diff --git a/.gitignore b/.gitignore index 6f716243..ce1a3128 100644 --- a/.gitignore +++ b/.gitignore @@ -20,19 +20,9 @@ carl.egg-info exp_sweep multirun outputs -testvenv -*.egg-info +experiments runs -*.png -*.pdf -*.csv -*.pickle -*.ipynb_checkpoints -*optgap* -*smac3* -*.json generated +*egg* core -*.tex -build -target \ No newline at end of file +*.png \ No newline at end of file diff --git a/.gitmodules b/.gitmodules index 7227e87f..091048df 100644 --- a/.gitmodules +++ b/.gitmodules @@ -6,4 +6,4 @@ url = https://github.com/Mawiszus/TOAD-GUI [submodule "src/envs/mario/Mario-AI-Framework"] path = src/envs/mario/Mario-AI-Framework - url = https://github.com/frederikschubert/Mario-AI-Framework + url = https://github.com/frederikschubert/Mario-AI-Framework \ No newline at end of file diff --git a/CITATION.bib b/CITATION.bib index 0b17f84c..f6a2b599 100644 --- a/CITATION.bib +++ b/CITATION.bib @@ -11,4 +11,4 @@ @inproceedings { BenEim2023a title = {Contextualize Me - The Case for Context in Reinforcement Learning}, journal = {Transactions on Machine Learning Research}, year = {2023}, -} \ No newline at end of file +} diff --git a/README.md b/README.md index e89b0fed..10df90d5 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ pip install . This will only install the basic classic control environments, which should run on most operating systems. For the full set of environments, use the install options: ```bash -pip install -e .[box2d,brax,mario,dm_control] +pip install -e .[box2d, brax, mario, dm_control] ``` These may not be compatible with Windows systems. Box2D environment may need to be installed via conda on MacOS systems: @@ -68,12 +68,12 @@ Different instiations can be achieved by setting the context features to differe ## Cite Us If you use CARL in your research, please cite our paper on the benchmark: ```bibtex -@inproceedings{Benjamins2023, - title = {Contextualize Me -- The Case for Context in Reinforcement Learning}, - author = {Carolin Benjamins and Theresa Eimer and Frederik Schubert and Aditya Mohan and Sebastian Döhler and André Biedenkapp and Bodo Rosenhan and Frank Hutter and Marius Lindauer}, - booktitle = {Transactions on Machine Learning Research}, - year = {2023}, - month = Apr +@inproceedings{BenEim2021a, + title = {CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning}, + author = {Carolin Benjamins and Theresa Eimer and Frederik Schubert and André Biedenkapp and Bodo Rosenhahn and Frank Hutter and Marius Lindauer}, + booktitle = {NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning}, + year = {2021}, + month = dec } ``` diff --git a/carl/context/sampling.py b/carl/context/sampling.py index 9cf7a349..31fec750 100644 --- a/carl/context/sampling.py +++ b/carl/context/sampling.py @@ -1,27 +1,16 @@ # flake8: noqa: W605 -from __future__ import annotations -from typing import Any, Dict, List, Optional, Tuple, Union - -import importlib +from typing import Any, Dict, List, Tuple import numpy as np -from scipy.stats import norm, rv_continuous, uniform +from scipy.stats import norm -import carl.envs +from carl import envs from carl.utils.types import Context, Contexts def get_default_context_and_bounds( env_name: str, -) -> Tuple[ - Context, - Dict[ - str, - Union[ - Tuple[Any, Any, Union[type, Tuple[type, type]]], Tuple[Any, Any, str, list] - ], - ], -]: +) -> Tuple[Dict[Any, Any], Dict[Any, Any]]: """ Get context feature defaults and bounds for environment. @@ -46,11 +35,11 @@ def get_default_context_and_bounds( categorical context features: ``"VEHICLE": (None, None, "categorical", np.arange(0, len(PARKING_GARAGE)))`` """ - env_cls = getattr(carl.envs, env_name) - env_module = importlib.import_module(env_cls.__module__) - context_def = getattr(env_module, "DEFAULT_CONTEXT") - context_bounds = getattr(env_module, "CONTEXT_BOUNDS") - return context_def, context_bounds + # TODO make less hacky / make explicit + env_defaults = getattr(envs, f"{env_name}_defaults") + env_bounds = getattr(envs, f"{env_name}_bounds") + + return env_defaults, env_bounds def sample_contexts( @@ -59,9 +48,6 @@ def sample_contexts( num_contexts: int, default_sample_std_percentage: float = 0.05, fallback_sample_std: float = 0.1, - seed: Optional[int] = None, - uniform_distribution: bool = False, - uniform_bounds_rel: tuple(float, float) | None = None ) -> Dict[int, Dict[str, Any]]: """ Sample contexts. @@ -116,8 +102,6 @@ def sample_contexts( 0.05. fallback_sample_std: float, optional The fallback relative standard deviation. Defaults to 0.1. - seed: int, optional - The seed for the sampling of the random variables. Returns ------- @@ -126,15 +110,11 @@ def sample_contexts( names as keys and context feature values as values, e.g., """ - rng = np.random.default_rng(seed=seed) - # Get default context features and bounds env_defaults, env_bounds = get_default_context_and_bounds(env_name=env_name) # Create sample distributions/rules - sample_dists: Dict[ - str, Tuple[rv_continuous, Union[str, type, Tuple[type, type]]] - ] = {} + sample_dists = {} for context_feature_name in env_defaults.keys(): if context_feature_name in context_feature_args: if f"{context_feature_name}_mean" in context_feature_args: @@ -161,21 +141,7 @@ def sample_contexts( # the sample mean. Therefore we use a fallback sample standard deviation. sample_std = fallback_sample_std # TODO change this back to sample_std - if not uniform_distribution: - random_variable = norm(loc=sample_mean, scale=sample_std) - else: - # bounds defined as [loc, loc+scale] - if sample_mean == 0: - # relative bounds are centered around 1 so subtract here for the percentages - loc = uniform_bounds_rel[0] - 1 - scale = uniform_bounds_rel[1] - uniform_bounds_rel[0] - elif sample_mean < 0: - loc = uniform_bounds_rel[1] * sample_mean - scale = uniform_bounds_rel[0] * sample_mean - loc - else: - loc = uniform_bounds_rel[0] * sample_mean - scale = uniform_bounds_rel[1] * sample_mean - loc - random_variable = uniform(loc=loc, scale=scale) + random_variable = norm(loc=sample_mean, scale=sample_std) context_feature_type = env_bounds[context_feature_name][2] sample_dists[context_feature_name] = (random_variable, context_feature_type) @@ -190,30 +156,27 @@ def sample_contexts( random_variable = sample_dists[k][0] context_feature_type = sample_dists[k][1] lower_bound, upper_bound = env_bounds[k][0], env_bounds[k][1] - assert lower_bound <= upper_bound, f"context variable {k}: lower bound [{lower_bound}] is higher than upper bound [{upper_bound}]!" if context_feature_type == list: length = np.random.randint( 500000 ) # TODO should we allow lists to be this long? or should we parametrize this? - arg_class = sample_dists[k][1][1] # type: ignore [index] - context_list = random_variable.rvs(size=length, random_state=rng) + arg_class = sample_dists[k][1][1] + context_list = random_variable.rvs(size=length) context_list = np.clip(context_list, lower_bound, upper_bound) - c[k] = [arg_class(c) for c in context_list] # type: ignore [operator] + c[k] = [arg_class(c) for c in context_list] elif context_feature_type == "categorical": - choices = env_bounds[k][3] # type: ignore [misc] - choice = rng.choice(choices) + choices = env_bounds[k][3] + choice = np.random.choice(choices) c[k] = choice elif context_feature_type == "conditional": - condition = env_bounds[k][4] # type: ignore [misc] - choices = env_bounds[k][3][condition] # type: ignore [misc] - choice = rng.choice(choices) + condition = env_bounds[k][4] + choices = env_bounds[k][3][condition] + choice = np.random.choice(choices) c[k] = choice else: - c[k] = random_variable.rvs(size=1, random_state=rng)[ - 0 - ] # sample variable + c[k] = random_variable.rvs(size=1)[0] # sample variable c[k] = np.clip(c[k], lower_bound, upper_bound) # check bounds - c[k] = context_feature_type(c[k]) # type: ignore [operator] # cast to given type + c[k] = context_feature_type(c[k]) # cast to given type else: # No special sampling rule for context feature k, use the default context feature value c[k] = env_defaults[k] diff --git a/carl/context/selection.py b/carl/context/selection.py index d66857bd..7be40f35 100644 --- a/carl/context/selection.py +++ b/carl/context/selection.py @@ -88,7 +88,7 @@ def context_key(self) -> Any | None: Any | None The key of the current context or None """ - if self.context_id is not None: + if self.context_id: key = self.contexts_keys[self.context_id] else: key = None diff --git a/carl/envs/box2d/__init__.py b/carl/envs/box2d/__init__.py index 6f727873..ad6a3424 100644 --- a/carl/envs/box2d/__init__.py +++ b/carl/envs/box2d/__init__.py @@ -1,42 +1,22 @@ # flake8: noqa: F401 +from carl.envs.box2d.carl_bipedal_walker import ( + CONTEXT_BOUNDS as CARLBipedalWalkerEnv_bounds, +) +from carl.envs.box2d.carl_bipedal_walker import ( + DEFAULT_CONTEXT as CARLBipedalWalkerEnv_defaults, +) +from carl.envs.box2d.carl_bipedal_walker import CARLBipedalWalkerEnv # Contextenvs.s and bounds by name -from functools import partial -import warnings - -import gym -from carl.envs.box2d.carl_lunarlander import CARLLunarLanderEnv +from carl.envs.box2d.carl_lunarlander import CONTEXT_BOUNDS as CARLLunarLanderEnv_bounds from carl.envs.box2d.carl_lunarlander import ( DEFAULT_CONTEXT as CARLLunarLanderEnv_defaults, ) +from carl.envs.box2d.carl_lunarlander import CARLLunarLanderEnv from carl.envs.box2d.carl_vehicle_racing import ( CONTEXT_BOUNDS as CARLVehicleRacingEnv_bounds, ) - -from carl.envs.box2d.carl_vehicle_racing import CARLVehicleRacingEnv from carl.envs.box2d.carl_vehicle_racing import ( DEFAULT_CONTEXT as CARLVehicleRacingEnv_defaults, ) -from carl.envs.box2d.carl_vehicle_racing import ( - CONTEXT_BOUNDS as CARLVehicleRacingEnv_bounds, -) - -from carl.envs.box2d.carl_bipedal_walker import CARLBipedalWalkerEnv -from carl.envs.box2d.carl_bipedal_walker import ( - DEFAULT_CONTEXT as CARLBipedalWalkerEnv_defaults, -) -from carl.envs.box2d.carl_bipedal_walker import ( - CONTEXT_BOUNDS as CARLBipedalWalkerEnv_bounds, -) - -try: - from carl.envs.box2d.carl_bipedal_walker import CARLBipedalWalkerEnv - from gym.envs.registration import register - - def make_env(**kwargs): - return CARLBipedalWalkerEnv(**kwargs) - register("CARLBipedalWalkerEnv-v0", entry_point=make_env) - register("CARLBipedalWalkerHardcoreEnv-v0", entry_point=partial(make_env, env=gym.make("BipedalWalkerHardcore-v3"))) -except Exception as e: - warnings.warn( - f"Could not load CARLMarioEnv which is probably not installed ({e}).") +from carl.envs.box2d.carl_vehicle_racing import CARLVehicleRacingEnv diff --git a/carl/envs/box2d/carl_bipedal_walker.py b/carl/envs/box2d/carl_bipedal_walker.py index bf54b776..33e66453 100644 --- a/carl/envs/box2d/carl_bipedal_walker.py +++ b/carl/envs/box2d/carl_bipedal_walker.py @@ -2,7 +2,6 @@ import numpy as np from Box2D.b2 import edgeShape, fixtureDef, polygonShape -import gym from gym.envs.box2d import bipedal_walker from gym.envs.box2d import bipedal_walker as bpw @@ -106,8 +105,7 @@ def __init__( instance_mode: str, optional """ if env is None: - # env = bipedal_walker.BipedalWalker() - env = gym.make(id="BipedalWalker-v3") + env = bipedal_walker.BipedalWalker() if not contexts: contexts = {0: DEFAULT_CONTEXT} super().__init__( diff --git a/carl/envs/brax/__init__.py b/carl/envs/brax/__init__.py index e381dadd..eee221fb 100644 --- a/carl/envs/brax/__init__.py +++ b/carl/envs/brax/__init__.py @@ -1,23 +1,20 @@ # flake8: noqa: F401 # Contexts and bounds by name -from carl.envs.braxenvs.carl_ant import CONTEXT_BOUNDS as CARLAnt_bounds -from carl.envs.braxenvs.carl_ant import DEFAULT_CONTEXT as CARLAnt_defaults -from carl.envs.braxenvs.carl_ant import CARLAnt -from carl.envs.braxenvs.carl_halfcheetah import CONTEXT_BOUNDS as CARLHalfcheetah_bounds -from carl.envs.braxenvs.carl_halfcheetah import DEFAULT_CONTEXT as CARLHalfcheetah_defaults -from carl.envs.braxenvs.carl_halfcheetah import CARLHalfcheetah -from carl.envs.braxenvs.carl_humanoid import CONTEXT_BOUNDS as CARLHumanoid_bounds -from carl.envs.braxenvs.carl_humanoid import DEFAULT_CONTEXT as CARLHumanoid_defaults -from carl.envs.braxenvs.carl_humanoid import CARLHumanoid -from carl.envs.braxenvs.carl_hopper import CONTEXT_BOUNDS as CARLHopper_bounds -from carl.envs.braxenvs.carl_hopper import DEFAULT_CONTEXT as CARLHopper_defaults -from carl.envs.braxenvs.carl_hopper import CARLHopper -from carl.envs.braxenvs.carl_reacher import CONTEXT_BOUNDS as CARLReacher_bounds -from carl.envs.braxenvs.carl_reacher import DEFAULT_CONTEXT as CARLReacher_defaults -from carl.envs.braxenvs.carl_reacher import CARLReacher -from carl.envs.braxenvs.carl_pusher import CONTEXT_BOUNDS as CARLPusher_bounds -from carl.envs.braxenvs.carl_pusher import DEFAULT_CONTEXT as CARLPusher_defaults -from carl.envs.braxenvs.carl_pusher import CARLPusher -from carl.envs.braxenvs.carl_double_pendulum import CONTEXT_BOUNDS as CARLInvertedDoublePendulum_bounds -from carl.envs.braxenvs.carl_double_pendulum import DEFAULT_CONTEXT as CARLInvertedDoublePendulum_defaults -from carl.envs.braxenvs.carl_double_pendulum import CARLInvertedDoublePendulum +from carl.envs.brax.carl_ant import CONTEXT_BOUNDS as CARLAnt_bounds +from carl.envs.brax.carl_ant import DEFAULT_CONTEXT as CARLAnt_defaults +from carl.envs.brax.carl_ant import CARLAnt +from carl.envs.brax.carl_fetch import CONTEXT_BOUNDS as CARLFetch_bounds +from carl.envs.brax.carl_fetch import DEFAULT_CONTEXT as CARLFetch_defaults +from carl.envs.brax.carl_fetch import CARLFetch +from carl.envs.brax.carl_grasp import CONTEXT_BOUNDS as CARLGrasp_bounds +from carl.envs.brax.carl_grasp import DEFAULT_CONTEXT as CARLGrasp_defaults +from carl.envs.brax.carl_grasp import CARLGrasp +from carl.envs.brax.carl_halfcheetah import CONTEXT_BOUNDS as CARLHalfcheetah_bounds +from carl.envs.brax.carl_halfcheetah import DEFAULT_CONTEXT as CARLHalfcheetah_defaults +from carl.envs.brax.carl_halfcheetah import CARLHalfcheetah +from carl.envs.brax.carl_humanoid import CONTEXT_BOUNDS as CARLHumanoid_bounds +from carl.envs.brax.carl_humanoid import DEFAULT_CONTEXT as CARLHumanoid_defaults +from carl.envs.brax.carl_humanoid import CARLHumanoid +from carl.envs.brax.carl_ur5e import CONTEXT_BOUNDS as CARLUr5e_bounds +from carl.envs.brax.carl_ur5e import DEFAULT_CONTEXT as CARLUr5e_defaults +from carl.envs.brax.carl_ur5e import CARLUr5e diff --git a/carl/envs/brax/brax_wrappers.py b/carl/envs/brax/brax_wrappers.py deleted file mode 100644 index 1013caf7..00000000 --- a/carl/envs/brax/brax_wrappers.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright 2023 The Brax Authors. -# -# 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. - -"""Wrappers to convert brax envs to gym envs.""" -from typing import ClassVar, Optional - -from brax.envs import Env -import gym -from gym import spaces -from gym.vector import utils -import jax -import numpy as np -from functools import partial - - -class GymWrapper(gym.Env): - """A wrapper that converts Brax Env to one that follows Gym API.""" - - # Flag that prevents `gym.register` from misinterpreting the `_step` and - # `_reset` as signs of a deprecated gym Env API. - _gym_disable_underscore_compat: ClassVar[bool] = True - - def __init__(self, - env: Env, - seed: int = 0, - backend: Optional[str] = None): - self._env = env - self.metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second': 1 / self._env.dt - } - self.seed(seed) - self.backend = backend - self._state = None - - obs = np.inf * np.ones(self._env.observation_size, dtype='float32') - self.observation_space = spaces.Box(-obs, obs, dtype='float32') - - action = np.ones(self._env.action_size, dtype='float32') - self.action_space = spaces.Box(-action, action, dtype='float32') - - def reset(key): - key1, key2 = jax.random.split(key) - state = self._env.reset(key2) - return state, state.obs, key1 - - self._reset = partial(reset) - - def step(state, action): - state = self._env.step(state, action) - info = {**state.metrics, **state.info} - return state, state.obs, state.reward, state.done, info - - self._step = partial(step) - - def reset(self): - self._state, obs, self._key = self._reset(self._key) - # We return device arrays for pytorch users. - return obs - - def step(self, action): - self._state, obs, reward, done, info = self._step(self._state, action) - # We return device arrays for pytorch users. - return obs, reward, done, info - - def seed(self, seed: int = 0): - self._key = jax.random.PRNGKey(seed) - - def render(self, mode='human'): - return super().render(mode=mode) # just raise an exception - - -class VectorGymWrapper(gym.vector.VectorEnv): - """A wrapper that converts batched Brax Env to one that follows Gym VectorEnv API.""" - - # Flag that prevents `gym.register` from misinterpreting the `_step` and - # `_reset` as signs of a deprecated gym Env API. - _gym_disable_underscore_compat: ClassVar[bool] = True - - def __init__(self, - env: Env, - seed: int = 0, - backend: Optional[str] = None): - self._env = env - self.metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second': 1 / self._env.dt - } - if not hasattr(self._env, 'batch_size'): - raise ValueError('underlying env must be batched') - - self.num_envs = self._env.batch_size - self.seed(seed) - self.backend = backend - self._state = None - - obs = np.inf * np.ones(self._env.observation_size, dtype='float32') - obs_space = spaces.Box(-obs, obs, dtype='float32') - self.observation_space = utils.batch_space(obs_space, self.num_envs) - - action = np.ones(self._env.action_size, dtype='float32') - action_space = spaces.Box(-action, action, dtype='float32') - self.action_space = utils.batch_space(action_space, self.num_envs) - - def reset(key): - key1, key2 = jax.random.split(key) - state = self._env.reset(key2) - return state, state.obs, key1 - - self._reset = partial(reset) - - def step(state, action): - state = self._env.step(state, action) - info = {**state.metrics, **state.info} - return state, state.obs, state.reward, state.done, info - - self._step = partial(step) - - def reset(self): - self._state, obs, self._key = self._reset(self._key) - return obs - - def step(self, action): - self._state, obs, reward, done, info = self._step(self._state, action) - return obs, reward, done, info - - def seed(self, seed: int = 0): - self._key = jax.random.PRNGKey(seed) - - def render(self, mode='human'): - if mode == 'rgb_array': - sys, state = self._env.sys, self._state - if state is None: - raise RuntimeError('must call reset or step before rendering') - return image.render_array(sys, state.state.take(0), 256, 256) - else: - return super().render(mode=mode) # just raise an exception diff --git a/carl/envs/brax/carl_ant.py b/carl/envs/brax/carl_ant.py index 001c4b3a..c53fd64f 100644 --- a/carl/envs/brax/carl_ant.py +++ b/carl/envs/brax/carl_ant.py @@ -1,103 +1,113 @@ -from __future__ import annotations +from typing import Any, Dict, List, Optional, Union -import numpy as np -import jax.numpy as jnp -from brax.envs.ant import Ant +import copy +import json +import brax +import numpy as np +from brax.envs.ant import _SYSTEM_CONFIG, Ant +from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper +from google.protobuf import json_format, text_format +from google.protobuf.json_format import MessageToDict +from numpyencoder import NumpyEncoder from carl.context.selection import AbstractSelector -# from carl.envs.carl_brax_env import CARLBraxEnv +from carl.envs.carl_env import CARLEnv +from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts -from carl.envs.brax.carl_brax_env import CARLBraxEnv DEFAULT_CONTEXT = { - "stiffness_factor": 1, - "gravity": -9.81, - "friction": 1, - "damping_factor": 1, - "actuator_strength_factor": 1, + "joint_stiffness": 5000, + "gravity": -9.8, + "friction": 0.6, + "angular_damping": -0.05, + "actuator_strength": 300, + "joint_angular_damping": 35, "torso_mass": 10, - "dt": 0.01 } CONTEXT_BOUNDS = { - "stiffness_factor": (0, np.inf, float), + "joint_stiffness": (1, np.inf, float), "gravity": (-np.inf, -0.1, float), "friction": (-np.inf, np.inf, float), - "damping_factor": (-np.inf, np.inf, float), - "actuator_strength_factor": (1, np.inf, float), + "angular_damping": (-np.inf, np.inf, float), + "actuator_strength": (1, np.inf, float), + "joint_angular_damping": (0, np.inf, float), "torso_mass": (0.1, np.inf, float), - "dt": (0.0001, 0.03, float), } +class CARLAnt(CARLEnv): + def __init__( + self, + env: Ant = Ant(), + n_envs: int = 1, + contexts: Contexts = {}, + hide_context: bool = False, + add_gaussian_noise_to_context: bool = False, + gaussian_noise_std_percentage: float = 0.01, + logger: Optional[TrialLogger] = None, + scale_context_features: str = "no", + default_context: Optional[Context] = DEFAULT_CONTEXT, + state_context_features: Optional[List[str]] = None, + context_mask: Optional[List[str]] = None, + dict_observation_space: bool = False, + context_selector: Optional[ + Union[AbstractSelector, type[AbstractSelector]] + ] = None, + context_selector_kwargs: Optional[Dict] = None, + ): + if n_envs == 1: + env = GymWrapper(env) + else: + env = VectorGymWrapper(VectorWrapper(env, n_envs)) -class CARLAnt(CARLBraxEnv): - env_name: str = "ant" - DEFAULT_CONTEXT: Context = DEFAULT_CONTEXT + self.base_config = MessageToDict( + text_format.Parse(_SYSTEM_CONFIG, brax.Config()) + ) + if not contexts: + contexts = {0: DEFAULT_CONTEXT} + super().__init__( + env=env, + n_envs=n_envs, + contexts=contexts, + hide_context=hide_context, + add_gaussian_noise_to_context=add_gaussian_noise_to_context, + gaussian_noise_std_percentage=gaussian_noise_std_percentage, + logger=logger, + scale_context_features=scale_context_features, + default_context=default_context, + state_context_features=state_context_features, + dict_observation_space=dict_observation_space, + context_selector=context_selector, + context_selector_kwargs=context_selector_kwargs, + context_mask=context_mask, + ) + self.whitelist_gaussian_noise = list( + DEFAULT_CONTEXT.keys() + ) # allow to augment all values def _update_context(self) -> None: self.env: Ant - config = {} - config["gravity"] = jnp.array([0, 0, self.context["gravity"]]) - config["dt"] = jnp.array(self.context["dt"]) - new_mass = self.env._env.sys.link.inertia.mass.at[0].set(self.context["torso_mass"]) - # TODO: do we want to implement this? - #new_com = self.env.sys.link.inertia.transform - #new_inertia = self.env.sys.link.inertia.i - inertia = self.env._env.sys.link.inertia.replace(mass=new_mass) - config["link"] = self.env._env.sys.link.replace(inertia=inertia) - new_stiffness = self.context["stiffness_factor"]*self.env._env.sys.dof.stiffness - new_damping = self.context["damping_factor"]*self.env._env.sys.dof.damping - config["dof"] = self.env._env.sys.dof.replace(stiffness=new_stiffness, damping=new_damping) - new_gear = self.context["actuator_strength_factor"]*self.env._env.sys.actuator.gear - config["actuator"] = self.env._env.sys.actuator.replace(gear=new_gear) - geoms = self.env._env.sys.geoms - geoms[0] = geoms[0].replace(friction=jnp.array([self.context["friction"]])) - config["geoms"] = geoms - self.env._env.sys = self.env._env.sys.replace(**config) - - -# # NOTE: this is not up to date! -# class CARLBraxAnt(CARLBraxEnv): -# def __init__( -# self, -# env: Ant = Ant(), -# contexts: Contexts = {}, -# state_context_features: list[str] | None = None, -# dict_observation: bool = False, -# context_selector: AbstractSelector | type[AbstractSelector] | None = None, -# context_selector_kwargs: dict = None, -# ): -# super().__init__( -# env=env, -# contexts=contexts, -# state_context_features=state_context_features, -# dict_observation=dict_observation, -# context_selector=context_selector, -# context_selector_kwargs=context_selector_kwargs -# ) - -# #self.base_config = MessageToDict( -# # text_format.Parse(_SYSTEM_CONFIG_SPRING, brax.Config()) -# #) + config = copy.deepcopy(self.base_config) + config["gravity"] = {"z": self.context["gravity"]} + config["friction"] = self.context["friction"] + config["angularDamping"] = self.context["angular_damping"] + for j in range(len(config["joints"])): + config["joints"][j]["angularDamping"] = self.context[ + "joint_angular_damping" + ] + config["joints"][j]["stiffness"] = self.context["joint_stiffness"] + for a in range(len(config["actuators"])): + config["actuators"][a]["strength"] = self.context["actuator_strength"] + config["bodies"][0]["mass"] = self.context["torso_mass"] + # This converts the dict to a JSON String, then parses it into an empty brax config + self.env.sys = brax.System( + json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) + ) -# def _update_context(self) -> None: -# #self.env: Ant -# config = {}#copy.deepcopy(self.base_config) -# config["gravity"] = jnp.array([0, 0, self.context["gravity"]]) -# #config["friction"] = self.context["friction"] -# config["dt"] = self.context["dt"] -# #for j in range(len(config["joints"])): -# # config["joints"][j]["angularDamping"] = self.context[ -# # "joint_angular_damping" -# # ] -# # config["joints"][j]["stiffness"] = self.context["joint_stiffness"] -# #for a in range(len(config["actuators"])): -# # config["actuators"][a]["strength"] = self.context["actuator_strength"] -# #config["bodies"][0]["mass"] = self.context["torso_mass"] -# # This converts the dict to a JSON String, then parses it into an empty brax config -# #self.env.sys = brax.System( -# # json_format.Parse(json.dumps(config), brax.Config()) -# #) -# self.env.sys = self.env.sys.replace(**config) \ No newline at end of file + def __getattr__(self, name: str) -> Any: + if name in ["sys", "__getstate__"]: + return getattr(self.env._environment, name) + else: + return getattr(self, name) diff --git a/carl/envs/brax/carl_brax_env.py b/carl/envs/brax/carl_brax_env.py deleted file mode 100644 index e266d6c1..00000000 --- a/carl/envs/brax/carl_brax_env.py +++ /dev/null @@ -1,82 +0,0 @@ -from __future__ import annotations - -from typing import Any, Dict, List, Optional, Union - -from brax.envs import create -from carl.envs.brax.brax_wrappers import GymWrapper, VectorGymWrapper - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts - - -class CARLBraxEnv(CARLEnv): - env_name: str - DEFAULT_CONTEXT: Context - - def __init__( - self, - env=None, - n_envs: int = 1, - contexts: Contexts = {}, - hide_context: bool = True, - add_gaussian_noise_to_context: bool = False, - gaussian_noise_std_percentage: float = 0.01, - logger: Optional[TrialLogger] = None, - scale_context_features: str = "no", - default_context: Optional[Context] = None, - state_context_features: Optional[List[str]] = None, - context_mask: Optional[List[str]] = None, - dict_observation_space: bool = False, - context_selector: Optional[ - Union[AbstractSelector, type[AbstractSelector]] - ] = None, - context_selector_kwargs: Optional[Dict] = None, - max_episode_length = 1000, - ): - if env is None: - batch_size = None if n_envs == 1 else n_envs # TODO check if batched env works with concat state - env = create(self.env_name, batch_size=batch_size) - - self.n_envs=n_envs - if n_envs == 1: - env = GymWrapper(env) - else: - env = VectorGymWrapper(env, n_envs) - - if not contexts: - contexts = {0: self.DEFAULT_CONTEXT} - if not default_context: - default_context = self.DEFAULT_CONTEXT - super().__init__( - env=env, - n_envs=n_envs, - contexts=contexts, - hide_context=hide_context, - add_gaussian_noise_to_context=add_gaussian_noise_to_context, - gaussian_noise_std_percentage=gaussian_noise_std_percentage, - logger=logger, - scale_context_features=scale_context_features, - default_context=default_context, - state_context_features=state_context_features, - dict_observation_space=dict_observation_space, - context_selector=context_selector, - context_selector_kwargs=context_selector_kwargs, - context_mask=context_mask, - max_episode_length=max_episode_length, - ) - self.whitelist_gaussian_noise = list( - self.DEFAULT_CONTEXT.keys() - ) # allow to augment all values - - def _update_context(self) -> None: - raise NotImplementedError - - def __getattr__(self, name: str) -> Any: - if name in ["sys", "__getstate__"]: - return getattr(self.env._environment, name) - else: - return getattr(self, name) - - diff --git a/carl/envs/brax/carl_double_pendulum.py b/carl/envs/brax/carl_double_pendulum.py deleted file mode 100644 index 3290307d..00000000 --- a/carl/envs/brax/carl_double_pendulum.py +++ /dev/null @@ -1,68 +0,0 @@ -from typing import Any, Dict, List, Optional, Union - -import numpy as np -import jax.numpy as jnp -from brax.envs import create -from brax.envs.inverted_double_pendulum import InvertedDoublePendulum -from carl.envs.brax.brax_wrappers import GymWrapper, VectorGymWrapper - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts -from carl.envs.brax.carl_brax_env import CARLBraxEnv - -DEFAULT_CONTEXT = { - "stiffness_factor": 1, - "gravity_x": 1e-5, - "gravity_z": -9.81, - "friction": 0.8, - "damping_factor": 1, - "actuator_strength_factor": 1, - "mass_cart": 10.5, - "mass_pole_0": 4.2, - "mass_pole_1": 4.2, - "dt": 0.01 -} - -CONTEXT_BOUNDS = { - "stiffness_factor": (0, np.inf, float), - "gravity_x": (-np.inf, np.inf, float), - "gravity_z": (-np.inf, -0.1, float), - "friction": (-np.inf, np.inf, float), - "damping_factor": (-np.inf, np.inf, float), - "actuator_strength_factor": (1, np.inf, float), - "mass_cart": (0.1, np.inf, float), - "mass_pole_0": (0.1, np.inf, float), - "mass_pole_1": (0.1, np.inf, float), - "dt": (0.0001, 0.03, float), -} - - - -class CARLInvertedDoublePendulum(CARLBraxEnv): - env_name: str = "inverted_double_pendulum" - DEFAULT_CONTEXT: Context = DEFAULT_CONTEXT - - def _update_context(self) -> None: - self.env: InvertedDoublePendulum - config = {} - config["gravity"] = jnp.array([self.context["gravity_x"], 0, self.context["gravity_z"]]) - config["dt"] = jnp.array(self.context["dt"]) - new_mass = self.env._env.sys.link.inertia.mass.at[0].set(self.context["mass_cart"]) - new_mass = new_mass.at[1].set(self.context["mass_pole_0"]) - new_mass = new_mass.at[2].set(self.context["mass_pole_1"]) - # TODO: do we want to implement this? - #new_com = self.env.sys.link.inertia.transform - #new_inertia = self.env.sys.link.inertia.i - inertia = self.env._env.sys.link.inertia.replace(mass=new_mass) - config["link"] = self.env._env.sys.link.replace(inertia=inertia) - new_stiffness = self.context["stiffness_factor"]*self.env._env.sys.dof.stiffness - new_damping = self.context["damping_factor"]*self.env._env.sys.dof.damping - config["dof"] = self.env._env.sys.dof.replace(stiffness=new_stiffness, damping=new_damping) - new_gear = self.context["actuator_strength_factor"]*self.env._env.sys.actuator.gear - config["actuator"] = self.env._env.sys.actuator.replace(gear=new_gear) - geoms = self.env._env.sys.geoms - geoms[0] = geoms[0].replace(friction=jnp.array([self.context["friction"]])) - config["geoms"] = geoms - self.env._env.sys = self.env._env.sys.replace(**config) diff --git a/carl/envs/brax/carl_fetch.py b/carl/envs/brax/carl_fetch.py new file mode 100644 index 00000000..272e6481 --- /dev/null +++ b/carl/envs/brax/carl_fetch.py @@ -0,0 +1,127 @@ +from typing import Any, Dict, List, Optional, Union + +import copy +import json + +import brax +import numpy as np +from brax.envs.fetch import _SYSTEM_CONFIG, Fetch +from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper +from google.protobuf import json_format, text_format +from google.protobuf.json_format import MessageToDict +from numpyencoder import NumpyEncoder + +from carl.context.selection import AbstractSelector +from carl.envs.carl_env import CARLEnv +from carl.utils.trial_logger import TrialLogger +from carl.utils.types import Context, Contexts + +DEFAULT_CONTEXT = { + "joint_stiffness": 5000, + "gravity": -9.8, + "friction": 0.6, + "angular_damping": -0.05, # Angular velocity damping applied to each body + "actuator_strength": 300, + "joint_angular_damping": 35, # Damps parent and child angular velocities to be equal + "torso_mass": 1, + "target_radius": 2, + "target_distance": 15, +} + +CONTEXT_BOUNDS = { + "joint_stiffness": (1, np.inf, float), + "gravity": (-np.inf, -0.1, float), + "friction": (-np.inf, np.inf, float), + "angular_damping": (-np.inf, np.inf, float), + "actuator_strength": (1, np.inf, float), + "joint_angular_damping": (0, np.inf, float), + "torso_mass": (0.1, np.inf, float), + "target_radius": (0.1, np.inf, float), + "target_distance": (0.1, np.inf, float), +} + + +class CARLFetch(CARLEnv): + def __init__( + self, + env: Fetch = Fetch(), + n_envs: int = 1, + contexts: Contexts = {}, + hide_context: bool = False, + add_gaussian_noise_to_context: bool = False, + gaussian_noise_std_percentage: float = 0.01, + logger: Optional[TrialLogger] = None, + scale_context_features: str = "no", + default_context: Optional[Context] = DEFAULT_CONTEXT, + state_context_features: Optional[List[str]] = None, + context_mask: Optional[List[str]] = None, + dict_observation_space: bool = False, + context_selector: Optional[ + Union[AbstractSelector, type[AbstractSelector]] + ] = None, + context_selector_kwargs: Optional[Dict] = None, + ): + if n_envs == 1: + env = GymWrapper(env) + else: + env = VectorGymWrapper(VectorWrapper(env, n_envs)) + + self.base_config = MessageToDict( + text_format.Parse(_SYSTEM_CONFIG, brax.Config()) + ) + if not contexts: + contexts = {0: DEFAULT_CONTEXT} + super().__init__( + env=env, + n_envs=n_envs, + contexts=contexts, + hide_context=hide_context, + add_gaussian_noise_to_context=add_gaussian_noise_to_context, + gaussian_noise_std_percentage=gaussian_noise_std_percentage, + logger=logger, + scale_context_features=scale_context_features, + default_context=default_context, + state_context_features=state_context_features, + dict_observation_space=dict_observation_space, + context_selector=context_selector, + context_selector_kwargs=context_selector_kwargs, + context_mask=context_mask, + ) + self.whitelist_gaussian_noise = list( + DEFAULT_CONTEXT.keys() + ) # allow to augment all values + + def _update_context(self) -> None: + self.env: Fetch + config = copy.deepcopy(self.base_config) + config["gravity"] = {"z": self.context["gravity"]} + config["friction"] = self.context["friction"] + config["angularDamping"] = self.context["angular_damping"] + for j in range(len(config["joints"])): + config["joints"][j]["angularDamping"] = self.context[ + "joint_angular_damping" + ] + config["joints"][j]["stiffness"] = self.context["joint_stiffness"] + for a in range(len(config["actuators"])): + config["actuators"][a]["strength"] = self.context["actuator_strength"] + config["bodies"][0]["mass"] = self.context["torso_mass"] + # This converts the dict to a JSON String, then parses it into an empty brax config + self.env.sys = brax.System( + json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) + ) + self.env.target_idx = self.env.sys.body.index["Target"] + self.env.torso_idx = self.env.sys.body.index["Torso"] + self.env.target_radius = self.context["target_radius"] + self.env.target_distance = self.context["target_distance"] + + def __getattr__(self, name: str) -> Any: + if name in [ + "sys", + "target_distance", + "target_radius", + "target_idx", + "torso_idx", + ]: + return getattr(self.env._environment, name) + else: + return getattr(self, name) diff --git a/carl/envs/brax/carl_grasp.py b/carl/envs/brax/carl_grasp.py new file mode 100644 index 00000000..f7795a42 --- /dev/null +++ b/carl/envs/brax/carl_grasp.py @@ -0,0 +1,132 @@ +from typing import Any, Dict, List, Optional, Union + +import copy +import json + +import brax +import numpy as np +from brax.envs.grasp import _SYSTEM_CONFIG, Grasp +from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper +from google.protobuf import json_format, text_format +from google.protobuf.json_format import MessageToDict +from numpyencoder import NumpyEncoder + +from carl.context.selection import AbstractSelector +from carl.envs.carl_env import CARLEnv +from carl.utils.trial_logger import TrialLogger +from carl.utils.types import Context, Contexts + +DEFAULT_CONTEXT = { + "joint_stiffness": 5000, + "gravity": -9.8, + "friction": 0.6, + "angular_damping": -0.05, + "actuator_strength": 300, + "joint_angular_damping": 50, + "target_radius": 1.1, + "target_distance": 10.0, + "target_height": 8.0, +} + +CONTEXT_BOUNDS = { + "joint_stiffness": (1, np.inf, float), + "gravity": (-np.inf, -0.1, float), + "friction": (-np.inf, np.inf, float), + "angular_damping": (-np.inf, np.inf, float), + "actuator_strength": (1, np.inf, float), + "joint_angular_damping": (0, np.inf, float), + "target_radius": (0.1, np.inf, float), + "target_distance": (0.1, np.inf, float), + "target_height": (0.1, np.inf, float), +} + + +class CARLGrasp(CARLEnv): + def __init__( + self, + env: Grasp = Grasp(), + n_envs: int = 1, + contexts: Contexts = {}, + hide_context: bool = False, + add_gaussian_noise_to_context: bool = False, + gaussian_noise_std_percentage: float = 0.01, + logger: Optional[TrialLogger] = None, + scale_context_features: str = "no", + default_context: Optional[Context] = DEFAULT_CONTEXT, + state_context_features: Optional[List[str]] = None, + context_mask: Optional[List[str]] = None, + dict_observation_space: bool = False, + context_selector: Optional[ + Union[AbstractSelector, type[AbstractSelector]] + ] = None, + context_selector_kwargs: Optional[Dict] = None, + ): + if n_envs == 1: + env = GymWrapper(env) + else: + env = VectorGymWrapper(VectorWrapper(env, n_envs)) + + self.base_config = MessageToDict( + text_format.Parse(_SYSTEM_CONFIG, brax.Config()) + ) + if not contexts: + contexts = {0: DEFAULT_CONTEXT} + super().__init__( + env=env, + n_envs=n_envs, + contexts=contexts, + hide_context=hide_context, + add_gaussian_noise_to_context=add_gaussian_noise_to_context, + gaussian_noise_std_percentage=gaussian_noise_std_percentage, + logger=logger, + scale_context_features=scale_context_features, + default_context=default_context, + state_context_features=state_context_features, + dict_observation_space=dict_observation_space, + context_selector=context_selector, + context_selector_kwargs=context_selector_kwargs, + context_mask=context_mask, + ) + self.whitelist_gaussian_noise = list( + DEFAULT_CONTEXT.keys() + ) # allow to augment all values + + def _update_context(self) -> None: + self.env: Grasp + config = copy.deepcopy(self.base_config) + config["gravity"] = {"z": self.context["gravity"]} + config["friction"] = self.context["friction"] + config["angularDamping"] = self.context["angular_damping"] + for j in range(len(config["joints"])): + config["joints"][j]["angularDamping"] = self.context[ + "joint_angular_damping" + ] + config["joints"][j]["stiffness"] = self.context["joint_stiffness"] + for a in range(len(config["actuators"])): + config["actuators"][a]["strength"] = self.context["actuator_strength"] + # This converts the dict to a JSON String, then parses it into an empty brax config + self.env.sys = brax.System( + json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) + ) + self.env.object_idx = self.env.sys.body.index["Object"] + self.env.target_idx = self.env.sys.body.index["Target"] + self.env.hand_idx = self.env.sys.body.index["HandThumbProximal"] + self.env.palm_idx = self.env.sys.body.index["HandPalm"] + self.env.target_radius = self.context["target_radius"] + self.env.target_distance = self.context["target_distance"] + self.env.target_height = self.context["target_height"] + + def __getattr__(self, name: str) -> Any: + if name in [ + "sys", + "object_idx", + "target_idx", + "hand_idx", + "palm_idx", + "target_radius", + "target_distance", + "target_height", + ]: + return getattr(self.env._environment, name) + else: + return getattr(self, name) diff --git a/carl/envs/brax/carl_halfcheetah.py b/carl/envs/brax/carl_halfcheetah.py index 1cc93f8b..aa014088 100644 --- a/carl/envs/brax/carl_halfcheetah.py +++ b/carl/envs/brax/carl_halfcheetah.py @@ -1,61 +1,109 @@ from typing import Any, Dict, List, Optional, Union +import copy +import json + +import brax import numpy as np -import jax.numpy as jnp -from brax.envs.half_cheetah import Halfcheetah -from brax.envs import create -from carl.envs.brax.brax_wrappers import GymWrapper, VectorGymWrapper +from brax.envs.half_cheetah import _SYSTEM_CONFIG, Halfcheetah +from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper +from google.protobuf import json_format, text_format +from google.protobuf.json_format import MessageToDict +from numpyencoder import NumpyEncoder from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts -from carl.envs.brax.carl_brax_env import CARLBraxEnv DEFAULT_CONTEXT = { - "stiffness_factor": 1, - "gravity": -9.81, - "friction": 0.4, - "damping_factor": 1, - "actuator_strength_factor": 1, - "torso_mass": 6.25, - "dt": 0.01 + "joint_stiffness": 15000.0, + "gravity": -9.8, + "friction": 0.6, + "angular_damping": -0.05, + "joint_angular_damping": 20, + "torso_mass": 9.457333, } CONTEXT_BOUNDS = { - "stiffness_factor": (0, np.inf, float), + "joint_stiffness": (1, np.inf, float), "gravity": (-np.inf, -0.1, float), "friction": (-np.inf, np.inf, float), - "damping_factor": (-np.inf, np.inf, float), - "actuator_strength_factor": (1, np.inf, float), + "angular_damping": (-np.inf, np.inf, float), + "joint_angular_damping": (0, np.inf, float), "torso_mass": (0.1, np.inf, float), - "dt": (0.0001, 0.03, float), } +class CARLHalfcheetah(CARLEnv): + def __init__( + self, + env: Halfcheetah = Halfcheetah(), + n_envs: int = 1, + contexts: Contexts = {}, + hide_context: bool = False, + add_gaussian_noise_to_context: bool = False, + gaussian_noise_std_percentage: float = 0.01, + logger: Optional[TrialLogger] = None, + scale_context_features: str = "no", + default_context: Optional[Context] = DEFAULT_CONTEXT, + state_context_features: Optional[List[str]] = None, + context_mask: Optional[List[str]] = None, + dict_observation_space: bool = False, + context_selector: Optional[ + Union[AbstractSelector, type[AbstractSelector]] + ] = None, + context_selector_kwargs: Optional[Dict] = None, + ): + if n_envs == 1: + env = GymWrapper(env) + else: + env = VectorGymWrapper(VectorWrapper(env, n_envs)) -class CARLHalfcheetah(CARLBraxEnv): - env_name: str = "halfcheetah" - DEFAULT_CONTEXT: Context = DEFAULT_CONTEXT + self.base_config = MessageToDict( + text_format.Parse(_SYSTEM_CONFIG, brax.Config()) + ) + if not contexts: + contexts = {0: DEFAULT_CONTEXT} + super().