP under Python 3.8 and PyTorch 1.0.0 & 0.4.1. To install DeepCP, simply run +We developed DeepCP under Python 3.9 and PyTorch 2.0.1. To install DeepCP, simply run ``` -pip install deeptorch +pip install deepcp ``` or clone the repo and run @@ -30,39 +20,9 @@ To install the package in "editable" mode: pip install -e . ``` -### Setting up the testing environments - -Some attacks are tested against implementations in [Foolbox](https://github.com/bethgelab/foolbox) or [CleverHans](https://github.com/tensorflow/cleverhans) to ensure correctness. Currently, they are tested under the following versions of related libraries. -``` -conda install -c anaconda tensorflow-gpu==1.11.0 -pip install git+https://github.com/tensorflow/cleverhans.git@336b9f4ed95dccc7f0d12d338c2038c53786ab70 -pip install Keras==2.2.2 -pip install foolbox==1.3.2 -``` - ## Examples ```python -# prepare your pytorch model as "model" -# prepare a batch of data and label as "cln_data" and "true_label" -# ... - -from advertorch.attacks import LinfPGDAttack - -adversary = LinfPGDAttack( - model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.3, - nb_iter=40, eps_iter=0.01, rand_init=True, clip_min=0.0, clip_max=1.0, - targeted=False) - -adv_untargeted = adversary.perturb(cln_data, true_label) - -target = torch.ones_like(true_label) * 3 -adversary.targeted = True -adv_targeted = adversary.perturb(cln_data, target) -``` - -```python - logits_cal = ... Y_cal = ... @@ -77,48 +37,21 @@ Y_Sets = predictor.predict(logits_test) # evaluate the prediction sets metrics = utils.coverage_rate(Y_sets,Y_test) - ``` -For runnable examples see [`advertorch_examples/tutorial_attack_defense_bpda_mnist.ipynb`](https://github.com/BorealisAI/advertorch/blob/master/advertorch_examples/tutorial_attack_defense_bpda_mnist.ipynb) for how to attack and defend; see [`advertorch_examples/tutorial_train_mnist.py`](https://github.com/BorealisAI/advertorch/blob/master/advertorch_examples/tutorial_train_mnist.py) for how to adversarially train a robust model on MNIST. - -## Documentation - -The documentation webpage is on readthedocs https://advertorch.readthedocs.io. - ## Coming Soon -AdverTorch is still under active development. We will add the following features/items down the road: +DeepCP is still under active development. We will add the following features/items down the road: -* more examples -* support for other machine learning frameworks, e.g. TensorFlow -* more attacks, defenses and other related functionalities -* support for other Python versions and future PyTorch versions -* contributing guidelines +* more CP algorithms +* loss functions for CP * ... - -## Known issues - -`FastFeatureAttack` and `JacobianSaliencyMapAttack` do not pass the tests against the version of CleverHans used. (They use to pass tests on a previous version of CleverHans.) This issue is being investigated. In the file `test_attacks_on_cleverhans.py`, they are marked as "skipped" in `pytest` tests. - ## License This project is licensed under the LGPL. The terms and conditions can be found in the LICENSE and LICENSE.GPL files. -## Citation - -If you use AdverTorch in your research, we kindly ask that you cite the following [technical report](https://arxiv.org/abs/1902.07623): - -``` -@article{ding2019advertorch, - title={{AdverTorch} v0.1: An Adversarial Robustness Toolbox based on PyTorch}, - author={Ding, Gavin Weiguang and Wang, Luyu and Jin, Xiaomeng}, - journal={arXiv preprint arXiv:1902.07623}, - year={2019} -} -``` ## Contributors diff --git a/assets/advertorch.png b/assets/advertorch.png deleted file mode 100644 index 2b6ad0b..0000000 Binary files a/assets/advertorch.png and /dev/null differ diff --git a/assets/logo.png b/assets/logo.png deleted file mode 100644 index 00a5021..0000000 Binary files a/assets/logo.png and /dev/null differ diff --git a/deepcp/VERSION b/deepcp/VERSION deleted file mode 100644 index abd4105..0000000 --- a/deepcp/VERSION +++ /dev/null @@ -1 +0,0 @@ -0.2.4 diff --git a/deepcp/__init__.py b/deepcp/__init__.py deleted file mode 100644 index 422de6b..0000000 --- a/deepcp/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - - -import os - -with open(os.path.join(os.path.dirname(__file__), 'VERSION')) as f: - __version__ = f.read().strip() - -from . import attacks # noqa: F401 -from . import defenses # noqa: F401 diff --git a/deepcp/attacks/__init__.py b/deepcp/attacks/__init__.py deleted file mode 100644 index 3c8a4d3..0000000 --- a/deepcp/attacks/__init__.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -# flake8: noqa - -from .base import Attack -from .base import LabelMixin - -from .one_step_gradient import GradientAttack -from .one_step_gradient import GradientSignAttack -from .one_step_gradient import FGM -from .one_step_gradient import FGSM - -from .iterative_projected_gradient import FastFeatureAttack -from .iterative_projected_gradient import L2BasicIterativeAttack -from .iterative_projected_gradient import LinfBasicIterativeAttack -from .iterative_projected_gradient import PGDAttack -from .iterative_projected_gradient import LinfPGDAttack -from .iterative_projected_gradient import L2PGDAttack -from .iterative_projected_gradient import L1PGDAttack -from .iterative_projected_gradient import SparseL1DescentAttack -from .iterative_projected_gradient import MomentumIterativeAttack -from .iterative_projected_gradient import L2MomentumIterativeAttack -from .iterative_projected_gradient import LinfMomentumIterativeAttack - -from .carlini_wagner import CarliniWagnerL2Attack -from .ead import ElasticNetL1Attack - -from .decoupled_direction_norm import DDNL2Attack -from .deepfool import DeepfoolLinfAttack - -from .lbfgs import LBFGSAttack - -from .localsearch import SinglePixelAttack -from .localsearch import LocalSearchAttack - -from .spatial import SpatialTransformAttack - -from .jsma import JacobianSaliencyMapAttack -from .jsma import JSMA - -from .spsa import LinfSPSAAttack -from .fast_adaptive_boundary import FABAttack -from .fast_adaptive_boundary import LinfFABAttack -from .fast_adaptive_boundary import L2FABAttack -from .fast_adaptive_boundary import L1FABAttack - -from .utils import ChooseBestAttack - -from .blackbox.gen_attack import GenAttack -from .blackbox.gen_attack import LinfGenAttack -from .blackbox.gen_attack import L2GenAttack - -from .blackbox.nattack import NAttack -from .blackbox.nattack import LinfNAttack -from .blackbox.nattack import L2NAttack - -from .blackbox.estimators import FDWrapper, NESWrapper - -from .blackbox.bandits import BanditAttack -from .blackbox.iterative_gradient_approximation import NESAttack \ No newline at end of file diff --git a/deepcp/attacks/base.py b/deepcp/attacks/base.py deleted file mode 100644 index a5f114d..0000000 --- a/deepcp/attacks/base.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from abc import ABCMeta - -import torch - -from deepcp.utils import replicate_input - - -class Attack(object): - """ - Abstract base class for all attack classes. - - :param predict: forward pass function. - :param loss_fn: loss function that takes . - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - - """ - - __metaclass__ = ABCMeta - - def __init__(self, predict, loss_fn, clip_min, clip_max): - """Create an Attack instance.""" - self.predict = predict - self.loss_fn = loss_fn - self.clip_min = clip_min - self.clip_max = clip_max - - def perturb(self, x, **kwargs): - """Virtual method for generating the adversarial examples. - - :param x: the model's input tensor. - :param **kwargs: optional parameters used by child classes. - :return: adversarial examples. - """ - error = "Sub-classes must implement perturb." - raise NotImplementedError(error) - - def __call__(self, *args, **kwargs): - return self.perturb(*args, **kwargs) - - -class LabelMixin(object): - def _get_predicted_label(self, x): - """ - Compute predicted labels given x. Used to prevent label leaking - during adversarial training. - - :param x: the model's input tensor. - :return: tensor containing predicted labels. - """ - with torch.no_grad(): - outputs = self.predict(x) - _, y = torch.max(outputs, dim=1) - return y - - def _verify_and_process_inputs(self, x, y): - if self.targeted: - assert y is not None - - if not self.targeted: - if y is None: - y = self._get_predicted_label(x) - - x = replicate_input(x) - y = replicate_input(y) - return x, y diff --git a/deepcp/attacks/blackbox/__init__.py b/deepcp/attacks/blackbox/__init__.py deleted file mode 100644 index 5501e05..0000000 --- a/deepcp/attacks/blackbox/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from .gen_attack import GenAttack # noqa: F401 -from .gen_attack import LinfGenAttack # noqa: F401 -from .gen_attack import L2GenAttack # noqa: F401 - -from .nattack import NAttack # noqa: F401 -from .nattack import LinfNAttack # noqa: F401 -from .nattack import L2NAttack # noqa: F401 - -from .estimators import GradientWrapper # noqa: F401 -from .estimators import FDWrapper, NESWrapper # noqa: F401 - -from .bandits import BanditAttack # noqa: F401 - -from .iterative_gradient_approximation import NESAttack # noqa: F401 - -from .utils import pytorch_wrapper # noqa: F401 diff --git a/deepcp/attacks/blackbox/bandits.py b/deepcp/attacks/blackbox/bandits.py deleted file mode 100644 index e6834cc..0000000 --- a/deepcp/attacks/blackbox/bandits.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -import warnings -from math import inf -from typing import Optional - -import numpy as np - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from deepcp.attacks.base import Attack -from deepcp.attacks.base import LabelMixin - -from .utils import _check_param, _flatten, _make_projector - - -def bandit_attack( - x, loss_fn, order, projector, delta_init=None, prior_init=None, - fd_eta=0.01, exploration=0.01, online_lr=0.1, nb_iter=40, - eps_iter=0.01 -): - """ - Performs the BanditAttack - Paper: https://arxiv.org/pdf/1807.07978.pdf - - :param x: input data. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param order: (optional) the order of maximum distortion (2 or inf). - :param projector: function to project the perturbation into the eps-ball - - must accept tensors of shape [nbatch, pop_size, ndim] - :param delta_init: (default None) - :param prior_init: (default None) - :param fd_eta: step-size used for fd grad estimate (default 0.01) - :param exploration: scales the exploration around prior (default 0.01) - :param online_lr: learning rate for the prior (default 0.1) - :param nb_iter: number of iterations (default 40) - :param eps_iter: attack step size (default 0.01) - - :return: tuple of tensors containing (1) the adv example, (2) the prior - """ - ndim = np.prod(list(x.shape[1:])) - - if delta_init is None: - adv = x.clone() - else: - adv = x + delta_init - - if prior_init is None: - prior = torch.zeros_like(x) - else: - prior = prior_init.clone() - - for t in range(nb_iter): - # before: # [nbatch, ndim, nsamples] - # now: # [nbatch, ndim] - exp_noise = exploration * torch.randn_like(prior) / (ndim**0.5) - - # Query deltas for finite difference estimator - q1 = F.normalize(prior + exp_noise, dim=-1) - q2 = F.normalize(prior - exp_noise, dim=-1) - # Loss points for finite difference estimator - L1 = loss_fn(adv + fd_eta * q1) # L(prior + c*noise) - L2 = loss_fn(adv + fd_eta * q2) # L(prior - c*noise) - - delta_L = (L1 - L2) / (fd_eta * exploration) # [nbatch] - - grad_est = delta_L[:, None] * exp_noise - if order == 2: - # update prior - prior = prior + online_lr * grad_est - # make step with prior - # note the (+): this indicates gradient ascent on the loss - adv = adv + eps_iter * F.normalize(prior, dim=-1) - # project - delta = adv - x - delta = projector(delta[:, None, :]).squeeze(1) - elif order == inf: - # update prior (exponentiated gradients) - prior = (prior + 1) / 2 # from [-1, 1] to [0, 1] - pos = prior * torch.exp(online_lr * grad_est) - neg = (1 - prior) * torch.exp(-online_lr * grad_est) - prior = 2 * pos / (pos + neg) - 1 - # make step with prior - adv = adv + eps_iter * torch.sign(prior) - # project - delta = adv - x - delta = projector(delta[:, None, :]).squeeze(1) - else: - error = "Only order=inf, order=2 have been implemented" - raise NotImplementedError(error) - - adv = x + delta - - return adv, prior - - -class BanditAttack(Attack, LabelMixin): - """ - Implementation of "Prior Convictions" - Paper: https://arxiv.org/pdf/1807.07978.pdf - - Gradients for nearby points are correlated. Thus we can reduce the number - of samples we need to compute the gradient, since the previous gradient - estimate can be used a prior. The gradient is learned online, alongside - the adversarial example. - - :param predict: forward pass function. - :param eps: maximum distortion. - :param order: the order of maximum distortion (inf or 2) - :param fd_eta: step-size used for fd grad estimate (default 0.01) - :param exploration: scales the exploration around prior (default 0.01) - :param online_lr: learning rate for the prior (default 0.1) - :param loss_fn: loss function, defaults to CrossEntropyLoss - - The reduction must be set to 'none,' to ensure the per-sample - loss is accessible. - :param nb_iter: number of iterations (default 40) - :param eps_iter: attack step size (default 0.01) - :param clip_min: mininum value per input dimension (default 0.) - :param clip_max: mininum value per input dimension (default 1.) - :param targeted:bool: if the attack is targeted (default False) - """ - - def __init__( - self, predict, eps: float, order, - fd_eta=0.01, exploration=0.01, online_lr=0.1, - loss_fn=None, - nb_iter=40, - eps_iter=0.01, - clip_min=0., clip_max=1., - targeted: bool = False - ): - - if loss_fn is not None: - warnings.warn( - "This Attack currently do not support a different loss" - " function other than the default. Setting loss_fn manually" - " is not effective." - ) - - loss_fn = nn.CrossEntropyLoss(reduction="none") - super().__init__(predict, loss_fn, clip_min, clip_max) - - self.eps = eps - self.order = order - self.fd_eta = fd_eta - self.exploration = exploration - self.online_lr = online_lr - self.targeted = targeted - self.nb_iter = nb_iter - self.eps_iter = eps_iter - - def perturb( # type: ignore - self, - x: torch.FloatTensor, - y: Optional[torch.Tensor] = None - ) -> torch.FloatTensor: - """ - Given examples (x, y), returns their adversarial counterparts with - an attack length of eps. - - :param x: input tensor. - :param y: label tensor. - - if None and self.targeted=False, compute y as predicted - labels. - - if self.targeted=True, then y must be the targeted labels. - :return: tensor containing perturbed inputs. - """ - x, y = self._verify_and_process_inputs(x, y) - shape, flat_x = _flatten(x) - - eps = _check_param(self.eps, x.new_full((x.shape[0],), 1), 'eps') - clip_min = _check_param(self.clip_min, flat_x, 'clip_min') - clip_max = _check_param(self.clip_max, flat_x, 'clip_max') - - projector = _make_projector( - eps, self.order, flat_x, clip_min, clip_max - ) - - scale = -1 if self.targeted else 1 - - def L(x): # loss func - input = x.reshape(shape) - output = self.predict(input) - loss = scale * self.loss_fn(output, y) - return loss - - adv, _ = bandit_attack( - flat_x, loss_fn=L, order=self.order, projector=projector, - delta_init=None, prior_init=None, fd_eta=self.fd_eta, - exploration=self.exploration, online_lr=self.online_lr, - nb_iter=self.nb_iter, eps_iter=self.eps_iter - ) - - adv = adv.reshape(shape) - - return adv diff --git a/deepcp/attacks/blackbox/estimators.py b/deepcp/attacks/blackbox/estimators.py deleted file mode 100644 index c1ac4a2..0000000 --- a/deepcp/attacks/blackbox/estimators.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import torch -import numpy as np - - -def norm(v): - return torch.sqrt((v ** 2).sum(-1)) - - -class GradientWrapper(torch.nn.Module): - """ - Define a backward pass for a blackbox function using extra queries. - Once wrapped, the blackbox function will become compatible with any attack - in Advertorch, so long as self.training is True. - - Disclaimer: This wrapper assumes inputs will have shape [nbatch, ndim]. - For models that operate on images, you will need to wrap the function - inside a reshaper. See NESAttack for an example. - - :param func: A blackbox function. - - This function must accept, and output, torch tensors. - """ - def __init__(self, func): - super().__init__() - self.func = func - - class _Func(torch.autograd.Function): - @staticmethod - def forward(ctx, input): - # grad_est does not require grad - output = self.func(input) - grad_est = self.estimate_grad(input) - ctx.save_for_backward(grad_est) - return output - - @staticmethod - def backward(ctx, grad_output): - # Note: this is not general! May not work for images - # Be careful about dimensions - grad_est, = ctx.saved_tensors - grad_input = None - - if ctx.needs_input_grad[0]: - grad_input = torch.bmm(grad_output.unsqueeze(1), grad_est) - grad_input = grad_input.squeeze(1) - return grad_input - - self.diff_func = _Func.apply - - def batch_query(self, x): - """ - Reshapes the queries for efficient, parallel estimation. - """ - n_batch, n_dim, nb_samples = x.shape - x = x.permute(0, 2, 1).reshape(-1, n_dim) - outputs = self.func(x) # shape [..., n_output] - outputs = outputs.reshape(n_batch, nb_samples, -1) - - return outputs.permute(0, 2, 1) - - def estimate_grad(self, x): - raise NotImplementedError - - def forward(self, x): - if not self.training: - output = self.func(x) - else: - output = self.diff_func(x) - - return output - - -class FDWrapper(GradientWrapper): - """ - Finite-Difference Estimator. - For every backward pass, this module makes 2 * n_dim queries per - instance. - - :param func: A blackbox function. - - This function must accept, and output, torch tensors. - :param fd_eta: Step-size used for the finite-difference estimation. - """ - def __init__(self, func, fd_eta=1e-3): - super().__init__(func) - self.fd_eta = fd_eta - - def estimate_grad(self, x): - id_mat = torch.diag(torch.ones_like(x[0])) # shape [D,D] - fxp = self.batch_query( - x[:, :, None] + self.fd_eta * id_mat[None, :, :] - ) - - fxm = self.batch_query( - x[:, :, None] - self.fd_eta * id_mat[None, :, :] - ) - - grad_est = (fxp - fxm) / (2.0 * self.fd_eta) - return grad_est - - -class NESWrapper(GradientWrapper): - """ - Natural-evolutionary strategy for gradient estimation. - For every backward pass, this module makes 2 * nb_samples - queries per instance. - - :param func: A blackbox function. - - This function must accept, and output, torch tensors. - :param nb_samples: Number of samples to use in the grad estimation. - :param fd_eta: Step-size used for the finite-difference estimation. - """ - def __init__(self, func, nb_samples, fd_eta=1e-3): - super().__init__(func) - self.nb_samples = nb_samples - self.fd_eta = fd_eta - - def estimate_grad(self, x, prior=None): - # x shape: [nbatch, ndim] - ndim = np.prod(list(x.shape[1:])) - - # [nbatch, ndim, nsamples] - exp_noise = x.new_full(tuple(x.shape) + (self.nb_samples,), 0) - exp_noise.normal_() - exp_noise /= (ndim ** 0.5) - - fxp = self.batch_query( - x.unsqueeze(-1) + self.fd_eta * exp_noise - ) - - fxm = self.batch_query( - x.unsqueeze(-1) - self.fd_eta * exp_noise - ) - - gx_s = (fxp - fxm) / (2.0 * self.fd_eta) # [nbatch, noutput, nsamples] - - grad_est = (gx_s[:, :, None, :] * exp_noise[:, None, :, :]).sum(-1) - - return grad_est diff --git a/deepcp/attacks/blackbox/gen_attack.py b/deepcp/attacks/blackbox/gen_attack.py deleted file mode 100644 index 2299e8e..0000000 --- a/deepcp/attacks/blackbox/gen_attack.py +++ /dev/null @@ -1,440 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -from typing import Optional -from math import inf - -import torch -import torch.nn.functional as F - -from deepcp.attacks.base import Attack -from deepcp.attacks.base import LabelMixin - -from .utils import _check_param, _flatten, _make_projector - - -def gen_attack_score(output, target, targeted=False, buffer=1e-5): - """ - Fitness used for GenAttack - """ - n_class = output.shape[-1] - y_onehot = F.one_hot(target, num_classes=n_class) - - pos_score = torch.log((y_onehot[:, None, :] * output + buffer).sum(-1)) - neg_score = torch.log((1 - y_onehot)[:, None, :] * output + buffer).sum(-1) - score = pos_score - neg_score - - if not targeted: - score = -score - - return score - - -def compute_fitness(predict_fn, loss_fn, adv_pop, y, targeted=False): - """ - Compute fitness for the population - """ - # population shape: [B, N, F] - n_batch, n_samples, n_dim = adv_pop.shape - # reshape to [B * N, F] - adv_pop = adv_pop.reshape(-1, n_dim) - # output shape: [B * N, C] - probs = predict_fn(adv_pop) - - # reshape to [B, N, C] - probs = probs.reshape(n_batch, n_samples, -1) - # outputs shape: [B, N] - fitness = loss_fn(probs, y, targeted=targeted) - - return fitness - - -def crossover(p1, p2, probs): - """ - Mate parents (p1, p2) to produce members of the next generation. - Children are generated by selecting features from either parent. - Select from p1 with the probabilties in probs. - """ - u = torch.rand(*p1.shape) - return torch.where(probs[:, :, None] > u, p1, p2) - - -def selection(pop_t, fitness, tau): - """ - Select individuals in the population according to their fitness. - These individuals become parents, which produce children via crossover. - """ - n_batch, nb_samples, n_dim = pop_t.shape - - probs = F.softmax(fitness / tau, dim=1) - cum_probs = probs.cumsum(-1) - # Edge case, u1 or u2 is greater than max(cum_probs) - cum_probs[:, -1] = 1. + 1e-7 - - # parents: instead of selecting one elite, select two, and generate - # a new child to create a population around - # do this multiple times, for each N - - # sample parent 1 from pop_t according to probs (multinomial) - # sample parent 2 from pop_t according to probs (multinomial) - u1, u2 = torch.rand(2, n_batch, nb_samples) - - # out of the original N samples, we draw another N samples - # this requires us to compute the following broadcasted comparison - p1ind = -((cum_probs[:, :, None] > u1[:, None, :] - ).long()).sum(1) + nb_samples - p2ind = -((cum_probs[:, :, None] > u2[:, None, :] - ).long()).sum(1) + nb_samples - - parent1 = torch.gather( - pop_t, dim=1, index=p1ind[:, :, None].expand(-1, -1, n_dim) - ) - - parent2 = torch.gather( - pop_t, dim=1, index=p2ind[:, :, None].expand(-1, -1, n_dim) - ) - - fp1 = torch.gather(fitness, dim=1, index=p1ind) - fp2 = torch.gather(fitness, dim=1, index=p2ind) - crossover_prob = fp1 / (fp1 + fp2) - - return crossover(parent1, parent2, crossover_prob) - - -def mutation(pop_t, alpha, rho, eps): - """ - Add random noise to the population to explore the search space. - - Alpha controls the scale of the noise, rho controls the number of features - that are perturbed. - """ - # alpha and eps both have shape [B] - perturb_noise = (2 * torch.rand(*pop_t.shape) - 1) - perturb_noise = perturb_noise * alpha[:, None, None] * eps[:, None, None] - - mask = (torch.rand(*pop_t.shape) > rho[:, None, None]).float() - - return pop_t + mask * perturb_noise - - -class GenAttackScheduler(): - """ - Parameter scaling for GenAttack. Decrease mutation rate and range when - search is detected to be stuck. - - For more details, see section 4.1.2 of https://arxiv.org/abs/1805.11090. - """ - - def __init__( - self, x, alpha_init=0.4, rho_init=0.5, decay=0.9, - rho_min=0.1, alpha_min=0.15 - ): - n_batch = x.shape[0] - - self.n_batch = n_batch - self.crit = 1e-5 - - self.best_val = torch.zeros(n_batch).to(x.device) - self.num_i = torch.zeros(n_batch).to(x.device) - self.num_plateaus = torch.zeros(n_batch).to(x.device) - - self.rho_min = rho_min * torch.ones(n_batch).to(x.device) - self.alpha_min = alpha_min * torch.ones(n_batch).to(x.device) - - self.zeros = torch.zeros_like(self.num_i) - - self.alpha_init = alpha_init - self.rho_init = rho_init - self.decay = decay - - self.alpha = alpha_init * torch.ones(n_batch).to(x.device) - self.rho = rho_init * torch.ones(n_batch).to(x.device) - - def update(self, elite_val): - stalled = abs(elite_val - self.best_val) <= self.crit - self.num_i = torch.where(stalled, self.num_i + 1, self.zeros) - new_plateau = (self.num_i % 100 == 0) & (self.num_i != 0) - self.num_plateaus = torch.where( - new_plateau, self.num_plateaus + 1, self.num_plateaus - ) - - # update alpha and rho - self.rho = torch.maximum( - self.rho_min, self.rho_init * self.decay ** self.num_plateaus - ) - self.alpha = torch.maximum( - self.alpha_min, self.alpha_init * self.decay ** self.num_plateaus - ) - - self.best_val = torch.maximum(elite_val, self.best_val) - - -def gen_attack( - predict_fn, loss_fn, x, y, eps, projector, nb_samples=100, nb_iter=40, - tau=0.1, alpha_init=0.4, rho_init=0.5, decay=0.9, - pop_init=None, scheduler=None, targeted=False -): - """ - Use a genetic algorithm to iteratively maximize the loss over the input, - while staying within eps of the original input (using a projector). - - Used as part of GenAttack. - - :param predict: forward pass function. - :param loss_fn: loss function - - must accept tensors of shape [nbatch, pop_size, ndim] - :param x: input tensor. - :param y: label tensor. - - if None and self.targeted=False, compute y as predicted - labels. - - if self.targeted=True, then y must be the targeted labels. - :param eps: maximum distortion. - :param projector: function to project the perturbation into the eps-ball - - must accept tensors of shape [nbatch, pop_size, ndim] - :param nb_samples: population size (default 100) - :param nb_iter: number of iterations (default 40) - :param tau: sampling temperature (default 0.1) - :param alpha_init: initial mutation range (default 0.4) - :param rho_init: initial probability for mutation (default 0.5) - :param decay: decay param for scheduler (default 0.9) - :param pop_init: initial population for genetic alg (default None) - :param scheduler: initial state of scheduler(default None) - :param targeted: if the attack is targeted (default False) - """ - n_batch, n_dim = x.shape - - # [B,F] - if pop_init is None: - # Sample from Uniform(-1, 1) - # shape: [B, N, F] - pop_t = 2 * torch.rand(n_batch, nb_samples, n_dim) - 1 - # Sample from Uniform(-eps, eps) - pop_t = eps[:, None, None] * pop_t - pop_t = pop_t.to(x.device) - else: - pop_t = pop_init.clone() - - if scheduler is None: - scheduler = GenAttackScheduler(x, alpha_init, rho_init, decay) - - inds = torch.arange(n_batch).to(x.device) - - for _ in range(nb_iter): - adv = x[:, None, :] + pop_t - # shape: [B, N] - fitness = compute_fitness( - predict_fn, loss_fn, adv, y, targeted=targeted) - # shape: [1, B, 1] - elite_val, elite_ind = fitness.max(-1) - # shape: [B, F] - elite_adv = adv[inds, elite_ind, :] - - # select which members will move onto the next generation - # shape: [B, N] - children = selection(pop_t, fitness, tau) - - # apply mutations and clipping - # add mutated child to next generation (ie update pop_t) - pop_t = mutation(children, scheduler.alpha, scheduler.rho, eps) - pop_t = projector(pop_t) - - # Update params based on plateaus - scheduler.update(elite_val) - - return elite_adv, pop_t, scheduler - - -class GenAttack(Attack, LabelMixin): - """ - Runs GenAttack https://arxiv.org/abs/1805.11090 - - Disclaimers: Note that GenAttack assumes the model outputs - normalized probabilities. Moreover, computations are broadcasted, - so it is advisable to use smaller batch sizes when nb_samples is - large. - - Hyperparams: alpha (mutation range), rho (mutation probability), - and tau (temperature) all control exploration. - - Alpha and rho are adapted using GenAttackScheduler. - - :param predict: forward pass function. - :param eps: maximum distortion. - :param order: the order of maximum distortion (inf or 2) - :param loss_fn: loss function (default None, GenAttack uses its own loss) - :param nb_samples: population size (default 100) - :param nb_iter: number of iterations (default 40) - :param tau: sampling temperature (default 0.1) - :param alpha_init: initial mutation range (default 0.4) - :param rho_init: initial probability for mutation (default 0.5) - :param decay: decay param for scheduler (default 0.9) - :param clip_min: mininum value per input dimension (default 0.) - :param clip_max: mininum value per input dimension (default 1.) - :param targeted: if the attack is targeted (default False) - """ - - def __init__( - self, predict, eps: float, order, - loss_fn=None, - nb_samples=100, - nb_iter=40, - tau=0.1, - alpha_init=0.4, - rho_init=0.5, - decay=0.9, - clip_min=0., clip_max=1., - targeted: bool = False - ): - if loss_fn is not None: - import warnings - warnings.warn( - "This Attack currently do not support a different loss" - " function other than the default. Setting loss_fn manually" - " is not effective." - ) - - super().__init__(predict, gen_attack_score, clip_min, clip_max) - - self.eps = eps - self.order = order - self.nb_samples = nb_samples - self.nb_iter = nb_iter - self.targeted = targeted - - self.alpha_init = alpha_init - self.rho_init = rho_init - self.decay = decay - self.tau = tau - - def perturb( # type: ignore - self, - x: torch.FloatTensor, - y: Optional[torch.Tensor] = None - ) -> torch.FloatTensor: - """ - Given examples (x, y), returns their adversarial counterparts with - an attack length of eps. - - :param x: input tensor. - :param y: label tensor. - - if None and self.targeted=False, compute y as predicted - labels. - - if self.targeted=True, then y must be the targeted labels. - :return: tensor containing perturbed inputs. - """ - x, y = self._verify_and_process_inputs(x, y) - shape, flat_x = _flatten(x) - data_shape = tuple(shape[1:]) - - # [B] - eps = _check_param(self.eps, x.new_full((x.shape[0],), 1), 'eps') - # [B, F] - clip_min = _check_param(self.clip_min, flat_x, 'clip_min') - clip_max = _check_param(self.clip_max, flat_x, 'clip_max') - - def f(x): - new_shape = (x.shape[0],) + data_shape - input = x.reshape(new_shape) - return self.predict(input) - - projector = _make_projector( - eps, self.order, flat_x, clip_min, clip_max - ) - - elite_adv, _, _ = gen_attack( - predict_fn=f, loss_fn=self.loss_fn, x=flat_x, y=y, - eps=eps, projector=projector, - nb_samples=self.nb_samples, nb_iter=self.nb_iter, tau=self.tau, - alpha_init=self.alpha_init, rho_init=self.rho_init, - decay=self.decay, pop_init=None, scheduler=None, - targeted=self.targeted - ) - - elite_adv = elite_adv.reshape(shape) - - return elite_adv - - -class LinfGenAttack(GenAttack): - """ - GenAttack with order=inf - - :param predict: forward pass function. - :param eps: maximum distortion. - :param loss_fn: loss function (default None, GenAttack uses its own loss) - :param nb_samples: population size (default 100) - :param nb_iter: number of iterations (default 40) - :param tau: sampling temperature (default 0.1) - :param alpha_init: initial mutation range (default 0.4) - :param rho_init: initial probability for mutation (default 0.5) - :param decay: decay param for scheduler (default 0.9) - :param clip_min: mininum value per input dimension (default 0.) - :param clip_max: mininum value per input dimension (default 1.) - :param targeted: if the attack is targeted (default False) - """ - - def __init__( - self, predict, eps: float, - loss_fn=None, - nb_samples=100, - nb_iter=40, - tau=0.1, - alpha_init=0.4, - rho_init=0.5, - decay=0.9, - clip_min=0., clip_max=1., - targeted: bool = False - ): - super(LinfGenAttack, self).__init__( - predict=predict, eps=eps, loss_fn=loss_fn, nb_samples=nb_samples, - nb_iter=nb_iter, tau=tau, order=inf, alpha_init=alpha_init, - rho_init=rho_init, decay=decay, clip_min=clip_min, - clip_max=clip_max, targeted=targeted - ) - - -class L2GenAttack(GenAttack): - """ - GenAttack with order=2 - - :param predict: forward pass function. - :param eps: maximum distortion. - :param loss_fn: loss function (default None, GenAttack uses its own loss) - :param nb_samples: population size (default 100) - :param nb_iter: number of iterations (default 40) - :param tau: sampling temperature (default 0.1) - :param alpha_init: initial mutation range (default 0.4) - :param rho_init: initial probability for mutation (default 0.5) - :param decay: decay param for scheduler (default 0.9) - :param clip_min: mininum value per input dimension (default 0.) - :param clip_max: mininum value per input dimension (default 1.) - :param targeted: if the attack is targeted (default False) - """ - - def __init__( - self, predict, eps: float, - loss_fn=None, - nb_samples=100, - nb_iter=40, - tau=0.1, - alpha_init=0.4, - rho_init=0.5, - decay=0.9, - clip_min=0., clip_max=1., - targeted: bool = False - ): - super(L2GenAttack, self).__init__( - predict=predict, eps=eps, loss_fn=loss_fn, nb_samples=nb_samples, - nb_iter=nb_iter, tau=tau, order=2, alpha_init=alpha_init, - rho_init=rho_init, decay=decay, clip_min=clip_min, - clip_max=clip_max, targeted=targeted - ) diff --git a/deepcp/attacks/blackbox/iterative_gradient_approximation.py b/deepcp/attacks/blackbox/iterative_gradient_approximation.py deleted file mode 100644 index e0880ad..0000000 --- a/deepcp/attacks/blackbox/iterative_gradient_approximation.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import torch -import torch.nn as nn - -from deepcp.utils import clamp -from deepcp.attacks.utils import rand_init_delta - -from deepcp.attacks.iterative_projected_gradient import LinfPGDAttack -from deepcp.attacks.iterative_projected_gradient import perturb_iterative - -from .estimators import NESWrapper -from .utils import _flatten - - -class NESAttack(LinfPGDAttack): - """ - Implements NES Attack https://arxiv.org/abs/1804.08598 - - Employs Natural Evolutionary Strategies for Gradient Estimation. - Generates Adversarial Examples using Projected Gradient Descent. - - Disclaimer: Computations are broadcasted, so it is advisable to use - smaller batch sizes when nb_samples is large. - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_samples: number of samples to use for gradient estimation - :param fd_eta: step-size used for Finite Difference gradient estimation - :param nb_iter: number of iterations. - :param eps_iter: attack step size. - :param rand_init: (optional bool) random initialization. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - """ - - def __init__( - self, predict, loss_fn=None, eps=0.3, - nb_samples=100, fd_eta=1e-2, nb_iter=40, - eps_iter=0.01, rand_init=True, clip_min=0., clip_max=1., - targeted=False): - - super(NESAttack, self).__init__( - predict=predict, loss_fn=loss_fn, eps=eps, nb_iter=nb_iter, - eps_iter=eps_iter, rand_init=rand_init, clip_min=clip_min, - clip_max=clip_max, targeted=targeted) - - self.nb_samples = nb_samples - self.fd_eta = fd_eta - - def perturb(self, x, y=None): - """ - Given examples (x, y), returns their adversarial counterparts with - an attack length of eps. - - :param x: input tensor. - :param y: label tensor. - - if None and self.targeted=False, compute y as predicted - labels. - - if self.targeted=True, then y must be the targeted labels. - :return: tensor containing perturbed inputs. - """ - x, y = self._verify_and_process_inputs(x, y) - shape, flat_x = _flatten(x) - data_shape = tuple(shape[1:]) - - def f(x): - new_shape = (x.shape[0],) + data_shape - input = x.reshape(new_shape) - return self.predict(input) - f_nes = NESWrapper( - f, nb_samples=self.nb_samples, fd_eta=self.fd_eta - ) - - delta = torch.zeros_like(flat_x) - delta = nn.Parameter(delta) - if self.rand_init: - rand_init_delta( - delta, flat_x, self.ord, self.eps, self.clip_min, self.clip_max - ) - delta.data = clamp( - flat_x + delta.data, min=self.clip_min, max=self.clip_max - ) - flat_x - - rval = perturb_iterative( - flat_x, y, f_nes, nb_iter=self.nb_iter, - eps=self.eps, eps_iter=self.eps_iter, - loss_fn=self.loss_fn, minimize=self.targeted, - ord=self.ord, clip_min=self.clip_min, - clip_max=self.clip_max, delta_init=delta, - l1_sparsity=None - ) - - return rval.data.reshape(shape) diff --git a/deepcp/attacks/blackbox/nattack.py b/deepcp/attacks/blackbox/nattack.py deleted file mode 100644 index 08c8d26..0000000 --- a/deepcp/attacks/blackbox/nattack.py +++ /dev/null @@ -1,288 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from math import inf -from typing import Optional - -import torch -import torch.nn.functional as F - -from deepcp.attacks.base import Attack -from deepcp.attacks.base import LabelMixin - -from .utils import _check_param, _flatten, _make_projector - -from deepcp.utils import to_one_hot - - -def cw_log_loss(output, target, targeted=False, buff=1e-5): - """ - :param outputs: pre-softmax/logits. - :param target: true labels. - :return: CW loss value. - """ - num_classes = output.size(1) - label_mask = to_one_hot(target, num_classes=num_classes).float() - correct_logit = torch.log(torch.sum(label_mask * output, dim=1) + buff) - wrong_logit = torch.log( - torch.max((1. - label_mask) * output, dim=1)[0] + buff) - - if targeted: - loss = -0.5 * F.relu(wrong_logit - correct_logit + 50.) - else: - loss = -0.5 * F.relu(correct_logit - wrong_logit + 50.) - return loss - - -def select_best_example(x_adv, losses): - ''' - Given a collection of potential adversarial examples, select the best. - - :param x_adv: Candidate adversarial examples - - shape [nbatch, nsample, ndim] - :param losses: Loss values for each candidate exampe - - shape [nbatch, nsample] - ''' - best_loss_ind = losses.argmin(-1)[:, None, None] - best_loss_ind = best_loss_ind.expand(-1, -1, x_adv.shape[-1]) - - best_adv = torch.gather(x_adv, dim=1, index=best_loss_ind) - return best_adv.squeeze(1) - - -def n_attack( - predict_fn, loss_fn, x, y, projector, - mu_init=None, nb_samples=100, nb_iter=40, eps_iter=0.02, - sigma=0.1, targeted=False -): - """ - Models the distribution of adversarial examples near an input data point. - Similar to an evolutionary algorithm, but parameteric. - - Used as part of NAttack. - - :param predict: forward pass function. - :param loss_fn: loss function - - must accept tensors of shape [nbatch, pop_size, ndim] - :param x: input tensor. - :param y: label tensor. - - if None and self.targeted=False, compute y as predicted - labels. - - if self.targeted=True, then y must be the targeted labels. - :param projector: function to project the perturbation into the eps-ball - - must accept tensors of shape [nbatch, pop_size, ndim] - :param nb_samples: number of samples for (default 100) - :param nb_iter: number of iterations (default 40) - :param eps_iter: attack step size (default 0.02). - :param sigma: variance to control sample generation (default 0.1) - :param clip_min: mininum value per input dimension (default 0.) - :param clip_max: mininum value per input dimension (default 1.) - :param targeted: if the attack is targeted (default False) - """ - - n_batch, n_dim = x.shape - y_repeat = y.repeat(nb_samples, 1).T.flatten() - - # [B,F] - if mu_init is None: - mu_t = torch.FloatTensor(n_batch, n_dim).normal_() * 0.001 - mu_t = mu_t.to(x.device) - else: - mu_t = mu_init.clone() - - # factor used to scale updates to mu_t - alpha = eps_iter / (nb_samples * sigma) - - for _ in range(nb_iter): - # Sample from N(0,I), shape [B, N, F] - gauss_samples = torch.FloatTensor(n_batch, nb_samples, n_dim).normal_() - gauss_samples = gauss_samples.to(x.device) - - # Compute gi = g(mu_t + sigma * samples), shape [B, N, F] - mu_samples = mu_t[:, None, :] + sigma * gauss_samples - delta = projector(mu_samples) - adv = (x[:, None, :] + delta).reshape(-1, n_dim) - outputs = predict_fn(adv) - losses = loss_fn(outputs, y_repeat, targeted=targeted) - losses = losses.reshape(n_batch, nb_samples) - - # Convert losses into z_scores - z_score = (losses - losses.mean(1) - [:, None]) / (losses.std(1)[:, None] + 1e-7) - - # Update mu_t based on the z_scores - mu_t = mu_t + alpha * (z_score[:, :, None] * gauss_samples).sum(1) - - adv = adv.reshape(n_batch, nb_samples, -1) - - return adv, mu_t, losses - - -class NAttack(Attack, LabelMixin): - """ - Implements NAttack: https://arxiv.org/abs/1905.00441 - - Disclaimers: Note that NAttack assumes the model outputs - normalized probabilities. Moreover, computations are broadcasted, - so it is advisable to use smaller batch sizes when nb_samples is - large. - - Hyperparams: sigma controls the variance for the generation of - perturbations. - - :param predict: forward pass function. - :param eps: maximum distortion. - :param order: the order of maximum distortion (inf or 2) - :param loss_fn: loss function (default None, NAttack uses CW loss) - :param nb_samples: population size (default 100) - :param nb_iter: number of iterations (default 40) - :param eps_iter: attack step size (default 0.02) - :param sigma: variance to control sample generation (default 0.1) - :param clip_min: mininum value per input dimension (default 0.) - :param clip_max: mininum value per input dimension (default 1.) - :param targeted: if the attack is targeted (default False) - """ - - def __init__( - self, predict, eps: float, order, - loss_fn=None, - nb_samples=100, - nb_iter=40, - eps_iter=0.02, - sigma=0.1, - clip_min=0., clip_max=1., - targeted: bool = False - ): - - if loss_fn is not None: - import warnings - warnings.warn( - "This Attack currently do not support a different loss" - " function other than the default. Setting loss_fn manually" - " is not effective." - ) - - super().__init__(predict, cw_log_loss, clip_min, clip_max) - self.eps = eps - self.order = order - self.nb_samples = nb_samples - self.nb_iter = nb_iter - self.eps_iter = eps_iter - self.sigma = sigma - self.targeted = targeted - - def perturb( - self, - x: torch.FloatTensor, - y: Optional[torch.Tensor] = None - ) -> torch.FloatTensor: - # [B, F] - x, y = self._verify_and_process_inputs(x, y) - shape, flat_x = _flatten(x) - data_shape = tuple(shape[1:]) - n_batch, n_dim = flat_x.shape - - # [B] - eps = _check_param(self.eps, x.new_full((x.shape[0],), 1), 'eps') - # [B, F] - clip_min = _check_param(self.clip_min, flat_x, 'clip_min') - clip_max = _check_param(self.clip_max, flat_x, 'clip_max') - - def f(x): - new_shape = (x.shape[0],) + data_shape - input = x.reshape(new_shape) - return self.predict(input) - - - projector = _make_projector( - eps, self.order, flat_x, clip_min, clip_max - ) - - adv, _, losses = n_attack( - predict_fn=f, loss_fn=self.loss_fn, x=flat_x, y=y, - projector=projector, nb_samples=self.nb_samples, - nb_iter=self.nb_iter, eps_iter=self.eps_iter, sigma=self.sigma, - targeted=self.targeted - ) - - adv = select_best_example(adv, losses) - adv = adv.reshape(shape) - - return adv - - -class LinfNAttack(NAttack): - """ - NAttack with order=inf - - :param predict: forward pass function. - :param eps: maximum distortion. - :param order: the order of maximum distortion (inf or 2) - :param loss_fn: loss function (default None, NAttack uses CW loss) - :param nb_samples: population size (default 100) - :param nb_iter: number of iterations (default 40) - :param eps_iter: attack step size (default 0.02) - :param sigma: variance to control sample generation (default 0.1) - :param clip_min: mininum value per input dimension (default 0.) - :param clip_max: mininum value per input dimension (default 1.) - :param targeted: if the attack is targeted (default False) - """ - - def __init__( - self, predict, eps: float, - loss_fn=None, - nb_samples=100, - nb_iter=40, - eps_iter=0.02, - sigma=0.1, - clip_min=0., clip_max=1., - targeted: bool = False - ): - - super(LinfNAttack, self).__init__( - predict=predict, eps=eps, order=inf, loss_fn=loss_fn, - nb_samples=nb_samples, nb_iter=nb_iter, eps_iter=eps_iter, - sigma=sigma, clip_min=clip_min, clip_max=clip_max, - targeted=targeted - ) - - - -class L2NAttack(NAttack): - """ - NAttack with order=2 - - :param predict: forward pass function. - :param eps: maximum distortion. - :param order: the order of maximum distortion (inf or 2) - :param loss_fn: loss function (default None, NAttack uses CW loss) - :param nb_samples: population size (default 100) - :param nb_iter: number of iterations (default 40) - :param eps_iter: attack step size (default 0.02) - :param sigma: variance to control sample generation (default 0.1) - :param clip_min: mininum value per input dimension (default 0.) - :param clip_max: mininum value per input dimension (default 1.) - :param targeted: if the attack is targeted (default False) - """ - - def __init__( - self, predict, eps: float, - loss_fn=None, - nb_samples=100, - nb_iter=40, - eps_iter=0.02, - sigma=0.1, - clip_min=0., clip_max=1., - targeted: bool = False - ): - - super(L2NAttack, self).__init__( - predict=predict, eps=eps, order=2, loss_fn=loss_fn, - nb_samples=nb_samples, nb_iter=nb_iter, eps_iter=eps_iter, - sigma=sigma, clip_min=clip_min, clip_max=clip_max, - targeted=targeted - ) diff --git a/deepcp/attacks/blackbox/utils.py b/deepcp/attacks/blackbox/utils.py deleted file mode 100644 index 03fd837..0000000 --- a/deepcp/attacks/blackbox/utils.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from math import inf -from operator import mul -from functools import reduce - -import numpy as np - -import torch - -from deepcp.utils import batch_clamp - - -def pytorch_wrapper(func): - def wrapped_func(x): - x_numpy = x.cpu().data.numpy() - output = func(x_numpy) - output = torch.from_numpy(output) - output = output.to(x.device) - - return output - - return wrapped_func - - -def _check_param(param, x, name): - if isinstance(param, (bool, int, float)): - new_param = param * torch.ones_like(x) - elif isinstance(param, (np.ndarray, list)): - if param.ndim != x.ndim: - raise ValueError(f"Mismatched number of dimensions for {name}." - " Expand dimensions to match input." - ) - new_param = torch.FloatTensor(param).to(x.device) # type: ignore - elif isinstance(param, torch.Tensor): - if param.ndim != x.ndim: - raise ValueError(f"Mismatched number of dimensions for {name}." - " Expand dimensions to match input." - ) - new_param = param.to(x.device) # type: ignore - else: - raise ValueError(f"Unknown format for {name}") - - return new_param - - -def _flatten(x): - shape = x.shape - if x.dim() == 2: - flat_x = x - else: - flat_size = reduce(mul, shape[1:]) - flat_x = x.reshape(x.shape[0], flat_size) - - return shape, flat_x - - -def sample_clamp(x, clip_min, clip_max): - new_x = torch.maximum(x, clip_min) - new_x = torch.minimum(new_x, clip_max) - return new_x - - -def _make_projector(eps, order, x, clip_min, clip_max): - if order == inf: - def proj(delta): - delta = batch_clamp(eps, delta) - delta = sample_clamp( - x[:, None, :] + delta, - clip_min[:, None, :], - clip_max[:, None, :] - ) - x[:, None, :] - return delta - else: - def proj(delta): - # find the samples that exceed the bounds - # and project them back inside - norm = torch.norm(delta, p=order, dim=-1) - mask = (norm > eps[:, None]).float() # out of bounds - factor = torch.min(eps[:, None] / norm, torch.ones_like(norm)) - delta_norm = delta * factor[:, :, None] - delta = mask[:, :, None] * delta_norm + \ - (1 - mask)[:, :, None] * delta - - delta = sample_clamp( - x[:, None, :] + delta, - clip_min[:, None, :], - clip_max[:, None, :] - ) - x[:, None, :] - return delta - - return proj diff --git a/deepcp/attacks/carlini_wagner.py b/deepcp/attacks/carlini_wagner.py deleted file mode 100644 index c45851f..0000000 --- a/deepcp/attacks/carlini_wagner.py +++ /dev/null @@ -1,245 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import torch -import torch.nn as nn -import torch.optim as optim - -from deepcp.utils import calc_l2distsq -from deepcp.utils import tanh_rescale -from deepcp.utils import torch_arctanh -from deepcp.utils import clamp -from deepcp.utils import to_one_hot -from deepcp.utils import replicate_input - -from .base import Attack -from .base import LabelMixin -from .utils import is_successful - -CARLINI_L2DIST_UPPER = 1e10 -CARLINI_COEFF_UPPER = 1e10 -INVALID_LABEL = -1 -REPEAT_STEP = 10 -ONE_MINUS_EPS = 0.999999 -UPPER_CHECK = 1e9 -PREV_LOSS_INIT = 1e6 -TARGET_MULT = 10000.0 -NUM_CHECKS = 10 - - -class CarliniWagnerL2Attack(Attack, LabelMixin): - """ - The Carlini and Wagner L2 Attack, https://arxiv.org/abs/1608.04644 - - :param predict: forward pass function. - :param num_classes: number of clasess. - :param confidence: confidence of the adversarial examples. - :param targeted: if the attack is targeted. - :param learning_rate: the learning rate for the attack algorithm - :param binary_search_steps: number of binary search times to find the - optimum - :param max_iterations: the maximum number of iterations - :param abort_early: if set to true, abort early if getting stuck in local - min - :param initial_const: initial value of the constant c - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param loss_fn: loss function - """ - - def __init__(self, predict, num_classes, confidence=0, - targeted=False, learning_rate=0.01, - binary_search_steps=9, max_iterations=10000, - abort_early=True, initial_const=1e-3, - clip_min=0., clip_max=1., loss_fn=None): - """Carlini Wagner L2 Attack implementation in pytorch.""" - if loss_fn is not None: - import warnings - warnings.warn( - "This Attack currently do not support a different loss" - " function other than the default. Setting loss_fn manually" - " is not effective." - ) - - loss_fn = None - - super(CarliniWagnerL2Attack, self).__init__( - predict, loss_fn, clip_min, clip_max) - - self.learning_rate = learning_rate - self.max_iterations = max_iterations - self.binary_search_steps = binary_search_steps - self.abort_early = abort_early - self.confidence = confidence - self.initial_const = initial_const - self.num_classes = num_classes - # The last iteration (if we run many steps) repeat the search once. - self.repeat = binary_search_steps >= REPEAT_STEP - self.targeted = targeted - - def _loss_fn(self, output, y_onehot, l2distsq, const): - # TODO: move this out of the class and make this the default loss_fn - # after having targeted tests implemented - real = (y_onehot * output).sum(dim=1) - - # TODO: make loss modular, write a loss class - other = ((1.0 - y_onehot) * output - (y_onehot * TARGET_MULT) - ).max(1)[0] - # - (y_onehot * TARGET_MULT) is for the true label not to be selected - - if self.targeted: - loss1 = clamp(other - real + self.confidence, min=0.) - else: - loss1 = clamp(real - other + self.confidence, min=0.) - loss2 = (l2distsq).sum() - loss1 = torch.sum(const * loss1) - loss = loss1 + loss2 - return loss - - def _is_successful(self, output, label, is_logits): - # determine success, see if confidence-adjusted logits give the right - # label - - if is_logits: - output = output.detach().clone() - if self.targeted: - output[torch.arange(len(label)).long(), - label] -= self.confidence - else: - output[torch.arange(len(label)).long(), - label] += self.confidence - pred = torch.argmax(output, dim=1) - else: - pred = output - if pred == INVALID_LABEL: - return pred.new_zeros(pred.shape).byte() - - return is_successful(pred, label, self.targeted) - - def _forward_and_update_delta( - self, optimizer, x_atanh, delta, y_onehot, loss_coeffs): - - optimizer.zero_grad() - adv = tanh_rescale(delta + x_atanh, self.clip_min, self.clip_max) - transimgs_rescale = tanh_rescale(x_atanh, self.clip_min, self.clip_max) - output = self.predict(adv) - l2distsq = calc_l2distsq(adv, transimgs_rescale) - loss = self._loss_fn(output, y_onehot, l2distsq, loss_coeffs) - loss.backward() - optimizer.step() - - return loss.item(), l2distsq.data, output.data, adv.data - - def _get_arctanh_x(self, x): - result = clamp((x - self.clip_min) / (self.clip_max - self.clip_min), - min=0., max=1.) * 2 - 1 - return torch_arctanh(result * ONE_MINUS_EPS) - - def _update_if_smaller_dist_succeed( - self, adv_img, labs, output, l2distsq, batch_size, - cur_l2distsqs, cur_labels, - final_l2distsqs, final_labels, final_advs): - - target_label = labs - output_logits = output - _, output_label = torch.max(output_logits, 1) - - mask = (l2distsq < cur_l2distsqs) & self._is_successful( - output_logits, target_label, True) - - cur_l2distsqs[mask] = l2distsq[mask] # redundant - cur_labels[mask] = output_label[mask] - - mask = (l2distsq < final_l2distsqs) & self._is_successful( - output_logits, target_label, True) - final_l2distsqs[mask] = l2distsq[mask] - final_labels[mask] = output_label[mask] - final_advs[mask] = adv_img[mask] - - def _update_loss_coeffs( - self, labs, cur_labels, batch_size, loss_coeffs, - coeff_upper_bound, coeff_lower_bound): - - # TODO: remove for loop, not significant, since only called during each - # binary search step - for ii in range(batch_size): - cur_labels[ii] = int(cur_labels[ii]) - if self._is_successful(cur_labels[ii], labs[ii], False): - coeff_upper_bound[ii] = min( - coeff_upper_bound[ii], loss_coeffs[ii]) - - if coeff_upper_bound[ii] < UPPER_CHECK: - loss_coeffs[ii] = (coeff_lower_bound[ii] + - coeff_upper_bound[ii]) / 2 - - else: - coeff_lower_bound[ii] = max( - coeff_lower_bound[ii], loss_coeffs[ii]) - if coeff_upper_bound[ii] < UPPER_CHECK: - loss_coeffs[ii] = (coeff_lower_bound[ii] + - coeff_upper_bound[ii]) / 2 - - else: - loss_coeffs[ii] *= 10 - - def perturb(self, x, y=None): - x, y = self._verify_and_process_inputs(x, y) - - # Initialization - if y is None: - y = self._get_predicted_label(x) - x = replicate_input(x) - batch_size = len(x) - coeff_lower_bound = x.new_zeros(batch_size) - coeff_upper_bound = x.new_ones(batch_size) * CARLINI_COEFF_UPPER - loss_coeffs = torch.ones_like(y).float() * self.initial_const - final_l2distsqs = [CARLINI_L2DIST_UPPER] * batch_size - final_labels = [INVALID_LABEL] * batch_size - final_advs = x - x_atanh = self._get_arctanh_x(x) - y_onehot = to_one_hot(y, self.num_classes).float() - - final_l2distsqs = torch.FloatTensor(final_l2distsqs).to(x.device) - final_labels = torch.LongTensor(final_labels).to(x.device) - - # Start binary search - for outer_step in range(self.binary_search_steps): - delta = nn.Parameter(torch.zeros_like(x)) - optimizer = optim.Adam([delta], lr=self.learning_rate) - cur_l2distsqs = [CARLINI_L2DIST_UPPER] * batch_size - cur_labels = [INVALID_LABEL] * batch_size - cur_l2distsqs = torch.FloatTensor(cur_l2distsqs).to(x.device) - cur_labels = torch.LongTensor(cur_labels).to(x.device) - prevloss = PREV_LOSS_INIT - - if (self.repeat and outer_step == (self.binary_search_steps - 1)): - loss_coeffs = coeff_upper_bound - for ii in range(self.max_iterations): - loss, l2distsq, output, adv_img = \ - self._forward_and_update_delta( - optimizer, x_atanh, delta, y_onehot, loss_coeffs) - if self.abort_early: - if ii % (self.max_iterations // NUM_CHECKS or 1) == 0: - if loss > prevloss * ONE_MINUS_EPS: - break - prevloss = loss - - self._update_if_smaller_dist_succeed( - adv_img, y, output, l2distsq, batch_size, - cur_l2distsqs, cur_labels, - final_l2distsqs, final_labels, final_advs) - - self._update_loss_coeffs( - y, cur_labels, batch_size, - loss_coeffs, coeff_upper_bound, coeff_lower_bound) - - return final_advs diff --git a/deepcp/attacks/decoupled_direction_norm.py b/deepcp/attacks/decoupled_direction_norm.py deleted file mode 100644 index 3e1fbc7..0000000 --- a/deepcp/attacks/decoupled_direction_norm.py +++ /dev/null @@ -1,137 +0,0 @@ -# Copyright (c) 2019-present, Jérôme Rony. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import torch -import torch.nn as nn -import torch.optim as optim - -from .base import Attack -from .base import LabelMixin - - -class DDNL2Attack(Attack, LabelMixin): - """ - The decoupled direction and norm attack (Rony et al, 2018). - Paper: https://arxiv.org/abs/1811.09600 - - :param predict: forward pass function. - :param nb_iter: number of iterations. - :param gamma: factor to modify the norm at each iteration. - :param init_norm: initial norm of the perturbation. - :param quantize: perform quantization at each iteration. - :param levels: number of quantization levels (e.g. 256 for 8 bit images). - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - :param loss_fn: loss function. - """ - - def __init__( - self, predict, nb_iter=100, gamma=0.05, init_norm=1., - quantize=True, levels=256, clip_min=0., clip_max=1., - targeted=False, loss_fn=None): - """ - Decoupled Direction and Norm L2 Attack implementation in pytorch. - """ - if loss_fn is not None: - import warnings - warnings.warn( - "This Attack currently does not support a different loss" - " function other than the default. Setting loss_fn manually" - " is not effective." - ) - - loss_fn = nn.CrossEntropyLoss(reduction="sum") - - super(DDNL2Attack, self).__init__(predict, loss_fn, clip_min, clip_max) - - self.nb_iter = nb_iter - self.gamma = gamma - self.init_norm = init_norm - self.quantize = quantize - self.levels = levels - self.targeted = targeted - - def perturb(self, x, y=None): - """ - Given examples (x, y), returns their adversarial counterparts with - an attack length of eps. - - :param x: input tensor. - :param y: label tensor. - - if None and self.targeted=False, compute y as predicted - labels. - - if self.targeted=True, then y must be the targeted labels. - :return: tensor containing perturbed inputs. - """ - x, y = self._verify_and_process_inputs(x, y) - - s = self.clip_max - self.clip_min - multiplier = 1 if self.targeted else -1 - batch_size = x.shape[0] - data_dims = (1,) * (x.dim() - 1) - norm = torch.full((batch_size,), s * self.init_norm, - device=x.device, dtype=torch.float) - worst_norm = torch.max( - x - self.clip_min, self.clip_max - x).flatten(1).norm(p=2, dim=1) - - # setup variable and optimizer - delta = torch.zeros_like(x, requires_grad=True) - optimizer = optim.SGD([delta], lr=1) - scheduler = optim.lr_scheduler.CosineAnnealingLR( - optimizer, T_max=self.nb_iter, eta_min=0.01) - - best_l2 = worst_norm.clone() - best_delta = torch.zeros_like(x) - - for i in range(self.nb_iter): - scheduler.step() - - l2 = delta.data.flatten(1).norm(p=2, dim=1) - logits = self.predict(x + delta) - pred_labels = logits.argmax(1) - ce_loss = self.loss_fn(logits, y) - loss = multiplier * ce_loss - - is_adv = (pred_labels == y) if self.targeted else ( - pred_labels != y) - is_smaller = l2 < best_l2 - is_both = is_adv * is_smaller - best_l2[is_both] = l2[is_both] - best_delta[is_both] = delta.data[is_both] - - optimizer.zero_grad() - loss.backward() - - # renorming gradient - grad_norms = s * delta.grad.flatten(1).norm(p=2, dim=1) - delta.grad.div_(grad_norms.view(-1, *data_dims)) - # avoid nan or inf if gradient is 0 - if (grad_norms == 0).any(): - delta.grad[grad_norms == 0] = torch.randn_like( - delta.grad[grad_norms == 0]) - - optimizer.step() - - norm.mul_(1 - (2 * is_adv.float() - 1) * self.gamma) - - delta.data.mul_((norm / delta.data.flatten(1).norm( - p=2, dim=1)).view(-1, *data_dims)) - delta.data.add_(x) - if self.quantize: - delta.data.sub_(self.clip_min).div_(s) - delta.data.mul_(self.levels - 1).round_().div_(self.levels - 1) - delta.data.mul_(s).add_(self.clip_min) - delta.data.clamp_(self.clip_min, self.clip_max).sub_(x) - - return x + best_delta diff --git a/deepcp/attacks/deepfool.py b/deepcp/attacks/deepfool.py deleted file mode 100644 index 37e5a2e..0000000 --- a/deepcp/attacks/deepfool.py +++ /dev/null @@ -1,183 +0,0 @@ -# Copyright (c) 2020-present, Pouya Bashivan. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -""" -This code is an adaptation of DeepFool implementation from foolbox package -https://github.com/bethgelab/foolbox - -""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -from deepcp.utils import batch_clamp, clamp -from deepcp.utils import replicate_input, replicate_input_withgrad - -import torch as torch -from .base import Attack, LabelMixin - - -class DeepfoolLinfAttack(Attack, LabelMixin): - """ - A simple and fast gradient-based adversarial attack. - Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard, - "DeepFool: a simple and accurate method to fool deep neural - networks", https://arxiv.org/abs/1511.04599 - - :param predict: forward pass function. - :param num_classes: number of classes considered - :param nb_iter: number of iterations. - :eps=0.1 - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - """ - - def __init__(self, predict, num_classes=None, nb_iter=50, eps=0.1, - overshoot=0.02, clip_min=0., clip_max=1., loss_fn=None, - targeted=False): - """ - Deepfool Linf Attack implementation in pytorch. - """ - - super(DeepfoolLinfAttack, self).__init__(predict, loss_fn=loss_fn, - clip_min=clip_min, - clip_max=clip_max) - - self.predict = predict - self.num_classes = num_classes - self.nb_iter = nb_iter - self.eps = eps - self.overshoot = overshoot - self.targeted = targeted - - def is_adv(self, logits, y): - # criterion - y_hat = logits.argmax(-1) - is_adv = y_hat != y - return is_adv - - def get_deltas_logits(self, x, k, classes): - # definition of loss_fn - N = len(classes) - rows = range(N) - i0 = classes[:, 0] - - logits = self.predict(x) - ik = classes[:, k] - l0 = logits[rows, i0] - lk = logits[rows, ik] - delta_logits = lk - l0 - - return {'sum_deltas': delta_logits.sum(), - 'deltas': delta_logits, - 'logits': logits} - - def get_grads(self, x, k, classes): - deltas_logits = self.get_deltas_logits(x, k, classes) - deltas_logits['sum_deltas'].backward() - deltas_logits['grads'] = x.grad.clone() - x.grad.data.zero_() - return deltas_logits - - def get_distances(self, deltas, grads): - return abs(deltas) / ( - grads.flatten(start_dim=2, end_dim=-1).abs().sum(axis=-1) + 1e-8) - - def get_perturbations(self, distances, grads): - return self.atleast_kd(distances, grads.ndim) * grads.sign() - - def atleast_kd(self, x, k): - shape = x.shape + (1,) * (k - x.ndim) - return x.reshape(shape) - - def _verify_and_process_inputs(self, x, y): - if self.targeted: - assert y is not None - - if not self.targeted: - if y is None: - y = self._get_predicted_label(x) - - x = replicate_input_withgrad(x) - y = replicate_input(y) - return x, y - - def perturb(self, x, y=None): - """ - Given examples (x, y), returns their adversarial counterparts with - an attack length of eps. - - :param x: input tensor. - :param y: label tensor. - :return: tensor containing perturbed inputs. - """ - x, y = self._verify_and_process_inputs(x, y) - x.requires_grad_() - - logits = self.predict(x) - - # get the classes - classes = logits.argsort(axis=-1).flip(-1).detach() - if self.num_classes is None: - self.num_classes = logits.shape[-1] - else: - self.num_classes = min(self.num_classes, logits.shape[-1]) - - N = len(x) - rows = range(N) - - x0 = x - p_total = torch.zeros_like(x) - for _ in range(self.nb_iter): - # let's first get the logits using k = 1 to see if we are done - diffs = [self.get_grads(x, 1, classes)] - - is_adv = self.is_adv(diffs[0]['logits'], y) - if is_adv.all(): - break - - diffs += [self.get_grads(x, k, classes) for k in range(2, self.num_classes)] # noqa - - deltas = torch.stack([d['deltas'] for d in diffs], dim=-1) - grads = torch.stack([d['grads'] for d in diffs], dim=1) - assert deltas.shape == (N, self.num_classes - 1) - assert grads.shape == (N, self.num_classes - 1) + x0.shape[1:] - - # calculate the distances - # compute f_k / ||w_k|| - distances = self.get_distances(deltas, grads) - assert distances.shape == (N, self.num_classes - 1) - - # determine the best directions - best = distances.argmin(axis=1) # compute \hat{l} - distances = distances[rows, best] - deltas = deltas[rows, best] - grads = grads[rows, best] - assert distances.shape == (N,) - assert deltas.shape == (N,) - assert grads.shape == x0.shape - - # apply perturbation - distances = distances + 1e-4 # for numerical stability - p_step = self.get_perturbations(distances, grads) # =r_i - assert p_step.shape == x0.shape - - p_total += p_step - p_total = batch_clamp(self.eps, p_total) - - # don't do anything for those that are already adversarial - x = torch.where( - self.atleast_kd(is_adv, x.ndim), - x, - x0 + (1.0 + self.overshoot) * p_total, - ) # =x_{i+1} - - x = clamp(x, min=self.clip_min, max=self.clip_max).clone().detach().requires_grad_() # noqa - - return x.detach() diff --git a/deepcp/attacks/ead.py b/deepcp/attacks/ead.py deleted file mode 100644 index ecb4c98..0000000 --- a/deepcp/attacks/ead.py +++ /dev/null @@ -1,302 +0,0 @@ -# Copyright (c) 2019-present, Alexandre Araujo. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import torch -import torch.nn as nn - -from deepcp.utils import calc_l2distsq -from deepcp.utils import calc_l1dist -from deepcp.utils import clamp -from deepcp.utils import to_one_hot -from deepcp.utils import replicate_input - -from .base import Attack -from .base import LabelMixin -from .utils import is_successful - - -DIST_UPPER = 1e10 -COEFF_UPPER = 1e10 -INVALID_LABEL = -1 -REPEAT_STEP = 10 -ONE_MINUS_EPS = 0.999999 -UPPER_CHECK = 1e9 -PREV_LOSS_INIT = 1e6 -TARGET_MULT = 10000 -NUM_CHECKS = 10 - - -class ElasticNetL1Attack(Attack, LabelMixin): - """ - The ElasticNet L1 Attack, https://arxiv.org/abs/1709.04114 - - :param predict: forward pass function. - :param num_classes: number of clasess. - :param confidence: confidence of the adversarial examples. - :param targeted: if the attack is targeted. - :param learning_rate: the learning rate for the attack algorithm - :param binary_search_steps: number of binary search times to find the - optimum - :param max_iterations: the maximum number of iterations - :param abort_early: if set to true, abort early if getting stuck in local - min - :param initial_const: initial value of the constant c - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param beta: hyperparameter trading off L2 minimization for L1 minimization - :param decision_rule: EN or L1. Select final adversarial example from - all successful examples based on the least - elastic-net or L1 distortion criterion. - :param loss_fn: loss function - """ - - def __init__(self, predict, num_classes, confidence=0, - targeted=False, learning_rate=1e-2, - binary_search_steps=9, max_iterations=10000, - abort_early=False, initial_const=1e-3, - clip_min=0., clip_max=1., beta=1e-2, decision_rule='EN', - loss_fn=None): - """ElasticNet L1 Attack implementation in pytorch.""" - if loss_fn is not None: - import warnings - warnings.warn( - "This Attack currently do not support a different loss" - " function other than the default. Setting loss_fn manually" - " is not effective." - ) - - loss_fn = None - - super(ElasticNetL1Attack, self).