From c38b893b1aacdd09678fa473ca5b6bfdc463d031 Mon Sep 17 00:00:00 2001
From: chy <308604256@qq.com>
Date: Mon, 1 Mar 2021 18:59:00 +0800
Subject: [PATCH 01/13] offpolicy update
---
examples/mujoco/mujoco_ddpg.py | 130 +++++++++++++++++++++++++++
examples/mujoco/mujoco_sac.py | 91 ++++++++-----------
examples/mujoco/mujoco_td3.py | 143 ++++++++++++++++++++++++++++++
tianshou/policy/modelfree/ddpg.py | 23 +++--
tianshou/policy/modelfree/sac.py | 29 ++----
tianshou/policy/modelfree/td3.py | 25 ++----
tianshou/trainer/offline.py | 5 +-
tianshou/trainer/offpolicy.py | 5 +-
tianshou/trainer/onpolicy.py | 5 +-
9 files changed, 354 insertions(+), 102 deletions(-)
create mode 100644 examples/mujoco/mujoco_ddpg.py
create mode 100644 examples/mujoco/mujoco_td3.py
diff --git a/examples/mujoco/mujoco_ddpg.py b/examples/mujoco/mujoco_ddpg.py
new file mode 100644
index 000000000..64a102ad3
--- /dev/null
+++ b/examples/mujoco/mujoco_ddpg.py
@@ -0,0 +1,130 @@
+import free_mjc
+import os
+import gym
+import torch
+import datetime
+import argparse
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+
+from tianshou.policy import DDPGPolicy
+from tianshou.utils import BasicLogger
+from tianshou.env import SubprocVectorEnv
+from tianshou.utils.net.common import Net
+from tianshou.exploration import GaussianNoise
+from tianshou.trainer import offpolicy_trainer
+from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer
+from tianshou.utils.net.continuous import Actor, Critic
+
+
+def get_args():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--task', type=str, default='Ant-v3')
+ parser.add_argument('--seed', type=int, default=0)
+ parser.add_argument('--buffer-size', type=int, default=1000000)
+ parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[256, 256])
+ parser.add_argument('--actor-lr', type=float, default=1e-3)
+ parser.add_argument('--critic-lr', type=float, default=1e-3)
+ parser.add_argument('--gamma', type=float, default=0.99)
+ parser.add_argument('--tau', type=float, default=0.005)
+ parser.add_argument('--exploration-noise', type=float, default=0.1)
+ parser.add_argument("--start-timesteps", type=int, default=25000)
+ parser.add_argument('--epoch', type=int, default=250)
+ parser.add_argument('--step-per-epoch', type=int, default=5000)
+ parser.add_argument('--step-per-collect', type=int, default=1)
+ parser.add_argument('--update-per-step', type=int, default=1)
+ parser.add_argument('--n-step', type=int, default=1)
+ parser.add_argument('--batch-size', type=int, default=256)
+ parser.add_argument('--training-num', type=int, default=1)
+ parser.add_argument('--test-num', type=int, default=10)
+ parser.add_argument('--logdir', type=str, default='log')
+ parser.add_argument('--render', type=float, default=0.)
+ parser.add_argument(
+ '--device', type=str,
+ default='cuda' if torch.cuda.is_available() else 'cpu')
+ parser.add_argument('--resume-path', type=str, default=None)
+ return parser.parse_args()
+
+
+def test_ddpg(args=get_args()):
+ env = gym.make(args.task)
+ args.state_shape = env.observation_space.shape or env.observation_space.n
+ args.action_shape = env.action_space.shape or env.action_space.n
+ args.max_action = env.action_space.high[0]
+ args.exploration_noise = args.exploration_noise * args.max_action
+ print("Observations shape:", args.state_shape)
+ print("Actions shape:", args.action_shape)
+ print("Action range:", np.min(env.action_space.low),
+ np.max(env.action_space.high))
+ # train_envs = gym.make(args.task)
+ if args.training_num > 0:
+ train_envs = SubprocVectorEnv(
+ [lambda: gym.make(args.task) for _ in range(args.training_num)])
+ else:
+ train_envs = gym.make(args.task)
+ # test_envs = gym.make(args.task)
+ test_envs = SubprocVectorEnv(
+ [lambda: gym.make(args.task) for _ in range(args.test_num)])
+ # seed
+ np.random.seed(args.seed)
+ torch.manual_seed(args.seed)
+ train_envs.seed(args.seed)
+ test_envs.seed(args.seed)
+ # model
+ net_a = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
+ actor = Actor(
+ net_a, args.action_shape, max_action=args.max_action,
+ device=args.device).to(args.device)
+ actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
+ net_c = Net(args.state_shape, args.action_shape,
+ hidden_sizes=args.hidden_sizes,
+ concat=True, device=args.device)
+ critic = Critic(net_c, device=args.device).to(args.device)
+ critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
+ policy = DDPGPolicy(
+ actor, actor_optim, critic, critic_optim,
+ action_range=[env.action_space.low[0], env.action_space.high[0]],
+ tau=args.tau, gamma=args.gamma,
+ exploration_noise=GaussianNoise(sigma=args.exploration_noise),
+ estimation_step=args.n_step)
+ # load a previous policy
+ if args.resume_path:
+ policy.load_state_dict(torch.load(
+ args.resume_path, map_location=args.device
+ ))
+ print("Loaded agent from: ", args.resume_path)
+
+ # collector
+ if args.training_num > 0:
+ buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
+ else:
+ buffer = ReplayBuffer(args.buffer_size)
+ train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
+ test_collector = Collector(policy, test_envs)
+ train_collector.collect(n_step=args.start_timesteps, random=True)
+ # log
+ log_path = os.path.join(args.logdir, args.task, 'ddpg', 'seed_' + str(
+ args.seed) + '_' + datetime.datetime.now().strftime('%m%d-%H%M%S'))
+ writer = SummaryWriter(log_path)
+ writer.add_text("args", str(args))
+ logger = BasicLogger(writer)
+
+ def save_fn(policy):
+ torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
+ # trainer
+ result = offpolicy_trainer(
+ policy, train_collector, test_collector, args.epoch,
+ args.step_per_epoch, args.step_per_collect, args.test_num,
+ args.batch_size, save_fn=save_fn, logger=logger,
+ update_per_step=args.update_per_step, test_in_train=False)
+
+ # Let's watch its performance!
+ policy.eval()
+ test_envs.seed(args.seed)
+ test_collector.reset()
+ result = test_collector.collect(n_episode=args.test_num, render=args.render)
+ print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}')
+
+
+if __name__ == '__main__':
+ test_ddpg()
diff --git a/examples/mujoco/mujoco_sac.py b/examples/mujoco/mujoco_sac.py
index c07877324..a3d82372d 100644
--- a/examples/mujoco/mujoco_sac.py
+++ b/examples/mujoco/mujoco_sac.py
@@ -1,7 +1,7 @@
+import free_mjc
import os
import gym
import torch
-import pprint
import datetime
import argparse
import numpy as np
@@ -12,42 +12,38 @@
from tianshou.env import SubprocVectorEnv
from tianshou.utils.net.common import Net
from tianshou.trainer import offpolicy_trainer
-from tianshou.data import Collector, VectorReplayBuffer
+from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer
from tianshou.utils.net.continuous import ActorProb, Critic
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--task', type=str, default='Ant-v3')
- parser.add_argument('--seed', type=int, default=1626)
+ parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--buffer-size', type=int, default=1000000)
- parser.add_argument('--actor-lr', type=float, default=3e-4)
- parser.add_argument('--critic-lr', type=float, default=3e-4)
+ parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[256, 256])
+ parser.add_argument('--actor-lr', type=float, default=1e-3)
+ parser.add_argument('--critic-lr', type=float, default=1e-3)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--tau', type=float, default=0.005)
parser.add_argument('--alpha', type=float, default=0.2)
parser.add_argument('--auto-alpha', default=False, action='store_true')
parser.add_argument('--alpha-lr', type=float, default=3e-4)
- parser.add_argument('--n-step', type=int, default=2)
- parser.add_argument('--epoch', type=int, default=100)
- parser.add_argument('--step-per-epoch', type=int, default=40000)
- parser.add_argument('--step-per-collect', type=int, default=4)
- parser.add_argument('--update-per-step', type=float, default=0.25)
- parser.add_argument('--pre-collect-step', type=int, default=10000)
+ parser.add_argument("--start-timesteps", type=int, default=10000)
+ parser.add_argument('--epoch', type=int, default=250)
+ parser.add_argument('--step-per-epoch', type=int, default=5000)
+ parser.add_argument('--step-per-collect', type=int, default=1)
+ parser.add_argument('--update-per-step', type=int, default=1)
+ parser.add_argument('--n-step', type=int, default=1)
parser.add_argument('--batch-size', type=int, default=256)
- parser.add_argument('--hidden-sizes', type=int,
- nargs='*', default=[128, 128])
- parser.add_argument('--training-num', type=int, default=4)
- parser.add_argument('--test-num', type=int, default=100)
+ parser.add_argument('--training-num', type=int, default=1)
+ parser.add_argument('--test-num', type=int, default=10)
parser.add_argument('--logdir', type=str, default='log')
parser.add_argument('--render', type=float, default=0.)
- parser.add_argument('--log-interval', type=int, default=1000)
parser.add_argument(
'--device', type=str,
default='cuda' if torch.cuda.is_available() else 'cpu')
parser.add_argument('--resume-path', type=str, default=None)
- parser.add_argument('--watch', default=False, action='store_true',
- help='watch the play of pre-trained policy only')
return parser.parse_args()
@@ -61,8 +57,11 @@ def test_sac(args=get_args()):
print("Action range:", np.min(env.action_space.low),
np.max(env.action_space.high))
# train_envs = gym.make(args.task)
- train_envs = SubprocVectorEnv(
- [lambda: gym.make(args.task) for _ in range(args.training_num)])
+ if args.training_num > 1:
+ train_envs = SubprocVectorEnv(
+ [lambda: gym.make(args.task) for _ in range(args.training_num)])
+ else:
+ train_envs = gym.make(args.task)
# test_envs = gym.make(args.task)
test_envs = SubprocVectorEnv(
[lambda: gym.make(args.task) for _ in range(args.test_num)])
@@ -72,21 +71,20 @@ def test_sac(args=get_args()):
train_envs.seed(args.seed)
test_envs.seed(args.seed)
# model
- net = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
- device=args.device)
+ net_a = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
actor = ActorProb(
- net, args.action_shape, max_action=args.max_action,
+ net_a, args.action_shape, max_action=args.max_action,
device=args.device, unbounded=True, conditioned_sigma=True
).to(args.device)
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
net_c1 = Net(args.state_shape, args.action_shape,
hidden_sizes=args.hidden_sizes,
concat=True, device=args.device)
- critic1 = Critic(net_c1, device=args.device).to(args.device)
- critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
net_c2 = Net(args.state_shape, args.action_shape,
hidden_sizes=args.hidden_sizes,
concat=True, device=args.device)
+ critic1 = Critic(net_c1, device=args.device).to(args.device)
+ critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
critic2 = Critic(net_c2, device=args.device).to(args.device)
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
@@ -109,46 +107,35 @@ def test_sac(args=get_args()):
print("Loaded agent from: ", args.resume_path)
# collector
- train_collector = Collector(
- policy, train_envs,
- VectorReplayBuffer(args.buffer_size, len(train_envs)),
- exploration_noise=True)
+ if args.training_num > 1:
+ buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
+ else:
+ buffer = ReplayBuffer(args.buffer_size)
+ train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
test_collector = Collector(policy, test_envs)
+ train_collector.collect(n_step=args.start_timesteps, random=True)
# log
log_path = os.path.join(args.logdir, args.task, 'sac', 'seed_' + str(
args.seed) + '_' + datetime.datetime.now().strftime('%m%d-%H%M%S'))
writer = SummaryWriter(log_path)
writer.add_text("args", str(args))
- logger = BasicLogger(writer, train_interval=args.log_interval)
-
- def watch():
- # watch agent's performance
- print("Testing agent ...")
- policy.eval()
- test_envs.seed(args.seed)
- test_collector.reset()
- result = test_collector.collect(n_episode=args.test_num, render=args.render)
- pprint.pprint(result)
+ logger = BasicLogger(writer)
def save_fn(policy):
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
-
- def stop_fn(mean_rewards):
- return False
-
- if args.watch:
- watch()
- exit(0)
-
# trainer
- train_collector.collect(n_step=args.pre_collect_step, random=True)
result = offpolicy_trainer(
policy, train_collector, test_collector, args.epoch,
args.step_per_epoch, args.step_per_collect, args.test_num,
- args.batch_size, stop_fn=stop_fn, save_fn=save_fn, logger=logger,
- update_per_step=args.update_per_step)
- pprint.pprint(result)
- watch()
+ args.batch_size, save_fn=save_fn, logger=logger,
+ update_per_step=args.update_per_step, test_in_train=False)
+
+ # Let's watch its performance!
+ policy.eval()
+ test_envs.seed(args.seed)
+ test_collector.reset()
+ result = test_collector.collect(n_episode=args.test_num, render=args.render)
+ print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}')
if __name__ == '__main__':
diff --git a/examples/mujoco/mujoco_td3.py b/examples/mujoco/mujoco_td3.py
new file mode 100644
index 000000000..1caac6ef0
--- /dev/null
+++ b/examples/mujoco/mujoco_td3.py
@@ -0,0 +1,143 @@
+import free_mjc
+import os
+import gym
+import torch
+import datetime
+import argparse
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+
+from tianshou.policy import TD3Policy
+from tianshou.utils import BasicLogger
+from tianshou.env import SubprocVectorEnv
+from tianshou.utils.net.common import Net
+from tianshou.exploration import GaussianNoise
+from tianshou.trainer import offpolicy_trainer
+from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer
+from tianshou.utils.net.continuous import Actor, Critic
+
+
+def get_args():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--task', type=str, default='Ant-v3')
+ parser.add_argument('--seed', type=int, default=0)
+ parser.add_argument('--buffer-size', type=int, default=1000000)
+ parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[256, 256])
+ parser.add_argument('--actor-lr', type=float, default=3e-4)
+ parser.add_argument('--critic-lr', type=float, default=3e-4)
+ parser.add_argument('--gamma', type=float, default=0.99)
+ parser.add_argument('--tau', type=float, default=0.005)
+ parser.add_argument('--exploration-noise', type=float, default=0.1)
+ parser.add_argument('--policy-noise', type=float, default=0.2)
+ parser.add_argument('--noise-clip', type=float, default=0.5)
+ parser.add_argument('--update-actor-freq', type=int, default=2)
+ parser.add_argument("--start-timesteps", type=int, default=25000)
+ parser.add_argument('--epoch', type=int, default=250)
+ parser.add_argument('--step-per-epoch', type=int, default=5000)
+ parser.add_argument('--step-per-collect', type=int, default=1)
+ parser.add_argument('--update-per-step', type=int, default=1)
+ parser.add_argument('--n-step', type=int, default=1)
+ parser.add_argument('--batch-size', type=int, default=256)
+ parser.add_argument('--training-num', type=int, default=1)
+ parser.add_argument('--test-num', type=int, default=10)
+ parser.add_argument('--logdir', type=str, default='log')
+ parser.add_argument('--render', type=float, default=0.)
