From 19756c1b14ba90a6e5cef26d1bfaac32e79ca223 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Wed, 2 Aug 2023 12:21:21 +0200 Subject: [PATCH 01/46] Remove low-rank from GromovWasserstein solver --- .../initializers/quadratic/initializers.py | 4 +- .../problems/quadratic/quadratic_problem.py | 54 +------ .../solvers/quadratic/gromov_wasserstein.py | 143 +++--------------- .../quadratic/gromov_wasserstein_lr.py | 0 4 files changed, 26 insertions(+), 175 deletions(-) create mode 100644 src/ott/solvers/quadratic/gromov_wasserstein_lr.py diff --git a/src/ott/initializers/quadratic/initializers.py b/src/ott/initializers/quadratic/initializers.py index 62950bc4e..db7ba0be7 100644 --- a/src/ott/initializers/quadratic/initializers.py +++ b/src/ott/initializers/quadratic/initializers.py @@ -204,7 +204,7 @@ def _create_geometry( from ott.solvers.linear import sinkhorn_lr q, r, g = self._linear_lr_initializer(quad_prob, **kwargs) - tmp_out = sinkhorn_lr.LRSinkhornOutput( + return sinkhorn_lr.LRSinkhornOutput( q=q, r=r, g=g, @@ -214,8 +214,6 @@ def _create_geometry( epsilon=None, ) - return quad_prob.update_lr_geom(tmp_out, relative_epsilon=relative_epsilon) - @property def rank(self) -> int: """Rank of the transport matrix factorization.""" diff --git a/src/ott/problems/quadratic/quadratic_problem.py b/src/ott/problems/quadratic/quadratic_problem.py index a14273a92..d7918e4c1 100644 --- a/src/ott/problems/quadratic/quadratic_problem.py +++ b/src/ott/problems/quadratic/quadratic_problem.py @@ -11,7 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import TYPE_CHECKING, Literal, Optional, Tuple, Union +from typing import Literal, Optional, Tuple, Union import jax import jax.numpy as jnp @@ -23,9 +23,6 @@ from ott.problems.quadratic import quadratic_costs from ott.types import Transport -if TYPE_CHECKING: - from ott.solvers.linear import sinkhorn_lr - __all__ = ["QuadraticProblem"] @@ -240,40 +237,6 @@ def init_transport_mass(self) -> float: b = jax.lax.stop_gradient(self.b) return a.sum() * b.sum() - def update_lr_geom( - self, - lr_sink: "sinkhorn_lr.LRSinkhornOutput", - relative_epsilon: Optional[bool] = None, - ) -> geometry.Geometry: - """Recompute (possibly LRC) linearization using LR Sinkhorn output.""" - marginal_1 = lr_sink.marginal(1) - marginal_2 = lr_sink.marginal(0) - marginal_cost = self.marginal_dependent_cost(marginal_1, marginal_2) - - # Extract factors from LR Sinkhorn output - q, r, inv_sqg = lr_sink.q, lr_sink.r, 1.0 / jnp.sqrt(lr_sink.g) - # Distribute middle marginal evenly across both factors. - q, r = q * inv_sqg[None, :], r * inv_sqg[None, :] - - # Handle LRC Geometry case. - h1, h2 = self.quad_loss - geom_xx, geom_yy, geom_xy = self.geom_xx, self.geom_yy, self.geom_xy - tmp1 = apply_cost(geom_xx, q, axis=1, fn=h1) - tmp2 = apply_cost(geom_yy, r, axis=1, fn=h2) - if self.is_low_rank: - geom = low_rank.LRCGeometry( - cost_1=tmp1, cost_2=-tmp2, relative_epsilon=relative_epsilon - ) + marginal_cost - if self.is_fused: - geom = geom + geom_xy - else: - cost_matrix = marginal_cost.cost_matrix - jnp.dot(tmp1, tmp2.T) - cost_matrix += self.fused_penalty * self._fused_cost_matrix - geom = geometry.Geometry( - cost_matrix=cost_matrix, relative_epsilon=relative_epsilon - ) - return geom # noqa: RET504 - def update_linearization( self, transport: Transport, @@ -344,21 +307,6 @@ def update_linearization( geom, self.a, self.b, tau_a=self.tau_a, tau_b=self.tau_b ) - def update_lr_linearization( - self, - lr_sink: "sinkhorn_lr.LRSinkhornOutput", - *, - relative_epsilon: Optional[bool] = None, - ) -> linear_problem.LinearProblem: - """Update a Quad problem linearization using a LR Sinkhorn.""" - return linear_problem.LinearProblem( - self.update_lr_geom(lr_sink, relative_epsilon=relative_epsilon), - self.a, - self.b, - tau_a=self.tau_a, - tau_b=self.tau_b - ) - @property def _fused_cost_matrix(self) -> Union[float, jnp.ndarray]: if not self.is_fused: diff --git a/src/ott/solvers/quadratic/gromov_wasserstein.py b/src/ott/solvers/quadratic/gromov_wasserstein.py index ca3c24090..adf1d3d24 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein.py @@ -16,7 +16,6 @@ Callable, Dict, Literal, - Mapping, NamedTuple, Optional, Sequence, @@ -29,9 +28,7 @@ import numpy as np from jax.experimental import host_callback -from ott import utils -from ott.geometry import geometry, low_rank, pointcloud -from ott.initializers.linear import initializers_lr +from ott.geometry import geometry from ott.initializers.quadratic import initializers as quad_initializers from ott.math import fixed_point_loop from ott.problems.linear import linear_problem @@ -113,8 +110,6 @@ class GWState(NamedTuple): linearization of GW. linear_pb: Local linearization of the quadratic GW problem. old_transport_mass: Intermediary value of the mass of the transport matrix. - rngs: Random keys passed to low-rank initializers at every GW iteration - when not using warm start. errors: Holds sequence of vectors of errors of the Sinkhorn algorithm at each iteration. """ @@ -124,7 +119,6 @@ class GWState(NamedTuple): linear_state: LinearOutput linear_pb: linear_problem.LinearProblem old_transport_mass: float - rngs: Optional[jax.random.PRNGKeyArray] = None errors: Optional[jnp.ndarray] = None def set(self, **kwargs: Any) -> "GWState": @@ -161,26 +155,14 @@ class GromovWasserstein(was_solver.WassersteinSolver): Args: args: Positional arguments for :class:`~ott.solvers.was_solver.WassersteinSolver`. - warm_start: Whether to initialize (low-rank) Sinkhorn calls using values - from the previous iteration. If `None`, warm starts are not used for - standard Sinkhorn, but used for low-rank Sinkhorn. + warm_start: Whether to initialize Sinkhorn calls using values + from the previous iteration. relative_epsilon: Whether to use relative epsilon in the linearized geometry. - quad_initializer: Quadratic initializer. If the solver is entropic, - :class:`~ott.initializers.quadratic.initializers.QuadraticInitializer` - is always used. Otherwise, the quadratic initializer wraps the low-rank - Sinkhorn initializers. If `None`, the low-rank initializer will be - selected in a problem-specific manner. If both ``geom_xx`` and ``geom_yy`` - are :class:`~ott.geometry.pointcloud.PointCloud` or - :class:`~ott.geometry.low_rank.LRCGeometry`, use - :class:`~ott.initializers.linear.initializers_lr.KMeansInitializer`. - Otherwise, use - :class:`~ott.initializers.linear.initializers_lr.RandomInitializer`. progress_fn: callback function which gets called during the Gromov-Wasserstein iterations, so the user can display the error at each iteration, e.g., using a progress bar. See :func:`~ott.utils.default_progress_fn` for a basic implementation. - kwargs_init: Keyword arguments when creating the initializer. kwargs: Keyword arguments for :class:`~ott.solvers.was_solver.WassersteinSolver`. """ @@ -188,27 +170,20 @@ class GromovWasserstein(was_solver.WassersteinSolver): def __init__( self, *args: Any, - warm_start: Optional[bool] = None, + warm_start: bool = False, relative_epsilon: Optional[bool] = None, - quad_initializer: Optional[ - Union[Literal["random", "rank2", "k-means", "generalized-k-means"], - quad_initializers.BaseQuadraticInitializer]] = None, progress_fn: Optional[ProgressCallbackFn_t] = None, - kwargs_init: Optional[Mapping[str, Any]] = None, **kwargs: Any ): super().__init__(*args, **kwargs) - self._warm_start = warm_start + self.warm_start = warm_start self.relative_epsilon = relative_epsilon - self.quad_initializer = quad_initializer self.progress_fn = progress_fn - self.kwargs_init = {} if kwargs_init is None else kwargs_init def __call__( self, prob: quadratic_problem.QuadraticProblem, init: Optional[linear_problem.LinearProblem] = None, - rng: Optional[jax.random.PRNGKeyArray] = None, **kwargs: Any, ) -> GWOutput: """Run the Gromov-Wasserstein solver. @@ -217,42 +192,32 @@ def __call__( prob: Quadratic OT problem. init: Initial linearization of the quadratic problem. If `None`, it will be computed using the initializer. - rng: Random number key. kwargs: Keyword arguments used when calling the initializer. Returns: The Gromov-Wasserstein output. """ - rng = utils.default_prng_key(rng) - rng1, rng2 = jax.random.split(rng, 2) - + assert not self.is_low_rank, "Please use `LRGromovWasserstein`" if prob._is_low_rank_convertible: prob = prob.to_low_rank() if init is None: - initializer = self.create_initializer(prob) + initializer = quad_initializers.QuadraticInitializer() init = initializer( prob, epsilon=self.epsilon, - rng=rng1, relative_epsilon=self.relative_epsilon, **kwargs ) - out = iterations(self, prob, init, rng2) + out = iterations(self, prob, init) # TODO(lpapaxanthoos): remove stop_gradient when using backprop - if self.is_low_rank: - linearization = prob.update_lr_linearization( - jax.lax.stop_gradient(out.linear_state), - relative_epsilon=self.relative_epsilon, - ) - else: - linearization = prob.update_linearization( - jax.lax.stop_gradient(out.linear_state), - epsilon=self.epsilon, - old_transport_mass=jax.lax.stop_gradient(out.old_transport_mass), - relative_epsilon=self.relative_epsilon, - ) + linearization = prob.update_linearization( + jax.lax.stop_gradient(out.linear_state), + epsilon=self.epsilon, + old_transport_mass=jax.lax.stop_gradient(out.old_transport_mass), + relative_epsilon=self.relative_epsilon, + ) linear_state = out.linear_state.set_cost(linearization, True, True) iteration = jnp.sum(out.costs != -1) @@ -267,15 +232,12 @@ def init_state( self, prob: quadratic_problem.QuadraticProblem, init: linear_problem.LinearProblem, - rng: jax.random.PRNGKeyArray, ) -> GWState: """Initialize the state of the Gromov-Wasserstein iterations. Args: prob: Quadratic OT problem. init: Initial linearization of the quadratic problem. - rng: Random key for low-rank initializers. Only used when - :attr:`warm_start` is `False`. Returns: The initial Gromov-Wasserstein state. @@ -294,7 +256,6 @@ def init_state( linear_state=linear_state, linear_pb=init, old_transport_mass=transport_mass, - rngs=jax.random.split(rng, num_iter), errors=errors, ) @@ -319,57 +280,11 @@ def output_from_state( old_transport_mass=state.old_transport_mass ) - def create_initializer( - self, prob: quadratic_problem.QuadraticProblem - ) -> quad_initializers.BaseQuadraticInitializer: - """Create quadratic, possibly low-rank initializer. - - Args: - prob: Quadratic OT problem used to determine the initializer. - - Returns: - The initializer. - """ - if isinstance( - self.quad_initializer, quad_initializers.BaseQuadraticInitializer - ): - if self.is_low_rank: - assert isinstance( - self.quad_initializer, quad_initializers.LRQuadraticInitializer - ), f"Expected quadratic initializer to be low rank, " \ - f"found `{type(self.quad_initializer).__name__}`." - assert self.quad_initializer.rank == self.rank, \ - f"Expected quadratic initializer of rank `{self.rank}`, " \ - f"found `{self.quad_initializer.rank}`." - return self.quad_initializer - - if self.is_low_rank: - if self.quad_initializer is None: - types = (pointcloud.PointCloud, low_rank.LRCGeometry) - kind = "k-means" if isinstance(prob.geom_xx, types) and isinstance( - prob.geom_yy, types - ) else "random" - else: - kind = self.quad_initializer - linear_lr_init = initializers_lr.LRInitializer.from_solver( - self, kind=kind, **self.kwargs_init - ) - return quad_initializers.LRQuadraticInitializer(linear_lr_init) - - return quad_initializers.QuadraticInitializer(**self.kwargs_init) - - @property - def warm_start(self) -> bool: - """Whether to initialize (low-rank) Sinkhorn using previous solutions.""" - return self.is_low_rank if self._warm_start is None else self._warm_start - def tree_flatten(self) -> Tuple[Sequence[Any], Dict[str, Any]]: # noqa: D102 children, aux_data = super().tree_flatten() - aux_data["warm_start"] = self._warm_start + aux_data["warm_start"] = self.warm_start aux_data["progress_fn"] = self.progress_fn aux_data["relative_epsilon"] = self.relative_epsilon - aux_data["quad_initializer"] = self.quad_initializer - aux_data["kwargs_init"] = self.kwargs_init return children, aux_data @@ -377,7 +292,6 @@ def iterations( solver: GromovWasserstein, prob: quadratic_problem.QuadraticProblem, init: linear_problem.LinearProblem, - rng: jax.random.PRNGKeyArray, ) -> GWOutput: """Jittable Gromov-Wasserstein outer loop.""" @@ -393,23 +307,14 @@ def body_fn( del compute_error # always assumed true for the outer loop of GW lin_state = state.linear_state - if solver.is_low_rank: - rng = state.rngs[iteration] - init = (lin_state.q, lin_state.r, - lin_state.g) if solver.warm_start else (None, None, None) - linear_pb = prob.update_lr_linearization( - state.linear_state, relative_epsilon=solver.relative_epsilon - ) - out = solver.linear_ot_solver(linear_pb, init=init, rng=rng) - else: - init = (lin_state.f, lin_state.g) if solver.warm_start else (None, None) - linear_pb = prob.update_linearization( - lin_state, - solver.epsilon, - state.old_transport_mass, - relative_epsilon=solver.relative_epsilon, - ) - out = solver.linear_ot_solver(linear_pb, init=init) + linear_pb = prob.update_linearization( + lin_state, + solver.epsilon, + state.old_transport_mass, + relative_epsilon=solver.relative_epsilon, + ) + init = (lin_state.f, lin_state.g) if solver.warm_start else (None, None) + out = solver.linear_ot_solver(linear_pb, init=init) old_transport_mass = jax.lax.stop_gradient( state.linear_state.transport_mass @@ -435,7 +340,7 @@ def body_fn( max_iterations=solver.max_iterations, inner_iterations=1, constants=solver, - state=solver.init_state(prob, init, rng=rng) + state=solver.init_state(prob, init), ) return solver.output_from_state(state) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py new file mode 100644 index 000000000..e69de29bb From a0797613a1595ba32b2eb3c22b3bc7e55400e33a Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Wed, 2 Aug 2023 14:31:23 +0200 Subject: [PATCH 02/46] First skeleton loop --- .../initializers/quadratic/initializers.py | 19 +-- src/ott/solvers/quadratic/__init__.py | 2 +- .../quadratic/gromov_wasserstein_lr.py | 135 ++++++++++++++++++ 3 files changed, 141 insertions(+), 15 deletions(-) diff --git a/src/ott/initializers/quadratic/initializers.py b/src/ott/initializers/quadratic/initializers.py index db7ba0be7..23e6fe3d0 100644 --- a/src/ott/initializers/quadratic/initializers.py +++ b/src/ott/initializers/quadratic/initializers.py @@ -185,22 +185,13 @@ def __init__(self, lr_linear_initializer: "initializers_lr.LRInitializer"): self._linear_lr_initializer = lr_linear_initializer def _create_geometry( - self, - quad_prob: "quadratic_problem.QuadraticProblem", - relative_epsilon: Optional[bool] = False, - **kwargs: Any + self, quad_prob: "quadratic_problem.QuadraticProblem", **kwargs: Any ) -> geometry.Geometry: - """Compute initial geometry for linearization. - - Args: - quad_prob: Quadratic OT problem. - relative_epsilon: Whether to use relative epsilon in the geometry. - kwargs: Keyword arguments for - :meth:`~ott.initializers.linear.initializers_lr.LRInitializer.__call__`. + raise NotImplementedError("Unreachable.") - Returns: - The initial geometry used to initialize a linear problem. - """ + def __call__( + self, quad_prob: "quadratic_problem.QuadraticProblem", **kwargs: Any + ) -> "sinkhorn_lr.LRSinkhornOutput": from ott.solvers.linear import sinkhorn_lr q, r, g = self._linear_lr_initializer(quad_prob, **kwargs) diff --git a/src/ott/solvers/quadratic/__init__.py b/src/ott/solvers/quadratic/__init__.py index 02a5f72cc..32512dfab 100644 --- a/src/ott/solvers/quadratic/__init__.py +++ b/src/ott/solvers/quadratic/__init__.py @@ -11,4 +11,4 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import gromov_wasserstein, gw_barycenter +from . import gromov_wasserstein, gromov_wasserstein_lr, gw_barycenter diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index e69de29bb..aa98dd883 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -0,0 +1,135 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Mapping, NamedTuple, Optional + +import jax +import jax.numpy as jnp + +from ott import utils +from ott.initializers.quadratic import initializers +from ott.math import fixed_point_loop +from ott.problems.quadratic import quadratic_problem +from ott.solvers import was_solver +from ott.solvers.linear import sinkhorn_lr + +__all__ = ["LRGromovWasserstein"] + + +class LRGWState(NamedTuple): + costs: jnp.ndarray + linear_convergence: jnp.ndarray + linear_state: sinkhorn_lr.LRSinkhornOutput + errors: Optional[jnp.ndarray] = None + + +@jax.tree_util.register_pytree_node_class +class LRGromovWasserstein(was_solver.WassersteinSolver): + + def __init__( + self, + *args: Any, + relative_epsilon: Optional[bool] = None, + quad_initializer: initializers.LRQuadraticInitializer = None, + progress_fn: Optional["ProgressCallbackFn_t"] = None, + kwargs_init: Optional[Mapping[str, Any]] = None, + **kwargs: Any + ): + super().__init__(*args, **kwargs) + self.relative_epsilon = relative_epsilon + self.quad_initializer = quad_initializer + self.progress_fn = progress_fn + self.kwargs_init = {} if kwargs_init is None else kwargs_init + + def __call__( + self, + prob: quadratic_problem.QuadraticProblem, + init: Optional[sinkhorn_lr.LRSinkhornOutput] = None, + rng: Optional[jax.random.PRNGKeyArray] = None, + **kwargs: Any, + ) -> "GWOutput": + if prob._is_low_rank_convertible: + prob = prob.to_low_rank() + rng = utils.default_prng_key(rng) + + if init is None: + init = self.quad_initializer( + prob, + epsilon=self.epsilon, + rng=rng, + relative_epsilon=self.relative_epsilon, + **kwargs + ) + + return self._iterations(prob, init) + + def init_state( + self, + prob: quadratic_problem.QuadraticProblem, + init: sinkhorn_lr.LRSinkhornOutput, + ) -> LRGWState: + """Initialize the state of the low-rank Gromov-Wasserstein iterations. + + Args: + prob: Quadratic OT problem. + init: Initial linearization of the quadratic problem. + + Returns: + The initial low-rank Gromov-Wasserstein state. + """ + num_iter = self.max_iterations + if self.store_inner_errors: + errors = -jnp.ones((num_iter, self.linear_ot_solver.outer_iterations)) + else: + errors = None + + return LRGWState( + costs=-jnp.ones((num_iter,)), + linear_convergence=-jnp.ones((num_iter,)), + linear_state=init, + errors=errors, + ) + + def output_from_state( + self, + state: LRGWState, + ) -> "GWOutput": + return state + + def _iterations( + self, + prob: quadratic_problem.QuadraticProblem, + init: sinkhorn_lr.LRSinkhornOutput, + ): + + def cond_fn(iteration: int, constants: Any, state: LRGWState) -> bool: + del constants + return self._continue(state, iteration) + + def body_fn( + iteration: int, constants: Any, state: LRGWState, compute_error: bool + ) -> LRGWState: + del constants + return state + + state = fixed_point_loop.fixpoint_iter( + cond_fn=cond_fn, + body_fn=body_fn, + min_iterations=self.min_iterations, + max_iterations=self.max_iterations, + inner_iterations=1, + constants=None, + state=self.init_state(prob, init), + ) + + return self.output_from_state(state) From 0d0c85f2b5a8e69c793ea7cde6726adc23007c26 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 3 Aug 2023 15:46:23 +0200 Subject: [PATCH 03/46] Add LRGW implementation --- .../quadratic/gromov_wasserstein_lr.py | 877 ++++++++++++++++-- 1 file changed, 803 insertions(+), 74 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index aa98dd883..601cc4ace 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -11,125 +11,854 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Any, Mapping, NamedTuple, Optional +"""A Jax implementation of the Low-Rank Sinkhorn algorithm.""" +from typing import ( + Any, + Callable, + Literal, + Mapping, + NamedTuple, + Optional, + Tuple, + Union, +) import jax import jax.numpy as jnp +import jax.scipy as jsp +import numpy as np +from jax.experimental import host_callback -from ott import utils -from ott.initializers.quadratic import initializers +from ott.geometry import geometry, low_rank, pointcloud +from ott.initializers.linear import initializers_lr as init_lib from ott.math import fixed_point_loop +from ott.math import utils as mu from ott.problems.quadratic import quadratic_problem -from ott.solvers import was_solver -from ott.solvers.linear import sinkhorn_lr +from ott.solvers.linear import lr_utils, sinkhorn -__all__ = ["LRGromovWasserstein"] +__all__ = ["LRGromovWasserstein", "LRGWOutput"] + +ProgressCallbackFn_t = Callable[ + [Tuple[np.ndarray, np.ndarray, np.ndarray, "LRGWState"]], None] class LRGWState(NamedTuple): + """State of the Low Rank Sinkhorn algorithm.""" + q: jnp.ndarray + r: jnp.ndarray + g: jnp.ndarray + gamma: float costs: jnp.ndarray - linear_convergence: jnp.ndarray - linear_state: sinkhorn_lr.LRSinkhornOutput - errors: Optional[jnp.ndarray] = None + errors: jnp.ndarray + crossed_threshold: bool + + def compute_error( # noqa: D102 + self, previous_state: "LRGWState" + ) -> float: + err_q = mu.js(self.q, previous_state.q, c=1.0) + err_r = mu.js(self.r, previous_state.r, c=1.0) + err_g = mu.js(self.g, previous_state.g, c=1.0) + + return ((1.0 / self.gamma) ** 2) * (err_q + err_r + err_g) + + def reg_ot_cost( # noqa: D102 + self, + ot_prob: quadratic_problem.QuadraticProblem, + *, + epsilon: float, + use_danskin: bool = False + ) -> float: + """For LR Sinkhorn, this defaults to the primal cost of LR solution.""" + return compute_reg_ot_cost( + self.q, + self.r, + self.g, + ot_prob, + epsilon=epsilon, + use_danskin=use_danskin + ) + + def set(self, **kwargs: Any) -> "LRGWState": + """Return a copy of self, with potential overwrites.""" + return self._replace(**kwargs) + + +def compute_reg_ot_cost( + q: jnp.ndarray, + r: jnp.ndarray, + g: jnp.ndarray, + ot_prob: quadratic_problem.QuadraticProblem, + epsilon: float, + use_danskin: bool = False +) -> float: + """Compute the regularized OT cost, here the primal cost of the LR solution. + + Args: + q: first factor of solution + r: second factor of solution + g: weights of solution + ot_prob: linear problem + epsilon: Entropic regularization. + use_danskin: if True, use Danskin's theorem :cite:`danskin:67,bertsekas:71` + to avoid computing the gradient of the cost function. + + Returns: + regularized OT cost, the (primal) transport cost of the low-rank solution. + """ + + def ent(x: jnp.ndarray) -> float: + # generalized entropy + return jnp.sum(jsp.special.entr(x) + x) + + tau_a, tau_b = ot_prob.tau_a, ot_prob.tau_b + q = jax.lax.stop_gradient(q) if use_danskin else q + r = jax.lax.stop_gradient(r) if use_danskin else r + g = jax.lax.stop_gradient(g) if use_danskin else g + + # TODO(michalk8): extract to a function + inv_g = 1.0 / g[None, :] + inv_sqrt_g = jnp.sqrt(inv_g) + tmp1 = -4.0 * ot_prob.geom_xx.apply_cost(q, axis=1) * inv_sqrt_g + tmp2 = ot_prob.geom_yy.apply_cost(r, axis=1) * inv_sqrt_g + lin_geom = low_rank.LRCGeometry(tmp1, tmp2) + + cost = jnp.sum(q * lin_geom.apply_cost(r, axis=1) * inv_g) + cost -= epsilon * (ent(q) + ent(r) + ent(g)) + if tau_a != 1.0: + cost += tau_a / (1.0 - tau_a) * mu.gen_kl(jnp.sum(q, axis=1), ot_prob.a) + if tau_b != 1.0: + cost += tau_b / (1.0 - tau_b) * mu.gen_kl(jnp.sum(r, axis=1), ot_prob.b) + + return cost + + +class LRGWOutput(NamedTuple): + """Implement the problems.Transport interface, for a LR Sinkhorn solution.""" + + q: jnp.ndarray + r: jnp.ndarray + g: jnp.ndarray + costs: jnp.ndarray + # TODO(michalk8): must be called `errors`, because of `store_inner_errors` + # in future, enforce via class hierarchy + errors: jnp.ndarray + ot_prob: quadratic_problem.QuadraticProblem + epsilon: float + # TODO(michalk8): Optional is an artifact of the current impl., refactor + reg_ot_cost: Optional[float] = None + + def set(self, **kwargs: Any) -> "LRGWOutput": + """Return a copy of self, with potential overwrites.""" + return self._replace(**kwargs) + + def set_cost( # noqa: D102 + self, + ot_prob: quadratic_problem.QuadraticProblem, + lse_mode: bool, + use_danskin: bool = False + ) -> "LRGWOutput": + del lse_mode + return self.set(reg_ot_cost=self.compute_reg_ot_cost(ot_prob, use_danskin)) + + def compute_reg_ot_cost( # noqa: D102 + self, + ot_prob: quadratic_problem.QuadraticProblem, + use_danskin: bool = False, + ) -> float: + return compute_reg_ot_cost( + self.q, + self.r, + self.g, + ot_prob, + epsilon=self.epsilon, + use_danskin=use_danskin + ) + + @property + def linear(self) -> bool: # noqa: D102 + return False + + @property + def geom(self) -> geometry.Geometry: # noqa: D102 + """Linearized geometry.""" + # TODO(michalk8): extract to a common function + inv_sqrt_g = jnp.sqrt(self._inv_g) + tmp1 = -4.0 * self.ot_prob.geom_xx.apply_cost(self.q, axis=1) * inv_sqrt_g + tmp2 = self.ot_prob.geom_yy.apply_cost(self.r, axis=1) * inv_sqrt_g + return low_rank.LRCGeometry(tmp1, tmp2) + + @property + def a(self) -> jnp.ndarray: # noqa: D102 + return self.ot_prob.a + + @property + def b(self) -> jnp.ndarray: # noqa: D102 + return self.ot_prob.b + + @property + def linear_output(self) -> bool: # noqa: D102 + return False + + @property + def converged(self) -> bool: # noqa: D102 + return jnp.logical_and( + jnp.any(self.costs == -1), jnp.all(jnp.isfinite(self.costs)) + ) + + @property + def matrix(self) -> jnp.ndarray: + """Transport matrix if it can be instantiated.""" + return (self.q * self._inv_g) @ self.r.T + + def apply(self, inputs: jnp.ndarray, axis: int = 0) -> jnp.ndarray: + """Apply the transport to a array; axis=1 for its transpose.""" + q, r = (self.q, self.r) if axis == 1 else (self.r, self.q) + # for `axis=0`: (batch, m), (m, r), (r,), (r, n) + return ((inputs @ r) * self._inv_g) @ q.T + + def marginal(self, axis: int) -> jnp.ndarray: # noqa: D102 + length = self.q.shape[0] if axis == 0 else self.r.shape[0] + return self.apply(jnp.ones(length,), axis=axis) + + def cost_at_geom(self, other_geom: geometry.Geometry) -> float: + """Return OT cost for current solution, evaluated at any cost matrix.""" + return jnp.sum(self.q * other_geom.apply_cost(self.r, axis=1) * self._inv_g) + + def transport_cost_at_geom(self, other_geom: geometry.Geometry) -> float: + """Return (by recomputing it) bare transport cost of current solution.""" + return self.cost_at_geom(other_geom) + + @property + def primal_cost(self) -> float: + """Return (by recomputing it) transport cost of current solution.""" + return self.transport_cost_at_geom(other_geom=self.geom) + + @property + def transport_mass(self) -> float: + """Sum of transport matrix.""" + return self.marginal(0).sum() + + @property + def _inv_g(self) -> jnp.ndarray: + return 1.0 / self.g @jax.tree_util.register_pytree_node_class -class LRGromovWasserstein(was_solver.WassersteinSolver): +class LRGromovWasserstein(sinkhorn.Sinkhorn): + r"""A Low-Rank Sinkhorn solver for linear reg-OT problems. + + The algorithm is described in :cite:`scetbon:21` and the implementation + contained here is adapted from `LOT `_. + + The algorithm minimizes a non-convex problem. It therefore requires special + care to initialization and convergence. Convergence is evaluated on successive + evaluations of the objective. The algorithm is only provided for the balanced + case. + + Args: + rank: Rank constraint on the coupling to minimize the linear OT problem + gamma: The (inverse of) gradient step size used by mirror descent. + gamma_rescale: Whether to rescale :math:`\gamma` every iteration as + described in :cite:`scetbon:22b`. + epsilon: Entropic regularization added on top of low-rank problem. + initializer: How to initialize the :math:`Q`, :math:`R` and :math:`g` + factors. Valid options are `'random'`, `'rank2'`, `'k-means'`, and + `'generalized-k-means`. If `None`, + :class:`~ott.initializers.linear.initializers_lr.KMeansInitializer` + is used when the linear problem's geometry is + :class:`~ott.geometry.pointcloud.PointCloud` or + :class:`~ott.geometry.low_rank.LRCGeometry`. Otherwise, use + :class:`~ott.initializers.linear.initializers_lr.RandomInitializer`. + lse_mode: Whether to run computations in lse or kernel mode. + inner_iterations: Number of inner iterations used by the algorithm before + re-evaluating progress. + use_danskin: Use Danskin theorem to evaluate gradient of objective w.r.t. + input parameters. Only `True` handled at this moment. + implicit_diff: Whether to use implicit differentiation. Currently, only + ``implicit_diff = False`` is implemented. + progress_fn: callback function which gets called during the Sinkhorn + iterations, so the user can display the error at each iteration, + e.g., using a progress bar. See :func:`~ott.utils.default_progress_fn` + for a basic implementation. + kwargs_dys: Keyword arguments passed to :meth:`dykstra_update_lse`, + :meth:`dykstra_update_kernel` or one of the functions defined in + :mod:`ott.solvers.linear`, depending on whether the problem + is balanced and on the ``lse_mode``. + kwargs_init: Keyword arguments for + :class:`~ott.initializers.linear.initializers_lr.LRInitializer`. + kwargs: Keyword arguments for + :class:`~ott.solvers.linear.sinkhorn.Sinkhorn`. + """ def __init__( self, - *args: Any, - relative_epsilon: Optional[bool] = None, - quad_initializer: initializers.LRQuadraticInitializer = None, - progress_fn: Optional["ProgressCallbackFn_t"] = None, + rank: int, + gamma: float = 10.0, + gamma_rescale: bool = True, + epsilon: float = 0.0, + initializer: Optional[Union[Literal["random", "rank2", "k-means", + "generalized-k-means"], + init_lib.LRInitializer]] = "random", + lse_mode: bool = True, + inner_iterations: int = 10, + use_danskin: bool = True, + implicit_diff: bool = False, + kwargs_dys: Optional[Mapping[str, Any]] = None, kwargs_init: Optional[Mapping[str, Any]] = None, - **kwargs: Any + progress_fn: Optional[ProgressCallbackFn_t] = None, + **kwargs: Any, ): - super().__init__(*args, **kwargs) - self.relative_epsilon = relative_epsilon - self.quad_initializer = quad_initializer + assert not implicit_diff, "Implicit diff. not yet implemented." + super().__init__( + lse_mode=lse_mode, + inner_iterations=inner_iterations, + use_danskin=use_danskin, + implicit_diff=implicit_diff, + **kwargs + ) + self.rank = rank + self.gamma = gamma + self.gamma_rescale = gamma_rescale + self.epsilon = epsilon + self.initializer = initializer self.progress_fn = progress_fn + # can be `None` + self.kwargs_dys = {} if kwargs_dys is None else kwargs_dys self.kwargs_init = {} if kwargs_init is None else kwargs_init def __call__( self, - prob: quadratic_problem.QuadraticProblem, - init: Optional[sinkhorn_lr.LRSinkhornOutput] = None, + ot_prob: quadratic_problem.QuadraticProblem, + init: Tuple[Optional[jnp.ndarray], Optional[jnp.ndarray], + Optional[jnp.ndarray]] = (None, None, None), rng: Optional[jax.random.PRNGKeyArray] = None, **kwargs: Any, - ) -> "GWOutput": - if prob._is_low_rank_convertible: - prob = prob.to_low_rank() - rng = utils.default_prng_key(rng) - - if init is None: - init = self.quad_initializer( - prob, - epsilon=self.epsilon, - rng=rng, - relative_epsilon=self.relative_epsilon, - **kwargs + ) -> LRGWOutput: + """Run low-rank Sinkhorn. + + Args: + ot_prob: Linear OT problem. + init: Initial values for the low-rank factors: + + - :attr:`~ott.solvers.linear.sinkhorn_lr.LRGWOutput.q`. + - :attr:`~ott.solvers.linear.sinkhorn_lr.LRGWOutput.r`. + - :attr:`~ott.solvers.linear.sinkhorn_lr.LRGWOutput.g`. + + Any `None` values will be initialized using the initializer. + rng: Random key for seeding. + kwargs: Additional arguments when calling the initializer. + + Returns: + The low-rank Sinkhorn output. + """ + initializer = self.create_initializer(ot_prob) + init = initializer(ot_prob, *init, rng=rng, **kwargs) + return run(ot_prob, self, init) + + def _get_costs( + self, + ot_prob: quadratic_problem.QuadraticProblem, + state: LRGWState, + ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray, float]: + q, r, g = state.q, state.r, state.g + log_q, log_r, log_g = mu.safe_log(q), mu.safe_log(r), mu.safe_log(g) + inv_g = 1.0 / g[None, :] + inv_sqrt_g = jnp.sqrt(inv_g) + + tmp1 = -4.0 * ot_prob.geom_xx.apply_cost(q, axis=1) * inv_sqrt_g + tmp2 = ot_prob.geom_yy.apply_cost(r, axis=1) * inv_sqrt_g + lin_geom = low_rank.LRCGeometry(tmp1, tmp2) + + tmp3 = lin_geom.apply_cost(r, axis=1) + grad_q = tmp3 * inv_g + 2.0 * ot_prob.geom_xx.apply_square_cost( + q.sum(1), axis=1 + ) + grad_r = lin_geom.apply_cost(q, axis=0) * inv_g + grad_r = grad_r + 2.0 * ot_prob.geom_yy.apply_square_cost(r.sum(1), axis=1) + + omega = jnp.sum(q * tmp3, axis=0) + grad_g = -omega / (g ** 2) + + grad_q += self.epsilon * log_q + grad_r += self.epsilon * log_r + grad_g += self.epsilon * log_g + + if self.gamma_rescale: + norm_q = jnp.max(jnp.abs(grad_q)) ** 2 + norm_r = jnp.max(jnp.abs(grad_r)) ** 2 + norm_g = jnp.max(jnp.abs(grad_g)) ** 2 + gamma = self.gamma / jnp.max(jnp.array([norm_q, norm_r, norm_g])) + else: + gamma = self.gamma + + eps_factor = 1.0 / (self.epsilon * gamma + 1.0) + gamma *= eps_factor + + c_q = -gamma * grad_q + eps_factor * log_q + c_r = -gamma * grad_r + eps_factor * log_r + c_g = -gamma * grad_g + eps_factor * log_g + + return c_q, c_r, c_g, gamma + + # TODO(michalk8): move to `lr_utils` when refactoring this + def dykstra_update_lse( + self, + c_q: jnp.ndarray, + c_r: jnp.ndarray, + h: jnp.ndarray, + gamma: float, + ot_prob: quadratic_problem.QuadraticProblem, + min_entry_value: float = 1e-6, + tolerance: float = 1e-3, + min_iter: int = 0, + inner_iter: int = 10, + max_iter: int = 10000 + ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + """Run Dykstra's algorithm.""" + # shortcuts for problem's definition. + r = self.rank + n, m = ot_prob.geom_xx.shape[0], ot_prob.geom_yy.shape[0] + loga, logb = jnp.log(ot_prob.a), jnp.log(ot_prob.b) + + h_old = h + g1_old, g2_old = jnp.zeros(r), jnp.zeros(r) + f1, f2 = jnp.zeros(n), jnp.zeros(m) + + w_gi, w_gp = jnp.zeros(r), jnp.zeros(r) + w_q, w_r = jnp.zeros(r), jnp.zeros(r) + err = jnp.inf + state_inner = f1, f2, g1_old, g2_old, h_old, w_gi, w_gp, w_q, w_r, err + constants = c_q, c_r, loga, logb + + def cond_fn( + iteration: int, constants: Tuple[jnp.ndarray, ...], + state_inner: Tuple[jnp.ndarray, ...] + ) -> bool: + del iteration, constants + *_, err = state_inner + return err > tolerance + + def _softm( + f: jnp.ndarray, g: jnp.ndarray, c: jnp.ndarray, axis: int + ) -> jnp.ndarray: + return jsp.special.logsumexp( + gamma * (f[:, None] + g[None, :] - c), axis=axis ) - return self._iterations(prob, init) + def body_fn( + iteration: int, constants: Tuple[jnp.ndarray, ...], + state_inner: Tuple[jnp.ndarray, ...], compute_error: bool + ) -> Tuple[jnp.ndarray, ...]: + # TODO(michalk8): in the future, use `NamedTuple` + f1, f2, g1_old, g2_old, h_old, w_gi, w_gp, w_q, w_r, err = state_inner + c_q, c_r, loga, logb = constants - def init_state( + # First Projection + f1 = jnp.where( + jnp.isfinite(loga), + (loga - _softm(f1, g1_old, c_q, axis=1)) / gamma + f1, loga + ) + f2 = jnp.where( + jnp.isfinite(logb), + (logb - _softm(f2, g2_old, c_r, axis=1)) / gamma + f2, logb + ) + + h = h_old + w_gi + h = jnp.maximum(jnp.log(min_entry_value) / gamma, h) + w_gi += h_old - h + h_old = h + + # Update couplings + g_q = _softm(f1, g1_old, c_q, axis=0) + g_r = _softm(f2, g2_old, c_r, axis=0) + + # Second Projection + h = (1. / 3.) * (h_old + w_gp + w_q + w_r) + h += g_q / (3. * gamma) + h += g_r / (3. * gamma) + g1 = h + g1_old - g_q / gamma + g2 = h + g2_old - g_r / gamma + + w_q = w_q + g1_old - g1 + w_r = w_r + g2_old - g2 + w_gp = h_old + w_gp - h + + q, r, _ = recompute_couplings(f1, g1, c_q, f2, g2, c_r, h, gamma) + + g1_old = g1 + g2_old = g2 + h_old = h + + err = jax.lax.cond( + jnp.logical_and(compute_error, iteration >= min_iter), + lambda: dykstra_solution_error(q, r, ot_prob, self.norm_error)[0], + lambda: err + ) + + return f1, f2, g1_old, g2_old, h_old, w_gi, w_gp, w_q, w_r, err + + def recompute_couplings( + f1: jnp.ndarray, + g1: jnp.ndarray, + c_q: jnp.ndarray, + f2: jnp.ndarray, + g2: jnp.ndarray, + c_r: jnp.ndarray, + h: jnp.ndarray, + gamma: float, + ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + q = jnp.exp(gamma * (f1[:, None] + g1[None, :] - c_q)) + r = jnp.exp(gamma * (f2[:, None] + g2[None, :] - c_r)) + g = jnp.exp(gamma * h) + return q, r, g + + state_inner = fixed_point_loop.fixpoint_iter_backprop( + cond_fn, body_fn, min_iter, max_iter, inner_iter, constants, state_inner + ) + + f1, f2, g1_old, g2_old, h_old, _, _, _, _, _ = state_inner + return recompute_couplings(f1, g1_old, c_q, f2, g2_old, c_r, h_old, gamma) + + def dykstra_update_kernel( self, - prob: quadratic_problem.QuadraticProblem, - init: sinkhorn_lr.LRSinkhornOutput, + k_q: jnp.ndarray, + k_r: jnp.ndarray, + k_g: jnp.ndarray, + gamma: float, + ot_prob: quadratic_problem.QuadraticProblem, + min_entry_value: float = 1e-6, + tolerance: float = 1e-3, + min_iter: int = 0, + inner_iter: int = 10, + max_iter: int = 10000 + ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + """Run Dykstra's algorithm.""" + # shortcuts for problem's definition. + rank = self.rank + n, m = ot_prob.geom_xx.shape[0], ot_prob.geom_yy.shape[0] + a, b = ot_prob.a, ot_prob.b + supp_a, supp_b = a > 0, b > 0 + + g_old = k_g + v1_old, v2_old = jnp.ones(rank), jnp.ones(rank) + u1, u2 = jnp.ones(n), jnp.ones(m) + + q_gi, q_gp = jnp.ones(rank), jnp.ones(rank) + q_q, q_r = jnp.ones(rank), jnp.ones(rank) + err = jnp.inf + state_inner = u1, u2, v1_old, v2_old, g_old, q_gi, q_gp, q_q, q_r, err + constants = k_q, k_r, k_g, a, b + + def cond_fn( + iteration: int, constants: Tuple[jnp.ndarray, ...], + state_inner: Tuple[jnp.ndarray, ...] + ) -> bool: + del iteration, constants + *_, err = state_inner + return err > tolerance + + def body_fn( + iteration: int, constants: Tuple[jnp.ndarray, ...], + state_inner: Tuple[jnp.ndarray, ...], compute_error: bool + ) -> Tuple[jnp.ndarray, ...]: + # TODO(michalk8): in the future, use `NamedTuple` + u1, u2, v1_old, v2_old, g_old, q_gi, q_gp, q_q, q_r, err = state_inner + k_q, k_r, k_g, a, b = constants + + # First Projection + u1 = jnp.where(supp_a, a / jnp.dot(k_q, v1_old), 0.0) + u2 = jnp.where(supp_b, b / jnp.dot(k_r, v2_old), 0.0) + g = jnp.maximum(min_entry_value, g_old * q_gi) + q_gi = (g_old * q_gi) / g + g_old = g + + # Second Projection + v1_trans = jnp.dot(k_q.T, u1) + v2_trans = jnp.dot(k_r.T, u2) + g = (g_old * q_gp * v1_old * q_q * v1_trans * v2_old * q_r * + v2_trans) ** (1 / 3) + v1 = g / v1_trans + v2 = g / v2_trans + q_gp = (g_old * q_gp) / g + q_q = (q_q * v1_old) / v1 + q_r = (q_r * v2_old) / v2 + v1_old = v1 + v2_old = v2 + g_old = g + + # Compute Couplings + q, r, _ = recompute_couplings(u1, v1, k_q, u2, v2, k_r, g) + + err = jax.lax.cond( + jnp.logical_and(compute_error, iteration >= min_iter), + lambda: dykstra_solution_error(q, r, ot_prob, self.norm_error)[0], + lambda: err + ) + + return u1, u2, v1_old, v2_old, g_old, q_gi, q_gp, q_q, q_r, err + + def recompute_couplings( + u1: jnp.ndarray, + v1: jnp.ndarray, + k_q: jnp.ndarray, + u2: jnp.ndarray, + v2: jnp.ndarray, + k_r: jnp.ndarray, + g: jnp.ndarray, + ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + q = u1.reshape((-1, 1)) * k_q * v1.reshape((1, -1)) + r = u2.reshape((-1, 1)) * k_r * v2.reshape((1, -1)) + return q, r, g + + state_inner = fixed_point_loop.fixpoint_iter_backprop( + cond_fn, body_fn, min_iter, max_iter, inner_iter, constants, state_inner + ) + + u1, u2, v1_old, v2_old, g_old, _, _, _, _, _ = state_inner + return recompute_couplings(u1, v1_old, k_q, u2, v2_old, k_r, g_old) + + def lse_step( + self, ot_prob: quadratic_problem.QuadraticProblem, state: LRGWState, + iteration: int ) -> LRGWState: - """Initialize the state of the low-rank Gromov-Wasserstein iterations. + """LR Sinkhorn LSE update.""" + c_q, c_r, c_g, gamma = self._get_costs(ot_prob, state) + + if ot_prob.is_balanced: + c_q, c_r, h = c_q / -gamma, c_r / -gamma, c_g / gamma + q, r, g = self.dykstra_update_lse( + c_q, c_r, h, gamma, ot_prob, **self.kwargs_dys + ) + else: + q, r, g = lr_utils.unbalanced_dykstra_lse( + c_q, c_r, c_g, gamma, ot_prob, **self.kwargs_dys + ) + return state.set(q=q, g=g, r=r, gamma=gamma) #, (c_q, c_r, c_g) + + def kernel_step( + self, ot_prob: quadratic_problem.QuadraticProblem, state: LRGWState, + iteration: int + ) -> LRGWState: + """LR Sinkhorn Kernel update.""" + c_q, c_r, c_g, gamma = self._get_costs(ot_prob, state) + c_q, c_r, c_g = jnp.exp(c_q), jnp.exp(c_r), jnp.exp(c_g) + + if ot_prob.is_balanced: + q, r, g = self.dykstra_update_kernel( + c_q, c_r, c_g, gamma, ot_prob, **self.kwargs_dys + ) + else: + q, r, g = lr_utils.unbalanced_dykstra_kernel( + c_q, c_r, c_g, gamma, ot_prob, **self.kwargs_dys + ) + return state.set(q=q, g=g, r=r, gamma=gamma) #, (c_q, c_r, c_g) + + def one_iteration( + self, ot_prob: quadratic_problem.QuadraticProblem, state: LRGWState, + iteration: int, compute_error: bool + ) -> LRGWState: + """Carries out one low-rank Sinkhorn iteration. + + Depending on lse_mode, these iterations can be either in: + + - log-space for numerical stability. + - scaling space, using standard kernel-vector multiply operations. Args: - prob: Quadratic OT problem. - init: Initial linearization of the quadratic problem. + ot_prob: the transport problem definition + state: LRGWState named tuple. + iteration: the current iteration of the Sinkhorn outer loop. + compute_error: flag to indicate this iteration computes/stores an error Returns: - The initial low-rank Gromov-Wasserstein state. + The updated state. """ - num_iter = self.max_iterations - if self.store_inner_errors: - errors = -jnp.ones((num_iter, self.linear_ot_solver.outer_iterations)) + previous_state = state + it = iteration // self.inner_iterations + if self.lse_mode: # In lse_mode, run additive updates. + state = self.lse_step(ot_prob, state, iteration) else: - errors = None + state = self.kernel_step(ot_prob, state, iteration) - return LRGWState( - costs=-jnp.ones((num_iter,)), - linear_convergence=-jnp.ones((num_iter,)), - linear_state=init, - errors=errors, + # re-computes error if compute_error is True, else set it to inf. + cost = jax.lax.cond( + jnp.logical_and(compute_error, iteration >= self.min_iterations), + lambda: state.reg_ot_cost(ot_prob, epsilon=self.epsilon), + lambda: jnp.inf + ) + error = state.compute_error(previous_state) + crossed_threshold = jnp.logical_or( + state.crossed_threshold, + jnp.logical_and( + state.errors[it - 1] >= self.threshold, error < self.threshold + ) ) - def output_from_state( - self, - state: LRGWState, - ) -> "GWOutput": + state = state.set( + costs=state.costs.at[it].set(cost), + errors=state.errors.at[it].set(error), + crossed_threshold=crossed_threshold, + ) + + if self.progress_fn is not None: + host_callback.id_tap( + self.progress_fn, + (iteration, self.inner_iterations, self.max_iterations, state) + ) + return state - def _iterations( + @property + def norm_error(self) -> Tuple[int]: # noqa: D102 + return self._norm_error, + + def create_initializer( self, prob: quadratic_problem.QuadraticProblem, - init: sinkhorn_lr.LRSinkhornOutput, - ): + ) -> init_lib.LRInitializer: + """Create a low-rank Sinkhorn initializer. - def cond_fn(iteration: int, constants: Any, state: LRGWState) -> bool: - del constants - return self._continue(state, iteration) + Args: + prob: Linear OT problem used to determine the initializer. - def body_fn( - iteration: int, constants: Any, state: LRGWState, compute_error: bool - ) -> LRGWState: - del constants - return state - - state = fixed_point_loop.fixpoint_iter( - cond_fn=cond_fn, - body_fn=body_fn, - min_iterations=self.min_iterations, - max_iterations=self.max_iterations, - inner_iterations=1, - constants=None, - state=self.init_state(prob, init), + Returns: + Low-rank initializer. + """ + if isinstance(self.initializer, init_lib.LRInitializer): + initializer = self.initializer + elif self.initializer is None: + raise NotImplementedError("TODO") + kind = "k-means" if isinstance( + prob.geom, (pointcloud.PointCloud, low_rank.LRCGeometry) + ) else "random" + initializer = init_lib.LRInitializer.from_solver( + self, kind=kind, **self.kwargs_init + ) + else: + initializer = init_lib.LRInitializer.from_solver( + self, kind=self.initializer, **self.kwargs_init + ) + + assert initializer.rank == self.rank, \ + f"Expected initializer of rank `{self.rank}`, " \ + f"found `{initializer.rank}`." + return initializer + + def init_state( + self, ot_prob: quadratic_problem.QuadraticProblem, + init: Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray] + ) -> LRGWState: + """Return the initial state of the loop.""" + q, r, g = init + return LRGWState( + q=q, + r=r, + g=g, + gamma=self.gamma, + costs=-jnp.ones(self.outer_iterations), + errors=-jnp.ones(self.outer_iterations), + crossed_threshold=False, ) - return self.output_from_state(state) + def output_from_state( + self, ot_prob: quadratic_problem.QuadraticProblem, state: LRGWState + ) -> LRGWOutput: + """Create an output from a loop state. + + Args: + ot_prob: the transport problem. + state: a LRGWState. + + Returns: + A LRGWOutput. + """ + return LRGWOutput( + q=state.q, + r=state.r, + g=state.g, + ot_prob=ot_prob, + costs=state.costs, + errors=state.errors, + epsilon=self.epsilon, + ) + + def _converged(self, state: LRGWState, iteration: int) -> bool: + + def conv_crossed(prev_err: float, curr_err: float) -> bool: + return jnp.logical_and( + prev_err < self.threshold, curr_err < self.threshold + ) + + def conv_not_crossed(prev_err: float, curr_err: float) -> bool: + return jnp.logical_and(curr_err < prev_err, curr_err < self.threshold) + + # for convergence error, we consider 2 possibilities: + # 1. we either crossed the convergence threshold; in this case we require + # that the previous error was also below the threshold + # 2. we haven't crossed the threshold; in this case, we can be below or + # above the threshold: + # if we're above, we wait until we reach the convergence threshold and + # then, the above condition applies + # if we're below and we improved w.r.t. the previous iteration, + # we have converged; otherwise we continue, since we may be stuck + # in a local minimum (e.g., during the initial iterations) + + it = iteration // self.inner_iterations + return jax.lax.cond( + state.crossed_threshold, conv_crossed, conv_not_crossed, + state.errors[it - 2], state.errors[it - 1] + ) + + def _diverged(self, state: LRGWState, iteration: int) -> bool: + it = iteration // self.inner_iterations + return jnp.logical_and( + jnp.logical_not(jnp.isfinite(state.errors[it - 1])), + jnp.logical_not(jnp.isfinite(state.costs[it - 1])) + ) + + +def run( + ot_prob: quadratic_problem.QuadraticProblem, + solver: LRGromovWasserstein, + init: Tuple[Optional[jnp.ndarray], Optional[jnp.ndarray], + Optional[jnp.ndarray]], +) -> LRGWOutput: + """Run loop of the solver, outputting a state upgraded to an output.""" + out = sinkhorn.iterations(ot_prob, solver, init) + out = out.set_cost( + ot_prob, lse_mode=solver.lse_mode, use_danskin=solver.use_danskin + ) + return out.set(ot_prob=ot_prob) + + +def dykstra_solution_error( + q: jnp.ndarray, r: jnp.ndarray, ot_prob: quadratic_problem.QuadraticProblem, + norm_error: Tuple[int, ...] +) -> jnp.ndarray: + """Compute solution error. + + Since only balanced case is available for LR, this is marginal deviation. + + Args: + q: first factor of solution. + r: second factor of solution. + ot_prob: linear problem. + norm_error: int, p-norm used to compute error. + + Returns: + one or possibly many numbers quantifying deviation to true marginals. + """ + norm_error = jnp.array(norm_error) + # Update the error + err = jnp.sum( + jnp.abs(jnp.sum(q, axis=1) - ot_prob.a) ** norm_error[:, None], axis=1 + ) ** (1.0 / norm_error) + err += jnp.sum( + jnp.abs(jnp.sum(r, axis=1) - ot_prob.b) ** norm_error[:, None], axis=1 + ) ** (1.0 / norm_error) + err += jnp.sum( + jnp.abs(jnp.sum(q, axis=0) - jnp.sum(r, axis=0)) ** norm_error[:, None], + axis=1 + ) ** (1.0 / norm_error) + + return err From 1f7f10d5350f313abbf0e564cfd64b7c13e4b226 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 3 Aug 2023 19:42:59 +0200 Subject: [PATCH 04/46] Add ULFGW --- .../quadratic/gromov_wasserstein_lr.py | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 601cc4ace..3c8a1760b 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -376,8 +376,23 @@ def _get_costs( grad_r = lin_geom.apply_cost(q, axis=0) * inv_g grad_r = grad_r + 2.0 * ot_prob.geom_yy.apply_square_cost(r.sum(1), axis=1) - omega = jnp.sum(q * tmp3, axis=0) - grad_g = -omega / (g ** 2) + omega_quad = jnp.sum(q * tmp3, axis=0) + grad_g = -omega_quad / (g ** 2) + + if ot_prob.is_fused: + alpha = ot_prob.fused_penalty / (ot_prob.fused_penalty + 1.0) + norm_g = jnp.linalg.norm(g, ord=1) + + tmp4 = ot_prob.geom_xy.apply_cost(r, axis=1) + lin_grad_q = tmp4 * inv_g * norm_g + lin_grad_r = ot_prob.geom_xy.apply_cost(q) * inv_g * norm_g + + omega_lin = jnp.sum(q * tmp4, axis=0) + lin_grad_g = -omega_lin / (g ** 2) * norm_g + jnp.sum(q * tmp4 * inv_g) + + grad_q = alpha * lin_grad_q + (1.0 - alpha) * grad_q + grad_r = alpha * lin_grad_r + (1.0 - alpha) * grad_r + grad_g = alpha * lin_grad_g + (1.0 - alpha) * grad_g grad_q += self.epsilon * log_q grad_r += self.epsilon * log_r From 07aeedfdc2f67beaa046fda87eae63ffa80a2fcd Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 3 Aug 2023 19:56:08 +0200 Subject: [PATCH 05/46] Revert change --- .../initializers/quadratic/initializers.py | 23 ++- .../problems/quadratic/quadratic_problem.py | 54 ++++++- .../solvers/quadratic/gromov_wasserstein.py | 143 +++++++++++++++--- 3 files changed, 189 insertions(+), 31 deletions(-) diff --git a/src/ott/initializers/quadratic/initializers.py b/src/ott/initializers/quadratic/initializers.py index 23e6fe3d0..62950bc4e 100644 --- a/src/ott/initializers/quadratic/initializers.py +++ b/src/ott/initializers/quadratic/initializers.py @@ -185,17 +185,26 @@ def __init__(self, lr_linear_initializer: "initializers_lr.LRInitializer"): self._linear_lr_initializer = lr_linear_initializer def _create_geometry( - self, quad_prob: "quadratic_problem.QuadraticProblem", **kwargs: Any + self, + quad_prob: "quadratic_problem.QuadraticProblem", + relative_epsilon: Optional[bool] = False, + **kwargs: Any ) -> geometry.Geometry: - raise NotImplementedError("Unreachable.") + """Compute initial geometry for linearization. - def __call__( - self, quad_prob: "quadratic_problem.QuadraticProblem", **kwargs: Any - ) -> "sinkhorn_lr.LRSinkhornOutput": + Args: + quad_prob: Quadratic OT problem. + relative_epsilon: Whether to use relative epsilon in the geometry. + kwargs: Keyword arguments for + :meth:`~ott.initializers.linear.initializers_lr.LRInitializer.