__init__( - predict, loss_fn, clip_min, clip_max) - - self.learning_rate = learning_rate - self.init_learning_rate = learning_rate - self.max_iterations = max_iterations - self.binary_search_steps = binary_search_steps - self.abort_early = abort_early - self.confidence = confidence - self.initial_const = initial_const - self.num_classes = num_classes - self.beta = beta - # The last iteration (if we run many steps) repeat the search once. - self.repeat = binary_search_steps >= REPEAT_STEP - self.targeted = targeted - self.decision_rule = decision_rule - - - def _loss_fn(self, output, y_onehot, l1dist, l2distsq, const, opt=False): - - real = (y_onehot * output).sum(dim=1) - other = ((1.0 - y_onehot) * output - - (y_onehot * TARGET_MULT)).max(1)[0] - - if self.targeted: - loss_logits = clamp(other - real + self.confidence, min=0.) - else: - loss_logits = clamp(real - other + self.confidence, min=0.) - loss_logits = torch.sum(const * loss_logits) - - loss_l2 = l2distsq.sum() - - if opt: - loss = loss_logits + loss_l2 - else: - loss_l1 = self.beta * l1dist.sum() - loss = loss_logits + loss_l2 + loss_l1 - return loss - - - def _is_successful(self, output, label, is_logits): - # determine success, see if confidence-adjusted logits give the right - # label - if is_logits: - output = output.detach().clone() - if self.targeted: - output[torch.arange(len(label)).long(), - label] -= self.confidence - else: - output[torch.arange(len(label)).long(), - label] += self.confidence - pred = torch.argmax(output, dim=1) - else: - pred = output - if pred == INVALID_LABEL: - return pred.new_zeros(pred.shape).byte() - - return is_successful(pred, label, self.targeted) - - - def _fast_iterative_shrinkage_thresholding(self, x, yy_k, xx_k): - - zt = self.global_step / (self.global_step + 3) - - upper = clamp(yy_k - self.beta, max=self.clip_max) - lower = clamp(yy_k + self.beta, min=self.clip_min) - - diff = yy_k - x - cond1 = (diff > self.beta).float() - cond2 = (torch.abs(diff) <= self.beta).float() - cond3 = (diff < -self.beta).float() - - xx_k_p_1 = (cond1 * upper) + (cond2 * x) + (cond3 * lower) - yy_k.data = xx_k_p_1 + (zt * (xx_k_p_1 - xx_k)) - return yy_k, xx_k_p_1 - - - def _update_if_smaller_dist_succeed( - self, adv_img, labs, output, dist, batch_size, - cur_dist, cur_labels, - final_dist, final_labels, final_advs): - - target_label = labs - output_logits = output - _, output_label = torch.max(output_logits, 1) - - mask = (dist < cur_dist) & self._is_successful( - output_logits, target_label, True) - - cur_dist[mask] = dist[mask] # redundant - cur_labels[mask] = output_label[mask] - - mask = (dist < final_dist) & self._is_successful( - output_logits, target_label, True) - final_dist[mask] = dist[mask] - final_labels[mask] = output_label[mask] - final_advs[mask] = adv_img[mask] - - - def _update_loss_coeffs( - self, labs, cur_labels, batch_size, loss_coeffs, - coeff_upper_bound, coeff_lower_bound): - - # TODO: remove for loop, not significant, since only called during each - # binary search step - for ii in range(batch_size): - cur_labels[ii] = int(cur_labels[ii]) - if self._is_successful(cur_labels[ii], labs[ii], False): - coeff_upper_bound[ii] = min( - coeff_upper_bound[ii], loss_coeffs[ii]) - - if coeff_upper_bound[ii] < UPPER_CHECK: - loss_coeffs[ii] = ( - coeff_lower_bound[ii] + coeff_upper_bound[ii]) / 2 - else: - coeff_lower_bound[ii] = max( - coeff_lower_bound[ii], loss_coeffs[ii]) - if coeff_upper_bound[ii] < UPPER_CHECK: - loss_coeffs[ii] = ( - coeff_lower_bound[ii] + coeff_upper_bound[ii]) / 2 - else: - loss_coeffs[ii] *= 10 - - - def perturb(self, x, y=None): - - x, y = self._verify_and_process_inputs(x, y) - - # Initialization - if y is None: - y = self._get_predicted_label(x) - - x = replicate_input(x) - batch_size = len(x) - coeff_lower_bound = x.new_zeros(batch_size) - coeff_upper_bound = x.new_ones(batch_size) * COEFF_UPPER - loss_coeffs = torch.ones_like(y).float() * self.initial_const - - final_dist = [DIST_UPPER] * batch_size - final_labels = [INVALID_LABEL] * batch_size - - final_advs = x.clone() - y_onehot = to_one_hot(y, self.num_classes).float() - - final_dist = torch.FloatTensor(final_dist).to(x.device) - final_labels = torch.LongTensor(final_labels).to(x.device) - - # Start binary search - for outer_step in range(self.binary_search_steps): - - self.global_step = 0 - - # slack vector from the paper - yy_k = nn.Parameter(x.clone()) - xx_k = x.clone() - - cur_dist = [DIST_UPPER] * batch_size - cur_labels = [INVALID_LABEL] * batch_size - - cur_dist = torch.FloatTensor(cur_dist).to(x.device) - cur_labels = torch.LongTensor(cur_labels).to(x.device) - - prevloss = PREV_LOSS_INIT - - if (self.repeat and outer_step == (self.binary_search_steps - 1)): - loss_coeffs = coeff_upper_bound - - lr = self.learning_rate - - for ii in range(self.max_iterations): - - # reset gradient - if yy_k.grad is not None: - yy_k.grad.detach_() - yy_k.grad.zero_() - - # loss over yy_k with only L2 same as C&W - # we don't update L1 loss with SGD because we use ISTA - output = self.predict(yy_k) - l2distsq = calc_l2distsq(yy_k, x) - loss_opt = self._loss_fn( - output, y_onehot, None, l2distsq, loss_coeffs, opt=True) - loss_opt.backward() - - # gradient step - yy_k.data.add_(-lr, yy_k.grad.data) - self.global_step += 1 - - # ploynomial decay of learning rate - lr = self.init_learning_rate * \ - (1 - self.global_step / self.max_iterations)**0.5 - - yy_k, xx_k = self._fast_iterative_shrinkage_thresholding( - x, yy_k, xx_k) - - # loss ElasticNet or L1 over xx_k - with torch.no_grad(): - output = self.predict(xx_k) - l2distsq = calc_l2distsq(xx_k, x) - l1dist = calc_l1dist(xx_k, x) - - if self.decision_rule == 'EN': - dist = l2distsq + (l1dist * self.beta) - elif self.decision_rule == 'L1': - dist = l1dist - loss = self._loss_fn( - output, y_onehot, l1dist, l2distsq, loss_coeffs) - - if self.abort_early: - if ii % (self.max_iterations // NUM_CHECKS or 1) == 0: - if loss > prevloss * ONE_MINUS_EPS: - break - prevloss = loss - - self._update_if_smaller_dist_succeed( - xx_k.data, y, output, dist, batch_size, - cur_dist, cur_labels, - final_dist, final_labels, final_advs) - - self._update_loss_coeffs( - y, cur_labels, batch_size, - loss_coeffs, coeff_upper_bound, coeff_lower_bound) - - return final_advs diff --git a/deepcp/attacks/fast_adaptive_boundary.py b/deepcp/attacks/fast_adaptive_boundary.py deleted file mode 100644 index b3c4ee6..0000000 --- a/deepcp/attacks/fast_adaptive_boundary.py +++ /dev/null @@ -1,590 +0,0 @@ -# Copyright (c) 2019-present, Francesco Croce -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import torch -import time - -try: - from torch import flip -except ImportError: - from deepcp.utils import torch_flip as flip - -from deepcp.utils import replicate_input -from deepcp.attacks.utils import zero_gradients - -from .base import Attack -from .base import LabelMixin - -DEFAULT_EPS_DICT_BY_NORM = {'Linf': .3, 'L2': 1., 'L1': 5.0} - - -class FABAttack(Attack, LabelMixin): - """ - Fast Adaptive Boundary Attack (Linf, L2, L1) - https://arxiv.org/abs/1907.02044 - - :param predict: forward pass function - :param norm: Lp-norm to minimize ('Linf', 'L2', 'L1' supported) - :param n_restarts: number of random restarts - :param n_iter: number of iterations - :param eps: epsilon for the random restarts - :param alpha_max: alpha_max - :param eta: overshooting - :param beta: backward step - :param device: device to use ('cuda' or 'cpu') - """ - - def __init__( - self, - predict, - norm='Linf', - n_restarts=1, - n_iter=100, - eps=None, - alpha_max=0.1, - eta=1.05, - beta=0.9, - loss_fn=None, - verbose=False, - ): - """ FAB-attack implementation in pytorch """ - - super(FABAttack, self).__init__( - predict, loss_fn=None, clip_min=0., clip_max=1.) - - self.norm = norm - self.n_restarts = n_restarts - self.n_iter = n_iter - self.eps = eps if eps is not None else DEFAULT_EPS_DICT_BY_NORM[norm] - self.alpha_max = alpha_max - self.eta = eta - self.beta = beta - self.targeted = False - self.verbose = verbose - - def check_shape(self, x): - return x if len(x.shape) > 0 else x.unsqueeze(0) - - def get_diff_logits_grads_batch(self, imgs, la): - im = imgs.clone().requires_grad_() - with torch.enable_grad(): - y = self.predict(im) - - g2 = torch.zeros([y.shape[-1], *imgs.size()]).to(self.device) - grad_mask = torch.zeros_like(y) - for counter in range(y.shape[-1]): - zero_gradients(im) - grad_mask[:, counter] = 1.0 - y.backward(grad_mask, retain_graph=True) - grad_mask[:, counter] = 0.0 - g2[counter] = im.grad.data - - g2 = torch.transpose(g2, 0, 1).detach() - y2 = self.predict(imgs).detach() - df = y2 - y2[torch.arange(imgs.shape[0]), la].unsqueeze(1) - dg = g2 - g2[torch.arange(imgs.shape[0]), la].unsqueeze(1) - df[torch.arange(imgs.shape[0]), la] = 1e10 - - return df, dg - - def projection_linf(self, points_to_project, w_hyperplane, b_hyperplane): - t = points_to_project.clone() - w = w_hyperplane.clone() - b = b_hyperplane.clone() - - ind2 = ((w * t).sum(1) - b < 0).nonzero().squeeze() - ind2 = self.check_shape(ind2) - w[ind2] *= -1 - b[ind2] *= -1 - - c5 = (w < 0).float() - a = torch.ones(t.shape).to(self.device) - d = (a * c5 - t) * (w != 0).float() - a -= a * (1 - c5) - - p = torch.ones(t.shape).to(self.device) * c5 - t * (2 * c5 - 1) - _, indp = torch.sort(p, dim=1) - - b = b - (w * t).sum(1) - b0 = (w * d).sum(1) - b1 = b0.clone() - - counter = 0 - indp2 = flip(indp.unsqueeze(-1), dims=(1, 2)).squeeze() - u = torch.arange(0, w.shape[0]) - ws = w[u.unsqueeze(1), indp2] - bs2 = - ws * d[u.unsqueeze(1), indp2] - - s = torch.cumsum(ws.abs(), dim=1) - sb = torch.cumsum(bs2, dim=1) + b0.unsqueeze(1) - - c = b - b1 > 0 - b2 = sb[u, -1] - s[u, -1] * p[u, indp[u, 0]] - c_l = (b - b2 > 0).nonzero().squeeze() - c2 = ((b - b1 > 0) * (b - b2 <= 0)).nonzero().squeeze() - c_l = self.check_shape(c_l) - c2 = self.check_shape(c2) - - lb = torch.zeros(c2.shape[0]) - ub = torch.ones(c2.shape[0]) * (w.shape[1] - 1) - nitermax = torch.ceil(torch.log2(torch.tensor(w.shape[1]).float())) - counter2 = torch.zeros(lb.shape).long() - - while counter < nitermax: - counter4 = torch.floor((lb + ub) / 2) - counter2 = counter4.long() - indcurr = indp[c2, -counter2 - 1] - b2 = sb[c2, counter2] - s[c2, counter2] * p[c2, indcurr] - c = b[c2] - b2 > 0 - ind3 = c.nonzero().squeeze() - ind32 = (~c).nonzero().squeeze() - ind3 = self.check_shape(ind3) - ind32 = self.check_shape(ind32) - lb[ind3] = counter4[ind3] - ub[ind32] = counter4[ind32] - counter += 1 - - lb = lb.long() - counter2 = 0 - - if c_l.nelement() != 0: - lmbd_opt = (torch.max((b[c_l] - sb[c_l, -1]) / (-s[c_l, -1]), - torch.zeros(sb[c_l, -1].shape) - .to(self.device))).unsqueeze(-1) - d[c_l] = (2 * a[c_l] - 1) * lmbd_opt - - lmbd_opt = (torch.max((b[c2] - sb[c2, lb]) / (-s[c2, lb]), - torch.zeros(sb[c2, lb].shape) - .to(self.device))).unsqueeze(-1) - d[c2] = torch.min(lmbd_opt, d[c2]) * c5[c2]\ - + torch.max(-lmbd_opt, d[c2]) * (1 - c5[c2]) - - return d * (w != 0).float() - - def projection_l2(self, points_to_project, w_hyperplane, b_hyperplane): - t = points_to_project.clone() - w = w_hyperplane.clone() - b = b_hyperplane.clone() - - c = (w * t).sum(1) - b - ind2 = (c < 0).nonzero().squeeze() - ind2 = self.check_shape(ind2) - w[ind2] *= -1 - c[ind2] *= -1 - - u = torch.arange(0, w.shape[0]).unsqueeze(1) - - r = torch.max(t / w, (t - 1) / w) - u2 = torch.ones(r.shape).to(self.device) - r = torch.min(r, 1e12 * u2) - r = torch.max(r, -1e12 * u2) - r[w.abs() < 1e-8] = 1e12 - r[r == -1e12] = -r[r == -1e12] - rs, indr = torch.sort(r, dim=1) - rs2 = torch.cat((rs[:, 1:], - torch.zeros(rs.shape[0], 1).to(self.device)), 1) - rs[rs == 1e12] = 0 - rs2[rs2 == 1e12] = 0 - - w3 = w ** 2 - w3s = w3[u, indr] - w5 = w3s.sum(dim=1, keepdim=True) - ws = w5 - torch.cumsum(w3s, dim=1) - d = -(r * w).clone() - d = d * (w.abs() > 1e-8).float() - s = torch.cat(((-w5.squeeze() * rs[:, 0]).unsqueeze(1), - torch.cumsum((-rs2 + rs) * ws, dim=1) - - w5 * rs[:, 0].unsqueeze(-1)), 1) - - c4 = (s[:, 0] + c < 0) - c3 = ((d * w).sum(dim=1) + c > 0) - c6 = c4.nonzero().squeeze() - c2 = ((1 - c4.float()) * (1 - c3.float())).nonzero().squeeze() - c6 = self.check_shape(c6) - c2 = self.check_shape(c2) - - counter = 0 - lb = torch.zeros(c2.shape[0]) - ub = torch.ones(c2.shape[0]) * (w.shape[1] - 1) - nitermax = torch.ceil(torch.log2(torch.tensor(w.shape[1]).float())) - counter2 = torch.zeros(lb.shape).long() - - while counter < nitermax: - counter4 = torch.floor((lb + ub) / 2) - counter2 = counter4.long() - c3 = s[c2, counter2] + c[c2] > 0 - ind3 = c3.nonzero().squeeze() - ind32 = (~c3).nonzero().squeeze() - ind3 = self.check_shape(ind3) - ind32 = self.check_shape(ind32) - lb[ind3] = counter4[ind3] - ub[ind32] = counter4[ind32] - counter += 1 - - lb = lb.long() - alpha = torch.zeros([1]) - - if c6.nelement() != 0: - alpha = c[c6] / w5[c6].squeeze(-1) - d[c6] = -alpha.unsqueeze(-1) * w[c6] - - if c2.nelement() != 0: - alpha = (s[c2, lb] + c[c2]) / ws[c2, lb] + rs[c2, lb] - if torch.sum(ws[c2, lb] == 0) > 0: - ind = (ws[c2, lb] == 0).nonzero().squeeze().long() - ind = self.check_shape(ind) - alpha[ind] = 0 - c5 = (alpha.unsqueeze(-1) > r[c2]).float() - d[c2] = d[c2] * c5 - alpha.unsqueeze(-1) * w[c2] * (1 - c5) - - return d * (w.abs() > 1e-8).float() - - def projection_l1(self, points_to_project, w_hyperplane, b_hyperplane): - t = points_to_project.clone() - w = w_hyperplane.clone() - b = b_hyperplane.clone() - - c = (w * t).sum(1) - b - ind2 = (c < 0).nonzero().squeeze() - ind2 = self.check_shape(ind2) - w[ind2] *= -1 - c[ind2] *= -1 - - r = torch.max(1 / w, -1 / w) - r = torch.min(r, 1e12 * torch.ones(r.shape).to(self.device)) - rs, indr = torch.sort(r, dim=1) - _, indr_rev = torch.sort(indr) - - u = torch.arange(0, w.shape[0]).unsqueeze(1) - u2 = torch.arange(0, w.shape[1]).repeat(w.shape[0], 1) - c6 = (w < 0).float() - d = (-t + c6) * (w != 0).float() - d2 = torch.min(-w * t, w * (1 - t)) - ds = d2[u, indr] - ds2 = torch.cat((c.unsqueeze(-1), ds), 1) - s = torch.cumsum(ds2, dim=1) - - c4 = s[:, -1] < 0 - c2 = c4.nonzero().squeeze(-1) - c2 = self.check_shape(c2) - - counter = 0 - lb = torch.zeros(c2.shape[0]) - ub = torch.ones(c2.shape[0]) * (s.shape[1]) - nitermax = torch.ceil(torch.log2(torch.tensor(s.shape[1]).float())) - counter2 = torch.zeros(lb.shape).long() - - while counter < nitermax: - counter4 = torch.floor((lb + ub) / 2) - counter2 = counter4.long() - c3 = s[c2, counter2] > 0 - ind3 = c3.nonzero().squeeze() - ind32 = (~c3).nonzero().squeeze() - ind3 = self.check_shape(ind3) - ind32 = self.check_shape(ind32) - lb[ind3] = counter4[ind3] - ub[ind32] = counter4[ind32] - counter += 1 - - lb2 = lb.long() - - if c2.nelement() != 0: - alpha = -s[c2, lb2] / w[c2, indr[c2, lb2]] - c5 = u2[c2].float() < lb.unsqueeze(-1).float() - u3 = c5[u[:c5.shape[0]], indr_rev[c2]] - d[c2] = d[c2] * u3.float().to(self.device) - d[c2, indr[c2, lb2]] = alpha - - return d * (w.abs() > 1e-8).float() - - def perturb(self, x, y=None): - """ - :param x: clean images - :param y: clean labels, if None we use the predicted labels - """ - - self.device = x.device - self.orig_dim = list(x.shape[1:]) - self.ndims = len(self.orig_dim) - - x = x.detach().clone().float().to(self.device) - # assert next(self.predict.parameters()).device == x.device - - y_pred = self._get_predicted_label(x) - if y is None: - y = y_pred.detach().clone().long().to(self.device) - else: - y = y.detach().clone().long().to(self.device) - pred = y_pred == y - corr_classified = pred.float().sum() - if self.verbose: - print('Clean accuracy: {:.2%}'.format(pred.float().mean())) - if pred.sum() == 0: - return x - pred = self.check_shape(pred.nonzero().squeeze()) - - startt = time.time() - # runs the attack only on correctly classified points - im2 = replicate_input(x[pred]) - la2 = replicate_input(y[pred]) - if len(im2.shape) == self.ndims: - im2 = im2.unsqueeze(0) - bs = im2.shape[0] - u1 = torch.arange(bs) - adv = im2.clone() - adv_c = x.clone() - res2 = 1e10 * torch.ones([bs]).to(self.device) - res_c = torch.zeros([x.shape[0]]).to(self.device) - x1 = im2.clone() - x0 = im2.clone().reshape([bs, -1]) - counter_restarts = 0 - - while counter_restarts < self.n_restarts: - if counter_restarts > 0: - if self.norm == 'Linf': - t = 2 * torch.rand(x1.shape).to(self.device) - 1 - x1 = im2 + ( - torch.min( - res2, - self.eps * torch.ones(res2.shape).to(self.device) - ).reshape([-1, *([1] * self.ndims)]) - ) * t / (t.reshape([t.shape[0], -1]).abs() - .max(dim=1, keepdim=True)[0] - .reshape([-1, *([1] * self.ndims)])) * .5 - elif self.norm == 'L2': - t = torch.randn(x1.shape).to(self.device) - x1 = im2 + ( - torch.min( - res2, - self.eps * torch.ones(res2.shape).to(self.device) - ).reshape([-1, *([1] * self.ndims)]) - ) * t / ((t ** 2) - .view(t.shape[0], -1) - .sum(dim=-1) - .sqrt() - .view(t.shape[0], *([1] * self.ndims))) * .5 - elif self.norm == 'L1': - t = torch.randn(x1.shape).to(self.device) - x1 = im2 + (torch.min( - res2, - self.eps * torch.ones(res2.shape).to(self.device) - ).reshape([-1, *([1] * self.ndims)]) - ) * t / (t.abs().view(t.shape[0], -1) - .sum(dim=-1) - .view(t.shape[0], *([1] * self.ndims))) / 2 - - x1 = x1.clamp(0.0, 1.0) - - counter_iter = 0 - while counter_iter < self.n_iter: - with torch.no_grad(): - df, dg = self.get_diff_logits_grads_batch(x1, la2) - if self.norm == 'Linf': - dist1 = df.abs() / (1e-12 + - dg.abs() - .view(dg.shape[0], dg.shape[1], -1) - .sum(dim=-1)) - elif self.norm == 'L2': - dist1 = df.abs() / (1e-12 + (dg ** 2) - .view(dg.shape[0], dg.shape[1], -1) - .sum(dim=-1).sqrt()) - elif self.norm == 'L1': - dist1 = df.abs() / (1e-12 + dg.abs().reshape( - [df.shape[0], df.shape[1], -1]).max(dim=2)[0]) - else: - raise ValueError('norm not supported') - ind = dist1.min(dim=1)[1] - dg2 = dg[u1, ind] - b = (- df[u1, ind] + - (dg2 * x1).view(x1.shape[0], -1).sum(dim=-1)) - w = dg2.reshape([bs, -1]) - - if self.norm == 'Linf': - d3 = self.projection_linf( - torch.cat((x1.reshape([bs, -1]), x0), 0), - torch.cat((w, w), 0), - torch.cat((b, b), 0)) - elif self.norm == 'L2': - d3 = self.projection_l2( - torch.cat((x1.reshape([bs, -1]), x0), 0), - torch.cat((w, w), 0), - torch.cat((b, b), 0)) - elif self.norm == 'L1': - d3 = self.projection_l1( - torch.cat((x1.reshape([bs, -1]), x0), 0), - torch.cat((w, w), 0), - torch.cat((b, b), 0)) - d1 = torch.reshape(d3[:bs], x1.shape) - d2 = torch.reshape(d3[-bs:], x1.shape) - if self.norm == 'Linf': - a0 = d3.abs().max(dim=1, keepdim=True)[0]\ - .view(-1, *([1] * self.ndims)) - elif self.norm == 'L2': - a0 = (d3 ** 2).sum(dim=1, keepdim=True).sqrt()\ - .view(-1, *([1] * self.ndims)) - elif self.norm == 'L1': - a0 = d3.abs().sum(dim=1, keepdim=True)\ - .view(-1, *([1] * self.ndims)) - a0 = torch.max(a0, 1e-8 * torch.ones( - a0.shape).to(self.device)) - a1 = a0[:bs] - a2 = a0[-bs:] - alpha = torch.min(torch.max(a1 / (a1 + a2), - torch.zeros(a1.shape) - .to(self.device))[0], - self.alpha_max * torch.ones(a1.shape) - .to(self.device)) - x1 = ((x1 + self.eta * d1) * (1 - alpha) + - (im2 + d2 * self.eta) * alpha).clamp(0.0, 1.0) - - is_adv = self._get_predicted_label(x1) != la2 - - if is_adv.sum() > 0: - ind_adv = is_adv.nonzero().squeeze() - ind_adv = self.check_shape(ind_adv) - if self.norm == 'Linf': - t = (x1[ind_adv] - im2[ind_adv]).reshape( - [ind_adv.shape[0], -1]).abs().max(dim=1)[0] - elif self.norm == 'L2': - t = ((x1[ind_adv] - im2[ind_adv]) ** 2)\ - .view(ind_adv.shape[0], -1).sum(dim=-1).sqrt() - elif self.norm == 'L1': - t = (x1[ind_adv] - im2[ind_adv])\ - .abs().view(ind_adv.shape[0], -1).sum(dim=-1) - adv[ind_adv] = x1[ind_adv] * (t < res2[ind_adv]).\ - float().reshape([-1, *([1] * self.ndims)]) \ - + adv[ind_adv]\ - * (t >= res2[ind_adv]).float().reshape( - [-1, *([1] * self.ndims)]) - res2[ind_adv] = t * (t < res2[ind_adv]).float()\ - + res2[ind_adv] * (t >= res2[ind_adv]).float() - x1[ind_adv] = im2[ind_adv] + ( - x1[ind_adv] - im2[ind_adv]) * self.beta - - counter_iter += 1 - - counter_restarts += 1 - - ind_succ = res2 < 1e10 - if self.verbose: - print('success rate: {:.0f}/{:.0f}' - .format(ind_succ.float().sum(), corr_classified) + - ' (on correctly classified points) in {:.1f} s' - .format(time.time() - startt)) - - res_c[pred] = res2 * ind_succ.float() + 1e10 * (1 - ind_succ.float()) - ind_succ = self.check_shape(ind_succ.nonzero().squeeze()) - adv_c[pred[ind_succ]] = adv[ind_succ].clone() - - return adv_c - - -class LinfFABAttack(FABAttack): - """ - Linf - Fast Adaptive Boundary Attack - https://arxiv.org/abs/1907.02044 - - :param predict: forward pass function - :param n_restarts: number of random restarts - :param n_iter: number of iterations - :param eps: epsilon for the random restarts - :param alpha_max: alpha_max - :param eta: overshooting - :param beta: backward step - :param device: device to use ('cuda' or 'cpu') - """ - - def __init__( - self, - predict, - n_restarts=1, - n_iter=100, - eps=None, - alpha_max=0.1, - eta=1.05, - beta=0.9, - loss_fn=None, - verbose=False, - ): - norm = 'Linf' - super(LinfFABAttack, self).__init__( - predict=predict, norm=norm, n_restarts=n_restarts, - n_iter=n_iter, eps=eps, alpha_max=alpha_max, eta=eta, beta=beta, - loss_fn=loss_fn, verbose=verbose) - - -class L2FABAttack(FABAttack): - """ - L2 - Fast Adaptive Boundary Attack - https://arxiv.org/abs/1907.02044 - - :param predict: forward pass function - :param n_restarts: number of random restarts - :param n_iter: number of iterations - :param eps: epsilon for the random restarts - :param alpha_max: alpha_max - :param eta: overshooting - :param beta: backward step - :param device: device to use ('cuda' or 'cpu') - """ - - def __init__( - self, - predict, - n_restarts=1, - n_iter=100, - eps=None, - alpha_max=0.1, - eta=1.05, - beta=0.9, - loss_fn=None, - verbose=False, - ): - norm = 'L2' - super(L2FABAttack, self).__init__( - predict=predict, norm=norm, n_restarts=n_restarts, - n_iter=n_iter, eps=eps, alpha_max=alpha_max, eta=eta, beta=beta, - loss_fn=loss_fn, verbose=verbose) - - -class L1FABAttack(FABAttack): - """ - L1 - Fast Adaptive Boundary Attack - https://arxiv.org/abs/1907.02044 - - :param predict: forward pass function - :param n_restarts: number of random restarts - :param n_iter: number of iterations - :param eps: epsilon for the random restarts - :param alpha_max: alpha_max - :param eta: overshooting - :param beta: backward step - :param device: device to use ('cuda' or 'cpu') - """ - - def __init__( - self, - predict, - n_restarts=1, - n_iter=100, - eps=None, - alpha_max=0.1, - eta=1.05, - beta=0.9, - loss_fn=None, - verbose=False, - ): - norm = 'L1' - super(L1FABAttack, self).__init__( - predict=predict, norm=norm, n_restarts=n_restarts, - n_iter=n_iter, eps=eps, alpha_max=alpha_max, eta=eta, beta=beta, - loss_fn=loss_fn, verbose=verbose) diff --git a/deepcp/attacks/iterative_projected_gradient.py b/deepcp/attacks/iterative_projected_gradient.py deleted file mode 100644 index 28d8446..0000000 --- a/deepcp/attacks/iterative_projected_gradient.py +++ /dev/null @@ -1,563 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import numpy as np -import torch -import torch.nn as nn - -from deepcp.utils import clamp -from deepcp.utils import normalize_by_pnorm -from deepcp.utils import clamp_by_pnorm -from deepcp.utils import is_float_or_torch_tensor -from deepcp.utils import batch_multiply -from deepcp.utils import batch_clamp -from deepcp.utils import replicate_input -from deepcp.utils import batch_l1_proj - -from .base import Attack -from .base import LabelMixin -from .utils import rand_init_delta - - -def perturb_iterative(xvar, yvar, predict, nb_iter, eps, eps_iter, loss_fn, - delta_init=None, minimize=False, ord=np.inf, - clip_min=0.0, clip_max=1.0, - l1_sparsity=None): - """ - Iteratively maximize the loss over the input. It is a shared method for - iterative attacks including IterativeGradientSign, LinfPGD, etc. - - :param xvar: input data. - :param yvar: input labels. - :param predict: forward pass function. - :param nb_iter: number of iterations. - :param eps: maximum distortion. - :param eps_iter: attack step size. - :param loss_fn: loss function. - :param delta_init: (optional) tensor contains the random initialization. - :param minimize: (optional bool) whether to minimize or maximize the loss. - :param ord: (optional) the order of maximum distortion (inf or 2). - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param l1_sparsity: sparsity value for L1 projection. - - if None, then perform regular L1 projection. - - if float value, then perform sparse L1 descent from - Algorithm 1 in https://arxiv.org/pdf/1904.13000v1.pdf - :return: tensor containing the perturbed input. - """ - if delta_init is not None: - delta = delta_init - else: - delta = torch.zeros_like(xvar) - - delta.requires_grad_() - for ii in range(nb_iter): - outputs = predict(xvar + delta) - loss = loss_fn(outputs, yvar) - if minimize: - loss = -loss - - loss.backward() - if ord == np.inf: - grad_sign = delta.grad.data.sign() - delta.data = delta.data + batch_multiply(eps_iter, grad_sign) - delta.data = batch_clamp(eps, delta.data) - delta.data = clamp(xvar.data + delta.data, clip_min, clip_max - ) - xvar.data - - elif ord == 2: - grad = delta.grad.data - grad = normalize_by_pnorm(grad) - delta.data = delta.data + batch_multiply(eps_iter, grad) - delta.data = clamp(xvar.data + delta.data, clip_min, clip_max - ) - xvar.data - if eps is not None: - delta.data = clamp_by_pnorm(delta.data, ord, eps) - - elif ord == 1: - grad = delta.grad.data - abs_grad = torch.abs(grad) - - batch_size = grad.size(0) - view = abs_grad.view(batch_size, -1) - view_size = view.size(1) - if l1_sparsity is None: - vals, idx = view.topk(1) - else: - vals, idx = view.topk( - int(np.round((1 - l1_sparsity) * view_size))) - - out = torch.zeros_like(view).scatter_(1, idx, vals) - out = out.view_as(grad) - grad = grad.sign() * (out > 0).float() - grad = normalize_by_pnorm(grad, p=1) - delta.data = delta.data + batch_multiply(eps_iter, grad) - - delta.data = batch_l1_proj(delta.data.cpu(), eps) - delta.data = delta.data.to(xvar.device) - delta.data = clamp(xvar.data + delta.data, clip_min, clip_max - ) - xvar.data - else: - error = "Only ord = inf, ord = 1 and ord = 2 have been implemented" - raise NotImplementedError(error) - delta.grad.data.zero_() - - x_adv = clamp(xvar + delta, clip_min, clip_max) - return x_adv - - -class PGDAttack(Attack, LabelMixin): - """ - The projected gradient descent attack (Madry et al, 2017). - The attack performs nb_iter steps of size eps_iter, while always staying - within eps from the initial point. - Paper: https://arxiv.org/pdf/1706.06083.pdf - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_iter: number of iterations. - :param eps_iter: attack step size. - :param rand_init: (optional bool) random initialization. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param ord: (optional) the order of maximum distortion (inf or 2). - :param targeted: if the attack is targeted. - """ - - def __init__( - self, predict, loss_fn=None, eps=0.3, nb_iter=40, - eps_iter=0.01, rand_init=True, clip_min=0., clip_max=1., - ord=np.inf, l1_sparsity=None, targeted=False): - """ - Create an instance of the PGDAttack. - - """ - super(PGDAttack, self).__init__( - predict, loss_fn, clip_min, clip_max) - self.eps = eps - self.nb_iter = nb_iter - self.eps_iter = eps_iter - self.rand_init = rand_init - self.ord = ord - self.targeted = targeted - if self.loss_fn is None: - self.loss_fn = nn.CrossEntropyLoss(reduction="sum") - self.l1_sparsity = l1_sparsity - assert is_float_or_torch_tensor(self.eps_iter) - assert is_float_or_torch_tensor(self.eps) - - def perturb(self, x, y=None): - """ - Given examples (x, y), returns their adversarial counterparts with - an attack length of eps. - - :param x: input tensor. - :param y: label tensor. - - if None and self.targeted=False, compute y as predicted - labels. - - if self.targeted=True, then y must be the targeted labels. - :return: tensor containing perturbed inputs. - """ - x, y = self._verify_and_process_inputs(x, y) - - delta = torch.zeros_like(x) - delta = nn.Parameter(delta) - if self.rand_init: - rand_init_delta( - delta, x, self.ord, self.eps, self.clip_min, self.clip_max) - delta.data = clamp( - x + delta.data, min=self.clip_min, max=self.clip_max) - x - - rval = perturb_iterative( - x, y, self.predict, nb_iter=self.nb_iter, - eps=self.eps, eps_iter=self.eps_iter, - loss_fn=self.loss_fn, minimize=self.targeted, - ord=self.ord, clip_min=self.clip_min, - clip_max=self.clip_max, delta_init=delta, - l1_sparsity=self.l1_sparsity, - ) - - return rval.data - - -class LinfPGDAttack(PGDAttack): - """ - PGD Attack with order=Linf - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_iter: number of iterations. - :param eps_iter: attack step size. - :param rand_init: (optional bool) random initialization. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - """ - - def __init__( - self, predict, loss_fn=None, eps=0.3, nb_iter=40, - eps_iter=0.01, rand_init=True, clip_min=0., clip_max=1., - targeted=False): - ord = np.inf - super(LinfPGDAttack, self).__init__( - predict=predict, loss_fn=loss_fn, eps=eps, nb_iter=nb_iter, - eps_iter=eps_iter, rand_init=rand_init, clip_min=clip_min, - clip_max=clip_max, targeted=targeted, - ord=ord) - - -class L2PGDAttack(PGDAttack): - """ - PGD Attack with order=L2 - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_iter: number of iterations. - :param eps_iter: attack step size. - :param rand_init: (optional bool) random initialization. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - """ - - def __init__( - self, predict, loss_fn=None, eps=0.3, nb_iter=40, - eps_iter=0.01, rand_init=True, clip_min=0., clip_max=1., - targeted=False): - ord = 2 - super(L2PGDAttack, self).__init__( - predict=predict, loss_fn=loss_fn, eps=eps, nb_iter=nb_iter, - eps_iter=eps_iter, rand_init=rand_init, clip_min=clip_min, - clip_max=clip_max, targeted=targeted, - ord=ord) - - -class L1PGDAttack(PGDAttack): - """ - PGD Attack with order=L1 - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_iter: number of iterations. - :param eps_iter: attack step size. - :param rand_init: (optional bool) random initialization. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - """ - - def __init__( - self, predict, loss_fn=None, eps=10., nb_iter=40, - eps_iter=0.01, rand_init=True, clip_min=0., clip_max=1., - targeted=False): - ord = 1 - super(L1PGDAttack, self).__init__( - predict=predict, loss_fn=loss_fn, eps=eps, nb_iter=nb_iter, - eps_iter=eps_iter, rand_init=rand_init, clip_min=clip_min, - clip_max=clip_max, targeted=targeted, - ord=ord, l1_sparsity=None) - - -class SparseL1DescentAttack(PGDAttack): - """ - SparseL1Descent Attack - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_iter: number of iterations. - :param eps_iter: attack step size. - :param rand_init: (optional bool) random initialization. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - :param l1_sparsity: proportion of zeros in gradient updates - """ - - def __init__( - self, predict, loss_fn=None, eps=0.3, nb_iter=40, - eps_iter=0.01, rand_init=False, clip_min=0., clip_max=1., - l1_sparsity=0.95, targeted=False): - ord = 1 - super(SparseL1DescentAttack, self).__init__( - predict=predict, loss_fn=loss_fn, eps=eps, nb_iter=nb_iter, - eps_iter=eps_iter, rand_init=rand_init, clip_min=clip_min, - clip_max=clip_max, targeted=targeted, - ord=ord, l1_sparsity=l1_sparsity) - - -class L2BasicIterativeAttack(PGDAttack): - """Like GradientAttack but with several steps for each epsilon. - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_iter: number of iterations. - :param eps_iter: attack step size. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - """ - - def __init__(self, predict, loss_fn=None, eps=0.1, nb_iter=10, - eps_iter=0.05, clip_min=0., clip_max=1., targeted=False): - ord = 2 - rand_init = False - l1_sparsity = None - super(L2BasicIterativeAttack, self).__init__( - predict, loss_fn, eps, nb_iter, eps_iter, rand_init, - clip_min, clip_max, ord, l1_sparsity, targeted) - - -class LinfBasicIterativeAttack(PGDAttack): - """ - Like GradientSignAttack but with several steps for each epsilon. - Aka Basic Iterative Attack. - Paper: https://arxiv.org/pdf/1611.01236.pdf - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_iter: number of iterations. - :param eps_iter: attack step size. - :param rand_init: (optional bool) random initialization. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - """ - - def __init__(self, predict, loss_fn=None, eps=0.1, nb_iter=10, - eps_iter=0.05, clip_min=0., clip_max=1., targeted=False): - ord = np.inf - rand_init = False - l1_sparsity = None - super(LinfBasicIterativeAttack, self).__init__( - predict, loss_fn, eps, nb_iter, eps_iter, rand_init, - clip_min, clip_max, ord, l1_sparsity, targeted) - - -class MomentumIterativeAttack(Attack, LabelMixin): - """ - The Momentum Iterative Attack (Dong et al. 2017). - - The attack performs nb_iter steps of size eps_iter, while always staying - within eps from the initial point. The optimization is performed with - momentum. - Paper: https://arxiv.org/pdf/1710.06081.pdf - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_iter: number of iterations - :param decay_factor: momentum decay factor. - :param eps_iter: attack step size. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - :param ord: the order of maximum distortion (inf or 2). - """ - - def __init__( - self, predict, loss_fn=None, eps=0.3, nb_iter=40, decay_factor=1., - eps_iter=0.01, clip_min=0., clip_max=1., targeted=False, - ord=np.inf): - """Create an instance of the MomentumIterativeAttack.""" - super(MomentumIterativeAttack, self).__init__( - predict, loss_fn, clip_min, clip_max) - self.eps = eps - self.nb_iter = nb_iter - self.decay_factor = decay_factor - self.eps_iter = eps_iter - self.targeted = targeted - self.ord = ord - if self.loss_fn is None: - self.loss_fn = nn.CrossEntropyLoss(reduction="sum") - - def perturb(self, x, y=None): - """ - Given examples (x, y), returns their adversarial counterparts with - an attack length of eps. - - :param x: input tensor. - :param y: label tensor. - - if None and self.targeted=False, compute y as predicted - labels. - - if self.targeted=True, then y must be the targeted labels. - :return: tensor containing perturbed inputs. - """ - x, y = self._verify_and_process_inputs(x, y) - - delta = torch.zeros_like(x) - g = torch.zeros_like(x) - - delta = nn.Parameter(delta) - - for i in range(self.nb_iter): - - if delta.grad is not None: - delta.grad.detach_() - delta.grad.zero_() - - imgadv = x + delta - outputs = self.predict(imgadv) - loss = self.loss_fn(outputs, y) - if self.targeted: - loss = -loss - loss.backward() - - g = self.decay_factor * g + normalize_by_pnorm( - delta.grad.data, p=1) - # according to the paper it should be .sum(), but in their - # implementations (both cleverhans and the link from the paper) - # it is .mean(), but actually it shouldn't matter - if self.ord == np.inf: - delta.data += batch_multiply(self.eps_iter, torch.sign(g)) - delta.data = batch_clamp(self.eps, delta.data) - delta.data = clamp( - x + delta.data, min=self.clip_min, max=self.clip_max) - x - elif self.ord == 2: - delta.data += self.eps_iter * normalize_by_pnorm(g, p=2) - delta.data *= clamp( - (self.eps * normalize_by_pnorm(delta.data, p=2) / - delta.data), - max=1.) - delta.data = clamp( - x + delta.data, min=self.clip_min, max=self.clip_max) - x - else: - error = "Only ord = inf and ord = 2 have been implemented" - raise NotImplementedError(error) - - rval = x + delta.data - return rval - - -class L2MomentumIterativeAttack(MomentumIterativeAttack): - """ - The L2 Momentum Iterative Attack - Paper: https://arxiv.org/pdf/1710.06081.pdf - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_iter: number of iterations - :param decay_factor: momentum decay factor. - :param eps_iter: attack step size. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - """ - - def __init__( - self, predict, loss_fn=None, eps=0.3, nb_iter=40, decay_factor=1., - eps_iter=0.01, clip_min=0., clip_max=1., targeted=False): - """Create an instance of the MomentumIterativeAttack.""" - ord = 2 - super(L2MomentumIterativeAttack, self).__init__( - predict, loss_fn, eps, nb_iter, decay_factor, - eps_iter, clip_min, clip_max, targeted, ord) - - -class LinfMomentumIterativeAttack(MomentumIterativeAttack): - """ - The Linf Momentum Iterative Attack - Paper: https://arxiv.org/pdf/1710.06081.pdf - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param nb_iter: number of iterations - :param decay_factor: momentum decay factor. - :param eps_iter: attack step size. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: if the attack is targeted. - """ - - def __init__( - self, predict, loss_fn=None, eps=0.3, nb_iter=40, decay_factor=1., - eps_iter=0.01, clip_min=0., clip_max=1., targeted=False): - """Create an instance of the MomentumIterativeAttack.""" - ord = np.inf - super(LinfMomentumIterativeAttack, self).__init__( - predict, loss_fn, eps, nb_iter, decay_factor, - eps_iter, clip_min, clip_max, targeted, ord) - - -class FastFeatureAttack(Attack): - """ - Fast attack against a target internal representation of a model using - gradient descent (Sabour et al. 2016). - Paper: https://arxiv.org/abs/1511.05122 - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: maximum distortion. - :param eps_iter: attack step size. - :param nb_iter: number of iterations - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - """ - - def __init__(self, predict, loss_fn=None, eps=0.3, eps_iter=0.05, - nb_iter=10, rand_init=True, clip_min=0., clip_max=1.): - """Create an instance of the FastFeatureAttack.""" - super(FastFeatureAttack, self).__init__( - predict, loss_fn, clip_min, clip_max) - self.eps = eps - self.eps_iter = eps_iter - self.nb_iter = nb_iter - self.rand_init = rand_init - self.clip_min = clip_min - self.clip_max = clip_max - if self.loss_fn is None: - self.loss_fn = nn.MSELoss(reduction="sum") - - def perturb(self, source, guide, delta=None): - """ - Given source, returns their adversarial counterparts - with representations close to that of the guide. - - :param source: input tensor which we want to perturb. - :param guide: targeted input. - :param delta: tensor contains the random initialization. - :return: tensor containing perturbed inputs. - """ - # Initialization - if delta is None: - delta = torch.zeros_like(source) - if self.rand_init: - delta = delta.uniform_(-self.eps, self.eps) - else: - delta = delta.detach() - - delta.requires_grad_() - - source = replicate_input(source) - guide = replicate_input(guide) - guide_ftr = self.predict(guide).detach() - - xadv = perturb_iterative(source, guide_ftr, self.predict, - self.nb_iter, eps_iter=self.eps_iter, - loss_fn=self.loss_fn, minimize=True, - ord=np.inf, eps=self.eps, - clip_min=self.clip_min, - clip_max=self.clip_max, - delta_init=delta) - - xadv = clamp(xadv, self.clip_min, self.clip_max) - - return xadv.data diff --git a/deepcp/attacks/jsma.py b/deepcp/attacks/jsma.py deleted file mode 100644 index 2a827dc..0000000 --- a/deepcp/attacks/jsma.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import numpy as np -import torch - -from deepcp.utils import clamp -from deepcp.utils import jacobian - -from .base import Attack -from .base import LabelMixin - - -class JacobianSaliencyMapAttack(Attack, LabelMixin): - """ - Jacobian Saliency Map Attack - This includes Algorithm 1 and 3 in v1, https://arxiv.org/abs/1511.07528v1 - - :param predict: forward pass function. - :param num_classes: number of clasess. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param gamma: highest percentage of pixels can be modified - :param theta: perturb length, range is either [theta, 0], [0, theta] - - """ - - def __init__(self, predict, num_classes, - clip_min=0.0, clip_max=1.0, loss_fn=None, - theta=1.0, gamma=1.0, comply_cleverhans=False): - super(JacobianSaliencyMapAttack, self).__init__( - predict, loss_fn, clip_min, clip_max) - self.num_classes = num_classes - self.theta = theta - self.gamma = gamma - self.comply_cleverhans = comply_cleverhans - self.targeted = True - - def _compute_forward_derivative(self, xadv, y): - jacobians = torch.stack([jacobian(self.predict, xadv, yadv) - for yadv in range(self.num_classes)]) - grads = jacobians.view((jacobians.shape[0], jacobians.shape[1], -1)) - grads_target = grads[y, range(len(y)), :] - grads_other = grads.sum(dim=0) - grads_target - return grads_target, grads_other - - def _sum_pair(self, grads, dim_x): - return grads.view(-1, dim_x, 1) + grads.view(-1, 1, dim_x) - - def _and_pair(self, cond, dim_x): - return cond.view(-1, dim_x, 1) & cond.view(-1, 1, dim_x) - - def _saliency_map(self, search_space, grads_target, grads_other, y): - - dim_x = search_space.shape[1] - - # alpha in Algorithm 3 line 2 - gradsum_target = self._sum_pair(grads_target, dim_x) - # alpha in Algorithm 3 line 3 - gradsum_other = self._sum_pair(grads_other, dim_x) - - if self.theta > 0: - scores_mask = (torch.gt(gradsum_target, 0) & - torch.lt(gradsum_other, 0)) - else: - scores_mask = (torch.lt(gradsum_target, 0) & - torch.gt(gradsum_other, 0)) - - scores_mask &= self._and_pair(search_space.ne(0), dim_x) - scores_mask[:, range(dim_x), range(dim_x)] = 0 - - if self.comply_cleverhans: - valid = torch.ones(scores_mask.shape[0]).byte() - else: - valid = scores_mask.view(-1, dim_x * dim_x).any(dim=1) - - scores = scores_mask.float() * (-gradsum_target * gradsum_other) - best = torch.max(scores.view(-1, dim_x * dim_x), 1)[1] - p1 = torch.remainder(best, dim_x) - p2 = (best / dim_x).long() - return p1, p2, valid - - def _modify_xadv(self, xadv, batch_size, cond, p1, p2): - ori_shape = xadv.shape - xadv = xadv.view(batch_size, -1) - for idx in range(batch_size): - if cond[idx] != 0: - xadv[idx, p1[idx]] += self.theta - xadv[idx, p2[idx]] += self.theta - xadv = clamp(xadv, min=self.clip_min, max=self.clip_max) - xadv = xadv.view(ori_shape) - return xadv - - def _update_search_space(self, search_space, p1, p2, cond): - for idx in range(len(cond)): - if cond[idx] != 0: - search_space[idx, p1[idx]] -= 1 - search_space[idx, p2[idx]] -= 1 - - def perturb(self, x, y=None): - x, y = self._verify_and_process_inputs(x, y) - xadv = x - batch_size = x.shape[0] - dim_x = int(np.prod(x.shape[1:])) - max_iters = int(dim_x * self.gamma / 2) - search_space = x.new_ones(batch_size, dim_x).int() - curr_step = 0 - yadv = self._get_predicted_label(xadv) - - # Algorithm 1 - while ((y != yadv).any() and curr_step < max_iters): - grads_target, grads_other = self._compute_forward_derivative( - xadv, y) - - # Algorithm 3 - p1, p2, valid = self._saliency_map( - search_space, grads_target, grads_other, y) - - cond = (y != yadv) & valid - - self._update_search_space(search_space, p1, p2, cond) - - xadv = self._modify_xadv(xadv, batch_size, cond, p1, p2) - yadv = self._get_predicted_label(xadv) - - curr_step += 1 - - xadv = clamp(xadv, min=self.clip_min, max=self.clip_max) - return xadv - - -JSMA = JacobianSaliencyMapAttack diff --git a/deepcp/attacks/lbfgs.py b/deepcp/attacks/lbfgs.py deleted file mode 100644 index 83fac63..0000000 --- a/deepcp/attacks/lbfgs.py +++ /dev/null @@ -1,150 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import numpy as np -import torch -import torch.nn.functional as F - -from deepcp.utils import calc_l2distsq - -from .base import Attack -from .base import LabelMixin - -L2DIST_UPPER = 1e10 -COEFF_UPPER = 1e10 -INVALID_LABEL = -1 -UPPER_CHECK = 1e9 - - -class LBFGSAttack(Attack, LabelMixin): - """ - The attack that uses L-BFGS to minimize the distance of the original - and perturbed images - - :param predict: forward pass function. - :param num_classes: number of clasess. - :param batch_size: number of samples in the batch - :param binary_search_steps: number of binary search times to find the - optimum - :param max_iterations: the maximum number of iterations - :param initial_const: initial value of the constant c - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param loss_fn: loss function - :param targeted: if the attack is targeted. - """ - - def __init__(self, predict, num_classes, batch_size=1, - binary_search_steps=9, max_iterations=100, - initial_const=1e-2, - clip_min=0, clip_max=1, loss_fn=None, targeted=False): - super(LBFGSAttack, self).__init__( - predict, loss_fn, clip_min, clip_max) - # XXX: should combine the input loss function with other things - self.num_classes = num_classes - self.batch_size = batch_size - self.binary_search_steps = binary_search_steps - self.max_iterations = max_iterations - self.initial_const = initial_const - self.targeted = targeted - - def _update_if_better( - self, adv_img, labs, output, dist, batch_size, - final_l2dists, final_labels, final_advs): - for ii in range(batch_size): - target_label = labs[ii] - output_logits = output[ii] - _, output_label = torch.max(output_logits, 0) - di = dist[ii] - if (di < final_l2dists[ii] and - output_label.item() == target_label): - final_l2dists[ii] = di - final_labels[ii] = output_label - final_advs[ii] = adv_img[ii] - - def _update_loss_coeffs( - self, labs, batch_size, - loss_coeffs, coeff_upper_bound, coeff_lower_bound, output): - for ii in range(batch_size): - _, cur_label = torch.max(output[ii], 0) - if cur_label.item() == int(labs[ii]): - coeff_upper_bound[ii] = min( - coeff_upper_bound[ii], loss_coeffs[ii]) - - if coeff_upper_bound[ii] < UPPER_CHECK: - loss_coeffs[ii] = (coeff_lower_bound[ii] + - coeff_upper_bound[ii]) / 2 - - else: - coeff_lower_bound[ii] = max( - coeff_lower_bound[ii], loss_coeffs[ii]) - - if coeff_upper_bound[ii] < UPPER_CHECK: - loss_coeffs[ii] = (coeff_lower_bound[ii] + - coeff_upper_bound[ii]) / 2 - - else: - loss_coeffs[ii] *= 10 - - def perturb(self, x, y=None): - - from scipy.optimize import fmin_l_bfgs_b - - def _loss_fn(adv_x_np, self, x, target, const): - adv_x = torch.from_numpy( - adv_x_np.reshape(x.shape)).float().to( - x.device).requires_grad_() - output = self.predict(adv_x) - loss2 = torch.sum((x - adv_x) ** 2) - loss_fn = F.cross_entropy(output, target, reduction='none') - loss1 = torch.sum(const * loss_fn) - loss = loss1 + loss2 - loss.backward() - grad_ret = adv_x.grad.data.cpu().numpy().flatten().astype(float) - loss = loss.data.cpu().numpy().flatten().astype(float) - if not self.targeted: - loss = -loss - return loss, grad_ret - - x, y = self._verify_and_process_inputs(x, y) - batch_size = len(x) - coeff_lower_bound = x.new_zeros(batch_size) - coeff_upper_bound = x.new_ones(batch_size) * COEFF_UPPER - loss_coeffs = x.new_ones(batch_size) * self.initial_const - final_l2dists = [L2DIST_UPPER] * batch_size - final_labels = [INVALID_LABEL] * batch_size - final_advs = x.clone() - clip_min = self.clip_min * np.ones(x.shape[:]).astype(float) - clip_max = self.clip_max * np.ones(x.shape[:]).astype(float) - clip_bound = list(zip(clip_min.flatten(), clip_max.flatten())) - - for outer_step in range(self.binary_search_steps): - init_guess = x.clone().cpu().numpy().flatten().astype(float) - adv_x, f, _ = fmin_l_bfgs_b(_loss_fn, - init_guess, - args=(self, x.clone(), y, loss_coeffs), - bounds=clip_bound, - maxiter=self.max_iterations, - iprint=0) - - adv_x = torch.from_numpy( - adv_x.reshape(x.shape)).float().to(x.device) - l2s = calc_l2distsq(x, adv_x) - output = self.predict(adv_x) - self._update_if_better( - adv_x, y, output.data, l2s, batch_size, - final_l2dists, final_labels, final_advs) - self._update_loss_coeffs( - y, batch_size, - loss_coeffs, coeff_upper_bound, coeff_lower_bound, - output.data) - return final_advs diff --git a/deepcp/attacks/localsearch.py b/deepcp/attacks/localsearch.py deleted file mode 100644 index 4e38581..0000000 --- a/deepcp/attacks/localsearch.py +++ /dev/null @@ -1,275 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import numpy as np -import torch -import torch.nn as nn - -from deepcp.utils import clamp -from deepcp.utils import replicate_input - -from .base import Attack -from .base import LabelMixin -from .utils import is_successful - - -class SinglePixelAttack(Attack, LabelMixin): - """ - Single Pixel Attack - Algorithm 1 in https://arxiv.org/pdf/1612.06299.pdf - - :param predict: forward pass function. - :param max_pixels: max number of pixels to perturb. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param loss_fn: loss function - :param targeted: if the attack is targeted. - """ - - def __init__(self, predict, max_pixels=100, clip_min=0., - loss_fn=None, clip_max=1., comply_with_foolbox=False, - targeted=False): - super(SinglePixelAttack, self).__init__( - predict=predict, loss_fn=None, - clip_min=clip_min, clip_max=clip_max) - self.max_pixels = max_pixels - self.clip_min = clip_min - self.clip_max = clip_max - self.comply_with_foolbox = comply_with_foolbox - self.targeted = targeted - - def perturb_single(self, x, y): - # x shape [C * H * W] - if self.comply_with_foolbox is True: - np.random.seed(233333) - rand_np = np.random.permutation(x.shape[1] * x.shape[2]) - pixels = torch.from_numpy(rand_np).type(torch.LongTensor) - else: - pixels = torch.randperm(x.shape[1] * x.shape[2]) - pixels = pixels.to(x.device) - pixels = pixels[:self.max_pixels] - for ii in range(self.max_pixels): - row = pixels[ii] % x.shape[2] - col = pixels[ii] // x.shape[2] - for val in [self.clip_min, self.clip_max]: - adv = replicate_input(x) - for mm in range(x.shape[0]): - adv[mm, row, col] = val - out_label = self._get_predicted_label(adv.unsqueeze(0)) - if self.targeted is True: - if int(out_label[0]) == int(y): - return adv - else: - if int(out_label[0]) != int(y): - return adv - return x - - def perturb(self, x, y=None): - x, y = self._verify_and_process_inputs(x, y) - return _perturb_batch(self.perturb_single, x, y) - - -class LocalSearchAttack(Attack, LabelMixin): - """ - Local Search Attack - Algorithm 3 in https://arxiv.org/pdf/1612.06299.pdf - - :param predict: forward pass function. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param p: parameter controls pixel complexity - :param r: perturbation value - :param loss_fn: loss function - :param d: the half side length of the neighbourhood square - :param t: the number of pixels perturbed at each round - :param k: the threshold for k-misclassification - :param round_ub: an upper bound on the number of rounds - """ - - def __init__(self, predict, clip_min=0., clip_max=1., p=1., r=1.5, - loss_fn=None, d=5, t=5, k=1, round_ub=10, seed_ratio=0.1, - max_nb_seeds=128, comply_with_foolbox=False, targeted=False): - super(LocalSearchAttack, self).__init__( - predict=predict, clip_max=clip_max, - clip_min=clip_min, loss_fn=None) - self.p = p - self.r = r - self.d = d - self.t = t - self.k = k - self.round_ub = round_ub - self.seed_ratio = seed_ratio - self.max_nb_seeds = max_nb_seeds - self.comply_with_foolbox = comply_with_foolbox - self.targeted = targeted - - if clip_min is None or clip_max is None: - raise ValueError("{} {}".format( - LocalSearchAttack, - "must have clip_min and clip_max specified as scalar values.")) - - def perturb_single(self, x, y): - # x shape C * H * W - rescaled_x = replicate_input(x) - best_img = None - best_dist = np.inf - rescaled_x, lb, ub = self._rescale_to_m0d5_to_0d5( - rescaled_x, vmin=self.clip_min, vmax=self.clip_max) - - if self.comply_with_foolbox is True: - np.random.seed(233333) - init_rand = np.random.permutation(x.shape[1] * x.shape[2]) - else: - init_rand = None - - # Algorithm 3 in v1 - - pxy = self._random_sample_seeds( - x.shape[1], x.shape[2], seed_ratio=self.seed_ratio, - max_nb_seeds=self.max_nb_seeds, init_rand=init_rand) - pxy = pxy.to(x.device) - ii = 0 - if self.comply_with_foolbox: - adv = rescaled_x - while ii < self.round_ub: - if not self.comply_with_foolbox: - adv = replicate_input(rescaled_x) - # Computing the function g using the neighbourhood - if self.comply_with_foolbox: - rand_np = np.random.permutation(len(pxy))[:self.max_nb_seeds] - pxy = pxy[torch.from_numpy(rand_np).type(torch.LongTensor)] - else: - pxy = pxy[torch.randperm(len(pxy))[:self.max_nb_seeds]] - - pert_lst = [ - self._perturb_seed_pixel( - adv, self.p, int(row), int(col)) for row, col in pxy] - # Compute the score for each pert in the list - scores, curr_best_img, curr_best_dist = self._rescale_x_score( - self.predict, pert_lst, y, x, best_dist) - if curr_best_img is not None: - best_img = curr_best_img - best_dist = curr_best_dist - _, indices = torch.sort(scores) - indices = indices[:self.t] - pxy_star = pxy[indices.data.cpu()] - # Generation of the perturbed image adv - for row, col in pxy_star: - for b in range(x.shape[0]): - adv[b, int(row), int(col)] = self._cyclic( - self.r, lb, ub, adv[b, int(row), int(col)]) - # Check whether the perturbed image is an adversarial image - revert_adv = self._revert_rescale(adv) - curr_lb = self._get_predicted_label(revert_adv.unsqueeze(0)) - curr_dist = torch.sum((x - revert_adv) ** 2) - if (is_successful(int(curr_lb), y, self.targeted) and - curr_dist < best_dist): - best_img = revert_adv - best_dist = curr_dist - return best_img - elif is_successful(curr_lb, y, self.targeted): - return best_img - pxy = [ - (row, col) - for rowcenter, colcenter in pxy_star - for row in range( - int(rowcenter) - self.d, int(rowcenter) + self.d + 1) - for col in range( - int(colcenter) - self.d, int(colcenter) + self.d + 1)] - pxy = list(set((row, col) for row, col in pxy if ( - 0 <= row < x.shape[2] and 0 <= col < x.shape[1]))) - pxy = torch.FloatTensor(pxy) - ii += 1 - if best_img is None: - return x - return best_img - - def perturb(self, x, y=None): - x, y = self._verify_and_process_inputs(x, y) - return _perturb_batch(self.perturb_single, x, y) - - def _rescale_to_m0d5_to_0d5(self, x, vmin=0., vmax=1.): - x = x - (vmin + vmax) / 2 - x = x / (vmax - vmin) - return x, -0.5, 0.5 - - def _revert_rescale(self, x, vmin=0., vmax=1.): - x_revert = x.clone() - x_revert = x_revert * (vmax - vmin) - x_revert = x_revert + (vmin + vmax) / 2 - return x_revert - - def _random_sample_seeds(self, h, w, seed_ratio, max_nb_seeds, init_rand): - n = int(seed_ratio * h * w) - n = min(n, max_nb_seeds) - if init_rand is not None: - locations = torch.from_numpy(init_rand)[:n] - else: - locations = torch.randperm(h * w)[:n] - p_x = locations.int() % w - p_y = locations.int() / w - pxy = list(zip(p_x, p_y)) - pxy = torch.Tensor(pxy) - return pxy - - def _perturb_seed_pixel(self, x, p, row, col): - x_pert = replicate_input(x) - for ii in range(x.shape[0]): - if x[ii, row, col] > 0: - x_pert[ii, row, col] = p - elif x[ii, row, col] < 0: - x_pert[ii, row, col] = -1 * p - else: - x_pert[ii, row, col] = 0 - return x_pert - - def _cyclic(self, r, lower_bound, upper_bound, i_bxy): - # Algorithm 2 in v1 - result = r * i_bxy - if result < lower_bound: - result = result + (upper_bound - lower_bound) - elif result > upper_bound: - result = result - (upper_bound - lower_bound) - return result - - def _rescale_x_score(self, predict, x, y, ori, best_dist): - x = torch.stack(x) - x = self._revert_rescale(x) - - batch_logits = predict(x) - scores = nn.Softmax(dim=1)(batch_logits)[:, y] - - if not self.comply_with_foolbox: - x = clamp(x, self.clip_min, self.clip_max) - batch_logits = predict(x) - - _, bests = torch.max(batch_logits, dim=1) - best_img = None - for ii in range(len(bests)): - curr_dist = torch.sum((x[ii] - ori) ** 2) - if (is_successful( - int(bests[ii]), y, self.targeted) and - curr_dist < best_dist): - best_img = x[ii] - best_dist = curr_dist - scores = nn.Softmax(dim=1)(batch_logits)[:, y] - return scores, best_img, best_dist - - -def _perturb_batch(perturb_single, x, y): - for ii in range(len(x)): - temp = perturb_single(x[ii], y[ii])[None, :, :, :] - if ii == 0: - result = temp - else: - result = torch.cat((result, temp)) - return result diff --git a/deepcp/attacks/one_step_gradient.py b/deepcp/attacks/one_step_gradient.py deleted file mode 100644 index d23f370..0000000 --- a/deepcp/attacks/one_step_gradient.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import torch.nn as nn - -from deepcp.utils import clamp -from deepcp.utils import normalize_by_pnorm -from deepcp.utils import batch_multiply - -from .base import Attack -from .base import LabelMixin - - -class GradientSignAttack(Attack, LabelMixin): - """ - One step fast gradient sign method (Goodfellow et al, 2014). - Paper: https://arxiv.org/abs/1412.6572 - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: attack step size. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: indicate if this is a targeted attack. - """ - - def __init__(self, predict, loss_fn=None, eps=0.3, clip_min=0., - clip_max=1., targeted=False): - """ - Create an instance of the GradientSignAttack. - """ - super(GradientSignAttack, self).__init__( - predict, loss_fn, clip_min, clip_max) - - self.eps = eps - self.targeted = targeted - if self.loss_fn is None: - self.loss_fn = nn.CrossEntropyLoss(reduction="sum") - - def perturb(self, x, y=None): - """ - Given examples (x, y), returns their adversarial counterparts with - an attack length of eps. - - :param x: input tensor. - :param y: label tensor. - - if None and self.targeted=False, compute y as predicted - labels. - - if self.targeted=True, then y must be the targeted labels. - :return: tensor containing perturbed inputs. - """ - - x, y = self._verify_and_process_inputs(x, y) - xadv = x.requires_grad_() - outputs = self.predict(xadv) - - loss = self.loss_fn(outputs, y) - if self.targeted: - loss = -loss - loss.backward() - grad_sign = xadv.grad.detach().sign() - - xadv = xadv + batch_multiply(self.eps, grad_sign) - - xadv = clamp(xadv, self.clip_min, self.clip_max) - - return xadv.detach() - - -FGSM = GradientSignAttack - - -class GradientAttack(Attack, LabelMixin): - """ - Perturbs the input with gradient (not gradient sign) of the loss wrt the - input. - - :param predict: forward pass function. - :param loss_fn: loss function. - :param eps: attack step size. - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param targeted: indicate if this is a targeted attack. - """ - - def __init__(self, predict, loss_fn=None, eps=0.3, - clip_min=0., clip_max=1., targeted=False): - """ - Create an instance of the GradientAttack. - """ - super(GradientAttack, self).__init__( - predict, loss_fn, clip_min, clip_max) - - self.eps = eps - self.targeted = targeted - if self.loss_fn is None: - self.loss_fn = nn.CrossEntropyLoss(reduction="sum") - - def perturb(self, x, y=None): - """ - Given examples (x, y), returns their adversarial counterparts with - an attack length of eps. - - :param x: input tensor. - :param y: label tensor. - - if None and self.targeted=False, compute y as predicted - labels. - - if self.targeted=True, then y must be the targeted labels. - :return: tensor containing perturbed inputs. - """ - x, y = self._verify_and_process_inputs(x, y) - xadv = x.requires_grad_() - outputs = self.predict(xadv) - - loss = self.loss_fn(outputs, y) - if self.targeted: - loss = -loss - loss.backward() - grad = normalize_by_pnorm(xadv.grad) - xadv = xadv + batch_multiply(self.eps, grad) - xadv = clamp(xadv, self.clip_min, self.clip_max) - - return xadv.detach() - - -FGM = GradientAttack diff --git a/deepcp/attacks/spatial.py b/deepcp/attacks/spatial.py deleted file mode 100644 index 78299a5..0000000 --- a/deepcp/attacks/spatial.py +++ /dev/null @@ -1,159 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import numpy as np -import torch -import torch.nn.functional as F - -from deepcp.utils import calc_l2distsq -from deepcp.utils import clamp -from deepcp.utils import to_one_hot - -from .base import Attack -from .base import LabelMixin -from .utils import is_successful - -L2DIST_UPPER = 1e10 -TARGET_MULT = 10000.0 -INVALID_LABEL = -1 - - -class SpatialTransformAttack(Attack, LabelMixin): - """ - Spatially Transformed Attack (Xiao et al. 2018) - https://openreview.net/forum?id=HyydRMZC- - - :param predict: forward pass function. - :param num_classes: number of clasess. - :param confidence: confidence of the adversarial examples. - :param initial_const: initial value of the constant c - :param max_iterations: the maximum number of iterations - :param search_steps: number of search times to find the optimum - :param loss_fn: loss function - :param clip_min: mininum value per input dimension. - :param clip_max: maximum value per input dimension. - :param abort_early: if set to true, abort early if getting stuck in local - min - :param targeted: if the attack is targeted - """ - - def __init__(self, predict, num_classes, confidence=0, - initial_const=1, max_iterations=1000, - search_steps=1, loss_fn=None, - clip_min=0.0, clip_max=1.0, - abort_early=True, targeted=False): - super(SpatialTransformAttack, self).__init__( - predict, loss_fn, clip_min, clip_max) - self.num_classes = num_classes - self.confidence = confidence - self.initial_const = initial_const - self.max_iterations = max_iterations - self.search_steps = search_steps - self.abort_early = abort_early - self.targeted = targeted - - def _loss_fn_spatial(self, grid, x, y, const, grid_ori): - imgs = x.clone() - grid = torch.from_numpy( - grid.reshape(grid_ori.shape)).float().to( - x.device).requires_grad_() - delta = grid_ori - grid - - adv_img = F.grid_sample(imgs, grid) - output = self.predict(adv_img) - real = (y * output).sum(dim=1) - other = ( - (1.0 - y) * output - (y * TARGET_MULT)).max(1)[0] - if self.targeted: - loss1 = clamp(other - real + self.confidence, min=0.) - else: - loss1 = clamp(real - other + self.confidence, min=0.) - loss2 = self.initial_const * ( - torch.sqrt(((( - delta[:, :, 1:] - delta[:, :, :-1] + 1e-10) ** 2)).view( - delta.shape[0], -1).sum(1)) + - torch.sqrt((( - delta[:, 1:, :] - delta[:, :-1, :] + 1e-10) ** 2).view( - delta.shape[0], -1).sum(1))) - loss = torch.sum(loss1) + torch.sum(loss2) - loss.backward() - grad_ret = grid.grad.data.cpu().numpy().flatten().astype(float) - grid.grad.data.zero_() - return loss.data.cpu().numpy().astype(float), grad_ret - - def _update_if_better( - self, adv_img, labs, output, dist, batch_size, - final_l2dists, final_labels, final_advs, step, final_step): - - for ii in range(batch_size): - target_label = labs[ii] - output_logits = output[ii] - _, output_label = torch.max(output_logits, 0) - di = dist[ii] - if (di < final_l2dists[ii] and - is_successful( - int(output_label.item()), int(target_label), - self.targeted)): - final_l2dists[ii] = di - final_labels[ii] = output_label - final_advs[ii] = adv_img[ii] - final_step[ii] = step - - def perturb(self, x, y=None): - x, y = self._verify_and_process_inputs(x, y) - batch_size = len(x) - loss_coeffs = x.new_ones(batch_size) * self.initial_const - final_l2dists = [L2DIST_UPPER] * batch_size - final_labels = [INVALID_LABEL] * batch_size - final_step = [INVALID_LABEL] * batch_size - final_advs = torch.zeros_like(x) - - # TODO: refactor the theta generation - theta = torch.tensor([[[1., 0., 0.], - [0., 1., 0.]]]).to(x.device) - theta = theta.repeat((x.shape[0], 1, 1)) - - - grid = F.affine_grid(theta, x.size()) - - grid_ori = grid.clone() - y_onehot = to_one_hot(y, self.num_classes).float() - - clip_min = np.ones(grid_ori.shape[:]) * -1 - clip_max = np.ones(grid_ori.shape[:]) * 1 - clip_bound = list(zip(clip_min.flatten(), clip_max.flatten())) - grid_ret = grid.clone().data.cpu().numpy().flatten().astype(float) - from scipy.optimize import fmin_l_bfgs_b - for outer_step in range(self.search_steps): - grid_ret, f, d = fmin_l_bfgs_b( - self._loss_fn_spatial, - grid_ret, - args=( - x.clone().detach(), - y_onehot, loss_coeffs, - grid_ori.clone().detach()), - maxiter=self.max_iterations, - bounds=clip_bound, - iprint=0, - maxls=100, - ) - grid = torch.from_numpy( - grid_ret.reshape(grid_ori.shape)).float().to(x.device) - adv_x = F.grid_sample(x.clone(), grid) - l2s = calc_l2distsq(grid.data, grid_ori.data) - output = self.predict(adv_x) - self._update_if_better( - adv_x.data, y, output.data, l2s, batch_size, - final_l2dists, final_labels, final_advs, - outer_step, final_step) - - return final_advs diff --git a/deepcp/attacks/spsa.py b/deepcp/attacks/spsa.py deleted file mode 100644 index 5465d4a..0000000 --- a/deepcp/attacks/spsa.py +++ /dev/null @@ -1,208 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import warnings - -import torch - -from .base import Attack -from .base import LabelMixin -from .utils import MarginalLoss -from ..utils import is_float_or_torch_tensor, batch_clamp, clamp - -__all__ = ['LinfSPSAAttack', 'spsa_grad', 'spsa_perturb'] - - -def linf_clamp_(dx, x, eps, clip_min, clip_max): - """Clamps perturbation `dx` to fit L_inf norm and image bounds. - - Limit the L_inf norm of `dx` to be <= `eps`, and the bounds of `x + dx` - to be in `[clip_min, clip_max]`. - - :param dx: perturbation to be clamped (inplace). - :param x: the image. - :param eps: maximum possible L_inf. - :param clip_min: upper bound of image values. - :param clip_max: lower bound of image values. - - :return: the clamped perturbation `dx`. - """ - - dx_clamped = batch_clamp(eps, dx) - x_adv = clamp(x + dx_clamped, clip_min, clip_max) - # `dx` is changed *inplace* so the optimizer will keep - # tracking it. the simplest mechanism for inplace was - # adding the difference between the new value `x_adv - x` - # and the old value `dx`. - dx += x_adv - x - dx - return dx - - -def _get_batch_sizes(n, max_batch_size): - batches = [max_batch_size for _ in range(n // max_batch_size)] - if n % max_batch_size > 0: - batches.append(n % max_batch_size) - return batches - - -@torch.no_grad() -def spsa_grad(predict, loss_fn, x, y, delta, nb_sample, max_batch_size): - """Uses SPSA method to apprixmate gradient w.r.t `x`. - - Use the SPSA method to approximate the gradient of `loss_fn(predict(x), y)` - with respect to `x`, based on the nonce `v`. - - :param predict: predict function (single argument: input). - :param loss_fn: loss function (dual arguments: output, target). - :param x: input argument for function `predict`. - :param y: target argument for function `loss_fn`. - :param v: perturbations of `x`. - :param delta: scaling parameter of SPSA. - :param reduction: how to reduce the gradients of the different samples. - - :return: return the approximated gradient of `loss_fn(predict(x), y)` - with respect to `x`. - """ - - grad = torch.zeros_like(x) - x = x.unsqueeze(0) - y = y.unsqueeze(0) - - def f(xvar, yvar): - return loss_fn(predict(xvar), yvar) - x = x.expand(max_batch_size, *x.shape[1:]).contiguous() - y = y.expand(max_batch_size, *y.shape[1:]).contiguous() - v = torch.empty_like(x[:, :1, ...]) - - for batch_size in _get_batch_sizes(nb_sample, max_batch_size): - x_ = x[:batch_size] - y_ = y[:batch_size] - vb = v[:batch_size] - vb = vb.bernoulli_().mul_(2.0).sub_(1.0) - v_ = vb.expand_as(x_).contiguous() - x_shape = x_.shape - x_ = x_.view(-1, *x.shape[2:]) - y_ = y_.view(-1, *y.shape[2:]) - v_ = v_.view(-1, *v.shape[2:]) - df = f(x_ + delta * v_, y_) - f(x_ - delta * v_, y_) - df = df.view(-1, *[1 for _ in v_.shape[1:]]) - grad_ = df / (2. * delta * v_) - grad_ = grad_.view(x_shape) - grad_ = grad_.sum(dim=0, keepdim=False) - grad += grad_ - grad /= nb_sample - - return grad - - -def spsa_perturb(predict, loss_fn, x, y, eps, delta, lr, nb_iter, - nb_sample, max_batch_size, clip_min=0.0, clip_max=1.0): - """Perturbs the input `x` based on SPSA attack. - - :param predict: predict function (single argument: input). - :param loss_fn: loss function (dual arguments: output, target). - :param x: input argument for function `predict`. - :param y: target argument for function `loss_fn`. - :param eps: the L_inf budget of the attack. - :param delta: scaling parameter of SPSA. - :param lr: the learning rate of the `Adam` optimizer. - :param nb_iter: number of iterations of the attack. - :param nb_sample: number of samples for the SPSA gradient approximation. - :param max_batch_size: maximum batch size to be evaluated at once. - :param clip_min: upper bound of image values. - :param clip_max: lower bound of image values. - - :return: the perturbated input. - """ - - dx = torch.zeros_like(x) - dx.grad = torch.zeros_like(dx) - optimizer = torch.optim.Adam([dx], lr=lr) - for _ in range(nb_iter): - optimizer.zero_grad() - dx.grad = spsa_grad( - predict, loss_fn, x + dx, y, delta, nb_sample, max_batch_size) - optimizer.step() - dx = linf_clamp_(dx, x, eps, clip_min, clip_max) - x_adv = x + dx - - return x_adv - - -class LinfSPSAAttack(Attack, LabelMixin): - """SPSA Attack (Uesato et al. 2018). - Based on: https://arxiv.org/abs/1802.05666 - - :param predict: predict function (single argument: input). - :param eps: the L_inf budget of the attack. - :param delta: scaling parameter of SPSA. - :param lr: the learning rate of the `Adam` optimizer. - :param nb_iter: number of iterations of the attack. - :param nb_sample: number of samples for SPSA gradient approximation. - :param max_batch_size: maximum batch size to be evaluated at once. - :param targeted: [description] - :param loss_fn: loss function (dual arguments: output, target). - :param clip_min: upper bound of image values. - :param clip_max: lower bound of image values. - """ - - def __init__(self, predict, eps, delta=0.01, lr=0.01, nb_iter=1, - nb_sample=128, max_batch_size=64, targeted=False, - loss_fn=None, clip_min=0.0, clip_max=1.0): - - if loss_fn is None: - loss_fn = MarginalLoss(reduction="none") - elif hasattr(loss_fn, "reduction") and \ - getattr(loss_fn, "reduction") != "none": - warnings.warn("`loss_fn` is recommended to have " - "reduction='none' when used in SPSA attack") - - super(LinfSPSAAttack, self).__init__(predict, loss_fn, - clip_min, clip_max) - - assert is_float_or_torch_tensor(eps) - assert is_float_or_torch_tensor(delta) - assert is_float_or_torch_tensor(lr) - - self.eps = float(eps) - self.delta = float(delta) - self.lr = float(lr) - self.nb_iter = int(nb_iter) - self.nb_sample = int(nb_sample) - self.max_batch_size = int(max_batch_size) - self.targeted = bool(targeted) - - def perturb(self, x, y=None): # pylint: disable=arguments-differ - """Perturbs the input `x` based on SPSA attack. - - :param x: input tensor. - :param y: label tensor (default=`None`). if `self.targeted` is `False`, - `y` is the ground-truth label. if it's `None`, then `y` is - computed as the predicted label of `x`. - if `self.targeted` is `True`, `y` is the target label. - - :return: the perturbated input. - """ - - x, y = self._verify_and_process_inputs(x, y) - - if self.targeted: - def loss_fn(*args): - return self.loss_fn(*args) - - else: - def loss_fn(*args): - return -self.loss_fn(*args) - - return spsa_perturb(self.predict, loss_fn, x, y, self.eps, self.delta, - self.lr, self.nb_iter, self.nb_sample, - self.max_batch_size, self.clip_min, self.clip_max) diff --git a/deepcp/attacks/utils.py b/deepcp/attacks/utils.py deleted file mode 100644 index 07fe763..0000000 --- a/deepcp/attacks/utils.py +++ /dev/null @@ -1,241 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import collections -import numpy as np -import torch - -from torch.distributions import laplace -from torch.distributions import uniform -from torch.nn.modules.loss import _Loss - -from deepcp.utils import clamp -from deepcp.utils import clamp_by_pnorm -from deepcp.utils import batch_multiply -from deepcp.utils import normalize_by_pnorm -from deepcp.utils import predict_from_logits -from deepcp.loss import ZeroOneLoss -from deepcp.attacks import Attack, LabelMixin - - -def zero_gradients(x): - if isinstance(x, torch.Tensor): - if x.grad is not None: - x.grad.detach_() - x.grad.zero_() - elif isinstance(x, collections.abc.Iterable): - for elem in x: - zero_gradients(elem) - - -def rand_init_delta(delta, x, ord, eps, clip_min, clip_max): - # TODO: Currently only considered one way of "uniform" sampling - # for Linf, there are 3 ways: - # 1) true uniform sampling by first calculate the rectangle then sample - # 2) uniform in eps box then truncate using data domain (implemented) - # 3) uniform sample in data domain then truncate with eps box - # for L2, true uniform sampling is hard, since it requires uniform sampling - # inside a intersection of cube and ball, so there are 2 ways: - # 1) uniform sample in the data domain, then truncate using the L2 ball - # (implemented) - # 2) uniform sample in the L2 ball, then truncate using the data domain - # for L1: uniform l1 ball init, then truncate using the data domain - - if isinstance(eps, torch.Tensor): - assert len(eps) == len(delta) - - if ord == np.inf: - delta.data.uniform_(-1, 1) - delta.data = batch_multiply(eps, delta.data) - elif ord == 2: - delta.data.uniform_(clip_min, clip_max) - delta.data = delta.data - x - delta.data = clamp_by_pnorm(delta.data, ord, eps) - elif ord == 1: - ini = laplace.Laplace( - loc=delta.new_tensor(0), scale=delta.new_tensor(1) - ) - delta.data = ini.sample(delta.data.shape) - delta.data = normalize_by_pnorm(delta.data, p=1) - ray = uniform.Uniform(0, eps).sample() - delta.data *= ray - delta.data = clamp(x.data + delta.data, clip_min, clip_max) - x.data - else: - error = "Only ord = inf, ord = 1 and ord = 2 have been implemented" - raise NotImplementedError(error) - - delta.data = clamp(x + delta.data, min=clip_min, max=clip_max) - x - return delta.data - - -def is_successful(y1, y2, targeted): - if targeted is True: - return y1 == y2 - else: - return y1 != y2 - - -class AttackConfig(object): - # a convenient class for generate an attack/adversary instance - - def __init__(self): - self.kwargs = {} - - for mro in reversed(self.__class__.__mro__): - if mro in (AttackConfig, object): - continue - for kwarg in mro.__dict__: - if kwarg in self.AttackClass.__init__.__code__.co_varnames: - self.kwargs[kwarg] = mro.