__init__( + env=env, + n_envs=n_envs, + contexts=contexts, + hide_context=hide_context, + add_gaussian_noise_to_context=add_gaussian_noise_to_context, + gaussian_noise_std_percentage=gaussian_noise_std_percentage, + logger=logger, + scale_context_features=scale_context_features, + default_context=default_context, + state_context_features=state_context_features, + dict_observation_space=dict_observation_space, + context_selector=context_selector, + context_selector_kwargs=context_selector_kwargs, + context_mask=context_mask, + ) + self.whitelist_gaussian_noise = list( + DEFAULT_CONTEXT.keys() + ) # allow to augment all values def _update_context(self) -> None: self.env: Halfcheetah - config = {} - config["gravity"] = jnp.array([0, 0, self.context["gravity"]]) - config["dt"] = jnp.array(self.context["dt"]) - new_mass = self.env._env.sys.link.inertia.mass.at[0].set(self.context["torso_mass"]) - # TODO: do we want to implement this? - #new_com = self.env.sys.link.inertia.transform - #new_inertia = self.env.sys.link.inertia.i - inertia = self.env._env.sys.link.inertia.replace(mass=new_mass) - config["link"] = self.env._env.sys.link.replace(inertia=inertia) - new_stiffness = self.context["stiffness_factor"]*self.env._env.sys.dof.stiffness - new_damping = self.context["damping_factor"]*self.env._env.sys.dof.damping - config["dof"] = self.env._env.sys.dof.replace(stiffness=new_stiffness, damping=new_damping) - new_gear = self.context["actuator_strength_factor"]*self.env._env.sys.actuator.gear - config["actuator"] = self.env._env.sys.actuator.replace(gear=new_gear) - geoms = self.env._env.sys.geoms - geoms[0] = geoms[0].replace(friction=jnp.array([self.context["friction"]])) - config["geoms"] = geoms - self.env._env.sys = self.env._env.sys.replace(**config) + config = copy.deepcopy(self.base_config) + config["gravity"] = {"z": self.context["gravity"]} + config["friction"] = self.context["friction"] + config["angularDamping"] = self.context["angular_damping"] + for j in range(len(config["joints"])): + config["joints"][j]["angularDamping"] = self.context[ + "joint_angular_damping" + ] + config["joints"][j]["stiffness"] = self.context["joint_stiffness"] + config["bodies"][0]["mass"] = self.context["torso_mass"] + # This converts the dict to a JSON String, then parses it into an empty brax config + self.env.sys = brax.System( + json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) + ) + def __getattr__(self, name: str) -> Any: + if name in ["sys"]: + return getattr(self.env._environment, name) + else: + return getattr(self, name) diff --git a/carl/envs/brax/carl_hopper.py b/carl/envs/brax/carl_hopper.py deleted file mode 100644 index 4fb5eaa3..00000000 --- a/carl/envs/brax/carl_hopper.py +++ /dev/null @@ -1,60 +0,0 @@ -from typing import Any, Dict, List, Optional, Union - -import numpy as np -import jax.numpy as jnp -from brax.envs.hopper import Hopper -from brax.envs import create -from carl.envs.brax.brax_wrappers import GymWrapper, VectorGymWrapper - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts -from carl.envs.brax.carl_brax_env import CARLBraxEnv - -DEFAULT_CONTEXT = { - "stiffness_factor": 1, - "gravity": -9.81, - "friction": 1, - "damping_factor": 1, - "actuator_strength_factor": 1, - "torso_mass": 3.67, - "dt": 0.002 -} - -CONTEXT_BOUNDS = { - "stiffness_factor": (0, np.inf, float), - "gravity": (-np.inf, -0.1, float), - "friction": (-np.inf, np.inf, float), - "damping_factor": (-np.inf, np.inf, float), - "actuator_strength_factor": (1, np.inf, float), - "torso_mass": (0.1, np.inf, float), - "dt": (0.0001, 0.03, float), -} - - - -class CARLHopper(CARLBraxEnv): - env_name: str = "hopper" - DEFAULT_CONTEXT: Context = DEFAULT_CONTEXT - - def _update_context(self) -> None: - self.env: Hopper - config = {} - config["gravity"] = jnp.array([0, 0, self.context["gravity"]]) - config["dt"] = jnp.array(self.context["dt"]) - new_mass = self.env._env.sys.link.inertia.mass.at[0].set(self.context["torso_mass"]) - # TODO: do we wHopper to implement this? - #new_com = self.env.sys.link.inertia.transform - #new_inertia = self.env.sys.link.inertia.i - inertia = self.env._env.sys.link.inertia.replace(mass=new_mass) - config["link"] = self.env._env.sys.link.replace(inertia=inertia) - new_stiffness = self.context["stiffness_factor"]*self.env._env.sys.dof.stiffness - new_damping = self.context["damping_factor"]*self.env._env.sys.dof.damping - config["dof"] = self.env._env.sys.dof.replace(stiffness=new_stiffness, damping=new_damping) - new_gear = self.context["actuator_strength_factor"]*self.env._env.sys.actuator.gear - config["actuator"] = self.env._env.sys.actuator.replace(gear=new_gear) - geoms = self.env._env.sys.geoms - geoms[0] = geoms[0].replace(friction=jnp.array([self.context["friction"]])) - config["geoms"] = geoms - self.env._env.sys = self.env._env.sys.replace(**config) diff --git a/carl/envs/brax/carl_humanoid.py b/carl/envs/brax/carl_humanoid.py index 93fc358b..873473ca 100644 --- a/carl/envs/brax/carl_humanoid.py +++ b/carl/envs/brax/carl_humanoid.py @@ -1,59 +1,113 @@ from typing import Any, Dict, List, Optional, Union +import copy +import json + +import brax import numpy as np -import jax.numpy as jnp -from brax.envs.humanoid import Humanoid -from brax.envs import create -from carl.envs.brax.brax_wrappers import GymWrapper, VectorGymWrapper +from brax import jumpy as jp +from brax.envs.humanoid import _SYSTEM_CONFIG, Humanoid +from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper +from brax.physics import bodies +from google.protobuf import json_format, text_format +from google.protobuf.json_format import MessageToDict +from numpyencoder import NumpyEncoder from carl.context.selection import AbstractSelector from carl.envs.carl_env import CARLEnv from carl.utils.trial_logger import TrialLogger from carl.utils.types import Context, Contexts -from carl.envs.brax.carl_brax_env import CARLBraxEnv DEFAULT_CONTEXT = { - "stiffness_factor": 1, - "gravity": -9.81, - "friction": 1.0, - "damping_factor": 1, - "actuator_strength_factor": 1, - "torso_mass": 8.9, - "dt": 0.003 + "gravity": -9.8, + "friction": 0.6, + "angular_damping": -0.05, + "joint_angular_damping": 20, + "torso_mass": 8.907463, } CONTEXT_BOUNDS = { - "stiffness_factor": (0, np.inf, float), "gravity": (-np.inf, -0.1, float), "friction": (-np.inf, np.inf, float), - "damping_factor": (-np.inf, np.inf, float), - "actuator_strength_factor": (1, np.inf, float), + "angular_damping": (-np.inf, np.inf, float), + "joint_angular_damping": (0, np.inf, float), "torso_mass": (0.1, np.inf, float), - "dt": (0.0001, 0.03, float), } -class CARLHumanoid(CARLBraxEnv): - env_name: str = "humanoid" - DEFAULT_CONTEXT: Context = DEFAULT_CONTEXT +class CARLHumanoid(CARLEnv): + def __init__( + self, + env: Humanoid = Humanoid(), + n_envs: int = 1, + contexts: Contexts = {}, + hide_context: bool = False, + add_gaussian_noise_to_context: bool = False, + gaussian_noise_std_percentage: float = 0.01, + logger: Optional[TrialLogger] = None, + scale_context_features: str = "no", + default_context: Optional[Context] = DEFAULT_CONTEXT, + state_context_features: Optional[List[str]] = None, + context_mask: Optional[List[str]] = None, + dict_observation_space: bool = False, + context_selector: Optional[ + Union[AbstractSelector, type[AbstractSelector]] + ] = None, + context_selector_kwargs: Optional[Dict] = None, + ): + if n_envs == 1: + env = GymWrapper(env) + else: + env = VectorGymWrapper(VectorWrapper(env, n_envs)) + + self.base_config = MessageToDict( + text_format.Parse(_SYSTEM_CONFIG, brax.Config()) + ) + if not contexts: + contexts = {0: DEFAULT_CONTEXT} + super().__init__( + env=env, + n_envs=n_envs, + contexts=contexts, + hide_context=hide_context, + add_gaussian_noise_to_context=add_gaussian_noise_to_context, + gaussian_noise_std_percentage=gaussian_noise_std_percentage, + logger=logger, + scale_context_features=scale_context_features, + default_context=default_context, + state_context_features=state_context_features, + dict_observation_space=dict_observation_space, + context_selector=context_selector, + context_selector_kwargs=context_selector_kwargs, + context_mask=context_mask, + ) + self.whitelist_gaussian_noise = list( + DEFAULT_CONTEXT.keys() + ) # allow to augment all values def _update_context(self) -> None: self.env: Humanoid - config = {} - config["gravity"] = jnp.array([0, 0, self.context["gravity"]]) - config["dt"] = jnp.array(self.context["dt"]) - new_mass = self.env._env.sys.link.inertia.mass.at[0].set(self.context["torso_mass"]) - # TODO: do we want to implement this? - #new_com = self.env.sys.link.inertia.transform - #new_inertia = self.env.sys.link.inertia.i - inertia = self.env._env.sys.link.inertia.replace(mass=new_mass) - config["link"] = self.env._env.sys.link.replace(inertia=inertia) - new_stiffness = self.context["stiffness_factor"]*self.env._env.sys.dof.stiffness - new_damping = self.context["damping_factor"]*self.env._env.sys.dof.damping - config["dof"] = self.env._env.sys.dof.replace(stiffness=new_stiffness, damping=new_damping) - new_gear = self.context["actuator_strength_factor"]*self.env._env.sys.actuator.gear - config["actuator"] = self.env._env.sys.actuator.replace(gear=new_gear) - geoms = self.env._env.sys.geoms - geoms[0] = geoms[0].replace(friction=jnp.array([self.context["friction"]])) - config["geoms"] = geoms - self.env._env.sys = self.env._env.sys.replace(**config) + config = copy.deepcopy(self.base_config) + config["gravity"] = {"z": self.context["gravity"]} + config["friction"] = self.context["friction"] + config["angularDamping"] = self.context["angular_damping"] + for j in range(len(config["joints"])): + config["joints"][j]["angularDamping"] = self.context[ + "joint_angular_damping" + ] + config["bodies"][0]["mass"] = self.context["torso_mass"] + # This converts the dict to a JSON String, then parses it into an empty brax config + protobuf_config = json_format.Parse( + json.dumps(config, cls=NumpyEncoder), brax.Config() + ) + self.env.sys = brax.System(protobuf_config) + body = bodies.Body(config=self.env.sys.config) + body = jp.take(body, body.idx[:-1]) # skip the floor body + self.env.mass = body.mass.reshape(-1, 1) + self.env.inertia = body.inertia + + def __getattr__(self, name: str) -> Any: + if name in ["sys", "body", "mass", "inertia"]: + return getattr(self.env._environment, name) + else: + return getattr(self, name) diff --git a/carl/envs/brax/carl_pusher.py b/carl/envs/brax/carl_pusher.py deleted file mode 100644 index a9cd2a34..00000000 --- a/carl/envs/brax/carl_pusher.py +++ /dev/null @@ -1,52 +0,0 @@ -from typing import Any, Dict, List, Optional, Union - -import numpy as np -import jax.numpy as jnp -from brax.envs.pusher import Pusher -from brax.envs import create -from carl.envs.brax.brax_wrappers import GymWrapper, VectorGymWrapper - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts -from carl.envs.brax.carl_brax_env import CARLBraxEnv - -DEFAULT_CONTEXT = { - "stiffness_factor": 1, - "gravity": -9.81, - "friction": 0.8, - "damping_factor": 1, - "actuator_strength_factor": 1, - "dt": 0.01 -} - -CONTEXT_BOUNDS = { - "stiffness_factor": (0, np.inf, float), - "gravity": (-np.inf, -0.1, float), - "friction": (-np.inf, np.inf, float), - "damping_factor": (-np.inf, np.inf, float), - "actuator_strength_factor": (1, np.inf, float), - "dt": (0.0001, 0.03, float), -} - - - -class CARLPusher(CARLBraxEnv): - env_name: str = "pusher" - DEFAULT_CONTEXT: Context = DEFAULT_CONTEXT - - def _update_context(self) -> None: - self.env: Pusher - config = {} - config["gravity"] = jnp.array([0, 0, self.context["gravity"]]) - config["dt"] = jnp.array(self.context["dt"]) - new_stiffness = self.context["stiffness_factor"]*self.env._env.sys.dof.stiffness - new_damping = self.context["damping_factor"]*self.env._env.sys.dof.damping - config["dof"] = self.env._env.sys.dof.replace(stiffness=new_stiffness, damping=new_damping) - new_gear = self.context["actuator_strength_factor"]*self.env._env.sys.actuator.gear - config["actuator"] = self.env._env.sys.actuator.replace(gear=new_gear) - geoms = self.env._env.sys.geoms - geoms[0] = geoms[0].replace(friction=jnp.array([self.context["friction"]])) - config["geoms"] = geoms - self.env._env.sys = self.env._env.sys.replace(**config) diff --git a/carl/envs/brax/carl_reacher.py b/carl/envs/brax/carl_reacher.py deleted file mode 100644 index a4820b97..00000000 --- a/carl/envs/brax/carl_reacher.py +++ /dev/null @@ -1,63 +0,0 @@ -from typing import Any, Dict, List, Optional, Union - -import numpy as np -import jax.numpy as jnp -from brax.envs.reacher import Reacher -from brax.envs import create -from carl.envs.brax.brax_wrappers import GymWrapper, VectorGymWrapper - -from carl.context.selection import AbstractSelector -from carl.envs.carl_env import CARLEnv -from carl.utils.trial_logger import TrialLogger -from carl.utils.types import Context, Contexts -from carl.envs.brax.carl_brax_env import CARLBraxEnv - -DEFAULT_CONTEXT = { - "stiffness_factor": 1, - "gravity": -9.81, - "friction": 1, - "damping_factor": 1, - "actuator_strength_factor": 1, - "body_mass_0": 0.036, - "body_mass_1": 0.04, - "dt": 0.01 -} - -CONTEXT_BOUNDS = { - "stiffness_factor": (0, np.inf, float), - "gravity": (-np.inf, -0.1, float), - "friction": (-np.inf, np.inf, float), - "damping_factor": (-np.inf, np.inf, float), - "actuator_strength_factor": (1, np.inf, float), - "body_mass_0": (0.1, np.inf, float), - "body_mass_1": (0.1, np.inf, float), - "dt": (0.0001, 0.03, float), -} - - - -class CARLReacher(CARLBraxEnv): - env_name: str = "reacher" - DEFAULT_CONTEXT: Context = DEFAULT_CONTEXT - - def _update_context(self) -> None: - self.env: Reacher - config = {} - config["gravity"] = jnp.array([0, 0, self.context["gravity"]]) - config["dt"] = jnp.array(self.context["dt"]) - new_mass = self.env._env.sys.link.inertia.mass.at[0].set(self.context["body_mass_0"]) - new_mass = new_mass.at[1].set(self.context["body_mass_1"]) - # TODO: do we wReacher to implement this? - #new_com = self.env.sys.link.inertia.transform - #new_inertia = self.env.sys.link.inertia.i - inertia = self.env._env.sys.link.inertia.replace(mass=new_mass) - config["link"] = self.env._env.sys.link.replace(inertia=inertia) - new_stiffness = self.context["stiffness_factor"]*self.env._env.sys.dof.stiffness - new_damping = self.context["damping_factor"]*self.env._env.sys.dof.damping - config["dof"] = self.env._env.sys.dof.replace(stiffness=new_stiffness, damping=new_damping) - new_gear = self.context["actuator_strength_factor"]*self.env._env.sys.actuator.gear - config["actuator"] = self.env._env.sys.actuator.replace(gear=new_gear) - geoms = self.env._env.sys.geoms - geoms[0] = geoms[0].replace(friction=jnp.array([self.context["friction"]])) - config["geoms"] = geoms - self.env._env.sys = self.env._env.sys.replace(**config) diff --git a/carl/envs/brax/carl_ur5e.py b/carl/envs/brax/carl_ur5e.py new file mode 100644 index 00000000..02ebd518 --- /dev/null +++ b/carl/envs/brax/carl_ur5e.py @@ -0,0 +1,127 @@ +from typing import Any, Dict, List, Optional, Union + +import copy +import json + +import brax +import numpy as np +from brax.envs.ur5e import _SYSTEM_CONFIG, Ur5e +from brax.envs.wrappers import GymWrapper, VectorGymWrapper, VectorWrapper +from google.protobuf import json_format, text_format +from google.protobuf.json_format import MessageToDict +from numpyencoder import NumpyEncoder + +from carl.context.selection import AbstractSelector +from carl.envs.carl_env import CARLEnv +from carl.utils.trial_logger import TrialLogger +from carl.utils.types import Context, Contexts + +DEFAULT_CONTEXT = { + "joint_stiffness": 40000, + "gravity": -9.81, + "friction": 0.6, + "angular_damping": -0.05, + "actuator_strength": 100, + "joint_angular_damping": 50, + "target_radius": 0.02, + "target_distance": 0.5, + "torso_mass": 1.0, +} + +CONTEXT_BOUNDS = { + "joint_stiffness": (1, np.inf, float), + "gravity": (-np.inf, -0.1, float), + "friction": (-np.inf, np.inf, float), + "angular_damping": (-np.inf, np.inf, float), + "actuator_strength": (1, np.inf, float), + "joint_angular_damping": (0, 360, float), + "target_radius": (0.01, np.inf, float), + "target_distance": (0.01, np.inf, float), + "torso_mass": (0, np.inf, float), +} + + +class CARLUr5e(CARLEnv): + def __init__( + self, + env: Ur5e = Ur5e(), + n_envs: int = 1, + contexts: Contexts = {}, + hide_context: bool = False, + add_gaussian_noise_to_context: bool = False, + gaussian_noise_std_percentage: float = 0.01, + logger: Optional[TrialLogger] = None, + scale_context_features: str = "no", + default_context: Optional[Context] = DEFAULT_CONTEXT, + state_context_features: Optional[List[str]] = None, + context_mask: Optional[List[str]] = None, + dict_observation_space: bool = False, + context_selector: Optional[ + Union[AbstractSelector, type[AbstractSelector]] + ] = None, + context_selector_kwargs: Optional[Dict] = None, + ): + if n_envs == 1: + env = GymWrapper(env) + else: + env = VectorGymWrapper(VectorWrapper(env, n_envs)) + + self.base_config = MessageToDict( + text_format.Parse(_SYSTEM_CONFIG, brax.Config()) + ) + if not contexts: + contexts = {0: DEFAULT_CONTEXT} + super().__init__( + env=env, + n_envs=n_envs, + contexts=contexts, + hide_context=hide_context, + add_gaussian_noise_to_context=add_gaussian_noise_to_context, + gaussian_noise_std_percentage=gaussian_noise_std_percentage, + logger=logger, + scale_context_features=scale_context_features, + default_context=default_context, + state_context_features=state_context_features, + dict_observation_space=dict_observation_space, + context_selector=context_selector, + context_selector_kwargs=context_selector_kwargs, + context_mask=context_mask, + ) + self.whitelist_gaussian_noise = list( + DEFAULT_CONTEXT.keys() + ) # allow to augment all values + + def _update_context(self) -> None: + self.env: Ur5e + config = copy.deepcopy(self.base_config) + config["gravity"] = {"z": self.context["gravity"]} + config["friction"] = self.context["friction"] + config["angularDamping"] = self.context["angular_damping"] + for j in range(len(config["joints"])): + config["joints"][j]["angularDamping"] = self.context[ + "joint_angular_damping" + ] + config["joints"][j]["stiffness"] = self.context["joint_stiffness"] + for a in range(len(config["actuators"])): + config["actuators"][a]["strength"] = self.context["actuator_strength"] + config["bodies"][0]["mass"] = self.context["torso_mass"] + # This converts the dict to a JSON String, then parses it into an empty brax config + self.env.sys = brax.System( + json_format.Parse(json.dumps(config, cls=NumpyEncoder), brax.Config()) + ) + self.env.target_idx = self.env.sys.body.index["Target"] + self.env.torso_idx = self.env.sys.body.index["wrist_3_link"] + self.env.target_radius = self.context["target_radius"] + self.env.target_distance = self.context["target_distance"] + + def __getattr__(self, name: str) -> Any: + if name in [ + "sys", + "target_idx", + "torso_idx", + "target_radius", + "target_distance", + ]: + return getattr(self.env._environment, name) + else: + return getattr(self, name) diff --git a/carl/envs/carl_env.py b/carl/envs/carl_env.py index 2393143c..e0f094c1 100644 --- a/carl/envs/carl_env.py +++ b/carl/envs/carl_env.py @@ -43,7 +43,7 @@ class CARLEnv(Wrapper): contexts: Contexts Dict of contexts/instances. Key are context id, values are contexts as Dict[context feature id, context feature value]. - hide_context: bool = True + hide_context: bool = False If False, the context will be appended to the original environment's state. add_gaussian_noise_to_context: bool = False Wether to add Gaussian noise to the context with the relative standard deviation @@ -274,10 +274,7 @@ def reset(self, **kwargs: Dict) -> Union[ObsType, tuple[ObsType, dict]]: # type """ self.episode_counter += 1 self.step_counter = 0 - if "context_id" in kwargs.keys(): - self.context = self.contexts[kwargs["context_id"]] - else: - self._progress_instance() + self._progress_instance() self._update_context() self._log_context() return_info = kwargs.get("return_info", False) @@ -285,7 +282,6 @@ def reset(self, **kwargs: Dict) -> Union[ObsType, tuple[ObsType, dict]]: # type info_dict = dict() if return_info: state, info_dict = _ret - info_dict["context_key"] = self.context_key else: state = _ret state = self.build_context_adaptive_state(state=state) @@ -299,7 +295,7 @@ def build_context_adaptive_state( ) -> Union[Vector, Dict]: tnp: ModuleType = np if brax_spec is not None: - if type(state) == jaxlib.xla_extension.ArrayImpl: + if type(state) == jaxlib.xla_extension.DeviceArray: tnp = jnp if not self.hide_context: if context_feature_values is None: @@ -349,12 +345,7 @@ def step(self, action: Any) -> Tuple[Any, Any, bool, Dict]: """ # Step the environment - step_output = self.env.step(action) - if len(step_output) == 5: - state, reward, terminated, truncated, info = step_output - done = terminated or truncated - else: - state, reward, done, info = step_output + state, reward, done, info = self.env.step(action) if not self.hide_context: # Scale context features @@ -379,7 +370,6 @@ def step(self, action: Any) -> Tuple[Any, Any, bool, Dict]: self.step_counter += 1 if self.step_counter >= self.cutoff: done = True - info["context_key"] = self.context_key return state, reward, done, info def __getattr__(self, name: str) -> Any: @@ -498,12 +488,12 @@ def build_observation_space( else self.env.observation_space.low # type: ignore [attr-defined] ) obs_shape = obs_space.shape - if len(obs_shape) == 3 or self.hide_context: + if len(obs_shape) == 3 and self.hide_context: # do not touch pixel state pass else: if env_lower_bounds is None and env_upper_bounds is None: - obs_dim = obs_shape[0] if len(obs_shape) == 1 else obs_shape + obs_dim = obs_shape[0] env_lower_bounds = -np.inf * np.ones(obs_dim) env_upper_bounds = np.inf * np.ones(obs_dim) diff --git a/carl/envs/classic_control/__init__.py b/carl/envs/classic_control/__init__.py index 1c34fe42..1acb7d10 100644 --- a/carl/envs/classic_control/__init__.py +++ b/carl/envs/classic_control/__init__.py @@ -7,15 +7,13 @@ DEFAULT_CONTEXT as CARLAcrobotEnv_defaults, ) from carl.envs.classic_control.carl_acrobot import CARLAcrobotEnv - from carl.envs.classic_control.carl_cartpole import ( - DEFAULT_CONTEXT as CARLCartPoleEnv_defaults, + CONTEXT_BOUNDS as CARLCartPoleEnv_bounds, ) from carl.envs.classic_control.carl_cartpole import ( - CONTEXT_BOUNDS as CARLCartPoleEnv_bounds, + DEFAULT_CONTEXT as CARLCartPoleEnv_defaults, ) from carl.envs.classic_control.carl_cartpole import CARLCartPoleEnv - from carl.envs.classic_control.carl_mountaincar import ( CONTEXT_BOUNDS as CARLMountainCarEnv_bounds, ) @@ -23,7 +21,6 @@ DEFAULT_CONTEXT as CARLMountainCarEnv_defaults, ) from carl.envs.classic_control.carl_mountaincar import CARLMountainCarEnv - from carl.envs.classic_control.carl_mountaincarcontinuous import ( CONTEXT_BOUNDS as CARLMountainCarContinuousEnv_bounds, ) @@ -33,7 +30,6 @@ from carl.envs.classic_control.carl_mountaincarcontinuous import ( CARLMountainCarContinuousEnv, ) - from carl.envs.classic_control.carl_pendulum import ( CONTEXT_BOUNDS as CARLPendulumEnv_bounds, ) diff --git a/carl/envs/dmc/README.md b/carl/envs/dmc/README.md deleted file mode 100644 index 1ab21757..00000000 --- a/carl/envs/dmc/README.md +++ /dev/null @@ -1,10 +0,0 @@ -# Headless Rendering -If you have problems with OpenGL, this helped: -Set this in your script -```python -os.environ['DISABLE_MUJOCO_RENDERING'] = '1' -os.environ['MUJOCO_GL'] = 'osmesa' -os.environ['PYOPENGL_PLATFORM'] = 'osmesa' -``` - -And set ErrorChecker to None in `OpenGL/raw/GL/_errors.py`. \ No newline at end of file diff --git a/carl/envs/dmc/__init__.py b/carl/envs/dmc/__init__.py index 2b2bef72..03225e66 100644 --- a/carl/envs/dmc/__init__.py +++ b/carl/envs/dmc/__init__.py @@ -14,7 +14,6 @@ DEFAULT_CONTEXT as CARLDmcQuadrupedEnv_defaults, ) from carl.envs.dmc.carl_dm_quadruped import CARLDmcQuadrupedEnv -from carl.envs.dmc.carl_dm_walker import CONTEXT_BOUNDS as CARLDmcWalkerEnv_bounds from carl.envs.dmc.carl_dm_walker import CONTEXT_MASK as CARLDmcWalkerEnv_mask from carl.envs.dmc.carl_dm_walker import DEFAULT_CONTEXT as CARLDmcWalkerEnv_defaults from carl.envs.dmc.carl_dm_walker import CARLDmcWalkerEnv diff --git a/carl/envs/dmc/carl_dm_finger.py b/carl/envs/dmc/carl_dm_finger.py index a30ae3a4..ed8469a5 100644 --- a/carl/envs/dmc/carl_dm_finger.py +++ b/carl/envs/dmc/carl_dm_finger.py @@ -9,7 +9,7 @@ from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { - "gravity": 9.81, # Gravity is disabled via flag + "gravity": -9.81, # Gravity is disabled via flag "friction_tangential": 1, # Scaling factor for tangential friction of all geoms (objects) "friction_torsional": 1, # Scaling factor for torsional friction of all geoms (objects) "friction_rolling": 1, # Scaling factor for rolling friction of all geoms (objects) @@ -30,7 +30,7 @@ } CONTEXT_BOUNDS = { - "gravity": (0.1, np.inf, float), + "gravity": (-np.inf, -0.1, float), "friction_tangential": (0, np.inf, float), "friction_torsional": (0, np.inf, float), "friction_rolling": (0, np.inf, float), diff --git a/carl/envs/dmc/carl_dm_fish.py b/carl/envs/dmc/carl_dm_fish.py index 973fab16..b7886baa 100644 --- a/carl/envs/dmc/carl_dm_fish.py +++ b/carl/envs/dmc/carl_dm_fish.py @@ -9,7 +9,7 @@ from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { - "gravity": 9.81, # Gravity is disabled via flag + "gravity": -9.81, # Gravity is disabled via flag "friction_tangential": 1, # Scaling factor for tangential friction of all geoms (objects) "friction_torsional": 1, # Scaling factor for torsional friction of all geoms (objects) "friction_rolling": 1, # Scaling factor for rolling friction of all geoms (objects) @@ -26,7 +26,7 @@ } CONTEXT_BOUNDS = { - "gravity": (0.1, np.inf, float), + "gravity": (-np.inf, -0.1, float), "friction_tangential": (0, np.inf, float), "friction_torsional": (0, np.inf, float), "friction_rolling": (0, np.inf, float), diff --git a/carl/envs/dmc/carl_dm_quadruped.py b/carl/envs/dmc/carl_dm_quadruped.py index 7949b356..29554d98 100644 --- a/carl/envs/dmc/carl_dm_quadruped.py +++ b/carl/envs/dmc/carl_dm_quadruped.py @@ -9,7 +9,7 @@ from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { - "gravity": 9.81, + "gravity": -9.81, "friction_tangential": 1.0, # Scaling factor for tangential friction of all geoms (objects) "friction_torsional": 1.0, # Scaling factor for torsional friction of all geoms (objects) "friction_rolling": 1.0, # Scaling factor for rolling friction of all geoms (objects) @@ -26,7 +26,7 @@ } CONTEXT_BOUNDS = { - "gravity": (0.1, np.inf, float), + "gravity": (-np.inf, -0.1, float), "friction_tangential": (0, np.inf, float), "friction_torsional": (0, np.inf, float), "friction_rolling": (0, np.inf, float), diff --git a/carl/envs/dmc/carl_dm_walker.py b/carl/envs/dmc/carl_dm_walker.py index 6b9d0662..524aa2a3 100644 --- a/carl/envs/dmc/carl_dm_walker.py +++ b/carl/envs/dmc/carl_dm_walker.py @@ -9,7 +9,7 @@ from carl.utils.types import Context, Contexts DEFAULT_CONTEXT = { - "gravity": 9.81, + "gravity": -9.81, "friction_tangential": 1.0, # Scaling factor for tangential friction of all geoms (objects) "friction_torsional": 1.0, # Scaling factor for torsional friction of all geoms (objects) "friction_rolling": 1.0, # Scaling factor for rolling friction of all geoms (objects) @@ -26,7 +26,7 @@ } CONTEXT_BOUNDS = { - "gravity": (0.1, np.inf, float), + "gravity": (-np.inf, -0.1, float), "friction_tangential": (0, np.inf, float), "friction_torsional": (0, np.inf, float), "friction_rolling": (0, np.inf, float), diff --git a/carl/envs/dmc/dmc_tasks/finger.py b/carl/envs/dmc/dmc_tasks/finger.py index 2c02cbb5..9bad7621 100644 --- a/carl/envs/dmc/dmc_tasks/finger.py +++ b/carl/envs/dmc/dmc_tasks/finger.py @@ -45,34 +45,25 @@ def check_constraints( limb_length_1: float, x_spinner: float = 0.2, x_finger: float = -0.2, - raise_error: bool = False, - **kwargs: Any -) -> bool: - is_okay = True +) -> None: spinner_half_length = spinner_length / 2 # Check if spinner collides with finger hinge distance_spinner_to_fingerhinge = (x_spinner - x_finger) - spinner_half_length if distance_spinner_to_fingerhinge < 0: - is_okay = False - if raise_error: - raise ValueError( - f"Distance finger to spinner ({distance_spinner_to_fingerhinge}) not big enough, " - f"spinner can't spin. Decrease spinner_length ({spinner_length})." - ) + raise ValueError( + f"Distance finger to spinner ({distance_spinner_to_fingerhinge}) not big enough, " + f"spinner can't spin. Decrease spinner_length ({spinner_length})." + ) # Check if finger can reach spinner (distance should be negative) distance_fingertip_to_spinner = (x_spinner - spinner_half_length) - ( x_finger + limb_length_0 + limb_length_1 ) if distance_fingertip_to_spinner > 0: - is_okay = False - if raise_error: - raise ValueError( - f"Finger cannot reach spinner ({distance_fingertip_to_spinner}). Increase either " - f"limb_length_0, limb_length_1 or spinner_length." - ) - - return is_okay + raise ValueError( + f"Finger cannot reach spinner ({distance_fingertip_to_spinner}). Increase either " + f"limb_length_0, limb_length_1 or spinner_length." + ) def get_finger_xml_string( @@ -107,7 +98,6 @@ def get_finger_xml_string( x_spinner=x_spinner, x_finger=x_finger, spinner_length=spinner_length, - raise_error=True ) proximal_to = -limb_length_0 diff --git a/carl/envs/dmc/dmc_tasks/utils.py b/carl/envs/dmc/dmc_tasks/utils.py index f5199955..ab449618 100644 --- a/carl/envs/dmc/dmc_tasks/utils.py +++ b/carl/envs/dmc/dmc_tasks/utils.py @@ -162,9 +162,9 @@ def check_okay_to_set(context_feature: str | list[str]) -> bool: gravity = option.get("gravity") if gravity is not None: g = gravity.split(" ") - gravity = " ".join([g[0], g[1], str(-context["gravity"])]) + gravity = " ".join([g[0], g[1], str(context["gravity"])]) else: - gravity = " ".join(["0", "0", str(-context["gravity"])]) + gravity = " ".join(["0", "0", str(context["gravity"])]) option.set("gravity", gravity) if check_okay_to_set("wind"): diff --git a/carl/envs/mario/__init__.py b/carl/envs/mario/__init__.py new file mode 100644 index 00000000..c59871f8 --- /dev/null +++ b/carl/envs/mario/__init__.py @@ -0,0 +1,12 @@ +# flake8: noqa: F401 +import warnings + +try: + from carl.envs.mario.carl_mario import CARLMarioEnv +except Exception as e: + warnings.warn(f"Could not load CARLMarioEnv which is probably not installed ({e}).") + +from carl.envs.mario.carl_mario_definitions import CONTEXT_BOUNDS as CARLMarioEnv_bounds +from carl.envs.mario.carl_mario_definitions import ( + DEFAULT_CONTEXT as CARLMarioEnv_defaults, +) diff --git a/carl/envs/mario/carl_mario.py b/carl/envs/mario/carl_mario.py new file mode 100644 index 00000000..1e8e9edf --- /dev/null +++ b/carl/envs/mario/carl_mario.py @@ -0,0 +1,77 @@ +from typing import Dict, List, Optional, Union + +import gym + +from carl.context.selection import AbstractSelector +from carl.envs.carl_env import CARLEnv +from carl.envs.mario.carl_mario_definitions import ( + DEFAULT_CONTEXT, + INITIAL_HEIGHT, + INITIAL_WIDTH, +) +from carl.envs.mario.mario_env import MarioEnv +from carl.envs.mario.toad_gan import generate_level +from carl.utils.trial_logger import TrialLogger +from carl.utils.types import Context, Contexts + + +class CARLMarioEnv(CARLEnv): + def __init__( + self, + env: gym.Env = MarioEnv(levels=[]), + contexts: Contexts = {}, + hide_context: bool = True, + add_gaussian_noise_to_context: bool = False, + gaussian_noise_std_percentage: float = 0.05, + logger: Optional[TrialLogger] = None, + scale_context_features: str = "no", + default_context: Optional[Context] = DEFAULT_CONTEXT, + state_context_features: Optional[List[str]] = None, + context_mask: Optional[List[str]] = None, + dict_observation_space: bool = False, + context_selector: Optional[ + Union[AbstractSelector, type[AbstractSelector]] + ] = None, + context_selector_kwargs: Optional[Dict] = None, + ): + if not contexts: + contexts = {0: DEFAULT_CONTEXT} + super().__init__( + env=env, + contexts=contexts, + hide_context=True, + add_gaussian_noise_to_context=add_gaussian_noise_to_context, + gaussian_noise_std_percentage=gaussian_noise_std_percentage, + logger=logger, + scale_context_features="no", + default_context=default_context, + dict_observation_space=dict_observation_space, + context_selector=context_selector, + context_selector_kwargs=context_selector_kwargs, + context_mask=context_mask, + ) + self.levels: List[str] = [] + self._update_context() + + def _update_context(self) -> None: + self.env: MarioEnv + if not self.levels: + for context in self.contexts.values(): + level = generate_level( + width=INITIAL_WIDTH, + height=INITIAL_HEIGHT, + level_index=context["level_index"], + initial_noise=context["noise"], + filter_unplayable=True, + ) + self.levels.append(level) + self.env.mario_state = self.context["mario_state"] + self.env.mario_inertia = self.context["mario_inertia"] + self.env.levels = [self.levels[self.context_index]] + + def _log_context(self) -> None: + if self.logger: + loggable_context = {k: v for k, v in self.context.items() if k != "noise"} + self.logger.write_context( + self.episode_counter, self.total_timestep_counter, loggable_context + ) diff --git a/carl/envs/mario/carl_mario_definitions.py b/carl/envs/mario/carl_mario_definitions.py new file mode 100644 index 00000000..8cdabafe --- /dev/null +++ b/carl/envs/mario/carl_mario_definitions.py @@ -0,0 +1,27 @@ +import numpy as np +from torch import Tensor + +try: + from carl.envs.mario.toad_gan import generate_initial_noise +except FileNotFoundError: + + def generate_initial_noise(width: int, height: int, level_index: int) -> Tensor: + return Tensor() + + +INITIAL_WIDTH = 100 +INITIAL_LEVEL_INDEX = 0 +INITIAL_HEIGHT = 16 +DEFAULT_CONTEXT = { + "level_index": INITIAL_LEVEL_INDEX, + "noise": generate_initial_noise(INITIAL_WIDTH, INITIAL_HEIGHT, INITIAL_LEVEL_INDEX), + "mario_state": 0, + "mario_inertia": 0.89, +} +CONTEXT_BOUNDS = { + "level_index": (None, None, "categorical", np.arange(0, 14)), + "noise": (-1.0, 1.0, float), + "mario_state": (None, None, "categorical", [0, 1, 2]), + "mario_inertia": (0.5, 1.5, float), +} +CATEGORICAL_CONTEXT_FEATURES = ["level_index", "mario_state"] diff --git a/carl/envs/mario/generate_sample.py b/carl/envs/mario/generate_sample.py new file mode 100644 index 00000000..39a95222 --- /dev/null +++ b/carl/envs/mario/generate_sample.py @@ -0,0 +1,122 @@ +# Code from https://github.com/Mawiszus/TOAD-GAN +from typing import Any, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +from torch import Tensor +from torch.nn.functional import interpolate + + +# Generates a noise tensor. Uses torch.randn. +def generate_spatial_noise( + size: Union[Any, List[int], Tuple[int]], device: Union[str, torch.device] = "cpu" +) -> Tensor: + return torch.randn(size, device=device, dtype=torch.float32) + + +# Generate a sample given a TOAD-GAN and additional parameters +@torch.no_grad() # type: ignore [misc] +def generate_sample( + generators: Tensor, + noise_maps: Tensor, + reals: Tensor, + noise_amplitudes: Tensor, + num_layer: int, + token_list: Tensor, + scale_v: float = 1.0, + scale_h: float = 1.0, + current_scale: int = 0, + gen_start_scale: int = 0, + initial_noise: Optional[Tensor] = None, +) -> List[str]: + + in_s = None + images_cur: List[Tensor] = [] + images: List[Tensor] = [] + z_s: List[Tensor] = [] + + # Generate on GPU if available + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + # Main loop + for G, Z_opt, noise_amp in zip(generators, noise_maps, noise_amplitudes): + if current_scale >= len(generators): + break # should not be reached + + # Zero Padding + n_pad = int(num_layer) + m = nn.ZeroPad2d(int(n_pad)) + + # Calculate actual shape + nzx = (Z_opt.shape[2] - n_pad * 2) * scale_v + nzy = (Z_opt.shape[3] - n_pad * 2) * scale_h + + # Init images list + images_prev = images_cur + images_cur = [] + channels = len(token_list) + + # Init in_s + if in_s is None: + in_s = torch.zeros(reals[0].shape[0], channels, *reals[0].shape[2:]).to( + device + ) + elif in_s.sum() == 0: + in_s = torch.zeros(in_s.shape[0], channels, *in_s.shape[2:]).to(device) + + if current_scale == 0: # First step: Make base noise + if initial_noise is not None and len(initial_noise) > 0: + z_curr = initial_noise.float().to(device) + else: + z_curr = generate_spatial_noise( + [1, channels, int(round(nzx)), int(round(nzy))], device=device + ) + z_curr = m(z_curr) + else: # All other steps: Make added noise + if current_scale < gen_start_scale: + z_curr = z_s[current_scale] + else: + z_curr = generate_spatial_noise( + [1, channels, int(round(nzx)), int(round(nzy))], device=device + ) + z_curr = m(z_curr) + if (not images_prev) or current_scale == 0: # if there is no "previous" image + I_prev = in_s + else: + I_prev = images[current_scale - 1] + + # Bilinear interpolation for upscaling + I_prev = interpolate( + I_prev, + [int(round(nzx)), int(round(nzy))], + mode="bilinear", + align_corners=False, + ) + I_prev = m(I_prev) + + # Main Step + z_in = noise_amp * z_curr + I_prev + I_curr = G(z_in, I_prev, temperature=1) + + # Append results + images_cur.append(I_curr) + + if current_scale >= gen_start_scale: + images.append(I_curr) + z_s.append(z_curr) + current_scale += 1 + + return one_hot_to_ascii_level(I_curr, token_list) + + +def one_hot_to_ascii_level(level: Any, tokens: Any) -> List[str]: + """Converts a full token level tensor to an ascii level.""" + ascii_level = [] + for i in range(level.shape[2]): + line = "" + for j in range(level.shape[3]): + line += tokens[level[:, :, i, j].argmax()] + if i < level.shape[2] - 1: + line += "\n" + ascii_level.append(line) + return ascii_level diff --git a/carl/envs/mario/level_image_gen.py b/carl/envs/mario/level_image_gen.py new file mode 100644 index 00000000..17378462 --- /dev/null +++ b/carl/envs/mario/level_image_gen.py @@ -0,0 +1,418 @@ +# Code from https://github.com/Mawiszus/TOAD-GAN +from typing import Any, List, Tuple + +import os + +from PIL import Image, ImageEnhance, ImageOps + + +class LevelImageGen: + """Generates PIL Image files from Super Mario Bros. ascii levels. + Initialize once and then use LevelImageGen.render() to generate images.""" + + def __init__(self, sprite_path: str): + """sprite_path: path to the folder of sprite files, e.g. 