+ parser.add_argument(
+ '--device', type=str,
+ default='cuda' if torch.cuda.is_available() else 'cpu')
+ parser.add_argument('--resume-path', type=str, default=None)
+ return parser.parse_args()
+
+
+def test_td3(args=get_args()):
+ env = gym.make(args.task)
+ args.state_shape = env.observation_space.shape or env.observation_space.n
+ args.action_shape = env.action_space.shape or env.action_space.n
+ args.max_action = env.action_space.high[0]
+ args.exploration_noise = args.exploration_noise * args.max_action
+ args.policy_noise = args.policy_noise * args.max_action
+ args.noise_clip = args.noise_clip * args.max_action
+ print("Observations shape:", args.state_shape)
+ print("Actions shape:", args.action_shape)
+ print("Action range:", np.min(env.action_space.low),
+ np.max(env.action_space.high))
+ # train_envs = gym.make(args.task)
+ if args.training_num > 0:
+ train_envs = SubprocVectorEnv(
+ [lambda: gym.make(args.task) for _ in range(args.training_num)])
+ else:
+ train_envs = gym.make(args.task)
+ # test_envs = gym.make(args.task)
+ test_envs = SubprocVectorEnv(
+ [lambda: gym.make(args.task) for _ in range(args.test_num)])
+ # seed
+ np.random.seed(args.seed)
+ torch.manual_seed(args.seed)
+ train_envs.seed(args.seed)
+ test_envs.seed(args.seed)
+ # model
+ net_a = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
+ actor = Actor(
+ net_a, args.action_shape, max_action=args.max_action,
+ device=args.device).to(args.device)
+ actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
+ net_c1 = Net(args.state_shape, args.action_shape,
+ hidden_sizes=args.hidden_sizes,
+ concat=True, device=args.device)
+ net_c2 = Net(args.state_shape, args.action_shape,
+ hidden_sizes=args.hidden_sizes,
+ concat=True, device=args.device)
+ critic1 = Critic(net_c1, device=args.device).to(args.device)
+ critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
+ critic2 = Critic(net_c2, device=args.device).to(args.device)
+ critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
+
+ policy = TD3Policy(
+ actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
+ action_range=[env.action_space.low[0], env.action_space.high[0]],
+ tau=args.tau, gamma=args.gamma,
+ exploration_noise=GaussianNoise(sigma=args.exploration_noise),
+ policy_noise=args.policy_noise, update_actor_freq=args.update_actor_freq,
+ noise_clip=args.noise_clip, estimation_step=args.n_step)
+
+ # load a previous policy
+ if args.resume_path:
+ policy.load_state_dict(torch.load(
+ args.resume_path, map_location=args.device
+ ))
+ print("Loaded agent from: ", args.resume_path)
+
+ # collector
+ if args.training_num > 0:
+ buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
+ else:
+ buffer = ReplayBuffer(args.buffer_size)
+ train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
+ test_collector = Collector(policy, test_envs)
+ train_collector.collect(n_step=args.start_timesteps, random=True)
+ # log
+ log_path = os.path.join(args.logdir, args.task, 'td3', 'seed_' + str(
+ args.seed) + '_' + datetime.datetime.now().strftime('%m%d-%H%M%S'))
+ writer = SummaryWriter(log_path)
+ writer.add_text("args", str(args))
+ logger = BasicLogger(writer)
+
+ def save_fn(policy):
+ torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
+ # trainer
+ result = offpolicy_trainer(
+ policy, train_collector, test_collector, args.epoch,
+ args.step_per_epoch, args.step_per_collect, args.test_num,
+ args.batch_size, save_fn=save_fn, logger=logger,
+ update_per_step=args.update_per_step, test_in_train=False)
+
+ # Let's watch its performance!
+ policy.eval()
+ test_envs.seed(args.seed)
+ test_collector.reset()
+ result = test_collector.collect(n_episode=args.test_num, render=args.render)
+ print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}')
+
+
+if __name__ == '__main__':
+ test_td3()
diff --git a/tianshou/policy/modelfree/ddpg.py b/tianshou/policy/modelfree/ddpg.py
index 9a4dad062..9d84c76d6 100644
--- a/tianshou/policy/modelfree/ddpg.py
+++ b/tianshou/policy/modelfree/ddpg.py
@@ -129,18 +129,29 @@ def forward(
obs = batch[input]
actions, h = model(obs, state=state, info=batch.info)
actions += self._action_bias
+ actions = actions.clamp(self._range[0], self._range[1])
return Batch(act=actions, state=h)
- def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
- weight = batch.pop("weight", 1.0)
- current_q = self.critic(batch.obs, batch.act).flatten()
+ @staticmethod
+ def _mse_optimizer(batch: Batch, critic, optimizer):
+ """used to optimize critic"""
+ weight = getattr(batch, "weight", 1.0)
+ current_q = critic(batch.obs, batch.act).flatten()
target_q = batch.returns.flatten()
td = current_q - target_q
+ # critic1_loss = F.mse_loss(current_q1, target_q)
critic_loss = (td.pow(2) * weight).mean()
- batch.weight = td # prio-buffer
- self.critic_optim.zero_grad()
+ optimizer.zero_grad()
critic_loss.backward()
- self.critic_optim.step()
+ optimizer.step()
+ return td, critic_loss
+
+ def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
+ # critic
+ td, critic_loss = self._mse_optimizer(
+ batch, self.critic, self.critic_optim)
+ batch.weight = td # prio-buffer
+ # actor
action = self(batch).act
actor_loss = -self.critic(batch.obs, action).mean()
self.actor_optim.zero_grad()
diff --git a/tianshou/policy/modelfree/sac.py b/tianshou/policy/modelfree/sac.py
index 68bef3971..ac81cbfc2 100644
--- a/tianshou/policy/modelfree/sac.py
+++ b/tianshou/policy/modelfree/sac.py
@@ -115,7 +115,11 @@ def forward( # type: ignore
x = dist.rsample()
y = torch.tanh(x)
act = y * self._action_scale + self._action_bias
+ # __eps is used to avoid log of zero/negative number.
y = self._action_scale * (1 - y.pow(2)) + self.__eps
+ # Compute logprob from Gaussian, and then apply correction for Tanh squashing.
+ # You can check out the original SAC paper (arXiv 1801.01290): Eq 21.
+ # in appendix C to get some understanding of this equation.
log_prob = dist.log_prob(x).unsqueeze(-1)
log_prob = log_prob - torch.log(y).sum(-1, keepdim=True)
@@ -134,26 +138,11 @@ def _target_q(
return target_q
def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
- weight = batch.pop("weight", 1.0)
-
- # critic 1
- current_q1 = self.critic1(batch.obs, batch.act).flatten()
- target_q = batch.returns.flatten()
- td1 = current_q1 - target_q
- critic1_loss = (td1.pow(2) * weight).mean()
- # critic1_loss = F.mse_loss(current_q1, target_q)
- self.critic1_optim.zero_grad()
- critic1_loss.backward()
- self.critic1_optim.step()
-
- # critic 2
- current_q2 = self.critic2(batch.obs, batch.act).flatten()
- td2 = current_q2 - target_q
- critic2_loss = (td2.pow(2) * weight).mean()
- # critic2_loss = F.mse_loss(current_q2, target_q)
- self.critic2_optim.zero_grad()
- critic2_loss.backward()
- self.critic2_optim.step()
+ # critic 1&2
+ td1, critic1_loss = self._mse_optimizer(
+ batch, self.critic1, self.critic1_optim)
+ td2, critic2_loss = self._mse_optimizer(
+ batch, self.critic2, self.critic2_optim)
batch.weight = (td1 + td2) / 2.0 # prio-buffer
# actor
diff --git a/tianshou/policy/modelfree/td3.py b/tianshou/policy/modelfree/td3.py
index bd6572205..09f288ff6 100644
--- a/tianshou/policy/modelfree/td3.py
+++ b/tianshou/policy/modelfree/td3.py
@@ -105,25 +105,14 @@ def _target_q(self, buffer: ReplayBuffer, indice: np.ndarray) -> torch.Tensor:
return target_q
def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
- weight = batch.pop("weight", 1.0)
- # critic 1
- current_q1 = self.critic1(batch.obs, batch.act).flatten()
- target_q = batch.returns.flatten()
- td1 = current_q1 - target_q
- critic1_loss = (td1.pow(2) * weight).mean()
- # critic1_loss = F.mse_loss(current_q1, target_q)
- self.critic1_optim.zero_grad()
- critic1_loss.backward()
- self.critic1_optim.step()
- # critic 2
- current_q2 = self.critic2(batch.obs, batch.act).flatten()
- td2 = current_q2 - target_q
- critic2_loss = (td2.pow(2) * weight).mean()
- # critic2_loss = F.mse_loss(current_q2, target_q)
- self.critic2_optim.zero_grad()
- critic2_loss.backward()
- self.critic2_optim.step()
+ # critic 1&2
+ td1, critic1_loss = self._mse_optimizer(
+ batch, self.critic1, self.critic1_optim)
+ td2, critic2_loss = self._mse_optimizer(
+ batch, self.critic2, self.critic2_optim)
batch.weight = (td1 + td2) / 2.0 # prio-buffer
+
+ # actor
if self._cnt % self._freq == 0:
actor_loss = -self.critic1(batch.obs, self(batch, eps=0.0).act).mean()
self.actor_optim.zero_grad()
diff --git a/tianshou/trainer/offline.py b/tianshou/trainer/offline.py
index 13f96faeb..802e34963 100644
--- a/tianshou/trainer/offline.py
+++ b/tianshou/trainer/offline.py
@@ -93,8 +93,9 @@ def offline_trainer(
if save_fn:
save_fn(policy)
if verbose:
- print(f"Epoch #{epoch}: test_reward: {rew:.6f} ± {rew_std:.6f}, best_rew"
- f"ard: {best_reward:.6f} ± {best_reward_std:.6f} in #{best_epoch}")
+ print(
+ f"Epoch #{epoch}: test_reward: {rew:.6f} ± {rew_std:.6f}, best_reward:"
+ f" {best_reward:.6f} ± {best_reward_std:.6f} in #{best_epoch}")
if stop_fn and stop_fn(best_reward):
break
return gather_info(start_time, None, test_collector, best_reward, best_reward_std)
diff --git a/tianshou/trainer/offpolicy.py b/tianshou/trainer/offpolicy.py
index 72a243d9a..444dbc08a 100644
--- a/tianshou/trainer/offpolicy.py
+++ b/tianshou/trainer/offpolicy.py
@@ -145,8 +145,9 @@ def offpolicy_trainer(
if save_fn:
save_fn(policy)
if verbose:
- print(f"Epoch #{epoch}: test_reward: {rew:.6f} ± {rew_std:.6f}, best_rew"
- f"ard: {best_reward:.6f} ± {best_reward_std:.6f} in #{best_epoch}")
+ print(
+ f"Epoch #{epoch}: test_reward: {rew:.6f} ± {rew_std:.6f}, best_reward:"
+ f" {best_reward:.6f} ± {best_reward_std:.6f} in #{best_epoch}")
if stop_fn and stop_fn(best_reward):
break
return gather_info(start_time, train_collector, test_collector,
diff --git a/tianshou/trainer/onpolicy.py b/tianshou/trainer/onpolicy.py
index dae20a741..ab731270e 100644
--- a/tianshou/trainer/onpolicy.py
+++ b/tianshou/trainer/onpolicy.py
@@ -155,8 +155,9 @@ def onpolicy_trainer(
if save_fn:
save_fn(policy)
if verbose:
- print(f"Epoch #{epoch}: test_reward: {rew:.6f} ± {rew_std:.6f}, best_rew"
- f"ard: {best_reward:.6f} ± {best_reward_std:.6f} in #{best_epoch}")
+ print(
+ f"Epoch #{epoch}: test_reward: {rew:.6f} ± {rew_std:.6f}, best_reward:"
+ f" {best_reward:.6f} ± {best_reward_std:.6f} in #{best_epoch}")
if stop_fn and stop_fn(best_reward):
break
return gather_info(start_time, train_collector, test_collector,
From 68a17f756f08429923a6a4c7edd7a14ac7667f07 Mon Sep 17 00:00:00 2001
From: chy <308604256@qq.com>
Date: Sat, 6 Mar 2021 21:38:54 +0800
Subject: [PATCH 02/13] upload benchmark
---
examples/mujoco/README.md | 130 +++++++++++++++---
.../mujoco/benchmark/Ant-v3/ddpg/figure.png | Bin 0 -> 223544 bytes
examples/mujoco/benchmark/Ant-v3/figure.png | Bin 0 -> 351723 bytes
.../mujoco/benchmark/Ant-v3/sac/figure.png | Bin 0 -> 218165 bytes
.../mujoco/benchmark/Ant-v3/td3/figure.png | Bin 0 -> 195348 bytes
.../benchmark/HalfCheetah-v3/ddpg/figure.png | Bin 0 -> 195322 bytes
.../benchmark/HalfCheetah-v3/figure.png | Bin 0 -> 350134 bytes