__dict__[kwarg] - else: - # make sure we don't specify wrong kwargs - assert kwarg in ["__module__", "AttackClass", "__doc__"] - - def __call__(self, *args): - adversary = self.AttackClass(*args, **self.kwargs) - print(self.AttackClass, args, self.kwargs) - return adversary - - -def multiple_mini_batch_attack( - adversary, loader, device="cuda", save_adv=False, norm=None, num_batch=None -): - lst_label = [] - lst_pred = [] - lst_advpred = [] - lst_dist = [] - - _norm_convert_dict = {"Linf": "inf", "L2": 2, "L1": 1} - if norm in _norm_convert_dict: - norm = _norm_convert_dict[norm] - - if norm == "inf": - - def dist_func(x, y): - return (x - y).view(x.size(0), -1).max(dim=1)[0] - - elif norm == 1 or norm == 2: - from deepcp.utils import _get_norm_batch - - def dist_func(x, y): - return _get_norm_batch(x - y, norm) - - else: - assert norm is None - - idx_batch = 0 - - for data, label in loader: - data, label = data.to(device), label.to(device) - adv = adversary.perturb(data, label) - advpred = predict_from_logits(adversary.predict(adv)) - pred = predict_from_logits(adversary.predict(data)) - lst_label.append(label) - lst_pred.append(pred) - lst_advpred.append(advpred) - if norm is not None: - lst_dist.append(dist_func(data, adv)) - - idx_batch += 1 - if idx_batch == num_batch: - break - - return ( - torch.cat(lst_label), - torch.cat(lst_pred), - torch.cat(lst_advpred), - torch.cat(lst_dist) if norm is not None else None, - ) - - -class MarginalLoss(_Loss): - # TODO: move this to advertorch.loss - - def forward(self, logits, targets): # pylint: disable=arguments-differ - assert logits.shape[-1] >= 2 - top_logits, top_classes = torch.topk(logits, 2, dim=-1) - target_logits = logits[torch.arange(logits.shape[0]), targets] - max_nontarget_logits = torch.where( - top_classes[..., 0] == targets, - top_logits[..., 1], - top_logits[..., 0], - ) - - loss = max_nontarget_logits - target_logits - if self.reduction == "none": - pass - elif self.reduction == "sum": - loss = loss.sum() - elif self.reduction == "mean": - loss = loss.mean() - else: - raise ValueError("unknown reduction: '%s'" % (self.recution,)) - - return loss - - -class ChooseBestAttack(Attack, LabelMixin): - def __init__( - self, predict, base_adversaries, loss_fn=None, targeted=False - ): - self.predict = predict - self.base_adversaries = base_adversaries - self.loss_fn = loss_fn - self.targeted = targeted - - if self.loss_fn is None: - self.loss_fn = ZeroOneLoss(reduction="none") - else: - assert self.loss_fn.reduction == "none" - - for adversary in self.base_adversaries: - assert self.targeted == adversary.targeted - - def perturb(self, x, y=None): - # TODO: might want to also retain the list of all attacks - - x, y = self._verify_and_process_inputs(x, y) - - with torch.no_grad(): - maxloss = self.loss_fn(self.predict(x), y) - final_adv = torch.zeros_like(x) - for adversary in self.base_adversaries: - adv = adversary.perturb(x, y) - loss = self.loss_fn(self.predict(adv), y) - to_replace = maxloss < loss - final_adv[to_replace] = adv[to_replace] - maxloss[to_replace] = loss[to_replace] - - return final_adv - - -def attack_whole_dataset(adversary, loader, device="cuda"): - lst_adv = [] - lst_label = [] - lst_pred = [] - lst_advpred = [] - for data, label in loader: - data, label = data.to(device), label.to(device) - pred = predict_from_logits(adversary.predict(data)) - adv = adversary.perturb(data, label) - advpred = predict_from_logits(adversary.predict(adv)) - lst_label.append(label) - lst_pred.append(pred) - lst_advpred.append(advpred) - lst_adv.append(adv) - return ( - torch.cat(lst_adv), - torch.cat(lst_label), - torch.cat(lst_pred), - torch.cat(lst_advpred), - ) diff --git a/deepcp/bpda.py b/deepcp/bpda.py deleted file mode 100644 index d350da6..0000000 --- a/deepcp/bpda.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -# BPDA stands for Backward Pass Differentiable Approximation -# See: -# Athalye, A., Carlini, N. & Wagner, D.. (2018). Obfuscated Gradients Give a -# False Sense of Security: Circumventing Defenses to Adversarial Examples. -# Proceedings of the 35th International Conference on Machine Learning, -# in PMLR 80:274-283 - -import torch -import torch.nn as nn - -__all__ = ['BPDAWrapper'] - - -class FunctionWrapper(nn.Module): - """`nn.Module` wrapping a `torch.autograd.Function`.""" - - def __init__(self, func): - """Wraps the provided function `func`. - - :param func: the `torch.autograd.Function` to be wrapped. - """ - super(FunctionWrapper, self).__init__() - self.func = func - - def forward(self, *inputs): - """Wraps the `forward` method of `func`.""" - return self.func.apply(*inputs) - - -class BPDAWrapper(FunctionWrapper): - """Backward Pass Differentiable Approximation. - - The module should be provided a `forward` method and a `backward` - method that approximates the derivatives of `forward`. - - The `forward` function is called in the forward pass, and the - `backward` function is used to find gradients in the backward pass. - - The `backward` function can be implicitly provided-by providing - `forwardsub` - an alternative forward pass function, which its - gradient will be used in the backward pass. - - If not `backward` nor `forwardsub` are provided, the `backward` - function will be assumed to be the identity. - - :param forward: `forward(*inputs)` - the forward function for BPDA. - :param forwardsub: (Optional) a substitute forward function, for the - gradients approximation of `forward`. - :param backward: (Optional) `backward(inputs, grad_outputs)` the - backward pass function for BPDA. - """ - - def __init__(self, forward, forwardsub=None, backward=None): - func = self._create_func(forward, backward, forwardsub) - super(BPDAWrapper, self).__init__(func) - - @classmethod - def _create_func(cls, forward_fn, backward_fn, forwardsub_fn): - if backward_fn is not None: - return cls._create_func_backward(forward_fn, backward_fn) - - if forwardsub_fn is not None: - return cls._create_func_forwardsub(forward_fn, forwardsub_fn) - - return cls._create_func_forward_only(forward_fn) - - @classmethod - def _create_func_forward_only(cls, forward_fn): - """Creates a differentiable `Function` given the forward function, - and the identity as backward function.""" - - class Func(torch.autograd.Function): - - @staticmethod - def forward(ctx, *inputs, **kwargs): - ctx.save_for_backward(*inputs) - return forward_fn(*inputs, **kwargs) - - @staticmethod - def backward(ctx, *grad_outputs): - inputs = ctx.saved_tensors - if len(grad_outputs) == len(inputs): - return grad_outputs - elif len(grad_outputs) == 1: - return tuple([grad_outputs[0] for _ in inputs]) - - raise ValueError("Expected %d gradients but got %d" % - (len(inputs), len(grad_outputs))) - - - return Func - - @classmethod - def _create_func_forwardsub(cls, forward_fn, forwardsub_fn): - """Creates a differentiable `Function` given the forward function, - and a substitute forward function. - - The substitute forward function is used to approximate the gradients - in the backward pass. - """ - - class Func(torch.autograd.Function): - - @staticmethod - def forward(ctx, *inputs, **kwargs): - ctx.save_for_backward(*inputs) - return forward_fn(*inputs, **kwargs) - - @staticmethod - @torch.enable_grad() # enables grad in the method's scope - def backward(ctx, *grad_outputs): - inputs = ctx.saved_tensors - inputs = [x.detach().clone().requires_grad_() for x in inputs] - outputs = forwardsub_fn(*inputs) - return torch.autograd.grad(outputs, inputs, grad_outputs) - - return Func - - @classmethod - def _create_func_backward(cls, forward_fn, backward_fn): - """Creates a differentiable `Function` given the forward and backward - functions.""" - - class Func(torch.autograd.Function): - - @staticmethod - def forward(ctx, *inputs, **kwargs): - ctx.save_for_backward(*inputs) - return forward_fn(*inputs, **kwargs) - - @staticmethod - def backward(ctx, *grad_outputs): - inputs = ctx.saved_tensors - return backward_fn(inputs, grad_outputs) - - return Func diff --git a/deepcp/context.py b/deepcp/context.py deleted file mode 100644 index 2d48aa1..0000000 --- a/deepcp/context.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from contextlib import contextmanager - - -class ctx_noparamgrad(object): - def __init__(self, module): - self.prev_grad_state = get_param_grad_state(module) - self.module = module - set_param_grad_off(module) - - def __enter__(self): - pass - - def __exit__(self, *args): - set_param_grad_state(self.module, self.prev_grad_state) - return False - - -class ctx_eval(object): - def __init__(self, module): - self.prev_training_state = get_module_training_state(module) - self.module = module - set_module_training_off(module) - - def __enter__(self): - pass - - def __exit__(self, *args): - set_module_training_state(self.module, self.prev_training_state) - return False - - -@contextmanager -def ctx_noparamgrad_and_eval(module): - with ctx_noparamgrad(module) as a, ctx_eval(module) as b: - yield (a, b) - - -def get_module_training_state(module): - return {mod: mod.training for mod in module.modules()} - - -def set_module_training_state(module, training_state): - for mod in module.modules(): - mod.training = training_state[mod] - - -def set_module_training_off(module): - for mod in module.modules(): - mod.training = False - - -def get_param_grad_state(module): - return {param: param.requires_grad for param in module.parameters()} - - -def set_param_grad_state(module, grad_state): - for param in module.parameters(): - param.requires_grad = grad_state[param] - - -def set_param_grad_off(module): - for param in module.parameters(): - param.requires_grad = False diff --git a/deepcp/defenses/__init__.py b/deepcp/defenses/__init__.py deleted file mode 100644 index 0586898..0000000 --- a/deepcp/defenses/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -# flake8: noqa - -from .base import Processor - -from .smoothing import ConvSmoothing2D -from .smoothing import AverageSmoothing2D -from .smoothing import GaussianSmoothing2D -from .smoothing import MedianSmoothing2D - -from .jpeg import JPEGFilter - -from .bitsqueezing import BitSqueezing -from .bitsqueezing import BinaryFilter diff --git a/deepcp/defenses/base.py b/deepcp/defenses/base.py deleted file mode 100644 index 064e5a4..0000000 --- a/deepcp/defenses/base.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -import torch.nn as nn - - -class Processor(nn.Module): - """ - Processor - """ - def __init__(self): - super(Processor, self).__init__() - - def forward(self, x): - return x - - def extra_repr(self): - return 'EmptyDefense (Identity)' diff --git a/deepcp/defenses/bitsqueezing.py b/deepcp/defenses/bitsqueezing.py deleted file mode 100644 index d8e44fa..0000000 --- a/deepcp/defenses/bitsqueezing.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from deepcp.functional import FloatToIntSqueezing - -from .base import Processor - - -class BitSqueezing(Processor): - """ - Bit Squeezing. - - :param bit_depth: bit depth. - :param vmin: min value. - :param vmax: max value. - """ - - def __init__(self, bit_depth, vmin=0., vmax=1.): - super(BitSqueezing, self).__init__() - - self.bit_depth = bit_depth - self.max_int = 2 ** self.bit_depth - 1 - self.vmin = vmin - self.vmax = vmax - - def forward(self, x): - return FloatToIntSqueezing.apply( - x, self.max_int, self.vmin, self.vmax) - - -class BinaryFilter(BitSqueezing): - """ - Binary Filter. - - :param vmin: min value. - :param vmax: max value. - """ - - def __init__(self, vmin=0., vmax=1.): - super(BinaryFilter, self).__init__(bit_depth=1, vmin=vmin, vmax=vmax) diff --git a/deepcp/defenses/jpeg.py b/deepcp/defenses/jpeg.py deleted file mode 100644 index 276d874..0000000 --- a/deepcp/defenses/jpeg.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from deepcp.functional import JPEGEncodingDecoding - -from .base import Processor - - -class JPEGFilter(Processor): - """ - JPEG Filter. - - :param quality: quality of the output. - """ - def __init__(self, quality=75): - super(JPEGFilter, self).__init__() - self.quality = quality - - def forward(self, x): - return JPEGEncodingDecoding.apply(x, self.quality).to(x.device) diff --git a/deepcp/defenses/smoothing.py b/deepcp/defenses/smoothing.py deleted file mode 100644 index f26e522..0000000 --- a/deepcp/defenses/smoothing.py +++ /dev/null @@ -1,149 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.modules.utils import _quadruple - -from .base import Processor - - -class MedianSmoothing2D(Processor): - """ - Median Smoothing 2D. - - :param kernel_size: aperture linear size; must be odd and greater than 1. - :param stride: stride of the convolution. - """ - - def __init__(self, kernel_size=3, stride=1): - super(MedianSmoothing2D, self).__init__() - self.kernel_size = kernel_size - self.stride = stride - padding = int(kernel_size) // 2 - if _is_even(kernel_size): - # both ways of padding should be fine here - # self.padding = (padding, 0, padding, 0) - self.padding = (0, padding, 0, padding) - else: - self.padding = _quadruple(padding) - - def forward(self, x): - x = F.pad(x, pad=self.padding, mode="reflect") - x = x.unfold(2, self.kernel_size, self.stride) - x = x.unfold(3, self.kernel_size, self.stride) - x = x.contiguous().view(x.shape[:4] + (-1,)).median(dim=-1)[0] - return x - - -class ConvSmoothing2D(Processor): - """ - Conv Smoothing 2D. - - :param kernel_size: size of the convolving kernel. - """ - - def __init__(self, kernel): - super(ConvSmoothing2D, self).__init__() - self.filter = _generate_conv2d_from_smoothing_kernel(kernel) - - def forward(self, x): - return self.filter(x) - - -class GaussianSmoothing2D(ConvSmoothing2D): - """ - Gaussian Smoothing 2D. - - :param sigma: sigma of the Gaussian. - :param channels: number of channels in the output. - :param kernel_size: aperture size. - """ - - def __init__(self, sigma, channels, kernel_size=None): - kernel = _generate_gaussian_kernel(sigma, channels, kernel_size) - super(GaussianSmoothing2D, self).__init__(kernel) - - -class AverageSmoothing2D(ConvSmoothing2D): - """ - Average Smoothing 2D. - - :param channels: number of channels in the output. - :param kernel_size: aperture size. - """ - - def __init__(self, channels, kernel_size): - kernel = torch.ones((channels, 1, kernel_size, kernel_size)) / ( - kernel_size * kernel_size - ) - super(AverageSmoothing2D, self).__init__(kernel) - - -def _generate_conv2d_from_smoothing_kernel(kernel): - channels = kernel.shape[0] - kernel_size = kernel.shape[-1] - - if _is_even(kernel_size): - raise NotImplementedError( - "Even number kernel size not supported yet, kernel_size={}".format( - kernel_size - ) - ) - - filter_ = nn.Conv2d( - in_channels=channels, - out_channels=channels, - kernel_size=kernel_size, - groups=channels, - padding=kernel_size // 2, - bias=False, - ) - - filter_.weight.data = kernel - filter_.weight.requires_grad = False - return filter_ - - -def _generate_gaussian_kernel(sigma, channels, kernel_size=None): - if kernel_size is None: - kernel_size = _round_to_odd(2 * 2 * sigma) - - vecx = torch.arange(kernel_size).float() - vecy = torch.arange(kernel_size).float() - gridxy = _meshgrid(vecx, vecy) - mean = (kernel_size - 1) / 2.0 - var = sigma ** 2 - - gaussian_kernel = ( - 1.0 - / (2.0 * math.pi * var) - * torch.exp(-(gridxy - mean).pow(2).sum(dim=0) / (2 * var)) - ) - - gaussian_kernel /= torch.sum(gaussian_kernel) - - gaussian_kernel = gaussian_kernel.repeat(channels, 1, 1, 1) - - return gaussian_kernel - - -def _round_to_odd(f): - return math.ceil(f) // 2 * 2 + 1 - - -def _meshgrid(vecx, vecy): - gridx = vecx.repeat(len(vecy), 1) - gridy = vecy.repeat(len(vecx), 1).t() - return torch.stack([gridx, gridy]) - - -def _is_even(x): - return int(x) % 2 == 0 diff --git a/deepcp/functional.py b/deepcp/functional.py deleted file mode 100644 index 5180809..0000000 --- a/deepcp/functional.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -try: - from cStringIO import StringIO as BytesIO -except ImportError: - from io import BytesIO - -import torch -from torchvision import transforms -from PIL import Image - -_to_pil_image = transforms.ToPILImage() -_to_tensor = transforms.ToTensor() - - -class FloatToIntSqueezing(torch.autograd.Function): - @staticmethod - def forward(ctx, x, max_int, vmin, vmax): - # here assuming 0 =< x =< 1 - x = (x - vmin) / (vmax - vmin) - x = torch.round(x * max_int) / max_int - return x * (vmax - vmin) + vmin - - @staticmethod - def backward(ctx, grad_output): - raise NotImplementedError( - "backward not implemented", FloatToIntSqueezing) - - -class JPEGEncodingDecoding(torch.autograd.Function): - @staticmethod - def forward(ctx, x, quality): - lst_img = [] - for img in x: - img = _to_pil_image(img.detach().clone().cpu()) - virtualpath = BytesIO() - img.save(virtualpath, 'JPEG', quality=quality) - lst_img.append(_to_tensor(Image.open(virtualpath))) - return x.new_tensor(torch.stack(lst_img)) - - @staticmethod - def backward(ctx, grad_output): - raise NotImplementedError( - "backward not implemented", JPEGEncodingDecoding) diff --git a/deepcp/loss.py b/deepcp/loss.py deleted file mode 100644 index 3521343..0000000 --- a/deepcp/loss.py +++ /dev/null @@ -1,102 +0,0 @@ -import torch -from torch.nn.modules.loss import _Loss -from deepcp.utils import clamp - - -class ZeroOneLoss(_Loss): - """Zero-One Loss""" - - def __init__(self, size_average=None, reduce=None, - reduction='elementwise_mean'): - super(ZeroOneLoss, self).__init__(size_average, reduce, reduction) - - def forward(self, input, target): - return logit_margin_loss(input, target, reduction=self.reduction) - - - -class LogitMarginLoss(_Loss): - """Logit Margin Loss""" - - def __init__(self, size_average=None, reduce=None, - reduction='elementwise_mean', offset=0.): - super(LogitMarginLoss, self).__init__(size_average, reduce, reduction) - self.offset = offset - - def forward(self, input, target): - return logit_margin_loss( - input, target, reduction=self.reduction, offset=self.offset) - - -class CWLoss(_Loss): - """CW Loss""" - # TODO: combine with the CWLoss in advertorch.utils - - def __init__(self, size_average=None, reduce=None, - reduction='elementwise_mean'): - super(CWLoss, self).__init__(size_average, reduce, reduction) - - def forward(self, input, target): - return cw_loss(input, target, reduction=self.reduction) - - -class SoftLogitMarginLoss(_Loss): - """Soft Logit Margin Loss""" - - def __init__(self, size_average=None, reduce=None, - reduction='elementwise_mean', offset=0.): - super(SoftLogitMarginLoss, self).__init__( - size_average, reduce, reduction) - self.offset = offset - - def forward(self, logits, targets): - return soft_logit_margin_loss( - logits, targets, reduction=self.reduction, offset=self.offset) - - -def zero_one_loss(input, target, reduction='elementwise_mean'): - loss = (input != target) - return _reduce_loss(loss, reduction) - - -def elementwise_margin(logits, label): - batch_size = logits.size(0) - topval, topidx = logits.topk(2, dim=1) - maxelse = ((label != topidx[:, 0]).float() * topval[:, 0] - + (label == topidx[:, 0]).float() * topval[:, 1]) - return maxelse - logits[torch.arange(batch_size), label] - - -def logit_margin_loss(input, target, reduction='elementwise_mean', offset=0.): - loss = elementwise_margin(input, target) - return _reduce_loss(loss, reduction) + offset - - -def cw_loss(input, target, reduction='elementwise_mean'): - loss = clamp(elementwise_margin(input, target) + 50, 0.) - return _reduce_loss(loss, reduction) - - -def _reduce_loss(loss, reduction): - if reduction == 'none': - return loss - elif reduction == 'elementwise_mean': - return loss.mean() - elif reduction == 'sum': - return loss.sum() - else: - raise ValueError(reduction + " is not valid") - - -def soft_logit_margin_loss( - logits, targets, reduction='elementwise_mean', offset=0.): - batch_size = logits.size(0) - num_class = logits.size(1) - mask = torch.ones_like(logits).byte() - # TODO: need to cover different versions of torch - # mask = torch.ones_like(logits).bool() - mask[torch.arange(batch_size), targets] = 0 - logits_true_label = logits[torch.arange(batch_size), targets] - logits_other_label = logits[mask].reshape(batch_size, num_class - 1) - loss = torch.logsumexp(logits_other_label, dim=1) - logits_true_label - return _reduce_loss(loss, reduction) + offset diff --git a/deepcp/test_utils.py b/deepcp/test_utils.py deleted file mode 100644 index 9cdf388..0000000 --- a/deepcp/test_utils.py +++ /dev/null @@ -1,344 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from deepcp.attacks import LocalSearchAttack -from deepcp.attacks import SinglePixelAttack -from deepcp.attacks import SpatialTransformAttack -from deepcp.attacks import JacobianSaliencyMapAttack -from deepcp.attacks import LBFGSAttack -from deepcp.attacks import CarliniWagnerL2Attack -from deepcp.attacks import DDNL2Attack -from deepcp.attacks import FastFeatureAttack -from deepcp.attacks import MomentumIterativeAttack -from deepcp.attacks import LinfPGDAttack -from deepcp.attacks import SparseL1DescentAttack -from deepcp.attacks import L1PGDAttack -from deepcp.attacks import L2BasicIterativeAttack -from deepcp.attacks import GradientAttack -from deepcp.attacks import LinfBasicIterativeAttack -from deepcp.attacks import GradientSignAttack -from deepcp.attacks import ElasticNetL1Attack -from deepcp.attacks import LinfSPSAAttack -from deepcp.attacks import LinfFABAttack -from deepcp.attacks import L2FABAttack -from deepcp.attacks import L1FABAttack -from deepcp.attacks import DeepfoolLinfAttack -from deepcp.defenses import JPEGFilter -from deepcp.defenses import BitSqueezing -from deepcp.defenses import MedianSmoothing2D -from deepcp.defenses import AverageSmoothing2D -from deepcp.defenses import GaussianSmoothing2D -from deepcp.defenses import BinaryFilter - -# blackbox -from deepcp.attacks import LinfGenAttack -from deepcp.attacks import L2GenAttack -from deepcp.attacks import LinfNAttack -from deepcp.attacks import L2NAttack -from deepcp.attacks import BanditAttack -from deepcp.attacks import NESAttack - - - -DIM_INPUT = 15 -NUM_CLASS = 5 -BATCH_SIZE = 16 - -IMAGE_SIZE = 16 -COLOR_CHANNEL = 3 - - -# ########################################################### -# model definitions for testing - - -class SimpleModel(nn.Module): - def __init__(self, dim_input=DIM_INPUT, num_classes=NUM_CLASS): - super(SimpleModel, self).__init__() - self.fc1 = nn.Linear(dim_input, 10) - self.fc2 = nn.Linear(10, num_classes) - - def forward(self, x): - x = self.fc1(x) - x = F.relu(x) - x = self.fc2(x) - return x - - -class SimpleImageModel(nn.Module): - - def __init__(self, num_classes=NUM_CLASS): - super(SimpleImageModel, self).__init__() - self.num_classes = NUM_CLASS - self.conv1 = nn.Conv2d( - COLOR_CHANNEL, 8, kernel_size=3, padding=1, stride=1) - self.relu1 = nn.ReLU(inplace=True) - self.maxpool1 = nn.MaxPool2d(4) - self.linear1 = nn.Linear(4 * 4 * 8, self.num_classes) - - def forward(self, x): - out = self.maxpool1(self.relu1(self.conv1(x))) - out = out.view(out.size(0), -1) - out = self.linear1(out) - return out - - -class LeNet5(nn.Module): - - def __init__(self): - super(LeNet5, self).__init__() - self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1, stride=1) - self.relu1 = nn.ReLU(inplace=True) - self.maxpool1 = nn.MaxPool2d(2) - self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=1) - self.relu2 = nn.ReLU(inplace=True) - self.maxpool2 = nn.MaxPool2d(2) - self.linear1 = nn.Linear(7 * 7 * 64, 200) - self.relu3 = nn.ReLU(inplace=True) - self.linear2 = nn.Linear(200, 10) - - def forward(self, x): - out = self.maxpool1(self.relu1(self.conv1(x))) - out = self.maxpool2(self.relu2(self.conv2(out))) - out = out.view(out.size(0), -1) - out = self.relu3(self.linear1(out)) - out = self.linear2(out) - return out - - -class MLP(nn.Module): - # MLP-300-100 - - def __init__(self): - super(MLP, self).__init__() - self.linear1 = nn.Linear(28 * 28, 300) - self.relu1 = nn.ReLU(inplace=True) - self.linear2 = nn.Linear(300, 100) - self.relu2 = nn.ReLU(inplace=True) - self.linear3 = nn.Linear(100, 10) - - def forward(self, x): - out = x.view(x.size(0), -1) - out = self.linear1(out) - out = self.relu1(out) - out = self.linear2(out) - out = self.relu2(out) - out = self.linear3(out) - return out - - -# ########################################################### -# model and data generation functions for testing - - -def generate_random_toy_data(clip_min=0., clip_max=1.): - data = torch.Tensor(BATCH_SIZE, DIM_INPUT).uniform_(clip_min, clip_max) - label = torch.LongTensor(BATCH_SIZE).random_(NUM_CLASS) - return data, label - - -def generate_random_image_toy_data(clip_min=0., clip_max=1.): - data = torch.Tensor(BATCH_SIZE, 3, IMAGE_SIZE, IMAGE_SIZE).uniform_( - clip_min, clip_max) - label = torch.LongTensor(BATCH_SIZE).random_(NUM_CLASS) - return data, label - - -def generate_data_model_on_vec(): - data, label = generate_random_toy_data() - model = SimpleModel() - model.eval() - return data, label, model - - -def generate_data_model_on_img(): - data, label = generate_random_image_toy_data() - model = SimpleImageModel() - model.eval() - return data, label, model - - -# ########################################################### -# construct data needed for testing -vecdata, veclabel, vecmodel = generate_data_model_on_vec() -imgdata, imglabel, imgmodel = generate_data_model_on_img() - - -# ########################################################### -# construct groups and configs needed for testing defenses - - -defense_kwargs = { - BinaryFilter: {}, - BitSqueezing: {"bit_depth": 4}, - MedianSmoothing2D: {}, - GaussianSmoothing2D: {"sigma": 3, "channels": COLOR_CHANNEL}, - AverageSmoothing2D: {"kernel_size": 5, "channels": COLOR_CHANNEL}, - JPEGFilter: {}, -} - -defenses = defense_kwargs.keys() - -# store one suitable data for test -defense_data = { - BinaryFilter: vecdata, - BitSqueezing: vecdata, - MedianSmoothing2D: imgdata, - GaussianSmoothing2D: imgdata, - AverageSmoothing2D: imgdata, - JPEGFilter: imgdata, -} - -nograd_defenses = [ - BinaryFilter, - BitSqueezing, - JPEGFilter, -] - -withgrad_defenses = [ - MedianSmoothing2D, - GaussianSmoothing2D, - AverageSmoothing2D, -] - -image_only_defenses = [ - MedianSmoothing2D, - GaussianSmoothing2D, - AverageSmoothing2D, - JPEGFilter, -] - -# as opposed to image-only -general_input_defenses = [ - BitSqueezing, - BinaryFilter, -] - - -# ########################################################### -# construct groups and configs needed for testing attacks - - -# as opposed to image-only -general_input_attacks = [ - GradientSignAttack, - LinfBasicIterativeAttack, - GradientAttack, - L2BasicIterativeAttack, - LinfPGDAttack, - MomentumIterativeAttack, - FastFeatureAttack, - CarliniWagnerL2Attack, - ElasticNetL1Attack, - LBFGSAttack, - JacobianSaliencyMapAttack, - SinglePixelAttack, - DDNL2Attack, - SparseL1DescentAttack, - L1PGDAttack, - LinfSPSAAttack, - LinfFABAttack, - L2FABAttack, - L1FABAttack, - LinfGenAttack, - L2GenAttack, - LinfNAttack, - L2NAttack, - BanditAttack, - NESAttack, - DeepfoolLinfAttack -] - -image_only_attacks = [ - SpatialTransformAttack, - LocalSearchAttack -] - -label_attacks = [ - GradientSignAttack, - LinfBasicIterativeAttack, - GradientAttack, - L2BasicIterativeAttack, - LinfPGDAttack, - MomentumIterativeAttack, - CarliniWagnerL2Attack, - ElasticNetL1Attack, - LBFGSAttack, - JacobianSaliencyMapAttack, - SpatialTransformAttack, - DDNL2Attack, - SparseL1DescentAttack, - L1PGDAttack, - LinfSPSAAttack, - LinfFABAttack, - L2FABAttack, - L1FABAttack, - LinfGenAttack, - L2GenAttack, - LinfNAttack, - L2NAttack, - BanditAttack, - NESAttack, - DeepfoolLinfAttack, -] - -feature_attacks = [ - FastFeatureAttack, -] - -batch_consistent_attacks = [ - GradientSignAttack, - LinfBasicIterativeAttack, - GradientAttack, - L2BasicIterativeAttack, - LinfPGDAttack, - MomentumIterativeAttack, - FastFeatureAttack, - JacobianSaliencyMapAttack, - DDNL2Attack, - SparseL1DescentAttack, - L1PGDAttack, - LinfSPSAAttack, - DeepfoolLinfAttack, - # FABAttack, - # CarliniWagnerL2Attack, # XXX: not exactly sure: test says no - # LBFGSAttack, # XXX: not exactly sure: test says no - # SpatialTransformAttack, # XXX: not exactly sure: test says no -] - - -targeted_only_attacks = [ - JacobianSaliencyMapAttack, -] - -# attacks that can take vector form of eps and eps_iter -vec_eps_attacks = [ - LinfBasicIterativeAttack, - L2BasicIterativeAttack, - LinfPGDAttack, - FastFeatureAttack, - SparseL1DescentAttack, - L1PGDAttack, - GradientSignAttack, - GradientAttack, - MomentumIterativeAttack, - LinfSPSAAttack, -] - -# ########################################################### -# helper functions - - -def merge2dicts(x, y): - z = x.copy() - z.update(y) - return z diff --git a/deepcp/utils.py b/deepcp/utils.py deleted file mode 100644 index 1e826ed..0000000 --- a/deepcp/utils.py +++ /dev/null @@ -1,391 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -def torch_allclose(x, y, rtol=1.e-5, atol=1.e-8): - """ - Wrap on numpy's allclose. Input x and y are both tensors of equal shape - - Original numpy documentation: - https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.allclose.html - - Notes: - If the following equation is element-wise True, then allclose returns - True. - - absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`)) - - :param x: (torch tensor) - :param y: (torch tensor) - :param rtol: (float) the relative tolerance parameter - :param atol: (float) the absolute tolerance parameter - :return: (bool) if x and y are all close - """ - import numpy as np - return np.allclose(x.detach().cpu().numpy(), y.detach().cpu().numpy(), - rtol=rtol, atol=atol) - - -def single_dim_flip(x, dim): - dim = x.dim() + dim if dim < 0 else dim - indices = torch.arange( - x.size(dim) - 1, -1, -1, - dtype=torch.long, device=x.device, requires_grad=x.requires_grad) - # TODO: do we need requires_grad??? - return x.index_select(dim, indices) - - -def torch_flip(x, dims): - for dim in dims: - x = single_dim_flip(x, dim) - return x - - -def replicate_input(x): - return x.detach().clone() - - -def replicate_input_withgrad(x): - return x.detach().clone().requires_grad_() - - -def calc_l2distsq(x, y): - d = (x - y)**2 - return d.view(d.shape[0], -1).sum(dim=1) - - -def calc_l1dist(x, y): - d = torch.abs(x - y) - return d.view(d.shape[0], -1).sum(dim=1) - - -def tanh_rescale(x, x_min=-1., x_max=1.): - return (torch.tanh(x)) * 0.5 * (x_max - x_min) + (x_max + x_min) * 0.5 - - -def torch_arctanh(x, eps=1e-6): - return (torch.log((1 + x) / (1 - x))) * 0.5 - - -def clamp(input, min=None, max=None): - ndim = input.ndimension() - if min is None: - pass - elif isinstance(min, (float, int)): - input = torch.clamp(input, min=min) - elif isinstance(min, torch.Tensor): - if min.ndimension() == ndim - 1 and min.shape == input.shape[1:]: - input = torch.max(input, min.view(1, *min.shape)) - else: - assert min.shape == input.shape - input = torch.max(input, min) - else: - raise ValueError("min can only be None | float | torch.Tensor") - - if max is None: - pass - elif isinstance(max, (float, int)): - input = torch.clamp(input, max=max) - elif isinstance(max, torch.Tensor): - if max.ndimension() == ndim - 1 and max.shape == input.shape[1:]: - input = torch.min(input, max.view(1, *max.shape)) - else: - assert max.shape == input.shape - input = torch.min(input, max) - else: - raise ValueError("max can only be None | float | torch.Tensor") - return input - - - - -def to_one_hot(y, num_classes=10): - """ - Take a batch of label y with n dims and convert it to - 1-hot representation with n+1 dims. - Link: https://discuss.pytorch.org/t/convert-int-into-one-hot-format/507/24 - """ - y = replicate_input(y).view(-1, 1) - y_one_hot = y.new_zeros((y.size()[0], num_classes)).scatter_(1, y, 1) - return y_one_hot - - -class CarliniWagnerLoss(nn.Module): - """ - Carlini-Wagner Loss: objective function #6. - Paper: https://arxiv.org/pdf/1608.04644.pdf - """ - - def __init__(self): - super(CarliniWagnerLoss, self).__init__() - - def forward(self, input, target): - """ - :param input: pre-softmax/logits. - :param target: true labels. - :return: CW loss value. - """ - num_classes = input.size(1) - label_mask = to_one_hot(target, num_classes=num_classes).float() - correct_logit = torch.sum(label_mask * input, dim=1) - wrong_logit = torch.max((1. - label_mask) * input, dim=1)[0] - loss = -F.relu(correct_logit - wrong_logit + 50.).sum() - return loss - - -def _batch_multiply_tensor_by_vector(vector, batch_tensor): - """Equivalent to the following - for ii in range(len(vector)): - batch_tensor.data[ii] *= vector[ii] - return batch_tensor - """ - return ( - batch_tensor.transpose(0, -1) * vector).transpose(0, -1).contiguous() - - -def _batch_clamp_tensor_by_vector(vector, batch_tensor): - """Equivalent to the following - for ii in range(len(vector)): - batch_tensor[ii] = clamp( - batch_tensor[ii], -vector[ii], vector[ii]) - """ - return torch.min( - torch.max(batch_tensor.transpose(0, -1), -vector), vector - ).transpose(0, -1).contiguous() - - -def batch_multiply(float_or_vector, tensor): - if isinstance(float_or_vector, torch.Tensor): - assert len(float_or_vector) == len(tensor) - tensor = _batch_multiply_tensor_by_vector(float_or_vector, tensor) - elif isinstance(float_or_vector, float): - tensor *= float_or_vector - else: - raise TypeError("Value has to be float or torch.Tensor") - return tensor - - -def batch_clamp(float_or_vector, tensor): - if isinstance(float_or_vector, torch.Tensor): - assert len(float_or_vector) == len(tensor) - tensor = _batch_clamp_tensor_by_vector(float_or_vector, tensor) - return tensor - elif isinstance(float_or_vector, float): - tensor = clamp(tensor, -float_or_vector, float_or_vector) - else: - raise TypeError("Value has to be float or torch.Tensor") - return tensor - - -def _get_norm_batch(x, p): - batch_size = x.size(0) - return x.abs().pow(p).view(batch_size, -1).sum(dim=1).pow(1. / p) - - -def _thresh_by_magnitude(theta, x): - return torch.relu(torch.abs(x) - theta) * x.sign() - - -def batch_l1_proj_flat(x, z=1): - """ - Implementation of L1 ball projection from: - - https://stanford.edu/~jduchi/projects/DuchiShSiCh08.pdf - - inspired from: - - https://gist.github.com/daien/1272551/edd95a6154106f8e28209a1c7964623ef8397246 - - :param x: input data - :param eps: l1 radius - - :return: tensor containing the projection. - """ - - # Computing the l1 norm of v - v = torch.abs(x) - v = v.sum(dim=1) - - # Getting the elements to project in the batch - indexes_b = torch.nonzero(v > z).view(-1) - if isinstance(z, torch.Tensor): - z = z[indexes_b][:, None] - x_b = x[indexes_b] - batch_size_b = x_b.size(0) - - # If all elements are in the l1-ball, return x - if batch_size_b == 0: - return x - - # make the projection on l1 ball for elements outside the ball - view = x_b - view_size = view.size(1) - mu = view.abs().sort(1, descending=True)[0] - vv = torch.arange(view_size).float().to(x.device) - st = (mu.cumsum(1) - z) / (vv + 1) - u = (mu - st) > 0 - if u.dtype.__str__() == "torch.bool": # after and including torch 1.2 - rho = (~u).cumsum(dim=1).eq(0).sum(1) - 1 - else: # before and including torch 1.1 - rho = (1 - u).cumsum(dim=1).eq(0).sum(1) - 1 - theta = st.gather(1, rho.unsqueeze(1)) - proj_x_b = _thresh_by_magnitude(theta, x_b) - - # gather all the projected batch - proj_x = x.detach().clone() - proj_x[indexes_b] = proj_x_b - return proj_x - - -def batch_l1_proj(x, eps): - batch_size = x.size(0) - view = x.view(batch_size, -1) - proj_flat = batch_l1_proj_flat(view, z=eps) - return proj_flat.view_as(x) - - -def clamp_by_pnorm(x, p, r): - assert isinstance(p, float) or isinstance(p, int) - norm = _get_norm_batch(x, p) - if isinstance(r, torch.Tensor): - assert norm.size() == r.size() - else: - assert isinstance(r, float) - factor = torch.min(r / norm, torch.ones_like(norm)) - return batch_multiply(factor, x) - - -def is_float_or_torch_tensor(x): - return isinstance(x, torch.Tensor) or isinstance(x, float) - - -def normalize_by_pnorm(x, p=2, small_constant=1e-6): - """ - Normalize gradients for gradient (not gradient sign) attacks. - # TODO: move this function to utils - - :param x: tensor containing the gradients on the input. - :param p: (optional) order of the norm for the normalization (1 or 2). - :param small_constant: (optional float) to avoid dividing by zero. - :return: normalized gradients. - """ - # loss is averaged over the batch so need to multiply the batch - # size to find the actual gradient of each input sample - - assert isinstance(p, float) or isinstance(p, int) - norm = _get_norm_batch(x, p) - norm = torch.max(norm, torch.ones_like(norm) * small_constant) - return batch_multiply(1. / norm, x) - - -def jacobian(model, x, output_class): - """ - Compute the output_class'th row of a Jacobian matrix. In other words, - compute the gradient wrt to the output_class. - - :param model: forward pass function. - :param x: input tensor. - :param output_class: the output class we want to compute the gradients. - :return: output_class'th row of the Jacobian matrix wrt x. - """ - xvar = replicate_input_withgrad(x) - scores = model(xvar) - - # compute gradients for the class output_class wrt the input x - # using backpropagation - torch.sum(scores[:, output_class]).backward() - - return xvar.grad.detach().clone() - - -MNIST_MEAN = (0.1307,) -MNIST_STD = (0.3081,) - -CIFAR10_MEAN = (0.4914, 0.4822, 0.4465) -CIFAR10_STD = (0.2023, 0.1994, 0.2010) - - -class NormalizeByChannelMeanStd(nn.Module): - def __init__(self, mean, std): - super(NormalizeByChannelMeanStd, self).__init__() - if not isinstance(mean, torch.Tensor): - mean = torch.tensor(mean) - if not isinstance(std, torch.Tensor): - std = torch.tensor(std) - self.register_buffer("mean", mean) - self.register_buffer("std", std) - - def forward(self, tensor): - return normalize_fn(tensor, self.mean, self.std) - - def extra_repr(self): - return 'mean={}, std={}'.format(self.mean, self.std) - - -def normalize_fn(tensor, mean, std): - """Differentiable version of torchvision.functional.normalize""" - # here we assume the color channel is in at dim=1 - mean = mean[None, :, None, None] - std = std[None, :, None, None] - return tensor.sub(mean).div(std) - - -def batch_per_image_standardization(imgs): - # replicate tf.image.per_image_standardization, but in batch - assert imgs.ndimension() == 4 - mean = imgs.view(imgs.shape[0], -1).mean(dim=1).view( - imgs.shape[0], 1, 1, 1) - return (imgs - mean) / batch_adjusted_stddev(imgs) - - -def batch_adjusted_stddev(imgs): - # for batch_per_image_standardization - std = imgs.view(imgs.shape[0], -1).std(dim=1).view(imgs.shape[0], 1, 1, 1) - std_min = 1. / imgs.new_tensor(imgs.shape[1:]).prod().float().sqrt() - return torch.max(std, std_min) - - -class PerImageStandardize(nn.Module): - def __init__(self): - super(PerImageStandardize, self).__init__() - - def forward(self, tensor): - return batch_per_image_standardization(tensor) - - -def predict_from_logits(logits, dim=1): - return logits.max(dim=dim, keepdim=False)[1] - - -def get_accuracy(pred, target): - return pred.eq(target).float().mean().item() - - -def set_torch_deterministic(): - import torch.backends.cudnn as cudnn - cudnn.benchmark = False - cudnn.deterministic = True - - -def set_seed(seed=None): - import torch - import numpy as np - import random - if seed is not None: - torch.manual_seed(seed) - np.random.seed(seed) - random.seed(seed) diff --git a/deepcp_examples/__init__.py b/deepcp_examples/__init__.py deleted file mode 100644 index 7da4698..0000000 --- a/deepcp_examples/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# diff --git a/deepcp_examples/attack_benchmarks/benchmark_decoupled_direction_norm.py b/deepcp_examples/attack_benchmarks/benchmark_decoupled_direction_norm.py deleted file mode 100644 index c7e1ebc..0000000 --- a/deepcp_examples/attack_benchmarks/benchmark_decoupled_direction_norm.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -# -# -# Automatically generated benchmark report (screen print of running this file) -# -# sysname: Linux -# release: 4.4.0-140-generic -# version: #166-Ubuntu SMP Wed Nov 14 20:09:47 UTC 2018 -# machine: x86_64 -# python: 3.7.3 -# torch: 1.1.0 -# torchvision: 0.3.0 -# advertorch: 0.1.5 - -# attack type: DDNL2Attack -# attack kwargs: nb_iter=1000 -# gamma=0.05 -# init_norm=1.0 -# quantize=True -# levels=256 -# clip_min=0.0 -# clip_max=1.0 -# targeted=False -# data: mnist_test -# model: MNIST LeNet5 standard training -# accuracy: 98.89% -# attack success rate: 100.0% -# Among successful attacks (L2 norm) on correctly classified examples: -# minimum distance: 0.006792 -# median distance: 1.388 -# maximum distance: 3.3 -# average distance: 1.38 -# distance standard deviation: 0.4716 - -# attack type: DDNL2Attack -# attack kwargs: nb_iter=1000 -# gamma=0.05 -# init_norm=1.0 -# quantize=True -# levels=256 -# clip_min=0.0 -# clip_max=1.0 -# targeted=False -# data: mnist_test -# model: MNIST LeNet 5 PGD training according to Madry et al. 2018 -# accuracy: 98.64% -# attack success rate: 100.0% -# Among successful attacks (L2 norm) on correctly classified examples: -# minimum distance: 0.005546 -# median distance: 1.872 -# maximum distance: 20.45 -# average distance: 1.917 -# distance standard deviation: 0.733 - - -from deepcp_examples.utils import get_mnist_test_loader -from deepcp_examples.utils import get_mnist_lenet5_clntrained -from deepcp_examples.utils import get_mnist_lenet5_advtrained -from deepcp_examples.benchmark_utils import get_benchmark_sys_info - -from deepcp.attacks import DDNL2Attack - -from deepcp_examples.benchmark_utils import benchmark_margin - -batch_size = 100 -device = "cuda" - -lst_attack = [ - (DDNL2Attack, dict( - nb_iter=1000, gamma=0.05, init_norm=1., quantize=True, levels=256, - clip_min=0., clip_max=1., targeted=False)), -] # each element in the list is the tuple (attack_class, attack_kwargs) - -mnist_clntrained_model = get_mnist_lenet5_clntrained().to(device) -mnist_advtrained_model = get_mnist_lenet5_advtrained().to(device) -mnist_test_loader = get_mnist_test_loader(batch_size=batch_size) - -lst_setting = [ - (mnist_clntrained_model, mnist_test_loader), - (mnist_advtrained_model, mnist_test_loader), -] - - -info = get_benchmark_sys_info() - -lst_benchmark = [] -for model, loader in lst_setting: - for attack_class, attack_kwargs in lst_attack: - lst_benchmark.append(benchmark_margin( - model, loader, attack_class, attack_kwargs, norm=2, device="cuda")) - -print(info) -for item in lst_benchmark: - print(item) diff --git a/deepcp_examples/attack_benchmarks/benchmark_deepfool.py b/deepcp_examples/attack_benchmarks/benchmark_deepfool.py deleted file mode 100644 index ffccb85..0000000 --- a/deepcp_examples/attack_benchmarks/benchmark_deepfool.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -# -# -# Automatically generated benchmark report (screen print of running this file) -# -# sysname: Linux -# release: 5.8.0-63-generic -# version: #71~20.04.1-Ubuntu SMP Thu Jul 15 17:46:08 UTC 2021 -# machine: x86_64 -# python: 3.8.5 -# torch: 1.9.0+cu102 -# torchvision: 0.10.0+cu102 -# advertorch: 0.2.4 -# -# attack type: DeepfoolLinfAttack -# attack kwargs: eps=0.3 -# nb_iter=50 -# overshoot=0.02 -# clip_min=0.0 -# clip_max=1.0 -# data: mnist_test, 10000 samples -# model: MNIST LeNet5 standard training -# accuracy: 98.89% -# attack success rate: 100.0% -# Among successful attacks (2 norm) on correctly classified examples: -# minimum distance: 0.004306 -# median distance: 2.356 -# maximum distance: 5.376 -# average distance: 2.33 -# distance standard deviation: 0.8195 -# -# attack type: DeepfoolLinfAttack -# attack kwargs: eps=0.3 -# nb_iter=150 -# overshoot=0.02 -# clip_min=0.0 -# clip_max=1.0 -# data: mnist_test, 10000 samples -# model: MNIST LeNet5 standard training -# accuracy: 98.89% -# attack success rate: 100.0% -# Among successful attacks (2 norm) on correctly classified examples: -# minimum distance: 0.004306 -# median distance: 2.356 -# maximum distance: 5.376 -# average distance: 2.33 -# distance standard deviation: 0.8195 -# -# attack type: DeepfoolLinfAttack -# attack kwargs: eps=0.3 -# nb_iter=50 -# overshoot=0.02 -# clip_min=0.0 -# clip_max=1.0 -# data: mnist_test, 10000 samples -# model: MNIST LeNet 5 PGD training according to Madry et al. 2018 -# accuracy: 98.64% -# attack success rate: 7.42% -# Among successful attacks (2 norm) on correctly classified examples: -# minimum distance: 0.02558 -# median distance: 3.37 -# maximum distance: 6.413 -# average distance: 3.199 -# distance standard deviation: 1.395 -# -# attack type: DeepfoolLinfAttack -# attack kwargs: eps=0.3 -# nb_iter=150 -# overshoot=0.02 -# clip_min=0.0 -# clip_max=1.0 -# data: mnist_test, 10000 samples -# model: MNIST LeNet 5 PGD training according to Madry et al. 2018 -# accuracy: 98.64% -# attack success rate: 8.41% -# Among successful attacks (2 norm) on correctly classified examples: -# minimum distance: 0.02558 -# median distance: 3.804 -# maximum distance: 6.413 -# average distance: 3.422 -# distance standard deviation: 1.416 - - -from deepcp_examples.utils import get_mnist_test_loader -from deepcp_examples.utils import get_mnist_lenet5_clntrained -from deepcp_examples.utils import get_mnist_lenet5_advtrained -from deepcp_examples.benchmark_utils import get_benchmark_sys_info - -from deepcp.attacks import DeepfoolLinfAttack - -from deepcp_examples.benchmark_utils import benchmark_margin - -batch_size = 100 -device = "cuda" - -print('Begin testing...') -lst_attack = [ - (DeepfoolLinfAttack, dict( - eps=0.3, nb_iter=50, overshoot=0.02, clip_min=0., clip_max=1.)), - (DeepfoolLinfAttack, dict( - eps=0.3, nb_iter=150, overshoot=0.02, clip_min=0., clip_max=1.)), -] # each element in the list is the tuple (attack_class, attack_kwargs) - -mnist_clntrained_model = get_mnist_lenet5_clntrained().to(device) -mnist_advtrained_model = get_mnist_lenet5_advtrained().to(device) -mnist_test_loader = get_mnist_test_loader(batch_size=batch_size) - -lst_setting = [ - (mnist_clntrained_model, mnist_test_loader), - (mnist_advtrained_model, mnist_test_loader), -] - - -info = get_benchmark_sys_info() - -lst_benchmark = [] -for model, loader in lst_setting: - for attack_class, attack_kwargs in lst_attack: - lst_benchmark.append(benchmark_margin( - model, loader, attack_class, attack_kwargs, norm=2, device="cuda")) - -print(info) -for item in lst_benchmark: - print(item) diff --git a/deepcp_examples/attack_benchmarks/benchmark_fast_adaptive_boundary.py b/deepcp_examples/attack_benchmarks/benchmark_fast_adaptive_boundary.py deleted file mode 100644 index 9ad96a9..0000000 --- a/deepcp_examples/attack_benchmarks/benchmark_fast_adaptive_boundary.py +++ /dev/null @@ -1,197 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -# -# -# Automatically generated benchmark report (screen print of running this file) -# -# sysname: Linux -# release: 4.4.0-140-generic -# version: #166-Ubuntu SMP Wed Nov 14 20:09:47 UTC 2018 -# machine: x86_64 -# python: 3.7.3 -# torch: 1.1.0 -# torchvision: 0.3.0 -# advertorch: 0.1.5 - -# attack type: FABAttack -# attack kwargs: norm=Linf -# n_restarts=1 -# n_iter=20 -# alpha_max=0.1 -# eta=1.05 -# beta=0.9 -# loss_fn=None -# data: mnist_test -# model: MNIST LeNet5 standard training -# accuracy: 98.89% -# attack success rate: 100.0% -# Among successful attacks (Linf norm) on correctly classified examples: -# minimum distance: 0.0001396 -# median distance: 0.112 -# maximum distance: 0.2155 -# average distance: 0.1092 -# distance standard deviation: 0.03498 - -# attack type: FABAttack -# attack kwargs: norm=L2 -# n_restarts=1 -# n_iter=20 -# alpha_max=0.1 -# eta=1.05 -# beta=0.9 -# loss_fn=None -# data: mnist_test -# model: MNIST LeNet5 standard training -# accuracy: 98.89% -# attack success rate: 100.0% -# Among successful attacks (L2 norm) on correctly classified examples: -# minimum distance: 0.001726 -# median distance: 1.423 -# maximum distance: 3.01 -# average distance: 1.412 -# distance standard deviation: 0.4805 - -# attack type: FABAttack -# attack kwargs: norm=L1 -# n_restarts=1 -# n_iter=20 -# alpha_max=0.1 -# eta=1.05 -# beta=0.9 -# loss_fn=None -# data: mnist_test -# model: MNIST LeNet5 standard training -# accuracy: 98.89% -# attack success rate: 99.55% -# Among successful attacks (L1 norm) on correctly classified examples: -# minimum distance: 0.007688 -# median distance: 7.61 -# maximum distance: 36.06 -# average distance: 8.365 -# distance standard deviation: 4.42 - -# attack type: FABAttack -# attack kwargs: norm=Linf -# n_restarts=1 -# n_iter=20 -# alpha_max=0.1 -# eta=1.05 -# beta=0.9 -# loss_fn=None -# data: mnist_test -# model: MNIST LeNet 5 PGD training according to Madry et al. 2018 -# accuracy: 98.64% -# attack success rate: 99.86% -# Among successful attacks (Linf norm) on correctly classified examples: -# minimum distance: 0.001405 -# median distance: 0.3509 -# maximum distance: 0.6404 -# average distance: 0.3476 -# distance standard deviation: 0.05255 - -# attack type: FABAttack -# attack kwargs: norm=L2 -# n_restarts=1 -# n_iter=20 -# alpha_max=0.1 -# eta=1.05 -# beta=0.9 -# loss_fn=None -# data: mnist_test -# model: MNIST LeNet 5 PGD training according to Madry et al. 2018 -# accuracy: 98.64% -# attack success rate: 98.35% -# Among successful attacks (L2 norm) on correctly classified examples: -# minimum distance: 0.003942 -# median distance: 3.04 -# maximum distance: 19.92 -# average distance: 3.205 -# distance standard deviation: 1.311 - -# attack type: FABAttack -# attack kwargs: norm=L1 -# n_restarts=1 -# n_iter=20 -# alpha_max=0.1 -# eta=1.05 -# beta=0.9 -# loss_fn=None -# data: mnist_test -# model: MNIST LeNet 5 PGD training according to Madry et al. 2018 -# accuracy: 98.64% -# attack success rate: 94.33% -# Among successful attacks (L1 norm) on correctly classified examples: -# minimum distance: 0.00622 -# median distance: 112.8 -# maximum distance: 441.9 -# average distance: 114.6 -# distance standard deviation: 52.85 - - - -from deepcp_examples.utils import get_mnist_test_loader -from deepcp_examples.utils import get_mnist_lenet5_clntrained -from deepcp_examples.utils import get_mnist_lenet5_advtrained -from deepcp_examples.benchmark_utils import get_benchmark_sys_info - -from deepcp.attacks import FABAttack - -from deepcp_examples.benchmark_utils import benchmark_margin - -batch_size = 100 -device = "cuda" - -lst_attack = [ - (FABAttack, dict( - norm='Linf', - n_restarts=1, - n_iter=20, - alpha_max=0.1, - eta=1.05, - beta=0.9, - loss_fn=None)), - (FABAttack, dict( - norm='L2', - n_restarts=1, - n_iter=20, - alpha_max=0.1, - eta=1.05, - beta=0.9, - loss_fn=None)), - (FABAttack, dict( - norm='L1', - n_restarts=1, - n_iter=20, - alpha_max=0.1, - eta=1.05, - beta=0.9, - loss_fn=None)), -] # each element in the list is the tuple (attack_class, attack_kwargs) - -mnist_clntrained_model = get_mnist_lenet5_clntrained().to(device) -mnist_advtrained_model = get_mnist_lenet5_advtrained().to(device) -mnist_test_loader = get_mnist_test_loader(batch_size=batch_size) - -lst_setting = [ - (mnist_clntrained_model, mnist_test_loader), - (mnist_advtrained_model, mnist_test_loader), -] - - -info = get_benchmark_sys_info() - -lst_benchmark = [] -for model, loader in lst_setting: - for attack_class, attack_kwargs in lst_attack: - lst_benchmark.append(benchmark_margin( - model, loader, attack_class, attack_kwargs, - norm=attack_kwargs["norm"])) - -print(info) -for item in lst_benchmark: - print(item) diff --git a/deepcp_examples/attack_benchmarks/benchmark_iterative_projected_gradient.py b/deepcp_examples/attack_benchmarks/benchmark_iterative_projected_gradient.py deleted file mode 100644 index 18dcf05..0000000 --- a/deepcp_examples/attack_benchmarks/benchmark_iterative_projected_gradient.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -# -# -# Automatically generated benchmark report (screen print of running this file) -# -# sysname: Linux -# release: 4.4.0-140-generic -# version: #166-Ubuntu SMP Wed Nov 14 20:09:47 UTC 2018 -# machine: x86_64 -# python: 3.7.3 -# torch: 1.1.0 -# torchvision: 0.3.0 -# advertorch: 0.1.5 - -# attack type: LinfPGDAttack -# attack kwargs: loss_fn=CrossEntropyLoss() -# eps=0.3 -# nb_iter=40 -# eps_iter=0.01 -# rand_init=False -# clip_min=0.0 -# clip_max=1.0 -# targeted=False -# data: mnist_test -# model: MNIST LeNet5 standard training -# accuracy: 98.89% -# attack success rate: 100.0% - -# attack type: LinfPGDAttack -# attack kwargs: loss_fn=CrossEntropyLoss() -# eps=0.3 -# nb_iter=40 -# eps_iter=0.01 -# rand_init=False -# clip_min=0.0 -# clip_max=1.0 -# targeted=False -# data: mnist_test -# model: MNIST LeNet 5 PGD training according to Madry et al. 2018 -# accuracy: 98.64% -# attack success rate: 6.8% - - -import torch.nn as nn - -from deepcp_examples.utils import get_mnist_test_loader -from deepcp_examples.utils import get_mnist_lenet5_clntrained -from deepcp_examples.utils import get_mnist_lenet5_advtrained -from deepcp_examples.benchmark_utils import get_benchmark_sys_info - -from deepcp.attacks import LinfPGDAttack -# TODO: from advertorch.attacks import L2BasicIterativeAttack -# TODO: from advertorch.attacks import LinfBasicIterativeAttack -# TODO: from advertorch.attacks import PGDAttack -# TODO: from advertorch.attacks import L2PGDAttack -# TODO: from advertorch.attacks import L1PGDAttack -# TODO: from advertorch.attacks import SparseL1DescentAttack -# TODO: from advertorch.attacks import MomentumIterativeAttack -# TODO: from advertorch.attacks import L2MomentumIterativeAttack -# TODO: from advertorch.attacks import LinfMomentumIterativeAttack -# TODO: from advertorch.attacks import FastFeatureAttack - -from deepcp_examples.benchmark_utils import benchmark_attack_success_rate - -batch_size = 100 -device = "cuda" - -lst_attack = [ - (LinfPGDAttack, dict( - loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.3, - nb_iter=40, eps_iter=0.01, rand_init=False, - clip_min=0.0, clip_max=1.0, targeted=False)), -] # each element in the list is the tuple (attack_class, attack_kwargs) - -mnist_clntrained_model = get_mnist_lenet5_clntrained().to(device) -mnist_advtrained_model = get_mnist_lenet5_advtrained().to(device) -mnist_test_loader = get_mnist_test_loader(batch_size=batch_size) - -lst_setting = [ - (mnist_clntrained_model, mnist_test_loader), - (mnist_advtrained_model, mnist_test_loader), -] - - -info = get_benchmark_sys_info() - -lst_benchmark = [] -for model, loader in lst_setting: - for attack_class, attack_kwargs in lst_attack: - lst_benchmark.append(benchmark_attack_success_rate( - model, loader, attack_class, attack_kwargs, device="cuda")) - -print(info) -for item in lst_benchmark: - print(item) diff --git a/deepcp_examples/attack_benchmarks/benchmark_spsa_attack.py b/deepcp_examples/attack_benchmarks/benchmark_spsa_attack.py deleted file mode 100644 index c6c2b39..0000000 --- a/deepcp_examples/attack_benchmarks/benchmark_spsa_attack.py +++ /dev/null @@ -1,133 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -# -# Automatically generated benchmark report (screen print of running this file) -# -# sysname: Linux -# release: 4.4.0-140-generic -# version: #166-Ubuntu SMP Wed Nov 14 20:09:47 UTC 2018 -# machine: x86_64 -# python: 3.7.3 -# torch: 1.1.0 -# torchvision: 0.3.0 -# advertorch: 0.1.5 - -# attack type: LinfSPSAAttack -# attack kwargs: eps=0.3 -# delta=0.01 -# lr=0.01 -# nb_iter=1000 -# nb_sample=128 -# max_batch_size=64 -# targeted=False -# loss_fn=None -# clip_min=0.0 -# clip_max=1.0 -# data: mnist_test, 100 samples -# model: MNIST LeNet5 standard training -# accuracy: 99.0% -# attack success rate: 100.0% - -# attack type: LinfSPSAAttack -# attack kwargs: eps=0.3 -# delta=0.01 -# lr=0.01 -# nb_iter=100 -# nb_sample=8192 -# max_batch_size=64 -# targeted=False -# loss_fn=None -# clip_min=0.0 -# clip_max=1.0 -# data: mnist_test, 100 samples -# model: MNIST LeNet5 standard training -# accuracy: 99.0% -# attack success rate: 100.0% - -# attack type: LinfSPSAAttack -# attack kwargs: eps=0.3 -# delta=0.01 -# lr=0.01 -# nb_iter=1000 -# nb_sample=128 -# max_batch_size=64 -# targeted=False -# loss_fn=None -# clip_min=0.0 -# clip_max=1.0 -# data: mnist_test, 100 samples -# model: MNIST LeNet 5 PGD training according to Madry et al. 2018 -# accuracy: 100.0% -# attack success rate: 10.0% - -# attack type: LinfSPSAAttack -# attack kwargs: eps=0.3 -# delta=0.01 -# lr=0.01 -# nb_iter=100 -# nb_sample=8192 -# max_batch_size=64 -# targeted=False -# loss_fn=None -# clip_min=0.0 -# clip_max=1.0 -# data: mnist_test, 100 samples -# model: MNIST LeNet 5 PGD training according to Madry et al. 2018 -# accuracy: 100.0% -# attack success rate: 6.0% - - - -from deepcp_examples.utils import get_mnist_test_loader -from deepcp_examples.utils import get_mnist_lenet5_clntrained -from deepcp_examples.utils import get_mnist_lenet5_advtrained -from deepcp_examples.benchmark_utils import get_benchmark_sys_info - -from deepcp.attacks import LinfSPSAAttack - -from deepcp_examples.benchmark_utils import benchmark_attack_success_rate - -batch_size = 10 -num_batch = 10 -device = "cuda" - -lst_attack = [ - (LinfSPSAAttack, dict( - eps=0.3, delta=0.01, lr=0.01, nb_iter=1000, nb_sample=128, - max_batch_size=64, targeted=False, - loss_fn=None, - clip_min=0.0, clip_max=1.0)), - (LinfSPSAAttack, dict( - eps=0.3, delta=0.01, lr=0.01, nb_iter=100, nb_sample=8192, - max_batch_size=64, targeted=False, - loss_fn=None, - clip_min=0.0, clip_max=1.0)), -] # each element in the list is the tuple (attack_class, attack_kwargs) - -mnist_clntrained_model = get_mnist_lenet5_clntrained().to(device) -mnist_advtrained_model = get_mnist_lenet5_advtrained().to(device) -mnist_test_loader = get_mnist_test_loader(batch_size=batch_size) - -lst_setting = [ - (mnist_clntrained_model, mnist_test_loader), - (mnist_advtrained_model, mnist_test_loader), -] - - -info = get_benchmark_sys_info() - -lst_benchmark = [] -for model, loader in lst_setting: - for attack_class, attack_kwargs in lst_attack: - lst_benchmark.append(benchmark_attack_success_rate( - model, loader, attack_class, attack_kwargs, - device=device, num_batch=num_batch)) - -print(info) -for item in lst_benchmark: - print(item) diff --git a/deepcp_examples/attack_madry_et_al_models/attack_cifar10_challenge.py b/deepcp_examples/attack_madry_et_al_models/attack_cifar10_challenge.py deleted file mode 100644 index aa8cccf..0000000 --- a/deepcp_examples/attack_madry_et_al_models/attack_cifar10_challenge.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import torch.nn as nn - -from deepcp.attacks import LinfPGDAttack -from deepcp.attacks.utils import multiple_mini_batch_attack -from deepcp_examples.utils import get_cifar10_test_loader - -from madry_et_al_utils import get_madry_et_al_tf_model - -model = get_madry_et_al_tf_model("CIFAR10") -loader = get_cifar10_test_loader(batch_size=100) -adversary = LinfPGDAttack( - model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=8. / 255, - nb_iter=20, eps_iter=2. / 255, rand_init=False, clip_min=0.0, clip_max=1.0, - targeted=False) - -label, pred, advpred, _ = multiple_mini_batch_attack( - adversary, loader, device="cuda") - -print("Accuracy: {:.2f}%, Robust Accuracy: {:.2f}%".format( - 100. * (label == pred).sum().item() / len(label), - 100. * (label == advpred).sum().item() / len(label))) - -# Accuracy: 87.14%, Robust Accuracy: 45.69% diff --git a/deepcp_examples/attack_madry_et_al_models/attack_mnist_challenge.py b/deepcp_examples/attack_madry_et_al_models/attack_mnist_challenge.py deleted file mode 100644 index 28d9139..0000000 --- a/deepcp_examples/attack_madry_et_al_models/attack_mnist_challenge.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import torch.nn as nn - -from deepcp.attacks import LinfPGDAttack -from deepcp.attacks.utils import multiple_mini_batch_attack -from deepcp_examples.utils import get_mnist_test_loader - -from madry_et_al_utils import get_madry_et_al_tf_model - -model = get_madry_et_al_tf_model("MNIST") -loader = get_mnist_test_loader(batch_size=100) -adversary = LinfPGDAttack( - model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.3, - nb_iter=100, eps_iter=0.01, rand_init=False, clip_min=0.0, clip_max=1.0, - targeted=False) - -label, pred, advpred, _ = multiple_mini_batch_attack( - adversary, loader, device="cuda") - -print("Accuracy: {:.2f}%, Robust Accuracy: {:.2f}%".format( - 100. * (label == pred).sum().item() / len(label), - 100. * (label == advpred).sum().item() / len(label))) - -# Accuracy: 98.53%, Robust Accuracy: 92.51% diff --git a/deepcp_examples/attack_madry_et_al_models/download_cifar10_challenge.sh b/deepcp_examples/attack_madry_et_al_models/download_cifar10_challenge.sh deleted file mode 100644 index a98d906..0000000 --- a/deepcp_examples/attack_madry_et_al_models/download_cifar10_challenge.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/bash - -cd $1 -git clone https://github.com/MadryLab/cifar10_challenge -cd cifar10_challenge -python fetch_model.py secret diff --git a/deepcp_examples/attack_madry_et_al_models/download_mnist_challenge.sh b/deepcp_examples/attack_madry_et_al_models/download_mnist_challenge.sh deleted file mode 100644 index 99569a2..0000000 --- a/deepcp_examples/attack_madry_et_al_models/download_mnist_challenge.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/bash - -cd $1 -git clone https://github.com/MadryLab/mnist_challenge -cd mnist_challenge -python fetch_model.py secret diff --git a/deepcp_examples/attack_madry_et_al_models/madry_et_al_utils.py b/deepcp_examples/attack_madry_et_al_models/madry_et_al_utils.py deleted file mode 100644 index 1bcbad2..0000000 --- a/deepcp_examples/attack_madry_et_al_models/madry_et_al_utils.py +++ /dev/null @@ -1,159 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import os -import sys -from pathlib import Path - -import tensorflow as tf -import torch - -from deepcp.bpda import BPDAWrapper -from deepcp_examples.utils import ROOT_PATH, mkdir - -MODEL_PATH = os.path.join(ROOT_PATH, "madry_et_al_models") -mkdir(MODEL_PATH) -Path(os.path.join(MODEL_PATH, "__init__.py")).touch() -sys.path.append(MODEL_PATH) - - -class WrappedTfModel(object): - - def __init__(self, weights_path, model_class): - - model = model_class() - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - sess = tf.Session(config=config).__enter__() - saver = tf.train.Saver() - checkpoint = tf.train.latest_checkpoint(weights_path) - saver.restore(sess, checkpoint) - - self.inputs = model.x_input - self.logits = model.pre_softmax - - self.session = tf.get_default_session() - assert self.session.graph == self.inputs.graph - - with self.session.graph.as_default(): - self.bw_gradient_pre = tf.placeholder( - tf.float32, self.logits.shape) - bw_loss = tf.reduce_sum(self.logits * self.bw_gradient_pre) - self.bw_gradients = tf.gradients(bw_loss, self.inputs)[0] - - def backward(self, inputs_val, logits_grad_val): - inputs_grad_val = self.session.run( - self.bw_gradients, - feed_dict={ - self.inputs: inputs_val, - self.bw_gradient_pre: logits_grad_val, - }) - return inputs_grad_val - - def forward(self, inputs_val): - logits_val = self.session.run( - self.logits, - feed_dict={ - self.inputs: inputs_val, - }) - return logits_val - - -class TorchWrappedModel(object): - - def __init__(self, tfmodel, device): - self.tfmodel = tfmodel - self.device = device - - def _to_numpy(self, val): - return val.cpu().detach().numpy() - - def _to_torch(self, val): - return torch.from_numpy(val).float().to(self.device) - - def forward(self, inputs_val): - rval = self.tfmodel.forward(self._to_numpy(inputs_val)) - return self._to_torch(rval) - - def backward(self, inputs_val, logits_grad_val): - rval = self.tfmodel.backward( - self._to_numpy(inputs_val), - self._to_numpy(logits_grad_val), - ) - return self._to_torch(rval) - - - -def get_madry_et_al_tf_model(dataname, device="cuda"): - if dataname == "MNIST": - weights_path = os.path.join( - MODEL_PATH, 'mnist_challenge/models/secret') - - try: - from mnist_challenge.model import Model - print("mnist_challenge found and imported") - except (ImportError, ModuleNotFoundError): - print("mnist_challenge not found, downloading ...") - os.system("bash download_mnist_challenge.sh {}".format(MODEL_PATH)) - from mnist_challenge.model import Model - print("mnist_challenge found and imported") - - def _process_inputs_val(val): - return val.view(val.shape[0], 784) - - def _process_grads_val(val): - return val.view(val.shape[0], 1, 28, 28) - - - elif dataname == "CIFAR10": - weights_path = os.path.join( - MODEL_PATH, 'cifar10_challenge/models/model_0') - - try: - from cifar10_challenge.model import Model - print("cifar10_challenge found and imported") - except (ImportError, ModuleNotFoundError): - print("cifar10_challenge not found, downloading ...") - os.system( - "bash download_cifar10_challenge.sh {}".format(MODEL_PATH)) - from cifar10_challenge.model import Model - print("cifar10_challenge found and imported") - - from functools import partial - Model = partial(Model, mode="eval") - - def _process_inputs_val(val): - return 255. * val.permute(0, 2, 3, 1) - - def _process_grads_val(val): - return val.permute(0, 3, 1, 2) / 255. - - else: - raise ValueError(dataname) - - - def _wrap_forward(forward): - def new_forward(inputs_val): - return forward(_process_inputs_val(inputs_val)) - return new_forward - - def _wrap_backward(backward): - def new_backward(inputs_val, logits_grad_val): - return _process_grads_val(backward( - _process_inputs_val(*inputs_val), *logits_grad_val)) - return new_backward - - - ptmodel = TorchWrappedModel( - WrappedTfModel(weights_path, Model), device) - model = BPDAWrapper( - forward=_wrap_forward(ptmodel.forward), - backward=_wrap_backward(ptmodel.backward) - ) - - return model diff --git a/deepcp_examples/benchmark_utils.py b/deepcp_examples/benchmark_utils.py deleted file mode 100644 index 5d4fd63..0000000 --- a/deepcp_examples/benchmark_utils.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import os -import sys - -import torch -import torchvision - -import deepcp -from deepcp.attacks.utils import multiple_mini_batch_attack - - -def get_benchmark_sys_info(): - rval = "#\n#\n" - rval += ("# Automatically generated benchmark report " - "(screen print of running this file)\n#\n") - uname = os.uname() - rval += "# sysname: {}\n".format(uname.sysname) - rval += "# release: {}\n".format(uname.release) - rval += "# version: {}\n".format(uname.version) - rval += "# machine: {}\n".format(uname.machine) - rval += "# python: {}.{}.{}\n".format( - sys.version_info.major, - sys.version_info.minor, - sys.version_info.micro) - rval += "# torch: {}\n".format(torch.__version__) - rval += "# torchvision: {}\n".format(torchvision.__version__) - rval += "# advertorch: {}\n".format(deepcp.__version__) - return rval - - -def _calculate_benchmark_results( - model, loader, attack_class, attack_kwargs, norm, device, num_batch): - adversary = attack_class(model, **attack_kwargs) - label, pred, advpred, dist = multiple_mini_batch_attack( - adversary, loader, device=device, norm=norm, num_batch=num_batch) - accuracy = 100. * (label == pred).sum().item() / len(label) - attack_success_rate = 100. * (label != advpred).sum().item() / len(label) - dist = None if dist is None else dist[(label != advpred) & (label == pred)] - return len(label), accuracy, attack_success_rate, dist - - -def _generate_basic_benchmark_str( - model, loader, attack_class, attack_kwargs, num, accuracy, - attack_success_rate): - rval = "" - rval += "# attack type: {}\n".format(attack_class.__name__) - - prefix = " attack kwargs: " - count = 0 - for key in attack_kwargs: - this_prefix = prefix if count == 0 else " " * len(prefix) - count += 1 - rval += "#{}{}={}\n".format(this_prefix, key, attack_kwargs[key]) - - rval += "# data: {}, {} samples\n".format(loader.name, num) - rval += "# model: {}\n".format(model.name) - rval += "# accuracy: {}%\n".format(accuracy) - rval += "# attack success rate: {}%\n".format(attack_success_rate) - return rval - - -def benchmark_attack_success_rate( - model, loader, attack_class, attack_kwargs, - device="cuda", num_batch=None): - num, accuracy, attack_success_rate, _ = _calculate_benchmark_results( - model, loader, attack_class, attack_kwargs, None, device, num_batch) - rval = _generate_basic_benchmark_str( - model, loader, attack_class, attack_kwargs, num, accuracy, - attack_success_rate) - return rval - - -def benchmark_margin( - model, loader, attack_class, attack_kwargs, norm, - device="cuda", num_batch=None): - - num, accuracy, attack_success_rate, dist = _calculate_benchmark_results( - model, loader, attack_class, attack_kwargs, norm, device, num_batch) - rval = _generate_basic_benchmark_str( - model, loader, attack_class, attack_kwargs, num, accuracy, - attack_success_rate) - - rval += "# Among successful attacks ({} norm) ".format(norm) + \ - "on correctly classified examples:\n" - rval += "# minimum distance: {:.4}\n".format(dist.min().item()) - rval += "# median distance: {:.4}\n".format(dist.median().item()) - rval += "# maximum distance: {:.4}\n".format(dist.max().item()) - rval += "# average distance: {:.4}\n".format(dist.mean().item()) - rval += "# distance standard deviation: {:.4}\n".format( - dist.std().item()) - - return rval diff --git a/deepcp_examples/instantiate_adversary_from_attackconfig_class.py b/deepcp_examples/instantiate_adversary_from_attackconfig_class.py deleted file mode 100644 index f38c655..0000000 --- a/deepcp_examples/instantiate_adversary_from_attackconfig_class.