'mario/sprites/'""" + + # Load Graphics (assumes sprite_path points to "img" folder of Mario-AI-Framework or provided sprites folder + mariosheet = Image.open(os.path.join(sprite_path, "smallmariosheet.png")) + enemysheet = Image.open(os.path.join(sprite_path, "enemysheet.png")) + itemsheet = Image.open(os.path.join(sprite_path, "itemsheet.png")) + mapsheet = Image.open(os.path.join(sprite_path, "mapsheet.png")) + + # Cut out the actual sprites: + sprite_dict = dict() + # Mario Sheet + sprite_dict["M"] = mariosheet.crop((4 * 16, 0, 5 * 16, 16)) + + # Enemy Sheet + enemy_names = ["r", "k", "g", "y", "wings", "*", "plant"] + for i, e in enumerate(enemy_names): + sprite_dict[e] = enemysheet.crop((0, i * 2 * 16, 16, (i + 1) * 2 * 16)) + + sprite_dict["E"] = enemysheet.crop( + (16, 2 * 2 * 16, 2 * 16, 3 * 2 * 16) + ) # Set generic enemy to second goomba sprite + sprite_dict["plant"] = enemysheet.crop( + (16, (len(enemy_names) - 1) * 2 * 16, 2 * 16, len(enemy_names) * 2 * 16) + ) + + # Item Sheet + sprite_dict["shroom"] = itemsheet.crop((0, 0, 16, 16)) + sprite_dict["flower"] = itemsheet.crop((16, 0, 2 * 16, 16)) + sprite_dict["flower2"] = itemsheet.crop((0, 16, 16, 2 * 16)) + sprite_dict["1up"] = itemsheet.crop((16, 16, 2 * 16, 2 * 16)) + + # Map Sheet + map_names = [ + "-", + "X", + "#", + "B", + "b", + "b2", + "S", + "L", + "?", + "dump", + "@", + "Q", + "dump", + "!", + "D", + "o", + "o2", + "o3", + "<", + ">", + "[", + "]", + "bg_sl_l", + "bg_top", + "bg_sl_r", + "bg_m_l", + "bg_m", + "bg_m_r", + "bush_l", + "bush_m", + "bush_r", + "cloud_l", + "cloud_m", + "cloud_r", + "cloud_b_l", + "cloud_b_m", + "cloud_b_r", + "waves", + "water", + "F_top", + "F_b", + "F", + "bg_sky", + "%", + "%_l", + "%_r", + "%_m", + "|", + "1", + "2", + "C", + "U", + "T", + "t", + "dump", + "dump", + ] + + sheet_length = (7, 8) + sprite_counter = 0 + for i in range(sheet_length[0]): + for j in range(sheet_length[1]): + sprite_dict[map_names[sprite_counter]] = mapsheet.crop( + (j * 16, i * 16, (j + 1) * 16, (i + 1) * 16) + ) + sprite_counter += 1 + + sprite_dict["@"] = sprite_dict["?"] + sprite_dict["!"] = sprite_dict["Q"] + + self.sprite_dict = sprite_dict + + def prepare_sprite_and_box( + self, ascii_level: List[str], sprite_key: str, curr_x: int, curr_y: int + ) -> Tuple[Any, Tuple[int, int, int, int]]: + """Helper to make correct sprites and sprite sizes to draw into the image. + Some sprites are bigger than one tile and the renderer needs to adjust for them.""" + + # Init default size + new_left = curr_x * 16 + new_top = curr_y * 16 + new_right = (curr_x + 1) * 16 + new_bottom = (curr_y + 1) * 16 + + # Handle sprites depending on their type: + if sprite_key == "F": # Flag Pole + actual_sprite = Image.new("RGBA", (2 * 16, curr_y * 16)) + actual_sprite.paste(self.sprite_dict["F_top"], (16, 0, 2 * 16, 16)) + for s in range(curr_y): + actual_sprite.paste( + self.sprite_dict["F_b"], (16, (s + 1) * 16, 2 * 16, (s + 2) * 16) + ) + actual_sprite.paste(self.sprite_dict["F"], (7, 1 * 16, 16 + 7, 2 * 16)) + new_left = new_left - 16 + new_top = new_top - (curr_y - 1) * 16 + + elif sprite_key in ["y", "E", "g", "k", "r"]: # enemy sprite + actual_sprite = self.sprite_dict[sprite_key] + new_top = new_top - 16 + + elif sprite_key in ["Y", "K", "R"]: # winged spiky/koopa sprite + actual_sprite = Image.new("RGBA", (2 * 16, 2 * 16)) + actual_sprite.paste( + self.sprite_dict[str.lower(sprite_key)], (16, 0, 2 * 16, 2 * 16) + ) + actual_sprite.paste(self.sprite_dict["wings"], (7, -7, 16 + 7, 2 * 16 - 7)) + new_left = new_left - 16 + new_top = new_top - 16 + + elif ( + sprite_key == "G" + ): # winged goomba sprite (untested because original has none?) + actual_sprite = Image.new("RGBA", (3 * 16, 2 * 16)) + actual_sprite.paste(self.sprite_dict["wings"], (1, -5, 16 + 1, 2 * 16 - 5)) + actual_sprite.paste( + ImageOps.mirror(self.sprite_dict["wings"]), + (2 * 16 - 1, -5, 3 * 16 - 1, 2 * 16 - 5), + ) + actual_sprite.paste( + self.sprite_dict[str.lower(sprite_key)], (16, 0, 2 * 16, 2 * 16) + ) + new_left = new_left - 16 + new_top = new_top - 16 + new_right = new_right + 16 + + elif sprite_key == "%": # jump through platform + if curr_x == 0: + if ( + len(ascii_level[curr_y]) > 1 + and ascii_level[curr_y][curr_x + 1] == sprite_key + ): # middle piece + actual_sprite = self.sprite_dict["%_m"] + else: # single_piece + actual_sprite = self.sprite_dict["%"] + elif ascii_level[curr_y][curr_x - 1] == sprite_key: + if curr_x >= (len(ascii_level[curr_y]) - 1): # right end piece + actual_sprite = self.sprite_dict["%_r"] + elif ascii_level[curr_y][curr_x + 1] == sprite_key: # middle piece + actual_sprite = self.sprite_dict["%_m"] + else: # right end piece + actual_sprite = self.sprite_dict["%_r"] + else: + if curr_x >= (len(ascii_level[curr_y]) - 1): # single piece + actual_sprite = self.sprite_dict["%"] + elif ascii_level[curr_y][curr_x + 1] == sprite_key: # left end piece + actual_sprite = self.sprite_dict["%_l"] + else: # single piece + actual_sprite = self.sprite_dict[sprite_key] + + elif sprite_key == "b": # bullet bill tower + if curr_y > 0: + if ascii_level[curr_y - 1][curr_x] == sprite_key: + actual_sprite = self.sprite_dict["b2"] + else: + actual_sprite = self.sprite_dict[sprite_key] + else: + actual_sprite = self.sprite_dict[sprite_key] + + elif sprite_key == "*": # alternative bullet bill tower + if curr_y > 0: + if ascii_level[curr_y - 1][curr_x] != sprite_key: # top + actual_sprite = self.sprite_dict["B"] + elif curr_y > 1: + if ascii_level[curr_y - 2][curr_x] != sprite_key: + actual_sprite = self.sprite_dict["b"] + else: + actual_sprite = self.sprite_dict["b2"] + else: + actual_sprite = self.sprite_dict["b2"] + + elif sprite_key in ["T", "t"]: # Pipes + + # figure out what kind of pipe this is + if curr_y > 0 and ascii_level[curr_y - 1][curr_x] == sprite_key: + is_top = False + else: + is_top = True + + pipelength_t = 0 + while ( + curr_y - pipelength_t >= 0 + and ascii_level[curr_y - pipelength_t][curr_x] == sprite_key + ): + pipelength_t += 1 + + pipelength_b = 0 + while ( + curr_y + pipelength_b < len(ascii_level) + and ascii_level[curr_y + pipelength_b][curr_x] == sprite_key + ): + pipelength_b += 1 + + pipelength_l = 0 + while ( + curr_x - pipelength_l >= 0 + and ascii_level[curr_y][curr_x - pipelength_l] == sprite_key + ): + pipelength_l += 1 + + pipelength_r = 0 + while ( + curr_x + pipelength_r < len(ascii_level[curr_y]) + and ascii_level[curr_y][curr_x - pipelength_r] == sprite_key + ): + pipelength_r += 1 + + # Check for fall out criteria + try: + if pipelength_l % 2 == 0: # second half of a double pipe + is_left = False + is_right = True + elif pipelength_l % 2 == 1: + if ( + curr_x >= len(ascii_level[curr_y]) + or ascii_level[curr_y][curr_x + 1] != sprite_key + ): + is_left = False + is_right = False + else: + is_left = True + is_right = False + else: + is_left = False + is_right = False + + if is_left: + if ascii_level[curr_y - pipelength_t][curr_x + 1] == sprite_key: + is_left = False + is_right = False + if ascii_level[curr_y - pipelength_t + 1][curr_x + 1] != sprite_key: + is_left = False + is_right = False + if is_right: + if ascii_level[curr_y - pipelength_t][curr_x - 1] == sprite_key: + is_left = False + is_right = False + if ascii_level[curr_y - pipelength_t + 1][curr_x - 1] != sprite_key: + is_left = False + is_right = False + if curr_y + pipelength_b < len(ascii_level): + if is_left: + if ascii_level[curr_y + pipelength_b][curr_x + 1] == sprite_key: + is_left = False + is_right = False + if ( + ascii_level[curr_y + pipelength_b - 1][curr_x + 1] + != sprite_key + ): + is_left = False + is_right = False + if is_right: + if ascii_level[curr_y + pipelength_b][curr_x - 1] == sprite_key: + is_left = False + is_right = False + if ( + ascii_level[curr_y + pipelength_b - 1][curr_x - 1] + != sprite_key + ): + is_left = False + is_right = False + except IndexError: + # Default to single pipe + is_left = False + is_right = False + + if is_top: + if is_left: + actual_sprite = self.sprite_dict["<"] + elif is_right: + if sprite_key == "T": + actual_sprite = Image.new("RGBA", (2 * 16, 3 * 16)) + actual_sprite.paste( + self.sprite_dict["plant"], (8, 5, 16 + 8, 2 * 16 + 5) + ) + actual_sprite.paste( + self.sprite_dict["<"], (0, 2 * 16, 16, 3 * 16) + ) + actual_sprite.paste( + self.sprite_dict[">"], (16, 2 * 16, 2 * 16, 3 * 16) + ) + new_left = new_left - 16 + new_top = new_top - 2 * 16 + else: + actual_sprite = self.sprite_dict[">"] + else: + if sprite_key == "T": + actual_sprite = Image.new("RGBA", (16, 3 * 16)) + actual_sprite.paste( + self.sprite_dict["plant"], (0, 5, 16, 2 * 16 + 5) + ) + actual_sprite.paste( + self.sprite_dict["T"], (0, 2 * 16, 16, 3 * 16) + ) + new_top = new_top - 2 * 16 + else: + actual_sprite = self.sprite_dict["T"] + else: + if is_left: + actual_sprite = self.sprite_dict["["] + elif is_right: + actual_sprite = self.sprite_dict["]"] + else: + actual_sprite = self.sprite_dict["t"] + + elif sprite_key in [ + "?", + "@", + "Q", + "!", + "C", + "U", + "L", + ]: # Block/Brick hidden items + if sprite_key == "L": + i_key = "1up" + elif sprite_key in ["?", "@", "U"]: + i_key = "shroom" + else: + i_key = "o" + + mask = self.sprite_dict[i_key].getchannel(3) + mask = ImageEnhance.Brightness(mask).enhance(0.7) + actual_sprite = Image.composite( + self.sprite_dict[i_key], self.sprite_dict[sprite_key], mask=mask + ) + + elif sprite_key in ["1", "2"]: # Hidden block + if sprite_key == "1": + i_key = "1up" + else: + i_key = "o" + + mask1 = self.sprite_dict["D"].getchannel(3) + mask1 = ImageEnhance.Brightness(mask1).enhance(0.5) + tmp_sprite = Image.composite( + self.sprite_dict["D"], self.sprite_dict[sprite_key], mask=mask1 + ) + mask = self.sprite_dict[i_key].getchannel(3) + mask = ImageEnhance.Brightness(mask).enhance(0.7) + actual_sprite = Image.composite( + self.sprite_dict[i_key], tmp_sprite, mask=mask + ) + + else: + actual_sprite = self.sprite_dict[sprite_key] + + return actual_sprite, (new_left, new_top, new_right, new_bottom) + + def render(self, ascii_level: List[str]) -> Image: + """Renders the ascii level as a PIL Image. Assumes the Background is sky""" + len_level = len(ascii_level[-1]) + height_level = len(ascii_level) + + # Fill base image with sky tiles + dst = Image.new("RGB", (len_level * 16, height_level * 16)) + for y in range(height_level): + for x in range(len_level): + dst.paste( + self.sprite_dict["bg_sky"], + (x * 16, y * 16, (x + 1) * 16, (y + 1) * 16), + ) + + # Fill with actual tiles + for y in range(height_level): + for x in range(len_level): + curr_sprite = ascii_level[y][x] + sprite, box = self.prepare_sprite_and_box( + ascii_level, curr_sprite, x, y + ) + dst.paste(sprite, box, mask=sprite) + + return dst diff --git a/carl/envs/mario/mario_env.py b/carl/envs/mario/mario_env.py new file mode 100644 index 00000000..127a75db --- /dev/null +++ b/carl/envs/mario/mario_env.py @@ -0,0 +1,227 @@ +from typing import Any, ByteString, Deque, Dict, List, Literal, Optional, Union, cast + +import os +import random +import socket +from collections import deque + +import cv2 +import gym +import numpy as np +from gym import spaces +from gym.core import ObsType +from gym.utils import seeding +from PIL import Image +from py4j.java_gateway import GatewayParameters, JavaGateway + +from carl.envs.mario.level_image_gen import LevelImageGen + +from .mario_game import MarioGame +from .utils import get_port, load_level + + +class MarioEnv(gym.Env): + metadata = {"render.modes": ["rgb_array"]} + + def __init__( + self, + levels: List[str], + timer: int = 100, + visual: bool = False, + sticky_action_probability: float = 0.1, + frame_skip: int = 2, + frame_stack: int = 4, + frame_dim: int = 64, + hide_points_banner: bool = False, + sparse_rewards: bool = False, + grayscale: bool = False, + seed: int = 0, + ): + self.gateway: Any = None + self.seed(seed) + self.level_names = levels + self.levels = [load_level(name) for name in levels] + self.timer = timer + self.visual = visual + self.frame_skip = frame_skip + self.frame_stack = frame_stack + self.sticky_action_probability = sticky_action_probability + self.hide_points_banner = hide_points_banner + self.sparse_rewards = sparse_rewards + self.points_banner_height = 4 + self.grayscale = grayscale + self.last_action = None + self.width = self.height = frame_dim + self.observation_space = spaces.Box( + low=0, + high=255, + shape=[self.frame_stack if grayscale else 3, self.height, self.width], + dtype=np.uint8, + ) + self.original_obs: Deque = deque(maxlen=self.frame_skip) + self.actions = [ + [False, False, False, False, False], # noop + [False, False, True, False, False], # down + [False, True, False, False, False], # right + [False, True, False, True, False], # right speed + [False, True, False, False, True], # right jump + [False, True, False, True, True], # right speed jump + [True, False, False, False, False], # left + [True, False, False, False, True], # left jump + [True, False, False, True, True], # left speed jump + [False, False, False, False, True], # jump + ] + self.action_space = spaces.Discrete(n=len(self.actions)) + self._obs: Any = np.zeros(shape=self.observation_space.shape, dtype=np.uint8) + self.current_level_idx = 0 + self.frame_size = -1 + self.port = get_port() + self.mario_state: Literal[0, 1, 2] = 0 # normal, large, fire + self.mario_inertia = 0.89 + self._init_game() + + def reset( + self, + *, + seed: Optional[int] = None, + return_info: bool = False, + options: Optional[dict] = None, + ) -> Union[ObsType, tuple[ObsType, dict]]: + self._reset_obs() + if self.game is None: + self.game: Any = self._init_game() + self.current_level_idx = (self.current_level_idx + 1) % len(self.levels) + level = self.levels[self.current_level_idx] + self.game.resetGame(level, self.timer, self.mario_state, self.mario_inertia) + self.game.computeObservationRGB() + buffer = self._receive() + frame = self._read_frame(buffer) + self._update_obs(frame) + if not return_info: + return self._obs.copy() + else: + return self._obs.copy(), {} + + def step(self, action: Any) -> Any: + if self.sticky_action_probability != 0.0: + if ( + self.last_action is not None + and random.random() < self.sticky_action_probability + ): + a = self.actions[self.last_action] + else: + a = self.actions[action] + self.last_action = action + else: + a = self.actions[action] + + assert self.game + frame = None + for i in range(self.frame_skip): + self.game.stepGame(*a) + if self.visual or i == self.frame_skip - 1: + self.game.computeObservationRGB() + buffer = self._receive() + frame = self._read_frame(buffer) + self._update_obs(frame) + + reward, done, completionPercentage = ( + self.game.computeReward(), + self.game.computeDone(), + self.game.getCompletionPercentage(), + ) + + info: Dict[str, Any] = {"completed": completionPercentage} + if self.visual: + info["original_obs"] = self.original_obs + return ( + self._obs.copy(), + reward if not self.sparse_rewards else int(completionPercentage == 1.0), + done, # bool + info, # Dict[str, Any] + ) + + def render(self, *args: Any, **kwargs: Any) -> ObsType: + return self.original_obs[0] + + def __getstate__(self) -> Dict: + assert self.gateway + + self.gateway.close() + self.gateway = None + self.game = None + self.socket.shutdown(1) + self.socket.close() + return self.__dict__ + + def _reset_obs(self) -> None: + self._obs[:] = 0 + self.original_obs.clear() + + def _read_frame(self, buffer: Any) -> Any: + frame = ( + np.frombuffer(buffer, dtype=np.int32).reshape(256, 256, 3).astype(np.uint8) + ) + self.original_obs.append(frame) + return frame + + def _update_obs(self, frame: Any) -> Any: + if self.grayscale: + frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) + + frame = cv2.resize(frame, (self.width, self.height), cv2.INTER_NEAREST) + if self.hide_points_banner: + frame[: self.points_banner_height, :] = 0 + if self.grayscale: + self._obs = np.concatenate([self._obs[1:], frame[np.newaxis]]) + else: + self._obs = np.transpose(frame, axes=(2, 0, 1)) + + def _init_game(self) -> MarioGame: + self.gateway = JavaGateway( + gateway_parameters=GatewayParameters( + port=self.port, + eager_load=True, + ) + ) + self.game = cast(MarioGame, cast(Any, self.gateway.jvm).engine.core.MarioGame()) + self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + self.socket.connect(("localhost", self.game.getPort())) + self.game.initGame() + self.frame_size = self.game.getFrameSize() + return self.game + + def _receive(self) -> ByteString: + frameBuffer = b"" + while len(frameBuffer) != self.frame_size: + frameBuffer += self.socket.recv(self.frame_size) + return frameBuffer + + def get_action_meanings(self) -> List[str]: + return ACTION_MEANING + + def render_current_level(self) -> Image: + img_gen = LevelImageGen( + sprite_path=os.path.abspath( + os.path.join(os.path.dirname(__file__), "sprites") + ) + ) + return img_gen.render(self.levels[self.current_level_idx].split("\n")) + + def seed(self, seed: Optional[int] = None) -> List[Any]: + self.np_random, seed = seeding.np_random(seed) + return [seed] + + +ACTION_MEANING = [ + "NOOP", + "DOWN", + "RIGHT", + "RIGHTSPEED", + "RIGHTJUMP", + "RIGHTSPEEDJUMP", + "LEFT", + "LEFTJUMP", + "LEFTSPEEDJUMP", + "JUMP", +] diff --git a/carl/envs/mario/mario_game.py b/carl/envs/mario/mario_game.py new file mode 100644 index 00000000..09f62da5 --- /dev/null +++ b/carl/envs/mario/mario_game.py @@ -0,0 +1,43 @@ +from abc import ABC, abstractmethod + + +class MarioGame(ABC): + @abstractmethod + def getPort(self) -> int: + pass + + @abstractmethod + def initGame(self) -> None: + pass + + @abstractmethod + def stepGame( + self, left: bool, right: bool, down: bool, speed: bool, jump: bool + ) -> None: + pass + + @abstractmethod + def resetGame( + self, level: str, timer: int, mario_state: int, inertia: float + ) -> None: + pass + + @abstractmethod + def computeObservationRGB(self) -> None: + pass + + @abstractmethod + def computeReward(self) -> float: + pass + + @abstractmethod + def computeDone(self) -> bool: + pass + + @abstractmethod + def getCompletionPercentage(self) -> float: + pass + + @abstractmethod + def getFrameSize(self) -> int: + pass diff --git a/carl/envs/dmc/dmc_tasks/__init__.py b/carl/envs/mario/models/__init__.py similarity index 100% rename from carl/envs/dmc/dmc_tasks/__init__.py rename to carl/envs/mario/models/__init__.py diff --git a/carl/envs/mario/models/conv_block.py b/carl/envs/mario/models/conv_block.py new file mode 100644 index 00000000..614f0fae --- /dev/null +++ b/carl/envs/mario/models/conv_block.py @@ -0,0 +1,31 @@ +# Code from https://github.com/Mawiszus/TOAD-GAN +from typing import Tuple, Union + +import torch.nn as nn + + +class ConvBlock(nn.Sequential): + """Conv block containing Conv2d, BatchNorm2d and LeakyReLU Layers.""" + + def __init__( + self, + in_channel: int, + out_channel: int, + ker_size: Union[int, Tuple[int, int]], + padd: Union[str, Union[int, Tuple[int, int]]], + stride: Union[int, Tuple[int, int]], + ): + super().__init__() + self.add_module( + "conv", + nn.Conv2d( + in_channel, + out_channel, + kernel_size=ker_size, + stride=stride, + padding=padd, + ), + ) + + self.add_module("norm", nn.BatchNorm2d(out_channel)) + self.add_module("LeakyRelu", nn.LeakyReLU(0.2, inplace=True)) diff --git a/carl/envs/mario/models/discriminator.py b/carl/envs/mario/models/discriminator.py new file mode 100644 index 00000000..4527e94f --- /dev/null +++ b/carl/envs/mario/models/discriminator.py @@ -0,0 +1,34 @@ +# Code from https://github.com/Mawiszus/TOAD-GAN +from argparse import Namespace + +import torch +import torch.nn as nn +from torch import Tensor + +from .conv_block import ConvBlock + + +class Level_WDiscriminator(nn.Module): + """Patch based Discriminator. Uses Namespace opt.""" + + def __init__(self, opt: Namespace): + super().__init__() + self.is_cuda = torch.cuda.is_available() + N = int(opt.nfc) + self.head = ConvBlock(opt.nc_current, N, (3, 3), opt.padd_size, 1) + self.body = nn.Sequential() + + for i in range(opt.num_layer - 2): + block = ConvBlock(N, N, (3, 3), opt.padd_size, 1) + self.body.add_module("block%d" % (i + 1), block) + + block = ConvBlock(N, N, (3, 3), opt.padd_size, 1) + self.body.add_module("block%d" % (opt.num_layer - 2), block) + + self.tail = nn.Conv2d(N, 1, kernel_size=(3, 3), stride=1, padding=opt.padd_size) + + def forward(self, x: Tensor) -> Tensor: + x = self.head(x) + x = self.body(x) + x = self.tail(x) + return x diff --git a/carl/envs/mario/models/generator.py b/carl/envs/mario/models/generator.py new file mode 100644 index 00000000..95c75eab --- /dev/null +++ b/carl/envs/mario/models/generator.py @@ -0,0 +1,44 @@ +# Code from https://github.com/Mawiszus/TOAD-GAN +from argparse import Namespace + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from .conv_block import ConvBlock + + +class Level_GeneratorConcatSkip2CleanAdd(nn.Module): + """Patch based Generator. Uses namespace opt.""" + + def __init__(self, opt: Namespace): + super().__init__() + self.is_cuda = torch.cuda.is_available() + N = int(opt.nfc) + self.head = ConvBlock(opt.nc_current, N, (3, 3), opt.padd_size, 1) + self.body = nn.Sequential() + + for i in range(opt.num_layer - 2): + block = ConvBlock(N, N, (3, 3), opt.padd_size, 1) + self.body.add_module("block%d" % (i + 1), block) + + block = ConvBlock(N, N, (3, 3), opt.padd_size, 1) + self.body.add_module("block%d" % (opt.num_layer - 2), block) + + self.tail = nn.Sequential( + nn.Conv2d( + N, opt.nc_current, kernel_size=(3, 3), stride=1, padding=opt.padd_size + ), + ) + + def forward(self, x: Tensor, y: Tensor, temperature: float = 1) -> Tensor: + x = self.head(x) + x = self.body(x) + x = self.tail(x) + x = F.softmax( + x * temperature, dim=1 + ) # Softmax is added here to allow for the temperature parameter + ind = int((y.shape[2] - x.shape[2]) / 2) + y = y[:, :, ind : (y.shape[2] - ind), ind : (y.shape[3] - ind)] + return x + y diff --git a/carl/envs/mario/reachabillity.py b/carl/envs/mario/reachabillity.py new file mode 100644 index 00000000..60fd2d17 --- /dev/null +++ b/carl/envs/mario/reachabillity.py @@ -0,0 +1,309 @@ +from typing import List, Tuple + +import numpy as np + +horizontal = 10 # when sprinting Mario can jump over a 10 tiles gap horizontally +vertical = 4 # Mario can jump over a 4 tile wall +diagonal = ( + 6 # Mario can jump over 6 tiles to clear a 4 block height difference when sprinting +) + +empty = "-" +ignored = ["M", "F", "|", "E", "g", "k", "r", "y", "G", "K", "R", "Y", "*", "B", "o"] + + +def remove_ignored(level: List[str]) -> Tuple[List, Tuple[int, int], Tuple[int, int]]: + """ + Replaces all ignored tokens with the empty token in a level. In case of Mario and the flag the coordinates of the + blocks below are returned and they are also replaced. + :param level: a level in ASCII form + :return: the level in ASCII form with the ignored tokens replaced and the coordinates of the block below Mario and + the flag if existing + """ + new_level = [] + mario = (-1, -1) + flag = (-1, -1) + + for i, row in enumerate(level): + mario_y = row.find("M") + if mario_y >= 0: + mario = (i + 1, mario_y) + flag_y = row.find("F") + if flag_y >= 0: + flag = (i + 1, flag_y) + for token in ignored: + row = row.replace(token, empty) + new_level.append(row) + + return new_level, mario, flag + + +def reachability_map( + level: List[str], + shape: Tuple[int, int], + has_mario: bool = False, + has_flag: bool = False, + check_outside: bool = False, +) -> Tuple[np.ndarray, bool]: + """ + This creates a numpy 2D array containing the reachability map for a given ASCII-Level. + Every solid block will have a 1 if Mario can stand on it and can reach the tile and a 0 else. + Currently ignoring sprint. + Levels are generated without Mario and the flag and as such the algorithm is not including these. + :param level: The level (slice) as a list containing the ASCII strings of each level row + :param shape: The level shape + :param has_mario: As levels are expected to be generated without Mario, this option has to be set to search for + Mario as a starting point + :param has_flag: As levels are expected to be generated without the flag, this option has to be set to determine + playability via reaching the flag + :param check_outside: If this option is set, playability will check if the player can reach the outside + :return: A numpy array where a 0 indicates an unreachable block and a 1 denotes a reachable block; + a boolean indicating if the level can be finished by the player + """ + + level, mario, flag = remove_ignored(level) + reachability_map = np.zeros(shape=shape) + index_queue = [] + + # find the starting point, either the block Mario is standing on or the first solid block Mario could stand on + found_first = False + if has_mario: + index_queue.append(mario) + else: + for i in range(shape[0] - 1, 0, -1): # start from the bottom of the level + for j in range(0, shape[1]): + tile = level[i][j] + if ( + tile != empty + and ( + reachability_map[i][j] == 1 + or not found_first + and i < shape[0] - 1 + ) + and i > 0 + and level[i - 1][j] == empty + ): + found, queue, _ = mark(level, reachability_map, i, j) + index_queue.extend(queue) + if not found_first: + found_first = found + break + if found_first: + break + + # calculate all reachable positions by applying a BFS type of algorithm + outside = False + while len(index_queue) > 0: + index = index_queue.pop() + _, queue, reached_outside = mark( + level, reachability_map, index[0], index[1], check_outside=check_outside + ) + if reached_outside: + outside = True + index_queue.extend(queue) + + # a level is playable if either the flag is reachable or if no flag is included, the rightmost side can be reached + # Bug: if the level ends with a gap, it might be playable but still wouldn't count as such + playable = False + + if has_flag: + if reachability_map[flag[0]][flag[1]]: + playable = True + else: + # look at all tiles in the last column + for i in range(1, shape[0]): + if reachability_map[shape[0] - i][shape[1] - 1]: + playable = True + break + + if not playable and check_outside: + # Assumption is that reaching the outside is identical to completing the level + if outside: + playable = True + + return reachability_map, playable + + +def check_blocked( + level: List[str], i: int, j: int, dh: int, dv: int, right: bool +) -> int: + """ + Checks for a given position, level and direction if a blockade exists in the range specified by dh and dv. + :param level: The level in ASCII form + :param i: x coordinate of the starting position + :param j: y coordinate of the starting position + :param dh: amount of blocks in the horizontal direction from the starting point the algorithm tries to jump + :param dv: amount of blocks in the vertical direction from the starting point the algorithm tries to jump + :param right: direction of the jump + :return: the blockade y value if a blockade is found, default max value otherwise + """ + blocked = horizontal + 1 # default value + boundary = j + dh if right else j - dh + try: + if level[i - dv][boundary] != empty: + height = 1 + dv + while height < vertical + 1: + v = i - dv - height + if v < 0: + # over maximum level height, cannot pass + blocked = dh + break + if level[v][boundary] != empty or dh + height > 10: + height += 1 + else: + break + if height == vertical + 1: + blocked = dh + except IndexError: + # over maximum level height, cannot pass + blocked = dh + + return blocked + + +def check_down( + level: List[str], + map: np.ndarray, + i: int, + j: int, + dh: int, + check_outside: bool, + right: bool, +) -> Tuple[bool, bool, List[Tuple[int, int]]]: + drop = 1 + found_first = False + reach_outside = False + found = [] + boundary = j + dh if right else j - dh + if boundary > map.shape[1] - 1: + if check_outside: + reach_outside = True + else: + y = min(max(boundary, 0), map.shape[1] - 1) + while i + drop < map.shape[0]: + # right and down + x = i + drop + above = x - 1 + + if ( + level[x][y] != empty + and above >= 0 + and level[above][y] == empty + and map[x][y] != 1 + ): + map[x][y] = 1 + found.append((x, y)) + found_first = True + break + drop += 1 + + return found_first, reach_outside, found + + +def mark( + level: List[str], + reachability_map: np.ndarray, + i: int, + j: int, + check_outside: bool = False, +) -> Tuple[bool, List[Tuple[int, int]], bool]: + """ + For a given position and a level this will mark all tiles reachable from the given position and collect all + these positions for further use. + :param level: The level (slice) as a list containing the ASCII strings of each level row + :param map: The current reachability map where the reachable tiles will be marked + :param i: x coordinate + :param j: y coordinate + :param check_outside: if the algorithm should indicate that the player can reach the right outside of the level + :return: A boolean indicating if any tile can be reached from this position, a list of all reachable positions and + if the outside can be reached + """ + found_first = False + reach_outside = False + found = [] + blocked_level = vertical + 1 + blocked_right = horizontal + 1 + blocked_left = horizontal + 1 + blocked_down_right = horizontal + 1 + blocked_down_left = horizontal + 1 + + # mark diagonally + for dh in range(0, horizontal + 1): + # check down as far as possible, Mario can fall down the whole level until he hits a solid block + if blocked_down_right == horizontal + 1: + blocked_down_right = check_blocked(level, i, j, dh, 0, right=True) + if blocked_down_right >= dh: + found_rechable, found_outside, positions = check_down( + level, reachability_map, i, j, dh, check_outside, right=True + ) + if found_rechable: + found_first = True + if found_outside: + reach_outside = True + found.extend(positions) + + if blocked_down_left == horizontal + 1: + blocked_down_left = check_blocked(level, i, j, dh, 0, right=False) + if blocked_down_left >= dh: + found_rechable, found_outside, positions = check_down( + level, reachability_map, i, j, dh, check_outside, right=False + ) + if found_rechable: + found_first = True + if found_outside: + reach_outside = True + found.extend(positions) + + for dv in range(0, vertical + 1): + if dh != 0 or dv != 0: + if dv >= blocked_level: + break + + # check if vertical path is blocked + if dh == 0: + if level[i - dv][j] != empty: + blocked_level = dv + continue + + # check if horizontal right path is blocked + if blocked_right == horizontal + 1: + blocked_right = check_blocked(level, i, j, dh, dv, right=True) + + if dh <= blocked_right and dh + dv <= 10: + # right and up + x = min(max(i - dv, 0), reachability_map.shape[0] - 1) + right = j + dh + if right > reachability_map.shape[1] - 1 and check_outside: + reach_outside = True + y = min(max(right, 0), reachability_map.shape[1] - 1) + above = x - 1 + if ( + level[x][y] != empty + and above >= 0 + and level[above][y] == empty + and reachability_map[x][y] != 1 + ): + reachability_map[x][y] = 1 + found.append((x, y)) + found_first = True + + # check if horizontal left path is blocked + if blocked_left == horizontal + 1: + blocked_left = check_blocked(level, i, j, dh, dv, right=False) + + if dh <= blocked_left and dh + dv <= 10: + # left and up + x = min(max(i - dv, 0), reachability_map.shape[0] - 1) + y = min(max(j - dh, 0), reachability_map.shape[1] - 1) + above = x - 1 + if ( + level[x][y] != empty + and above >= 0 + and level[above][y] == empty + and reachability_map[x][y] != 1 + ): + reachability_map[x][y] = 1 + found.append((x, y)) + found_first = True + + return found_first, found, reach_outside diff --git a/carl/envs/mario/sprites/README.md b/carl/envs/mario/sprites/README.md new file mode 100644 index 00000000..6e2eda67 --- /dev/null +++ b/carl/envs/mario/sprites/README.md @@ -0,0 +1,4 @@ +## Notice + +This folder contains the sprite images from https://github.com/amidos2006/Mario-AI-Framework/tree/master/img. +They are necessary for the Mario-AI-Framework and our level preview renderer. \ No newline at end of file diff --git a/carl/envs/mario/sprites/enemysheet.png b/carl/envs/mario/sprites/enemysheet.png new file mode 100755 index 00000000..7846c9b6 Binary files /dev/null and b/carl/envs/mario/sprites/enemysheet.png differ diff --git a/carl/envs/mario/sprites/favicon.png b/carl/envs/mario/sprites/favicon.png new file mode 100644 index 00000000..6bdeb815 Binary files /dev/null and b/carl/envs/mario/sprites/favicon.png differ diff --git a/carl/envs/mario/sprites/firemariosheet.png b/carl/envs/mario/sprites/firemariosheet.png new file mode 100755 index 00000000..3e8431bd Binary files /dev/null and b/carl/envs/mario/sprites/firemariosheet.png differ diff --git a/carl/envs/mario/sprites/font.gif b/carl/envs/mario/sprites/font.gif new file mode 100755 index 00000000..213d7a01 Binary files /dev/null and b/carl/envs/mario/sprites/font.gif differ diff --git a/carl/envs/mario/sprites/frameworkAD.gif b/carl/envs/mario/sprites/frameworkAD.gif new file mode 100644 index 00000000..51881dcf Binary files /dev/null and b/carl/envs/mario/sprites/frameworkAD.gif differ diff --git a/carl/envs/mario/sprites/itemsheet.png b/carl/envs/mario/sprites/itemsheet.png new file mode 100755 index 00000000..cc5c4b84 Binary files /dev/null and b/carl/envs/mario/sprites/itemsheet.png differ diff --git a/carl/envs/mario/sprites/mapsheet.png b/carl/envs/mario/sprites/mapsheet.png new file mode 100755 index 00000000..6a5d77d4 Binary files /dev/null and b/carl/envs/mario/sprites/mapsheet.png differ diff --git a/carl/envs/mario/sprites/mariosheet.png b/carl/envs/mario/sprites/mariosheet.png new file mode 100755 index 00000000..fa9877cf Binary files /dev/null and b/carl/envs/mario/sprites/mariosheet.png differ diff --git a/carl/envs/mario/sprites/particlesheet.png b/carl/envs/mario/sprites/particlesheet.png new file mode 100644 index 00000000..8421cdbc Binary files /dev/null and b/carl/envs/mario/sprites/particlesheet.png differ diff --git a/carl/envs/mario/sprites/smallmariosheet.png b/carl/envs/mario/sprites/smallmariosheet.png new file mode 100755 index 00000000..ac7cd549 Binary files /dev/null and b/carl/envs/mario/sprites/smallmariosheet.png differ diff --git a/carl/envs/mario/toad_gan.py b/carl/envs/mario/toad_gan.py new file mode 100644 index 00000000..ac290a83 --- /dev/null +++ b/carl/envs/mario/toad_gan.py @@ -0,0 +1,129 @@ +from typing import Optional + +import functools +import os +import sys +from dataclasses import dataclass + +import torch +from torch import Tensor + +from carl.envs.mario.generate_sample import generate_sample, generate_spatial_noise +from carl.envs.mario.reachabillity import reachability_map + + +@dataclass +class TOADGAN: + def __init__( + self, + Gs: Tensor, + Zs: Tensor, + reals: Tensor, + NoiseAmp: Tensor, + token_list: Tensor, + num_layers: int, + ): + self.generators = Gs + self.noise_maps = Zs + self.reals = reals + self.noise_amplitudes = NoiseAmp + self.token_list = token_list + self.num_layer = num_layers + + @property + def original_height(self) -> int: + return self.reals[-1].shape[-2] + + @property + def original_width(self) -> int: + return self.reals[-1].shape[-1] + + +GENERATOR_DIR = os.path.abspath( + os.path.join(os.path.dirname(__file__), "TOAD-GUI", "generators", "v2") +) +GENERATOR_PATHS = sorted( + os.listdir(GENERATOR_DIR), + key=lambda name: [int(index) for index in name.replace("TOAD_GAN_", "").split("-")], +) + + +@functools.lru_cache(maxsize=None) +def load_generator(level_index: int) -> TOADGAN: + import carl.envs.mario.models as models + + sys.modules["models"] = models + gen_path = os.path.join(GENERATOR_DIR, GENERATOR_PATHS[level_index]) + reals = torch.load( + "%s/reals.pth" % gen_path, + map_location="cuda:0" if torch.cuda.is_available() else "cpu", + ) + Zs = torch.load( + "%s/noise_maps.pth" % gen_path, + map_location="cuda:0" if torch.cuda.is_available() else "cpu", + ) + NoiseAmp = torch.load( + "%s/noise_amplitudes.pth" % gen_path, + map_location="cuda:0" if torch.cuda.is_available() else "cpu", + ) + token_list = torch.load("%s/token_list.pth" % gen_path) + num_layers = torch.load("%s/num_layer.pth" % gen_path) + Gs = torch.load( + "%s/generators.pth" % gen_path, + map_location="cuda:0" if torch.cuda.is_available() else "cpu", + ) + return TOADGAN( + Gs=Gs, + Zs=Zs, + reals=reals, + NoiseAmp=NoiseAmp, + num_layers=num_layers, + token_list=token_list, + ) + + +def generate_level( + width: int, + height: int, + level_index: int, + initial_noise: Optional[torch.Tensor] = None, + filter_unplayable: bool = True, +) -> str: + toad_gan = load_generator(level_index) + playable = False + level = None + tries = 0 + while not playable: + tries += 1 + level = generate_sample( + **vars(toad_gan), + scale_h=width / toad_gan.original_width, + scale_v=height / toad_gan.original_height, + initial_noise=initial_noise, + ) + if filter_unplayable and tries < 100: + _, playable = reachability_map( + level, shape=(height, width), check_outside=True + ) + else: + playable = True + assert level + return "".join(level) + + +def generate_initial_noise(width: int, height: int, level_index: int) -> Tensor: + toad_gan = load_generator(level_index) + base_noise_map = toad_gan.noise_maps[0] + nzx = ( + (base_noise_map.shape[2] - 2 * toad_gan.num_layer) + * height + / toad_gan.original_height + ) + nzy = ( + (base_noise_map.shape[3] - 2 * toad_gan.num_layer) + * width + / toad_gan.original_width + ) + noise_shape = (1, len(toad_gan.token_list), int(round(nzx)), int(round(nzy))) + noise = generate_spatial_noise(noise_shape) + return noise diff --git a/carl/envs/mario/utils.py b/carl/envs/mario/utils.py new file mode 100644 index 00000000..0470d3ab --- /dev/null +++ b/carl/envs/mario/utils.py @@ -0,0 +1,59 @@ +from typing import Tuple + +import atexit +import os +import socket +import sys +from contextlib import closing + +from py4j.java_gateway import JavaGateway +from xvfbwrapper import Xvfb + +MARIO_AI_PATH = os.path.abspath( + os.path.join(os.path.dirname(__file__), "Mario-AI-Framework") +) +_gateway = None +_port = None + + +def find_free_port() -> int: + with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: + s.bind(("", 0)) + s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + return s.getsockname()[1] + + +def load_level(level_name: str = "lvl-1.txt") -> str: + prefix = ( + os.path.join(MARIO_AI_PATH, "levels", "original") + if level_name.startswith("lvl-") + else "" + ) + with open(os.path.join(prefix, level_name), "r") as f: + level = f.read() + return level + + +def get_port() -> int: + global _gateway + global _port + if _gateway is None: + _gateway, _port = launch_gateway() + return _port + + +def launch_gateway() -> Tuple[JavaGateway, int]: + vdisplay = Xvfb(width=1280, height=740, colordepth=16) + vdisplay.start() + atexit.register(lambda: vdisplay.stop()) + free_port = find_free_port() + return ( + JavaGateway.launch_gateway( + classpath=os.path.join(MARIO_AI_PATH, "carl"), + redirect_stderr=sys.stderr, + redirect_stdout=sys.stdout, + die_on_exit=True, + port=free_port, + ), + free_port, + ) diff --git a/carl/utils/doc_building/plot_radar.py b/carl/utils/doc_building/plot_radar.py index 276caa98..f33815ea 100644 --- a/carl/utils/doc_building/plot_radar.py +++ b/carl/utils/doc_building/plot_radar.py @@ -4,14 +4,12 @@ from pathlib import Path import matplotlib.pyplot as plt + import numpy as np import pandas as pd import seaborn as sns - import numpy as np from carl.utils.doc_building.plotting import radar_factory - plot_legend = True - env_context_feature_names = { "CARLMountainCarEnv": [ "force", @@ -295,9 +293,6 @@ 5, 3, 14, - 14, - 14, - 18 ] env_names = [ "CARLMountainCarEnv", @@ -323,13 +318,11 @@ ] n_cfs_d = [11, 5, 8, 6, 10, 16, 1, 20, 7, 6, 5, 9, 9, 9, 4, 3, 14, 14, 14, 18] n_cfs_r = [0, 0, 0, 0, 0, 4, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] - - n_cfs = np.sum(n_context_features) - n_dynami_changing = np.sum(n_cfs_d) # 129 + n_cfs = 131 + n_dynami_changing = 129 n_reward_changing = 7 - n_float_cfs = 114 + 14 + 14 + 14 + 18 + n_float_cfs = 114 percentage_float_cfs = n_float_cfs / n_cfs - print("percentage float", percentage_float_cfs) env_types = { "classic_control": [ @@ -380,14 +373,6 @@ ) data = pd.concat(data) - asvals = list(data["action_space_size"].unique()) - asvals.sort() - print("unique action sizes", asvals) - print("number of context features", np.sum(n_context_features)) - print("perc changing dynamic", n_dynami_changing / n_cfs, n_dynami_changing, n_cfs) - print(len(n_cfs_d), len(n_context_features)) - print("reward changing", len(n_cfs_r), np.sum(n_cfs_r), np.sum(n_cfs_r)/n_cfs) - # normalize values cols = [c for c in data.columns if c not in ["env_type", "env_name"]] # type: ignore [attr-defined] max_values_per_col = [] @@ -398,8 +383,6 @@ max_values_per_col.append(max_val) data[col] /= max_val - - cols_plot = [ "state_space_size", "action_space_size", diff --git a/changelog.md b/changelog.md index f5e94fdd..d5696ace 100644 --- a/changelog.md +++ b/changelog.md @@ -1,5 +1,4 @@ # 0.2.1 -- Make sampling of contexts deterministic with seed - Add Finger (DMC) env - Readd RNA env (#78) diff --git a/docs/source/environments/data/context_definitions/CARLAcrobotEnv.csv b/docs/source/environments/data/context_definitions/CARLAcrobotEnv.csv new file mode 100644 index 00000000..b54b4d2f --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLAcrobotEnv.csv @@ -0,0 +1,11 @@ +Context Feature,Default,Bounds +link_length_1,1.0,"(0.1, 10, )" +link_length_2,1.0,"(0.1, 10, )" +link_mass_1,1.0,"(0.1, 10, )" +link_mass_2,1.0,"(0.1, 10, )" +link_com_1,0.5,"(0, 1, )" +link_com_2,0.5,"(0, 1, )" +link_moi,1.0,"(0.1, 10, )" +max_velocity_1,12.566370614359172,"(1.2566370614359172, 125.66370614359172, )" +max_velocity_2,28.274333882308138,"(2.827433388230814, 282.7433388230814, )" +torque_noise_max,0.0,"(-1.0, 1.0, )" diff --git a/docs/source/environments/data/context_definitions/CARLAnt.csv b/docs/source/environments/data/context_definitions/CARLAnt.csv new file mode 100644 index 00000000..9352bcae --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLAnt.csv @@ -0,0 +1,8 @@ +Context Feature,Default,Bounds +joint_stiffness,5000.0,"(1, inf, )" +gravity,-9.8,"(-inf, -0.1, )" +friction,0.6,"(-inf, inf, )" +angular_damping,-0.05,"(-inf, inf, )" +actuator_strength,300.0,"(1, inf, )" +joint_angular_damping,35.0,"(0, inf, )" +torso_mass,10.0,"(0.1, inf, )" diff --git a/docs/source/environments/data/context_definitions/CARLBipedalWalkerEnv.csv b/docs/source/environments/data/context_definitions/CARLBipedalWalkerEnv.csv new file mode 100644 index 00000000..b1d8a28e --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLBipedalWalkerEnv.csv @@ -0,0 +1,21 @@ +Context Feature,Default,Bounds +FPS,50.0,"(1, 500, )" +SCALE,30.0,"(1, 100, )" +GRAVITY_X,0.0,"(-20, 20, )" +GRAVITY_Y,-10.0,"(-20, -0.01, )" +FRICTION,2.5,"(0, 10, )" +TERRAIN_STEP,0.4666666666666667,"(0.25, 1, )" +TERRAIN_LENGTH,200.0,"(100, 500, )" +TERRAIN_HEIGHT,5.0,"(3, 10, )" +TERRAIN_GRASS,10.0,"(5, 15, )" +TERRAIN_STARTPAD,20.0,"(10, 30, )" +MOTORS_TORQUE,80.0,"(0, 200, )" +SPEED_HIP,4.0,"(1e-06, 15, )" +SPEED_KNEE,6.0,"(1e-06, 15, )" +LIDAR_RANGE,5.333333333333333,"(0.5, 20, )" +LEG_DOWN,-0.26666666666666666,"(-2, -0.25, )" +LEG_W,0.26666666666666666,"(0.25, 0.5, )" +LEG_H,1.1333333333333333,"(0.25, 2, )" +INITIAL_RANDOM,5.0,"(0, 50, )" +VIEWPORT_W,600.0,"(400, 1000, )" +VIEWPORT_H,400.0,"(200, 800, )" diff --git a/docs/source/environments/data/context_definitions/CARLCartPoleEnv.csv b/docs/source/environments/data/context_definitions/CARLCartPoleEnv.csv new file mode 100644 index 00000000..1fcb4d0d --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLCartPoleEnv.csv @@ -0,0 +1,7 @@ +Context Feature,Default,Bounds +gravity,9.8,"(0.1, inf, )" +masscart,1.0,"(0.1, 10, )" +masspole,0.1,"(0.01, 1, )" +pole_length,0.5,"(0.05, 5, )" +force_magnifier,10.0,"(1, 100, )" +update_interval,0.02,"(0.002, 0.2, )" diff --git a/docs/source/environments/data/context_definitions/CARLFetch.csv b/docs/source/environments/data/context_definitions/CARLFetch.csv new file mode 100644 index 00000000..84a11cc5 --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLFetch.csv @@ -0,0 +1,10 @@ +Context Feature,Default,Bounds +joint_stiffness,5000.0,"(1, inf, )" +gravity,-9.8,"(-inf, -0.1, )" +friction,0.6,"(-inf, inf, )" +angular_damping,-0.05,"(-inf, inf, )" +actuator_strength,300.0,"(1, inf, )" +joint_angular_damping,35.0,"(0, inf, )" +torso_mass,1.0,"(0.1, inf, )" +target_radius,2.0,"(0.1, inf, )" +target_distance,15.0,"(0.1, inf, )" diff --git a/docs/source/environments/data/context_definitions/CARLGrasp.csv b/docs/source/environments/data/context_definitions/CARLGrasp.csv new file mode 100644 index 00000000..3bf2cac4 --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLGrasp.csv @@ -0,0 +1,10 @@ +Context Feature,Default,Bounds +joint_stiffness,5000.0,"(1, inf, )" +gravity,-9.8,"(-inf, -0.1, )" +friction,0.6,"(-inf, inf, )" +angular_damping,-0.05,"(-inf, inf, )" +actuator_strength,300.0,"(1, inf, )" +joint_angular_damping,50.0,"(0, inf, )" +target_radius,1.1,"(0.1, inf, )" +target_distance,10.0,"(0.1, inf, )" +target_height,8.0,"(0.1, inf, )" diff --git a/docs/source/environments/data/context_definitions/CARLHalfcheetah.csv b/docs/source/environments/data/context_definitions/CARLHalfcheetah.csv new file mode 100644 index 00000000..57186d5c --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLHalfcheetah.csv @@ -0,0 +1,7 @@ +Context Feature,Default,Bounds +joint_stiffness,15000.0,"(1, inf, )" +gravity,-9.8,"(-inf, -0.1, )" +friction,0.6,"(-inf, inf, )" +angular_damping,-0.05,"(-inf, inf, )" +joint_angular_damping,20.0,"(0, inf, )" +torso_mass,9.457333,"(0.1, inf, )" diff --git a/docs/source/environments/data/context_definitions/CARLHumanoid.csv b/docs/source/environments/data/context_definitions/CARLHumanoid.csv new file mode 100644 index 00000000..bef83ec5 --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLHumanoid.csv @@ -0,0 +1,6 @@ +Context Feature,Default,Bounds +gravity,-9.8,"(-inf, -0.1, )" +friction,0.6,"(-inf, inf, )" +angular_damping,-0.05,"(-inf, inf, )" +joint_angular_damping,20.0,"(0, inf, )" +torso_mass,8.907463,"(0.1, inf, )" diff --git a/docs/source/environments/data/context_definitions/CARLLunarLanderEnv.csv b/docs/source/environments/data/context_definitions/CARLLunarLanderEnv.csv new file mode 100644 index 00000000..eba5d5e8 --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLLunarLanderEnv.csv @@ -0,0 +1,17 @@ +Context Feature,Default,Bounds +FPS,50.0,"(1, 500, )" +SCALE,30.0,"(1, 