From 542ce2708da8fd4687057c4a2932bcf5f2ea9649 Mon Sep 17 00:00:00 2001 From: cif Date: Thu, 27 Apr 2023 15:42:01 +0200 Subject: [PATCH] =?UTF-8?q?Actualizada=20pr=C3=A1ctica=20a=20gymnasium=20y?= =?UTF-8?q?=20extendida?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ml5/2_6_0_Intro_RL.ipynb | 8 +- ml5/2_6_1_Q-Learning_Basic.ipynb | 1384 ++++++++++++++++++++++ ml5/2_6_1_Q-Learning_Exercises.ipynb | 138 +++ ml5/2_6_1_Q-Learning_Visualization.ipynb | 368 ++++++ ml5/qlearning.py | 274 +++++ 5 files changed, 2169 insertions(+), 3 deletions(-) create mode 100644 ml5/2_6_1_Q-Learning_Basic.ipynb create mode 100644 ml5/2_6_1_Q-Learning_Exercises.ipynb create mode 100644 ml5/2_6_1_Q-Learning_Visualization.ipynb create mode 100644 ml5/qlearning.py diff --git a/ml5/2_6_0_Intro_RL.ipynb b/ml5/2_6_0_Intro_RL.ipynb index f3ab50f..5487645 100644 --- a/ml5/2_6_0_Intro_RL.ipynb +++ b/ml5/2_6_0_Intro_RL.ipynb @@ -48,7 +48,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "1. [Q-Learning](2_6_1_Q-Learning.ipynb)" + "1. [Q-Learning](2_6_1_Q-Learning_Basic.ipynb)\n", + "1. [Visualization](2_6_1_Q-Learning_Visualization.ipynb)\n", + "1. [Exercises](2_6_1_Q-Learning_Exercises.ipynb)" ] }, { @@ -64,7 +66,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -78,7 +80,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.10.10" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/ml5/2_6_1_Q-Learning_Basic.ipynb b/ml5/2_6_1_Q-Learning_Basic.ipynb new file mode 100644 index 0000000..dd174f9 --- /dev/null +++ b/ml5/2_6_1_Q-Learning_Basic.ipynb @@ -0,0 +1,1384 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos Á. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning V](2_6_0_Intro_RL.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "The purpose of this practice is to understand better Reinforcement Learning (RL) and, in particular, Q-Learning.\n", + "\n", + "We are going to use [Gymnasium](https://gymnasium.farama.org/). Gymnasium is toolkit for developing and comparing RL algorithms. It is a fork of [Open AI Gym](https://www.gymlibrary.dev/). Take a loot at ther [website](https://github.com/Farama-Foundation/Gymnasium).\n", + "\n", + "It implements [algorithm imitation](http://gym.openai.com/envs/#algorithmic), [classic control problems](https://gymnasium.farama.org/environments/classic_control/), [Atari games](https://gymnasium.farama.org/environments/atari/), [Box2D continuous control](https://gymnasium.farama.org/environments/box2d/), [robotics with MuJoCo, Multi-Joint dynamics with Contact](https://gymnasium.farama.org/environments/mujoco/), [simple text based environments](https://gymnasium.farama.org/environments/toy_text/), and [other problems](https://gymnasium.farama.org/environments/third_party_environments/).\n", + "\n", + "This notebook is based on [Diving deeper into Reinforcement Learning with Q-Learning](https://medium.freecodecamp.org/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe) and [Introduction to Q-Learning](https://huggingface.co/deep-rl-course/unit2/hands-on?fw=pt).\n", + "\n", + "First of all, install the Gymnasium library, which is a fork of the OpenAI Gym library:\n", + "\n", + "```console\n", + "foo@bar:~$ conda install gymnasium\n", + "```\n", + "\n", + "If you get an error 'No module named 'Box2D', install 'pybox2d'.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting started with Gymnasium\n", + "\n", + "First of all, read the [introduction](https://gymnasium.farama.org/content/basic_usage/) of Gymnasium." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environments\n", + "OpenGym provides a number of problems called *environments*. \n", + "\n", + "Try 'LunarLander-v2' (or 'CartPole-v1', 'MountainCar-v0', etc.)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from warnings import filterwarnings\n", + "filterwarnings(action='ignore', category=DeprecationWarning)\n", + "\n", + "\n", + "import gymnasium as gym\n", + "#env = gym.make(\"LunarLander-v2\", render_mode=\"human\")\n", + "#env = gym.make(\"CartPole-v1\", render_mode=\"human\")\n", + "env = gym.make(\"MountainCar-v0\", render_mode=\"human\")\n", + "observation, info = env.reset()\n", + "\n", + "for _ in range(100):\n", + " action = env.action_space.sample() # agent policy that uses the observation and info\n", + " observation, reward, terminated, truncated, info = env.step(action)\n", + "\n", + " if terminated or truncated:\n", + " observation, info = env.reset()\n", + "\n", + "env.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will launch an external window with the game. If you cannot close that window, just execute in a code cell:\n", + "\n", + "```python\n", + "env.close()\n", + "```\n", + "\n", + "The full list of available environments can be found printing the environment registry as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['CartPole-v0', 'CartPole-v1', 'MountainCar-v0', 'MountainCarContinuous-v0', 'Pendulum-v1', 'Acrobot-v1', 'CartPoleJax-v0', 'CartPoleJax-v1', 'PendulumJax-v0', 'LunarLander-v2', 'LunarLanderContinuous-v2', 'BipedalWalker-v3', 'BipedalWalkerHardcore-v3', 'CarRacing-v2', 'Blackjack-v1', 'FrozenLake-v1', 'FrozenLake8x8-v1', 'CliffWalking-v0', 'Taxi-v3', 'Reacher-v2', 'Reacher-v4', 'Pusher-v2', 'Pusher-v4', 'InvertedPendulum-v2', 'InvertedPendulum-v4', 'InvertedDoublePendulum-v2', 'InvertedDoublePendulum-v4', 'HalfCheetah-v2', 'HalfCheetah-v3', 'HalfCheetah-v4', 'Hopper-v2', 'Hopper-v3', 'Hopper-v4', 'Swimmer-v2', 'Swimmer-v3', 'Swimmer-v4', 'Walker2d-v2', 'Walker2d-v3', 'Walker2d-v4', 'Ant-v2', 'Ant-v3', 'Ant-v4', 'Humanoid-v2', 'Humanoid-v3', 'Humanoid-v4', 'HumanoidStandup-v2', 'HumanoidStandup-v4', 'GymV22Environment-v0', 'GymV26Environment-v0'])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gym.envs.registry.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check the environment specification with spec." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EnvSpec(id='MountainCar-v0', entry_point='gymnasium.envs.classic_control.mountain_car:MountainCarEnv', reward_threshold=-110.0, nondeterministic=False, max_episode_steps=200, order_enforce=True, autoreset=False, disable_env_checker=False, apply_api_compatibility=False, kwargs={'render_mode': 'human'}, namespace=None, name='MountainCar', version=0)\n" + ] + } + ], + "source": [ + "print(env.spec)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The environment’s **step** function returns five values. These are:\n", + "\n", + "* **observation (object):** an environment-specific object representing your observation of the environment. For example, pixel data from a camera, joint angles and joint velocities of a robot, or the board state in a board game.\n", + "* **reward (float):** amount of reward achieved by the previous action. The scale varies between environments, but the goal is always to increase your total reward.\n", + "* **terminated (boolean):** whether the agent reaches the terminal state, which can be positive (e.g., reaching the goal state) or negative (e.g., you lost your last life). If true, the user needs to call *reset()*.\n", + "* **truncated (boolean):** when the truncated condition is satisfied (e.g., timelimit or mechanical problem in a robot). It can be used to end the episode prematurely before a terminal state is reached . If true, the user needs to call *reset()*.\n", + "* **info (dict):** diagnostic information useful for debugging. It can sometimes be useful for learning (for example, it might contain the raw probabilities behind the environment’s last state change). However, official evaluations of your agent are not allowed to use this for learning.\n", + "\n", + "The typical agent loop consists in first calling the method *reset* which provides an initial observation. Then the agent executes an action, and receives the reward, the new observation, and if the episode has finished (terminated or truncated are true). \n", + "\n", + "For example, analyze this sample of agent loop for 100 ms. The details of the previous variables for this game as described [here](https://gymnasium.farama.org/environments/classic_control/cart_pole/) are:\n", + "* **observation**: Cart Position, Cart Velocity, Pole Angle, Pole Velocity.\n", + "* **action**: 0\t(Push cart to the left), 1\t(Push cart to the right).\n", + "* **reward**: 1 for every step taken, including the termination step." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([0.02466699, 0.02517318, 0.04970684, 0.01225195], dtype=float32), {})\n", + "Action 0\n", + "Observation [ 0.02517045 -0.17062509 0.04995188 0.3201944 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.02517045 -0.17062509 0.04995188 0.3201944 ]\n", + "Action 0\n", + "Observation [ 0.02175795 -0.36642152 0.05635576 0.62820244] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.02175795 -0.36642152 0.05635576 0.62820244]\n", + "Action 0\n", + "Observation [ 0.01442952 -0.56228286 0.06891982 0.9380878 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01442952 -0.56228286 0.06891982 0.9380878 ]\n", + "Action 1\n", + "Observation [ 0.00318386 -0.3681544 0.08768157 0.66783285] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00318386 -0.3681544 0.08768157 0.66783285]\n", + "Action 1\n", + "Observation [-0.00417923 -0.17435414 0.10103822 0.4039946 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.00417923 -0.17435414 0.10103822 0.4039946 ]\n", + "Action 0\n", + "Observation [-0.00766631 -0.37075302 0.10911812 0.7267452 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.00766631 -0.37075302 0.10911812 0.7267452 ]\n", + "Action 1\n", + "Observation [-0.01508137 -0.17729534 0.12365302 0.47030163] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.01508137 -0.17729534 0.12365302 0.47030163]\n", + "Action 0\n", + "Observation [-0.01862728 -0.37392715 0.13305905 0.79925877] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.01862728 -0.37392715 0.13305905 0.79925877]\n", + "Action 1\n", + "Observation [-0.02610582 -0.18085699 0.14904423 0.55121744] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.02610582 -0.18085699 0.14904423 0.55121744]\n", + "Action 0\n", + "Observation [-0.02972296 -0.37772328 0.16006857 0.8869 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "(array([-0.0286674 , 0.02992311, -0.01944724, -0.04998275], dtype=float32), {})\n", + "Action 1\n", + "Observation [-0.02806894 0.22531843 -0.0204469 -0.34873745] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.02806894 0.22531843 -0.0204469 -0.34873745]\n", + "Action 1\n", + "Observation [-0.02356257 0.42072514 -0.02742165 -0.6477972 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.02356257 0.42072514 -0.02742165 -0.6477972 ]\n", + "Action 0\n", + "Observation [-0.01514807 0.22599575 -0.04037759 -0.3638739 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.01514807 0.22599575 -0.04037759 -0.3638739 ]\n", + "Action 0\n", + "Observation [-0.01062815 0.03147022 -0.04765507 -0.0841912 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.01062815 0.03147022 -0.04765507 -0.0841912 ]\n", + "Action 1\n", + "Observation [-0.00999875 0.22724174 -0.0493389 -0.39152038] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.00999875 0.22724174 -0.0493389 -0.39152038]\n", + "Action 1\n", + "Observation [-0.00545391 0.4230279 -0.0571693 -0.699342 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.00545391 0.4230279 -0.0571693 -0.699342 ]\n", + "Action 1\n", + "Observation [ 0.00300664 0.6188939 -0.07115614 -1.0094596 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00300664 0.6188939 -0.07115614 -1.0094596 ]\n", + "Action 1\n", + "Observation [ 0.01538452 0.8148897 -0.09134534 -1.3236117 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01538452 0.8148897 -0.09134534 -1.3236117 ]\n", + "Action 1\n", + "Observation [ 0.03168232 1.0110391 -0.11781757 -1.6434273 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.03168232 1.0110391 -0.11781757 -1.6434273 ]\n", + "Action 1\n", + "Observation [ 0.0519031 1.207327 -0.15068612 -1.9703763 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "(array([ 0.04422636, 0.03184601, -0.00627003, 0.03030435], dtype=float32), {})\n", + "Action 1\n", + "Observation [ 0.04486328 0.22705732 -0.00566394 -0.26435024] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.04486328 0.22705732 -0.00566394 -0.26435024]\n", + "Action 0\n", + "Observation [ 0.04940443 0.03201666 -0.01095094 0.02654087] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.04940443 0.03201666 -0.01095094 0.02654087]\n", + "Action 0\n", + "Observation [ 0.05004476 -0.16294654 -0.01042013 0.31574863] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.05004476 -0.16294654 -0.01042013 0.31574863]\n", + "Action 1\n", + "Observation [ 0.04678584 0.03232227 -0.00410515 0.01979785] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.04678584 0.03232227 -0.00410515 0.01979785]\n", + "Action 1\n", + "Observation [ 0.04743228 0.22750285 -0.0037092 -0.27417746] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.04743228 0.22750285 -0.0037092 -0.27417746]\n", + "Action 0\n", + "Observation [ 0.05198234 0.03243402 -0.00919275 0.01733326] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.05198234 0.03243402 -0.00919275 0.01733326]\n", + "Action 1\n", + "Observation [ 0.05263102 0.2276866 -0.00884608 -0.27823585] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.05263102 0.2276866 -0.00884608 -0.27823585]\n", + "Action 1\n", + "Observation [ 0.05718475 0.4229336 -0.0144108 -0.57369566] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.05718475 0.4229336 -0.0144108 -0.57369566]\n", + "Action 1\n", + "Observation [ 0.06564342 0.6182546 -0.02588471 -0.87088335] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.06564342 0.6182546 -0.02588471 -0.87088335]\n", + "Action 0\n", + "Observation [ 0.07800851 0.42349413 -0.04330238 -0.58644974] , reward 1.0 , terminated False , truncated False , info {}\n", + "(array([ 0.00689714, -0.004671 , -0.03079236, 0.01346231], dtype=float32), {})\n", + "Action 0\n", + "Observation [ 0.00680372 -0.19933812 -0.03052311 0.29627305] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00680372 -0.19933812 -0.03052311 0.29627305]\n", + "Action 1\n", + "Observation [ 0.00281696 -0.00379464 -0.02459765 -0.00587795] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00281696 -0.00379464 -0.02459765 -0.00587795]\n", + "Action 0\n", + "Observation [ 0.00274106 -0.19855535 -0.02471521 0.27894375] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00274106 -0.19855535 -0.02471521 0.27894375]\n", + "Action 0\n", + "Observation [-0.00123004 -0.39331618 -0.01913633 0.56373024] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.00123004 -0.39331618 -0.01913633 0.56373024]\n", + "Action 1\n", + "Observation [-0.00909637 -0.197931 -0.00786173 0.26508042] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.00909637 -0.197931 -0.00786173 0.26508042]\n", + "Action 0\n", + "Observation [-0.01305499 -0.39293987 -0.00256012 0.55527335] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.01305499 -0.39293987 -0.00256012 0.55527335]\n", + "Action 0\n", + "Observation [-0.02091378 -0.5880258 0.00854535 0.8471486 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.02091378 -0.5880258 0.00854535 0.8471486 ]\n", + "Action 1\n", + "Observation [-0.0326743 -0.39302143 0.02548832 0.557165 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.0326743 -0.39302143 0.02548832 0.557165 ]\n", + "Action 0\n", + "Observation [-0.04053473 -0.58849174 0.03663162 0.85776806] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.04053473 -0.58849174 0.03663162 0.85776806]\n", + "Action 0\n", + "Observation [-0.05230456 -0.7840931 0.05378698 1.1617405 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "(array([ 0.01617916, -0.01409287, -0.04266112, 0.00518782], dtype=float32), {})\n", + "Action 1\n", + "Observation [ 0.01589731 0.18161412 -0.04255737 -0.30064413] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01589731 0.18161412 -0.04255737 -0.30064413]\n", + "Action 0\n", + "Observation [ 0.01952959 -0.01287623 -0.04857025 -0.02168084] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01952959 -0.01287623 -0.04857025 -0.02168084]\n", + "Action 1\n", + "Observation [ 0.01927206 0.1829074 -0.04900387 -0.329284 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01927206 0.1829074 -0.04900387 -0.329284 ]\n", + "Action 0\n", + "Observation [ 0.02293021 -0.01148394 -0.05558955 -0.05244839] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.02293021 -0.01148394 -0.05558955 -0.05244839]\n", + "Action 0\n", + "Observation [ 0.02270053 -0.20576656 -0.05663851 0.22219047] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.02270053 -0.20576656 -0.05663851 0.22219047]\n", + "Action 1\n", + "Observation [ 0.0185852 -0.00988272 -0.0521947 -0.08780695] , reward 1.0 , terminated False , truncated False , info {}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.0185852 -0.00988272 -0.0521947 -0.08780695]\n", + "Action 0\n", + "Observation [ 0.01838755 -0.20421918 -0.05395084 0.18796247] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01838755 -0.20421918 -0.05395084 0.18796247]\n", + "Action 0\n", + "Observation [ 0.01430316 -0.3985294 -0.05019159 0.46314988] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01430316 -0.3985294 -0.05019159 0.46314988]\n", + "Action 1\n", + "Observation [ 0.00633258 -0.2027354 -0.04092859 0.1550786 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00633258 -0.2027354 -0.04092859 0.1550786 ]\n", + "Action 0\n", + "Observation [ 0.00227787 -0.39724815 -0.03782702 0.43457374] , reward 1.0 , terminated False , truncated False , info {}\n", + "(array([-0.01675624, 0.00657571, -0.01191216, -0.03199182], dtype=float32), {})\n", + "Action 1\n", + "Observation [-0.01662473 0.20186645 -0.012552 -0.32840922] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.01662473 0.20186645 -0.012552 -0.32840922]\n", + "Action 1\n", + "Observation [-0.0125874 0.39716482 -0.01912018 -0.6250239 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.0125874 0.39716482 -0.01912018 -0.6250239 ]\n", + "Action 1\n", + "Observation [-0.0046441 0.59254843 -0.03162066 -0.9236667 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.0046441 0.59254843 -0.03162066 -0.9236667 ]\n", + "Action 1\n", + "Observation [ 0.00720687 0.7880829 -0.050094 -1.2261168 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00720687 0.7880829 -0.050094 -1.2261168 ]\n", + "Action 1\n", + "Observation [ 0.02296852 0.98381275 -0.07461634 -1.5340647 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.02296852 0.98381275 -0.07461634 -1.5340647 ]\n", + "Action 1\n", + "Observation [ 0.04264478 1.17975 -0.10529763 -1.8490696 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.04264478 1.17975 -0.10529763 -1.8490696 ]\n", + "Action 1\n", + "Observation [ 0.06623978 1.3758619 -0.14227901 -2.1725085 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.06623978 1.3758619 -0.14227901 -2.1725085 ]\n", + "Action 1\n", + "Observation [ 0.09375702 1.5720552 -0.18572919 -2.505514 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.09375702 1.5720552 -0.18572919 -2.505514 ]\n", + "Action 0\n", + "Observation [ 0.12519813 1.3788872 -0.23583947 -2.2750359 ] , reward 1.0 , terminated True , truncated False , info {}\n", + "Episode finished after 9 timesteps\n", + "(array([ 0.01553306, -0.03829413, 0.01700553, 0.01151424], dtype=float32), {})\n", + "Action 0\n", + "Observation [ 0.01476718 -0.23365578 0.01723581 0.30951375] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01476718 -0.23365578 0.01723581 0.30951375]\n", + "Action 1\n", + "Observation [ 0.01009406 -0.03878359 0.02342609 0.02231595] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01009406 -0.03878359 0.02342609 0.02231595]\n", + "Action 0\n", + "Observation [ 0.00931839 -0.23423353 0.02387241 0.32229704] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00931839 -0.23423353 0.02387241 0.32229704]\n", + "Action 1\n", + "Observation [ 0.00463372 -0.03945951 0.03031835 0.03723709] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00463372 -0.03945951 0.03031835 0.03723709]\n", + "Action 1\n", + "Observation [ 0.00384453 0.15521485 0.03106309 -0.24572802] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00384453 0.15521485 0.03106309 -0.24572802]\n", + "Action 1\n", + "Observation [ 0.00694883 0.34987968 0.02614853 -0.52845335] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.00694883 0.34987968 0.02614853 -0.52845335]\n", + "Action 0\n", + "Observation [ 0.01394642 0.1543998 0.01557946 -0.22764695] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01394642 0.1543998 0.01557946 -0.22764695]\n", + "Action 1\n", + "Observation [ 0.01703442 0.34929568 0.01102652 -0.51537514] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.01703442 0.34929568 0.01102652 -0.51537514]\n", + "Action 1\n", + "Observation [ 2.4020329e-02 5.4426062e-01 7.1901991e-04 -8.0456305e-01] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 2.4020329e-02 5.4426062e-01 7.1901991e-04 -8.0456305e-01]\n", + "Action 1\n", + "Observation [ 0.03490554 0.73937273 -0.01537224 -1.0970197 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "(array([ 0.04527323, 0.02190002, -0.03927047, 0.01146642], dtype=float32), {})\n", + "Action 1\n", + "Observation [ 0.04571123 0.21756251 -0.03904114 -0.2933436 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.04571123 0.21756251 -0.03904114 -0.2933436 ]\n", + "Action 1\n", + "Observation [ 0.05006249 0.4132187 -0.04490801 -0.59807944] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.05006249 0.4132187 -0.04490801 -0.59807944]\n", + "Action 0\n", + "Observation [ 0.05832686 0.21875295 -0.0568696 -0.31987342] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.05832686 0.21875295 -0.0568696 -0.31987342]\n", + "Action 1\n", + "Observation [ 0.06270192 0.41463676 -0.06326707 -0.6299348 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.06270192 0.41463676 -0.06326707 -0.6299348 ]\n", + "Action 0\n", + "Observation [ 0.07099465 0.22045206 -0.07586577 -0.3578286 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.07099465 0.22045206 -0.07586577 -0.3578286 ]\n", + "Action 1\n", + "Observation [ 0.0754037 0.41656595 -0.08302233 -0.6734364 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.0754037 0.41656595 -0.08302233 -0.6734364 ]\n", + "Action 1\n", + "Observation [ 0.08373501 0.6127377 -0.09649107 -0.99106103] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.08373501 0.6127377 -0.09649107 -0.99106103]\n", + "Action 0\n", + "Observation [ 0.09598977 0.41903025 -0.11631229 -0.7301758 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.09598977 0.41903025 -0.11631229 -0.7301758 ]\n", + "Action 1\n", + "Observation [ 0.10437037 0.61555123 -0.1309158 -1.0570843 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.10437037 0.61555123 -0.1309158 -1.0570843 ]\n", + "Action 1\n", + "Observation [ 0.1166814 0.8121419 -0.15205748 -1.3878263 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "(array([ 0.0463822 , 0.03024504, 0.03519755, -0.02215011], dtype=float32), {})\n", + "Action 0\n", + "Observation [ 0.0469871 -0.16536354 0.03475455 0.28142697] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.0469871 -0.16536354 0.03475455 0.28142697]\n", + "Action 0\n", + "Observation [ 0.04367983 -0.36096355 0.04038309 0.58486557] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.04367983 -0.36096355 0.04038309 0.58486557]\n", + "Action 0\n", + "Observation [ 0.03646055 -0.5566272 0.0520804 0.8899912 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.03646055 -0.5566272 0.0520804 0.8899912 ]\n", + "Action 0\n", + "Observation [ 0.02532801 -0.75241566 0.06988022 1.1985804 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.02532801 -0.75241566 0.06988022 1.1985804 ]\n", + "Action 1\n", + "Observation [ 0.0102797 -0.5582641 0.09385183 0.92859185] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.0102797 -0.5582641 0.09385183 0.92859185]\n", + "Action 1\n", + "Observation [-0.00088559 -0.36452585 0.11242367 0.6668154 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.00088559 -0.36452585 0.11242367 0.6668154 ]\n", + "Action 0\n", + "Observation [-0.0081761 -0.561017 0.12575997 0.99267435] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.0081761 -0.561017 0.12575997 0.99267435]\n", + "Action 1\n", + "Observation [-0.01939644 -0.3677815 0.14561346 0.7419863 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.01939644 -0.3677815 0.14561346 0.7419863 ]\n", + "Action 1\n", + "Observation [-0.02675207 -0.17493759 0.16045319 0.49844098] , reward 1.0 , terminated False , truncated False , info {}\n", + "[-0.02675207 -0.17493759 0.16045319 0.49844098]\n", + "Action 1\n", + "Observation [-0.03025082 0.01760164 0.170422 0.26031297] , reward 1.0 , terminated False , truncated False , info {}\n", + "(array([ 0.02045374, 0.02493249, -0.02570103, 0.03014062], dtype=float32), {})\n", + "Action 1\n", + "Observation [ 0.02095238 0.2204134 -0.02509822 -0.27053916] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.02095238 0.2204134 -0.02509822 -0.27053916]\n", + "Action 1\n", + "Observation [ 0.02536065 0.41588435 -0.030509 -0.57103133] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.02536065 0.41588435 -0.030509 -0.57103133]\n", + "Action 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observation [ 0.03367834 0.22120321 -0.04192962 -0.2881138 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.03367834 0.22120321 -0.04192962 -0.2881138 ]\n", + "Action 0\n", + "Observation [ 0.0381024 0.0267035 -0.0476919 -0.00894437] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.0381024 0.0267035 -0.0476919 -0.00894437]\n", + "Action 0\n", + "Observation [ 0.03863648 -0.16770318 -0.04787079 0.268318 ] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.03863648 -0.16770318 -0.04787079 0.268318 ]\n", + "Action 1\n", + "Observation [ 0.03528241 0.02806809 -0.04250443 -0.03907115] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.03528241 0.02806809 -0.04250443 -0.03907115]\n", + "Action 1\n", + "Observation [ 0.03584377 0.22377296 -0.04328585 -0.34485587] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.03584377 0.22377296 -0.04328585 -0.34485587]\n", + "Action 0\n", + "Observation [ 0.04031923 0.02929264 -0.05018297 -0.06613071] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.04031923 0.02929264 -0.05018297 -0.06613071]\n", + "Action 0\n", + "Observation [ 0.04090508 -0.16507524 -0.05150558 0.21030648] , reward 1.0 , terminated False , truncated False , info {}\n", + "[ 0.04090508 -0.16507524 -0.05150558 0.21030648]\n", + "Action 0\n", + "Observation [ 0.03760358 -0.35942435 -0.04729946 0.48630762] , reward 1.0 , terminated False , truncated False , info {}\n" + ] + } + ], + "source": [ + "import gymnasium as gym\n", + "\n", + "env = gym.make('CartPole-v1', render_mode='human')\n", + "for i_episode in range(10):\n", + " \n", + " \n", + " observation = env.reset()\n", + " for t in range(10):\n", + " env.render()\n", + " print(observation)\n", + " action = env.action_space.sample()\n", + " print(\"Action \", action)\n", + " observation, reward, terminated, truncated, info = env.step(action)\n", + " print(\"Observation \", observation, \", reward \", reward, \", terminated \", terminated,\n", + " \", truncated\", truncated, \", info \" , info)\n", + " if terminated or truncated:\n", + " print(\"Episode finished after {} timesteps\".format(t+1))\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Frozen Lake scenario\n", + "We are going to play to the [Frozen Lake](https://gymnasium.farama.org/environments/toy_text/frozen_lake/) game.\n", + "\n", + "The problem is a grid where you should go from the 'start' (S) position to the 'goal position (G) (the pizza!). You can only walk through the 'frozen tiles' (F). Unfortunately, you can fall in a 'hole' (H).\n", + "![](images/frozenlake-problem.png \"Frozen lake problem\")\n", + "\n", + "The episode ends when you reach the goal or fall in a hole. You receive a reward of 1 if you reach the goal, and zero otherwise. The possible actions are going left, right, up or down. However, the ice is slippery, so you won't always move in the direction you intend.\n", + "\n", + "![](images/frozenlake-world.png \"Frozen lake world\")\n", + "\n", + "\n", + "Here you can see several episodes. A full recording is available at [Frozen World](http://gym.openai.com/envs/FrozenLake-v0/).\n", + "\n", + "![](images/recording.gif \"Example running\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Q-Learning with the Frozen Lake scenario\n", + "We are now going to apply Q-Learning for the Frozen Lake scenario. This part of the notebook is taken from [here](https://github.com/simoninithomas/Deep_reinforcement_learning_Course/blob/master/Q%20learning/Q%20Learning%20with%20FrozenLake.ipynb). You can get more details about this scenario [here](https://gymnasium.farama.org/environments/toy_text/frozen_lake/).\n", + "\n", + "First we create the environment and a Q-table inizializated with zeros to store the value of each action in a given state. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 16 possible states\n", + "There are 4 possible actions\n", + "QTable\n", + "[[0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import gymnasium as gym\n", + "import random\n", + "\n", + "env = gym.make(\"FrozenLake-v1\", desc=None, map_name=\"4x4\", is_slippery=False) #no render so training is faster\n", + "#env = gym.make(\"FrozenLake-v1\", desc=None, map_name=\"4x4\", is_slippery=False, render_mode='human')\n", + "#env = gym.make(\"FrozenLake-v1\", desc=None, map_name=\"4x4\", is_slippery=False, render_mode='rgb_array')\n", + "\n", + "\n", + "action_space = env.action_space.n\n", + "state_space = env.observation_space.n\n", + "\n", + "print(\"There are \", state_space, \" possible states\")\n", + "print(\"There are \", action_space, \" possible actions\")\n", + "\n", + "# Step 1. Initialize QTable\n", + "qtable = np.zeros((state_space, action_space))\n", + "print(\"QTable\")\n", + "print(qtable)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define the hyperparameters." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Q-Learning hyperparameters\n", + "total_episodes = 10000 # Total episodes\n", + "learning_rate = 0.8 # Learning rate\n", + "max_steps = 99 # Max steps per episode\n", + "gamma = 0.95 # Discounting rate\n", + "\n", + "# Exploration hyperparameters\n", + "epsilon = 1.0 # Exploration rate\n", + "max_epsilon = 1.0 # Exploration probability at start\n", + "min_epsilon = 0.01 # Minimum exploration probability \n", + "decay_rate = 0.01 # Exponential decay rate for exploration prob" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now we implement the Q-Learning algorithm.\n", + "\n", + "![](images/qlearning-algo.png \"Q-Learning algorithm\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "state, info = env.reset() #reset returns observation, info\n", + "step = 0\n", + "done = False\n", + "total_rewards = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10000 [00:00 greater than epsilon --> exploitation (taking the biggest Q value for this state)\n", + " if exp_exp_tradeoff > epsilon:\n", + " action = np.argmax(qtable[state][:])\n", + "\n", + " # Else doing a random choice --> exploration\n", + " else:\n", + " action = env.action_space.sample()\n", + "\n", + " # Take the action (a) and observe the outcome state(s') and reward (r)\n", + " new_state, reward, terminated, truncated, info = env.step(action)\n", + "\n", + " # Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]\n", + " # qtable[new_state][:] : all the actions we can take from new state\n", + " qtable[state][action] = qtable[state][action] + learning_rate * (reward + gamma * np.max(qtable[new_state]) - qtable[state][action])\n", + " \n", + " total_rewards += reward\n", + " \n", + " # Our new state is state\n", + " state = new_state\n", + " \n", + " done = terminated or truncated\n", + " # If done (if we're dead) : finish episode\n", + " if done == True: \n", + " break\n", + " \n", + " episode += 1\n", + " # Reduce epsilon (because we need less and less exploration)\n", + " epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode) \n", + " rewards.append(total_rewards)\n", + "\n", + "print (\"Score over time: \" + str(sum(rewards)/total_episodes))\n", + "print(qtable)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we use the learnt Q-table for playing the Frozen World game." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************************\n", + "EPISODE 0\n", + "****************************************************\n", + "Action 1\n", + "Action 1\n", + "Action 2\n", + "Action 1\n", + "Action 2\n", + "Action 2\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "****************************************************\n", + "EPISODE 1\n", + "****************************************************\n", + "Action 1\n", + "Action 1\n", + "Action 2\n", + "Action 1\n", + "Action 2\n", + "Action 2\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "****************************************************\n", + "EPISODE 2\n", + "****************************************************\n", + "Action 1\n", + "Action 1\n", + "Action 2\n", + "Action 1\n", + "Action 2\n", + "Action 2\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "****************************************************\n", + "EPISODE 3\n", + "****************************************************\n", + "Action 1\n", + "Action 1\n", + "Action 2\n", + "Action 1\n", + "Action 2\n", + "Action 2\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "****************************************************\n", + "EPISODE 4\n", + "****************************************************\n", + "Action 1\n", + "Action 1\n", + "Action 2\n", + "Action 1\n", + "Action 2\n", + "Action 2\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n", + "Action 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cif/anaconda3/lib/python3.10/site-packages/gymnasium/envs/toy_text/frozen_lake.py:328: UserWarning: \u001b[33mWARN: You are calling render method without specifying any render mode. You can specify the render_mode at initialization, e.g. gym.make(\"FrozenLake-v1\", render_mode=\"rgb_array\")\u001b[0m\n", + " gym.logger.warn(\n" + ] + } + ], + "source": [ + "env.reset()\n", + "\n", + "for episode in range(5):\n", + " state, info = env.reset()\n", + " step = 0\n", + " done = False\n", + " print(\"****************************************************\")\n", + " print(\"EPISODE \", episode)\n", + " print(\"****************************************************\")\n", + "\n", + " for step in range(max_steps):\n", + " env.render() # render according to rend_mode specified in gym.make\n", + " # Take the action (index) that have the maximum expected future reward given that state\n", + " action = np.argmax(qtable[state][:])\n", + " print(\"Action \", action)\n", + " \n", + " new_state, reward, terminated, truncated, info = env.step(action)\n", + " \n", + " if done:\n", + " break\n", + " state = new_state\n", + "env.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* [Gymnasium documentation](https://gymnasium.farama.org/).\n", + "* [Diving deeper into Reinforcement Learning with Q-Learning, Thomas Simonini](https://medium.freecodecamp.org/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe).\n", + "* Illustrations by [Thomas Simonini](https://github.com/simoninithomas/Deep_reinforcement_learning_Course) and [Sung Kim](https://www.youtube.com/watch?v=xgoO54qN4lY).\n", + "* [Frozen Lake solution with TensorFlow](https://analyticsindiamag.com/openai-gym-frozen-lake-beginners-guide-reinforcement-learning/)\n", + "* [Deep Q-Learning for Doom](https://medium.freecodecamp.org/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8)\n", + "* [Intro OpenAI Gym with Random Search and the Cart Pole scenario](http://www.pinchofintelligence.com/getting-started-openai-gym/)\n", + "* [Q-Learning for the Taxi scenario](https://www.oreilly.com/learning/introduction-to-reinforcement-learning-and-openai-gym)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© Carlos Á. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "metadata": { + "datacleaner": { + "position": { + "top": "50px" + }, + "python": { + "varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])" + }, + "window_display": false + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/ml5/2_6_1_Q-Learning_Exercises.ipynb b/ml5/2_6_1_Q-Learning_Exercises.ipynb new file mode 100644 index 0000000..021c3a3 --- /dev/null +++ b/ml5/2_6_1_Q-Learning_Exercises.ipynb @@ -0,0 +1,138 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos Á. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning V](2_6_0_Intro_RL.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercises\n", + "\n", + "\n", + "## Taxi\n", + "Analyze the [Taxi problem](https://gymnasium.farama.org/environments/toy_text/taxi/) and solve it applying Q-Learning. You can find a solution as the one previously presented [here](https://www.oreilly.com/learning/introduction-to-reinforcement-learning-and-openai-gym), and the notebook is [here](https://github.com/wagonhelm/Reinforcement-Learning-Introduction/blob/master/Reinforcement%20Learning%20Introduction.ipynb). Take into account that Gymnasium has changed, so you will have to adapt the code.\n", + "\n", + "Analyze the impact of not changing the learning rate or changing it in a different way. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optional exercises\n", + "Select one of the following exercises.\n", + "\n", + "## Blackjack\n", + "Analyze how to appy Q-Learning for solving Blackjack.\n", + "You can find information in this [article](https://gymnasium.farama.org/tutorials/training_agents/blackjack_tutorial/).\n", + "\n", + "## Doom\n", + "Read this [article](https://medium.freecodecamp.org/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8) and execute the companion [notebook](https://github.com/simoninithomas/Deep_reinforcement_learning_Course/blob/master/Deep%20Q%20Learning/Doom/Deep%20Q%20learning%20with%20Doom.ipynb). Analyze the results and provide conclusions about DQN.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* [Gymnasium documentation](https://gymnasium.farama.org/).\n", + "* [Diving deeper into Reinforcement Learning with Q-Learning, Thomas Simonini](https://medium.freecodecamp.org/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe).\n", + "* Illustrations by [Thomas Simonini](https://github.com/simoninithomas/Deep_reinforcement_learning_Course) and [Sung Kim](https://www.youtube.com/watch?v=xgoO54qN4lY).\n", + "* [Frozen Lake solution with TensorFlow](https://analyticsindiamag.com/openai-gym-frozen-lake-beginners-guide-reinforcement-learning/)\n", + "* [Deep Q-Learning for Doom](https://medium.freecodecamp.org/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8)\n", + "* [Intro OpenAI Gym with Random Search and the Cart Pole scenario](http://www.pinchofintelligence.com/getting-started-openai-gym/)\n", + "* [Q-Learning for the Taxi scenario](https://www.oreilly.com/learning/introduction-to-reinforcement-learning-and-openai-gym)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© Carlos Á. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "metadata": { + "datacleaner": { + "position": { + "top": "50px" + }, + "python": { + "varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])" + }, + "window_display": false + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/ml5/2_6_1_Q-Learning_Visualization.ipynb b/ml5/2_6_1_Q-Learning_Visualization.ipynb new file mode 100644 index 0000000..b607f22 --- /dev/null +++ b/ml5/2_6_1_Q-Learning_Visualization.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](images/EscUpmPolit_p.gif \"UPM\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Course Notes for Learning Intelligent Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos Á. Iglesias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## [Introduction to Machine Learning V](2_6_0_Intro_RL.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section we are going to visualize Q-Learning based on this [link](https://gymnasium.farama.org/tutorials/training_agents/FrozenLake_tuto/#sphx-glr-tutorials-training-agents-frozenlake-tuto-py). The code has been ported to the last version of Gymnasium.\n", + "\n", + "First, we are going to define a class *Params* for the Q-Learning parameters and the environment based on these values." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlearning import *\n", + "sns.set_theme()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "params = Params(\n", + " total_episodes=2000,\n", + " learning_rate=0.8,\n", + " gamma=0.95,\n", + " epsilon=0.1,\n", + " map_size=5,\n", + " seed=123,\n", + " is_slippery=False,\n", + " n_runs=20,\n", + " action_size=None,\n", + " state_size=None,\n", + " proba_frozen=0.9,\n", + " savefig_folder=Path(\"./\"),\n", + ")\n", + "params\n", + "\n", + "# Set the seed\n", + "rng = np.random.default_rng(params.seed)\n", + "\n", + "# Create the figure folder if it doesn't exists\n", + "params.savefig_folder.mkdir(parents=True, exist_ok=True)\n", + "\n", + "# Environment\n", + "env = gym.make(\n", + " \"FrozenLake-v1\",\n", + " is_slippery=params.is_slippery,\n", + " render_mode=\"rgb_array\",\n", + " desc=generate_random_map(\n", + " size=params.map_size, p=params.proba_frozen, seed=params.seed\n", + " ),\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Q-Learning algorithm has been defined with two clases in the file *qlearning.py*:\n", + "- *Qlearning* for learning the q-table\n", + "- *EpsilonGreedy* for implementing the epsilon-greedy policy \n", + "\n", + "First, we check the environment." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Action size: 4\n", + "State size: 25\n" + ] + } + ], + "source": [ + "params = params._replace(action_size=env.action_space.n)\n", + "params = params._replace(state_size=env.observation_space.n)\n", + "print(f\"Action size: {params.action_size}\")\n", + "print(f\"State size: {params.state_size}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the environment" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "learner = Qlearning(\n", + " learning_rate=params.learning_rate,\n", + " gamma=params.gamma,\n", + " state_size=params.state_size,\n", + " action_size=params.action_size,\n", + ")\n", + "explorer = EpsilonGreedy(\n", + " epsilon=params.epsilon,\n", + " rng = rng\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will be our main function to run our environment until the maximum number of episodes *params.total_episodes*. To account for stochasticity, we will also run our environment a few times." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to plot the policy the agent has learned in the end. To do that the function *qtable_directions_map* perform these actions: 1. extract the best Q-values from the Q-table for each state, 2. get the corresponding best action for those Q-values, 3. map each action to an arrow so we can visualize it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function *plot_q_values_map* plots on the left the last frame of the simulation. If the agent learned a good policy to solve the task, we expect to see it on the tile of the treasure in the last frame of the video. On the right we’ll plot the policy the agent has learned. Each arrow will represent the best action to choose for each tile/state." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a sanity check, the function *plot_states_actons_distribution* plots the distributions of states and actions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we’ll be running our agent on a few increasing maps sizes: \n", + "- 4x4\n", + "- 7x7\n", + "- 9x9\n", + "- 11x11\n", + "\n", + "Putting it all together:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Params(total_episodes=2000, learning_rate=0.8, gamma=0.95, epsilon=0.1, map_size=5, seed=123, is_slippery=False, n_runs=20, action_size=4, state_size=25, proba_frozen=0.9, savefig_folder=PosixPath('.'))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Map size: 11x11\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "map_sizes = [4, 7, 9, 11]\n", + "\n", + "\n", + "res_all, st_all = run_frozen_maps(map_sizes, params, rng)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The DOWN and RIGHT actions get chosen more often, which makes sense as the agent starts at the top left of the map and needs to find its way down to the bottom right. Also the bigger the map, the less states/tiles further away from the starting state get visited.