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import torch.nn as nn - -from deepcp.attacks import LinfPGDAttack -from deepcp.attacks.utils import AttackConfig - - -class PGDLinfMadryTrainMnist(AttackConfig): - AttackClass = LinfPGDAttack - eps = 0.3 - eps_iter = 0.01 - nb_iter = 40 - loss_fn = nn.CrossEntropyLoss(reduction="sum") - rand_init = True - clip_min = 0.0 - clip_max = 1.0 - - -class PGDLinfMadryTestMnist(PGDLinfMadryTrainMnist): - # only modify the entry that is changed from PGDLinfMadryTrainMnist - nb_iter = 100 - - -if __name__ == '__main__': - from deepcp.test_utils import LeNet5 - - model = LeNet5() - - train_adversary = PGDLinfMadryTrainMnist()(model) - test_adversary = PGDLinfMadryTestMnist()(model) diff --git a/deepcp_examples/models.py b/deepcp_examples/models.py deleted file mode 100644 index 2cf3135..0000000 --- a/deepcp_examples/models.py +++ /dev/null @@ -1,158 +0,0 @@ -import math -import torch.nn as nn -import torch.nn.functional as F - - -class LeNet5Madry(nn.Module): - # model replicated from - # https://github.com/MadryLab/mnist_challenge/blob/ - # 2527d24c4c34e511a12b8a9d7cf6b949aae6fc1b/model.py - # TODO: combine with the model in advertorch.test_utils - - def __init__( - self, nb_filters=(1, 32, 64), kernel_sizes=(5, 5), - paddings=(2, 2), strides=(1, 1), pool_sizes=(2, 2), - nb_hiddens=(7 * 7 * 64, 1024), nb_classes=10): - super(LeNet5Madry, self).__init__() - self.conv1 = nn.Conv2d( - nb_filters[0], nb_filters[1], kernel_size=kernel_sizes[0], - padding=paddings[0], stride=strides[0]) - self.relu1 = nn.ReLU(inplace=True) - self.maxpool1 = nn.MaxPool2d(pool_sizes[0]) - self.conv2 = nn.Conv2d( - nb_filters[1], nb_filters[2], kernel_size=kernel_sizes[1], - padding=paddings[0], stride=strides[0]) - self.relu2 = nn.ReLU(inplace=True) - self.maxpool2 = nn.MaxPool2d(pool_sizes[1]) - self.linear1 = nn.Linear(nb_hiddens[0], nb_hiddens[1]) - self.relu3 = nn.ReLU(inplace=True) - self.linear2 = nn.Linear(nb_hiddens[1], nb_classes) - - def forward(self, x): - out = self.maxpool1(self.relu1(self.conv1(x))) - out = self.maxpool2(self.relu2(self.conv2(out))) - out = out.view(out.size(0), -1) - out = self.relu3(self.linear1(out)) - out = self.linear2(out) - return out - - -def get_lenet5madry_with_width(widen_factor): - return LeNet5Madry( - nb_filters=(1, int(widen_factor * 32), int(widen_factor * 64)), - nb_hiddens=(7 * 7 * int(widen_factor * 64), int(widen_factor * 1024))) - - -# WideResNet related code adapted from -# https://github.com/xternalz/WideResNet-pytorch/blob/ -# ae12d25bdf273010bd4a54971948a6c796cb95ed/wideresnet.py - - -class BasicBlock(nn.Module): - def __init__(self, in_planes, out_planes, stride, drop_rate=0.0): - super(BasicBlock, self).__init__() - self.bn1 = nn.BatchNorm2d(in_planes) - self.relu1 = nn.ReLU(inplace=True) - self.conv1 = nn.Conv2d( - in_planes, out_planes, kernel_size=3, stride=stride, - padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(out_planes) - self.relu2 = nn.ReLU(inplace=True) - self.conv2 = nn.Conv2d( - out_planes, out_planes, kernel_size=3, stride=1, - padding=1, bias=False) - self.drop_rate = drop_rate - self.in_out_equal = (in_planes == out_planes) - - if not self.in_out_equal: - self.conv_shortcut = nn.Conv2d( - in_planes, out_planes, kernel_size=1, stride=stride, - padding=0, bias=False) - - def forward(self, x): - out = self.relu1(self.bn1(x)) - if not self.in_out_equal: - x = self.conv_shortcut(out) - out = self.relu2(self.bn2(self.conv1(out))) - if self.drop_rate > 0: - out = F.dropout(out, p=self.drop_rate, training=self.training) - out = self.conv2(out) - out += x - return out - - -class ConvGroup(nn.Module): - def __init__( - self, num_blocks, in_planes, out_planes, block, stride, - drop_rate=0.0): - super(ConvGroup, self).__init__() - self.layer = self._make_layer( - block, in_planes, out_planes, num_blocks, stride, drop_rate) - - def _make_layer( - self, block, in_planes, out_planes, num_blocks, stride, drop_rate): - layers = [] - for i in range(int(num_blocks)): - layers.append( - block(in_planes=in_planes if i == 0 else out_planes, - out_planes=out_planes, - stride=stride if i == 0 else 1, - drop_rate=drop_rate) - ) - return nn.Sequential(*layers) - - def forward(self, x): - return self.layer(x) - - -class WideResNet(nn.Module): - def __init__(self, depth, num_classes, widen_factor=1, drop_rate=0.0, - color_channels=3, block=BasicBlock): - super(WideResNet, self).__init__() - num_channels = [ - 16, int(16 * widen_factor), - int(32 * widen_factor), int(64 * widen_factor)] - assert((depth - 4) % 6 == 0) - num_blocks = (depth - 4) / 6 - - self.conv1 = nn.Conv2d( - color_channels, num_channels[0], kernel_size=3, stride=1, - padding=1, bias=False) - self.convgroup1 = ConvGroup( - num_blocks, num_channels[0], num_channels[1], block, 1, drop_rate) - self.convgroup2 = ConvGroup( - num_blocks, num_channels[1], num_channels[2], block, 2, drop_rate) - self.convgroup3 = ConvGroup( - num_blocks, num_channels[2], num_channels[3], block, 2, drop_rate) - # global average pooling and classifier - self.bn1 = nn.BatchNorm2d(num_channels[3]) - self.relu = nn.ReLU(inplace=True) - self.fc = nn.Linear(num_channels[3], num_classes) - self.num_channels = num_channels[3] - - for mod in self.modules(): - if isinstance(mod, nn.Conv2d): - n = mod.kernel_size[0] * mod.kernel_size[1] * mod.out_channels - mod.weight.data.normal_(0, math.sqrt(2. / n)) - elif isinstance(mod, nn.BatchNorm2d): - mod.weight.data.fill_(1) - mod.bias.data.zero_() - elif isinstance(mod, nn.Linear): - mod.bias.data.zero_() - - def forward(self, x): - out = self.conv1(x) - out = self.convgroup1(out) - out = self.convgroup2(out) - out = self.convgroup3(out) - out = self.relu(self.bn1(out)) - out = out.mean(dim=-1).mean(dim=-1) - out = self.fc(out) - return out - - -def get_cifar10_wrn28_widen_factor(widen_factor): - from deepcp.utils import PerImageStandardize - model = WideResNet(28, 10, widen_factor) - model = nn.Sequential(PerImageStandardize(), model) - return model diff --git a/deepcp_examples/trained_models/mlp.pkl b/deepcp_examples/trained_models/mlp.pkl deleted file mode 100644 index f649a2f..0000000 Binary files a/deepcp_examples/trained_models/mlp.pkl and /dev/null differ diff --git a/deepcp_examples/tutorial_attack_defense_bpda_mnist.ipynb b/deepcp_examples/tutorial_attack_defense_bpda_mnist.ipynb deleted file mode 100644 index fc91368..0000000 --- a/deepcp_examples/tutorial_attack_defense_bpda_mnist.ipynb +++ /dev/null @@ -1,422 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Attack, Defense, and BPDA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Copyright (c) 2018-present, Royal Bank of Canada and other authors.\n", - "# See the AUTHORS.txt file for a list of contributors.\n", - "# All rights reserved.\n", - "#\n", - "# This source code is licensed under the license found in the\n", - "# LICENSE file in the root directory of this source tree.\n", - "#" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "import os\n", - "import argparse\n", - "import torch\n", - "import torch.nn as nn\n", - "\n", - "from advertorch.utils import predict_from_logits\n", - "from advertorch_examples.utils import get_mnist_test_loader\n", - "from advertorch_examples.utils import _imshow\n", - "\n", - "torch.manual_seed(0)\n", - "use_cuda = torch.cuda.is_available()\n", - "device = torch.device(\"cuda\" if use_cuda else \"cpu\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load model that is trained with `tut_train_mnist.py`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/gavin/anaconda3/envs/dev/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", - " from ._conv import register_converters as _register_converters\n" - ] - }, - { - "data": { - "text/plain": [ - "LeNet5(\n", - " (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (relu1): ReLU(inplace)\n", - " (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (relu2): ReLU(inplace)\n", - " (maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (linear1): Linear(in_features=3136, out_features=200, bias=True)\n", - " (relu3): ReLU(inplace)\n", - " (linear2): Linear(in_features=200, out_features=10, bias=True)\n", - ")" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from advertorch.test_utils import LeNet5\n", - "from advertorch_examples.utils import TRAINED_MODEL_PATH\n", - "\n", - "filename = \"mnist_lenet5_clntrained.pt\"\n", - "# filename = \"mnist_lenet5_advtrained.pt\"\n", - "\n", - "model = LeNet5()\n", - "model.load_state_dict(\n", - " torch.load(os.path.join(TRAINED_MODEL_PATH, filename)))\n", - "model.to(device)\n", - "model.eval()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 5\n", - "loader = get_mnist_test_loader(batch_size=batch_size)\n", - "for cln_data, true_label in loader:\n", - " break\n", - "cln_data, true_label = cln_data.to(device), true_label.to(device)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Construct a LinfPGDAttack adversary instance" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from advertorch.attacks import LinfPGDAttack\n", - "\n", - "adversary = LinfPGDAttack(\n", - " model, loss_fn=nn.CrossEntropyLoss(reduction=\"sum\"), eps=0.15,\n", - " nb_iter=40, eps_iter=0.01, rand_init=True, clip_min=0.0, clip_max=1.0,\n", - " targeted=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perform untargeted attack" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "adv_untargeted = adversary.perturb(cln_data, true_label)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perform targeted attack" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "target = torch.ones_like(true_label) * 3\n", - "adversary.targeted = True\n", - "adv_targeted = adversary.perturb(cln_data, target)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualization of attacks" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pred_cln = predict_from_logits(model(cln_data))\n", - "pred_untargeted_adv = predict_from_logits(model(adv_untargeted))\n", - "pred_targeted_adv = predict_from_logits(model(adv_targeted))\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(10, 8))\n", - "for ii in range(batch_size):\n", - " plt.subplot(3, batch_size, ii + 1)\n", - " _imshow(cln_data[ii])\n", - " plt.title(\"clean \\n pred: {}\".format(pred_cln[ii]))\n", - " plt.subplot(3, batch_size, ii + 1 + batch_size)\n", - " _imshow(adv_untargeted[ii])\n", - " plt.title(\"untargeted \\n adv \\n pred: {}\".format(\n", - " pred_untargeted_adv[ii]))\n", - " plt.subplot(3, batch_size, ii + 1 + batch_size * 2)\n", - " _imshow(adv_targeted[ii])\n", - " plt.title(\"targeted to 3 \\n adv \\n pred: {}\".format(\n", - " pred_targeted_adv[ii]))\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Construct defenses based on preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from advertorch.defenses import MedianSmoothing2D\n", - "from advertorch.defenses import BitSqueezing\n", - "from advertorch.defenses import JPEGFilter\n", - "\n", - "bits_squeezing = BitSqueezing(bit_depth=5)\n", - "median_filter = MedianSmoothing2D(kernel_size=3)\n", - "jpeg_filter = JPEGFilter(10)\n", - "\n", - "defense = nn.Sequential(\n", - " jpeg_filter,\n", - " bits_squeezing,\n", - " median_filter,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Process the inputs using the defense\n", - "here we use the previous untargeted attack as the running example. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "adv = adv_untargeted\n", - "adv_defended = defense(adv)\n", - "cln_defended = defense(cln_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualization of defenses" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pred_cln = predict_from_logits(model(cln_data))\n", - "pred_cln_defended = predict_from_logits(model(cln_defended))\n", - "pred_adv = predict_from_logits(model(adv))\n", - "pred_adv_defended = predict_from_logits(model(adv_defended))\n", - "\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(10, 10))\n", - "for ii in range(batch_size):\n", - " plt.subplot(4, batch_size, ii + 1)\n", - " _imshow(cln_data[ii])\n", - " plt.title(\"clean \\n pred: {}\".format(pred_cln[ii]))\n", - " plt.subplot(4, batch_size, ii + 1 + batch_size)\n", - " _imshow(cln_data[ii])\n", - " plt.title(\"defended clean \\n pred: {}\".format(pred_cln_defended[ii]))\n", - " plt.subplot(4, batch_size, ii + 1 + batch_size * 2)\n", - " _imshow(adv[ii])\n", - " plt.title(\"adv \\n pred: {}\".format(\n", - " pred_adv[ii]))\n", - " plt.subplot(4, batch_size, ii + 1 + batch_size * 3)\n", - " _imshow(adv_defended[ii])\n", - " plt.title(\"defended adv \\n pred: {}\".format(\n", - " pred_adv_defended[ii]))\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### BPDA (Backward Pass Differentiable Approximation)\n", - "BPDA is a method proposed in [1], which can be used to attack non-differentiable preprocessing based defenses. Here we use $f(x)$ to denote a non-differentiable component, and $g(x)$ to denote a differentiable component that is similar to $f(x)$. In BPDA, $f(x)$ is used in forward computation, and in the backward computation $g(x)$ is used to propagate down the gradients.\n", - "\n", - "Here we use BPDA to perform adaptive attack towards the defenses we used above.\n", - "\n", - "[1] Athalye, A., Carlini, N. & Wagner, D.. (2018). Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. Proceedings of the 35th International Conference on Machine Learning, in PMLR 80:274-283" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from advertorch.bpda import BPDAWrapper\n", - "defense_withbpda = BPDAWrapper(defense, forwardsub=lambda x: x)\n", - "defended_model = nn.Sequential(defense_withbpda, model)\n", - "bpda_adversary = LinfPGDAttack(\n", - " defended_model, loss_fn=nn.CrossEntropyLoss(reduction=\"sum\"), eps=0.15,\n", - " nb_iter=1000, eps_iter=0.005, rand_init=True, clip_min=0.0, clip_max=1.0,\n", - " targeted=False)\n", - "\n", - "\n", - "bpda_adv = bpda_adversary.perturb(cln_data, true_label)\n", - "bpda_adv_defended = defense(bpda_adv)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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i+NLHKO6oo45KYv/8d7LNXlxXf+yxxybrXn/99SQ+55xzkriMtel8ggwAAABEmCADAAAAESbIAAAAQIQa5A74zGc+U1v2PSV9D+ann366LWNCfUaNGlVbfuc735msGzhwYBL7usCzzz47idt5GVD0HHvttVcSH3/88Un86KOPJvEdd9zR8jGh/R5++OHa8gknnJCsa7TmOIuvIfa1pRMnTmzZfUMaNmxYEu+5556Z2/vzmtop7pnta+qnTZuWxHnXlygDPkEGAAAAIkyQAQAAgAgTZAAAACBCDXIbvOtd70riL33pS11ue9hhhyXxX//615aMCc1x7bXX1pY32mijzG1/9atfJTH9QNEd+++/fxKPGDEiiW+99dYkXrVqVcvHhObzPfC9PfbYo00jSZlZEvtx5o37G9/4RhJ/9KMfbc7A+gh/LsuYMWOS+Morr2zncDJts802Xa7riXMZPkEGAAAAIkyQAQAAgAgTZAAAACBCDXIbTJo0KYn79+9fW77zzjuTdQ888EBbxoT6HHrooUm86667drnt3XffncRnnHFGK4aEXm7nnXdO4hBCEl9zzTXtHA6a5MQTT0ziN954o0MjyTZ58uQk3mWXXZLYj9vHHPcas2zZsiR+7LHHkninnXZKYn+OwsKFC1szMEmbbLJJEh955JFdbnvvvfe2bBytwifIAAAAQIQJMgAAABBhggwAAABEqEFugUGDBiXxgQcemMSvvvpqbdnXZ7322mutGxgK872Nv/KVryRxXE/u+Vqx5cuXN29g6LU222yzJN5nn32S+Omnn07i3/72ty0fE5rP1/Z20sYbb5zEO+ywQ23ZH/PyvPzyy0nM77TGrFy5Mol9//wjjjgiiW+66aYkPv/88+u+7x133DGJfZ/jcePGJbE/P6K768qKT5ABAACACBNkAAAAIMIEGQAAAIhQg9wCp512WhL7vpG33nprbfn+++9vy5hQn//4j/9I4okTJ3a57fXXX5/E9P9EPaZMmZLEvtfoLbfc0sbRoC84/fTTk/gzn/lMt2/7/PPPJ7HP35kzZ9Y7LKzFmWeemcRmlsQHH3xwEl9xxRV139f8+fOT2NcRjxw5stv7uvTSS+seR6fwCTIAAAAQYYIMAAAARCixaAL/lcbXvva1JF66dGkSn3XWWS0fE5rj85//fLe3/exnP5vEtHVDPXzrJG/RokVtGgl6q5tvvjmJx48fX/e+pk2blsR/+MMf6t4X8vnn+8Mf/nAS+5JO35qtiLzL2P/85z9P4mOPPbbLbX27up6AT5ABAACACBNkAAAAIMIEGQAAAIhQg1wHf/nhCy+8MInXXXfdJPb1Xg888EBrBoaOGjFiRBI3eonVJUuWdLk/f4nrYcOGZe5r+PDhSXzKKad0exyvv/56En/xi19M4hUrVnR7X8iXdwni3/3ud20aCVrJt+daZ53sz6sOOuigLtf9+Mc/TuJRo0Zl7svf1xtvvJG5fZZDDjmk7tui+R599NHMuJmeffbZbm+70047JfHjjz/e7OE0HZ8gAwAAABEmyAAAAECECTIAAAAQoQa5G3xNcXypaEnaaqutknj69OlJ7Psio3f6y1/+0tT9XX311Uk8d+7c2vKmm26arDv66KObet9Z5s2bl8TnnHNO2+67N9pnn32S2L+26J0uvvjiJD7vvPMyt49rz/NqhovWFBfZ/pJLLim0b/Revo7ex7GeUHPs8QkyAAAAEGGCDAAAAESYIAMAAAARapC7wV/L/B3veEfm9p///OeT2Ncko+fwPaw/+MEPtu2+jzrqqLpvu3r16iTOqzG84YYbkvjhhx/uctt777237nFhTYcddlgS+3MefB/Te+65p+VjQutdd911SXzaaacl8cYbb9y2sbz88stJPG3atNrypz71qWRdfC4E+rYQQmbc0/EJMgAAABBhggwAAABEmCADAAAAEWqQ12LcuHFJfPvtt2du72vH4n6V6NkOP/zwJP7CF76QxP379+/2viZMmJDERXsX/+xnP6stP//885nb+vrGuKYQnTd48ODa8qRJkzK3veaaa5L49ddfb8mY0F4zZsxI4mOOOSaJfW36ySef3LKx+F7mP/zhD1t2X+g91ltvvS7XrVq1qo0jaQ0+QQYAAAAiTJABAACACBNkAAAAIGLt7FtnZj2iSZ6vx/ryl7+cuf3uu++exFk9ZHuSEELXF1Zvo56SN6ggb/LFteu+r/FLL72UxB/5yEeSeMWKFa0bWAeVIW/KnDMHHnhgbdn3Jp48eXIS+77m//3f/53EZulT/eSTTybxzJkz6x5nO5UhZ6Ry500rzZs3L4n79fvnaW1nnXVWsu4HP/hBW8bUHd3NGz5BBgAAACJMkAEAAIAIE2QAAAAgQg1y1T777FNbvummm5J1Q4cOzbwtNcitVea8wZrIG9SjDHlDzvQsZcgZqe/mzY033pjEF1xwQW35rrvuavdwuo0aZAAAAKAOTJABAACACJeartp7771ry3klFdOnT0/i5cuXt2RMAAAAZeTbC/Y2fIIMAAAARJggAwAAABEmyAAAAECEGuRumDp1ahLvv//+Sbxw4cJ2DgcAAAAtxCfIAAAAQIQJMgAAABBhggwAAABEuNQ0usRlPFEP8gb1KEPekDM9SxlyRiJvehouNQ0AAADUgQkyAAAAEGGCDAAAAETaWoMMAAAAlB2fIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABAhAkyAAAAEGGCDAAAAESYIAMAAAARJsgAAABApNdNkM3seTN7X5P2NcXM7m3GvrpxX00bN4ohZ1AP8gb1IG9QD/Km/XrdBBkAAABoBBNkAAAAINJbJ8gTzexJM1tkZpea2XqSZGbvNrPZZvYVM5tf/ej/2DdvZGYbmdkNZrbUzB6StE28UzP7gZnNqq5/xMz26WoAZnawmT1a3XaWmZ3p1n/MzGaY2QIzOz36/9FmttLMRkT/t0t1vP0bf2rQBXIG9SBvUA/yBvUgb9qot06Qj5V0gCpJsL2kr0brNpM0UtIYScdJ+m8zG19d90NJqySNknRC9Sf2J0lvlzRC0uWSrn4zQdfiFUkfl7ShpIMl/ZuZHSZJZraDpIslfUzSaEkbSdpckkIIcyQ9IOmIaF8fkXRNCOG1bj8DKIqcQT3IG9SDvEE9yJt2CiH0qh9Jz0v61yieJGl6dfndklZLGhKt/42kr0laV9Jrkt4SrTtX0r0Z97VI0s7dHNf3JV1QXf66pCujdUMkvSrpfdX4k5Luqi6bpFmS9u30c9tbf8gZfsgb8oa8IW/K/EPetP+nt36CPCtanqHKXzJvWhRCeGUt6zeW1G8tt60xs/8ws2lmtsTMFksapspfbGswsz3M7H/N7GUzWyLpX6NtR8f3Ux3Pgujm10jay8xGS9pXUpD0h5zHjMaQM6gHeYN6kDeoB3nTRr11gjw2Wt5C0pwoHm5mQ9ay/mVV/gLzt5UkVWtyvijpw5KGhxA2lLRElb+C1uZySTdIGhtCGCbpkmjbufH9mNlgVb6KkCSFEBZLur16Xx+RdEWo/smFliFnUA/yBvUgb1AP8qaNeusE+TNmtnm1GPwrkq5y679hZgOqiXGIpKtDCK9Luk7SmWY2uFpLc1x0m/VVSbKXJfUzs69L2iBjDOtLWhhCWGVmu6uSDG+6RtIhZra3mQ2Q9E2t+VpcrkqdzxHVZbQWOYN6kDeoB3mDepA3bdRbJ8iXq/JXyrPVn7OjdfNUqa+ZI+nXqtT0PFVd91lJQ6vbXCbp0uh2t0m6RdLfVPl6YpXSryy8T0v6ppktU6Uu5zdvrgghPCHpM9Vxzq2OZ7a7/Q2StpP0YghhajceMxpDzqAe5A3qQd6gHuRNG1mJP91uOjN7t6RfhRA27/RY0DOQM6gHeYN6kDeoB3nTGr31E2QAAACgLkyQAQAAgEifKrEAAAAA8vAJMgAAABBhgtwiZna3mX2y0+NAz0LeoB7kDepB3qAefSVvmCCXgJkNNLMLzGyOmS0ysx+ZWf9OjwvlZmbHmdkjZrbUzGab2Xlm1q/T40K5WcXZZvZC9cpZd5vZhE6PC+VnZqeY2bxq3vzMzAZ2ekwot548v2GC3A1tmHR8SdJuknaUtL2kXSV9tcX3iRZrQ94MlvTvqlzmcw9J+0s6tcX3iRZrQ94cJekESftIGiHpAUm/bPF9osVanTdmdoAqv6v2l7SlpK0lfaOV94nWY37TtT47QTazYGYnmdmzZjbfzL5rZutU100xs/uqf/UslHRm9f9PsMr1yheZ2W1mNi7a3/vN7KnqX9YXqevLNK7NZEkXhhAWhhBelnShKr/AUDJlypsQwsUhhD+EEF4NIbygSnP4dzX1AaMpypQ3kraSdG8I4dnqVbZ+JWmHpj1YNE3J8uY4ST8NITwRQlgk6SxJU5r1WNE8JcubHju/6bMT5KoPqfKXza6SPqj0RdtDlSvVbCLpHDM7TJVLOx4uaWNJf5B0hSSZ2UhJ16ryV9FISdMVTVTMbAszW2xmW2jtTGnCmaTNzWxYow8QLVGWvPH2lfRE/Q8LLVaWvLlS0rZmtn31q87jJN3arAeJpitL3kyQFF/5bKqkTc1so0YfIFqiLHnTc+c3IYQ++SMpSDowij8t6c7q8hRJM932t0j6RBSvI2mFpHGqXFf8wWidqXJ5xU92cyxnS7pPlcTcTNIfq+Mb1enniZ/y5o27n+Ortx3Z6eeIn3LnjaQBkn5QHdNqSc9J2qrTzxE/pc+b6W4s/avj27LTzxM/pc6bHju/6eufIMfXG58haXQX66RKovyg+pfSYkkLVUmUMdXb1bYPlazIupa5d46kRyU9Jul+SddLek3SSwX2gfYpS95Ikqp//X9b0kEhhPlFb4+2KUvenCFpoqSxktZTpY70LjMbXGAfaJ+y5M1ySRtE8ZvLywrsA+1TlrzpsfObvj5BHhstbyFpThT7K6jMknRiCGHD6GdQCOF+SXPjfZmZuX1nCiGsDCF8NoQwJoSwtaQFkh4JlfpAlE8p8qZ6mwMl/VjS5BDC40Vui7YrS97sLOmqEMLsEMLqEMJlkoaLOuSyKkvePKFK7rxpZ0kvhhAWFNgH2qcUedOT5zd9fYJ8mpkNN7Oxkk6WdFXGtpdI+rJV2yGZ2TAzO6q67iZJE8zscKucEXqSKl8ldIuZjTGz0Vaxp6SvqfIpD8qpLHnzXlVOzDsihPBQPQ8EbVWKvJH0J0lHmdmmZraOmX1Mla/Lnyn6gNAWZcmbX0j6hJntYGbDValJvazgY0H7lCJvevL8pq9PkP9H0iOqfPR/k6SfdrVhCOG3kr4j6UozWyrpr5IOqq6br0rrpG+r8tfRdqrU3EiqFbEvzyhi30aVrx5ekfRzSV8KIdze2ENDC5Ulb74maZikm6vbLTezWxp9cGiZsuTNd1Q5weoxSYslnaLKH1mLG3p0aJVS5E0I4VZJ50n6X1W+sp+hHjLR6aNKkTfqwfMbqxZR9zlmFiRtF0LgUxN0G3mDepA3qAd5g3qQN83R1z9BBgAAABJMkAEAAIBIny2xAAAAANaGT5ABAACASL923lm1cLxmq622StbPmDEjid94441u73u99dZL4lWrVhUdXpcqbf/+abvttsvc/m9/+1sSb7/99l2uHzYsvdrixhtvnMTPPJNdY+/HVuQbgbzH9fTTTxe53nrL+Lzx/PNbhH+tiu477/ZF9pXn73//e215m21K8XydAAAgAElEQVS2Sdats076t+7ixWlDgg033LCh+y6iLHkzfvz4JG+KvFaN8q+1P7b94x//aNtYiujfv38Sv/baa5nb+2NILO9YNGTIkCRevnx5x/Nm4MCByaDXX3/9ZL0f88yZM+u+L/9c+9+HWV588cUkXrJkSeb2w4cPT+JFixYl8ciRI5N4xIgR3R5L3u+7ZvL3FULoeM5Ia/6O8s+Bn8v443WWFStWJLHPm+eee86PJYnz5iu9wdKlS5N43rx5mdt3N2/4BBkAAACIMEEGAAAAIkyQAQAAgEhbu1jk1eksXLgwiefPn19k30mc97iy6oJbWUOVZ/r06Un8+uvp5cq33XbbzNv72uvZs2fXlovWJpW1lrSZOlmXmmflypVJPGjQoGYOp26+ftbX15a1LrCVNthggyRetmxZEvua8fjcgrz6UC/r2CVl1wX797y/ra+N9LWpCxYsSGJfDzlmzJjasq+T9XWYAwcOTOKFCxd2PG+GDx+e5Mwmm2ySrM+q/fXH5iJ1pt0R16LGx3VpzXH6Y4V/rot46aWXMu+rlXwtqa81LeuxpplziPjcE6n31hT7uY4/j8Yfe4rwx8Q33niDGmQAAACgKCbIAAAAQIQJMgAAABDpaA2yr9nK6/nr9pXEebV1eTpVd9xoDazvy/nKK690+7Z5z39Z6rsaqUFuZ42x18la9jx5z8uAAQNqy6+++mqhfZclb4rWIG+22Wa1ZV/7uNFGG2XGnn9+hw4dmsSjR48uMrSEr4PddNNNC42lCJ/Dvrba9wlu5H7LkDf9+vVLcsbXjntx3aSvsfb12Y0q8nxuvvnmSTx48OCmjcP33S3Sv7nZynKezLbbbpvkzbrrrtvt2/oaYz8nK/PvkUbk5bM/Rs6ZM6fb+/bzQ1+Dv3LlSmqQAQAAgKKYIAMAAAARJsgAAABApK01yKNHj07ubO7cuZnbZ/XzbHTcZanr8ddZz6sVy7ume1bfSP+Yfb/Vl19+OYnLUBMoFa9Bbmfd8eTJk2vLvvfo2972tiQ+9thjk3jLLbdM4jvvvDOJH3jggST+5S9/We8w2/qclCVv8mqQs44Beb2F844/w4YNS+K8OuFWKvLa++OJrzHOexyrV6+uLfvjS15/5zLkzbrrrpu8sGPHjvXru7xtv379mjqWrB7V/vfA1ltv3bKxlPl8nrLUIBftg9zM8wLydOo8nFGjRiWxP1/Kn/Pkjz3+HCmf80XOWfP3vXz5cmqQAQAAgKKYIAMAAACR5n4nlMOXVOR9bRm3j/GXUC7S8qNRRb+iKHLJ0aLtd/xz5L928JcBjVtXef4Stz4uq062bvvmN7+ZxEceeWS3b+svHT1t2rQk3n///TPje+65p7Y8c+bMZJ1/ThppI1ZU3uXPO8W32XrttdeS2L93nn322dpyVnlXd5SppCLOs5NPPjlZd/vttyfxuHHjkrhoq7L4q/281njNvhRzM+S1h2ol/7ptvPHGSRyXwOVdbvz3v/99EvtjSXyJbEk66qijkvjhhx/uxoibo8jxvCylkZ4fV14pZCPy2sJ1UiOvj3/Omvl7vkj721j5jlAAAABABzFBBgAAACJMkAEAAIBIW2uQ8+r6/Pq4JrCoRmphGq19aWVtna8dGzFiRN37ii+TKknTp0+ve1+t1M6WOHGbKkmaMmVKEhepOX7qqaeS+Lbbbkti35rprW99a+b+4tZhec9JM2v0fasrnzdlrCWV1qw59vX5M2bM6Pa+fNssv++8PPPbx+dU+Np0P07/Wufd19FHH53EcW3fCy+8UGhfjShrXmTxNdf+0tovvfRSEuddijpL3nvYt90sclt/7skTTzyRxBMmTEhiX8catwctKi9fO3kOSav4FoYLFixIYl9f24iiNcdxi7PrrrsuWXfggQcm8XbbbVf/wHJ84AMfSOJG82CLLbaoLftzcpql5x3BAAAAgBZiggwAAABEmCADAAAAkbbWIHu+Rs3XNrZTK3sx+lrHuM7V1xv6vsh5dcHz589PYl97tsEGG9SW8x5jWXtMNsLXd86aNStze39p3Q996EOZ28d1xIceemiyzr82y5cvT+L77rsviT/zmc8ksb88ZiP15kXF701/CW2fR76Wuix8Pvv3kq/li2N/PoSvwy56Kd/nnnuu29suXrw4c72vZ/SP653vfGcSx+8BXwudt29/jG5lf9cy8L+D/PHAx0X4Xv6+f3iRS+d6/nyd+BoCa+N7sHtZv4tHjhyZxP441xdqjj1fLz506NAk9sf+djrggANqy76vdytrjj3/+9H3+T7jjDOS2OeRf3/EdcdZl4CX6p9b9q6jGwAAANAgJsgAAABAhAkyAAAAEGlrDbKv+WtmzfFOO+2UxDfccEMS/9d//VcS+3qwuI9hVv/JeowbNy6J4zq+RurO1mbevHmZcV+TV3PsjRo1Kol9Laqvt43ru+bOnVvovk499dQk9jXHCxcuTOKbbrqp0P6L8LVpcc7m1RA20q+8lfx7K69/aFzj7fuY+trdojXIWQYMGJDEr776ahL7Ol+f075376WXXprEd955Z7fHknc88rWV/pyJuE+wr4P1dYJlrF/efPPNk7iRmmv/+833oC6aQ/Gx5rDDDkvWrVixIvO2vg+yfxz77rtvl+unTp2arHvxxRcz76sv1BznGT16dBJn5dHf//73ZJ0/TvnfQb5u2D/fO+ywQxJPmjSptrzffvtlDbulHn744ST24xw+fHgS+2ORfw7XW2+92nLe8bnefuXlO0IBAAAAHcQEGQAAAIgwQQYAAAAiHe2DnCeuvcmrHzzvvPOSeMstt0zi4447LvP2J554Ym152bJlyTpfvzV58uTMfTXixhtvTOLZs2cnsX+cfqxF+Dqpnlo7ltW/uehj8tufcsopSeyfb18nnLUvP86jjz46cyx5tX5F+Do2b6ONNmrafZXFmDFjktjXqfn6/KzX0tdo+9dm0003TWJ/HkNcLyel/ck33HDDLu+3O/zYnnrqqST+6Ec/Wlv2j9nnhc9RXx/p+7lm9XfN6/1cxr7rvp67Ef/4xz+S2NckFz0H55e//GVt2feQzcrd7jj88MO7jH0ff3/c8r9H4j7/UnnPUWgm/z7yvcnj97uUnvPk+Rz0x6283yvvec97kvgTn/hEbfnaa6/t8n7r4euCs85h8H3/P/jBDybxK6+8ksS+T7jnj00x/97zOdxdfIIMAAAARJggAwAAABEmyAAAAECk1DXIcb8/X2/iewH6vscXX3xxEj/++ONJ7Hvw7bLLLrXld7/73cm6KVOmZI4zryYoy2OPPZbEe+65Z+b28fXHJenHP/5xt++rjDV/3dHMcRfdV5HaJZ+jvu70tNNOy1w/bdq0JP7jH/+YxHPmzKkt+37Mvq+prwXz75ciemre+F6Yvm44qy7br/PvO9+r2Ndd+td2iy22yB5sA77whS8ksc/ZrF7oeXnh+4f62kqfd3GfZF+X3RvF/fR9juTxOebPs7nggguS2Ncdx+Ie3mszYcKEJL733nuTeO+99+7ytv41fuihh5LY92T2Ncd5fb6z+Psu63kyedd4yKo59nzNseePx/4aEBdddFESx+c1nX322d0ex9o08vwfeuihmesnTpyYxP48jqz7zqtX9n3tu4tPkAEAAIAIE2QAAAAgwgQZAAAAiJS6BjmWVyt35513Zq73ffFuuummJI7rR++4445k3TXXXJPEvjbU18qMHz8+iX3tY1wTOH/+/GSd7xXoa8uef/75JM6rD/V9CnuiRmq821k/6+vQPv3pTyfxJz/5yczbv/TSS0nsczR+LL7OdJNNNknieq8935v494rvRerrBON+xL5mzfdUzuuX28z3nT92+ePNbrvtlsT+/TJq1Kjasq+NzuNrQBupZfe10I30cG8VX0vuH7+P4+fTH2vyesQOHjw4iddff/0kjvvX+v358xX87xxff/+5z30uiW+//fYkXrJkSRK/973vrS2feeaZyTpfI7v55psrS97xO6u2tKf+/vLHmqVLl3b7tkV/Z51++ulJPHXq1CSO+6AXGcfaxlJkbP513W+//ZLYv7b+OOf5825WrlxZW/bvSy/etgg+QQYAAAAiTJABAACASEdLLHz7F3956GaK2w9J2V8V+JYsd911V+a+/Vdls2bNytw+LqvYcccdk3XDhw9PYt+erujXknlfPcTyLkfcKT2lzZgfp//qO8+KFSuS+C9/+UuX244bNy5zX0Xb8fixx19J+ZxbvHhxoX2Xhf8K218mOX7vNXrp7bz3XVzese6662Zu648nu+++e+b2vgTDP+4s/qvIQYMGZW4/e/bsJI7bnvnjSdFLK3eCL01oJv+e9WVSvqSqyOvmSyb8JYV/9rOfdXtfknTFFVfUli+//PJk3RNPPJHE5513XhJ//etfT2J/LCpybOopx37fgs+XTnrx48q6PPPaHHnkkUm88847J/Fvf/vbJC5aVhFrpLzxU5/6VBL7koq77747ifPKIHzJXDx/9O8Vf1/xcakIPkEGAAAAIkyQAQAAgAgTZAAAACDS0RrkIjXHvk4n79KCjfCtfppdlzZ69Oja8o9+9KNkna9dfPTRR5N44cKFTR1LLK91Vac0UgfVzvu6/vrrk/itb31r5vb3339/En/1q1/t9n35y1p7efXkviVdWS/h2gifz74u0NepFanX93yNW147tby645gfp7+0rOdrQmONvs4+r/xzHB8r4/Zy0po1t2U83vjn2o85PnZL6eP1l+H2v6PyLls/cuTIbo/TtxE755xzkjivBraIAw44IInPP//8JPbn4Pj8u/nmm5O4p9QVF+Hr/osoOpfxx5annnoqiS+++OIub9vq5z6e0x177LHJOn8Ogj/HJu8S20WeJ/8+rvf8Kj5BBgAAACJMkAEAAIAIE2QAAAAg0tYa5EbqX/zlLPPqVXw9YZGav1b2wpSk733ve7Vl3z/R92D2l7l+4YUXktjXxBVRtP9ip7SzZq3ofcV1lv7yxP5ysL4u8Fvf+lYS+768RfiaYl9zlbfe53z8/vI55vtqNjLuVvLnOPh+zr7Hb1y3llePnNWTU2rsfekdffTRSXz88ccnsa9/bGY9+cCBA5PYXw52s802S+L4efA1h1tttVUS++ewjPzr6PMifh+NHTs2c1t/bPGX3p4wYULmWOL+4yeccEKyrkjf3bXJyhnfY/mRRx5J4okTJ2buG2tq5D265557Zq6/88476953o+Lex76m3v8+/P3vf5/EefORrN9pRc+56S4+QQYAAAAiTJABAACACBNkAAAAINLWGuS8vqO+tjGuZ/M9M33d3UYbbZTE9fa9W5tG+yK/613vSuK4r+SLL76YrIvrk6U1H8fQoUOT2D9n/prkr7zySm3Z96v0z79/fbAm33847mX80ksvZd52yZIlSez7pnpZvXVHjBiRrPP1z9tss03mvvP6RMa1pj5v5syZk7nvsvB1fr4G1j9n8fkAvj7W19PmPb/NdPDBByfxXnvtlcQPP/xwEhepo8+rhRw3blzmep/Dm2yySW3Zn0/h65fLKO/claxjv69pz7PPPvsU2n6PPfaoLfvn0teKF5WVM/53jn+O8ur1H3rooST+6Ec/msRZtf/t7IHfiOHDhyexfz38c9jI+T9jxoxJ4iuvvLLbt817v/vnt+j2Tz75ZG3ZXwfAH3/zavR9jvvzzuLfQ9ttt13mOOut+eYTZAAAACDCBBkAAACIMEEGAAAAIm2tQc6rVfJ9IePex75G0tcFF62LyuLrM5999tnM7fPqoiZNmpTEcd2x71noe0zm1V77WsgVK1YkcVw/+txzzyXr8nrjYs0ev/61jvtS+9fq7rvvTuKf/OQnmfflc3rmzJndHeYa+R7Xnktr1qa/+uqrSRzXN0tr9tvuifx5CT7//WOOn0Pfd73oeQdxz1ppzfr0rGOGP/7svPPOSfzUU08l8fe///0kzqq38/fbaE3nkCFDknjhwoW1ZZ9z/nHl9bLvBJ/3vt6zCH9+yAYbbJDEJ554Yt37brTmuAjfd3uXXXZJYv+6+viMM87I3H/W72qfn2XMGWnN6xl4/jmJjz3+WJx3LtBjjz2WxDvttFMS77jjjkn817/+NXN/saK1un77D3/4w7Xl8ePHJ+suvPDCzH35+Z/vW+/Fxx7/uzLvHJvu4hNkAAAAIMIEGQAAAIgwQQYAAAAiba1B9vJqbeKaTr+trxH0/Wnz+JqUbbfdtsttfR1U3rh9D1VfNxXXHD3wwAOZ2/p95fUyzlrvt/W9Xeu9Xnm7NXId+0b9/Oc/T2JfdxwbO3ZsEvt65qKPI85Z/1r5nJw7d24S+1pRX7PlczzuK+nH3VP6IPs+x96oUaOSOH4/+Mc4evToJM4752HDDTdMYl8THsvLA1/P7PsL//rXv868fSt7x2bVCfp1Za0fjfm+x3l9eOP3nX9P+RrsvNf5iSeeSOIJEyZkD7aJfA3tDjvsUFv+7Gc/m6zz4/T8eTKNvO7+uOZ/H5aFr5/dbLPNktifxxQfy32e+H75nu89fswxxyTxqaeemsTnn39+5v5ikydPTmJfzxz3OZbW7JMeH0P9Y/a/s/z7wc/J/O8sfwyNz7fKO5+q3r71fIIMAAAARJggAwAAABEmyAAAAECkrTXIRWsus+o7PX+d77yev1tuuWWX+yraQ9k/rqOPPjqJfd/IuD/un/70p2Td7Nmzk9jX0uT15cwau6+L9LWMebXVndLOmmNfY7jXXnsl8cSJE7u87fXXX5/E11xzTaH78jWMq1atSuKsGnGf//69459D3wM4y9ChQ5PY1+s/88wz3d5XmfhjRJFt/fOdd8zIet/6PPC1p5tsskkS33LLLZm3b4R/nL6W0tcV+17T8THG1yAOGDAgif/xj3/UPc5W8T1pfZ26f37i+lpfj5lXS1rkPdhqp59+ehJvscUWteW8mmNfF3zttdcmcZF+7p4/5o0YMaLufbWS73ntjwf+nKms197f1v+ePvPMM5P40EMPTeLjjjsuM27EPffck8QjR47sclt/jtOll16axP645X9H+fM+/Pwkfp78OSP+vJl6r4vBJ8gAAABAhAkyAAAAEOlomzcvq9VaHn8p2LhNlbRm6xnfzqcI/5WyH/fXvva1JPZfvzz44IO15bxWJ/5x5PFfd/mvDGPDhg1L4ryvBDslr3ymiKJfR3/+85/v9ra33XZbEvuveRoV56z/+sq3wJk/f37mvuKvUIvyX1e1so1YM2299dZJ7EsA4sfhLw3tyxy8rK//ivJttfzz6x9HI/K+/vaXNC5yieO857uMl7b3ZR++XWIWX9bkc8bH73znO5O4nS3Mbr755iT2bbCyyip8CZAvnfnpT3+axI0cH/zvaV/SUxZ+nHnHi0bKBv2x5u1vf3sS+xLPelucSdK5556buf4rX/lKEh977LG1Zd/ez/+OyivN8+/FGTNmZG4fa9bvJD5BBgAAACJMkAEAAIAIE2QAAAAg0tEaZF8n4mtM4hqURmtK/KU0fR1PfPnG4cOHJ+t8mxXvwgsvTGJfo/XII48kcXx5aV+/7Ov0fK2pr6X2l2P0bXA22GCD2vLChQszty1rDbKv7ctrD9Opmtj77rsviXffffdCt/d19L7FUZwL/jnJa8Xka+ROOeWUJM6qdfc5+MUvfjGJ40t+lpl/frPyJKt90do0UnPsvfe9781c/+c//7nufefVPjZyHoiX17KyjJe2LzqmuA1c0RyYOnVqEvua5GnTpiXxQQcdVFv2lx+/7LLLkvjpp59O4vHjx2eOxd9Xkctct/N4+/e//71t91VEXovU1atXJ3H8XvDvi6LtVv3z/9BDDyXxo48+2uVtG22h6ucr8SXKPV8/nleDnFf/H9d5+3NGmnVOCJ8gAwAAABEmyAAAAECECTIAAAAQaWsNct7lb7P4+mR/Wc+ifE1KXLOS10P2V7/6VRL7Pr2e74scy6vDiWujpTUvvzh48OAkzqq18X2P/aWMe4p2Xnr6bW97W7e39XWBjVxiVVrzMuRx7fqmm26arLv66qsbui/vrW99a5fr5s2bl8TnnHNOU++7WXpKf+Z99tmnpfuP3y/+GOx7lWYdF7uzPu516mvXe6O4z3zWZajX5i9/+UsS77HHHknsn9sTTzyxtuxr5H3Nsa959eco+BrjInWvn/vc55I475wQv29/nIxzKr5U+dqUsW5dWnNceef7xNv7GuS8Wn1/PlU7fx96hxxySJfrfF37H//4x0L79o/bX547rjvOO9bX+xzxCTIAAAAQYYIMAAAARJggAwAAAJG21iBvscUWSex7s3px3Yi/Lnde71vfLzGrz6tUrLbp4IMPTuK417AknXrqqUl8yy23dLkvX2/kDRo0KImHDh3anSGulX++fVzWms121lj5PtPf/va3k/iDH/xgEse1uo3WHPv6zokTJybxAQccUFv2NW6er0HMqzG84YYbkvjhhx/uctt77703ifPG0il5eRP3sJWk9ddfv7bs+3tuvfXWSexfK19vm3dsix122GFJvHLlyiS+8cYbk/iSSy7p9r49Xxfrj8m+vnyzzTYrtP+4L3v8fK5N3vkXPcHSpUtry/495/mcue6665L4LW95SxL7WtOY7/talK8P9ce9uIf1pz71qWRd3nEu7/eIr8ftjfz5Pln8sdkfO/KuhdBO/rXNmr/kzany5j7+/VR0/hird27DJ8gAAABAhAkyAAAAEGGCDAAAAETaWoPsaxV9jdWiRYvq3ndevWHe+rhGxW972223JbGv0/vNb36TxN/73vcy7yvm63S23XbbJPZ9kJGvmTXLX/7yl5P4wQcfTGLfVzY2efLkJPZ1rUcffXQSjx8/Pol9nWDc6/T5559P1vk6VV9r6vfldbKXZrtss802Sbxs2bIkjmv9/PvQ8z3E45rN7tw+7l8+adKkZJ2vVb3qqqsy95UnHouv+/X1pL7WOo+/fVbvX3+uRqO97Nshr+9z1mPwz7W/7R133JHEvi+yr00/+eSTa8v+WOH7IBfl73vKlCm15YEDBza07zzx+UX+vvxzlve+Kgt/rC/yHPak/uG+N/GTTz5ZW867zkLRntZFao6bhU+QAQAAgAgTZAAAACDCBBkAAACIWDuvbb7uuusmd+Z7/L7yyistu+8i1+q++uqrk3VHHnlkEj/11FNJvPvuuyexr20soky9iJ9++unsRoVtYmbtS9ImauVr6ft8+x6RResGm1mDHEIoRd5st912Sd74ev6tttoqifv16/qUDF8L6etLfW2kr1314uf70ksvTdb5Hrdf/OIXM/fllekYksU/p3//+987njfrrLNOkjO+f35eXXErxfnqexP78x18X/Pnnnsuif3v3rh2VGq8p3sW/xxm1b3n9eUuQ85Ia/6OaqRWupX9wZt9bPC17/E5OmeddVayrujj8u+tvGNqEd2d2/AJMgAAABBhggwAAABEmCADAAAAkbbWIPuaQF+TMnz48CRupC+yl1d7s88++9SWf/KTn2Ru62uQfa3N5Zdf3rRxNSruqVi0j2BZapC33HLLJG98rbqv+Y77ajabf71eeOGF2vKYMWNadr9Sdp2wv669r50ssq+i/HNSlrwZNGhQkjcjRoxI1g8dOrRl9+3r5+bPn5/Eixcv7va+fN3lyy+/nMS+v3Mz5dXYFqkLzNtXGepJhw8fnuRM3usU90HP6gHdaf496t///vix+eab15Z9vXI7+XH6vrsrV67seM5Ia9Yg5/1ej2vCi+ZNmc4x8P33L7jggtryXXfd1dT78n2VfS4UQQ0yAAAAUAcmyAAAAECkrZea9pdf9F8N+hIAfynqLP6rmAEDBhQa2957793tbd/ylrck8fLly5O4lV+B+K8pZ8yYkcSrV69O4naW0LSKb1nmY//VeVy6U7QNU/zVopReEnhtmllWUaTswb83fHlSI/v2yvSVXhG+1Gbu3LlJ7I8/cd74yyL7Mh7fmsrfV1x6IxV7H+YdF/3YivDHhzlz5iSx/7rXj9u/n/xX83HJhb9krm995VuPlUFeScXIkSOTOKsM0D83/rn0r+tGG22UxL7dX5YibUzXxo9t1qxZteWiLQw935Iy673gnzMf+3aWZVH0GOlbTPZUvr1gKzVSUlEvPkEGAAAAIkyQAQAAgAgTZAAAACDS1hrkRi6/mKeZdZK+jdvUqVOT+Pvf/34SL1y4sGn3ncfXf/laJl/vFddw+dZfvi6tp9aaell55usi/SVV82qOi2hmKzXPt/rycW95LRvhax19PWNcZ+nXL126NHPfvn7W501em712aiQP4zZm0pq111mtOn1rO7+tr7ntCRYsWFD3bf2xO68934Ybblj3fXmN1Cg3eonfIvX3veGcmd7En7PQr19bp4wdxyfIAAAAQIQJMgAAABBhggwAAABE2lpQ4utjy1Snd/zxx3d720brO+Nem0V6Xa5NXq/NWF4tYitrZsvC9x5tZz9Knzf+tc/rwdrKuuJG9l3WvPH1s+PGjUti37s47gk8ZMiQZJ2vSfb9gH0vdF8T7vvlxnXyvja30Z62XnyMyKvx9P3jfd9ZP1YvrtH177W8vr89ga8bzqrP9TnSaC1vI/y5F/6aBM08tvj89OeELFmyJInj94rPiU5e5rqV4ueo2cf1559/Ponj53DTTTft9rgkafTo0Uk8b968JPbnJGTluD9vw//u9fvOOw8kft78uP11AbL6lWfhE2QAAAAgwgQZAAAAiDBBBgAAACJG30EAAADgn/gEGQAAAIgwQQYAAAAiTJABAACACBNkAAAAIMIEGQAAAIgwQQYAAAAiTJABAACACBNkAAAAIMIEGQAAAIgwQQYAAAAiTJABAACACBNkAAAAIMIEGQAAAIgwQQYAAAAiTJABAACACBNkAAAAIMIEGQAAAIgwQQYAAAAiTJABAACACBNkAAAAIMIEGQAAAIgwQQYAAAAiTPNwqMMAAB9gSURBVJABAACACBNkAAAAIMIEGQAAAIgwQQYAAAAiTJABAACACBNkAAAAIMIEGQAAAIgwQQYAAAAiTJABAACACBNkAAAAIMIEGQAAAIgwQQYAAAAiTJABAACACBNkAAAAIMIEGQAAAIgwQQYAAAAiTJABAACASK+eIJvZZWZ2dje3HW9mj5rZMjM7qcnjmGJm97b7tqgPeYN6kDeoB3mDepA3rdev0wMokS9IujuEsEunB4IehbxBPcgb1IO8QT3Imzr06k+QCxon6YlODwI9DnmDepA3qAd5g3qQN3XoVRNkM9vFzP5c/RrhKknrufWHmNljZrbYzO43s7dV//8uSe+RdJGZLTez7c1soJn9PzObaWYvmtklZjaouv27zWy2mf2Hmb1kZnPN7PjofjYysxvMbKmZPSRpGzeOt5jZHWa20MyeNrMPd/e2aD7yBvUgb1AP8gb1IG86IITQK34kDZA0Q9IpkvpLOlLSa5LOrq7fVdJLkvaQtK6k4yQ9L2lgdf3dkj4Z7e/7km6QNELS+pJulPSt6rp3S1ot6ZvV+5okaYWk4dX1V0r6jaQhknaU9IKke6vrhkiaJel4VUpcdpU0X9KEvNvyQ96QN+X4IW/4IW/IG/Kmd+dNxwfQxATaV9IcSRb93/1RAl0s6Sx3m6cl7ecTSJJJekXSNtG2e0l6LkqglZL6RetfkrRnNTlfk/SWaN25UQIdLekPbhz/JemMvNvyQ96QN+X4IW/4IW/IG/Kmd+dNbzpJb7SkF0L1Wa+aES2Pk3ScmX0u+r8B1dt5G0saLOkRM3vz/0yVF/hNC0IIq6N4haSh1dv2U+WvqK7GsYeZLY7+r5+kX3bjtmg+8gb1IG9QD/IG9SBvOqA3TZDnShpjZhYl0RaSpleXZ0k6J4RwTjf2NV+Vv6AmhBBeKDiOl1X5emKspKeicbxplqR7Qgjv9zc0s3VzbovmI29QD/IG9SBvUA/ypgN600l6D6jy5J9kZv3M7HBJu0frfyzpX81sD6sYYmYHm9n6fkchhDeq219gZptIkpmNMbMD8gYRQnhd0nWSzjSzwWa2gyr1QG/6naTtzexjZta/+jPRzN7ajdui+cgb1IO8QT3IG9SDvOmAXjNBDiG8KulwSVMkLVKlFua6aP3Dkv5F0kXV9c9Ut+3KF6vbPGhmSyX9XtL4bg7ns6p8HTFP0mWSLo3GsUzSByQdo0pN0TxJ35E0MO+2aD7yBvUgb1AP8gb1IG86w9KSFgAAAKBv6zWfIAMAAADNwAQZAAAAiDBBBgAAACJMkAEAAIBIr50gm1kws22btK8zzexXzdhXN+6raeNGMeQM6kHeoB7kDepB3rRPr50gAwAAAPVgggwAAABEevsEeZKZPWtm883su2a2jiSZ2RQzu8/M/tPMlpjZU2a2/5s3MrOtzOweM1tmZndIGhnv1MyuNrN51dv+n5lN6GoAZna8mU2r7utZMzvRrT/NzOaa2RwzOyH6/z2r97Fu9H8fMrO/NOF5QdfIGdSDvEE9yBvUg7xphxBCr/yRFCT9r6QRqlzv+2+SPlldN0WVyzaeIqm/KlelWSJpRHX9A5LOV+XqL/tKWibpV9G+T5C0fnX99yU9ljGOgyVtI8kk7SdphaRdq+sOlPSipB0lDZF0eXXc21bXT5f0/mhfV0v6Uqef2976Q87wQ96QN+QNeVPmH/Kmjc91pwfQ4iQ6MIo/LenOKInmqHolwer/PSTpY9WEWy1pSLTu8jiJ3P1sWL2vYd0c1/WSTq4u/0zSt6N127skOlvSz6rL60t6RdK4Tj+3vfWHnOGHvCFvyBvypsw/5E37fnp7icWsaHmGpNFR/EKovjpu/WhJi0IIr7h1kiQzW9fMvm1m061yDfPnq6uSryqi7Q8yswfNbKGZLZY0Kdp29FrGGLtc0uFmNlCV67D/OYTgt0FzkTOoB3mDepA3qAd50wa9fYI8NlreQpW/rN40xsxsLevnShpuZkPcujd9RNIHJb1P0jBJW1b/P95X5T8qL/61kv6fpE1DCBtKujnadu5axlgTQnhSlcQ6qHq/l3fxONE85AzqQd6gHuQN6kHetEFvnyCfZmbDzWyspJMlXRWt20TSSWbW38yOkvRWSTdX/4p5WNI3zGyAme0taXJ0u/Ul/UPSAkmDJZ2bcf8DVKnleVnSajM7SNIHovW/kTTFzHYws8GSzljLPi6XdJIq9UJXd/eBo27kDOpB3qAe5A3qQd60QW+fIP+PpEckPSbpJkk/jdb9UdJ2kuZLOkfSkSGEBdV1H5G0h6SFqrywv4hu9wtV/vJ5QdKTkh7s6s5DCMtUSYDfSFpU3e8N0fpbVCmEv0vSM9V/vSskvVvSXSGE+fkPGQ0iZ1AP8gb1IG9QD/KmDSwtVekbzGyKKmd97t3psaBnIGdQD/IG9SBvUA/yprl6+yfIAAAAQCFMkAEAAIBInyyxAAAAALrCJ8gAAABAhAlyi5jZ3Wb2yU6PAz0LeYN6kDeoB3mDevSVvGGCXAJmNtDMLjCzOWa2yMx+ZGb9Oz0ulJuZHWdmj5jZUjObbWbnmVm/To8L5WZmO5rZbWY238yosUO3mdkpZjbPzJaY2c+qF4wAutST5zdMkLuhDZOOL0naTdKOqlyzfFdJX23xfaLF2pA3gyX9uyqX99xD0v6STm3xfaLF2pA3r6nSv/QTLb4ftFGr88bMDlDld9X+qlxlbWtJ32jlfaL1mN90rc9OkM0smNlJZvZs9ZOU75rZOtV1U8zsvupfPQslnVn9/xPMbFr1r6DbzGxctL/3m9lT1b+sL9JaLs+YYbKkC0MIC0MIL0u6UNIJTXuwaJoy5U0I4eIQwh9CCK+GEF6Q9GtJ72rqA0ZTlCxvng4h/FTSE819lGi2MuWNpOMk/TSE8EQIYZGksyRNadZjRfOULG967Pymz06Qqz6kyl82u6pyDfL4RdtD0rOqXLbxHDM7TNJXJB0uaWNJf1DlSjAys5GqXJf8q6p8mjdd0UTFzLYws8VmllyPPGJKE84kbW5mwxp9gGiJsuSNt6+Y9JRZWfMG5VaWvJkgaWoUT5W0qZlt1OgDREuUJW967vwmhNAnfyQFSQdG8acl3VldniJpptv+FkmfiOJ1JK2QNE7SxyU9GK0zSbNVuaJNd8ZytqT7VEnMzVS5VGSQNKrTzxM/5c0bdz/HV287stPPET89I28kbVv5FdD554ef8ueNKhOjeCz9q+PbstPPEz+lzpseO7/p658gz4qWZ0ga3cU6qZIoP6j+pbRYlWuZm6Qx1dvVtg+VrPC3z3KOpEdVua76/ZKuV6VO8KUC+0D7lCVvJEnVv/6/LemgUNJr2kNSyfIGPUZZ8ma5pA2i+M3lZQX2gfYpS9702PlNX58gj42Wt5A0J4r92d2zJJ0YQtgw+hkUQrhf0tx4X2Zmbt+ZQggrQwifDSGMCSFsLWmBpEdCCK8XfUBoi1LkTfU2B0r6saTJIYTHi9wWbVeavEGPUpa8eULSzlG8s6QXQwgLCuwD7VOKvOnJ85u+PkE+zcyGm9lYSSdLuipj20skfdnMJkiSmQ0zs6Oq626SNMHMDrfKGaEnqfJVQreY2RgzG20Ve0r6mqQz6nlAaIuy5M17VTkx74gQwkP1PBC0VVnyxsxsPUkDqvF6RruuMitF3kj6haRPmNkOZjZclZrUywo+FrRPKfKmJ89v+voE+X8kPaLKR/83SfppVxuGEH4r6TuSrjSzpZL+Kumg6rr5ko5S5WvuBZK2U6XmRlKtiH15RhH7Nqp89fCKpJ9L+lII4fbGHhpaqCx58zVJwyTdXN1uuZnd0uiDQ8uUJW/GSVqpf57QuVLS0/U/LLRYKfImhHCrpPMk/a8qX9nPUA+Z6PRRpcgb9eD5jVWLqPscqzTI3y6E8Eynx4Keg7xBPcgb1IO8QT3Im+bo658gAwAAAAkmyAAAAECkz5ZYAAAAAGvDJ8gAAABAhAkyAAAAEOnXzjsbNGhQUs+xatWquve13nrrFVqfd1/xen/bzTYr0ipyzftq5HE2cts8ec/hokWLLHODNhk1alRmHdDw4cO7va+VK1cmcaOv1eLFi7tc55/fvOd71KhR3b5f/zjyDBo0KHMs/nEX2b+/7dy5c0uRN9UzuTui6GvfiFYeIxpRdFwhhI7nTd6xxotf17zH648VPic23HDDJPbHtUWLFnX7vvI0csz0/LGlmfx9++ewLL+jttpqq7qPNVm/Q6Q18yTvtfN5FOdK0bzJO27NnTu30P5aJe859FauXNmtvOETZAAAACDCBBkAAACIMEEG8P+3d79skRxfG8f79y4WR2Rwuw4cyJXEbVxwIW5tVsUmLpErF7fISHC7bnFEBhmXl5BHPXuduoFzc6ianm74fhR99dDTf2qq65q5+xQAAAhmzSCPzByPzvTF7W0yL7gkS80uqpHXo5o5rmabMi5L5o4z7qvm/jSr53KBIzPH2K7q8xZrea+1cefGZY5VzBzr9lw/5ra9Vks9LnetR+rt6yt67hPV/VjiuItvkAEAAICAATIAAAAQMEAGAAAAglkzyFU9uWCXd9Es08j8SyVLCq+nzmaW45umsRlj9e233zbLlTrH03S7xmSl5mp127TJPu4ZiTnzddl79V7n5963Zcev/ZR7TsDJcsbVzLF73iHLmlav6ci6yJussbxJc2aSN2mTmWP3etdGN3nv/n98gwwAAAAEDJABAACAgAEyAAAAECwqg5zl9qr1ayvbnqY866Q5nGq+SPNflXxXb93RTeYRlyrmjkefX319zBnv7+836w4ODppllxPU9z4/P2+WY+bq77//Trelbfaff/5J30utNTMXbfMYtplBVrFv03bhcnw92Wptc09Rdt+otoGe5yV0nT7/oMuV/mObGeS1qOZnH7ruMfT5ktg2evulOY+rUkd8U3lkvkEGAAAAAgbIAAAAQLDViIX7uj8ra1Plft7Oypm4r++rJXfiT1C9pYAqlvSzb4X+ZDSSOydamu3169fN8vHx8b3bdtN06rXXKI5uO0YudFs3Nzf37sdoa2k3c1rSOdF2FNuw+2l95M+9L168GLat56jys7FGKF6+fNksf//9983yx48fm+Wrq6t7t12d1lqjIpXSgE+lPNomj8Ndj02WP6tELCrju2mq73fcnrbBUf0Y3yADAAAAAQNkAAAAIGCADAAAAASzZpDnzMBq/q3yXpqFcXmiSsk41PVkqlweXK+d5jdd5jgrRfjXX3+l7625QZd/jkZnjteaT89s8xjmLKGox/nNN9/cu17XaSbZ7bfrC59b3xefBdBjd88gqEo/p/2ayxzrdT85OWmW3717d+++uDah+10tZ1n5rCy1X1pSmTcV24buV29eWdu85s+j7H42TXkOflv4BhkAAAAIGCADAAAAAQNkAAAAINhqBnkkzRzrNL+a87m+vm6We7I4mltVbqrqh657yHqVZWSfA5ePc23y8PAwfX2s0Xx5edmsu7i4aJZ1KmqlOUI8HXN+9rJ2pLn4So3aqqeYR86ypq6f78neTlPbd+n97ejoqFnWzHFl29PUHlclTztNt/vc6v9n1nIPm7Oes25bn22J2V8dJ+kzCL3Tw2djId0vpfvingWbA98gAwAAAAEDZAAAACBggAwAAAAEs2aQqxmS7PWamdJ6ta7mXpYl1ZyevpfbtjMyS9NTQ7I3E7cGLvvlsufn5+fNstZBjutdHcfPnz83y3t7e+nrs7nqq5m2kZ89jOXOtbvWrp521Fv3+LnLzm31XFave8wdv3nzplmnGeRq/6B1kd+/f3/va3vr1Wb3IV3naiqvVW/94cjVz4/3LK1TnN1j7lrfkyd3GWR9ZmcJ+AYZAAAACBggAwAAAAEDZAAAACBYVAZZ8y1ZjqqaMVaabYrLOo+9ZmdcjclsPnKl+6kZwbOzs2b5zz//bJYrGaC1Zo7nzKHpOdG8nWa04rXWa6c0J6/1arVNf/r0qVmOdVarWbCRtUjXwuX8sj5ik3VLq1y/qLVH9drG5yvi3yNk7Uhrqq4hT1q97rHW8+i6x5orjrljXafnVmtQa43myntrzeUPHz40y/psheq5R43M6m7SyHrilXHQNN3+/Gs+Pfv/7H72ENqusuvlxmxuXKX9Sc/zAA/FN8gAAABAwAAZAAAACBggAwAAAMGsGWQnq7mnGSvNvrj6ni4f6mr0zUWzY5onUppJrmTo1pLv2ibXjrLMlrZZzRy7zJXW49YMVsbl2HRZ212mmmdcqqy/cfU/nWqOsELbjbYr5TKi0Saz6r3ndOlGn7vd3d1mOV7nak177eu/fPmSvnfP/bDS3pyn8qxEdhzVvlozx/psS9Y2tNawPufinhPo6furzyBoP9eT434svkEGAAAAAgbIAAAAQMAAGQAAAAgWlUHOahMrzaNoXlOzopqx1G3H/9esp+Z0evOFcd9dPUvNjul6995rqfWa2eZ+auZK9yVeH702em01K+ZoDeYs71zNsSk9zkomea1GfjbmbKOaQdwklxus5IrX0t9UxM9Nb93jw8PDZln7C1dHNtJ61+fn5+myXuc4F4DmkXVZ91Pf++bmxu/wE1O59tV7umZz3bMsMXfs5lFw4w0nttEXL16U/je7t24L3yADAAAAAQNkAAAAIJg1YuG+rh85FWlPeZjqV/vVnw7jcWrZFf1ZXl1eXpbeK/upfK1TT2+Su5ZZzEF/6jo+Pm6W3U/j+rNnNi1wT7TmLpUpXtfSTkbGJHrjAZUyT1Wuv4rvPfozv4bpo0fKzlf1XOp1j7GGaZqmV69e3ft6fS+NAZ6dnTXLep+plIw8PT1tlrUf0zKDeg/Tfi1rr9Wf5Zei+rmKn5tqpMKV4NNrG8vuuf3s/TzHY9Epyquq015vAt8gAwAAAAEDZAAAACBggAwAAAAEi8ogZ3rzXT3Zu55peR2X09G8lis79hTLcy25PFQsa/Pdd98161z5HS258+HDh/T1MfvXm89yn4e15Ix7zFnmrac0m/abLqep0wjHtuKuc+90yZXztsQ2Vt2nnmy59g+a3c3KummmeGTmeJraZ130nvLjjz82y7pen73Q93bTXEdL7vujTX5OlLuvXF9fN8ta8jbSvsT1U26q6ZhH12y60v2qPAczF75BBgAAAAIGyAAAAEDAABkAAAAIFjXVtKrUnHT55t5sXQ/NksWMluaJNJejWTInmzJY1y0xA7h0mrfb39//+rdOFauv1enQK/VBq6rZ0mx5LTlAtaQ6yD1cVlWvnWbZK59z91p3HkbWCV4bN7279gcuS6p10GOWXPsOrYOsuV+XW6+8Xl8b+8C7aN1ezcg+9XbhuOOvTOc+TbfvMz1c5ljbtKvRHF1dXTXLWd3/beEbZAAAACBggAwAAAAEDJABAACAYNEZ5MjlM3X9yExyNSOl+S3dlyyno7VxNVtWzUJm9XKfe/brMfT8Z9dS81t6bfXa9NQ2dp8P1yaXUHNybpvMHPfUI9dr8/r162ZZa5VuMrvncrVZH/IU21SWB3WZY/cZdHnPWOvYPZuyu7ub7gu2q3Lv1c+365sqnzv3WpdNr+TqdVuaQa7W6p7juRC+QQYAAAACBsgAAABAwAAZAAAACGbNIFdrbMZll5l083hXMsn6v9Uayro+y6lq3WM3T321JmJ8vat9C99u9HzHPKibW16vrWbLemrQuv+9ublpll1d1LXWPl6j6udSs+ruOYVKu3nufULP8Wse02WS3T1JM8mVrLnm1N1x6etjllTru7taty5LWslDr6U9uv6y5zNY/YxmOWCtkezaoNJ28ssvvzTL8Th1P3uesdkWvkEGAAAAAgbIAAAAQMAAGQAAAAi2mkF2uZ0sq6TZGZfTqWaSs23p/+p+ak5HjzNmVbU2bjUrpjTns5YMV2ab9RH12h4dHTXLMcur51rrPLptu1x9XNZjrGbJVJazfwptaNsq+dFqjerLy8tmOes3Ndda/axU2sJzyLFXsqV67k9OTtJt63WP/aDL8VY/s9rm9vf3v/796tWrZt3Ozk6zrG1bn8XQZT1nWf3ntbShnv2sPtP0888/N8u//vprs3x8fNwsn5+fP3hfNL8c28Fd67NncnQsou25t3571i+Oajd8gwwAAAAEDJABAACAgAEyAAAAEMyaQVYuk1zJJrlaxZVMlstzunqX+v9ZZkvrEjp6TnRfyAiOpXkuzWRFeu41e6dZapfB6rk+LreqdZC1jWY1K8kkb5Zei6qs30RN5bkZPe+jM5cV7hkdPY7Xr183ywcHB1//1rrHrr6t1nt397hKZh6329Uff/zRLGf3rJcvXw7dl+yZKF1HHWQAAABg5RggAwAAAMGippruKcWmXOSi571cqST9GUN/Wos/Obmfn6rTkaqe6YnXIjvGyrS703T7ZyGNVGRTuGo5HfeTqos19FwfF/PRiEU21awr14Pa9LlKz73+3O3M+TN0T5RtDf2NOz6VfRZ6Sy+q3d3de9e5qeO1jWlsQstXxs+/9mPa3j5//twsa3lLfb0rVZr9L25z5UTjeMTFt3Q8otPYazvRviqbhlwjh9X+IPtsVu/zD8U3yAAAAEDAABkAAAAIGCADAAAAwVbLvKksJ9ybMXF5sOy9lOZ4Tk9Pm2XN4eh00hcXF/duuzcrTem29hxUpzPX1+u1znJQWl7n7Ozs3tdOU577naY8w1zNb2lGUd9L9+Xw8PDr35pL08wh+mjOPSslOE23+xNVKU3W66lNUe7OVXZMo8+zbi9O+by3t5e+1k0J7J5JiPSeo2XcNKeqWVOVtc81tpml0fMf+++e8rfTdHvsoveNWB5QaZnTXpX7/GPxDTIAAAAQMEAGAAAAAgbIAAAAQLCoDLLqySNpJsXVKY35GH1fzWC9efOmWdbs6fv375tlzWhl29bMscsMuewNmeQ+ev6yOsj62rdv33a9t9ZVjlyGUDOIyuUEM9VaschltUPvUq0nusnp5yv90RraibtPVI7BnRvN9mv9fH19bCfuHqW033J1k2P9Yc2d6jMII7OlWvd4DW1madwYYuS2K9z8EU5lvoNR+AYZAAAACBggAwAAAAEDZAAAACCYNYPcU2NSsy/VusguW5bVYoz1J6fpdr7T5XI0M5i9vjdLU5nn/qmq1CrVXJR69+5ds/z7778/fscMvXaabY91kUdn8zRHeH19/fVvba89+eXnKvtca3/i+gvNgLq2MGcd5Ljs+uwlGvlMh3t+RJ9VOTk5aZY1VxzvYe7ZCOfq6ipdH9ugZqVdHe7qdf73338f/FoyyT7bPvIcaRvUZc3Nx2vpxkVkkAEAAICVYYAMAAAABAyQAQAAgGDWDLLmV1xWJsuwuHyXvlfMb961PtJczQ8//JDu58ePH5vl3377rVnO6kS6+pUua92TOXa57LXIMlnVueddbuqnn35qluP1c9emWu82axuaU3W5Vbdes9g3NzcP3U08QpYnVZr5dPnRrJ8dndXTPiS2o2p//9S449UsudJMctye9i16f3M0V5z1J/pad1xZm7hruWINOfa5jfxcufrY2u6y57G0n9L/rWTP7xLbwqbGMnyDDAAAAAQMkAEAAICAATIAAAAQzJpBVtV8aIXLkmo+bmdn5+vfb9++bda5OqWaEeyZr1zF/cLDjGxHmu3ryc8dHByU3kuPI7a7avtWuu2RbXapNplfrLaLeK211rZm1T99+tQsu2uLx9O8rcrakOt33PrLy8tmWe8ze3t7X//e3d1t1uk9SlVq8U9Tmx/Vtt2b96zUqV+rSm1i/TyPrh8etz96noQs+95bL9/Ve57jGSq+QQYAAAACBsgAAABAwAAZAAAACGbNILtMSeV/e2l+5fT09OvfR0dH6f9W85qV46xmjjUftslcN2r03F9cXJReX13/2NdOU56Zc7W415IbrPQ/PX3VXbTPiNvXrJ7WB3X74q51zCD2trFKdn0t7WKU6nVxtBZ5XNZ6tdVznbVH5WrjVo87u0ettW9xNb97Mskjz0n1WQmX8z07O2uWYxb+y5cvzbpq3WOXOe55HuCh+AYZAAAACBggAwAAAMFWy7ypyk8J1TJVWhZHtx2//teybVp2SUsB6Vf/lSlW9bWupM5zjFTM+TPbnD/xjYxUVPfTbTturzod+lqMjKy4Uk2VbY+Mx1T1tslsmve1tpNRRl4nbV8uBjHyPrHNPnEteq71kmImbppwPa5Y9q06/bmWoHPHPUdb4RtkAAAAIGCADAAAAAQMkAEAAIBg1gxydZrDSga5Ov2q/n82xaiWYdJsjZtSsaec1HPMHKttZpCXatP7mW1/LeeoamQmdE6uT8j2rXrMm8zFPzUuh9qTNe2ddrfn2myyzTjPvU0tjY5PslJuvdcu6+fcOOmx+AYZAAAACBggAwAAAAEDZAAAACBYdB3kTdLMSpZBjtMnTtPtKUBHeg6ZYixf/CyuNfc352dpLTV/t5kffeqqmePq9oClqTwTpet2dna6th3HcCPnFIj4BhkAAAAIGCADAAAAAQNkAAAAIPjff//9t+19AAAAABaDb5ABAACAgAEyAAAAEDBABgAAAAIGyAAAAEDAABkAAAAIGCADAAAAAQNkAAAAIGCADAAAAAQMkAEAAICAATIAAAAQMEAGAAAAAgbIAAAAQMAAGQAAAAgYIAMAAAABA2QAAAAgYIAMAAAABAyQAQAAgIABMgAAABAwQAYAAAACBsgAAABAwAAZAAAACBggAwAAAAEDZAAAACD4Pw6Pvo2zOtLQAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pred_cln = predict_from_logits(model(cln_data))\n", - "pred_bpda_adv = predict_from_logits(model(bpda_adv))\n", - "pred_bpda_adv_defended = predict_from_logits(model(bpda_adv_defended))\n", - "\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(10, 8))\n", - "for ii in range(batch_size):\n", - " plt.subplot(3, batch_size, ii + 1)\n", - " _imshow(cln_data[ii])\n", - " plt.title(\"clean \\n pred: {}\".format(pred_cln[ii]))\n", - " plt.subplot(3, batch_size, ii + 1 + batch_size)\n", - " _imshow(bpda_adv[ii])\n", - " plt.title(\"bpda adv \\n pred: {}\".format(\n", - " pred_bpda_adv[ii]))\n", - " plt.subplot(3, batch_size, ii + 1 + batch_size * 2)\n", - " _imshow(bpda_adv_defended[ii])\n", - " plt.title(\"defended \\n bpda adv \\n pred: {}\".format(\n", - " pred_bpda_adv_defended[ii]))\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/deepcp_examples/tutorial_attack_imagenet.ipynb b/deepcp_examples/tutorial_attack_imagenet.ipynb deleted file mode 100644 index cd2fffe..0000000 --- a/deepcp_examples/tutorial_attack_imagenet.ipynb +++ /dev/null @@ -1,200 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.models import resnet101\n", - "from advertorch.utils import predict_from_logits\n", - "from advertorch.utils import NormalizeByChannelMeanStd\n", - "\n", - "normalize = NormalizeByChannelMeanStd(\n", - " mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", - "model = resnet101(pretrained=True)\n", - "model.eval()\n", - "model = nn.Sequential(normalize, model)\n", - "model = model.to(device)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from advertorch_examples.utils import ImageNetClassNameLookup\n", - "from advertorch_examples.utils import get_panda_image\n", - "from advertorch_examples.utils import bhwc2bchw\n", - "from advertorch_examples.utils import bchw2bhwc\n", - "\n", - "\n", - "np_img = get_panda_image()\n", - "img = torch.tensor(bhwc2bchw(np_img))[None, :, :, :].float().to(device)\n", - "label = torch.tensor([388, ]).long().to(device)\n", - "imagenet_label2classname = ImageNetClassNameLookup()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from advertorch.attacks import SparseL1DescentAttack\n", - "from advertorch.attacks import LinfPGDAttack\n", - "from advertorch.attacks import L2PGDAttack\n", - "\n", - "def tensor2npimg(tensor):\n", - " return bchw2bhwc(tensor[0].cpu().numpy())\n", - "\n", - "def _show_images(enhance=127):\n", - " np_advimg = tensor2npimg(advimg)\n", - " np_perturb = tensor2npimg(advimg - img)\n", - "\n", - " pred = imagenet_label2classname(predict_from_logits(model(img)))\n", - " advpred = imagenet_label2classname(predict_from_logits(model(advimg)))\n", - "\n", - " import matplotlib.pyplot as plt\n", - " %matplotlib inline\n", - "\n", - " plt.figure(figsize=(10, 5))\n", - " plt.subplot(1, 3, 1)\n", - " plt.imshow(np_img)\n", - " \n", - " plt.axis(\"off\")\n", - " plt.title(\"original image\\n prediction: {}\".format(pred))\n", - " plt.subplot(1, 3, 2)\n", - " plt.imshow(np_perturb * enhance + 0.5)\n", - " \n", - " plt.axis(\"off\")\n", - " plt.title(\"the perturbation,\\n enhanced {} times\".format(enhance))\n", - " plt.subplot(1, 3, 3)\n", - " plt.imshow(np_advimg)\n", - " plt.axis(\"off\")\n", - " plt.title(\"perturbed image\\n prediction: {}\".format(advpred))\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "adversary = LinfPGDAttack(\n", - " model, eps=1./255, eps_iter=1./255*2/40, nb_iter=40,\n", - " rand_init=False, targeted=False)\n", - "advimg = adversary.perturb(img, label)\n", - "_show_images()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "adversary = L2PGDAttack(\n", - " model, eps=1., eps_iter=1.*2/40, nb_iter=40,\n", - " \n", - " rand_init=False, targeted=False)\n", - "advimg = adversary.perturb(img, label)\n", - "_show_images()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "data": { - "image/png": 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zKd9e3zeT7SJmvaXfBjLJ/jX66KroH58j511t9fxyIsOFmEUy/wn4WJJ2ftKm2xN5n46ZOX0wSX8Bi69s/unklwMux6z79M0512t2FfVrMTPvKuAqzEy8P7tMOZ7SxkVkugsze/GVmOUV/rG9bHI9ipjlIz4E3NeW9lFgAnhOcr8+AXx6mffrYuDFSb19wDeAD620zq62X3JP9gNvSa7xMzEzQ1+x1D1o06kvAF2YPjgGvGw59wAzG/t3MQvE9mA+3X5HkvYcYDoprzCLKW9O0j6P8TDlgTVJHW9P0t4BPIKZJb0H83zQgL2Ifo4lbf4WcG1b2j8D75mTvwo8a4G6jvSvtj52YE5778Z4xdYDo8C9wDOSa/RVYFuSdz0wDtyQtP/FyX5f0u4Z4LIk77rZ+5X+TksfSXnm2PN8FKgAP5Xo7V+0yTxbt72AXD+H4YNnY4zLi4FNbf3jeswzaB/wM8lxBfwAM62Dm7RpN/DSha7FavqtuACnQPlf1rb/TuDONmXdNyf/F4FfadtXmCGATZip++9uSxPMkgbzKf/rgP9aQKallP9O4J1taZdhFo+12xR0Q1v6d4HXLuNaWEk9l7Udex8LGFnzlP8Q8MFke1E5jkepk7Z/oG3/csAHrHnydiXn7Uz2Pwp8pC39BuCRZHvR+zVP3T+70D37cf5hluPYOefYX3OU7Be8B2069YK2/TuA31/OPcA8VH+xbf9PMTM9z8rwwXnqWIshhGzbsdcBX0u2v0piqCX7L2FxI+sajhr5b8KQx0VJ2p3tdSXHDtJmiM1JW46R9Ya2/X8E/rJt/13A55Pt95CQYlv6lxIZ88AU8P+1X4f0d9r6SMozx57noxz7olXAzI+4kaWNrC8Bv75AvXsw82UdAF7Udvyaea7xe4G/W+harKbfWT1cmGB/2/ZezFvxfGlglPwvEvf7FOYNXTBvjQPt+bW5e3PLz2IjZuLNE8FAIme7zDaGPGYx3LZdxyjxUuhL6mmXeSH5EbOQ6dcS9+s0xgNQmpPtROSYD3PvkYNxN1si8oFkKGgG08mYI8dCMix6v0RkjYh8OhlOmsEsxju3fSlMn7hmtk8k/eINQH9bnqX0YN70Zd6DhepeqI9twujP4TZ5/xrj0YI5esGxfe0p0Frfo7WuaK1b2gQNfwtjSILxWnXMKdKBMcROFCNt24159mfbvwn4uTn35QXAOq11DWMcvwNzHf5NRDafhEwplkbKM8eivQ1VTBsHFs5+BEu16R0Yr9/X2o5tAgbm9IWbObYtqxbngpG1sW37PMyCs7PQc/LuxwwrdLX9slrrb2PcskfqEhGZU/fceubGpSx0zrk4hFGadplDjn3YngjGknra14NbSH4wC53+C7BRa90J/BVtsQFLYKk2zsXcexRghmdeD7wa4yLuxLwFsUw5lrpff5LIeZXWugP4xWXW++OG/cDX5/SJgtb6VMzafzL3YKE+th/jySq1yduhtZ6N5TtGLzD6djzQbTI+hBlKAUDM9CceZrjodGM/xpPVfl/yWusPAGitv6S1fjFmqPARzNBPitOHlGeORXsbCpih1EOYRa3BhKzMov2FbbE2gTGyzhORD84p8+Sc61nUWs++DB0vH51RnAtG1u+KSLeIbAR+HfjMInn/CnivJMHVYgJofy5J+zfgChF5TRK0926OVY52fAHoF5HfEBFPRIoick2SNgKcPxsYOQ8+BfymiFyQKOctwGf0Sa71p82K6/8EDIlILnmzfeMiRYrAhNa6KSLPwRg8y8VSbZyLXxSRy0UkB/wR8LlE3iKGMMcxnfKW45BhqftVxHgipsSsh/W7x1H3jxO+AFwqIr8kIk7ye7aYhb5PFidzD/4GeIuIXJcE8a4Xkc1a68PAl4E/E5GOJO0iEXlhUu4O4N0iskFMEPnvL3QCEekSkZeKSEbMumlvwMSZfCnJ8gnglWLmu8pjdPeftNYLebJGMPEipwIfT8790sTjmxETSL9BTLD8qxKZWphrHJ2i86aYHynPHIsbROQFIuICfwzco7Xer7Uewwyp/2Kit7/MsUbVR4DfEZFnicHF0vZRAMZL/DLgp0TkA8mx7wIzIvIeMescWiJypYg8O0k/Xj46o1iVQh0n/hkTFHcfRoH/ZqGMWusdmAWMP50MXzwIvDxJK2OC8j6AIf1LMEMH89VTwQSivhLjcn0MeFGS/Nnkf1xE7p2n+N8CH8MEAT8JNDGxGEsiedhXF8lyE8YjNJyc41OYh/B8eCfwRyJSwQQU3rEcGRIs1ca5+BhmHH8YE8D57uT4P2Dc2AeBhzFBwcvCMu7XdkwA5TRGL/5puXX/OCHR5ZcAr8W8iQ5j+oh3Cqo/4Xugtf4uJhj/g0n5r3P0zfyNmADYhzHrnX0O49EB49H5EnA/Jqh8sXM6mLjF2cD3d2E+/vhRIsNDmDfrT2CC1IuYfrMQhoC/T4Y0fn6RfEtCa70f4+W9OZFvP8ZIVcnvtzH3awJ44RJypTh5pDxzLD6JWch5AngWJsRgFm/D6Oo4cAXw7bY2fRZ4f1K+gvmI5Zi557TWU5h2v1xE/jh5IX8lcHXSljLGWOtMihwvH51RnNVrF4qIBi7RWj++0rKsRojI/wT6tdZvWkEZ7sIEJX5kpWRIkSJFihNFyjMpTgbngicrRQIxc4tclbhhnwP8CrBjpeVKkSJFihQpfhyRzk57bqGIGSIcwAxv/BnGzX1asYhr+eWn+9wpUqRIkSLFasVZPVyYIkWKFClSpEixWpEOF6ZIkSJFihQpUpwGrIrhwute/Hxd6tpANqOoV0JyXTmiMGQqqBL5FS67dDMHy9/nks4STtDCdsAOOvjOf32f3kuu5p47v4sEms6Cw7Mvv5Ssp+hdu47d0zWmymMUPIuC6xLmcuwZHmbPY+N0dndyqLyHll9FBzmUstGBj8Zizcb1NMvDaLEoNwK0QKZgEVkt1q/t5+J1G9m43uVb3/shB/aD49mUx8fIZj3WXTBAZXQSW1vo2EaJJlaCZUNnZ5GJ8Rl0GBOKhlgDip6OHP0b1/LAg49iRxbNqMVMrU4u63L+BRfiU+fQ1AiemyMKq1i4eFEOz3No1OoQ2ITaB5SZaheNZBStZgw6pLuvj7VriuSzvdSDKZoKkIjIauFpB2l4PPqjJ1hT6uXwvlEAchmXZtBECxSKRWrTDZAYkZgwjNBa47oOITESKWwBPMGyHCzPI2M5eBkXQZHJOrhuBuKI7s5NtOIGuYxLaGmyVoOMdJPrs3l09w9pVW3qtYMcbNaJY5/SwACTe5s0KnWcADas7ePxA4fp7e1mZGwvrnZQcYjoIvmukLybga6IkcMVmjWXQiGP011HaY98T5HJ/QfJtFzW9OVRQR9BziEIAsZHRpj2I8JY4egZKtPhis6pNSRDGrbDtm3mwG3AjUniFmDnQgWT30K4GfMxd5KvvANKW+fk2Qrlp8OYD4OLTapxS1LfLLZDSUN5yGyzDUo3lyjfVjbfA20BrltCvkHM4jrHmzaLElCGXWW48VbYdSv0PQK7NkN5xw7+/M6t3OICHxtk+2t3sW0blHdBafBo8RuBW2bl3QVbgZ0luOlduxjaNsjQ0BbuvH8nO24ZYvMdQ9yUNPYz22DX7L0pwS03lbmZD8PQtiPi7QS2bC3B7WVu+bC5fIOtQXbdYhpWBsZ2weBgcm1/KWn3KcSCl7HMkaliTYu2Q2kbbIGdT4c7GVrRPnHdtc/XpZ5+snnX8EQxR6QTnggqXHbJAjxx//fpvWgRnqjUmBodo5C1KNguQSbH3tE5PBFUieMcChtai/BE1iJy23ii3+Vb9/2QA3vBsW3KU2Nk8x7rNiU8gYXWNkqffp7Qvk1s+ehIoTXYosGFMIa4GdLd30dPd5Gujl5q/hQtiyM8kdUOuu7x6KOGJ4b3jaKBgu1Q1y1sW8jkC1QmAyTycaMmkVi0LI3rOYTa8ITYGiujsXTmCE84GRdLFBnPwVIZLB3R3beJVmh4IrA0WdUgq7rJrbV59LEf0qqdIZ4o5VGh4YlGEDA9cpBpXwi1wpIZahNL88SqMLL2P/oEM+vG6YxtRkYPMN6q4doZoriL3p4Mk8Nl+tf0MVyN8fJZXMumWDqPS57bT75vHbmeTfgtTdyqYXtNInyaRZucyjLTFJpxgGOHhI0aA90efVcPMNYI2DPiQWATi0K0RTZbpBk16O7p4FD5AKKEMGpguw4qsrE8hZ1xcLTGafoMdPWwZ+8YUTNEK03kQKvaRIcKX1m4IogSQGPZLjPTdUBANGb+NE1GBNdTVKtV0BZaFHGkQEdoHeFkXFrNKlnPIgpDPNci0BExEV6ug1qljsSCioVIa5QCLG0eBnEAIijbRyuhZTep+SF2bBHFIaLB0gJa4YoDjoCy0LHQCEK0FuIIvIxLdapOGEQoZRPHAhLiui5hvYUOLQJiglgjukVHNkNTC+V9hwl8H2xNHMdYlqZzzaNMzVRxrQwZyyGftYhqPqrgcOn6y4nCESzVw9XFC+joKdCKYsbP81EiZByXrrzHmk0bKHb3cGh6E/VKlUajgfabNMI65BRTehzWReTtBpOTDXqnLArFHFJr0JICga+p7a9j64P0biyhwpBL1hZQbpGJmTqHJ1fewXvjuu0MfHAbN+3cRenWQUpbjUEELGhgDQK7hhapdBC4JeHSIeA2KN04T76nb6d05zZKO5NzbVmgvpvn7G8zBlYJKCd2xREDa1buZHvXdhjcxlOxBbbvMt+GPwULGFjl7XDrtsR2S+ofLMGOIdg8CDtvh9IW2LV1K7cMwW1b4cbNu9j2aWNgbX4NDG6FXbdB+fat3PLDHQz9Ggwl59uR1PsZyrALhu7cCdffwlbKlH+tBB/exp1Xwa7bdsLOLWZhn18rE3wmuShDiSxlYCvsvL3MlhL8uS5R/oUy7DjasBLG4CsB5Vtg+w7YNsji9+E4saCdmhhYW4EtQzu57iaHoe07KW/byleffopOfhI4+PiPqG8YJxdZjE4eZLxew7XaeOLwAjzxnKV5Yrqe8ISEhK15eCIGHbugLbJekaZegCewsew2ngh8Bjp62BOPEQUJT1hzeALDEzrSWJl5eEJpRB8fT7jaQrtzeEIL+ILW2sy262hEecTNgEgJyvGxPaFpN6n7IQ4WYRAikSCRYLfxhE54ohYGaBGaPnQULSSsEegIyBLqGB36uDmXsNpCa4UfxBAIIi0K2QyNWBjdf5ig6aMcTezEWIGmc2JhnriwP+EJzgBPHDA8UdpYwol9rlhTJLQ6GK832V+zlqW3qyIm69JNa/Vzn/tcxnY/wZOTk4w1pvG0jR+A62ledP1Psu/wI1wzsJaOOCSX7+Fg1eXfvvYdtrz0BvrzAZ/+/JdZk8uyeV0OJ+fQVejlvuFhshkHR7k4dh4dxWQ7cnR2r6FWDbGcXpTtEcVNLMfGUhlq9UN09vUycngPreo0h6fGmWlViH2PRjjJlVc8C6/RZL0Xsndqkm/9aBQVxtSrDTp6usk44Lc0GfKIipBYCNH09XcyNVEhDFoQCpEyfUhpxabLBhgvVyiPjQM2rUaduNUg153hsiuuYnL6EI3mDIViFzOtSXpyncRNhe1k2f34IRzJEiWeLCUaccCzXBqNFpFuMXBeJ52FfjIdNpWa0KRCpFvkvQw6itENh/F9o7iFPFOjFXzfB4kJ4gDHdVm7bg2H9g0jYhFpnzhWxHFIf/8aRscOYsUWsXJQtoUVadZe1EulFtAYqxGjabVaoDRe0eHSSy/msYcfRSnQhQrZ3Hp0Y5wrLnkGo6O7iRpFqs1JntxfxnGFwYs3s2f4CazQJo4UA/lubM9ipBnQmprAAhzHpRlN0dHpkXFtYhTFYiddXV14noPQAz50lDqYinxc8kxOzOA4DlaXS7PuU609iaMsolYNxOeeu55cWU/WTUOaMQyxlo9NK5ehdCILBG2H0legvJAX7FShzSNyJlDasnCbEseWcU/dZg4M3lhiiwu3DSUXdifGAJ2Vea6nsAylMXOOchlKNyeVjnHELbT1NYkN2W7B7MLMxb61XZC5gp09GBpaWU/WpeclPPHkU3nCcTU//eIT4Amvl/smhsl6p5knHhlFRWeQJ/xJerJL8IQHniQ8IS02nN9JMZMp99JrAAAgAElEQVTwREVoSoWIhCfiGF1v44mRhCd0jB8HuFmXtf0JTyiLKPbRWhHN5QlxUJaFpX98eGJVeLK8jMK2lbmgvk9edeKgwA/wii7KzuAqC4k1WDYt0YSVSRq1kFalTk8pS5ctiI5pNRTZTg+xhTVrN1IdOUAmlyX262QVdNk2a7I29x7chx08xvceeoRWWCSPIrQDyMR0FTvpVRmyWYveXA/rOjaQz3WhsOjuLuGtsbEIuPS8HLkL9mGLYnJyBGVDtdng8ORjxI0maipDHMfEuklncRP7Dk5Q9PIE+OZtQ7nEWFgi+M0WlsTYMdRjH+VFNP0WtjRxW03E6cEOLLoKG+i2bKaaPnGgcJSFo4Q41kRBC6U0Spl5JEU0iEWEJpYmnqwlVE1AUVeCDmJEuSggchR+WCfQLQIiBEEsDy0a1xZsK0sQRFh2FltBEEVYWcFzssSRjagWOtK4eY9iscjkyCGiSBAx97UVBBR0AdfBPDgcTdTyEC8maGWoNH16rQEyazPUWx4HHysTWxlKxSwHxjRh6EAsNJsN+rt7sPuK7JqZwVaKVhwQRx1MTzXIbFzH3j3DZIZHcPLD5KwcYfFxcLIUh2vMNITWjID2cARynRlqjSq5TJaeXAE3p+juWr+i/QGAW4Eh2LnTGBFbZh1CW6G0g2OHBduHEhfDNihfz7Fuou0s4DY6DgxtgaE2q+QMrxBZ/i2MITNf2uzGbUcP7LqlzK5Zo2p2WHAWJZ7qKSwZz9iuQbh5K9yyI8mXtLM0CDtu46hBvLkEfWVT5kYo7TT3cXCw7Rxl2LkDtmw1ttgpHhE85VgN9qBXUNiOAoG4nSeCAK/DRXltPMEyeSIrrOneSHWijSf0KeCJvj48sVaOJ7Ib6HaW4AnaeAKLIGzjCXsJnqBFKBEowXYSnnAE2z7KExIDEmFl5vBEbHiiUHgqTzRaAYVCAecs4ImuZfLEqjCyenv7iKMQP2xRbQb4oqmHEXEcszZTpDI9TqACxBVEafKFLM1mjUgCpsbH+eq+ffzKG1/P7R/9GFY2Z1Z0tRRhLaTZAOIY0aBdG5nysdQopd5uutwSAxdeypfu/DaHRqbIRnn8uMnTX3IV9377m2jtUZODWHaIVyjgui6XXXgx2Zm9DOR7eGh0hsfLMxBb1OsNCh05tFTxHJeubA63w8XzPLTViysRL3jG0/AsRba3hG07RAKecnELHWy4uMpE+CjhTJOx8VFqjZBqq0F2U4nv730QwglcgfpwgzCjybUyXHHB5UTKA4Q4stEotGiEDEHUQmuNbVmE4qHcLNqOEC8A30GCFmJZuBGghKzn4vshsQ82Fj4hEoPrKkIdEgVNFCASEccgSlPM5DjcOoQAtmsTSUTgx2StTuJ4GAjROibWxk3uZRQj02VQijgKcewMYSME5VCZajExMU2+q4POjh62vPAqfrjvCeJClkassFWEUkI1inGo4mc8XIlRoYByCJWFUgEdnRlir0qsbBqisCxBCCkVCkyELVRNiFsttAghEWs71jE+UcGvNalNtYhcjbN7dOU6Q4LSrfDIDtjyNtj+ru0MzsZmzQ4ZDrVlTgysZZH1traMg5y8gQXHGlgrgQUMrHY8xXFUwlyHXcAdia25bW6mBEOwc8hs3jLPrHOvAQbKyS0pAeUyOx+B0uajRu1g240pl022LYnci96zoeR3vN7BU+wpWw0rq/d29REFIWHUYmq6ic7M4YmJp/JEYzk8oUOadU4tTzRWmCf8E+AJlfCEFSF2QNh0EFmAJyKwtOGJOISMcyxPWBIR64QnsoYntAbXswkDwxM5+yhPxAlPiG14YnQxnpieJt/qoLN4dvDEqjCy8tkMjVqVWj3EFptANJHWxBEUenpotQIkBGKzQFeMplINwI7wWxX2HBymv5RhYEM3lVrEtB2DbtHyI5rNJiKCY2do+jFIgNcKyfR24cUNdNTiRc9/Jt+4535G980QOCGup/DigKYV47hZlFLYtiCOorenQKwz1CWiomJELJrNmDAS+tau49DBJ8B3qUQNiCKgQagtCmsCpp84gG1ZqKiOpcEnxlEtvFyeINQUC914YmEHEeucLrzCGromLF71tOuZ0jZ2Mv7e0dWJFoue7nV09N1PtV6jOlljplpHBz5RDIQWURAa76DUcexuXMsjdF3qwTCRUjStEM/LUp2qEWctKhPTxBKjtYZYUCK4uSx+LCjRaIG4FREQI5bGtgRQiAhaC57toHScxAkEiJg3lCgw9zmTz2EFEU6oURaEorC0xlaKOD5ErDwOjY5Qbw6zpncjg72bGJuZMLEKcUykFVoJjRBcGwJLo7QCCVHYuK6LUkLB8hBf48cxlhOAgoJlU5kKafgWWDY60ijbQjkKhcaxNBEByvbQcbxifWEW5XISkP5bsG3bNnbcAlvnxkDNwbK9IbMZlwoiP4dwjEfrRo56qwZNsP62W1nYKBlavO7b4CmG3pZScg//z1PrPWKwLCfOavbcx2vlnEIDq/3DgJVEPpuh2ahSq4RkMx61WBMvwRPV5fJEq4moH1OeaIYoBzQJT9geYaeLHw8TRQvwRByjWZgnwlme4ChPWMrwRCb7VJ5QomgG5t4tyRN65XlCrOXzxKowsrBdxitVJA6wCjmiWgVRmkAFDKzpY6K+l9iFMFDk8kV0aHNwej/5DofJiTEaNY+pxjCvfukNfPLLdzJR1+R0k6iumakZI0CsCD8TYDeE6UqTTDe04iaIRd7L8oKfuJr/nP4elWlQkVCzFFnlEmY0lqVxspqWrpF3FRXtoWLBbWpKPb08OfMYYZBjzdouhkeyEMXolkNogYNN1tKcv67ED0em8P0pstk11JtNkBArtiis28jUnjH2HZpCt1pU/Dq2fQA7Y7EpupxHn3iQjG3jihBlbJyMh9P0yeS7mBmrUsg7VKdaOHaeVtQASyGxRmIbhYVtuTixR6yaNJ2IrO2S68jRajWIWyEbutfj9XfQOL/KcHmCelSjUQ8I/Sa5zgwFq8DI5CHiUGPXQAeC7VrkcjkaNMgqBcrFEh/LjoilhqKFq1wiHaMthScKVbSZmpwEz6YeRngqJrY9HNuiGBcYaUXEDZ8JZTFWfhzLEq796Reh4wZ7dk+QixwCv0HQABVHOEQEOkS0hQaEEM+2aDVslFK4VgRiof2Y2FFoT2Ep4x6PAdEKP9BE4hLFTZSnkCCEYEVDTwATmP6uge1s22JcTUsZWAtjK8dEnLfhtu1wY7snq837cePQELcNDZ3oSRf9AHJFMc+wankIhoZg6CaO35gZ4lgjbK4HaTFj54zEks9+TnriKM0G3q80PJfhqUlEG57QtQqiT4QnXsEnv/yVeXki1hFSmJ8nisXCSfHEntXKEzIPT+iIDtfF8XK0/OPkiVhjV06QJ+wzwRPxSfOEHYbQWh5PrAoja6C/jwfvf5iuCzbT3D/D5ESFUClwYwY29HPwvt3YKsZ1Xbx8jgDQ5OgoekT1EL8FT+47yAuedRlT1Sqh6xEHeWqVCWIHJuvThI6DG9sE2RyNsEFPEBFNNnAyWTJeTLenuOGFV3L3A0/SqMzgYBPGEVkcPOXi2EJR8tRbkImEehwy7YRs6OtjfHqG6Zlx8kWLRqtKxsqiXIdsFNMKGgRYFAsltH4Uzy5B7NPZ4TI5WaGpCvTmu6lnJ3CaNrnuDJVDE/Sv30i+kCErIeu7ChycqmIXi3Rnc4RxhFso0uV1MRVO09nZz/CB3WTyoCWikO2gVW8QS4S2YjwrRyXyceIcPbqD0fgwVuRQxCNfcnnogcfhsSblkQmjiKIJY03W9ZBag1o8TpeTxSt42J0O4rh42QxhHHHdC34G18mhlaCpo7Wmq6+PXO8AUaNFFEWUa3uYClpsuLCfBx8fpkkL0RCKRa1eY43XQTNoMN5q0J0rENUCRioBuaLFt+/7AbZf4eU/dR2jUxP84HvfZ6QV0DVZR3CxtCLWPpJ0lGYjwo8jbImxRROqGOW4NKKAKAoIVYYojtECYkc0mjM4BFiWYLseQTPCUvWV7hKUh2DbjSc/lrd1+w7KTomdN3LUgNgJbIUbv8JTArwB2A63LeG+KSWeGoChUpmhd3346HQTLMzJt5Xhxs2cmSCfpYbYkmHTEsbIomSmfhhcqMx8Q3BDwJZB2Lnr6DlXFU7cwNq1Ewa3wC3bd3Gzs/KurIG+Ph48NEbXRSfHE1uuvIzp6SpBxiO2E56wDE8UXQe3Pg9POFkyuWDZPBG2NJl6TN3Rx/BEPA9PuGFMGJ4hnti/m0xhEZ4I23giPIzlOBS0R+F4eMLzsDscxF6tPCEnzBNKBMf1aM6EuNJYlt6uCiPrgvOeTl/PBThdHVSedoDLJzvRls3kxDSdvZqetRoJMjgFi6YXEkYxWjUoFl0kBy0/z94DB3nBs+CSjQP84NBuOu08OraTwEGPlmhcFDNBk5zlIrEQNUNEtfC1hx07KCyuvPgihqeaoG1iiWjVm8RZIWopgjggaDVoxj4ZN0tOW0xOjlCZqhLqFjm7A1s5qFgRRgERNnY+j3YgjGpkcw5+o0YcWlx2wfkoK2aq1mRjX4HuwkXsPzTGuoE1eA812Lz5Aur1Kuv6L2DXI9Nc98It3PfgQ7TqDayuDJ7TyWUXXYbQxLIUXb0d+K0Yy7bIuln8RhMAWyCINBmtcRwLQovYgQl/ilKmgCMOURRw3sb1jI03ifwYsEBa1Fs+Pev6qExWqU/71JRPrGcIdYyTtSnmstQnHyeKtPnaWGK8DOQ7bJTtIVqD5aGDkO6OIs5kyPM3XEuj5AMg+Q6sOENeWUROmVIrIqcadOXz3PPgfTiuwvMs9hy2ufuhr7NhzYW8/KU/zb0PPEC+WCB2DxNGtpmpIo6JxCOIFOgAFVuo2MLCwxYHz8pihxYtrc0cX3YG0RFhLChxEScCpQjCOrj5FesLpxo7fhr4avlooPZsoHeZhS2hZdh25TZjYqhcOsbAWgw3nsmv6pbySiV2Q7s4CxpYczO2Y+dZNu66zHswmOjJzdtW3sCCU8gTz4CL1w/wg7E2nogTnmARnmgunydqrSaREy2LJ0TOIE+UluAJUp5YjCeUG4EoYuUTLJMnVoWR9fjex/jOXV8j09tJ3HI4OPYkli1UK02mD48SZ1soK8AvNqnqDLGGZ1y5Ga8jj467UVeVwI14eG+N5/3U9VxZschm8kzXRyk3xhgbPYiKIyp+BV1rkO/vJbCrjMgU2SBDt+Pitho4YuE5NdYNdPLA/dPUVJaM7dFQIRYeylLUWlN02yE6blKdHKca1whaPtlCBm1ZaE+R83NEKqJYyNCRtZCiRbaQ58orL+c73/06F65bQ1+X4plP+0mef93z6RKb8/rXUfObxGEMaLxMFhEBiahWX4OlXGotnyCKeWz3Y6zpG2Bqappm7RDVap3LrnweI/sn+dHjT+DlPDL1HL72ER3hCDjah0gRah9bcuRdi/61F+FJDS1NGg2wbUUQxcRREze20FpjWRZ+o06sY3QYobVFVjJ0FRycYoHySBURM1+IxMlcL6qH+miZLs8i0BYj02Usrwpujsfv/Q8z4ZyKyQiElk0xm6VZUahmHTtrETYiJv0ZLMsimxXyjovj2cwcHuPh0TG6s1k63SLPu/qFNBoNOnJFWl6EE3fRkVeUNg4QxhHTtSqN0Oe8ooOb9wg9wZmcpp6t40cNbCuL5UY07GmKXgd+UMdSycSp5wq2cHRYatV4WVbn/AVPcXydQjEHgV1bho5G0K8kjqdNsxdk6DTIcZw4ZTyxv8bzrkt5Yjk8kTvdPDE2QZcrZydPZJbHE6vCyOru6SAIG5QPTONZBcSCWAtexuKZVz+P793/NQq9nXhukWy2kyiEB/bvJnIyPP7kd2lW6ly8Nk/4tI286IZX86mdn2bfzCS9rkPG8ugu9qLcHrq7unE7BTs/QJebZc3aZxMG4EjdBC26FiVHY9uKVl44cGAfEzMVWsrHp4mlbZqtGq6VZ9z30cUMVs3Gdh3cjEe1EpAvWlRGy9zw8hdz73d/wJve9qs4ysd2sqgYfvOm1xM1avSvWYNS0F3IkbUzjI6OUlrbiy0OURSSsY3yhmFMX6GbFjFBEOB5HpdtOp+WH+N4Ltc86+nUGj6PPrmXu77+PXJ5h+/94D6yxW5qYxVcbGyxEa2wxEw26gcRXs7Fs2wyVgcaRSQhWmsUglgWgRIz4Z1jYeL7FCImXCmOI1S+SD3ykzsYE8cxnmXj2jYSQhjHTFciAtXC9Rzqfp3OriyO1iCaWJSZrA5FodjB9MghSh15pppNwiCk2fTpKa2hEk5SbQlSaeHEEUHk42UdxqtTbNo4SHfX+ey85+Nk4vXgVIilSVQNsD0Xr+CQyXkcnu5E8jYZlSHvbET12mBbWJk8+WyB5toqQUdMxnbIWDmCeGwFe8McbAf0jTB025JZzwYMUl6V8fZPcWDNTos+xCkxMEqwOgysE0R5iBU3tI7wxJ5pvFzHOcQT3+dNb3vn2cMT1qnhCRVCGIVMV/RZyRP+MnliVRhZlih05BMHIY2oQZwTdABxGFIPauYTUxXguBqtA/x6gJfrpLNvHY88eoiaWLiuywP3/YDXvO4XuOGnX8y/PLSP+790Dy4+F1+YI4weY3h0hHw2R+nSZ7J7ZpxmdRgnCOnySmQLeTIaJkJNZ16h/QYzT+zD8lzWFDI4roWX72Tjml4cq4dLOvrYcElAwe5Hi83Ahl66uvr5uVf9JP39G1jTkcN751vIZj06PY8wDNCxj+XlsCwL27Zp1hpkMhl8ZbPm4ssgCLAsFyuO8TFvBzqKmKk3yWU8LCdGRxFuTlBuSBzHlLp6KbgN1v1EL1dvHqRSq1Jrtjg0WuHWv/4wkxPTZLwclqWwbYsoiHFR1KdrhD0tIp1BK8GvzqDDCDAzAtuAjsEONFoLECFiYUcBaEXOytKcnMa2TH7LViAKL5shaFWxbZuejgLjtQlyHTmCiTpBo0FsK1yliUSjLXCUJpfJ0513ybgOyvdZ01eiuq9GsaObscMj5G0FShDROJInn/eYmpyiOv5dSrkH+YnBaznvqqez82v/Quw3GZsZo1pr4ER1Mk2X3p4uJNIcPHCYVj0iDs3cYR0dBRq1JjoOib2QqB7j2g62t/KB70ewjXPGwAL4J0pLfgU5uMXEAy0VU7WcVXaWi6c4d2YrHjo19S80MpvM+JDM6cDRbxRWw5wJbVgN4hzhCR3SaNTOGZ5w3/kWckvxRDaDLzZrLroMwhXmieYZ4Akr4QllvlZ05OzliZVfPwTwfeNFEXHMUi5aI7YQhUIjmCRWIUFDoyRDFNr4jZgn9+0l4wkSCjnbpbu7n85sCTvOsXn9lXRmLfYfHuGJ4Qm+/s0HuPe+Ecpli/ExuKjQgZ6cIed2YllFpizNWK2K1kL32jyBYxNUPYYnhZEK7Dlc4ZHRJqMzMYeeGGZm727GDu7l3/7jC9zxH3/NpP8ow7X76Rio85zLN3PZpnVs6M6zrruTXi9DJpuhkC9SKHTgOlnEdrHsLPnedWAVsJ0cSrvgFghVFu0WyWT70OQIY5eevnVoO4/K96AKa4gyvYhXIiBP7BTJFLuxLZe+7m429Pdz3oZ1XDjQx8+95hX8zCtfTqhjenr7cDIeuUKWomNRtGwiFTI2WcayBL9eR2LTYZVS4AhiC9gOIjqZsC5GPAffAjuXodbyzTEsBItYFJbnEkZ18zYU18g5WTZtOg+lIGhqfGWhdYQdx2CB6wiepfA8D2JBopj61ASaCDeZQM+JHazYJY4EHTfIuhZBNYS8g+8XuPf+u7n332/nqvMHueTSK6j6TVQ+Q6BstOXg5AIiJqhb01g6xlYhjhOC18SnipMLsWOwIoE4pljwVrpLrB60Tbp5QphjvRyJeUrisZ85ZOrfuWOHMWhKJV57/fYj5y6BCSyfBydjB21ZzpjZ9hP8AHA5hRK7uQxJI7cnax7uOHrNh47OobooVkfI1GnHOcMT647liYFZnnANTxS7DE9oZWNlDE8EYdbwBAlPWIvzhHMCPBHpaGmemKyjToQnZBk8saGNJywLrZvYUQxWjOsmPJH3iM4ynlgVnqzK5Ci2lcUhJgRUbBFFEBRC1CQ4sUPGzVBr1SlmLKpT48y0IvLd3diWmVitb0MR6VzP3d/+Ii+87tWU7E6yWRc/iJnxIyq1FlKLgEmeF+f56g8exXUsJBJ6urIUCy5+b4P1XZcTzBxkxp8htiK0b9OyLLo9mzV9vYSTkwy+4PmA4tpXv4prn/scDuzfw+UXn49Cm4WSJcRxPPOJrjafgWoFsc6AcrBFCLUNUUzsJoqHwnEc/DAAMV8lxlrQ2mH/vlGmpybo7Oxk7ZoN7Nuzl87OTpqNiHvueZD+9QNUpqa46spLOHzoIANre7nk/A42rl1HEDd5xUtfwujYOF/++t2EsVBrzeC4GTb1rOO+0YdQoc1YZRyxe4iljmVpVFOILYWD+WnELK0QgeWA67pUG5MonQMNlghx2KSYX8vUuE2r0aLSCOnqzNLbm0e5HhO1CdzAInYxn0uHGqeYI58vcjCMODxdw7ZtLBWTyxfp7M2RGfWwlAILdEvh+wosF8t1KCLojimoexycdMhPT3BoeD+vfc1bGa3t5+4HvkogNlLI4NdmEMsmQkMIYajJeR3M6Aoqr6hXfbLrLaxsA3tVvLdjZl+YZ/LLM4oTiEk6xhFTMlMAlMcGKf8/8t49So7rvu/83EdV9XOePQPMAJjBiwCaL4kEKUqURqIl62HZsQ3Kke3EdnLWEezE9q5W8TrncO0QjB3m7PHZ7HpjZx04XifxemM7srCOHcnUWitKI1GORfAtzpAESbwGM8DUPLu7uruq7r37R9UAgyEAAnwB8v7OwWlMd3V1dXXf/nzr95yeysYBhZyfe/jEoex24l8eOD988MF1SfQhcHCqdkmxsdZm6hCvkz51iX4Sk+s+40NcRqg9eJWdC9Z3gZ04TFbKeWULD9aorR3xA8DD2Xuu1Q8wPXXh/F1NI/8bMv76Nlhj6Rxaf3dy4kPvfQ8zl+CE1gHCWpx1WJVNtLW2ANrDl4I00SAtsuLDOk6kaYq7BCeWlhfpzzlx6kqcmJ1hdPjynOBynIivkhPpOk50QqStvi4nBobXccIorM7yzaxx6DVOWMPiwncXJ24IkaVMg/q2Kqup4WyjhXEOYzUmcrzSPkvbd+AClLOY2NDqiKy/hfZoNtpErsPAQB9eUfP4Y49x110fIG11GOrz0H6JVitibr6JdQIkuDTCGEMztUgp6S62OLu0SrjSYmTPzcwvrXJuPsni78pjYFMFvJhKX5XhkRF27d7HTDjH3j07qRYkt+/bjYdFYjEuG+mAU1mCo3M4FE4KoICUPkaQNwnNvnRCCJwVJKkFoZHSwytXWTwzy9HHn2Jpucni4hkEkiAo0zGOMFykUukhXDqH53msrq7yF49WSGPDti2b8IRg795xTBqxpdaD19vP+PAQm7Zs5c+/dobF5ZDlgYiRwX6Cep2CVyNOYLk9C6LLUjMmilPKw/0UGksYYzDGELUbpLZLl5iuM1RdFyEUAoHSEt9TJHGb2BlwEJk2T599EeMpVCdFKgtO43AEKqBIEZFa2m0HwkMaS8HzWUhX8QsBWmuw2eyu1DmQgqBYBOdwRlH2AyLTwvV2cNrSDBs8+sU/YvP4EB++4wcxOmSpEdNuLVEqGlqrWd8TIaCcJ406ZSgNa/yqwksV/g3h3+X6C6w3aOt/eiaPhEzk7dXPC6xL/TZtVDPrtjv8C5OXrHic2LD5Ze11lNKhKz/8+rbek/SR+69KmdXWH/G6Lgvh5LrT86YP7A3YDRiqXDNlGtS3fndyoqcgGcw5gTU4AaAR5JyQV8EJsmaeSWpxqUIF3yWcEO6qOPHMek6IdZyQAaU1TqxcB04Eb44TN4TIGt+3i51jfcRa43UEc3PzdJ1m8rnn0UsRqmSJxQqOXoxIaSSL9BRKxM0IF0OiEnbu3kXr9Bz9xWH+85HPs+PWe/jM3/kBuknM4kI2zPKxJ45x7NRZonSFFIeQEicgcSkKzblGTNAzyDeefBVXFvQUe0iCBNVe4patt3Ln3jv42H33smV8HJUmVHt7sM2VrARVS5AaLRSWzJWtUDiZT+rWHgKN0B5K5tPVhZ/dWgvSofDpdA1f+tL/w8vHjiOcYeuu7ejBXu68ZS/9/VXiOGa11USrIvPhHB/b/lHKfonjp0+TtCKGhoaZmTnN1u1jxHGHE6+ewqwGbN26lb23CJ566inue+/HORfOc+qVaZZXQ156+RjzcwavK4mlQwiBUqukzmP59HG8GKRWlIpFSuUBCr5CtLrcsetWCrpIEJRRXkAxUJR7qmzZsoNYF2l3Q2Ik/cNldm5fRStBZX6WRpRAmhBFHVRvgRURQpqAs3QEFApF0qalsdoh6SqsiJHCRwuJrySFckAqHc12yK7BvZxdaFD0LMl4k3QkobugOTG/wEL4TQYGHXvq9zLSV+T5F57BeJDGErRFK2gbh5qNcUGB6FiK8mK8Lf71XA7X3a40cPlK9tBDh3nwwcz3MjUB9Ul4oFbj8MMh9YM1OFCjNhleWRCteZ3Wg/6tGP1zJTsAtekNw53fqB26SoVyo4iZjV3nb4RjuoxdV07IN8YJog59Q/3Y5gqZK0eiVD7aRkqscCj3OpyQPricEy7nhDN86b+8jZx4+i3kxPbbro4TQzcIJ6Y2cEK/OU7cECKrXAmwXgXhFGkxRSQKz1i27xmksRxhhUNaD+sUHSNoJxpVtsTGsujadJKUoZ5hVH/WSfbUiRPc9ZEfoOEZZJIwUNSsri4z8Z7t7N7Vi2nEKAvS8zDG4JTESovSUJJVYluklHpUKmXSoE1tcJgfvP/7ueOW2xkdqFHxNFqliG4TP1AYVKbSbYoRDoXASY0VYAEdlPP+ID54HiY1KM8Dm4BUuDQlnF3g6DhS2zYAACAASURBVBPPMDu/wMJSyNbxbWzdupWR0SF6enqy2UrOUfB9EBZjLTfvGsUKUAh6y2OQe8VGR3oQWqH1ELt2bCVud/GCgL7B2+kb6EP7AU8+/RQ3jW3iyaeeo9tWxNGrLIVtkF1A0TUFigWBcD7tThsjDc2oS5pm8fXxrVs4ffIk0ktxUiCEwpkOvZsHWZidR6oKSRxTqXpsu2mEbrNNuVymViwzVBQo6aH6C/SWayBTer/nThxQLhXwkMydO8HY2BimEmC6C3Q7Bt9a4qhFb18BqRNsschMskQr7tBxilpXIPwObljTWQQTG6ImVJeX8EXCrbs+xtPurwlPz+LSMuWyohwUEHGRbrNFoHy0LqPc9R+rcz3tWgTWxAMwmXtifu7BC8Gtei5YJutQn8jJPRky/RDU/hD4AhfmJ64XN29VZ/EJMk/g1YiGIw8RXlbJvcnxzZd7+o0iZq4y6exGONzvFk7cedvtjPZlnFBV/RpOYFKsAgU4oTEu81Zpv4yAS3NCvQWcEILe0lieHP56nKig/fJlOBGBjHnDnEg79I7cwJy46WM8Ld46Tgjn3Nu3Kq7S3rO7x80stuh2RdbALRCUvYChcUW1bxPFnkE2lwao+oJSXx+dFpT7N9FXGMZqSbMdsW97nfbSMu30DB2XoryA3oLFpgZpusRxSmyXWW20abZTTszM8ZePPcHsXAeRZrHwclVw8Kf/B377t/41mzYN8tEf/iD731UHk/KjP/gpAl8i4hgd6GxUj7MgPZACnEQ4iSNBSA2ehxAaJyVOyMzvS/7PCdqNJkUZsHBuiT/4T3/MUhRRLJXYtmWMak+ZPXt2Y13C6OYapWIFZdOsZNlapDBIpbDOIoXIrnLIBN2aScBah1QeJk0BMBbSNEV5Bebnz2FNg0Zc5olnn+ZL/+WLnHj5FWZmTpKmDmU1Pf1l5hcWsrg4Fik8ilriFTyqPX2cPDGTza5SUPQ9tCfYe+fNPPPtx2m3LJ1OQqXkocaLbKlt4tT0SRJTQnsADhfEbB/dzfyZc7DYxPMDuk7QjDrcesc2zrWWOH3sLGUFWmp8VUD4Bq9czq5WAkNJeXjVHlrdgFp5Ew4o9PeQCEfaaVMWEn+gQLRksPY0teowwcAoR595lKGt2znx4vO0G5JWI0QIDyO77NxyE9/62reva4nhoUOHrs/CvJp5OJfyfuTuqakjUN93cdv0yYdgYr2GWddpnVr+1DpZlvdbncQdAu9Uh/mNdjWzCb+L7NChQ9d1TVw3TnzrCWZnX4cTt9XBXZoTGItQGpQ8zwljEpS3jhMi82xdxAkE7ZUmRfUWcyKbLQNcB07kVXn/f+LEDeHJ2rPvdtTpU5wJI1babWJZpt1ucPfI3Tw+9RTlWkLiL3HHtnE6s6s88/wJgv5TNJZCnnjhBf7RP/wpeoIxmukrPPv8N2k0IzwB27ftozw4TKO1Qkn2UQlG6S9rto1s4aaxmHvf9+OstmNaHKcZdYk6bbaPlfnpn/kBXjj+Er7vs3tsB3fcejMFz4GJKZTLGJdkLlzrEE4DWRdYgcrcvVrjlMZZg5A+Tlik0DhjEKnh3FzIUG2EP/nTP+PVV0/RaLfxAo9zS/N84IMTjI4M09dbRciUUqCxSQe/4GcrIjFIKbOFozVYg82vTAR5Qrm1IEC67KpJaUGaJGgl8bRCCMeWrSNEcR/V2DK65RMsnVtmdMs2nn/+CZ558hli0yUoD8CqZdfwOLGJSRLL6vI8Jb9AuawoVQRpAsIppBKUqgVSYXEIEmkolEukLsVrFahuq9GOzhCU02w+mBAU2h6DxT7C7hmsMggSKtKnUPZpnFvg+z74If7v7iOcnZ9HG4mNIkrpMFs3b+X49Atoz6ETx/A2nzMzp5gfmWPxbBuXLFIe9OjbUaTRWkUeKyDxKOsKr8SnKHlHUT01VmbPUakMs7WvQqDqlEsDOB96enqu84p4E3aIN5fLczWepHXCYepw7oyaDak/WGPiAIzVJzi5b4rJSajfn+um3GNVq0OYC7lwvZfnCNcmsGrANOcF3tQXstd6jctl/XYb7W0O2R2YeAvS6tY+j3darN0o4cx19oY50Qx54rm3mRPbL3DCxTGFQpk0iaEYIKRDOo0TBodESIX0NnBC+Tgskvzv1HBuNmRo6Bo5EfiZiyw2CCczseVdghMIrLsEJ+K3lxNKvwWcsAkVcR040Vsh0NfOiRtCZKWpYc+23Zj4BbYPV3nfvbuJzyUUx7bzV9PfJjYRHRFwrrFET3WQYtlj244xHp85hUs8fvd3/4gd/90gq82z3LtvP88cfwXdNZx98VmsF3DvRz7JieNPEbV7SEghPkngF6HbxYu6xIsrnJo5zRPHp3lldBv9/eP801/8FW4aH6dS9in42fBQKXtJbYISJQQCIx1CKZxTWGMQ0iF0gPCCTHBJC86StGMCT7ISNpn8+mN87Zt/RaPbJU0cQ5sGKZYU4eI83//Jj7N71xilUgkh2ggr8FUBvGzuknQWoRzOuuzKZO1qxJFdpSgJ1mHiFOl7ZDkAgPDQUoDJL8UwOGMIhKMYKEya8Pf+3qdwVjH94kv85y9+kcef+Do9pSL33nU7x58+xpNnXsJ1S9y9ZzttAdt2bGepvcxyYxnpFO12l6rzIemweWAYFkMUAcZpXLtBb1ExtqWCkh6FQNExLZotQ1BIEaqDMymJM8SugxWSwcpWnp96hgP3fITpuVm+8e2/xkNSKguciEjjCF0MaHVTmo02ng99xSLDo5o99dvodCOausUpNcM9N99NoDRL7RUWmm3saopzDXoKmyhoSV+1H2HbdFdnWGyt8PKMuV5L4WK7XMncw2RlZ5eC4KGr2O0BCN8I/Y+QVTyus/rB3Gk1nbmkhmrAvinCIzD98CRDByeyAcNro/3WhwZzUVUDwvy9XCk4Vz8Ak+8OqT1Ye40IqK9FKieA7+XiPK4a1A5CeJiLz+lbJCIu5bCqHYAjP8WF8/VGRcs7Ka7W3sja7UUfxsNc726kV+TEd75NnEZ0gktw4htXwQk/4N4PvwFOfO5XuGnHBk4Uck7IMsJlnEArnFWkcYL0HVIHcJWcSBLH8OU4IdvQBr/0Wk7gck5IoJP5BS7iRJoi9QZOFN4aTuzfs53uRk6giJpdKmuc6B8G3gAn5DvICZNzQkr6evsRpk23scaJ+PJf1nV2Q4isSuBR7XH80If2QNxgeWaeucWEsbF93LXvVnyvxqunnyHxLbGwmC54BcVSawnnCZodzb//w9/ne+66jaAAA4PbmJ85RZpIPJty+pVpRvfew4P/5J9y5/47UC5ldGSYmZPzTE+9SqdrKZUK/Mwv/ffcPHYT97z/A/iiix9ofK/AmvvWSYnWxfxLalEOsg63AukrUApjMjdvZzUrBW2uRPzp5/+cV+bOMnduCStgfv4so9tHabbOsqdvnLvfcyedTof33bWfbrdNKZBI65PEMU4mGAmik5x/XQCLQaZ5gFBkasta8l5X+X3OZdPFnUE4cEIipIMkRgiJwGCNRGlNVXuk3ZTb9+7kpt0/y+nZT/PVr/wl52ZPs6wfZ3h0iPhcC9/36av1c/bsWdIUhKewJsYvGSpbSix2F4lYwPMknUabbpISlH2c0Rx/ZRaNwvMEOtAEVYcvHbft3c3C0grzMzNIp0kR+MWAxaUlHn3kUUbGh/joPXfxrcefptgb4GyXVIIz4GtFq5XN+5q4fQ9j/WUaSQubBCRJLzdXR1hprFIeLLFrZAuIAugCs1Gb2bMvM1wpM3P6ZTo2pbd4E7FZQdF95xfBpexyIa4HLnHf1XbmrL2OwJqAsA61w2RCbn3vhHUC68ADcORzwBfgC0NT1PfVoZ4lvPM7IeyrcYAJJu+GiQ3C7Lwdyd7LeuF1JWfW1BH4gle70NZg8sIxTVFj4oGQySCk/oe18+fiyEMhB27Pa/kudz43CqCrCZlu2Pw1u9woSN+IwHqnw40TG24v+jAu9aV7Z+2KnLjlbeTEqXmmpl+l27aUKgV+5hcvzQkhJc5InJMoXUBoBc6iIAuXaYmvSlitcO4ynDhzlrnwYk60WmfpvwInOmodJ6IEvIwTDnDSINoG4WX+K4A0dihAZQlgZEVYGzghHKTrOGGvjRMF32fgUpyorOOEeAOcWFhhfvZG4UR6Vd/bG0Jk3byjgo3aLC63sMZn+myTVxfPsNcv01Py+evnvsLO0d1sG9nM8WOnKAqBLpRZaXRotFMqgWZpxbC82mK75yMp0iok0Bsg2wlzJ75Db+8mxm65BaoVvv7lSQQKpQsY00WiwJOMjQyza9cOSgWNbDfxRBmpgszlKhXZlzFrvymEyqoKpQYpyJaSQkmLi7oUyj18/Svf4C+/+k1OzMzSdY5us4OvDeViAWkMQ739eFqwY/sYACJQBDLApDFOymzhOgFJgrMGlMwXAaRxF7/oZ60irM2Enqfz6ou8EgWRJcO7LJzphMBhkJ4HqUOkBiXBWAOxQSqBdpaqp9i+aYCf+PSn+avHjxJLwdGvTbLqJ7x88mWG2EK7ZWkstFhtNUnjDp4WbN2qML5G9PrIQDAwEBA1I7qxw3kOXQyQwkNpRSIMUSvCL1RYmpth1+Z+Cn7M7OwKxVhTrlSYn5tFkdJoLTO6pcYPfOQeljoxPQWP8aEBjj73FPt27kQZg6r047otZmdW6BnsJbWWTtLGpNBIE44+9zh9hUGSbpMdo1uxfpVquci2beMMbxqlsZowv3ycou6ht3fL9VwOb8yuILBqNZj8hcPUHzx4QVBcdtgx1Nb2taE51UQIk7lYOPI54DeBA1CnDg9nrxPWgIlaJhAeWGv6mT/pYS6wek2EXHTcl3D3PETmlcqPdzYBDtRgMiRc68E1UaM+FfILwRF+jgMX9lmHA7dPwrcPZK9d49JidKMAWi+w3qKw2cEJOHytOWfXO59rKsw+yxtkzORbzQmR+NfOCXUxJ1TURK9xwlhkQYHJWghYt44TSoMQWKFASpRzGScKPXz90ZwTp2bpio2cSF/LCZ1zIsk5oQsIMk5YZ1BCZj/9QLvZplgsABc4of08xcW4rF37tXJCvjWcUH0+0r9GToz2UyhcR06sxMyvnLgmTtwQIuvYiVewroenX1nl7LlZVJrww5+6jzhd5NZddXYMbWFh9gSxbeP1KFoLLaRTxHHCjtow+/bu4vgLTxMtNuh0EwgSgoKESoVYtiiLgFde+ja/+FN/l2fPLvK1v/gW5aCM1AIv0FR7Stz3ve/nttvrbN+6Hc8IbKWGVFmLB+l5Wb8NK7Py2rWUZKkwzqGkj3AWE3eRqsiv/to/Z+b0WSIE7XaHwGpanVUcAiV8hoYGCDzJrbfexm233kK1WqZYDFBSgTZIZ1GpwEqT5WA5Qeoc1iRYa9FCooQhaXZAKzydteeT3SQTfU5m7l5pMEmKUgKkQEpFa7lBua8PQUKSgG8dAptddeFQOuvXUi1rzp48yYcn7mH/e+9i8e/8ff7rt/6KV089j40DFqM5esa28uq5Y3TiCKctPZuH0K1VElnBL8WIxTaNVgNMD6YLIzvLeB3BrGigVxyx8bBGMTQ8StgIGSgUYAskjRRPawY2FfnA3vcRiza+dvieR9BQ9JXKjA54HD9h+djEeyn6HrOtiKKOmZ9dZKG5jJIFPCtIhGJztR+9OsSW3TdhraXP15xuOarlAp3mEolQ9PdV2DT4bl5dnGdsy+7ruBreegtDMoEFwFTmqrriEy5995rAqtXJGmZ+JfPY1PZB7UCNMAwvJCGtCZPaOkivd4as8/JMPAQ/+WCdg+vVzxQwBHw6//801D4DDz4IHMl2WAvJROHhaSan9/HhDx9g34EahCGTD4fUH6jBgQMX+lCF+b6uJJzWVSWGR7J2FhfbG+sQe/hKOWfv0Lzsax1DNFWvUb9BBBa8HZywOSfKxDJ6Y5yoXuCE8DNOWCuRQq8FGEDknNBrnIhflxPa+vSf58S+jBOVnBNKgTFIa1Eu699k4pwT1mHTBGsMEoWnHGkn44QWGqsM0q3jhAWUwcRZTtYlOZGCbxxCWJAK+RpOnHpDnFgUFfzidxcn+vorbKpdGyduCJGlK5t49cQcZ86dYNf4CPXRIQarAb0Dg8ydfhxPKPbu2Yn2AoZHu3S6hqC3yqd/8IM8/cwxXjw+ze7tOznVCrlb95IKR9HrQxZbkDritEucJDz52COUtt+GFhaFI/B9rEq46dZ97Ni5k9rAECZu4+sSygJa4yxYJFIpnHA4JFJlXW0RgrQbozwPE8W0Gk3+1f/2PzN9YpZ2NyWNO4AjanfBGToOok7EaiNi964xPvyhDxH4joKWSJfiW0kr6iACmTWeIwWy2LqSFuckWujMrSsEUmaT2F22NrA2QYqsnBZtwDmEzjxtLq8cKZfLkFhQGqkicCKbrWQMzuWJkcKC5zOyY4xuFFH1BaYn4K733EWpUualF59le08Pm6s+u6tFOl1HbC2rCYwOjEJJ45sCjZEYebNP06xSrRQY2lqjGSXc1onxCwWOnpjCL3mgSmwb3kI4N0uPX8bvK0ChyNZgGO0ZVCLRKQijSIxFiDLdVpcUh592CQKLEpqbRkfYOjzG0W8/xssnTrKlp49YBOhSmjl2OwYrLfPdmMgU6HeSNIlJrWHZpBRkAWstRX11buDvSpuYgMnXJ2eNkHC9EjkE+CF3/n6Nn/2xI8x6B3jwOHzmI/C5n88GP9fOCysuzOBba8l+6BIvklN/8kE4yI9etNGhev5XDXgYDhyHd41OcOg3JzPxNZU37qwD9RoTQ9McPlyjNhFCnawv11T++htzsK7gmQo/d0FY1TaOFJoAjhx5Q56t+loEcy0Mu+ahu8bQ5JsZ2Dj1OuHHAwfhSO69nCRzYr3V+Wtvxt42TpQ8SLkKTuy9mBNBCZVkv7FJ1+K0xCsohHSkRqKkyDxFa5zQHqYZ02o1+Ve/9TqcIGLlpQ67d209z4mSJxGk+E7SanaQFQlKYs0FTkhhcVaipYezKSAQOSdQl+CEl3PCuzwnBBEIgRRknBDrOOH7jGzfRrf9JjgxnHOitUq1VmBoW41m6x3ixKsn2dJ3eU603QVOxNYQvwFOqEOHDr0d6+Ga7JXjxw6Nbt/Bx773e9g+NkxhABajhLHNu9k0OkzX+qxG83S6CZ4f0Ek6lCr9DHpNtm0ZZVg7Xjh2kpIQbLtphGLPILIT03UdpJAYE+ELn9WoSbFvgL237OLxo09SG93Mf/Oz/4DP/qOf4YP3vp8ASVCo4JRC+X7WrV1phJQIoZAyEz7WOJxzSKmwXYvyShz+N7/Hf/z8n/HS6VmarQiZOubOzRDHCVJpjJT0Dfbz3nvuYKBWYdeunezdvYNiKSDwfaQUKAVCWTytsTikNdmgAi1xaQzO4GyC1AJjLEoKnHUIrRDWZuuMtci7htSQxjHSORwOJwXSC0ApbLeDUprUWqRQ2bOkQSCz4c22i+vEKGlQUlEpluitBOy8aS+jW8Y592qTMm22VxR9hYj+oqHTTejXMX60iEzOUEqXefmVp5g++TJzK2f41uP/lXTuBXwtmFtZRCURtGfpqaaMDG2lZ2QT0XKTRtwm7SZ42pE4RSOusJwq2qIIXi+FYJBEBFT6ByhW+0mExqQBSjo8UWDzpq2M7bmVF2ZPsiIlekXQsQKNRFgvq6aRBaoFCLTFV5qS0HgCWqkhsIJ7v+cHHrqea+LRRx899Lbs+GR0VZtFlIB1RX+fB0qfZfevH2VoZIL7/k0E74Pp03Ue+fEjTPzj8QtAjsiE0BP5kx+9zIuEWURqqMRrNjo7AWGU7av+APzk90Hjx09CWGNqMqL+2TqUQihlow2fiEqE4ST8cZmTny9Rfw8wnh/L+pechNLJ/LF1x5G/Xf7XP4aJT+Z3ja97OMzf0x9wDSG/GhP1iJNhPgS6BOzPH7ovvz1JJnxOXnE3F97HRn287thf18av/PD00XWb1utwct2L7Yf7dt53XdfE28YJe7Wc+Fk+eO/78ZAUChUcCuUXcMaC9lCeRCiFEAKTZEnRr+HEb/8e//FP/oyXTl6eE70D/bz3vXcwUCtfxAk/WMcJL69EFA5pDMI5RCDBZJzAJtlcR5MircClDumrrO3QBk642GC6MZJ1nNAXOCGExmKz0KfIOAESS86JdoxSV8+J7honWovI9Awlm3PizDpOnH0TnOAaOLH3dTghLnAi2MgJd3WcuCE8Wcvhq5Qqghef/Q57dr8LvyjZvmkrq40IVfap9tXxCuMUKhWEpxlwilJB0OjpIXAR1W1jnN0c0BPHLNiI0VqFRtIGs4ITJZJ4iaSlabuUouqhONhmz+1b2bR1Gx+85/1sHhhEChBFD2stnu+BCpAiwWARUmCTNe+RB1riXEzcbSGc4k//6E947jvHCBstOs0mS81VUhxuzUsStagO9tJuLPP1x77Fx7/nI9x2+y1MHZvm5j07KQQSrMBKhZJetjCkxgmFTbooZxA2zpq5qQCsQ+us06nydN7x1M/EoLW4pItwWW6W0iDSBKFLgMB1m4BEihRrs0b12Q5sFtcnRRiLcNlUdSkENu3ilEFriRQJ/f0Be/bv5pVjgnOnT9BXNrhOk8HBgIrXYrVhkakgLUnevW8X242i03R4Lcdqa5XuXMi7duzhZHUTQdqmaDz6SyUqhc2Utvfw1F9/HVkJWD75EpVN4LFM2esnboQsywg/KtHuwkvHnmdIJiwVA2bn59F7x4jx8SjhfEd93x6Oz83iSgUGxBbAsOQsJQ39vZtppGdYjYv0FktIBYEoIgsNGvHfUE/WG8gvmli/fQ0mPc5XnoW/GcLkJAcfnLi0d+UAF3lqQmBfbZIwnMiiiBe30wLygrYpmHroCFM/d4B6LRvNc/CBfFr0fMjEZ2BotJblf2WHQO0zE+wLJxj6zCS1MPdyXcJrUwt5TYXk+nPywIOvuSt7zgRwGGrPQPglLuR4rbd153fiQI3JIyH1H60z+fNTlz/1a16/K9mVHI9r3rYrbfNG8sompy5+ziTwkWvcx1tsbwsn4mvnBBs4IeiCzDhhuikCgacDUJfgxNQxwuYFTiQ4SDoXcaLTvJgTzx+b5pabdlLQ4KTCFhSSLK9KSp2N5Um7qMSe5wQyQDjwPJ1HQgTOAP5rOSGcQ/kbOBGv44TIaq4u4oRLEc4ijMNZgVQCG3dxegMn7tzNKy9nnOivGFy7SW1TQIkWKw2LMm8DJ8xVcML5eOIaOVEoIfU6TnS/ixLfkxXL3OlZRnbsots8hadhbukbxJ0KW/ytKFZxySqd+YQktTTbEYnxKPvZhzH76su8NH2cPr9IkYQpC+ObxjHWEpsWhZ4BStVhCukwRpe5edcHObjpvbzr7lvYNjIKOgUrESLAKYWQOgvVOZXFvxGorJQwnwIPJnLELcmDD/9LZmZm6KQxp2dn6DQbFIMChVKRpNyDtYZS0aezGlHsVfyt7/8Eu+u7GRoaYKS2C78QYE2chci7CdKH1CW4NEFKEDKBPGHR2RSJwtm10lGJQ2WFhN0mshBAKhDSA6EAi3SORBiES1CpROTJ+w6XeebSbGo6Ln8NHFKJLLseA06ghCNOIrTykC6mT1tur9/Enh27+PaTz/Lqc0cpaJ++oEWvF9Dvl2ilDbqdDjrwcTjG+nx2bt7L6nLKi3MzTD75TW4Zu41gWz/VWj+druP0ued5+vFnWFltMtI/wDmXEjTPkragXFpB+UV6attx3SbaxCy1u5Am9FlFeWgPvalCl0qUygXQAu0HxO0Oo4N9fO1b32IxCfE7VbQzzBeOM1ItEZSLtDtLWCk43VgldoK9Ozdfn4Ww0Q7x1lbNv4GQT20doGvhYUhqhL8PnKvDv52idj/rBFYIB2sXJ8yvhbfCbDdHwomLIngbbYIsZezQgwf4o8MhU79foza1L1Nkn+N8rlS9HhJOh+yr5YHNcJKJCQjzTvK1jWG+yfz4DtQuuu/OB+CJ9bMDJ0JqZIn72WDr/IHDwAEID+T/33Aua4cgPMR5QTOZ540dPpSdnH0TeRXllfp2ATUOEq47gRdFEx8gE3eT63Lg8rd1PvwIF743a+HB3+Kyo4kmDtaYPHyJT+MGCA9utGTFMjc7y8jY9eWEXM+JbF4PqpLF4pRyWb3eeU5Y4rbkwV9bx4mzM3RWM04US0W65T7cFThxe84JYWKUcLg4QXoeiU0yZigQIsmEE4BNkULibEJeYogVCgHYJOeEEQjhgZCgXMYJmXFCW4mwFzghFLiULPS5xgnhkAjwcs+WESi5gROB5fabc048dYETPaZFrw7oD9Zxwvdx4nKceBfBtt7znJg59zxPrXFiMOfE6lnS9gZONJtomXPC5pwYzDnhlyj1XpoTSybEi946TtwQo3Cfmz5KddMmzpyeodFoMLe6QhfBvptvpza4BeFBSny+VT/SMdBbxbRjZOwTGw2dAc6cBen6uGnvuxkZHmJTzxCDpT781CBa80RnTzPz8l/ztce/yO69m9lcq6GUl3Xh1R5K+/g6qya01uKkw9gEYxIQIl84ApvEfOXLX+dXf+1/4qWXTzO3uMzsuTmSTptW1KEbp6w2WpioRVFCUcLwyCjbdu3gmaln2VwbolQsYowh6XbRQiLJpo2nJkYACoe1FoHDuaxvk3Dkcfb8ikIIpJeLKSmzpEuTgHC4pEs29wq0kIi1fvDSA5sCEoRByGzHQgjA4pzB2hRsinVxti8p0FrjsGATCh4EMqXgwS037+XmO95D1+sjcVAIKuhAUpUFikEBGwg8WaBXl+gJigwMOu7euon33XsvJ9vzdDsRptnlyW9P88iXHyNdjrECGssrtFcti1GDOJG024KVZouOWaLVXKTTTZBtQ9KOWGw3iZJVWvEyq62Q1UaDOE5pNWOsDBjoHeATH/s+jIVGt40UCiE92q0OvlSksSFeXqFa0OzcPMpwMHwdVsEl7ND1PoDcclUUMC1ZBAAAIABJREFUAtN5jG/qH09lHF4TWJMAtddUJHKYixRV+NAUhx7gsipr7e4zIUwdrOWd4dfayWc57xP1THPNf2EejkDIvvOPcz9QrxEOQbiWe7aWH1arXRAPefuHJw5nzwvD/A1O1S543taS5PME+PB31u/r4uOeFGHmIVt//9q5qa1rUxECX1g3umjDeQg3nMCLnFxrYnCCCwJrzdaLqEMXtmOKK85+vKTA2mg3SGPS56aPUq3dIJzwsmpCay3WcxiTkMZJJkDWONGN+cqXJ/nVf7aBE9EGTjSalPXrc0IhkUpi7TpOOEdqLBKHdQYsCAPWGs5zQmaccOe9UFlyPHm05TwnkAhh84JDD2eSLDkemyXxr+eEMVhSXJpibbaPjZwIUkegUgr+Ok7onBOFDZzwBJrLceLsRZz4i/WcWMw50bkEJ7rrONHKOWFWaXWXWe1enhNpehWc2HT1nLghcrK+8qf/6dB3nv0ripHj2SdPsG10K8tLTeJkgSef+ipedZShoIiUioIr0I1TigKcS1hpLBOvtplfbbMcdRkZ20bfYJFasQ+Hw4gU03YgO3h9/fQPb+G//ewDbBkdo1Iu01heoKfag5BePhbHYq1DS4mQBSBLflzrjjs/s8j/+9XH+Pf/1+d55eQsxnXptFu0uxFKa95127uxzhEEBbygkPVAaUU0l5vMzp7lkx//KNVSka2jm9GBh1YQ+DqbUyVt9kW2STZ13RmwKQ6DtQYpRJ5fpZB5yywhBMalSCkgcYi80ZxwMUncxtkUJTXCiqz/icrKc7N9W0AinCNJuiTtDlrpvPOwRJAJN0d2VSYEGGdwZAvY932KBUmhUkUXejl3dpVC4GFshESifUVRV7BOUgksnpZ4aIxKqRY9qiPDDEiPVrvDsydf4fjsEiZJGKoOMrswTyPs4JV9khWDc5JGAr4s0lpdIjJNwoUFal6FBB9dgOjcHM5KotYCSXuJVtKhmyQMFxQl59EzXOXuOz/OF5//U/rtKNY2mWut0m01qVWHSaUETxFIyz0fPvA3MyframwSauMwfgDCIXjoU4d5bGo/E/dD6UeAcp5HtT7f6TL5PutThmrA+H11Hp2MMg/XFfj+M4/A/jxEd/TLMPTHUP8XIft+qcS0gfBHpqn/xH7CoYjSvhJRuJ/ol07Cr0L4QARHofbZUpZUNpQda1iDj/98jScezTPOhjjf0Hfs4yUioFTP31eUHW/pgRolIsKTUNoPfBAmT0KZKUoMnc+Xqv9IiV/+oZCJ+7J3Wyc7d5c8Gfuh9Ak48rmHqB+8L7v/WnKrHuZ12zscIstyC/81lO5bu/cAPDR9IR/s9awG7D9IPTxKGMF9913fnKzryonFBXp6ehAq40SaJhgDnpZIXQBxCU48emlOyA2c8AsFkK/PCaU11hgUNhNyaYLSIGXOCZNiRTZCJ8uv0kjtsmtqnXFCaQHdC5zAxRjbxqSv5QQy40SWx7WOE92cEy67uBdCYp3LJ5uIC5zIqx4LxeDynHASHSiKXgWHpKQTfE9nnJDXwImCT9K4MicsPqoArTNzwBonlmm123TNNXBCafDkVXPihvBkTR07QX3H7cQFxcziGaZffAF0gbmww3vf93fRsspCK2KpGRHFXaQfYIQDmZWVCuWhpEQLSaFQoOT30uk0scpS6alhPQsqodo/wvs++FGMudDRu6e/D8jESl5bkCf35V3MAJMkJFEH00r43//tv+P//IMvsbIcYV2XleYyrU6EspJA+gSeTxonxJ0uzmSLIUkSqr1VPvW37ycxMcObNzNz5hRpt4kXaLppNxtZpXQ240pqjDHZeJzctFRZIr7MqODc2m12zM44kAZnU+K4g0OilY/WfuaZkg4pdd7LVObhQAnWYOIunVZEoVLJfiykBGERinwkRNZVPmt0moVQpZRZZ2Esg/193LRrB5u372TFFog7AqsUVb9AoeDjF8ELQAeS/lKF3kqV3mIZrYoUiwED/QEfuX0XP/7JCXpqBZZFxMJqROQ0M2ci5lsRy1ET4zpEzRbLiw1WGjFWBLRTSxzHdNqGpCNYmW+ytNxkPlxmIVzGdhyzi4ucbS5TocSmos+7x25Hiw7LUYuk2absl+iYDkksabeatFdW394v/PW2PHF76uFsrmB9fRVdPrT5h2tZ80+m4NMPHOSB+7NxOFNheMGbcyXLe1JNTeQOmLyTw4HwglcKLrGfEKam4OCB/LE6PPD0AfjDGlPUCGOoDWXhst95aJr6BIRHpqkxRe1pMq/TmjPoCDCRpyzNh9Tq0Ho6pDYFtbCWPZ6H8MK8WnFqMrxQ/VeH8CfDLGn9MxfyzrI8tfpFxx+GMOFd6EQ/FV44DQATB+uv8XIdeODBbJ8PwJoz7mqs1nz9bQ7lt595EHionv390JErerXW22Q9fw+Th7PbjR7K62DvBCccCeXSJTgx0JdPExSkLkUqhVYCp2T2G+yunhO+8tHrOEG6gRM/soETnSba16Q254TWqKIHnsbk3jQEWfsFm8mrjBxZvhSWrIs7ZOkhKuNEN26DkGh8POWfr1I8zwkncVbkrDGYKOdEMeeEzqIoQoFSDunEazihPIW0OSf6LuZEEgusvpgTQUllnKhU6K1eAyfmX58T7ZwTJhWszDZpLTeZP7PEwvLVcaLol2ibDkkX2s0m7cWr48QN4cn6F7/yTw4tzC1R7iuy/57bqVWqqME+RoNNPPLI7zF38hn6BsbwPI+FhXNQ0vQEPTiX0l5q0mp0CVstlptt7rznNrYOjLJ5ZBMmijDtmCDwELqH/q11du+p01+tYm2adeD1CwihsGQlqmma4nk+uMwFrLQHRvCdZ17kd/+PP+Sbjz9Fs73ESmMFZyBcDDPPtMi8PotLi8RxlySJSdMEIRzvf//7mJs7w9j4CKOjo4yM1Bge6qenVMCahMCTOGvQwpHaGCGy1qbZLpNMJGXB9iz+jsjbLeQjdqTMO7xbhFIk1mZDqi1IJOisUaogm9COTbL4eprdSi3QmixfK8/PstZlbSqArAmrQ0iySkTrkEqjpMBXApyhUinRN7SJ77z0KqutBBu30ViQgsQKej0fXwk8VUT5Hlp6nI0dNd8hRUqqoOL77Nw8RP/AMJuH+/BJmV1YIE0dzZbB84ssNFaJVju0opSuE5g0Jaj0opQjaizQTWI6UZeo1aVjFKJQxjRXiOImOM3c4inKxV5enT9JeHaR3kof1hq67Q7GKpSwpKbDR3/o7//N9WTlAmBoAsbvy6v4ShAehVIeGnziJHCCC16rGpSmYahWek3F3iUtyvYzfhKYgvFydvd0/lrnbaP3ZgyGylky+/hadd+905TCiD/45Ukm7h8nnIRHpqcYOz1O/YmQKKpRGi8x/Y2IaCxkrFzKRunshyiE6CiUhkocfhj834DS/RCNRdw5UWP2kYjwZI0PjJf49d94hE8cmIBHIviHEK316poKCSdD9h+sE5ZCahFMnQwZKpUykfQ/Qniixv0/ETF9FEptzguqtVN18ugVVOkkV3dO107tNbR8mAb4WsijS8DPXv3zxkMYWvM2jgH16+/Jeic4If0eBrZf4IQxKcrPOAE5J2TOCd9HOIc16TVxQjjH4uIiSc6JJEkQch0ntr+WE84meCq78NfCkeScEDb7/XYmITUJwma/x1KJfI5t3sLBueziOevhgJAq66nlNKRkDNEKIRTYLKQoXJKN3rFZ1EP66zjhDM7arMJwrWLR2fMRD6kUzji01kgpCFQm1C7iRPMKnJDXyInw6jnRaizQjWJanS5R9+o54TZywl0dJ24IkfXbv/lbh850UqJwhWh5FmVSgqE+akXFrvp7CPwiX/3yVzh7ZhatHaoS8OgXv0pztUlfsZfAL3DiXMhKO+auiTsIrGH3TTcjPY1Tho6FLZtv4db33MnopiFk6lCexFibLR6pcc5hTTer1nAXji1JHMLCf/gPf8wTL72CsI6uBeFSymWPglemEBTodCOSNAaZ7VdIQaVcAWcJZ2fQvmbPnt3s2b2H3p4ifb09CCSKFJckYAzGJGglkBisJXM9O4MQoNdyr3DZ1HYrs8R4skGbzmbtGayxeIUiQrgsX8tarPSQQoOwWBuTNQR2CGvyREayakIL0tc4HEpk4xakFAhkXnkIaC/zmuVuYessWitSYyn6ilvffTsvnZon6jqazmCsxqphlCiDX8Q5i/J9rFCsCkHNk1mDO2cINMjAsCpThgqC7bUeekYGGewpcWJmlkK1TNiMIM5+2KwEJQTVwWFsYmg2VlhtdykKBxasH2B8DeEKgpilboeZ2VcpeJo7734vacdydnaOtk0JjEZ4Ds/zUe02H//0P/ibK7JyqwOTD+cwBUpD8NBPP8x9j05AI0tPGvoDYH+Wj3Q+lLYmPi5jk4fgpIF/F8F90QHKQ4LSmrJ7nZBYGGWbfPafQ/i3sv0wDkd/AybGxzn6hZCDB/dz9MdCwp0ljlKi/ExIuAnmo0nG77+f6HdCiCA6meUu7f9siekn4OBXINoTMvm5EuPjIbN14JdCxgZqvCxC9k+ME/5OROl/gclfDhkfL1GbhKhUIqpDFIXwDTg5MUU0WUYciSh9osTUUYiOnoQDJUrrm46G+fm6mjDgQ7w2jBcCv36J+6/V7uO1bSeuJjx5viXHJBwa5z6ur8h6pzhxy/472bJpCGkyTnTjhEKlBEJjncMmXaTSWHN+ohlJfGVO+EGZICjQ6kaYNAGZDW8WUlCpXJkT5JwQNpv8kZoETwuEMWAFxpisZ6JxqPz3W1qbcc2CUiJPihc4IxA6a0+kgyIqcCgtsYnFKZ1Vz/smy8daqypMDUibXdCTJcFL38OKnBPWITSIvLmpkAKnPFxqQQmyn+OcE6ml6CluveMqOCF9rNrACWsIBMjim+RE3KUoN3Bi/lo4oVFxh4//7dfnxA1RXfjJH/sAz02/SCo173v3+7DhcdTmHh7982/RWAlxQvGpH/lh2mlEtViBcpVdn97OM89/h2OLM5xebvLSuTm6DqqlLXznhS8z1F9g246bGRrcgVLz9G4eprdQBGPwfIWVCk9rsHksWUpIZabKhcqS3ZFMPfMd/ugLf87U9MukTrGwsoKJG/h+gSRJ6CYpSdzB9326cUzaSSiUCiRYonaTwPNJpI9N4aZde5mbP83Y1k14gWZhdpbBWh9KSSQWawUmSbNGb9IhTJaHhZWQghIeJjVI7UB2sTarLBQYhFA4k5X0WpNkJ9aCkF4WwzfZPD4pFLhMxEkpETbFmvT8dHaSNIshS5F7yFR2hYLK8wDSrPpwzaMlJNZZPM/iS01BOD587z0cffpZzh57mlZ8DpJTDA334xJF18SYjkNqzbnpY2y5eSdtK7HOo9GxNKxi+Vyb8vAwjVKX0d4R9GYYq9+GkgXatkDS6SKEIpEdPCcZ2TTK0rnnSf4/9t49SK7rvvP7nHPuvf2YnvdtgAD4AEGQnEtSpESKlmipbZlUeUN7nd2Bs6RSXqo2LnOqUqIqJW4qlUx5g0HV1mRrHZPZEpW4BltOrblOJCfB7KbWS1shKUstyZYpQSQlsofiGwDxmL7znunu+zjn5I9zGxiA4EOiSEJOzj/AvHp6LtDz+d3f7/v7fkdLdHpb9EiRuWRo9zhSwsrmGoubhtHqECLPOXaqTTg6wG/e9Rn+1/ZxFt84Q29IMj4ySnxiiUqw8eG8ED7g06IosMJzWcBfOOhs2eMW1Fstmq2IRnyBHcKbfKLO9xB4cgZ8islUPH/+pt87nInC2mF+N7Rmim8172q6EKhPhSy0WjQeb7DQbNGah0nbYH53i6jRIGw1iafPGZEeebBNSMhXvtJi7isR0wdDomlgLoQJCFshCw9A+EDxzVkgboY0ijiZuLg+Yd9zYiKkTeQc6B+GsA3RAtAIiRdcEPV2wbu7mLgIopm3+KHj4mJdaMUQ8qag65+bM/xPJWRv/Pw3XX+G84FxolIBo/EChZWKSlAG7RaRpBBoI4ub03fPiU6So9Me1SAgSVL0z8AJ4UtEblxh1cudQai0zkrBCpRUCAMKH6tcbI6QOcZId2MtNEIpdK7xpEeWpZAIvEAifB9pnO8VOc7vy2iMdUUTxQ2/MzcF8txlMkpRON47TkhVcCLLUZ57vxAWScGJwBBojzKWO3/1E/zg+2/BiTxF64twAp+NzLCRfNicWHnXnBB2e9vmQzpf+vyvWKV8jMlY754hTSqY4Qo7h6/EakPJeuzcEVIqDyC9gKwsGDOj+OUKqizBpqS9DQhqVAbGaZ/+PuHwDnTeRaHwy8PsvPpqrtu3D8C1If0AKSWeVZiitWqzLka6OASTQ6eb8MUHfp8uluX1DbCCjZVVpPJd5yrVSGVRhQO8STL8ygDGaPI8Z0e9jpSw/8b9mCznN3/9Tj5++y3YLKc2UMILJEoosKnzKxGiGNmBR98y4tzGCoBSqrgDAgqHdoRF4BVRCQIr3ecKiyuGBMXX98d/LtS6b1x3doJfbMUIUVg4iEK3JXDzeWuxUhXP073thPpghWKr26FaqZHkmp7xmPvKH5P3YkZqmh1l90vKo0RiUgwex9bX2VsbAC3R6RarQtNZ73GqJ9lZC9BZj4AEYxV5Bic3UkoDI3Q6Hb7zwx9zevkN0gSuv2aCtdPH8a1ABT7BQJfAlCiNDFAuCUpUkEEZ283JKxVK1UEGVA9MjaAagPXoJgF+pUygIMsS/vjffLUfivGhnJmZmQ/ghRkxM9NiZobzncQnIX4wJgzdXl/4Nuabb7V4Fk07zdfPupl2aA6+/HBIvE3c1CImInRu9M0JwkbsxOVHCsOtCCbvhfkHOFc4ToZnY3iKH9ltE87HhA+5T7r1ATg6s+2bNyCcdbqkxgRnNytpnROzh4Xm7NZ2xLGoTTzvCrDWfEzUL0jf61Zev/K9RM7MzMyH+pr4oDgRTexDpxdwAoXG3ZiarOtMqt+CE9YKNi/GCV9gc4PpZfjVn54TWZ4SyPM5oYqNQWtT+nJ3ACWdGN0VV8KNAKUFCt2veTMnrHVbhk52AlZv4wTaaYwFwDZOGIEtOOGYVGy5K4WwwonitUWoghMotnrvgRPJFqvyF4sTl0Qnq1QeYXlxkyRdYVc5JB2sUtod0n5NsL62SN5ZwV+rstmz1EZCumWLGRpA+TWWVldYPHWaQHrEqzFX3nATQ2MZLx9/gV179lIulfjIjTdQ33k5SZZTqQZIpTC4VqHFYrRzcg88D2PdWA6rWF1eRPsllk+foWcMaa+LrySpTDEqR6scYQXdNIPcYhJD7ndRSlEdCEizjPGxIcqB5Zfv+jX27NzNqz95mZs/chO9zhaqHCCMJDdOU+VvuyZO5CgxQrqOksnd21jnvIsFIRFnBfruP/fZpPU8x7cCK03xGNAvss52saxzrheFmL4I2MHo/my9+P8jhJNlCeGS5otMUSeI7JuYSkqlEpnNEJ5Bpik33f5RfvLCK9j119DdDBnUsMogZYDOFWOVIQwpiW+oyoCy0fh+zmoQUM0EOiiTmxQ3JhUMjA0ymHUYCnr8xidv4pVjYxw/dZobrprgiddeJM+GoCQpJwo8uGLfVSwuLrK+cYZM+JBJBgcCqvUET/gc33idca/CyEjIsz/5IcPDo3iepJx+qCz5AE8LIQ5x8OBBJhuTfH52nslJ4DCEDVeAhFPh+TE8czibhEIsH26zajpP130fTE4X9U1fQ9Q3fXqb4mOyAfP3tfjyyWkaJ+dpAl+cbXLz/VM0vjjLHJNMfbXBIVo8Tsz8nBPBhw9MEz8wy3wbmkdcbVKfiolnW4RRdG7EGbpu1cRk6AqjMOTYIyFhGDtbhQjiWWg1QqI4Jo5dVdZohGwRc7QFEBG3mxDCv2rOER6OaIRtYqIizsddlCiELx2CAzfDxGTh+n7Bzx1vu4bAed2qmUcbiNKDHDy4zT21KBJ/tgTFX+zzQXFiaanL2NgAyvfIjcWD4gZTkuc5QeCRmW2caC+ivRLLZ87nRHIhJ7oZwlh0asiDghO1ghOjb80JrxIgUoH0FJocz1JIUiQ26bmFKU9hhMS3rrNmBG4TUjs2WKT7uxJY7bpw2IITQmCFxgiFNO7Gub983teQGW0RnhO29/flrDEIOCslKdoCbhvdFoJ7SWF/VHBCSDyvRKoLTiRv5oR4G04MeSX8zZRh33sTJ8QlyolLYrvQF0OUR4eQFcEG48jRKxkbuoF05VXM4nHkasyZVxZZfO0M3/nudxnduY+XfvASy6eW8L0yw8OjSL9EjsfmpuHp5nNk7dOUNlfZN7abnQMDkPcQntucENqgs2LbQgqMTTFoNBKtNcJC6/mf8PBX/pj1jVWMTUi2Nsh6WyRZD2EUwnrkqabTSRBGkGWZ2zTJNYHy8L0Km901wt2Xk6ynvPr8i3iBz77rrscoy9DYECIHq3soeig02uRnO04ShbASZQ3KuuJIIjjx+jFXJBoQwrrOFR5GWtJsC5Th5IljTrzv27OrxWg38kODEhryxM3LlQeiBARYgfN9sc4/y5gUazNsrguhY47Ns0I4nyBlisSltiMSlJAEMsATJUqe4lO33sg/+oe/gS6PsBlU2eots5VvYvKcnu1Ssk4bFmjNUm/NjV9LFVZXE77+/Sd5ZSVmz7472XHZbaihcTxviJGddar1Ov6A4CM37eXXPnUbwztKfOYTH2dg1JJka8SrOfFal5HxXZxePM3mqkB1fQIESyublAcvQ5tVrhge4fTiCj9uvcJY+TL2X3sH7VM9jsfZh/dieKfT4mzHaernoJA5+OWDMAXzX5rn/sdCN+6a4FwX5cKcw6ltRp/x+e/ffsK6i/mjKFyAc8XWWxRYrTmYfTAmejiCJ+a5e0+LaDLinvsneawOj9inePiNiNbnjnBPE+YW2hDBVBQS3/sg8ZEWTEVEUzAxjaty+mHYxdiPOVfpuFqv8M2KYjfqA5iE8LGQiJjmV+YJW+5iN5sxR6emoRERHzjCxIGQ9kJIgwa/0wipR8X24GR89udrxjD1VQgnYOHQOa+sMAQOFbZdF4vJKc4De5sc/Or0+R9vnX8p3/L8vMaKl9B5S04sv4o54zjRfuG9c6Ja85CeB6l2m4MSrBIYk2KERtsLOPFHf8z65kU4wZs5kaYZVm7jhCo4sefinBgcHsImFkuCMl1smmNw8g4PQ1Ap4fkSmWkCbTAWpBCcPHYMaUHnRRdJClAeRkKadhwn3jiGHwSgXGElsVhtHScMrneXJ+AJZMlDiBJGuxmLMQZtNHiy4GcK2jhRvTEInRcOpgnSO8cJS0LgSUoqQOUlArWNE8pxovM2nDi9sYpR+qKcqL8TJ+ofDicuiSLrlR99A5I1btn3cUZrmxjZZmX1J7z60gucXlljbTPj9OoGS/EG/tAg3W6Xoy+9TPOpZ1j44TNsrKxTrZS5YSJikx6vn1oh1yVeOnGCV84cJ5c1pLD4QZlelpHaDOW7FVOjceK4LEPrHN/3WVlZ4S++/k1OLi7xxpnTroAqCiDf97HaOaF7fkC5XCbPnUhvcHCYSm2AUqmEsTnVapUzJ06xvtVhaGiIjfVVqpWArNfF8yVZlmFE0V3KdWGr4AzknAGpKTZEXNFljGHPnt3OK8VXQDEGtBqJQSkPMOy58kqsTl0BVowZ3QjQFqNHjaiU3EZKXrjHSw8RBEhf4XkeIJEycKL3s4alFqEMQp17fhQmeFmWYYuNE6szAuVRChSjQyXu/k8nMeUxchRZlrtiDEngCXwvwFOKiiw5Swir6G0ZPnLNx/nYdR/npZef5IWXXucbze+yvtnh+Weeo7smuGLHdVy5Zz87d12OEobr91/Fb/3GXfzSJ26hNKbo0GG906O3ZeimmiR3d52pFhghWVzqsLHcYcfIKMbkbCZbqLIHWvWdOy7NU1gsAMy9y3X8tzzTDQfjOfeYC7NFN6fOOe+mi5yLvbu9bcU/BOoXM+V8hxNNQVR00BYebNK4M6I52yRuxsx+aRaemYNWm4efPsDX6hA1pggnJ2g2mzTiEBoRzQdbhCHMH44JGxD2m0AR0Cy6ScX3CvujwMIZfaEOrQeB2ZhmK6bx9CTxPMTErgt33+3EzSatFiyEuI7VAhxtQ6vtHr8/mgwb8Mgj59zlw3uKgqoJ8QJw8Nxzeasz8Wjo/Cwuct6xhroEzEN/3uctOfHyC5xedZw4ufUeOaFwnEgyUjKkVChRcCLPMelPwYnsAk7on54Tftl1z4wwLiPRGKx2m3xa585U1Bq8wFnqKOFjsezavQubG1TJCfTpb0FKg+dv40SWFjYNxfa4LDiB81OkUsKkGXmaYqxFlgKEFyB86R4nN845HokTaTkDbfqc0JriCWDQGApOWAMio6Q8Sp5idLDE3b/9zpyoBu+BE/LD4cQlMS6s169m40Sbb734LKMjO9g67XP1xE3sv3of33pmgZKW9IRh2HqMAJvxCsYY9t1wDRUTsH76VU4tvs5dv/kPeOOZFwmHhzhzfIvquEeSa3zfMhgEWAuVSoU8MxgEeWH6qfLcCeCxJF3DGyc6PP6332FjcYVSuYrveyRZitCCztoW+JZeLyVNcoZGB+h2M6rlGtZqer2MPM/Juh1qtRqluuKf/ff/DQvP/YT9+/eBcS/QLMvwggDle3jKYnKN7fWQpGCsM40DlPDQRheGoBKpFMWtVVFgpbiXhI+UAp1lSOm6YUYrpOfuStg2+rNGuJGj8pGqWJGxAvCLFwSFDssDpd0dSqELs1nu7vKM86VHulGi58miIBOgJViPoAy9TpcBv8Rtd/w9nmn+B1QW08sNXasZDXz8Qg9X8SCQms1uwlJvnaFexo+P/pjv/fCbtGPNzsvGyPco/uq7zyPsc4i8jFdZZnTnCDff+ilWupJr913LjvEd/PInP43na05vriLkjZw4vkSvs4VfMqSvb2EDj5OrCYHNGSsL/LLP8mbP/fJKLVp++DrF93zOy2O54PQCKowDAAAgAElEQVQBfF+TQ3/W5OBXnQdUGHKuIGps+9x3IPo0MDtb/IUL6rNDwMF3r9huTcDcAjRoMD0VUb+3ATNzHPqzScL5OlPNFnc3Z5l+7VGisAmtiCOH4AAh4eN1orjJ640JogiadzeZnGwwPweTDzufrzbQagLzTRqTDeIWhF9rED/QJCw+zlRI4wjEszHtyQnqcQx3NWgxSdSqE39xlvDxkHYYciULHJueIF6Iac3GhPc+Qv3eB4iZ4IGmi+hpHlogvAei+ws3+X7tNeP0Y2+1GBA340vGbf1SOO+ZE2feBSeUT9Y1DAw7TlixjROm4IR4Z0701rYw0tLLLsKJnqan3z0nVCkAz8OXZff7OUvB9sgyi18SSI3rEimJFcb9HrYB1kqkcE7vVqQgBSZXSOmR9TI834I0boFKKkzeF66DUBKTOTE9nuOEtIVUxPMhzcErpLvGd5owa93zME6/JZRXjByDwuIBx7BCciKFAOERVKDXdZz4+Kf/Hj/8xn/Ay9+CEwoCT7PZ+8XhxCVRZLXO/IjR6hA33HoHedIjKOWMj1RR+wb4lfBafF+RbijOnFxjq5QQb3R5ZWODq/MuS+1XMRuGE0tv8NR3H8MfuYajLxxjrDzKTjvOP7r2FnRmSMsBgdYk3S6qHBStUUNqLDpPCAYq5KlmYyvln//Lf4HJUiqDNVJjWVlfg8ySIjAKAmMpVQepjQYknTWCoIyUEq1zKl5AlmcMDo1QGSyRih7PPtPi1ddeYsfOEa7as4ehoRrrax1GRoYwxpDlXTzhge8iDYQsuldSkBuNkGcNFNwRAkyKk8c7Y1Br3ZaJFDghPV7hjwJI5bZDUG4jRQlIuwBIUaGfxWC1S26nX6EL589llEAnCVmSUhmsFH4oEmtS5wgsi8JPWDdeFGBljjUSv1RmZNxQGhxg+bobeelH36IkNJ2sw9iQQNgyJs3xSmVk3qPbtWTdTb6z8DzpVsJWqpFeBe1V2ciWCRTk2pCKLr2tKukJyUsDi7z0yg8YHBphpDbA/tHLuOyq3ezefzV7b/wUW1FGt7fKytLr7N65wUYvJ01TNnuSXlZBKMH4+Bg6zeglXSD4wP7vv2/n7WqaGOcQOgUHv1AUGvOci4Xpg73BOwp/5mi4VlB/XHhhUXBw25M5xDuaYUaNFlEcMfmU02aFYZuwEXIPDzIzBY+0JvmT16ZptppEd0U0F1r84eOP8q8/OwcPtuChkKtm68QsMNmYYiFuMTlVdKPCEBYginCOou2IMGrRemCeaGICWrEzZp1zxU14IHSDxVZE694W9akYmjGNOydgPib6swa/zwKn7p7l4NQUjcdCmJmBRydgNiacj1loxEw+3aC50HzTEkFz5q0LrLPn/y+wzp73zIn1d+BEz5CWAgLP0ut28YKLcKJWIU/enhNdJEJqAgpOjAQk3W2ckDkVVXCiOkJl+B04MTqI0RatuyjjYVUOBjxr3Fa68IoukutEWQ3KVxgtXEGmPNBFlwrnk+V5gE3czXkxTcGThWWQwvZv9NNuYfFTcYJ4abCmh/Cls2iQAAkCgREC3Uvp9XqUh0p4VqJ8ic16CKUKj0WnExaexRiLkTnY8zmxdP3fLU5cEtuFs1/6J7Y2MMRG5yRlsUxaHufKgSuQbLCWLmG0RMsy5JozqaRcGWSp/TKqPEjJSp76yXO88doKRvf4/D/8Lba6XV555UU+dsen2X/tjdw4EVHfsRPP81C+hxZO62TzHKktrtD2eOXFE/zVX/0NRx57kpVkgwFVIel20dYgjMUPAjY211w0giihrYAsoVQuI5RE+h6+VAwOVSiXyyilKJcUYTjKtfv38w8m/z5pp8P42BCBJ1GA7yt8L0ehSLMtlBBYnaOkE7hbRFFEiW25WNa9MIQb6/XzpPpbh2e3A8F1mopRoSuE3F6K6LvGIbEmL4q6/mOBE3UW3SqRY7Uutli0EzWCa10Ld3eCEMXSgAQlEXgY61qu1grwDMdOrfPX3/oBx175PlluqFarVPBQgUc3y6gKj+VMsZhZTr96lLSXkeZdMr1JuKtOODrG9775XbqdhE5vi6RrqA0NUB4cZnnhFLkxBJ5HpjKwHh+742YWnn6Ka666khuuibjl43cgyFnuKOzWMvFqmxOnjrGxtkIqyozv3MMPv/Nt1rZyXl/c+Lu/XbhdW9UPE+6//2JF2oUFVL8wo0WL6NwiXKsIV/5pn09UjPCaMP/AJNET8zSimLm9bRpTdVqtr8CTwD33QKMBzQUOHAj53OeaHCTkShocuyem+Wcx4dMT1CNol2J+58vw9XrRpWqF1FsLhAcmXAhis3mue7ctzJoQovmQ5sQCIRPEkRs9xrc0CGfmz16KmMJ2AmgRsvDFJuHNdRrTELcazB2aI9oV0piOCOuc/V7vqkF1iXWxPuztwg+cE1aixDtworfBgLeNE7nFLwdsbK7SSbqoPifShFLlZ+SEAN9TqEDjWUmWbyJygbA5UhRbhbLkulDFUlOWapfdLC14HsYIpO+SPEzPoALX/TrLCYFbohK4KcWFnLDO2d7kBqGEC6AWuKxDIRFaYkUO2tk8UHDCAjbTWAlKSayQ5LlwerVAIbdxwliBeCdOpBlV+YvFiUuik/U3z32TvVfu5cZrP8bWesCptTZ608dmawyPjKNNxqZeZ8AzkFcIVMrdv/LbrKyvs7x0jPHKCIs7TrC0ssozz/6AT//yr7LvqqsId+1i9+49hcbIOjsDwDMGaw3WZmQYfFEmTTLK5QqZgFKlTNl0UUIyUK3SS7tkWUav18PzfTwGsJmmWvLxa4MEJec9laWabpohN2FkeIzR0Sq//Mk7yHTGR66/nixJkVJSUhIlJaWyj01zkiQhUAGu/2oRwi82+JyHl7HaWTTQ3/izWFskoQu35WIpbB3o67Q4V5TR3zp03SxRXIciiwEhPWc6R39jRGDPfr7G5nnxYnN3aG5N1wVMG60RWKSV7i4K68zpRI4UHrbIXEw7hh3Dg9z+qYitjVd47vmjVFSVRAeYVCL0JotpytGFF+n2MrY6JYYHx5HkjFQG0RsZmehwwzWfRvo+QmkGapdRKSmMKNG7PSXTKdYKcnOaoWqJYGiAUmmQrSTl6KnjPPfnr7F75xgf/+inGayW2LlrH5ftHqS3tcGJ9QzPr7D/hr2k2aUsyvr5nPjQIcKD29pKTwAprsPV12RdeArgx0VTiElcdE4rOt9pILr4Jt2FZ7trBLg3+uLwKJgnnIaJOOShpyaJm3PMtR8n8ltMEtNcaDLdaBDdG3HnfTHhNNw6GXP0MES7QpoPuq7Ur9uI/0zcxx89/hDHWjFxFDMRhsTNmOaRORqNiCYx4VxI9H1o3dcimnIVVWuiSSsuAqrjBo04pv3EAtXJFtHCAVpHmk7TNQnEIfUQ4jvrNKZCosmI5pEWk/eExAtNQg4ALaIGHGm6knR6BmZnOL+ovbDYveD6nL3QP0/PrF+Q835zQlk4jxOBwWbvjhPVSpUk65LhOKH8AB95PicCBfzsnEg3e4ggQFgfpXB+WabQKAmDMTlK+EglUEqCcjIPjUUKv9gqNKgy5IlGBW7D8KyUxBi3ECiKPT3jge/060JK57noW2fIapx8xRYeWla4sGj3SBKhLFjlOOHhUkQECC3wlMRYjTTScUJ6GJ3jCUmeXoQTokpi34ITWyWGhy5tTlwSnawv//Mv2k53g+XlFXRvnXavh/AU1dIItcoItfII1cowg7UxrBSUKwJpU+r1OiazWJVy+vQp1noJi0s/Ac8S+AN86qOf4dqJ6xkcGaTklfBKJQzgS59cJ9g8w1qB5/scf+0Uf/AH/zPHl5bp9nKCIGBjZZ1EpxiRYzNDLg1DlUGyNHVuv9qQFCGZee4EfeVyFRX4GKWpD9QYGxtm79VXsjOsc8cnf4nohv2sLS2Sm5xdO+qYLMX3ZLGx5ypra9zmo7QgAg8jhQvFka6AcaJ0OOd75WSFUvZXa3EBn/0ho3Vuv0J6INRZCwc86V54UmK0dq3xvprPZEVB534xGJs7N2BLodWSRVdLu06V9Yoiz1lJ5DZHWolBgFRYoUi1QWeCp55dYOG5p9k8/TxVkRN4JTIjkHnKYl5mQPXodnvEKx1eefUNdLrF7v3Xc+Z0zPqZk2RGkyQZG52MHbsuY7G9QimzdE2O0IatnqYcAllCQMDeHVU8FfBcu4dQHXbUx9AdiS5DqVSi6tUYr48zPjzKWG2AXEr+5cOP/t3tZDUgbBaMjp2NQHOyyWSzcT64ZzjPgPJscXXeE42JHwhdPTCBy3HpFwPvUGSFTYijbSt2/cKhBdEBaH4uhIMx7RbUm/BI/RDp/XfSmprkcNyA+5rc31hgPpwA6nC48DagsJ6YWyBuNIjnWy70+qkQ7nPeWuEERIQuizGE2cMtpp6JCJkhfmSGVjMkok7zQJOJL00Q12Oipxq0Zps0mrhx48wC8ROhe/oTwGGI748JF9wPfishX5+F9rMx8Z0tGmFETEx44Hxt1tmO4EUK07D497nYiSadL9cHdT7sTtb7yonoegaHC04EJbQATylMnp3PiROn+IP/4RwnfD9gc/UCTgSaQW/IicUNiNyQGINUkly/AyfG6txxxy8R3XgBJ9IU35cuHURmYDxsnuOJzN00lwNyKVE5KM/JTopQNZAFJyTuprfoeFkNsiRA92/Dz+dEllu8QCHKPqabgZBuoiGMy1sUIKSTlEijQGqMyhA2wE0gPRASI3KkMW4aYwsHSCuxGAxFx0sUxtey4EQieOrHCyz8+Gk2z1zAiSxlUb8FJ669njOnLi1OXBKdrNX4GMlmRmflJFZXGQ3HGd+1k5Isk2+t8car3+OV117j1GqXa6+7gfj4qwQ1Z722a/dOaoO7Acvq5iYlJUiFoH65QZUClpZXGBsfRyoXuqykT667WJMjhEQhcXpGCZ5PmicYY1la62Jsis41lsyJGbe6JGsdZ/ePxZMKJHTTlHIlcJ0jJan4Hnlu+HSjQby0yFV797Fv79V0eylKBoT1nRhjSLME8hxrwFf9LpIt8qU0KInWmWsHCw9h3KjQ2m3mocV40BrOGoi6zMPCfLQQQhZf4CIXpOuUmSRDeRJ07kSI52F9m0eWUEjrijZri49Iz3XXdOLGh+KcR5f7MlHM8EXhSeaEk16lwnUT19LrdTieJmyeeQFJjucH6FxjpAHPMFIJGKkNooQHucZWB8lHNgiyQXp5xtZWD8plxnaMsbKy4vKvcgBJ7kv2hnsI/BXGNyRGSlrtFdrLOVddVqHd7rB8ehMjAjLjfGQ++cs38aNnXiZb2WL3ZZX387/7+3dC4AHe2Zn7waL7As6yoAn302AhdkJtKBoqFzzO9gIrnITmAkRfC8+aNrVinBFniOuITfef1MULgbjB+R/rK+Yj57PVvid2tVoMPApf4wtMNL5CczZieiai+Vidww/GhHFMFMZsTcOf3geNhyBu1YmimIXZltNX3R9yqPRFvnrwy7TDIvQZV2A1myGTf1KHJ1rMPXgv0TMHCBfahDRptCcICy1Wc77pgqUfqtP86BEiYsLJiOiukNYT0Gq14HCdcDqEI/CvmjEhLepfaFBvHyA+0KL9OXe9G49Bcx5ah+DAPTB7aJbJg+fbNbxVNzA+5LYVW/8fa2X9zJzwLbvq78CJ9gpjYwUnrEbhY3SCMRlSKBSSPAeTnc+J5fVtnBAZaEFvpUfPdvEI0MLi9zmxmVKuvQtOJBdwIk1A59jcac6dTYJFBM59XimLzlKU70GxMS9FERMtnY2D8gtOILC544T0IO3l+MUYEXuOE9paPN/DasjWunhVhUjzYs9JOBWJse6GHcATGO26X266KM6aVAuhQKWIvF/GUUh4i1Fk5qYwVjlOiFxSqlbYf/U+x4nsHCeUF2DEmzkhcw+BxlYuPU5cEkXWv/33Xyfp9hgZLHPVVXv4tU98lN7aJp3u8xw/tUqKx62/8mlOnVxmdHwnp9rH6Cyl1EqKOI55/bVlTi+tEn3ko3z36WfY7Cl+93f/EwRQrVbRQiGtIvBdRqGkhEaAzshzTXt5lUf/7f/By8ePI6SPJ1JqgyU2VzRplpLlPTy/hLCK4eEBbA4b3S38QOJXFF5qkVLheyVqA1WWVhYRvsf/88TjJEnCQG2Iy3aFXFa/nOX4DFIZxoaHwRN4qoQ1OVqnxV2GhwCU56ONwfN8TJZjZIYVLrTZrdk6wbuzaXAaKCv6LvG6yGAsRntCsF06b6122gDjCjtri5BpZCHfsu7FQT9iyHl4uWR1390C2R5GS1ewSe2+p/DOtp+FDZDKFr+wJIUnPVIIhiqK2z72MU4fP4PfjdnY3MAzGViX3h5kgrVej25mePV4Dw/Nnv2CG6/did1/GWudhJNvtFlbX2KsPsTJ1zxAcNlAiRGp2RFkcO04r//gVV7oVDm20SUTPsqmlAbKHHuj7Ywycg9rFR4Z+/bs4dkfPU9nK+HMS5sf+Gvg53Ji3l30ySRFtykmnneRMfE8roA45zrw5jOFq75aEM9D1ASmQsJmDGHE9EzI/Eyx0ni2XvgpC4F+UTHtppaTkzELrZBWEyamG9x9X0jUjJkPY6YWnJ1CjCvw4kMTxHcWG3nNFq1wWxxQHHPw4EFmD8VMHqQYc+K8JuoxPBjSbsF0BNzV5PCTIQtfgDBeIJwNqUfQmIRm4wHi2RkimkzPnOTIA0/Qur9FszlPY7JBY6rBfAC3xi2m6yHN+WL2en9MHMbUbw5ptiBsRsRxE24OaTVDGvdMvulSTExC/FngYNHR+or7+xG/xfTDDxMfmXvXl/ftOmK/KOc9cUK8AydqVTQKaSW+dFiUlNyN69txYrjE5tI2TqiCE0OOE5tJ5xwnSj8jJ3yB553jhLA5QngIC54fkGQGXyryVCNF6uxAjUFJgbGe02JZ4Rzc8SjmohirCYIAnWmkdxFO5M5CyJdAT6NzkEEhGzFua7HwNAWrnA2R7+o15SmMyJDCYK3CqgBkjjAaYYsvksVWvAe50SgrMRY8IVBWMDzsv5kTNoPMogJFkArWOj26xvDyG11KPuzZt/nuOeF/MJy4JIqsX7/1Nmr1IXbt2MNyvEpXZ+y4bITdtX/MzbekaJNSHahDLnju1e/z+f/in/IXf/G/ka2ucvnoley4aZihwXFOLC5ir9/Ht390jPrg5XR7PXbsqOPJIg7B9s3DPJTvkxiD9S3PL7R4/bWXMb2cDdlF9DQyT50LlDZgfVYWO4wM1Vjf6mCynCwTJDphlEGUX0UFkPS2kF5G2R9ioGIQDHBZfRefuOVWqr5ioDyAJxW1ahWtM0zeQViFxsUZSDwykxFIUbRuLXkOgeeR6g6e8pwYvrhTMAKUCs76jki/AjZH4JGbFM8fIN1axysVXTDlFzmFLlhU+AKT23OdLuGqdSEE5DmmcPsVwqKkjxQSJ4h3bvmy34YWnlsBdrkKWCtRQroMRSPcWrG1WHJs7n4GKaAjMuKVHrWKoru5htIl7GBOJ9Msns5YWltlC03SzfDfkOwaqlLySozXFANX7+b0Ygk9UKMe1sjTLvtkSkUoVjcsZR1wqlfFSyylkkKSIGSJwbHdeCfbmCqURQcPn2q5hl8bYmx8kHE/oOpdEi+L9/VMfg3mHwmdIWa9YPXnePvtv90Qt1zcTutBiB7COW6GAA0O3zvnipo6nJ0ZvkuPLOBcwRCe+yM+PEFzYQHiNvNzMHUgYm6hxakvzhJ9YZqYJpOPhXAf0Ijd2G571M72blALGgeh1XTxPIQQz8VEUdGlexTiuyaZnPks80/eTDh5v/P7asDcfJOp5qPErUdo0SK85wB7xddo3AOFmRazxEw3m4R3wdFmi4HHGvxeaxKiJt8BmvNtGo0JeKgO0b1wpE39KwtMvhExPxe9qecXz8LU0zA3Dwtz8IiNgDbzRMRH5jj0YMzB6Qt+xrc6RZbjL/J5vznhByCtwrpWB+ChpE9i34YTmy5jz2qDzX1WVjqMDPY5YUhTSy8zBScqqED8dJzIC05wjhPK+GSkBFaAlEhhyTT4QqJt6m6Gfe/spjoShAqchxcG6VWAghM6RVUHSDfW8Cs+wlgQfjHp0e5rSwqbZAirCnG8ASmdFZDpd6cMwhPoXLnAaZzdhcY4va9VGOvE7mgFwqCRSFzaihDC9QNMEU4deEjpvZkT62v4lLB+zlaiWVrJWFpdpaM0a1sZ8ph1nFDvghNrHwwnLglN1n9+9612dSulVlYYv8sNt97O1tIina5k787dDA0Pk6Rl4pVj7LhiCCVq7LvyZra2Nvje0a+jZJkffv+v2R9di1lL8Wo+Vkvu/y//KZft3oVAuRm4cromay06t4WWKufwH32Nb/7tU8RLS/hWsNlJQILRCZtrHYwxdNMtqtWaKzhKPqboACm/hJSSSlBBZzmd7jp+pYonoVat8PHbb6W9+Aa333obv/n372ZgqEZZCIYGK2xsrlIuKaS2mDzFGI1SCk/1twhdYLRSCs/r+8a6FHBwrru+H5BpF+psrcWr1Mi76wjlo4oY9TRNCYLAFUdFxwoo5u+y0HnhOlRCbdtE1BjSQjT5ZpGf64B5ziKCYluFQp+FcS9UC1pbhCl0Wp4i0RIjAk61u/y7//3fsHx6gTwBKxOULPPai6dY3sxZ7/RYz1JKvqSk4LboCvbvu6xo4Qsya0m8MtlqTLr4BlsbG2xZwaZU7Lj8OnRvlaHxYXYNj1LyA0YGRji5tsFYSWHzLkmSsb6+zqlTpyiPXMbxV17ETw1o+JPvvPh3V5PVP9tsGuZnYyanL0LqbWLr2QjCNhyYKiwfmhTVSd+iYRIOvtnzYYZ3mS3cgnDa1SzMhHDvAtQnoFEvOlMxzdk2DWKmGlM0H2rBPMRzEE4V+c2NbXmHxWM2Y7eMePZnjp3p6ZG5JlFUrEE+RGFX0SCmSfNLTRonH6X5tSNMTsTEh4suX7hAg5BmHNMK27AQEU5GxDSBCZqzTRrTEY0ohCMLMN+mFdVpTE8Q37fAlU9M8JdpzAGgHUwxf8t9NJ6KXGB1f1wabnuuC8WfTWeg2mpD6wBEu0OYiQlnIH5XF/e9nw9bk/WBccITCGHRuSl+ZV7AiXgJH8cJXXCis9HB5oZuskV1YBsnUotV5k2cSLvriHfDiVqFjc4q5UAhhcV0E4wxKE/hSYHBODPRLMP3fFRJOk8q7SHLFmudcel5nDAWWa5hkgs4kaX4nu96WcJtibvoOQlSYDONCDxs7kxarXbRbojcSWqEhymMuoWkyFEUmNygfN95PJr+0pZASB+Ra6zUzipIO+alvR5e2SPJlOPEYpd/99VtnBAJSpV57YVTLHccJ9Z0SrkkKVm47aYr2H/VpcOJS+KW/eTpTda3lsnWMzZywf69HXZUriAb3uCF7x/lmVOn+Oi1H+O2xu1cfvl+Trx2lD96+L+jk2qMMdRqVVbOnMJeez2raZfb9+zn20dbjIyNUq3UEJ5y0TD4xfZe7ow7teXYsZM88/yzbGxu4ilLL0tI88wFe2YZnvSdn8nwCNZqPvPJT/AfH/8evq+plspgoRKUyKzBBAqZ11AiwxODhGEdrCS6+Vb2XrOfpNtloFRCe4Y0EQwEAVKCThK0tUhPYbTBKp8eBh+BH7ixoYt1yFHSOfhKKZ1RnPDRWrO4tETFC/jeY01u/Mh+rrhyL0myRalac4VQcYzpB4yqs3c3FFovpKXI63GWDAYoDPBEESAqVGEHURSBiBynxep/DwkiAzyXrZVrpHQRQNI6/69AKLpZQjgSUB4ZRS8PkXdX2Mig/fIJjsXLdHspGsi1IJeKvBLQap3gmit3gxSs9zrkwkcKzUBthHJlEC+zVLOM23bVSfOAq8bLKM/dnQIkOsNDMFiukVifQGUsnllCqoCgWqOCj1f2SbNf0HHhuzyNCJp9q4LP4/IKtxdY88D9nNVH9Vfepvtdqdniz34kTdsVMpP3zML5e4bAhQVW5FYIL9Z5iYoCC+CRmDgKCSdj4tubhA3Xbnq4EXIkhNZ0y404cQUWEYRHOL/AKh7zvAZO8eFWM2ZhPnJdrOmQeC4mboaEB2LCNjQOTNJ+pu02C28JCScWiB94hLlH7qL9oNumbExF3Drd4Oh9c9CYIm40efLJOiExzRD4szaTj09Da554dgEa8Kczdf6ryQbHDjeJJ+Z46KEpPnp7m5ufnadNg6mD555mWBRc8XxMK2xx6HMRB78cErUo4oHeXGBNzoTMz/zd1Gq9n5wYqNSgzwntI5RFeTlGu5vEY69t44Rn6WVd0lyfzwmTMTjiOPGrn/wEf/749yh5OdVy5U2cMHmNYDsnzFtwIhUM+AUnugk5oAKFTjQqCOgBgRB45RIap4G1JkcFBSeERF6ME39zEU4Iz3WnpMUIp0PGKsdPkwPWhUVLl0oiJFi/TJ5apC0hjUbJwrPLOY86XRgSYTK3BKUVInDjS4zz8LJWIo1xi15W4weKNLV4xtLJEsJRx4m8zwkNiy8e5/V4iU43AyXIjUBvFZz48Qmu2XOOE9rzER8iJy6JWJ3nXjzO2ppFyip2M+MHf/Vtfvz0t/g/v/l/8eL6K+zdOcIbJxY4erTJ2qkz/OWfP0664jGx+xbWlnLql+/HZoN45TGuu+pqTp9a4rfv+W0CGZBlmXMst5o875Hnhkw7zdLWesK3v/k3rK6ukvW69BJNr9cjEz10Dp4KqFYDarUamU7p5Zb/+Pg3MKaDzhKstZT8Mrm25EkPvbUFeUrJL1Oq5NgsRyc9rti1i7GxEbTWjI+O4fs+xlrIM/KtFONJgiDAU75biVWSirL4UiC1oKSls/C3JbR0gsNO0sEWyehbnS4nXj3B97/7I14/9QYP/Yv/iZWlZfxKjeX2Bnl3kzTPQEryzLisQuG7DULbz0B0nSqLdu1gkSKlcRuNQhQ+XapYy3WRC8ZmhYeKi9cRAqcZ6JuTWhCePCvEV/0nGPUAACAASURBVNagtMuK9MvgK8tHb/slqkOXc8UNt7Jj/HLWusuUZEpPZ2wkXUq+R6fXJckz2ps91nswNHIl4dh1DI5ewWh4OUMju6kOjzISDuKXfZZyS9ezHFta4+WVNU5vrrG4tsnJ02fopTnLm8v01lNW2jEm2YBcY7SP8AQjtQGGK+UP8+Xwvp9msxBPxxC2QuZnganwrH0CfxKd66iEQONJzqbmnDfPAlrwSP/tKCKa4W3HUtGhtyiwtp3WXH+TMaJ59zyTkxFxHBPF80w8FhI14IFbpp0W7OwXvf33vdiZLHRj9VZhPtqIaX3uCPPNFrBA9OBhGpMREU3maNOeuYXpBxr8ztQE8XSDW8MGR1sxPNSAqMWRzzb58leBSbcdGX21zuEHD/NwBDTbtAhpNI/wpw/O05w4zK1TMPvZL/H0MzGTXz5XYMG2S9SC8NmQJ55ocM8XQuLDEEYNpydr8CZN1tsWWL/gmqz3kxNpmmFTA0aTZz2yxJBZjRAFJ759ASeSDr0+J+Q2TuSOE3/Z50SeYj1LKXgbTnRzdPr2nMjWU7QnKQUB0iqEJ7FKUJWaIBAoI6gIDzJ7PifSn44TuS04kTpOCOljkxRrLEIYRJ7hpMA5WhjQW/i+8zu1Xn9KIkE6fa7OMoRyhZAbKTq9GCYtfCDdJMcKUVhOWESWUZICVE65do4TA31OjF3O+uYKtUpOanPWu11Kgc9Wt0uiM9ob53OiNvThcuKS6GTddcd+4rUer55sI2sDbGQBpr3Cb332XlbjNtdddy2tHy3QtTn//uvzPL3wIr/xmTv5wTM/4LUzK3ymVGOz3GPHkOLEaycwA6N85MZfQvgCbTLyxCA83xUMJCihyPOcNM8oVyusLC8jPN99nlaQJAwPVdAWhNAoK/ByD0+CLlVI0x5pnrCxuUZq3Uqs50ukBzIIWO8kDHk1hsZDBsfHCcd3MlgbZWBAsLyyyFClwlanQyINptulZEukOifwfEyeY3EmpEKB8YvRXq4plz2MTekZw+k32mAEO3ZdwVf/9ddY6qzxre98n/1XXEUqKvzjz3+BvVfvYXBggN7aEv/17/+3XHHdXkzeQ1hT6Lqk02pZMFaA1kXkgSmWBQXCGLdJKHKszV14KLhOmCiiGKTAmgyB59aedY7UOUapQq/lIfycPO2AMO6uJXV3MTfduB/fVzz2J/8LiyfOELd7ZAT0uh0Sk7KUtJGqQpqmCGM59vrrjNYEOZpTp0/SyzU7xobIPYE1Q1QHxlhZOgXKo7pjD520x8qKwYiUXBuqQZUT6wnjwyHdgcvwg6spZyljO4fITRVRVoykKx/ei+GDOCFM3wO7mzDTd2qfi88aiIZxC+YgPgDhQ9CcO8ipZuGUHobnttqK0dtMEyYbwAy0HuGcN8SFVI8LQ9C32poDwhbEdai3gKjF5KMTcFfIxNdgoTXptglDeOCRBwkfCs850v+0vlHbTLoaURG1E8MfJge4otRk8h6Yb4VM7P0czYfvpDEbEz08SfwM/LN2TIM6R1sNrqTJUdq0WaBxcIrDYYtwrkk0HcHdMPlUg9nZNhMPRUSxC56OWi1a9Qm+fl9Mw05x+JZzGYuznLUfO/c8D8LMI05i1pyA5myTyQPAXcXnvGNa9LYL/At83ldO2AwvM0jlI7yCE1ahdU7S50R7GQKf3BhsXsJ2OwwPVtBcwAlA+xVs3iO1CRsra6QDGqkknq/O54SqMRSGDI5dnBObnQ6JMOSdLmVRIjM5ge9hjJN2aBRORiXJsKCh7L93TmDOcUJUFHnH4KIKJaKXInyFyLWbVkhXOAnpObsgk7sxIhbl+W53IM3xfIVNM2TJx6IwaGSuMcpNQgRlLDnGB5sapHCc0Mrjxgs5seo4kfQ69HRKe2kRz6uQ5ikiv7Q4cUkUWYvxFlmScts1u6nuKHP9TRE76nvYf03E3B/+jyyVPWrlEbrJKrZ3io/sjzi19Dp791/G3puuQQc+uVWM79zD8WMnuemmmxioKpdULpWzdjIucsZag0TS6/VYWVnh2LFj+F6Jte4WSS4oSUmtMsTq1lohUXLtToxwYnXrnM3LpSq+76OtQFiBUD6pThmplDE+dDY6NL/b5Pdu/CekSZdXXn2JiWuvROSGLOkxMjbC+toKQ9UBPAHWExhl8fwSmTZUrESfjcoR5Apym5NlFiU9vvFkEyt86vWX+dtnnyPNE0SpzF//4BmsX2ZjY529+yf4xje/h0y6/Pn//Zf83u9+Dhn0IxgM0vqFI7xF5hnO/d2ZzNGP5HGWvW5kWGwbuuO2UaTsb6WA1TlCFfmFuUb2Z+/CtZe9oAQWsqyLTjOEqkDWY2igxsY6vPr6aXKh6SQbpNqNRhEWXwmX4YViZXGJzp4hcjQ9FOMjO8j/X/LeNUiO6zzTfM45mVm37kYDyAJAkABIgpdOiRJFQrLWskumLVkkfVm7aFv0jCzvxoTRsbOixjZ3w7GBnVmCG174h2Lp2RUdE9tw7OzYHodoy2jbsixTFmVapctIJCCREqea4r0B4tYJ9LUumXku++NkXwCCEiRbIiyfCEQD6K7Mquyufr78zvu97/x5MinYsaNBEIScSPuMjI1y/tWUflFwfv4c1ghyLVDRyzz19NPUUChZZ6xRI6opdu/ZxFilwunjcxRy5I14G3xf11QT+JNSKL5hhy8GD+1TpdzqkN9ObAGdD0P8ofTCAmlV1wXe0DTd8ImLC58Y0m8hrJ8CDnSgNbn+uPQ9TTqPQSuOSdPUHzLGJzRf3FXbuDacu5N6W6sL1gZB/tSGcOvfn+7yoY/6c7WbkPIh/8SYo9tNSbvQIoa5Oejcz2wrZq48T3OiQzNprencukmX5nSHuBWTdjuk97TodtvESQpTXeI4ZuZeiJ+AByuH+NBPTHLgNU+UsiD09hqtBOLphLTV9eax/8S7U9/J+l5zwjgwxpSmyhZpPScW5ueZfWWWUFVYHJacUJLxxpiP0nEggtfhRFgnDFY5IV/LiZU+nS93+LW3XpoTo1vG6Z2fZ2y04T09A4GNLMpV0MZSNRJjvfBcIjCXyYkvPPkUInp9ThAFZWqMBRMSVBVOWAI0jginyxBpactINzwnjEYoh1uNnBFe96uCcotQWqwuvD5L+BxI6UKcdP6GXjiUqiAaMFhZAWMIVI38kpxYIjeaUAYIV3IiL1DuyuKEOnjw4PfkDfGdrEce/dhBXYdX+4vMzy/wzHPP8redz/KNp77ALW9OGBvdxp7rr2Xn1XvZtj3m6H/5G2pjTc4vLUMoWV46wy/fdRefefRxrr75ZnZdcz27r72KStjw0cnKJ5N7QaDEipAsL/jLT3yKF188wfHzZxnqApMPWc4yBoM+1UBRq2+h0RglDMAWGicdA52Dztm2ZTNREBJUqt5MVBtGqg1y69B5Qa0a8DM/9zP0lhdZnM+oj4ScX5xnfPM41ShiYT5lpFZFyxxJSK1SRziBRBEo3wp2ovTBUgKJpd9f5uTJeZSwvPjyq8yeOMEf/+lfMcgtsydT5s4ss9AbsrzSx2QFzz77Iiv9goEN6D73Ell/meZYg/Ht2yCIfHr6mp+WKwusMlLBWb+NiPOZhqoUP5ZvKoffEhSrDskiQJRTOcLKdZstp73nl3Ol3YRBEeBUhBMGheKzf/4XPDL955xb7tMvYFCAyQcoqTDW+3cNhj2EcGgZEAqLqAgKDX2jmX31BKObNtNb7PHKyy+yaCCoC4594wlOnjrFqfklzpw7S1jkxNe8iZdeOc380grzvQWKZY0Sdc4v5zz99CtIF/Dm667nJ37uAw++Ue8HgMcff/zg9/QE+2B6e51kD9CehJmjTD8IyR0+YvCOO3ytMAv0Z6C1B9I3Q1yHfnmITgyNOtSP+gnFO/sxM/3++jn6XP6K/TnYA3E7pn+sT+cjsCc5ynS9Sb3TJ+nDbOyPW69/m+P14RAxbfo06z7mp3/Iv24S1os44IJDdaC+B4726+z5QMzRR7vUf2UPyW9M0kz7zM5CsidldzOhP3kn3Q9/Hq6rM/3/Pcqdo/vYfWyaQ0dniVt7SH7l3fz7f/soXWbZ85GE7lfqzPZnEf+5T/0D+2DQp74P6KfM/9Sd7PtA3QvdV1e3A809MJPyYD/md17xNg6pSaHh5wFmH/WvNf4OL/d3s+6444439D3xPedEEFKGwuBsgBUBg37BJz75Kb754gleXThLtsqJYcZw0KcaXsgJXXJiWORgcrZv3kz4D+DE0nzKSKOKVjnShVSCupdEiYBAesd1Jz0n5GVw4qWTKemZZRZXOdEvePa5Czkx7C+zbSMnCMvpcOf1WsYbq4pgNRO3jHkzBSLw3loucBhjvdWEBet8p0uoAGu8HZHfLiz9Hk3hb+JtWbw5jZIRIryIE0eOcG55QD93LBUCkQ1QwTonhlnPm2FfQZy4IjpZk7/4r3zquKyQkRNFVXJdIJXmm88+zZn0NGbFp4rv3nkDP/kzv4atrFCv1lla7nFq7mVmTr1EfMNOTF5w9VXbCBgtp+9CtDX46BlJUA2phj5raqRepxKE2MxRjRpoWSE0Bj3MqEchmc3pDy15tgJSIQhoVCNcxXJucYlGrUagSoG4UAyGBSqAeqNCkQ35YudzvGniRt552z5GxxskN19PoxIhhSBQMBwOGK1u8vvlvWWCWuhNPR1EIiizBP00pHMKSQOdz3H49/+CsbFt/P3nnuDU2QXCcAWde3dbbS3OWmwYkGUDpJNENkcsWD4+/de8+PxL/N9TH0HnGTJat2TwxqblViGU24KlpYMM8TmJ+IT3jUo+p8qpxA3u886sZSBaa5FoL6gMI28XoRShxUcV9fr82ZE/p9/vo7VFa4cw/vtWWO+9NShypCqzrYRgdHwzYSAYieos9Xu85brrkKMBKwsDRsZCpKpRDavUogYvnHgON6yhwirbRsaoVywuW2ZTrUHgNrG0tMiyXWE5FSwuZJxdPEN4menqV9xaMwD99ivtrpuLpoenmIih/X5gCj52CroPXLgL1Vn9+tVpt5b3yYrBu8YD0zPppTsrl3Iyx9c6nY1fU3pR3f+2lPbHUg48HDN9X8tvRcYxc2m6Hjvz7WwLUnionZKWJ0jn4NApmJxOibtzpPckxBfJw5IWdA8CB33nK51Jif8woTkV0211SJIWCTNwX5v03ilIJuj8xByTyRyJewrue5jOXMxknMBM4qN/nmzDb0L68AxxK4WZLnMkxJ0myT0wFc+QJLF3kW/G6xcz9tqrFEinYj56AA53D9F+5CGmD6fEEylTU35KEsprcrnbhv9E13fFiWCFeuPyOFFY413InURFfhIw6w8YqdRpOM+JStRAXcwJU3IiX8FJn8fXqES4qiVdWKJRvwQn1KU5MTbeYGIjJwLIsgH1YBNOGIZ6GRmECAqMg4jAGyGUN8mOb8GJYAWtDdlwnROFCiiKPkpJwpITfzb917y0yokiQ1YkaIt0PlJHhsqHQ+OLKNCgwvLmOsQqixISWS2NpPHdKqzBCoMKfcfPauN3PZznhBI+PcRFIUIohFSEQJEVDFdKTgxKXXXmiJzFBRKtDbiAgcm8+fUqJzZfGZy4Iiwc7nrXHhcGkkhVMaFkJKpjjMGFQ2rVcaKwglBbyfUyYyMRzobs3B1TkQGj46PkiwFBvcrY2BhBpc6OnVu55pqrqNZGUEr5wOIyPkYEIUopXn3pFNNH/opP/91jpIt9jChdyrWh0JrxzWNorXFW4WzO8soQZQyFdWS6oF7xRUelVqdwMFJroIc52uXUahUC5T1D9u7dw23vSHjH225nR3MLzeZWdDGgVqnizJAoDNHDHlIKhLGgJFIFyCgs3dz992dpfonjs2f57GOPc/SpZ3jx1TnOpylWhyznQ1yRI4OQwlmcsSghKIrMX2Alcc4xFoZsHR/hE3/+++y5YfdqYg6rPwNCyrKzBaUlr7dncKrMEV0PkPbxO6ttYlMK5+Xa55xzyDXjUreu85IReaGxFgpt+aWf/SWWe5rj80vkeU6WFxjj3e5tOT7snCGSAmTE1dtGuba5iW1bamzatYfuc1+nmvXYstmwd89NbNmyhePnM65tbqGuDE5JzNAyLCTHz5xhZPMugiLnkb9+nLnBOeZSzS3JjZw9d4JBDxZ7fSSCwdD84Fs4cIla5SKvqtdd36PsvNXTrkX+dOH3uikP3BN/R1tjcQozByCegsmDbaYemyaehsNHfONu+kHY/0BKuvGgCXBvQnqwy0QLOu/t0nygSdyK6XZTb//VnKB7pAl0aLUmuP3+lGPtlDSNoeWL0YmWL3460zOkid/qfF8Sc2znjA+YfmgOuilpMyaeS2FmtaqC5EBMdyp97bWdBp4ADkHShiNv65B8rMV0uZ37/VhvtIXD94sTSIncyIk/KzmxdAVwQgjQFiMFURgiK98BJ7Ih1uYoGWKUw+ReR5ZlGVKBtRIRWMbC6EJOhH4nEJzfJgyl7/atWf14Z3ecl4oII0FRWjWULAAEBms1qxwRGN/hcpHPxLXO35Q7QITkhcE6KKzll376EpwoDFR8YosTElsUVEKFJWLXjiuHE1dEJ+v5Vw1F0SPPz+EKTbNRR0WORatR5iyRFNRUjqxXiIIKkXZ0n6rQaCje9e53cOwrXyeXjsBa3n3XXey4qkWgGiilSk8sTUhAICs449DKkZ4/x9y5FAKFjELyPKOmIka3jtPcsZ1nn5shMAErK0tYM0TYAIlBKUtUkVidI2WAQTG2aZSV8/OE2pFHArM8ZKQWsrTQ43hljjvu/nHOL/XQw4z5xUW2x2NgHSP1kN7yeaqhRBs/zUgQYFXgM6JWba2ARr3KmbPHeXn2DM89f5JeIVhZMtQU4DTGaXTuKJzfmy/wdwnG+CDOIAjRwrE86PPZz32ROysBO/dsA4IyAqcshFTpkyUCELa8Kygjc8TGn6fVdpZhNaV9/f/N+jGdj1Hw0T8WoTMcjsIaTp1KyQc5C0t9hsbRzwqUlIgAtPYRCkVhCJR3ildKMRwO6Q+rnD+fsf3mmxjfEbO1fhU3vuXtbGqM8dKzz5IjOTN3mrrukZkIE2U4GbCUwWi+gLU9ZGWZH96xh933vp2l4iwvfCXguedP0JOKIAi/Pz/43+9VuobHD3jfqOnpsju12gWZBKYufEhysOzu4EXpSdNvE5J6x/Sk6Xe1krb/0532n4vj16nBpvDdtvKTF5wyLqNkgHiqSzqZELfggcn4OxB3p7TaMVMdSD8I8YGU7iPTMAf3T3WZfCghvR9a7+/QSVska0alZYvsMW8hn6aQvL8J5SRf0pwgjb01w/4DHXYfivl0mvLvki4T0ynNAy0m4pSJVou02SG9O6X1uy3SIx9k7pFP8el77/cvNp4gpkmn4ztnt3dSPt1pEU96kVj30PpVW9XmT07G3N1OaU3F0JrhyKGY5vsTJu+NSdpNuoe+E8fXf7rre8IJ2fAGpM5hjUbZgCio4GzJiYVzzJ1PIdzAiShidFPJiW/OEJqA5Z7nBCYgxCBlyQmTI0WAcd+CE+cu5IR9PU5IgZaCIIxQQYCVl8mJRU09BOc0utBoaTGZ35YbGo0KJXlukIGflFzjxN9/njsrd7Dz2m3e+1CUPpPW4ZTyhZYMccLrd8t9EawEaZ2fKHQCISTCaZ+VKJS/QZcRVheUBo++YHP+9zzG4EyOk47CGE69+lpOhIHESbyPZOiH1oJAECiBCK4sTlwRmqz//bcPHhzqDG0Nzc2jvDq/wtmlgl4+RBfgnOJ8mFFXI/QGEa+mA2bnzlCp1nji89/k6ItznDqzzMKS4Y73/hjX33g9URCsxcVYHCg/9qpdwcpSTiBDHjnyCeZ7Ga4oqIaRtxkIAoa9ITq3GGsR0mKFzzvMjKbX9z5TYbWGDBVRrULW6zEc9slswabGJrTWLC6vcPObrqPWUGzdspkoEGzaNEKtooi3bkbrAU4bCltQ5BlSBAiliKREWOsLC0o3dguzLx/n2Ndm+E9//pdgQxbPpVgBg2Loq300DjBGI5UvemwpVg9VhAO0MVRrIzzz5DHe9uY9XJ9M4HTua6cyZ3A9VNqrE4QK/N0JoozcsWWtVRrd+Z36DUWV3vCdLTtkPn7dPx6L0d4VPreaL331G7x04iyD/tAb1VmNRGDxEzRSCqT0bsE4Q71aY7QK+374Bmw1YldzK7t2XsumcDNbt+xh166EhZU+u/fewvbd72Lz1RPsuCphdOteClFla/Mmdu68iV07buL4/FkWTp9gk6pw78//PO+782eJti7Qm1vhf/w3//MPnibrj2Df70Dah9n+Bi3SbPnx6Gsfkj5efgQQ0IhTmkmdNIVmE+hD8hvQn4V0pvzC34P+LVwkdirXPv8YpoHkolP28cXUHujf2YS+79LMrAqnXkd01G7BzOprqNeZfdQn5fT/CpJ31Gn8SkJabxKn0+wZtKh/oE//03327GnS+Qj8yuNtZs/0/fFnvd4r7UC/Vad+LKU5mcKjM9T/VZ+ZX5+hcTjmhbRPly71TsKPFvuI/rjP0ak6g7tSdu/bDSl0ZqG5507uenyG+rsT4n6T7qNdBo/Cnsk+6Z49LNZn2dfy5+5fJBBL4wSOphw91qfZgHi2Tv3/gsmHoHHfPh697xjp0fKiPAjccenr84+13mhN1veEEzIAWXLCOcQqJ2zJCXEJThjPiUHJCe1M6S0VYuyATGv6WeF1XpUaMlBEUYWs/zqcuKXkxPhmolAwdjEnckPhCorCF2wyUIRCItzrc+IP/uIvcRs40c+HfvfAaRBgnOeEcJSTgZZARiAgzw31sVGeeaLkxMQE6Lz8LnjdrvdLBIHGGeG3DcubcIHwU+uhNz51gvKeXPpBKACry26WZwpOeIWv9JxAeMNwZyW5u4gTxmG1LhXBAbYwqEAgQ4XREmzJCQX7fuSN58QV4ZN19ZYx3nLtTm65psm1O2K2NKqM1iO2VjcRhSFLWcZN267mnUnC+DhQnWfz1nHufu+PcHIwR32sx5tvbXL9deOEURWnDYNBz2fuGUMQhYRhiCgNNucXzvLss8+QF30WFs+SFwWF1ljnqFQqzJ54hX5/mSwbUqnUwIDWFotg25btjI2Oo6REioBGVCWKIqrVKrVKlSzvU60pdu2+ije/5a3kec473/F2rLWMjY0BEmOsd5EfLJd5gOt/1rZvhUAbX+GfPHmKZ776dbZt3kEY1Fnu9TESjCn8R+f8ZIkxhGHo78okPuZGBQyHQ/I8xznH4uIiZ88NOHr0WZbPnfNvTmNLtadb/8Pqvy1+7Mb6/XVn1wKqhfDFlWQ1M9Gt//9qBwvvveWc1wBAGXFkLcvLy4xvibHWUliDFRbtLFa40lHfe3MZa73lhtEs9gYUeoS3v+NnmLhuHzdf9w621rZjzHlOnnqGV08/Q1UZisEKw0FKb3iclZUheWZZKQzPvPJlPvP5z/FU91MMw9OoSHH27BL/7x/9EZ997A/4pR/7VT7yf/729+1n/7te381U2aTP+AMggQ7dy469iYGkC11iuhdNF6YXdb94oHzA9Ld4nhu2uKY2/Jpql/4FaSkAnz6M930od9SmL/ErbXpjlyv2FhAAzQeADszd32V6pkuTNml3hu5h78F14GFovR86j6wKzTYcJoE4if305W+Wx70X4s40cy3oTkAruYfkoZQBh3jw6pTmTEw8lTLTTWEuJqHDRJxyJE45/Jsph49Ac3/CXCvm9m7M++gSt2Jq9/nXnOxnTUMGwEzXn7gFE/f6Nl93DlJiOvd6kdzqPfL0Va9znX+A1veEE/kGTlRCVBgguDxOHD/xCv3eMlmWUalu4IQQbBu/iBP1KlElohp9C068c50TJlvnxDDznJDSM0KKCzmRD1/LiUBdyAm9ygklMNoQyhBjHVZKDAInAobDDG1yhHQsnF94DSeEtaUe14H2nHAGMKXm2Tqkdb5rZQvyoXdy99IlURaD5eNwOOuLMCFFWZ8ZbxvhSiG/lFgsS4sbOGFKTmBxyovwrfWc0LlFG4NxnhOZvTI4cUVsF07++q+wOH+Wl145QXzVNna8fILdu27g7770FcZHq1RkgGs4Gs1N/PRbf4ookzw7d5qXXjjLj//Ibch8wIsvpxT5ImObRnxsAQFS+xzA0FX83r0VLC/16Pczjh8/RZ5ZBCFF4btTzjlOnDiFlYrhYIBzhqzfoxr5YmpoMmygyXPfdalGAf2Vgc+JMmAzTSFAF5Y8W+Ll2VkQgvTcWfbtu43m5s1YnVPoFdK502weGUPbHqNVP+5qrbdrANBFgS58AGe2NODY8yf4xGc6mKHFWs1QWKwAVxqrOmvXXoO13u+lKHJcWdAoJSmKAick80rxHz72l+ze3qT93/0LwlCBsUjp1htZIgAswmU4W/XThaXA3W8elj86opwwsaY8/4bX4VZF+16/5ZyFIPA1m/SPPXduASEVUaVGlmUYa/wvKzNEyRCtDRaHEt6DKzeafrbM1H/8CP2BZW6hz8imCjWh8HaqAdu2b8a5Ho36NqLqCGGkGB2Pscs5wo0Qb6tw7myfvdu30KhVOH32LCfnhzyXFnz8k3/Cth3bueu//bXv63vgslcSl2nI/8DjTEErScqcwdUVQyclfsjn5l1s3t4t/70aK8N+vNg+ufAQa6vNZT3PyQ+t/32644+3Ksonhk6pkE+mof0xYK40K109XwdaHYifhM4Rb/HQjbs0O026XbjnD9sceM/9dCee5kDzMxy6u0PyGHSf8qah997b5d6DXRISL34vC53uezskH0oggenDKQcOQHdmP03S8iV3vTjdxfzEfR1ub0/wn98b05rx1ya+7wDTD0Nr+jD3HGjTmekyN5OSxDN8mhYTKSRTKYeIaSXeHmIt0Lp8bXEr5f5f7pL8ckLrgZjmPSmdQzM+Pmga7hWQPAD7J//J22B92/U954Sp4ISlMI7l5Q2cGFiECCnyS3BiOMBhyAc9KkFAjErs9QAAIABJREFUo1JlWGTY8HU44S6TE8UGTjTGKDZywpWpHA5sVmCdxeYw3MiJgcUVmkxZjAZrjI/C0T72xglAaBQBVltssTo5LilMgQwk5530nNjRpP2rnhNuaFAhoMCWebiu5ISwVQik11rVBFJrhIywhe9SIfzEunUSYYz34GI11s1inM+4xVh0EPgCUYBQGzhR9ZzIh17Xq63nRJFrn+XrApxW5FIz1FcGJ66IIkuZTWwba3LND7+DLXGDwY0Zg6WM4I4xsmKJSlDDGcMwD3iu+3W+8fJXGB25hm3bdjDIob5jnKtGQtLzkq1j49RrNVQQes8OE2CdQFqNLoVxeW+RCDh78hREIVEtAimIN2/n9Inj1FSIkwG59unrS4MCYX0nSEcZjUYDpUKsG7LSz71hmjWIWgjZkEpUJaxE3HDdTYzd9ma277iGUAj6vfPs3nUtJ08sMz46wvn5czRqI4xWamitKYYFo2PlfoGIICwwzrI3uZlrvvgk20arfOWVEwQEhAhy47AGLJbCgHACZR3GQV70vbGocxs6ZBKLJHQFxUBxdOY57hoMEQSoqgLrPbN8EVVu+zmBENp3s/ARDdYWCGlxQiKsf9PilN8SdH6v3Ns2WLAWKVQpeCy7XYVFOEuh+/zQD+1jpdfj+edeYe5MgQ0EIqhDnmOFxhrp72iEpjDQiOpIBIHbxNXXbGeh9w1qahMnT/Y5cy7FGsm737edL3/xabaPn/VFYVZjbGyMsfERZo+/yNKi1/Tv2lFh+47N9JeGyMYYvWzAk8+/SH3sCtZkXRwb852uVaV7gu80bRRNT6fQLqNtVgun9kFa0we9JKosfDqrodAbv27jStrQmb7sblu6UdPexB90quuP3YLuB713VtxirfO2UXffakJ3svTPSiBOUiZ2zjATJdBO6CYdDvIQ3Nrl5FNdknZC974pYjrQ/hSPPdIhbSdw6wE69x1aK/CSB9Zf3P6HJuh0UpKJlLgd07k7pXWgBTMd5h6G9oGE2UPQ+mib7vRhEmKmb52h1Yb0oQkmplO6cZM4AaZiYrrMpCmtJiRzG8YPkoS42/GC/JbvbB14f4uDj0zynvt+kXZ7N2kn9tq1ltd1rV62H/Qi63vGCa29tQACWWi02cAJC2dPl5yol5zYcjVnjr/kOVHdwInhBk7kF3OiQGaXz4ldu67l1Conzp+jUR9hNKh5D6xhQb1R95GxKgLpOXFDcjNXf/FJmqNVXn75BIEIfL6hLbtHwpBrr5HCWFCK3BRIvKRGIXBG42SANgIZaIo84sn/ejEngLLbhNOlqF1gnfZD5k6UQdsGYSwyUGC0j2orJEIZP60vfCA0WJy2KKEwrii7XRYKt4ETt3tOPP8Kc6cLopokaIxQnM9wqsBotcYJLaEhrxxOXBFFVveZz7GyMEe9LhmrjbKwnCNEjYGusbJ4mt7COTKGxNt3s2PzVt5x649QCyqMN7YiLZhNkkZ1FCGrjI7ECFFhaCyhtFSrVUxhEVKhdcbS8nm+OfMsi4M+m7c3OXX6LMOeplKvcPL4LLVqtLataHsZCMfIaINhLnFIpKoglWU4HJJlA7ACGYaEYUQYhvSEQ1RCFnsrPPbYY2waqzNWjeicfZUf++F38tzMDM3mVqphxDVXX481BVGlTqVSIbM5MpRIoTDOEskIM8yZT+eYX17izJlFNo+N0lvJfGfOOVQgMNbgnEGuFkhCEpQJ4a70wVottLwlBAwKw1//zRf4xX/xEjfcuIexqAoGpLBYYVHOBz2jfOdsdaoQ45+fz9DRWFb3xh1ob/8gZXmuspNlnf/F49vDfrrEaoNSAUePfYl9+97MyROn/GivdmB65R2j314MQ99tNNpiAxhScMdP3wl2yI237CLtLfE2OYrOezit2LFNcfX2EYa5ptdbZnz8GlZ6A/besIdrTu7kmede8NdDr5DaZYb0EAsZC70Bw0Jgzw/fiLfBa1Z8oOwo/aMe1E+7xZMwHZdxMpSAvtSU2mqBtXGt2ihsWBtypGkx7cXxl/+ULvpHF07hheLA5Em88eb9rMfnxGW2XwqdubLzlSYcmu7yqfdMENMlnfR6rbgTk7dbRNF7+OD999GanCHttOl2WzTpMNUtD5asF1hQ2lyUnlrpVErcjMtzJ3STKVqHuzCRQLPLzFSTORKSw4dpTcSknSbtVpeEmM5Myv1THX79d5v8D1Mxkw/FtJtt7j8yw62tDgeSFlMfnIMW/nmvVbPldU1SHn5qitbc7aTT0D0CNH0tu/H6/6Cv7jOfY2Vpjnr1H58TtWq19HW6iBPZJTgx++JrOeFeywmxgRPiH8KJa0pORHUqdc+JsCqRKIy2RCLC5J4TC8tLnD2zyPjYKP1ehnEGbRxhTWK0BmFR0vlCiYBQVLxOS3ouKBmUOlu/cTEYFnzq0S/wS//Sc2I0rIL1t+tWWB8ujde1GTcs9bei1GkpnJRQ5DhhShG85wT4iUCBxRjvv+VEgTEW5Vx5876RE/9ljROy5IQZLqOExBmLMxCEIdpceZy4Ioqs5ZM9nLAs9s6RqSWu2r6DagTXbG3S2HIjaW+Fv/ry5/j689/g718qENEYm8cMRaapRhIZStRIxt43v4Wfuusn6fcXCIMq1c2xD8kMcgYDQ5qepdFosJAu8aWvPc3pdB5TOGq10gNKVhkOhwQqYmWlh3UFSkYMs4JACDJrWVlYYn4hIwxqRBVJLdpCbnOyrEe+0GfbVTuxAipBSKEHVMfGeG72LJtqipOnz7P3xmtoNmN0ltMfzhNvbWKyjEFuKYqCiqp5915rMNZnOn3161/j83/3GGFYYJaEn+JQktApssKU47Ol4RsWpEEQrm8dinWtlCvFnbnOWRnC8898k11Xb0cH3mw0FIG3fCj3zjEaWU4GImwZIFr15sauQInAG8g5So8vR1FoAum8Fgu7to24OkGyGi49Ho3wb+7715xfXiTLMh75079h6cwi9bCGVY7CWKSUaG3XisZIOnbt3MHyqUWsGFKNGuysbqeqIqLRUTQhUmlGr92OIEDiUEIRhDUMhus2X8ftN9/O1rFxlpfnEI0Rzs8vcPrkKWpRjZHqGPWxK0Gq2CU9lKxP/sX4dsXr0fQyfKOIITmY0nmKtZDn7oN+u2ljzEw8WeqsEnjwl6d44DOT0IGpQzB5AJID5RRhvH7o5MD6MTsbn2MCcfn8L6fT0joInfuAj/npxQMHYPpWfAG4OgkZX/RyV6u7qS75/pin3gOH4hYJHe6eSonjKSbbCYdyyB9rkz8F908lpPHdpHNtZrqHaU9eWGEmMRwhJY5jkukunYcSJrqQtvze5eT+pi+GZqCVHmCmc4iZ9BBJq01KQtqdJqbNVGeKVieh/eQ9HJs7QosJ0sMpnXiaVjfl3mZM0uly6E9SWn8SE18FSVlcdmjR7t7Nh6cP8NEYWo/EdB5L1yci/5mt5ZMrnhMr58jkZXAiHGPzpm/Bid4CYVilum2rl6WKnMHQMHf6LCObLuKEcdSq/neQtSHDLCeQEYuLPYQsUCpiOCwIpOfE8sIS80sZofScqF7MiR2Xz4mBmWfraBOTZ/RzizEFFVFDyAs5cexSnBCSeqgYrhiM9F4MuXYIoVHSYl1IGIhSZO7zAwUSawxhEL6GEzUlMEr6rcNIIVe1YaYgFH4L0Xsv+gxDFxgcGkXoJyGRWKeRDrTSPg8Rrz2TUiCdQ1ufu2i0xWnBJrXOiUGW86d/8lcsnFpktFrD1R350KJCibFXJieuiCIrnU+x0iJQWLWFrz73HNu3biZyX0dWBFu2xNx58+1sevuPcn7Q45Uz53j61VMcf/YE54M6Y8px065redPE2ymMJIxq5HrIYLhMOLqFItfo3BJFVZaW+swP+/QGCoVgZNMYThmEcAxzg9CSrBhi0X5MVvg9dC0tURQQbamTZQFBECERZMPzuEAysmmccPNWtNNkRuMi2Ll1G1VRoV4ZsnnrVbiK5Pxyj9GxTdTCgGqtysrKClFQoYJAhRFGQ+Y01UhhTEGkQt56y1tZWg5ZXLEgQ3KzhLEWYwxCKozWOGtZNVbyoc9+0tDiCFXgPUtMKSpUxt8ByIgXXn6FHy5+iN6woFYdw+gCaSVGUcYc+A4ZLlg7uDOZbw0rH7Igpd+q9Losn57urENStpYFYDXWOKxTWDMgz3JqtVHCYJ7NtTGOv/wqt73jragn4Uw6jzYOnMLYAiUNTgZEUjA6UiPXy/zd45+hVgmROAqTo6IKWa5pRH4PvxABkaqijEHVM1B1BnbIqBynQBOGFQI9pKgq+v0+NhsiRIW6GsGEGfe/Qe+FtdVKoNMh7bTWDD/XKosytiWdhLmd3mbh223NTcYw1e7QJfHbguUDktWYmxg6xLQoDTxToAMPfOweXzW1EiZLo9NuB6/Hml576FqBBeWXrxqGdr0R6OVuHXbeA+3H2kwfnPY7mnf7c3U/nJI8sF7VXZzs001SmJvjMe7h0GchvWqa9HcTDsSQdlo8eHdK2Jrjmt+f4T99cj+tdsyR6f00J1M+9UiLQ/eud60oZW8HHpmg83DKXLsJd0+THmjTSqfpEMPhmA5d9rd9QUXS4qEWpN2UTieldWCCI3d3aE0mpB1IOx26LejS4VQnhW6LqyZj7vlgl+TlFiv3wP1TvpbslL3FNtM0kzYPJAlp2qV7HzABBw8mPMwc6bcKg/4BXOnCOazQgMJdzImqYMvmmPclt7PpnT/K/Mq350SlUmNo+gx6y4SNdU5U6iUnsj7Lq5wYHSsn3xxDaXBakOkhQmmUKjmRa7T1nGhcxInB4Dwi3MAJ+x1wIqjSy3qERFQQBCLCWBjmBbVasMaJWzdwQsiQzCxiVs2pUegixwpXxssJ8swhlWNQ5AglCGSItX4HQQq8fY6UOLHOiUFRUBVjfvJ+oDGRRDqDExZpS9PsMpbIkYMxCBVQWIsKBa4AISwOhzIWoxzCeZG/MwKLweYWFwQIMaQwOY2RUcKe58SrLx/nth96G+rJr3MmnafoO//zIAsQBueuPE5cEUXW9t3bWVycZ9uO7YxV6+z6yX1Eqsr5YkB65gQvP/sSn/3qE5w5v8RLr55my6Yab75+J1fd3uTmm/Zy6vgS27ZvZWLXdi8CLwzWCCpBSD7sYzAEgSAb9jnxyileeOkFhsNllNT0+wUyUASRwuSGbNjHrlXElooKsJFiMDT0ih6DwYCgWsHl3mSzVouQgcLpAlmRuFwj9ZChNryaH2eQjXLvv/wFrtm81euYrCaKQmxRYHODdd591xRDGrUGQkpCqVAqwLmcLBsiBDTGLHF1nBeee5FA1TE2J4pC8tyP1irlM7iMMd69V0VoAcM8Iwz8D7FxftvQGgdCY13ByyeeZ3kwIKqOEWgBGMIAr7GS/q5GG00g/bQfzk8iCiMQtkA6s5Zf6JzFOgNC4oTFOIWTpYeWdMjATyjiJAEB55ZT3y0rCu5417uYmZ0lPZEyd27BF4E4r4l0yheJTvg7ziJkuVjhxVPzhAps7oNKlwtHTThc4AidRBtH31mKbEC9NoKgYMtojM0KlvpDVlxOJB2jUcSNu3cxzOeZG5zHutob+G4oVwfKlLwLV7r6OW+0eblNjSmA6dZ6oPIlztciXe+UrflHxUDsE14odVEdfJHXwWfqTV6YU9hKILnP+2sdTOHgpXRbl1plATl9cHq9s1YWPsmH4tf1P02BpOt1SmkKbeY4nMT+6nW7tA606X6wQ5I+zO/9/Ek6M0dIP3w/B97a5v5D03TY7/MESbwZWLmm35P687UmaE36qcpON+bgfW0OPpEyd3eX6UNd4nbCzJEObe6BrtdhTXVS7x3WgThu0el2aHfnaCcprVabw1NlbmQ74ZevTsm+5jt+nRZrrzIlLrcOLxSiHTz4z2Fz8LVr+65vz4m/O3r5nDCFwWpFRUbkw8FrOfHiC+TDZZS4mBOWbDhY65wIUXIiVPT7hny4ggov4kQ9QlY8J6JIUujvgBPKeK2SMJ4TVc+JqBK8Lieef+5FQlXHmIwwjMiLHIQgVAqE3/VQQKSqZEh0MSCwFicFhdOEYQjWYZzGsoET0VgpDzFESuG0wEpHECgKpxHOYrT308I5pBOgc5SwYCUCgXeZtiAFwlkQkiBQOCP98FUk0EPfCAhtwLmVjZz4kZIT5zwnSt2xl3YpXHDlceKKKLL23XYLYSBZ7htcJOhrTbpylpFqyPio5JbbbkToKqYYMr+4xNXX38TNu/cRjVQ5/ewMW65/getv2IsLG2TZgFoYoRwMBwOCsEK/6EGu6feXqdQke3fvBX2aE6+eolLfRKMaorUmaFQJQ4W1ljzPqQR1+v0+gZAIaZBOMtIYR5s+2hiQPkBUBop6fYTzZ85RoAmCgEplFKszhHE88+RXOblljO3bm9x4416GK8uEgWSQOS9MVI56vUYhHFIIbFFQWEEQBOTDIZvGR3lLcit/88Uvs6m+g4XhMlIGFMW6J5UXuZvSODTE5AXtX2jzyU9+kmHuDUCFo9zWkygVoHPBlq1N8jzHakMue4hyWjCIfMtWyAqOEPDxOMaVgc8OnPWu8La0ZkA4sK60I9UY40X3vn3sxfFWWLSFQTbEOuNN45aWuXbvboLNozz5xNPcevttfOPrT7G81CeKKlhTIERAGAaMjNcJR+ske8a55dYGyyvnKBYzRscaPHPiFOfOnmUsGMUJy8pK30+eFJBrH/VQyCFBTbG40kfKgMwJsuUBS93naFS91UdYyV/7Q/pGrHRVeHTRugyV80aN1AVrtcha3X67L/aV0OoDygd1u5AkMNmCqU6pk5+apk17/dxdiFsd0k6beJL1yB2gK4AH4KmHgEOv+2wuer0XvQBYL97i8jlfokhcjfuJibn34AT3do/QnkyYeu80J7M58rTDXOsPue/D0GUnBx++h/cwA3MprZk28b33kDw2RbfTLTVRCXSh/YeTdO6eIiWlu798fXNw8NZp4hb89kOTzM6kJMxxhDJ6KJlgZrpLknSheQ+dmS6tA12m3hvDW+HlJ9p88NAMBw50SNjPB1N4/8eaVH61w4e6R4gfm4SD3/7y/HNc+267hVBJlgffHSc2X/8Cey/FiV7JCVNyIis5setCTtQrIcZoopF1TgwHQ2rRCP1eyQnhPZsatXGM6/vIF1VyIlPUR0eYS8+R24IgCL87Tkhv2WN1QcGlOTFe38HCYNm7uzuDU77gcRaCUPtdCuowMPzCL7b528c+yVKvj3QOaSRSSJxwJSfwnMhybyRd9JBKUWAJpOdEkVVwBASB34o0TpTFok8C8fITgytNfYSwuFxhpMbbM0qk0uAEFgWRpdd3DIY9rPynzYkrosh64fmvUQvHGdtxFaPBFmQQUlMF1dBRq8TkxRBZUYQEXGUNTllePfc0InUQaUQ0zounz7F11xg7lMIIqER1nCyjEnLK7b8aiAFHv95leSkjCCsUZoX+oI4zPqNQCIcIBSoQrAz6WJ2T5YbKSJUsy1hI5wirFVQQ+epbg7OKhcVlhIBMaz/NGKzQWxzQ7/f5wtGvctdd72PnjqvJ+gPmV+a5asc2pJOIigIgz3Oq9RGkkwRh1XeohEEoyTDTvPvH38bsy8/TSV9BuMwXMNKhjSQIfPCy1l7DJHG4AP5s+khp31BOF8pydJccY6tEo77qXz53hkhrNm0eQUZVZK2KERIflK4hqKD10Ef/CIW1DucKnLA+rwrfSdMWlAEtjDenK7esVz1dVBggrEQpb02xPMwBy/hVMV/5whNs2nIVD/4f/5bf+l8ewFgIwwidZchKxGhjjIm9O7njx/dRD0M2NUYYBhHVkVG6z3yZZM+bebcIEDJABhn9lR5xvIPBMEMIRa6HWBSRFFg0TgW4oWRlecDZMydQIwFKSYZZn/n5ue//m+BSK570o3XJxgIlpdOJaV3cwtpYx8SQrBYmF5O5y1qBdehQl6QzQztprz+2/PrVU3buB54GPhTTbu33raKJhNbMHMnJJh886Js/cae0eZ/203d8zAvHDx/yW29rEXvT0HmdGJgY70LfOlCKzvGnm4v9FF36el24FJiDqbTLQQ4yM5PSeW+XyY+2ORl1+M1uzO+mKQ/zFJ8lJRJdDnztYe4/dJDW/pTOe+6Gyf3+2qYJSek8P331FHzIN/OSUluWzgDtmJlu6icG6dJNuiRJi7TTId6/nzYJnfsPkU52mejG/EgrIXnrFK39Le7/YMo9SZf7pydot2eYebDD/o/tJ/mDGGZev8Dy37gm351B2g/G8pzYwtj2bYwGWzdwwlKJYv87aiMn5IWckK/HCae89YCGwhQIKp4T3yg5EVQo9AoDV3JisILAISqCMFKs9PtYk5NnhurWKr3ljIVzc0TVCBVW/JabBSc9J3Aw0JqKk8hghf78t+YERhLWfJhynudUaiMIpM9uzL41J3JtqdUkRSYQKGRgvf8iARXht+v+9Mif4awhqkbookAqLzURymBslXBE4pxgKT1DlGs2bR1B6CqyUcUgURKE0jhVQRfD0vdKlnY+eamL93mHMlQYA6EQ5GiUCNEOAuHQBqR0SKmIVIVgzBKFgpWz57k0J/43jHVXPCeuCMf3l15+5mAzfhMyFKioAipAhgEOSd9otAoohobCDslMxjBXGOtFdoW2CKeIaqOMbopp1GsoKcgz71FidYEtoCg0J0+e4eyZcyycH3Dq1CyDYUYka96/BFBSoYUFETBYHqKNJi9yDDAYZN5GX9oy10pQi6rejHPVLRioVGrU6zUG/Yy3vOUtLC4tML55lNnjL/Hf3PY2hLM0GhGD/hKjIw1wAbVaDWdBW7MhE8q7qVvrpwjHt25HAl/48hfJcotxgiBYNTA1aK0JVegTzJVYMxRd30JUaxOJQjkUAY2KYu91V7P3+msYHan66RMpCMLA5zwKVRqOrsbkSN+Qwk9/iNKjVAi/HamNQVg/mSJdGcPjHE44fPyOwpX/Z6ylElTRRc78+QXCsEK8dQdffvIrjI9v5flnZwmjgP5gQBDClvEGO3eOEZLT6/eYffUVgoEjPXmCoMgZ6gG9lWXybIVhP2Ph3DxYmD9/jmG/R28xhUKTDTOyfp9sMEDbFYzoY+WA0aBCPaywpTFOo1bjx97zc2+ou/V/+PGPH0zuOEq/eYGRFd1OnX0bC6JVh3Cx4e+rzugXu6RPA/cAvwEMoLWvSfKR34XHj3p296H7KDT3rT+klUB6C9TjPnXRp19+3ew+OPrxWWZpsWfmGLsnY/p7gFfq7GnCbLr+FPbU11/D7LdoaPW7sKcFHIZ6Kyb5rT53VmC6n7Jnpk6clK7oF624k7LvE3Wm//uU/+ngFt71c/Dpjzb5w/8H/v1Yg8nrjtFIDtD5i2P86z/+WSZ/8SWOTX2cNO0ye6zBf/zSb/P4x48B0CyvYX0P8O/8NW4eLRuKTejvgTi5E47OUE9SGnUQs036aZ+5mTrHHvwdDv1FQis9xtHGbv7XR+7h8x+f4QMTCWk6S5zAnnoDun32/NYxJuI2M7Oz7GvuJn2lT9KHdFUb1mbN7j6mySz1S5ro/6Ou17Pl4I13fH/xuW8cbG5/EzKSF3DCWsXAlpwYbOBEpjDu23BiMAQc1npOaG04NZdy9lS6xon+IKOi1jkRKEUhLciSE9ZzQgP9lQwVRL6jVYlKTtQQ8kJOVDdy4q3fghO9JcbGGmAC6vUaTnrPq7UhIl6HE1/6ArlxFDlUKsrri3UpQrchSuALCGuRShIoKIoCpXzgchiWnSwCRqqeEzfsvYbRsZITYgMnpPIfyyg3UXLCOocwFpdbCuOF+Np6Hjjtk0NEyQnhHOBAaYQIsaU59ms4EVWIt25f48Rz3w9O6BWM/O45cUV0sqxr0HPncS5A+AROCp3jhEBVqijrECrAFAEWMFZipUYXOVFYp5cvY41jmGsK46jIgCD0xpVRoCCUjFY2MTKywDPfmEPQJ6zWsEsDEDkQkhtNrVbDrhhynRNVA6xWVCshVjuUcjS3b2P25VfQRUYlatAf9jCFJivytTdfZKsgCiqVkG/812doxlt405vehJA5n/rs3zIxcRNbazXi5ggr/SEjI5LesEcURdQqtTXndqVCpPBGokZbMH0qFcNbk+t44uuz2MLinKEwPgRaCOHjgwQI4zAbXNmllGuFllIKbQZY5djSjNl7wy4GgwHnzs3RaIxRqdcptCEMnffNkj7TS8jQe165MiBa+NBoYZ3PR5SSQEY4bRAy8B0+Z7EYnPCxFaYAggBQSKWohDk7mrG/y3OOTeMj3LR3Ny/+/9S9a4xk6Xnf93vf99zqVHX17dTcdnaaNy2nKHJFaSVKll3yWkxCyk6A9EYJJUQUAgQ7MSAKUGTACSZ2pgeBVx+C0AlEAvFsgiRMDEi0vP3BjpxVSGrlkhOZ4tIkJapalETu9t5mus9MX+tUnXPeSz68p7ur57KclSPu+gUGPd196tyqqn9PPc//+T9/+h3+yl/9UXb39vjnLwwJVUQ2v0x25gK//5WvcevOIZSaIniRg2lB4BSBhUAGOGfRtiZQgjgOmFZ+hFIY4bOEAURhSCShjrzxYFlrOmGLOIhJoxQVVvzX/39bJ7zF1Z9px+tfbuyxRie+SMC96u+7V95Im67ncC07sWiYyQjln7rR+E/51Ff/aZ8FG93I4UrGBiPyQQ+2ve0DN4B+7s9pBKvZkHyQnTiV9/3Pj0uPz+GF+g9T6zr25sK7m6/B5XzA4LkhV/4+DFf75Nuje/aV9zNYG3HlWh8h/i2uPf6T/H51hfVq5KuOo8/Rz2+w+lKfH7jwGb5Inxs/M+TKeRh85Sm+/nqjxzr2bGhqlcPj29gIvyBbh95gg+EQBk9Btu2NUIeDnMuDjN7XP83qr49Yfy7j6tNw41OfJM8GDNdzhoOM1X7GCMj729xYW2WQw+DpjO31nGwAzw2h12Tx/POUHZ/D9ySH9T0aNv3nWU5VgtMcAAAgAElEQVR0GJs7ODvDibrCyRlOtGc4Id+EE1NL3AoI4oYT4oQT7XiXb76xjWBMmLRwR5wwQcOJhPDQB1ZRO8BWDSea6sIxJ6qSuN2mKMeYSlPqhhNSEUV/fk6kSYq03ohahCHS3YcTH3gPX/nDlzE4puMpTlhUJKlrgbHOB0baV4VkY0KKBes0YRwgVYCpa6y8ixO550TUajgROe8YKi3GGmToOaGMZwdSQRQQaEttLKGSOHcUbCqM8Tpf3VhCCK3AAFHoNV2BIgorzp7ixNwxJwb/BnDindCr7h3BXch0OuFwvMOkNGgrESpAERGFbVSySJR2qK2kciHWRhgh2Z/ugBAcHBzSasUgaoSpKUpvxulnWjnfMWgMK++6QC1BU6OU9iZpypJ0U6aTEtVSKBUSxylBFFE5KHWJNoI/2fgDdvNtLpzpoawkEiEIRdrqkLZaBEHgI/jKEljJ0tISKoj46r/6Bl/76h/yvu97P3Nzbc6/6xGy3nmSJEFImB6OESJEl5rKWIyEoh6zV+xT1xXW1SStiB/6yA/x4Q8/zrmlJVqRwhrpk0WBI44ighDCAAIJqgmygGZEjfes0lojRAspJZ25mN3dXW5t76BQpEnoP9E4cM1Lo7YOSeAd4YXAOj/uR9cOoS229gFYLRV1WSGUwhmLdhqHwdoajMRpL5R3VKAsMgQdSqQMOHO2R6c1z97tbZxxnDu/xBMffoyd7VdZXloilhHLS13iKOUHn/gBfuRHvo+lrMX7L53lsUd6fOSHHuXiSsL3fXiR7/uhRZaXFhCEhK7DctTDSEUpOoigRVulLCYtws4yoZ5D6hZumrKVG169dci3X9pi44/eAeqXX4DBZbxX0swonAdZOPQ5+d2N/MRDaXQdOO9F46zB+hVOgpl1uHzZP250heNgaHh9Ha56pI/yphaXw0YzPif7RGPhAAxnbtXRqR0HBDPC9bdq5JRd8Tv55eeeoT+AGzdg+MwINiC/zulAK4OMPtk6fM051r/xJaLXM34mzsgfHzD8/LN84jNrrN+Aj4qc/Ivr9K/1Gfz9K2wPR/QvfJQsy8iehuH1kxMdNp9Rsz4M13Mf9DyNF9F/JafXjPsZfcl7VPQGOaOvj9jY3qDfh+Gz66xe7XMD2P70NpeB0SiHbdjONuj3c7LLMNrIyEe+HNofNBYYR9d39+fkhwmCHrbR4N+w5YS3pplOZjjhJEK+CSeM58TOZAdkw4kw9hkTU1PUBQQznCinOOs5UQWgheeEqyqInOdEMT3mRBQ2nBBQ1p4T35rhhGw44WY5ETacKN8iJwrPiXJSUjlLDYzLMXvjfaqiwtoZTvzg45xbWqYVKFTcwmkLCoSLUIlAYb2thbHUUz+Nw6lGUmIcBoMQKTKQpMkJJ0TtOREGUBo/WxAJdW1RR5NDhA/kdDNmSNQGU9dIFVIh0LrGKeXn2DrtOwpt7YM1DcLVOFf5qpECE0mECDhzpuHEzbs4kb+zOfGOyGTdublFkvhEeBhJhJtysHMbK3wN2iJoRynWlOjaoo0kmosJgpD5ZJnaCS6eO0toQ1LVpi4NCodzgjAIKKsxsqw43DlgvF+yd/sQPXHUtcTFEa3K0nICWjFVMQEsVVmgqxpTV77sqDTE8/xXv/Az/L3/4R+wvHSOqpoSxxG10VirkVIiTEmUJFRUmDFMikPOnFnhxd//fT7/+V/jL//YD5OGIUFviaSlkFVAq53inKMGAgPl4djbRShFaWsAzNRQFRWL7Zjvf88i5k8LXD1FRi1kGKDLCmyAtRYrDTKOqEvvRHxkDmqb0TutOCZwJd93cZnzF86QJilJ0qbSDkSEEOHJbCw4Ls0qfAVQCNGM33EIIZFaEzqHkQF1bY6PE4gAgUI3cwullKjS4hBYqQicZOL8ZPaF3iIWx8HhhPddeg+/9f/8LktnztKdP8/kYIcLF5d5//suEgrJS9+RfGDl3RSTbW7deoPx7W3OP7LE4x/8AAvded77H6/Q6ixzMPWNAVtuh53dMcXuLrs7E1759rdIOy0Oxo5pXVGbikvnHqOzMI+IUh599Oz39PV/v9XnOhvP/AL9K5cZNU7swAMzQp+Y6eK7AvzM+skIwSP3h/wzQG/IaNSnT0b/62uMGrlA70aTJ8kgu7ZK9inI14BsQPaJEfkXG03SgBPdUO7tGdauwkeHMBiO4KkBGfnp0zxymL+fi+nMOpGV9cmfGbH6mT7k8MwzMFiF7Rs++MquAfmIfNT3MwZpXCkuww98Zo2nf/In4cJHufZ4zoiM4a99Bqovsrb2CT4zWmOUZ3x6LWf4KZ8y+qmPrjHI/LEG1+49rzyHfNA4sD8LV/6Zd38fNcHsgE+z3R+yAQx/eR0GfUbXn+F1V1F9/TMMnhmSX4WMnN5om41+n40bTzOgR/7PRvSH+fHIouyjsHrUPcqA/NpdVrAP0qXdfSPhTYPyh14P2bPwvVh3br5BEnVACcJghhMOKn0vJ6SW0PWcOJssM0Fw8expTkhcM/BZUVUFUpsTTmyP0cUJJwLTcCJtUU0LEJa6LLxRctVwQmpcNM+1//JnuPbf/wN6i2ep6jfjxPRNOaF6S7SOOJGkoBwGhauhKsdEQUAUKUpRg3PoyQknPvDuRbQpuH17D+bbRGnCVExxNkSHEFAjkxaTwyna+BKhrf2HcjNxdNotAkre/+4ZTkSeE44IKSMvaLcgA4HRwmuAhdfo4vxUEGs0CAllSRyGGBFSTzVWWO/KLnx1w5d0HTISpzmBpMabXC/0ljwnbs5woneWbvd7yIn5eUT88Jx4R2iyfue3/re1crpPOd2jKiYoYYiE487ebYR0vP7GK8Rx57i7YXFhkde2XqKua/Z3t5gWE4pin/2Dfbpzi6g4IklaGOddbI0pKXXFnb19/uzVV9n41gb7+3sEgUIEAiUiSmOwdYm1BqsdVVVjXE2oQpCCOIxwBn77C1/gL/34gNv5HSQWtKYVRd7p1jhEoIji0LvVWs3S0hLb2zd5/EOP8zM/++9DpVm5eAHlHEkaE6mASVURxy2UkiglcViscxjrCKSi1ppqOmVvd4+vff3rvPzK6+wdaoSIiKKQuU5Kd65FN5YkIRhdIaUgEQqHwDhvQiqlbATqFecfWWLwEx8miRKiMOLs8gJxq0Ucp4Sxb99VUvm0cjN5HU6yYtZYdF154zghsMaipEIi0FVN0Li0e5d55WdUOYEUshFFGlSjUVBKEbZihAzY2T9kdzxmaekM29t3ELomVvCjf+nHeO97H2Ouu8jy0jzaVGQLGQtnzhL2zjDXPsfe+JDNW6/yhd/7Ci9t3aLmNq+89lUutHtcXJzn/OI8Z5bP0u7OUamahTjmzOIy55a7LC9ULHY0UtyBuMVg8DfeVv3JN7/M2uUnUobXC/ibJz/P/1dIn+REB92sF2bFOik8efTfJ6F4AuhDWkB/c4VelkIB+QsvzD7Er0bDVZz8iuI3esfBXdrmJNBLYaUHL+Reg7XS78FmcVorNrNPWHnTaz6+nDwnz2Dzv8lhxWd3funvw+aRxizzW6d96H8MNtf9rlevwP/4W7D5+RW+PHiB7MUeP5wWFJd+g9/48U2+2f9Dnr/xX7DyvzxJsZZSFMDH4JfWMja+uXnP+az8FjCE9JKPM4bPwseLPs+z6QXwKeS/Cy9f2uCrmwX5s/D8yyvk//RFBo9/HHvry6w/VVAMe6Q/14chrPR6PP/fjrj819u0P56T9aG9Cet/Z0S734Mv+y7JIi8o0nvP6S2tN/ugffdz9KA18xp7uzVZv/Nbn1urygPKwz2q8oQTt3duIwLH66+/QpyccKK7OMOJvS2qI06M9+nOn3DC28QIz4mqfDAn3AwntD3mhLWnOYGFL37hC/zYjw+4c8SJWtOK78cJ9XCckAFFXXltWCAJQolQFsMsJwx1OWVvb4+v/auv8/Krr7M/1jgXEaqITrdFt9NiPpa0Ikd9WEIAka9hYkzDCSUJY0Wt7+XEufMLxInnRNRIaQKh/JicxmQaB6YZ9GxrS1XV1NrzxGiDVArpfEYrCEJ0rTHaIlXg7YDwnHCNZ1dwxAn5F8yJ9CE5MffWOPGOyGThYtIETC0RQc3t3S3mkphL8wuESYuQMcX0FnpaIGvFzviQcOrAlARpQiBrinpMVywwnu6jwoA4jFBRQDWtcbVE2IBxsceZpQU/oFJG6NognWDqSpKkxbSuMMablsUpaJsQioBAKnAhUlWE8SU2vjUiTtoIWkQSjFIYVaNqQx1Yb/ImIg7395mMD3FhyPLCIv/yy1/jfSvnKIopJlacDXvUdU2n0wXp0EIgnO8WUfgXWK0tWluqcoIxJe9613sJ04Tdf/ENqltbXDifYU2IKQuyxYzJdIdLWUKxbdkup4SR5PXdnWaItGFpeZ5uFPHomQXO9s4RIHjk7Bmq0hKkkKYpVVXR7naptSbCl/+8lqvGOYFwUBuNUIp24OcueucHTaVrrLSYSnIwOSBN5lCRaz7BhQgnwVZ+HEUQEEUp0/EhYZKQnemRznU5t73F5rff4D2Pvpf/+4tfYHm5zcql80Shom610Af7rLzvg2gcC6bmkpQ+9R54Yz5bC2pToJkyHj/K5l4Ae/vsHt4mCCZEbp5eegY6gkqWRKKF04Y9c0gaRLjxnbf7HcHoOmxfw6ejZlb2a7x5duHI5qDJYhxzdoQvPT2O7xa8T8bmriPB9Q24lp0MLh7is1hrzYDmGzmjq96fapDhRfVDGJH70TIPk3W5z2GPR+Y07u48Bc9FnKTkjlbuy5Z5H8j6rK+P4LNw+Rrkw4xnv/RJVs+vcuWf9egz4MInBnxie0j267DRBKnZENaHD4hGZuufI1i9mvHJ557jatb3TZarGRvPNp5Ww5znvjGkH2as/sKAPMsZrmaMfmrIYHXAaJSTkzP87HMMrg3I1nKucJnhr28w3Bh6L691r/HaYMPrt2ZFWHkzG7x5GrgK2TMzL4NGQ3df8daRlu7u+/zd1nWOXyfvgAI61DGtFIw6zYmVpe/CCVsStB7ACReh0oCqqHHuITihEqbGobVDm4o4Aa0SQhughEKYANGuUfElvvWtEXHaRtgWoXTYIMCUf35OzHW6EDScEBVWGZT10zhOcUKXrLz7vQRtz4lyusWFRz0n6v0DzpxZZmp2eHeWoPdhc3/MoVW8NvWcwBoW5x/AiaklSBpOlBXt+S7TuqaFwhpLECpqXQM+mCx1jVSSuajt+aEcpqrR1meydCXYP9yj3ZlHKNtIhwK8vNg3IzgbEKoWk8mYsDXDia0tNr/jOfH8F7/Acq/NpVlO7O55TjjHgr2LE67GlndxYj+A/e/CifKEE/YhOfGOCLLCwHfvGVez4BIiC9SOSTQmVh2y+fNM4ynBvCPPdznYP+DPvvPH9M4v877zFzGtlHaaePGdLpEKtHBEQqBNham8d1USd7h56w2sNtRlRWUscRzjnKEsp0wmE7B+Vp6uKlSgqOsaGQUYN6XWFdYYpqWh1mOUCqlEM27PGO+T4izGBUyqgmk1Yb6b8u/+jb/BV178Mh/68PuZm1/k8PCQQLXZ2d8jSWPMdEKrPYesDZoa4RzTqiQI/AxDrB87EMctrHMc7tc8cu48kRQsLnZptzvYYkxZjulEHYrxmLlLKdNdwe4beyghicKIaTFhMi75az/6w3ygf56AmPe9a4WyukMxlVxYWDgWyDvnCIKAsiwJ4gQA57wLvXCQJAkWh7aWymikhGo6JQgCrJXIJKIlukRhjNY1VVkTx4rSTVBCUmkNxuCcRknJZDz1ZcYgZGlunslZx2RS8q6VR9jZuUO3O093fpn57FFupovs7t4hVclxhq0u9gijCOMsxmqC4Ay4EFsDosZa7Ydw2xJrSqpqB2ViDspdnHYEKmIhMLhKU0wO3r43w9E6PyQ/Kq3NWjHMln+an6+vD1m92mx7FNgM8WaXzDz2GnA9YxD2Gd49kbAptx2L7bOc0U9m9Af+kFkOGwPIXm8OnQFXM2/1cKQhakCeZ5m3zLp7APXDrJxj7dPRuYxuXKe/eo0rawNurA2bY+RkuY8Usiswembkz+matxcj/xRXr+XcuD6iv55xY/05bvziJ3mKq/wicO0/Alb7MBwdxxv3BBI/hQ9QmmvYeC7n6qBP1huw1h9x4Uee5cpgldXPrPHL62sMwlWuXM0Y9dcZrA54pjMkW+1DnjO4Aht5RvarT8EgY/jRIdnVy2wMM7LBKv1PZOT9nOEQVrPLZOSng6Ps5KkfwCmHfcAHWM12x+so4L47wDpaD3J3PVozgXj2DigZhqFlMqkfihNbW3c4OLiLE8l9OBE7IiswtkbX9WlO1PfhhC6ZlBMwEAYhta58FqnhhJYluvJO57OckD7mwNaGQP05OJGc5kRdT5AoqrryTUXOe1IdccIJx+He3ZxYwBZ7TA4Paas5ioND5HLEnHLcfnWPQEiiOGI6fhNOHEouvGsBYw0q8HY+cRhSlhVxElNpi7AKKw3WWNKmxKmtpnYGVznqqiRs3ANUFNPuLhBHCVVZUuuaKFRoSlQgvSu+NQg0gZJMJlOsbjjRPeHEexpOzH83Tkz2CMOGE+3vDSfeEeXC3/vtf7pmZUC22KMsx+xPK+J2myReYDIu2Nl5jd7iGeIgoq4nbO/cQiSKdqeDikNacYv59jzCWjoLiyStNkEUUU9rrJkShBFJq8XLL2/y6uYr/Mm3X6Kqa1ToPS9anRTtNMp5I7O61kjZtJbiO/KM1ei69p10jQ2BFGC0QzZTw53zJp0qCLEYWq0WUZTw7T/5I97Y2uLKf/afkMiQixcv0E5aJGmLSls6aQdrDEqEGFODABUqKl1TTQq0nqJrg1IxhpAgUtR1gTMgZUyrFdPuKM4uzdONE3pLHc61WvQW5xmXmlt3DomVII4VZ5YzzvUWicKaRy8+QnGwTy9boPfoo8RxzKSsUGFIEEZYIIoTdG0aSwiHqQ1JGFNrQxCEWOeafxYhvS9MGISoIPDDQaVEyRAESKXQ1qFEQFVpjHUoBdoJlIrQ2mDqkiQKUXHKH/zBN3jk4hnOZmfpzs3TWeh63YAw5NtbFMU+QlgOxwfs7NxhPL3DweFtivGUnTs32cr/FON2ubN3E02JcDW6HuOMIAnmMcqRpl3idI657gJx1EWFMa3WPD/0kb/6tpZG/vDgO2vFUUbqPhU4Gi+nFOj3V/zw5P5MB35TWhtuwkrqsyD5JvC3CorPb1I8iQ/Ejip4d9k95C8CT0D+BLABReHLZrOx2epV78/JzwK/A3zQ72flb0P6G805n3ag+O4r98c9Opfh80Oe+Lmfh+e9ZURT6STdTE9uyP8BvRXIs1XSYoO8yEmf+U3y1ZSVv9VrrqXHL/3KFfKzOU8+iS+DNT4TxelLP1mNlcX6z8Lgp4G233Dz2ed55GNtVn7uKgUbbLzwAisr0Pt0xlfXN8hHPeil9H6pR28I2VOw8cuQrcDgYymb67Dy936OJ35+w9/fHDZeKMhy+HgKm0dP+GwmreDhynv4LGjvSZon503WfS/6ASt/+8uFv/fC/7lmCciWPCf2phXJXZzIFs+QBBFaT9nObyFailanQxCHtBLPCWcsc4sNJ8KoGT4/IYgikrjFy5sNJ75zFyfaKVrcxQklgBNOWLTfX2maTmw/dszimrEzFm/O/BY40WpR2RNOCKewTWOTVPKYE6aeonXDCRsSxoqqnuCMQ8iIVhTQngs4d2aBbpTQW57jQieltzzPxDwkJy55TozHFUEUEkSeE2EYoY1tghmLqTStKEZbg5SBZwTOeyuKCBXFBCIkCAO8IaMkCGOEdcjQc0K6gLrSjaQEtJ3hRFWShA/PCaxlXBywc+d7z4l3RCZrYaHL3t4Bk8ltjClZaMcoStAtCDRzi0scVlPs1HevnXv0Ao+euUA1qQiSDmEaIohIkzZJ2iIQIWZaYV2Fc4KyrNg73CNbWuQPrSYKApywlJOKuXYXakEqY+q0ZlIaotihrQHr3zhV6RDW0ZIxh9OCKJVMJxNqLZCB8sNDY19mC8LED7oU3uhTWIUI5vhLP/5B/t8v/Q5/+sd/zJW/+Ul6S22snNKN2ziMz4bVB8flOl3s44RChhHUkiAJ2N/fZSvf5s9eeYXbu4LlM2eJ0jZhS3Gu3aEVOKrpbfbGh0hr6AYJf+3sWV4+fIF6p2Zxqc2P//C7SRLF+UcuYCjJzmZYoaCqiBbmWZjvoXXlU6raa7AiqVCBRWvBtKyJk5RJMUHIAG0qQhUQRjFaSm+GKhTCWaQGpMI5CKIIU9c46aiVRUQSW2qKwwkyignjgChtMZ44ZBIxH7f4Kz8xYHvrdRAhZ5aWsVYTBCFFccju7k12i12UDXAiIdRjBAGTaYEtNVYUyFYI4xaKgOmewMQloUyoTMHUaGpziFUh0gpi4UcxOCoODw/f7rcE+TpcWV3jBmvQ9+W40WzyqeeF1DTZHFbh+jNw7erp/Xw684GXd2jIyUYZ+Wv47MbAZ6TyozLcbBbkqaYm+Qze0yHLGHwKhq/DlQsZ//lnfLfd+hBWfwHy5/zDsgxfUhw2JcYjR9GHNSCYdXgHVj89OC47jrKcfnZUEuuTXx+xeg2GR6LxfL2JTVbJ6o9C/ikY+fRcfwPWeyOewyeI8sY79eSGnxzzOMFzdA7Xmks4OsXVPutDWF9f5+nPZfzih5/h1752heGzG2S9jGwwIh9l0Muh51NQ2Zcy6PcYbfjIqX9jSL7a3NtRxuUbkD8Fw8uQzTjnA/e5dfdRtDeu92Qz8yj/dddMpiu/zpsYpX5v1kK3y8HB+JgTi517OTGuJthp7TmxcoHz2QVsdZoT7VlOlCXW1TgpKCd3cULdxQmN50RSM6k0UeK78LCKMFaUEx9IxcQUtiBMJNNygi1nONE64kTc/K18SE5EM5xwY9JmP/V0D0RwzAkVBhyOd9m67TlxZxeWzpwlTDtEQcC5hRatAKrpbfYPDxHOkDaceOngBfTuDCfih+eEwxEKhZAW6wSHh1PiXkqxX9BOA7StCFVIGEXoWCGVRFrlB0QbjjmhkghTVzjp0FhEKDGVpj6YIOOYMJjhRCtiPvkunNi5ye7kL4YTBzsPx4l3RCZr+KX1tShKiJRCRi26i2eIW20QjYlaDXemY2QQIuMY1epgTYUTkkpbimnBXLeLNbDUO4sKYowtsdpgncUJwWRS860//hPyO7u89kaO1t7XI1BNGrGqvH8IkrIokDJE6wnTcgzCIQRMq8ILybUmiBJUEBIK725r8YFEMZ6gtcY6g3OWSAFCczCesH8w5okf+VHOnz3P8tIyrSDCWoiTFlYblPQCcmcd1XSCQ3mhOCCNRDjHZL/mjde36LTbbP7ZJgvLy7zxxms8eu4saUuSJCHduTkC4T8NGBFw6+Yedqr58A9+iPe+61E67YRHLj7Cu9/9LrCKs71zxEkLYyV13YzmMRbhBMV4jAxDRBCgrWWu0wEhaKUtHI4giKi1f1ELQElJoBROepNShxdCWmsQ+KyXthbhIAhDynJKWVXIMCJQIWEQMS0nBCoiir2TbxQEOKcJk4hOp0Ons0A5hvOdi9hJBdUeu7vbmLrizp1bHBZbFFPNXPsSSbzE4fgmh4cH5Ns3uXXz2+wXO9TTkoO6YGdvm+l4j0prJuWUohyT77zBx/76z76tn9pfuPXC2r+Xv8ALqS/V/aebGU82GY68gPQJuP7syzy54lNFz/Qz/u4T96YmjpMZ60A/9cmLlzkRuM8alzb5sj6Q543YvQcMUvK/OSR/bAVehOf/u4KVjwMvQv8J/zX9OKSp/z+9Pv2P5z4w2aT5xeyaTaHdtdYhe8Lvpt9vgpsNyJ+HzWHKV4fNMYc56Xdg80lYW4UXmgt9GUjZZPDkk7T5MvlmQf/lIdk/bAMpKyLnxRfTUyb6q2uQfxuKJpt3fBdT4Fl8fa4R3I9ehCufXeVX/p1fYXXQp/jFgp/+1Y/R/njByhOXeGpQMNzsMfg4bD7vs23Fi9D/akH+Ys7msMnuDaF4fkSxuULWPtKVwcf+NqwcwOZTMHzxAXfpKIU5u44yXbMNEQOabF2zccZJxnIEg55/eu5Zz6zT/+KA/DdPXk/pT8OTH3l7M1nD315fi8OYsOFEe+EMSauN990LsAa2xmOC0HOCVgdh7+WEOeKEjDHOjxSz9j6cuJn7rIm193JCKKYHh0gZoesJRTFGKM+JsvacsFYTRglKhYRSIGTDiTCiKKZ3ccKCMPfnhGyqCkecEMIL742jLKZYq1BKIiyo2o86qw5qXms48fKfbbKwvMTNW69z6dxZ0kTSShTd9hyhDNDGUruA7VsnnHjfex+lnT6AE1pS6xNOYAXTaeHHtYUhurLML3ZBCVrtFCEcSsVoqwlUhBCghCSIVTOBxM++xYDVupnO5tDOIoAwCSmnU6a6giDywdrdnJg+gBMFnG/PcGJvG1OX3LmzdS8nDm8yPjxg+36cuHOLabVPVc1wYv/hOPGOCLK+8rv/ZG18cEBZGRQG6QICLHd29wmjAGcFS5154iBirjVHS0VESRshIqyWpK0EKQRKgDGCMPXlKqlCpFTexsA5xtU+h4cHvPTK6+ztj5EuRGAJoxgZKMrC14SdsUwmFVJECOE1RmVVUpqSKIpQMkDKABkExEEI0k90F1LhnA84wijA1D6YCJOYlXdf4NVXXwNVcfl9l1hY7OCqkjhOQSqsNSAck+kUFSjSJAEhKMuSupriWilBO0aolMpWnLmQsX17mw9+/4dYnu9y8fwynXYLGSgSldBKWiwsn2dxuUc2l7DYUVxYWebxDz7Gud4ZWq2U8xceYXFhESEDlno9giAhSgLiOEUbgwrl8fggIQIC5UcgaOuQQci0qsFAEAYY7ct/4E1Ia2eoqxqjLZPJGIf1Dr7OUmnH4cE+VekIkgQhQw6LgumkAiSBCrCmJlQBKkqRGKR03gQwaIFy3HLNhZwAACAASURBVHz1m+iDl0iiksnh67TjCBVCN02I4phut8vewetU9U3qSQlmTF2WKCzFuMBVNVIlSCtI4hhdjXFmClISKcmT//Z/8PYGWS+8sPbkJXjhxZy0n/KRYhNIyVPIUmATfnqldxIQbL5I8XyPfD2nP7hEkRcnIB4CH5vZ+QPLRP4B99gvbEA6WCEbQPFVSAU+mfJVWP+HQ/qDFfJREzz0mj3kzWHuW+J6ky7Dng92epy4u4+AlX5O+4mUJ37JXztf5bjr0mycBAu9YU5vJWUzu0Va5N7BvbdCMfA7LZ5P6X/MJ/KOjO03XmiCzbvvS8Fxufbo+14PNl7YoD/okz+bk/5dP980H0L+YkGawubLsPnJ5gC5v9wjs9aj8mmxAps/1yNN/bGzbSgEbH4Znm9c5mfv0mjYOPEX+EjyQWXYHic140049QTMXuODAiyAfp/8y8WJxxn+NfeRtznI+srv/pO1yeEBk1KjhPHmklhu7+0TNZzI5haOOZHIkChue82N9X9TjzlRC8LOvyYnnGMy9ZyQBFgnKaclk3pCHMdI8QBOqPtxQrw5J5IUMcOJopwShJ59QjWcKKe4zr2cyLe3+OAHH2dpfs5zouNtf5Kw5TmxeJ6lXo/FuYSlhhMf+sCbcCI8zYkglCgVEYYBWEUQR54TxiHVDCdEgLF+pi0CL9h3xlslaevnFArrx0/bhhP7+1RTh0oSpAgZz3BCiaDp7AxQ4Ztw4vAlkqBkMn6ddhShQvHWOeEkSfTn48Q7wow0FQGLcx2vPRIxUx2wPzEsdbvoqkQo77Ohosh7QIUQhxYVTInk2LtnasPWzTeQylJVU6wBpyLCuE1AwLQQLGeP8PJ3bmJt3YyZsSRhgjF+AKUQAhUJgkjRaickrRBHiWOKM4JW1EFXNVpXVPWUaT1lrzhEW8P+3h57t3cRQqC1Zjwe41BUVpLvFLz4L7/JrVfuUI+nLHTmMFoSxHNg/Ugc7SSV0VgB4+mE8XiMNjVRrHy7rtHosqQ6OODC8kXuvFbwocc+xP7+Lt//gfeTRAFKhSRxh3bSJUhaRHHajNFp8X2PXeb97/8AaEW32+Wxxx7zQ6Z1QXZmGVyAsSVaV7z22muoOKG0GisV0+mUqvTdK86c/KumJdbU6NJ/lUGAkwKD8Y0AQYB0EEWR914xNdoa6rpGCOFbqqcTkIJWq0UQhRRFgXCSsi4xTtNdXmSuu4Q1BqwgUBoVxJzrPcb2TsU3/+zbvLr9Mq+9+hKvvPIy44NDJFCOd6mKA8zE0Y1aSKdZmFugly1w8UJG3DJEukTUBcXhLracoOqSwDkSZd7utwSM4FPrz0Dfd+9lNJr2o/INp4OhK5/p01+F/tXGpWpWB31kTXUFGML16/dvThw0O54tjc3C/Kn8ZMM+PnB4+unBPeL2EfhS2FXor/mfra83BqPfZWXZXdWxvLnOgTdUHRwJ879+cpLrjS9V1gdWM/qMyPIRV1Y5dnK9fn0E/VUGmbduv3IVnnkrFup3+03lkP3vl33XX/O71VX/81EfuOK1UcMbTYvnED8Ecabkm80Mwe43mawbQ/j83cdeh/5VjnVap0To97Mde1CzwdUH/Pz4hGa+jjh1zcNnv8tjvwcrJWCu3UKb8pgTuxPD0pznBHdxIkgESWwJ4tJzQs9wImw44cCJCBHcnxOBUhjtR7vcjxPtI04IzwmrBe2ki65Pc2K3OEQ7w/7BHnt7nhNVpRlPHoITyb2cMALG5ZRiPMbohhNtccyJ6eEB5+fPe05cfpz9/R0++IH3k8YBSnpxf6zmCJLUC+WdIwhbvOetcOLVuzgxmVJrh64trmEF1lJNSt9YoEtMWfvSoBAYazDaEMgTTrgjTjiD1jVCCiaHEw7v5sShNxsv6xJjNd1shhPuPpz4zgM4MXlITuiC4uA0J2LxcJx4R2Sy/vGv/89rVkZ02m0SJanqMbEKidIWSZISR7HPoliDMRZTGsp6grABxlTErTkOiwOMrGi1F0niDlGS4HTF+HAf4zTCaf7g61+jOzfPt199jel4QhiEfpq49qJ2Jxy2rDg8LMEaJtMpVVVhjAEJQeCnrEsVAA4lFA6JcBIVBQQthda1F7ErRRQE0KSCo7mIv/aTf5n+By+ztLjAXKdNKAxGaKIwoqoKnK5Jk4QoiPx8J+u7FrUxqCAiDjuEUYJBsJQtsrC0RCuOcVqznC1SW0srjgmTAJwgiiLCOOHMuYw0iWmlKdn8Ipfe8x7G4yntdoSTLZKoxdzcApOyYr7TJZ3rEAchzlqEtVhAKLDSm8MKKamrkigKm9E5DhH60mcopR9LICXOGoypUSJA4GdjWeMIVYxwEXGaIqxhOi0JwgAlY9oLy8wtzBO1EpyDCOdHZoQBxmmCMCYQgko4Xvy9IdQVRqQsLCwTqIBpXbLYyZgcTFjsLNFJ5wkTRXfuHLo6xLmAqpoShG2C0JIkCab0z72KAqzWOAU/8dH/8O3NZD32G2tp8bHjmYLFakF/IyVNfXrhUh+KAfArkPeg+E3gkt82v57zsZ9O2SwgnZ339yLwcXjy+5vMyooPapok2XHi41R17yiTsQbPfM5rv9ONprC40mSANuBSv0/RHKg/yCieL2AI+Qv+8f2+L5v5nfHAbFpBc753JWB6bWAAm0PIsoz25yBvtrm0ucEPP/kERf4ExeYGg3SFn7gBN1b6XG3/HYYM+J3feZYX/lGP3/jNlNUrfXKxycfIKDa/u/q7j7/H5L6UeXx+Tbaw3/f2YBs/76tzR3Fp70lYGfT8tv8Q+InidHTb3Id+89QAPPFL8OSGP+haDi8cncBxqu6uW9f8PL/e+Kfdb+X4TObf4XRm7u71xOnjHJ/mANrp2y98/8f/6H9aczI54YQ+JFEhcXuGE0GI1gZtLGZqKLXnhJ6WJO0uB3dzIk5wpmIy3vdDkWk40Znn26/NcCK4DyfKElM3nCgbTqgZTgQnnEBIhPWaqSD2nHDWoORDcgJNFJ3mRKwinJzhRDXLiRgrJUvZEguLi6Rxgig1i71FjNUkcYKUkqglCVVElLTonV+m81Y40e0QK88AYS1WNJxQvqwqpKSelIRhCHhOyDjCIQiVRBpJoLxn4ptxIumkyIYTcRggZEx78U04YT0nlBHU6gGcqEoWuxmT3e/CiahNEDScqGY4YTQED8eJd0QmSxjL7k5Onm+xtbeFdpB02lRVRVVNKcsJZTX12wqBVM6XFfGDkg+K26TtLtK0mRQFk3LCpDjE1BqsIwpCDsqS5bPnUElAaCydVuLbcoUlDiPCUGFNCVKQthMkFmkNrTjx3R9xgJT++GU1JgxjgiDwqVGrvQln6XwUXlusUY0TcIkzhsSE7GzdZu/2HW7d2ubg4MBbJWiH1hXOWNLOHGU1ZTLep6qmqECemIhK78KuTU0YhozHY4rikIWlNpdWHiGKEpbn5ogAV5YI5+cJzs/PEyeSc+d6rLzrIsu9RdpzHS488git1jznL5zlYHLA7b03iAPF/sEBgZBYC4GKaLVavuZeaara0Gl3OTw8PPlUpwLKWnNwcNC0Egu0NZS1pqw1FoF1mrquEMJhbIU2E4LQYkyJCEJarTZhFNGZS2m1YsbT0nfryJhaW5QVdDpd4rgFxhJKRV2XzC+dpTO35LOSU+3/WAQxRb2Hii1W1FS2RCLAaOKW/8PSaS2wMNchTTu+GyZUdDodktg730sdvr1vCCB/tkkr5MAVyNeP0gwjWPdO6/nPA6uNgHvG2j27ljHMfcYjvztbc8oHYEaTPutpdR/Rdb7mxfenjnXqfE/SHvn9fKfyB/z/7rV61/HxZqQ5/npHI8g3coajRsfUB7jMIMvI10eQZQz7GTeuAPmIda5yPYdrv/D7COF9D9afHZLfgI31mbQg3GuL0KxR/0TEfyTwn13DI+HbVVj94kkKcX3WOuHqvdd1bFExG3gdPQ+jt6Yzz95M7J7hM2gz5b/7rtnGiplzzX/qLZzIX+Sq3QknDraorTjhxNRzYjqZgsQ3HwUNJ5zFBpL9I07YB3AimuFEKyCsvwsnooQAi7KGVnIfTkxnOBHdhxPVQ3IiUDhzmhNVPaUY71NOpkglcbaZvnHMCU0cRowPxhSTMfNLbS6+x3NicW4B5SDAMNn1FhDduS5xJDl7puFE1nDi3JtwQkqsg0Dey4l22nACQ2lOOLG3O8MJYSj1Q3BCn3BCvgVORNGbcCKMKao9VPJdONF5ACeCh+fEOyLI6rZDIqUJpMEcjNnb3uab33iRN26+zBtvvMarr77Od17/U27e3uL1rW1e3dpie3ubV26+zP5kh/19zWtvvAJun707OyilqKuSymiCOGJ/fIiuKg52czqxQgiBk94ETTiY6sIL8IKYMJlDqgicRAuJdpKysoRBjJIxgUqJwoSqNIwPpzCZ4qpJk/UJaMUpc50UrKN2JTJWpO1FDqclGy+9ikoX+NAP/hC9c+exoaLVahNI781VVz6iDwIJSKrSoGtBoFrUuqSuS7rdlEcuneOHP/IEH/pwn/e+990krQCpDFZMUalCLizQXeyRdjq0WzGtcJFs6RzduXlW3v0u6mqC0VPiKGJ/a4fl7hJRoNB1ozkTAVpbptOKra0try0wfmTFzv4ecdQiCCJ06d8g2jrKyQRj/Ogei/Qagcbx3QpHGCdY16KT9ojCti89Kk0ct+jMdREuxE0N1XifqthDoujMLSCSBCFK4iCk3Z4jUC20tjx64VHOvetRbu/tEgpL4SqCJCBJIupSk4Qt5tJ50rhLOdnnYC/nzp0ttD7ACMdhNfUfuwy0kxaRirC1RjpHoNTb/I4Amq66GyO48UzO02vAIOPGENZHkPUge7p/khjJYXTVR0H9qxzPO/bgzE7PwLtPMDH8xsw3pwIiHzwNbzTBzoMg/fVTm/vAcPb3d3fyHa27y3B3G5g2patsG57+3ICnjjoJV5ty2wiGec4nB5A/7X/Odn782CFw/tOQZeuEP3mVUdOBt3oFPvvZEatrGdc/23hlNWW27O5rHMHGEF967B+VL5tB2Hdd2/qncgZNMHN5tjx3NO97lXsD1JH3EgXfVXr0pN64Pjq5n6Oje5OdfJ9zOjPWb0qJ6w85Syfz53Nq62FzhNlzrHmw19b3cHW7EaHQCGGodg85uLPNN//gRV59/WVee91z4qWbM5y4tcXW7W1eufUy++UMJ2zDCTfDieBeTjjzIE5EhMkcEAGS+kGcaHlOHB40nNAT7wV4xIm5GU5EM5z4zl2cCO7DCV0TBtJXYirPiTBsoRtOzM2lnF05xxM/9gQf+sHTnDBiippTyMV5ls6do9Xu0E5j0niRxe5Z5hfmuXhxxXPCNpy4dS8nJAG6NkzLiltbW0gZIHTDiYMTTtRjTdVwoqpOc2LaeGM5e5oT7bSHCjwnrDjhhK0C7KThxHgPaWc4QUmsHsCJnZ2H58TOFvUsJ9x9OGEcAQ/HiXdEkKVaXRaWztNOF8jOXiSdnyOdX0BXgloLkAJhIZKGd1/MuHhumUklcDZgOjEEIZQHBflOSdhK2N/bQ0iFihTG1owP9wliOH/+PNu3t3j8Bz7ITn4bYxxOSJSIiGREKiKMLtC6RGtLmrSJw4h2mjCdVkzLgqqaoivj/UeEQwuHjGKstWhdsLs3xhhHHEoi1fLbujHtdsyF3gL//IvP8/JLr/JHoz+gqiqKcooK2ggZYKqCyaRkUhsEEuE02AlOWNrpHOcvrtBZyDDGYG1BGsU4U4J1VFXBfLeHImE+jhFYBAalYKErKUVFp7vEzt4+YeC9YablgTfXdYY4aOGQaG3Z2dtHNd00Ve04GBfIQCClxGlHK0mo65KoE3Gwt0sUhCwsZYzHB9SmwmlBEESEYewF8pOaShtaqSJsApg4DomjLtZa6tpQO5iYGmNhob0I0jEu91FWUDvJpJoghEBIi1J+2HX/8odJ0w5Jy39aNHWFCCPSoIsKOsggxjqNJSRMQv8JLoqwbsK0qtk/2EYGhkA5DuqCiZ1QlHeY6rdiIPQXsy7fGDJYhSt9uPqZjGfXgGdynhrA4GoD/eE2oxnD0s//yAaQM7qBdyhf9V9H9E87vN8nqzK4xjFITxtP9sn6IwZXYXuYsz3iJGCYXb9+12OPYH2kD5p1qf/sqd2/6Vo7MtnMYf3nh2yvZ2Tb/ROdE94l4dgtYn3YZOdy+FQG+TpXIp/RuzKA/uf8Y37qBvxq2Wf9Ezm/+rWB1731jw91z7qcAb/emJ82G/Xve+6jYxPYU78+ksmtc/reZ15r9tTTzbff8MEsGVy51j8+p9W8CSCb7NuRhutUhDRqgtzVh3QObYxfT239pftc/6893O7+olcQz7GYnafbXuDsxUdJOnOknQWsFmgncEIgDETSek6cX2ZceMfw6dhzojosyO+UBK0W++MZToh7OfHhH/kgO7fux4kYo8dYV6JrQxp7TqR3c2LqOeGneThkEOOsRZuGE3qGE8UMJ868OSfQJ5zAeB3WMSeShhOLGc4YnC1Iw7DhhKWyBfNzy0QNJ5S0SOU50elITFLTTpfYnx4QqohaP5gTu3v7qMiHEHXtODgsUOH9OBFysO85sXgXJ8IgIgxiZHiaE5FSSOk5kYRdLJ4TNhBMrefEYroIgWM8aTiBZFI/gBOduYfnRGfB+38dcWLvPpywD8+Jd4Qm6yv/4v9ai+OYMIpptRRJa56lhUXCdos4EnTaCa14HikVh/sHVGWNsX7uUSgjjNUU07E3g3OGMGmzuNAjCAKqsiIOY6qp5vbODvvjfV58cYODYkwUR8RhhHYa56DUFbpyBNIRYdHOt+RWU4NxliSOSOIYGfgZS0KAdCAV1HqCdZowUFT1FGu1Dw5RjCdjyrKkLA3bWzmPP/5+P7YgaZGkCdoYdF0hEbRbbUIZYDE4J1BhGyMDOq05rDHEocI0xqDtVuv4PJKkg64r5hfmsdKipEMbQXfhDBcvPsbi/0fdu8VIcqV3fr9ziWtm1jWrm+TMsDWjGZu1WMv2EitrH2pBreTVSH4wipYlr2XJgIVpCJAE+PZEG2D1C18E2CtoBjaoBxv2CtrRytMPO5BEGSMTLmm9Ky0lWYtVtXbuTbIvVVlVeY37OccPJ7Iyq7qqu8mZcXMPQHZVZGTEiZMZ9fviu/y/9U3qeoy1BRiHVj6vqttdRSjJ4dEhRTlFaSjLjNOTIVvPXaMwDRZLGMWUedF+Yr7sOEm6hGEXGUboyAvARnHSit9Jn69V1QRhShiG1FWBlQ4ZhEgd+bi50oRRjLCWWT5BJyGFaaA2ONv4nlgodBv7l8I3/i7rmvff+zbGGu4/fJdZPkbWlm43pbYF0/yUrDrFqpxsmpMXJSoIEVWNEhKpHMaFbYugGltVPgdAxAgsf/vvPtucrN/5nbf3bt70ZfzZ222K0g3/b9pvf9/KWK4Re2W7TVJqy/TvDGDQgRvc9ZpYd+EufdJWp+As9WmAz+fahsHBgG3SM9AO5oqiA/iNLD3z0qR7gPFz4m57jFv4ponb4C2xbKEhMa88HLBorPiY3CwAduHtn8gWZXa/BdkvZGRb7ez+sz7czfw13Jlf943W2krh7cznsL3tz5OCF059BV4+OCC7I+BOSvaOP8c56YYLI8toE6TmC3PJfAdwsL9FNtdRuHhtl4XrWg2NdC44+pNtvlwGuzf73PnvfdLWH3baec0NwQt5a+fUap+0ro8br1yy7bY3QH/wP37G1YV/9Ht7SeL/biSRIklW2Vj3nIjnnIhXkVKeccK585yY5TOkUjhnCJIO66tbKK2pW06URcPJ6SnjacuJfEYQBuc4UdQVpvac0G2ukRBQ1y0n4pA4bDlhJEI/BSfUU3KiqRBG0ElbTliDkwIVtpzo9LBNywk750S64ETYpalrVleXOJEJVjav8YmPfcZzomo5YZ+CE8dP4IS1xHGXMOp8YE6oOSfCCCU9JySW2XSCTkMy0yBq3zXE905UaL3ECftd4oS4hBMuRoin48RHwpOVFxNORwPKMmeWQVlNsXXFRmedzd463aTLysoq3c4Kvd4qnU6H9d4a/ZUVunFAIJXXOqlqIh3hBCgtfO+nxlHMCsJIs7K2wdraBiDbPCdIogCNRAkQDsIgwMmAWkWYBsoy94ZbEFAUBdPplMl02qoEN61nqwY0UoYUReEFTJuasixpmgYpNLWDyWTEtWt9nBCESYoNJI2DIIgIg5jSlRgcTjqCQKF1SNRZZWPrOVQYoMKAsrYEoSIIIrJsijGubarZ3jB1ibAKRMDK2ibrG32KKifLx3SSLs4qyrIBFxDoLrPZjPF4zNbWFkHYpdPdoDaCF178FPcfHNPtdNjor+OQ3L9/n6ibkpeFb0sgJALfC6qscrSAMssxVUaRz3BIrAxQ2pcYV5WlqQwY3zwUQIcB1jXgJGsrG9jSEqkQsLjGh0/BkRe+bZC11vc9tA2zrAChKQtL1OmhZMJ4kmNrTahXcKZDNQt8+LMumZwOGFcZtfGtHUw5ZTY6YXx6TGNr6iqjrnKsqZ/dzdCO7V34rz82OCeYuT+Ag/39ywG/5I64dWYhLYV+9j2YX2LAzd1tdvveO+KtLxYJ7tv9c86R3d2F22oXuPXL3n3UhzNP0lmB209xVgE3r+oDn4jP/lI13dkLj18Dfp3znpo/h3mcbHtvz59j7s2Zi3EO8OHQuadrME8ia4fDr9X2Nn1aF9gHVaVfHhfy17Z3YXCw/WgYdD71q8Z83e74JPZ+v8/tNwd+0Q8uCKf2bz+a37WcJ9eGXG8/YX139tofrurdOD/0zTb/7RmPvJwwGByd40RTVawnq0/kRLzECVfVRCrCAUoJ8qzAGUc5K4hDzcraJmvrC05oJc5xQokFJ4yKKKd2wQntOTGbc8LVmPpRTuTTHCU+BCdUTGmXOJF4wyLqrLDx3AuooOVE1XJCR2SzKaZySCHBWpS4wInnW07UBVk+odP5LnICi1USnEOrD8EJ67w6vvCccEaytraBLSyxXuZE2UogLXHCgTUtJ5znRJA+nhPGtpwoZo/nRJNj7dNx4iPhyfryP/pf96RSFEVNbnLKcU5uGo6HD5kVJY2BapozzoaEQYislM/yj2JEoJBETGczTo5nJJ2YXtpDyIAwDDCmQUcBCMWdf/kvOR1NKU0NWKyVZJlBKKjqhrIoQClMU6CkRkiBE+AwSBQgfOVKUyKloKpKpFIYa3FWUleOTidBCNmKcyrCSJMkEVGkCFzA8WjA9Rf6/M1/62/Q7Sb00i5NnVOXBVoryrKgqkuCoIsII+IkIdIhURT6apAwIlAhUSBQYQSmoSwLrGuIwy5SSGph2dx8jmvXP4EKQ3SgCXWEsxAGCTIOCZKQujaEnZiyaphMc1QYkxUlaadHoDWzKkNKKGc5pixYWelRl6Xv/dQ4AqnITU0viDg+PsHZGh0oqqJmms+wxiKVZFbk5EVBGsde2C8KkbLt3C4ESE0Qhxjnn16cc0ymE4TUREr79jtSIxzIIMBJBc7QW13nvffeReQTTGmoXYaQAq0CLL4VgxQNTd14kVjnMEWBccY3MRUWJSRBHOFbYwiEcJSu4Uc++58+06f23/7t39578T+Hwd2Fu+JGClufvUF/ANlcz3O3D3e826L/X/rtf/1gwIv/XUb2TtqKTPn391OAPvt3Mu5kGXf/AXCj07pRLh+/8dY73NhaWCGvvOIPdvdtX7nIO60vbW5A3IBFG5g+g0FG/5MpvPwhnCtzz00Kg9sDMlLeYZsbfRj8zttthR8+Xng3W4htpl6wdLAFvJV5j998Gd/mzBiZz2fequdDjUveeKYX1ubG8RaQHgCCs4kcDHjtCym328vgtfZzzCD9Cbx3bb77BSPw5t0t3snSRz1u89/bz3v7Eo/c8rj7dvvDy49fgGwA2zcH/PXeTzzTe+LLv/W/7KngPCcK23AyOvScqKGa5YzzIaFuOaEcKoyRWiJlxHSacTKYkXRbTijPiablhLOKO3/5eE5kWY7Q+owTKpQ4Ada1nHCCujE0dcuJukTKlhNOUpWObi9FyA/JicBzoiwLgrCHC2OSJCVSwXlOyJAobDlhG8qqwNIQBxc4ce0SToTfXU4UpqYXRhyfnnjvolJURcU0n2KN85zIM8qmIA5bTiQhUnhOSC1AaIIoxPAoJxKpaYxDqSs48b7nhH0CJ2bTlhPlU3BC1PzIj/3Mvx6eLBE4qrqmQePqAmu8DoeuFUpKrGtohEXrEKUCamtonKWsGo6HMybZKaOTjLoRaAG2KWhMiTE1cRwThhqc4tOfeYnVXo9ilrG6uoqSAWGoKcuauq4J4wCtBFEUUZuCuvHzMKbGWtdqiUg6nR7g/1VKEQQBOoAgtNR1CU4Shd5F6xt2RpRlyca1NX7u536GT37sRb75ja/jnGM0HdEIQ9iJyPOaTmfVvzcIUUJiTY2xxVk1X1HmVEVOPitoyqr19EASp7jAEUQhq9111tY3AYi0Qjrv+TGuAtUghKAoKoI4ItARUdoh7Kas9bqsdFI6cUSdT9lcXaPOa7ANxtbgDNPZkIeH74No+Na3v0ZZ5pycDIjjkGwypSlyinJCEPhm0k3ToBCkaUpRliAsDx4cemX5QOGUxqnQP2XoFGcFs9kM3X6RndL+D0YUtc2nrW+cbQzT2Yi1tTWsE15XRXg1+dxMKeqCsjFUVqHTlLDbJe12UCokGxdEMqA2DY1rMNYitC8nDgDlnt29MB87WzA4+ALbc4/F3D11AIMdvAfmJgx+zsfj+oN9fj3yu/Rf6zN4wXukBtv9M2/WnTfwGfMHA9685d/PF1odpys0o3bmiUf7/tzbO/1F0vWyZ+as0o8zsat+f+Dzr74DL0i/79/f3211suZZ6X0voDVoxcNuHnl7E4AtONqa98Tx1/bGrTfgzdu8eWspg3upgPOycXPvgwhpXTLaAoNBn9bdd7R4bbvPG3uLZb8V/fL5XLntpd3b76GTXQAAIABJREFUvLbtbWB/wJfos9O6Dw+e5A287Opu+Q+uf3GXyy63vYaD/ne4Ft+FIQNHVRcLTlTFghNKYkVDoyxKBC0naqrKc+JknDHJhwxPMirTcqIuvKfC1MRRjJIapOLTn3yJ1e7VnIiT0HMiiKibgtqUNHWFaS5wotsD0XJCK4Kw5YS21M13wInCcyKOO6ggJDjjRI4xhjzznGiKnOIyTkTOq6JHK6ytrGOMeWpOxKvfISdCz4m6zCmqC5wQkjhKKVtOfPur96iNQYQtJ/QSJ9wSJ5zFqIAo8pxQ6kmcsI9yopJPzwnmnBBP9b0V87DNsxz/7c//uFtb6zKZjRFhF20sFQ3KOKyAOI7J8xIpnO/GrRVV6Rs4V02NlorpSUWQapLUN4184ZOf5saL349AY10NVcVfffOblLXl6//qq3zjvYfcf3CMqRvKuqJpLHXT4BpD3VicrJAEKKXaeLbAGOOT6oQXRbPWq5mDxBqgrYKw1jfKNBbq2guxdTtrlOWQf/9Hf5gXX3yBKJD8B3/37xCgcDRn71FKoLUGFbLS6RLGCU5IokATRr6BtHRgJNCUDIfDM+HPQCekaYoIQnCGSEfUdUldZb6iUkBlGopJjhaa3voaVkgINfnxmKQXexHUpkFrTW0qlDFYfDd6AItgc3OzzR0oCWKNqAwiDilnM0KlyerSJ4KGHcrGN0DNqpIoisCA0AHWWtJeD6Rs+0vW/gksiKmqinx6gsWRdjYJwpjGWSIlfRWlE+AMD4+HnA5O+P1//L9Rjo+opPAK+0EANQQ6hUDSlBVNU7GiA0SoKZ2hMBmR9M1Gz8LHQnpDzij2/v4Xn+4O+h6Nn/78nuOXYGsPdndgvw3NnSk1vAH9J4lLXhgHB7B1B+YNC7+06+2sx46BryzceQ1u3Rrw+ustbLfx8N/nfOn/pSfmOzK05qPfh8G8r18bEuvvtkV2u3Aw37bT5jVdaPG3fxt2dq+Y0rZ/T3/e3BAWsgdPsC+utFEve2F52/znc/stxf0uTvIxxvCHHvNjvsHjxUoPYO+Le8/0nvhvfv7H3dpGl8lkjIq6qOY8J6Igpqg+GCee/75Pc+MTC07YpuJrT+JE3WAqn3/VuJJAhQtOKIFpTOvteAwnwgTrvkucSLuESYJzLSeSlhOAEXhOjIZe+PMCJ6wxJKHnRFVlvpfiEicUmpXVNaxuOXE0JlmJmWWP54RB0L+ME1FImV3gRNChNC0nypIojqABEVzCiaJGqiVOTE6w4rvMCRUgIk3ZGAoew4lGsferT+bER8KT5SrD6GRCU0qaaUmeFdRZTWUs9axiMpry/HN975KsLJNpSWO99TuaTjh5OCbWAS4rETKkyiuwzoep8hHD4wHjcsq1a302+yt0V3s0pkJqhcUxX7xAa//FFV7PJIpToihCyQAlA+IopdfrESQxRdlQliVxPL/BFIFOEChM45BK0TiLCiN6SYf1bkIQrPD7v/N/IqXmh/7WD3IyGDKaTmicb8Q8LcYopbxyexzinKFuKqT2XwgpFMIKGgd1VlBkOaao6KYdkrjDxtY1orRDFIatkn2NwD/J1Y2lLEtm4wlhHCBDxXSaMcunaCeJehEy0CSB13lpmgqNA9swGY1J0xWMEShnyLIpVkBZ1ExOfN8/U5aMRiNmTYVAY4yhcgYhJZM8I45jrwumIiKtSMIIYZz/AkpBnHQQQUhZ5WTjkRcdDb3Rl+VTsDUWi62tb8tgJEkYYa1p8+RKhg9PGR4NmY0zslHO+OSE4eAhRTmltjXTomQ8GzObjLGTkqIocQbk/GlISF85w+gZ3xGw9SrwlZZ/rfEwGHi9qgHewHpzXvf/2sK5xE288bMHt3bOM/nVm9671N8Z0N+FV68oyz+n4ND3BhYD+IGf6nOr710og7ki+I94Paiz9/S9sdMHeAP6O/0zQ+FD2QfLek0DGLwJr20D/4O/zvl5l5tn35lLVFzIgdrZ5UzK4qLNNziC/kuctzrnJYsXp3QhzevcLksK7HsXVNMfMZLmx3mp1eECGAwW3qmLwqXLrqdHdCaeclzMC5sf86KBdXHe30ne2ndpuMYwOp5gSkk59pyoZjUVlmrmG7tf29r0nGgs42lJbWsa2zCaTTg5HqOD85wQOLIioyhHjE4HTLPLOWGWOKEDTRhpoEFJiYo7vo2ODFAiII5TOmkPvcSJKGw54RRBkCDEd48T1hhMUyEjCKIlTtgFJ5rZ5ZxIIy+yKbBUFzgRxAEqUkzzJU6sRsjwyZzQy5woF5ygKBkNlzjRGCqWOJG0nNARUdByonEIh+dEeoET4ZwTU7J88uE4cXyBE2XLiewCJ9wSJ9zTc+IjYWQRB4ggACRCR4hAIoOGUEnC0DcevnfvHlXlhdOUUqytr6OkpBd32OpfZzSZUjvJbHxK0AsJhKDJMq8eay3TosQ5x3Q4QjvpxdS0Rhvt3YxhSCNBIehGHVZ7G4R4q1VKCVIilGI0nlJNpxT5lKIeMxyOfZlqpJAKkJIgCsnKAmENgRStjEFFqCQ/9EM/xOnxEV/96lcxxrDa69KUhU8gRzMajdqbMsUJzfrGFmGQUDuDxYKSOFdjTE2ez9qKF0HaW8M1PlmwKSucFG0TbIiiiEl7vS+88AJBlKDCiLgT00lXmM1yL3ZqLXlRUFSlf0rISqQOWV+7Tp55T1TjQoSKaIxjZXOdMAwxxuCco7e+Tm0agjhC6Yi8rMnqEh0GlGVJPs2ZFacMZxPGWYbFURQFzliklBRFQd00rG9uoMMUYxzGFuTZhEBLaHsjYp2XsTA13bQDIkGrmF53nSRewdW+abcT0je9zkvqvGA8npIXBRZHhcWUgmw6oywyyllGMZ6SZRnuIyBG2v8SbO9742Fnrm+5A+wt+Hfz1faF0PP4pZt4GYbbfr9fnB+sNdK+9OP79Ol7B88+vHTv/DlvtcbJPv2zENVRe4r9PuzswOt3Fonvg31/ns/9j22EcA8vbfC51vh5jXPCpDf3+ktZ+ZzX7rpinNPl6kP/d+GNM/0v5hPmrDkg0P+D84ni2zuwf+vAa1s9f/l5+kuSEFdP5op5LY+lY+zNdavm+7509THPpt+H//3zOxdPd+6Xne0vnLVbmr8HeFRj7LLxJI/i9oV/2+NvfygL+bs7RBggdYCzEhV4TqiwIUQStJx4+OD+GSe0UqxvrqOQ9KIOW2vXmc45MfSc0MbRZHlbyGSZ2fOcOB76v5vKaKL4PCfSuMPaygapEyghUdL/zRFCMZlNqadTisZzYjRuOZF6aYI5JwpRIPJmwYmTJ3NCXeAEgWZ1Y4tQL3FCSJwrzzhhzAVOWEuVlTglqK/gRBglqOAJnCgXnFi7ihMbC040OHob69RNy4ngMZyYtpwQjrouccYiruCEtTPyfIpWFzhRPwUn5FNyolziRJ7hzL9GYqSIEqEcVT1Dyow0iTClQAeORvhGxVoq76aTClM3jE5GSBGQpGs0wpCbmm8+eJ+60VQnOffvfcurxmJQgSYNEuIoIk1TxrMxP/CZT1GOh8hI+cqPukQZ461UqahKA6Fuy3MFpq4p8xwlwQkLToJThFGCxZEVM4y1zKYjcDUSS6QVTZVRO0veVBjX8Cd/9uf8yZ/9KX/5r/6KaVUxnuaoIEYqn9C+sdEHJRmNRoRxgrHWJ7prTVXmlNmMfOq1uOKkR7e7xsrqOkEYIgNN4ywy0Iynp+TVjFk2Is+9CN5sNmM6nVLWDVIrGmtQQnrl4HKMcFDaxj9JUNNQ8f7h+zw4vA+qRgUSIQ3T4Sm2rJlOM2rTYJwlLwvKrCRWIdPTCZPRGFPnVNmE6WTEZDLBCol0vmt7bWpGkzFVVTGbzZhNx8SBxhhHWRviMERZST3LGZ0eMjwe4IwBrXBaouOINErJ85y19T5RFOMo0YEhSiRWNTSuQAmfuOicwwSaxiiamSUfVkwm/kmkbhoqa/xcplNmo9NnfEMAtwfsvXaBiQcA2/TnMJ2D7/N4Ffi5Z2ruFWmFS89yer61y6D1er36Fdj6Yp/9W2+c7fB6683YZeCr3IDtweKQ/Tss4pbt6fcHrVer9bIBC4NgMPAna7e/+cUB/MM+B3Pj6nEq5Rcu8ey4lxhCO1/pw8FO29evD6+fN4IO9mHn9W3Yh+2r4qPLnps9FlWLF42ptkrzTPPq4lj2Qs0LM/sXXruw/+4FL9EbX9mHW5dE71qB1/2j13215sXj7l5+iksO8+h8aZdgvg4XjLEn5379/zBcQRA46ks4YVzLCdVyAoUpG0aDlhOdlhNNzdfvv09tPSceHN3FCM8JnWhSmRCiSdMO49mYl1/ynNCRoioL6rpEt5wQUlEXBgJ5xglb1+TZEieqC5zIpxhjmeWeExSWqKOp85YTwZM5EV7kRPQoJ6pqRj7JMU3LibULnLAWHQeMJ6cU5gpOVEuc0C0nijHCXuCE9Zx4eHgfxCWcmCxxomo5oR/DCSTqAieKsmQ2nlFc5EQcIq2kmtWMTg4ZnVzgRBqRPAUngmVO6AUnssdxYvh0nPhIGFmdeA2FIu30MM621jQ0mYCmpKOgqXKEklDWdNMYG9TkbkyqK8oyB2Fw5ExOT8gnY44nIxoD2kRoFdPUOVJKep0NPv7J51DGsLbaZzYtcFah44RASIytqeuSOBU465+ITBtvBtqmlwGOGilCBKCEJlAaV5UEOiIvGqIgpqodQsQ0eY0T3g17ba1PXcDhvSkYOJ36L/ZwmoGBbDbBVCVR2qGqDWXue2IVuW+q7PCeORE4qqJmllWEYYxsGpqyojYVk8mE0EgC4eP3WgaEQUC328VZQaA0UZwSRylFnYEQHA1OOLx/SJOXmKJBC40WmrXV63S6EWEYY5FInbLWv46Tgmw2oqpLiqykyi0YBS5EKH8T1mWDqS2mETgnmEynxDpFRxFx2qOT9giDLknSIS8K6toQRRFREnM6GRMkMZ3eBr3VPnlecnx0SDGd+dh5XnFyfIpUDVvXXgARIUUMVmCahjjsEIqAVGkc0re6oKCUhkbURGlCGqWEkSZNQsIwxdaOpjJoEz3bGwK8++pNzmB3E1/aD0ds716IVw1YiH4uJaAf3YadJeX1wZu+Hc0bbw74WbHPQX/Azq/d9Nnjc+Nlry3XPzovuXDwBn6fzy26Dw8GsPO59vwXIOxDan0vrPn5dv82fLb9d6645t3Ftc7HU/SUZn9vcHbdg4MBb97eZ3fZQjlgYXheJaOwnLi/B7zqf+/PRUKBvaP+eamEZQtw7uqZbztnsTzmnH24vc/59TsAXofzHXwG8KU2Lw1Y7pe0fJanyU//0nya+97L12/ncc6uegP//WtDnIPPPfm43+vRSdeRVpF2PSdmkwkKzwlhL+FEd4kTQcsJZxAyZ9xyYjAaUTUgqhDRhDRNjk40vXSdj99oObHeZ9pyIogWnKiKkjgROJozThgMUoJrLJhLOKEDXF0SiPOckPIKTtz3nBgtcUI+BSesrX3OV+g5MZ4WRPESJ5qKybTlBFdwQmuipOVEkYESHB2fcPhwwYlIaLTUrK08hhPZMifMkzkxmxKpFB0ucUJ3SbodZuc4EXE6GhMmMWlvg97KghP5EidOn4IT8ZwTgBALTsTLnIhCQpViK88J5Z6OEx8JI8tIC6EvzwxkTJJ2idIe9BTx+nPIlQ2+/1N/DaVjOqsr6DgglB1WV7aoUai0YmNzhW64QWYto2xMPppxMh4xno7Jq7HXucor4lSxphOCTsJpNaYT+/JZ1xiQmjhKEVJTlQ0gW10mi8DHw2vT0NicOI5J4w44iWm1m4RSKKWRUlGVDUoL6qZESIetfeL8sBiRFQX/3t/6QQwl1rqzp4YSxywryGcFk9EJuApURS1yhHJeE0unKBEyOzlBULK2tsLx8UNG41OaqqScjXFNzSgbUpiaMs8Q0iEUDE6GnIzGWGs5Phr4vlgyIC9mpGmKikJUHGKVbyfhpACk1+sKEwCGg/cRpuLh+/dQVtLfeB5B0CZyVsyyEXVTEAdeULURhsnpIcLVSFtz73BA1TScHh/x4MEDJpNTZrMJTjoqU1EbS1b4/oxIgQoDkjDxLSqcIQxj0iSiocZFiuPjnL86+FPG1ZC8KTAI6spxOh5xNDpFdzuAxBiBsAlhBdr53lujbMhonHEyzBmNZzTWEhBQyqfrrv69HK8Bn28Bd/sA/qvb+P50261FcyFeNZhrQx1wpty+vQu7Bwvu9/uwM4CbN+G1X9v1RtMve8Nh8Jp3Ur251xpXA85Rd3u3NTgGi2OdeXPmXh/wxsztwZmu0n+yz2KuB7C3B7f+4OBy86PN5VpOFTu4KgS2bEycW4oDbu7ucPsNH15tnX9+nsvG4P5izucU7udetnYSg6V8uM9vD3xu0pE//fa8wg84aF09ezuLZTvY3vL23TwRH7+Ot2694a/1AK8hdsFmnl/fYOnC+jf7Z/PvD1jKyeqfX7BLPG/nxu0Bg7mh1rYGGrT6XOfGLt7C9TUSfOlpQpHf4+GkxYRe7kWJmDDuEq8scaJ3nhMqCAhFywnrObG5tUIv2CBvOVGMZgzHI6b5mMpNvc7VqCbuKFajBJUkDIox3TknzIITUmuq+jwnJGCNpbYNjWk5kXTACIxrNf6kOsvjrYqGMBYUVQGXceKHPCeaJU4U9ik4oZY5UbC5scbg6CGjySl1VVJmY1xVM5oNKZqWEziEvIQTw5YTWcuJsOWEFDTqAidiz4nTwfvQciJwC06EYYJxFVk+oqpaTghD2VRMjj0nRF1x72hAZS7jhF1womrOOKHDgCRa4oR6DCeqC5w4PVlwwi1xwl7gxDhnNJvROIsmIBdPx4mPhE7WH/zeF/dUEKCFQgQKhENqTSgEkYO6KpFIdJLgrKUGQh35pHUlsWVIEsVUpqYYzqirjLS7Qq+3hrOStLdK3RRsbF7n6PguD+/l/LM//nNMECEaKJsanKMsauqqQCqBDiRhkILzjSt93hGA853UlaIoKnQYILRGSUGgAxDC5we0rkelfANQIQVKe62N564/z4OHD7CuYa23xupqjyhNiXSAaWp0ENBZ3QI0mxubCOs9MXMB1Gw2oip8xaBxEqU02WxKVczQ0kcyy7xgNMspq4YgUDjnSNIOUZRiTAP4KpLpZMrR4JA4TsBBXRUUeYm1qtUSkZjacHh4SHdllV53hdPTISsrawxHY6qqpKpKrDUIAXmRM8umBHGKQFA1DVXp0Dokrxqef/4FjLNEYUivu0JezGhMg7MCJTRJHFMWGdZan68lZdvbqs1vk3itNK1prC+rff/dr1NNJrjSoK3wn5MQiEAyywtMY2jqGufA4SibkgaBaxqUUxi8m98JgzWWzBp2f+rnn6km0P/0w7+9N3jFCx1tb8HWDl4H6bPAOyw0ot4CtiHdAV7mvJclgzt3l7bf9e9LSeHvZ3672ocbL5PeyHirDzcv6iW14uVszcXJ/Q7Zxf3miuR/Ctsvpwzu+jm88lngrtfrGmzBH/8xvPLKFn/4hjc4LuozXTzs3atyiJZ3bPWg+tuQDba8oXcX3vrVAS+3YlH9H/N6T9zwTqfBXMuKdvt8vLK0funSeeZSUq1uVcZiqZcOxdufhcE7/sWtGylbO3D3LUjb9RncgVd+cudMvR+VLhTtl6/t7GTtprd4VBer3Xnr5SvWCB499vYVmliP0dRK8V+VV155torvX/nyP9xTkeeEDBTIlhNKENmWE06i4wTnLI2EMLiEE01NMWo50fGcwErSdJXaFGz0tzg6eZeH93P+nz/5c7jIibymrgukfkpOZBU6Ds84EbX5x0EQgLBY47z2VVEj9ZM5EQZPwQlzgRNIlNRMJxOaMvOcUC0nspYT4RWcUFdwomg5oQRan+fEyhInTsdjqrKkKkuvJSYhy3OyOSecoLGWqvKcKCrD8y9cxQm54ESWYd0VnFCP4UR1gROh+sCccMaSPyUnPhKerCRM0UKTBAmhUCSxL2mVMsUohQgEhckpZmMMIKxECJ/zFCpNFFve/fZ7RDIlqwyTqWUymvLOP/kj/vLOP+fo8FtkRcW997+JLRWlHfPx77tOrByNqogihVIOIRVxmhApqBsoyrEXKAW0lghtAYutJVVVoQOwpqTOM4RQFHVDWc2YTYdtSaxvm5CmMUWZIWrDRrfLiy9+nBuf+Dgfe+FTRHKFyTSjqjKm0yl5VjCcjFHSoqRXEjamJs8yBNDkOTqJCKMVrE5YW1tjfX3Vf8mN43Q0oixz0mSFF5+/wfX+FkVRMDg8IpARvZUV1jf6qDDAVDUWw/XnPgbgVfcrx9a154hjXyJsEQRBQNxJyYsJeVXSXd1ASs3a2hpFUbCysc6DwRFZ5XXJVrurgOHeg0PvKneSbDzB2oav/dVfcDp4iDGG8SwjKxqslYRBQK+bYurGhzODCIlAYuh0OhjXUOQzZuMR09kQ2zi01nS6IUUtmaKIhSKva0aznLq2SBtgjYLKIQtH4zJsnbPaWyEODYGOKZ0hL2sKWzFqppw2OU02fFa3wtn4pV+Dnf3W1bKz8JrQ9iXkFvCF2/C7fnN/h3Nxoz599g+ALx0sFNFhkejzKuzvH8DOtk+s2oebF5LMgXPerMEtf47dmzzqIZnvf+RzoLbBe0fm+22fD2XtthWLH3gMdq5+6c32ZK3XZWd3ccLB3HN18JT5RZeEQB8/2nktx/h+ejEXjlh8Pq0MxZl+1kWZhguhw22uSDzfw6/vhRjhm5c0AH+q6V+IQJ+d49yGZzc20pTAKc8JqUiSlhOkmFbYuChnFGXLCfcoJ779rfeISM5z4p+2nBh8i2lWcu/+t7C5ojRjblzGCbXgRFVAUYypqgLsBU40S5yoW06gKKqWE7MhTbPgRLf3ITkh3NWcCLqeEyueE2GoFpwoctJ4hRef+15xYtNzYmWJEydHZOUSJ1zLCZY4wQVOTDOyfIkTnZYT+hJOyIZi9ign4uRDcGLlPCeyOSdsy4nZ03Hio+HJ+v0v7iklvPCoFYzHU6K4gxA5zkqcDclsgwwTsBIh/ROBMRavwBqgRMi9e4c8GJ1gHVR5hlCOjY0+Sdql1+khgZPhkLxusBYms5LT42ErMmcJggiEoyhKdKAQQhNojcNby9ZZcA5rXJu7VWOtj8c7vBfEOdr+SQCCIAgpy4owTFBKMZmM+erXvs3Xvvo1irzik5/5FFEsmU6nuMaglUQFmsY6VtfWUIHCOotWivF4jKlrbNMQhRHdtEMYRdRVSV1X4BqklgSBJgg01lmOBg9RQUR/8zorK6scn574L+5kzPB0SFX7Hk3Xt66xsb7e5haUvH/3Hu+99z7OGsbTGRvXNmmamrpqEx5tzSwbE+gALSVaaeIgZDwaIpSiyguSOCWIIpypkEoihSCKuigdUFYVSafH6uoqxhjqpiHLMhpjKauSpm58H6vGYIyll/ZQ0nsPAcI4QomY48Ex99+7i818aLi2DZ0kxNqapi7R1lFbg1WCbuTbEllX4SqFsQJratIoRAeOugDhDFrBq3/vF5/pU/sXf+eP91787BZ3BkAH7vwofLuErZehfwOyH4TdX93hD3/D9+7L5i0MWyX4LMu48beBG1se7h24/Q9g+y7ek/ESXsl92yuHn2urdwDk7X5z1fUBpK8AW3DnHRbK7j+DNyQE3P6VAdu/kIKAQXr+vfPR34Zs3mcQuLkH77z9lItyANy4e/XrN1q3zdwrM/fCzYfgUc8OPgftMaL3Z2Nnu1W3v+gJGgDp3cW5U9/SZjsC/hTfF7ID+6LPjbcy9r88oPOpjGwnJe20H9n8/GkffiXzHrXW87bFFdldb/vjMlhStN+Bly+5xuWxvCxvAi/3gTu3Oci2z7afObbeXmx41p6s3//939pTWp5xYnQ6JYo6CJnjnMQRkkuDCFpOiJYTtfUeCwK0DLl3/5AHwxMsUEwz5JwTnS4r3R4SwcnoAicGLSeq85wIopYTSuOEpcgLX91nPCfqqqKxzYIT4gpO6AUngtD/rX8qThjH6vrq1ZyIYs+JuOVEVQFLnNC+++JFTgyekhP37t7j3cs4UTYoJc9xQgovkxTrlhNaUWQFaZiiVIhzla8YFYIg6qLnnOj2WO2uegX2uiHLH8OJpOVEXoFoOSFjTo4fzwllHU3LiSQKqGuLdQW20tiLnMhbTuin48RHwsj6v37vN/cCKfn6179KEAs6nQ7ONUi7gpQOHThiLaCpEHUBpsQYgVQSJwVSGLodRVFMeTAY0zhBEkYQBRyfnNJd7XmlW9eQdhK+/q27KB3wzW+86wXaVIJWvr9doDVCKaTwOhtWGEzTisBZ3/pMaoWQgf9ZKIQA61p5fhnQGIPSGmctxpi2K3kD0hHqGBk4PvOZT/KJj38MJxV33/0GH3v+ebJsBjgmkzEbG2tsXruOdf5JReAF0JqqQumQrWtbaKVxrsFUJcZU1FWOaRqmswxoaIylk/boX3+BqigoihmHg/ukYUhTFqz31yiznHw64nhwwnA0ZDI9ASfY2rxOknbIsgyBRgeayXjM4MEJhw8ekGcTrPHqxg8ePPAl01pzejpECE2eF0gpSTodimKGaSxKaHQcUVUVcZJQ5DOOj49ROiBJOr5dhfDNVmdZhlQKqQPKvEI4H4401gCONE6JOwmHh4eYZsr7Xzsgr0pqDIFwrPdXyPKcQAagwVlHrBPKuqapoCwn1A2EYegrZOoKbX2D8ViH/Id/7xeeKVD+5x9+e+9lCXe2vBem/xttP+hsEaq784cZ6a/swys3fHSwD2ydRQp96tY+pD/mN2zf4NEwWJt7dS7FawtvGMCZF+zgN3zIcpnnpHivzKtwcAd2fqINfS0bIYPzP2dzw60dT21gzef1pDFvNzTf/2wxuDIklsLlIbT5aK2Su4ML+82PnXIWopyHcbfTpd9fBv4LuPETGTyEG6+kpG+lZG9B+pJfk62z42aLkOXSfC+b3gBI0/P78RgbdD43ptXjAAAgAElEQVS2ltbkxvwY2TZbuzyam7U0nrWR9Qdf/s29UEm+9o2voiNBd6XjHyyXOBFJgcgLhC0XnNDnOZFnUx4ce06kYQRxwPHpEzhhljhReU4gFEpammVOuJYTss3R1S0n5BWcUBrnLMYuOOHEd5kTQuO4ghPick4cPYkTkxNA0L+KEw9POHz4gHy24MTDBw+QwuuMnQ6HCKUpqgIpJGmvS5nPMLVFyUs4cfp0nKANR57jRPphOSEoqwl1/Z1x4iMRLkzjDspKbnziRYSKwEZolaCCCqUBJxEyIoy6GB0TdNdQSqK1RGqHlAHGCPr951hPOmAF47JkkpVUTc0oGzMtchpjGBwd8fwL11ldXSEItBdzsw1SglQWpYT/8iOIooBer4NWse+xB75ywzU45xszS6GRIiTQESApqxyEJS9mbTuehrouEcJRNf7LcXw05l/8xR32/+9/yj/6zf+Dv/ZvbvPtb3+TpNuhKAqiKCINI+qi9FWXQXQmdSClZHVjnePxkMYuJd5ZR56VnJycoAmYTqcoGdDp9Dg5HSCEYzw6ppOEzEZj6rzgZHCMMTVKO7qdiE4S+KacjWM4GTLNJuggRiE4PjwijRP6m9d5+eW/yfXrzyOsIEwTPvHii3S7XUSgCaOEOEyIOilJt+e1Uaz/DLWOKKoJSgse3n9ANp7SiWLqvCKfZV5Com2uGoahTzDUms5KB2PqVh8lRyp8W4W8Ik7WuXd/TOMsvSjl+lYfrWOcDGgCTWEEwklU7RhnBQoJLmGlH9PpJKA0jZQ+KTPC68A8+7x3+m6P2608eX/bJ7afSTQs5139Qx+mug3eWOK8XfO5r9xchMcuCzld5cGZC2e2nqjtXf9zHx7Rtzr4WR/O6g9g+6f7vmUPbRuYecI5QH/gBUWXQo2D74I2wD4swqA7LJL2B8CvX/ABzRPZb3FeWuEWV+pM7QwuLNx8XVr/0mAX9udqqIOz/52b4OCn2+LJeSXh/7vN0a5fs9u/vEhx7+8++iFdtUJ9gJ0ny3s9crxBGzKdFy/MT/Drl+wLH7izwPdqdFZ7SKe48YkXkTrCNRGKlhMKzwkVEfZWMDpGdddQWqIucOLaCy+wNudEdQknmidwQntOGGdQQhBHAb30PCcwvurQWYcMJM4uccLKtiLekpdTmsbn2s45UdbZd8wJpGS133IC470DAMaRT4vznMBz4vjoAidOxtSzyzkhmqs4cUgaJ2z2r/Pv/o2XlzgRn+dEmBDJhLibkqQ9nw+lAOE5kc3GyDknJk/PCWtqirKg/ICcyI0A6zkxWebExqOcCCK88fyUnPhIGFnWgJQaGYR0Oj0irQhUg5AaHSpU0JBlM5qqQkuBsQ0q0IAkIETJACklUaQ4nc4YTCdMyopyXGALga3h4fvvoaIOz3/fDVZ7XbLREcoq8srfYM45pI6950k4KuuIgph8UoLzrwshiNO5ArwFV9OYgqrOfLJ2Y4jDBCU0oVboMCBOE6TUOAE67FIbSxQpXvz4Jzg6Pcbahn/yx39CZQXv3zuicprxNGeUZ1RlDs4xOJkwnkyQShF1U/KsYK2zStNUlNkM0/hQ6tZzN3jxE99P0k1YXdukrgrevfsNqH3iZ5SkVHnDeHxINhtiS4PWIdZosqahsgKlFA/uv0s3Sdna7NPf3CCMvILwydEQk4/4y794hzzPMc4xPL7Hu+9+lcPBffLpjLgTU8sSHUZYJzgdnRAEEQ2SwhSY2vLw/gOSJOb+wwecHA/BGk7HA4p6xng8JM9nZOMJ09MR7773Le6/f5fT8TGT6SmnxyccPnjIaHhIqCrKakI5O6abdFGhpMkqnMyppydsRoLrLySUVU2OQ8fK66bEljJLUEKgrEEYS+MEjdVM6wJi9axvCRC/xP4v3z4zBPrb0G+rvW59YZGrNddyem0Pr5104TC399709sYFQ2t3l4X0Q3u43Xluz9wuGfhixu3WMNj/0X1fVbikbzVotaf6r/kco58Vd7j5i37/oyPOWu/stsft989X8+18EJXLc5IJS8cAeOOCBPtZ378LmfPztjqv84j1srt7/vf+vjeC9ncGPu1pbs0M4OAI3nx1m4M+9P9t6LdJc4PBAN7sP/JB9Olz+6f9+Qe7wBcP+C3mtuy+V8YH7vw7S5beRevqYOl65obe/DPf822Pnmb0aXO8rtr94Py+gzf6H9yS+x4MOzLg1Bkn4kARhp4TQkrPibzlhBBAg9IacYEToRIMpzOOJuc54Rp4+N57qGTBiXzOiXKJEyrG1AaJo6iM50S24AROkPQilA5QymLKCucKqqrlhDXEQYIWmlApgigg6Sw4EUS975gTSTclny5xIl/ixAvfd54TjeeEc49yIs8vcKJqqIxABi0nogUngkhhkRwfDbHFiIN/8WfkeU5jLcPD+49wopEliggrBCeTEwIZUc45URkOH3hO3Hvv3oITowucGE2YDi9wYnLK8PiEw8OHnBw9fCpOPPdCQlU/hhNmwQljNdOygPDpOPGRMLICrduqsYBQaJ/jIwRaCUzjXY1xGCEUSOHQzifZCSFpGoOwgrKocM6RJgmiNhSTnKaG0/GYk4enTGcleVZyeO8eVT6kMZLDwRG2DhBSYp2gyivKzOt5lGXNcDimLEvq2ndjDyN9pm5ujEMI39ogipKz/+Iobbugh967NMtQwv8u8Ynsn/70p/j6N75KXZfs/kc/SZbX/OMv/y7jaYZxlgdHh2it6a70/BOPdKyurIBzTGZTOp2O18xyjsHpEGMtdV3TWIeOO1gEdV6ghCUKJGGoGZ4MGJ4OyIspoyqnlFCbBltbNvrX6a9fo6oth4MhOog4Hg8ZnByT5TlJFLO5uYkKAypTAV4E78HDe8ymBdms5tr6dYxxTIYjlJSsrKyAaEiSjk+ITCKapqETpmxsbmGtZW1jnSAKvTpyZZiMpmSTjGyS0TQVTdO0OVL+s6nLiiSK6XZiLwyYw9rqNYQsSFJF2o1YXU/pdNdZX7tGr7vBxuo1VtZCXvj4Gr31hDhSrG9uobUk6Ep0GtDpJKytJyitWe8kRNGzV3zn117ydsRgKekdbyy9/vq2tyyWVML3l9Xbl4HYGg5n+kqt/tPt2wAH5/a93Vpig9cGZ0bCQR+Odnxi/c7rO48qsLeeo0GbbL3zAwu9qMHcrfYXrZNop8/2dn8hWTB4opLU+XFRS+rKF1l44q4Yj7z0/KOOrMGglTdof1427La24NW+N0Jv3+uzNZe2mMvzL4U2d/b69Le2YMtXF/YPgCOvxXrrFtx8fRd+dODlGV7fWWh6XTTUtmnVZdvraw3JfYA9Fn0lP8hYNiwvKXbw2wfn2gU9qxH0ApRoOSEXnFBKgPAVYXHQckI61DInqiVO4DkhG0M+yWmM58Txg1Oms4J8tuBE3XLCGL3gRFFRFiVFXlI35hFORIlvF+NMK6cgtedE3HIiTIiDFCElYRS1UYiWE8H3mBPN1ZyILuFEcRknqiVOTM5zYmtzEx0GFKYC5znx8PD+I5yYjheccDSksedEN4nIZw0r3S4bq1tYY1m/tkkQt5yoL3DCVDT1o5yIophuEmNs+cE5sbHEiaDlROcSTiRPx4mPRE7WP/+jt/biXuSNFiEJVISOQmIZ01iBdoI0TIliTagkuvEVHNbkBIEvPS1dCc4RxB1G44xpNiUvStajiFI4hCtZ7XaJlOL0NOOf/cnXmRYTpNWgfSsCURuaQBLHMQKIwhCtQ8IgwWKQIqCsLEI2hEGEkiGORd6V19OSGFtRlhVBEGBxOKVQDvIyI9SKd997QK+zQmMcd/7yzxmejrnxiU+yuprw7t1v8ulP/RtEUYc4SZFCIIWkLHI6cYKV4Ixrk91LVlbWEM4QRiFVMSQrC7pRwnQ6pqgLVBDhXMPoeEA2ntJYwdpKn9PjMa4swRnyouTet96lLktWextce/45hqMhTVUzG085HQ4ZZUOqfEwlLUVeMR5PUYlmOB7TW+tSlr4VweZGH6ykKRoUAlN5/RiEI4pT3wMqCZlmM2RpyKc5ZZZzdHrIw4f3mU5HDEcnOCdQYQSu9iXbBFR5RTUdYmxJZ6UPoiaMJZOjjGqUoWVC0unQW+2SJDG9bpc8L1hb2aQThFxbe57OSvD/UfeuMZKd533n7z3XqlOXnp45PTdehmRkacpQbAX0ZePdMujIWNnCIkATgejYkr0B4tlkJWFtL+BguQHYxAKzH4KVbYgKjBEWztqJsTLWbGwCxJY3kpkt27EtkRYlWdXiZcjpme6Z7jrdXddzfc/77of3VHdNc2Y0lO0M9wUanK46dW7Vxd9Tz/N//g+OZ2E5CY8un2ZpqcWZky0arYClls3Z5dOsNFz+7kd+5oHqT16a/ObaL/wfH+Hllzbh10LilRiuQfA56P0YNH4zIj4TEKxgSl0tDCTn9g4V5Ls/BZvfhCv/rMelP7jAxj+Hn/nlLhsvxURBg3+9fpknu93bRD/BRkAYGK1Qb/0ynQ5E0ZGaOgYudeFX/2GPC//gdpX1hQ63aYS6H4HNj2KCghgi4kOLgtUYokdD4nf4QXwXq9IZResQ3Mn24ZgI/h3yrrkNQgcTiL4MV1YgeBZEBEEMXDt6YRBBMPe+ejk+Es73MDYbmH2wAo0yJl5ZIQ4igsgEppsr8JGf6PBLj8OP/nLAy7wM8Qo8CtFPQfzN6pjVtfW+2GflggACwuhIl3dlHbgEFxoL4v1jzQaHK+IdurQwqpomAszf0X/kSBMGJgh7xWQsP/pzD1aT9dU/+f21YKVmyjrqiBN1t0aeC5wUgnpAre7gaQunrDghE1xP4vqGEwKN6zfYP5gSZzPiWcapdo1MaSCnGdSpO85tnLAXOKGznNKzqNXqt3Gi5tUpigLb9siKI0449u2c0Ciw7swJR0OS//VyIs8y2kt34IRXZzq7ByeWFjghSpIk4/rb15FFRnuRE8UCJ2aGE4VQZHNO1ByG04oTqeHEyQVOONo0J5RKIWxthldrEIHLNI6x8pI4SchmCdECJ/YP9oCKE6rAwXAimeXk8X1yonYHTiydo9GqOKErTiwvcKJtOBHWXX7kJ74zJ94TQdY3vvLlNc+pGdPJWg3X9XAsG8t1aQR16o2AeiPAdX2CoE17qU0zaHHixArL4TlOLp3l/OmHWWqu8ND5kP3dHXajCZ7wKJSGUjKzLFwlmKYZ0TTGcxU/+KEPcTDeJ0klaI1Tq+E6prvBFoJMSjzXI5cFpSrJ8hzPs9FKAApNSVkUyLLANYIeyiI/7BhRqkQrM4C4VBrH9pBS4dVcbNtCKEWczkhnOQ8/cpr/9OcvszsYEjRqXPzAEzjC5tTJJdJkQq1WZzQc0gha2AJmswlZmlDzaqAFUpUcRJEZ9KkS8iQlmUzJkhRVmMnlwrKwtObgICIIfJxanTxXWLYFno3r+dhCMNjbIdrbI0sL9gfbWL7H/v4BjldHxTMm8QG1oMn29SHC0TQbAcPhPkutBkmaILBI8ylJFhMnE1zXQ+YFEoEdOGRxhlaavYN9hrMRmSq5NbhOOh1Tb/sU0zFJmiPzhCxWlDpDlxkHB/soYVGvNQiaLRzbwfeb3Nh6i8n0OpajyIoYnedgCbKiwLWNWaztNSi1orQ9tC5ot06TI/EclzRLQDlYTo1JGlPg8GM/+Q8fKFB2nvjztS9++BW4EBB3YsIgIv7RAP4vuPAUBL8dEHwcA/XfxgA9pjJtqiKKLmz+T0ADnnzqAl3g1zYimER0L32ET//4z/I//pNn36mq7sBHYtgI4MKFLjH/CniqIrKh9MubcOGpCxCbMma8aBwFR2LxzYiwF/ALK/AJ4MnYZIWCGDYuQhx9lwFWBN3gSOfd/eUur7y0eViKDMPqnOZi7ncx4DiKIq4R8BOrsBKtc010TKZqsalgcfs+xKISkK8YLVq0Cax0jA5tA+KV6FAIH8fGm2ulH9HrxHzx52OCzRXCrnk8KOGLn494+kJgbmkADRERBKZz4eNf/FUeffJJNoAnO/DkJvyLFXhqfv/vVga8g/A/jqE3t+r6IPDRYxtUQvjgwoMXvn/jlS+vuaV/aDrpeq7hhOPSbNSpLzeotwJcxydot2m3K04sr7AcnuXk0jnOn3mYdrDCQw+FHES7DKIJDi6y0AhdMsoVNdu5IyfS1Ghx3Xod18HMKxQcciIrzHDiLM/xaja6PMYJWZhua1uYocwa48OkSlRp5iGW+q/AiXhCvX7ECccTTCcT8uwYJ/YihH53nMiy0jSE+TauZzoFB3s7RPt35kQ5mzGec+LGAidGFSeSO3DC8ZBZgRQC4TlkMzNHcu9gn9F0RCIlO9GN2ziRZgUyTyhmBSUFqswYDfdBWPi1Bs3vkhOF7YEuaLdPk1vHOGFXnBD3x4n3RLlQC7BdGy0sfNcjTVOEEHi+i2VrPM+UE33HxXVd84foOFiWhdAamU/I0wllkTAejnA9G9/1yHRJDkivxnAas7O9y/7+kKRIefThc3zrm3/JNI5NKlmDUCWWY0qCSkqwIUkzAMqyxLZF5WmiqhbWAtu28RyXIk8pktiYj+YlZVmgq04HAKXk4e+O45AV5g2eO9ZGgwPOnTlLu95mf3ePjY0NpDST5QF2dm6iK0NTIYwrsO171Ot16nXfjAxqneBg74DpKKYZNBC6ZDLeZzoZMRruMxmNGI+GFGnMZLRPKlPazRqz0ZDdwYD94RZvv73BYHcT3/fZ3b3F3t4BOze32b11kzxNuHZ1m/3ogL2DbR574gwr7VMk49gM48xASUFZavJSMksTHN9DKWX8YhyHbJri2jZlUeC6Dp7ncTDaZ3d/i+FowNXXNpgc7JOmCQB5ERMNtrmxtYXnedTrJgUe7e4g8wLXFniNGq5To8gFNbeJEIIizXFFHVlUA761xnZ8HGHh2z6BbeP5AUoI7Oo8xgcH2ALaJ5oP4mNw2xqsQbQwqiUiNJqm+QYhZvRJF/hFjpXPqkijD+u/c0Tc9R6srhr/rPVP/BJaT+88MLhvLAiO1Ngfg1XMXMA7VKQ2PhEdaaCq1x/tN+RTHeMm/1tr4ZEf01zjFB573f2uiwuVzgh6a73bdrUxf/JuTuXRPf59MTQx2TrQWaUzONroTvFL+IXbrar6h9fRP+rWW7w2E3sxiDhyzO8u6OYiWO2G9BeE+eHF+XsacWX10qFGfX6ZH1u8/+9yHVYC71VtfA/4ZGkFlucscCJ7JycA33VxLdd8sT3kBIYTyYQijQ854TkehVCkQG67JKq4KyeK2zjhUaoSVZZ35EQWJ7dzwrLx3DknEjzHJk9LhCjQlFg2IECVxziRvwtOCLg154RtgQS1wIlareJE8y6cGC9wYniME6264cTugP3RTa69vcFg99pdObF5bZuDvbtwIr0DJ2oees4JyyGbpbjuAidcj9Hk4DZOTIcHhhMa0jJjt+KEU3HC/itwwptzwrkHJ5bujxPviSCr0TpBUUh836GkoH1iiaC9hNYFtVpAKS10IZFxTJllyLKgKAVZViBzSaEBx0Nic/7sKXBLppV/BtqmjBVeLNidztidjinG+0yzGcO0II+r/SgzcTseTwAoBZADSlOoGKVy8jw1HQm6JM8lrtOgUBqJoiwLpMwpJdiOMIMybRvPcxDCfNiVUgBIbTxTbNvFcesIXLa2b7G/f8Ck2OONG1dpt9ucCpdRyhzr5MmTYGnyPCeOY6ZJjLAttAM4NpnK0a7gzPlTlPGYretX2YtuMp3sMdzfZTIacrAfEU/GRAf77ER7xOOUqzdeYzLdYxYfsLszRqaCbCa4/tYbxMkQt+kymu7RXg54a/N1cmuA5dioxGP7rWtEowMm05jGiRDfrxsBas3BtlyWT5xCK8EsHiHLmMlkiJzEpMkERUmOZjgZ8eUv/Qd6f/CHfPVP/pTd7S1u3bzB/nCL1954lRu3vk0SF7hujaIskGVCmozQcsb+eECaxrQbLZaXz3FiSdBs2Dh+G8cOTADaatGs+Txy5iQ1W2NbUAt8cp1Sd32UcjmYSEqV0m41CIRHOSoe1EfhcHWfhZC+GaYcmXE0wNHA4Z75d2+uFXq6euF8vA7ACvz8J49FMVfg2Q4QdfnCq+tGczQPePpH2/bWzU46ndAo29errsD5dOQFIX24aoKnPpjM0QrQ65vgAFjrQI+I6Etd6HWOWF4Jtw+73KoZeve1jmnDjq/wTsHjwms6Aw7LaodjhIA+q4TrC1NrHoJwZa5JumL6DBa/u65Bn+cPg8rbYpEurHeB52G1egPDTiUiv1xtuwo8y9FwbaDzDIf3+HJlWhqtQH8NrlTir8HhIar36W5B0PHv2fPtViHqVg0T5sKPno+O7t/84UvvgQ7D5tIJZFHg2YYTJ8JjnCgsVCHJZzFlniFVxYm4QGZHnFC2s8CJAs/xjKP3DOypZmfOieE+0/SIE0mSH3FiMgONmWiba8OJ8ogTrldHU1YTNypOqJJSGk7kEiwHtJLYlo0/54TroMoFTtjfJSeKd3JCuHfgxOYdOLEXEY/HRPuGE7NxxlvXX2M658SNMTKFLBZcf+vbzJIhTn2BEzdfJ6vfPOTE1iInlkPcWh2hLZyWd8SJUjCbVJyYDZGzmKRY4ERyxIk/+5M/ZWd7i5s3b7A33aL/xtdu40RZ5sgyIbsbJxrHOFGrOFH3efjsSQK34kS94oTjo8ojTrQqTujp/XHiPRFkCZlTq5lau6UdUCWqzFHaIS9LkiIBYZM7NsJ3cRyHpBgjixFxMSHPMmSaUavBaBLzvocucqIeIBVIJJnKQJagBCKWhMtniPZmfO/Fv8Ukn+D7Np5jgyyrLkMbx7JxfQfHByU1nlejXm9gIfAcF8t2KbXEdjRCg227uF6ALDNc18ZzGxRFiVIgpTz8UUpS5iW2wJTQ8oI0mzKbZUSDMbZuc+2NbVaWz5KmKUEQ4Ll1hoM9AruGKxS3trdo1BqcC88SeC0mcYKlIZumpDOJ12xTFJJcZsTxlEk+ZHd/h0IXBEt16u0mp0+fJJ0O2Nu5xe7BLvn4AJ3nZLZkPImYjW4yuLHFYHeLbDJib3uXg909JiPFzs5NSlK8oIHjWLSXT5JNZmQyw7FtZsMxQgnyREIJlnDY3Y1wFOhaDZ0rpJSQK6aTGXtRzvYtze7QoygtMjsli4ekcUyeTMnzITuDq+zuvs3m66/z9tub5LnEc2qMpjNsbTNJJEoKZmmBJQSeZTOMI+LJDfyax/Xrb5PGByRpzts3tiitgLwUOLZNo1an5rc4dfIs7tISrtd40B8J1q8AFzt0Pmm498wzRxR9PuKwqyyaO4zPg6VnOQo6+izOESaiQ6cT8uFnQuh0uLI+oL9+lC1bJHW3EnD3+5FJQ1VPRVFIVDmWE8HcZJ25y3uVmYnCjgF59RydkP76BoQD+j1zLlWKjsNRjPOA8X4DrTsFNtVaXbvH6yLo97omuLpYBTGhyed0WT+6fZ0ubB1dQ6d7k6dXnyckhPXq4GvQ4fug//xtxvrmXnVMMPQc9NbWuLQKG72IcBX6qxBdvgI9GPRg8MLCua0dXdTqp6DfjRgQ0lmDSxdNRHoUQ4Z3bfwLAT52LFk5P8F1eLEHl+f3aa7jqrKMc8H//LXflYv8X/eqfJOEsBDaoczkISeSsiTJE4Swye1jnFAjYrnAifoRJ5bqDaQG5UgynaGKEi0rTpw8Q7R/xIl6zTnihFJYtuGE47nYHqhS47k1vFrFCdvFtt2qG14DAtutOCEzXM/BswOKoqSUUMw5UVScyEozALt495xw7sCJaXEPTsymTNKR4QQFwYk6taUWp0+fJJvuHnIiGw/RuuLEOGI23iLauka0s0U2qjixtcc0gp2dG0hS/AVOpOMZqcxwPJvpYIitBVksQYGwHXZu7uCUoP0aOq44kSmmB0eciKYeuRJkdko+HlIkCXkypTjkxDXevhcn9DFOJBUnfI9rm28TTytObG1RioBcCRzniBNhxQnbvj9OOH+jH4r7XIUqsZSx59euiywLIyTXArKCwLYQnsJRLmWpcNCcDtrEhUaXDsLLSZOcWR7z9rUtbm7tEqczNBrfr5EmBbbtkA5nZMMpRZZy+tEneOP1b9Ns+ZS5AMvGd20sbIpMUkiJktqkcbGQqqzacy0ExlhTqsKY3VmO8UcRAqeaE2gJj7I0Ja+iKLAcG0vYxmzNcyhLhaYgzzWWJYjjCWfPLbGx8RqPP36GUysN8kyRZRLbNTMSsyIFq07r5AkeuvA44+kUoVNsx8OyLKbjaxwc7DE6GLB0KuTUmdNMr10nPcg4d+4cSSG5uXOL2XhCHM9otZdQpSDLS4L2MjLP+Nqf95BJgmd75IVksDGgsD2ytEQBZTHlbz3xPShcfH9As2Hcfx1MW7Rt2zTbLWZZhuf4xLOpCVp9jzie0j7lEh1McRsBtXqdr/3l63z76g20LMl2R1zYC1Gxoh242EKglCKLU/z6Eieap3BEgcJBCQ/P82g02wzrE+peg9GsjvZq2JbC8jI8WWI7dXYOYhzvFFLNaLg+zTOPE9QDJvGM1lJA+2STslDkUuLXWiAe/HeP3uWI1acvEoWRyazMg57n4bnnMMHMatXRv1b93Kuk0zWWBINLK7zwqT6Dz0Ss9jtE3T5hBzbWjxJaqwsDjQ9X9WTYNefwrIYrXpXhuMNxw4Us1dEp9E281cWkkg43OtouWvz1trLjHVZ/Ydtja33t2Lkv2qaHsHqpZxr1OlT+X+bJp3twpQr0nu736PdhvROyCjzTf461Tz8Pz82jQnPt6y+s0g9X+UwUEZkQzMQr/f6hbRfAixtVl+c6rHRDer2brFLZZHQ79Ht9E/StV12bl2Dd6/PsZzo83414bi7e74esEx02Bt7tbY8w13a3Kuyl4w+Ex/4dYSzh37Hhg1lZIXEwZpY2LlIYTpSFwFYFgTCc8JRLKe6DE9u7JOkMbWlcq0aSFziWQzyZUUyn5EnKmcee4F5PHGQAACAASURBVLXXv02z7VNmxziRS4pcopT5om1ZpttdWxp1B07Y9js5YVuGE8I3wZTlOlj2AieUQumCvNDYliCeffecsISH8O7OiWyccu7sORIpublrOHE9nrHUXEKp2znxyp//ESpJaNgeszhlMH6DwvLIshKloZBTnnjie1Bs4/vubZwYV5xotVtMFjghHBvbd484MZziBsZM9C++UXFClcTXhzx2OqSIFUsLnEjilHrFCUsU8NfBieAOnMgqTtxnjurB0wSouR6Neh3H98CysR0fYdm0mk2WmksErZP4zjK+4yJUxnh/xM7NbxMX++zvvcXm5luMxvu88hdf4Wt/8RWErUiVxLZqqNKYw02TKVOZIV2LVthmN9qhFTTwK32TUoq8VPi+j+96KGFq4kVuOkKQCl2UeFVtvJQSAWit0bpEypw0mzKdTinLkjhOqNfrxlYiCHBtz1ysJYxzrFSoElzXmJ7WanXefusmQcPh7NnT7EVjsiwhzxPGoxTbNu2ik/GURq0BqqSUGbrMydKY8cE+WZaQJROSeMLV17/J1Y0b+JZPvRnw2tU32Z9OmeaaZmuZk6dO4zZalJZFNNhmMHiLne1rnD21wkr4MIVl0zp5hqyoMUlsDsaSjTd2uTVw+ItvvsFgMMBWLs1mm0ajgSUcmu0lLMvB0YLhcEg8m5EnKao02gmJTXRzB9d2SWYxN29s8frrb+JaNfI8RQs4SBIeeuJ9TNME4XooXMZpihaKnVs3GI/HzJIpjXadLMu4dXOL17/1dUoSirKAUtKu2zQ8sCxBUF+i7jkUxYQCgRQlCs1wOkSrgmQyxpY2yTRDWJo0jpnaD3524aXtz7ByERMEdI8wuvJJ89/bwLoGhNAP1+8elEQQRj16P9Bn43NXiH4Qost9iDpEl+Hi6qE0yAQ6i9FBtTqfgegKdD4GnxC9w6xZZ+65Nd++Evn0+/OAymwXzUuJYNww+z3oVRqmHvA8XFybl0A770qsfs8VwfE00+fh8F6FcBg0XZlrxnqmZEsEq314/gqsbUTw2ecIu9D7XI/eZbj0Aoe6q14lzDq0Fu0cJebWnr1kdHXVCjsRP7/9tcN79syHqxPtVAEW0H8Rnv2McZj97OfN/eEXIXw2YtGzdBVMMAR39bPqH7/e77Tm72UVYIUv3HXL/2wrCOo0gooTtnXEiSXDifoiJ3LDiVs3+3fnhKVIyxyrrFEWCi1hOpsyLTKkZdE+bTixFDTwEZUG6xgnHEwgVZaoHNAVJzwXhKAsKk5gOFEUOWl+xIlZcowTlgsKtBBoVSBThQYc52+eE34z4LW3FjjRNJywWy1K2yLa2WZv7y12tq5x/tQKJ5YfJhYWrTMPkckao5lg70Dy2lu73No1nNgdRBUnWoecaLWXsC2HhrBv44RWGbbvIbXhhO+6JLMZN7e2eONNw4ksNYO5D5KExx57H9NxgrA8lOUyXeDEdDxmNq04kdyZE63vlhNuxQn//0ezC6++8qW1RGkEZryY7bo4loNG49iFEcWjUHmB79Zwaz5OYwnbcWgsnaURBAhX89DDF3j4wuMMdvfYuhlRKgulJQpQWhCnU4Jmg7K0eOSRh7l+/SYCF6TCFjalSslzRS2ogdagCmwLMx5HSxzXNfPulMB2tTF3E8ZIVSmNQCCEQAiTRp5PVrSwKJTpUHQ9F11qpCxwXAcQZFmOUmYmVpYnfOLjP81SM+Dc2RWEEDSbLWQeo7SZEL4SniHLFa2Gx2Q0Jktj9vcG+DUfx6lTb52g1qhjuRazOMXRPqPpkJVT5/AdC+VYLJ86w3QUk6QxzUZAKQVFVnCwv8+taJ9xovijr2xwY3fMYHuPwcGYQglGwzFJWuJZNqWUnF45QZEVZIWZI3biRJtZEjOe7kGeUuYJRZaRZzmW1ijbJpmOcIWgKCXDTPHVP/sqbs2nlIoiy0nygh/64R/G8er4tkXgN1BZjlP3sIQmS1IQHvV6jSI1g1V3b25jWwotM25sfYuar5F5zLff2uChhx7nRHuZ6XTfdIYKsJTPNB6Tq4y9/QGNug/USNMY317i7/7YRx9oJ9Xf/v7/Ze21CwEdYjo8SlT18wfBUSDUuWDGt8SYLrH1oMOTdxurEkEcX2ClA4/+7JOsXMAEMZ8CHoqJXzk2DWfTHKsTRdWxA6I/MvYI0QVYeeoCQTUTMfo8BuDz7rVXgCfN7qMNEzRdiKGzApsvV/u/YhwLgi786A9AFIeEH43Z+OkOH//Sp4heeol7z7r5DutONgaLNhULnZIf6WFmRC64UYQN6FV2GCHw0SchDgKiGPoNePLfXOACfV5+aYVOAGEQshLF1VSdRwmITfAVmH7El9Zfhn8Ts9qFjQvQ+/uwwsbhPfvmj6wQ92JohHQqm4tXYnPf6JjRiL/8FSg/2mdjY4UoDgnCmKhyxAji6vzvMrdQcGTx8AtUsdidWiVvW0cbxC89+O7C177yH9YSrbEAIW7nhGsbDawWCl0ccUI0Thg38LtyYg+lLfM6UXGiuDMndFFxokwqTtSh1KCLSriuUariRCFQWmB7FScwnNDacEILgSVsXPuIE6K0KLQZxuz5FSd0gY2DcByyNEPpv0lOeIecqFkWyrZYDm/nhCwERW44MdjbZ5xoel/Z4PquGaUTDUfkpWC8wAklJStzTuRmLvGJE23Gsxnj8R6iSE0n+czMVpxzIp6OcLFu44RT81ELnPgvf+iHses1PMuiXnHCrnvGOT9JwfKoB9+ZE6+/tcH5d8sJcX+ceE9ksn7vD9ZRJJRCIbRGKI2lIajVcW0PS1vYtkO9XkfqjFrTwfXqNOpNPNuiUWvQDFo0anUeOnmGuiewLR8tACGwSo0jLN73+BP4jsdSq47MUvx6nVQWKFszSzNsq45fq5HnObIoDo3cpJQIYaOUQusS17bRpaoGfpqJBUprSq2wHIHlCLDmrxOUWlIWCsdyEdoyHlC2TVkWlYGdWWVZ0G4u8corr9BeCrBdB9d2ydOCNIsPM2a3opuU5OxEe9g1h0ajjtAlSSZpL51CKo3nthiOY86cPU+92eDMubPsRhH7o32ub77J1bfeNI76boNZUdBoNLBtl1xqVlbOsRtNGI2mDIdjprk0QVhSIIVkPEm4trnDww+f59Vv/CWb168ym2YsLbW4fv06cTIFNLN4SiZzsjzFdQRJOiSJx/gNH23Z7Bzs8btf+Ld85MM/jutY+L6P7QSMhgnXr15jvL9HqRTbt95CqozxwT6z0T41X6CLFI3gsSceR2kLt7HMeDhA5hkfeN8Pc/78+/ng9/4XdN73I/hujUajGqatC5QtUJbCrrnUGiEnVx5DOA3yoiQIAmpCPqiPwuF67tVLfD3a4NIKDKKITuXm3V83BqKdS0anXunL6V+KuLQYVCxkNLrApf4qq1XmaaHSxWXRI+xBd5UjF3EgXMXoqBjQDU0JrFNtE17msAQXzQ8QwaWqk+/5z0W31ai61T57Pehfhs6asX34+SoD1u9jlOYh9Ojz4gtrhyf5XQmuO9C7V7qmw21itagLYXf1tk7EKKSyRb89SdcHOn0qg6pONTvIBKPzPV6cdyJ2Q6J1cy+6ITB4lg99GkI6dD8bstrpQKdLGHZNVpEO/OKn6a/Ap3mW7lpohPN96H7OiKJ6nQ5Rv0P4fMTczPXF7ir97kJVb+2dlxzOOzoxvRHAPecUmg3NMd4DjYUA/D9/+H9T6hgtFEJpRK6wNNS9Gq4wkgnbcajVDCf8tk1tkRPeEScePnmGhmvhuD66itqsUmMLi8fPPY5ve5xo384J7RxxolarG06UBcISII84USqFFiWuZSMKwwlZ6Ns44VSc0EJTZBJhC5Ql0eUCJzCckGVBnqWH90Hr744T9eZ34kTziBOTfa7fuMrVNxc4Ie/MieFoymg4ZprmlNI65MRkkrBZceLrFSems4x2q8X1zesk6RS0YH9gOFGoFM8RJOmIJJ7g1ytORDv87hf+LT/+4f8a37dwa4YT490pN65eY3SwjywVN28YTowO9pmMD/DrAjHnxPvfyYmLC5z4wJwTwXfghGiQ5yX1d8GJ90Qma7Q3WHOcpglcHAtbW+TatLWWUlFqjSUEhSyxLJfxLKVVP0lWpHh1D1cYN17HNjVsWQhe3XgNrQVKajJpylWlUEgpGE1jDsZj0iTB9zyGoym261NakKYpnueSpxml1iAEtuMAuqqna7QsEEKDsEwnSDUXyhICyw2QpcYSNpZrAyV5kWE7Nq7nmA8lpn5fFPKw41AIgeNYBF4dr+bw2IXz1P2AIsuIdrexEFhCENRbxgjV0STTCfu7Wwx3dkmzjOFexPWtt4l23qaIc7QucWyHvf09lKVYXjlDve5x7qH3c+b0GfbHe9iuh1MPePvNLSzPo3Q8xuOUV169SpIlWJZgablN4PkoDZ5wsT2bvfGUncEBr3/rOk/9vQ/SqC9T832KIkXmBcYdzMKt18mVJMslSa6wpMUomWLZNq+9uUfLdRlsbfPTP/tz/PEf/Ylpi7VKZBYjZcKJkydIkyl+3ce1NI5l3oN6c4maWyfNYq6+9iZlNiNOYvJMsnKmRp5r3nrzbYKaIslj0tmEIilBKCaTIaPJLVxbGI8zW9JqBkiVYmvJ+bNP0PmB7gP91v4/iH+/9s9/9gL9zYgoAJ4K2OzBhY+scOGL8PI8c9EBXoYfjR8lmjtUXgY+frSvzRBe3txgYxNTNgwg7pjMRvcpwYWP/wx///0bvPS/wktzTZSAuB8TBStEm3Dh0ZDBRkwcQf9Jc+joUQifjGDTZHgGHzc+T089VaVnIoiuwZOXjgYgNwKITkL4Ex1e+aWI1Y932dg06bfNfwbBz4V0otj4fjXg5ZfNfu423PmOK4IL9xqUfCxq2ARiNg71S4dVygUPrz7wedb4OC/RvRLR+J1/x7r9FL8R/AwvHavRxgAhBK/ExE+a7N9mDDzZ5amnegQrG8Rxn41NCFdiNr74R/yLf/ky//SpgF7wT/nc8xFr//EyL11e4dKFS8TRy7zy0S47/yBkPQ7YWNmk8xSwChc24MnNDVYwtyzEZJ2AyoLCXO/zz8NTP3TsPhy/p90Qfi3m0gq8HJggfuUCBM9Dx4YPPmAz0oObu2u+30TNOSEssrLEdgUqV0hl9K1FaYyjJ4uc8Dxc54gTUimKQvBq/zW0EqjyiBPKVkhlsb8zY5qMSeacGBpOSEuQJhUnkoxSabRVaYrRxrZHgJIFwjacMFOjAW3+X2/XAqQ8xokyw8LGq7kU0nBClBZSSbRSR6+1LQL33XNidOv+OVGbc+LMGfbGezh34MRonPLVV6+RZzG2hnZ7mZpXRyBxhXdnTtSWqdV8ZJFS5AVYNo4ncOs1ciVJ85IkV4gC9oYTHM/h228eEOKyd+06P/WJ/5b/9Md/SrvdQHuaTMbkMmHp5AmybIof+HhC41mA0NTmnEgqTiQz4jQmTyXLYQ0pj3EiPsaJ6S1cIVBlgeNImq2AUqc4UnL+zBN0fvA7c+I9kclSZUEmE7SW5LmZoWfZNtp20JZHqUGVHpQWWmhc3yfJ9rFtmzzNieUUjSTLY2ZFQj3w0W4dpYX51iNshC5JU9PWmk0l+bSEUmBpqDkuqjQjEep+jXiaUHME6Iw8j5GlxlTWFT4u2B5SWeSFmVtlIZCqAFdga4kuYmxLohSo0q7G79ggzUBp13VN1wTz4MoIIgtpcWtvn2h3wub2gGg4wBaKEy0PB42Mc9KyxPZcJtMRcTHC1h6WHyBKBSrBUw5xnKItG6fWYJbFNJdOcuGh9+EqhYXNzd3rbLzepxW0cCybzetvohFYfp1vfesqf/bq15mmMWVZ8tDZFZZsl//+H/9j/pt/8suk2GjbI2jUub61S64V4xhEyyHXJU6twTSJyZOUoN4k8AIatTa+4+LXbISl0baDLAoG0QEnT51gdzbif//sr3AqbLO/O2Tl9BPsx4LcalGkCUt+C5kZ2wttOyRZSZGXzNKEg+GIet1HTg5YCnyCIGDz7T0m4xTbrTNNYDweUxQFhShMAGwJRtOCNE6xahZZqbAcsHRJkY64fuurD/TzALDq9rnUW8g6hfOxKsDTlRC+Fx125PUX590cz/5U28yTMxtXqqTGqtlf/wfW+ayG8583x+v3e7eLykPo9yJWCOmHHToYAdfFpzEkjkymZGNuhRAe/YRPm+xVH1hZ7x+V8a6Y4/cio44PexHhxyJWwgguVrqkeWroblmp++xCjO4lnl/cTWiybJUpwm3brHTh2SpFFPEZnuFLfOnLa3wqevUOJ1J1TfYru4po/uhlrlyO+KVfDIlWqrxTb4N+ZxWtP2OyZBt9zN3qslZ0iPpXiMIOfeATG+uEleB9DY6GXS/cq08tnsZq5XcWVs0S32GF6z34Oly5gsmWzqPNvwf9L99vy+ff4NIFRZGg1REnbMdGCwfteJQCVOEaTljHOFHcgRONihMY4bQQNoKSNM6JRzOklkzHVbehUoecECjqtRrxxHBCk1HkCbLUlJYArfG1i3A8CmWR5wuc0AXCE9hSoooYmwKlQUkboW0jjSmE4URlVYEAYQkcz0FYFSf2/2qccN8FJ9rtFvadOPH1r5OkE8qy5PzZs5wSDr/wj/4RP/nf/c9350TTobiNEzG+18C3GwS1E4YTvgW2wG36yKIgGhxwanmJaDzht371Vzh5qsn+YEh4+gkGM01htSjThGW/hUwNJ5RzxIlkkROzA1p1n6ARsH39PjgxzklnKXgWWaGw55zIRlzfuj9OvCeCrDSbIfOMPC+RSUlWDBlPDhhFuyTxGCULZuU+uUiYjGboTFPkislkQiFTXO2jSptSO/jCNSW4rECpaqSLbZEUOUmakpUFcT4mzmJmScFkViAxGRJRlCidonVBUUrQHg0/wEcROB5C2KRKolWO6zh4jk9W5OQyw3EcKEGVFkHQxrI8XKGwhKRR81HS1NptC/I8P7x2xzlq8GzXPTxXME1GyHSGgyZPJQKPm7dukaM4ceokWZYgShs1EcTJAXt717l2420EHlLPOHP2EZrtOgfjXfaHB1iuxa3BLtdv3WKW5cjclEJPnV5h+eRpLj7xXxHtjdjfOeAgipnuKXSpEEIwHE842J/wm7/+6/zp7/4mbQs8Jc0EcuHiWja+4yJnOdeuXUMWCTYa27OZTEdMpiOy2RjHcbDtgHrQxLFcSgWvvtbnq197lSRJSEpNJiVLSzXe3HiVyfiA2WjM6ZXznHnkCRy/ibB8iqJkqbmE7fn4tTqqlJT5jNxS2DbYIqHebDGbJaTZGG0XONpiqVGnLBPqNZcz4SM8fGaJlXaNCysh2pYMrm1BmjKOZ0z2Jw/gU3D7ioqIy8YFk06nD583gUz394wIeeNKBN3wnV2AwKW1o0FznYXOsCg0nYFhBE8/A1yOCD9jPK4626bcFK5Cp9u9zRjT7AhYjejQh7DH8xsYIfc8qOIoIGM9pHfZBGzRFYypU99YEYD5/fNX+kSX+1z6hDlIdAnohqY3byM88gO7V2fborHpwnpHyHMvo87eQuUwgs6rVTRaFdVMl6CxqjgsGT59Gb6vx5dYI+SFw5t0ZR36z3ePGhU6fVOmi+bHCekWIatPw+DFdTPv8QWjsl87v043grVX11h7boW1T5rOy2eiVdOlGMKzFy+Z0C+8vSIY9g5PgTXg8pWj51bvEP/ddo+qv58oMrYbIZh7Pm8UiIAurLoPvmiYpFPyYs4JSZYNGU8PGA93SbIxZVYw0wdk+p2cKMmOOCGOcaJQxpLBt0iyjKTIyHRBko/Jspg4K5jGJVJXnMgrTlBQKInQHo1aHUcvcEIbTniOg+f6ZFlOmmXYtoMuDCcaQRvLcQ85UXd9yiyHsuKEzEEIBODYC5xo3IMTO/fHifJenNg+xolTc078CNHeiL2dAw4GMeNIoeacmI44GI/5jV+/wp/97m8cckIWoAsP1644EVecyBNsZTgxm4yZxSOy6RBbONheg3rQwCodVAnfvPo6f9x/lZFOiKUyDGjXuLrxKvFkRFxxYmWBEzI/4oRbq1PkR5wwbv334ERhOHH61CM8fPYEK80aF86EaEsy2NyCJGWczpgO748T74kgS2qT9o3zhLgYk2eSoijIsozRZMpkFpPnkjTXIGyyeIaUkrKUzKYpmSzIczMgepYmHMRTcC3KQh5qqsAynYClxLZclFLYtk2WFdVPhtYlpZQ4roVUpqSX5hJlGUG7JTxj4wBoXaJ0jpR55ShsoUpQWlKWJWmSg7CxLCMqtCwHz6+j9VE3o23buK6LbZvthpMpnufRbp2gUauzubmJsBy2b+3yvg+8n3a7ze6NbaMNswS1wEcWgtkk5X3v+yCTeMJwOGQ43Gd35yZFntBs1NmPBqSzmAsXLiBsi729faJoj9/7/T/g937/3/Hb//pfMRwO+eorrzKZTpFlbNLmCmQpGGdTLEczjd6iXvexbYFSEtd1ibOEKIrY2ztAoIgnU1rNZRCumT9Zq1FvBGjbou75CMclqDeoN5qcP/8ww/EMhM1HPvITTKcx00nM93zPB6g7DeJRQhynDMcjM99LKVShGI3G5Llk8/oWr/W/RTwbmgHZUlPkJaiEwCr53icu4DmaZt28P9nUQqQF471dTjgBWB43r+0wfmOb1954hZvbb1ELlqk1lh/sBwIIt7pE/YgwhD4doosGhL0rsP6C0TRBpZ06th4Slw8p2l8ALhFmKnEXXiTkyu/ACiag6V3hyMNhIXAiNCL7sAO9zx/t6LlV4MW7gPdSRPcSdLvz3VRpse5R8Lf6LIS/0uHys1V80B+YzEw/IpwHKVVwcte1zu2CqYXLvPcDC+tYF+WL65XIjVVWQ+j2gU5I9/n1w+36wKdWIWSNTj+iXz1+aRU637d+tN8rVSZyUP2+EfFbOqQzH5Rd9FnZ7hJ2+9Dpsx4Cax/GpABfBB3y4S9/iF63OvThtOq7XEv1+LP3Ckw3ju2ieksOHeuf4/bgtnp8/T1gRlq6NqksiYuEuJiQ5ZIiL0iTjOFoyjQ1nMjkOzkR3ZwdcaI8xgkl0WjyXFIqwwlVSixclFbYVsWJvOIEJTI3nCjkESeEIxD6LpxQOY5rYWGhS1C6MJxI5SEnkiw1Yv7AVGHM5BFzfNdb4MToHpx4/18DJx6/nRP//vfmnPhNRsMhL7/8KpPZlDKfIjCcKErBXjHFckumg7eo1QwnBBKvbhtO7EXsDRY40V4GXPyWaWIKmg20Lah7NSzXIQgC/GaT82dPMxzHYFn8+OpPMhyOmEzvkxOZ4cSbr92DE48f40RsODHZrzjhe9zc3GH8VsWJm4YTfvv+OPGeCLLy8QTSArvI8bCRlfeI1i6WbaNUgZplFLMZKouxpKQoJQ4aRMZ4tk8hU6xScjDcY3snwi4UwvUoNJRCguehtTAiSMfB9WzyIsa2NRb6MOtVKI3rt1CWMhPOWzUsx6aQiiyfgdYIbVEqkKrEc1y01ng1F4SZNxXnBV7QNEFfnFOrmwnjeTapAhgNpuex+p9AWXUXCrIk52B/gMJle3ubg+Et8CSvffsqe7t7qDQmmc7Yv7XJ8GBAOh3TqgVcfe0bJDrGXVqm5rucWF6mVm8yniQk0wTlOkjtsFQ/xfs/8Ld5/4f+Dg1Hsru9xbWdAf2/vEYUxUgJqnRQlAjbeHzVl1qMRA1v6SxbwwElkqBeB8dFeBbNdkirXccVLlq5WJZDu97CsWs0T5ziZHiek61TZur58jlOnjxNKgXf8/ijhwHqH/6/f0LgNSltm1f738CyclotQTLZ5+BgwHi6w2iyg3B98B2CRpssy3j7+ibjyT6O1EySKcJzULnNYDLlL9/eZJZZTEqPvWlJ+8QKhdAUSAazKZNsxmQ/ZjCZsNQ6i91aYpYmTEezB/2RYO38M/R+p6o69eFiBzN6Zb7BIXir3xeCkeee6x4Ji+5QKgu7EL3a4+mtAS/2evR7Pa4sKgsWsx9rHbN9NdKnPxdCHy8NzldlfdXvG6F72Anh6e6h2eg8TpiXGKFPdzUk6q9wuQdhdx2e6UNoAsx3OHze53o3L1ntzC8IfuvKJV6oROXf/2l4prfGpyL4svt1Pvc56BDyhVWTDfx+YPBl6D7bod/vmnLuaki/V+1uHphU713nBXhsLSIMI/rPQ3erQxj26XQ7xses/zzfH3bpdzo8zwriCxHRZ58zb+UvQfh8JbqfC/Sj6u2NqljpeJbqXd6YeeLq9hXd8W/oQSw1SdFTwwkXm7KQKK1Rc07ognKRE/kRJ/yguDcnBJQUWA0PpSDwA2xxF04IhcRwQlsK23ZotGpY9j04YbsopXF9FyyFV/eYZQVevYlSC5zwXPJsTKliwIjkERKpC8pS/mfjxIn6Kd7/gQ/y/g/9HdpCcXDtBtduDfj2y9cY7qaoAsrSoigMJ1RZELRbjKnhtc6yPRkgdWk44XngWjRbIa3lihPaxbId2q0WWtRoLp9i+cR5Tp0MaTZPcPLkeU6Gp8mKkscefwxhWZTa4ot/+McEzROGE9/8BpbKaTUFyajixKTihOMjfIegeR+cuPZuOdF+V5x4TwRZrushlaYUFuN4Rp7nlJmJ9KWU5HkBUlIqSWILJr6ZRVVqC0TNaJosGywPpMVgMKIM6sZZXVu4wkMVuWnrLFKyoqBIM2wFltKAhRA2WgscyyaJpwS1BmCRZwqlzEwqIYwuy9g0iMPuQtu2mU6naG3G3nieg1KSLMtwXROEFUWBUqCVW2m0XLQ23YvzIEsDZ86dR1gu/9uv/kv8YInd/SmFFCwtLdFst8ESxHGM69RA2KRlRiwnZPmQdJCTDGP2JiOubW6jSoul9knOngupBx7xbMStm9eJh0P2NrfJhYDS5pTfYpYmZi5kaRoOhBDYwiHP8yrLZ4T/Dz10lnPnznP27FkeOX+Oxx55mLNnzyJ0E8fxyPOU4XAfrUvOnj2PLBRouOpqjgAAIABJREFUG20ZQaflaBzXp9lY5sa1bVQJnufhOA5lad5zSwjGs5xoNGUwy8mzjHiUIBOJLDMcu85wfMBuNOThhx8mTVNKPyMTKaWtCR9/hHOPPkG71qQpclZqNdpNn0KnCMemVT9Jq1GnFZwiWPb5vvd/gNMnz3B2+XFajRMshw892A8E8GGxxtfPGSJ2LoWs94ABdNeoUlrHcNg9toN5iey4E2VV4Ys6QNihG3bodjpc+tjtHXTz1/Ve6JusRtccojPXhi2WExdPpXJR73QhvAjr/chkpy5Cl9BksD4F4WpkriPs0F+PYD1k9VKHaKNL5/wKzzI4KoV+py64+1nR3UfPdPocdglevlml9AbwwidDvtBZ45nPRVA8x8ee69K7EiF+3IwnWnsOuv9nyPpPvsjKeo+Nngkce2DGHHV6R7emA/0XTAdmJ4LOJyG8Ar0rEfTgctghuvQ1VnsQhX0+thryXNilW8055FmInrtEf71zWAM0WU5zuuvzlsHwu+8GXHxdB44it8N5SQ926VJQYDgxSWekqeGE0iVSFuRZfjsn6u+OE47lmSHJAgppxrflFSeEqoyohU0pjQN4khgvKiEsiu/ECQscx2Y2m1Iqwwnfd9BKUhQZru+iURR5jlICXboIbfS7WlmQaXRpfLo0cObsXwMnxiOuXbszJ3ZuXicejtjb3CYVGkqHU36LsU4Qnk1elKbr0LewlMNkmpElGaWjsVzBuTNnOHfmHGfOGE48+sjDnD1/FqED7DknRvtoWfLQ6fOUuTLvDdUAalsj3BqN1mm2396mVFD3fWqua5IuZYnlCMaZJDqYMkgqTowrTvx/zL17kGTXXef5OefcR+bNrKzXze5Wq9UlS6bV6Qey3bZ5uby9NqxsdieWEgZ7sSB2maUXFhFjtDFM0GPWpVhvExuzAziwgoj2RMwAq1hgvS4mAgxyYNNsGXbWptsWI1NlG0vdpX7XrWdW3sz7OOfsH+dmVVap9cBjkE5Ed1W+bt57M7M+v/w9vl+TIfkH4MT0YY5M3ldx4tgret++JqYLv7T4x/Oe52FNgfI8hIBAKbQYYMoMaTWl56E8j1BLGlpCzcMPagijnG2BkCgEhRSEqs6zy1coqjc6pUv/lroEa/GEcmq61rgJQWMreQaLRVSZJokxIK0TobMSpPRRvgQDpdYoT1JqjaxMonVlt5Blfdf8GARIJHmeIYRLKxtjEYLddPJwCSEoS02WDeh2d/gXv/yL/OWX/oK3vOkNKKmpN8YZDDKarRaHDh0hrEcUZUl3c4s07ZOXJWvbN9lIrtNbu40uQQ8ysn7OTtZjfS0BbUBAPfLxhGR1vct2mnLt9ga9XNDvbTkVYuUa9LEWhFMzzkpNljn7BWEV0xMTNOse//1P/wShX2dq/DD9fIeoETHdblOUTlyv1Wq5byHGTYbmxtI+fA/x9GHieIrPPvWneJ6PNgXaOM8ogeXIdJ03v3GGqWYdPwzR/QFhEBKNT1CLJvj6c1e59OWL3HN4nMkxD09IpoJJ6qKO6G4g7YBk5zqF6JMJi8Gwfu15imyHVmuSIFAU5QAjMjxKJqYmsMIibE6a93nXD/7YqzpJ9dzsM/OnPwXMpiz9DyvM/HrE8QQ+8VnoEJEkEfFxXqBzNIubltu9fo79JsUzwEWIltscB5ZFTPRQDGnKcffDaWhVEkkzp3BRwzKsDKfRhjQeyihVP+ME0moasPM+SH7VSVG1Z2D2lzosTa2w8FFoTEG6EpHGEckCXHx3QnsxYuULCTO/GrHyxSssrXRIT1XP8/eZLKzWC+SfIkie4o5ZmcW56tykwGm4cBrm/iVcOp/yC194nJ/9sw+QvSPl47+0QuMtHd74VdzwwXFYSWdIHumwkiZ0Ztz5O4U7x3G7gRsNdZdZAU5BI3GDiysRnP8X8NA74NSjMdFnL0EDTqXQuOS0ungodfv2EHSWU5LZ4Y66/5Pz8OFTCSudyJ2n/wRpsdGV4PZ3Tylr+VXXyfryX/3xvC89rC2QKIQvCJSiYIAtc4TR6DtwIjjICTHCiaWKE+xxoihLEOAJhTfKCWuraXCLtQLhZglfmhOlRsmKE74ikBUnrCEv+1hrnUyRkORZhpBOWVFbg8RNPQo1FL0WCFlxIv8OcaIAnb2QE1ZCvV5xYrPLZpZyfW2D3kCwvb6GDATKk/i+44QSjmu7nOj3wCriSceJn/npnyD0HCcGec9xIm5TFJaosZ8T1viUWA7dfQ/tycPE7Sn+5E//FE/5aF1gsCgBQliOTNV485v2cyJQIc2JcWqNfwBOTI5yIn1FnHhNZLKUD0pa6l4dz68hkBgENRkQ+Q0CL8RSEHo+XqDIA+NGQAdbZP3bpL0uYRgiheDm89dYXV2jrzWqtJTGuOlAA55yhpQGKAuDFB7d3g5ZuVM5n3tgrAuGMg2ixPgGFSqs0khlKHONRVEP6tjC4vu+61/ConyPwoL0nCKxEJaizJxiPOx6FwpKJ2AnZfWtpzoPSpHnOUIIzj3+v3P6vT9MFDQQZZNGGFELfDZvrHDxr//Kict1t+ina5SlIVAKRQkYjJbkRY/BIKU0PUqdUatFHDp0nHqtxVYv5erqDY7dcx+HDh0iDATWM4RhA0/V0MJDqRChJEEtRAR1Wo0mE1FEHB9iarrF+37oXfw3P/5PuPu++5iZeR1Th2KiaIqoPk6ytk2j0UAIwWAwQHqS191/P81mk2azRXNsDD9UnD79n/HAidcTSIHOSoqy70xTraQ36PPsyjU2tnrUopCw1cSrhwRWkA4Knr1yhTc/+N2sPH+N3tYmoddA1XK8esFmnnEtWYFMMyYPM16rQb/kHW/7UV53/K1Io2nVYqbGpohqbURzkr4wlMri+yEz8b2vyudgdM2/92E4m0AnpnMm5iwu2TIX40p4BzMW1Ud9t2o4LAsNr6jsY0iANsTELJ/vuI6syvQ5aceuD+yD7BpOLw4nHKtM2Xy8uM8P0U0uxk6byg3FkXScmDtzLqO1uATngs+w/JNuSo+qnJYsxMRn4ddOxpw9G9P5bqfBdf5vEpZnRxvSXzo3s9skPzzUg3cfTvftJmPivQNagKUF9p/M+ZiF83DmDPCenyfpJHz+UVj44CJzs4t05paYPQnJYswyS8zOLvGFP0g493iyb7AzmY1392txwU1NMlvpnlUG1Q//OHRm52F+T63/0UfdVpZXE5Z+EpJlOPf+JZZY4vHRP+kd15u3uBQzF79U89pQi8z9S86MJDgTRlJiL5zE3D0tc69+43utJlG+44Rfq+MhsUIQyYAobBD6IWaUE8pxIj/ICUY4YfZzoijBVwFK7HFCoOj2d8iM4wTCwxqLVI4TSuToUU5IQ1k4TtT8OlZbvMB3/UvS4oUeWVXVsFYipKUoHCesAW1KhNV4qkSIAmklVSINS8WJLHtJTmwc5ETvACdsxQk9yonccSI+Ti08wIkjhwg8gQgMtVYLz3N9U9L4KE8QRAEyrNOKmoxHEe32ISYmxvih0z/Ah0c5caS9y4nbq1s0mg2EFAz6/V1OtMabjI1N0Kw38GuOE2/onCCQgjIrybM+QuH6zAZ9lp/dzwm/GeIbN8TwUpzYyPY40fQO0xpy4q0VJ7TjxOSLcaL9Isq/B9ZrIsgqtUAbKCwIa2iOhTQbilJrkM7ryRc+aEOv3yfLc2ypUdJHyhpHjhwlK3tspLfJBjskt6+DySgoMbZEChc4DZvgpZRVVsngSUUjaFKrRSjPByRFUbiGRWPcGK3WmMySDwo8P0RKUdXGfdfsrgErsUbQCGt4CMpBRlmWKCV2m/KtrQI462NEJWQ6ktEyxuwGZD/wvd/L7z/5B3zl6a9iPcNqcpvV1VWu3rzJG9/4RvJiwI1bN6mNTTPVjllbv0W2k1ILQyya8biFCgM2ul2MreGHTbpZRt8YNrfWocxYvf4sZa9HUwTUTYAWzk/RahcMKiXwlcdYPUIphVcL8aQiz0uazRbt9mGnP6Y13bTL5OQknucxPj6OECG1epNSG+666yjra5sYw275sSg0l5/7Jvfffx+1RoTBBcFFURKFAfcebnFsLGD91rf45tNf4faNKwyyHQaFwFpB3u+T3NqgNTbNoWNvINMBVoxjbZ1Gs83hw2/kyF0dWpOHaTaOcejIPWwXl5k+1KY1NUmhB2A9elvbhNLDp460Huvrt/nm5b96VT4Ho+uT8+eJWSI5vwxxwvkFF/C4GX1HxHiUex9jN9AC9pfzhg8ZmkefhGQxYXV5CZZiFs8lblx/Nak8EuPdKlFn2HO16AK8309mOfmoC0DiZbctKsHNzueBTtU/RvWYM06MtDP3ME98Yc98uRMDc07NdOGcE1OFhMU4YfZjS6wuLHFyeCxnX77D6tzIuVh6kWnDvbskJCxxJq6mKVncDUY7MfBowiJwfqnDezqu/vd0HhM/PMdissoSHRYegzNnE1YXIDkHH/uzmLPZ3L79GFrdJDHM/kw1QXDuPEkcs9SGziLEbVh4x7zz86n2YX7+HEuJk7vozLoY6MuXZ93cwp9V+zl8TYHFPzjPgyMl3IU7fLd+eHh74sqUu7FUjAtiz1ZB7Z38ImNYSF7+NfiHXv3MulKbtgiriRohjUhRCk2V2iAY5YTOsf2X4QQVJyiR1iKFrNo+il1OWGudQXDQJKxFeENO5AVYTWEg8D3HjIElzwo85ThhleOEHXJCOxGtsVoNX0ryfh89yglhK1V4iRY+RrjATCgDlSuKMabS1nppTpwc5UTr5TmhbUgYNukWGQNTsrkxwontHpEICE2AlRZriqonziCVwFc+jSDC9xVeEOKhMFYzPj3BdDzCiR3HCeV5TE5OIIRPLahTGus4kWxiDQzSAVlaUOSOE/feey9hVMcI8L2AsixpNUNmDre4b+qFnOgPDMIK8tRxohm8kBPNsT1OjI8fZmzIibLiRDxJ/hKc+Prl//cVvW9fE+XCv/zCv5+XSrK2toqnPGxpKAun3FYUGiEkvvLIS00UNfE9j1oYukZCPyQv+zQbbfLMYywa43ayw7W1lEHex1QlQG0N2rhyWa6dEjvWUg9DpOeRlyVGGzxPolRALQiwwqKLkjIvqIU1Aj9wpUVbuilET6KrXiWlFEII8nyAlIIwdI32WTbA9z1Ko1Geh1QScN9edKF3M1nDcuUw+NtY22In7fGmk68Haen3NpAKJg+5Pqd+d8up0mYGm/ep+x6bRY70A0QQ0NvYotQwOTlNvrXOoLfOreuX6XfXyUtBLazT7W7T7e/QT3M2exkWS6FLQi9ASUupNZ6qIZV0iXElULbg7rsO8WMfmMNiabfbJDduMTY1xo2rV5FWs3b7NkeP3YtVHkePzVAUlqI09Ae5S8HWItf/lQ14y9vfxsrVFa5cvoLWlprn0whDpqdD6vWQ6cNHUFZTD2uooIHXilhN1nj6qzeYGLO87tgk1mjKIqVWqxH4EklGGAjCoOYmhWyBlAGGAC0sVki8sIZUHs0goDk2gecLpPKIJ+9jcjLmu7/voVe1NPLZC8/Mp50G6ZWIqBHtlcyeAT77Ig86/TIbXcSJlMbADIgnEqIPQFrZy3RSaD8EKx9P4SloH3eCpdFDMXwxJQFWEkg/m/DU+Yi448pjfAS4CJ1bsPCZKluVAm1YrhQyo37C4bE27XbE+cWE93VSIpESfRTS2ZhLj6WcnD3FpfQpPtJ5mJVLbTqPxHAqIr2UvmSjUcQLW9JGV3qHcmNEysVHOqxcTIiYgaeW4FTb6Y+mMHMcePIcpx6Z4ZEPnuJnf2IFcT5lebZNdAo+/OvwgZ9KiGcjZx+0AtHiygtLm1FCSlRZ2jRgZnZ3p5MogeMR6UMJUccZ8gw30I7apCvOTocI3h2nfOhX4fSHYf5DHT59Ye+EtE/PcCvp7FYmO6dfeLzJaB9d3HGqsaNr2VnzRPDCc53GdNKU0+98dcuFjhOKZC1xnNAVJ4ybFrsjJ+ovw4kkZVBWnBCCstAYYUAIJ88gnPNg3QuRco8Tvi9RIqAWBlgsOi/I+0X1NyjA4Cx2dFG6jFclZq08hcBxQklRPV6Q9vuEgfNAVNJDehKrJbIaPvKkh7VgqTjBy3PCvBQngjtzot9f5+a1y/S7G+Q51OoVJ9IdBv2czW4KArTQKAKUtRRW46saKpAUpUH6Es8WHD1yiB//0TmsOMiJ5/dzwgscJ0pLoTX9rKA1MUFQb+D7HjrLKk48z5XnrqBLx4mxMKQ1EdKIQg4dP4IoRjgx0eB2ssHTTztO3Dfz7XHCD2uoF+HE1CvkxGsikzU1OY41OdPxOBMTEqkK+r0UKrhKBLkuXTmuKPANpFlajYhqPK9GsnaZRq0Hvma9m5DZFF8FBCrAVPYEw39OeE4gcanXQTlA2xLlWYTU5LlrUh+W73yl6Gc7dHvbKGt2xUOttU5cE02pc/JisNvsOGzaN8ZQlk4Xa++5cwSu5j7czt7Ir/t9oDPuPnaMr33lb8n6GWWZs7W1geeHrK+vozzB5PSUM7vevM03r96i6JeQWTau3ubas9cY7PS5/I2/Y2PQw2vU0MYi8Gk2JmgfuQevPsZEs0mzFuDZjFAGWDwKbRkMBthSY61gUDW+W2vpvOG7eN199zAxOcbx40dptVocPXqUt7/jbe5ceYq3vuVBwprixIkTbrR6p8fNm7fIMqcEPz09TRAEBEGNyclJ7j5+F57nVedD0Wg0CJUl2UjorW1TCo8gbLC+lfLli8/iBRGp2aTbz1BW0FCS8aDuvrUaSSQkgSjwhUWVASbP8FRJveaBktTqLXy/gQwVzdY4RgvCxmHGJqaRKgMvfPU+DNVaeDxh/lzVZH6wPPfiF/ev4eNGa1jngffD7Adj4msxyS9AZ6GqGCWudNZ54vH9k4sLy67MtFtli+FPKr+/OUh+0V21tOSyVvHCnpVOPMeunlXnTAzzCW9vxKxykvYctGdjOsDJ35llgSU6D3c4t7jE7O90uDSTkDzYIT7YvP8K1vmXu8MscH6pOkUxZ8509kYfd/WhzvILH1ri/CeXYHaJJ24scP6csxh6bBHmZqukz+P7X6Kzu2XJJRbacWU/FJMsuHsNHYziJCZerk7P3Khuxt5uDK86fy7h937N7df8fJXJjKnKhydZ2vdlef92lhZwGaqhmv/DS67KO8+eg/Wop1Al2bG3ku/M8MF/4pqojyPLnLj90pyQr4QT4QgnREBgA6wSeJGHtAqvslETOMFqZaE/yglRklfDTMpT5HmBHyrSwQ7ddBtZ7nFCYBFYEBUnyoHrvZICYw2lLcA6P1uk254xBukVWAxKSAyAtJjKheTb4cSNl+NE1sOr1yofXp9Wa5L24WOOE2MVJyjxtI8uPQpj2Oyl6MxxojfIXD+ztXROfBf3jXBiYnzIibcyyAt8T/GWtzxIEHicOHGCrSEnbtwmGxQIJNPxFEEQ7uOE73nIyh+4Xo9oBI4T29f3OLG6mvLli8/h1cdelBOyrDhhK06YAJ1neMEoJ5p7nGjegRPqlXHiNZHJWvoPF+bj9iFXEsTiSY/xsYjxiWk2ups0WxFBYbHFgDDy6OuUVjRGWaZErSmszpA2qPqsFPH4GM98/Xn6eUlZFGhrnMWOqUqAnsQY7b4tCIvRGmxl4InTTSlFie955EVBUZT4QYjvKwalRRoJypmUWmOriRA3JSmlQCmfwSBz/VdCIKqmSYwlUB4gKUv3bUlrva9kOAy6tNEktxOiyQk0HpOtGrYQ2Dylu7PJ8XtPsJmscuPGbayBtLdNLQrZWFtlY2uD5kSDsijxohDPaLLtAYfvvpvb3Q2kztnaWsevCYrSMuhmbGz3KK2lnxms1BgrETJAGyegN64Up95ykh/50R/h+989S12FHDp2jJXLKzSaESsr1wmVz61btznReSPWC9jqZRw5EnNs5l4Ggx7j45P4nqAx1sLzBdpC1u9x9cplvv7VL3N4POTY0Yh23WAV1JWiVBZPBKQobt5c48ixe7mxusOt52+g/ACharz+dXczPjZG1IoQykMEIUqNUwgopKIR1cEKtMnIdjapBT4oCIRrdA38kNL2kVLTz/soK/nu7/2hV/Vb+635Z+Z/6+dS2heApxKSNCKqvAKjkdJPdDBzMtroPmyCHrbrDD1jTsHKMyksQnS6iqXaULkbk2Sniaqm89l5WLngnmT2l2JWPp0SPwQ86Z47+SLEKSx+AhqzVe91CulyzG/8RsqZMzHpgynpZMzZ90Yk8+D7CU/NxXw4eYjFmUuc+6cpf7B+iV89dZzf+NlF1h8+xfF2wo3/6xL3HyqY/VCH5QuJM56+qzqWvaTPHdep0QtnR85BtRYq2x0BRIspF2dg7wRXT/FQyg//1CU6736Yf3XuPD//Uw1OPfkk59qzPJLC4790nMMnUtpjIxtOFllcmak6xttOxiKGmTR1r1v18jySJCzGEenxJRIanFqJnfXOgUPrzMfwTMpTT0ZE707g4kUWmWFmPubiZ1M+cBoefzzldHKhcooefeHdap+thhKEuynFZSiTC9XrfWqJK0/N0n63u3/c3muDW1qCdhu4Aqd/7NXNZC1/9cL85KFDBPhgDEp6jLciWqOcyO/ACZ0SjR3gBBUnlp+nX5SUZeE8B43AotFVtsoY7TL50vEDC8r3sMZxQvslnvTIdUGZlwRhiOcpp9VVSvD3OJFnOUUBtTBwVQHlk5cZutBIAVa6pitrDcp4GCGw2mJMJeVQMQRwvofGTaYnqxUn7EtzglFObKyysbmfE1I7Thy6+25ub20gTMWJcI8T6zs9jDT0c0thCzwlkXjktiCQHg3f4+1vPcncB0Y4cfdRrlx+fpcTvvS5fes2D3TeCP4eJ44fn6GfpYxPTOB7gubY+C4nBv0e14acmA45diRiOjDgWepSkitXRuxZxe3VNeJD93JrtcutlRfhhH+AE6LihHGcGPQ2qPkhYsgJs8cJIf5+nHhNBFlf/tL/PX975yZdvQENQ2NynLDZwuaCKJQM+j3GDx1hfGIKTyqa0Rhpus1YVGOiFbOxsYHFMtZsApYi7/J3lxM2tnvO/0nJ3QDLBT0uYyQVaF2ihI+nFEVeIqRkfGKCRqOJzg39wcBZT1nQxqC8kCAInON6JV5qrUV5PrLyxcqyrDILZbcEaLH4nnuewYgkwvDn8N9weUpy7Ng9rKxcY3V1gze8+R6OHDpGlq4iw4AwaLK2eh3PDylNH61zNm7dpruxhs6hlJoorIFU9Ld2WF3fYG2ji0RAMUDrgm6aUmRdRDZAWEt/kJHnglz3kVaiyxwo2dnp8tDsOznReT3f965ZjBQ0Gw1uriXcOzNDvR5RD+pMtqdR0gNPEjUaCKWYGG+xsdnFk4pjx45TlJnL4QloBCGrSYI0msvffJqp8Tq1MQ8lDNaC9Hy8WkCaFnzrRo+Z7zrBN77xLXr9grIoadVCZg7fTasGY40mxlOUxqDQYCVR5NEcG0dYg7ElgedT5gNKrbFAIHEBuBBIrbGFgcJQD0M673jvqwqUC/Ofne9MQroMNCLSJyGahSjGTakNe6UOTpO9VMZhVDur8jEkcmWi3dsiXNN7BdmVC9X1Cax8qZpqG/EFHAZjMx2IhtOOxyE6E9GJUla+uEL02xFpkhKfSFm6AitJxOyllJVTy6QfhVNX4QNnPsJHP7XI9T+Z4dmFJaKozf3/3ZtZ/kRK+paE9CJ03jPSA//3mTi8Q094ghu0PNmpyolV4BpH7hAWcROA6Ydm4V8/xc/92/eRXoBo7mFmU4hn4E8+nXI6qkp61ZqNZnanOxNgtg3tlRfu78VhdPzRNpyKdgOsg4eWXEhJL8KpFVhpRETHZ3jkFKS/s8IKp1heSvipD1QPutM5SYBL1dvkuLuq82hMcmHvCaOZtvOWTNgNwoarXQ1KzP463Kde5enC/7CfE9H4OGGrBd9JTug7cMKjmkysOJFVnBifoFF/cU6EBzkhHQMEAk/6u5wAFzt5QmKExRc+vlLkRUGpDYEvd5XVhRJghft2YMHzvk1OrL+QE+nWDsn6BusbXaR4ISdsNkAYS7/MyftQygylXbApK0784LveycnO6/n+EU7cWl9jpuJELagzfWgaJV7IifWtHceJu2YoTP7inBirU2t6eBgMoPyAoB6Q7uR860aP468/wbe+9RKcUIpSV5xAEjU8muPjCLPHCV1kLgHCCzlBYaA0RFFI5+0vz4nXRLmwPf5G3nzfu3j91Bs5Jo7S6IUU633+6m//PVPxBv/Vex7gR979Zh445FPmPXJTMN6cpp9nrKerRM06fkNxe+MWIvS5+/gDxFPT1IIAJZxWipTg+z6eF2C0B3hgJb7vEwSBa4L3JKEfkGUFW90UcClfaxRW+oS1iNCTrgy427RuEML1aeXFYHeCMAi83elBa22lMg9FoXdNoQ+WCIHd23RpeH5lhdIURM2QC3/xHKs7a4hokpu317hx41t4QR30DlvrN+jvbKPKnHJn+I3L58bzV7l54yrdfo+xyQnQJbKXsbW1QX97k80r19m6uo6yhjASHD8U8aZ7m9w72WKy6dOOm7z1bW/gn/+Pj/A9730nD3x3h+tXrhGPT/P85eep1xtYa5mI21y/vYqUgu964ATH77mLq1eepyxzBnlJktwmy1O63S1aE1OAZGfLjS8bXIl2shkQmAKRlni5YivLKa1HWYJWBds7m/w/Fy7S2874u6VvIijJlGB1cB0bCvKiz9b2NoaCUln6WUqe7rC9dgtjXSAc+SFhvUnNr1HTAluCQbip00aA8AOEpygG2T/+h+DgWjjPmeWqkrMK8bDkN7SScfJTfFvUW4Izi5U1S1UCHKq6c97pRj1+nhFPvKV9CvD71slqp2L2ArwElj9TqZaejXcbuBcWXVbkiSdcuWr5/ZDMLTG/M8ejy6v8+tmY3z+3wMl2h0ufq55zDtqfiTk7D0/CvqbspJO8rC/haJlsdA2t+HZAfhpWAAAgAElEQVTFUU8mJPESn6r0x2aTSvT8gwnEsyw9usjsXHtPMX2pEkdfxL0Iwyb0zt5E4W7jfjyiqbqvvru0W8qNDxR+F6rHuboePPrrHTh7hqVFeG98lmQppsMSv7ac7JVGR7vhR577YOf/OZHsu7xrrXPmwP1jd6Dzc/BYwKu+2mMnefB1e5xoDipOLO1x4r9+95u5/9vkhNLgDTnhB+ii4kQpK2eOAG2HnPDJ8hfhRD0i9BSlLjHscQLrMiK5ydxt1lVLXHN9FdAZH43FYnbtdbTWlbK668dCgimd32p5gBN//hfPcvvlOFHklNsv5MT2QU5sr5Nub7D+3DW2rq7jG0M9EhyfjHjTvWPc22wyNebTnt7jxLve+05OvrnD9W9eI245TtRGOHFjyImTJ7hnyIlilBM9uumQE2IfJ+yQE7pA9EtqWrFVlhTGw5QSLUu6O5v85Z//Ndv7OMF+TnS3MWI/JzZvVJwwFSdqjhOhvQMnPB/lu2TJK1mviUzWlb/94nw+6CE9j6A2hl8PGBub4A2v+x62tuGrS8/y3MotjBfgRx4bGwmIGtKr06hPEzYydDYg9C2Xn7tGsrnB8jeukKxt4fsBhS2dCag1GKsxpgSsK+NpsLZESkHNr1caKbYKfAwlGikUY606Uig8z6PUGcJIZwhaOukHrS1BEIK1aG0oS42UrhzoBa4XrMwyjLRuDne4ZKW2UmWxdjNb1VQLFra3ujz0/h/g2eeuUVMezzyzzAOv/y5W125RaKAs2F5PuH4rAR96vT7dbpfJ1jQCRa/Q9De7eIFPOciwZUE3SwmsRFtNmmf0+xlGFdRDxd3HppmZGefBN72eKCjx6yVHjzzA4SPHmXng9Tz37Nd5/f3H8RSs3l5D64KwJgmCBuOT0/TyjKnJaSampqmpkOn2NF7oE3iK8bFxsv6AQb7DjdWbYGD5a0v83d9+FfyAQlr6oiDwA3YGkloUcuHC1xibjEl7PbJt5+dVlgqlJBP1JndPTxKNNakFglZzjCPxXRSlIS8giOpk3RSsRyYEoR+ijUYXOaWwmFJS6BKd55g8RxrAV7zpe17dcuGFr3Xnl9KEhMhlmYZZq2V2Uw1tXr7X/cXWxUsJ/8upiPelLmMTzVS90DOQnILTM/DQDDQWIBHtve7y3UxZAp0IFmHx8RVmDrvHgisxzTTY1W1KF2A5huMpXErhiCd483hEvBQzf3aGp5dX+PTCCjNRwsV3d0hn4K7ffp6Ln/gbTv3aDCtPpSS3wF+EOIpJ09Q1068MG8pfYrX39gtwzVqn4Bupi0miYSnxiynHZ2d435mI5YsQXXRZs04HVi6mRDMzvO9DH+Gp//IpotMHUkangXd3IEmIk4j0N5xYVjrayE51ebS+23HlSXcI+7fZwZ27x/9VzIU3rcCpR/jlt5wj/lrE135lCf5pCqfhi7//cb5wYZHTc9VxxG13vC9yXpLFhFM/FZEQE0X7hbWii8PdiOFiCg3gVJvfWoGl5+B/etOrm8m68rd/OZ8NdpCe/5Kc4NvkRG4KCiGwFSesHeGEAXBTgDU1wglGOSEZa0VIoZBWoU2GsI4TZeEa2rWxBH4I2slG6FK73ixdOk5IhSlySmmxutJURDhOGKdZKJT7HSvcNCR7nHjf+9/Fc6+EE8EIJxrTCKnYKTSDzS5ezafsZ9iioFv0CYWkNJr+wHHCegW1QHH3PTGvu2+KN5+4j6im93PiDQc4cWMNbQrCmjjAiSkmpkc5ERB4itbYOPkBTny94kQpA4Qy9JVGWUVvoAjHAi58/muMTcf0sj79rQwpBWXh2PtSnMgyS22szqCbAgc4kY9woqw4URRgwHiK734FnHhNBFl//Rd/OK+kmxRRgUBY8D1JL03JBjnj4xNEjYjtrMCaFnHjHuLDhxlvjZEXCTUREYRTTEzfxYmTJ7nryN3cXt3km5ev0RsMQBqsKdGlKyN5UmKN84RyeicBnhdgpXDCokGAsZBnJRKDtQWeChBAnvVQXkivt42xJSinmdJsNChyZxbtBx5+4JHlGZ7yEIZdo2ol5L5JQlFNEwr2SofDNVpCvHjpP3Lr2ho6z/jB97+HnZ2ETuckt577FmsbN+mmPQabPcoiQ5cGJQyb3R5ZXkJZILUh1X20KTDWEJSSvmvZx5cCL1Q0ozr1mkApS5lp1pMbPPCmB7jv/gc5PnO/K7PpHkUOZW5oNDzApyxKNta3Eb6Hr3yytMfhw4cQyuPm9WsIJFnaZ1BoEHD95lUa9RrkfXQJN2/eYmXpP1KogJtrPfLUImt1droFy9+8SViLaLamuHHjNgZDqQtUXTIejRE1IibHa0g8ms0mRT+n1+9hjBP8y/oDVCCp1wPywYBGvUGrNY3wFMYKetsb1H2DJwPCIERJSb0xwYm3zr6qQDn9Ixfm3zkbcQFcIqlRBUHfhjDnHdeZlFMrKSujG4xxKZRKw2nlU5WW5jBIWWGk5+sKJK7GOKOAP2I34mu3XVYsStw+x+9jt2T14Y8k/NFPxrR/GGYeSfl0J2V5eYaZjz/FlfNtLiZLJE9+kYff9h6e/HjKqU+0iSL4w88l3HV/xErV2zT70yOlzJdZycJweg+SP3J9aKdieOoUtD8BURTD/7FE2na1w8WFp0iZQVyMiU9FuFpazEPPLJLeF+1O8e07b5cSeAhWnkqJ/j/g9B0kD+ZPwchU4DAQeqmX9PTPpfyzj0Z8//96HH5zhfabcPXddwLnIPr0Im9qj5Qsh2W/F5wE90TRTFQ9ZzVGuYh7feNqQrIDrKT7ysdfTJyA6s+9ymKkT//5H84rz5AXFuU742RfSXr97xAnPA134oSoOOEFKFlxwqs4YSDPh5zI8VSIAIq8h5Ihvb7jhPAOcEJn+L7jRF4OkMZHCjAYrC9RSIzQWM1u6RILVgnQe727rs4pXDO4gosXXxknimyA1o4TG+s9Ml0iygKMIc37TlW91ARa0reWwuT4UuL5imazTlRxQvdL1tdu3pkTmcu0RXUPjE+pSzbWu/s5cfQQQv79OKG9gKurKXlmUUGd3k7FiSii2ZjixrVb2FfCibTngmkOcCId0IgcJ1DOCeYgJ0TFiQdeASdeE+XCetRCKA/pefS7O+R5yerGJs9fv4oRJbeSW9TH2sRTh4lalryxTalT+ukOykwR1GLGJw8hZMgg7eMZuOvQGDVlCX1FlhtM6TRGlHTluKFUQlk6W4MsyyhNBsZiSu0mSnwFxlIPI6fsawVae/R2+ghhsVYTSEUtDKtvNsJNyVXbVsKVC8sqc+V53m5ZcKi/MtTFOqgAP7xu6KmohGB7p8eOLjn3v/0Gab+kKD2SnR2mDh/FV3Wk8KmFEaJUaI2zUxgMyAtLUVh3DrRhUCm4yzJHa4POSzzPqeHWw4jQi/DDgMlWi6xbopTixs3nGaRdisLSmp4gmhyn28vxAo9ms8HRo0eYGp9gbGyMEsvm5iZaF0xNTeH5EAaKZjNide02R+5qU5Yl22mfWrPBVneTQkKWFfT6JRsDTS817PRTwnrE5s4Oq7duMzk5ThAE+MJZOZTGIpRicqKOJxVbGxv0swwr3HSRlBIlfPqZpl/k+EiyLOP22jqFNQwGA6YPtWmOT4CyDIo+W70t8nLwj/G2f8n16K/FfHIudiKSZ0YEN79DPnLJ++NKFGp4BbtwPt+pTJJnDzxfwu4QWjx6wxzM273S29xch2R17/JuSW4Bzt27SnyXK0XOnYTOYofk/CInH3uY2d+d42Eehj/5HRaX+5xdPEtnNmH5TMKHj69y6VJCPOuEVBc/z/4JyJdY8Uk3EcjjcDKbJTnnHneyOpbk3LITkkoSkiWYm5ulHUN8JqHdqVJN55Y5n8AHF2Hpcbet2WqKcGmZXS2xeBb4+b0dW3qcvVHH69WJGFrgzDsD6vkFN5F5x/UZZ6b9lrsXWCpg7umYTpK44zkLnT+JeeIJSEYFW+fvEOB95uBJqfb9LLv9aCc/ya4n5eIQHYmLq19KIuMfa6mxFsbzEMqj36s4sfmd48QgtRgn9u44Yfc4kZWF40SRUdoRTlTThY4TjT1OWI9B3sepw2tCVXGiOMCJ0iBKiVSCAicL6UnP9WBp4RhkQQbOnk0Y50hipeOZ44bjhC6NsyTrvjwn6rWG40QfRN2j33OcKDPHCXLNQAiywnHCWtBFiR9WnAgiQlFHhQFHxloMNkoUipu3HCfy3NKKJ4gmxtlJc7z6HicmwwnGoooTGxUnxitO1BVjjYi11dscOfwinBgU9POS1bWSnR1Dr0wJ63XHieQ2k+1xlArwEHuc8O/ACXtnTnhqjxOlrDjRbhM19jjR7W1RvEJOvCaCrCAIwHoIWxI1WighqQc+01NHCFXA9MQkN29e5vaNZ1ldeZ7e7Q2u37hCL11ne3CbZP151rdvUW+ESE+Q0+PEd93P0UNtCivxhctaWKoSnzUUuqSsmtadTx/URA0v8JGeQCiDwlJvjqERZFlOaQ3allgKgqBGVBsjDJwXU1EUlLbEVwEYZ+AJrp5uTYlR1QfBsBtYAe6binXaWXdaezpaYEzJX3/pa9RUi+b4EX75X36cifgQ3fUuSjXoi4LN7W22ypRBYcnKwo0zk1NKg9AwKN23o1RoShkglY/1A1RpKLKCTFuC8YijR49w98wMYTNAa82RI0c4ceIk9WgMhWSsEdFotpCez0a3Sy8bcOPmCldWvkHoS67fuMX6jVv0BiXSC9jcSZ21w06f9VtraK2ZOnQYYTW5sazvSNa3S4x2b/CvPPMcg1SwtrnFWGuCfj8j3U5dWTZqYLQmy3N2trdZX9shHfQotEYIQzHooaTE6MJ5HeKCy16WkxUDEAWD7R2iwCfL+nT7KVJFeCrgrsP34ddefQmHkyR0HkzozDoxzHi+uuGgnMG3KRIZn4XkM3BmGCg8UbXgxHCGzv7nGUo3jDzVKHQ7j8J7H3WVKoCFx5acvsPIFCQx/JsYOmc6zJ6Bk7OzPLZ4hqWlJeIzJ+mcjekEbaeFeX6BM7/zXzgj5bmY9KMrnPzkwxx/m+tyWl5N3Dlh/z4dXLvBXaeKGa9Bcm5xt7+tE7vJv9nLLgibP+nkLNrLMSc7Lp48X/UonY/dMed/43wHIWFxobO7nb0npTK+plI+cD1p5xacmj3A4tl598s8LP0BfHJ1ntm/gfjFvhMn8LHfhFkbszCfsLoISXXfpXMJP/7z8MQTe71gXB8JuIZyEmdeuE1wgqi7au/zlZ5WDHNf3bvrLOc5+wqC2X/oFQaB+zJOSRS1XObhTpy4/u1xIvAUxrhp8FJbytxxoihLnIToAU74AuHtccKMckKW5GVBWHEiUBFK+RRlQWlKfBmAFfT7OUIJDNoJm1ZC1wYXYAlZtZHkFiU9BG7SEQ3CcwKsQDXCCNaAsS/OCSkrTnQdJzJlyfIcVXOc0MogDWTCYDUMpKYkQEofggBRGrK8IDMWNR5x99EjHLpnhtp4gC5KDh92nIgaBzjhV5wYDLi+usKV57+BpyTXrzpOdAvPcWIrZTvZYqdbsHF9DZ29CCdKSSkzvvp3z9HvCtbWtxhrVJzYcObaKmzemROm4kRxZ06kgxFObO5QD3yyvE+v3OPE0cMz1MJXxonXRJBlbI7yLIN+l253u5q4kARS0N3uURqJTwtfTTAxHSN9mGxO0N3sUvdr1OshW+sbPH/5CuvJGlcv3+bSxb9CBRLPk0hv75sDuMBHa43BZZKcCbX7cBjj7Ab6aQFKkltNaQ1hvUGeFQhjqdVbKK9GqTXa5BRZDihsKXc1sobipMMgSVWNi7JK8w4nWKzVlVei4aBm1ugaLSP2Bn1+7//8YyanY9K0x9XNdZ67cpnNjR20CRDGI9cugMxL12dQSucIr4SzDhK2KmGWGoTGSkPohURhhLSSQanJyWmN34XnBUT1psu8CY/x1iRlWdLv97l5/TrxoTbK92g2xzl69B6E8KjVIq4/fxWlc9KdbWyRsbW+StpdZ2drjdvXr7G2tkY/y2iOT7PW7bHTL9kZlBRWEbUarG6uo7Wlt5MRhnV8v75rqK2UAiXJBjnZoDqvYkAv7aJtiTaGrJr6qZLwlEJT9AfYskB6CmNKTGGQWpKZHFUfYzO9TdZ/Ze7q/5Br4bGEhQ/GsOyyIcuPjty4LwB6If3iF8t2DR+3K7sO8YMuAml/DBa+7jJFLLrbWKp8kxN33dLI8y6cZxfUS/PAE8nuduPfnaNz/jPwOJw7z2525IEzLohrd2I+94FPcHZYl/wMPLYEC588T7K0Cudizj0W8+TnnmR58d9w/J+9jYVPLhGzSLwI8edgblQFPuGOzjvxw/svL46KZ1WB31wMi+eAkzCfwOrPJCw9kZAMMzjVds+cca3pwceq8/exSh49cYc3O189Z7x/X+Y+lsCs0856OE5IFmDp8Uf3LG4+Nkv7xjwPEr8wWzS0QjoJfAp+QTwGMTz6+ZizH6t0rmJYIOY9w2OC/e+PKkN2/oBw2G7W8UxnXwA91CRbHJXNmj3LL55/9aOssswQxjJIu2wn2xTpCCe2Rjjh/T048dcHOOF7SN9JKWilKaqpQImk1CV5NsKJUpL29jhRlIawVnGisNSCMZRfcUIWFP0BlIoylwgEutTO47DihNWGAHbbR0pjKfMSp/6pd4WwwZUJXXXG9WMJi5tuHHLCwk7/hZy4vLKfE1kxwgkcJ4wBaZwnobXuryeZBjRCGurKccKXynHCy2lN3IVXDx0njEUIj4mJEU5c2+NEa3Kco8fuwVMeUb3OtStXkfnWHie6CenWbfrba6xevQMnBiU7WUmhFb6qsbq5Dgr6/YywXscP6mhrsWaEE/0RTuA4kZf7OWGt44RWmmKwxwlbcUKUe5zY6K+R9nZe0fv2NdGT9fSlz8/v9PoEXh2Bh6cU2mhUKPGlj1Ieviz48qWLHDl6L7WgSV5k+J4CqcgGOYHUaN3HGp9a4Boiv/HN59jqZ5i8RNuRN6CpGs2FG7eVUlKr1wjCGr4v0MZQC+sUWebGbZXCYPA8hURhTE5ZFniVyJ0unV1Mve4xGPQwpqyCJxfUKeUsBpRQ7k1rNOD2Z68JH1yimt3bRn/CXkmxKApura2S9fukpSHyxzD9nLK0bHZz0izD0xJdgi0FvudRFAaFwuRugsKUGmEENhBI6VHzAowvaY2PUQtDxqenmBw/TL1eZ3N7i+ZYg7AWoHyn8aJLmJ6KAUtUqzNI+wBkWcatW7fY2UmIahEDnbO12WWQD2g066T9nH6aMj09TW9ni1azyb/93d/juecuM8hLytLsTm8KJONjE+RFgZSGwK8RhP6uX+SgzGiN1bj/7jEmp5uEKqJVr1GvUSn1lwQ+lEWOLtwfPuFLoKAmawxwUhGlNoS+otA5dpCBkLzxnT/46o6r/8pvz5/8sQYrX0qZ+cR+mYDdfpkXWQfFvF/wuJEZ/aVbCWml9D0TV91BM3v3Hw0Yljswk+Im4lLgVExMSprCzEgz+CP/RPCzJ2boPFmJkqYxxCukRMx8apaFzyzwvf/8KBefXGHp3yU8cekjhE89xRUi5jqf4ounZvj6VxL+5//cZ6VxnNkkYeV9/y2zxx8mWT7OxRvLsBnx0C9FLF+odLtGj3nYu1ZN5i0sAP8OnrgQ88wzKSsrEFWaYcspRGlMSkocxySJaygfPj5+BC5+1J26lYfhwkpVQT0D6W+47beBld8CBJxKYlbi6gQPT8kizM/AvyblVCdi5nTEQuoU9mGF5DRc+IsU8ZtuCIG4kpIY6bOKf3+ea49/mvY7O3zpQsIikdO5AuaOp7TfOXL8M4uwst9X7dQp9jKLOEkwgMeeSjjVpnIV31v73mIrKe3Z6FU3iH766T+b7w0GruTlHeCE+s5xAlv97TUWJaTjhL0zJ4JanbIydj7ICUuxywklS/JSUJicRlRxwhZYaxDWiY8qX6G1RhqFpyRGO3kHJS0aXU2pg8up4XrDKqYALtiSTpMLeYATxX5OrG/lpL2MyJPkOZjCWagVpeOEPciJWsUJFWBqkrGm40QrnmKiVXFiZ4tm1CCsB3h+gBQCXUA8FWNFxYl+xYl+xurtW2zvJET1BpnN2djskuUDGs0aaZbT20iZOvoSnMBgrGsZaTXGyYoCKRwnwtAnCEY40Qq4/+7WPk5E0X5O6NJxQmfWBb8HOKEPcEIo9Yo48ZrIZDXCBvVgjLKwdLtdCp0x3T5EmqbIwKderyFrTd7+zu/DF4JbN24wVm8hRUiaphgpEDLCV+Mo4TPQGSWWY4enaVqI6hNOTdyC1cZ9hqTYlViQEqzVFLlm0DdorRlkqct0aI0oLD4SCoPWzoRZKZd12un10TbHc/p4KCnxPI9areYmQgRYJREqdL+LvRLlUCR175/7tBwMrIaX9zJxLtulC8Oz33yOr/zN19kq4W+vrbGeSXqpoZtr1vuGtR1Xt+73SjZ3LEmWsZEVpJVbetYrCAMPVQsIwwaqD3mWEYYhzbEWUb3JyfsfYGoiZmuzy+REi3KQce3qFda2ElqtFltbW5Rl5j6QFna2u2yubdMrMjbXN3h+5TKeCrh1c5UwDKmFDZA+oReS9nNe97p7yQYlWZahdeEmMdMBEsv29jq+B6WxFDajik8RQtOqj2EGsLPdo9jcIfRysv4mN28kFANne2SVpFYLCcIWXuR8KUPp0T48hbCaeuQzOR7hBRK0RPpNwkj94735X2x9vsPDT3dYOldlm6px/rn5F0lTzVfaTq9w87OdOVh0GY1kgRHl7wMbGY70L0HnF6oa3TnoPA6dc8t0RrJFyZmEjktMcfIz7n5xPMtiDMQxZ8/ME3854czljjNIjhdJ/IRHw8c4f2mRxYWEX/nULD8zd4l7jn+OT1xK6MymLDx1nEs/+yt0HjxKhwfpvC1m+XMJv/LJxFVLF9g1pt7dZ3BK7EuznAQ6H4NPPuruG58FfhfOPz48UPczSRKWKlX2pMp0JYvQ+RlI/oA9UfQY12c1mnqqJkAXD6QRkyVIvgDzSfL/U/fuQZJd9Z3n55z7yMyb9ey+2a3Wo0svcCUIhFXA2utNTw9iLY0XNqJ6sYUHcEwwQc3MIhbB7qxjK/CoOsw0eJkQZi12vC3v2A6vbOPxdDEbsXYIg0d2Yq8H6DZgIEtCUquru7q7um698nFv3tc5+8e5WZVdXS0JdgbJJ6K7svJxM/PerPz87u/3/X1/+HWfteLxHyoyhYOnn/11Y8kx+D0IoDX0BA8n3+LbzOLvJgxNEtOHpUFptl68pnowpN8bSm+19taaYc4H1vb71PgQzJoZ5dfVqF+dVXVHqNqjpH1NJyw44R8i6t+AE1uXqVbGEK+QE9XKJFIZTqh0DyfYhxNZTtztFhnxHBFpHCUQ+S4npGO+w9u9CKUSjHxLICyJbTt4nodwQbgCLImwHZTUJGlsxsBpQZrrndPvXGEMtLUwFQkpMLNLNCBQCoSUoCBXxiE+TxUvPFdwog/fW9lgM5aEqWK9m7PRU2xEGd2+oruVXseJfmQ44To2VsWl5FSxYxMolZwhTtzxYxw4sIcTK+cJ2uvXcCJPc8ihvd1ma72zw4mV5RexZcEJt0Rl4npO9Pv5DifM7McESw9xIjecyLMBS3PGvFFUPMyJuODEGmk/vY4TpaokTZP9OeFYyEwineor5sRrIsiynTKOU+LgwRq3Tt1OpTrG5uY2bqmCUIJep8fKhRdxLNhqb3PLbbfSCXvkec6ttVvwnAoVbxzHHcF1XSbGDzI+dpAkVVRGqvT7ZtxNlmVmhmGu0VqQZebyYASO7QBCFQNCE5Q0H+qsEEwnSWKyT1oQJxntbsf4niR9SqWKObDCAWziOEeijYdWliC0KVHq3GS2BmXBvQHVjcqFg9sHS2hI0ozDIxNYueb75y/T7ipWVtfYDCM2uoKgk7KVZISRIEoE7Z4iSWyyVJGnJuWNMC73rlOmWq3g1qpUR6uUhEPFLnP48BFGJw8iLYeDhw5z7oXzaKmZmByh7ErCMCRNU4QQrK1dZX094JZbbiZMYvwDB7l65QqHDx8hDENsu0TY67PZ3qbT63Dw0EG8kRGCYH3H+d62jZkrFDO7imYBWzqoVJBmxiROCAvyjLJrY5dcym4JpSCME/rxFhcuP49dclm/2qZcdvGqLlme45Ztkixm5eoKB0bHSfoxnldlbLSCsPpsbp7jzqnX/+f8uL+i5Tfh9MfX8B+DYCCY9mFxYQh4J4bgeHNR9fELcL7Maq4tcnIwZmUaI8YebG66EHA34MRHmjsZoc+DMSoFWo9C47EGp26B+oIJBmZP+7ROQu0rMP+7DaNJ+gdNgtYSwcN1Pl56nObJ0zSb8NDD07DkM598CP8PZpl7X50lH+57bJqP13r0PnqUB+4L4fRR/I/6vO+j9/GJX0pgAXxqhP5Z7vuSKafWirKfvwbNRX+nglqPIVho8oWv+CagKiKUxmngYzD3qBFaBafNA+rzUH/M3McflPNa0AzAfxSzAxaN0P26sGRnnzd3b5z14Xjx2NOtQXURjgcs+U34wu4+X9wnO1mnaQ7qPCyMmOCtMQ+N4gAHa6ac23i8cOQqyrKN5iw8Ufw+HDnP3yBA31fXZ7ocFlvQYh7qe0VdP/rl2GUsWcI/VOPW2wpObG3jOEOceO7cLicmbzUdZHnO0VfAiagfoTCcUCjydIgTykzhyPIMnSnDCTRhkqDsghO2JM0S+p0E4RgBfNrPabc7qCwjy2NKboWsrwAbLW3CborIFaARynT3IU1KynFsFIUvViHbFcLoeE19EMMcazeVJXSRySr8SqWGfpxxuFpw4vJl2mHOlfYaQTtkoycIuinb/YywK0i0pB0q4r5lOJErtDCcQBhOeNUKdq3KyESVknKoyDKHD+3DCWvACUHYKzghBWvBVTa2A245cmSXE1cvc+imw4RJiG0ZTmzswwmVZ2g0tmsTdY3uOVMpTtEYYEThstkAACAASURBVEsHlQkynexyIsuo2Ca7ZjghCk5scmH1hRtwwrkBJ8po2Wdz8zx3Tk3v+zndu14T5cKv/8WXF0Cz3VkjVanRTGUZ0rIYHZtk4qCPY1cR0iZP+nTaHdxyiVxAriVKZaR5jG1DLiVJmuGgwC7RzzNWrqyCpugAHGgFCw8SrRgdHaPfT5FSF/ookNLCksJkwGyJ1imIjDRNiOMcENiORAMVzyOMepRKJZIsLjyvNGmcorWx+zduwAIwo3w0GPf1PWug4xp2gt87dgfMhAUtJMISpHFCPwWEIEoSbLfMZq9DkmkziTxT9MloxwlKQJqBhRnbEMcRuZDYuGZoaZoihY1yHY5O3Y1jVxmdGMNxbEqOhVCw3elx6KYjSG0hLQvf9xkdrTI2Os7hw4eJ45ia79Pe2qIyOoLtWKxdvsThI7eAzrn19im2221uPXo7Za/K73/h37J66arRHCijA5BS4DjOrjlrmuE4FlpIcqWwHZdSycVzBHffeYhqdRy7UsKxy4xXxxk7cIA0S7j55iOsb66jU4WNhWM5uG6V1bVVRKaJ0cRZRpQIklQwXj3C6uoq9/wXr7Lj+9NPL4S/NUPta8s78LxuHfN2Td8Hc2RCY3H0UquOKUs1GnDyBDTew24X4UngAfi1X2jy735nCqs1VcyegWP3TEELTp04wcwvf47zzSbVewKCrxlh0nLVbCdYDlhqLdM8Accv+Kw9GfBIOSTcCPnLmYizi+P8x4961Gb+JeFvLBI+9QTLn5xirt5g5kydM62niH6vSpZ6bDke/u8t8dXnU+6bPkOk/oS73/Embn7HuwjGl/FCj6C+TMBRZmoe/tEaQT1glgbNX1uG78LT3wl5/88e5exyCA/6LC+H+Md9wuVlZj/ocfZWj6dOwloItSrwQeBpUyrkDEwNyo/PQv17wNHCib9YfgAzXlFx8zEur9+B4GyIX6sTBAHeA1OEHpwKYeaMh7c8BU9D60wxUHuZ3bFHw6uKyb75Td7+Hlg+A8GMj/dUiDcDTy7C+d8IER54C8AqfOKpFjMzezc0ta8p66CEGADeyd27gnnPwRTQWILwDMdeZZ+sr//Flxewod2+SprfgBPlIU50dzmRvUJOSAH9KBvEFJjgxwxm9kojpDrDstjhhGPbWEKg+wqr4qBUgpApaZIQ93PTSegas9FyxaPb7lEeLZHEfWwDIJI0RSuBLVykLSHTxrjUMt/3UotipM7uvhg6NUeIYqqIaTc0t4vCjnGIE1mcEsUm4xWGCW65wka3Q5obz60kV/R1SidNyaUgiY2GVwtBnEfkWmJLlzTuYycpUtoox+Ho3QUnxoc4kUN7mBMVC3/SZ2SkyvjoOIcOHyZOEmq+z+bWFtWRURzHZu3SJQ7ddAtCFJzY3ocTuUblxqPMtgW27ZixQwryLCuSGJJcK5yy4YRrwevu2suJCcYmb8wJx62yeqXghBjiRCYYH7mJ1dUrr4gTr4lM1kjVoVyyqJTHqLgWI+UylVIJB5ukG7IVbOKWJLYNY+MHOHDwJuxyCatSJRd9pG2BtrAsQcmxcRwLhEOeRVy+skamFZlSSEshJdgOWJbYcYHvdrtYVjEUOTWRuy0dUMYELs0iUq2IVUIubHIUriVRmcZ2XRRQrXik/chMgU+KgdCObTonwcxEynO0zo0mjGvH6ADX/X6j8qG5zgi3N3sddKlEFPVot7tILHphgqBk7ClQdNKUKBH045RupKCfEvVzUuUgNFSERZZlWCXBxMgoVy6tUCl5bHa3qE6Oo7TAG5ukH0YcvvUmDh06hGXZ5DrDcRwuLq8wVpnAts17HxsbY2z8ANWKR9lyiaOUH3/bfeRC4rgeNmZ4dJorxkdHuPWmI6bD07LQeWF9YdnmLASJFmYafZxmqDRBqNx4lilNN8uRwiW2Q7rdNrlusxI8y8bWKk7mkOYZllVF2hVicqIoQmjFkUkfy9W4tkSniiwKseiTRFsIuX8m8Ue5TgUtFhea8HF2DSsHgdYe54UfeNtDl+c/XFyYMxmceuKzGATMf7nBxxcwED5dCOCLTrXPPvooLC6aBNfj5sW0iiBtEKs1PxLQ+DCsNQPqvs/iGvitgB87eZT/Zx5qjYDG3CJLjYDm/CytxSaLJ2HxiV/kLxfhfY2jvHjfGvWzPl8CYIpGDc5/o8JiuEiTe/Ebx/FnG9Rb0wStgFYQAC2C0wss1ZqcKjob/RqsBQFrp80VrVPQ/ICptS0uwOxDPrPzsHSSIvtTp0GwI5T3p4t9Pgetk0V5dWgFp022a+eAPF7siEVgrbV7uIJF5oZKjC2ME/7OQdwv0VQkLmuLxcYbEHwgAL9u9n8A9UI413rY/Lz0+Beu387eZNWez9LOIO/Ba2gN/Rt2gX8VV8WzcW1JxTGcqJbLVNz/NJxYGeKEUyo4UdKGEyWwbJsoCUFp+nEfHRecsA0nLM8mTULDCSslFzaZuJYTWaYZHfVIwwjXdoiTBIrsS6XsmkwVmkzn5EKjs9wwSCu0KjggjCKroIDRZilQWoGwzGgTISicHQDDiY1uD1Vy6YY92ttDnFCuafzKFe0oJUokUZQSJgqZpoS9XU54QpImBSdGC054HpvtLaoTezhx803UakOcwOHihRXGSoYT6Q4nJhjzdjnxlrfdh7KGOPFjL8EJLRGWhZAWZBItDSeSJEOnMULlkGkSpUlt8fKckAUnhOGERHHkgI9VKjiRKLIwxNIFJ6xXxonXRJCllCTPzcBMkbt0uhlJ7pAhyEROrEJ6YZ8kycjJyfOUuN+jLG3KVgmFBEvST3QxuibFcSymjt5K4813cGh8jBHPlCQd2ybPCk1UrncyJf1+Yqam2wKJQ5YrpK1J05SyU8XWDpayKduCSsmiF3WR0pQWsyQxswotSZ4b0VBWRNRgAqPB78MdhHv/7bdudP1gudKis7mFKsp/SWLSpEnSx3FKlNwKIAnDvimnhX1SoekmCZu9Hlc6Fuc3I7o6ZtI7SOJaHDx4kI21dfPH1o2wLIder0OahVxaPk+/t8366mX6UYQUNv6hgyR5gl0u4TgOXqmMVy1z+913M3XnXdzxujsBKKMYqZbodDp4pREsy2Jra4uf+/mfx9JgCYlddtGOhcDCshzKZQ9bOjudmLlWpDonVwpHSMarI4SdLocma1hWiU5PcOSmNzB+4DZkxSUVOdJ1yFSGbZWRlRId1Wc9DUmkxcQB33yhuoIw7tOXycvu8x/FqgON2cHsF1Mm2qmD3SiyKmB4XcJiZ6aLWQ3Y1RcNMt43+1CD1kKAf9InCArwtoDjhZynCZw6RasJJz5/ksYCrN1r9EANwK/7tHzjY1X/eWMpEJwOYB7mG9CYm+b4Z1vU6z5+rUX9/jX8OtT9gNuXZ/nQfAv/W7P8zINTrFV8jnOc03yJj/5MwH3/5qME1GnU6zR60zx2M9SaEDSbtAjwaz5fKip1040v0Gr6zNWh7rdoPhmweBLqn50mOB0w9ydwfMh7rJ5Aq2kCLWahvtCk5RdOYD4sfiTYf58XAVMwBzTqtALTaVmfK2w3vlXftUp4GIJglmAwwoYhHforCJobSZ1ma42F+wG/RYPT+IvQ+N1ZWr7P2gmof8jHbwUIcT+Lg/PrE8X2h7tTGbJ7GHxY7i1e0Frx+0DjxfVB5au1tJIoVXAic+mGGYmyXxEn3L2cyK/lxN978x0cGi044RacKEw/88JjEA1xnBiDabfgRKaQIiONUyxRNgL8vkXZFnh7OJHnKVmeIaQg12YodK5zbFfuVFVylWFbFlIana4QyhiQDsa4aYoJISBsg++BUakFFHN8dvdZcbFkCTobW4jMuNnHcUya5EVmz6XiVRCWpFdwIur1SdCEecJm2OPKpuTFzT69ghN9KffnRGg4sbJynn64TXDlMlEcIS0bv3aQVAxxolKm4nlM3WU4cfvr7kQApT2ckDfghKhaaG3hug7lSgVH2qZaZCkyrc0IOzQOglJaortecEIXnLhlDydKBSdkwQndZz0b4oQFdslwIhKvnBOviSBroIFKkj6JTrBcEOzqqJSCdmeLJIuxLIcoSc0fGhYKkNLYM1iyRJ5rSqUSSinCJMSbGMGrWCRJQtkpIzCjcVRxZiCljRGyWyRJxna7a9xfbYVSUKlUjGWALSiVSvT7ffI0oVL1UAh0YSfQT2LyorUXTIZsYDcwsGvYmT0F+wZWNwq49g6RHg7UqpURbFsSF50oAwuJQWDX7YY7fmBaa1zXpZfAVpTTDTVpGqMU6CTjb5ee5cUXznPlyhXW1pbZ2LhImrfxvDJRFDE6Oo4WkGQxleoooyPmrMTzPHpJRKVSRWnBdreHEII4h1yYTFSmBG6ljOuWmaz5rG2sU6lUKHsVDtR8xEB/UKxS2UEIjW1Lym7JHF/LWHHY0sIqhKjdXo80U1y6vEbaj4vAOSOKOmRZn3a7jaUVoyOecWMWFqVSlcnxA2CXWN1cxx0pI7GQCYy5VXJp/+f7sL/C5a/VjdfUHDBtMih70wmtE7v6rHohjC+SHdeuoa6ynTUIrgZVpXvNj8VCRE7L6MKoFcFWAVx/fg5/Dj786/MEj0PQ8PFPD4TiAf4S0Arw60VMeNwnOGm2u+gv0VprcPJtASdP1fjYQ02Y9vnQ/XXWWhfMjD5moVe05dXXmP+/Pwq1X+FhTtJ66CGaQQvuDVhceBwaTaILTfz5BsFDS/yMD8GnG3z8I6fx1wIWW0b9/dn3+VQfNDvnQw0IFn2adZ/phk/waXhraYn6wAiqCS18guM+iy3zHmZXpvH3kyTdz2680mpRA5r3G+8qmsBCa2e/tR43Oi+f4JoYx+faY3OjhNGphRaNuToLC0CjziJ1ggb4rUXqszD3B9BcbNFc8nn0yw1md6wmiu0vDG9tEPkNXbXAkFh+aNXhQws+J9756ls4oCGJE5KsT0KCZYMgvoYT28HmvpyAPZxQ+3DC28MJbTihtUBaBSdkwYnuECeERaVaQUgjASmVy/SjPnmSUBnxjEdjlmPbZt5djibPlBkK7dhoDWmck2UKlZuslLFmMCUxMYwDUYzV0QKpcrRlSpFIyPNsJ7siLGnkwZiAsmxXsW1JlBlLCFvKnZmuKs/odEIjp8GU4oRwCLOCEz1NmiYoII8NJ5YvLnPl6j6c6BtOMMSJankCW9p4ZY9ef4gT7R5SCpJckwsJBSdKezjh3YATKoeK56Jyje1YlNwy0pZm/qRr47qWka4JQV9H5LLgRFZwIn0ZTjhVJicOgLOHEylMjFZJ1N8h4XucpQgsUp1w5123k+cpG5sd8sxMAde5YrJaRSoTNHiei8ht8jQmSyFLQuNvoVJypcgzQZpqJsYmuf32O3nn338rtYNlMjS2VDsZLBMI5ViOjeXYYDum/dS1SPo5lu2aGU66TxybYKRUqmDbLlmscSwXoRUqy7GETZ4qkjTHdkogrGuCKoXe+aevOdP4wbMmJugyf1lb7W2ckio8TXYzc8COoH84yEqShF6cIYRFL87YDDOWV9u8eDFlq6+J04zywXG6qc23v/E9VlYu8+LlTUgFwVrE1maXjdUtgtU1Wt9/jk53i24nQQqLi8sv0gvbXL5yBWFZRP0+Wa7pdSO8kSqZlEjLwcIh7UecP/cCly5foOxagCLOY1xdYqRcQWiFcFxkbpljZJex3RLYFkhhhk+HESrPOOiPIlSFickRDh30yVWCKy2ktBmRDpWyTS9qU3EtyrZFWbgk3T5lJbjn9tsZHRlBWSWs6gSJEOjo1Xd8r9dhbZqhMTZDLB4w8tFd09DWEANnT+wD6/3oPevTPGWyNIOsVaO4b3ASPn7Sx38MllrsBFIBhcfSUotmwwQMSzXADwiKTNJcrQnNwGiNmjAd1U0mLJhl7uGA+p/4/O7Xfer1mnFL/8KTHL2vwlILHn+oyQP+NHVqtJoBdVq0ppss0OD4pW9By6fxlTp+4zhnP+bTuu04dbdJ4/EGS1Vo+U1mj3yBJ5pzfPuZgBZwyve5zzcpmsWmz5Nnv8Q0AUEzgJ+CuT/wTbam2NcLs8CpgOkgMLu6GRDs8ZkCTGBSBKn+iUUefwy+8ZXi+Az8xgpnUv/hFjtppaIrcCdwPQH+Qp3Wfs8xdPiCjxe/BCazOAs03xnQ+gicfC/MNurUi2COE7xkLTkAWHuJu5xkp668eKnOh+NXv16Y5ClCWKQi4c47DSfWB5zIDCcOHBhB5tdzIo9eISf8MolS2EKRF9+nruOgMTNqkTaUHNyyi+1YxNEQJ1REP+mjNLilCrbjkPUNJyydoZLMzC9MFGmaY9sl0yglFNrSWLZAC73zvFBIsXI9sMYqPLG0uZ8QyMEEEQVCSHQuCt8tDSnFfEVBu9vFcnZP5DOtdnRdWaZI05Q8M7YNGpN16/YzhLTo9TO6Ucby5TbnLySGE3FGeXIfTsRDnHhxg06wxnPnn6PT2aLbS5Da4uKFghOblxFWRtSPr+eEcrAsh6T30pwg2+WEUjm2XcYSLlpb5LlA5SlxXHBi/KU5US44Ub4RJyojKAwn4kwisr9DA6K/efY/LCR5HyUzsqRLnqeMjFbIM4ssM50XUkhylRJGKf24h+NW6HRCcqWNhkaazg6hHfpxjGMJ0qJEtL7R4/vPLbO+HZGnqclADYKfYkin0iYFm2Upju3g2A65ys1ZUp5gO6UdC/40TXYF6VKj9KAmnlLxvMIHq0jtDrJTg+BqT0fhjdbezNWNbi/ZNrZtE4ZmdvvgtuGfey8Plum+ML5daZISxX1Gq1XzRxolbK1voe0SR26+Ba9UxrIt+lkf23boRhEZCq9cJs2g3dlkbHSEzc1NRkdHWLl4kVxBnuYolRL2esZGYnMDrRWXLq2QqYxqdYTqyBgvvPA8P/72t/DM954hkynkxiDQsS2kBa5bRimNhcS2HIS0yIVDpexxaNzj0CGf8TEXy3IBgWOXybLUfGGoDMd16HQ7RFGEZVsIYY7t6tol4n5MrrTxPdM5Ub7NfT/57ldX+P6v//VCLagZQXQIzLA7DHnIf2lH8I4PfgghfPXZ4vqBYLsFnGVH0BwAYQ2OvneG2rkAQjO32GsUgvjfAv4xNPyQ8Kk6DzxSY2kpwAvBX4CnvgP1YInwgw28p0O8EIIzHl7xnE2mCIKQ8LzH/G8F/Jf/cIr3HFsiPBpwphQQLPs0ZjyCBnxsyeOp0+PMTPk8sbzErz7YABHgVacIWOapr4V4bw2Z+toyXjPgve9/mN+5N+en75xi+8+f5H0L8OR/G9L4Vfji50PGnwmoXeow86kZjs004Z9D/axH8K6I+v1HWfrzkLv+pyMUFlV4Ajhj9m19Dj59BtpLpikgnD8KLY/wbLjvgMFgFsIWHD0DD3+uzkOvDwg5Q/iZKfgr8B+BcMl0IYSrT7Dw8C/z9DEgB0QTpqeYfS+cnYVfOgLv6XjwtBl+PfAyC1rG0yrEHB8zm/Akc781xxP/MOCB3/QI3whTxyCcAr9TlPeOYdKbQciJE3Ds2LWv3Sv+8x4pPh8PMDT8GxMkzhRJ0DPLeC049vZXV/j+zTN/tpAOOBF3yVXK6IATRYee1JJcX8+JLNcI23TtpVmCUA79foxjG05YBSeeO3+Bja2CE0LsfKdiqUIKopGAUim25eDajvGwQqCyGLtcJo1TpJAkcYIoBjtriTHI1ArpJthOFXSGFnrH9BO0kQErjVYUxpkay3hHMJC7DzJXJvASWJZtHmcZWwdpY8YDSUAb3VbJsnDKDv3ImKsOPLWMqEvsOMsLIXaDOczzpFlelCtz0iwl6vcZ9fbnRKVcxrIs4rRP1XNZ3wzJRI5nVwwnekOc8DyuXFklE5AnOUrFhGFIP8nY3Nqg31Nc3Vwhy67lxFve9haeHXBCFZywwCoJbFlGC7AsieM6CKvgRMnj0IFdTujURdr7c6IbdojCyDjuM8SJOCbXBSdURpS9Mk68JoKsZ/76rxbCXgeRKuK+IuyFaGWmoQ8Cm27UB0nxwVXESZ9KxSVNE9I8p9vrodKEUa9CkscopRkbr2LZkiTLcZXF9y9cop+EpGm+E3QYd3nQqjA2ExKtNLZlYVsOSI3jlEmSBNc1IkHHcU12xZIoIZCW0XAJS2BpiW1ZJHG8k1myLAuVq50C+cCf6wddezsNwaSb0zgjVaYwP3yf4S7FvWv3evOHmaLp9VLaUcbY6CiHDx9k+eo6z15cYXKszIGxccIoIlNmCPbm1hbd9jZ33D7FxOQhtjpb9NoRYRgRx4put4MCemGXtJ+Q9GNUBlke0t5uo8nY3FjHcUr0uhH/22f/D77Xepa3zfwkTsWl3+2ikxQtwXUc+nGK4ziMVsfMjC7bpjziIASMj7scnBgt3o+kUqpgWQ5uxcMplchTTaagVLKoeFUsy8Ep2UinAhIq5So6U6RJm8kDPiU9xvRbf/rVDbK++92FnTLfwFhz75oa/iXcAbM3CLwGgcH/Dvzi7j294l/49LJJrARw6jMB9bd5eHWYej0E/x1wAVpHAsJ6QPBP4GgLeCfUlsGvT8EfhYbCIXgPgv8UhI9AcBZm5z2WmwGB8vl77wk4+UtnmHn7I5z6hZA3WE2cdIqzzRla4RJhFaYeOcNPnHmQT/3eGd79qMfyUg0xVYWgTsAyBBCFId9tfYf337qMNzrFX//seb74S1/iHf/mHQRPL3PE83nwv4KP/MkSX/rNM7Te4TPjTnH+6RBvCqoq5AHfp3bGY1mEUPcJlkIC4XPXwx5P/lTI7PsCCDyWl4FmyI5T6z7LWzKzmpdr8LVlqB3zCJkyAY4PM/8Slj8wOH4Nnv5joIQJjmtTcD8sPQ6chWPvCeGPw51DufMcw02CIXCqCY33s/RHAV4Dloc/F8vXdj0ap9XrAyyKeZicZbdzdQkWm1CfuvauwSJGq9eCn/u5VzfIeuav/9+X50T/BpzI93CiXCHRu5ywbUma5biZ4USUhPT7GZZl9FJZlkFmOJGjEVKiEo0jLWzHSBsct0IaF5zIMxzXNa7jjiRHYLtuEbBIHCERSLIkJUs1SuVGJJ7tVjqkLZBIcqWRRcDEsGm1iZVQWiGF6RDUUhuLh4F1FhTDoy10npLogWy+EM8LcztK7wjrdxAjjUWEMUA1XEz1S3Pi4Ng4URSR6oQojdnqbNNtt7njrikmDhxiu7dFb6PgRB862x2Uhl6vS9pPSdIY1Tec6PXaaPUKOBHFaNt0BMZpij3MCcumXB3ixLjhhLT3cMI1UqNMmTm7A05Yjo3l7uFE33DC1WNMv+3lOfGaKBeWRgVH7zrMRG0EnWe8/u7bOXrrTfSiDbq9bS5fvszG1iZJYsRm1WoVoXLC7jaCvpkTmCtQihfOPcPW1jpRHHLuxefI8wREn6lbDyFkbjQ9tr2jXTKRe1FiKzoATX1fodIMaQviOMZxHPr9fhEwsbMNMOagaAvXGSlSr+ZTrrU29y9SvzfKSg3WDxN4ZVlCji6c41/ZtgYCfGCnfJllOQooj42z1enSaUe86Y1vwtMWURaaYaZJTJbEbG9uMjk2ys21w6ysrBAE6wRrGwhspHS5eOEy290e250trly9zNWrARsbW6wFV+l0OkT9HlEU8eKLL/Lccy+wtLTEnbffShR2+Np//Crf+9vvkKYxXrlCkmq2NttGR6czwn6E67pYQiJTC9fy6ISa9fVNsjQny8BxLEqlEnkG0ipTKY9hWxXSfGDqapNnAq0gSXPiMCNPcqrlCp3tNqF6GQ+EH8Xy4eR7fzgdzMLehz1a/Bz+OvChtQjBEviFKVSz6K5rnYSF+TpnMSLuoFlnrQZLl2HpCSDwCdaKEuCpwrPpNATH2TEGbdzrM/8Fnw+8t0n9IZj7+VkaLajf2+S+T9b5wHubrH1pkdkLLaLoLIvMYyTsLU43oTXbYo01apzdMU01cqY6J9dqBDRpEPDJX72PtcdNubT+MPjHW6x8ucU/uOUEDwd1mo83qdWXCBZ9nvynZ/nlz50tOgEDaJoxQvfdV4fPBvgrMD9biP736fS7oSh9cN8FdsuydTOuZ/44NOagNejQGwjNW7D40OA9Xbvx+ktV5j7buEYsHzTN88/vM8F5/vEbvIEGtAavYwnmi8uzjd3bwZRamTU2WvV9/R9+tKs0KrjltkM/FCccvYcTywUnutdy4ujNh0DkOI6NbTnogglCCxxLFeGJMN81uRnLkse7nLBth35oOJFn5iRYFKZVSmmktLGkZ/S7uS7m1ips1zbc0GoHygpZxEmmWqLlrj3DkHwVgUBpQCiEBssWhQC+yFZJjRAxcZyCygsfrUGEZl7X4HnQZvtmnq+FRCClKLJw13OifQNO5ANOTAxxYn2dtdUNLGlj2S4XLl5mu9dju73LieDqD8GJapU41WxtGU7keUbUjyiXrufEZmcPJ9yCE3aZcqngRCaQynBCq4ITWU4cpmRJTtWr0G63idTfobE6f/WlxYUwjOm0QxCSyyuX2Gr38MpjoGFkpEqSRsYbSmdsbW3S3Y44dMAniHqoNCbTNnmWkSnTxpomEVEU0uuGbHd6hP2IF1+8Qj8tXHJ3/KZMqtZkfUydOs9zlDZjDixpIYTcCciMbwikeZ88lTi2DXmOLTVZv19s26QrBxPchwdCw7WZLK31NbfvLRHuF5gNX5cr43K7X2lwb9Zr8LhhrRi6CDCLvzuLnLe88Y1cvHKJlSsrbG1GdNptEiW5/a7X0d5cpzoywsrlKzz73HPYAv6H//Gfc9Mtd9MNA/pZzHee/x5XL6/x9bPfpL29zTPPPoNdkawFK1xc2eDbrWcINrZZ/Ld/yHdaz3L69BdZW9tEaolTsvHKFVKlkK5LksaMeFXiJClGE0kgp1KpgpURhxF3H53gwIERHCkpuxbKgizPKdkCkogkTbEcF8cto4FSRdBu9+n3tjg4cRAtcnBsUiUQWqBlxpve9l+/upms0tMLjcO7KZR5ru8aNPZKTQYeSMGUNaPWugAAIABJREFUqfr8cQjhftmXf8YuaEOo1U2ZEEJm7oGZOZ9wMeTUby/Suehz5CeA8yFHqwHLy/D+Z+YJfieAWgjeMsHHYKkZ8mDdx5sK8Z6E5bPgfwhaH/V48s9DvLfP4P+xRxgGLAdLPDgzB9QI5BlmnJDgHQ1+4r9/F0HLIzgreJfTYjmo8uTJpwiDOq25D/PT725x+62PcNcXmvzkxx7k67/5Vf7w95/n8t84HF1t8OAHz9O8x2P5YXhv42OM3gv/7qyNvCvl7Jc87v7YIwSrPu/48Mc4MjeCt7rEDB7LAUQRpL/9L/Dm7iN6T4szizU8b//RRDdIaBHUzH71f8WUYQdrgRP8ineM5WUj3RrYYQ1WvbZzKK7d3vAVRQDXPBMwVTOvwAsxwWwdvCmTzWoO+WydPAWNGWg+vf8bOPkIHP9J4BjMvRN+7R+fhGMmwgqCQVna2+mJ8HwgmuLYz766may/+tPFhX5/lxOrK5fY3MOJNI3QWqF1ztbGJp12xOEDPlejHirbhxP5Hk7Eu5yQlihKcJjgoiirabXLCRzzXb/DiaJ0aEsbLSFN+2SJxHVtdJojhSaL48LCx/gBWpaNypV5PjBlOCmQxVi2HfurQerJksatASPsllIU0pTiPkogLUAKYy8hLTRgxiAWXYvSQmCc53dLj4OnMFovhTZGpHooEfGDcuLiFZ59fpcTN99yB+1og34a893nv8fa5TW+NsQJ5wfgRLlUNtZMrkuaxoyUq8RpgiJHCokmp1I2nAg7Ea+bmmByYn9O5FlEplKskkupVCZPc0ojkna7T9Td5YQoOCG1QIv8FXHi1W+joghwUmPzEcZ93vimN/K33/omaZaRZxKtE5xSiV63i8pTpIY46fH1b16hfNMhKhosR+NYml7SR2bGoypJczbWr5AJTdyNqXglkmAdIe2dQGNQNtv7czcDJdF6t0MwyRPy3ARqlm0T9yMsywixLVeAljtlwsG2hi/v994H295v7fe4/cxJr9mX+wRX+z1WysFr1UgpqJRd3nhPnW+c/Tajo6McmBxHp1v0oi7LFy+ytNTi0JEJVlavkKeKarXCi+efZ9RzeezTn8atWLhehZHJceLNHolOOXTQpzIyyjPPn4MsI8tyLl2+TBSGHKhU2VzfYnx8nCzK6EYh1WqVAwcO8MJz54hEhO2WAahWq3TDjjEk1Zo0z5AoHGmaD8ZGPLxyCSE0JbdCr9PDGx/DLZcRcUqqNQgLx1HEURsUVEYn2Op2kFLuBL55nu0Gnq/mWhyoo03b4Ml97mKCriL90Lg2UbLveog9XWZDy4elUwHTszA/+yEjCi+2GzwBtfMBp+7/APW68WsI3rlIPTDGWWvHjTA8qBun+ObJgLnjUJuuM91co7kYUG/41Anwg0UC33RONuZnabVMKmf+/nv5QLPJqS8GvPV9MN9o8OTZszROBzz0MDx+vsltnGWBFvWfqfCNz63BfUdprjWpX6rjN1s8/PgsX/nKKXx/HvwGYRRw1GvBt1qcXT7Fws2f5fHHW8w93MBNfILZJabdJktHf8pk5ZZrBA+wMyR577DnG67i/sGebNLjC79epBXNDJ1mqzhcAZw6AXNf3ueADbpE92y7UfNpYrRi/hPwcKM4lAOB/eBpgPQyu+L6fdb8SXbLxEEA75jfffp9HhMscuM03o9wCXhZTtj7cOJr37xC+fAhKuzDifJLcyLPFFqYgGtgnyCkQBejbkQOylIgJCpN0SgsIUh0Qp5qkALHtYnCEMeW5HmCUxLoXILQRsurNEJCnuWmtFd8X5sZsWY0TuHxwMAcVVqmCiHQ5LCbmYKdjkStNJZjBlGbDssiE4Z57QqFIyzSzLw/E0qZpipdjC9Dm05FflhO5Ndy4l995jO47hAnNnokGE649ijPvHAO0lfIickDPPf8OSIZYTtlLFFwIio4oTSpypBCIYWLUDfmxEi5TD9OSZRGCYlTdnc44Y1dz4kszUy3wStYr4lM1rfO/NlCpxMRpQnKqfDtpb/ltttuY+3qClVvgiRJ2E4iE1UrRa7gykaPjuqTdTdoRyErV9ZI0j6ShOBqQJgo2tEWSmkQNlLnJLlLGCW0uz3AnInY9m6cORxkGS2VIEtN9kwISZplCFtgWw5Kp6hcIESKY7tkCrJMICxAYER3QwL7wSgdIYqzB1GI5YtM1g9TKtz7umF/A9P9smKD92jOpCRZpqh4HufOn2e706fd7dFe7/LfvHPGjKi49Q6+/OdPM+Z6eOUKSiRsbKxyeX2de+95A2Ur5tz5VWMbsb7JxvoWiJz1tYBg9QKrV67y4rkLbK5tkqs+90zfTqeXgpRY2mJ9fY3RkVG63Yh+FFMdHSFNMlzbZmJ8nLVgnepIFXKjs1BSorWNZStcx+Umf4KDByZxyyWSfmbODrUgivukeQh5hq0lKo+Ng2//AjeNVjh06CCTkwfZ2Nqm0+lQchyEcLnn7a+24/vXFswlk9YI6gFecG0uxfeNZOi6VdwtALwh+wb/7zcJjxWimzoEgc8jCx7/1x+F1I8CNQjf7/PUx2s8+Wcw+zmP5Sc8vOWQ8y94zM3XOPskhP8rTM8dx3sqYPm3A05d9Jj6KvgzRs9U/fwMU0/DdBRS/WDActnnfHMRfrrK55ZDGg+HPHDgEfzpKmcXq4TLTxF2XuCpTzzB/Ec/yT/68AxPfPrf84/G7uLP5BP8+9/fZvsd28wEbyIIl5g541F/x5sJp2rUgxrL9/u8954ZFhL44OuOs/qdJs4LnyJdSwkfCDn+kyGX/rTKsWMhnXMzVA88xdc+scS7393gk48v8+TnalRvP8s75t/FVz+1xJEjHixAWAJf7MlqnQCeBX9qT7ZwUDI8YzJbRvM2ODgBCws+j3035MgMMAUzd8DCB+d5+ulro6xm6DNVHHMzftujUQ/53C/BJ38PwidP8fGnZshniuM/0GB5u3myY8e4cdptsAZSM8+DB3c/I4tzUD9zrfX8+WrA1Cc9jt30Kg+IPvMfruHEt5a+/QNz4uJeTnQz2vH2DieEzslx6XYTOr0eFF5WUhS2PxZGG7UjRDff6UYhkiO0JIlTlC1whIMmMZxwMlzpkueQaQHSuMg7rmNCH6mRSCzbZLUY6L6KE3BZCNQtIVGFOemOMakuAj4G1wFCYFuCXGGGTSsTkGltUlZCDMT1FH5cGpTRgRmdm3lv0pame96SWG7BicoeTlwqONG5ASfWV7m0vs5b7nkDZbHLibizydW1LZA560HAenCBrfWA559bvoYT270UsYcTvW5E2I4ZOzBC0o+xpMvE5B5OpDFKCrS2cd0fgBNIlNrlxKHxCodrB67hhOvYSPuVcUK8FowXO2sv6jBqU5IJSbvN1uVzpComUznfP3+BLFVcvnyZpJ8wMVnjxZULjB32Ud2ENO+ic4U3Okq73SXsxFy+so7jCiqVCkJrLl3dQMYZ/czh+YvrXGl3gEJHKAR5mpHneZEdETvlu3K5TLnsAjZJEuK65WLWYbqT5UoSU39P8wzbNj4sWZIX21BmJqJtmxp8Zk4PhGU+uIOgZ2BgClyXURu+bnB5eA0HTXsDrb33Gf5dSonjOIVJqjAtvENlx4rn4Dg2d91+hDfX7+L8uQvkysO/8xbi7YCf/vE6W1tbnFs5x/p2QnViBFtlBGttvvfsJZLUIkz6JoDUin4cc2CyxG23TaKwWF5epdsGt+xQsh1INdK2UELt7BfXKZPFGcrSjIyO097exhI2UlhI28GqpDhC8oY77ubNr7+Jmn+Agwcn0bYymrl8kEG0QGcobZoYdnV3mekmdVw8e5xEhbi2IBcWv/DPFn74qPc/wVpYWLj2IBaZiWCxxee/XefRR4duG2Q/hocOs8/txaoXmpwAMxu40TCWDfXZQgddr+PPnoaTDWZbcPIvAxorUNcLnPrCQ9RP1aDhE5zwaXwzYPregMWv+Du6qbXFJo3HGhxfnIf5Jh84dZJG0KDVgBp1pqehEUzz8Q8E1I+3OD0PjdNN6jVonK6x+IdrVP/nBvet1ZgOfJo/0+Ls2YCPHoUn/DozZ5/k/NH78OstmK5RD6ZpNgPmfVisN3iq9Tned+EozV99jHn+Kc1vebSaH6LOIq3AZJzm7p/nEx9c5Dc+2uI9TwQsr8P/8lPw3o98nj/4g0dNZmuQJWqxa7ewX3Zrwfw4uWDmRvqFhUPQhGar0DoV4vE6xXZ9aNwLQQKnTwbU53xap661qfKbdaOMahRZsiZMz/rUg4BWMyBgMMw6YHp6mkUCGsG1r3e/5Rc+aEEDgrdhBma/xJqdB9x57sV9Vf8mruPEpXOkOibNMpaevwji/wcn0Fxa3UDEGf3U4YWVda5sdUwwJABh7ADyRJHlmQl4kIiKjWtZVFwXpEMchpSqZZQy3/15XnCiH2K7DnmqsEuWsfOJtTnhxgyUdmyHJFVolaEszLieLEdigp68kLOYjJMqLmm0EMbWgUKvVdT9tDaVQFWIuKQGbBtBZlziVfF4zGOMjkxhSTNc2gRq0mTBhHG/T7PUmHkX0dwNOXH3LcTtgJ96c5321hYvrpxjvZ3gjY9g5RkbgeFEnFpESR9pGU7EaczkRIlbb5lAS5vzL67SG+ZEXHDCMhGiQFByy+RxRi4VI6MTtLe3kVhY0tgyOZUUB8kb7nwJTqCLbF9Gnmrc8ktzouQKcunw3rl/8bJ/E6+JTNa/+tSHF2wJjhL0ww2IVinbOY4rOHL4Jm4+coBbb6rx+rvu5Jtn/wapFRudDdJun9W1VVzbY31tiyuXNkl6Mffeey8XV1bodntEaQxIsjxlM1Rs9iKyopNCDcp6gCUtrCLwyAuDUaRACJPpkkIQhn0z4DNLGAjb3VIZdj782ogK0QjLpGl3hkEX2iyFNmcjSu3MmrpRFuvlLBiGr385Qf3ejsOBUWqe54yOjhRDnuVOmdSSFq5TIumHTB4+yG21UcJum9azl0lTzeThm/j++UtoO2HS8xBRTDvcRMqMgxOTjJYSqq5Fyck5ND7OXUdrHKmNcGW1z9rVLkdvm2JtdcsM4lYpXrkCWmFJiS0t4tR4b1kl16TfkxSvWiUMO2iRY1kCx/aoehWO3lLlliMTHJgcZW19hcsXr9DrdPDHDzLqjdBrb1MpW9TGD9KNu/R6baTUXLz4Am96wzRXVi7SiS7SbQdYlgbpcu9rYHbhNVcUmQmvXrumWyzwh6wdblTSCXebFKlDEPr4tZDFWp1GGJimtd9uMXWsxmzDzMdrLVYRMx4PLjzMU7/zNJ+54nOX+BDv+UQDQo8gCODr8In/s0V9smqybHXwnoLa3BTLT8HHPn+K4I6QRlhl+XzIctOjfjQgPBvwqbM1HvkTWD6zSD2sMhV+FaqP8UVC3rW1jP3cFG94fJnndZWQkDf/ywe4f3WJPw6/xPijR5kJjrLUfCtf/eISaXqEOh73eiH3HK/yV94Rvv0cvO/YKZ74wt0st97HI3NLPHkGPG+KMIB3v32GC3+zyJklSI963Od5LK2HvP3EzxKeL3bW0tB+FcBnWoRv32eS89PmX4NCLwXGHgKoL8GJGhwLjAbuxEfg2BPAIpw9B2d+EerHPYJfg6njXCPa8n474JFfNHFeuFw4OyyFVJdhmRC/fhRvLmTxn3hM1ULeP9ug+tPLBMvXD0E8eQIahbWHV4XaB6H5G1D193QwDtlHDNbiSVg61+Rnj726mazrONE3nHBLkltu3uXE6+66k2+9Ak68+d57WRlwIjGcSPKU7chwIu4rLEei0ajcmFNbloXjOEbnJHNUopGuhRDGfFoi6XQjygUnhJCgNG65YjoTM2Ushwo/KkRhaC0to62VaqcyaAuBUoJcgFAmsBFySPMuBVIXyQITBwGC4XNsiRG/a2GKhNLSqNx0YUupr7mvZUtUrookndgpEWqhybMbcAKJWypfz4lnLpMmmgPDnKh4yCim09tEWRm1iUnGSgllLCqVnENj49xxW42bDoywerXP1Stdpqb2cMKpIKUqTGUtkgEnXBdLYDgxWqXb6RQNDBLHqVD1yhy9ZeQaTlw8d4Ww3cGfOMhoZYRef5uybVGbOEgveWlOCDRaOq+IE6+JIOsbf/6nC1dXN/naN/6S5e/8BbWpu0G4dMMc203JycF2qYx4vGH6x7jl0CGWz58n7qRYpQpSmY4MKTRprlnfDEhQpLlFXymk5TI5UWOzfRVXOmz14iLIMpkOhTQHyrJBgrQktuNg2xJLOkgLLF2iXCqTJiGKBK1MFBwnxmNLq5SSa6O0ZQKsPEUWhqQD8XumclzXNXYRSiEtqxA1vnw28eXKiS+l09pvDcqklq2xbRP153lmzoQwIwnSNKNSKhOsBRy59W4O+hM45RRhQaU6yeUr61xd6XL+wjrKytne1kiq5Ok2mXAo2SP4tQNs9ULW2j3OXdkmSbrkqcN2O+D2O+5ivb2JZdlkeULJ87DdMmmuKDkloriPFjmO/v+oe/souc76zvPzvNyXutVS6+WWZNlYDdgkfXkz4waSCVOMj53YwCTZtA6L2bWZzDJLH2biHIIzQ85oyLq1MJ3d5ATHi8kmrTnLBOLZMcOqckhiYs8YzBYzm5BIYGJTBSQBtbGNXNeSWt19q+q+PM/+8dzqbsmSUQKsvY+PLHVV9a2qe2/V93t/v+/v+5WEXsAwH+MHEZWBwA8RckRVwnSjwdUHYmYOvhxJyP4r9tHa3yLL16lkQalCRChZ3xhTFCClj1IBgb+btY2MxtQ0zUaLvTv3glgnUIZXvfGtLyigPPbYI4srv5IS3VCzqyM4a4BtK2lfMLJfrxQ4yfkwO3GBiFLoktFaAfp93hxFpBnM3dAi7qWcWIk4fNvt9FeOk92Z8f7/6UO8/4Nv4bb3rPDB7x5i7oo22Q2C+G0zdOOU27MWD/52ROuNkKzEpEkGD8ZkKxnJSNA7fJwvHm0zNwe33w4rIiXtRPzRp+7jVd9Y4uTCIR68s0OUHSaN7+PGmdfyL491KN48Q/dgivfOL3L7G3+a4/ce51szM0T9A0CMON4inRvw9NNPc2Dao9XOeDyNeTxa4TXM0EpTusEC8zel9F835L574BovI00zyFIeeaTrvKcmO+cDcPK/RPzuu6H3D1wXbUVAdJwtq4y3nU9clnF2ZBc5NAD0jkDyj+HJI0t0/32bmfvgz8fw9pYL8Z5pQosU5g4SRduY8HF38LJf7dI9MeP8rAAWYro/u8Itv/ph5t57khNJStaFT820+UI34z91+szMQbzS4pZF6L8vJTl8kLSf0b6BTU+1LIX009CKYK4P6cq293hh+3kR7v75BU4+cpw3vsAk6y++8J8WTz1xmj//8n91OHHVtaAuwAnv4jghgwbiApw4fSZljKGsFJkxKOWzezrm7JrDidVR7gTu9UV5hUAa5dpqvsAiCZo+nq+QSqN8gSQgCkLGG0Os2oYT48q1JG2BH0qqUjsCZ52nVlVayrJACetMs5XGGqhMWRMaV3GywjrRu6j9ty6cNNz+3e9kZEwam6665VqDVti6Jbi1THUBDklQSqM0SOtc1aXUVMU2nKjOx4l9L7mWON6FCAvURXCishXn1i2yDCnsOUrr4flT7Gvt4cxGRnpug2999yx5cQFOrNY4wQg/bKKDkLIyBEHAxtoQoS1eIQiDgOF4jAqa5JXF1wFSOZzY1Wxw9b69zLz0GqQNueKqfbSu2IYT1uHExtqYIgepLoETu/aivAxfVJeFEy8KkvXAH/3+opQepSmogl088Z0nefTRL/Pw5/6Exx7/SzbW13nFy19B6GmqasSO3RGvf0ObH/uJN7DDL1hfX+XJJ54AHVBWklAritGYMGgSalAYpKcwZUkQ+WgsRV4xHBo838dFDFpnfGlBWUNVuRO7MkOUDNw4p7UYWwKuP28sKA21dz9F6SpaZVGgVW3/7+Zhkba2fZASO3Fl337FUYvQLzZp+HzrYtOIFxKuCytim+0430crzXA4qv3C3BSllM4TZao5RTYcMxwXnF09hx9Irt6/h4YXML1rF9995kmGG+s0d2jOrGc0tGQ0OsdUY5pde/dw8qkB6dkR59bGrK2PCL2APKcWeRp00CA/l2ERlKhN472oEZANR/jaQ0vXHiyExQ8CrKmcxsAapPBA+0z5kn37dyGEs8ywZkRZ5O7KzkiqosTmbhQZIUGUnDk94OQTf8O++AqMrYAKgwc2YGACfuzHX9hK1gMPPLL4S796kOMrNerd8NzHpNuqHksptCfVLmqCtSnT6REvtOCgC54+RJ/FD8zw+AOOYHESjvw2/Np9Ef17oHPkONHb4eDvwsJvvoXjX+qRnICZKuVnf6zFjuWTNNdWmPnAAvM/H3FL0qSfwi8udVGZYCaDk3HMzFtg7p0zzL0u4nhynCxLaR+cob+S8raXJaSthObttxEvteEtD0L0GmaaKxyYgaeKFp9LMuJPrPKWx6a56Y6H2PPyA3RO9Rm2mpz9qxxIOeBNc/iw0zKtDFNa7YSfueo+/vyZp/n8H7+W1a/3iece4sBXPdI0Ik6cv1T/3ydM35iSpRCvQLYW89tLGWe+BfO3LHCi/yBREsEMnOjDgWm2MVW3nyd2ZF+48PDU1aCPPQIPPAILD7URx2E5hXvuh86nYe4D0P003P7rc3zwX60w13S/Q7al6UpmZrZak/OQ3pORvC3ivf+0y8dMxCM3RHSWM/5h+918o/sAt8xBMufOi/6HgFsi0i/W4qsLmGD86zHZp7NNu4oscu3NaIbz12Ow/J4PspJ2+W9//pdf2M/EH/3+ovZ9iglOPPX8OBHtinjjGx1O7JzgxHcmOCEIfU0xGuP5EZE2eLJuRZUlQcMHYzFVRbZhUNJDe4C2BIFGGNDG4cR4BKZyOKF8H6TF4nCiKKwzOtbCWSkoSWmc8XVRuBxER57qqXMr0L6HtKqWtAsHLxInV1cCKmfKLLXd9MpylaetfSXPq3iBp+ufrROxC9el3FxCirovWv9c/2eMwPd8PM9nuJG5VqmQlFWFqjFrKpoiGzmcWK1x4qr9e2heBCeyPMOXkrxcZyqcZufePTz5zIBnTo9Yy8asnhsRhSHZ0L2HCU6M1zJAUAq9hRNRwMbGiCDwoFR4KHJl0TpAAFoAwh07tE9TSfYd2IWgxonxiLLahhO2xA5rnJAOJ06nA05+5wKcsB6m0Awqjx/7+997uvBFocn6zV/9p3acD8HXfPvJp7nqigNsnD1HaTYQxhIFAQKNLS1rpwekp5/h2le+lJ+64aewGxl3/IsPcf2bf4pADxmOCvJRwRtfm/DlR/vonQ3GGxkbwzXGQ48SSxD65KZiOKp4dnDaEYH1HB2ClQphRB2V41K9pdCgJ3YMMC4qlBBInDfKdmuEyZpUsJRStU6rcAHS1tTju1s+XWVZXpIkbV8Xu/35phK3b+9Cy4jJa3W+IlttzSDwamM/Xfu+aLzAJwxDlDTMXL2Tfa2Y2Wtfxp/9P3/KFftiGlMNvvOdJ2jtP8gz3x1QliVPfvcMwyGcPee0bFmeEQW+C5KWHp7yXfK5dSX4YeECun3fR0vF7p27ycYZRZWD9Tf1c15tv2GwjEYZjWbE/j0Rr3/N1Vx7zUEausGOnRHWSJDOzdnHYKzGig08bwdrZ9eYjqZYqywNT9MMfM5uZDRCj689cRpUyIc//JEXVpN176L9via6YiaBgps39QCSWq5zzIHrhZ3GGIiX5knbHWhD90iP9kcT6NTTgzGwAOn7If4UpDfC/OGUHjGtuEe3lRAvORuH9Cgc+VjKR/+D02stkHJVJ+Y/fCJl+Q0DksPJVi7iEiwvA3ensHyMuxfu5tiJLq+/PuMvuhu0VhIOH0y5p53xofn/g+X33kHyO/Okfo/2U3DsmHsHC3e0WSbmEC2W730vK0nMwXSDmflDbCwNSCfZgSmkJ1KSm2PiO2LuvC7lPZ9wu6wNdO5JiW92Qc9JcnGp20XXpXRxUE9lQnxXLZKbHJ4U+BjwDvf7yykszEKn22M+ceKqI0eWueujC653mACpC4NOF+DI++GuOluxe6RH+67EveYeLCzU+/WCacM0vcgkYdfp87rgynT1NuMYukePcP+j9gX9TPxgcOInCfRoEyfeUOOEt7PBeD1jY7SFE36NE6NRxbPfPUO6OuRcliOVRQQaUbrvcC0l0gNh3EV0ZQ3as4wKgxIghCTPxgjlLGLycYWeyNuqinFl8X3pgL4qkVpSYZCmbhdSOs2VmXynw6QtqLBUwsldTFU5vS8GaWECR1IKrBRI4XDNirq1WE9Jum1uCeidt4StJyDrLF6tKQqnL7bGEoQ/IJw4dYbsHKxmNU6MM6LQJy9zNB6e51OYEgz4WrGRj/E9D9/3ETnE+2KyUUZhcjC+q9opZ7GgtcOJbG2D5q7mJk78yCsOEsgGO3ZEWLuFE541WC7AiXCKdWsJPU1T+5wdbeGEFZp/s/S//f9Dk/Wn3T9YDHxDkRdI4aFNxb54msFggzDcgcFjVJRURqLDkKtefjU7opfw2Yce5vG/eYyynHbsnIq1jdMEzT0EUvLM6WdJ18+ys9FkLRsyFWmUlFSVwfdhuL6KLCtae3eya0eAGdWmb1Zt+YQgUFo5zxILphJo3BVHVRo8rbfd51zjpax9taw7+cvCZQcqrSmrEiXVZojzZD1f1er5dFeXsnCYbO9S04bbRfcuMNvUBMyRxaouxRZFQZEXaE+TbYzwRMRfPv5NZNDg8W+cJLeCM6dHPDs4S5YLvvPUWfJSsHou49x6htYeeZkjlazN+TzKceFGhZV0+0srtKrN74TEIKhsiRSSYTZECoutSiTuCg5Rx00oiRAlkfbY5QccvPoAvu/jazeWrPwGeTECa/EUhHoHpqiIwhCEoSxzimJMqH2Mp/nm06cwQjPMx9zyUz/9wmqyHrhAk/W3WPPz0D8BEJ3XO2wBrdQRjJUkJc4islqsHi3EzL2KaPKJAAAgAElEQVRzhv6nU5IP9zczClv3tYhOxDCTOSf5PtCGaAW4AU7+O5gZRTRnoNttER/E6ZFS15b6Z287yImlFbIPZwzSATtuT+F4k+bvzhDP9Ug/mHDy6AziwT6H2xl9YpLbXYbQm//JQZ76R6uI/peZuyvizf/8ALK7yswrpzg+20b0j3PiEwlPnRSkrYQsyyDo0zqV0nn8JtpC8vmjf8bvvuMupIATWcriHYJHHiic5OrpPtf+ywMcfyDj/nvnWTMpJ4666k70moj4Doj2OD3U8SP/FvHIzdxyKmOlD+1fh+wRyGLozbj9mvYgW6oH/XpbFSlSSE+6alH8Rjhy5Fd49T2KaGXGDTP0Um57VYQ45MjPzMmY378v4y1vaZGlcPJkyn33PEy/cwqSlCNHOrz6jTOQOBPW9/7hAjM3nITdGa1XtSDrkqYzpEDvuDsnehG0FnCtSGrtWASwDPNz7rjObJOEHWervTiT0pp/Gze8/IVtF34/OPG1bz1GUU7XPk8Va+tbODGocWJHFDmcaDicMNbgB5CdW0VZhxPTUwGmdF0M923kLBZsbfsjlYTSico1EozAXIATTuhukJ6rCEkBWihKWwEGKTVFXrrvOuvMPycdDqlkzXtcNUpIsRmCTF1ZmtgIwbbvfOpWIU7v5SnlEk4QKC1cK3PyWCWwFXiBdAJ3uw0nRI0T9cBYKSwNP6DIC+czpRxOaBHx2OPfRPgNHu+fJEdw5tkRaVrjxJNnGdc4sZZlWCspyZG1iavSmiovMaLGCWOxQuF5woU0C4HwfEpTO/2vbiBV7QSAxfOc9Mdai/YkQuRE2mfaD7j6JQ4nPE9RGovyapwwFk9DqCNMaR1OSEMxwQnlY/wJTiiGZXlZOPGiIFl/8sD9i2urY6xQGIZ4hPT6f4MMLONxThgEIArGG2vYQDNaqzg7OkNuSgLZYGpXk0pLms0djEYFVGMsHs+eXkdrSW4qZK4oZEE2KgmFQvke+UbBNa+eoShL9u/dw85Y8bID+3n5S6YZDw3kJVKGbqpBuRFXgcv6M6ZCynoqzxqsFKjAd6PD1iBV3f5TTjipA49yQqy22URcytfq+Xy1no90XWw68VK/ezGjVDcNIzaF8QC+71PkBWDIRiOUVpw7O+TcqtM4DZ49x9n1gkG6yno25NzaqLa0qKhMhZYKU7ovE6XcwEG1rdzswrpDKlMSNhpu5Bnc9I5SVLgvBiHB8xto7VExpiotoa+5Yu9urvnRA+zcMUUU7qE4exrp+5CXlKZkKtqBsc7YdDTewI8Cnl0/RyA8Ai0ZCc2X/+oJZ0DnN0ApfvLGF1aT9Rzh+2RdSgC0bfW367QmLa5tcqKMlCiLiUjJREQ6A1E/I30kJYqg9ZaY9GczorfXovpshd5yRNpM+GKckhzf2pb4AmRvhzSJue3dGV/8dMrBmYjWjCMZrVZE1kzJVmLS44JmNsMtt0d0eylffOsKD55IeGnzQX76l2Y4ed9xkv/rLdzz9hbNrMVfdr7IR//5AY6mMXN7b+RUlsIX/5rsmmm+3p8h77+ea/8HmEs7dB58iMS7hoe+PIP3mibZ3CkyjjP9+ad5ZHWV5GeapI83+c1TXyVZiUiB6QMHeOgk7Hr2BKe//Nf81lLKobvnSE+mdDs93vySFisdp3tr/c/Xc9urV5h5JOI4sHKqtnbIHMECJyCP4npfTwhWvHVfNgMHU3j5Da+GNOGWOWgmCXNpxopwRIgIombGLX8IPOa213ooIv2NR7j98RY/84UUPpoQ1c6jHWJajy0RPdAn7c+4lmPchCw6z8WhP++evzVxjDjpKnSdbI4ZwValK3OVrJk5lwjwr9twfCaCLtzwAmuyLokT2jIunh8nfLENJ6ZqnDDbcUKQG4PMJYUo2RiXhCiUV+PEa2YohiX7W3uY3qt42YF9vOzALkYjlwyixBZO2KL2sarbS4DDCc7HidI6mwZrQXnKSSCkpiiNG6aqK05WWqxxQnRrHMEx4Aw3rfPE2orDkQgJcjI1CGz6OtRwIKzLUZSq1nXBea3GyeNtZV01TLrsYGcDAZgaJ6RECIMpXHXM01s4MZzgxOr5OLG6HSfWt3DCYNDSw9Z5us5+YgsnwIIyeNrhRCNsYApQBWghkYHGCCfkN9K9Fq00lc3Pw4lrt+HEePU0MvAR4204gc+oyBmPzscJT0vGSnPiG09AWeEHEcjLw4kXRaxOMc4xvk+xYTGVx+nTZ2nFe2iEU2gVMhqVFLkkGxo2MgPaI1srifw9lJVEWY2m4uzgDFHo04g0o1HOwSuuZHdzmkBqrC/QKqA1vRclC6rRBq2d+3j6ZMquHVPsjAKa/k5ko2RHc5q/96rd/KOf/FGuu6bJ1Vd5tCKfPfFefC2pe4ibPfZJy68Yj/G1hxKyduLd8jkpxrmbMtHeJX2rLmeaEM4nUtvXhdUr4Dkk6sLHP9/2J2srEb4mXlaSZaN6tLWkKArKstwkTG502U0uGmMoqtKNLGOxVAjh4iXyPEcpVeuocqJGwHiYE/g+aDACFAIhLVIL8nKMkJbKFPg6gHIMleGKPXvQWqOkz8bwHEZ7GCyBr6CqWNtY5dnVZyjGJUr7FOMSW5R4oWQ1r/j8nx5nkJ7hsf43uf8/HuMLF3gXvXDrIs6Qdz33puesC9uMF4zzx3HsrAyI6XxsW6xLu00M9K5K6d3lfmlxAeJ27Bwi4h5fjYEk3fyd+EZcG6yX0lmEuBWTzuIMSLvQ63XoxXEdPdMjnod0AK1uzGefmmfhswkf+sQhllsprYWEzjIstHvc/J5ZbvvwLXS7PWCFX/5ffpn77jtBd/Z6OkdTTg5atK4fMOj2SWc/wqFbPk164iFuuTrmRKfNCR/apCwcbJCcgM5ij0PzcPdN8xydh4XFmMXFmA8twMbGQboPxbS/Dt17BvSI+ezDbX7tl7ukK5AehJUHEzqvg/efcO3GJIYkdjt6M4InTuh+6oJD0Y6ZXwTilNlbay7TjolT6N0Eve4Saepae6Sw9IvL0EvpLwJ3dEnaKelXYcEu0mPA4o3OHDTtJrDc5ZMLCYNOmzRxbrQpQDfmyJHzT4J2F5JO7fRR39VL4ZOL8VZ7c+I/W592yTws/yLQgeRFcDF+SZxoXgZOlBfgRONCnNiFLzTWl8gqYN/0XpQoqMaZw4lvpEzvdjixI9yJjCqmmtNc/+rdvPUnf5TrrokcTjR89u7fi+9JrFDYeiJQaFfFKouSYjzE83y0kAjrKvJVZRBIrCmREsLASSRQzr5BydoFXm99n7vuQz05aGrNlnCTi87QS27WsqyVdfShRYlab1W6TETl/ucGvLSTI7nfqR3ojcs51J5w04fUxvLGQukid6D2fcQ+ByfK8nycmDjJl2WBsdVmV6esSiphMbbC2AqBw4nhcIiSEonCyhFR5DPayGlMhdgASgnSuHcqhcWYHIGgMhW+DrDlGErL3qnd5+EEysMai+8rKLdwwlbPxYm1GifS02f46mPf5P77j/GFL/7XyzpvXxSVrM8/+HuLUw3FWpnhG5/BmdNYJQkDVRt9lozHJb7wOXN6wA5PsTGeJK6PGWUjshykzBmOLKORxcqK8XiEFSWmKqASPPn0k8R79xBEjuEO1zfYc+UOVKNinLkk8SunW2zkpwjkLlTYYP+BiNZUwEtfMo0w6wwLj7ySlNWIsiqc564xtV+Hz2icuZFXU+F7PuA+DEJJlFYIKcGaOhBU1i667qye6KUuJEqX8sm6VKXrYi3C7T9frJ14qcoZgOd5NdGyridvBUXhKnlCuKs03/eRUlIUxXljwUKI2ruv9jWxLutRa7X5vpVS+L7CZR8KRuN1fC9Aag+pPJSgzvzyajJXkY8K/EaIkYq904IDLcF0cwfTOxVSeuTFEAmMjWFXaw+FHRNUHtpXBArWy4xTq6v8xz/u8o2/eoKv/eXXOfPMWdbOjThzZo1/+YFfeWErWaceWTw/X+Uy1kToPilh1Ik7F64sA5G5CbrkhvrGCFhZccNlN0BrMYVH4JEuHH8Q5t7iWllvA+I0ch5NmQuIXnov3DIHpND7lZTW/ojooHsRD6ZNolt+n97xiGZ/hoNzt5B9cIXOiR5zPzHkzp84ypu/AQfnBqxwOytHj3Pow22a3Yhe3GXu9oQ7P/5VXhbH/PiNPvEdr+GXvtDnZ/71azgVdkmyjJnjK6xkHfjx1/DKN6f0/uwertz3Z7zmun9CM4j4rceOk8Qt0hR+7XCHaz8/5OzXD/DATySI7gorcweJVzN6N0fMXJuRvDtj5f6MbGaGu9puoOBPr+0SeU1aNwIPHiSdWaF734Dkl1p0OjG/++FbSFcSTp5+ENGM+K3fAL4A3dMZw9+BqBWx8r/DbXdD/z63r2bWYOHdbY5+aYWDvxQR/XeQMceJb0TMtSF95ONk/S9x/BuwsvYID746o5XAr3885kvfymBuhgezFZiD1uw8/Dd9Dt7gqmZvvCHasmNoA19k04A03ZyMgOOPXOQcm9yUAK8C5qD/6lO8jVe/oJ+JvxNO5BZptuHECKS6BE6UBRjBU4MaJ5qayowdTly9AzVVMV4/i1EBB3a0yMpT+GIX3oU4wTrrQ4/SSMpyRDHKsfUwkRACz/MZ5esYHBnx/RAqZ47tSJSL1BEYhw/CkRwXs2PrqcGJ5ANQAjkJka6LVhizWcuS0kXGOePUGmvqfWqtc4bHuiqQ3Sx31aUxKV2AdG1eagEt67aj2KYRswLPcw75SEeOpJDkuUVK95pHQ+c1hZQUo8KRNVPPIiHqnw3Sd/vKWNdGFTiyqrXC04GLExKC0XgN7Qco38MLPDc5qjykcLIciyFfL5xvmVLs27MNJ6YVUnjk5Tac2Odwwi++B06kDidOn13lA//ie+PEi6KSZbTPOIf9u5uE/pD9rV1oEcAoI98Ys35ulUiHiMBj31UHMKFP6FnQBb7UqEBx9YEpXvvaVzG106MRQhB4+A1FaXKEaNAIfV7xspczHo7IzozRZQN/KuTU4DTjZ0s8McWUKhmPKhpyN9P7NKPRdxmmGzzx9FOcXR+wZ0cD5ZUoWSDx8FS0KWKf+IZoHVJZgfQDMJbKGioBofYdWy/LenRWIqUGXM6Wq+5sHY5L/ftSETnbb/tebcLt9yvhogKQWy3GC5+vqgo39mstVjgrCqcrqCiKCmsFw3GOmFw9bWtXGmOwlSOhwoInJEqJ+mpNbV41VTLY1IhJ7QJUAaJGAyuV0wdIiTU5ZTHG4PalRuIpj3DHVeSVYH0k8ANNNbKUo4p9UxHrZ9Z5Nl3hqh+9gmE1BOH8Vs6sS06efIYzZ9cYVwUb4xG5dWZ7L/jqXfKHy151ceOiK54k9py3lt1fHeD+bdupKzXtWaCT0uqAK3ukpMDhw86dnBhav+CqXl2gfwTm2wPubh/mk4sJswsxSbfrZF1fTei+7hgFbQZJTP/YIUhSPteLGfRhqdsi6UK32+UdScJ7SEj4Q1q/8hDd62/jdS/pc+KelLSbcuW7BgxawCDlT34/pX19zH8PdO5NWU4OMfzRhLQNsz3gekiJOLGSEj/Uo9uB3p1A2qbfTWA2Jc279I7BLe9LWT6R0kti3nNLQhzDwjws3t9ioT1PEiekXXj43jZT74J3vvNdLDwcwywcfkdM+z+3WWjjxP3UkUM+zC+4fdW9r8dbRYf5+dhVp34BPvKVReYX3O6Ne4vQg/bddQVq3v19/5Udki6knZjurcAAx4DvmhyVek3+0b3IsZ4Uay95guBKXTEkt8bw8N/tHPxBrvNxIvu74cRVl8YJKS+GE77DiVOnGT+zhRP52OHErv2aUfY0w2cmOPEMu3c08MMaJ4SHFzYdiRACaRVYQ+A1QUhUGCIqQ2kNBkGAxlQuU88Ypz8VQoM1VMa4SBzhROzOOBso3fdyZZyWykXhbO9EWKdlxxEqI1zwtKjJ0pZ0qyZPBjDCVdYsIKy70BUSoaEy7oJZawX1IJ72BdZWbqrRWJSnGBYVRZFjhaEo3UX5uCrQ0hUbHA7ZWmNr3NS9hWpkCKRCSYHU0Nzho7V7qBG6JmQVQkdOIwYEKsCi0J7EettwQl8CJ4YCP7wAJ569HJzIHU7Igiq/PJx4UUwXHv5nt9qiqDCqopSghmOU9qlUQVlKKjtinI3xfR9TwHhcsCMIKGvX3NxkTO3cwbmNAilGYDWjTOCHzhxgNCwZliO0ljUh8lAG1jfO4QcNytyRBaEA5dpbfqRoBlOIXLGerSEDRdMPKVTE8a98i2wjZ1w6sd+k3GntZATYTQ0WRVEbmjriUhXOUd4J+bZacNsJyXaz0O9nXaoydeHtEuFM7bTCVs/Nc5y8jk3hpa5N6IRECelagca49pzn11YQbk3e9+Q2KSWekFjlBgPixjRWGM4N11BewHRziubUTjbyEUVeIZVLSi9LQxB4VJWlLAxFOa6JqWIqDPgHcy/jmpfu56UvO8j6+jpaTyHw3HMqg1rLKK7Isc8UqMZOdLibe37v41jjc+qpZxkP18nGbhoUqfA9wfra6IWdLrzQ8f3vsi5weo+JN6fr6EMvdkRsc2INnGAbONyD5T4kjyb0FnvECY70LOPSquuVAvcuJty7OCCdTx1BW0zg/p6b4ouh24FWmjLbi2uiEBOnfZbn5+HOO7k/OczDcY9eG+glHO0OmG+36XaOErcHJMkh/u3yMtc3m7zvMzdz388+xG3cTOfmlI8kCyzd9F7i3zhIP45Z6Zyg/b6D/MU9cPdnDjN4GLqdZZI5uOm2mMU7U1Jc4PX7PjPLQ7/R4voG3PfQMeI4odWCb/+vMQv39+ncOsssXbo3QevZ/8LgmTfBJxO4Bw7dljLoztLvpcx+NqXbiUlqHtJLIZkFWgndbo92DHHH7be0Rz3aCAvdmOXa/T3tu31/bzzPYtKBIykp88x70Em6zjn+k9DrxbCcunDpejKzs7QIs4vMt9xUZ79zae503oH7Xg9KYuildHtdPvepNta+sCkIP3ScyEqyaoTnOcH3dpzwggZ2XDHOK0QDsA4ngoYi8qYQ1RZORH6IaUT8+Ze+RZbljIuCkoKqqLB1O00ohSlKZG2UbWXdmtOaajTGEwq0m1TE1l5d1HiBcd5atWZWKoGpLBLhJBZCYmQFVV2Zqi0brJnEu7mKkXMXshgjtqpeWmAmIlhgwsCkdjZEBBoqZ8Jqbd3iVE6/JYRFCrd9FTrvL13jRF6WWOtwwtM+pnITg26OydkmlGXp2n7a4YSRkqqs2B/topIV59bPIb0Gu5tTNHbsYH08cgNmCjyp3LBWw+FEPtYU1RrCVgilaAYB7e+FE+cyigOXhxNGuODt8fB748SLopJVsYH0cvI8Q2OJdjfI7ZhiaBgVOVVVEPl7EaVG6ZI9+wMMGxiZgS7xtaA4t4YYjjBlSVlkaJ1jhWBsLKWUNKIpsBqQZBsjstEGQmpKIylKi+c3sEozHgswATb3OX3mWQqTMdVsMNzIGOcZ3/za15x/B5Xz1RAglERqVZOn8rxKzqRSNLltUoHZTnY277tIUPTFRO4T8nPhn+2/c7GK16TidrHtTYjQ9jbi5N8TgmWMcVYUthZkbqt8TVqFkynFyReAa4HWtiNia5tCCNLhKibQhFNN9u3Z7XRbAhqNAE9LgiBgXBmKoiAfOzuMvBzXegCD+zYAaQUqCFgdjxlbSzl6FlMM8Lx1qvIseu8Ue/ddy0kvQ2rBWnGGp59a5xtf/xvy4QhP+aj6GBVFwWiYf9/n9A98pbj4ls6l739OBWMbkG7WnVIH6Mk8tHpb2+t1cSHPqSuMLIOzcLi1Rzwfkx6Dbsx5BIt684uLPecW0akjW+53bCPuAV3qsOkY5qHTgUErpZvM0u70SD/X5uH5Hr04Ju3G9PsDZunTTpeIaZOQ0Okd4+cWoJHEnLg35bYP3Uzy6YS4lcLwPpJHD9I6OM/h2xI+/ZnfodGLuftD8/SI+dV73k9ybxtu6bG41GVlJYHZjOtvbrN8b0rjfQN6idtJG9fH9Hon4P6Uzq/1mL21S3pTi9bhNu0vvYmk3SLpJnAzLgKnl7LS7DF8e0yr3s+9uCZS3Zh0qcf8e4B2SvfZlGQQE8cxSdvpoJZ6dZUwhXgW6MH97a7bqXfFxK/tES+4StjsYYi77jmTwziCVUfotNuLzNdasPRyCNbkwG2uS2TwvNOdSO2FNr9wOVrAH/J6Dk7sapAHY4qNS+DEvoBqghPquThRTHACwaiylFrSaExB5XBiY33ERrYBwuHEuLR4QQNjNONcQBVgCp/T587HiTzP+PpXvoYULvewonSDO0o6A2opqUrnnD4xBVXCDflInPVCJS1FWda2Da52I+sQ6IkJqZhopwxu0lrVGYcYMLWmqu6QOLXGJOWkcnYSajIlyWY1y1SOtKnarFrIutpUObF7NSqgcBoz6UkQjtwJKZDIWhJWUQ5Lp7U3uIDrzRaodLF0tsJYg5ngBHWOotjCCVnH5jwzOutwYnqK/Xt3YaTFKoimAjwlCf2gNh8vyEemjrlbc+HdxkBVIbgMnIgvAycqudlxKYvLw4kXBckiVIgowA8D7Lgg26gQXgihwKtyQqkxZhUhxgjlc3YtJ5OKsdCMNkaMSwFKIyIocsDbQW4V5AY7yhFCoKkQukTLiogm1VgShhHj8RAtJKUtQWvCUIKo2MhPI3STqgj57nfPoa3HM6tjdGM3XhigpQTkpr/UZMRVKY/SVJSmwgt8hBCEOtyM0TG4atfEcUYItdk+BOoswfOJ0MWmBS82ZXi+manc3KbWft2alCjlIQMFnsD3NcrTaK0J/QAt3XuZVN8ma5Mw1p5fk8qVqa/KNluC2wgUsEm4pHB6tUmPP/QDGkEIvmY0GhFFUwzX1tnZaLJ+Zo1s1d0m0HhC4imfsigohwU7m1M0wwCke96N4ZDB2WeRwC4VElrNOGzyVPg039HfYeiHlOU6zXVDevJpvIMNBs88y7k1y3S0G+UH5JRoH0K/gdIRwr44PhbPWYs4cK39i84PutvCzU7sSNTkMWm62fkhjmPie9t0uzHdT22J3tttYD4lnnUtsV6r3mASkx5N3XN12yzGbdIu5xO4GOZnU5IYWmnsXlYvoZVA92hK+3MxvR50+zAbQ//9kC71oQ3JO9ostyFtpXRJaXf7tD+1TLcHJD3iw/CexTbz3Xk+uXEb13eh101cxmJvnqVvN7jvaMytg5TBQz3u+/gHWbgDrrzuHtJHl/n0p+8mfjRhuduGpMWHPzPPykMp8YJjjd23HmWRmPe9L6G11OVDtx0kAeKfa9M6lBAvuPfZXUqYT94DvQGkCX/w9Vl6H2lz2/VtTt4Mf/DzsSsIHmrXpcAUZiG9E5IetP7HGFotZ7vQBVox80+7/eL2M8QL0KPO3wHmv5pw3cdSYJ52DIc+htNS1fs+naXOM4T22P38nFzFbat7yfvObwWmNTnmLjb7j5dF3H7Y62I4UYUQCTx7EZxYzxnWOJFlI8ZVjRNNyMdgdY0ThYGyAASqqnFCVDRFk6oQNBoRRT5ECUlJCZ4m9CXIio3RGbSeohyHfPfpLZxQjd34jRon7DacMC5Cx5O+c0uvKvzQByMIZODE40pQCovQ0kXqWIusnGdWPTqIsRWe9JwpKXWgsxAYK2qdsEDXXu/WGjerJV31SUpZp5E43FG1U6n2nZ7JlOApD91wNghhoNGexvN9wiBE6zoqDomUalPgVTnBljPdVpKqMK6KWFspmLolKExtdarqi3dr6qlJScPbhhNBQBSdjxOjMxvEnpOADFdHTAXn40RRFpQbBdM7ppjaGWCVpaoujRPfCS4PJ6QXkJsS3YTQa6BUhLxMnHhRoImyPuSa0epZgsBD2AJFgS8lU7t2oXWEUj4WhS1KppRPqEJ0Jdk5HbmpNGXwpUYEHrLMEVVF5Rm8MCDAMWZRQuiHFCrDm9LkpiCMGhBUWEYU2dm6AiPxvQbW5JzbOIP2LEZZlLb4getFS08S+BJbGRpBiK89xsMRorJEfognFPloDEBROWMVYak/ENsrUE7f5PrZ8ryJRGCLxFxGC3HSclRK0Wg0CIIA3/cBgzEuCFopQSB9mv4UU409eI2QMAzBWJSn3bSfkOcRuE1itc1s9WJC/O3vS8qtbVQICmORnr/5fnzfJ5IevlSsnjmLDZp898wq1hdUuiQbZ2hfMjYj8jyjEgUbZsjaxlmMMUThDoRnCQNBOTxHHDY5la/xzLkznPv2XzBrryI8pXj6218htWs8du4Jbpz7e5z+dsqn/ujziKpkbXTO6QuET5V7rA3XeMX+KeZe9bIf0Jn9/a0OMROudB7KLePIVo2N8aKLcIl7Ke1FmD8CvbduPTweQHvgKkrpcsqS6BDTZ/4u6PegV29oNnXjaZ2u6xTRi2kf6ZPOJo6kJV0W066rVrW3ON6g3+POFHoL0FtOufUYsNyjuwx3PzpL8gsQd2MOAUkSM3sopjcbk3ZTZv9zj1b9Phe6MPsotL/yEViAhXZKkia07khZnm+z/AfLLCfztJ+4j7Tb4z3thDg9wcGVEzzamqXfSOl0NujcCx9/983cmhwi7V0JrZhDtEl7swzo8Cd/+CccSu/m9S/t044HPBy36Lw1hbsT0vgw7Vtj4gTiQUy/G3PvdS3mF2PS5S7ztGgvtPm5N/XpH4U4bRGnCW/a2ycBlq/qkrTbLFx3mObwBGmc0q31T71el7TbJ+knMA89iwvapiZHbUeEjtSh0B0v5o53uPJfmsKxG2st3WJCfEebuANHPtel1Yu5aTkm7tT3pzirjwvOnfZlMqU4oQ6xTlm41znBx8/H3v4/Wsr6mFwznuAE23Bi5y60tw0nTEkTn1AGeFayqxEReVs4IQMPXeaIwgULSzxCKhAVFBD4AYXK8Kc8clPgNxqIsMLaEeW5M5SmwlqJ74eUZsza8Aza34YTPgitkIEk0BJVGho6xJMeZTVGWEvTC/GFoq2vTNgAACAASURBVBiPERJyU0FlkZXDCWEtorKgLXiANC4bQ0qkkZSu+egCoI2b0nMtRSdyd/N5ta1BBVW1VU0y1qCVJmxE+L6HVtphkynwtPPj8oxHs7GDqaiFP+VwQhiL1B6+r/C08+wSgFTO67AqJxpc6ulAgcm3S04c2TKmbptOdGEWKinItUX6fm3zY/ADn1C6rMjVs2cxUZNvr61hNVS6ZL3I0IFkzIi8HGJswdnRBmvrZ6E0NP0dSN8S+oJy7bk48UouDycc+fWpcs1Gsca1+6aYe+VLL+u8fVGQLE9ptC7Z04rJjUWrkDyrKCtYz3OqUFN6EhNIrC8pKACJpxsMi9wBuHURBr6O8IIQYQVFJclKSSV9iqqBJWKUKaSweEhUZaEY4UtFQ3ns9EJCLdgZNfCFwuTGtQG1YLSeQ15ClePZHDAo7W9O3k2IA1JQVOVmTqEQgrIuiW4nSxe2CLf/sdZN8V1OxM6FVa7tE3vbyc7k9jzPqaqKcZ5TGeck7Nzw5Ga1Smv9nKrUptXCttbj8/l4SUlt41Dr0CxgJtOJ7m+kG/91MRKur24rQzEcMxoOAUmgA6ampmoLDOP67bkjrWEQ4GuPa17xcs7lBWfX12ntuZKX/chPMdYR8RUz7N37KvaYabxzBf/3X38J2Rzz139zipJVqqpy06CFZSrSXHPlfq57zcu55iUXCQF+AdY86SW7g9vvSBfhkIV+GzpXduEXtj2uh2NDCSQLs/DtmMM2hk5cR6r0SGYT+stp3fJLmZ3gaZIyeG1MvDRwdgXtbdvtwuCQe2ASJ8yn0HsDEMMdfeAQzC5A71EYzMKhTzq90NKRlDROWTgE8wspD73pBO20z9HllGMJdBbh55eO0ksTlmcdAezVhK7/ZJf+kaP0G7cRx22Ij0G8weHPfIZ0vku3FwND7hue4OTBlDvefozlpUXnbt/ucHhxgdfddIyFuw7zrquWaFzdJm4dZul17yL+yCzzs22We2+lc2WftNemnbje6cM58FSL3kJKhx795WXiwzGz7S6d5QFpr0fy2QHdLnzyyRh6ywwWl+jFtxEf6hEnMYMeLCQD4jjmwawHvZh0MSWOazuM2LX62sBrj0G6BHHSIU6WalsGnFVGB9KbenRvXYIE7vpom16aQnvb8Yk53+rj78KPYqAfs7zoqo/p7Atfy/KUxtcluy+FE8E2nFCS0i+wQqJswJBtOJFbfC/CC0OEkBRSMtaKioC8bIBoMB5qhDV4VqIKg5jghPbYGTRoCMF0M8SX23BCCUbZGPISYQoCk2NLg/J80B6T+r+nfFetoqSiQkkfIYVr40nrMhit+65DOquFoo49s9JVrFwMjouXEQascDqySatNaeVc2zdbJjhCZly7UCIR0uL7HkVu0L5HVbg8XVAMswyDk2oU1Qg7rn2xvBpPhEIrTW1pX1efRK17dpF0QuOmDgMB1pEv6tcopXu9Wro2ZFmVSCsQFbUp6CQ+R9cG2dZ1YbD4yllJFBtjRuMhGIlPQLPRoMhzPN9NsI+LEqhxwvO4JvkB4ETD42X79/O6176ca67ed1nn7YuCZBXjDDEqyQsPHTax2hA2fUIpaYopzAbI0kOUIdUGGC/EKk2BYTwKqDZKfB0gVIBXWigNO3YJ9u0OmVIGUY3Bz5H+iEqNKdcFptCooEnlBYxzR5hGomQoDdYWRDrC01POPE5rbCDxvRDfD9njC3ZGIUIoBIaqKigrF6o8IVkTnZPneZS23CQ8StWN8M1xXm8zVmeiY5pUpOB8W4fthOz5lhCCUb6BsKZ+DoXv15E1WuM1QmQUIKTE8zyQkmYjcuHVyonZJ2RrMjU5WVqqzXbmxLV9Ipx3E4jG3VM5QznBlhZMSomhorAVWTYiaPiIOnTUCEO0YwpjnF2DEIIzZ85QVorKwvT0NJiqDuHOqWwOlUJGHnmeIxolU82QUJSM8g1G44Knv3MSbc4RToVYb8xL5Sv4vX/3Z7R27UHbJlp67Ni5Bz+QvPIV+7nxza+jsatF/6+/9UM5z39Yqw10r4PZPswebsM8bNokpVvNoF6vT9rp0xEx8awDTz6WcDiFXtp2oqt2TNpJSea7tJOUdB46dIjbs7QGrqKySQqWYxZrBE8HcPgTQOLaV+l8TP+tkPZT0j4MBn1uum6B9o2QLPU4thzTurJN4w8b0J2Fz32MPl2SdJ5/1Z7nk7fGJMzCYJ6kDZ00JeZuPsEAFmLSbos7l+4gZYHBoy8Bv8/hQwu870OQHHRtUa7vwiHodJd417vaHMPH66XEC/O0v9Km3erR7aZ8ji5HfjGh89YuraPQbQ9oL3fovKvH8qeO0H1DF3oDV3FqpczGPQ794x6HZhMGC33idgrdhEEv5V1XpcTtFkcX4fqoC702bWIOHUrozif0fxFOppCmKXECreug1+8x6KW0P+IOweBTy8TthLSO/4nb0Psc0DsM84scnu/SOuSmGBcOQXseFjeJVNdptr6fdYTNlmSc4vRmL3wh62+FE2YDKkKQmlLBOA8Yb8MJvYkT0JoOiAqDMGNEkCPVGKvGFOsSk2tUOEWlfMZFhdYeI1WyIUssJZEO0arGCU9jPUXoBZs4sWtHiJAKKQ2lLSjGljyvQAiKyg1GGVPiSe06HgIkEiWcwNyaCoEi8H3noSVkbZPAZltQ4ewUhFAYq5yBqHWCcCUlcjvKi9oCwhqqSpLlGcJUSCUJIk3gKzzPR6CQXgN8H4FEBx7CQsNroJXCWEFRliitUFqhlYTCGU4jLZ5y78FaSz62SE9iPIGqL+ad2arBWIHaZhsxcc2vbElhSobjEV7DB+lc4I0wRFNTTuMmNALFmdVVrHUB3tO7ppFUhIGm3MQJgZz6PnGiUePEj+znxvbfDideFCSLRkgeeQhdogwI60I0C19S+hYRGoJoSNjM0V6F9gSykihriCKN9SuKckhZZYzKNdY2MjbWJeeeHWEq5Vi+ETWxsbBHk6ucfGOVsHS5S8NxTiVC7BiKoiIvx0hRYssCISt8aRG2QKqKPIBSGQwFqh53dXXTLRd1r85XAtdTN3Ua+4WapbIsnxMOPfl5kim4/XbYqhZdSLYuFMFPKlyT5wDn3l5VFi3AC90HQaEo82pT/D6pWE22o7XcbGtKKTct7ibtTyVc29S1O+s2onCCR1lPEk62p2VQT2HW1TXj9s0oHzMuMqxwwwTZ+hrWlIyH64zHQ6y1hOEU4Hy7xuMxvrVsDMfM7DtImo9QSlEaA5VB2og9u6+mwmOYFewI9vJ/fuZBVk6eQgjBjukILxDI8Tlu/vuv500/Pkfc2sWVV+3ljTf8wx/4Kf5DW3XWXDwPrXjgqlFduGsCtItw6LATWi9zmI/cGhM/mZImTujOO2CpDbwHmHcMKv4I3Hq0luMsLBI/3SPtpgzumCe+QPdzL85Yk77LKiSF/nxMnKbEn4TuO1NHePoxt163RJwmdOOE9uGU5cUlkne0GcRduq+9kY8cS4gPdxgkPTpPtWjTgn6f2SsX6L2zQ+/GAZ1fTzh2rEN74UrkaYjbXZaO/vH/S967R8d1nVeev3PfdasKAMFbFEVSgB62jLIUyW34laTLlh8dyun0ZMBObE0sOWuSIeIe08uS1upkBp1ehlbHTE+7R7LHctqBMt2JFWUsjUM4j7GbiqXIXZ70WnHIZcePKj8lliRKYl2CBFC4Vfdxzpk/zgUBUaJM+RGp15w/iEJVoap4b1Xt73x7f3uz/BAsHbmF9q8ep3MftA7FJL0hUT8iIuLz90Y078649+Q8i3ug/ZrDtGnR+lTEpz++wHUsEbViopkZvrxwgM7f9Xn3yQMM0o8TRx3i5QbNZkzrYATNJv0WvPPwEQ4szDB3aAb6mGKKZWCGu5njDz4LzcMxRDFHOhGd5RbR++HhvzDC+XYbOndD4+A8rQMH6SxBa/kwdz05T2zCHmEGOALNd8HyA4ehs8htjTZR3ziLLi1B5x5olWam0KJd+xErom1dsNgMGL481sXiRC3DdqX5fKsSJyoOdqXECRJStc76IGFt4LKxnqIcG21ZiLz0hLI0RA6Zm5ElqwQ6xbMckjSjsEN0qskzSWbn2FaBzrdwQusCy5JkHhRCoXSOpUFIgXDMBtQE3NtY2jKbWwSWpSmyAtdzjajdsrCVQIvCBDNbFpQaVyFMB0sJTSHMtKHjGH2UZQuUstHK2Cpg2EMzPa+3cMK2DR1puQJHCLJhhrAchA3VegVVSFxb41VsfNvFcm2EpRCWoSnNJGHJFwqB7ZoNurC1iaLD2EzYWmFh4Vk2slBY2ojaNaWGTGgEFkUmDY0oNY5TgVL87qCxHWPYOipS0iJB6wJsGJxZRcuMZLhOlm6gtcbzamiFkfCMUjwtGKyNfjScUNtwYteLw4mXhRnpg8t/tGhas9qMngK2FnjCgXyEozXpxgjXq6IKjVcJGa0NDSBLTRAE9E+dZaxeA+HheQ5CjHDx8VyPQuaoXOHYCsexqEhNxXNILUlua1Se4Fd9itGAfFTgOBaFyvA8m3SUYGOTYrRaGzl4rodOFbawSTKN1NJ0hcooAONgXgrcwUTKbNcoSXmukDKFi5ngEKUW6vxO1naN1vbrt6/tRZfWxjXXczyyPMNxNj28XLTWuL5v2rdKgTZ/V+Q5wil/6i0t1uaUh+PZjFcr+J6LY5dCfSHY/ARrvfUahRDlc5Uh1LYJPvVcB6RCWCaaKMtzAtsjzQuqYZ1kOMD3fbJUUq1VyfMU3zNBpLZtdljpKDW8PwKlJbWxKpft3YGe2slejL9Wlm5gO4K1wSpZXuBYAbYb8IX/+jVIFZftGucfve5a3v7GN7Dr0p3s3HcJe/ZcyeWXv4odu3Zz+Z7LuO61b3xpzUgfecQ0icIfcMfe1sUYI6yOMD6hd9wBN0QQH02gC/tnj3H0czAbwvSvQ+NXIU5Mvt3Nwy7towmiFUI34eqpaY4eDwnbjzD9hv0QJvS+HhF2k2fpwxJMJh9N6JWxMCcaCY0TwHGYem9IfDQhaUL8QEL1fQlTs026x2PC9zRpNhN67QaN1jTLCyGf/T8S9keztI/1aL/vQao3u9x+cIpb3zvLgVuP86U/jNm7p8HfP1xj4/EvMup1+eDMNOLB47jXvZ8wScijhOprfopXPPA9vrWaM/6KDsN/doipv/0P8IZprrr+fbz+t2cYHj1GHFcJe3DjZ+dZPjrLzfc3SSaPcd+ru/zsOxKSLrTjKr2laXrNhN57Y6KbQ443GjQ6DaaThKVP9Wg2TX5jckODE8vwxc98iZlWxNFjCczOIhYTWteGJJ/r8atfipgKE/79HX/AJW94LdVuj+hEzHScsDzdIvznMeGtIZ0jUK0CR2Hhrha9K3rEXZidbZFMt86d+2QWej1gypyQcC7cMhT9UdYS5/IL4aWP1Xlw+Y8WhXqxODF6Fk6c+P5Zdu6oAR6e42BZQxzpG/uZLEdJgxO2bVGVisCxGdmS3NLoYogX+shknSKVOI5FnmX4rk2anocThdnQqmwbTiiJEBauJ5CF8ZLSmTKDSBbITJaG1WbMTiplbByKLX8sXRhjayUNNcemvgnOabLKSqo0GN22ETf1Y3nZ5PIqleP7PoNkiOc7KCmxHdekk9QqqNKaAUAXGHywzSS5+a8KhBImU9ESOK7NeFjBcxyDE8ICxxh2G7qwnJa0BNjlMZIS4YDrWGRphu+7ZiJQGGpzVBRUtEsmC8JKneFwgO/6FIWiOl4lS1MC3zedtW04URQKLUqcmKj9eHDikiu5/IoXhxMvi06W8Cx820K6FtLKEBZ4rmX4ZtsilZI3vfFnCcOQajVguLGCFyikLigQ1KuCK6+4FMtJCYIC285NwKeTItUI2zKCd6WM39LZwYDV9Q1EClV8AneCYiQRuaI2HiKLjNAPqIVVqmGdMKzx9jddx5W7d7B7rEauQdsOWZlFhbCxBXiWfU6jda4TZFnP6jxtUoObRZazOSq7TVu1SRtu0nKbNOP5HawXog03tU+b9990ZDehzyk2NpbwUMaMwkQElfd1XfdZHTQhzIfH9138wOjQLNv4sTi2jW1Z+J73rKlIE51QTqxse/1QlJOW1rnXFQQBw1GOZbsMNoY0dl1KmhU4rk+aFWaqZDRiY2P13HFyXZdA2gRJhusKVtfW0WnBKMtw/QnildPUwho7a3vIsgy/FhD6Dq9/7U+xO6ozvW8XY/v28I9e9yauvLyJ9izW0yFB4DG2c/wn9VZ/cetCMpgXSP3Z3ml6/6Y2a3OCjYioZYTMnXeVk22tJvP3N+n0+xyYK7tS0Qy3dE38y1bmDkTnjZk9y/hy22oumeeYu9t4Ni3HsLy8ROvj0F5ucc8dbZY+Ac1mk/4SNBdjZuKY5vISUxs9+v17oA3NhZ8DWjSJ+YNOm+ieNv/xg30ue0+b5lyb5S/0edV/iYgfvI8/Ob4f+m1aH3wtd9/957T2HCKZew/9qTnmh02Wrj/E9YdgaTHmumiG+fgAc60mB+cjaM3RP9Il6t7D8kNvp9GBBk063YjXvaND6+4Imn0zGPBljP5pBqL7od+CA60WZmYgph9Dg4h+dIQjn+iYKc9mh9Zin85XO7RaLTq3dOk2I97//v8JSscG7olZWu7QjGOaj7VozmyZwDYz8N9xO23TFDPFT8eciHiZLT64a87z4fKkvKj98/NARfzUtl9eekmWwQnnAjghXgAn2MKJa64tccIvsJ0SJ9wUKUfYDgRugNKKStXgxNpGgpVClQDHmaBIJaLYwomqH1CrbuHE2950HVc8D06UgTY4DrhlJmJR5OBopAQlDfVnAp8Fhc5N1mtZMDllLI/lWKX8ydB1KIXv2iZ6zBYIcW7eEG3sR7Fs0+WyRLkxFhaWFqZT5TvIbMv3MPB9HMfCDwLydISjBRCgtTTZiwIKaV6H67k4lo3lGSsHYQncwMYPXIKKbzpytonRdmwb2y6nFt0S1zYLSUvg2GYi0fNchBYIClShjLE34FV9gkrAKC1xIh8ysWMXw7TAdX3SUUFYMTgxWN+GE47BCbfEiY2NF4ETE8/FCeW+eJx4WXSy/ubzRxbdoMJI5VhFSK5NLpHycoQE33E5deoZ0lSRZRrPctm1axJVbCCsDEuPkYsCNwiQucSza2hdUBEutmVMLTeyEcKtUHHqSOlTHfNxLR/LhvXkLJWKz9VXTjEcrlFkNkKFrA7PkGcS4Wi+990nuHTfZTzaO0mAT5bl1Ko1skySZwpNgdYuWZEaR1shzMREnuO6ruGMpREv2qUZqeM4pRCx7GKVYdKmFSvO2Sk4rmPE8+Uu5EKC8+2XtZLnOG4jNtyiID3LQcoCgdkh5XlhHr+MRJBKgmMiG2qBjUazsxIwOVYDVWYUSjOxaNmGF7csu+xsCWzbwrFdBKVA0jWFpNbmtXiBgy4ktuXjeL7JQFRDM8qr4fTK07gOZNmoLFgVqpAoaYorIVyQQ6Rn9GFOXTJKn2RXsJdgR0DvyccZGx9jOBoysXOMjUzzta9/m13RGJdO7eaVzVdTn4w4s3Ka8YmIM6srJnTUs4iiXXzj21/nH//sz730nawLreeJyum0oXEzJEe3rgtDTEejYcKa20cTDixC7zNANaHTMHqb3ud60GicK5ii6YRGAmGckEQYZJ8uuyPJVnst5LxG22GMQKwJx/59h+mFJr3jCbM3Q3NmluSekBsXqsxYIdP/OiShRxjD0Q+eoNqa5o9/a5arftFl+qMh/P6baR5PiI93+X1uZKPVQSz+HpPBx2jtGee9v9Ng4XerMAtx4TL3pw3evO8A93025jO1SWZ217lx5Rn+5rMd/vqukHtvnWffa9p88mCDqNVE9Dqc6DU53plm9tBPc/vBz3DznSHzhx9hKWkxdXODbj+m94WYSDaYnZ+mGvf4TXGCT713mve0Yrp3Jhw7fozmrSFxD459LGT21pD9/+4Y4S/eTPOSaWZvhOXfiJmdjjn+2/Dr9/W4pJFQbUe0Fqbp/BY0rg05+pcJ0VsE0zf26X7UHOaoA0kD4q/Dx363RfLrCQvvg2O3QnLctKr2A/d1D3Pz8RbHb44Jj4bcKaAbwiOPvIg33A3PvSosr7sDuCSBX36JO1kviBPquTjh4nLJ7klUvg0nrBInConr1JCyoOq4WNrgxCAbIbwKgV1HquBZODEYbsOJ0SpF5pQ4sUKeKYSj+f53n2DPdpwY5tRqNbJckiYSbIlSLoVKce2yoBAaVRR4wRZOaGli2IocXM9G55seUib5wkKghXlOrU0XSVgOWmz6E4LtGe8qpTRq03Saki4sjZ6zUYFli/I72jAUShk609Y2mgJbyrK4MjihMyPQVxiccNGEroMQiknPZ0e9ikCilUbmGiiwPRshzBQjloWSRoZi46KUKDfshqpUhcESx3dASRzbx3ZKnJAlTkg4s/oMnoJCjUwBqAxOIA29aOOhVIJybVzLxhmTJKMXgRPXPBcnFAYnJqJdfPPbX6d1ETjxsiiyPv/gny7ato1KUjJRUPFt0AWFykEWuOW4bn28znoyQAjJ6kaCa1eYqEecOXMGHLAcUHlOnls8/fRJKjsq2L5LpVYnz0bU/JDVs7Ghy0QGAsYmQoTyENpmOFxhfbhBWBdcdpnH5EQD3x1jI1ljfGKMftynWgnxQ4uNdI00HzJIJFIKhKWg2BxPVedoPtu2y6BkWbZuVfnTUIWb2YVmCnHLI2uzyBLWloUC5xmOwrONRjd/FyVnv3W7MkWVMDSd0ls0ZCmfNPQhqjShU9j4eJbDmGsxXquwa2cdx5LoQuF7FZI0w0aUSfL2OYNQI5Z3zk1cbtKNUppJFq20MeTDxvODMponoxKExghPCFzHNnEMWpsQUWUiGSytSfMU2zJBqhXbY7JeY3p6J/HaU1x1yTWc3lhh775peo8/yeT4Lk73z6Bth8EoY9/UXizfxcJlfbCG63pkqSIvFDvHJylkwamnV4iffJR/8s5fenkWWXOYjsV5qzHNOfpoM7Ju+wpjmG3Bl/5XCJtACI0Qwyuet5Ky2ko2/0lC07JK4AX5yzIOhgSmr20Qn+gRxlPE9yWEVWjuj2j3etBISOIeUyKk24jptRNuvLFB9S0tZqdjun/VYP+tMb/x0YS/aMzxK9Mf41/9Zof7+DZLrYTeT1dpHV+me+A9LP70kDf8k5jPfbVL539u86XxhF94W0j8uUe59p+12HXFPbwtfD9/9Ee3ced/nOfqq95B+lcrTO2fZn7xAPFnupz4vqRZ7ZLcDkdvbJMc6xCfiJgaJkxPCfi9Exz+K/hP+2Pu700b6rM3xdH3xMz2WnTfnnBiJmL/XSG//dEe08E0s82Q+45B+KWE6dYU3WMQHYB/0Yzg3ogwiekej5m+ISSagrie0CBkak5AHBKbQUlzuBOImwlHPwm9v4He78WE14YQQi+Omf3T/dzUgL9th0Sz8OB8RHjsx8EXmnUDxp7rpaYLL4gTOofiuThhWZLVwQvgRGbxzDMnCeoVnIpLpV6nuGicSC4OJ/ItnFBaIIREFBqpTFGgMZcdxzZ5e0qV38/GDt3k+plJQ8e1jDu7lFD6XgHI3Awg2cIweLbQ5Xe9NqakYHhCqc9RjCaMWuF4DnlmCiKjtTI4o5Q0dGOhjYfkJvYoBcJQoFJrPMvDtlzGPIMTUaOOa2mUVnjCTP9j6h4s2wwxaalwS08uLUrvLKd0fZcaPzCylk2c8Cs+UkqyPCMMQjMcJYyVUjoyOJGnGUpL083TikzluJaFkpKK47FjvMb01I8PJ+KnVzh9kTjxsqALA+WZ0X3fIc0UV13eJBkIXHsHGR5Pnj7LqhCcOnUaD4vc1dR2VBlruFTqa+ze41MJfMaqEyhpo8SAaOdegnAc36tQpCm6DtouGNtTI81XWV09w0aSkeWKjeEK9bpNbayBpybw7IinHj/D06dW6PVP0V87y/raEMutkkrFMMkJgpDRqKDmugSOmRC0Aw+v1D2Z4E8jfD9Hl7FFp7muuxWLUPpjbacEt1N9eZY9y84Bnj+3cPOxzW2bE4lFWeQpsixDKUUujQWFySQUCIz2wLIsbCnZuyPi1a8Y55pXTPK610xz/eV1rrt6B1HkcN110zQurVGpV7E9F6QR7+eyoFqtlNYTpnPl+yaPMMvKKcsiww9cBqMUUfFI0xQpJUEQsLExxLFLKtIyni6OHRIEAYWSDGXOSCiwIVdDM80TBGhPMxA5nuMyTAuC6hhydZ19kw1coaiGFsN0yNjkTjYyRbGRM1hPySSsDXNsW7P30nEKtc4w1xR6wN69l/1k3ug/jnVBT4dydc6j8DY5vYjSsoHncnwxLDehPc8586tOjDEE3eZTuXwBY/Dtz33uZwTLHwAOxkSfgs4MdLrGssCEG7ZoHYGDRyIW5prMRBGtAzHLS006/z20Dxtfp7f3l3lg6Qi3LcLM9fM89KoWUSOCgy3u3HMPhz7S59cWezSuP8jjr+1BP6LfjiBq8pdfXaa90WDPnj3QbNJ56B7e9fEPsfD5z3P4A20OZ30OH2nR7TbpzMW0W3Bnf467WWBuoU2rBXc8EIFusnAgZjGe4Ugblu+IiRegFZdjd3dDc65F97aYhWZE+za4/fUxzS7G8Z6Yux9qEgP9uQbc3iXuNolKN/xGM+b++82wQLwc0Vw07u7bvdDiZYg+3YSF8qoZTIfxQETsweImJRxD9M6tE9yGZ1tv/FArPt/o/yVZF8QJ6wfgRHQeToQTqMJGWQYnKtVxfPc8nLi0xjBf5cz6Fk4MkhWqNZtafQsnnrxYnHBcAtclGxbYoYfvu2X6h8T3XEMFYpvpbe2Y3D7L5LDmuekkCW3h2GUsmdYoLSAXuJbpZOVFgesYOk8qDcJCbdIfChACKVWZW2g2xlqC4zrGpyvLKaRkNCqMVY5OUcI4prvCxrYkFdcmz2xsRzG1cyczr5jg1VdO8tprp7j+yjqvuXrS4MSrp9m1t04QNrHjyAAAIABJREFUVvF8o01LRwVFUVANK9iOgx8au1S/4iMUJtzbdo3OreIyUilW4JKOMqSWVPyAjeEQxypxQoCyBI4X4gcBhZYMZcbI0uDASA3RtsEJrC2c2EgLgtqPihPrXHaROPGy6GT99V/fv2jbPirTeEGFRztfYXy8xmCwRp5k3PTL7+bbXztOvV4jXxuwd99uzjyzCjIDawd5prGVZuWZmOo+H+Wss3diJ48/1cNxXdKhJlM5cphTJIaX3VEbA5EwOrvCxMQYeSqxnICRtjn51FMElQn27tnFL/7CfhwtWVlZxRHGvyNDonSAzmGQ5ihL4bg+WTY0YZclzacLjN+JvSn2M50p29LApmmnodfMlIY+Zzmy3REXyuIJsyuxra3sw3O3ndfl0kikNN4mSilcXNMuLoyJXhh6eD789M+8hl2Bz2uv3cWrphpcNXUp11+zF1Gs8aqrpxGOTb3msyFtwqBGvVZhlPo80z+FRUBWDBGW4b4tIbCFTeCHSJXjuSY9XUuFbYNvV7At83lHmakb360BNlrn5Hl6btfgBx5KSaTMcWwXVZgJFUsIQ2fiElQ8amM+r57Zy9PxU3zjK12u3LWLPEiYqOyin5zBrYwzwqLIhxSjIYXyyFRB4FVp7Jzkicce59LLphkN17j8ssuwvZCnzzzFW9/yCy/PTtYPWtssvlox9CJMNyQyIvfwORwfMAUP/Bq8vg6Nq81VzS40E0i2FVbNHzBldq5+iyFqwPQNpgMWDaHag9Z+6ImQOIRW3IK4SRLGvOP3eqw8YXIUw48m9B7ez+zvNrjxo1Xi1zX4+fmEuBlzW9Lgb2fh9lsSGh+OOPYrdxC/6Ur2t36KWy6t8qE7/kfyPR2Sfo/uTTFv3d3h+KdcHnnkEcIvNfmT/z3huhn47P/Q5he+cCsLvTZxt82x/7fD/lZEHCbcFCc89JcxnV+qMjwR89tXniD+foOYkOYdx2j9boPwmpBWe5qkF5MA4TFoHohJphM6+2H/gXlmwxNEScz078NH/zt4dLlN1Hwzvfs6JK2Qftym223Q+B34aAyPfrJH0owIge6vdAhvbEC1Q5Pfg9YNJEehQWn78JbyBHZLCvcZ4APAtVtDCJsraULj2It6Bz3PCmnfATcsvrSdrB8aJ9QWTjhSs3JqCyf2TOzkiefDiaFgNV8nqo6BVeLE+BhFKrHcLZyoXCxOjAxOuL5Plg6hNKUWtsEJrRXKxtCHtqaQYFuc60AJYeF4FhQSbWls2/heORiPw6IAbBDSiL0RAqRtFBybhZaD6U6pMrtQi3OMg1ICLPC1YwTyRYHAIqx6BB686Q2voVELeO21Da65MuLKyy7l+mv2IYarNF99OcJzGKv6DEqcGKtWGGUez5w+hRDGsNWyFJ7tYrkCChvPqaB0YfwRhUAXEmEJAq9iXr4sE0V0jieqpjDUGXmxgesFZENFUOKE0oXBibzECS2QmQRR4sT4s3Hiqh8DTjx18ine+o4fjBMviyLrL478n4sbyYBq6KPyIWHgcrZ/Btt2qddcvvrVv6NSGcetujzz9DMkuUVYCzhzOqU+HiC1Zn19HT/06X/nKVTm0x8MGQt2IvOMZDTAD/eATAkrNhsrK4wKga09wn2TqCJHyhrXv/EqHnvsW7zq6r2cXX2Ck8+c4ZlTMSeeeJozZwuE55FZBXokCeuCYVqQZCALkIVCSU2aJQjhoDXGUE0rHN83xUHZuUKbHYUQFp7ns6mZUlJTBg6ci91B6dIdYtMZ97kZhc83aag17Noxxt4ddV71qgbTu6tcNVXh52+4lisu34ePJI/7XPnK3Uzt2Y0UilpYp1KvISoVGo3LOH3mLJ5TxxY1hsWAZDSkXp9E2YrvPvY0ySghk5JcGpFlLiVSm52S5zqlxtLGtmyKIjcUqG14eVnkCMuIVgM/wPM9tIZRmuJ4giColF9CGI7dKt1USumX0iBkxnjV4/FTPXQRsv/GtyG1ojK2m6eeOUlqV1ldzwk8jyzXeEENN3CY2DHO2dUVKr5DkUnGx8epBjUuv+JK/vODn+eVl03x+je+9b/NImvb6m0vphIIN3nE86cWE7jhEWj8THnbsgHrpPHcu77QOleDiZKFLBmrZH5bQy2BuAdHu114uMvx9/X47Oda9D7VpZtA0utxYKXH0V5C8ubjfLSdMNdq8ro/bhL2Yo7G+5m+8SitTpXl+27gL//wKNhVLsnafDNJqJ0+zof/1YfxH/oGvS83oNFgutWi+qYIN+zC4iJvW/wMjc8dp3FgnuRjx5j6nYR72iGtMGGHiHjfEzD95oRD1YQv9nocj6YJPxoT//w01fmI+FhCj5gkgua06RDGSQ9aJ5hvNjjePUav3YM/nCX+Qsz+98Fcs8E9nZjmfkPdikRwYH9IKCKarZCk7BomTYgONMoWVIM4uoFk2WjqkimgVxbL+zGTf6XLe/KG8kTNA9uKqsaPy37hhpeeLvxx4MTahXAiey5OjPrbcGLvJErmSFXjujdexYkfFifyEieKBDZxwjY4Ybs+loZcF8bAs4CijCsLPN9svi1BUWiTiauNhldh2AhLGt2rVuV0IVu44DgOWqot+NhmaLpr5w72RlVedXXE5ZfWuXJfwD9927VMT++jIgxOTL9yN9N7d6OFpFqpEdTrWF5AY/cUp9fO4tl1rO04UZtE2ZLvnniKYZqQ5gVZodDKIs8lyjL/L9txsC2wLDNAJVVuzKtdF9tykXkOloXjWARegOf7aG0xGqbYnsAvcQIbk1pi2wiFwYnSklLojPHwJ4ATV14cTrwsiqz/+vCfLbqujx3YiFTjVCqEtSp+vUbggSw0WZaRpBLXCpianubs2Sep10LW18+gVU5YGcOybE4np9m1q85Ov0oyShmub5ApG603uHTPBKNkiGOHSN+iiosKQnyhGKvX+do3v0U6dBmOck6dWsWzfGQxQlgVHEeR5AnCcQkcycozfWrVMWqBDzInySRabDnqIjS6pAxzpUsHX1naOwAYmwPLElueWMJMkNg2ILcVUZSjtxeI1jlfCL9ZkO2ohVzSqNPYOcGOcAeVMOLxx04xtqOKV6lxxVXTONpiNMrQjktaONhCcerpPlm2gmMN2XdpnSxbY8dEndFGiO8HnHrmJHGcU+iCIldIbVrZ5wJLSw8YVZhxYFXqCYQWuEGIlsbyIsszLGHyGtNhgkYjXBeEg7XpG6YVjuuaYFOtTThoUVBxAmrjFSbGPHZfNolYyxD+AC+s8MTTZ8iLDKwqll4lTSTZKOPs6gph4PHdR79DfWIcLSGsjVPIlGR9QKE1YWARDwRvfcvb/tsusu4AbigBeFOes/nzQlVTm2cBNG0In0dkf8FVVmSL/w4e8c3vrXk4/hbgj2PCOCRsQWfa6MIa10AyHdL5N13ipEU0HfORxh/zsdmj/PoD+7nhClhot3hzs8mxRo8Hq4eZunWW9j3TvOdAwq1xROe+WaNFm2kw0f0S03HIz/7z1/PoZxOSkyfoxT3ibgx/cpzZdzX52MxniH5mlribkEQnOLyU8IfViI+12hw69Ga++PM97vZ/i0eOX8t/mI64I/xPfOUDP8dD33oPX+8l9E4k0ItoTiXEXYiTJiQN5vYnLB9ucHg5ZjqZYubDEb07j7L/PsHyr4d0/0VMcy6kOQ0IiMKQahxzPIbwRMKJaXN6po8CJzAUX2KO53KvyfSNLWamuyTTcKLXoXFTA94D8Z9GcDQx3atlwyC+GDVWFJfdr4tYL3WR9SPjRJYTVi+AE4ML4IRnUdXbcKJa5+udF4ETTxuc2DnhU+QGJ7A1MtOovCiNQSXuJk4IhZSmG6NkmZ7h2Hg2ZNJoaj1tYwljm6VKI1HK719bgy6zAk0bSxs5uzC2EHpb4SXKsI8xv8LuXWM0JieYCCfw/YjHHjvFjsmQsFpn6pXT+FozHGZI1yORPpbMOHP6NKNsBUcM2be7TpavsWO8zigxONE/9RT9Z3JSmSNzjbKEMQZ1QUuBEhLbKm0jbKPXdVxRusRv4USeljght3BCOy5YLnbp3YiWOBUPlWmkrVGixAnrh8CJsyuElR8fTrwsNFkSQS308AgQYcBgkGDZEDga26mQFTl7L5tg18QElRDOnO5hU2E4TMmkz+n+KsPkLGm2wd7oKrSs89SgQMqcyliFy/btwrUqnF1ZxXV3YFUnqFZCrHGPJB6wugFnTq9hWx7a0lS9kKuvupxozy5Cv4bQ4FdDpnZeSs2qAB47d00Rr5/C8y2T/VcuIUQZL2BGUtNComUKhRmTNcagZrLDto2lQ56nhv5zLLQtKDRou+xmWSU95ljm9232DefrtM43KD2bJqzlIwoh8SYqSHKqjUnSTJFmA5IkpUCwsrFBnm7gyIJhkuI5Lo89dorBesD3T67h2Ds5+fQGg+IMX/y7r/L1755mNRmQpsa81MFBaNBlRqOWirwokMqiyHKwHdAO2CCzIVqAFopKUMUSHkJonMAjcNzSLV+SFwVKlRqy8vhIacxfNSbQO7IFO8dtnkrPYFUt9l36Cux1i4nqJLsuuQJLSE6fTUllhu9Z/MzPvgHbCdi39wpEbr4k15OzOK7GC6s8/sQpnjm1hspP/6Tf8j/RFcE5Q8mLjZx73gn9H6TButCDLGKsCeZM3dXvQ3RXBO0mcdsEJt//7nk67TJSpnWQg60O7aUOH59rcXhhAf3kv2XurhgWoH0H0Ippz0TEdOHhZeLjc7z+gWXmr2/RAJpVCCs/x9Rf/GcOLfdpVyL6zQbNHvTo0v1kk+U2HHwsIr6lTXRHxJHb27TuWubjM/dw01IbOMJNX11kzwcuZf7uGVqtNu86dD13pvdy/1LM7URErSbtI6Z6XDjYZPlwh1aZCd1YaLFwbwSdLtwCneuadIiI3wX0TZRRZ7lJHDUhjugQMTMf0ywDv5stYK486JtuGV34+wc6cGSZ5dJwtNlsQmYCpaPLyxO8Gbtz7mRsXbpj85w8z7rYPMMLPsA/4PqRceLMjwEnVl4kTlxicMLCdOxNvE0pB3EtHMspN5wSixydSRxhUUiF4xhpiOfapFojGGLG7S0kgkKAcoyZJ5isv8ICsRl1g5lGtIBCbZVXAmFeR9npWVMJq8MhhZK49QBlp4zvmmBjVJDn66wNCoZScDZNKPJ1fD0iKwosy+V7j51iMAj47hNrOE6Dp09tsJae4b8cK3Ei3Sgn4zVOYSYYlZSmUJJmsl1KiyLfxAkX4WpkMSzxT1EJz8MJ2yVNC2NWnRcomaGVJh+lZvBsVOKEtijEdpxYOYcTat1iov78OPGz//hiceLsRb1vXxZFVuBmJOtnyIYDPMdmfEeNQTJC5JKzK6usrY7ox2vEp88glcVgIBkMzlKru6zGTzOxI0JqiYPH+ESIVjkTIViBx2qa4NQrYAcMkTg1iSfWmaz7rJ09y0QUotKEHIGjQmoOrCerZNpmNV5DSk3o+Tg5jIYbCJHi+sbUtB5OMhwa01HX1viehWOZd26e52ZUVinjfOuYKUOLLb+sTT7cskx8DdrCsU2kgZYKlKbIcjP2Wq7tAvnzdVjbl+l+bX6SBIPVNfI8R6qCM6dX8UTAKEvI8xzHcShSm7WNNVbX1xCex55oD/VKgKccHj/5KN/89nfofOcJ+isbnFkbkkmFLPdFUmeoMk/SKlu7jmuhhQIHJCbiwXMDHMej4ntYChxl47oBCBe8CoMkQ6uMIl1HoFDKwrVNAXvOvb6MI6pVArwd44wGNrP1K7l8+jK+c/x7xIkgyRVn1xPGx3ZRCSbArSC8Cl/71qMMc80gSRiO1tFZws4dO6gH4whL4fmaWtXGti+WIHt5rh+GIXrev/kh41QWATrQKIXfNGKiCCI60DHi+5McodGOaLabEHdpd5q0DhzgULMBt7U5dPh/4fDhPs2FGD7dhQ4sNA7QakPr/RHtyjIRsLTvn9Jvw2tPNGmzTPT+BZKj93D8wX/NoXe3iN4zx2tbcxwE7l7sc8/e25k72eLQF2LiVpNO/yCtLvAwvH0PXPfOdzN/HUR77iE+0uQuvkx3Gdq0mG936V7foTUfc+T1t+G/pk/rToijPktL0IpjDl8e0W+3WY67RH2TaTQ3D1EXWIqJ5js0Ox3iZmwKzmiGpdtjWu+GzjJEzT5LsVG7xx1TeH3oQ9Cfj2kdgPgILPXNbXEL4gPbztVzTp45GR86d1Keu37QHMV5Z/UlXS8LnBACR754nBgMzbSca2l8xwjY0SVOKGPmbKGxbAdZbJqKlnmGWqHyAq0NNgghcDwXoW2EBCE1anMyXYPKTTahY9lljI5hBLRWuKXZ6WbxZdugRwrLMxmB6+vrZLlEyYKVp1dReUCWrKGKHEdZFEOHjWHC2dU18Hz2TV5KLQwIhE2v912++s3v0P3+E5w6s8Hps0PyQpLlhoHJdYrWmcE5x8K1LRzLQttmoEkiQYDvVrCESxh6iAIcbWM5vsEJt8JgmGHbGUW+hm2ZeCDLDoz/qtAmF1IZ/fOzceIqgxPf+B6riSBJnx8n/v6iccK/qPfty6LIcis1xnY0mLryClAuw/UBKhvyvcdOkmaSRqOBa9cI/CquB56v8IMacX+dvdFVjFUrhNUIx/WYHPPRMkW5YAvBWHWMb3/rcRzfww0niM/mnFwfkNo+wvJRuaJeCQmqLlm+TiFNF6p/6jRn1hNSRpzeeBo7tBnfMcZ4pYonXCwlqYcOFU9QDwMm61V8tuJxHMs22X7lJKGiDH1my819S7xunWt5oiVabRmWbkbcbLeF2B6bc6FlrCM0w1GGyg1VmVGQu1Cr11EowvEKjuNRWAVDK8OqeHiVEKSiNuaTyBHDVLKROWgRmmmLUW6Cni2BJZTxarEtRFkISpmfoz8LrVCAgwalyVRBXijSPMPxXLAFeT4yRZkFYRiYKAgMb+85LnkxLH3G9LOc8NGaSS+g5mtWRiMGKwmTe/fh7KgzGGygpaJ/6km0GpIOzoJMyVOFUEOK4TpOkbN79148zyctQCsLz3XZs3svFefCx/X/d+uFOh3LL1CBtYxfZvvd0GhFxG04dHKOpbap247c32CmGbP0icN0oj7dA22igx3E3jZRo0WfNl0W6C8f4aablmgeiDncafGOuE+rP0+ze5i5Tze5/2//lnurx2n1b+JTrYdp3dmk9Z5p/vzPPwns4e1vhwZtuu0Z7n93lzufvJPlt/d5qAN3tmCh1eK9nTt526V3sfDUAXiqy+KX78W7d47WoYjb7r+F1i0QRW2OfH6OI4di5hYj7sqafFrPMwMsLXWJmxHtdpvPP9Zi/s45mq2IxvwMhz/QofuONp1OhzviDvN7oB3HRC3TtGofjpmLZujeHdHoQ7cTszDXMiajmAYYQLMd0SmNS5sNWLjexCJFd247QXPmR3tx23nYfnn76lzg+gutl0G0zssGJ4oXwInAujBOVAMmx6p4toUGLGHhBQ55uXG0LRcttqLLpJZgwTCVlNk1xgtLFsgiB0wAtrKML6FWBVgax7YRWiJ0gW2yz4xRqG2Xui4AjVbCcIuOxcZahswUKIkUkpFjM75jAqE04XiAF4RITzHycrRr43shQkrCyQqDbMgoV4xwsfwaG+spo8RYK9gCPM+ERVuOjfBcpNQUiWEnUlmgtNmwO5Y2Nj2FmW4cjQxOaEugpLHucSwIq0H52m2yVOI7LkUxJAiMNMVytuEEBifC2jaciC6AE+svEifsi8OJl0WRVQwLVuIz9HqPsba6iusIPC+k0WgQVCsmqdxRKCejoGD3rjG0GuB7gtODM6xn66ytJeSy4O++/h2SFNK1IeOTY9SqNo0xi3zQw1lPUesZVavG048/xc4dk1jKwamEqPUhNV9geRqkxY7AY/eecWwdUrHrZGnB4/3TrCdDskKTZSN8XALPRxUZ61oii1IzpSXCKrCFhRAmONpk/4HtGmptM8fQshXmLWYKqu2O8Js+W5vFmed5eJ53Qaf37XmDSEkuM7I0RVCYCT+VU6zGCIw2jELiFTmXBOPY2nDfeZ4yGA44u7HOYJiwMhhy6pkV+qcz8qKkRYUqbescUKXhqQ2O6yNsB13SokJLiiyjKDIUGpkrktEIgTI0oiVxPZthMqAYbIAlqHiVshOYMkjWsR2PPM8RwjbPKGCs4uM4NqfOxuS5Zm1FUdjjZDjkGxvUKi6+bTEcDLGtgB07GqAq+E6KHGVUnYDxSy7hiadPkiQjJicbVCo+ush54P/+v5DFj89j6EdaLxWwXezzzkUXrMFaMXALtA+VddpcxPWHDrMwHxMtlzmLB2dovesgrfkGUb9Fdzni/V85SPQJaMy1WJjp8t7bunzhC3O0b5khWp7hbZ27WCZipv8Y3SNL0DmC92/+gj3zQPPjHF5a4sSDHfjIYSDjocVlorjFbBWONCOa72xx97338I4YlqNFll6/zEInosVtvOND76alPw+H38n1ty/TeegODrTbHJlrG9fWpQ7zzHD7You9XoclcQu3f6BDdO8MdGLaHbjnNhPgONeGmSbMf7zJwa/M0+rEfGgB89rjObq3QbsPxB0OxzELh0zhNdONzIDAHHRKd37a0G7HzL8bmIeH3j3H4cWYqAPt129z4V/GXL6fFy6iOhivtYtUWEUR5wq4l3IVw4KV/kuEE/IicSKXF8QJXWSs55JitClMl0iV4TkCoTV5Ic8VWY7vYGmBbTs4ro2NPicWN7Ct0NLCwkZLgbBtXKeC0BrHdfACI0dBmEg1Sq2wiXlTYKJ20UpRpDmWHCIpjA4sy1BnY2wydOAhLIGfDZl061iFohiNyOTI4MT6GsPhkP7aiCceP80zTw4Zpb6hLDdNuqQZKXctG2GDHwTYoYfhLI08RKYFeZoh0chMsj4c4TgKmecIYXBiNBqQb2yAEFScinGElymD0Tp+xWc0zLCwUcU2nLBLnEg0a6dfACdEwI6JBmQvAifUxeHEy0L4/vCDf7YYVkLQBeGOcWSaUA3r5FlqRIACikKD9liNN8hGmlotwvUr+LWALM2w7CoIQc1xGA0VeD5rqxmn46dpNMYIAofxSyKyTGNXoeI5hMKHDJJixKuueyUrJ9cBizAIsITmTDJEZSZbcKJeJXQrIE0A1MSOOmE1YC1bJ7AU2gLlQD4yxnHGQg5czzWTHBIs1yEvTPK6XYYjG8M1YXYXmnOdKiFslJJlF2dzJ7O1tmcZbo/Z2aQRbcsuj52gsaOCRhjTVO2BJXF0waU7QsKxOn7gsGv3TmpuldXVdWzbI5UK2/KQRU6a5ww2UrQGqeWzOmnCcbGFbUZzHetckbhZIG4JMEuXe2GRyxRtmRwutOHoc1mQphlWmXNlu7YJBVUFsihK+lWye9dOqq5gLPC45IrL+Mp3nmDckWjXw/Y9xibGUTo3mq9ajSxPsR2L7z/2KNgZAg/heAjHIvA8LEshi4xvdo9z1eVX8qorX8nqaIO33vAysHDYzlpewIT0J7Hmwm1PNYd5612g8AoxQ27T8bOnGXuYG5pJh8PEzE1XSWLB9HxEtwPh8YSjywnJwz161/Y4kPTo3hzxiatv5+ff9XlayX2cmO0xe+B3uOedH6M62+SBh6tccU2DuH0fv3nbUfa2ZlHvbFO/6/vwyojdj8IH39vj3i/8S+78f07zv30h519+7bMc/9rf8/c74fif/BYPTl/C6d+I+dUHb6X73t/hw78/S6/ZoXXgXt7H1zn6xquZ/KLixMeu4SMnfpfPLN7Il75xlGb3S8xGPSIaNOaOcm2zQfyrH+b3f+ljJNdPM/vLEbP7mkRhDxG3SA40mKbHfUdhutnmaPcYjVtvpv3amGqnR/i60kV/tsH0sQ79zwiS6Sl6bZhqHCCJjtFswexN0HsUetUTdD4n6C4nfO7Oo8zeP0v4yyHi6oTkS0a8HoIxgo0xVh7nj4aWwxDnbD6uAQ7wrPzL51tJYu7zUgvfH37wzxbDcBtOZAnVyj8MTojtOPHUOugXxgkhBQLNxOQWTviWQtugHc1oZIyoofR6ck1Chs5NxycrcmPA7NjkowLX87DQ5XS1RZEZ0bzSxjzatR0cYTpbotAI2zcdLARSloyhFihtonrMHJURxRfK2DrsalRR0piggoewBZ7O2F11CGshXtXj0kt2EQZ1zqyu4VgumZLYto+SKVmRMxgW2JgIHizz+ForhONhWSZoGttCyhwwNZilSnGYhSkKtbGmyLMUbVmoXCGwUbIgV4UZmLINlWqyHo32VyHJVYkT0U7CbTjx1e88wbgr0c55OCEVlbEaWVHiRO9RcDOE/vHhhPOD7vAPsTzPodAFlmUxWFvHxufs2RGFGGJ7NXzfpxgNORvHhNUKo3yD9GwCwkZIC+lpdl9S49QzTzD7uuv51rdPsHrmLG5Ns2vXZZx66gzV8XFWT8dMTFRJUk2WpiQKRjZUXJv+k8/gVB0owLIUgyJj0q9i1wTDImOUJcYkLtAMk4z1DSikxsNBWBlV30NvgKwac04cB88LGA6HOJbNUGtIC+NxhRnrUBpEYbKlNh3fz2mttIXrbkYJ5PheeM4CYruma3tXa7v7e6Eklu0itUUy8qnZAagMKQvOrA8QKmc1qbCzuouq75HFZ1krJAobmQlW19dxvQBEQZKApUGpAs+vUBTG4BShEKJ0tC80WArfNRmRSksjB9AADlIZTzClc5SwoCgY5jmu4+P5DulohCUFo8QUWuQgHIvh0NCJQpjJm3SUkbNBUPFZOXWS2Zl91Br7+Na3v4HjWOy9ZBfDDRPA2u+fZJSk1Cd8lFKMzua4Ncg1jGFRn5hgfbDG5OQktUqVb3a+gxvYuHnxEnwKfsC6kHimNBp94RWz2IyMYeXFPlWMMbz8AaKdmNJt4PleQxOWbmqw8K6IRgu67+hzII44/IkOd336AJ32bTTe1WSBO1lqt2l/4B5wI7rdNh944BO8/9Ofpn/kMO08ohnHvP+6ZfrczsJjB8Gdant3AAAgAElEQVRr0RVL3E6L+c4i0fUR0OKrWYtf+wjEnKA1s8HhygdpLsKBj8D8V1LaD8VEn+wQezGdz0csxX0W334I4S2SZk3uWv40RH2ah1vQh8NtM+h3R3MOlg/jLjRZuL4BzYgFL6Lz0Bydt7eJH56jdR3EXz5IfLjL8tISrfkWC3dB57YD0Gwyd32TTqvNHQ9EfPpOE4XUiaA136TbAboxRx5e4kDUJqJphha65uA2+036d8FcG7pRRPyQ0XstdGEpgmgR4sXzjn/07HOxOQxx7nc2T95FrBc7BPETWM/BCX0BnOhfDE5cx3e/3WPlxeCEU+JE+INxwgo0SZKhnwcnijXNZFVzeiBR2iYIKgzWEhzfZqQLRG50uFKbFBHtGOsHicK1PZN5iNF0OaKk4eyCTEscGZAjsTBh1FJJLBdkYTpLQoDU/x917x4kWXXfeX7OOfeVr3re7O7qprsakHClhYSkkrAtOyUCsAHNjHeqrQHNGBQx9lCWR00Y8M7arpCCJtYuO8brxgqBLVVPxHqN2RCsltSGbeH2qhk8Ke+MHo1AEmSBUENX08+83V2vfNzHOWf/uNkPEIgGPUAnorsqsm5lVt1bmd9f/n7f8/kKUKBMvrlKSRfjSHqZT8H3MTpBWMOLxyI8lbK6XmC8vIGC57NyfI2TJv+5O6lideUkXqGIcjO6fYGroJsaXOUP4uY01nXySUmagrVIx+CrQp5bSDoo9MBxXfTAS2XJ0EJBmhH3+vhBhu+7xHEfpQU6TRBKoI3AlYJuv4/rSIS2SAxJnJB5HQrqFXTCPU8npKV99DydsIb+6R+tTrwlxoWnooikl5FaKJVK+VisqBjZVGXLcJmwDCWvRLFYpFgZZ3zzZsqVUQqFEk4lwLM+zz39LI7S/I9vPEuv36c8VGFsuAhrGUOjFqG7tJe+xX//p3/idLdPpnucXFtDnD5OIpbpJsdYXjlOUBrFdx2Gw2HSzgpaa1YSC6qI1injbo+NpRJWOWROSnm8zOi2DZRGfVK3j+eDcB2E9Oh0eoOxX4ZSYhAGLQm8Ako4SBRCOZzdJCjtoMI3CCcPwAaJwEHrvIOUpulZU/0rohsGt+WoiIxOt8tKp0tiMhIR4w57WCfAFiqsZIbFI4c4eOJ5+u4Kfdunbzv0suNUx8fo9k6jE0jSTl5YWYGxCm1yBonrS6Q0IDXKV1jloAXg5CPOTFu0kCAyjE1AGBIyZGYQVqKtIE569PtdlJEIofJ3W1pjTEa/3z8bdC2FxZES13XZPLGVS9++nW1hFb+kcJKUamWEcqHM4ecPYno9lk+cZOX4MTqnXiRqr7C8fJpnFg9w4LknKHspXaEplAr4xSJYj3JxmMrIMKPeMMXiT5Hx/YJ2h716gdU4cwe1l93X+ab31stuv9DVgvpECDVot6D+iRrNhYgdn6jSfHiBWr3OrbU6zfk91HdUmbNPYpMX2D0X8cQTlqlWSBTVqH95jrA+Qx2YXZyhOV/lmp0NFt5VpU6N1p+0CNlBrb7IkaTJtm0RYbtLa7HE43MteDJi83+6Bg+PxgOf5F9cWae58xaiCJqtiM0P3sSNs028fQvMP3oTm6my8K0m4iikDy0S7mhxY30edteYrU/RpM19NLl21y1Ur5mjWt/HzBM16rfWaVOjRStH5tcjws1NqDep0mB+X5MdsxHvmljkljKECyH1KyAKYaYKYRvmPrOD3Q/WaC5CqwWNJhBF3PdQk1orN7zXw1kajfz6LAxO9fcVWK9wLX6o9cN+/49gXbBOlC5EJ77L+qvoxPFX0wl5nk4U37hOaDfGC0A4DtLxWV/r4vgSrRMcR+G5HtZAwS/gFQIKfoDjKoIgGDAULUbm+YHSGcTzGEmWSFKbm+bTOCHVGjD5m93BObSWvBtmQSqJ67joLGZ1tcfyqVUSk9EnxpQUwvMgKHEqhWeOHOLA8QOsqhW6aY+YdTJ9hA0bxun3l0n70EvX6PdjtFQYEWCVQLoS11d57JzQqMKAWC8tOAIpVc5WlAKd5TsFhcx1wskM0kpwFJnp0+11kVpiUVgjyNIUq3OdcB0PrXOdkGd0YvytoRNviSJrJBynUCigtMv6yVMDfogkPZnwQnud9umM0+srZDam34kQ8SrlssNFW6qUPUlhJGDLRSEeQwjW6PU79DqG6MgyfaXZUNxE7dJx2n3LO97zHlQvxXMrOBJGLtrOd58+wtFonbTTw/ZXEDi45YTt75kk6a2xwYsZLrrEVvPMqYReoBC9hFE5jl2B5ERCurbMhqqL0Q6e52FsXnnnPimBsRlZliAlpGmM1ilapzmawBoMFiXy7pfr594nx3FIBpE6OcLgTPinfan/6hWW1hop88T24+0TrKx1yVKXpK8YHholcMtU/CoSyXMHTnLwuYRn9h/kqaeeYbmX8fQLh1h6YZkXDh4h0ZK+1SAtyqQUHIVnPQqygquK+E4FYXxcIVGWPNfBDLxpg+4c5EWgQz5CFNbgObkPLY3zcyOERkh99vc0Ju8KWi1BKJTjMzxSplQq4QjJM8dO4gxNYMqS0bGQ4lCFoZFhelmXTmedoaEh/FIZ1yj0+iqXVEfpxSkXb7+MsdIQR44dQ6AIij6FQoFt41VkyaId86rn9adlXagmzgwqqejsfy9d9cbAXwUD1/UF3vGgQ1I7kkfkRIsQ3teiPdOiufAwrWqdfffWmCWCNtTqIYsP7uQmcSfvnw9ZvAGqu64hrNWJbmgSLjRoh3PUd9eIvrjATGsKboXa55vMjM/yj7/xST7+kYdpfqrG9eE1vHfbe4lo8eutCK5ok9wL+x48Qv0fDvIrv1Lj7nCO3a2QcL7JN25psGOqDtEs9971JDx4E9yVC9LVLFC9aY6H5lOgDgtNwnurfGIhgqjBR3fNQyPhtvl5WjvnadywHZoNwh1T0Jpiz021/Pw1Q2qNFu1wivr9IfUwojpbJToS0roBbvhoi+ZixGIzpHEF1HdDtQZzdeBWeNcTMzTmQ2Z21WlX57m1DizA/H2Dc904d4le02s1t4sfdFjrzB3tOu+6vwXWD6sTwQXqxMmBTshX0YlsrQe983TivReoE8dyndi41SVLnLyY0il+IWdFGatwpMZk8YALlZDFfZJOnySzZKnGuOBID0/6uI4PKFzfIen3cYQid/jmtHaTZGSZQWfnvwG3+XRBgtF5lI4amOqPRCdZX+2i+wrbEYwMj+M7Q4wUQ5Tr8NzBkxw7lPDc/ud5qvUc0XrGUy+8yNKB0xw8cASdSnomJTMalSW4Elyl8E2AKwJ8bwihA5RROBZINSIzYA1SaOTAbiaEQGUq74QZjasUwih0lpHpBEdqhKMxwmJ0Tr7PdIwwkhyb7zM8VKZUOE8nCq+hE5UxXKOIX0UnOKMTldevE28JT9Z/+/L/vUsqi84SxsZGEVYMSLd5yreQKVIWsFIS+EMk/QxLxtFjx1hf6yLTDI0kdgxFx8f1HMLxMhsnNnL8xEmCAJ793rO4xWG2brkImSUMhRsYqUjW0x4jw2XCUcXPv+8STq6cYvn0KU4c6pOuden2BV6xzHrSYdOmDRxfOsbSkWU2TYyydOQAP/dLIWnSpb82RC8usra+zFq3n3ugsjyr6UxrVymFUJJA+iihcKWDch3yWGmBsgN+ySByJ03TgbfqXHH1cv/Vq628wMpHdHGa0303ThQpVDKSboxSljTrkcaW8Y3D/PwH3kPaX2HLlk0sRz2SzDBSLILrE3c1mclI0izvVEkHIySp0SAykGYQgG3PPrYRBj0Id3ZUHrkjhcBR7sAnpch0ihJqUDAOIKZS4Dr+2ZBrYwzaZDhK4joST2gcxyFNenR7GS8cOMKQr1BuAJ4LVjM07EKaUSmW2LQxZL23RqlQYLhQZdu2cdxySHlsiOr4OCa1jI2PoPsWzyvQ6a7QWVnn6l/5n958T9YPsaqvfchLVvFVPFdLwFIXmCSnXL6Gf+fsOgMxfQwm6xDuhKWoyuTkJLWZSZZqXdaemqa7owtzRb7yN3sJ997MfZ+u8h/3HuTZ0vNw/Av80bZplrpLzHzjOpb2foS9+yGiSjP8L8z96Z+x9wtLdPcv8b0D/5UjK8+w96tf5NtLX2XzRInuUY9/OeLSeKzLnuZjHI0OMlnfQfrg41z+hzdx2wcinn3+Tzn4kd+G9xV5bGGRv3zger7wv/4HJud63HjpA3z48G5u/bMSz3Yv5sXFvdxOjVZxisnFaZ7d3eXDzS5L9Y/y/n/8EpNPXc/fzpU4fvPnqLYe57rfiKj+Tp1oskh3OiIqQiQmCQ926daKND8ZMX13l1IFZv44QjwwydQslKZzvtl107DntyKK77sOrr2TmRfr/OqnIvw1eHw/UIP6VYNzPSh+u0B41WsASZuPAbk965VW9cwdPca5O+XN92T9sDqhftQ6sTzQidUL1Il0oBPdIuvdZdb6fcyAG6V1hhUSayzKVQhP4ePi4OJ7DtZxsEJjLHjkgGocMCYlSVKkVKQmRTnnkkPOkbHsS7RDIgavt/ltVuSpuolWJBo2bfIIhi1xL0EJQ5zE9DsZ4eYh3vO+KzB2na0Tm1iO+iSpZqxUAjcg6RhSkaGTjEzKHEhtFYY80FpIjRUZNgWEyD23A52wWuO4LmiBMrlOpDZGSQdtcx08oy86ywGsLgqJRLkSow2ZznAciedJPKlx3PN04oXX0IkNo6z31qi8ik7YMzrRff068ZboZJX8MjaDcqnAM989QKlYZMPoKG/fejmO7jFRHccruDm4rN+j7ARksSQcHcMtlUiMi18sUTAOqQfG9emvrPCNJ57hbbVLSdJjWLdCuVzk+PE2qbB0Tp6gv9xjpbeOxhAMb+CbreP0ChUyX7Dp7aNs3TSGU0gYrngUgwKnXjzJlrEqb5sYpXu6zfTUVhp7v8UzL3yPanWdizZkjI1XGBouM1wOcBwH1/UR0sHzC4xWN+EXKmglkEGAVyyjHB9FgCtdpAry4y0kSXLWW8WACp9DPM9lFb68o3X29kHtle9SzMeSUXuZ7zx9iGPHMuIUVtbW8AtFEAYphvnqY9+mF/s8d+BFipUi6ITCUAmhDVoJbKrwlUdBuSgE0oI1GWmqyZIYo2Pymb8lyfLgZytAKHnWzK+tQRuDNtBNUpT0QeZbbvMlMTrHN6SpPrsN1/EEpWKFDUMVgvIwlgzhCg4uHUGqmOHKCJnNGB4NWO2t0o6WUYFDYbjC2MZtXLRtC35xGFWRJNJHKsPR55c49N3vMT4yjun3CQqKbnKazRs2knn6J/8k+Emt1zv2qfHSgOHZN/i4D0J78PDRbW1ad0yx544m125pEjVyRlREi5vm29xFk1unpqjfHdGabzL1aJP5W24gmptkoQ5Lj0I9+g809n2MqdoMNWbgD/6A0nvfS/33/57P/k6NTghR1GKxGRJW4b3/HDH1RWjWFpiP/oZbrwDcj5KSd+oWWg2YmWPHI3cwO9Nk8doFnrhrhvYC7Ll7gXq1wf2tKaaiKe7ZCfMPNzi6vcZD8/DonbuIuJ+INjubbRrNeZq1Ntcwl5NXm+S5OHdXad+xCLSo7wqJZqFxuEmt2mTxtgYz99eI5iNuuS1vAy4sQLM2RRg22fHl+2mki+x6MqLx8CDA+/zCOByc3JcHhL/Gimb4vk7Vy5uVIcDCm1pfAT9Gnfjm69SJxeP0SufpxOY3qBOVXCeU4+AIH6yL4wQUyxvw/QqZEIjAR/llfMfDFQU87SDw8RwPm2UkWYqRgkxrpMi7PQyyC4VS+YkTkjxB5xyO9EzsjhjEiVgpcYTiVBTx7acPc/xoRi81rHS6BBUfqTJEWuGJf36KXs/n2WeWKFcCVJZSrJRxrCERlrTr4OINCiAHaQek9jghSeIcWC0MAkuSxuhEY80gwxGDlZbUydA2I0sMfZ2irJdPZhyBMWCFwhhBZgXxoFtnDShPUCi8TCccwcGDr08n5NAF6oSKL+jv9i1RZBVLPv1+jyyFiY0bSVJDZThESSgVSnTWupSUoiAFI8MBpYpiy+ZNyDSjG51kYptDO3qRkbFRyn6JoLtMP+1z6bbNvPj8YTZsfj9XTv8S0bHTnDh6EikqZG4FISuMyDG2bNzEWhRj0yEKscSuWYbVMO2T67iuR3TyNBaXrmPJfHC9IcpDmzl0PKZW3c6Hf/lfMzRZph8cxFrN+NgwOgHPVfiepFB0GSoNkayv4wGelxdgCI3rCKSwKAlC5iNBYyDwfByp8ifBgKNyZtfeGRaXEDk+4Qz5PQebnvv8LFdK5kb71bU+KytxThdWPmvrfYwjWdPrpDIjyWK2bt2KEJZquIm1tS7aSkyWg1aHhwp4hRwjweC+8xFojqPQg+gghzM/mzvYYZh7rLIsIbMZyhE45L9D/vuasyNROIe3OMs6MQLfVSAFE9UxHOny7aefQylBsRSQZDFjoxtpHz6NTBy6mcfT3z1IOzoFWIKCS6lSoFwpMjpUIUtSJrdfhFMMOHLyRQojAd995imK5RLR6Q6bNlz6k38S/KTWOaD4q64fKNIPv7GHXVyAarsOd0P4mRr1qMpMfYYbb6xTJ6Q2W6P+UJUai0QzNaq1eeZZoHYP7PjyLIuLVVqNDsw/zLbPTBF9KIT5v2YxXCSaiWj/7jx/+/8cZCa6k/eFDcJGHQhphRHRQkj9ayE7/77K3MwOvr6+m49tnscmli9//vM0aVMjpNV4mOYtD3HNPXM8aRMarTr1cBFurLFr6q+ZmqtTvRdIYDczHD4yxyfmdkC4i7nd8wCEe2rsngqZnQL2bc5RDHNVamEbPjPFjtmQmR01mjfdyVQtZK5dp717lvqNszRaLXa9MMMLd+0jXAyJIrh1FmohtG5oMnMXNG+IYDG/jLX24OTOk48La7xug3o48Hadv15eh8/fDczexZu9fmw6MZnrRPUH6MTwD9KJ9hvTibGRYXQi8IQiKCpKRZeRygg26eIDflDAFQ5CZDieREqNUAbpGqywaC3wXQ/HioFZ3CKMAxI0BqvzXelKGIwSSPKiy1iDQCIcAcLgKIlUAq0zglJAr5eyvBKTphohXFaXuyjfJfUSEqFJsz5bL96GkpINGydY7+Y6ITLByIjL6GgRv1CgUHBACGxmsTo38wsr8imHzcn2wpE4nofjOpgswZoMm2Zk0uD4CmkNrp+DUs0g7xdyhJCDQrmCLNFgQRhBcEYnxsZwcPl26zmU8/p0olJ+bZ04HnXYNPEzF/R3+5YYF37x83+xq1AsIpWDcjKSpIeQlkz38wiYvqZYCfBcD9eVHDp6GO15TG7fytjGjUhdYLgySvv0MVApgdrEI//t77lso081LLB05DAvvniMjRMhGzaMs+2SiykMF+mcOsWp6DhZ39LvrTF52TCKCplxibMYbRykAiU8bK9LJZBI4WGNw/JyRK/bZ627yvOHDuF0DOJUzHpc5IUjx/C8CuDg+UWcQokkS8GVOKUCnnQxxuC6Dq6rcB2F6wVYq8kGI7YzO/jOHwmaQXtXDjpWZ3ha55vdAZSjzmaDWpsntmdZBlbS7ca4nos2kiQBaS1KGhxdQJuEXprk+AQpEFKy3uuBVYyOlChXApCKfpzQ7/dJdQJn29D5+A9rc2KxkkiRG/3FoOhCgDYWJSV2MAYUgpx4LwcvAAMKvhhERSgl8T2PiybK+EEeIi2soJ9aHFVidHgY1y+ghObEseNceunFjOiES7dtY3TTOO1TR9Fpj5WVDkanBJUy4egER6MjbBzdSKVYYP3UKcrDZda6XUrD43Q7Cb/4ix/6qR4Xvtb6Pstmk7Mjvh9o53yDCLHiNBz8x8epfahIrQR3NyM2fnsSrurSnayx944u7UtKLG7rMndPnXq4RHPjr1PaAb9whWBMh7Run+Z90zto3fk4/7zUYvx2mNxb4vH6Qd42czO//dsH6UZLTP7qk9x80df5l//+UYrT11OrfZrWnRHPf7PL/v8yjeRx0p+f4MNXfolL33kb/3jgIzRHr4e9n+RzH/xjvnPTIp/dt4X6A/NUr2/RWPgLLtqh2L8XaluKtL4C0WSVxmV7CYugFtcIZ5fYv9Cldt08j0cz7Hl8kuOTf8bjf1xj28wkkzdPU/3kV2g+sI3u3kkmazXe1+2yv9Fi7ydbbJu9Hh5o8p0d13HNdZv40t4vUWq0aDVL7C9BbTewWKQ6PWA1ROflStbPXbsfx5r+J94SAdE/bp049HKd2H4xhZEfn04cPHoMzy0hpIvnFxF+iTRJwZc45SKudTDW4HgOQeDgORInKGKFRps0R98YjbE2R1LZHINgTU49V1IhxAB06ngoaRHSoBwHIfJ8xBzrKAYB1QKrE5LEkiQpjpebyXWq8i4TBjf10Tam0+/jeCq3gSjJWreHRTE8VKRSCRBC0c9iet0+me0NINO59ys5g58webEkrANGIkReTIElTQ2Oo7CZIUXjDAxbeb5v/obeCoOwNs9sVBLf9di6eaAT5PiKH5dOVCrjdHsXphNvCYRDqVTB931WVlcplIYoFRQnjp6gOjFCnKYov8DpfgfHOKx3+oyObiTrpBw5dJil423CoTL9nsUplKkkil6wzL/9V7dyPDpOvKKRnkvRkcRJD6fgcOzIiwilGA9HsYGifypmdHgzcepw6MALjE9sop8sUxkZI9WS/rrGHVWUhKTLKN87vMSVH3onrhQUVIXO+jqP/F2TiYm34dhlRkbHieOELOuTJIb1TkohdZBBQGIS0l6XICiipEec9jDaECcJOtWDXYQa6ahzZHhtzsbxOI6Tm8rhbEcLONv5OTMydJx8R6LjOKQ6wfXd3GxvLTo2dPodBA4nV9tUxsqMeQWUtAwPjRBnXeKeBemSxhKhBIVSjmZYWVumsx4TpwlZluGqvFhzHAeTaeyZ7EYh8owsJFLIgWfL4DDYDWPz8M+zv4dSL0FUwLkishT4GNtDG4/OcgpZylCxRFenIGJQCX4h4G1v34oWfex4gVg4DFHg8q2X883v/g/iOKYSFDFIllc7CM+jp23+QqMdDh9dYuOWi+h1ugyPBt/3N/qWXy9DOcxybufZBa06eQvjtToh5z/Oqx0/yNEjHHxazY+tzoVEbWAnMLeLB7c/yK52xEKrySPfmOPOLQ1qUxFXNBrQnqI63+SWu6s8fGOVBx/cRfuahOaj83B1yO8/UocrYefXbuLJKIFbGjTqs0Q0qT+5j/CaW+DIbqKwSas1RaNeZaYVUvv9Bv9wQ8jttfcibruWu/gyD4sFHqLJJz5/hBvmr2AfT3LDgmD+W1fzyE37mG3eD+07ua0dUq/uIGzO07i7QevunNB5JL6GXQsRc7tD3v/uWR65ehFmIVqocd+3FrnxhirMzbCz1aJVi2hM1bl/9xTzNJlaqBJVob6jyQ3zUJ2P2NPaCU2YCXfQrreohTkZvlYLaYVQ28mrk9zDwfl9ta+fOeb1zBTf/CYW8AZ0YuSlOrFhqEz39ejEsRcR8gJ0YniM1Ax0YkRRkq+gE6JCp5vrxMbzdKIfJ6T9PklmWE4Nw10BxYCejbFxH1cGONIljXsYben2eqRphrE69yLFEm0zrAKERmQ58Fo6eaK0lS7GGpQQSAeEccmMRiDRYvC6bTXSuug0xZCHSadxTNzJSKUGk7DeW8Yd8hhzJvB9xdDwGIleo9O1OK5PGq9jrMV3ArLE0OutsL7aJdEp/X5KwZFkxqKUS5Zm2EwiXIWHzHcWovCUJDVZ7s+SBkGWo4iMILEarMRXLtrmOyYHlRSQ72L8gTrh/oh1on/hOvGWGBdK4WCsZGRoDF8olNAIm7K8nuC6EldqAr9CqVTAei5x0kGWPVRlCMeFkeFhSoUiQ0GBDgm256DtKiIALSUuDgKX0eFhfK/M0XZE0a/wvRcO4wiP2hWXErNMdzVmYmIzJtOMDFexrsJxhigNj0BSZm3do99dZevoOAeePMiz3zjMN765yHcWD3HJz1zCpokC+IoRXzJSLKMyF8c4+BpiUjrryyS9NTzHJ+nH9PrrCAbdG50iRb7lVil1dovymYJJDFhaOss9V57n4fu5QTxN03O+p0E+olIKz/PQ1gx4W+S79Izi+KmIVGuEsIwNjVK0Polco6clJ06cZP1Ul7jXobu+RpZldDspR46ts/TiMmvLXTKT5pwV6cPg3ZLW6dkntzg7IswzqazIScYaPQgqzREXxgwAecbQi/tYcc6w7yiBcgTeSJF+Ium1Y7aOFTBpH8/zWF1dJltbZXV1HWU1rrORg0sneO7pJbpLJ1k/tkY3W6a19B1cUcTDEq91yPodlk8f4btPPMXhQ4t0liMMHYYrIxw/dJAXnnsWT7hv9lPi9a+X+WpeV4F1Zl2IW/7l7KWXfW3Xrhl27YM5gOZgZ+Jg/tQOQ9gD7XtD7rrlQZ68p0VtFmajGoubPcInWrSjkAebIUfqNZq1GumNbaq02bfvSdjX4iEWeObRFpHfZvFJ2Ne4l39xQ5lodoG7776WmVpEuOcWGhdt4JY7dnITM5QOlZiZn2JXbYZmc4qH0xa/9q0Gn7n6ar74RIv5iSqfuLrGzAxE32pxxeYrOMrVzE2ERJt30WrdSVSb4a4qfOyO7TTqUzxKjRc+P8tRbmPXdo/64hSLjSa1iVtp7l6g0XyYdr3OXZ8PWZxpQ9TkwR11oluhtriHttfMR3U7QqaY4d1b2kSzc7BwJ3PhFDOzU0TVNrUqOSurGsIMVGshM+TG+LOXonXeRYkgfPI1rt2rFVhvvu3qB67XrRPpS3Vi6MelE955OpF+v04caB3hW08t8vTiIS657BI2n68TpTIqcXEyh1Ki6auUTncZ3V3DdXxSHdPrdQY71C3aZEiV6wQGSsNFpJNn3To2nxxYa0j6CVYZlBR5VqzVxL2MNM0nATrTKC3xHBfX9UkHXEVHKYwUaKs4efok2ubZiaWgTMEGaLlOt285cfwEa6f7ZL0OayunSLVmvZPRPtnl4FKb0+2my1cAACAASURBVO3l3MwvLY4sYF2BRaNNhhe4CC+HiGqR87+ENBiRgTVoadBG0O87CDfAZHlRCJZu0geRRxJZC44jUK7AHy3Si8/TieRlOnH6DerE1354nXhLjAu//F//z11CCtZ0TFDwiW2fxApKbpnhgkftsst4/y/8HBPVEbwgIO2v8Mwzhzhy/DhhdZwto1XW+jGODFhNUqxw6CUZxkBlqIRCcNllNU6utun3U0YCRdErYa0EaTjaPo0bBHhqiNhmWF/SxyCNxGaSJE4oFMpok4B1cJSPcTwc1yCMi0POcFpeWcdmKdL2KPkeK3HCaqePlgJnEN4pB9E5OjOAxnU9IH9HIaVFSA+lXKS1eZFkNEIphMmjGAQC13GBvP2qhIvrunmbVeaGR51maGMIggDHdQch02oAQLV4jk+aGoS0aKPpx2ug8xcwADmIY4jTjCQzSJWHiyIMCoGQCmvykFM56FwJOZj5W4E1gHKQwubbc5EDTovBczyMsYPdhuAole+ocfJulwAcR1EeKzF91eVcenFIWMrJ7etaUAgKtE+sUi4Ns7LexfMqxP0+/fgwo8USmZFY31AaGSZZW2HMHyIVGb1OhyHfoTxSQQrN8MgQnlvEyiLDwyPEvS7vec805VKFk6cifvGXrn1rjAtfTu3+ca438jjnjRnpwmOPLfLYY3BNBI/tJcfBD1b10126dajVukQLEdHn6nz8I48zd/t17J35LNNzLT79mw2qlWk++nsP8Mljkxx+qsbf/UXEh9//FR7/6yUuvniabe+I+OCnJul+qMvjS1XaD8G2T+/nj+Mr+fjvNbjuw9Ps/Uabz91xLf/u/XtJJ46wuKOEDAXTk11qH5nkj/6vGo2/nmbb7V1qkzXu+9O9VDZ+icnfvYeb7/t13vFvriP8gzvpdlssfa7OA3cuUe3uZ7IruO5/uZ5Ka5q90eP87u9uZHHpszQW55l+cAdT1/453Xf/A/d8rcZvXPmv+Es9ytzOHnv+jz9n2/XThHuXCHdP8+kvLDG5tMTeX+tye3EHH764RHWpxfTnpon2RzDdpbjUhbkaLEVEfwrVjVBc6rL4GBSL501ua92XjHEf+FgeLP2SNQM8/hrX8qrX+PKbPC48qxNZTFD8CemE/wZ0IkuAczohMZjMxbN5R2R57TydcDyWk4S1Th9tBcpz0JnB9RzSfoKxGp0meF5Alhn8oouUFms9HEchtCUIgrwYchUCOxgfytzLhMxBn8pDKQfXyTmHSNA6QxtNIQhw/Tz7UAoH4eb+LiU80kwjXImRliRdh0RgrMAqCYNJRZJmpNailEXbDOXmkWv5VEPhOiC1i/LySYxFoVS+S1AMzo+QFmslEovJNAUnQJBPcJQjcAa7Cx0lMSLXCaUV5WqJ933oci6ZDAnLr64TjqqQxC/TicBQGn4NnRj/4XXiLdHJcmMXN1GovsUXMcr1CIfH+a2P/wa/ePUHWDz8PX7zX/8mf/if/oQnv7rI8omYt22/iNrbL6Xoubx44gR9Ek6tnKDiZWwKC8TdFTxf0OuvIBW88L0D9JIYr1QgGC4SrR5j4pItEJRy2Jt0CbwUtMGzEqcvB5RzQ2r6SN9iVEww5FDyDaVCgLAS4YBBY6zACYoMV4YIJ8aRRUFZuQwHFcqqiMDFc4so6ZNlA/O344CVFAoFgsDD831K5SG0EWe7UlYICoUCruuejalJkgQxAK65bkDgl/DcImeopo5ykFKeZWwFnn8W0HYmOFqnBptpkiTF9wroVBP31ul3OxgNcZ+zs29jJNYorFHn0BGDnSqOEkjs2c7VOfO9AQyDhjBCWKTwzvK7gJccb3XeAlYyL9w++MvTeCMBL3zreYaExiu4VIIiI2MxE5vHaS+3cQKf6oaQ0ZEyK2lIrxfj25T+apfADUik4FS/zxNPfJvTp3uUhqqMjk7g+mWULIKTcXI1Yr2/RqwNx0+cpN+PQXtvzhPhldYFMopeDu6eeaUb38h62Q62s3e5K58ILjz6yt+2677w+3Yi7npylnB3RP0mmHshb3Gl7ZBGtIfwEeDOGvffOAtEWLuPxQcjvh7Bu7ibdiOEZsjcLNRrNcI7Fmk190BU5+tPzPIn83Pc0K4R3lUnnJlh7sgczRmYSyLazR1sq83y0T3zzM9Dc36R1pE29+6bgeYewvZH+QQRczsfZOrOBmHSgJsWiXaH7Pl6lbDZYvcjdRaAe/c9QuPJKaZmW3Bfi/mFFrfuq1FvzTK1q06tWaN2NSzc2+Lem67hoRvez7U7FwjrM9QI4cE6t727yUyzxiy3Eu6eoXH/w9z0aJt6vcrcNTtgB4SLIbP3w0zUJlqA2l0ALaLw7rPnPx/7zcJtnOtC1WBm7tw5n901aDeeT+6vc3acuOt8BtpbfJ3VifjCdOLUy3Ti8IkT9M2PQCfcl+rEmTi0VPRRviXllXUiTXUeoXOeTvhVRcVzGC1WGAoKOXvLKSGEiybPv/UDH6xgvDqK77kUPZ9KuYDOcuSDtRqkpOgFOI5CSRelBP2eRkuFG3i4XpFSZQjXLyIDAUbgKA8D9PoJAoHv5ekbrqNQyoLOizXilDRO8GWANppe2iHurCOEoNcH13VJjcWS70LXWY5VUChElnfOHEcMMmn7JElCEmco5eThc8pC5uJYixIC5QSkIs03UA1qOWMNUsjcS2ZBWokKBB/85ffijji5TrgDnfDLjJRiJjac04mNE+d0IpYDnVg5TyfiPk98O9eJ0cqPVifeEp2sJ7/+6C48RT9L2DA8QvtURLd7mkPPL9F6pkXRDahu3cjPvvtyCtUygR/w4osvUvC8HAEQlNBpn6Lv4Xge/bhPqVxCp5ry8BBGZ+hUcHX9cp5ffI6YISyCzvoynhAMDwek/T5xX2KdDN/x8rTzLMEKS+AXSHopaDmgkSfE/Q5Waia2lPjlX/kQx08cRtiY9fUETxVxUayv9/ADhdUa13XopQmOK3AdbxA9MyhGhCLOLJ7rARYpFQZBoVCi6Hs5sVfJ3MMkQEmLsYZMp1iRDfhTHq6j8N18XOd53qCYyZOkECIPplYKpRwQFm0tWZqwutYltQmptqQ6Ie53SXWcZ0hJL+etCDDakpgEJd0Ba0VgB9ENyso8JkhKlJNnJ0qcHI4n1GAUaFGOwtgsN1KaHD+RZRme54M1BJ7g8ndcxnPPLlFOAjqrPU6tnc67f6kiWtes9VMuGh/j4okK27dWiE72SddX2TQ2zIYNG1jt56GjR58/RNrrYTGMBAXkhgqlsoeyimLRITMukxMb+epXv8qll/wMxhogI3ATrvyFt0gn6wetkLMdjJf7nhdf6cY3ss7vbnXhrxg0PB7LkVnXXfUqXvirvv/WLzy2n2JUZO8X4Fej/Vy55ytc3qoRdrtEDxRp7QiJogfgwUmufXKNT15bZOcX4an4ShrNFvuXepRuLnLdZJ39jUXuuONm5h/4c6KZaX79ga8wOR3yR60uSyzR9HtEi10eWIOHlpr8cW2S/3fmP3PP7WsU98O//Xid6LKv0P61KcR1FcIr6/zNdzQ3X99k7xX3cfcnn+JT//M00/MlqmKJ37qjwaSocVzPs39phq985Vcpfv0LzHysTuk7+/mN66r8zgOfpjs5TeOvHmCy0qU4t4Mn/rHBMzffzNLSzUxGb2fx+CVc9YUDdD9S4pPNCMR+djQnuezD0yy2GnzsDx+m2hLUHqzR/bM6S0uPc930mXSdKsXoqvxkPpZfD4r74Z8465tqVXOI6Rme2f7jUV4otzg3Dl7iLAPrsTPX9wdsaLgb+G3g8je5k/V6daLwSjqR/eh1IgcpWwKvQNxLkbyKTtzw/TohUklnrYfrK+IsI1CKfpLiDqCjOfYGcA1WQ69rCYoe1uYcQW0FxVKZUuCDzhlbVoBVIve/CovNcp1AgFIejnJwXZl/DAq4jgSr6GX5hiOR5Q0Ax/UwA8N7mmlOL3fQIiVLLWkW011fJ9NJPsKUQf76n4fGkegY13Fz/y0yr9esRgkHYwye4yIdkWuJdcAYtFJkNtcJRyqs1DnEO8m1QqPxnABrNMVA8I6BTlSSYq4TKwOdyGSuE1muE9snKly8rUIU5TqxsTzQiV6GZaAT3R7W5DphL0AnMjKKF6gTb4kiq/Xf/37XqfZhMBmd9Q6VwkZIQffW8YoFunGfsZFRVpdX6PW6nDreZmxsMzrT+F5AIPM4Fxn4nD7RIQyrrK11cF2P48cOMTIyRDc+zNe++jTvfOd2JsNRep01tPJILchMUCi4dNe7FIdc0jiGVON4Mkf9G8P4+Di+59Dr9zm9nuG7hmJQ5uTRDksHDnP44BFKfok4M0jXwQhLOXBxTEI6gJIq5dCP4wFEL0OgMUbn7Kgsw/XyOXOhUBw0iizCaAoFH8fJA0QFAsfx0RqU9IA0Hy/KAEdaFAIj8518jsozA33PxVH5HoecHJ9nWMVxH1covMDNf0atUSoP8TzDWzEM8p0BYwWKPPrG2jzBXdt0UMzl9HYh7eD4AdQL8nm6BTAD71juH7NndsQAOksYHq6w/ZKLiE618ayfd6Qqw6Q2xRiJ7xcR2uA6HmOew8YNIa50eOLJ58HXVEoFVvtdrJdvG5bWMrJxmNXVLo50Gd1cBW0ZHiqTakhEnom4actFGC3YvHkzVqd0V/t84IM/BUXWG9zp95K1wEtGeq+1VB0mzwOSLnHhU8YzxxW7sLE2yR6qHHyoyPSnimzbBqXpLt0Hakx9aT/7oyJ/9U9FwishWlric/uuJ7xsielikY83H2fy7+ocfMfDTAP7m0Va0y2qYY2/Wepx+5/ezK/9eoO5XfcwPxYSV3ey989/iVrNhwcirvvfd/KXn9rElz57GbdfV2RycpFJruPmHfNc9oFJPj3zNdTfQtSK2DlbRNPlf3voUU5tjJmb3E2rW2TmD5/i+o3w0dtn+fr+/bx3XvDB26cpTv4N139kkqhRZH9zkcnbp1ma6jJTm2Jv4zh33/1ZrrprmoXmQabrS/zhdJ07f+/PaZxe4rp9t7L3P97J9PuvY/GzEYtLi3QnzwvrfrXrfVX+IQJK9XyUePabznSqXi+d9mV3H9Hiqqv+zZv6nPiJ6cT/9zTvvOJ16IQvMefphPcGdUJrTZxlSOESpzECQWYyMAarMzJjSZMM1887WOWhEoP3vxidUQyC3BeLyLtHno/WAiFdpNJYq3C9EkJalFBk5GkcrlAYRzFU8pE2j3mzRmM0KKVZ68QESuF5Lkma5dBopXBQaJ0hpYMBlMpf+61QSCtJM/LpitEYkSFFHofjuA4om7O7jEWnFuEIjMl5XlJYsjTLR6BKYGKdIyoMZGnC8MgF6ITNdWLUc9i48VV0wn/jOiF0Sne1xwc++Ms/HePCrgvWGcMJRnAro3SyVVSlSOYNs9b3sWKYk90OMgjoac1YtcrocJBX4wWfU0kPjyJe11LdWCBOT9PrL2MyzfDQEMK6jFcm2L59K889f4LFAxHaHSPVUFCW8VFNHGv8UgHSgMx6BMUh+lrQ04L1zBDHMSsrHbqdjGJZURoaJRWW0lAFjaBQruAVAkoFF9dJqG5wKZcdLv/ZSS67dDMXXzxK4OT8qzRNcZUctHk1WRrjuYp+T2Ot4OTJNtg87zC2lk56bvegtZZKUTFUVmTJGmliybLcYI5ywFF0taaTJdg0o+T6YDW+l6e1O54CMrTO8w/TAdsqUAFl38eTFqkM/czQjTPSzJBoQ5qmCJOeBaRqmYGTm/RTfYYtnCGExZrcsH82FsfksNI0TXPm14D9BbnR3XEcSqWAwHPpnlynTIByLLLi0zm9TOdUl87pPr31Ptqus3lkmAPHD3Lg+CHaJxN+9rIq2zdUCRDYXoo91UFmMZNbLiI7rtn2zg1c9nMTmOOncIVideU0p04tY9rHiQ4cwKz0sEmXw89/D51oRkc2vVlPhR9uvZHx4OuEi9YHj3FmunRBU6Zo8O/ukNZtA9Vvwlw1ZPYw0IqIdsLdjXnCGlCrEzXh6ruaLH40Yubee3mfeJj6TSFRKyJstdj1+RbVeo3FKjQfWqTa2MHdtzXZUa+zXTQ4+mhI44o7qc8vkt65k3vuv5/mR5vsuncn7xaCncCt93rUZuGWWx6mWW/xvu0tbrwa5j/WYCZs0GCezXfOsLhjkU8c/jzpt26lNtuAKGJxvs61ty3i+TfQqkXM3bNAuxrSWJijPjVLa65GM4JZHqbemGHPfINdtY/xmc+HLLQijuyLuGcGalMR4dUhddpENzWYcmehCvVZCOvkDKwLOclhfli4B8L5MzfO/4BveH0rnH/zE6LfkE5U3oBOXPr6dCLJXqoTqxeiE4GLqxKqwy6lQOU6cclm3nbxGKUCBJ5HqtOccYgGZcniGL8g6a1rhONz7MgJhNBICbExxCafJGhj0ImhEjgEgUSn63TXNEnfYk0OLbUCYiFY78VIa3FjS5paCkU3zxlUAitS+v0EhSXOsjwCSLl4ToDrGNyioNuHXpqQpoY4tXR7CVm3lxdFIkXLDCPBdVwSa9GAJEEi0JmLIkMoQ5ymOCpGmJgsS4B8imgTg5Ayt7t4DuVKQKAuXCeeP36Q548eot0+oxNjPzqdqExc0N/tW6LIErGiGg6T9HuIzNLvgycsftElcGIcOlw0spmRQoURv4hQikwbgoJktX0aaQxZ2iNxTR4v0FNctOli+rqH8n1Ora7hGI/+eoeRsWFGt46z1jmBazQSxbMHTlMeHSfwJVnSoVxx6eo1TJoxVhqnICXWZrzrXZfzgQ/9Ape/9z38TO1dVEc3UVQCJTSV0RLRcsTI2DCBFJxaW0a5gn6aoMkYDXwuvajMxKjHpg1Fhkb8vFhJc5xBv9/FcQTWZPieh9G5o8mRQLJGZ62L1pZe3Cda7kFmMDgDI2GKI3OGS5pqbC/BN4rVbo+V7iqrax2OHW/T68V01rokA7OnMxhbIhw8VxJ4+e6SOCEHiUrOAkaxDkI5CGHxFEhjkVh0muGK3EclhAIjMCbFmIxUJwgYME1AiHwcacn5X1bnzWghLAUvQEmPF0+cYD2LOXV6jecPLDEWFLj93/87RjdWGJ8YYeumi3nu4HNUKlUue9s7aT3/NKWhKhdt2Mi2t28nDMv4ZZ+C6xCLlA2XVCn1inz76ecZ+tkxVs1Rnn72uzz59DfRrqQ8NkwnPs3oSMCWrVspDg1h3J/S7MKXm51/jGvP4P3bWXDled6thVlonCneahGzIXAf8C6o3ZVXDHXIEfALkG9LhKvDOQhDiEJaMy1mazXCTwDJvdQnqrCvyU3cRxjVuKY9w0dviZiZmqN24wz1eshn7BwzO6D+mRnuunqOhVtv5f65kHfR5KbN1/DCJxpcdJFHfPgw94l7+fTOI/jbP8Y9jwDzDY4k91CrT/Hg7pBrZmFq9hEeebJByAz3bZkhtnXet+VhHrpvgd31Jp/5xBR1ZmjOR7QenuGecA9heCdb3r2d+gLUwxbNdp3GbMju+gIPzk/9/+29aZAk53mY+Xx5Z9bVR1b3XJieGVxdwOAQh8TqYFGQSRMkZdHqCYZA7wLeCNEYr5egJVJeKWIitGjsaifCq11IDoDa8CA21iFKlqhgoG3LKxprkguyJK1EEhBBAqjGDfSc3Z19VFdV3pnf/sjqmcYABAeUhgPJ+fwAeurIysrqmued732/96X12CK/dazNWaCzWDSCP946RuuhFt1Ol+YUzD9ynG4X2i2KIPjS+KYNnfk334ZXXFOvBW26uBznB/MWWw0v/be5B97lHOoK80N5Qv7oPGEGhSdu2fbEj130hKkLhLrtiVUmJhtYUrAebqLZCmESk297Yk+V3eMmu1yH8TGHVGZkcbH7Lg4DdEMg0wDbNMjToteULgRxPGBrY4jMBWEes9YLMRRA0TBsA03LEJogiQZkSYoa51QVwVqvRyhDNtY2OHtmhc0tnzDwicMYVVXRDRNF5CiqhmkaVKsauqoz7Cs4leIckiyFPEVTdBTdQOY5uqKgywxFpPjDoKhhE4DQyREIWYzlSdMIbTs4BIQiyfMUqRZ1ujLPwRIIIXEMC1UzOL16iSdsm3/+zy7xxNJL1KojT7z2HNW6y76pPZfhifE3e6J+iSdqdXLrb9HswsXvfmN+sLWFoxe1Poqhk8cJSq6QJxGrqytIXbK+sY5MY3Q1R8qiQRooLL3yHK47Rr/fo14dBwWCMELEIR/+0F2cO3uaV1Y3qNoam+dWIJKILCKVCklqFvVCeUSuCAxTR8kqBEmMVdWBAaZRJ/CHDIZ9lpfPs7Hisbm2Tp6GbIYZ/d6wyBvrBkJoZKGg6rgoao7f28Kp7UI1UyqayXilSt02CPpDhsMQU9OIM4mqCuI4xjQNVCHA0Ij8IapQyTMVRRWEoY/MIqDYYaMJBU03UBRBmqQIBFEUIEVGkoRomkKSxAihXCikv9B7a7QqlpOjqqBZEkM1UKXAMg0MXUXX1WJbr1Kk+yTFbsQsl6RZTpYVTe9Mw0TXdRAayAyZR0WpGQqKohbBl5BkWQ4UtWFZJhFaTr1SRVGgVjUIwpDq2BhJDlpWtKnw05ynFl8gzlR6a2uEwZCZ667lxZfPEPRWue7aA+RZQqLF5NIgFQpBAGmY8OKrr4CioArBxkbCn//5s/zZ15/lZz/20xwc30cucg4euAGn0sC0awx8n1hCRdG47ciPv/vThfNcnC/3I+M4uB1adxR/upCF2pEzPPIkNO8CvwvOksOTUOScWj4tD7zXwW+OnuODNw+PPwF3+eA96MMdPqLbxHdhvu1y7Q0en/iYQ/PXfp/77vh1nvw3j3PsDpc7jrR4/NcWODK9SAePpXyBhceP0Z7pcI/zcRbu+SU+80+f5OZjX+b25S/h3/8F9n/Q5cj1Lp9ofYT/+09/j995+Jd53Z3hT2nyB7/x//Cvf+Es89zFT/7eIh+4p8sn/onglytHOPjTHfz+e/nAQRfl6/+KxYP/F0tPfhNe9ZgJfY58BLwj+3G8RQ6rR/A6Hkf8Jr/vVfjj+5dQ+3cxsbRE50mfyu98iic7nyjSozPFhdj/+BJ/+jVB+4TLh+9b4MgXHJY6FAHWk5d8BEsw88Qlty2Ormdz+yHNy8wmv0Wy9863fsjV3l14WZ6wJOtrP2pPmEAfq3KJJ1YvemIryOhvDUmjHHQThEYWC6oVF0V5syfGxqrUTIOoP2SwFWLqGnEKqirIkhglM9D1omH0MIwQ2agHuq4Q9IeQh0gh6CchKhq6rqGpCnEWkWXF2DYpI+I8RddU0ixGVbTRDm9JPipkT9MMQU4KqEKiOxJVGhi5gm3rGLqGYaioQkFTdWQmC09kGTmSJJdFp3ZNxdBNDE0vFgeUDJHFkI12FWay8IciyfLi8xI5pIlEMSWO7qDpUKsYBFFItfFmT/zVM5d44tqRJ7b+Bjxx6If3xLtiJSsKtrAMwdr6eRQZMdjaQCElQyXOcur1OuFgiG1ZF0bJkEtMU6fmmlx/4+2EUcquqb0oeUKtotF+/x2MT07znx7/KhubWxxqNogTheqea0hEQpxluBNVKlbI+ISDYtgoWQp5hi/6NNQGaZwhsyK9pdsOfhYxHA7IM1BUkyRR0QX84rFfZLI5QbNeR4qcXElJ8ow40ag6U6R5RBRKckVFWApOo8rhw9fzgffdzOw1kziGiq4W42qEqozqlBQMo1hpSvOMPE2AHGXU8sGi6NEhpaRWq4EYpfSEQFGKeqwikBoNBR11ht/uQ3Ux6JKkSU6YCMI4AaGCCpqhomsKuhRF2wYhIM+QMiEnQ9MVhKaiy+K+oi9cseqV5JI4SciRpFk2qr8Sbxr5o6kKmhSMO1X8KEM3LfxhSBxFSFLSPMTJc1SRIP2IeGvIzL4pVs8vsX/XBDceuoHp5jSmaZIGEToGwzgEkWLoKnv27KFem+DF185zbiMkXo/Z41Twhmv8wZf/Dfv376e/tYmqZQwGA5IkLKbeZ/FV+Bb8EMxfjRc9cVmNLN0T4Hqw8IZVGI/u6oMXV2ZGx3HnYW473fUA8NDo53aLPfeegIeB49D5w+O0XWjLOZhv0jl5kvbDs+AWaczmvdA6+RjtOY+T3v3McZRf+KOj8NmP8tHPfZaF+zvcf9v9/DoLNNsL/If/cD8LnUdpLcB7jh7jPR/+MPOdFvMfbHLgj1p0O02+dXSOez/ZwWt6eCc/y8mFJscfPk6nc3eR2nzAo3tuFVptuic85o8d5/ixNu5xl85R+N2vHufppz2+2IUDX/szONbkk7c+QPMLj7J6dwvvsZN02x0WHoG5r7jQbHP0PhfvXi4UrLvvZIVyR/q3M/cOnve3gMvyRO9qeCK5LE986p/8Im5zgsk3eCIlTqBaeaMnFFWhOvLET//4zcwemKRiqOiaAYoKFaUoatd0LMMAA1KRkUYRQpcohoHIwcp0yHIQkkrVQRGSPEwReVYUostix3gqi7Futm2O2jcoF4pxkzRHE5IglASRIE0y0lwpeiVqxY5wU9EQeV60EhIpuUzIKUb2aIaBJRQUTSFXQNMFSZISZJJhFJOrkkiAVDPkqIW1VCSZyFENBU1VMYTCmLnDE/478MR1Fz0Rhz96T7wrgqzl85vEqUC3bXJd4bbWBJoW0GiozFw7DkpE1amQpD57911LGErCeBMSlY31LbIsQzUtQj+g7w/pbQ35//7iG/TDdSo1qDVcwlRn9946ti7QDY1rD+4hSVNqukIc9XGUjPObHqYpELkglBuYio6mjZEjGHMczFxFVzWqNRO70iNlA9UM+ff/8Y/Z6K3jOBaOYRc7M9KYipbRo4eqSOxKFcUyMPQKqmJgajqmbbB/ZjeHD17DzKRdNODMJLpuomcJeZKT5QF5EuOHAQBZkqIojIoWi5mHcTRERaIZxd6OLCuCNChy2eTywvzD7ZUsQfEQTSlGQphSIDSB1CBDR2YKMs1IshzV0NENiVCyYs4iElO1qZs1nGoNV75FQwAAIABJREFUZEaeJRfqvIodxUVqUdE1VIViNiPFX3r5qOO7qpkEkU9vOIA8J/FjNJnh6BLbqNCwGoix4ngfeN8tHLhhD2NjY9w0cz3jTQvbrOD3ffYf3I0UDeyKw+7mfs68dprnl09TrdlsbK2SRAmzE2Ps3jPF+z/0Myz+xQv8D7/xIOv9DWqT46ipZKu/SRxnGCqESXi1vgrflwevfknMm/i+sdaOO+Z2BAieu4rXeqCoHZvjYkAwX/yvs117dALmjrfxFrocO3qUP7y/xUIHuh0PmtB8Gppf7XLs6Bye58HxWRZb0H6oBUc9bq/C8dU5PGbx/rDL8W9/gS/PHYOTJ/nqB+/nw3h89GMdHvnqB5lrz9EBXLfLUzSZn59HiHs59gttvMceo9PxOPawS/vuNp3mUb7w0Byd1hxffag4+XbH5ctfadG+u0Nnscttn23x0RMec/MtPPckt4v3wm1tHnvU4/itP4/XmeXh+1xW752lc3eHzgkX77NekT71PLrdDrNQBHDb1237el0Oj3EhiHX/Jlp4jJh7F7R4uNQTN70LPdH4fp4wLnqiWr3oCTWNcXRBL++hqhK7NvKEVXjC2PbENbu55dr9zEzYmLqKqUlUzURL48ITaUAexYRxMbQ4yzKECpmaoajF36FpFKAAqiOKnlpSKXqaxjmaKkjjnDgKURV1tJpU1M0aFZM4FuiGwMglqCBMQa4Y5JmKKiVxLrFsE92QKOQYOmhqjq5Y2Fodw6kh1Iwsj4iGPlJAnkh0TS+CU1NDzQWKlJBpkIwmg+QpqmYQRj5b/g5P5BmOcZme0C96Av7mPBFdpifeFUHWnj276G15HNo3SxYnbPZCMmx8f4Xe6hq7m/vIlJiJySmWz53HNDNuue0wqinJEh9pKjRsG80QZFKnN8iwdZdd7i7Onj6HoabYFUiSkCxNURWD+vgEL59+je88/wyqbuLHKbumZxj6UDEVUkMHoaIqObqW8VfPfYcwjZhyp0CahAMdUzSLXh+5YNeuXSS5RNGhXneYnpqgVjWZbDTYs2sKNUmLfsKWfmHGoKFCKE3Gdxvs3T/JbdftYvekRV2NyYMtiIbEgz6pv4Uqi/ScYtgg8os9r2ROEmfEcUwaS1ShoSn6aOOtLPpcUbRaKFa1iuBLFg10i19w1SgGTYsiILJVgW2qNBo1LEtDIULIFEs3SLNiOTeTKWmeEscxURQRRQFRFJGmxY4VqWqoiokmVcRo1S2XMVKKoteMqZFGAZVKBc2yGUYhqcio2RZmLjAdHc0R1BA0pytspVtYMiaPQ+yapN5oUHUNLCXmqe+8xObGOYZr60TeOX78tsMcnDqAldTonV7HErBrepKltTOcXj7HdQdmyOIIvz8gThNW4j6KzEnThDSKi9Tn1Wa7BmYktwcundr7LuCCd+eg236rO4CToyWqE+C2Wtw3DxyDbgc62+/pLNCCzv0UHctduP32ewHotl3cR7q0F+CPHnGh6/H52+GxSov2XLFaNn/b/bhui1XPo9WFB46D1+4wy6O0ul0+99HP8lHXY4EWHzw+i/v0LA+9f5bOf9ui3VrAPdbBO/Hv+G/uX2X+4/M8IGPmW59ltnW0GMvzmRMszHusdk6wevIknFjg7o/eS8ttQtflcx96kFY8x7GHmrTbJ2jPdek83aHlJTz88HE+d/cJjh738J6eBTy8VodHvSZ0Zjl+tkm7+RgnDZfuJ8HrFt3cizdfXAwXOLE9APoNDa/egh1jj3bG5d5fZ1VrYcc5XUX27HujJ4bvQk98Z9sTzSkkOzyh7fBEOvJExWHX9CSNhsnkeIPd01OIeOQJUy/a3ehgKIUnaq7C3v2T3Hr9bvZULRoiJo37EA0INvsk/ha6yMlSiWFVEUoxH1dVVFBykiQliUOyOKfYZqmRAlKFLJUIoZDnKkXXbFG0OjQgHkbouoGtqcUud0VFzTJMAbYhqNcqWLpKng4RpFiaQRTE5JlEKimZjEjSiNAPiUOfNE+JwhhFFeSmXngCBYkOuUDoxUxcJdMxVY0k3OGJMLjgCS3b9oT8wZ5QC0/0dnjiv7r9r+GJ4PI9Iba30F9NHviln5U/c9dHiIZ9zrx2hkPX7uX5xVd5xTvDdRO78MOEJ59+hoPXHmCquYe1jdOE4SZTky5pBK8snUEROrv37gOZEMUplq0zVjXJhIGmqGRJRJQYDP2UKA3IEx+rMoGZJazHAY6hEWUBjmqxb+YASy98l7X+gPH6BJGUVGrTrHkr1CtVer0+ExMGS6fPYFsVFGmhaAKpGsgshzxFQZAGA7b8Ibt2TxJkGWEvQNdNgkGAohkY1Sq95SGaEYOwcKYsMtPAsiwcf5UXX1qlXh9jq1esYiWZZH2rj8hSojgjjTPiNGEQJAzTgCQqthE3pxqs94qmb3meF2N5clkU1OdF+4Y8zzEVDWmoVEwDoQkMIMkzNM0iSSJ0RWBZOhEqw2FEmkiCOCoaklI0Rk2yYqdiMcxaHQ2kVkdjgCQyL/pi5XkOeVZ88RUbhRTbrqAqsLm5iaXrGKZG1SlGBZmOjiZyLMciwyQLIpoNhVtvP8w3v7PI6sqAydokNbfKNXvGOb85JAsCzi6vMNaosNwfMGHUaU65PPO9Zzl4zRRjlRp+EhX9yqyUYEIwq1/Lrj27GOQ5imYxVneomhX++b/4n8Tb/9ZeWebn56/+F/MyaLGj+P2HwaMoiv+KCx3v4m0/aOXEpag/mgX3NvAeKZ7kHQX3seIYrbk2q14Xz13kwdu7/OGn26w+4tJ6usnnbv8kc195gBMfahFLl7Nf9aD1GCe64HotvMc83DYsfAa+i8sDeMxFbT53oEP7uIu34OK6i3AUul33QkDT9Vz+6PMeDzxcdF3vfA5mj3eh06bjebRnXbxFD48urDZxF2bh2CKe6zLbLlatFqAY8fM+cL8wusBtik0Cl65oenDMfbsxStsDJi/O1PlhPjMXuH9+/qp+J/6L8cRmgC5MgjhAUUeeOD9EMy/TE7lkfdBHxAlRXPS4SuKRJ7KQJErI84yJ8Tq9QUycxaNm0AJVVdAwUE2JyCW5zNCEBrqGY+goukBLIc5zTN0kJUbJwKkYhLmK74ekqcSP42IzFCp5LomiFJSELM8gL4I+VdFAUYoO9plKJovxcCLNEVqGpjqoIsW2KggFtnqbmELHdEaeUASmpaMpl3iirnD7jx3mL76vJ3zOLq/+yDzxrgiy/u3DvyJfP73Ec995kUzmTExV2bd/L7feegevnXkRLdXZXF9nYtzhme+9zJg7znBzmcCPmNh1APIAgUHzuhl6585TM21O95aZqNZJBiF1u8HiK69y4ODeori87iL0iN7KBlVtnEz3EaMCc6FlmFqxhfbA3hm+1vkW5uQ4+SDHdiLOvdYnNSNmpnZzbnWVal3jp376Azz5l99lsBag2RAnkKQagk3UtEqiDtnqZ9QbBlEMs9ftJbYVNHs3uVMFFGIlo+nuZTNcJ9kc8o3f/QLVqsNEw6JiTBBEIVubW1i6QSQlcRZR121Wez0gQ6gWZDlxEhJFAcMoJREZpmGgx4BU8WWKMDVqeoDMxxCGShIMsHWNwVCSGGAgUdEI0gjL0BGKzlYQIOKMKAlJYoVUUVCzlEj6qKLKMA7xA4EUPkITCJmjKhZaopIqkOUR6AIlkxi6TmW0khVnGZouGA4CdFulbttUagaKodJwHKTMGMZQrTqMWzX83ipmpcZffu8FDk3tY9+hfWysnmZtdcDeg7u57qZdSGFAbmP3erzWO0fDGMOLUhq7xtH6Gadfe52YHHtqgqxyGuVMgzve+16CPEVYFQw1IhgmnPjf/88yyLoMWh503beIii7X5B643ZH6W903HuYHBVtzFAXfs108r4VbxDK0OsUKFxTP77ag2d2ei+ziNj06J6H1EKye8OCoS+vkSTzazB5vQws+99FFvvCQS+dz4M7BsTmPk14b94NduLs4tQUPfverHo9+0WV2FRY7Lu6iR/e7Hkc/7dKlw/F2m3s7XdrdYyx2Fzh+do7HznbhxCLtOZdFYHV0nRa94rUefB888C3evGj1FtfDHd3u8eb7ikv0IAs8AJ47Cta8iwO8KRZMf2CjBw84CfPx1Q2y/tZ4wo449/oWqRn/cJ6oG0TRyBOOgubs8ITIaDbfxhNxyNbGFpahE0pI0oiqYeNt9hA7PBGlIVEY4EcpsciwDAM1BKGo+FmCsHTq6oA8n0CxNZJwgCFUghBiFUxFoguVYRpjmTpCFp6QUUZKQBSoZIqCkqbEuY9CBT+N8AOJUMLtOAtFGJiKTowkS0KEqSAyiaEVnjBNjSQvmmlHYYBuXeIJ20HyFp5wRp6YfrMnrr+pRi7GIbOxN3u81j9HQxvDSwpPGP2MpR/kCSUi6Cec+O0f7Il3RZD1G//iv5ZxusHk2G6EGqMrRdomjmN2zdzIuddWSETG+ESdjY2Aod/j/NllJibqTExO84df/Bo//3PvR0t72I0KtclJlDDgtmtv5y+f+jbrGVTqFTzPY2LCRYlDklQjzmOqNY080YjjhN7mkOldEwyiAFt32BiuUderjI+P8/Lps2QyxKlWGR+z+NY3utQbFhO1SayaSRDGbHh9zq+f5T3vO0yapmx5A6rVKpEMSIOE627az3eeX+Q/P/4koZ8wYVuc9yP6g4TpisMe0+Gun//7vPryi9SqBqZRIRUZaxshYw2H/sCnOmYxXA+QalG8XrEd/DBgeXXI+LhdFLangKYyDANWVs4zMTmOKhWiLCQnQZEOYRxQb9jEWxmKnjMIQkgFwtAQsih2VIVClKQgUpJYjgZZq/hpTEXV2EqG2IrJ2JhBFErOen0SCXkqESrEMsMWkEmFPE+wdJNUlWioVGyLoL+FU62S5pI0ykmUjJoquWHvXlb9TSanpqlrKrWqSZgo9MMBu6aaxP2A06vnuGbfFOveCoZWZ7o5zpnTPTRNI0gS1thEX+lx20/cSZis0t8MOX1+QGYIXMfCJmNy1sA7X6MiJLkuubF1K+u9s0yOT/E/zn++DLLeAQt0uY8m3k7Tu+B9Bh55eDS+5RhvXnLpAq1j0D2J1wT3JDAHx5pFphF4Q1CwffjOCQ+v/RhzXzxGV3RoPtzG9cCdPwZPn8TrLHCseR8nu4vgunQXoNl2cUexxvH7PRbuL4rKH713gVl3jt/6Njx2ALyHwFuEWdelc2KRlluMCGp5Lt1VrwggPY8uLi0X5ubbfG7PvcwdP4bnebTnZjnxPo/2ax6tVZfuPwav7bH6eWg/7LK4ALNfg7aER2+D1Tk46hYpwdlWi5bb3XFtAA/mPw/zD7zdJ1CEkDuZ92B+dnRzd66IPi885hjznOR+dsRmC7xt7df8VV7J+tvmicaYyZPf6FJv2G/wxPpyn7XeWW5932HSPGVr5RJPtPbznRde4D//p28R+AmTpsX5MGI4SGhWR574+N/n1Vff6IlVL2Bs3GIYRFQqBsNeBKpECkHFsvCDIaurAY1xq5gqEgOGWvhj5Tzu7nGUUCHKQ9IsQpVVEhlQdWziYYbQcoZhiIxAMXSEAkky8kSaAglxTNFsGo2+71MzTLaSAY5qUasaJFnO2dUBSUrRFFtCouVYEvJMQeoJBiapLtFRsU2LcLBFpTbyRJiTqBk1RTK7ay/L8TvzxB53nKUzI0/EKWtiY4cn1hhu+rx+fkC+7Yk8Y/Kmv54n3hVB1qP/8rNyY/0lbHuSOFOpqQa93gbuXputwMR1bM4sr3DLe25lebnH/n3TPPe9Z8jSkFdfO801Bw/hjjU4v3GO3NfIMdDUiHpd47t/+U0OHLyFRFdxm+OcOnWK3fv2EvsJVVuh119BauMINIJwyLDXR3U0pmpjBGnM6fNnmHTHkCkEg4Ra1ST3czIFKo0amg66oXDqtTPIXMOpV4nTFC2H4VBi1wQr5/uYjoEmMsIwZKwxwcSEQ6BljMsKq0MfP09INwZYmk6URgyJqKg1Ir+PYdVRVZWtzS3Onn+JiYlJTr98GqteZXx8kmrFRGCxvLKOahokSoYBxapXGpPlEarUyYA4zzAVg1SJ0XKHMAuI04iKaUKSsxUmiDzGT2NM3SJOE9QsI5ISRYGNngRyHM1gLfDRkOiqxLTHiP0BiqEzHAbkEmQMucipj1fRRdGYtK7peMOITAiyvJjNlac+Nx/cSxRFHDx0iFdffZXDt8/SqNgsnfFZXjlFRRfE/pD23/sgmqry2uunUAyLrz3xNT7+Dz7O0tLrHDhwkOFwwEvdV3jvjTN85c87TO+9gebUOJu9NWI08ighlCn7xiuMXTNZHOul59kIVFZOrXLghkPceF2Lf/lb5UrWBd7s77emzTtviLrz2NvBlAdt9y0OtR10bLP9szt67jEX7vXe2C/MhXbbpbOw/SIeruuy6MFsCxY70J5zWe16xWmMasI8b/v9jJaPOuB9DXjgkgUjFxY+t8DcsTnmHinaUXQuHKCoPWs9RBHAtF3aH/JY+AWYPVbUX7WbcLLzIO1HPo03X9SveU0Pt/22OcB3TgtO3A3H53/4Q1ztIOtvpSdUqNT/Gp4YcwiMjPG8guf7DC/1hIyoaG/hieWXmBif5PSrp7GcKuOTb/SEZhnEYtsTOlGakOYRmtRJJSRkmOItPGGZEGdshSkijRnGMaZtkVziibX1oklqxTJYG/ioOzwRDQaots5wEJADDCHVchqTVXSK6SdVVWEtTMikUoztEQKZB9w88zfriR+7aYb/t3NlPfGu6JP1+jPfmh8OEsab01h1h5eXnqTemOCVU2eZnpri0HW38vrSa5xbW0dNIl58YZHV3grNaZfZGw4QxgpeL8LUq9QqKZqmUKk7nPf6TOzeg+0I7EaF4XDIWG03Mk3YHPYJ4yGhmpEMM6LhFvaYSd02Gas0yIYJ7jXXYqFgCA2ZmxhVjcgPUY2IQOYEvk9/q89WL2RsvIntWIRJimEVE9JNRSGN+rhju0lkiqE7aLaFbdlkMiMd6sRyiJQ+SppRsQ38KEZRbQghiNbQjEl0vWiNoCkCy6pTbzQYb05SbYyhINAUk5iUWrXK7mmX5Z6PKlKkaRLHxXJukMaESYpl2iTJBlpuI9MeY5OTDHsxMQp54KM6NTLdoKoKBBpS5vjCQMsskiCiXrMRuYJq5SgKTFgallVBZjmqrqHnOYpI0WwFbbQzRopi5mISZgzTDDHaEVPTVXa5TSpmhb1TTXbtP8Da2iaHdu9hmAteXTxFRsj0xF727p3m5iMtpKWy2tvCVi0yIbntvbcidYOXTq9gpTlry69SbUyyNQiZbE6z0ltlar+L25zAVAQbww1ElKLGIGqgGhtc496MSBVyLWdrfRMTnZ+/+553f5+sHxELR6C1VOxW83fOQ/R4Y5ulJd45O5s5HQHXB9+Hjzg72kNtz2g8Chxx8f6Rj3Pn6PWb4P4c+E9A90mfxaUWR2Y8fHd0Pi489ZSPw/aikIN/j8v+x328Jdg/B79/0ufYb7osvurjNV26Ty3RdBzm7mnzaGeRSms/juNz5OswM128pn8SuAd4Co7e1YLX4alnwX8Qlr7s4KhLcMRh4QNw10lYEh5HmrD/hS5PtZrQcfEqPt4iNB86jL8MHHGgBc6TThFQzvB9gttLLvxljkZq3QHOEzuHGF4eo8W0q94n693kiapjMl5pkPUT3P07PJFd4on8r+kJRp7Ihwh8xKWeiAtPqOokurHDE9U69VqDMXcSpz6GKkaekCm1WpVdb/CERZzGWPrIE1mKodtk2QZaZpOnPcYmJhj2YhJFJfGHqE6dzDCoIhBCQ5Lj5waatEj8iEbDQWQKipmjZDBualiWg8wlGBpGmiNEgmaraJqG4ZiAghSQBBlDmReeSHNqhsquySkqxtt5InijJ3SV1a0f7Im+HzI5OcVKz7tinnhX7C78q5efRHEkg16fzbUe44195JjceMNhkiTh69/4KsvLZ9BUCJOURFVp3XAzUQhnzw/RFJWzS8+ytfEKp5dXmd7bJEnWGa8ouGM1rIrL1rqPqupkQjIMoVJ3caxJpsYPUm80aExMkvgZvaHPVhiSKnDmlUX2HGiyMvDB1BEpxOTEuYk7MYGpqdw6ewMVC5J0C0VPue3mg+QDn4Ef4cc61q6DKHZAxVHRbYFhSRIJdqVGFA6pOZPEgUAVJmmuYloWuiGwbZt6fQrNiJDCR5eSSXecib1TCE3QbNhcM2nQmp1i77SBY0hUW7LhDbl+okHdMnCAGhpGnFNVVMYqJlVDoWpMFAWGRoU02GK8ppGFm+CoKLmPGvYYDFKSJEERAXtqGudWTvPCWY/l5WVs20YVCpZaFMUP4pBBNGRrMGA9TUGxRz2xcuI4ZNjrsbG2Sd/3SbOcquMwPjbGzP5JDsw0uP22GarNKoaacvjWg9BUqFvg1DS+/dwiq/46iRSEvuCV506hS5NESM6ePkU8jAg2Ntlbq2IooBkG1UYFxc6oT1SIo4xgM2ZXbZLEAEWrYEyYbCFY/PazPH3G46++t1h0eo90gqFk6eXXr/ZX4qrgvemH4se50ZKSd2m/pu9XL3XJ7W+IE95ul9t2bVH74iKO64K3XZvU8WDBw9096qM177Jwt4c378E8ND04PtehddxlvtWi9UhxTLdVpPgutIhYhc/g0VnlQh+qE18sUnucXKQ1W6xunXjfSb46fx9zq8CDLqufhk6rOI3ucZcWHu4suJ5XDGUGvAfAHVWjz7W6fLlTdHW/75H76D7W4dHZFq4LtD1m28V77ZyEB1ehe5KLRe5et+iIv93m4tLls50cY8cD31gZf6I1uuYeuI9cuhR4eay+42dcGa6GJ5xtT4xd9ETsZ/QHI09ol3jCujxP3LrTE8lleKIySbjDE4Yx8oRlUa9NoW97wpKMT48xMd1EmIJm3eZAU+emm6Y4tMvAcQpPbHoDrp9oUNv2hNRQ4gxHURlzTGqmgqONPKE5ZOEWE3WNZLCOXtXRch910GMQpsRpgiBg95jGuZVTvHDOY+X8eWxn5AlLJ85VBmnEMB7g9wtPKHql2MQoJEkSMtzqsbm+ST/wiZMcy3YYq48xc80kBw40uP22/YUn9LfyxPNv9MTzp9Cl/ZaeUBXQzMITqp5Sn6heUU+8K9KF9/zDw3Jq0mXmwPVkqkrdMlCxGQwj+gMPw7C59tpdPNd9gZefe5kbbr2ZWq2GoRn4vYDmLp3NrQG6bhFFajHSJQ7I0xC70sD3IUpiDEMjTQRSyclViZULMgRR4lG3J8kzk4yAFMHWssePfeAWnvzTb7J3zyH6iY9MoqLRWl5DkzFCCKLYRxKjGAoCg0Fvk7GxaU6dW+WZp15g099k7h98hISYNEto1GxOnVlGyoxadQzDaSClJE1T0jBgbNwmHPr0fUkYSKbdCvVKlRVvGREmqKZFLCosn36ViaZNvW4WXYWVCo5mIHOLl5eep1qpoFsmulRB6hiOyTAYksQhplEHJWWz38cQKjKDKAtRsphBoiMqFfJkhSySGHqFMAxJZY6j6uimibfaw52qsbK8TmYagGBtbQNNqsWOF8tm3e8RhxlJFpPJomeWpmk0a1WmJ6pYdgV3wqJer6KrGoduvJaVM8vkZKQiQ9UUrj1wPf/q0T/g9Vdf55f+6X1E8QDSgDDNSGKfROj4w5A/feLrHLl9lub4JBPNMfx+zNmz57F0g36/z3veewe7xsd56sXnmdRrvLSyRN2tka/0eGrjJcwebOnjmBH8vZ9q8bq3yZf++M/+y00XXs7uPt4ii3jp895B+rB7AlqXjG5pjeqnWCyOO9eCR0/A7HHofLJD89Nt3Nki6HG7Lq1jHic/BMc+PXpumyKmWOXiwOTO6BwvBDNFT6rFTlGAT6uo2O+uQuuoi4uH1y3eXJfRLsJRMOhuV51vDw70eMM18E6Cux37dIs73GZRg37herUv3nfFudy07/fhaqcLr6onpCBKfwhP5DFC2eEJU0HIy/PE0tIyKN/HE2M2oe8z8CX+ULJrqkLNcFjZXEWJEoRpkVBh5eyrNGo2467J5vo6uVrB0Q1kZvHy6yNPmCY6KuQ6RuWiJwy9jhApm4PCE3kGcb7TEw5ZskoeSQytQhCGZOQ4qoZuWhc9cX6dzCo84a1soCsjT5gW6/5W4Yl85Akkmq7RrFWYnqj9nfDEuyJd6J15cX7MHkM3VPRKFffAPvzBGmaaMAh8VFvjlVdeRDOrvO/H30ceQ6akbGyssu73qelVzp49jR9lGEKh0Rgj8SOiLEbVHHKRECYBQisKsvVMUK03ULIMkedUNRujqmDbMYMg5MzSEtO7G5x7ZRV7bJzvPvtdPvTBO3jl5RcI+z6mllJtCIKwT73qIHIDmaiIxMGo65iGQiwjDt98I9fffB0yFeiGjkyDYrefqtGo7kal6DmiIZAyxkSSpyqzh1sMVs+zuXGWxliVJB5Sr1psbMX4mz2iNCLY2uDUubOY1jiV+l5kGBCToSspQQTN/WP0ewq245DXE9JBQK5UsNWcgUwhVRirOkRxgJAquQpOvYHUJboeFXMVhQShoKGyMdhE01T8GCqVjHgrxtANgjAglAJVJljCZMIKmNYtVgY9olRQN0ymGjb1hsmhmSmaEybXHjjIbreJSDNUM6XWcPCClDRKsaemsHMbwzL57vcWWTtzjttbs3zzm9+mYtrIZEDSG2JpdSaaLi892+VnfuI9jLtjVDWV5bVNbvvJW3DUcfbfuBfLrDGQQ85ubGGmKUOp0twzQbrWJ5syObT3Brw05Ceuv5mxSZOV4CwHG/v58Nwnr3q68EGKCSdzHjSf7LI00+KN+bULzZPeMR7gzLvwxMXjuYDvUWw5u3SUy3bKbscfPVy8B0epO6C932NpyQfH4UQX5nz4beY5Mpr903ZhyQHPh/91fp5s/AkqjwIz8FQbZtpt3E8t8atPwJf+jwX8SouFX3uQL/67O3ni51r8o1s8vrXHYaiWAAAH4ElEQVQMd0xD82NL7Hdm8H34X+bhqWeX+L2POLS/Ap+/B37pM11map+Hzz6BG7jM/KrPZz7c4XDLx3EEHHVwF+Hxk13EL7fA93E+7/Hgz/42n/jmEf63G+DwSZ/uvIv3hM9C60k+svgUJ3+zxW98DCq/ucBnvt7h8J+3cNoO3pMuzu/4zJ0G70hxHX+5A87XWlRUD2/GgaVinNCJx4HjLvf4PktL0D35OAtHWhyh2GDp7czmeXDcKWJD70FwVFj41/BHLty5uONxO7KAOz+q423ofAq609CsvMUvwnYuEOg+CM073/yQk2048otwJ1c3XeideXG+ao9hGiqGU6U5s5fhcP0STzyPZtZ+KE9kIiHKA4RyiSfSDCFyqurleeLlxReIhoUnamPgB1vUaw55ZkCikic1zLqKaSgkMuLmm2/kupuvQ0FF0zVkFGCaBpquMVbbg5rqmLZajKAhQY9TVKFxw20tNk4Vnhgfr5JkAxpVm42tmKjfI0xCBv0Nzpw7i+OMYzl7IIqJs7TwRAbju+sMthTsikPeKDyRSgfbKDwhpKBqmCQyJENFKuBMNpCqRNED8iwlzkBRFbREcHZjHUvTCFJwnIx4I8bUDfwoIMwFRp5g5SNPGBYr/cITFd2kOW4zNvKE2zA4ePAQe90mIslQrR2eCFMqU1NYuY2umnzve4usnj/Hra1Zvr3TE5tDLP3NnqjrKufPbnLr+2+hor29J5LlPvmuN3viXHiWQ+5+PvwPf7An3hVB1jNPfWV+YqzO5ESDtVOnsAwNQ1HJRE7VrWILCSmse5u8/srzZHFGc881kKVsbG2i6zruZJ2KbbB79x6Wzp7GrNoIqaKoGmE0ZNNbRddUTNNEm9AY5AEHdu8h8n10SyXMY5Y3tphu7mbX9G5sx6HqVEjVnNkbr2fljIdtN2iMNxmvT5AkkjRRSPIMVbNQVNB0Ddu26W1u4NSqBH4f3dAJhwEKOqZd7LJzpybZGvjEcYQfDMmTDFUoJECaZJz1zrO6vEJzapp91+zjplsP89pLLxDEKQeunSFOA2zDpFKtcrh1gDBe5ac+sJvl5Zw4CqjWJsnyjGrNxB9uEQXFfK8UiTvVIN5KyHNJkmwhUDF0C0RKlkuEqmCZJnmQoaMTbcUIKag3KiR+wPpmnzQZctPsLXjeKpmUCFRCEt5/036OvP8mlrxzrK8N2L13F/WKzsy+PVyzby91wyjOu9Fg6PucWe+RqTnV+jgrax77p3cR5im6EGz5A2I/olGto2kK1x46QByHNPfNECQDrrluN73VDaZ2TfPcc89Rq4yjCI16cx/DcB0DHaELguEQXTdIUgXTMDFrFWK/xzOvvchmf5XTZ5fRx6sMz6zgNHSG6pDg9R6f+NR/f9WDLLUNM0uw6MDSTJM3RTkXBgC+cxwXun/iv6E6xz8B3AW8zsV6oO3yn0sG4fmj/24HWABLSw64xfm0m+Djcs/JP+Ez/xHu/HVYehTco8ASPPHhL7HUdPBmwHdhxgev8ou460/wzT+hWFFyofUr03zpxCLOsw6f+PM2tXyJ5j8Gz5kBH7oLHv4/cyDbz+OP+hydgWUHnDuaeL95J87gCX71Wz5fAu488hSO08Y95uDfBv4NMNNusv8nZujeASKGX/mfj/Cpp+HhZnGev/MzPs884fInv7TITPsu7qmB67ksPDvDxx44wl1thwrw++IeWh97Es+HRR+cCjy+H7xnPZo1l9aRGZ56ysM5AixAq+njzXjgOTT/uMWRZ4qLeiHA6hSfgevA46PPwLmzuG2xC//dx4FndnwuHkXA5BQ1bdu3ufcXi3jNxdFneik7VraaT1wMunfEXhzx4cE/gfmrXJP1zFNfmZ8Yr+OON/BOncI09e/jid4P5YkoGrKxcoknkoADe/cQDXd4Yv3tPeFULnoijiFNC09oeuEJXVHQTJt+fwPbqRKEfXRdJ9gcIoSO6RSeaE5N0htEJHFAfzgkH+34TjVBFKWcW11mfXmVqfEp9h3aR+vWW3j9pRfww4QDNx4gSQJMzaRWq3LzTQeI0lV+8sf3snImJY0DKrVJJCnVmo0fbBGFhScyBZq7G0T9hCyXZHqfLFVxjMITcSgxKwqGYiAjiSF04q0YmStMNmukYcjaZp8kCbj55sOsrq6Sjjzhi4T3t/ZzpH0Tp9bO4XkD9lyzi7qtc+CaPVyzbw91w8SpWNRql3iiOs7KeuGJIBt5IhwQBxH1Sh3TVLj2wA/whDOOomnUp/fhB+sY4u098ezpF9ncerMnAmVI79Ued1+GJ94V6cKSkpKSkpKSkr9rvCsK30tKSkpKSkpK/q5RBlklJSUlJSUlJVeAMsgqKSkpKSkpKbkClEFWSUlJSUlJSckVoAyySkpKSkpKSkquAGWQVVJSUlJSUlJyBSiDrJKSkpKSkpKSK0AZZJWUlJSUlJSUXAHKIKukpKSkpKSk5ApQBlklJSUlJSUlJVeAMsgqKSkpKSkpKbkClEFWSUlJSUlJSckVoAyySkpKSkpKSkquAGWQVVJSUlJSUlJyBSiDrJKSkpKSkpKSK0AZZJWUlJSUlJSUXAHKIKukpKSkpKSk5ApQBlklJSUlJSUlJVeAMsgqKSkpKSkpKbkClEFWSUlJSUlJSckVoAyySkpKSkpKSkquAGWQVVJSUlJSUlJyBSiDrJKSkpKSkpKSK8D/D1OmwaAjIyrdAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "adversary = SparseL1DescentAttack(\n", - " model, eps=1000., eps_iter=2*1000./40, nb_iter=40,\n", - " rand_init=False, targeted=False)\n", - "advimg = adversary.perturb(img, label)\n", - "_show_images(50)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/deepcp_examples/tutorial_train_mnist.py b/deepcp_examples/tutorial_train_mnist.py deleted file mode 100644 index 63cb1b1..0000000 --- a/deepcp_examples/tutorial_train_mnist.py +++ /dev/null @@ -1,127 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import print_function - -import os -import argparse - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim - -from deepcp.context import ctx_noparamgrad_and_eval -from deepcp.test_utils import LeNet5 -from deepcp_examples.utils import get_mnist_train_loader -from deepcp_examples.utils import get_mnist_test_loader -from deepcp_examples.utils import TRAINED_MODEL_PATH - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='Train MNIST') - parser.add_argument('--seed', default=0, type=int) - parser.add_argument('--mode', default="cln", help="cln | adv") - parser.add_argument('--train_batch_size', default=50, type=int) - parser.add_argument('--test_batch_size', default=1000, type=int) - parser.add_argument('--log_interval', default=200, type=int) - args = parser.parse_args() - - torch.manual_seed(args.seed) - use_cuda = torch.cuda.is_available() - device = torch.device("cuda" if use_cuda else "cpu") - if args.mode == "cln": - flag_advtrain = False - nb_epoch = 10 - model_filename = "mnist_lenet5_clntrained.pt" - elif args.mode == "adv": - flag_advtrain = True - nb_epoch = 90 - model_filename = "mnist_lenet5_advtrained.pt" - else: - raise - - train_loader = get_mnist_train_loader( - batch_size=args.train_batch_size, shuffle=True) - test_loader = get_mnist_test_loader( - batch_size=args.test_batch_size, shuffle=False) - - model = LeNet5() - model.to(device) - optimizer = optim.Adam(model.parameters(), lr=1e-4) - - if flag_advtrain: - from deepcp.attacks import LinfPGDAttack - adversary = LinfPGDAttack( - model, loss_fn=nn.CrossEntropyLoss(reduction="sum"), eps=0.3, - nb_iter=40, eps_iter=0.01, rand_init=True, clip_min=0.0, - clip_max=1.0, targeted=False) - - for epoch in range(nb_epoch): - model.train() - for batch_idx, (data, target) in enumerate(train_loader): - data, target = data.to(device), target.to(device) - ori = data - if flag_advtrain: - # when performing attack, the model needs to be in eval mode - # also the parameters should NOT be accumulating gradients - with ctx_noparamgrad_and_eval(model): - data = adversary.perturb(data, target) - - optimizer.zero_grad() - output = model(data) - loss = F.cross_entropy( - output, target, reduction='elementwise_mean') - loss.backward() - optimizer.step() - if batch_idx % args.log_interval == 0: - print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( - epoch, batch_idx * - len(data), len(train_loader.dataset), - 100. * batch_idx / len(train_loader), loss.item())) - - model.eval() - test_clnloss = 0 - clncorrect = 0 - - if flag_advtrain: - test_advloss = 0 - advcorrect = 0 - - for clndata, target in test_loader: - clndata, target = clndata.to(device), target.to(device) - with torch.no_grad(): - output = model(clndata) - test_clnloss += F.cross_entropy( - output, target, reduction='sum').item() - pred = output.max(1, keepdim=True)[1] - clncorrect += pred.eq(target.view_as(pred)).sum().item() - - if flag_advtrain: - advdata = adversary.perturb(clndata, target) - with torch.no_grad(): - output = model(advdata) - test_advloss += F.cross_entropy( - output, target, reduction='sum').item() - pred = output.max(1, keepdim=True)[1] - advcorrect += pred.eq(target.view_as(pred)).sum().item() - - test_clnloss /= len(test_loader.dataset) - print('\nTest set: avg cln loss: {:.4f},' - ' cln acc: {}/{} ({:.0f}%)\n'.format( - test_clnloss, clncorrect, len(test_loader.dataset), - 100. * clncorrect / len(test_loader.dataset))) - if flag_advtrain: - test_advloss /= len(test_loader.dataset) - print('Test set: avg adv loss: {:.4f},' - ' adv acc: {}/{} ({:.0f}%)\n'.format( - test_advloss, advcorrect, len(test_loader.dataset), - 100. * advcorrect / len(test_loader.dataset))) - - torch.save( - model.state_dict(), - os.path.join(TRAINED_MODEL_PATH, model_filename)) diff --git a/deepcp_examples/utils.py b/deepcp_examples/utils.py deleted file mode 100644 index a583db9..0000000 --- a/deepcp_examples/utils.py +++ /dev/null @@ -1,247 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import os -import sys -import pathlib -from torch.utils.data.dataset import Subset - -import numpy as np -import torch -import torchvision.transforms as transforms -import torchvision.datasets as datasets - -from deepcp.test_utils import LeNet5 - -# TODO: need to refactor path to keep a single copy of file - -ROOT_PATH = os.path.expanduser("~/.advertorch") -DATA_PATH = os.path.join(ROOT_PATH, "data") - -path_of_this_module = os.path.dirname(sys.modules[__name__].