__call__`. + + Returns: + The initial geometry used to initialize a linear problem. + """ from ott.solvers.linear import sinkhorn_lr q, r, g = self._linear_lr_initializer(quad_prob, **kwargs) - return sinkhorn_lr.LRSinkhornOutput( + tmp_out = sinkhorn_lr.LRSinkhornOutput( q=q, r=r, g=g, @@ -205,6 +214,8 @@ def __call__( epsilon=None, ) + return quad_prob.update_lr_geom(tmp_out, relative_epsilon=relative_epsilon) + @property def rank(self) -> int: """Rank of the transport matrix factorization.""" diff --git a/src/ott/problems/quadratic/quadratic_problem.py b/src/ott/problems/quadratic/quadratic_problem.py index d7918e4c1..a14273a92 100644 --- a/src/ott/problems/quadratic/quadratic_problem.py +++ b/src/ott/problems/quadratic/quadratic_problem.py @@ -11,7 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Literal, Optional, Tuple, Union +from typing import TYPE_CHECKING, Literal, Optional, Tuple, Union import jax import jax.numpy as jnp @@ -23,6 +23,9 @@ from ott.problems.quadratic import quadratic_costs from ott.types import Transport +if TYPE_CHECKING: + from ott.solvers.linear import sinkhorn_lr + __all__ = ["QuadraticProblem"] @@ -237,6 +240,40 @@ def init_transport_mass(self) -> float: b = jax.lax.stop_gradient(self.b) return a.sum() * b.sum() + def update_lr_geom( + self, + lr_sink: "sinkhorn_lr.LRSinkhornOutput", + relative_epsilon: Optional[bool] = None, + ) -> geometry.Geometry: + """Recompute (possibly LRC) linearization using LR Sinkhorn output.""" + marginal_1 = lr_sink.marginal(1) + marginal_2 = lr_sink.marginal(0) + marginal_cost = self.marginal_dependent_cost(marginal_1, marginal_2) + + # Extract factors from LR Sinkhorn output + q, r, inv_sqg = lr_sink.q, lr_sink.r, 1.0 / jnp.sqrt(lr_sink.g) + # Distribute middle marginal evenly across both factors. + q, r = q * inv_sqg[None, :], r * inv_sqg[None, :] + + # Handle LRC Geometry case. + h1, h2 = self.quad_loss + geom_xx, geom_yy, geom_xy = self.geom_xx, self.geom_yy, self.geom_xy + tmp1 = apply_cost(geom_xx, q, axis=1, fn=h1) + tmp2 = apply_cost(geom_yy, r, axis=1, fn=h2) + if self.is_low_rank: + geom = low_rank.LRCGeometry( + cost_1=tmp1, cost_2=-tmp2, relative_epsilon=relative_epsilon + ) + marginal_cost + if self.is_fused: + geom = geom + geom_xy + else: + cost_matrix = marginal_cost.cost_matrix - jnp.dot(tmp1, tmp2.T) + cost_matrix += self.fused_penalty * self._fused_cost_matrix + geom = geometry.Geometry( + cost_matrix=cost_matrix, relative_epsilon=relative_epsilon + ) + return geom # noqa: RET504 + def update_linearization( self, transport: Transport, @@ -307,6 +344,21 @@ def update_linearization( geom, self.a, self.b, tau_a=self.tau_a, tau_b=self.tau_b ) + def update_lr_linearization( + self, + lr_sink: "sinkhorn_lr.LRSinkhornOutput", + *, + relative_epsilon: Optional[bool] = None, + ) -> linear_problem.LinearProblem: + """Update a Quad problem linearization using a LR Sinkhorn.""" + return linear_problem.LinearProblem( + self.update_lr_geom(lr_sink, relative_epsilon=relative_epsilon), + self.a, + self.b, + tau_a=self.tau_a, + tau_b=self.tau_b + ) + @property def _fused_cost_matrix(self) -> Union[float, jnp.ndarray]: if not self.is_fused: diff --git a/src/ott/solvers/quadratic/gromov_wasserstein.py b/src/ott/solvers/quadratic/gromov_wasserstein.py index adf1d3d24..ca3c24090 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein.py @@ -16,6 +16,7 @@ Callable, Dict, Literal, + Mapping, NamedTuple, Optional, Sequence, @@ -28,7 +29,9 @@ import numpy as np from jax.experimental import host_callback -from ott.geometry import geometry +from ott import utils +from ott.geometry import geometry, low_rank, pointcloud +from ott.initializers.linear import initializers_lr from ott.initializers.quadratic import initializers as quad_initializers from ott.math import fixed_point_loop from ott.problems.linear import linear_problem @@ -110,6 +113,8 @@ class GWState(NamedTuple): linearization of GW. linear_pb: Local linearization of the quadratic GW problem. old_transport_mass: Intermediary value of the mass of the transport matrix. + rngs: Random keys passed to low-rank initializers at every GW iteration + when not using warm start. errors: Holds sequence of vectors of errors of the Sinkhorn algorithm at each iteration. """ @@ -119,6 +124,7 @@ class GWState(NamedTuple): linear_state: LinearOutput linear_pb: linear_problem.LinearProblem old_transport_mass: float + rngs: Optional[jax.random.PRNGKeyArray] = None errors: Optional[jnp.ndarray] = None def set(self, **kwargs: Any) -> "GWState": @@ -155,14 +161,26 @@ class GromovWasserstein(was_solver.WassersteinSolver): Args: args: Positional arguments for :class:`~ott.solvers.was_solver.WassersteinSolver`. - warm_start: Whether to initialize Sinkhorn calls using values - from the previous iteration. + warm_start: Whether to initialize (low-rank) Sinkhorn calls using values + from the previous iteration. If `None`, warm starts are not used for + standard Sinkhorn, but used for low-rank Sinkhorn. relative_epsilon: Whether to use relative epsilon in the linearized geometry. + quad_initializer: Quadratic initializer. If the solver is entropic, + :class:`~ott.initializers.quadratic.initializers.QuadraticInitializer` + is always used. Otherwise, the quadratic initializer wraps the low-rank + Sinkhorn initializers. If `None`, the low-rank initializer will be + selected in a problem-specific manner. If both ``geom_xx`` and ``geom_yy`` + are :class:`~ott.geometry.pointcloud.PointCloud` or + :class:`~ott.geometry.low_rank.LRCGeometry`, use + :class:`~ott.initializers.linear.initializers_lr.KMeansInitializer`. + Otherwise, use + :class:`~ott.initializers.linear.initializers_lr.RandomInitializer`. progress_fn: callback function which gets called during the Gromov-Wasserstein iterations, so the user can display the error at each iteration, e.g., using a progress bar. See :func:`~ott.utils.default_progress_fn` for a basic implementation. + kwargs_init: Keyword arguments when creating the initializer. kwargs: Keyword arguments for :class:`~ott.solvers.was_solver.WassersteinSolver`. """ @@ -170,20 +188,27 @@ class GromovWasserstein(was_solver.WassersteinSolver): def __init__( self, *args: Any, - warm_start: bool = False, + warm_start: Optional[bool] = None, relative_epsilon: Optional[bool] = None, + quad_initializer: Optional[ + Union[Literal["random", "rank2", "k-means", "generalized-k-means"], + quad_initializers.BaseQuadraticInitializer]] = None, progress_fn: Optional[ProgressCallbackFn_t] = None, + kwargs_init: Optional[Mapping[str, Any]] = None, **kwargs: Any ): super().__init__(*args, **kwargs) - self.warm_start = warm_start + self._warm_start = warm_start self.relative_epsilon = relative_epsilon + self.quad_initializer = quad_initializer self.progress_fn = progress_fn + self.kwargs_init = {} if kwargs_init is None else kwargs_init def __call__( self, prob: quadratic_problem.QuadraticProblem, init: Optional[linear_problem.LinearProblem] = None, + rng: Optional[jax.random.PRNGKeyArray] = None, **kwargs: Any, ) -> GWOutput: """Run the Gromov-Wasserstein solver. @@ -192,32 +217,42 @@ def __call__( prob: Quadratic OT problem. init: Initial linearization of the quadratic problem. If `None`, it will be computed using the initializer. + rng: Random number key. kwargs: Keyword arguments used when calling the initializer. Returns: The Gromov-Wasserstein output. """ - assert not self.is_low_rank, "Please use `LRGromovWasserstein`" + rng = utils.default_prng_key(rng) + rng1, rng2 = jax.random.split(rng, 2) + if prob._is_low_rank_convertible: prob = prob.to_low_rank() if init is None: - initializer = quad_initializers.QuadraticInitializer() + initializer = self.create_initializer(prob) init = initializer( prob, epsilon=self.epsilon, + rng=rng1, relative_epsilon=self.relative_epsilon, **kwargs ) - out = iterations(self, prob, init) + out = iterations(self, prob, init, rng2) # TODO(lpapaxanthoos): remove stop_gradient when using backprop - linearization = prob.update_linearization( - jax.lax.stop_gradient(out.linear_state), - epsilon=self.epsilon, - old_transport_mass=jax.lax.stop_gradient(out.old_transport_mass), - relative_epsilon=self.relative_epsilon, - ) + if self.is_low_rank: + linearization = prob.update_lr_linearization( + jax.lax.stop_gradient(out.linear_state), + relative_epsilon=self.relative_epsilon, + ) + else: + linearization = prob.update_linearization( + jax.lax.stop_gradient(out.linear_state), + epsilon=self.epsilon, + old_transport_mass=jax.lax.stop_gradient(out.old_transport_mass), + relative_epsilon=self.relative_epsilon, + ) linear_state = out.linear_state.set_cost(linearization, True, True) iteration = jnp.sum(out.costs != -1) @@ -232,12 +267,15 @@ def init_state( self, prob: quadratic_problem.QuadraticProblem, init: linear_problem.LinearProblem, + rng: jax.random.PRNGKeyArray, ) -> GWState: """Initialize the state of the Gromov-Wasserstein iterations. Args: prob: Quadratic OT problem. init: Initial linearization of the quadratic problem. + rng: Random key for low-rank initializers. Only used when + :attr:`warm_start` is `False`. Returns: The initial Gromov-Wasserstein state. @@ -256,6 +294,7 @@ def init_state( linear_state=linear_state, linear_pb=init, old_transport_mass=transport_mass, + rngs=jax.random.split(rng, num_iter), errors=errors, ) @@ -280,11 +319,57 @@ def output_from_state( old_transport_mass=state.old_transport_mass ) + def create_initializer( + self, prob: quadratic_problem.QuadraticProblem + ) -> quad_initializers.BaseQuadraticInitializer: + """Create quadratic, possibly low-rank initializer. + + Args: + prob: Quadratic OT problem used to determine the initializer. + + Returns: + The initializer. + """ + if isinstance( + self.quad_initializer, quad_initializers.BaseQuadraticInitializer + ): + if self.is_low_rank: + assert isinstance( + self.quad_initializer, quad_initializers.LRQuadraticInitializer + ), f"Expected quadratic initializer to be low rank, " \ + f"found `{type(self.quad_initializer).__name__}`." + assert self.quad_initializer.rank == self.rank, \ + f"Expected quadratic initializer of rank `{self.rank}`, " \ + f"found `{self.quad_initializer.rank}`." + return self.quad_initializer + + if self.is_low_rank: + if self.quad_initializer is None: + types = (pointcloud.PointCloud, low_rank.LRCGeometry) + kind = "k-means" if isinstance(prob.geom_xx, types) and isinstance( + prob.geom_yy, types + ) else "random" + else: + kind = self.quad_initializer + linear_lr_init = initializers_lr.LRInitializer.from_solver( + self, kind=kind, **self.kwargs_init + ) + return quad_initializers.LRQuadraticInitializer(linear_lr_init) + + return quad_initializers.QuadraticInitializer(**self.kwargs_init) + + @property + def warm_start(self) -> bool: + """Whether to initialize (low-rank) Sinkhorn using previous solutions.""" + return self.is_low_rank if self._warm_start is None else self._warm_start + def tree_flatten(self) -> Tuple[Sequence[Any], Dict[str, Any]]: # noqa: D102 children, aux_data = super().tree_flatten() - aux_data["warm_start"] = self.warm_start + aux_data["warm_start"] = self._warm_start aux_data["progress_fn"] = self.progress_fn aux_data["relative_epsilon"] = self.relative_epsilon + aux_data["quad_initializer"] = self.quad_initializer + aux_data["kwargs_init"] = self.kwargs_init return children, aux_data @@ -292,6 +377,7 @@ def iterations( solver: GromovWasserstein, prob: quadratic_problem.QuadraticProblem, init: linear_problem.LinearProblem, + rng: jax.random.PRNGKeyArray, ) -> GWOutput: """Jittable Gromov-Wasserstein outer loop.""" @@ -307,14 +393,23 @@ def body_fn( del compute_error # always assumed true for the outer loop of GW lin_state = state.linear_state - linear_pb = prob.update_linearization( - lin_state, - solver.epsilon, - state.old_transport_mass, - relative_epsilon=solver.relative_epsilon, - ) - init = (lin_state.f, lin_state.g) if solver.warm_start else (None, None) - out = solver.linear_ot_solver(linear_pb, init=init) + if solver.is_low_rank: + rng = state.rngs[iteration] + init = (lin_state.q, lin_state.r, + lin_state.g) if solver.warm_start else (None, None, None) + linear_pb = prob.update_lr_linearization( + state.linear_state, relative_epsilon=solver.relative_epsilon + ) + out = solver.linear_ot_solver(linear_pb, init=init, rng=rng) + else: + init = (lin_state.f, lin_state.g) if solver.warm_start else (None, None) + linear_pb = prob.update_linearization( + lin_state, + solver.epsilon, + state.old_transport_mass, + relative_epsilon=solver.relative_epsilon, + ) + out = solver.linear_ot_solver(linear_pb, init=init) old_transport_mass = jax.lax.stop_gradient( state.linear_state.transport_mass @@ -340,7 +435,7 @@ def body_fn( max_iterations=solver.max_iterations, inner_iterations=1, constants=solver, - state=solver.init_state(prob, init), + state=solver.init_state(prob, init, rng=rng) ) return solver.output_from_state(state) From 6d257ffc0922914c102c1782cf2e333d0ba74bed Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 3 Aug 2023 20:15:49 +0200 Subject: [PATCH 06/46] Add a TODO --- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 3c8a1760b..60e49819d 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -115,13 +115,14 @@ def ent(x: jnp.ndarray) -> float: r = jax.lax.stop_gradient(r) if use_danskin else r g = jax.lax.stop_gradient(g) if use_danskin else g - # TODO(michalk8): extract to a function + # TODO(michalk8): extract to a common function inv_g = 1.0 / g[None, :] inv_sqrt_g = jnp.sqrt(inv_g) tmp1 = -4.0 * ot_prob.geom_xx.apply_cost(q, axis=1) * inv_sqrt_g tmp2 = ot_prob.geom_yy.apply_cost(r, axis=1) * inv_sqrt_g lin_geom = low_rank.LRCGeometry(tmp1, tmp2) + # TODO(michalk8): include the fused case cost = jnp.sum(q * lin_geom.apply_cost(r, axis=1) * inv_g) cost -= epsilon * (ent(q) + ent(r) + ent(g)) if tau_a != 1.0: @@ -181,7 +182,7 @@ def linear(self) -> bool: # noqa: D102 @property def geom(self) -> geometry.Geometry: # noqa: D102 """Linearized geometry.""" - # TODO(michalk8): extract to a common function + # TODO(michalk8): extract to a common function (also in other places) inv_sqrt_g = jnp.sqrt(self._inv_g) tmp1 = -4.0 * self.ot_prob.geom_xx.apply_cost(self.q, axis=1) * inv_sqrt_g tmp2 = self.ot_prob.geom_yy.apply_cost(self.r, axis=1) * inv_sqrt_g From bf0a50d5d909e2732facb8cb74290eb484e1186a Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 4 Aug 2023 11:06:28 +0200 Subject: [PATCH 07/46] Fix `grad_g` in the fused case --- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 60e49819d..d7ae2a3be 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -393,7 +393,7 @@ def _get_costs( grad_q = alpha * lin_grad_q + (1.0 - alpha) * grad_q grad_r = alpha * lin_grad_r + (1.0 - alpha) * grad_r - grad_g = alpha * lin_grad_g + (1.0 - alpha) * grad_g + grad_g = alpha * lin_grad_g + 4.0 * (1.0 - alpha) * grad_g grad_q += self.epsilon * log_q grad_r += self.epsilon * log_r From 7b8a0f4088bbb5e5c6a78b59a86b61042a7b8540 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 17 Aug 2023 17:57:32 +0200 Subject: [PATCH 08/46] Update docs --- docs/references.bib | 9 ++++ docs/solvers/quadratic.rst | 3 ++ src/ott/solvers/linear/sinkhorn_lr.py | 5 +- .../quadratic/gromov_wasserstein_lr.py | 54 ++++++++++--------- 4 files changed, 42 insertions(+), 29 deletions(-) diff --git a/docs/references.bib b/docs/references.bib index f2f59d870..1fd796f0e 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -794,3 +794,12 @@ @misc{klein:23 title = {Learning Costs for Structured Monge Displacements}, year = {2023}, } + +@misc{scetbon:23, + author = {Scetbon, Meyer and Klein, Michal and Palla, Giovanni and Cuturi, Marco}, + eprint = {2305.19727}, + eprintclass = {cs.LG}, + eprinttype = {arXiv}, + title = {Unbalanced Low-rank Optimal Transport Solvers}, + year = {2023}, +} diff --git a/docs/solvers/quadratic.rst b/docs/solvers/quadratic.rst index 4fcc6b077..b147acac5 100644 --- a/docs/solvers/quadratic.rst +++ b/docs/solvers/quadratic.rst @@ -15,6 +15,9 @@ Gromov-Wasserstein Solvers gromov_wasserstein.solve gromov_wasserstein.GromovWasserstein gromov_wasserstein.GWOutput + gromov_wasserstein_lr.LRGromovWasserstein + gromov_wasserstein_lr.LRGWOutput + Barycenter Solvers ------------------ diff --git a/src/ott/solvers/linear/sinkhorn_lr.py b/src/ott/solvers/linear/sinkhorn_lr.py index 6afdc9f01..7ed474e56 100644 --- a/src/ott/solvers/linear/sinkhorn_lr.py +++ b/src/ott/solvers/linear/sinkhorn_lr.py @@ -165,7 +165,7 @@ def solution_error( class LRSinkhornOutput(NamedTuple): - """Implement the problems.Transport interface, for a LR Sinkhorn solution.""" + """Transport interface for a low-rank Sinkhorn solution.""" q: jnp.ndarray r: jnp.ndarray @@ -279,8 +279,7 @@ class LRSinkhorn(sinkhorn.Sinkhorn): The algorithm minimizes a non-convex problem. It therefore requires special care to initialization and convergence. Convergence is evaluated on successive - evaluations of the objective. The algorithm is only provided for the balanced - case. + evaluations of the objective. Args: rank: Rank constraint on the coupling to minimize the linear OT problem diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index d7ae2a3be..f151a3a01 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -11,7 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""A Jax implementation of the Low-Rank Sinkhorn algorithm.""" +"""A Jax implementation of the unbalanced low-rank GW algorithm.""" from typing import ( Any, Callable, @@ -43,7 +43,7 @@ class LRGWState(NamedTuple): - """State of the Low Rank Sinkhorn algorithm.""" + """State of the low-rank GW algorithm.""" q: jnp.ndarray r: jnp.ndarray g: jnp.ndarray @@ -68,7 +68,6 @@ def reg_ot_cost( # noqa: D102 epsilon: float, use_danskin: bool = False ) -> float: - """For LR Sinkhorn, this defaults to the primal cost of LR solution.""" return compute_reg_ot_cost( self.q, self.r, @@ -134,8 +133,7 @@ def ent(x: jnp.ndarray) -> float: class LRGWOutput(NamedTuple): - """Implement the problems.Transport interface, for a LR Sinkhorn solution.""" - + """Transport interface for a low-rank GW solution.""" q: jnp.ndarray r: jnp.ndarray g: jnp.ndarray @@ -246,15 +244,16 @@ def _inv_g(self) -> jnp.ndarray: @jax.tree_util.register_pytree_node_class class LRGromovWasserstein(sinkhorn.Sinkhorn): - r"""A Low-Rank Sinkhorn solver for linear reg-OT problems. - - The algorithm is described in :cite:`scetbon:21` and the implementation - contained here is adapted from `LOT `_. + r"""A low-rank Gromov-Wasserstein solver :cite:`scetbon:23`. The algorithm minimizes a non-convex problem. It therefore requires special care to initialization and convergence. Convergence is evaluated on successive - evaluations of the objective. The algorithm is only provided for the balanced - case. + evaluations of the objective. + + .. warning:: + This solver only for the **unbalanced** case. Balanced case is implemented + in :class:`~ott.solvers.quadratic.gromov_wasserstein.GromovWasserstein` + and will be unified here in the future release. Args: rank: Rank constraint on the coupling to minimize the linear OT problem @@ -277,14 +276,13 @@ class LRGromovWasserstein(sinkhorn.Sinkhorn): input parameters. Only `True` handled at this moment. implicit_diff: Whether to use implicit differentiation. Currently, only ``implicit_diff = False`` is implemented. - progress_fn: callback function which gets called during the Sinkhorn + progress_fn: callback function which gets called during the GW iterations, so the user can display the error at each iteration, e.g., using a progress bar. See :func:`~ott.utils.default_progress_fn` for a basic implementation. kwargs_dys: Keyword arguments passed to :meth:`dykstra_update_lse`, :meth:`dykstra_update_kernel` or one of the functions defined in - :mod:`ott.solvers.linear`, depending on whether the problem - is balanced and on the ``lse_mode``. + :mod:`ott.solvers.linear`, depending on the ``lse_mode``. kwargs_init: Keyword arguments for :class:`~ott.initializers.linear.initializers_lr.LRInitializer`. kwargs: Keyword arguments for @@ -335,7 +333,7 @@ def __call__( rng: Optional[jax.random.PRNGKeyArray] = None, **kwargs: Any, ) -> LRGWOutput: - """Run low-rank Sinkhorn. + """Run low-rank Gromov-Wasserstein solver. Args: ot_prob: Linear OT problem. @@ -350,8 +348,11 @@ def __call__( kwargs: Additional arguments when calling the initializer. Returns: - The low-rank Sinkhorn output. + The low-rank GW output. """ + assert not ot_prob.is_balanced, \ + "Balanced case is not yet implemented here, please use " \ + "`ott.solvers.quadratic.gromov_wasserstein.GromovWasserstein` instead." initializer = self.create_initializer(ot_prob) init = initializer(ot_prob, *init, rng=rng, **kwargs) return run(ot_prob, self, init) @@ -416,7 +417,7 @@ def _get_costs( return c_q, c_r, c_g, gamma - # TODO(michalk8): move to `lr_utils` when refactoring this + # TODO(michalk8): move to `lr_utils` when refactoring this the future def dykstra_update_lse( self, c_q: jnp.ndarray, @@ -550,6 +551,7 @@ def dykstra_update_kernel( ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: """Run Dykstra's algorithm.""" # shortcuts for problem's definition. + del gamma rank = self.rank n, m = ot_prob.geom_xx.shape[0], ot_prob.geom_yy.shape[0] a, b = ot_prob.a, ot_prob.b @@ -637,7 +639,7 @@ def lse_step( self, ot_prob: quadratic_problem.QuadraticProblem, state: LRGWState, iteration: int ) -> LRGWState: - """LR Sinkhorn LSE update.""" + """Low-rank GW LSE update.""" c_q, c_r, c_g, gamma = self._get_costs(ot_prob, state) if ot_prob.is_balanced: @@ -655,7 +657,7 @@ def kernel_step( self, ot_prob: quadratic_problem.QuadraticProblem, state: LRGWState, iteration: int ) -> LRGWState: - """LR Sinkhorn Kernel update.""" + """Low-rank GW kernel update.""" c_q, c_r, c_g, gamma = self._get_costs(ot_prob, state) c_q, c_r, c_g = jnp.exp(c_q), jnp.exp(c_r), jnp.exp(c_g) @@ -667,13 +669,13 @@ def kernel_step( q, r, g = lr_utils.unbalanced_dykstra_kernel( c_q, c_r, c_g, gamma, ot_prob, **self.kwargs_dys ) - return state.set(q=q, g=g, r=r, gamma=gamma) #, (c_q, c_r, c_g) + return state.set(q=q, g=g, r=r, gamma=gamma) def one_iteration( self, ot_prob: quadratic_problem.QuadraticProblem, state: LRGWState, iteration: int, compute_error: bool ) -> LRGWState: - """Carries out one low-rank Sinkhorn iteration. + """Carries out one low-rank GW iteration. Depending on lse_mode, these iterations can be either in: @@ -682,8 +684,8 @@ def one_iteration( Args: ot_prob: the transport problem definition - state: LRGWState named tuple. - iteration: the current iteration of the Sinkhorn outer loop. + state: the current state. + iteration: the current iteration of the GW outer loop. compute_error: flag to indicate this iteration computes/stores an error Returns: @@ -732,10 +734,10 @@ def create_initializer( self, prob: quadratic_problem.QuadraticProblem, ) -> init_lib.LRInitializer: - """Create a low-rank Sinkhorn initializer. + """Create a low-rank GW initializer. Args: - prob: Linear OT problem used to determine the initializer. + prob: Quadratic OT problem used to determine the initializer. Returns: Low-rank initializer. @@ -783,7 +785,7 @@ def output_from_state( Args: ot_prob: the transport problem. - state: a LRGWState. + state: GW state. Returns: A LRGWOutput. From 5855a3245ed9e363f5ade115f1a71e19c6231f1f Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 17 Aug 2023 18:27:55 +0200 Subject: [PATCH 09/46] Remove duplicate citation --- docs/references.bib | 9 --------- 1 file changed, 9 deletions(-) diff --git a/docs/references.bib b/docs/references.bib index 1fd796f0e..f2f59d870 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -794,12 +794,3 @@ @misc{klein:23 title = {Learning Costs for Structured Monge Displacements}, year = {2023}, } - -@misc{scetbon:23, - author = {Scetbon, Meyer and Klein, Michal and Palla, Giovanni and Cuturi, Marco}, - eprint = {2305.19727}, - eprintclass = {cs.LG}, - eprinttype = {arXiv}, - title = {Unbalanced Low-rank Optimal Transport Solvers}, - year = {2023}, -} From e4a70c3c70eda5c9cdd3dbfd79ab3fc63da3ef08 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 18 Aug 2023 15:23:34 +0200 Subject: [PATCH 10/46] Fix cost for the fused case --- .../quadratic/gromov_wasserstein_lr.py | 42 +++++++++++-------- 1 file changed, 25 insertions(+), 17 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index f151a3a01..768f9e45d 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -109,20 +109,24 @@ def ent(x: jnp.ndarray) -> float: # generalized entropy return jnp.sum(jsp.special.entr(x) + x) - tau_a, tau_b = ot_prob.tau_a, ot_prob.tau_b q = jax.lax.stop_gradient(q) if use_danskin else q r = jax.lax.stop_gradient(r) if use_danskin else r g = jax.lax.stop_gradient(g) if use_danskin else g - # TODO(michalk8): extract to a common function + tau_a, tau_b = ot_prob.tau_a, ot_prob.tau_b inv_g = 1.0 / g[None, :] - inv_sqrt_g = jnp.sqrt(inv_g) - tmp1 = -4.0 * ot_prob.geom_xx.apply_cost(q, axis=1) * inv_sqrt_g - tmp2 = ot_prob.geom_yy.apply_cost(r, axis=1) * inv_sqrt_g - lin_geom = low_rank.LRCGeometry(tmp1, tmp2) - # TODO(michalk8): include the fused case - cost = jnp.sum(q * lin_geom.apply_cost(r, axis=1) * inv_g) + lin_geom = _linearized_geometry(ot_prob, q=q, r=r, g=g) + + quad_cost = 0.5 * jnp.sum(q * lin_geom.apply_cost(r, axis=1) * inv_g) + if ot_prob.is_fused: + alpha = ot_prob.fused_penalty / (ot_prob.fused_penalty + 1.0) + norm_g = jnp.linalg.norm(g, ord=1) + lin_cost = jnp.sum(q * ot_prob.geom_xy.apply_cost(r, axis=1) * inv_g) + cost = alpha * norm_g * lin_cost + (1.0 - alpha) * quad_cost + else: + cost = quad_cost + cost -= epsilon * (ent(q) + ent(r) + ent(g)) if tau_a != 1.0: cost += tau_a / (1.0 - tau_a) * mu.gen_kl(jnp.sum(q, axis=1), ot_prob.a) @@ -180,11 +184,7 @@ def linear(self) -> bool: # noqa: D102 @property def geom(self) -> geometry.Geometry: # noqa: D102 """Linearized geometry.""" - # TODO(michalk8): extract to a common function (also in other places) - inv_sqrt_g = jnp.sqrt(self._inv_g) - tmp1 = -4.0 * self.ot_prob.geom_xx.apply_cost(self.q, axis=1) * inv_sqrt_g - tmp2 = self.ot_prob.geom_yy.apply_cost(self.r, axis=1) * inv_sqrt_g - return low_rank.LRCGeometry(tmp1, tmp2) + return _linearized_geometry(self.ot_prob, q=self.q, r=self.r, g=self.g) @property def a(self) -> jnp.ndarray: # noqa: D102 @@ -365,16 +365,14 @@ def _get_costs( q, r, g = state.q, state.r, state.g log_q, log_r, log_g = mu.safe_log(q), mu.safe_log(r), mu.safe_log(g) inv_g = 1.0 / g[None, :] - inv_sqrt_g = jnp.sqrt(inv_g) - tmp1 = -4.0 * ot_prob.geom_xx.apply_cost(q, axis=1) * inv_sqrt_g - tmp2 = ot_prob.geom_yy.apply_cost(r, axis=1) * inv_sqrt_g - lin_geom = low_rank.LRCGeometry(tmp1, tmp2) + lin_geom = _linearized_geometry(ot_prob, q=q, r=r, g=g) tmp3 = lin_geom.apply_cost(r, axis=1) grad_q = tmp3 * inv_g + 2.0 * ot_prob.geom_xx.apply_square_cost( q.sum(1), axis=1 ) + grad_r = lin_geom.apply_cost(q, axis=0) * inv_g grad_r = grad_r + 2.0 * ot_prob.geom_yy.apply_square_cost(r.sum(1), axis=1) @@ -880,3 +878,13 @@ def dykstra_solution_error( ) ** (1.0 / norm_error) return err + + +def _linearized_geometry( + prob: quadratic_problem.QuadraticProblem, q: jnp.ndarray, r: jnp.ndarray, + g: jnp.ndarray +) -> low_rank.LRCGeometry: + inv_sqrt_g = 1.0 / jnp.sqrt(g[None, :]) + tmp1 = -4.0 * prob.geom_xx.apply_cost(q, axis=1) * inv_sqrt_g + tmp2 = prob.geom_yy.apply_cost(r, axis=1) * inv_sqrt_g + return low_rank.LRCGeometry(tmp1, tmp2) From 4ecdca0d2f4778ba06cc0afa9b8e41e2a60a73bc Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 18 Aug 2023 17:49:23 +0200 Subject: [PATCH 11/46] Fix bugs in TI --- src/ott/solvers/linear/lr_utils.py | 72 ++++++++++++++++-------------- 1 file changed, 39 insertions(+), 33 deletions(-) diff --git a/src/ott/solvers/linear/lr_utils.py b/src/ott/solvers/linear/lr_utils.py index 9fba08c87..5a5b26e64 100644 --- a/src/ott/solvers/linear/lr_utils.py +++ b/src/ott/solvers/linear/lr_utils.py @@ -14,11 +14,11 @@ from typing import NamedTuple, Optional, Tuple import jax +import jax.experimental import jax.numpy as jnp import jax.scipy as jsp from ott.math import fixed_point_loop -from ott.math import unbalanced_functions as uf from ott.problems.linear import linear_problem __all__ = ["unbalanced_dykstra_lse", "unbalanced_dykstra_kernel"] @@ -36,8 +36,8 @@ class State(NamedTuple): # noqa: D101 class Constants(NamedTuple): # noqa: D101 a: jnp.ndarray b: jnp.ndarray - tau_a: float - tau_b: float + rho_a: float + rho_b: float supp_a: Optional[jnp.ndarray] = None supp_b: Optional[jnp.ndarray] = None @@ -105,16 +105,17 @@ def body_fn( iteration: int, const: Constants, state: State, compute_error: bool ) -> State: log_a, log_b = jnp.log(const.a), jnp.log(const.b) + rho_a, rho_b = const.rho_a, const.rho_b - if translation_invariant: - rho_a = uf.rho(1.0 / gamma, const.tau_a) - rho_b = uf.rho(1.0 / gamma, const.tau_b) + c_a = _foo(const.rho_a, gamma) + c_b = _foo(const.rho_b, gamma) + if translation_invariant: lam_a, lam_b = compute_lambdas(const, state, gamma, g=c_g, lse_mode=True) - u1 = const.tau_a * (log_a - _softm(state.v1, c_q, axis=1)) + u1 = c_a * (log_a - _softm(state.v1, c_q, axis=1)) u1 = u1 - lam_a / ((1.0 / gamma) + rho_a) - u2 = const.tau_b * (log_b - _softm(state.v2, c_r, axis=1)) + u2 = c_b * (log_b - _softm(state.v2, c_r, axis=1)) u2 = u2 - lam_b / ((1.0 / gamma) + rho_b) state_lam = State( @@ -129,8 +130,8 @@ def body_fn( g_trans = gamma * (lam_a + lam_b) + c_g else: - u1 = const.tau_a * (log_a - _softm(state.v1, c_q, axis=1)) - u2 = const.tau_b * (log_b - _softm(state.v2, c_r, axis=1)) + u1 = c_a * (log_a - _softm(state.v1, c_q, axis=1)) + u2 = c_b * (log_b - _softm(state.v2, c_r, axis=1)) v1_trans = _softm(u1, c_q, axis=0) v2_trans = _softm(u2, c_r, axis=0) @@ -155,8 +156,8 @@ def body_fn( constants = Constants( a=ot_prob.a, b=ot_prob.b, - tau_a=ot_prob.tau_a, - tau_b=ot_prob.tau_b, + rho_a=_rho(ot_prob.tau_a), + rho_b=_rho(ot_prob.tau_b), supp_a=ot_prob.a > 0, supp_b=ot_prob.b > 0, ) @@ -242,18 +243,16 @@ def cond_fn( def body_fn( iteration: int, const: Constants, state: State, compute_error: bool ) -> State: - if translation_invariant: - rho_a = uf.rho(1.0 / gamma, const.tau_a) - rho_b = uf.rho(1.0 / gamma, const.tau_b) - c_a = const.tau_a - c_b = const.tau_b + c_a = _foo(const.rho_a, gamma) + c_b = _foo(const.rho_b, gamma) + if translation_invariant: lam_a, lam_b = compute_lambdas(const, state, gamma, g=k_g, lse_mode=False) u1 = jnp.where(const.supp_a, (const.a / (k_q @ state.v1)) ** c_a, 0.0) - u1 = u1 * jnp.exp(-lam_a / ((1.0 / gamma) + rho_a)) + u1 = u1 * jnp.exp(-lam_a / ((1.0 / gamma) + const.rho_a)) u2 = jnp.where(const.supp_b, (const.b / (k_r @ state.v2)) ** c_b, 0.0) - u2 = u2 * jnp.exp(-lam_b / ((1.0 / gamma) + rho_b)) + u2 = u2 * jnp.exp(-lam_b / ((1.0 / gamma) + const.rho_b)) state_lam = State( v1=state.v1, v2=state.v2, u1=u1, u2=u2, g=state.g, err=state.err @@ -268,12 +267,8 @@ def body_fn( k_trans = jnp.exp(gamma * (lam_a + lam_b)) * k_g g = (k_trans * v1_trans * v2_trans) ** (1.0 / 3.0) else: - u1 = jnp.where( - const.supp_a, (const.a / (k_q @ state.v1)) ** const.tau_a, 0.0 - ) - u2 = jnp.where( - const.supp_b, (const.b / (k_r @ state.v2)) ** const.tau_b, 0.0 - ) + u1 = jnp.where(const.supp_a, (const.a / (k_q @ state.v1)) ** c_a, 0.0) + u2 = jnp.where(const.supp_b, (const.b / (k_r @ state.v2)) ** c_b, 0.0) v1_trans = k_q.T @ u1 v2_trans = k_r.T @ u2 @@ -298,8 +293,8 @@ def body_fn( constants = Constants( a=ot_prob.a, b=ot_prob.b, - tau_a=ot_prob.tau_a, - tau_b=ot_prob.tau_b, + rho_a=_rho(ot_prob.tau_a), + rho_b=_rho(ot_prob.tau_b), supp_a=ot_prob.a > 0.0, supp_b=ot_prob.b > 0.0, ) @@ -328,8 +323,8 @@ def compute_lambdas( ) -> Tuple[float, float]: """TODO.""" gamma_inv = 1.0 / gamma - rho_a = uf.rho(gamma_inv, const.tau_a) - rho_b = uf.rho(gamma_inv, const.tau_b) + rho_a = const.rho_a + rho_b = const.rho_b if lse_mode: num_1 = jsp.special.logsumexp((-gamma_inv / rho_a) * state.u1, b=const.a) @@ -338,8 +333,8 @@ def compute_lambdas( const_1 = num_1 - den const_2 = num_2 - den - ratio_1 = const.tau_a # rho_a / (rho_a + gamma_inv) - ratio_2 = const.tau_b # rho_b / (rho_b + gamma_inv) + ratio_1 = _foo(rho_a, gamma) + ratio_2 = _foo(rho_b, gamma) harmonic = 1.0 / (1.0 - (ratio_1 * ratio_2)) lam_1 = harmonic * gamma_inv * ratio_1 * (const_1 - ratio_2 * const_2) lam_2 = harmonic * gamma_inv * ratio_2 * (const_2 - ratio_1 * const_1) @@ -359,9 +354,20 @@ def compute_lambdas( const_1 = jnp.log(num_1 / den) const_2 = jnp.log(num_2 / den) - ratio_1 = const.tau_a # rho_a / (rho_a + gamma_inv) - ratio_2 = const.tau_b # rho_b / (rho_b + gamma_inv) + ratio_1 = _foo(rho_a, gamma) + ratio_2 = _foo(rho_b, gamma) harmonic = 1.0 / (1.0 - (ratio_1 * ratio_2)) lam_1 = harmonic * gamma_inv * ratio_1 * (const_1 - ratio_2 * const_2) lam_2 = harmonic * gamma_inv * ratio_2 * (const_2 - ratio_1 * const_1) return lam_1, lam_2 + + +def _rho(tau: float) -> float: + tau = jnp.asarray(tau) # avoid division by 0 in Python, get NaN instead + return tau / (1.0 - tau) + + +# TODO(michalk8): rename +def _foo(rho: float, gamma: float) -> float: + gamma_inv = 1.0 / gamma + return jnp.where(jnp.isfinite(rho), rho / (rho + gamma_inv), 1.0) From 04a3e8bec4bca323b49c1200ff070377428b5d68 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 18 Aug 2023 17:54:04 +0200 Subject: [PATCH 12/46] Remove unused import --- src/ott/solvers/linear/lr_utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/ott/solvers/linear/lr_utils.py b/src/ott/solvers/linear/lr_utils.py index 5a5b26e64..98b35fe8d 100644 --- a/src/ott/solvers/linear/lr_utils.py +++ b/src/ott/solvers/linear/lr_utils.py @@ -14,7 +14,6 @@ from typing import NamedTuple, Optional, Tuple import jax -import jax.experimental import jax.numpy as jnp import jax.scipy as jsp From 4fb9ee49b85248bf585715033969367a72ef5a38 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 31 Aug 2023 16:10:28 +0200 Subject: [PATCH 13/46] Change way array extraction in LR init works --- src/ott/initializers/linear/initializers_lr.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/src/ott/initializers/linear/initializers_lr.py b/src/ott/initializers/linear/initializers_lr.py index d4c3aea3e..e7b671699 100644 --- a/src/ott/initializers/linear/initializers_lr.py +++ b/src/ott/initializers/linear/initializers_lr.py @@ -373,9 +373,7 @@ def __init__( self._sinkhorn_kwargs = {} if sinkhorn_kwargs is None else sinkhorn_kwargs @staticmethod - def _extract_array( - geom: Union[pointcloud.PointCloud, low_rank.LRCGeometry], *, first: bool - ) -> jnp.ndarray: + def _extract_array(geom: geometry.Geometry, *, first: bool) -> jnp.ndarray: if isinstance(geom, pointcloud.PointCloud): return geom.x if first else geom.y if isinstance(geom, low_rank.LRCGeometry): @@ -407,7 +405,11 @@ def _compute_factor( ) if isinstance(ot_prob, quadratic_problem.QuadraticProblem): - geom = ot_prob.geom_xx if which == "q" else ot_prob.geom_yy + if ot_prob.fused_penalty >= 1.0: + # prefer the linear term if it has a higher weight + geom = ot_prob.geom_xy + else: + geom = ot_prob.geom_xx if which == "q" else ot_prob.geom_yy else: geom = ot_prob.geom arr = self._extract_array(geom, first=which == "q") @@ -415,7 +417,7 @@ def _compute_factor( centroids = fn(arr, self.rank, rng=rng).centroids geom = pointcloud.PointCloud( - arr, centroids, epsilon=0.1, scale_cost="max_cost" + arr, centroids, epsilon=1e-1, scale_cost="max_cost" ) prob = linear_problem.LinearProblem(geom, marginals, init_g) From 43d39eb3c58f17d5130f321224d8d89f599721ae Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 31 Aug 2023 16:12:44 +0200 Subject: [PATCH 14/46] Disallow LR in the old GW solver --- src/ott/solvers/quadratic/gromov_wasserstein.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein.py b/src/ott/solvers/quadratic/gromov_wasserstein.py index 6e4f816af..7c0976055 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein.py @@ -198,6 +198,9 @@ def __init__( **kwargs: Any ): super().__init__(*args, **kwargs) + assert not self.is_low_rank, \ + "For low-rank GW, use " \ + "`ott.solvers.quadratic.gromov_wasserstein_lr.LRGromovWasserstein`." self._warm_start = warm_start self.relative_epsilon = relative_epsilon self.quad_initializer = quad_initializer From 0747c56a08ffe72bf7ef7ba6ace2d270ac978410 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 31 Aug 2023 16:22:33 +0200 Subject: [PATCH 15/46] Disallow LR in old GW class --- .../solvers/quadratic/gromov_wasserstein.py | 21 ++++++++----------- 1 file changed, 9 insertions(+), 12 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein.py b/src/ott/solvers/quadratic/gromov_wasserstein.py index 7c0976055..f9b498e05 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein.py @@ -158,24 +158,21 @@ def update( # noqa: D102 class GromovWasserstein(was_solver.WassersteinSolver): """Gromov-Wasserstein solver :cite:`peyre:16`. + .. seealso:: + Low-rank Gromov-Wasserstein :cite:`scetbon:23` is implemented in + :class:`~ott.solvers.quadratic.gromov_wasserstein_lr.LRGromovWasserstein`. + Args: args: Positional arguments for :class:`~ott.solvers.was_solver.WassersteinSolver`. - warm_start: Whether to initialize (low-rank) Sinkhorn calls using values - from the previous iteration. If `None`, warm starts are not used for - standard Sinkhorn, but used for low-rank Sinkhorn. + warm_start: Whether to initialize Sinkhorn calls using values + from the previous iteration. If :obj:`None`, warm starts are not used for + standard Sinkhorn. relative_epsilon: Whether to use relative epsilon in the linearized geometry. quad_initializer: Quadratic initializer. If the solver is entropic, :class:`~ott.initializers.quadratic.initializers.QuadraticInitializer` - is always used. Otherwise, the quadratic initializer wraps the low-rank - Sinkhorn initializers. If `None`, the low-rank initializer will be - selected in a problem-specific manner. If both ``geom_xx`` and ``geom_yy`` - are :class:`~ott.geometry.pointcloud.PointCloud` or - :class:`~ott.geometry.low_rank.LRCGeometry`, use - :class:`~ott.initializers.linear.initializers_lr.KMeansInitializer`. - Otherwise, use - :class:`~ott.initializers.linear.initializers_lr.RandomInitializer`. + is always used. progress_fn: callback function which gets called during the Gromov-Wasserstein iterations, so the user can display the error at each iteration, e.g., using a progress bar. @@ -363,7 +360,7 @@ def create_initializer( @property def warm_start(self) -> bool: - """Whether to initialize (low-rank) Sinkhorn using previous solutions.""" + """Whether to initialize Sinkhorn using previous solutions.""" return self.is_low_rank if self._warm_start is None else self._warm_start def tree_flatten(self) -> Tuple[Sequence[Any], Dict[str, Any]]: # noqa: D102 From 83a1c3631b85f270f66f17d4e52bbf1483b506ea Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 31 Aug 2023 16:23:07 +0200 Subject: [PATCH 16/46] Remove `is_entropic` property --- src/ott/solvers/linear/sinkhorn_lr.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/src/ott/solvers/linear/sinkhorn_lr.py b/src/ott/solvers/linear/sinkhorn_lr.py index 206251f43..761be474f 100644 --- a/src/ott/solvers/linear/sinkhorn_lr.py +++ b/src/ott/solvers/linear/sinkhorn_lr.py @@ -396,10 +396,10 @@ def _get_costs( state.q * ot_prob.geom.apply_cost(state.r, axis=1), axis=0 ) grad_g = -diag_qcr / (state.g ** 2) - if self.is_entropic: - grad_q += self.epsilon * log_q - grad_r += self.epsilon * log_r - grad_g += self.epsilon * log_g + + grad_q += self.epsilon * log_q + grad_r += self.epsilon * log_r + grad_g += self.epsilon * log_g if self.gamma_rescale: norm_q = jnp.max(jnp.abs(grad_q)) ** 2 @@ -728,11 +728,6 @@ def one_iteration( def norm_error(self) -> Tuple[int]: # noqa: D102 return self._norm_error, - @property - def is_entropic(self) -> bool: - """Whether entropy regularization is used.""" - return self.epsilon > 0. - def create_initializer( self, prob: linear_problem.LinearProblem ) -> init_lib.LRInitializer: From b783f3a2a1f967c87a281d721a302b7a6a26412e Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 31 Aug 2023 16:26:55 +0200 Subject: [PATCH 17/46] Use `jnp.linalg.norm` --- src/ott/solvers/linear/sinkhorn_lr.py | 6 +++--- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/ott/solvers/linear/sinkhorn_lr.py b/src/ott/solvers/linear/sinkhorn_lr.py index 761be474f..f4fc4521d 100644 --- a/src/ott/solvers/linear/sinkhorn_lr.py +++ b/src/ott/solvers/linear/sinkhorn_lr.py @@ -402,9 +402,9 @@ def _get_costs( grad_g += self.epsilon * log_g if self.gamma_rescale: - norm_q = jnp.max(jnp.abs(grad_q)) ** 2 - norm_r = jnp.max(jnp.abs(grad_r)) ** 2 - norm_g = jnp.max(jnp.abs(grad_g)) ** 2 + norm_q = jnp.linalg.norm(grad_q, ord=jnp.inf) ** 2 + norm_r = jnp.linalg.norm(grad_r, ord=jnp.inf) ** 2 + norm_g = jnp.linalg.norm(grad_g, ord=jnp.inf) ** 2 gamma = self.gamma / jnp.max(jnp.array([norm_q, norm_r, norm_g])) else: gamma = self.gamma diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 768f9e45d..24c4c9a19 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -399,9 +399,9 @@ def _get_costs( grad_g += self.epsilon * log_g if self.gamma_rescale: - norm_q = jnp.max(jnp.abs(grad_q)) ** 2 - norm_r = jnp.max(jnp.abs(grad_r)) ** 2 - norm_g = jnp.max(jnp.abs(grad_g)) ** 2 + norm_q = jnp.linalg.norm(grad_q, ord=jnp.inf) ** 2 + norm_r = jnp.linalg.norm(grad_r, ord=jnp.inf) ** 2 + norm_g = jnp.linalg.norm(grad_g, ord=jnp.inf) ** 2 gamma = self.gamma / jnp.max(jnp.array([norm_q, norm_r, norm_g])) else: gamma = self.gamma From 0f661cc90c4755a52873c633260c6e8619000607 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 31 Aug 2023 16:35:20 +0200 Subject: [PATCH 18/46] Simplify initializers in GW --- .../solvers/quadratic/gromov_wasserstein.py | 26 ++----------------- 1 file changed, 2 insertions(+), 24 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein.py b/src/ott/solvers/quadratic/gromov_wasserstein.py index f9b498e05..f3dcecfe1 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein.py @@ -30,8 +30,7 @@ import numpy as np from ott import utils -from ott.geometry import geometry, low_rank, pointcloud -from ott.initializers.linear import initializers_lr +from ott.geometry import geometry from ott.initializers.quadratic import initializers as quad_initializers from ott.math import fixed_point_loop from ott.problems.linear import linear_problem @@ -333,29 +332,8 @@ def create_initializer( if isinstance( self.quad_initializer, quad_initializers.BaseQuadraticInitializer ): - if self.is_low_rank: - assert isinstance( - self.quad_initializer, quad_initializers.LRQuadraticInitializer - ), f"Expected quadratic initializer to be low rank, " \ - f"found `{type(self.quad_initializer).__name__}`." - assert self.quad_initializer.rank == self.rank, \ - f"Expected quadratic initializer of rank `{self.rank}`, " \ - f"found `{self.quad_initializer.rank}`." return self.quad_initializer - - if self.is_low_rank: - if self.quad_initializer is None: - types = (pointcloud.PointCloud, low_rank.LRCGeometry) - kind = "k-means" if isinstance(prob.geom_xx, types) and isinstance( - prob.geom_yy, types - ) else "random" - else: - kind = self.quad_initializer - linear_lr_init = initializers_lr.LRInitializer.from_solver( - self, kind=kind, **self.kwargs_init - ) - return quad_initializers.LRQuadraticInitializer(linear_lr_init) - + # no other options implemented, use the default return quad_initializers.QuadraticInitializer(**self.kwargs_init) @property From d79ce31231935bcafcf10cd57df8ba3453670c8d Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 31 Aug 2023 17:03:04 +0200 Subject: [PATCH 19/46] Simplify initializer creation for low-rank --- .../initializers/linear/initializers_lr.py | 22 +++----- .../initializers/quadratic/initializers.py | 5 +- src/ott/solvers/linear/sinkhorn_lr.py | 47 ++++++----------- .../quadratic/gromov_wasserstein_lr.py | 51 +++++++------------ 4 files changed, 45 insertions(+), 80 deletions(-) diff --git a/src/ott/initializers/linear/initializers_lr.py b/src/ott/initializers/linear/initializers_lr.py index e7b671699..923d91413 100644 --- a/src/ott/initializers/linear/initializers_lr.py +++ b/src/ott/initializers/linear/initializers_lr.py @@ -39,7 +39,7 @@ from ott.problems.linear import linear_problem from ott.problems.quadratic import quadratic_problem from ott.solvers.linear import sinkhorn, sinkhorn_lr - from ott.solvers.quadratic import gromov_wasserstein + from ott.solvers.quadratic import gromov_wasserstein_lr Problem_t = Union["linear_problem.LinearProblem", "quadratic_problem.QuadraticProblem"] @@ -127,7 +127,7 @@ def init_g( def from_solver( cls, solver: Union["sinkhorn_lr.LRSinkhorn", - "gromov_wasserstein.GromovWasserstein"], + "gromov_wasserstein_lr.LRGromovWasserstein"], *, kind: Literal["random", "rank2", "k-means", "generalized-k-means"], **kwargs: Any, @@ -140,22 +140,14 @@ def from_solver( kwargs: Keyword arguments when creating the initializer. Returns: - The low-rank initializer. + Low-rank initializer. """ - from ott.solvers.quadratic import gromov_wasserstein - - if isinstance(solver, gromov_wasserstein.GromovWasserstein): - assert solver.is_low_rank, "GW solver is not low-rank." - lin_sol = solver.linear_ot_solver - else: - lin_sol = solver - rank = solver.rank sinkhorn_kwargs = { - "norm_error": lin_sol._norm_error, - "lse_mode": lin_sol.lse_mode, - "implicit_diff": lin_sol.implicit_diff, - "use_danskin": lin_sol.use_danskin + "norm_error": solver._norm_error, + "lse_mode": solver.lse_mode, + "implicit_diff": solver.implicit_diff, + "use_danskin": solver.use_danskin } if kind == "random": diff --git a/src/ott/initializers/quadratic/initializers.py b/src/ott/initializers/quadratic/initializers.py index 62950bc4e..54c9ac613 100644 --- a/src/ott/initializers/quadratic/initializers.py +++ b/src/ott/initializers/quadratic/initializers.py @@ -23,6 +23,7 @@ from ott.initializers.linear import initializers_lr from ott.problems.linear import linear_problem from ott.problems.quadratic import quadratic_problem + from ott.solvers.linear import sinkhorn_lr __all__ = ["QuadraticInitializer", "LRQuadraticInitializer"] @@ -189,7 +190,7 @@ def _create_geometry( quad_prob: "quadratic_problem.QuadraticProblem", relative_epsilon: Optional[bool] = False, **kwargs: Any - ) -> geometry.Geometry: + ) -> "sinkhorn_lr.LRSinkhornOutput": """Compute initial geometry for linearization. Args: @@ -199,7 +200,7 @@ def _create_geometry( :meth:`~ott.initializers.linear.initializers_lr.LRInitializer.__call__`. Returns: - The initial geometry used to initialize a linear problem. + The initial :math:`Q`, :math:`R`, and :math:`g` factors. """ from ott.solvers.linear import sinkhorn_lr diff --git a/src/ott/solvers/linear/sinkhorn_lr.py b/src/ott/solvers/linear/sinkhorn_lr.py index f4fc4521d..3cc5b187c 100644 --- a/src/ott/solvers/linear/sinkhorn_lr.py +++ b/src/ott/solvers/linear/sinkhorn_lr.py @@ -29,8 +29,8 @@ import jax.scipy as jsp import numpy as np -from ott.geometry import geometry, low_rank, pointcloud -from ott.initializers.linear import initializers_lr as init_lib +from ott.geometry import geometry +from ott.initializers.linear import initializers_lr from ott.math import fixed_point_loop from ott.math import utils as mu from ott.problems.linear import linear_problem @@ -289,13 +289,8 @@ class LRSinkhorn(sinkhorn.Sinkhorn): epsilon: Entropic regularization added on top of low-rank problem. initializer: How to initialize the :math:`Q`, :math:`R` and :math:`g` factors. Valid options are `'random'`, `'rank2'`, `'k-means'`, and - `'generalized-k-means`. If `None`, - :class:`~ott.initializers.linear.initializers_lr.KMeansInitializer` - is used when the linear problem's geometry is - :class:`~ott.geometry.pointcloud.PointCloud` or - :class:`~ott.geometry.low_rank.LRCGeometry`. Otherwise, use - :class:`~ott.initializers.linear.initializers_lr.RandomInitializer`. - lse_mode: Whether to run computations in lse or kernel mode. + `'generalized-k-means'`. + lse_mode: Whether to run computations in LSE or kernel mode. inner_iterations: Number of inner iterations used by the algorithm before re-evaluating progress. use_danskin: Use Danskin theorem to evaluate gradient of objective w.r.t. @@ -322,9 +317,9 @@ def __init__( gamma: float = 10., gamma_rescale: bool = True, epsilon: float = 0., - initializer: Optional[Union[Literal["random", "rank2", "k-means", - "generalized-k-means"], - init_lib.LRInitializer]] = "random", + initializer: Union[Literal["random", "rank2", "k-means", + "generalized-k-means"], + initializers_lr.LRInitializer] = "random", lse_mode: bool = True, inner_iterations: int = 10, use_danskin: bool = True, @@ -730,7 +725,7 @@ def norm_error(self) -> Tuple[int]: # noqa: D102 def create_initializer( self, prob: linear_problem.LinearProblem - ) -> init_lib.LRInitializer: + ) -> initializers_lr.LRInitializer: """Create a low-rank Sinkhorn initializer. Args: @@ -739,24 +734,14 @@ def create_initializer( Returns: Low-rank initializer. """ - if isinstance(self.initializer, init_lib.LRInitializer): - initializer = self.initializer - elif self.initializer is None: - kind = "k-means" if isinstance( - prob.geom, (pointcloud.PointCloud, low_rank.LRCGeometry) - ) else "random" - initializer = init_lib.LRInitializer.from_solver( - self, kind=kind, **self.kwargs_init - ) - else: - initializer = init_lib.LRInitializer.from_solver( - self, kind=self.initializer, **self.kwargs_init - ) - - assert initializer.rank == self.rank, \ - f"Expected initializer of rank `{self.rank}`, " \ - f"found `{initializer.rank}`." - return initializer + if isinstance(self.initializer, initializers_lr.LRInitializer): + assert self.initializer.rank == self.rank, \ + f"Expected initializer's rank to be `{self.rank}`," \ + f"found `{self.initializer.rank}`." + return self.initializer + return initializers_lr.LRInitializer.from_solver( + self, kind=self.initializer, **self.kwargs_init + ) def init_state( self, ot_prob: linear_problem.LinearProblem, diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 24c4c9a19..fe343b13e 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -29,8 +29,9 @@ import numpy as np from jax.experimental import host_callback -from ott.geometry import geometry, low_rank, pointcloud -from ott.initializers.linear import initializers_lr as init_lib +from ott.geometry import geometry, low_rank +from ott.initializers.linear import initializers_lr as lin_init +from ott.initializers.quadratic import initializers from ott.math import fixed_point_loop from ott.math import utils as mu from ott.problems.quadratic import quadratic_problem @@ -263,13 +264,8 @@ class LRGromovWasserstein(sinkhorn.Sinkhorn): epsilon: Entropic regularization added on top of low-rank problem. initializer: How to initialize the :math:`Q`, :math:`R` and :math:`g` factors. Valid options are `'random'`, `'rank2'`, `'k-means'`, and - `'generalized-k-means`. If `None`, - :class:`~ott.initializers.linear.initializers_lr.KMeansInitializer` - is used when the linear problem's geometry is - :class:`~ott.geometry.pointcloud.PointCloud` or - :class:`~ott.geometry.low_rank.LRCGeometry`. Otherwise, use - :class:`~ott.initializers.linear.initializers_lr.RandomInitializer`. - lse_mode: Whether to run computations in lse or kernel mode. + `'generalized-k-means'`. + lse_mode: Whether to run computations in LSE or kernel mode. inner_iterations: Number of inner iterations used by the algorithm before re-evaluating progress. use_danskin: Use Danskin theorem to evaluate gradient of objective w.r.t. @@ -295,9 +291,9 @@ def __init__( gamma: float = 10.0, gamma_rescale: bool = True, epsilon: float = 0.0, - initializer: Optional[Union[Literal["random", "rank2", "k-means", - "generalized-k-means"], - init_lib.LRInitializer]] = "random", + initializer: Union[Literal["random", "rank2", "k-means", + "generalized-k-means"], + initializers.LRQuadraticInitializer] = "random", lse_mode: bool = True, inner_iterations: int = 10, use_danskin: bool = True, @@ -731,7 +727,7 @@ def norm_error(self) -> Tuple[int]: # noqa: D102 def create_initializer( self, prob: quadratic_problem.QuadraticProblem, - ) -> init_lib.LRInitializer: + ) -> initializers.LRQuadraticInitializer: """Create a low-rank GW initializer. Args: @@ -740,25 +736,16 @@ def create_initializer( Returns: Low-rank initializer. """ - if isinstance(self.initializer, init_lib.LRInitializer): - initializer = self.initializer - elif self.initializer is None: - raise NotImplementedError("TODO") - kind = "k-means" if isinstance( - prob.geom, (pointcloud.PointCloud, low_rank.LRCGeometry) - ) else "random" - initializer = init_lib.LRInitializer.from_solver( - self, kind=kind, **self.kwargs_init - ) - else: - initializer = init_lib.LRInitializer.from_solver( - self, kind=self.initializer, **self.kwargs_init - ) - - assert initializer.rank == self.rank, \ - f"Expected initializer of rank `{self.rank}`, " \ - f"found `{initializer.rank}`." - return initializer + if isinstance(self.initializer, initializers.LRQuadraticInitializer): + assert self.initializer.rank == self.rank, \ + f"Expected initializer's rank to be `{self.rank}`," \ + f"found `{self.initializer.rank}`." + return self.initializer + + init = lin_init.LRInitializer.from_solver( + self, kind=self.initializer, **self.kwargs_init + ) + return initializers.LRQuadraticInitializer(init) def init_state( self, ot_prob: quadratic_problem.QuadraticProblem, From 1be699f4acf04a26c592de24d8d6919656f49ed0 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 31 Aug 2023 17:08:08 +0200 Subject: [PATCH 20/46] Remove temporary name --- src/ott/solvers/linear/lr_utils.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/src/ott/solvers/linear/lr_utils.py b/src/ott/solvers/linear/lr_utils.py index 98b35fe8d..8ade265c9 100644 --- a/src/ott/solvers/linear/lr_utils.py +++ b/src/ott/solvers/linear/lr_utils.py @@ -106,8 +106,8 @@ def body_fn( log_a, log_b = jnp.log(const.a), jnp.log(const.b) rho_a, rho_b = const.rho_a, const.rho_b - c_a = _foo(const.rho_a, gamma) - c_b = _foo(const.rho_b, gamma) + c_a = _get_ratio(const.rho_a, gamma) + c_b = _get_ratio(const.rho_b, gamma) if translation_invariant: lam_a, lam_b = compute_lambdas(const, state, gamma, g=c_g, lse_mode=True) @@ -242,8 +242,8 @@ def cond_fn( def body_fn( iteration: int, const: Constants, state: State, compute_error: bool ) -> State: - c_a = _foo(const.rho_a, gamma) - c_b = _foo(const.rho_b, gamma) + c_a = _get_ratio(const.rho_a, gamma) + c_b = _get_ratio(const.rho_b, gamma) if translation_invariant: lam_a, lam_b = compute_lambdas(const, state, gamma, g=k_g, lse_mode=False) @@ -332,8 +332,8 @@ def compute_lambdas( const_1 = num_1 - den const_2 = num_2 - den - ratio_1 = _foo(rho_a, gamma) - ratio_2 = _foo(rho_b, gamma) + ratio_1 = _get_ratio(rho_a, gamma) + ratio_2 = _get_ratio(rho_b, gamma) harmonic = 1.0 / (1.0 - (ratio_1 * ratio_2)) lam_1 = harmonic * gamma_inv * ratio_1 * (const_1 - ratio_2 * const_2) lam_2 = harmonic * gamma_inv * ratio_2 * (const_2 - ratio_1 * const_1) @@ -353,8 +353,8 @@ def compute_lambdas( const_1 = jnp.log(num_1 / den) const_2 = jnp.log(num_2 / den) - ratio_1 = _foo(rho_a, gamma) - ratio_2 = _foo(rho_b, gamma) + ratio_1 = _get_ratio(rho_a, gamma) + ratio_2 = _get_ratio(rho_b, gamma) harmonic = 1.0 / (1.0 - (ratio_1 * ratio_2)) lam_1 = harmonic * gamma_inv * ratio_1 * (const_1 - ratio_2 * const_2) lam_2 = harmonic * gamma_inv * ratio_2 * (const_2 - ratio_1 * const_1) @@ -366,7 +366,6 @@ def _rho(tau: float) -> float: return tau / (1.0 - tau) -# TODO(michalk8): rename -def _foo(rho: float, gamma: float) -> float: +def _get_ratio(rho: float, gamma: float) -> float: gamma_inv = 1.0 / gamma return jnp.where(jnp.isfinite(rho), rho / (rho + gamma_inv), 1.0) From 87284aee35a1f1be22abb95d234688617372a14c Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 31 Aug 2023 17:45:57 +0200 Subject: [PATCH 21/46] Fix norms --- src/ott/solvers/linear/sinkhorn_lr.py | 6 +++--- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/ott/solvers/linear/sinkhorn_lr.py b/src/ott/solvers/linear/sinkhorn_lr.py index 3cc5b187c..9424e7fb7 100644 --- a/src/ott/solvers/linear/sinkhorn_lr.py +++ b/src/ott/solvers/linear/sinkhorn_lr.py @@ -397,9 +397,9 @@ def _get_costs( grad_g += self.epsilon * log_g if self.gamma_rescale: - norm_q = jnp.linalg.norm(grad_q, ord=jnp.inf) ** 2 - norm_r = jnp.linalg.norm(grad_r, ord=jnp.inf) ** 2 - norm_g = jnp.linalg.norm(grad_g, ord=jnp.inf) ** 2 + norm_q = jnp.max(jnp.abs(grad_q)) ** 2 + norm_r = jnp.max(jnp.abs(grad_r)) ** 2 + norm_g = jnp.max(jnp.abs(grad_g)) ** 2 gamma = self.gamma / jnp.max(jnp.array([norm_q, norm_r, norm_g])) else: gamma = self.gamma diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index fe343b13e..8ed39b0c2 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -395,9 +395,9 @@ def _get_costs( grad_g += self.epsilon * log_g if self.gamma_rescale: - norm_q = jnp.linalg.norm(grad_q, ord=jnp.inf) ** 2 - norm_r = jnp.linalg.norm(grad_r, ord=jnp.inf) ** 2 - norm_g = jnp.linalg.norm(grad_g, ord=jnp.inf) ** 2 + norm_q = jnp.max(jnp.abs(grad_q)) ** 2 + norm_r = jnp.max(jnp.abs(grad_r)) ** 2 + norm_g = jnp.max(jnp.abs(grad_g)) ** 2 gamma = self.gamma / jnp.max(jnp.array([norm_q, norm_r, norm_g])) else: gamma = self.gamma From 30f275ee15bb9ee6b812f3d85fbfa075e4cda427 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 31 Aug 2023 17:50:37 +0200 Subject: [PATCH 22/46] Fix linkcheck --- docs/conf.