.../benchmark/HalfCheetah-v3/sac/figure.png | Bin 0 -> 193003 bytes
.../benchmark/HalfCheetah-v3/td3/figure.png | Bin 0 -> 188388 bytes
.../benchmark/Hopper-v3/ddpg/figure.png | Bin 0 -> 264777 bytes
.../mujoco/benchmark/Hopper-v3/figure.png | Bin 0 -> 432804 bytes
.../mujoco/benchmark/Hopper-v3/sac/figure.png | Bin 0 -> 207377 bytes
.../mujoco/benchmark/Hopper-v3/td3/figure.png | Bin 0 -> 218176 bytes
.../td3/figure.png | Bin 0 -> 198636 bytes
.../benchmark/Humanoid-v3/ddpg/figure.png | Bin 0 -> 126946 bytes
.../mujoco/benchmark/Humanoid-v3/figure.png | Bin 0 -> 311853 bytes
.../benchmark/Humanoid-v3/sac/figure.png | Bin 0 -> 205693 bytes
.../benchmark/Humanoid-v3/td3/figure.png | Bin 0 -> 194940 bytes
.../InvertedDoublePendulum-v2/ddpg/figure.png | Bin 0 -> 147620 bytes
.../InvertedDoublePendulum-v2/figure.png | Bin 0 -> 335613 bytes
.../InvertedDoublePendulum-v2/sac/figure.png | Bin 0 -> 219913 bytes
.../InvertedDoublePendulum-v2/td3/figure.png | Bin 0 -> 235334 bytes
.../InvertedPendulum-v2/ddpg/figure.png | Bin 0 -> 289283 bytes
.../benchmark/InvertedPendulum-v2/figure.png | Bin 0 -> 359678 bytes
.../InvertedPendulum-v2/sac/figure.png | Bin 0 -> 164198 bytes
.../InvertedPendulum-v2/td3/figure.png | Bin 0 -> 167250 bytes
.../benchmark/Reacher-v2/ddpg/figure.png | Bin 0 -> 175778 bytes
.../mujoco/benchmark/Reacher-v2/figure.png | Bin 0 -> 237945 bytes
.../benchmark/Reacher-v2/sac/figure.png | Bin 0 -> 178285 bytes
.../benchmark/Reacher-v2/td3/figure.png | Bin 0 -> 180480 bytes
.../benchmark/Swimmer-v3/ddpg/figure.png | Bin 0 -> 186342 bytes
.../mujoco/benchmark/Swimmer-v3/figure.png | Bin 0 -> 308777 bytes
.../benchmark/Swimmer-v3/sac/figure.png | Bin 0 -> 187664 bytes
.../benchmark/Swimmer-v3/td3/figure.png | Bin 0 -> 180358 bytes
.../benchmark/Walker2d-v3/ddpg/figure.png | Bin 0 -> 214338 bytes
.../mujoco/benchmark/Walker2d-v3/figure.png | Bin 0 -> 364329 bytes
.../benchmark/Walker2d-v3/sac/figure.png | Bin 0 -> 199054 bytes
.../benchmark/Walker2d-v3/td3/figure.png | Bin 0 -> 192889 bytes
examples/mujoco/mujoco_ddpg.py | 3 +-
examples/mujoco/mujoco_sac.py | 3 +-
examples/mujoco/mujoco_td3.py | 3 +-
41 files changed, 116 insertions(+), 23 deletions(-)
create mode 100644 examples/mujoco/benchmark/Ant-v3/ddpg/figure.png
create mode 100644 examples/mujoco/benchmark/Ant-v3/figure.png
create mode 100644 examples/mujoco/benchmark/Ant-v3/sac/figure.png
create mode 100644 examples/mujoco/benchmark/Ant-v3/td3/figure.png
create mode 100644 examples/mujoco/benchmark/HalfCheetah-v3/ddpg/figure.png
create mode 100644 examples/mujoco/benchmark/HalfCheetah-v3/figure.png
create mode 100644 examples/mujoco/benchmark/HalfCheetah-v3/sac/figure.png
create mode 100644 examples/mujoco/benchmark/HalfCheetah-v3/td3/figure.png
create mode 100644 examples/mujoco/benchmark/Hopper-v3/ddpg/figure.png
create mode 100644 examples/mujoco/benchmark/Hopper-v3/figure.png
create mode 100644 examples/mujoco/benchmark/Hopper-v3/sac/figure.png
create mode 100644 examples/mujoco/benchmark/Hopper-v3/td3/figure.png
create mode 100644 examples/mujoco/benchmark/Humanoid-v3-0.4clippednoise/td3/figure.png
create mode 100644 examples/mujoco/benchmark/Humanoid-v3/ddpg/figure.png
create mode 100644 examples/mujoco/benchmark/Humanoid-v3/figure.png
create mode 100644 examples/mujoco/benchmark/Humanoid-v3/sac/figure.png
create mode 100644 examples/mujoco/benchmark/Humanoid-v3/td3/figure.png
create mode 100644 examples/mujoco/benchmark/InvertedDoublePendulum-v2/ddpg/figure.png
create mode 100644 examples/mujoco/benchmark/InvertedDoublePendulum-v2/figure.png
create mode 100644 examples/mujoco/benchmark/InvertedDoublePendulum-v2/sac/figure.png
create mode 100644 examples/mujoco/benchmark/InvertedDoublePendulum-v2/td3/figure.png
create mode 100644 examples/mujoco/benchmark/InvertedPendulum-v2/ddpg/figure.png
create mode 100644 examples/mujoco/benchmark/InvertedPendulum-v2/figure.png
create mode 100644 examples/mujoco/benchmark/InvertedPendulum-v2/sac/figure.png
create mode 100644 examples/mujoco/benchmark/InvertedPendulum-v2/td3/figure.png
create mode 100644 examples/mujoco/benchmark/Reacher-v2/ddpg/figure.png
create mode 100644 examples/mujoco/benchmark/Reacher-v2/figure.png
create mode 100644 examples/mujoco/benchmark/Reacher-v2/sac/figure.png
create mode 100644 examples/mujoco/benchmark/Reacher-v2/td3/figure.png
create mode 100644 examples/mujoco/benchmark/Swimmer-v3/ddpg/figure.png
create mode 100644 examples/mujoco/benchmark/Swimmer-v3/figure.png
create mode 100644 examples/mujoco/benchmark/Swimmer-v3/sac/figure.png
create mode 100644 examples/mujoco/benchmark/Swimmer-v3/td3/figure.png
create mode 100644 examples/mujoco/benchmark/Walker2d-v3/ddpg/figure.png
create mode 100644 examples/mujoco/benchmark/Walker2d-v3/figure.png
create mode 100644 examples/mujoco/benchmark/Walker2d-v3/sac/figure.png
create mode 100644 examples/mujoco/benchmark/Walker2d-v3/td3/figure.png
diff --git a/examples/mujoco/README.md b/examples/mujoco/README.md
index 3f116fd6b..67c36feb0 100644
--- a/examples/mujoco/README.md
+++ b/examples/mujoco/README.md
@@ -1,27 +1,123 @@
-# Mujoco Result
+# Tianshou's Mujoco Benchmark
+We benchmarked Tianshou algorithm implementations in 9 out of 13 environments from the MuJoCo Gym task suite[[8]](#footnote8).
+For each supported algorithm and supported mujoco environments, we will provide:
+- Default hyperparameters used for benchmark and scripts to reproduce the benchmark.
+- A comparasion of performance(or code level details) with other open source implementations or classic papers.
+- graphs and raw data that can be used for research purposes[[0]](#footnote1).
+- Log details obtained during training[[0]](#footnote1).
+- Pretrained agents[[0]](#footnote1).
+- Some hints on how to tune the algorithm.
+
+Supported algorithms are listed below.
-## SAC (single run)
+- [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/pdf/1509.02971.pdf) 389bdb7
+- [Twin Delayed DDPG (TD3)](https://arxiv.org/pdf/1802.09477.pdf) 389bdb7
+- [Soft Actor-Critic (SAC)](https://arxiv.org/pdf/1812.05905.pdf) 389bdb7
-The best reward computes from 100 episodes returns in the test phase.
+## Offpolicy algorithms
+#### usage
+Run
-SAC on Swimmer-v3 always stops at 47\~48.
+```bash
+$ python mujoco_sac.py --task "Ant-v3"
+```
-| task | 3M best reward | parameters | time cost (3M) |
-| -------------- | ----------------- | ------------------------------------------------------- | -------------- |
-| HalfCheetah-v3 | 10157.70 ± 171.70 | `python3 mujoco_sac.py --task HalfCheetah-v3` | 2~3h |
-| Walker2d-v3 | 5143.04 ± 15.57 | `python3 mujoco_sac.py --task Walker2d-v3` | 2~3h |
-| Hopper-v3 | 3604.19 ± 169.55 | `python3 mujoco_sac.py --task Hopper-v3` | 2~3h |
-| Humanoid-v3 | 6579.20 ± 1470.57 | `python3 mujoco_sac.py --task Humanoid-v3 --alpha 0.05` | 2~3h |
-| Ant-v3 | 6281.65 ± 686.28 | `python3 mujoco_sac.py --task Ant-v3` | 2~3h |
+Logs will be saved in ./log/ and can be monitored using tensorboard.
-
+```bash
+$ tensorboard --logdir=log
+```
-### Which parts are important?
+You can also reproduce the benchmark(e.g. SAC in Ant-v3) with the following command:
-0. DO NOT share the same network with two critic networks.
-1. The sigma (of the Gaussian policy) MUST be conditioned on input.
-2. The network size should not be less than 256.
-3. The deterministic evaluation helps a lot :)
+```bash
+$ bash run_experiments.sh mujoco_sac.py Ant-v3
+```
+
+This will start 10 experiments with different seeds.
+
+#### Example benchmark
+
+
+\* Other graphs can be found under tianshou/examples/mujuco/benchmark/
+
+#### hints
+In offpolicy algorithms(DDPG, TD3, SAC), the shared hyperparameters are almost the same[[6]](#footnote6), and most hyperparameters are consistent with those used for benchmark in spinningup's implementations[[7]](#footnote7).
+
+By comparison to both classic literature and open source implementations(e.g. spinning up)[[1]](#footnote1)[[2]](#footnote2), Tianshou's implementations of DDPG, TD3, and SAC are roughly at-parity with or better than the best reported results for these algorithms.
+
+### DDPG
+|Environment| Tianshou| [spining up(Pytorch)](https://spinningup.openai.com/en/latest/spinningup/bench.html)| [TD3 paper(ddpg)](https://arxiv.org/abs/1802.09477)|[TD3 paper(our ddpg)](https://arxiv.org/abs/1802.09477)|
+| :---------------: | :---------------: | :---------------: | :---------------: | :---------------: |
+|Ant|990.4±4.3 | ~840 | **1005.3** | 888.8 |
+|HalfCheetah|**11718.7±465.6** | ~11000 | 3305.6 | 8577.3 |
+|Hopper|**2197.0±971.6** | ~1800 | **2020.5** | 1860.0 |
+|Walker2d|1400.6±905.0 | ~1950 | 1843.6 | **3098.1** |
+|Swimmer|**144.1±6.5** | ~137 | N | N |
+|Humanoid|**177.3±77.6** | N | N | N |
+|Reacher|**-3.3±0.3** | N | -6.51 | -4.01 |
+|InvertedPendulum|**1000.0±0.0** | N | **1000.0** | **1000.0** |
+|InvertedDoublePendulum|8364.3±2778.9 | N | **9355.5** | 8370.0 |
+
+\* details[[3]](#footnote3)[[4]](#footnote4)[[5]](#footnote5)
+
+### TD3
+
+#### implementation
+|Environment| Tianshou| [spining up(Pytorch)](https://spinningup.openai.com/en/latest/spinningup/bench.html)| [TD3 paper](https://arxiv.org/abs/1802.09477)|
+| :---------------: | :---------------: | :---------------: | :---------------: |
+|Ant|**5116.4±799.9** | ~3800 | 4372.4±1000.3 |
+|HalfCheetah|**10201.2±772.8** | ~9750 | 9637.0±859.1 |
+|Hopper|3472.2±116.8 | ~2860 | **3564.1±114.7** |
+|Walker2d|3982.4±274.5 | ~4000 | **4682.8±539.6** |
+|Swimmer|**104.2±34.2** | ~78 | N |
+|Humanoid|**5189.5±178.5** | N | N |
+|Reacher|**-2.7±0.2** | N | -3.6±0.6 |
+|InvertedPendulum|**1000.0±0.0** | N | **1000.0±0.0** |
+|InvertedDoublePendulum|**9349.2±14.3** | N | **9337.5±15.0** |
+
+\* details[[3]](#footnote3)[[4]](#footnote4)[[5]](#footnote5)
+
+### SAC
+|Environment| Tianshou| [spining up(Pytorch)](https://spinningup.openai.com/en/latest/spinningup/bench.html)| [SAC paper](https://arxiv.org/abs/1801.01290)|
+| :---------------: | :---------------: | :---------------: | :---------------: |
+|Ant|**5850.2±475.7** | ~3980 | ~3720 |
+|HalfCheetah|**12138.8±1049.3** | ~11520 | ~10400 |
+|Hopper|**3542.2±51.5** | ~3150 | ~3370 |
+|Walker2d|**5007.0±251.5** | ~4250 | ~3740 |
+|Swimmer|**44.4±0.5** | ~41.7 | N |
+|Humanoid|**5488.5±81.2** | N | ~5200 |
+|Reacher|**-2.6±0.2** | N | N |
+|InvertedPendulum|**1000.0±0.0** | N | N |
+|InvertedDoublePendulum|**9359.5±0.4** | N | N |
+
+\* details[[3]](#footnote3)[[4]](#footnote4)
+
+
+## Onpolicy algorithms
+TBD
+
+
+
+
+## Note that:
+[8] Supported environments include HalfCheetah-v3, Hopper-v3, Swimmer-v3, Walker2d-v3, Ant-v3, Humanoid-v3, Reacher-v2, InvertedPendulum-v2 and InvertedDoublePendulum-v2. Pusher, Thrower, Striker and HumanoidStandup have not been supported because they are not commonly seen in literatures.
+
+[0] Pretrained agents, detailed graphs(single agent, single game) and log details can all be found [here](https://cloud.tsinghua.edu.cn/d/356e0f5d1e66426b9828/).