\n", + "\n", + "To check if our agent is learning, we want to plot the cumulated sum of rewards, as well as the number of steps needed until the end of the episode. If our agent is learning, we expect to see the cumulated sum of rewards to increase and the number of steps to solve the task to decrease. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABcwAAAHjCAYAAAAe+FznAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3wb9f0/8NfdaXnbcfYOGQQKSYAEAiQkhNECpW1+KRRaKA0UKLPsMNovScvoAMIIO2GUGSBsSoGEEUbIDhCy97SdeNtad/f5/P6QLVu2ZJ9kyRp+PfugsU+nu7c+Gj69733vjyKllCAiIiIiIiIiIiIi6uLUZAdARERERERERERERJQKmDAnIiIiIiIiIiIiIgIT5kREREREREREREREAJgwJyIiIiIiIiIiIiICwIQ5EREREREREREREREAJsyJiIiIiIiIiIiIiAAwYU5EREREREREREREBIAJcyIiIiIiIiIiIiIiAEyYExEREREREREREREBSLGE+WOPPYYLL7wwZNn69etxwQUXYMyYMZg8eTLmzZsXcrsQAg8//DAmTpyI0aNH4+KLL8bOnTuj2gYRERERERERERERUcokzJ977jk8/PDDIcsqKysxffp0DB48GAsWLMA111yDhx56CAsWLAiu89hjj+HVV1/FXXfdhfnz50NRFFx66aXw+/2Wt0FEREREREREREREZEt2AKWlpbjjjjuwcuVKDBkyJOS21157DQ6HAzNnzoTNZsPQoUOxc+dOPP3005g2bRr8fj+eeeYZ3HzzzZg0aRIAYPbs2Zg4cSI++eQTnHXWWe1ug4iIiIiIiIiIiIgISIEK8x9//BEFBQV49913MXr06JDbVqxYgXHjxsFma8rrjx8/Htu3b0d5eTk2bNiA+vp6jB8/Pnh7fn4+Dj/8cCxfvtzSNoiIiIiIiIiIiIiIgBSoMJ8yZQqmTJkS9raSkhKMGDEiZFnPnj0BAPv27UNJSQkAoE+fPq3W2b9/v6VtFBcXxxS3lBJCyJju2xGqqiRlv+mIYxUdjpd1HKvocLys41hFh+NlHccqOp09XqqqQFGUTttfuuDxdnrgeFnHsYoOx8s6jlV0OF7Wcayiw/GyLhljFc0xd9IT5m3xer1wOBwhy5xOJwDA5/PB4/EAQNh1qqurLW2jIzQtOQX6msYvVFZxrKLD8bKOYxUdjpd1HKvocLys41hFh+OVfEJIVFTUd+o+bTYVRUU5qKlxwzBEp+47HXG8rONYRYfjZR3HKjocL+s4VtHheFmXrLHq1i3H8jF+SifMXS5XcPLORo1J7uzsbLhcLgCA3+8P/ty4TlZWlqVtxEoIiZoad8z3j4WmqcjPz0JNjQemyTdfWzhW0eF4Wcexig7HyzqOVXQ4XtZxrKKTjPHKz89KWiEGERERERGFSumEee/evVFWVhayrPH3Xr16wTCM4LKBAweGrDNy5EhL2+iIZJ0xMk3Bs1UWcayiw/GyjmMVHY6XdRyr6HC8rONYRYfjRURERETUNaV0Kcu4ceOwcuVKmKYZXLZkyRIMGTIExcXFGDlyJHJzc7F06dLg7TU1NVi3bh3Gjh1raRtERERERERERERERECKJ8ynTZuGuro63HHHHdiyZQvefPNNPP/887j88ssBBHqXX3DBBbjvvvuwaNEibNiwAddffz169+6N0047zdI2iIiIiIiIiIiIiIiAFG/JUlxcjLlz5+Luu+/G1KlT0aNHD9xyyy2YOnVqcJ1rr70WhmHgL3/5C7xeL8aNG4d58+YFJ/q0sg0iIiIiIiIiIiIiopRKmP/jH/9otWzUqFGYP39+xPtomoabb74ZN998c8R12tsGERERUVcjpYQQAkKY7a/chQihwOvV4Pf7YJoyLtvUNBtUNaUv7CQiIiKiBBBCwDSNZIeRUtLheDulEuZERERElFhSSng8dairq2ayPIKDB1UIEd8JP7OycpGf3w2KosR1u0RERESUeqSUqKmpgMdTl+xQUlKqH28zYU5ERETUhTQeuLtcOXC5sqGqGpO4LWiaErdqFykl/H4f6uoqAQAFBZx0noiIiCjTNR5z5+YWweFw8ni7hVQ/3mbCnIiIiKiLEMKEx1OP3NxC5OYWJDuclGWzqTCM+FW8OBxOAEBdXSXy8orYnoWIiIgogwWOuQPJ8tzc/GSHk5JS/XibR+tEREREXYRpmgAknE5XskPpchoP4tnDkoiIiCizBY65m47/qHPE83ibCXMiIiKiLoeXhHY2XoZLRERE1LXw+K9zxXO8mTAnIiIiIiIiIiIiIgIT5kREREREREREREREAJgwJyIiIqIMNm/ek5gwYWyywyAiIiIiyliZdsxtS3YARERERESJcvbZv8Jxx52Q7DCIiIiIiDJWph1zM2FORERERBmrZ89e6NmzV7LDICIiIiLKWJl2zM2EORERERFZ8utfn40zzzwb9fV1+N//PoDfr2PChJNw88234803X8OCBa/B7a7H2LHH4pZb7kBBQSEAwOfz4tln5+LzzxehtLQEdrsDhx/+E1x11Z8xfPihAIC7756J/fv34Wc/OwvPPvs0amqqcdhhP8HVV1+HESNGRoxp7949eOSRB/D999/B5/Ni2LAR+MMf/ojjjz8RQODy0GeffRpffbUCq1atwLXX/insdnr37oM33ngPAFBSsh+PPPIQli37Fn6/D0ccMQpXXfXnNuOgzLN2Wzle/HgTpp85EocOLEp2OERERNRFdJ1j7hI8/vjDKXnMzYQ5EREREVk2f/5LGDv2WMyceQ82bFiHJ598FBs3rkePHj1xyy23Y9eunXjssYfRrVt33HjjDADA3/9+J9asWYU//elq9OvXH7t378LcuU/gzjtvx0svvQFFUQAAW7ZswlNP7cTll1+FvLx8zJv3JK655nK8+OLr6NGjZ6tYhBCYMeN6FBd3x1//Ogs2mw2vv/4qbr31Brz00hvo339AyPqHHjoSTzzxbMiy5cu/xbx5T+IXv5gKwxCoqKrE5ZdOh9PpwvXX34KsLBdee+0VXHXVZXj66ecxePCQBI0spZoHXvsOAPDPl1fjmVunJDkaIiIi6koy+ZgbAKqqKnHFFRen7DE3E+ZEREREZFl2dg5mzboXNpsN48Ydhw8/fB8HDx7EU089j9zcXBx//ASsXLkCP/wQSDbqug63243rr78Zp5xyOgDgqKOOgdtdjzlzHkR5eTm6d+8OAKirq8OcOQ9gzJijAQCHH34Ezj33lw0Hz39uFUtlZQV27NiO3//+Yhx//AQAwGGHHYFnn30Kfr+v1fo5Obk44ogjg7/v2rUT8+e/jClTTsPvf38xfLqJN15/BTU11XjppXno3bsPAGD8+BPxu9/9GnPnPoG77vpnHEeTiIiIiKi1TD7mBoBXXnkJ1dXVePnl1DzmZsKciIiIiCw77LCfwGZrOoTs1q0YOTk5yM3NDS4rKCjAtm1bAAB2ux0PPPAIAODgwYPYs2cXdu7cgW+++QoAYBh68H69evUOHrgDQPfu3XHkkaPw3Xerw8bSrVsxBg8+BP/8511Yvnwpxo8/AcceezyuueaGdh9HbW0tbr31BvTt2xe3335ncPmqVcsxfPgIdO/eA4ZhAAAURcH48Sfg448/bHe7REREREQdlenH3CtWLEvpY24mzImIiIjIspycnFbLnE5Xm/dZunQJHn74fuzcuQPZ2TkYOnQYsrMD25FSBtfr3r1Hq/sWFhahrKw07HYVRcGDDz6K556bh8WLP8OHH74Pm82Gk046GTfddCvy8wvC3s80Tdx55+2ora3FAw88CperKf6ammrs27sHkyePD3tfr9cbsj4RERERUbxl+jF3dXU19uzZnbLH3EyYExEREVHC7N27B7fddhMmTjwJ//rXg+jXrz8A4M03X8fSpd+ErFtdXd3q/pWVFSgq6hZx+92798BNN92KG2+cgS1bNuGzzxbhpZeeR35+Pm666baQdaWUEFLi0UcfxKpVy/HQQ0+gd+/eEFICDd8hcnPycNRRx4S9HBUIVO8QEREREaWSVDrmbtTymLu5vLw8jBlzNK6++rqw9032Mbea1L0TERERUUbbsGE9/H4fLrhgevDAHQC+/TZw4C6ECC7bt28Ptm/fFvz94MEDWLv2BxxzzLiw21679nucffbpWL/+RyiKguHDD8Vll12JQw4ZGrZCxjAl3n/vXbz22iu44YYZGD16DEwhoBuB/yAljhw9Brt27cCAAQMxcuThwf8++uhDvPfe29A0LV5DQ0REREQUF6l0zA0AH3wQeszd0lFHHYPdu3em7DE3K8yJiIiIKGEOPXQkNE3D448/gvPP/x38fh3//e+7WLIk0E/R6/UG15VS4rbbbsSll14BTdPwzDNPIS8vD+ecc17YbQ8ffihcLhf+/vf/w8UXX4Zu3YqxYsUybN68Ceecc36r9df9+D0efPCfOOmkyRg58jCsXfsD/IYJRQGkBIYNG4Fp087Hpws/wnXXXYnzzrsQBQUFWLToE7z33lu49tr2+zQSEREREXW2VDrmXrv2B9x337046aSTMXLkYfjxx7UhLWFGjDgU559/AT788IOUPeZmwpyIiIiIEqZ//wGYOfNuPPPMU5gx40bk5+fjJz85Ao888iSuueZyfPfdagwdOgxAYAKi8877HR5++AF4vR6MHXss/vnP6yP2RXQ6nXjggTl44olH8NBD96Ourhb9+w/AzTffjjPPPLvV+suWfQtd17F48edYvPjzVre/8NKb6NmrD5544hk88cQc3HffvfD7fRgwYBBuvfWv+PnPfxnXsSEiIiIiiodUOuZeuvSbhmPuz7B48Wetbn/99XcxYED/lD7mVmTzFD9ZZpoCFRX1nbpPm01FUVEOKivrYRii/Tt0YRyr6HC8rONYRYfjZR3HKjocL+uaj5XH40V5+X4UF/eB3e5Idmgh7r57JlavXok33ngv7tuWUsIwJYDAv6oCQFECPc2FhNLwMxBoZZ6T5QiUnMeRrvsjjn23bjnQNHZKbCnZx9u/v2thcPkzt07p1DjSBT+LreNYRYfjZR3HKjocL+s4VtHhMXd0bDY17q+rto63geiOuVlhTkREREQZTTQkxqEEfg/kwiVEw1yfstmkn43rUKilS5fi97//fdjb+vfvj0WLFmH9+vW4++67sXbtWhQWFuLCCy/EJZdc0smREhERERF1DBPmRERERJTZJCAhoaChklxRoMhmiXIlcDugMF8ewVFHHYWvvvoqZNmmTZtw2WWX4U9/+hMqKysxffp0nHrqqZg1axbWrFmDWbNmobCwENOmTUtS1MkhpcRDb3yPHJcNl579k2SHkza27K3GMx+sx29PHY4jDilOdjhERETUhTFhTkRERERJd8cdMxO2bSElGvLkTf+iqeuKIgFAgao0FZpTKIfDgR49egR/13Ud9957L04//XScc845ePLJJ+FwODBz5kzYbDYMHToUO3fuxNNPP93lEuYlFW58v7UcAHDxWYdBU9lux4r7XlkNvyHwwGvfsQUOERFRgiTymDuTMGFORERERBnLMAVEhPaIqgIoigJTBNLkmqbCNNmj04qXXnoJ+/fvxzPPPAMAWLFiBcaNGwebrenrxfjx4/Hkk0+ivLwcxcWxVQzbbJ2bbG7sa9myv2U0cajN7mu3aVDVzL1uIdJ4xcLfrI9pZz/vnSGeY9UVcLys41hFh+NlHccqOs3HS9My929/PChK07+JmFlT05QOH0swYU5EREREGUMICQkZrOo1hYRsqDAHGirLEWhVrqpKQ8JcQFX4xcYqn8+HJ554AhdddBF69uwJACgpKcGIESNC1mu8bd++fTElzFVVQVFRTscDjkF+flbI79HEUedvSvwWFuVAC5Mwd3t1PPnWD5g4ph/GHtYr9kBTRMvx6qhkPe+dId5jlek4XtZxrKLD8bKOYxWd/PwsOBwKDh5U45K4zWTxPhkjhAJVVVFQkA2Xy9WhbTFhTkREREQZQzcFICU0R8MBuARE4w+NPcqlhFSa+pXbWTkVlXfeeQc+nw8XXnhhcJnX64XD4QhZz+l0Aggk2GMhhERNjTv2QGOgaSry87NQU+MJWV5ZWW95G9XN7ltZWQdNVVHr9qOy1oeBvfIAAK8u2oxPV+zGpyt24z9/OTU+wSdB8/GK59UZ0Yx3ukjUWGUqjpd1HKvocLys41hFp/l4eTweCCFgmhKGwbFrSVGaruyMZ4W5aUoIIVBd7YbHY7a6PT8/y3KSnglzIiIiIsoojQfehikAyKbJPWXzdWTo9aBk2dtvv43TTz8dRUVFwWUulwt+vz9kvcZEeXZ2dsz7StaXzJaJgUhxbNpdhY27q3DW+EHB1itms3V1XUBqwNWzF0NK4M4/jMOg3nk4WNWUVM+EL9KmKeL6ODJhTCKJ91hlOo6XdRyr6HC8rONYRcc0A4lyiix45WeChikeJypYTkNEREREmaMhNy6khGGK4OSejcuCq0mAafLoVVRUYPXq1TjzzDNDlvfu3RtlZWUhyxp/79Ur/VuORPKPl1bhrcXb8NUP+9tcr/Glt25nRSdERR1V79Xx0bJdqKyN7eoIIiIiSm9MmBMRERFRRpAyUEYuJaAbTZd4qkrgP9mwIJg2Z2V51FatWgVFUXDssceGLB83bhxWrlwJ02y6/HXJkiUYMmRIzBN+ppPSCoutY1hwlhbmvb8e8z/dgvteXZ3sUIiIiCgJmDAnIiIiorQipIQpWl9m6TcaK8oDJeVCyOAMn2qriReZuYzFhg0bMGDAAGRlhU4ANm3aNNTV1eGOO+7Ali1b8Oabb+L555/H5ZdfnqRIU0OiLjWmxPpu60EAwP7yzu2hT0RERKmBCXMiIiIiSiu6IWAYDdXiDa1Xnn1uLq6/7k8N7VcCfcslJHbt3omzzzoZJSX7mlLkUSQxKysr8ctf/hSrVq0Ie/s//vF33H33zA49nnRy8OBBFBYWtlpeXFyMuXPnYvv27Zg6dSrmzJmDW265BVOnTu38IFMY8+fpQWHDJiIiorCee24urr76slbLd+3aiVNPnYD9+/fFtN1UO+bmpJ9ERERElPJMIaAqCpSGNiqyIfVoConX5r+MZ+Y9iSOPHBNcX0iJXTu24Y7bb4TP6w2kvxonGLK4z5KSEsyYcT3Ky8tbx2OaePzxR/D+++/gjDN+HvsDSzMzZ86MeNuoUaMwf/78zgsmLYS+2iRLzomIiChNvfbay5g370mMHn1UyPKtW7fg5pv/DK/XG9N2U/GYmwlzIiIiIoKUEn69Y7PJx8phV4OJ8HBMIWEYEjYN0LSm9faXlOBf/7obP3y/BgMGDATQmJBU8NKLz+HVV57DoEFDcOBAaeA2BCb6VBVAUdq+0PL999/Go48+jL59+7W6bceO7bjnnlnYt28vevXqHfXjpa4jlvy4lBJz3vwB2S4bLjnr8PgHlSQVNV489Mb3OOWY/jhpdN9kh9MmRQEvByAiorhL5vE20P4xdyQHDpThH/+4C99/vxoDBw4Kue355+fhP/95BoMHH4KystKot52qx9xMmBMRERF1cVJK3PviKmzZW52U/Q/rX4Dbfnd0GwfwsqGivOl2KYENG9YjNzcPTz39Il54YR5KS/YDADRVwcqV3+K222chLz8fN15/ZXCCT0UBFrzxMp5+6jE88uhcjBx5OIQQuOmGq+B21+PBh58Gshz4+usvceWV1+KYY8bh3HN/GRLNmjUrMWLEofjnPx/AX/96ayKGhLqw0koPVm8O9NCefuZhUDNkctpXP92C3WV1eO7DDSmfMCciIoq3ZB9vA1aOucPbuHED8vLy8Nxzr+C55+aGtF1ZunQJ/u///o68vHxce+2fQu738ssv4Mkn5+CJJ57BYYf9BEIIXHvtn+B2uzFv3vNQFC1lj7mZMCciIiIipHTL3paVnjLwpWP88RNx7PgJrdqtKAAeevgpAIED7UY2LdCZ+Nfn/Bbffvs17vvXXXjiqf/g9ddewqZNG/DEU8/DbrcDAO69934ACNuH8Ve/+nVcHx5lrliKlIXIzNJmn99MdgiWZcg5CiIiSjVp+vdlwoSTMGHCSWFve+yxuQAQtvf4eef9DkuWfIV77/0bnnnmJbzyyovYuHEDnnnmRdjtdhiGSNljbibMiYiIiLo4RVFw2++OTtmWLEFKIJkYmNQTCGTOAdn8rqGF6KF3b9iHqiiYceuduPSS3+Hf//o7Pv9sIW648Xb07z8Qppm8y2QpA3U0993G65kSiT1ZiIgovpJ9vA3E3pIlVqqq4o47ZuEPfzgP9947C4sWfYIZM/4SbKWYypgwJyIiIiIoigKnQ0t2GBFJCUACuikgZUMqS8aeT+zVqzeuvOo6/Ptfd+HECZPw05+dFdd4iWKVqWlambGPjIiIyJpUP95OhN69e+Oaa27Avff+DRMnTu7UiTs7ou3ZjoiIiIiIkkw2+zdQXS6hKo3LZUg7lmhSchs3roemadi4YR1qa2viGDFRQEeTxEwyJwdbshAREcXPhg2BY+4NG9ahpiY9jrmZMCciIiKitGCaja1YAFVVYNeaTQLa8K+mKlAtJLuWL/sW7737Jmb+7Z9wZWXhwQf+Gf+AqcuTsu3fo71/e7burcbXP+yP7k4paOXGA/hxe0WywyAiIqIOWrp0Cd5++w3cffe/4XK5cN999yY7JEvYkoWIiIiIUp6UgICEEBKK0tCGRVGgQAmpwVUtZMtraqrx73/fhTPP/AVOOGEicnNzccN1V+D4Tybi5CmnJ+ohECW8XvzuFwKT3PYozMKIAYUJ3lviPPrWDwCAZ26dkpT9s8CciIio42pqqnHvvX/Dz3/+K0yYcBLy8vJw9dWX4eOPT8KZZ6Z2O0RWmBMRERFRGpChPzX0TLBpCmxadOmtBx/4JzRVxeVXXAsAGDXqKPzil9Pw8EP/RllpSbwCJopNtGXlDbbvb7rEuaTCHa9o4ofdZYiIiLqUf//7Xqiqiquv/jMAYPToozB16q/xwAP/RElJal8RxwpzIiIiIkoJUkr4DQG7TYXaVhPh5ok3RYEC4Nbb/i/sqmPGHINFny0NWfZ/M+9ptd61f74Z1/75ZpimCFnep09ffPXVioihzJnzVOQ4qcuLMfcd9f2r6/34+/NNr1PZ0R13dSwxJyIiCuuOO2aGXX700WNbHTP//e//aLXeDTfMwA03zIDNpsIwmo67U+2YmwlzIiIiIkoJQgYm9DRNCaEEWq9oqgrZOK+njDwFosJZ+igdRJ3ItrZ+RY03hnt1rlSMiYiIiCgctmQhIiIioqQzTAHDCEzqKaSEYQrohoApBERDxlzIwKSe0bZgIUqejqWJf9gW48SXKZadrq7zpdUkngpLzImIiLo0JsyJiIiIKClkQ0U5AAghISGDBbhSBv4zTNlwGwBIqKrCanJKPRFeki3z1lby2M3XmfPmDzGFk2L5cjz4+vfJDoGIiIjIMibMiYiIiCgp/LqAv6F3oURjt4rGanIZrDQXUgKQ0FQeulKKSrEMdXs9zD9ZsRv/+d+GTut1vrO0Nqb7Ja0XO8/JERERdWnsYU5ERETU5aRKdk8CUoEpREO1eSAyEaw8VwLrNGSvVDV9s1ichLFrStbT3t5+X1m4GQBw7GG9MHJQUSdElF7S95OGiIhSCY//Olc8x5tlOkRERERdhKZpABT4fN521+0MgbpxGexd3ry63KapDf3KVSgKoKZ5Gxa/3wcA0DTWq2Qkqy/PFPve7PEbyQ6hTckarjT/uCEioiQLHHM3Hf9R54jn8TaP2ImIiIi6CFXVkJWVg7q6KhiGDpcrG6qqJaUnuJQShmFCykByKtCzXAZ/hxZa1yEBGJ2Q2zNNAV2XccvUSSnh9/tQV1eJrKxcqGwrk5kaXi8VNV58sGRHXLbVEcJqhVWKJfBTBzPmREQUu8Axdy7q6ioBAA6Hk3PwtCCEAtOMz4FIIo63mTAnIiIi6kLy87vBbneirq4KXm990uIwTAHZUE2uKkpTwhyBhLmWpPYrQgBuu4Z4ZxKzsnKRn98trtuk1POvl1ejrMoT/L3lpcGdlp/OkHz5g699h2t/PQo2jSeaiIgovTQe9zUmzSmUqqoQQsR1m/E83mbCnIiIiKgLURQF2dm5yMrKgRACQphJiWN3WR18hoE6t478HDv8uoTfMGEKAU3TUJzvTEpcNV4dPxnYA4ppxq3qRdNsrCzPdA3nd5ony4HkJaSt9vBM9daqa7dX4OEF3+OGc8dEdb+XPt6EvGw7fjFhSEz7ZQ0gERF1lKIoKCgoRl5eEUwztVugdTZNU1BQkI3qanfKHm8zYU5ERETUBSmKAk3Tgj0WO4PHZyDLGTj8FFKFITTowoQubNCFgW4FOdhf7oaECijJOUwVEIHLZk0ThhHfqhfKLNV1zfqSpljiWViOJ8UCD2Pttoqo1t9fXo9Fq/YAQMwJcyIionhRVRWq6kh2GCnFZlPhcrng8aTu8TZLXYiIiIgoITw+AzVuPwCgxu1HSYUbPn+gol1ICdGQ1TNNASEQ7O0orGf7iJLmPx9tbH+lFi9lK5XfLdf4cXt0CWMA+Gj5LkvrpXqFeSz8ese/eLPNLBERUdfGhDkRERERJURppRsHq7wwhYBpSpimhNtn4ECVB0JImA19C326CbMhSd6zMAvFBclpx0IUjZIKd9MvERKsLfPR7329AztLaqPaz/3z12DRyj34futBy/eprvPjQIv2MERERERkDRPmRERERJQwphDYVVqHyjofTCFQWetDebUXhgAar8A0TYmCXDuAwCWaDnvntYkh6kwSwKznlkd9v5c+2YQHX/8+qvt4fOyXSkRERBQL9jAnIiIiooQQItBeRZcmdFMGinANE35DwBQCwhToUeiCAgU2G+s4KAMlseeJlV2LDsb3/daD6NPTjx55mdWbVWFPFiIioi6NCXMiIiIiirtatx9SSkgpoZuAMAPl5IaiQDdMCBlI1tk0lckpylixpKOt9DlPBQeqPLjvlTUAgP/85dTkBhOBlJKfL0RERBQ1JsyJiIiIKO7KKj0wpYSQDZN6Sgmf34TDocEwAxN+di9wMZlFlKYOpmiPdBnTaQoiIiKiJrz2lYiIiIg6TEoJo6GKfH95PYSUMAwB2ZAwl0IiJ8sGv9+EkBLd8p3sVU4ZL5nF4uESx1JK7DlQ1+z3zoyo88X68Hgej4iIqGtjhTkRERERdVhlrQ/V9X4U5DhQ79EhpYRPN6EqCnRTwqYpyMt2IMthg6IAmsa6DUpv6ZhsXrRyD15euDn4e6RqbFMI1Lp1FOY6Oyu0uInH88J8ORERUdfGbypERERE1GE1bj9MU6Ky1ge/IeD1mfD5Tfh0EwqA4gIXAMBmU5ksJ+oE4RLHHy7d1WKl8Pf918urccOcr7FtX03k7Xcgtk6TFkESERFRquG3FSIiIiLqENnYq1wIeHwGpAS8ugmbTUW3PCeK8tOvSpW6Jikllq7dH7f+3LFM4JkKleub91QDABZ/tzfiOikQZrti7mfOnixERERdGluyEBEREVGHCClhmgKGkHD7TWQ5NBimQPdcF2w21mdQ+li58QAefuP7Tt2nlDIhk982T7w37qNlAr/5b4YpsHF3FYb3KwguE23lm5PZn11KbNxVhf49c5GbZU9eIERERJSRmDAnIiIioqgdqPLA5zeRk2VHbUM7FtMQEKaAFCp6FmYxWU5pZ8POyk7f521PfYsjhxTjd6ePANCBqugWGrfzwZId+HTVXtx+wTHhVgqa/+kWLFq5B8eM6NF0cyqUu4exdF0pnnpvHQpzHXjg6gkR14s1fNaXExERdW38FkNERERElkkpYZgCNfV+eP0mqut90A0BU0gYUkJKQDcFFB5lUhqKd3rYSsK2rNKDRav2RHWfaCz4Yhsqa31468ttrR5f8+T8opWBGFZuOpCwWOJl5cZAjFV1/iRHQkRERJmIFeZEREREZFl5tRe1Hh2GKQAAmgy0XzGFhBQSEhJZThs0lRlzymxWcsnxqhaPSYtdCylbLWsvId5WhXlSH1uiS8BZYk5ERNSlpcU3GV3XMXv2bEyePBlHHXUUfvvb32LVqlXB29evX48LLrgAY8aMweTJkzFv3ryQ+wsh8PDDD2PixIkYPXo0Lr74YuzcubOzHwYRERFR2tlVWos6jx783e0zYJoy0LdcSAjR+G8gaQ4A2S7WZBDFKl5V3eE2E+2m2+xhnkRt5bPjMX7MlxMREXVtaZEwf/zxx7FgwQLcddddePvtt3HIIYfg0ksvRWlpKSorKzF9+nQMHjwYCxYswDXXXIOHHnoICxYsCN7/sccew6uvvoq77roL8+fPh6IouPTSS+H38xI+IiIiokiElNANgeo6X3CZlIApBHRDwjADyXLDlPD4TJhmimbXiCyKKlFqJTMbxVuisZq7Myu3O1JhntQCc4uTpKZqSxkiIiJKbWmRMF+0aBF+/vOfY8KECRg0aBBuvfVW1NXVYc2aNXjttdfgcDgwc+ZMDB06FNOmTcMf/vAHPP300wAAv9+PZ555Btdccw0mTZqEkSNHYvbs2SgtLcUnn3yS5EdGRERElLqq6/zQDRGSF5MIJMl1w4RhmjBMAb9uQlGBbvlO9CjMSlq8RB0V9x7mUaz79+dXhG2bYkXY/HHYEvPoNt5WhXkyc9Ft5ctDb4stSqsJeSIiIspMaZEwLywsxGeffYY9e/bANE3Mnz8fDocDhx12GFasWIFx48bBZmu69Hf8+PHYvn07ysvLsWHDBtTX12P8+PHB2/Pz83H44Ydj+fLlyXg4RERERGmhut4Hr24AMlBpWufRYRgCQgK6IeDXA8lyv2GiIMcBp0OD3ZYWh5dEKWdHSS1KK9wxpXjD5cGtVKq3t45sI2PefJ9tVqJ3shQKhYiIiNJUWjSYvOOOO3D99dfjlFNOgaZpUFUVDz30EAYOHIiSkhKMGDEiZP2ePXsCAPbt24eSkhIAQJ8+fVqts3///g7FZevkL4Sapob8S5FxrKLD8bKOYxUdjpd1HKvocLysi3WshJTQNBW6IaGoCvYcrAcAuH0mbJoCTVNQkOuETzfRLz8XqpoZFZlaQ2UpX1vUHmuTfiZowzFquen2kssizAqmEHB7jfgFFYOOtmSRUqKm3o+CXGccoyIiIqJMkRYJ861btyI/Px+PPvooevXqhddffx0zZszAiy++CK/XC4fDEbK+0xk48PH5fPB4PAAQdp3q6uqYY1JVBUVFOTHfvyPy83mps1Ucq+hwvKzjWEWH42Udxyo6HC/roh2rsgo3cnNdyPIYyM11wjAlbJoKjyHgsNmgaBp6dc9NULTJ4zMD//K11TXFvVI6yu0FOrJEH0PYCvM4PJRw2/j78yuwq7QO5586vGm9ju8qKm1O+mkhmpc+2YRPV+3FZWcfjvE/6d16+5lx/o+IiIhilPIJ87179+Lmm2/Gc889h7FjxwIAjjzySGzZsgWPPPIIXC5Xq8k7fb7AxFTZ2dlwuVwAAr3MG39uXCcrK/YvQkJI1NS4Y75/LDRNRX5+FmpqPDBN0an7Tjccq+hwvKzjWEWH42Udxyo6HC/rYh2rgxX1qKz1ob7ehwPlCqQEnA4V9XU+eBQ/fLqJGpeWwMiTw+MOHEd25msrPz+LFe0EIJB4jiXRbTXJHu22w1WY7yqtAwAs31DWPIA29pmAdLrFhHakPX+6ai8AYMEXW8MnzGMMi4iIiDJDyifMv//+e+i6jiOPPDJk+ejRo7F48WL07dsXZWVlIbc1/t6rVy8YhhFcNnDgwJB1Ro4c2aHYDCM5X9BNUyRt3+mGYxUdjpd1HKvocLys41hFh+NlXbRjJYWE12fCNAXq3H6oigJVsUNKCZ8hYJgiI09WmA3JPb62KB46rfI6xh21l8y2mutuK2H/4/aKaEKyJF4JbU7uGeD2GthzoA7D+xdwTIiIiJAGk3429h7fuHFjyPJNmzZh0KBBGDduHFauXAnTNIO3LVmyBEOGDEFxcTFGjhyJ3NxcLF26NHh7TU0N1q1bF6xYJyIiIqLWDCGgKIDbZ8CUEqaQkBIwTQkHJ/ckal+0iWwpY6rIDnePVtsJOzFoe+F0vHK9zqtb2kZb9FYnr5qSum9/uQ0i0uSkMZ+x6FpJ4789vxz/eGkVvllbkuxQiIiIUkLKf9MZNWoUxo4dixkzZuDbb7/Fjh078OCDD2LJkiW47LLLMG3aNNTV1eGOO+7Ali1b8Oabb+L555/H5ZdfDiDQu/yCCy7Afffdh0WLFmHDhg24/vrr0bt3b5x22mlJfnREREREyde87UIw8aQoMM3Az0V5TgghoTdUlPcscqFbvqvVdoioY2LO71qtBLeQRG9z/RjEoyPLfa+uDvm9eRH0u1/vwJIfwyd622tVw2LqgLLKwLxfIW12iIiIurCUb8miqioee+wxPPjgg7jttttQXV2NESNG4LnnnsOYMWMAAHPnzsXdd9+NqVOnokePHrjlllswderU4DauvfZaGIaBv/zlL/B6vRg3bhzmzZvXaiJQIiIioq6mstaHylovBvTMgxAS+8rrkZdtR71Hh2mKYLLLFBL1bh1ZLo39tokAS0nqaHPF//12J9ZsPhhDKK33FI9EdaTCbSC0BlvKyDXZ4ZLuHp+BLKf1r6Kb91RH3DfQlPAN7NDyZqFEiJqJdCIioq4t5RPmAFBQUIA777wTd955Z9jbR40ahfnz50e8v6ZpuPnmm3HzzTcnKkQiIiKitOT1G/D6TewqrYXLaYNhCpRWeGAKCSElivKcAAKV59kuG3Kz7EmOmCg1WJloM9oK7W9/LI0tlhjarVhZx3JLFsiIyedwm7hq9mL860/Ho3thlqXtt9JiV5GibDd8JsaJiIgoDJYHEREREXVhqqpACIl6rwHdMKEbAjX1fqgq0L3AhSynDQ67ColAwpwTwhGlr1YJ5HYyym1VmIduJ/JN73y1PezypetjOzkAhKsMj62cnp9mREREFE5aVJgTERERUWIoSiBhbsjAhJ5evwFNU0IqyTVVRZ/inCRGSURRs1AdHq8K80gqa304WO3t0DbCallhHmuYEU4AdtXzgvFo40NERJQJWGFORERE1IUpAAwpASEDiXNTonsBJ/Qkiod4JyAjJbDDLW9VTB5uWbuTflqMK8Jyv2Fa20CU2spnN49lyY8luP2pb7G/vD7sumoXTYwTERFR25gwJyIiIspwbq8eTKiZQmBHSQ18eiCRJQEIU0JIQEgJm6ZAZRaJqF3JqMYNt8vKWh8WrtiTkP2JNh5kyKdEmNW+Wbsf2/bWxDWefQfrcdd/VmDt9oqQ5ZHCfPHjTSipcOOZ/66Paj+R+rFnuq5aWU9ERNQSW7IQERERZTDDFDh4oA6qEMjPdkA3BExTYn95PRQokAhM7qkoCnRDQDdFskMmyhhxz6lLtCqv/tfLq1Ba6bG479Cl7bVcsdqSpaLWiy17qjFuZE/YNBVb9lZj7vvRJamtePydtdh7IHy1eFt0PfznGudkICIionCYMCciIiLKIG6vgWxX6CGekIBpCFTX++GyazCFhKoqMIWAlIFWLKoK1Hl0mCab2BJFLdLbphPK0MMlywP7DrMoynCsTvp56xNLIGWg2v3M8YNQWuGObkcW1br1sMtl8wcbxWOMmC5nHp2IiKhLY0sWIiIiogzh9hoorXSj3tuUVJISkELC7TNQXu2F12/AFAIenwEhJXy6gGhImudm2dm/nCgGHy7dFWxzZMWu0rqY9iM7WLMerq95m+u3lWFvVp3duNoPW8tjisuqiPHEPOlnVItjtnLjAcxftBnC6hmIJOGkn0RERAFMmBMRERFliMbEd/Mq8T1ltTAbsiCGKSARaE/g9RuQUsKnGwAAh11FtssGh11LRugpoaLGix+3V6R8UotS0+rNB1oti/RKKomxAvuT5Xsw44lvcLA6QlV5yL7bb7/SXoJURNmhKdHvnFjz5ZFuj1xhHt+U+aNv/YAPluzEkrX747pdIiIiSgwmzImIiIgyhKIApgykyeo8OnaW1EI3mhLohilQWeuDgIRhSggB+PwC3fKc6JbftSvLd5XWYu7767FoxR5s2VOV7HAoDYWdKDLOGeTXPtuCA1VevPbZ1vZXDrfvKOOJuqK9IaO9bH1ZdPfrqJjHuXN7r1TWeOO2rafe/RFPv/dj3LYHcNJPIiKiRkyYExEREWUQYUpASlTV+qAbAoYQEA1looYpUevWISVgmoHJPp0OFS5n157WZv3OSrzw0SZ4/Sb6FGdjcN/8ZIdESRJNS4pktq8wLUzOaym8BDyIqjofftiW2NYsLTVP7IdL8kfKA0dKEKd63ri6zodv15ViyY+lIS24OootWYiIiAK69rcjIiIiogwiZaAtiwSgaQoM04QAYAoJYQoYphnIoqkKcrJsMNl6BMs3lOHDb3cBAA4dUIgpY/vDYdMA03o/aqJIkvkOC5f8bNWmpQPbD5dUlgA8PqMDW21bpB7m7SV6o23JkuqV1s0/u8Ne2UBEREQdwoQ5ERERUQYRQkJKQFUUmCLQosUUEn7dhG5IaKoCVUo4HTb4fF03KSylxBdr9mHxd4Gewscc2gNnHDcQNZ74VWsSHaxqv9d4MsW7olgCsGuJu4jZSrxRPaY0zTWLZg8y1ZP7RERE6YgJcyIiIqIM0TjpZ51HhykEhJSBf00Z6GUuBFRVg5SBPJHRRa+/F0Liw6W7sHJjYJLGSWP64qTRfaAw80QdEO7l88ibPyRoX1Zeq2FLzKMTw/paAhPmsYpYSZ6mGXNeHERERJRYqXc0Q0RERERR0w0RbMmimwJCBhLDpimDyfPe3bIhGyrOFUWB7IJZF90w8dpnW4PJ8jOOG4hJY/oyWW7R22+/jTPPPBNHHnkkzjrrLHz44YfB29avX48LLrgAY8aMweTJkzFv3rwkRkrhW7LET7i3THmNF+99syOOewkVKf7mjzVcXI03m0KgovnEm538to/XOcrmn93xPO/Jj0EiIqIAVpgTERERpbkatx/l1V7kZzsCPcyFhKIEkkNCSJgCgAwskzLQjkRVAFMCWhdKkNR7dbyycAv2HayHTVMw9aRDcNigomSHlTbeeecd3H777ZgxYwYmT56M999/HzfccAN69+6NwYMHY/r06Tj11FMxa9YsrFmzBrNmzUJhYSGmTZuW7NATJLVPOIVNmLdYFm6CzI6orPXh89V747rN5iL2MIe1BPL9r67Bhl1Vwd8j9zCPzwfjwWoPfthWEZdtNSdCHmT8nsMuetERERFRK0yYExEREaWpxqSJrotgKxajoW85JCBEIJVimgKmDFSVCyEh0fCzKaCpXeOCw4oaL17+ZDMqan3Icmr4zZRhGNgrL9lhpQ0pJR566CFcdNFFuOiiiwAAV111FVatWoVly5Zh2bJlcDgcmDlzJmw2G4YOHYqdO3fi6aefzuCEeWqzlAxPswRpR8NtniwHYk+Mf/jtTpRUuPGHM0a2uY2/zl0Gnx7/uSJE8wrzuG+diIiImDAnIiIiSlM7S2oBAC6HBt0QUNVAEtw0BUxTBhPqNpsKtyfwc48iFxQELr0XUsJp05IVfqfZe6AOryzaArfXQGGuA789dTi6F2YlO6y0sm3bNuzduxdnn312yPLGtiuXXnopxo0bB5ut6evF+PHj8eSTT6K8vBzFxcUx7ddm69wTOpGSn+HiaJmo7Mze3ara/tioqhKyjqooaBm10mKdVpTEPQct42uPKWTE7LCqNG1LC3PZjBLhcUQax+Yvg3C3v/75VgDAlGP6Y2i/gogxh0uWx+N1ojbbhqapcXuOIo1TMjSOUyr2xE81HKvocLys41hFh+NlXTqMFRPmRERERGnGMAVURYGUEl6/CdMU8BsCDrsKKQG31wj0MxcSiqqgZ2EWtIZMk63ZgWnvbtlQ1czuybJlbzVe/2wrdEOgT3E2zjtlGPKyHckOK+3s2LEDAOB2u3HJJZdg3bp16N+/P6644gpMmTIFJSUlGDFiRMh9evbsCQDYt29fTAlzVVVQVJTT4dijYXeEP4EULg61xdUZubnOhMQUjt1ua3dscnKcEM1itDtsCDQhaco6u1z2NrejqmrE2232jp1s232gHgUF1j+DFq7eG7HHtsPRNB65FZ5Wt+8qrQv7OGw2LfzyZp+TLW83m1V35+a5on6N5ud3/GRdlccI/lxQkI38nPh8ptntrcfj+y0HsHxdKX5/5mGwJ+EEazzGq6vgWEWH42Udxyo6HC/rUnmsmDAnIiIiSjO7SmvhdNgghIRfN2EYgYS54dAgIeHXRbCCPMumQdNUuBwaTFOEbCfTk+U/bq/AW19uhxASh/TNxzknD4Wzg0m+rqqurg4AMGPGDFx99dW46aab8NFHH+HKK6/Es88+C6/XC4cjNGnndAYSyD6fL6Z9CiFRU+PuWOBR0v1G2OWznv4G1/56NKSUKK30oGdhFkSL91N9XWyPMxa6bqCysr7NdWrrfPjD3z4O/u73G616gHs8/ja3I0wR8Xa9g61Gvl1bgpf/tw5nHT/Y0vq79tXA6w+/T69PD8ZZW+sNu055eV2rZWaEx9e85UnL2z2+pteIr53xC6emxtPqszhaVVVN74vKynqYfr1D22uk62arx3PH498AAJw2xfJzFQ+apiI/Pysu45XpOFbR4XhZx7GKDsfLumSNVX5+luWqdibMiYiIiNKIkIEkuZSAkIFqR6ECAoFJPnOy7Mhx2eD2Gshx2TpcBZqulm8ow4ff7gIA/GRIN/xqwuCUvuwz1dntdgDAJZdcgqlTpwIADjvsMKxbtw7PPvssXC4X/H5/yH0aE+XZ2dkx79cwOvcLp4jQ8mPFhgOorfdj2fpSPP+/jTju8F6tuoOYke6cAFK0PzZmi9vDTZhpmrLN7QgZ+XYZh8f78bLd+Om4gZbWXfJjScTbhGiKM9IXb90Ik2yX4cex+VC1vL3O3ZScVsLc3hYJCdMUMAyB1ZsO4JE3f8DPjhuIc08eZnkbAKA326dhiLi9T2SE8QCA0gpPp78fAQTHi9rHsYoOx8s6jlV0OF7WpfJY8VsDERERURrZU1YHUwSS41LKQK9yUwJSwhSBBI5NU5Gf44CmqQ19i7sOKSUWrtgTTJaPPbQHpk4cwmR5B/Xu3RsAWrVdGTZsGPbs2YPevXujrKws5LbG33v16tU5QXaC97/ZCQBYuq40qXFIREgANxPNZJOrNx+wtl8Z38km4/Xx1DyWMOcFIi+PYf/xmMTT6zfwyJs/AAD+t3RX1PdPyqSfkQaWiIgoA/GbAxEREVGacHv1hv7kEkI09C8XErohIIRs1SKiqzFMgbe+3I5v1gYqUScf1RdnjB+Y8a1nOsPhhx+OnJwcfPfddyHLN23ahIEDB2LcuHFYuXIlTLMpmbhkyRIMGTIk5gk/k6LdpGCzVh21oS1YOvNVtmrTAVz70FfwRmghAwDzPljfalnLh9f46yMLfrC033inTOM2ZjLsjzHvv61Evq9ZW5hYc8g+vWOf1SFXMzCRTUREFHdsyUJERESUBqSUKKlwB1qwSMBvSJhCoFe3LJhCwO0zYZgiZLK6rsTjMzD/0y3YVVoHVVHw8xMGYczw7skOK2O4XC788Y9/xKOPPopevXph1KhR+OCDD/D111/jueeew7BhwzB37lzccccd+OMf/4jvv/8ezz//PGbNmpXs0OOqE7uutMunm9i8p9ry+lIGWoI0ZxgCG3dVWt5GSXm8e8on4DRDhARyuMWx7L35SYpwbW6s6Ghlfbwr/S3pYlcrERFR18aEOREREVGKK6t0w+0z4NMDFeVSSvgMARWBiTtVVUOWBKrqBWxdsGV5RY0XLy/cjIoaHxx2FedMHoqh/QqSHVbGufLKK5GVlYXZs2ejtLQUQ4cOxSOPPILjjjsOADB37lzcfffdmDp1Knr06IFbbrkl2O88bbSRFBQpWMnb0Uls3/tmB977Zoeldf26ib/MXRr8PVVbskRcJ2zGPHwAbYUlYqxmt7p9K5pXmHfayzIFX/9ERESJwoQ5ERERUYqrdetQFEDXBQRkYMJPUwAtqsmFKSDtXavCfOu+arz5xTZ4fCYKchw4/9Th6FmUleywMtb06dMxffr0sLeNGjUK8+fP7+SIOk9got3UShpGkzCvrPV1KMvt9rVo/5JKQ2Gh4rqjLcx/2FaOWrcfRbnOtjfalob1lTbOFPj8Jj5dvQdHD++BXt3CT5grUulSByIiogzEhDkRERFRCjNM0TDBZ+Bn3ZSwawqElFCbJU3sNhVOh4ZcV9c5vFu2vhT/W7obANC3ezbOmzIcudn2JEdFaa2NhPhbi7ehus7ficG0z2azfoJsy17r7VusaNneJRYJmfTTykrt7T/M8tmvBfr3//6nh7a/rw5444utWLRyD978YhuevuXksOuEVpgzeU5ERBRvXecbFREREVGKc3sNZDm1kOrDPQfqYJiBST6NhgpXIRXYNBWm2ZQoURQFRXnOcJvNOFJKLFyxB0t+LAUAjBneHWceNzCq5CFRtD5bvTfZIbSS7l2llTg9AmkhY/7wgu9b77/hs9YwBdZsPohDBxYiL9vR5r6q65tOmiSih3ljT3mzjSpykYyWLERERF0IE+ZEREREKUA3TJRWulGU50Rhs0v+hQhMzGdKCWEKqGogad6j0JXEaJPHMAXe+WoHftxeAQCYcnQ/nHhk7zZbHBBlqkTnSttMxqZUorZ5S5bwga3fGXly0/e/2YF3v96BXkVZuPfy49tM5IdMuJmAMbCyTSHbf7xEREQUOybMiYiIiFKAkIGqwao6HwpznRBSQjdEoLJcCEAGEikKGv7tgglij8/Aa59txc6SWqiKgl9MGIxRQ4uTHRZRlxSXNG28WrJY6skSZvcN+1+x8QAAoLTSE2H78U1Kt7U5K33yzXjMPEpEREQRMWFORERElAJq3f5gEmRHSQ0AwDQldNOEYUooigIJCVVRYZhdL0NysMqDVxdtQUWtDw67inNPHoZD+uYnOyzKMB15ZyVlQtAE7zPRj6gxX15e7U3wntref8u8fcvzkc2HOeTnKEco0trl1V4U5Dpg01RYmc8zpCVLVBEQERGRFUyYExERESWZx2egrNIDVQEABVIqUBQFFbU+SCFhCglFCaRFivKc8OtmUuPtbFv3VuONz7fBp5soyHHgvFOGoVe37GSHRRSiq/WSjsvjVRRs3FWJf768umOxRPjZyv6tLA5tgdJsX3F6zm9+/BsAwL+vOMFSNXvMj5eIiIgsYcKciIiIKMlMIeHTTdg1FcKQ0DQlUF1umOhRmAWbpuBAlRc5Thtsmgqb1nUmt/xxewXeWrwdQkoM6JmLc08eipwse7LDImol3m07rPj6h5JO32eTjj9eBcDi7/bFNZRongarHWFkhJ4v0T7ljetHeq3MfHYZspy2ZuvLsO23ZKSSdyIiIooLJsyJiIiIOklZlQemKdCnOCdkuZQSUkr4DROGIWGzKYAEuhe4YLcFkuM9i7KSEXJSrd50AO8v2QkpgSMO6YZfnDi4S50s6CghBFSV4xWNjrTUTkbe8n/LdiV2B208qHg8XkWJT4W0jLHmOpiMbvXEhy4QotnWLbRkWfDFVuRlOyLuN1KE9V4DLkfTV/QVGw9g3Miere+fhHw50/JERNSV8AiaiIiIqJO4vTo8vtB2KpW1PpTXeCEl4PMLGEI0/Cu7bHJYSolFK/fgvW8CyfKjR3TH1IlDuux4xOrXv/41Fi1alOww0kra9TBPopR6tLFWmIc5Q2KYotWyiM9tmMX7y+vxwZKdeHXRZkv7a73Jpo1u2l0Vfh3O+UlERJRQrDAnIiIi6iRCNl1Kb5gCQkhU1voABBIg3QqcsKkKPH4TtfW6peRKptENgbe/3I71OysBABNH9cHko/qGbUtAbdu9ezdyc3OTHUaXken58paPT1qZndLShuO7iVg21/zT5bkPN7TZw7y00tPmvvx664R7cP3GO7QRZMiEnhFeVBLtr0NERESxY8KciIiIqLNIGUyY7DlQByEBQwgIU0IAcNo1AEBuloocl63LJYnrPDpeXbQF+w7WQ1UVnH3CIIwe1j3ZYaWts846C08++ST69u2LAQMGJDucjBepPUemaJmYNeOQqFXj9BkXayjh9v7N2hIc0jc/4vZXbChrtjy2Hbd1LyvV48nIkXetv0ZERNTVMWFORERE1EkCfXCbMh3CFPD6zED2o0W1ZldLlpdVevDKws2orvcjy6nh3JOHYVDvvGSHldZ27NiBFStW4PTTT4fL5UK3bt1CblcUBQsXLkxSdJknEwt923pIIh4V5nHqYR4iqpYsjZ+zoZ+3LT99Iz3WcM952x/dwVk/I64RbWufeL7u2jrpk4EvbyIiooiYMCciIiLqJLIhHVHn0WGaEoYpYRhmIDvTtfLjIbbsrcYbn2+FXxfolu/E+acOR3G+K9lhpb0+ffrg7LPPTnYY6aUDWcFMb43R8uGZcWrJEp9xa9aiJAGp3c7sTx+yq0it05ut5PYaiQ2owRdr9sFp13DeKcM7ZX9ERETJxIQ5ERERUYIJIaEbAlJKCAEcqPKgzqPDpqkwhISqKOhZlJXsMJNixYYyfLh0F6QEBvXKxblThiHLyUPUeLj33nuTHUKXkuH58laJ7XhUmMfrPKGM+Es7+1dC/226IfTXyBXmgeWfrNgNwxQ447hB7VwdpLQbYvNxjtjDvNnif7+6Gk/eNLmNLVqntPOMfLx8NyYc2Qf9e3JuBCIiymz8NkJERESUAPVeHQeqPOjfIxflNV7Uew0IAfgNASEl/EZgYjjTlICa5GCTQAiJT1bsxtJ1gX7Ao4cW4+cnDIKmdcHBSLCtW7fi66+/RllZGS688ELs3r0bI0eO5ISgcZbxFeYtfo9PhXmcUuYW+n53aPMRK70Bv27ilYWbAQAnHNGnnUck29weEFrNHrGHebOfdSPyJKOJ4DPMTt0fERFRMjBhTkRERJQAHq8Bt9eAboiG9iuBRLlumPDpgGEISATaB5idm+9IOr9u4s3F27BpdzUA4OSj+2HCkb27XN/2RDNNE3feeScWLFgAKSUURcEZZ5yBRx99FLt378aLL76I3r17JzvMjBGnDiWppY1EdFwqzBPwlo/mvEXjZ07rAvPQJZFassgWt+l628nkee/+iPXbyjFt0tCI6zQf1siJ+vi82DbuqsSLH2+K6j7tVaETERFlApbwEBERESWCEkh2lFa64TfMQIJcBqoyFQUoynPANAUKcx0oynMkO9pOU1Pvx3MfbsCm3dXQVAXTJh2CiaP6MFmeAI8//jjee+893HXXXfj666+DSbYZM2ZACIHZs2cnOUJKdSH9wFu2ZIlD0nZ3WR2WrS/r8HZijSTip07LlixtlJiv21EZGkc7H2XfrC2Bv43EurTQxDxeFzP88+XV2HuwvtneMvGsDxERUfRYYU5EREQUB3rDZep2mwYgUIVnCgnTlNA0BUJI+A0TQkh0y3fCpqno08V6de8vd+PVRZtR69aR47LhN1OGsRduAi1YsADXXnstpk2bBtNsStCNHDkS1157Le67774kRpd5OnNiyGRoNemnmTqP10qSOSylxb/hf41YTb/nQD1eWbS5zftGSzS74igjr1ogIiJKA13rWxoRERFRguw9WA8pgSF98gEEWg1IGZjsU9M0GELArwfasmhq16um3rS7Cgu+2AbdEOhR6ML5pwxHYZ4z2WFltIMHD+Kwww4Le1uvXr1QU1PTyRFltgzPl7fuYZ6iDziqliwWl0faZstkudVdW530M9KKiTo5Y6XdCi8GIiKiroAtWYiIiIjiQMhAUqWx0lxICVMGepf7dRO6ISGkRN/uOV2u/cjy9WWY/+kW6IbAkD55mH7mSCbLO8GgQYPwxRdfhL1t2bJlGDRoUCdHlPo6kobM+Ek/W7ZkSaHy55iHPtjDvO3P5GgS1B19GVi6f4KGni1ZiIiIAlhhTkRERBQjIST2HKhD727ZgAwklHaX1aMgx4Fatw4pJPzShCkkDEMg29W1Dr2klPh4+W4sXRfoUTxmWDHOOmEQNJU1G53hoosuwv/93/9B13WcfPLJUBQFO3fuxNKlS/HMM8/g1ltvTXaIGSXD8+WtpFTCPMLPMWt2UtMwBQ5UeSwGIjtc/d38xIQvQq9zKydnftxRgX0H63Ha2AEdioeIiKgr6lrf2oiIiIji5ECVBzZNhWFKVNb5IKWElIE+5bUewO0z4NcFFAVQFQkJicLcrlNV7ddNvLl4OzbtrgIATDm6H048sneXq65PpnPOOQcVFRV44okn8Morr0BKiRtuuAF2ux1//OMfcf755yc7xIySiRXmjQ/J7TWgGyLkNjOFEubNz1ZE8zworX5o7Z4XVmJHSa21MGDtxElbMTa/ZfmGMlzRzjott1vn0ZGX7cD9r64BAAzokYuRg4raD4qIiIiCmDAnIiIiikGtW4eqAkII1HsNSBloy2I0TISn6yYkJHp3y0ZJeSC53lXUeXS8unAz9pW7oakKfjlhMI44pDjZYXVJl19+OX73u99h9erVqKqqQn5+PkaPHo3CwsJkh5ZxMnXSzzqPjmsf+jLZYbQp1tx9pPN3zRdbTZYDsFze3tFzDZFeam8u3oYPluzEpT8/PLisvMbbsZ0RERF1QV3nmxsRERFRHAkRqLY0hYSumwAC/cpNU0CIpsS5oijo1S0LPQpdSYy285RXe/HMB+uxr9yNbKcNF/3sUCbLk6zx6gdFUeBwOGC325MdUkZa8MW2ZIeQEBt3VSU7hHat2XwQT737I+o8ekz3j+d1L5ZOnHS0bUuEzPwHS3YCAF5euCm4LJqLeqxM+klERNQVxFxhXl9fj5ycHADAhx9+iJKSEpx88skYPHhwvGIjIiIiSklCSphCwoZAwlw09C/3+gOV5oYQIf19VbVrJCF2l9Xh1UVb4PEZKMpz4renDUdxftc4UZCKpJR46KGH8J///AcejyfYBiIrKwtXXHEFLrvssiRHSOkgHaYcEFLi23Wl0FQlLifoNja0koqW5ZYsMW3d+ga0GK9okgj0YN+2twYDeubC6dBarcOuWkRE1BVEnTDfvn07Lr/8cpx55pm47rrrMHv2bDz11FOQUuLBBx/EM888g2OOOSYRsRIRERElnZASFTXeYP9eIQHTFNANCVMEkpR+v5la/X07wY/bK/DOV9thmBJ9u2fj/FOGIyeLlczJNGfOHDz99NO44IILcNppp6G4uBgHDx7E+++/jwcffBDZ2dm44IILkh0mpTgtjU74lVS48ZNDuiU1Bis91Dvavae9Kvbmz1m0VeOfrtyDlxduxrB+Bbj9Qn6vJyKirinqhPl9990HTdNwyimnQNd1vPLKKzjjjDPwt7/9DbfeeisefPBBvPDCC4mIlYiIiCjpdpbUQiJQWS4loBsmJAItWIQpIGSgNUu3fGeXqCyXUuKLNfuw+Lv9AIDh/QswbdIhcNhbVyZS51qwYAGuuOIKXH311cFlQ4YMwbhx45Cbm4vnnnuOCXNqVzp9jvkNEVX5tikk9hyoi65PeRukjNQspfV6iRRykiPKp6/xs3zL3uo4RkRERJReor5Wa/ny5bjhhhtw5JFHYsWKFaitrcVvfvMb5Obm4rzzzsPatWsTEScRERFRShBCwuc3YJgCXr8B05SQIpAsV1UFvbtlozDPCadDg92WBr0MOsAUAu9+vSOYYDnhiN74zZRhTJaniMrKShx11FFhb5s4cSIOHDjQyRGlvkQnMtONlICWRj04/IaIav2VGw/g/+Yti2sMnVNh3vbtzVuyRPvspdHTTURElDBRf4vTdR0FBQUAgC+++AJZWVnBFiymacJmi7ktOhEREVHK8voNVNR4IaWEz2/CMCU8PiNYbS6kRE6WLa2qMTvCp5t4ZeEWfLelHIoC/PyEQTh1bP8u8/jTwfHHH48PPvgg7G3ffPMNjj766E6OiNJROr2na+p9WLkxuSeCOuWcSxQtWeI9jycnBiUioq4g6uz2oYceio8//hiDBw/Gf//7X0yYMAE2mw26ruOll17CiBEjEhEnERERUacLTORpIstpQ0mFG6YZmOBTINC33DAATVMghETf7jnJDrfT1Ht0vLxwM/aXu2G3qZg26RCMGFCY7LAIwNtvvx38ecyYMZgzZw4OHjyIM844A927d0dNTQ2+/PJLfPTRR7jtttuSFyiljXRKmHt8JlZuSl7CvHEC6PbX61hWvb27p1PfeSIiolQUdcL82muvxZVXXomXXnoJDocDl156KQDgpz/9KQ4ePIgnnngi7kESERERdTYhJeo8Og5WedEt3wkhAI/fhF1TABnoX96zmwsHKr0dTn6kk6o6H178eBMqanzIdtnw21OHd6mTBanu1ltvbbVs8eLFWLx4cavld955J84999zOCIvSmMoeHZbJhr8N7a/Xwf20c3vzhPmydWXYe6Aev5wwBDat/QvM+WwTERHFkDA/4YQT8N577+GHH37A6NGj0a9fPwDARRddhPHjx+PQQw+Ne5BEREREnW1HSS1UJTCpp083oRsmvD4DfhUQApCQ0FQV2S4bbFrXSDGUVXrw0iebUOvWUZDjwAU/HYHifFeyw6JmFi1alOwQKKNIZlCjIC1WmFubGrSt/bTTkqXZ36Q1Ww5izZaDyHHZ8bPjBra/8Xae71nPLcfsayagIMdhJdSYfLh0J5atL8M/rpqQsH0QERG1JaaG4wMGDMCAAQNCll100UVxCYiIiIgo2Uwh4PMbyHLa4NMFfH4TPr8J3RBQVUBRm6r08hOYNEglW/ZU440vtsKvC/QodOF3p43oMo89nTQWsxDFTde5gKbDpJSwNO1oRyvM27n/1r01rZaVVro7ttNm3v5yGy762ci4ba+l1z/bCgB464ut+Pl4C0l+IiKiOLOUMI+2v+G9994bUzBEREREqUAICVME+tH6dROmkPA1JMt7dctGWYUn2SF2qpUbD+C/3+6ElMDAXrk49+RhyHZxovd08NFHH2HVqlWoqWmdQFMUBffcc08SoqJ00oU6TnWY5QrzBLdkSTS/bum0QIfpRufsh4iIqCVL33SWLl0a8ntZWRkMw0Dfvn3Ro0cPVFVVYffu3XA4HBg5MnFnmomIiIgSzTAFDFNCCAnR8K+UEgqAnkVZAABTJDtd0TmklPh89T58+f1+AMDoYcX4+fGDoFnog0vJd99992Hu3LnIzc1Ffn5+q9sV9qYmCzraPqQrkZCWstmJbsnSEfvLo6tEl1Lys4SIiDKOpYT5p59+Gvz5vffew3333YdHHnkEo0aNCi7fsmULrrrqKpxxxhnxj5KIiIioE1TW+lBV54OiAEIKeBuqy71+E5qmBJMCLqcGhy2zk8amEHj/m534bks5AGDSmL44aXQfJkbSyFtvvYVzzz0Xf/vb35IdCjXjsKnwp1HlLCvMrZMycGWSlfVSkRQyqqrulz/ZhJWbDmDWxcciN8se93j414aIiJIl6m96s2fPxo033hiSLAeAYcOG4c9//jPmzp0bt+CIiIiIOotfN1FZ64NpCvj1wH9urw4JCb9hItvZVGdQlOdETgKSA6nCp5t4deEWfLelHIoCnH3CIEwa05fJ8jTj8/nws5/9LNlhUAspmisNSyKx1cyZxmpLlmc/3NCh/YgEPSfRXj21cOUeVNb68PHy3QmJh4iIKFmiTphXVlYiLy8v7G02mw1ud/wmEyEiIiJKJI/PwK7SWlTV+VDvNWCKQDsWn27CMAVcDg2apkCYEmpmF5QH1bl1/Od/G7F1Xw3sNhXnnTIMR43okeywKAann356yJWiRNEKJICTHUX6kFJaGq/Siui+M2/YWYm/Pbcc2/c3zEWQYs9JvVdPyHZ5jpaIiJIl6tmaxowZgzlz5mDMmDEoKioKLi8rK8MjjzyC4447Lq4BEhERESWCKQRKK90wTYmySjdURYVpSphCQjdMFOU5keW0BXuaq13gm3tJuRvzP92C6no/sl02nH/qcPTrnpPssChGt99+O8455xxceOGFGD16NFwuV8jtiqLgqquuSlJ0XVe6TWTICnPrpIx/z3e/buJfr6wGANz36mo8ev2khOXLrT/VoSu6vUbENU0h8NS76zC0bz5OP3Zg7MERERF1oqgT5jNmzMAFF1yAKVOm4KijjkJRURHKy8uxevVqFBQU4PHHH09EnERERERxIaUM9iV3ew1oqoI6j44clx2mDLRfkRLIamjBoqkKfH6R8e1I1u2owDtf7YBuCBTnO3H+qcPRLd/V/h0pZb3wwgvYvn07tm/fjuXLl7e6nQnz1pgbbo1DYp2wWGEejT/d/0XwZ4/PBJD8kxgt995WhfnqTQexfEMZlm8oY8KciIjSRtQJ85EjR+KDDz7Ac889h1WrVmHv3r0oKirCxRdfjIsuugiFhYUJCJOIiIgoPsprvKh16+iW74JhCAgtUFmumwJSAn5dQFGbkuP5OQ64HAKqmpkJcyklvlizD4u/2w8AOKRvPqZNOiR4woDS14svvoizzjoLt912G7p3757scChN8SSCdV9+vw9jhiX+vZao5yTWzfr8ZsTbPP7I1efJUF3vR1WtD4N6h28zS0REBMSQMH/iiSdwyimnYMaMGYmIh4iIiCihvH4z0HrFFPAbAnYEqgLr3DoUJfCzKpuS44qiwOnQkhdwAgkh8cGSnVi9+SAAYPxPeuHUY/pn7MmBrsbtduPcc89lsjwKGX4hSdQqa31xbzGSyb7+oQSjh6ZxwjwRG06xl8/1j3wFAJg5fRwG9mLSnIiIwot6+qq5c+di//79iYilTW+//TbOPPNMHHnkkTjrrLPw4YcfBm9bv349LrjgAowZMwaTJ0/GvHnzQu4rhMDDDz+MiRMnYvTo0bj44ouxc+fOzn4IRERElCRSSlTV+aAbgSo4UwSS5YYpYDZcQt+nezZ6dcuC3aZ2iYpK3RB4/fOtWL35IBQF+PkJg3D6uAFMlmeQE044AUuXLk12GBSDP5wxMtkhBJWURzdBZVfXGX8+EnUSIxFbTdU/pxt2VSU7BCIiSmFRV5gPHjwYmzdvxkknnZSIeMJ65513cPvtt2PGjBmYPHky3n//fdxwww3o3bs3Bg8ejOnTp+PUU0/FrFmzsGbNGsyaNQuFhYWYNm0aAOCxxx7Dq6++invvvRe9evXCv//9b1x66aV4//334XA4Ou1xEBERUefw+AwcqPJgQM9cKIoCr99ERa0PlbU+KApgComaeh+yXDaYpggmHxRFQfcCV8YnzD0+A68s2ow9ZfXQVAX/b9IhOGxQUft3pLTyy1/+En/5y1+wc+dOHHXUUcjNzW21zq9+9avODyyFRfveL8h14I8/Pxz3v7omrnEM6ZMf1+11xPqdlckOIa10Rn/xpFeYZ8DfyGT3gSciotQWdcJ88uTJmD17Nj777DMMHz4cxcXFIbfHe/IgKSUeeughXHTRRbjooosAAFdddRVWrVqFZcuWYdmyZXA4HJg5cyZsNhuGDh2KnTt34umnn8a0adPg9/vxzDPP4Oabb8akSZMAALNnz8bEiRPxySef4KyzzopbrERERJQaquv9MMxA5biiBJILwhQQABw2FYYpUevW0b3Qicoaf8h9FUXJ6LYMVXU+vPzJZhys9sLl0PCbKcPYyzVDXXvttQCADz74AB988EGr2xVFYcI8DnJd9rhvM5U+gzRedRIV0RmJ2ETtIsbttvUK6ch4tDfZtikENDXqi+YBdNLzREREaSvqhPmcOXMAACtWrMCKFSta3R7vhPm2bduwd+9enH322SHLG9uuXHrppRg3bhxstqaHMn78eDz55JMoLy/H3r17UV9fj/Hjxwdvz8/Px+GHH47ly5d3KGFus8X2xzlWmqaG/EuRcayiw/GyjmMVHY6XdRyr6LQ3XpqmACqgagpsmop6nw4oSiBpLgBAQlEAh90GRfFD09SMHfvGZIKmqiitcOOFjzai1q0jP9uOC356KHp1y05yhKlFa0jQZMLrYdGiRckOoUtw2OP/WmkvUdiZ2KYpOp2TL09uS5ao9m61aF3KqF7363dU4N+vrsHvThuBU47pH01EAAJzeBAREUUSdcJ8w4YNiYgjoh07dgAITFp0ySWXYN26dejfvz+uuOIKTJkyBSUlJRgxYkTIfXr27AkA2LdvH0pKSgAAffr0abVOR3qxq6qCoqKcmO/fEfn5WUnZbzriWEWH42Udxyo6HC/rOFbRiTRebkNCKF4UFGRDUQC12ofsnEBrFkgJh9MB1SaQn5eFGo8BKYH8vMwe+/2VHjzzwXp4/SZ6dcvG5VOPRFGeK9lhpRxfoM19RrwX+/Xrl+wQ0o5Niz457HJE/ZWqXamUo2aFeXQ6IxEbS1LeyrPYsk2J22tEv6OW27Swzo6SGjz4+vf49aShmDCq6Xt7W/nzJ9/9EQDw0iebYkuYM19ORERtiPvRXW1tLfLy4ndZb11dHQBgxowZuPrqq3HTTTfho48+wpVXXolnn30WXq+3VR9yp9MJAPD5fPB4PAAQdp3q6uqY4xJCoqamcyfA0TQV+flZqKnxwDRFp+473XCsosPxso5jFR2Ol3Ucq+i0N17V1W5U1/rww6ZAuxW314ApJHy6CcOUsGsKXE4bamo9gBCw29TAzxlIU1Vs3luDF/+3HqaQGNgrF789bQQ0yIx9zB3hcfsAoFPfi/n5WQmpaG+8MrQtV199ddz3m84mH9UPn67aG9V9nKwwp2bMBGdihZT4bHXr12g8enK33MRrn23p8DatZPefeOdH1NT78cx/14ckzKPxxZq9KMpzYtTQ7tbCYsaciIjaEHXC3O/347nnnsOyZcug63rwD7OUEm63G1u2bMF3330XtwDt9kBPwEsuuQRTp04FABx22GFYt24dnn32WbhcLvj9ob1Hfb7AF53s7Gy4XK5g3I0/N66TldWxyiHDSE5CwzRF0vadbjhW0eF4Wcexig7HyzqOVXQijZdhCPj9JqSQMEwJwxTw+U0IKZGbZUdutj14/xyXLfhzJvr2xxL879tdkABGDizE1JMOgd2mZuzj7Siz4dg2E96LbSXMc3Nz0bNnTybMW+jXo/XEqG1RADjsWtzjSKUUdTIrzPv3yMWeA3VJ238sEl1hvmrjgbDLo8mXr9lyEB8s2dF6Gy1+31Vaa32jEVgJyzQ7NmZ7DtTh+f9tBAA8c+sUS/dhD3MiImpL1Anzf/3rX3jxxRcxYsQIVFRUwOl0olu3bti0aRN0XY/7QXfv3r0BoFXblWHDhuHzzz9Hv379UFZWFnJb4++9evWCYRjBZQMHDgxZZ+TIkXGNlYiIiJKnsf+paKgk9+smFEVBvVeHpipwOTXohkBOVvzbJ6QiKSU+XbUXX/8QaE83bmRP/PTYAawW7ULCtVJ0u91YuXIlZs6cib/+9a9JiCq1xVLYbUvA1QEpVGAONYnBDOiZA4/PQHmNN2kxRCvRFeaVdb5Wy7bsqYbezknQ5lE9/Mb34ddpkUSO9EiiqWa3tmr0Y9b8HlVhxqQ9TJgTEVFboj66+/jjj/GHP/wB7777Li688EIcccQReP311/Hxxx+jX79+ECK+lTiHH344cnJyWlWtb9q0CQMHDsS4ceOwcuVKmKYZvG3JkiUYMmQIiouLMXLkSOTm5mLp0qXB22tqarBu3TqMHTs2rrESERFR8uwoqcWOkhrsKqttSJoL6KYJwxRwOjTk5zhQXOBKqVYHiWIKgXe/2hFMlp95wmCcdcIgJsvDkFKi1u2PSzuDdJCdnY2JEyfiqquuwr/+9a9kh0MRJDNJnVrSbxwSXWGel2VvteyeF1fi36+s7vjGW4Yeh4di5bM1Gd1R4py2ICKiDBN1wryiogKTJk0CABx66KH44YcfAASquS+77DL897//jWuALpcLf/zjH/Hoo4/i/fffx65du/D444/j66+/xvTp0zFt2jTU1dXhjjvuwJYtW/Dmm2/i+eefx+WXXw4g0Lv8ggsuwH333YdFixZhw4YNuP7669G7d2+cdtppcY2ViIiIksPrN2CaInhZt083ISHh9wvkZduRGybBkKl8uolXF23Bd1vLoSjALycOwWnHDuoSJwpi4fYaqHMb8PpNCCE7ZcK+VNCnTx9s3bo12WGknM54m5xx3MB210ml92uiK6YzTaLHKyfGv2db91bDFAKllZHn4dpVFtr+Ji590Ttw37beBx19h3SVk6RERBSbqK9JzsvLC/YMHzx4MPbv34+6ujrk5uYGf4+3K6+8EllZWZg9ezZKS0sxdOhQPPLIIzjuuOMAAHPnzsXdd9+NqVOnokePHrjllluC/c4B4Nprr4VhGPjLX/4Cr9eLcePGYd68ea0mAiUiIqL0U1Pvx8FqL3xGYPJOaUjouoBNU+FyaMhxdZ1keXWdD68s2oKySg9smopfTz4Ehw3uluywUpIQMpgc1VQFhilQWetDliuzW/ZIKbF//348/fTT6NevX7LDSXuxJLat3Kf5KtdMOxJffrcfa7YcjHpf8ZDMk0gpdN7AMiPB80PEOiZ7DtRjwefbsGxDqeX7WH7m2wrKwkbaS157/QbqPQaKC1xtrhcNtmQhIqK2RP2NYOzYsXjhhRcwbtw49O/fH1lZWfjkk08wdepUrF69Grm50U2UY9X06dMxffr0sLeNGjUK8+fPj3hfTdNw88034+abb05IbERERJQcB6u9KK/yoLLWB01TIIWEqirw+E0oCpCf03VOjpdWuPHSJ5tR59GR47LhvFOGRT2BYVdhmgJllV5ISLgcGqAqqHMbyHLa4LBpME0Z/UFyCho5cmTE5KyUki1ZwlA6oQWIlYRnyPMmgQt/emjyEuZJTywme//ReXPxtsTuoMVwaKpiuar9f8t2RbereLRk6cB+Gt8F1z/yNXy6iX9cPh49i7Itb7ctbMlCRERtifq7wFVXXYULLrgAl19+OV544QX89re/xf/93//hhRdewMaNG3H++ecnIk4iIiIiAIH+3Lv318GV7UR1nRf1XgN+w4QDGoQwYbdpkFKiKM+Z7FA7za7SWry6aAu8fhM9i7Jw/inDUJDbdR5/tA5Ue6FpCkwT8PpNOO0ahAI47RpsNhW6YcJmS8PS1hauuuqqsAnz3NxcTJ48GYMHD+78oFJdijztami+PKmV1sluyZJe6fLEazkeiTyhIePTxDzsYp9uYtn6Uowa2r3dvfj0wHxlG3ZVBRPmHZX8E0FERJTKok6Yjxw5Eh9++CE2bdoEALjxxhuRm5uLVatWYcqUKbjsssviHiQRERERAPj8gd7kuiFQXeeDKQDDDCQ8DVNCykCbjW75TjjsWrLD7RQbdlbizcXbYZgCA3rm4vxThsHlzIT66MQRQsLl0uCXAoaQUFQFqqpAVQCbpgZaKtjS//VzzTXXJDuEtNMZeWlrLVma1pEyuXl8tjBPLS3zvAnN+0bYdjT7jLTqa59uwWer96JXt+yY+omHvCdiGAMmzImIqC1Rf5t66623MHnyZJx44okAAgdzf/rTn+IeGBEREVFLew/WQ1UBv26izq1DCAm/LlCU70R5tRdZThtUVYHdFvW85mlHSolvfyzFJyv2AACG9cvHOScPhT0DEr2dwWHToBsSEICmKDAVBaqqwqYpKTXhYkdJKbF+/Xq43e6wSalx48YlIaquzcqrS2mZDUziazKpPcyTtudU1nnPh9U9tfU8RcpLr9p0AECgnZjViblbdCqyZO+BOtjtGnoWZoXGxTNBRETUhqgT5n/9618hpcSRRx6JKVOm4OSTT8bw4cMTERsRERFRkBASumHCbtdgCgmPz4ApBGyaAqddQ9/uOckOsdMIIfHRsl1YviGQcBg7sgd+euwAaGrmnyiIF4dNhVcFdAQKQFQl0AtYURT0Ls5BXa0n2SF22Pfff48///nPKCkpAdA0sZ6iKA1XYyhYv359MkNMOZ1xsiTaHuZJrzBnYjGldGZhdCyV3622ESm13Tz5bXE/aqQ5GSKsX+fR8dd5ywAAz9w6JeQ2q62Gyqu9sNvULjUnChERxZAw//bbb/HNN9/gyy+/xMsvv4wHHngA/fr1w8knn4wpU6bg2GOPhc3Gy4CJiIgovgxTQDclFMWElEB1nQ+6Ibvcl1i/buLNxduwaXc1AOC0cf0x/vBeGVUVnSg+v4nyGi8UKNA0FTZVBWBC0xRomg2apiDbacuYKxTuuece2Gw23HvvvejduzdUnlCJu2jfdseM6GFpPTWF3s5JbV2hdG6COB105nDEZezbz5dH3k8H3wfl1d6It1l5bG6vjpsf/wZA64Q7ERFltqgz27m5uTj99NNx+umnAwA2btyIxYsX4+OPP8ZLL72EnJwcrFixIu6BEhERUddV59Hh1034dQMKbIFqc1PA7TOQ7ew6k1t6fAZeXrgZew/UQ1MVTD1pCA4f3C3ZYaU0v24GKwkra32waSocdhWKAmg2FTkuO2wN2clBvfJgz6De9+vWrcMDDzyAU089NS7b27t3L6ZMaZ00uuuuu3DOOedg/fr1uPvuu7F27VoUFhbiwgsvxCWXXBKXfSfLryYOwdtfbo/b9v549uH439Jd7a4XUmEOJLXEPNmTflILzZ6OHoUuHKiKnBSO464sLY91Xavba15h3vwtEZp8l8H3T1uTllo5EVRWlf5XGhERUWxiLgX3er1YvXo1li5diuXLl2PDhg0AgH79+sUtOCIiIiLDFCir9EBVAd2QUBUTUBT0756L6hp3l+lxW+v246VPNqOs0gOXQ8P5pw7HgJ65yQ4r5UgpISVgCgG7TcPB6kBFuYREjssOU0pkN0yKalMV2F022DQVpikyrkq/uLg4rlXlGzduhNPpxMKFC0PGKi8vD5WVlZg+fTpOPfVUzJo1C2vWrMGsWbNQWFiIadOmxS2Gznbm+EFYuGIP6jx6VPf7/c8OxX/+t7HVcgXR9zCXUnbZlixKl/mEt64xCTykTz58upnYfUVKKkdReh5pG6FthyKs0/L5j9DDvOXPjau1FSYn/SQiorZEnTCfPXs2li1bhrVr10LXdQwdOhTHHXccLrroIhx77LEoLCxMQJhERETUlew5UAcFgN8QyHLaYAgBVSgNleUSmhZIKDUmOjNdRY0XL32yGZW1PuRm2XHB6SPQsyir/Tt2QQeqvDDNQCIkL8cOBYFJYCWAHJcdXt2ETdOQ7bIH11MVABnYruS3v/0tnnrqKYwfPx7Z2dkd3t6mTZswZMgQ9OzZs9Vtzz//PBwOB2bOnAmbzYahQ4di586dePrpp9M6Ya4qSkzzbTrbmHw32h7m4X7vTOxhnmIang5F6YS5YDvpqW+eu26rn7mlh9s8Yx6yDxnyPrLyurZywkZIiQ07KzGwV57lyUuJiCj1RZ0wf/LJJwEARxxxBP70pz/h5JNPhqZlzqWrRERElFxSSvh1E6qqwDQl6j06TFPAb0pISBiGAKB2mbrDnSW1eO2zrfD4DBTlOXHB6SNQlNd12tBEyzAFbJraMEmsgKoqsGkNPcttClzQoKpAjwIX6jw6VAXo3S07I5OCO3fuxNatW3HiiSdi+PDhcLlcIbcrioLnn3/e8vY2btyIYcOGhb1txYoVGDduXMhcRuPHj8eTTz6J8vJyFBcXx/QYbJ3cT17TQvfX7v5l+HVULfwnlM2uttpHOM376Kuq0unj0Fwy3xlqKjVzTxGNr63AZMXJGR+lxWtSUSK/V5onqVvep1Hz1imNk3Q2av5+CXyOt96P1uz9ptmU4ATYocvVkPFSlfbfV83vH2ndL7/bh6ffW4du+U48eO3ENreXKI1jZOWzhThe0eBYRYfjZV06jFXUCfNXXnkF3377LZYsWYIbbrgBmqbh6KOPxnHHHYfx48fjiCOO4IRCREREFBUpJTw+Ay6nDZCBnrlSAoZpAlBgmIEkOgBAka0qxTLV6k0H8MGSXRBSom9xNs47ZThys1nBFolhCChQYFMVGAj0fLfZNNjtGuwNB+Q9u2XBadOgKAr6dc+BqgD2NqqB09nOnTsxcuTI4O8tKzfbquQMZ9OmTejRowd++9vfYseOHRg0aBCuvPJKTJw4ESUlJRgxYkTI+o2V6Pv27YspYa6qCoqKcqK+X0eNGtYd3285iD/+8ggUF+e2+VmjRIgxJyf8Sa2iwhxkZbU/UXHzbWZnO1FU2PErBGKlJDFp7XTaUvKz3unQ4PMnth1KJI2vLbtNTXhbkUjP/fL1Zfh4xZ7g7za7FvG96nI1/c1qvk5ozqBpP/sO1jctVYD8/KarqfJyXcFtND+ZkpPTdDKwsDAHtobP+7xaf9PyguyQxIzDYWv386W8vqkV01tf7cDFZ/+k1Tqrt5QDACpqfEn5vGqu+VhR+zhe1nGsosPxsi6VxyrqhPlRRx2Fo446CldccQV8Ph9WrlyJb7/9Fp999hlmz57NST+JiIgoal6/iT0H6tG7WzayXYFJPU0p4PWb0BrarhimRHGBK5AMzcBq4OaklPh01V58/UMJAOCIId1w9omDMjaxGy9+w4TdpkJVFagNLxG7Fkig2xoqBfOzm5KVzgya4DOcF154IW7b8vv92LFjB7KysnDLLbcgOzsb7777Li699FI8++yz8Hq9cDhCE8HOhgl5fT5fTPsUQqKmxt3h2KOhaSru+tMJqK7xQAqJysr6Nk8smKZAZWV9q+X19eEfc2VVPbze9vuhV1Y1bbOu3oeqqs4dh+b0hpOVY0f2wIoNBzp1336/ASFSr+3WkzdNxh/uWZSUfdfWBV5bpikSfmVMWy3PXvzfhuDPhm6GfR8AgNvTlLRuvo5sFrts43HU1DRNvFnv9gW30fyx19U1TXxaWVkfTJg3v29Fs+UA4NeNiDE3qm12/7c+34KpEwa3WsfnM0L2nQyapiI/Pws1NZ4u0aauozhe1nGsosPxsi5ZY5Wfn2W5qj3mST8BoKqqCqWlpdi3bx92794NKSV69erVkU0SERFRF2QKCd0UwcpyU0i4vQYEANUMJGuEkMEEp8ORuVezGYbAO1/vwI/bKwAAJ43ug0lj+qZklWWq8OsmDlZ74XRocNhUQFFgg4DfCPS5VxQFudkO5LpYnR8rh8OB5cuXw2azBRPjRxxxBLZu3Yp58+bB5XLB7/eH3KcxUd6R/umBFkydS1EUSCEt7VvI8DEKM3wC0DQkjhnRHa9/tqXN7ZpG0/1NUyT1i7fRsO9Yi5l7FWWhtNLT/ophCCGT2hImkmS2cDIbXm+dEUE0V6E0vg/e+HwrfLqJ350WuOKk+XvBMAR0Q2DPgTqU1zQluSM9ywpCk/aR3pdms33ouhkcHKPZfQ1DhAyajPDebe61Fu/TcOu32kcSmaZIegzphONlHccqOhwv61J5rKJOmC9cuBBLlizBN998gx07dsDhcODYY4/FFVdcgUmTJqF///6JiJOIiIgylG4I1Hv14BdeIWWgwlwE/jUgoUDJ6CR5I7fXwKuLNmPPgXqoqoKzTxiE0cO6JzuslObzmyiv8cJm06DrAjm5NpgSUBQVNk2FXQv0+S3Odwb72lJswiW+R4wYga+++gq9e/dGWVlZyG2Nv6d7QU1bOcNo29oAQM+ibDx07QT8+eGvLAYAWJzuMCEak8OxnrQ7aXRfvP751niGRAi8IlJtzk/DFPjvtzsBAKePG4AehVkh23B7DVz3yJcwWpxQivQ2evPzLahtdrWGlZdgyLZkhOUWCCmxdltF++tl+BVvRERdVdQJ86uvvhp9+vTBSSedhFtuuQXHH398qwmEiIiIiKwqrXDDbwpIKVFR40VVnQLRkCwvLnAGJ7/L9ArrWrcfL368CQeqvHA5NJxz8lAM6ZOf7LBSlpQSfkOgstYHVVHg0BToUOGwa/DrAooamKTN5bRBiMx//STahg0bcP755+Ppp5/G2LFjg8vXrl2LYcOG4bDDDsOrr74K0zShaYErQZYsWYIhQ4bEPOFnOjh17ICY7peX3XYf85YTIibz5Ws2JsxjvH+H33spko8c2CsXu0rrkh1GkILEf651pEW6r6GVT/OTSmu3l7dKlre1H6/fxHtf72i2pP3HGyFf3uq3qDbUBjPBfeSJiCg5ok6Yv/322yGTBxERERF1hCEC7QZMIVDj9iMvyw6fIdCjyBXSbzSTVdb68MJHG1FV50deth0XnD4CPQpTdxKcZKv36qiu80OBAqdDhRCBCeRUYcJh12AKCVUqcDTkMWwNVeYUuxEjRmD48OGYNWsW7rzzThQVFeG1117DmjVr8MYbb6B79+6YO3cu7rjjDvzxj3/E999/j+effx6zZs1KdugJddb4QVGt35kvwx6FLhyo8ra/YjsaC2hjjb0jj1lRUiZfnjKC46EoUJXEjk60V1A0Xz+aVj5W9xN5/tlmSflt5Tjm0J4tF7eKo3FT9V4dPr+JbvmhRYDh2sSYQrS6UokV5kREmSnqb6EjR45ERUUF/v3vf2Pq1KmYMGECNmzYgDlz5mDhwoWJiJGIiIgyjJQSQkoYpgj0LDcFhAB0U8LrN6EbZpdJlpdXe/H8/wLJ8qI8J6afMZLJ8hbcXh2lFYFJD+u9OurcBlwODaqqwG7T4LAHflYUID/HAU1VoSqApgJ9inMwsFdekh9B+lNVFU888QSOPPJIXHfddZg6dSq+++47PPvsszj00ENRXFyMuXPnYvv27Zg6dSrmzJmDW265BVOnTk126B3WVsJXjZzBi5tYC1j/cfnx+MMZHS90anzvKYoSU5U5r+6Ir8bkcqdUmEe5fmVtU/uURSv2YPOeKkvbsLwfCw/30bfWNttus4lFI6x/zYNf4qbHvkFNfegcDOHed4YRLonOhDkRUSaKusJ89+7dOP/88+Hz+XDMMcdgw4YNME0T27dvx2OPPYbHHnsMkydPTkCoRERElCl2ltZCSqBvcU5DdXmgX6hpSuiagh6FXaPd246SWrz26RZ4/Sa6F7hw4U9HtNuqoSuqcetQoASuQqjTYbepKMhxosbth8MW6FXu002oigJNVZCfY0dVrR+qogQniu2qli9fDk3TcPTRR2PPnj3429/+hv379+NnP/sZrrrqqqi21a1bN9xzzz0Rbx81ahTmz5/f0ZBTTip0XIglLxprgjueMXTkfqkmvqPZcUqKNTH/39JdIZNkfr22BF+vLcHPT4juKoy2dOgEQTuPZXdZHYb2y4fLETlFopsCToT+TWGFORFRZoq6dOuf//wniouLsWjRIsyZMyd4hvv+++/HlClT8MQTT8Q9SCIiIsocPt2EkGiY2FPAbwj4dAOmKSEhIYXsEtXl3205iBc/3gSv30S/Hjm46GeHMlkegaIAmqrgYJUXmqpAVQFNCyTDA78HEuWqEvive0EWFIWVre+88w5+//vfB68CnTlzJpYvX45BgwbhiSeewFNPPZXkCMkKKWNP1sbzPaAE/y86HWuHlELv4RQJpfkJnET/pYymJUvzZHnoNuIVTfRPQfN9i3YC+fL7fbjygcX4fPXeiOscqPJgzeaDIePChDkRUWaK+m/skiVLcOWVVyI/P7/VAdhvfvMbbN68OW7BERERUQaSgd6mphAoqfTAb5jw+k10y3dCUQBDZnaiU0qJT1ftxTtf7YAQEocPLsLvf3oocrLsyQ4tZSlQYNOUQKsVVQn2kFUVQNVUKEqgNYbNpsJhD9zWsygLvbplJzPspHv22WcxdepU3HLLLSgvL8c333yDq6++GnPmzMH111+PBQsWJDvEzNXBj7CQST8DGXNLehVloX+PHJx78rBW2+moWD+XO9y1JhVK/FNIY5sRRVFSriVLezoab1wfb4tNLVtfBgD4z0cbAYR/2f39+RV4eMH3wXUBtmQhIspUMZ2U1rTwl7b6/f6M/oJLRERE1gkpoRsiZJlumCitdEPXBYSQ8PsNGIZEj8IsOB2B4wthinCbywi6IfDmF9vw1ff7AQATRvXGtEmHwG7L/Ir6WMmGXveapsJhU6FpSrBvtKqq6JbnBABomgqXQwsm0/OyHSjI6doV+9u2bcMvf/lLAMDixYshpcQpp5wCADjyyCOxf//+ZIaXNmL6ehPnHJrVEHKz7PjbJcfhZ8cNjG8ADTHEUuk+tF9B7PvkpJ+tlFV6gj8n+qt3rVu3tF5bOYC4VpiHnEhqfx/NK8FbrrN1bzXe+HxrG3uLHPj6nRXBn1lhTkSUmaLuYT527Fg89dRTOOGEE+B0Br6gKIoCIQReeeUVHH300XEPkoiIiNLPwSoP6r0GhvTJBwDU1PtRXuOFaQropgkpVeimgClkMGGc7bSh3mskM+yEqffoePXTLdh7oB6qquDnxw/CmOHdkx1WyiitdKMwxwmPz4BPN5Gf44CUEqqqwGELtF5RNAWqaKpY1VQFOS47crPs0A3BEw8t5Ofno76+HgDwxRdfoG/fvhg8eDAAYNeuXSgqKkpidOkjUQm/NtdrlpiWUdyvpY61Q2mxrTCl4kcN747Vmw+2eb9MmXQ3VcrC3v5yO4DG10RqRNVW6xYZx1MeHXs5h8ZxoMqL/367M/LabYTd/DZWmBMRZaaoE+Y33ngjzj//fJx++uk47rjjoCgK5s2bh61bt2Lnzp14+eWXExEnERERpRmPz4RhCkgp4TcEDtZ4YRgmdENCyEAvc78ugi00AKAg14n8DKwKPlDlwSsLN6Oqzg+XQ8O5Jw/F4IYTCdRQoScV1Lr98BsCmqqipl6HqiowTYG8HAc0VYFNU2EKAdFwEYKiKlAUwKapXaLvfbTGjx+POXPmYPPmzfjkk09w8cUXAwA++ugjPPTQQ5gwYUKSI6SEi2M+1WYLtD9qnnfs1yO33YR5R3R2Org434XyGm8n7zU2KZQvb1PzCuyOhrt4zT4cMaQ4sC0LGwupQkd0Pdmtrtleb3QiIkpPUX+zGDFiBBYsWIDjjjsOS5cuhaZp+OabbzBw4EC8+uqrOOywwxIRJxEREaUZiUBLFo/PxP7yepimQJ3XQK3bD10XMEyJglw7igtcIffLtPZuW/dV45kPNqCqzo+iPCcuPuswJstbEFJCUxUYDYkVTVWgqAqyHBpsmopclx2aGmjHoqkqVE1BfrYDPQpcTJS34Y477kBRUREeffRRnHDCCbj88ssBAPfeey/69u2LG2+8MckRpoeYPpIi3MdyS5OWPczbuN+IAYUR9xvPT1Ob2vq9llmf1mmW/FSia5AztF9y/u4YRtOYGqJjLddWbDwQdnmkZ635cimjbO/TxsrNPxNYYU5ElJmirjB/++23MX78eNx///2JiIeIiIjSkJQymOhu/FkIQEigrMoNvy4ghIAUEr2Ls6EbApW1PrgcUR+KpJVVmw7ggyU7ISUwoGcufjNlKLJdnNyzpQOVXmQ5NfgNGUyIq1JC0xTYbAqyXTbUeXRkOWxw+wxIM9AeIi87865GiKeioiLMmzev1fKXX34Zffv2TUJEFIu2WrLkuGy49XdH4+J/fBr29niegLTZkpAeV5ROnfMzmgrkRMtyavD4zIi3ByrMrT8n50wehn+8tKrjgYWLpY04dLPpMTz17rq47TOmpyoRT28KvWaIiCh+ov6Weu+99+Luu+9G7969ExEPERERpaGdpbXIzbKje0EWdpTUAghchh34D/D6jEB7jYbvlXabip5FWUmMOLGklFi4Yg+W/FgKADjykG44+8TBrIaOQFMVaA1jY9cUOG0aJNDQgiUwngN75UJRFFTX+VFZmx4tE1LFF198gZUrV6K6uhrFxcU44YQTmDBPtA7m0ELSjx3YVjwv2AlXYZ5pJeZtFQt39sVPgQmMIyfMoUQ3CasWpgd9Z2g5+Xc87CqtRZ3H2oSkjYSUlvqpNz7PlnuvZ9hVcUREFBB1wry4uBg1NTWJiIWIiIjSlBASdR4dhblOmKaEKQQUBTCFQJ1HD0zuqUvkuDK7ohwI9GZ/a/F2bNpdBQCYNKYvThrdJ+NazURLStnQekUNXoVgmAJen9nQakWBqigN/cibepMLEeh5rzUk61wODYqixHUyw0xVVVWFSy+9FD/88ANsNhsKCwtRVVWFxx9/HBMnTsScOXPgcLBKv1PF8LINvF/iH0q0Gt+XmaztCvPOffDtJbgVRPd8hJu0tTMkImE+89nloQsi9mQJbWJupRi88W+NlUk/F63cg5p6f/sbJSKitBP1t9Zzzz0Xf/vb37B06VIMHz4c3bt3b7XOr371q3jERkRERGnCFBKqqmBXWR1MIYI9Pf26gKJKQEjY7SpysjK7HUllrQ+vLNyMg9VeaKqCX0wYjCMPKU52WCnhYLUXhiGRl2NHnVtHz6IslFV6YFNVOOwqbLbAv06HFpjME0DvbtnYX14fksTKctrQozCrS5x86ah77rkHu3btwpw5c3DKKac0tEoSWLhwIf76179i9uzZmDFjRrLDzEzNcpM/GVyEH3dURnf3FplQq5XELdeL54klm01F4IGFzyTmZtmjrvpNNT8Z3A3fritNdhgA2k9wK0p0KfxknWSMd8J8VstkOVqf6PD6DbgcNjTfs9ULNaxW4m/fX4OXPtlkcatERJRuov6m8Y9//AMA8M4774S9XVEUJsyJiIi6ECkDrVcAQNdNeP0mbDYVpimR5dRQ7zEgIdEj19XOltLbvoP1eGXhZtR7DeRl23HOyUPRv0dussNKKlMI1NTrKMx1QDcEVEWBbghkOW3w6yZsqgq7XYWqqnDYVDhsgUk+Gyc5zHLaUFyQhWxn6CFrboafeImXL774AjfddBNOPfXU4DJVVXH66aejoqICc+bMYcK8A84/Zbil9cYM7xF1wry5zuyQ/LvTRkRMAoZtyQLg3itPxEsfrsfPTxgc9x7ZCtqu+h7evwC6IYKtwDpi7KE9cNq4ASmTMLeSuI3myqVktWQxzPgmzHeWtn6uW75CrnxgMZ68aVLoa0dKixXmjfOxtL1eVa2v/Y0REVHaijphvmjRokTEQURERGlKSAlTSJimhNtnwOc3ka3YYQqBHJcNedn2jG9Hsml3FRZ8sQ26IdC7WxbOP3U4J6QEUOcxoOsiWGGoNvQpt9lU6KaA06FBVRU47SoKcgLtfBRVQfOywIIcjmNHhLsaFAD69OkDt9vdydFkllgS2bF8EhbnuyLesb3P1mg/eof1K4h4W6DCPJSqKDhiaHfc8rujUVHd+XML3PibMfhkxe64JMyPP6J30pLK4bTfkiU6mdSSpaVwJ1VKKjwtO7JY0jRO7dwjdV4qRESUAFEnzPv165eIOIiIiChNSRn4WlnvNWCYgT7VhhAwTQlVUTI+Wb5iQxk+XLoLUgJD++bj1ycPhdOuJTuslGGzKSiv8UGBAhWAx2cg22mDWw/0M3c5NGha4N++3XMgpERphSfZYWeEqVOn4vHHH8exxx6LnJyc4HLDMPDiiy9i6tSpSYwu/bXd77rjbjpvDHaV1mHU0NjbOkX7+dvW6ja1dc/skCRsAj7qjz60B5atj1zxrShxbDUSx6fTpikwzI5tULMwSbRutDEpaAtJS5h3cBysCPdWrHP7Q96jgR/bjyVYYd7Oepl+bENE1NWx+SMRERF1SFmlB1JIeP0GzIYvxoYhIBr6mmcqKSU+WbEH3/4YSOaMGd4dZx0/MDhhGAGQEnabFjiRIiRUBch12aGpKrw+E3k5DUlyIZHVrO2KojBhHg8ulws7duzAlClTMGXKFPTs2ROVlZX46quvUFJSgoKCAtx2220AAsmfe+65J8kRp5cE58tx+OBuOHxwtw5tI+oq5DaSgDab2mp7icyXD+yZi5+08/iVaBt5d5J4vDbarTBvaHHV3IlH9sbXP5SEXT+TK8zDqXHrcDpCT15basmiWZ0vgIiIMhkT5kRERBQVwxQ4UOWBTw9UthmGgJCAaUoUFzhhComqWj+yXFrGJsx1w8TbX+7A+p2BvsQnH9UXE0b1YcVZC0ICWQ4NUkh4/CZUTYHLqcFuD0zwqSoIVJi3OMnQv0dOhC1SNN59913k5gb66C9dujTktt69e2PVqqZ+03ztRi+WCvPOHueoK8zb+My2hal4Vpu9d+P92Ab1zrO0XqQJUdvqxx6Lzn6LWGkP42+RjJ5+xmERE+Zasib9jHMP83BEmPeiXzfhsDe9PqWUli4iaDxp1N7bmx+ZRESZjQlzIiIisqzG7cfBai+EKaBqKnx+A1IGvpj27OYKJj6d9sxNllfX+fDqoi0orfRAUxX8YsJgHHlI7C0TMlFjIlEICYdNhXRo8BsCDpsGVVEgZaCiXFWUsBWtdhtb2sTDp59+muwQMlq4JF04SU2sRbnvtj62w71XE3lBTePu2hrmQNuvSLfFss/U+btlJWHeMhmtqoHTB+GGLJF/k9saNiOKtjHxpCgKWsz5aantjvVxSp3XChERxR8T5kRERGRZdZ0ffr8Jv27C6dACVcMIXHLdvEo4U5Pl+w7W49VFW1Dn0ZHjsuHXk4daroLsKoSQKKlwQ1UUOOwaCnKcOGB44LBrsNtUqKqK3Cw7hJAwTJlSCapMJYTApk2bUFZWhqOPPhqGYaCwsDDZYaW9eLdk6ZbvREWNL67bjOe7S1Vab7B5Ej3eb+XGz4Y2h1lpI8kdZUASbY9XZ39StZcwl1JC11tXb2sR+qcn8u9yW++Fzpn0M8wyyNAe5g3/a09wys923uD800VElNmYMCciIiLLdEPAr5vBy8ANXcAQAn2Ks5McWeL9uL0C7369A7oh0LMoC+efOhwFOY5kh5VShJCoqffDpqoQUgb72KsAnHYVmqaiMMeBbvku1Hl0aImfC67Le+edd3D//fejrKwMiqLgjTfewCOPPAK73Y77778fDgdfw7FqK6GmRPi5LXHJv7XYSFxPSIXZVPMkbLzzh1ZiD5PDD7ktnbWX4JYI3+5EU1UYZuuqbisV64nQOZN+tt7H1r016Ne9WXsvGd1JrrZWlWDCnIgo01lKmE+ZMiWqg61FixbFHBARERGlHiEClVm6YcJnCBiGgAQgFYmehVkZXSUshMSnq/bim7WBvrBD++Xj15OGtppMrCvy+AxU1/tRmONARa2vod0K4HSogKICQkJRAokfVVPhsKkoynMCALrluWAKZswT6b///S9mzJiBX/ziFzj55JNx/fXXAwBOP/10zJo1C4899hiuu+665AaZxtpKvoXcZPnz0dp6V/7qCDz29lprW2xnk1dNPRKPvvVDs/Uj30Fp+F9zoW1a4vt3wMqwKW20ZIkpnChKzO/4/TH450urYYRJWnfGpJ9A+LZAke7X1oSuidQpFeZhli3+bl+764QV7AXUzmppf0qGiIjaYilhfuyxxwYPnoQQ+OCDD5CXl4dJkyahR48eqKqqwtdff42Kigr85je/SWjARERE1Ln8uom9B+sBAIaQDclyCdMU6J3hleUen4E3v9iGrftqAAAnHNEbU47ul7EtZ6JV69ahQIHHb8KmqVAVpaHtigK7psLtN6AqCgpzndANgV7dml4v+azOT7gnnngC5513HmbOnAmzWcXp//t//w/l5eV47bXXmDDvgJwse1y3ZzWfGc0JyvbWbLmpNtcPU87dPDkb95YswZ/aa40RfseJ/pQe2rcAv548FK8u2tzqNmvTS7atrQlYg/sJc9LRpkVImHdCv/lwwp1QiDsLwy1h7USG5dNbPAwgIspolhLm//jHP4I/33fffRg9ejTmzp2LrKys4HJd13HFFVfA7XbHP0oiIiJKCt0Q2FdeD78eSLYJIVGU54AhJNxeI8nRJVZZpQevfboFFbU+2DQVvzhxMI44pFuyw0opihJImPkNE3ZVhaIqsGkqNFWB06FBNwVUBeiW70p2qF3S9u3bMWPGjLC3jR49Go888kgnR5Q5xh/eCyeN7mtp3Xgn4KI5XxfPq3/C7TfW7f/ixMF49+sdba7TuO3pZxzWZkV95BbmUfYwl6nVxqW9inApgXAX6Wha+Mx4IluyrNtRmbBtW2FlAt6aOj9yXdY70ra1xbZaARERUWaI+jzz66+/jksvvTQkWQ4AdrsdF154If773//GLTgiIiJKjoPVHlTUeFFR64XPb4b857BryM2yo2dRVvsbSlM/bC3HvA/Wo6LWh4IcB6afOZLJ8ggcdg2mKWHTVNg1FYoC2G2Bfx22BJY0UruKi4uxdevWsLdt3boVxcXFnRxR5rjsFz+BvY3Xd0zdQCyXmEdxUzubjG791jfGWmF+6tgByGknedm4vbEje+Kffzq+jfUSV2F+1vGDgj9nOaKY/isOLVnaywFLBOaJaCliS5YufmXUffNXW3pagi+nNp6AQA/zrj2eRESZLqZvMRUVFWGX79u3D06ns0MBERERUecTUsLrNyBkoHK8us6Pqjo/PD4DfkPAawgYUsLl0DL6S7cQEh8t24W3vtwO3RAY0icPfzz7sC4xqWk0fH4TtW4/AASTXppNhd2mBirMNQVOuwa7TQ1bAUmd48wzz8TDDz+M//3vf/D7A8+XoihYu3YtHnvsMfzsZz9LcoTUnPVK9Pi1ZIlmfUVpfXvzNh/R9nR2tZOAbl5h7WpjzoiIe43trEXEX887ZTgG9c7DJWcd1u5mpp50SAw7D9VuWxeZHj3MO4OVCnO/LvDD1nLL22xvixk8nEREBIstWZqbMmUK7r//fnTv3h0nnXQSgMCs1AsXLsSDDz6Is88+O+5BEhERUWLtL3fDp5tQFcCmqvD4TTjsKmxQoBsmDMOEpmjIzon60CFteP2BfuVb9gb6lU8Y1QeTx/TN6BMEsap263DZNRiGhNrQhsVhUxsSNQpsqore3bJRXe9HdhSXwFN8XXfdddi0aROuu+46qA2ZzQsvvBButxtjx47Fn//85yRH2EXEuSdLNJ9I7SbXo2nvgtYhhiRho2oVE7+EY8SWLFFmzPOyW/ekb/74ivNduPMP40JXiJCoPev4QThqeHds2l2FFz7e1OZ+/3rRWPz9+RWtb2g/Xx42URzub9asi4/N7Ipoiydmn3pvnfVNtjfpZyaPJxERRZ8wv+2227BlyxZcdtllsNvtKCwsRGVlJUzTxIknnoibb745EXESERFRAuiGCUVpSoqrigLYAL9hQlOBeo8Jwwx8azSFzNiKqooaL15dtAUHq72waSp+NXEwDh/MFizNHaj0wuFQoQBw2VRkOTWYIjCZm9Ouwaap6FGYBVVR4LCrUBom+6TkcTgcmDt3Lr7++mt8++23qKqqQl5eHo499lhMmjSJCZ9OYjlfbnW9BFaYt/khH+am5snZaPfVq1s2DlZ7I97ePFnd1mOO2JLFYkD5OQ5MGt0Xw/sXYH95G/NxRdk7vl+PXGzeW93uuj0Kw7c3azcHLCVEmPk0w1WYD+iZ224c6awrXMhUVulGUZ4Tdlvkqy2IiCh+ok6Y5+fn47XXXsMXX3yBFStWoKamBkVFRRg/fjyOPz5ybzkiIiJKLVJK7DlQH/gZgG5IaCqgKBKQEqYp4fE1TewphMzIBNu2fTV44/Ot8PpN5GXb8Zspw9C3e06yw0oJUkr4/CZcThukAni8Bhx2FQ6bBlVRoCkKclx2uL0GVEVBblbrCk1KnrfffhuTJk3CiSeeiBNPPDHktgMHDuDtt9/GpZdemqToyGFX4debMp6WW5jH8WM4XBV2XrYdtW49zH5brxua1I5mv8DFZx6GGx/9OvI6HW/pbsn4w3tFbKHSVp96K6zEFvFxWmgzIi1WmGe6cOMQq8bXeXvb7KwWN0JIfLflIB558wf07Z6Du/54XKfsl4ioq4vpGllFUTB58mRMnjwZPp8Pdrs9eJknERERpQdTSBimCH7pM0wBRVFR6/ZDysDthbkOZLvsqPfqqK7zJzni+JJS4pu1Jfh01V5ICfTrnoNzpwxFXrYj2aGljHqvgdp6Hd01BSoAm00N9h1WFAWqpiI/24HyNqpEKXluu+02zJ8/H0VFRa1uW79+PR5++GEmzBPg9guPwcEqT/D3SCca77l0PDbtroqqTURge3FcV2n968zpx2LDzkps31+DhSv3tFg19A6hyVnrLWVcDhuyXW2vr0RIxue4bKj3Gs1WDH9/qwnNkK4yLe7iaFbNm6iJXCPmy9u5nwSimvQzkyWlwryThvmfL6/C5j2BKxX2HazvnJ0SEVFsCfNt27bh4YcfxjfffIO6ujq8/vrreP311zF06FBceOGF8Y6RiIiIEqCi1gfTFJCa2lBRLqCpCrx+EzZNhddvIssZSB7nuOzIcWVO9bBfN/He1zvw445KAMDoYcU4a/wg2DpYTZiufH4T5TVe9O6WDVVVYBgChilQU++HTVNhmhKqCjjtNjjtWkMrH0BTAZtN6ZIVjanq8ssvx5YtW4D/z959x8lR1/8Df31mZvtev8tdei+EdBIIJQRCk56AggUUVBRFEEVsqMBPUFG+gIKgUhRRehNp0nsICRCSkEJ6vcvl+t22KZ/P74+52+zebZu97fd++sDczk757Mzs7Mx73vP+wLwpdNlll8FuH3gTqLW1FWPGjMl180reyFoPJo2siAqYx1Nd7sTCQxvSCJhn9/tWVebAkTMasLu5J3q5iNXpp7UM83MXT8CJ80endMyIN79JIyvwSUTnjXED4ylnqMcfMTLDPHurPfaMkyVNCxE7UCwPxUS2DEbMM9zlwKD1BcsJIYTkluWA+YYNG/CVr3wFNTU1OOOMM/DQQw8BAGw2G37zm9/A6/Vi2bJlGW8oIYQQQjKr26dCNwRsTPRmmwsosgAX5n/11bHrqha79u4QHn1tC/a3ByAxhs8dMRqHTa0ryXIzqfKHzJIqnAsEVB1dPRoEBBRJgsQY2rtVeFw2yFJfvXIGATP7UpakIZnRWKi+/e1v47HHHgMAPPXUU5g+fTqqq6Pr8UuShPLycpxzzjn5aGLRKbS9e5SFklHJjmuWPluMeY1rKLMyB9htMhy21GowR2WYRwxPNTaa8mdLMGJ0SRbre8Jg9p10y4wMxRuYmSzJ0rfRknb6mecjwwvv74RNkXDi/NF5bQchhJQqywHzm266CTNmzMB9990HAHjwwQcBANdccw2CwSD++c9/UsCcEEIIKXBcmOVYNINDkc2MYoMLGEKEM4pL0bZ9XXjiza0IhAx4nAo+f/xEjK23FvApVZLEYHCBzh4VdkWGqhtQFBbOVnTYJEgSQ02FEy6HgpBmhMsBMIlBlodekKYQzZs3D/PmzQu//u53v4vRoymgMhiDPRpmqjPPW793NAKqgYoEnekO9lsYHZgWcd8DgBsvOQLV5c6D72f4EMASvEpjBmmNNtgM82Q10KvKHHHnm2x/iBckHoo3MLNxxtJ//+8vn/fYO3pCeOyNrQCA4+aOHLJPxxFCSDZZDpivXr0at9xyCxRFgWEYUe+ddtppePbZZzPWOEIIIYRkhhACms5h783s29nUDZ2bwxw2GarOYbdJgADqKp2p9DVWVPrXKx9R48Z5Syah3DN065WHVAMO+8FMT1liaOsKwabIsNtk6AaHLEmwKRIYAEWWogI4kVmiDVWu8L5FCsdvf/tbAIDP54PHY2Ylv/DCC2hqasKSJUswduzYfDavNA0iiJZs0gqvAxVW55lkpv3fT3ToZyx6/OE10Znumc64TVRbPN54ydpz0vzRaOkM4OPNLZEjRswreprIGuaxJPupTHRcvOr8ORibIEM/3UzxoZlhnrl5hddeGvP85T0rcMHJUzB1zMB+IxIxOEeXT0NVWfybYZFU7WAcptTO1wghpFBYvhXpcDgQDMbu2KmjoyNmjURCCCGE5FeXT8XO/d1o7w6hrSuIQEiHYZhZ5sGQDlU3UFlmR1W5w+zMsYQuuEOagcfe2IpXPzSD5bMn1uCiU6cN6WB5IKSjrSuEUO9FtxACUm+WuNL7nyQxOO0y7IoERZEgywwelxIVZO/jdtqgyJThVmi2b9+Ok08+GXfffTcA4NZbb8UPfvAD3HTTTTjrrLPw4Ycf5rmFQ0TKKeZZbYVl/QPOSctWpdJ+C8G9eLXJUw0Qxpp8ZJ0HI2rjB/r7TzLYDHO7Lf5x8ZBxVfC6bHHnm26m+JDMMM9C1DjhHOO8ubfFh5se/Njysm579BNc9ed3sWFne2oTRO00FDEnhJBssHxlc/TRR+NPf/oTmpqawsMYY/D5fLjvvvtw1FFHZbSBhBBCCBm8oGYgEDLQ1hVEW1cIvqAOVTNQX+WCy6lgRK2nJDsKa+sK4r7nNmDjzg5IEsPpR47FWceMG/KPL3MhIMsMhiHQ1OZHUDUgMzNQLjNm1imXGBx2GTZFhqJIKHPZUV/ljt/BHik4N998M2RZxgknnABN0/DQQw/htNNOw6pVq7Bo0SLcdttt+W5iUbCyxw/m25FOhnbyDHJr8xxM6C3TR4agZiQfCbHX24mHjUp5OYlWUVTAPI1PmChDPdnckgW+48WIh+IxOrMh49yvv74OyF//aE9K40eFyyM+vG5wrN7SgtufWINuv5rBFhJCyNBjuSTL1VdfjfPPPx+f+9znMG3aNDDG8Lvf/Q7bt2+HEAK33HJLNtpJCCGEkDToBseBjgA03YDOOXwBzSy30VuCRZYluEo0M3jL3k48+eY2BFUDXpcNXzh+IkYP8+a7WXnR0R2CP6DB1ZsdzsCgSAydPhWyZHbgKffuB5Iswe1U4AvqqK1wweWQsWt/T8JMSVKYVq5ciRtvvBEzZ87E8uXL0d3djfPPPx9erxdf/OIXcfnll+e7iUUhrWBcOiW3MxGn6zeTZLO0kpib6eB8Mh7nwUvVRLP2BbUBw46ZNRzN7QHrC+23nKgnkWK0IXL9HTquKhz47JOog9O+9RUvEJ80YA5gVJ0Xew70RE83BPuTyEZZkmSzzMYyB/N031sf78Ef/nXwqSGvaysuPu2QTDSLEEKGJMtXPsOHD8d//vMffO1rX4MQAmPGjIHf78cZZ5yBJ598kjoVIoQQQgpIl0+FP6gjqHGAC+icoyegwe2UUVPhTD6DItRXr/yhVzYjqBoYWevBJWceMmSD5QDQE1DR5TuYbcZYb4BcmOVUPE4FjAGKIkGRzAxFu2LWL5clCWPqvShzD90SNsVK0zRUVJhVr9988024XC4cdthhAADDMKAolnNnSFIDA16pZibnI8yZKOY3sNNPlvGgeCw//co8nHrEGBw/92CWeOQ67N+ujp6BmbTxgs2xSnck+kh1lU5cePIUfPOMQ5Jnbsd4fzA3GuVkN7OFwJVfmDVwOirJMijhGxCJ5pmlVZzOtutrZWSwHAA6fZRhTgghg2H5LHnfvn2oq6vDD37wgwHvhUIhfPTRR5g3b15GGkcIIYSQwTG4WadcCAEuAAigzGODvURLkqiagWfe3YH1vVl+cyfX4tSFY4Z8fW2pX7mdkGbAaVNgcA5FYmCyDElikHrr1zvsMux2OVyOoBTL9QwFU6dOxUsvvYRx48bh+eefxzHHHANFUaBpGv79739jypQp+W5iyQnHTEWMYUmMGubFruae5CMm0j/IlzTF3MK8cxSHHVtfhimjK+Mvu1+b+welj5hejxG1HjS1+WPOf+D2iF/DXJYkHD8v9fIu/SXKMO/T/wbAwWUnzzCvLndi/rRhWLWxOTy8lPogSVW3f+BTBoOxZU8nyjy2+COI+NttMNL6raUS5oQQkhWWj8gnnHACNmzYEPO9NWvW4OKLLx50owghhBCSvv1tfviCGvxBDb6gBt0QMATAYZZocdjknGQJ5ppZr3wj1u9ohyQxnLZwDM44auyQD5YDgMTMQJCmcwRCOgJBA4yZ9XXNTj4l1Fa4MKrOizH1XlR47BgzzDska+GWkiuuuAKPP/44Fi9ejM7OTlxyySUAgFNOOQXvv/8+Lrvssjy3sDhku4b5b761EFd/aS5G1nmSj2xRsux2K5m5Vg4HC6YNAwCcd/yk1CcKL8ja6CceNhp1lQefmFp6zHgwxmDwgZ/NSmzxq6dMtdSOWDei7SkEzONJFvju+yyHjKmMGi5n+LhdDDfYn39/Z0bn19ETyksgOtWbHdH3jyhiTggh2ZBShvlNN92Ejo4OAOZJ1Z133omqqqoB423YsAFlZWUZbSAhhBBCUiOEgG4IdPSEEFB1CGEGSA2DA4zB0DmcjvQv3gvZrv3dePS1rfCH9CFfr1wIMeCGCJMAMKClIwi7zSy1whiDLDPIsgSbLKGqzBE1jTIE6+CWmqOOOgr//e9/sXbtWsyePRsjR44EAHzta1/DwoULMXWqtYDgUJXtGuYN1W40VLuxs6k7nSUBMLOqV6zfj9OOHBfdjCTtsJZgzlIOmi+YNgxfP+0QOOyZ+c1JkGAOt1PBpWfPwK/vX2WO2zuypvOBM4rxgaM+UsQHHGXxN2TZoglYt70Nh46rDg8bTLDZ60yQ4Rxh8ZyRcNhlTB5VCWBg0HVq/2x9i9gQzFg3eAph6CzEqSOfKtB0A7YYncbyfje5slFLnRBCSIoB84kTJ+LOO+8EYHZOsm7dOtjt0XUsZVlGWVkZfvazn2W+lYQQQghJyB/U0dzhh2EIaIYAVKO3HIuAIQQkAMOqnSVZWuOjzw7g+fd3gXOBETVunH/CpCFbb7ujJwR/UMeIWjNTNRDS0eXXUFPlhsQYFLtZcgWi98KcSVBkyXJgiBSP0aNHD+hj6Gtf+1qeWkOy5VtnTscXT5iMCo+1Y9+AYFuC6JvVxOV0g+WxFhO17GQRwt6R1VgB8xhLyFRC9qhhXtzyvaPhjPjctkEEzE87cixWbNiP9u5Q7BF6V4MkMRw1Y3h4cGTQ9ebvHoVKr6P/lJYMvXC5GZTOZF30VPU91fX+p03423/X42ufG3hT0zBE1EahgDkhhGRHSgHzz3/+8/j85z8PAFiyZAn+/Oc/45BDqMdlQgghpFAEVR26zhHUOLjBEdAFVJ1DlsyMQJ2LkiuvYRgcz723Ax9sMGu3Th9XhbOOHjeoR+CLnT+oh7dzSDPQ2aPCpkhmsFw2/4UQkGQzu5zx4njcnpCiEeMwm4tDL2MsZrA8aYZ5gmjbYAJxg4nhxW5z6iuxb8yYGeYpTmttiQd5+mWFp1L+zGlXMG9KHT767EDUcK/Lhl98dT6u+vO7MaeLlwMdmWFe7rFHvf7FV+fjhn+uStqmSCV26pASHqOcT3+JxjA4x9a9XZgwotxSWbi+bfW3/64HANz/4qYU2kYRc0IIyQbLV0ivvfZawmB5d3f6jxISQgghxDouBDp9KlSdQ9MNcGEGy91OBZrOoelmiY5SqlvuD2q468k14WD58XNH4NzFE4Z0sBwwAxtS78W54KK3404ZYGbpFbsiAYzBaZcxvNqDMrd9QCkWQki0Yj5yJqth3l+i0BtjhbsuIn/eDgbMjQHjxfp8ufptHNCRaYTvnTMTXzph8oDhCZsWZ2MlepJswojy8N/DqlwJZh7VihTHKx2x6t9HSlaw5ZHXtuB3//4I/3hho6XlJuvo1Wwbj/peU7icEEKyI6UM80iqquIf//gHPvjgA2iaFs5KEELA7/djy5Yt+OSTTzLeUEIIIYTEtqOpG5pmQNM5VI3DZpNgGAJelw02RYLMGJQSyiLu9Kn490ufoaUzCIdNwtJFEzC1X6dnQ5nCgG6/iqBqoMxlAxeAIjEoklkeQDc4RtR64bDLqLOnGjAhZOiyWucbAGZNqIVNkTAxIkCZdNo8xCUtf7YUGzm4chYDlxG52JhB74hp+gLgsWuYD5w6blZ5hreHy2H50jthE+Kt4VSCrlbaMwRLmINzkfwJiwTvv7JqDwDgvXVN+OYZ08PDP9iwHw6bjNmTamNO9+IHuzBncuz3+uj9gvnx2pnJzdYT0HDro6tx1IzhOOGwURmcMyGEFC7Lv9q///3v8a9//QtTpkxBW1sbHA4Hqqur8dlnn0HTNHzve9/LRjsJIYQQgoEdOmo6RyCkQ3AB3eAwOIdsMNRXm4FQR4llXDe3B/DgK5vR5VNR6XXgKydPRk25M9/NyquegIZASEddpbnNbTYZ3X4VDpuZXW4IASYBsixBksxSLIOpq0sISc7tVHDHlcdCkRne/3R/vpsTV//AdqIgW64C+snLyKQ2fbySLANmn6PP5bAlOe4mLd4eLd56iCzBkmhdDsE4eMq4SJxDbuXJjc6eECq8DnT2hPCX/3wKALj3J8fHfbLhd//+KHHbBgTMs59j/ux7O7C9sRvbG7spYE4IGTIsXy299NJLuOiii/DMM8/gwgsvxIwZM/DYY4/hpZdewsiRI8G59VpxhBBCCEmuuSOAnfvN0mchzYBucIQ0A5pmQNUMcGFmEGs6j7pgLhWfbm/Dvc9tQJdPRW2FE1ecNwfDqtz5blbedflUGIaAqhmAYHDYJCiKGSx3OhTIEoPDpsCumCVZZEkakjVpCcmJiO+WTZHMoFiK37d0vpbJprEafC7U8g7pHLMaqgf+PsTOTo/9wmo5m2S8yTqjjtG4dFoQFTBPMIdU12kplXNLlcFF0jszycqy9Nm4qwMA4AvqB6eNmNRqwFuI5E9cZFr8DnQJIaR0Wc4wb2trw+LFiwEAU6dOxSOPPAIAqK+vx7e+9S38/e9/pyxzQgghJAv8QQ2cC2xv7IKA+Zg0F4BmCOi9HXzWVjphlNjNa84FXv1wD5b3ZmmOG16G85ZMQlW5E13dgTy3Ln9UzQh3JibJElo7Q3DYzWC52yHAYAbsQjpDuceOMoeErh4ViswoYF7Cpk2bZinAtWHDhiy2pjQU89cl2b6QKOjXf1LGBh9CzlZwL6qGee+Lo2cOR3dAw7QxVYk7uoyzjjJ1nPzikkl4e00jli0aj9c+3GNp2sRtiL02o0qyFPPOm0ecpxoOT13ktuRCQOrdOMnqpfc3IMAeryRLBn/oaTcihAxFlgPmZWVlUFUVADBu3Dg0Njaip6cHXq83/JoQQggh2aFzAQYBwxBw2GUEQjp0nUNAwOBmZpkklU4ZFn9QxxNvbsX2RjOz/qgZDVgybyRsJVZqJh2tnSHYFPMyVmEAUyRUep2QGGDvrVUuMwaZAbWVLgT9Ici92a4SRcxL1mWXXRYOlIRCIfz973/HuHHjcMopp6Curg4dHR147bXX8Nlnn+E73/lOnltbemJW1ch5KyzoH2yLynyNfouF/y+7Yh2eIkP1Fd7Emdp900sSw2kLxyadf3QN88x/wJMPH4OTDx+TvC+RGItOVI88XpxVkSMzzAdvKP5c9C970t87axtxzKzhluYZGcDmXAC9pzF9Tw6mKi9PhQzBfYAQQiwHzOfPn48HHngACxYswKhRo+ByufDyyy9j2bJl+Pjjj+H1erPRTkIIIWRIC6kGAEDXORgDVI1DkhlCmoGaCgckiSW9wCs2ze0BPPzqZnT0qLApEs46ehwOHV+d72YVBCEEbIoEVTfAwKDIEnSdo9xjQ3W5E9v3dQFgcDsV+FUdNkVCEIDdJqOeytiUtMsvvzz8989//nMcd9xxuP3226OCNZdeeimuvvpqfPrpp/loYtEZ9JE11WBTFiKTyeaYqBrE1DGVeGnl7oPzstC8wZRVjhm0jhh09MzhcDkUTB9bFWd6a+2Kzk5PrY3h+WUyXBmzbQkC5nF+8+1KqjeUU/uwQzFWapZkycy8+vaR/hnmfW7854eW5xf1HclBDfOhuA8QQojlGubf+973sHr1anz729+Goij48pe/jF/96lc455xz8Mc//hGnnHJKNtpJCCGEDGmNbb5wNrmmmx18ahqHbgjYbTIUWYK9hLKuP9vdgfue24COHhVVZQ58/fRpFCyPYBgCisxgU2RIEoPDLsMuM9hkCRJjYBKDLElwORSMH14eLt1ChpYXXngB559/fsyg29lnn4233347D60iOZWshnmCqOCcSbX95sVQ4UlShzsTEsfLITOGC0+eisOmDoto2iCi3gUsUYa5bsQuv2aL6Fw0UcA91dVUWrfiU/PkW9uwektLRucZnWGe/nyEiP7e5mL7ZOPJC0IIKXSWM8ynTp2KF154AZ999hkA4KqrroLX68VHH32EJUuW4Fvf+lbGG0kIIYQMNbph1iRnjEE3OPxBvbdjT7O3J4MLhDQdRol1xCSEwDtrm/D6R3sBAGMbyvCF4ybC7bR8ylLSDnQE4XEq8LpkhFSzlnlf8BwAqsscuUg6IwXO4/Fgx44dWLRo0YD31q9fj4qKijy0qvhYCRUlKyeSqeWkajAZ5v2DrQzApWfPwP0vbMQZR41LPN8ch1lZnL9TmjbLNcxTb8jAQYk68DaM2OvYluIN0lQ/Xqk9vZaqJ9/alvB9q511Rq5vPogfaCGiM8xz8ltP8XJCyBCU1tVnXV0d6urqAJgnGJdeemlGGxXP9u3bcc455+CXv/wlzjnnHABmR0U33ngj1q1bh8rKSlx44YX4xje+EZ6Gc4477rgDjz32GLq6unDYYYfh2muvxdixA+vZEUIIIYViV3MPyt021Fa40NmjQjcEDC4gBMAFh2GYdcydjtLJKtd0jmfe3YFPt7cBAA6bWofPHT4aMmVHhy/MGWPhcixgDA6bAiEAWWZw2uXwUwaVXkc+m0sKxOmnn45bbrkFiqJgyZIlqK6uRmtrK1588UX8+c9/xiWXXJLvJpagAossWYz6Jjp2MAY0VLvxk6/MG2yrEopZB97C50g0qpUgZ0o3OjIZrIwxr0T9Tehx0pRTftosxVU6mOAuQXi7Zi5gDoioiiyx55XJGz4l9NAGIYSkLKWA+R133JHyDBljuOyyy9JuUDyapuFHP/oR/H5/eFh7ezsuvvhinHjiibj++uuxevVqXH/99aisrMS5554LALjzzjvx8MMP47e//S3q6+vxhz/8AZdccgmeffZZ2O05eKSQEEIIsUjVDGiagZ6AeWF0oCNgBsn7AubcDJRXeOwlcxHT5VPx6GtbsK/VD4kxnLpwDA6bWpfvZhWMptYAFIXBMAS4EPC67JAlBkkyO/gcVeeFqnE4SqgsDxm8q666Co2Njbj++uvx//7f/wsPF0LgvPPOy8o5+1AXM8M85RrmGW1KSrOMjLX932VHw2HP/zHESnC8T2TIMNH0U0ZX4uPN0aU2orLTC+xHNVFz9EFnmKf2WQdTPoTEJgaRtc+FgJTjG3NUkoUQMhQVTcD89ttvh8fjiRr26KOPwm6347rrroOiKJg4cSJ27tyJu+++G+eeey5UVcV9992Hq6++GosXLwYA3HrrrVi0aBFefvllnH766RlvJyGEEDJYe1t80Ho79+Qc8AV1yNLBx6+ryx0FEdTIlL0HevDIa1vRE9Dgcij4wnETMG54eb6blXeGwaHqHC6HAllm0HRull0R5g0TbghUlzvR1hWEIktUp5wMYLfb8ac//QmbN2/GqlWr0NXVhaqqKixcuBBjxozJd/NIAYjMTq0qS/xkSqJs54EzTrdF2XHr5cegozuEMfVl8AW0qPfi1QLPuZg3WxKVZElew9zq8mIuZ4iWZMm0yLU42FUaOXkuHgCItxv6ghp+96+PsOCQYTjr6PHZbwghhORQSgHzjRs3ZrsdCa1cuRKPPPIInn76aRx33HHh4atWrcKCBQugKAc/xsKFC/HXv/4Vra2t2Lt3L3w+HxYuXBh+v7y8HNOnT8fKlSspYE4IIaTgqLoB3TCzyVs6g6j0OnoviiXUVjohhCipzj3XbmvFM+/sgMEF6iqd+OIJk5MGbUpdl09FUDUgICC4WcNWYuZ/HqcC3RCQGYMkM5S77Sh30xNzJLHJkyejoaEBzc3NGD16NGS5dI4hhWYweZhZqWGetNNPKzMbTEsyJ9Znigz8x3q/wmMPd1h6yLhqHDK2Cht2tgMAAqqRlXbGcsKC0Xh15e7Yb1oMfMbLMLcrma1hHrluFZnFXe5Qk2qgWvT7FxhcXXguADli4bnuLyDSyyt3Y2+LD3vf3h4VMO/yqShz2wruiQ1CCLGi4HvQ6urqwo9//GP84he/wPDhw6Pea2pqwpQpU6KGDRtm9pa+b98+NDU1AcCA6YYNG4bGxsZBt01J8WQkU/pquFIt1+RoXVlD6yt1tK6sofWVOlmW0N4dRGOLH2pvdvnwWg9aO4NgvZ1+OR0F/7OdMi4EXvtwD97+xPw9njqmEucsnghnipnzsiRF/VsqfAENAdWA3SaZJVYcEjp9Glx2GW5JQZnHDlUzzBrmSO1chL6H1pTa+lqxYgVuvvlmrFu3DowxPPbYY7j77rvR0NCAn/70p/luHskzazW9LczXelNSn3eSmadSPmL6uIMB83gxvWzE+i4+41C8unI3qssHf2M4Xmb86GFeAIAnQ51lU4Z5hkTsuIPt9FPkuNPPeN+FWDdPVm5sxl1Pr8OSeSNxwclTs9wyQgjJHsu/oj/72c+SjvPb3/42rcbEct1112HOnDk488wzB7wXDAYH1CF3OMyTj1AohEAgAAAxx+ns7BxUuySJoarKk3zELCgvd+VlucWI1pU1tL5SR+vKGlpfifkCGnxBDZ09QSh2BYJpcHlsqK50o6rCjZBmQAjAVSIB86Cq498vbsS6ba0AgBPmj8ZpR42HJFmPTng8pZONHgjq0P06KspccDoUqLoOpfeGgNtpgyQxTBlTiZ2NXXDYFYyuL7M0f/oeWlMK62v58uW45JJLMHfuXPzoRz/CzTffDACYPn06brvtNtTX1+Piiy/OcytLX6pZltnIxkw2T2sZ5sWRLWq1mZEB9mx/xAqvA3f9aHHsGtQWlx0vkO20K/jzD46FnOQ3NZ2PSv1/HmR1VUSuu8F2+jmYdqQj3nEk1uDH39gCAHjto70UMCeEFDXLV94rVqwYMMzv96OjowOVlZWYOXNmRhoGAE8//TRWrVqF//73vzHfdzqdUFU1algoFAIAuN1uOJ1OAICqquG/+8ZxuQZ3EcS5QFeXP/mIGSTLEsrLXejqCsStWUdMtK6sofWVOlpX1tD6Ss22fZ0QArA77ejo9MPnVxEKMjjl6CsRTdXizKF4tHeH8ODLn6G5PQBFZjjrmPGYPakWPb6gpfnIkgSPxwGfLwSjyHskE0JA1TnauoLmjRG7DK7rAANUDthtEjTJHK+nO4hqjw0A0N7uS2n+9D20Jh/rq7zclZWM9ttuuw0nnHAC/vjHP0LXdfzhD38AAHzrW99CT08PHnvsMQqYZ1qBxZSTNsdCtM3SR8txYLWYArkepw26HuPYksHPkNIN9iK5AVL0RNQ/AAZXkkUIkfMi5lb2FOoglBBSKiwHzF977bWYw7dt24bLL78cS5cuHWybwp544gm0trZG1S0HgGuvvRb33nsvRowYgebm5qj3+l7X19dD1/XwsMiOjZqbmzFt2rRBty/miU4OGAbP27KLDa0ra2h9pY7WlTW0vuITQkBVOTgEIBsIhHQYBgc3WMkFN3c0duGxN7YhENLhddlw3vETMWqYd1Cf0+C86NfTvhYfGBhkRYJhGJAlMyDuUGT4gjoEFxhV64EQ/c89rF0k0/fQmlJYXxs2bMBll10GYGCG4NFHH437778/H80acrIaPkoW9MxgDfNCia8m/cgptDMyxhhZAzpy0lQy/vMZpx9WObgEsALZnMUrxUD1m6v34sgZDVHljwZT5aZ/vPy/7+3At846NP0ZpsBKhjntWISQUpGxZ7snTJiAyy67DLfffnvGOtO8+eabEQxGZ5ydfPLJuOKKK3Daaafhueeew8MPP2xeXPZ2XrR8+XKMHz8eNTU1KCsrg9frxYoVK8IB866uLqxfvx4XXHBBRtpICCGEpKvbr+JARxBcCAgIhDQdhiEwrMqV36vwLPhw0wG88P4ucCEwosaN85ZMQrmHOqts6QiEO/RUJECH2akaY4AsM/NviYExVjDBKlI8ysrKcODAgZjvNTY2oqzMWlmfoaqmwokuf2pP+AwmuzIb3/HIWf7ky3Px1if74LQreP3jveZASzXMU29g3I4Ic5ENm8KK5NER88IQ0ezj545MOvqV580e3OLS3N9mjK/Guu1tg1r2UPLZnk60d4eihonBZJj322HfXduEeVOHDRiv73ug6QZufng1DhlbhaWLJqS1TCv7Cp2qEEJKRUaf/fR6vdi7d2/G5ldfX4+xY8dG/QcANTU1GDlyJM4991z09PTgmmuuwZYtW/Dkk0/i/vvvx7e//W0AZu3yCy64ADfffDNeffVVbNy4ET/4wQ/Q0NCAk046KWPtJIQQQqza19KD1q4gOnqC4FxA1QVUzezsU5GlnHcsnS2cC7y0cjeeW74TXAjMmFCNr506jYLlvXRDwKZIkCQGRZbNwLksQZEl2GQJVWUOVNC6Imk64YQTcOutt2Lt2rXhYYwxNDU14S9/+cuApzhJbN8+ewZmTazJWCeKORUR6ZowohyXnHkoKr0HjylW4napBM1G1pp9PM0YX5P6jDMslYBdZEmMqBh+xIdMZT4OW2odVackoh0XnpK89nNDtTtzy46Q7HN/Z+mMrCy32FgJeQdVPeq7Ntga5v07623tDMQdf8X6Zmze04ln3t2BQEhPe7mxxarFTyFzQkhpsHw1vm/fvgH/7d69GytXrsRtt92GiRMnZqOdMdXU1OCee+7B9u3bsWzZMtxxxx348Y9/jGXLloXHueKKK/D5z38ev/jFL/ClL30Jsizj3nvvHdARKCGEEJIrQgh0+zVwLqBz80IqGNJRXeZEfZYugPMhqOp4+NXNeP/T/QCAxXNGYNmi8bCVyM0Aq/rXLFU1A5LEYFNkKDKD16XAbpMgywySxFBT4cTwGg/qq0pnnyC5ddVVV6GmpgbnnXdeODj+wx/+EJ/73OfAGMMPf/jDtOa7fft2zJ07F08++WR42IYNG3DBBRdgzpw5OO6443Dvvfdm4iMUhGGVLlz5hdmYMroy+ciDiB9lI8zkjqhlnY1ORfu77usL8OcfHJvVm6KxYo1RZVUGUZLFqkWzhuPQcVU4f8mktOeRL/H2h99eemTCgL3LoeDQcVVJ5//FIlwnWRVVkiVznX6a4u/0ekTZun/+b1Nay4z3nRpsRRZNN/CX/6zD8k+b0mkWIYRkleU0iSVLlsT8cRVCwOVy4fbbb89Iw+LZtCn6ID9r1iw88sgjcceXZRlXX301rr766qy2ixBCCOmj6RzN7X4Mr/FAkg7+Znb5VHjdNnT7NeiGgMENCC6gcoGKcgc8Lhu6ujOd/ZMf7d0hPPTKZrR0BqHIEs4+ZhwOHV+d72bljaZztHQEUV3ugMMuIxDS4Q/psCsSbAqDEDIkicHlUDCixgN7JrMWyZBVUVGBxx57DE8//TTef/99dHR0oKysDBdeeCHOOeccuFzWayBrmoYf/ehH8Pv94WHt7e24+OKLceKJJ+L666/H6tWrcf3116OyshLnnntuJj9SwZMLLLvS67LhW2dNh633yZX+rATuUgm4y5IElyPfN0WtlWSJXAVRU6awKe02GVd9cW7qTSsCwypdGDZ3JB5IEFxlUvKVk4sbNPm0a3+PpfGjO/0Enn1vB+xpJBC8/2kTGtv8yUeMYdXGZnw7rXrnqdcwt7LZX/1wLz7Y0IwPNjTjyEMb0mgXIYRkj+WA+W9+85sBP36MMXi9XixcuBBerzdjjSOEEEKKUU9AQ0jjUHUDTrv5UxtUdbR0BdHWHYSuc3AhoKpGOOs4Mguw2O1s6sZjr2+FP6SjzG3D+UsmYUTvY/pDQd+j0pHnS7rBIcsMbV0h1FQ40NGtQkCguswBRZbAhYDDJoNzUTLleEhhsNvtOO+883DeeedlZH633347PJ7o7/Ojjz4Ku92O6667DoqiYOLEidi5cyfuvvvuIRMwv+DkKXj2vR0pldLItYXT+wWiIq/lMlySZTC+dMJklLltSceL9SRWVNA7hXbyeNm+LOafpJ9kN4Y8TqXkV+Cjr2+xNkHEbtbZE8KTb21La7mvfLgnremA9LsQyNam7A6oWZozIYQMnuWr83POOScb7SCEEEJKhhACnAswMPQENARVHW6HAm5wMFlCe48KhyKBMaDMY0srw6gQGZzjjY/34d215qO1w2vcOH8Idu7Z1BqAyyGjsswBTeewKRKE6AuEc7R1hSDLDACD3SabgXWDo77KjaBqQCrxrDySO3fccUfc9yRJgtvtxtixY3H00UenVK5w5cqVeOSRR/D0009H1T9ftWoVFixYAEU5eGmxcOFC/PWvf0VraytqatKvZ53rG0hybxa2HCMbO/KJof7tOvnwMThpweiYWbWR80r0eaQUx4vELIwbbk/E51g0ZwSeeXc7Zk+qTTofRRnYv0ai9RWLJLO4yzn1yLEJp/2/y45GQNVRVzXwyQjzmGqy2STIUuL2RG4nxg62KTILX47xeQcj2bqS5Pj7Vyzptu2oGQ1YvbkFi2YPx4ad7XHnq8gMujEwwhprP+jvrGPGR22ToU5RJAgcLI1iZLjzWylGxr8kmcuNfO/guUhqAiEdL32wCwc6DtZIl2UW/v7EOiZGnsMkW1aiY2o2WT1uDWW0rqyh9ZW6YlhXaaWz7d+/H+vWrUN3d3fM95cuXTqYNhFCCCFFTQgzYy2kGeGM8m6/BoMLqLoOTTcgywzlHnvMR+SLkS+o4Yk3tmFHk3luMHtSDU49YsyQLC0iSwz+kA6XU0FbZwgAYFMYHHYFiiRBYgg/fdB3sShJZt1ydzF2KkgK1jPPPIOmpiaoqgpFUVBZWYmOjg7oug7GWPhpiEmTJuGf//wnqqvjl03q6urCj3/8Y/ziF7/A8OHDo95ramrClClTooYNGzYMgNn/UboBc0liqKrKz9Mp5eUDg7K2iOOZlXZ5vY6UpvO47SmNF4mlsY5croPLGT2iEv/+f6emdMFaWelGVZkz5nux1lfsZTvitjfZ50j0flm3GjGeN+qmQCx2uxL1d9+8JdvB4eXlrqzsf/HWlduVePufsnAs/vf+zoTjpOJnFx8Bw+D4+LMDMd/vm6/dJkM3BpaJq6rywOlI/CSA252585uzFk3AM2+nl41dKCoq3AipRvi1x+NIMLZ1LufA7WGzmfu1O2JZAtb2mwcf/wQvLt8RNayi0hP+frli7LNyROD7xgc+xBXnzcGYhvKY84/cj/JxrE/1uEVoXVlF6yt1hbyuLF+VPf/88/jpT38KVY39+AxjjALmhBBChjQBM8O8pTMIIUS4XrnBBXxB8+KTG6JkMon3tfjw6Otb0eVTYVcknH3MeBySQodgpUgIAUliYIJB1zkcdjPTMaQZsMkSAGEGaoIMTrsCr9OGkGZAFTzpvAmx6vvf/z6uvfZa/O53v8Mpp5wCWZYhhMCrr76KX/3qV/jVr36FiRMn4gc/+AFuueUW3HDDDXHndd1112HOnDk488wzB7wXDAYHZKg7HGaQJhQKpd1+zgW6utKr1ZsuWZZQXu5CV1cAhhH9vdS0gwGv9nZfyvP0+Q6ug0TT+f1qSuNFElxYagsABALWlwMAXZ0BQDeihiVaX7GXHYq7TKufI1J398EM2I4OX9LfV3/EOggGtfCyuyO2QVdXAO2OzN30Tbau/Em2i6pGB68Hs74AoKcnGHN433zj3XRob/dB77cf9BcIqBkLmFd5i/8ptc5OP0IRx4/IY0ImBIPagGGapqO93YeAP3pZVvabdVsG3lRpa+sJb9vI5fbNl0fs25t2tuOG+1bgpu8cFbvdoYHT54LV49ZQRuvKGlpfqcvXuiovd6Wc1W45YH7bbbdh5syZ+PnPf47KykqrkxNCCCElTQiBnoAGLgR0zs065b1P3nLBofVeZHIhsl4PNhc+3tyC55fvhMEFasod+MLxkzAsxuPyQwUXArLMoOrmzRGvywabIpmP/PeWYZEY4LRJkCSgpsJp7it6Zh/PJgQw641feeWVOO2008LDGGM48cQT0dLSgj/+8Y944YUXcOmll+Kmm26KO5+nn34aq1atwn//+9+Y7zudzgHJNH2Bcrd7YL1pK3Q9PxechsEHLLuvzwnAWrvKIrIwE02XzvyFxbakuxzA7Ish3vix1lfMZRsi7niD2daRx1BD5+BJfmCNyPH5wTYZRvR8srH/xVtX3Ei8XSK3W7xxrOg/v/7ztcUpkaHrPOn5C+cAZ5n5XRMZLl+SD7rOo/a5TO9XsdaR4L3LtbjfxOqHJZKmGeF67CKFY0m3X4v7XrrHokxJ9bhFaF1ZResrdYW8riwHzJubm3HNNdfg0EPT6V2ZEEIIKW1dPrW3U08zy0bVDejcrAWq9j6Oa7fJ0DQe92KkGOgGx/9W7MaHvY90TxldiaWLxoU7OR2qOEe4di7nAk6HAokBmm5ub0liKHfb0eVXMarOC8YYZMYgF38CHSlAjY2NGDs2dl3okSNHYu/evQCAhoYGdHZ2xp3PE088gdbW1qi65QBw7bXX4t5778WIESPQ3Nwc9V7f6/r6+kF8gtIwYUQ5vnjCZNRVxi5nEpajn4R0f3qy8VTU1V+ai/ueW4+vfm7aoOYjInpUTOW3lUf2wFjgAdkKjx2dPhVzJ9fhzdX7MjbfZGspUYZ4sk4/WQrzH2oi99H+QeysSGMDCCFw04Mfg3OBn10wL+Y4PDKuFXMZFhZc2F89QsgQZ/mqds6cOdi+fTsWL16cjfYQQgghRaknoCGkGuj0qfAHdTNrmAFqb2BcMKDCa4cim0HTkFqYd9JT0eVT8djrW7G3xXx89vi5I3DMrOFFfQNgsDg3nyyQZQabwiAxBq/bjiqvA5VeOzbt6gBjDCNrPbDbZFSXJwmcEZIBkyZNwmOPPYZFixYNeO/xxx/H+PHjAQA7duwI1xyP5eabb0YwGF2+4eSTT8YVV1yB0047Dc899xwefvhhGIYBWTZLWCxfvhzjx48fVIefpeTkBaOzMt90jrqL54zEKx/uwbwpdRlvj1WHjK3CH757dM6XG5kZm4vY5WDceMkRaGoLYPzwspwuN16GORC7k8koLLUbF6lIZT6VXjs0nYfL3hUazkXUfZmMB8wzdPrlC+r4bHcHAPNcLxae5AZTLk8FX1q5Gw3VbsyaSL8zhJDMsxwwv/baa3HppZeip6cHs2bNgss18LHrBQsWZKRxhBBCSLFo6QzAMERvzXIOMCAYMlBV5kBHtwpDYnA5Dv7sup3F2dnnjqZuPPHGVviCOpx2GeccOwGTRlXku1l5IYR5ASxJDN1+FYGQAVliqKlwwa7oUCTA7VTCmeWMYUh2gkry5/LLL8dll12GpUuX4pRTTkFNTQ1aW1vx8ssvY9OmTfjTn/6E9evX4w9/+APOPffcuPOJlyVeU1ODkSNH4txzz8U999yDa665Bt/85jexZs0a3H///bj++uuz9dFKUq7iTF6XDf932dGWM8YzEQgbU5/boG88kUG/Qi/54XbaMGFE4k42syFWhnnfqkoWMGfh/8uNKaMrMWtiDe55dkPuFmrBHU+twyVnTA+/zkmGeRoivxcszjaO/L6ksomzFUD/bHcHHn51MwDgvp8uyc5CCCFDmuWA+Y4dO9DS0oI77rgDQPQdXyEEGGPYsKEwf6gIIYSQTOsJaAiEdBiGQEgzzNIrOocsm3WrXQ4F7d2hqE6QipEQAivWN+PlVbshBFBf5cJ5SyahqsyR76blzYGOIAxDYHitGwJm5zVCCMgSg92uQJGlcKdpisxKomY9KS7HHXcc7r33Xtx+++244447YBgGbDYb5s2bh/vvvx/z58/Ha6+9htNPPx1XXnll2supqanBPffcgxtvvBHLli1DXV0dfvzjH2PZsmWZ+zAkSlWZA+3dIcwYX53W9OmUVxnMIeyGbx6BxlY/po3NUofQFuOPkWUlIicdSsfpZKvMlqAkS9KAOWMZi5enMh+JsYINQgPA/jZ/VEmWePXj05VobVvZDpFPXpjHiIFTJ2t6tr5DIdWAw34w6aCtK3antYQQkimWA+Y33XQTRo0ahW9/+9uora3NRpsIIYSQotHcEQAABDUDus6hGwxMMi+G+q4nq8sd0TUfi4yqGXh2+U6s29YGAJg5oRpnHDUWNmVoZ0vrBociSwhpBgJBA06HDEWW4bDL8AU1yLIUDipUlTl7O/0kJLcWLlyIhQsXQlVVdHZ2oqamBpJ0MBC2ZMkSLFliPTtv06ZNUa9nzZqFRx55ZNDtHcqslLC45sLD8OFnB3DMzOFZbFF/6R/DRtR6MKLWk8G2RLMafiymDPN8ifWb1beLJqthbo6bw988lvkgdKZF7mbJyppkQjprP5VVGNX2GNu4f/A+E3vBCyt24rHXt+K7S2dg/rT45cPyjXORvFwRIaRoWA6Y79u3D3fddReOPjr3teYIIYSQgiMEOAd8AQ0SY9ANDo9LQbdfQ5nbfIS6mDvCbOsK4tHXt6K5PQCJMZx8+CgsmDZsyNYrNwyO1s4QbDYJEmOQJYb2rhDsNgl2mwy7IoHBzMzq+w/AkM7EJ/kVDAaxadMmaJoGIQR27NgBzjkCgQBWrVqFH/3oR/luIklDdbkTJ83PTl30eAr5sO9xWvudjQqSR8b/MtSebJEYy0mwFUDMm+Ipl2SJ87YiS2bZugxjYAkD5gumDcPKjc1x38+FDTvbw39nOrj/wP82DRi2atMBNLb6LM0nsl3xbiRFZqFb+b5wIbB5dwfG1JeFSxSmuhYee30rAOCe59YXbMD8vuc2YPWWFvzmWwvhdeW+hBIhJPMsX8FPmTIFTU1N2WgLIYQQUjSCqo6mNj84N//WDQ5FkuBxKfC6bChz2/PdxEHbvKcDT721HUHVgMep4PPHT8TYAqk/m2uabsAX1OEP6lAUGSHVgCQxKLIERTaD5C6HAokBHpcNnT4Vdltx1qknpeP999/H97//fXR1dcV83+PxUMC8gJQVYJDlsmUz8Oen1gEo7ID58BoPzjt+Eso9qa1DLmL/XegkiYEbuQqYp1aS5cTDRuGVD/dEvR+vJMsZR43F029vt9SOVPY7xhJvx0LYd/e1HAxe5+qmx2//9RHOXTwh7vucCzzz7nZMHV2JQ8ZVRz95gdjrLen3pf80vTN5/aO9+PfLn2FsQxmuvSi9Pu8YGD7Z0oLdzT2oqTjYefqP7nwXnzt8DE7M8U3ESO+sbTT/XdOIzx0xJm/tIIRkjuWA+c9//nNcddVVMAwDc+bMgdfrHTDOiBEjMtI4QgghpBAEQjr2t/sxpr4MDEB7dwgdPhWqqsMQQLdfQ7nbjm6/BpddKfrsa84F3li9F++sMW+Qj6rz4AvHTyyJmwDp6gnoYDCz4+wKgwYp/NqmmBlzo2o9sNvk3nI8DPSUP8m32267DZWVlbjhhhvwzDPPQJIknHPOOXjrrbfw0EMP4e677853E0mE2ZNrccJhozB+eOHcmBwdcZM0c1Wps8NKkCo6wzeqinnG2mPVuBS2uyQBMLLfFiB2SZY+wZAe/jtWxni8Tj/ltMpVpFL+JXZHmrMm1mDJvJF4b13+E/4qvQfPoXJVPqYnoCV8/911jXjm3R0AzI4zozPMY08TOU6s093+m7jv5bu9AeWdTd0J25QQA/74+BoAwJGHNoQHt3WF8OArm/MaMCeElB7LAfOLLroIuq7jV7/6VdyAAHX6SQghpJQEQjo0naPbp6KtOwTGzIvFYEiHzgXsNgletw0OuwwlQUZWMej0qXjyzW3Y3dwDAJg/rQ6nLBgNOUHnX6XIF9TgtMnhzy1LDG6Hgp6gBkWWITEBRTYzzO02CQ6bDLvNfHydseSPqxOSC5s2bcKvf/1rnHTSSejp6cGDDz6IxYsXY/HixdA0DXfddRf+9re/5buZpJfEGL5y0pR8NyO+EjqsRWbSFkqG+eRRlfj+52ehvtodd5x0OmtNV6IMcy0iSB5z/bHctpUxBnuM9l586jRUeB1Y/un+nLUlHsPIzz6XKInjQG8/PH0ibzqs2doSc5rkNf9jLy/TySTt3dTpJyEkuywHzK+77rqiz5wjhBBCrFBkCZrO0d4TAucCQgiouoGQxiEgUOY261MnurgsBpt2d+CZd7YjEDLgsMk446ixOHR8db6blTONrX5Uee1wOhR09WgIKDpqK13wBTX0BDR4nDbYFBkSMy/8nHYZjDFUeh2o8BzMHGOMQZIAt8WauoRkGuccDQ1mFt748eOxZcuW8HunnHIKfvKTn+SraaQIldIVYGRwNTIAmO/L3NmTahO+73EqCKq5STEfP7wc766NnZkdHQ9IPfqbThwhpZIsAI6e2YB/9q/l3TtxIey7a7a2hv8WhXKXpt+aicwe//vzGzFm2MBqAjzq+5LixkFmvluadvBGDT3FRwjJNstXcuecc0422kEIIYQUpEBIh4CZdaNqBoQAgqqBQNBAZZk93HFRMTMMjlc/2ov3ezOwhte4ce7iCagudyaZsnQIIcBgPr7ssMuQJRbOAOvx65AYA2MMDkUCB8AMAZdDQV2lC7LEBlw0jq0vowQDkndjxozBpk2bMH/+fIwdOxaBQABbt27FxIkToes6fD5rHcKRIaiAgsmZ9PnjJoWzjosp8Hb5ubNw97Prcc6x8etSZ8pxc0ZC1TgOGVuF6/+xMu54sWK/DLH3l+wUZDEDt7E6KQ23oQD23eaIbO5Y5WPyof/DcKm0iwtg0652NNR4Yr4f7ziRieNHstrvr6zaTWVZCCEZY/kqf+XK+D+WfRYsSK8TB0IIIaSQ+IIaGlv88LgUGJzDH+KwKzL8Qb1kguWtXUE89dY27GvxAwCOOGQYTpg/CsoQK8HChYAkSxAQEAJgEoOuc3T7VTjtMlSdQ5IASZLAuYABwGlX4q4nCpaTQnDmmWfi5ptvBuccF154IWbMmIEbbrgBF154If7yl79g0qRJ+W4iKSqlc1yrKnOE/05eYqJwjKkvw6+/cURG5pXsY0sSi1sXXkTVuh44I7PTz9gR8ys+Pwt/6q1DnSlxg7T9/i0U/QO/EmM56wg0sg07+tUTTyVgvmZrKx5+dTNkiWFZxI0bIUTMzl4PboMYWyHDH7kQ6piLTH8oQkjeWL7Sv/DCC8EY6/foWvTBj2qYE0IIKVa6wc1Mcpi1Hf1BDbLMwLmArgsYhoCAKIlg+dqtrXh2+U5oOofTLuPsY8Zh6piqfDcrL4QAFAkwuHmxIzOAKRK6/RoqvQ7YbWZgfGStp/fmgiipbEtSmr75zW+ivb0da9aYwalrr70Wl1xyCb773e/C6/XirrvuynMLCcm/qC4/6bieUF8wMDKuGivOG6/TTwaGOZNq8dtvL8TP/vp+agtNpepHnA1XqDevBwTMJQZu5DbQ+vzynVFlYoCBnZG2dg2sE762t7a5wQVe/2hPeLhA7O0e3gZJNkVfwD1VmVpbBzoCuO2xT3DygtFYPGfk4GdI8XJCSoblq/1//vOfA4b5/X58+OGH+M9//oM//elPGWkYIYQQkg+dPSpauwKQmBkgDaoGFEWHbgjonEPnQE1FcZcqEULgnbVNeP2jvQCAsQ1lWLpofFQd7qGky6cCMC9Yg6qBzh4VksSgSBIYMzv8tNlkjKz1wKbIGNtQhs6eEMqG6PoixWP79u1RdcpnzpyJV155Bdu2bcOECRPg9Q6sT0tIJI/LFv5bKtEHj+Im9hZosLUQiKhOU2NHzBOtPStrNmZmcorzC8dqC2xbCh79WpYZ9NyUpg97YcWuAcMMHt0wX1AfME5kTL21K3TwRW/EPN722rKnM2F7wgH3yGFC4M6n1sWZIPYX12rg/aFXNqOx1Y/7X9yUmYA5IaRkWA6YH3744TGHH3fccXC73bjrrrvw17/+ddANI4QQQnKtJ2B27tjRrQISg9epQOccwZAOIcza3oV20WWVYXC8uGI3PvzsAADgqBkNWDJvJKT+hSyHkEDIgME5ytx2KJIE3RBw2iQoMkOZ2wnD4BgzzBtVfqXC60gwR0IKwze+8Q1ceeWVWLp0aXiY1+vFrFmz8tcoUlQ8Thuu+uIcKBKDXKIR86gnpwuueEdhMqJKsgx8nyWNmGd2PccvyVI4nX5G6l/6RJEYQnHGzaWUapjHGYcLASnGdmcsfu1xTT8YoOdcQJKjJ97R1B0+X03V2m2tmDUxcee5kdRc36kghBSNjJ71HHbYYVixYkUmZ0kIIYTkRJdfxYGOAAzOwZgZWNZ0jnK3HUHNsJyxUoh8QQ0PvPRZ+OLjlMNH48T5o4ZksLzLp2Jfiw/+oAZFNgNBssRQU+GE0yaDSQxOh4L6KhdqK11DrqY7KQ26rqOqamiWWSKZc+i46pIu1zWqjp60SFVfADoqwzxGAJXFyTTuO92wlGE+qJIsfX9YWGAO9A8gy1k8D/vHCxtjDo+1xHjB8Ejxav6v29YWd76GwQcM+/vzG/BqZEmXGLPVY0wXHj/O8L4+eXJdE54QUnoyWoD1lVdeoUc7CSGEFB3OBZpafZAlCb6gDi4EFJkhpHPUlNthtzmL/sR7X4sPj72+FZ0+FXabhHOOnYApoyvz3ay86QloUGQJPX4ddrsEBkCRJcgyg8upQNM5RteZJVjc+W4sIWn6/ve/jxtuuAEtLS2YPHkyamsHZt2NGDEiDy0jJP+uvWgBPt58AKctHHtwYIEFVgtNrBrm8c6PYsawWXYyvpOWZCmwDTsgYF4gN+VTyjCPM8qfnliD+366ZMBwBkCPUZ/97TWN/eabufNsX1DDr+79AHMm1eLCU6ZmbL6pKO6rBUJIJMsB869+9asDhnHO0djYiH379uGSSy7JSMMIIYSQXNF0jqDKwbkO1RCoKXfCEAIdPSFIjEG2FcaFTDqEEPjosxa8uGIXDC5QXebA+SdMQl2lK99Ny6lgSEdbdwhOuwynXQYA2BQJnAsokgQO8yZJXaULBzoCkGVpSGbek9Jy3XXXwTAMXHPNNXEzMDds2JDjVhFSGMY2lGFsQ1m+m1GUIjOR42aYxzjmhBO+M16SJU6GeYEFyvuI/iVZ5Py38+m3t2F4jSfpeOkEthNliveJl7ked/w4wxkD3ly9D+3dIbz+8d6kAfNMr3mrn4MQUrgsB8xjHQAkScLUqVNx6aWX4txzz81IwwghhJBcaO8OoaMnBC44gqoBIczOlxQmob6quHOLNd3Ac8t3Yc3WVgDA1DGVOPvocXA6MvqAWUETQoQ78pQlCRBAl08DA4NdkaEbHDZFgsEFJInB47TB02Aza3EWeQkeQm644YZ8N4GQokJH/dREBk1jZSWzeKHqcCecmW1P3BrmFpdXX+3G/jZ/ZhqVwIAa5nnIMPeHojv0fObdHRhZl0LAPEEW+sOvbsbmAZ17spgZ5gPnO3BYwhsecWbJYPF7TOd6hJA4LF8xP/DAA9loByGEEJJ13X4VvqCOhmo3hBDY0dQNIYCgqoNzgQqvHXabVPS1ygGgtTOIx9/Yiv3tATAGLJk3EkfNaCiJz2aFP6ij26+BCwGHIkFRZIABsiTB5VCg6QacdmVAp08ULCelYNmyZfluAiGkYKWRCds7yelHjg3fjI8ZQM3xT2imAuY/+8o8XHn7O5lpVAJBNfqcI9cB80+3t8UcvveAL+m0iTLMX1q5e8Cwvn6BkhEW98d445vnuZTlTQgZPMsBc7/fD7c7OuPuk08+wezZszPWKEIIISQbWruCMAyB9u4QOn0h9AQ02GQJQc2ApgtUlxd/5rUQAmu2tuL593dB0zk8TgXnLJ6A8cPL8920vOBCwKZIUDUOmyJDlhgkSYHTJqG+2gVfQEOFx4G9vTXsCSk1qqri8ccfx3vvvYcDBw7gN7/5DT744AMceuihmDVrVr6bR0hBoXulqZk8qjL8d+wM89jrMuclWSz2+lnusQ8YNnFEObbu60q3aTGt3Ngc9TrXAfMXVuxMe9pYmeDJ6KnURk9hnChxa7JYmw0hhMST8pF5w4YNWLp0Kf7xj39EDe/s7MSXvvQlnH766di6dWum20cIIYRklME52rqC8Ad1BEI6VM1AMKSjunzgRVKxCakGnn57O/7zzg5oOsfYhjJccub0IRMs51wgqOoIhnTsa/GhsdWPHr8OW289ckVmkCRAkcyLaI/ThmFVbjjsMhqqXGioLu4SPIT019bWhnPPPRc33ngjdu7ciTVr1iAYDOLNN9/EhRdeiI8//jjfTSSEFJOIYOSIWrN8x5GHNsQZL0YN875OPzNdkiXZ+2kub8qoCvzkK/PSm9gCW45rmA8mQG81ExxItYZ5Oq0ZiOLlhJBMSelIuXv3blx00UXo7OzEpEmTot6z2+34+c9/Dr/fjy9/+ctoamrKSkMJIYSQdLV3h7Bzv1l+RTcEgpqBoGbAMMz61i6HApsi57uZg7KvxYe//Xc91m5rA2PA8XNH4MKTp8TMlipV+9sD6PJpaOsOQZEkSIzB41TgdiqwKQyKIkGRJUiyBNavQ0+30wabQhnmpLT8/ve/h8/nw/PPP4+nnnoq3BfRH//4R8ycORN/+tOf8txCQgoNhdtS9cuvzsd1Fy/AnMm1A96TGIudYd5XIiXDbYmXYT7Y8mq1la6cZH8rOT7/sA3iM1nOBEdqAXOrnYnG7/TT2janbzwhJJ6UjpR/+9vfUFVVhaeeegonn3xy1HsulwsXXHABHn/8cbjdbvzlL3/JSkMJIYQQK4QQ4ELAH9TR3m2WXzEMgZ6AhkBQh24ICAg4HDIqijioLITA8nVNuO/5jWjvDqHCY8dFp07DotkjIElD7DJAAOVuGyTGoNgk2G0SHHYZiiLBblNgkyXIcm/QfIitGjI0vf766/j+97+PsWPHRgURHA4Hvv71r+PTTz/NY+sIIUUnIkrpsMsYU18Ghy12wkGikiyZTjGPW1FtkAH6XJXoyXVJlsEE6K3GyxlDSp1+Ws0wz1RGOiGExJNSsdbly5fj0ksvRWVlZdxxampqcPHFF+Pf//53ptpGCCGEWOYP6ugOqNANAU034LIr8IfMTj11ZkA3zLre/qABmyIVdbC8J6DhP+9sx9a9Zm3NQ8ZW4YyjxsLlKP5a7OmQZQanQ4FbF5BlBgbzgr7C44BDkVHhtYeDhrm+OCUkH0KhUNzzd1mWoWlabhtESIGjGubW2RQJv/zafHAucOMDH4aHswRh6oxnmMeZ42BrpueqA/CcB8wHUQJGWIyYM7CUOv3MVOexg91kXAjc9dQ6DK9145xjJ1qengL5hJSOlK6oDxw4gLFjxyYdb8qUKVSShRBCSF61dAag6RyMMXDO0a1rCIR02BQGzTAvSCq8DpS5i/uMdtu+Ljz99nb0BDQoMsMph4/BvCm1Ge9Iq5DpulmPvtxtQ3NHAAwMLoeCcrcdLZ1BMAY0VLvhtCtAmSPfzSUk52bOnIkHH3wQixcvHvDef//7X8yYMSMPrSKElJr+faUwxuB2xgg1ZKkmS7xTHzbI5eXqnGowAex0DKokSxoR4dQyzEX43/tf3IhhVW5MGV0Zd/ztjbE7Yh3smty8uwMffnYA+AxpBcwJIaUjpYB5dXU1mpubk47X1taWMAudEEIIyTbGGDSdQ5EZdEOACw5AQNUEbIqZhQygaMuVcC7w5up9eHtNIwCgrtKJcxdPxLAqV55bln2qZkCWGOTeC70uvwpV4+gJaJCYBEfvI8ZVZQ60d4fAGIO9yGvTEzIY3//+93HRRRfh7LPPxuLFi8EYw7PPPovbb78d77zzDu655558N5EQUoKqvA5UeAc+wdd35pXpzO1kge10l5arU8XcZ5gPotNPqwFzlmKnn73/bt3bhbc+Mc9xf37hYRZbB+sp5v1G1/TkbU2kuNNxCCGRUjpSLliwAE8++WTS8Z5++mkccsghg24UIYQQko62riAMzqEbHAYXUHUO3RCo9DqgyAwh1UBdRfEGlrt8Kv75v03hYPm8KbX45hnTh0SwHADaukJo7giGX2s6h02R4AvoUGQGu00GhHnhLMsMisyK9sYIIZkwf/58/P3vf4fL5cI999wDIQT+8Y9/4MCBA/jrX/+KhQsX5ruJhBSUIfSQVlZcfu5MnHf8JEwaVYFKb4wnu/K0fmstnPtddOq08N+5yzAv4RrmAIwUJuoLxAdVPWpaqwa9xegYQAjplVKG+YUXXogvfelL+N3vfocf/OAHcDiif/xUVcWtt96Kt99+G3/729+y0lBCCCEkFt3g2N3cg9oKJ5ra/LArEkKaAUli8AU0yIqEMreCqjIHOBdFG0D9dHsbnlu+E0HVgN0m4Ywjx2HGhOp8NyunbIoEVePQdA4uAJ0L2BQJui5BZmZwvO+SbFxDecJ5ETJULFiwAA8//DCCwSA6Ozvh9Xrh8Xjy3SxCSAFaesz4QU0/d3Jd+G+bIuHCU6bCH9TQ2OrH+h1tOOKQegDRNyY8TgW+oN5/VpYkO7M74bBRaO0K4tUP9ySd17iGsvDfuaphPpgSKVZxIdDSEUh7+p6Axb4vGItdn7yfvsT1yDHTydYe7CYbSqUNCSGJpRQwnzlzJn72s5/hN7/5Df7zn//gyCOPxKhRo2AYBvbt24cVK1agvb0d3//+97Fo0aJst5kQQggJ84d0BEI6DnQGoRsCqq4hpBlgDCj32MCFCJ/8yjmuEZkJqmbgueU7sXZbGwBgRI0b5yyegOpyZ55bljtCCOiGAJPMzPGWjiAURUJNlRsqN8vvuJ0KGGPWHxUmpISdeeaZOPvss3HmmWeivr4eTufQOW4Qko5EHVUOBcfPG5nwfau/sMfPPTg/LkQ4AG1E1LS+5qvz8e7aRjy3fCcAM9AeWRYjE/FLmyLhKydNCQfMjzy0Aet3tKHTpw4YNzJgmqvYaS7PTx9+ZTNWbTqQ9vRWS5YwpFb3PFNnb4MNeA/tIwAhJFJKAXMA+MpXvoJp06bh3nvvxauvvopQKAQA8Hg8OOaYY/D1r38ds2fPzlpDCSGEkEgHOgIIqQa8bpsZKFcN6JzDJjOUu+3oCWgoc9thG8Rjp/l2oCOAx17fGu7ActGs4Vg0ezhkqXg/UzraukJQNQ6PUwFnDJAAt1OBx2WDMAzohgxJYr2lWIbWuiEkkYkTJ+KOO+7ALbfcgsMPPxxLly7FSSedRBnmhKRgKAbOsnnLOTJb2+1UIEsMXAgMq3Rh9qTacMC8rtKFfS2+g23K8X3wyHhrrrKNc3mu+koKWfaZllLAPNzp58FhRfkdpMQNQkpGygFzADjssMNw2GFmxwvt7e2QJAkVFRVZaRghhBASj25wdPlUGEJAMzh0zsE1DsMQqC4zy4bZbVJRB8vXbmvFs+/thKZzeF02nHvcBIytL0s+YYkJhPRwrXIwBklmkCSgzG2D22lDKKhC1zkkiWFYpRtOO3XySUif2267DX6/Hy+99BKef/55/OIXv8D111+PJUuW4Oyzz8YxxxwDaYjdgCMkoaKM0BUfRZbwp+8vgtRbTm3CiHLMnliD+mo31m1vsz7DGNvtR1+ck9YsImeVqwzzXNcwz6WmNj827uyIGrZpV/uA8fqqtgz2SUGrm6z/UyWD3eQULiekdFgKmEeqqqrKZDsIIYSQlBlcwB/SYZMlqNyArnMIAbidB4OldltxBk41neOF93fiw97HZccPL8M5x06Ax2XLc8tyj3OBzh4VTocMCQwSA5w2GVwANeVOjBtZgUYF2NXUDcDMWCOERHO73Vi6dCmWLl2K9vZ2vPjii3jxxRfx3e9+F5WVlXjnnXfy3URCSKHIYbTP5Tj4my0xhu9/wXxa/Rf3rIgaL6Wgdb92e102TB9nrZ+XcDZ5xAJz1e+NUoQlA61465N9Ua9vevDjgSP1ZZgPcll7W3yo8NjTn0HE9hcRZR0JIUMPXVkSQggpOiHNgOACXBLQNQ7OBYZVuYq2Q88+rZ0B3PvsejS2+gEAi2YPx+LZI4r+c6WrvScEmyJBkaTwBYvDJoMxFr4h4rQraKj2FGV9ekJyrbm5Gfv370dbWxt0XUddXV3yiQgZQuiXJP9y1RdJ/6X0xUUlNnBYqso9dnTFqIueTClnmKcqvNkjNkwgZL0z2JdW7saJh43KWJsoXk7I0EUBc0IIIUXBF9TQ3hVCbaUTjS0+6ELA0AxompldXuxB5Y072/H029sRCOlwORQsO3Y8Jo0cWmXPgiEd7d1mRrlucGg6R6XXLLHjsMkIaQYqvQ5UlTuiniCgzHJC4tuxYweee+45vPDCC9i6dSuGDRuGM844A7fccgsmT56c7+YRUrDK3EPvya6i1XsK+KUTJuOhVzfjkjOnW59FjMioZCFaetbR4zB7Ui1+ff8qy8tONWDOGPDFEybjoVc2W15GoePhDPODEfNbHv0krXntjaiB358QAq2dQVRVxe7LI3KLcyEg0W00QoYsusIkhBBSkDSdo9MXQm2FC76ghub2AAyDo7HVj26/Bt3gkBiDojDUeBz5bm7adIPjlVV78MGGZgDA6GFenLN4wuAeJy0yms7R0hEEALP0jBDQey9QnHYZms4hywyywVBV7rB0AUvIULZs2TJs3LgRLpcLJ510Eq655hosXLgwHBjSdR2KQpcDhPRhjOGXX5sPVTNQ5i7t3+FYidzFXn/5pAWjcfy8kWllbEvhiiwHzzH6/rzmq4fhv+/uwHnHTxpQMqbPzAk1afejkmqfO6PrvBhVS502D8ZTb2/Ds+/txLeWzsQxM+oHZJBHvqb+OwkZ2ugMmRBCSEFq6w7CF9BR6XWgx69BMzhCqgG7IqDpHNUVDnR0h1DhcUAu0kdZWzoCePKtbWhqCwAAFs8diWNnDx9yuSwhzYBNkcABOB1mgNyjSPAHzScHFFkyb470/ksISU11dTVuuukmnHTSSXC5XOHhe/fuxaOPPoonnniCapgT0s/44eX5bkL+lECEMJVgeaxTib5AeeRbfeccE0dU4MovzE5cLobFzlJPhS3F81gu0l9GodONvl4/s7ucZ9/bCQD429NrccyM+oTj8nS+D8X/FSKE9KKAOSGEkIKlc46QZiCg6lBVA/6gDs0mAUzAYZNRX+3OdxPTIoTA6i2teHHFLmg6h9upYNmxEzB/+nB0dZuZ9KWqpSMAj8sW7uyry6dCMzicdrMzT5kxcAY47AqcdhmKLMHtkGC3yejoCeW59YQUl3vvvTf8txACr7/+Oh5++GG8++67MAwDEyZMyGPrCCEkM6yGkGOOP7DPzwHB6UTBagaWdsJDqhnmQoiiL0EYzw3/XIUbLzmioOLNyerp6wbHgy9/hpkTaw5Ok+1GEUJyhgLmhBBCCorBzdrVAMANjgMdAYRUA0HVgME5mA4Uc7dcQVXHc+/txKc72gEA44eXYemi8agsc+a5ZbmhGwJdPhUuhwIhBFSNgwsBT5kNnAswxqDIMhgDxtSXQZYOXkT21TMnhKSuubkZjz32GB5//HE0NTWhvLwc559/PpYuXYpZs2blu3mEkDwZWTewtMdQDvaF49AxSrKkLM3TUyXFgDkXoqQ7oXz67e04/JBheW1D5E2RZAnmL6/cjTdW78Mbq/dluVWEkHyggDkhhJCC0eVT4Qtq8Ad1SBKDqgsI6OgO6DB0jkqvHZ0+FWWu4qwruru5B0+9tQ0dPSokxnD8vBE4akZDyT5e24dzgaBqwOWQIcsSOOcQwiytY1MkOGwSKr0OqLoBuyIjENLhddmiguWEEGveffddPPzww3j99dchhMARRxyBpqYm3HHHHViwYEG+m0cIybPaCheuvcg8Flz/j5V5bs1B+Ton6ssPj1y6lbYwln46R6oZ5qVckgUwbwjksirQ//vHSmzZ0xk1LLqGeeLG7G7uyUazomzZ24ktezpx8uGjqSwhITlGAXNCCCEFQTc4mtv9UBQJvqAOuyIhqOoIaQwMAsOqnZAlCW6nLd9NtYxzgXfXNeGNj/dCCKDSa8c5iydgVJ03303LiW6/Cn/QQGcP4LBL0A2gsdUPBgav2waHXYHLIaOqzMwg7/uXEGLdPffcg0cffRS7du3C+PHjccUVV2DZsmVwOBw4/PDD8908QkgBGdtQhp6AFn5dtCXMMxBIZDFKsliufpLlGuYijxnmiswO1hnPEs6zvwPKEoPRu5z+wfIB7UnSnO6I7062/OaBDwEA5R4bjpoxPOvLI4QcRAFzQgghedET0KDIDE67WZoj2Ft2xSNL8AU06HYJXAg4FAkVHkfR1mzs9Kn4z9vbsaOpGwAwY3w1TjtyDJz20v4Jbu8OweNU0B3QEFINKIoMhQGKJEFnAm6XDbrB4bBJGFXngU2R891kQkrCzTffjKlTp+KBBx6IyiTv7u7OY6sIIaTA9Ls70Je5HZnFazWjN+sZ5lzkLcvYXG52A9qawbFyY/Og55MoM9xuM59kjCeyEr2mc7z/aRMOGVeNCs/Ap1u7/eqAYZv3dGDzng5MHlUZt20GFyl1ThupsdVvaXxCyOCV9tU6IYSQgtXcEYDEgHEN5djR1A3GAFU3wP2A3S4hpHLUVTktn1AWCiEE1m1vw/PLdyGkGbApEk49YgxmT6op6cdpAfOCTtM5WjqD4e2nSAiXWREAPC4bgqoORZFLfn0QkktnnXUWXn75ZXzzm9/EwoULsXTpUpxwwgn5bhYhpEBFBmvttuI858qEvlORyAQNq+cnOen0M18B8xwkrqzb1pb1ZcgWPsdTb2/DO2saUVvhxO+/c9SA92Nl3K/f0Y71O9rxl6sWw24bmAxyyyOr8dmeTtzyvaPhKcKnZgkZSihgTgghJC8EF4BsnrT21bJWNQ7dEBhW5YLBedHWsA6qOp5bvgufbjdP/EfUerBs0XjUVAyNjj01w9yeEIBdkWAYAk67Er5wkGUJZW6zk88Kt71ob4oQUoh+//vfw+fz4dlnn8WTTz6JH/zgB6ioqMAJJ5wAxhjdoCKERHHYZHzzjENgcFG0AbxMHNViZZhbmz79hlirYZ7eMgbLSqC5kCX7GDwiO/3DTWa2e0tn0PJyNIPHDJh/uqMdALB6cwuOnkklVggpZBQwJ4QQknOdPhWGEJDBsOdAD4KqAUAADHA7zJ+mYg2W723x4ck3t6G9OwTGgGNnj8CiWcOLtqSMFX2PwLZ2BuF12cFlAZdDARigKBJG1HjQ2hWEqnNUeOxgYKgqp3rlhGSax+PB+eefj/PPPx9bt27F448/jv/+978QQuAnP/kJzjjjDJx++umYMmVKvptKCCkAxVYb+dqLFmS8o9JYGeaWpu/9XzpSrWHOuchfp6hFerN1e2MXxg8vx+7mHny4qRmawVOelqc+6gDp7guEkMJRnNEIQgghRa29OwjD4OACCKkG/EENgZCB6jIHvO7izG4SQmD5p034+/Mb0d4dQoXHjotPnYbFc0YMiWC5L6ChsdWP/e0BKLIERWaQJQaHXYYiy5Bg1o2sq3ShpswstVNT4czbo8WEDBUTJ07ET37yE7z55pu44447MHnyZNx77704++yzcdZZZ+W7eUXl7GPGAwCOnzcyzy0hZGjrf16VzqnExacdEj3PGBnmwmLN7nRPaayVZElvGYNVTBnmG3d1hP++48m1AIBr7/sAz7y7A4GQkXDayPrniWqhJ5PP09vtjV14/eO9cdu/Yv1+XP+PlTjQEchxywgpLpRhTgghJOt6AhoCQR2VXjs6elRwLmAYArpuQDcEuBAI6RxlRRosb+0M4pl3d2B3cw8A4JCxVTjzqLFwOkr7Z5ZzAUliaGzxgzHApsgwDA6nXYYsMUi9TwnY5INlIPoC5YSQ3JJlGSeeeCJOPPFEtLa24sknn8TTTz+d72YVlTH1ZfjrjxZTJ8WE5NmgY5EMmD2pFn/90WJ8++Y3o96KCgxnt4/LMGslWfITiZXl4gmYRwqqBl54f2dK43IuoOoH08p5jrZ/pv36/lUAgEqPHXOn1A14/6/PfAoA+Of/NuGq8+fksmmEFJXSvpInhBCSdwYXONARgM+vosunQjc4NN2AbnBwLqD3Pu9o6LzoMrGFEFi9uQUvfrAbms5hVySctGA05k2pLdpHV1OlagZaO0OorXRAlhkUiUFRZAS5gMdlAxcCdsVcB2ZwqUivOggpQTU1NbjkkktwySWX5LspRYeC5YQUgAydYkV+n6VwSZaD71s9c0n33C/VvlyEEHnLXC7WJwINzvHYG1tTGvfav3+AvQd84dexMrRDmoH7X9iIeVPqclp0JZ1k932tPszFwIB5n2BIH0SL0teXcENIoaOAOSGEkKzad6AHggu0d6uw2xSENANBzQA3BLjgMHo7+Sy2jh+DIR3PLt+J9b2d94wbXoazjh6HSm/p1uTuCWiwyRIcdhm6wSHLDB09KiTG4HAosMkMusEgywxMMFR47Kgqc2JvSw8UiYJMhBBCCBm8wSYlRNaXnjWxBmu2tuLE+aMBRJd7sVKSYzBNSrXcCRcircD12Poy7NzfbXm6SEUbMDdS34aRwXIgdpD6fyt24f31+/H++v0YWeuJO69BVHMpae+sacS/XtqEKz4/C9PHVee7OYQkRAFzQgghGRdUdcgSg6JICKlmgFxAwB/UoBkcoZABxsyTyYYaV9FlY+9u7sFTb20LB4uPnzcCR81oKLrPYZU/qEMIoL7aBQBQZAZNF7ApgMwYZEmC066g0uuAwyabHX4CGNdQns9mE0IIIaRI9A9Sx+o8MZPJqZctm4k9B3owtqGsd97pzzzdSSPPH0fUerCvxRdzPM7TW0YmTk+LNSPYGERdlVg3TNq6Q6lOnfZycyYPm/S+5zcAAG5/Yi3uumpx7htAiAUUMCeEEJIR5mOiDCHV6D3RZ6itdMIAg6qZZVd6AhoYYxAQEAIo99iLKsjMucB765p6O9IBKr12nLN4AkbVefPdtJyQGIOkMKiagc4eDV63DYxxuOwKFFmCLDPYhQyvy1Z0TwwQQgghpPBY7XjTKpsiYfzwgzf2I89LrcZaM3FGe9L8UTC4wL9e+mzAe+lmmGfiXLtYA+aDEWvzG5wnfD+V94rViyt2YcWG/bj6i3Pgdg6u36kiuvwjQxgFzAkhhGTEjqZueF02dPs1dPSEoMgyFIVBSDI0w4AiS6gudyCkGrDb5JQ7OCoU3X4VT729HTsazUdaZ4yvxmlHjoHTPnR+SiUJcCgyWjtD8LptcNhluCBDkswyLVVeB4KqTsFyQgghhGRNwZYHyUC7hIgfeJ87Ob0+cjIR6x6C8fKYeIp3UTJdkmXVxmZ8/riJmZ2pRY++vgUA8L8PdmPZsRMGNa+C/Q4TEmHoXOUTQgjJCk03YFNkaAZHd0AD5xxB1YBdAXr8GmSbgKYLSMzs1EhxFV8w9bPdHXjmnR3wh3TYFAmnHjEGsyfVFFV2fKoMzqHrAg67WXM8ENKhGxzdfg0uhwKbTYIkMZS5bRACqKt0gXMBr9sGiTG4nXRqQQghhJSShmo3mtr8OHRcVdaX1f/cKlZJlnKPPevtAGAp6pko0J1IX/m6PlyImPO58JSpWDi9HoE0OmrMSIZ5CZ7zpmMwJV4Go7kjgJ1N3eHSQZnQ/7vV5VexcWc75k6uS5jYpEdk2adrKD6xQIoPXdUSQghJW5dfRWtnEPVVLnBDoMMfhKf3ET1N5/CHdFQ57aj02iEX4XmRpnO8smo3Vm48AABoqHbhnMUTUVvhzHPLsqetKwRdF6itdEKRGTp7VHAhoEgS7DYZMmOQJAaP04a6Sle+m0sIIYSQLPvRF+dg+adNOHb2iHw3BYCZgHHHlYvwvdvezupyLGcJp3Gue+v3jh64zBjzOX7uSABAUDUsLyMTsW5GAU4AqQfMk3UYm8422d/uz2jAvP9+9rt/fYSmNj9OP3Iszl0cP5s9nZsnnT4Vj/dmqJvzsDwLQnKOAuaEEELSpmoGdIOjsdUPLgSCqpltXum1w+VQoHOgusKJnp4gDGPw2Qi51Njqw3/e3oHmjgAA4Ijp9TjhsJElX25ENzhkWUJLRxB2uwSbIiGkGVBkBodiZpc7bHJJ1mYkhBBCyEDV5U6cfuS4fDcjjDFE1VA+ZGx1VpZj5VxHIHZmeDJ2mxz1mnMBOUGWSTqZuYUQmxxe40Zjqz/fzRi0yJIs8TpnBYqzhnlTm7l9Vm1sznjA/IH/bcJHnx0Ivy7Fp3RJ6aGAOSGEkLQFQwY0nUPXOQwhUOGxQzc43E4bGGNw2qWie4TTMDjeXtOId9Y0gQsBt1PB0mPGY9Koinw3LauCqg5dN0/vFYlBYgyqyuFyyGYpHdkMlpe77dB0I/PFGQkhhBBCLLj18mPQ2hnEhBHlyUeOlOKpabIs4ehxYwcB7TYJE4aXY+OuDgvLjN/AtE6rGYPTLqeVnZ4J5xw7AacdORbfvOn1vCw/k1IuyZKB0+Rd+7sHP5ME2rqC+M8723H83JFRZY6SNT2dmzZ9wfjBzIOQXCvtNDlCCCFZFVR1qLoBX1BHIKTDaZdR4XVAlorz56XTp+IfL2zCW580gguBQ8ZW4TtnH1rywXLALMXiC+oQApBkBrsiweOyQZIkVHjsUBQJjDFUeO2QJakoM2cIIYQQUvz6gsYVHrv1YDkATxb6W4kXW7/zB4sxqs6b8nySxWPTTUS55sLD0pquT7yljqrzJJ+WFUaWeyakXJIlA8u67u8rMzCX+Nq6QvjPO9vx12c+jX4j6T44+GW3d4cs3ZAiJB8ow5wQQoglHT0h6DqHwy5D1TkCQQNet4Iyd446YMoCIQTeW9eEd9Y0IaQZcNplnH7kWEwfVzUkHhns60DKJjPIkgxFlmGTGRRZgsE56qs9aO0MwOOyQZYYZJmhzGVLMldCCCGEkMJxyZnTsfzTJpx51LiUxrcSzxMQMbO/rWbS8hgLPXpmQ/jvdE9L+zpzT0elN9E5fvIGSYyVzPk0TznD/OB4XAgYhkjYkWY+bdzVHvVaJImYZ2pb7m8PoKHanZF5EZINFDAnhBCS1PbGLlR6Hagqc6CtKwTOOUI6h6YZqK10wKakfxKeby0dATy7fCd27e8BADRUu/GF4yeiqsyR55Zll6ZztHQEISAgS2bpHKdDgW4IuHovqmoqnHDYZLgcCsrcNjCYJ8njGqxncxFCCCGEZAJLM1/5yEMbcOShDclH7JUscNhv5LitijcXOUYwXQgRFai/9fJjUO4+mKSQboZ5/1rpVkwYUYG2rmDM91JpTqybAMUqnT6Zfv/vj7CtsQu3Xb4oCy1KbuPOdtRWOlFb4Yr5vtV9KlN19FMub0NInlDAnBBCSEIG5wiEzJIrXT4VumEgEDKg6QY8LlvRBst1nePttY14d20TODezPk45fDTmTKot+bp6nAsc6AjAaTOfEnDYpN5/ZQgYqKt0IaQZqPDYw1kkxVaLnhBCCCFkMKxlmANxQ+Zx5vPD8+cMGNY/g7nCE53dnXaGuYWA+e8uPRI//cvy8GshYmfPA6mVWmnrDqW87EJnpLhTRI712Z5OAMCnO9rCw9K96WPVlr2d+P1DHwMA7vvpkpjj9L/uSfYRM3ZJUEI3UkhpooA5IYSQhLp8GnRDoCegQmKAwc0SHrUVTigF+mhhMo2tPjz11na0dJrZMpNHVeDUI8agssSzyvvoBociSXDYFXChm516KjJkmcEhJJS5bShnxVtihxBCCCElrBDv4Yv4gcR4meo2+eB59ImHjcKqTc04ft4orFi/P+5i0imHwYCUy4FUeO2oKbdwPpxCc0opLppqSZZC+cxbeoP1APD6R3tiltDUdI4n3tya8jwlxqBqBm584ENMHV2JL580Ja22FcgqIhFUzUBbd4hK5fSigDkhhJCwvs5XdIODCyAY0tHeHYKuG3A5FHT6NDCJoarMUZTBck038NYnjVi+bj+4EPC6bDj1iDGYNrayZGorxhNSDSgKQ0tHCE6HDEVmcDkVGFxAkhgURcKoOi84FyW/LgghhBBSnH79jcNz9tSb1RrmVkV+jC+fNAVfOnFy0nOwdB+CTHWdXf3FuQPakGg9pHLOWEqdOw6mjMhg10M6k0dungde+izueM8t35nyciTG8OGmA9jd3IPdzT0JA+bvrWtEhddRmDe5yAA3/PND7DnQg598eS6mjqnKd3PyruCjHR0dHfjVr36FY489FvPmzcOXvvQlrFq1Kvz+hg0bcMEFF2DOnDk47rjjcO+990ZNzznHn/70JyxatAizZ8/G17/+dezcubP/YgghZEjrqy24u7kHu/Z3Y0dTN3bt78aBjgCCqg5V56j02lHptaPKax9Ux0H5sml3B+586lOzBIsQmDa2EpeePR2HlHDHnh3dIbR3h7CvxYeegIbmtiBcdhm+gAa7TYbEAJvCwJiZ5aTI0qBqXBJCCCGEDIbXmTinb2SdN0ctsRYEFwkyzOPqN34q56PpnLOm+ikWzRqOEbWemLHNZYsmxG5PKsu3GOm9+NRplsbPpVQ/SjZuEryzthF6GjXUrRvY9sjPI0kspbr0+1p8uOfZDfi/h1enupjUWicE7n1uPR5/I/WseJKaPQfMPr1WbGjOc0sKQ8EHzH/4wx/ik08+wS233ILHH38chx56KL7xjW9g69ataG9vx8UXX4xx48bhiSeewOWXX44//vGPeOKJJ8LT33nnnXj44Ydxww034JFHHgFjDJdccglUVc3jpyKEkMLBucDOpm7saOqCbnCoOkcgZKAnoKEroKHbr4Ex8wTdYZcLtof3ePxBDU+8uQ2PvLoFnT4VFR47zl8yCecdPwlupy35DIpYSDPrzyuyBJdDgcdlg9OpgIFBkhhssgSbLMHrsmFMfe4uQAkhhBBCYvn66Ydg4sjC6Fx8eLXH0vjx6lLHC/Knkynff5oJIzK3rs5fMglA7KD8jAk1MadJqdNPizHeRbNHYP60YdYmyjLd4Phw0wF0+VOPIxn9PngqMfT+00T6dHsbXlixK+XlpytWMyPbLrHUOv7s6DlYuz7W2OneUtjb4sO7a5vw/PuUCJst9iK73s+Wgl4LO3fuxLvvvotrr70W8+fPx4QJE3DNNdegvr4ezz77LB599FHY7XZcd911mDhxIs4991xcdNFFuPvuuwEAqqrivvvuw+WXX47Fixdj2rRpuPXWW7F//368/PLLef50hBBSGAwuoGoGDEMgpBnQDQ6DczOTQAhUl9tRVxm7V/VCJoTA6s0t+PNTn+LT7W1gDDhyRj2+s/RQTB1Tme/mZZ2uc0gSA4NZbkWRJTjtMmyyBFdfSRaHAkmW0FDthiwV9CkBIaQAtLa24uqrr8bChQsxd+5cfOtb38KWLVvC7yd78pMQQpIZVuXGNRfOz2sbrrnwMHzhuIk4akZDytMIIeKmWx87ezg+f9xEHDt7eNTwdB5w7D/NL76ayXUVu0GJs6WTf4hEQeC4CqyMy/8+2IU/P7UWnT2pBcyb2vz43q1v4y//WRceFvXEQsRqa+8O4b7nN2D5p0347i1v4em3t8Wd76fbWi233apYqz6yFE1QNbB5d0fS+STbM37/4EfWGtYrN1n2Q1uxJchlS0HXMK+qqsLf/vY3zJgxIzyMMQYhBDo7O7Fu3TosWLAAinLwYyxcuBB//etf0drair1798Ln82HhwoXh98vLyzF9+nSsXLkSp59++qDal+v6vXJvpyCyTDtvMrSurKH1lbpSW1eaztHlV2FwASYJ6IaAEAKawTG8xlpWTSx9QdhcB2P3t/nx7Hs7sGu/+VhZfZULZy0aj1E5fIzXqsGuq46eEDSdo67SBX9QQ6dPg0ORYFPMALnTLsPlVKBIEmSZoarMiQqPHU6HAlcRZtqX2ncxm2hdWUPrK77vfOc7kCQJd999N9xuN/74xz/ioosuwssvv4xgMIiLL74YJ554Iq6//nqsXr0a119/PSorK3Huuefmu+mEEJKyiSMrMHFkheXp4gUIZUnCaQvH4s3Ve/HWJ40R46fRgWcWywjG77Q0vlRqqutGYQW/07HSYomKm3vLkHwQMV28ewB/e+ZTbNrdgXfWmPvGM+/uwNI4JXCshIpfXrkbXX7NwhTxRd70eCzFUiiR+2qsj+4L6oNtFskSKtFpKuiAeXl5ORYvXhw17IUXXsCuXbtwzDHH4NZbb8WUKdEdDAwbZj66s2/fPjQ1NQEAhg8fPmCcxsZGDIYkMVRVDT6YlI7y8uLL9MwXWlfW0PpKXbGvK93g6PFr6Oj2w2ASnC47uBCwQYLBOUZWeeCN0Yt6ujweR8bmlYhhcLz0wU68snI3OBew2yR8buE4HDtnZNEEv9JdV10BHW67DRwSNM7Q0HtzQGIMbqcCt9OG0fVlA6aL/YBt8Sj272Iu0bqyhtZXtPb2dowaNQrf+c53MHnyZADAd7/7XZx99tnYvHkzli9fHn7yU1EUTJw4ETt37sTdd99NAXNCSMnrfTDTkrgB6gxnV6caYk+nRMwZR43DHx9fk3CctDrKLLD+hYKqMfiZxFkNfXWjU5pFkn0j8v1MBcuB9LZh5CbkcaYXQli+CRR5oymd6UlskTdFHJRhDqDAA+b9ffjhh/j5z3+OE044AUuWLMFvf/tb2O3RAR2Hwww0hEIhBAIBAIg5Tmdn56DawrlAV5d/UPOwSpYllJe70NUVgEGPoSRE68oaWl+pK/Z1pelmTevWriA45/AFdOgGR0jjsMkMDocCj0MBNwx0dQcGvTxZkuDxOODzhdJ7HNOCls4AnnxjG/a2+AAA08ZW4dSFY1DpdcDnDyWZOv/SXVf7Wnyo9NoRCmoAY+juDoIxwCGbJ492G4On3A6XwtDe7stW83Ou2L+LuUTrypp8rK/yclfB39SrqqrCLbfcEn7d0tKCe++9Fw0NDZg0aRJuv/32hE9+1tQU++05QgiJTwgBWU4cuOsf2Cu4QF/cAH78Seqr3UlnWwolNJo7Bn9d9PrqvTGHW7k/kuwSIRP3WmJ16Bkv4J1I5P4dP2A+uHsj6U7PhcAnm1vw/IqduPDkqRgTI6moTyCkw2GTU6rbni9dPhUrNzbjyEPrLffRpRscdzy5FiNrDyYE2yjDHEARBcxfeeUV/OhHP8Ls2bPDJ+tOp3NA552hkBkUcbvdcDqdAMxa5n1/943jcg0+a0jX83PgNwyet2UXG1pX1tD6Sl2xrqs9zT3QdANB1YDEGFTNQCBkAEygrrL3R1IIGBl+dNLgPGuBJyEEPvqsBS+t3A1N53DaZZx+5FgcOr7aXHaRnaRbWVecm1kVrZ1BOGwyGARkCSjvfTqAMWBYpRsORQbnIq2T3UJXrN/FfKB1ZQ2tr/h++ctfhvsSuuuuu+B2u9HU1JTwyc90A+ZUArGw0fpKHa2r9LB+QapcHxNSJckSnA4FP/3KPPzu3wdrM0e2V47xWWJ9Hiki8J7s86ayPhhLbTxbnPYkmj6V+TrssqXtpihSQQcn07Vlz8GkTVliB9dJjI+aaH0lei8TNydY7/8psnTweJUgKi3JLObTCZHtjBfIl2QGxeIxUVZY1N/plLO8+aGPsWarWQ/+zqfW4ebvHR1zvJaOAH54x7uYOroS13wteX8B+TrO3/rYJ9jZ1I1Nu9txxednW5p21aZmrNnaGl4fAKDILOvH2mL4TSyKgPm//vUv3HjjjTjppJNw8803hzPGGxoa0NwcXUuq73V9fT10XQ8PGzNmTNQ406ZNy1HrCSEkPzp7QhAAKr0HS3wYnEPTOfxBHQ67DC4EhlU703oEsxC0dQXx7Hs7saOpGwAwfngZzj5mPMo9mSsnU2gOdATgddngcihQdQN2mUFwCTZFhsE57DYZbqcNqm5AkSS4HEXxU08IKSJf+9rXcP755+Ohhx7CZZddhgcffBDBYDDhk5/poBKIxYPWV+poXVnj7lciMF/HhGS8Xgeqqjw4usoDRATMI9vr7ld2r6LCFfPzuF0Hx4v1/uO/OwPPvbMdC6bXx10fP/3aAvzu/pUAAMUmp7Teamu8MYNXiaavqEi+P196zmxUVSXPRO9TVeXJag3lr50+Hfc/tz5r80+Fx+MIr9NYTxrEW98sye+iqg2+dExHj4qv//Y1/PiC+Vg0dyQAwOWKXzKyvNwds5PIis6Dv/3xwvgPv7YVl31+tqUbJO3+g7XPKyo8aXVQGRkc7glqcdfpKx+ZTwVsUuGclgAAZVNJREFU2t1h6diT6+P8zt5r4Y82HbB8jGTywO+a02XP2bG2kH8TC/4q+sEHH8Svf/1rXHjhhfj5z38OKeLu0YIFC/Dwww/DMAzIvRt5+fLlGD9+PGpqalBWVgav14sVK1aEA+ZdXV1Yv349Lrjggrx8HkIIyZX97X5IjMHlUOAP6mAMMAwBzRDQdA7d4GAsvbvy+SaEwMebW/DSB7uh6hw2RcLxc0fiiOnDCu/x1gzRDd57Y4Ohvdvs4NMX0FHmsUOSJNgVBkCBJAF2m4QRtYV5QUkIKX6TJk0CAPz617/G6tWr8a9//Svpk5/poBKIhY/WV+poXaXH748+rhRqebnu7mDMtkUO8/crEdjdHUS7Y2CwKnK8eJ/3+DnD475/1RfnYProg52W6poxYLwLT5mKB/63KWpYV1fssiOaOnD68DSdyUuV2JiwtN3a230ZCfzGY2Rx3qny+dXwOhExngCNt7503dwW+9v8UGQJNRVmJYW9LT54nQqcGUyU+f2/VmH2xGqUl7vQmeC3uLWtB44YNzh6eoJR7Y7lpRU7cciYChw2dVjK7YrcT9vbfQkD5s+8sx21lS4cNaMh7jiKLMVd35HHn1T24Xwf5xmzXoKzxzcwqaGnJ5T1Y22+1pWVMogFHTDfvn07fvOb3+Ckk07Ct7/9bbS2HrwL5HQ6ce655+Kee+7BNddcg29+85tYs2YN7r//flx//fUAzNrlF1xwAW6++WZUV1dj5MiR+MMf/oCGhgacdNJJ+fpYhBCSdbrBe3ukF2hs9cEf1M2SHMLMMmcSMKzSlV4nPHnW0R3CM+/uCGeVj6n34uxjxqOqLDcdi+YD5wIH2oNw2CU4bDJkyex8yOzM0/wp5waHTZHNRzGLb7MSQgpca2srli9fjlNPPTWcqCJJEiZOnIjm5uakT36mi0ogFgdaX6mjdWVN/2BiIa67YVUuTBxREbNtkcP6l8bjcfaFyNKI6XxeXY+erxAD53P83JEDAubxlsWFiPteKtcSVj+DrvPMFOOOoxByhZ57bwcOn2YGiWN90rjbggu0dQVx9Z3vAQDu++kStHQE8LO/LAcA3PnDYzPazr5AZqIbGKpqQGYMQgj88fE1sMkSLjtnZsJ9P1Jnj2ppH4kMrmqaEa5ow7nAgY5AuK7+jqYuPP7GVgAIr+tYbDJLuL77WG1jPo5VshT/s8Sjxdi2/Y8h2VTIv4kFHTD/3//+B03T8PLLL+Pll1+Oem/ZsmX43e9+h3vuuQc33ngjli1bhrq6Ovz4xz/GsmXLwuNdccUV0HUdv/jFLxAMBrFgwQLce++9Ax4ZJYSQUsCFwJ7mHvPE2BCAENB1gc4eFZLEoMgsfBIuSayo6gNyLrByYzNe+2gvtN6s8uPmjMAR0+uL6nNYIYSAP6hDkSXYbRKCqoHaCht0g0EAcCgyRtS4wbn5REFNhRMHOgIZzS4hhBDADH5fddVVqKmpwZFHHgkA0DQN69evx5IlS1BbW5vwyU9CCElLEZzi/eZbC6PKG37u8DF48YNdSacrmqciEwSvi+QTRCmEp2t3N/cAMEstBkJ6krEP2nPAhyv/9E74tcF5OIkIyN59hkQ3RgwuEAjpuPa+D9DSaWaVd/tV+IJaStOL3kZ/9NkBPLd8B7515qEpdSZrTnvw77/8Zx1WbToAWWL429XHobHlYFb8J1tawLnA3Cl1A+ahKMXZwSXnAm+s3ospoyrDw5J1PBxzPjE2Tf+OX+99bj0ggG+cMd3y/ItZQV9RX3rppbj00ksTjjNr1iw88sgjcd+XZRlXX301rr766kw3jxBCCgbnAkZvt+lBVYcsSdAN825xR08I1eUOgJknJLou4LDn/0TRiqZWP55dvhP7WsxHw8bUe3HW0eNQXe5MMmXxUjUD7T0hMDDoBofXZYemc8iSBEUGwBgqPXY47eZP+YgaDxx2GW6nUrQ16QkhhWvatGk45phjcP311+OGG25AeXk5/vKXv6CrqwsXXXQRHA5Hwic/CSGkVPU/74r31CPrF17O9enaRadOwz9e2Iizjh5nabpEMdh8n3HOmVSL1VtaLE2jpBFUzJZbH/1kUNPrRvTWERmMmEfun4YRf76cC7z+8d5wsBwArr7zPaiRGeYptOuOJ9cCAO55dj2u+WryDjYBQETsnas2HTDbygX+/sJGvLOmMfzeHx9fYy7jykVwO21R87DluNPJkGbArkiDvmH29pp9+NdLn0UNS+caMFb2f+T26gloeHdtEwDgvCWTUOYeOsnHBR0wJ4QQkpoDnQH4gjo8TgXBkAGbTUDTBRgzy7M47BGB5SL6jdN1jrfW7MO7a5sghFmb+8TDRuGwqXXFk5WThmBIR3u3CodDhl2WzM49FQbukCFJDMNr3HDY5Kh14LD3lkgo4fVCCMkfxhhuu+02/N///R+uvPJKdHd3Y/78+fj3v/+NESNGAEDSJz8JIWQo63+KFu+MLZ0s0VQcO3sE5kyqRZnblnzkVOX5tPPyc2filkdW49Md7SlPk04nkdnS1Ba7NngqAWYA2LK3M2q/ymS1TSUikNyXmBWLwQVCanRZD7VfiY2EGeb9XvcEE2fcR17/xFtNkcHySIGQMTBgrsTfiTOdsL/3QA9+ee8HOGbWcHz9tEMGNa/IJwv6JHrqevOeDnictgH9XMXa16JK0USUwCnl6+9YKGBOCCElIKQa0DQDHZqBnqAGuy6HT2yKtbb3lr2d+N+KXWjtMjsimT6uCqccProk72qb9eU5fAEN7V1mdoYsM7jtMhTZDJLbFBmSJIExhLPKCSEkl8rKynDdddfhuuuui/l+sic/CSGEHBQviHj0jOF4c/U+zJyQ+XJW5R7r59GJYrf9s+ZzjTGGq744F9+++Q1oKdZBTrXDv2x7/eO9cd9bvq4ppXn838OrcdmymeHXmcwwjwqYJ8kwTxZHTVTDvH9UOlYnqHEntfh5r77rPdz6vaP7DU19H1Y1A9sbuzBpVEVapX2ef38nADOgf+HJU/Doa1sxe3INZozPzHc9XsC8rSuI3/7rIwBm3XsAaO8OoduvxjwORW6vyL9LtApqXHTFTQghRYoLgbbOIFxOBVwI+IIaJImhrtKJjh4VFV47PM4MZpDkSKdPxQvv78JnuzsAAF6XDacuHINDxlblt2FZ1N4dgiEEvIYAYwzdfhUuh9IbJGeo9bqg6RyabqCmhMvQEEIIIYQMFf3LafRx2GVc//XDc9ya+GorYp97fnHJpJyXlYnHStzUUyB9/fTvdDXSJxbKzERug0zWMI8sXZOwhnkKC02QoD4gi7sv41kIgb0tPjRUu6OC9yzBtKl4adXuJC2IfCv6vbueXodPtrbijKPG4ZxjJ1heduTsXl61B69+ZP7XF8QeLDlORPtAR2DAsKv+/C4A4MhDB3bMHrm5E97sKHGFcaQghBCSEBciXGqDc4Fdzd1w2hU0twdQ7rGhq0dFUDVQ7rbDpsioq3TlucXWCSGwdlsbXnh/F0KaAYkxHD59GI6dPbwkM6r7sjECIQOqxuF0yvC6bAhCIBCSYFdkyDJDQ7W7JD8/IYQQQkixmTu5Fmu3tUWVKUhV/+ByOvPIhy8cP2nAsAtPmYrj545EW1cwxhT5kHpQb1hVEVwnWbgTERVAznCGuRACz72zDc1tvrjjpRJQTVhiJs57r3+8F/966TMcNqUOl50zM+Y46Xzc/tNYmccnW1sBAK9+uDu9gHnE37GC2IMVL2CuKNHldSKz47fs7RwwfuQ2jbwhMtRC53QFTgghRWBnUzdkiWF4jQf72/3wB3VoOkcgpMPtkBHUDAyvcRdtXbEev4YXVuzChp1m/cGRtR6cdcy4ogz8p6q5PdCbLSFQ7rGDCwGXQ4Gh6aj0mo/Lup02CpYTQgghhBSI750zEwYX+NYf3rA8bf/yJSNqPHHGLBzV5Q54XQOfWO275CiUaw8rQU9JYrDbZKiakXzkPLGyViO3QSaTgWWJYfXmFvzlqbUJx0uUfZ6OvqD/iyt2AQA+/OxA0nHTmf/gWN/vX/94L1as35+xdsRqQby+rCI7NtV1ATmiMlOiTj9DmgE9otRRJp9gKAZ0FU4IIUXAMAQMQ2B3czcCqoGQZsAf1OF0yOjoUeF2KgVzwmoF5wIrNzbjjY/3hbPKF88ZjqNnDk/YaUmxCqkGgpqBcrcNdpvUe9IhweNS4A/qcNhkcKcNAZjrJt4jsIQQQgghJPcYY1GlKqyorTx4XnfnD48Nd9iebalcIvSvl+512dAT0DB5VGXG2zO8xo3G1tidXfaxel1jNWBehJdNcan6wcC/kcGnFhx2GXsO9CQdz0rN8ZjT93vdN7tUrgXTCeD2Lw+TTutT2X00nWP1lhYcMrYKXpdtQAmebASf462zyLr9qm5EHXti1jAXAv6gju/d9lbU8Mggf/9M9VJEAXNCCClQbV1BBEI6XA4FusERVHXYFBkh3YCqGmCMoTaNjnsKxa793Xh++S409z6ONqLGjdOPGofhNe48tyx7WruCUCQJjQENXpcdjJmdx1R6HWAMKPc4UO2xwefXoBk8bpYAIYQQQggpLpNHVeLCk6egPsfl9lIJzF3x+eiSF7/62nwsX78fx88dGXP8aWPMvoWSnarGOq/PRpKPlWxdiTFMG1uFTzanXic816ysogdf/iz899ptrRlrQ6XHnrCzzz5qip2txtN/07V3h/D2J/viXgdFjp6RDPOIl91+FXsP+DB1TCUYY3GD6Ym2z+bd7YBu4LWP9uDZ98xOPk9dOCZGOyw2PAXxSrJEDu3fOW68Tj/7+hOL1Dfmx5sP4M9PrsPXT5+Go2YMj9seIURRJvX1oYA5IYQUEF9QA+cCHqcNXX4VhiHQ6VMhAKgahwDADQEhMlujLpf8QQ2vfLgXq3tPUl0OBUvmjcTcybUlmVUOmEHxvs3lcirQNANuhwJJMk/a7YqE8cMrUF3pREeHHw67DAdyk3VECCGEEEJy4/h5o7K+jJF11kq9MIYBmaK1lS6cedS4mONPGlWBhmozEJ7ozH3WxBp8Z+mMmMvLNCtXRZLEcNVXDsNDL27AC+/vynxjMqDbr6U8blfEuA+89FmCMa0xuMCTb21LOl5rVzDj2/TvL2zEyNrY+7EYZE3tRJfQv7r3A3T6VHx36QxUljnw9Nvbw+9F1mH3BfWY0+9v8+PqO98DANRXH7xZFGs/y8a1/J4DPuza340x9WXRy4r4W+v3FII/xmfhHLG/3L0zuv0Js0zPPc9uiBsw37q3E7c/sQbnnzAZRx7akOpHKCilnT9PCCFFprk9gP1tfuxo6gbnAkHNQE9AQyCoQ8Asy6IbHPXVLtRXF1d9b84FVm1sxp+fWhcOls+dXIvLls3AYVPrSjJYrhscHT0htHaG0NGjAjBryFWWOzGi1o0KjwNOuwxFlnofDy29dUAIIYQQUmoK9SnAay9agNoKi9cIFuN2DVURWeMJ1sPIOg8ctoEJIJFTfPnEydYWngGyxFBV5sSXTpyS82Wnqq9fp3zauKsjpfGa2xOX10nm2fd2YHtj14DhqZZk6far8AdTv8HA++3wIuJ1p8+8Xvt4cwt+88CH0dP1y8Ru7w4NmPeu/d3hv11JSi6tT7CN27qCAzLBU3X3s+sHDowIzqeUYR4nmG/lUPHnp9aiy6/h7v/GaE+RoAxzQggpIIYhoOocnJu1xXwBDYYhYHdIqKlwoak1ALtNKqrgMhcC67a14tUP96CtyzyxqK9y4bQjx2L0MG+eW5c9ms7R3h2CbnC4HAoYAMbM4PiwSle4Q09FNjseIoQQQgghxUGSAF6AfUaObShLPlI/g8lzTXRFEv+mwsHh/TNhY5k/bRhWbWy21rAECvVmR7EKaQbS6QSzT6dPxa/vXzVgeLw5RsZyQ5qBq/78vqXlpZLY3e1XY0wXPWFQ1QE44s7D5Ugcbo0VcAeAPc09+NV9H2BErQc3fPMIAOZT6O+ta8LHnx3AFZ+flbCkU/+Mcd3geO3jveHXqQTi4wXMrdSRidWZaLGhgDkhhBQAVTN6g6sGdEPAHzRrWOsGR3WZA87eH9yGmuLJKhdCYPOeDrz+0T7s7e0wxu1UcOzs4Zg/dVhRBf1TYZ58CARVA4GQEe60szugwe1QIEsSGDOzJeyK+YCXJDFUeOOfaBFCCCGEkMJjBl0LKyBUU56jzuJZnL9TnZzF/jvSCfNGYcX6/Zg4ohyTRpRnNGDOqM5CRoVUnpUObHc1H+xw1B/U4XYODF8e6O0Ly4r+ge9d+wd2bLpue9uAYf07C40pYod2prlOPti4HwCwr8UHwCxtcmNEtvurH+7B6UeOi/vlCarRd/Je/3gvXv/IWsA8XkeuJRADt4QC5oQQkkf+oIam9gAgzLrkXX4NYAw2mwSbIqHM5YKiHDyrK5aSHbube/Dqh3vCJyAOm4SFhzZg4aH1MR/NLAWtnUFwIWBXZCiKBIUxKIqECo8DbqcCu2JuU5dDgSLTmTohhBBCSLFiBZj4MWFkeczh2bx8SDTreNctkUPjZXtPGlWBW793NMrcdry0cnf6DYwhXseIJD0vr9qNzx0+sFPLTPrbfz/FlV+YDSA6+zmdLObOnoHZ4z0BDV6XLeF0/UuX9L2M7Ngycs+S07ze6/+d+N8H0fXP+wfE+wv1e3/r3s6o1zsau5JejxtcDOKZgdJBAXNCCMkx3eDo7FFRXe5AY6sfQc2ArnMImPXJPU4FHpetKIOq+9v9eP2jvfhst/nDrMgMx8weicMPqYOzxALlQggEQgZ0gyOkcdgVCapu/isrEhSJob7KDZsiFeW2JIQQQgjJt0IN2sgFlMQyrNKF5o4AjjikPifLi0owT7AeasrjPEUZlWEef/q+pzDjlodIE5VkybxNuzuyOv81W1tjDk9n31i9pWXAMF8wecC8/7KEENjT3IPfP/QxzjxqHE5aMHrA++kYbIJc/3ba+l2HPvzalqTziF+RZWilmFPAnBBCckAIszsRiTG0dYfQ2RNCIKQjqBlQNQOBkA4GhrpKJ2xK8QWW27tDeOPjvVi7zXx8jTFgzqRaHD9vJEYPr0RXdwCGkV7HJYUqpBno6u0Ypsxth8EFJJlBkiU4bTKYxKDIjILlhBBCCCFpEgAuPftQ3P/iRnx36cx8NydseK0bW/cO7KgwH359yRHoChkYVmaHYeQ2oBUrYPn9z8/Chp3tOGbW8JjTRAYEpRROkzNdC7lYntgtJu3dwZwtK3KXS6lMSgpUzXpd770HfHj1oz3oCWh46NXNOGnB6OinOdLcbfvvnv1nk2rM+qWVu/H6R3swLLKT3hQNtcB4PBQwJ4SQHGjuCMAX0DF6mBf+oIagakCWGLp7A64jaj15bmF6uv0q3l7TiI8+awmfzE4fV4Xj5o5EbYUz7UfRChXnAgbnkCUJ7V0qnA4ZhiGgyAwOmwRJtkFwgaoyB8rcdtiU0vr8hBBCCCG5dvgh9Zg/bVhBZQZfetYMPPbGFpyS5VIUqXA5FIxoqEB7uw+5qKseuRli1TqePakWsyfVxp0+siJK/206J8Z0mQzeUTmW7PCH9OQjZYiI2McztW+EVCPpjZn++/pfn/k06vVnuztw55PrDo6fZluS3dARKc754Vc3AwD2t1uv885F7FJOVlZ3KYTcKWBOCCFZphscPX4zSL51byd0LhBQzaxysASPKxawkGbg3bWNeP/T/dB7M1kmjCjHknkjizb4n0ggpEPTOXyBgyeDZR67ecJvAxRZgiwzjBlWBgEBOZV0GUIIIYQQklBfzKaQguUAUFPhxKVnz4j5XqGdCxdesujBbRkZHPzCcRNx3NyRA8bu335ZYgPqSafq+q8fntZ008dVoaNHDXfESKLldB+LzDDPVMBcM5J2hplsn/vdvz+Kep1uML+x/z42IMU8rdlaEq/tH2zYj5kTa6KGvbu2EUdMry/Jp6opYE4IIRkmhECXX4PHqWBfiw+6IRDUDIR0A7ohoEiA2yHDYZOhyKyoHgvkXGD1lha8/tFe+IJm8Hj0MC+OnzsC44bH7mioFHR0q5BlBrtNghBmbXa7IgHMDJY7bDLqKpyQJIbCrbZJCCGEEEKybfq4Klx82jSMqvPmuylZV+F14KgZDXhvXVPK07CoDPODfx81czhcjoEhqv5B0VF1Xuzc3225rf2XbYUsSThm5nA8+nry+s9DUbJgcyZF7g2ZKtcTVA3oSeq7WA3OpxMvP9ARwPvr90ctMw/xcry/fj/GNZQNGP7YG1vx2Btbo4bd+9wGtHeHcMZR46KGx/uqPfPudmg6x/knTM5Qa7OHAuaEEDJIQgjohoBNkcCFQCCko7ndD1mWoOscwZAOVefQDQ5JYqgud+W7yWnZtq8LL6/cHX6sq7rMgRPnj8LUMZVFFfRPlaoZEDB/7GWZwWlX4HTICPWW05ElBiYxjK7zQJGlklwHhBBCCCHEGsYYFs0ake9mhGX+FDV6ht88Y3raAfPI8+d4zRwQE7XweRjLTPYzY9HBfZI/kdnPmcpsD2l60nlluJR+TH/rV+ZFj3UjIkfZ/Kl0Dtrn0+1tAwLmsZqp6Qaefns7AOCUw8egqqqwnsbpjwLmhBAySB09Ktq7Q6ivdqG5PQC7IqGzR4XLqcDQeTgTu7bSUZSlOva2+PDah3uwvdHM5HDaZRw7ewQWTKsrqRrlQgiEVAM2m4T9bQEwMDBmnsi7HArcDgWKwiC4gE2RYVckDKtyFWUnrYQQQgghxcDjsuW7CUWvtrKwknVYVEmWqDdi6l8ewkrcutLrQHt3KPy6wmO3MHX0MhlFzAuCyEJJFlXjlmuYJ/PpjjZL4ze2+rC7uSdqmG4MDJj35ZwX0t4oy6m1JjKJX4vx2QoNBcwJIWQQhBDwBzUEVB0d3SGomgHN4FB1AwgCqm6godrdW6qjuLR1BfHaR3uxfkc7AECSGOZPrcOxs0fA7Sy9n49AyEC3X4PTIcPeGwSXJJiZ5XYFtZVOGIZAmxFEbYUTZW4bZZUTQgghhGTBJWdOx/6OIGZNrIFhFFwR7qLwky/PxTtrG3He8ZMyOt9Mnv5KqWSYDyK1NzLYft3FC+B2pncDhjFWcHX0SfK64qnSDZ605rjVAK+VMjVb93bixgc+jLHMgW0KhAw8t3wH9rf7LbUnm2IlBSb9thTBYb30Ih6EEJJlQggIIcC5wN4WH1TNgM+vwWmT4Qvp0HUOt1NBIGhgWKWr6ILlTW1+vLeuCZ9ubwvfwZ81sQbHzRmByrLi66A0VYwBHqeCbr8Gr9sGSWKwyRLK3DZUeOxwO23QDY6gqsPjUihYTgghhBCSJYtmj0BVlQft7T4URWSlAE0dU4WpY6ry3YwB4mWVxzu37p9FbOUcPHLSMfUDazJbUWzXdKUqMnC9cWd7RuapGyJpyZVs1ml/Z21jzOG6PjCQ/9Yn+7LWjnTJMb4bsVZnpp4IyBUKmBNCiEX7Wnxoau6Cbgj4AppZz1phaOkMwuActRVO2G0yKjyiaIKqQgjsaOrGe2ubsHVfV3j4pJHlOOGwUaivduexdZmn6QZ6AjqqyhxmGRZFgqZz2BUJNRUOcA7YbTLsNgkN1e7wdlRkCcNrCrvWGiGEEEIIIdky2KubmgonNu/pNOeVwrVS/xhbskkmjizH1r1dGD+8DC2dwXSbOWCZFC/PPyEE/u/h1eHXkR1kDobBk2eY72hKr6PZVLy5OnYQ/L1Pm+ALaFlbbqbECpj3Z/b7dvCmw8DuTAsPBcwJISRFnAv0BDT4dbOTT1U1EAjpcNpkOBQZlV4ZEmPhE79iCJZzLrBxVzveW9uEfa3mY12MAdPHVeOoGfUlFxzu24YhzYAQQFOr33y8UjJrkztsMmrKXej0qxjXUEaPXhJCCCGEEJLE8Bo3Glv9GFmX/NrBrki4/cpFkBhDT0QwMF7MzWpW6vfOmYV31uzD0TOH41f3fmBp2kTouiD/spWhrOk8aaefD/xvU1aWnchTb23L+TLTkUoN8/ue34B316beOXAhoIA5IYSkQAiBbfs6IckSnC47gqoOVTXMEh0CqPDai6pDT03n+GRLC5Z/uj/cEY4iS5g7uQYLD21AVYmVXuFcgDEgqOoI9m63Sq8DvqAOl12G22mDqhlgjKGmwonqckdR3PAghBBCCCEk33543hy8+tEenHjYqJTG9/TWEu+Jyp6Nfe4t+lXCSHaGXuGx4/Qjx5nTZjDAStcG+cezVBXF4KLoyoUkMpi6/+mIlWHef0j/YHkxrG4KmBNCSAxCCBhcoK0riJ6AjkqvHf6QASF06ALoCegwdI6qMgckxuCwy/luckoCIR0rNzbjgw3N8Ad1AIDLIeP/t3fn8XGV9f7AP2eZfTLZt+6lbdIt3UsLpS0UilzxqoiCSwtCAVm9yFYQFBBBL0JZymVXfooiCgpyuYIXVODKZikVWqClLG3plrTZM9vZnt8fJ5nOJDPJTJplMvN5v159NTlzlme+OTPzzPc85/ssnFqBhdMqYp3XXCKEQH1TGLJid87dThUR3YTbqUJVZThVGROqAvhkbxuszl4YO8RERERERD0FfM4ey0oL3RlMLpp8os9U3e8eicwMuukDlQRVZAkjaGxUzhqspHY6k36OJEOd/E82cDAXosmEORFRnIaWMHTdhKrIaA1pMAwLkagBSQIM04TX44CqyCgvch92/b6h1NoRxRvv1+PtDw/GJiwp9DmxeEYl5k4pg9MxMhL+6TBNy57JXpYQ1U1YloDLpcDQLbQFNZQXeeB2yvC6VRR4HXCq9nN3OxVEs79EHBERERHRkLvolJl4a9sB/Nui8QO30zS+UB1O8m+gBtrKssSSLMPsR/9vA05YkN4dDJkyTDEiRjyna6ifS38mxB0JI/qZMCeivCeEwMHWCIr8LrQHNRimBdMS6IjY9ckjuonmtggMS8DndiBQ4EFbeximOXgzZQ+U+uYQXtu8H1s+bYp9cFYWe3B0XRWmTygeUWVk0mFaFhqaIxAQKPA6EIrYCfPiAheikglVyCj0u1DodcKhygkf7lWl3ty4FE5ERERENMDm11Zgfm3FYe8nVd451fLxlQWJ62UwbEn0M2O+oLYcdUeU4pHntgLoGmGeeVKwyO9ES4fWrzZQoh372/Hwsx8Myr5N0xoRCdx0DfVo+XRqmHc31GVj+oMJcyLKexHNRGNrBO2dk0FGogY0w/7QjAiBqhKvPVJ5BH2INjSH8X/v7sN7nzbFlk2oKsDRdVWYNCqQsyVHWjs0uFwKdN1COGrC41KhKhLcLgWmEChwKigNuJJeKJAlKaNbPImIiIiIRqqqEi/2N4UwsTowbG1IJ/m9dHY1ftXHhIujy3zYczCIcZX+hOX9zckVeJ1YOntUXMJcHnHfnxRZgjkCkpLZQDf7nvRzJBnykiz9eG2MhHgzYU5EeWnPwSBKClzQTQsHmsMIRXS0Bu2r/4oMFBe44HTI0AwLsizB48r+t0shBHbub8erm/fj471tseXTxhfj6LoqjC7re9b6kabr6rklBCDsix+lATc0xYIiS3A5lNjfzzAEqkt9OTeqnoiIiIgoU5efPgcvv7MHx80dnDIXqaRKraVKoCmyjCNG2fMNpdrB906bjZf+1fO59HekbfftFEXq176GM8n+n+cfhSvufS3j7RZPr8Qb79cPQouyl2nm1qSfQ/1UXty4G587chxKC91pbzMS4p39GSAiogGg6SZMS8DjUqEbFsIRHfWGCU23EIro0AwLlSUeGKYFh3qonrdrBNT2FkJg264WvLp5P/YcDMaWTxtfjGNmVaO61DuMrRtc+xvDsds3ZUmC26HA0Zkkl2CXWfG6VFhCwO1QRsSFDyIiIiKiwVZa6MZXlk0a7mZkLFkKuiSQ/Ln0NynXfav+lGMBhr40RrySQPrJy3xn5NgI8+G4s+APr3yM8/59RtrrsyQLEdEwaw9p8LpV7G0MwrIExpT7sftAEJphIRgxYAkB3bBQWuiGJEkJyfJsZ5oWNn/ShNe27MfB1ggAQFUkzJlchqNmVqG4wDXMLRwcbUENDlWGYVpwuxT43CqEAKK6BbdTgVORUVLsASDB67Y/5hRJQqE/N+NBRERERDQSqXG1j9OdVPO0FZNx86824uSj+p58tL9J0O7bKZI06CNiz/78NPziz4NTo5t6pxvWsF7cGGjXPvTGkB/zjffqUVHkwb8tGg+Xs++cCkeYExENI8sSaGgOw+9xwLLsD8LdBzrQFtIgBKDr9uSeAZ8DDnXklOnQdBNvf3gAb7xXj7aQDsAeCb9wWjmOnFYJv8cxzC0cPB0hHVHdQihiQJYl+NwOjK0oQHtIQ0fYQIHXgQKvA24nP96IiIiIiLJOXGK80O/CygVjoapSr0m2+NzapFGFeOCKYwf5+1vPkiw9hp1nvJfeTRlbmPkBhoE8BBcPhlowauTUCPP2zhzBUHvm1R1oD+lY/bnaPtcdCfFmRoGIclZUNxHVTSiKhIhmwDAFIpqJUMSABAmyJKG6bOSUK9nXGMTb2w5iy6dNiOomAMDvcWDxjErMrylP60ruSNTQHIaqSCgJuBGKGnA7VUhOBRLsCwUelwqPS0V5kRhxk/EQEREREeWzb5wwJeNtBnuwU/dk3phy/6Am+GrGFsGZJXc6z6+tSFnDfPqEYpx81AT87LebhrhVg6s9pOfcRYDh8vdNe9Iqg3r/n97DaUEdC2vKhqBV/cOEORHljFBER31zGG6nggKvEw3NYRidpVeCnVdZTUugvMgDRZYwEnKrmm7ivU+b8PaHBxPqk5cUuHB0XRVmHVEKdQSNjs9Ua0cUAGCYAi3tUThVGV6XCq9bQUSzS7B0YbKciIiIiIgOV1fq9NrV8/HhZy04amYVXt+yf9COd0YaI3KHyuhyH2678GjsbQxi3e/eSXjshAVjUehzDujxfnDmAtz95LtoDWoDut9MtAe1IUmYf3HJBDzz6o4+15tfW46N2w4MensGy5/f2IkCb+93vdc3hfDHv29nwpyIaLAZpoWGljCimgHdtNDaEYVmCBiWBcsQCPgdUGUZiiJlfWJVCIG9B4PYtP0gtnzSBM2wANiTzUwfX4y5NWWYUFWQ9c+jvwzTwoHmCGQZUGQZXrcDumFCMywUFbhQUeJBwOu0JwrJzRAQEREREeWkfk6f2a+trvj6HDz4zHs486Spfa577r9Px0P//X7C4SaNLsSk0b2XShlT7oOqyNixv71fbQTsKjX9nFe03yqKPGhoCfdsC+wJQxuaez4mS1K/J0BNZWJ1AMfNG42n/+/TAd1vJkJRAzf/auOgH2fF/DFpJcxHUrnYdLz3aVPS5aqS3c+TCXMiGlE6wjoM04Lf44i9wXbVJtdNu7a1JEvQdAuWEHA7FBSNkMkew1EDmz9pxKYPD6I+roNSEnBhXk05Zk0qzen65IZpoaU9ClWRUeBzwjBNuBx26RXZocChKlBlGQGvPaphoDtrRERERESUO6ZPKMEdlxzT50Cj0WU+HDWjKpYwF0kS9KkGIB83bwyWzqrGb1/cjr9v2tPj8ePnjcFf397d6/FlSUI6tz9PGh3Ax3va+lxvXKW/18ev+Poc7KxvxxN//7jHY701Q06vmRlbOmvUsCXMC33OpKPbJWng62ynO7GtI8sTyemIr6N+++/+lXQdpyM7yhClwoQ5EY0Ymm6ioTkEwxRobo+irNCNjrCO5vYoVEVCOGoiqluQJHvW5VFlvuFucp+EENhZ34FNHx7ABzubYZj2p7IiS5g+oRhza8oxvtKfs6PJw1EDLe0aCv2OzuduX+woKnDBNBUIYU+yo5sWHIo8Yi5+EBERERHR8OvX96gkiVKRKnsqBFRFxurP1fZMmAtg+dxRfSbM08yXY2JV3wnz4+eNwZeWTux1HUWWUieDOxuS7GFJTrxbuyTgQlNbtNdjpaO4wIWHrjoW59760mHvK1OjynxJE+ayJMEc4Ix5uqdiro0wT4UJcyKiw9QW0hAM6wj4nIhoJiK6CZeqoMEKo7UjCksIWJZdn9zhkFHkd2Z9pY5gWMc7Hzdi04cH0BjXyago9mBeTRnqjihNa7KMka4tqMPnVtHSoUGChKICJwxTwO9xwKEqsCwBp0NGfXMY1aVeuJ25HxMiIiIiIjpksMtLd999ssP1twnxo4oDXgfa4kbedpEkKa3vr/FtSCghE2fB1PI+70q2BOzylkn01g5ZkhCfyq0ZU4TVn6uFLEnY9lkL7nzinR7b3HvZMly47pVe2wPYpTgdqgy9sxzpUPGm+M49GOdcujmKfEmYu5xMmBMR9YsQApIk4WBrBJpmIhQxEIwYMAwLkagB3VChmwKVJR5ougmHKmf1SGzLEvhkXxs2fXgQ23a1xCYWcagyZk4swbyaMowq82X1cxgoEc1AW4cOh8OuUW6YFlRVga/zZ0WWEyaUGV0qZ/0HKhERERER9a6q1DvcTehhQlVBwu/JkqWpymlMHV+ccr+qIiWMKl77rXm49qE3e6xnjzBP4ztgXLtSJbzTSfSGIkbKEfO9Jdvlbu3UDCs2yGvWpFLcftES/O3t3fif13fG1slkwFO6JUsG0pwpZdj4Yc8JNlPeUdBPmcxBljcJc44wJyLKXFNbBC1BDQGvA5GogVDEgCUEXE4FBV4VbqeKUMSA122/jWXz7TxtQQ2bth/Ev7YfTLjda1SZD/NqyjBjYknWf1gcro6wjo6QBo9LRVtQg6rIKPQ7oSp2IlxW3IAQKC10QwLg7naln8lyIiIiIqKR65pV87DlkyYcN3f0cDcl5qY1R+KN9+vxb4vGdXukZ7J04bQK/GXDLkwbV4wXNx4qsVJdmrwMaEnAhQtPqUuYdylVQliWpLTKdVhx7QqGe45UT9fYCh/2HOjosXzWpN7vcpa6Tfr5drdEc3GBK+E5XnfGgozaJQ9DnnhidSDpcgHg84vH4y//3AUzxcWJTFyzal7a+3Go+fHdN9tzIEyYE9Gw6Loi3n3ixohmIBw10dweRWswaifLowYKfA5Ylkj4AO9Klmcj07Kw/bNWvL39ID7e0xq70u92Kpg1qRRzp5ShsiT7RlcMFMsSONASRqHfhZBuQdPsMjodIR1lhR60BjX4PCpKA57Y3zSiGSy5QkRERESUg6aMKcKUMUX92nawKrKMLvfj1OU9J8hMdjyXQ8FNaxYBAP66cXevbZozuQzf/eosAMCBlvChB1Ikxbt/J04p7qAdESO9beLcftEStAU1VBR7Y3c7x5s+oSTuWD0fl+W+E/vxjx8xKnkyOpXhGGHeW+y/euwknHjkWFx69z8O+zgOVYGlm+mtmwOTfqYjmwc9AkyYE9EQiuomJNhvjLsPdMCwBCZWFSCimQhHDRxsi8AwLGi6PXmnaVkwLaDI7xgxV1mb2iLYtP0g3vmoER1xV/3HV/oxr6YcU8cX58UtVsGIDkmSENFM+J0qigMuaJoFp0OB0yEj4HXAqSoJF0CYLCciIiIiomE3gBn6+CRwqpIcPrcD4WjfCfD4MiHLZlfj2dd2ZNSW4gIXigtcAOw65t3Fty7ppJ8SUOR3JV2/i8/de/303pQG3AhGeo58H0x9XatIVeO8P1jDPJEzy58nsxNENKAM04IQiW/ypmVBkiTsORAEJGBiVQEM04JuWPhoTysimgmHIqGlQ4PHpcDvcaCoQIZpWQiGDahZfoXVMCxs3dWMtz88iB3722PLfW4VsyeXYe6UMpQWuoexhYNHCAEhDl2ZF0KguT1qJ8rdDnjcDhT5XXBIgCMgobVDg9etQlVluFlmhYiIiIiIskyf+XIpnZU6V43LksoA/uOrs3DXk+8mrONQZchyz/Tcj89ZhDufeAcHWyM92lVW6MEPzlyAm375VlptP++L0xPXi0u+jyn3YfeBIObWlPX6XLqPAE92rGPnjsIHO5sxa3Jpr/tK5oIvz8Sv/3cb3tvRnPG2/dVXXfF0chEuh4JzvjAN//XUlrSP1dsEp+necDDSKVme52HCnIgG1O4DHbAEcERcLbCd9R1QZQmGacIwBXbWt0M3LDS1RaEoElwOGUHdrk9eGHfFWpFlBOImfsw2Dc1hbPrwAN79pBHh6KHbqyaNDmDelHLUjC3M+g+BwxHRDLQGNUiQ4POokCUJze1RqKoCt1OBx63C5ZRRWuiBCgumKeA9jBEHRERERESUhwarJkuqwx3mhI8JSfL47KcEzJ5chh+dfSR++It/JmyjyDL+63vLcNEdrwCwJ4kcVebDd0+dFVu3e7P6KmkxsTqAT/e1YVSZD4unVyU8Fr+vH357oT3gKW7Cz2TlUdIpmeJQlVg5mr50/65fWeLF5V+fi7N/+re0th8IA1EGZkJVAebXVvS5Xvyhrvz6XNzy640p1suPjLmS5VcGmDAnosNmWhaCEQMBrxOWJWBYAkIIRDQT9U0hGIaFiGkhEjUQ1S0osoT2kA6nU0ZpwA3LEqhvCqOoIHuT411aO6J4e3sjNry/H3sPBmPLA14H5kwpw5wpZQm3qeUSIQR0w0IoasCpymgPGVBkGX6PilDUgKZbKA24Y3cYeFwqxlT6UVriRXNzEEPe0yUiIiIiIhpgEiSINL/bJJRk6SzKMabCD49L7VGGJb5cZVfSND7h3j2R31e+8ZJT6/D3t/dg+ZxRPR6L35eqyPB7Egd6TRlbiJqxRfC4Vbyz/WBnm3o/XiZuu/DorJiTLO368b0446TajI9VWeJJuV6e5MuhKNn9RIf/7CSiEa+pLYq2oAbDsGBaAoZh4dP97TAME21BHU5VRkdER0WRB03tUbSGdBiGiZLO+mmyLKGq1JO1V1Lbgho+2NmM93Y0YXfDoSS5JAE1Y4swr6YMk0YVDsiHbTYyDAuaYaKlQ4MsSVAVGaGIBr/HCVWR4HapsARQ6FMQ8DlhWgIuh9JjlnQiIiIiIqJMLZpeiZ317RhV5huS4yWr791fqb4jRvqoWd61Wfwo3EybVeR34ZRlRyR9LNmkn/EUWcZ1Zy6ADglrfvyC3aYB+r571TfmoiSQHSVL0/m6KktSr/GqLk3vvJQlCT+74GiYlpVwcaRnm1I3qqLIg4b4iWRHsGzPnzBhTkT90hHW0dASRmnAjahuT9ophIBmWGjr0OxZu4WAJAFhzUTA64SqyigtdCEUMeANOKHIh65iZ1uyvCOk4/2dzXh/RxN21R+aeEQCcMToQtSOK8K0cUXweXKzxIhlCZiWBVmS0NgWhRACqixDVSW4nCrMkIBTlaAo9khy0xSYUF2Q8DclIiIiIiI6XCcuHIsx5T4cMSrQ98oDoY9kspRBDfP4nGD8qPS06qSjW1Kx20aHMzgp3aoz8ccYqMFQU8cXD8h+DtcZJ9WmfE4Bb1x5GhmwzKSrZaxrbjOrl6syvYX5ujMX4Lt3/d/ANGaYZXvugAlzIkpJCPsjvetDJBjRIQSg6Saa26Nobo/CMi2EIgZCUQNR3YQk2aPFhQAimgmPS0EwYsDXebuVIsso8GZn6ZWmtgg+2tOKrTtbEibvBICxFX5Mn1CMmUeUYkxVIdrawzDN5JN0jDThqAGnQ4YiyxBC4GBrBKYp4FBlGKYFj8vuLEiSPZO106GgpMAFw7TgdTlQ6HfC61Kz/gOPiIiIiIhGHlmWMPOIzCeR7K+BLCSZMDAsgx13bZc4wrzbDpIkVidUFaS1/+VzRuF/N3yGmRNLel0vPmHf9VQK/U60dmhpHae/7Tscc6eUYVNnGZlkvC4Vd373GKiKjI6w3uPxWZNK8dXlk2K/y5lcIUlXL0nx3i5M+HNowF62pw+YMCeiBPubQtB0E+MqC1DfFEIoasDndsDtVFDfHO4ceSzsEeUQONASgdejorzIDd2w4O68tUiSEKtJlq1v6kII7DkQxLbPWvDhZy040BJJeHx0uQ8zJpRg+oTi2IQkuTaJpxDC7uxIgM+twuNUAUjwex2QJSAYMeD3OCBJdhehtMAFv9fRIzmerRdBiIiIiIiIMjKQJVnikp/xw63GVfqxq74DU8cVJd+u6/+EGuaJ68Qn479xwhQsnVUNtzO9NF91qQ/3XLo09v09lWQTmF522hw8+pdt+EqKci+pTKwO4NrV8zPapj+STYZaWexBfbNdykTArtsO9BzNXVzgwqVfm52wTBqE0iG97XEwb74//0szcP+f3hu8A2Qg2wfcMWFORDGabiIUNWCZFixLoDWoIaKZaAtp8LocaAvaV5F1w4Lfq/ZIko6EZLJumPh4Txu2727F9t2tCVeUZUnCuEo/Jo8pxPTxxSgqyM3JOy1LoC2oQTMsKIoEp1NBJGpA1y0EwxF43Q6oMuDq7GyVBFwo8tsjytUR8DcmIiIiIiLKBgk5wbiM93dPnYVXN+/D8rmjk26XbIR59zra8bt2qnLayfIuXnffA9sSJi3t/HlshR/f70fi2+dWh6Ru9deOnYQ9BzpgmAL7m0IAul9sOPRL99HcEa1n7ZXeRnwfNy/5368vvZWkTfXY3Cll/TpWvCOnVaK5PYrf/e0jAMDXV0zGq1v2oyOso7k92uu24yr82NXQ0es6mVBYw5yIsoXRWUKkK+l5oCUEp0NFaaEbhmlhz8EgdN3+gNhR345w1ICiSIhqFpqiEfi9DjhV2Z74UR05idNw1MCHn7Vg264WfLSnLRYHAHA6ZEzprEk+eXRhn1fYc0EoathX3SVAhgRFleHt7FwVqjIkCfC5HSgv8qA1qMXuEGCynIiIiIiIcl1fA8z7GgEsJUkyd99vScCNf18ysc9jxCcVTTN1SZbBGq07EAnub5wwBc+/uQvfXFkzAC3qW0nAjR+tWYTn3tyJJ/7+MYAk5Ww6dU+Ga3qyhHnqYw1GiZlU59d5X5wxIPuP/5ueeOQ4nHjkONz4yIY+E+YDPdKeCXMiyhq7GjogwX7Dl2UZ7SENTlVGRDehtEQQjhoIRnSYFiAsDaoio6jAiQLvcLc8c21BDdt2tWDrrmbs2N+ecEW50OdE7bgiTBlTiAlVBSNiZHw6dMOCaVkJIwssS0AzTDhVBaZlwTAFQlETxX4nVEWGqtgfUsUF9uQjLocSK6VjL8/NUfZERERERETxXE4FUc1E3RG91/W2M9Xp1W2JT04WZFCqtCvRHp9wN7tNFBmf7B2s5ONATPS5csFYnDB/TK+jqtPldMjQ9PTmEvPFjaAXAvj2v03Fr/93Gy78cl1seffrDN1jDPQ+GvxwlAbcaGyL9FieLOY/PmcRXElKzfRGVWSs/eZcPPDMezjYeug4Bd6e52E6T3GgTzEmzIkoK+iGBdO0IEsSNNOCZujQdRNtQQth3YIkyzAMA4oio8hv16werA+GwaAbFnYf6MCu+g5s392KvQeDCY9XFHlQO74IU8cVo6rEM6KeW7oOtIShyDKCqgFZkhDVTMiyBEmSIEsGFFmCYQqU+F0oCbgQ1U2UBtxQFXlIbo0jIiIiIiLKVj85bzE+3duG2YdZ+iL+m5UsSbj+2wuhm1ZaJVAObXdo+y6mmZgojv9OqyiD831uoL42D9T3b5dDSTthfvTMKvy/57YCsBPmy2aPwpK6qoTR+N3b5XP3TJMO1ndlR4q79iVJwiWn1mH9HzbHlo0q88V+nl9bjo3bDuAn5y3GNQ++kXL/hT4nJo0u7LF84dQKvPdJE6aMLUo4Zl8kScJx80bj72/viS2bPqEY7+9o7nPbZLI9B8GEOVEOagtqiOomivwuhCI6mjuiMAyB9rAOv9sB3RTQDRNej4oSp4qoYaGs2IdQKNrjQzibdYR1fLK3rbPUSit0I7HtYyp8mDquGFPHFaEk4B6mVg4eIQQsS0BRZJimBVU+NGJc0y0UF7gghIAsyygJuNDaocESAhOqC1hehYiIiIiIKE6R34W5NeV9rpcqtzhrUine/bgRx88fk7B8fD/KdnSVClXVuBHq3eYQizcQI8GT7jfLkppOVQGg97kekFhStKskS/fSNd3jduS0yh77Gayxdqm+k0sA5k4px1eWHYE/vvJJj8cv/PJMRHWzz5r1qdqtyDLWfGF6wrJ0KvpIErBqZQ2+duwkXLjulc7t+h8cmZN+EtFQsYRAR1jHvsYghADaQzpaOuxbbwxDABLQHtIgBFBa6IpdRfS61BGRQI3qJvYcCOKTvW34ZG8r9jeFEx4v8DowvrIA46sKUDu2CP4ktxrlgn0HQ/C6FZiWgGHaSfOuv2lEs+B2KYAQUGQZlhAYXeaH163C73FAgjQi/tZEREREREQjyXe/OgvtQQ2F/sMva+npTJgrsozvfnUWdMPCtPHF2HOwA0fPrAaQWCLDM0hzcSXUYx+UI2QmflT2xOoAPt3XltZ2IkUFne5J5dOOm9xjnd4uRkiHEZUpYwqx+0DPSTS7SpWkKvojSVIsWT6qzNfj7vr+iP87X3b6bKz73TtJ14k/dq+NTANLshDRoLGEgKab6AjpaA9p0C0BYQkEIzoMUwCSBJ/LTqz6PQqcDgWGaY2IhKllCRxoCWPPwSD2HLD/HWgN9/igqyrxYtLoAKaNL0Z1qTenSq2YpgUhAFWVYVkCpiXQ2qHB57bvClBkoMjvhGUBhiUQ8LngdVswTAsuh4KobkKyEKtJnums6URERERERJQo1TdOWZIGJFkO2IPausyZfKhEzA/OXHioHXHfff0Z1EfPRHyy2JmihMhQis9l/ODMBTj7p387rP3Fx3DxjEq4nD3rhB/u6P1Um3/12EnweVQ8+9rOhOXlRR4AwMyJJXjqlU96TSz/8MwFePKlj/Hixt2H1cbRZT58tLu187ilKPA60B5KHMmf7K9/GPnyQSsjNFCYPSEaQUzLQkQz0RHWEYoYsCyBsGbCsgSCYR2qKsEwBBwOGX6PA26n0uMWmWxMlkc1Ew0tYdQ3hVDffOj/7iVWACDgc2JCVQEmjQrgiFEB+AapY5AN6pvDsQ9nIewPFL/HAa9bhWEIFPgc8HscEAIIRQ0U+10JV9xDEQNWqkvpRERERERElJUqij19rxT3Vd/nGZz0nkOVccZJtYhEzQG7GJCu0oALF39lFiQJuOGRDQCANSdPw89+uwlfXjoxo32Jw/hefDiVQ67/9kKUBJLHzeNS8ZVlk3okzCtL7L/9xOpAr9sDgNOh4LQVkwEJePGt5EnzdPL9Xzt2EhRZwuIZVQCAi79ShzufeBdfP34yHvmzXQd+oMvzcIQ5EWXMMC3IsoRgWIck2f+3h3RYQsAwLUR1E7phwbIEfB4VAkBZkTvlpBHZIhI10NQeRXN7FA0tYTQ0hVHfHEJLh5Z0fadDxqhSH0aX+zC6zP6/t7ptI00oosPqHAEuyxI03URjWwRC2FexZUmC26nEnrMk2RcMAl4nTEsk3HaXbESBN8mEJURERERERHQYBjHPd94Xp+P9T5uxbPaoPtcV1qEksC+DCUUzdcKCsTCSDGYbCl31369dPR8el4pRZT7cfenSjEd9p5MvT7XH0oAbB1oiybfpoxmZ1K8/emYVPnfkODjUQ6Pc09leVWR884SalAnzM06aitsf/xdOWXZEyn143Q6sOrE29vuUMUVY3xnnroR50rv5kwT2zJNq8cvnt8V+n1hdgMoSL954rz5hvWyrj98dsylEWUIIe1LOcNRAR0iHqkhoDxuQJXtySwEBywS8HhVetwpVsROq2VSCRAiBYMRAc3sUTW0R+//2KJra7CR5OGqk3DbgdaCixIuKIg+qSjyoKvGitNCdVc9vIFiWQFtIg8epoiNsQJEldIR1uJwKIlETXrcDqirD7VDQ0hFFwOfCmHIfnI6et4YRERERERHR0PrWyho88uet+MLR4wd834unV2Hx9Kq01i30u7Bi3mi4nMqg1TDPFpNGF8Z+TpYsrzuiFJIELE9xoSG9O9OT5x7OPnkarrrv9aSPDeQN3ROrAxhb4R+w/XWFacaEEjxwxfKERHw6usc53dTMkdMqsauhA39/ew8A+2/z5aVH9EiYc4Q5ESVlWQIRzUQ4aiCiGYgaFsJRA4Zhl12RJMDlkCFkCaWFrh6zOQ8nyxJoDWpobIuguc1Ojtc3h7GvMYSobva6rd/jQHGBC2WFblSWeFBR7EVlsScnP+BN00JbSIdDkeFyyrAE0NgagduhIKpb8LpUuJ0qJAkwLQGvS4XDYU/Y2bW8utTLZDkREREREVGWWDprFOZMLsuKu5/jRwXns4DPgTUnT++x/NKvzcaf/vEpzj55Wp/7UFPU1C4rTKM8DuxyMa9u2Y/ls0fhgWfew9RxRWlt12Uw88eZJsuTSTaYUU6Sp5JlCatPrI0lzFMNgsymHFcyuZehIhpGLR1RBCMGRpf5ejymGybaQzo0w4IQAm1BDZphQTdMGKaAEHYJEr9HRaF/+D94ASActUeLN7ZGcKA1jAMtERxsiaC5IwrLSn0ptdDnRHHAhZICF4oLXCgJuGM/53LyVzcsyDIQjprwuVXUN4djdeRbO3TohoUCrxM+txqbyEOWJbgcCmRJgm5agLAnc3U5FFhCZGXNeSIiIiIionyWDcly6tusSaWYNak0rXX7U+K2qODQebB87mh8eUUNmpuDmDgqgJKCzGq+Fxe4Mz5+vB+cuQBbPm3CU698AgCQBrh2UHzee+WCsfh4bytOO24SNn/SmLBe98R/qpHpLMlClEdaOqLQdAumZUHT7VrjUc1Ea0izR47rJnTdgkOV4HQocDpk+DrrVw8HIQTaQzpagxrCu1qxp6ENTa2HSqlEtNSjxRVZQknAhZICN0oC9ojx6jIfygrdeZHkDYZ1CCHgUGQEowbcTgUt7RpMy4JTVdAR0uH32Mlxl1OB161CMyw4FBnjKv1Jr6a65MSLCfJgFscjIiLKUEtLC9atW4eXXnoJHR0dqK2txeWXX44FCxYAAD744APcfPPN2LJlC4qKirB69WqsWbNmmFtNREREI1Em1U4GIjmcSR7jolPq8FlDO2ZMKEn6eEVReqPSAeCSU+uwY187Zk9OL7GfysTqACZWB2IJ84EWX6LlGydMif180zmL0NYRxc8e/xeAniPKU40wZ8KcKEfohoWmtgjKiz2xNwrLEojqJgzTgmEKGIZdZmXbrhYYhgXdsmJv2y6ngkKfY8hvO4lqJlo6omjp0NAa1NDcbifEu/4ZZu8fQz63ipKAGxVFHpQVuVFe5EFpwIWAz5lz9cW7E0JANywostQ5elxCW2sErWEDWtSAZdqTsHpdKppaoygucEGS7A9aSbJnvS4ucMHlUCBJUmxUfrZ/MBARESVz2WWXobGxEevWrUNJSQkee+wxrFmzBn/84x9RUlKCs846CyeccAJuvPFG/Otf/8KNN96IoqIinHrqqcPddCIiIqJeZTLCfH5tOebXlg/IcedOKcfcKQOzr8GUaqLV0WW+hNh1Xy9V+sPZjxH9Q4kJc6I0tXZE0RqMQpLsN4BQ1EAookMzBAzLgrCE/ZgsQZIUeNwqCpTBm5TTsgRCEQMdER0dYR3tIR1tQQ3tIQ1tnT+3dmh91hSXJKDI70J5sRcBrwNFfmesfEqul1BJRggRuzvAEgJRzS6h03W12emwS6yUFLhgWXb8dNOCosioKvXGarE7VbnH356JciIiGql27tyJV199Fb/97W8xb948AMC1116LV155Bc8++yzcbjecTiduuOEGqKqKSZMmYefOnXjooYeYMCciIqKMZfTteQC+ajty7U75AUo/LJlZhVe37Mfnj0o9yW1FkQcnLhwbK0kbL9V8ddmea2LCnCgJIURnfXELmm4iFNXRETLQEbaT00JIUGS75rjbKUNVBq6sihACwYiBtqCGlo4o2oJdCXE7ER6K6AhGDIQiRtr79LgUFPntUeHFBa6EhHih3wmnQ0WgwIO29jBM0xqQ55HtgmEdTocMh6ogEjUQihqIaCZkSYKqyPC6VXSEdRT6nLFJOd0uFaoswR/wwKdKcKh20ZSu0jv5MOqeiIjyU3FxMR588EHMnDkztkySJAgh0Nraii1btmDhwoVQ1UNfLxYvXowHHngAjY2NKC09vNuMiYiIiFIZiG/hapaPeB4uZ588Dd84oQZed+8p5K8fP6XH71s+acQxddVJ12fCnCjL6IYFhyqjqS0Cv8cBVZGhGWZsVLGuWwhrBsJRA7ppwbIEZFmC0yGj0O/s10QQhmkhGDHQ2hFFKGInZ7uS3uHOkerBiJ2QD4YNWCL9al0+twqfx4ECrwMBrxMBnzP2c6HfiUKfM+vfiAabbpgIR037IohuXxDwuVW0aTpMoUOVAa/LAb/HLpkjSRL8HhU+twq/xwmnQ4Yi20l0WZZQWupHc3MQhmHvy+1U4Xby7ZSIiHJXIBDA8uXLE5Y999xz2LVrF4455hjccccdqKmpSXi8oqICALB3795+J8yH+sur0jm6TMm1UWaDhPFKH2OVGcYrfYxVZhiv9A1XrObXlmPjtgP4t8Xj0+4HyLJ02H0Gp0NJex/J1su2c0uSDj8mXRz9yCl9/qjxvY5KdzrkrIlVMnmT4bEsC/fccw+eeOIJtLW1Yf78+bj++usxfnzqPx7lFssSiGgG9jWGoCgSIlETTW0R6KYF3RAwTBNC2JNZyrIEl0OJJUi7GKaF9pCGcNREOGoc+qfF/27aCffIoeW6kfmobb/HEUt4F3gc8HcmwX0eB7xuFX63o0f78pFpWjAsAQj7DdeyBNpCeuwuga6ZQnwehz1aHBJkRYLbIcO07CvRsizB41Lh96hwqL1/EOR7vImIiABg48aN+P73v4/jjz8eK1aswE9+8hM4nc6EdVwuFwAgGo326xiyLKG42HfYbe2PQCD9ybqI8coEY5UZxit9jFVmGK/0DXWsrluzGJ/Vt2NCdaDPO7gLvE60hzQsmzf2sPsMi2eNSrmPmy84Gtfe91rs996OlS3nljKM/ahkvv/thXjt3X146e3dAACXQ8maWCWTNwnze++9F48//jh+8pOfoLKyEj/72c9w7rnn4tlnn+3RuaeRzTAtWEIgGNbR0jmi205e24nriGZCWAKhiA5I9vq6YSFqWIjEJcJD8Qnwzt/7k/juIksSAj4HfB4HfG4VXpcKb2fS2/5Zhd9jj3L2edQhnxx0sAkhBrRcSbCzbrskSVA6k9i6YcHlkOF0KHA7FRimgNOhQJKAQp8TRX4XBOx2pJqwgoiIiHr34osv4oorrsDs2bOxbt06AIDb7YamaQnrdSXKvV5vv45jWQJtbaHDa2yGFEVGIOBBW1v+lKk7HIxX+hirzDBe6WOsMsN4pW84Y1XkUdHS0ncf4D8vOAr7DgYxudq+C7w/7v6PpWhqj6LIo6bcx9hSL26/aAmufegNHD9/TNL1su3cMi3R75gMhqljCuF1yLGEuUNVhjxWgYAn7VHteZEw1zQNv/jFL3DllVfGbiW94447sHTpUrzwwgs4+eSTh7mFlAlLCESidl3xtg7NnvQypKO9M4HaHtYQ0UxYAmgPRhHRzFgtck23ENHNw0p8SxLgcarwuBR4XOqhf04F7tjvSsJyj0uF26nkdX3r/Y1heD0qCn29X6AyDAuKIkE37AsfsiRBCDsZrigSQmEDphCAJMHrdsDvUTvL7CiwLAs+jwMuhwK/xwHTEkkmmMjfvwEREdHh+vWvf42bb74ZK1euxG233RYbeFJVVYWGhoaEdbt+r6ys7PfxjMPosx0O07SG7dgjEeOVPsYqM4xX+hirzDBe6cvmWLkdCiZWB2CaArHbyzPUNXCxr+dYXODCPZcugyxLva6bTfHKlnZ0iWpm7GenQ0ZY07OujV3yImG+detWBINBLF68OLYsEAhg+vTp2LBhw4hJmG/afgD7mz9DMBiFYQoIIey3AwEICHSVvRYCPR+L/WyvYHUuELFl9noQgNW5fXwdbRG3bfy+ROc6h44tEt6iRNx+RfdtY8vtJLhpCViWfVz7/852dC7XTXt0eFQz+/k22JNTleHpHN3dleD2uuzEtzc+Ge5S4O1Meqeb+O5ehrzr74Ku5x57oNvbuoj/L/Fv0LVEiltVCAGpc4lI3Di29aHjJh6n63dZliDJCtqCGkzTgiTZx5Mke3SXJEnoesqW6CxjIkkwOmuaxI/W7vo7d00EJiy7XW6XglDYgGWJQ+tZgCRLsYaYQkBRJMASUBXZbgAEZMluo2UBAb8TLoc9elyRJVSXeiEE4HLmd512IiKiwfbYY4/hpptuwurVq/H9738fctzdcAsXLsTjjz8O0zShKPZn8uuvv46JEydywk8iIiLKCSOhRKvHpSIcNTBtfPFwN6UH0zqUlXKqCsLD2Ja+5EXCfP/+/QCA6urEmVkrKiqwb9++fu93KCchiuom7vr9uxlNBpnr7DrjdvkNV9w/p0OG26nA53VChj1xg9Mhw6UqcDllONXO9ZwKFEmCkOwEcCyynT90JY3tX+z/dENAN4zOx+2tuhLLPUmH/uvMcsf9GLeane5Oto8ei7q11U5US3GJcHsLST70W1dyX4KUkAjvOp4sAYqqwOVUUOC1R2VLkDrPNQFFkSFLgGEISLLUOeLbft4WBGABqnoooS7BrgFvmBYUWYJDlaGbojMRL0GVu9a1/1cUOfb8heicrEOR4VDt5ZAAVZZjiXlVkYZ1pH62TeSR7Riv9DFWmWG80sdYZYbxSu7TTz/FLbfcgpUrV+I73/kOGhsbY4+53W6ceuqpePjhh3HttdfinHPOwbvvvotf/vKXuPHGG4ex1URERET55cazFmLjhwewfM6o4W5KD36PI/Zztl98yIuEeThsX7NINhFRa2trv/Y5HJMQfeeUOmz++CCAroSnFEsoSp2Z1K6kaGwdxCVM45KUCdshPoEqQZYP7Ts+MSl133/smOhx7ITjxR5Hj+Rw1z66JtpUFDshK8sS5M7nKMsSHIqdGPd5HLF6371OzpgkMx3/XOP/T9gsllxOvW1iPA+NwO4u6Ws/PmMNJMQiWVu6PyZ3Lusa5S3FGip6tD/29CUp1pZ8Lgkz0LJ5copsxHilj7HKDOOVPsYqM4xXor/85S/QdR0vvPACXnjhhYTHTjnlFPz0pz/Fww8/jJtvvhmnnHIKysvLcdVVV+GUU04ZphYTERER5Z+yIg8+d+S44W5GUlUlXqw+sQZFAfdwN6VPeZEwd7vtP4SmabGfAXsiIo+nf1+GhmMSomPqqvD5JROzZgKB4Zd6tH3yyRa61ynJ/EiZjO83+14la2Tb5BTZjLHKDOOVPsYqM4xX+hirzAxHvDKZgGi4nH/++Tj//PN7XWfWrFn43e9+N0QtIiIiIqKR5rh5Y4a0Ykd/5UXCvKsUS0NDA8aNO3SVpaGhAVOnTu33fjkJUfZjrDLDeKWPscoM45U+xiozjFf6GKvMMF5ERERERPkp+1P6A2Dq1Knw+/148803Y8va2trw/vvvY8GCBcPYMiIiIiIiIiIiIiLKFnkxwtzpdGLVqlW47bbbUFJSgtGjR+NnP/sZqqqqsHLlyuFuHhERERERERERERFlgbxImAPAd7/7XRiGgeuuuw6RSAQLFy7Ez3/+8x4TgRIRERERERERERFRfsqbhLmiKLjyyitx5ZVXDndTiIiIiIiIiIiIiCgL5UUNcyIiIiIiIiIiIiKivjBhTkREREREREREREQEJsyJiIiIiIiIiIiIiAAwYU5EREREREREREREBIAJcyIiIiIiIiIiIiIiAEyYExEREREREREREREBYMKciIiIiIiIiIiIiAgAE+ZERERERERERERERACYMCciIiIiIiIiIiIiAsCEORERERERERERERERAEASQojhbsRIJISAZQ196BRFhmlaQ37ckYixygzjlT7GKjOMV/oYq8wwXuljrDIz1PGSZQmSJA3Z8UYK9rdHBsYrfYxVZhiv9DFWmWG80sdYZYbxSt9wxCqTPjcT5kREREREREREREREYEkWIiIiIiIiIiIiIiIATJgTEREREREREREREQFgwpyIiIiIiIiIiIiICAAT5kREREREREREREREAJgwJyIiIiIiIiIiIiICwIQ5EREREREREREREREAJsyJiIiIiIiIiIiIiAAwYU5EREREREREREREBIAJcyIiIiIiIiIiIiIiAEyYExEREREREREREREBYMKciIiIiIiIiIiIiAgAE+ZERERERERERERERACYMCciIiIiIiIiIiIiAsCE+YhgWRbuvvtuLF26FLNnz8bZZ5+NnTt3DnezhkVLSwt++MMfYtmyZZg3bx6+8Y1v4K233oo9fs0116C2tjbh37Jly2KP51ss9+zZ0yMetbW1eOKJJwAAH3zwAVatWoU5c+bg2GOPxc9//vOE7fMlXm+++WbSONXW1uL4448HwHOry7333ovVq1cnLBuI86ivfYxUyeL1t7/9Daeeeirmzp2LFStW4D//8z8RiURij/f1ugVyM17JYjUQr7tcjBXQM16rV69O+T729NNPA8ivc6uv/gLftyiZfPksTwf73Oljfzt97HOnj33u9LG/nRn2udPH/nbfcrrPLSjrrV+/Xhx11FHipZdeEh988IE4++yzxcqVK0U0Gh3upg25s846S3zxi18UGzZsEB9//LG46aabxKxZs8RHH30khBDilFNOEevWrRMNDQ2xf42NjbHt8y2Wf/3rX0VdXZ2or69PiEk4HBZNTU1i0aJF4tprrxUfffSRePLJJ0VdXZ148sknY9vnS7yi0WhCfBoaGsQ//vEPMX36dPH73/9eCMFzSwghHnnkEVFbWytWrVoVWzYQ51E6+xiJksVrw4YNYtq0aeKBBx4QO3bsEC+//LJYvny5uPrqq2Pr9Pa6FSI345UsVkIc/usuF2MlRPJ4NTc393gfO++888RJJ50k2tvbhRD5dW711l/g+xalkg+f5elinzt97G+nj33u9LDPnT72tzPDPnf62N9OTy73uZkwz3LRaFTMnTtXPPbYY7Flra2tYtasWeLZZ58dxpYNvR07doiamhqxcePG2DLLssTKlSvFnXfeKQzDEHV1deKFF15Iun0+xvK+++4TX/ziF5M+dv/994ulS5cKXddjy26//Xbxuc99TgiRn/HqommaOPnkk8Wll14qhBB5f27t379frFmzRsyZM0ecdNJJCZ2GgTiP+trHSNNbvC6//HJx1llnJaz/9NNPi+nTp8c6Bb29boXIrXj1FquBeN3lUqyE6D1e3f33f/+3mD59uti6dWtsWb6cW331F/i+Rcnk+md5Jtjnzgz72/3HPnci9rnTx/52ZtjnTh/72+nL9T43S7Jkua1btyIYDGLx4sWxZYFAANOnT8eGDRuGsWVDr7i4GA8++CBmzpwZWyZJEoQQaG1txY4dOxCNRjFp0qSk2+djLLdt24bJkycnfeytt97CwoULoapqbNnixYvx6aeforGxMS/j1eU3v/kN9u3bh2uuuQYA8v7ceu+991BYWIhnnnkGs2fPTnhsIM6jvvYx0vQWr7PPPhtXXXVVj20Mw0BHRweA3l+3QG7Fq7dYDcTrLpdiBfQer3ihUAi33norzjzzTNTW1saW58u51Vd/ge9blEyuf5Zngn3uzLC/3X/scydinzt97G9nhn3u9LG/nb5c73Orfa9Cw2n//v0AgOrq6oTlFRUV2Ldv33A0adgEAgEsX748Ydlzzz2HXbt24ZhjjsGHH34ISZLwy1/+Eq+88gpkWcby5ctx6aWXoqCgIC9j+eGHH6K8vBzf/OY3sWPHDowfPx4XXnghli5div3796OmpiZh/YqKCgDA3r178zJeABCNRnH//ffjzDPPjMUj38+tFStWYMWKFUkfG4jzqK99lJaWHv6TGEK9xWv69OkJv2uahkceeQQzZsxASUkJgN5ft0Buxau3WA3E6y6XYgX0Hq94jz/+OILBIC644IKE5flybvXVX7jjjjv4vkU95PpneSbY584M+9v9wz53T+xzp4/97cywz50+9rfTl+t9bo4wz3LhcBgA4HQ6E5a7XC5Eo9HhaFLW2LhxI77//e/j+OOPx4oVK7B9+3bIsozRo0fj/vvvx9q1a/Hyyy/jwgsvhGVZeRdLTdOwY8cOdHR04NJLL8WDDz6Iuro6nHvuuXj99dcRiUSSxgKwO7D5Fq8uf/rTnxCNRhMm9+C5ldpAnEd97SNXGYaBq666Ch999BGuv/56AH2/boH8iddAvO7yJVbxTNPEo48+im9+85soKCiILc/nc6t7f4HvW5RMPn+W94V97tTY3+4/9rkzw8+u/mF/u2/sc2eO/e3kcq3PzRHmWc7tdgOwX3hdPwP2ieHxeIarWcPuxRdfxBVXXIHZs2dj3bp1AIBLLrkE3/72txEIBAAANTU1KC8vx+mnn47NmzfnXSydTic2bNgAVVVjbzAzZ87Exx9/jJ///Odwu93QNC1hm643HK/Xm3fx6vL000/jxBNPRHFxcWwZz63UBuI86msfuairE/Xmm2/i7rvvjt3u19fr9qijjsqbeA3E6y5fYhXvn//8J/bu3YvTTjstYXm+nlvJ+gt836Jk8vmzvDfsc/eO/e3+Y587M/zsyhz72+lhnztz7G/3lIt9bo4wz3JdtyY0NDQkLG9oaEBVVdVwNGnY/frXv8Yll1yCZcuW4aGHHoq9sCRJir3Jd+m6dWP//v15GUuv19vjalxNTQ3q6+tRVVWVNBYAUFlZmZfxampqwqZNm/D5z38+YTnPrdQG4jzqax+5pqGhAd/61rewadMmPPTQQz1u+evtdQvkT7wG4nWXL7GK9+KLL2LWrFkYO3Zsj8fy7dxK1V/g+xYlk8+f5amwz50e9rczxz535vjZlRn2t9PHPnfm2N9OlKt9bibMs9zUqVPh9/vx5ptvxpa1tbXh/fffx4IFC4axZcPjsccew0033YRvfetbuPPOOxPeiC6//HKsWbMmYf3NmzcDACZPnpx3sdy6dSvmzp2Lt956K2H5li1bMHnyZCxcuBAbN26EaZqxx15//XVMnDgRpaWleRcvAHj77bchSRKOPPLIhOU8t1IbiPOor33kktbWVpx55ploamrCY489ljDBCdD36xbIn3gNxOsuX2IVb+PGjT3OKyD/zq3e+gt836Jk8vmzPBn2udPD/nb/sM+dOX52pY/97cywz5059rcPyek+t6Cst27dOnHkkUeKF198UXzwwQfi7LPPFieeeKKIRqPD3bQh9cknn4gZM2aIiy66SDQ0NCT8a2trE3/7299EbW2tuPfee8XOnTvFSy+9JFasWCEuu+yy2D7yKZamaYqvfe1r4gtf+ILYsGGD+Oijj8Qtt9wiZs6cKbZu3SoOHjwoFi5cKNauXSu2b98u/vCHP4i6ujrxxz/+MbaPfIqXEEKsX79enHjiiT2W89w6ZO3atWLVqlWx3wfiPEpnHyNV93itXbtWzJgxQ7z++us93scMw+jzdStE7sare6wG4nWXq7ESome8hBDCMAwxY8YM8cwzz/RYP5/Orb76C3zfolTy5bO8L+xzp4/97f5hn7tv7HOnj/3tzLDPnT72t3uX631uJsxHAMMwxK233ioWL14s5syZI84991zx2WefDXezhtx9990nampqkv5bu3atEEKI559/Xnz5y18Ws2bNEkuWLBE//elPRSQSie0j32LZ2NgorrnmGrFkyRJRV1cnTj/9dLFhw4bY4++884447bTTxMyZM8Vxxx0nHn300YTt8y1e119/vTjttNOSPsZzy5as0zAQ51Ff+xip4uNlmqaoq6tL+T7WFZO+XrdC5Ga8kp1bA/G6y8VYCZE8XgcPHhQ1NTXilVdeSbpNvpxb6fQX+L5FyeTLZ3lf2OfODPvbmWOfu2/sc6eP/e3MsM+dPva3e5frfW5JCCEGdww7EREREREREREREVH2Yw1zIiIiIiIiIiIiIiIwYU5EREREREREREREBIAJcyIiIiIiIiIiIiIiAEyYExEREREREREREREBYMKciIiIiIiIiIiIiAgAE+ZERERERERERERERACYMCciIiIiIiIiIiIiAsCEORERERERERERERERAEAd7gYQEdHgufrqq/HUU0+lfLyoqAhvvvlmWvtav3497rnnHmzbtm2gmpfS6tWrAQCPPvrooB+LiIiIiKi/2N8mIso9TJgTEeW48vJy3HPPPUkfU9X0Pwa+9rWvYenSpQPVLCIiIiKinMD+NhFRbmHCnIgoxzmdTsyZM+ew91NVVYWqqqrDbxARERERUQ5hf5uIKLewhjkREWH16tW4+uqr8cADD2DJkiWYN28eLrjgAnz22WexddavX4/a2trY75999hkuuOACLFq0CLNnz8bpp5+Ol19+OWG/mzdvxpo1a7Bo0SLMmzcP559/PrZv356wzt69e3HxxRdj/vz5WLJkCR555JGkbXziiSdw8sknY+bMmTj22GOxfv16GIYRe7ypqQlXXHEFlixZgrq6OnzpS1/C008/PQDRISIiIiI6POxvExGNHBxhTkSUB+I7uvEURYEkSQCAv/71ryguLsa1114Ly7Jw++2344wzzsD//M//wOv1JmxnWRa+853voLy8HLfeeitUVcWvfvUrXHjhhfjzn/+M8ePH44033sA555yDhQsX4uabb4amaXjggQfw9a9/Hb///e8xadIkhEIhrFq1CrIs40c/+hFUVcVdd92FXbt2Ye7cubHjPfDAA7jjjjuwatUqXHPNNfjggw+wfv167Nu3D7fccgsA4Morr0RjYyNuvPFG+Hw+PPPMM1i7di2qq6uxaNGiQYosERERERH72+xvE1EuYcKciCjH7dmzBzNmzEj62H/8x3/gwgsvBACEQiH84Q9/wLhx4wAARxxxBE455RQ89dRT+Na3vpWwXWNjIz7++GOcf/75WL58OQBg1qxZuOeeexCNRgEAt99+O8aOHYuHH34YiqIAAI455hisXLkS69evx5133omnnnoKe/fuxZ/+9KfYaJpZs2Zh5cqVsWO1t7fjvvvuw+mnn47rrrsutp+ioiJcd911OOusszBlyhT885//xIUXXogTTjgBALBo0SIUFRXFjk1ERERENBjY32Z/m4hyCxPmREQ5rry8HPfdd1/SxyorK2M/z507N9Z5B4Dp06dj7NixeOutt3p04MvKyjB58mT84Ac/wGuvvYZly5bhmGOOwTXXXAPA/jKwefNmXHTRRQkd6EAggOOOOy52K+lbb72FsWPHJtx6Wl1dnVADctOmTQiHw1ixYkXCyJ0VK1YAAF599VVMmTIFixYtwvr167F161YsX74cy5Ytw9q1azMNFxERERFRRtjfJiLKLUyYExHlOKfTibq6uj7Xq6io6LGstLQUbW1tPZZLkoRf/OIXuO+++/DCCy/gqaeegsPhwAknnIAbbrgB0WgUQgiUlZX12LasrAzt7e0AgNbWVpSUlPRYp7y8HAcPHgQAtLS0AADOO++8pO1uaGgAANxxxx24//778dxzz+H555+HLMs4+uijccMNN2Ds2LF9Pn8iIiIiov5gf5v9bSLKLUyYExERgEMd5XgHDx5MGAUTr7KyEjfccAOuv/56bN26Fc8//zweeughFBYWYu3atZAkKdYJj3fgwAEUFRUBAIqLi7Fz585e2xIIBAAAt912GyZMmNBj3a4vCQUFBbjyyitx5ZVX4pNPPsFf//pX3Hvvvbjxxhvx8MMP9/HsiYiIiIgGF/vbREQjgzzcDSAiouywadMmNDU1xX5/7733sHv3bhx11FFJ1z366KPx7rvvQpIkTJs2Dd/73vdQU1OD/fv3w+v1YubMmfjzn/8M0zRj27W3t+Oll17C/PnzAQCLFy/G7t27sXnz5tg6TU1N+Ne//hX7ffbs2XA4HKivr0ddXV3sn8PhwO23347du3djz549WL58OZ5//nkAdj3Ic889F0cffTT2798/0KEiIiIiIsoY+9tERCMDR5gTEeU4TdMSOsTd1dTUAADC4TDOPfdcXHDBBQgGg7jjjjtQU1ODL3zhCz22mT59OtxuN6666ipccsklKCsrw2uvvYYPPvgAZ5xxBgDg8ssvx5o1a3DOOedg1apV0HUdDz74IDRNw8UXXwwA+NKXvoRf/epXuPjii/G9730Pfr8f9913HyzLih2ruLgY55xzDu666y50dHRg0aJFqK+vx1133QVJkjB16lQUFBSgqqoKP/7xj9HR0YFx48Zhy5YtePnll/Gd73xnAKNJRERERJSI/W32t4kot0hCCDHcjSAiosFx9dVX46mnnup1nSeffBK33norhBBYvHgxHn30UQD2JD9XXXUViouLAQDr16/HPffcg23btgEAduzYgdtvvx0bN25EW1sbJkyYgNWrV+P000+P7fvNN9/E3XffjS1btsDpdGLBggW47LLLMGXKlNg6TU1NuOWWW/Dyyy9DkiScdtpp2L17NxobG2NtAYDf/OY3eOyxx7Bz504UFhbiqKOOwmWXXYZRo0YBsG89XbduHf7xj3+gubkZ1dXVOPXUU3HeeedBlnlDFRERERENPPa32d8motzDhDkREWH16tUAkNBhJiIiIiKigcH+NhHRyMFLgEREREREREREREREYMKciIiIiIiIiIiIiAgAS7IQEREREREREREREQHgCHMiIiIiIiIiIiIiIgBMmBMRERERERERERERAWDCnIiIiIiIiIiIiIgIABPmREREREREREREREQAmDAnIiIiIiIiIiIiIgLAhDkREREREREREREREQAmzImIiIiIiIiIiIiIADBhTkREREREREREREQEAPj/jktYX/X/TmgAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_steps_and_rewards(res_all, st_all,params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* [Gymnasium documentation](https://gymnasium.farama.org/).\n", + "* [Diving deeper into Reinforcement Learning with Q-Learning, Thomas Simonini](https://medium.freecodecamp.org/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe).\n", + "* Illustrations by [Thomas Simonini](https://github.com/simoninithomas/Deep_reinforcement_learning_Course) and [Sung Kim](https://www.youtube.com/watch?v=xgoO54qN4lY).\n", + "* [Frozen Lake solution with TensorFlow](https://analyticsindiamag.com/openai-gym-frozen-lake-beginners-guide-reinforcement-learning/)\n", + "* [Deep Q-Learning for Doom](https://medium.freecodecamp.org/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8)\n", + "* [Intro OpenAI Gym with Random Search and the Cart Pole scenario](http://www.pinchofintelligence.com/getting-started-openai-gym/)\n", + "* [Q-Learning for the Taxi scenario](https://www.oreilly.com/learning/introduction-to-reinforcement-learning-and-openai-gym)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Licence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", + "\n", + "© Carlos Á. Iglesias, Universidad Politécnica de Madrid." + ] + } + ], + "metadata": { + "datacleaner": { + "position": { + "top": "50px" + }, + "python": { + "varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])" + }, + "window_display": false + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/ml5/qlearning.py b/ml5/qlearning.py new file mode 100644 index 0000000..660c595 --- /dev/null +++ b/ml5/qlearning.py @@ -0,0 +1,274 @@ +# Class definition of QLearning + +from pathlib import Path +from typing import NamedTuple + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from tqdm import tqdm + +import gymnasium as gym +from gymnasium.envs.toy_text.frozen_lake import generate_random_map + +# Params + +class Params(NamedTuple): + total_episodes: int # Total episodes + learning_rate: float # Learning rate + gamma: float # Discounting rate + epsilon: float # Exploration probability + map_size: int # Number of tiles of one side of the squared environment + seed: int # Define a seed so that we get reproducible results + is_slippery: bool # If true the player will move in intended direction with probability of 1/3 else will move in either perpendicular direction with equal probability of 1/3 in both directions + n_runs: int # Number of runs + action_size: int # Number of possible actions + state_size: int # Number of possible states + proba_frozen: float # Probability that a tile is frozen + savefig_folder: Path # Root folder where plots are saved + + +class Qlearning: + def __init__(self, learning_rate, gamma, state_size, action_size): + self.state_size = state_size + self.action_size = action_size + self.learning_rate = learning_rate + self.gamma = gamma + self.reset_qtable() + + def update(self, state, action, reward, new_state): + """Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]""" + delta = ( + reward + + self.gamma * np.max(self.qtable[new_state][:]) + - self.qtable[state][action] + ) + q_update = self.qtable[state][action] + self.learning_rate * delta + return q_update + + def reset_qtable(self): + """Reset the Q-table.""" + self.qtable = np.zeros((self.state_size, self.action_size)) + + +class EpsilonGreedy: + def __init__(self, epsilon, rng): + self.epsilon = epsilon + self.rng = rng + + def choose_action(self, action_space, state, qtable): + """Choose an action `a` in the current world state (s).""" + # First we randomize a number + explor_exploit_tradeoff = self.rng.uniform(0, 1) + + # Exploration + if explor_exploit_tradeoff < self.epsilon: + action = action_space.sample() + + # Exploitation (taking the biggest Q-value for this state) + else: + # Break ties randomly + # If all actions are the same for this state we choose a random one + # (otherwise `np.argmax()` would always take the first one) + if np.all(qtable[state][:]) == qtable[state][0]: + action = action_space.sample() + else: + action = np.argmax(qtable[state][:]) + return action + + +def run_frozen_maps(maps, params, rng): + """Run FrozenLake in maps and plot results""" + map_sizes = maps + res_all = pd.DataFrame() + st_all = pd.DataFrame() + + for map_size in map_sizes: + env = gym.make( + "FrozenLake-v1", + is_slippery=params.is_slippery, + render_mode="rgb_array", + desc=generate_random_map( + size=map_size, p=params.proba_frozen, seed=params.seed + ), + ) + + params = params._replace(action_size=env.action_space.n) + params = params._replace(state_size=env.observation_space.n) + env.action_space.seed( + params.seed + ) # Set the seed to get reproducible results when sampling the action space + learner = Qlearning( + learning_rate=params.learning_rate, + gamma=params.gamma, + state_size=params.state_size, + action_size=params.action_size, + ) + explorer = EpsilonGreedy( + epsilon=params.epsilon, + rng=rng + ) + print(f"Map size: {map_size}x{map_size}") + rewards, steps, episodes, qtables, all_states, all_actions = run_env(env, params, learner, explorer) + + # Save the results in dataframes + res, st = postprocess(episodes, params, rewards, steps, map_size) + res_all = pd.concat([res_all, res]) + st_all = pd.concat([st_all, st]) + qtable = qtables.mean(axis=0) # Average the Q-table between runs + + plot_states_actions_distribution( + states=all_states, actions=all_actions, map_size=map_size, params=params + ) # Sanity check + plot_q_values_map(qtable, env, map_size, params) + + env.close() + return res_all, st_all + +def run_env(env, params, learner, explorer): + rewards = np.zeros((params.total_episodes, params.n_runs)) + steps = np.zeros((params.total_episodes, params.n_runs)) + episodes = np.arange(params.total_episodes) + qtables = np.zeros((params.n_runs, params.state_size, params.action_size)) + all_states = [] + all_actions = [] + + for run in range(params.n_runs): # Run several times to account for stochasticity + learner.reset_qtable() # Reset the Q-table between runs + + for episode in tqdm( + episodes, desc=f"Run {run}/{params.n_runs} - Episodes", leave=False + ): + state = env.reset(seed=params.seed)[0] # Reset the environment + step = 0 + done = False + total_rewards = 0 + + while not done: + action = explorer.choose_action( + action_space=env.action_space, state=state, qtable=learner.qtable + ) + + # Log all states and actions + all_states.append(state) + all_actions.append(action) + + # Take the action (a) and observe the outcome state(s') and reward (r) + new_state, reward, terminated, truncated, info = env.step(action) + + done = terminated or truncated + + learner.qtable[state, action] = learner.update( + state, action, reward, new_state + ) + + total_rewards += reward + step += 1 + + # Our new state is state + state = new_state + + # Log all rewards and steps + rewards[episode, run] = total_rewards + steps[episode, run] = step + qtables[run, :, :] = learner.qtable + + return rewards, steps, episodes, qtables, all_states, all_actions + +def postprocess(episodes, params, rewards, steps, map_size): + """Convert the results of the simulation in dataframes.""" + res = pd.DataFrame( + data={ + "Episodes": np.tile(episodes, reps=params.n_runs), + "Rewards": rewards.flatten(), + "Steps": steps.flatten(), + } + ) + res["cum_rewards"] = rewards.cumsum(axis=0).flatten(order="F") + res["map_size"] = np.repeat(f"{map_size}x{map_size}", res.shape[0]) + + st = pd.DataFrame(data={"Episodes": episodes, "Steps": steps.mean(axis=1)}) + st["map_size"] = np.repeat(f"{map_size}x{map_size}", st.shape[0]) + return res, st + +def qtable_directions_map(qtable, map_size): + """Get the best learned action & map it to arrows.""" + qtable_val_max = qtable.max(axis=1).reshape(map_size, map_size) + qtable_best_action = np.argmax(qtable, axis=1).reshape(map_size, map_size) + directions = {0: "←", 1: "↓", 2: "→", 3: "↑"} + qtable_directions = np.empty(qtable_best_action.flatten().shape, dtype=str) + eps = np.finfo(float).eps # Minimum float number on the machine + for idx, val in enumerate(qtable_best_action.flatten()): + if qtable_val_max.flatten()[idx] > eps: + # Assign an arrow only if a minimal Q-value has been learned as best action + # otherwise since 0 is a direction, it also gets mapped on the tiles where + # it didn't actually learn anything + qtable_directions[idx] = directions[val] + qtable_directions = qtable_directions.reshape(map_size, map_size) + return qtable_val_max, qtable_directions + +def plot_q_values_map(qtable, env, map_size, params): + """Plot the last frame of the simulation and the policy learned.""" + qtable_val_max, qtable_directions = qtable_directions_map(qtable, map_size) + + # Plot the last frame + fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5)) + ax[0].imshow(env.render()) + ax[0].axis("off") + ax[0].set_title("Last frame") + + # Plot the policy + sns.heatmap( + qtable_val_max, + annot=qtable_directions, + fmt="", + ax=ax[1], + cmap=sns.color_palette("Blues", as_cmap=True), + linewidths=0.7, + linecolor="black", + xticklabels=[], + yticklabels=[], + annot_kws={"fontsize": "xx-large"}, + ).set(title="Learned Q-values\nArrows represent best action") + for _, spine in ax[1].spines.items(): + spine.set_visible(True) + spine.set_linewidth(0.7) + spine.set_color("black") + img_title = f"frozenlake_q_values_{map_size}x{map_size}.png" + fig.savefig(params.savefig_folder / img_title, bbox_inches="tight") + plt.show() + +def plot_states_actions_distribution(states, actions, map_size, params): + """Plot the distributions of states and actions.""" + labels = {"LEFT": 0, "DOWN": 1, "RIGHT": 2, "UP": 3} + + fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5)) + sns.histplot(data=states, ax=ax[0], kde=True) + ax[0].set_title("States") + sns.histplot(data=actions, ax=ax[1]) + ax[1].set_xticks(list(labels.values()), labels=labels.keys()) + ax[1].set_title("Actions") + fig.tight_layout() + img_title = f"frozenlake_states_actions_distrib_{map_size}x{map_size}.png" + fig.savefig(params.savefig_folder / img_title, bbox_inches="tight") + plt.show() + +def plot_steps_and_rewards(rewards_df, steps_df,params): + """Plot the steps and rewards from dataframes.""" + fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5)) + sns.lineplot( + data=rewards_df, x="Episodes", y="cum_rewards", hue="map_size", ax=ax[0] + ) + ax[0].set(ylabel="Cumulated rewards") + + sns.lineplot(data=steps_df, x="Episodes", y="Steps", hue="map_size", ax=ax[1]) + ax[1].set(ylabel="Averaged steps number") + + for axi in ax: + axi.legend(title="map size") + fig.tight_layout() + img_title = "frozenlake_steps_and_rewards.png" + fig.savefig(params.savefig_folder / img_title, bbox_inches="tight") + plt.show() +