100, )" +MAIN_ENGINE_POWER,13.0,"(0, 50, )" +SIDE_ENGINE_POWER,0.6,"(0, 50, )" +INITIAL_RANDOM,1000.0,"(0, 2000, )" +GRAVITY_X,0.0,"(-20, 20, )" +GRAVITY_Y,-10.0,"(-20, -0.01, )" +LEG_AWAY,20.0,"(0, 50, )" +LEG_DOWN,18.0,"(0, 50, )" +LEG_W,2.0,"(1, 10, )" +LEG_H,8.0,"(1, 20, )" +LEG_SPRING_TORQUE,40.0,"(0, 100, )" +SIDE_ENGINE_HEIGHT,14.0,"(1, 20, )" +SIDE_ENGINE_AWAY,12.0,"(1, 20, )" +VIEWPORT_W,600.0,"(400, 1000, )" +VIEWPORT_H,400.0,"(200, 800, )" diff --git a/docs/source/environments/data/context_definitions/CARLMarioEnv.csv b/docs/source/environments/data/context_definitions/CARLMarioEnv.csv new file mode 100644 index 00000000..e7fbff60 --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLMarioEnv.csv @@ -0,0 +1,5 @@ +Context Feature,Default,Bounds +level_index,0,"(None, None, 'categorical', array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]))" +noise,None *,"(-1.0, 1.0, )" +mario_state,0,"(None, None, 'categorical', [0, 1, 2])" +mario_inertia,0.89,"(0.5, 1.5, )" diff --git a/docs/source/environments/data/context_definitions/CARLMountainCarContinuousEnv.csv b/docs/source/environments/data/context_definitions/CARLMountainCarContinuousEnv.csv new file mode 100644 index 00000000..736fd40f --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLMountainCarContinuousEnv.csv @@ -0,0 +1,11 @@ +Context Feature,Default,Bounds +min_position,-1.2,"(-inf, inf, )" +max_position,0.6,"(-inf, inf, )" +max_speed,0.07,"(0, inf, )" +goal_position,0.45,"(-inf, inf, )" +goal_velocity,0.0,"(-inf, inf, )" +power,0.0015,"(-inf, inf, )" +min_position_start,-0.6,"(-inf, inf, )" +max_position_start,-0.4,"(-inf, inf, )" +min_velocity_start,0.0,"(-inf, inf, )" +max_velocity_start,0.0,"(-inf, inf, )" diff --git a/docs/source/environments/data/context_definitions/CARLMountainCarEnv.csv b/docs/source/environments/data/context_definitions/CARLMountainCarEnv.csv new file mode 100644 index 00000000..1cf3d747 --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLMountainCarEnv.csv @@ -0,0 +1,12 @@ +Context Feature,Default,Bounds +min_position,-1.2,"(-inf, inf, )" +max_position,0.6,"(-inf, inf, )" +max_speed,0.07,"(0, inf, )" +goal_position,0.5,"(-inf, inf, )" +goal_velocity,0.0,"(-inf, inf, )" +force,0.001,"(-inf, inf, )" +gravity,0.0025,"(0, inf, )" +start_position,-0.5,"(-1.5, 0.5, )" +start_position_std,0.1,"(0.1, inf, )" +start_velocity,0.0,"(-inf, inf, )" +start_velocity_std,0.0,"(0.1, inf, )" diff --git a/docs/source/environments/data/context_definitions/CARLPendulumEnv.csv b/docs/source/environments/data/context_definitions/CARLPendulumEnv.csv new file mode 100644 index 00000000..9a69ae61 --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLPendulumEnv.csv @@ -0,0 +1,6 @@ +Context Feature,Default,Bounds +max_speed,8.0,"(-inf, inf, )" +dt,0.05,"(0, inf, )" +g,10.0,"(0, inf, )" +m,1.0,"(1e-06, inf, )" +l,1.0,"(1e-06, inf, )" diff --git a/docs/source/environments/data/context_definitions/CARLRnaDesignEnv.csv b/docs/source/environments/data/context_definitions/CARLRnaDesignEnv.csv new file mode 100644 index 00000000..f303c13d --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLRnaDesignEnv.csv @@ -0,0 +1,6 @@ +Context Feature,Default,Bounds +mutation_threshold,5,"(0.1, inf, )" +reward_exponent,1,"(0.1, inf, )" +state_radius,5,"(1, inf, )" +dataset,eterna,"('eterna', 'rfam_taneda', None)" +target_structure_ids,,"(0, inf, [, ])" diff --git a/docs/source/environments/data/context_definitions/CARLUr5e.csv b/docs/source/environments/data/context_definitions/CARLUr5e.csv new file mode 100644 index 00000000..5b681908 --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLUr5e.csv @@ -0,0 +1,10 @@ +Context Feature,Default,Bounds +joint_stiffness,40000.0,"(1, inf, )" +gravity,-9.81,"(-inf, -0.1, )" +friction,0.6,"(-inf, inf, )" +angular_damping,-0.05,"(-inf, inf, )" +actuator_strength,100.0,"(1, inf, )" +joint_angular_damping,50.0,"(0, 360, )" +target_radius,0.02,"(0.01, inf, )" +target_distance,0.5,"(0.01, inf, )" +torso_mass,1.0,"(0, inf, )" diff --git a/docs/source/environments/data/context_definitions/CARLVehicleRacingEnv.csv b/docs/source/environments/data/context_definitions/CARLVehicleRacingEnv.csv new file mode 100644 index 00000000..8c55a66a --- /dev/null +++ b/docs/source/environments/data/context_definitions/CARLVehicleRacingEnv.csv @@ -0,0 +1,3 @@ +Context Feature,Default,Bounds +VEHICLE,0,"(None, None, 'categorical', array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]))" diff --git a/docs/source/environments/data/screenshots/ant.png b/docs/source/environments/data/screenshots/ant.png new file mode 100644 index 00000000..4883ada3 Binary files /dev/null and b/docs/source/environments/data/screenshots/ant.png differ diff --git a/docs/source/environments/data/screenshots/fetch.png b/docs/source/environments/data/screenshots/fetch.png new file mode 100644 index 00000000..5e5889f5 Binary files /dev/null and b/docs/source/environments/data/screenshots/fetch.png differ diff --git a/docs/source/environments/data/screenshots/grasp.png b/docs/source/environments/data/screenshots/grasp.png new file mode 100644 index 00000000..d0648f9c Binary files /dev/null and b/docs/source/environments/data/screenshots/grasp.png differ diff --git a/docs/source/environments/data/screenshots/halfcheetah.png b/docs/source/environments/data/screenshots/halfcheetah.png new file mode 100644 index 00000000..3f99a44e Binary files /dev/null and b/docs/source/environments/data/screenshots/halfcheetah.png differ diff --git a/docs/source/environments/data/screenshots/humanoid.png b/docs/source/environments/data/screenshots/humanoid.png new file mode 100644 index 00000000..2f0b6cc6 Binary files /dev/null and b/docs/source/environments/data/screenshots/humanoid.png differ diff --git a/docs/source/environments/data/screenshots/learna.png b/docs/source/environments/data/screenshots/learna.png new file mode 100644 index 00000000..9030d232 Binary files /dev/null and b/docs/source/environments/data/screenshots/learna.png differ diff --git a/docs/source/environments/data/screenshots/supermario.png b/docs/source/environments/data/screenshots/supermario.png new file mode 100644 index 00000000..b05858dc Binary files /dev/null and b/docs/source/environments/data/screenshots/supermario.png differ diff --git a/docs/source/environments/data/screenshots/ur5e.png b/docs/source/environments/data/screenshots/ur5e.png new file mode 100644 index 00000000..86e1dc0d Binary files /dev/null and b/docs/source/environments/data/screenshots/ur5e.png differ diff --git a/docs/source/environments/data/screenshots/vehicleracing.png b/docs/source/environments/data/screenshots/vehicleracing.png new file mode 100644 index 00000000..7bcd3c3e Binary files /dev/null and b/docs/source/environments/data/screenshots/vehicleracing.png differ diff --git a/docs/source/environments/data/tab_overview_environments.csv b/docs/source/environments/data/tab_overview_environments.csv new file mode 100644 index 00000000..2dec5e5a --- /dev/null +++ b/docs/source/environments/data/tab_overview_environments.csv @@ -0,0 +1,17 @@ +Env. Family,Name,# Context Features,Action Space,Obs. Space +classic_control,CARLMountainCarEnv,11,Discrete(3),"Box(-inf, inf, (13,), float32)" +classic_control,CARLPendulumEnv,5,"Box(-2.0, 2.0, (1,), float32)","Box(-inf, inf, (8,), float32)" +classic_control,CARLAcrobotEnv,10,Discrete(3),"Box(-28.274333953857422, 282.74334716796875, (16,), float32)" +classic_control,CARLCartPoleEnv,6,Discrete(2),"Box(-3.4028234663852886e+38, inf, (10,), float32)" +classic_control,CARLMountainCarContinuousEnv,10,"Box(-1.0, 1.0, (1,), float32)","Box(-inf, inf, (12,), float32)" +box2d,CARLLunarLanderEnv,16,Discrete(4),"Box(-inf, inf, (24,), float32)" +box2d,CARLVehicleRacingEnv,1,"Box(-1.0, 1.0, (3,), float32)","Box(0, 255, (96, 96, 3), uint8)" +box2d,CARLBipedalWalkerEnv,20,"Box(-1.0, 1.0, (4,), float32)","Box(-inf, inf, (44,), float32)" +brax,CARLAnt,7,"Box(-1.0, 1.0, (8,), float32)","Box(-inf, inf, (94,), float32)" +brax,CARLHalfcheetah,6,"Box(-1.0, 1.0, (6,), float32)","Box(-inf, inf, (29,), float32)" +brax,CARLHumanoid,5,"Box(-1.0, 1.0, (17,), float32)","Box(-inf, inf, (304,), float32)" +brax,CARLFetch,9,"Box(-1.0, 1.0, (10,), float32)","Box(-inf, inf, (110,), float32)" +brax,CARLGrasp,9,"Box(-1.0, 1.0, (19,), float32)","Box(-inf, inf, (141,), float32)" +brax,CARLUr5e,9,"Box(-1.0, 1.0, (6,), float32)","Box(-inf, inf, (75,), float32)" +RNA,CARLRnaDesignEnv,5,Discrete(4),"Box(-inf, inf, (11,), float32)" +Mario,CARLMarioEnv,4,Discrete(10),"Box(0, 255, (4, 64, 64), uint8)" diff --git a/docs/source/figures/CARL_logo.png b/docs/source/figures/CARL_logo.png new file mode 100644 index 00000000..bc753ad1 Binary files /dev/null and b/docs/source/figures/CARL_logo.png differ diff --git a/docs/source/figures/concept.png b/docs/source/figures/concept.png new file mode 100644 index 00000000..65c88e92 Binary files /dev/null and b/docs/source/figures/concept.png differ diff --git a/docs/source/figures/experiments/CARLMarioEnv_mean_ep_rew_over_step_visible_inertia.png b/docs/source/figures/experiments/CARLMarioEnv_mean_ep_rew_over_step_visible_inertia.png new file mode 100644 index 00000000..c3f4c03a Binary files /dev/null and b/docs/source/figures/experiments/CARLMarioEnv_mean_ep_rew_over_step_visible_inertia.png differ diff --git a/docs/source/figures/experiments/gravity_distribution_exp1.png b/docs/source/figures/experiments/gravity_distribution_exp1.png new file mode 100644 index 00000000..b6244dc5 Binary files /dev/null and b/docs/source/figures/experiments/gravity_distribution_exp1.png differ diff --git a/docs/source/figures/experiments/gravity_sampled_gravities.png b/docs/source/figures/experiments/gravity_sampled_gravities.png new file mode 100644 index 00000000..4f75aba0 Binary files /dev/null and b/docs/source/figures/experiments/gravity_sampled_gravities.png differ diff --git a/docs/source/figures/experiments/policytransfer_hiddenvisible_exp1.png b/docs/source/figures/experiments/policytransfer_hiddenvisible_exp1.png new file mode 100644 index 00000000..f156f9b1 Binary files /dev/null and b/docs/source/figures/experiments/policytransfer_hiddenvisible_exp1.png differ diff --git a/docs/source/figures/logo.png b/docs/source/figures/logo.png new file mode 100644 index 00000000..65c88e92 Binary files /dev/null and b/docs/source/figures/logo.png differ diff --git a/docs/source/figures/radar_env_space.png b/docs/source/figures/radar_env_space.png new file mode 100644 index 00000000..85a16f53 Binary files /dev/null and b/docs/source/figures/radar_env_space.png differ diff --git a/environment.yaml b/environment.yaml deleted file mode 100644 index 7159b54d..00000000 --- a/environment.yaml +++ /dev/null @@ -1,49 +0,0 @@ -name: carl -channels: - - pytorch - - defaults - - conda-forge - - nvidia -dependencies: - - python=3.9 - - pylint - - rope - - torchvision - - cudatoolkit=11.3 - - cuda-nvcc - - torchaudio - - pytorch - - pybox2d - - pip - - pip: - - gym - - scipy - - ConfigArgParse - - numpy - - pandas - - xvfbwrapper - - matplotlib - - optuna - - dataclasses - - numpyencoder - - pyglet - - pytablewriter - - PyYAML - - tabulate - - brax - - protobuf - - RNA - - Pillow - - py4j - - ray - - seaborn - - stable_baselines3 - - sb3_contrib - - pytest - - debugpy - - hydra-core - - hydra-submitit-launcher - - hydra-optuna-sweeper - - hydra_colorlog - - coax - - wandb \ No newline at end of file diff --git a/examples/try_dm_control.py b/examples/try_dm_control.py index be259a43..48ff1825 100644 --- a/examples/try_dm_control.py +++ b/examples/try_dm_control.py @@ -11,11 +11,6 @@ from carl.envs import CARLDmcWalkerEnv from carl.envs import CARLDmcWalkerEnv_defaults as walker_default from carl.envs import CARLDmcWalkerEnv_mask as walker_mask -from carl.envs import CARLDmcFingerEnv - -import os -os.environ["MUJOCO_GL"] = "glfw" -# os.environ["SDL_VIDEODRIVER"] = "dummy" if __name__ == "__main__": # Load one task: @@ -37,30 +32,14 @@ hide_context=False, dict_observation_space=True, ) - carl_env = CARLDmcFingerEnv() - # carl_env = CARLDmcWalkerEnv() - # carl_env = CARLDmcQuadrupedEnv() - # carl_env = CARLDmcFishEnv() action = carl_env.action_space.sample() - s = carl_env.reset() state, reward, done, info = carl_env.step(action=action) print("state", state, type(state)) - def render(env, **render_kwargs): - frame = carl_env.render(mode="rgb_array", **render_kwargs) - plt.imshow(frame) - plt.axis("off") - plt.tight_layout() - plt.savefig(f"dm_render_{type(env).__name__}.png", dpi=300, bbox_inches='tight',transparent=True, pad_inches=0) - + render = lambda: plt.imshow(carl_env.render(mode="rgb_array")) s = carl_env.reset() - render( - carl_env, - camera_id=1, - height=400, - width=400, - ) - + render() + # plt.savefig("dm_render.png") action = carl_env.action_space.sample() state, reward, done, info = carl_env.step(action=action) print("state", state, type(state)) diff --git a/examples/vary_initial_state_distributions.ipynb b/examples/vary_initial_state_distributions.ipynb index 5c5ba3f8..08a2e42d 100644 --- a/examples/vary_initial_state_distributions.ipynb +++ b/examples/vary_initial_state_distributions.ipynb @@ -2,12 +2,6 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - }, "source": [ "# Initial State Distributions\n", "For the classic control environments the initial state distributions are narrow. With CARL (Contextually Adapative RL)\n", @@ -16,17 +10,17 @@ "\n", "## Collect Initial States\n", "We collect the initial states, once of the classic environments and once of the contextually extended environments." - ] - }, - { - "cell_type": "code", - "execution_count": 3, + ], "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - }, + } + }, + { + "cell_type": "code", + "execution_count": 3, "outputs": [ { "name": "stderr", @@ -151,18 +145,11 @@ ], "source": [ "import sys\n", - "\n", "sys.path.append(\"..\")\n", "import importlib\n", "import carl.envs\n", - "\n", "importlib.reload(carl.envs)\n", - "from carl.envs import (\n", - " CARLCartPoleEnv,\n", - " CARLAcrobotEnv,\n", - " CARLMountainCarEnv,\n", - " CARLPendulumEnv,\n", - ")\n", + "from carl.envs import CARLCartPoleEnv, CARLAcrobotEnv, CARLMountainCarEnv, CARLPendulumEnv\n", "import numpy as np\n", "from tqdm import tqdm\n", "from typing import Dict\n", @@ -195,10 +182,10 @@ "contexts_mountaincar = dict()\n", "for i in range(n_contexts):\n", " # Sample for Acrobot\n", - " initial_angle_lower = np.random.uniform(-np.pi, 0)\n", - " initial_angle_upper = np.random.uniform(min(0, initial_angle_lower), np.pi)\n", - " initial_velocity_lower = np.random.uniform(-1, 0)\n", - " initial_velocity_upper = np.random.uniform(min(0, initial_velocity_lower), 1)\n", + " initial_angle_lower = np.random.uniform(- np.pi, 0)\n", + " initial_angle_upper = np.random.uniform(min(0,initial_angle_lower), np.pi)\n", + " initial_velocity_lower = np.random.uniform(- 1, 0)\n", + " initial_velocity_upper = np.random.uniform(min(0,initial_velocity_lower), 1)\n", " context = {\n", " \"initial_angle_lower\": initial_angle_lower,\n", " \"initial_angle_upper\": initial_angle_upper,\n", @@ -209,7 +196,7 @@ "\n", " # Sample for CartPole\n", " initial_state_lower = np.random.uniform(-2, 0)\n", - " initial_state_upper = np.random.uniform(min(0, initial_state_lower), 2)\n", + " initial_state_upper = np.random.uniform(min(0,initial_state_lower), 2)\n", " context = {\n", " \"initial_state_lower\": initial_state_lower,\n", " \"initial_state_upper\": initial_state_upper,\n", @@ -250,39 +237,37 @@ "# Collect initial states\n", "renders, states = get_renders(env_specs=env_specs)\n", "renders_classic, states_classic = get_renders(env_specs=env_specs_classic)" - ] - }, - { - "cell_type": "markdown", + ], "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - }, + } + }, + { + "cell_type": "markdown", "source": [ "## Classic vs. Contextual Initial State Distributions\n", "The contextually extended CARL environments show a much higher variety in the initial state distributions\n", "thus aiming for generalization starting from the first state.\n", "The figure displays the average of 50 initial states." - ] - }, - { - "cell_type": "code", - "execution_count": 4, + ], "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - }, + } + }, + { + "cell_type": "code", + "execution_count": 4, "outputs": [ { "data": { - "image/png": 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xpC3YFl4L02TfBABAP2FfuqjSeZ1zrqpYHlcpT5JESZLMBMrDnUvi6uqj0Uhpmlb77+ouYdF+/dprr9XHPvYxXXTRRfqZn/mZqdA5+30cBDsfPHfOfZOku0g6tes4Zvb59bUIAAAAAHAYDRmQXlZL3cG1SpwrKlsP3pHVXK1pqGlLe2uaNqRiewjrL3VOXuv/jOSa1LKnqhcAADiMquMps/YAeBl+Lv7sF2CTNAlpt02/5fbeoTr6wmGCPC9C6KGN9fHnVD1XWfV8bvVxsyJ4HlU4r4ffOyuXtzW1LbynKHxuzhXhdELoAICO+u6zQ1XUeX0toUor1U8BAOgnyzJ575eqXB6E4LiZKUmSuReDpWla7d/DsG0XpZmZbrnlFv3e7/2eLrjgAv3gD/6gzjzzzNZpA7tq545QnXPHJP2YpB+U9GhJd+05CdMOvi8AAAAAAJqEjivTdsLvTsNWma7fKnBoIZBcrxCB9Uidk8zUEj0alJXVL8O85sSQAAAADpTGwHktKO6mRygC0OG1rse8cWC85Xh83lH6THi9YdwQ/G6qbG7xcAuaumgYy7KiOnkUxC8buWDK8URsOnQe/vZeSRnii9eNK499w/MuSWRlyJ3fHQCAIcSB8y59Z/Fw7IsAAFgsVDkPFcv73pUkCJXP6/vsetGceiDdOTcVPm/y4he/WBdeeKG++7u/W/e+973Zx+NA2qmAtnPuqZL+QNLZ8dNbag4AAAAA4IjZVs1zkzYS7K1za6t2vt53U3TseUk9Kx2it0Sbr8RvZjI6WgEAwCFhUfi5CkA3DTcZQVIU6i4D6F2O2acCbB0DbX2ZWXXizsxmwucDzUTWVnW973Rqy1+SlOeylorslTwvwuhSr3UAAECTEFoLwfM+vPdVJVX2RQAAtAt3CwnBc0lVADzpeeesOEAeKqeHR9v+OMwjrpAephX84R/+ofI813d913fp/PPPZ9+OA2tngufOuX8u6U9UnNOsf6IaCz50eA0AAADAAbC/v6+rrrpq280A5LXBALiZEklJWeF5K8Hzsg3DdmyZNhdVTkV3wPplkrJ1BIoahE6hRFQ8x8H3bd/2bTr11FO33QwAwBbFoee+IfBQodtF/17bCek4lL1JbceYQ4TOy2qyalgHzrkiWN4QPp9qXtkOlySTKu+EAgAAPYWQWgit9RWqtfYNzAEAcFSE/Wue51P723q18q770jh0Lklpms7ctWTeb8Om1733+vjHP64rr7xS/+W//Bc98IEP5PclDrSdCJ47545JeqWKc4om6f2Sfl7S1ZL+WNL3STIzS5xzZ0i6t6THS3qupO8ox3m5pP/DzPY33X4AAAAAq7n11lv1nOc8Z9vNAIoqy5s6iWM2CVQkyUZCvXUuX09APE03E1rJcskRPF87S9Ni+0yS9Vci976omul98fd65was1Yc//GHd85733HYzAABbskrovD4dZ7be4HPfCuMDhdTNe7l6+NtMPstWm3AInZfTq68DM5NlmZIFwfPQRqm83JXwOQCgh7DviYNqy8rzvNr/sB8CAGBWqHYuTQLm8f43BNLTBb8DnXNK03RmfxtXP/fez1Q1j4Vq66EdeZ7rS1/6kv79v//3+sVf/EU96EEPomAJDrydCJ5L+t8l3U1FgPxqSd9pZiel2YNmM/u6pE+Wj1c6535Y0iskPV/S+c65J5vZNgrFAQAAAFjSOeeco3/4h3/YdjNwxJmZxhus8pc6p0RFpfN8C9UFw/yHPllVVHsYDzrN1nm5vbW8B0yYWVXDfrxiaKqreNvcY90CAIADaKjQeTU9Sc77KvgsLXEM3Ddc3nPaW6mY3iRe5t4P8p6bwudt+G0CAIj3/ePxcH1kWZYpTdO5QTcAAI6asN/NsqwKfEuToHgWXdgcLgYL+9MmzrnW/WwIpcdV1duGjaut/8M//INe9KIX6VnPepa+7du+bWH4HTgIduVePN8b/X1ZCJ13YWb/Q9IzVfT3fIekXxq4bQAAAAAADM6pCJBsK56xKx0Cq/C7Em455DiVCQAAsIQVK5vWWTnNnRC/r7b3uOx7X2WZxeH6gYP2lufyWSYLjzyX8ry6U8/OBO8BADvBzAYNnQMAgHbj8bhxv+uc02g0W5c5z/Op8HisS0X00Wi08E4kIeT+3ve+Vz/5kz+pvb09Pe95zyN0jkNjV84zP6z875fN7H1tA7mWT6qZ/YWkN6g4F/pvnHO7UskdAAAAAIBGTkW1822Ep9NDdGtekzZShfuoiqt7pJKSDWwz8QUZXFwAAAAOpDxfywWmVk5729ZWOd17+aga3VLMJsHwgdqkPC/ec6igXr7/6reI2dRw/D4BgKPLe1+F2dY5fQAAUPwe895PVTWvc85pb29vJvAdxps37jxpmipNUznnWu9G8vGPf1wf/OAH9ahHPUq/93u/N9Um4KDbleD5WSr6Zz7V8Fr86T5tzjT+pPzvXSVdOkyzAAAAAABHwTaCAZmodj6EXIuXo5nJmykrH+gv0WZC59J08JxTqQAA4CCJb6W9tnlIg1dTX4WVlcV7tWfesCtUSrchK4+bFUFys8nvjRAwD3+Hp6OH4oA6AOBIiaunrms/Haa9bEgOAIDDIuwTx+Nxp/1ukiSN1cbDftWv+BsuBNDD4/Of/7ze/va362tf+5pe+MIX6vTTT58q9AMcdLtyrjlUKD/e8Nrt0d/3mjONz0d/f/OqDQIAAAAAHC2bjm1sIpRS55w7VNXOpclFA/VlGcLmuZlyTQLqJqpoL2OTHUgh3LMrYSoAAIBevF/7b4tQZXtrx0u7eJwWKo8P0bYyYF6f0tS/W0IJFrWF6ucAcHTkeS4ffe/Xw2fhMUTgLPQprhqQAwDgIAsVy/vsD0P4vL4vDvvVoe4qcscdd+iKK66QmemHf/iHda97zYu8AgfTrgTPv1r+904Nr90S/X3+nGkci/4+e+UWAQAAAACwJts8LbSJjgDbwFxMSRX88Jrc5j4Ezn35fP21MDz6CR2xTpu/aGEbF2kAAAAsa2PHLbt+fLSN9g1x3BhC43MHmT+P6iLKlotkAQCHVwiWd32sIgTd2c8AAI6aOCjedz8YXww21DTr/uiP/khZlumxj32sHvawh600LWBX7Urw/NMqzl1+c8NrH4v+/p450/jO6O9/GqBNAAAAAIAjZJNh5G0En51zVWh4qOBwCHnPVBt36+5ucPJRl0YImlcVzSXlUdvi9sXVtDkx149T0ZG0idh5WI8SFwoAAAA0abrzz8YuD+xQWbz3sXZZIXylNq1q4EryVlZOr/4GABxa9YrmbX1v4bVVq59bQ58XAABHgZkpz/PqbiPLaAufS5O7mCw77b/5m7/Re9/7Xl1yySV6/OMfv9Q0gINgV4LnHy7/ew/nXP3eAm/X5O51P+Gce0B9ZOfcQyX9VPTUR4dvIgAAAADgMPMbOlFj2t6P8aHmW68e7jUdpEiKiPtAc2tpQ+1vb6ZcUlYGzhchzNxfIikpL2BYNwsXE5gp5yQqAABAs/pxUpJM/V0/iT7IcdyaQm7mvazLbc2bAnplaH2l4HqYzsDvzTqE9AEAh0PfIPmqhSHMTFmWVX8DAHAUhP1f3uX34xxJkihN05n9cQidh3B7H7fccov+83/+z3ruc5+rRz3qUTr11FNXaiOwy3YleP726O+nxC+Y2Wcl/ZWK/rA7S/qAc+4XnHP/rHz8mqT3SDpDxbnmGyT99UZaDQAAAABAT5nZ5ioRRpyK0PCq4hNZmcrAdxn6jl/LlWo94XNXTrsIj2cqlmmu7hcPxGFmTsx1t43tNsa6AgAAmGbedw9LOzcdTF955jb99waO1ZxzrcHzpaYV835tx5tG1XMAOPT8kvuRtmqrfectsZ8BABx+ZqaTJ0+uHDoPnHNK03TquXifHgLoXdxxxx267LLL9MxnPlOPfvSjdZe73GWwuw8Du2i07QaU3ibpdkl3kvRcSX9Qe/1nJH1Q0mmS7ibpl2uvh0+pSfoZM6NwGQAAAABgZ226qylxTuniwXrJNH1Cy5vJnNNIxfszSV6JkqJu9UBzdcqVKJdmqmB7M6nn+zRtP0x9ECWS5NxGKpGHdcS6AgAA6GCDJ7XN+/Yg+Ib1rSrunJPb25s8keeD/WJpVf5eAQCgLkmSpYPrkpTnee9q6wAAHET7+/uDXGgV7zedcxqNRlWF81BRPU1TjUYjJR0u4L7pppv0r/7Vv1KWZXrmM5+pM844g/0yDr2dqHhuZickvUDSf5T0VufcabXXr1VRCf2W8ilXe0jScUnPNbM3b6TRAAAAAIBDwczWHzIojbdUeWio7q2wrOqh8/j1LPq3l5NXMkgLcjnty2lss6HzqTb0mib6cM4pUXEhwya6TK2sZC+xrgAAAFq1HRtv6iT3EL9xWoLjLklm30f933leBMf7tmPTIYCyOj0AAE1WDY7ny+wLAQA4IMxsKhi+LOeckiSp7jgS73vjoHmapsX5MLOF++ebb75Z/+t//S/d7W5305ve9CadfvrpS7cPOEh2peK5zOyKBa9f5Zx7kKSfkPTdku4raU/SjZLeJellZnbj2hsKAAAAAMAStnXqJy3DwqtWVwih81zzb91rZsqieVkZPnfycj2XQjW/MLbNn0LfqudWDt+l8xCFuALAJhkVIgEAAJqVoW2XJNsLnPU4Vmu6k43leRHMXmbWS40V2US1c3EHHwDAfK7sn1pWCORJ6lSZFQCAgyLsH0+ePDlI6Hye8Hq4m4j3XnmeazQaNc77tttu09VXX603vvGN+m//7b/p9NNP51wTjoydCZ53YWa3S/qt8gEAAAAAwCDWHTQIAeqDqmvovD68U1i2TqZEiawMn8+fhi+HMEm5OVkZd+6ynvquy1zqHFTHhFNR+dxvMNxkUhGqouMWAADsMJckkvcbu/DUJLmyEttC6zqOCvNeYfqN7V80vZZK6b3mu8Q4zjkuigQA7JxQmZUCCwCAw2bV0Lk0Kc4U7yPjv8P0Q/jce68kSbS3t6ckSeS9r4YJ+9q//Mu/1FVXXaWf/dmf1TnnnNM4D+CwOlDBcwAAAAAAhrY4Bj3QfEKn1QY7nBLnisj2igGQKgjes2PPS0oUqvsVVctdGT6fjpFbtR6sHM+secgu+oTJvVmxTjgp14lzTq5cVkm5bayTmSkvq9h7cZEAAAA4ALZxTBkC2Nua97aU77vP7xTnnBSq3PWssj4VOF+ymmzV1nltJqwAAAfK1u440oDgOQDgMAl39Ah39ViGc27m0TZckCRJ9QjPh0B62O//xV/8ha677jo98pGP1OMe9zjuOIIjh+A5AAAAAODIW2fV5hCkDjZ52idZcX4hdO7V/ySa06TKewifS5LJlf9XxMzD85NwezH2MoHz0GaqD66X0/Q6XTdvprSsrh4upgAAANhF4YT0puNnoeq5ypPouxSAW2iFquW2zLjOyaXpZPy+Vql2Hs8vanu4W1Q1/XJdxq0jQAgAu2s8Husb3/iGTj/99G03RT66qIoAHADgIDMzee+VZdnK01oUOq8PK2kqdG5m1X41SRJ9/OMf1/vf/36dc845evrTn67RiAgujh6ONAEAAAAAWCPTdLB9U3EBt2K181VC57G8oSq2ScrlNFaisRLtW6LMEuWWyCuRXzJ0Xp9HVyH0ju7CVrWNAMyBClIBAICjaZsXy23y+GxRxfEux209q47Xp7/UkWEId/cZpRzPLRs8D8vJe1l5i/b47luhQq2F1/NcyvNJOJ1jYADYWd/4xjd0/fXX6+TJk9tuiqQifO7LfQ0AAAfVOqqd9xmvqT2S9OUvf1mXX3657nWve+mSSy7Rve51r6XbBxxkXG4BAAAAADiyDvMJmGV/8IdlMkToPMjNZM4p1aQC/FqrzJspk7TXsSPRl7cg5lbE3cRVNBNNKttvwlQ1SAAAgI56371n1WPCJClCwxtkZnJ5Xsx7l4VK36FyXJ4v95vDrAhnL9uMvoH3EDhfJnju/aSKeX39hOXRFDB3TvJeLkmkNK1e4zcLAOyWJEn0tre9TWma6oILLtiJSuPhgqaAfQcA4CAJofMhq50v04Ywfvj3eDzWH/zBH2hvb09PetKT9JCHPGTl9gEH1U4Ez51z1w84OTOzbxlwegAAAAAALCXXbMB6k6d5+namrSN0LhVh4bGZ9rUjHRENQrVBTsP1s6nguQ/zKi8OYD0BAIAuZo5nQ9g4qpQdKllLqkLbqwZ8nXOyJapqDyJuc5LIrbHi6VR4uxYoX7fewfGpkZdYHstWOy9D5zMXuYbq53FbohB6XA3dSVKaKhmNCKADwA66853vrL/927/V3t6ejh07pvve9769pzH0vjoE9kajXe2JAwCgWdiHmZnSNF0pfO6cW+mCMDOT915Jksh7r//3//1/9apXvUpveMMbdMEFFyw9XeAw2JWjzG9W/7ta13tTwvnhw1uuDgAAAAAwuMP4I7Jrpe8m44FPdGWaPnmWObexzogQVu40LNXOe3GSRs4p21CYKjdTwvoBAADL8L6ojB1VlK6O+5wrfg+EQHGeF68NUam0vFhu43dZOkh3dVrU1rbjvzK03ZdLErk0XT603jN4blFAwiVJsV2FYHnchgUXB5gk5bm893JpKpemUpou8QYAAOvyspe9TD/90z+tz33uc3rxi1+svb29XuPXK5QPwcyUZRnhcwDAgVK/Y8fe3p7G43Hv6Sxb6bzJeDzWZz/7Wf36r/+6rr32Wt31rncdZLrAQbb9e/xMuJ6PgKJkAAAAAICd01TtfJd5M61+08LiB3quInDe9P5toPl00TdOYjpY62zbQkdMuuFAuGkLAS4AAHCgmJksz2XjcRH+LcNcFoXPLYR/Q4g5qkytMqi+0jHHFi6aM2mpQHb3GVivYPvCgHdUeX6RuNq4eb9ceHzZCvZJ0m/csA3V5x1VNI+H67qdVdt1nsuybLWq7wCAQaVpqn/7b/+t7nGPe+jnfu7neo3r13l3kvJ4xnuv8Xgsz74DALDDwj6rvl/c29tT2vPi26GC52amW2+9Vc9+9rP1tre9TXe5y10oYgRodyqef2ePYVNJZ0p6qKQflPRIFX1pr5H0B4O3DAAAAABwaHn1Dyd3tY1YrHNOafR3V95MuZYP84YrwuNlaWatV4ib2UYqn5uZrKw02Wl49auSfpQ556rtZVNdrKHqQNjOqPEIAACaVKHkOEg+b/j4mMZMSpLiGNL7SUV0qagy3YOLxt2kqXmGsHaHyuLx8d3yM7duAe1wAUCW9asuH6a9bDt7Bufj+XYOn5cXMtSr9MVhcwvt8L7/NlKGz6duAT1gJT8AwHKSJNE3f/M360lPepLe9a536bd/+7f1f/6f/+fC8dZR6bzu5MmTGo1GMjPl5QVPfcN7AABsQthPNe0bk/K34zov2Gpyww036BWveIWe97zn6cILL6zaARx1OxE8N7N3LTHaGyT9inPumZJ+T9KPSLrRzPpdPgoAAAAAONLW0UGVtzyfbCAM0HcO3kxeyy0Hqz/6VD80Ux4F5dfFq3tA2cyqwA3BjW4S5+Sj5bZOYV1a10ATAAA4UkKY17yvqk33PT6VVATOQ/g8Gt+8L8LHfZQXQW78bi3le2huUstxW5eA+hx9jqGri1T7zi8Mv2y11h4V1mdEFdcXzqP+vqJla6HC/pJtCRdjmll1cYSSpHieY2QA2KpTTz1VD37wg3XHHXfoT/7kT/Rnf/Zn+oEf+IHW4dcdOg9VY733yrJMaZpO7vpC3xcAYIfElc7n7Rv7hs9X3d997nOf0/ve9z455/SDP/iDOuWUU9h/AqUDfwmGmf2xpKep6Gv5d865/8+WmwQAAAAAOOKaQtjOubX+CA/T73P7wFDp3Pc8yeXrjyVPlIXQ+zr5noGOeuV2zFdV2N/AvOJ12fdCBwAAcLhZHEgOFdLKcO/Uo+u0yorpVh5/WDlt61tZrWtYeWjz2rhqe+Jp15frJo7PVqlut8R77xUqaNjO4vHDtmU9f6O0zWdqOxfHxwCwTaE/7q53vasuuugiPeIRj9AVV1yhv/u7v5OvXTAV991VF76Vz7U9+ogD52He8d/hdQAAdkHYF4Zq54skSaIkSXpd+LyMW2+9Vddcc41uuOEGfdd3fZfud7/7LTUd4LA68MFzSTKzd0u6UsV5zhc7Li0BAAAAAGzJNk7buLKSYtrj53AInfeqAqni/eVmys06Bc4XtSjfQPi8V/A8qgBPcGO+uPvFafMVFlk7AABAmg6dW57L8ryoeB6C4uUjhMm7hKNngusqL3yLAltdhOOjjZ+0WuOtvwc7Rl624veS83fLXgTQNdAQhcFrM548P9At4cOFEJImF0Pw2wUAti6ExO91r3vpmc98prIs02te8xodP3586vV5jxCmi0N1fQLoIVSe5/lMuDw8F1c9BwBgW+J9Ud8Lour7yUXz6Wt/f18f+tCH9KlPfUr3uc99dOmlly51MRhwmB2K4Hnpz8r/nifpO7bZEAAAAADA7lvXyZV8zombdXZJdf2BHzrz+oTOvaRcUlYGzvu2a1FnXF62Z102EW4/ytZdzT8Wtr5NVMsHAAC7byp0nmXyWTZdpTweVlGgPM+7BdAbguqW59PzXsS5tQbBW22qkumyoedl2rfsvMxWqz6fJIvHDRc41GcdguEDhc4bRRdKECIEgO1zzunss8/W5Zdfrj/5kz/R9ddfryzLZiq0JuXxwbzgXBxIXxR4awuctw2blcdNAABsWrhQKsuyav/VV5IkGo1Gnaqf9w22X3/99Xrb296mU045Rc973vOqfTaAicP0qfhi9Pf5W2sFAAAAAODI2tapGicp6Vj1SJLGHQIJpjJsLlXVzVdp3yJ+zeHzPsxM2bYbcYCk2ly18/pFBJwgBQDg6KqOA/JcfjxuDP22jitNqqAvOi4OQfXpmU+3YY6lK22vagNVsG2FMLXP+h9xVxcO9J5Zt0r3da5ruKDllvDVMXLHW8bPVYbXVVb0t/ixvz91MQVVbAFgN4xGI73tbW/Tj/zIj+iGG25o/G7uE2Rrq3xuZsqyTHmH/U09mM4+AwCwafW7c5w4cULj8Xjp6SVJojRNG+cT7+M6F2LyXi984Qt14YUX6gUveEF14RiAaYfpU3GX6O+7ba0VAAAAAIADoakK4qqyLZyoSXpWmx4vaKMvh8nKsPmqJ5+6RmwS5zTbNTicdaxvFMI67lLdfkisUwAAjjizIoBbhqeWCSSHStRqCpfXh4teb6qEvkjnEPMQ4mOyekBt3cdra/pNVK2r/iMuP1Pn5NJ0/jKbM30rw/8rLZGwnZfbunk/CZxnWVXp32eZ/MmTRQh9U9XuAQAL3e9+99Ov/uqv6iUveYn++q//euq1vtVXJc0Ez5epXJ7neRVSX6bCLAAAq4gD4fH+azweL7VvlIr9497eXut8wr+7TP/xj3+8vuM7vkOXXHKJjh07ttFzHsBBMtp2Awb0tOjvW7fWCgAAAADoKVRXDj/Q6MQ4mLZZ7XzViuKmInBu2k4F6bRneH4Z3kzqEG53ZVsO05X6m5Jos2FwM1O+gW0HAADsnhD8DsHelaYlVdNweS41VEorBrHp172XkkSmjr/htvE7z/tivs6tvfp5q/I4fOa5vtNYpdp5mkrLBv8Xrbe2qu8hSLFKCLwMmoe/4xD7TKvCa95LWVa0O02L4DwAYCvC8cFTn/pUXXfddXrXu94lM9O3f/u3Lx2si/UNnMfiMF6WZRqNRvRJAwA2Yn9/v9oH1feH3nt573tVGQ/7L+ecTj31VGXl3bXicHsYJuzv2qqhv/jFL9YznvEM/ct/+S913nnntd5tBMAhOY/qnPthSc+LnvrAttoCAAAAAH2E0Hn1X20n+HsUhaD1EExqDXWvU+JcETyf0+llZeXyXNPbVmhzVv73MIfOY03v0Dmn1DmNnNNIk8rddCYuFpbTJqud+/IBAACOpipsHsLIAx7DVsHdRfOOh93h3287+9uyQ7umKsQvu57zvPuFAfG86/OfN/16u+LK7MuGCkOF/bC9lfPpugSqSutZVlRG39XtAACOiFNOOUU/+qM/qizL9N73vlef/OQnl55WqNa6anA9rvoawnfsLwAA63bixInqbh1NFc7D/sh7X92do00cCk+SpPp7NBpVwfX4PE/4dxg3HibPc73zne/Uddddp+c85zm6973vXYXUOU8ENNuJ4Llz7r49H9/inHu0c+5/d869RdIVklIV/XwfNrOPbfcdAQAAAMBiVgsDm1kVqKSjfzOGWs6LQttuTeHqRPOrnYdtamo7K/8dVzhf98mltnD8NkLncTdmUgbOU02WJR2Jy9noeqxtq56TowAAHC1lFWjL8+KRZcVjPJZlWRHYXSEQbnFwuGX+9fD5oqrW1cntZatu77p4WXdY7pb3vGx3ycr21Rih6nsfzs1fX02VzuN29giKz0yjnLaV2/Iy06l+54VpcLwMAFt1z3veU0972tPkvdcVV1yh48eP9xrfzJTnufI8r0J6q/RfhcBdMEQFdgAA2sQXToV9WfzvpuHnXWwVh87r+8N6qDyeZpM8z3XjjTfq937v9/Tc5z5X5513nk455RTOEwELjBYPshGf1WqF5sIn/euS/vXKrQEAAACAFr7WMRF3O/TphKhC5g0BysQ5eUlJdPs37C5Ttx+0Q6/JRdXOQ0W8eDsLba1vd+vmykc812TDoXNpckvFqkK3JuuFz9owwja57mBLPHWvohoBAAA4vCwO82ZZEfSOAugKAVvnZOHEczjJnCS9Q8dmJud9MW7Ta6E9zhUVtctw8MJg+YaPOZ1zG63IbmaLf/eYybzvvqyWvchwiPfd1sbyPczMLzyWPR6OQufzwuJO6rwtWRROXKb6OwBgOA9/+MP1hS98QX/5l3+pK664Qs973vM6jdcWvFvmO70tqOe9n6oYCwDA0MbjcbVPiwtyhb+bguIWnSdtC5k3iauch/m1DXvLLbfoiiuu0AMf+EBdcsklM5XRATTbleB5sMqn9e8kPdfMPjJUYwAAAAAgFsLipiisGv1dP7E9LxDsJeUtJ5F93JFSBtExrCFDsV2D3IMHz1ueD+8tbGNVZXMN+777mPqcqKw0vsn5R5+h0JaUz9WgnHPF91X57541LJdiUhXsAQAAh1d1DOu9LMvkx+Mq4Guh2ngIM5fHBeZcUZk8SSSzIuTcs+r13PB5HJyOw+dzTk6Hi/PqF2SuTZJsttJ1FLge9Ohs2cr1cTXYZaqdt43TVn09DsivMXQe2tcaim+atPdSnsulKeFzANiyb/u2b9Ntt92m17zmNXr0ox+tiy66aO7wodJ568VIHSq5xsOGCrBN8jwnaAcAGFwIm2dZ1nqHjfB80wVQYV8YVzHvcufaeLg8z5WmafV88JWvfEUf+tCH9P73v1+veMUrGocB0GyXgud9PrFjSbdL+rykj0j6E0lvMbNNnNcEAAAAcESFrvtQSTrmQvXp6NHW2T8vdF7Ny0y5JmHOMA/slqZq501rqYyiVK+tGv2otreWk0smaRxC5zt2S3W3wdB5dbKsfITTanEIn8/VcBKVIa8NbHO+DDalKtdlCDmxPgEAOFRmQucnT849vp0K/objkhAeTxK5NB0+fB5Pr8txSZJUFdIPtPj3RwibN73nFS8UDBcW9JpG1DaX9vv1UVXJbwrlRRc8ND1fzbfv++0TOm9r86JZ5Pns9goA2LizzjpLT3ziE3XrrbfqV37lV/SKV7xCZ5xxRuvwWZZVf7eF7Ky2T64L46RpunCf4b2n6jkAYDBhv3TixAlJmqp2Xhfu7pGmaeNFUuH1vb29Xm1wzmk0mkRkzUxJkujEiRN6//vfr7e85S36v/6v/0tnnXVWr+kCR91OBM/NbNN31wYAAACApbSd/q2H0ecGXvvMz0xjSXuEKgc1VMjDa7rauZMpkZVB88mcnJxGcjI5eTm5aIhlxNGJeuA8l7TfUjVi25xzG+2ISNReGR7Dq1e236T4ThQAAOBwqI5z81w+y2Rzqn22TKD6DWVRKHjw8LkkJUlxfO99FXRu/f22jd904f33qJLdOI0hmtLnt0pbdfE+81gibN1aJb0pdC4VYfE8712JvJpmnk+m06Fa7cw8Or5HKyvZUvUcALbrPve5j5797Gfrhhtu0Cte8Qr95E/+ZGuILk1T+eiuLk3iKuVWHv/E+5N5Vc7rwrzohwYADCXPJ3WER6ORsiyb+7sn3Omj6UIo51wVXu+zf2uaxzve8Q598IMf1MUXX6xLLrlkqekARxnnXwEAAACgo1zdq0eHDn5vptxMYzPtm+mkmbKGiumLjHesavVhsOoSDeMXFxaYRsqVKpcr6oxPzcGVYzh5peVwRUB9OfXxvKRMxXayq6HzRJu5+t05p9Q57TnX2ulh5WcS65FISjdwcjJegxnrEwCAw6cM5PrxeBLMbeNca2VrC2HhMhxsWVZUQe9x/GBmUksbzPup1xa2dVP6HI91HXaZEHgcHi8r1/cYuVhX/WbYb/hIa+BckvK8MXRu4fllqomH0Hm5fS19t6oQOFzQBvO+X/AfALA2Z511lv6f/+f/0R/90R/pgx/8oE6ePNk4XFuV8zZh+PDY29vrHcrL83wqJAgAwLLMbGYfNxqNNBqN5u6fvPfKsmzqzh+SqnHMTHmeLwyxt/nzP/9z/eEf/qHyPNfzn//83uMDIHgOAAAAABuTS1UQPTNTpiIsnEtVVHmerBx/6ZPRqPS5iKCNl2Tmlah49KxlX43Xp/a5c06j8mSTVxE0zyTl0Xa1i9Ky3cmawsghbD4qK6r36ezg8zQcV4b9E+c2Vn2c2AwAAIdTCItbls0PyjpXhG3Lv+dOM4TPQ3XqvuFztYfKZ4LpLcNV4bE03fzdWpasHt46uXnrZYj5ONc/JN1WkbzPbJNktqJ4Syi8Co2H1/r83olC5+Z9p1+FTW2bueAihPXLCyIsz4vtMXqud5gfADCYOEh+yimn6C//8i/1zGc+U1/84hcbh5/Xb9X2WpIkVahvFZ79BQBgBVmW6cSJE42vOeeUpqnSlgvIAzNTlmWt+6Twep8Lpm655Rb99//+3/Xt3/7t+oVf+AXu8AEsaSeC5865+5aPu68wjbPDdIZsGwAAAABIq4dTs4ZpVLc+VRGezDUJojeF0c1s8jxh2a0qln4cHG9fH641hGtyMiXlo9N8zarq5r723C6eCkpC4FxFlfahu++SWti87/R3cZkddJvsog3bfvVv8d0IAMChESpj96y26RZU9JwKn4cgbt/jhw7hcyuH25nq0qGSd/leD8SJ9VAFvO84EZem/auQS9PjtG0fIXTu/VKVzqttr+M20lqNPVqvIVwe9zVIk+Pk6oKOMpDOsTMAbEfYDzvndMYZZ+i3fuu39F//63/VNddcMzWcL7/T2zS9liRJFeTrW+m8Pm2C5wCAZfnod8k84WKpRcJ+yTmnJEmUJMnU79pQIb2Lyy67TD/0Qz+kpz71qTr11FM7jQNg1k4EzyV9VtINkl65wjReXk7j+gHaAwAAAABTqmrjS4y7qLp2dQI4fmgSRo+D6KFiOuHz7bIycO7Kf80zP3Bt5XTmh8/DthAi7mE78Tt8MUISBc6DIeItrqycPmqYfl+cPluPsM2vq8J9G24CDQDA4WChEnTHKtCKKod2CgCXYd8qgJtlvSpAm9Q6vEXVr62cV1P43DknJclmq57HAeVtBM/73r2r6/qPpj8TPO8RuAvrZGbZNLUjPBcCgW2h8CbRxQ+93mPD9F2STALw0oJLomviz8GO/ZYEgMMsDp2H/373d3+3zjvvPL33ve/Vxz/+cUlaGNarvxYqx8ZBvLi6el9h/l1DfAAAxPI877wPcc5pNBrNXDAVQubx/iy+KCt+Pfx7kZe97GW63/3up8c//vE699xz+7wlADW7EjwPVu3pWkcBNQAAAABHWNXJHgV8w6PLqdkQFu81T02H0UPgPcw3l5SZFdXRw+t9T+IfYasup3BK31n30/rzf6hOwudu6tnJ+g7bm9Ok/RY9v2vrvil0vooQNg/THaqCerjIY9eW30EWd+5uqtOpquTIegQA4HAIodw++/Zwstm5xVXPQ0A5Dv+WQfeubF6l6qjtYV6Nxyl9wsrLqgXO5oXmO1m0TsLrTcP1qO5dDN7z2K7pQvE+yzcsq3icEGqI2xLWZ/maawqrt7QvbBth++76DpuqnbvyQoIq+N5TuDBialsFAGxECNCFffTd7nY3ff/3f7+++tWv6sMf/rD+4R/+oVMRkyBUfq1Xfw3zWla9UAoAAF3kea48z3vdOaNeybz+CLz31aMeQJ93p488z3XNNdfowx/+sL73e79X97vf/bS3t7f8mwSwc8FzAMAcjdVQ5zwOg6lbgMYPa3scrvcPANgNcbg3rji+KIDuVQTDhxCH0H0ZhB+X/43b4Q/pMcFu8b1C591MwufxRQbxhQv100S7Gjp3A4bOQ+DcqejASKVBA+3SsGsRBafyJOqG5me1v3ftMwEAAPqZVwl6bngqqnq+KGQV+hurYHEIn/c4jmgLn1dB5aawctf3MpSGebQeKy1qz6Lflw0Vx2fG7xN67zNs27z7LuM44N3U3rDOw0ULfeYRLkIIofOO21pVib1xktYYSu+qOnYu3wvH0QCwWXGY7lGPepQe8pCH6POf/7ze8Y536Pbbb28cJ+7vDePPC9sNcbzRJzgIADjawt0yltl3xPu0+sVUUxdUm1XB9i6/Ybz3+trXvqbXv/71uuiii/TIRz5SZ5xxxmZ+kwOH2GEKno/K/3KvHwC9HYQw9/S8y6BTfBLD/NQjDmEfVFU1T5/LfDb9yLPiVrh5Xr6el6H04m8ZtwjdlL4XRHRdL7v4OQRw9ITvnKbweBxCjytSB0OGzue1L5c0LtsRqrLX28N36bSV373lvabSvevKJOXVtuOjdeVUhK6D0IJdW5fOOaUa4HZm4cSZivc9xDTb5EbV86GFCwWk9QeqrPz+q/691rkBAICNmHeCel4183DhWxnU7XIcEleutiHD56HqeTimj/+uN7vz3HZbVUFb6hcwb5xYz9+tNls9vM9yrbabeHp5PjOPEBwP769z6Dser0/oXJrd5qNtdl4ovQ/C5wCweeE4JQ6f/9AP/ZDufOc7681vfrM+/OEPT70ehqmHzkej0Vr7XkK4L543AAB14dxjn0B4kyRJtLe3pzRNZ0LocRjdOVecp41+e7aF3b/xjW/ofe97n6655hq94AUv0LFjxwidAwMYLR7kwDiv/O8/bbUVAA6c2QMei/7TEDKbc83OWg9OzGTWo8P+kB0oxScuas/OrCbnOt7eFCuLLw6Y3v7d1H/qp3qqEyNa1FE1/Xksbl+bTo0PALsgBMClSWeHaTMBgjAvL2kvmncQqg7HjzCeasOhi+FPsFQVzsN+VV7ztp7xjp7kSQaqdB4C55u8Un5Tn9ejwjlX3hWguGhgkxUC8vI4kfUJAMAhVYasWvuT0lTK80n4vMMJbzOT815Kkips7NK0c/9iPP7U897LJUU/pZkVx0dprV8rSYrA77qO8Vveg4VltCk9g+S92heC/TVu1OMUaJJU66qaZsN8iiIofmq8Lu2zPJfyvNevycbQuffTF1Ykw/WDV9sofQMAsDH18JyZ6cd//Md11lln6SUveYke97jH6dRTT50KnkvFd3aaphvtzx2Px9rb2+P8HABgRvitNx6Ptb+/v9J+Ig6bh2B5HCg3s5kAeizPc6VpOvXv66+/Xpdddpn+5m/+RqPyd2J93wqgv0MRPHfOXSrpYSrOFX9qq40BcPD0qIw9feBhtXC6KwIrHJysj/koFORCKZpttgiR2ar8as3muSRtHicEzIsXB28jACyjXsl84fBmVYh40wHasZn2asci9SC61BxGb6y8d0iPa3ItX6HHbJgIbXn/GnnZzC4vVa4s+rkeAt3SbofO08WDFZXbnZu5G0D4rMQXR2xSCCuLk2eD29TSrF88wIlQAAAOsQWBcleGuS1Uo+5wDG1mUp7LpWlV9dylafdq0iH8XP895n0VIDZJLs+LcHxoq3NFO9d5nN80/Q0fJ1VV37sNLMvzKji+sKq4983VzvtUAg/zKJfVTMX2PG9+D4uWYwir16unL2qONB0qDxXJy+eWDZ27BeOEiyU4zwEAmxcCcGeddZYuvfRSHT9+XD/zMz+jl73sZVPDxWG6TfPeKxngThsAgMNnPB7r5MmTVbXzULG8jxA4D5xz1XSybHJuLuyPwoVb9d8u8XPvfe979fu///t66Utfqjvf+c7VfPi9A6xu48Fz59wfzHn5ogWvT01K0mmSHijpEdHzb1+yaQAwRxGINTPJhUjDjlcJPWxlG0OnfhWes6lVMFn+rlxH2JxVlrf1O/FUze8wbdwAdl1e7nu6BJVN06HmUAk91+aqOI/NNFpQ7TcOo8fHME2V0TEsk6rQdde9X3UxgzZbBbyrNArGdxFvW0NVSR8CRxjDarvIYF2y6MIb1iUAAIdcGRBuu4B2qtp5OBndMfhreV4FbyUVFaA7BHxNmlQ9bwqfh2rnKsLnFlcpXVTFfV1WqW7dt60huL3MtOe1seFC607jxYPFYQjv20Pn0cUOVfB7nnJafUPn5QymQufmfXEhRH2YJSwKnwMAtu+8887TU57yFH3iE5/Qy1/+cv3ET/zETgS+8zynQiwAYEq4Y0eoTB5+M+V5Lu99rzt0tF1c5ZzT3t6exuPx1HzDa/ULo8K/r7zySr3xjW/Uve51L33nd36nJO3E/hQ4LLZR8fy5aj6/7iSdK+nHV5j21yT9zgrjA8A0i6qlLOyH37V4Q6ijuUttWs28UxOTExdWpLIkuUSSG/7K/+LkQ7ls6WBZLXcu9T9RxS1fAWxIHM72c4csh9f8StpmJu+cvNYf8M6lziH3qfY6N/W17sym2ui0gxfb7bikXF4hOF7d2aOHybjFf3fp1mWLLnJoEgLJu9a9F5bx9upGHT6Jiu+jTQbQJdYlAABHQlnVvLVfKYTPy991VSXzDkKV8nBHvpnq023jaU74XJNq523h843f/a+lrYNZ1IfX9HpTtfF5s+gxbCvnijB2U3GMhtB5PF5zo2zyPpYInU+F2vO82F7iqn9J0q+aezxevL21Wfd2AQBYyDmnc845R5dddpl+9Ed/VI9//ON1wQUXaG9vr/e0hr6wLS/3jdusvA4A2C1ZlunkyZMz+xwzU17+JhqN5p/Z6rJf2dvbmwm4S83h84985CO65pprdP/731//7t/9u5lq6gBWt61PVFvOov58n8c1kr7XzG7cyDsAcCSEkwtFh3OHysybPjlwJIRK53NOJM0fc9jWhG0hzMG8zOcy89XVnEPNx8oTFOb7naCwUE1n4DbNmeOap1+b2xY/Z9V6qdY7n3ngsAvfwF0+7/NC50F15X9ZwTpUQx/gVPnMfML0+4439SjblscPM+Xle9jMfmY4m2xvteyq9R2WV7/phNB5vF6WqJe3FmmP0LlzTolzSp3TaAdD59L0nQqwuvgilU2t7+pSVNYhAABHQwgMz3l9atgeASkzK0K/ZcVqy/NOfZMmNQfiw4XN5Un30O/cdFvwTVnbMVPTe+9T7bxrmLxlmk6SlgnD1dsZ1llDqKE1+B2Hzr3v3WvqQqXzEHaPn9Py1crDNLqMX50TAQBs1Wg00jnnnKNf/MVf1GWXXabbbrttqekMvb+n/wwAEHjvlWWZxuPx3KJcZqYsy1qH6fObOATIwx044n+HaWVZple/+tW6853vrOc///k666yzpkLnFNgChrGNQmm/3PDcL6noy7hO0h92nI6XdIekmyR92MyuHaZ5AI4eV/blrvYDuThJMFCThkJl6OGF7aR2G9diKRdV0C2qNN/noDWE2qfK7Gu6s3/R9KppTEacaVPfdi1ode/PzvS8t3Qr4Z6mKuqXy7Vasrba8pyss/7bDID1MnU/PugSOp+Zvk2+511ZaXzIKuhWHgeE6ufLTHOmOoEm31NxFfRQBXDqG/6Ifp9Z7ZEo2iX3FIfOp54v1+2y63UIXSuWu2i4dVX4H9JuH5EcTIlzxTa7AV6TKufhO/yofhcBAHCghZBsrf+tcTgVYdzWytflxZLh99HcYWOhUnoI/5bTcGm6sNq0qbh7VNzG+jQVKmyXw4e7Kq6rj6z6zVmfx7LVrVuW4XQPZHhy0p+6qAK6lSHvTsdwc6rdz70gYWrQ8m6WTdNqq3RezqNx+Dh0vkyfaZhuHIIv30sVGl/m+DYEMZYdHwCwUWF/PRqN9IQnPEEXXnihXv/61+tpT3uazj333M7TqVeEHYovjwNCO6l+DgBHT1UoKc+rquaLhs/zvAqLx0Hxvn349fHr/7388st1xhln6PGPf7we9KAHTb0OYDgbD56b2Uzw3Dn3S+Wfn2l6HQDWas5tTKtbqvbQ1DFe/1G/7EFN35MPOxmG72uF/pD1vPXmoLWZV9hiXLXluKmA3uw48XQmgebZ18rne5xwqU87bpMaNvmNHmjPzGuZT9pmgkSNgfPZhbfa9C2cqCvWcTx5fgAB2xN/1jcRlwzVxV0ZiogDuqt8E4RwRePJ/1WmqclyiYPocZutDKOrNty2rWt9xmFzP/1lvvT0mkLngY/2g5tcsmEbXRTjiLflJHpOKt7TJsPIfYTlzumy4SQqluly63ze8LNbvjdTGrYzsS4BADioXJpOgreLjh+ik8utAWHnigB5CJ/36GM1syp8Hs9rYdXo+PdQva/Y+6mLj6thkqSqiD6oKMxc7wuv/27rau7Si+fjXBHE7rK84/Vdq1bfNOzcddj1d1gUUK8H8q2seD892ZZ1v2LofGo6UVtcfZtbstp5VaV9R36XAwAWC/uAU089Vc9//vP10pe+VOedd55OOeUUnX322QvHX+edH7338t5X1WPDvmZX+n8BAOsXh859x7tWxfumpvB4H23jfPSjH9XVV1+tpz/96br44ovZNwFrtI2K502uUtFP9bFtNwQAprhETst1FDcGh6ugVtL/AGdOQP4wM+t4a9UZw1dvadwOymrVZiaXpLXhysC3m427VMNE48+ft5drmM5s29qC8fUAdU3Z9v4V2pfhZv+5Y5t2p8C5wsm5SVh0iTlNz68KEboyhD6ZJz+KgM2z2n/nSSXZANXpQgBdmg2hL/stYGbKiwmupeJ0PYhezMpNhdBDVfS6w/Dd1ho4X3GaWYdp5WWAe1OVz8N6bToiiddlCJs7ta/jEEbeNWZWfZYPw/a5C8JSTNTne3LyybKpCzRd+f/t344hwBXWJQAAOICiys4Lq55LVWjbzQv8JolcnhfHB4uGrQnhc4Vq6XlehONXCJ9XoewwTHRR6Y51k/U2E2YPIYRFx2ZdLjQIgy4KNqzSx+l9UXm9aR4hwD07kdVD52E61azcpFL5kqFx59xkW+XYGAAOJOecHvrQh+pxj3uc3vOe98jMdMkll+hOd7pT6zjrDJ2bWRU8d85pNBopyzLt7e3RnwYAR0TYF2RZpvF43Hv8cEeOJEmqi5iG8E//9E967Wtfq8c85jF6+MMfrrve9a6DTRvArJ0InpvZpdtuA46eoSpQ47Do3qW/uGKoTfeP14LFxba2bN3Rw3DqYTld3/V01ZnVP9f1yuGTSksNnTahmk+3KXe7rW9De1b+vrIQ4Im3yU1F1uJmVKdRlhl5LSdLpgPn6+uYq+bVMA8rt6+iAHrYnl3tOwTAunkVoV5F/11kJCkbIHwexCF0SSsHjMP7GEWh8HWptz1IamH6tmW1zu+6ob7ZvcpIbMs1XavIekzQl+HaRIurkK+qKXQer6uwfSYd1t8u781C5HmX23hQhGqirvNFtEWd8vhYtZpWeD1cFFjNI2z9k0rn8brjpCcAAAdPFbQNvyu69KF1qRiepsXrIdSu7oUVzKwKnFs5j17h86aq51IRZo+C7UqSbu+3j5Zjsarye9dq5D2Fyu7F6M3LIZ7+VEhu3nIdsL/OwkUEQQid96k8H0LqK4bOZwpQhADGEKHzAcMcAIDNCfvqJEn0Yz/2Y/rxH/9xXXnllZKkf/7P/3njOFbfpw4oBM6n7hZa9rvQ/wIAR0OodD4ej5WvcMeuMJ2wn1t1X5Jlmd7ylrfoK1/5ip72tKfpHve4x9JtA9DNoetpcPPKwQJquMLXvGQrVqHAgTbkj+A4lCzzMp/LbPYH+ExHOhbYznKaDiF7WZ41rtPJ8E23Xh1yV7toOcyrADW9TdZP0FTVavtuk30H93nxvbui5Svhd5i2L26j231ZrDecXtyOuHx0qI4PYHWrfM5G6ha6XYY3U2amTCH0vJysS8XANfFmysv3MS7fS67Z9xOfIBn6ey9fapomKZdZJi/Tvt9X5veV+0y55fLyylVUlg/vJzziiuhdjM2U9/ypbmG59hqrn9S55krnKsLoo3KYTqHzHT8JFe4SwD53OF22aJOX2eRYtbg00pTKa6RcqbLokStVrkResuKzGWro59F3XJ/PHgAA2DFpKpemckmPO0eGoO28QcLrSwZxLc+LgHGey7KsU0jczBqHC2HlqWHWfawcT9+54uS+n+0rHEzX6XZtg80v5uE6bAOtw4b12hKecOHCgFgInZeBid5aLggIYXO3aui8qc0AgAPFlfvrNE31mte8Rueee67e+MY3TlWYDcPEF3wNLQQM6+e8Q+gwy7K1zRsAsDtCtfM8z1cKnsfTy7JsZh/TR57nuummm/SiF71IL33pS3WPe9xj589DAYfBTvQ2OOdeNNB0zpf010NMC4ecxQHMySlpfgihu5ZtxaxTaJUtrZ9+lcGHPYCM12f3mvhduLI6Y5+2zF8OjSeJFmyTVTh+E99/Ubh9EkA/+HEgK/cp/Ufsd0vnEEJvu/ABwHBW/WZKVVQVX1cAvQoZR8Htvu3NzLS+S3i6M7PGMPoqwfphFYHW3DJl5jU2r3yqYSYnL2e5EsvkLJOZV16+J5Mm66p8f+PqfU7WX65inZws10su1zuwLpUddEO99ciorKgeOOeUOqe9Mox+WLvvTJwsG0qicrvR7IUHJslbLlnxbVYEzn0ZLM/lGr8RTE5WDZcqjD/d0R0uIgAAAAdUkhTh845BYklV+HzeSWaXpsUxbBls76uqbh3C5x1OtoeK6W3TiodZpk2DWMOxb6dQe+jzCn2fq/yW7lrdu35Bcpj/vLbG7TKTZVlVHb33kmu5GCGudB4q4vflkkRuNCoq/BO2AIBD56d+6qf0iEc8Qj/90z+tJEmqKrFx8DwIzyVJojRNpx5Jx4v7vPdTIfc6M6teH4/H9KUBwCGX57n29/erC46GEvY3y0z3H//xH/WsZz1Lf/7nf67TTjtt0HYBaLcTwXNJ/8k59w7n3H2WnYBz7gWSPiTp4uGahcNiEtgrgo5WC5l3vp0kjqb6D/WmKplmZRi0Y4Rr2aAq/cQ7z6xHRGygjv/GTpy+26TUf5s0m1tjfXZwX/v3JEy9S5atJLSxzjR2V8Daxd/ky37kiuBkUQF6nargttQ7sF0EnHePVeHr4j1lKkLZefleN3PXGC9fhs0zefly3oFzIzUdmLmyOnNSxvpd41CF4ieIVZXsxzLlJu1bMhVWDxcY1B959IgrrHsz7Q94YcHIueJ9lBdTjJwrKvtXy6L5xNZBR2B5PeLPhCl8Z/nyUotQ4bysZN7jG9iV47kyfL69+zoAAIChFMUSyhDuEuFzLQpTJUkV7F02fG55Pvlvli08x2BSc0i9JYS8dtHymQp+DyEsiy6h7K6/77r2Iy76bVLOr1rv4TfmnGIyLg5xR+1oq46+cP5hPlFbp0LnzhXB8Z5cebHGKlXOXdfwPgBg45xzut/97qcnPvGJOu+88/Sbv/mbU31zoTJ6Wl6EFwfTm6Y173XvvbIsk+94fBDfWZnwOQAcLuEio5MnT2p/f7/zvqHrtOP9Rp7nvS5k+uAHP6gXvvCFesxjHqOHPOQhh/J8FbCrdqnn4BJJH3POPavPSM65s51zfyrppZKOraVlOLCsDF6qfFQV6xp2UPGPIeyucAHB4BV/3WzlvakXZxtSf6L3xQub3OaO3HYdTkwNManqBET3270WTXCzFy0Moq3Dpjl83mvKy2yTXSt2+7KCZb3Ke3TXifBd3T1ktK7OqyXXWcfg/iqdbs45uYQfSsA6hTtchP+uEjzdVPhcmny3hMB2CCPP+7YZ4j2uS3VxUnhIU2H0XKqqivuO36vdv3u9MsvlVeyXnPmpqsrFw5SWhxuza9eUlJWYu6z5Yl2ZzFwZv22YYn15lO/bR8smPML63y8D6E1B9XpY3WqPYFRWNk/LStWhuvlQYfN01zsA+Y04iLCOQ9XzyefZitB5efwXPjfLX/ITPnfhgvOJrt8TAABgt8THistUPp8bPg+vl38vXWW8DJ5X/+0SPq+dpK/OWXg/eX0Xj11Cm3q0rcuQ9Urjrb8QhlgmYVmH/uMQ+u96W/dQod6aK9j3Gr++bZb/Xjb4PUToPG4HAGA3jUYjXXjhhfre7/1efeQjH9H73ve+mX1YCJV3EYYNx0xmpjzP5cPdoDvuf/Nyv+jnXMgFADh44v1ClmU6efJk9Z2/jPjCp/AIz4d9Uah+vmg+n/70p/WBD3xAZ511ln72Z39We3t7S7cLQH+7EjwPvWxnSvoj59yrnXN3WjSSc+6pkj4u6anR0x8fvnk4iCaB86gq4qIfOctUoMbGhHVYrctBK17Oq0fZZXQ39YN8HdvSTodydkw4YTTg1HqGsafHbZxqfCKhj7Z21J+fezFF1IZNbFdT38dtnw03/V29pU6pKkjXc7ze30PmV3iLfBcA6zRzadkA30ebCp9LUQC9DKGHIHJblHOXw+exxtC1amFrm62KXl9/HeLpslrgPJGVcfDpRyIplSlxVg4zPZ3iufnh87yc4yR0vkSlxYZQelwJP28IqjeF1ev/Tsr9YRxIj6uqx4+m+Xex63u0g/DZOEicJtujSZJZFTp3K4fOA1NiXlZt1QV+6QMAsBviu99VjzyfVA4Pj/iYMoTCy//2Coh3CZ9Hj6Uqn4d+6hA+71L5PISdG6ZThc+H1vWiz659j12m06WfL7znmb7NWt9ph/5Ct2gdhuVu0e+3sD3Om264O2I5rpXh8V5LJJq3pCIcHl9YEW3j9de6GCp0vr5iKgCAIR07dkwPechD9IxnPEMveclLdPz48aniUsv0aTvn5L3vHTgPQjDRzKrpAAAOtup8U/m9Hqqd53lefed3Vb/QqX7Hjvi5eJym4STpjjvu0Ac+8AHdcMMNet7znqcHPOABVDsHNmxXguffIelz5d9O0r+U9FHn3BObBnbOfZNz7nck/amke2hyvvq3JD12vU3FgWH9f1hR9Xx3TdaNr/5tUXXk7d+2azq43rktW7zYYRIM2s0rzydtWrIy9ABtWDocLuve6noF8EVTrgfoWr+3ulxM0TBMj6rdHQestrFqnA4n36xL5fO1brfL/ShZtFym11e/9rf94AIwrP6fzm4SFUHeTYrDxvXq1k3DHbSAbb3yd157n+G9xpWOF9UeNOWS5WXV5Hot8OlhJcnJRWH0esjcqtB6k/IoVnGl86G3vbgKvkXPtQXVwzCJpjsL6kH1+qMeXPdSdRFA2yNu4y7zO96+g6C6k4Smv4OKI1Er7w4x5LevyVnxWY7nDwAAtmem2EAI4cbBprgSdBzwNZsEYctwrQuVyrvoED6vijMs2ecSh8/DY9nw+VT176Ern3fp65wzv97nWroMv6hfPwqeL5zavCIb8fKOqp2Hix7mtjVJqu02DNuvSIlNj1Nr51TYe4lCIc651UPn8foKYfzahSC7/tsNAI6au9zlLvqe7/kenXbaaXrDG96gkydPLv1dHUKFq37nh2nEAXYAwMEWvtOzLFOWZdPnlTp+37cFyJsC5SGcHv7b9EiSRO973/v0uc99ThdeeKGe9KQndb7TB4Dh7MSnzszeJ+lhkl4TPf3Nkt7pnPs151x1/0Tn3MWSPiLpX2uSmPuipO81s581s/2NNRy7rUO130b8ANopi8Kqk854Pzt8X6tmwZx6dwpv42KH+jI1P1l2O6lvs1z1P1sTKjiudR5dpr8T2+TsSab4opE2xff3/LYvE97ubOlw6Jo/Rz0vVADQTwjpxt+BbVfSLyNVWUV6CxePVOHyKJxd/8YKAfWDLH6fuU1qHsfB6PZvai9ZJrN87lDTisisnBrD53GF9NqclJsUVzqv10wfSlwhvuldVR11zimRNHJOp0R3/2gLqdcfedNDqh5NgfV6GLnpsSt29lh5h1XbjCYXI0hx4Nyiv4f+9okvl5huEwAA2Kypvt04yKrp473qGLApiB7C51IRIi+rQne+a9288HkI/UbDLfs+q1BylnUPnzf1eYfbia8hdD71d3159AiLd7Ew0K3ovS6aX6e+0I6h7XIdV6HzuZOcDagvFTr3vnEbq7bp8PwmQ+fxRSBRn3AInNcvBKle55gaAHbG6aefrv/4H/+jXvva1+pTn/rUVOXzrkKlcu/9IP3WQwTYAQDbF4Llobr5eDxufb1r+Dz+uylYHiwKnX/hC1/QX/3VX+nMM8/UD//wD1O0D9iSnUkvmdnXzezHJT1b0m3l06mkfy/pr51zD3bO/byk90k6X5M02uslPczM3r7pNmPX9a8MIZWBSB2tE9K7+uNvclLCy3xerZvWYW35DtDFByJdtqWi5mV/617uDcvEbGqZmp9UA9w189b75tqw4ASJnw6oFXnvxduCazvJs7hB8/8dtWSr26S1V9SvXzQyY8sB62WrW3X7Pt29zxmAQv3T6SSNVAbGNfl+XyWInmrbl0dpEhLWJBAchOcPyzdVvSL6vvdT7zu8T5OXt/FSxx3F3jaVKWkM0Dr5sppzoQqdSypqqycbWd6+Yd2GoE5aPkbOaTTgPOeF1afC6WbKokccWG8Lr28ynL79o9GDJT4W8pKysM41Oa5Oos9Ksqb7LZjlMpt0hh+W7zUAAA6Sqn8kBM6LJ9sf8bjhEfqQvJ+Ea9NUbjTqF9SdFyp3TkrTyd8rnLSeCp9n2UxV88bhm4Ypw/Zqe30ZXd/XvH6tHv3uC0PlYd0uEradBVzbugsXPYRlGsLVi9pXbjPVRQR9K7fGgfUwvThsIU22u1VC52m6eOCGtqlcBlPtiwdR9JsuDqJrN88jAMBRlKapLrjgAj3jGc/Q7/7u7+q6666bCQa2CYHBUL02WLUAS1zgKs9z9hkAcADFofL47zZhf+Ibft+FsHj896L9TJIkrcOdOHFCL3vZy3S/+91P3/Vd36WzzjqL4DmwJUOeUx6Emb3OOffXkl4t6RIVfS+PlvS35SDh2+J2Sf+Hmb16863EQeCcW+GHjGn7kaDNmCyjsvKGFu/kN96m6sSDzQ2jVtWPTXKu6Kg1s07vp5iHymVgxfgxV0SKprephu3EqXeiwbyXS9LObV1JGQRueVFHZbvvqst3SBXa977a7lRuK05ecos7/vt+X5kV85pcRzDnBI1zjdukS0KEsmH6nbfJRW22hYMUn73i5I1LkqW+t9f22SnDeEN1ilWdbSucLNz29zNw2MWhyFjTPRiqEIRz8j2/J1IVn+d8y53uocqgVLz3UPE6VMdOy38fNvH7Lp+RLJPKauWL77kxy0lKXKLMXFXFuamCeS6nzBTVet4sM1PuXBUyj9fvLq3v+HPY51Pi6iGOHn/Pk5spaTheY788Kw6c5w1/B8VnZlOBFZOZlEnaY50BALAxVZ9u3L8bKimH5xVuXlhWHS8rmTcFcE2qAtjVcViaVuHsTv0tZVC3LXAcXnNpKq0QlCr61pJqPqGqdevwZnJxsD6aRvX3gmkMJV7Og0zP+2J5Nr7YMcC+aiOiwHiffjnn3HTovM88y9B5Na2wXYdbyktVYNyF7biHpUPnbQH6DttW+ByH6uwbOZ8BAFjIOacXvOAFuvjii3X8+HH9q3/1r/TYxz527jje+9bqtOG7fZnjoCRJlEb7JjNTlmUajUZT0wYAHAzhe7zrRU2h+nmaplMVyqXuFzaF3xkhQ+O9r6YhSf/jf/wPff3rX9e/+Bf/Qg972MOWe2MABrFzwXNJMrMvOOe+S9K/k/RiTZ/7N0nvlfRjZvbZ7bQQB0fRWd035BdCl0dGXJHYfPttRzfapDz+R+9OTDNf5tQ7rkezsiJeqHifN4xbS5UvzGn3CXJ3HdaVgeMe23RxtqD4c04nweRkxi796F/mtEL/6jBdFEHs+u1ha7G1sN2lfSNrq7R3XWGdRdvkokR5uHXygm01uhjC8iJQ79IehydrvV5iiStJJK2jUUXFpl2JAwKQpn+gpKEzvv6Ys99NJGkHwuexEDiXVIVs0zKkfJiZZVNVkYOkvACp617dqQi1epXr1aXKNdk2cjmNbXvf5aG6ef1oL1xwsK0jwFRSvtJFwxPrDKznml12mFVVpC+/T9q+48K2aGuqdj5RfPp2tPsLAIDDLQqah2rJjXfFC8NG1a+rUG1bCD2ukB71ZfcJn7e1pwqmp6nciuFzRe9tUUjYzOTK+c5Mo/zbmS1X3bquS99pqA6+jIYK9k3TXVhxXN2D4m3hbcuy6eHK8HeX6VbV6/ucX6pty9GMJ6HzemX2vqHzJCn68ftsC+G9NF1MG6ZTu/tA+GxO3bXTuaLn0/uV7w4AABjW1Vdfrac+9an6yEc+om/91m/VsWPHGocLocCgKQgYVy3veiw0Go1az+VzsRIAHCx5nivLsip0Pq/aeV0Iq9cvRgoVzOP9QdP+Zm9vb2aaIXh+/fXX6zd+4zf0hje8QQ984AOXem8AhrPLCaYzJD1ckzaG/IYk/ZOkO7bRKBxEy/6I6f5D6qAyXwStZ6t4b6ryXK09VfWb5o7cea81mala3jZP3/EEQm1Tag7UuqlKNF1V1dqHZibvs+4dA7addd9qibYM32/R1obmkw5V9fLys7W25Rkq9HeYfv0iiuKzsWBBLSxmPn/ey2zT4aKKqQtPdsq8OwaEQRYtl2UrRtEhB6yTmfWuWt4kBClTFRHHkXNFFemWq/gTFUHldAc73b2ZMjPtm+lkFEg/yJrXcC5F787JlMgrVa7EMjnLJMuKcLqyclhTpvZlkqhY96Mydm7lWENsY8tIym1spLLafvl8Xq7XbYepd2Hrr24fX34XhEdePrykfTONrdgKwmO8zmO9AyQst3G5vLJyOe3ShTXBoouCAADAMKwM3lqey7Ks+G+PfbCZFeONx0VwuKmfNYReo2B3a2XtpnHnFF+pppOmVV/vsqrlUC6LucNK0qIT+ps6lllxPlVl+3nDLHqvPauTz3QMx/MPgfCuQfK2APmicRqq7zeG4st+Ajfqd4GkS5J+lc7Nqur98WfQxXcY8L7Y7sKFIhbdpUDR76XyAhKV23PnOw0AADbCOadf//Vf12c+8xm97GUvax0uSZIq/JfMOx4qX+8yzN7e3tzQeZ/AIgBgu+K7YoQA+irTyvNcaZo27k/iquhpmk4F1YO46voP/MAP6JWvfKXue9/7ckETsAN2MnjunHuSpI9JerYmZUP3NTkn/c8k/a1z7p9vp4U4KMKOZqnO4XUFgbds0kmYT6rJzAy0+c7Cya1Xa0H42r+jWhuLzTnQqDpKzUfTdNNh9U0GRPt05Hcuou5lmlNdpnmkzZ28OCjiDnlFJzDallNU9VYdQtTV91Tvatbld9Qy66vTNrTCd6D52c+yGt6jRUHu+JbKYRqd5r++bTacsHLRSczwPTW/SXMu4Fmhrfx4AtYr1/BhxKYgelMYPZF2MnwuqapavG9x7PqwMSUypfJK5ZXI5Gbr1xf7LXmZciXmZfLKynrN9WXiVKxvlWNsI3SehO1NzT/807LSeVP7Ma36DRX/zTHzJKyvYjvKy++JruFup2I73SROcwIAsBlx4HzZvpDqmCJMqy2AHsKzZSXNzucCFoXPk6ToQuszzRYhfB7ey7xlYtLs+4xC0+oQ6B7CyoHirqHyhYUtOqoHz8Oy6itexn3GD0Hsen9oLXTu4u2u53ZVhc4XjRfaHrepFjqv2hRVQu+yvONQenVBBb+PAGBnnH/++XriE5+o48eP6/LLL28drqnK+aJhm8KCbSHBuhAaZH8BALvNzKrgeZZlK184FPYfIcC+7H7g1ltv1Y/+6I/qiU98oh70oAfplFNOWaldAIaxU8Fz51zqnHuxpHdIuq8mdzX//0m6l6TfiAa/u6Q3Oed+xzl32sYbiwOm/8nsovPscFVDqyoQ22xn48xwDaHR9bXLVyHThfOc0+5YCLg23Rps8t6t03KIptrjR/gyFzusYXvrOb2wjexKpZJtfv7i2/pUnCu+TcrgUbNJML36LHWpqt/z1qR9O/Snt8nF81nlO7B1HDc1kKql6Jxc9aKL5r/48762uwVUM4g+F/MC5U0jxv9aoZ3ONd9WGsCwNrHPCT9uEk0C6WmoRl0+dvEik1DNOLOyArQmdcIP0pHybFuLd5GUVc4nj7wKoU/C6GUgvdw3OXk5K251bzLlDQF0k5TIb7Sityu3pxAqb5t3Ur4uFet3uu472nixnIIQxK8C52UAve8x6qY7pQ7T73sAAHaVRYHX+X1oHaenKIAegtv18HXcr9b3d1Xb8HFAeIDfahaqQ4dq0YsC14vC50vqFaJf4dip9T2EP+e8h679gnMmMB0CD5W6+4zXddsN22bDcbBLpvv0ZkLnPbapTqHzsMznhMmr7Tjanlbq/w0XVQAAdsKpp56qxz3ucbr//e+vj33sY/roRz86yHTj8HkcOO9zfEQxBwDYfSEg7svfT30uVKqr31kjVFD3DRfsxnztt9tXvvIVvfOd79Qdd9yhn/qpn9KxY8d28lwqcBTtTPDcOXe+pPdLepEmdwC/RdIPmtn/18xuM7N/J+l7JH0xjCbpX0v6sHPu4i00GwfApJrwMjueNQcaN2gSOu9euSL+7/raNaeiRkvn7rywuKTWAO90VfWWg5mZu3E21a+ctG9uG3obeHtbJvyu2ZDt9tSD/1tqQ/2Z6LPUaQoWgvxDL9Oe28tS34XN85j7PmzeScWp5Hm1DKvqUbUfLvHFMpv+Li6+k3zVztnvp25Vzxc+1wW/m4CNiKuQb6LDoh5CT1RWRG9oy658DfgQUC6D6CGEGx7bPnKYp3mP5pVYETQvHlHAXH7qEZ6bvC45TQLo4UKlsCzCcnJlNfV1blOu3F7isHlb6DwMWz9KjNuOdts/Pt6+cCFKuAAli74X+i6fbX23VeE1AAAwqCpMlOflnSZXD51PTV9lWDkEXaOAraTpIG8tXNsq9CPPC5+Hx5zq6J3fQ9/weXg9tCEKFC8VPu/Q/qkhlqmuHrV7+s6mNrkDa2j/vGn3eH9T68VqfXlhm1z0PmrDdQ6dtxwHT1UVD/9eV+g8Xp5zzrVM/3P1vupqGuW2zDE2AGxX2B/e85731MUXX6zzzjtPl19+uU6cOFENs0r4OwQI0zSdChL2UQ8TAgB2Q3V307LaeQiHh+/+pO/dmuq/haL5xNOf1x5JOnnypK6//nq9853v1HOe8xw94hGP6HSnDQCbsRPBc+fcT0r6sKRHadKv9ReSHmZmfxYPa2bvkPQwSf8jevoCSe9zzv2847IWtFkihGtzOg4PkriKda/3son3Pfdgor3z2dpCn2U4qzHAWgZJ5y+Dhq+QePjqZZvbvsZxF+heYbpHp/QB/Upc9sKH4v0O+J4b599+0qxteS/aVpYKFlpoX8/Pac/qA32/A4vql00n9aZD59WrYZ05V3xPt9ylYFEbhvqenswreu+u+e4J85Z9vA2vcjFH2KYP6mcZOChClegQAG86llh7G8p575WVz1MVV+MmUVvabmu6SSFwHoKb3ooq6HH4fFePnH30PVwsbx8FzosQefs7sCponlSBdE2C5WVkO1SGH0fLyalYn0OvuzC9+OKFeVXOw7FG2zDbDJ+Hz9xBsMvb+LrEx2OmSZXz8PBLHYcVn6hi3Q/b3ul5zDpq6w8AgI0yqyqSr2ufG4o8WAhvxwH0MgTl6mGoeQH0ReHzeFoDHLiE8Lll2eLweS2cbdFz9dc6m/ceamHpOLzdx0w18/g9zAtHT43SY55xkDssm/B3WMbz3ncYrs8yDe+jLUAXLUtXtnGp0LlzcqPR/NB5+Ey0nf+pLf+hz3dZtBwO+rk0ADjI4uOY888/X5dccomuv/56vfvd766e73q+remOGc45jUajpUPnkjqFDQEA2xF/R4e/JVXB867f/9W5q5bhw/QX/R40M33+85/X3/zN3+jUU0/Vs5/97OXfHIC12InguaSXSjpNRf/LCUkvNLN/ZmZfahrYzL5mZs+S9FxJt5dP70n6FUlXrb+5OGhWq3p+GCyo7NI2lq2jSnNN2wUBnebZUEmkbZohDLsEm6m2HHek5o3tKBrj2gPy7XOb+2rvbXiVqudlSP9A/fhvCC4voz0oHOJjc+bf+lqHdrl+nTULq/83tmF934Pmy9uqNrUpPtcXncSpqp03DTg18WKbXKepSkhTP3aal1uoZD9niqs36sjut4DtCFWj20Lfm5JqUvU8Kf89itu0pXYFcfg8CCH0rBZC31WJvFLlSsvQeb/WWlUd3ZXjhvC5qagAHQL5uSbHoPE2tYo4cD7S/LD51Dgdht1W+HxXOia6CNv/UREfD1cVzs2q8PnS0y2D55KUrOn4NFwqUpfbsNVXAQBAdAF+qEi+oXlOBbhDf1OaFn1NaSoXwrpRNehGiwLB4bfXQL/BqvB43PYFw0qTY/mqb3pBcH2e6l0M/bsyXHww7/U8H25+0mQ919ZxdXFCkhTDtBUNCeugFrxvHricR6he39aeOCge/l4mdL631zxOaEO4w0BbW8Nra+47sCxbelsEAAyj3lf8gAc8QJdddple/OIX69Zbb1262ngID45Go0H6o/M8J3wOADskfB/H389NFyB1veNFHDqPK6bH43aZzte//nW9613v0oc//GH93M/93ArvEMC67NL5XSfp45IeY2a/3WUEM3u1pEdKel80jW9fT/NwKHTpOKyJA7gH1/Jh06oTfE3vv61CdZfAdmvHbtM6dm5eXHixuAJyPF9rD5+6pavsD7y9LdkBsPUtfqmCPWvuQJ8KIvexjnYtUz172d1+UwC/ZUjfcuIojDKzbYcTdwvatsT3dx+TE6R58/dP27znrIb6d8VyqHYObIMrQ9+pc1Xge9NV0EP16ql2lc+FIHpoWwipb1IImTd964fQdWamTLsXQncypcqUrNgyVwufS6a8tg8xk3KTchVhZadinS01v7BdahI47ypsN122EivX33iDv3/We2kclhF3LntJ4+gznQ/w+7D4xEQVINdgOp7V8PqB/o0PAMAO6lsgYajZquiD8VkmG4+lPK+C5C5N5UajomK0tDDkXQWU65yrAu0a6LbeVdX2LCtCu3PC2FX4PGpbCG8vfE914bdjmNaC35JVuH3J6ueN01zU3hC073o79/AeQogtDvUvaksZTq+ODTsEz6eGrzelFjoPgfd5wffG6bSFzvsEzruG6QdC1XMA2L44yHfaaafpoQ99qJ71rGfpl3/5l3X77bcv/I6uB8LjoGH8HOetAOBwCN/54/FY4/G4OD805/dOkiRK07RxPxBC5m2vx8MsCp5773XllVfqxhtv1I/8yI/onve85xLvDsC67Urw3CT9lorQ+Sd6jWh2g6TvkPSLkuaUUcBR1xZwPjJ2+Qegc1OB2NbgaoNi2LIjelF4daXcuW8MpFb/3uG+1KXDxma91sWwbIlq8c3TGWpU81EFoT6dKuWJhYUnVHq1bbmLE1ySLnnxTX1hzIaNFm8rZbA7Wq+uR4X6xbGkIU569d/ezTrcgteG2p4BbEsIC1dVxzfUuZ5IGi2qHKBJqHjPOY3KwPy62mgqAqjhm8+XQdTW4aMQ+nhHKkXvaVwGxoNEzi0XHnFl9fMQOk/Ld5jWlr23RJklVVi//vrceTRcBNHHqKyOvoxNhs8PknCpwa50qKxL+LyHz+8QgfOYV6Kx9tbyK724l0Go8T/7+WbLBgBgeFWQepttMKsC6FYG0EMQ2I1GVR+dZdmkCnpdW/hckitD5yHIPkibQ/C5DKG3DlcviFI82fz3LgntDn2M89rp/dwA/lxxUK42n7bQd7zMFwbDzaR5Ye+2aUTbYFcuSWZD51GYvLEN4fW4jWn/vuCV7ep2CABHSBwMP/300/X85z9fb37zm/Vrv/Zr+tKXvtQ6Xj1wPhqNqirnQwtVdQEA21evcD4ajaYuOKqL9xFhuLjKedt0Y977uXe/eMtb3qLPf/7zevCDH6xLL72UC56AHbUr50m/38x+1sz2lxnZzLyZ/ZqkJ0r6zLBNw2ESfmj13SmFKtQHVXi/LlkuUFMEK9f8/td8nBAuPOiyDGaC2lZ05oYO8qYgd1v4tHNVltkpLjles9WuPl9fxfvhDbQhdQkK15Zn13U9b1kuc8te6xlqXnZbmL8NTKqFt8xUziWzr7vJe55n0UUljeH4JSxah22vz/2ODHdLWKo9w1TQArCa+PjROTcVQg8h77XOX0WgvOt86lXRw3hDhNFzSVn5fZtFIXJbED6fmkYIsEobDaGbwrLxZVC8bvnlYjJ5y8spmJyKgG64PDJ+SJMwvtRtD1GF9zsOHxu5le75I0k7c8HALjGbVLA/jHy5/WbRf9fBpPITOWzXlJWh8+h06cwwXpv9DgIAAJtloSp1XPXauSJ8XobHQ1C+MYA+p/paFT6fU8Wtd3tDGH5B+LwYeNLW6m6lcbC7q7a2Nz0fBbrn9hEuej0s81XbGA+SptMVyGvVwF2azoa+o+1jal5t8wuB73n9uiF0Hs7FaLKt9Aqdl1X6q7bUAuczbQjvpXzNQluWPi+xorYLOgAAGxWfCxyNRvr4xz+uN7/5zfrsZz+r/f3ZSE4cDkzTdG2B81ie58oWHfcAANbKzJRl2cz3cdgXLAqgLxouVFBve63pN9YXvvAFvepVr9IjH/lIPeMZz5gKtwPYLTsRPDeztw00nQ9IeuQQ08JhttzO6LB0lS1d/VrTlYqHVIXCXbJUwH1yG8z5t2OZzG/BMognUassPT9629S573q/p6FCtNPNWKnc+3DtWKNVtu1Y4wmETuuw+XZCU+1acjrDWjJ03tr2DhcmNISvixC3ix7NugTTh7NoPu2vty2jpjslADjY4k77EPIOlcbXWQk9Ub8q2dJ0sNJrNjDfNYzuVdxays/5vu8TPlc5rTDdTYbQT9G45RW3VNXzXCZvVlY+LzrPQtXzIrxrGsspl2s8jnSarKdFR1wWQsAdhpWGCZ0HhHRnLX9p2e7yZXXz6qFNvEenTCMlQx3Ll0F2U/H7oO1zfXAurAUAAKuwqAq0ypC5S9Op0LiVoV7l+XQ/6KLwuXNzh1mmrV0qn0uargoeh8/XVDm0MfDcJATop0au9a23TafrPBbNv14RvL5+yuUcL8O5xShCoHtR2+LQuXNFtXFF4fMFnHNKwoURzk0FzhsD/WUF9nidu1BdfYuBDI6yAWD7XLQ/CtVnjx07pt/8zd/U7//+7+sDH/hA1cedJImSJJGZdQoZDs3MqHwOAFvivW8MnQdxsLzNovB5+C3V9nuqaT/wsz/7s3r2s5+tJzzhCVPzJngO7J6dCJ4Pycy+se02YLdNQs7LhS8P6gnq6v0uuTMOncJrff+u+p9+Orap+zII1URaKoi0dZ+G1wZaRvO2t6UOqlapdN20LNZk6fnMzy+voG091ELl9VcbAtODLEPz/TexcNJByx+Qm1q2yeIFLTqtMPvey+2xoTnOxSdIuq/YVZbvqneGaLdktXPHlbvArqvfSSdUQk81THXxJiF8vmi6pkmYO3w3htByaGsSPUKb6+1umk6sHkaO59FF1eFUTicvH0MfcRTTtzL6HU+9HhKZv2+vy2VFUcFq7LDHmswjs1S5JdV7a3p/YTxreX3qvZTLrFovLcMNGToP8w3raF1HhE4Hb7+XH9DfhrGwTcXVzTNNPvfr/g1QfCoTeY1kS3RRFZ+for650+QikNSFuue+fK1l3odgHQIAgPnqdyGyLKtCwtVvn3DcEwfQFwTL4wrXQ4bPFYfPm45VnKuGC++vCieHvxeYClp3bXvX46bacFWRlfixaNrLtCkEzuMgdn39hdB5PeTeVPgiLOMORWKqsLgmfcLOuann544fhg3rpRY4t9r7rCqgK/o9GqqcH7DfVACA9Zjqty7/fvzjH68HP/jB+shHPqIPfehDU/3baXlR3jb65gieA8Dmhd8ZvsMFwM65xmB5/Y7RSZIobbjwNlQ9nzef0JZf+qVf0tlnn62HPexhOuuss6rXw4VUB+0cEnDY7XTw3Dl3gXPuOc65/8M59wvOuV/cdptwSCyzM5pb8fdgmPzAXKIDMhwErPPEfOjQ72lycLE4GDG1DBa0pXVa8zqbGwO4S4S9q2m16bv+wjhLho6j0NpGLDWbgZLn9XU47z1X1zI0zbe9PXNvy9rphFVDhZs5308u+t9yJv0Pyhu3+2hZzVtOtdcmn7+WZVSG5TZb7bzb98O811o/t8tsz/xoAg6UqY4V56ZD3eVzQwnTbfseDyHupkBlqDI+1XbNBtHj6VeXATXML4SR42n6Mrza56uv6uAKD2lhsLoPK/8nmXn34e4bQbEkuuwji1BuvX02NQ+Tk1daRGFNk/eo6fdW3xt2qYAeL/upaZUXEKxjLxJfJLCOX0Vru4ZwAw5qeDms01xFiD4rt6tNX3BdbFeJ8jIm3mVLCIHz8Jg8a0pc9F1Whc9nA+hhewYAAMM6ECeCQ1/nzNM2CUqHEFRb+Dz0r5Vh37WEz2sVratZl5VJZwLn5b8Xhs+X7Rdc5hgxLOt5Fdnbpr2onWF6Nqn4PhMoD+GIltB5Y/9j2Z4ux8VxhfGqXze6KGGRqYB6vE7r866/1tIGAABicb/1Xe5yF33f932fvvGNb+jDH/6wvvjFL1bDJT3u4jJkv1FVZGNBIBEAMKy+371xsLz14uza3TTi4UKwvB5WD/b393X11VfrIx/5iJ7xjGfo3HPP1Wg0ahwWwMUiliYAAQAASURBVO7YueC5c+5Ozrmfd859QdLfSXqtpN+S9MuSfqllnCucc+9zzr3XOXfvzbUWR83Gw7frsmQYpejQ7Nbh2ne6cWi8z0FD0Zk76TzuMWK/YO/Mqzb1r5kx6x3cfZnJyiBx47LuPUmrxlv6oGyj2/02P2Px9r1oW3fT22B4tqWSdzHJLu+tfR21jd3loovJul/2AoTaySGb/fw2jDT1eZk6mbOoGdv6ATHvxNC8NlWf28n2M/nvAd9vAOilKYCeRNXEV+0gaQufzwudB3lD+LzNTCi9oUPIGqYXwtXLfvPFAfQ4WL3aN2kcTo3V18fi8LmpWI7NU/PRX6l8w1y9WbUe4vcWlnOY/qIAehxkD9MI62tdwjzb3v8qDmrw/CAGl6uLPTQJnXsV2+a2fuuanMyl5ecmKaufOzVtGW4yRvUJC38Vn93waYqHLcLnrgyfO02C9wAAYGBJstvHdVEQua0St4UAd3iu7XdcOJk+cPC3Cp+Px82B7fI9WPR8CJ9bqPw98HHdwnMCXV5vCZ5PHZd1WY5lCDtcJNBYxVzRb+Y5wyiu2hdddNDW1pnpJ9FxZ+hTjAPvXcYPofM5gXMr2z4VOA/T2IEQBkEQANh9zjlddNFFuuiii/TlL39Z73jHO5Rl2babVYURw98AgPUKofM8z3vddSKEytvC43HovC18HoaL78rhvddtt92m173udbrkkkt06aWX6owzzpiaH4DdtFPBc+fct0n6qKRfkXSups8yzvsmeZ+kx5WPH1tzM3EIFDumJUM/B/wHz9TOf9n3v44A/nSJjh5tqx2oWLdbwZR/zAmXzq+058pqesWMay9GofFdMVkmbukKPCFQu/Yf/dsMvEzNt/1kyJT6sqy2q5ZKtJ2XY9P4SyyXhnW90ndgU5i6tVkNMcG4AtBCS1RfWlH1I2ne/Be03aIOsqlKWT3aEP8XwMEVV0FPJaWa1Nhe9Sr9evjcpKJacYf9aFtoOATX82gacYQzfg9h3uE7cxL4LKwaPpemQ85DVkCf1nTMl7Q8P1nO7Yp9bK5EWVntvE383uLgeKqys60hoN7EomW9yT1HRmhXUvF5kQ7GibHq2ERF4Dwzqx5+B9rvlMi7ovJ5eExHxSfH2OF/J7XMVR6jTULns6ya6mQaB2PdAQBw4Ox6n0YZ+K1+T4WgcRTYthCUDv2Dbe8pTGfgKmxh/jYeN1Y+r+YVtzn8WyrGGfo4Z14f14LXZsLUXcab15SyWl6oDN8YOg8B/XnB9CicPhVoXyCevqRJ5fuOFyGEoLpzbhIqnxM4b51/h4D7RuxKOwAArUJf7g/8wA/oPve5j972trfphhtu6D2dddwpLw6fAwDWJ3zf9g2dx9I0VZqmUyH0eiA9SRKNRqOpquWSZoZJkkTHjx/Xxz72MV177bW67LLLdNppp81ME8Bu2pmeAOfcoyT9paT7aXI28TOS3ijpHxeM/ipJ4/LvZ66piThs5gRD55lbhfoAcUm6dDBlyFB1VZXEWjrOF1nyIKM4QJlffWduG5wWhpLN57X5LbG9RdOYacASXLXdL7n2N7rZb/kz1nn29QqMq9frbNtWFlcrajgJIalekb2ciZZqZxSknlQ7b79N7ux3ZbjwY87FHc41t3lus6Yrq6/KJemcouddlttybWmqoA/gcAjHAqlzGjmnkWZD6H2FMLi0KAw9qx5kjoPr88SxzlH5SKLX6uHzEBhfVQihZ2YaLxFqn790E7mZ795E3qVR1eUuofOirnKuVPmC0PnUOGXAPCuXl1Qs16Aevm9+B8Ujj5bPJhA+PxjiE4KZpPGOBc6D4iKWZKrquS+j4plSZdVdBJx8+agukXFOrro0ZtF8JtXPpa3/6tiaqYskAQAYSvg9cBCqkoXweZoWD+fks0yWZdPVz0MA3awIFbdNq2OV677Me1mWzYbPQ6i6DCjH4XPL89D49vb2bohN/7f+chkAb3ttpmp7eK1v2MH7yXzStHGecSXxsOwaj3ni9TUn5N04/XpRmzh03mX5lsPNzG9B4Lz+/nbBrlRdBwAsFkJ83//9369/9s/+mf7Df/gP2t/f7zy+77ivXEZe7tPppwCAYcX9wOPxWPv7+ytf7BMHy5OG38HhuzxNU51yyilK01R7e3tVYD3sj7z3+vSnP61f//Vf1+/+7u9Wz4fp7ny/AnDE7USyyTk3knS5pDuVT10j6XFmdr6Z/ZCkj88b38xuk/ROFecpH+Gcu9sam4tDougMW3bsQ/KDxy2/ow6B6GF+/DVNY3FnZVNHv5Uh9s7tmrcM5s5/UgklVLBuEi+nVZbVkD+yV+kINvNrrUi+7HRnw2LLNiC6RWuHCyxcOKEw/WSH2XS43W15cUQfbSdqmrbxZb8DfZ7JfHSyprX8akMVoY4n39wq301Dbput7eiyjpuqSnVrG1fuAkdDCKGnmg5vb0qoSO41qXy8rBBGdyrey14Zrk/LauheRdh1SCGonUlVxfA2iaR04XHdbPg8lZNcKu9SZUo0tqaLtkIINqkC53kZll1GHK5Py2r5VRXGMoA+jgLqkjRybmZuYbhN/GrJNhh031XjHT0hFv8G8SraGS5iyHewzV7hYkJNfabCBSAhih7q+5uczCUyl6q4nKf78VOIrsu82i71Pcys/P700b8BAFiVS9Ppas8HoX8jhM/LytHJaCTzXn5/XzYeT4WlqwC6NBuijkLHbjTS0KrK51k2VYE9/q2gWn+j5fl0xfZBGrJ8dfKpquzx9PrOP/57UWGYENhv41zRD5xl/Y+Hon7OqW1/3jZfrg9XG79orEnlOpvXlrCt7oqZyu8AgJ3nnNO97nUvffu3f7se85jH6Fd/9Vc7jbfO0HkwHhe1JumnAIBhhO/TEDjPskz7+/saj8dLh8+bKpu3hc/NrAqSj2q/lZ1z+sAHPqDXv/71ev7zn69v+ZZvUZIkStN0ZloAdtOu9Ab8qKQHqehz+oikJ5rZB3pO46/L/zpJDxuwbTjUGkKjHfSuwrHTVun8H+BH35yQ+OLwactrVRXmFQJU5XZRVD1ub8O80Hk0VDGtpU+2rOfH9TLbvhTC55v/DLh5FyM0PG9Lnggp6wL1a1lSHPyG/w5m6m31fy+LLzLoH26fMiegH1o72e4nYb1Oy2lXwudLKm7RPDmx1WV7pDoQcLQlqgW2O3wfeE0C43tLfH+MzbQ/UMVj03SQNYTRQ7B+VA4TwtRDiSuhdwmht3OSUjk3vY8Ke8npqsuj6FEEY+PqzMuGzuvy8r017dd8GSxftJ3EFeLXKYSZV92SnDTo9oGyUr/KKucq1tUuHCsF4eKXEIqPFUflTnn5+fNyU589cyMlSjX/Plbtyuh6Vfn8KMk0uZjlKAbvAQBrVF58Gv4+EOFzqap8riRRcsopcklSBNDHY9n+fhFejo9VkkSqV/F2rpiGVP13SFYGpH0IoId5zgmfK4Tl24pVJP2PpKycbq1x8xre2o5e51ni6ZhNgvU1Vf9aOUwTF9bVnGHauCSRovVbret5/XpRlXaXplPjh9fM+4VHpS5Nd6rvsKny+9TFJwCAneWc0/3vf389+9nP1utf/3r99m//dhX6brKJ0Hk8LwDAMEKV8/Dfb3zjG9V3ep7nypa4CLcp95Sm6UywPATPw/d6/fv9mmuu0dVXX61jx47pWc96liQ1BtgB7K5d+cQ+Pfr7J83sG0tM42+jvx+4WnNwtCzbCbZbJ+yX4arO/yW/CkKVkiWXQ5eK0m0WhqY7tq1YBrMVLqe2iwUB2EXVqydtaKqS2UG9avRgHberTWd9239LJe019ldP3WZ+Y2GcfvNZrk0dtrmey7W4o0C44KKl0r/VPxNOLuk2o8HD+yto/37ocRKl93rjxAxw1MSVAVwZ0khUhrXLEHpTEDeXZsLEex1D6yGI6kNoe5B3Upg3rVD9PNV0yH6oMHqolJyX7Wi6PNBr0X4mKYdx5bRMZuU6Kds6y8mqYGxRpXkdQoX68J5G5W3ZczVvD7G8Vt14Le2L2rKsJY+Wd0Ko1r0LwgUZIdAdLj7YlfZVd1oo/+5y8Usabb1p+P4YoC2uvGRkiAtwDgILd0OIq6Gq/A45IssAALAe4TdIqMZc/SYJFcUPQhA1hJHLquUuLQqSWAg5h8B32GeW4WELAfTy+Woa6fyCJsuwUBU7y4q21KZfD59XIe2hKp/H4e/6822jjMczw1f96Ysqe8e8nwqdt72fUJl+Yah92dB52NajNrYGwr2vqpi7UA0/SSbV2vO80zF6FZTfIVXofIm7gQIAtqs6bnNO5513nt761rfqP//n/6yvf/3rU/uluM96k31Kmwy5A8BhFt8RNc/zKoCe57ny8rdI+HfXi36S6Pd9/bvaOae9vb3GYcJz4d+33Xab/uRP/kS33nqrXvjCF1bjxdMCsPt2JXj+iPK/nzOzDy45ja9Ef5+5UmtwZEx+WC3xUbC+VZF30yrh8yKk26Xid+sEFo/ZGvpeEKiq2rbYZBnMmWbD6/Eym/s+yrYsH9BfLeDfZOpk0LLWUfV8mx0JZSX3ri2oV6dO0iVuozvv5EpZJbzazroum97rpXfyvArnN7ZoJnSucjkt2Oai76HVfkgMuw21f0e69iUXrQOLAvp9vpcAHF1NIfQQeg7h7FBZuunYIAzf9l0agsFTocMBK9627h8a5hW/v/Ae47D9MneLiS8mC+HWEMoujloXTy9xibxLlSlRbkWV5fC9H68Lk6sqoRcVz11VoXkdEhXLLNf0+g0h4/B+2/Y01mGYVcXh/2Ud1O7EFX4VDSoEzkO1+/B53/YJu/pFIT76rC4SPllJ+R0RPgvDtczU/VfAwWXR9lB/3tT9IgAAAOZyrgrnTh3LJ8lq/ZCbEgLjUQV0F6pnl2HhKvRtVr3PKvBdBqOrgPIaKr5PtaMhuDxT+bwlfN67MnV87FafZ0v1camhQnociu8qvM84dN4UxA6VxRdMe5nj46l1KhWB6+hihWjiVag8LLNqeyrbWK2TRfMM892xz44LgfNau6ogPiERANh5od81TVPd97731c///M/rV37lV3TrrbcqSZIqIFjvn3XOVa/XH0N9//cNQQIAmsVVzbMsU15eeBsHzkMA3Xtf/d2m6Zxd0/Bpmk7tH9JwZ7Bov/I7v/M7OuOMM/T0pz9dZ555Zut+B8Bu25Xeirur6H+6YYVpxMX1lkj/4UhbYsdVdHTuTtW4lS25715mOVRVR4p/dGhbdHDRIyTfO7A9dztoqL841aHcoep5WSl6qQOlgcKqs8tiuRUfL9thPwPx9Db72aqCcr3eTxRKdv1O1lh58crC5eekPssiTK8ITM/v6O998U28HbZUJWraxqYj2s3tcUVDZtrWy4JKSSvpdR6udjItulCprXWdLoABcOTEAfT4SKT6d8t3RlP4PK5o27TvCWHVISw6agnzmr43xuQR2p+G/y4ZRK9C6JK8qQyJS/mCn8Gualtx5wtzRbDcKy0D5qmkVM6lxf4/Cpyvq9p53La9chmEEHl15NQQQK+v6fow69hrxvM4SsK2tq3fh2amvPxshf+2fd431iZp+gKQHmHzwElKnRXfA1rThQkmuUO+xVq0TbS9Xq2vHbhQAQBwMFXH6yFkXj9+L0PpO9/3Ed5Dmk6HzzXp1w4B5xCeDr8hQjhaZjNFK4YUh8+bQtZx+Lzq7wyPMHx93fRta2P/15xh6s8v7BN11bDhPcSh8taK6PG48+bb93gnDp2Hv+Pwfu3ig3DcW4W0o/lPegrnzE6qwu07p2HbXuf2DgBYnxA+f+Yzn6n9/X1dddVVuvnmm2fCfyFEOHP3ztojHmYVIQRJ/wQALCcEyb331aNeibxe8Tw82r57m77b2/r7512YdOWVV+rzn/+8LrjgAj30oQ8d9OIlAJu1KwHtE5JOLR/LOjv6+yutQwE1oSLJcreJMh3cmnwTxXvXkstAmnSTLl4Wk8BynxP7Ybo2E07tMMNOw3daBq6I0Da+bpI5m1cDuQydlwHlnsu52EZX3d6mx59s+0lRlblvmyS5jsu3v+5hh/iWcEvNKT7ZsMTmH2+Tk6DakNySYeqGiyVaB3WdGl59bkN7XPU/08/PNGP+OppUd199Wyq26ZUnM2Xu94NbvH7Mun53Hfz9CYD1CSemnXNKohPV5txU8DgIp+C9c0WIUIsr2frymHjVq5O7TMeXxxBtQVJX+298WZqFjrFo+HnHDZPXXBF8Dfud8tiyPn+v8hAyaoUvLwQzi46lVCxn78J7Wm/wPFyEkGq66r3Vl2O5vuOq+dL0+6wuuGu4sCGeX12f3wp5tI4Pm7Bs66rP6YbaUf220iQwHC442FoAPvpv9ViyLcX3iCmVrXk7Wvf0t8s67gPiO1aEyq50+AMAluGckyVJ0U/mi/sHVccDUR+apEmoeNeEdpaV2UySi07Am5mU59V7jU3tT6Ow8tDHZ2ZWtKn4x0wFczMr+m41OUY174vhlujfmhGqjs/7LZa3XN68qE85DnOHsHYI/DeF+kOlc80JQIdpLXF8U7+Aorq4Il6WoZ315fH/Z+/fw25Z6vpe9Pur7vHOOdec68pCFve7hMCCCGRBBAS8iwgaJcegJ26zT8IJKtHweDYxbqMm+ihk763boBwT2G4FiXhDHy85+ogIRq4qKJKAAgoCwkIWsNaac77v6K7f+aPqV11dXX0dY7zvmO/8fZ811nzH6Oqq6qrq6urqT31bzoWZiy/3ETqP23T4zW0Ynf9VqVQq1f6KiHDf+94XX/mVX4nf/d3fxT3veU+cP38eFy5caJtdTYhHtJy9aCTweZF7y4lKpVKpeiX9Z/wBHAwu29MP0PTjzJxdSNR3LQj3aIlyv73vfe/D6173Ojz5yU/GrbfeigsXLug9hEp1BWtfnq19HG5+4uEbxPHE6O8Pb5Yd1VWpqY6/kbhvMvFKFS1bSXYs5UDLJi0dKDvxFe5EI2XgtucnfBnO9XwCUM9bgKS3JBKnIfdlbmYCiLyNfC2OI+R7gwFpOJaJefDAWjyhHrvyz017JMD8B3BzHmRMfSgQgfkMX16cBknbf4Sx+fMrk4H2v8n++3SudPoHMt1WN5RucFXKhVG3c5VKlVfsQhu7FwtQXKBxBk8ngQJ8jnHgUCSQ+qYSAHZI4oo9JT25orSc0P2/bo1T212nNxYiWAYqJtTsywYNICtu0XG6khaBOpcr2U4gcHBA375i6Bxo13nsdB0fiziPp7/Hsn7f3HbKfAwNuxqln3pGHafHu+/K1bSVRSHHcH8YQ+c1gIpP1uVc2k9wOPftb0le4rc9yBsQVMsUX0Pm1ER1jG1ZpVKpVKdTZAwgjuHpGN2DuzGUvZeS/BUFqCzd8STjVObIhds7XbcAZAGddzS+lfS5qpz7eXLt5ihPnOYLM8bdmTFBHE/vbnXdBrP9v5xxae+NQ8q277XvUgZDbudxmjPrgogcYC9tlSKncylTnz4nxyqg9tQRVZhJ3TPoPNxnx+cuEuh88F5cpVKpVFeCvuqrvgrnz5/HW9/6Vrz3ve8Nv89+e92WrgkpEKlSqVSqaZK+M3Y9F8Uu5HFfnTqky99T++Ap4eq6xs/+7M/i+uuvxzOf+Uw8/OEP13sIleoK1744nr8ZwCMA3ExET2Hm35+zMxGVAP6x/1oDmLW/6uQUX3xO8oISVtwucfY4Va7nS8vAr2KDBaMf3A5whO1xOhnO4eLJVnFy4anA90gZEBkwt4/BHT/5pjDQJpgjZ/SZ5Zw6siysq6zIgNguBM1O+hyg1j9zJG3S1uv5KYaHDREKM7McxZ27bxVoiHamKwCzBcGA5sBvI+2J2aLjeB7XfWbf+IZlAAHsX/CxzTa+LZEBYHvz1fc2B9e/ejDRWpCJHBr0hkqlUvVIgMFqpC+MoWwL53IOeIicGSWAOVe6ihnllgCQsVGCOJDPhUtzy844+sR9a+ueAwUYFdzIVa4z3No/lw+BvhmEOgrvZLxfcgkDd0O6TRnKO8PLb7UfJ8SAabxP/DsReYi+LVkEEG+XxQpDbYHH/iZy+YnyPHTdk7eE7KIcj0u7HrnE7VkA77E+Yqf5QQOcb+NBICX9wR6jaHuv+NyXxSlzJgHlWjAbTFOpVCqVyis4nxOB67rlGO4DhLkf2leoKIHjGQBimFp+j4Fjax1wnzihb+r6OSgB4JlBq1X7fkhczn3+qXZ3NFQU3fqYm78h+LwHVp8zopBj4qrq5tWnwVXVhsrTMUvc7paMZ2KXVXE692XVAejj8vCLL2Zpz4DzGLbvtGf5XUB8lUqlUp0Kfd/3fR++4Ru+AYeHh3jkIx+Jc+fOLTYVEG0y/hHosSxLnZdQqVSqCRLgvKoqVFWFOvMWKiJC4e8HY0d0wMHhAqdLHyzfx9IF2q7q6faPfvSjeO1rX4s3vvGNuOWWW7a2UEmlUp2c9mU24Feiv19CRHPfl/NdAO4PN2/2O8x859ZyptqZmsnYxjH5pCeXl7t9T1/ptc9yx7+/F3Y38FjYbU2snrgMhtKSbWmYSe2YG6fwOUqd5We314FsbTKgkwn2zc+BBfuf2EC0cXmZ8nqhKeotP0fPzcsdOdRsXp0MpBHFEy8ckf4P6GnTcRkNuM8Ol9v+9EmT3eH7xJxdeDNcPiqV6moVexB4LlDq8Gf3WRE5aND/PUdLXKpTTXVPF8fuasP0BFgu0JRBAaAg8k7dHiAnQoHa4+IWBjUIFZgrgGtUA4vICA7ELkiWERrUKFB7L3IDV9ZmS/16H3Qe56fw4WJZDyNXaNcl+9/XnHekl+3x1Uq+58qEoo+JPuLEL/UQO9MXaDv1mzgOapyur1SJy/wu7g/DokkAa1+PJwGdC8xcwbePLd3LG6LQXlSbKYbOK99O2LeZOaqivuI0zHmoVCqV6vjVekNgUeQBVQ+17jW8aoxzPi8KmNVqNKytKvB67Zy6gTYQPcPte7J83FxVsEdHXTA+dj6HA7o5AyDEcTU7DxhVWOuONXNMXGXu8HKwdpy0pC8gvY+ntZjY10UKnVMMhUtykUt6um1U5Nzuw9eo/bYc1uW4ot+krczR7PztWAECySyi8AH2/7xVqVQq1WwZY/DiF78YdV3jh37oh7LA4lRtEyjUOQmVSqUaFzMH2HxKvykAelm27UoEXBfX87qusV6P21sRUctNPb4O3HXXXXje856HV73qVbjpppsUOlepTon2YkaAmX8FwDv91ycC+AUium5sP3L6LgDfE/3877afQ9Vu5AA8Xgjh7kYK/wFwTrwbQPiDYRa5nXuQlpZPvjL3OxRn0zPFMO8aPzTJTMYPH+cmzu87vLGmmQ7ZkZbWazuSTYGVhW1jSd5J/pd36Z4zSB7uAxf2j1vtx4bzwD0PjMjI64dznrQSZlcPVHY9ARWd97Fb+9T2G7nrqlQqVZ/EoXYTCQQtAPqZGQ+ExQF7G/D51CvtEihyTAJmr8A4C4uzZHFAhIIMDAE1CGsYVGz8ld2iQA1whYotauTLgUAgKgEqYalA6ttXwDuFb3CdK3rcyfuOscikFaD+CEIX1QMAuvXb6ui7xLFJDQVndbRh9RhGLwEc+HYbt18B/uVjwoKC45+gPKnlugKcH0ndHeNDt13B5qKVb++q7amGay/ptWQ9cVFQiGdGP65SqVQqVVYe4CVjHKhalg4STsdwUbi9nKEnApUlQARzcDD89kJxj6sq8NFRcO4GABjjvu8AQBeo3B4eduJna91vPh8Cn6dvjOqLd3Ze0vSHYHdRMsfG63UvqM4p8B7P00fg+hKFewwxofB1xqlrfwqc+/Y91+mcimXPY3YhEuC+BzgP2xU4V6lUqlOrxzzmMXjKU54CZsYrXvGKjeLaxrwdM28EwKtUKtXVoPg+JQbHp4iIsFqtUCT3MQKdi+u5xCcO6GkfL/sLgC6fT3ziE3jJS16Cr/3ar8XjH/94rMYWc6tUqitGc96yu2v9MwBvAHANgGcD+Asi+ikAvwvgWglERJ8H4F4AngTgGwA8BPBmc8DLmPkPjjXXqkUS5+boBwAWIALzZq7BS9WkSU0eJ8q9ItICs836T5+YGbB1G8aU3xdiKrGzNDODTLEY2mbm0fYV0qECTP2vC12Sj/A6UVr2Ckpm+LY25H3Zs29AonZ3fk0p331R6IdmAjPBeb93skQuScvyJHGmfRCRmQWhMxjEmPTQonVuxYs0EqAo395le+z8bprfNnDzXvoK4l22wyZeA0AeNpH/e1r9MFvAkofz3bl8pZw3KpXqeFTNhAKHJA7dFTMKAOeMCe7iPJKOdQPzQcftKZp7LBW2d6NqwCD/kZw41/MCFQNEFoYBJh+Km/AFLJgJ1scCuHBSIuw9053juINyYxCY/HEwUWfbmApf7vOO1V2n+hyw2cOj1sct8QtUmnNXt8z+mH14ZtT+mjUFis9J4pQ6Tq+BuWt//zK2OFB07xHHN/BbX3qbSlrbNq7ucp7GzvPH6fIkCy+2ka60MXGEFwd8HQVtT1JHskCgr9+pMe8cZh9nwby1NzqoVCqV6upRmN8RYFWgYgDEDKQwr7iji0P3njlcUlk2ULcHyLNj2KIABOyWB/4xyGwtUNcN4LzpNdbPN7O1zon86MhB/hHUzMwgaxswuq6np52rB19/nf1zsLjMw4qL9kAa4c2fOSd1f3yxKHbeztTHIvMLoiY/1rbj6EljDpAd3N33BOAmyUtPOaVlqHOZKpVKdXpFRPiCL/gCXLp0CS95yUtwv/vdD1/+5V++OL4YVlySFwEXVSqVSjWsqqqwXq9hFy5yFphcYHMBy8VJXe4BrLUoiiJ8H1pk9PGPfxxvetOb8Fd/9Vf47u/+7pCGSqU6Hdob8JyZ/5CIngvgtQAuALgHgBf5j4gAvCPZVXqkXwLw7TvOpmoLElfsdJoyALlgME7yYrMZNApc2ZNueahyntwgJAN/Ln1IsKXydA8z7PT25SGboVyTMUAt7TnBS1gA8d69J4RJtQHCIhP7fbkh8rHPA5yb+Ocey4YiAi11OQ8PMSxmo300xeNyZj/CDE+Jxz+2zxmiSVEG6HukvmdkbmATh/O9lVb8ECL6fyufZofAtVuhcQyi0NfNFlsA++NmpFKp9ktuFLa9dyMQHPBp/Xi78GMcG6XTBycKfL7J8kr2sPLUOJgZ1RaA1Bx0LnIAr3FjCXKLrsKDfwAWAmy6GKwHzcECkrcR7eDenYHMh7bltAQ6j9MqR9JhZlgfRvJGaIBwAC0wXSChWsL7sqoTgH2O4jpGcs+wdOFZLOr5u5WHJsHub0N/T8ybxWavmIsBYivQ+TFAV5x84rxsojK6tzJAuNfSkdD2JdD5UK1Jvzz1HJZ7+9p9cXWn41iVSqVSzVDLaCEGy60Fi8N5ChqLGck+AugC0cPP1vbA5yiKsI2BZq4vAr6Z2R3jtgB0L7YWqCqX3RQ+r2sHfwtcPVa2fdtjmDz6jX26Qb4MxsYPLCB7D+TOfhslr4KPyzGb0xllGhYCSB5iAxmffprGbOh8y3W9iVp5SfJDQAdG1zGgSqVSXR1arVZ40pOehBe+8IX4vu/7vo3A86Xqc9RVqVQqVVvs75etv++y1i6Gz4kIZVlmn9PEjud1XXcWBqX3fHVd421vexte85rX4KUvfSmuueaakIZKpTod2qulgcz8XwE8DsCbgPAMUnqc2DQs/twF4MXM/FxeREqqjl2Rk253Ezdg+pYmktlPSIa4R7T0IrdH095bUewyvkjR6RgcShbUKXnIN14tB2CRW/hQ2xvIwMgEcP8k8WSX9z16aLJJvcvxzq3nqedmVpsMSqWNzkx72O1cynBBX5LmgxdCPkm6U+OQiRuSNp1CeuklltlD++2HLa3jpvC/JK0Z0PmeTihRet7PrCp3bXLY4z4en0qlOlnFoPK2FI+cCM7ptiAKkHNBBNPT5wr0uokmjoqa8Mwbwfdj0HkDZTfO5Ryczd310MgbfEiOgVDDoGYDywTLHtZGA/HL1ZKovTxObl6lrHPlTFF9bKIp7UfGXwxXHrXkXyYogeCunYaXbTYTbo7iOs6NV3btqpxOLIRyy3yK6FNOqKNQVgvHuFI2a+bG6XzH9wzSFuIFKRuN0+HatPHtWkBlaeNz3qTAO566cufjXk2PLVJ4q4Fvf2N1J+1s6kQaR/3DlPhVKpVKpcopzD/JvIoHdsVNmXLuZxJun5zRZA61KBwEHbm95cJmj8laB2h7N7kAXfeB1xPz1UrLp8FV1YozpOVhAnmG0utq3iP2eU/DpOOESeMGf+xsbRNvlAeu686i1VYee6DzRW0mAurJmCb+UwSdkxybfBK4nKLfW+etSqVSqa4a3XjjjXjSk56EJz/5yXjZy1622LV8rogIRVEodK5SqVQTJPdNdV0H4HzpvK28ZaIsSxRFgbIsO2+ciKH2uH/OPUt44xvfiHe961344i/+YjzsYQ9r7aN9u0p1OrR3T9aY+S+Y+WkAngTgZQD+BAhmbNLzXATwegD/C4AHMvNLTiKvqvlyawPGH17H8PkmDzMD8Bw5B0+Lb8FNjM/z6dLyiz23ynsuahRnIQ/lUACB5uUxhqOH2kLzAMREIG5fWBOVVAoPjxw3mVb7nKxdP+RfvOBgg7qeqaWts70QYi50PhXMn+/fGJ8zfW1z/gB8QX1IGkk+Og+M4nYbP2CiBG3MZXn2cSxcELTj86R5UISFWeTlCwxUKtWplkDLBtubfCF0Qd4A1EYgqADLKYQukPFS8YL9Jc25+7luOQ+d55zA3VjPgMnB59aXPsUf8m+5CHB6+9iYnUO6AJ+t7dGnBTcnD/bngLhjmrp4oZV3RAB98lsMoMfbav93nYSbqlYanXo5WeXA9HR1/Jjmtl0pX3GsDhDxjsYK0i7Tet8WcN7pVzC/X3P5O46pq72bHpulAJELGD6x/ubC58DweatSqVQq1RwFuFXm2yKAl3KgUTQvd9JjRQDdfPfB57n5RJlT8sA313UbQM/A3LPy5cVAA3JXlYO7ZZtA7oBLqwd6z4HlrThk/2aHJNA0Yxb20DlS6DzNawp4j8U/BQiPjrE1FvZl2Tc+Jr/44EqCztPzrgOcR7/v1WIPlUqlUh27jDG4+eab8Q3f8A1429vehne96104OjraeZrxp+86tKmjr0qlUp0mWWs3Bs/lXiDth4uiaC0Gkrhz/XN83/Snf/qnePOb3wwiwnOe85xWGnqPoVKdHpXjQU5GzPw2AG+T70R0PYDzAD7DzHefWMZUi9SCkGc8ACVYB4FEThbxRXLogtTApe6Gg8EgmODiMXgx85O+c+XixXj8V4jcwIHCMc2WdwVZClambufdAAbEdjbg4qAfQX9G8gCAifpf0+nzAfKT8km0ri20f2SgeX1qCGchLtCj+fftbFc4jqt3tAaOU+XOW3++zTkHhsp3J4qA6TmaWEdE5MHsJfmi6O9OxH7TNDBd6mN2U+nEnyys4e6xMVuQaQ8r5BzO5XFW+3CNak8VIWizrx1yYFf+9UKlUm1f5PsUA8AuuCbnVKALFwqgzD69+CrE/ndJufZj3KW9lmV2QPvMfeSaMRUL7YPOGe4YuuGlbCgcL4NbXbqkbYhhYQbrI7eNo39b8LJcF7F97FWOC0STnLdj9z6BhGvfJmIwXuovxBmNlSXcnGOJ4zHxvd6MOPZR7Mf7QxOxIRyac66OgPOd5S39bDGteOGMidp3EaW9MOaN9u6Ndeye8wpQeEtBBJ/P3n9mP5uet1dy+alUKpXq5CWgq8zni8N0+O7DyfhK5l9I5v9PNvMtqJwBUAacDuP+OL8e7OW6bgBu7/7OcbxzQeUcACBAOTNotWoB1QKdh3HrAuidpT4krTTt+Dv8WD88O+AAloc6j4+hrhu38/T4YkA8c9yTSk2Ot29hQM9uc6DzkO8lb3HdkkKZJu7mrW25RRIqlUqluqp15swZPPrRj8YTn/hE/OIv/iJuueUW3Ote90JRFOM7z1AMPA5BieFNLcmisNSRV6VSqa4GSV9Y13UAzzd1O5f+Pe6Hi6Jowe3CEeW4OGst7rjjDvzyL/8yLl26hKc//em43/3up8C5SnVKdcWMwJj5M8z8UYXOrzyFC9vCCUuBHRu3j8Z1I3UIbt9oZGATtmBbt+PLKMCSiy58e0tHLlLL/WKmmK2vv6XuLMNdVGtCdF7GwoT39IHXcBoNNJ4BjWxSBuxd+NOYF0HKO9RI+fcpfmPBvH3iMspmaFF++vM208lR3GBmaO7guXEJ6iu/kfh66mz2DUaaftJfcs953XFuym2neU45OXh9knYIa6UKq3MX5JP9NU3dIlUqVU5LIN4pcXZ+g1uRHG8TcLTwH3EprjYEO6qFizunOvK6fNfIQedjacfHS3CLLynZfgB25TFjkiyGyi0zKn887H8viVDscMJN8jtHaT6ZOQDRaekGUDoJN1fpfqdlCnKo1cXQecWMI3Zu57uCzhkIdShu9dsag8gChdBneABdvm8il+8C224VzThzuw9Lj1MxdN63uGZOPHN9wgR217GsSqVSqbYhcWJuuZ4bE0DZ1oNqgXj3xZHZQ8ijzucxpOWPgQQ2B8BVBbteB9A6uKFnnMjnipld/EdH7bgi4BviOt7duXu9z3wP+e0Lk8+Y29e7vrcAbWthq6oNmMs+ktZQ/Q9BcbnjjRc7DDwTCdD5BOiOgNBWT0LhvDHG5TdaHNpaEB29aUClUqlUqlir1QoveMEL8M53vhNve9vb8JnPfGZrccfAeeyomyrmOmLAUkBInZdQqVRXm6Tfi9/+sPQtELHTeR8gbozBarVqOZ/3MXe/9mu/hne+85149KMfjS/7si9rWAqFz1WqU6e9cDwnogf4Py8x8+0L47gZwDUAwMwf2lbeVN0HiEsvBMzLLnIufb9qKlwo2QF+1LgNsq1Bpoh37L3JcGEdKNzrTk4EYmAuWsPWgkzRH+8Ja1l9UnCxnpyOL2O21k9mLiuL0fwtdD0HmjYzWFdEAScnmOF2PHSc3L8JZECwYLbBlX807wsWcsT7jpXrErfzpXk5Li0B4mPJ4oLp57Wn1bZ8jLvxekwTkTbZBfRl8U5WUTs3pmiB8AKdH5fcWwR2CxG12lRwQpq2ryxiYGsBAxCK4BSrUqlUsci7yS4BeXMqAVQ91/kSwDqXBzTwNBM58JgZhQfj58piPkzPUgajaXaPS6DMqRIMlUFgQgeqNP5ehOHd6DFtTCMO0HF+SmruR4xM1k3O6XQVcG1pLpRqPVQaHM89hGHRuKK74Y6Hz6V+/H5z24hlBnt4+TRcEdmD5Ctq33fG7cUCWO8Q3pXl2EuB5KkyUV3HbWNb8rMCPo1tHYvr2a7U8Vc473y/sWRhTy6+8X62rVrO2z2dA1GpVCrVlakwb8IMil3bUgBbHJojo5ETk8DI/m+Ogek4WFE0cLYAv36bOJNzVYGrClSWrTn2qQ7orTQiMQDUNezhIcyZM008cRln9ssq52yeHG8fxJ7bzz1XiUYh1sKuM3eoAqkDi4xCAugu+fDzepJ+vNghp0XQ+QmNkUJ7yZWRtD0dv6lUKpUqo9wc1k//9E/jy7/8y3HDDTfgaU972uRryBAISUQo+hbtJekLZN4XTq9pKpXqalNd1zg8PAx9YFmWYTHO1GcNcT885e0Rq9UqOKvHILn8W1UVXvrSl+JHfuRH8MVf/MW9C4pUKtXp0F6A5wD+Em7O678C+MqFcfwnAM/28ezLcZ0adaDuBftvloFo4ji+0eF2vGxrD4eOX0jZvwJkNyDkEGV88orB+8maAVRKfdu68pOr/TeMvcnNaW8tSHbbijBfolH4XFzz0jAdqLynibhwOwR0ZzZNMsWi85eZQbDAlsHfsAhgwgOWUc0xO1/sSLPEqXvkIZkshugsIul3O59TF532zb1f2nkiMwn2Pr03Fv71yJPPl7Qc9vu6oVKpTl4rIqy3NNYpANQ98PmKCBX6AdgYshag1fTE1aeaebYDd7xv7fOZygHj7X5YIHkHi7PHxSX3bi/51YVqxxcD9xYO8CxQo0LZ8km2fvscAL2I9omBcwfYNnnY1qgwB75PVQBS0UDoApfnAPQaDjqH/z4HQGdm1AsXNVxJsnCg8KawcE5ybu7aiZqIWufBLoDzWBYm/H9T+NyNnXeZ291LSmDJOT2koX62T7JoZLW1XKhUKpVKFUnAbD9mJmMCgB7GOgIDnzSATgQqS8Bad5+0XmfHY4PweQRUcVW5YxZX9Mj9fRMHbbbWwecHB82oSsrNmJAHGoCrbVW14xRn9oF8hdmvuJ4y+7C1DtyPJHnpQOeZeWICumC4tBmffvitiIxpRsD+2JF/TNk8HJNC+8jdN58gCK9SqVSqK0upSdk97nEPfN/3fR9e97rX4e6778ZXfuU41tMHP8bOulPiqJJxQbq9rutJwKRKpVKdFq3XaxwdHYX+Ud4cUfj7m9gJvU9LXcilv42hc/k897nPxfd///fjCU94AooTuh9SqVTHp30DtDed7dDZki3L3QxY/7cNzn5LIOJN4fMAQ3LiYuEdeQXUZVtFeRzOpwCZDOpAm+TdC0ddrvvi3cPJO3Hnlb9bEO+A5MZyUj2mjiXMQF05+HyDxQtjeWsB4jPkQNkZN6J9AL6HLobCtKFywUGotb/cgDtQeDxfHSeYLaqpd7PYYV0mE8bPhyWgxMxzLHY7j/q2aenQ7PbbKr8ZD7zY1iN1n2nrWwDxxd284fCoU05TjyN9gCGLfDbpFwVs3y+l7ZZgihK27p8A64uGsZvFGiqV6nRIriljUPjk+DA8cupzRR8C38sIzJ6iNTPKePw0UxUw6opdg0FsPU7ely8B0t3fDtYtMj28S09g8SoJIAgrTywHAToNutB647juAO5anCOA1mepDADQfPdzUQ4ub7mi+4/AqyFMAimPpWH38H5qqSq484p9WVW+bLYJhR8XbC5KIfNNzuc5ctg5w7gjXRSHu/8r/N9XZjuzch4y78zNfko/KzJEk85tlUqlUqnmquW2GcPlsn0AQIdd9pbMrSgCyVFV2Tm1DnzuPzm3cra2MdIxxgHNKSydxj8yJ8nWwh4dNXPLMhdY182/SyQO4rn9fb1A8hbPHfv04R36OvvlxjxT5kRte044djxvgfUCsvfMtZuynOxevunCgKXqBfEz7WTJOLhTjvJ3JyPNM4+wUEKlUqlUV5RkPtoY0wIXv/RLvxTve9/78Kd/+qe47rrr8NSnPrU3jj7ovCzLjkNuTgJNTpnnYmas1+tW3CqVSnVaVdd1p3+UPlP6waIoQh/eB6BLuKWK01+v13j5y1+ORz7ykXja056Gm266SftjleoqkC77U/WKo0k4/wMcHWfBPN1dOtw4mGI5GNmCDT08LROE4m7OtgFL5UZm4o2Ic9jITGgCy6gOX1bH8cB/qjjNU1yfE2/aAHlI35sKwrQ/21b5M1uwHYcxiRrAd+pAhMIE5oIubUFd5UHvZDJ3CqTcSrONEE3NzfEM1pZBJPFCh5GQE39bptRFdI5cezyJNVE7cL7smeBJAkVpU+v3Xug7OfdcuKY9b8WdPsnOdO24H861qwn9UNqHMDjEtfEbQlQq1amVuAWU2M71vwAGXcdLOGBYFmPmRnHWQ7TS1xZw8GlBBDPh4fImPbTAz31xWFgYroOX+cRYQXCO5ibA6F0Z9B+ngNWl35aKiFAmvwsCWyT7xLA74EFTf9wVHPi9FL2VY9hUNXOTlyh/cd1YyS9zgGUnjRD9tXHmcq69lADna2astwydh/LF8UDnxrdh6UMKIqyOCToXMcifpfMcy10/WuJKhs5lTF/DLWDY5bJMnni+yiIElUqlUql2qZYLm4ezyZgAClORvHGTCJAwJ3XNl3ytVqAeCIpiQF3AZ6Lu/HP8zKOunZO6B9rZ2uDm3Y58goOotbDrNex63X4OJGBCXfeD4H35G3kuwwC4rl3+PeQucXBdd4F1OcY4Tt8eco7sAfq2tsm7HJPEhbZxRnAy7ymzqdA5jcDru1LLiV2OKc1PBPgNnRNhLt2XFVvb1It8bPx8Dp0P0jL39bBPz8tUKpVKNa4cHF6WJb7xG78R6/Uar33ta/GBD3wgu2+4nkR9f1EUk6BzcTivZ14/cmmqVCrVaZP0kVVVZYHyuq5D/0lEwQk91RKn8778HB4e4t3vfjd+8zd/E9/6rd+KG2+88Yqce1epVPN1mp4NiXv7aXgufeIKzrcJxsDJ5OEc+Lz5zJzszUDu4sTOEdzMHh4PoKuH0t334Xw2x2VbNyTimr4caN4PtWHz9u+Ny/FwfcYT+wMJteojt81BlTsqm6VjlynHPSkh+d23n8wEs5t4bVznB/M0Bdre8XgtDDgXDjy5rz20A6U/zE5nPCOZhy+TtdwJZvGAmrv9b5qn1sO20bc7YLA9xf1fPg/9D4zSlPN52caNy37dnPT1q0Df4pRWiDSy1r86MaZSqYZEEfC5qcxIPLEzel+/xEAAjWUfcf+OYercdcBiGoDcpz4okmFB4R5gSX/q3JRpAD6Pj1PKMQXQBe420b1Qn3twq9yiuOT3IvpdrhMC/tfRZw6ITklaSyWAeMhHBJgLkM5xXqO/x/IqOdvhHcSxqAJw6CH9bcDh7OOJYfNdjx9IFlvAg8bSF+0sxX65M5tgYcKHs+8DkKUnBhxczscdtfZVUr9yHg31zdtMc6ivjp3vr8QyValUKtWVp9arwAUu94A5PIjeAdA9fHsi1yqBz+WT5sFD0rEjdZ9rNYAwhy+wOVdVANDRB6BPkcRlo3lrb1bTgb59Plpf67q17+BbEz1gjhgwj6HzOG7bGA11lJt/i/IbxscelO6Y7+Sg89zigATo7tOQ+/wuFM6FpL2EcVkEnLfOG7TvH1LAvNWOBB6XsMhA5hm1wmTiUalUKtWVpRROvPnmm/GFX/iFuPHGG/HjP/7jnfDpHBURoSxLGD/mGYIdrbUt4HzudaPP1VelUqlOi9brNdbrdegrU4XnR97pXOBz6Ydjbese+fbbb8dLXvISfOu3fivuc5/7bOSirlKpriydJvD8/v7fz55oLk6NhkHkNqw8fQBPJBN0EwGH3jx4KDeGymNoPIL4XFaHXTaa5HrA6cXA7R5NpA241Df1Oexm36xAzk3qJq+s7EsncqvPxj9yw9mnTRYJZKHb0fTy6XD4H5Bt5zzgvE3twd1U1/5pruKtHC7QBoPOkfx1FynMByiG20t6Hk6D0GKn7k0H3ZPbpe+zXN0PRtj8mXzviXjk/M+VOUdh+t3OO2mTIIrtc/pEtMv+d3DBSv/DzP6y4Ob/UxanqFSqq1qCVm4Kn6eIZqz4QfFQWgE65rbzbsgj+uHs+MH1UsVQZOhJJyw8nRDzKHwODB9jrgwCyD8lvuR6T5nfWxB6VBYxhD5UEiFfG16vszA8c2gXFgh5zG0bU3DXX5zDk5G4nFf+IxD+EsWwudTvJvHNkcDFsjAibs8nKVcm5MDyAKE3QDqTd0WnJsfbcnI5KVk0/e1xjRX74HOFzlUqlUp10moB6BHgHQPoLRhY5p2PP6Mub2XZD58neQ3hBq6xMSgs7uEpODz1Gh0A5KoKbucBbo9d1V3grCt5FFn7ezuhkM94TpJjF+0oDoHIKQG/w/hDfovA5jjvAZ5P4LdWexgAy8ec0FtxToDTt6G03adwORKHcyCq39g9PgOYt4D0BDbfRC0AXeFzlUqluqIUu5PHcyqPfexj8cQnPhF33303fvu3f7sTRsIZYwJwHscXS645dV3DZq7dcySgpV5rVCrVadQYdC6KwXPpEwVAjxcB5fabq4997GN4wxvegOuuuw5f8iVf0urzVSrV6Vc5HmT/RURPB/AYuPmL951oZk6BeOLkj8DKBEKM0o5dRIgowLlEQxevgSmtdLKws9k6193g4ODic/DncP7ifZl9fv2FePaFlvnYHC76szCnPuEncovwPVuf5ByW433i+IcWIwT4PEDi8Y3mQnf5kC3ycNT8umLm0JL7jrkFmqZl0EQEJjSuz2QAJK4wAvp30pHv7bIcbrfkw4wd4WZy7Z9Gztm83PmDcB7Fvy/MDDrlMQDDdQDhKRMWIQ15ELHhOi2iSTP0cxbyNOjaPKX14H/t+e770J6HRZQii+FhT/x988a5qP+FHKsgk9sVD7jAgwxAHg9L+o3hk5Xlv53lW6VSnQ4REYwHAZf2kSGuTBwCuMrDYQGeuSctcR4nAKsMyBFfDazEI9/9eHkT/wNmhiW5K9kmnuw8lQVYHVwThvwxSvnFozyO/u4bDYaRR1RW8HGxjx/ojqcEDGh2b67WcVrx3zLStJLehg9m4jyYaAwdIFW/XbaRT3ds1CBtZR+A5zEFSNwD56Ili0XiRSD2mB+aSfsJiyqwH8B5KpmZcGpPmnvcfCtviThJpYt8jvsBatPPtvsphc5VKpVKtQ8KLs7ui7tWyfwfc2ssLnO8dNzwq8DxZenuA1JQQGAsZjfWFyC6rv2zlAGoQObtrHXAsR83B2Ba5kfHrtmRw3ko0xjmNgYUg90hA8m8ty/bTmox3A64/AnwHIHgLOUQpdGCv2PoWtIWmEJ+66nf1kKFuIzScECor6Fyk3uZrPv6DpQupGjlQf6NYXxR8vdJoXicnI86jlSpVKorQzE0LtfXa6+9Fk94whNw+fJlvO51r8OjHvUo3Pe+9w1gY13X4e+h/j5e7FSnC9sWStKO865SqVRXsqSfPDo6QlVVk+9lBUCP++OiKFr9ZBp+Tr95xx134J3vfCfe9ra34Z/9s3+GM2fOANC+V6W6mnTs4DkRvXJg860j21tRATgH4GEA/l70++8szJoKDRQ8Z9LV7VODTBG+T4bP4dHJXHp9k7/pxBgzOEDlMbArEEP4n580HXeeCJPSZCLzagPwvBueUZh5x2qg83mvlWJbu0laUKc+w01lmMBM4Z7xtsPWOrg9rY9tldGCuvI5QxajIAqAzJJ0cniwg8UziJW01xj+8uU1rDmQ6kKgVeBpd9LO2tW1qWL2YDWbDcnLDhUvXNkUpli+eKUNf/dEvjhfQNP3dsDyVv+bz3POdSntK1LwZ1NtClduQ7PejIB26UWPg7Lxxm079JNbOGdUKtXplMDnAGA36B8J7qawiuKo0e3vcuFSVb4vWw30W/I4PsC0W+rjHPwuPsfbk8fHR3zP24qPsU4W3Qm0mXMPjoFO+btAeyGAbCuj+MecLYZA9Dg9uZvaziMeJ3EqNz6f1qdVeAAm3ha7ag/FxxPCnaQEOK/RBcWbZQzjkno/btgc6ALngGuL+zwi6cLmvl3T6YHOK3/OnESbkHzURCgR9SVXeNmqVCqV6nQpXJcKP5/ioek+AD1sP74MAsbAlCWsz18HPjemcQsvCpfnCfC528wurLV+bh8NeB4tCO3MJwrwTeQAbg/FC5Qt7uYyP55e/9na1mJZtjY7ZynQOdd1yF+A0Ev3qJIBVy6ZPIY/gQb0TgD1PvOMVjwCwGWcweP4J0Hnx+hyDknP/dDkIYXN5ZnQznO1TPEcqM57qlQq1ZWn2CX3Pve5D5785CfjHe94B37hF34B3/It34LVagUAKIrh59rxm0/EkXeb0muMSqU6LZL+8ujoCOv1GlVVhb7YTFgAK9C6tRZlWQb4PGcYKWGn9J+Hh4d4+9vfjre85S343M/9XDzhCU9obdc+WKW6OnQSjuf/E/IUGwG4D4Bv2iDuTwP4iQ32v6olDuaL97d1gIgH3bK9HEAIPymWOkL3w+99EHXjDI3gRBvcjqlx0m4czadchKeHHYgFJ/GIvqnPZXUaQ+FpfQpQyrbqLBAYndxtQro42Lo/TeHnvze7EWyB8XPhaHaulox09XUOHQ8JtibtCQadaV0yLt4kP/E5E6flUuug6hhqR80DhfFFFdFajFlqOc7M332LmpF5tsvO312csjm3mZaS9pGAyLn4hkDmbAq2zixiGKhNZjhzftPte7P5otY/W9eC8xrY0QTTlGz0nPvT02B9CKNSqQYVw+ebwsIGQAW03JlzGoPP18woPBg8pBh6BhAcsJcvMGIw16j9W2W2C587mL0OsU7rk2NAnNFeICDgtIDeKSAe4ohA8cKPxQQ6lfgt0WQH5DEQ3bgfUW8ZbLXR/YRBM85pAehoQOcYcjZol5047FsPwO6LBAzOAeci66/pfS0oXmRwkovtpC6kvvYV8hdJPtN+ZZ/UWcwzcWwXQ+cMbP3cnCtmRkWEcyeaC5VKpVKpxkVEDYAuIK64d8v11D+k7wDgu82YA8qZHYCdcT4nY5o5dmNmwefwYbiuXfxFEeILsHVsKjO0f1XBeMe6Vtml4LmHx1tly9w4sAMOaF+vHcTu7wG4qsLxSpgA3w/kL8xPiyuqr1sa2U+2Szn0hi+KXif0EJfA3sc1XxcDJVE9sMyVnvAYca7CwpBjcopXqVQq1e503/veF//m3/wbPP7xj8czn/lMPPShD0VZDs/YxRDktoFzkbinjzmuq1Qq1T5L7hWttbh8+TLW63Xrd2YeXegTq6oqFEUR+kbpH+M3TlhrJ/Wdf/Inf4Jf+ZVfgbUW3//93x/Ca7+rUl1dOqm7+pzJWu73OZ93AvhSZv7YsRzBKRNHE4KbTLIyW7CtMRV2bkDmpCmS6f4G+LhH0veweOtixtzal+X7RDhDXL1pwURYs/8JaFv1OTWOGWm1XpUpdb3Fcsq1n+mafhxEycCpbwzV16Zzjuk9jjD9+TrmgdvCgWL+/F3QPrPJZ37MtccJb3SIAX73EGHzS6Us1hjS9EUbrZgX5SdKtZMu2zr0W8zWX2UT5x9KFzlQq38kKjZezJTXguPdwXOXKdc5Mv4BFUU3bpPaUrSQ56SuHSqV6oqSAJfb0Bh0LhK32z5dtrbj5j0mcbteEaGgJW/M8FAmCtTMWG/9wbs4JFIEoE+TAN0lgJLa7ssU/V7kxoDMsMzOQdv/7XLjgHUZXRXAonKL4698uYVFALNimp6epFUDIc0aaOVhHeWlF9LeST0vUwXgss/PmBt1bnlpDbdoo/L7nxR0XhCFc7DwYP++oiAkeYzyue/T2RXmD00tGuh8ah+9S5Ev730591QqlUqlmiQBjT1QLDB27CItvx1blsoSZrUCrVbddGMYO877gjlZrmvY9RpcVbBVFRzHJz0XsRb1pUsOEPdwN9e1i0fAhHhesc+ghtnlQb5bC3t01MoHe6f2wXwJzB7FExzURyDwAJnHxjqZ+jZlOQ0636XTuYDk0eJdWUgQwP9jclrfpVjqU6VSqVRXvC5cuIBf//Vfx3Oe8xzccccdo+GttajremfQuaiu6xZMqVKpVFeimBmXLl3KPjOw1qKqqlnPE+q6xnq9bvXBq9WqBbDzhGcUr3jFK3D/+98fP/zDPwwAkx3YVSrV6dJJGIR9X+a3fwv3/O39AF49MR4L4G4AfwPgj5j5f2wne1evnPP0dh4eioNG1zm6K3GoJlO080AEosJPWLqLXifMwLEIEByDe87tt3kk3TiaD0OhwfF7g0fZx+5YOwGwnRyVLydG4xwP+PqwMgnKmAtJBvflYAQtjuOuPjYtL/IOLnPKQdwXxQtyUh6muhqLI3qrTdrJXHPsQp8PgJ3TFvKmgpbzzhwJQOSPYRlsPTEdtrPbUBeo3p7CWx5mSM6JbcDvabzuj94Q2T+D0nKl8L/O9tPk1j23T3V9SfuvKfG3Xm2VXLdUKpWqTyuiRSBgDDPOiaMEUBP1QraXrcUZYzDVa4GZYSN3ZQOAxeEbY32wDYv5OOovK3bu69voQQmMAjUqlK005sfTQOIWzbEFx3F/zOKCPhZPHEesGu1V3nNyHDuqr5lhElf0bckyhwUKBSKHbW5c2Rne6X2gLNbMKLdUz3NlARxNgM1b+zCDfVs/afdqkbjMA5j0xoKTkrTFfc1fn6xfXMHMqKWfmzBGDgsRsF/QOeCuF6nEnb3G+AIllUqlUqmOS2EOUoBdgbqtdXOtHvCV7VQUAbLeuQR+JwJimNtlvJl/Ffjcb1o6JwsPjovrORkz7jjNDHv5sgPkyxLw4Hkop9h1PGsCwm3I3Oeh5URfVaCybPKTGmAURXPMfjzYKYOx44jqnooiD52vVi04PRvNtoDvuKykHEJW21A8T62rK1Snae5YpVKprlYZY/Dwhz8cL3zhC/EDP/AD+LZv+zY89KEPzYZdr9etZ2Htt607xU6+22AdxL1XpVKprjQxM46OjlBVFSp501QmTOxkPlVVVcFaG4BzYwyMMR2QPTdW/57v+R7ceuutePrTn47rr79+dtoqler06NjBc2bugOdE9G/9n3+R2646HjngckNYmS3gQW73ykPbgZXzacvFSl44H91IkNtfJhNdXEmYXFYEPk8AWbYMkExmNRADgYfhTmZPWsyne92uFqDprznZWFNh6IkKQDZ7WBwyCVoCqME2P9AZz2cbYZF0HPptmnRmRSn1Og32TJWHfefG1dNOMtEEAD8O1gN2M/ykfNY93YJwXG4n811IG+h8xxAtM3hBvQPIlt02J777+9n5+XWLEOCLdsYDJ/ZtO1dOEk9mMqd5LW/GhSmqz1ZbPobFEGNy5wy2eF7MqSsCUQyfL02SwciUvUqlUnnJ2GdF5Bx1J47/BFqOVRBNhmELOBDUAlnwtvLwxhz4vIrAxgBjYw6EDlgUMKg9AMkw2A5I60aI2+uLBbC3cmwR8F3CHXNf2cZxUBRWykag9NTTJ4XRx65OZQrIy8OgKO1tqPYLD4jdHYA44Ausa0fSrNGU53FIXKjHFgjEYv+xAIh58nmxS5E/Pwn7C5zLWx22vfDhuCQwdvzQ1PrzqpBFJ5lxqgDnuX76JCSLE6inT5eFJHKsFRFKhYhUKpVKtUdqAeiAA7kFQGcGebftFoAuv+02Y8HN3BI5d/F4m+TR5zm4fi9IB/DAdl27OIka4DsK05IvE16vYZhDHmLgHEShrFLZw8PgZC5py5y3OF73pu/3iesCxgAeeg+HNgI4iIN5gM4zxzkGnfc5pI8qmH8k/8ZBetIIixJGYPgrWtKei324O1KpVCrVUsk46xu/8Rvxnd/5nXjjG9+IsizxwAc+sBO2KArU/m0lfXMGMYwu8PlS3sFaC+sNE1er1aI4VCqV6iQkbuaHh4e90HkseZNEWU7DQKVvlf1Wq5UzHinLVt9pjGn1w7/8y7+Mo6Mj3HbbbXjEIx6BoigCvK5zwSrV1aeTcDzP6Y1w8yt/ctIZuVoVJl5hHIA9AnVn5R22ydHJAJObvISDlQWUHbrYEDWvdHf7Nd9aIG4EPAzC5x44FFjdhWW0EgE1QOyIs3CAmdl2gOlhuf1ybra7UuM4H7m2bKgQh03qk8h9p5kArIsUngrtpCP1scT1omnTNNpO8tlybVnSHnczF9DfL2LoBW4pA5VzM0kfhevJ2IlPNDdvKZjoes7NwwSCDetTlh3HlPOOs5P4Y28BSB9SjL2idatacn5K/0VRnzYpKXb9QqacmDlTTq0Os1MmFP3WBaPlXNi8HCedhzlt+5yZkwfXiTRtacoiAfaIUVSW7twhd03TGzaVStUj6R8Kdo66w2NktADBWAJCVx4AHut1BAYGdd3PLRxQLODoFDFzuIdI05GPQOi5/ANu/Cf3NQ7ydAOzzaBa8vFuvx+WZbXidh67oIsL+NDxpmUT7y8AuuzL6N6L5ED0+F/Jn43bFVGA2qln37kKgK5vS8bnXY5B8pIDpAXmTY9j25I2XaO/Plr5Sj6SVym/k8IrYpi7iP7eF5moD7hSgXOgadPp6E/aTc79PN6nFph7S4vJl0oWglDSVmR8HvoYtAF7jsfCKpVKpVLtiTpumsZ4kw/qAujiDM68WwDdA8emLN04IIHPYQxIAHkPXrfc0ReI/TFhvQZ7CD/8loZzfzinc9lgDHi9bmDwXBp17cIY4/LrXeYh6VQVkAPApLw9lBwMiaQ80rHFABCehc6T/U1ZDkPnU1zO+8Dy6Htf+2kB5pnvJ/0sYJfqfYSiUqlUqitORITrrrsO3/qt34qXvvSlWK1WuMc97oELFy50ws1xxc05oU+VgJICTxIRCr/gT6VSqfZZAoSv12vU/o1RU/cT9/N4EU+u34sX9ojTeVmWoZ/OvZXiox/9KH7u534O3/It34JHPepROHfuXAirUqmuTu0FeM7MTz/pPKic+sHvccUPGF1c8Myhmz5yjuI0ChG7h5cuAx34XAByIMCMDijpAfkCuMIQV15uQbAh0ei3cUiRAT8h7fI1Rc0+x3fR7S/LzdQ4glOrflwZm8nweWgHPWXZTqcLyUyTb4hLxMMLEbpJCeQuafbl17v4x+7SJIh8HF0eGhtuS9NA3+28wpJGYODuoofmHFtYJ1Mn+bObxtLMxb3d89WVF4VFDVPylnfg3zCPWTC/3Yf7H1tnz5iDUFo/6QKObWgxgL6h0mvcPHlkamJ3FBZxtX+N4lKpVKp+GQEBB8YRfdB5COOhCgFjl8Ln7CF48teBqT2YwMZ9aUl3yiFNao1L3TE2gOTm8Dl5t3NaOqqckELzL6FxQYe/ZwlQp0DoAwA60MDbQBccT/cVGD13pen8nYkjhtmHIPapSgF0cXwWJ3TLjIKo0zal3Uva23TvTkHgIRd6Cd/65ACeE5iMlfMwdpXfF7CborrblzxtqnghSXa79NVAcPsHGkf9fYLOTdR25HxPXc5TSV++zXsBlUqlUqm2qca4BG5eVwB0a9uwuSym2iWALoB5WbpxUAwXxPA5PEzNPM0QpJNM+z6RmRsHdP+33Icg/hcAvOsdJA/Wm/OIK3l8vWfnkt6KP4LOBXJvjeetdfc+4uzuw5HkIQUaegCKsNkD4+TB8xxcTkXR67ZNPs30uLJ/R9+ntI8WRH8VAucqlUqlOr269dZbceutt+Id73gHrrnmGvzDf/gPW9uXzHN03lwzQdaPbeJ5TGttgN51nkKlUu2zxO28qqpFi26kv4uh8HQhT25hT+xwnva91lr8xE/8BP7BP/gHeOxjH4vrr79eoXOVSrWXb1JWnbDchYEAaq9iGlYGamF2yCK7x+3M1oG8E9zUmzxQZzJR4N54Am4wn2EiU6JsX0CbPMpvTT5784cGBBkCXbv5OP6XZC+rz3HJ5DDLpLTEPVYfrcyZKK7hdJDcHE6VHP+S4+bQdpuB1khqELh03CGd2oO1XFvqA42l3ULy1c3zrkXR+ZdV/BChu7E55q2qXV9zdKwD4sVtMY6i3a7n5d/1RV0AKvNKXEeRNOma5EGM9C9RHvJ52WJdL6yrrUA7S+JIHmotbWs8eE6pVCpVW8HBOF0QhAaC7AUh47+5cXYeUwyztuLzcczx4xOYcVJ6AApq0nbjUESguLtWMQMW4/HmUpK4jguFJThYMz4mgT/T33MySZi4rKa4W9j4g2ahQqyWw3G0f26/NOxUSXw1HPC9Tj4BzM3sM3w3N08Wrv2ufV76oHM5Pht/Rq7dx3lVlzZUEoU+4iSdzoma+0YT5Wff3NeXSpzARxcpJOfMPjmdxw7n8WKO9Dzry+eUvlylUqlUqn1QGCML4F0UDkj2TuDxtg78vN2MBOdzpG6ckjeZfy+KaU7cafw94ZnZOZTXNbiqgkN5S9Y6ML2qwOs17Hrd7GOjeUVmF8ZaB5yv123oXOKN7kFC2Ohf9u6kg/nvOZ5QTkCot9b8HByYTulr6HPz9Mzu2OUYJI9x/pkn3e9QlLfwrEvA+DifCm+oVCqV6grWV3/1V+PcuXN429vehve+970A2nOPSzSHcRDoUuDz9Df5rlKpVPuoGDqvqir0W3OU3quk7uexo3m8Lf1dPuv1Gm9/+9vxZ3/2Z3je856HG2+8cdbbK1Qq1emV9gSqrJrJVveYcWwg3+uuEU8cxn9j/OYiBaajLe43xO4P4xNyAaqkaLI45FFgc/mJ25Ol3cwF98R03yk67puZufU5VQ6EjMop1EPjJjKQqTS2wXTicp4Nn5vlxx3a9tR9IxB3QsbaaeXA396McTtvJzAZnYf6XZvIgc0hhG83/a48y86P4T5lpK9JIf8NFiyMqRPn5PYchZv4kCUbS9QPd7cN9GWZti39cKsMM8DhdoHpJXWynbSXLB6i9BrWuab1ppZJX86vkwPQ0xtVlUq1v4pBY8D1KpWHBYeulpUsuJPfFsDnaT9nPSg8pxetB/LZl24BcWhvwBFO4PN6Vj4EOjfgE0Bh28cUTQqiAdD7HrpQtK+J9iuRr6M+pUC5ABUCY/fB5RK+lk8mzOQRkG+TAn/L55AZRz3tU9rtprIAjphxZG0vRB7D5nK8Y8B5vO+uJW2kQLMoRdrPSSkGmUM7xekAzoEGOp+70KKKFlWc9DhL2kzoe9DUT3Bkn5DPfYHoVSqVSqWaoiyALrB5AqDvbFkqEVAUg/C5hKGiaBv0TI2/bxP8+D8By2ODkTDPV1XgoyPweu0g7Ag+57qGPTpqwkWO5535YA+yp9C5zJeSuJELnB2eByX/tg4xKidfj51wAp3HoHkEx4skz3MB81zZhrpK21QGjFepVCqV6krWwx/+cHzN13wNbr75ZvzyL/8y7rzzTgBYBE/GmuKsy8yo4zfHRKrrunebSqVS7YOYeWPoPH5mJAtuBCrPfVoGMdFbIeRT1zU+8pGP4JWvfCWe//zn4173uhdKv4BX3c5VKlU5HuRkRET3BfB3ANwA4BrMmMdj5p/eUbauOhHJK9cJhD6QdNrg3EG1DsILbhXUfk1HLn2XggGQQLkR/A3JI5HzMRwEXmuQKcSzvLXSVbbFeSZkoNRcvLCgkXA+NE7qkfq0+pyhABdzC0B1v4mrSD6dtKyYHdYzVNZNGGpY9zlAOAPL8A6pM5q0PxkDtnNRF0kjbR/DabKtPVgf/TahLLchbiohLECY2qZCu6H8q0yXZWhgU9+gvAfan/3QZraaep0KM7O1Uf+UQmYz8prtxu1wmyXq7d+oU4bH0L8RzQD2N9dGfSX51wO3Fg4YgIf7iHZ9n7xaZeAXA5EpBq/hKpXqZEVEMBGwO2VkUkXneuEiCWBhBWA14XwnuJvMirqvcL/MjGtmuCAsGTULcA0AFgTr80FgGI+C1syAB3D703bAOcJdw8mpfUxNXcriAguE4+zbl30Yy9zsR+TKYkF+SjgIPLd3CqkS4K4dSTgThRur53QhRM0MQxRg75IIJVELXmZmrOEcvue2I1mEsR4onwCcLxwnyLGkbwnYpgQejh3FT0LxWIEA0FXwgG8ubB0WB/nvJ+0SEUPncdsZcv3vUxgJ67hRpVKpVFeI4jc9MpEbu7Azcgh/F4W7tu1qwVhRhPEuxYCUB7DZWkDyUNfgGXNlYf/OhsRMoqocFE7OYT2GssXRnA8PYc6cCTOdYR+g5XTOdd2OP3IgBTNYAPGePOXc3dM58XQ/AtrQucDsklYE1LfiDIuYtygBzBG1L4XNVSqVSnWKddttt+HOO+/EL/3SL+G3fuu38NVf/dVbibf1NvFE1lrU9fAseFVVICKFJlUq1V5KwPP1er14sU4MngtYbq1FWZYBLJfn+9KXGmOwWq3CvvHC249+9KP49V//dVx33XV41rOe1WuGpFKprk7tFXhORBcAfCeAbwJw/4XRMAAFz7eocMEhk4WI+12L8xIA3blis5vobPHkPS4VMOAOqEcOzPMQnAtrADAY3DvZGmBdMiA0+7a2Ib5xkdc6Jq61SCZIub1vPu2ThfVimH9T+Dzek7mnvMmAqDuJOxzrQPn5dMi7qkucY2XpoHtexJ27Opvz6J8mh3dtwYbDTqFxd36MOKGfALfRAsnkQcGstuTbg8VW4Nr5oL8TRf/PbdmZiEDZPm3Krib5ngfNcmqVUzjV2n3gaPqm8OdEBykbS31iuIG0ZxxrK+VwXm3Q1vYEkBLXc9c37P460gbObXLu1wqfq1R7rgCf+/6z6gknwGOqwschcPKaeRJ8DrgbzdqDzrEuWjsZPp8CiA8pwJIUOZez8y+vmcHUAN1h7A+CDS7p+ycDBxHH0LMcJ3swJQeFBgf0CNaWuGogOPfN0crvm6bXF5cA6ZIfd31uvsfAet/oLI7XsnM8N0SoIvi89LC1SLZNHa3UHlgfcjfnnu1LZLF9yDh2xqekPI5bkpd4GW+9cEy375JjqjCvfUgfLO0PcID3kkUT21ARpSsAuvQbS2R9X74/SypVKpVKpZqu8CYlds8xxJGbrHVjU7lu2i2YvKQyBubgwIHcHvSGpOnhcSoKN+M2J/2Bezpx4I6f+YgDOvw1XbazB7vqS5dgzp4FvPs5iMI2juAvmS8PMLq1MAcHw3NKM7cFcJzZgfmAc1UXoKIsO8fX2XdLc1wth/MdpaFSqVQq1T7ri77oi3DhwgU873nPw9Of/nTccMMNG8eZe04ooOZUiQNwUejzLZVKtR+Sfm29XqOqKtR1vQg8L/w9UOxwLpI3PhTRm7XifyU92W6MwV//9V/jV37lV/Cyl70Mf/EXf7HRMapUqtOpvQHPiejRAH4TwH2wc+JPtVgpRLwJtOzdxLlm92rBCe7MDlDNQMzejbc1+eofj/ZNuDrXdeP3LVpAZuPI7h+PR+m1IdmuG3Uc78DRYy+aORmXTZ4/MT4P9BX388YlNxvnRIjchfUO+rOgZfLQ7AJImcP/pqXk4dxJYckAAfwNHo2Y2kZy0LDbe8ftrLPgY8kDFvG/m+CgakzPAob+/Ydilrcu5DeOZmcLWrIQgnv7FiIzCyAPb3TA0AIizpdTMH6a5swTwLI96PaWaR6cLwr9U1hoYFvbRheV9LZgDtD3sWjBNUKlUu2XDBzAGEONoj7ovE9z4PMUXBcd+TimxOIA8TFg0Xi2oD3GcyN5Dh/3o+tbrf/lKNqay1EzMvPgevJ7LuxxKAXJLQAwd+DynGLg3Pp9hqD1IUkdT3F3HgXS/XghfPzvw8t6XZ4tHOxrmFEQ4cB/ZARTeUB9KK7Kh+uA9NjM2XxM4t6+LRVynP68OYnh1xw3+9OmuSNGOVdzffGUdrsLyXmzkr4Emy+0EPh8byYhVSqVSqVaKmMCgA4PUAfHbiAA1VsTEcjD0ojcxLPwOeabBOXSayk23alrB5L7+dnYhby++26Et60yuzxH+0q5MODKbW62IudygePT+AUoJ6IQPi4Pc3DQP68aO6NvqCxwvsX4VSqVSqW6kvR3/+7fxU/+5E/iWc96Ft70pjdtHfKe4nKeqq7r4NhrZhnPqVQq1e60Xq/DZwl0HvevMVweSxbelGXZ2i6LcKy1wSUdAH7zN38Tb37zm/Hbv/3bC45IpVJdDdqLZz5EdB2A3wJwC9oY3kcBfATAxZPIl6pR+6IDD9HZxa/3aOLyoLGtwZH7eb/reTv99sNP6oJ8HpTvm3AN4CWZDpDcOGu3oUtm20DyGZhQwgDyipHMDZS4g5ygWq+iGimnRQp1EB0nEYwphx9aM4MRlfFoOq6sXbOYVqZzAV0AmO8BSfPSabUxbiaokdRVbwZts1DCBR4pk2VQerPgZH6JdJIX+Ki1mnwo1u5CDwBgy959O5thcAb/aBaWZFIhh3bscoW7W5E/f7/YGTQT6WDxZRcoDALFvo1kEjTGY0wzy2jO4pIhkc/XccDQrs1vIaJM8x1bsDHFaWEXbgzNmzxs+3s2sEXvAg6VSrUXEhcYccWOp+EDsDwgAVhjILICJgOt4jgeQ9CVv55t182XWn+ZgJYD3Y6cA1jplrUyGN75270joxlL+XBWft8ww3OA9alhc47nApdLHef68iIKI2MMQ/MBdIFil7pY97qte+hWRrjxMs3cXRYzO5gero0dAjhjDM74OBiu/QcnfK8abkFF7GIuSyOnAPXb0DaWi4pLdQDPN8/WLEldpaOCdJxyWhezyXHNaTOy+EPaZk7WL75J2+0uRBEYXhCFt2Rsq84Yvn9S6EqlUqlUV6A687MCoHugmsTFGx4y3qYDuofMqSzdmH+9bv0ew+dToW7KuX5n5kslDNd1eOslr9fByZzK0qW9XqM+OgIAmNUKXFUO9Bbg3Fo3Pp0Kdwk87vMa4HK4+57c8YRyMqZxO/c6Nug8gvEDIK9jn16FMlKpVCrVqdX58+fx+Mc/Hs985jPxAz/wA3jRi16Ec+fObRwvM8NuMN4SWL0sy+AQrFKpVCch9gt7q6rCer1e3K8JMF6WZWdRTXju4f+tqgpFUbTCCXRe1zXKssSrXvUqfOQjH8HXf/3X4/73v//Co1OpVKddewGeA/iXaEPnPwbgR5j5gyeXJVWf4lcYkXGg42aTqOw4bFjnfk4EmPwKrFb6ZIKDdnu7gaACLl804JQskIX1E5MFYhfn9nYjP8A9TvYTsXkWtu1im3nYTsS9kP1xqqlPOf4JN2lTYGoiFy5A9hQg/lGXdWaIG+WYXFnC1xuFV3bmsyRtZxjQ7c2TOFRPrjP28LmgK8OKH2C0211fI4tTgge4gRg+z657AEA923rjj4Dz8L1zACazIGQwx/6hgbypwDQw+uSM2fHqyOZnwH/xGE5JOecmVG1LG+F5uXKIHtrkw/eU0wLQuNOXbiJna7ssD4v63PlpyeKZ8YUj5A+np36Gsuuh723B5w1wHl9Dx/fZl+uZSqXql4yBgus5nMPuVEBSYFaBx5kZ9Qw3ZYHXY6C5chEFSHZIkufhRwDuLUbEFUxAlKeNSRqQmWFh/AIniuBj74ruF85tojDCjRf2bhRjuw5k4aFA2GE7UQBc031b+fHQ+lQX8zieAoD16WxLEhdF+Y9HKALLx6C1gOzsHTqOiHCGCCu4tmipeS+QAOdx3PHI/bgg6RrLJ2fk+M2Mc3Jboqjs03SvxnFBhXnQucwsjJ1rzBza7a4ehYpDvvwdetEtnwM1/L0ors42olKpVKorX6krXACkIxd0ttaB6TLvNne+M5+wf2usG3+14HMZ3xoDlGXjij4W39DvfdujYwIArirYixcdmO7np+qqcpB8Xbecz5s5acrHJ3Pi1obj5br2ZkXJ+CEzRiEPnneg89WqczzBlGTAnGSOwjMJycNYOapUKpVKdZXIGINrr70W3/iN34h//a//Nd7ylrfgtttuw/nz5xfHWVVVB6KcK3kWLwC6wucqleokJH3R4eEh1us1an9fNVfyFoccdC7b5d/U1TzNDwD8wR/8Ad73vvfhgQ98IJ7+9KejLPcFLVWpVPumfekdvir6+8XM/NITy4lqouRi5ybmcgD47Bg96EYgb2JtwsRpqhggzjmOC1jcwHzD+WwcptHEiWgCNYLTAWrBzgJU90LtApamx5FOsp6gOu7nI/U5fbBDzj8ywJPR8Uo6UTkniUwun2biGW5iv6fduDBSf/Ndz+cqvHJUgPVRBVym2X9OE4lh5vBbBvSdWLZNPbfPhUF5p8PhcMnDFvY+ow0tNJq3EI+vyzkacjv3IWbFt1zTFle0JAs6kmNu2nWP02iP23nzey4flP2ZovMrTWuaO/cmAPgJaCls0zm+nmvFGEQ/cL66a9Pm15JF53omj1dUvapUV6HI9zelB8gdQz39XE/P7rnwOaFxP7cysU+Nm/XgOhvpZwbChXjCGH2uOHJKd/8nwP9rHI5O24GSGUvzOBBfJCmjZjlu+/cUrh6KU8DYnIbqLN5navsYUuxEHoPtRARibrUjknblF1fIgoc1EVZRm02Bc4n/JBy5mZ2r9ZyraAx9l9G5tGtRct5Ssu1qFM/sUy38gpoZC4DCmwFo+4726eINSW/bCjAadgfQq1QqlUp1nGrPqTdguMz1yDw1mAOEvtEVVmCBsoRhhhXA3BiQtWBjgqv4lDebduap+mBpCROD9V728BD28uXggC77m7IErVYwBwcOPpeorG0Zx3BVNfn1sH5839eZx83M64rLeVgAEMmUZee3bUHnYQGv/2wMnJ/AfcjO0p9QtlIPV+s9hEqlUl1NMsbg/ve/P77hG74Br3jFK3Dve98bD3/4w2fD3rHL+TbmLawf0xBRL4SpUqlUu1Zd1wE4N8bMfpuDsBvGmCx0nmqov2NmfOYzn8Ev/uIv4uEPfzie+tSn4qabbpqcF5VKdfVpX95h9jD/7x0A/reTzIhqniiaSJsLf/bJOStbMNcYcsUeTTs3cTWQz5ajMxEI7X1zjs9TnKyZBa62rbCy7z69ajwt0+yAY05+Ywg57MbJ9vzkYlPe0xXqw9fNYNkuunnsd1TJJ+EfBmDGJDYlwEZIa+okbLtNbrLSO8Q3pTyDJiAvjspwobPHmol1qC3OrcqBcoxXe+5WbWBq1p5j7boTX0/dSf/aFx311CUZiBN/J+97PymzrE/ZWj/dd50cfMgydo1x15a5eeTWee3721nnejuuE384plLtQMwM6z+88PzYRwkALoDurP2S8OyhyblpSzyWOYCXUzQEQHtP8t6x5fQ8ugWnMoqDd0APeYcDJuU4jO/D9+nBRAyax27UJPlFe8Q21L5TWN1G54XUXfzhZJ90e+4zJxynf/t8VMxYR58q+qyZcciMi9birrrGnf5z0VpUcgzRuX5Smpqy1KOBexOBvDVgly0wpOnTCp+o/e/TOXCcEiB8Tl9Yy3mE+fdrNjrvtiHpE4DNF9VMkVxbT2qRh0qlUqlUu1AYDxnj4OmiaD4CI8dg+maJuXjK0n0kvgh6D+mOKeeElwsXzxnVdWNaUNewh4fg9Rq8XsNevhw+9cWL7nP5MriqwAJPWOvc0T3AHuKLxwXxuFyOb+gZkS+TcDwy7yy/SfxxnHG8Sz5NBtrzerJNwnlYf9JnaV7Sz5w0t5B+uJ+UOs3FlWtnG4L/KpVKpbqyZIzBM5/5TKzXa/z6r/86PvjBD87aX6DzOl7stqHkGmatDRC6SqVSHZek/6mqKvRBAo9PXQxDO5if/7Vf+zVYa/G4xz0On/u5n7uVOFUq1enVvjien4F7xvVu3rUFsWorImocwZ1pBweIGNj8AWKA2Mh/TPMoPb5gBlcROLi3m66f+EMEsPt8sksom27sTkEhGLe3d441xiw6BxTSIxOt3h3Y5aQUl6mr3/Zxzj9FKZrHzcDGRAju9D5NEbMF0fzVzgCD4Oowl+aoO/RYGh5qGlUM8ksbGInZtVkT2mwoA++OMxoDczNxu0Ct8uCFbzKgvvMxCSZgc2hjQ2l1nXYm1Z3vmzpp9mdqPM4NJf1I0ycZgMcnStKbhby7tH+zQztgJhPRAwDXYNLE8vvFNy3NSTua9yTjG5Vz0yct2PmY+tzcAobh/oZi+q+1xdXzSMZ9mU51HE/P83B920DMziGYcRwLN1Sq3UuAaosI2m02tsJeKW1e4MgaCMAqTxqfNCoAcNKfMVw5TV2CKjA0iAJAWfv+ayiOPtfzNnQuWw2I5rlCtPPo4HOLMsQapxf/y2iuhEztsPsCVUq5hrxG4L+NvodxM3xdA6iJUCZhW/Xfc4wGjdP4pqUQj23ThQq9YEy8vb4TVN8B2Iuo+TKYKxgwzpgSB+YsyuJalOWNMOYCTnJtvpwHQz2KnCdh4QN2M7RpjWHSzxXS5x2nGG0n/iEFt3Ms7yNCWiP95pji9janH9+GKmasZoxfVSqVSqW6UtRy6CZyQC8Qxqhs/SLXTcfJRQFD5O5Z5XXsft4uOJ9HzyRmHEDzd5RnBlpu52ytA8zvvtvB5kdHATAXmdUK5tw5lBcuoLhwAVSWzbxRUYS3EAmsTB5ezkLckrcwn9kA961t8rcH8DvHHsexVFLHUVmpKUNbrXpMngUR4OrtKl68qlKpVFej5BnZv/gX/wIvfelLcfPNN+Pmm2/GDTfcMLpvDIdv2xxG4hTI82o2V1CpVMcn6cvqug79kPSTxpjQ70nYIS0xVszNyTIzPvShD+EXf/EX8eIXvxiPfvSjZx6VSqW6GrUv4Plfw7me69t291ydi5qHOAl+8tG/0J1yYZemxzWYLYwph0FimCYfnXx69CECPQULyeWzeeWjgOvtmxg3cWbak2aS7sgFnW0d4PN9BvXcJHkBcaAHsGzyNAV9mcFct+D90I6YwQ3p36S5oGyYrQPaQ1lzp82ACsDW89pqmBGfkq95+XbQuAdQIxi5aTOS+Hg8gG+jmJbXrQDnQf4YBsrVlX8ObegBbFv5jxEv5MOHkMkigdE3M+zXedhS+uAnqdM8kJ2fgOGo3edKPF1w0vxuBspwBOoPWXeLKY4bMnHpzd1pAbTY27Zlcx+A7t8QkTvHR7lzj0Ue63meTQF7fQ6pVDMkV5ocmJ06Rnf7yv07DySPVZTXEv4YZy7EM3DAZBy39cc8Bz4vgACf136R3hh0y8yoAKzSa6AHxdu5xAbwub8OTujTclc/aT9SLhtDJVuS5FWgcFkcCf/dJm2B4NpJHFaOawpkKwsM6k0XNwEN9IL2OcdpuOgbcwW2d6O++Aeo7vyvMId/Alp/yEHoqHCZzgOr++PsNY/H+eu+EmfP34aivBlEBzhJAL1PRIQC2KnDedx/pW75qq5kkdLUNs4+bLXhOSGq/T3B0tYa+gJMHclvVzpyVKlUKtVpV8txW+Bqv42tBQnQvVTGwBwcgI+OgLp2cRnjYHeBrpe4giZO6OwdseN/64sXUX3mMzj8xCew/sQnUN15p3M3X69dukUBc3CA8sIFHNzznjj7gAegvPZa0GoFMgZkDJgoOMSzfC+K4OjOde22+WMgZnCUNyoKsLUwBwcQkw8xFCJjGgheADK/7+IFyuJonwHPVV2Fco6f58k5oVCfSqVSXVWKn4s9/elPx+233473vOc9eMMb3oBnPetZKMt+ZKnP5XypudxQOsyM1Wqli+RVKtWxqKoqrNdrAEBZlqGvkz6oKBxPUVVVdn8igpnypqseWb9YVED3y5cv4z/8h/+Ar/3ar8UjH/lIXHPNNYvjVqlUV4/2BTz/AwAPB/CIk86IKq9mkkgm65ILmAe5HRSACNbeEtTGDFuv/aRkGfKUBYk5v/LLAZMcuW54qLz1W5RkDJcTgci0wjloXCYY3Ye5bsHOvYdj6zy4voeKoX5rl72+SgDouD1k4X3ybvSR+7ODVJejFQ7advtnbxQjd/GJR+NgVDBmubHLOTLjnCAyUXjvWgMzLY4A+BcYc46Pz+/t3aTn60sm5nvTiZ1p+jR5NXvSB0wceO98MiFTzq2HIb2KXXSsbx+Z/HpojzlabNNKvu4+1Jq4kKLTt8VtclaxnRxmkn3zwkC4JRqMe8jhnii7tkQWKQ3VkVyTGN1Xb+0eOG/SIVh3LVaprnCJU3N2WwzmJhD6PoLokp915nhWPv9HM8HzFFBmZtRoymOqCr9PzYzKO8+lUHlO8VXEwCbQeZxTA6IafQux8nETLAowAIMaNWTx33TFgDeA4IQeL2g4aYlrNotLIgDDHL7H7bwTlhkFEWqMuzwb+DrGbo87Ll/LjNreDXvp7aBPfDeKw3diJeHioSbfCRy9B5cP34PDT/8M6OARuPYe/y+cv/45WB08aGd5zUlg8j6VRCg8eL5LKWw+XdLvTXU6Z7jFP9schY21myFJ37tpPJtIXc9VKpVKdbVIHL4FQue6DnA2CZS+PHLQwQFQVYB3HBdYm8rSpWfzc4QAWmB3kN9HPqjr4Ghuqwr13Xfj0gc/iLv//M9R33lntJufcwPcPpcu4fDiRRzdfjsufuADuOZhD8O5Bz4Q5bXXgsk7ktc1uCzBRQErC2Ojt9FaD1lQDNILWA7nqh7AdIGageaYfFj4PMlvU8o1/JnC0gqdLxIzh4XP4buWo0qlUl01Eoiyrms897nPxY/+6I/iD//wD3Hve98bT3ziE7P7iBOwKIYkY4lz8NK5R3EdLssyXJ/0OqVSqXYlgcnTRTUCmtd1YyBIRFitVp1FOLm3M1hrF4HoAp2/7nWvw9HREb7ma74G11577YIjU6lUV6P2xcbr5f7fexDRs040J6oBceP8aGsHeLcG8A7Opsj1gUyx1UE5Wwuu1w0An5Vzm82nm8tTBqaU9NhGaXXDcZi4dWECYG3r3sncNO52GvsqcQwpGhftiRKXkRyvw2xdWXUA4aScBybHp6ipo+3CLjwJxI/S9AsY5oRv0vL5X3w65Y9dXgPKW4XOneJ6dH1D4z4/rNTRPNpi03wOAeqdHI2ke5JakLfeYqSwvVvWmYdNzC2X7Wx/SAQyPXlckvVRyH6Cevv5k9M2XsE3tnBpXH2LOqx3ut/N9Yb8NX/8rQIq1ekSMzvI1TvYrgVExLZHHcvFyEPnohLAmZmTUSXyUGq1wNnbwEG17MtvKK9xOsDUS1ABoARR2bOY0S3asTCoUaL20HmzdfMRhEDoLicO+C+InHv+CV/LxH0+OGl7uHnl/86FLaNw5YT8i3N6Gt8SGR8Pw51nFRDazREzLlqLz156Ny7+zXeh/PBXoTh8ZzsvESOSrnfko/fisx/7/+D2v/p/4o5P/QyWLbmdJvJlWBK5su4JU/jtqx1B55JGnI/9Gl2dDsXQ+aZvAIi1pK7SBTDlCUHnovWC64ZKpVKpVFe0iEBlCXNwACoKN3fu/118b0AEWq1cnN4xlAo3egwO43Hcfj4Y3sEcdQ2ua3BVOWBbfpdtzLBHR6gvX8bhxz6GT7/1rfjsH/1RCzp32WjAbI5+AwB7+TLueve78ek3vxkX3//+7lx/+l3AdMlrDJTJ+MHfRwaQP/rOde1AcwH75Rjjv+V7+pGy8Wm05ugFyo/jGPgg/sTl2veJjrf3c6XJtwvyiy9a5atSqVSqq04CRb7whS/ELbfcgh/4gR/AxYsXe8MWRdH6nhsvCYxeFMu5FGbGer3udRZWqVSqbamqKlRV1VpYIyIilGXZ6c+MMVitVq1wg0Z0E8XMuOuuu/DWt74VL3rRi/ATP/ETOH/+/Ox4VCrV1au9cDxn5rcS0Y8C+JcAXkZEf8zMHznpfKna4mRii9lBsMGlgCKvR3JO4m4S0PS6ii/Nh3MMtw5wS+DD8GqlIYfpjMs1mSLrBOvCWJ8GBQfpTlnAO3OLeXTHET1/LGALGAJ4/yBKQPIY/UBd9/dhUfiXTJGFtdmvUI6BRQdfctTuNnNIbly961abiR3dl4HXw/nqrIgmzCLRWu2NXVq514dJGunvzvHdvXa0u3DCNhP1O5q0XgLROtdk9DjHTM2nP5elnifkY3Pgd1yL2lgPlMZgECNbTu4hSf5NDtkyTJtx4gxAQHjbRCaDrk9YgIzstWPA4sUY/cfTuCQMv7kgv328D5Rzp1UXbHd7jqd9977Wp0o1Q5suxLJp38bcchEGju9cqSe66xYADozB0Yzxes75HHBwZUnznM8JDn4UMLPC+I1qBeAANWjy9cdh05n3fgTlthhYWBjwlnHcGPRkGcf5z1QH5W0qdmkXZ3Owcza38IspIuC/gAPAxS09DZNTAQBEi45PRjAM164F3hWQtwZw0VpUd/42znz2/8a1l35rsG14LiW3BdWlP8Tdn/ghrNd/g2vu+SKcNWZrEyeG2m9JGAoXFgNsKe0pedDr+HR1l0/nJW1U2u62NLeflbwATf+zJI5dSEB4bX8qlUqlOu1qzd96AD0A4B7wJsCB0kvGDcbAlKUbO6/Xbh6qqtqAds9+LdBaXMOthT06gr18GfboCJf+6q9w8S/+Ake33z6YjaEZrPUdd+Cu97wH9cWLuPbWW53jeroiVCTzukURzfE693EypoHrjQFFEAYk/SJjgjJSruSPn2WcRNTMy+fGKj15Dsm1vkyo0xn1PmvkNGWcNXUsNmXxsYQRcD/dRxYEUORIr1KpVKqrRsYvRHr2s5+Na665Bv/8n/9zvOpVr9pa3HbpWAoITsRluRcYlUqlOmWy1vZC57FkoY04oIvE/bxPdV23FuxM0V/8xV/gh3/4h/Grv/qroe9b4pyuUqmuTu1Tb/GdAF4B4P4A3kFEzyOi3VOAqlE1wG4OVmw7TbRdpb3zuHGozVahTknXegdXWzf5QHtiq3fiKnZnD79R49CdSS92NqfUbbcHWo/36zkYfyxV6xj2RgH6jiXOtnL8ad37UJmy73P8dsmk7u8Uynkr4GSArG23rJc6To5NVmdcNYcmU3PtRVYsupQWAPghPg7/uvPGdhaUbEvkz6Wl5316vrkfc4tI+iJo52VvxLa3uPvaBUX/b8c1s944D6P7jQA45KG91oSAsXpcZLd4/M42HP2/N4xcX3aZkbE2mV1ogEll1n4rye7PcYXOVadR2zhj4td7soepazhYuoYHwnc87hPofGoac6/YQ87IU4HMWAKfA36CfySOpWVXUNttvHlVem9Ki9KZoxj6Fhfxkk7OFT12NhfwOXZEj8MZNGVa+r+H8ivhh9zPA4CP6Lzx54wVgBcILueHzPhkVeHiZ/8rznz2/8b5S28A8eHwMQ4WaQV79Jc4vOP/wsVP/RQuWYu7rd3ImVnczQsMnzuGGofzbTpRx87mcR4oOg/0Oj5d8naHsX4oLMgAtuqgX8wExmVBj8X+QedAU04qlUqlUl0tao2/yDlB02oFU5ZtsDo3PpN5nsSVPDhqAwHKZmYXl6TRByBIOjEEX1Wwh4fg9Rr28BCXP/QhXPrgB7H+278ddaseHGMwo754EZf+8i9x13vf2ziDIxqfihN8kr9W2ZRleJYjLu+QBZbeRX6OwjyXuM/LM6Lk3h7pJ3UsT13Oh5zOc/HNEM/5JMcx6Vj6PrljTI619Uwxc2wcbdu752EqlUql2qliY7j73Oc+eNKTnoSHPvSheNnLXtYJt3Suqs8ZfYpqedvLBvC6SqVS9amqqsl9i7zNoSzLFgje94YH6bvGoPZY73rXu/Dbv/3b+Kqv+io85jGPCfHrswKVSjVVe7FUj4heGX39WwD3AvAzcO7nfwjgbwAcTYyOmfl/3nIWVRNhN0AmFj0c62wh3EQdAzDsGGVsAYRjBhNAzGAmx74RhWgbV2iXft6h20/utpzRqZlr9emE9AAQxGm9wUE7cD7bBsZjBoOa/XqOJd5vX9y+2EPnfYOf4NJiuXFkCXXf4+NH1MPvcFN3SBxEyHhnffbRb1A2rXokb1ZCUXMcb5ex47g77oG6zeaVRh2P0/Du/w04ng+Tz7vLI4d/dyl5M0B83POOtVFA7AmY7z4d9Ucz2suuz7th8HkRvd3tL5h999t2oR0FkLlvUQP1LhhphZmTbZ8fogULKSTF1nk7OVHZeVGaY/lZvIAlF1963WKelu/Qb6O/X9okX9lz/OSvVyrVNtXzboiNlC52a/V+fgwVj5w2Pa/mQufwaZ8xBoczXc9zDtbMjNoDrnOOJFrSCDDDevC2Lw5x216SRist/wtT+7fWvcAxKs5jyIuMu+VzDPmSfBQ+bYvG0Z/lbzR1IK6A5Osu52wewgKh7cgxAWj9jWhb/KmZUbFzPT9kxmfrGp89/Cvcctcv45rLb0bJd27h6Nfgo7/C0ad+EsX5L0B58CAwDNbMYWHA6OhIygPj7VRcyMuRNj9HQ+mf5LX7Sn5kF+4BMX4OCkxt2Tnyb/OcnTrCkwUckrLgV3PB9V2LfZ8B5tDfqFQqlUp1tYiicb5A4iBy8G5dN88eBub1OuMMcm7qBoBdr128LqALL4558TMNa908FIvZjw15WH/qU7j0oQ/h6JOfdA7q6RzV1DmrKHx98SIufeADOHPPe+LgnvcEnzvn0ozBb6D5V+bdxOlc4P2yDA7tzaLimXPBRO2y79t/g4Wo2Rj7xodj+R/aPuPYtzE67cy9Dy2WSMqX4e4d4etdpVKpVFePZPyzWq3woAc9CM985jPx8pe/HO9617vwmMc8BkX0RhQBLwWkHJvTSk0K587HiLtwDL7rMzCVSrWppG+p+95CNSDpB4ko7E9EKIoiLJKRDxGF/nLMtfwv//Iv8Sd/8ieoqgrPetazcPbs2dZ+2vepVKop2gvwHMD/hPY8h7AY1wN4xoL4FDzfkmK4du4+AgnHLoKEAgzbgB7bgM/jtOQRrAeDm5uKEfiVCIR4u5/I9ABnCm82YLQc11AYlz8XVT+gzABgbQugP/mL+Vj9eGwkhVz9975J5zEQmdm6usxNWvbCsfMUQHmK5niJOrBuVhTvtBCg7QXwh9OMb5jnnZcWZNlDS9t3NKTofIjjZ47OlbnnO8v/aCa4HsPF03C0nQPnM1aupnDiaP59Gbf63nQhAtsJ5Z87V2c8rFlYhse90MalNxZoSnnlNK2tyTEPtwuaeZ5zOGeafmLirhPUekXuDvoQlWrftEuoNwYXAQSItwWey3kchZmqJdC5qACwInJu4xP2H7pKCUg4F6KVK5iAzfAAe06WGYbmxZ9LL/27BZ6TLNuj1jbgeOHvOG+MNoi+y7y02iUcIB3XjQC4rXC+3sW1Ps1beuMfg7Gte6rkU/t7v5qd0/lla3HRWtxeVbjHnf8FFy7/Plb2k7OOb4iZYV7DXn4nDj/1EzD3+ncAzqBG48QuLvBx+xTYW8prykjU+DjMFqDzvvT1ur09TbkriaHzpf1xn4beFpDmIV7IIW1L2tm+idndq+7LInyVSqVSqbat3vFAbrEmkRvvG9M4Y4/F043EwefM4KoK8REQgHJJPzhgJ2/HZGbUly7h4gc+gKPbbwcfHYW4WwNnIvz3T38aH777bnx2vUZlLc6VJe5zzTV4wj3u0Zonjv+uPvMZ3P3nf47i2mtRnD8PLsuWe3n4V/IuQHrk4r4UOo/DB3fzXY1B5gLrydxwJ1edueOebX1h+tQTjvq2j3xP2yplwoZZbB0DqlQq1VUpIsKFCxfwmMc8Bk95ylPwn/7Tf8IP/dAP4dprr22FMcZ0gPIhpdeVufMy4hgcw54qlUq1VNIHVVU1y408VrwYJl2YE7+lIeX7+vrCixcv4s1vfjM+/vGP4/M///PxoAc9CEDjdq79nkqlmqp9Ac+B/uerc3u03T/9v+q0sEiZvbM5AGocI9xjTg4AOvuwG+fSWrFddNAzJe7nfotLLj8B19kuK1mTfdpgtByXDcfiYEEb8oEoXnHa7jTtUF5u4paMaeX/uCWTzNNuxigwx20Yx5dBBCS74OOAd678EL5vR3KMFDnYp1D59Mgwu7eaC4872HPuGRnVIxHIFAESoiWZzuWKmnpK22r4zu6snydunE9m7Ra5gE9pL0TYhTN0VxOOgwyA5oGSO236j0Eg6hg6b1LxZ2R4gNR/I5MtK+mzJ5SN7J9/s8SAehanTBclxz8n6e4DjSWLrVq5mXMsZNx1YyitXJhcmbHDirpxbX6OK3CuutrEcx8IbylNoH2VSJ2KmzfLNNt749swPytyLtR24jhFoNZcWIGTl8LnIR70O/wKWLnN3imFveUuIoY4Gc09RidPOwTS07uIGETvXAW2nI/QHtG00dBWfHpSTwLtW3bu93JuxeXX+js59+LFBzb6uwawZsZFa/GZusZFa3HX+g486u5X41z91/OPaYCVcONui/Xf/hjMTd+GMwf3gUGBmp3bOhHhAK7sBRwnYFJ7lzGeQQOvL23DwX0p/eg1e6uSNlqPnFeymELabs75f6loYCFOLIsmnxS1MwIm7X9SkrLb5zyqVCqVStWnSWPvdH4n+bsVQ+rA7UEC8nNvk9IjAq1WDXQgY265f0s/kheZ56sqVJ/+NC596EOwly71zuO981Ofwm/89V/jnZ/6FD528SKOrMUNBwf4uzfcAGbG4+5xD5QJtEU+rUsf/CDOP/zhsNdeC1qtuvlAA1lQUThHdCLnkO1dssmX1yQJyC35MSZ7THujsTmKkXZAfc86xo6570Yp/XeGwrMY05hH+Q1bmCNWqVQq1ZWsCxcu4Ju/+Zvxkz/5k/id3/kdfPEXfzHOnz/fChPD50Na6nSeSsBziVPn2VQq1RJJX7TU7TyVuJzHkj6qruvAP+Te/hD3Y+985zvxvve9Dw95yEPwjGc8I2wr9E1EKpVqpvYFPP++k86AqqsAwSxadeVRAZkY49oDCiY4SAT3c2b3WvstwedEpgWAxwC6gzoZTXJJmq3tYSc/6ZW6iySu3GSiyVsHj4YLOhDCtSfYBqAha0OYk3F8mFIfLUTK1Stz67CYGxyo5RTfQYoysefA8FhLIfFOOm3IfYoTcT6OmRPVCWScxNhNJ5qMnfowI4aNm/L0cW9IaVHU9lvfc2FNAa7q+Tf6zGBbL594nrjfcZxfU/vS8TNjThrDwLlLkFptq1lwQ/3nXj6i2Ysp5O0QArDN1pb6gI2VW0y0A7HvX4PXUViwlC8Ddx1ZeIMoD+IUOFddZdqDHgVAG8KNoVIZPXacw6JztIBzY8bMPjnWARGOJsLnAjJWPdstOxfbuTeeAWj33/uGLWOu6NtUX2+fQtS5a+dxOJLHeUnzsc30JU0DwBIFR+fag+YSpgRQ+2t1DdcuUwhX2o/A6TFkDrQB2hrAkXc5v8ta3FXXuMtanL/8Jhi+5I+zW/zpb31MQ/x74G38tjs++wbcdMOzcVBe31oQcsiMNRHO+M8k6DwCgAta5j6dQjuSrl6vd6PgSjPhPKqi9rrV825CPxdD7/E+0ir20ek81b5ch1UqlUqlSjX5up6Dy3Ng+RR5wJrE+dzacJ9HdsTMIJIpS+eAJ3GSc/nmqhqcr7SXL+Pok58M7ugM+Ll/Cvn4xOXL+M63vx13iBu6198eHuJNH/843vTxj+M1T3saHnThAlYeOE6fdRzdfjsObr4ZfPasS4u5eTbi06OybFzPI6dzAAFAHy/O5tj3Hjjfkgad9ony9y4y35rbnlkY0Nl3LE8C8vk6YMAtqJi6eEClUqlUp1JFUeB7vud78K/+1b/Ca17zGtx6661YrVZh+5w5lj7TqTmKwXPJn867qVSqJWJmHB4ebhyPAObSH4nDufRxxhhUVQVmbvVZ6eKZT3/60/ipn/opPO1pT8NznvOcDnSufZ1KpZqjvQDPmVnB89OmHjCW2YJrP1APLujGgelbeijLbAFG4xgeAHSZuPITjLA9rF68vQlAHkpvg7yJKzd5910bwaMBDEQLAAwTbKmbcDRpF4c5Lvi85fg7Vie5eiYDMhnn47AIQR6CT59IbMq5vQ95J/VttJ0Gym7a5IJYMBc+DUBVa1FDewVisoef+x1wAuF+2JhtDYBdHfWmMS3f4RyevNOYw3MewGe2IJqKk0Xta08Gxbt0PW3it926IIJb1YPBc5mi/7uyNtGGmWV4IiD4Jqh+V+4cmZsDmtXemtXFUwIn542v76ntim09Gz6n1jVtP84jleq4tG1QcBuKHdHl6i4OtjEAG0vg86VHQnDw+Zq5FyhPw5dEvVAmM6NaAJ8zc3CRroFeV+jjhM9zGgPSZczlbk122756QfQdpW/8h309Wf+pBCJnt6jX+Pq3aJ9n0q5jR3Rxio6dpdfMuNtaHDLj0FrcbS3WzLhsLZ58xwtQ4Ggj6Bxo/56GOffx5+PiNZ8Ha67FGWOCw7mchxUzamux8sfZB5Qb306XAucub21ndb1WH4+mnDnSR1fYzbk+VNPpOQM0fWbcVvdVsTO7SqVSqVQnoVnX7gQoXwyWT5UA6EXh4PO6duNvgc97YPfWs43VyoG9R0duWw80TMY4+Nta1Bcv4jN/+IfObR3JfQYz1tbi2b/zO6NvhPnHv/d7eOWTn4xH33hjuL+L9dk//mOce8hDwv2iwOcwBlSWwSGbpAzk+wx4nATgv4rh5vZbDTP/ZhRqNlfHY4vUB+onuPdfJQsAVCqVSjUuYwye/exn46Mf/She+9rXwhiDxz72sQDQcfedom04n4s7sQCeOgenUqnmSKDw9Xq9cVxEBJPcyxj/FidrLeq6BhFhFb/xighlWbbg9Lqu8QM/8AN40pOehKc85Sm48cYbURRFiEulUqnm6uqdZVFNEC8C8CbFzC5uW1dgWwEgmGK76yDYWkeAR+m1JsPIDMPPue1EHuBruwe2J3i9a0YGyk3Ls8lXG2ZPw7jtxwlBTQQa0x+IQMbhLmSK4UlLOU7uc/xOw3fLCkvg5ynpxPDtzH2RAb6HX8G1JO+xE3UbWGe2vdB5FHDxuU1yDiwonyn11GkJ8qxkan6lLU196EC0qK7na8b569u0q+OJefNOQOlvkm5fmwiv7E2ySmQc7D/73Jrfnt1ioSVv1thEewKVTu2/JFx07ZyliRNrrXan0LlKtddiZtTMqJgDHJ6C5gK3LhX5/afGMRaKfV7nynogOTi598WPvendgwQOLvynBByY7MvV+M8u+9s4D2n620hXwPGwKAJR24EDXuHzIOmv/HFX8IB6sgAzBmgrZtxlLT5d17js3c4vWgvLjDutxR3R6yl72JnB71NFYNxV1wF4T52kJe9H1uISMy55SD4+L0siHMjxL8hD4euuRP8iDNVuxBh3O5d2u+5ZhL+JZOFGX95qdKHzVQR19S2O2RcV/vh0glKlUqlUeyOZ6xeX8boGxx9r3cdf94/1XsQY0GoFc3AQoGyZM0KSt1REkXN4UTgYvR0AIGcoU1+61N4UbQeAu6oKn/8bvzEKnYv+6X/7b/i1D3+4vRJU/hWQqywbUwvvdi5u68HVXMxjimnzlmRMKKurBTqXZxEUOcRTWboyFHBfgO9dzv3J+dMDC7KcY3u2+F+lUqlUJ6vnP//5WK/XeOMb34j3vOc9i6Bz0fDz+Wli5gB1qlQq1VxZa1FVM5/rR5J+LIXOgYZTM8a03hAhrugClMuHiPCa17wGVVXhS7/0S/GIRzwCq9UqG7dKpVJN1V44nqtOmXi6EysgsG4NRjP43577uYu7cQ23cN60FCbWiIp+qNVvTx2k3Wse2w60qas5mcLz4glsbuuOG3PjWCwX9baHiDsOADTfuXaJpridcwYYj52TAfjj5I7DSj4uuPIecQ6OyyoA4mRA6Hf5nq1QnwOWiAP5I9gWMDzWnl29zztv3CQ8wmtHA/Q+EeInzFvMkLrzz71RD8c26noe9vCrxxcMdPcQlt29e26z8KX57vuNwbS7GArbGlSU+02neInLwFyxZb9IJvotAvXn5eEYIG2/oGRpH9cspsnnsVmAQO3fVKqrSPvmcj5XAXaIzl05qw1RC+ydowJNfzAFKFgRjcLla2asZvYx1jvylcn+FgUMbBhb1ANw5lJZFOAtXxSzEChRuArFTum7ck0WIF3SnZpmHK6vXcUu6OJwLlCuuJ0buPZSw9Vp3L6kbC5ZiyMPcVv/fe0XXAgAflxyzuLuuNfMKLw7X669cbQYZM2Ms0Q4awzKmcB56myebrtSJQ73xyEzs8xzsr7dDoaBa9+7WEopLvmpBDjPlWUZtY9N3PV3rfTYruR2rVKpVKr9V+uaGc3pt/5N99lxnhZL5u6JQEUR3j7EACidh/dgb/jNg9gyVjdnzoCPjsJ2rirYS5ccsH3mzPbzPuSA7UHpENRD02ERgMxfEYHrutdRm4gCXL+P88Tb0BL38uMWA8255R0YU/g/OJ8XJ/X+MpVKpVLtm4gIP/iDP4h/+S//JT772c/iW77lW3DddddtFN/SZ4lA47ZORFiv1yjLUucvVCrVqJgZVVVtxe18CAyP3c3F7byu6wCby7a6rvGWt7wFL37xi/GOd7wD973vfVvbVSqVaqn29fmTai/knI2Pxw0YENC0gbm354LbcqIWuDCCdEdhbu8Q0f7NNI4irbTagLorw24YTl3MA9zeB2lzcP3mdAJ5Swrg5ZS4U+i81yFDXMl72lHs+CsuMWMAdYCsozZCO2ivRFtrg1PSmjuokzbJdTXbGcRN/E47NnHwD983Hnz27B8veMgcDqeLWmShQjuz/fH35WaHg+l4ccosUf7ByVA66QO08QUPPedsaIvLyjHXL07R8UKfPWktycMGzWe0nHwf58omcx2aob4FRc35TSFPeoOpulq1t1DBDMXXA7dUr3EMF/h3rgwQHLKnaEq4Jf4OAvOW3ikb6NaZhNm2jqttxEB4zqFcXNK33U+nacaO7NKOKv+RdjRlMUO8+CE+tjBmSMIwnKv0of8IdF4DuBhB50ceBL5sLS71uuctu6wPqWbgkBmXPRBfjbQ3gitLgdXlM7T8k3zZ55zNg3OhXqePTWFRz0BjEuhczpVtK1fbsqAjl6+CmjukuYsdjlPyVgRAx58qlUql2o6C+zhz4/ptbXApF+dyyN/itCzX+8xnbySDWzmG2CXaw+e0WsGsVsEFHYBzQM/NERI1rul+XzLGlZW48Q3BDds+PqJ2upEbd3Aqj+pWjp2lPCLYgsrSHY+4p1/hyrqXp67lx+FevgUxc6u+WttOID8qlUql2l+dOXMGz3/+8wEA//E//seN49tkzsEYgyJaILUrRkOlUp0u1XWN2t+PLVWf03kq6Zfk3kEWyMT91d13340XvehFeM1rXoNbbrkl9Gs6J6tSqTbV3jueE9HfAfAwADcCOAPg0wBuB/DHzPzZE8zaqVdY/UnkYF5G4wLdp5lu58MZMAEwZshFb/mFr3GipnhetnEHiSYwezLkJmBbcIOfAI3dttk/cvYgH5Hx/uWJI7ef1A6vqQz7AuAaMPnJwvQ4tj4YmASd9z1SH8hLaEc9k92IwXeAyTm0hHg78D77fWrnXBy5ehCl9bRczinGO6okbu597sM51/NxiS9NK2H0lqkvKyIDa2sQzT/vmNkfWzef4XwjSn5bKH8e+4hA6JZf2M7ecb6b4e5vcZZ6jqVP/Ysltq35deMWUEycvIgXahDaZR1+y6aSTZdDe5+a17QcexPsVXOeLXC38QsQFjmf+5uwAGkuun5t2IZa/V/IGPL1T00dzWzv7oIh+fVwT/iuN5YqFdAP8V2JSo9DRjEx8BqPbMZ6AAMHCTLGgWMDjLoDizv53OVicm9SAGEcbUEwMP5tLn6SjTpL0xaJYbbudr5EcV3F4ylOfgv/btCOBbKRUZqka6JtU+MIHz9uD0Ast52hS3JO+QwHtx9FkDnDOZ1X0XZxSK8B3GVtNk/xZc03m24+e36Pt8dxVf4+pgJw5MGfcx7yiAFxcXGO3bZruHOngCtbE20XMF/CxqC56uTEvp0O3VXKdlncs+1rSOpWLudmDoYnaU/RvvvWgnIu/trOVSqVSjVVnetsZx6ls0P76w7ytFO1DDo4+rnnSDyA3J4q9YB9T3gqS7j3cPo0jo4cAC7Qdw/ksJ17rXZc4k5Oxrh8CTwOuHxIXqx7/iS/MRFIvhdFrwv6PqvjXt5syAXefYZ2KAacw3laT1t6lqNSqVSq0yEiwiMe8Qh87GMfwzve8Q78/M//PJ773Oceax6MN9mKoU9mhrUWRVGE54sqlUqVSqBzu8EYN2dCk769If47Z+wh2z/xiU/g1a9+Nb7sy74MT3rSk7BarcI+KpVKtan2Ejwnor8P4FsBPBtA37tzmIjeDeA/A/gpZr7ruPJ3NUkuNt5sVfA0yNRgOtG5C7cLb83sJko9YLh0gk2gB4k7gPVygKAoySzC0AChcZgEpI3hcFdu5OGYBLZl9oA1wjE1Dsk1iGKEIX8cDZS/mcQNd8rD8txD7ml1QqEdtePooDQBzHfxI2mEmbJC/OrMCM7cUH5ZQQPBxxPBQ2swmFtFQmn9ZhPrAvjdcuXmnGgtmmjimLZ4wGELri011E17ont7znOdc2oQFuYRQgjIP+JYktdjGFAvAVAIbvXFaK+aOp3Hi38E98oB5j3nrPSxA2se+jPcvmbMenWdr+/4dVDzNB92z2Ri9h6UnjeLFOc9Ob/zifpThKd1u9koqHvOq1SqUwOd90nAYRv1tSl4HgPOsQgIwOyU0VVBDgMfdAn2ff9c+Nz6vDQQtIyzm/5UwmwiN5rfP6/gdKkX0L6CMZCF0oF8fcQweWsEEU9aonO1Ass134flTFx99R8gdL9IQFzAaw+b13AQr0Dm1u9zFG0XCPyuemyZQ/9tytglMN1esbsnqH2eCYBhxlm486nwxyWwcAsYljIigmV2bxLwiRBzgM8BvTbvi+KFEzlJf2h9G932NSRuE5KfvrcNSH8u4VNgfR9EUX9vtI2rVCqVKqPstXTKb2ImsIM8HbtS0HziXH3YR+ZTybmgA2i5u2fNYDx8Xq/XLfiXjAF5ICHEv8VreBqTvEGRjIEpy+Dc3hobczMfDj8ep7IMDuCyyFWObZ/UGePP/X6KlMLnrWd2KpVKpVLBXTfPnTuHxz72sbh48SLe+ta34tGPfjQe+chHzopnqTt5URRZiBMArLUdIF2lUqlEcb8zi5WIJP2PSe+H0P/soC/sJz/5SfzhH/4hPvjBD+IFL3gBzp8/r88fVCrVVrVXIyIiOk9E/wnAWwB8I4Dr0eUg5GMA3ArgRwH8dyL6kuPP8dWjZkVV/Nq+dJUV5yeDN0+8ScNaMNfe7TVGG6bLXextZwK3gZT9A94hQoEyYZJVZwJyS5ytY0knTEPZRSvU/OtA+44xPo5NX+vUuP1OA7W7N1t9mFJ2b4C6Ax+3niAfB8d1xD1lxe3XarbqY5PBE8UY08w23irPqWB+0oZa8cnigPabBdwCBUGvZmQvqffG5dyAcnW0bfWA5clZkwbI1sHxuZdP11IX7aYeJoBH6eIfmyxs6XvYkom3WXQgaOIGmuX2H2thuouqPv+gcl66mw2jOi7nmfM7m+zcth76lfa1SG8sVSqn0w6cxzJAa5xh/afmxtk3uOkm+wosPAUYnAqTB2Bz+iGEfdpApkDiFMJsVqsE9rfKV0LrSG+SDRx4H/9L6I7h4zqvo/YwBIxLGkX0XRTD52EclKQhUEHcFiXtGu32V3nQvEi+S1jAuaH3qYdJCrcTfad+3+81OIC/lW+HR9biyLc3Q4TSA+i5EYIcbygPNO25de5teG+n2lzB7bynHmIIfCjcUsWQdnwO9aUTzim0nfb3QeSvG9I3KXSuUqlUV6+a+d38pzVIi2Bp+S5z5Z19cWWM2bNKjzlzrJPjiMoGRA7ELssAccOYLsgNhLAAwj4S1hwcNHOIyX4lEZ56r3tNnpL7vJtuwi3nzjV1lR5bUYCKwoHkq5X7W1zN48F7br4/WRTbajvHrNiZsOVSKB9xb4//NtEztz2c496FGGjX0VVwzCqVSqWaLrl+3vve98bf+3t/D7fccgt+6Zd+CRcvXtx5usaYLMAZq65rnb9TqVRZWXlD08T+JJWEnbJf554j+e3w8BDvfe978da3vhVPfOIT8ehHP1rZAJVKtXXtzfMoIroGwP8PwD9F9xl2DeCTAD4K4M54N//vfQH8BhEd7zt2rlK1AXT3eHPpaq0ZqYJMEdws2NZgWznIcqGrtYOY26iLA/48hDuSH5Dpuq/7csnD502Yzn4CG6bgBPMgfN4+jk3BqRn7RsfpJk4xf3JQwOawX9OeBnMZl1UCbTr4PJqUJ+PLewGomeY1Tntim1tWHzmIn/1x9Txw8EBpDPlMV+RWEwH729LwjffAIoyBSXZ3XjULRdr/TlS0eGa3WgIzx+fEQP4yZdu0zfbvreOkbhuj0J9LPMf/2I5DfzdPi+swwGfuWJctEFiWfuu8kH5lTh7kGjwhvHPApGYfIPSTOimmUjW6Ws4GggeGM32XgOip63Q8Ei3gAIMpfV8MFg9JwM05qj1gIrEz4Jyko2tntRg+dxC7BV3x7SKF0eORdlrmuUnKIXHyb5yOjL0FmBWwXMLLb0fWovYgenrHU/vwhc/LOoKKxO2ciFAD+AzdC9Z73MeXtmSY2WFmetZAhn/juC6ZW7BG2QLFpR0eeji+b3IllCv8OeTDMjPWyUfOPQXQT0ZS5mMLYmqMw+mbKIXO43MoVex2Ls77+yLJm4F/y4E+2FCpVKpTrVGwvAnYhsutBeq6ga174PJTpQxkPws2j+IJ++W2EzmguyxhDg4aqDydjz06AhWFC7NawXi3dFqtUJ4/3wK7RefKEv/73//7eOCFC6MLy+57zTX4X269FX//nvcM8XC0j7nmGpjVykHnkds5imZkI3OGFB0TisK1j7puwebsw3eeH2xbQ4B5DJZ7R/YOYH6Vq/XsTF1jVSqVSpVIrq0PechD8HVf93V4/etfj7e97W2wE58j2pnjKiJCURQoimkzK0vNx1Qq1elUmFeO+p64X5kzL7oEVs/1Re9///vx9re/HZcuXcI3fdM3TY5PpVKp5mif7ub/vwA+P/r+VwD+NZyr+Tlm/hxmvh8zXw/gcwB8FYDXojFEKwD8DBHNe8eOarHak2kpRLyjNE0BKkoHvgNgW8PWNbiuPHA5b3DfclNJfp90o5B9eBqVR4gr8WUcgDrYNq+LD47mI4B9A+LOdz8P0OVs4NMfpyldvQCL4fO4HMhMj8MB6B42jx9KRGUo6YxCvOOZjRP2aYyXcycvk5Jq0nKLLCY4IC+cnGX/SjAfy6I4NhHFjtHRmwhoZn6mLgYI6brEZ+2zSIvmG2KgeSiPSb8lbS33UC+KhxKX7rjvXgaDdPuyTaGS45ioabWZRWtENgTOES9kIXnXxozkzWi7J7/4Js0rx//XSTGVCoADWU+bBBxPj2wIPo+VguhrD8QaOHB21G0B06/kAlfOUQyWyyIbJgOK8Or5tdpA51e6ArAKoIIrrxhejZ3RS/8pot9N5l6lFaePr/UWHnTrnaK42O97yBxcwi95x3Dr4W1CU7cGwEEEucRxS6olEX7h3EtxF93UgstTpb+HtVi5dcIZMP1dN/zvuNPcG5WUgz8nDIBzRO4Y/O+yYAP+2KWM5fhSCYS/Zg51JX9bdCEu1e4l5Z7T2tdDhd1A54Br+7Er/lhYWeizb9B5Ad+vKFilUqlUp1aDYHkMl9c1WD45uPzkDuH4lILmdd0qh7lxyf6T5CFoWq0cXH5wEOaSeb0GV5Vb2LlawZw9C1qtguv5Pb74i2G8U3nnKQgRfuEZz8C9z51zC5TjbXALz84WBX76qU/Fw667Lvwe/vXHfdMXfAFWN9zg8lWWAS5vpSWO7eKE3po/9+VZVQ2AHm1Lf9tEATIXGCSGy2PAPIbMVb0Sh3otJZVKpVLlJNfde97znvj3//7f4+u//utx1113je43d2xljEEpY5CJqut6MgSvUqlOt6TPWa/X2f6HiCb3MWYB85O7p1yv13j1q1+NP//zP8f3f//3z45TpVKppmovwHMi+vsAvgHN8+OXAfg7zPzDzPxnzNwiN5n5k8z868z89QD+AYCP+H0PALzkGLO+VQ06geyxZNBvihVM4SHknU6oOffz8CEPINeV+3gQeSpq0jhoDwDCg3Xh85PAnA7KjlejpUC0AOrd09AdQ9tHMAtUp8dhM+TEFG3S1Egc8N2x5I5nJIKkHJbCnNIG1ghlHZeFz+fitinHGac72XFfANN5K6unwu1uBwOiQjI2MQ3j3iQgcJZPb/t9D084jHkPmdyktDsf3DHM1W6nsxsX7WWTDq3FGLlzqtOeogcqvORhSrNQJhPlMi0+34752sdL62nOwogEOPcLStp91IIs9OWMoreE5DO0yF1epVJdORIg0npovEq2C3w+FwYUEF2gAwGR+3ruAo1j9ZCCc/Cs3DTpCqxSeG9dIjc2r6cMQQAABIZBjeKKhc5jKDw4Z/s2MHVsF7uWByCdCMZD4/Enl34KpFMUF9BAtOJwftHalnP4JWtx2VoYAGeIcNa4tw6tmbEiCm1JXJRFHyofhwpnwrV06HBT0HwKC8IAPnvweNR0DpUv05IIB0Qovet6zYxL1qJGBNN48HZqi2oB6Mytf+Nz40qaK7gSVWEYOh8Ls6lKIrfQZ0I9l0Qt6Hwfei/yfUaJ/AIWlUqlUp0OpW9yCyB1n3P5yWb35CSgeQreLx1HRMD6YhnjHNDPnoVZrWCPjtqLSYsCxTXXoLjmGoAIq5tucs84/PHk8v4rX/RF+JZHPhK33ngjLpQlSiLc+9w5fPUDHoA3fcVX4PqDAxcwXpQAhIH42fveF8WFCzBl6YNF+TEmOLHDmAAqp5I3KUo5o26eDYS3LM4A0FuAeVG4j3dZzzqYqxZJ4H29v1GpVCpVqviNIufOncNtt92GH/uxH8N3fMd34Pbbb+/dJ503G5pHE+B8qst5KoHP9TqmUqmm9APS3/TNly7tiyT9um5Ysn/7b/8trr/+erzgBS/A2bNnF8erUqlUYypPOgNe3xD9/Upm/rapOzLz24noiwH8EYBrAHw5Ed2Dmf9225ncuQL4diVPVnnYDQVIYL6ZwO30pIwrJiY/YenT4RrsXQ+DI/moc7AT29q7fXQt8ZjFGbrfRo/IJBO/Pg8eQHXb6zYom93PwaNuTw9WRC7vlHGw9Xv5shboZkI7Wgxd9gC/RB0Aerp7fOH2McUyp3B4EMZ65IOc7WGrTsmAaCF0SQRC22VY2ly23UgYa6fD0Ry/fsfM2tc1T9dmGl/I3DHEfQy3wjoH9L72tUxjZU2mgK2OFsQ8FWToKYtda0G3l3WuJ0ri6kYcl7FrM92+KteOQl2LU2yrbVvnGrvxmwLmFcSs8yVKZ4oD+IkqTG71lcf8Y3Dl1LRvd72bvvjHlfVerEFUqU5MpxGCyELBzKgSMHGOI3mqeKTBaBzjjQeC47jF8XrMtZfZOUXDg5RTVDOH8MyM2sOOgtYTsVvaRoABe8YgGvf4sbb7XFlKof9t329J3FJvnfbiYXSLxqE8l4MKjUM0ovChzfi47qzrzpsHCA5WP2tMALBXflxSEWEFB6jfbS1+6uz/ga89/Hd4oP2TwaHL2LAmdTu3KPCmm14LmGtAAFZEOGNMyMchM84AKD2MU7Fzbl/5B11VaJPTVTPDErn7aTQPzeRc6r/rUG0iWQDTdy7JCG295XMtlrShKVpFjXmT/nyb2icAXqVSqVTHIA/xXmnj6J3Lg9VbW/Av8e1gDFJfvgxz9mxwCw8mCUQwZ85gdcMNsJcv4+Yv+RLc8fu/j6Pbb89f55nxvAc/GF//4AeHe2wZu8aSeVxqfsDnPOtZWN10E8zBgZ8DdcdLRA70zsxbMTOorjuu6PE2NsbFI4A4ZB7fjasFHCefD+T+Ve1UrTcoq0GGSqVSqQZERDg4OMDXfd3X4R3veAd+/ud/Hl/1VV+FBzzgAa0wOfBcxh+xizAzbwR4xqrrGsyMstwX7EqlUh2nrLW4fPnyIFAeyxgDY0znrQnbMu9gZrz85S/HwcEBnvKUp+BRj3rUVuJVqVSqPu0LbfSF/t8KwIvn7szM7wPwk/6rAfC0LeXrWBQPgJm9768A27b9ysV9XzEpg3r/xTmBByf0HTU3ccv2DuMAAkzt3FVq95noAsweYu8kQ8YzukOTYD0us4n7bBcszLumB6cOTqH0fB6jAB5gHob+hyHIYUlew4RxtPK4CdTUy+TBUgi/sL20zidfBuKEH46VFoCtYdfmzyiPY+7nUoe9x5Wrr7kDzAg6zb/GR86VdrzdsMfc1/hFGYt2XbBwQia1d+++t6QMpy4WidyzO8cvD7cSeCspY1cOUaoLyiOOI79teTnPboNLklnYB8q1ZuzYwpsq2KIfyYsjnp0VyAIMd42Zex7t7uGlSnUl6bSdATXyfaiA3bH7ucE0R/KcxM02jkMc0SWdGe9uCZr+3qImvFwJmVN3d+dk7rDzEjUK1OHfAhbmioHOY0fzuGy3eY8Yx2+BwcUCEtYydxzSS3/9P4oAcwZwxIzDyNFbfr/oJ1hl39IvYDj07UlA6wMinCHCipzb+FkiXFsUOCDCh80j8Obya/BX5tFbKQsAWOMavPvc/4w7Dz4PZ0yJsz5v4u4uzuulf6gFOGj8kBlHkbti+saBKWL2byxAA+rHf4vL+tiCDtU0xQsjchJn/l3hMBbeRX1i+Bg6LzzsfZIicm8kKOEh+GO511KpVCrVSSg4R3s3aR2JALGrOep6lqv2aLw+vq3P2zDDHh66v4lARQFzcOA+ZRnmCM2ZMzi45z1x8Dmfg/N/5+9gdc97dvPo4yj84syDosCZosBBUbjFmT1jAipLXHjUo3D+cz8X5uzZxhhDnMUjYDx/CL58ctuA0E5DewWCg3lwKveQvbqXH78CdK7lrVKpVKoeydxCDIyXZYl/82/+Df7gD/4Ab3/72/GpT30qQJwy5hD4M/4tHo8YY7BarVAUBYqiaIXbRPqcTaW6+lRVFS5fvhzcxu2MxZRFUaAsy9AHGdPlxJboZ3/2Z/GKV7wCT37yk/GEJzxB52lVKtXOtS9L7+4DNx/0pxs4lb8ewLf7v++7jUwdq2LglNsPHIkQTVYSGs+I7a182rbifDGzh8Odo/VOXNCpcQ0nY3wZClDng3gndMBI6eUntoIrh/XbW6RxBJ/3T4w5wNNBhumrIeXYGRkHdeo6arssMWArkJFT1h1XNo/RPrGndbatbFIHA22vU//BfV4a9xgg4yaZueZleWSP5pBp1SfX7BcBOHyFTBHA0+nt0e3P1iI1c3Zl7usuBUBdhfXktT/9eQs2Gm9R12/YcO71tROpj84mmXjfQFPLlJO0Wm4nPWqFkfNxqo6j39zg3KKkLJg5csJOHupxklYvhE9N/xX/NlpuPBJmQhzyIGeOfNudp/n1usghve9cCnHKtdyf2+HvSZHPzIqRVQOz9mvL1TF7NymV6mrUafLV6oPORbKt8sC4fEDk3MNnSvZl5pareVgEiOa6JuA0YfjqIc7lU11zmZ07NMPBz/H+TboOEE4h+6EjHiuN3u1bAnA4+bST2N59lNRLHGNf/ByF7wvHcHC0hDHR9/Y9rmsvF61ttT0ZXgv4Xvi6lXubghqn84II543BdUWBIz6Dd5hnYG2O8PkMPJTfPbcoWrpEN+HDB1+Evz7/TTgwZ1GSCQstBLSXNlaxc2Kv/W8WCOD5AS13PgeacXIFwPi2ZYlgBMj35Shwvl7Ll6uv/5TFLXHb35biOKfC43E/VtD0N0TsSrELv7Y/lUqlOt0St2hYq8C5lEUydtjKOD2KeyflzOzczdfr5rfYPMIYmKIAE8EeHQXn82se+lDAWtxNhKOPfzwPDU8cC5gzZ3D2AQ/ADU96EszZs86EpiwdcC7uoxOgjdj5vGOq0fcsJM23AOwCnqt2rhQ6l3anUqlUKlWq5hlpY/pw44034ju+4zvwyle+EnVd4+u//utb+8Th07GZQOnpc9h43yUS6FRdz1Wqq0dVVWG9XkeGmAxr7SzQW4DzPiMpCTNV6/UaP/ZjP4bv/u7vxhOe8AScOXNGwXOVSrVz7cvo57z/97MbxBHve80G8ZyIpgAigEDoQAyKpRNi+3bhaPLjoHnnHC4gosDh25hGpQB8M9rQdTxRS1y7rUQeQRHH3gREicDtzqSl1EEEVXez48I55rJZOECm8E4ltkE6k/g78LnA09wGzXvzGB8Dyf5SPs22ecB1nMXpCx/aN2wh677sh6BMt1ABsGERwRyFY4++g+BeWW99OyRf90xJPY0eVfJvkg5ca+/WSYIOTaqDpecz+aZJIwCvr5QI9JWyCws5dqmonVNYWOO+jSqA55tD8tvU0nNr0PU9LHqJz4UuitZKs+lgonPW9S0xuD8K+p9A+YY2OBGGjiedZia0+NjSfMUu9NMW2PTHO7ZfWLS0hXoJC2aWvmVCpbrCJUDradFU9+MY1iYsh8/jfaVPTfMQl6/1/Xo6kkpBdMmfwaQRQbgnkuOx/nsRbY/TjfPfG+eEdLPhR/rmXLzSDnNX+F20zhRqHzsHpoQXOFdcoSXM2n+PYXT47Wt/zAKXy+8SHmjAdYIDXOEh65VvH+eMwTXG4Fpj8KnyFrybvwBrEGxd4OH8rvmFA+AzdB985OAL8NfX/D9ABw/GgTEogdZiiHjEWsFPrDCD/XXcEmHtF2SU/rjqGW06VnDjljbuy42JQHI/6Nt3+I79mxfYV8m5l+s/pV3bpF1vQ/FijAlLOd2DCDSvLTxp6Jyi9qzQuUqlUp1+Beh8VzD0vkvGAMm/W72XjOb8dnaPygyua9ijo3waMYBeljDwix7PnsXBzTcDzKDVCneXJQ4/+tHRtHJzt8X58zh7//vj2kc/Gmfuda8AnFNRtN9mC4Cs7QDJnTGHfI/D5cYl1rrxs7UN9BzNwbV+74tDtZHCc7ANFi2oVCqV6upUMDbx1+3HPe5xeMc73oEPfOADeP3rX48v/MIvDGE5M56KHdBz8xdp/HMV3oRY1yiKk34vnUql2rUEOk8dzgU+n/MmBemXiAg2edvVHPh8vV7j5S9/OZ761Kfi6U9/Om644YZW3CqVSrUr7Qt4/kk4l/KHbBDHg6O/l7qmH7vmDmDb4QVwbcPT6UB6n9TAcogmqy2cE/ZcZ9hsAu4fwKPnDPLAMceT4wIgs0xyUcSex6B0AyvHwHcLPhfIMAOvt/Ijx0UGIHZwuUDy3ISV/WR7PKEdQOAord48xscQyiRyhl5azqMg89CukucmGxT9nZ4PzlnclQ9Lm5mjFCqN25h7Ot6CfefAq2NhmS0IJpO+3QhKnZKvJk3TaZLdfMaAb/t312hcm9lVX9Iqg9B+R9KKj5HZ5z1qSIO7+vN9z/rGIYg4bmttsJw739uKbkbihyaMzDmcT7tpF8vhbHcaLgTDd64Y95mgDLTYuS6n9TJXAm92NzRNJNPXbzIx5uBzbjkTq1RXiwLMigj03XJ/NbOnWay5TrwCW4bFSUDLtXyq4qu3GchHHH8okyhtisIBDTw+B9S1aFyDBXKXKx777fJ9rL9bWl9TIG75tw9Cj9PvXHcwr69O23UKkSMTX1/YPodzAc5jx2j5TeDyOvN75SHyOK4aCG3ECMSNpn2xvxYXLqM4Q4SzRLjeP9T5FB6M99ABmM7gcn0OD7L/HRfwmcz1u7nkyuFfwgV8yjwQf3PwD/Dxs1+G9dnbcJ0Hxws/QStlIUCy8ZHJQolQrh40F/jcYFmbbue5OWelPkIfJqB+HP+MiemrVQE6z21DGzqf2zf2phnFLW+LGKshWTgj54uhk4XO1eVcpVKpri7Fb3bbz7mdHek4YPMk/p2WL7ODr6sKXNfj4Y0BHRy4Me7ZswAzDu51L5gzZ2DOnAEVBY4+8QnYw8NuUmjGrAIb08EBVtdfjzP3vS/OP+xhuOahDwWtVs7tvChac70y/yZjpaC+v4d+Q/NciIzp3p/H82n+HnI0HdVktco5A5xLeWspq1QqlapPses54K7ZZVniK7/yK/Frv/ZrePOb34z73//+ePjDH57dVz5mxGhNwm0CnwtwKvGpVKrTJ2stqqrqQOfxdulLBECfYjgnfUcOPh/rTy5fvox3vetdeNvb3obv/d7vxYULF0K82hepVKpda1/A8/fCgef3J6KnMPPvL4jjG6O//8d2snVlSNx1wQ1UmYPQRftycQmTeCyTihax+/HyiVY/WcUconYFYiXiBkD205/EBPZAZjO5GIGt/l8y0SrVCD5ndjCxc+VIy1cmz2wTj0y8MiL4nNswaDTh1kCnFuIW30qrL4+Rgjt6qP+FbrzYvA2lLviAPyY5HJfhEDaU1Uz43Dk759EOAcPFe3H2MZFp6nQofTSgfnAHaqbdZ6TXtLfBMFF6s5UD9X2bmuo8nY1z6jYiTOHHQz/HHBzS3aKMKavIj6P/W9B3BS4811ZjRE3+tJ3zt/WWBMg5T51DbqXRApiX5XtSu+iFqQeUtscp2ZkzKcQ8+0x0fWj65ghgK8B5O5X2L/HCgd4c+7pePCkmfeL+jBNUquMQEaHw/UEARnsg3CUSkJaSz7YVg72z942gWQF7Z7+1A77vkHFuz/5FEn8MOItiCLOeAeqmrufp/nKcu6oD0ZTXswbwNNm+rbxx8re06xb0H6XVyaWvoxTITY9N2vc6qc8+6Fz+PvLbcrJwDuIClwsSY3zdWn9PQ3AweEmEs8ag8HkrifBZuh/+mJ6Nv6JH4Eurn8bD+d04x3fiAJdRYo0CNUAMC4OKVjjCORzStfh48Qj8+cEz8dmzT8GFg/vgeh9vER13AJB9eUq+1h6Cj+FyMIdjOePb3qbweVz2AuELnC8AulzLA6gclbVe47vKnYvStuWc2SZ0HkPsNLFPiqFzouZtDsep1iIlbGdeQKVSqVRXkK4W6Dw+Rjnm44DBd5dCKy2uKnBVzduvKFCcO+f+JgKVJYoLF7C66SZ89p3vxOHf/A3s4SF4vXZAu7XhGYQxBrRawZw5g9VNN+H8534uzj3wgVjddBOKa67JgsgxoEzJ98HDC4Y9/eHYNm+BZEmDqOWWngXQT3jMc0WOuNL550wZ0sS6ValUKpUqnn+QuecHPehBuO222/CmN70Jr3vd6/D85z8f1157bQcwn+M8bIzpQJ9zFcPnKpXqdImZUVXVaB9RRwt9y7IM+45J+qu6rifD5+v1Gh/+8Ifx2te+Fs95znPw4Ac/GGVZ6rytSqU6Nu0LeP7rAOQdOP/Zw+efnLozEb0AwBf5r3cA+IMt52+32uLkZQyhA/ATdA2I3oSRzSd/wQkrVGEcJOyByhjUXhgx/ON9fzF2HnSdi7rAqwwQmcgRW8ouAixs3Qa+26Q02K9sywKwZPxr0QUw92EFoGULcJ3sK5Os3IVKrYeqUzgqzWPrUNmlsXDFrpsM3O7NUgxUBnd2ZscVAwAawDuuz6kKk8498LksGOhGOYWAlnj78yNlDkTA9ILTzu3L/adDaK8FYAhsmwGtaxPjeMIQqL9UabuNtiTbfL811r42bX877vPCmxUWAHvbCtXZpfMAJ3L5j/u4wSwvWCwhaZBzPZ8Ln0t7dN3xxLRnpBHeKLGwTTTX290+fO0shurPkVudswTyj+O4Mh9nqVQbScA/UYA60QXRZ41B0Dja5sDqbY6oNu2FmDkAviUR6pljRYK7sayIUDKjGoHPh1CHGPAkgXt9vsbKjD3ou4rqLIXPa3IO1osX1Q2knf09/RwDsCNHJQB4/PuEq4lrt0BYjJBuBwBLhCNrWy7yTBTAcgnLaKDxI2txJJA02u1W4O0j5lYbELdzGXHLuWOIUMPB54UPd+ChkRURzpkL+ET1WLzc/DD+Lv8Jbq1/Dw+y/wP34I/gPD6NkmtcpmtwB90Hf13civetnoE7D/4eblxdj3uUJS4YgzKC2YdEcOUsILj1f5f+b4HPD5LtxYT6GFK8OKDw138B0MmfN/I93LVEwPPVLgH2c4t26mj7NqHzKolrShtI3cVPYhJP2osBWgsxVCqVSnX6Fea5etzcTo0iwPxYjvW4y9RD57aqlqVLBLNauXvn1QpUFDh73/vimgc/GJc+/GFcfP/7cfjxj6P69KdRX7rk4PPVCuW11+LMve6Fcw9+MA4+53NQnDuH4tw5mHPnWmC3OJ4LOC7/hlmqieOPSfB5DJaLA3pdd/YLiw6i/GSLZloJZnbc8phqm/Ed53hPwXOVSqVSzVBYFO//fdKTnoTLly/jx3/8x/Gbv/mb+Ef/6B+FsEWxbNn+Nl3PVSrV6ZD0CXVdTwLPRQKQnzlzJsDoU9zPy7KEtTYshJF9pG+JjZ0+8pGP4PWvfz0A4LnPfW6IQ93OVSrVcWlfwPP/C8B3AbgJwOcCeBsRfRsz//rQTkR0E4DvBfAt/icG8H9yP+m4V2q5WO8ukQBVAyn4ul+O6A2AXPiJ3gLE1sPBG4B9ZEAkk7nkANxQLomzmYDIvpy4oZ8D5N04YwTPMVAO9M1B2kQOpPTOH5z5PQsJ92xvHLXbGEk3j23ZuspC61O0y/bRLEIAAIoAdNPA9636nKgRljIP8ZOvx5FyGubOm0UFgJ9ILdxN60QYvJ0QsomleRSXdUl3XjpdSV7nAFqD5yt3F1L4DcsyKHtPLNN9G2QPnavhOhH3L75PbIWzPa/IjR6SAFFbmOCYHudhzkOePm0yWTMjlfm7TGSt2/W02weEZIrBRUQ5ufMTHtaf8Mrkvjhg/UKw/TpPVKrj1BCIbmWsMqE/Y+SBRfmt9mkFoHBhfhnL3c5zqphREMEu6LdLAGs0EHr2XmNGfDFYW/t4BdwfKrM1cws+l/oT8JaTOl6q3PGFttKzfdcSaHwuLGsxLc8CzwpAK/VQ+99rDzxXfvxQ+H0uWRvc/wugM64Qd3SByjn6rfTfYyieAZCv57PGLfAtiXDJWhjfhksinDcGf2Mfhw8Vn4ciWvQh8CwBuKkscZ0xuFdZ4pwH15s7vWgUHi9K8e2Io3DS7prRQgOlSxwCn8siiBzcv0S1XzwiADqA8CYDAdLht8siDNHVeM2XxTa580Tc+Pu2z04LyALsxcR+KPR3dDJO55KHAldnW1GpVCrVKZbMme3YUCBN8yQgfq5rWHEkXypjAGNA3gHdHByA6xrnHvAAnL3f/Vw667VzVa9rB3evVm5uyxiQMTBnz8IcHLQcxkPcIhlvWAsqywYSn3ywESw+QZSk3wuZMwdAXrW5QjlreapUKpVqQz396U/HPe5xDzzjGc/AM57xDHzO53zORvEJrLnUwG+1Wm2Uvkql2i+Ft7xai6Ojo9l9g7UWly9fxrlz51oweS6d+L5H3M8lfM45/dOf/jR+//d/H7/xG7+BX/3VX3XPG/1+Oo+rUqmOS3sBnjPzp4no2wH8DNxzuQcB+FUi+iCA/wrgTwH8LYAjANcCeDCA2wB8CYADNM+C3w3gJceZ9ytNqSN6CqLvncgEp3LYGECfO9inAPK5r+IOnZ9cbjla+PAxlM7epTwGfgPQ7uHN2F28DYZLXuAAP+bW72DbAxwmx5DkNYVXm/QTtw4rq+kat+8pdb8Lt/MJiQIgZ+Bra1BYLJEviz45QH/M7cRD/JEDdFynAU1J4nAu+V0QOAdXC9QJXlKO3FqA4NLuqxOfzyXPTCJIufXzbFB+MJHen5r22n0QE86PUNYZYrgn/yHuXSsDdE9V/w1AGwyaGv3Yoomli0+Wa35afW922FRT+47Mnjt/QNh6le/SOGb0j6lCP0UnhTapVPspGcnFzs4BhM10zBW8A7Lfp7eH58ZpHGhAxKm9QJ9b76baJM6SCBUzSgA1UbZ8VkSoMA/MZmasAaRT933wfuzEnTpMVwmYvoliEPokQPNNQPcWyD2ybwyWpzqKIN4UsGUAd1sbzoe+fFTMnbbSvoNq3KJDvfq6lzZniHBg3Fh5RYQD/5H01z6f7CH188bgjDG4qShwoShQADhnDM4RNZB60k4qD4xLngmNI3rt4wUasLyM9hMJnB7Ofxp3858qAdANEYwv8/A93Y4lo7PTLYHO5/ZPfRqC16eUfSnXBNrcIX+JzAnC7iqVSqXaE/U8FL8idRKguegky7GuYQ8PganQed/YpSjc8xnr30xalm6uuiyBunbzZQJayRg6Bh6KAubMmQCTx+lR7EgaQePhd9NvkMB9eWYGJjidMuAgd3EPFEOfDIDemP0oML2JUuicgEl1pVKpVCpVnx72sIfhV3/1V/EVX/EVeOtb3xoAzeMSEaEoCgU9VapTKmbG5cuXN9r/0qVLOHPmDIqiCE7o8XZm7rypQfqW9Dfpa37qp34Kf/Znf4b//J//c3BD135IpVIdt/YCPAcAZn41Ed0DwP+Ghs94CIB/MbCboxOd3gPgy5h5eY+/QzUAWAp5n+zELTND+GvJC3M06XNCF6Y43Rg0Bhgkk8MeQJ8zaduAxJHvIVHjpp1KAAmB0AX6ZNlXHLibyTIHBjdwfHiduaO8W3lhy92JURJ377zLsCuHuOnH5WQ7cG3jit7vqJxL5ySVtjtm9sdt/AIE2/zGeff6VNPgc1dvrfKSxQcBJBdnOmomwm3UTjLAeZKKHzxawGI6zB0Ya5q8WCAViSvjQFm59tADb7N1pbMJwN0DZjeu8CG33rlZIPtpaTLLOZDJ/x61cRGFfmMgb8mNx9D27O7w4FbnTQrT8+niaIP+veWc0aZu53Pc9ufHPd43hPN6a4svuuouUto0PjPSFw0rXLv28LxRqY5b2XEJmrO18NfX2Gm6jgBdjh6mxvB6TvFZK2GHliUdx52EpDP16k9wZWIBFL7/zsHKBljkql6hfRMbw/tSVwL1lhHQKyBwgM8BlDOuL8HZAvmyP26QRBzKl6Q/Bzi3cGB5LqwFcOjvrXKO6RbAXfI6yIE0Dpkd6Bvlr2LGnKuY8YscVkRgIlz29X+NMa23AhQeBpcFC9cXhXNMB4LbeSxK/u2TjJIqf99YIFos4eOMwfn4zQo1M2wEqW9DstgCcE7V8l36lXi7ifq0q+W6Xw38tg3ofOwNAlMWvgh0flKLBMqojV4t7UKlUqlUp1ARbN78dIzjdnmd+vGl2EnfrtcBFp+sqIwIaBzBi6JVnlSWbq5+tXJzZ9YGt/N4no2MgTl3rg2YR4qdxAP0jQZQJu+0nstn6ylJpm5J8jxRYX5OjjMHvcs2n9fWk77TslBjB+oD+vdxzl6lUqlUV5bOnDmDW2+9Ff/0n/5TvPjFL8b/+r/+r7j++uuPJe2iKBT4VKlOsTaFzuN4Dg8PcXBwgKIoAmxuI8M7a23oT8b0Iz/yI/jsZz+L5z3veeFND9oXqVSqk9Ax2xcPi5n/TwBPBfCu6GeKPrnvlwD8BwBPYOa/OaasTpZcMCIM2U9MWTBbcF0FgPrEpiAljwEIcPkT5232oKh8jlvtV4E4IJj86xnd3wWI+l0vMhGCTAJ6EoV4ehWAdxvqTABitpWrSwgkn8Tf2dcnawqfbneyzf3m9+lmJu8CHKDnFEp3ix9cHjPHFdLpcTQhX+4nuBBB3PndcTf1LmU1LW/T2m+oqxBejt805wuS+pTzeup5LHU1EQyVeMkY0JgTsZjKJAPTZsHGeGrZX5l9Fza1H+ggWRP380rO0ala9laEzbRZ/9jj2BMWMkgb8xB+y3k/v9Bh1O3cFL2LUXoy0207C7qDqa+5TRKfFvcu+qf4HPXn/7ZFsoDFg/xyHL2v9h2Qe8Al+V0OsTd94PGDlCrVlSCKxh7hnIWDO0s0wLgJixabcbaFd0NHA/7Gisfc7MPJJ3dVPY4zVHqTXB76FPf2AqIPhZkj9pBy3zaBoGs4YFoAZgGn6zgs8v1ciMcvIKj8fhXawPZx3yNJmwj5mJl+3P7G9mO48jvy5dBy4PDxHPnfg8M/h7tJ1HBO53YEOl+zcyJPXdJlEUf6W06thSBwUK98zhqDG4oC1xUFri8KXGsMLhQF7lEUuPdqhWsjp/MDD3/HLudxy43P2Tgv8d81d53bW9vhym2dlIuU4zZbktRxaDPSpv33KjlfxJ37tF/7ZQFA3F4FNt8GdB6fnznl+sNULegbxwedExEMkULnKpVKpbpyJfNX1jroO3qD6rGN26P0T/CJTwOCr9eDxy2LD8PHQ97hU5YOFjYGVJbuI2H8b/DfzcEBzNmzztncg+JkDMzZs73QuQuUzAH3fXw+wqcogDiv6ce4ubz0t/QTu29zmg9fltn0BVAXAD0tu548zfok8w9Dn30DuFttKi6zJMyoKYtKpVKpVCMiIpw7dw5f93Vfh0uXLuG3fuu38Ld/+7c7TdMYg7IsFfRUqU6x6rrG4eHh1u4jmRnr9Rp1Xbt5WGNajuYpiN6nH/3RH8V/+2//Dfe73/1w2223wST3DSqVSnWc2hvHcxEzvwXA44joSQC+GsATATwMwA0AzgD4DIDbAfwRgDcB+C/M/JkTyewcRQ812z8zGsdxCSHUqPx2jBeHDhhA3uW2yRdHfx/nhavtgg4/IcU+aw6+JO9xMe4uQfJfe6BABP/i8/wAQsqHAGIpB0bw1rCIXIDJzz1GwASzm1uTCUlxdQYyruuSwehYWpOwCN7brbyGvzkCSwWU73H2Ze+Al0tHEtsXEUU8uAfRGWBikLVZCFfkmgpnji8Tljk6BSn8G7cPOas5cmGffhgeRLPW86b9+FULupe2S8alO5zK5Px01FtOEYw/JY7k+3gJpZPPfeUyEJOcm7HdTKjCHbblmW1gQoSIj5Oj85OkLIcIrO6f0Y9RHFNzI/3XRloYwcTzFmjOrf64piz2EOjf/b2LdiPntRxXNw3/No45kSblRIZmLhaJoxp4+4FKpWop535GRDAy/idqwaoc/R7vmTraxlAkEaFGAyAOQY27UJyugPVjKgDU8iAejSN1LszcYxH38j4X4BZQ6usiwJ7++lf6Mi38bxJe/k0B45OCcTn5zM1Luv/YvgxXz7UHlNPwtd9mo39Tl/PaA+s5p/s0rhQ6t0CA1dO9ZZtIFjUcELXc0aV9yjlj/HZpvysinPVguovIAephwUgUVwwJt8o/AlMsfDuKyyD6rUYz8SJtV9rcKkqP2Tmfb9vdOrj1+zyLwzmkX+HoLQ4yLmEO5XhaJqvjfiFdSDG0EGVy/JjWN4/1n0XUJtL2uEtR1PbMKalzlUqlUl0lEtg8/o4TGL9LevuwkM/D73a9dvenQHd+J3e9l3FqPL9kTPaZQetNeaYxawhz7wIUl+UwdD4kax1cbq2LbyyevjEMc+PanglDcTgJ0wNtZMfGyX6T8zilncwYl3WOY4k2HA+HdpBC5n1luYfAvEqlUqmuTBER7nWve+Ef/+N/jFe/+tW45ZZb8Hmf93m4cOHC1tMqikIhT5XqlKuua6zX60kg+BxZa1FVFay1KIoigOcxdM7czya8//3vxx//8R/jC77gC/C0pz0N1113nfZFKpXqRLV34LnIA+hvOel8bEVDjhLkIFYgnphxlGTn0nASIDo4yr6/wHFrayc/x3FRkzQ4Sp5QeACd/QSn5BE9ddDA5y5IewI1IH9DADra1cLWejKn/+IeJmXbB9Sk2Uov0PFAmiJ7sDzi09OJ9RY0GLYNwOfujwiebyYA92GwIkApkYkAc6lHck/xmfwEf2YQyAwOdT4NPge4XV7U7M1sm7TCQ44Yeu5Lg8NEPrN1CxZ64PPGrYSS6BoX1SFlw3iAoW/fBjDu5l+AfJ4A47bjn7IgJKM++J37tkVh5B+Svna3mvtQqVlQkC/nFjDc8+aDHJYcgGb3BTHc3Nme9e2cfATzQofzdx5gKO1x6CYrSWiwnY2m7c/j1gO0LXZ/REndxL8lYVja+eKHPu5snVvmImYLomJ62atUqmhhWtN1hHGa/5cjYLUFOkaAYfpvC0IHgmu3hDmOM9SgcWqfki75fazvx9wwrd0fxWHm9lPWAw1jkK5lt8AyABc+H2B2YLCPA0jq6QRhkRR8X5KfpcC5OGLnXLstnFt0zdwKL5Lt4mI+lt6audWW03zEitsd0IwrCgAHRM7FOrrGFj4Pxtev8XEYuEUHZTSmP2tMA6GjKS8agH7j+rHsFjbI+Wmj3+DjsnE78/C5AN9x2lPb9RKFMvXHRdE5IYCzjPXjtznEC3Kv9PFAbqGEONkvfYQQn2dpu001BnSbqJ0YIixEtGZJ2rn0x1d6HatUKpXqKlEEecu/JzZ+j/PCMw0EtqDm2UTzDIDhn1PUdQNcp+F6I/T3TtHYOpifyByVMaDoNwrz3Nz6TVzQEW+fMyeIZjyK8Mxn/lglPC8aexNjX9wpHD2SB5oQBplj2VrbGUp7rPw32Ld13CNAOUVhdPypUqlUqm1I5gqf8pSn4B3veAd+93d/F2fOnMFtt902K56+MWWYKyNSl3OV6pRLoPO6Tp9ebEfWWljbGBoWRdGYx1jbAsnjPqmqKvz0T/80Hv3oR+NZz3oWHvKQhwCAgucqlepEtbfg+WnSkANzA56xcz2PrgdtEB2eVRV0JQbhju8i0lzYBGyO89O9+O36AtfE3/jCyqRVgIajcu3eLEi+OQFxXfkSO47Z75zJQeOI6+rHgGE9tJ6DOyOgQfJHTSmCDAi2C+jG8HhUzkRN2ZOHquP9Ghf49rE38Hk7T2G7tc6VAuTytG8DFaIMpA+fVzhI1yK/cMBPcpOZ/hg/lGM4X32NZR5quLpzfopN8bZBFkqgaQeuS9DkZrUHRmdZeMC5AW8yEZ7kMWAkob0knc+IHHxiwci3jdxN+bg7O7KT0Z1BNZEU8mCeAyATLVbYlTZ6sNVbfm3kLP8GBnTbN1HUZqhptxJna3vUlqYeQvJQZFHfQB79ml1uU9vpkv6KQ7vKXie2cJ2Nr1dhAcuEh31znekBuIdxfuKLYTDN5b0/ruY82rNrgUq1hxKn47QvoeTfVk8fgegCLYrzbLo/0MDfMRgb91S7PFMLABWco7UAkkPpyTEIfJ5zOI9GVrM1FdIV0LeUcMyoAL+Ey8GdlIzXTkKSbg68nhsPA6OO43F4cTLP7RM7oAt0nkK2Ap0fMTsIfCw9n1YaB0fxA+16JTSANAFgogB8lxEgI2PAlY9LnMwPSN5d1JRxSYQzHkSXtOQcExA3Vx4pbB6PUjrtzR9vCrJbIMDxLcf0HcLnEr/kRdKQegjgs6/vNFxrYd4VJGlXcfuO28EUaDwbL6afrzQCksftYCzstqQu5yqVSqW6opQC3icFmscSkHrHwHnnKp1et9Pv1oJz0HlvAu1xKqfQcHz/ZgxQFN3jjRYySjharWAODtxvde2cy23m2UeqPsB8IXgO+OcdwPQyQX7sG88598LV8e/xc500TPJ775HNuK8b1SZjvi2NF0M9KCCjUqlUqi0qNmB6wQtegG/7tm/DW97yFtzvfvfDfe5zn8nxcGacKVCnMUYBT5XqlIuZtwKdTzFwrOs6POczxoRFLfJWhZaBirX4oz/6I7zxjW/Eq171KtzvfvfT/kilUu2Fdm8Bq5oomagy/kPtCSwvGewyW7CVT+2gshhSPya189PNR7OdswP1bcpdWI0DismE724SywxPBoKaMO1IXZwTH/lLObiPjWA/gTvFTy3epylDF9TDvJ18ujyyjQY5cVRhYNHej5l9XtJ0bfb3sD0ChfdiIt+L0knbbCADKkpXd7l6XSApRznnAvgdxx0P7qLztFXvnHepDu7WoR20AWEJ0yQ1EVzNLSzweRjal9MEM3GA7cB5zT1/D+Q0yQ8lfWKIalZzjCbvdzrwXnKODOQnlCv3QPv5feO+ynUHyQKHsE36GOpsG9LUcH0Kr56buZ84G23cF/W0VbYWzHU+/k2bTdwvkHEP3ibfCM5PXM5vOTcn9ZmD8XWvWyqVql9TQV8BOQu4lcAFGjhWztvaA7w12k7jddQfsgd3JayNwu1KJVx/HpyxR8LH8G4O5O2De6fK+uMeE/lBhIFFgRoFaoArgCsQahjUMLBh+ehxSuDVGg7eXgqdC0AuruRTVAM4YucOntuHARz67QLopsA4wwHUhxOgc8Adaw5Ql3IAGhDWeLi8BFAkMHbsXl4Q4cAYHHiQ/BpjcM4YGADnjcF5Y3BtUeB8UQQAfUWEs8ag8GMTieuACGeMwRkfnwC56eg2Bpk5+j0uk/S4w3YB7wEcWttpw3J+7eoKHPcdcTo26nc4Clcl4XZ9b78LVUmblUVCcxZpxJJzdhfQ+a4dImQsGl97VCqVSqXaWzE3wHJd+zmcEx6H+DxJXraZm3hhcZg/InLPODz0Hf6WT/Qsga0FV5UDz6ekJ2mk8SV5CmGLAlSWoCIzuiFq5r3KEubsWeeObkzYDx6i6H1Wk9ZttOBg6FnGFLFvR5vuF5639OQnu90fK0l5xOUSfTp12/dJy5Dai9LnfI5TCp2rVCqVahdKrykHBwd44QtfiE9+8pP46Z/+adx1112T40oNFsMcSlEE8FylUp1OxTD4JooXqxRFgbIswyfuS4gI1locHh6G/cqybHFAAqPfcccd+PZv/3b82I/9GG655Rbtj1Qq1d5oL8BzIjogog8Q0af8v9fM3P9Fft9PEdHzd5XPudrsgkQeQI4/fdC0PPiNoFhbO1jsmIH0NB/gBLpF85B65xB6C0B302id8uzs50HldMrNxzfVNdlNLtawvi6c+56Lt1U2MRQLCmGbvGbyaIowYcmcTm5ScLltMmNdncjigE5eY0A+dzA2HNO+KCwqGBlMkfGTthHsKcqVxbA8aG2rBOBN4XbqtJMArUt9ZSekI+Ac+fLu1jeahQpz5fNEZPLdwxbP0Ulu5wDi8y5A563vhDl9WdPP7H7QPf0Y20rrLgb63Q+9KfZFmP87k54r0xmZvQI0vnrYxl+a61Xu3NpWnuR6borZ5+rWbhhnLKDKybXvKw8wU6mOU2F8u3D/HIhe+DFsDIgeRrB3Li0Jt2bn6L2rpSMCnws8Ww2kkz7Mzo2mBfpdKuvz0Je+gYXhGkAFCqXictxA++6NLsZD6bsG0AWyrtCA4kv72TieqdC6AOWH1vamXQG4FME04niexnPRWlz28YyphgOy45EToXEYFxC2jKBYSQdw50VJhJVA6QAOPCx+lhr3cgPgDBGuLwoURLiuKMK5deCh9LPG4IwPW8hkrqTv4135OA98e+87zzvl4s/VWDazrzjIXxbnxySOassgUypGGzaP81pF/Uj82zoB068ECL1T7mjqaAl0vp55zg5BPcFlHscHnUsbjxc7qVQqlUq1V7LWOWTX9f7A5kAHON9UMVwuIHKAy1MIeeDZEIAGzK9r2PV6OF1JL05nKH4JG0PpPWHJGNDBAczBQZygA86LAqYsnRO6Byo6zzISyZzr0rnXbHxL4HNmB/PHixnlt5742KcX7zc2do4Bt7RtDAHrMdjf+YyA6/Gb1HYlaesoCoXOVSqVSrUTxYAmADzqUY/Crbfeive///34oR/6oUljt9ThWIDzGARVqVSnTzJGt9bi6OgIdoN7D+k3+harSF8lELo8Z7t06VJv3m6//XZ813d9F57//OfjUY96FA7iey2VSqU6Ye0FeA7g2QAeBOB6AP+FmS/O3P8VAM4CuAHA/3urOdsXUQSim2KaY2p4CNwG0o8TRg8Pok/UmT2G/nyTp/ZvrXKMnDm6UdE8eFAA9HoNrtc+K8ZvimHk2DHDNgB6AOG7+fChkStLB93nId08fC6O6xEI38pnjUE4/YTUB+cnoZq6T86faaBpDKfGDivNhLGLL8pLX57kfJQ6jlYrkilCmFYaUzTSFzTtIb9vr4v1SPHwUB7lMGYD/n1qgPy5+M0+zgVMcuIP52VfJAlIHk2ouL+78W/nIR3n+6U5WrpgYqNrR+NB2izOGo6vrxynyD0IGjj3Jke04GFM7tyk7qKYuXGqVKphxQ66myqGX1cC0pJDpmOH8wrodR3nTLhtnskFGpdcAWRTh+f4eFZRX7bK9G2EBv5dIs7A5wYcnMzdmK7rQg008Hnsh1wEB/TtaluwucQ1F4AVJ+5LA6C4QOlHfoK1z9mZAdxt7WQ4WqBzga0LD3YXHiAnIHvdDM7m/m8T7Svw+DXGBBi9JMI5Y3ChKHDeGFxXFCEO4+OR3yWe9IpPPh6B3EtyjupnfDq5dpEDx4+SMhOX81jixH2U2Sbbj+NOzCZQOdD0I+ligRyYvhcwWEaMttt5hQY6n/t2ATnn5ih2M89tk7Z3XNB5vKBDpVKpVKpRCQy9a3mgG3Xt3Lpl8eO+jC8WAuc5sDyGhltQcAQCL85mXYMz0HkMmndg86H8G+PczeM2EB9LzvVc9snBEAKge7ieVqt8HL0HyL2mLnPEsrBh6b6J07m4zA8B7SmAfqyK25Z8UhDdt4ss0J5C7ykYj+7saXBUl8UF0uZUKpVKpdqxYvj8677u6/DVX/3VePe7341XvvKVg/uloGkMhapUqtMvZsbh4eFG96AClE/tN2JI/ezZs9kwH/7wh/FzP/dzOHPmDL75m78ZxZz7J5VKpToG7fq51lR9RfT3z87dmZk/TUS/AeAfAngMEd2bmT+2tdxtpF1NJDWTkO4f9pDieJos4LcEaz04Fwfc3QyiW68nguS3neY2B/ASV0iXCEQFmvJiX4DkOe0I9CTj85iZMCTjw0+fbHYOGFUAlFsQs/ujgfLJeCgympxjZPIi8dgOUEhkmrpO82LrLvgalUfO9Z2ZAVtvAIyesMiAyL/msnX+UO95MwZNN3Xk6xRNGmSKceg61A9F/3bzkNYVM7cWnkw573uzYOsGeu9uBXO3bWUi8cfeLIgYBKZH1AdlE5meUhrJ3oJ9Jse9dCFNX4akTxnoWxpgOl6AkkaaL78mDkbzJgiadQRs7byHQlsSM0++PLm2Ej2IYgZzvcAdaYlDefsh4eb9ZX4R0awY5JqzQVSu/N316oq8BqhUx6Bqh2AEwQGSJQD2/bZ8BJys4c7PADFG+4tLs/VhBGzf5GyWOEDUgjcrNHBwqpIoAKAlgDrZ1/jjWCpmhvVlYGBhehZpVswdh3Xrf48BeTe6syGmRXlKP5tCEmic7OfEJcc3tDhCFk8cRdfLHGQrx3JR3NJH0jZ+wWeNpm3GI4k6ap/WX+/jtiywukCzUkOFB84vW4sjX6crNKDvigilMTjkxgm+AFAag9KfBysirH2ako/0/CAgAOgC7RpyLujxyKJmdm8qiH6rmHGQtLX4DiBXzgLCx6r9fdpxoBrWQ+YUzqUmDzbpP+Scs/43449jX8YKKVgu0Ln8O1UpvD5VaXtIFUPnuxxdp3Upv6lUKpVKdWLKAMR7A5nH8oBxX846Cyfl7+O+znrwWdzhXRYSkH1GnmjMAT2FmGUMaIxzMz84cM8YisLB1qmMcWVnbXg2Jc7hzAyy1oHKIj8WDsdWbG70wMygum6nM2NfyNyqSeZb63p4EYE/Zvbbtz0m2zS+7HmYxtmTxljKOv5UqVQq1XHJGBOuaV/6pV+Km2++GS960YvwJV/yJXjAAx7QCpu+zc8Y0wI7d3X9Wq/XwRFZpVKdvC5fvrzxPekSKFwc0HN9zcc//nG88Y1vxBve8Ab8zM/8jI6nVSrVXmpfwPPH+X8/xczvXhjH78GB5wDweAC/tnGutqFjmzCViSqggco9zjyWB5k8dSQ1qAOxbhtG55R7b+c3QmW2dfGM42EBNolAxG2gOwXT4WHNLPSNADODJ4JGzODwIvmYAGwmgN0Eqp9EBUVQAnmYvA+cTPGF6DjRbQcuHpncbAPofVXtwEMb6uikBzcB9sbUBQDeWTxaYMDMjaux1CPPc9Vu14mkYYGB+nKT/8Y7L0taDDABxC0YnMGgsEiii1FPKYcAF2e3c2j3nf0H2kMTRNpFMiheAJ9T36T8JhKQO83fNuNfoG6/tIAJ9n1z2/k8c1PTKtctoPib7k4ERr5vGlLchgfrkggk1zaWNw3MK9057uApcL79dracGI8XqjC7h4DLX0/sL956Y6tSZZW6bW9T7ZFG0w3LGL4FomfCtvbxY4o6CbNkijsHn8cgagwJS/iCKDhtF8m+SLYvkbvicy90DjTlFB8zM6P20MQquma6Y7SwIP9taj7adbFN4HxOfALMiot13341HFhcR/vlFgHI75eshc1AOALRxu2OARz5fORc0+UXk+wrEid8RgN9l0m4M9F5YIhwxoepPfh9WRY8eICcongLOLfxIgLJ05oOwLyPV1zXCzRtqe/quk4WNFh/DqYjtgoI45wys13aVHpe7UodqBzt/iP0HT7PElZ+k7ZwkorvtpdA57l+da76SkDasMm0+W0qdVVXqVQqlWqOZD5xs7kErwxs7n7eX+AcQJhbl7+R+/sk5Z20xXE7QOOiifmM52RZ4OmJ+0kbodUKZrVq9h2Cz4HgqB9MLozpuIkD2JkJxibweWt/KfNoIavA8qnLNwNunl9g+qjc92Gstg95UKlUKpVqqWJDQnkudnBwgEc96lH43u/9XvyTf/JP8PrXvz5AnnH4uq5hjAkg+K6uicyMqqpCmgAUPlepTlDMvDF0Ls7lm6iqKpRlG9/8lV/5Ffze7/0efvAHfxDnz5/fKH6VSqXalfZlFPMguDmXP98gjvdFfz94o9xsUSczcSqOCsaB0caAprhUC3Abf9yGAG5CnHb//+z9e9QtS1Wfj39mda937wOHi4AgkIAgAoeLqCSKgqDAgYGagKgDZRDv5hsyvIYBiTpiYvx5IRqRRNEgiLeoiBCC10QjgoiAxBsoYgzKGEEFVMAA5+x3ddX8/VE1q2fXqu7VvS7v++695wPrvGuv1V1VXV3dq7r6qdk8LnIsZSM/lVd8fGUYLLMv+jGAWczeqCNSdRcHPcclRL3sjIuQLDenwXpGes+DZbL8nOvBqyjFm2luyK95H6Xy1cqvROvN74oy5VX69nARbgzIoPiiC8C0zycXWVqQvE/lpkTtUbTS9ioRvfPxl9q+TksL8VPHnqqHzfqYiLaX5fKxety+n7M4zSxW06L2kdvn1H7M0ddn7h2ivNWD8h2IXdMc3YZ8fg3jNc7lzRfZRtnvM37WD1EHB6nGHQZt9Llz66LqWBo7z00Wb3v5BsfzkW4SxXPcjulW64rmtZNacun8s/fNZsO4xjjGb8wgfYxLj1oad0COxqyFX5FVPYaRsuUl0Y19ei3dipx/MaGKk8Rc9ijLM1C57t4XqAwwwtZJhLWeblCCtk6Qksi+7Uws9Z23+wDXMOW+WZJehyhUd4jbVluP0zJr2V+qPZTLh7TsrSrSuYizjXpJe5T68qmd1XqxjD4qNGHYfp1Kj9BL4xJ5XETwEyKcpCjml5zDDUoOd+pvm5ZtVboSGX2lyt2gl4FLyrJJOi0RWtmW4tqkNilF2tqgLtK+6pjzZIHye31cnQX5PIHNNjja3tG3Wdn350E+36Fvz1MR/2vr621birSNse90+zrGwFxuozDp3DAMwzgAS8degX4sJoT+Vd57OOexZX2fIG+fFoZFKNbv1RjQuSPXot73gYXK1xby01b18uV6ZVpyPyWtR0kwr0Yil+/GyqLTci6nM3WvhaU9HQCWNrrr+kDfvnWa8U1dpJf19PFxQY4JwzAMw7gW0H07IsKNN96If/gP/yEe+9jH4ru/+7tx5cqVwfcijZ6FdO59H5BO/h3snpthnAshBFy5cmWvY3DjenIHatfIP/IjP4I/+qM/wmMe8xh87Md+7M5pG4ZhHJuLIp7L9JwP7ZHGB9X72+2RzjVIP9BI1AvWs34AK4PBWRnIgvgBRfRaXjofHHZwuhTQ+zqSwdReaibXxNekgD6zUzEQvtVAZCkJ5u1Mkjf70W0uhy83lpsQnnM9YzgbeEzWZFX+izEgukuMuL4+OBQSfdkWZsFpd6o6IxejT+vjrbYPypSYgeD7tAZSd9gsL9C341wP8+sj7+uxVebuX9Z6x3LJdyP6fpk8INbE5HI5SWBY10dppzukWez/obCoJuDUcqt9ric41NqWEvA385NFlh4/+9fltv09mvOI4Dk4F6nz09z2OFXvNYaPLN7/onJLbtinrnIqYzcQF6d5Uc79hnFxOObQcP6F33LslhJ6Kf/K2TColxzFWUBXgml9CuJ43qVAntNV+QmNOmfW1i3TWQIhXjdsKz+PLBOS9FssneKd18XtUL4OcI4UWVant0SYXYu8PLFeQB8NXe//mmir0wzYFK/H2t1AnOf+2VaOaCPSc/mdU/9uC2G8lM5X6f0lJZED/YDHSfpeRNw25SHb0JZ5YvqXV76TtHL50mdZXifqhecijaqIL8dg2ne1c8tY2z0mXLYPKCmd62K6Xj5PPjijvkP1mEx/t6GP6V37O46mpXM3Y7l9cOq4PG4f1TAMw7ge0GMJs39TRLYVoTaEcxlHKMVykay1ND0qWS+Qt8+KmijPRXT2ueXtx6pnbCdVJmXK+LnI4m27GW29XHbsO5HOCwEdRTTSuMHj9yx2ZV+RXfrAWjLP96+KiRcb68m66mVjboZhGIZxGLRUfvvb3x5f+IVfiP/zf/4PXve61+H//b//N5DPyyjoh4aZEULYEFzHPjcM47h477Fer/OTB/bhUE8tkPPA61//evzxH/8x7nOf++AJT3jC3tHUDcMwjkm7fZEz4f0A7pxeu6LX/eDoUmdMrXN6foNGpO6eE6I8XniZwPZBOy7jB8q/KMkYMhgNYAdRbjwvKa8I4TH9UbFuATrSueRI3FcOp80iaqJcG2J5NusrDaIizKpLTnlQ2h/cf7FZf1IX7JUYXywj9S9lr8nnDDAVZWP1GEjVMRLBkFLU9M3yBxCSvM0j0djPgPhIz1jORccXEUhL9IPy07ApY87xkeRzMCSqNblm00bLDQr1/YReMpfcKbWt2DQCwDH2Zr8pNKyHdLwUG5wO/Xp+OXq85D/4bmKzdZkBLI+GL/Uxt1NOw2Y/NxcOIGoO21Z3PJ1v5C83JSYHFjYzk302tU0bAr7kpz8jB/D8CytpE3vXJWH5cVspi/pXf4Nm8NGM9JnBqTxT29TfcD1u5IMyz/oxPQ+9n0i1l/36I+r33jAM+CP37xskEXLmsVv2ErnyIvVXLy9iJikpuNbrrOXpAHBRRjkn6/Rcyjv/hhXrNuk7qde5