+
+[1] We used the latest version of all mujoco environments in gym(0.17.3), but it's not often the case with other benchmarks. Please check for details yourself in the original paper. (Different version's outcomes are usually similar, though)
+
+[2] We didn't compare offpolicy algorithms to OPENAI baselines [benchmark](https://github.com/openai/baselines/blob/master/benchmarks_mujoco1M.htm), because for now it seems that they haven't provided benchmark for offpolicy algorithms, but in [spining up docs](https://spinningup.openai.com/en/latest/spinningup/bench.html) they stated that "Spinning Up implementations of DDPG, TD3, and SAC are roughly at-parity with the best-reported results for these algorithms", so I guess lack of comparisons with OPENAI baselines is okay.
+
+[3] ~ means the number is approximated from the graph because accurate numbers is not provided in the paper. N means graphs not provided.
+
+[4] Reward metric: The meaning of the table value is the max average return over 10 trails(different seeds) ± a single standard deviation over trails. Each trial is averaged on another 10 test seeds. Only the first 1M steps data will be considered. The shaded region on the graph also represents a single standard deviation.
+
+[5] In TD3 paper shaded region represents only half of standard deviation.
+
+[6] SAC's start-timesteps is set to 10000 by default while it is 25000 is DDPG/TD3.
+
+[7] We use batchsize of 256 in DDPG/TD3/SAC while spinning up use 100. Minor difference also lies with 'start-timesteps', data loop method('step_per_collect'), method to deal with/bootstrap truncated steps because of timelimit and unfinished/collecting episodes(contribute to performance improvement), etc.
diff --git a/examples/mujoco/benchmark/Ant-v3/ddpg/figure.png b/examples/mujoco/benchmark/Ant-v3/ddpg/figure.png
new file mode 100644
index 0000000000000000000000000000000000000000..a7323780d460df5b6561da6f6b3d1a505dfc01cb
GIT binary patch
literal 223544
zcmeFZ_g|Ce`!;TCTUxDBL`9h`D2T`s!-lkqL}jTEK$(I95{AqGVK}O6QYy+4C?YE?
z5!p)w64@ez5g=3|AR}PdE6H=-uJ`-z^=XIXPah%6_g`YRX
z?Ajr?W81cEyUw1`xwviHk6G~V@gIMHS8R-V;lJC|yic2Z8@oGr{N*A{sUAAfv)OZfKl
zufJO%Pvq0jd4d0Z`~OoIq%4(ZFNjj^Ttp`{+E9K
zdC5I5#rsVzE1frG=tZI6WGZ1UMy2L0Hnzi|oSM2?BhWNNU+Mh8x4H9czS-5?-!HT}
z?1gt3uK4YTAAZO<{|bY_IC}f|H2-~Ak8yMR?ufp=KHc)4cJ+*oip>Y~50Oqv^9bKK
zUo)vS?`~^mU+&9Hs^D@68Rg|F0R%!uW~SOihs7^?@p933cB>ysN=nkz>gQ~V{W9<0
z#lyHG7T8rr`(9w{$O?V)0*jaXp(fyY*LOdCPJaqhb*$*3=JL8_8`Sdl-Ks}8oAvcQ
zH)9gylwD~D(3^wTJev2_NAZXS+Gh7>UCbQ_;cg#$-jm9gofhoyyd1B8A^x>rvz;QH
z%fVCNxs*}&%UK=}W51Wm$h;PKs{orP8sXKs{`;^`e+qV4KJaZGtAe6+KZK(<7cVKIM@}%Ach1Sy5C~e88j9yMG0DEqv{q@+}-km
z=DgCPv{o$-VnmjooSjN*k@%d|UpzUwKhk6HngDUFxO$%BwKXn}r`U@TUi2!TZXEX<
zTw5uxKZxFXyDFa67458_YZ6gCSEV#pHPgAa(O)lI6vDO`8|(^pOVPMH^)`8QD`4v_
zuZkI!zDegrR)&Hay(^F^X0zTZK#F;h8+tPR7aPukz}U;3A$!L?{p0%`O9m_6-kTY_
zCsY`@omW#89tkhSn!qe+^C$|S&WsU5woVNPZx-;eKCs6c#e!`DCpFMf-|yUSxa6u3
z?kj@&RGe-qR;BZl|J^h3!?NVmd|94rcA%8bQmxQ&jNwp=|xuHP+nS+bewM&UV(ncX*pRR7MZfbC&mAII@>S
zw)CME*>yguWXW?Eij0@{+n&32JW+M>5wGd@Pan$L@VWanT2M6mADCH-rfu|6>}~%E
zIc_0*q0%yDwRYahW2PV^e|_K(~2hPk+qe2#3-Rc**0Sk|!s_pgeaDeyF*uT0S*b?ntl*n@
zgFn=>BFt(eQa5!~quP}5K(nxG7dv=n{*+iF-R;We6ijZogwo(&>zkntEBt5yxr_T5|lcW2P
z__k~LJ9EftwcdDy_C)5vIgk0;)e}|G*O_&jbI~?iW7*i!tH%#QV#pq=?9$TM6W(M<
znJ=a+tj#)dl~x8PFQCLWVS_5>*1q(9>NY~F`1iJ$DK$oQ%!AdNEsCC2bE{h?`?cs<
z|5Cp}l_yQ7q*$?HiGhLoUeTsTU43hP<(z8L!*iKqXbz)<^*lS}X;1Ouq591*9I`u*0(Hg!yn-kf5VO$=V(?(Y|-o9>rPyA5Hh#6vt%=;O;&}(u@!E0|Z
zWdBL_uc@4+`;rCy^{bE~`5hQ;N}V?pzqH=bO~C2f?~v!jp1
zw^a*s*C=B9jDi=8NzUJl7xhz_IhtvnCja<@8b&Vm)<1S
z;#x=W8e~T^<6D+yWcl6DzkS7t0&F+A{K|kMTTr-$H!bx
ztzD%{8LCgkk!bgJ%`i4*LR3gBdH)s}l6CyK%(XP`dS61oKiY4i-9ACGn}cs3N6O^{
zjJ$dCjQL~esoiQPOkqFv6upxW4jt34uHF+*
z)YmoZe`Nmb>UH7359y5Qw;%UmRy9FyX=TNPYMO6;ur0pmz@jDG@5(5**rRb*KbBhV
z#!Nj=GzN&-B!VB_FQnoYR$x(`VR97X=;)YlUhF7*jmB}h520bB_&DU)<n%}Nr|IS0$g%$VI_=|$`b(>;r)_L(4&7&aQLqLyjXVjB0h-<+4td9UYfP9q)^rU%`hyH8~_kg>adb+R)>
z2(XUn#~bxuoN&#npFqh+qPgoaq?zE29vMu+m4&6{WwLz8dI^b^N+`@%OLce-(cZfj
zAL4K1?dxl(OVIFPmYVhV#%Qfncy#y-T*2r-VEw#%w_6@mT-1f54|JDHKMYT%icF?%
zrn?qJHrs8@b2+Nkn>3Y}JTb}8jKELpnD7)3!9Q8PwCj#0wc6~Z66f1L#akY)q^xT$
zD3q8VBWG(bv;5jDtjl|NdieMroIC7RIljt@!PiLFtu=S)R9dF?rV29uK4P^eT_z9+$1KV)nk&UTP4=QC+=~98;ME6VVe5+mQG~i&
zPtH0zEWYO5_pCM>PGD%mUtys{k#+XGdjw=816DUN*ze*=72{f8@z|W)WmrU=CNa(p
zubQ=HM+z}`iJfxqqPTcp(Z{smI2P?5=tGs$%YdRk7HPAkU-NcXQIwEdg8QN
z3(}y9_0r=>O0)>
zM6G}1W(*!eA+KiLl@F|vyzx{90pBdnYJK&Dd3@rtl~EyK;iw_^dK7)VvpnX294!$_
zkt~#%eqH?P!wXh6xh7-wVw-2S-eX3^jDJZlFPEaT=gKLl{w+!IF>cJ(dNT8l)$)hr
zQA?~b{3$q+hv917vm=4UkKYwZ7&!IeIL
z-qei|Gse!$%|SA%a2;kVc*HDx;z+h9gISSjO@OSh%!6GyBO-^z$D)){_o<9{pTaT$
z#6&gnqCGDLw2VLVczbt}^1w`6xath7#THA7Tuc!aKp>&IMz#3;gAvp`vu2-uD`HzpYNM^NeJQBWco0X!M?8Pz5v+YzfZk6Pt4}VH&N~^19IbgzA6)f@$
z0|{dTEyy8frBVcqf~I@yAT8)vSXdzY8rdTCn5Gv&C;j;JxWmZ0Kp4W-I0kG)cE21(n!
zJw?O)W$iG2)P{lZp|CK7TWWu}MQj2_Wb8G9@KXesKk^_eG2kiSsD*ogMh35xw2kd6
z@-v_&uvdoUwK*&zMn@<8e1wpDO|fLcf;)F5{ipiS0Kb>~sRttW%f`rBD7)tsSl1q%
ztDKzU!%Gb$+7%x#v+_A@5QGVt;4i(>xUsgBjlO`@lf2J*6JPfjw%NUlxqAVTI^ujq
zEH
zj*jM}S{G5Jc8b|OJZw4A0HB&7y85SI!%mL0_U51w`C7b{$ubM!n8C|=Hod~UE2~x2
z?^(^2>rM{Qq^42z_v
z2qy$qM#bc7&Us*(7sh3DYwB`yr9YB4?v|KK-uUAB+LV!eq(I$=X8yU!ScU?^WY)GO
z>-R93OpRKO2a#~{W8HKQWZrmU3P!T@J2c9p3+~xr%3W#kEV4V91>uok#1$gut5RnUuLBJWG-?2SY3c&DK-G{qH
zWSsTlvjYd-r(M7i-!<@vg@uKcbO!j=0J^46m3DM=unO_DDu|$=Lie+xQaUx}>lwYq
zX?)>2c1^{jkR<`5n^IQ>?EM25?xzu<%rkFvG)Kla0kRqNTdf`yMbJQ*l|9E={KBYh
zD}Y36V~uxzotmkdxO#9ZAv0WRfHno(w6
zUV1?6CufhxEtChRVi%uoLg|XMD^`_e>kDM3SyvA=tx&9@$otHCMHxy5t1}^7-p|qZ
zl+!n|*XHnDk!H2ihgqN%pvv5sbYBDYlo$Z@9jt};`hb@+)Si82ThFq5$p^cJuPMCa
z1NMusIh^A#P2-tu!%IJg(5NyAhY%)snxyqq%Mu44L|KVe1vRJ%Yi4C_90K88-*diI
zA}KlfBZr-6PpocCou29nDcqF>IK2JGEWmQqUP$tiCmhYpm}{|pgNYzkJ;_>`M31V?
zr6wHsEXO90Ap<$5$6az(u!$%du_S!=QvaC`4!;H5u#n{A(-F>y(YTV$SQ)|BCxsT`
z`tl<{(wR6rHS^@YVfX`?Rl()uNCElqYi$qBs0-_sz^$%d+(?vL8CmUSB~WH}PMvIyy-PF0VK!}DAsntyk0z2qF#AXea=7_+rL=Y4
z?!iH|=@~>*h*6(@pR^%C@phf>3aL}7s;fa`smpyoMttCwsJBRRoc)n
zxXQ-Uu9sG4MLq9nim~+SaJTnUHO6Xi*7&8ieht8^40~OxVKe6=#JFp3)&?lrBAd+B
z_f`2yiE}lJD@XS3z?NQXTL}ZA=cvEls1SN&9
zh|LfSu`$+|&dHE?a4!0KM~@NO%Vf2v<#{l>>c|7q_8f>ptxPR_o%Fwoa#1Lhfm5e<
zmG9P>nc4O;Z<|m!Q)0E0r0(a8c2iL%zRhDQQ-IprrD{J(S<@VA;x`H}E1OnmgkqgF
zJ@Al)pbgLfqWvu~7^d6qM16-pWgN?l4b^HVuCO77`9Q!kVL%*j2s26~AgE$MsqA4D
zaW`UsovO)~xeQvCmrAy6=N&dP)>wMo2&jr7#{KhP$w~}@lxIu(2kz{b5V=+ouuS4B
zvMQZUPoljI6;qo)RaaGwoi$Q-XlF>;G03}6rCN|?&JhyAVq-LgaKAd8U{C^{(?ki!
zigF8V>fMI56cs-!EDF&gbuLR7F@;6p;{3dB*{C4|8GTfunf2Unin$z~DCa=H>YD5+
zDJ&Jx!PZiCykSle#6Ct(Zzh3KTGkgM{eZmktG8qVI^+*cRyY;}!1Qk~c4}re?
z5u~}UFYNPZ;v)*-(L}=V1&@s>NAgs5+Cizbz{|p3?ZvDd4AY{NaS|XaVQyV7kcs0S
zl=o{WL6At1BKk?{IbO{hAqK9^5i}8hUHkc3F_e8DJ(fu%3KopF;qH8&tn3;USm!$#
z75l9WBWRRI4ED9YmTQPuw4Z8wiH6>xJ^ZQmpdX>WiH|+`yMDBHhR`Rz<2zf5DTtaM
zE&J_<#Y`)k_NX_5k0Sa-QFT1oJq^tQHs_6nru`@g#&SGzRk*BxoCJjZk2
z%8Iem%Yc06bt1^sonbRYTq`UyLPl=%+KYFvTPa15VDCJa@#b1M3|GB@%{6)jtlL1ahCF%xm_8GZ#)k5_%KBd
z&TqgLjxafj^=UT%{c&zi7UdQ5{C=ljzt+Dui{~p&@JTTDlsa`kmS$cN*Htfb+{_3k
zJ3smNlDbxz(SInmdrb2YVqai^XRmRQtPN@TBYx?pj9l2{S
z5ArmndQs|1C?A!wq!gv3WQ(zP_l+R)gbI6_&z5x6VM~{1`pH0<&~tQhiAC$&#DIO;
z{*)BzH>smr6RlR-`gG2LG#zn7$DeqzD!}@X#<-ul^ll9lFaEUk_7L*RyF2r=gBLqg
z$Ow;~dA6bx?fqTvO4@w4v#YB}Pw|mxO9BQi8eQlCGBJ%tbDnN&Y?SDA9>rrYl!`tP
z_j(5E&E|F2f}qhJte2OULBaW)5vs#6UE03v%F1i)i!R*7e-JG}W!aJPsTr3r(HS^9
zrW$z3ERa2cVrB2zUhJXJ|Nh~D?93EdkEUmZe#{Gm&VlpibeaywZ@I^{jgx3SslF$@
zeWKY;kCnOt9kwPzf(sou8ANRdaoK15va+%sFMpD11}s)n09p}$`LL&#uupHdM9d(7
zxZ!{qu{>ua=8Zkyy}E$WLXew0WyX3n0>D=pwWw~RAJBJwn6cqOi(g4}-5qD6Cet@0
zE?ZdGJ2^S^3n=?BMXuw7xP{8RtJXsO{p5p@&fa&+f;^Kuh3uYQWXIXkQ%a@B}FNP^~s^(cUsRQ^?