__file__) -TRAINED_MODEL_PATH = os.path.join(path_of_this_module, "trained_models") - - -def mkdir(directory): - pathlib.Path(directory).mkdir(parents=True, exist_ok=True) - - -def get_mnist_train_loader(batch_size, shuffle=True): - loader = torch.utils.data.DataLoader( - datasets.MNIST(DATA_PATH, train=True, download=True, - transform=transforms.ToTensor()), - batch_size=batch_size, shuffle=shuffle) - loader.name = "mnist_train" - return loader - - -def get_mnist_test_loader(batch_size, shuffle=False): - loader = torch.utils.data.DataLoader( - datasets.MNIST(DATA_PATH, train=False, download=True, - transform=transforms.ToTensor()), - batch_size=batch_size, shuffle=shuffle) - loader.name = "mnist_test" - return loader - - -def get_cifar10_train_loader(batch_size, shuffle=True): - loader = torch.utils.data.DataLoader( - datasets.CIFAR10(DATA_PATH, train=True, download=True, - transform=transforms.ToTensor()), - batch_size=batch_size, shuffle=shuffle) - loader.name = "cifar10_train" - return loader - - -def get_cifar10_test_loader(batch_size, shuffle=False): - loader = torch.utils.data.DataLoader( - datasets.CIFAR10(DATA_PATH, train=False, download=True, - transform=transforms.ToTensor()), - batch_size=batch_size, shuffle=shuffle) - loader.name = "cifar10_test" - return loader - - -def get_mnist_lenet5_clntrained(): - filename = "mnist_lenet5_clntrained.pt" - model = LeNet5() - model.load_state_dict( - torch.load(os.path.join(TRAINED_MODEL_PATH, filename))) - model.eval() - model.name = "MNIST LeNet5 standard training" - # TODO: also described where can you find this model, and how is it trained - return model - - -def get_mnist_lenet5_advtrained(): - filename = "mnist_lenet5_advtrained.pt" - model = LeNet5() - model.load_state_dict( - torch.load(os.path.join(TRAINED_MODEL_PATH, filename))) - model.eval() - model.name = "MNIST LeNet 5 PGD training according to Madry et al. 2018" - # TODO: also described where can you find this model, and how is it trained - return model - - -def get_madry_et_al_cifar10_train_transform(): - return transforms.Compose([ - transforms.Pad(4, padding_mode="reflect"), - transforms.RandomCrop(32), - transforms.RandomHorizontalFlip(), - transforms.ToTensor(), - ]) - - - -def get_train_val_loaders( - dataset, datapath=DATA_PATH, - train_size=None, val_size=5000, - train_batch_size=100, val_batch_size=1000, - kwargs=None, train_transform=None, val_transform=None, - train_shuffle=True, val_shuffle=False): - """Support MNIST and CIFAR10""" - if kwargs is None: - kwargs = {} - if train_transform is None: - train_transform = transforms.ToTensor() - if val_transform is None: - val_transform = transforms.ToTensor() - - datapath = os.path.join(datapath, dataset) - - trainset = datasets.__dict__[dataset]( - datapath, train=True, download=True, transform=train_transform) - - if train_size is not None: - assert train_size + val_size <= len(trainset) - - if val_size > 0: - indices = list(range(len(trainset))) - trainset = Subset(trainset, indices[val_size:]) - - valset = datasets.__dict__[dataset]( - datapath, train=True, download=True, transform=val_transform) - valset = Subset(valset, indices[:val_size]) - val_loader = torch.utils.data.DataLoader( - valset, batch_size=val_batch_size, shuffle=val_shuffle, **kwargs) - - else: - val_loader = None - - if train_size is not None: - trainset = Subset(trainset, list(range(train_size))) - - train_loader = torch.utils.data.DataLoader( - trainset, batch_size=train_batch_size, shuffle=train_shuffle, **kwargs) - - return train_loader, val_loader - - -def get_test_loader( - dataset, datapath=DATA_PATH, test_size=None, batch_size=1000, - transform=None, kwargs=None, shuffle=False): - """Support MNIST and CIFAR10""" - if kwargs is None: - kwargs = {} - if transform is None: - transform = transforms.ToTensor() - - datapath = os.path.join(datapath, dataset) - - testset = datasets.__dict__[dataset]( - datapath, train=False, download=True, transform=transform) - - if test_size is not None: - testset = Subset(testset, list(range(test_size))) - - test_loader = torch.utils.data.DataLoader( - testset, batch_size=batch_size, shuffle=shuffle, **kwargs) - return test_loader - - -def bchw2bhwc(x): - if isinstance(x, np.ndarray): - pass - else: - raise - - if x.ndim == 3: - return np.moveaxis(x, 0, 2) - if x.ndim == 4: - return np.moveaxis(x, 1, 3) - - -def bhwc2bchw(x): - if isinstance(x, np.ndarray): - pass - else: - raise - - if x.ndim == 3: - return np.moveaxis(x, 2, 0) - if x.ndim == 4: - return np.moveaxis(x, 3, 1) - - -def _imshow(img): - import matplotlib.pyplot as plt - img = bchw2bhwc(img.detach().cpu().numpy()) - if img.shape[2] == 1: - img = np.repeat(img, 3, axis=2) - plt.imshow(img, vmin=0, vmax=1) - plt.axis("off") - - -class ImageNetClassNameLookup(object): - - def _load_list(self): - import json - with open(self.json_path) as f: - class_idx = json.load(f) - self.label2classname = [ - class_idx[str(k)][1] for k in range(len(class_idx))] - - def __init__(self): - self.json_url = ("https://s3.amazonaws.com/deep-learning-models/" - "image-models/imagenet_class_index.json") - self.json_path = os.path.join(DATA_PATH, "imagenet_class_index.json") - if os.path.exists(self.json_path): - self._load_list() - else: - import urllib - urllib.request.urlretrieve(self.json_url, self.json_path) - self._load_list() - - - def __call__(self, label): - return self.label2classname[label] - - -def get_panda_image(): - img_path = os.path.join(DATA_PATH, "panda.jpg") - img_url = "https://farm1.static.flickr.com/230/524562325_fb0a11d1e1.jpg" - - def _load_panda_image(): - from skimage.io import imread - return imread(img_path) / 255. - - if os.path.exists(img_path): - return _load_panda_image() - else: - import urllib - urllib.request.urlretrieve(img_url, img_path) - return _load_panda_image() - - -mkdir(ROOT_PATH) -mkdir(DATA_PATH) diff --git a/docs/Makefile b/docs/Makefile index dda79d4..697b8b0 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -20,5 +20,5 @@ help: rm -rf $(BUILDDIR)/* rm -rf _tutorials/ mkdir _tutorials - ln -s ../../advertorch_examples/tutorial_attack_defense_bpda_mnist.ipynb _tutorials/tutorial_attack_defense_bpda_mnist.ipynb + ln -s ../../deepcp_examples/tutorial_attack_defense_bpda_mnist.ipynb _tutorials/tutorial_attack_defense_bpda_mnist.ipynb @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/conf.py b/docs/conf.py index beb79c9..e69de29 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -1,312 +0,0 @@ -# -*- coding: utf-8 -*- -# -# Configuration file for the Sphinx documentation builder. -# -# This file does only contain a selection of the most common options. For a -# full list see the documentation: -# http://www.sphinx-doc.org/en/master/config - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -# import os -# import sys -# sys.path.insert(0, os.path.abspath('.')) -import os -import shutil -if os.path.exists("_tutorials"): - shutil.rmtree("_tutorials") -os.makedirs("_tutorials") -os.symlink( - "../../advertorch_examples/tutorial_attack_defense_bpda_mnist.ipynb", - "_tutorials/tutorial_attack_defense_bpda_mnist.ipynb") -import sys # noqa: F401, E402 -sys.path.insert(0, os.path.abspath('..')) - - -# autodoc_mock_imports = [ -# 'numpy', -# 'numpy.linalg', -# 'scipy', -# 'scipy.optimize', -# 'scipy.interpolate', -# 'scipy.ndimage', -# 'scipy.ndimage.filters', -# 'tensorflow', -# 'theano', -# 'theano.tensor', -# # 'torch', -# 'torch.nn', -# 'torch.nn.functional', -# 'torch.optim', -# 'torch.nn.modules', -# 'torch.nn.modules.utils', -# 'torch.utils', -# 'torch.utils.model_zoo', -# 'torch.nn.init', -# 'torch.utils.data', -# 'randomstate', -# 'scipy._lib', -# ] - -from unittest.mock import Mock # noqa: F401, E402 -# from sphinx.ext.autodoc.importer import _MockObject as Mock -Mock.Module = object -sys.modules['torch'] = Mock() -sys.modules['numpy'] = Mock() -sys.modules['numpy.linalg'] = Mock() -sys.modules['scipy'] = Mock() -sys.modules['scipy.optimize'] = Mock() -sys.modules['scipy.interpolate'] = Mock() -sys.modules['scipy.ndimage'] = Mock() -sys.modules['scipy.ndimage.filters'] = Mock() -sys.modules['tensorflow'] = Mock() -sys.modules['theano'] = Mock() -sys.modules['theano.tensor'] = Mock() -sys.modules['torch'] = Mock() -sys.modules['torch.autograd'] = Mock() -sys.modules['torch.autograd.gradcheck'] = Mock() -sys.modules['torch.distributions'] = Mock() -sys.modules['torch.nn'] = Mock() -sys.modules['torch.nn.functional'] = Mock() -sys.modules['torch.optim'] = Mock() -sys.modules['torch.nn.modules'] = Mock() -sys.modules['torch.nn.modules.utils'] = Mock() -sys.modules['torch.nn.modules.loss'] = Mock() -sys.modules['torch.utils'] = Mock() -sys.modules['torch.utils.model_zoo'] = Mock() -sys.modules['torch.nn.init'] = Mock() -sys.modules['torch.utils.data'] = Mock() -sys.modules['torchvision'] = Mock() -sys.modules['randomstate'] = Mock() -sys.modules['scipy._lib'] = Mock() - -# XXX: This import has to be after mock -import deepcp # noqa: F401, E402 - - -# -- Project information ----------------------------------------------------- - -project = 'advertorch' -copyright = '2018-present, Royal Bank of Canada.' -author = '' - -# The short X.Y version -version = '' -# The full version, including alpha/beta/rc tags -release = '' - - -# -- General configuration --------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.doctest', - 'sphinx.ext.coverage', - 'sphinx.ext.mathjax', - 'sphinx.ext.linkcode', - 'nbsphinx', - 'numpydoc', -] - -# Add any paths that contain templates here, relative to this directory. -numpydoc_show_class_members = False -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The master toctree document. -master_doc = 'index' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -todo_include_todos = False - - -# Resolve function for the linkcode extension. -def linkcode_resolve(domain, info): - def find_source(): - # try to find the file and line number, based on code from numpy: - # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286 - obj = sys.modules[info['module']] - for part in info['fullname'].split('.'): - obj = getattr(obj, part) - import inspect - import os - fn = inspect.getsourcefile(obj) - fn = os.path.relpath(fn, start=os.path.dirname(deepcp.__file__)) - source, lineno = inspect.getsourcelines(obj) - return fn, lineno, lineno + len(source) - 1 - - if domain != 'py' or not info['module']: - return None - try: - filename = 'advertorch/%s#L%d-L%d' % find_source() - except Exception: - filename = info['module'].replace('.', '/') + '.py' - tag = 'master' - url = "https://github.com/BorealisAI/advertorch/blob/%s/%s" - return url % (tag, filename) - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -# html_theme = 'alabaster' - -if os.environ.get('READTHEDOCS') != 'True': - try: - import sphinx_rtd_theme - except ImportError: - pass # assume we have sphinx >= 1.3 - else: - html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] - html_theme = 'sphinx_rtd_theme' - - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -# html_theme = 'alabaster' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -# html_theme_options = {} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -# html_static_path = ['_static'] - -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# The default sidebars (for documents that don't match any pattern) are -# defined by theme itself. Builtin themes are using these templates by -# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', -# 'searchbox.html']``. -# -# html_sidebars = {} - - -# -- Options for HTMLHelp output --------------------------------------------- - -# Output file base name for HTML help builder. -htmlhelp_basename = 'advertorch_testdoc' - - -# -- Options for LaTeX output ------------------------------------------------ - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - 'papersize': 'letterpaper', - - # The font size ('10pt', '11pt' or '12pt'). - # - 'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - # - 'preamble': '', - - # Latex figure (float) alignment - # - 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -# latex_documents = [ -# (master_doc, 'advertorch_test.tex', 'advertorch\\_test Documentation', -# 'tracy', 'manual'), -# ] - - -# -- Options for manual page output ------------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -# man_pages = [ -# (master_doc, 'advertorch_test', 'advertorch_test Documentation', -# [author], 1) -# ] - - -# -- Options for Texinfo output ---------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -# texinfo_documents = [ -# (master_doc, 'advertorch_test', 'advertorch_test Documentation', -# author, 'advertorch_test', 'One line description of project.', -# 'Miscellaneous'), -# ] - - -# -- Options for Epub output ------------------------------------------------- - -# Bibliographic Dublin Core info. -epub_title = project - -# The unique identifier of the text. This can be a ISBN number -# or the project homepage. -# -# epub_identifier = '' - -# A unique identification for the text. -# -# epub_uid = '' - -# A list of files that should not be packed into the epub file. -epub_exclude_files = ['search.html'] - - -# -- Extension configuration ------------------------------------------------- - -# -- Options for intersphinx extension --------------------------------------- - -# Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = {'https://docs.python.org/': None} - -# -- Options for todo extension ---------------------------------------------- - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = True diff --git a/docs/deepcp/attacks.rst b/docs/deepcp/attacks.rst deleted file mode 100644 index a980501..0000000 --- a/docs/deepcp/attacks.rst +++ /dev/null @@ -1,117 +0,0 @@ -:mod:`advertorch.attacks` -========================= - -.. automodule:: advertorch.attacks - -Attacks -------- - -.. autosummary:: - :nosignatures: - - Attack - GradientAttack - GradientSignAttack - FastFeatureAttack - L2BasicIterativeAttack - LinfBasicIterativeAttack - PGDAttack - LinfPGDAttack - L2PGDAttack - L1PGDAttack - LinfSPSAAttack - FABAttack - LinfFABAttack - L2FABAttack - L1FABAttack - SparseL1DescentAttack - MomentumIterativeAttack - LinfMomentumIterativeAttack - L2MomentumIterativeAttack - CarliniWagnerL2Attack - ElasticNetL1Attack - DDNL2Attack - LBFGSAttack - SinglePixelAttack - LocalSearchAttack - SpatialTransformAttack - JacobianSaliencyMapAttack - - -Detailed description --------------------- - -.. autoclass:: Attack - :members: - -.. autoclass:: GradientAttack - :members: - -.. autoclass:: GradientSignAttack - :members: - -.. autoclass:: FastFeatureAttack - :members: - -.. autoclass:: L2BasicIterativeAttack - :members: - -.. autoclass:: LinfBasicIterativeAttack - :members: - -.. autoclass:: PGDAttack - :members: - -.. autoclass:: LinfPGDAttack - :members: - -.. autoclass:: L2PGDAttack - :members: - -.. autoclass:: L1PGDAttack - :members: - -.. autoclass:: SparseL1DescentAttack - :members: - -.. autoclass:: LinfSPSAAttack - :members: - -.. autoclass:: FABAttack - :members: - -.. autoclass:: LinfFABAttack - :members: - -.. autoclass:: L2FABAttack - :members: - -.. autoclass:: L1FABAttack - :members: - -.. autoclass:: MomentumIterativeAttack - :members: - -.. autoclass:: CarliniWagnerL2Attack - :members: - -.. autoclass:: ElasticNetL1Attack - :members: - -.. autoclass:: DDNL2Attack - :members: - -.. autoclass:: LBFGSAttack - :members: - -.. autoclass:: SinglePixelAttack - :members: - -.. autoclass:: LocalSearchAttack - :members: - -.. autoclass:: SpatialTransformAttack - :members: - -.. autoclass:: JacobianSaliencyMapAttack - :members: diff --git a/docs/deepcp/bpda.rst b/docs/deepcp/bpda.rst deleted file mode 100644 index 6d64189..0000000 --- a/docs/deepcp/bpda.rst +++ /dev/null @@ -1,22 +0,0 @@ -:mod:`advertorch.bpda` -====================== - -.. automodule:: advertorch.bpda - -BPDA ----- - -.. autosummary:: - :nosignatures: - - BPDAWrapper - - -Detailed description --------------------- - -.. autoclass:: BPDAWrapper - :members: - - - diff --git a/docs/deepcp/context.rst b/docs/deepcp/context.rst deleted file mode 100644 index 5b9c3e8..0000000 --- a/docs/deepcp/context.rst +++ /dev/null @@ -1,23 +0,0 @@ -:mod:`advertorch.context` -========================= - -.. automodule:: advertorch.context - -Context -------- - -.. autosummary:: - :nosignatures: - - ctx_noparamgrad - ctx_eval - - -Detailed description --------------------- - -.. autoclass:: ctx_noparamgrad - :members: - -.. autoclass:: ctx_eval - :members: diff --git a/docs/deepcp/defenses.rst b/docs/deepcp/defenses.rst deleted file mode 100644 index 9c6a4f1..0000000 --- a/docs/deepcp/defenses.rst +++ /dev/null @@ -1,48 +0,0 @@ -:mod:`advertorch.defenses` -========================== - -.. automodule:: advertorch.defenses - -Defenses --------- - -.. autosummary:: - :nosignatures: - - ConvSmoothing2D - AverageSmoothing2D - GaussianSmoothing2D - MedianSmoothing2D - JPEGFilter - BitSqueezing - BinaryFilter - - -Detailed description --------------------- - -.. autoclass:: Processor - :members: - -.. autoclass:: ConvSmoothing2D - :members: - -.. autoclass:: AverageSmoothing2D - :members: - -.. autoclass:: GaussianSmoothing2D - :members: - -.. autoclass:: MedianSmoothing2D - :members: - -.. autoclass:: JPEGFilter - :members: - -.. autoclass:: BitSqueezing - :members: - -.. autoclass:: BinaryFilter - :members: - - diff --git a/docs/index.rst b/docs/index.rst index 1431c35..e69de29 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,44 +0,0 @@ -Welcome to Advertorch -===================== - -.. comments original size: 626*238 - -.. image:: ../assets/logo.png - :width: 313px - :height: 119px - -.. toctree:: - :maxdepth: 2 - :caption: User Guide - - user/installation - - -.. toctree:: - :maxdepth: 2 - :caption: Tutorials - - _tutorials/tutorial_attack_defense_bpda_mnist - - -.. toctree:: - :maxdepth: 2 - :caption: API Reference - - advertorch/attacks - advertorch/defenses - advertorch/bpda - advertorch/context - - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` - -.. _GitHub: https://github.com/BorealisAI/advertorch -.. _minimal working example: https://github.com/BorealisAI/advertorch#example diff --git a/docs/user/installation.rst b/docs/user/installation.rst index c7ba90d..e69de29 100644 --- a/docs/user/installation.rst +++ b/docs/user/installation.rst @@ -1,37 +0,0 @@ -Installation -===================== -Latest version (v0.1) ---------------------- - -Installing AdverTorch itself - -We developed AdverTorch under Python 3.6 and PyTorch 1.0.0 & 0.4.1. To install AdverTorch, simply run - -.. code-block:: bash - - pip install advertorch - -or clone the repo and run - -.. code-block:: bash - - python setup.py install - -To install the package in "editable" mode: - -.. code-block:: bash - - pip install -e . - - -Setting up the testing environments ------------------------------------ - -Some attacks are tested against implementations in [Foolbox](https://github.com/bethgelab/foolbox) or [CleverHans](https://github.com/tensorflow/cleverhans) to ensure correctness. Currently, they are tested under the following versions of related libraries. - -.. code-block:: bash - - conda install -c anaconda tensorflow-gpu==1.11.0 - pip install git+https://github.com/tensorflow/cleverhans.git@336b9f4ed95dccc7f0d12d338c2038c53786ab70 - pip install Keras==2.2.2 - pip install foolbox==1.3.2 diff --git a/external_tests/test_attacks_on_cleverhans.py b/external_tests/test_attacks_on_cleverhans.py deleted file mode 100644 index 8bfc770..0000000 --- a/external_tests/test_attacks_on_cleverhans.py +++ /dev/null @@ -1,601 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import warnings -import pytest -import random - -import numpy as np -import torch -import torch.nn.parallel -import torch.optim -import torch.utils.data -import tensorflow as tf - -from cleverhans.attacks import CarliniWagnerL2 -from cleverhans.attacks import ElasticNetMethod -from cleverhans.attacks import FastGradientMethod -from cleverhans.attacks import MomentumIterativeMethod -from cleverhans.attacks import MadryEtAl -from cleverhans.attacks import FastFeatureAdversaries -from cleverhans.attacks import BasicIterativeMethod -from cleverhans.attacks import LBFGS -from cleverhans.attacks import SaliencyMapMethod -from cleverhans.model import Model as ClModel - -from deepcp.attacks import CarliniWagnerL2Attack -from deepcp.attacks import ElasticNetL1Attack -from deepcp.attacks import GradientAttack -from deepcp.attacks import GradientSignAttack -from deepcp.attacks import L2MomentumIterativeAttack -from deepcp.attacks import LinfMomentumIterativeAttack -from deepcp.attacks import LinfPGDAttack -from deepcp.attacks import FastFeatureAttack -from deepcp.attacks import LinfBasicIterativeAttack -from deepcp.attacks import L2BasicIterativeAttack -from deepcp.attacks import LBFGSAttack -from deepcp.attacks import JacobianSaliencyMapAttack -from deepcp.test_utils import SimpleModel -from deepcp.test_utils import merge2dicts - - -BATCH_SIZE = 9 -DIM_INPUT = 15 -NUM_CLASS = 5 -EPS = 0.08 - -ATOL = 1e-4 -RTOL = 1e-4 -NB_ITER = 5 - -# XXX: carlini still doesn't pass sometimes under certain random seed -seed = 66666 -torch.manual_seed(seed) -np.random.seed(seed) -random.seed(seed) -tf.set_random_seed(seed) -inputs = np.random.uniform(0, 1, size=(BATCH_SIZE, DIM_INPUT)) -targets = np.random.randint(0, NUM_CLASS, size=BATCH_SIZE, dtype=np.int64) - - -targets_onehot = np.zeros((BATCH_SIZE, NUM_CLASS), dtype='int') -targets_onehot[np.arange(BATCH_SIZE), targets] = 1 - - -class SimpleModelTf(ClModel): - - def __init__(self, dim_input, num_classes, session=None): - import keras - self.sess = session - model = keras.models.Sequential() - model.add(keras.layers.Dense(10, input_shape=(dim_input, ))) - model.add(keras.layers.Activation('relu')) - model.add(keras.layers.Dense(num_classes)) - self.model = model - self.flag_weight_set = False - - def set_weights(self, weights): - self.model.set_weights(weights) - self.flag_weight_set = True - - def load_state_dict(self, w): - self.set_weights([ - w['fc1.weight'].cpu().numpy().transpose(), - w['fc1.bias'].cpu().numpy(), - w['fc2.weight'].cpu().numpy().transpose(), - w['fc2.bias'].cpu().numpy(), - ]) - - def get_logits(self, data): - assert self.flag_weight_set, "Weight Not Set!!!" - return self.model(data) - - def get_probs(self, data): - assert self.flag_weight_set, "Weight Not Set!!!" - return tf.nn.softmax(logits=self.model(data)) - - -def load_weights_pt(model_pt, layers): - w = model_pt.state_dict() - layers[0].W = tf.Variable(tf.convert_to_tensor( - w['fc1.weight'].cpu().numpy().transpose(), tf.float32)) - layers[0].b = tf.Variable(tf.convert_to_tensor( - w['fc1.bias'].cpu().numpy(), tf.float32)) - layers[2].W = tf.Variable(tf.convert_to_tensor( - w['fc2.weight'].cpu().numpy().transpose(), tf.float32)) - layers[2].b = tf.Variable(tf.convert_to_tensor( - w['fc2.bias'].cpu().numpy(), tf.float32)) - - -def setup_simple_model_tf(model_pt, input_shape): - from cleverhans_tutorials.tutorial_models import MLP, Linear, ReLU - layers = [Linear(10), - ReLU(), - Linear(10)] - layers[0].name = 'fc1' - layers[1].name = 'relu' - layers[2].name = 'fc2' - model = MLP(layers, input_shape) - load_weights_pt(model_pt, layers) - return model - - - -# kwargs for attacks to be tested -attack_kwargs = { - GradientSignAttack: { - "cl_class": FastGradientMethod, - "kwargs": dict( - eps=EPS, - clip_min=0.0, - clip_max=1.0, - ), - "at_kwargs": dict( - ), - "cl_kwargs": dict( - ord=np.inf, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - GradientAttack: { - "cl_class": FastGradientMethod, - "kwargs": dict( - eps=EPS, - clip_min=0.0, - clip_max=1.0, - ), - "at_kwargs": dict( - ), - "cl_kwargs": dict( - ord=2, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - LinfPGDAttack: { - "cl_class": MadryEtAl, - "kwargs": dict( - eps=EPS, - eps_iter=0.01, - clip_min=0.0, - clip_max=1.0, - rand_init=False, - nb_iter=NB_ITER, - ), - "at_kwargs": dict( - targeted=True, - ), - "cl_kwargs": dict( - ord=np.inf, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - L2MomentumIterativeAttack: { - "cl_class": MomentumIterativeMethod, - "kwargs": dict( - eps=EPS, - eps_iter=0.01, - clip_min=0.0, - clip_max=1.0, - decay_factor=1., - nb_iter=NB_ITER, - ), - "at_kwargs": dict( - ), - "cl_kwargs": dict( - ord=2, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - LinfMomentumIterativeAttack: { - "cl_class": MomentumIterativeMethod, - "kwargs": dict( - eps=EPS, - eps_iter=0.01, - clip_min=0.0, - clip_max=1.0, - decay_factor=1., - nb_iter=NB_ITER, - ), - "at_kwargs": dict( - ), - "cl_kwargs": dict( - ord=np.inf, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - CarliniWagnerL2Attack: { - "cl_class": CarliniWagnerL2, - "kwargs": dict( - max_iterations=100, - clip_min=0, - clip_max=1, - binary_search_steps=9, - learning_rate=0.1, - confidence=0.1, - ), - "at_kwargs": dict( - num_classes=NUM_CLASS, - ), - "cl_kwargs": dict( - batch_size=BATCH_SIZE, - initial_const=1e-3, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - ElasticNetL1Attack: { - "cl_class": ElasticNetMethod, - "kwargs": dict( - max_iterations=100, - clip_min=0, - clip_max=1, - binary_search_steps=9, - learning_rate=0.1, - confidence=0.1, - ), - "at_kwargs": dict( - num_classes=NUM_CLASS, - ), - "cl_kwargs": dict( - batch_size=BATCH_SIZE, - initial_const=1e-3, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - FastFeatureAttack: { - "cl_class": FastFeatureAdversaries, - "kwargs": dict( - nb_iter=NB_ITER, - clip_min=0, - clip_max=1, - eps_iter=0.05, - eps=0.3, - ), - "at_kwargs": dict( - ), - "cl_kwargs": dict( - layer='logits', - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - LinfBasicIterativeAttack: { - "cl_class": BasicIterativeMethod, - "kwargs": dict( - clip_min=0, - clip_max=1, - eps_iter=0.05, - eps=0.1, - nb_iter=NB_ITER, - ), - "at_kwargs": dict( - ), - "cl_kwargs": dict( - ord=np.inf, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - L2BasicIterativeAttack: { - "cl_class": BasicIterativeMethod, - "kwargs": dict( - clip_min=0, - clip_max=1, - eps_iter=0.05, - eps=0.1, - nb_iter=NB_ITER, - ), - "at_kwargs": dict( - ), - "cl_kwargs": dict( - ord=2, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - LBFGSAttack: { - "cl_class": LBFGS, - "kwargs": dict( - clip_min=0., - clip_max=1., - # set binary search step = 3, which can successfully create - # adversarial images and the difference between advertorch - # and cleverhans is within the threshold - # the difference of the two results are very small at first - # because of some rounding and calculating difference - # with tensors and numpy arrays, the difference gets larger - # with more iterations - binary_search_steps=3, - max_iterations=50, - initial_const=1e-3, - batch_size=BATCH_SIZE, - ), - "at_kwargs": dict( - num_classes=NUM_CLASS, - ), - "cl_kwargs": dict( - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - JacobianSaliencyMapAttack: { - "cl_class": SaliencyMapMethod, - "kwargs": dict( - clip_min=0.0, - clip_max=1.0, - theta=1.0, - gamma=1.0, - ), - "at_kwargs": dict( - num_classes=NUM_CLASS, - comply_cleverhans=True, - ), - "cl_kwargs": dict( - # nb_classes=NUM_CLASS, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, -} - - -def overwrite_fastfeature(attack, x, g, eta, **kwargs): - # overwrite cleverhans generate function for fastfeatureattack to - # allow eta as an input - from cleverhans.utils_tf import clip_eta - - # Parse and save attack-specific parameters - assert attack.parse_params(**kwargs) - - g_feat = attack.model.get_layer(g, attack.layer) - - # Initialize loop variables - eta = tf.Variable(tf.convert_to_tensor(eta, np.float32)) - eta = clip_eta(eta, attack.ord, attack.eps) - - for i in range(attack.nb_iter): - eta = attack.attack_single_step(x, eta, g_feat) - - # Define adversarial example (and clip if necessary) - adv_x = x + eta - if attack.clip_min is not None and attack.clip_max is not None: - adv_x = tf.clip_by_value(adv_x, attack.clip_min, attack.clip_max) - - return adv_x - - -def genenerate_ptb_pt(adversary, inputs, targets, delta=None): - if inputs.ndim == 4: - # TODO: move the transpose to a better place - input_t = torch.from_numpy(inputs.transpose(0, 3, 1, 2)) - else: - input_t = torch.from_numpy(inputs) - input_t = input_t.float() - - if targets is None: - adversary.targeted = False - adv_pt = adversary.perturb(input_t, None) - else: - target_t = torch.from_numpy(targets) - if isinstance(adversary, FastFeatureAttack): - adv_pt = adversary.perturb(input_t, target_t, - delta=torch.from_numpy(delta)) - else: - adversary.targeted = True - adv_pt = adversary.perturb(input_t, target_t) - - adv_pt = adv_pt.cpu().detach().numpy() - - if inputs.ndim == 4: - # TODO: move the transpose to a better place - adv_pt = adv_pt.transpose(0, 2, 3, 1) - return adv_pt - inputs - - -def compare_at_cl(ptb_at, ptb_cl, atol, rtol): - assert np.allclose(ptb_at, ptb_cl, atol=atol, rtol=rtol), \ - (np.abs(ptb_at - ptb_cl).max()) - - -def compare_attacks(key, item, targeted=False): - AdvertorchAttack = key - CleverhansAttack = item["cl_class"] - cl_kwargs = merge2dicts(item["kwargs"], item["cl_kwargs"]) - at_kwargs = merge2dicts(item["kwargs"], item["at_kwargs"]) - thresholds = item["thresholds"] - seed = 6666 - torch.manual_seed(seed) - np.random.seed(seed) - - # WARNING: don't use tf.InteractiveSession() here - # It causes that fastfeature attack has to be the last test for some reason - with tf.Session() as sess: - model_pt = SimpleModel(DIM_INPUT, NUM_CLASS) - model_tf = SimpleModelTf(DIM_INPUT, NUM_CLASS) - model_tf.load_state_dict(model_pt.state_dict()) - adversary = AdvertorchAttack(model_pt, **at_kwargs) - - if AdvertorchAttack is FastFeatureAttack: - model_tf_fastfeature = setup_simple_model_tf( - model_pt, inputs.shape) - delta = np.random.uniform( - -item["kwargs"]['eps'], item["kwargs"]['eps'], - size=inputs.shape).astype('float32') - inputs_guide = np.random.uniform( - 0, 1, size=(BATCH_SIZE, DIM_INPUT)).astype('float32') - inputs_tf = tf.convert_to_tensor(inputs, np.float32) - inputs_guide_tf = tf.convert_to_tensor(inputs_guide, np.float32) - attack = CleverhansAttack(model_tf_fastfeature) - cl_result = overwrite_fastfeature(attack, - x=inputs_tf, - g=inputs_guide_tf, - eta=delta, - **cl_kwargs) - init = tf.global_variables_initializer() - sess.run(init) - ptb_cl = sess.run(cl_result) - inputs - ptb_at = genenerate_ptb_pt( - adversary, inputs, inputs_guide, delta=delta) - - else: - attack = CleverhansAttack(model_tf, sess=sess) - if targeted: - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - ptb_cl = attack.generate_np( - inputs, y_target=targets_onehot, **cl_kwargs) - inputs - ptb_at = genenerate_ptb_pt(adversary, inputs, targets=targets) - else: - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - ptb_cl = attack.generate_np( - inputs, y=None, **cl_kwargs) - inputs - ptb_at = genenerate_ptb_pt(adversary, inputs, targets=None) - - if AdvertorchAttack is CarliniWagnerL2Attack: - assert np.sum(np.abs(ptb_at)) > 0 and np.sum(np.abs(ptb_cl)) > 0, \ - ("Both advertorch and cleverhans returns zero perturbation" - " of CarliniWagnerL2Attack, " - "the test results are not reliable," - " Adjust your testing parameters to avoid this." - ) - compare_at_cl(ptb_at, ptb_cl, **thresholds) - - -@pytest.mark.parametrize("targeted", [False, True]) -def test_fgsm_attack(targeted): - compare_attacks( - GradientSignAttack, - attack_kwargs[GradientSignAttack], - targeted) - - -@pytest.mark.parametrize("targeted", [False, True]) -def test_fgm_attack(targeted): - compare_attacks( - GradientAttack, - attack_kwargs[GradientAttack], - targeted) - - -@pytest.mark.parametrize("targeted", [False, True]) -def test_l2_momentum_iterative_attack(targeted): - compare_attacks( - L2MomentumIterativeAttack, - attack_kwargs[L2MomentumIterativeAttack], - targeted) - - -@pytest.mark.parametrize("targeted", [False, True]) -def test_linf_momentum_iterative_attack(targeted): - compare_attacks( - LinfMomentumIterativeAttack, - attack_kwargs[LinfMomentumIterativeAttack], - targeted) - - -@pytest.mark.skip(reason="XXX: temporary") -def test_fastfeature_attack(): - compare_attacks( - FastFeatureAttack, - attack_kwargs[FastFeatureAttack]) - - -@pytest.mark.parametrize("targeted", [False, True]) -def test_pgd_attack(targeted): - compare_attacks( - LinfPGDAttack, - attack_kwargs[LinfPGDAttack], - targeted) - - -@pytest.mark.parametrize("targeted", [False, True]) -def test_iterative_sign_attack(targeted): - compare_attacks( - LinfBasicIterativeAttack, - attack_kwargs[LinfBasicIterativeAttack], - targeted) - - -@pytest.mark.parametrize("targeted", [False, True]) -def test_iterative_attack(targeted): - compare_attacks( - L2BasicIterativeAttack, - attack_kwargs[L2BasicIterativeAttack], - targeted) - - -@pytest.mark.parametrize("targeted", [False, True]) -def test_carlini_l2_attack(targeted): - compare_attacks( - CarliniWagnerL2Attack, - attack_kwargs[CarliniWagnerL2Attack], - targeted) - - -@pytest.mark.parametrize("targeted", [False, True]) -def test_elasticnet_l1_attack(targeted): - compare_attacks( - ElasticNetL1Attack, - attack_kwargs[ElasticNetL1Attack], - targeted) - - -def test_lbfgs_attack(): - compare_attacks( - LBFGSAttack, - attack_kwargs[LBFGSAttack], - True) - - -@pytest.mark.skip(reason="XXX: temporary") -def test_jsma(): - compare_attacks( - JacobianSaliencyMapAttack, - attack_kwargs[JacobianSaliencyMapAttack], - True) - - -if __name__ == '__main__': - # pass - test_iterative_attack(False) - test_iterative_attack(True) diff --git a/external_tests/test_attacks_on_foolbox.py b/external_tests/test_attacks_on_foolbox.py deleted file mode 100644 index d066b7b..0000000 --- a/external_tests/test_attacks_on_foolbox.py +++ /dev/null @@ -1,166 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import warnings - -import numpy as np -import torch - -from deepcp.attacks import SinglePixelAttack -from deepcp.attacks import LocalSearchAttack -from deepcp.utils import predict_from_logits -from deepcp.test_utils import merge2dicts -from deepcp.test_utils import MLP - -from deepcp_examples.utils import TRAINED_MODEL_PATH -from deepcp_examples.utils import get_mnist_test_loader - -import foolbox -from foolbox.attacks.localsearch import SinglePixelAttack as SPAfb -from foolbox.attacks.localsearch import LocalSearchAttack as LSAfb - -NUM_CLASS = 10 -BATCH_SIZE = 10 -# TODO: need to make sure these precisions are enough -ATOL = 1e-4 -RTOL = 1e-4 - -loader_test = get_mnist_test_loader(BATCH_SIZE) - -data_iter = iter(loader_test) -img_batch, label_batch = data_iter.next() - -# Setup the test MLP model -model = MLP() -model.eval() -model.load_state_dict( - torch.load(os.path.join(TRAINED_MODEL_PATH, 'mlp.pkl'), - map_location='cpu')) -model.to("cpu") - -# foolbox single pixel attack do not succeed on this model -# therefore using mlp.pkl -# from advertorch.test_utils import LeNet5 -# model = LeNet5() -# model.eval() -# model.load_state_dict( -# torch.load(os.path.join(TRAINED_MODEL_PATH, -# 'mnist_lenet5_advtrained.pt'))) -# model.to("cpu") - - -attack_kwargs = { - SinglePixelAttack: { - "fb_class": SPAfb, - "kwargs": dict( - max_pixels=50, - ), - "at_kwargs": dict( - clip_min=0.0, - clip_max=1.0, - comply_with_foolbox=True, - ), - "fb_kwargs": dict( - unpack=True, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, - LocalSearchAttack: { - "fb_class": LSAfb, - "kwargs": dict( - p=1., - r=1.5, - d=10, - t=100, - ), - "at_kwargs": dict( - clip_min=0.0, - clip_max=1.0, - k=1, - round_ub=100, - comply_with_foolbox=True, - ), - "fb_kwargs": dict( - R=100, - unpack=True, - ), - "thresholds": dict( - atol=ATOL, - rtol=RTOL, - ), - }, -} - - -def compare_at_fb(ptb_at, ptb_fb, atol, rtol): - assert np.allclose(ptb_at, ptb_fb, atol=atol, rtol=rtol), \ - (np.abs(ptb_at - ptb_fb).max()) - - -def compare_attacks(key, item): - AdvertorchAttack = key - fmodel = foolbox.models.PyTorchModel( - model, bounds=(0, 1), - num_classes=NUM_CLASS - ) - fb_adversary = item["fb_class"](fmodel) - fb_kwargs = merge2dicts(item["kwargs"], item["fb_kwargs"]) - at_kwargs = merge2dicts(item["kwargs"], item["at_kwargs"]) - thresholds = item["thresholds"] - at_adversary = AdvertorchAttack(model, **at_kwargs) - x_at = at_adversary.perturb(img_batch, label_batch) - y_logits = model(img_batch) - y_at_logits = model(x_at) - y_pred = predict_from_logits(y_logits) - y_at_pred = predict_from_logits(y_at_logits) - - fb_successed_once = False - for i, (x_i, y_i) in enumerate(zip(img_batch, label_batch)): - # rule out when classification is wrong or attack is - # unsuccessful (we test if foolbox attacks fails here) - if y_i != y_pred[i:i + 1][0]: - continue - if y_i == y_at_pred[i:i + 1][0]: - continue - np.random.seed(233333) - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - x_fb = fb_adversary( - x_i.cpu().numpy(), label=int(y_i), **fb_kwargs) - if x_fb is not None: - compare_at_fb(x_at[i].cpu().numpy(), x_fb, **thresholds) - fb_successed_once = True - - if not fb_successed_once: - raise RuntimeError( - "Foolbox never succeed, change your testing parameters!!!") - - -def test_single_pixel(): - compare_attacks( - SinglePixelAttack, - attack_kwargs[SinglePixelAttack], - ) - - -def test_local_search(): - compare_attacks( - LocalSearchAttack, - attack_kwargs[LocalSearchAttack], - ) - - -if __name__ == '__main__': - pass diff --git a/pytest.ini b/pytest.ini index 19b7b16..32c687a 100644 --- a/pytest.ini +++ b/pytest.ini @@ -3,8 +3,8 @@ testpaths = tests external_tests addopts = - --ignore=advertorch_deprecated - --cov=advertorch + --ignore=deepcp_deprecated + --cov=deepcp --cov-report term --cov-report xml:cov.xml --durations=10 diff --git a/setup.py b/setup.py index ce719f9..f3807cb 100644 --- a/setup.py +++ b/setup.py @@ -11,14 +11,14 @@ from setuptools import find_packages -with open(os.path.join(os.path.dirname(__file__), 'advertorch/VERSION')) as f: +with open(os.path.join(os.path.dirname(__file__), 'deepcp/VERSION')) as f: version = f.read().strip() -setup(name='advertorch', +setup(name='deepcp', version=version, - url='https://github.com/BorealisAI/advertorch', - package_data={'advertorch_examples': ['*.ipynb', 'trained_models/*.pt']}, + url='https://github.com/ml-stat-Sustech/DeepCP', + package_data={'deepcp_examples': ['*.ipynb']}, install_requires=[], include_package_data=True, packages=find_packages()) diff --git a/tests/test_attacks_running.py b/tests/test_attacks_running.py deleted file mode 100644 index 4f446e5..0000000 --- a/tests/test_attacks_running.py +++ /dev/null @@ -1,305 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import math - -import pytest -import itertools - -import torch -import torch.nn as nn - -from deepcp.attacks import GradientSignAttack -from deepcp.attacks import GradientAttack -from deepcp.attacks import L2BasicIterativeAttack -from deepcp.attacks import LinfBasicIterativeAttack -from deepcp.attacks import L1PGDAttack -from deepcp.attacks import L2PGDAttack -from deepcp.attacks import LinfPGDAttack -from deepcp.attacks import SparseL1DescentAttack -from deepcp.attacks import MomentumIterativeAttack -from deepcp.attacks import FastFeatureAttack -from deepcp.attacks import CarliniWagnerL2Attack -from deepcp.attacks import DDNL2Attack -from deepcp.attacks import ElasticNetL1Attack -from deepcp.attacks import LBFGSAttack -from deepcp.attacks import JacobianSaliencyMapAttack -from deepcp.attacks import SpatialTransformAttack -from deepcp.attacks import LinfSPSAAttack -from deepcp.attacks import LinfFABAttack -from deepcp.attacks import L2FABAttack -from deepcp.attacks import L1FABAttack -from deepcp.attacks import DeepfoolLinfAttack -from deepcp.utils import CarliniWagnerLoss -from deepcp.utils import torch_allclose - -# blackbox -from deepcp.attacks import LinfGenAttack -from deepcp.attacks import L2GenAttack -from deepcp.attacks import LinfNAttack -from deepcp.attacks import L2NAttack -from deepcp.attacks import BanditAttack -from deepcp.attacks import NESAttack - -from deepcp.test_utils import NUM_CLASS -from deepcp.test_utils import BATCH_SIZE -from deepcp.test_utils import batch_consistent_attacks -from deepcp.test_utils import general_input_attacks -from deepcp.test_utils import image_only_attacks -from deepcp.test_utils import label_attacks -from deepcp.test_utils import feature_attacks -from deepcp.test_utils import targeted_only_attacks -from deepcp.test_utils import vec_eps_attacks - -from deepcp.test_utils import vecdata -from deepcp.test_utils import veclabel -from deepcp.test_utils import vecmodel -from deepcp.test_utils import imgdata -from deepcp.test_utils import imglabel -from deepcp.test_utils import imgmodel - - -xent_loss = nn.CrossEntropyLoss(reduction="sum") -cw_loss = CarliniWagnerLoss() -mse_loss = nn.MSELoss(reduction="sum") -smoothl1_loss = nn.SmoothL1Loss(reduction="sum") - -label_criteria = (xent_loss, cw_loss) -feature_criteria = (smoothl1_loss, mse_loss) - -cuda = "cuda" -cpu = "cpu" - -devices = (cpu, cuda) if torch.cuda.is_available() else (cpu,) - -attack_kwargs = { - GradientSignAttack: {}, - GradientAttack: {}, - SparseL1DescentAttack: { - "rand_init": False, - "nb_iter": 5, - "eps": 3.0, - "eps_iter": 1.0, - }, - L1PGDAttack: { - "rand_init": False, - "nb_iter": 5, - "eps": 3.0, - "eps_iter": 1.0, - }, - L2BasicIterativeAttack: {"nb_iter": 5, "eps": 1.0, "eps_iter": 0.33}, - L2PGDAttack: { - "rand_init": False, - "nb_iter": 5, - "eps": 1.0, - "eps_iter": 0.33, - }, - LinfBasicIterativeAttack: {"nb_iter": 5, "eps": 0.3, "eps_iter": 0.1}, - LinfPGDAttack: { - "rand_init": False, - "nb_iter": 5, - "eps": 0.3, - "eps_iter": 0.1, - }, - MomentumIterativeAttack: {"nb_iter": 5}, - CarliniWagnerL2Attack: {"num_classes": NUM_CLASS, "max_iterations": 10}, - ElasticNetL1Attack: {"num_classes": NUM_CLASS, "max_iterations": 10}, - FastFeatureAttack: {"rand_init": False, "nb_iter": 5}, - LBFGSAttack: {"num_classes": NUM_CLASS}, - JacobianSaliencyMapAttack: {"num_classes": NUM_CLASS, "gamma": 0.01}, - SpatialTransformAttack: {"num_classes": NUM_CLASS}, - DDNL2Attack: {"nb_iter": 5}, - LinfSPSAAttack: {"eps": 0.3, "max_batch_size": 63}, - LinfFABAttack: {"n_iter": 5}, - L2FABAttack: {"n_iter": 5}, - L1FABAttack: {"n_iter": 5}, - LinfGenAttack: {"nb_iter": 5, "nb_samples": 10, "eps": 1}, - L2GenAttack: {"nb_iter": 5, "nb_samples": 10, "eps": 1}, - LinfNAttack: {"nb_iter": 5, "nb_samples": 10, "eps": 1}, - L2NAttack: {"nb_iter": 5, "nb_samples": 10, "eps": 1}, - BanditAttack: {"nb_iter": 5, "eps": 1, "order": math.inf}, - NESAttack: {"nb_iter": 5, "nb_samples": 10}, - DeepfoolLinfAttack: {"nb_iter": 5}, -} - - -def _run_and_assert_original_data_untouched(adversary, data, label): - data_clone = data.clone() - adversary.perturb(data, label) - assert (data_clone == data).all() - - for Attack in targeted_only_attacks: - if isinstance(adversary, Attack): - return - - adversary.perturb(data) - assert (data_clone == data).all() - - adversary.targeted = True - adversary.perturb(data, label) - assert (data_clone == data).all() - - -def _run_data_model_criterion_label_attack( - data, label, model, criterion, attack, device -): - model.to(device) - adversary = attack( - predict=model, loss_fn=criterion, **attack_kwargs[attack] - ) - data, label = data.to(device), label.to(device) - _run_and_assert_original_data_untouched(adversary, data, label) - - -@pytest.mark.parametrize( - "device, criterion, att_cls", - itertools.product( - devices, - label_criteria, - set(label_attacks).intersection(general_input_attacks), - ), -) -def test_running_label_attacks_on_vec(device, criterion, att_cls): - _run_data_model_criterion_label_attack( - vecdata, veclabel, vecmodel, criterion, att_cls, device - ) - - -@pytest.mark.parametrize( - "device, criterion, att_cls", - itertools.product( - devices, - label_criteria, - set(label_attacks).intersection( - image_only_attacks + general_input_attacks - ), - ), -) -def test_running_label_attacks_on_img(device, criterion, att_cls): - _run_data_model_criterion_label_attack( - imgdata, imglabel, imgmodel, criterion, att_cls, device - ) - - -def _run_data_model_criterion_feature_attack( - data, model, criterion, attack, device -): - model.to(device) - adversary = attack( - predict=model, loss_fn=criterion, **attack_kwargs[attack] - ) - guide = data.detach().clone()[torch.randperm(len(data))] - source, guide = data.to(device), guide.to(device) - source_clone = source.clone() - adversary.perturb(source, guide) - assert (source_clone == source).all() - - -@pytest.mark.parametrize( - "device, criterion, att_cls", - itertools.product( - devices, - feature_criteria, - set(feature_attacks).intersection(general_input_attacks), - ), -) -def test_running_feature_attacks_on_vec(device, criterion, att_cls): - _run_data_model_criterion_feature_attack( - vecdata, vecmodel, criterion, att_cls, device - ) - - -@pytest.mark.parametrize( - "device, criterion, att_cls", - itertools.product( - devices, - feature_criteria, - set(feature_attacks).intersection( - image_only_attacks + general_input_attacks - ), - ), -) -def test_running_feature_attacks_on_img(device, criterion, att_cls): - _run_data_model_criterion_feature_attack( - imgdata, imgmodel, criterion, att_cls, device - ) - - -def _run_batch_consistent(data, label, model, att_cls, idx): - if att_cls in feature_attacks: - guide = data.detach().clone()[torch.randperm(len(data))] - data, guide = data.to(cpu), guide.to(cpu) - label_or_guide = guide - else: - label_or_guide = label - model.to(cpu) - data, label_or_guide = data.to(cpu), label_or_guide.to(cpu) - adversary = att_cls(model, **attack_kwargs[att_cls]) - torch.manual_seed(0) - a = adversary.perturb(data, label_or_guide)[idx:idx + 1] - torch.manual_seed(0) - b = adversary.perturb(data[idx:idx + 1], label_or_guide[idx:idx + 1]) - assert torch_allclose(a, b) - - -@pytest.mark.parametrize( - "idx, att_cls", - itertools.product( - [0, BATCH_SIZE // 2, BATCH_SIZE - 1], batch_consistent_attacks - ), -) -def test_batch_consistent_on_vec(idx, att_cls): - _run_batch_consistent(vecdata, veclabel, vecmodel, att_cls, idx) - - -@pytest.mark.parametrize( - "idx, att_cls", - itertools.product( - [0, BATCH_SIZE // 2, BATCH_SIZE - 1], batch_consistent_attacks - ), -) -def test_batch_consistent_on_img(idx, att_cls): - _run_batch_consistent(imgdata, imglabel, imgmodel, att_cls, idx) - - -def _run_vec_eps_consistent(data, label, model, att_cls): - if att_cls in feature_attacks: - guide = data.detach().clone()[torch.randperm(len(data))] - data, guide = data.to(cpu), guide.to(cpu) - label_or_guide = guide - else: - label_or_guide = label - model.to(cpu) - data, label_or_guide = data.to(cpu), label_or_guide.to(cpu) - adversary = att_cls(model, **attack_kwargs[att_cls]) - torch.manual_seed(0) - a = adversary.perturb(data, label_or_guide) - - _vec_ones = data.new_ones(size=(len(data),)) - _mat_ones = torch.ones_like(data) - adversary.eps = adversary.eps * _vec_ones - if hasattr(adversary, "eps_iter"): - adversary.eps_iter = adversary.eps_iter * _vec_ones - adversary.clip_min = adversary.clip_min * _mat_ones - adversary.clip_max = adversary.clip_max * _mat_ones - torch.manual_seed(0) - b = adversary.perturb(data, label_or_guide) - assert torch_allclose(a, b) - - -@pytest.mark.parametrize("att_cls", vec_eps_attacks) -def test_vec_eps_consistent(att_cls): - _run_vec_eps_consistent(vecdata, veclabel, vecmodel, att_cls) - - -if __name__ == "__main__": - pass diff --git a/tests/test_bpda.py b/tests/test_bpda.py deleted file mode 100644 index 7beea38..0000000 --- a/tests/test_bpda.py +++ /dev/null @@ -1,139 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada and other authors. -# See the AUTHORS.txt file for a list of contributors. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import itertools - -import pytest -import torch -import torch.nn as nn - -from deepcp.bpda import BPDAWrapper -from deepcp.utils import torch_allclose -from deepcp.test_utils import withgrad_defenses -from deepcp.test_utils import nograd_defenses -from deepcp.test_utils import defense_kwargs -from deepcp.test_utils import defense_data -from deepcp.test_utils import vecdata - - -cuda = "cuda" -cpu = "cpu" -devices = (cpu, cuda) if torch.cuda.is_available() else (cpu, ) - - -def _identity(x): - return x - - -def _straight_through_backward(grad_output, x): - return grad_output - - -def _calc_datagrad_on_defense(defense, data): - data = data.detach().clone().requires_grad_() - loss = defense(data).sum() - loss.backward() - return data.grad.detach().clone() - - -@pytest.mark.parametrize( - "device, def_cls", itertools.product(devices, nograd_defenses)) -def test_bpda_on_nograd_defense(device, def_cls): - defense = def_cls(**defense_kwargs[def_cls]) - - defense = BPDAWrapper(defense, forwardsub=_identity) - _calc_datagrad_on_defense(defense, defense_data[def_cls]) - - defense = BPDAWrapper(defense, backward=_straight_through_backward) - _calc_datagrad_on_defense(defense, defense_data[def_cls]) - - -@pytest.mark.parametrize( - "device, def_cls", itertools.product(devices, withgrad_defenses)) -def test_bpda_on_withgrad_defense(device, def_cls): - defense = def_cls(**defense_kwargs[def_cls]) - - grad_from_self = _calc_datagrad_on_defense( - defense, defense_data[def_cls]) - - defense_with_idenity_backward = BPDAWrapper(defense, forwardsub=_identity) - grad_from_identity_backward = _calc_datagrad_on_defense( - defense_with_idenity_backward, defense_data[def_cls]) - - defense_with_self_backward = BPDAWrapper(defense, forwardsub=defense) - grad_from_self_backward = _calc_datagrad_on_defense( - defense_with_self_backward, defense_data[def_cls]) - - assert not torch_allclose(grad_from_identity_backward, grad_from_self) - assert torch_allclose(grad_from_self_backward, grad_from_self) - - -@pytest.mark.parametrize( - "device, func", itertools.product( - devices, [torch.sigmoid, torch.tanh, torch.relu])) -def test_bpda_on_activations(device, func): - data = vecdata.detach().clone() - data = data - data.mean() - - grad_from_self = _calc_datagrad_on_defense(func, data) - - func_with_idenity_backward = BPDAWrapper(func, forwardsub=_identity) - grad_from_identity_backward = _calc_datagrad_on_defense( - func_with_idenity_backward, data) - - func_with_self_backward = BPDAWrapper(func, forwardsub=func) - grad_from_self_backward = _calc_datagrad_on_defense( - func_with_self_backward, data) - - assert not torch_allclose(grad_from_identity_backward, grad_from_self) - assert torch_allclose(grad_from_self_backward, grad_from_self) - - -@pytest.mark.parametrize( - "device, func", itertools.product( - devices, [nn.Sigmoid(), nn.Tanh(), nn.ReLU()])) -def test_bpda_nograd_on_multi_input(device, func): - - class MultiInputFunc(nn.Module): - def forward(self, x, y): - return 2.0 * x - 1.0 * y - - class DummyNet(nn.Module): - def __init__(self): - super(DummyNet, self).__init__() - self.linear = nn.Linear(1200, 10) - - def forward(self, x): - x = x.view(x.shape[0], -1) - return self.linear(x) - - bpda = BPDAWrapper(forward=MultiInputFunc()) - - with torch.enable_grad(): - x = torch.rand(size=(10, 3, 20, 20), device=device, - requires_grad=True) - y = torch.rand_like(x, requires_grad=True) - z = bpda(x, y) - z_ = z.detach().requires_grad_() - - net = nn.Sequential(func, DummyNet()) - net.to(device) - - with torch.enable_grad(): - loss_ = net(z_).sum() - loss = net(z).sum() - grad_z, = torch.autograd.grad(loss_, [z_]) - grad_x, grad_y = torch.autograd.grad(loss, [x, y]) - - assert torch_allclose(grad_x, grad_z) - assert torch_allclose(grad_y, grad_z) - - -if __name__ == '__main__': - from deepcp.defenses import AverageSmoothing2D - test_bpda_on_withgrad_defense(cpu, AverageSmoothing2D) diff --git a/tests/test_context.py b/tests/test_context.py deleted file mode 100644 index eb5fe11..0000000 --- a/tests/test_context.py +++ /dev/null @@ -1,123 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import pytest - -from deepcp.context import ctx_eval -from deepcp.context import ctx_noparamgrad -from deepcp.context import ctx_noparamgrad_and_eval -from deepcp.context import get_param_grad_state -from deepcp.context import get_module_training_state -from deepcp.context import set_param_grad_off -from deepcp.utils import torch_allclose -from deepcp.test_utils import SimpleModel -from deepcp.test_utils import vecdata - - -def _generate_models(): - mix_model = SimpleModel() - mix_model.fc1.training = True - mix_model.fc2.training = False - mix_model.fc1.weight.requires_grad = False - mix_model.fc2.bias.requires_grad = False - - trainon_model = SimpleModel() - trainon_model.train() - - trainoff_model = SimpleModel() - trainoff_model.eval() - - gradon_model = SimpleModel() - - gradoff_model = SimpleModel() - set_param_grad_off(gradoff_model) - - return ( - mix_model, gradon_model, gradoff_model, trainon_model, trainoff_model - ) - - -mix_model, gradon_model, gradoff_model, trainon_model, trainoff_model = \ - _generate_models() - - -def _assert_grad_off(module): - for param in module.parameters(): - assert not param.requires_grad - - -def _assert_grad_on(module): - for param in module.parameters(): - assert param.requires_grad - - -def _assert_training_off(module): - for mod in module.modules(): - assert not mod.training - - -def _assert_training_on(module): - for mod in module.modules(): - assert mod.training - - -def _run_one_assert_val(ctxmgr, model, assert_inside, assert_outside): - output = model(vecdata) - assert_outside(model) - with ctxmgr(model): - assert_inside(model) - assert torch_allclose(output, model(vecdata)) - assert_outside(model) - assert torch_allclose(output, model(vecdata)) - - -def _run_one_assert_consistent(ctxmgr, model, get_state_fn, assert_inside): - dct = get_state_fn(mix_model) - output = model(vecdata) - with ctxmgr(model): - assert_inside(model) - assert torch_allclose(output, model(vecdata)) - newdct = get_state_fn(model) - assert dct is not newdct - assert dct == newdct - assert torch_allclose(output, model(vecdata)) - - -@pytest.mark.parametrize( - "ctxmgr", (ctx_noparamgrad, ctx_noparamgrad_and_eval)) -def test_noparamgrad(ctxmgr): - _run_one_assert_consistent(ctxmgr, mix_model, - get_state_fn=get_param_grad_state, - assert_inside=_assert_grad_off) - - _run_one_assert_val(ctxmgr, gradon_model, - assert_inside=_assert_grad_off, - assert_outside=_assert_grad_on) - - _run_one_assert_val(ctxmgr, gradoff_model, - assert_inside=_assert_grad_off, - assert_outside=_assert_grad_off) - - -@pytest.mark.parametrize( - "ctxmgr", (ctx_eval, ctx_noparamgrad_and_eval)) -def test_eval(ctxmgr): - _run_one_assert_consistent(ctxmgr, mix_model, - get_state_fn=get_module_training_state, - assert_inside=_assert_training_off) - - _run_one_assert_val(ctxmgr, trainon_model, - assert_inside=_assert_training_off, - assert_outside=_assert_training_on) - - _run_one_assert_val(ctxmgr, trainoff_model, - assert_inside=_assert_training_off, - assert_outside=_assert_training_off) - - -if __name__ == '__main__': - pass diff --git a/tests/test_defenses_correct.py b/tests/test_defenses_correct.py deleted file mode 100644 index b190469..0000000 --- a/tests/test_defenses_correct.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import numpy as np -from scipy import ndimage - -from deepcp.test_utils import generate_data_model_on_img -from deepcp.utils import torch_allclose -from deepcp.defenses import BinaryFilter -from deepcp.defenses import MedianSmoothing2D - -data, label, model = generate_data_model_on_img() - - -def test_binary_filter(): - assert torch_allclose(BinaryFilter()(data), data > 0.5) - - -def test_median_filter(): - # XXX: doesn't pass when kernel_size is even - # XXX: when kernel_size is odd, pixels on the boundaries are different - kernel_size = 3 - padding = kernel_size // 2 - rval_scipy = ndimage.filters.median_filter( - data.detach().numpy(), size=(1, 1, kernel_size, kernel_size)) - rval = MedianSmoothing2D(kernel_size=kernel_size)(data).detach().numpy() - assert np.allclose(rval_scipy[:, :, padding:-padding, padding:-padding], - rval[:, :, padding:-padding, padding:-padding]) - - -# TODO: correctness test of GaussianSmoothing2D and AverageSmoothing2D - -if __name__ == '__main__': - test_binary_filter() - test_median_filter() diff --git a/tests/test_defenses_running.py b/tests/test_defenses_running.py deleted file mode 100644 index 68d0496..0000000 --- a/tests/test_defenses_running.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import itertools - -import pytest -import torch -import torch.nn as nn - -from deepcp.test_utils import vecdata -from deepcp.test_utils import vecmodel -from deepcp.test_utils import imgdata -from deepcp.test_utils import imgmodel -from deepcp.test_utils import general_input_defenses -from deepcp.test_utils import image_only_defenses -from deepcp.test_utils import withgrad_defenses -from deepcp.test_utils import nograd_defenses -from deepcp.test_utils import defense_kwargs -from deepcp.test_utils import defense_data - -cuda = "cuda" -cpu = "cpu" -devices = (cpu, cuda) if torch.cuda.is_available() else (cpu, ) - - -def _run_data_model_defense(data, model, defense, device): - defended_model = nn.Sequential(defense, model) - defended_model.to(device) - data = data.to(device) - defended_model(data) - - -@pytest.mark.parametrize( - "device, def_cls", itertools.product(devices, general_input_defenses)) -def test_running_on_vec(device, def_cls): - _run_data_model_defense( - vecdata, vecmodel, def_cls(**defense_kwargs[def_cls]), device) - - -@pytest.mark.parametrize( - "device, def_cls", - itertools.product(devices, general_input_defenses + image_only_defenses)) -def test_running_on_img(device, def_cls): - _run_data_model_defense( - imgdata, imgmodel, def_cls(**defense_kwargs[def_cls]), device) - - -@pytest.mark.parametrize( - "device, def_cls", - itertools.product(devices, withgrad_defenses)) -def test_withgrad(device, def_cls): - defense = def_cls(**defense_kwargs[def_cls]) - data = defense_data[def_cls] - data.requires_grad_() - loss = defense(data).sum() - loss.backward() - - -@pytest.mark.parametrize( - "device, def_cls", - itertools.product(devices, nograd_defenses)) -def test_defenses_nograd(device, def_cls): - with pytest.raises((RuntimeError, NotImplementedError)): - defense = def_cls(**defense_kwargs[def_cls]) - data = defense_data[def_cls] - data.requires_grad_() - loss = defense(data).sum() - loss.backward() - - -if __name__ == '__main__': - pass diff --git a/tests/test_utilities.py b/tests/test_utilities.py deleted file mode 100644 index c4e6045..0000000 --- a/tests/test_utilities.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright (c) 2018-present, Royal Bank of Canada. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import warnings - -import numpy as np -import torch -import torchvision.transforms.functional as F - -from deepcp.utils import torch_allclose -from deepcp.utils import clamp -from deepcp.utils import CIFAR10_MEAN -from deepcp.utils import CIFAR10_STD -from deepcp.utils import MNIST_MEAN -from deepcp.utils import MNIST_STD -from deepcp.utils import NormalizeByChannelMeanStd -from deepcp.utils import PerImageStandardize -from deepcp.utils import torch_flip -from deepcp_examples.utils import bchw2bhwc -from deepcp_examples.utils import bhwc2bchw - - -def test_mnist_normalize(): - # MNIST - tensor = torch.rand((16, 1, 28, 28)) - normalize = NormalizeByChannelMeanStd(MNIST_MEAN, MNIST_STD) - - assert torch_allclose( - torch.stack([F.normalize(t, MNIST_MEAN, MNIST_STD) - for t in tensor.clone()]), - normalize(tensor)) - - -def test_cifar10_normalize(): - # CIFAR10 - tensor = torch.rand((16, 3, 32, 32)) - normalize = NormalizeByChannelMeanStd(CIFAR10_MEAN, CIFAR10_STD) - - assert torch_allclose( - torch.stack([F.normalize(t, CIFAR10_MEAN, CIFAR10_STD) - for t in tensor.clone()]), - normalize(tensor)) - - -def test_grad_through_normalize(): - tensor = torch.rand((2, 1, 28, 28)) - tensor.requires_grad_() - mean = torch.tensor((0.,)) - std = torch.tensor((1.,)) - normalize = NormalizeByChannelMeanStd(mean, std) - - loss = (normalize(tensor) ** 2).sum() - loss.backward() - - assert torch_allclose(2 * tensor, tensor.grad) - - -def _run_tf_per_image_standardization(imgs): - import tensorflow.compat.v1 as tf - tf.disable_v2_behavior() - import tensorflow.image # noqa: F401 - - imgs = bchw2bhwc(imgs) - placeholder = tf.placeholder(tf.float32, shape=imgs.shape) - var_scaled = tf.map_fn( - lambda img: tf.image.per_image_standardization(img), placeholder) - - with tf.Session() as sess: - tf_scaled = sess.run(var_scaled, feed_dict={placeholder: imgs}) - return bhwc2bchw(tf_scaled) - - -def test_per_image_standardization(): - imgs = np.random.normal( - scale=1. / (3072 ** 0.5), size=(10, 3, 32, 32)).astype(np.float32) - per_image_standardize = PerImageStandardize() - pt_scaled = per_image_standardize(torch.tensor(imgs)).numpy() - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - tf_scaled = _run_tf_per_image_standardization(imgs) - assert np.abs(pt_scaled - tf_scaled).max() < 0.001 - - -def test_clamp(): - def _convert_to_float(x): - return float(x) if x is not None else None - - def _convert_to_batch_tensor(x, data): - return x * torch.ones_like(data) if x is not None else None - - def _convert_to_single_tensor(x, data): - return x * torch.ones_like(data[0]) if x is not None else None - - for min, max in [(-1, None), (None, 1), (-1, 1)]: - data = 3 * torch.randn((11, 12, 13)) - case1 = clamp(data, min, max) - case2 = clamp(data, _convert_to_float(min), _convert_to_float(max)) - case3 = clamp(data, _convert_to_batch_tensor(min, data), - _convert_to_batch_tensor(max, data)) - case4 = clamp(data, _convert_to_single_tensor(min, data), - _convert_to_single_tensor(max, data)) - - assert torch.all(case1 == case2) - assert torch.all(case2 == case3) - assert torch.all(case3 == case4) - - -def test_flip(): - x = torch.randn(4, 5, 6, 7) - assert (torch_flip(x, dims=(1, 2)) == torch.flip(x, dims=(1, 2))).all()