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index 8c5dc72be..2e1257ff6 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -125,6 +125,8 @@ "https://doi.org/10.1137/17M1140431", "https://doi.org/10.1137/141000439", "https://doi.org/10.1002/mana.19901470121", + "https://doi.org/10.1145/2516971.2516977", + "https://doi.org/10.1145/2766963", ] # List of patterns, relative to source directory, that match files and From f11e19f7f79f7e545d4c939818fd740524fe1985 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 12:07:24 +0200 Subject: [PATCH 23/46] Remove old initializers test --- .../linear/sinkhorn_lr_init_test.py | 36 +------------------ tests/solvers/quadratic/fgw_test.py | 3 +- 2 files changed, 2 insertions(+), 37 deletions(-) diff --git a/tests/initializers/linear/sinkhorn_lr_init_test.py b/tests/initializers/linear/sinkhorn_lr_init_test.py index 948046b0d..d0b4c00ba 100644 --- a/tests/initializers/linear/sinkhorn_lr_init_test.py +++ b/tests/initializers/linear/sinkhorn_lr_init_test.py @@ -15,7 +15,7 @@ import jax.numpy as jnp import numpy as np import pytest -from ott.geometry import geometry, low_rank, pointcloud +from ott.geometry import geometry, pointcloud from ott.initializers.linear import initializers_lr from ott.problems.linear import linear_problem from ott.solvers.linear import sinkhorn_lr @@ -23,40 +23,6 @@ class TestLRInitializers: - @pytest.mark.fast.with_args("kind", ["pc", "lrc", "geom"], only_fast=0) - def test_create_default_initializer( - self, rng: jax.random.PRNGKeyArray, kind: str - ): - n, d, rank = 27, 2, 3 - x = jax.random.normal(rng, (n, d)) - geom = pointcloud.PointCloud(x) - - if kind == "pc": - pass - elif kind == "lrc": - geom = geom.to_LRCGeometry() - assert isinstance(geom, low_rank.LRCGeometry) - elif kind == "geom": - geom = geometry.Geometry(geom.cost_matrix) - else: - raise NotImplementedError(geom) - prob = linear_problem.LinearProblem(geom) - - solver = sinkhorn_lr.LRSinkhorn(rank=rank, initializer=None) - initializer = solver.create_initializer(prob) - - assert initializer.rank == rank - if kind in ("pc", "lrc"): - assert isinstance(initializer, initializers_lr.KMeansInitializer) - else: - assert isinstance(initializer, initializers_lr.RandomInitializer) - - q, r, g = initializer(prob) - - assert q.shape == (n, rank) - assert r.shape == (n, rank) - assert g.shape == (rank,) - def test_explicitly_passing_initializer(self): rank = 2 initializer = initializers_lr.RandomInitializer(rank=rank) diff --git a/tests/solvers/quadratic/fgw_test.py b/tests/solvers/quadratic/fgw_test.py index 5dee71293..b746d2374 100644 --- a/tests/solvers/quadratic/fgw_test.py +++ b/tests/solvers/quadratic/fgw_test.py @@ -22,7 +22,6 @@ from ott.solvers.linear import implicit_differentiation as implicit_lib from ott.solvers.linear import sinkhorn from ott.solvers.quadratic import gromov_wasserstein -from ott.solvers.quadratic import gromov_wasserstein as gw_solver class TestFusedGromovWasserstein: @@ -264,7 +263,7 @@ def test_fgw_lr_generic_cost_matrix( lr_prob = prob.to_low_rank() assert lr_prob.is_low_rank - solver = gw_solver.GromovWasserstein(rank=5, epsilon=10.0) + solver = gromov_wasserstein.GromovWasserstein(rank=5, epsilon=10.0) out = solver(prob) assert solver.rank == 5 From 6d4775e3edc8c45e6e6a0efe3affe88b9c421c43 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 12:23:39 +0200 Subject: [PATCH 24/46] Fix more initializer tests --- .../linear/sinkhorn_lr_init_test.py | 2 +- tests/initializers/quadratic/gw_init_test.py | 77 +++++-------------- 2 files changed, 19 insertions(+), 60 deletions(-) diff --git a/tests/initializers/linear/sinkhorn_lr_init_test.py b/tests/initializers/linear/sinkhorn_lr_init_test.py index d0b4c00ba..b3738a22a 100644 --- a/tests/initializers/linear/sinkhorn_lr_init_test.py +++ b/tests/initializers/linear/sinkhorn_lr_init_test.py @@ -23,7 +23,7 @@ class TestLRInitializers: - def test_explicitly_passing_initializer(self): + def test_explicit_initializer(self): rank = 2 initializer = initializers_lr.RandomInitializer(rank=rank) solver = sinkhorn_lr.LRSinkhorn(rank=rank, initializer=initializer) diff --git a/tests/initializers/quadratic/gw_init_test.py b/tests/initializers/quadratic/gw_init_test.py index a1d8c582a..e802fb33a 100644 --- a/tests/initializers/quadratic/gw_init_test.py +++ b/tests/initializers/quadratic/gw_init_test.py @@ -14,80 +14,39 @@ import jax import numpy as np import pytest -from ott.geometry import geometry, pointcloud +from ott.geometry import pointcloud from ott.initializers.linear import initializers as lin_init from ott.initializers.linear import initializers_lr from ott.initializers.quadratic import initializers as quad_init from ott.problems.quadratic import quadratic_problem -from ott.solvers.quadratic import gromov_wasserstein +from ott.solvers.quadratic import gromov_wasserstein, gromov_wasserstein_lr class TestQuadraticInitializers: - @pytest.mark.parametrize("kind", ["pc", "lrc", "geom"]) - def test_create_default_lr_initializer( - self, rng: jax.random.PRNGKeyArray, kind: str - ): - n, d1, d2, rank = 150, 2, 3, 5 - eps = 1e-1 - rng1, rng2 = jax.random.split(rng, 2) - x = jax.random.normal(rng1, (n, d1)) - y = jax.random.normal(rng1, (n, d2)) - kwargs_init = {"foo": "bar"} - - geom_x = pointcloud.PointCloud(x, epsilon=eps) - geom_y = pointcloud.PointCloud(y, epsilon=eps) - if kind == "pc": - pass - elif kind == "lrc": - geom_x = geom_x.to_LRCGeometry() - geom_y = geom_y.to_LRCGeometry() - elif kind == "geom": - geom_x = geometry.Geometry(geom_x.cost_matrix, epsilon=eps) - geom_y = geometry.Geometry(geom_y.cost_matrix, epsilon=eps) - else: - raise NotImplementedError(kind) - prob = quadratic_problem.QuadraticProblem(geom_x, geom_y) - - solver = gromov_wasserstein.GromovWasserstein( - rank=rank, quad_initializer=None, kwargs_init=kwargs_init - ) - initializer = solver.create_initializer(prob) - - assert isinstance(initializer, quad_init.LRQuadraticInitializer) - assert initializer.rank == rank - linear_init = initializer._linear_lr_initializer - if kind in ("pc", "lrc"): - assert isinstance(linear_init, initializers_lr.KMeansInitializer) - else: - assert isinstance(linear_init, initializers_lr.RandomInitializer) - assert linear_init._kwargs == kwargs_init - - def test_non_lr_initializer(self): - solver = gromov_wasserstein.GromovWasserstein( - rank=-1, quad_initializer="not used" - ) - initializer = solver.create_initializer(prob="not used") - assert isinstance(initializer, quad_init.QuadraticInitializer) - - @pytest.mark.parametrize("rank", [-1, 2]) - def test_explicitly_passing_initializer(self, rank: int): - if rank == -1: - linear_init = lin_init.SortingInitializer() - q_init = quad_init.QuadraticInitializer() - else: - linear_init = initializers_lr.Rank2Initializer(rank) - q_init = quad_init.LRQuadraticInitializer(linear_init) - + def test_explicit_initializer(self): + linear_init = lin_init.SortingInitializer() + q_init = quad_init.QuadraticInitializer() solver = gromov_wasserstein.GromovWasserstein( initializer=linear_init, quad_initializer=q_init, ) + assert solver.create_initializer("not used") is q_init assert solver.linear_ot_solver.initializer is linear_init assert solver.quad_initializer is q_init - if solver.is_low_rank: - assert solver.quad_initializer.rank == rank + + def test_explicit_initializer_lr(self): + rank = 10 + linear_init = initializers_lr.Rank2Initializer(rank) + q_init = quad_init.LRQuadraticInitializer(linear_init) + solver = gromov_wasserstein_lr.LRGromovWasserstein( + rank=rank, initializer=q_init + ) + + assert solver.create_initializer("not used") is q_init + assert solver.initializer is q_init + assert solver.initializer.rank == rank @pytest.mark.parametrize("eps", [0., 1e-2]) def test_gw_better_initialization_helps( From fdbafe978c88c99017f4c6f144f33802140437f8 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 12:42:38 +0200 Subject: [PATCH 25/46] Remove `LRQuadraticInitializer`, `reg_ot_cost -> reg_gw_cost` --- .../initializers/linear/initializers_lr.py | 2 +- .../initializers/quadratic/initializers.py | 57 +------------------ .../quadratic/gromov_wasserstein_lr.py | 31 +++++----- 3 files changed, 15 insertions(+), 75 deletions(-) diff --git a/src/ott/initializers/linear/initializers_lr.py b/src/ott/initializers/linear/initializers_lr.py index 923d91413..f7c07cc6e 100644 --- a/src/ott/initializers/linear/initializers_lr.py +++ b/src/ott/initializers/linear/initializers_lr.py @@ -397,7 +397,7 @@ def _compute_factor( ) if isinstance(ot_prob, quadratic_problem.QuadraticProblem): - if ot_prob.fused_penalty >= 1.0: + if ot_prob.geom_xy is not None and ot_prob.fused_penalty >= 1.0: # prefer the linear term if it has a higher weight geom = ot_prob.geom_xy else: diff --git a/src/ott/initializers/quadratic/initializers.py b/src/ott/initializers/quadratic/initializers.py index 54c9ac613..eea46d458 100644 --- a/src/ott/initializers/quadratic/initializers.py +++ b/src/ott/initializers/quadratic/initializers.py @@ -20,12 +20,10 @@ from ott.geometry import geometry if TYPE_CHECKING: - from ott.initializers.linear import initializers_lr from ott.problems.linear import linear_problem from ott.problems.quadratic import quadratic_problem - from ott.solvers.linear import sinkhorn_lr -__all__ = ["QuadraticInitializer", "LRQuadraticInitializer"] +__all__ = ["BaseQuadraticInitializer", "QuadraticInitializer"] @jax.tree_util.register_pytree_node_class @@ -172,56 +170,3 @@ def _create_geometry( epsilon=epsilon, relative_epsilon=relative_epsilon ) - - -class LRQuadraticInitializer(BaseQuadraticInitializer): - """Wrapper that wraps low-rank Sinkhorn initializers. - - Args: - lr_linear_initializer: Low-rank linear initializer. - """ - - def __init__(self, lr_linear_initializer: "initializers_lr.LRInitializer"): - super().__init__() - self._linear_lr_initializer = lr_linear_initializer - - def _create_geometry( - self, - quad_prob: "quadratic_problem.QuadraticProblem", - relative_epsilon: Optional[bool] = False, - **kwargs: Any - ) -> "sinkhorn_lr.LRSinkhornOutput": - """Compute initial geometry for linearization. - - Args: - quad_prob: Quadratic OT problem. - relative_epsilon: Whether to use relative epsilon in the geometry. - kwargs: Keyword arguments for - :meth:`~ott.initializers.linear.initializers_lr.LRInitializer.__call__`. - - Returns: - The initial :math:`Q`, :math:`R`, and :math:`g` factors. - """ - from ott.solvers.linear import sinkhorn_lr - - q, r, g = self._linear_lr_initializer(quad_prob, **kwargs) - tmp_out = sinkhorn_lr.LRSinkhornOutput( - q=q, - r=r, - g=g, - costs=None, - errors=None, - ot_prob=None, - epsilon=None, - ) - - return quad_prob.update_lr_geom(tmp_out, relative_epsilon=relative_epsilon) - - @property - def rank(self) -> int: - """Rank of the transport matrix factorization.""" - return self._linear_lr_initializer.rank - - def tree_flatten(self) -> Tuple[Sequence[Any], Dict[str, Any]]: # noqa: D102 - children, aux_data = super().tree_flatten() - return children + [self._linear_lr_initializer], aux_data diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 8ed39b0c2..0e28e0e3c 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -30,8 +30,7 @@ from jax.experimental import host_callback from ott.geometry import geometry, low_rank -from ott.initializers.linear import initializers_lr as lin_init -from ott.initializers.quadratic import initializers +from ott.initializers.linear import initializers_lr from ott.math import fixed_point_loop from ott.math import utils as mu from ott.problems.quadratic import quadratic_problem @@ -62,14 +61,14 @@ def compute_error( # noqa: D102 return ((1.0 / self.gamma) ** 2) * (err_q + err_r + err_g) - def reg_ot_cost( # noqa: D102 + def reg_gw_cost( # noqa: D102 self, ot_prob: quadratic_problem.QuadraticProblem, *, epsilon: float, use_danskin: bool = False ) -> float: - return compute_reg_ot_cost( + return compute_reg_gw_cost( self.q, self.r, self.g, @@ -83,7 +82,7 @@ def set(self, **kwargs: Any) -> "LRGWState": return self._replace(**kwargs) -def compute_reg_ot_cost( +def compute_reg_gw_cost( q: jnp.ndarray, r: jnp.ndarray, g: jnp.ndarray, @@ -149,7 +148,7 @@ class LRGWOutput(NamedTuple): ot_prob: quadratic_problem.QuadraticProblem epsilon: float # TODO(michalk8): Optional is an artifact of the current impl., refactor - reg_ot_cost: Optional[float] = None + reg_gw_cost: Optional[float] = None def set(self, **kwargs: Any) -> "LRGWOutput": """Return a copy of self, with potential overwrites.""" @@ -162,14 +161,14 @@ def set_cost( # noqa: D102 use_danskin: bool = False ) -> "LRGWOutput": del lse_mode - return self.set(reg_ot_cost=self.compute_reg_ot_cost(ot_prob, use_danskin)) + return self.set(reg_gw_cost=self.compute_reg_gw_cost(ot_prob, use_danskin)) - def compute_reg_ot_cost( # noqa: D102 + def compute_reg_gw_cost( # noqa: D102 self, ot_prob: quadratic_problem.QuadraticProblem, use_danskin: bool = False, ) -> float: - return compute_reg_ot_cost( + return compute_reg_gw_cost( self.q, self.r, self.g, @@ -293,7 +292,7 @@ def __init__( epsilon: float = 0.0, initializer: Union[Literal["random", "rank2", "k-means", "generalized-k-means"], - initializers.LRQuadraticInitializer] = "random", + initializers_lr.LRInitializer] = "random", lse_mode: bool = True, inner_iterations: int = 10, use_danskin: bool = True, @@ -346,9 +345,6 @@ def __call__( Returns: The low-rank GW output. """ - assert not ot_prob.is_balanced, \ - "Balanced case is not yet implemented here, please use " \ - "`ott.solvers.quadratic.gromov_wasserstein.GromovWasserstein` instead." initializer = self.create_initializer(ot_prob) init = initializer(ot_prob, *init, rng=rng, **kwargs) return run(ot_prob, self, init) @@ -695,7 +691,7 @@ def one_iteration( # re-computes error if compute_error is True, else set it to inf. cost = jax.lax.cond( jnp.logical_and(compute_error, iteration >= self.min_iterations), - lambda: state.reg_ot_cost(ot_prob, epsilon=self.epsilon), + lambda: state.reg_gw_cost(ot_prob, epsilon=self.epsilon), lambda: jnp.inf ) error = state.compute_error(previous_state) @@ -727,7 +723,7 @@ def norm_error(self) -> Tuple[int]: # noqa: D102 def create_initializer( self, prob: quadratic_problem.QuadraticProblem, - ) -> initializers.LRQuadraticInitializer: + ) -> initializers_lr.LRInitializer: """Create a low-rank GW initializer. Args: @@ -736,16 +732,15 @@ def create_initializer( Returns: Low-rank initializer. """ - if isinstance(self.initializer, initializers.LRQuadraticInitializer): + if isinstance(self.initializer, initializers_lr.LRInitializer): assert self.initializer.rank == self.rank, \ f"Expected initializer's rank to be `{self.rank}`," \ f"found `{self.initializer.rank}`." return self.initializer - init = lin_init.LRInitializer.from_solver( + return initializers_lr.LRInitializer.from_solver( self, kind=self.initializer, **self.kwargs_init ) - return initializers.LRQuadraticInitializer(init) def init_state( self, ot_prob: quadratic_problem.QuadraticProblem, From 17b447a03aa823aac64a0dbde7fa0c37c94a3757 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 12:44:11 +0200 Subject: [PATCH 26/46] `host_callback` -> `io_callback` --- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 0e28e0e3c..f86445d86 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -24,10 +24,10 @@ ) import jax +import jax.experimental import jax.numpy as jnp import jax.scipy as jsp import numpy as np -from jax.experimental import host_callback from ott.geometry import geometry, low_rank from ott.initializers.linear import initializers_lr @@ -709,8 +709,8 @@ def one_iteration( ) if self.progress_fn is not None: - host_callback.id_tap( - self.progress_fn, + jax.experimental.io_callback( + self.progress_fn, None, (iteration, self.inner_iterations, self.max_iterations, state) ) From 37140573cb6000ce9cb5701e1b775353a1e02526 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 12:44:37 +0200 Subject: [PATCH 27/46] Fix more initializers tests --- tests/initializers/quadratic/gw_init_test.py | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/tests/initializers/quadratic/gw_init_test.py b/tests/initializers/quadratic/gw_init_test.py index e802fb33a..17531407b 100644 --- a/tests/initializers/quadratic/gw_init_test.py +++ b/tests/initializers/quadratic/gw_init_test.py @@ -38,8 +38,7 @@ def test_explicit_initializer(self): def test_explicit_initializer_lr(self): rank = 10 - linear_init = initializers_lr.Rank2Initializer(rank) - q_init = quad_init.LRQuadraticInitializer(linear_init) + q_init = initializers_lr.Rank2Initializer(rank) solver = gromov_wasserstein_lr.LRGromovWasserstein( rank=rank, initializer=q_init ) @@ -66,19 +65,15 @@ def test_gw_better_initialization_helps( epsilon=eps, ) problem = quadratic_problem.QuadraticProblem(geom_x, geom_y) - solver_random = gromov_wasserstein.GromovWasserstein( + solver_random = gromov_wasserstein_lr.LRGromovWasserstein( rank=rank, initializer="random", - quad_initializer="random", epsilon=eps, - store_inner_errors=True, ) - solver_kmeans = gromov_wasserstein.GromovWasserstein( + solver_kmeans = gromov_wasserstein_lr.LRGromovWasserstein( rank=rank, initializer="k-means", - quad_initializer="k-means", epsilon=eps, - store_inner_errors=True ) out_random = solver_random(problem) From 0fa6be2d63474ea1254da9b1385afc6070e8abf7 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 12:56:45 +0200 Subject: [PATCH 28/46] Fix more tests --- tests/solvers/quadratic/fgw_test.py | 9 +++--- tests/solvers/quadratic/gw_test.py | 49 ++++++++++------------------- 2 files changed, 21 insertions(+), 37 deletions(-) diff --git a/tests/solvers/quadratic/fgw_test.py b/tests/solvers/quadratic/fgw_test.py index b746d2374..821c7cc0e 100644 --- a/tests/solvers/quadratic/fgw_test.py +++ b/tests/solvers/quadratic/fgw_test.py @@ -21,7 +21,7 @@ from ott.problems.quadratic import quadratic_problem from ott.solvers.linear import implicit_differentiation as implicit_lib from ott.solvers.linear import sinkhorn -from ott.solvers.quadratic import gromov_wasserstein +from ott.solvers.quadratic import gromov_wasserstein, gromov_wasserstein_lr class TestFusedGromovWasserstein: @@ -227,9 +227,9 @@ def test_fgw_lr_memory(self, rng: jax.random.PRNGKeyArray, jit: bool): geom_xy = pointcloud.PointCloud(xx, yy) prob = quadratic_problem.QuadraticProblem(geom_x, geom_y, geom_xy) - solver = gromov_wasserstein.GromovWasserstein(rank=2) + solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=2) if jit: - solver = jax.jit(solver, static_argnames="rank") + solver = jax.jit(solver) ot_gwlr = solver(prob) @@ -263,7 +263,7 @@ def test_fgw_lr_generic_cost_matrix( lr_prob = prob.to_low_rank() assert lr_prob.is_low_rank - solver = gromov_wasserstein.GromovWasserstein(rank=5, epsilon=10.0) + solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=5, epsilon=10.0) out = solver(prob) assert solver.rank == 5 @@ -277,7 +277,6 @@ def test_fgw_lr_generic_cost_matrix( assert geom.cost_rank == rank assert out.converged - assert out.primal_cost > 0 np.testing.assert_array_equal(jnp.isfinite(out.costs), True) @pytest.mark.parametrize("scale_cost", ["mean", "max_cost"]) diff --git a/tests/solvers/quadratic/gw_test.py b/tests/solvers/quadratic/gw_test.py index c43cc92d1..e8d7c7ca4 100644 --- a/tests/solvers/quadratic/gw_test.py +++ b/tests/solvers/quadratic/gw_test.py @@ -21,7 +21,7 @@ from ott.problems.quadratic import quadratic_problem from ott.solvers.linear import implicit_differentiation as implicit_lib from ott.solvers.linear import sinkhorn -from ott.solvers.quadratic import gromov_wasserstein +from ott.solvers.quadratic import gromov_wasserstein, gromov_wasserstein_lr @pytest.mark.fast() @@ -199,16 +199,21 @@ def reg_gw(a: jnp.ndarray, b: jnp.ndarray, @pytest.mark.parametrize(("balanced", "rank"), [(True, -1), (False, -1), (True, 3)]) def test_gw_pointcloud(self, balanced: bool, rank: int): - """Test basic computations pointclouds.""" + """Test basic computations point clouds.""" geom_x = pointcloud.PointCloud(self.x) geom_y = pointcloud.PointCloud(self.y) tau_a, tau_b = (1.0, 1.0) if balanced else (self.tau_a, self.tau_b) prob = quadratic_problem.QuadraticProblem( geom_x, geom_y, a=self.a, b=self.b, tau_a=tau_a, tau_b=tau_b ) - solver = gromov_wasserstein.GromovWasserstein( - rank=rank, epsilon=0.0 if rank > 0 else 1.0, max_iterations=10 - ) + if rank > 0: + solver = gromov_wasserstein_lr.LRGromovWasserstein( + rank=rank, epsilon=0.0, max_iterations=10 + ) + else: + solver = gromov_wasserstein.GromovWasserstein( + rank=rank, epsilon=1.0, max_iterations=10 + ) out = solver(prob) # TODO(cuturi): test primal cost for un-balanced case as well. @@ -316,10 +321,12 @@ def test_gw_lr(self, rng: jax.random.PRNGKeyArray): geom_xx = pointcloud.PointCloud(x) geom_yy = pointcloud.PointCloud(y) prob = quadratic_problem.QuadraticProblem(geom_xx, geom_yy, a=a, b=b) - solver = gromov_wasserstein.GromovWasserstein(rank=5, epsilon=0.2) + + solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=5, epsilon=0.2) ot_gwlr = solver(prob) solver = gromov_wasserstein.GromovWasserstein(epsilon=0.2) ot_gw = solver(prob) + np.testing.assert_allclose( ot_gwlr.primal_cost, ot_gw.primal_cost, rtol=5e-2 ) @@ -342,9 +349,10 @@ def test_gw_lr_matches_fused(self, rng: jax.random.PRNGKeyArray): prob = quadratic_problem.QuadraticProblem( geom_xx, geom_yy, geom_xy=geom_xy, fused_penalty=1.3, a=a, b=b ) - solver = gromov_wasserstein.GromovWasserstein(rank=6) + + solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=6) ot_gwlr = solver(prob) - solver = gromov_wasserstein.GromovWasserstein(rank=6, epsilon=1e-1) + solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=6, epsilon=1e-1) ot_gwlreps = solver(prob) solver = gromov_wasserstein.GromovWasserstein(epsilon=5e-2) ot_gw = solver(prob) @@ -362,7 +370,7 @@ def test_gw_lr_apply(self, axis: int): prob = quadratic_problem.QuadraticProblem( geom_x, geom_y, a=self.a, b=self.b ) - solver = gromov_wasserstein.GromovWasserstein(epsilon=1e-1, rank=2) + solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=2, epsilon=1e-1) out = solver(prob) arr, matrix = (self.x, out.matrix) if axis == 0 else (self.y, out.matrix.T) @@ -371,29 +379,6 @@ def test_gw_lr_apply(self, axis: int): np.testing.assert_allclose(res_apply, res_matrix, rtol=1e-5, atol=1e-5) - def test_gw_lr_warm_start_helps(self, rng: jax.random.PRNGKeyArray): - rank = 3 - rng1, rng2 = jax.random.split(rng, 2) - geom_x = pointcloud.PointCloud(jax.random.normal(rng1, (100, 5))) - geom_y = pointcloud.PointCloud(jax.random.normal(rng2, (110, 6))) - prob = quadratic_problem.QuadraticProblem(geom_x, geom_y) - - solver_cold = gromov_wasserstein.GromovWasserstein( - rank=rank, warm_start=False - ) - solver_warm = gromov_wasserstein.GromovWasserstein( - rank=rank, warm_start=True - ) - - out_cold = solver_cold(prob) - out_warm = solver_warm(prob) - - cost = out_cold.reg_gw_cost - cost_warm_start = out_warm.reg_gw_cost - assert (cost_warm_start + 5.0) < cost - with pytest.raises(AssertionError): - np.testing.assert_allclose(out_cold.matrix, out_warm.matrix) - @pytest.mark.parametrize("scale_cost", [1.0, "mean"]) def test_relative_epsilon( self, From 3843609bb7e5f29165e1fb3e7d9ac37fb156ad28 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 13:06:42 +0200 Subject: [PATCH 29/46] Remove initializer mention from the docs --- docs/initializers/quadratic.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/initializers/quadratic.rst b/docs/initializers/quadratic.rst index 0222cc00b..637a9d2ca 100644 --- a/docs/initializers/quadratic.rst +++ b/docs/initializers/quadratic.rst @@ -6,7 +6,7 @@ ott.initializers.quadratic Two families of initializers are described in the following to provide the first iteration of Gromov-Wasserstein solvers. They apply respectively to the simpler GW entropic solver :cite:`peyre:16` and its low-rank formulation -:cite:`scetbon:22`. +:cite:`scetbon:22,scetbon:23`. Gromov-Wasserstein Initializers ------------------------------- @@ -14,4 +14,3 @@ Gromov-Wasserstein Initializers :toctree: _autosummary initializers.QuadraticInitializer - initializers.LRQuadraticInitializer From 62aad99de95a4d429f6b836d14ffc696cb58a3b7 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 13:07:29 +0200 Subject: [PATCH 30/46] Remove mention of LR initializer --- docs/initializers/quadratic.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/initializers/quadratic.rst b/docs/initializers/quadratic.rst index 637a9d2ca..79466d757 100644 --- a/docs/initializers/quadratic.rst +++ b/docs/initializers/quadratic.rst @@ -5,8 +5,7 @@ ott.initializers.quadratic Two families of initializers are described in the following to provide the first iteration of Gromov-Wasserstein solvers. They apply respectively to the simpler -GW entropic solver :cite:`peyre:16` and its low-rank formulation -:cite:`scetbon:22,scetbon:23`. +GW entropic solver :cite:`peyre:16`. Gromov-Wasserstein Initializers ------------------------------- From 4e57972ad347d04f180c692c887d5658dc714d52 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 15:04:48 +0200 Subject: [PATCH 31/46] Start incorporating GWLoss --- .../quadratic/gromov_wasserstein_lr.py | 24 ++++++++++++------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index f86445d86..35ec41abd 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -117,8 +117,9 @@ def ent(x: jnp.ndarray) -> float: inv_g = 1.0 / g[None, :] lin_geom = _linearized_geometry(ot_prob, q=q, r=r, g=g) + # `2 * ` missing here because it's already present in the geometry + quad_cost = jnp.sum(q * lin_geom.apply_cost(r, axis=1) * inv_g) - quad_cost = 0.5 * jnp.sum(q * lin_geom.apply_cost(r, axis=1) * inv_g) if ot_prob.is_fused: alpha = ot_prob.fused_penalty / (ot_prob.fused_penalty + 1.0) norm_g = jnp.linalg.norm(g, ord=1) @@ -360,12 +361,14 @@ def _get_costs( lin_geom = _linearized_geometry(ot_prob, q=q, r=r, g=g) - tmp3 = lin_geom.apply_cost(r, axis=1) + # TODO + tmp3 = 2.0 * lin_geom.apply_cost(r, axis=1) grad_q = tmp3 * inv_g + 2.0 * ot_prob.geom_xx.apply_square_cost( q.sum(1), axis=1 ) - grad_r = lin_geom.apply_cost(q, axis=0) * inv_g + # TODO + grad_r = 2.0 * lin_geom.apply_cost(q, axis=0) * inv_g grad_r = grad_r + 2.0 * ot_prob.geom_yy.apply_square_cost(r.sum(1), axis=1) omega_quad = jnp.sum(q * tmp3, axis=0) @@ -863,10 +866,15 @@ def dykstra_solution_error( def _linearized_geometry( - prob: quadratic_problem.QuadraticProblem, q: jnp.ndarray, r: jnp.ndarray, - g: jnp.ndarray + prob: quadratic_problem.QuadraticProblem, + *, + q: jnp.ndarray, + r: jnp.ndarray, + g: jnp.ndarray, ) -> low_rank.LRCGeometry: + h1, h2 = prob.loss.h1, prob.loss.h2 inv_sqrt_g = 1.0 / jnp.sqrt(g[None, :]) - tmp1 = -4.0 * prob.geom_xx.apply_cost(q, axis=1) * inv_sqrt_g - tmp2 = prob.geom_yy.apply_cost(r, axis=1) * inv_sqrt_g - return low_rank.LRCGeometry(tmp1, tmp2) + + tmp1 = prob.geom_xx.apply_cost(q, axis=1, fn=h1.func, is_linear=h1.is_linear) + tmp2 = prob.geom_yy.apply_cost(r, axis=1, fn=h2.func, is_linear=h2.is_linear) + return low_rank.LRCGeometry(-tmp1 * inv_sqrt_g, tmp2 * inv_sqrt_g) From b312126701b3d3a0c8fe3e1d83ef8e2b52c61d76 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 16:28:56 +0200 Subject: [PATCH 32/46] Simplify reg GW cost computation --- .../quadratic/gromov_wasserstein_lr.py | 83 ++++++++++--------- 1 file changed, 44 insertions(+), 39 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 35ec41abd..a2a313b12 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -32,6 +32,7 @@ from ott.geometry import geometry, low_rank from ott.initializers.linear import initializers_lr from ott.math import fixed_point_loop +from ott.math import unbalanced_functions as uf from ott.math import utils as mu from ott.problems.quadratic import quadratic_problem from ott.solvers.linear import lr_utils, sinkhorn @@ -113,26 +114,17 @@ def ent(x: jnp.ndarray) -> float: r = jax.lax.stop_gradient(r) if use_danskin else r g = jax.lax.stop_gradient(g) if use_danskin else g - tau_a, tau_b = ot_prob.tau_a, ot_prob.tau_b - inv_g = 1.0 / g[None, :] - - lin_geom = _linearized_geometry(ot_prob, q=q, r=r, g=g) - # `2 * ` missing here because it's already present in the geometry - quad_cost = jnp.sum(q * lin_geom.apply_cost(r, axis=1) * inv_g) - - if ot_prob.is_fused: - alpha = ot_prob.fused_penalty / (ot_prob.fused_penalty + 1.0) - norm_g = jnp.linalg.norm(g, ord=1) - lin_cost = jnp.sum(q * ot_prob.geom_xy.apply_cost(r, axis=1) * inv_g) - cost = alpha * norm_g * lin_cost + (1.0 - alpha) * quad_cost - else: - cost = quad_cost + out = LRGWOutput( + q=q, r=r, g=g, ot_prob=ot_prob, costs=None, errors=None, epsilon=None + ) - cost -= epsilon * (ent(q) + ent(r) + ent(g)) - if tau_a != 1.0: - cost += tau_a / (1.0 - tau_a) * mu.gen_kl(jnp.sum(q, axis=1), ot_prob.a) - if tau_b != 1.0: - cost += tau_b / (1.0 - tau_b) * mu.gen_kl(jnp.sum(r, axis=1), ot_prob.b) + cost = out.primal_cost - epsilon * (ent(q) + ent(r) + ent(g)) + if ot_prob.tau_a != 1.0: + rho_a = uf.rho(1.0, ot_prob.tau_a) + cost += rho_a * mu.gen_kl(jnp.sum(q, axis=1), ot_prob.a) + if ot_prob.tau_b != 1.0: + rho_b = uf.rho(1.0, ot_prob.tau_b) + cost += rho_b * mu.gen_kl(jnp.sum(r, axis=1), ot_prob.b) return cost @@ -148,7 +140,6 @@ class LRGWOutput(NamedTuple): errors: jnp.ndarray ot_prob: quadratic_problem.QuadraticProblem epsilon: float - # TODO(michalk8): Optional is an artifact of the current impl., refactor reg_gw_cost: Optional[float] = None def set(self, **kwargs: Any) -> "LRGWOutput": @@ -231,7 +222,24 @@ def transport_cost_at_geom(self, other_geom: geometry.Geometry) -> float: @property def primal_cost(self) -> float: """Return (by recomputing it) transport cost of current solution.""" - return self.transport_cost_at_geom(other_geom=self.geom) + geom_xx, geom_yy = self.ot_prob.geom_xx, self.ot_prob.geom_yy + + quad_cost = 0.5 * self.transport_cost_at_geom(other_geom=self.geom) + if self.ot_prob.is_fused: + alpha = self.ot_prob.fused_penalty / (self.ot_prob.fused_penalty + 1.0) + norm_g = jnp.linalg.norm(self.g, ord=1) + + lin_cost = self.cost_at_geom(self.ot_prob.geom_xy) + cost = alpha * norm_g * lin_cost + (1.0 - alpha) * quad_cost + else: + cost = quad_cost + + marginal_a = self.q.sum(1) + marginal_b = self.r.sum(1) + cost += jnp.vdot(geom_xx.apply_square_cost(marginal_a), marginal_a) + cost += jnp.vdot(geom_yy.apply_square_cost(marginal_b), marginal_b) + + return cost @property def transport_mass(self) -> float: @@ -358,32 +366,28 @@ def _get_costs( q, r, g = state.q, state.r, state.g log_q, log_r, log_g = mu.safe_log(q), mu.safe_log(r), mu.safe_log(g) inv_g = 1.0 / g[None, :] - lin_geom = _linearized_geometry(ot_prob, q=q, r=r, g=g) - # TODO - tmp3 = 2.0 * lin_geom.apply_cost(r, axis=1) - grad_q = tmp3 * inv_g + 2.0 * ot_prob.geom_xx.apply_square_cost( - q.sum(1), axis=1 - ) + tmp = lin_geom.apply_cost(r, axis=1) + grad_q = tmp * inv_g + grad_q += 2.0 * ot_prob.geom_xx.apply_square_cost(q.sum(1), axis=1) - # TODO - grad_r = 2.0 * lin_geom.apply_cost(q, axis=0) * inv_g - grad_r = grad_r + 2.0 * ot_prob.geom_yy.apply_square_cost(r.sum(1), axis=1) + grad_r = lin_geom.apply_cost(q, axis=0) * inv_g + grad_r += 2.0 * ot_prob.geom_yy.apply_square_cost(r.sum(1), axis=1) - omega_quad = jnp.sum(q * tmp3, axis=0) + omega_quad = jnp.sum(q * tmp, axis=0) grad_g = -omega_quad / (g ** 2) if ot_prob.is_fused: alpha = ot_prob.fused_penalty / (ot_prob.fused_penalty + 1.0) norm_g = jnp.linalg.norm(g, ord=1) - tmp4 = ot_prob.geom_xy.apply_cost(r, axis=1) - lin_grad_q = tmp4 * inv_g * norm_g + tmp = ot_prob.geom_xy.apply_cost(r, axis=1) + lin_grad_q = tmp * inv_g * norm_g lin_grad_r = ot_prob.geom_xy.apply_cost(q) * inv_g * norm_g - omega_lin = jnp.sum(q * tmp4, axis=0) - lin_grad_g = -omega_lin / (g ** 2) * norm_g + jnp.sum(q * tmp4 * inv_g) + omega_lin = jnp.sum(q * tmp, axis=0) + lin_grad_g = -omega_lin / (g ** 2) * norm_g + jnp.sum(q * tmp * inv_g) grad_q = alpha * lin_grad_q + (1.0 - alpha) * grad_q grad_r = alpha * lin_grad_r + (1.0 - alpha) * grad_r @@ -872,9 +876,10 @@ def _linearized_geometry( r: jnp.ndarray, g: jnp.ndarray, ) -> low_rank.LRCGeometry: - h1, h2 = prob.loss.h1, prob.loss.h2 inv_sqrt_g = 1.0 / jnp.sqrt(g[None, :]) - tmp1 = prob.geom_xx.apply_cost(q, axis=1, fn=h1.func, is_linear=h1.is_linear) - tmp2 = prob.geom_yy.apply_cost(r, axis=1, fn=h2.func, is_linear=h2.is_linear) - return low_rank.LRCGeometry(-tmp1 * inv_sqrt_g, tmp2 * inv_sqrt_g) + # TODO(michalk8): below is for squared loss, handle KL loss in the future; + # will need to be updated in many other places as well + tmp1 = -4.0 * prob.geom_xx.apply_cost(q, axis=1) * inv_sqrt_g + tmp2 = prob.geom_yy.apply_cost(r, axis=1) * inv_sqrt_g + return low_rank.LRCGeometry(tmp1, tmp2) From a499102f3ef2fc5daec6bfde2892f08934c5f06a Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 16:39:18 +0200 Subject: [PATCH 33/46] Finish `primal_cost` --- .../quadratic/gromov_wasserstein_lr.py | 22 +++++++++---------- 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index a2a313b12..dbc4f182d 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -223,23 +223,21 @@ def transport_cost_at_geom(self, other_geom: geometry.Geometry) -> float: def primal_cost(self) -> float: """Return (by recomputing it) transport cost of current solution.""" geom_xx, geom_yy = self.ot_prob.geom_xx, self.ot_prob.geom_yy + marginal_a = self.q.sum(1) + marginal_b = self.r.sum(1) quad_cost = 0.5 * self.transport_cost_at_geom(other_geom=self.geom) - if self.ot_prob.is_fused: - alpha = self.ot_prob.fused_penalty / (self.ot_prob.fused_penalty + 1.0) - norm_g = jnp.linalg.norm(self.g, ord=1) + quad_cost += jnp.vdot(geom_xx.apply_square_cost(marginal_a), marginal_a) + quad_cost += jnp.vdot(geom_yy.apply_square_cost(marginal_b), marginal_b) - lin_cost = self.cost_at_geom(self.ot_prob.geom_xy) - cost = alpha * norm_g * lin_cost + (1.0 - alpha) * quad_cost - else: - cost = quad_cost + if not self.ot_prob.is_fused: + return quad_cost - marginal_a = self.q.sum(1) - marginal_b = self.r.sum(1) - cost += jnp.vdot(geom_xx.apply_square_cost(marginal_a), marginal_a) - cost += jnp.vdot(geom_yy.apply_square_cost(marginal_b), marginal_b) + alpha = self.ot_prob.fused_penalty / (self.ot_prob.fused_penalty + 1.0) + norm_g = jnp.linalg.norm(self.g, ord=1) - return cost + lin_cost = self.cost_at_geom(self.ot_prob.geom_xy) + return alpha * norm_g * lin_cost + (1.0 - alpha) * quad_cost @property def transport_mass(self) -> float: From 4bcb71bf8a4c415b7d4c7c59b7cf74af1c5f5a38 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 17:19:27 +0200 Subject: [PATCH 34/46] Don't calculate unbal. grads in balanced case --- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index dbc4f182d..9be3ce9ab 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -368,10 +368,12 @@ def _get_costs( tmp = lin_geom.apply_cost(r, axis=1) grad_q = tmp * inv_g - grad_q += 2.0 * ot_prob.geom_xx.apply_square_cost(q.sum(1), axis=1) + if ot_prob.tau_a != 1.0: # unbalanced grad + grad_q += 2.0 * ot_prob.geom_xx.apply_square_cost(q.sum(1), axis=1) grad_r = lin_geom.apply_cost(q, axis=0) * inv_g - grad_r += 2.0 * ot_prob.geom_yy.apply_square_cost(r.sum(1), axis=1) + if ot_prob.tau_b != 1.0: # unbalanced grad + grad_r += 2.0 * ot_prob.geom_yy.apply_square_cost(r.sum(1), axis=1) omega_quad = jnp.sum(q * tmp, axis=0) grad_g = -omega_quad / (g ** 2) From eea362f826d678052113c738376ddd359639b75a Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 17:26:49 +0200 Subject: [PATCH 35/46] Fix `primal_cost` in balanced case --- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 9be3ce9ab..47b9527b3 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -223,8 +223,8 @@ def transport_cost_at_geom(self, other_geom: geometry.Geometry) -> float: def primal_cost(self) -> float: """Return (by recomputing it) transport cost of current solution.""" geom_xx, geom_yy = self.ot_prob.geom_xx, self.ot_prob.geom_yy - marginal_a = self.q.sum(1) - marginal_b = self.r.sum(1) + marginal_a = self.ot_prob.a if self.ot_prob.tau_a == 1.0 else self.q.sum(1) + marginal_b = self.ot_prob.b if self.ot_prob.tau_b == 1.0 else self.r.sum(1) quad_cost = 0.5 * self.transport_cost_at_geom(other_geom=self.geom) quad_cost += jnp.vdot(geom_xx.apply_square_cost(marginal_a), marginal_a) From 3f14488a2c1f6671b27f127b853faf09c726e399 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Fri, 1 Sep 2023 17:57:49 +0200 Subject: [PATCH 36/46] Update GW LR notebook --- docs/tutorials/notebooks/GWLRSinkhorn.ipynb | 47 ++++++--------------- 1 file changed, 13 insertions(+), 34 deletions(-) diff --git a/docs/tutorials/notebooks/GWLRSinkhorn.ipynb b/docs/tutorials/notebooks/GWLRSinkhorn.ipynb index f5d261c76..b87db9d1e 100644 --- a/docs/tutorials/notebooks/GWLRSinkhorn.ipynb +++ b/docs/tutorials/notebooks/GWLRSinkhorn.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "E_-S77MmiOou" @@ -14,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +36,7 @@ "\n", "from ott.geometry import pointcloud\n", "from ott.problems.quadratic import quadratic_problem\n", - "from ott.solvers.quadratic import gromov_wasserstein" + "from ott.solvers.quadratic import gromov_wasserstein, gromov_wasserstein_lr" ] }, { @@ -79,7 +78,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "y4aQGprB_oeW" @@ -120,13 +118,12 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "dS49krqd_weJ" }, "source": [ - "Solve the problem using the {class}`~ott.solvers.linear.sinkhorn_lr.LRSinkhorn` solver class." + "Solve the problem using the {class}`~ott.solvers.quadratic.gromov_wasserstein_lr.LRGromovWasserstein` solver." ] }, { @@ -148,18 +145,17 @@ }, "outputs": [], "source": [ - "solver = gromov_wasserstein.GromovWasserstein(rank=6)\n", + "solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=6)\n", "ot_gwlr = solver(prob)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "vxDoBrusUHmq" }, "source": [ - "Run it with the widespread entropic {class}`~ott.solvers.quadratic.gromov_wasserstein.GromovWasserstein` solver for the sake of comparison." + "Furthermore, we also run the entropic {class}`~ott.solvers.quadratic.gromov_wasserstein.GromovWasserstein` solver for the sake of comparison." ] }, { @@ -197,28 +193,11 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "colab": { - "height": 545 - }, - "executionInfo": { - "elapsed": 785, - "status": "ok", - "timestamp": 1642798323297, - "user": { - "displayName": "Marco Cuturi", - "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gj0UBKLFbdRpYhnFiILEQ2AgXibacTBJBwmBsE4=s64", - "userId": "04861232750708981029" - }, - "user_tz": -60 - }, - "id": "HMfUh6uE8kdG", - "outputId": "3feef227-b93c-4783-fba0-09e366f416ea" - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] @@ -241,7 +220,7 @@ "def plot_ot(ot, leg):\n", " plt.imshow(ot.matrix, cmap=\"Purples\")\n", " plt.colorbar()\n", - " plt.title(leg + \" cost: \" + str(ot.costs[ot.costs > 0][-1]))\n", + " plt.title(f\"{leg} cost: {ot.primal_cost:.4f}\")\n", " plt.show()\n", "\n", "\n", @@ -266,9 +245,9 @@ ] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ott", "language": "python", - "name": "python3" + "name": "ott" }, "language_info": { "codemirror_mode": { @@ -280,7 +259,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.6" }, "vscode": { "interpreter": { From 8207696a5a20173f01a35716380b811d3917f925 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Mon, 4 Sep 2023 10:13:25 +0200 Subject: [PATCH 37/46] Convert quad problem to LR if possible --- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 47b9527b3..bd6f115c7 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -352,6 +352,8 @@ def __call__( Returns: The low-rank GW output. """ + if ot_prob._is_low_rank_convertible: + ot_prob = ot_prob.to_low_rank() initializer = self.create_initializer(ot_prob) init = initializer(ot_prob, *init, rng=rng, **kwargs) return run(ot_prob, self, init) From ba272f0a58df2d06c743b25f9f1b7fc7d8c6d93d Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Mon, 4 Sep 2023 10:15:04 +0200 Subject: [PATCH 38/46] Convert quad problem to LR if possible --- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index bd6f115c7..1184c64ff 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -29,6 +29,7 @@ import jax.scipy as jsp import numpy as np +from ott import utils from ott.geometry import geometry, low_rank from ott.initializers.linear import initializers_lr from ott.math import fixed_point_loop @@ -352,10 +353,14 @@ def __call__( Returns: The low-rank GW output. """ + rng = utils.default_prng_key(rng) + rng_lrc, rng_init = jax.random.split(rng) + if ot_prob._is_low_rank_convertible: - ot_prob = ot_prob.to_low_rank() + ot_prob = ot_prob.to_low_rank(rng=rng_lrc) + initializer = self.create_initializer(ot_prob) - init = initializer(ot_prob, *init, rng=rng, **kwargs) + init = initializer(ot_prob, *init, rng=rng_init, **kwargs) return run(ot_prob, self, init) def _get_costs( From c565ee363f73d1c430e70137e41e55e9445c40cc Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Tue, 5 Sep 2023 10:26:09 +0200 Subject: [PATCH 39/46] Regenerate GWLR Sinkhorn --- docs/tutorials/notebooks/GWLRSinkhorn.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/notebooks/GWLRSinkhorn.ipynb b/docs/tutorials/notebooks/GWLRSinkhorn.ipynb index b87db9d1e..e0a8deb29 100644 --- a/docs/tutorials/notebooks/GWLRSinkhorn.ipynb +++ b/docs/tutorials/notebooks/GWLRSinkhorn.ipynb @@ -114,6 +114,7 @@ " geom_xy=geom_xy,\n", " a=a,\n", " b=b,\n", + " fused_penalty=1.0,\n", ")" ] }, @@ -187,7 +188,7 @@ "id": "w35fLv3oIwLW" }, "source": [ - "One can notice that their outputs are quantitatively similar." + "One can notice that their outputs are quantitatively similar with respect to their primal cost." ] }, { @@ -197,7 +198,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] From f508d5df38242f9b225767bffba55c7da67ebf53 Mon Sep 17 00:00:00 2001 From: Michal Klein Date: Tue, 5 Sep 2023 09:12:14 +0000 Subject: [PATCH 40/46] Regenerate `LRSinkhorn` --- docs/tutorials/notebooks/LRSinkhorn.ipynb | 29 ++++++++++------------- 1 file changed, 13 insertions(+), 16 deletions(-) diff --git a/docs/tutorials/notebooks/LRSinkhorn.ipynb b/docs/tutorials/notebooks/LRSinkhorn.ipynb index da624b339..f74172104 100644 --- a/docs/tutorials/notebooks/LRSinkhorn.ipynb +++ b/docs/tutorials/notebooks/LRSinkhorn.ipynb @@ -126,7 +126,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -149,9 +149,8 @@ "solver = sinkhorn.Sinkhorn()\n", "ot_sink = solver(ot_prob)\n", "\n", - "transp_cost = jnp.sum(ot_sink.matrix * geom.cost_matrix)\n", "plt.imshow(ot_sink.matrix, cmap=\"Purples\")\n", - "plt.title(\"Sinkhorn, Cost: \" + str(transp_cost))\n", + "plt.title(f\"Sinkhorn cost: {ot_sink.primal_cost}\")\n", "plt.colorbar()\n", "plt.show()\n", "plott = plot.Plot()\n", @@ -193,7 +192,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", "text/plain": [ "
" ] @@ -219,7 +218,7 @@ "transp_cost = ot_lr.compute_reg_ot_cost(ot_prob)\n", "plt.imshow(ot_lr.matrix, cmap=\"Purples\")\n", "plt.colorbar()\n", - "plt.title(\"LR, Cost: \" + str(transp_cost))\n", + "plt.title(f\"LR cost: {ot_lr.primal_cost}\")\n", "plt.show()\n", "plott = plot.Plot()\n", "_ = plott(ot_lr)" @@ -270,7 +269,7 @@ "costs = []\n", "ranks = [15, 20, 35, 50, 100]\n", "for rank in ranks:\n", - " solver = sinkhorn_lr.LRSinkhorn(rank=rank, initializer=\"k-means\")\n", + " solver = jax.jit(sinkhorn_lr.LRSinkhorn(rank=rank, initializer='k-means'))\n", " ot_lr = solver(ot_prob)\n", " costs.append(ot_lr.reg_ot_cost)" ] @@ -310,14 +309,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -351,9 +348,9 @@ ] }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "ott", "language": "python", - "name": "python3" + "name": "ott" }, "language_info": { "codemirror_mode": { @@ -365,7 +362,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.10" } }, "nbformat": 4, From ec4582526e21eb3340d2f681277501dea1dfc63c Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Tue, 5 Sep 2023 22:15:46 +0200 Subject: [PATCH 41/46] [ci skip] Fix linter --- docs/tutorials/notebooks/LRSinkhorn.