zHqxXD3WR8!I$`guZo+X_bibX5In
z?(VzqJWCtm5}z;joY829!foW#3H8QND!HtT)@2W6Gpsn>{Jb{nH**a0bo(LHE#*
zaX;!@=EbddQt`b$(IX>}|M);_%OL?LsE@HLUP%wpVJd2ih=?Ha?D=wz?n0E~BW%eg
zIcRl62-jbj_(t3J+LK>j#b_77-g5i3$Ac-7_^Mb=LZtfm#T!pDGkwhxSfYc+-?)j)
z4*Y$%^qZF;(n+0|gjNa0+;J*la!^hWv;Avl%UQrCuputr3pzrWT$7}~OMT6
zXr_;Pufw!3-^$TUcSA7YY;||Lm%)nT5d1Bl%9Q*>y`_+B!u3|GZO4$3P@JL>s>gqHshc%KB{ZfqRRXF$4*=6
z+*~-bQ(>@dlzP~1tNI2I*^UHQZU|aS22=`Y{3L;(?`VL4pMbKVSJ!R-LBnQNneb6@
zNiff`%TG$i-H~SOqMmpev5%k&+n2fFv`M}8vHO#@
zq+Vpt+4>$;3bZ-~S6Hn5uRF3lZ{`Ftiv1U}a$NBH^#DB|v#fYJK|8GXkX+@1E4RQi
zD|`om2@g>;9mI8MTNs9o}GO~Q(f{FgA
zOnmCu%!?AD_|t5)R-TXHf77b<>T!gj=jEpPPbf)UYEN2%DYW_9A961!Nxt~y_!0c8TNqP14@^iK28}FY2YL0-%13EnrV~cXr
z`K%LfCX;f%ctT#teGIagcdD~bCG22*ApL+g!k98bXOc|i;u&xBfp~Xza`1Lh6&7
z%!mCL%SsMR5&!q;Jh*Sa%>ePdR3koWi6br!Ewbt?-!@r$-h_|3dwl&cYQlrR*n8?(
z5>0>SJ0N2$R4G9Dj*5w@iKJ28ToV#WBxEbxKGhMn5NZ(+8l@t@ZtRP=H;@@AU#LZ%
z1Za-R#Xa)SWzNYU^7Msc`=xwDI9YjlGN}u3*{0OO>bWFWOwQJXiWxR&7WyIqy?^{A
zr8U)pDn+DcXx@{lGbwnwb&35spj8mI-j<8hwm*18E*ca5y|syG2lh!)plc<&30kN=
ziN(#I0empZS?RYI#Um-GHxZxYl;=D$VlhU;g=pl?^!Dig-0i}BT@Ia*kB#yXJXZ}EviWjxwt?2^DEuL-YK^G^(g_LdAoM
ztq1OQ|JwS(cTpm3rl|(#E?Ne;(IEE#nH;4Yt<1@FvvM
zwWbU}oT~@D(0OJ>q+Dj@WUFFVuCA{4>-x?#gvJKn308M<
zb1TgC!Mk7oU5bAZ`r~R7T{h>j)gTCxVwS?g5|kX>pyl49tK|-B%U$JKTN2$+|XzpU#d>FJl2}*8EkxNL&f*~Xk
zy0^^i=z}r9nSKWBR}x}kP#*{0Rv<(q%NIl@D}L_-Er?bg_)cP;7$zbnU?MLoTTl!g
z6D$?cFD%O1^9e19j@o$+n7=`_ezw-(UYJkX*EU{}O*kStTR`t{XnWYf$~B^LDxpzY
zBxZ(|U{{KHziwb8MTURP)1ez10Sth_&$5Ea2k#T2x+DzI?5L!X?MQk;lAq5dcX6l(
zHpbPQ4V=*HmL}kIANxSgD#IVDlYV^#s;r=a0fhIaf7d1@$!f>Z2j@Vi;0lgz(#!Fc
zk-Eys(ego4i>&po5YmJ_f=h*AquNh9k~B95y9(Af5P554J{Y69^!kp52!XxaPp;o;
zvyg~yJMHDQXxMn5$CVii{M4F{k2Nx%DqzE&4ng{-Z%ZpJl{xHDwCb3fG<03^@Kv2?
zc8}iUjEsQQM=r`j?jf5(48)dEX<6o=4-)L!m;jo8-j9-O=jW#iDQK&$zQ*x{JMN-L
z;0>mS37+o18Mms)hX_Sw<+UYQoLFKF8hSf=<+av5X^}qL*N?=@3^}ik;N5Gc42lYV
zTG4`A0F#HXO~TbDwyn2#`b2%2d;jAa$|_3ZEIoTQnMYhgHXh@zQ_f;f4DUhzq)Cw?
zEYxqgSk5581GDsO;gW^uA0Afat66W(hx|V5JCsIpMtmjEWkXt8zrC6AUIN2M5-JZ7
z)dv$KF;fVife7RovL5xH6uAKDuF#*XMCOQIhVC$b+L}wvoKI2Mty>(|Wkl%&!wNi&
zF5-Pdb<}S3)+=++JiWcWn_j;@57kKSFxwsi7x^QyQLyJ(
zxUxEj7zPooD7so9HS3+C<~2c{vEe>k>&@0S#fk|EMsx+Q+-GIV2mQ8N_1_ofPi?Op
z+7k{A2`i*XT#$<|x7q}WOMPW%UXeebp5gJKA(RY^(HvV1wRh~gk;WDMMZo`QW1!9Z
z#rM00j7Ipatz{v2MMDG+ExM*lj#`9JG4#p+V{EpBYx5|CGdg#8Jy2VFrI^^XKFR$x
z20iusjO?(?vkt2ZF4B~Zp(+L`FL+k=Y}=iQd(;}!df1+9$Wb`9U&9wd1QSbk_EU6x$@
zaOy*SKj=x!JJQ6U*>8~C(2Vpb_%19z)fTs_XN&S9uQQSekQKw>A)y&T^8~!Xds`@m
zEcSc|VlkPI3aCT$8pIt@4CugPSqTfdc9H_^f%uBqXOVf+gD%Rzryo}=y@~Wy=OD>S
zo-mjmavD9m8DXvh4k+n(bf5+;L)Iv=*_v!nVdeqrHoY~cp$vG
zX*6}xOgX-BSAAY(s0hdxkoj8P9ufP*R&>1)N(es;ZE%)NK@gcc*|U
z6)^Ewd3`OZa3+R|v;vS8MaEFV34}O^1kXk(btb(YM$CB7e;$qOG0f8luP7-sP-(&pl_#L^p7(=*!VsYHsbD4WE
zqU&pG@1)S^F_xLeBK&e#d$qC%dQU?+l$w=N_b9LojUp&FqbeBbRfE+e*RN5Cy#L!o
z{l&>HB6tOANGo7Pm|J+CIdvpapG+9tY=90hoe#LLy(kwb);`E5A!FAP^FXlbVz32+Zc6vp;(Hev99X1y>E0WJz_th8vUYUZ?0;IULq6
zxhGr|w06e#@@v_uoV9u_9h-_97dlFzyA3v?=T?M6h`7ku9IgKq@jf+o1^ZrtnD!6t
zpDXRBvxwZa$EqL@5pf0gjkINv)P)E<(TMKv!DX+W2K&%~0|&y&%2trnC=9g{(SRfE
zW7L%2Ox0^C%g5vDiT7KIR~1YtV%#N=iQIBYRzb@}jAr>@|B?Q}(2}m;D~Jk50e@9v
z#YWO-(=?Ws`GE#8MefF+4v9KOEe526^wI~+w{tACmeE3~QgoGo6
zltYl$)j(y3GC&?{jD8IDgSWu4(kzg>DG(#`NXaTr=0OE+(RPb7x0zAn<9hXn0DI=d+<-l2SoLb!c=2Sa
z45OP!4|zjpAWjR!2xvmJ-WrRi;A}R@XA?uJF4t6mN;(^tcJs3?@dr}Opjs7lySA&!
zn9iy(eE&!c1M`6DbdD6$7e~B08YCvNIj6ynxhg1}&%QfZSh#^+O4Gnwe~f70H8fKT
zD0>Jw@mT7n^aim5E}{R5^kG*H5XM^*>bZ4%e8pArLWC~Y=u`0qoXO?eJA+M@)l&>9
zUa5;)XKg}@4-Vb3`*c0q6!b>$qqV3~fC2g~G{-dd%cPW3auxrE8b9;Z`gtOIf@!oix^BQW(qa(oWXl`@tAeZlQ~?8*O#iXtz8hrHt0v-Pb(*jEDk_Vz97pN
zByIy;i|Xgl1df2xSxoX}ME6r{IG(F<`RK&f2j}wVI>=cKfybCmsehyD*-UqnTEonX
zEU^9rp6hd%{6dhnC3MTxeAx1%XwZebg60xETBRw?dDhEg6?0WM$_e1>k$HtxkF$bb
z{T6Cqnq0jE{#mu8WbXcVFqDCmWVhD7v}4Jk3&p{|3V2<9)BUJ0C`#
zs?UhojRvjeZPI3wzRqVT(O$o{9ro^7F7{yDgAW4OmmwD9KExG`7UO)VR#bRxgC!tY
z9_eadB#b1Ex0y99rmhmPr50}ML6!t2)wr)w=Yhq2=0z
zrK)RaFt9}dRwMlm-v4!{h8oN$Qi`s)IMT>in>OQmnJ|j#IkYu!6y{}dZVS}hxVRw{
z8DBg50J69YpfhM41CaDH3g#zh8VFT}DTuls`G)V2mtJuzrC$h)Hgk||=3k?%xvH^D(h4O?FLMG3`VG@s_2uX{T$!=4E1_HHVHsQ@{&e
zGj+-tU(Ar(e3qbFcsQ7|n0CU24Ul0#_O^8NmM}&~*#8~ldt<>_uyENU34{fmybIWR
zAmC)gBY{|Si2=QJ-QC>>8ygeN%iUauL%2Z_qN_brl$7>DED@tVl7N{G74g;C>Ot3@
z_~h=k3S@onG3-I1@Td8Cp9&C+=C~*-J1%sC>~Hd{t~wJ`IMvl*-sTTC94uTRjUhc6
zPE}#sz!yDc+s%VTV7Jv?OyovU(426I;J}fpQdae-6@R=9XR!jgKF}LA?2cSRq^6?a
z
zHF()I+`j8$lU)3Te{ZujeLp)J^$~Yj&2N;06Qm`9+?lAPV!iRkj0_@AIYL)+jqce0
zWth!)b!$3|6#w
z%8TDabaaM;R?`>^F|-uieu(=GJQZ1=`?5`AF%wmVUR_#v3IuFAiRP6?@I^clMKe`V
zHjj_Lq3dibc{S0oJ=*PxX@W|TR#MVMZ0X7Ly|uN8B2n9X@pD2i{3`I)%#NaULTcBS!>N&bo>`db=x*w
zlcVziMUk?JflEIN*azc!_IfwzGrr)zZU5+IosXz@5sg^Mx;m>=uXXuNDZwVL^}&E@
z0`X+7ut-NPpTiaW*ZALNA|2Q2!R^z}pKjAV8XKmcE=;`qdjDeC(7tPWl42H0>k0i1
z<$HA{zs{Dq7=y^wK!0majgS7`)gbnqq#f2q`QFa)0h~4NSX$4X79DZmelCLT6x;N@
zk?Ki8Bg#E-U(?{zj_EoV&0Zzpj@+y5*Mn3|U;phEy=!~rD;GJkZQX&*cK1lQ28(+;
z6^lxx_k`=*sdlK^^L55aT-BkDSlcjO-uT_nB=ZX
zOTO6M4cqXIoAhkL*UN3U0~q#$XXdMit=;svsFf^}L*+YDu1027lxy${@hleC&9=(Bgvfov9b^C8Rxb8Rwf^-r9_o^ABVewJB=!Q=l0Kmy*0ZMoBR4pi^kFUT=ks=#G1~eaG?_
zwRGDyrSp{+!|5)HRZ8mvkYnpy6kGWe%MXhXI&cwJ#fuw@!FqhTC)`P-xmZhq?`uU@
z=y<0${wlsTHcaQx9=`A}?&f?`+3&4c>6S=^`HSF(k2@{W(Jk)u^2C>kmu=g6nr@`;
zO}@7PZ8EXO=8Kv&lV``~Z%gwEwa1H_9iu$z^F}&Kx$k1|m|MDEqvYlfLak`wGlNhj
zQ}urX2Xai7iHSJelIGUvh438Bz#ua{^@l#0f`iASBEGKliC4CTYazGd0$n=xia*eA
zy1kO+gxjNj#rKQUw(aH*0}6jdZk9PbCRF?a;d^Y#1%mBaw`;?b|9rQ!X`zW7`**pr
zt2>GV4fRfBe|IqZuxef!(X?eu@f%q7q(h;=2OCdE>gNz>k&~ueJ|5M
zbWmw?{pcqhf7?yz{Z9@szB+T`H`p@?J<|uoe0l$~X5U9-
zdb)_VV#N8nA1zd*j@*F>*iSzR-)c49ec!g<-Y|0H;y}>X@vj%(?V5eT-99q$)()_a3gLMGtKH+m{-CNdtmZy
z>b1+j3;_O>Ly2MtXA8uYKZO1M
zy#-V)qN+ekRrNsmh_3~L{lR<)(^M4TmLTGs_5eKC^!Rk-beZy}htR%1Z?mL0SV2Hf
z!veyq>6F&sk%!{ylh4VIvqFip((y77xI%XU^D1kQLq{TQcl30EMk74=$jk7fJG#KU
zSiG=mnsnA8!%~hi9&aNmVC8)QK%zlx%T-@L=E8Uz19~};;6^@NJQ}=d;KvNq7K^v^
zJPU0|bhw3E<>7_(t=&ZHs{tP3T9j1b@{JsUwlt6X7_RvDK!A6mGDufp6PG949j-BM3a5A;X(2=F3NV-HOF
zd%-NkwZIQ|#G;^cN4#!nW@c6c!%xvbtJN5n@#+IL{{M8#w(nS+D8(2X``j)^Mrh|_
z?pVE;>j>I@fOmDbZT_&zP1cS1ZRA#X;cU7|jE3@V@~NQ4;{M^XGl#h=!t#(oZ7TP)
zO_nv=@5htR*BvM?oFQtdTiB`I$JzErmw%-{KB%e(mtdr8ie0I%T=h*@7!dwoe#aD_
zzOeMT7DRTT?6m%HTQT&axG4SFICw`)Yo@V95iZIa6QCKbcQFpbOi02hX+FM6OF3=!