Z5uYTvwtDem3bkogDul8WH6/Zo5S8uBThkNASJ9u1OmB2oCeILA0TVnPV+X5Ib5YLsu2tX4i+ijcAcOo5LWyS9tCWs+r32XdDoJatpyIAPVdCwz66SKdt0ToUjtpiPIyDAzaDgG4rNff2LcpvVQvQD8ZInB57au2NbUfedIA1DZIGoEZPpWxOjGjUpa8H+NCWFXaKKd0JZr1WSHtwxXnBznOuTzmVP2JDH0W4yFZGi8+28bUcTAXmpDJtWhOW85Pu+SLlF5TfGYYhmEY+xLHJdL7kf4RgF6cPUNxZvL3bjD2NbKcEoUvIlTbBjWezF23SMLO44T7SvWyvgjjSRQfxTkghI1x50F6+S1l6VyuGeVzuX5kDO/f7Aunsu1TL1I2SDrqczD3T0cdu2eox1nP+X6LYRiGYVxrtG2LBzzgAbj55pvx3//7f8dd7nIX3HTTTbjhhhuOnrfI5WOCa1AB5w4lsBqGMY48baDras8qXc6h+uwhBLz73e/GL/3SL+G+970vHv3oR+OjP/qjD5K2YRjGsbgo4vlfAbgLgAcR0W2ZeZfI55+s3r/7MMXajzggK51DEcY2B4tmyd5HoZe3pSQyWDcQoOeSB51FPk25aEG6lKl3IEew5TJtUt/3LP2hF7E9D6Er6TaK4g7UOHDwqb4q+5Bc2soZAi7HiOOUBHRO/0PoReCyzvpo6X155XNyvVjLY/I5au0ubXEIUZYu8iOEkbKELKafr3zuknQ9X3rm4GXlKHkzKgO/fdsa3d/VtAMoRTynhrJI3kdrjhL0lPzJal/0UcnTPuAoCuvvB5MyaknKxIOx20Oy/fmUwOWXk9sMAOy72cuWZduS8mDRnU+ZB7oh0Ud9XnYjbeP8r9rD1jyrN+3iPs/R9GcS258blGE5hxCPtRK5JGse3HiKnw2P/f4w2GH7yrrUwtcZCuclO8nilTYv/ZMl58thknLD7MATOQzjKkTExvUZ9OVFUPQjfd4pyh7cQG4ulilF0pyGkiTHjnqRhmtD6MyMDn203wYYRIEWWVKG+hyAtXo/50xTRiSfI597tYzmSgi4wbmNegMYHrSXmFpjH+FV9qdPdTzVNiSfK/pR8Ogjkmvk/C5R0bXQWqP8Vc+Ru2VdLV8gif9pOb2P5K9eXk/uaChGNs/SNxEuE+FKIbnr5U+cyxG4pQ1rsbxJZdJyOKU62Cbx53zQ944klUCU5axaGwxJSB+0sySka6lrSj4/j0EdPUFFImvnMqX3rjj2dGR0fa45eNmKv9In3HZsyQSLfRk73+gJD3Tg/SbHqsPwuDEMwzCMQ5Llc+dAqS+5cQ+h8pTGQ+U98eXm+22/hyLIqzH2i8SGbF7K0Enu566LdT4nzbG09i1r08CdnAwmHI6S5PPROk9lk7IyADRNbm/x43yjJo6dHzD6XxyL3/600sk0tEBeiGPxfgFN7ofB+kQb1+SGYRiGYewOEeFpT3sa3vzmN+O1r30tLl++jAc84AFHjSa8TTrXy3nvB0GkDMM4PHKsXcSnDJyenuIVr3gFmBmPeMQj8MAHPvC8i2QYhrGViyKe/yaAhwBYAfhiAC9YsjIRXQLwDPXRGw5XtP3YlK7LG/oMSnehz09AF5RgCyCrFXuWqV+f82j0oWT0etpukOauInoZXaIX6tNNdtekAeqQ62tDHiUXq3RGBOgcXTzLoKEQzOuDnrlsIUbN4BAbVJQBY11s5i3RsYNKvxdXOfiqfB7XxEY5tJjOfM4XQ3Os5Nr+mCPtJrF7dH8PFpVI8FEej1HwHYjKqD9zhOvhjYW4CfHikwODqCkk3N00jl5GplSNfR1xZULCWHn7QfT5IjRtWbavs6iO5MH3kYuCsfxlG6eayFHbrypTHyG+36eLkkptLG5rW22PW/fDPqf3A3jnu0bylokb8dw/8vjZvWYoFEkB6Xx+ngM9Mqlk2TZNRvqn3eVzoP+tMPncuN45y947IV7ALYl+PpaOSOJAIaJzHwVcC5RaKJ2S0N2WsomI3IrsTb3ELXKyLMMpX4miPNVbiFNqNn9L58jnsv3l92uO0aZlmQAGs89Rzw/BmPS9ZP211NeWNEQE1xMl5DMi2oxiDeDDIQxk3Fo9jX0mkePbQkIRSb4WAVvagLSPdZKVgdhmWiI0SeBlxLZ4OyIEAI1aVnODc30Ec6io3ekzqY9W7WspI6XPa0Ky/sQz5zJLPcr1veTLKg8d0b5T7SynzX1E8y6Vo1xGllundM/jl1jOC3JM6zJk0Z+GkdnzpIPKeeYQyD7WVw9T0rne34egdp4qn6BwaOlcJoVYf8wwDMM4Nnk8jigGL1Hy9jGinOvx+cHf8v1cjljWQ7FNTAYz2Huw9wjr9eb3Y2nOjZ45NsZWS1ek89QeZo1ZpUjmPCVfpfKS7CvncKhgItvIkc/3jDbKzCDvYzqq3Bti+sg25eWkLDSM/G4YhmEYxu5813d9Fz7v8z4Pbdvizne+M+52t7sdJZ+50rlefr1eY7Va2X03wzgCckx2XTf7uDwrmBm/+7u/i1e84hV40YtehI/5mI857yIZhmHM4qKI568C8P+l9/+OiP4nM799wfr/AcDfR7xf+FZmfuehC3g8evG6d6VFwjuM+L1f2UiNfXEfueIIMjogg2b7yuhhU3CXdPdCBE8A3Ed1IUoCOiZEyEUCepR2OdsKw+0ai4IOIoTg46B/u0KMnh7TilLvppwZP6+XKYrNxeAnc5ZEq2J6GgCO9XBeTOznGfW/TWhWC+b9HdOkrKRviPlJEo8ibD9pYQ414TtNLYhJ5LZHINfmvMk1dZGZCAQ3Ks1LpHakdjNcbotpzDNvMGwUadeB/KljuvZdOqd63wvb+lvmmXL9ILm94Rn1tlVKT22r8sVUxsMlx9pMmaLrJ8fIRIX9qLW1bTA4dFvypnEpfW7JRib8nB+7lmXz2N0perphGBscUlZcgpx9Ax0m8vZARCfKErrIzKVoXkropTzaAui2nGc67iM9O7UdDsCtIgIjRngPSm6tybfbCEleH7v4le+1HCtlBDbOoIvzr+aJ/doOAzhlnp3GKfNAnqZUr2NCc0CUzsv0a1uvP5OI4R1zFsfLZXWEb/29iOVlz9Az47JzaBCldFLp3KFp8CHvc3RyAkDMeQLDZedwKYnj8hmKtqnFconIz4gR97X0XZPra4hU3ab0ApD7XRKN2qU2J5K0x2Y0eXlKwKqQ42v7oEvi+3n2WrqRMsp2AEXU+zS2ENR3+z5MeJ3SlHOXnpZbw2O3pwzUGItiXkrnu5zDxiijyhuGYRjGmSIycLphnyOiiyA84zd2QyzX7w85HsN8tGjsh2Aghk9td5LOwQyeIZ0vEs4XQERA08C1LdA0gPfgFAl/Vh0TgZpmWj5Xy0lUdxmTlAkEaKafJrornCZVYM/opwxEIR/YiMzO6djZKqDrNPaMxm4YhmEYRs/znvc8POtZz8Kf/Mmf4D/+x/94lDx2jaos8rlhGIeFmXF6enpho53ffPPNeM973oPb3e52510cwzCM2Rx+1GkHmPmXAPxO+uedAPw6EX32tvWI6I5E9KMAnqk+/tYjFPGgUBIey5daIkW0cFFYdk16OVCKuHo+MxwpCdROleewZWGOUaQ5BHDw8cVBCa3LBhE5SYccQoogHtT7+ULixnbmfeOyUNwLq6T2m9tYj2YM9vbRyutR7aR+NgbwmaNr6Tsw+74Og09SbSVvcnBNWxVuY31V8oGIsJX6Y0ZI+Z31YH5+/FS5LWk75pRHyj1fQI7SrGtWoKYd3b9RzJdYh0htZt4peEMGrtRtnKHZRRk35TOafvWQLeNQoiLQB4wdg1xI/XIsz2LJOYSH5SS5kZXOE/1x7iuv/viXOtQvXfY55VgmSveCfX9O4LxvF6fldLRz139W5jdRt7sfo5vC/t7MbgM82JfHIP+2uaafyHFuv7uqXPvkP7KLpP3sk/bStmsY1xoMZKHyrCFEUbUlyrLvwdNGlCSlV+Shomunz5gZIUnN+rsW2FqukMTpoARRTnnKuUkiSAtrtewSovw6EfVYyfSDMuJgc81yeusFwniNDsAtFSl8LL8rHKM/SyTxNskZtam5Iu3WpPMxHFFOmxBF8a4Q3Z1I5ZXJEg0RLjuHk0I8lrZwG+ewUuk7Ipw4hxudy+XX2yLvV0RZ8hVBt02ftapNEXqhXZe1XE4fa6xeW+snlaVJ2y7lkMjtIrWPpSXSeXnslXRcj/h+1nRJNK+VxRfnifK7sfXmIOuJdO4n0pJz9yGl89qVl+xjWeaQ0nmrJstchL6qYRiGcX3Rj8clydy5KAE3Daht419978O5/qWWQ9PEl3P9i2jZWOEUIUQp2s8bFz5Lcl1IPWzb7hB66bzrxp8CqdJeLJ3PnSwg+/LkJH5Y3gOZA1Hc9rFy6LI4dU9FtTeZUEptu3kfZk9EDD9Yen7kHotMJhj5foC05ws8icIwDMMwrhbuec974hu/8Rtxt7vdDc9+9rMPnv56vd5Lbu26zn7vDeNAiBNx6623Xkjp/K//+q/xmZ/5mfjVX/1V3OY2t7FxXsMwriouSsRzIEY8fw2AGwDcDcB/I6I/APAKAP8LwHsBXAFwBwAfC+DRAD4XwG2AfJ/2lcz8s2df9MNQ+wEZdigJOhJ4XJxxftHR+8FQiqEPARwmIvoAkUvSXiaM18m2dACACaBBnZX1Op6W/k6iXEf5E1F8TZGsexFPfZ8joSNHvGbwZF1NCQixDCm6ezFxgYjAgUHE6JUSAnwAuXYgCuoI6kT1R40yhxjNu4hMLcLKxqA4p9h1ROn+w9l3johcluN3PS7m7Cct9xICGC4fD5tCJqm6ibHpouDeP5Z2nNTeppBjhX0eeM83gBYQ92vIEyjmRMGuNtTBttbpZWmupJOO0eD7j3UdjZz78pMJJovL8TywQ9vc+Txbnj+yYbflAqfIT6KM98du/qbIb7diLmJGs9zGvMhE3LfvuQXg5aogpYkkus1etIs7cm4H6V7qYXNbYkQyF89fu7TtdJzHc9/FqivDODYSTfe8B4BFEndEA9H30HkQokDLRFm49eo7eUn0cFkPmPdzQSk92YIGMdo5pL+p8KksS2dRMwMdMdqR0jAzOhpGLea0Pfuc4VKPaC+BXdKYIxdL5O8uCe5aymaMR1rntM6VCTFe9qWOgq8R6Vy2V0col/WFAOCSc9V0Shlc/ooQPiXwyrq6jbSqvyGR/bV0LJ9RkYaUWepDoq5r8bzcNvl3wLCN6uPEEYE4Rv3X188DsV2Vr0McvFmnvn0ZnV+Qej/vKNjMHJ/IgPqxGtRxRep7vV753TZEXM/SearfEpksdOhzd1lOHdmfiDYi2u8CFe1aPjMMwzCMsyaPOU79ns6J4H0sQv90wfO+XgPU77Uey1tSL6q+2ftqtPM8TrhnfU/VVx+Ax8GJdB6/yH9pSxobac6JfK7ykLqUNkhNerJoisBPUk8H2O/MDPJ+78jnGRnPq0QuZ2ZQCP0+rN0rTH8ppcMyCcD6g4ZhGIaxGOccbrrpJnzoQx/Cq1/9ajz/+c/H137t1+6dLjMfRBqXdJqmgTvCE2wM43rj1ltvPdq1YQhh5+P0z//8z/EjP/Ij+NzP/Vx84id+IppDXXsYhmGcERdGPGfm/0VEXwDgpxHlcwLwcek1hvZyXw3g6Uctoxa9BmM580TlXRhbv/9RLKVpLW9yZfkjIvIyEM3uQwvxMlg8yHMokfcfpr8j6eSliaLIrstaxP4b2weDQU4gSroioDMqA5wiocf8mNJAKCbEZsl7W/1JBGfEPRB9WvXoyfQNg4DgK7Jqr1iMSY3MyDKy/jCK/L2E338lkhCfg5DYqyD7iJRIUnmWSTbk5mFbGZySKAmsqW317STJ5xwQK8+BqAHgJydtxCj4ksd02XM6nLaBk7qR149lG4jxhRHGAIhFPldtomaOieCbo56rc09ggFIE/1K3Ilfd3qF7XtuHUghdp4OEK4Usk+CR88Rck3rfc1qamLD1RghjQ7em/ngdK6vI6duLoeowrbcsgnU6fx7g+K6fe/pzY7WeRvfjsj2Ub5xdYOG8Z3m54vmY4oSL0e3SXapd0k+/QRe23gzj8ASMC7znASHKtQGHi+I7lo+cMWQITEu4QOwb1mRxLd6WMFIEdPTSbAMgJGG4Icr1reXUMQF3DGbAU7+eKMQ5DY5dtEZ9mx5svlh0FwlY6mWXfikDWaLN6ZV9UgzrlTGMDC/7a0p+90COUj7WrrVsXgrhjD7Stazd0lDYl7ap22pN1pWJFKHY7y0RTihFTsd4NGuXxHSdt0vbKDJwYMZpsZ5EN5dtlDwkLVJp5eNfX//o+khttBTP9XJOHa9acs/HmAjuqb8YiODSMTImn3Mqk9Ttef4qSzsNaTtKiVwm71Dxvb5u6q9kp88dHkWk85FrjIDdjsMpalK508fKyCSNXfLR0rn1uQzDMIxzJfRj0aOc9W9VIcOft3A+CBih62KXeuH0BNQknQ/uOOwqs+8CUR+l3fVPdhyMe6fyzksuXcs4FyO6p8/juDTXt0fG8FgF0KH+6S8sk5cPJKAfUj7P12cimBdySr6fItu+TUBnjvWmlrM+omEYhmHM59KlS3jIQx6C9XqN//pf/yt+9Vd/FY973ON2/j1lZvgDPmlH0gNg8rlh7Agz48qVK1iv10eRupl5Z/H8937v9/CKV7wCf/qnf4qv/MqvxA033HDw8hmGYRybCyOeAwAz/zwRfRKAHwHwD7B5b7bGLQD+PYD/Hy+z5XYpYfpbSo7jncfN4dd5HdVtHdpxIV1n00tkNKhKWfaYg680KIN+ePiUXLuYygA35awGlVGXE2vr51LGbciRzVGv940o6OSS4BeSiMfF9lJfPWLOJ9F5c5+IKrgF7vdnLK0MzGqTWL4PUZuhsk30+ohIwsPysJLPVdpZGJDMi/pAXEeE/mMNfuayisyv9tsuMqWIlHlbSddmfdB3k34lOQZEBuEQgBBALqXFMmljQrDVhZjhR+dtiDmDJquhSFDvV6Q3HGVpku1Px5kM8se/PEixL/uQjYkbezK8yTFvncmbGJMrLiwzDaMd9tHttYo2UciaXDZy4yGL47M2aaAI5rIuOVxkQkT8u7dOMyybtK+Y0Xj+w4NzWY7q+EaKdN5P8rh4SPuZFyW+pDzXF+ki/eLsfEzKjV27yWVcP4jseJEQcZOSeHrM/j4V7weCNepnHR3FmIp1taQty2XJMsnH+vwnf32SMZcMGcYo5pxkUC3Jc/4jvxH53EilNjuRvt6mPfaByM0SCV5+A0j3LbBZx555IN6GLWXxiKL6ulgPQDWv2lm+lOLHakrEcpLJmIomfSfRyr0SviXKuXw3hrQZkcgl6jWhl85jnzvJ66oMpRxMqSxgxqqQh2VK5fBqaR6yXp78kH6Hg6o7aesByAK6HBci4o9F0dby+fxWezy0YA70kw9q3zP6/TC4vlX7rmyDjPQkgFRfNelcztWHnpQzJpWX0vk++0Bvu7QNwzAMwzgv9PjrMe8sLELJ5ocQjXdl8Bt9SBk8hCidi3iu+oIbeR0Tiawt4rn+3Pv8NBuRp2ftBb0NzoGUfL51/JViAB0U9UDShzyggJ6jkR9I+Mpj0jrCucoLUP1L5tF8czpKVM/X4dZnNAzDMIxZ3OEOd8BDHvIQvOc978ErXvEKPPCBD8Q97nGPxRKpyKdh8ROL56ULmHxuGEsJIWC9XmO9XiOEEMdqj3Ac7SKfv/e978Uf/MEf4C//8i/xlKc8Bfe6170AWD/eMIyrjwvXO2HmP2LmTwLwOAAvAvD2ymL/D8CvAHgOgHsx8787vnQ+dqOeqy9GFDQHj50UgXBDOixfyFHNlr6yiJBf6ZZnjpAgg3HlcscTgvv8JXp0keeB892slwCJLCyRwaeET71eub9qdT7YUtku50CuiX9Ht7OvFyIHuAZEtcjg+tZ6vQ0Oh/pT2UKAxEDvt0siLHuAQ5+X1I2kIwO1tYgag/Zb1NuGZL+5zqEH//v9ELchH3dpO/ZpXvq0QklKlQjgyyj2dfqb26YI7hP1nkqEgdI1oy7zNhTHQ84375fi2JBB+eAHN2/AcrMjpRX69Lg4RlC++sqEtMut553qNlbWkUjVS3Z4pR1v9cB3aMeky5cykfrZFu18o0ByjqCRNpjbz4x6OMebcaPIDJbcVue18w3yemOCvjovZ7GwTOKC1Y1mhxPbtrqkfI7a7aS5874yjKsUfTP4uP3o5Whh+yzLNshXfS5nBvnl8sxZpg7q5ZOg0KCP7i09MZGGa79+XCw7p5yEkPq+AX1M5aJvC5HSAxxkMp8sX0fkYNmeXX9LGCoCuSpZFqjTq4GSdDGMWq7LM1WWgLp0Lm1H8qrtW11eeVUn6qa/DZDl8ZKGYiTzVuTyJFUHpEjnzuVI57W05b2ko8sqcrb+jFCfrOBS3lDruFSmtpD+GxGCSV1v6XoZEbIYsY3LlZrsxyblI3lIvuV+lv3apeOoBjMPouRfBDgd+3kiROX7oM4NZV2GyvpSD7KtZbT+fEzy5qSKQ1DrdTvVRg4hnefj/YL91hiGYRjXMRfl2r8Yd4xjlGdbto17HEmghgjR+/52622UICZAn58Wr48IUYx0jqaJf2t56jrYpUwin29bTo3766jrZRlIL3OA+18SROZQ7T/eKkwBXUbup+TvJvLN12LFGHzt3pVhGIZhGHXucpe74IlPfCJuvPFG/PRP/zSuXLmy6HdUpFOJTn5oJO1DS+2GcS0TQkDXdTg9Pc3H5jGPoyVPO1iv13jTm96Ed73rXfi0T/s0fMEXfAGaprGxX8MwrkouVMRzDTO/GsCrAYDiGfYjAFwC8H5mvuUcy4VFt27VzeY4IKhWZ6BMi9V/R2/ZU+WzQfm2QWJCDnPm+phgLOahBqnKvFMUZvGfD5rXkL5uGGqniP5aqZP6On2EXCqWGYoWcX83ALsotiQBuLqNSchHk6LZBt8vR320kNyAotUhhRwZnAwxmDp6mbmXz5HugDcYRDdnkWpkkBaDaHODemFOAjbpL8CQCMQjj4okPT68X+epj7IzcWNhUH875ZJkcZnIkT6rpVlu4Gh5CHAEhLh/+0jhum1tDij3dShtL4BoLL5n33bzxJRyCWk7Um4HgIulGIBr8j/6aNAxhuLcOT+xrLG95IjpWZCeELiWzCmSup0jXUv6wG5Rz3ekvwkw42YYF20sl3FLWcek9OmC5fQno2lPTorYQ1iW5IEdLvg2887thtKjd3UO8Q5dsc7VdiG3x40y3hKdnracw7aljQDG7gK7YVxNiDAqYqM+R16EG7xaVA1T5/YjUJ5l5QrKQQml6fysBVP5BZPfIo+hbD22LcwMT73AO12uAJc0dZ/61LV1JMI6yRbk30ra6L1oGXjfemb0ArnqHU72cEQe19KtlKkUccu8TplxqqSEMrryNogoStSyP4vfdVL1218B9svJMici2Ka/etLBDc5tLQuhj3Ku60rXWUuUJWRCFHl1PaP4K1six7lI4LqvJFLw2G2lgE3BnZkRiODRD8g4tXwoP1PXM1ymkdrpWP1wmrzQKhn6vJH9r6OFD84X6dgndezr74NqqzKRpWzredSGefLY2Qd9rOjPmuL7XdMGhlHxDcMwDOOiwBdBeNHC+TnI5uof/d9D/14zg5Mgwd7n92clmwskeQKgto3ieXXBwaD/bmNLIp9PtLEcfVyXw7lh9PCUv6TDEkQmifuSzlI4hPHt3wNO0Rdrkc2H92Ewue/zsqoe9HWeYRiGYRjj3OEOd8C3fdu34aEPfSg+4zM+Aw9+8INx+fLlresdWzrX+Xjv82+6/bYbxjjMjK7rcOXKlY1jU/59jMjnEvV82/H5J3/yJ/j1X/913Pve98aXfMmX2NMMDMO4qrkqzmAc+Vtm/svzlM73ZSM6cBIPxyIEj0bWZqjlAnKU5xB6wXnwEjF5SpqXwSinXr1YsSnpHYphvhIN/cwGT3P9ShRnn8RsHf25XEWW930dy5SByn7LUWRdm7dxfPui3EzNCs6pWW00XCbLyhxG96hsn97ODYKP2wHEfEHF9nEqU7280xE5Ni+wBu0cOw7wbhwv2wTePdsSI4m8OjIKqWNkmNdsvUDahURBl+NZJp7I52Vx9L4pzgP6XCD7Zvje5+g/nPeBavth5ByRbxpIpHbO6yyhF+xTcvk/h4Gq+2RboXggt28X3Xe7mdW3nbk3xDb3Bc2K6k75SQtz2ZhQNRH1euopCEuP58FvG8v5d7dzArbuN6CPbrQZrzUe1ldFl2gYxekoaR/iN3i3p7ZsexnGRUKfOUVAb3HxJEGRNg9/a3y3cjgAJ6luvLySOBqYc7TuUjKVHoSIvzWYgVN2owJwLEMvnQsSQbmGSMk6j3VKB+glcdmOfc5VkpYI5LpNjUUaB4AOwBXelM5r0Z/L/K4w40rqGxL10bW3CfxAFMRFZva8GVlbC+VlBHJ97Jw4h8vpe8lfxOSGCDfOkM6R1r1MhEspP0m/LY5F/S/JU7aHKp8Lsg2tyOdFOjofXQ9jUbZLSX+QFnrxXc4vjSojYSjol+20RncBf0eZh08/qH0vEdA9NnvhnWr35Xf5mDx0oRXlcUJEeSIBVdrJXETIb5H2+QX5PTEMwzCMCwEzEEIUsc86wjmp+xbORfFZRzc/MFk0F+lc8j3rvkGSINxqNS5dS6RyiuOSs8tYW25EwJ5TzsGYKFG/j9Qy8tnOfawDRj3XMKeJBmPXD6ndz8lfxu6h0rOxNcMwDMPYTtu2eNWrXoVnPvOZ+N//+3+j67rJ5cekc/1UnEOO64hMK+8Nw9hEJml0XTc6IcR7v3iyyJxjOoSQA+yNLXvrrbfim7/5m/HQhz4UX/7lX46maUw8NwzjqubCRjy/iDCHkRuHVH17aFF7TuTfeh9ToslJnMDNCIGbP3rDyB1ZEM3RkHcXhycZyO4pn7MeFMs3qLmwJ3R0dEqL9mJ3vxjl7RgsA4Bc04vCtGXbyIEaF6P1sgdAA9GXnAP7LpZzRJiM0We5Mogr+QZVHgdyDYianM+GkDoSWbiX6Yvvgt+UgTlGRY/Fbnbct7wsug65GIV3QV4yYQBI2+HcxvGVo8VLFMyFkEtqDaW6EJlbZPdUbRuC96A+J84zYqyo9QaRT9JEh/7cEsBBPkfep4PI6kQguMr5qDheZpCmNWxZYhlEbqf9Efdxf/NktK0sPB9JO+pF/zmTJcbz0WWs5ufkOFy4/TjML9ay47l+Dhy2yZ0KMfw3qSj7o2yeI4GzjQ68lBhtFViyr3VE8um0XfrdmZ3yII/4szPVVueVOS/FPOv3wsQs4yyRqMklIokCABNlGfe8zycEYJXKMyUjnxUnROgwlGF1hOvAfTR0ACDuz1wN0WgkbwZlUX2lzgkEhovfVsvjmcEjUnuXBOj83BsGmEIqA+0ltjqR8JNcy7wsOvOHK/1hxrRkLL37W0KI0biVzLyNMkI1I4rywPCYkPYmMraURwvxhBjJXEvUUgYRxm/j3GxhWoRwjz6iuSPK7URH/HZEuawroo3JDlKGRq2P9G+R0glR+tfLt1RvD7WDZJCEAAEAAElEQVR+VozST1gzD9qq4NDXdxbxU94SFZ3RD+jUIquXrJkvVORzQSKce2BwrOnvpe2I0K2vUvV57azOcU1Rj1o6B3YfaCvldcMwDMMwEmk88zyirevo0WcZLCdlnu/dnMk1ZS0Poiidt7GXUt0Hum7kmsC5yf01WZMijW8rq9yj8b6X4l26nyJ5E8XvvO/76il9kokMC+o2R1w/1oSDiejn8j2AXvCfKAMDfZT3tLxFQDcMwzCMcYgI97///fGFX/iF+Bf/4l/g2c9+Nm6++ebRoFze+6pkWi5/yABLzIz1eo3VajXi+BjG9Yscl1euXMF6vZ5cVo7dZuK6g4hyBHN9rOljunyS+6VLl+CcQwibQWiJCF/8xV+Mpz3taXjc4x6HO9zhDntsrWEYxsXAxPMljEbM4j4SbW9J6X8Mv0psldg3/jmv46g7uQdj4gb1sJMchbPAAb7z8CmC7Xq9fUbo9KxR7gW7lJ9z7mxnf5HeA5TL0LQtmiSDNm076MR0XYd116HrfG4/q9UqihAuyr3R7R5GXRZ8it4SOEarXrXpNjszGidliB0dDlwVoLquQ+f7SIZt28KnyNYSGYacgyOHwIwra4/1+hSO3GDsMgR5hFOUhZtWTh8M5xq0bYumaVLam4RK+dbrNXwIcK7J+3P6sVX1GwxN0+dfo2katE0zayxYC+eD8jOnoDpx5rK02fV6HQOxEOV9IfsmT1Vw/fETmzEP0g3ySB+KkncIAW3bwPt+VqRzFCWZnD9w65UreV/GY2xgmed2tlqt0HUdOLUb6RgHZpyernN5fegvep0jNE2bl2uaFiGwKn+K9JmWb4pjsXZMO0kvxBsDzjn4wGib4boyw3TVtnAutlkgHjur1ap6s2W9XudyNs5t7OsxYZuZceuttw6Xof4Com37n8n1ej2oPwAby9TSl2OCiHDS9ttam8EqddY4GsySl33jXFO9oSD13fmQ1y/LecMNN+TtANK5o0hHyiptomTs+I4XVozTiYs4KSMRcOnkJJfPOZePXVlmLP9t9e1Df26UG4PNSBSj1WqV8pTju982Isp1WUPWXfq9Pi7atq0OPi2hbYrtqqy+7tZy4kl5AH5mH2FsP2xD9tNUHS5O04XJAbSpdmEY50UpoY/J0meJQ+xvdDh/GV5Tis/F1UWOekxEcBVJWOPRoIHHWgnjNCGdC4EZTEBbST32fAic9mjcjx5YENOYlOAtrw5RBl66LzyAK8W5XHpnYxG29TLrVIdzI+GLGA5g0Kddc+zfr9N1jktScKu2da3Kw0gRvZ3LNVeK0ESEFcYl7hItucukhSyYAzkCvpbaqVhfJOJSetbr62VzNHTuI73r/VoyJoUzM8KUKJK+l8jXsn/l6QGyPx36CPEu1dtYmyonUlw0ZBLG2IQIZo6TViDHdbo2hjwtgdFC2g6l/x1+awftpJi0UptIsA2ZZHBR94thGIZhnBvyFMczvnaZI/UejSTZU9OAu26xGH1IqGniq22juLwoEMx+dSfyNVciEWb5u7gXwCHEfSdjgVpAF9Fct6cdBfSc/4Qgvg/MDHi/VUAfROCfSg/YENANwzAMwxjna77ma/DXf/3XeNnLXoYPfehD+JzP+ZyNZeS++5woyPr7cKC+Xdd1dl/MMAq897jllltmRzOX47E8lkQ4H3PR9DHtnMvu0A033DCYgFLK6s961rPwaZ/2aXjc4x6Hj/qoj9plEw3DMC4c1hs5EBLBeVHk0clPaRC0libWKHGUBkf7uN0bydfiA072cUel+5JYbkcNuJEqCfCOhqHeirpiKsRcVdBhvv2AL5HbObrxTgzKogRF9GN1jSME10ehdURwFPdJX+64ug+hf3Q2UYwOy8Po2RLdTaTOzockOLucUAgMonhh0xBtiKEiuWgBQf5GKTBFvmsIDQOXVg18R/AhZKFaRF/mAGYCgbHKvsVwzznqJe1hOZK8ocqXozoS8qBv7WIrlqEXRDd2TbFdNaS+3YhIGcedGSAg9i1FHo//caAoTIPAMdwegqOhmMnpCavlxeXE8ZPzBYrg5YSmcbmzGgKDSdcPpybFKo3NyS4OUPXK8AEgSgI6UtuEpNOnH8fuRdnoBVQRdDkwnOujRcaLa71dm/sypDYlaY7N8JZ2UZ6Xts0G58Bgxwgc4DDv8WU6zYZi+6R4FG4I7lIHtfKOp99/3260vXpbFok/t4v0vvMBK3lqwpZt8Z7zRIiynHlfMW3YPHn90B93c7c1HqMT32OYpi6PXi++HztO553rQ2A0raiG00rgWBvUx9rmOmHsq/jx4EbccNvyEwy4f4rCrr9gHEZugurf2JHjZl5d7ncjyjk5bx6GiyTJGkYD9IInNvsnHsOurwiUEgFahNHzatctgJBE1vMog4jvDRD7VEUZpN5yBGv5IkmpkoYsM5TWCQEODiFG1qaAuXHJmYGOeEM+99z3qTUNAjyaauquEGf1ugHAqUjLC+o/IIrD5bBpjoA9kpaUveMo507J6eU2lMI5gBxVvUvbILK4jgKtLmUz/USAunTeEmGVrmkk6v022iSdi3IiaYsc3s7oD66IchT22ndantfb16ayln0N2fc5CneSvWsExLY8NjDDzOiSfC7nHZHdocosE0ralBelctXahUT411HmLxpebtwBG+X0YBAHMAI2p1/Ev3KmdSC4tIdCercvWizXEn8Z9XwuWly3KFWGYRiGkUjCOXA21yvnEtW8VgakHg0RuOvA6/W5XK8RkKVut1otk6uJcrTzbVHPZyXXNFX5fANmqEHHvC+JqF9fPpsS0MvvRrNLfU6Jfn4EmBnk/agsnsswscxgeSAK8wA4LW/9T8MwDMPYpGkaPPvZz8YP/dAP4a1vfSvucY974JM/+ZMHy7Rtm+XWJb+nOhLyPuRAXpUgZ4ZxvSGRx2+55ZbFAVrlWBL5nIjQNM3s41qCsZWiuX5/66234md/9mdxyy234OlPfzo+8iM/0vrhhmFcM5h4figOPgA4NK45D5rl/0xCYsuiuJk86qdRf5eeN77dzHFSlotllx9XRpoNxv33LNKjFrglSi2rgtJmLvIvmWU2UBK0lKuWP6T4NsawIwEAlKJUOzQuakmMGOGPwUrY7tfhPkY2GMPHaDJSlFrn4LJM2XdgoqsZheIN8VtVdRnpVmRux1GUbxxwsmrQ+QCfopQPS8pgUI4sTBSFXOdcFrCAuuRAKX1dvigUpGjhrqwTytK+GnpfRJZDqJdsnXovYjBJPiL2Y/NwIQeAKUcmLL8PzKAkdm/I5xPli5MHasun+m2aKJ4zg9nnfRInNtBI9NQx3Ta2PQnK0ovglIXrjVKkZRpHUcCnKHxwPla375cgchn644NoYl1aLl7koz+dPpf210mk6AP18+XYAmJE+DkX/rkMgzTC1nNYPj+of08L1/Vv+kkm3M+6mMtIhefjGPXASGWVEylBi/RSfXqjpXJyLgQcufSXBmWQtEQeH53QwKH+20cjNz3Vvarh75J6q/I6xI3DwLtFxnSO4P32/HUE+V1wROCx+jKMqxyJnqalTzn3ihDK6sb32FHQ9+N460SZQyMyJ4/2JY4DY3hecEBVPt/2fYMob3dpGS01h3R2dIiD+J5iL3tOhG9mwJOKVo94PRNSP72IqRd75SSRlSPlX0HEcRGHZ0fUQxTwO+aN9WSSQ63fLb+xEuW8Y561r7U0X26DyMzyrB2HKGDXIqKLHrIRSbyQ8oEo866IJid0lIj0XZPLx/aBfOZUm5L1x46/Uhp3iPUr/dva03gorSf1PdozT+1hquPKSRSXOpO6DNRHN5fPAwBK13X6GC+vzWQ/XmT5PE/CVgI6I4A4IIAR5o6NyARp+Hws74puC4OJFjTv/KLR+0jSMAzDMIzrnjMUzgeyef/hUfOcLIdIzwDg/flK52kMc5F0Tn3wG/3Z/gUiUNNsj0guEdC1TK7X10K5EtAHYrxeZ4aAnsVvLb0fmCyLE1X3hZRPhPJtArpsjaTJaR3rixqGYRjGkDve8Y542tOehle96lV4xStegbvf/e64173ulb/fFuV8Cn1PYB+YOcvvJp8b1ytyHDAzVqsVTk9PFx9bkkbTNIuk8xrlun/3d3+H173udXjRi16EF7zgBbjLXe6SnwRvGIZxLWDi+dXCEQcZo1vI/b+oyIsramlFBuyl+JiGo+iWey5XHmScpZ1GorRvLFv5YSegbRycSzEO8w94LyiLssDMWHeiZERRT2fTu5V9xocU1fvx6yQLULxtTqTka8hmUCpzvEHuKcT9oeXyXMZeUo3yE8As4myK2pzESi1XVH1JTuP6BBA1IFrFaNfkYxRpDnldSnXFHG/258fFgBDN7BR1nzkJs0nuVPoHqfbGYginCPZOotiLmJx3/7J9IpHNCfFii5RRTOQAilG7NUPBphfFehkAG3J8SWDAJYHEqXZZlj63U5ItS/tmIITIYLeT0OVgphh1M00AaJyDD/MeF5QnOxDFcpJDYJfbkHMA50F8ysuLcOWci+eCLORKlPMwnAAB2ab+wjkEwDXppADk/aGl1n7TZb24/xlSSTX9SSQ/yi9pV4FrEwBo8AfkwHApaRefGjt5vi33pZRpcx1Ox0mcPEBpML+PihnYZeFfcKlepP4YQAjjMl6f2eaxHQX8+qQGuanVryL7JR630/rVlvac7+8kBU+OYxYZPO0XOXc4l57SMawDcjXBnAbHVcxVtj1K0j7I75Ae9KlMdEhtJKT7eqS/yG9rJ8yRz/UqZ3RjUCT7Ov3xW6w1W7eS8/y8iTS6Bvtj1REQUP/tMYyrncH5NfVnpdsrsjTks4qUWt4M1mepQT8VE4f6nki6IvDOjYS9D+UcpNgzBsbk87HvCbHcEgGc06Ql3ZfL8jgDIEYAbZ2wI30d6b/nPZM6wgxK8mk6z6U+WbnPym32qX+4S5Rzn7ZRb7+Iw2WdidQtZRHZfZt0rkX1eo+rb9+STkN1QV2WY+aNqOC15SXSeVu0w6laytHYC6ldX/fItVctor5I2kAvz4vovJFXUZa8DdRPRqhN4NBl8diMzi/49Joabg7qe0lDyiViuaTP6qXrm1X9inBz0eVzoBfQQXJ9uz0CZSWVtI3xLDC/NzQk171u+7Q8enxTrG8YhmEY1zV5MHG76LsP+Td37O8ZsyG+S58nBLD3e0cKX4wSskXWpradXz96UHDw8eYkzVnFAaL8rcuE4lpKCd+yPKPSv1L/HqRBMTr7oN0VExE25PSynMxR4p4RcXxX4rAn9wJ6JZ+BgF6bVDGWJhDbnB6rtf6pYRiGYQAA7nWve+HTP/3T8Su/8iv4T//pP+E7vuM7spS6NKqypva06l2RSM+AyefG9Ye0f30MrFYrdF2XJ2XMJYSA1Wq1c19YyuLUpM7T01O84x3vwI/92I/hGc94Bm666SbraxuGcc1h4vls5NbtgsXPnV0KMSEjDJbSoo1eqJcoRVCOUWNLI1IlmgYl+yjTdeEEpOQbovTooDZFgxd9IInYWUUNvSSbOu7raO0WN4aT6NwrwEfahQSQyzJMlo5TZjG6d6wH6ZjEQefhoHuWkjiWOsq86fGIDDhikGOw7wVASqL1poKYaooJ4CiAhiTiOufAFMDBJ4GWskgr+TMD3hEcxweZ9wOUDMcx7xAN2IFYTHlbo0zNaVnXNFki3Sgnz++IaTk1Cvoup+MZMXI3jx3XNGjjQ9GEBhESa8R64bQrqW/Ag+1JbS7X5TAKfn5APLteqJbjhYDAHgEOjhh9PMkp+kF4cEqPo1DOSdCO4n+URPsyUDqSmqhvEYHRxfKnCQ7ox/jz9utt8JzEcHmygi5VbgNAf/T3+SK3DSixvN+aXjSXc0A6fgA4BnKYa5Ggc/2mVIhTO4x1QQ6gsHmTyzlCF/o2petVzh5j589Yt01s52o/ROklak/9ebOvg5COy5AlqC03C8rPuI+OOaRXkKQ99F/F7YhSUppEUqxbE3U4twOph9ieKLXbLD45OR/JzYy0HLmcUjxH9rLYRulF4Od0rmYa3hyLsT4H20gpRV378bwngzqlMNgLpBcZKfsmuq2Vv95xy+be+OPAYDeMxF/PD7kspPKPpyw5n130GjWM3ekn4kUp18kNavRiKOS9nNdHjgmuvC+l19r7ncueXvGnuI+gfAxqorSUYY58zsW5q03riFQtZ8Um/VbI1QVBJASGCAtl3ZW9jSjkpjMaScryC97354FeKC+RXpqIx0vrtRtZlzGsS/00EC3AeqSo8MyjfVdpu7LetjYlqbTof9/HlmuSTL7569zTEuGScwNpehsi+jYVuYWI4JRA3qC+bwbrIInoKb361cGwfPrffZtD7rdxSrOJhZrcLhYxfeLHNqRrsVKylzIE9RnQj17k/pSaHKPlnMD9MXGRb00R4uRIpOti1ZMdbPNwjXo6DgFhB/k8y+ZKNJf3c+pO/0644jPDMAzDuJrYVSTe4AyE8wsnm5dlKAfZk+TMCyWJg5JkbGrbaoTtufs/LzcipG9FSeUAYllCDJYhTxSkchm9bvmd1Hfon38ziH4uwXMqAvqGnL6RXSpLsd6hyRHWJ/IZLLNlWUDuFcT6oiTPZ4nd+qqGYRiGgYc85CFYr9d4/vOfj1e96lX4nM/5nNwX2Yfeudi/bx3Uk2FMPjeuF0T0LgVziSauJ2XMQT+JYNd+sPe+HwMmwjve8Q786q/+Ku585zvjK77iK3ZK0zAM46Jj4vkCzjzKxIVF38QtOsKsBAQ1YBoHAQdJ5Dd5wDVHt+0FN0r/GQoKSfhoWpBrsrA8FIVFIo8Dh3FsMMRo5xTUYOdQNTyuZrgpbA4kYETh1yHe1OYkq0YRNuTItrFK+xoJSdgFEVzeN4yGHJgYgR0YIUWu5cFN8Zw/i1zsAK/HgmM0bLgkOXCAV8dBFJMABIJjkXP67SVq4JoGFAJC8IOotwwHogaARx+5PX4Wx6N9vkgSpWCXyHQD4Tm1Tw4hycBaRO3LPZ5PGoh2lEL513OUfeNDlGizmjRIdtjmmJEjM+YysOhPaq8lSTfuzwYx8t867Uu1WJGXjsQp9emDjMETcvD3JP32de7S+yRJs5SHcpskavJxmjUX2Xes0lbbLVJ5HvNX8nOUkl3ebk5PCNC6EgPpsySaM/obCCIxD+7jlBfaKa2BuJvSdfEmhGYYibzcGjnbVEQ5IjjXIHDtQl+E+QZEPtaD1A/HJy94n542MNH2Zdn6l2rzclnVtnDR3rkf7AgM/cCFJHpPHxsxAn/aL7QpT/aTCEQFrKQjEh8nCa4ov7QVOacJWVcnV+xXLW5m7bo/fxVkQZ0YxMf9VTgEm9Lf9Dmsl9DmRSEXKU1H0N1Mc+oGFqWnK4QLX5eGcSh0RGvdhxURVKIOC3pgubo81HksDXqRWl7/Ld8vIf4iJaH5UDKJgoHpiNuoy+Vl+bri8wYVETu9upSeiKEOMfp3Q8MIxf1zU5KcS5TF1Ib6Pq78gsZpmjQpr0qk8g5YXJeyPaeV60+9rdIWatGWA6J0XktDGERtXlC+MjJ5iaR1SQnOtTI2RLicpHNhTk3FyxMaRC3X38n3uj3U0tWfS/n8SPtrR38HY99Jop7LZIGgJHE5rqbqWCYvTN0a8sXxr7dDjgOBVRmyVB+/yMty+lz2ZS3ti4C0Ik5P5ZFpuX3PdPNabniVv9lTilfI0xMCNI76JyjoyPVzj53yWDWJxzAMw7iqcQ7k/e7X+EcWzge/s3ms8Hx/e3PuIjknIYi1cB4H+IDziHauykeUop3XHv+uBwnH1ucYCAPOAd7vVfccwuApiRvyefm9updAIQC1bUgSeZ6YKduSPqeUTt5CaT/MvZyu8qqW9cjyOUSGGRHLBsssmGzBIfTrmIBuGIZhGJmbbroJX/M1X4N/+S//JR760Ifivve970HSPdiETsTff5Fe7bfbuNYZk84Fkc/X6/XsiSLOuUHk9H0inxMR/uqv/gqvfe1r8Qd/8Af4vu/7vp3SMgzDuBow8dxYzKhglszXLMik/zAYLke57ReP70Xwi+/m/n5HUboBidxYiYQtN5xjuoQuNHAUBgNurAe6Y0lBW2Pi7cKImFdIn3G7wkDUlWjdzsXtWHsAxDk6OovwIpE+KN3SDgEcbdwYNTmkcqiodjE/AHAISvnofEw/jl06OMcAGnRo0DipNxHWXZ5jEEJAowY8RVomR2gdIQSPtQ9gJeESNWgaB0duMAuXqAHBZxk7i9DsQDRvH2lhO5ax71gG9NEwe2Fhu9zOSRmIkcYr3ycJ2LkkaHOMwJmPgYkyhhBLQ46SdO7ANf0kycyOOgQGPBO8T/IFpcFgTmJpIUSJdBzfR3G3odhGJDh4jspPTTqGZZJAUjfSBAGGBzOhbaM4TsT9MQZgDUKX2p1s6yCmeWrDoAZEXd42DjLA7dKf9DQD18SIOxTbXgDFtqCFaKJ8NycA4ACsinsMug7GIOfUEiLbT0cb0uePEOTRrg6g6Z/aKJC1WDnA+9P4WZKq+2jnsk0ObsE5qmylQ8m83t5jdHaPKPhQitIb4HlM24o4IgTXgFjdhKmWKbbreCNq5DiCiztPfiASATGKaSm6Ubppx2kyA3OpJfZ4jr8F8fw4ln98soGPJ8rRtC4Ccr4RsWrOBB3ZvrkqeLxhhepNqrn59b87F7s+DeOYlL8+IhDrY2tMQkdaVqT1qejVNSFyyRDZNgF3FxhR+N6GCLRjZ/HadhD6aNOSR5cmzDjEyWlMDh5qOh/H+mwJaBAjmsdfOUpKeS+ma+lctiUKwEBMNT0tSJXOA1hXfq/mwGn9mjAuMrsjyvJ8jW3SudTN3HaRB1nTJNapNihpr9K/G6KNCQciat/g3Oak2Mp7jURRb1M+ul01qc169O1Cl2GoJw/LJssy82hPq5HfMgzrTrqaOQJ/ZV2H6ZtJIU1SWG2Rv4dC9RD5TJ78FJjRJUlmlY4ROa6lTef9SfHJAasLeWMqILCHTPrYEj9e9Ybk+mtzggjl6Z5ua2/IqWNtcI6dcRzpKOeN+rdhGIZhXK3kKHD7JJKE4UNyEWVzoBDOx+SF0D+tlb1H6MbHtI6KlM25KJ3vUn9K0tbrT48oLkTE8YpkMjs6YSGTZ5G89rlep4lj5TQir5yVfB6LyyDvY32M5JUnNshkhznyeSmtF9cw1p81DMMwrkcuX76MBz7wgfj6r/96POtZz8KLX/xi3PGOd9w7uvghxXMg/o53XYe2bXP6hnGtsU06F5qmgXMO3vssoE8hx/O+8rn3Hrfeeit+4id+Au95z3vwr/7Vv8Ltb3/7xekYhmFcLdizVowJkpSXI29EIZqDr7/YgzlEWZAlwnZSNp1D0xAaR2gah7ZxaJxD4wjOUYo+PbdUDowmC6ugNgmp8tlQswncIKQ5FmXUYZn1Sc7l9Znagw4M6lvfmhiJfOxzRudjHXJOQ+RfoPMBXQDWXqJW99E8AjN8CPCB0AURV/v1fYqi3IUkJIzIzYFjxG5O8ikQ91lAE8d3QfCBlJMZo+d2Pqh0dR3E/Bs3lHBjG2jhmnbzAi1J6bpTx9jcjzVkm6Q+9GOmgKHYJeVbElGdXCViiipjWachcBJdN3GuiReXolFMSeeDFRu0jUjgLkbIDqmzHRghYCD5bwjXIuOnf8S271L5GxA1cK6N+6yoGiJCk+ogcIsuMJjTBIZ0HMbjVEnkzgGuBVwDpGPWNSuQc2jaE5BzcK5Nx6LMyO7LLJoJK2GaZ7QFaetxK+drVkQOTdPmWbFz0PssTqpYTSwty8WJBJ77fBgAh6EgNcxDyVETN+uYOUUg12XTMV3rONdPNGAQArejF2RElNphnBji3LhoP5gMAgIqk4b0sjqOZZxgEUZEN4keL+XfbLNxO+S84DAWJL5W1otMlsJnSud5PSy7YA5hU6BcUkdxwooNchnXPksGikVEbNNLnnozhlPLNyORS/TEJc+MLr3WSWr16CMrT5WUpFwHiJCSBfuZUMp3jJoY69Lner2Qtl2igzPHiXIBDdZpQmUHByYHQgNCA4dGprbmM6pPaZT1JcJzVNVjL8UDuMKMK2Hs92oaD+CWEDaEcV2HbZK2a2dgkdZvCQFXijSI4jWXrD93rzZEGxHJxyLXr4hwosTcWpRwIkIL4DaFdA70kdzHpoY1SbhvAGzrnYlULe/HlhHhXIvYk8dhRfgfSO6I9VArH6cJn2PH1PiUuJ5SoK8hE3dX1EeFX6fP5LjW25jPRUTwM8pwlsSrNJ+mSfst0nmJXFHpK9OeeNzOOzsR+rYrzDmOdN1aP8gwDMO4pmiaRZNbAUSxuusOKp3LuD6ci1Jw00yKuGdOKhc1zXB8W8RliRzvfaybLfLEsZDyUdPArVb1SOHAvHrV/UwZ6y/aCwGj0bpnkcoKYDNghP73traWZPJBZHD9ea2MMolg5HsOAWlQfubG7A6n/La2mzSpAd7PLpdElC/XsYAShmEYxvXKbW5zG9x888144AMfiGc961l4z3vec95FqsLMWK/X+b1hXEtIZP9t0rlARGjbFicnJ4vyCRW3aAnPe97zcOXKFTzlKU/Bgx70oJ3SMAzDuFq4OqyqCwFluXmX1/lRyuPxEdWj8vjgFeKL5cXTA1NpwDSmHyOIDQRzkbyPuLUSHVxEcomQnCXWLZnnxw9RXJdBWbY7y665iKIiiUtUOhFMtewdmKNw7EMWm3sBnbHuPNZdihxOwOWTJOEHRucZ3sflqlE6GPC+lw2J4sSBznOul7jucL3OB4TAA2shyj7xg7ZRUb65X5+K7wTnKMscS/CpnGPNtvNz4/3WqZV1isDc183oMv3+X1IOorTfQ78/JT8feFKy9Z5zxPq8XLJvYoe8SZG7Kf1NEjk1cE08VpqmjXEDyaVJCXFbomwqwml8Mdcvdhvntt6/4LApw/gp8Rpnf2EtdbjrPS457uc2g+2LDZdYUi45v0ymPmOZXYntOeRjucacp2Ucs4xXM41bMukLo8fuXBzR4KkYhmEMEbFcBOopQdUhCqSy7JQoK0jfwCspfV1I6SKm6+6AiO67IML0Uglb5POx7R+Lyiz1pvHMebuQyhLrwKNjj1tDwK0h4JTHI12Hke/W6RrBI2DNHuvgR6XsKSRCeSmL6+snB0xOn2NEGf7WEAZRwLVsvlQ4v+zcoK496tL5Ki3riFI0eMqRu8uyXCLCDYMny9Qp24yjPvK8vNdIBPQ2ye9z22y5VCna15avpSzyuYjpzUgZRESuHbNyXG5TsaRNz1lG2k180smwDKviPCOfi2Q957xyTGTqKXM/uaNJAnoLjxZdenk0SU6viemU5fPNmh3bn4K0h1I63xaZHsDiSR6GYRiGcdUxNwJcHHQ8mFSdg8hcRNEcqX8xI2o4i3Qu91TSvZizJpfTOVDbjkvneYUFdX3MMSAiUNtutCuW9rbtXpbGufyUzY08ikkD+jups1JAlzKc5VMUeYZUziKgLyjbQG4/h/ZpGIZhGBeJtm3xnd/5nXj3u9+N7/iO78Db3va28y7SKCKfG8a1RNd1kwH5xmiaBpcvX14YkC3slNcP/MAP4MqVK3jsYx+LT/mUT1m8vmEYxtWG2T9nxG7Cugx4yQOwRR6fK47X5PE9B7yyXN4L5n3Ec4l2LrHaLorkp2R00pHRRd2YGADmodgdI2fXJe2DUgnUEWXy/jMtfEttB07CqnzHUSzvfMiCeSnpxojm/TbWxKA+irpI08PIt4HDhizNaZ1+W/ry6jLEz1RaFTmUK5F2p4hSfcCc6QI8IabPoSZkbEtP9lO1PGlfLJFkpe7lyQE6yrWUJ0dDl0kUEhEoi+RNnHBB6ThJn7VtkwRykc77SOQS6VrXg85nirGvtykctX06p6am5PRDw8xoGlo8MUHoI0tPb5lEtN1envpxNZfxuKPDshwaOY63TcSIc4rmXChelN+kwxPbwvHz4UPkkya0GMa1yqEORRGN58jlWiKV5ZYeZ2WkdA+gUy9G/1u/ZBt3kc6FLMFOSOb6u6mI1SyyffpVk2fdyOenzLiSpO0rabmy5xDS5+X2fThFFw8coycvPcN1iNJ5jqCeZOCWKE3Q3F5/WVxX6Yhwvv2Kp0fyvuQcTtK6WdwfKccl53Jkc5GvRVXRa6yScK4joi9BpPJaNPFS8CaiQbTzqYjnba0NTYjr0o/W30tPqZZnGb1f2lVTKbdOaw7blgtpUom8F6FcHzdyninPMdLuapM5zg4P4g4uieUx4nl+BpJaTp5fFdRyZe3U5XMaEdKBNJEA6VhSn49NfNHraTHd+j2GYRjGtUiOEL3t2kMk4D3G5fqnlV5c2RxQ0deVcF72vQBsCtEhnFu0cx2wx61WfRTxYzAmcO8JteNPXgQw/14YUTU6PYBeTB9bVwnosu65yOeVCOWjy+0SmZ05RuffI/qjYRiGYVzNxCdYO/zUT/0UPvShD+ENb3gD3vnOd553sUbZVdI1jIuEuGnr9Xq/4GjO4fLly3ALJseGENB13fYFE7/wC7+At7zlLXjqU5+Kf/AP/oGNCxuGcV1g4vl5kOTxba8seCc9ItlaWRIdFdWdRBieGXWkXkgMRPdSXB8I5lcHWnGQQV+J3gxyScpOEbJHtou5jySuRWyeKYXMpcw/ysRRnpV8atll5T9J32uR5Zljx8h7rLuwEeWWK+sGiXIPTk0hbndNBJXxcslL0gSGZQaGEc51/vK3lFqkzreRo3iGujw/xr7CrHO7RwDtRkR/ZsZULHYC4k2BFD08/jMe+841IIqTKzxHDUPOCxKNPDDFseVozoCol9fzUZ1Ei23BYaLA0v9b2trUOsMWotKiWJ+Lz1szduEuu1mE5qXlWSL8xUEKFUl0ySSLLftGo9N0NE8/y9uwQx40UwUvYfTCeT6XbBPsRzan/GjnNnAVXBPmc/VOLD3e9oseL0KfYVyrHKtnLBJ2S3009NrvjSwnUusuEjrQD+bJq3z6jwjpOkq6jpQu1CTwJYyJ5Pq7Uj4XWbQGM+BTjPKytCKWn3KMAn+aXiKhc1pf/i3feyWpR/l1njDCAG5NeYnYn6MrpxLOQcT1Ncdy7CKcA71cu1Lr5okHKW39eywR0XUUdsIwT9mGVZLZp6I/61+ysh1Ju29H9q2I70Af4bu2fbW8y8+kXY0pN3Jdo3/PdHklmnlOpxC39bEk32vkiQTbbgmNReGvllnSTuXPx0l57ACDySuMeFyLoD/1FIZjQPBo0KEZiWQ+vl6MjF6LgB6vhuamk45L+Xdqg6PLF220JpoZhmEYxrXEVvlcxNsd086iuUjmF1A2B5RwriTusX5AHiOXevEeoevOL5K0CPJte7j6Lba9jAa+mBkDemNSOO/SBlNb2yi3iOlTkf5TO5BlzkU+B3qpfGo5GfQXWX1m2jn9A98PMwzDMIyriTvc4Q74xm/8Rrz1rW/FL/3SL+Fv/uZvzrtIVZgZ3nuTz42rFulvdl13kL4nEeHSpUto0mRTmUxSvvT1HDNvlc+99/hf/+t/4Xu/93vx5V/+5XjAAx6A1Wq1d3kNwzCuBkw8X4CWuQeDSzMk8oFQDt6QOWov6Ne8EvavPPDrhkJ6RUzPg8T9Bqk/RVn091cZVBOE1aZrsUZHbZbow1wI03lsjmMEWIlSXS63d7lJJHFW+U1LhvJtCMPt6booond+XAzWu1tvv96+WrRuTuWTiN0b5VD1OJ5vRcJWaY1ubyrrrtHLazL8HJj3iyQNxNNCYNm+ePxyksrlGNYvGvxNcTVTp7jvIPf6j9R/Ty/I9PsYeZKLHAeEeMyUEwlKyCVphXoJf2vE9pGvnAzIbznH7Hp8hR3axy4i/NJ1tAblZz5RoRQAlzC3aPGnYf526HJXz7eT6w7PN4CeDFM/NwAidtXzmivYT0EHSOOis0xKpRRTVI5DGn9R/SXr9/2QiZfqtxjG1cKxe8lylIlcOyaYz1lmKQPhNv3Nfb308hjK6FIf++SbBeoR+bz8VMu+XNFMmWO/dVM973+POpZI3/H9Oonda2achhgZ3UOixEe9tZfPt/9yeMQ0c3kxrNOA8d8+TZfS8SqtnYRzoj5ivipjl/qFOuI5pWW1oC6itc6X0zaIdK4F9Rpj25yFfJVf+b3+TEc711QF89SmSplYvpuaNFFuq7R3PVlCyjI1AaMmn9cmcdSQKRSTy6T9p98H3fYqArpuR7JteXkc5nyyHQ8HD7coBrwmHv2b8nmcHLKt5LrNAX27qk5eoOGkEfnMMAzDMK4HSvk8/wIq4XdOvzbL2jIGIBK0fl0wxoTzMbR0LvIuex//nrXAK2UnihG+k1B9yPTH3uc6OuQ+ZY7Ct/63bn9L61fadE0yr7X3yvcDef085PMZ0vvgXuSCMmq53eRzwzAM43qEiHDf+94XT33qU/Ge97wHP/7jP76T3H0Wv6Min/tzeLqOYeyDHB/e+4MeK845nJycoG3bwaRhZh78u/xu7BgKIeBv//Zv8dznPhdf8RVfgYc85CG44YYbDlZewzCMi86W59AZm9R+1JYNktGCVfIg0V7oW+Jzl+dc0LKoGzHCKslyDsV7jA4zA7Qk3f5mM81arxc/OUmQsUYIpGKRE2RwLnV8qJfQpZYGg3cYRhmPadUEmqKMSgInYjD3mg9zAGEkGgmHgbQZyxDQ+ShqkkMvGFf2kzQ7TmmFFOGd2SMERptE5TJrkXkohDy4GoV+iZpfuRHPDGYPZld8nCZqlHWU9iOLfMsMxwCTXpdn3/CX/bfkWNYTFSrfgjkg6hkcJfEa1B9PqYnkQevYia1EVFcdXC2A5QkU6SXbHkLIEyPywlk+535/xYWhv+0jl9cjAxLJsRHXcwR0HPLkCOZQ2XYujsO04RySkDouh2eRI2s9SPU8jmwLECV6RwHMbta9jbzugnNwFvgDANLH7PQ66YQxcyLDbk9Z0MfE2PlHIPkNSO1jziOR47km5IkIY+lKvpTPY9KegTA4xzJKUahMWCbmcCXteCwN25Psj3kRwuX8eHyRdBwa/JlYQtVbKc8VZ/icZPxVo3j4TaRchxGP+bIdxVOMTAjoy98/xSWtc/HuGRvGQRDZ9iwg9TdP1qH+ly++4clldi2rU+mVwrDuK5c/uFx+X9mesfwAIORJav06fb+JB8u7tDx4eCZ3CLn/g4owDSQxN/3AyO9R7kKlpKJkSrkeOS2Qni8DrujPUl8ygUyLrGP1OYZHlOOD2sdLTq0i0opcW5ZW0mbIdUi/XKv6q7U8ZVtaIpykSOfbYPU3txEtbo9sX1nufr/zoF2MtRNKdRAq7aoBJqOKE9LESUh/jvrP1bp5cmXKJ/fXVTmYhhM+Y791+gkykuZmD2lzOZ/EaGnblNKXYxlqO3T/R7903ejzyaHPe4wkh/NcBX88pf5al9Q2lHtgiEx20JNetkXSL5++YBiGYRjXE/KkFEgfTI1ZbY7pynieGrcd+3sBGVzjKAF5NiLqchxr4wNLFHPIkwUkiIlEOz9kJMxSPE9tY/D3gDBzngRASewnXa+75KnKTVyMjZbflftQtxER32WZM2rfcs+EkpS/LV8OYTCRZOvySHUcAngqCrxhGIZhXIPIGOMjH/lIvPe978Ub3vAG/NzP/Rye/OQnL0rnrPqBMWhg/K13FujJuArIHk4IB43YLyK5jniu85HjY0nf9n3vex9+6qd+Cve4xz3w+Z//+Tg5OTlYeQ3DMK4GTDxfwKjguNOgytg6ww4mbbuLXK4lg7obHVUefL+ofGr7+lvpNFw0yak59Ry1d0QiperbmSWLSgYlqTOKkfVUOMnOjgJEHJ+Tvr6ZHtMB8vapMUIZWBSxXN90Z5boxUFJyKTqoxfa+7xVBnkphg8ewfu0K1wSJ5Ps66LiMtxPUfRG3gdR7AYHdD5qEC4EhJCEA+cmBmDjNsQOl4fv1gABPvgkENdVEuYA4ig1Bo7rxosaAGhUh40RggdAhaSc6q4oS9znUe6VSOW1CQXMHkQ67tw4lB8iL4qJzrH4B0vkd5/2b7+UXpbzthIaFYEcqFdzHKzVqUjb2byBkQWnJGNEATxAR5vPy6ZlJEp+bCubciozI/j4mCByDiF4dJ1E766VlgEOCJysVU7HW450wqmDToN9nTJTYo5EWg/wXmTUsFE+JOkr2vgidMW2NQbp9VR9zIpizf1xE6JVM718IgSPgFhvDTHIyTEydo7irKbF42D6HNWLMssHI+IxEX/2p8+dsn/7mxSBPcht18aYQxb1RpYY5JEnbxQ/FFXpvHKOknOvpNu3G2lXGwVMf7bP7B/9vai1n+I3bfKCVNr+or7DlmXz1+VEl3QTdONHV/3+7DDxJqaA6rkEUDcxZVn1G3rG91MN47pBn5kGvxRFP14LIFoahfpulzyDEkgE+Q0pCfK5kmrlTF6eUfS/dSRuVD7XUnqWRTluo5bPnVKJo1ANSGw8nV9I58cm/Sa5VO5cYwwQORAIDi66BCS9rYBQaKqctr1j3pBY5bv6hMZNAmKkc5kguxQRjnWE842yqLwoScsS1RnoZVxK26SR2j4h2mnAQVKTSOFjAnb5i1wu59C3l6l60pK4TrshilHfR9aTKweu9FHyd/I3SSdZ1C7KlJdJ6QWKVyjbel+y7LbltMgemNGlfaOvgBjpOOoT74/xosyD4x/1c8DOsEd6rtNBcGCEYjJIKaNrGvS9d5lcUR4n+vOtfT/DMAzDuA7IQnn8R5SY02S3DQkZMgyhxg4u+G9pLm/6uzQ6uESUzn0l+fchZe85aOkc2DnSeTmhs7oM+n69fp9l7LkskcZl26ReZaKmCpCzGJlgoK91tUROFPMbu27Q4v05wACgt3+iLmVSxKCuppZHvD8m2259YsMwDON6QQurj3/847FarfCyl70M97vf/fDgBz94Vhq9T3I2fQQdsdnkc+MicyzpHOiPXQBomgbOOXRdN8hTHx+6f9s0myPw73//+/GmN70Jv/7rv44f//EfHwjthmEY1wsmnl84ajLbHHGW851g733+EaZ+gUoONaF0TgllEK1MtFD+UnRrX+kQUFEwvZmkFhorXpQSReaWMrmNZbQ24eC3PoqcJmPaDRFXMjDDRzMVrgnwIQospJaJFw0egR1cmlEqy2T5DwBRSGnlXPIrypmAowBQvMUt+7lxSJJ1v+1acAd7hOCTsxu/8MzwXQeXHvvYOgfmssMk+zHWJYcul8P7KP87x0lc36yjEDycI8Ro5vJ4SwDwubwiE8f7EX6wbr89fW2IELxNzImu0HRU55h2SPnF/dY0YTAOzNm+TTJGCOh8B4DhUxT4aroENK4XHMY6mJQ1jhQxP/SieOMCQgCca4bra+kcAeDYboKKsKKXl22U/UggcJZT5WhO7Sww2Mf2cunkBD40cOQBdbyJkuRDL4CDPa6suxQcRw/ED/c196eqPq3gk8Au4/BamaO8roirYIBcjNhSi4Oo0y51kni/oZ8IsrF2vv8TJ3ygk4sQdUyNNCl9/gghCeWB0TQMUFtZsb/ZxOxxuu4gHrVecvheJj7UyzBFPCb6JyEM5OPBgkFFW83qIsait/ZbIk8uIASWc3Nt/3DMI92UcMXGkCxTlD0goCGdXzyPEUt08/4cEhjwnoufKZmYEbdjUzFUy1Lov6NyqYkLxmxjUfFB3pJeDB97EsIe5Ajm+pee9LM6ZLl+4s4+g1reB7QNRtuFENvF+dxgM4yz4iK18VLGHZzN5fykhFL5bldxNEcXx2ZE8lrZWgBdWm5U6C2k9LGzqi4DMJTSGwCeCGCXvPP6lQAz4NEL6JIWgVPE5QBHIqAD+pfNM4HIxyjqcAhMCHBoaNj/k20NvHnG5FTuufXOGEY6X4LI4iKc1+pTpy9tY5XEZlLpyL83hO2KpL6E2E/pI6y3GB+0EDE8513ZJi4+k75J2cbK5UUqLvOoMXUFKdHOpd2DGWNTDVv0x5FMRGhmXLDPiY4OxAkCEq0+MKMDADU5gKAE9PSdtMs8wQObEracAxjpmMPu50QGo0WHeg0tR45Zl9KWo9ghwFdaDBFtRPSvSec5Cr/dRDAMwzCMTVJfjq+B30l9nwI7RnWuSeccAsJ6fbByzkUEegLi9jTNpFg8ntAMkVpHUZdI5Esk8sRsaVzSTvsKIcSXCCI75D2gaSATBjbE+xTUR6KtV9GTLnCo3u58ZJIDVYSZ0WXnyOpAFO+dM/ncMAzDuK5wziGEgBtvvBGPeMQjEELAv/k3/wYvfOELcac73Wnr+rUnW0/9jh7i/oPI5/np2Pa7bVwwzko615+tVisQEdbrdT4+asuV3HLLLXjd616Hn/mZn8Fzn/tc3HjjjWjb1o4rwzCuO0w8v+oQCTZpfpVOpu6oave4mtYIpRg+eEeyrvzoDm/V9zHcxuGiYGqTJstHA0M9oCGP/nZw6AXupKNKpG4g9EFdN/QDKJF9P0Tik+3zIaDzUoYopoDbDfGTKEoJgB605CTqBrCS0QMziD1cij4sonJLvdDY+QACw5EHhy5ORmDe6OgwgM77XNPOBTjXQiKIs8g2CJAI3yUhBHi/RtM46Jh8cbsCfJciRqsb+3GMtoMjD+8ZrpGIvAwO60KaTiVliTK+bD/FaHlaNE7bzpwF2742JPJlJ3tmsHzs6M4UKJNBHPPfbHMijwcwOj/cphBi9PHGtXGiATOQorcThyhOpb8+eJx2Hdadz5JO3j70HeFePheZJ00Y2JCzQ4wi7qXOO3g4+E72cYwSE3ycTHAq8nmO0qPrLR4LElmU4PpjnWJh5KLBkUiwSHIwp4kLDgSf0yT0Ue9zlE+S1qOP8U15WdevcyELOkM5Oj3qVgnCckyH0KVJFmkt6tchcP34YEboPBwFtO2qaFcc9y8CTtdJmE559OeyYR5As5Noprfd+y6VPx13ch5nVhHk+3aRYofDB5lMsXlsxP0Vn6IQAicZWbZB3VIhRvA+3neizQkrMUpOf07iwXdIEwCi3D7cwyEfUwzAkRuI3dIu+oj3Ae3qZLAdOcf0pAER35DrSBdm/KJxVkT9IxGlxpB+T1zxa9w/oeOQgqwcT/JLOigPyxMtDMM4T8qzUgD6qMvcn9VFHOUkve4ioBMQZdYttOjl2tHrmZF1KYnApZhOiJKwlz5QKlMgAjhNupt4UooI6EzACowGUSiXiXOegEb9qgU5z3JIknWAywp6LIsDcOuEJB6AXN4pRIANSVpe73Aed0monXoekEeUk6VMHTNucG6wPNEwirkuv+SxdJCh3BrJoyUaFa+1OF79vvKZtIdam5O2U67XKvF8LL8pDYaTPO4L+Vx+o8s05TjySMcSM1YzBqu7Bctp+XwNDORzoBDQiQbtV7bVpbz0d4R4bMt6fV9qATznDLIL8ZjuJlonEeFSMSGhnKCh27/dRDAMwzCMOmcexfuYuPiU0V1/9yWCtPoA7D24O1afZwsiZhOBVisgReRb1GebI53LcrX3tX8fGqIoins1JrWveK7TDWGzDtJ3pCcZVMiTYOW64IwnsbP3USifURecJPss848thySfN83g/oRhGIZhXOvIb96d73xnfOqnfire+ta34ku/9EvxMz/zM7h06dLoeqHSX9Cyq/4tHTxh9EDy+Xq9xmq1st9t40Ih96+9P04ws6lI/yKMX7lyBd77HLlco6OhMzNe/vKX4y1veQv+2T/7Z7j//e9fjYhuGIZxPWDi+b5s6Ywt7apFKVMpd3UjW2VfyWH2YOiy0tUk9j4i7hAf+oAWVEZ1rWXLUYCeyHXj48BA55MSORC+eVBtQ+munhx4U/49JLlcjBhFGRgIs0QpDjXFTotErZV1ymIzYjoEgFyUaYIPcC4+ir6hKMF6H0VtGdAcLx+j8x4UPBrXgX2HtnFZvggYRtOure99h/U6SgiNI8BFqTWEoWzjiOBcLEvnfZIW0zg+ZJuH0oekESpi7xR5n1IYPA5zClksR9jmKHIudiZyeqne2Oe2OoegZh0TAY5FTPbwPj7yZ51uUuQya2Ffve98qLbvrouCu15W6pkcI7BH4xwAD/Aap6dKWk0Cr6x56n1u57GcASF4EAHrLqBtHYgYlCKBcyD4kSYpcj+R3B/oz4mr1So50sP24EOsC+/rbX3lis5+9okZRCG287D9pk85u5ZERN9ykyYw43R9isYR2qZJk02c+PeDdSWPOPAQstgTP9vvQkuOxTihBRttkiiJ4iT/TkelnjHAAaEocw1S+43T9pxKNCfHkLi0vXoVqQn8AnOIEeRFiuf+GNNrBXiQ6/LxE2+KxbTbRkQo/RSG3aLIXzR6UR5gxIGrkM4XxyJG743TN0jtl+pPumFc41wtpxHdM88COpAndGUJnfrnB809jxBidGx55tDUeiJ0lmLrNpgZDcblazn9DIV7AqEpZ5uNZBDA6GIfYZBvFNNlck9QNemTxLAiBkV9HQEtbmEa/X3xwNbtdkleZ0Rh+FRJ4XNpkgw+FuFcl8er39OGaCAy6yjngt4HDRFOkii+dJKcjigukaQbqkebBob1glSmluoiOlXey/ryXvKtSekSIV7WHduyFY1NeYxpSLtsEaOCT9WRFr+X1GQtGnmNLsnwDulmU8qnFNelHA3RRnuVaOxyvOt2KesB/USWOW2i47BTlPwljD0Ri1L71fWnj5la+zcMwzAM49qEgCycHwSJgs2co3DzOQyaSHRzStL5IPL10nGjXepGRz0/Ajkyui6bEusHEvW+SPT7lO7Gd8Awyn1ZVrn3MWPZY5Ajmm8RygE1eSIEUGu3kw3DMAyjRMvnX/d1X4cv//Ivx1d+5Vfie7/3e6uRz51zcM6h67osftciLJfpy9+atL4LIp8bxkVBopwfo188x51rmgY33HADbr311hgYkWjDC5Fj9od+6IfwN3/zN3jCE56ARz7ykVVR3TAM43rBRgpm8hd/8Re4013uet7FuLZZPMCZ/wNtmZ+n9LM4QsgCtkueGNwJP0YxdBm2dZ62yRmj62xJt1YPh+7IbUvvrCORjHGomc1j7FOvmzO1gUOoGmNlKiO8X3Ro+J8L06aOzXntJ30+ItJvjGuJq+X4PyR/93d/d95FMBQey87nImcDQ0m6/HtMdL6NltABIN0MdwBA8jyheQKpyMNzBVsiWixUr4jQYbPOSyk9y/MACA6UnpQ0FNOl3AEtOjiE+PAXAtzw2R1Yc3yezSo5C/K9TNZsQQgICGENQpvin/eIRD6FiNGEGPW6Yx5EIp/DnAjnukyBObfHS84N9ptI4FSsI7REuOTcQAbfBX0ed9gUoQfLFnnpJUUWr01OyN2Aynpjy7ZF++Q00aAkH0NFviJ1S3ud0+alvmObmxfN3MvNqq1L9lI7pTJ1ab2xASItoMs2emZwamOuIqDLdsi6UxH+A4CmeAJZSFPbHA43QbyMes5wYHKxjGo5Xd8ubSNwffY1jP15wAMegHe/+93nXQxjR6y/bRjXD1lKHgnssRPypNEk77L3COcQ7XwgnTfNUDo/Zr7O9WK21G3qs8K5MxHwmTkFDmEdvWX/hNP2EFF9O7ZI5cwco4SniDyyrDwd6SxgZpD38ydaSHlraQExLYv0aJwx1te+urG+tnG1I16IiOO3uc1t8OIXvxjPec5z8PKXvxyf/dmfjbvf/e5VsbxtW/jUh1jS53TOHUTONencuCjEYIPHlc6nop2Xy16+fDkfk/p4k9dP/MRP4N3vfjce/ehH4zGPeQzaNDnTxo0Nw7heMfF8Jh/5kR+J//Hf//uCNXj4PnvRxx82Ct4vjjJ3WGijDHE22HhEWWag69Y5UrkMyE5txaFnjnWVQV/p5IzhnEPbtnnWW9M0g3RCCOi6bpBGuyAyhDxKxnuPtUQKXkDXdfDe5/yljGX6MrtWr1ci5ahth9TD0kfIyGzepmlyGU5OTkaXL8sgNE2T06ixpK0Q0eQ+CiHkMnRdV62rkm37vGwzUidj+csyZTnGOuNy8Vr7fqz8ZVvR2zC2HzS1Zco2M7a/pL3KcSXrNU2TH3U0th1j+3psHzAzrly5Ul2mrINaXZXLLKFWB2VbKM8/U21pqi0SUX6s25w2uyvb0pbvp9rCtjTmnkO3LTfWlg6Vdg19XCz5LdiHY+7vKc5q+4TrdTb5zTffjPe85z3nXQwjsXRQbCCoTkRLHhPSj9HiRXbVEc/l5VJZXfH51HaLYLtNWHdpuW1Cdi39WsT0VqWVpfF0bRGy0i0KLbLcmuI6ZyE3cLx+izIvJYmccpTsBkPJNzBwKxgtYmTlFh4doqg+kPoriDAsv4gina+TdL7k+q5N0nlNrq+Ro3GndXRetUjirNY5cS5Hip4b2XoM3f6akXO6loB1GWvCs5R16pgphXUd9ZzV5/vIKVKGBnEyBjPnNs8Yl7GlfGN1UcOnfLatEZI8L0cDM8d9umWyQo7EriajSJ4OvYBee+JB3mbmXA+AHBeMpjgLxno77m+75KFF81ZuLBBtRD03jF145StfudN4jnExsP62YVz7lML5IX7zOUWI5v4DsPdRUD7j+zZaOodI56X8pKXsaxHZ1pEJpPumTU3TTy4o69E5UC0yOpT4LWPLaXyUzlBAZ8kP2C6NS9T+Kfn8oKUzjO1YX/vqxvraxrVA/+ToOIZ829veFt/0Td+Eb/u2b8Ptbnc7PPrRj8bf+3t/b7Bsue5S9pHP9X13G+syzhtmzvexL0qQPn1ciAciZXvJS16CV73qVfiqr/oqfMqnfApWq5UdR4ZhXPeYeD6T1WqFhz70IVuWYvWnEsUAOJMBPC0aj0Pawii+Ir3U5nrjX+YPve8QgtQHo/PdIBtOdaRF8zg4wLOjlk+JxkvRnRrNtroUEVbE09VqNRjk8N5n+VtYMoNU8vfeZzl2CSIk623Q+Yus7JzL2zBWFyLR63T0bL+2bReLhlJXWjy/fPny6PJa+tY0TYPVajXaHpYIptvEc10/6/V61qDWtvx1GiIbj5VB6mBJOdq2HZ1MMLYeEQ3ait4GfVzIhXStnGWeus1M1bNsn3wv5WjbdrQDL9sxVtdjxx0z45Zbbqkuo48LnYdmW3uZoky/zKN2/pk6f0y1ASLCDTfcsHW5fZlKW7dZvR1z6lkz9xy6bbl9ZvNPrTvVJsbq4JhMTUo5FvscF7tyvV7cW1SKa4ep45TRC6pC2dqp8n7fI0JHXAYKEV3dBC8/L5Eo2HMiPEvE6Lnycha1C/mckARYkeU3lokaMKeSEzxElxU9RLRtBkBMIHJwIDRJRgUAD84Rz0WeZQY6opRugINHQNPL56UgXcqt6e9S6VzSaVR6c6VzqS/Zj7VyaWQ7VkQ4SaLyrr80sp7I1aTyLhn9vPJv+cxR/YlUteNk6rjaNs0wT9BAkrL1JOz0eSmfS/rNlmND1p8DMyNM7LuyzAH9tgUAlGScKfmc1EvSEPkc6LeVK8ezrNegl9c9T0xQB9JRdrio533aDo7cYHKFvNfH+fXYvzAOy0033XTeRTD2wPrbhnHtcgzhHFDSuXRwQjg/6VyPlTsXBemRiftHKVmZjxbeiYYR0aeYK4yLFF1b9ph9Or1NNQksbWuW09VXWfwu9hWYY98cx5dw8jVgCINjYmM5uYaZkM8N46yxvvbVjfW1jWsJ3b+65z3viS/+4i/GK1/5ShARHv/4x+Oud73rYPntLs+8/Jb0E0SitbEu4yIgwdouinA+BTPjd37nd/ALv/AL+KIv+iI8/OEPx4033njexTIMw7gQmHi+M6UdzcN3Z/QDOZBQNvKkwd3iUYm8OhC39YNtJQOyygGE0Evm6g90FPSroVNhGMYQuzg1DMMwrnd26cMuXaPMY6PXPzJxdK5UO4UWdEvZHIhSaRbP1edckU05lXNMpBZJvRTJt5WvJp/nqO3p5rhLCwd2cFGxzcq4S3HQAZeuYChHa2MQGASKBjqIASbAgQCGEtV7PMfrMJekdk7R0vUmSYTzXDapN0SRd650LjK2RJueIxzrvMq8mRmuiL5ersOIgu6Jc1k21tL6EliVGxiXywFUt80R1a+90+cOfXR2SWOMqXpraPypBEBfd3qSJWtRRIn1OrL6nAkXsr6beVzIvtgqnqd0RWwXaR1pO7a1JS34S7t1xXd64oyUndQ+y8cApacKbJzrpHyE6T2wDEaUnhy5HIldJm7otm/XOoZhGIZxbUFAL9YeWDgHMOz/xTfgEKJcfaRHx0+StpOSdE7nLQuXUvOB+1qjfeAQhtG8jxT5HECOcL4R/Vy2XYRyPZmbeVM+18unMh+79ejJsVvl82PUoWEYhmFcQ3zyJ38y/uIv/gJ/+Id/iF/5lV/BU57yFNz2trfd7C/uiIw96jHIbctPPY3cMM4SCcS47wSMs8B7j/e+97346Z/+aTzmMY/BE57wBNzhDnc472IZhmFcGEw8X0LutB0u0sBQHO/TLhbq325+mT5TAz05ckQZUWL0H/uhbqL3H6WIeSEOsIWJKGbGxWXJBYtxcdD7bezmyZJ9OrasfK7zmMpzyTKGYRiGcS2zq6Q7mp76ra6lS7X+evG3/LxGuY6WSrWIHjC8zpHy5cjYVI/+LWnXRPIpZB1W/VYtvvb/JniIeD5c143ECNefhCSaOxAcOMmpMQdHIsciRjeX/hgCwKEvQdoXtYjSu0jnDYZRzuci8q/IzFIjc+R1B+CSc4PBhIDdr49F+BVRZCzaea1MY5HIy/2f01bRgGr9Ub2OXk+/auh1pPxh5Dqq/N4hivwdxuVziUzuJo6dXBbmPAlkjnzu0ddvEIFEZPcZaeTtwabsM9gPck4ozleyvhzDABXLkIp6fogzJ4GpQaOkc0LcB/lpAXatYhiGYRjXFFk4T3+PIWDrCOfqw/jvc5DOJaI7EfXSuY44fobl2BCw1fuzuPfAInsXEyCPkmtxL68W/RxEQAigUj6vydwTwvox4JQHEY1GNc9llfIZhmEYhlHlcz7nc/ChD30Iv//7v4873vGO+MzP/EwAhwvGuFQ6L5/CbRjnATNfNdJ5CAHvf//78Yu/+Iv48Ic/jGc+85m4dOnSeRfLMAzjQmHi+QKYKz9+MrCyqINI8n8MxfH03dg6lbc1ZYSIwUcZ79Hl3vx8WMaoTYRUZ+YtX53UIvYZFx+936aWmbtPx9KpyjozBptN5DAMwzCuNZb2kraJo4cmR1KZWEaLvWN/N9YpvhPhXKIni0gt10w5wnP6fEzMlWWWyuctgE6lWabRC/N99OQGfiCizyFGeWc44hTVPF3+JPlcoqQHjhprkyKse2qrwnksU4zM3TFjPUM6d0k6b0eE7DnrMhG6om1M3X7Qonspne+DyL5T+s+UAF2bPLGtLWvhucyn1maA6ajn5edj4rv+XqKxS1mm5HOvBPU5+nVI8vncAZ+OObclLZ8vSWNbNHmZeOLTq5ysIOszHIjidJbsb6UadXtrNgQmAqEFwQ0inTew6xTDMAzDOARHE3p3IP+yq8jfR0VHuZZo5113PtK5iEXOxdeBpfPZY8tK9q7iHOD9+PdL2RaJW9WDFr8PipbFg9wfK3Jybvg9AA4hTxAYTbMQ1o9FNQq7/n6qrIZhGIZhZJ7xjGfgZ3/2Z/E93/M9ePCDH4x73/veZyqem3RuXCSYGd77q0I6B4C/+7u/w5ve9Cb82I/9GH7913/9vItjGIZxITHxfB9IHs+ONMAy7Njp6HrFipW3F+uxNjL4NtyGimBuXPeYkH7x0JHIBxFQjxBp3KKXG4ZhGMbFESv2gbfcvNZR08dkXn1FIxGXRS5lRNFcpFoRifVnJRLhOWA8EnRJC2BdpEFE8El6XxGh4wYNulR2xrhOvEmMvRyirM4MIs4yOTPBo4GjDgSHgACQA8DoUkz1Gowo/nbp79S2ivwt0ZmX4ohwKQnnIp0HTIvdsp4WdDX7RjtvibZG8q6VrU37dSlTEyom60D+Vvq+EmVcp+PQS/k18VzwzFildafkcz05gGZsOzOjWyifSzlEPqeFaQh5PKF4ghYhtqGAKPrkpzTpdUFoZHo+yUQPIKQ9sHSiyLBcDg4NiGJaciw1dj1jGIZhGIejaUDen/s1EgFHEa5rSJRoVgIFew/uuqPmW0NL5wTEaOdNc9g6UGL1nGX1nTOR/3O/f05a22TywaJ1YZq9BzUNoPufzoFCOF5blX0Rwnj086bpI+NjhtCdtovOKor+trpfsG8MwzAM43rl5ptvRtu2+IzP+Ay87W1vw2q1OpN8iQhN08Ade/KlYcyAmdGdw6RcibC+9Djoug6vec1r8C3f8i1485vfDMCClhiGYdQw8XwBmz8kNPhTxvy78D87KjreUB7tBfMLvw3GuWMdrItHKXqUn89FC+yGYRiGYYyzi/x6tbFL1HQduRxIUc5TGvKXmCflckljjmgrrIiwVsuK7Crrt0RgNEDtiVZj25YEdYmQLrI6M9DlbY+fema0FHApTU7mpHZ7tODiCisAOJ0R5Xxf4bwhwirtn7WSzt0MYaRJYm4tWvs+0fvbQtQeS6UW1X1u/3Q0SvrMSPGlMN6M1P86RQzXyxOSSM4cI5YXZZaJENJWRVQfa++dar8uLTNnUka5DVPIdkjkcybCCv3khLmwal8OgFauHOJ2y2AUp+1wrNtDm46zEOWgVIbABIJLTxpYcPySQ55eQTHnE+dGn0BgGIZhGMZuXJSnZmpx90zGNQsJmLsuy8RnWR9ENBCu6eQkytbHyWy/dfX1mhrLrsEhxGWOIc87l6XvoyGyuPf1bSSK+ymEPIGh3Je1NAnYmPBwSJgZxJwncGx8L+W0CKqGYRiGMckd7nAHPP7xj8d//s//GTfddBPe/va3n0kE8rZt7R6/cWE4D+lc2CXf7//+78cb3/hG/I//8T+ytE5nMKnZMAzjasOmty2BXPGSwS79ungQuX5gLv0YihgRP3Mg1wy364Jui2EY2+H0WNd9O75TneeLcBPJMAzDMIyLBSd5OiTRtitejNi/cCJRo5eat0UdlsjYc1mpfoxc3WjxlxCvfeakKFHOW3Ro4DcipDMInh3W6KNWE0QUlnU9WnSDaM1rZlzZIp27VDcnSRzf5QL+knM4Sdsu+0HKuU1abolw2bksrZd0O/QJKW2TCNFtEo/nRjsXCX8pIkFLNPLR5YDcFmtTz5vUhsfQWyHLT7GivkW1qpzb2rtMyphKn9Ukg7nIpBBZf52O5116/4EZHkk0VxNQ9L7uJ5e0uV6jaB6PPL3MigiOHAI5MLUgStHLyzogSuMgDYhayQFrrNBSbM8indvNAsMwDMM4LCKinvUvLCEK5xLVOpflyGxIv+nf4fT0fKTz1G/P9bClDiRa+1FxxXUXUY58Lt+fCXpb5X0qy5m0123R552Ly8gEjjlCeZLW6Uh1yEAW4ieLARxvkoNhGIZhXAPc9ra3xWMe8xi86EUvwmd91mfh/e9//1HzO6uo6oaxDWbGer0+6LUREcE5B+ccmqbJL/ms1t/23s9O/xu/8Rvx/ve/H//u3/073PnOdwYAe3KAYRjGCHZ2vIYYSKJKMO//7dJNWRPMDeNa5ixmW9IZ3sAxDMMwDOPaQCbH6UFGp14ioAPD6Oj5sexI0Z5n9j8kcrSsW178Rvm8mZQxJMq5RDrf2CbEibyOgFVaFtyhQYc2vQc8KKUR02F0iFGgx6RzkbNX6bVLZGaRxuX2P6tXS4RtQ60nzuFyGlCtDQzvMlRMhfzdbIk8XpOv5ROpt9XMvq+++p1aeuo7n/bX2DK1OnET39XyFJlc2rtGJnHolqhF+TG6yW+HhCSZa/k8tuLd9rnkLyWUaO2b9FH9myRJafkcav0WBNe3IhBaEK36F0Q2l9YWp4CcuFWW+k06NwzDMIwjc0YyL4nEnERrIjqTsVFASdtKZuYQYsTzs0aCDgGx7pPkHL865z5PLf9jl6kmtpSfyb+TtH8mtSR5bYtmLt/PjWauBPRDb0eWzw3DMAzD2BkiwsnJCT71Uz8VX/RFX4R//a//Nd7xjnccJR+Rznd5Evp6vUY446f2GNc+zdTkywVo4bx2zSefjQnoc8rwtKc9De973/vwyEc+Eh/90R+d07No54ZhGHXa7YsYFwn9Y7bxyGyRKpwDBesMGoYxzpwLxkNETTcMwzCM6wGJ3gxg8++BhN1rGf38KJfEcl8Itlo+R/EZKt/JvxsAIUX2lujTfrBPCIQGTBLFbZiqCOeb0nkUO6IQzqn8/boevVAfCXApWnoHRsct1lxvHxIRXuTjXXpjJ0nezZI2elHbpa0cjTCeZPeTJImPCerzY4SotNGL0p5Lpbi+fFk2V3xeV5hVHkVkdxGOp/rDU8voiO3lpAFpQWWZ5kTrb1Tkd13+ss1uttLUzjbatioXM3iL5K8JwKCeRT7v2/x8pA59qgNJlyt1GNDAJd2cQGAiAByDUVYmfsyCYqtz1GaZ36RzwzAMwzguJNdHzoFCOMr1j37Ca/73GZH7iCkSNMcPwd4D3p+5pEtOPQGGCNS2x5fOl6ZLNB01W9rKASQn6U+XZWRmUIpyzkB8r8t3xPY6QNps0+TJCtVlpM5SmWen3TRx24pJ3/vAACiEanR6mewQs7c+tmEYhmGMQUS4fPkynvCEJ+B973sfXvnKV+Lmm2/GQx/60J3SK3/niWhnwTeEkIVz773JtsZBkfa0z6SGpROM9XJley6DIwn//t//ezzsYQ/Dox71KDz0oQ9F27bW/g3DMLZgEc8vIPpHU0cVju/TS/6dX67/zqKYG8Z1zVSHXb6bFRVyi5BjGIZhGEbqp2MYuTvG4O0F2ybJ1BKp23rrdaQeWyCLzydJdJWrHYnGLAT18umlP5N0pe/jgCJierx+IuinQ8XPmiScO50SyZOkKO1rhgMPpHMgugEBDI8YIZuZETigYw/wGszdRj/LpbaySmJzKVhvrb+0/olzG9J5KSuPqTBEMVa01LuI0DWW9hOd1Fkq27Yo3bXvq1e7I+lIpHfZ/zna+UwBe2ywJE+SqHw3KvPPyC+nrc8pqs4EmURQ7sMcQX6kPpboT5zS1+uENBlk18jnpfyvn3Qg28dqGocsQ6D+2FxyRKRjtY+K3vSTOeyGgWEYhmEcnXzvYuRR5zulmdIl56LoulA+OCgi9sp7kdDPOEJkls6lLlLU66NHFD8Ag/12VuXVEeqBPEngGO11EnVvbzL6+a5lUWkfanuq7Vpvx1XQ5gzDMAzjInDXu94Vj3/84+Gcw2//9m/jLW95y07p6N9mHeV5l3SYGSH1i+S9fpkvYOzKtijkS9PZNe+aeyf/DiHgZS97GW655RY88YlPxMMf/nDc6U53sskXhmEYM7CI5+dE+ePEKWqBmne1uSwNP6+lYxiGIeeFWsTyXTrkhmEYhmFMM4ieoD7XA7I5AjoRKEVhLr+rrXe9IrItI0ZKDkl4zdJriv5G2JSjdXS7wf7AMBo155csS0kgZ4AYDSQitxtcp0UhdlM2L2FO6VMs4xpR3GUwHAOUUukl9j7a+VKI+ijpev28jamu9L/L9QlR1j5xLqdRRvQWlsZwlG1slFCv90OJCNe1Mi7INLeRjbQwPclyWz6EXqCXNGqRzpfAqZwyUQBI8nzaDlbRWLyS0zXy71DZtpCuDebefmJmBBG1VRo+lWlp5PMakrYc07Ft6mO3PzoZDiDOx2iWvAYJ6nVjDUX5vMltyq5vDMMwDOPsyJHPkX6fi0mks9OJiUmi5yq5sgjnOkq1COfe16NXHwkdmIiAKOM3zTJ5+iwl+fID52KE+LzAsH+9FxOTZ3XUc7lekHH03F53bKuL0fuPp/u48S3VlxlLW96mv3vXrYp6TuqYtD62YRiGYcxDfjMf+MAH4gMf+AB+8zd/E69//etx+fJlfOzHfuzsdPJ4pIz3OYem2WVUuRfPS3zqpzVNk4V2+803llK21V0CH+4jf5frlumcnp7i937v9/CGN7wBT37yk/HgBz8Yt7nNbarLGoZhGJtYxPMjk3/IBj9KapBUbonmm+gyWIhhVALncmS9pY8RMQzABKbrkbM6R1jbMgzDMIw6ut8uEc9zFHSoqOjAQMYt+/zXc9+f0EdAXxHhEsUo6C36COENhv0eEatjlPE+4jgAeGb4dEM/AOjg0CWVNQq/UXBNMdBBKVIy1B6bI51rPBNOGViniNGngeHQoYVHQ3H7WopRzneVziWNcn2RofPExFQHJQ6xji85l2enjykzY2lMla9BL2qXEafLlMbkaJHzZ+c78b4mbS9FWoSwrUbK7S4Ryb9cTtr3RjsfkWGknmp5+YUCDbNMlhim4SsR1/dBJj00AJhclOzVeElcJh6VsoUSCX1wnszLxNSIGjg0aMihvY7Po4ZhGIZxnuhI0tD9wdqyxXf5vdwnSdG8z1U6Bzakcy2enymqTpHvIV3QPs9IFPbBJxv30fZkhzHrQeTzw5VkOymK/7ZrBmD7dcXmCur4WzqZt2Djaaqp3RmGYRiGsQznHD75kz8ZH/dxH4e/+qu/wste9jL8xV/8xez1Zez7ENK5RDUv8d4jhADvfX4v+ZobYGxDtxNpR8KSvqy08X3Q0fx1uqenp3jnO9+Jn/zJn8QjH/lIPOpRj8Jtb3vbi3tNZRiGcQGxiOeHQH50KlHjYnSyGJ9rY9Ash0Rzw3UM4whY2zKOhbUtwzAMw5hPNTo3UL35K9Gp5b2+nrjeBndFQA8Uoz87IqwQ6zMQwTNjjc363UiDCJ2K/r1mBx7EmI6cUAefBPN+ijAvls6BKLev2aFDLxd7Zpy4NZhEkt2dSyOiNhHBMaNDuomAujDeJKl/VaSzRC6fQiZVaHFc76MynykRaS6lsLRrWk1qW1P5CPp4HU0Psb2ORZIH+jrS2lKT0m7QPwGAkzRSG9SROu8q3wX1/Vw6ZqyKyQI+leGQg0qyPfFIYzAFEEuOeqltpZcJPw0caPCECcMwDMMwzp48CVKEgSRrUy2ys0jm3vcC8AX4Lc/917LMIYC7LkaCPmu0/Ns0+XVV4Vxfd+la7xAwAAohRoDf+DK2P6k/9n5QbzmquHOgEM4m8nnMOJY3hNHI78ycRe/F0eHz/cp47O01piDH6QU4Ng3DMAzjaoWI8PjHPx7r9RovetGL8K53vQvPe97zcHJyMrmeCOBENIhGvgtj/QEtmYcQ4JyDS/1yyXNjQpphYNimSuG86zq0bZvb1bb+6CEFcDlmdFn+9E//FD/6oz+Km266CZ/3eZ9nbdkwDGMHbCr6AeijbA2jbQFQIeZcfpFr0mszirlhXDS2dfiuN+npIqMfVaT/jn0/N72ly1ibMAzDMIzDoGXdBikytrxP0b7ddXYdIZG5ZZsdYp1cIsKNzuE2LkY2HoMQZWKk9YgIAQ6huDR27FPE9D7Cs2dGYJ8jl3dgeDCCUtfLXtAaDqdMWIOzbNwQ4ZIjnCAAvCm9z+UkbW/tor6l+MjKtUShGcmlTRHkS+l8TJsJiBLyEiTKv0OMej61fxzVJfpmIjLf0s/nMqY3s/p+pbYn97Un0mQg10ONoJbR9SSTJpyKHj+n/LW6DgujngtrfdMgTWQIaWLDoYnnPoJDkyZnLIuCQ5Qinpt0bhiGYRgXinwfRETpth2+VETz/O8L8FuupfNBtHPvY6Tz9DozUv3kf4pwvot0tIdYfZBI1+X+Te1jA5HFDwAzVycKlBEQc6Tws0ain4/kndvartHGZULHDseXHL85b4t2bhiGYRg7I33jJz3pSfju7/5u3O52t8MP/uAPTt5nd86hbdv4BMy23Vs6n4p2rgkhoOu6/Fqv1wPvwNwAo4xwvl6vN9qRLLckSv8hxXP9/o1vfCNe+tKX4p73vCee+cxnXojrTsMwjKsRGxVYAOXHFRayOIeqYB5fc6JxGcbFZeujHa0TdmGQfVH+Hft+bnpLl7E2YRiGYRjHRa4wtJAuEatFSL/WpfQWSUYutq8FcNskZF92DqtUF3o5EaGBFGucHDq0G/K5hsBoVAxqRvQeopge5fQuvdbpdQszbgmA54AGHpfI44b0Okm6O4HBLPr6fG5wrhppmlI7kHIIIglrVkS47BxOCtlbolkfglVqh1o+35byvtHOS7R4TCNi+z5I6nq7xrZRS+s1tBQuYrvOR6RzmZDii/1cK5vsA41nxuaw/3b0baiQ0jiWfC44OBC16eV6CUjIYzNxuTSlJIr6RyyXYRiGYRjXGYV0zl0X5YquO1PpnIgG0bndahXl3/O47lsqLc+JyF729fYgPjhn5nXN2HIyUeIgJVqAksNrcAhRnncuTtLYVToTAd2pe566GBhOGBm0tWt0rMEwDMMwzhoiwn3ve198wzd8A/7sz/4MP/iDP4grV64MvpeI4/Lvtt3/GYRjwvh6vZ5cR8RzedXEdeP6RNpGTTgXvPcIIcT7GKvVGZau58UvfjG+7/u+D957fNVXfdW5lMEwDONawe7DLUHCxM0UzLWcblHNDcMwDMMwDOP65awif9SipIuQrqOkXwtSukQ7byrbISL+KkX0vkSEkxQNXeTjXBeI0qpHMyqf0wItnMDwHBAYaMnjMq1xW5zitnSKG+kUN+AKLqXXCU6xwhrEHgFhNNI40EvlN1TEB4kk3gDoMBSfO9Sl80vOodQYGBgVkseipo8h0bYlwvc2QXuXaOdzKMUJzbZo2DpC/hhybHmMR4oXRCwvI5pvS1+Xp6zPOTTY3I6l+xOIwrreRk7yOY/I53GUhNEgoEFACz94xSOOZ26LqPctCC2IVvE1eBZEmlCS6udqP8cZhmEYhnFBKCNkS3THrjtYFO45UBGBm5qmjxB+FV/f1cTuUqTeWe6fWk/vu8pyuT5FAt+tBPsh0f8rYjkzAyL17BjBXOcjkdaRJghQ0wASSb+Y3HC1tznDMAzDuChof+iOd7wj/u2//bd4+9vfjpe+9KX4wAc+AJcmh5XLHsI78t7vdb/Ce59F467rTEC/DpF7XtIW5rYnfa/srOXzl7zkJfi93/s9PO5xj8NznvOc2ZHXDcMwjDr7T4W7nrhKIvs2TbPXY3UOVQbdSZjT0VzasT103Y/lP1WuWge/3NZSMlpSbv2Iol066zXBSec/Vq7aNk+ls+vFjaSp63Bb262VbVv+S8q2LS1dp3MFsm35l4/2mVqnlve2chDRzu279n7bemPfj0VgH1u3jNCuL663rbMtb40cW9uit49t867notp6ZR5Tx922dUvk2Dqm9Di3XUy1hW1pHCpi/z6/k/s8CUKiEJxV/+G8LpgvYv/IMK53qPibz7bpeNUSaozmffU8HtMhnnfKqN7l5yIRtwACUYz6zIyOCJ4xKp27FJ18G5xqUWJWn5BPwm8qi1pWRPYWHVySctfcoqMYC70sSUN9BOfyDKvFbNl3Xv1bI/L6Kknqm9swcV2ETYG9Bqlytqls8u8p2XlzKndk3ytbQhS1pez6WJjza7VtOfmemQHJZ6R+y/Wa1A410tLcyHISNV6Xq8P2wR0HxPKhv34IM8pZUpaP0zHUyt9cNs6vHv2eBt8zKGno+2O9EMMwDMMwDgV7H6Od6w9DiMJvCGd23ZSlc30vQuTg8xKAl+Ypy5d1VqZzwG2RfnP18xCiWC3/Tvu5NibOQJS7z2hC+QYyLl60OWYGeR+3QwR55thmj1TOjScQGYZhGIaxN+InfMRHfAS++qu/Gi9+8Ytxww034NM+7dNwz3ve86B5TXkFXTf/uYaSRkj9kxACnHMXwlUyjku575cGXZL2Ivewm6bJaR2TV77ylfizP/szPPGJT8QnfdIn4Q53uMNR8zMMw7geMPF8AVeLRGWR1Q3DMAzDMAzj4nGRVO6NiHIoIlHrSUHl3wsopWe5uRB55XMU4rHIyA0RXIrW7OHgQQhJQt+UZof09ZIEBTDAAY6AlpBF8alhdgLDIaR8GMwETzEWdCNlhIrgXKyvPw/pJRGokf7NzIMo7ycT0vmYXr80OraOyl0T44UsMS+M4K3Ztp6ut1q73zd9IG5jmLnstrSZeXD8yXJani/btEjvc/JziBMvmHm2JF+WL8jEUPWZJ0LDjEBAC1YK+VjLKSZYIk7yqOnqS7Bo54ZhGIZhHIoc9EN/KHLFGUrnAIbRpoEonRdRqI0F1PrPE31qor5/miednjUjYjkzR4le2gMRwHwUAV1H3bf+tmEYhmEcHiLC/e9/f3zmZ34mXve61+GWW27BIx7xCNx0000HzacW8HDXJ7bKOt77LA+LgA5Yn+FaQ6TxXYRznQaAPFnBOTfo2x4aZsZrXvMavOUtb8EjHvEIPPzhD8fd7na3g+djGIZxPWLiuWEYhmEYhmEYhlFE5O7ZEM8rUnr+9zlK6TnydJIC8pM20Iu68jmpz0XK9kToUrRwRgOCyCTFE0nUK/6bkkAeE20BuEqU8+myMxoEtOgABtYggBwa9FHDB8urSOJSpgDAi0yc/h2UdN5ORDrP2zUR7XzOnpW8gGHdjqFF6mq08z1vTDQSERIjT7eZmc7WJ6lgfmR2qUs9AWJbJHnZ15yE8XJChUT231YGLeFr+Xyp+C/y+SA9ZjgScZ7zNi5INenmfaz3pWcTJ9H27YaWYRiGYRh7kPvESdxVX0Th3HvwDk8n3ZUcYVr6bM71L+zW9zm367YUNGkjf6Iccbz/aPwpnouYmqQpwjbSdWIl6vmgPPIe5yifV8rAIfRtQ5ZLEdIPVVYiypHVrb9tGIZhGMflMY95DP7mb/4Gv/Vbv4X3v//9YGY86EEP2jvd/BTEEfF8n3R1+iIS66cyW//h6kaL5oeITq6j5AMx6rn3Pn+nl9un7XRdhz/+4z/G61//etzvfvfDIx7xCNzpTnfaq+yGYRhGj4nnhmEYhmEYhmEYZ8DFixM+j7Fo0RtCOjB6Q/8sb8o3qSyhEM0bJBm7EBgksrhL70/hQFgB7MEUYgzmHO0w/i8OzRMIBAdOUcmjgt5grnC7WaMuyefxKVYnVfGaaJiHSOehEDW8ks5XRGi3SecTJZXI6ZNboyK8ixg/J5L21MDx3EjcZcR++ewkTSbYlzmTCGSbt1GWpkEvlOtlSnE7T6xQ/5Z1JeJ9LSp+DVG75VhYLomrKOe6bBwAYoRo6yyKpC70seNjrS/Ze7vkZxiGYRiGUYU5Rjcv/g1mcNedWTFyhGnpaxKBnIvRzjHdl76wOAckqWTwWapfADm698ZyuzAinnOaWCB1ObWsMJDPi+uvM0NHNU/SmEThz5MUBOc2llueHeW0qLEet2EYhmGcFU996lPx7ne/G7/927+Nrutw17veFXe5y112Tk/6AV2lLxtCyNLvvjAzvPcDCd1dzX3X65hyQkEpnO8rhes0iAjOuY12uE8ep6en+L//9//iF3/xF3G7290OT33qU3H58uW9ymsYhmEMmRuUyzAMwzAMwzAMw9iDOfLu1YSOGi7Sdptejfpcok0PXmdQtizEqoFJKWc5WKmXPyEC0IKoAVHcokAtPMWY5IwWhBWI4jINObREedvnbpuvXI4TgIYCbkceJynieoCSjWlTbPdIkc6BQbRzpG062RLpXJYfi7o9N5ajls5bbIrYY3L7WJ0d4kaEiPt6/+7C2KQLTYsYYX1blHbZp+W6ens5Rd4vkUkS5bqBeVHbA/pjAYjtZ5czky4nIYAQBm1x19tVBE7pzS+V1J/dwDIMwzAMYx90BOmBdK6inZ8pSToHEKXzpulfV1u/p5Si5yx/AJZcg4uMPrVOlrudO/p17SQSgdy53BbY+2GU/spy+jWdPA231frbhmEYhnHmPPOZz8QXfuEX4r3vfS9+4Rd+AbfccstBIpOfBSEErNdrrNdrdF2XpeVr6f7ItYreT8yMruvyZAKgn6iw72QFmaQg753qr8pnu3J6eoo/+7M/w0/+5E/iypUr+Nqv/VqTzg3DMI6ARTw3DMMwDMMwDMMwDspY5OUcHV2ikcu/jzTgLGJtIIJPeUStHOiKKNOEGBn8lBmnaNByl8R6129PsVEtOjQpUvmy2MxAhxasEnQ5KnmMX73CKQIuIbBDQJSaLxVpBPVXxHFGFIlbIpykNKfUAC22j36/Zf80Eu1cxPgkn5fpaLmd03o1auuPMbVtc2baN6ptHIpdUnPYjMZfQ+pXl3lFMU740sgC0ubXzOiYsdpFImEGE+CUTu9TpEiJ0L6LmiJnCJpxZFGaXGEYhmEYhrEPWa4oBQpmsPf5dVZQ02T5mpR0jms16rT0H/U1WtP0db4lGvnO7JBujnzeNIeJyr4PSSwniSzqfYzi7lx1OQBZTqck2sePVDRJ/VeuJ6/VdmcYhmEYF5ybb74Z97znPfGCF7wAH/zgB/HMZz4TwLLJYFPRzo8tg4cQEEJA13VomgZt2+YI6IBNartIlNHMdXvREc9lOSJCCGGwP3fNV9pB27YDqV3k9GZBX5SZ8epXvxo//MM/jPvd7374zu/8zr3KZxiGYYxjEc8NwzAMwzAMwzCMM0FHSdcR0lciXaeXmxmFbS4u5aGjRUuEapEGPIA1cyofIaAZiOGb2xKF8xZ+IN3OwRdpt0S4lKXzSAMfxXaKErkD0EHJ+ogy95p5IJ2LQCyRzrfV4JRYPueWg0t5uFRGico+h2MPSFDxvhpZfWZazYz22KjX0ts1tboYS8NhU9qX9eU4WoJI28tasYK7jbL6JLN77BZNHUA6CqdFniWTFAzDMAzDMLaxIZZ7Dw4BOONo56QjnQNRIi4/uwCQioQ9f6Uo0dc+39i+8t+7SlET69UmGizJh5pmp4mWB0dFwpc2O4qKZI40mYHaNr/X7Y2ATYndMAzDMIwz5aabbsK3fMu34O1vfzv+w3/4D1iv14vTGBv/FTH82IjIfOXKFVy5csUin19QJJp513VZNi+j1gs6YvmulII7ADjn0LbDEd8l1xwvfelL8fKXvxwPechD8O3f/u17lc8wDMOYxkYLDMMwDMMwDMMwjHNHhPRSSm+VlO6UlL4LTUoP6CN9M/NGNO4VEYhaMI3L500WzpcNkgc4dEmVbYlw2TmcjAjilyhgpUrAzPCp7B3zIOp1QJTnV0k4nxMBusN0NPNuRsQbh17qlhz3ES+WiOu6DCVNUadj7WZuWfU+GKsRRj8IPlVrPPJ9m8op+UwN21flc6K8zi7y+fLWjLxGV6kXBhAYe8nnwPg+ojTZwTAMwzAMY1+4JhvLv0OIIu9ZoqNAOpdf2EX0HuMQss8ZiPA7ye0FW/dfIdDMqZtBmS6KfC7Rz52L27ynCJSlcxV53zAMwzCMs4eI8BEf8RH41m/9Vrz3ve/F85//fPzt3/7t7PXHBGHv/ZlI52VZQgi49dZbcXp6Cp+e2mKcH9I+uq4b/NWRx7etvy9aapeARG3bxiddrlZxHHjGdcGLXvQivP3tb8dnfdZn4TnPeY71Xw3DMI6MBYYyDMMwDMMwDMM4AxoAkEdyJ3T0ahtgrVOTmRnIN78HdYjpeizFWnkvkbq1yN2QQ+AGTFGaJR4OsjbwaOBBC5TaAJejnZ+4GOGcADAIoVBooyAfEBBSbPVeSD5FFOWJKG+vg5L0Z5Znqq7mbFWb5O4mCcBuRASupTUqE098N4WuC6pJ5yPlWIK0k7F0POLj6puiLZUwM8LIfnIAQDSYCDFVHlbLMoqJAEToFpxXdKT2OfuAwGjhQX3rHETtBxOYCB0TupT+8sF+BtDlyRoaxxzrCjiMOGXsDaGf3GMYhmEYVwtZNNbSTfo3ex8jYp9RX4MkCrX827kYxTpFob5mxImx7ZAnUun6di4K1PLZMeoghI2I8pyut6YYlNU50IyJu2eCtBWRz2sR5rdAEhX9gE8iMwzDMAxjd5xzuP3tb4+v/uqvxkte8hJ813d9F57xjGfgwQ9+8OR6nPonZR9F5F5XebJJbfldEGlZ8soBO1LaIr43TZNfxtkhkwD0q4xoPgfv/UaE8qWEEOCcy31waSu1yOfSBy/L9/znPx9XrlzBYx/7WHz8x388brjhBuvHGoZhHBkTzw3DMAzDMAzDMI6MiLDMvClQ9wsNJOpymQtxA/uCQJX3uXZUPQb0EapL6VwirAciQPaLEoYJgKMmy+xEYS/hJMAhoAGowSUiOGg5mnJcc5GkReSlrNT3kdqDRMNmzjKviOcO86ThbbFKtn0vEcVFNncTeeu6l2OhJlYfMoJ12Uam6sTNFL23pcNJhJ4jz4/lptdlZvBINHzBIdavzl+ntU2CL/PWf6dwYDh4tOhyG6XUTGPbTp9y3CIGwZNDs9MhRBsx0506pxoXCLuZYxiGYVyNJPGci8+YOUaNPiOZOEvnKrp0jnR+jN/YiSf5zGaPcuWo3LU0y/qufbaUoq88/Kp4zpVMRJghP5VPPLowfVRpTzKxYua+kutik84NwzAM4+JBRPj7f//v48lPfjJe+MIX4uUvfzk+9KEP4ZM+6ZNG1xGhWKdBRAMpfCyvfQR0va6e0KcldC3FhxDgvTcB/QyQOhfxXwvnu+zvQ/R9pTw6qvlY2yzldGbGD/7gD+L09BSPetSj8OAHPxh3vOMd9y6TYRiGsR0Tzw3DMAzDMAzDMM6IcrCsHDrLA3z6s37lDTFgsNxFuLl9jpR1WSoMAxFZ1SWl6NOMGEGZiXrZHADgwNSmAU2JtDe/rokoSecOhAYN0SAqNCcplwggTIvjItCLfC7b0aCPOj5XC9gmWk+1JxHE9d8p2bqMRK9l6UG6E2mMlmXhskuir++znEvH+hyhfWz9wIwATEaw18sCsV5z1HOi3KbnliPL50UEov57TtJ5jN/v4PMkifyHKKnnskaMic4MBHJwO8W0V2VY2NYNwzAMwzDGqIrlIcTP0+vMpHN5Cc71ryMIwOd+/bhke44snm8sCgyil8+p+8E1Ji5A/QLDSanyvhhzoHJZ1Q5NOjcMwzCMi8nDHvYwPOEJT8Bv/dZv4Td+4zfQNA0e/vCHbyxXRjsvpe8xyj7ALv2aUmYWWVjE4rIsIkGLhN40zUAuNvZnLML5IfqtErG8hIr+aJmX/ncZ9XwM/d3p6Sl+/ud/Hu9617tw880342EPexhuf/vb77MphmEYxgJMPDcMwzAMwzAMw7gg5GgOI99rMb2U07cNGF/r6GjxARhE8CuFZr2sI8oid1DRuEVGBwOMBoGAhgmgLqa5rapJ1FuHGAPaoSFXRPROGm9adkowztI5M3wafG3Uti0Zgq9J33O/l3yBXnB26bUNEdSXfrc13Rl5Ti6D+cfPnLomRFk8t8WFSJ2KMN5sucHSAEBaNkdJl/okQiMTKhaeH2T9fHMKjAYeDTxc0sndYAuVbE8izHM+DhgBgQGm2HJ23d8mnRuGYRiGcQhYIp3rPpKOcu63PQPogBTSORGBmiZHO7/ahZtF5Zcn2xSf7Qszg6bkc2kH+vsFsnpc9WLL5+XTkTaWM+HcMAzDMK4anvzkJ+M2t7kN3vjGN+LXfu3XcMMNN+BBD3rQYBktf8+VzgUddVoipi/p2+go61IOyVvSFgldC8tdF8feT05O4JzL31n/ZHfKqPJlFPxD4L0fTBTY9ndMQBf5fM7+/uAHP4g3vOEN+Lmf+zn8k3/yT/Apn/IpuHTp0mE2yDAMw5jFoZ5ibRiGYRiGYRiGYRyZPECc/h2Kx6PrATlWr7DlxSOvqwG9fV2SsnnLY+NFHhZhugVwQoRLSapuibDSUb3JgdDC0wrACoQWoAZE8tj79MoD+A0IDYhaELVoqMVqQzrvo527LdJ53k61XVLOVsnyc9QYBuAnbhJs+x5Q0bSxTBjPQnXlu7ny+tY8UrmEFvNE8dnpY345pZ7G2NZGlzxUtinykrYiZW23lGWjbGn9mG4szwodTrBGiy4L6P0rSuiUWjUzw6spHjE6elyG2YMRdjrH7DNBwTAMwzAMQxDpfCOKtkz09f5g0f+2kftoahIypQiPqEQNvCqp9UOn+qa17T6ybMSFfMNAjHqPZZKVjl5/kfqtLBK9c7F9lS8VfdQwDMMwjKuDm2++GZ//+Z+Py5cv4/nPfz7e/e53D74vn94if0XuLhEJXAvhzrm9oo/rqOciPnddB+99fq3X6/xelj89PcWVK1fyZ2fVN78W0PUl0eRPT09xenqKrusOLp2XSFtxE/1LvYz8G8DsKOwf/vCH8eY3vxlf93Vfhy/4gi/AZ3zGZ5h0bhiGcQ5YxHPDMAzDMAzDMIyrmLFBOBp5r6Vy+Rsm0hmkUQwS1oaaRyNaL1h2GyKbMw4bRY4QBXQR2RsAHkniJQKjhQeB4MHcgmO88kH4c8oi+nRcZoYDyKGdUQseUQYXKbwlwkllwJaZsQaw2lF23oZT0c5lMEGiro9RG8beJrbvy6DtH+FJAHParQjwzIzarRz5fGpf5QkmmCe7twB8inyOlL8D8oSEFkC3oD6YGZ6AFQCHK2ixnlo6Seex5CEdA7rsMUI64uccwBSPgyU6lQ1iGYZhGIaxD4N+UCgmwqVo5+z92UY7dw5aOkfTxNf1HN1xTFTfty4WRjDflRz5vGmAs2xL2zij7TcMwzAM4+x4wAMegDvd6U74tV/7NTzlKU/Ba17zGqxWKxAR2rYdRD0fY5tUTkRomgY+9Wu2pae/H4uuLUK0pB9CGERlF+n9ypUraJoGbduiaZpB2tdlP3mCcr+I1H9s0Vznr/fhXJxzef9rYb6WhrSbX/qlX8Lznvc8POc5z8ETn/jEQ26GYRiGsQC7Z2cYhmEYhmEYhnEdIVr05hfpMYfYFNOz4F0+AvEY5RsZlJQIzHqYdK7MvnQIWoTdjggNM5zI6MxwFFVrhwYuybZLCXBw1Mwqlwj2IsOvUjT2KdbMaIrI38C8aOfdyPcinZOSz5sZUfxkUoOUp5Z6ray7UrbvFpjUpQUHIBxYUh891hawIsI6tcE5OABIoktQExHWabsaxO0Mcx6pC8BxwIo6nFCHjuce82lCBjOYHBgu10MvnxMCAwGEQJg1AaOxm0mGYRiGYRwK7zelc+/j35EIkMeAmmYoAhPFz9L7a4YRaZyaJkYar/RNyblhFPID1Aczg0IYjyQv5VDXxrSnrE1Nc7YTGSbgEPr2ZRiGYRjGNcNd7nIXPPnJT8Z97nMf3Ote98I73/nOHP1ZxtplzHO9Ho6UugVP2HHO5cjlU2OoYxHVp5atSefybxGom6bBpUuXFpX5ekNE/7OIbF7ivc/7bSkinwPIf2v7+QMf+AC++Zu/GX/5l3+Jl770pbjHPe6xX6ENwzCMvbBfZMMwDMMwDMMwDCMjsbodoqTaIMq7KyK06dWkl0uvQ0YXkYgWgRmeGR0z1unVpc+D+r58dZXXunh1QPXlixdhKMJTjobu4OlE9NqBrD9FHCxv0cyUzj0aeEQZnNFL53MEXM8MrTcEjEvlwti3rhDDJfe5Awpui6B+TKVGt89yf+5ThrF9UE7ckGNmn2OkXbCujrWv9/9Kbt6k77eJ7BKvvIEH8Wkux7LNYBB7MA9Fmyifc3p5MG9vm4ANYBmGYRiGcSBK6TxFOQdwpoJwKZ1Tks7lr3x24dmnrzt1nVAT1Q987VlSleB3FHYG15HNvOs/wzAMwzCMXSAiXLp0CZ/4iZ+I3/zN38Q97nEPvO51r8OHP/zhPNZeY6nALf2buetN5S3fy98QArz36LoOp6enG6+u6+C9x5UrV3DlyhV47w/+lMurDalfEfOlrs4yyrlG2sWu+8U5N4huX/K2t70NX/IlX4L73ve++OEf/mHc/e53vzqulwzDMK5hLOK5YRiGYRiGYRiGMQsq/g6/pNFo6cC8AUdZXw+LHmMAeSxN/amOBiOybscc/wI45RbAKkVqlrjkpYJOoBz3Wt6P5bj5yRV2WMOBQFgRYwXGigAPRpuXJfV3GH89MINnRhOXiOo18haoaOdLZGgRoWvMiZo+F6qkVQrzhLpgv6QMtXw08oSAQIR2S9o8I29pYXNvBentZMT22qZXRwSkdoyRyOeUpfCQpj4AHRgtCC0InhiB45IBDm605Uh6AQEEp7ZAIp8zmlxWKefG2uRmRUQ3DMMwDMOYgpmjZD78MP/lEHaWjJdCzlWlczQNsGOUwLkc/Poq9S+vVSTqOWP5RADST1RqGtCW6KCGYRiGYRi7QkRomgb3vve98T//5//Es5/9bDzjGc/AzTffjLvd7W55ubZtDyIlS/Tzsm+j/71NDp/6XsooEc8lyrpE1fbeZ0lZR0e/HpB6F2EfiJHjtch/1kj9S3lq4viSdPS+ZGb8xm/8Bl7wghfgy77sy/DIRz4SN95443Wzvw3DMC4yJp4bhmEYhmEYhmEYB0GEV2EwxElUldI53cQP+vMLcDO+LAMhStIMwCWh23OTozfLlvOG1qxqhGUbp7YvfifKrwPQEKOlGH2eEeCSDK/X4Cyeu1wi2Q79cNNJCbpS700S113xd86wLmNTOp+onb0p29/S9Mek9CXo9ZkZSBHqmRleiydq+TllXFpPongHRFFGxKYGQEjCeU0+j3XIIITUtlP0TwaYJBJ6jHzOYDDPKxlxSGUYtgZKUc89mlxHgzZDAMGZdm4YhmEYxl4wc5TLy/6uCOfMMRL6WV2HFNJ5Fs4LIf26IE3qrNU9VfrPU8vPRvWPq9+FAOhInlPLb0FPaD5I2Q3DMAzDMEYQ+fyhD30onvOc5+C1r30tPvShD+HRj340brrpprzMoH+yR17MvNFf8wd+gpDkAUQZPYQAIkLXdWiaBiEENE0D59yGgH4ticlSx0FNZNTiv+yH8+pnHqqu9fYAwOnpKV74whfij//4j/H0pz8dj3zkI3HnO9/5IHkZhmEY+2PiuWEYhmEYhmEYhnEUahJwKZ/XliWiTYH3yIzlUPs8Rj0n+KjxokmiboTqaXFNSh/Lk8Dk4EBoCGiJk0oeB49dUbKkL6QlYmzpgAYh1apI/RK5vRZ3vbqdIgFTH7ddtn8OZZTuMg93oGjnpKIsTqVXE9N3QW54uErEcAbgmeHUYPuUqD+3ZRNifclkjTk49FHsZV9IGxDh3AFgdYNIop3rv1BpNOjrMUQrHMxOtf8xZHpJH+0mphNA6rOQhBzXb/Xs9mYYhmEYhlEj953KPpQI50k+PytJY0MuF0EmfX4tCTKzmIqY7hyg5SVZbp8o6zqN6te8eb2UPtPi01L0Na7J54ZhGIZhHAuRz2+++WZ0XYc/+qM/wmtf+1p8+MMfxsMf/vC83CH6IrVJgsfo44hQXkb0ds6h67oso4t8fq1I6KVgrl8lYxHoz4oyQvk+/Wbhgx/8IF7zmtfgv/23/4anP/3peOxjH4sbb7xx36IahmEYB8TEc8MwDMMwDMMwjKuQq/E2NZcvJQuTWkb/BaIYi8rn4E2Ve2691Jabk5YuM8OhQ4MARsNDSXd3CCAHgkNLhIYYTaoxrySIYY0MpesGAR0YQAuPXlZmIEu9mxHTi1JQrwO79KKFongtmrfO59BC8cZEh4WD24SRqIojaLFbCCOD6o4IjhmhSD8wo1lQTgLi0wMWlLGU4UU+5yTsNIDENYdLLcalaOdaj5Ftc4O0CYFcjKq+pf0zBxDp1sejdchEkP8ZhmEYhmHsTSmWa+E8hCihHxkiirKziqSdZZizls4PKaTsW+YF6+frC4kcvkN2c584tLni7lHPBZPPDcMwDMM4S570pCfhxhtvxG/91m/hta99LVarFT7u4z7uYH2Q2jiqjk6u/71rnjTSRy6joMtnWjyXSOiSxtUioS8RzjWyjRehj7mveP63f/u3+J3f+R287nWvw8d+7MfiGc94Blar1QFLaBiGYRwCCxxlGIZhGIZhGIZxlXH+Q4fLYESxteMoT4eJgVKR0J16NemlPxPhQOchkb3lvby8enXMuRzlKxSvqcHdBkBDBEctGC1CjgW9Tz3F+M9D6Txula6tWEciB8fY5g384HWCNVqswfAbonNXCPu1KNoyWJCjnae/DeZTLsspf53uMVk64LE0IvqSZR1ie9lnm3PdLVwvC/XFZw3iIdSA0VBAO2hDIb8k8jmlMoQiHZci9M9jztkrHgds4rlhGIZhGHvCIpiXkRh97CNzCID3ZyNnlNI5EP/t3GYUdCMzWivuyLc3yzaTIuTv21b0BATb44ZhGIZhHJtHPvKR+Mf/+B8DAF784hfjz//8zw+eRyl1S/TtmvQtzI3OLWnUPi9hZnjvsV6vceXKFdx666249dZbcXp6iq7r4FO/P6RJqecZIXwMKVMIIZc5zHw6kwj3+6D315zX2DaEHSf2MjPe97734fWvfz1++qd/Gg960IPwghe8wKRzwzCMC4pFPDcMwzAMwzAMwzAOjpbAxwZGpyKMj77fIqxLnvr91Hr7EPMkMLVgEDyfokE3uU4Z0V3/ZRAcXIoDDYAD/HB1EHoZfSoXAmOFUwQ0WGMFn+KmCx0z2iQj11KSIWrR6ZdK58C0IH1oTaSM4r4t/xrHFD+mpPYlURd3LaNDnHih96dLbckjxP1BpXQlrVIi7ZMsOSgJpRw4TZ2YgtmnmxLjWxInJcQ96lOUeBu8MgzDMAxjZ8rrAPm392cmnVfF8qYBpdeZRju/2mgakPeb1yz71FeakDCWBjODQgCapVdA88iRKMe27UhYCzMMwzCM6w8iwv3vf3986Zd+KX7+538en/3Zn43Xve51uO1tb7tYUq71m3UfthblWgvpS6J319LXzCm7REPvui6L0s45tG2LpmnQNM1k1PbzgJnRddP3F47F0qjwOjJ7jRDCojYWQsAHP/hBvPCFL8Tv/u7v4mu/9mvxyEc+cvb6hmEYxtlj9+4MwzAMwzAMwzCMndHidECMiFzKzHPE8UMxqrSqiM8DIX3PckkMaqIWTIQuEAgdYpRyGXydLjFRjOPekAOBsCKCKwTeKAqHrWKvxjMnEZ5BST4Pqna6kZsFMlDQpgjdjmixKN5QlIZr0A7pbaO23+XfKyKscZy21xJV61HQUrlLZemAQd0sEc+Rlt2Wb4lnzhHXY7T8XhKnlFYLQkc80V4ZDh7MhEAOhD5KY4yqHtsX8baJERPbRvJsA5UrM9aIdWcYhmEYhjEXZgYq0QHZ+xi5+qylc9WXIeeycA6cvdzCO0YgPBrOgWZGcjwEDOwklnMIB4tOr+Vz+HK68ZE4dpR4wzAMwzAuHNLPvOMd74inPe1puOMd74iP/uiPxs/93M/hEz7hE3DDDTfMTmtKPBfJWy83JjGLDL6Ntm2r/eRmh8mBZSRxAFlCFxFdxOtDiOjnFUldIsQvkdfHIphvi1S/Leq5936ReP62t70Nz372s/HxH//x+L7v+z7c9a53nb2uYRiGcT6YeG4YhmEYhmEYhnEdMyfKuP43F8KsvMIFeyxlDSr+Dr+kwfZIpHb9b6BeDz0ORJfg0IJ5jaTiF2siiQp9VGeiBkQOLUQwH4oHURQOoAUyr1Z/m5QepzKGEe1bS+ciKcc1lkfHK6XzHIn+CNI5MF0+Kt47otnt1al6ADaPi231oqXysWU9M9zMGxgikDtMy/01CEBAQJOimOvPGyKAgRYET5icLUFggENsY0RooNuKiDhxKsrsmyxEoByPvc6a2eRzwzAMwzD2Q6LynZXoC2xI5wD6KOdHiqh91SF1NNZ3FDG9XK1pjrsvQzgTWZuSfH70K2rrSxuGYRjGdYtzDqvVCk960pPwlre8BV/2ZV+GJz3pSfhH/+gf4b73ve/kumXUcp1mLXo5M08Kx865wbqjTzit9F0kavkhCCHg9PQUp6encM6haRqsVqssb2sRfYl8XtueOZ/pf/uRSbJEtFW8l2X8jH7yWH2OSeXyeSgmssqytW2aU3ff8A3fgN/+7d/GN33TN+Exj3kM2tZURsMwjKsBO1sbhmEYhmEYhmFchZQidPn5tu9m51MRiMt4xucVwWMJtfqqflZGNEGvwg4EdB1BfSDjEzxaEDkEjhHKiaI0TmoZIEaNdtSA4HIE6uYA0jmwORHAgVPaUQoOheBLFLXffvmIRD2fi6QzFY37rOPsyT5k9e8xHABsEdPn7AkRzgOG2+sQ6xTYlPOXslQbCQhYpWjnJQ4Ap0pqJPHJ4sV2GdhFUR1QAjrlF1E/dUMfJ/lmBByiuj5veKoDtujphmEYhmEYiRCGInOKgC7fnUm0cxXVHEh9mKaJInUSPM462vlRmIhYfpAI4dvE9CPAzPU+Z5KjDrHfBvJW0xw16vs11d4MwzAMw1iElqebpsHd7353/OiP/ihe8pKX4CUveQke9ahH4YlPfOLouqWELZGstTg+GB/fEhGbmTfE6LIPVBOP95HOdUT2siyCcw7r9ToGZyny19tSiwI+xi59O2YeRCzXZZnbl3POzRLPl0jn5Xq6PvVkA73NXddhtVqNptN1Hb71W78Vv/Irv4If+IEfwMMe9rDRSPeGYRjGxcPEc8MwDMMwDMMwjKsIz4yOeUP+3kqK3r0LZSTwmNz5yuZLI7WPprMwYrSW0PsvaPCZRBaPOjlDYteJ2E2QyN+U4p6LDMyDlGP0c14snXts7msCo0FASII7oRksk1VhJaCX0b63IdHMa+sMt+ywOAChjLhTCj7q/bZyLB3WrkUeF/GcmTdkI92OdiFHj0eU2Kck/0G+HCYzbgH45PMQKEWGd3DYvCkjJXEI8BzbkidObVqyGSr/Ug1ELfqCOATMj/TJzPCpnZ31BAbDMAzDMK4+Nq4Nknx+JtJ5IVyLdJ6jnJtMMUTJTItWcw5ckYi2wQBoS0RzZgYV/fk8geFA0eoH0SEnBP6d04+ZWHszDMMwDCP3O9q2xUd91EfhaU97Gt70pjfhzW9+M975znfin/7TfzpYthSt9WelaC4SspbU9bqaEEKWyPW6kqZEHK+xT7TzUoiupTvVD9NlKsX0Q19fzBXHp5iKer4tovncMsq+01Hsw4z+LDPjve99L779278dd7/73fE93/M9+LiP+zhcvnzZpHPDMIyrCBPPDcMwDMMwDMMwriKCvI4sS2yI5ji+bL5NGh/LvSqUHJna8Kcotlm6pRjdnNT3Wjru0+Ac2VyzS6RzAAhqFVHiKb1iRPaoxPuktYtgTkSD8i0dxi8ji2vks2PIwg4Y1JwI8LpcmPh3+d3SGi/F9zliu0tlDGqduUPqWjyXtLadD1yKcS/rSv5amCfESQc+GvMAHBwB4CmRJ8nncNHBIYnnXwromv5I4DTtYgnMjEB9ynYrwjAMwzCMraho57tIyksh5wZCMwF9lHOi6yv69Nxt3BbVnChK4LXPsfvTvbaWrhDP9QTTQ+6/LN3L9hzgmjZL59dTezMMwzAMo4qOeg5EUfi+970v2rbFbW5zG/zhH/4hnvvc5+LpT3867nWve20VkHXEcy2dy99y3fLfIpfLulo+3zUK9xI2nvIqY7Qp/1pfTEdyP/b9h0Ns6zZ5/VB5jE02GNtnH/jAB/D7v//7+OVf/mXc8573xOMe9zh8wid8Apqmsf6qYRjGVYaJ54ZhGIZhGIZhGAaAQjKX1wEGUWcL49v+fc5R1ueQpXIlxgrT9TmMbC5a7lKFQjSaoXAu74Em5xLAaEHkBvJuk97tEu1c0hkbzo4R3K9NStllaq9JPTTo91cAZsf9ZmawGoQvxftafpSWCDy8qVBK6yTlIgJznKAAcuAJ+TxG0E8x+znJOMQpmv+4GB4nQTQ7S0IhtbmpPAzDMAzDMASJdj4qNh+SUpgQ2VyiNx5YnLkQTEnjh0pf6qyMQn7EvDmEPFFg+AVv7uc9IeqvCCXlXa+BdaTza66tGYZhGIaxE1oIlj7Gve99b9z+9rfHbW97W/yX//Jf8DM/8zN4ylOego/5mI9BM/GEF5GKdfRyoI9mXlu29pkWzUVkH4vEvU+0823outmWz1n0rXT97IOOSl6mPZbn0nLq+tISei29d73rXXjrW9+K3/md38Gtt96Kz//8z8d97nMf668ahmFcpZh4bhiGYRiGYRiGcZ2jRfNdIqnPEcu59tlVIJIvRQvnNdG6qzyOtJrODjouI0awFmmdiojpZTw/R5xinktkPYqx0IlmS9B9eeO2jrWhYw4dU0U02Sc/GnmvKSOUS8R4Zs510EwMmOsJCsyMwLxY9tdpuangkCrauZQ9JFGmAcDFunFfEgKlL+FAxNOPmk3R9IUooHOS0LWA3sf7D3AIe0xFkLqW9mq3JwzDMAzD2AYzHz3iuYjl+d/xw14APqIws42jXX8dUPyeFGyI4uSBmtif+oZHoYx6zhzzO4LULe2HmYGUDzB/OnIujbS5c2xvhmEYhmFcXEQYZmbc6U53wqd/+qfjPve5D/75P//nODk5wWd91mfh3ve+N05OTqrranT083K5bX0lvUwIYVT6PnTE8zLtqX+fVTlKtkUsH0OXr2mawX7RUdun1ptLrd/u1NN29PfveMc78PrXvx5vfetb8VEf9VH4nu/5nmqkdMMwDOPqwUYcDMMwDMMwDMMwrkMYMUqyB9Axwyf5dWp5WSeodfWrYx68vHqF9GL1upYgIrgk2LaIUaNrF9ztyOclDh5Lo52LDB2VXr9VXj9J2q9EuV6B0ZDHCiKvz2dqWSnX1TQA4SYGumu1qreNmQdR32vLE4CGaOdoAHIsSlorAA0FNPDp1eXXCdZJ8Y77NTAP1nVp73mlb0t0/EAOc/ZebG8j5Uzng3jOcCnSuds52vlGHil9wzAMwzCM84aIgDIypHOgpgE1TZbPTaoYoZD2azAQ5fOF640nOKMfeQ59zTyBQdoNpq+58vcyucE5k84NwzAMw5hER6VerVa43/3uh1/+5V/GX/7lX+Lbvu3b8Pa3vx233nrrIGK2kyf4JJg5f1+LfF0ydk/AOYe2batR1ss8D41zblE09bMUz2uM1WEZadw5h6Zpcv2d5XWIzs97jw984AP4uq/7Orz61a/G4x//eHz91399rne7PjIMw7h6sVEHwzAMwzAMwzCM6wSRVUs5vBTJO/VaM2M9Uya/3iCiKA8ngXhMNi9xANoDD6gGRMHXpTjSc5DI6C0xVklYbuFxCadY4TQJzL2wPEaThPsxjtkypqKKTzElli+V7ndlnwEZZkZI0epbdFhhjcvosKIOLdZo0aFN+7NJmrfL72MkfGZGp/ZtbT81OSq5A+85hOSTkL+GQ4cmxWE/HOtjRrk0DMMwDMPYQk06pySdS5Tz61IEXtJf37bsESJ4z4mAzyFsyOcMADtEoFxCFnaaJrYtEXOgnyOkJHUlnJu8YxiGYRjGHERQFgG4aRo897nPxed+7ufiK77iK/D93//9+Ku/+qv83RT6/sCYdN51XXVd5xxWqxVWq9VAmNbvj4WOuL1tG/XyFwktm5cit9Sp3tdnyRvf+EbcdNNNuOc974mv//qvxxOf+MSjTyYwDMMwzoZdg2sZhmEYhmEYhmEYFwwu3uco5SoC8/UoiB8Sl2707zs8S4gX5J5obzM7AGBwEornSecnFKNUN9RHvHZEkKF1kdcZLhWPwCmWellcPUS8LRr4oSnTJrUN///27jxQlqo81/j7Ve99OIdJJmUQBRQQBSMREUVRcVYccYpDlGgSY0zUXI0mxiQmMYNDNIN6vdEYNQ7EOYoDGpCgOKEoowoyiRMCCggI7N313T/WWt2ra1d3V/fuaZ/z/O7dobu6etWq1d3H1dVvfTXIsMPa4xz2bim8ntXPWKr4XvuceIA9haabbjdUJG9rU6WyvSl/DVLl+jKOSXj9TKGPpRVqeSHPNtqOUfP8fdSy8OxCUimTealx3rShwvmSSl+Sy1SYOhX3J2XVfeiJEAAAAJNmRbEmNN0Jnaew9KIEKxb9++C441QUsrLcur/vViq7L8g7CgAAbCXyAPbjHvc4HXPMMXrta1+rF7/4xXrMYx6j5z73uWuek4Laq6uraseT8sapHp4HolutlsqyXBOKn8aVVFutVs9+L1oYemlpeKQvjdMgRVGojHPltJ/TLiZ0ww036LWvfa1OP/10nXnmmdprr720vLw8te0BAGaP3+IAAAAAYAOpC2qm6uRtheDlirtujX+pWvm2WpV8vcwsBLLNtBxDzZP8It1SrBg+7pXhFULn8qahc9d25mrFwHLal6ImrJsqY1t8nsljxezu+2gpBvFTX5rF3mdvKb6O0voqmg/6BPVrtz3gc5fG3Mw6J4oMU8g7Vc2rNb7T+6mex8rnZYygh//e4nlUPb3S1TZaMmupUCG3VqyC3nwUS7W0ok1qa6nTdjohZlXjn3vRc4JN/Ev//nX+Tcz+AADANmRGVfQ6ofLqtuKy9PiihVg2rFTZe5KafE+uqXouhWros/ie3al+3uAPAABgVNX5RFEU2nXXXfWqV71Kz3jGM3TSSSfp+OOP180337xmvpHfH3UukqqMVyuN14XB8yB6v79xtj/KurOuFt6kf036VLfOtOaNN998s8444ww94xnP0ObNm/Wxj31Mt7/97bW8vMxcFQC2MlQ8BwAAAIANpO7QnLt3Qpf5Mownv3x5fgnzqW1PhUwtFVaqHOF1a8fYsXldHfJeLpOs0HIWb14yCxWsY+i8bh8LlWp3HvXOshRLrz6n+r5rWYjDz+r92C/8PU5l7aLmQHjn6gETPEjeihVm2hpcHV0Ko95SWy2F4EuZVazvrhP61y/wnuqeu0tlelmt++ODd2r6lz3V1KV4SVaVMZweq+TEZ3hlK6mttlpa0bJKFVqq9DW9L1alzvuwqnolh6baHk6s6GmTHzcAAMA0VMM3rVan2vk2M//IKieuu6mikJcDTmntN6bjVj0vS6k1+LpJYd5bWSbJGjwXAABgIyqKQjvvvLMe/OAHa4899tBJJ52kxzzmMXr5y1+uY489ticsnoLN48wF68LIdSfV5ffLOFfMQ+lp26kATzloPtlne+tdZxpSBfi6wkKjBOFTO/nYpUrokjrV0JuqG48vfOEL+sxnPqOrrrpKv/Vbv6UjjzxSu+22G4FzANhKETwHAAAAgA0oHWIspU5glbD5+GYdNs+lKtOmQi0rB1bITtqSSs/72+y1N5lcrlZWqXxwBfBuwCKvi12oVGGtypp125ueFAxvaX1Vs+sMGpOhVc+t+4o02f+mofhCrpbKEDpX+iGl/smmMD79/k2wWCPcPVTMb8vU6nkn9Qufh/epman0QuqcLOGdExS6/zaFiHs71slPrfYVwzydv8oPEuP8+1amy/WO/EwAALDRmZk8znlTiGLS35Y61c4ryzpVzre1audmzaqHD2tjxn1oOm+X+5r+peeOGtQBAADYKHbbbTfd61730k477aSDDz5Yp512mi655BI95SlP0a677tpZrygKtdvt2jaqQfAUeG61Wj1zqPz4X17BvFr5vNVqrTlWOCiEXqdf+4PMY75Xd4x0nKvd9Fs3H4Nx+7aysqKPf/zjOuWUU7R582Y9+clP1tFHH6099thj5DYBABsHwXMAAAAAWHD5wdEQF+1WNy/dR6qSjaBzQDXdz/7mwWNId0ml3GxgurmUOq+5rQkG17FQbFEuqVBh3SBuYcP32WJl9XzN0IYP3HpdxfBJ6u6DyYZ8BsZ5bddW8e7qF05JfSoHrFOnMKlVCV5Xhfh2Os2kq9TaYHVqY3BIPrzr5IXaasutpVasYp+fDLH2PRZbN5fc5OaxEnw4xJSeW4bTE1SOMPLVrfT8d53vp8Js6u/JbR2jCwBYOHn4JAVLJliVu1rR3FLYPNvWwoWRt5LvjoOqq49Veb0mVF67Tt16ZSmNUG0SAABgo9lxxx11xBFH6PDDD9e73vUuff/739cHPvABHX744TrooIO06667Dp1/VSttS2srdqc2qkHz6m0z61QBr1Ot5l1XPT3f9ryqmTeR+pbv67j9LYqiE8ZPt6sh9kFh/brtX3jhhfrSl76kiy66SHvssYeOOeYYPfrRjx65bwCAjYfgOQAAAAAsoOrBPVcWOidsPrY8cD7vsHkuBHVbcrXVUgozePr/2XrqqYhunXfGAGYqpBghDv/Nlw8TgsdFp/Z5qoUdllu8tbYXxfCercsor9s4r/Gw4PYwTZ9rCgdnzLwTBOoGva1zf6lT1723xn3bvTZQXUgqTXK32tMDvPN/Q+XzW9y0bEVPNfz0aq8Nn6f3Znz9TSq9FR+xzl/qhxSrETUck3wfJvUZLfL2FvSHJAAAMFmdKudF0Q0HF4XUpxJj0zaV2syXhwdD2Lzm8UUxtatk1VQaH3vOVROuWdNuv8rmaZtTCJ67ezjhta7qZPov80wAALAVa7Vaet7znqfTTz9d//RP/6SLL75YRx99tA4//HDd4Q53GPr8VK08/TdfXq12nj+W/lsNoUv957eponketE7rF0XRc79Jv+c9z0v7UlcpfpQ2Wq1WpzJ9arN6AoDUf1zSc1ZWVnTxxRfrlFNO0cknn6ynPe1peuhDH6p999135H4BADYmgucAAAAAsCAGhc37XTpyasGBrYyZdQKsixkBCYHdVS1rWSsytWRqy807mQmXtDryZeOLEGqOFajVU+G8pbEivVkbhdoxMG89J0Ok8W5rdu/RomGQftrS2FRPGKiOdIqPt7LYfntNQDzcX9JKXE8xAt4/jp23UMi0qkKtnpa9c7t0qfRSqzKVcv3KSy2baaknfJ5aSicb9G453G/JzVR6N3BeNepr4+5KkbDWBF7bVffOfhEKAgBg25HC52YmtVohdD5m1fNO4LkuVJ5VO0+Pb/PzjfXs/3pOEBhju/1C5SM0sL79BQAA2EAe8IAH6H73u5/e8IY36B3veIcOOugg/fEf/7F22mknbd68ufY5qcp4vzlyXoE7VSuvBqKrVcuHVTyX1Gknr37earXWVPyuayO/XVd5fFSDvh8MajeFxSdy5SYzLS0t9d1m3se636RWVlZ0ww036IorrtBrXvMa7bfffjrxxBO100478f0HALYxBM8BAAAAYE7qDux1guZ9HpdCCNPiY25raxnXPWvYOltjgH0jhM1zLqmtQqaWluRqdSqTu1a9t9J5E2aFChWd/INJ3TYlVaPFzdrsbaO392md0Stbr0c1BN30OS2zkce0aduFpBUvtWSxco7W9jOcClB2lhWKn+f02e6s6zK1O2um2Hh4djoBpf++u/rvZ76NQm25Wlrx8G/QciXsHU6EqfskmVbV6lRY71ddcj0/O7RjCH29AfQUPi9E+BwAgG2JmclTyGVpKcwDynKk70CDQudmJmu1pKLoVD7fJucZ1X2e0hi4e985Z2fTRSEvy4HrjLnx2pC5l2V4DwAAAGwjWq2WXvGKV+ioo47S29/+dh177LF6+ctfrmc961md0HeaExdFoaU4Dy8rc7Q81L1p06bOOvlcPQ+SD1Ndty403mq1OscGq9XDqxXVU//zCunpr2l/qm3Whbvz/87qt5p+31n6hc/dXWeeeabe8pa36KyzztJb3/pWPfjBD+5Ur98mvwMBwDaM4DkAAAAAzFmTsHk/JmmpEqAd6/BezUHBoWH1IfflXhuCnyaLodL+9aAX32r8qh72oS31rSHdn1lLUqGWuaxmNMzGPRywNq5eqK3uqKe10n89q5Kd3g3dGLbHNVxrT6AYZMmsp/p7ofoTLtZryXoruUvdz2t9BDuFuF1tL9WyMvbPKiPn8USDbptSOMGgur0lrdZUQg/vDVOhUoVW5X1D2YWkVbV6KqunbaZ/N1pqq50dImq7h/C54okuNe12e9EN2CxJateMWVhz/SYRQG+7y636jgUAANsEM9mmTeGEuZWVRuHzTuA8/VUfS4H29N8xL3uPiiFjmMbc+1VFn2LofV2V0QEAALYyD3zgA/XABz5QP/vZz/SXf/mXeve73603velNOvTQQ7Vp06aedftVEa+Gs6sVuZvOr+sC6tX23V1FUagd55EpVJ4HvqttpErt1Qrp1YB8075Ul1VD9nVtp4B8Nbi/Xnk/8zGo9vWCCy7Q29/+dn3xi1/U4Ycfru9+97sDK9gDALZ+BM8BAAAAYMY8hjpdWQx0DiHtYaqHDEc+hFitAld5uEll9trnVUP2W0HYvGpVS2orVA4vPMSyly3E0OtCvUk40NuSZDKrj2OHUProI1X0CfvmW0jVzlNo3nvC5tl6KlXET4CpW0277AmiF30/E5N4nZu00e1bkCLzpbvKnqsNeKxeHva4jAHpVU/thAr2Rdwrk8dIfu8+Fur+uNCkGnuqmF6qGBioXrJCpVzmaS+Gc3etWCjoWB/0Xtt/Kbz7CjOVGvxeXY92HP9C413NIIX+y1gBHQAAbP3yQIdt2hTCJSsrUrsdvotVwx6dywatDZx31slD563WtlvpfF4mPdbpPTCs3T5Vz9N3et4DAABgW5LmPre97W31+te/Xt/4xjf0yle+UgcffLBe8IIX6JBDDlmzfjVoXp0/FUXRqaLdtAJ4k6ro1W3lwek8fD3ouXnwuyiKzv1hFdrr9jN/rNpG3vas1PXxpptu0mte8xqde+65Ovroo/XOd75TBx98cOf1Sc8DAGx7CJ4DAAAAwAytVgLmk7hs4kY5rDdukL06Qp2D0w3baBxwn9ElLJtYlSQVWvVNaqnQktoq1I7B5CyU3KlGnUajiMtCJDdVTA+rZuuMqW68y/gXHvcQPFYZgs5Z9DxVA++tb+7ZY2UMeocq3lK7tl56naJPle1cqxKGHvTe6UTfvVShUkue+i21ZFq2QoW3VKrorJvq0qdweitrL+VSis6+exwrVxkD93mfRnknFvL4/8IYuIW69NUAeqGWZOHVavped5dWLYxHqe5JBenV6leLv/Mua/C6jMtj+DxVnx/lXe3Z52eV8DkAANuMnkDE0lIIo7fboWp2XaijLnAuSUXRDZm3WrL8/oLyFJbeQKzV6l/RPK1TFGtOHMgf81HCOqNUMi9LqdVauyy+FwAAALY1ZqYdd9xRRx55pN785jfr4x//uF75ylfqkEMO0ROf+EQdddRRtc+pq4Kebjc9hjko1F2VqpuncHt1+1Lv7wStVqsnRF4NhFfvjxo6H7TtunbygPwkvn/k451bWVnRBz7wAb3tbW/TE57wBL3qVa/S/vvvr1122UWbN29e93YBABsfwXMAAAAAmLFFCjhvBCb1BM0tW95E09H2AQdqm4bXB1Wub9qP3qrmpjLGzlsqulW1zVV6CNvm0e1wgLjoBIJLdYPC3okjj6+ufnnprjIGn1OoupT3nGSRlqe/+rHw+H/LGKQu4pp5yLkb7k4RlLJmzFOQPQ96L6vUstoqs5j8LUoR8O5f6q+lKuZeqmXdiHUhxZMBYjWbSv/S/bUnWpTZOHT3uYjR6TyOL4WgfJOq56mNUq3QeqxqXsbPTOXCsLEzZePgkbtrVRZC8pZ/CvtXpFdnjfgjUaMtjS79W5qqzw87rSKt0/P8GD5PsaFFDowBAIDJMTN5CpoURah8XpZr5vPWfUInUNwJmscq54seOp+lnoDQesekyfPN+s9rR9y+S7IG4fN+IZ/qXBMAAGBbkuaBO+ywQydsfuc731mXXXaZTjrpJJ166qk67rjjdPe7372zfvX5owTIpW6IvK69fn1Mf+12e03V9brwd1271VB8XXi9us0m+oXPU9X3UcdnlG0mV111lb7yla/ok5/8pPbcc0+96EUv0uGHH6799ttPW7Zsmei2AQAbG8FzAAAAAJihFKImfD5Y56Bvup/9jdxWzbImde8GvULrDZf3k1fk7rZpMVRssRZ4DG+bx8rg3VC0V0YpxtGVamD3q1A9SAq2W+xfdR9jFF5SKXNXGTeRguEWHxscOl+71W4QuxspTn0p3TvjVKp7MD4PjBedkSvVUluKofPVGAD3uE/tOLa9W8+qeVvZM6oWA/ahAn3a81ZWudxiHfSi81nv1myvP4WhW7e797UbXN+xq9t2N5gffoxQ7E8uVsXpfAqGvyJlDOCUnj6blrU0qF+zCd94DIjl/240PjEl7ZviiRwTqhYEAAAWn5nJizBns6II1bVTZfDq97UU8ohB9U5gXZy41mNQEHwa7TWoHMl3bwAAgNlIc6+iKHSXu9xF++23n773ve/prLPO0qWXXqqTTz5Z3/ve93Tf+95X++yzT6d69zih81Ero1erqVe326/y96D9rPal7vmjflfotz+d48yx6nldSH1U+T5feeWV+s53vqNzzz1Xl1xyidxdRx11lI477rg1ldcBAJAIngMAAADATKUK1ev56bsaXt+aYg7V4Oi0gqt5WHlc/aqd9F1/WJ9ieLbo00aoOV3IY3C6e7i3HaqE52+s2ESRjWKqhD0qt1aIaPep5p6WpjhzGtZu4Lpb6btp6Fzxed4Jm8c64lbER3qluHXREwEP4fAltdXSqkylVmMt9WRJUqGyU609Rd1LFZ1AutVULw/7m2qbp+B3Ky4tOlspZGp3Hu8vhfM9hs87NcXNGr9kqeZ3Tz11D+8OZVcMSL0Pd8pO+/Wb6QbZV91UWCHz9INE88/nUgx2V0+qmDR3D+Nt1onxN+lfep7i8wifAwCw7UhhcncPlcxj1fNsUtsNN6dACoHzmTE1mw73DRwVhazdHu1bUIOq5wAAAKiX5shFUWjLli06/PDDdZe73EUXX3yxPvzhD+vUU0/V9ddfr4MPPlgHH3ywdtttN2233XaNKovny6vrNznWnweo1zuXHxQ+X+826trOK7u3Wq3OY2VZjvU7RxrD6667Tpdddpm++93v6pxzztHVV1+t/fbbT7/927+tQw89dKz+AwC2DQTPAQAAAGCGzEwWQ43jBp9TmHJrqds2q7D5sO2P9Jx13q9K9cCHf0k3Sa0YSg5hbutcyj5WfV4zkut4p5jF8Hq91PLamuGpt93Q+cibVtmJg6etpaB3dUQLcxXeVqF25xmF2tqkFbW0mrVZ3YZrWaudCujpb0mrcplWtKwVLVWe2Q3U5+201O5UKXcVMpWd/1s0qLFv2WkFshBab3XC/sNfx0JlHO21r0TbXUUWxnZJpZtMLZUWQ+9e9FZ67/RKnRMASpda1q0M3jZTq2Zc1+5biOX7jCpOVgPoTWvytGPIiPA5AADbns7/7rdaW9WJvTM1jblTUcj6hWlSQDydMDAB6eRZgucAAADrk4LN7q4tW7bosMMO0yGHHKKzzjpLf/M3f6OrrrpKz33uc3W/+91Pe++9t3bYYQdt2rSp5/n9jiOOdTy/ElZP4e1BldYHFpmpCYebmcpsXjqN4Hm/Po5yzNXddfPNN+vaa6/V2WefrRNPPFErKys69thj9fznP1/77bffWP0GAGxbCJ4DAAAAwIxtbcHx9Rg1GDruNjq3438Ld3kWeK8+3u/+pIXI7/BK0Fbpa7oXQhEtWaz0vTrhUO+wWHFbMfBtRc96qfZ2sc76/oXaci31FHIvKtHoUN18tRNy74bOb1Ur1LHOd6inO8vmkrc7Vc89BsWlFEpfkSStaqmz1RAyL1Udl1RhPW0ghM89BtR9zfp10tZLtXqWt1XEbY4/lqW7Sq2tqF/EWHjblkNsf8gm2vGz01L4kWJVUit+jodZktQ2m3rl8yQF0NsKVddTAH+QtH9FaIDwOQAAwDz1mYv1zK6HzddSeH2S/QIAAEAjebB706ZNus997qNPfepTesMb3qBPfOIT+shHPqIjjzxSxx9/vA477DC1Wi0VRdEJW1eD1XnV8iaKotDS0pLa7XZPOLzJMb9Wq7UwxwabhOOHhc/dXWVZ6qabbtJpp52mV73qVdp99931kpe8RA94wAO0++67T7TPAICtG8FzAAAAAJix9Vb0XoxDnePLKy8PUj2Y2jQg3mR8ZlV5eWAf1K10XtQGy0d5rcMB92UrtZLtV6lWpwr4qMyG17JOwXdPIfjIpbWh7zFZJ8bebT2EsFsqVGrZ2mp7GWuWh9D5slaGbj9VKfdYjbyIwXWXqa1Wp1J5NXw+aERC+LyMbYTWli1UCldsu9FrGkPiLTOVWlLpbalB1fQmylhF3dU94aOtlop4zy2+Lwd8PsoYzk4Hldox4L1kw/evpfB+b3LCxSSlkzKahOTzkD4HzgAAwFZjzt9/Jsq9W/F8GLPwtzXtPwAAwAb30pe+VC996Ut1/vnn6xOf+IT+6I/+SLe5zW30whe+UA972MNCwZqiUJld/Wac0HmS2iuKQq1WSysrKwOf1zScPispjN9PXmFe0pqK6ZJ00UUX6VOf+pQ+9KEP6cYbb9QJJ5ygF7/4xVpa4ggoAGB0/K8HAAAAAMzBeg9ZLs4hz7XqAuOFQoizGrqfdYXxWaivTt773zL+2YTCDyHEHqLDy1Z2Qr3e2eJo2wnVuocfyA+h9taa5aVXa3aPr1BbpUK4PQWCQ2g6BNBbKjuR7EKllrSqJa0ObDPW+NaS2lrttNcNnkvqVEAvVGo7W5VkKt069dD7t+0x9G66tTRtX5jM2mp70fkMDO5bqUKu0loqJLVlWjYpRLZLua8/gO4KYfEUbl+NVeUt+/NYIbxvG+5arQSzV2NF8WEnloTxD5+VQduYhlTRPP27NEgZK7ovLdCPTAAAAGNxn/uJt2OLc7dq/70sZSlMVBQyd3k5mZM1AQAAMF0pJH23u91NBx98sJ73vOfpjDPO0Otf/3o9//nP15/92Z/p+OOP1+67766iKDrVuvu1JfXOF/PQ+dLSUmd7eRt1Qe4UUG+1hh/dXl5e7tz2ON/Ol+Wh+VGYWU871cf6LcsrxOfrffSjH9X73vc+/fznP9cRRxyhN77xjTr88MO1vLzcGRsAAEZF8BwAAAAAZsxitbXWHEKXgwyqMN7kfr7MspD5eiu8L4JqmDy/PWq19RSJnnTwIw+fFwoVWdqSSi9iELhJCCOGra1ZbDyEztfudWtC1bn7acu1pFtDiD8G7E2lWlrV8pDQeeA9NdSrtwuVaqmt1XjYpKVSy1rVqgqpQaTelEbS1ywb9dOwbCZpSa62FH88mNR7x2W6OVZyz0PYnc+tWSekXvv8mvB5mQW7l4ZUFy/iNlZn/O9gmcZR3RNj+nFJK+6dfeSHGAAAgBlrUq182Dp9wuuTZEXRrAI7AAAAJHWrdC8vL2uPPfbQwx/+cB1xxBH60Ic+pHPOOUfvec97dOihh+qRj3ykjj76aO2xxx4Dw+d1ldHzY3n57RTsrra3yMf+hvUtPb66uqpLL71U73jHO/TZz35W97rXvfSkJz1Jd7/73bXXXntp55131ubNm2fRZQDAVozgOQAAAADMySQOYdYFovu13XRZ320Nebxa0XxxD9EGPQed8+V9bqvB8mHamm7YIYXPTRaD2K7SJPfwyuRB6F7hEbdCRcO9MysGVN+ecpDYXW0PVcWXzDoh76JTD72/VJG8yMLxqdp5vk76c6Xq2GUc1ybhcVehdhzLVraNYbXAq62ktU2mQq4QsvdKf0fhSiNkaqtQ21uShQr81dMI8gB6qqK/pj13ta237r27S/HEmmLYDyIKAfVZh887l52NAfR+r0yqFNRWGJ+8chAAAADqWTH86knNG2s494rBpdrvW03C6wAAAJgLM1Or1dKOO+6oHXbYQU996lN1zTXX6Ic//KEuv/xynXHGGfr0pz+tO9zhDjrwwAP11Kc+VVL3+F46VtdqtXqW1VX/rs4X0/18/aZ9rt6vW5b3s2m7Tdqucnf94Ac/0Ne+9jWdeeaZuvLKK7XDDjvoJS95ie52t7tp//3316677qpNmzY17gsAAIMQPAcAAACAObB1/vBtklrZwcYiWz7oOZOUqpqnthehsnntpSZr7nuskt3v8WnoF9ydtBQqlopueNpiyNbzwHI88J2WmKlp7Nws1FWX+cwDHCl47So70e6iJkxeJ4SoQ7er1cirCpUqVKodI9UpsG7x0WGqFc9HDeO7S6Xl9dUtjHtMo/e+liO0q/BebKtQW63QQnwN29aNxlcD6Kk6eQqu5z+YlDFoXg2flzGwPuzfhvTv2ayvAJH2PZx0Ub/vYRXPPiPqVJ4HAADAWmY2n8rfabv95pQTqnpet39pGXNEAACA9TEz3fGOd9Qd73hHHXbYYbr00kt1wQUX6OKLL9ZVV12lD3zgA7rooot0t7vdTQcffLD2228/7bTTTj3PHxQkrwuej9vPuvvTLDpT54ILLtB5552nyy+/XL/85S9lZtp55511wAEH6IADDtB973tf7bzzzj1V4AEAmASC5wAAAAAwR4XZWGHkajiy1a+y24TlFdZnGTbvdwB4WBX3fqHyYkbjJXUrTM8yVBu2aZ0K3alyt8xiYDmtZ3FQ0rrDWMxY9K/cPc29DKFpV+ml5KVkhUqXLIbEBwXPu3H7Sr9NtZ2uBsdD8LxJtfO6tkw+xsiU7tkJJjH+nfpbCZ9XY+5925Sp7aa2WiorAfoUFu8EzXv2obvpUuqEbdLnqIw/6FR/wkhtDqv1XqhbfVw+zmitj7urrd4rN1T7mwL2hM8BAAD6mFbovGlofFjVc0k2iblmpWqmCPIAAABM3PLysg4++GAdfPDB+uUvf6mzzjpLV199tc4//3xdf/31uuKKK7TPPvtozz331G677aadd95Z+++//8A5o1Xmi3lAvenvBYOqm+ft1C1r0m61Qnve51tvvVVXXnmlrrzySv3kJz/R2WefrQsvvFA33XST9t13Xx111FG6973vrQMPPLDRvgAAMC6C5wAAAAAwR4U0pEZz83baE2inTn6gs19F4Em0PXC9yn8H3m7S5gxD57OqdN5v+569Yq6W3EqZSsklsxBPHxYKltQJewwKnU9LGscU4F9115JCTXdTeO+XQ+IjIbC+tudNIyKFTKWNFlLpvhULWXzuqCNXKu9jN3xuLpVmUgyflyria13bE0nhvRAqnRcq+6ybqnunCuDV8TGFKux5AD39cNLOg9n5PrirVDhBZtB4t2KbPsMTQ6rSZ7XoE5YnfA4AADDElILnkoZ/j4shcCvL/uFzs7HD551q59kJ2Z3bzAsBAACmZqeddtIDH/hAPeABD9Att9yiz3/+8zrttNP02c9+Vttvv73udKc7aY899tCTnvQkbb/99tqyZYu2bNmi7bbbbk2176IoVJbhV5lqEH3YMUkzq22vX8g8r8A+LBCf/zdZWVnRDTfcoJtuukk333yzrrnmGp199tn65je/qTPPPFO3v/3tde9731tPetKTdOihhw7sOwAAk0TwHAAAAADmxOJlwIcddGzU1oT61NOm9caMx9nGsB/f68LjtSHzDfgj/rxD53XC6xhfUStrg8V1z+pWOR++9ng1wQdry9X2bvvdnnWVLrXN1arZ+qBXoEmd9+q2mlrqeTen8PdobbXdVeRVbmL0u5SpUKnV2F54NddGz9PJB6EefCuG9IdLFcDdTK2ax3sC6NmVG9rufZ+THht06kIR29SYV4OYlBSWt7gvVvNYYaZWTQUjAACARdTnQj8bQ/zu3HjdopDafU7NLgqpLGvbG1q9vBo6L4pOlXUAAABMn5lp8+bNeuxjH6vHPvaxuvHGG3Xeeefp4x//uN7ylrfoDW94g4455hjd//7311FHHaWDDjpIO+20k8xMS0tLarVaKopCrVZrTUA86fdbzbBK50kebE/rNP39Z3V1VWVZqt1uy9114YUX6vOf/7y+9rWv6Tvf+Y5uvvlmPfjBD9YjHvEIvfGNb9QOO+zAXBQAMBcEzwEAAABgzqZZrXwcRaxsPc7Fwi3/EX7Af/N1t0Zl/JtX1ebhTEu2JIux5PQXuutZdfP0Tqh/rarhFVcI5daFjscRxjFUKk/tt7MNrq1GLXkMn/cEhbM2qs8xSd4whTNOWKcbNB+/UvyKu5bMej5HLStUqlChUre6ZdvxzlopdN79S48234sUss63n0sB9MIsBNXdO1XB6w469YS2+2wztWlmoYr6HLm7VuPt6hik/WzF9bbmf9MAAMDGZkUhN+sfxp7oxqY0J4qVJL3JPpiFfS77nHJZFKHqeb/H65qMIfPOd6WiCMsAAAAwNzvssIOOOuooHXXUUfq7v/s73XrrrTr11FN1yimn6OUvf7l++tOfysx0yCGH6PGPf7wOP/xw3eMe99CWLVtkZmsC4kldKL0aPE+Vz/NlKWTer6p6+ssVRSF315VXXqlvfOMbuuCCC/SVr3xFX/rSl3T7299ee+65p4444gi96EUv0rHHHjvB0QMAYHwEzwEAAABgziYRPDeNVjljTR+GVCG2SuhVldvELbtWFjZs3rVs+SuXHxgftaWWzNpTCdincSxjvelSahRCdg8V0tN7erUTqO9vUKB8uWdQCpm1aquq9+2P0ghbpx65DQh+u4pYvXy4QpLbkkymdoP9TP0Zx6r7wH8nTOEgk1t4rdJJCP3eVGVWGX1Q9XMz0+qCfKbqxiDtx5IInwMAAEydmazVahY+L4owz3evr5Ye21JZhnlcCpbXbTavbD6sKjoAAADmZtOmTXrEIx6hhz3sYSrLUr/4xS/0uc99TmeccYa+8Y1v6H3ve5/OPfdc7b///jrssMPUarV02GGH6fDDD9cd7nAH7bPPPtpxxx37VkPPQ+jDpGB6Hja/6aab9L3vfU+XX365vve97+miiy7SxRdfrEsvvVS33HKL7nSnO+khD3mInve85+kd73iHdtllFxXxBMyCOSgAYIEQPAcAAACAbZDF8GQKdjapUD5K24uukFSuI6jfz+rwVebK+lShXkeLqsa2XSEsXox5OkWqct5tL6/U3dVWq+++pNBzCkBPjsVtu5YafjrKGFbuPN9MJpd7/+qKdX1uq1rJPVUxL7Rsksm0qlhtvF9ftL4q/Hm18kEB9FTJ3My0qv4Hn9xd7ezfojqmEP5flBM6UtA8vxaAu2s1VXAnfA4AADBdw6qZ54pCFtfrOw8uimy+HoQLQXknbG6tVqfaebcbzPkAAAAWSR4KTyHtPfbYQ8cff7yOO+44ubtWVlZ066236tJLL9Ull1yiT3/60zr11FN1+umn69prr9XVV1+tTZs2ad9999VNN92kAw44QHvuuad22mk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7iCem6dbc9ZFa6wsmWrnWenuSryT5Sinl/0vT5bl/nbm6VnRD5++stb58nHFrmkD6bOqGzs9I8oxa64a+Me9I8i+llLOSnJfk/p1FeyX5xzTfADDvhnW9LqX8WpL+5+aNtdbX9a/beU/+z1LKV9K8pnbpLDo4yZuT/NUAQ8/F38dZf2/MRCcI3xvQvKrWes+A+1iS5CE9s1ameY1/L8kza60/69+m1np9mpuJdklzw1TXIyJ4znaulHJykgnf97PoRbXWk2d5n4dPMH9JmvfvI5L8bSnlbUne0PZ5r8ce2fqbHTak+XaEQfyib/r+4641vsvS3NzTdWQSwXMAAABgzo1MvgoAAADAgvXv44XqetVaf5zk//TN/q25K2nu1VrPnWIgprv+l7J1V99jSykTBXDmRa31vCRvmmDxAUlekuSDSS5Ocmsp5cullL8vpTx8vmrstQjO+Z+mCfd2vWai0HmvWusl2bpz77IkfzG7pc2+Tqj6pr7Za2Zh14f0Tb9nqhvWWu+ttV4xCzUM4utJ/nqex1yX5Nn9ofNetdafpOmO3utFpZRd57Syhefl2TqU+B/jhc571Vq/neYbE3q9eBrnbrb/Pi6098b+2fr/b1wzjX0cni03B3VdmuSJ44XO+5zcN73HNMYHFpaVSf4+yZmdb/CYTP86d/V/+8MU3DnJPttc2zd94IBjAwAAAEyL4DkAAACwmP1/U1zvY33TD5vtQhaBU/qmHz2UKnp0QpEvS7J+klVXJ3lCkjcm+VYp5eJSyh+VUhb6v23NyzkvpYwm+W89s36e5O1T3b7WekaarsddvzNLpc21W/qmBwlrTWRl3/SE4eoF4h+mEXKbqTd3ulm3qrX+Z5qO110rkzxnzqpaYDrfQtD7XqpJ/mYq29ZaP5bk/J5ZOyX5gwFLmO2/jwvtvbFf3/S6aezj6L7pjUme0+k+P5n+8e6dxvjA3KppbtB6TZInpfnmhVVJViTZJ8lvJ3l/tv0cenyS0zqfr9r0f+6Y7PPseO6eZJ9t+q9D/ddFAAAAgDmxZNgFAAAAAEzTRZ1uzVNxcZpAWfffQnbEYMaVfdP9gbuhqLX+71LKp5P8XZLnJplKV98HJzkpyZ+XUn6n1nrVXNY4A/N1zo9MsnfP9Glt3agn8P+ypb5DSyl71FpvnJXq5s4dfdPLZmGf/d1D/zDJd2Zhv3Ph+mwd7J4PG5KcNsD6H05z00jX8UneN5sFLWCPytavyfM6Hcan6oNJHtkz/etJ3jvFbefi7+NCe2/0/63ovx5MRf81+SO11u9Pcdv7901Per0spXwjzXP6o1rroVMcZ2CzPc481v21NDdo/aTW2t9hn4Xhrdn2prq58MNZ2Mf/S/JvLdfdazs//1FKeXOav22P6Vl+QpI/z7bfCtFrRd/0dG5Auadvuv8mnzb9170d7VtFAAAAgCERPAcAAAAWqwunumKtdUMp5ZYke3RmbTfBjFLKsUmekeSoJIcm2S3Jzpn83332mGT5vKm1XpPkv5ZS/jrJk5M8PslxSR6a9jDxMUm+WUp5dK318rmvtLEAz/lxfdNTfm/0+Hnf9GFJzp1eOfNm577p/vDWdHwlyaYk3S6nLy+lrEjytlrrFbOw/9l04RC6nf9git2gu87umz52FmtZ6B7RNz3oTQJf7pt+5LhrjW8u/j4utPfGqr7p/q7BU9EfPH/3ANuu7Zv+UdvKpZRlPeNdMMA4A5ntceax7pE0N1ElC/dmnx1e54aWqd7UMlS11q8PsO41pZTfSHOde1TPoteWUk6qtd41wab9Hc6ncwPc8kn22aa/rp2mMT4AAADAwBb61xEDAAAATOSGAde/s+fxIN0EF6RSynGllO+nCYL9XZLfSvKAJLtnas0Gdpu76qan1npPrfXztda/rrX+Wppg8cOTvCzJ5zN+J8n7J/lUKWV0nGWzagGf88P6pj9eSqmD/GTbwOV95qjW2TQbHY+3Umu9Ok2n6V5/luTyUsqFpZT/UUp5aillIZyf/o768+HiQVautf48yW09sw4opZTZLWnBOqBv+geDbNwJc9/eM2u/Ac7drP99XATvjYFeV51zeVTPrHW11kECz4f3TU/WofnIbAmYfnOAcQY16TillPM71/7LZmN/s2RttoRmZz14PuAxL9oxmb5a6/okz0/zDRBd909zQ+RE+j939HdAn4r+a+4gn2V2lL+nAAAAwAIjeA4AAAAsVoN0BOy3qIMapZSXJvlqmo7g09XfYXHBqbXeW2v9dq31f9dan5Zk7yT/M03X3V5HJnn2XNaywM/5fedgnwv6WwE6odH+DvLXztLu/zLNjQ79jknyt0lOT3JjKeW7pZT/Xkp58CyNO6jbJl9l1t00jW16O6SPJNlllmpZ6Hbvm75xGvvoPd+j2bbL/0Tm6u/jQnpv9Hf6HTTweVC2fi2eM+D2D+t5fFMnmD+hWuu3aq2l8zNIZ/WBTDZOKWV5tnQwP3+m+5tFvedzVoPngx7zYh2Tmau1/jTJ5/pmDxI8XzWNm6v6u5QPEjzvD63fOe5aAAAAALNsKp2YAAAAAFggSimPT/LebB0O3JjkvDSduK9K0+12fZJ7+jb/0nzUOFdqrTcleVUp5Zwkn00TxOz6wyQfnYtxF8E5320O9rnQG1Ycmm3DWpfPxo5rretLKU9PczPDq7J1V+Subrfko5L8XSnl9CR/1QmtzZcN8zhWV3/Ydyr6g3Crk9w6C7UsdKv7pqcTCOzfZucM54aDJAvuvXFL3/RUQ/ldR/dNf3uqG5ZSliZ5SM+s7w449jAdnWRZ5/FCCkTPWfA8wznmhXqemdyXk5zYM722Zd0bk9Rs+Xy4NE2X9OsHGG+fvulBvrGi/+/MjvC3FQAAAFgABM8BAAAAFpe3Z+sA9OlJ/rTWek3bRp3um9uFWut/lFI+nOSPemY/dg6HXOjnvD8M/OoMEKKcwA9nuP1cO3acebMW/qy11iSnJjm1lHJ4kiclOT7N66y/03qSnJDk10spJ9Raz52tOhagVdPYZibdXBez/uPsPw9T0b/N7dOsZdYsoPdGf4fxNQNu3x88HyTs/JBsCRUniyt4/siexwspEN0Nnv+s1vqr1jUHN4xjXqjneUY67/m952GoH9Za183DOOPpv7bcb6IVa613l1J+nuSAntn7Z7Dg+f5905cNsG3/c/HzAbYFAAAAmDbBcwAAAIBFopRySLYOy12c5MRa671T2Pw+c1PV0Hw8WwfPV5dSdq21zmq3x0Vyzm/sm76y1nrmPI09LM/qm/7pZDcCTFet9ZIklyR5VymlpOm2/uRODb03POyc5JOllINrrdtruHq8YPFket8HYxm/Y3edXjlJpheGnw83903fdxr76N1mUxZA8LzXkN8bV6d5PXW/nWHfAbefSfD8YX3TrcHzzrm5Nc15OK3W+py+5UvS3KiwPMkptdbnlVL2T/LiJL+d5AGdZT9JcnKSd9VaNw0yTinl+0ke2l93s8lWHlVrPX8qdffUflyaGxCOTXJImqDuaJKbOufmlCSndm5amMhRnd8TPg+llIcleWGSxyQ5KE2359uS/DLNuTkryWdrrVfM4JhndDzTGbNv+12TvCjJbyY5Is01d32SS5N8Ksl7J3sPDXqeBvSqJC+YxnaDelGa1/ow9H+byNJJ1r8sWwfPD0/yrQHGO2yc/U1Vf/D8ZwNsCwAAADBtgucAAAAAi8cj+6b/dYoB6CR58GwXM2Q/G2feqjQhudm0GM75lX3TD5yncYeilHJQkqf2zf7MfIzdCRpe2vl5VynluCSfzpZA9v2TPC/Je+ejniF4yCArl1IOSLJLz6yrJgifru+bXjnAMBN2ox2yq/qmj8wAr9PO63znnlk/nyS4O1Tz/d6otd5TSvlRtoQ29y+lrKi19r+WJtIbPP9ZrbX/RoE2AwXP04SXu8/leMHqw9MEy5MmpPyXSf4p295UcUSab+A4KsnzpzpO59s3Dp+kxqS5AeSiAepOkrck+esJlu2V5Lc6P88ppZxYa+0P9aaUcnCS3SYap5SyNMl7kvxxtv72kaS5seU+SdYm+S9J7k3yzzM45mkfzwzG7G7/x0n+V7a+ZiZNd/1HdH5eXEp5cq21//oyrfM0hVp3RHv1Tf9ykvW/l+QpPdOPTvLhqQxUSlmT5MCeWRvS3MwzVYf2Tf9ggG0BAAAApm1k8lUAAAAAaDHWN71NW8tZtGff9I8G2PYJs1nIArDTOPNuGmfeTJ+f+TrnM6nzrBmMuxi9LVv/u+ZYkn8ZRiG11nOTvLpv9mPHWzfze62YK0eUUgbp5P+4vulvTrBefxf0/vddm18bYN35fA76uxkP+r7sX3+b7sgL2YDvjen6ds/j0Uwt9JtSyl7ZOlz67YnWnUBvaP3OJD+eZP3eoPp4Ae7e/f12knelCZ/+SZJHJfn1JG/Mlhs0nldK6e+q3TbOWGeMo9N0zk+Sz6YJsvf+PLjWeucAdSfJw9P8XXxXmhsLHtPZ7ilJXpumM33ShJ3/6wT7mGycD6Tp/l6SfC3Jnyf5jc52x3WWnZLmGwG6naane8wzOZ7pjplSypuT/Gua0PnlSV6ZJuB+bJLfS3J6Z9VDkny2lDLe/9ubznliW/3XqavHXWuL/+ib/o0yTov7CTy5b/qsAb8V4oiex7fUWn86wLYAAAAA06bjOQAAAMDM3Nk33d+hdDb1B1mWTWmjpgvnH81+OUPVHza9boJO5DN9fubrnM+kzm8muTnJ7p3pJ5RSDq+1DtI1c1Eopbw8yTP7Zn9kyGGrr/VN7zHuWvN7rZgrS5M8O01X3al4Qd/0VydYr79779FJTp7iGL8/xfWS+X0Ozk/TVbh7zXhsKeWBA7xW+68fE527hWyq743pOjfJH/ZMH5OJA9K9ju6bnso2SZJO4PfInlk/qLX239DQNt543dF7lx+f5B+SvLmvw/25pZTb0tx4kzRB4v7uxuOO0+nKfXEp5cg0Af0k+Wqt9eIZ1p0kf1prvXSCZf+vlPLeNN2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\n" }, "metadata": {}, "output_type": "display_data" @@ -295,7 +280,10 @@ "\n", "title = f\"Initial State Distributions ($n_{{initial states}} = {n_initial_states}$)\"\n", "\n", - "data = {\"classic\": renders_classic, \"contextual\": renders}\n", + "data = {\n", + " \"classic\": renders_classic,\n", + " \"contextual\": renders\n", + "}\n", "nrows = len(data)\n", "ncols = len(data[\"classic\"])\n", "enlarge = 3\n", @@ -305,7 +293,7 @@ " for j, (env_name, _renders) in enumerate(renders.items()):\n", " ax = axes[i, j]\n", " _renders = np.array(_renders)\n", - " render = np.mean(_renders, axis=0) / 255\n", + " render = np.mean(_renders, axis=0)/255\n", " ax.imshow(render)\n", " # ax.axis('off')\n", " ax.set_xticks([])\n", @@ -319,12 +307,18 @@ "\n", "fig.set_tight_layout(True)\n", "plt.show()" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } } ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.5 ('testvenv': venv)", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -338,14 +332,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.5" - }, - "vscode": { - "interpreter": { - "hash": "a36dcd8ce3fbd929ee047bf0480a76de49c6a54240010afb1cc974faf11ebb5e" - } + "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/setup.py b/setup.py index da867e13..ae5cd938 100644 --- a/setup.py +++ b/setup.py @@ -22,7 +22,6 @@ def read_file(filepath: str) -> str: extras_require = { "box2d": [ - "swig", "gym[box2d]==0.24.1", ], "brax": [ @@ -33,12 +32,9 @@ def read_file(filepath: str) -> str: "dm_control>=1.0.3", ], "mario": [ - "torch", - "py4j", - "Pillow", - "opencv-python", - "xvfbwrapper", - "jdk4py" + "torch>=1.9.0", + "Pillow>=8.3.1", + "py4j>=0.10.9.2", ], "dev": [ "pytest>=6.1.1", @@ -55,13 +51,6 @@ def read_file(filepath: str) -> str: "sphinx-gallery>=0.10.0", "image>=1.5.33", "sphinx-autoapi>=1.8.4", - "gym[box2d]==0.24.1", - "brax>=0.0.10", - "protobuf>=3.17.3", - "dm_control>=1.0.3", - "torch>=1.9.0", - "Pillow>=8.3.1", - "py4j>=0.10.9.2" ] } diff --git a/test/test_all_envs.py b/test/test_all_envs.py index a39c51a5..141b06b4 100644 --- a/test/test_all_envs.py +++ b/test/test_all_envs.py @@ -16,7 +16,6 @@ def test_init_all_envs(self): env = ( # noqa: F841 local variable is assigned to but never used var() ) - obs = env.reset() except Exception as e: print(f"Cannot instantiate {var} environment.") raise e diff --git a/test/test_evaluation_protocol.py b/test/test_evaluation_protocol.py deleted file mode 100644 index aac1d6bf..00000000 --- a/test/test_evaluation_protocol.py +++ /dev/null @@ -1,57 +0,0 @@ -import unittest - -from experiments.evaluation_protocol.evaluation_protocol import ( - ContextFeature, - EvaluationProtocol, -) -from experiments.evaluation_protocol.plot_evaluate_on_protocol import ( - plot_evaluation_protocol, -) - - -class TestEvaluationProtocol(unittest.TestCase): - def test_context_creation(self): - cf0 = ContextFeature("g", 9.0, 9.5, 10.0, 11.0) - cf1 = ContextFeature("l", 0.4, 0.5, 0.6, 0.8) - seed = None - n_contexts = 100 - context_features = [cf0, cf1] - modes = ["A", "B", "C"] - for mode in modes: - ep = EvaluationProtocol( - context_features=context_features, mode=mode, seed=seed - ) - contexts_train = ep.create_train_contexts(n=n_contexts) - contexts_ES = ep.create_contexts_extrapolation_single( - n=n_contexts - ) # covers two quadrants - contexts_EA = ep.create_contexts_extrapolation_all(n=n_contexts) - contexts_I = ep.create_contexts_interpolation( - n=n_contexts, contexts_forbidden=contexts_train - ) - contexts_IC = ep.create_contexts_interpolation_combinatorial( - n=n_contexts, contexts_forbidden=contexts_train - ) - contexts_dict = { - "train": contexts_train, - "test_interpolation": contexts_I, - "test_interpolation_combinatorial": contexts_IC, - "test_extrapolation_single": contexts_ES, - "test_extrapolation_all": contexts_EA, - } - for c_id, C in contexts_dict.items(): - if len(C) != 0: - self.assertTrue( - len(C) == n_contexts, - msg=f"Number of contexts {len(C)} not equal to desired number {n_contexts} for {c_id}.", - ) - - def test_plot(self): - cf0 = ContextFeature("g", 9.0, 9.5, 10.0, 11.0) - cf1 = ContextFeature("l", 0.4, 0.5, 0.6, 0.8) - seed = 1 - n_contexts = 100 - context_features = [cf0, cf1] - plot_evaluation_protocol( - context_features=context_features, seed=seed, n_contexts=n_contexts - ) diff --git a/test/test_sampling.py b/test/test_sampling.py deleted file mode 100644 index f622a9a6..00000000 --- a/test/test_sampling.py +++ /dev/null @@ -1,34 +0,0 @@ -import unittest -from carl.context.sampling import get_default_context_and_bounds -from experiments.carlbench.context_sampling import ContextSampler - - -class TestSampling(unittest.TestCase): - def test_get_default_context_and_bounds(self): - env_name = "CARLPendulumEnv" - env_defaults, env_bounds = get_default_context_and_bounds(env_name=env_name) - defaults = {"max_speed": 8.0, "dt": 0.05, "g": 10.0, "m": 1.0, "l": 1.0} - self.assertDictEqual(env_defaults, defaults) - - def test_context_sampler(self): - env_name = "CARLPendulumEnv" - cs = ContextSampler( - env_name=env_name, - difficulty="easy", - n_samples=1, - context_feature_names=["m", "l", "g"], - seed=455, - ) - contexts = cs.sample_contexts() - true_dict = { - 0: { - "max_speed": 8.0, - "dt": 0.05, - "g": 9.748400206554313, - "m": 0.8727822986909317, - "l": 0.9215523401261485, - } - } - for context_sampled, context_true in zip(contexts.values(), true_dict.values()): - for k in context_sampled.keys(): - self.assertAlmostEqual(context_sampled[k], context_true[k])