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/notebooks/LRSinkhorn.ipynb b/docs/tutorials/notebooks/LRSinkhorn.ipynb index f74172104..94968d36a 100644 --- a/docs/tutorials/notebooks/LRSinkhorn.ipynb +++ b/docs/tutorials/notebooks/LRSinkhorn.ipynb @@ -269,7 +269,7 @@ "costs = []\n", "ranks = [15, 20, 35, 50, 100]\n", "for rank in ranks:\n", - " solver = jax.jit(sinkhorn_lr.LRSinkhorn(rank=rank, initializer='k-means'))\n", + " solver = jax.jit(sinkhorn_lr.LRSinkhorn(rank=rank, initializer=\"k-means\"))\n", " ot_lr = solver(ot_prob)\n", " costs.append(ot_lr.reg_ot_cost)" ] From 68d3ce2f00ba96d14c96b36c672cdeb52c88f421 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 7 Sep 2023 17:27:52 +0200 Subject: [PATCH 42/46] Fix convergence metric --- src/ott/math/utils.py | 6 +++--- src/ott/solvers/linear/sinkhorn_lr.py | 18 +++++++++--------- .../solvers/quadratic/gromov_wasserstein_lr.py | 10 ++++++---- 3 files changed, 18 insertions(+), 16 deletions(-) diff --git a/src/ott/math/utils.py b/src/ott/math/utils.py index 3641ac0d6..8e7ea90ee 100644 --- a/src/ott/math/utils.py +++ b/src/ott/math/utils.py @@ -26,7 +26,7 @@ "norm", "kl", "gen_kl", - "js", + "gen_js", "logsumexp", "softmin", "barycentric_projection", @@ -116,9 +116,9 @@ def gen_kl(p: jnp.ndarray, q: jnp.ndarray) -> float: # TODO(michalk8): add axis argument -def js(p: jnp.ndarray, q: jnp.ndarray, c: float = 0.5) -> float: +def gen_js(p: jnp.ndarray, q: jnp.ndarray, c: float = 0.5) -> float: """Jensen-Shannon divergence.""" - return c * (kl(p, q) + kl(q, p)) + return c * (gen_kl(p, q) + gen_kl(q, p)) @functools.partial(jax.custom_jvp, nondiff_argnums=(1, 2, 4)) diff --git a/src/ott/solvers/linear/sinkhorn_lr.py b/src/ott/solvers/linear/sinkhorn_lr.py index 9424e7fb7..5185a9faf 100644 --- a/src/ott/solvers/linear/sinkhorn_lr.py +++ b/src/ott/solvers/linear/sinkhorn_lr.py @@ -55,9 +55,9 @@ class LRSinkhornState(NamedTuple): def compute_error( # noqa: D102 self, previous_state: "LRSinkhornState" ) -> float: - err_q = mu.js(self.q, previous_state.q, c=1.0) - err_r = mu.js(self.r, previous_state.r, c=1.0) - err_g = mu.js(self.g, previous_state.g, c=1.0) + err_q = mu.gen_js(self.q, previous_state.q, c=1.0) + err_r = mu.gen_js(self.r, previous_state.r, c=1.0) + err_g = mu.gen_js(self.g, previous_state.g, c=1.0) return ((1.0 / self.gamma) ** 2) * (err_q + err_r + err_g) @@ -385,12 +385,12 @@ def _get_costs( mu.safe_log(state.q), mu.safe_log(state.r), mu.safe_log(state.g) ) - grad_q = ot_prob.geom.apply_cost(state.r, axis=1) / state.g[None, :] - grad_r = ot_prob.geom.apply_cost(state.q) / state.g[None, :] - diag_qcr = jnp.sum( - state.q * ot_prob.geom.apply_cost(state.r, axis=1), axis=0 - ) - grad_g = -diag_qcr / (state.g ** 2) + inv_g = 1.0 / state.g[None, :] + tmp = ot_prob.geom.apply_cost(state.r, axis=1) + + grad_q = tmp * inv_g + grad_r = ot_prob.geom.apply_cost(state.q) * inv_g + grad_g = -jnp.sum(state.q * tmp, axis=0) / (state.g ** 2) grad_q += self.epsilon * log_q grad_r += self.epsilon * log_r diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index 1184c64ff..d3362bdac 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -57,9 +57,9 @@ class LRGWState(NamedTuple): def compute_error( # noqa: D102 self, previous_state: "LRGWState" ) -> float: - err_q = mu.js(self.q, previous_state.q, c=1.0) - err_r = mu.js(self.r, previous_state.r, c=1.0) - err_g = mu.js(self.g, previous_state.g, c=1.0) + err_q = mu.gen_js(self.q, previous_state.q, c=1.0) + err_r = mu.gen_js(self.r, previous_state.r, c=1.0) + err_g = mu.gen_js(self.g, previous_state.g, c=1.0) return ((1.0 / self.gamma) ** 2) * (err_q + err_r + err_g) @@ -398,7 +398,8 @@ def _get_costs( grad_q = alpha * lin_grad_q + (1.0 - alpha) * grad_q grad_r = alpha * lin_grad_r + (1.0 - alpha) * grad_r - grad_g = alpha * lin_grad_g + 4.0 * (1.0 - alpha) * grad_g + # TODO(michalk8): check + grad_g = alpha * lin_grad_g + (1.0 - alpha) * grad_g grad_q += self.epsilon * log_q grad_r += self.epsilon * log_r @@ -696,6 +697,7 @@ def one_iteration( The updated state. """ previous_state = state + it = iteration // self.inner_iterations if self.lse_mode: # In lse_mode, run additive updates. state = self.lse_step(ot_prob, state, iteration) From afd8ea691c8574b5a2a61aa2f731b1cc49caf0ed Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 7 Sep 2023 17:58:26 +0200 Subject: [PATCH 43/46] Undo TODO --- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index d3362bdac..f0a3765b5 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -398,8 +398,7 @@ def _get_costs( grad_q = alpha * lin_grad_q + (1.0 - alpha) * grad_q grad_r = alpha * lin_grad_r + (1.0 - alpha) * grad_r - # TODO(michalk8): check - grad_g = alpha * lin_grad_g + (1.0 - alpha) * grad_g + grad_g = alpha * lin_grad_g + (1.0 - alpha) * 4.0 * grad_g grad_q += self.epsilon * log_q grad_r += self.epsilon * log_r From e006eae084faa45736626d9a245e2c7176d7d489 Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 7 Sep 2023 18:32:34 +0200 Subject: [PATCH 44/46] Fix factor --- src/ott/solvers/quadratic/gromov_wasserstein_lr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py index f0a3765b5..ec56d430c 100644 --- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py +++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py @@ -398,7 +398,7 @@ def _get_costs( grad_q = alpha * lin_grad_q + (1.0 - alpha) * grad_q grad_r = alpha * lin_grad_r + (1.0 - alpha) * grad_r - grad_g = alpha * lin_grad_g + (1.0 - alpha) * 4.0 * grad_g + grad_g = alpha * lin_grad_g + (1.0 - alpha) * grad_g grad_q += self.epsilon * log_q grad_r += self.epsilon * log_r From e06f1b08a74c33b8ece2550dea18b7cbbcec330c Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 7 Sep 2023 18:40:54 +0200 Subject: [PATCH 45/46] Regenerate notebooks --- docs/tutorials/notebooks/GWLRSinkhorn.ipynb | 4 ++-- docs/tutorials/notebooks/LRSinkhorn.ipynb | 14 +++++++------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/tutorials/notebooks/GWLRSinkhorn.ipynb b/docs/tutorials/notebooks/GWLRSinkhorn.ipynb index e0a8deb29..0252b9c4b 100644 --- a/docs/tutorials/notebooks/GWLRSinkhorn.ipynb +++ b/docs/tutorials/notebooks/GWLRSinkhorn.ipynb @@ -198,7 +198,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -225,7 +225,7 @@ " plt.show()\n", "\n", "\n", - "plot_ot(ot_gwlr, \"Low rank\")\n", + "plot_ot(ot_gwlr, \"Low-rank\")\n", "plot_ot(ot_gw, \"Entropic\")" ] } diff --git a/docs/tutorials/notebooks/LRSinkhorn.ipynb b/docs/tutorials/notebooks/LRSinkhorn.ipynb index 94968d36a..2a23aa3bb 100644 --- a/docs/tutorials/notebooks/LRSinkhorn.ipynb +++ b/docs/tutorials/notebooks/LRSinkhorn.ipynb @@ -126,7 +126,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -150,7 +150,7 @@ "ot_sink = solver(ot_prob)\n", "\n", "plt.imshow(ot_sink.matrix, cmap=\"Purples\")\n", - "plt.title(f\"Sinkhorn cost: {ot_sink.primal_cost}\")\n", + "plt.title(f\"Sinkhorn cost: {ot_sink.primal_cost:.4f}\")\n", "plt.colorbar()\n", "plt.show()\n", "plott = plot.Plot()\n", @@ -192,7 +192,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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fz4EH7Medd97J4JNPWqcgiUajDD75JO68804OPGA/xo8fX88rNRj+zPtVSsGclAFPR+UNsYAMW6mbTEsNzyAlRnzrejCDwWCoY4wY2QDGjx/PIQcfSO8mMV7/Wxoff/ThWgVJUoh8/NGHvP63NHo3iXHIwQcaQWJoUFwXPquC3iHI9sFYAK+cN02/kmdBjXf7kPev1RCLNRgM2yVGjPwFtYXIp6eFGNw7wDsnhf4kSGoLkXdO0u0+PS1kBImhwfktCosTcHw6zAGKLK+k11GPkZAFTZAYsZAYMULEYDDUJ0aMrIc1hUhWSIfoQd1WFySVlZWrCZFB3WT9ywpZRpAYGpx3KlUxc0g6fOxA3JJfpKkNrQGfBemkGp6lYQ4MBoOhfjHHnHUwadKktQqRJLUFSadOHf8kRJKsKUjq09Q6ceJELMtizz33XOdt7r33XizL4vbbb6+3dRnqj4gL39bAHmmqmPlfHOVlgIBPLd8DXufVZI+RNExkxGAw1C9GjKyDDz74gLLySq7czfcnIZIkKUgy4iVrFSJJskIWV+7mo6y8kg8++GBLLns1dt11V3bZZRfGjRvHr7/++qfrXdflueeew7Ztzj///Hpbl6H++KEaCh0YnA7FwGxXg/FCqFqmwmsDX+3dPtl51YgRg8FQnxgxsg5uuukmjjv2GM4aGeGjWbF13m5QtwDzrkxfpxAB+GhWjLNGRjju2GO46aabtsRy18kll1wCwH/+858/Xffll18yd+5cDjvsMNq3b1+v6zLUD+9WQgsf9AvBBBcqPL9IExcyXChFA/LKSaVpkkLFYDAY6gsjRtZBMBhkxBtvcuSgozjhzfULkvXx0awYJ7wZ4chBRzHijTcJBut34sdpp51GdnY2//3vf4lEIqtd9+yzzwJw4YUX1uuaDPVDSQImR+CgNLAstYB3AceCthZkIdFhW7CClBjxYcp6DQZD/WLEyHrYXEHS0EIEICMjgzPOOIOioiLefvvtPy5ftWoV7777Li1btuToo4+u93UZtjyjqpR+GZypVu/fJo0hFgT80BIJkRxgqXeVv9aPwWAw1BdGjPwFmypItgYhkmRtqZqXX36ZaDTKueeei99vtp7Ghuuq0VnXALTxw3RgmQU+R56QGiDX1XC8TNTwLDmx1wzJMxgM9Y0RIxtAMBjklVdfI6dJHkM+3TAxMuTTGDlN8njl1dcaVIgA9OnThz333JPRo0cze/ZsAJ577jksy+KCCy5o0LUZtgwLY/B7DI7M0P+/cSHi+UWauRBKVtRY0AqIoQ6tPlKixGAwGOoLI0Y2gGg0yhmnn0ZpcRGPH75h48MePzxAaXERZ5x+2l/OsqkPktGRZ599lu+++47p06dz0EEH0blz5wZemWFL8H4VYMEJafr/e3GwHEjY0MWCbBcKXXVebYaESLJFvJlLYzBsPyxZsoQHHniAm2++mbFjx+K6boOsw4iRv2DNzqrrq5qpzZqN0RpakJx00kk0bdqUF198kSeeeAIwxtXGStyFL6phlxCk+WAZKul1AccHmX71FylGM2ni/Dk9YyIjBkPjZ/To0XTp2o2bbr2df/7fU+y7775cc801DbIWI0bWw6YKkSRbkyAJh8OcffbZrFixgtdee43mzZtz3HHHNdh6DFuOX2qgIAF/81I0E4BCCwJx+UWKUWomiCIi5azuFzFpGoOh8eO6LhdefAlW8860vuwlWl76Irn7n8ejjz7KTz/9VO/rMWJkHWyuEEmyNQmSiy++GMtSO6uzzz67wb0shi3DO1WQacN+Yf3/UwcSXuS1uasS3hzUBt4GlqN/LSCMrjefDIOhcbN06VLmzJpJZv9jsEMZWJZN9m7H4g+l8eWXX9b7eowYWQf33nsv7418n5ePXb8Q+WhWjE7/qvrLxmgvHxvivZHvc++9926J5W4Q3bp1o23btgDGuNpIqXTgxxrYO01luzXAuIS6rsZ90A/IdMHvVdIEgcWkoiFhlLYxYsRgaNxkZ2djB4LEipb+cVmivJB4tIYWLVrU+3qMGFkHRx99NNlZGfxrQoLyyNoNPcny3Up/7nrLfssjLv+akCA7K6NBe3qMGzeORYsWsd9++9G9e/cGW4dhyzG2GkpcONVL0UwBlrgyrzp+yA+o2VkNkG5pDs0SlK4JkErdhBpi8QaDoV4YD+SmZ+GcdRYl3/+PkrGvUj7lUwrfvoPmzVtwwgkn1PuajBhZB/3792fUF18xrTjA4a9F/iRIavcRmTdv/jr7kJRHXA5/LcK04gCjvviK/v371+fLWI177rkHgCFDhjTYGgxblvcq1Vekhxfa+N5VD5FAQubUAleRkSUuNLc0tXcFiowESIkQExkxGBofy4A+wABHJx089n/se9FFRCaPpOizx9mtZydGf/0VWVlZ9b42I0bWw+67775WQbJmQ7OMjIy1NkZbU4jsvvvu9f4avv/+e84//3wGDBjARx99xC677NIgqtew5VkRh+lROCRd/3eB0V7XVdeFpq7Mq22BKhQZaY2iJBF0MDCREYOh8VEBnI6+79O8SCnAS+Ew3zz+L8rLSqmqqmLMN6Pp1atXg6zRiJG/YE1BMmLa2jurrtmpdcS0WIMLEYBZs2bx/PPP89tvvzFo0CDeeecdbNv82RsjH1dJVJzipWjmAbNcwIWED/awFB3JRX4RC1XVJND9AmhonoO8IwaDYdsmCtyNRMhrDpAAKwGuBS9ZcJZXNuf3+0lLS2u4hWLEyAZRW5Cc8nb1Olu81xYkp7xd3eBCBOCcc87BdV3Kysr48MMP6dChQ4OtxbDlcF34pAp6hqCpd4CZABSgFvCJAPT0qwV80NXgvBgSHTYyrVrIT2LEiMGwbZMAXgW6IjFS7oIV03fcteBZH5y1le3+W9lytl523313vvr6G26//fb1zppJCpLbb7+dr77+pkGFiGH7YXYUFsbh6PTUZT+4EHUgGFdlzTIkNhJA0Gt2VgCkIwHioGZoCYwYMRi2RVzgG2AAcCmqlIs54K8G16/r/88H5zfkIteB6fq8EfTv33+DDKjBYJA77rhjyy/IYPB4rwr8FhztRVpLgZ/iYDs6ADVBk3lbe23gc9RuhjlIeHgpZJOmMRi2UaYCw4Af0YlGuQs4EIhALKzy/gd8sLWWLxgxYjBs40Rd+KYadgtD0It1/gTMT15vw4FAkavc8TygLwrZLkDh0eSAvKQwadjsscFg2FDmA/cCn6LvcimKhthx8CcgGlZfoVt8cF1DLvQvaJRpmoYa9NPYMe/r1snEaljpwMkZqcvGo8oZnwOJEOzvl8Bohb70GRa0QWFcG5lXk+W9CZS6MRgMWy8rgNuAI4FRKLKwAAmRQBR8LkRDqoy7xqfbbs00qsiIzyfnXiwWa3BncGMkEokAcl4bth7erYImPhjg1ePGgakOuAkIxyAWkjBp5iryEfaqalojH0kyMoL3uxEjBsPWSxnwMvAa+v52Br4HaryS3VBU3Zbjfn2PL7DhPhQJ3ZppVLtKIBAgFApRWlpKVlbWH3NYDJtPIpGgqKiIjIwMI0a2IkoTMCkCB2eoQgZgBjDDawGPqzk0M5F51XLlpneQGClDAiWABImFxExmvb8Sg8GwPmqAd4BX0He8I/J4jQYc12tsGIVYQNO5s4FTbXiIrV+IQCMTIwDNmjVjyZIlLF68mJycHAKBgBElm4jruiQSCaqrqyktLcVxHFq1atXQyzLU4qtqGdVOr6UeJpCaNxMNwH4WrASyvaZnfkv/tkblvaBmZ5Z3nwQ6yBkMhoYnDnyGIiFTULTjEOC/qD8QDmTEwY0pLePYOgE51obH2HY2+Y1a5/Dhw3nnnXeYMWMGaWlp7Lnnntx///3rnXPy4osvcu655652WSgUoqamZtNW/BdkZ2cDsGrVKpYsWbJFnmN7w+fzkZ6eTosWLcyk362MDyqhYwA61Pom/+pClXemVB2Eo3zwjgvNgUVeJU0EffkT3k8IHfTAiBGDYWvABcYCrwOTURfVI4FPgOdRNMTvQjgKcQciYUVHmwJH2PA421Yn5Y0SI9988w2XX345u+22G/F4nJtuuolDDz2U6dOnk5Gx7sNXdnY2M2fO/OP/WzpSkZ2dTXZ2NrFYjEQisUWfq7Fj27aJLm2lLIrB7Bick5O6bAUw2+uymBaFmrAOSFlAJ9SDoL8FRSh6EgYK0ZlUBB0AHYxnxGBoKFwUAXkN+A1Vv/VH5vN/40VDvBlTRBTdjIW1medZcKCliMi2dkKxUWLk008/Xe3/L774Ii1atGDSpEnsu+++67yfZVm0bNly01a4GQQCAQKBQL0/r8FQH3xUpQPRSbWUwwRgFvKGOK5EyK9ACxeyLYmPPCQ45iDRsZRUFU1Supv4l8FQ/8xGnVN/R4KkNXAz6qI6DwkVnws5DlRHlJKJBlUpl2vDvsDDaOTDtsZmpZNKS0sByMvLW+/tKioq6NChA47jsMsuu3Dvvfey4447rvP2kUjkj8oNgLKyss1ZpsHQ6Ei4MKoK+oQh25e6fAqwwtVBqyaoKMjvQDt0EAtZipS0RpdnIgFik+oxkizxNRgM9cMSFAmZD0xCG/MVSJxcgmbM4Ep0ZMShPAb4oCYgo2qODXsC9wMt6n/5dcIm9xlxHIehQ4ey11570bt373Xernv37jz//POMHDmSV155Bcdx2HPPPVm8ePE67zN8+HBycnL++GnXrt2mLtNgaJRMi8DSBJxQKyoSARa7EE9AMCEz26k+KAfiLlR4lTQgMbKIlADxs3qjs23F9GYwbMsUAU8AjwJfosjm3sAdwIPedXHAdqGJq9RrRQz8fqgKQDMgzxMid6KJ3NsqmyxGLr/8cqZNm8brr7++3tsNHDiQs846i379+rHffvvxzjvv0Lx5c55++ul13mfYsGGUlpb+8bNo0aJNXabB0CgZWQUhGw6p1bf9F2BmQo3O0iKArcZm6a7CtouQwa0ciZElpIyrFimfiIsRIwbDlqQSVcM8gHqEjEbp08eAKuBE1EMEIORqjEM0AtUOhANQ5peHJNeGgcDfgS71/SLqmE065gwZMoQPP/yQMWPG0LbtxmmxQCDAzjvvzJw5c9Z5m1AoRCi0LfmADYb6o8qB76thjzTw1zqdmIBSLwCuoy6rS9BBrrMFExxobsFydCBbgQ5gFinTatLAasSIwVD3RIGP0Hc1AnyL0io3oCjH6cAq77YWkONCEwcWR71UagAKbfUYCdoaiHcJ0Kd+X8YWYaMiI67rMmTIEN59912++uorOnXqtNFPmEgkmDp1qulXYTBsIuOqNWfm1Fp2eRdFPso9v0h1CLqjg16+K89IAmjqlfWGUBOlGmRWjZJqdGYiIwZD3eKgNMwdqGHZ9yiSeRzwNPA+8DeUtvGhkt1WLjSNw5KIegMFglDs0wlEmg17AWchQdIY2KhjzuWXX85rr73GyJEjycrKoqCgAICcnJw/2q+fddZZtGnThuHDhwNw1113sccee9ClSxdKSkp48MEHWbBgARdccEEdvxSDYftgZCU090PfWsHDBcAKB6wYBONQEYYTffAVmtgbciHTUk65hFQEpAodBGIolZNsfGYMrAbD5uOiOVEfAC1RZVsZsCNwOTAX+T3KSA2rTHehuQtlcVgah7CtrqplNnR1IWDLV3IocEADvKYtxUaJkSeffBKA/ffff7XLX3jhBc455xwAFi5ciG2nAi7FxcVceOGFFBQU0KRJE/r378/3339Pr169Nm/lBsN2yIo4TI3C0VmrXz4B+NmR0S2zBsrTYVfgc9QArcqrpMlBTZEqSLV+T5b1NkHixERGDIbNZzrwBhIhK1CPn47ARUiAnIeamlmkhEhzF1q6MDOq8vxMH5QE9P3shoTIPsDuwNH1/YK2MBt1zNmQqa2jR49e7f+PPPIIjzzyyEYtymAwrJ2vqmR+O3WNjkbTUSWNY6m/SLqXjsnxeo0sRNfZyLw6H6VlqlAVTZzVG58ZMWIwbBoLgP+hklsL9Q1pC5wMnA18CPQllSJNoJOIlkALByZENcyymQ/m+iFoqWFh0IL9gK7AKfX+qrY85phjMGwjuC58WAVdgtCq1je3HEU+oo4OfpVhTfKciIRHZwvmORIjNd5lc5BIWY7ESAUq8w1hxIjBsCmsQCIkgcyo/0Opz72AC9H37UTkFbFQRDIO5LqKSoYTMDGqtExHP0yxdf9WKKp5IBrpcAHbxuC7jWWTS3sNBkP98nsM5sfhqDV6tf8EzHXAH1cfguoQDPLBVNSbYAckOjJQuWAb1M0xE4mQpHHVT0qEGDFiMGwYZcCzwHMoDTMWeA/ogXwh96JBd7siz0iIVFqmrQs7uFAag9+iEPZBbz/85JXlt7IU5TwEiZkhNE4hAuaYYzBsM3xUqejGcWuIkUnALy7YDmRFoCRT5rbxqEqmKRIi2RYUoHDwIhQRqfGus9HBwOf9bgysBsP6qUFVMNORj2M8MozvAOwGnIpOAvZHRlUbpWXiQNhVSW9XFz6P6v8BH+zoh28s6IXK9tOAg7znuxp9PxsrRowYDFsZxcXFvPfee4wfP55Jk6dQXFyMz++npFVH2u+yK5OOPoi9994by7JwUDlgoQuWDxIJ5ZvTkaAodeUDybRU3jsdfemXojy0490uKUZcdOZlDgwGw9qJI2P4dyh1YqPoRxdU5XIe8ogMR2W7FkqBJlDVWktX0chuDrwTVbm944P2PgmRvVHlTBaqlokCw2j886LMMcdg2EpYsmQJt99+O6+8+hrRSIRwi/bYzTphZ+eDkyA2bxlTfniUfR+8k+49enLjDdcz4JxzqMHCist9X5EG7S2diTVzFfVYCAS8st4Z3nOVkKqicUlFROxavxsMhhQualL2KYqEdEezYNoA/ZAfZA8k+AcgD0kyyuh4j7GD1w2ZhIRIM5++o5YPfrThKBeWel6R/ZDB/EZWH9XQWDFixGDYCnj55Ze5/IoriLp+0nc7keY7HYovs8mfbue6LjULf2HxTx9y3nnn0fOVV2ny7PMEm7UnGIfiNDjQVlOlDijnvNCBhK1oSTPvcWLoQBlHB4FkdAQadyjYYNgUpgBvAzsDBwP/Qt+bPkB/1LDMRm3ZR6DvU7INUBzI9NIynV0YF4dVccjxQ0cL5vtggQWnuzDDVlXb3kiIXI2qcrYHjBgxGBoQ13W55ZZbuPfee8nsfSAtDrwQX1rWOm9vWRZpHfqS1qEvGfN+Ys7nj5MYOICmH35JXpterMpS/4EngAGeefV9r9Q3hs7iEt5jVXn/rilGzEHBYBBzgNdRinMw8AiKeOyIohfnAvnAj6gtewX6LoXR9y0KdHB12d4uPBWDbAeCftjHgq98sMqCS1yYYKuqZiD6bl5G6uRhe8BEYw2GBuSf//wn9957L00OOJ+mg65ZrxBZk7ROu5B/xsP4SKdw0EFUL15CyFJEpAKoRr9XoR4FBaistxAdLGvQQbJ2x1UHExkxGJYC/0RVMKehmU/XotLaHZEw+TuKYlyKzKpR9L1KTsK2gV5eWmagA/+KQHsXanxwkg2f2FBiwVBUPdMUpXkiwPnoxGF7wogRg6GBmDJlCsNuuonsASeSvfvxm/QYvoxc8k+6E6pjLLnpQlriUohCu6tcmeeaoRTNUlI9RrJRhCSIzuCSogSMGDFsvxQDT6JGZX9DXo3Lvct3Rl6Qf3i/f4GqZsailExygHYERTh2cOXTSkvAExHo7YNVNpzjg+ctiNlwPTDO0nd0V/QdPAX1CdreMBFZg6EBcF2Xc887n0DTduTuc/pmPZYvowlND72MlW/fTf6bI/jplFNo44WGF6NKmvbALJS3/gKdrSVFSAwddCOYib2G7ZNK4B3Uf2cw+t5ci6pe9kSVLWehCdjlKBryI575lFSzwCiKhkSBA1x4NQEFMdjBD6UWDLbhUSBkw1XA154Q6e09xkGorHd7xERGDIYGYOzYsUyZ/BPZ+5+P5dv8rh7pXQYQ7rwLCx96mElIfHS2YIGriZ/tSKVg5qPISCU6AEbQQbcGk6YxbF/EgJHAfSj9chKqkHkCeTfaoyqZoUiIvEuqYiaERLzrPY4P6OdqUz3dhYdjEIlDkwBkWbCnD/7PghxPiIyxlJrp5T32HsgMu71ixIjB0AA89dTThJu1I9yhb509Zla/QcyZOIFZkycTcNX3YIELcUvpmaTIWIzER5n3b5RUAzQTGTFsDzjAl8AdKKUyBHgLpU26orLd3sBdQE9gJXA86veRiTbObO9xIsjf0cHzh+zvwtAI9HQh4oddvTkz/3VVdn8hSs00Rd/R9t6/+9bLK996MWLEYGgAvvz6a4JdBmBZddfcOa1zf2x/kBXffEM5sIMFS1yIeAPy8r3bLUdndeXowOqQipAYMWJozLhowvVtyOD9dxTlOB95qPZCgmIYcARKwbyAen4kvzch73ILCfhdXQi58pPEHLi7Bg61YY4NZ1hQYMOHLvS24CQLfrL0HB1QaXC291zbO+a4YzDUMwUFBawoWEazPbrW6eNaPj+h/E5UT5rEMm/4lh+walXSgNIzMbwqGxQhCaODcwJzUDA0Tn4D3kBRj2HAaDRFtylwJCrZHUzKPLoIRTGWeLeJoe9UDYomZgHdXH2fLgJuj8OyGBwakJ/kVguetWEasL8FO9laQxYafren95znbukXvo1gjjsGQz2zZMkSAAK5Lev8sa3sfJxFiwmhg2grLyqyFJ2JQarrapxUeiYHExkxNE4Wogm6TZD3Yz6qkKlGImQuSsVciqIdDmpq9jQSDUHvJ/ndqEApHJ+r/1/kwrkxyHWgd0Di45823GLBAuQ5aWprHelI2ByObnc5jXfw3cZijjsGQz2TSHhtx+owRZPEsmz8icQf5tV0S70RliJDXg2pL30MhZwT6GxtBSYyYmg8rAJeQyLiHCS+7wFmoim45Uio34JEAmhcwiVAKYokVqNqlzLvtlWoSmYFmsrb2oVTo7A7sDII1Q7c6YPrgJUWXO5Cpa3xCyEk+k8ExqBqHSNEUpjjjsFQz+Tl5QGQqCyt88dOVJeSmdeeLsAcF3y2KmnmIFEyl1SPkdozaTK8+0cwBwXDtk058CZKTZ6KxMTzwNdAX+Ao5P84B/UBAQmVf6A0TgeUeqk9U6bKu7ytK5FzBvChA/+Kwik2jPJBXweO8cGVrgbd3Q7MsvUd86Mo5FnAB2jejDFsro55PwyGeqZz586kZ2YSXf57nT6u67rElv9Oi513prMFC71KmvakpvHOQWFivP8H0MEyKUZqaPzTQQ2NkwgSIf9EkYob0EyZc1HE43QkOvoAN5MSIhNRJcsnSLiXo/RMBRIQq1Bzs6ZuaoLuP+LwYQQu8cOnPjgG2MsH17tQYcPjwFRL908axC9FvUyuI9Xx2JDCiBGDoZ6xbZvddt2N6MKf6/RxYyvmkagqI23X3WnrhZLL0Zlh8uA3Fzn5k/nvNCRK0pFgqSY14Mtg2BaIo0m6dyGBcTuKbJyPxMkpqPQ27l23B/rMVwPXoAhJ0r0VQBVmEe/2ceBgFyKuhMqlLhwVg8o4nBrUULzrLPDbMNyFuK3U0CeWqtei6Ht2NfAyEiLJTq2G1TFixGBoAM4952yq5k0mVrSkzh6zfMrHZOW3pOvBB1NiqZGSY2kWTfJgu5CUWTWEoiJrRka2h3Hlhm0fF/gWCQwbiZGWaMN/ENgH2B91Vb0IpWySUb+vUbnuBFQ9swoJmXJS/qnOqJvqUjR8socLR0ShjwM7huAzFx6zYaINzzlg22qg9rylip0K77mGAf9BwmfDJ09tfxgxYjA0ACeffDJN8ppS9t3/6uTxYsVLqZz+NYddcgk7BAMscKGNpYNvciYNqMImm5QYSXj/1vaMGDFi2Nr5BYmQ5d6/u6GJutejqpkzUNXM7t5lLbz7laNy3atQo7E4EuN5yKSa7j3mAUCmV7Z7PTDaUSOzsywoC8DiBDznU+nuSAfSfYrOPGDBLugEwPHW9m/gCu85DOvGiBGDoQFIS0vj0UcepmL6aKpmjdusx3KdBCWf/ou01q0ZdO11f3ReTbd0tldbjBShUHTSoBdHgiUdha4jmDCyYetlLjKa/oQ8IUejzqmXojLaS0mZs+8A+tW670hSkZJu6HvREQmUMKp4SQMOBkpceatuBy5OwMgI3OGHz/3QxIF/++U7GeNAM1vdXIdZqeZoEW+d/4eiMnVfxN/4MMZ5g6GBOPPMM3nzrbf49JNH8WU1I9Rq45ugua5D0agnqV48nYO+/JLijAz2s+A9B3JtmVd/QQdJUJ48ig68ydx4EB2M/Zg0jWHrZBnqFRJCpbdNUXnsC0h4nIUiIdOAK1k9CrHSu2wmqqaZ7V3eEgmHVt5l/YA8V6JmX2APF/aOQ3oc7gwq9XOAAxf74DJXgyc72xJDF1twLLqsEt32MRShSfb3MawfExkxGBoIy7J47dVX2aVvH1a9cTOVM77dqPsnqstZ9f4DVP78GWc+9xz999+fRZ7RLllJ0w5FQ5qgg7aLeiiUkErXBNCBIISESsZanstgaAhKUPOx/wInoyZhhcgQ+hRKp/wNGOf9fhUpIeIi0+ih6DPfDQmWTkiUO95PMWp+FnT1+yVA2IX9otAzAWeG4QHgAuAMH5xHSry8a8EFljq3zkFRln8CT6IKm+5b4k1ppJjIiMHQgGRlZfHlF6M46+yzefed+6iasRc5ew4m2KLzOu/jxCJU/TaG4tEv4ESr2H3gQBbEYuxfXsFv6ZmADrarLFURgFIwxSgC4qKDZidSYsTnXWfEiGFroApNyP0dbfTdUYTjHmAqsBMSJ5+geTJ3svqZ9SLk01iK2q5PReK7PfJNdQEmocZlOwG/u4oU3gbc7cAbUTjbhuIgvJqA4Tb4LLjQhUJL7d0ftRSRuQCYjL5fjwHPIvNsvy3z1jRajBgxGBqYzMxMXhzxFnOfeYM5t1zDsheuJNi6B+F2vQnm74Cdng2JOLHipUQL5lA950ecmgoC+TuQlr8D0xYtpuriS/jxmmvZ68ILmXH77bRJzyZGqscBKPycPGuMIoFSiaprEkiMxDCeEUPDEUMCYwJwHHAaSh2+CIxCjfuuRpGQX5BvpHaFigM8ATyHxHYPNA9mByTAoyhKOAPYG5lU5yJBchpwVALmReHBAPzXhooEPOuDyRY87ECprem9t1lwJpr2+yOqxnkUlfXuiASSYeMwYsRg2AoYV2ORt/dgrvrXCUwd/QGfTXyAskkjIR5L3cj2E2zRkcydDiWz3+EEmrT+46p42QrKp3zK6Cee5IA33+Ts514g79CDWEbKvPo7yo/bSHxYpObTJI2rLqYhk6H+cYBvgC+QgfQu9FkchTZ4G6VHSlHVypmkBtolmYnSNOXAgcjkmo6EyEIUDfkJRUeORBOtVyIR0tKFneMQjsOzQbjFhuYJeNkHr1rwX0dt3S8CLrTUQO064DtUBvwv4EMklg6p+7dnu8CIEYNhK+CDKmi7AuJWgKJVvxP9+UfSuu1B7t5nYfkDWLYPX0YTLP/apYI/uwVN9j2LzJ0Opfiz/+PhIw7lvGefZem55/4hRuYj41+yK2QyEhJEZ59JMWIOCob6wkXpkveAAagCJoCiHs8iz8ghQFfURv0QlEqpPdMlhjwdb6N0ThvUebUL+lwXePefiAysvYFZrp7nFuA7F06KwU4ODAlJZPRPwL1+uBf4OAE1Pt32SC8iciua+rsMpWZGI2F/fF2+OdsZ5rhjMDQwS2Iwswp2LoKJnz/BdyNvIGfgYHL2OQNrI4fpBXJb0vykuyga9STPnX8+WRkZXHTyyYDy6DuSmj+T9IgEWT0yYg4KhvpgJupg2gWVyaYhP8ezyAzaC7gYRRwSSAysWek1CfUBcVC0YxwqZ+8GLEZRwZUoMnIwEHD12J2RUfVGB16Pwqk2dA/B3x2lh67w63G/S0DUpx4m/Sy1lv8H8Ln3+I+haMti7/EMm4457hgMDcxX1eBbDqXzpjL61aFk9T96k4RIEsv2kXfoZbjRap44/3zO32MPaN+epagVdjk6K0xWzyQjIyFMmsaw5VmEUi9NUMltLvpMPo3ERBMUnfgVRUzOIWXETlKDoihfoEhHCPgBRUbiyB+1s3f9jkB/ZFKNot4kewKHJeD3KDzghyl+eCoB11hwmA8uAyYnwLHhJSDfUormQeRpWYQEykxkXh2KmcC7uZjSXoOhAXFc+LQS2hckePfJs/E3aU2T/c/bZCGSxLJsCRJfGldfcAGu6/4xMr0aRUHCpIblmTSNYUuzCjUBews4G23umcD7aDOfiFIgJ6LpuTsAN/FnITIGletOQuWzi1Fkrzvyb0TRXJhxSHwPAGa7ip5cDXRyoW8cVkThzSC8FYBxCXjQ1rC784EpDvh88I4FeZaMqo8Bn6GmaQ95z/slElRmI918zHHHYGhAfovCihLIHvsRKxdMJv/0B9fpC9lY7FAGuQdeyBfv3sv48eOJDBhAFSqbDKGIiA+Z/GrQGaoRI4a6phwJkKVoPkxn9Dkbj/qHVKIeIQOB11Ha5nb+PD26DImTCahsNgCMRZEPB1XF7I4EQgeUlnFcGbfzkWj4yIUrY9DTgYfD6tiaGYf/+KDcgotd9ehJtyWSVlrwd9TSPVlq/BAq430HzZ0x35e6wbyPBkMD8lkVZC6Hnz5/nFDrHoTb9qzTx0/rugehvFb86/F/Yw8YQBEa4JUUIzbqK2IiI4a6JoJMp1NRY7Jzvcvno9LbJcjbcQrwFdrsLyI1RyaJi3wj96Ey9L8hwdEGle4WIRPrLqjSZmckSqa58prsi7qjXuPC/yLwNwsGh+FsB7o78IhfAme4q46sTW2te5alVNCzKFIzBzU0q0HN1P6OmXBdl5jjjsHQQNQ4MLoCWs4vZv7UUeQdfkWdP4dl2aTteDBvv/02h77wPBV+P0XICFh7Ym9tMWI8I4bNIYG8GmOAQUg8WKgy5mVUKZOLfCEFaKLtyajSZU0KgWtRx9P+6PM6CuiDhPQs5Bn5DbWC3xfoCfzqyjtyESrlPcCBORG4yw/pfrjM0WX3+OVfecaBIgs6WPCepajNfcDzKFozE1XsgDq/Xo9pDljXmFSXwdBATKyGyEqomP0TAOG2vbbI84Ta9iJSXUVgxgwsdPaX613nR2maZDWNgzlDMWwaLvA9Kr11Ua+QPVHU4k3UoOw3lKq5BEUbylH31DWFiAu8ChyFeosMRoKkDKVlKlBa5gBUVtsKzV/KcWGO1031TiSs+8WhICJ/yIIAPOjAOa6Mq48BTzhQbEFvCz6xJKIeQCmkN1CDtPuQgH8M9TLJrbN3zZDEHHcMhgbiw2potgKWzJuMHUzDn7emVa9uCObvoF8mT8bt3ZsKVo+GZKKQeRCTpjFsGr+gPh99kBhJVmaNRRt6BHU8PQIJk1/Rpt5kLY+1CEVDlqO26qC0SX/v99mofLctSt/sjkTPJFfX74QEz9ueP6SbA8+H4UYLFsXhZhsG2fJ7jEpApU/3f9HSZN9nkBB62VvnfUh83IdE1JppJEPdYI47BkMDUBiHX0qhRwnMqSzGl5aDZW2ZQKUdSscOhggUFf3R6MxHKhqSNLDaKJy+LR0U5s+fz4QJE5g8eTKFhYW4rkvTpk3Zeeed2W233ejUqVNDL7FRMxdN022N0i7J1uwzUQv3YqAjKs/9CXUqPR15PdYkgYTAS2jA46lIhLRGqZio93yHIPNoR9R2vTsSIjFUjdMXuM6F16KKrAwLq3onFIeHfNDDhitd+D4BVT6lkv7PUrrmVe/1vIC8Lveirqr3oTk07Tbv7TKsh23puGMwNBq+rQarAEIuWFhsZiXvX+K6kGnb5KFcuoNKfEOkoiQOEilb+0EhGo0yYsQI/u/xfzNh/I8AhHKa4cvU5J1ERTGR0pUA7Lrb7lwx5HIGDx5MKGTshnVFAdq8A6gxWTPv8pVIhMxDn6shSOT+G0VG1hxol2QmSuNU4lXBIMExAH1eF6I0yV5IMAxE3pBVwFyvm+r16PN8kAOzonCzDwYG4EQXOiTgAR+EbBjiwk8JiPgkMO62ZFJ9B/lDnkORnruREHoAdVbtuvlvm2E9bO3HHYOh0eG68GEltF4B0QRkZjcnXlGM6ySwbF+dP1+iuhw3FqFFs2Z/mO4S6MAfRJtGHJ152mzdBtaJEydy1tnn8Nv0X0nvtDPNjv074XY74stYPeCfqCyhZtE0pk/9nLPPPpt7h9/Hf19+id12262BVt44KEVdU4vRTJdkpKAGpV/Goc/Q8aiq5RUkItYcaJckipqHjUSRjuNQuqcVSre4pIbaTfQefy+Uvvne1ee3PYq8zAUGxcGKwWsBWBWAcxOwuwPD/VBgwfUuzPSEyLXAFRY8jjqqJoXIz0g0dUDD7w7y1mLYshgxYjDUM/NisHQV9KzRwXaHLrvwZTxCbNVCgi3qPq0QLZgDQNf+/XFR9COIzIBZKE1joUjJ1hwZefTRR7n2uusItehEq3P+RTB/zVFpKXwZuWT02JuMHnsTXTGPhZ/9iz322IP777+f6667rh5X3TioRqW3s5GZNJlmcVCZ7Qfe77uj6pnPUTTkLDQ9d21MQeWxFvKSxFG0ZW8kblZ6z3sc6s46EHlMugE/eNUyxwB7WvCmA1fFoXMC/huG1yx4Mw7HuHCTX0LmPkc9RCK20i6nWOqo+oP3vM+ibqq3oV4oT6F+JgM2540zbDBb63HHYGi0fFUFoQLweQfUvXfdmf/YPmoWTt0iYqRm0VR82dlkd+lCKQplh1BkJJtUiWI1OqvdGg8K9913H8OGDSN79xPI3fcsLN+GrzLYohPNT3uQkrGvcP311xOJRLj55pu34GobDzHUu+NH1KvjVFJtz39BKZNqlM44BzU2uw91SF1zoF2SauAeVAXTBXlA3kDejP6kSnb7oMjJi6hkty8yt87zuqleA7QErnXg1SgchhqZXQn8nlBDs/P9irr824WVLkQseMaCgy3NmJmGojfPIk/LLSgd81/Ux+TAzXjvDBvH1njcMRgaLTEXRpVDqyKIO5Dhg117ZtJ832Mp+uUzsvofvdmt4GvjJmLUTPuC1meeQbFtU42iIkF0oHdQZATUmXVrjIy8//77DBs2jJw9TyV3n9M36TEsn58m+5+DFQhxyy230LNnT0444YQ6XmnjwQW+QT09DkZlukmvx1JkMl2ORO1FSEi8iMTBrcgYvTbGeI+VjmbExLzH2h+V+VahdMvJyETaDkVEDgC+chXJy0Jt3SPAIQ7MjML1NpwRhJMAKw632nC4LUPs/1wocSFuKwKymyXRMd977mdQ07ObkBn2XfQ9OGZT3zzDJrFR9v3hw4ez2267kZWVRYsWLTjuuOOYOXPmX97vzTffpEePHoTDYfr06cPHH3+8yQs2GLZlpkagsgAyEvp/XhCy8iB47uXEVy6geu7EOn2+yl9HEy0vouell1KIUjMZpKpp/N6PizaCrS0yUlRUxAUXXkRGl93J2fu0zX68nD1PIaPbQC66+BJWrVpVBytsXLho5sttyB9yBxICNurt8SwydBagKpQ7kMfiOeAMVLWyNiFShkp5b0FpmzNQemQ+mh/j8363kfh5DKVHeqBoyRgvGjIA+TzmAbsmYFEEnvPDYSE40oWMONzvg0N8qoR50VGzNdvW0L1dLflXlqKGZs+iFM6NyBA7CqWHTt2M99CwaWyUGPnmm2+4/PLL+eGHHxg1ahSxWIxDDz2UysrKdd7n+++/59RTT+X8889n8uTJHHfccRx33HFMmzZtsxdvMGxrfFIJ2cvBSYBtwU7tYWIhRA49AN8+B1I06kmcSFWdPFe8ooiyb55nv1NOpXvv3kRQ6+wwOruMsno76xpSg/O2Fu644w5KyivJPfTyOokYWZZFk0Muo6wqwm233VYHK2w8zEQVJDNQD45j0Wchjjwht6C0Rj+00WegVEdX7/Zr65KTbOV+DOplczwagPccing0Q2bqicDhSAB9jATQrt51C71pu5cCB1nwtgMHxiAvCh+EIBaA0x3okYDHfdDdhmEuvJeASgsyLPjQgi6WBvKVoGjIc6jT6nWodPh71FfkQswE3obAcl3X3dQ7r1y5khYtWvDNN9+w7777rvU2gwcPprKykg8//PCPy/bYYw/69evHU089tUHPU1ZWRk5ODqWlpWRnZ2/qcg2GBqU8AWfNgXY/QSABTfxw/kHwf7/D012hZtkCqnffkbQOu9DsmOs3q++Im4ix6u07SStfwg0zfsNq2pRqZEAEdat8HVUl3IY2oT6oD8THqAKioSkvL6dl69YE+gwid98z6/SxS759lejkkRQsW7bdH1MWo/RFNpoTk+td7iKR8Ib3e3NkSI2j9EZXJC7WHGiXZCUSKfOQ6fQw5M/IQZUzIdSR1Y+iLA+hz6Pt3fYTNyVWLvduf6Mrf8gBLjweVmroVQf2ceA2P8QtuNWFHxJKy7Sy4HVLr+lyJK4eQRGRH5E46YfMtJ8iYbI1RQYbAxu6f29Wl6XS0lIA8vLy1nmbcePGcfDBB6922WGHHca4cePWeZ9IJEJZWdlqPwbDts6P1eAUQNiT/61zoGUzmOQHOwFWhw50v/VlqmZ+R+HHj+E6iU16HicWYdXI+4gtns4bI16nqGlTOqKzvVUoKpIclGehzSXA1mdgHTFiBNWVVWT2O7zOHzuz72HU1ET43//+V+ePva1QiCpe3kDNwi4hJUTmI4H6JkqhnIV6hnyESnsvRlU1axMiLmocdiJK/Z2AoiFPoGhIc/QZm4iEQCckEA5HHpFdgS9cPW9XJBASwBEOvB6BIRY8F1Zq5a0EnOTC/X4o8Up3f0ioYqaLBe9YElkXoUjOYyg98wNwhff8M9GE3mvYej772yObLEYcx2Ho0KHstdde9O7de523KygoID8/f7XL8vPzKSgoWOd9hg8fTk5Ozh8/7dqZvneGbZ+PKqDZSkg4ELRh506ambEoBywXcKF9/xPYe/irVP72DQWvXk905YKNeo7I0pms/O/VRBf9wvvvj+TAAw9kCap2yEImwWwkPgJIjCTbwlezdXVgHTt2LGmtuuDPbl7nj+3PakZa626MHTu2zh97a6cCRTaeQgLgGtTXA5TC+Lf3U4KqSe5BUY7hqHT3OiQo1sYi5Ad5CVW/nImqZqYjL0gcpWJ+A85HA/Xmod4he3hrW+zqs3gGcLSlyprdEzAvAk/44fKgyn3nx+FyC270wXQLbnZhcgJqfPKGvG5JdF+AWrj/E6VmxqEoSX8kul7xXtO6IjyG+mGTxcjll1/OtGnTeP311+tyPQAMGzaM0tLSP34WLVpU589hMNQnS+Mwdzlkx/T/Jn7o1h6+L4bCHEjY4I9DMAZtTjyF5iO/xUmzKHhpKIWf/h/R5XPX+diu6xJZ8hurPvwny1+5no5tm3H/hPEcfrgiCiVIbOSh6oWkaTWADsDJtvBJQ+vWki//cfxE7Bbr7iWyufhadObH8XVrGN6aiaKGYg+giMDNKGKRvO4tFA2Zh8ycd6PS27uRSLiDdTf/SiBxcwaKaJzoPfYjqG9IPhIGU5AwOAJV1eyDvCbHAV+6kO7q83or0N3zhxwUg+wovBuCHgGlcEJxGGbD6T74yoJ7HJiRgGqfRM8L3of4XBR5uRdFRMahqM7upCYGX0eqoszQcGzSSdCQIUP48MMPGTNmDG3btl3vbVu2bMny5ctXu2z58uW0bNlynfcJhUKmdbOhUTG2CgLLwOcojN2jFWSkw8S54OSD64OMGogHYXYGpPUdQNYHU4j838MUvP1vKn7+DF9ea0ItOhPIa4flD+DGIsQKF5JY8TuRkhU06diJWx95mL6XX062P/XVrkKVCMlW8EmxYaMheTVooyhl64mKACxYMJ+0gXtuscf357Vl4bQvttjjby0kgK9QhOII1F00+RlwgW9RmsJPyheSifwYCeSryF3P489C3pAosAvyfzyHBO4g9PmLIbPoGciT9D/vupbeY3/iyqfUHLgMfV6vd+CVmPwg/w7DFBtudKBbAm71QS8bRrgq3V3iQrlPVTB3WoqsnI2iHzeiMuHvUTRmIDJy/x8qEc7ZyPfTsGXYqGOP67pcccUVvPvuu4wePXqDhlANHDiQL7/8kqFDh/5x2ahRoxg4cOBGL9Zg2BZxXPikGJoVezNifNDH++r8lAB/DGI+yC4DNwTLg5BWDX4rTIszb8K99gaW/fwRiTHfUDVxIsz+GmJRCIXZt0d3mh99Nj0OPAj/4Ydwo23zEqkOmUnXSQkSHBY60MdQVCQdHbjDqG/E1iRG4vHYRjU321gsn59YLLrFHr+hcZE34kMUgbiT1f++s1DTsrh3+SmoqiTZ5Ox01HdjXUSRB+NjFH3YD0VQHkAD6pajz9UkJIQv8NawNxIAB6G+I9WuGvCdAexkQakLJzvwaxQusuGmMDxnwfMJ6O/AHX5oacHjLnziSFiU2mp2doWlMuKzvdd8NRJV36GmbHujVOXDSPQkZ+oYGp6N+qZffvnlvPbaa4wcOZKsrKw/fB85OTmkpcl/f9ZZZ9GmTRuGDx8OwFVXXcV+++3HQw89xKBBg3j99deZOHEizzzzTB2/FINh62RGFEqXQp5nXG2RDu1bwYIYLM70oiUW5JVAKAhxF3wRpW38MajO9mMdfSz2oGMBsAKQsNTB9XUbRsZhFxs+9ZKuy0h5AIrQGWcR2gD8SIzUeJdnoPRMGqk0zdZCKBzGidZsscd3ozWE0xpngH4aMp/uyJ+bkK1EPolVSFAchLqgzkCpmKRwWV+6bor3uCFkOB2EymUDqMJmFRK7Y5AvpQhVbQ1GYuFIb32dXQ3B+zuQZ8EMF/6WgGgUHvbD3wKKzExOwGEO/N0PAQvucWGMo/UXWXA7cLql5zkbpXIuR96V75F3ZV/0uf8ncB5rL0U2NBwbdex58sknAdh///1Xu/yFF17gnHPOAWDhwoXYdsqKsueee/Laa69xyy23cNNNN9G1a1fee++99ZpeDYbGxBeVkF4AlgM+C/p1BJ8PviuCoix1jLSAnFKw8iFcA7YDoRpV2VR7dba+BMQD6k9iubpPIerDsL+VMhXW7h+yGIXBi9CXPYTOmMvQBpWc2Bvy/t2axEjPHj35beX8Lfb40ZXz6d5jbcPst13moxRIPvJC1B5OV4N8IVO9/3dD0ZAaFOHIRCmNzPU8fhUysn6H/CQHoPTecCQwCpEIWYC8I1eg+S/ZSJS0RmW9H7opcXwb+l687cDQBLSKwSsh6OqD41wNkxwMXOpX35DbXBjn6PFX2Uq3HGZJZJ2NZuOch1q6f4fuewCKBv4TdXfdck4kw6ay0Wmav2L06NF/uuykk07ipJNO2pinMhgaBTUOfLccmnkn+Dl+6NFBv/9UAk4uJAIQiEMoCivTIa0SHAsyvX/jfkVObO/rZ1uQcPXvEldnoZWoLNJl9TPaOegMsAgJjqRptRhFQ9JJVdNE2Loang3YfTd+HTFyiz2+s2IOA46v+7LhhmAF6hVio3RI7WoXB3lGPkd/81zkC2mB5rZM9/7f8S+eYyyqrGmCOqEeBTyJRMEpSIDkAF8j70g3NBn3JCQUjvLWkOelZQ4BBliKBN7gwKsxTdh9IqzS3MNcaBaHi2043tbU3XtdGJ+AsK1S3he8xyhAQuRMlF56BQmRv3nP46AJvIehaJFh62NrOhEyGBodk2ogvgxCjv7fMQ+a5srLMT0GoWqoSoNADBJBqE4oKpKwIbsU5rbX/SzAtfWTxEatuJsBiy3l7QtRfj7JfDQKfRXasNLRl76I1JC8crRJRdm6DgiHHnoo//73v4kUzCHUskudPnZ0+VyqVyzg0EMPrdPHrW9KUZ+QImTebL/G9VNRX5AgEg3Ho0qaSajvx2Fow15fSibZFv5XFA05CInZu1F6phh9nguRILkEiZxRyAdS7a3tf0AvV+JnKJDv+UNOcWFaBM604LYwTLTgGgd2SMBQHwy05W95yIVJCZXF11gSHL0tlROfh3qJnIR8MN+iDrKHI4H+BBJQu23wO2uob7amY4/B0Oj4tAyyVwKuzuZ29uLDMxOwKAz+hK7LLofKDAhWQHWGNofMCljumT+shISIZen2LoqM/OrAQEt5933Rgbl1redfiDaMQnT2mAzblyIRk4HOWnPZ+iIjRx55JK3atKX8pw8JHTm0Th+7fPJH5LdsxTHHbJvj0GrQrJWZKO3Qc43rl6I0RQSlJ/ZEwmMlcD/yFK1voB3oM/YJiii0Rpv50agHiYMiEb+hiNwYJITORqmePbyfLkjkjnRVOVOG/B1BC6a7MNiBmgjc64czAvC8BU850DsBt/igi60hdk+68EtCM2YCFrxmKZIzF4mQK1HL+ddQBOcolDZyUfSkM/p+GLZeNqsDq8FgWDeFCZixBDITEhd5Qejs9e/7vgyKMsHxoh3NixUhsRJeSsaBUMTzi1jyi1jo7BZdhI16QnS0dHaaizah2sa8AhQZsVF0JBOlayLeY2SQqqaJsnWJEb/fz7VXD6Vy2ldEls2qs8eNFMyhcuoXXD30Kvz+bet8LI6qY/6BNtjbWF2IJIfZPYkiXq2R6DgIRSZeQKmMs1i/EFmJIhyPo3TLCUhY3I4Mq72856pBUYiTkDgYiqIhFkrdTAFWek3MdgIutSRE3nbhiDiEI/BKEE4Pwo0WvJiAPRPwoF9C5FPg3w5MTei70syCEZ4QmYnmyFyPhMj/vLUchkQTyCOTiYSJYevGiBGDYQsxrgrsZeB3dHDu3Q7CXpvHqaWqiIl4jtJmJeAmIBqQGAnGoTxdvyfFCZZXDeOmxMgKF9p5MXYLiZHakZEKUj6BIpSmyUbCw/H+n6ym2drECKgar2+/fpR8/BhObPMra5xYhOJPHqVb925cffXVdbDC+sFF0Yfb0d/qLtS4K5leSQ6z+wcSoOlITJyPBMFdqEx3GKt/Ptb2PCNITa3dw3uMd5Hf4wLv8dujBmIxNAX3NeAb5NeIev++DHRyJRouBvbz/CE3OnBlFPrG4Z0w7ORXS/epcTjchfv8Eh2vAK858JsDER90tuBVSxGWqWhw3i1IfIxAQuRAlIoCRXXKkVAybP0YMWIwbAFcFz5eBTll2jCy/NC7o66rBubGIb1KgsTvyDsSqoBoUOLDH4dZ3QBPeGDLzBpEIgJLX95yINtK+UQK0ME6SQRtDk2RMMlidTFSu5pma/OMgKIj1w97Cad4JYXv3ocb3/S+IG48xsr3hhNZuYCZv/3Gjn124rHHHqO8vLwOV1y3uMBPKAJSjMTIQaQO3C5KY9yGUiY2igIMQ3/ffyCBejt/7ZdYjNIsryGT5/Eo+nIzimrsinxHYeQH2Qf5Mi5C7dw7ohbw7YB3XQmRhd5zt7OgxIVjXfhfFAa78FoYLM+o6sYlGob5lIb5lwsfOTDPhUqfDLEvWTLP/oTSMnejKpk3kBDZB3V+BQm3OagD6/r8MIatByNGDIYtwPwYrFyioXgW0DITWrXQdVNdWBBQCa8/oZ8okFklIWIBOLCyBUrRxNSh1SU1TddG5b0RCwotbQCQamAFEj1+tIklW8EHSYmRBKtX00TZuuZzuMCLldDyo97c3/89ogunsWLEbcRLV2z0Y8XLVrL8zduoWfAzTQddS/MTbmWZrwXXXHstPXr24vPPP6/7F7CZzEYb7q+oD8exrP73mY+qW75C71UvUhGQZ1Efj0uRp2R9f9c46hFyDvpsDEARkLdQQ7NLUIqvHYqyLEXzbOZ6z3+Fd/35qIKlyNVnrS1qOhb2/CH7OTArArfb8M8Q/GrBCS60iau52Xl+fZ7vcWG0A6tcKLQV7XjGknD+AZUsP4AE0FtIiAxAaSHQAL4fUVMzI0S2Hba2EyGDoVEwuhLCBWpM5regf2cZTgF+rIDCdMiIem3ay6EsU5ESn6sx6JEAxH3ykwSjEAvowJps0WVZgJf+mYfOSh1WP7tYiQ7ghajUM4E8J8mNKTmHpvZsmq0lTVON+lN0eg4yS6Bpr0O4sPMoXvnwFAqeH0L2vmeSudMh2IH1OR/AidVQOfULir95GTuYRv7guwm3U4+j9K4DiJcup/izxznssMP4xz/+wc0337zFX9tfsQRFJzLRpNw1Z6KXoIqRQvQ3z0cbbybwJWr7fjLrniNTm1ko1eGi7qtHo86pN6IZL2lI9LRAwqQnMocORVU5RyExcxqa87KHK3FwIZqaC/CWC9fFoVkMng/CPn6t/zEHeibgKp+a9hUB97vws6PP5mJbkY7bvfTkGNSM7REUgXkbmVV3QWkhUKXOp976k/4qw7aBESMGQx0Td2H0EokNG8j1Q7cOqetnl4ITgpqwIiFNiqEsGzJrAG9gXlETr8TXUllwwvZ6iHgNz0AbkR/l5PdB4qN2f4l5qGKmEPkEkmeJQVY/U06maeKkmqU1JCtQE63mE2Gn0TAtF5Y3hS7O3tx0+q988tW1jPnyGUq/e5WMnvsTatebYMsu+DJyAYtEZTHRgjlEFv9K5bSvcCJVZPQ5iLwDL8AOr97Sy5+TT7OT7qL0u/9xyy23EA6Hufbaa+v9NYM24/+hv8fp/NnbEUUzZKaQmrx8BopYzEYei13Rhv1XG3EUNQv7DKVkWiFh8ShK/Q1BUY7dUOSlBkVOlqCoyRDU5XV/77K3XEVmpiHDbKbnD7nFhVfi0DcB/xeGTrZSSqMd6J9QR9UOllJED7sw2YEmFsy0lP4Z6kUKRwH3Af/y1vsuEiI7odSShSI1I1CKamsR1YYNx4gRg6GOmRqByFLI9URDt5aQnaHfV6E28JlxqPCGxeStgAUdIBGGaCYES6AyC9KqdH0iICHis+RFSUY/kpGQJcgTMoXVN7C5aKMqYvWoh8XqYiSZ2nFp+IP4dFQ6mlEBxzwBE1tASboavlku5JHDSYc+y/JHb2bVO09T8tGrlP/04Vofy5fVjMx+h5PZ7wgCuesezGlZFrl7n4Ybj3DDjTdywAEHsMsuu2yZF7gWKtFZ/kKUalizo4qLhMHHKELhIN9If1SinSy1vYYNG/o2GaVzMoA+yBuyDBlR90cCdrq3jg9R5OVa1GukCrgK+B21W38VaOt9ztNQOsmyoNiFM12YGtXj/yOsdM1pLpQ7sKfX2j3XktflSRcmOtDGghmWBMXpnhD5CEVDnkSVPe+iKEkv1F8kadx+znv+9cfKDFsrRowYDHXM5yWQVqiUS7oP+nZKXTfFhYV+pR6q/IAPMrxOq9VZYPtU3hv31EEoCnYgJRTieCkalHYJoA1ibZU0C1AjtIUotB8mNTgvOTSvNg0tRr5AFSG5wLHPwegW4KuAuC1fTUa1WuY/ew5Y7TqRd/19tPn3fTz20zgO2nsfQp13Jb3H3vjSsgnmd8aX0WSjnj93nzOJLZjC2eecy89TJq821mJLEEUb7U+o8dhZa7nNLJSySYqMHVBUwkJCITkJt9sGPF8lSn2NQxGFZDTkYfT5uBJFG7qgOTUzkRm2nfccJ6H0Wcj7/d/APq46rp4F7Oh9oKa7cJoDVVG4wYaLg7DKUmv3FgmZTq/wq8T3e+A1FyYkoLUNcyxFQAZ5j/U28DRKAXVCzdTGeq/3Qu99WOWt5VpWb39v2LYwBlaDoQ4pd+DnhZDm+TmahaFjLYUwqQZWhSU+fN7EXjsBWdWwMh/SS1RRAxALykfi98yrQWQMtNH/E0iYJEPya4qRxWjzclH0JClmkqIjKUaS/7o0zNmJAzyP0gGtgIN/hK9rIKsQKtMgLQJpntl3UVtFkSygsgl0C8A7//sf/vQsmh97I5k7HkBa5/4bLURAU3yzD7iAaVN/4auvvqrbF1mLBEo73ImiEHch/0VtVqFowLvob5KBogVHkxpoF/IeY0OEyFg0o2Uxiob8DfWfuQZt8scj42dfFIGpQb6Lmd6/NyIvyfHos/OGC/1dGUWHkRIib7kwKAF2BJ4MwOUh+MUTIm3icJwF1/gkRD5ApbsTEtDUhqWWUkdJIfIaEiHPe2t833sdnZGp1kJN1B5h7d4aw7aFiYwYDHXI+Cqwl0LAq6Lp1wGSfbVcYGkJOH6oyJAgyaiCqkzIrYHluZC2EqqbaKheTRjyShUZcF21wU5GRpJjohJWyuexHIXxkxSSag++DIkZC3kCQqQqc5I0hBipREPaEugMvFM5jPkZus+CRXmQXSkzb5oDTYvhiQsl0KrTIScdBsQi3PHii4R7H47l3/xaoFC73qTld+SZZ57h4IMP3uzHq42LIhnvo0qQO/nz+12DogGzUTQkhqIO7ZFA+Q86+/87Eih/RSkq752OREgb1JH3Ie/xhqIUUACJwlEo/XMEMsV2Q1Uys7zbvgS0cxVlq0b+ENuCmAu3uvBaHHrF4V8h6ObTa3nAhR5xOM+G/Xyprqg/OTDJgQwbKiz4j5Uy3b6AKmWeRwL7AyRE2qH0kIUigg+iCElySrVh28WIEYOhDvl0OYQrFa3I8UOvjqnr5gFLY5AVgfKg0jFZFVCYBy1yvfQMiogE4tqEEyEJDjyvSLICBlfP4aL7gDaT2sbFSrShBZEYyfbum6yySWd16luMLENnwumoHDUH+Opb2HUiLGgCmdVQE1JDOF8cxu0BJTkQjkFxS+gQhFa/TKWyvJyWXQbUyZosyyLQaTe+GfttnTxekl9RqW1P1t6G3UWVMF+hktgosDcypMbRxryhA+2Sj/cpen87ItPnyWhcwFDUzOxQUibVD9Dn6xKU3jsVeUNmoL41A5C59SBXguVEoL8XwShy4WwXfonCES4MD0OeDfcCnzkyr17tg562xNW/XJjtKp1j26o2e9qCrt66n/TW/jzyq3yEhEhrVEZsee/PP5HRd0PeD8PWjxEjBkMdsSwOSxapMZMFtGsCzWplC6YAc/3QZAWUNwPHB82Xw6IOkNkGwqVQkak7W47SE056qttqUiwkf/chARJBG1ZtIZIUKdUofP0zMrmCIiYZpM6s3Vr/1pdn5BfgdbQpD/Que38W7DUKFmVJcLhAk1KVNedF4PMDoGkRlOTrfnl+KJs8Gcv2EWjRaV1PtdEEW3ZhxQ9vsnz5cvLz8zfrseajCpnmKCWSvZbbTEWNu1qjv20bVEniR6mTd9HAt78aaJdkBWo0thTYGUUTjkDRkAIkMsajKEwuEiLtvee8xbvNHcgkepG3vjeBvT1/yLWoQyrAry6c6UBFFK6yYUhIkZJz0DiEnR240Q+tLInjf7owz1G6qNrS5U9YEmAuEjxjUGSkKUoZfYNEyVXe+5NAqZmjgR4b8H4Ytg2MGDEY6oix5RBYDn4vpdK/c8psCjAtCiuD0DYuY2o0oA6tM3KhIAzhxbAySwIj4YdmxZDIkdjwIXERsFIbUvKyKNqAam+bJSgNU4TESCnacGzUBC05sdetdZ/6iox8iqbG+lHKoAwYUQn7vA1LXRl/syuhIqyNLaMSPt8fyrJ0eVEL6ORAWxtKVq0ikJaJHai7omRfhtwHq1at2mQxsgKJEFCao8VabrMMtUwPkopS3YD+NstQSqQNfz3QLomLSltfRJUmPZFPZAEyp+6KNvDRyET6Pvrs/A19dk5AM152QVG8a1CFSntXUavFSOT4vTTh28CNCciJwqMBOMIPRRac6kLzhDwl1/lV5rsKTd2d60gEFdoSEv9nyTfjIuPqT0iI5CIhMgYJuaGkRPjjKGq08wa8J4ZtByNGDIY6wHXhywUQiutL1SQAXWrNc48CJaXqK1KcpZSM39to3Qy1w04rh4TnF4kGILsGqiyVtfotnRHaSIw4SJi4qM37ElY3ry5HYqMInWFWoaZYmWhTaeldn+zKCltejCTQ5uZ46zkHnb2/Aez8BSwvkXhrUQwrc6D9Cs3naeGD7/pCi5WwqrPej9yQfBcrbBvXdep2oa5qjjalmqYMvZ6VKNXRcS23qUBRoQK06Zag4XUd0Eb9X/Q3OocN90Ik265XINHREaVhHkafjatQZG4ZMjW/jQTGDSgFdDcSPd+g97U5SoMc7ko8HgHs7angmAu3ef6QbnF4JAS9feoxcpEL3ROam3OhX5/bBcDjrmbMZAHzba3xUUv/d5F/Zgb6fGSj5xyLooxXk0pJPotE1l4b+L4Yth2MGDEY6oCZUais1VukVxtIq3WyPh0oiEJ2DMrTwI5BMALREGSGIGOFOq/ajqprsCCRJZMrpMyneOIE9OVNpmIWoSqDJL8jwVGIKhFi6Ow6G214PlJD8pJn3VtSjFSgEHwP1OdiCJod8hbQ7XeonKQX07QcirKg7Qooz4DmNfDxXlCUB62LoToTsuIQTpfX4Zd27YhVlZOoLseXVjeFnfGSAizLok2bNn99Y48aVHb6G/Jm9Frb46JN9gckFqqRYEjOjPkWDXc7DomTDVorioS8RSpScDrqMXMVMoSegJqbHYr8F5WoSd6ByCOSD9zkPf8VKDX0NXCwq+jEENT/A6DQhfNdmBKDgx24LwzNbW+SsAs7xuEYWz+WpXTcy67Mqh0tmGgpKnO/lSo1vwWJr2eRWP4MiaJslBJKfiZHIGF96Aa+N4ZtCyNGDIY64ItCCJTIc5Hpg506r379FGC2D3KXyfNg+SC9DFa2Bp8P0lfAomwJjbhPpaylebWanNlKu7ikPCQJK+Ub+R2FrpMkG54VonLNZLfW5Fwai9WH5DlsOTGyBHjCW9/36Gx8GvAO0LoaMl+HsirIqfLm79RAPAjRdGhbA2P6QPNCWNlB/pEOLoR8Mr0G+vcHILpsFmmd+9fJeqMFs+m8Q1eys9fm8FidOJpm+x1KcZzM2vu3TELejy7ob9cMRT78KHLwEjrjv4MN9+3MQtEQGwmarqiF+0MoUnIFijbMQJGIN9Df+irkZTkepW8KUBrlGjSjpqObmox7BxDyXtA0F85xoDwKl1hwdVjXPQy850CfBFzggwFeQGkM8KELPzjQ24LvLQmtW61Umfnf0Wf0aSSOR6E0UiaaQZP8PH6AhHNy/oyh8WH6jBgMm0nEgYnzIeQJhxYZ0HYNk8DcBCz3g78KAjFwbMgrgpUtFf1wqqE0xxt+F1YFSTjolfJ6CiQTbWxJgZKsnomjDb92K/jFKCJS6N3G9v7N4c8Te5ND8mzq3sA6BW00+6CIyM0oSvQuiiLt/DIUlanPSrNSKE2HNkVQngn9lsFbA2B5C8iJgZ2lviztg0pJBYCuXbvStn0HKn8bUyfrdRMxIrO+54jD13/+7aLN9nYUtbobVZysKUQWoGFyE9B7X4PO9o9Dm+uzKGVyGWoktiHvfwQJgCuRIOuEqmxaIAGShjbyL1E5bwGKdPT01vk0aqv+EIpcHICqa+4H9nIlQtoCV1oSG66rviLHJ8CJwH1+uDGkFMxFqLV7n4Qm7g7wxha8C3ziwlgH+ljwg2dqvcMTIjEkfkpR9UxSiHzt/X59rffiaxT5W1tTOEPjwYgRg2EzmVQD7jIZV20L+ndSyWKSUiBWBgkHinMgHPF8D2VQ3ByaFip1k/ApRVMThnZl8oSELQkHB8j2drpkdCQ5+C5KqgtrkiXIG1CNPAnJJmfZaDNzWV2MVJEqHa4LXBS6/woZImehiMjPwHvec570LYwpgpxKaFkMC5tD10VQmAO+NHlFvt7Je5/aQVUAsmLgD2jzBPk6hlx2KdUzxhKvKNrsdVf+NoZoRTGXXnrpOm8zBc1XKURi5GD+fCAtRdGgN5BIXIWiJpeiv8EoNH9nANqUm23g+qagNMw0FA3pgQTIM8hvcSlK1/2IWru/4T336Si9cTRKdVyINvnrkXB9AzjK1d/mHOAQ78MUddXU7IY4dIjCcyE4PgDlFhztQk1CHpG7/NDZ1uf0WdQ/ZLSjZmg/WorGXG3pfYogIRVHnVPDpAb8hdDnJOlj+hGJ2GSTM0PjxYgRg2ENysrKWLp0KStWrCCRSPzl7T9fDL4aHUBz/dCz4+rX/wwsrYJMr3TXctU3I6cMIpnQYl5qMJ4/rkm9GdmpCbtJ4ZGLNvlkRCTm/T/q/b7aa0DmRwudVYa924a9+8fRGWhSjFQiIVIXYiSOznYr0cZXjjbMychX4QOGFME/50HbpdCkApbnqMFbdTos2QH2nwWvDoRlraBNCVQ3h4QLrfwSbX1rPd/ZF15IWm4ORZ8/gZvsBrcJJCpLKBv9PMefcAK9ev3Z9TEbNRD7BaUXjmf1GT+gv8VbKHKRhYRgP+SL6IhE2R3ofb8TRS42hEoU1bgZeUNao7kseSiq4ke+j6+RGEkgf0hL73l+QVU9t5EqAb8CCYdqFzq7ivTcBnTydv1VLgx2YUQU9o3Dy2HY2afXcKQL7RPQy4W7/dDU0mt/CJXujvY8ItMsCbZzvSqwGtS0LIia3QWREPkaCeYbSTXxm+pddyVmo9oeMJ4Rw3ZPVVUVI0aMYOTI9xk/YSLLli7+47pwWjp9+/Zlv3334YILLqBr166r3bcoAfMWQrqrg23n5pCzho9yMvCLC3mrYFE7tYH3JSAtqgZnWQugsmNqGq/tQElzbShBV5tHBEi3UpU0WClhUrsi5o/XhDYqF0VJQqQiKbZ3n9qRkaSpdXMPCOWoB8QB3uvuguabTEJn3S5wnQsX/Qz7T1J6JhCHmA+6FMPMbtB7IWSFYFQfyKyESEsIBsFXDV2Cei+Sf4XpwL/y8tjhmaf55YQTKJ/wLtm7n7DR63bjMYo++icZQR9PPvHEatctRa3J09HGv7a24y7yjXyCGoyB0mp3oE22FFXJwIYPtEvyLapsaYWiTN2B/bzLfgcuRlVTo1Cp9Jvoc3EYqjq5GKVuHkZ/g3PR52g4cLxXLbMTikgkS9F/ceFCF0oicI4F14f1+fsCTeLtF4eBFpzpUzSwHPUQKXc1f6mJ1959uGdYBQmqIUig3oc+a1+j6JkfCZGkmXo2Sl8Nw2xS2wvm72zYbolEItx33308/MijlJWVkt6+N/52A2jW/xSsUAY4CeLFS5lWMIef/v00DzzwAIcedhiPPfooPXqo3dK3JWCv1IYTsmGXNYyrLrAiDgUB2LECfDGIB1TSW95U/pGIq7P9QFQD8jIjUJajTbe5qyFjyZLe5Im/ZaXSNcmpu0mSBtWk4CggladP+kKiSKAkq2mq2Xwxsgh4Cg1VewdVawxEvSPeQ2LoGmDobNj9c8isgTaFMK0D7DQbFreB4rbwty/g5QM1h6bnAli0O1S5StHkZaQ6cD6DvBjLgJbHH8/cG/5O8QP34cQi5Aw8Gcv2/XmRayFRVUrRRw8RW/IbH3zy8R+9RYpQBUcFcBrq+bE2ZqPptR1QKWoxKcGRQObLid770nUdj7E2ipFgmIHMv5XABSgycRnyityB15DMu/4t9HcdgnqFHIlSNz4klm5G6ZCfgRNcvb7zgB7JFKCrlM2tjj6HdwZgsF+ftyeA113oG4cTbDjUe3tXAI+4UOhI+NoWRCx4yJJxFiRWLvXeo7u89XyNIh8+JESS4wkWIUPvMFJREkPjx4gRw3bJzz//zKmnnc7MmTPI2PkoWvc/er1j5t14lMoZYxnzwxvs1Lcf997zD6655lq+XGDhc/RFahqCzmvsWEuA8sVAGMqzIb0GqtMgtxRWtIW8QljQFnyOIgTxALSIQoalg/wOng8lOTvEdfV7MpKR3Jhrb7uF6Cw+2WNkHjrQR/CiLaSETI132yrvMTbVwDoReUQuQqH/U1F56xRkZoyj8PxTpdD8Y2hRBO1Wwoy20KYAKrJh5i5wzNcy7n68i7w14SzIzICyKLQLQJFPm/BNKNqwEvktRjuQM+Re2s0L89ubd1IzdxJNDr6IUKt1j5FzE3EqZ4ylfPRzpPktPvjkYw488EAqkZiaj6o31iUgVgGvIIHXDm2iZ6L1gdIMI5B34w423PPgomjFE2g2TF/kDdkLRTdmoZRLFIm8Y73niaEUzmC04Y9H7eA/9O57tPeYnVxFbz5B6aOkFynqap2vx6FTDO4NwgC/PndDUcOyXgm41Ad9vbzJXOBpFxY6WnepBU0seMCSYRaUqrrUew23eu/XN0iIgIRIsunbCiRor2fDZu8YGg9GjBi2O7755huOHDQINyuf/LMeIdii81/ex/IHyex9EOnd96Jk7Ctcf/31TJoxi+BRT5Hu2thA3w4QWGM3n5iA6cWQnQ4luZBWLTGSVwzzdoa2c2FWazU68yUUGUlP04HYAs2gsVS54JBKtbikIhm1e4WA0go5SJTkoU2zuXefSlY/26zxblPCpkVGXOQDWYDOsJ9GZsN2yKfwjne784CfXPhpDBz7m+cTyVVkqGU5TNoNui6D7lXw4pGwoA10XwirdpLx1x+FLiGd0YPeiyIkeF51oaYMdvnN4oQWt7PwiIN47OfzKHj5GsItuxHaYVeCLbvgy8wDJ0GseCnRZbOJzP6OaFkhxx53HE89+SR5LVvyHkopnYAiGWsTEMlhdnORWPgFOMp7jZb3fr/k/Q2GsXGb6gokJJYhEbNmNKSNd/1byIvSC6V/bCRQMry1DEAek3e9v0cNirL8zZUIaI+iJMm0zEoXLnFhcgz28PqHtLcVFTodyEuo6+31fmjn3WcyMMKF6Y68Kb/Y+rv/00o1eyv0nn8X5LOxkDelthBJvj+lyEdyFfJHGbYvjBgxbFf8/PPPHDloEDTvSrPjb8EObkij7RR2IEzegRcQbN6J159/lF5LszjktIfI9EHvTn++/egSWJgNLQqUdghHwI5DXgn8mAu7LISfuigq4tiKkEQzJC4CFtS42vCTvUAcFL1IipEgqdk0SeYgg2OyFXwZqbP1Mmo1UGPzPCMxdBbbBs1OeQalJ5qiao+30SZ5IjqLv3MmDPkIwlENwVvUDHabAbO6wOLucMT/JObe3Vkpq6Y2rMqFMkfv2ezMVAnyKuSJ+IcL0XJouQSO+xhyimH53/Zmn0t/o9u/PuHz5U+z/NdPWPld8Wprb9e+I0edfjKXXnopO/bp84d34TC02a9NhLjebZIVQsn02B2k0mDvAjNRGWqHjXgvHSQwXkb+jRwkNPZA0ZAZKOqSjrq3noCEXpX3PBeiKMkTyGT7OxJut6CN/2fUpv0VFO3pV+sF/uKqc2pxVOmoG8OQZSkydKar+TJZDtzghxzvfl8C37gw0Svd/cZStOWfVmoswQokRPZGXVQt1FX1C+/13ohMvuDNrfFuv7bW+YbGjxEjhu2GaDTKaaefgZOZT/NNECK1yexzEE5NOdM/eZhefY+k1z4Hkb+GszHqwOwVUNYEupbDQgtq0iCQgKZl2rxKMyVAQjUQDcvQWeKDlg6kWVDq6Ow1WeIbI/V7cmiejc5gHe/3+WiDKkIbRDIV4yIfQmatNdYWIxtT2luKOqoeiTaZEaSiANORjyEEHILEym6lcOWzEl0dV8CkLrDDQkWLZu8Gh3ytGSjPnw1L8qDzEoh20ea3IAFNA1BpSzBVAGcDF7uQqAB/NZz1GmRVqiqppAl0XOJjl/ZH0ePVo7isucvChQspLCzEtm3at29PXl4eLkpl3Ia8LXeu5/VP815jT5QWKkAbbK53/QQkBo5A/UI2pgx1IRJAFcBBSGBciATIZSjqcA8SOh1Rz5YXvfv+DQmjy9Hf8DnkXzkK6I3SNJ2B3TwvyPWo8gWU8hsB3OnITH2TD84IKBI3FrjBhQEJPefl/tT4gTeBWS587cC+Fnxkqf37A1bq/Vjqrf0Q718LGXFHIRFXW4hEkBA5E0VWDNsnRowYthvuu+8+Zsz4jfwzH9ksIZIka9djqJ4zns/+cx7XnDIDy0pb7fpRBeAUgpULVWmQUQMRz7yayIZQNSxoD7jQpBiWtYZ2CbUUj6IBY6VAlp0q742TMrOGSHk/HBT1yEXehaNQ6qQpEjBZyKRa6N0mWSq5KZGR+cB/kD9kNjrrHoaiA7+hDS4b9cHoB+ziwN9e1gTeTsthejvIKodm1TB2IORWQc9lQBt4s72qilrEYXwrqHYgGIWcIJRY2qwuQekGqwqsGBz0tcp/wyXw4Yl6T3b5CcYfDre0AAuLDh060KFDKlaRXGcPFD1Y16chOcwuB3lH5qD0Teda17+ImoTdup7HWRtxlM55B0VAIigq0h9VJE1HkYqW3hpORq3SJ6O/4eUoVXQoEmddUNTkKvS5uRc42SvZLUWiy+8JkYjnDxkRhw4xuCsI+3h//OeBF1zYLQ572nCSLUGcQGm4chc+duBgC973qmX+YaXSLQu9tR2LUkwg8+wo9Fm8kVQ1URxFfo5HKS/D9osRI4btgurqah56+BEydj6KYP5fe0Q2BMuyyTvscpb+52J++XEEA/ue88d1rgtvL4dyv5p2rWqq1ESNHzIqYHkryClUd1EsyChXI7T8TJhvQYWrDqWrLIWtq5DB0IeG7Vko3ZJAX+IEioTkovB4J7RpZSChEvR+CpEwSYqR2tU0G9KB9UdkrrwebYzlKDVjofTE6yhM3xmdwQ8CdhgNvX+G5mWaOxPxQ7858HM/WNoZThuhUfIvDIFlAWhboNbvYRtKEpAZh2CmojtDUIoiUA1uDWQUw7HfKkIyo5siS/5icIJQcMSfK2AWoGm6TVFkY10ltpXea1mJBMsE77WcScr4+waKAJzDhg+0SzIDRTssJCaS/Td+RZGE5khMfIxE5tFIuADsi8Tm/Shd8pT371IkrD5HAvFsV0LpWGBArVDNChcuc2FyHHaNw/Aw7OA1LPs78KsLfeJwsg/29j4oNSgSZjvwvgsHehGRo1F792Rp+VzUv+RUJNpAIwA+Q0LkelLREwd1gj0QiTDD9o0RI4btghEjRlBWVkrrXY6q08cN5LUhvXN/nn76cS6+6Jw/Lp+7BGbbUNAc2i+DBa0hs0hn/U1L4Pd2EK6BijA4ASjJ0YHeyYEMFyodaOeT8AhZOpBXujroJ6MjISRSkgPzkq6ISrSZxb3fk1UyaSiS4ekfIDWbpob1Nz1zkQekAEVBnkNn7Mkz39moF0dnlAY6HPWtWLgYbnxdaYDsSvljev0OxU1hSW/YYzJ0qIHE8fC6A3EbsiKwtAPEPa8Ifg1ja4d6ZGRUg1sNq9Lgzv9ALAH+GpjcX83kDvgRxg+CQbWcoyuRCHGQ0XRdvoQEqWF2e6DupHFSbd9dlML4FImijW1RXoMawn2JDKrFqAKmLzJvTkMRkB2QkDgZCcB30d/wEvT3OgaljJ7ybneK994/6v27ryvxciXQspYQ+dkTIoVRrf+WNKXCqlB0JdOBjgm40gfdPSFSiqIXfge+cNVf5DNLguNqK1XJNQtV3ZzjrRtgHCkhci2pHi0uirL0RWZbg8E0tjNsF4wc+T5p7XYk0GRjz2H/mvTeBzH5p0kUFBQAiop8/CtYcXVczalSc7OITx1Wm5TCqmylbAJxmTVXtIJgApYH5BUJWxIuCTsV1agkJUb8pMyrNkrrFKGDfDXqd+Gis+UQqbk0FSgSktynN8QzEiXVLfNClELYEYXWQamLl73LLORj+C/wcg0M/Zdec+cC+KWTBt7lRmH8/hAJQp9foGlr+L9jYWkAWi+HUHMoCkKpC/4I7BhUw623gJyI5vusSocrf4DMJRAqhfG7Q+dFkFsMRS1gyT5wkKXIzXOo3PgYtFmuTYi4qDz5Nu89bUrqLP9E77XPRxUqy1GKY2PH8k1GG/WvyEthIcHmQ9GQFSgaUoTE0KlI4M1FKZi7UM+WE5HI2A+9J8nS2HuBw1wochUZuZ2UEHFd9Qg51YGaiETE8JCEyGIU9WmfgCYJuM2fEiLLgPtdzV8a40JPC8ZZStFdW0uITEPpoYtJCZFkFC3Kn1vev4KiVgdt5HtoaLyYyIhhu2D8hIkE2uz21zfcBEKtuwMwadIkBg0axKICVRlklinaUROGcAwqM7QxNyuDuAU12RCKQmYFLGkFbaphiQ3NXMixocRVj5GEpbPiKiQskqIkOZcmKUqKkNhIlu7aaKMJk5pLU3tiL6RKhZOiZs0DQjE62z4G5fSHIxHS17t+LjoDH4C8KpcgU+g1wA3/hfRVEiIz22juTvelMHFPiOTAYV9BawtGDIW3CmTgza2Eqn6Q5ui1tEzArkGlNJrHwV8BS8JwYCn0GQXUQGUAlrUBHNjjB/jhGGiWpXX8hjbv3uv5+y1Am2NrVMEyFZWzdvGur0Sm0AqUSmm6nsdaGxXeezgBzbEpQH6aPkjk/YIG5/UDXkBplXlI4LneWnqizX65t5bXvbXeiDb8n1HH1OdQ2uOAWtGQpD/krQS0icKtQTjI+0P/AFzjwv4JCLtwrV89bkCRjhddKHEkaptbMNPSOk6yUtG1yShaNhRFxEBC5GP0ebua1Yc4vos+a8ds5PtoaNyYyIih0VNeXs7SJYsItOj01zfeBHzZLQikZTJt2jRcF774BZY2gUg6NC3X4Lcm5Wp57nO87qh+9RZJ+CRGLDTXJmIDLnS2NBskx1IkxIeiFwHv/wEkHGLedZVIjKxAQqMUiY9kK3iXlBix+XPviwhaQ23PyFxU5XABSpHch9ISSSEyH22e+6GN6xK00Z4AnDgB2vygAXiVISjMgl7zoLwFFHWC/EJouhy+OA7mtYcKC/JXQHYu/JwJcRdaxiHoh/ttyE5AqAKWB2GHBBz2HVjlECyDMfvCTrPkxZnbAwp6QrGtiqLbWbcQKUWlsG8jITAH3ec2JERc5L+4D9iTVNnyxjAWRUMKkNEz6cuwUTRkGYpoOEhUnIOalP2KIgm3ob/LIUh83InMwyehqMLD6HNxqKuUzUWsLkSWu3CGC2/GYKcoPB1KCZHXUGv+vbyKmZtrCZEJwH9dmO+JwoQNRRYMs+DkWkLkR9SA7gZSQmQ8KSFyJatHokahlNlpG/k+Gho/RowYGj2VlZUA2KH0v7jlpmFZFr5QOhUVFSxbAXNXQEUaLMuHDsVQliF/iO1AWg2UZEA0AGkRCMaUygkkIOgDn08D4fraUOwqcmChlE1SlEAqgpEg1da9BEUn8kh1X11GyuiagzYIl1THy+SmsmZk5Ht0Bv539LyPoTPipPV3AUp9HI42rivRpngQ0KMI9nxJht3cSpjRDtotg5wEfHGEUlS9voFYW+h9BoxcDFVhr9x5B8hytfZoDcwIQo4DblTdPdNtuKAMWoyFUCUsbSaBNn0H6DQfpvYFp7kGtu1R6/XVJooEyCMo2lOBxNztqCeGhcTV7Ujs3cH6IytrowhFCx5GQiLbe+zzULntI97l16DIUgeU5noapdn2B65DkY6rgMfRe/IlMqmCRMwRrgbdjfbW2a7WC57squpoWhSOSsB/wtDdp7//bUhs9I/DfhZc6FNJL8jj8ZkLPzuKaCyx9dm4zZLZNskY7z26GUVjQJ+FpBAZwurG3u+RyLoQM4HX8GdMmsbQ6PH7vY+589cTeDcV14nj9/sZNVV+h9wILMxQJUgkBImEBEWTUihLlxixHYmUwqYQ9lypARuq45qOWuVCJ1vm0CiKWtieOEmmaEBiJOb9/I7KTAvR5vUdEiPJOTUxtBmtGRlJekZ8KAVQjDbT6aj09O+k+kIsQmfnx6KW4n/37ncY4MRh8FMQKoeOy9VPJK0KOq2C8Qfq9babAe2isHwo3O9AJAbNSyCcAXPy4BgH3vLSA+39sDwKMVcv9DGg4j2V9DoVMPYI2GcSTO4Kv/SHts0gEEy1Il/tb0RqmN1e3vszHW2aSWNlKUqP2Mhwua5qm3XhogjHMyi6si8SczejjXgIEoL/QOmgN5BAeR39PTNITeH9G0odvea93/sgH8knKC1zqasy3F1RpCHZTdV19Xj3emXRl9lwcQiClp7jXCDgQOeEBt3taqfW/hqqthntwN4WfG5pDbdZq1e8fIEE3x1I9IE8Nx8hYXs5q1cyTUEt4K/HCBHD2jFixNDoycvLIz0jg1jx0i3y+E6kikhZEXnNOjK7AFbky//xqwNVlkp2K9LAn4CcUljeDHCVognXqD18uxoot3XbuAtNXIhZ0NVSFKLCTU3e9ZNKzfi8nyp0kF+AKjFqt4JvicpDk7NsYO1pGtBZ+46oOmMMqoaoPbBsMUoHnExqqmoQbaAzgGEfQtYc2KEAFrSAmgDsPBeq82F2G4gH4ehp8NuJUNYNlvwOpRmw4xLo0QtWWvBpApbGIM+v6Ag2VAfhuSjMngM9ZoKvHKZ3g46LYHwfaL0EFu8EbVqqOmXNDS85zK47inKMR16M5NyZODqj/2mNyzeGAuSpWYpKXn9HEYMdUGRjMhJsByMRsSdK3TyB/qY7I3/LZ0is3IjKpJ9EKbBcJAA6A8e6+ltdgD4jSWo8f8g7CWgVhb8H4HC/hMpy77Xt7EBNAq71QUc79fqfAPwuvOvA0Ra8Yel9uMtK+Wfw3qfHUWRmZ++ySSi9FEFzaNrWuv1M4H0kWs2GY1gXJk1jaPTYtk2/fjsTLfh9izx+dLketyqtP5EElDTVULym1VAehOwqqEqXGGlSBPM6qa+Iz4FYAFwbcnxQ5jWXyrNhlaMz1aCtM+kIEgTJVI2PVG+QpHfERVGLHUi1gq9ApbZRUr6QKKk0TZIydPZ6COqq+h46k7+OlBBZgjbG00l180wj5bu4eraG4LVdBVE/LGwOnRbJQPvSsRD2wbFfQkF76HgafB2HshpoWgh5IfC1kRibbkFejdfkzYKiEFxTpZRNx3cgGIdIFKbsAj0Xqhppyl7QNAx2tiIISVYh8+hnqMvqNLRR3k5KcPyCNvCMNS7fUBzv/bgERVIGor/Trd7f5Qrkv7kTpS2eR2W0M1EUxYeiIyegaMwTqCFbAYqA3IYiV/cCR7qKsL3nPX5tIVLgqn372zHoFYV/h+CIgD5Tk4HjXdg7ISPxnf6UEKlGPUtCLrzqyJz6uiWD7YNrCJF3gH97t08KkZ/QZOIo8qy0r3X7pDn4OlK9SAyGtWHEiGG7YL999yG2cApOLPLXN95IqueMJzunCWW+HhIQIZjXBtoWQnEm5JZo7gzIZFmco+m9TYrUDj2QHCzjA8fVBjPTkReiDImJKnQwjyLxYaGNzo9EiOv9Xog226RnpIbUJlDh/V7lPWbyfrPRhtzX+3kOCZdLSHlUlqFN8hy0uVyD0jZfoU323ErIfw5yStRPZGpHTSZuUg4/7gu7F6hBmb8IulwNr/ph1lJFRdoWwb5t4UUbxjuQ4yqj1syFBWmwUww6ZkP2F2AXaSjepF1h38kwZjcvmpQGbfPVkyXZAv9VFMXp770fJUhs7OO9fyuRQXc8ivAcxManEOajSMB7KK2SQNGP01Hk4iFSrebf9tZ1kreuVSjycQcSNIeSioQke3BciEyfbwBXuIowJLz1ZtZa7CRX3VZ/jcGhCXgmDL29P97b3n0PSaiHzV1+yPPuW4RETo4D/3XgFAtetFQd9YC1eqrldSSkHkJCBSRy3kefxQtIzUACialnkBDZMm4tQ2PCiBHDdsH5559PrKqcqhlj6/RxnViE6l+/YN8jzieKn5pcaB+FFSE156oKAVE14wpFFB2JeX6RpoVQnQ5No95kWlsbTR8bfnHUW6McnbHXuOo9kizlTYqR5MwZvMtKURVGCTpLT5BKyZSTKg1O9+6/BJ2Fd0MC5RF0ZjuY1MZcgMLyF6AGW1cioTMHneEf4EKHVyBjiaIi09uB60CHxZDWHFa0hx/awEFjgJPhpS7gJqC8XAMDc3wwqqPO7HNdcCJ6L2alQYco7BaCr4qgySjIrFEH12XtoEWxXt+0PSDThZqmeh2jUT+Q1ii1MQl5GAaTEnRvIEEw2Htda6at/ooYEm1DUSXKzqSao9WgaMgcFNXogaIJp3p/g2TztUFIyLzsreEBNOPleWRa7YM2/hokNB5BnVdPtFb3h7zmwnkOVEXgPBceCkML78h+L/CMC3vHJXKv80mwgVJu/3Q1BO8tF/5mwUuWqqPut1bvC/Ii8pM84r0eUCTtPe91n0PK3AwSOf/H+rvcGgy1MWLEsF2www47cPgRR1Dxwxs4sZo6e9zyCe8Rr6mkw66XAFDVHHZqAkTVTTQWgKhX0ptVAUtaK0zu+iCvCOJ+2DWgplJpPlVG7GrBDEcDzcqBNNdr6W6lpvWCNuI0UpUwMRRyT7aAT1bO5CBhUc7qTdNeQqJlmHfbn1DU4JBar28F2lQuQpvkRaQ8KEfhDWH7FppOUD+RZTmwKgs6L4R2Nrx8LFilcOxEWNkGfh0swfH9ClUZtVkFVa1hpF9ehioX0quVmmkeV4+RMTbsPFLCJFoBY/eDI7+FL/eAimxIj0H35lDsV4SnDImDb1C0YSipktwJKErRBlWltNuEv/kMFLHwtBWVyMx7MhJtDyNT6T3IX7EIpWGe837PQIbWbkgM/YA29W+Rr+MW7zXcg9Iy6a6ayP0d6L2GP+Rm4K6EDNO3+OHakERrUiD85kKPOBxqwxm+lIj5DXjKC4195cIhFoywNOjvXitlVnaRaBuJWrcnU1g/o34hDmqRXzu1Ve69B5exuqAxGNaHESOG7YbHHn0Ut7KIkm9erpPHi66cT9n3/+O4024grdkOpPmgLA9WNYHcCvkm0iJQniUxklMM03pAIKZ0TUkTHey7p0tQpNkyr3azYbEDbT0xEnd1Npvch3xoE7CQGEmmYmqbVC1UEePzbpNBKjISQ+mJbGSgLEUbbB/UjCvJClS9cinqJ3ImKkGNIyHiA45ZCu3ehPZLZdad3QZarpKBd9RA2GExJJpA5ixwr4alfnAcSBRpWJ4LjOsIeS4sjSplFQEsG9qH4HcfHPk79JoMpVWwMk9VRYE4lGfAnD5qHDe9hSI4g1FVRwsUpejmvZalyFw6C0Ur9mLjUzI13vvxdyR2eqL1345SX1d57+MwFOF4BBlZs1G6IoEm7N6I0htHomZnN6PN+2BkHP4URauGuupzshL5Q5rUWvAyV2LjrTh0j8IjITjO84cUoqhLvgM5cZXtHuhL3fd74A0XihyY7UIfCz611Mzudis17M/1Xu8oJEQ6epf/grwjoCqe7mu8R/9EFTtrzgUyGNaHESOG7YZu3bpx3/B7KZ/0PhW/fL5ZjxUvL6TovXvo0qUbPQ+6A4DcEOQ3gS8saFUEhelqTx4JSYw0WwWL2qkLa/5yWNhO1QsB20u5+BSxyHI0GbWDLZ9H0rya7ClikYpuhL3rg0h82KRMrsu86wNoQ0yKkzHoDHhPdFb/EDJW5td6fUnj5+UognIC2thdtNEsAIZEoOWLkLsErBqY2RHCUZX0ljT1GpDlw8AvoPAUGNsZTnVhXCGszIDmK2BFC+iQBpkxbWSRhNIw1wBN/JDrwE4jID2hUt5v94VBY+HLPWF5vhrGOTmQnab3owiJjX1JDbR7CUUWzkVejhAbz0/A+agcd7D3/pzsvS9PAA+i0tf7UURmCirjfQMZgf3e/w9FkY/hKO2RAbyJBEpnUmmZM1wJlL2BM61UDxCAiS6c5sIvUdg/Dk+GYRdPbExDUZr9E3ovh/mhd62j/AfAdy7MchQNy7VhiqUU0vVWqtrF9V7TOBTtSUaQpiIPCsj7UruEOilyT0ImaoNhY9hoMTJmzBiOPvpoWrdujWVZvPfee+u9/ejRo7Es608/yTkeBkN9MnToUC666CIKP/kXpT++jes6G/0Y0ZXzWfX638kL21w7/COiVoiwD8qbw355XuOxCihNV0t4AMeCjCpVzvgS0HaJ+otkJtRpFcDvUwpkjgtYamAVQcIk2dgs2do9KUySreAzUWQgjERHE7SONPQlz0Y9Naahs/be3u/foaqYAKn0TyE6qx+CNsyDSfWYSE6KvQFIjITATKWbludr1kynBZAWgNFHQbc5kL0K3FyYfKLuM9+FglUQqJJvpn8npaScGkVWiMGOPtgxXesOzoVOy2BFJczpAl0XQ00IFreGBR3UWC0/X9GDK1BkIdlxdgzyjvRGm33Ljf5LK5p0Lyq13Q9tsj4UDSlH/pmp3nt4ENqM90G+ikf1cmhHqoHaYWjDH+G9t5a3tlUoLTMIGXefRn6LXWuJENeFV1y4wIWKqATLo2Fo7R3FPwIuduGYBFQ5cLcf2iS9JSi6tcSFMV7UrcSWYD3Hgkut1GbgeK93MkrRJRuXTUOzcCxk1q3dCM7xXu9hbHyDOIMBNqHsu7Kykr59+3LeeedxwgknbPD9Zs6cSXZ29h//b9FibeOqDIYti2VZPPnkk+Tm5vLAAw8QnTuB3EMvJ9D0r90DTqyGsgnvUf7963Tr1o133v2YEZM6ANA8AMu7QBs/OFEI+KA6AMQlPgJRKMvSphCugSbFEAnD7hYscLQRVFvQ0YKfHEVL8j2PSLGrEt8KJC6q9LCkkzJkNkGloq1INTxb6t0eNOtkPNpEipFP4WN0dp+bfG9QZOFhFBF5F3kfklNVR6Kw/YXAvJ+h92joslht75c0hVbLID8BK3cGfyFM6QFnvAmfPgi3emU/z5XA0hC0XwCZeTA5DLEYlPuhxoJucTg+W5UwkWrY6ysoiYEVhSn94by3YMTRUNAKWpVrnk3LbJlVk11A55Ma3Hcnm9bbwkUejn+jLqQnI5/F+SgF9IT3fvZH6ZJ30d9nKFp7suz6FBQ9eAV5L+5HwvBB77E6kWpidq2rSIqNKmz8tYRItavXMtKBFhG4LAAn+1MekEeAj11V0qQB1/hT948hUdHcVenuIRZ8ZSkqc7GlVFKSuPeezfPuk/R8TEMRHB+aKVO7AZqLKoB2Z/U0n8GwMWz09/SII47giCOO2OgnatGiBbm5uRt9P4OhrrFtm/vvv5/DDz+cs889j0XPXkp6p11I730gwVbd8ee2xPKO8k6kkujy36maM56qaV/iRCr5+403cvvtt/Pl1BCVMQj5ICeg9u/f1UB2uab0Wg5U/j97Zx0mR5l18V9V6/S4Tybu7sEJEUIguMtCcHeHLG4L7GKLs8vu4rYs7hLFAsQgITbxTMbdWuv9/jg9zGQywVc+6PM8/Yx0d3VV9Uy/p+4959wU5UIkNcOmrgo6y6uAyvin/G5e+FdMC0edJXLyjSP9SKv9tgFVLlpJRwtaYFJpyw3JRCRkAG223mWIrMyO3z8YLawL0EyR3yG9SCtaUJvgLKRb6I1iyUE6gfNQdWBxNRz4HAxbqw+QL3tAoAl610BGBty7A4wsht3mwydHwO97aYF908CyMh1rWgSKu2kfd4rAbB8UtkAfH2x0iXClLoMxa6G+CRaOgx0WQ3UarB8ke/SALbB3d6i0tcBHkTajiZ820K4V1ajCsQq1PDagast1aFG+BVUCLkJE7g60QAcQkUtDC/0F8e2dEN+3N1D1ogK1agxtIWYnG30/DSWftscWo/yRRVHoH4GrvbBT/JM7hoSijQZGR+XEOsRuIymtbbj+Bh501PJ52lJL7py4c6YVkfh+lSIikhn//TJERNxI5zKqw/l6HOlJJpBAAj8d/7FAvFGjRhEKhRg2bBjXX389u+2223YfGwqFCIXa8iDq6+v/E7uYwG8MkyZNYtWK5bzwwgvcffUDLH79DgAsXwDblwyxKLGmGgC8yVn0POJMul97Btf27YsTgi9Xazu5HijoBzUeeCUEhdVQ44XkJmhIka03vU5kxHFBz42wtrfyMcZ44dFGSLV19dsHeDMGabYWK8eIUAQtLYCt6asxtBBG0e+zEGnJoC1rpJi2cfW/Q1fPn6BF53x05d+qn4iils3T8cekoIUHJGQ9Ai3u1VE46mkYsAb8IVjcU/s5YLM0M4/sBQcvgvcGwdBNsMNhIiJ1Bs5uBI8Hum9WlajFC9k2fOTV3JrBMaj06XWX1EC3cqir1oDBzd1h78/gyaOgLAt6Nata1D9HJMpCVYejUUXkp8CgKsVjSKh7GCIkp8XP78OoojQi/ru3USvsQqTF2BLfxh5IIDsfkYiTEKn5c/y+Y1EI2t+A4xCRuAe1xbp2ICKfG7jSQFkEdnXgOr+0RCAi+TtgqAPeGBzsgl3bNd5bdT+DHXjAwJkW3G8pxOwiS1WdVoSR8LYWiVXT47//BlVrvOjvqP1zQG2bJNReSiCBn4N/Oxnp0qULDz/8MOPGjSMUCvHoo48yceJE5s+fz5gxYzp9zq233soNN9zw7961BBLA7/dz/PHHM3bZ8axYW87zOQv52Ps14XAjfTa6sXr0JLfnWFK6D2Lmvi76ZErAl/MNNIS1IKZ7INpfi9SfDUyuh0XdIKcYigsgqQXS62FNb8BA1y2wYCx4DAxwa8R7ga3Fo5elSatpthYIx0COpatzq93NIMJQFf+5NTek9XeFiFzsgHQOyai6sTcK02oVd6agRW0DiiJfjK6mT4yfnyC6Wq9DQ/GGfgCFX0NaBWzIENnqs1EBXEV9tIi9MwROnwlf3gh7uPTc3Q34y6R7SA5BbXfIDUhI6XE0rK3cgUKvgtRGfgxNzWA1wUeTYa95UJ4J68YCHkirgl45MNOtSs8FaMFvZxr5UShBOR9b4udhOao8XYsI3U3x83sOInp3IifSTmjBb63CXIYqCjcDs1DVoBIRkTPj78tbqC1zmVFgWA1qy/jaERFjJLq916hFdQRwiR9S449Zhdo8BzqwIQYXuKB/OyKyAVWJejmy8J5vKU11OBKqtheeBpFupSV+LK223uVI2+JHFbIdO5yzd9B7e/J3nNcEEvih+LeTkYEDBzJwYJv5a9ddd2XNmjXcfffdPPnkk50+Z8aMGVx88cXf/lxfX0/37j8lESCBBL4fxkBlJXRPyaPryH3I3nkfjEsukMpcLXB12VCVoavKFxwY2VoV8ULfHvBFkgSMsTAk29Dohe6NEmlaRnbekE/ExO2oMtATleyNA8mueEKqBREHcl1aHKKIjKxAi4JNm9AwgBbPVnISQz3+xeiKtpC2oXn/QERhR0Q2QIuQH+kXCuOP24DK/q0i2clIP/BHoKoIct6DnCId3+ZChZZlNkFeCvx9AoxcAvnp8NUBcFB35WbsYaA0qNfIK5XOY88cuM0DSWHY1Q1NYWhwwVgXfL0BepVAUq3aMZYFvUrhlVOgxQc5QSW0Onma/7IHcrX8FMSQTfUFtEAfhBb61mj3R5CjZHD8vMxBVtdzUWVpdvz96Ie0FyXI9jwMtWWejN9/LSIzrW2Z0+PVkN2QZdpqR0SajcjPqw7kheFkFxzraXPUfIBaRkfFpDe6zg257Z7/FQoxyzLwrJE49Y+WbMVXWfq7a0UTIlCgyllr+NsKlLgaiO/jLh3O27z4eTqPxOC7BH4Z/FesvTvuuCNFRUXbvd/n85GWlrbVLYEE/l2I1UMoBH6PbKUxN9hRkQjb0UJR2k9Xrk8BoVpYlBevirhh+ECVt98LQ2o9hL1qLYQ90o1YDoQ8CujKrVAOCRb0tWFpTLZfx4I8CyqMckXyLZGRECIYQdoitQ0iSP747z3o6tsgcrMQiRBBLZy3UMshLf681gWnFrVlTkVVmBKkF7GAjehquAgtmuuaoOA5yCrSFcyaHuCOQGEpDPTB8+Nhp0WwaiTsVAX+g7VQjwbqDQwu1ZyZlGbIL4Cnvdq3/VECa0lY03YXBCFnLZSmQ/8VCjg7cB40Z8HCHSHoQHId1KfDSH9cY/ET3/d1aDF9GbVL7Pi5vgoRs4tRZslpqApxT/z8nYZaNpsRWTwVaUZeQhWMi1Gl5hakrzgVtXNa3TL9jcjd8UhM2p6IFBs9/pUo9A7BjR443ttGRB4EbjVwaFQuq5s7EJG5SJsTcxRmNt2CeyyRiZs6EJEGpHtxI61L69/FSpQSm4Ke1/H8LkAE7RwSRCSBXw7/Mc1IeyxevJguXbp8/wMTSOA/gHUV+lB13BBxg7Egs1bpqJYB44GG7rJornFgxHJY3APGl0PvblAbr3I8EYHu1UoP9QY1d8YV06Id8kLQD302wPre8RHuPvjCERkJ22rVrItXUvJsiThb4t+3IKdMa9KqB+k9WofebaItdXUoIiotaNEYi67cTfwWQFWYF9FiVIdaBQegSsFzyDmzFi3Wqw1kvaCKSE4DrCuAZi/0XQcD3fBVPlTkQKobDvkcHrkIgrbEmtkGsiNQHpVw1+2FmjxY54WzW6DUDxtjEHa0zRPeh821UBqAigLoXwxpjfDqRdAUg1gMrAhMz4diS1qGnX/k+x1GpPIt5BbaFVWSzkUtir/Ez1t/tOAuQK6a09BCfV/8HKYg8hYDzkAE5jVUmfpb/Nxm0daWudwoxGwtqpQkd1jJPzNwtYHSKOwQlT6kb/xy0UHalFIDe0Qhw4ITXW0kxaAU181G1ZI6Sy25v8RFqldZba4p4u/5xaj6cytt2qGVyHKchiope3Q4d8vjx3MFP70llkACneFHV0YaGxtZvHgxixcvBmDdunUsXryYjRs3AmqxHH/88d8+/p577uHVV1+lqKiIpUuXcuGFFzJz5kzOOeecX+YIEkjgZ2JxRXwYnRticeFoVnV8hoyBmq6Q6hNzj6HhbR4D67vDiEHKYxgFrDPQrw6KkyCrBhoDmkWT0qgUVlAlYVM3RbwPsPVcr9Fi40dkx2drkFmj0ZV3KlpA/bS1Yzy07Y8fXcG7UVslhLIvtiCdRwYiH61wIbHnTvHvX0HtnHqkXWhAQs2p8dep/wT6LIZeGzXYbnMW5FVCzwhUuuGtveDwBbAlDWaOh+auukJPR5Ni68og6IasBnDnw4fJ0MWB3ZNgpQWlEbBd0L0OopsgpVxzexaNgQlfQG0hvDIGgiEYWg9+P0xMEyEI8+NmnyxHBGIu0jo0oaTQK1FV6RJk2T0ZEZFHEFG7CC3SS+LnfP/4Y76Jn6cRyE78JG1umSTaQszOMQoP86K8lfZExBh43MD5BurD0oHcm9RGRBpROqrbQPco7GjDKe42IuIAj6IK1McO2BYMtuBflkSn13cgItWocpMD3EYbEVmFKmWZSFc0qcO5W4cqJpeSmMCbwC+PH01GvvzyS0aPHs3o0aMBuPjiixk9ejTXXnstACUlJd8SE4BwOMwll1zC8OHDmTBhAkuWLOGDDz5gzz33/IUOIYEEfh7WV8jy2uKGmAW+oObIxFyqmFT1hVSjFoEnDMv6wbhiWN0XktMlcExG+SIZQI0fCkpVZXFFIaNeuRjuqCy+demQ71J7osJoMXDZkGZBiaN2UBLSW6SjxSVG25VohLZheWF0Re5GV7OVyP3yBNImdIs/p4G4qwVVPo6Pb+dVtOiWIv3F71DJfiAK7lqwBca/AQNWQ8iGZd0gOQg9KiHqg6JxsMNq+MfOMGYTFB0g4awbXeW/FlVeSEE5eNzwYQ/oEYV9XXCrI5dQahjqvHDZC7AuX7N7gkmw+2KIReEfZ0BuUOfEtMCAPFhsK+9j9A98j1uQS+RatNBORO/bxUgT8TfUOslBGhoPsuweiSzOt9E2IfkqJAS9Cy3qD6EqxC20Rbqvp60tM9zo+0OBgzq0ZZoM/B64w4FACE634UafKh/Et7MfsIcD4Sgc64Jp7UoSYUR4sgz809EwPLel92Af4Gpr6yGA5fF97h7fv1ZSsRpVi3KQPmZKh/O3BRGey2jLrkkggV8SP7pNM3HiRIwx273/scce2+rnyy+/nMsvv/xH71gCCfwnUAu4KrToN3hFRnKqAaNFw7ghlBcPfwor0n1LgcK9mtNUlneAN6KQVgdNPmj2QFJQVRFjaQ5NY6oSWGvTNSQvNxYXqJq2xFAP0qgEXCIK1UZXqe1dNDF0XzK6Aq9HjpkV6LFzEIn5PbrSTYlvt5WMvIiEmCmoOnA5WsxSkT7gRFRxGALMD8Ghz8CAjSIUy3uKGPXYDMk+SEuDmQNg7FLYYQt8ejp8bGvfrgWedSBcBkEv5NbB2gJVRBpTYIiBWZaONxaD0bWQvRzqu0K0i0SufVZA/WCoGghOKfStlzX6yBzpPLJosx9/FxYgIuJFrZZ5qAVxImpL3E88gh1Vix6Nn8srUaWgHhHB0ajlUYXsuT2Ad5GrZHn88Sls3ZaZhzQ8vwfSO7RlNhs9Z0EM+oThIi9MafeJPC++zRMd+DoGl7mhZ7ttNMTfu7EG7nFEdOZbqvZMA86ztm6llKCqxkBEqFrvK0LktQARr707nL8q1Ka6mDanTQIJ/NL4rwhYE0jgfwVfAvkVWrBL0yDqksi0dfGPpOpKM2Ag2qC2DRYsHAQH+VW27g+8EoHeVVAdDzurTpNeJOoCd1iakcwaWNcLvFERnRpk3TVAN5f6/H4jN04ELSrplhZDT3x/HPT4JiQyTUOLchP6Z16PrmxLEalonUtThcjJSNSWuCu+vQ2IkKSgFkN1/Pn1wL6vQ//1ECqRrbYpGfLKVEVKdcE9e8Fxc2HFKMgbDP/oou3cjOaffBkBuwnyK6HFhvJCGB+AsZZizX22zk2SDce9DbPyoPc6WD4Q9p0pG+vSs8DTAiELvI2QnA1ZLjlz6vjuqbD1KMr9NhTRvjOqhlyGXEV/R+2qTKSbyI5/3RvpSP6AyF8zymWZiIjGQUjQenP8PNq0aSha2zIXGPgrarFczbZE5FMDpxhYEoExYbjLtzUR+RtwjYETYrAmBjd2ICLl8eMaa+BPDpxswXuW2noHWXBBByKyGZGJYfH9ab1vDbIfFyJy1ZHc1ceP8Vx+eohcAgn8ECTISAK/acwCelfo+4oUEQhvWD97YuAk65+kqhEIQ0sypIbg61wtcFWIVKxzpGfY4leLpypbLRp3FBrSJErNrlXqaJoFSRasNfq9A/RzqRIScyA/njHSYNT2qaStnB5FC1wMtVFahazNKFUzBZU7W6PgbaQpeQxdEXuQ0yaMqh+nx7dVhMjNjkiMu9ti6DUfomvA8cCmfAg0QH492B4RhuE1sHgo7FoK0/fTlfVtqDqzNARWlUS7qdVQnQn3J8Mij7QPlqPFzR+GLrWQtgRWdgM7BKkt0LcUInvAvHwINkDfBi3y++TDXEti3u3NQDHxfTgbBb+dgfQOw5D2owxVCD5BQ+9+j5wwn8a/X4JaWUmoFXQNIiwXIBfNy6iC8Mf48w+Ib7+1LTPWwI3EWzaWqknf7puBxwxcZKAmDNNicK8fBsbZgYOIzZsGDohJB3Kju61tAxLA/hkY7cDtjnJD/m5pH4+0FLLWnvusR1qYcajSYrfbzj9QK6+ArWPhQZW7O1A1KWE3SODfjQQZSeA3ixgSjBZW6ec6v4hI6we5F7C8uorc2CQtg8uCsbVQ45LIMwWV000Ish0oSYLCEuVhWI6srJu6g8tAeo0cNv3i+pAVKDnTGOjlUtm90VHCZjNaDNItkREfqpasQ+QiAwlVXWg7Bl25l6NFtDT+uCgabDcStU8eRe2YO2mrKtyBFuihSD8xtBpSXgZ7s4jXyu46WV1LoacPuvth9o6w00rYlAY3HqXx9nehDIymsNoKyU0iIlgwqhA+Duicpxslr4YcuY6yiqA4AvVp8OWOsP9sHfCXJ4IrCE0WpDeAOx3286vCswVVLzqiEpGHB9FV/kjUQvk9as08jghTGqp89EKZHuOAgxHBqIuf+9+hALSVSKTaFRGR9xGJvQaRkrdQONgVQInROb4UGNWhGtJklHJ6lwNJITjegpv9Oheg9/woFII3OAqFFlziUvZMKxahlko/R4Fo11lwmyUNyPGWLNztsRpVgnZHlZHWTbUSkV6IFB7S4XlhpJ05Jv6YBBL4dyNBRhL4zWIlkFQDqdF4Od4dr4rEJVGpRot9fQgaLfDHIDUCPdPUZvkrss2ui0kvUuuFJi902aLpvDaQUaPgNE8kbq21YbgFARvqbLlqPJZCzpqNrLz9bAlb/fH7ytG2itAimoEWlSakeekS/7lf/OcAbc/5J3JFFCFSMhFd4bd2BO5BC3cOIilWFMqfhawKyC+Donxo8kD3Ehjhkv34qUlw9Bx4ZAIUd4Ut+bLDvoNst7NbIBBUJH52FTipcHgX7XcukOZAqQV1YfCVQ/flsk+HfDre4SvAdTDMDECoEbo2iqQNzoctlio8pai10AqDSMF5iEicgVoyO8R/twURhI/Qgn0d0nu8jaoF5UiImho/l9eh5NqH49u6E7lnbom/7vmoitHalrnYwBNGVYjrUFBde2wy2s7rMegWkv7jHG8b0diMqio7GzBRmGrDMa6txa4fAu8bSHXg6TgRudpSW+1MS86Z9liOyM9eqM3Suql1iIj0QWT68A7Pi6EAtP3ZOqk1gQT+nfiv5IwkkMD/Aj4HelWIcDS61SJxWnMdbA3AcwE1zQolywyB7Ye1ySprL0BXsiVR6UUqfNKIhD1qzwCkNKhdY1xQmS0bb5Zb4VYRWxkjtq1/RL+RgLavpQTN1jklxYhkDEd5Im505bosFiNtxQrKV6/GiUbZnJlJ/ahRZGRnsxJVCfaJH+eXaBHOjB97EC1uq1D7wouulmMfwMHrIWstbA5AWabEp/1DkO2D5d10jF9nQNjA3D1VaXgHcKLwWiN4A+DdBJ4mEbhjesHelhbw2wxcb/T66bWQXA/d1sKGAglbu1dAUjJ8eTC4IqomDKiHBj8cmqYgtYFsrYcoRlWZkvj7sQURzatRW+pJ1LbJQ8QjhNoo+6DWxP20zfmZhAhmLUpGTUOkZQlqjZyFyMha2mbLZBi1vg4AdulAQkD6mZsNFEdheBSu8sHwdgcwH1UtTnPgixic44Ih7S4TDSKV1QZqHFhmweUWzLAkqj3H2nqKLiiF9Qakb5lOGxFZj7QyA2ibKtweBolVd0OVpAQS+E8hQUYS+M1iPnBAXC/S7NHiF/Hqg7suHTwhjbePRiU6zQ9DOA02W7Jp3osEoC1R6FYP6zKlgSjPlfbEiatgUxqhwQ2lhWpR+C0t5I4FJqar3woLMh2ot6GLpfk0o9DVbQXScSShhdTMn8/m+x8g8tJLOM1NrI0fT6tn7d5Bg4mccTpDTjyRpRkZdEH6kpFIF7IaVUO+QVfHZyH3i78I9p0NbIK6KGzuCSkh6FkBvb0QjVdFDnod7j0YKseLZCxGwWXfNMJkP3zYAs0u6F8BeWlwYJ4qDJnIflprQ2EU8rZAtBny6uCfh8AuSyDqgbTj4X0bonWQ1gJNUUgpVHbGi4g47Ymu4F9EOSl9kKh0JlpghyNx5gOo/XUAutJ/BRGwi+Nf/0RbxsoVyFU1CxGZ81H74jHaIt3dqAKzOP74ZUZk5zz0vrWHY9QW+quBWAQmO/B7v0LsWvE0qiqdHYMvHbjavfV2YijrJNvAQkctq+OBayxVfS62VMFpjwVIXHskarO0YgMiUIMQ8Tquw/NM/P6B/PRU2wQS+KlIkJEEfpOoQovUgEr9vKU1UcySqLQ2E1xl0BwfHp0eAW+SrvpT0OIHKvO7IpAehYUBOXFqMgBHFZHaDJGRYDJU5cDOtohIBXKRtMQg26WFM9VAs93mftlkSUcwBLURYjU1FF9wIS1PPoE7qwupYw/B320InuweWC43saZawmVraCyaT/Oll/LlLX9g3F8e4ahDDuFD1IJYihbwJWiBvRMtvO5G2Pd5cOogqQzW9xBJ6loKQz0SYb6wGwxbAC/sAp4C5X2sRcSrqgEmeuBDFwSrIKUFcmKwUzcIuvQ6w4GlBnJs6F4KjU0wsAhaLJ2btCbIyID5u4AJQ0MMRtRBnQv2yYYllgjaIrRIX4D0HdMRwVqHdBxulJkxBzmNro8f603oiv8CVB2IxR/bD4lNHdTWWIQErFb8OQeihb8ZEdBeaMjdE6jCcz1b6zpAgXU3A+84kBWGA2w4yy8iSvxP7TpgsYFjY7DKwE1uDRxsRRC10UYYeNzRuICd4tqcnVF1pGuHv+vPkEPoOLbWj2xEbcVh8ePoKHIF6V4y2dbam0AC/wkkyEgCv0ksRCX8gnJdJa5KVciZQTHwQb8Ct6IxCVHTHAjkQcSSvmIeWui+MdCzFpI9Eq0O2Qyr+oE3BN4IlOZDWrMsrI4LJtsSORZb0hUEHcj3aFFPdmTrbUDk5HQLvkAtmU1FRby75xTCFVVk73shycMmY1lbS75sfwqe7G4kD5lAtKGS6vce4uNDD2Wnyy+n7rbbeNWy8KEWRgkSgN6GFs59/gk9ayB9HdRnKj02rwoGxiDggeIs+DwfdiyC0gkwdFct6Ec7MLMR0lyQ7IWSFsiwoXc5+AKwexe1TmJAvlELZVwYNmzRYjvyG3hzH9j9S1jXE87eRYu0FQQ7DOEQePJgL7eu2ndGbZmr0cJ6CKqGTEdX/Otoq4bsg0Sp76HzeDYiebcgArIeDcTrgs7/6cgh9SaqjnyCItiz2botkxt3y0wCJnfSltlgtH+LYtAzDKd64GB3m/4jjMhAuoHdYvr7u8YtC3kr6hDpmGAUiLazLUHyk4hQXWlta7Wdi7QepyL9SSs2oerLSNR+OpVticgbqOrWsW2TQAL/KSQErAn8JvEZciBQIZ3G5hSRESwtxFjQ3Kx/kBQHktwKLktCZf3F6AM/EoEuldDg04C8zHIFndkOpNdJe1Kfofu8wDA3bHKkF0k30ox0dUO5AccBbBEEDxq6VgdQWsrCSZNxmh0KT7yXlOFTtiEiHeFOzSH30KvJnHwqd/3xj3x4000chCosnyAh60JkE97xYxi1Avyb9Lqb8iHQAr1qodAtgnbrVJj8PryxN2TtBE9acLaBlxvl+rgvAI86kF4FqUFIiUJSF/i9R1qNsQCOhLvRNdAUgaQIRAxU58FOiyHSFxb0VkujIQyDa+OVoXyl0q5FYV21yG6ahIjJdYhcPIvOnY3IwMT4z2FU8XgXhdSlInfStYiIPI5aHzci58m9SG9zNW2zZZ5DbZmQkfvoVDonIvOM2l5fRWFoWGmqh3jaiEgZcvmMNjpHgyw4y7U1ESlBrp7JBm524EBL5Gomap9c2wkReR8RkbPYlog8jLQlVXRORGajFs7x2x5OAgn8x5AgIwn85hBFH75jAacS1gagIaD7jAXNyWrVxML6mmQgMwPKLS1eBi2SB6FWTEoDbPCJgNRn6Hd2DPwtClRr9mvKbKqjcLNaB2IeiDr6B8yzZRleZaDUDWcakYJUC2qM4Y0zziBW20jekTfhTsv7wcdpWRZpOxxM+u7HUnb99bz96acsQG2gz1AuyX5bYOe3IKkecipgZVe1XbqVw+B45vwjo6HHWljaF3L7QnE29DPw90Ylkj6SDIeHoTmmc9W1LO4q6qqS/ypgByNNxLAG2FirczRwBczdA3JrwXbD8OHK1/AFIRQDTzMEMmBPv/QwS1Ep90y0MB+IFtAtiGzMQbN5/oAqJPfE7x+DiIaDtCEHI9tuEFVU3kfttlxUNZmKKgRBVJ1oAa4w2rcPEPnp2WFFdwz8zcBVBqrDMD4Kd/thbDuh6iKkNTrRgQ1ROMwFB3VwzKxClZ2JBq5x4FwLPrdUKdsNuWc6pqC+iTRAF8b3vRWbERHZEZGgM9iWiHyONCZndXJfAgn8J5Fo0yTwm8M3yI0xMgpOFazIU4sFNNAu6kFx8I60En4L3CkiIYMMfGjpynom4I/oSr7EBylNUJEnUmLi2pMuJbBqIDTHI9B9lhY5y4I6R4uYx63gtHJbV9+1RiX5GLDkjTeoeO01cg/+Pe7U78ob3T7SdzmSltWf8Y9jj8O3pogx8XZNXQgyn4HkMOSvhY0FcgIVlClzxG3BnGT4dAgc+jK8f7ZhdegLFt82C+fzBbBmDR/EIjwXSKZ2+DC69BlLdLf9SAp3p0d3RboXoHyULQYGWuBfLI1IwBFZ61YhXU3ZYdAUgGhEOSX9a1UBieXKcrsUaT1eRlf416PF8wXUUklG5y4HuV4KUeXjHVQBykPVhatRReUTRHBOBU5AM3pWIrdNKlu3ZQqNSMpYRFKsDqt2Q/z+9w1khGFfC87zbz0M71+IHF3swOwYXOSCPh0uBb9ApGicAzcauNGC+yxZtXe3JKjt+IH9LzTA71JEVlpRjM7bzqgd1d7a24qvERG7gsRVaQL/fSTISAK/OSxAlYfu1VDiguJkiLlFIqqz9BVH0e8uC7okS1iabrRQ1aNWzSsG8msh5IL6JOi3HsqzwIqpPRPygbdBA/OMC/YwUGW0GOfY0BzR/rwfzyTpFoZBfo2A91qKJm+658/4ug0iMLCziK8fBst2kTnpFMqe+z37zJlD14kTSQPWvg7DKyC8EZq9UJkO6Y3QtxkyvUokfW4qTHvf4cnUp3j33LtpXLoYyxfAl98Hd0YhJT4PTnMzrrdmUlz+DzDnENxlf7hxBvcP3pl9kKtjjQNJlbA6DJZPNuB1feWgSXdB3Tjpb1JDchKlN8BqP7SkwaGWzs/LSNexN6psPRB/L3ZHJGERChw7EbVXbkekJIJCzXbR28qNSF/xBGp33Iassa1upPZumeI40TgVGNBJ6WC9UaVkoQM9QjDdDUd5tiYsfwA+MnCOA585cJ172xySd1EuSjcH7jZwhyXHTA9gB0vH3fHln0Vi5CtQ9aMVxahSsivKlzmvk+cWISJzJYlFIIH/DST+DhP4zWEZssqaClieDA1JqmR4otCQAmn1qmr4TJwkpKrsX4gqGNVoHs3nMdi3Asq9Erz2XgfFedJC2EgI25gGQZ8yREZ7oCSmK/ThFiyLamptmg0eBypt6S8+NLK/jiktpWHmh2Tve9HPPmZfj+G40wtY8sADTJw4kXmL4cQvIKUO6mpgY2/wRaBbJeR5pCNY0x/c69bzh3dP5uviWQT67UDe4dfj7z0ay3Zt8xpOqJmm5XMoX/gmr+y1K6ELLyR88800JQWwouAsg1ofBMKAA7stgqLecMhIuM8tglIZVv7ICgMNeXCLLWdMX6T9mIwyN2aiCselyFHyCKomXYcs24/Hn7MF2Xiz0CJ9KhJyti7+f0OW4D6o9fYQcstcaaQvWYYcOqmdEJG5RtbgTVEYHIGLvbBzu0/UaPz1PAb2j6lCcZO7zVEDqrY9gxxWMQPPGbjbggstBY5NtOLVGLZ+zmOINF3F1nkgWxAR2Q0l817ItlWPzfHnXxk/Zwkk8L+ABBlJ4DeFcuQaGAXEKqAoVe0BECEwdrzFgloxfo9aK1HUcliLRJ/pAFHo2qDKStQFKfWAUeBZoFnaEzwQ8mtK78AkeDmkKknAgkpHC5/PUriX29ZV/MuWQqlWf/klAP4ew3/2cVuWhb/XKDbNmcuHVXDBy0r6ZB2si0eZ5lZCui0LrdcLL2XP56Mb9yGY5CXvyJtI6j36O1/D9gVIHTWNlBFTqf/iFd6+7wHS5n1E37fepqYmm8Zq8ObqvHlQ0upnUyFztIhCv5ByO/rWg9cNPbPho7h76USUGno1InM7I93HSmShPRoRigfRAutDGpAT0Xv5T9QmuREJW59FpPKa+OPbt2V6xElGXxQj37EtEzPalycNhCOwswMz/Irxb0VVfP8mGQhF9X6f6dp6Tk00vr89DXzkwGoLbrUUYjYa2M/aWowKIiIPo9bUdWw9n6cEVYv2QC2Yi9iWiFQgwnUpsqgnkMD/ChKtwgR+U1iA9CKjgPWboDJJFQyXo0XHFeXbrBGvJVurE6+Q5CC9SQbKtfCHoSWiibyeKGzsJjuvKwq+kHQi5TnSoHQFervUrmhxaTvZDmTZmlPThPI4HjNQ6EjjsGjZMmx/Mq603F/k2L15vQhXVnDZswZvEJI3QkkKNCVJhJsRUkXGBzwy4Cvm3DiVaG4hXU6893uJSHtYtov0nQ4j/9g/0rR8FdfstTeb1zRSk6pzuakrHPkWlBXCsEHwhA3BKHwWhdQGyI5BMBtGuiVW7QrchxbYCNKOHIPaLLNRdcCgdkg+IjYnopCzEHLe/BMJPUeg7JBuSIPhY2u3jC9u290XONzqXB/ye+BhA54wTDPwpw5EZBkSNx9noCwKu9hwontrItKC2kjDDLzuSBx9mQVnW3FtynaIyL1IqHsD2xKR+xHRWoIqIh1rV3WIkJ1PWxJvAgn8ryBBRhL4TWERKu/nAgsr5aKxLGk8Ym5VNQzSi6QHpBmJoKtrP+q1j0GCypw6qLFUWcmsguIuIiKOrYUqtwa2FGgR2sWlbS10IOKCnQAnBkkuxb9vASrcWkQzLLUFNgSDWF4/VscV8SfC8vjBGJLXR7FrIdYAG3KUiZJXCyG3rtZX5gd597GjMOk55B5xPbb/p11D+wr6kXvEjZQvX8aKe6/ASZIoOLNBoWcr94PBOcrOCIch3YEhNbLV9siV1uFVFG0+C81YuQ0tsjegELXTUZXjQ6T/CKOKQXf0Xk9FVYYXUJrtvchVMhG1ZVrdMlca+MyI4FyB2mgdsdZICPqOA11