zlo3T^biMNkQQ{AOy8mRe(C8W&`D1bDyNqeg`x^yr^rVR{nt$GJ1)^#%9nWI)utyGy
zBPqUiqOAF0df;%W$FIt}-#wO24Dxhm7Jt}v41F+O@^p&#mW*BjJ
zaADrsvvHS7)5j|YH`Fprr+kbdXu*VmA?Gys{gDj=a|C;{(_(F)gBW7)@wy?ABO!j@)dymhPbiC9LHruKF@B<}S
zz0qatNLtI}5wQWo^z&f=(o0y61!{97;}&2lRZgz*-QQG?%K|mX(oFS$yOUMw6zeue=E)KJWF&oR!ToHrN$V#$L)wAI
z$C0274Pj6e@M4G}mzw7cP6ZYd?BCf8&^kdg;|603N=lSII<1eQ79Ls;X&|XDzoe5y
zD!5eQ5dTf;=3KRWFsOK@$oLc=cg3BnO!AQs2R;)|2EBicM3XHa-qUXr!p@oev>9&>
z+MU3xZKHl?oZtzC23uqXe+YXSKPKz$-!Z)D*w`I8$zzu+Q_4*HJy#vD$6MSJcN=ZC>LkfXsg|~I%(%6DYi&^
zQ~bOn7oca917)v6DfsRG$y&()qy0%QH*_5|*I+44yhcFCMCanl%gV?ACGwm>Dn+Jh
ztBPtp$6J%E9CSB^KW|2TpefCFY8w$#8f((vPItTv8EBPa5%$RUlIIleUUH?9u&7dI
zqlkSeh|fv1GY#OxfSE6_dlIED5jv7k*rNL_SC3g$c@NJhqfpHNlt|7n;ndyJqco@X
z={El9eg-JMS3|hm?LZjXT{sE^reo7iG2>pBA4`vWsO$fLSy#wovtu=gfZX2xIbm7j
z$I9uu-b;gR29Sv4ymSAtu&bVSXjK7uYhfW}mz+xks1N)}0-quv>vEk*%<}e1bC59$
z5>M)g+xevZl%91(1Xo>aSYZ7nF2pxx^urZ4w;L-2fZI2_VtZ~(zZgLsSeRh*ih
zPI_IHVMVA)QYorM5JoQF#fDsyH|8^-zzIvRm*+pUcwp6s
zw|ScW_LYE4@oG)k8RAo+JqqiP;t=btfow|ZKzz_qZ1OM+!1~zy*Pnu8C_H+5WknI;
zNZ<0Loiz@q6-uQ_cA((LtQQ9k?};9|jKf=f-Zo^C`B6&HNFoC1H%ivcwX;!|dt!*U
zawQX9=EKE7PrHz03Pu(0=N)G>{&qo+o>?TGa4Rd&vf8&JQ@30}=E=pDGm?4R&y9N?
zK9}kFKQ<7MCGm$$FkIrVz^OXSKXp~y)Voyo_?`>L3`bU7k3R@|=UcyJRcP|qlB9o$
zw+9|R`1jr&q#ts2PNLtEX@Cv&@ZrNG+jH7!Y3Kj3+gumDnO}8?M9_
zX=rFjJRotkV?zTDR6+vB`;->6lW;`Uv9PmNn|&7U1-V*t9z-D)t;Kq?Y{*S$nfCd*u{$z7H3u|=M-orwC-eyB
zJ~_WbUj^BqpeJY8O})v)mLfGn((?Fa03AUF%ogbThV_g3zl%jxCnDq!$WJ~_ENH14
zRSJV+9_RXygRkJ2sN843-+-5!56}<6Vu9#ZLWkZabZmgcQq63%3M-LWEov_eImPi6
zG2g#OfL|7=ESSCF!L&r&YY{SqIV6;d3WY@xLys*(lO5los0#=p&93dCti3hC$>|9A
z#;QIJdg=~8__$&RA&pULeY!39JEz3lkBb
z9(zOibDw)xZ<>tm>W$wfo-6zNe%}LvT?rjQfnXYR!ZH!?pFpI-2Wj&^0Jqf&uwcJz
zdxY^MW&DXC0KX3nJeXQHNdGX?vWQx?DqsUZmj|CW$r+FkGVK==z(i;iG=HqIH~{Hz
zPCJkJZux8xWJy^)dkrZ-c5(Z5pu;bD1AX4VP-Eh%8b>LeBz$F6i-PzR4#
z2y*7jz*I$6R+b-o#>NJV93ucjOG?iMXLI^EFuAuEY&wou<{E=Ylptz)W-~ys{zX2Z
z8;YowmKL02QK!;6zL3+W4dQ_vdt(_}3UR9eh*goq`f^E?c52GI#wAkpb=9wz?@j3Z
z&Uio;TuB}t)45>x>&w5y50)i8WHxy8Z3QF=pg5~z+r`#?$0F0A80cD@k;JoQy
zU{cK#TMc5#7!0NPz@Z`@p@_2Ig-N&Phr@oY^yKrkL~YZy4ypkq^gJkA)Iq=^Ex1P*|)07uSm
zz{1c?2yzCuc0rXkXeBT}J*5g5ShscsEKk|U0Udjg5~U5=67u>OqRzm`iv+YHkWWCi
z2EuKUEHJtjCX`-Ox7s@9w}4KHus8Pu&59S~vo(B8XwD
zIiMs==yJz-YiRUR2L-3Rm*uMjBF7IL5FTmSQ9=bheO9X+wcDzX`rgzbx;Zj?b@HOZ
zUFl^AdR4cosjR9FE{vc`A&0UsKQQu43nCDv3P(yLc@!ZJN0PG+wwHDWjLnq!b>OUx
z`3(;iz-cDXUfV=OqPGbY4)_zp;S{BU3~g;E0$49tz-}es6o4ll(e5W2;#%IyTmO%Y
z^~KVgWZ5RMeqJ4Map?S_s!~Mu=pRh9ZGD$K*RU6}{?uX+R&NYADG`?QO1nLBAOToO
zxQd0y7z9M&e(>`W8LuRW!ixi-6p*1`x9o8jbdne}XKf9^>M(SlLDBTBXRkye;ijY<
z_ZigAzDbeR2`K};N|>=->DIi7uLd%V22HU%aGv*5)+f`%z|QLmLjXiSS2*$kX7-LC
zxp6IoJI`$%uJn?6zS8_+F!)CzE0BrtmAw9EOiYaGM1sBGv(JKQ6c+H1!JYLZ4_kY*x41
z7oT+TD8MG4KdZ;I*fIusO7A=N*}372Glsq)zY@iK{-6392y@;wZf_MLhh)pdGdegL
zeWyate|`3n!_ML|w15zgCju^@6Y$=d+#?-w=zm1(
zhdv3>*7y4hd_uo&A>w&SY4Dxj*|n)Xq|01-t%LeNX6w88-d
zr8^GYf^dE#UDvgQ^Pc_gnLY8$GqVqz
zJu2lEQso+Y9(tSR92AnEG9b_Ww^T%trkB%@Tl;x9f*C1X!VmlZZ4W{5t;-h{;N4)=
zbGOramQXI2|I`%fq9`HJRPeb{(&z{kp=(uCDT;rg`&dV$Ms)}9ZnQ4V(-&a5HU8EL
zA
zmXbw7c@k$^b$uqNCz2UP5x};`4#kH-1&dDV4qaE(eLWkp*5))wvgxTlO!Io(9OO8Qc0?LF=kQc}kx|RYn|2T4A4W3c#zKz90(?
z;u1;~WS|UaU7oc}QXUNj5TuW@d
z-S?d6wP0mxq3D_3Jel{HBm6UifFx}
z92z(g<@KjHZ~a}~>tdT?VxJLiV#?!dD*0;0+let2iu9D@q?~ep55;2BRf1^~WIq%7
zxLyv>-ZpX!tUT1wMJ&{1Y%aD+RTAA~{-4>Si&&)UNp+<`D3$ns)W}tA{{&9cVwj}N
zopM3{O8?e}>+G_x>4vb^KaTB-2t$sOtFN}~pV5Uz*UIQ4!vT1iMmL?vrv0PszT@p6
z5I8_RgHa(!{Z@4E-?GgrZm;zuus2l?VkKW5ZFl_l(oaF(pn`kuTu;R2d$Mc}H}V5b
z^c|OW!=9}fxw)_ZBu3Z%9*!3YOqwjkcbG1W+!R2^V?ymt3={nOCI*kv#@S9{V?}E;
z=m8iVSDb%e23D_{&%u$Z_1zAx}I7;1vAU=PKmO2<`woBn-@9*zYR75^hGzjm#P#5dq?
z4KuDsGyj#<~Mcglby#7*20MczC&yLS?GNmF4D4Vd{tDw=l%&#)Oy#c_!j3S
zU|@icxy#ycLI-(
z0FPv2uM)9e&QOM_TmQ=p;S?j5odSmx{MlGwx*U1Kx-0*dbCwbuoIqswp
zkuSANKuWp4l_b7g{}cs_@uhe@F%8Uj>3La{##@mz@l)%n?DRXXTT|F
zH~M=*`sBMDxPXoV*3r(H6?OjK^Fo1MF&xn%C5NPbM*pMbl3DdOc!FQK6D(KBkcWDFJ^?+(39vILda?T?>lh7u&mF>_6&gTv7#dX%sN1&
zyhWHp9mHu`EK<6WurANPpWz(_8!tR!>|Ay-6IBYO^n))EeYkNum@AgGDRJ5vOkS+o
z->o80%TYrp1G3SwyjjBUaLq2b@g01|_xOAlNjisSNFnKKcvkq|XW<~v0+s~glnPMt
zf0;MF$C;y9dxX^jAk4gT%_{S53FLN58PlRzPM1^7lBWw*b3hq(767W`|Gn4f?pRxrHcx0VUJ-5rz4EbUK=L$gO}UJSdv-@(Upv$$@Pn0ZhygZMg#19X*ye@1wXdMlPY3W=1TQ4q1
z5@%pn@_-7eW9~NFf>j_S1j6-=NSlVZ_#%Mo&A1T1&e|w8B39)+sj)$x10L$N
zKaw{Vk6BNde7q02XlhxB08NQnVRQzkOzP8pHqgw^q?En2-J7aJWh;P5RJ
z2Y#P?4kM0t#|oK|b?(yy0kp>-jen$yYBHMt-L$V!bpeRd!oa%G1Pt&_N9W2RQU4E^
z8vbqymiCH`SRlWd2NTR4LBW_Ry+DLNOWN8q^2P1jQf~@W>A2RWlcUQ2d^lUJu;HO_91=Uj(SE9^_#5VCL8C%Cqi%qL
z*f!!6T>1c%`=R{bba|$^xI3{?)gjbl@x&|VqR$d_5KL_A8B7cbgMLBXVTGLAy!4Ss
zEO#>p2}O;1)mft9%zjcAIjchAib-wTob4or|VL@1mfsvsAxb0
zRL2IiXX8oHD+o<;%nClcdYF;L(m*ylxM4oItRRLogm^lai_e@EA-x<8b}2dlxB=iJ
zz=QhA^g&G>Gzp!6c`6GLpHcN6WfTPwp78LW002+7O*71{SOv*k`RRNvdhE#3W#PU?6yplnlH5Vy4uBmXscYS>9pF2lUX-oWi@
zmdX1&*HP^>z)H5EU=e>kuKG9pOX=KkYD7>ah-g5{G*4so313m48KFyK!~{4tA|LYt
zr-bm{e-6fZf2FQ-{-n4caw(f5{|*df;h73x#8u7NC`$O$l0B?cZC^c0=t~Y7Ag)w#
zc3f^OWeJ!3tsowF^5~&D@v?5a6u3SL9Z_b
zfRP$M%xQJ=8>%&$JCDfY9y&Q04K|T#U0L8g!huL1i&IVNU~b+r
zQUM}>D|C+zv;VR1C7`t6>SJFfEX4p*Ax2RWO1B2c2Rm#4;8+s9f{s`1DM`3kdV-Vc
z)#Sd#l23EYBtp!h9+T9NU4^c!ID#?{I&(vF$y3%CIH)Lb0N}L325Lsg+M|tc>@Hup
zh-*RjXQlmzR-q!(ZH)G4l}<10rKDjeXreqJf;!&fybvsyKwFvc%j>uW@t;pY$0d>F}9&*5!aOS
zc=^kA5sm>6tZiDprYk!p5~4yeY|l*8R~+^OfX!(Lj$i2hIqq~w;m;HrW?X)Zvri$=
zD_QHai!(T)Vod=bq7ZFh5c(mAihDM<`l8h9Lq(G28%LdigT~@>6?Bh)ZEu
zD$MYZ6!yDL-&4gMVHwnX5g??4NcPv>9P=4$JU+Arg%yr(1qk~}I-S^ueq)dARl=AE
zSwI9Jd+NIhaSQZe6q{YXkickQN~^S3T)eMsnD#PN(>Qa&p<@be(rqASKGoCq77)cfY=yHIOSO6
zB+wBZWkw-n0OYOej)6Zw{5kj2Z@MF6+PytU(7LLubQLUJPkcrCX;h>+ge=Qr@es8!