CcLytaPf2LZw34tu/woH5UTjRBVM7sIIaRJz2NPCQo6Czoy21k3YCTrJU3WgPB1mKP0dkqv3MmFYiMhmR7YvZtuTdFH/+GYiwJZDA/xoSZCSB3wwiqHzeHQiFYUMLtCSJjMRcELbiZMQSGUkNaHBbJG61LbEkmJwEVMagTxk0eKE5CQYUQV2GXifq0eC8xmQ5RXwGRrl1VVrjQD9LFYJwTK2ZCkvWy/EmPiDPUl9/S0oKJtSMMc4vcvxOsAnLcuF23Fgb4jZeB3KrJcK1AY8Nf6m8karyNWQecAm2L/CzXtOb35eMPU5g7TsPUv3FbCpy4bB3VflYvLuEmsaBLlFwWjRor0+GpvO+Yik11IvO+cnAS0jIegUilDeidkMlIh4nILJyB1qUH0a6jYdRNeo62kLObkEVkP2NSEopattkdUJE5hi1Pb6KwYAQXOaB470imK24G1VwrnDggxhc4YaRHT5hi+P7dqCBGx0YZStV9SYksG2tjLRHDJGwJagl1b/dfaWIiOyJtDKXsC0RCcdfczoSxCaQwP8iEmQkgd8MlqHqxihg4QZotAFLc2fCPlUGXLE2MmLbWggt9AFfF99OwEhvUdigPI2wB/quV0XEQs9PadZjHZcW/DleGOFA1EB/l6yoLgfWuvS79Cj0teETA4tstSZ2GTECJ9RCtKbkFzn+cPkactO6UbXRYlOWtCtp9WBHIcnSsS4bVcOXb95D6s6H4c3t+Yu8buqY/fB1GcDKZ2/EF4GhK2DuAcp3iQLdo1DhwA6VqgAEc+ETS6RjEDANCTJvQETiYpT38QwSIm9BrZNhSBN0MIo/fw+5n25EbppTEFFp35ZJM7IJ7wYcb21NLkD6kL8YWW3LIzA2DLf5YI92K34MVRyWGDg+Bp/F4EY3dO+wreWIFB1gRFgOsZX58ggiIhdbOt72iCKisgK5q3q3u68ckZ+pyKp8KSLNHZ9/Z/ycDCCBBP53kSAjCfxm8CVaOIYDnxVBi7etGmAZVTTcMYlYW9car6UFyY+uvgtRKd8Xlmum2ScCYkXBHVHYWVKLHCNZVdKjNPvhQlsD94wF3W0FnoUdpb/uF3dSrLYVMz/OSIw5ZNw4LJeLlnULf/axGydGy9qFjOuyD1YIalM1CDC1Hvy2WlOZWfBc0ePEnAipYzoqFn46LMsmddyB1C+cRdf3l1PUB8onq921xVFUfn4Y0oOqVK1K03kZgCoV/0RW2wtQe+JmpAUJoaFuFyFy9zqKhz8NZY3MQULTixEZad+WmQF8ZSQgvQjZZzuiPq4P+ZtRNP0UB+5Igr7t2i51KHytm4ERMSXpXueG9A7b+xRVdfZ04HIHLrJV/XoD2XKvsLatWrS2nNYjItK93X3l8WPcO36clyEy2R6tGpOJyEGUQAL/y0iQkQR+M1iLBIvhWiguVw5IUlRf3WF9dcU7Iq3/GJ64kyaAnAgjkK00t16LWosPkpphdb+4eDUmJ43lhnXdtSDsHIYNcSuvcYmQzHQg2UBvW1beSo9snN2d+FRfYGBmJl0OPpjGJW//7FZNS9F8nOZajsk6g9UFakdl1kCuKz43BvjLXlD+wYv4++6AK/mXlTgGBuyG7Uum6b2XST0UnCSFfHWJQYEDBZWqXFXlwQRbLZQqZNH1IlLyFXKLDEAC4vOQtiIS//5RNJV3MmqZVNEW6d6+LXNgvNqxFFVF8johImuMhJ7vOpAfgiNtuM4nPU8rViGR7NEmPrnZggtd2w7NewMd6zAHrjXwB0vhZl8jknSVJcFyewTjx1yChK7tB+K1EpFpSEtzOfq7bg+DKi4jUdUlgQT+15EgIwn8JlCCqh19gI/XyQVjA7nNUO0XkQj5VBnBVpsG4poRpEuoRi2eMgcGlEKtG5oD0LUYSrq06U1cEfh0R9jYU2Snh1tXr8scpZrOBlJjcYLjktCyZ1SR6Q7SsNQDHwPWxRcTqdhI46K3f/KxO5EgNTP/xqDsXbH7jcaF3DO5DjiWbMqzR0Luuhirqhfh7dqxWfDzYbk9eAv6UVW5gOWjlNsyxEAwBiVRaGpUOFzXTLUlnkXBZiPRjJ47UevFH79dgzQj36A2RU9ERGpR5WQfNKDPYuu2TLZRu2cIcKal97cjZhm1PL6KyW58vgdO8249P+YDNOzuKgNfRGFfFxzZIdq9NQ+k1ihx9h4DD1hwvyV7+EhLRCS9w+s3I4dQNZpR015wWoGIyH7xfbiczrNCnkYtrT07uS+BBP4XkSAjCfwmsAC5aIY7sGS9dBouYGSphtZ5ItKNuJy2QXegK063kZ03hFwJsRgMrIdqrzJEBq6EujQtPhGPUlxr0yDilRbE41Ip/lOjPJLdUXui2oKJLujtxGPiDfSwtBi9isK+6nfdlaQzzqBm7mOEy9f+6OM2xlD9/iPQWMv5Y/9B0AeeFshulD5iGFASgE+HwQ6z1xGONuPN6/Mzz3bn8OT2Yk3tEqZ7dIxERB58VSINI3NgHzf8ydLsnD+jttUsNAhvNSIYh6DHP4isv3ch4eYrqBUyA5GNjm2ZVUYVkzOB8Z2QkGi8YvKHuD5kVBhu9sGUDorQB5Gg9CYH3ojC2S7YvcMnaQQJlvOMBiO+aOCvlqbtZgJDLe1TR3lwY3xfm1BFpP0AgMr4NvdHoW2Xo7/pjnglfn4O6uS+BBL4X0WCjCTwm8BitCj5S6C0RR/WqSHI2gjGqKoR9rWV2Nu3adxGi0sSMNuSFTY3rhfBBZnVxIUj0psUd4WUWk3qzY7C7yxY6kCJDf1tjZivc2RFneCC0ri9c52BtZZsqBuRmHEo4P/THXj6DaLshWsIFa/4wcdsYhGq33uQpq/f57KhD+PpOQA7ohaTy6XAtaXAW3vB0R/C87s36th/opX3+2D7U3AFG3gRTb/9PCb3zOg6kbEtOfCVBYehtsXL6Dz0RAvxdaiyVYOqJYvRotwHaSp8aIFunS3T2pY52MBTBj6Kb6NbJ0Skzqga8ZgBKwzjHeWHDGqnD3GQbuVj4KIYvB6Da9wwqMOnaBMiKzsZ+MCBLy1VRE6z5IQZYqny0lHjUYdSUSOIiGS1u68SEakDUYvncrYlMqBqSRkKXEsggf9PSJCRBH71CKOqhgeYvwaCjghGjzqoDakl44lJwGpbW1dGvBa4jPr0WagtkNmgcnkwrjWpTVPImSsK3jBUZsOWLhCzYYwFybbmgHhisMWGQw1kxrRf/W3ZMxtthV69EP+P7IXaD0VA99oUsp//ANfAwZQ+cwU1cx4jFmzc7vEaYwhu/oayxy+m+av3uXD0Xxk47kSsGKQ3QZqjhbAC2NgXMpsgHIJPpsVLAE70F34H4nBiWC43DwJ2BMI27FCtalRzBhi/EkiXoGTVRtQe2w2JUr0oT2R/VCH5C9Jt/BGFoO0bf5n2bZkCo7ZPHiIS/k6ISFHctvueA7khONCCG32Q3e7TsRFN3E0zsGcUFjhwsxvyO2yvEhGjAww86iiH5g+WnDpjgVGWsko6BpJV0zaw7na2bt1UISJyENKfXI4Eux3xKdKhdDbHJoEE/teRiINP4FePr1FpvKAFlpaA44A/Bn3WQlmWnDTuaJyZx+PgXe00I7Zp05xUOzCiFNZYcn5kVUFxdzlT3FFIbQR/FErztSBke9U6CBhVYFJtyDKaVeMCVtnwkVs24hgiIQG0+L0DZDRCNAi+pCy8784l7abbqXroJhoWvE5g4G74ug7Gk90dy+Uh1lxLuHQNzWs/J1JSRGHeCP584BdEk0bRaMvlk9EEmbau3h0PfLwrTHwa7roFLu7SjVOASE0Jvq6D+aURqSnB6tkDY6BXGBo8kFsl4jEwF06wNPjtKUQs3kQizG7o3MxAlZwXUDT/k6jN0xrp3n62zAwULf8kst322c7qPDPuqFkXg75hOMkD+7q31n6sR/klJxoojalycZV7WxvwBkSQjjFwnQM9LTjO0nPHA7taajF1RCVy7SQjvUt7olGNWk0Ho1k5l9N5jPsSpEW6jAQRSeD/JxJkJIFfPb5ERCOwHqqiIgGZzZC0BSqHoawRByyXdB+tcfCgq3Fj9PwKIBqD3nWwxgdhP/ReA1XZIiIAuRWQXg816aq+LPCowjEuLM1CD1ul+4gDDTacakHE0uLaYmC0pQU3BHhbwKmH3GL4cgyk13rwnn81Teefiv/+R6l973maPpgjEUsrMjLx7DKeqafdy2HhvUlbYbM+RdH1GXUwLJ6d8g0wfyKMmgfzJkNwMEx3ZXB2776ES4tg2ORf/H2IVqwhMOUQdohBnQ12MzRFoVsSNKWoLTMEEaXeKI20G2q5nI6cM2+gxfsm5JqZHt92+9kyg4wqUUXAtUDydvQhjwIvG2iMwdAIXOaD4R1KFnMRsbnWwAdR2MOG/e1tY+K/RkTqGAOXOjDJ1vycc+L7PdWCKZ2ck9agtfT4MbXXgFQj4e4hSAdyGWpBdcTK+P0zSHygJ/D/F4k2TQK/ahh0xVptYN06kQCfBT03gYlBTbYqIV4jO66hQ2XEVpm8G2rVeMKQEVFGSdirSb0NqUpwjbq14BcWA5baNH0smGGkGUkGMi34hw2bDWDDREdkJ9mS+8FnSRMRMWBvhMJNsHyIqhouA/XpkJpcQOjGq7EXf02gpgG+WgYLF8OGjVBRRfqTr9Jz5DRosNmYI6KU3CwRqA9pCmq7wBo/dK2Cp08Htwv2jkJ0/Hia137xi6W+tiJSuYlw5WZydx9PchhW2jCsVK0zO0/EbLSlatAZqCU2BrlRjkcL9bVI2Hp//DF7xN/ft4DnUZujm1GbxB3/uTMiUhvXhzxpgAjsHJU+pCMR+TvSmNweF6oe6YIDXNsSkbmIJB1k4DwHjrKVfzIDiZUP2w4RKUYVkRykb+mMiByKSNqlbOu6Af1tP0XnGpQEEvj/hAQZSeBXjS3ow95XBZvrtPCnGShcrfurc8AdX3ddLrS6ddCMVMRzIJqQXqQS6UU8EYWGWUbC1YgXGtJhTV89122kCVkT15zYNvwr7pZJjUGBS1UbKy5sHGtgIVqI0ipg3GxYH88qCTRByCMC5I6K/FiAE0jCHjIERo6Ebt2xsUSoGmBzrmbBeKIwulkVoRLUpnlmEuz5Drw0A2JJUBODTx3wnHgGsZoSgmt/ftBaezQsfgt/Ti7hAw5kQxSSHejXAMVu2JIpd0kIEaYDUMvhcaQReQfpLR5CSaTXIuK21WwZoCyuDzkEONjaljQArDYKQfvQQF5IFuBb/JDf7pPQoHbIW8D1Djwfg4tdsEOHT0uDiMJSNLn3Igcui7fb7kJalxMsVXg6YgPKQOmGrMbtc0JqEBE5DLX4LkFuro4oQ22hS+lcQ5JAAv+fkCAjCfyq8SW6ovSvg/qYsiICUfA36fvaNGk8wl6JGw3K3mjNGfEB9ZbIRNhA11ItAiEf+Fs0eyapRRUT24GXDpKY1UJ6ExdwhVHse7MtZ0d+DFribZogkGFk711h62rZ3QS7zoay3lCfDNkVgKUpwCkNUJ0pvcf2/nkdAyYENVnKTSmsVqJpMVooH90RRi+Ghokwb7gsxWsiImpX7bATo8aOo+GjJzGxX0bIGqnaTNNX75F21plkunz0d4O/UfHoXXOhxQ1HWQrx8sTfsweQUPVJtDjfiOzRJ6OqR3u3zKEG3jbKJrkKGLwd0cQHRpWIJTHoHYTpbrjQu7WotQU4Ep2LY2PwZgxucCucrj0cNDcnAuQ7cL2BP9rSbryAWjRnWVtP1m3FGlRx6YWIVfuKRi2aI3M4ajVdxNaumlZUo3TVC+m8YpJAAv/fkCAjCfyqsQRojkD9Jog5msGSvEX3eW0t6t4YBL2Q5NYiBG1uh6AbPI768uEYpNQoS6QlABk1UFIoq683IidNeiMEU1UVcaHprB/aqgSMsSHJQKktB0mLCw4NiwDNsqDMUuz4uM8hwwNvjIDUOvCHlGcSdSlu3rjBdn3rJsa0W0yNUTWkKUUC27QmOYSqUdz4hnQo6gY9SuHhMyHHQCQIYTfcEoKrUyxmPPIwLRXrqfv0hZ99/o0To/qde/F078bul11JIAKLY5BVB1EL6nLkNhmAMkVWoarEDNSeeZ+2SPextLVlWt0yvY2qELWo0tAxhh2kD3nQyJGyJQpDwnClDw71bF092YzIzVQDvWJy2dzo3nZwXhhVLvoYCZofNPAXS62iz1F76UJLluOOWImI1QBEnNprPOqAPyHb8ouIaOSwLRrRMZ+Ngt8SSODXgAQZSeBXi2D8a0kxVMV/8BvILFFVxAZwyXIb8oLP1aYZaW3TbEiGlJiu4t1hCMTD0UJe6LYRyvNURbGMnDTGgoZkVSCC8UrLDhG97lpbyaOO0STfYTbMsaHJVp5GpoFp70BKFB7fUfkl/qCSSZsDagtVZ8VJlNExRInvq9HNBmLueEsnBfJadJcHWG/gxb3g0PfgmcvA5QUT1sTeIS1wUapsspcNHUvXk6+m7pNnaVw26yeff+PEqHr7XkIlK5n6+GNU+QLU2RBoAW8zFGRANy/sAGxCi282EqFOReShhrZI99a2TDMiK7VGce6TUZaL3QkRqYnrQ54zEAvDuCjc5oexHfQh85Fd+EoDm6MiNZe6trUCNyCCOcnAJw68ZuAJC26x1L4bBFxuaYZRRyxF1ZxhqK3UGRE5Es3iuYDOiUYQVU5OYuuI+AQS+P+OhPg6gV8tliBXxlfrFTvusTSgLq1BC3okXUTCG4XaALi9tGlG4hWSomTY5BLZKGyQbiTsVXWkx0ZYNhTSa+PW4BhUZEGLR1keuwOLjcK5LAN5thasrJimtVa64BtbWok0YOLn4FoP7xyqSbrpdRLHNqVIHOuKAC5VSPwOhCywYtq2FZ95g6NDaEyDzAposKQnqDeweAj02wwpu0H1MOgS1uyVJGCvFDk+NrRA7jpoPv9aaus2UvXPu4jVV5C202FYdsd0jO0j1lxH1bv3Eyyaz9Qnn4Rdd6coAj0dqI9Adgh26AU5FjxvqdIwDYWFnY9cJsehwXiwtVtmMIpsn4n0EjnbacusNCJXXxnoHoadLDjXv62o9WmkvbjbwDNROMCGPTs51NaZMMcZeMiBUktVm9MtEYNcS/vTmeNlESJXY+PH134X2hOR51EOScdZNaCWUGsLp2/nh5xAAv9vkSAjCfxqsQBIbYBYuabiJpn4It+kFkpzVy3m7lg8fdWLUlTjbprqJPg4A9IjsMUPvctUiWjxQtSWTsQV0/C75mTY1E2v6zYw1oEhbphnoNkN2UEosMCJiRA1AqkWVLlUDei+EXI+hzlDoC4F+q/QwL2KXGhI0UTgFr+i5Y2lVoFjwB+BoEfkKYaIiWPrhqWKSMRAkxeWjoL7ZsHHl0JVDFZH420rLzxmQa9G8FWCrx6a0m34y6P4unWh9p7baC6aT+akk/F1HYLVmTI0DhON0LRiLrVzHsPjhlNfepkFBx1Iv5CcQrk1qgolBWBlMnwUF66eg+bQ3IdaSrehPA2DEkcXo7aM30hP4kO6C/d2duV9I+vuGgd6h+AwNxzp2bp6YlBlZTFwhwOPx+AMFwzvpF7cSoZOMRK1ei24z4JjgdHIJXUxnc+J+Tx+XLsAZ7E1EalnayJyNhLndoRD25TeznQoCSTw/x0JMpLArxIGaQC866EpPhTPigK2gr88HqjPERnxxqQBSbf0RGOJEPyzJ0ypg1kpEHIgpQoqbGhKUnWlJB+SG9ui5EcvhbKueu0MW1NorThJ8BtItyEUhWKjBafIBb4YRKJw0DsQy4bPhkF6TdzhY+TaiXglqsWWVsQxIh/+IKqEeOOtGpf0Io5L1ZtkdDXtj8GsveH82WBfCH/wQGW8fVPogxUuKAxBfYNyU9Z1h5ICyK6zMZffQmzn/am79FTKnr4CT15vAgN2xVvQD09WNyyXByfcTKR8HaEtK2lZPpdocy299zuEQX95kGVdCujvwIIYdLflRgq6oCAPnrHhUkuzYi5HrZpK1AZJoS3ErCdqy5QZpZPuhwLEOkPUwMMoJr4mCoMjcI4Xdu7wSRcGTkStkAti8LQDM9zQtZPtLkKzgk4xcIkD/Sw4z1KFYiLSlJxH5x+mH6H9mQicwtZEpIGtichZdN56MfHzMA61tBJI4NeIBBlJ4FeJTUA3B77ZoGwRj9GCnQH4IpCWCWXp+qB3RyGa1LZQ1KXBJitGy+Wns6ZyBfZLb+D4MmmK6HHNAeixHjZ3l2jVHwRPMMaCh08ndOcKYq++wfrMTEah9kuTo8XPWDDfpdknMUvuGm8Mdl6g/bh3AuSVQWMq+IL62hJQzHxzKmCr1RQz4G4R0fBGUUibA5YdF7TGqyLumMhMZXfICUG/cXDlYNgQkk7F7xFxSY1CVQTGroCiXlCWB/3XQ1lPsBugZ+EupD3wNcUrP2T9uw9RP/9NzEe125zzpNye9J82nZGXnE3j8EG4klWJ8IUh3Q0D6qDMBy3N8EIW3GHJhvsWCmG7H2V79GTbtsxnRgmk5wFdtkNEqo0qKp8bOaZGxOAKP/TsUOkoQxWNQwxkOjDLgZvcqlR1xIfIbn20gbMd2NMWGToa6VoKLUXVd7ZLM+PHs3f8ODoSkT8incrzKDelW+eHxRPxczJxO/cnkMCvAQkyksAPhuM4FBUVsWnTJhzHITs7m6FDh+Lz+b7/yf9hfAlklEO0KT4ILwxuNxQ2g8cNSX4obiUjMYgGIBZVVSBsxag+bzprP36OFJ9N8MBJFPxzFhaZtHhVGem1AZYPhoxacIVirLhjOiUfPUeyz6Zlr0nwwSwuy8pkvgPlBsKWZoe06jqiyFGR/DUEKuDx8Wr7ZNdI7+GJQTCgPJG4fAWPHR/YF4bkaqjMUduJuIC2dbUz8YTQSBSqsmDBBDhhDnx4J7wcVfUhCejqhiFBOVgy62FTIdRkwgEfwsw9VZHJroCUJsBtE953L/yn7MXk2Ya8z9ezsOta9n0zzFtTkumTMZRe3my+2RPWeWGUFz4BdjQSyZokuZiW5sBAD+xjtVl330UVjyjSQryDqhFXoBj9vyOtzvW0DTLsiBVGupMlcX3IKOBC/7YEYxHSZFxtYFlM7Y9r3SJ57WGQkLQOmBwnIifZyoM5BRGRQZbIRGe79A4KI9sfEZf26EhETkFOp87wImpJ7b+d+xNI4NeChJsmge+E4zh88MEHHHLooaSlZzBw4ECmTJnC1KlTGTt2LCmpqeyy62489thjtLS0/Ld391t8DWxepwXZ5WjBCHggqwlSU/RzaZraIZYjO+6iVEiuj9FwrojIs4f6mXO8n6SiZVQfOIm6hhqCPg3UG7lE1tmQHePru0REnjvUz7zj/aSsWsaGKZNYU11DtVHUfCT+n1ZnQZIDKTZkrIKUTbCmh7JDemyQS8exIZisSHmMMk0sWwtnLwdSSkVqDLIUQ5ujp/UfOmrruOZNgmnzYeY5cJ4byhxNHE5KguFhmNMkohZxQTAJdl4EcyeDNwW6VENWhSozVVnQkAGBRpgy0+LrQ3qzX/OeFA6YRmzqHqQFsqnoCr5UhaotcMOuwJgIVLvACsJXER37CTkwMu5A6YHyNoaioLMViHjMQFqXG1FV4Dxr+0TknfjjFsf1IQfa8HvftkTkRSQevdfAnCj0tuBc17ZEJIbaIh6gr6PWzOW2bLYXozTVnSyRjM526TVERA7nu4nIC8gV06vzw+IdZFk+ajv3J5DArwkJMpLAdrFy5Up23W139tprL975eBGeMYeQd+RNFJ7xKF3P/DsFx91B2sRT+KosxEknnUTvPn156623/tu7TTNgh2BjiQSjrpjEkzkeSAtDIJ67HXIpnbQuBVZ3hd61MTZePZ26d0REjhjqYWSBi9nH+XBtXMbiKybR0lJDUijubgnFWP2H6ZTNFRFp/3hn1TJOnTKJYG0NxqhaUYPEmlEHkiKwwyyozoP1PaHXeoh54lWRiKovYY9ubuT+wQJWS7Qas/RjzIonwNLmAMKIMAUzNTfHMxTu7g8lBroHIStVrZkPmyC9TCFtxoJACOgJBQGIRiEcVPJrzCVHT/cm2PdN6Vdq0mDcanhnKgxfDsVdgSFQ76jykWYpjGt5CL70gKcUCpKgnwve8MJyS62JHD2NtShx9Xi0iC8x0lOcDOy5HRISMXCf0TC9TTEYEIKzPXCCd+shdgbpUP4BPOjA41HFuh/WSbR7EL3uMNTeu9HAn+y2abyTketoXzrHi4hkHIOG27VHIyIiR6Oqy3Q6zyKBtsyVjjqTBBL4tSJBRhLoFC+88AIjRo5iyeqN5B11M3kn3kf6LkeS1Hs0nowC3Ol5+LoOInXM/uQecQOFpz1CQ6CQ/fbbjwsuuADHcf5r+74YKNwIzWFVB1wxSPJBqht6++O6CtQ6qUuHT8ZA/4YYCy6czpZ5bcSiFSMLXMye7sMqWcbGUyeRt7KGoh4xKi6bTs0H23n8cT5Cq5bRsPck3FU1uC0ludrAgBh0XwthF3w1GHLKpTupT1XIWdgLtemqkMRcbc4YbzPkl0KTXwTEE1IrBdoqIyBtTHMSLBkAhZVw8tEaZd+lBfZKU95IRRP0Wg391silE/XCcRuhppeszL2qIa1KlZfmZEgLgr8GRi2CuRMk1i3K13OTmyWEXZsNeRG5knKRbuMN1CbbuxrWByA9Vfkrt1oS8c5ClYhHkJBzsFEmyPsonbTXd+hDrkKD7kIRGBKBG30woUPjOYKqDxuAGxx4JAbnuWC3Tj756hDh2NfAEgf+auBRWyLUx9Hk3cMsfe0Ig6ohryJCtV+H+5sQETkGEZHfoZZPZ1iAWlznkCAiCfx2kCAjCWyDf/7znxxzzDF4++5M7gl/JqnXqO+0cwJ4srqSc/j1ZE45g3vvu49zzz0X0xpn+h/GFwYq14ETFRFx25DrhYAfcuIcqT5Js1u2FMDEeTGWXDKddW+1VUQ6YmSBi1nTffg3LKPk+Em8/fyxVH+4LRFp//jZx/lIXbWM8H6TCNfVgNFE2cZyyK6H1/fQ/nUpU55IbTqkBMFxqyLSnCSdiHHpKn3gKg3fsyxpSvwhZY5YxG8GMLqvKguyquC1yWqTJIXgsBRlanjrYfQiGLwcPt1Rj9+9BIomQYmlxNmSEKTVqUrjj0FLFkx4W6Rn6TCYMhc+Hisx8JrBkN8TpkblIvrErZyQp0MwzgtDGhSzXpqsqsvvLVV76lFLIwnZY3c0cDNy0lwKBLbzJ7fcKDRsroHssGzUt/mhb4dskCo052Y4cFAMno1JH9K/k0+9EkQWjjfwogPvAU9ZqqbMRi6W4y3tZ0cYpGt5nzY9SXs0IU3MUahychQapNfpsSFB70W0pQAnkMBvAQkyksBWWLt2LcefcCJJA3cna/+LsT2dJSd0DsuySBt7AFlTz+Ghhx7iueee+zfuaecwQGkNbKmTbdcVg2QXpLlh2ACgSnqKh/ZRi2OnRTBv3t1seeVZHtnP1ymxaEUrwaB0GZVzXtguEWn/+A+P9RH6agnW+WfiN9BcAkmNsLALVGVAWj0E/bC5EHpuVBWi2a/UVeOSqDNmQa9KOXdS43Nvgl49N+aOO2niVmDb0a0hBSrSlNhqLOjhhcctVTt2/1jx7+9Phj6blS8SGijHSpUN+dWQWSn7cjAJanKgxwrougXW9IPMGljdAwqqJXzNyIa8PIiE5dBZYenc7+JArQUpZQp3G5GkNtUo1Ja5ADgQtWXmGc1iORLY3+p8yJ0x8JZRimmrPmSqDdf5lPPRHkuBg4CzDKRHYWHcMZPXyXZXoeySswzc5cBqS0TkaksVlcFozkxnBMIg6+5c5KqZ2OH+1orIUej4DqUtyK0j1qH5OokJvAn8FvGjycjcuXM54IADKCwsxLIsXnnlle99zuzZsxkzZgw+n49+/frx2GOP/YRdTeDfDWMMJ518CsafRtY+52FZP42rpo7ah5TBe3DW2edQUVHxC+/l1qiqquLTTz/99uf1QPoGqItC/aYVNJSvJj8J/F7o3wvqGuHuAyGnHnpukKbE2W8a7swM7l/gUN3y3dWckQUuvjjFz0cnJn0nEQGIOoY/fRrB2DbdjziatGooWAMtPlifDz3XiyhU5UByVBWIkFfVj8YUwJbWpHetIuxbAoqmdxzNqvFEtm3PWCgEzTKwfJCEqVkxWO+G3BI46hX4eiAUF8DkjyErCZILYa2lQXo9DFQHIbtS7aHKXJGlKe9qgfxwAuwxDzb1kNbG1RWOzIJKA19G4CmvXEKTQ5DlheIgZDZDJBVGB2A3C96xNFumL3CMgceMrNjXAgO3Uw0JGwWH/Q3YENeHnOKB07zbBp+9gbJL7jHwdVTnZIa780rLF2hfzjJwpaN5OX+14FR0vD0tuNiC7p3sk4OG1c1HYWW7d7i/mbZZMy8jDcn2AstKUEjbZahSlEACvzX86NWmqamJkSNH8sADD/ygx69bt4799tuPSZMmsXjxYi688EJOPfVU3n333R+9swn8ezF//nzmzplN2uTTsL0/7yMxY8oZNDY385e//OUX2rttUVZWxvjddmHXXXf99nU+j0J4A5R98zFv3DyGl/8whqr1nzOkP5S0wN1T4aCPIVoPxTnwrwOhZM+h7P3mbFY1BZj8VGgrQvK3hWFu/yi0Vcupb5bNLt3bxAklDQ6nv97CisrYt7+LOobpr4R4/psouX9/ntT9D2H/t5UdUpmmqPf0emWWRHJh5FpoCEgYGnOBz5aWIsMBXyn0qoBwElTFpwVH3ZDaEp+jY5QYazuqonjiOo8Wr9xCzX7osxG6bIIFgyVA3WUxNAyCXgNgjQ1bHCi0oXcNZFRKK1KXAb1qYdd5mkzcEICGdKhOh10WQmMhhHpAVgZ8GlZFoacbTnBgUUxzdzIaIcsBb57IzgpL1YILEcH4G6pi3EDnOR8gonM1mgMTjMCgCFztgymdBBPchfJKHjXwbFTDCU9xby1obcW7SCh6ooFzHOhhw+2WKjV9UJjZ5XQezR5DVuLFKPtkpw73N6OKyOGoFbU/MKLzw6Mqvs8X03mUfAIJ/Bbwo3NGpk2bxrRp037w4x9++GF69+7NnXfeCcDgwYP56KOPuPvuu9l7771/7Msn8G/Egw8+hD+rC0l9f37OoyuQTtLgCdz/4EPMmDED2/5lO4JlZWVMmjCe2pL1HD/SwxlnnAHAxn1OZ+WSj5n5p73YsSBG1Ilx90170muPD1mXtyNHvQkrsuFvByuI7ND3IZYEyQeNZN2Ls1lx2EQmP9XMzON8PLY4zCXvhQAobzLcMdW3jXampMFh8hPNrKh0eH1VlFknBOiXZX9LRJKefp6d9zmcvWZCUQNs6qr2Sm451GTJpVJYKRLR6p4Je5Wy6jWQWgrJIegSgaUx2JwHyU2yKzug1kzcTeNy9Fx3TITGFVWmSrfNUJIGwz+H4kLosQXKB8GQwfCWC1oMBJBT5K0WERLbkWahJgn2na8KzZzdYcAKMB6oKYAcL2Rmw5kW9AvDEC+UW/B5EOo9MLIFGpuhwgJvqsjKBZbcM88YTbC9CnjJ2v5Cvcyo+vCVgR5hzRq6zAfZHf6cYqg6EUKtlvtjcLJLZKQjDPAMErfuZ+A0B/axJSo9OH4esOK6lU72KYqIxhqk7ei47y3x+49ARGRv1JrqDA2IQJ2LxgIkkMBvFf/20LNPP/2UKVOmbPW7vffemwsvvHC7zwmFQoRCoW9/rq+v/3ftXgLt8N4HH+Dtv+tPbs90RGDgeEqXvMfq1asZOHB7kr0fj/ZEZPbxXvpn2aR5Lc444wx2OGoVS155kB0LYrzzOx8xA3s/HeK8aXty9H0fcs30HWlMhTFrFMLVvULD7j73QqDvSEbcOZvFl01k+EMNbGmIMWN3L4WpNue9rbG/7QlJKxFpCBk+PjnA6a8HmfR4M2O6uHl7bYykZ58n/aDDsbZA8Wr4bCeRhECLSENJAThe2HkNLEuRiDXoVbUj2UiDkVsHA0qhKgCuZoj5FT3fOqsG+Db0LOwWIWlM1q9dLvC0QEkKDF4J5fkwZA18PAl6doXPXbLWljgwxIZP6iC1WhH3ER+4fLDjbPA50OKCpUNhxNcwaS48dzYk5cEXfhjvqC3WnCwSsykKA5KgvArymqA0G1bb8FCciLTOjXkctU4y2PaDyBgJOV8AljvQPwy72HC6d9u8kVqUcLoLMNGBh2NwmWvb5FUQkXgQVT7yDZzlwCk27Iz0HAcivcqFdK7bCCPHzSbgEnQ87dGCxKqHA28Ce6LheJ2hBbVxTqXzeTQJJPBbwr9dwFpaWkp+fv5Wv8vPz6e+vn67IVm33nor6enp3966d++sY5vAL4mysjLKSrbgLdie4fDHw1vQD4AFCxb8YtsEOOXkkykqKuLD47wMyHZhWRb3TvNxzg5evnj+zm+JSLLXIs1n8e6xPvLdLTx14TQO/8zhjTsgq16iz1oPfDIMCiLQoxlcY0aSdNgp3xKRWyb7OHdHL/dP83PXZ2EufU8tm/ZEZPaJyeza3c3MEwJk+C3eXRMh7fa7CBx2OEk1YNZCUR/Y0BNSGvW6VdlKO+1TD01u3SJuRdEHLIiFILdC+SJdKmFtQK2TVluyNyyRqtUub8TlSHOCpaqGEwNPUHH3janQtRTWj4RgN5GLY4ENRq2BNUBNE+RVyF1TnQ12IwxYCyEbNvRRe6j3JijrAZVJsKJQ8evDwlqcl3pgQhjGuiElpIrIlhCk5EN/A+MM/NlAEXKcdLfgM0Qi2iNsNBTucWBdXB/yO7dmzHQkIqsRgTjRwMAYvB6DG9ydE5FWojAOBdFd5sCVtgjFcagqkmJtX0AaAm5Cx3o52xKRIG2tmXeQBXjH7fwNh9EE3mNQtSeBBH7r+J+Mg58xYwYXX3zxtz/X19cnCMm/GcXFxQC40/O/55E/HK6kVNxJKcwrLqYQLaKtN4i3GeI/t//++26DTzqJd959h1s+ivD4QTYu28KyLO6b5mO//i726Okmud2q9djiMOurY0z43WmUptncuQcs7Al2GD7dFZLimoJ1GbDiH3fR8rc7vyUirVWQc3bU8nTu20GaI4bZG2LfEpF+WVr58pJt5pwYYI8ngqy7/RZS9tybobWDMMCnoyGjOj7EzgfleZBsQ6AS1vpFLJqTNNwu5EByEAqqRJJaQrIiGyC/DDZ3VVBZqDWF34jIeKN86we1Y2rTBD0Sx1oOFA+GpqGQY6uF8DYQdER40hvBrhHhqE1XVWXKe5BsQb2BueMVfb/TF/DAWTA4E3ZzwWILPgkrZG0EUByB7ABYm6DeBV0CkJYEww3cYGkWTYUFmfFdX4hi31tRaUQYlsQtyv2icKEPRnTic/0ATe69x8BnMZGFG92dJ7XWoHbIMQYWGvi7gbviQXTnoP3Ks5QR0pl0pQURkQrg92wbVtZKRA5DtuCd2ZZktSIG3I2ySLbnrEkggd8a/u1kpKCggLKysq1+V1ZWRlpaGklJnYskfT7f/+S8k18zvhVofk+eyI+FY1nMdhxq0NC41PhXf4dbUofvfSiOu3VvWi90LaD/4YeT//QzXHns77CAxw7yfUtIpvXf2uFy7/wQF7wTYuxll3FJz1vpsgxCAfh6AGzIgX1nwrrJUBCCufNn03LNJZwx1rMVEWlFe0LSNdXaioi0Ii/ZZu7xfkY8Uk3jPvsQfn4dK3pb+IKQUw2lXeRwaUpV6mqTBbhU0WhMBb9bqaepzbp6zypWGqonpinBYxfoqxuREcdS68cdbWvP2AaSgtDkRZN//WAlQ2MvuMAFf0U5IEuMMj1iFrQ0QI9KtYiCSQply6kHHLl4NnWBU/8GK/tCczcYlSc3zFMO1EehWzL0iMpGvToKTUE5USbkK1p9iiXSkW9pxszvUc6In7YqxNdGFtuvDPSIaNDhZT7I76TK8RCyyj5ulKg6xIaj7c7/fItRa+ZMA/808K6Bv9qaU/NnJC7tHydKnaEJCWzrkZC2Z4f7W4nIIYggjWNbZ00rDDrG3YAx23lMAgn8FvFvJyO77LLLNhHh77//Prvssr3rhgT+G2htpcUaKiHeXvm5cELNOC2N7J+fzxh0VVmJrkYj7W42Ih6e+Petyece2khJK6z47wJHHcWpwKPH/o6ABx7Zf9s8lH8sCnPBOyEuvOwy7rr9dhoutXCq4Y3eUJIDEz5T9eHTFJgfAPfI4QR6D+ClFUVctLPDwJxtL8fP2dHLwBybgdk23dM773I+83WEsoYY48YdTlF3i6hbxKM5GSIe6UQCEUgph9J8iKZowfcZ6FsJa1Ihv1wWX28l1KZKExJxSfxqIceM5UhY6ljSeLjRVbflgHEUL++OidR4e8LVLiV7piBCUuioBeM0QahBjpmoR/sy9QPItqDKgjkTVNUZWASvnQCjU+ELW7bYMRFZW1M8UN4EtX7oUw4bAuCvhTXxEsjtlmbAlAJZiIDMQS4UY2TH/RfwjYGBIRhlwzl+8HcgFw4SjVaghNQ/R+FgGyZuJyFsORKrXmDgHgeWWfCUrfCxF5G4dCdL2o7OUI+ISDNwDdC1w/0h2ojIbGAksMd2tmWQe2gA2ycrCSTwW8WPJiONjY0UFRV9+/O6detYvHgxWVlZ9OjRgxkzZlBcXMwTTzwBwJlnnsn999/P5Zdfzsknn8zMmTN54YUXePPNN3+5o0jgZ6Nr165kZucQLi0i0H/nX2Sb4fK1YAyTxo5lGp2Xvw36oK9rd6vt8H2w3XMNbVWSzO7d8fh8bGnqPHp+S4OqPV/268edxuKAKlg4GN4fDmO/USrpzHGwIgsObobFpBPpMYSaDauY+Hgzs08IdEpIpvTZ/r/NPZ+FuOjdED63xdoN7+MK11BYnUlSC2zqCXjB9kDPNVCfwreD8OpTId+BzHLwBOS28YXAF4Ti/gbroy9oWfUyb31TSfmHLfj86cQG9iN8xnFYObkYWzZeJ36iWsWtgSjkpEIPN3yFyMonwO5Gke2vIW1I3zIRnoZUtWK8QMiILH66I1x0N1QPh419oEuWiEpXC+aHIMsDlQ60WBp8160EFncBOws2WHCO1TaMbi4wIX6uvgDONnAPIjRbYjAwDId44BD3tlWORqTtGAGc4sAdMTjbBUO3o3z7FJiJiMjVDjTEicjdBj5HhGAva1tbbitqEBEJowyUjiLTViJyMLIIDwYmbWdbIDFuBrDPdzwmgQR+q/jRZOTLL79k0qS2f7lWbccJJ5zAY489RklJCRs3bvz2/t69e/Pmm29y0UUX8ec//5lu3brx6KOPJmy9/2OwLItJEyfw9kfzMbsf+73x7z8Ezas+xZuVzaLBg5mPWjSjkc2x1cZoIZ1EMlD4A7bpIDvkh598whVTp7BjfoznDum8pff78V5KGg0PnHEGFUFYeOjpxGxIL4f1WbB6jOLMewehuj7KisuOJzjvVR7e188988NMeryZWdshJJ2hlYhcsZuXo4e6mfT0NzQfPonM62dR2TOTlizo1wK1Ucgrhc3d5appTgJfDAo2wfJ8cAU1KK/HV80sW/Qcn/71AVpWLsSVmsXm1FxwewmFmwm//CjcfiUccSSuM8/GjNsZ47JwLLDcgIEBfihxa0FdgKpKBSjyfY4NKWGw6iWIdTnStEz4BEiCOgcWD4OUFqWyLtsN1hcoXv0i4AEHKqOQmwwbInCED7LqYJlHc4Gu7aKKyGHx82PQNOXDUeujyShNdRlgR6FvBM71wdhOTvd64ATgdKAgBn934Go3dNnOn+kbKOX1NAPnO5Bhwd8tuNSoOjcKOMLafghZJZoE7KC2UseskTBywhyISE9fNM13e3gT6U5O/I7HJJDAbxmW+W8NEPkRqK+vJz09nbq6OtLS0v7bu/Orxfvvv8/UqVPJP+ZW/D2G/6xtOeEWNj98Eknnn0PP226jB/qw7ocitqvQVfRoND7+u0Ln7733Xp5/9hme/+eLdOvWjU8++YS9p05hbF6MN4/2fStWfW5phJvnhnjucD/D8sSzjTGc93aIB74IM/nkhzm4zxn8/SBY0x16bZY2Y+nQKNETjif4UtusmbJGuWVqWswPIiTticite0pvsqQ0xsSnQsQKh5LxwiyyhmTSrxLWVEKgAcpylTNSn6FwsH2WwvuDoWsZ5MxaxbybplFXu46kPuNIHb0v/t5jsOy2/Yi11NP41Qc0fPU2seoSrBNPwTz8ELg9pLao3dLD1hV8AE2u/Suw2Why8AAbvqiAPqshu1rR8TsshJ2WQb2tFs9NV8JJz0J9Onw9GRgAO1uwqw0Ph2BFE/ROgZwwpPjA/gaWBiDXB2d2g0cs+GecMKxFlYpTkYD0ZaDGQPeIqkKX+aBrJ1WOeWgWzR0GimJQZOAyt5wvHWGQC8cDTIiHmQ2zVdk4CU0SzrS0Dx1FqK0oQzNyLCSQ7Zj/EUYVkQNQhaUr25/iC2rfLEM5KL+sIiuBBP738UPX7wQZSeBbOI7D6DFjWV1aT+70O7Fc3x13/l2oev8hmpbPIn/5cqI9e5CE2gLJSAB4GDAIBV8tRe2DIUjU15u2D+277rqLSy65hFSfTX7XHsyaM4+TTzqJ2bM+ZPW5yfTM0Or17NcRjnu5hYBHgWGfnRJgeL4ISSRmGPxAE2VNyez2Xi1r+tiM/EIaiJo8+Lj0Dr6acRl/PcDPqWPaTJ1LSmOMeqSJY4a5eeawzuKvhMawIf22BsZ0sfn81OStqkoLS2Ls8Ncm3AcfQeYLLxAohq6bFG62qSu0JEFaDNKqwW+gNAOyPvya1adPJBZIJueQq/Bkd/vOc22MQ+NX71P9/oOwzzTcz79MF5+bYrcWUoMyNL5BLa6NMehnKxukciP0XQOeqCYYn/YEtLgh14FF3eCJ4+Hye+DrXWD5fprOe4IFL1owv0GTZ7d41XpYFIReK2F+IVyeDnMC0N2Gy+On4x+oPfQNWuyNgaFhGGzBeV45dzriH8ATKFH1XzH9/Zzh2jYCHtRSuh+1S3oauNCBfW0Rj6NRFcSylJi6vVyPYkTavIiIZHS4v7Uisi8SwOYiUrI9fIHIyCUkBoEl8NvED12/E/8fCXwL27Z5/LF/EK7aSM3sx37ydpqL5tO48E08N99GpEcPCo0WkTI0g2M98Bf0AT0H9dkvRcK+eagsfidwVpyIzNjdy5IzAoRripk0YTxXzphBZlYW+z0fprLZ+ZaIHDfcTdF5KfRMt9n5b818XRYl6hh+93KI9fUWOXc+wdRVNhO/hEBQM1uSfZA/bRrejK1n05Q0OBz1YgtdUy1unPTdwwJTvBb37uPnyy3OtzkksPVsmuzfHUOjUaBYMD5/piVJM2bsFhi4GiqTIXXZFpZfPA0nJYO8Y//4vUQEwLJsUkfuTd4h18A7b2MuPJsyt1oMDgo2extVSHY0anO5gOZKyCuXGLY2A8Ys1CKfFK+cvHoA7PExrBgO2f2kdakGyoGPHUiJqKViHHjbBbuWqh0S88DIZGlIdomTBgOsMtKoPAdUGBgahGk2XN4JETHIffMW8LSBR6IwwJJGpDMi0oQyT3ZHEfrnOHCyDdMRWdgZsOMZItsjIhsRSUpCWpGMDve3ZoPsi/Q3mXw3EVmKIucvJPFBm0AC34fE/0gCW2HUqFHcfdddNHz5KjWzH8M4se9/Ujs0r/qUildvJ2mfg5k64hyCjkKiHAMD4gFbJeiDvxH15v+E9ADvoFHt1wPBu+7i4TgRuWWyj96ZNrOn+wjXFHPqaaew53MvUB5LZfQjLd8Skb8flER+is3ckwLfEpL9ngnyr5Uxsv7xIudaB7PrHOkp/FHNc2nyQ2jEULq8NpsVjZpN801FjEmPN9MYNp3adzvDOR2C0SIx59tIeO+Tz2MfdgjuJmV/OJZ0IiEfZFZJL7ExX0Pamu64Aae5mZwjrsOV9OMmlST1HUfWlLOIPfpXnPnzcaN2xab4eY4B/3AUpe6NQksQAs2ac+MOw05LRZLyLPhygKzGkzdqaJ+rH2TYinufb8HQCASMtBBJNuwfgUidwtlG+9WOSbOkLwGYa7Q4zyZemQnDmV442iuS0B4taHqvAW534NYoHOqCg12dW3crUSrqUUC5AzMMXG0r5+NARBgiFsxATp7OsDa+jVT099fx+q2ViOyDKjvJbN8KDAqRexGRn59eX0wggd8OEmQkgW1w3nnncdddd9Hw+b+ofHYGkcpN3/ucWLCRqrfvpeLlW0jfY3+6//E5Vg2wGbdai00pqoikGhhl1LuvQh/sNejnjeiKdKe77uLqdkSkte3RnpDMOu0UDj35dIrrY98SEVd8VcsJtBGSD9dF2OGuu7l2w8E0e+GTSXFbbFQunmiqdCzuwSMZ9ZfZLGvws8Nfm34UEWlFe0Iy/OFmnlsaIfXvz8ORh1MPZNVA1AVY0mDgAn8IBi+D6kxwVdaxefZTpIzeF3fqT5tUkjJiL1yZBfDQQ4CqIA5ahPsaRZOnA6tqIKe0jRgNWw6pjhbdzS5YOAoKS6G2F1T1gsVeWIWssGuBYAhsN5TbsJNLsfoA61LhjBRZdFNQdWGxUT5HuYG0iFw7d3hhl07k85tR5WEf4AgH7orBhS7YeTtvwwYUIHa2gU8cuMvAPbYEp0ehhNOWOBFJ2c45W4k0IBmIiHR8XARV6vZG58CFRLjbw2bUXrqU79ZCJZBAAm1IkJEEOsVFF13EnDlzyPME2fL3s6l48Xoal80iUrUJ48QwxiHaUEVz0Xyq3rmPLQ+eSHDlx8zo+zCfVL2I7fORUg3VHhhUrrCrOmAdivDuZhQOlY6yHFYiYlL+6qt8ccklXLyzt9Pgsd6ZNvOm+/DUFPPI7bcyfaRnKyLSilZC0ifLzaqbb2Sv0iC7zYEtNizpqeyNZBvq09Q+KAhBWk5X7PR0Ur2dB5q14u3VEdbXdm4nbiUkKysdvNMOJHzE4QQs6FEGrohCxVr8qor4WiDq1eTeqBsy7n0CJxIiZeRPd5pZtovUEfsQffY5qKoihK7M84DNDuxnK4ck1KJcEU9Us3J2+hpq4kEvX47Ueem3BVamwtKBsNlWtWA40OzAmojsvcNdqpCUlovImAyos2CIJXfUywbuQ+/70DD0c2C8WzkiHTEfEYjrDOTF4PkYXO+Gvtv5lPoa+DtyyDzlwPMGHrVFwE5DzpWwpej27UUoLkPJrLlII9JRGRRBFZGp8WOoraqi/4IFzJ8/n+XLlxONRrd6fAUKZLuY7ZOfBBJIYFv8T8bBJ/C/gfHjx/PNsqU899xz3P/Agyx8485OH5eX0o19h/+ewFGnkVlTQPqL8Nh5cNxD0LUEWsKQnwK5AVVHyoyuLtONdCJuSx/01YB7wAB8GenM3tRCXQgyOrm07J1pM+94H+8UuTh1jGcbItKKJaUOG+sc+k0eS22tjwKgzxaloC4aAWnzgQDkAzVhqFi/jPDmTRw4xkPfzM632eqayU3zMG+6t1OXzWFD3Nz0kU3V8q9w2YZujkX6OgWtgYSiGKWmDvkGirsoDG3DB48SGLAL7pTtNRN+GFJG7EXtx08TfeYZrPPOA1SVGAF8BBTXQX6FUlojXui9Xm2bJo/OR4tPlZsxFrw8CLxu6GPDClThiIblIFnpgoEWdKvSsL0NXhgfgA8NDLM0d+ZTYIkDA6MwwYbBHiju5NQ+AzyCElU/jOlv4Wb3tqFnrZiL8lIuMXCTA2sseMLS692OtCK2Jb3G9hwsi4GHEWmawbbzaKKoItJ94UKufPBBFrz3ATWbNmz1GK/Pz6hRo5h+3LEcOH06j6Sncz7bbwclkEACnSNBRhL4TiQlJXHSSSdx0kknUV1dzcKFC9m4cSOO45CTk8PoUWPwPtadki0WrwIbesGGHtBrLdz5B7joahhUBOEohAfBCA+ssySCDBkFmvU3Eid6gdWDBzN05iy+mTyJyU+1MPM4HxmdrEg9M2zOGNfZODPhw7VR9nsuiGfyFM567BWSbrPoUgkmHZIqYUAlzN8FugWgxKhFUbX7BHx/upe/XnY+6T6LP+61dWWmlYiknncede+8y4Qn1jHn+K0JSWmjwx6PB6kO5ND1n++y2WORvEXtGbejr2EPJDdBbZaqI83J0KcIPqlaQ3r/o3/2e+YKpOPOKiS6di1utDBWG9hkwUoHskKQWh9PeA1D940anGcDc8ZIu5JVJ/LUlAJ94uSxdXjDwLAI5AavWl3zS6Tz+CYP0lxQ48BMSwJPT0zHfaELDnDL7XJEu301qDWyBHjBwMNR6GHBFa5ttSStj38Ftf3ONHCJAyELHrfhRSPnzZGoanMk2yciX6A01O6octJR1xEFrlq3jpmnnc6XH36AJyMPf/9dyRl1JO6srlguN05LA+GytSzbvJTzL7iQi6+8kitvuomc88/XcJ8EEkjgByNBRhL4wcjKymLKlG2jnVoOgvBfoE+ltA8z94BTnoIdvoBrH4Ebz4CxS6UVKB+mq+ZKCzZbsMWoV7/eyAkxwILho0eTM3MWc76HkGwPrURk18lTOPKVV+lS7GfeENiYA192B18dDCiC3VfAvw6GDKOFsyoVMo89j4JlcMdj5wN8S0haiUjBFVcQvOVWDhxcwaybxrPHk+uYG6+QlDY6jH8ixAZXFvkffkRLt/64YlAXASsNcOLuGQMZDVrQi7upKpJbEsNpacLyJf8C7xTY3gCu2lq86PUyERnpVwdJW6RVccU0RbhrGRg3FOdDRhm8diCM+Bq+3g3WJkGaG2ahVk9PwIpAgw25NhzZCOvDqmT0y5azpsSSrqRbTK/td0ONrW2spq1qEELZHznAQwZui8pdM3U767iDCEQ6cLiBsxyRjgdsDcubhfQm/a3vzv34BJGWXkjX0fFDMApMf+YZXj7tdIw3mZyDZxDov/NWGS+t8PcYDjscRLShkvr5/+KmSy7hgxf/xSsvv0ReXseotAQSSGB7SJCRBH42/KPA3wv6r4P1IbVBvukPg1fD3l9AXSr8+RiYsACG+eDrflBoQ44F31haoHKNJrimGqVj7jJ6NA0zZ7HwRxKSD9dG2f+5IMMnT+G6V15loN/P7M1w5Fy1iGqPgJIUWD4QGjMhasNqC6rj2o3uG6D/tPNwR+GOp0RIuqZZXPRuiD6XXIFz660E1lvkO3nkPDOPjaeMZ/yT63jxUA+nvhllg5WJPfsjwn36EwyqBZJZDisGxaf2WuANQWMGpFdJQ+KJQFOmjcv2YqKhX+Q9MbEwJhAggioVjqVgseZmKKgF2xEZGbBGrZAmC1YOg94r5FiZ3EXTdj83mr4bAIptqA8pvTUaUJUifYsqWrUBtXlWGnAbGBSDiTEY5wOfBQORkyaCxKJ1KPRsd2AXB2bENMRv9Hb0IWE01G4M0M8oDn50PMzsSqP3dndkJf6uuS9zgGdRPsqFfDvk+FtEgWP+/ndePOUUkodOImuvs7B928+YaYU7NYesKWcQGLgbC17/I7uP34OP5s1NEJIEEviBSAhYE/jZsCxIP1gi1f6bwEnWyHnLgkgZHP4lnPQWzB0DzpcwZQtscEQ8dkBX3BFLdsh0lIkxy8Cw0aM5f+YsVjR4Oeif4e/dj5WVMfZ7Loh/wiRir7zKlX4/9wCLypWUGQFMBFKrYZfPYfhyKHGBicKmbKhPBhxoCkDXQ86j8Ip7uePTMBe9GyL9sisYdMut5G2y6LsKFo8Ep0sehf+YR0NebyY81sxGMkib+RHDB/YnaGtR7r0ejAsCjVCVo6CvvArIrNBsmrp06FIKhaUWab4conVl33WIPwjGiRGrr8TOySFswGUp/8PdBBnlekxSENKboWsxeNywsh8MWwJvHAT9N8ARQ+BpIMmCQy0YZClG/tiQ2ijDPXBWCLIaRBS+KVTrYy0iBptsWO6DP1oSia5Alu7Lgf1QdeIeYC8HXnBgiBvesBXBfhcamvcFaufVI9vtFCDfwOkO7G1rcN2paH7RYGC/7yEi76OMk6Eozr4zInL+Rx/xr9NOI3X0vmTvd/EPIiLt4e8+jJxjbmNDSQUHH3IosdiPs8YnkMBvFQkyksAvAv9ASBsMhXXQswEqu8CiYeAPQqQWjpkJh34MH+wE1XPhpAZY4Shgaxi6Us20YK4FW1DpfqOBuuUrCDY2MTjr+4OCM5MsclJcRNeuwVtZSQ0KzVpWCBt7acJtzEbi0Rg0+MGxoVsECmtVLYi4oSwPvh4JzvnnkX/bY0z54510v+VWGkIWXZeIrJTmg2/FBsovPBTPyeeTfdTZWH+8lyZbMeWFUXBimj0Tqaum8oSpOHf+iaY0tbJ6bNC5qalZS+6L8xi3BPbIP4yWb+ZgYpGf9V60rJ6P01RH9JBD8BiRiBYDFWHIqIXURlVkBqyVVqLMDZu6QZcGqMiBQCY8EICyeHVjMcooSTHwdRh6e6Dagv7lIo81bvBlKGzsgJDSXhe5YIYlgbIVfx+eQJkfE1HuzC0O3GtgvAvGWnAyIhinoUpKBfAYsD+yfz/uwO8cmGLLWntE/O8k34JjLVXUtoc3gZfQVN1z2faDLwrc3tzMKyeciL9wEJlTzvjJ85k8mYVkHnA5n37yMffdd99P2kYCCfzWkCAjCfxiSD9IgVmDVoOTAvMmQNAFnjJId2D6azDlK3hrHBS9B7+PwqJ4hSQXkZJ6tLgstyD52Wf56/RjmT7czQPTtmfObENesmy/GTXFrJ0wnprNm7GBghZY2h/+djQUd5eDBMCTDb0s2Ugjfsis04yWqAcKSiC9DrocdALhSy+m0baoqIHVPeV+CXyzgXVnjiey9BMarjqPYABCp/6OyK47ULxoEWUhbWNlRjUrz5sMn35Iwy2Xw803E/WK8FSULaVx7x145b4JrJ39FFN7nEW0qYbmlZ/8rPehfslbWDvvQubIUdioIhGLQnKdWjPJTXLPdN0kcrZwBOw7D547HAoqoGognGKg1sAwl4hHkwU7hqHMgYwAjDPwZaVyN0pyIRq3417jg24uaVS2AGcgm+u5yM3TA7VyDo7BeAcusiWYfQk4B9gTOCj+nL8gserBQLYDnxo414ZuaPJvPQpgS0ZurOUoibUjXkZkZFx8fzpSjBhqAZU8+CBlGzeSuc/5nepDfgz83YeROmY/rrr6ahoaGn7WthJI4LeABBlJ4BeDtyfkjYGkJhhSCrWF8Pk48IWhuhp6O3DSC7DLGnhtJHwyG+624CMHIkbl/CPQ1bb/2Wd5OU5E/n6gf7v23Y7onWkzd7oPb00xLRPGc8PmzVw0G/KCUOeDlhgU58EHE+GzXtJDeByo9UNuORSWQGaNXEFJLTCpSfbVQ76CbpthQ09oqtnAqnPHkx0uZcXZyZw62k3TPx5khwKLESktrJs8iZSPFuHbUk3t0ZNxbVnG/JOTuHGij5p7rsG+4Wa2VC2l+ugJDExq5Njhbu5Zcjxr6hbQZcBEGhe8jjGd55h8H8Ll6witW4zr3HNoNhJ91gHBiEhIRi0E/dCtRLNwalPURkoOwJK+YPkUSLY8ovOyxSttSBUQbIFcFyyyIVAJ6xwJkbtkAgZuc0PAJZEraErwWNQeOxV92Dxh4M9R6GnBbS44Kp4D8gAiHjPjtxtQRec0A3Md2X79tobgnYUIRYWlNl8KminzGcr4uAHFuv8NuAoloY5BlZftEZEdHIcXH3iQpIG7/6AI/h+CtJ2OoKUlyFNPPfWLbC+BBH7NSJCRBH5RpB8ol8XgIlkzP95LJCCnHDbFYGAETn0WRm6C17vDG4vhIgvedmBvIzGk69lnWXzcjycirWgNRsuoKebkPcbzQM/NDC6DLm9+Q++yCJZLw+lCPi203/ig2Q05ZWpf+Ftkfa2qWU0v08TyRmAdbMkA15oNNB00nvymUuYd76Nnhs0j+/s5dYyHL4pjnDbCZnhyC6XHTGD1abvg2biMOdN9jMh3cc0EHzdO9LHx79ew4Xc7MsjXyMzpXh4/2M/xIz3c89Xx5PbZmWDJKmpm/u1Hn/tYcx3lr/4BBg4i5bDDiSAiEIiCJ6hgtUCzWlGFG5Wg+uVo2O1TeHySWldlPeEdS3kiITe8Z8uF43JgflizfIqAZaWauNuSDqk+6GYpnGwe0Bclo2ah6sV+KIn0jwZujMI0Fxy1nWh3EBn5CKWpbnHUQvnE0sRhg0LIDrCkORmNWjifAP9E7aA58ef/BVVcKpB1+BqkPXkS5ZSsRfqUHYGVn39O2fp1PytwriPcaTkE+o7jiScTZCSBBL4PCTdNAr8oPPnQbVco+wjGboIvesLcfWDfVyGtEhryYVAznPks3H8svFYAqzbCvJ5wogNdZs3k7eOOVcT7dxCRWeuiPL4kwj37+Dt12fTOtJkz3cf4J4p569I9KJ14AS+/diGFn+7LwSe8RE7Ax7IBYuNdkcW4Ig+qs9XKaHn3FYovO5xrh4wg7YWZ1PfPoLZ6Ay3HjCc/WMq8E3z0SBeXty2LR/b3YwHnvBXktik+bprbiLtsNXNODDAiv63kf80EtZuund3C/mO9ZAe0jb8dqN8//u7tZE+bTtVbTwCQOfkULOv7rxmideWU/et6HFcM++23CHp8+JF91glCWqM0MY2p0KUcRlbD130kni3tDWsGwOCwKh9XGng5CoMctd2+ALoHJSZd6oPUFoiGNNunMB/qXCIgMy14FdgVhYkZFBo2Gqhz4Aij6PjZtgiDv93NF78tQILYaXHHTKUFN1rSrdyA9CQbLWWTbE9aalC1Yx4KNMtAowZWo1ybVr9SBWonvQus++ILLLcHX+HA7z3XPwbebkNZ/NlzRKNR3O7Ex20CCWwPif+OBH5xZO6ndNP0tbCkKyzYEUZ+Ab3LoDgburuhXy0c8xL87TBYWQCP18HL6bB/Sipuj4fyZog44OpkHW7NEQlFHL6utPhwO7bf2qChMWwgEOXl1y7k8CFuXvvyHV6wD2XQ/S+R2eTDa5R/YsdUwk+th6Klr1B+2eHs3dvmszVfU370ZGr/9T7RfSeS3VjKvJPaiEgrbMvi4f0VF3vFByGykyxmnrA1EWnFNRN8RB3DjXPDDM6xmT7Si21ZbYTknSdxHX08DS88RWjLclLHHEDywN2w3NuGvEXrymlY8g4NS97Bzs2i27tz2dSrNzFHVREXapt5ohBzK3Bt4GpoCcDHQ2Hy+7BiH6hPBbcX9rRgvpHrqDwgDUeRgZIW8HmgxIaditXi8afAfWlwoIFbLC3sG4HL0EyYTUir8bQDxTH4k1t6IFB7JEQbOWhCAtd8YLiBuxxpSU6x4XUDLwB7AG9bmsB7FyIdHWGQc6oaaZCGI5IzjDbS40GumkxUuVkERL/6Ck9uTyzXLzvWzpPfl5qWZtasWcPAgb8s0UkggV8TEmQkgV8c7kzoPhFWvA87b4C5feHTAyHnH9CtHDYUakHoVQLnvwV/mQbPeCApCd7baQcOeO0tPjxwXw56IcSrR/rwt5sZ/+HaKPs+FyRv8hQuv+4GLt13n05zSBaVxJj0dIiWjALCWzZx8c5e7pjq4701MQ58/h1Wn38oWU+8RLXlI1y8noLGDAYVZfDNylcov+FwDh3o4q6pHoqqDIe+9DWNh03BPWAQlXM3sqLS2YaMQBshGZnvYlJvF0NyOxdBBqOGz7fESHKz1fybrQjJC0/iuegKIgu+oOqNO6mZ9SjJg/bAnZaH5fHhhJoIbVlJS9HnkJKMddIJWFdfg1WQh2Wko2hCWpz0OvBGVBXJqIO+FTBrJAz7ClaPgoW9oasDlV7Y34L7okrMTba1UHuiUB8FdypkRyC1Brrb4C+Ar4AuKHxsDdAbtXi+RvbsN2NqsbzplsuoFS5U2QggQvI4su4ONHCeAwNs+JOlVNWPUZ5IgyW9yPbqRDHgXqRZOQORl2C7WwjNrXk6/hiDZiIdCjzR0IDL98tPk3H5NXm5vr7+F992Agn8mpAgIwn8W1C4D6z6CIZuUOrpsl4waCcY8TFEcgCv8h6K1sKx8+CZ3eAvPi1wJ+y1J5WvvcXMA/fl4BdCvBInJK0VkR6Tp1D4yqvc7vdz0oezeHzPrYPRFpXEmPx0CF9WAXWbNn9LRCzLYu9+bl47ys+Bz78DJx/K0KsuYs5BB+LO6wJHX8W6P53OoQNdzNjNzehHmklyw+P7+znh9aU0GQMjxrL/M1/wxu8CTO277b+PbVmcs+P2Y+qDUcMhzzczZ32M148JsEv3rbfRSkhixvDU3beT+epsRs94gI9fepiGN1+C5VVSkqalQ9++WA89hHXs78BYJK1YQUF+HhvRsLrkpmaiy77BkzIO28hm3HstlKZDaVfoswlW58I3vWCHCERc+kAoikF1Eoy3pL0ItcgW7XVBrxoYZEOFB07IgvsMHBYnGYtRm2ZTVRX7PfkUk+fOY/OCBTRXlpNnWXQpKGSnHccxceJEjjnmGFJSU6lErZx9Aa+RdXeIDadYcJVRUNoEYJkF4+PbbwEaEdlqvTWiikg9aruVo1TXekRAmoyeVxc/zw6qHNko1ybqcuN1fvlMkFabtsfzy1ZcEkjg1wbLGPP9AQ7/ZdTX15Oenk5dXR1paWn/7d1J4Afim7eg8jWo7AHvD4KBJTDyHRheDpXdZOcNoF7+gt3gzWHw2Wg4KgPucMOpH3zIEwfsy5494LxxLg57McSgyVPo/sqrrPb7mYQmvaYuWsTCyZPon9zCXVPcHPpimKSsArZ0ICLt8W5RlP2eawHLYuduLtZVG8obo0ztYX/HSwAAkLNJREFU6+HGiR6mPtVC7wyLpgg0hAwP7OvnhDcitBT2I7q+CLcT5fVjOick20NHIrJnn86fW9NimPx0iKVNSQx5/gsiO/dnvUvTft0OuB1DyGOBpat7q6Ee39S9CM7/nC73P0DZ2Wfjqm/AmbY3sU8/pfDye8mYfh61GXDhn2H9EBiwFDaOhhU94fMdYIAD0STYx4HlQXjPAz3csuf6q8FtQ2EKDFoByUEoKYSjuoowzIhbf2+oqcF/xRUUP/4EOA6+roPx5vXFlZoNxhCtLydSvobg5hW4A0n0P/ts3Ndfz+BAgLBR2msPS5kzi5AwNRcluGYZVTea41/DtFU7woiMxFDFzULnxRX/2Rf/OwuhNlCWkRZlNBAMQWUtfHPjNZQ89SBdz378J+eLdIbGpTOpevMuampqyMjI+MW2m0AC/1/wQ9fvRGUkgX8bBk6GtbNg2Gb4tCcsLIABA6GuDnoEocivhacXEPsYVqfo6vzFHWBcAP62155kvv4W9xywL++ubqHv3lMZ98qrVPj9XIDK7WcCT48ejXvmLFZMnsSkx+sIdO9GzXcQEYAkD3gtw45dbd76nZ+SBsMejzWxqirK1Kei9Mm0eO+4ZIJRw+QnmjnnrWC8QlJEQ58+RNeu5YBnm38wIfmxROTrpiR4bzbNPftT7FVCLZb+YZ24QdUA1Nfj2nsv3F8t5NjhHp4+9xxobsZ65SX8i77gkBEenvrj+UTdkH78eZTkQ4sNtZkQCsBnw+UkWgtMMvCKgdVe5Y+UWBANAg64kiRajQb1+0AevGa00H9uwT8/+IDa46Zj6htJ3+VoUkZMxRVI7/QYo/UVNCx+hxX33IvvpZepf+ZZKsaNIxXYbNT6iSEyUQ94jL5vFbkmGchGraF81B6ygIMsmIREq15LRKqY/2PvvMOkqs/+fZ8zfbb3wrIsvfeOdBAUFBEQUSm2aOzGJMaSV40xsSXGN1FjlyYICCqxoNKkiSBFOiwsu2zvdfrMOb8/nhnYhaUZ83vVzOe65gJmZ86cc+aw3/s85fOII+y7wW06PZBXA1E1cLgWjG7Zp5iMvhQ0VBGor8AYnXTe7/NC5S3JplXrtmEQCSus8yjc2hvWf0wGK8ROgBIN2h0Ta/EDHeBEJ3D5xLwqD2nd9APTv4S+xdB9LzzohQ/98JdLx/DHz74g9f4HGPLBR3itVmYBBcAzSJumDrTo3Zus9V/R4vbb8ZaU0D1Z5fmzgAjA3Z+60XR4d4oNu0mhbbzKhhsj8PihTayASJxNIS1K5c0rrRTW63x21M9fxhjRDh8h/q778BuMXLnYyZ7S84f3b/rIxbrjFw4i1pXrUXv2os4shZwBA6CAVxFQCIUz1RnTMe3ewdobLCy42sr9A83w4G+x7tzO6hvMzJ8sz5X9+V60jz/iu94weAcc7QgVkVAXDbZgsWgeUKqDV5XP0vxgdIm/Rw8TRFfL5F5PPHQzw35d/v3u8hVUXn45JnsaaTe9RMyga84KIgDG6CTihs8i7ca/o7kN5I0aSecNG7kOiVoM02G6Aq8pcESRmThlqljMH1VhrwE2GmChCjYV4lQYp0pb9lwFfqvADQioPuqBp8ugMhu828H6DWQchrhSiHRLbU28Blmdh6GYLDgObjjvd3mh0gN+PNlbuHz8pT/YNsMK6+eqcGQkrP+oLENh12oYUwyFreBYIvRNhIMe6Fcv499zkG6HSg2m/wuc08AVBfd1kA6IiaNGcHjkCPZp8IQORxXohPhItEXulncDE3r2ZN2rr+IeOJB9t9zCnZ94eGWiBfU0IFmT4+dwpYamw8RFTtbNEfBoG69y6O5ITCqYDPKeY1UaM5a76JCgckUHI9d+4MV2yRD633cvX676GHvuEaLM5w/rp0WqeANQUNe8mVm1S2dMMDVjWLUeuvVCV6DaAjYvNNiklVbTG3WR6KB174Fr1efsKgnQv4WBF8Zb6JSoMqCFgd5pBhxene2lYLbaUFq1wuaCgvbQwgB7+4FVk+nBmgIHNXArUk+h6JISiQQqjbDBDVm1cFCHnCTY7YcyBZRvd+C97jrs7YeQOPEBFMOF/0oxxbcg5do/UbH8jxyaNJHI7/YwLSsLnwLXIjVFOlLncRSJchQCJUi3zHokVZMR/LtHB78PvLWg14KxBsxOcX01IsAbgXjZ2HTwm8FtgnoTFLVJwHbVdOrXrCK631UXdRxnk/PIFrx1ldxxxx3/9rbCCuvnrnDNSFj/Ma1Hajrab4U2c6EqCZb1hhY1MGY71NuhbS44kVSNBbk71+3w6hT4ujccT4VrTPC8AXJ1+IUGM1XYrEjovj+ywBg4ZWr1FnDwnXcouuUWbutjagIka3L8XLHYycgsAw8OMXP5uy46JaongaSxjlVpDH3HgarAa1dYufYDL1rvgUQuXkzN5ElEZu9n/UwLPVPPbx2u6Tq//NjNmzt9vHOVlTm9ThW5hkBkr8MGn68nNbMXVZHgtgE6dKqBQ3FiEqYGJJwZMMjiiqKj3HMP+ssv8/oVVn7R99R2HV6d8Ys9bC0zMPWm1Wz57RB67warHwIx8MloCARkkJ5RlyF+nmCEIVoDxQNWD3htEOeAzsdlmODozmIuVuT1YO3blxPVHlJueP57t8VqHifFc+8mo3c3hq5ZTX9FwUewGwiBIxWpC6kI/rkXCOiQ7gNjHbhrwVwDdsep9l03cl0Yg9vItIEvEhxGcGtShHvCLsW5fg08e/dQdlkfYobMIPaS677XsZw8JncDZXPvZuTgfqz67LN/a1thhfVT1oWu3+E0TVg/uHTE+fIg4jkxbAAcbwHtKiCjBgpi4VASuJ2QPERAJC/4vkzA54SbP4aO30FsA3zqg90BmRz7uArPaZCmS/tmBTIsLRfpyJiHeEv0uekmOrz0Fq/v9HHnJx40XW8CIh9ca2dUaxN/v9zKd6Uar+04cyrwkxs8lDToFNfrTH3fjbf3QIxzLx5E4FTb7619TNz0kZt5u+XzGkdEjF+sx9ytFxantOLqikRCio0y0C8QBAYfQRABQEH589Ooycn8cbO/yWduOhFgc64X9cZfEt91CImVkN1e4KOuN8ToAh2qAq1UsV+PDUB7pwy06xOQws+WOkQ7JUqlpMAiRcAw/Y03yD50iPjL7vu3/DlUi534cfeQu24t/g8/xIXUe+Qhs2+ygUJdHF9rfLC3Esw50GI3xH0DKQegQyG0dIjBmQU5b2lW6J4Kzg6Q1AX8aVBtgCNm2J4AFdGgGKWNWQ9AZJseZMx6mNot7+EuOPC9j0fXNaq+eAVjwM3rr732vbcTVlj/TQrDSFg/qHQkMuFHcvYqYg9ffqVEQIZkyx3vkXYCIdu9YO0p1uHFSCFiIlBeA3d+ARO3gTkAs3zwZAAOKbBJhSMaLNekNmALMgm2GPGhiEJqSn7d5yYGPHgKSJ7d7CGgwSsTbFiNCrtLAjy8xkO/dJXbG0UUQnpsuIWW0QomFXwGC8o7i/Fee/EgElJzQPLGTi+7Cr3Ynv0HSpdegLS46hFy7gyA2y4nxqLIc4rSKL9aX49y+Xhs9VUsndL0GMa1NXD/QDO+V/7GNztfIVIDZyREpkJRPKCB0xD03/DL9+JWoYcuAwytSEdNRj10awDdBAfjwa5DnK6z66WXsXcYjDm59UWdh+Zka90bS0Zn1r/0MkVAhA52TfxNjlbB7lw48R14tkKn/TCgANrWQ7QejBTpkGyBYcmQ0gESBoGxJ+yIg1ZOqKqWNuXtiWCJhEijgI3PA9YApJdCZgF0m/Y/xHQdQvnyJ78XkOhagKpVL+E8tJF33n6bzMzMf/vchBXWf4PCMBLWDyYf4oyZBUyn6VCyYT1gd2vQaqB7uQxoK0gD9QQUjIbEJPGByAFqkHHwthKYug4u+waqVXjbB6MCkKbAIhXKdXhKg9lVVbyXnc31wDvAUOB54C9t4NYWN3HtjW/x2k4fCXaVlEiFy9518uUxH2PmO092zYRSNE6fji8gmcu28ZK+SbArqPFx6NdMxH4eEHH7dX61ys3a4/5mf346kGTGqPRqYcb5u3uIXb+LmDrIT5UBfroGFhe4guSh6VLboSNREwD1umsx79jG6uvNDMow4vDq3PCBm7m7vSiKwgvjLdwzwMyuN++idPu/8BvA21VqL5w6BBSI9AoophnkM/aYwRmAXX757MMaeJywPwneUqGHBo7vvsN75BCRPS+7+AvlLIrsMZ6StWv4LLuMz/IgZy8kb4Uh+wQ+IuqksyYSqQ+xmaFXEtzaHm7rD5kD4LMO0qKcUA7Z+aDVwQY7HEqELpGQYobjfqhzSNQnswoyiiCmGqIc4I800//Fj2nZow9l7z1MzaZ3T3qFnE/eihOUv/tbnPvXMG/ePK655pof7NyEFdbPXWEYCesHkQOp1xiBOGk2lg4cVODjyeJ8OeaoPLmhrYDF/tXgeEAGs9UgqZoIJG1gzoGuG+CmPWA0wLU+yA5IZ84bKpgKChkwoB9vde/G6i++wIgUOB4GXl8BywaC5YabaDvxDt7b5+PZMRacPp3JS1w4vDofXms/CSLHqjQ6vdTAkLcdVLtOAcmya2wEiorRS0sFCM5SZRVq333xGy8T3nXyxbHmgQSkRkFRINoC62Za6Bbhour6UUTs2EWMIkWlkbv2YC4tPPkeS7B41aCc+o+rdeiE2xvgQLmGw6szbpGbRXv93PSRm9d3eHH54btyMJttKAktqI+Cf0WDokkdhd0DdlUKWmuBGLcYhGUhKSG1Fvx+SYPVJsNNAbFmr96xHRQVS4tO5702LlSWlt0A6P7BDobkQ1otoMk1ka+JnX2XBLi2Lfy1H9w2EKydYWMqZKtwqBxic6GmDD7zQX4cNCTAxChIt8E3PiiqFQjJ8kDbAjDVQUQN2N0SgYqIhlHtotmx/kt+/8gj1G9dSulbv6R26zL8tWWcXmKn+7248/ZQsfI5SufeQ0akwuZNm5g1a9YPdl7CCuu/QeEC1rD+bVUCfwNmAx1O+5kXGeveCojSQHkF2u+Hld1gXxqMPQxJJ+CLmXCXG8yvSB1IS2QB9AExwOYhkD0ZFmZCZAA+NIOpuJBRI4bhrCygUwJsLlC4dOW/6D1uHOOBlWtgxDJ4I/pDPnphGld3NPDeVAt5NTqD32qg2g3DMg18fL2d4nqdkfMc2IwKVS6dljEKa2eLz8iwuS5yzYlYl36K7/5fYD20h/UzLfROOxUdaewjsny6jZe3+1id42flaT4kmq5z60o3877zMW+ylZk9JLVS49YZvdDDvgYbls/XYS4uomb61ajxcWhfbUTr2AFFAyUAZhVQwa0Dmgb33AWvvkqHRAPZNQq6348prQW+4kI6Jhk46jAz8E+fow8dxo7eUrCZ4pX2arcZTIpERU4AuEExSAdPQIO4CjB7wWiCmtZQr4HVADX334t/yUe0uOmVH+w60nWdgv+dQe8b/4du1/+OgEGMZstiIS4W/icCKoOusBVApwBENMCBOtjilaLUCiuUREI/Awwxwzcq7KsHrxssBsgwQWweuP2gOcHolOJdTyTExEH3WLgnHaKCX9nevXt54YUXeHfRYnxeD+bIGIxxLWTksaceV/kJ9ECANu3ac+/dd3H77bdjtVp/sHMSVlg/dV3o+h2GkbD+LeUjE1rvQsymGqsKmRVyJdAX8OrwZD7c9gzUWuDVS8TLYuomcEXDiCfA/hYcWi2Rlg5IhCQXiAaWTYTKq2BRNFhOFOIcPwxfZQHrZ1toEaUweamHdSfgmpX/ou2l47ji9/CY9iFfPDeNqzsZWDzVglFVyK4MMHKeE5MKFU6dvukGcqo17CaF9XPslDZoDHrLSadEFacPjtdoaENHYP7kcywuF4HLxqAe/I71s6z0TjM0a2jm8etMXepqAiRnA5GQQkCyp8aI7vFyWRuFIzUKx9VYAl9txNq6A15VokKaAq7Q/1xNQ7nzLpj3NrrXy539TLy9R0OPS8VfXwMff0Zf+zAq0uB4hiy+Nh/oRmmtNugQZYCADzQvpJmhtl7SRKoHskrgUAfQ7OJHoqjgvuVm9C+3knbD8z/o9VT46k2MnvNLnnzqSRIi4O+qREa6IqmkbjpkeiC7DnY3CBztByqjQLFBvArTLLBdhd114HRJfU3LCOhQCcXl4NfBUgP+YCeRHgVx8dAuCu5Oh5hmunqrqqrYvHkzO3bs4Pjx4/h8PqKjo+nZsyf9+/enb9++P6hza1hh/VwUdmAN6z+ug8AipGMm9rSfZQNzkYLSjOBzZgU6Z0BJP0jaDhkFcDQTSltBnxz49Ctw3QLdjoPtmLRngkRVsoHrP4PXEuGyboW8OW4YcVUFbJxtoU2cJC0+nG5h8lIP7026kjeX/osndSdfngVEoi0K6+bYya7UuPxdJy2iVdbPsZMWpZIWpbJ6tp2x851oOjx8iZlnvt6I99opGJetIOEP/6Dw6uGMnOdg1Q12ntzgOcNZ1WJUWD7dxtSlLiYtdvLhDDtL9/vOCiIAsVaF3w02cMNyJ1e0N7JsupUql86wBTUcHzGM6DUbqenQAV2R2glX8H1GFAy2GDxeL38bb+H+QRau7eZn/KISYrsMpa5XX/Lc4E6U9E6KH4ZWywTlPBXSg506NW6oMkGiJtGujpVQFAmaFXpHQHJwra1SoNpsYWfgzA6kf1dqwMewZAsVUfAQ0p57CzAiAIX18HU9bPJCnQZFJjgaC3YjzDHCVqukYBY2QFWDHGuWTQzavIehwC8RH2uNuNCaDGCOgugEyLDDXWcBEYD4+HiuvPJKrrzyyh/8mMMKK6xwZCSs76lvgC+BXyMunI21Ifi4Hyk2bKxKHZ4ohSv/CElGeGeoPH/zRthvBvOTMK0B6n4L+Q1iF68g6ZpjgOotZNSuYVgcBWxoBCIhefw6k5d6WH1cQw9oXN3Z2CyILLvGitmg0CHBQF6NRpQZoizKSbMzgEPlAWZ+4OJYtcaTIy38eo0PfeAwtN076GB3Y1M1dpVo2Iyc1VnV49eZtNjJFzkBVIWzggjAJ0d8TFnqYkI7I0uusWEO7ktJg8awBV7y1FhYs5HITh2oQ+zN0XVMv3sY31+fPQkiJ7+HPD/jF3nwDx6GYdkntI2xc8QIGQFIdkF2hEQ/4g3SKn24DogQ2/d4LySUSYRKS4T4eDgUbCt2KqD974voDz1E5v1LUdSL6yo6mwKOagpemsWly5ZhmDaNljrc7ob9dfCdQ4pWqwJQHAnlERBhgHFGaG2Bl3Qp9i2rl1RWigU6x0DXMthcKB1DqTVQ6wa3EaxGiI0BU7y89p50iAvfmoUV1g+usM9IWP8xrQK+Ru5cG4OIjtizHwEe5kwQ0YHVCuQnQdpQSPTCgDxoMMCqNtDfAXVrQEuGqAcgTpFiVJAoiclVyBXrh2Grbx5EQCISN/dU0QJ+JndST4IIwF2fuil36Lw60crERS56vepg7XE/MVaFCYtc9HjVQXH9KYfUTkkGvpgVAcDrO32svMaCtvkrtIYGGtwBPr3Bxk29THxy/dkt3p0+KHXLfraIMXJ5u+ZfdxJE2jcFEYDUSJWNs8y00mrQxwyDI0dOggiPNA8iAMNbGfn8egvGrzfiu3YihV4nRgVKFai2yjRbFKnDaAjIIu1QIcoLplooiQCDF3JiZYhdHRKpiPRCZpt+4PPgLc1p9ni+jzxFhwFI6dWX1BpIy4f5RbClHop12BkN+9OhWwxca4cRkVBugWfcUFch7eCxKgxNgFmxkHoI1hSC0QdtCqHaI46rEWbISgRrAiSb4e60MIiEFdb/tcIwEtYFS0cGjpUA99E0x+cCnkM6YG5F/DEaqxb4E+In8rQKa8eLHXdSLlh8kJ8BHiv0XgOf1YHaE1JmSJ2AAylunL7lUtx6PutvbB5EQCIRsz5wkxWjcFd/00kQAXhkmAVVEQt4iwF6pqpcvtDJgDca2FMaoN6jM3Ke8ySQ+DWdez5zUe+Bx4abWX08gKbDLb1NFNfrDHzTySsTLYxqfe5ZMwecdqIWLaLaEM2IhV4qnU0t4ZuAyLSmIBJSCEiytBoco4bBu+/Cww/Bc82DSEghIDFv3ohz0kQUpxMfwem2mvwC8AfguAfcFlB84NLAViVGa7Xx0MEL7RqgXyG0KYbBO6F71ABM8Sk49q1u9nO/jxz7VhPdqTtlShb2Sij0QqkZvk2A4ylwdQR8ahMPlm1GyPbAJ5WgVUGEBsNj4YpEmOKAjd/Bbhe0qYOUIsg3gdcMMWbomQ6eGIgzSWom/vv7tYUVVlg/kMIwEtYFKQC8hBSS3khTD5ESBDQmAc2NBNuHgMrM4M87AbtiYNVoaB2AsTmy8G1sC0keqF4lxa+GqZDZT4pkdUVhQNwQyp0BthWefTCdxahwdScjx2t0Ll3g4qNDpzwiMmNU4qwKyREKL463cqhCw6vB0Sodlx+GtTLg9AmQ5NdqzPnQxZJ9fhZPtfJNocZfv/byj8utvDnJxvLpNgrrdDq+5MDtP3PezEkQcdiYvG49/WdcR9KXGzjijWwCJKtz/OcFkZBCQJLpq0SZPROef+6cIBJSCEgMmzcSuHIiAS0gnTM61AQLYfWAeJfYfJDoAosRkmsg2g2td4KvHpLyJK1j1SDeZCbu+tto2L+OQEP1OT//QuSryMeZvY0W199JK0WhwA47U6A+Dh62wWcRkGmH2wxwyAe5NbCzAiJ9cEk0jEmG660QlwNv5IDXCwNLwFMLJyKC4GuC4VlQbJHakLvTITEMImGF9aNQGEbCOq/cCEz0RjpjGmsv8DISKel82s90xJNiNTI3Jgtp9X1DgUQFIsdAhB16F0CUG/amQVkkDNwA71aID4f9XkhOlbbTl3q/xvUtZ3LDCjfv7TsFGbVuHS1Y+vThIR/vH/QztbORyZ2MTF0qQJJTrTFqnoMoi8JLl1u54QMXnRIN/Hqw+aR3x3v7/AzNFCDp+FJDsyBy9wCp97iqk+msQCIW7272OWxY162ntlcv8r3g6NiVNms2cNgbycggkGzM8+MNwPQupnOCSEipkSobb7QxvJWBf1xuPS+IhNQnzUDnJBX/9u3o9XU4gu3BJqCFFyL9kFAH8RWQVAB1wShJaoVM9k2pB4MNsjRISoUdnaDu9/ehREVQ+eXLZ/hvXIx0LUDlZy9iy2pHyo1z2JYMSdHwlgWWRIDPCs8qMDcAx+rgRDk4XNAlAiYnw7AomO2CJfthdRVkNsCgAjgagAo7BMyQaYbLOgoYRxskNZMUBpGwwvrRKJwpDeucqkVcVachM18a61PgAPAoYh1++vv+AQwErkEiKbnAG8AU4CYFfmODK8aD9gEMPwafdIWv2sE1u6Htx7D7RugVAckPQt3voM5n4PW+7wBww4qFALSJUxm3wMGgDAM39zZxwwo3V3cysmiqVLPMeN/F1KUu4mwQZ1V5cbzlJIisusFOtEX8J/68yYuCAMl13YwU1OncO8DE1mZAJCQBEpi61EXHlxwcvjsCl09hzHwHe8o0jPNfxturFwcC4PZClh2KunQlefkGjl41nJELG1h9vYnsKo1ZH7gwG2Bql/OvkKmRKutvjLiAb0/U4NW5bLGHvbUmkj/6kurYOJQAWB1SH2J2yIIdEQCjH2xO8KWI/XycHw6nQzcN3O1kIV+lQKsAGNQEYl58leqZU6nbuoyYwdMveJ9C0nWd6jWv4ynJptOXmxiZYOOXFilO/Qh4HzBpsNIJ7nowa2C0wlXREGGCG3T4uhD+UAqqFwZXgsEB31rAbwWfEbpYYVQH+LQOIg0SEUluvoY4rLDC+j9SGEbCOqtKEZ+QXyBRjZD8CFTEId00p9/P7wMWN3qfjiwsB4AHERMzFBiowK7h0GktdC+CrVmQnQQFMdB1G7x7KXRpAeZWkHUHHP47RCoG/tb3HWqB65cvwG6C9gkq644H+DInwNTOAiKhWpH3ptmY8b6LDw75ubW3sQmIxFjlNU+NlujCnzd5MSiweJ+f2/sY+bogwAtbfc2CSEiNgaTDPxwkR8ChCp2ABnpMDEYdIt0w3AJbdTC6ILJFV7o+v4GvfjecMYsa6JEobqjTl7tZyoUByYUqBCJbK4z0fHs1xoTBVATA3gBWL6g+MHplmm3rSgj4xfNFVcHbCvqapKV3tiIW/yYd+gF7AqBqEDtqCtp9j1Pzv39A8ziIHTbzgofmaV4XVatfw7F3NbP+8RqvjxqMT4EPkVbuaB2+cUFuPeCHJDNERUFbC1ymQDsPvJgLR+sg3Qm9K6AoAIftgAW8KgyOgyGtYUXVKRBJCYNIWGH96BRO04TVrHIQELmfpiBSB/wZ6AnMoCmINJeWqULqSUxI901Mo9dfpsAqE0RPkAtxZLY8v7YDKDqMXynTfwHMIyD2ctgFFCkGprW+A7PBSLdklbevsqEBl7YxNAERAKOq8N40G1M7G3lms/cMEAFQFIWnRlu4u7+JgA5GFV7b6T8viIQUStkU1et8Vyr1J/ZXX4UrriAiAIVmOGSQSbTt9ssibmrVFWswZfPe/gCmt+fDNTOYvtzN8gMXNgvlfGoMIvqnq7GnDcYV/B9v9YNBA5cVtCjwWcTp1KqBI0k6VqbbJH1VDNysB1M6CgQcUGgAkwuq7WB+4gksT/2Fum8/onjBr3Hl7j5n2kbXAjiPbKF47r24j27hrbnzeOnu21iiwF8QO/pdbphfAbnVYAamxUFEAgy1wjOAoxJ+e1h+3q8C+pfBkQAcigLVKiAyPgNGtBUQiTDAXWli6BZWWGH9+BSOjIR1hnYjd6cPIRNwQ8oDXke6ZU6f09pcWuYb4GPgNsTe/XTFK5ACnBgM8WugQxmk10JBLBxLhHZ7YPMxyG8LO4GtN0L/Y3Bs6zbu2zKOPmkqq2baMKrQN83A1oIA35Vo9E1v2stjVBUWTbUxqaOPSR1NRFvOrM3Ir9P59KifVjEKmTEKG09oFwQiIV3VycRzl2r8+gsP6qxZBG67ndgAOBRI0OGAJmmEBhOYfKAnQnTPrri3buf6oiL2DBjK/unXYQCmL3vv346QhEDkm3IjfLYaBg/G8y3UxEmhapQJiqLEiC7PAH2dEOWHPj74wAZXxsK8AOTo0BGwGUDVIeCENWYpaLZ4waqD1Q2mm36NtdMYqn57M2VLfo8poSW2tv0xp7TFEJ0Iuo6/tgxvSTauY9/grymjzYiRLH3rTfa0bcvfgCwdDvnh4zpwe8Rltk00TIuAhaqkA3v54G+FsLsSEhzQrxJUP+xQoTISjEZxib26I3SMhsVlYFPhzjRIv7DymrDCCuv/QGHTs7CaaD0CEb9C7khD+gbxF7mfptENODMt4wbeRupIZnHKSbU5HdZhpQZ37YbKNyEvDt7tB0n1cOtWKGkPv/0VDFeEnHt8uY3ZV4yhe7KXVTMtJ8Gi1q0zfqGTQxUB1syOOANIzqUTtVLcqumwfk4EqZEKhys1eqRc+Db2lQUY/o6DuoR0tF17MMUnoGjQQgefQRxN+24CQxrUREN8DOTbxATueQ1+44IGM9gb/NTfORuWvcfSqdbvBSQnIyLlRiwfrSap32DyosDihnQD5BohQpc0zS+M8LwBbqmC/Cpo44aPW0K7WMgPQIQKNQZAA7MLynSIcUBZNMQ7IL0GfHVQlgQdcsBv1Knet5Hala9Tt3sjNUUnmuxbYpt2tBg1gmF33EGPvn0pBjrpsCoABxrA4ZTv2R4B/SPEnKwAeEiH0gaYXwA19dCjCtoE7eC/s4DDLqkl1QATe0E7A7xbBtYgiGSGx8WEFdb/icJ28GFdlHQkGlKC2LurjZ5fBpQjd6bG096zDBlH/3vEAO0YAiLXAL0u4HM7IAu1ozeYW0KrfGhTAUcT4ctUaJUNnQ9ARleI2baNG6aNoVeql1XXW5pEOGKsCp/PtDN+oZMx8x0XDCSng0irWDny7wMiDQnpWHftwZOYwGCf1D20MsFWTbpSqltAehVYbbDZAj5NgO8vmgyl0xSI8xnxvP42ARWmL3mPzyxKk0F755Ou60x8TyIi+mer8V0ymIZgo4/HIg6mCUh0JE6DIwpEeWCzETp4YGMUNERAug5bVEhW4X4v/CUADj+0rYeDiRLdaVssKZuidEgtBVUBu6JgHzSctKuGUxEJqdWVTK4qY6hFZXVaKsdjYigDkoE0YHcA1jigxgHokGqF7lFQYYSWilwbd/jh0zL4phwi62BspURxSjU4EAFei3x2tBWG9YEOAVhQChYVfhkGkbDC+kkoXDMSFjrwFmL1/UtOXRQeZBqvHbiTpiBSi9SOxCHREiuwHOl++B0XBiIg7buXKRJ1iZkshZydj4rR2fZ2EKHArz6CN7/Zxi2XjqF37JkgElIISDolGhgz38HO4ub9SFw+nY+P+DhRG2gWREKqcet8ftR/zvqHfWUBRsx1Uu9TiJq/AHdSAiYdDqqS2tjiFxtzezXEF8GhDPguVYqAI5CJuW5VoCRWAc/az/GmJWArKEDTdCqdFxe41HQoc4ExMhI9KYGAAiMMp2p7NCSC1QDEmeEbBWLcUOWH4yYoj4WWZvhEhUwF3mmAfzqhTocOTiiPFHBqnwsBt0RzFAUyK8FogSMZsL8lZEXCYit80jKBW3t25uNOHfksJobjyHTnhgD83QFbyqGmAWJM8IsE6BsHR03wawWcOnR1wII82FIE7UthZKl4i+TqsC9G2n5VBVrFQv9B0DkAC8tksvHtaZAVBpGwwvpJ6HvByMsvv0xWVhZWq5WBAweybdu2s7527ty5KIrS5BEesf3jkQ9p3W2FdEuEFq0K4ClgDHAFTQtVQyZmNyAmZpXAHxH79wcRY7SL0SWK2I1v6QRfdwBzPbQvAc0G+zIgL9fDkUvHkWlx89lZQCSkGKvCn8dYqPXAO7vOHOTm9OlcudjJlYtdzHjfTU61zoNDLGeASKVTIiaXvevkifWeZoFkX1mA0fOcpEcpdEs2Uj/lagLf7sSlg88Px1XwqJBYCfE1UJwByQYwmCBGhV6q3P07fOI+a/n8c0rnXEGM7qJu40b+Nt7Cdd0vLk1jUBXW3WCmpV6DOmoY0dlHGOAH/Z//RO3VDVv2EbzBSEmNUYbeeb1gWPAeh67qjPPQDsoVGKTB+Hr4hR/KDNC+ASq9EKiHKCfY3aBpYNTAFoDdneFAa/EsebgOXouEDkYoA65F2sB7AT11eMkNayqhtBbsCoyPhb8mwAYLuBVYAnwQAG8FfJwL9SUwshA61QlsZQcH5AWMAsh9W0CLPtDPLSBiVOD2VGgT/jUTVlg/GV00jCxZsoQHHniAxx9/nJ07d9KzZ0/Gjx9PWVnZWd8THR1NcXHxyUdeXt6/tdNh/TByAE8Dw2nqnHoYiYjciXTNhBTqlvkSSctkAVuA/0Umq47nzDbfC1GBAiWKFMeemCzFrkOPSkRhXxtoa7Ywus1kjlQE+DTbf85tfVsUYOpSJ4MyDDw1uulq5PTJ0LqtBQF+d4mZrQUBWscq3POZu4lTa6VTY+wCJ4V1OvcPNPPkBu8ZQNIYRNbPsbNutpUeUW4MY0fBzp3UmAENbDp4I6XQc9AJyE8UaDMB3wIWIMEPsQEoffABDAE/1S79gpxVz6bUSJUNs8y00WpwjhjGn/7wGNx9J9HHDlIzZigrDh/Br8BxHQw+UN9bTMFvr8dScIT6CaN4aNsOjjnhAx8o9RDtAXcdBDwSwYl3gcUDFbFwrK3MexlQDiOOwBAnDIsTCHkJuAmJrKXrcMQDqyrgRKX4hfSMhv9NgnZ2eEWR6/B54BEnHC+A/QWQXgyjiyHWJ9fDoQjIjRLHXrsC4ztCoBMMd8L8UvmFdlsqtD19emNYYYX1o9ZFF7AOHDiQ/v3789JLLwGgaRotW7bknnvu4aGHHjrj9XPnzuX++++npqbme+9kuID1h1clAhyzkbqNkNYA24B7kTRCSLXI4tIfARcP4jUSjURIvk/xUT2wEKmtcOlQpcF9KuS9Bp2/g+2dYHtLSdv0Oxbgyf1TWbPlIxZNsTGj25kRg2+LAly6wNFs+25jEPn0BjvDWxlZ8J2XOR+6yYpVOFGrs3y6jaGZhpMgsnaOnW7JBp7d5OGhNR4eG27miZEW9pdrJ0FkzWw7CXZh+hq3zph3Peypt5K4eh0lffrQukTOY6UZ/AZoiBPAywt+B0YdTA5Iqywhu0dHAnV1/xaINFZJg8bweW5yKv3M7mni6TEWhi/0cpwY9PWbMHXoQMw/F1N67w3M6m7kxfFWxi328F2thahl62jRsi+xBjicIMWuQ4/Cus7SPeMwQawTYgIw8iAYY6DaBjExkN5euoiKkQJlpx/0enC6pXsnxQ53RUoL9epgR1U0MEWDe2qgoQKsVdCnHBI9cixuFQ5GQnmwlCfRAJN6wtY4mOSEeSUCwrelQgf7v33qwgorrB9I/5GpvV6vlx07djB27NhTG1BVxo4dy9dff33W9zU0NNCqVStatmzJVVddxf79+8/5OR6Ph7q6uiaPsH445SOpmTs5BSIaUniaj9R8NAaRxmmZcQg8/BEYCczh4kFEQ8L2v0eiML2BuxSBky3ApEkQpUDLHBnilpMFiWaVg/t2YTUo3LDC1cQOHi4eRABm9TQzb7KV3BqdzBiFqUtdDHjT0QREAH431MIzYyw8ucHLo2s9jJ7nIMHeFEQAYq0Ka26w0DPKTfnYUSTu3Y9XgyIrtCyCqmhJixUgrbFWYKQXnJUlHOvT5QcFEQhGSOZYaRev8vkxP7UenQ0zzbShFkYOJfD8cydB5O1JVuJsCquvt9AjxkP9tFHE5OzgRDK4bdCxGL7sCi4z2Oqh92GYegSi6yAuGoqtkJ0OZakwVIFvdVjnh4pacJeD1w3JNrg9EZ6Phi0GSRH1R9rH+7thVjHUFEHLfBhdeApEvBGwO/oUiLSzwi0DYWMcXBkEEYBbwiASVlg/WV0UjFRUVBAIBEhJSWnyfEpKCiUlJc2+p2PHjrz99tt89NFHLFy4EE3TGDJkCAUFBWf9nKeffpqYmJiTj5Ytm3OpCOv76CCSDvktkB58zoEYSbVGhuCd3knzBQIOmUiaZiXwMGfaw1+I9gXf+0Xw829DIgRfAU+q4NDg3XTYORDaemHkCXAa4YPWCmPbXI/Lr9MvTW0CJIV1GpcucNAhQT0DRHyB5kEkpMZAYlBlrkxjEAkpBCRPb/ISbVHIrdH4rvTMAXmxVoVxWRCorcOydT8mPxj88F17KWgdjqQw6pCBgQUlJQT6dMFXVf2DgkhIYh1vJ8aiMGqek1qPzldBIPE+9LuTIGIIGsXFWBXWXG+hvd3N1hvGUOl2EFsnhbeZtdD+BLQugq4uOBEFaRpsagFH0qTVto8Vbg3AwXpQK0BxQpwFrkyAN2JBN8GHCtyFpKjKNLBWwwPFYCyGS3Khd7VEjABcCbDVDHWKRD4Gx8DNg2CpDSY7YUEjEOkcBpGwwvrJ6j/eTTN48GBmz55Nr169GDFiBCtWrCApKYnXXnvtrO95+OGHqa2tPfnIz8//T+/mf4W+QTpeHgFig88VIl0x04BRjV4b6paJRjxH6pFoSAJiAR95kZ9dgbhrvh7890wERj4AJiM1J0cVSRN11WHyRHAaIP64tJEWZMJ1w//MtL6/YVuR1gRIbCaFWKtChVOnztM061ju1Nl0IkDvNAMDWzTfrjurp5n5V1vpmKCy4aaIM0AkpBndTMTbFKItCkMzDUxc5GTt8aY1LH9Y7+HpTV5Mf/wTHUZNp9dxSAiAzyz+K7sAA7KwVpSUcrB/D7Sqal4Yd+Eg0uDVuekjJx8e8p3/xQiQrJ1jJ8osQGIzKWycZWbuVdYmIBLS1wUBsisD6EOGYjfb6VgOj6yGjDLpnokzQIoXvm4HVfECKWl+2JcKr3nBUQGGBinWHRAH/4iHURapC+mJFDlvAw54IL8U3imDFifgsjxIccs+mCxQmwJfa3LeDMCUdJjRF94wwBQXvFsiUbabUqBLGETCCusnrYuKsCcmJmIwGCgtLW3yfGlpKampqRe0DZPJRO/evTl69OhZX2OxWLBYwnaJP6RWAYcQV9XQl74LgYEHEMgIaT+wiFNOqxsQSPgl4g1xMfIGP2MDQr6XIQvxh8DVwHXA58CbwCTgSQW+0OCjeBgzDDqth4LjsKUDHGyr8Bf3c5gzYNFHf2FAugDJu1NsrJsTwah5DkbOc7B+TgQtY4Sz/RrE2WBLvhS2Lp9ux2I8s8x2Zg8zM3uc3W01r0Zj5DwHsVb4cIad5AiFKxc7mbjIySfX2xnd2sgf1nt44isPWU/9Cfddj3DUK63JpXboZRAgK0QW0DalpRSNGo61oQofkFOtoes6inLuEuAGr86Ed51sPBFgwXd+3psG0y7AGK3KpVPj1kmOULAYINqiMqfXmce76qifSUvdKJdezug/LyfXp3DHFtjQBgqtMkyvPgHWJUIrB6Q2SF2PWwe3G9QAxBugdTT80gZtVZirQDvgD0gB7L90WFMHrlo4US1Tm7u4Tu1DXALsM8ABr0TnbAr8ogN0zpB04VUuWFIiqa4bU6Dbhc8MDCussH6kuqjIiNlspm/fvqxZs+bkc5qmsWbNGgYPHnxB2wgEAuzdu5e0tItd1sL6PtKBdxEzs/sQENGRVMt6JP2S0Oi1yxA4+D1iTPUiUkfyP1wciOjAViQd9BVSFzID+Bq56J5A0kOPI7UTf9DFbvxTHb7WJX1TNgGOW2BIPqR4YG86fBOhcE/L5/jl3RIh6RuMkByr0lg3JwJNh5HzHOTXaicNzaxGhXFtDHx2NMC0pU48/ovz7giBiKrAujkRZMaoWI0K/7rOfjJCMucDF0985SH5j39i6B2PYHZC12KojAF3lPiKpAXPP6WlFIwcTlTRcb69xcprV1h5abuPez9zn9PTJAQiu0sCbLzJzvSuRma87+L988yyOVAuXijxNoW1c5qHMTgFIoZxl3P7/cvRzBZaVUKdBda0hoIWYFdh2HHQLFBph33x0OCDep9M120ZCfclwGsRcMIACxSB2hkIiLzrhUWlUFcBNQUw9NgpEFFVaNkGtiiw3yfXULwBHu0FPTIksjbBDe+XgE+H2cnQMwwiYYX1s9BFp2keeOAB3njjDebNm8fBgwe54447cDgc3HTTTQDMnj2bhx9++OTrn3zySb744gtycnLYuXMnM2fOJC8vj1tvvfWHO4qwmlUAeBlJtdyIRCR8SFeMFzErC90b1yJtvqG0TC6SphnHxXfL5COwMQ9IRIpcS5Hhe48AqchdchXwmA6Juvy5S4e7VLmbfgroHQW9x4BXg6yj4hpa2A5a+BX+NOg5rr7/fr4t0jCqEGGGrFi1CZCEDM0ubWNk1bEAt/c18WVOgGuWXTiQNAciITUGkoV7fZhuupnOjz5CQgH4TGCtkTH2UaqYjBUgbctRTz2F59AR3ptsokOCgdv6ms8LJI1B5POZdoZmGpl/te28QHKgPMDwBR5qfCo+1UCCrXkQya4MMGmJG/OI0Vz36HIabBZa1sD4A/D4BJn7kuCEEfnwRTc4mAR+N9RpEhUx2eCyRFgaBW2M8LQixdEPBb/vgA7P1MGiMjkvlmzoWwQtgmU3kXbI7AwrasSGHqC1BZ4aCBnxAiJj3LCyWK6HmcnQ+2JzhWGFFdaPVhcNI9deey1/+ctfeOyxx+jVqxe7d+9m1apVJ4taT5w4QXFx8cnXV1dX84tf/ILOnTszYcIE6urq2LJlC126dPnhjiKsM+RGQtq9gCuDz9UgE3QHITUioWXpQPC11wFjkRTNKgQcLuZbciDtvk8hcDMdiEeiIbcFP/cFYC+ySHXQ4U86bNbhZgUMqtxJz1EgRoO9GvxlNCgRMKEY0hxwJAUKY6BynZfajUcwGRRWzrAzKENwqTGQhEDkjZ0yffeViTY+nGHni2MBpi9znTMKAdIaezYQCSkEJKPbmNAXv0vqB2vZagOnBfKSYHKkpJ+swXNSDbR74AEiMtK5+VM/hXWyGp8LSE4HkcEt5ViNqnJOIBEQ8VIXk4LfH2BkhnLWNFByhEpWvBHD7u8oP5LLwDzY3RIOpoDJDRYNAhbY1hKKTOANiOmZywKBSHjQDg+Z4HVFOqQeR4YmKkCZD+4ph6+qoW0ppB2E1g0SeQPITAdzJiwogurgYQ+IgccGQaRNrpmBHlhVDB4NbkiGvmEQCSusn5XCg/J+hqoD/ooAR6jjJQexfL8d6YoBCYMvB04AdyCL5atIy+7Ii/g8HVgd3JaORFO8wBHgeqRNeBFifjUDGQ63WJeBe+MV2KRIGmkqkKLDR8C7AZipwiwFnGuh5n04mAwrukHyCQ8bnpnC1upVfDTDyvh2Z8Zt6j06T2/08PRmb5Ppu5VOjUvedlLt1sm7PxLrWVIWAFsL/Ax5y8n13U3Mv9qKeo56Drdf58olHjacULjrhU28PqcfkUb4oxU2Igt0HLAWSYvVH8ohMHYYqZ4KNs0y0yJaQOf1HV5u/9jN3f1N/P1yKw4fzYJIY/k1nalLXHyc7WfJNBvTuphOgkhNTDKBwkJm9zA1W6zaWKUNGiPme8kPxDDq1Y20SupIjQqf9wCzDmYPJLqh1ArWAGgRYNXg0joY2AqyFbgZyAhuT9fhKwe8Wgt1bhiYB0qVdNGkAAYD9O4G2+pgfY2ksgzAlWlwTWdAEUO9Nh74ugicGlyfBAPDvwLCCusno/CgvP9SlQJ/59QEXYDNyCL4EOLpAAIeLwN9kHTNeqTI9E5O3bFeiA4j0ZAaJAqThURCJiJQshSJmFwHeHV4VZfU0FQFvlYEYKYA8TqsAAp1uEqBV1RYoUu0xD4YKpZCi+0QHelh3rtTKD0HiABEWRSq3ToGBVpGywIcclatCrbvngtEAAZlGJk32cqcD93YjPDalWcHErMBMiLB6/OyzV2NArSySG3OLqQQuAI5tz4dLBltiFy9kcKxwxi64BSQ3NZXoOn2j90EdNhXpp0TRAAK6nS+LQPsdq5d7uLZGo1ntmrUR184iACkRKp8NdvMiPm1fPXLYXRauJGy3h2xeiXC0WCFODf4bWDxQqoOUfVQngQZioBm6BOq/TC/Gja6gTq4PBtq/fILJwWIjYZePWHpUfjOLRBrVeDWdjAkU/79BpDmga3FAiLXhkEkrLB+tgpHRn5GCkU/7geSkF/oixAYuJlT5HkAKWq9Nfi614AWyKTdC51VWwW8g3TepCH+GZuBvkiL8ErgOLJAWYOREA0ZirdNkYV5CjLK/n1kENsUBbohg9cAHnTAdd+Cvg304xA46OHO7CnsLV3FymvPDiIh+QI6N6xw8cEhP29PsvLCVu8ZhmYXopBT6y29Tc0Ciabr3PovD3N3+0h5YT6Om2fiskGkSc5vGeI0+3vgKNDSD/VOcSy1H86h/rJhtPBUsKGZCEmUmXOCSG6NxtD5Hkpi0wmsXo/x0Yfxv/cextZZ+HNzub6bifmTzwSRL475+fs2H69OtJAR3TT9VNqgcck8DwVqPFHf5pDgt1MXBe0DcMIEAQP0VqDUDQk+eCsZUoMNPboO25ywpAby/RBbCIOLxI1VRa6V9lmQ3gpe3w95wfqQOAM80B3aBqup3wV8XsgukqF61yTC0JgL/srCCiusH4nCkZH/Mu1G2mVD0Q838A8kTXND8DWN0zK/B44hLbWzgY4X+Dl+JI3yCVIHcTUCJPkIBG1E5otcDYzWYYkuxZuXKbBLgc8QCFF12ReC/+4QXA91HeqOQ+k2aO+CD2OgrwuOxXtYenAK+ypWsfIcEZHGMhkU3p1iY9pSJzevdBNrVVh3kSAC4kMCMOdDMcFoDCSNQaT7c/PRLp+JG9CNki5zIYD1p+B5sgODvbBXB8UP3jZtaPvJRkquODNC0jJaoUW0So+U5vc3BCLlselo6zehZmSgz18Al47D36U7xonj2FvpoNYD8Y1mtYS6Zvy6yrAFXjbOMjcBkpxqjeIGDWO3digWCwkOiI2DXqWQ1wLcZlA06F0FA62nQKQ+AItrYJcTXF7IOgxdnVCEREwyjTC4L9Qq8Ne9UBW8DWplgQf7QFzQK+QjoNYLRUEQmRIGkbDC+tkrDCM/A61HDM0eQVIgZUiqZgayEIIsjC8haZl7gPeQYsrfAxc6U2wHEnlxIdEPB+JdMgtJ1/wVGZZ3jw5LdYl+jFNgryI1JVN06eZZggDT9ciYeoCAFyr2QNk2cFUI9FgV+KoTpO7XWbJiGrsqLxxEQlqd42d/uUaM5dwgcqJWY+l+H/cMMDfb+toYSKxG+McEWxMQ6fj8fGJHzkTzQpxVrM73Ix1Ne5HIk4J0K+3wQoxJwKRdLWR1bsPhRe/x9fDhPLVB559XyDdyeftz+4c8ts5DYbUP6xfLsbfIwAlgNMKNN2H1gOmzDRy4fDgjFzpYP9NCvE05CSLauMtJ+stfKR89muHzK9gwW4Dk63w/Yxd50HoMJOOdVUR6DHQJQAs/fJwA1VboF4B2JWJG1y/Y7/2dC96tFiCxV0FiNrRFIiI60DMOhg6ArYXwfiGyr0C/aLirN1iCX+kaINsLtcWyrasTYEQYRMIK62ev/7gDa1j/OemIodhBxM/DjCyAf0cG3YVA5ADwLAInPRAn1dbB11wIiBQDjyGeI1nAGCTdMAwpdH0VWVzu0+GQBn/XoYcCqSpsUGCUDmN1WKBLXcptwK8UARFXJeR9Drv/BnmfQlkF7EEcOg069KyBVaO97C/dRka0Su+0C79klx/wccViF7k1OlmxSrPdMCDtu0PfdvDbLz1cs8x11rbfnqkGoi2wt0w7CSLv7PbR7q/zGdljJg4blGZAG5NAl4p0E1kRcAsA5QHINUOMUaJA7XV4oLiA4zfOJj3OxIOXXLjZ36PDzCRFm/DPuh5zWSmqLteEoksqxdC1G4G1GzjgjmDkAg+L9/pOgcjyFXjbdsT26SbKjIkMn+9l+QEfYxd58PYeiO39z0m0RXLzDvBnwQITVFugTzU844ctUWA2QWYkzKuCVytA9UOLoxAZBJGS4DFf2h5GD4MPs2FxEERUYFIq/KrfKRDZCnztg7piqPPDpAQYGXvBpyOssML6CStcM/ITlY5EKaKRWg8FmfeyG4EMK03TMrcjQ+i+RopUEy/gM9xIzck6pPZhIAIKlyLeEUsRZ83ROnymywC90Yp00dQrcLUuUZqPdeigiO17vCKLcG02lH4LtUEj3pLgfipAihEKsmBPS0iqhZwU6P/uXB79x020j5dZK8kR54aS5Qd8XLvcjabptIlTyK3R6Zeu8sWsCKItpyIfeTUaw95xYFDhseEW7vjEzdg2hjOcWveUBhgx1wEo7L0jgsfWe3lntw/r3PkM6TwTUw00JIOlA0TYBACLgcsRWKxAIlH4pfhTN0OqB7KyC9g+dRiR1YVsmmWhddzF3R8crggwbIGX6vTW+NdswJqcghsd85IVaEP6o7fKRNu3D3XUcAJV1SgTJ2JcvoII1Yy1Fmoioe3m4xybORR3SRHGIUOwL/2cTCUScwCG6vBeOrQqh86V0DdV/EbWusGRDG1cUqzayQPefdDgF9AtBfwWmNUPYmLh9V0SPdEAiwI3t4NhmaeOYw+w3Ad6kRS6ToyHcXEXdSrCCiusH6EudP0Ow8hPUD4k+tEDAYMAAiYRSOpDoWlaZghSpNqaYL3GebavI6mf+cFtjUYiIV2D23sfaVOdqMM6XdIQoxSpQWlQYLIuYPGFLoWOVyJTeP1uqNgNZd+Cu0pSMbkIsEQB9hjY3Q6qIqFvLvSrhxZ94anatbx99QT6JmscrfSTFCETc88GJI1B5HeXmPjjKAtXLXHx+dFAEyBpDCJf3RhBpFmh35su8qr9XN7BzPJpFixGRUBkgQd3Wibe/AKSTH5KHTo95s/HNGEmhkKojQRTJExPEl8MPxKpugWBtIPI+VG8cv4DChhyC2D8MBJrCtn4PUAkpBCQVKW3xrLqK5xv/RP++CTGFmmwbiP+Nm2xHTqAumIFjgcfJE4zE1UPZdFgdUK0F+L35uL84h1MM35LYadIehXCrkxoZ4KGeph+UBxwX0yFV7PleytMFdOyCXlQnicdWq2D36eeBHcNgBo3/PM7OB4sVI1R4YEe0L7R/IFs4G0fmIIgcnkcXBb/vU5FWGGF9SNTGEZ+pnIgtRmXI+PX6xEvhpHA0OBrDiLdG7cEf74UuAmJYpxPxxCIKUNMylzIULzxSPGpD7hKh+06fAsMVyAPcCpwpS7Q8pUOlygyh8amgLMMyrZLTYjmk33KCR5LkgK1LWFXa4hzwJBs6JwAKQMgph2sW7+WiRMnMDxD56NrLeTWaIyc6zwrkJwOIn8eY0VRFAKazqT3nCeB5I0rbUxc5GwCIqMXejjgjkB56k8E7r+Py1orPDHcxKWLPLjbdyV+zVoidu4m58ZZGJ5+lg7Xz8RXDp33Q0kGTGgFC6wyhTgLiY5YEdAqBowa+ALQWoXqvALqxwqIfJ+IyOk6GSExR+OvqOSRoWYWH4J8SwL+dRuJaNeWNE2GEcbVSAosoILPAu1KwZIACflQooDdKvNmBnnhoAX65cKQEsjrAeOc8FYlYITLImCDG3ochIAH2gDlCqid4f7OsK8Y3jwkvjIAmRb4bR9IaDTULh94yQ+WIqj1STRkQtypjqqwwgrrp60wjPwMVYXcdc9GrLbzkXqNm5EcvY54deQhILICgYmbkUXxXKpFOmu2B7edhHTBTEaKYwuRAtSDwBYdhijynFuBCbrUqmzTYYwiNSUmDaqPCITU5cpnFAf3WQFi7ZDdFk4kQrcCGFgIGd0guT/YgnfNu3fvZsjgQQwLgkjIF+RQRaBZIFl+wMe177vQdJqASEiNgUTToXWcwro5QRBZ4CbXHYn/868w9u1OYNUqnFdPQvf5MPToiW3tWi6Jj0cNSB3MlSpUHYHDdojQQI2CTnHiKaIjaZkGpEI8GqmTsPrA64dWZQUcHjWMpAsEkXqPjqpAhPncK/Shcj9D33FiMijsus1OjQcmLPGRb0kgau0WEltmkW2B1GLwRoHFB62qwJ0KlgYoVsFlgrG5sKs91EdCfKVEeQZUQ2oq7C4EfwCu8EF8Dhw3wdq2MOsgOKygD4B7EmH1EXi/QIATxNr/3j6n6kNAgPd5P1iLoM4HY2PhivgwiIQV1s9J4dben5lC4HEn0pmxHWmv/S0Qi6RlXkYG0k0jOFQMON/4wgDwL6TDJQaJsBQg9SH5wGIkEpIAvKlDfwVaKnBMgct0mSczV4cJCjyjgO6E8l2SivHWyUJ2HChHIiymNNjTBowBGHQMphRDSn9IvBoMQWKqDxa6fqDL/p2uTokG1t9oZ+RcJ2PmO1kz287GvAAzlrvom6YyvauRBwZbzrA+N6hiHT/pPSefZgf41SAzkWaFkXMdHHbbuWTNV2zu1R2bDspll6Gs/AR17jz8//g7I+PiMWuwWQFVgZkueCkfolqBpoor6SEk+tQRAZI9wePWEBi0BcChQsGzz+I7nsvC2fbzgkhujcaw+R5MBtg4y3LSh+SM71HTeWqjjwYveAI6Y+a7OF6jcXNvEy9vL6Zh3FicaS2gXSb1nfuRPGQcEe0609EuqZTVMZBVAR4T7G0BA2zQuxL+EAn9C+HTljCoEmxeaHdcUjuVikREBpfBlu7QsxXcYYCFu2BdtbjwqsDlqXB9l6aQUY2AiCUIIqNiwyASVlj/zQpHRn4COogUkj6AgMcKxLvhl4CJU2mZm5Hi0R0ItJwv7f4d4kXiROpKipFUj4rAwDhd7mzXBGs/KgC/IgWr3wK5OlypSDrHWQyl26FqP2j+pqmYGDOUtobD6dC+FAYeh8xMiYJEt5YFKKDDTsQptg4BFwdQt3YNb1wxkREZOh82io7AqQhJhBlO1OpM62JkwdU2jOdxGg1oOjd+5GLRXj+ZMQpFATutN35NRdfuKB5IsUCOCjYfNJjECO4XAZirQi8F9imQngMtcmB/Z0jXIbalnJ8lwExksVWQu38rUKuD2StOrZHH8ym4dBjJdUVsnG0hK7Z5wAj5iFTFphPl82F3l7PpLEDyy49dvLnTR89UlZ3FGgYFeqWq7CrR0K1R2Fr3BXR8tSX4So+D30tmnxG4nn0ce/dRDDwGm7qAX5fal3dU2LoHlqRCcRRYDZDkhuFfQpoXrFaJ1LVTwdYD3msLv/bA1p2wK1ioagZuagcjWjXdVwfwZACMRdDgheExMCUhDCJhhfVzVDhN8zPRN8CXwK+RRfEVpEhwUvDnK5BiwuuRKbmdgKs4ZcvdnMoQCDmIRFK8QHtkouyXwGBdIOdzHboqYvWuKzBMl/2p0OFqBbprUHNADMoaCmXboVQMgDEBDrcDlxkG5ED3akjtBcl9wRLslMjVBUAOIbUVXiBSEUfXfgRt69es4c9BIFnZDJCMme9kVJaRuZOt5wURkFkuM5a7WX7QjyE6iqEbN9Gme3e+CkBkKVQlQoUqMFdmhAQNdA9Um2WeygQX7C2CHiWwoRP8PgJetEpdzhTEit+AwFg5Yn/ewwMbVXAZIcYN7Q/ls2fKMOJqi9jUDJCEQKQsNh3rhk3c4vXyyohhpJ8FSK5c5ODzYwGWT7fxxTE/o1sbWbzPx/sH/STN+DO2zB4nX6v7fTiPbKFu50q8hYeJueGXDHzgr5xIsVMZB11s4n+yrQGsLrDFQUEEJGXLYLwpeyXi1TMSogbC/ji4shpurIP042AKQLQKv+oOHU9r23IDfwyAVgROLwyNhmmJYRAJK6yfq8Iw8jPQKmSRvhuJFvwvAhp9OJWW6YXUd3yA1Im0Ocf2PEiE5WNkWF4iEoHog5hNddZlAf5Cl3RDfTAlMUgXq3cvsti2bYCyHVC+A3yOpqkYqwHqWsGhTEivgSFHITNCUjHx3cFgEuv3rxBfCSX4UIGBiniXaMjnfYsMVYsBvlmzhkVnARK/pl8QhIRee/0KD8sPB1AXLyPqqiuZaTKyFxjoh4/KITsJLAHwmaXrJUaXaM/YAHxqhb7ZkJEPW7vJ8XjipI4nM3geqxDQi0CiTlGAzwNlCgRMoPvA7oXYqnyqLxmGzdEUSEIgUhqbTuKGTSRkZODQIfdYDsaRw8jwNAWSI5UBhr/joNIF6VEKefdH8eEhH1cvcRHVfzLxo29tcg4Cjmr89ZWYU9rQsOszqr96B3vPftg+/Izb4iPZpEJuHThNMKwI0lQocMD+RIhwgkWFP9eCtRdsNcHYInjnEORYIT8ZLi2G3/WBxIjTzj3wp4BE0dweGBIN08MgElZYP2uFYeQnLB2BBh8wB+lQmcupepFQWmYWYr+uATciC/fZtrcZiaoYgc7BbY9AvEeSdWiFQEgm4FDApEDvIIRYkOLV5EKJglQfBF1rmooxRcGJ9lAZBb1PQO98SO4gEBKZKbUfOxDPkqrgNv1Ad0XcXKOD+/INp5xKa4L7OwBJBW1ds4YJV0xk5GkFrReqEIh8cDhAp/eWceyqq+kBNBilTqatBsccUG8TkOhplO4irxacWqtJjURUKYwqh69ToCQTPAY5Tw8ifinZCOjNDX4vyTocrQZ/jACewQ9dAqAYIOd4Ps7xw0isEyABGDrfQ2VsOoENYvFu0EEJSMeS6VAO2mXDyHCWsmmODYdPZ+RcJ26/Tp0H3p1i42BFgMEZRl7Y6mF1jkbSjD9hDUZG/LWlVCz+Hd66ShIm/Y6ITkPxFB6kbNnjRA4fSfLKj+lVoLArCuqsMOQ4lMXCZRtgdScoSofuMZAQKcDaKxtW5EuxLoAhE65pA5NOM7rVgOcCUF4Mfg8MioIZSWEQCSusn7vCMPITVQD4JwIHVyIRhE3I3Bc7p9IyE5BhYlchLb5nUy7SClwI9ETC5MORiIuqQxekJiQV6YwxI89t0aXtdrIfrPukHsRZItssRnxEUMCVAUdbQ7QLBh+FNl5I6iOpGHM0HNclWnCQUwDSShHvkiykEHcLErWJRgDHiBTQDkQiN421YPUabr1yIqOCQNKcdXuz51XTuW6FhxWHA/xh6TJeuPpq6gPQ2gHHoyX1NUuDZ73gNkCKAkkGKNXBooNThQYdun0HXaphc08Y64CVLcEbBKrtSMQqEYnwvITAo8sHiQ3gjYVoH5Rp4DfJBNxaBUx5+TRMHEZiTREGoDgYEanPyCDaBxWapMl8RsQx7r57Mf7zJTJjFFw+iLUqdEpU+TTbzyUtDazNDWAxwJCWBtbnBjAltCBl1gto7gYqFv+OVLWGfqkKKw75TwKJ89h2yt//A5l/f4P0KbfSey+s7gwWkxSttjgCMS6IvxT22aFYg56FYDwiETMFuCwFbugKf1ZORYpAYPjvATheLOmu/lFwfZJE3cIKK6yft8Iw8hOUG/gbspANARYgcHIjEn14CTE6CwD7kEhJ7Fm2VQ+8jhSitkNqGPoi0YZKXdI7m3VJgXgViQS0Abbq0EaBCXWgbZfOGL9LIim5SCpGtUFZWyhMgq6F0P84JCVLQWp8F6g3iGna1wh8gCzQwxUBoj0IYNUjKQwnUqNyNgAJyeuGqhMw6rHbObTkdTbeZGdo5oU1hB0oD9D1FQeWqVMZuGwZOxSFFA36V8On8XLMbj/k6WDwQTczHFXBp8FvVPinChVeuG4NqNGQlwonsiBBEW+OCkXOn9Pno4PJRDISJVkHZDggWgdrJBQ5oEaBLjoc9vuotxvJdCn0q8nno5HDUADPhk0oGRkkuKBVPbStCrAxS6XYokBBAWS25JKWKvl1OhEmhbVz7Ly318evv/Sg6/C38RY+P+bn82MBdF1gIHrwDDwHVpOq1rBxjpW0KIVZH7hZsv8UkFR8+iK+3O3Meb2QTf0jGJ4DOzpBlUHSM6Mi4MY0uNsH3iooVaBlKbQrgxvbwqgsOdfVCAA/hsDtWxrsKgaDG/pGwszkMIiEFdZ/i8Iw8hNTHfILfBriGfJ3JOIxhlNpmWmI8VgPYCLNF6lqSMvv24hLajJy129F6jr66PCtLlEKTZFoSyZiYtYdGHkC3N9AzRG5Ca9HUhVOwJ0CJ9oK2Aw6Cu0rIbErJA8Aa5pEBtYji7ABAZzBirQX5yAppYrg825koQoBSFQzx6JrUFsGlXlQkQd1pbBszV9546Pf8MhQM0+NPrN991z66xYPv/nSw/BHHmHnU0/RF4VKJ1SawGkAT0AW7ig3pERI9MiiQw8j7NYhuRh65kF+EsSbpDamQpf5PvkK2HftombcWExXXU3q66/TSVXZoYOxFiba4EMzeBzgNoL90DHqrxyJpUsf4pctwxdtptbhYIaisMJmxxkAox9aHS0h75qRKMnJOD5aiXL77airPiVQ18CjQ808OtzC3N0+7l3lJsmu8P41NoZkGvFrOkX1Go+v8zDvOz9GezTpFicb51hpGZzR49f0k0CSeNXDmFPbUfjarfS+95/Y59xGlA0cZqg3Q200DDODyw8ddkj3DDp4bfAHI9wU2/Rcf4uk5eI0WFMMVjf0joRZyWAIg0hYYf3XKAwjPyGVIvDxCwQSXkbaQzsihak5yIL9efA1rZrfDPuB55D8fXskatISgZleOuwP3iWjyOKfgiyy/QMwYC80bJWJuTqnumJ8JqhuDQUtoE0ZDMqBJCMk94PEPpBvlzTMXiS9ogJ9gt0wtUiaqQCBHi+ycPdHakCaAxBPA1TmQ0Wu/Olzn/rZ+2u/P4iEFAIS2yOP8PpTT/GcptC6AdZZgwWbTsj2Qcco+CoIay1UKPHC9I2wqgvE1MHxdjDACEW6wJq2exfK6FG0MjjIqQ4QM3sOyW+9Rb2u4m4AJUqAzuAE/fgxtEuHkuStpLQ+QMb4y7GtWEGk2UyaJr4v9loI1Ffgv3QwsSV5OLwaTmskhvp6WsSoFHjNBBqcXNPFyLIDEn/682gLDw9rWjmk6Tp3fOJhwwmNL244BSIhHa4I0P2fDkxZfUm65g+ULfsDdruVxFUbuKYIFvWEoXlQGgWHYqVwV3FBi3LY0w0GxkGpGf6MRNsa6x4NciuhTR30jIDZKXCRZT5hhRXWT1xh07OfiHKQuTL3IwCwLPh3C/AMUr8RARzmVNj7dFUiUZXdiHtqBlKkehDQdLDq4gyqKpKWiQMO6pDVALd9DfW7odzTNBXjioOi9uC1SBrm8vUQlwkpE4GO8JUqRZseJELTEZlPY0BSMC8Hj8GHpF1CRainA4gWgJoiqDwh0Y/6iubP0w8BIgC/HiKL9W/+/Gf+oOuk/fFPbDcr+DWIUaDeBHgFrnRgsgJLdBh4zEOcZsDqNVKUBgO94DJILYe+cxfqmFF0j3Sx5gYbHx/xMXv+PDxA5MtvoZtUbCrU+4Djx9DHDaWFVsXGmyzsKdW4aulnREydQmDZCnapZjRVWoijjx+i8MhRruxt4pbeFm7+2MGz11jYXaLx+HonluHDWbZhA9a2/TFGJvDo2lWkRCrc3PvUVaIqCq9dYUXX9TPO2dEqjZELPBhi04i7/D4ALJldqf16GaP0ACv6Gbi2RAzRIhugczHsbAWpFdCiFXwbD7NMEnl7FGkXD3VzrdOgpkTM4MZEwuzkMIiEFVZYZ1cYRv4PtRv4EPgdksI4BPweAZSFyBC8L4GpSPvt6fIiRaxLgTQEQLoi6RiXDhG61JYYFAEQO1JQ2q4QbtoEjmypIakLfma9CtWtoKgVpNbCuIOQ5oKEHhA/EfYly5C8QiQC0gIYoUgqaCtS46IiNS1RSDSnOQBx1Qp4VJyAqnwI+M59nr4viOi6jjfAGUWuJ4Hk6aex+/w4nnyWgFnBrECUBvsiBDKMKnyjgPVoPrvHDmN/fAJDXl7Npsw4tuqgaeDfswvlJIhYiLUqzOwhMDBn/jxifOB68y3SUKk/cgz3+KG0DFSxcZaZ9CiV9CiVj6ZbmbT0M7SpUzAsXYHZYKaHHXIHDyXq6Rd56+H76ZGicvAOOx8e8vH4eg/WXz2AqVUrPJu3kDT5YRSDERSFW1d+BnASSAKazsEKjW7JTdtbjlZpDJvnpsaYRMJ1z2KIiMNbnospKQu/x4HizWOo3obPTBCdBy4buE3QIwcKOkG3CFihiEPv1Uiq7wGkTilPgzdLIMEFUw1QE64RCSussM6jMIz8H+krpI31QaQFNBH4FQInx5D6ja3Bn8ec9l4dKQ79a/Dv7RAQqUCs1CN0ARujIr4hZqDID8MOwZD14KuWVE4oFdMQCSXtoD5W2nLHb4ToKEgaBLW9YZVFwAkEaq5QpK7lO6Q+JYDUqsTSPIAEfFBdKABSeQIc1ec/P1Vm8bVYue6v7PkeIOLx61yzzMW3RQHWzrHTKfHUYqzrOkX1OmYD7PnL8yQEwPz0s9gUhYPV5XhLizE4nWhx0RTr4L38cpJcxdQfL2TNvWPIWL+GEzFxaDt3oV46iq6NQCSkk0CyaB5JJqh55BFclw4n01fFxtkCIiFd1s7IyulWJi35DHX6FHzLVnDYYUb3+FC3bsJoUOmcaKCoXqN1rIrdasC3bStER6Na7ahG+az4cXcAcOvKzzAoMLOHiZtXupn/nY/HR5h5YqT47R+vPhNEajcupPbrJdjaSm+Wy+HA44CUA3AiDdLrQDdAu9bwbKSAxygERpYhXV0tgdk6RJYJxHa2w61JsE6RLrBpF/TNhRVWWP+NCteM/H+WjgBHCTADCW2PQyIaLyEh72ykrmI8Zxap5gN/QqIfrZH22AAyhVXRxSbepMgsGRWoaoBLdkPaJjHb8gXfW6ZAVToUt5E5I5dkQ8sqiG0D5kGws43UTLgQmBkE9FTE8+RbpABVR2AnBCChb0bXBTgq8qT4tLpQ0jHnksMABxLgcBKUJUBkJMRs+5y3b7iMu/ub+Pvl1vOCiF/Tuf1jDzVucPkCrD0eIDNGpc6js/5GARJd1/n1Fx7+ttVL92SVQxUaPg0S77yP6iMHCaz+osk2jSqkRhnYeKONGrfO6IUeDB2743r5FbyXX9YkItKcFu7xMucjD7rJROtoTkZEmtOqo34mLXWjjL2CxPc+JGvj52y54nJ+M9hMRrTC/Z97eGCQmaxYlXtXuVGnXYP+0Ue0/NX7KIps01dTQslbdzIkPUCbOJWFe3xc08XIkv3+k0Dy6rde7vjETdyYXxDVd9JJEJnRTV6n69BrzyHKEzuSUgpZ5VDeEi5Lh9HREq27CkknDkGuwWuA8ToUOSEiAHc0wB2pYFLlOvkr4hrc4dyXQVhhhfUzU7iA9UcoHakPiUZg402kINWNpDh6IW2vtyN1H43lROowPgfSkbvQSMAbrAnJ5xSEBDRwV8KQryH5OwGaUCqm0golbaA2BboUST1INBDdG04Mgq+iZVsGJDrTTxGTsm3BbYAAyCCkSyb0bfg8knKpPCHFp+6QC9ZZ5FPgUBwcSoLCODBGQncrTIyHQTES1i8qKuKSwQNRHaWsn2U5o/iysfyazg0rPCw/5EfRdVRF51/X2emdqjJ6vpNyh866OXbe2Onjb1u9vDzByi29TUx+z8kXOTLF15TajqheEzAntULzOKn59K8kG+rZeKPtpDvq7pIAIxd6ICWT2pwcfjXIzF/HnTti894+H2/t9jPvKstZQQSgpEFj0DseiqyJvHM8j3WqwrKZM2lYuhRN10m/9FKKvvwSg6qgDB2G6a4HcF07mfTbXscUl37S0CxFqaZ/msIHh/y8O8XGjG4mnt7o4ZG1Hh4fYeaxERZuWelm7m4ftvaDcGVv5dmxFh68xMK7e3zM+sCFesNssn7/DppNJUWHdxMhJ1JAdAKSGpwO/Aa5bmN12FoG26MhRYfhVnhRFSAGicQ9jdSW2M99aYQVVlg/I4Vh5EcmH9Ix0wOBiC+BexEfikNIsWc80kXTOHemI/btLyG/xFsiBmUNOkTq0qliVCBeB48PlEIY9BWk5st7ixC4KEuC4nZgVWHgMehYArZEqB8K33aGXUZ5fRZwiSL7uw1J/ShIGmkwpwBE16G+TOo+KvOkWFHXzn78GpATDQcT4XgC6DZoZ4YxMTA+pelo+cbKzc1l1Ihh5wSSxiDSp1cv9ny3i5UzbIxrKxstd2iMnu/kaJWG2w8vT7ByZ39JbXj8OlcvcfL5sQCmpNYkz3wezVVPxeIHSaKajXOsZ8yNCQFJQ2wKgcJCHrrEzJ/HfP+iWhAQGb7AS44ayxVfbeS2Dh2oC8DTXj8HH7gfW2Iqnt8/Cm+8hXfLekwvv0aHXBf7+iSRMOF+rJndzwoiITUGkv8ZbqbP6072lGonQSSkd/f4mPWhG8P1c+j01FsMbqliNcBdwe9xIXAtMhRwCvA7HfIckOqCS/3waaoY5g1AaqBCZ+UA8AUSUQkrrLD+OxSGkR+RHEiY+jIkRVKFpGheRRb548F/9zjtfQeAJ5FumUwEFOp1iApGQoxIDYerASLyYNB6SKo+lYopNEFRFlRlQNsKGHwM4l2gd4ODg2FzikRcEoBhiniR7EIKVBWkMDUEIDGA1xmMfAQBxOs6+zHrQJEdDiTB0QRwWyHDCJdEwhXJkHgRX+O5gKSxxfvf313E7++5iwS9li03Wki0n3pduUNj1gcupnY28Yu+TXuSXtnu5a5P3ZhUBWOLzmj15WcFkZBCQFIfm4L2bwJJCEROqLF41m2kR0oHPLqkyKotMmjQpkMrp9TlbIuClABc+zl8/JvRHK8rAFf1OUEkpBCQDG9lYENe4AwQCSkEJDGz59Dv7bfooqq4EX+bwQhYTwBW6vBNFRy3SKv4NDtcpcqogiikTuT2RttdhBRbj7rosxRWWGH9FBWGkR+JqoAXENhYhbTqtgHmI+kWF/BLmhZ81iC+DVuRjpU2SHdMZLAmRAXiNairlEmqg7dCvPNUKqYgFgrbgWqHfrnQPR8MdsgfAVu7QJFNwGMIkKhIC3AOAiApwCXIghOlQV3Jqc6X+jKJiJz1WINFp4eToN4KCSr0tcIVCdA2BVTD2d97PjUHJI2H3rVe8j5dp0zGtH8/X44YTpa5gTUzzU2ApDm9udPLL/7l5s5+Jq7tauSyRR4SItRzgkhIq3P8XLrACcOGwcaN3wtIQiByXI3FtGojnq4dGFMKmTqYImBJBPRWxaL+eLB1WgtATCVMWgNVa5bzxltSGvqLPibe2OnjF31MvH6l7ayfGQKSs4FISLd85OLt3T66bt2KOnAgoxGwboNcs6/qsKoG3AHoBjjipcamN2LWdxsSzfsVUmMCUt/0RwRQ0i74LIUVVlg/VYVh5EegfCT6MQPJsU9DFv3vEJv0EcBYToWx/cC84COWoGdD0OGzRBcIiXNCTSVkHoAhe8UttAjIUyG3JZRnQcsGmROTWgulHeG7IXAgHVRV6kBaK5AHHEHC7unAUARALPWnIh+V+TLU7GxyGOBggtR9lEdCpAbdjTA+Fnq1AMsPUBwQQFJRB4DPc3N5Z8Qw4hylrLvBwu/WeFlxOMATS9+naNJkXlZl8Nr+/fsZNWI46eYGVp8DSBqDyEsTpEA2p1oj2sJ5IabcoTFioYecQDSRX23E//Eqah984KKAJAQiBcTS9vONnOjYAdUM/9JgrhEqFfFy6YlcIwFdrNaPuSClGh5aDSm1Gg/8dSRHS3ageZ0MbWlgc36AtyZZuam3+ayfXeHUznmMb+30cuu/3LS+4w6GvvwyhYpCMdAJicaZdSiphBo/xFlgXIykALcgAN4fGWtwH5KCfBaBXBAfm38gvjnhdr6wwvp5Kwwj/8c6iISkJyKOmjcCyxGgqAfuQCAgpM3AU4iJWGvArksXS7kuC1FcOdTUQIf9MOQwmHwCNscjxKLdkwD9TkDvPPBY4MAQqQVxx0h6p6si9R8HEABJRWBokB+0RqZjDZVnPyafAoeDRacFMTLFtoMGIyNhSBrEJIFy7jX8nAqBx16kXuUIsvhakbvo3kB6bi53jxhGSWEhmqKybNn7TJ48mVcCMEgV91dNh43f7WPcoL6MyYRPr7ee8VnbCgMMfNPBzB4m5k8+f6dOY5U7NEbMdZLtNNPimx2kdutCrga1Dz+C+7mnWTHdxtWdz0yRnK7LFrlYmwd37NpDUZdOrEAgdAACsAuRFupNujzvAq7dA092hJQ6eOZDCOTDXv8xnn6xB6o5gkBd+QUDydkUApFbb7+DR/75Ms8oCqXItXscQAeHEwIBGOWBG+Jho0GifnkIhGcjkbcBwEMIUL+OvAbkej+KTKUOK6ywfr4KO7D+H+obpEB1MDKrZRrwBgIXHRGPhtCJLwT+B4GElkDbIBpW6aD6Ie4EVDmgxUGYekzmpxwEjqZDURtI9sOlRyF9PxxsB+9eBxVpcrc6ABmCtxexbE8L7kvvGvAFPT92FpzddOz0olOALDcMt8DIBEhpCaYz1/kLUgBZtPYEz1c2Ah52JDXVDzF7a8tpg/OysuixYRO33nIzd959L4OuuIrdLlAD8KAKGV7I03UOvvk2Xo+XyR2b38H28SrdklW+PObncKXWxIfkXCp3aIyY7+FonRHdoGLK7MARDzhzjhFY8A5tkswMaXlh27qxh5Evj7lZ8vyzuN96C01VGY9A7HfA33Sp5ylCrp2lR2DdXrC0hgYz7DZAUgx8M6wtY+I/Zu2jEzFExLEpvxqzAW5ZKV76FwMkIRCJv/kOer34MnG6wiuKmN0tQq7PjT5wmSBLh4YE2KFKQet85JprQKI565Fo3+8R0L4juI0WSJRkd/A4e17w3oUVVlg/V4UjIz+wViFgEYHc0UchBmUgd4Hdgn8PTej9AClibRn8Fmp0MNdCdD7UuGHAEan7KNMg2wJH2kB9KvQqgQE5UBEF23tDbhswJUBXA1gUmVPjQaIvo33QOR88wdoPV23z+3560anHBOn10EuFMXGQkQmR8ZIKuRiFwGMXEvHIRupiIpEW5n7BRxuab/v061DhF+O2Y344pEG2DicAR9DHwqhDrRGG+HXyHv01a17+Gy9dbuWuAWdfiMscGmOCbb8hH5JzKQQixxw2IgbNoHrN66QWlKHX1uG8dChJvqpz+og0p4V7vMz50IMyew68+Rb3GlTGAE8AdeXllF83A9OkSTw58z6ylkFhBPxmMqSWQOfjkNsKJq6GbX2gZs82Dj56HXVVeZjskRjTO+PO3nrBEZLGINLiny9jNiike+BWI1xugjJgogtKdYhSAQvEqRLJiwfuQQzQOiOgYUO6Z4YjEP4Ccq0vQyI9bqR+pDljv7DCCuvnoXCa5v+zdOSurwHJifdFFt8apCj0lwig6MBKZKCdCrQJnn2HBlEnwFICDRoMzRaAyNODkYl2EGWES3IguQZ2tIW9XYBkyIiVUfbZSCg/VYfRldA2D9x5UFN8dtOxM4pOndDVDSMioX0GxGWA8fwZh5MKIJCwAwGPo0hhbSSSLurLKfA4vXTSr0O5Hwr9kOOHAxocDbYvNwTXd5MOKRq0UaGjAl0MkG6CNKNEgH7/m1+z+m8vNAsi2wsDdEhQiWlkUNYYSL660U7HswBJYxBJmPEMgYYqypb8ntlr17Ji5vWk+KrYcJEgEtLCPV5mf+jBcMMckv/5FgaTSm1VOd6xI1COHsbl1eh9x/Pc2PU3VNjhb9Mgvhoi3RDvAK8fzIFgTdDXTratfJLdW/6Jz12HYjSjBrzUPRyF3XR2imzw6sQ8U09Uj/48sOgbBnZWeEOBHA0MAejuA8UJtbUQEQVliZKSK0Ku+R7I93kbErkzInOOYoCPECDJBF5D4OU9BNZzkHqq39H8FOqwwgrrp60wjPx/VAD4J7LgZiO/eD9FwONqYCTyi/Yg8ku3EGitgwlweyDpAGh14DXAqMPQogiOGGBfFpRnQpdKiY4UxMO2dtCQCrGpkG6DE4p0OKR5YNQJaJkHzjwZVd+cnEGn00NJUBYJUV7oWA2XWKBrGiRmgj32wo7bj4DHtwh4HEPAIxpZcEIRjyyaDvjz6VAWjHQc9cnQvmPBdmVnI+hI06GNItDRySitwalGKZSsRBbC0KMEcDpdPJMQR9cYP9t/YcfcaFZ9qFi1e6qJtbOatv0u2+9j+vsuHhtu5g+jmk/rzPrAxcI9PlLnvIgltR2OQ5uo+OgZUlOSiNZqWfc9QSSkhXu8zP7IwxWz5uD74zN8NX4U9vxsNs2xsHCPjz9t9DJ+5vMM7/Eb/nQnRDXA/YvA5YcvR0BpOgz93EVajQ3FAH61gU/eu56DX//rvF0zIYW6bDo/9Dgrnn6Ctkjk7m0ddvlBc0MvF/xvLKSaxcRsB9JKXoCkGROQWqTWSHTOjfy/WI6kZpKQeUp9gLcRaPkI6RK64nufvbDCCuvHqjCM/H9SKN2SjBTvtUOK8xIQU7NUoBapC1mHuFNGA9RA2i6JiBiMMOoQ2MpgXzQcag9qJAzJg2gXfNMKilPBngBJ6VBukOhJiyoYlgOpeeAtab7t9oyi0wC0rYK+fuiTBEmtIC4N1PNUD/mDx7cN2I6ARwNyLG2RWoF+yN1vaFPeRtCRHYSOoxoUKaegwxyCDiTK0cEALYLQ4VSbAkcxsvApwfObhtx5G4L7Vw7M/XQVa66exGWtFZZfY8FsUE6CSNT11+P9+F+0szhYf6OdRLvK+lw/E951MjTTwEcz7NjOEj3YfMLPpQvdKGldiL/6fyiddz96XTGZsSobb7SfE0TKHRpb8gNM6mg8Z6FsKGVjjInG4Kwn4NdYNNXGlM5G/medhz9t9JL8m+epeuo3dM6GiRvA4IdtY3T2/u1hyuf/jWk3vU/3LlewdcNjfLziqQsGkZBCQNL2sce58oknuBn4VxW8p0FtJNjMkOSDOQpca4adCjyPXAtFCIC355RF/JdIusaIzKcZiFwzHyKDIF8MfS5wffB9YYUV1s9HYRj5/6A64C/IwuhCDMSKgNFIQR+I5fvLiGlVImAthPQdUG2HSBWGHwRvNexoCQVZYmzVswhy42F/CpgiIaoVNMSCwwMti2DwEUjMA8V95j7pnOZ0qkCrauhaKzbrKZmQkAnW00fpNpIfCbFvRaIeOchiE4vMFukffGQgqSavDiV+KPJBth8O6BLeL1TAHVyjLUHoaKdARxU6GSDdCFYjVKpNgSPUTRyP3Enbgp/jQyIiZcF99CMgYgr+GQAcOqz4dBXHpkxifGuFCW1V7vzUTeoddzD1oYd4e/BA/CUldElS+Z/hFmZ94DoviIS06YSfcQvd+E2R+J116NBkAF1zKmnQGD3PycEK7bxtv7Vunf5vOMip1gjo0DNFZX+5xnunAYnlj8+TfsNvSHZAXYRO+V8eouKV5+iRYmBvBXTtN5V93yy9aBAJKQQkmf/zONZHn8Dtgstd8OskeNcIKzXxvWnjhXuMMNgIf1GkYLUe6dpqjXx3twKbEGh0IBHD3khKZzVSR/UwAuzPIe2+F7/HYYUV1o9VYRj5D6sUiYhYEMjYiyyI9yIFfBuRlsY6IMUP0TmQ+S0Up0GyDwYchFIf7G4LzkToXwxGP+xOB68RLGngyQCXG1rlQf+DkFQqC29j6UBxsOg0O1R0WgudKqG/EVq0lNRLdKrUFJwuPwIbW5GoRy4CVXGIp8QApM6jBeDToDgY6TgSgIMBiZAUcwo6rDqkB6GjkwrtDRBjgoARypVTwBEyb40JPsxIxMOH+FTUBffNHTzHhuCx+jgFHxakHie50Z9JwDsB8H++it9ePQmP18eAO+6g8qGHaBgxDHNdMW9NMHLDCjflTp1L21wYiIS06YQYnbWPU7i0nYkXvvby4ngL9w06cwktadAY/o6DWjfc1NvEs5u9ZwWSWrfOpQuc7CoJ4Nfg2bEWHhhsZuYKF8sP+s8Akm63Pc/g63/Nh0sfovyV53hxvIU7+pno/qqTI5VnWrzvKQ3wwtdenh5jIS1KxRfQMRma7sPc3V6yKzWeHGXhuc1eHlnrIfq3jxPz+BO0sEI3VWqfEhXxDVmvgR6AwX74lQk8Rim8LUP+f1iR1M0VwWtKRUBlPTIYUkeuu4eBm5Aaq61I101YYYX181AYRv6DykFmxRgQENmN3O3djbSn3oe0LCa6IOkotNoltuytGqDLITgSCUfaiotqhyrIjYUSG5htAiA+BTKPQ7/9kOY8E0CqGxWd1gWLTjuWQ08ntEmHhCxIaCnbayw/UtPyNZLrP44s9nGI/8MgBDxiNYl0FDaCjhygWAFPcP2yadBCh7YqdFYgwyTpJo9RJgIXIQsQSIeMnVPH4UUKe93I3bLKqdSOD1nEzEhEJAQZjR/nu3M+ocM7GgzfsI5du3aRPW0ac0cMI6mumI2zLLSKVTlQHuC9fT4eHmq5YBAJadMJP5ctdNI/XaVPurFZIClp0Bg1z0l2pcaE9kZWXmfnb197eOALzxlA0hyIhEDCr+lnBZIOA0dy5Jv1Jz97a4GfwW85ubmXibeuOvXl7ykNMHqek0qXTocEldv6mHh0vZd/Xm452WXz8jYvd38mobYbe5l480ort6x0M+87H2NXbcN+SX9KrFK02lGBOwGbAn/UYacOVj9M1+BGMyxWpdakComoZSHtu92R2p46xBytDRIF28cpe/m3EQgeclHfSFhhhfVjVRhG/kPajfzCBLmzK0fu5AYhbYqLNYhwQno2tDoIpZnQvgpSj8O+VKhIh/a18uZjcaBq4I2HgBWy8qBPNqR7mxZ8Ni46LY+ESA90qIBuVdAuDhJbQUIriEo81XbrBw4jv/R3IhEPD5JSCoFHN006MYr9cNgPBzUBlGJF/ElAoKMl0FaRYtJYE2AEpxFKFVlsdATMrPIjNAQ4HMGHN/i8EtwvKwIakTSFjRQksvHvhuk9wKMBuEKFmoJ8Zg4fSlxdMZuCIHIu6bp+QQZo5wKSUGrmWLVGiyiFzTdHkBasKTkdSOo8cOkCB7tKtDNAJKTTgeTqTgba/sNBbo3eBIK8AZ0pS1yszvGz8jo749oaT4JIq1iFVyfamPyek1KHjtK2PYFj2bx1pRWnD+7+zM2vBpnpl25g1gcuxrQ2sD5PY8zlE/n7kuWsD5j52gPlZiixQ0CFTopc+z4F/hSsB0r2wz0KdDPBE4pEziqQFF9bpKD7EBIZ246k+hqQ624RUtj6BwToEy/o2w4rrLB+zArDyH9AXwGLkYW1FCmgfDT4/BN+8DmgRQ5k5kBNKnQqBVM5HMgCgxXSnZJSaTCCbpANZZZA70Jo6TkFIF4VjsSeVnRaCV0qJP2RFISP+AwwWSSacAgBjx1IoakP+WXeHeitQZofXD44EIDDGuToUKLKQgIQocnCkKVAqgoRRvAbZV8bFNlegFMpEw2JfISgQwk+NAQ2IpEFqHEKJQQbF+8JKhBTg0SeQn9WN/q3t9FrVbebxbNmErBHYMk9zqEtGxmbZSQlUuH1K60Y1eZho9yhMW6Rh3axCu9OsTTpxmlOn2b7mLjIxQODTJgMCs9u9vLECAtL9vuaBZGQQkDywCAzW/L9fFeq4fI3DyInj78RkHRJUtlTqjWbHvL4daYuFSB58TIrv1/roVWswpezIoi3KRyt0hg610l1UhaBQUMILFoIwK8GmfnrOInWLNrrY+YKF+kZGWQfOYLNJlGW6gCsdsM6F5wwCpR4DNBBFSipVOBZHco16OyF3xlhvxHeUQTa/UiB8yQEUFwI3Mch0ZIK4BOkBf5NxCzt3zD0DSussH4ECsPIOeTz+di8eTMlJSV4PB5iYmLo0aMHbdq0Oet7ViD+ICoSap6BpGbucEC+B1KKIDMPXHHQvggcPihoAXGadLmU2wCfLOStyqBHFWS5ZGEOFZ0eSJLCVR1oVQOdy6FdAyS3kKLTxFZgioMDiqRaQhGPALLYd9agvR9ifXAiAIeDhaRlQehQEOhoAaQokKiCJZha8RjBrciibgkepwuJMviCDzW4r6GoRqij5fSajQuFDQ0pXDwdMEJ/bzwU2ITATdxZ/gx9ptvtZsrVk1m35kv8mk73vv35bvs2TKrMdrm6k5FFU21nAEm5Q2PEPCfZtWY0v5cr2htYdo31rEBS59EZO9/BoQqNdXMi6JOm8vAaD89u9mI3gdMHvxpk4oXxzQ+sCwFJtAXqPHBdNyOLpp57mI9f07lhuYulB/xnrVMBAZJxC5xsKQjQPVll9WwBkZCOVmkMne+hwhJHoKysCYiEtGivj1kfuJk9ezZvv/NOk5+5ddjkhi9cAsGFEeAwCij/UoFsBV7VwKnBWD/MNME/DJK6rEEgeQhy/dQhLcDW4M88SNfZPgROriWssML6KSsMI82oqKiIN954g3+++hqlJcVn/PzSceO4+667mDhxIgaDmF/piFHTWmShNgO/8cPfKmCzAaJqodUJ0K2QUQrVJqhLlFRKpRU0DaxuyCqDrjXQziML6+lFp2m10KkCOlVBfIxEPmIyobAFfG2S4r4Twf1J0KC1DzL8Mh/keDC9UqaAvxF0JCHFhrGq1HO4TBAwSArGiNypOoLbbOyJFoKNJCRFczGwoSNh97NFMOobvVZFilfjaAoXocf3cZq/7777+Pvf/86n19vwBmDa+25UBT69zka5U+e65Wf6iZQ7NEYucHO0Grxev+ybAle0N7Jsuu0MIAmByN4yjfVz7AzMkIoXXdd5b58fq1Fn9kdeXF6NR4ed3bvko0M+Wscq/HWrj4V7fCyaYuPabud2mAtoOseqNToknNst9vF1bp7c4GXRFBvXdT9zm0erNMYv9jC1g8KzY88sqPX4dYa+42JvpUpZWXmz/+80HXZ74XMXbA9AbgTUmmQQ4y8UsflfpoMxADcGIM4Mr6oSVTQghd4DEOg4zCk4tQIbkBEKE5AakrDCCuunqTCMNJKu6zz33HM88uijqEYz1s4jiew5HlNcOorRjOZuwJWzA9d3n+IsPEz7Dh359JOPadWuHX9Gfqk6gRH1oJ+ABfFAAFoWSE1FTC1U2QEzuI0ShbC6oX0udKuQXLnTCvuT4HDiqaLTTuViaBYDxLaEirawvyXsiRQDMC0A8X5Jsdj80rZ6AqgIQgeAVZM7zVgFIlTwmkALpoE0Rfa7MWgoSDFpBAIYrREvlBBsJHImbOhIlOJsEYw6ZCEJbT+K5iMYccHPvZhy0RAwNTT6s/Gj8XN+4Mhnn7F40iQmd1BZPMXCtsIAVqNC12SVKxc7+So3wBezbIzMkgW63KExZqGXskAUa9dvICoqiuLiYtasWcPjjz3GxHYqS6adStmcDURO1zcFfsYudJ8XSEAA4+aV7gsGkvNJ13Xu/czNy9t9GA2wcoady9qduZ9nq4/x+HWmLPPwRa7O7BUr+c2Ey+iknPt7y/FJpGS9Dw7bpMg6QxU/ki+Br3RI8MNdwGoTfK0EzfqAcci1lI0UNdchYww+RlrnH0Gum7DCCuunpzCMBKXrOg888AAvvvgi0QOnETN4Ouo5Ztt7io9Q8+kLWDUX479aT0HXbiTmw7DD8FY7qI6A9GIxI7PVQ200+IJtjTY3dMyGnifkF/HRTDiU3LTotGsFxLmhNgNy2sLBDMiPhoAG0T6IDAC6/EKuUgUkAgh0xP6/9u49vok63//4azJp0iuFSluK3EQqbhcpghTLqiBwFEG0iIrHC7XoihxgxXJ2BW+s+1tO9bjrQQEFRNlVQBAVUBEQKlBRlJsooiCo2Ar2ghVaSpu0M/P749u06Q1aLQ6pn+fjMY+0wyT5ZBoy73zn+/0OagSDpoMjCMqdYDlUS4fvj+hCfXCHocJFV9R5el/rRltqjmppqAXjGOrg7hNKwy0YEZz63L6XxoWJEqpDjY9e+VrCK5cw1JwtvuG9DmoO+y0F1r/6Kk/eMpqHL3fx/wapEDDpnVJmby9H0yA10cWCEW4KS60aQSQhIaHGc69evZobRqYwrJuDZaPclFXAkJdK+CzPZPOd1UHkYKFJyqsebvu9g2mXV5862ZJdweUL1ZiiGYPcPHh5/adVoPkCiWVZ3L/Ow9Mfe+nxzDMUr13DD++uY9Xo4HoDSW2eCosbK4PIn157kx+HDOWQBaE6dNfhOh2StYZbx340YEMprPPAp8FQ4IZoB4zW4C1LzUL8u3L4Dx1e0lVn6VDUaZsQVAvfcdT7/2JUGFkD3I9MFy9EIDqjYWTOnDk8+eST5ObmkpiYyKxZs0hKSmpw++XLl/PII49w6NAh4uPjeeKJJxg2bFijn++XhJGnnnqKKVOmEPUf9xLRu3ETThulReS/+jAOw8O9D23n4yvj+KoDRP0IbQtBM6AkFMpCIKQUun0L/T5TH9h7e8C3ceCqUJ1OLzwKmgnfRcPX58HhaPCEgNsBTlNNjV6mV3cQdZkQrIFeeWqlQletHJpDjZRxoMJAK1TA6IJqefG1brRGHdSPUTNo+G491S8TNw23YERSPdzWqrxfQyHCf91JqoORf0AKtdTzVQ7GQac6TPiYlvpm7JtAznfrpeaByIE6gIXUvtVALytjxsjr+fS9Day7LZgrOqtX8c6Bcq5fVkZUn77kb9/GrT2C2F2g8Y0RQe/MLNr/PoF2qG/kHTS1XzsAO95Zze0jU7jmfAf5J2HnkQqCdI0Nt7u5tIOTg4UmA1/y4HFGcPTHQv420M0jA9x4DYubX/fw1lcGpqHapjIGu5l62ZkLJP5BZPazzzJg/Hg2eL08NeoG8tatYdXNpw4kviCy/pDFylVvMnToUAByTfjEgPcN2GaoP0AHJ1ylwzBNtcrVVmZClgfeKYUPguBIsDpdOFRT4SLfUhdwPBkEWyrf/11RF9T7DnXqphgYjjpVcw4wpEl7QwhxNjhjYWTZsmWMGTOGuXPn0q9fP2bOnMny5cvZv38/MTExdbb/8MMPueKKK8jIyODaa69lyZIlPPHEE+zatYsePXrU8ww//8XUVlhYSFz7c3H3uIqoIfc0+n4AFcVH+WHhn3COSSP64Zm0zQePG8pCwe2F876D5O0Q4YEvEyC3A5xbDOecUH0zDrWB3Gg42QpwgxUEFY7qib00wFnZyuH7eh/kUCGklaZmH42jOmy0UQ+DhvqQ9g8YJ/3qdlJ/uGiNCgW+VopTBQrfxK6mpVpHyisf1z9IOCoXX+KwUK0aFVR3fPXnQPUF8A8PvgARQj3Bwu82iMZfKdjXZ2TNbaEM7KJzx0ovYUHw/LUu3viyglteL2XkyJGsWrWS6HOiWL4xi8iEBHIs+LbyNNgRS03QdsxSrU4nVq/m2xtT0EOC6bnyLb57+EFKP9nO/GuCmJJZgRHTiXs2v8+OF15g7SOP8NcBbnbnW7x1wERvG4cjJIaKvAN0DjnJwYmn7qT6cwOJfxAZ8eyzDB4/Xl37CDjm9TKvb1+Of7mHn/4c3uC8Ko9v8TAt08Nd//43Y8eM4QLqDq8ts2CPAVsN2GioGXHP0aG/Djfoavi3/6MbFuzyqlDyrgbfhEKoA/ppql9IuQlDDHjfpYaKR6DmuilA9S05ibr4XhhwF6rztRAicJyxMNKvXz/69u3L7NmzATBNk44dOzJp0iSmTp1aZ/vRo0dTUlLC22+/XbXu0ksvpVevXsydO7dZX0xtTz31FH/+ywO0H78QPaxNo+/n89Omf1G8911idh0hyB1G5xzot0P1FTncCXK6gScMTrrgaCsoCYGKYKhwAUFgVl7e3lF55A7SVOAI19VVdttpqkUjGhUYHKgDelHl4uNAtYREog7o/i0MvmnSfcGi2FLN3KWV672ogFD5hbbqPppVfdAwqe7E6kKFDZ/6wkFDAcL/ZxenDhCGX31eqkfs1P7ZV39jtisH9q5dy6IRI7j+AgcnvLAxB0zDJOUCB0Ve2Py9xqh312M5HEScey6tu3SpcbrHN0TZ12JTYakD4qHtO6iIiMBxQXd+LC5m97CrKdq6leD484l5732C4uJAg59m/J3CRx/BEeQk7m9/5/C0qbgjo4moKCQrNZjfRZ+64ymoQNJzrpoWvmhqRJ2ZUuuzNaeC/i+epP2Yu0mY+zyWS73/gjT4Yc4c9kycSPqlLv55dcN9V76uHGVT3LYjfdZvoXWHOFrrapRMD01dCqAb1fPAWBYcMlXn1Q2GmqcmRIeeOlyvQ39HzffSwXJYUwpvmPBlKDh1FbT3aBBhQAcLdgWpSxhciHqP5KHey1NRwepR6k4CKIQ4e52RMOL1egkNDeW1114jJSWlan1qairHjh1j1apVde7TqVMn0tPTmTx5ctW66dOns3LlSj799NN6n8fj8eDxVH+3LioqomPHjk0KI6Zpcn78BRQEd6DtiD837gXWUn4slyPz/0jS2Pn06ns3ee3g+05Q1ApKg1VfEcNZfRoFp2rtCNbUdWeiHBDnUM3Y4agPZhc1WxvcgLPyL2BSfXD1hYkagcKqDBKVi+8P56x83KDKxde/okZA0KpbJvzDjP8CqmNsfQd7/1uP3+JrBSmjZvAxqe7vUlHr1vfvVq3XYfktvr4jpt/i276hQ7PvcfLefJPdo0bhcOpc9PZqzBMn+PzGG9GcOpesfoeYQYOqWnb8W3lqL77r3gT57V/frVVczEfPPMNlaWOJjIujDPBYquXg45f+TVCnzhzato0vHn6ANiF6o4OIZVlM3eDhfz9seJr5+lSYFrev8LD8S4PuTy0j+qpRBGtQ8MocPnlsYr3Dd+tzsNBkwEsejLYdGbJ2Cz9Ex3Ec0JwQ4lSnImMccLEGiZo6rdIe9Tc5bqnTOe9VwAem6sh9ng5X63CdA8Irn7rAUJ1dX6mAnSGqw3VrVP+R2Ar4SYdjumodbIOaXt6Lmja+PTC2UXtECHE2aGwYOX2PNj9Hjx7FMAxiY2NrrI+NjWXfvn313ic3N7fe7XNzcxt8noyMDB577LGmlFbH3r17OfTN18SMTvvZjxHUuh3BnRPZdeh19j95N5YDgipUeHA6IMQN7iAVPMIcKoj4Dm6+A+6PFhTWOpUB1Qc73+JGhQVfy4cLFRxMrbrVwtKqT4X4H+g9lSMTah/way++gFD74A+nPsj7txRotep2+t3WPoXjoPpgHkT1lPD+B3b/n921bn0/+/ZNUK3nqR2mqtZddx07srIIcblI6tMHDfhw82aCg4Pp3bt3A6+yiSIi4KGH6u4ogLRUCgoKuPiOW2jt1s54EAFwOjQWjXRjWR5eTx9N3NPLyMvP5dO/NT6IAHSLcrB5jJsrXsrhvWsuY+bmLZwbG0eZAfsN+KwCDhjqWkyWE4J1CNOhowaXa3CxE/7iVNco2mfCFgPe8MIsC6J1uEKHG3W4LRxGmpBVBi+VwqYQ0HU4rkOpBnFeOBIExzXVN+on4H+Acaj5dXpX7q8dO3aQnZ2N0+kkISGB+Pj4Ru8zIcTZo0lh5Ncybdo00tPTq373tYw0RUFBAQDOyNjTbHlqzlYx8MNRoo5DURvwtKnuUOlrpSig5qkPX0dT38++A73vPjpqHgv/x/Dd+nfu9P9mHkR1i4bv2i0u1Omb4FrrfNv6ltoHdrff9r6DvP/Vb2sf3ANxFsyByck1fu/f/9e72klBQQGDBg6gvPgo76eFnvEg4uN0qJljWeFh+Z9uwjKtBoOIp8Liofc8DO3mZEjXmh8D3aIcZFUGkrsvu5z+WZ+jtwom2AltdBjignM19X7PN+CgR13DaKYGpg4uJ7RxQLwGg5zwvy5wm7DTgDUV8J8e1W/kYiekBMO/guETL7x8AlYFqz5XhU51H9OCwzpEaCpozwOKysv53fz5zJ81mwP7a34JumLAQP57SjojRoxo8v4TQtinSWGkbdu26LpOXl5ejfV5eXm0a9eu3vu0a9euSdsDuN1u3O5fdoWS8vJyADT99AeCU9KdhJse7uqsmpn9T32EVf4cQXWLhv+B3tf/whc0RMvnCyIFOQfZNObMt4jU5nRoLB7pJue4wdYcA7Oek7CeCotRyz2s/srLrB0V9Y6ysSxLtZpZGiWFGo4Tqk9SAbBXV5cK0N3gcKlRX6EO1a+krQVB5aol44AB7zvUiDC3DtEaJDrhL0FqUr6tJjxWrh43Xoerw2GsCW+XwL+CILey35XThCJHZSguK+VfKSmYGzIJu+BSYkb/HVdsVzANyr77jB27V3Pdddcxbdo0ZsyY0ajWICGE/ZoURlwuF3369CEzM7Oqz4hpmmRmZjJx4sR675OcnExmZmaNPiPr168nudY31+bWunVrVV9pMbSqO8qnsczSYn4XE8VD8pkmTsPr9TL4ShVENt7hanQQGfHKSVYfMBodRI6VWbQObvgN6XRoZN0ZyqULTvL0x+qqPf93tWod8Q3f3XDIYtWqVcx/fj4pr65hpV8gOfCjwYCXvZixnRmTmQUxboor1EUVy8uhzAtl5eApBatEDXUvA7IdcLDy0gJmEGgu1X8qSFMhvVyDjQ54y6H6iYQ4IM6pTrmEmbCiHGaa0M4Fd2jgLIXFOnzrBodVeYozNRU2ZRFz02OEdOlV43WHJQwgLGEARdveICMjg7i4OCZNmnTa/SmEsF+TT9Okp6eTmprKJZdcQlJSEjNnzqSkpIS0NNU3Y8yYMZx77rlkZGQAaqjlgAED+Oc//8nw4cNZunQpO3bsYP78+c37SmpJSEggJDSMkwc+whV7/s96DLO8jPLvPiH5uv9q5upES2QYBiUlJYQEQWgDw2drm5bpYfUBg1svcp42iPiG787e5mXRDSHccophv06Hxta7Qmn/1ImqQPLEEHedeUSGDh3KjaNuqAok57fRuPJlL5GxnXlvUxZxcXGAGiF2jOqJ8QqBoyZ8b8L3hprsrMiAYgOcFVBeAUYpWAaUG2qItOFQLSpm5WR9pQ74WoN9Onh1Ncw9XFenfzaYUOJQQ4sv9MAXwDdf7MZYvpxzht9fJ4j4a5V0A+WFR3h0+l/54x//SHDwz7mwgBDi19TkMDJ69GgKCgp49NFHyc3NpVevXqxdu7aqk2p2djYOR3Uvg/79+7NkyRIefvhhHnzwQeLj41m5cmWj5xj5uSIiIkgdcwcvLllOZPJoNL3p3WNOfplFRVkJ48aNOwMVipYmJCSEjZvfZ+AVl3Hly7lsvMNN59YN97iZvc3LEx94CQ8P55U9Jxh6vpc7Euuf29R/HpGQII3b3ihFgwbnITEti/vWevixFG6//XaeXrSIlfsryD2p15jQzOVy8drrb1QFkjYhDlq361IjiID6oGhLrXlHfL2U/f5rWah5cAqBfBO+q4ADFXDIgCOmmrulxIRSEyoMsExwaaBraiRXsQOOaSqwGJWdqZyWWlxznsPbqi1hCQNP85eAVkkjOfLpWl599VXGjBlz2u2FEPZq0dPBf/bZZyQmJtJ2xJ8JSxjQpOe0TIOCRVMY0CueNe+809SSxW9YdnY2A6+4DIobDiSzt3mZtKaMtLQ0nn/+ee4dN44XXniBf6cE1wkk/kEkNlzHERZFv0v78+Zbb7JkZHCdQGJaFhPf8TB3ZzkLFixg7NixzJ07l4cenMbiJa9UBRF/Xq+XW//zFr7av4916zNrBJEz5ScDDpTDXi98bUC2qWZ7LUSNojmpQblDhRRDh+KELoTHXUzUoLsb9fj5i6Zww8BLWLRo0Zl9IUKIBp2Rob2BpmfPngwbNpz1G57DFdOVoLaNG5FjWRY/bXwRT943TH1gwRmuUrQ0nTp1YlPWlgZbSHxBJD09nX/84x9omsa8ytOWqS+8AFAVSGoHET38HDZlbaFr166MTUvj1sXqQOsLJPUFEYB7772XcePGNdih09dC0tDF886ENjok6ZBUz1kUy4ISAw574WsP7DsJ04qL0M+PbPwTBLeiqKjo9NsJIWwXiKM2m2TJksV0O68zR199CM8PB067vWUa/JQ5n+Idq3jmmWcYMKBpLSpCQHUgIaIdV77s4btjaoaZ+oIIgMPhYN78+dx1112krizj5U+9DQaR+Ph4dF3nxYULuf2227l1RRnLPi9vMIj4NCZknC2jTzQNwp3QPRSGtYH0WIiJjMQ48VPjH6T0OJGRTQgvQgjbtOiWEYDIyEg2b9rIsOHXsmvxfxNywR8Iv3gY7g6/r/HBa5SdoGRPJqWfrcFbeITnnnuOe++918bKRaDzbyEZ+HIuqRdpPLbZWyeI+PgCCagWksV7yln3tVEniPj4AgnArYsX8eJunfXfGPUGkZbgumuHseDlpVhXpqHpp54U3ns0m9IjXzFs2F9/neKEEL9Ii+4z4q+0tJT58+cza86zfH3gK0KiO0KrdupDzVuC58g+NNNk1KhR3H//ZPr169fMr0L8Vvn6kHz7XU6DQcSfaZqMu+ceFlSesmnfLqZOEPFnGAZj09J4edGiFhtEAD7//HMuuugioq6eSESvuv1e/B1d/X8E537G4e9zfvGcRUKIn++MXSjPDs0RRnwsy2Ljxo0sXbqUvLw8TpaWEtWmDYmJiYwdO/aUk7EJ8XMdPnyYLVu2cPPNNzfqVIhlWXz//fds376d3r1706VLl1Nub5omhw8fbvJMxYFmzJhUlryylKjrpxLaLanOv1uWxfEPXuH4B0uYN28e99zTtKt1CyGal4QRIUSL4/F4GH3LLaxauZLQrn0I63UNrpiuWJZJ2aHdlH66htLcr5kxYwYPPvig3eUK8ZsnYUQI0SIZhsGiRYt4ZtZsdu3cUbVe0zSGDR9O+v33M2jQIBsrFEL4SBgRQrR4X3zxBTk5OTidTrp3706HDh3sLkkI4UfmGRFCtHgJCQkkJCTYXYYQ4hdq8fOMCCGEEOLsJmFECCGEELaSMCKEEEIIW0kYEUIIIYStJIwIIYQQwlYSRoQQQghhKwkjQgghhLBVQMwz4puXraioyOZKhBBCCNFYvuP26eZXDYgwUlxcDNDiLwImhBBCtETFxcVERkY2+O8BMR28aZocOXKEiIiIRl3x9GxVVFREx44dycnJkWntfwHZj81H9mXzkX3ZfGRfNh+796VlWRQXF9O+fXscjoZ7hgREy4jD4WhR15xo1aqV/AdrBrIfm4/sy+Yj+7L5yL5sPnbuy1O1iPhIB1YhhBBC2ErCiBBCCCFsJWHkV+R2u5k+fTput9vuUgKa7MfmI/uy+ci+bD6yL5tPoOzLgOjAKoQQQoiWS1pGhBBCCGErCSNCCCGEsJWEESGEEELYSsKIEEIIIWwlYcQmM2bMoH///oSGhtK6dWu7ywkoc+bMoUuXLgQHB9OvXz+2bdtmd0kBJysrixEjRtC+fXs0TWPlypV2lxSwMjIy6Nu3LxEREcTExJCSksL+/fvtLisgPffcc/Ts2bNqgq7k5GTWrFljd1kB7/HHH0fTNCZPnmx3KQ2SMGITr9fLTTfdxPjx4+0uJaAsW7aM9PR0pk+fzq5du0hMTOTqq68mPz/f7tICSklJCYmJicyZM8fuUgLe5s2bmTBhAh999BHr16+nvLycq666ipKSErtLCzgdOnTg8ccfZ+fOnezYsYNBgwZx/fXXs3fvXrtLC1jbt29n3rx59OzZ0+5STs0Stlq4cKEVGRlpdxkBIykpyZowYULV74ZhWO3bt7cyMjJsrCqwAdaKFSvsLqPFyM/PtwBr8+bNdpfSIrRp08ZasGCB3WUEpOLiYis+Pt5av369NWDAAOu+++6zu6QGScuICBher5edO3cyZMiQqnUOh4MhQ4awdetWGysTotrx48cBiIqKsrmSwGYYBkuXLqWkpITk5GS7ywlIEyZMYPjw4TU+M89WAXGhPCEAjh49imEYxMbG1lgfGxvLvn37bKpKiGqmaTJ58mT+8Ic/0KNHD7vLCUh79uwhOTmZsrIywsPDWbFiBQkJCXaXFXCWLl3Krl272L59u92lNIq0jDSjqVOnomnaKRc5aArRck2YMIHPP/+cpUuX2l1KwOrevTu7d+/m448/Zvz48aSmpvLFF1/YXVZAycnJ4b777mPx4sUEBwfbXU6jSMtIM5oyZQp33nnnKbfp2rXrr1NMC9S2bVt0XScvL6/G+ry8PNq1a2dTVUIoEydO5O233yYrK4sOHTrYXU7AcrlcdOvWDYA+ffqwfft2nn76aebNm2dzZYFj586d5Ofn07t376p1hmGQlZXF7Nmz8Xg86LpuY4V1SRhpRtHR0URHR9tdRovlcrno06cPmZmZpKSkAKpZPDMzk4kTJ9pbnPjNsiyLSZMmsWLFCjZt2sR5551nd0ktimmaeDweu8sIKIMHD2bPnj011qWlpXHhhRfywAMPnHVBBCSM2CY7O5vCwkKys7MxDIPdu3cD0K1bN8LDw+0t7iyWnp5Oamoql1xyCUlJScycOZOSkhLS0tLsLi2gnDhxgoMHD1b9/u2337J7926ioqLo1KmTjZUFngkTJrBkyRJWrVpFREQEubm5AERGRhISEmJzdYFl2rRpXHPNNXTq1Ini4mKWLFnCpk2bWLdund2lBZSIiIg6fZbCwsI455xzzt6+THYP5/mtSk1NtYA6y8aNG+0u7aw3a9Ysq1OnTpbL5bKSkpKsjz76yO6SAs7GjRvrff+lpqbaXVrAqW8/AtbChQvtLi3gjB071urcubPlcrms6Ohoa/Dgwda7775rd1ktwtk+tFezLMv69SOQEEIIIYQio2mEEEIIYSsJI0IIIYSwlYQRIYQQQthKwogQQgghbCVhRAghhBC2kjAihBBCCFtJGBFCCCGErSSMCCGEEMJWEkaEEEIIYSsJI0IIIYSwlYQRIYQQQthKwogQQgghbPX/AWboH066YV2aAAAAAElFTkSuQmCC", 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GGDGREYPBUNcYMbKFTJkyhcMPO4Q77riD0049ZZOCJBKJcNqpp3DHHXdw+GGHMGXKlDoeqcHwe96vUgrmz6nw34j+tpBfpMxbl6bc2zYhRnz1NViDwbDbYcTIFjBlyhSOPOJwejaK8vqfUvnoww82KkgSQuSjDz/g9T+l0rNRlCOPONwIEkO94rrwSSX0CkGWDybhVcu4kOJt08RKNjwLeb+t3+3JYDAYdgxGjPwBNYXIJ2eGOK1ngLdPCf1OkNQUIm+fou0+OTNkBImh3pkfgWVx+FM6LACKLK/rqqseIykWNEJixEJixAgRg8FQlxgxshk2FCKZIZ2ih3ZZX5BUVFSsJ0SGdpH1LzNkGUFiqHfGVKpi5ogU+NiBmAWuBY0taA74LEhD1TQ2kIo5MRgMhrrFnHM2wbRp0zYqRBLUFCQdOrT/nRBJsKEgqUtT6/fff49lWRxwwAGb3Oaee+7Bsixuu+22OhuXoe6IuDCxGgakqmLmtRhqJgIEfIqOBGx1XoWkGDGREYPBUJcYMbIJ3n//fUrLKvjrvr7fCZEECUGSHlu3USGSIDNk8dd9fZSWVfD+++/vyGGvxz777MPee+/N5MmT+emnn353v+u6PPPMM9i2zYgRI+psXIa649tqKIyrt0gRStMkUjF+oNyBbAuqvO0TnVeNGDEYDHWJESOb4KabbuLEE45n+LthPpwf3eR2Q7sE+PWvaZsUIgAfzo8y/N0wJ55wPDfddNOOGO4mufjiiwH43//+97v7xo0bx6JFixgyZAht27at03EZ6oZ3KiDPD3sFYaqrihnLhUYupLtQ7FXSlKHoiI+kUDEYDIa6woiRTRAMBhn9xpscM/RYTn5z84Jkc3w4P8rJb4Y5ZuixjH7jTYLBul3x48wzzyQrK4uXXnqJcDi83n1PP/00ABdccEGdjslQN5TEYXoEjkgDy4I34srQODa0trQQXtCSZ6QAiZFESa8p6zUYDHWJESObYXsFSX0LEYD09HTOOussioqKGDNmzG+3FxYW8s4779C8eXOOO+64Oh+XYcfzRRVUukrRxICvHX7zi/j9kOdKiGQDK727/DV+DAaDoa4wYuQP2FZBsjMIkQQbS9W8+OKLRCIRzjvvPPx+M/U0RN6thM5BaO6Hn4BVFvgceULCQBaQYitCUk5yxV6zSJ7BYKhrjBjZAoLBIC+/8irZjXK57JMtEyOXfRIlu1EuL7/yar0KEYBevXpxwAEHMH78eBYsWADAM888g2VZnH/++fU6NsOOYWkEFkbh2FT9P8GVALEsaAKEXHAcrdbbAoiirqw+kqLEYDAY6gojRraASCTCWcPOpKS4iMeO2rLlwx47KkBJcRFnDTvzD9eyqQsS0ZGnn36a//u//2POnDkMGjSIjh071vPIDDuC97zymJM9MfKutyRvzIbOqKS32IVcW+IkkcGxMOvSGAy7EytWrOCBBx7g5ptvZtKkSbiuWy/jMGLkD9iws+rmqmZqsmFjtPoWJKeccgqNGzfm+eef5/HHHweMcbWhEnfh80rYOwVSfPKDLHAlMlwbUv2QicRIJvKT1EzPmHVpDIbdg/Hjx9Opcxdu+ttt/OPfTzJw4ECuvvrqehmLESObYVuFSIKdSZCkpKRwzjnnUFBQwKuvvkrTpk058cQT6208hh3HrDCscuDP6fp/KrDWAn8MAkAJMq+m2OrEWsb6fpFEusZgMDRcXNflgosuxmrakZaXvkDzS54n59C/8MgjjzB9+vQ6H48RI5tge4VIgp1JkFx00UVYltpZnXPOOfXuZTHsGN6uhEwbBnor3n3iKFoC0MxVCW8WYFs6AaxGvy20cF4cmVwNBkPDZeXKlSycP4+Mfsdjh9KxLJusfU/AH0pl3LhxdT4eI0Y2wT333MPYd9/jxRM2L0Q+nB+lw78q/7Ax2osnhBj77nvcc889O2K4W0SXLl1o3bo1gDGuNlAqHZhcDQenSmxUAd96hpCYD3oDGS74XK1XEwSWk4yMpKC0jREjBkPDJisrCzsQJFq08rfb4mVriUWqadasWZ2Px4iRTXDccceRlZnOv6bGKQtv3NCTKN+t8Odstuy3LOzyr6lxsjLT67Wnx+TJk1m2bBmHHHIIXbt2rbdxGHYcX1dDsQNneCmamcAKF2wXHD80C8gnEnEh1dY6NCtQNCSARIiDurAaDIaGyRQgJy0TZ/hw1n3zGusmvULZzE9YO+Z2mjZtxsknn1znYzJiZBP069ePz7/4ktnFAY56Nfw7QVKzj8ivvy7eZB+SsrDLUa+GmV0c4PMvvqRfv351+TLW4+677wbgsssuq7cxGHYsYyugdQC6esG8b1wodyEQkzm1wFVkZJkLzSxoibqvgsRIQoSYyIjB0PBYBfQC+ju66ODRfzPwwgsJz3iXok8fY9/uHRj/1ZdkZmbW+diMGNkM++2330YFyYYNzdLT0zfaGG1DIbLffvvV+Wv45ptvGDFiBP379+fDDz9k7733rhfVa9jxFMZgVgQGp+l/F5gQ1x+uC41dKAaaA9VoTZqW3t9hdDIwkRGDoeFRDgxD3/fZLliObn8hJYUJj/2LstISKisrmThhPD169KiXMRox8gdsKEhGz954Z9UNO7WOnh2tdyECMH/+fJ599ll+/vlnhg4dyttvv41tm7e9IfJxpUTF6Z4YWQTMdwEX4j4YYCk6kgOkWDKstkAekTCKjKQjMZJS56M3GAy1TQT4OxIhrzpAHKy4quhesGC4Vzbn9/tJTU2tv4FixMgWUVOQnD6mapMt3msKktPHVNW7EAE499xzcV2X0tJSPvjgA9q1a1dvYzHsOFwXPqyCHiHI9U4wU4F8ZFaNB6C7H3JcCLgyt0aR6PAhQWIhP4kRIwbDrk0ceAU1OPw7UOaCFdN9rgVP+2D4Tjb772TD2XnZb7/9+PKrCdx2222bXWsmIUhuu+02vvxqQr0KEcPuw8IILInBCWnJ2751IeJAMCbxsRKJjbgLQVsCJB9IQwLEQSW/cYwYMRh2RVxgAtAfuARVykUd8FWD612k/NsHI+pthJvGdH3eCvr167dFBtRgMMjtt9++4wdkMHi8W6UVeId6KqIEmBFTFY3rQiNkXmvpQpED2d6JaSHyh3gpZJOmMRh2UWYBo4Dv0IVGmQs4EIhANKTI5wM27KzlC0aMGAy7OBEXvqqC/VIU8QCYBixGQiTqg0HAWlcekfku7OM1PFuCficWyEtBYqR+s8cGg2FLWQzcA3yCvsslKBpix8AXlxDxu3CLD66tz4H+AQ0yTVNfC/00dMxx3TmZVg0FDpyWnrxtKqqc8bsQC8JAvwRGU8BvqZKmFQrj2si86nq/4yh1YzAYdl4KgFuBY4DPUWRhCRIigai8YtGQIp9X+7TtzkyDioz4fIo9R6PRencGN0TC4TAg57Vh5+GdSplW9/NsTDFglgOuAylRiAahCGjiQqqrSho/ctivIhkZwfvbiBGDYeelFHgReBV9fzsC3wDVXuVcSljR0Lhf3+PzbbgPpWl2ZhrUrBIIBAiFQpSUlJCZmfnbOiyG7Scej1NUVER6eroRIzsRZQ58H4Yj0yHxcf8ZmOt4vQQcyAbmI3OqC1i2UjEt0YktBUVEfOiEFQMy6vqFGAyGzVINvA28DMwF2iOP13jAcSEQB38Uon6ZVbOAM2x4iJ1fiEADEyMATZo0YcWKFSxfvpzs7GwCgYARJduI67rE43GqqqooKSnBcRxatGhR38My1ODLSih14MwNUjTLXQmPSAAOsaAQyHa1rd+nFE5LVN4LanZmocfE0UnOYDDUPzHgUxQJmYmiHUcCL6H+QDiQHgMnBpEguLYuQE6w4VF2nUl+q8Z577338vbbbzN37lxSU1M54IADuP/++ze7zsnzzz/Peeedt95toVCI6urqbRvxH5CVlQVAYWEhK1as2CHPsbvh8/lIS0ujWbNmZqXfnYz3KqFjENrV+Cb/5EKld6VUHYShPnjHhSbAEheybJ3E/Eh4xFFe2WtDYMSIwbAT4AKTgNeBGaiL6jHAx8CzKBrid71UrAORkKKjucBRNjzGrtVJeavEyIQJExg5ciT77rsvsViMm266icGDBzNnzhzS0zd9+srKymLevHm//b+jIxVZWVlkZWURjUaJx+M79LkaOrZtm+jSTsryKMyPwnnZydtWAwu9LoupUahO0QkpE2gH/OLCABQZWY5SNGvRlVQYnQAdjGfEYKgvXBQBeRWlXH8F+qFKuP/gRUNcSHeBCERdCRE/kGvB4Rb8i13vgmKrxMgnn3yy3v/PP/88zZo1Y9q0aQwcOHCTj7Msi+bNm2/bCLeDQCBAIBCo8+c1GOqCjyqVZjm1hld7KvKH4OrKKROYAzTz/k63obGlE95CJDpWkqyiSUh3E/8yGOqeBahz6i9IkLQEbkZdVH9F31ufC1kOVEXUTTUSUqVcjg0DgX+iJR92NbYrnVRSUgJAbm7uZrcrLy+nXbt2OI7D3nvvzT333MOee+65ye3D4fBvlRsApaWl2zNMg6HBEXfh0yroHYIMX/L2mXir8FpQHYB+lk5sbVADtJClSElLdHs6EiA2yR4jiRJfg8FQN6xAkZDFqEeQH7gciZOL0RozuBId6TEoi4Llg6qALjJybDgAuB9oVg/jrw22uc+I4zhceeWVHHjggfTs2XOT23Xt2pVnn32Wd999l5dffhnHcTjggANYvnz5Jh9z7733kp2d/dtPmzZttnWYBkODZE4YVsThpBqx2GpghQuxuFrAR0Jwmg/KgJgL5S6/2epbAsvQyc1BJ7+axfC7iunNYNiVKQIeBx4BxqHI5kHA7cCD3n0xdCHRyPOHlMXA74fKADQGGntC5A6gdT28htpim8XIyJEjmT17Nq+//vpmt9t///0ZPnw4ffr04ZBDDuHtt9+madOm/Pe//93kY0aNGkVJSclvP8uWLdvWYRoMDZKxlZBiw5E1HGo/AvMddV1MiwC2GpuluQrbLnVV1luGxMgKksZVi6RPxMWIEYNhR1KBqmEeQD1CxiPj6aNAJfBn1EMEIOhqGYdwBKodSPFDqV8ekhwb9gduBDrV9YuoZbbpnHPZZZfxwQcfMHHiRFq33jotFggE6Nu3LwsXLtzkNqFQiFBoV/IBGwx1R5UD/1cNB6SCr8blxFRgodck14lDuiXBkQt0tOAbF/IsmVxboHTOHkiI1DStJiIlBoOhdokAH6Lvahj4GqVVrkfVbsNQGT7oe5ntQo4DKyKqlEnxw1pbZvSQrQXxLgZ61fUL2QFs1TnHdV0uv/xy3nnnHcaPH0+HDh22+gnj8TizZs3imGOO2erHGgwG+LZa68ycUSNF46K0S5krU1tVCLoD3wN5LjRH3pBmFqxDEZFq7yeITpIZNfZlxIjBUHs4wFcoFZOJoiEB4ETgHOAGJFIcvOaDXil+KA7LouC3weeHIhv2cCFgqyruTCRIGgJbdc4ZOXIkr776Ku+++y6ZmZnk5+cDkJ2d/Vv79eHDh9OqVSvuvfdeAO68804GDBhAp06dWLduHQ8++CBLlizh/PPPr+WXYjDsHrxTCXl+6FWj5GUJWp+GGISiUJ4CJ/t0AswFQi5kWcopryNZwluFTgJRkg58Y2A1GGoHF5gCvI8uCH5CXY/3BEYCi5Dfo5TkYpVpLjR1oSQGa+OQaquraqkNnV0thnkQMBg4rB5e045iq8TIE088AcChhx663u3PPfcc5557LgBLly7FtpOx4+LiYi644ALy8/Np1KgR/fr145tvvqFHjx7bN3KDYTekMA4/huGEzPVvnwr86IDPgYwwlKXBPsAXQIXXBC1oqZ9IY9RAKdH6PVHW2wjlq01kxGDYfuYAbyARUgBMQC3cL0QC5C+oqZlFUog0daG5C3Ojio5k2rAuoO9nFxQRORjYDziurl/QDmar0zR/xPjx49f7/+GHH+bhhx/eqkEZDIaNM65S5rczNuho9BNqAe9Y4DiQaiknne31F1nqqk20jcyri1FaphJV0cRYv/GZESMGw7axBHgNrQ1job4hrYFTUUrmA6A3yRRpHFXLNAeaOTA1InN6Yxt+9esiogP6fQjQGTi9zl/VjseccwyGXQTXhfcroUsQmtfoLVKGoh9RRye/ihQZVqch4bGHpSobx6cTYEvU8CwTmVlTUaQkBXlJjBgxGLaeAiRC4sjv8RpKfR4IXIC+b39GVW8WikjG0AVDLvKHTI1KiLT3wUxbj2+OVto+HGgKnM+usfDd1rLNpb0Gg6FuWRSFX2Nw7Aa92qcBix3wxSAlAtUhGGrDbNSboCOwyoVMS+WCrVA3xwwkQhLGVT9Jr4gRIwbDllEKPA08g9Iwk4CxQDfkC7kHLXS3D4pghkimZVq5MqSWRJWaSbVhTx9M98rym1uqijsSiZnLaJhCBMw5x2DYZfigUmmYk1LXv30autryuZAZhnUZOnlNQVUy2Z55NduCfHSllWh4Vo26sNroZGB7P8bAajBsnmrgPeQNORh9375E5fL7AmegyOOhyKhqo7RMDEhxVdLb2YXPojKYB3zQwwcTLeiByvbTgEHe812FRExDxYgRg2Eno7i4mLFjxzJlyhSmzZhJcXExPr+f4pbtabf3Pkw9dhAHHXQQlmURR10c17pqDx2PK6Sbhk58JS6EXciw1RJ+DvrSr0RXcQ4SHgkxkmjSak4MBsPGiQGfAf+HUic2in50QlUuf0EekXuB/6LvUwpK30SRQTUd6OrAmCg0sSBuQztPiBwIlNmKhByGLihG0fDXizLnHINhJ2HFihXcdtttvPzKq0TCYVKatcVu0gE7Kw+cONFFq5g5+REGPnAHXbt154brr2O/c88ljIUVBcuBslRoYykN08Q76S1F5rfWwFzvudaRNM+56IorERVJ/G0wGJK4qEnZJygS0hWtBdMK6IP8IAOQ4O+PPCSJKKPj7WMPV0ZxKw5vR6GpT99Rywff2TDUhZWeV+QQZDC/gfWXamioGDFiMOwEvPjii4y8/HIirp+0ff9M070G48to9LvtXNeleumPLJ/+AX/5y1/o/vIr5D79LMGmbQlGoTgVDrdhMurS2MKCpXGZV9OQsQ50hWajq7yEVyQhQBpyKNhg2BZmAmOAvsARwL/Q96YX0A/4E/r+3AiMRt+nRA/xGJDhyny6hwuT47AmBtk+aGfBUh8stmCYC3M9IXIQEiJXoaqc3QEjRgyGesR1XW655RbuueceMnoeTrPDL8CXmrnJ7S3LIrVdb1Lb9Sb91+ks/Owx4vv3p/EH42jUqgeFmeo/8ATQ31X++m0X0m0JkFYoGgIqEYbfixFzUjAYxELgdZTiPA14GEU89kSi4TwgD/gOtWUvR9+lFPR9iwDtXH2nBrrwRBSyXAj5JTi+8kGhBRe7MMVWr5/9kRC5lOTFw+6AicYaDPXIP/7xD+655x4aHTaCxkOv3qwQ2ZDUDnuTd9Y/8ZHG2qGDqF6xgpAlL0g56q7aBrWIT0Hm1ZbAWvR/GJ04a3ZcTaRsDIbdmZXAP1AVzJnAL8A1KLqxJxImN6KUyyXIrBohWR7voMm1h7dI5YEuPBqBtkCVDX+24BMb1llwJTDNlvAYgL6XI9CFw+6EESMGQz0xc+ZMRt10E1n9/0zWfidt0z586TnknXIHVEVZMeoCmuOyFoV2C12Iu5BnqzxwJckeI1koQhJEV3AJUWLEiGF3phhFFV9BqZdUVJ5bjFI0/YG7vL+/QFUzk5AASfToiaAIR0dXPq2UODwWhj1tRUHO9cFzFkRtuBaYbEnk7OM9/nRUjr+7YcSIwVAPuK7LeX8ZQaBxG3IOHrZd+/KlN6Lx4Eup/uJjmr05mmmof0EKWrU3w9IV2WpUTvgLulpLiJAoOumGMSv2GnZPKoCXgH+jCpm9USRkPGrdvgdaWfckFHE8C0VEfOg7lGj9E0XRkCxX+5kSg4lR6OSHEgtO9cGjaOG7a1H1TBNgLyRmjkFlvbsjRowYDPXApEmTmDljOlmHjsDybX9Xj7RO/UnpuDdLH/on05H46GjBEgf8ltI1iRVBF6PISAU6AYZR47PqGtsYDLsDUeBd4D6UfjkFVcg8jrwbbVGVzJWoS+o7JCtmQkjwu95+fEAfV5PqMBceikI4Do0C+n4d6IP/WJBtwxXABEvrRPXw9j0AmWF3V4wYMRjqgSef/C8pTdqQ0q53re0zs89Qfvl+KvNnzCDgmVeXuuph0JKkyFiOyglL0UkyQrIBmomMGHYHHGAccDtKqVwGvAVch9Z+6Qr0BO4EugNrUFRkFPrO2EjQu0jMtwLaemW7h7lwRQS6uxDxwd6uSnhfclV2fz5KzTTxnqst6lEysG5e+k6LESMGQz0w7quvCHbqj2XVXnPn1I79sP1BCiZMoAytSbPUgbClL3qet91qdEVXhk6sDskIiREjhoaMi1a4vhWlW25EUY4RyEN1IEphjgKORg3LnkM9P1aT9IZY3k81sI+rDqoDgJgDd4RhsA2/WDDMggIffOBCT0vG1emWnqMdKg3O8p5rd8ecdwyGOiY/P5+C/FU0GdC5Vvdr+fyE8jpQNW2a1qJBzc78VrKSBpSeiaLywSCKkKSgk3Mcc1IwNEx+Bt5AUY9RyA9yDkqVHINKdk8jaR5dhha4W+FtkzCmVnt/ZwJdXFWuXQDcHoeVUTgqAN+6cLMFz3prRB1qwV62xpAFtEBelAJUHmww5x2Doc5ZsWIFAIGc5rW+bysrD2fZckLACldNz0Kokqadt00cRUBiJNMz2ZjIiKFhshStoNsIeT8WowqZKiRCFqFUzCUo2uGgpmb/RaIh6P0kvhvlKIXjc5X6vMiF82KQ7UCvAPzowoM23Grpuf4M5NqwhGTjwaOQSBlJw134bmsx5x2DoY6Jx722Y7WYoklgWTb+eJwOnnk13dIJdSUy5FWT/NJHkVCJo6u8AkxkxNBwKAReRSLiXCS+7wbmoYUky1D68haS1TBzUfOyEhRJrELiodTbthI41JWHpBuqWjsjCvsBBQGocuAOn7wnaywY6UKFrf2lINH/Z2AiqtYxQiSJOe8YDHVMbm4uAPGKklrfd7yqhIzctnQC5roqIWyDeos0RVeBiR4jNdekSfceH8acFAy7NmXAmyg1eQYSE88CXwG9gWOR/+Nc1AcEJFTuQmmcdiiVWXNNmUrv9taumgYOAz504F9ROMOGz2zYy4HjfXCFC6U23AbMs/Ud86Mo5HDgfbTejDFsro85HgZDHdOxY0fSMjKIrP6lVvfrui7R1b/QrG9f9rBgmQMxr8dIYjXehSj/nTDgBdDJMiFGqmn4q4MaGiZhJEL+gSIV16M1Zc5DEY9hSHT0Am4mKUS+R5UsH+N1LEbRxDIkIApRc7NcV88xCrg7Dh9E4BI/fGxL4Bzkg+tdKLfhMeBHS493UQTyEuBt1F9k+4v5Gx5GjBgMdYxt2+y7z75Elv5Qq/uNFvxKvLKU1H32o5ULq1yo8EoIEye/RcjJn8h/pyJRkghTV5Fc4Mtg2BWIoZV070QC4zYU2RiBxMnpqPQ25t03AH3mq4CrUYQk4d4KIGEeRtHDGHCEC2FXQuVSF4bGoDwGZwThNReusSBow70uxGx1b/3Y0j6j6Ht2FfAiEiIpO/Zw7LIYMWIw1APnnXsOlb/OIFq0otb2WTbzIzLzmtPpiCNYAzSxwLUUVk6cbJeSNKuGUFRkw8jI7rBcuWHXxwW+RgLDRmKkOZrwHwQOBg4FfgUuRCmbRNTvK1SuOxVVzxQiIVOG0pgF3u09XPmtjvX+PjqqdEyvIHzqwKO21pV5xgHbVgO1Zy31Dyn3nmsU8D8kfLZ85andDyNGDIZ64NRTT6VRbmNK/++1WtlftHglFXO+YsjFF9MpGGCpC61tnXwTa9KAyhQzSYqRuPe7pmfEiBHDzs6PSISs9n7vi1bUvQ5VzZyFKln2825r5j2uDJXhXoEajcWQGM9FJtU0b5+HAemutr8WmODCXyNwjgUlflgah//5VLr7rgNpPkVn7rfURbUYRR9vA/4DXO49h2HTGDFiMNQDqampPPLwPymfM57K+ZO3a1+uE2fdJ/8itWVLhl5zLZ1Q59U0S1d7NcVIERIoCYNezPs/kaYJY8LIhp2XRchoOh15Qo5DnVMvQaWzl5A0Z98O9Knx2HdJRkq6oO9FeyQ4UoB1SIgPcmGdK2/VHcDFDowNw51++NQHjRx43K/GaRMcaGKrm+uNVrI5Wtgb579RVKb2i/gbHsY4bzDUE2effTZvvvUWn3z8CL7MJoRabH0TNNd1KPr8CaqWz2HQuHEUp6cz0IKZDuT4ZF79EZ0kQXnyMDrxZnh/B9HJ2I9J0xh2TlahXiEhVHrbGJXHPoeEx3AUCZkN/JX1oxBrvNvmoWqaBd7teUg4tPBu64NMqkuQoXWACwfGIS0Gfw/CAy4c6sBFPnlH5gMdbYmhCy04HhnEK1Ca6FEUoWmHYUswkRGDoZ6wLItXX3mFvXv3ovCNm6mY+/VWPT5eVUbhew9Q8cOnnP3MM/Q79FCWuRIgi10tUd4GRUMaoZO2i8LR61BuPIwiJDY60UdIpmwMhvpmHWo+9hJwKmoSthYZQp9E6ZQ/AZO9v68gKURcZBodjPp8dEGCpQMq1XVRKqUYNT8Luvr7EiTOB0ahRxzOCcH9rqpyzvbJGJsQL+9YMMLS2BahKMs/gCeQOOm6Iw5KA8VERgyGeiQzM5NxX3zO8HPO4Z2376Ny7oFkH3AawWYdN/kYJxqm8ueJFI9/DidSyX7778/iaJRDy8r5OS2DGFDhnVhbeY+x0P8p6ARchk7KCTHiQxERI0YMOwOVaIXcX1CL9q4ownE3MAvYCwmAj9F6Mnew/pX1MuTTWInars9C4rsN8k11BqahxmV7Ab+4ihT+DbjLhTcicJ4NawPwchzutiFgwYUuFFpq7/6IBWejhe9mou/Xo8DTyDzbZ4ccmYaLESMGQz2TkZHB82+8xaKn3mDBLVez6rm/EmzZjZQ2PQnm7YGdlgXxGNHilUTyF1K18Duc6nICeXuQmrcHs5ctp/Kii5ly9TUccMEF/HzrbbRNyyJmJRfBA4WfE1eNESRQKlB1TcLIGsV4Rgz1RxQJjKnAicCZKHX4PPA5atx3FYqE/Ih8IzUrVBzgceAZJLa7ofVg9kARwQj6DswFDkIm1V+QIDkTONaBRRH4RwBesqHUM6rOtOCfDpTYWr33b5ZSMJcD36FqnEdQx9c9kUAybB1GjBgMOwHfVlvkDDyNK/59Mj+Of5/PpzxA6bR3IRZNbmT7CTZrT8Zeg8nocxSBRi1/uytWWkDZzE+Y8PgTDH7zTc56+jmaDhnEKpLm1V9QftyHxIdFcn2ahHHVxTRkMtQ9DjAB+AI4ApXpukiAvIqiHn9B6ZZPUERiw9jhPJSmKQMORybXNCRElqLqmekojXkMsNxr6z4MGUz7xCAlBs8Gtchdkzi86INXLXjJUVv3C4HzLT3mWuAbVAb8L+ADJJaOrPWjs3tgxIjBsBPwfiW0WgNRK0Dxml+I/PAdqV0GkHPQcCx/AMv24UtvhOXfuFTwZzWj0cDhZOw1mOJP/82jxwzmL08/zcrzzvtNjCxGxr9UdPJPQVeiQXT1mRAj5qRgqCtclC4ZC/RHFTABFPV4GnlGjkRplfe9v29l/TVdosADwBiUzmmF0iad0Oc633v898jA2hOY5+p5bkFRllO8/iGXh+AaF/rG4V4/3At8FIdqn7Y9xpJZ9hYknlah1Mx4JOxPqtWjs3thzjsGQz2zMgY/V0KftTDt08f5Zuz1ZO9/GtkHn4W1lYvpBXKa0/SUOyn6/AmeGTGCzPR0Ljz1VEB59B4oVO1H4iOCxEjNyIg5KRjqgnnAaCQabkYieQUSIQvRZ/UiFHGIIwGwYaXXNNRHxEHRjsmonL0LsBxFBdegyMgRgN/Vvjsio+oNLrwegWE2dA7CDQ6cAPzVr/3+XxwiPvUw6W2pW+tdKGKzHAmR6d7fF9fu4dntMOcdg6Ge+aoS7EIo/XUWE16+ksx+x22TEElg2T5yB1+KG6ni8REjGDFgALRty0rUCnsluipMVM8kIiMhTJrGsONZhlIvjVDJbQ5KrfwXiYlGKAXyE4qYnEvSiJ2gGkVRvkCRjhDwLYqMRJE/qq93/56oEdkvrj7vxyJPxxAHFkbgQT9M98ETcbjSgqN9cCkwIw6ODS8AeZaE0YPI07IMCZR5wAzgSswKvNuLKe01GOoRx4WPK6Ftfpyx/zkHf6OWNDr0L9ssRBJYli1B4kvlqvPPx3Xd35ZMr0JRkBSSi+Ul+ouYyIhhR1GImoC9BZyD/BcZwHtoMv8eeUH+jFbP3QO4id8LkYmoXHcaKp9djiJ7XZF/I4p6iExG4rs/sMBV9OQqYA8LesehIAJjgvCmH76Nw4M2HOyV7s50wOeDty1oZKmk+FHgU9Q07SHvecchQWUm0u3HnHcMhnpkXgRWlUL2hA9Zs3gGecMe3KQvZGuxQ+nkHH4BX7xzD1OmTCHcvz+VqGwyhCIiPmTyq0ZXqEaMGGqbMiRAVqL1YTqiz9kU1D+kAvUI2R94HaVtbuP3q0eXInEyFZXNBoBJKPLhoD4f+yGB0A6lZRwvLdMMmVs/BkZGYE8HHgoptZIZg6d8UG7BRa7XvdiWSCqw4EbU0n0sMoE/hMp430brzpjvS+1gjqPBUI98UgkZBTD9k8cItexGSuvutbr/1M4DCOW24F+P/Qe7f3+K0AJeCTFio74ixsBqqG3CyHQ6CzUmO8+7fTEqvV2BvB2nA1+iviIXklxHJoGLfCP3oTL0PyHB0QpFQ4pQNGRvVGnTF4mS2a68JgORD+Rq4LWIIi+nheAcB7o68E+/ojL3uurI2tjWuOdZSgU9jVb/XYgamlWjZmo3Yla4rk3MecdgqCfCDoyvhKaLi1ky63Nyj7q81p/DsmxS9zyCMWPGMPi5Zyn3+ylCKZmaK/bWFCPGM2LYHuLIqzERGIrEg4UqY15ElTI5yBeSj1a0PRVVumzIWuAa1PG0H/q8fg70QkJ6PvKM/IxawQ8EuiMhEkPipq0FhzmwMAx3+SHFB5c4au1+t19t5p9yoMiCdhaMtRS1uQ+1m38deUMe8Mb0JDK3muaAtYtJdRkM9cS0aqhYC+XzpgOQ0rrHDnmeUOsehKsqCcydC+jqL8e7z4/SNIlqGgdzhWLYNlzUd+NW7+87UffTKIosXI9EwxkoPfIGSuHcwe+FiAu8gsymJagL6wKUqtnTe9wilN4Zj/rnHAJku7DQ66Z6B+qa2icGq8Lyh/zqhwcdGO7CA371B3ncgWILelrwsSUR9QASTm+gBmn3IQH/KEr35NTeYTN4mPOOwVBPvF8JTQtg5aIZ2MFU/LkbWvVqh2DeHvpjxgzo2ZNy1o+GZKCQeRCTpjFsGz+iPh+9kBhJVGZNQhN6GHU8PRoJk5/QpN5oI/tahqIhq1FbdVDapJ/39wJUvpuK0jf7IdEzzdX9eyHB8zZwWUSpmOdCcJ0Fy2Nwkw3H2ion/iwOFT49/nlLK/v+F3gZ+Vl+QkIkx/t9Mb9PIxlqB3PeMRjqgaI4zCyDrsXwS0UxvtRsLGvHBCrtUBp2MESgqOi3Rmc+ktGQhIHVh8Lpu9JJYfHixUydOpUZM2awdu1aXNelcePG9O3bl3333ZcOHTrU9xAbNItQmqMlSrskWrPPQy3ci4H2qDx3OopEDENt2jckDjyFSmnbIEHxvrfvnujz+itqfPa2t98DkW9kmqv7hwN9LLjWhVciqrYZFVKVTigG//BBdxv+6sJkT4gMBf5tKRLzCkrLPIe8Lvegrqr3eftus53Hy7BpdqXzjsHQYPi6CiiAFAcsLLazkvcPcV3IsG1yUS7dQSW+IZJRkjgSJDv7SSESiTB69Gj+/dh/mDrlOwBC2U3wZWjlnXh5MeGSNQDss+9+XH7ZSE477TRCIWM3rC3yUa+QAOq/0cS7fQ0SIb+iz9VlyAvwHxQZ2XBBuwTzUBqnAq8KBgmO/ujzuhRFQg5EgmF/5A0pRP1DAsjHEbJgkAPzI3CrD/r74U8utI3D/T5IteEyVz1Eqn0SGH+35Ft5BzVhewZFev6OhNADqLNq51o4boZNs7OfdwyGBofrwgcV0KIAwnHIyG5KrLwY14lj2b5af754VRluNEyzJk1+M93F0Yk/iCaNOGoIZbNzG1i///57hp9zLj/P+Ym0Dn1pcsKNpLTZE1/6+gH/eMU6qpfNZs6szzjnnHO45977eOnFF9h3333raeQNgxI0YRejheUSkYJqlH6ZjD5DJ6GqlpeRiNhwQbsEEdQ87F0U6TgRpXtaoHSLQ3JRu++9/R+I0jffeCKkHYq8/GrBMTGwovB6AAr8cF4c9nXU2n21pY6r8zwhcg1wuaXeJ5+jiMgzwA9INLVDi98N8sZi2LEYMWIw1DGLY7C0CHpUKa++R6e9+TIWJlq4lGCz2k8rRPIXAtCpXz9cFP0IIjNgJkrTgCIlO3Nk5JFHHuGaa68l1KwDLc79F8G8DZdKS+JLzyG920GkdzuISMGvLP30XwwYMID777+fa6+9tg5H3TCoQpGDBchMmkizOKjM9n3v7/1Q9cxnKBoyHK2euzFmovJYC3lJYijachASNwXe7xORj2N/5DHpAnzrKt14PHCABWNcuDwKHeLwSgheseDNGBznws1e6e59jnqIhG2lXU631FH1G+95n0bdVG9FvVCeRP1M+m/PgTNsMTvrecdgaLB8WQmhfPA5OgEfvG9fnrZ9VC+dtUPESPWyWfiyssjq1IlSlJoJochIFskSxSp0VbsznhTuu+8+Ro0aRdZ+J5MzcDiWb8tHGWzWgaZnPsi6SS9z3XXXEQ6Hufnmm3fgaBsOUdS74zvUq+MMkm3Pf0QpkyqUzjgXNTa7D3VI3XBBuwRVwN2oCqYT8oC8gbwZ/bzHzEdm2AhK+wxEFTfLgEVeN9VrgOaWFrZ7JQJHoUZmfwV+iSt9dL5fzcsed6HAhYgFT1lwhKU0zGySQmQ6Wv+mMzKvtkKr/xrqhp3xvGMwNFhiLnxaDi2LIOZAug/698igycATKP7xUzL7HbfdreBr4sajVM/+gpZnn8U626YKRUWC6ETvkIyMVLJzRkbee+89Ro0aRfYBZ5Bz8LBt2ofl89Po0HOxAiFuueUWunfvzsknn1zLI204uGhV2s+Rh+NOkl6PlchkuhqJ2guRkHgeaA78DRmjN8ZEb19pwHFI7LwAHIrKdSuQKfZUZCJtgyIihwFfeiW7maiBWdiCIx2YG4HrbTgroIZmVgxutuEYW4bY11xY50LMlnja15Lo+NV77v+hrq43ITPsO+h7cPy2Hz7DNrBV9v17772Xfffdl8zMTJo1a8aJJ57IvHnz/vBxb775Jt26dSMlJYVevXrx0UcfbfOADYZdmVlhKF8N6TH93yQFshpB8LyRxNYsoWrR97X6fBU/jSdSVkT3Sy5hLUrNpJOspvGTFB+V7HyRkaKiIs6/4ELSO+1H9kFnbvf+sg84nfQu+3PhRRdTWFhYCyNsWLhozZdbkT/kdiQEbNS592lk6MxHVSi3I4/FM8BZaM2ZjQmRUlTKewtK25yFFrZbjNaPsb2/fUj8PIrSI91QtGSiFw3pD/zVkj9knzgsDcPzfhgchKNdfa/u98EQnyphnnfUbM221c59H0v+leVI7DyDUjg3IEPs58iEe8b2HETDNrFVYmTChAmMHDmSb7/9ls8//5xoNMrgwYOpqKjY5GO++eYbzjjjDEaMGMGMGTM48cQTOfHEE5k9e/Z2D95g2NX4pBIyV4PrgG1Bn7YwZS2EhxyGb+DhFH3+BE64slaeK1ZeROmEZznk9DPo2rMnYdQ6OwVdXUZYv511NcmF83YWbr/9dtaVVZAzeGStRIwsy6LRkZdSWhnm1ltvrYURNhzmodTFXLTmygnosxBDnpBbUFqjD5ro04G7UFpjFL9f0A6SrdyPR71sTkIL4D2DIh5NkHl6GkqzlAAfIQG0j3ffEq9s9xKUXhnjwuFRyI3ARyEI+2GYA13j8G8fdLNhlAtj41BhQboFH1jQydKCfOtQNOQZ1Gn1WlQ6/A3qK3IBZgXe+sByXdfd1gevWbOGZs2aMWHCBAYOHLjRbU477TQqKir44IMPfrttwIAB9OnThyeffHKLnqe0tJTs7GxKSkrIysra1uEaDPVKuQPDFkGbaRCIQ6MAXDwIHlkET3aA6lVLqN5vT1Lb7U2T46/brr4jbjxK4Zg7SC1bwfVzf8Zq3JgqZEAEdat8HVUl3IomlZ6oD8RHqAKivikrK6N5y5YEeg0lZ+DZtbrvdV+/QmTGu+SvWrXbn1OWI99EFlonJse73UVRgze8v5siQ2oMpTc6I3Gx4YJ2CdYgkfIrMp0OQdU12ahyJgTMQYJnKFqA7mB0hTwE+NiFxigiMhKV7d7g+UMOd+HfIaWGXnHgQAdu90PMgltdrcIbtaGFBa9bek0jUdTvERTh+Q6Jkz7ITPsJEiY7U2SwIbCl8/d2dVkqKSkBIDc3d5PbTJ48mSOOOGK924YMGcLkyZM3+ZhwOExpael6PwbDrs6UKnBWQ4on/9tkQ9NGMNUnM6vVrh1dbn2Ryvn/x9qPHsV14tv0PE40TOG79xFdPoc3Rr9OUePGtEdXe4UoKpJYKM9Ck4ufnc/AOnr0aKoqKsnoc1St7zuj9xCqq8O89tprtb7vXYW1qOLlDdQU7GKSQmQxipK8iVInw1HPkA9Rae9FqKpmY0LERY3Q/oxSfyejaMjjKBrSFH3Gvkflvx1Qee9RyCOyD/CFq89iFyQQ4hYc5cDrYaVpngkptfJWHP7stXZfZ8F1XjOzaluRkLctiawLkU/lUeBZlCK6HAmRecjkejU7z2d/d2SbxYjjOFx55ZUceOCB9OzZc5Pb5efnk5eXt95teXl55Ofnb/Ix9957L9nZ2b/9tGlj+t4Zdn3eL4fGBRB3IGTD3h1gTRSWZQEuWC603edkDrrvFSp+nkD+K9cRWbNkq54jvHIea166isiyH3nvvXc5/PDDWYGqHTKRSTALXY0GkBhJtIWvYufqwDpp0iRSW3TCn9W01vftz2xCassuTJo0qdb3vbNTjiIbTyIBcDXq6wFKYfzH+1mHqknuRlGOe1Hp7rVIUGyMZcgP8gKqfjkbVc3MQV6QKErF/AyMQAvq/Yp6hwzwxrbMlYg5GzjOgvkW7BeHxWF40g+XBJRC+jWm1M2NPvjZgptrNDPbx4uIhLznaYYiL8+iXiUjkRdlMYrWXMumIzyGumGbxcjIkSOZPXs2r7/+em2OB4BRo0ZRUlLy28+yZctq/TkMhrpkVQx+WQPZEf3fKABd2sDXJVCcCXEb/HEIRKHln08nb8zXOKkW+S9cydpP/k1k9aJN7tt1XcIrfqbwg3+w+uXraN+6CfdPncJRRymisA6JjVw0GSRMqwF0Ak60hU8YWneWfPl3U77HbrbpXiLbi69ZR76bUruG4Z2ZCGoo9gCKCNyMIhaJ+95C0ZBfkZnz76j09u9IJNzOppt/xZG4OQtFUv7s7fth1DckDwmDH5AwOBpV1RyMvCYnAuNcSHMlVm4Funr+kEFRfW/GBqGLXymcQAxG2XC2H7604B4H5sahyifR85z3IT4X9Qy5h6QQuQiJqsSKwdeSrCgz1B/bdBF02WWX8cEHHzBx4kRat2692W2bN2/O6tWr17tt9erVNG/efJOPCYVCpnWzoUExqRJ8+eB3lAPv0RJSU2DKYog3AdeGtDDEgzA/DdJ79yf9/ZlE/v1PVo75D+U/fIovtyWhZh0J5LbB8gdwo2Gia5cSL/iF8LoCGrXvwN8e/ie9R44ky5/8aleiySLRCj4hNmxUKpmIjKxj54mKACxZspjU/Q/YYfv357Zm6ewvdtj+dxbiwJcoQnE06i6a+Ay4wNcoTeEn6QvJQH6MOPJV5Gxm//ORNyQC7I38H8+gz9RQ9PmLILPoWciT9Jp3X3Nv3x+78ik1BS4FHAuud+GlKBzswBMhmG7DDQ50jsMtPuhpw2hXpbsrXCjzqQrmDktRvnNQGmgUqpz5BkVJ9kdG7n8DVyEPi6H+2apzj+u6XH755bzzzjuMHz9+ixah2n///Rk3bhxXXnnlb7d9/vnn7L///ls9WINhV8R14aMSaFoEjgsZftirvSaC6TFd5cVCkFUKThAKAvobO4WmZ9+Ec831rJr5IfFJE6j8/ntY8BVEIxBKYWC3rjQ+7hy6Hj6I0FFHcoNt8wLJDpkJ10kJujK1kBiKoqhIGjpxpyBRsjOJkVgsulXNzbYWy+cnGo3ssP3XNy7yRnyAIhB3sP77Ox/13Uh4hk5HJuZEk7NhqO/GpoggD8ZHyPdxCIqgPAAci/qQpKBKmVzgfG8MByEBMAj1Hal01V/kLGAvS5/V0xyYHYGLbLgpBE9b8Gwc+nlG1eYWPObCJ468LyW2mp1dbqmMeDhqlHYVElX/h6IkB6FU5T+R6EmsqWOof7bqmz5y5EheffVV3n33XTIzM3/zfWRnZ5OaKv/98OHDadWqFffeey8AV1xxBYcccggPPfQQQ4cO5fXXX+f777/nqaeequWXYjDsnMyLQPEq6ODo/7w0aJ0Hy2KwPA3sOLgW5BZDWgrEXfCHNZn4o1CV5cc6/gTsY08AwArI0Odz4XUbxkShrw3jvKTrKpIegCJ0xbkWXYH6kRip9m5P5/dpmp2FUEoKTqR6h+3fjVSTktowA/Szkfl0T37fhGwN8kkUIkExCHVBnYtSMQnhsrl03UxvvyFkOB2KGowFUIVNIRK7E5EvpQilXk5DYuEYb3wdXViC2sLnWhrDn+IQjsAjfjjJr8jM9LganI3yQ9CCu12Y5OgzW2TBbcAwS88z3HvOkci78g3ynwxEn/t/AH9h46XIhvpjq849TzzxBACHHnroerc/99xznHvuuQAsXboU205aUQ444ABeffVVbrnlFm666SY6d+7M2LFjN2t6NRgaEl9UQOpqGVR9FvRrDz4bvl4HRZnqGGm5kF0CTgsIVYPlKGLic6DKmy99cYgFlF6xXE0Wa4ElDhziS5oKa/YPWY5ESBGaKEJI5JSiCSqxYm+InS8y0r1bd35es3iH7T+yZjFdu21sMftdl8UoBZKHvBA1F6erRr6QWd7/XVA0pBpFODJQhUrGZvZfiYys/4f8JIehSMa9SGCsRZ+zxeizdDla/yULCYSWqKz3fVe3+ZCQ8FlapfeKGLSIwqtB2MMHJ7paTPI04BI/VHqlu5MdPbbQVrpliCWRNRxV75yPWrr/H3rsYSga+A/U3XXHOZEM28pWp2n+iPHjx//utlNOOYVTTjlla57KYGgQhB2YWAiNKwEXcgLQrZ3um1YCTiY4fplXg1FYmwZp5RIM6ZXKncd8+t/2vn62peiJbanyoMCFaktlkS7rX9EuRFeAxUh8JEyrxSgykkbSMxJm52p41n+/fflp9Ls7bP9OwUL6n1T7ZcP1QQHqFWKjibhmtYuDPCOfofc8B03azdBquXO8/9v/wXNMQpU1jVAn1GOBJ5AoOB1FOLKRN2VvJHauAU5BQuFYbwyNvLTMYKC/pTTR9S68HIX+nj+k2oYhLjSOwYU2/MmGfAvucWFKHFJslfI+5+1jFUrDnOX9vIyEyJ9Q1MdB/UWGoGiRYedjZ7oQMhgaHNPDEF0FIS9Fs0dTaJQpL8ecCISqoCpFQiQehMpYMm2TXQq/tgIsCQzX1t/6RxPPdBeaWbDSUt5+LcrPJ1iMlkIvRBNWGvrSF5FcJK8MTVIRdq4TwuDBg/nPf/5DOH8hoeadanXfkdWLqCpYwuDBg2t1v3VNCeoTUoTMm203uH8W6gsSRKLhJFRJMw31/RiCJuzNpWQSbeF/QtGQQUjM/h2lZ4rR53ktEiQXI5HzORIGVd7YXgO6uyrrvQrI8/whp7vyh5wD3BqCqRZc7UDHOFzhgwNt+VsecmFaHIK2xPfLQE8LliJj6gUo6vEKMuWegKIxrvda+wP7bumBNdQ5O9O5x2BocHxcBllrAFdXc309z/c8B5aFwO+Vt2SUQ2U6BMsgnCoxkl4KBf30WNvRbQkh4qLIyFwXBtg6IQ9EfR5a1nj+pWjCKESljIn+hyXIvJeOrlobsfNFRo455hhatGpN2fQPCB1zZa3uu2zGh+Q1b8Hxx++ay6FVo7VW5qEJuPsG969EaYowSk8cgITHGuB+5Cna3IJ2oM/Yxyii0BJN5sehHiQOEg8/o4jcRCSEzkGpngHeTyckct91lToqQ2mZoKWIzKkOhMNwnx/O9MOzFjzpQM843OSDLrYWsXvChR/jWmMmYMGrliI5i5AI+SsSH6+iCM6xKG3kouhJR/T9MOy8bFcHVoPBsGmK4vDTSsiISUM0DkFHTylMLoe16eB4KZhmaxUhsbwF9GwgrQoq0wBLfhHL0tUtukmLi7nQxtLVaQ6ahGoa8/JRZMRGgiQd+UPC3j7SSVbTRNi5xIjf7+eaq66kYvaXhFfNr7X9hvMXUjHrC6668gr8/l3reiyGqmPuQhPsrawvRBKL2T2BJv6WSHQMQpGJ55CZczibFyJrUITjMZRuORkJi9uQYbWH91zVKApxChIHV6JoiIVSNzOBNV4Ts97AJZaEyBjg6BikhuUPOSOgBeyej8MBcXVU7WKrsudxB2bFwbGhiQWjPSEyD6WkrkNC5DVvLEOQaAJ5ZDKQMDHs3BgxYjDsICZXgZUPAVdCok9bCHqz/Q8l8oNUe07TJusgHodo0KuiiUNpuqIhriXDKhb4LZUHJ8RIAdDWi7FbSIzUjIyUo0mnEQrlp6HoSARd3aaRrKbZ2cQIqBqvd58+rPv4UZzo9lfWONEwxR8/QpeuXbjqqqtqYYR1g4uiD7ehlNqdqHFXIr2SWMzuLiRA05CYGIEEwZ2oTHcU638+NvY8o0muWjvA28c7yO9xvrf/tqiBWBSJiFeBCagcOOL9fhHo4KpC5mLgEM8fcoMLf41A7xi8E4KePjjFhdlxGOzAvX5o6qVhXnNgjgNhH3S04BVLvUlmefu8BaViRiMhcjhKRYGiOmVIKBl2fowYMRh2AK4LH66FnBLAhUwf7OkZV6uAX6KQ5i127XMgGIaUCoh4C8bYcVjQ2RMhHg7K/Tt426CqmCw76RPJRyfrBIkwfWMkTDJZX4zUrKbZ2TwjoOjIpS+8QHzdGta+cx9ubNv7grixKGvG3kt4zRLm/fwze/bai0cffZSysrJaHHHt4gLTUQSkGImRQSRP3C5KY9yKUiY2igKMQu/vXUig3sYf+yWWozTLq8jkeRKKvtyMOq/ug8RvCPlBDkYRiQtRO/f2KPrRBnjHhfau0oS3o+jdOuAEF16LyCfyWkgDHuKCE5N35SavdPdfLnzowCIXKnwyxL5gSVRPR2mZu5D4eAMJkYNR51eQcFsInMfO01HYsHmMGDEYdgBLYlCwElK9KEarLGjeWPf96MKyAITCEh3+uARDRoWiJT4XcGFNM5SiiaL8jJVcTddG20SQ/yOxelOigRVI9PjRJJZoBR8kKUbirF9NE2HnWp/DRSmHVfk9uXv/sYSXzqbgjVuJlRRs9b5ipWtY/eatVC/5gcZDr6HpyX9jla8ZV19zDd269+Czzz6r9fFvLwuQSfQn1IfjBNZ/fxaj6pYv0bHqQTIC8jTq43EJ8pRs7n2NoR4h56LPRn8UAXkLNTS7GKX4WqMoyyq0ns0i7/kv9+4fgSpYirzPZWtkVE3x/CGHODA/DHfY8GAQZltwsgstYnCBDef7IeJVzIx3oNCFtbYEx1OWhPNkVKHzABJAbyEh0h+lhUAL8H2HmpoZIbLrsLNdCBkMDYLxFRDMl/HUb8E+HZSqAZhSCWtTIN1rbJZVDiUZEiO2C9gQCUDUrxRNKAKxDJ1YEy26LAtwZGJd7OXQHda/uliDTuBrUalnHGmaxMSUWIemZtOznSVNU4X6U6xeA2e+CLN6Hcnwjp/z1pjTyX/2MrIGnk3GXkdiBzbnfAAnWk3FrC8onvAidjCVvNP+Tkob9ThK69yfWMlqij99jCFDhnDXXXdx88037/gX9wesQNGJDLRS7oZroq9DFSNr0XuehybeDGAcKq09lU2vI1OT+SjV4aLuq8ehzqk3oDVeUpHoaYbSHt2ROfRKVJVzLBIzZ6J1Xvq7EgcXolVzQYLh2jg0jsALATjAr/E/6kA3r2Kmn6004v0u/Oj1EFluK9Jxm6XP6kQUZXkERWDGILPq3igtBBI9n3jjT/irDLsGRowYDLVMzIUvV0FGWOKgUQC61Ki5nF8KTgCqUlWum1sEpVmQVeGZVKOwppHSN3ELgk6yoteykqkbB4mHeShEvYb1+0v8iipm1iKfQOIqMcj6V8qJyEiMZLO0+qQANdGqdOGM/8APjaEwCzrmHMTVI35i3MfX8PW4pyj5v1dI734ooTY9CTbvhC89B7CIVxQTyV9IePlPVMz+EidcSXqvQeQefj52yvotvfzZeTQ55U5K/u81brnlFlJSUrjmmmvq4VVrMn4NvR/D+L23I4LWkJlJcuXls1BUbAHyWOyDuqf+0UQcQc3CPkUpmRZIWDyCfBaXoSjHPsBX3pjORULpfO/+2cCh3m1vuRIqP6GUUYbnD7nFhZdj0DsO/wlBO1v3f+XA3nEY5dNty4F/ujDDgUYWzLMkaK70yto/R5+JfyHR9A4SInuh1JKFIjWjUYpqZxHVhi3HiBGDoZaZHYaqVZDtiYZuLSDDy68UAksikBGBcm+xmMZrYHE7iKVBJAMy1kBFujqxYoEbUITEh7woiehHIhKyAnlCZrL+BLYITVRrWT/qYbG+GElETFzq/yQ+B5WOBoDjPoMZUajKUMTIciHXzeb4455mzaE3s+6N/1L4+SuUTf9go/vyZTYho89RZPQ5mkDOphfmtCyLnIPOxI2Fuf6GGzjssMPYe++9d8jr2xgV6Cp/KUo1bNhRxUXC4CMUoXCQb6QfStElSm2vZssWfZuB0jnpQC/kDVmFjKiHIgE7xxvHhyjycg2KSlQCVwC/oHbrrwCtvM95GkonWV5113AXfoioEueuEIQsONOFUgcOcOBGv4THz6h0d5oDLS2Ya0lQDPOEyAdIJD2BUlDvoChJD9TWPWHcfsZ7/s3Hygw7K0aMGAy1zGclkFYo70eaD/q0T943A/lFMoqh0u+t1lshsRHOBJ9PreCjfgi5EIxoLRoXCYgYyXRPHH2BK9l4Jc0S1AhtCQrtp5BcOC+xaF5N6luMfIEqQnKAPqthxk+QGoN4SKXN6VVqmf/82ZDSqAOt/nofTf53H49Nm8yggw4m1HEf0rodhC81i2BeR3zpjbbq+XMOPpvokpmcc+55/DBzxnrLWuwIImiyn47Mm8M3ss18lLJJiIw9UFQiMUknVsLtsgXPV4FSX5NRRCERDfkn+nz8FUUbOqEKmHnIDNvGe45TUPos5P39H+BgV56V4agBGUjIDHOgPAI32nBhAAotmVebxtWa/a+eUfUb4FUXpsahpQ0LLbgPGOrtawzwJPK0dETN1CZ5r/cC7zgUemO5hvXb3xt2LYyB1WCoRcodmL4MUr3UStNUaFfjonxaGNYEvWhETE3PfDHIqIQ1eZBWDOGgHhsLQFol+L2wRcCSCdVGEZI48owkQvIbipHlaPICRU8CSMwkRMfGxEh9XJ04wLNoUmsB7O3Cd5Og0XKoDEFKGFKrIRCFX9vD2sYScevyoLMP3n7tNfxpmTQ94QYy9jyM1I79tlqIgFbxzTrsfGbP+pEvv/yydl9kDeIo7XAHikLcifwXNSkEHkZRAD+KYoxCno7EgnYhbx9bIkQmoTValqNoyJ9Q/5mrkWA9CRk/e6MITDXyXczzft+AvCQnoc/OG673PgE3kRQiY4ChcbDC8F8/XBqEHy2tMdMyBidacI1PQuR94DVPiDS21UX43ySFyKvIh/IsEiLvea+jIzLVWqia7GE27q0x7FqYyIjBUItMrQLyIeCZS/dur2gHaLJfUaKJtDxdlTOplVCRATlhWJADTddAdYY2rk6B3BKVNjquJoE4iowk0jVxK+nzWI3C+AnWkmwPvgpFVizkCQiRrMxJUB9ipAIt0hZHV+BtgPdmQb9vYXUmZFZAOCBxl1UBL52hbrUVWRBKgwNjYW5//nlSeh6F5d/+WqBQm56k5rXnqaee4ogjjtju/dXERZGM91AlyB38/nhXowl9AYqGRFHUoS0SKP9DV/83IoHyR5SgEtg5SIS0Qh15H/L2dyVKAflJiqR+wNHIFNsFVcnM97Z9AWjj6vNThRqq2Z5IvsWF12LQIwb/DkFnW6/lARe6xuAvNhzqS3ZFne7A9w6k21Buwf+spOn2GbRw3rNIYL+PhEgblB6yUETwQRQhSaxSbdh1MWLEYKhFPi7QQnc+IKtGbxGQoXRVBDLjUBoEKw5ZZbrSb5ENxCU6Yn6t2BsOKkXhLWuDbdVYmsYTIy6KdoC2q2lcrEATWhCJkSzvsYkqmzTWp67FyCp0JZyGvACZwGslcMgbsCJNHWirgtC0RCXQ03tLxNk2lDaCxqnQYtYsKsrKaN6pf62MybIsAh32ZcKkr2tlfwl+QqW23dl4G3YXVcJ8iUpiI8BByEAaQxUpW7qgXWJ/n6Dj2x6ZVE9FywVciZqZDUZCZF802dso4rAENT27AkVhmqPS2UeAQa6an50C9PMiGEXAOS78GIFjXLgnBXItuAf4xIG94nClD/a0JVr+5cICF+a4ei/9FvzXgs7euJ9AlTvPIr/Kh0iItERlxJZ3fP6BjL5bcjwMOz9GjBgMtUR+DJYuh1yvt0j7XGhcw1E4A/jVD43WQlkTtYJvWgDL2kFmS0gphcpUtb224pBSDa6nGBLCw08yt+pDAiSMJqyaQiQhUqpQ+PoHZHIFRUzS+f2VdV16Rn4EXkeT8v7odbzowhEvwVILUqLaLrcEogHIjcOUvaHpWihoCVYQGgWhdMYMLNtHoFmHWhtbsHknCr59k9WrV5OXl7dd+1qMKmSaopRI1ka2mYUad7VE720rVEniR6mTd1CX0T9a0C5BAWpythLoi6IJR6NoSD4SGVPQZyMbCZG23nPe4m1zOzKJXuiN703gIM8fcg3qkAoSWWc7UBaBq2wYGVTk7lygMA59HBjlhxaWxPE/XFjsSBRVWbr9cUsCzEUpl4nA8+jz+hHq7JrnjdtGEZyHUcqq2xYcD8OugREjBkMtMakC/AUyrgZt2Kfj+vfPjkFBANrE1XE1EtTKvHNzoCAFUlZASYrESDygFvFOtkL3PnQSDlq/rZWHHwmOCJqAak6b61AaJrGKbwmacGxU6ZBYsdet8Zi6iox8glaN9aOUQQmasA/7Gn4tglBcKZnykK6cUyLw7V6wLke9WSoyoZkN7XywrrCQQGoGdqD2ipJ96XIfFBYWbrMYKfBeEyjN0Wwj26xCLdODJKNU16P3ZhVKibTijxe0S+Ci0tbnUaVJd+QTWYLMqfugCXw8qpp5H312/oQ+OycDx6O+Hb8i8fQM0NbVmJYhkeL3hMibwI1xyI7AYwEY7IMiC85woUkc+rlwrR8yLaWEHnJhkaPP81pbQuLflnwzLjKuTkMpnEZIiExEQu5KkoL8MRQ16rsFx8Sw62DEiMFQC7gufLFMV/R+dNW+R+vk/RGgpFTbFWV4zdCiMqiSDk5cq/QWNwcsNT3LqoZKS2WtiZy+TVKMBLzf5cigWtO8ulq7pQhdYVaiplgZyMTY3Ls/SjIasqPFSBxNbo43nnO9cY8G9i6C+VMhM6K0zJpsaFMAxTnQqgJm9oAWq2FlewinQUZAvosC28Z1nU084zbiquZoW6ppSlGUYw1KdbTfyDblKCqUjyqH1qHF69qhifol9B6dy5Z7IZaiaEg5Eh3tURrmn+gYX0Gye2pH5OXIQeLnLdTp9W8oCnEgEgD/AI5yJR6PBg7yREgUuNWFV+PQNQqPBKGHT31HLnShS1ypn4v8Ei5LgMdc+NlRKm6xrTE+Yul/F4mceSg1k4WecxISJVeRLD1/GomsA7fwuBh2HYwYMRhqgflRKF0Bjbx5ca/WkFLDTzkHyA9DVhjKUsEXgWBU0ZGMFEhbDVHb68AaAyyIZ/Cb8vDbXnrGEycAPksTfBW6aq0ZiPkFCY4iVC0RRVfXWSQjLTUXyYMdK0bKkeegG0pXXYZMmm8BnR1Y+a7KnRuXQlEmtFoN5RnQthQm9YLCJtBqLUTTJFhSsjTh/dimDdHKMuJVZfhSa6ewM7YuH8uyaNWq1R9v7FGNyk5/Rt6MHhvbL5pkv0VioQoJhsSaMV8jr8SJSJxs0VhRJOQtkpGCYajHzBXIEHoyam42GPkvKoCBqM36xSgqcpP3/Jej1NBXyB/yEXqvWnlCZC0wwoWZUTjCgftD0MT2VhJ2ZV49zoYTbaVrfkTpt+kOdLBgqqXS3vutZKn5Lejz+zQSy58iUZSFUkKJz+RoJKwHb+GxMexaGDFiMNQCXxRBsFhRhgwf9G6//v3TgQU2NCqC0mbyi6SVwZqW4LMhrQCWZamxV8yvUtaSRsnVeV1LaRcXbWPjrWODIg2/oNB1gpoNz3p72/hJrktjsf4ieQ47ToysAB73xvcNWvJ9Nro6bwH4voSUXyGrUq8zpVrG3YpcaLwcvu8OzddAfmsoy4Q9qiHdJ9NroF8/ACKr5pPasV+tjDeSv4COe3QmK2tjDo/1iSFD5/+hFMepbLxkehryfnRC710TFPnwo8jBC+iK/3a23LczH0VDbCRoOqMW7g+hSMnlyIA6F1XIvIHe6yuQl+UklL7JR2mUq1E/j/auhOxsbzwh7wXNBs51oTQMl9hwpdfI7J/AWAd6xuF8HwzwAkoTgQ9c+M6BPS34xpLQ+puVLDO/AX1Gn0Li+HOURsoAriX5eXwfCefE+jOGhofpM2IwbCdhF75boiZlNtA8E1o0XX+bRQ7k+8EuB39EYqRRMRS2kBCIV6klPC6EU+QXSQl5QRIv2pIIabtowktUz8TQhF/zKZejiMhabxvb+53N71fsTfVus6l9A+tM4L+oXf0MtALsHFS2mQP0WQyrZkBmFTQpgZI0aL0WCpvBwDnw2Z6wqgXkVAEZqjJqkaKUVADo3Lkzrdu2o+LnibUyXjceJTz/G44+avPX3y6abG9Dno+/o4qTDYXIErSY3FR07KvR1f6JaHJ9GomyS1GFypYc/zASAH9FgqwDqrJphgRIKprIx6HW6avQBN/dG+d/UVv1h1Dk4jBUXXM/cKCr21oDf7UkNlwkZE6Kg1MND/rg+oBSMBei1u57xuFGT4i4SHh97MLXjnqQfOuZWm/3hEgUiZ9S1NQsIUS+8v6+rsax+ApFTjbWFM7QcDBixGDYTmZUgZMPAVflt/t20O8E64BYOcTjUJKjhe9sB3JKobgJ5K6DsjT1H/HF1V+kbalMsCHkM3EsyPL2+VuPESQwIiS7sCZYgRqeVXnPn2hyloUmM5ekGEnxHm9Te5ERF4Xuv0SGyPnInzATTVSpwGkxeGOSoh4timBpU+i8DAqaQnYcAunw3Z7QpEhRkZIsyA5DIFWTJ8jXcdmll1A1dxKx8qLtHnfFzxOJlBdzySWXbHKbmWh9lbVIjBzB70+kJSga9AYSiYUoanIJeg8Sa630R5Nyky0c30yUhpmNoiHdkAB5CvlxLkHpuu+QSfVNb5zDUHrjOJTquABN8tch4foGcKwLY4HzgCO9D1MENVu7PgbtIvBsEE4IQJkFx7lQHZdH5O9+6GRL5D6NWruPd6CHBd9ZisZcZek4hZGQiqLOqSkkF/gLoc9JIsP5HRKxiSZnhoaLESMGwwaUlpaycuVKCgoKiMfjf7j9xyvBX6UJP8cP3duuf/8PwKpKyCiFsgxFOvxxiZFIBjRdJKOmHVdHVscHaZmqyklcHTpoEnNRozMbncxdNGFEN3wNJM2Py9AJ3/V+OyiakkYyTVOBhEhtiJEY6hVRgSa+MjRhTkdCxOf9f9V30Gs25JbB6hyJsqo0WNoVjvgR3usNy1tC2zVQ1kziq7EPbL9STwnOueACUnOyKfrscVy3Zn3Q1hGvWEfp+Gc56eST6dHj966PBaiB2I+o6dhJrL/GD+i9eAtFLjKREOyDfBHtkSi7HR33O1Ajsi2hAkU1bkbekJZoXZZcFFXxI9/HV0iMxJE/pLn3PD+iqp5bSZaAX46EQ5ULHVxFem5F3g6QgDrNhdERGBiDl0PQx6fXcIyrqrBuLtzthyaWXvtDqHR3vAPtLZhtSbCd51WBVaOmZUEUnQkiIfIV+qzfQLKJ3yzvvr9iJqrdAeMZMez2VFZWMnr0aN599z2mTP2eVSuX/3ZfSmoavXv35pCBB3P++efTuXPn9R5bHIdflkO611ukcx5kbtDAYwbwQxxyC2FZa7V/t+Nqcx4NQPZSqGin9I2LxMq6pskVexNRkHSSlTSWFz53kBDZcFKsJNkeewU6wSciKQkhUzMykjC1bu8JoQz1gDjMe92d0Pom36Orbhdd+f5pGQz+ROkZfxyiPui0Dn7oCwfNhHg2TO4BOSVQ3Az8WRCsgvae2zbxLswB/pWbyx5P/ZcfTz6ZsqnvkLXfyVs9bjcWpejDf5Ae9PHE44+vd99K1Jo8DU38G2s77iLfyMeowRjI93A7mmRLUJUMbPmCdgm+RpUtLVCUqStwiHfbL8BFyKj8OSqVfsMbzxBUdXIRikT9k2Tkw0KRmZO8apm90PuSWPfoR+ACF9ZFJHquDUGqpfWDbnGhTwz6W3COT1HAMtRDpMyFmS40stXe/V7PsAoSVCNRFOg+9Fn7CkXP/EiIJMzUC1D6ahRmktpdMO+zYbclHA5z33338c+HH6G0tIS0tj3xt+lPk36nY4VUbxsrXsns/IVM/89/eeCBBxg8ZAiPPvII3bqp3dI3ZWAXgN+FFBv22aD3lgsUxGG1H3qUK/IRD6gNfHkTTcRVcYj7IOhC3A8ZYaVzqoGmLhTaEgoWStHUFCMWikTU/CInDKoJwZFPMk+f8IVEkEBJVNNUsf1iZBnK/5+FPCGHo4Zm073/Y2givqga9hkD2eXQci381A72WgC/doR4Juy7HJ4+CZa1gL3nwuz9odSGrCg0ykp6XJ5CXoxVQN5JJ5F57Y0U/+M+nGiY7P1PxbJ9Gxnl74lXllD04UNEV/zM+x9/9FtvkSJUwVEOnIl6fmyMBWj12naoFLWYpOCII/Pl995x6byJfWyMYiQY5iLzbwVwPopMXIq8IrejVMyB3v1voffzMtQr5BiUuvEhsXQzSof8AJzs6vX9BeiWSAEiMfM3Rz1d/u6HU/z6vD0OvO7CXjE4yYYh3uEtAB52Ya0j4WtbELbgIUslvCCxcrF3jP7ujecrFPnwISGSWJ5gGTL0jiIZJTE0fIwYMeyW/PDDD5xx5jDmzZtLet9jadnvuM0uM+/GIlTMncTEb99gr959uOfuu7j66mv4dImFP64JvnEKtG+5/uOWA+UrwA1CWRakVkFVOjQqgdVtoFEhLG0tr0gwIjHSNAqZtsLkma4afwXwKl681E2YpECJsH731bXoKj7RY+RXdKIP4zVOIylkqr1tK719bKuB9XvkEbkQhf7PQOWtM1FqJo4myCddsD+BDkugzRqY1xpa5UNZNizsC2e+A+E8mNQW0ivASYPURrAuDtk+qPAnS1FLUD+PJsCEKGRceQ/N8lP45eU7qF40jUZHXEioxaaXkXPjMSrmTqJs/DOk+i3e//gjDj/8cCqQeFqMqjc2JSAKgZeRwGuDJtGzkUgApRlGI+/G7Wy558FF0YrH0dowvZE35EAU3ZiPUi4RFOk4wXueKErhnIYW35uC2sF/4D32OG+fHVxFbz5C6aOEFymCUiqjY9AhCvcFYV+fPndXIhN2jzhc7IM+Xt5kEfBfF5Y52q7EgkYWPGDJMAtKVV3svYZbveM1AQkRkBBJNH0rQIL2OrZs7R1Dw8GIEcNux4QJEzhm6FDczDzyhj9MsFnHP3yM5Q+S0XMQaV0PZN2kl7nuuuuYPnc+9olPkuHaWBb0beetsFuDaXH4qRiyUtVBNK1KvojcYvh1b2j1CyxsoXJdn6N0RUaKl/axwHVU+uu3vKobkg2gEpGMmr1CQGmFbJLdVwuRidJFV881rzarkWApZtsiIy7qr7EEXWH/F008bVCofwyKiIwAprnw4U9w7gTILZdPJBCB5mUw+RDo+zN0rYQnz4HleYqWLO8NMR+EyqFjCsyykpVERUjwvBqDeBG0yLc4seNtFJwyiGe++Qv5L15NqHkXUvbYh2DzTvgycsGJEy1eSWTVAsIL/o9I6VpOOPFEnnziCXKbN2csKsM9GUUyNiYgEovZLUJi4UfgWO/1W97xfsF7D0axdZNqARISq5CI2TAa0sq7/y3kRemOurj6vGOc7o2lP4pAvOO9H9UoyvInVyKgDRIiibTMGuAiF2bGYEAcHghBa1tRoWFAozi0c+AGP7TxHjMDrd47x5E35Udb+/2HlWz2ttZ7/r7esbCQN6WmEEkcnxK0aOIVqNLKsHthxIhht+KHH37gmKFDoWlnmpx0C3ZwSxptJ7EDKeQefj7Bph147dlH6LEqk0HDHiLLB3u1//32X62DpVnQNB+WtVL/EF9MkZEp2dB3GczspJJV11JDs+oMyLMgYEG1o9sTvUDiJDuv+lCUI7E2TYKFyOCYECOlJK/WS0mu3gvb5xmJoqvYVmjtlKdQeqIxqvZ403ueM9BV95UlcOVrkBqB9CpY2hj2mQtz9lLvlf0/g8iBMCmodvkZDlS0kA8hGIFFWckS5ELkibjbAbcIUirh2E+hyVqYc9JB9L/gZzo+/jGfrfkv5T99zJr/K15v7G3atufYYadyySWXsGevXr95F4agyX5jIsT1tklUCCXSY7eTTIO9gzqJDkcpiS3FQQLjReTfyEZCawCKhsxFUZc01L31ZBS9qUQT/wUoSvI4Mtn+glIxt6CJ/wfUpv0l7/3oU+MF/og6pxZHJDxuDEG6pcjQ2a7Wl8n0hEi297hxwAQXpjqwlwXjLUVb/mEllyUoQELkQPS5sFBX1S+813sDMvmCt26Nt/3GWucbGj5GjBh2GyKRCGcOOwsnI4+m2yBEapLRaxBOdRlzPvon3XofQ69DB9EkZ4Pnc2BBIZTmQOdSWN4KqlPlG2lcpsmrJEMRkZRqpWjSwrDOBy1dSLGg1MunpFgQd/WYVJJNzGzvp9y7zUaTSDsUOehJMhXjoghIRo0xJqppqtm60t4S1FH1GA2P0SSjAD+RbLA1GPWs2CsG574M6WXQbjVM6wQdl8O6JrBsTxjyBbTJgCeOhGUB6LoICjtBoxAsr4Imfqj2SzCVA+cAFzngWwtWFA79Ghqvg+Is+W3a5Pto0f9Ybrr2WC7xuSxdupS1a9di2zZt27YlNzcXF6UybkXeljs28/pne6+xO0oL5aM25Tne/VORGDga9QvZmjLUpUgAlQODkMC4AAmQS1HU4W4kdNoj/8jz3mP/hITRSPQePoP8K8ei9/7fqLpmX1fvyfVA4xr+kNHAHY4E4i0+GOaX52MScL0L+8X1WbrMr3WRXCQy57vqL3KIBR9YsB9KzSSOx0rkVRnsvQYLGXE/J9nsLCFEwkiInI0iK4bdEyNGDLsN9913H3Pn/kze2Q9vlxBJkLnP8VQtnMJnT/2F68+ci2Wlrnf/Z6vAKQQrW6vxpldDOCDh4WRCqFJ+EcuFnLVQ1BSau7qyjiDRUOpqobFERU0MTRYWXg8SkmmLUjQZLEOT0RIUpYiiE38VipbkkCyV3JbIyGLgf8gfsgBddY9C0YGf0aSXifpg9AX6unDYV9BmPrRfDXPaQmY5NK2ECYdA3lrovgrC18CEYrAaQ5MwfLUHVMYVTcoMyY/QGlWH/MlVx1srAs3WQN+5kLoOvjhWPVn2nA1fnAsv+MHCol27drRrl4xV/Iwm4m4oerCpT0NiMbts5B1ZiNI3HWvc/zwa1982s5+NEUPpnLdRBCSMoiL9UEXSHGScbe6N4VTkJZmB3sORKFU0GImzTihqcgUSi/cAp7ryZ2Qg0ZVY5O43f0hc/UPuCsCB3pv/LPCcC/vEYH8bTrOVzomjNFyZCx85cIQF73nVMndZyXTLUm9sxyNRBTLPfo4+izeQrCaKocjPSSjlZdh9MWLEsFtQVVXFQ/98mPS+xxLM+2OPyJZgWTa5Q0ay8n8X8cPk0fTvee5v97kujCmAsoBW5i1sDJnVUB2ArFJY3RKyimF1M8CGtAoobgpN02GpBeUu5LhQaKmHQxVJ06rriZEgmiD83u8iNEkVoLTMDDRBON62QSRGMkmKkZrVNFvSgfU7NCFeh9YQKSMZgp+HSmDz0GR9CHC0C5mL4aCPoGmp1p0J+6H3Aph+AJTmwrGvQ94B8GRzWFoNHZfBquYQTIOiGORGwZ+t6M7lqAokrRjsSihNh9M/g0A5LOwIGdVgl8LKtpCxx+8rYJag1XQbo8jGpkpsK9DEvgYJlqmobPZsksbfN1AE4Fy2fEG7BHNRtMNCYiLRf+MnFEloisTER0hkHoeEC2hdmWNRx9RxKFU2zhvLLag9/Q/AOa5WwD0RleEmKAAu8fwh+8S1vkwHr2HZjcBsF3rG4BQbBnoeqGoUCbMdeM+FQRZ8aGlcf7OSpeWL0Ht0Osn1db5Bn5Uo+tzkeLc7qNfI4UiEGXZvjBgx7BaMHj2a0tISWu59bK3uN5DbirSO/Xjyyce48IJzf7t90QqtRbO6KbRZCUtaQVYh4MgvsrAdhKrAF4R4UK3gbQfcbJlXK135PlxbKZoYUOEqGhJDwiGEQvp+77aEK6ICTWZR7+9ElUwqimQ0Y+Oekc01PXORaTMfRUGeQVfs53v3z0eVJR2Q2DkGuM6FOZVw/Usy7mZVKD3T4xcoaQ7r2sCA6dAqCJGL4KuZEGuiNWrm7A8xx2ud74dmfomR81zIKgG7AtZkwYivIWUd2NUwu5eqkvaeA1+fBqfXaL6yBokQBxlNN+VLiJNczG4AqoaKkWz77qIUxifIt7G1LcqrUUO4ccigWoyiR72ReXM2ioDsgSIup3pjeQe9hxej9/14lDJ60tvudCQAH/F+D3R1+xVA8xpC5AckRIoi8GfgppB8IJUoupLuQPs4/NUH3TzFWoKiF34HvnBhfws+seQ9ucpKVnLN957vPG/cAJNJCpFrSPZocVGUpTcy2xoMprGdYbfg3XffI7XNngQabe017B+T1nMQM6ZPIz8/H1BU5MOfwI5BWTrkVKq5WZVPfUUalcDadK3YG4jJnFnYDAJxKAjIPBgCcL3GZ+inwvudME4mzKs2CrsX6SFUoX4XoKvlEMl1acqR+EiE1GuKkU15RiJoogyisPvDyFx5knf/QmSM3NPbx5+Bl1x41oXL3oLU1dAhH37sAE0LoXEEpg6EmA29foS8S+BfJbAkB9ovBzcD1uYqRRWshk4hyLBk8Mwtg2AZFGbBMQXQYZrEyPS+0H4VZJbCgh5AW/kvypBwehpN4FeycSHiovLkW71j2pjkVf6fvde+GFWorEam1a1dlm8GiqL8BByJBOH16L25FEUs7kHv47dosn8VlWZ3Qr6S6d54/ooiT2+RLI29BxjiQpEr0XE7SSHiokjPGQ5Uh+FaC+4OSogsR1GfNnFVzdzqTwqRVcD9LoQdmOhCdwsmW0rRXVNDiMz2xnQRSSGSiKJF+H3L+5dR1GrQVh5DQ8PFREYMuwVTpn5PoNW+f7zhNhBq2RWAadOmMXToUJbmq4w1vUzm1OoUSItARbrESNNSiFkQzpQQyShTuianAlbZ0MxVamadox4jcUtXxVUkK2mCJNelSYiSIiQ2EqW7FppoUkiuS5NoiJYQIwnTa0LUbHhCKEZX28ejnP69SIT09u5fhK7A9/We61JkCr0KuGAK5M6Ajvkwr5W6znZbBVMPA8sHQ76E7IPhyf3h64kQaQqNyqBwAKS6SlXlxaBvtgyOTashUAr56dC3AnpMA385VASU9rLj0P5XmD0I8tI0jp/R5N1zM+/fEjQ5tkQiaxaqKunk3V+BTKHlKJXSeDP72hjl3jGcitaxyfeOVy8k8n5EqZQ+KK1ygndcX0Tv9TAUBbkCCaFXkLDogYTIJ0h8XOBKeB0OHFYjGhJGwuStOLSKwG0BOMx7o78FrnbhkDikuHCtX2IYFOl43tXncCXQ1IJ5lsZxipWMrk1H0bIrkYEXJEQ+Qp+3q1h/Ecd30Gft+K08joaGjYmMGBo8ZWVlrFyxjECzDn+88Tbgy2pGIDWD2bNn47ow7kdYkQPhNG9yzYZGpeqXYbsyA8Z8YHmdV9PLZWLN9kPY62TW2dYVbpaXovEB1W4yMhJAwiHq3VeBxEgBEholKBKSaAXvkhQjNr/vfRHW067nGVmERMD5qMrhPpSWSAiRX9HkdwiKjlyCrqRPduHI1bDHWC2CV54KazOh+yKoagHhXJlW06vhfxfDmlVQmAmtV0AoCLNaa1HBvKhEyyMBdV9NXwdrUqBVDPqVQ+5PECiBbwdAz19U3vtTXz1HkaUqkNvYtBApQaWwY5AQWIgecysSIi7yX9wHHECybHlrmISiIfnI6JnwZdhIuK1CEQ0HiYpzUcfWOSiScCt6X45E4uMOZB4+BUUV/omiWoNdpX8uYH0hsho4y4U3o9A7Av8LJYXIq8C1LhzoVczcXEOITAVedmGJIzEVt3VMR1lwag0h8h1qQHcDSSEyhaQQ+SvrR6I+RymzM7fyOBoaPkaMGBo8FRUVANihtD/YctuwLAtfKI3y8nJWrYFFBVCRCqvyoP06KM2AULUER2oVFKepqiY1rBV8K9OVogn5tQhc3NWEX+xCC9s78bsQdZPVEIkIRpxkW/d1qJIml2T31VUkja7ZaIJwSXa8TLBhZOQbdAV+IxI/j6Ir4oT1dzFKfQxBk88V3j4GudA2Cge+BpmFivbMbQWtV0FTB8YNgsIM6DEB5o+EUzNg8iKtWtx0HZR0UUomxwK3EhaFIDsGVGul2JAfTkqDFp9BqAJWN5aAmtdeUafSHPDlasG2AWy8xDaCBMjDKNpTjsTcbahs1kJRgduQ2LudzUdWNkYRihb8EwmJLG/ff0Hltg97t1+NjKntUJrrv3qpHApci8TeFcBj6P0ch0yqIBFztKuF7sYjodK2xguegaqOZkXguDg8lSKR6yKR85ILe8fgYAsu8iU/W58Cn7kw01E0ZIWtz8atlsy2CSZ6x+hmFI0BiZiEELmM9Y2936AU1QWYFXgNv8ekaQwNHr/f+5g78R32HK4Tw+/388WPUBSEnDAsSYeMKFQHIe5IjOSWQGkaRIP6P6USihpDKAYRFwK21qrp5VMkpIOtBlYRF0LeFalFMkUDEiNR7+cXVGa6Fk1e/4fESGKdmiiajDaMjCQ8Iz6UAihGk+kcVHp6I8m+EEvR1fnxaGG4Ud7jBrsSLid9DJmLoMNq+L6TzKt7FMK3R0BmCTRdDOwPTfaHuwugNAVarYSAD37qAkMdeNvR8vTNA1BYLTNrNANuj0LBbMjIB6ccvjsU+s9S47i5vWCPRpDuT7YiX+89IrmY3YHe8ZmDJs2EsbIEpUdsZLjcVLXNpnBRhOMpFF0ZiI7JzWgivgwJwbtQOugNJFBeI/keXYpOzH9CqaNXveN9MPKRfEzSiPosWv/lTJLdVBP+kHscpQEvs+GioJroRZDBNOBAhzic5YP97OTjXgUKXPUQOciCzyyN4VZr/YqXL1DU7DbUowXkufkQidKRrF/JNBOVGF+HESKGjWPEiKHBk5ubS1p6OtHilTtk/064knBpEblN2jM/HwryoFUYZjtQaasKoTxNZtXsEljVDHDU5Cy1Wu3h80qhwtakHkPdRyNAZ0uTfxkSFYlmZ4nUjM/7qUQn+SWoEqNmK/jmqDw0sZYNbDxNA7pq3xNVZ0xE1RA1Fyxbhio4TkXmyZu8+y52Va468mdoMkkNzRY3g6oA7P0LxJvD0myIBWHQSph0E7RwYd0iKMqGjr9CbnvI8sNnMVgTgcY2xCrBCUBlCtxTDAszoMuX4C+F+R1VqTSjG4RTNMmH8lSdsuGEl1jMriuKckxBXozEujMxdEU/fYPbt4Z85KlZiUpef0ERgz1QZGMGiiQdgUTEASh18zh6T/sif8unSKzcgMqkn0BVNDko4tMRON5VWewF6DOSoBpFSBL+kBsDWtDOspSyGQb0dSR4r/FJ7CZe/+NAwIV3HDjegtGWjsOdVtI/AxIcj6HIzN7ebdPQGjhhlK5rXWP7ecB7SNCaCcewKUyaxtDgsW2bPn36Esn/ZYfsP7Ja+61I7Uc4DusaQ3kWNKmEsqBKVSvSJEqyi2FJW/lEfHFV2TgWZNhQ7lNDs1xLK/26llYCTkNRkkRZb0KAJHqDJLwjLhILe6A0QS5KQWQgYZPwhURIpmkS81gpuno9EpXljkVX8teSFCIr0IR1JmoYdr23n/+4ip5cVAJ5b0Hr5RDxw9Im0GGZogsvDoYUH5wwDj64DIZkwpfrYJ0LzfO9xm495amZa0FuhYy7YUtprRFFEnm542T4jYRhVi/oskK+m2VdoUkaBFIVQUhQiMyjn6Ir+NlooryNpOD4EaVi0je4fUtxUITjYvRa9/fep79578vlyH9zB0pbPIvKaOehKIoPRUdORtGYx73jm48iILcikXEPcIyriNq73u01hUg+at/+Vgx6RuDxIBzllxCZAZzk+UOsONzpTwqRKtSzJMWFlx2ZU1+zZLB9cAMh8jbwH+ABkkJkOvK5RFCVTdsa2yfMwdeS7EViMGwMI0YMuwWHDDyY6NKZONHwH2+8lVQtnEJWdiPKfN1wgUAIFrWAlkVQnAE56yQsXCCjHNZlKyKSuxaKG0EwpgkGnya2LhYscBRWL0ViohqlaSJIfFhoovOj/bre32tZP01TTXISKPf+rvT2mXjcAjQh9/Z+nvEedzHJ0s2V6Gr4XDS5XIN8EONQyemwOOSOgdylkF0Bs9pBTgk0KYXJB8GAlZBeDHN6w9n7yhy5+hcobATt10D7PHgjHabG5RGxopARhxVZ0L0KuudBeCXkToOqMpjZFw6YDZP7wJo89TBp21Svb09v/K+gKE4/JM7WIbFxsHf81qBUwxQU/RnE1qcQFqNIwFiUVomj6McwFGV6iGSr+THeuE5B0Y5CFPm4Hb3vg0lGQhI9OC5Aps83gMs9ERL3xptRY7DTUbfVn6IwJAZPhaCH9+aN8R47KA5proRIrvfYIiRysh140YHTLXjeUu+PB6z1Uy2vo8/GQ0iogETOe+izeD7JNZBA4ugpJER2jFvL0JAwYsSwWzBixAiilWVUzp1Uq/t1omGqfvqCgUePIIKf6hxoH4GCFMiohMqQWpb7vH4i/piiIZYLjQu1Vk3jsFbntX2eedVSiifVUnomAwmIhBgJkhQjaUhQ4N1WgqowEtU0cZIpmTIU5ajwHhdF5bijkZEzAxkr26I0TWKuW4Um1vNJNtJqgqpPznFVTdNsIjT6CVrnw09t1Wul3QrIbQzFefBtKxjwE+ReIF+CUw4lYbVyz3Bhak/IdyHLBap0Nb80E1pVwj7parIV+khdbCM+KGiptWhKMqGkFaTaEMvW6xiP+oG0RKmNacjDcJp37CJocn/Su+18tm5lXbxj9wwqZ22PUiyJ5mjVKBqyEEUvuqFowhnee/Ca954NRULmRW8MD6A1Xp71jnEvNPFXA6e4MsMeB/zZWt8f8gpqBlcZ1sq9D4WgqXdmvwd4yoUDYjo21/vURA/03v/D1SJ4Y1z4kwUvWHo/77fW7wvyPHrfHiHpx5mJRFgMidSafY2L0GfmKrbed2PYPTFixLBbsMcee3DU0UdT/u0bONHqWttv2dSxxKoraLfPxQBUNIW9csGNQMyvEt5qr6Q3q0yL5dlxdVbNLYKoH/oGNLml+BQy38+C+XEtaFaG8viOmyzRTUQr4qiraqISJooen2gBn6icyUbCIuE7STRNewFFC0Z5205HUYMja7y+1cibcBGaJC9CqYYSYKiribjHYmgxDjr+AqtyVC3TcQn0cOGFo8EqgRO+h48vhnXpanM/+1dY21iNykpz4YPG0DcuL0N6OZSkQuM49EmByT5o8QPsvRQiZfDdADjyW5i0L6xqDTml0LMxFPsU4SlF4mACijZcSbIkdyqKUrRCVSlttuE9n4siFhORd6YC9QY5FUWP/olMpXcjH8oylIZ5xvs7HRlauyAx9C2a1L/2jvct3mu4G6Vl0lxFo0YBPTfwh9wE3BmHnGr4mw+uDki0JgTCzy50icFgG872J0XMz8CTrj6XX7paZ2a0pfLce6ykWdlFkZqx6HOQSGH9gPqFOKjte83UVpl3DC5lfUFjMGwOI0YMuw2PPvIIbkUR6ya8WCv7i6xZTOk3r3HimdeT2mQPUn2aWAsbQ065fBOpYSjPVPOzrHUwp7uMrJnlStFgQedUb40YH8Rc6GrBChdaeWIk7ipKkviy+tAkYCExkkjFbGhSLfb+T0UTYCIyEkXpiSxkoCxBE2wv1IwrQQG6Er4ECZHhSHzEkBCxgWMqofXb0PZXqLRgQUtoXggdq+CjfWGPZRBvBPPaQL9+irJEqiFeCjnFgAvfd4NcF1ZUe5EjC1w/NA/CshAcXA39P4N1lVDUSK/VH4dVLSGaDYEozGmqtMBpqKqjGYpSdPFey0pkLp2PohUHsvUpmWpU4nwjEjvdNXxuQ5GrK7zjOApFOB5GkYwslK6II5/FDSi9cQxqdnYzmryPQBGpT1C06kpXXpc1yH/SqMaAVyGxMSYG3SLwaBBOCEhsrEVRl2YOZMXgQh8c4Us+9hvgDReKHJjnQi9LkaeTgNus5GJ/rvd6v0BRjvbe7T8i7wjIP9R1g2P0D1Sxs+G6QAbD5jBixLDb0KVLF+679x7Kpr1H+Y+fbde+YmVrKRp7N506daH7oNsByA5BXiOdvFsUQVEaNFoH1SGtO9OkEJa3VMqmeT4sayuR4vfSL+k+9cywXHUfbWvJ51Ht6vYYEhMWyehGChIyQSQ+7Br3rfLuD6AJMSFOJqIr4APQJPoQinbk1Xh9a9BkOhKlEf6EJh0XpQSWApe5kPM+5C4Cq1S9PlLCKuktzYLFnSE/D3pNh+gIpUtOd+GHxbCyMbRaAesyIbuVyn+rgXhE0aKzwtA0VWmWlhNUheSWKypyxLcw8QBY1QLSyoAcaBTQay5CYmMgyQXtXkDt6s9DXo6EIXdrmI5SILOQ4ClEkZCTkeH0QVT6ej+KyMxEZbxvICOw3/t/MIp83IvSHunAm0igdCSZlhnmpWUOBs62wFdDiHwPnOnCjxE4NAb/DUEfT2zMRlGaQ+IQjsMoP/SqcZZ/H/g/F+Y7EqU5Nsy0lEK6zkpWu7gobfQNEiKJCNIs5EEBeV9qllAnRO4pyERtMGwNWy1GJk6cyHHHHUfLli2xLIuxY8dudvvx48djWdbvfhLreBgMdcmVV17JhRdeyNqP/0XJd2NwXWer9xFZs5jC128kN8Xmmns/JGKFSPEpRXNoLix1VQ1SnArpJZoUXTThuraqaFqthLVNZNIscrSN368y3PlxwII2toRGmas8fxhFOSySzc4SreAzUGQgBU3IjVBKIBV9ybNQT43Z6Kq9p/f316j3Q4Bk+qcQTYQj0YQ5iGTX1ftcmVavBdZNA/8P0GQxrM5TI7cOS1XFM+Eo6LJQiwNOO0e9VW4AFkahaC2keNVEeZ1gngP+SkVW/BFobcFBTbw29eXQdzIUVMCvHaDjKom7uV3UvyW9Elo0lXi6HEUWEumsicg70hM9d/OtfqcVTboHldoegiZZH4qGlKEOo7O8YzgITcYHI5/II2iCbkOygdoQNOGP9o6t5Y2tEKVlhgJNXBlYrwL2qSFCXCSqznehPKLKmUdD0Nw7i38IXOTCsXGodODvfh3LxGOfQxG3SY5uL7a9CIsFl9SIvDne652B0k4tvdtno3JuC4nTmo3gHO/1DmHrG8QZDLANZd8VFRX07t2bv/zlL5x88slb/Lh58+aRlZX12//NmjXbzNYGw47BsiyeeOIJcnJyeOCBB4gsmkrO4JEEGv+xe8CJVlM6dSxl37xOly5dePudjxg9rR0ATQOwuhO09OvqPmBDdQDcuKIi/hiUZ3jlutXQqEiT6n4WLHM0EVTb0N6CmXFFS1p63TLXkVwoLwVFM2LIhJowZDZCpaItSHZfXYnECKh1+xQ0iRQjn8JH6Co/J3FsvMc+hK7ixyDvQ6Kp1VjkGxgBzC6Abp9Aj/lqe7+8CbRYCe2jUNgD/GthZjfYrwhW9FHEwHLh1aWwJBe6zIVQCsxsAU4EigJaOK9ZFI5tCq/aWrm43zcyuvrC8NNecOZ78MaJUJwNzcr13K3SNeEnuoAuRtGcPZE/ZFt6W7hIqP0HratyKvJZjEApoMe949kPpUveQVGsK5G/I9FE7nQUPXgZGWbvR8LwQW9fHUg2MbvGlUjxJcZdQ4hUebe960BeBEb64M81PCAPAx+5MDiu9/wav6qxQCLo32jNo1ccONKCLy1FZS6ylEpKEPOe51ckRBKej9koguNDze7+n72zDpOrPr/4597RdZfIxp24AAECSQghwZ0iwd1dCi1eoMVKcSjFrWhxiWMh7p6NrbuP3Xt/f5wZdrPZ4P21hTnPM89md2bu3HtnMt9zz3ve87YNQIv5SnZnxzJfHHH8GPzo/6dTp05l6tSp3//AdsjNzSU9Pf1HPy+OOH5pmKbJ3XffzZQpUzj19DPY9tT5JPYcSeLgiXg79cedno8R/Za3g02EyjbSvOEbmldMxw42cd2113LTTTfx2XIfTWHwuSDdC8V58FVQRtWwT0bVpuRoDHwzbO2qGR955ZpKiwH7+OCNFsnwtaYW/tWW/CPJyLhaayudNEY6WtACk0JrbkgG8gr0o5WMrERkZVb0/oFoYV2IZoqciPwiMTSjK/sL0QLZk9YFfikqy4wDvgnD/q/DqC1SMhb0k+m0XzVk+eFvo2FYEYxcB59cJb+JCfzLgm2l4EuEtDAU94B1bhjQBItTIL8eOvugwqd9ccpgj4VQ0wgrhsLw5VCTCpuGiLgkNsFBBVBraIGPIG9GEz9toF0M1dHzsA6VPLYgteUmtCjfgZSAyxGRuwct0AlIUUpFC/2l0e2dGt2395B6UYFKNQ6tIWZnOPr3VJR82hbFqJV6cQT6huGPXhgTlbEsZBRtcGB4BHYz4WizlaQ0RV+jnwOP2HCyAS8aKsldGO2ciSEc3a9iRDpjqbQrERFxI5/L8Hbn61nkJ9mPOOL46fh/C8QbPnw4wWCQwYMHc/PNN7P33nvv8rHBYJBgsDUPor6+/v9jF+P4jWHChAmsW7Oa1157jftuepil794DgOFLxPQlgRXBaqoBwJOcSffjz6PgxnP5Y+/e2EFYsF7byfFAbm+o8sA7IehcBbVeSG6ChmR5RNJrYVtXpa722AqbeqmTYaQHnm4UsWgxoLehKPSUqCpiI/NqyBAxiaWvWmghjEQfk4nKBum0Zo0U0Tqu/kR09fwlWnQuQVf+Mf9EGEWlvxj9ewqtV8zlKMMiCy3UUz6BIZshoQSWdNd+9t8OnQx4dBIcsRg+GQCdJ8HNfhGROgduLAY7HfpugIAb6nIgyYTlSZAUgb4RBZwNMWChBb0LobpWxKO4C0xcBM+fAk1u6N4EXhf0yZBiYSDV4XdIEfkpcBAJewbNijkaEZKzo+f3MaQoDY3+7UNUCrsMeTGKo9vYFxlk5yEScToiNX+N3ncSCkH7O3Ay8gc9gNSoLu2IyDfAdQ6URWAvC27xqXwHIpInAoNs8FpwhAv2blN4jwW+DbLhIQfOM+AhQyFmlxtSdWIIIWNuLVJEYu24q5DvxYs+R22fAyrbJKDyUhxx/Bz828lIp06deOyxxxg9ejTBYJCnnnqK8ePHM2/ePEaOHNnhc+68805uueWWf/euxREHfr+fU045hcFbT2HD8nJeyVvE1+ZywsFGuhe5MQu6k9N9FMndBjD9IBf7pap8kbNKdXuvCekeiPSVr+KvDkxshMVdIGs7FHWSKpJaD8HoPJouxTB/FHgd6OuGoA25LqkBPYByWxN8Q4iAZBnKhDDa3ByknFRFf4/lhsT+1hmRizHI55CEuiAORPsZM3cmo0VtK1p8F6Or6TOi5ycATHH0mMlAz9XQfT6kbIAtGSJbvbZCtgVru0sV+GiQyhpLhuh46oC9bUgtgqYkSA5ATWdwpWnxc9uS+ENAJx9MN6DfWohUgdEI8/aB8V9DaTZsGyZ/SVID9MqGuaaUnkvRgt+maeRHoQQZNotRJPtqRMj+iAjdbdHzeyEieveiTqRY10ysnHE1UqFuB2Yi1aASEZHzou/LB0hputpRiFkNCj7zdeAP+asDZkjn80pfa9DZOlTmOdSGLRZc6oJ+bYjIFqQS9bThUQcuMZSmOgQZVdsaTwMoTTfWLRRr612NykZ+NLhv93bn7CP03p5BHHH8fPzbyUj//v3p37+1+WuvvfZi48aN3H///Tz//PMdPuf666/niiuu+Pb3+vp6Cgp+SiJAHHH8MJRVQo/kXDoNn0LW6ClgwJ6zlYXhdqAmG2pTdVX5TxuGxlQRL/TuBt8kqKRhhXS13+iFbo1gRP2xsbCzhBYZNxtSFC5WgR6TZMrAaRgQtiHbbC3HZBtKSfUjEhFbcxLR4hkjJxZaFJegK9rOtKaxPo2udndHZAO0+CSgRbhz9HFb0BV6zCQ70ZEH406gtA6y34XkzdDkwPZO6hbKaYR8F/xjPxi2FPJ6wPRpUl/KgH0daC4HTzJ036yY924F8FmikmiHueQbqTFhkBfmtcCYFWBWQV2ajrdbBXx4oQLksoJqd3aydV72RV0tPwUWalN9DS3Qh6OFPhbt/jiazzMQlUNmo0TUi5CCNBMRoD5ISSoBDkEmzvcQoTARqQnTWpY5xxGJ2Qe1TBttiEgzSrX9lw25ITjLBSe6NSoA1K31Rwd+Z8NmG25yQ26b5y8DXncgy4GXHJlT/2yorfgGQ6pPDE2IQDlof2Lhb2tQ4moiUnnGsiPmRs/TxcQH38Xxy+A/0tq7++67s2HDhl3e7/P5SE1N3eEWRxz/LoSDYNVCokcdIWE3mBGRBJcVHTLWSyWNF4BALSzOlSqS5obB/XV1Oz0MKfUQ8qq0EPLKvIqjzJGGVMipgKoMwIA+Llhhqb3XdsnPUeZowm9uNAAt6IiMxIhDjHi4EDkJoE6YyujfK1AbakxXtNGV+DFItXDRuuDUEu3OQKpECbryN5BSsh9KEb0X2GRDxpvgK4OUKtjYHdxhJa4OB14ZB3sshnXDoMfeMMQvr8oIoN6BQdsgYihfxcyGj7K0b/uGIderybwuHywwIKcQKhKh5wa18k75GhoLYNlACEbA36DW4eHeqMfiJ77vhWgxfQuVS0xE5m5AxOwK1EZ7NlIhHoiev7NRyWY7IjNnIc/Im6it9Qqk1NyBzs1ZqJwT65bp46ikdCoyk7YlIkXR13rbgl5BuMMNJ3taicgjwJ0OHGVBvQ23tyMic4D3HbBtmO6oLfgBQ2TitnZEpAH5XlzI6xL7XKxFKbHJ6Hntz+9CRNBin5U44vgl8P/mGWmLJUuW0KlTp//ES8cRx05YVx0NEnNDOKrzp9VrAJsB2F5o6qqOjY02DFkNS7vBuHLo1VVZGT2A5yPQpRpqfOANquPDtBTKFfTr1qtQLapuG3p54OsoGQmb8lxssrQvuYaukFscyDFETDrRGgPvQeQoNvRuG62pq7shotKCFo1R6Mrdid4S0UL0OlqM6pAX5FC0uL6COmc2OQo822SAaw6kFULPTVCYB80e6LUZRtmwKAcqsiHFDUd44MlBev330dV5XjVUeaFgqzpEirvD1iQ4qQrqM6AwAhEbAn6Ysh1q1ytOvzoHepdASjN8dgkEmyES0Qk4LQdKDHkZ9vyR73cIkcoPULfQXkhJugiVKJ6Inre+aMFdiLpqzkYL9YNo4U5G58dCybRVaE7LEuQHuRx5TWJlmWschZgVIjNsUruVfB5wgwOlEdjDgpt9rcPsbORNKXVgXATSDTjd3Zo/4kTfs+0ObLVl6j0QeCJqUr3BaO2aAr3nVyD1505avUNrUctxKlJS9m137lZHj+dafnpJLI44OsKPVkYaGxtZsmQJS5YsAaCwsJAlS5awdetWQCWWU0455dvHP/DAA7zzzjts2LCBFStWcNlllzFjxgwuvPDCX+YI4ojjZ2JxeXT+i1uKBkSj2qMzZGq6Qmo0VMsCtiYoon1zAQwZIJ/FMKQe9K2H7Ql6flOikkKTG6AxCTCgcxls7yqVo78LltpaUG1TRtVN0RC0bEMdEmFHC0+I1mRMC5GR2P740RW8G5WKgij7ohiYghahxjbH66J1DoqJlIEClOB6c/Tn144SQROAzZth4BwYtBXqvLAtE3KqYLcAlBvw4QFwzELYXgD3H6TjmYMWtCMcsLZAsxey6iCYAV90hZwwTEyHDSZUBbRTaS4wl0JiFWTWwPKhMHYx1O4G73WHliAMrAdfAoxLFqkK8eNmn6xGBGIO8jo0oaTQ65CqdCUyjZ6BiMjjSPW6HC3SSxExOCT6mFXISzMUtRM/T2u3TAKtIWYXOmqv9SF/Rlsi4iBvyUUO1IfgcBsebENEGlE6qtuBLhEYY8JZbYiIDTyFFKgvbCktAw14w5Dp9OZ2RKQaldCygbtoJSLrkIE5A32eJ7Q7d4VIMbmK+ATeOH55/GhlZMGCBUyY0PoxjXk7Tj31VJ555hlKSkq+JSYAoVCIK6+8kqKiIhITExk6dCifffbZDtuII47/JDZWSD0o8YBtaKBdcqNKJwZQ3VNkpcIGTwhW9oGjl8D6AZCUpnbPgcgvkoGUkYHFsKo/+KKtvut7yzfib4G6dHXNpJpQFdFiapqQYsiM6DMh0YBSW0TENEQ6XKh7JkzrsLwQuiL3osW/EnW/PIe8CV3RItJAtKsFXb1fiRaWt9F8k1eAjaiUcJQjVWAQMKcFjnkTBtRAsEbHntwCvSu0L+tHwZj18I89oWAoJPlknHUB1ztwd732s+t2kawvB0CXIIxLhPscCBiQGtA5u30xvOuF7LAyWPZYodd48STIbYGQA5EgDOwKqwzlfWT8wPe4BXgSeR3GRc/VSqQOJCEl4wtUxrgEKQT3oJJJNVq0cxFxuCH6nPui5/JRtKDfgZJMB7Njt4zLkaH1dEQS2qIJmWPftyE7BCebcKqnNWNkMyrnHGZLNTvDBaPaXEKGkOm0twP/sGG4AXWGjmUKcKWxI3EoR+99d0Q8YwvAeqQWZaOk3Untzl8xIjzX0ZpdE0ccvyR+NBkZP348juPs8v5nnnlmh9+vueYarrnmmh+9Y3HE8f+BSgdc1ZBmwFqvzJVZNVJEACwPhLLl5zBCinQvzoc8C1rStBg5wPsWpNZJAWj2gD8k8uEASU3QlKqfdSlRf4itBTLiaLEwTV35mjYkukQ4qhEZMWi92ei+JHQFXo86ZtaghXk2Mo3+Hl3pJiMVJUZG/omu+JOjj70WXb2nIH/AqY4WmyGoVHHwv2BQDQQ2w5ouIkYFxWrzTU6FGf1g1AoY3gmW9FXrcBIybL5og2cT1CTBgPVQlgZ+P9Qla+DbXAuMiPwNfU3Inw4NoyGcBxEvdF8P9eMgkAlOFfRskJ/n6AyVCjJR7sX3YSEqrXhRqWUuKkGchkjHQ0i9OBnYAy26GWjhfSF67lzI/7IfIn8nIQPyx6irZHX08cnsWJaZizw8N6DPWFtsj57/xRb0DMGVHpjY5ht5bnSbp9qw3NLE3e5tiEhD9L0b5cADNhxuwDxDBGcqcLGxYymlBBGRAdH9id23AZHXfJQtc2C781eFylRX0NppE0ccvzT+IwbWOOL4b8ECNMbeCxSlK5Qsu6J18Q+lKckywYFIg7JBMGDRADjMJ+m+D/BWGHpWQ5VPxtfqNJlfI27wBtTWm1ELm3toUJ5pyEBqR7ttOplQY4DPgURThKPJkWpST3RmDSIjDlpwtqIr/Mzo7ya6kh6IouETaZ1LU4XIyXDUOXMvWoy2oLJBMvCcIwI0EKg3YL8FsNtaqNsMFckqNeWUQY+QygwPHAAnz4E1+4Kxj1qek5AKMNeGJXXgRKBrsY6nqADGJMFol4iK1wRfi9JqT14GM5Oh2xbY0Bv2/wISE6H4aPAEZeQ1myExQ+bezkjlibXUdoR6FOV+F1JD9kRqyNWoRPU0MpJmIN9EVvTngchH8qfo+W5Gasl4RDQOR4rJ7UgdMWn1UMTKMpc68p40oJJNeyLyFQo6WxaGkSF4wLcjEfk78AcHpllSRG5170hEyqPHNcqBv9hwhgGfGFKSDjfg0nZEZDsqNQ2J7k/svo2oRNQZkav25K4+eowX8dND5OKI44fgP2JgjSOO/xbMAiZX6t8VyVImfNG8PY8FkWgjV00jEILmZEgJwvIcLXCPI1JRaMMJ9bAkG1IalLDqsgAb6tOktGTWyC+SZsgfsjGqhDhAHzcsdcBjq7skhDwjvUyVXrzocRHkIUhEV7hlqETQjFI1Y16SWBS8iTwl/0DprG7UaZOHyjDnoO1vQAvPFBRJ37UMCj6GqmrIbIQ1vZWyWlADLhNW94chNbBkMPQZCNd65Tu5G/jUgSVNkLZNx959k/JFzsuGp5OhmyPCluWGloDIgGsGbBwAWZXKIelZDp6jYW4CtFRBjwZ5YY7Jhi8MmXlzdvGeOsgT8iQic+eiFtzDkPKxHvk3mlEc/n5IGbCQovRu9DEJqDRzXvS+S1H55a3oef8zIiW92LEsk+DoHJ+Ayibt9+1ZlP1BGA6y4Vo/ZLbxf1wPrHc0Y6YRERFvm+3EXmuEDXc7cK2hjpnOwBGG8mLaYjMinLsjZcRos51/oPctlx1j4UHK3T1ITYq3G8Tx70ZcGYnjN4uIoy/9rlEyUpuguTGxEo0PMKPG1S3Navd1A6NqocatNM5kJKfbIcixoCQRupRAsx8cRyms2wpEOtJroDYd+rqiZSEDfFEyUuAWGWiyoZsZ7aRBZZqq6L5EkP/DjwyJQXSFuwZtYzy6Yk6gdWheGOVSDKfVGzEWXe3GVIV7UEvxINQ9MSAM3tehKQBdtsDaAr14l1LoZ0BXH8zaHfZYCyv3hDu6a3/ui75WZSNsCst7k1WmxS89F9bnaVFPtSHLhGAY/DYkFkNJQNH5S0bAAZ+DnQfL9pdHpxlIaQJ3Khzok8JTjNSL9qgE/oBaYA9CRszViGSMRETgLqQW/Ql1Qd2GumoORwSjLnruT0Qt0WuRSbULIiKfInLzB1TW+ACFg10LFDs6x1ezMxFpQkTjPhsSg/KC3OZrJSIxchR0YEAEOhtwpWtHIrIYEae+NjzowE0G3GWIUJzSARFZjwyn+9AxEemBFI8j2z0vhObnnBB9TBxx/LsRJyNx/GaxCrXgpjVqMW906/fYF3ayo/8g9UFdofpsSIlA91Q95glkfC205Rep8UGTFzoVaSCeyxEBqcwGb1jkxHbBblFlpNbQYmwCXVwaDBd2oI8J5Y5KNj5DV+EmUi9S0cJvoMWtltaW3z6o8yMRkRITte9OjD53BlIBLqNVEr0feMTRgrQX6sRY8zFkVkCfzbAhC5o8IiJ7R9Ry/MIE+N1seOhwmD1MpOhJ5J0INsGiAGSUQ3kq5JcAHjhkiIhVDiIjJSY0BsBqhs7LwHQpl8UDDCqErCNglgtamiC/SZkag7JVyuqPyFbnNu+lg0jBxYhInItKMmOifytGi/LnaMG+Cfk9PkS+jHKUHZISPZc3oeTax6Lbuhd1z9wRfd1LkIoRK8tc4cCzjspeN6FuqLbYFt3OuxYUBOEaN5zvaSUa21EGyR6OSlsHmHCia8cMkulIdUqx4YUoEbnRUFntPEOdM22xCpGfyewYTlaIiEgvRKaPafc8K/q5OIQdk1rjiOPfiXiZJo7fLOYDPSqkLoRMsC20gjuaIeNz6z9ITbPIRUZIc2Y2JcnstwjNQimJQI8qqPTKcxLyyheCI9OqJyJDZmW2IuCz3BrlHjbV+msYWpT8FoQNkZE3HV2JgxbSWlTv34oW7BCw0rJIXbOG8vXrsSMRtmdk0DB8OOlZWaxFKsEU1Ko6Hy3Cse6TAFIx1kYVET/KEyldCScthJRq2B6CsnzIroURjSITq7tCxAXLM2H+CGj0SGn4CGhugVn1EE6GtDrItETghvSA4xJFIv7kwC2OXj+1Bfx10HkrFOWqlNOlCvzdYfFIMMMiaL0aoCERDk1SWa0/O/ohipAqU4KUhWKkZtwYPVfPI7NuLiIeQZRwOgWVJh6idc7PBEQwa4FpiPx9jAypf0VtwZ3ZsSyT7qgz5TBgbDsSAupsuc2BYguGheEGL+zW5gDmIXPoWTYssOAClwbexeAg43G1o6GJKwy4xoDrDZlqLzR2nKILSmG9Gak9p9BKRDYjr0w/WqcKt4WDzKp7IyUpjjj+vxAnI3H8ZvGVAwdX6d8tbnCF1K0BiiH3BCAcBiuiQWS5IQinQpGh+PEH0ZVwiwVd6mFThvwmZTnyi9jRBSWlAeoyoKSTFjc/kuJtQwTIbWoBTXOgwYSuBpTZMMxQiaECtWImGGpvDc6bx/aHHib85pvYzU1sih5PrGftwQEDCZ97DoNOO40V6enkoSvcYYjMrEdljJWOro7PA24xlCx77XvQGAZjO2zrDkkh6Fuux0VMqSKHvwtXXQNOJ5k9lwCFQSisheGJsLwcKtIVDZ/uhSP7SWHIAF63NZ24UxgKqqCyBfJr4MNJMHolRBKg+1HwlAGRFrUR11uQkAnDTakfXmB/dAX/OmpP7oX8GzPQAjsEmTMfRibSQ9GV/tuozHNF9OdfUPtzPSqzpKESzI1I/TgSDc4zUYeQG+3DkujjVzgiO5eg0Lq2sFFZ6EkHrDDsb8PvfZDThmi8iBS28y1YYMMNbpVnYrCQLynLgcU2NBkiF38wpPpcYUjBaYuFqPR0PCqzxLAFEagBiHid3O55TvT+/vz0VNs44vipiJOROH6TKHc0JXdg1C9SHO2CcUx5RmrTwV0m34QDpIYUtuVLlLTdK7qdDxGJSY/AokTFvddkAFGTZm2GMkuak6C6E+xp6sq80oAEE8KWBu1tQPJ7wGztftke9Y4MQmUEq6aGoksvo+X553BndiJl1JH4uw7Ck9UNw+XGaqolVLaRxg3zaL7qKhbc8SdGPfE4xx55JDNRCWIFWsCXOK3hZzcbELHgwDehpQXSNkNpnhSbrqUwMhqk9freMHghPHkohAbKDLoJtTzX12gC8RyPVJV0BzJtyC+A9GSVM4ag+PtsN3Srgep6TeZtAWoylbSamQ/zeoARhiYL+jdArRumpEsRGI58EzFDaR1SMNaj8sMf0JfaC0gNyUQKgYkW6L2jz3s6ug03Km9NotU8uhjlrhjR5xyGFv5mREB7oCF3z6F9v5kdfR2gst5twCcOZAbhUBPO97UOw3NQOWeJAydasM6B29zKmokhgCLohzrwnK0k3t0NqUB7InWkCzvia6RUncyO5ZetqJQ2OHocp7NzlPuriDC2b+2NI47/D8TJSBy/ScQSSztXquSxOlV+DlAbbjBBIWaWrXbeNBsScluH0c1FC90qB7rVQZIHWnwwZDus6SvviT8IZbmQ3igjpu2CCabCzIoMXe1W2bpS3kTUzOnSlXyhoZkmixF52bZhAx/vP4lQRRVZB11G0uCJGMaOli/Tn4wnqytJg/Yj0lBJ9SeP8uVRR7HnNddQd9ddvGMY+FAJoxgtaPcYWjhHzYH+28BfJdNrY6JSVkc1g9+Aokz4Jg9GF8Ki/eEYtxb0o8PwRQ0kuSAhEQJVYKTAiFUqc+3RF35n6Lzl2rDdUFfHxhIttoPWw6cTYfelsLk3HLenCAUBIAShEHhy4AC3VIQ9UVnmRrSwHonUkGnoir+QVjVkCnAE8AkqU12ASN4diIBsRqpQJ3T+z0EdUu8jdeRL5K/JYseyTI6jMs8EYGIHZZktKMdjiQ09gnC2Gw5zt/o/QogMpDkw1pJK8Ud3a9AZiGTdB4x34B4b9jClqD2PCNV1xs6ttnOizzkLqUAxbEPqyzBUfjqLnYnIe+hz1r5sE0cc/1+IG1jj+E3iGyTPU6Er4i0p8ns4RKPbkSpiOpBkgd8NTSkqEXRFJOEgIBSBThWa0hv2QHqFtmHakFqv7dSny0fiBYa4NTsk7IIUJ0qI3FJqLBswNQjN5cBxhhYlSktZNGEidrNN59MeJHnIpJ2ISHu4U7LJOepGMiaexX1//jPTb7uNw5EZ9gtHRtYliIj03gz7z4FgCLJKYFsuJLZA/0qVHhzgzskw8VN49EIYnqVF8ewIfFSjK/Zbk0UWssshtQnSwxDKgb9lyasxCnBsZaiEKiBUF70SCkNdJoxaCWZfWJAlA2dDRNH6TQYMyFD30Sa0yNeidtMERExuQuTiZaQKmIisjI/+HkKKx8copC4FdSf9ERGRZ1Hp41bUBfMg8tvcSOtsmVdQWSbgqLRzFh0Tkc8R6Vkegd2CcJsXDve0EpGy6OdmuANJEehvyCPSloiUIMVqogO323CoIXI1A5VP/tgBEfkUEZHz2ZmIPIa8JVV0TERmIQJ1ys6HE0cc/2+Ik5E4fnMIObDZgTEG2JVQmCCDJIbKNM1JKtVYIf1MdCAjXaWSzoDtSOo+AnlDkhtgi08EpD5dplSXpTbh/DJoTlDeRooNXd1QGzXIWpbITo6pOSPrgVKPvA9eZBitcRzeP/dcrNpGco+7DXdq7g8+TsMwSB1zBGn7nETZzTfz4VdfschRGWg+kGfAAc2wz5s6ph5bYG1n7XtBuUyRDvD4COi2CWbuA90Gigz0tuClGtjdgT+nwWlhcNdDxA89tinmPb0vTDGkxIxxYKGtksOmIk3w7bcOvt5DM2vIhhHdNHHWH4SgBe4Wxe1P8MkPswIRmPPQwnwYWkCLEdmYjTqH/oQUkgei949ERMNG3pAjUNtuACkqn6JyWw5STSYjhSCAFvgW4FoH3nPU0XIz0L0Df8jfUQR+VQj2jSjIbEQbo+pi5DU61YYtETjKpVvbjpl1SNkZ78AfbJlTvzHkf9kbdc+0T0F9H3mALmPHEst2RER2RyToXHYmIt8gj8n5HdwXRxz/n4iXaeL4zWElSlUd7oBVAWuTNQvFcKA+JVqusUUuXIZmxbiTtTD3RxK+Hy2I/pAmyRb7lYVRnguGJXMqQOdSWDtQYWkDo88LogWo3tIi5nbrqrXKVItona3HWcDS996j4l//IueI3+NO+a680V0jbexxtKz/mn+cdDK+DRsYYRgkGOrO8PxLs3MSKuWbCbohvwJ2D+jYZyfBV4PgsHdh1UUOlQvns3jGTJyvFuJs2kiTHeafCUk0DB1Mj7xRWOMPJilYQGoWGHnqOoqgabL9DXBvgWBAXUW+AHSqlDLSciC0+GX0rI9Azzq1KVsZ8Iyp9+xSlPNRhQiBAbwWfT+SkHKRjbpeOiPl4yPU9ZRLaxpqAirBXIOUglOBdxBpug4t9m3LMp0dkZRRyBBqtFu1G1Aa62eOOq4OBS72yXAcwxuIHF1uw0wLLnepa6ot5iNSNMaGWx241YC/GWrV3seQSbb9F/brKAX4KkRWYihCM3P2ROWoi9iZbCxHROxa4lelcfznEScjcfzmsAApDwXNUBSCbSmKbTcdmU9joWdm1C/SOQmaTdX4U9DVdVfUmZFXD0ET6hKh30Yoz5Rx1XJBKBqYFnKD45IfocrRlXaWqat/A/jU1Ot0DcLABNhsKSL9T0DTA3/F13UAif07ivj6YTBMFxkTzqTsld8zefZsCiaMJxUomQ9j1kJNCIwqKOmszJWh1ZBmKrL8lckw9VObpwe8wKwJ99O8dAmGLxFfXi/c6Z0p83qwA82YH85gY/k/4O4LaR55CENuuJ5HXXsyBTgOWG9BogUrSsBrqzNnewGMWQmp2dDQS909KSEot6FXE5QmQigJxhsy276FfB0HorLCw9H3Yh+kZCxGc2VOQ+WVuxEpCaNQs7GI/N2K/BXPoXLHXUgFinUjte2WKYoSkbNQ4Ft7bEZlosU2dAvBKS441r0jYfkT8LkDF9jwlQ23uHfOIfkYEa4CG+535OX5g6GI9jGGjrv9y7+ESM61yEwcQxFSSvZCxuiLO3juhuhzryO+CMTx34H45zCO3xxWONDNAKcC1iRBg19/N21oSJXXw7DVIeE2IDdZtfdOqERRjabazrNgahlU+KSs9C6E7XmQ0Kwv/7BbA/KCPgWYjfJAsa25L0MMWB1ROSPFBR4HqgwoMGC6DXUuGFVaSsOM6WQddPnPPmZftyG40/JZ/sjDTJwwnndK4bxP5Ftp2QIb8hTM1r0C8kz5CDb2BXfhZm6dfQarn55JYp8x5B5zM/6eIzBM106vYQebaVo9m6JF71N49F5ELruM0O2305iQCA40b4RmFyRY4A5rKu+mPnDSbvCQG4hApQVJdbDGgcYMuM0l9aM38n5MRJkbM5DCcRXqKHkcqUk3odyOZ6PPKUZtvJlokT4LGTlji//faY10b0ZqQg/gOkf+kpXIUJvSARGZg/wj2yJKrb3SC3u0OS2R6Ot5HDjYUunodveOiomDSEXIAcuBlx24z4DLDClp4w0RrbYv76B24/eRh6ZtHkgxIiJ7o2Tey9hZ9dgeff510XMWRxz/DYiTkTh+Uyhx5FcYaUCkAtYnQ1N0JrrbUbiZY+jL3wckuvUzjEoOhcj0mYYmznZthOJEmV8T6qWquG0RkmACVLog6IdsCwYkwBshqSSJBlRZGv3uN6HFAbdLr/O2Cf0cWL9gAQD+bkN+9nEbhoG/x3C2zZ7DeyG44g1lnDgVUBSNHc2uVpbKEgO8Xngzax5zb51CMNFL7nG3kdBzxHe+hulLJGX4VJKHTqZ+/tt8+LeHSZ37OT3f/5BqdxYNm8GbLn+K24CB62HxFMjsJaLQLwQrbOjeAH4PdEuVXyILqR3/QGWWBlR+OBGVVm5HC3YvpJYkRN+7nOjzDERgHkCqyHhkdq1GRMPPjmWZbo4MpH1QjHz7soyNOomed2RgHmspP6SgzapfFd2/8Q4EI3q/z3dpQGIMEUQcujvwhQ3rDLjTkE9kBHCwoVTWtnCQD2QmIl5tPxkl0ePfF5VgLmdnIlKBCNdVqEU9jjj+WxAvFcbxm0KspXcYsG07VCSC5YlO4zXBHYl2wzgqlSR6tXiayI+wCqV1rnPAF4LmsJQPTxi2FqiF14yotbclCcqzIeKBLgb0cMFGS1kiq4AsC5INSDc1oTfogqcdyLNkLl28ciWmPwlX6q5Gwv04eHN7EKqs4PqPHDyV4A5CSaPIWFIjdK5RRL0PeLzfMmbfOhkrtzOdTn3we4lIWximi7Q9jibvpD/TtHodN085kHXrGqlL0vkpyYdDp0NpfxhZAM+ZELDgiwgkNUCGDcEMGOaWWbUrGmy3HJG1S5F34zmk4NyA3rM/IcJYhEjIIcifczYiI++jcsxt0W1ego61bbeMz9GQu4OBY4yO/SG/Bx53dCwH2fCXdkRkJUo+PdmBsgiMNeF0945EpAWVkYY48K4NZQZcbcAFhrwpv9sFEXkQGXVvYWci8hAiWkuRItJeu6pDhOwSWpN444jjvwVxMhLHbwqLkV8kF1hQCo0JWnAsl3wjrghgiIykJIi4RKLP9aNa+0i04GTXy2TZmAiZ1VDcSZ0ojilykl0LxflahMZGO2YWOWrr3QOwbZlj+0QTWCs8cIqjwWkpBmwJBDC8foz2K+JPhOHR9D7/ggi2o5TVLZkiTl0qFE8fAdbmBfj4meNx0rPJOeZmTP9Pu4b25fch59hbKV+5ks13XAteCLnkS+lWAcXjYWC62oRDIchwYECdAuG6pMFlpoyly5AScADyd7hoXYzPQSrHdKSghJBiUIDe68lIZXgNpdk+iLpKxqOyTKxb5jpHibzPofLFkA5OeSEygn5oQ34QTjMV7Z7c5rHvRbd/jQ1fR+A0FxzYjhXUIOK0vwOP2Hr+7wyVk/YATjekbrSFjQYafo3IVNuZMTEiMhF1xlzBzpJ3U/T556KJzXHE8d+GeJkmjt8Mgo4MpP1MCEdgUzO0JEjGt9zKwXBFomUaB1ITRSBaHBGYUgPqHYVdPWrDnmXKF2nxw+BlsLYfJDYpbyS1DoqylOSa6MAIj65Ka23oHc0zCVsqzZQbuh1iQ5mpss12oDg5GSfYjOPY35sr8kNgB5owDBcu002oAramgcuG7BqwIyJNbhOeqLyVqvKN5J32AKYv8We9pjevN+n7nsrW9x4ha/9jiew/nqM+heZesHIQzDREyvLDUBNQxkjfDNjHB+8aSg0dhc75Gah7ZBtSMapQ2WUIKrOcgILQHLTwvo9KGn2iPz2IqLjZsSzT0xFJyURlG9cu/CH3AlstlZMu9cA+7b4970fdO9dY8KkN17rlTWqLmLn0CAdusmFvU+Wl25DB9jxD4W1tYSEStgqZaXu1ua8UEZH9EVG5mp2/1EPR8zENGWLjiOO/EXFlJI7fDJYh8+BwYMk2dWJgau5MyKerdrelxczlgMts/WJ3I/MqSCGxI9CpAZp86prpsznazhu9pdeL1FgubWu2F4bYEHGgvwmpjlSULS79LT0MvVzwtQ1LTJkyxw4dih1sIVJT8oscf6h8I9lpXSkMGVSFwHFrkF9ynTwNXmDl8BoWvP8AKXsejTen+y/yuikjD8bbqR+bnrsVF9BvO8wbB5sSpcR0i0gNGV4tBaAhHRa61D0zAJiKDJm3IBPxFagl9SU0s6cYlU4GoyyYI1D8+Seo++lW1E1zJlJV2pZlUqNlmb2BU4ydiYiFCNHNDlSEYXQY7vbtSEQspDgsdWCapffw9g6IyGpEig5z4FobjjKV+fI4IiJXdEBEIoiorAHuZEciUo7KV5NRq/JViHC1f/690XPSjzji+O9FnIzE8ZvBArTYDQU+3wRBj/4DuKO5IhF3lIy0WZQ8hjwjCehKvDOae+ILQ0IYmr1R/hGW38SMmlcTQpBZJcWlxQeXG8ojsQ3oakLQhrAj4+tkW6+x0YTPo56BXsCg0aMxXC5aChf97GN3bIuWTYsY2nsKKSVQlyzPSG6ZlBgHyMiEVzY8S8QOkzKyvWPhp8MwTFJHH0bd4pnkz1zNmn7QPFzlriJbnTWdw5AWhEASbI6WzvohpeKfqNX2UlSeuB1d7QdR7svlKGfkXRQPfzbKGpmNTK9XIDLStixzPSIPj0SfP6YDNaQekZynHM0fmmTDPT7o1eZbsw6Fr3V1YKilJN2b3UqMbYuvgDdRaeZqGy43pfC8h0LJrjV2Vi1iJafNSBlpe3959BgPjB7n1YhMtkXMYzIeeaTiiOO/GXEyEsdvAo4DGx2pGpEG2FomRSMhoq4Xd0SR7aYNGFIzQO29EURGytGX+gwgp16LWrMPElpgXV8ZWl0RkRHTA4UFWhD2CMFmA1ZagCmyM8OCZAe6uWCWI7/IBAM6W9HpvED/jAzyjziCxqUf4jj2zzr+lg3zsJtrOabXuWxI1fFmVUcTZZGP4IkDoGz66/h7j8GV9MtaHBP77Y3pS6Juxlv03RvsBKV/drG1kGdVyYdTlQl7u1SOqEItul5ESpahbpF+KK32YuStCEf//RTKfpmISiZVtEa6b0LbPAgpE084er2bgdwOiMgmZPT82Ia8oDwdf/TuSDLWIZPs8Q40RzRt93LXzkPz3kOBZoNtlWb+ZCjcbDkiSTcY8jC1RSB6zCWIiLQdiBcjIlORl+YaZMRtCwcpLsOQ6hJHHP/tiJOROH4TKEID33oZ8EWhumAMA3KboDJBnRFBv9pyMVqDzzyGyihJyHg4FF399itTFkhLInTdDqWdWv0mLgu+HANbu4E/At3dunpdZan0MwtIjrZ7VrpktOwWic5vQapMPZpzYlxxBeGKrTQu/vAnH7sdDlAz4+/07LoX7uwRuAB/M3RrVIkhDZg1DHIKLdbWLsbXpX2x4OfDcHvw5vehqWQha/trsR/sQIsF2y2obxI57JQEfzJlSt2CFtNjifo1EJn0o4U6B/koJqNyzduolHY7GpJ3PFKt2pZlshwRkIHIn+HpgIjMAq4EllnQNwiXuuFM747zYz5Dw+5+78D8CEw14Xftot1jeSC1DiTZ8IADDxvwkKH28GGGiEhau9dvRopMFeq4yW9zXwUiIgdH9+EaOs4KeRGVtPbv4L444vhvRJyMxPGbwAKkbgy1YdEWDaVzOTCsFEIGeCIQji040W4a0FWuy1E7bwCpIVYE+tVDtU+qSt91UJuq54U9UJkJtWkQ9kFaBLwuzWb5EpVl9kGTe6uAfVzQzdKVbaMjn0EzWlhfABrG7kXCuedSM+cZQuWbfvRxO45D9aeP4zTWcv7Yfyj2PgzdS0V6BgMlifDVYOhdVEgk2Iw3t9f3bvenwJPTg60lS5nm1XkkrIXaXaP7R2TCZLdCv75Gi+5cdPXfB6khx6NSjIFKLOeh0suV6Jy9iUowg9i5LLPWaR0mt28HJMRCasIdDpSHYXgI7vDC/u0coY8gteIWG96LwIUu2Lddx0wYGZZzHdhuwz8deNJQOSYD2M3QPrW3BzdG97URZZ20bequjG7zEBTadg36TLfH29Hzc3gH98URx38r4mQkjt8EFjsiE4llUNIk5SMlCGnbdAXrjkDQK/IRyxmBVjISK9XMMqJtu2Fo8gKmvCEYUkVsFxR3hpRabS/LghMMpb6WGNDXhK0ONFhqRd3bpfjzJAO22FBoqg11mwMDHS2qvr/cg6vvAMpe+wPBojU/+JgdK0z1J4/QtPxTLtrvMXyd+2GGNATPYyppdAXwwQFwzFx44OBGgJ/cyvt9MP3JuAIN/BN1Ns2zNcdnSJ1KU9vSYLULjkaL8FtIDemOFuKbkJemBqklS9Ci3AuZO31ogY7NlomVZY5wFFD2OVJFunZAROqQGvGMI7K2rwV/8UO/NiTDRr6VL4DLLHjfgj+4YUC7b9EmRFb2cJSmO9+QInK2oeTeQYbMpu09HnVIvQmjZNfMNvdVotLTYajEcw07ExmQWlKGAtfiiON/CXEyEsevHgFHZkePAV8XQsBWRHePeqgNyyfiscCOBVN1oIxUoMVhFZDRoN8DXpV36lI19M1lgS+o+TZF+WCbSnpNMjUHxGNDsalZLRmWfCG9XPIANLrgehPeNOVv6YFyNTYakBNJJuu1zzD7D6T0pWupmf0MVqBxl8frOA6B7asoe/YKmpd9ymlTnqT/oNMwLEhtgZygFsIKYGtvyGiCj8ZDVV5UArAju9z2z4JtYbjcPIqC4UIGjK3T+W3KANMNfzEU2rUOqQPJqNPlbLTP04n6NIAnoo/7M2rrPSj6Mm3LMvmOulHyUBCYvwMisiF63ydRf8gRwC0+yGrz2EY0cTfVgYkRTSC+3Q357bZXiYjRoQ48ZUONIY/IKVFj8nBDWSXtA8mqaR1Ydzc7lm6qEBE5HPlPrkFlw/b4CvlQOppjE0cc/+2I54zE8avHUjSYrHMQlhcr18JvQc9NUJYmMuKOgGnyrcLxrYEVqShlaAGpdmBwKWwy5BfJqIKiruANqdST3AD+MJTma0HI8qp0kOSIZKSY8i14bS08G13wpQsMU1fe3dBC0wh8ZGhwnFkHvsRMPJ/OIfXWu6l+5DYaFr5LYv+98XUZiCerAMPlwWquJVS6keZN3xAu2UCnvKH8+ez5NJnDCZrqFOpcpsTXZsD2wBd7QadF8K9pcFGkKxcD4ZoSfF0Gtj+NPxvhmhKM7t1EtoJSlpKrFOk+MANOcClH5AVELN5HZZOuqIRyPVJyXkM+iueRRyQW6d52tsz1aPDec6iU02sXq/MMZIrdbEGfEJzhgSntvB+b0WTfUx0otaRc3ODe0UMC8rg8AZwYzRDpbsBJhp47DtjLUImpPSqRKpOI1Jy2RKMalZaOQLNyrqHjGPelyOtyNXEiEsf/JuJkJI5fPRaiL+ikrVAZFqnIbAFvCVQN0p1eB0yXSjRtDaw+U2FoJlISIpbG22/0QDgB8jZCZXbUvApkV0JaA9Ska9bNIrcMlqNDipDvZsLHjrbTCFxsqMU3D0XCD49GxYeiRli7DnKKYNEwSK3z4LnsRpovOgvvo09R/9GrNH02Gyyr9WDTM3DvNY6J5z/IQa4DcZWatJgiSl22wxBTBGsV8PV4GDQPHjsLUlLgXE86V/boTah0Awye+Iu/D5GKjSROOpIxFtSZYASg0YLuyVDnhY9M2A2VOXqiNNKuqORyDuqceQ8t3rehrplp0W23DTEb4EiJ2oBKO0kdrM4RlB/ytqN9GBKGq7wwuJ1kMQcRmz868EkE9jPhUHPnmPjliEid4MBVNow3NT/nwuh+TzZgUgfnpBR1/KRHj6mtB6QaGXePRD6Qq1EJqj3WRu+/nvgXehz/u4iXaeL4VSPW0lvnwKZCCNvgM6D7VsCCyqhD0GOD4VbeR1tlxINk8q6onOIJQVpYqatBD/QshMYUlWQibkivg85FgCGzai8Drnc0AC4RyDDgKQNKbDBcMM4RiUkAOjnqsKk1dPXtKoFOW2F1f5WB3BY0pUBSSj7hm2/EvXg5iVUNsGwlLFoCW7ZCZRVpz79DwYipNHhNKgypPqmN0D9qlC0DajvBBh+UjYEtvcHngf1bwB43juZN8392K3F7hCu3EarcTu64cSSFYK0b+ldEladMCLhU0voIBYhlohySZ4BT0EL9R2RsfSj6mH3RufsAeBWVObo6ilp3R3/viIjUoXk2L0b9IWMjmi/Tnog8jcjMXTb8KwLHu+Aw185EZA4iSYc7cIkNx5nKP7kemZWP3gURKUKKSDbqAOqIiByFvDNXsXPXDUiNeYGOPShxxPG/hDgZieNXjS2oru+tg621IiepQN4G3V+VI+JhAK7oYuTQqox4DagylQPRBKQ36so86AVvGFLr9QTHpYF5jSmwsbee63ZkWF0fK/OY8Ea0WybZgmyXVBtQt8goBxahhSi5HobNgG1ddX9iEwR8IjympWRXwwE7MQFz0CAYNgy6FmA6Bg5SWRoMMKKtymMrtECXABkmvDQeemyFeYeA6YXaICw0wHvquVg1JQQ2/fygtbZoWPIB/uwcQoccxhYLUm3o1QDbPbA9EQa55OvpAxyKSg7PIo/IR8hv8ShKIv0jalvdYbYMUBb1hxwJHGHsTBpAHTmXA9MdeWemOHCHD3LafBM6qBzyASq3vGrBVS7Yvd23pYOIwgpgrA2X23CVqZLSfcjrcqohhac9tiBFpDNKiG3bnluDiMjRqMR3JVJO2qMMlYWuomMPSRxx/C8hTkbi+FVjIfoiT9gM9RGZWBNt8Dep5l+bBj4Lwm6ZG20AQ0QCVKZpMFSiCSHPRTkQ8IO/ReWYxGaliLosePNw8AdEbjyGSkLXOVDogiYTjnSgsy0TbWPUu5HuQC8H1pqaSWOGYPc5UNUNahMhvVrEI5CofJCG1DaThjtYcG3AaIaGFJGk/ps1vK8ILZRPjYHBq2DzmVCYBMEwFIakCl242x4MHzWahs+fx7F+GSNruGo7Tcs+IfX888hw+RjkAXeTiEW3bAiZ8lZMRUrUAuTjOAT5QmrQgr07mk8Tmy0T65Y5yoEPHGWT3IC6VTrCdKRELLOhVwBOdcGlXillMbQgg7HjwIkWfGDBLW7o2e6b0kZlnjCQbysu/s+mvBuvoRLN+dHW6fbYiAhVD6S8tFU0atEcmWNQqelyduyqiaEapateRseKSRxx/K8hTkbi+FVjiQMtEajdChFbhCMhOurFa2pR91qKbPfFlBEjmsSKygduW3X5kAVJ1VInWqIkobSTSjeesDpp0hohkBKdbYNKBjNMhV6NMlWqKXa0/RYXHBnU681yNEYeG4Yslcn0w0GQ0qCE10Ciyj6GLYLhNjo2KjqOIuctl4YA5kUVkWpkjt2cChu7wLrR8E1PyLLBbpbqclU13JVncN1jj9FSsZm6r1772effsS2qP3oQT0FX9r36OhIisMCGtHqImCKDN7mUqjoXdcdcg0ocp6AZNP9Ake6xULi23TI9HKkIdUhpaB/DDvKHPIweVxyBgUG43gtHeHZUT7YjcjPZge6WFK1b3Tt21YBI6b2IQNbY8LADjxsqFX2DykuXGTvOkYlhLZqF0w8Rp7azZOpQS++xyH9yGSrhtEcjUl4uYMcckjji+F9GnIzE8atFU9SPUVIK1S1avP0OpJVrMTcNwAXeiNI//W4t5m1be7clKLhslQPuECSGo0P1vDKEluVGyzw2JDWKWNQnixAEDL3+6DD4HHXgbHIAR96NgW6Y64JmA7YakGnAuK8hvRleGKmsEn9AQWoBn0yyTSngie6nywQrZraN3ozoP4NuCCVDQZ1+9wCbbXjzABhaAosO0z60NImIDaqFGzqpTfbK4aPIP/dG6r58mcaVM3/y+Xdsi6oPHyRYspbJzz5DhS+ROgOSWsDTAp0zIMeAfaJzWi5D2SsnI9Pv/UgViUW6x8oyzYis1DrKDdkfODH2frZDDVJDXnXACcGYiAbdjWjnD5mH2oWvdWBbRKW8a1w7twI3III5wYGvbHjHgecMuMNQ+W4AcI2h8kt7rEDekMHsbDaNEZHj0CyeS+mYaASQcnI6O0bExxHH/zri5us4frVYAvQxYPlmxY57DCWvJjdIFQmliUi4LWhOALf72zX9WzKyPhG2u7Xgd27SQhjyQsgtE+zqgWDWqi3YZSt9tcUDKTaMc0mZWYo2mhtdsDIsLXZVJqxxQdBQ6+/YjeBaCa8cpDCwlEbtV2OyDLI4Mr1aptSccIyI2GoNdmIeEaA+HXK3Q70hP0G9DUsHQUEFJJwDaSYkNMESlwjW+HQ4yIBNEcjeBi0X/5H6yq1U/fM+rPoKUvc4GsNsn46xa1jNdVR9/BCBDfOY/PzzsNc+bAirS6Y6DD1CMDoDUl3wuiGlYSoKC7sEdZmcjGLbYcdumYHATEdtuVejtu2OsBaRq2UOFIRUOrnIJ5NwW7yIvBf3OfBSRN0ykzo41NhMmJMdeMxWiN0/gHMMEYMcQ/6NjjpeFiMiNQoRjba70JaIvIpySNrPqgGVhGIlnN4dH3IccfzPIk5G4vjVYhGQ2gRWOVgOJDhghiGtSVHozZ216HssmU890cAxJ6qMVCfAl2mQHoKGROhZpgWh2asyiCeocovtguZk2FoAOPKbjLJhkBvmOtDshswg5JsiD1a0rdfrgmpT8eBZtZA1F77oC3Up0HODPCm16SoJGXY0rj66SEYclTl84VYzqxNd4WyXOmgMQ4pI2BFBWj4c7u4Ci7OhPAgbLfklUtzwvAe6hCGxEnw10JRiwhNP4SvoRO39d9GyYR7pE87A12UQRkfO0CicSJimNXOonf0Mbjec9uZbLD38MPoF9Vo59SJtKcmwwqv5PhFUchgO/A2VlO5CeRoOShxdgsoyfkclFx/yW7TP+ojhUzQ4b5MNPUNwtAuOc+88O+bm6LbvseEfFpzngqEd6MUxMnSmoxh4jwF/M+AkYATqkrqCjufEfIP8HXuhKPq2u1zPjkTkAmTObQ+b1im9HflQ4ojjfx1xMhLHrxKOA5sdSNkKTSHVIw1LCkJGC3jdUJ/dSkZCiSqlYOhWnwRvdINJ9fC5V4bTpEqoMKE5UV00JXmQ3BiNkvfD6GXw8YHyFKSbioA3HC0kCY7UCMeCRbZk/E0m+GyVc474DEiFb3aD1Do9zxNtIbZcIhgY2n/Q/f4wGCFwElu7Zkw72mbskSISdiAhBNMPhrNqIflEuDkCdS16fI4H1voh34JAHXTZCFu7QFkOZDaYJF90By0jDqH62rMoe/FavLk9Sei3F978Pngyu2K4PNihZsLlhQSL19K8eg5Wcy09ph5J7yceYW2XfPo6MN+Gni6pUg0m5GTJ6HmVoVCya5BnoxKVQZJpDTHrjsoapY5IyiEoQKwjRFBQ2kdATQQGhOEiL+zRTukIAaehUsglFrxgw+/dHUfFLwbeQUTkKltq20WGFIrxqLx2MR1/mX4ePYYJwJnsSEQa2JGInE/HpRcnuo3RwJiODzuOOP7nEScjcfwqUYi6FVZvVbaIx1G3S5JbbbIp6UpfdaJlGisBtZo4Uia2uyxarj6Hwso1uP75HnZSBk1hsPzQlADdt0BRgZJXfQFwBy0WPnoOgXvXYL3zHpszMhiKyjFNNoQceRrmuvQ6loHCyBwYuhrMFnjoQIWmNSXJx9KULJJjOGC5ZVz1xDp+LBlyPRbfkhDLbPW8YOi4Ql6o7AppLhhwAlxuQVELNLuihl0/pDhQG4Kh66CwACqyodc2qO4GzjbomzOW7DuXs239dJZ9+Sj1C97H+bx2p3Puz+1On0OnMfiCC2gZMgAzVSUofxAyXDCgCTZ7oSkIbybB7S7FuL+PQtgeQtke3dm5LPO1I0JwCdBpF0Qkpqh840BiBIZbcI1PQXNtUYYUjSMdSLdhhg23uSG1g+1ORwrbCQ5cYMP+pibm/g75WjobiqrvaJdmRI9hSvQ42hORPyOfyqsoN6Vrx4fFc9FzMn4X98cRx68BcTISxw+Gbdts2LCBbdu2Yds2WVlZ7Lbbbvh8vv/0ru2EBUBGJUQao4QjrDJMl7AMoIl+KErXQu+OQCRJpZktXSBsWNRcOI1Nn79Css8kcPgEct+YieFk0OKVMtJ9C6ztr5AzV8hizV+mUTr3FZJ8Ji0HTIDPZnJtZgbzLSh3IGhqau+3RALobYCzRcP6Xt5TSkh6LdSnql24tHNrmBpRw2oYRdn7atWJ4ooSKAOVIEzUxmsYEA5BVRbMnwCju8KnyfBuCOpNSAlAdgYMtGBdtLtle77KQgd+Dl/uC55mdRKl14Ibk/BBB+C+6gB6lTmM/udm1mRtYsL0ENP3TaJb1m50z8pi025Q5oI+PuWW7O4AIXASwFMGK9Ohrx8GOXCkqdbdj9E8lgjyQnyE1IhrgURHBKUJdaF4d0FE1iDfyVIHuoVU8rnUByntHr8YeTJudGBF9H242S2S1xYOMpLWAfs7cL4Np5sadHcmIiIDDJGJjnbpI3RshyLi0hbticiZqNOpI7yOSlKH7OL+OOL4tSDeTRPHd8K2bT777DOOPOooUtPS6d+/P5MmTWLy5MmMGjWK5JQUxu61N8888wwtLS3/6d39FsscKNoCobA8CoYDCR6VaFKSAANKUkVAXDYEkmBZGiTVWzRGicjLR/mZfYqfhI0rqT1sAnWNNQR8yiQZvlTm0qBpsfweEZFXjvIz9xQ/yetWsnXSBNZX11DtaA5NOGowrTUgwVbaprsSMjeKANVkKLk14NfiFkqChiR15URMIPr8boC3OlqOiaofoOMznNb/0JGoP2X2RNgjAEvHwhURqQf5NUo9HWLD3CZwBSESDW0bsxy+GQeeBMirgtwSRcnXZEBFnhbpAz81WHdwTyawP3mDpmJN2pekrCzqEsGXJAPnMg/saaiTqNqlpNOFIXk+TkmDkS64K3o8PVAM/CxEKppQWSbsiIB0R7H5uyIiH6EckiU29ArC4aZad9sTkdeRsvJXB2ZFoIcBl7h2JiIWKot4gN42XGHDtababK9Aaap7GCIZHe3Sv1Aq6jF8NxF5DXXF9Oj4sPgI5Y4cv4v744jj14Q4GYljl1i7di177b0PBxxwAB99sRjPyCPJPe42Op/7FF3Oe5r8k+8hdfyZLCsLcvrpp9OzV28++OCD//Ru0+AoOGxLkTwaLkvGwhyPzKiJ0dztgFsLbW0SbOgMvWsttv1+GrUfi4gcu5uHYfkuZp3sw7VlJcuumUBTsIaEgBZvV8hi/R3TKJ8jItL28fa6lZw7aQLB2ppvI+ZrHZUtLEtBV2NmQX0GbOmmNuGIW6qIPyjPSsSltl6vDZ6o56GhApKbomQDqT6xtNhYBxCOttWcCWWdoPZweDkgP0aPGkjupATUGc2QXAbeoIhNYggS+kCeH9z18smk1ml7NemQ5oFBqyC3TKWs4Ztg5r7QfyMU54O7DzREoLtP/phMYHUI5nvBKIdOfhjghn8ZsMFUaSILGITKMs+ibJFjkMLxZ6Qa7L8LEhJGhtfHgG0W9A/ChW6Y5pFBOQYH+VD+ATxiw7MRONQFx3YQ7R5APo7BqLx3qwN/MRV6dyeahzPZaJ0Q3B6vI5LxO3YeiteIjul3SHWZRsdZJNCaudLeZxJHHL9WxMlIHB3itddeY+iw4Sxdv5Xc428n97S/kTb2OBJ6jsCTno87LRdflwGkjDyEnGNvofPZj9OQ2JmDDz6YSy+9FNu2/2P7vhgoKIKmsIydLgsSvOoa6ZWoBchBw+jqUuGbYdC3yeKby6dROruVWMQwLN/FrGk+jOKVFJ0xgby1NWwssKi8aho1n+3i8Sf7CK5bScOBE3BX1+Axoc5QEFpvC/JKwQnDyn6QWSkCUpMOGbWKmm+MkhHH1A0DzIjaiVu8WqC8Yak0Mc/It6TElmqzvB80FcCdHnWtdKmFvbOh0ICKFijYoNk6Ab/C385rgooc2GZCtzLIqAB/CAIJYLqhyQX7zYB5e8LgNbA5S/vqNjXxdmMmdI2Ax6t228SIVALDgUk1sMUPSckiEX821TUyCykRjyMj50AHXnHUDXMTUi86QjXKD3nLgXAYdgvBLT4Y167wHEbqwxYU7f6YBRe7lG3SHnWIcBzkKKX1SQeeMmVCfRZN3j3a0M/2cJAa8g4iVO3LKk2IiJyAiMiJqOTTERaikt6FxIlIHL8dxMlIHDvhn//8JyeccALe3nuSc+pfSegx/DvbOQE8mV3IPuZmMiady4N/+xsXXXQRjuN853P+XVgIVG0BJ6I5Lh4Dcr3yicQyKeoSoTgTSvJhny8tll42jU0f7kwsYhiW72LmNB/+zSspPWUCH756EtXTv/vxs072kbJuJeGDJhCqqwEHBthQVw/pNfDxXlqocyqhLk2JqWkNYHlFRJoTpYpYbnV/9NqiyHZcIhy+gLpmDDvqGXFQa7Elr4jLgHndolkjzXBgCrzmBW8TDF4OfTbA/JEywe5fD8t3U3ZGUos6V7KqpJpE3FDdA/otgPR6eWX2/Qq+Gao25pICyOsKE0IqP833KpPjhRCM8cLgJsWslyZDrltzXtyorfVf6DkjgDGOQsGSUF5H+zyQGFYjP8lcB7JDaqO+2w+9232bVSHPxmAHDrPgFQtuckO/Dr71ShBZOMWBN2z5WF4wpKbMQl0spxjaz/ZwkK/lExRXP7nd/U3IE3M8Uk6OR4P0dnVsH6AY+B+e6hJHHP/7iJOROHbApk2bOOXU00jovw+Zh1yB6ekoOaFjGIZB6qhDyZx8IY8++iivvPLKv3FPO4bjwPZaKKoBIyJVJNkFqW4Y3A+cKpU4njhA5tAxS+HLOfdT8ubLPH6wr0NiEUOMYFC6ksrZr+2SiLR9/PSTfASXLcW4+DwSgPJ6SKqD1dlQnQop9VIeynOgS7HySgIeKQ6mKdUgYkKXBkisg5QgYEgRSalX26/pRFt7HZEUl625NCt6Sk1xDCgw4NUElV3GzlO5aPbe0KNYqkZjV/jaVhBbfglkVap9OeKB8i5gB/W8zd0hrQ42dYGcWkiIQH4C5OVBKAAur0owpq3hcQ0GeMphlQkjEqDRhj1MlWUuBQ5DZZm5jobCHQccanQ85C6WOXIHsNSGnkGVTP7o3TkGfgVwOHC+A6nRdurb3Aqea491KLvkfAfus2GdISJyoyFFZSCaM9MRgXBQmWg2cA5q4W2LmCJyPJo1cxStQW7tUYjm68Qn8MbxW8SPJiNz5szh0EMPpXPnzhiGwdtvv/29z5k1axYjR47E5/PRp08fnnnmmZ+wq3H8u+E4DqefcSaOP5XMKRdjGD+Nq6YMn0LywH05/4ILqaio+IX3ckdUVVXx1Vdfffv7BiBrG9RFoH77GhrL15OfAAk+6NcT6hrgrwdDVj0UbAXbgsjBU3FnpPPQQpvqlu9Wc4blu5h/pp/PT0v4TiICELEd/vJVGMc06Xbs7zAD0Hm9oue350DX7coPqcqGhDCktECzX/6NpiQgmhlSEICM7TLZJtkiKCGfOl1i5twYETEckZmQT+3DIQ/kNcLGVMiqhiPeg+W9oTQPxs2HzumQkA0bHSgyoFcIaiwREoDSfCjrCuM+gwQLvhgLe8yD4i5QmwHpaXBcJpRasCYCb/iUw7F/CLK9sCUEaQGwU2A3D+zjgk8NzZbpjVpm/+EoDv6PQP9dqCEh5A95CthqQb8gnOWGs7w7B5+9h7JLHnBgeUSq0e/dkNTBtuejfbnAgettiBjwpAFnIULQ3YArDCjoYJ9sFGb2NSqp7NPu/mZaZ828BRzBrgPLSqLHdjVSiuKI47eGH73aNDU1MWzYMB5++OEf9PjCwkIOPvhgJkyYwJIlS7jssss466yz+Pjjj3/0zsbx78W8efOYM3sWqRPPxvT+vK/E9Enn0tjczBNPPPEL7d3OKCsrY9zeY9lrr72+fZ1vLAhthdJVX/DBLSN55/aRVBZ+w6A+UGLDA3vDoV9DpAGKs+BfU6Fs4m5Mfn8W65oSmfhCcAdC8vdFIe7+PLhDyal3psnYglZzQkmDzTnvtrCm0vr2bxHbYdrbQV5dFSH76VdJOPJIDpilFNWqFEhshKQmlWaMVBi6RabQxkSVRWKTZJNNcJdDj0oI+6EyEbwtekxGXTQC3lGsfSxrBKScBNxKaK3NhB7lkLcFFvcV2RmzHJzBUNAN1qNz08mE7sVqifYHlfyaZEOnYui9HhoSNBunLhVGrYBgJ4jkQkY6zA9oO129cKoNCy2YaUJ6E2TakJgFW23YaEotuAwIOlqAV6KumfbdLzFUoqFy/3IgEIYBIfiDF/bvIJjgPpRX8pSjaPcRJpzt3tHQGsPHyCh6WjRDpMCEuw0pNb1QmNk1dBzNbqFW4sWoQ2ePdvc3I0XkGFSKOgQY2vHhURXd5yvoOEo+jjh+C/jROSNTp05l6tSpP/jxjz32GD179uTee+8FYODAgXz++efcf//9HHjggT/25eP4N+KRRx7Fn9mJhN4/P+fRlZhGwsD9eOiRR7n++usxzV+2IlhWVsaE/cZRW7KZU4Z5OPfccwHYeNA5bFz0BbPvPoDd8y0itsX9t+1Pj32nU9hld479CNZkwtOHarbLoTOVYJo8ZRhbXp/F6mPGM/GFZmac7OOZJSGu/CQIQHmTwz2TfTt5Z0oabCY+18yaSpt310WYeWoifTLNb4lIwouvstdBxzBxCawshc9HyKSaUat22UAC5NcCFgSiXpGwV797LEiohuQW6BSClQ6Up0JiC9RGW3txVKaJTRoOecGJztjxRMDwQecKKPVD/+1SOrqWQuMQGNgD3jPUNZNgwEQLZoahb4XKWwEPbOkFB78hhWbeGOi1AXBDdS/ID0F2lkoYvQPQ2wvlplp4GzwwOgSVzVBpgpkEWyy4zlT3zIuOZsfcCLxp7HqhXonUh2UOdA9DLxuu9O88SddCUepBVGr5mwWnu2B0Bx87B3gJmVsPceBsG6aYMpUegTpmMKK+lQ72KYI8IBsRgWi/7y2IiByLiMiBKPekIzQgAnUR6iyKI47fKv7toWdfffUVkyZN2uFvBx54IJdddtkunxMMBgkGg9/+Xl9f/+/avTja4JPPPsPbd6+fXJ5pj8T+4yhd+gnr16+nf/9dWfZ+PNoSkVmneOmbaZLqNTj33HMZfcI6lr35CLvnW3x0og/LgQNfDHLx1P055onp3HzS7jQkw7AtkFwMBZWwejAscENS72EMemAWKy8fz5BHGyhusLh+Hy+dU0wu/jAAsAMhiRGRhqDDF2ckcs67ASY828zITm4+3GSR8PKrpB1+DE4NFC6ChSPVGeMPKNOjIhscD+y9EZYni5g0+7VYJlsiCbnV0KsMapLACIoU2B4ZSwOxrLloe2/Yre1bBoqO92rScKkf+q6HylzoXwiLJ0BWLnxlQJYjVWSgCxZth9RamWgxoDEDOm1U3kizC9b2g4FrYOw38O4Zmi+zyA97RyAQgeoUmS63hGFAApTXQlYzVGRJNbnfpW6ZT1Ey6bPIpJrOzl9EDjJyvgqssaFvCPYy4WzfznkjtSjhdCywnw2PWpq4272Dj3EEeAQpH/kOnGfDmaaG6B2FPCyGIeWmI99GCHXcbEFkZVC7+1sQUTkGJcvuj4bjdYQWVMY5i47n0cQRx28J/3YDa2lpKXl5eTv8LS8vj/r6+l2GZN15552kpaV9eyso6KhiG8cvibKyMspKivHm76rh8MfDm98HgIULF/5i2wQ484wz2LBhA9NP9tIvy4VhGDw41ceFY7wsePneb4lIktcg1Wfw8Uk+8twtvHLBVI762uZf90NGA2Cr5fWrAZAXhIJmcI0cRsLRZ35LRO6Y6OOi3b08NNXPfV+HuOoTlWzaEpFZpyWxV4GbGacmku43+HhjmNS77yPx6GMwWyC8ATZ1g21dlKxqOFCdAY0pUNAEARMavCq9NCZDoglWGDKrlRrbqRI2JIEvKLLhiiiGviH52+R3bENx8VZ0VXehhdcTAFdYr9WpDMpHQXMONBpawLc6Ko9ssqEuoOA1dwQqs6G4E4z/XOmxJd3BG4DuRbBlENQ7sK6TFub+AS3Oa7ywXxjGeCAtAsVAUQhSs6GHpXkyf0W+nsnIVPs1IhFtEUJqyLPA5mh+yMluuMCzMxFZjwjEaQ70s+BdC25xd0xEYkRhNOpSuspuVWpORqpIsrFrA2kQuA35W65jZyISoLU08xFqAd69ow9w9BjvQa2+PXfxmDji+C3hvzIO/vrrr+eKK6749vf6+vo4Ifk3o6ioCAB3Wt73PPKHw5WQgjshmblFRXRGV7uxG0Sj0aO/t/33990Gnn46H338EXd8HubZw01cpoFhGPxtqo+D+7rYt7ubpDar1jNLQmyutth32tmUpprctw8sLQBXLnyzOyR5wHTB5nRY+Y/7CPz93m+JSEwFuXB3LU8XfRigOewwa4v1LRHpk6mVLzfJZPZpiez7XIDCu+/AP+VABjUPABvmD1Eni4lKKbVp4HdBaimsTZLK0eyXeTFkQ1IIcmsgP6zZNk0+mSs7lYgkxNQVoqWaoEedNTF2YtoiIQG3Ol4MG6oHQ1NvlTiOQ50pAVvTg/NKwFWrElDAD02pMGw+pIdFPL4eo+j7gevhpeOhX7oyPZYa8HlAxzTIgOIw5CaAq1S5KvkpkOBWZ80dhoLAyg0YFn1vFqE23RgqEWFY6oDXgr5huNwLgzvoc/0MZZE84MCXlhb4W9v4bdqiBpVDTnBgsQN/d+A+U3+/EO1XrqGMkI6sKy0o5bUSpcP2bnd/jIgcjVp892RnkhWDBdyPZtzsqrMmjjh+a/i3k5H8/HzKysp2+FtZWRmpqakkJHRskvT5fP+V805+zfjWoPk9eSI/FrZhMMu2qUFD41KiP/3tbgnt/u1DcdyxvYld6BpA32OOIe/Fl7jupBMxgGcO931LSKb23bHD5cF5QS79KMioq6/m0t3upMsylUOW94ctmTBlDmw9AHIC8NW8WQT+cCXnjvLsQERiaEtIuqQYOxCRGHKTTOac4mfo49U0T56C/VohKwoMfAHIrIXqTHlCmpOhT4kUCq9bSavNyeBzQ0IzJAR19Z5WCmFLakV1Foyer/IOhkiDbYiEOIZKOBCd6BuAZq/IW9AL3lRo6qJY9SeBUtQem2xocWxphF6lem5zokLTRq4EbJ2v4lw48SVY2R+sLBiRB32AFyLQEhHp6GFBqgs22lDVAt0MmJwD9ztwgEshZXkG3Iz+XR99v2MqxApk5FzhQEEYutgadJfbgcrxKGqVfcZRoupAE040O/74FqHSzHkOvO7ARw48acp8+ldkLu1r7JyYGkMT2uc65HHp3u7+GBE5EhGk0ezcWRODg9qI9wZG7uIxccTxW8S/nYyMHTt2p4jwTz/9lLFjd3XdEMd/ArFSmtVQCdHyys+FHWzGbmnkkLw8RqJI7Up0NRpuczMR8fBE/x0NHMVDKymJwYj+LfH44zkLeOqkE0n0wOOH7JyH8o/FIS79KMhlV1/NfXffTd2tBpFqeG8klGbBPvMhrw7m+WFeEriGDSGxVz/eXLOBy/e06Z+98+X4hbt76Z9t0j/LpCCt4yrnS8vDlDVYDNvzGDZ1MYi4oWuRSiWWGwxTRCO1TC22ER80JWqCb0EzbDMguxrCLnDVQZNfXTKWC1IbdRI80XRZx5Tp1TJbZ+CYNjjRsDSXBX4DfJ3h94aSPZMRIelkQ8gF3nIINitmHmRynTBHCkqVAV/vIS9JXi1sHQ3DU2C+qXC5oQFYCiR7oTwIG33QqxZqEyGhAVYm6f28x1D4XCmKifeibI490AL9HiIXqxwYEIQRBpzv077v8JlCgWAVwBMO/DUCR5gwYRcJYauRWfVSBx6wYZUBL5gKH3sdmUv3MOTt6Aj1iIg0o9bj9pN1g7QSkVlI8dl3F9tykFemH7smK3HE8VvFjyYjjY2NbNiw4dvfCwsLWbJkCZmZmXTr1o3rr7+eoqIinnvuOQDOO+88HnroIa655hrOOOMMZsyYwWuvvcb777//yx1FHD8bXbp0ISMrm1DpBhL77vmLbDNUvgkchwmjRjGVjuVvB33R17W51bb7d6DNcx1aVZKMggI8Ph/FTR1Hzxc3SO1Z0KcP9xoGB1bC0gHw2WAYtlrtqzNHwLpUOKgeVhlpBLsNombzOsY/28ysUxM7JCSTeu36v80DXwe5/OMgPrfB1k2fYgZryK/LwB1RLofXAjzQZavaeEHKRXMiZLshcwsUd4bsciWpJrbA2h4OvlnzaV79Fp+uqqT8wxZ8/jSs/n0InXsykU45GpgXbfN1gJYoN0uyITcJupqwDKkgXwJ720qjfQ9IboS+xSIxDcmQ3ghdylX2iACLRsCZT0GgHxR1BSsDcgzoYsDXQUjxQqWjILWeQOdSWJCjYXwRB84xW4fRzQH2i56r+agD5gFgHjLSDgjCUW443L2zytGIvB1DgTNsuNeCC1yw2y6cb18BMxARudGWCvW8KaXmG0QIDjB2bsuNoQYRkTAqB7U3mcaIyBGoRXggO4eetcVryKw75TseE0ccv1X8aDKyYMECJkxo/S8X83aceuqpPPPMM5SUlLB169Zv7+/Zsyfvv/8+l19+OX/961/p2rUrTz31VLyt978MhmEwYfx+fPj5PJx9Tvre+PcfguZ1X+HNzGLxwIHMQyWaEajNMdbGaKD47ySg8w/Ypo3aIad/+SXXTp7E7nkWrxzZcUnv9+O8lDQ6PHzuuZRFYMGB50hdKIet2bBxmNSEniFoqI2w5ppTCMx5h8cO8vPAvBATnm1m5i4ISUeIEZFr9/byu93cTHhxFU1HTyDl7pnUdMsglArda6DOhqxi2FYgVaPFL1Uksxg2psnr0ZQIvZY2s/rLV1h0/8ME1izClZLJ9pQccHsJhpoJvfUU3H0dHHscznkX4IzZE8c0lD1iovh5PxS5RCwWIlWpEwpMm+uCrBowm1TW8YZhaw4cNkPG2Vpbs3OSWsDnQG06bM2HodFuk79ZUBeG1BQoisCxXshvgnkuzQW6KR9uN+C4KFlwgOXI4NmESOjtwCpkyu0Vhou9mujbHpuBU1HKaZ4FT9twgxs67+Jj+h5KeT3bgUttJbT+3YCrHKlzw4FjjV2HkFUij4iNCEn7rJEQ6oQ5DJGe3mia767wPvKdnPYdj4kjjt8yDOc/NUDkR6C+vp60tDTq6upITU39T+/OrxaffvopkydPJu+EO/F3G/KztmWHWtj+2OkkXHIh3e+6i27oy7oP6ryoQuPjR6Dx8d8VOv/ggw/y6ssv8eo/X6dr1658+eWXHDh5EqNyLd7/ne9bs+orK8LcPifIK8f4GZwrnu04Dhd/GOTh+SEmnPUYh/U5l6cPhcKu0K0IkgOwekAEe9optLzVOmumrFHdMjUtzg8iJG2JyJ37y2+ytNRi/AtBwl13I/X1mWT3zaBvCWxoBH8jlOVBiw+a02RYnbASvugFOTWQ9uU6Fl4/lfrqQhJ6jSZlxEH4e47EMFv3w2qpp3HZZzQs+xCrugTj9DNxHn0U3B6Sg/KUdDN1BZ+IJtc+CWy3pVj0M2FVoQLNcstVRjJMOGCeyhNuCx64EI57V7Nzto4A+inOfR8THmmCTQ3QJQNyIpDhBWsjLPNBXgKcmg1Pm/B2lIxsQkrFWWiWy5tAXdQfkmvDNV7o3IHKMRd1r9zjwHoLNjhwtbvjkDQHdeF4gP0cuNCGISb8AQ3MSwMyDO1Dr128l2Woa8ZARKR9/kcIKSKHIoWlC7ue4gsq36xEKtAv68iKI47/fvzQ9TtORuL4FrZtM2LkKNaX1pMz7V4M13fHnX8Xqj59lKbVM8lbvZpI924kADlIAemOug4GoOCrFah8MAiZ+nrS+qV93333ceWVV5LiM8nr0o2Zs+dyxumnM2vmdNZflET3dK1eLy8Pc/JbLSR6NEzu6zMTGZInQhK2HAY+0kRZYxJjp9eysafJ4MXQbwNU58OCrfew7NqrefJQP2eNbG3qXFpqMfzxJk4Y7OalozuKvxIaQw5pdzUwspPJN2cl7aAqLSqxGPNkE+4jjyXz5ddI2QY5pSrLlOapiybJgNQq8NlQkQYpc5dTeNp47IQkco68AU9We6fCjnAcm8Zln1L96SMwZSqu196ii8/NdpcWUgdlaKxCJa4tEejrgqZGqNsC/deq5XhrN5g6G5yQyMHSfHjjGDj5DZ2n1QdBpwQ43YA3DJhfpQW9OAkOMWBhBDqthwV58Md0eNcFvdzw+yjBeBq1u65CiojjwOCQyhsXezsejPcP4DmUqPq6pc/Pea6dI+BB5ZSH0PZ6OHCZDVNNEY/fIRXEMOBidp3rUYRm33gREUlvd39METkIGWBzECnZFeYjMnIl8UFgcfw28UPX7/j/jzi+hWmaPPvMPwhVbaVm1jM/eTvNG+bRuOh9zLvuItStG50cLSJlaAZHIfAE+oKejersVyFj31y0CNwLnB8lItfv42XpuYmEaoqYsN84rrv+ejIyMzn41RCVzfa3ROTkIW42XJxM9zSTPf/ezPKyCBHb4cS3ghTWGWTf/xz7rzfZZ4m8GBkWJPsgf+pUvOk7zqYpabA5/vUWuqQY3Drhu4cFJnsNHpziZ0Gx/W0OCew4mybjdyfQbEFikwiIbehn0A+uFuhZCHV+SFxbzIaLpkJyOnkn/fl7iQiAYZikDDuQ3CP/AB99CJdeQKlLJQYbyEZtvEFgtCPvhQsIFUN+qfJL6lM1PTglCAkORGz49AAYtRSqsyG9j0LU6pB59AsLUsMwxCOz7Hsu2LdCqoPjhR4+eUjGtSnRrAPeQcPgKmzYLSiycE0HRMRBLb8fAC848HhEHS8X7oKINAF3IR9Iui1F5HQTpiGysCdgGvqc7YqIbEUkKQGVaNLb3R/LBjkI+W8y+G4isgJFzl9G/Is2jji+D/H/I3HsgOHDh3P/fffRsOAdamY9g2Nb3/+kNmhe9xUV79yNf8oRDJtyIS3AdqRW9HXkGylFX/wNqDb/F+QH+AiNar8ZCNx3H49FicgdE330zDCZNc1HqKaIs84+k/1feY1yK4URT7R8S0SePjyBvGSTOacnfktIDn4pwBtrLTKffZ3zXEcwdq4yRXyWfBNNiRAYvBud3prF6uhsmlUVFhOebaYx5HTYvtsRLmwXjBa27G8j4X3Pv4rr6CMxA+AOK9ws6NMtpQ6MEBTnqBOm8d5bcJqayT7mJlwJP25SSULv0WROOh/rqSex583DjcoV26LnOQI8Z0GWC3zNaslNjnbnNCbD7quUZZJrwJKeGtY3sQpCbjD6QLoJZQbMM2BAQKWfT12Q6IKjLaivhcIEGO2HGRakuyBW7JuDShWzge0R6BNWiNnxnp2Nqi0oB8UB7rbhzggc6YKjXB237lai8LXjEcm53oEbTOV8HIYIQ9hQPkjmLs7dpug2ktGcnPbXbzEiMgUpO0nsuhUYFBX/OiI/P11fjCOO3w7iZCSOnXDxxRdz33330fDNG1S+fD3hym3f+xwr0EjVhw9S8dYdJE88hPz7X6E60aRfra5IS5EJMcWB4Y5q99Xoi70G/b4VLQR73HcfN7YhIrGyR1tCMvPsMznqzHMoqrO+JSIuU4/LTmwlJNMLw4y5936uqjqCFg/M209GTSMiA6WVKknfO2gYQ5+YxcpGP2OebPpRRCSGtoRkyGPNvLIiTMrTr8Jxx9CIQsPCbsCBhhTNkElsgf5ror/X1FH6yQskjzgId8pPm1SSPPQAXBn58OijgAifjRbh3o48OmnAlhLIi+WKJEBOBeQ3a9EtNmHZYMiqg7APKrrDci+sN7QYbwSCAU0cLjdhT1PGXAfYmgJnJsDKqGk0HbX+3giUO5AW1vHf64E9OrDhbEfKwxTgmGjHzGUuGLuLt2ELChC7wIEvbbjPgQdMGU6PRwmnLVEikryLc7YWBa2loc9f+8eFkVJ3IFJ3XMiEuytsR+Wlq/huL1QcccTRijgZiaNDXH755cyePZtcT4Dipy+g4vWbaVw5k3DVNhzbwnFsIg1VNG+YR9VHf6P4kdMIrP2CC4c/xvvO65DgI6ERAg3QI6Cwqzp0BboW6OwoHCoN/X0tIibl77zD/Cuv5Io9vR0Gj/XMMJk7zYenpojH77qTacM8OxCRGGKEpFemm3V33MqhpQH2mgtFJizrphbbZK/aazc4kB+AlNwuGGlppHg7DjSL4cP1YTbXdtxOHCMkayttvFMPI3TsMSQC+VXgCSqcLOhTicbbAhGPVImIG5IefQ4nHCR52E/vNDNMFylDpxB5+RWoqiKIrsxzgO0WHOwCMwihMCQ1KlY+4oahhVBjyquzdIDIUc8yWJcEqwZCian3agjQFIHtIUh3yxyabEBhhYgMqVBmKzG1qwFvoWj3zVF/SB9bya3DOji18xCBuMmBbAtejUa799nFt9Ry5EO5yoEXbHg1GmbWAJyNOldChibv7ipCcSVKZs1Filx7Z1AYKSKTUXmxtqqKvgsXMm/ePFavXk0kEtnh8RUokO0Kdk1+4ogjjp3xXxkHH8d/B8aNG8eqlSt45ZVXeOjhR1j03r0dPi4npSv77v17cg45m4xQPqn/gr/+ES66Qz4Ey4bMrpDjljpS7miBTHHkExlg6Iu+GnD164cvPY1Z21qoC0J6B5eWPTNM5p7i46MNLs4a6dmJiMSwtNRma51Nn/1HUVHtIx/oUQKlnXTln7oISJCnIhCE0sKVhLdt47CRHnpndLzNWNdMTqqHudO8HXbZHD3IzW2fm1StXobLdOjsGCRu1SA5gIZoDcATgX7rZWQNeqHmg6dI7DcWd/Kuigk/DMlDD6D2ixeJvPQSxsUXA1DhyMD5BVBVBvkVkNSs5Nf0ekhvgCYTzASRpboMGL8e3hiuNt8eLqkCNwJWQJ1QG9zqyOnRoC6XbV7Yzw/TLRjhVivxV8AyG/pFYLwJ/d1Q1MGpfQl4HHjWgc8sfRZud2uacEeYg/JSrnTgdhs2GPCcode7G3lFzGgL8q46WJYg4tAJJcK2n0cTQYpIwaJFXPfIIyz85DNqtm3Z4TFen5/hw4cz7eSTOGzaNB5PS+MSdl0OiiOOODpGnIzE8Z1ISEjg9NNP5/TTT6e6uppFixaxdetWbNsmOzubkSNH4ppRwPrlBv9K0bj4zT2g13q49SG48RLouRUwwO6qK+nNBpQDQUemyr4O9EUzRdYPHMhuM2ayauIEJr7QwoyTfaS3j+EEuqebnDu6o3FmwvRNEQ5+JYBn4iTOf/ltUm436BQEIxX81dC7ChaMgW6JUO1Apg3F4/bD+5cHefLqS0jzGfz5gB2VmRgRSbnoYmo//Zj9nitk9ik7EpLSRptxzwaoTsym8+sfU+Q28Feqc8RtKyk15IakJi34voBUks5FsKRiI2l7/u5nv2euxDTcmZ2JbNqEGy2M1TZsN2F9BLpEIL1Gia6WC7KqZDZ1AfMGK0Y+pxqKs6E+BQrQfQl6G+kXBLcJW7zyd8wqlyl2ba5Uklrga1MGT48FLhuuMOFgt7pdjm2zrw5SJJYCrznwWEQx8te5RCbawwHeRmW/8xy40ta+P2sq6v055DeJzd/ZFRGZj9JQuyHlpL2vIwLcUFjIjLPPYcH0z/Ck5+LvuxfZw4/DndkFw+XGbmkgVLaJldtXcMmll3HFdddx3W23kX3JJeD6Ydk0ccQRhxAnI3H8YGRmZjJp0s7RTqGDoH4FdKuAdV3h69HQawuMWgZXPwt/OQUGrQefB+pyYTeXYsa3G9DoyFewGdjbgX4GDBkxguwZM5n9PYRkV4gRkb0mTuK4t98hv9rP5wNhWxYsKABPA/TZCD3Wwzv9INXWVXF1EqRPu5hOq+Gepy8B+JaQxIhI/rXXErjjTvZ+o4Jll49j3+cLmRNVSEobbcY9F2SLK5Pc6Z8T6NoXlwX1ITDSZFQNebVApjWoK6S4M4R8kF5mYbc0YfiSfoF3CkxvIq7aWryAGT3H2w0YWAwpRZAQiMbGG9C1XN09pVmQVgFzD4I95sPSfWCrH9I8Mp7mAd1slXZqPZBjwikhWN6i3BgzDRwLylywyYGCaDtPgguqTJiJpuzGVIMgyv7IBh5x4K4IHGjClF2s4zYiEGnAMQ6cH02RfdjUsLyZyG/S1/ju3I8vURZJT+TraP8lGAGmvfQSb519Do43iewjriex7547ZLzE4O82BMYcTqShkvp5b3DblVfy2etv8PZbb5Kb2z4qLY444tgV4mQkjp8Nby5k7A67fQ3FXaAuE1b1hUHr4IBVUP8WPHEEjFgFvf2wPgW6ubWQrDK0QOU6ym1IdTTfY+yIETTMmMmiH0lIpm+KcMgrAYZMnMRNb79Df7+fT4vh2M9VCqo5BopTYF0/aMyAiAkbTGiIRrJ33Qa9p16MKwL3PCdC0iXV4PKPg/S88lqcP90JFQa9G3LZ/spcik4bx7jnC3n9KA9nvR9hi5mBOetzwr36EggoGj2jGNb0h9wyDbPzhPXaqVVSSgwb8Ju4TC9OJPiLvCeOFcJJTCQM1NgiG3kRaAlB9yqREAPIrYIUGxoM2NQfskvBH4YJncCdAAtsaHSpe6TIhMYm8EeH59kGvFku1aQuRROCV4TA44IBEZhgw2gveA3oj/I2wigwrA75SfZBE32vt+ASF4zahT8khIbajQT6OHCWDSOiYWbXOSKz+wBjje+e+zIblYT6ohk37elFBDjh6ad5/cwzSdptApkHnI/p23XGTAzulGwyJ51LYv+9Wfjun9ln3L58PndOnJDEEccPRNzAGscvgqzJkOeOlmSSYMEowAXN5XDUYvjdDFjaDxoWwdggbA5DkqNW3jzUerkJtVR+CMxwYPCIEVwyYyZrGrwc/s/Q9+7D2kqLg18J4N9vAtbb73Cd388DwDcVSsqMAFYEkmpg94UwcL0WWCMMW3KhIREMS+2+XY68mE7XPcg9X4W4/OMgqVdfS7/b7iS5zqDvGlixG9idcsl/bi71eT3Z75lmthjppH76Obv170sgWvbovQksj8oyVdnR3I9KSK9UtkdzEuSXQadSg1RvNpG6su86xB8Ex7aw6isxs7MJOdqPdAMSyyGrAjCULeIC8spVcinsDv3WwZz9oH8hHLMb/BN1gxxpSG3IB44OKCV1kFdZHul1Igqr82ChDVsN2OLANhNWeeFuQybRNShj5hrgYKROPABMsuAVGwa54QNT+R73oaF581E5rx613U4C8hw4x5aCciMKNKtFQWcHfw8R+RQRkcHIYNoREbnk88954+yzSRlxEFkHX/GDiEhb+AsGk33CXWwpqeCII4/Csn5ca3wccfxWEScjcfwi8KRDp32goAZyG6EpBxYPgqR6tYEePxumLoSFfWHrQjjagTUhzTwZjGLi0w2Ya2jRykKLWt3qNQQamxiY+f1BwRkJBtnJLiKbNuKtrKQGhWaty4Ft3eWPiJgiHK4INPg09bZLELpWqWwRdkNFLqwYAvYlF5P9l2c44M/3UnD7ndTbBp2WyuNRlgv+NVsov+Qo3GddQvqJF2De/SDNbkg1oFNExt2IC0L11VRPm4x931+oT4eadOiyXZkjddWb6PzKXMYsgX3zj6Zl1WwcK/yz3ouW9fOwm+qIHHkkHgfyDWhxoNKGzCoREdOBzDpIDkKpC4o6qXzSkAwJGfBwIlSEYS9bRk8HSLJgYxAKfGrH3q1SuTE1PkhIhLQITIko7XWxC643ZFA2ou/DcyjzYzzKnbnDhr8B+7lgtAFnIKXjbKSkVADPAIeg13vWhhNtOMBUgu+xqMSTZ8BJhubN7Arvo/j54cBF7PzFFwHubm7m7VNPw995ABmTzv3J85k8GZ3JOPQavvryC/72t7/9pG3EEcdvDXEyEscvhsz9NZOkzxbAC4v2lBnSVaY6/4nvw7g1ML8bzF8BF5uwOAQJtlorh6Cr4DwD1hqQ9PLLPDntJKYNcfPw1F01Z7YiN0ltv+k1RWzabxw127djAp2aYWUfePZYKI16NAzAnwE9TcixIJSorpKsarW65pdCWh3kH3kqwauuoNFlUFEP23LV/ZK4agubzxmHtfxLmq67mJAfgmedSGivMWxfuJjSMOSVwLr0ajadNxG+nE7tndfg3HE7EQ+U5kPdthU0HjiGf/11P7bMeIHJ3c4n0lRD89ovf9b7UL/0A4w9x5IxbDgmInd2CyQ3gMtS9LvthrxKkbNVA2D8cvhoX3XZVA2AsxyodaRYVBvQZMDogNp20xOlaM2tUodNSZZm3eRYcKsPurpkXi0GzkUqxEVo2m43YK0Dh1uwjw2XmwpaexO4ENgfODz6nCeQWfUIINuGrxy4yNQsmP2in5V5hkpIhcBqlMTaHm+hwXmjgfPY2dRqoRJQySOPULZ1KxlTLunQH/Jj4C8YTMrIg7nhxhtpaGj4WduKI47fAuJkJI5fDK4k6Dleakj3agilwTe7Q0ITlDdBTwemvQmjtsCCDHh/C9xhwpdhaLEl5x+LiIv/5Zd5O0pEnj7Mv8v23fbomWEyZ5oPb00RLfuN45bt27nyS8gNQJ0Pmm0oyYGZ+8I33aDOkEpS44esSpVM0mtgSw9Nsp0UgP4GHL4Zum6F7V2gqXILG84fR3awlDUXJHHWCDfNTz/CmHyDocktbJ40gaQvFuMvrabu+Im4i1Yy74wEbh3vo+aBP+C69XZKKlZQe9x+9Pc3ctIQN39dcgob6xbSqd94Ghe+i+N0nGPyfQiVFxIsXILrwgtpdlQWqgNCEZWKElqkBvlCkNEM9QnysSRnwOouYHrhbi8sCqv7pzLasFQNBJohww1LXcooWW+pe6p7qiLhb/aC36XZQ6DW3lHIJ3IW+rJ5zoG/Rjtm7nbB8dEckIcR8ZgRvd2CykJnOzDHhscAvynj6fmIUFQYIkXJaKbM16hV9xYU6/534Abgteh+nMGuicgY2+b1hx8hof8+PyiC/4cgdY9jaWkJ8MILL/wi24sjjl8z4mQkjl8UmftBdjL026wv/pWjoSxVU2G3Af0jcPrrMKgE5hvwXCVc4IJPwzDeUleL6+WXWXLyjyciMcSC0dJrijhjv3E8kredAeWQ//4qepeFMU110ISiYsvaJGjyQla5fk8IQGodVFetp5fTxNogRNZCSSq4Nm2h5fBx5DeXMvcUH93TTR4/xM9ZIz3ML7I4e6jJkKQWyo/fj41njMW7ZSWzp/kYmufiD/v5uHW8j+1P/oHSo3dngK+RGdO8PHuEn1OGeXhg2Snk9NqTQMk6amb8/Uefe6u5jvJ3/gT9B5B8zDGEERFIblHAWtitbJFAlHiZLlg+GAZugLf6qw23tAd8asBtjgLZPjPlA3FFYElQ5ZiNwJIytUS3pEOSC/IdGODSbKHeKBk1E6kXByPvyd0O3BztmDlhF9HuIDLyOUpTLbFVQvnS0MRhB4WQHWrIczIClXC+RB6XD5BJ9XOkrLyJOn2WohLQncDzKKdkE/Kn7A6s/eYbyjYX/qzAufZwp2aT2Hs0zz0fJyNxxPF9iHfTxPGLwvRB/wOg/C0YUA6rc+GbA2Dqm5DUoGTP/i1w5hvw2HHwZR5sboRPUuDsCOTPmMGHJ5+kiPfvICIzCyM8uzTMA1P8HXbZ9MwwmT3Nx7jnivjg8n0pmXAp77x9GZ2+OIhDznyT3BQfq/qIjec7UOxoIFxdBrgj0Pzx2xRffQw3Dh5K8uszqOqZTm31FgLHjyMvUMrcU310SxOXNw2Dxw/xYwAXfhDgrkk+1s9pxF26ntmnJTI0r1Xy/8N+YkB/nNXCIaO8ZCVqG38/TH9/9uO7STxqGg1vPAdAxsQzMYzvv2aI1JVT9sbN2C4L84MPCHh8+FH7rNmsEo3lFiFx27BnBazoJDNtZQFs7QkDQ5pbc40D/4xAbxs6GzKSdm6WUXSVD5LDEG4BlwM9c5Tc2tuBOYYG4e2FFApQaNgIoNaG4xxFwM8xRVr8bW6+6G0hMsROjXbMVBhwqyHfyi3IT7LV6DgtNQYHqR1zkHKSgUYNrAcC0XMC8qRkoGF2hfPnY7g9+Dr3/95z/WPg7bobS75+hUgkgtsd/7qNI45dIf6/I45fHDl7QfJs6L9V5tFNPWD9QBi8UW213YHedXD8O/DMEbDFDX91wT+T4IjEFNweD+XNELbB1cE6HMsRCYZtllcaTN9F229twKEx7OAkRHjn7cs4ZpCbf83/iDfNo+j3yJukt/jwObpCNy3NaUmthw3L36bi6mM4sKfJ1+uXU3XMROrf/JTICePJaipl7mmtRCQG0zB47BDFxV77WZCsBIMZp+5IRGL4w34+IrbDrXNCDMw2mTbMi2kYrYTkredx/+4UGl57gWDxalJGHkpS/70x3DuHvEXqymlY+hENSz/CzMmk60dz2NazJ7alDiWXAwnNir+3TakiPYqgxQfz+8GgFRDsr9bc7l7Y34CvbSAMlUmKQd/gQHkL+L1QbcLIEvDZkJwGD/lhqg23mUoy3QpcjWbCbEPG0RdsKLLgz27oFH2bLEQKYuSgCRlc84ChDtxna//PNOFdR6WWfYEPDU3gvQ+RjvZwUOdUNTJGD0UkZzCtpMcDvIKISD1qKY8sW4YnpzuG65cda+fJ601NSzMbN26kf/9flujEEcevCXEyEscvDtMN/abC0hdhaAks7gzrxkB+ORTUwuZ0lWMKyuHCj+HJyfCuR9HfH40dw+FvfcD0Iw/i8NeCvHOcD3+bmfHTN0U46JUAuRMncfVNt3D1QVM6zCFZXGIx4cUgLen5hIq2ccWeXu6Z7OOTjRaHvfoRGy8+iowX3qTO8BEq2kx+Uzr9CtNZs+ZtKm46hqP6u7hvsocNVQ5HvbmcxqMm4eo3gIrZW1lTae9ERqCVkAzLczGhp4tBOR2bIAMRh2+KLRLc7DD/ZgdC8trz+C69ltDi+VS9dy81M58iacC+uFNzMTw+7GATweK1tGz4BpKTME8/FfOGP2Dk52IASYYW+IgN6bWAAS2J+jmgEub2gl4bobI7rO8JXWyo8Kr8cV8YghFINWGJoVyU6jC0pECGBUnV0M2EtFyY50hZ6uJS+aYn8mksB2Y68L4FXxnwvlvprDG4kLKRiAjJs6h1t78Dl9iKmf+zoVTVL4A/oiyU89l1bdlCc3C6Aecgk2ugzS2I5ta8iAixg2YiHQU819CAy/fLT5Nx+TUDoL6+/hffdhxx/JoQJyNx/FvQcyQsmg4Dt8HKPNiUA92HQMoSGVsxYDdg4xY46XMtEK/0FSE5fcr+VL/9ATOOOIgjXgvydpSQxBSRbhMn0fntd/iz389p02fy3P47BqMtLrGY+GIQX2Y+ddu2f0tEDMPgwD5u/nW8n8Ne/QhOO4rdrrucOUcchju3E/aJN7Dl7nM4qr+L6/d2M+LxZhLc8Owhfk59dwWNjoMxbBSHvDSf905MZHLvnf/7mIbBhbvvOqY+EHE48tVmZm+2ePeERMYW7LiNGCGxHIcX/no3mW/NYvjlD/P5h4/R8N6bsLoKAi2Qmga9emM8+ijGSSeCY+BfvYa8/Fy2ImNuUmMz1opVmP7RGIbSX7OqoMIH5fnQfzNUu2FldxgTgZBLPp91toyt4w0RgVCTQtLcHujZAANMtfMenwJ/tpRDAiqlvANsq6ri4OdfYOKcuWxfuJDmynJyDYNO+Z3ZY/fRjB8/nhNOOIHklBQqUSnnIMDrqHV3kAlnGHCDo6C0/YCVBoyLbr8Fxc83tbk1IkWkHnXbVACPOiIfDY4e04LMvCBjbxgRmw+BiMuN1/7lM0Fibdoezy+ruMQRx68NhuM43x/g8B9GfX09aWlp1NXVkZqa+p/enTh+IBYuh7J/yJPwVTfoWgM9FsLoaqhJ1iTZBFTLX747vD0SlvaFQ73wZAKc+sl0Xj38IPbvBhePdnH060EGTJxEwdvvsN7vZwKa9JqyeDGLJk6gb1IL901yc9TrIRIy8yluR0Ta4uMNEQ5+pQUMgz27uiisdihvjDC5t4dbx3uY/EILPdMNmsLQEHR4+CA/p74XpqVzHyKbN+C2I7x7QseEZFdoT0T279Xxc2taHCa+GGRFUwKDX5pPaI++FPqhxa98EG/EIejR8TgGGA31+CYfQGDeN+Q99DAVF1yAq74BTj2R/wAAkJZJREFUZ+qBRL76ii5XPUjWSRdTmQ0nvwTl3SF/OzR0h805MG8P6GdDJAEOsGBZC8z2QneP2nOTyjQsLzcD+hSqI6eiCxybDTda8HsXtBhwS00N/muvpejZ58C28XUZiDe3N66ULHAcIvXlhMs3Eti+BndiAn0vuAD3zTczMDGRkAPfOOqy6YtKJzXoM+IzIMORutEc/RmmVe2IlXksdHVlItXDhUoyPqTABFHSbyZQCYw0oDkMFfWw5pY/UPrsI3S54NmfnC/SERpXzKDq/fuoqakhPT39F9tuHHH8r+CHrt9xZSSOfxtG7AbPdYMh22FRJ1iTAb3zdNXaM6AY9r5I2re+0bj6oA8+7gEPtcDzk/cn450PePTwg/h4fQu9D5zM6LffocLv51KkppwHvDhiBO4ZM1kzcQITnq0joaArNd9BRAASPOA1HHbvYvLBiX5KGhz2faaJdVURJr8QoVeGwScnJxGIOEx8rpkLPwhEFZINNPTqRWTTJg59ufkHE5KfQkT4ZBYtnfqyNRFCJmCAxwGn7fHU1eOacgDuZYs4aYiHFy+6EJqbMd56E//i+fxuqIcX7rkE2wW+cy6mOh2aTajKUkz718Ogya3OkgkO/MuGDX7ljxSbEGmWn8ZMhFAYQi1QZ0JaJvzLAr8BCwz452efUXvyNJz6RtLG/o7koZNxJaZ1eIyR+goalnzEmgcexPfmW9S/9DL/x955x0lVX+//fe/02d47LL13kCJVEAQUEVBRKaIm9hKTGDVGE0ssSYwxGnsBkSooVlCagIqF3tuybO99+tx7f3+cWXZXFkRjft9o5nm9VmDLdPfznnOe85zygQOJBAoMmXzRDTApUumwGOJ9sSri+3CEgCIRSFLguCGPzRTgPAXSEYApQkZ+c0OvFQ1wBSGnFqJqYW8tWN1ym6IzBlDYUIVWX4E5Ouk7n8+zlb/kCG3bdQiDSFhhfYfCo71h/cekqpA+Ccp0yCqQd6eHs6HaAd5syERGQAPIQXHJRjj3qCzc+6sGL3jhnxPGcu+HH5Ny+50MXbkKv93ObKAAeAwxPRpARr9+ZG/8lIzrrydQUkKvZJW/nAZEAG750ItuwJvTHDgtCh3iVTZdHYEvCO1jBUTiHAppUSovX2SnsN7go6NB/jrWjH7oMPE3307QZOaixW52l353eX/eKg8bjp8diOxxOYhYuRG1V188ZgEDPfR/qh8xdjaWM9UrLsOycxvrr7LxxiV27hhshbt+i33H16y9ysqCqfK54sdvQ/tgFQe7Q7c8cEdBRRTURkvoXFCRQ7scAR9FASMoh7Vdha52GXd2AP546KHCfl3+/eaKlVROnIjFmUbavGeIGXLpaUEEwBydRNzI2aRd/TS618SJMaPpumkzVxgQb8AIAy5V4TlFwu8KVSgzydbhoyrsMcEWEyw0QYQKsSYZF1ZUmK/AXQpchYDqvUF4qApKj4NrJ1i+hKyDEF8M0W6INCBeg+xuI1AsNlwHNn3nc3m2MrQgviOfM3HC+T/aZYYV1s9V4cpIWP9RRXeC1Z1h6hEoS4fiKOgTD/sa4NyOUHBU3pX3ACoNmLoGPFbwRMCfneIhmXHeKI6PGsW+INynQ54KXZEciY6IGXYXMKlPHzY8/zzewYPZe+213PSBj39NtqF+C0jW5QQ5VKmjGzB5kZsNcwU8OsSrHLwlEosKFpP8zLEqnZkrPHROULmws5nL3/bjOHcYg26/jU9Wv48z9zBR1u8u66dFqvg1KKhrPcys2mMwNlQRMX+0Eb17XwxFMlrsAWiwyf+sGpxM7jIAo2dvPKvXsKNEY1CGiScn2OiaqHJOhol+aSZcfoOvS8Fid2DOaitm1HQBwT0DZemdWQFNgYMaeFTxUygGuAwxrFZZYHMQsuqkTZKbAHuCsnnZ2LYN/xVX4Ow0jMTJd6KYzv5XiiU+g5TLH6FixUMcmjKZqF27mZGdTUCBy0OvCQPxeRxFqhyFQAkyLbMBadVkIt4SnyGGXW8d6LVgqgWLC9oaTY+d05C2jR3QrOBTwW2BguwEHBdfRv261UQPvPh73Y/TyX34c/x1ldx4443/9mWFFdbPXWHPSFj/MW1EPB2pJ6DjP6AiGVZ3ggQPjN4Ox8+BntshUAnZyCFxAsABr02FjQOlJTDRCs/a4YAOtwRgphm+NoknYBASB64iExePIsmbB157jaJrr+WX/S0tgGRdTpALF7sZnW3irmFWJr7poWuiehJImutYlc7w11yoCrxwoZ3L3/aj9xtM5KLF1Fwyhcgj+9g4y0af1O+ODtcNgxve9/Ly9gCvXWxnbt8mk2sjiOxxOWDNRtLT+1IVDV6ngEGnejgSIwyiGvKhqRIGBgbKbbdiPPssL15o5xcDmi7X5TeYsNjH1jIT4361lt03DKP9cUgvAy0aPhgLmibtELMh24M9JtnGG6sDPoiqlymcSA0650FdFExsD+8FoFjz4Rg4gLxqHylX/eUHj8XqPjfFr99CZr+eDF+3lkGKQgDxgQSQx0BFTKoVoT/3AJoBaRqYGsBbC6Y6yVOxGeIV8SKvEbMh9yk7QmL/PQr4NKgywYlIqTwFFHDv203VuP7EDJtJ7LlX/KD7cvI+eRsoe/0WRg8dyOqPPvq3LiussH7KOtvzO9ymCetHl4EkXx5AMifGt4G9faBzGSS6ocIhYWgR26HbFZBtFggxkLFMvwdmfQDd9oLJBBv8sMYnK+PvtcBTQUjQZO9JBeIdOIFMZMxHdtz0nzePzv96hRe3B7jpAx+6YbQAkbcvdzKmnYWnJ9rZVarzwrZTtwI/uMlHSYNBcb3B9Le8BPoOxvLq9wcRaBr7va6/hXmrvMzfKdfXvCJi+ngj1h59cYRi27VQlaLELIephngpGttaCoCioPz5UdTkZB76LNjiOrfkaXyW60eZdwOJXYYRUy87ccxAXT+IMSBKl3ZaWxWSFVl219ENUxToHxDQS7NKS8MBWJJgIZBvQPrLL3Hk4EHiL7j938rnUG1O4sffSu6G9QRXrcKD+D1OILtvjiBekmoDaoKwsxaUPEjeBzFfy5+dCyC7DmINsIdi8DOc0C8NPF0gtQsEY8ClwTETfB0PJTFSHfEoUlGJzu5N5ux7qP18Cd6C/T/4/hiGTtXH/8KseXnxhRd+8OWEFdb/ksIwEtaPKgOpTASRnr2KBF3VTwCXCQaekHe8x9tAtgYbD0LSlWJILEYO2CSgtA5u+gAuOgyY4dc++J0PjquwzgJHgrAoKN6Az5FNsMVIDkUU4in51dB5DLqrCUge/8yHpsO/JjmwmxV2lmjcs87HwHSV6wecOo57/0gbWdEKZhUCJhvqa4vxXfH9QaRRrQHJS9v97Cj0E/Hnf2Lq1heQEVctUiogJkKx9YqYMlXEz3GyiVBfjzJxAo76KpZNa3kfxncwccdgK8Fn/87uL/6FU4GAFSJSoDAR0MFjDuVvBGUZns8EvQzYpoFNg1QzZLmhmxsUGxyMAqcG0arBzmeexdl5KNbkdt/rcWhNjnb9sGV2Y+M/n5EJHgOcOtRqcLgOthdAzn5wfw1d98LQfOhUK7H+JkMW9aU4YGwqpHWF5EFg7gLfOKB9NdSWwxcm2JoE5mhw2qXN5A2CIwBZRdAuD3pN/wMxPYZRvuLBHwQkhq5RtfoZ3Ac389qrr9KmTZt/+7EJK6z/BYVhJKwfTQEkGTMbuIyWS8nOT4atg0Cphg71kgCalwbWb6CwE2QOlxyIHGSksy9gLYOL34NxJVBtgWVe6OWHbBXetEKFBvcHYHZlFUuOHOFK4DVgOPAE8NdMuKbtPC695hVe2B4gwamSEqlwwZtuPjkWYOwC98mpmcYWjTtgENCkc9khXto3iU4FNT4O7bLJOL8DRLxBg1+t9rL+eLDVr38bSNrEqPTNsNJw763Erd9BXI08LnVWWT5n8QowgLQldOTDQP6jXnE51m1fsfZKK0Myzbj8Ble97eX1nX4UReHJCTZuPcfKnudupu7T9wiawd9L4u9dCDRG+MBjQJoq17HHCm4N9gfAa4MDQfB5YHeigGZnDdy7duE/fJDIPhf8gFdK64rsPYGS9et4P6eM9wvhyEFI+hqG7oFz8iCqBiy6mE59BkTYYVAK3NQFbhkE2f3h/bYQUCGuEHLzQKmDTZGwLxm6xkByBBxVoMYj/pH2ZZBZAHFVEN0AgUgrg556n6ze/Slbcg81W948mRXyXfJX5FH+5m9x71vH/PnzufTSS3+0xyassH7uCsNIWD+KXIhfYxSSpNlcBnBMgY/GSktmVK58/qssSFHh89WgXQlVbWX/SRukPZAKqCeg2ztweQNggRs9sNUPMQq8YgVzQSGDzxnIK716svbjjzEjBsfDwHMfwFuDwHbVPNpfeCNL9gZ4fKwNd8Bg6lIPLr/BO5c7T4LIsSqdrs80MOxVF9WeJiBZfqkDragYo6QU3ZBWSWtqHN996ks/k9508/Gx1oEEpC2gKBBtgw2zbPSK8FA5ewzObTuIRlonUTt3YyspPEl19tDPmpUm0NM7d8Xr19hfruPyG4xf5GXRniDzVnl5cZsfTxB2lYPF5kCNy6AhCt6LEX+IYYDTLxMp6WaBwCiPQGG2Js9bwA/BIBxXoCEertEk4r1m+9egqNgyup75hfE9ZMvqCUCfldsYdgLSqsHQoMaQtlDQCr2TYE4n+OdAuGEAWDvChkTYF4T9RZCQA65S+ESH4wlQmwwTYyAjEr7QIK9OIKSDGzrlgbkeoqvA6RXTdEQUjO4QzbaNn3DfvfdSv3UZpa/cQO3W5QRry/i2xc4I+vGe2E3Fu09Q+vqtZEYqfLZlC7Nnz/7RHpewwvpfUNjAGta/rUrg78AcoPO3vuZHlqa1Rcr+vg+g+6ewqhvkxMGQfEjJg/dug99GQuAhqGyALGTUNAjEAF+PgqPTYakii9oWREBKaSFjRo3AXVlA1wT4rEDh/Hffo9/48UwAVm2B4Uvh5Yh3eP+vM7iki4kl022cqDEY+koD1V4Y0cbE+1c6Ka43GD3fhcOsUOUxyIpRWD9HckZGvO4h15pIxJIP8f3qF9gO7WbjLBv90pqqI81zRFZc5uDZrwOszQny7rdySHTD4Lp3vczfFWD+VDuzektrpcZrcN5CH3sbHDg/3ICppIiamZegJsShf7oZvXNnqYRoYA29hfAaSPnk1pvh+efpnGjiSI2CEQxiScsgUFxIlyQTR11WBj+yBmP4CLb1A8MEKT4J/PJZZRopxQx5Big+wCRVAkMHqxdsXlAc4EkDVwCsZqi58zaCS1eRMe9fP9rryDAMCv4xk77z/kD3q36HboXYGCiNgfgYuN8hHqEtyJ+dguCog/21sMMPHh3KnFASBf1NMNwOX5pgVwN4fTKi3MYEccfle40GGV02GeCLhOhY6BkHt2ZAdOgp27NnD08++SRvLlpMwO/DGhmDOS5Ddh746vGU52FoGu07duK2W27m+uuvx263n/5OhhXW/5jO9vwOw0hY/5bygeeBm5GwqeaqQnaFXAQMQA7P39TBrU9AhQpv9pUKwcSvoT4bLr4B7Pvgq3/ICGlnJJskFwGS9y+D8vNgWQAcJwpxXzQCvbKAjXNsZEQpTF3mY0MeXPrue3QcP54LHoM/1r/DJ4/N4JKuJhZPt2FWFY5Uaoye78aiQoXbYEC6iZxqHadFYeNcJ6UNOkNecdM1UcUdgOM1OvrwUdjeW4PV40GbNBb1wC42zrbTL83UaqCZL2gwfZmnBZCcDkQa1Qgku2vMGD4/F7RXOFyjcFyNRft0M/YOnfEDDlWqM55Q2Be6jnrjzRgLXsXw+7lpoIVXd+vo8alodTUY73/EIOcIylPEq2M2ZFwYM/gUMGsQaYFAAIyAVEncVeAzgxKEjHI40glUC+gaGFbwXnsNxidbSbvqLz/q66nw+XmMnXcDjzzyILF2+IcqFZuegBvoaUCqG47Vwt4GcOsSFV8WDYoT4k1wqR2+VmFHA9R5wKJAOwd0rICScvGX2CvltWdWxJ8TlwAdogREYlqZ6q2qquKzzz5j27ZtHD9+nEAgQHR0NH369GHQoEEMGDDgR01uDSusn4vCCaxh/cd1AFiETMzEfutrR4DXEUNpZuhzdgUGRkDRaEheDYnlUJgEZW1g4FFYdhjUXhB3MSS80/TibBu6vEuWw5spMCG+kPkXjiC+qoDNc2y0j5NSwTuX2Zi6zMeSKRfxytvv8XC9m7WnAZFom8KGuU6OVOpMfNNNRrTKxrlO0qJU0qJU1s5xMm6BG92Ae8618tgXm/FdMQ3TspXEP/RPiqaOZPR8F6uvcvLgJt8pyao2s8KKyxxMX+ZhymI378x0smxf4LQgAhBrV/jdUBNXrXBzYSczyy+zU+UxGPFGDcdHjSB2/WaqOnXGoOX/uGZDweyIwev38/cJNu4YYuPynkEmLCrB2Xs4rr4DyPWCN1H6sikajKiGvATIVyDNJHBT7ZadM4lBOA60aYAyG+gRMNAGCTpgktyPCquN3cFTJ5D+XalagOGJNkqc8BukZXQdMDwgLZYva+GLANQZUGCBI/HgsMJcM3xpF3hd4IEKlxiAOzqghxmCByBfhwgv2KvBawKrCcxREJ0AmU64+TQgAhAfH89FF13ERRdd9KPf57DCCitcGQnrB+pL4BPg18jIZ3NtCn3cAXx7D2qJAXe74cK/QrIPlvWXz1++HbYlQtodcJkC+f+EE7skLl5B2jVHAYuvkIu3jMBWW8CmZiDSKF/QYOoyH2uP6xiaziXdzK2CyPJL7VhNCp0TTJyo0YmyQpRNORl2BnCwXGPW2x6OVes8ONrGr9cFMAaPQN+5jc5OLw5VZ0eJjsPMaZNVfUGDKYvdfJyjoSqcFkQAPjgcYNoyD5M6mll6qQNr6LaUNOiMeMPPCTUW1m0msltn6gwxm4KB+a57CD75+EkQOfk8nAgyYZGP4NARmJd9QPtYJ4fNkKlBskfi9xUN4syyefdQvVQXlHoZ8Y2sFy+QngjxEXBMk2V6bhWMp57CuOdu2tyxDEX9flNFp5Pmqqbgmdmcv3w5phkzyDJgnkvaMAdcUtGo0KEoUiohESaYYIZsGzxjgM0HJQ2ADmk26BYBPcvgs0KpAKVVQq0XvGawWSA6GuwJkGyDW9IhPrzLLqywfnSFc0bC+o9pNfAFcDctQcQA3kDMo/dwKogYwKcKVNmgzThI9UGvEsl5+KQNjC6A/F0yLZJ5LUQniRkVpBJgcxUy870ROKpbBxGQisQ1fVR0LcjUrupJEAG4+UMv5S6D5yfbmbzIQ9/nXaw/HiTGrjBpkYfez7sorm9KSO2aZOLj2REYBry4PcC7l9rQP/sUvaGBBq/Gh1c5mNfXwgdXnj7i3R2AUq/czowYMxM7tv59J0GkU0sQAUiNVNk820pbvQZj7Ag4dFiSWA0D7m0dRABGtjWz5kob5i824585mUK/tKZKFai2Q0OozVOB5Hd4zTLeG+kDww3ldmnTnIiEnYa0znwGRPgho9NACPjwl+a0en9+iHxFhwBI7j2AlApIPg5vFcGOeskd+SoW9mRCjwSY6YRRkVK5ecwPNdVQUidtmpGxMCsC0g7A2iIwB6FjPlT7wWuBCCu0SQBnIiRa4eYwiIQV1v+5wjAS1lnLQBaOlQC307JV4EHGaVORsvq33yvXAo8geSIPqbBhABgJkFgAZh0qksAVAeeshveCoEZAt5uhwSrvzg3D4JqPz8ev5bPx6tZBBKQSMfttL9kxCjcPspwEEYB7R9hQFYmAt5mgT6rKxIVuznmpgd2lGvU+g9Hz3SeBJKgb3PKhhwY/3D/SytrjGroB1/azUFxvMPhlN/+abGNMuzPvmtnvdhK1aBHVpmhGLfRT6W4ZCd8CRGa0BJFGNQJJtl6Da/QIePNNuPdueKJ1EGlUI5BYP9uMe8pkDJebAKDq8mGEItTz/GJmNXsludTaAJoJ6uKgow+yXdC9AtqWw5Bd0DP6HCzxKbj2rm31en+IXHvXEt2lF1VGNjGVUOaHAitsTYbcDJgWDR85wXDCl2Y46of3asBfI7kkY6NhUizMqIfPdsFOn2SRpBRAngX8FoixQo80CMZCnEVaMwlhEAkrrP9zhWEkrLOSBjwDRANX0zJDpAQBjSlAayvB9iKgMiv09d4K7DbBigugQwCGFgrofN4WUsoh7xuoMMCaBd1ni0nWUBT6Jw2j3K3xVeHpF9PZzAqXdDVzvMbg/Dc8rDrYlBHRJkYlzq6QHKHw1AQ7Byt0/DocrTLwBGFEWxPugABJfq3OnLc9LNsXZPF0O18W6vztCz//nGjn5SkOVlzmoLDOoMszLrzBU/fNnAQRl4OL1m9k0MwrSPpkE4f9kS2AZG1O8DtBpFGNQNImWIkyZxb85YkzgkijGoHE9Nlm9Asno+kahQqgQ50q8IEOhiKZIzF+sJkgsR5iPZC9G7QGSM6Xto5dgwSLldirfknDvg1oDdVnvP6zUaAiH/eRr8i64mYyFIXcKPg6AxqS4F4nfOiELAf8wgSHg3C8Hr6qhsggjI6E8+LgMhskHYMXcsWMO7QIvNVQ4JSx4EQLDM2GcodMy9ycDklhEAkrrP8KhWEkrO+UF4GJfshkTHPtAZ5FKiXdvvU1A1gGrAX+gISh+YGXFEhWIbkXxKXDgCJwBGXUtzgaRn0Mr4cYIn4YJI2BPOCJYS8wvcMsrlrpZcneJsio9RroIevTOwcDvHUgyPRuZqZ2NTN9mQBJTrXOmPkuomwKz0y0c9XbHrommvj1UCsGsp9lyd4gw9sIkHR5pqFVELnlHPF7XNzVclogkYh3L3tdDmzrNlLfty/5fmjo0oP26zdxyB/J6BCQbD4RxK/BZd0tZwSRRqVGqmy+2sHItib+OdH+nSDSqP5pJrolqQS/+Rqjvk58H6pMk6T5ICII8Q0QVw1xZeA2AwFIK4OGSEhygdUO7YGUdPimC9TfdztKVASVnzx7Sv7G95Gha1R+9BSO7I4kXTOHr9MhKRZetcNSJ/jt8LgC8zU42gDHqqDeC30ccEk8DHXCPC8s3wNraqB9HQzNgyMaVERA0AZZVhjfVbYAR4VAJLl1605YYYX1f6DwNE1YZ1Qtkqo6A9n50lwfAvuB39MUyNX85/4JDAYuRSopucBLwDRgjgp36DBtEvhehnPy4dN2sCUbLt0DWVtg2xgYoEC3mbD5BNTnmHhq+GsAXLVyIQDt41TGv+FiSKaJa/pZuGqll0u6mlk0XdwsM9/yMH2ZhzgHxNlVnppgOwkiq69yEm2TKsCft/hRECC5oqeZgjqD286xsLUVEGmUAAlMX+ahyzMuDt0SgSegMHaBi91lOuYFzxLo25cDOngDkO2Aom49SFuyiaMzRjJ6YQNrr7RwpEpn9tserCaY3v2736qnRqpsvDribJ4+ABr8Bhcs9rGn1kLyO59QHReHEgSrB6L8YHWBboGIAJj9ki3it0meSVwAjqRCd1V2u1iA1Qq01aDAlEDMU89TPWs6dVuXEzP0srO+TY0yDIPqdS/iKzlC10+2MCbBwQ02cJpgFfAWkrr6rhdcLrDqYLXBtEh53uYY8FUx/LFEbvuIclBc8LUdgnYImKGrHUZ2hk/qINIEN6VDShhEwgrrv0rhaZqwTqtSJCfkF0hVo1FBBCrikFXv334/vxdY3OznDOBdBFxuQjJDAOYHIQXo8QLUH4fXB0C9FS4+AO28sOBeeMABVgW8VbDlQehaDx5d49Yt8/j46Bs4LdApQWV/mU7QgOndBEQavSJB3WDmWx7ePhjkrmFWnt/mPwkiMXb5HsMwuG+9jz9v8WNSZErl+v5mIqwKT24NtAoizbXqYIDpyzykRykkR8D+cmn7qKvew3zhhbTzwAArbFVktX10OWSu28faP4ykk72B3ok6S/YGURSFZdPtZwUkZ6tGENlaYabX/LWYugxlVydwNoA9CHY/2NxQHQedyyXoLBiEykSwJ8FMVUZi51lgJpJR0tOQxNMyBZIqoPIvf6T2H38ievB0YkfMOuulebrfQ9XaF3DtWcvcZ17ghZt+iV+Bd5BR7mhDFiQebQA0qWTEOCHbAjMU6B6AJ4/D4XrIaoC+FVAQDE0JWWV8d1AMDOsAq6oEcG5Jh/SzKyaFFVZYP4LC0zRh/VvKQUDkDlqCSB3wZ6APcjg1B5HW2jJViJ/EgkzfxDT7/okmWG1AwkQ55Ibmyec/awt4YOJGeWcMYI+H9OthhwJFqomLutyI1WSmZ7LKqxc70IHz25tagAiAWVVYMsPB9G5mHvvsVBABUBSFh8+zccsgC5oBZhVe2B48KxCBppZNUb3BrlIBEedzz8NFFxJhQJEVDppkH0zPnZKiamvTA8fH0rJZsk/D8soCuHQml63wsmL/2e1C+S41BxHjw7U4UoeKPwTxfZg08DhAj4KADaqipVTqi4feMeLBUIBiFa7R5WuZCuCGQpOkl1ZFgOVPf8T2yF+p+2YVxW/8Gk/uzjO2bQxdw334c4pfvw3v0c955fX5PH3zL1miwF8BlwE7/TC/Go7WyoLAK6IhMgaGWeEvQKAa7jwIuTUwpAwGlcFhDQ5Fg8kuI8jjMmBUpyYQuSkMImGF9V+rcJsmrFO0E3l3ejeyAbdRJ4AXkWmZb+9pba0t8yXwPvBLJN7920pWZKPvkWxI7AHd9sG2DKhwwsEk6L4JNp0LedECIZ93g4gZEPevr7jvk/H0T1NZPcuBWYUBaSa2FmjsKtEZkN5ylsesKiya7mBKlwBTuliItp3qzcivM/jwaJC2MQptYhQ25+lnBSKNurirhSfO1/n1xz7U2bPRfnk9sZqMysYB+7RQBcIO1gAoEeDs1wPPF19zaWERewcN59ClV6ACly1fwjLOrmVzOjWCyJflZvhoLQwbim871MSLUdVpk9h0mwL5JuhTB2lB2UnzoQOmRMDrGhzXoJMJnCpYDNDdsM4mhmZrUCAz0guWeb/G2n0sNb++hrKl92FJyMLRYRDWlA6YohPBMAjWluEvOYLn2JcEa8poP2o0y155md0dOvB3INsQoHjPBW4v6Cp0joTpdnhDlXbgAA3+XgjbKyG5AQZVAAH4RoXKaLCYZWHjlM7QJRaWloHDBDemQWYYRMIK679W4TZNWC20EYGIXyE7Sxr1JZIvcgctqxtwalvGC7yK+EhmIwfW6bRHh/d0+HU5nPgbHI2D97pCjA/mbofSoXD/NFnAZ1ag55dfMXf0WHon+lk9y3YSLGq9BhMWujlYobFuTsQpQHIm5dWKuVU3YOPcCFIjFQ5V6vROOfvL2FumMfI1F3UJ6ejbd2OJT0AxIFWHgEmWvY34BIJtoSYWYiLgRAxUGfDHIDzkhno7WD1BGm6ag7JsyQ9u2ZysiJSbsb27lqQBQzkRBVY/ZChwwizL4ux++KUFnlBgVgWU1EKmDmvSoVMk5AdkiV6tBRRDPCblhuytKYuBeDek10KwDkoToOsx0EwGVXs3U/3+i9Tt3ExNUV6L25bYviPpY0Yx8sYb6T1gAMVAVwPW6LDPJcZUMxDpgEFO8a0UAvcYkqr6agHU1kPfKmhfD3Ua7LSBO0KWC6oqXNAHOllgcbnA1o3p0Da8LiassP5PFI6DD+t7yUCqISVIvLva7PPLgXLknan5Wz+zHDks7kMC0I4hIHIp0PcsrrenAq8aUJUKUf2hwzZIa4DCSPg4FbK3QtooSE+AuK++Yvb4sfRN9rP6SluLCkeMXWHNLCcTFroZu8B11kDybRBpGyv3/IeASENCOrbtu/EnJTDQL5tu21thqw5tC6G0PaRXgNUCn6VBwACrAc9qsvNFUyFGM+N/8VV0BS5buoSPbEqLRXvfJcMwmLxEKiLG6rUEhg2lMcfNb4GqIMQbUh2J1SXSP8IH2yyQHYQvosBtl0TWrxRIMMMdfvibBu4gtKuFg8lgCUD7YklrPZ4GGSWyhdhmUkgbPpKUaSOpiIC06kqmVpVxrk1lbVoqOTExlAPJQBqwW4N1XqhyAwak26G3A8pMAk5VwC06rCmDLWUQWwcTKsRsW6rDvggI2EFVIMoG5w6AzhosKpOdNNeHQSSssH4SCntGwsIAXkHK2zfQ9KLwIdt4nYjxtPmRWIt4R+KQaokdWIF4PH7H2YEIyAE2SYXVGiROkI2yHXNlKdqeTLnu+1bDK199xbXnj6Vf7Kkg0qhGIOmaaGLsAhfbi1vPI/EEDN4/HCCvVmsVRBpV4zVYczR4Rv/D3jKNUa+7qQ8oRC14A19yAmYDjpjAboLPguA3wFEDSfmwv43cryBSnTCr4DfLwRmjQmD9GgKpCTgKCtB1g0r39ytc6gaUecAcGYmRmICmwGhTk7fHAK5VoAGItwlwxLmhOgDHrVARA1kWWKOKP+TVBnjBLbtgOjVAZaSAU5ccUBug3iLPWUYNqE44nAX7MqF9JCy1w/tZCVzbpxvvd+3ChzEx5CLbnV06POOBzdWypTnODDfGQP9IOGyGXykS4d7HA6/nwqZC6F4Co0vAGZDdOXtjIegQEMmMgUFDoXszELkhDdqFQSSssH4S+kEw8uyzz5KdnY3dbmfw4MF89dVXp/3e119/HUVRWnyEV2z/9yiAjO62BS6j6dCqAB4GxgIX0tKo2hhidhUSYlYJPITEv9+FBKN9H41UYasBn8bD+qEQURc63CywNx1yvvFxePx42ti8fHQaEGlUjF3hz2Nt1PrgtR2nLnJzBwwuWuzmosUeZr7lJafa4K5htlNApNItFZML3nTzx42+VoFkb5nGefPdpEcp9Ew2Uz/tErRvtuPVIeiHXAX8KqQVQXwtFLSFZANwCHj0VaEN4AmAxwn2j9dQPutCYgwPdZs38/cJNq7o9f3aNCZVYcNVVrKMGtQxI4g+fJhzgmA89xxq357YjxymMRKlxiSVB38QlDeXcPTCbvj2b6NChUEajGmAGzSpUnSqgxov6PUQ5QanVyo7hhkcOuzoBvuzISMA99XDcxHQ0QxlyMTVB0hOTR8DnvXBJzVQUAcRCkyJgSdiYL1VVgMsBd4zIFABb+eCqwTOL4AudTLpdNgMx2JBM0vSb790yBoI/b3wZpm0836ZBu2/vTQprLDC+q/V94aRpUuXcuedd/LAAw+wfft2+vTpw4QJEygrKzvtz0RHR1NcXHzy48SJE//WjQ7rx5ELeBQYScvk1ENIReQmZGqmUY3TMp8gbZls4HPgH8C1wAROHfM9GxUpUKHAizqUjYNMKww+IRHzRzIg22ZjdM+pHK7U+PBI8IyX9U2RxvRlboZkmnj4vJbQ6w7I0rqtBRq/O9fK1gKNdrEKt37kbZHUWunWGfeGm8I6gzsGW3lwk/8UIGkOIhvnOtkwx06vKC+mcWNgx3Zq7PKAOYPQEC8H9vAcOJEBkYqYQbchY8vxfogNQslv78SkBan2GGeVrHo6pUaqbJptpb1eg3v0CB750/1wy01EHztAzbjhrDx0mKACOQaYA2BaspiCu67Enn+Y+olj+PWX2zjugg+DYKqGaC8E6kD3g0mFWJ+YV0sSITdbdtoMLocxR2GIB0bGCYQ8A8xD2nfpBhwNwOoayKkBmw6DouCpWDGWPq/AaOBvCvzBB8cLYHeBtLfGFUF0AHw67HfCiSgxtzqA8Z1B7w7DXfBGqYTX/SJVtvWGFVZYPx19bwPr4MGDGTRoEM888wwAuq6TlZXFrbfeyt13333K97/++uvccccd1NTU/OAbGTaw/viqRIBjDtC52efXAV8BtwHNY7VqkcNlEAIuPiRrJBqpkPwQ81E9sBDJlHAbUB6EO82wew30+QS+6gz7E6FtEQzK0fjL3umsW7eKRdMczOx5asXgmyKN899wtTq+2xxEPrzKyci2Zt7Y5WfuO16yYxXyag1WXOZgeBvTSRBZP9dJz2QTj2/xcfc6H/ePtPLH0Tb2lesnQWTdHCcJTmH6Gq/B2Dd97K63k7h2AyX9+9PtOFisUGmTFkpNigBeriFVCTNgq4PkqhKO9u2CVlf3b4FIc5U06Iyc7yWnMsicPhYeHWtj5EI/x4nB2LgFS+fORD2/mPJbr2J2LzNPTbAzfrGPXbU2YpdsIDNrANEqHEoChw8G5cOmzvL3Bou0d2KDMOIoWKKgxgGx0ZDRHuoVKAYOGuDVQG+ABr8YStMdcLNdEmDXKuIdiVFgug631EJ9BTgrZFw3PlTcciuwPxIqQy+0eBWm9IEvE+BCF7xRIiB8XRp0df7bD11YYYX1I+k/kjPi9/vZtm0b48aNa7oAVWXcuHF88cUXp/25hoYG2rZtS1ZWFhdffDH79u074/X4fD7q6upafIT14ykfac3cRBOI6IjxNB/xfDQHkeZtmfEIPDyEvJOdy/cHER1Jb70PqcL0A25RZAx2iwFzR0G0EzLypBVQlAYxVpV923dgMylctdLTIg4evj+IAMzuY2X+VDu5NQZtYhSmL/NwzsuuFiAC8LvhNh4ba+PBTX5+v97HefNdJDhbgghArF1h3VU2+kR5KR83hsTd+3CZoMgB7U5AeYK0xQpCj4EdGOEDd2UJOQO7/6ggAqEKyVw7HeNV1hwLUusz2DTLSntqYfRwtCeeOAkir06xE+dQWHuljd4xPmouH0PEsW2cSAVPBHSogPVdwGsFRz30OgaXHYEoF8RGQZkdDqdCeQoMU+AbA9ZrUNEArirw+MWcenMcPOaET01QrcgoeLQCQ3xwVQlUFUJ2HowrbAIRlx12RjeBSDsbXDMEtiTA5BCIAFyTGgaRsML6qep7wUhFRQWappGSktLi8ykpKZSUlLT6M126dOHVV19l1apVLFy4EF3XGTZsGAUFBae9nkcffZSYmJiTH1lZraVUhPVDdADJCvktkB76nAt4DMkOuZpTJ2k+RsChDdKmeRe4h1Pj4c9Ge0M/+3Ho+n+JVGk+BR5QoUGHhQ74cix09sI5JeBV4N22CqM7XIk3aDAwTW0BJIV1Oue/4aJzgnoKiAS01kGkUc2BxKTKXpnmINKoRiB5dIufaJtCbo3OrtJTF+TF2hXGZ4NWW4dzyz4sQTAFYHs3sJulJVaGhMd1BQpLSggM7E6gqvpHBZFGSXS8kxibwpj5bmp9Bp+GgMR/z+9OgogpFBQXY1dYd6WNTk4vW+eOpSLgIroeDmZAm3rokg/plTCgAY5HQYouk0EHU6BdA/S3wXUa7HODUg2GGxKtMD0WXoiUMLKVCtwKOBTxJkXWwm3FYCmC0cehXxWYDKkkNcTB1w6oD70oz4mGa4fAW0642A1vhn7tzEuF7mefkB9WWGH9l+k/Pk0zdOhQ5syZQ9++fRk1ahQrV64kKSmJF1544bQ/c88991BbW3vyIz8//z99M/8n9CUy8XIvEBv6XCEyFTMDGNPsexunZaKRzJF6pBqSAPwaMat+H1Ug6Zovhv49C4GRlcBUxHOSq8AGQ3InZg+DuhiIygdFh8oUmH7+n7lo1G/4qkhvASQOi0KsXaHCbVDna9l1LHcbbMnT6JdmYnBG6+O6s/tYWXCJnS4JKpvmRZwCIo2a2dNCvEMh2qYwvI2JyYvcrD/e0sPyp40+Ht3ix/KnR+gy5jIGHoHEIPgdkr+yAzloFaCitJRDg3qjV1fz5PizB5EGv8G8VW7eORj47m9GgGT9XCdRVgESh0Vh82wrr19sbwEijfqiQONIpYZx7nAcViedK+GujZBdBjU2SNMhMQBfdICqWFlMlxKEAynwXBDqa2TSJk2Fc2Pg6WgYboF/KtKiukeBrxU46IeCMnixFNrkwuRcSA5FxJosUJ0sr1kfYlS9OBWuGAQvW2CqGxaXSIVpbgr0DINIWGH9pPW9KuyJiYmYTCZKS0tbfL60tJTU1NSzugyLxUK/fv04evToab/HZrNhs4XjEn9MrQYOIqmqjU/6DuBt4E4EMhq1D1hEU9LqJsRLcgPS3/8+8oeuYxNCvhcgB/E7wCXAFcAa4GVgiiLVkfeD8JYK554PvZZDSRHszoRD2QqPu5/AngXLF/6Vc9IFSN6c5mDD3AjGzHcxer6LjXMjyIoRzg7qEOeAz/PF2LriMic286k221m9rczqffq01RM1OqPnu4i1wzsznSRHKFy02M3kRW4+uNLJee3M/Gmjjz9+6iP7oUfw33AvhwMQpUKFDXqbBMgKkQO0XWkpxWNGYndVEQByqnUMw0BRzmwBbvAbTHrTzeY8jTd2BVkyA2acRTBalcegxmuQHKFgM0G0TWVu31Pv7+qjQaYs86KcP5Exj60gN6Dwyy/h87aQ55Rx5Po42JAIWW5Ic8PXmTKq6wmA4oFEM3SMhhtsMqX1qgKdkOksqyKTMuvrwVUL+dUwMA+6e5puQ1Qc7DPDwaBU5+zANZ2gZ1tpF17kgWUlMlkzOwV6f18yDiussP7r9L0qI1arlQEDBrBu3bqTn9N1nXXr1jF06NCzugxN09izZw9pad/3WAvrh8gA3kTCzG5HQMRAWi0bkfZLQrPvXY7AwX1IMNVTiI/kD3w/EDGArUg76FPEFzIT+AJ50f0RaQ89gBw2fzIgoMO7Onytww0qeM6BI8kwtBDig3A0HrZEKVzb+Ql+cadUSAaEKiTHqnQ2zI1AN2D0fBf5tfrJQDO7WWF8exMfHdWYscyNL/j9sjsaQURVYMPcCNrEqNjNCu9d4TxZIZn7toc/fuoj+aFHGDX3Xqwe6F0o+17q4yVXJC30+FNaSuGYkUQVHeeba+28cKGdZ74OcNtH3jNmmjSCyM4Sjc3znFzWw8zMtzy89R27bPaXSxZKvENh/dzWYQyaQMQ0fiK/vHsFusVGm2qot8HGtpCXKsvrhuWBYYcqBxyIhWo/1GqyXTc7Eu6MgX/ZIEeB+Yq04q5SBEQWBWBROVRXQl0ejDzSEkRS28IXKhwIgUicCvf2g35tpbI20QMriyFowKxk6BcGkbDC+lnoe7dp7rzzTl566SXmz5/PgQMHuPHGG3G5XMybNw+AOXPmcM8995z8/gcffJCPP/6YnJwctm/fzqxZszhx4gTXXXfdj3cvwmpVGvAs0mq5GqlIBJCpGD8SVtb43rgWGfNtbMvkIm2a8Xz/aZl8BDbmA4mIybUUWb53L5AK/AmZJrnfgCgd7tJghwG3m6GTGR4zYKAJRlwAgSCkhixGpdmQXaPw+CVPMPX2O/imSMesQoQVsmPVFkDSGGh2fnszq49pXD/Awic5GpcuP3sgaQ1EGtUcSBbuCWCZdw3d7r2XuHwIWsBZBZpV1tbXG1BgyI6eyIcfxnfwMEumWuicYOKXA6zfCSTNQWTNLCfD25hZcInjO4Fkf7nGyDd81ARUAqqJBEfrIHKkUmPKUi/WUedx+UMrqDXbyKiD8w/Aw+dDaQwke+DcQtjQA/bFQdAnz6FXAasNJsfBMge0McEjoWrIfUC6AoYBTzTAwjKwV0HkARhQBBkh243TAWndYFUdFIY+19YKDw2BrAQBkTFeeLdYTM1XJkP/qFbvSlhhhfUT1PeeyLz88sspLy/n/vvvp6SkhL59+7J69eqTpta8vDxUtekXdnV1Nb/4xS8oKSkhLi6OAQMG8Pnnn9O9e/cf716EdYq8yOjuCGB46HM1SKVjMjKi26j9SPXkOsSkuggxWd5Ly6ma75Ir9LPfIBkQl4Uu+wvk3XENTQFrvzPgsAH365CqwC9M8LkCS4A5KrwRkImMr3vAjEyYXAiV6VAYAydiIfihn4Ydh7GYFN6d6WBIpryUG4FkzHwXICDy0vam7btTuliYusTNZcs9vDPTcca2SEnD6UGkUY1ActESLxsXv0nq2Kv4pv95uO1wIhEmxkgI1+eGROp7DOh4550cfmcl13xYwZbZKhnRKr8cIFh4/ftimnh6ov3kbfs2iAzNkvtqVhUWXOIAPMx8y3NKy0ZAxE9dTArBhkJGZ5pOe3+TI1Sy482U7tpF2b5cJlm68NJwiX03+8FmEsDalgGFZvAZko7rsQJWuNcKk03wHJAJPAg0+ogrgvBANRz1QNdyiDoBMRo02uDTU6EhGpaWSLYMwIAouLm/eEf+CpzjhTVFAiJXJElGSVhhhfXzUXhR3s9QdcDfEFNq48RLDhL5fj0CHCBl8BVAHnAjUh15HhnZHf09rs8A1oYuy0CqKX7gMHAlAjSLkMNrJlCkwyIdYhWYqMKXirSRpiN7U97R4U0NZqoCKf4jUPgC7EmBte0hqszH5w9P45uy1ayaaWdCx1OZut5n8OhmH49+5m+xfbfSrXPuq26qvQYn7ojEfpqWBcDWgiDDXnFzZS8LCy6xo54BXLxBg4uW+tiUp3Dno1t49rqBRKrwp0jxyxwG4gzx3iQADQdz0MaNINVfwZbZVjKiBXRe3Obn+ve93DLIwtMT7bgCtAoizRXUDaYv9fD+kSBLZziY0d1yEkRqYpLRCguZ09vSqlm1uUobdEYt8JOvxzDqhc20Te6CzwqruoFNA1sA4gNQYgGHAYYTbEGY0AADM2Xk+1ogK3QVhgGfueGZWqj1wLDjQLW05VKQpXa9esBX9bClVlpZKjApFS7vIasC/gG088LWYvBocHkyDA3/CggrrJ+Mwovy/kdVCjxN0wZdgM+A9Yh5tfENZS3SwumPtGs2IofmTYhX5Gx1CAk/q0H20WQjlZDJCJQsQyomVyCtiqc0Cb661ATbFPGuTAMiDVihQ54Bl6jwrAlW6bJjxNIZTGnQZjdY7T6WvjaN0jOACECUTaHaa2BSICtaTsfGZNWq0PjumUAEYEimmflT7cx9x4vDDC9cdHogsZogMxL8AT9b/dUoQJZd7t8OxAhcoUgcfNAAW0Z7nJ9spnD8CIa/0QQkzSskmgF7y/QzgghAQZ3BN2WA08nlKzw8XqPz2Fad+uizBxGAlEiVT+dYGbWgls2/HEHVks0UdumCwwcoUGeHGD/odnAEZSNxRD2UJko15EoEIABqNFhYA+s9oNbChYehJii/cFKAqEjo0weWH4M9PoFYGzCvI4zIln+/BKT64MsQiFyaFAaRsML6uSpcGfkZqbH6cQeQhPxCX4TAwDU0kWfztkwS8AKQgWzaPdtdtVXAa8jkTRqSn/EZMAAZEX4XWWY2E1k/v0SXQ3iSCXaG8iWmIWOuK3WoNmC6CgMVOdAMA37thzm5oH0DDUcguM3HnXumcbBwNe9efnoQaVRAM7hqpYe3DwZ5dYqdJ7f6Twk0Oxs1JrVe28/SKpDohsF17/l4fWeA9L8toH7eLDwREGEWz0y5Ikmz9xlwFMgKQr0HaqzgOJpD/QUjyPBVsKmVCkmUlTOCSG6NzvAFPkpi09E+2YjpvnvQlizB3C6bYG4uV/a0sGDqqSDy8bEgT38V4PnJNjKjW7afSht0zp3vo0CNJ2J7DvGaE1ckdDMgxwRBFQaoUOyFBD+8lgwpzW7eNx5YXAMn/JCQD0OLoQipeqQD7dpARja8tB/yQrsMY1W4vTd0TpR/vwn4fXCkCNwaTEuCkTFn/ZSFFVZY/yUKV0b+x7QTGZdtrH54gX8ibZqrQt/TvC1zH3AMGamdA3Q5y+sJAquQxWd2ZDx3H2JavQPYDPwl9PlzDVimS0Vkogr7TNKmuMSQscyluoy5zlChp9r0rtpXD0W7ILsC3rRD/xI47vCxYtc0DhWt5t0zVESay2JSeHOagxnL3FzzrpdYu8KG7wkiIDkkAHPfET9HcyBpDiJ9H12AMX4WXh1cJqk+uYGeBjwSepycwBA/7AnKHhVPh/a0X72Z0omnVkiyohUyolV6p7R+extBpDw2Hf3TLagZmbDgDTh/PMHuvTBPHs+eShe1PohvtqulcWomaKiMeMPP5tnWFkCSU61T3KBj6tUR1WYjqQaSzdCrBI6mgM8C6NC3CoY7mkCkQYclNQIjXi+0PwA9PAIiCpBlgsH9oUaBJ/dAdej6sqzwmwGQGDIorQJq/FAcApGpiWEQCSusn7vCMPIz0EYkHOpeZDqmDGnVzAR6hr6nDpmi6Y+kXy5BDoP7ELPp2WgbUnnxINUPF5JdMhtp1/wNWZZ3fQg0ykIQclCFTQpMMwRM5usSAT7TBF2a+QtqCqBwB1QcEaOiRYUtoyD5oMHK+TPYW3z2INKotTlB9pXrxNjODCJ5tTrL9gW49Rxrq6OvzYHEboZ/TnK0AJFujy4gYdQsNB847FCnCnzohqTOpiMHcpQB2/0QZZHHvW0DdOrSngNvLuGLUSN5eJPBcxfKMzKx05nzQ+7f4KOwOoD94xU4MzLxGGCYzTBvHg4PmD/cxP5JIxm90MXGWTbiHcpJENHHTyTpib9Rdv55jFxQwaY5AiRf5AcZt8iH1nswWQtWE+Ez0VODLB3ejoFqO5yjQ6dyUANwTijGd68X3qiB2iBEVUDKEWiP7KcxgF4xMHwwfF4I7xQJpAH0i4Jb+oM9dFfXAUf8UFMEDRpMSYDRsWf5ZIcVVlg/Wf3HE1jD+s/JQALFDiB5HlbkAHwaWXTXCCL7gccROOmNJKm2C33P2YBIMXA/MomTDZyHtBtGIEbX55HD5UYDdmriC+mtQLYJtqoCLiN0eFGDzQbcYIJ7QiCiBaB4D2x7A3YuhZzDsNOQZX0WHXrUw9rhfg4WfkVmtEq/tLN/ya7YH+DCxR5yawyyY5VWp2FAxneHv+rit5/4uHS557Rjv31STUTbYE+ZfhJEXtsZoPvDCzi/+yzqHFCQCZ3tMkKtAvGKTJW4kFHrCg1ybRBtkuevkwF3FxdwfN4c0uMs3HXu2Yf9/X6ElaRoC8HZV2ItLUVBLhMDgiYw9eyJtn4T+70RjH7Dx+I9gZMgkvjWSvyduuB8fwtllkRGLvCzYn+AcYt8+PsPxrliDYn2SObtAr0tzFclfXVADfwlCFsiwGGFNg54swaeqQT8kHUIIkMgUhK6z+d1gPNGwttHYHkIRFRgUgrceU4TiGwFvvBDXRE0BGFSApwXd9YPR1hhhfUTVtgz8hOVgVQpohGvh4Lse9mJQEZog/3Jtsz1wOeIufQmxMvwXfIinpMNiLdkMLAb2dqbiphTOwIjDHhfl1Hd81WJda9X4OJQtsZ7OnRVYKoKaaGig6cWinYKiAR8cnA1hv6nWKAgC/ZkQGI95KXBqGWvc/dD8+gUL7tWkiPODCUr9ge4fIUXXTdoH6eQW2MwMF3l49kRRNuaKh8nanRGvObCpML9I23c+IGXce1NpyS17i7VGPW6C1DYc2ME92/089rOAI7XFjA6exbWOqhNA6UjRMYKABYDExFYrDBCbYkg2P1gWCA1AO1yCvhy6ggiqwvZMttGu7jv9/7gUIXGiDf8VKe3I7huE7bkFHyKgX3xSoLnDsJo0wZ9317UMSPRqqpRJk/G9NZKIs1WbLVQ54S2W4+Te+VwvCVFmIcNw/HWGtoSiUWHkSZYnARZ5dC9BganQoILPvaANwnaeQSwerghsAfqNQHdUmQU+KqBEB0PL22D3SGjqhWY2xFGZzfdj93AWwGgEOqCMDEeJsR/r4cirLDC+i/U2Z7fYRj5CSqAVD96I2CgIWASQWiigZZtmWGISbUdYhr9ruPOQFo/C0KX1VgJ6RG6vLeAOGCCAZ/osMeA81TIV2Tz7hQDjhmwWocBCkxRISFkSq3Ok1ZM5TG5H7lIWykKsMfC3myojIT+eTDKCu16w++PrGf+xZMYkKxztDJIUoRszD0dkDQHkd+da+GhMTYuXuphzVGtBZA0B5FPr44g0qow8GUPJ6qDTOxsZcUMGzazIiDyhg9vWht8+QUkW4KUugx6zV+AfdwsLMVQEw0mB8xIh78r4q2xIqOuR4D9hhiMlQCohjzGSl4BTBhBYk0hm38AiDSqEUiq0tth++hT3K8+Bw89iDkjDTZsJti+A46D+1FXrsR1113EYCWmFkqjwe6B6ADE7c0luOY1glf+lrKOkfQphZ0Z0NEMDS6YfggOpMHTKfCvo5BjQFEKZBow6RiU54lHpl3o+dQT4KbBUO2F53fDiZBRNVqF23tB16Sm238EeDUA5kKoD8L4OKmKhBVWWD99hWHkZyoX4s2YiASX1SNZDKNpCjc7gExvXBv6+jJgHlLF+C4dQyCmDBiClNSjEC/IRwhAXGjAFzp8bcAoFYoV8CgwyYCDuiy7G67AZFW8IUE/lOwTCPFUy23KCd2XBBXqMmBnG4h3w9AiGJQJmX3AEQfr169n8uRJjMw0WHW5jdwandGvu08LJN8GkT+PlfAwTTeYssR9EkheusjB5EXuFiBy3kIf+70R8PAj6HfczgXtFP440sL5i3x4O/Ugft16nNt2cnzebEx/fpwul89Cq4BeO6EoG0ZmwtIYmTRqi1RHHKHHr9gAswFBDdooUHe8gLoQiPyQisi3dbJCYo0mWFHJvcOtLD4I+bYEghs2E9GxA2k6HFUgtg5MOugK+O3QsRwciRBTDGVBsNtgewYM1WC/BQbkwZBSKO4JE73wr3JQzDDRAZ96oM8B0H0CIuWA0hXu6AG7C+C1w7KVGSDDAncNgMRmEe75wD8DYCuC+gCMjYML45vMzGGFFdZPW2EY+RmqCkkwnQN0Rn6RP4+M7XZA3m2vBE4gILISMZteg7RtzqRaZLLm69BlJwENyEbdL5EFbxcbsEeHLQaMUKFMAZ8iFZLdOnwRatOMV8CpgLtKAKRkHwQD0rbIR6ot0RGQkw0nEqBXIQx3QeeekNINTKGM+h07djJs2JCTINKYC3KwQmsVSFbsD3D5Wx50gxYg0qjmQKIb0C5OYcPcEIi84SXXG0lwzaeoA3phfLQa97QpGIEApt59cKxfz7nx8RgabFEkRyWwDXYnQ4QOOKBLCuxQ5HmoCD1+ZgRGPAbYgxAIQEZZAUfPG0HSWYJIvc9AVSDCeuYT+mB5kOGvubGYFHb80kmNDyYtDZBvSyBy/eckt83msAUSy0FzgsMPWbXgTwNbAxQq4DXB6DzY0wHqIyChSgB0cC2kp8C2Ionnv8QLMccg1wprO8DcA7LDxjgHbk2Cjw/C20UCnCDL7G4f0OQPAQHevwTBXiggMiZWDKthEAkrrJ+PwqO9PzM1gsdNSCbI18h47W+BWKQt8yyykG4GEqE9Cfiu9YUa8B6wFIhBKiwFiD8kH1iMVEIidXjOgMEqdAq1ZMYasF2DlwyYpMLfVbAYUJkDR7ZLS6Z5KyZSAUsK7MkGswbnnIDZfsgcALGZzQKzDFmut9KQ0d9vq2uiiY1XOxn9upuxC9ysm+Nk8wmNmSs8DEhTuayHmTuH2k6JPjepCu/OdDJliZsPj2j8aoiVSKvC6NddHPI6GbLuU7b27UWkAdoFF6C8+wHq6/MJ/vNpRsfFY9XhM0VgalqNbI6NiQJNgcpYeEeR6lMXBEh2I5UCw5BAL4cGbhMUP/44geO5LJzj/E4Qya3RGbHAh8UEm2fbTuaQnPI86gYPbw7Q4AefZjB2gYfjNTrX9LPw7NfFuCaMIzctAzq0wd1tIEnDxuPs0o0eUVDjgzWRkF0Jvgg4kA6DHWJW/UMEDCyGDzJgSBU4fNDnKET6oVKR/TTnlsGWntAnW8zJb2yHT2skhVcBJiTDVb2gedRJNfBEEGwhEBkZGwaRsML6X1a4MvIT0AHESHonAh4rkeyGGwALTW2Za5DY8W0ItHyX/28XkkXiRnwlxUirR0XSWMcZUBvyhfRXoVaR0v4IQzbrHjXgYhVGKGB4oWQvFO4Eb13LVkyUDcrawuFU6FgGw0qhWydI7wv2UCRswBDAWm9IlSZaEQNt/fp1vHThZEZlGrzTrDoCTRWSCCvk1RrM6G7mjUscmL8jaVTTDa5e5WHRniBtYhSKNCdtN39BZY9eqF5ItEGuCZwBqLcIsV8XhPkm6KPIqG7nXbK8b2dvyPKCs7OEmy0DZiGHrYJAmD10n6x+SWqNOpZP/oQRJNcVsXmOjezY1gGjMUekKjadqEAAp7ecLacBkhve9/Dy9gB9UlW2F+uYFOibqrKjRMewR+FoNwAwCNSWECg9DkE/bQaNwvvoA1j7juHcY7C5s8CjLRJeNcFX+8S8WhwBDjMkeWHEasgIgM0u97GDAvZesKQz3OmFrdtgl1cg0gLM6QDntWt5W13Ag0EwFYHbD+fGwIzEMIiEFdbPUeE2zc9EXwKfAL9G0lH/hfTmp4S+vhKpPFyJbMntClyMHISnUxkCIQeQSoof2bCaFbquIYbkY6zRoZcCDaFAssEGbNWh1IBpKgxRwFUhrZjSA6AFW7Zi1AQ40g48FhiYB4M1aNsHkrqCySwVg6MIgBxAKjNBRUBkJDCQUGz9unX8OQQk77YCJGMXuBmTbeb1qfbvBBGQXS4zV3hZcSCIKTqKYZu20LF3LzYEIboEKpOhQpUdMmVmSNAFtqrssvRuQiUcroS+RbC+B/zOAv+Mk83I05ClgCYExsqBFAN6+WCLCTxmiHNBm5x8DkwdQVxNEVtaAZJGECmLTcf+6Rau8ft5bvQI0k8DJBctcrHmmMaKyxx8fCzIee3MLN4b4K0DQZJm/hlHm94nv9cIBnAf/py67e/iLzxE9KwbGHLn38hLdVIZC90d0KUOPq8Dmw8i4yDfAcmHwWODGXsFHns5IXII7E+AyZVwTT1kHAeLBlEq3NYTun9rt4AXeEgDrRC8fhgWA5eGQSSssH62CsPIz0CrkVCxW5A2zD8Q0OhPU1umL+LveBvxibQ/w+X5kArL+8iyvEQgMnR564AuhoRyrdGhswJuVXbD9DPgMx28hqSl9tJlGqZwB9QWtmzF2MzQkAWHMiGtFoacgJ5pkNkPotLk0KkKtWE+N6TqoCjyLnqwItklOhIt/w3S3ogBvly3jkWnAZKgbpwVhDR+75Urfaw4pKEuWk7kxRcx22JmNzAoCO+Xw5FkWQAXsEkLJsYAPQhjdFhjgXN3QkYJbO4nj48rVXw8WcgOoCoEriKQqlOEAZpfHh/NAoofrBrEV+RTNWwETk9LIGkEkdLYdBI3bSEhI5MGA07k5GAePYJMX0sgOVypMfI1F5UeSI9SOHFHFO8cDHDJUg9Rg6YSf951LR4DzVVNsL4Sa0p7GnZ8RPWnr+HsMxD7qo+4ISGSzxTIqQOXBUaWQKoGRR7YmwiRbrCp8FAVOPrDl1YYUwALDsJxB+Qnw3lFcPcASIpscbUEgUc0cBVJ1PvgaJiZFAaRsML6OSsMIz9hGQg0BIC5SPXgdZr8Io1tmdlI/LqOvCs/XVyWgRzu/0IO/26hyx6FZI8kGZAWgpBsBXyqjKV2D0GIXZG9MR08ULQbineBr0GAKAc5cE3RMlFSFg39CmBQFbTrCWm9wRYB/lCQ2QZDDmuHIre7tyKhaNGh2/IlIYMrsnzPDJyDTPZsXbeOSRdOZvS3DK1nq0YQefuQRscly8mbcgldNPDZxCfTUYdjLqh3iim1j1Wmi/w6WA15x6/WQ2QFjC2FLSlQ3A48VrntdyGVoaMI6L0OXG3I33MrwR8r+SvmAPRUZALleFE+rgtGkFgnQAIwfIGPyth0gpu2YMrMxGSAooFbAcvBHPQLRpDpLmXLXAeugMHo1914gwZ1PnhzmoMDFRpDM808udXH2hydpJmPYA9VRoK1pVQs/h3+ukoSpvyOiK7D8RUeoGz5A0SOGk3Ku+/Tp1Bhe5QYUs89BmVxMPFT+KQbFKVDj2hIjIKOBvQ5DO8UiFkXQM2CGe3h4m+Fx+rAExqUF4Hmg0FRcGVyGETCCuvnrjCM/ESlAc8ho6EXIRWELcjeFydNbZlJyDKxi5ER39MpFxkFLgT6IGXykUjFRTWggwFrdQkjC6oyitrBgC06pCjSjkkskSpI2SHQ9aZWjK6CNx2OtYFoLww+Dj0dkNUPEjuBooqHZb0hybARiOckW5HskmzEJ/I5UrWJRrwmZsRAOxip3DTXa5+s44YpkxkTApLWottbfVx1gytW+lh5SONPy5bz5NRLqA9C5wrxsmQDcw14zC8TJckaJNkk0t5qCAi4DBiyGTq7YcMgGFUO73WT1tKY0H2pRSpVI5AR6XQDfAEJb/PFQYxPLjNgg7h68aRYc/OpvXAEibVFmIDiUEWkPjOT6ABU6gKUfgvS27r9NszPPUObGAVPAGLtCl0TVT48EuTcLBPrczVsJhiWZWJjroYlIYOU2U+iexuoWPw7UtUaBqYqrDwYPAkk7mNfU/7Wn2jz9EtkzLiOvrtgXTewWWTqJuMwxLogbjzsiYBiHXrng/VIk1F1XDLM7gWPKlIpahN67A3gaQ2OF8kT3T8KZiW3NLSGFVZYP0+FYeQnKC/wd+QgGwa8gcDJ1Yjp7xkk6ExDTJQ3IYbW1lQPvIgYUTsiHoYBSLWh0pCqx2Yd4hSBigigTagS0lGBi3WwHBEIqS+RSspxQjkSTqhsCwVJ0KMIBhZB+/aQ0Q+ikuXyNwKfhcK9UGRt/EhFgGg3Alj1yNirm1CbhtYBpFGaBlUlMOKu6zm06EU2z3MyvM3ZDYTtL9fo8S8XtunTGbx8OdsUhcQgjCmCFRnQTgW/BrkGmPwyMZRnFW/Eb1R4ToEaD8xZA8RATirkdpQQr3KTTJa0B9yBAJ0sFlKQVNn1BmS5INoQs25xHdSaoIsPjhKgLspMZr3CoLp8Vp03AgXwbdqCkplJggfa1kGnKo1P26kU2xQoKIA2WZybpZJfZxBhUVg/18mSPQF+/YkPw4C/T7Cx5liQNcc0jNBzED10Jr79a0lVa9g8105alMLst70s3dcEJBUfPkUg92vmvlDIlnMiGJ0D33SFSpO0Z86LgjkpcEsAfJVQpkKbUuhYCrPaw7hQj7AaAeD7kQrbKxpsLwaLF/pFwqwUWRIYVlhh/fwVhpGfmOqQX+AzkMyQp5GKx1ia2jIzkOCx3kjORWu/z3Vk5PdVJCU1CTkk7UhqZq/QJIxdkcpFJJBqwJe6TIpc4Ab/LijeDQFPUyumAfCnQH62TNsMyoXeHgknS+sFhl12i2wwxBthUeSyhykyXpyDtJQqEPDxIgdVI4BEneZxcdVCRT6U50NlISz76G+8uvI33DvcysPnnTq+eyb97XMfv/nEx4h772XHww/Tz1CoroFKO7ht4NPk4I5yQ6IdjljkNvYxwU4dMvKgzwnIS4GkIJT3hHJDHttCBZw7dlAzfhyWiy8h9cUX6aqqbDPAXAOTHLDKBlotuOwQsfsYtdNHY+/Wn4TFy/ElWKl1uZipKKx0OHEHZfw5+0gJuZeORklJxvX2uyg3Xo+6+kO0ugZ+P9zK70faeH1ngNtWe0lyKrx1qYNhbcwEdYOiep0HNviYvyuI2RlNus3N5rl2skI7eoK6cRJIEi++B2tqRwpfuI5+tz1HxNxfEuUQ30i9DapjYLQVGvzQ5RtY2jFUrXHA/Sa4Lq7lY/0NMtUVq8P6InB4JWtkToqYgMMKK6z/DYVh5CekUgQ+foH4Pp5FxkO7IMbUHOTAXhP6nranuZx9wBMIOHRCqiZZCMx01yWwTFHApIopNN6AbbpMxYwqhvodsjFXN2R0uADwW6GmDRSkQ/sKgZAOiVIFiW8Hh1V5978bObjNikTAj0RaFp+GLseJlPMdCGQNoXUACQagsggq8gRAXLVNX1u59oeDSKMagcRx7728+PDDPBRQ6FQFn8bJ1M8It2yNzXbC1tBm3QygzAez18N7fSC+Co51gv5O2b1z3AB95w6UsWNoa3KRU60RM2cuyS+/Qr2h4msAosFtgK0eAnnHMMYPJ9FfSWm9RsaEiThXriTSaiVNl9yXiFrQ6isInD+U2JITuPw6bnskpvp6MmJUCvxWtAY3l3Y3s3x/EIA/n2fjnhEtnUO6YXDjBz425el8fFUTiDTqUIVGr+dcWLIHkHTpnyhb/iecTjtJqzcxowAW94Oh+VARBQeiIfMEKD7ILINdvWBwLJTa4M+Imbq5btUhtwI61EHPCLg6NQwiYYX1v6Zw6NlPRDnIXpk7EC/G8tDfbcBjQHekknCIprL3t1WJVFV2IumpmYhJ9QAyAaNokgxqMQmgROmw14A2OvzqEFRvh5yKplZMGeCNg9J24LVLHPjkLyGzK2RcBp54qYBsBoKG9P57AmMUaQdtQYDKhlxmJE0m1G8DiGFAQ5WAR3k+VBWDrp16H38MEAH49TA5rH/z5z/zJ8Mg/sFH2BmpoGsQY4V6M+CDg6GtuhcpsEKHwYd9xOgmHH4zhekw0AC3LtUlY/sO1HFj6BXpYd1VDt4/HGDOgvn4gKhnX8GwqDhUqSpoucfQJg4nQ69i8zwbu0t1Ll72ERHTp6EtX8kO1YqugqpC5PGDFB0+ykX9LFzbz8Y177t4/FIbO0t0HtjoxjZyJMs3bcLeYRDmyAR+v341KZEK1/RrepWoisILF9oxDOOUx+xolc7oN3yYYtOIm3g7ALY2Paj9YjljDI2V55i4ohzWRENUHXQvgG3ZkFYBehv4Og7mWGXU/PfIuHjjNNcGHaqLocwBYyJhbrgiElZYYZ1BYRj5P9RO4B3gd8jBfhC4DwGUhcgSvE+A6cj47bflR0ysy4A0BEB6IEBRZ4BFlyV2VpNMdNhCQWXneWD0DqjcDQW+plZMrQlqs6AoE9LqYMxR6KBKFSTuYvjaBgsNKDLkYMkERiuQjLRo3kBaOBoCHYNpHUACPqgokOVq5fngdXFG/VAQMQwDv8YpJteTQPLoo/T2B6l76HGCZgUrMtq8N0J8NGYVtilgP5LProkj2B+fwMi/rmXT2Di+MEkFKbhjB8pJELERa1eY1VtgYO6C+cT4IfjKK6Sh0nD4GJ6Jw8nSqtg820p6lEp6lMqqy+xMWfYR+vRpmJatxGay0tsJucOGE/nYU7xy9x30TlE5cKOTdw4GeGCjD/uv7sTcti2+zz4naeo9KCYzKArXvfsRwEkg0XSDAxU6PZNNLR6Do1U6I+Z7qTEnkXDF45gi4vCX52JJyiboc6H4TzDCaM/7JojJAY9Tpob6HIO8rtArAt5WJaH3EsSseifiU8rV4eViSPRATxPUJYc9ImGFFdaZFYaR/yN9ioyx3oWMgCYCv0Lg5BjQCzng70JaKs1lAF8g1RADMah2Q/wY1QZYdThgSB5EsgpmHQoNGF0Oo76E2mNirixCpmLqo6A8G+pioW8BnL8VMttA+jgoaSsTPDuQF0ucAlMV8bXsQvwpGuJViaV1ADEMqC0PVT/yoKZUPncmVVslTOv99X9jzw8AEV/Q4NLlHr4p0lg/10nXxKbD2DAMiuoNrCbY/be/EKeB45HHsaFwoLocf2kxqtuNERdNqQa+yRNJ8hZTn1vIx78dS/rmdeTFxKFv34F6/hh6NAORRp0EksXzSbJCzb334hk/kqxgFZvnCIg06oKOZt69zM6UpR+hXjYN/1srOeC1ovsCmLZuwWxS6ZZooqhep12sitNuIvDVVoiORrU7Uc1yXfHjbwTgunc/wqTArN4WrnnXy4JdAR4YZeWPo2VD0fHqU0GkdvNCar9YiqODzGZ5XC5K3JC2B06kC5waZmifDY9FCXiMQWBkOTLVlQXMMSCyFNI90MUJv0iGDaq8hmac1TMXVlhh/S8q7Bn5/ywDAY4SYCZS2h6PVDSeQUreRxBfxQRONanmA48g1Y92yEiqFrpgTZeqhUMV8yoGVAdhZC6kfga+6qZWTIkC1WlQ2lZWyJ9zHDo1QFpPsPaFL6JhA1J9sSky3dNHkQyNbxADqoFEzjcCSPNnxuduWf3we8/8uLhMcDAeDiVCWTJERULcV2t49dILuGWQhacn2r8TRIK6wfXv+6jxgiegsf64RpsYlTqfwcarBUgMw+DXH/v4+1Y/vZJVDlboBHRIuOF2ao8cILju4xaXaVYhNcrE5qsd1HgNzlvow9SlF55n/4V/4gUtKiKtaeFuP3NX+TAsFtpFc7Ii0ppWHw0yZZkXzr+QhKXv0OHTNXx24UR+M9RKZrTCHWt83DnESnasym2rvagzLsVYtYqsX72FoshlBmpKKHnlJoala7SPU1m4O8Cl3c0s3Rc8CSTPf+Pnxg+8xI39BVEDppwEkZk95fsMA/ruPkhFQhdSyiC7DMqyYHwajIuVat3FSDtxGPIavBRZmFjkhggNbqiHG9PAqsrr5G9IanDnM78MwgorrJ+ZwgbW/0IZiD8kGoGNlxFDqhdpcfRFjKDXIy2Q5nIjPow1SEumDeLF8BhS+chvBiFBA3weGLYPEr8EPdDUiil3QHlbqEmGbiUw4ASkRUFCP8jpCmvNUjExKzK1M0iRkLKvkMsAAZAhyJRM47Oha1LxaPR+1Jaf+bEIKHA4Dg4mQGECmKOhrwMujodBdoGwoqIizh06GNVVysbZtlPMl80V1A2uWuljxcEgimGgKgbvXeGkX6rKeQvclLsMNsx18tL2AH/f6ufZSXau7Wdh6hI3H+fIFl9Lakei+k7CmtQW3eem5sO/kWyqZ/PVjpPpqDtLNEYv9EFKG2pzcvjVECt/G3/mis2SvQFe2Rlk/sW204IIQEmDzpDXfBTZE3k15wQbTQrLZ82iYdkydMMg4/zzKfzkE0yqAsNHYLn1TryXTiX9ly9iiUs/GWiWolQzKE3h7YNB3pzmYGZPC49u9nHveh8PjLJy/ygb177r5fWdARydhuA5spXHx9m461wbb+4OMPttD+pVc8i+7zV0u0qKAQsS4USUgOgkpDV4GfAb5HUba8DWUvgmRkL0RtrhH6q07UBM1Y8i3hLnmV8aYYUV1s9IYRj5L1MAmZjpjUDEJ8BtSPXhIGL2jEemaJr3zgwkvv0Z5Jd4FpCKeELsOuQZUrlIUMCrgakGBm+DuH3y80VAHlCaJIZUuyoTMV3LIKkDNAyCrSkyhqko8i53hCK39yuk9aMgbaShtAQQT30TfFQUQNB/+vuvA7lRcCARcuNBj4IuNjg/DibEyihwa8rNzWXMqBFnBJLmINK/b19279rBuzMdjO8gj2S5S+e8BW6OVul4g/DsJDs3DZLWhi9ocMlSN2uOaViS2pE86y/onnoqFt9FEtVsnms/ZW9MI5A0xKagFRZy97lW/jz2h5tqQUBk5Bt+ctRYLvx0M9d37ozLgId9Qfb/6g4cSal47/s9vPQKgc83Yn72BdoXeTjUI4mESXdgb9PrtCDSqOZA8oeRVvq/6GZ3qX4SRBr15u4As9/xYr5yLl0efoWhWSp2E9wceh4XApcjm56nAb8z4EQDpHphXAA+SoMkRUzL99FU3dsPfIxUVMIKK6z/DYVh5L9ILqRMfQHSIqlCWjTPI4f88dC/e3/r5/YDDyJA0BYBhVoDHDqcMCQrJBbwBKRPP+ALSCyU1spxoNACJW2gMgM6VgqEpOlgGQAHe8FGu1RlEhUJWnMo4g0pRA6QZJoAJAZZhFdV3NR6aag+/X02gGKn+D5yEsDrhCwbjIiCqUkyuXK2OhOQNI94f/rNRdx3680kGLV8frWNRGfT95W7dGa/7WF6Nwu/GNDyyv/1tZ+bP/RiURXMGd3Q68tPCyKNagSS+tgU9H8TSBpBJE+NxbdhM73TO+MxwAPUWMBrAYcB2T55Hr5yyvK96Rvgw1vP40RtAXiqzwgijWoEkpFtTWw6oZ0CIo1qBJKYOXMZ+OordFdVvEi+zVAErCcBqwz4shJy7fJ6meGEqaqsKohCfCLXN7vcRUhlb8z3fpTCCiusn6LCMPJfoirgSQQ2ViOjuu2BBUA6cuDcQEvDZw2S27AVybhoj0SRO3TJtbAoUkWpdUNyPvT7HBJrJdfjGFAQB6XZYDhlW26vQohIgaKh8FkWFKrgVOBc5B3sAaSFoyBJqecSqoAYTaFjZXlQVSRAcjo1mk4PJ0KdAxKsMNgOUxOhbey/t4ekNSBpvvSu3dK36DFtKpZ9+/hk1EiyrQ2sm2VtASSt6eXtfn7xnpebBlq4vIeZCxb5SIhQzwgijVqbE+T8N9wwYgRs3vyDgKQRRI6rsZjWbCbQvTOjKsVbYXHAQjsMVMFtguOh0Wldh+hqmLgeatat4LUXxBr6i/4WXtoe4Bf9Lbx4keO019kIJKcDkUZdu8rDqzsD9Ni6FXXwYM5DwLo98pp93oCPqsGnQw8D3AnisemHhPX9Eqnm/QrxmID4mx5CACXtrB+lsMIK66eqMIz8FygfqX7MRHrsM5BDfxeywXQUMI6mMnYQmB/6iEV+6euh6Zji0DhtjA61tZB1DAZsg1iPtGKOmyA/A8raQFYDnJMLmXVQMwB294bdUWBRZUqngwInkL0xOgJFwxEAifBL0mlj+8XdaBRpRc1Np+WREKlCXytMioV+KWA69Y3595aGhKbtB9bk5vLaqBHEuUrZcJWN363zs/KQxh+WvUX+hVN50SyZJ/v27WPMqJGkWxtYewYgaQ4iz0wSg2xOtU60je+EmHKXzqiFPnK0aCI/3Uzg/dXU3XXn9wKSRhApIJZOn2wmt0tnTGb4EHhZlYrY54hxWEGi6WuBo15IrobfboDkOp3fPT6aY8Xb0P1uhmeZ+Cxf45Updub1O335qcKtn/E+vrLdz3XveWl3440Mf/ZZChWFYqAr4kuyGlBSATUaxNpgfIx4lj5HAHwQUm27HWlBPo5ALshKgX8iuTnhcb6wwvp5Kwwj/8c6gJSkJyOJmlcDKxBDXz1wIwIBjfoMeBhpm7RHyvImA0p1yWiI9kJtDXQ8CAMPgN0nrZicCChoJyFl/QuhXz4EYyBnKHzZFgIWWUzXA9mfsh8BkFQEhoYaoFY2tV6qS1sPHYOWptOiGLAY0MUE4yJhRDpEnC7T/SzVCB57EL/KYWTPiR15F90PSM/N5ZZRIygpLERXVJYvf4upU6fyVBCGqjBYlS27m3btZfKQAYxtCx9eaT/lur4q1Bj8sotZvS0smPrdkzrNVe7SGfW6myNuK2lfbCO1V3fydKi99168TzzKysscXNLtu0nsgkUe1p+Am3fupqBbV1YaAqGDEIBdGBqh3mIIALgNuCwHHsqElHp46H3Q8uCAdozH/9Ib1RqBVld+1kByOjWCyC9uuJF7/vUsjykKZQgQ5QCKAS637Aoa44UrE2CLSap+JxAIP4JM2pwD3I0k2b6IfA/I6/0ospU6rLDC+vkqnMD6f6gvEYPqUGRh3AzgJSQ9tQuS0dD4wBcCf0AgIQtob4AWghBVgZgaqK2DlP1wUQ4EAnBYgaNpkgGSpMGYXMg4CLm9YMVwqIiHBFWuP6DI4b4eOdBnAIO8EAhVPr7JlzHc1tTCdBoHKJCtwViHbGhNSgHV1PrPfpc05NDaHXq8jiDg4URaUwORsLcOfGtxXnY2vTdt4bprr+H6m2+j36SL2eoCgnC3Chl+KDAMDr78Kn6/n6ldTgURgE7xKj2TVT45FuRQpd4ih+RMKnfpjFrg42idGcOkYmnfmSMBcB8/hr7wNdonWRmWdXaXdXVvM58c87L4icfxvvwKhqoyHlisSPXs74a044pCJuWVRbB6O9hTwGWFXQqkxcA3QzowLvJ91t03GVNEHFvyq7Ga4Np3ZZ76+wBJI4jEX3sj/Z96lngU/gW8hcB1RwM2+8FtgWwDGhJhuyqG1gUIfDQgCxE3ItW++xDQvjF0GRlIlWQncj/7nPWtCyussH6uCldGfmStRsAiAnlHH4UElIG8C+wZ+nvjht6VyDK7zBCEVOuABrGlUOeCfgdl/LZCg8N2yGkLtSnQp1Q+Xx0Le/rDsSywR0gbxq7InhofUn05T4de5eANVT9qy1oPHTOAEifsazSdmiHdCwPMMCEesrLA/gPmMhvBYwdS8TiC+GIikRHmgaGP9rQ+9unToSQAxQHICcLBUJJsEeKlADAbUGmGwX6D/D/8mo3P/Z1nJtq5+ZzTH8RlLp2xobHfxhySM6kRRI65HEQMmUn1uhdJLSzDqKnDff5wkgJVZ8wRaU0Ld/uZ+44PZc5cjJdf4XaTyijgQQPqK8qpuGImlilTePDa22mzAvJscM9kSCmDbrlwIhMu/Bi+HAC1u7/iwH1XUFt1AoszEnN6N7xHtp51heQkiFxzI5nPP4tVVUj3w/VmGG+WHUqTPVBqQKQC2CFelbybeOBWJACtGwIaDmR6ZiQC4U8ihu3lSAXIi/hHWgv2CyussH4eCrdp/j/LQN71NSA98QHI4VuDmEJvQADFAN5FFtqpQDtDfCH1OpgbIKIUXB4YckR2geTrsC8R8tuB0wzDciGpHvZ2gj1dgQRoZ5WJmCOIITYVGOuCTvngDVVAAr7Wb3dz02m9HeLd0EuHsdHQJQNikmT/ytlKQ0aJtyHgcRTJJ4lEpoEG0AQe37ZOeppBx/EAHNDFkFtME3RYDIjX5QDsrEJXM2SYIdUCexT4029+zfqnnmwVRL4u1OicoBLTLKCsOZB8erWTLqcBkuYgkjDzMbSGKsqW3sec9etZOetKUgJVbPqeINKohbv9zHnHhzprLsnPvYLZolJbVY5/7CiUo4fw+HX63PoXZvX6DRUOeOZiiKuBKJ8Yl/2a+IoUBXptdfPVuw+y84vnCHjqUMxWVM1P3T1ROE83Pw00+A1iHqsnqvcgfrP0SwZ3VnhBkU3PZg16B8BwQV0NOKOhNFEWLhYir/neyPP5S6RFaQZyEchYhQBJG+AF5LlbQmiTNOKn+h2tb6EOK6ywftoKw8j/R2nAc8iBewT5xfshAh6XAKORX7QHkF+6hQiEKIArCJElYK6CgB+GH4M2RXDYBPvaQHkGdK+GPgVQEgPbuoM7CRLiIUuFPEUmHFI1OK8E2uaBOx/qKlu/rW4THGhuOvVBVxeMckLvVEjIBGvrnY1TFETA4xsEPI4h4BGNHDiNFY9sWi74czeDjqMBOKTDMQNKVPCEznKLAQkh6OiiQhczpFsgzQLRKlQpUhlp/CgBXG4Pj8fH0T02yDe/cGJtthCl0azaM9XChtktx36X7wtw2Vse7h9p5U9jWr/zs9/2sHB3gNS5T2FL7Yjr4BYqVj1GakoS0XotG34giDRq4W4/c1b5uHD2XIIPP8bG8WNw5h9hy1wbC3cHeGSzn3Fz/sLIfr/h0V9AdD386k3wBuGTMVCcDiNXe0ipdaBbIaA2sHrxlRz87L3vnJppVOOUTfd7HmDFn/9IB2Rr9KsG7AiC7oV+Hng6FpKtEmK2DRklL0TajAmIF6kdUp3zIv9frCA0vYXsU+oPvIpAyypkSujCH/zohRVWWP+tCsPI/yc1tluSEfNeR8Scl4CEmqUiExB/QALOUgypkPh9EH0MdDeYNBh5FKJKYXcMHG0HRMKQfIGFHW1keV1EAmREiRG1AUhrgFG5kJ4P7kIIBk69faeYTjXoUAPnqHBOMiRnQVT8d4/dBkP37yvgawQ8GhDw6IB4BQYi734b/TCuEHQUhtorBzR5J1yqgLcZdCRq0F6Brip0DkFHqgWCKhQ3g45iJIxNCT2+aaHrN4U+Xwm88sFqNk6bwgXtFFZcasNqUk6CSNQVV+L/4D062lxsvNpJolNlY26QSW+6Gd7GxKqZThynqR58lhfk/IVelLTuxF/yB0rn34FRV0ybWJXNVzvPCCLlLp3P8zWmdDGf0Sjb2LIxx0RjctejBXUWTXcwrZuZP2zw8chmP8m/+QtVj/yGbofgok/BFIAvzzfY/dQ9lC/4O5dc9xY9e1zINxvv5/23Hj5rEGlUI5B0uP8BLvrjH7kGeLcSlupQGwUOKyQH4RoFLrXANwr8BQHiQgTAO9EUEf8J0q4xIy3JwaHn7B1kEeRTjdcLXBn6ubDCCuvnozCM/H9QHfBX5GD0IJHtRcB5iKEPJPL9WWQ6Jh4w6iD+gMS4R+pw7lEIVMHODChsA23d0L0MCqJhXxZYoyE+DbxWcGmQXgbn5kBSHgRrTr1NBnC8menUUKBtDfT2wrBYSGsDCRlgPsOwRxApsW9Fqh45CHjEIvkXg0IfmUiryaVBUWOlQ5P2ynEDyppBh1WXmPD2NEFHqgUiLFBnagkcjR2leOSdtD10PQFkbLQUqUb5DQERK03bgusMWPXBao7PmMKEdgqTOqjc9KGXlBtvZPrv7ua1YYMJlpTQPUnlDyNtzH7b850g0qgteUHGL/QStEQSdNdhQIsFdK2ppEHnvPluDlTo3zn2W+s1GPSSi5xqHc2APikq+8p1lnwLSKwP/YWMWb8huR7qIwzK/no3Fc89Qe8UE3sqoPug6ezbuux7g0ijGoEk6w8P4Pj9H/G54QIP3JkEiyzwniEttQ4BuM0Mg03wF0UMq/XISHI75Lm7DtiCtGtcSMWwH9LSWYv4qO5BgP0JZNz3+9/isMIK679VYRj5D6sUqYjYEFPeHuRgvA0x8G1GRhprDTmELWWQvB9qHZAcgP5HodwHe7PBlQgDSsCkw54U8EVCRCro8eDxQlYRnHNI0lVN3xq7bTSd7k+AowngM0N6PXSrhmFOyMyEpDYQcRqHYBCBja1I1SMXgao4JFPiHMTnkQE0aAIcxQE4EjKS5gDlCvhC0GHTIdmQSkcXFTqZINoChgVqTE3A4QldfwzyTtmGVDz8CHDUIbkavtDX1NB99SPvslXE7Joc+khp9veXNNA/Xs09l0zB5w9wzo03Unn33TSMGoG1rphXJpm5aqWXcrfB+e3PDkQatSVPgs46xSmc39HCk1/4eWqCjduHnHqEljTojHzNRa0X5vWz8Phn/tMCSa3X4Pw33Owo0Qjq8Pg4G3cOtTJrpYcVB4KnAEnP6/7CuVf9mpXL76b8X0/w1AQbNw600Ot5N4crT414312q8eQXfh4dayMtSiWgGVhMLW/D6zv9HKnUeXCMjSc+83Pveh/Rv3mA6D/+kUw79FTF+5SoSG7IRh0MHYZr8CuztAD/CJQhbTMH0rq5MPSaUhFQ2YiMmhvI6+4eYB7isdqKTN2EFVZYPw+FYeQ/qBxkV4wJAZGdyLu9W5Dx1NuQkdWEIEQUQNp+qEqANi7ofAyOOeFoNsTp0KEWCqKgJBJsMaCnguaHrDwYcABSauDbRYwaq0y8NDeddq6AQQZ0SIOkLIhLA9O3BreDiKflC6TXfxxpM8Uh+Q9DgP4GOEPtlaJm0HHcgHJVoENB9uI0Qkc3VUykJgsEzFAVgo7GiWFn6KPx5vgRY6/XkHfLauixBKl+mBEAiaAJMprDhuM7uOGoDvM1OG/LBnbs2MGRGTN4fdQIkuqK2TzbRttYlf3lGkv2BrhnuO2sQaRRW/KCXLDQzaB0lf7p5laBpKRBZ8x8N0cqdSZ1MvPuFU7+/oWPOz/2nQIkrYFII0gEdeO0QNL5nNEc/mrjyeveWhBk6Cturulr4ZWLmxJYd5dqnDffTaXHoHOCyi/7W/j9Rj/PTbSdnLJ59is/t3wko8BX97Xw8kV2rn3Xy/xdAc7/6Csc5w6ixCFm5i4K3BR6Hh4yYIcBDg0u12GuBd5UxWtShVTUspHx3V4IpNQh4WjtEdjcS1O8/KsIBA/7Xs9IWGGF9d+qMIz8h7QT+YUJ8s6uHHknNwQZU1wMOPwQnweZB6AyFTrWQFJ+yDSaBh1CqabHY8BkgmAiGCpkn4B+ByQrozmAtGY67VQJveuhWxIkZgmAOJoFcgSBQ8gv/e1IxcOHtJS6A4MN6KKDP1TpOBQUI2muAeUm8CtN0JEaaq90MEGMWaocPgtUqHLYGAhQ2BGQ0JEWSgMCJF7k/iih22VBQCOKlqDR+Kf9+7HBKfIBvw3AFBM0FOYza+Rw4uqK2RICkTPJMIyzCkA7E5A0tmaOVetkRCl8dk0EaSFPybeBpM4H57/hYkeJfgqINOrbQHJJVxMd/ukit8ZoAUF+zWDaUg9rc4K8e4WT8R3MJ0GkbazC85MdTF3iptRloLTvhJZzhFcusuMOwC0fefnVECsD003MftvD2HYmNp7QOe+CyTy9dAUbNCtf+aDUCqVO0EzQVZHXfkCBRww4pkvuzR0KdDPDA4qAewXS4uuAGLoPIpWxr5FWXwMCxosQY+ufkOTWxLN6tsMKK6z/ZoVh5D+gTxHYMCNtmjRkJfqnwAMG+D2QVACZR6EmGbqUg1oDx9JAcUCqB0odEqNuOCUwLLsA+p6ALF/TxIlfhSOxLU2n7augewV0jxDTaVIbiE0GVZVqwkEEPLYhRtMA8su8pwG9NUgJgDsIB4NwWJdf/pWqHCQge2/SDFnIl6aC0wJBi4RbuUPXEUSAw4RkoriQKkdjdQMERKzIBEWj5+PHgI0gUk2pbvZndbN/N18YrHq9LJo9C80ZgT33OAc/38y4bDMpkQovXmTHrLZ+A8pdOuMX+egYq/DmNFuLaZzW9OGRAJMXebhziAWLSeHxz/z8cZSNpfsCrYJIoxqB5M4hVj7PD7KrVMcTbB1ETt7/ZkDSPUlld6neanvIFzSYvkyA5KkL7Ny33kfbWIVPZkcQ71A4WqUz/HU31UnZaEOGoS1aCMCvhlj523ip1izaE2DWSg/pmZkcOXwYh0OqLFUarPPARi+cMEFJBPhMMmJ9I2KsfsKAcgO6BuBeE+w2wWuKtG40xOA8BQEUDwL3cUi1pAL4AAHVl5GwtB8+nxRWWGH9NygMI2dQIBDgs88+o6SkBJ/PR0xMDL1796Z9+/an/ZmVSD6IipSaZyKtmRsCkOeFhFLIyJVY9val4A5AUSrEGFI5qLIBusBDdjH0LYNsjxzc3zadokBWDXSrhK5+SM2UykdiFqgOGZn8gqaKh4b4UrpoYiqMCUBBEA4Z8vWqZtDh1AQKUhVIVMFmlq2wAYtAkB+5TYohh4UfARE/IQihZQsllZawkcTZw4aOGBe/DRiNf/c0+14L8u467jR/NoKc1+tl2iVT2bDuE4K6Qc8Bg9j99VdYVAGoS7qaWTTdcQqQlLt0Rs13c6TWih70c2EnE8svtZ8WSOp8BuMWuDhYobNhbgT901TuWefj8c/8OC3y/P9qiIUnJ7S+sK4RSKJtUOeDK3qaWTT9zIlyQd3gqhUelu0PntanAgIk499w83mBRq9klbVzBEQadbRKZ/gCHxW2OLSyshYg0qhFewLMftvLnDlzePW111p8zavDFi+s8Uj1rTASXGboqMANChxR4HldTNrnB2G2GZ4ySdpqDQLJw5AqXR3yeraHvuZDps72InByOWGFFdZPWWEYaUVFRUW89NJLPPf8C5SWFJ/y9fPHj+eWm29m8uTJmEziYjCQoKb1NB3UvwX+1gCbghBZAxmFYNghvQLqzFAbD5EBARBFA0sAssuhTyV0cMvB2mg6PRZKOk2rhy4V0LUGUpMFPmLbwIlE+FwRc18eEpCWoEF2ANIDENQgN9ReqTZBMNRecWihyoQCMar4OTwW0M0QUCWxNIBUNUAqD433145UNlIQA2LzqsZ3wYaBlN1PV8Gob/a9KmJgjaMlXDR+nGXcSQvdfvvtPP3003x4pQO/BjPe8qIq8OEVDsrdBlesODVPpNylM/oNL0erwe+XR0JV4MJOZpZf5jgFSBpBZE+Zzsa5TgZnihvGMAyW7A1iNxvMWeXH49f5/YjTZ5esOhigXazC37YGWLg7wKJpDi7veeadNppucKxap3PCmdNiH9jg5cFNfhZNc3BFr1Mv82iVzoTFPqZ3Vnh83KmGWl/QYPhrHvZUqpSVlbf6/51uwHY/fOyGbRocj4RaC7RT4BeKtGGWGWDW4VoNYqzwbKhKYkKM3ucg0HGIJji1A5uQFQqTEA9JWGGF9dNUGEaayTAMnnjiCe79/e9RzVbs3UYT2WcClrh0FLMV3duAJ2cbnl0f4i48RKfOXfjwg/dp27Ejf0Z2p7iBERpQCq9bQfFDWrFUCaLqZUoGC/jNcsjbvdCuSACkkx88rZhOu1RI9SPJDnFZUJENe9JhtxXyDFlYFx+A1ADYNcntyA9BhxY6O6xa6CBXZGuu1wKGVTwoqHK7mw/gKMgv+wikqtEOAY3mBlHbt2DDQKoUp6tg1CEHSePlR9F6BSMudL3fp0sTRICpodnHt//d+LkAcOSjj1g8ZQpTO6ssnmbjq0INu1mhR7LKRYvdfJqr8fFsB6Oz5YAud+mMXeinTIti/cZNREVFUVxczLp163jg/vuZ3FFl6Yymls3pQOTb+rIgyLiF3u8EEhDAuOZd71kDyXfJMAxu+8jLs18HMJvg3ZlOLuh46u08nT/GFzSYttzHx7kGc99+l99MvIAuypmft2MB+NgDnwbgkEOSfTNUmKdIJPymEETfBqw2wxeKPGdpwHjktXQE8RfVIWsM3kdG5+9FXjdhhRXWT09hGAnJMAzuvPNOnnrqKaIHzyBm6GWottOXw33Fh6n58EnsuocJn26koGdPbC4YdxReS4Aau+wF+X/t3Xl4FFXa9/FvdXW6s5IQIAk7IqiTQcIAsrkEkVEE0SAqPi5E0BF5gEcMM4+AC+O8wxt9Z2RQQAFRRgUEGQUcERQykMi4BRgQUDYFiWBCMEJCSLqTqnr/ON1JZ4NEg0XH+3NddfWSXu6qJF2/PnXOqQgvhBVDYSSYujp2HuqBLoehxzHooMHXrVSn0xOREOHrdPrr71XAON0JvroI9raGIy4wy6FZOUQYarhkIVDoUC0dJip0RKNaJTSnWspCwNIrD8FYqJYbf8tGAqrjaWDrRisqw4aXulswTlLZWgJqNExdLRhRnP3Yvpezh4jA22a15+qoHVGkb4mw1Do6fT/TAYfvL7gMFZo2vvkms/5rFI9f7eL/DFIhYNJ7JczNLkPTIDXJxaLhbgpKrCpBJDExscp7r127lltHpDC0i4MVI92UlsPg14r5PM8k877KIHKwwCTlTQ93/9rBtKsrD51sOVLO1YvVmKKZg9xMv7r2wyrQeIHEsiweed/Dc5966fb88xStX8d3H7zPmlGhtQaS6jzlFret9LDhsMXDb73D94OHcMiCcF3NDzNch35a1Rl1A50wYGMJfOCBnW44HgpxDhilwbuWmoU4sRx+64C/65CrqSHAA1CXh1CtI4Wow6B/BdYBjyDTxQsRjM5rGJk3bx5/+ctfyM3NJSkpiTlz5tCnT586H79y5UqeeOIJDh8+TNeuXXnmmWcYOnRovd/vp4SRWbNmMWXKFGJ/+xBRPes34bRRUsjxNx8HPNzzt2x2JbXmQGuILVDTcDsMKAmDUjeEl8BF30Dv/RDugr1d4NvmKjx0LlCtH5oF37aBAx0gtzl4Q8Btge47VFLmUKNXTNR5QNyaGmXj0FUnUlNX5wFBUzv9ZqgA4G/ZuJjKvhvNgTNazc6e/svAU9S4qbsFI5rKobgW6htrfcKE/7BPoBBLBSS37zVDUOvuW6WKOUQMSwUX/wicM1bl9cCQEoracYVr6np4wG29tJT0Ebfw+aaNvH93KNd0VGvx3oEybllRSmyvKzie/Rl3dQthR77G10YUvf6VRdvERBJQ38jbaWq7ttEge+1a7hmRwo0XOzh+BrYdKydE19h4j5t+7ZwcLDAZ+JoHjzOKE98X8KeBbp5IduM1LO54y8M/9xuYhmqbSr/OzdSrzl8gCQwic194geTx49no9TJr5K3kvb+ONXecPZAEBpHVa95hyJAhAJwwYbsJWQZ8aqpfWDsH3KDDEE21ylV3xoQPS+G9EvjIBcdC1eHCoRq8p0G+CYPKVQfpD30dpDujTqj3DerQTREwDHWopgUwuEFbQwhxIThvYWTFihWMHj2a+fPn07dvX2bPns3KlSvZt28fcXFxNR7/0Ucfcc0115Cens5NN93EsmXLeOaZZ9i+fTvdunWr5R1+/MpUV1BQQOs2bXF3u57YwQ/W+3kA5UUn+G7x/+BMHUPzJ2fTvAC8brW4vNApB3rvVDvYL38FxTHQ7iTEFqvTu+c0h+MtoCQCNJcKFKbvMH85gKX6JWi6at3QnOB0qNDRTFP9PfxhozPqtn/yr9Na1YBxJqBuJ7WHixhUCCjj3IGi1Pdapj8soVogQlB9TfwtEppvPfwXJlDu6/haQtXDQy58ocH3TbgiQKBae856G7Wt6sPfZ2Td3eEM7KRz72ovESHw0k0u3v6ynDvfKmHEiBGsWbOaVi1ieXtTFjGJiRyx1DDUHOCYpUZLFWoqHJ1au5ZDI1PQw0LpvvqfHHl8Omf+k83CG0OYklGOEdeBBzM/ZOvLL7P+iSf4Y7KbHcct/nnQRG/RGkdYHOV5B+gYdoaDE8/eSfXHBpLAIDL8hRcYNH48Iajf5SmvlwVXXMGpL3fxwx8i65xX5ektHqZleLj/1VcZO3o0l1BzeK3Xgt0mfGxAhqFG0LRwwJU6pDjUvDOBr25YsN0Da0tgo0PNsRPugL4afKhBmQG/NWFziJqxNwo1yV4+6ndwBnXyvQjgftTke0KI4HHewkjfvn254oormDt3LgCmadK+fXsmTZrE1KlTazx+1KhRFBcX8+6771bc169fP3r06MH8+fMbdWWqmzVrFn/430dpM34xekTzej/P74fNf6dozwe02HmMkNAI2h2F3v+Blt+rs+ieaKsmgSpxwveRUBIO5W4wfE0AhgNMU7Vw4ACnBk4dIhzqA7yNVnnoJAa1g7eAIk01U/v5W0OaoXbMofiCAeqDvwz1oX0aNR26fyRKue9nZZYKBr4y0C31PP9Ow0QFD8N3n79rZAiVAaJKCwR1Bwj/fWHA2UbGGlSO0vGP2Cmr5brXtx71eVwZsGf9epYMH84tlzg47YVNOWAaJimXOCj0Qua3GiM/2IDlcBDVti0xnTpVbhcqt0vFUGVf68yhrVspj4xCu/RSCgqL+M+wGyj86GPCul5Mq00fEtK6tfqbmflnCp58AkeIk9Z/+jNHp03FHd2KqPICslJD+VWrs3c8BRVIus9X08IXTo2qMVNqbT7OKWfAK2doc+8DXDb/JfAFYKcGufPmsXviRNL6uXj2hrr7rnzlG2VT1LI9PTdsIaZda5rrapRMN02dCqALldO1W5bqw5RtwAYT9pgqaHTXIcV3OEcPeOzBclh/BlZZ8EW4+l+4GNitQZSh5hzZ5lSnMLgM9TeSh/pbnooKVk9ScxJAIcSF67yEEa/XS3h4OP/4xz9ISUmpuD81NZWTJ0+yZs2aGs/p0KEDaWlpTJ48ueK+GTNmsHr1anbu3Fnr+3g8HjyeygMKhYWFtG/fvkFhxDRNLu56Cfmh7Wg5/A/1W8Fqyk7mcmzh7+jx0EK6932A79pCfjycDgePC7wuNTrF8s9PrvtmD3VAhAaxDmitQ3NN9Xfwn0fF0KrONKr7hv8avsWDmp00cIdb7gsL/kMbGuoD3v/LC/Etoahvl1HUbIXw33b76tA13yETKoOKpdW+sw+89AQsJf56qQwOBirg+NenvNql/+eWfz1862AFLP7DMmbA4n98Xbtm/+vkvfMOO0aOxOHUufzdtZinT7P7ttvQnDq9175H3KBBOCp/ZRXXqy/+1qAQ3/YKvLSKivjk+ee5euxYmie0ptS3DUot+PTVVwnp0JFD2Z+x57FHaR6m1zuIWJbF1I0e/t9HdU8zX5ty0+KeVR5Wfmlw2bMraHX9SFwa5L8xjx1/mljr8N3aHCwwSX7Ng9GyPdet38J3rVpTaKmWuzCn6jsS54DfaJDkUIdV2qB+J0UW7DRgowlbDNWX6SIdbtThJk39TwDk+fqVvFEG28LV/1AM6qSICYYain5SV62BzVGjb7yoaePbAGPrtUWEEBeC+oaRc/doC3DixAkMwyA+Pr7K/fHx8ezdu7fW5+Tm5tb6+Nzc3DrfJz09naeeeqohpdWwZ88eDn/9FXGjxvzo1wiJSSC0YxK797/F4ZkPYGnq/DFOSx1SCQ2BUF19QEfoEKL5+kJolTvc7y34wdcS4W+BALXj9IcAJ5Uhwe1rhfB3RMXXl8TUAnbwWtUdvRfVT6T6Dr/64n9+9Z2/723q3MkHthT4W04Ca/df+q8H7tBDApZwau7YA6+7q136r7up2hpU/T1r3HfzzWzNyiLM5aJPr15owEeZmYSGhtKzZ8861rKBoqLgscdq3q8BY1LJz8/nN/feSYxbO+9BBMDp0Fgywo1leXhryigSZq/geF4uO/9c/yAC0CXWQeZoN9e8lsOmG69iVuYW2sa3ptSA/Qbs8sABQ80D8qpTzVMT6etDcrUGPZ3we+AJS03Ln2nCW16YbUGcDskOuE2HuyMhxYTMUlhSAplhqp/USQeUaNC6DI454ZSmOl//APxfYBxqfp2evu21detWjhw5gtPpJDExka5du9Z7mwkhLhwNCiM/l2nTppGWllZx298y0hD5+fkAOKPjz/HIs3M2i8PKPUGzYiiMBm+k+sbn30EXAZpWuTPXAn7mvw2+/iFU/TauaVU7cWpUHSUS+M08BLVT9rd++HfUMVQeugncubsDluo7djdVA09IwHtVDxb+Rp9gM7B//yq3Bwz4+c52kp+fz6CByZQVneDDMeHnPYj4OR1q5lhWeVj58O1YplVnEPGUWzz2Lw9DujgZ3Lnqx0CXWAdZvkDy4FVX0y9zN3qzUEKdEOOEQS7VyVez4LihhvXuN+B5TbVyhDihuQO6ajBIh3QnRFqQbcJ6A+4sgzAH9HLAiFD4e6iar2TJaXgnVB32LNBVJ2/TgKM6RGkqaC8ACsvK+NXChSycM5cD+6p+CbomeSC/n5LG8OHDG7z9hBD2aVAYadmyJbquk5eXV+X+vLw8EhISan1OQkJCgx4P4Ha7cbt/2onEy8rKAND0c+8Izkp3Eml6eKCtOvQREdBXIgJ1PYrK0SKBO3oXAcHjp1UhgoQ/iOTnHGTz6PPfIlKd06GxdISbnFMGH+cYFS1xgTzlFiNXeli738ucreW1jrKxLEu1mlkapQUajtPqkFwBsE9Xw8odLtU5Ww9R/aAu06CloSY5OwXsM2CLA8p0cOtqxt8kHf7XCVEWfGzCkx7VIftSBwyJhAcMWHMGXguB70LAdKjWyEKHLxSXlvD3lBTMjRlEXNKPuFF/xhXfGUyD0m8+Z+uOtdx8881MmzaNmTNn1qs1SAhhvwaFEZfLRa9evcjIyKjoM2KaJhkZGUycOLHW5/Tv35+MjIwqfUY2bNhA/2rfXBtbTEyMqq+kCJrVHOVTX2ZJEYlxsTwhn2niHLxeL9ddq4LIpntd9Q4iw984w9oDRr2DyMlSi5izTIPrdGhk3RdOv0VneO5Tddaev92gWkf8w3c3HrZYs2YNC19aSMqb61gdEEgOfG+Q/LoXM74jozdmQZybQgPKysDrhdIy8JSpczFZxWqouxc45oBDTnUSRcM3hjtEh4gyFdDLNNjsgHcdqgUlTIcEHXpZquXkrXI1k3CCG+7VwFkCS3Q45FZzyZgAqamwOYu4258irFOPKusdkZhMRGIyhZ+9TXp6Oq1bt2bSpEnn3J5CCPs1+DBNWloaqamp9O7dmz59+jB79myKi4sZM0b1zRg9ejRt27YlPT0dUEMtk5OTefbZZxk2bBjLly9n69atLFy4sHHXpJrExETCwiM4c+ATXPEX/6jXMMtKKfvmP/S/+b8buTrRFBmGQXFxMWEhEF7H8NnqpmV4WHvA4K7LnecMIv7hu3M/87Lk1jDuPMuwX6dD4+P7w2kz63RFIHlmsLvGPCJDhgzhtpG3VgSSi5trXPu6l+j4jvxrcxatfaOEAk9U+AOqhSTfhG8NtXxvQKEBRQbo5Sq4mKWAAYahWj8MXbWSWL4WjzMONZx6nw5eXQ1zj9BAM2ED6oSSLTW4zANfWPD1lzswVq6kxbBHagSRQM363EpZwTGenPFHfve73xEa+mNOLCCE+Dk1OIyMGjWK/Px8nnzySXJzc+nRowfr16+v6KR65MgRHI7KXgYDBgxg2bJlPP7440yfPp2uXbuyevXqes8x8mNFRUWROvpeXlm2kuj+o9D0hnePOfNlFuWlxYwbN+48VCiamrCwMDZlfsjAa67i2tdz2XSvm44xdfe4mfuZl2f+7SUyMpI3dp1myMVe7k2qfW7TwHlEwkI07n67BA3qnIfEtCweXu/h+xK45557eG7JElbvKyf3jF5lQjOXy8U/3nq7IpA0D3MQk9CpShAB9UHRkmrzjgT2UvbXiepHVYAaNXPYgINl6vKYCacsdVqDEhOMcjUizFGuRnYZwGldze9i6GpoPA7VKdxpgeuFF/E2a0lE4sCz/yKAZn1GcGznet58801Gjx59zscLIezVpKeD//zzz0lKSqLl8D8QkZjcoPe0TIP8JVNI7tGVde+919CSxS/YkSNHGHjNVVBUdyCZ+5mXSetKGTNmDC+99BIPjRvHyy+/zKspoTUCSWAQiY/UcUTE0rffAN755zssGxFaI5CYlsXE9zzM31bGokWLGDt2LPPnz+ex6dNYuuyNiiASyOv1ctd/3cn+fXt5f0NGlSByPlgWFJiw3wtfeuGgATkG5FlQoKlRNCWo2YnLNSjXoejXnYhs/RtiBz1Qr/c4vmQKtw7szZIlS87ruggh6nZehvYGm+7duzN06DA2bHwRV1xnQlrWb0SOZVn8sOkVPHlfM/XRRee5StHUdOjQgc1ZW+psIfEHkbS0NP7617+iaRoLfIctU19+GaAikFQPInpkCzZnbaFz586MHTOGu5aqHa0/kNQWRAAeeughxo0bV2eHTn8LSV0nz2tsmgYtdOgfppbqLAsKy+GoF74qhf0emF5UiH5xdP3fJLQZhYWF536cEMJ2wThqs0GWLVtKl4s6cuLNx/B8d+Ccj7dMgx8yFlK0dQ3PP/88yckNa1ERAioDCVEJXPu6h29OqmncagsiAA6HgwULF3L//feTurqU13d66wwiXbt2Rdd1Xlm8mHvuvoe7VpWyYndZnUHErz4h40IZfaJpEB0CiREwvAVMSYC46GiM0z/U/0VKThEd3YDwIoSwTZNuGQGIjo4mc/Mmhg67ie1Lf0/YJVcS+ZuhuNv9usoHr1F6muJdGZR8vg5vwTFefPFFHnroIRsrF8EusIVk4Ou5pF6u8VSmt0YQ8fMHElAtJEt3lfH+V0aNIOLnDyQAdy1dwis7dDZ8bdQaRJqCm28ayqLXl2NdOwZNP/uk8N4TRyg5tp+hQ//48xQnhPhJmnSfkUAlJSUsXLiQOfNe4KsD+wlr1R6aJagPNW8xnmN70UyTkSNH8sgjk+nbt28jr4X4pfL3ITn0TU6dQSSQaZqMe/BBFvkO2bRJiKsRRAIZhsHYMWN4fcmSJhtEAHbv3s3ll19O7A0TiepRs99LoBNr/0Zo7ucc/TbnJ89ZJIT48c7bifLs0BhhxM+yLDZt2sTy5cvJy8vjTEkJsc2bk5SUxNixY886GZsQP9bRo0fZsmULd9xxR70OhViWxbfffkt2djY9e/akU6dOZ328aZocPXq0wTMVB5vRo1NZ9sZyYm+ZSniXPjV+blkWp/79Bqf+vYwFCxbw4IMNO1u3EKJxSRgRQjQ5Ho+HUXfeyZrVqwnv3IuIHjfiiuuMZZmUHt5Byc51lOR+xcyZM5k+fbrd5QrxiydhRAjRJBmGwZIlS3h+zly2b9tacb+maQwdNoy0Rx5h0KBBNlYohPCTMCKEaPK++OILcnJycDqdXHrppbRr187ukoQQAWSeESFEk5eYmEhiYqLdZQghfqImP8+IEEIIIS5sEkaEEEIIYSsJI0IIIYSwlYQRIYQQQthKwogQQgghbCVhRAghhBC2kjAihBBCCFsFxTwj/nnZCgsLba5ECCGEEPXl32+fa37VoAgjRUVFAE3+JGBCCCFEU1RUVER0dHSdPw+K6eBN0+TYsWNERUXV64ynF6rCwkLat29PTk6OTGv/E8h2bDyyLRuPbMvGI9uy8di9LS3LoqioiDZt2uBw1N0zJChaRhwOR5M650SzZs3kH6wRyHZsPLItG49sy8Yj27Lx2Lktz9Yi4icdWIUQQghhKwkjQgghhLCVhJGfkdvtZsaMGbjdbrtLCWqyHRuPbMvGI9uy8ci2bDzBsi2DogOrEEIIIZouaRkRQgghhK0kjAghhBDCVhJGhBBCCGErCSNCCCGEsJWEEZvMnDmTAQMGEB4eTkxMjN3lBJV58+bRqVMnQkND6du3L5999pndJQWdrKwshg8fTps2bdA0jdWrV9tdUtBKT0/niiuuICoqiri4OFJSUti3b5/dZQWlF198ke7du1dM0NW/f3/WrVtnd1lB7+mnn0bTNCZPnmx3KXWSMGITr9fL7bffzvjx4+0uJaisWLGCtLQ0ZsyYwfbt20lKSuKGG27g+PHjdpcWVIqLi0lKSmLevHl2lxL0MjMzmTBhAp988gkbNmygrKyM66+/nuLiYrtLCzrt2rXj6aefZtu2bWzdupVBgwZxyy23sGfPHrtLC1rZ2dksWLCA7t27213K2VnCVosXL7aio6PtLiNo9OnTx5owYULFbcMwrDZt2ljp6ek2VhXcAGvVqlV2l9FkHD9+3AKszMxMu0tpEpo3b24tWrTI7jKCUlFRkdW1a1drw4YNVnJysvXwww/bXVKdpGVEBA2v18u2bdsYPHhwxX0Oh4PBgwfz8ccf21iZEJVOnToFQGxsrM2VBDfDMFi+fDnFxcX079/f7nKC0oQJExg2bFiVz8wLVVCcKE8IgBMnTmAYBvHx8VXuj4+PZ+/evTZVJUQl0zSZPHkyV155Jd26dbO7nKC0a9cu+vfvT2lpKZGRkaxatYrExES7ywo6y5cvZ/v27WRnZ9tdSr1Iy0gjmjp1KpqmnXWRnaYQTdeECRPYvXs3y5cvt7uUoHXppZeyY8cOPv30U8aPH09qaipffPGF3WUFlZycHB5++GGWLl1KaGio3eXUi7SMNKIpU6Zw3333nfUxnTt3/nmKaYJatmyJruvk5eVVuT8vL4+EhASbqhJCmThxIu+++y5ZWVm0a9fO7nKClsvlokuXLgD06tWL7OxsnnvuORYsWGBzZcFj27ZtHD9+nJ49e1bcZxgGWVlZzJ07F4/Hg67rNlZYk4SRRtSqVStatWpldxlNlsvlolevXmRkZJCSkgKoZvGMjAwmTpxob3HiF8uyLCZNmsSqVavYvHkzF110kd0lNSmmaeLxeOwuI6hcd9117Nq1q8p9Y8aM4bLLLuPRRx+94IIISBixzZEjRygoKODIkSMYhsGOHTsA6NKlC5GRkfYWdwFLS0sjNTWV3r1706dPH2bPnk1xcTFjxoyxu7Sgcvr0aQ4ePFhx+9ChQ+zYsYPY2Fg6dOhgY2XBZ8KECSxbtow1a9YQFRVFbm4uANHR0YSFhdlcXXCZNm0aN954Ix06dKCoqIhly5axefNm3n//fbtLCypRUVE1+ixFRETQokWLC7cvk93DeX6pUlNTLaDGsmnTJrtLu+DNmTPH6tChg+Vyuaw+ffpYn3zyid0lBZ1NmzbV+veXmppqd2lBp7btCFiLFy+2u7SgM3bsWKtjx46Wy+WyWrVqZV133XXWBx98YHdZTcKFPrRXsyzL+vkjkBBCCCGEIqNphBBCCGErCSNCCCGEsJWEESGEEELYSsKIEEIIIWwlYUQIIYQQtpIwIoQQQghbSRgRQgghhK0kjAghhBDCVhJGhBBCCGErCSNCCCGEsJWEESGEEELYSsKIEEIIIWz1/wFsgH0xGCVeVwAAAABJRU5ErkJggg==", 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" ] @@ -218,7 +218,7 @@ "transp_cost = ot_lr.compute_reg_ot_cost(ot_prob)\n", "plt.imshow(ot_lr.matrix, cmap=\"Purples\")\n", "plt.colorbar()\n", - "plt.title(f\"LR cost: {ot_lr.primal_cost}\")\n", + "plt.title(f\"Low-rank cost: {ot_lr.primal_cost:.4f}\")\n", "plt.show()\n", "plott = plot.Plot()\n", "_ = plott(ot_lr)" @@ -362,7 +362,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.10.6" } }, "nbformat": 4, From f04757f82da497f1aac45b21c547e4f35ac872fd Mon Sep 17 00:00:00 2001 From: Michal Klein <46717574+michalk8@users.noreply.github.com> Date: Thu, 7 Sep 2023 19:03:27 +0200 Subject: [PATCH 46/46] Add tests --- tests/solvers/quadratic/gw_test.py | 74 ++++++++++++++++++++++++++++++ 1 file changed, 74 insertions(+) diff --git a/tests/solvers/quadratic/gw_test.py b/tests/solvers/quadratic/gw_test.py index e8d7c7ca4..fb3825ce1 100644 --- a/tests/solvers/quadratic/gw_test.py +++ b/tests/solvers/quadratic/gw_test.py @@ -407,3 +407,77 @@ def test_relative_epsilon( else: assert 0.215 < out.reg_gw_cost < 0.22 assert 0.19 < out.primal_cost < 0.20 + + @pytest.mark.parametrize(("tau_a", "tau_b", "eps", "ti"), + [(0.99, 0.95, 0.0, True), (0.9, 0.8, 1e-3, False), + (1.0, 0.999, 0.0, True), (0.5, 1.0, 1e-2, False)]) + def test_gwlr_unbalanced( + self, tau_a: float, tau_b: float, eps: float, ti: bool + ): + geom_x = pointcloud.PointCloud(self.x) + geom_y = pointcloud.PointCloud(self.y) + a = self.a.at[:2].set(0.0) + b = self.b.at[15:20].set(0.0) + prob = quadratic_problem.QuadraticProblem( + geom_x, + geom_y, + a=a, + b=b, + tau_a=tau_a, + tau_b=tau_b, + ) + solver = jax.jit( + gromov_wasserstein_lr.LRGromovWasserstein( + rank=4, epsilon=eps, kwargs_dys={"translation_invariant": ti} + ) + ) + + res = solver(prob) + + np.testing.assert_array_equal(jnp.isfinite(res.errors), True) + np.testing.assert_array_equal(jnp.isfinite(res.costs), True) + + @pytest.mark.parametrize(("rank", "eps"), [(5, 0.0), (10, 1e-3), (15, 1e-2)]) + def test_gwlr_unbalanced_matches_balanced( + self, rank: int, eps: float, enable_x64: bool + ): + del enable_x64 + + geom_x = pointcloud.PointCloud(self.x) + geom_y = pointcloud.PointCloud(self.y) + prob = quadratic_problem.QuadraticProblem( + geom_x, + geom_y, + a=self.a, + b=self.b, + tau_a=1.0, + tau_b=1.0, + ) + prob_unbal = quadratic_problem.QuadraticProblem( + geom_x, + geom_y, + a=self.a, + b=self.b, + tau_a=0.9999, + tau_b=0.9999, + ) + solver = jax.jit( + gromov_wasserstein_lr.LRGromovWasserstein( + rank=rank, + epsilon=eps, + initializer="random", + min_iterations=50, + max_iterations=50 + ) + ) + + res = solver(prob) + res_unbal = solver(prob_unbal) + + np.testing.assert_allclose(res.transport_mass, 1.0, rtol=1e-4, atol=1e-4) + np.testing.assert_allclose( + res.transport_mass, res_unbal.transport_mass, rtol=1e-4, atol=1e-4 + ) + np.testing.assert_allclose( + res.primal_cost, res_unbal.primal_cost, rtol=1e-3, atol=1e-3 + )