z$2jv!$&L2hLG^_S@*I4gU|JUNAW8Gm?#4}sfng|Xmoh#?dOsf$fK|*4NFd)I-
zqZBp-fao`S-<=_R0eB1ITEvtecgjSPdrc=asS9r-K#T}r&G6h1MFU!SeHr>uVAmi>
zcL|}c<2}ryIgf||$ZZzvDmU_290nJ!&vi9E6t2JXDI!doF%q$?@b1I>Pr{+a51@#~
zh_LDRKpz6UtSQyY`6_*S9v@ZkXo87~C#K!j)@pt1NXm_?gj16f>~X^^QAasV{+wb_
zpF%RR$Exkt8(jsjdlO}w08rQBc@n%itws#?2dwkNXXAETg=T~AIxndjyP^37tOP_U
zpw9@)8^KUs)zEM1cawsPp!jKrinW4hZ_C<^0gQvzbxkH*BkZ?p^oSIEPXRR?fx>ko
zQyMG~RfBB(hkXaqj{^_Q|Ikw32+bi&v(JqI{@Zm@nY|)ap%zv%{fqtm(YnQ}5aS!Z
z<|#I1y2LtNA*lmTZgRNHh&;KDe=2ASv`f-6(!{ID*wWmIH!}k1tYBNVpx&^r&Wa4w
z0(_r)+wrGMoAQS@F8m<%q}RA9J)k>CgbPKrMXoFfc%Zs2LSRFS
z?tCDE*LCC+|32@+{WJi$Xf#my6&{b!#AkPAO0Nmfg4gkh&Y36OAXUwjzQ?*PNPRz+`yPz?w-
zZ%;yc072xnWdWoN0^w!w=#n2UXXGJ97@`6c66n`~^eZDGj3o9`Ir(#KjYhS^-^Y4g7D;_`4(kP+
zlHMc6r>8DX@=%rU7li~gewa=|Qo^NoR`EU+Gn5i6IWIkYtm<4Ys7SG*6cmM!+7NW9+)?_Wd^`9ZGiL;n0YF97z#219|9g8$p(m-OnH0WLGW_D_
zzTT*iir-`EY|-)!YtUE@KLX=9+qo!|*bBW$z&M#>*k9|m*7UHGz)FcV-LWN|qeRIJ10
zZ$#diPgTzsNF{Px^>9^M(Frtpmp&i7Av
zPd-R1VDjRmPCCG9yD;6-ga4G08N*2C4~5*LQFOJfNF9~pe_YRB}MzCym;=RW05jr1HS?z
zc2v1o40%yw-(4J9VThE9r%>ARZ8dLOE*Hl1AkFRyy9(0lrGYxrm=@-WCb?ui5#9+&oE$T|#EX{;2Mrj%M^|Yx1
z17d`uQzF8&OEr?v+W5~$8rU!kf{|-Km5cEr3{a@h@H|G|8d|Ba+4;Pp!kz0t!O8-b
z6pzVI7{qSBPaX=c{Rl&^-YfuxDZY;@Y;qR*dSZ}<&C
zfSyRVQEmR^exAT*<2l(ug+9gqI(}bba$-%bdR>)9-)bYP8;{lBG_1YgbN+-@-%(nP-ALqn6%(F6pY2c-BkCxkP
zfxY)a;*QJ#orX7o^E97^b#iV=-6MwoFwH@K{{&>l?9qH6RUi7uR&a@+1ZvX#^iWnNhOa7Q7Pq5V~?t7!>mu2
zlU2Y_TL;FSRN+GB_ON4RK8*Z1@3GdxxB-{3$YWa+4v691@s6g3kymZKDD(|=#sb0{;5a?HK&FK&q3!UQretp*UC!_eYtN|jVpMXHov(sZfkCUBuo2NyaJ
z8DypInMU!3V%id5;Og02TJktdMDi^%&^e}vqp0j-fy@>jG0QQG(D%rrLsBhhT
zwUcGDbcrKZkOV`y6xV%J5-+StuCR*8$vP)8P%0`=>g(41R)dt1OH8_(&UDtSR`F{O
zl-v4>&sR8FhU=njUZW$}^8`uMX>MK>6Fyn5T#qXy&^%P7NaTk?4b;b71uFp=3STUMop}+swd`~)JFO&
zdQ{MMEg6~QsFWaS`6JVxc%E;EBzvv3nBS{llk?er=EK~EUOnYjK!jmnWZXcyyhgRq
zNrN!upeM3vcVS=meD*f_n!+v$q=7-u4JO@>%u%AWu`l$es0`!CB(f7^8n~lh$P<{^
zl3Xkr{ESlP({P6W`r{IH%cC%Xv62!p1TES0ZM?is*jcT=d*q#fB}UTQcai|>CYF^5
zxUWYpW4*zHW#Ml7Fm#`-`}=J9
zGYS_fGrj+gHNVH9ZrlpD;na|u6uCFDPFHvO5l}jRetbcZa;)6?*%@jf{*~c_HBq!=
zEHP_tMg?NnGs)>`VnUQD+M}noZWPoX$u&;^YL_;Pq7eRS5Ek)}yKE2E2
z`7x4sg`lwL^IjE4P`Tj^#3@IS-nVsVmQ|$mgr379H5QlN$uRp#T6P8F)^FclAQW*+
z6KL`ognPMW$|p!P^|(wHp%pW6XdgiWk7ri$m3d`LcIZVbDi@S{K&z*RiLkXN!|!_-
zSV$_DCy&5UF&S*|#R#d8O-yRs!e+JC)C=QB^05mt!9U4iU7Ybg2Sl~X1
zY#2?h>FiaYv%NU>(!Oc9DU@=i#(?H&cdmLKHZ(LO1TMAf@Ko5}4_`@617~$doXoTj
zvbL@eusDWgJ-vBXlFv9rWr|9v{;pI@c20oyt`+l}LSgEjHD`J7JHqIQp_2t=xt)YJ
zsaj%S)!8veQCg~A^C(8-3No8f2@7-ctb4zI_`LZtsWi~q{9Oo{%0F7^lm)ca=ngZI
zWB047s`|&r3!vXpbF9KP(zJu!di&ewdY&eYLOp;h!(JLU;=$ckN|b``-%^0hUX|i<
zXrt77HuzN5z(w=qwxn&7`K4~UtAV44Bv;aRQPV#fTl6$br-DtX*@~()KSD(=Z*-EP
zJ!T3w6~vq?pZMbg%lpyfGm3x$`7ExWsnT`uP)2i4Vvi=)5&CS2?Ez;w_Ua}$3vV6c
zmYl@w=&oAtU8K2s0Rpc<=DaZACF?tN@a;Sl*?JhnS{@P-(oP1<@Eo?;^Ro*Jc@_u#
z0lsEj+Kzlv?(?z^l;!Hq3^ZsVQ|pXRPhubVnS0hkaH)+=)ylY+cfFAflP5@oj%Qs6
z?0L|7FJ*&DMq5eUef6QT-P!lw#qQq9jx}YGz>g2wzLEVazkn8F8cG~q^U~#8<#Cjj
zvDX@^^qaRKV$HKc3-n)Wz~!RWGi9ANfy{7c;Ds`i<}-HUKfu%bV?7t>ZD7m6vU6Q^OaG%PXJYmmG
zc}maSv5$pXHg;2I;AB@qhwKbV8|(jTHFO@;w%6larGk>pfB~&>S)kUGQOgLrJb5;2zu*k)3prUbyw@O{b1Pk{&y4D{PYU*(=J%tMHfm_y{xPI!bKov)}Tyh
z%wnfY!Co~w%-Tp>{XAv$^aUz~Xkqp)I*Mgso4Zms+xT{I#yBBWs?PYFmd>Z2V?Mgq
zFI$uBbnzmGRhPR;*oE1$L4)6QqclJ6(&=W#jswNzeR%mKpB@wI$(xOoi}$aP
zO1ZJ*7N@1B>QxS=qMTyN^{L{Sa=spyZBAIY|NIu&yrKkOgd1SCx;M@y@A#5_Igz>EX^4hYxHm#P@N%bL-WEyGX!*+e{qZya63ts>mz>J}$1+_E!s4
zJI{Q!FCfmUZ@O#H(Y%6%#ffi>BDjatg
z#Rc0u7ydftskY__)ic7;Q4tosn2=>e#I!T+btS4B?pacZm1`l~YCrT)#%
zN887Jn_5$b=A;YUqAoghs|>5QQY>
zjZe7*`_gl!xo-F^F6*XBQNI3N0ja`8%Pw&KW19Aw`8mJ$a6&U8oWeA|?%o&bnjxQ{
z2^ze6lLbyPCa5tDXY$2wz5Rx|}!2dh(r8t|3j9RjPu;I&BAnR_2j=hBk{_7n6k2G7TRkour|RD
znlzogg}GF_Q<-(!dbzg4@)9Qp(fPT-JGGkxrUO`IZ`XQY+5G%91cM
zTNgT-pq_UpP|E&W&PS6SA?)cS+wJ)Hvx7q1(HLfwCv#oBe*L%i0Fxtlf4U_znJ6S73B`F83s9uRzaPoVLf)IQOYkN
zv7#G?8AEGA_M^kl`=gp}p=eX#Dg+nB@iehmZu49K{}G>VI
z3NDnjOHqp&!^U2r3THYgwqf4ZDmalth|0BTb#>vLVWKXMvip1+-Np+!E3c{p@2!^;
z)=uE;FKcQ5NT>Cv>k3MA?+XU+$vTXyaBmt$Mm>Nq9Ge;arByXGheC?KcZv8k4Z}T^
zLMz@dnl+W&1g`KZ{Tr0mFjv)rYN)CN+b5c6w5f6dYy;p1yLQ}>4cgOU1h?DbnJVWM
zC9SCxJ6srO;!A`fCuQFlZGR?A6TS|3Yk)4!lfVAA1cf}(A>md4K*$cqTQXix{e^70pd)moOkU1IS7hHhhYuxl&
zqGpAw)dqu{6CcTi}UR<#TW{nKO?KxQ6
zu?oxPFskncD{#RcFh}FB4)*kG6!Sqono
zcUm9B#r^3BI_5m(aX&(Z2;*R^-;&K>tGTDGYjCz7mYj0=vy7fLEEoE;
zc(%J9j%6wL^9xb}S+lloWP4B2Q?-oFTL&YjtNZL$+0C{w7@iYS3>Tx=zFk-zf2|2~
zsS=P;C06e4S~t$l!knA9A0BYWRbNQ)3=CH0?iev_LYgzpL%gAHGU9&o)p6!abnAZ_@IFna
zm4~el;sT+Q%M;&n*HaGQ%hc#Zg{7eWkMnvQ@*N~o5D9q8kZo3HW(n)>4YF8HE
zR^S!$kT;(R?6cho4prfrfnzj>r&YLw6%`(ZsHYIk5TT2Et48ey3R^MdOFu97iwuFhYDyK$#%xSBFRRK08_SRA=-3;d-yy|~
z((;u**V@eUC;7vEeO3A;4kHr2xSXLvXSmfY=;MX45>$Ebx(dUPM}g0GAO?gH@MBq_
zm4$i8FrO0J*J(A-9;yV*qWXPJg|EKg*?z38c{h8QC+U7uqrAj%+S1=Qh+VE3B6nwp
z+s_Kxq}G$2>Pn2%R`IsidxSs$TitSqOfkYeeaXG~cem
z%eOugo=V
zZ0OR5N(=(@nCEXV2zB+r^dDw#=m1YHUO54GoMM*o_a)QKRAQ7R
zyZ!Ckx0?W?GKeaN`Ae!bQ|$IVa7BWM0_DdV&zH!^p#>OjJOD%Zb9lOhFmPcdUcEBR
zjzKbDyEl8T!au*&^9=Hy7K3r6J_Rsb?75>)Tgf`i0lf*AL+0@!QQDU9j}PfE=>Kum
z=BTyH#(<_Zv=7TNL{eP5*bh@xijZkM^D8UQi&y+be%^xI&o$ZSaLC1md7ASgBl#TrGhy@%61v9|iH&t(%hGdP7mr1aPKGS;~AwN0%YP-P@yhUNYdfY3vNT1&z`1&|*op{?Z
z7Sdpzrm@jcQrmE(`e~8Ya%W%|T;wu6uzqx**(p(WDAy@UUJt13!r1
zZ5wbYgsqOFY_V4VwKv-e-Z1)IXo>=BIS+7aH(Xi$CFsXK-wBoNc7x2SZ1z1jfrNXv
z|6MJ(Qs=Vn;M<&t&Uv{$16*~L&7%ZgTh|^uIhK7lKtdiM=NT895qxJlT@6j{+|JI%
z&^)5TOBO5cGJq;WkWrS1gW_eo{49tx4GwV>tvb7#{AtPxOnK<
z0wm0%i4m&Fpqf`9y*BLITpFA4T7>xD1+HYDhXK7yiQ$JUG>1_A!DJr0tnTS{LU!*L
z#DL0&l2UD8)5_0ePOXkdSk_
zXg;{AX0_V31~&su?SM_PhilWRc3=`fA@uSaR@gpYkA-k%9xi_#MAG>r@MHL+K9Cv1
z%&6K7m{u(Y7qJDqE)J)v=I}1)wUs#k+MZc}`C@&nhTAaKJ*wnjy&a4z?krVD2tA80
zm#yF6Bhfd+sWg#Ml8Km&Ni?MZkINV9)c>4)VZEW(%fS6cO3kDM*s>gUvmDyDyLhOS
z;#C>Hm_C{z}SgVUZV63@CMM?k;Vc@>Iv;&p`O4)ub
zjHCGnNqBovBhI%nL;E==GkYDvI88<=BUK4O;wT-}z!W+Bh@Z8}eQF*=@Da*w0?tCz^uM<`R;ADoFqa
zo-B8(&i0P1AUYSBL5vdZ`y_wZHY?i3%wLNaRzzID^+LKTLZ$Lvc0HzI%@jcElvAf?
zs1%~~Tp|;doxq-XvZX=J6=v3^K2lG36S5P_kK!_1ZOVkU(wtg76H@ucFgmPN+08DrgX8NilCt%Z!Wa_IfpcP`N{Ot3tu>_6UaN9gKmj
zg|hA>y=J|d!ds!rdns=dX@>~=Nz-*?g=Hk!>Dk1jAhd1aP?US_HG=V}VB&}>N+^{0gO2}$XNil7|HsU^o_gC
z34IiWmuBBb6?lzkYDUn}p+ub~BhMT>QM3)vYpji-%;;81EBJ3H&V&+QEU;372#?n{
zD>4qxF2!K9s$aOCvrOpxvzb?;*Hlzn@XJzJweQF!Lq!@=iyB+5I6svsiz7yJA6y)6
z{;u(qR|+BRj|;F6(^P#{xYFyS@zUwc#5>3+4!*RT8%h%q3w=!|)Q3n*c{QAQW8KPx
zsGgaweIjSPhE!P`1|U?stde@GteI9;3yW06g$;2Tb6ltbBWtI>n=0*%l)U3qgNBzf
zRJ<6O-_Dyi{Q34G*Vo#IGnVG+G72s&Jy)hNo)@r2J8%14Dd=plCS$g`|4+YZSGHKH
zuG@0IphLq;lTd|{hSO)E7>WE+{qRkvE*dVNb6)gW7@xyf3!Vq(hJ>{bCM~_qRpYkW
zn;jrxmL9y98&9-?kBVk$b)IWK)?
zRPNqTZk$yn+-f+S(%Vf0+O0Q|{!%z51X;Z}>^js3tf|h-tC9Ipv_?ml3%k%UIe(l*
z6P-hl*XpnHJx{BtFLkgQUXLN3+qoYmKhhfQ?SiSq7d^i{)suMqduMgnUQ^*M?=7j_
z3j#F#lth@rS1fy!HMW%@E&rAXz9@=y7d8+tC;q`Cn0u%?Y?#|-PLd8J#*6oJmt$#D
zp`hQTB|}I!Gw{5e?+L5*i%;3tNHV$4_+lN$mV0oievfz(T0fwh_}}C8#LX+ZS~T$F
zONnA<`Fdpr>K?ElUoiz4s5vc?EJjIu596}zZq9x&+)!*^bO;o8^`J5qPB1ed;*wvW
ziB>edi0YuV_S~6!+k#tg?osbJz8=m-C@bgT8z^v`76Y{EG`SjGUBrUCIIpQE)Ud3n
z@a7vbmgajtKiCX=1~na>LDI+@@D;?R@&%URtj2K^td;O@c`nkZ7um}nu3fp7!g5hD
zEK2^*V~f7x!X{=KdubBNbwQSA9ODOJbA&0oQnNJ`k(C9f<{?0`TPGsizWrTL2|G>EW24y>~tnx5#M&YnN2YR&(-Ps==M6
zLuE;sBe`e@fjZ4Qc9niVQwp8&3~m)EdLZ
z5a-t)ndT_V$`2KbZ&B=RU}!qtIKd)WD{Jq)@~KX%w68QO@0_*mQ#vC*`#Ci+Y?_MQ
zK6tLte0^U3nT@jNgR;kF&2Mh~+2evwJFSAX5h{5A%m&5!X}Hk!iR
z<{J*pmxUFCWN8bmBvY)r&GsmYH&AwLORuC5qfhs_LWt$B7_D%Jo-N_ZibFQH7oY>QjAl3yrWAgI
zZVVtsS%Vb45ljz(+9r}lL0wI8LD5p1a^NaO2cU6MC1F(<6$$oRrV3B$#32E%X?)YM3%7rBq9?Ifo3v#vf&&%J0=uBOu_1@)XLJ}^5Mt008hN1C?8dGgl6(Z)>eX>1q;&e=>#$hN0N&kp08k#
zOCEHlv!XhKO1bgq7HB5@__Zekg{jpDyCG5~sb>gV(AJAie_1d+Q*Ll}epA?lq@%{w
zsfaDoi4-br2j86%82!PdK-tP0=MsNhfF+Z6L=Z)crG>ePcdUrXG!c4&gG%g=TAKTHKYw|lYaae;l3NZD5D^rjpBomapBraRDy>$S
zVUx?dMuPdk4Q!+!U97Rv?;`aYtNU;Gg)(Kv@+_9|6<2a(Pg@B6JC{22AkY)7-I6#2
zot&o&+8g*(9m+hbnVJcmb=-n7@+g3o%=d&nkKJr<{&{xhEM~wCJl%X(%m8UH6M4DS
zMRrsQHB{+d6DrP9hM!qjefr=XUikM1%HiRp>suB#J`VGFy}Jv+mK%5Ogqm87ZL!Jk
zgrHnTl0JYp5~?CgH7xTQY)L9s?+&(xXqbKZK8n-fpU3q_QhBq-9ru*Es3+284$_En
z`)WFllVz1zpoT7I5AN^Nk~>eVNUSD&P5ACvne2gQ4I&Y#h*s>O5}^rfmjo*{y`0CHtD2`9r>
zm2Qe$>6HL=9tzJAFiV=U9?{4-SxFK;0;>2D*oE1$Wwin^t5Zshnes70h|+Kq2;57F
zpk%W4r05hZIW2aN42*lV6+1+(p-Vi1XEgYjGl?Xwb%%qkkUGIY%txUAq6!@GP>$T7
zYpyVtC*IqdJJZDH|GZn70Kd0&s0p>JShn`yz8K)?nD
zIx6hW>o@WDgqcmjn+#%3*+UT9&ONZ&FDA5-ljTAO=E3ZHx2VQ7k#55UPVAkB%7SN<
zESoZh2f3o0vg1|hon&N>pg!Hc
zzSoC?=GD%1AtQlM#Cz%~+#KvCX)l{#!4oj)5OZikQkY4`MGG&fN?ueb0JcSso;HNt
z+Rk*DC=?4{6o)nBpCctjr8}c#sXYJHZw<^e?MX3gWE!67=X{s>J;*6$2z{*ozfRRd
zW6GNeQAxq^_z47xb_$D8;WO>|+LpYj5G4(ZN>uCF*x26P-56(-{Q6rbJlTcnRLz}T
zmYUf_5*BpruP_@|
zZ|oF6c(WLFOib1!uo%nF`FjqdNkzDpAz?5RNcMQv3V@pynhnw0I+#I0>pdYnR
ze4i4bf>rPJyEhdwPs`Z*bn4VBE}90HP{&^||D)8f8~9&$)C5Hr+_6@&APIcFTb`}a
z!eA63szRlN0a{GCBABL2O=YlNo{mlZp;vXMXRH}$Pf%zAw2@Ax>*H{Nh}SY&_^N96
z_zA}KKCwv1M8E3@T2Xkcm{P9)3>^<9=Z+^1Wd19G!A;V=rt8Wp$vN?=OxBo?T;}he
zPc)BiK~*GM!k7~EDb{PGPwHN^;SYk_c_qJoyAN6wEXb>wE=>ZNqM)sBv`VPpqt7PA
z5;@1*u4l7z^90va>pcnUj5O@8H+hPEo6+F?NIM?<%_|fDuaEv9dMYRaYdyuig9m$HN*R;%S=hLEU(6iqGmX0H#-eWRz7hW`6V>v>Nxe
zP<^>XJ-`t@t>`ZA#+?@av&k%JLX3!wr8$n|;N^U=s*X4#wMG1(UpL_V#kNG`L0xM!
z3>ZE~Q2qBWU5f2)Ub%jG3_rKz-UsgWWw~qp5}9wb8v4}}s$2%Jb`XW{4K16ft2G4`
z(zQHshJV`oRXUW-+A>2o?f376GQaKsAvuohrAUv*y&9|5l8V^g>N70UJ4m(5I7a&d
z;mISYa>#0bP5rv(Z9Al_{^M@jW9u@kx24Wjd*l4(qkAh426o2H3wot{fX+Nn74|4X
zCAaw2J$~8)#0b@`CTnnch>#>5z;-o=Um>eJ!6=6=y`~T$@fe~;fiVn2r`R==h3R>E
zO`na)y`8B~fny_Ve&01RHX?X-bE}$fLY!=B%f)UTJb)(V<^QMf(fLcOm#Nv2a!Xvu
z?0YrsIW2MEOT!`jPkyuzl3Qx(+9RSE#HWUS1%M?Ze~I}?2B(_CqL1^;1XrlgK&{yo
zc`=X4?L5G{*9~7wkvw8d9wRMZFPyP`V(HKsH26Qk)h*9jD103oQ0@K#Oko0kQ>x^W
z0qXEoGGpKCH0UfH
zVNmH(`D^d(kv)gs4I!1bTymFc%u2z72UFlEcm2zwd1dV`kNqHFm4x@cZyFRJ;semP2n(?af4{Lk+<&T3EyM%T92fQi4XRje_UR6`x-W
zHB*(fwQtPj-&~AU(NL~`bY0n9KA;jvgJcgeDei7&pOx=
zYIcx+S&qw)tKq~Axc2~?syyWCbyPR;nyC`viI7*sv)hOe?KF(*A<(o@-91G_skC6(
zN8ZNM*CfQDd_@T)kN@x7N`kG}x^Juo6-X-U3(5}ORGp)(4eabneOEuZOAHOUw*bZA
z=9e=l-uwPx)9csVmWHHz3g)jDjaKQs+07p)k-skJi}{c1P`^a`fS*MpbPvNZ$hQxaO$fN}PpfGpo
z2rp%4$)R20eOz)O`|EMUvBWm>R=kW?Y-LGw%j?VX=DJJhitR(0uwp?trIAU
zk;ru1afg}YT!zei+wg^lnn~D=q!#3vwEqYhfe9swVXCaP
z;ZS?`rGk+&0TM{NvjA9Vv}{1RQ=g2Z5{fV7&0EwQDBfJ~fD@j(cBX9u+crD|b3d+V
z6%ZO!24)Z&CG+?F!PyBt1P#X{SPZWcQ1N&b+LUw$5yFbFv0U?2L
zpMPdrvAjU9xf$&ul$itkKLY3zSf6>hGT4*IQ#ARqsD1Wvf@eAW-hVBebbXw+rAy?+
zAg7{FFGy-Q#YWm$@kc(@hw|A#@n?35tAW)3#%vn`<7Syb1rxG2)49PtL6ol~6nblZ
zhmE88ExYDkL7|+3zh)*5A}qOwe=Q(E(bw$~Z^Ki-io}2tQYAV=6yN_WsN!~XD%3{}
z97!If8k4S2qPi`9eV*}O|EhIIvH9nqw)f6M^CH_ht6QQ44i$&5p7NQ5%CO=B%Uc|?
zPf@4qmC<%UH)qn^1hFW5r82~rj&1=3*#idi!SvMUeOyDy6X`ES{4ZpI#Lvw4em
zOSI;n9O~Oflhd$05$rO)+mLS>$K<3wpdGU=$Yll>$-h>w%r)wBL{zM@NF-0c{&CQZ
zAp}7__qHWjGEsa@K>jjqDB*`0OTF|
zOefu{wfO4w!fIc{8c9Q0^#V(Nj4^pHVuL781&EG+%KwA^B8#}lks&Y0Nf`GkvwG6{
z)hF0edKGw!(d5xDLixx3kFoELr?UU!z9pYi^@Kku*4<@w=#Cq;Rz
zv;kUC>gd;x2()%>msX
zRc~&zL>+J(2^cl>Hcx=+1Sa@UrGCf>lI@wmngB9p#;xk>-&k2D$EAL~y+d`dZBi-8
z>cs_sU13f|FT;%Hi~&3Jp1VN0eoy;^1e=6~vcFo}y0?_`A88>8=YpiSPabD<$Cf75
zA18<++cQ(yNy81xgf2{PIuCQoTJR-Z?E^M`+vKurPE|$#Tc0{qtzSU56FH*I2gfd9
zz31Y_^skPBIi<$W=+ckneClA$um1yni2A&SX4_P8vu+xmX?9Lp!n(P@V&?#JCH`1*
zd?0;YcMUlb3XL;=qyg@|)dJK?46jw`yeB&+fhFCuqhm|Uv*H&R+2lfj0VMU#tN0A%
zr>?X4>S6*`Q)&LelSAGjs%?KXIyyLt&*wU7Afth~+pSs%m?{%W>-uGK$O5#ADnGEdrjbH9jt_Qx$ja^t0i
zl^k=$!{g0A0dGMAu4gOHrYch;(r*|!G&>g7x-XJ;a2Uqzyc9LS<{FOssx$5(oB;sd
z^I1<^DasC()z~sjR&7c>czBKTz4r(#je$KCn>Ip_fNi2!*e+i4~1sriCtgZQp_
zXMHp7R;g*>l)jZ9Oo4gP^puQLf0j!5cwWW;qR?k9sfWym^+ME&@xT7k)qT*IrjAyK(KIQ@orOh{a*-aLj(@um;gkNXA
zr4XQI?{z@dvv(
z{h3rzm#cy7(!l)q^Uay}1R|SHI7(ydvsw%iFR+^uxIE?9kDn1B$2Q8ixvT5iS*KBJnq2bT|O%U?HGvfmo^%)poy=
z5XJ)jpZ&slB!me{E$*`m8|A}*7%9JV>B@J{fjeoZvA5K0j^ApE=?*MfJYTPiWCreez4RXk*x%m&
z?kRH$N8BLL(S53Yr))o&o!SyMGg;S`1cm4vJC(*Ou~4*|aFd09F24C?|6^aJ+U-Bm
zM*Si(bHaDNauUZ4(Y+iPaTbQvGL?Xp+uP(xm;;7>gS-?$#(KJdCr_r
z`|G0gTKOYDw70=9<0&yO5P>)Or^mtSU~^}w)l}t);_dn*GLLAQp{Xeay%V%>O1+&q
zJVNJv{RS_SEga?%niA)U^=nIMr;3}7`Ah<}YkLn!S3yPB_m?(Ebavi|_JclFctwgp
zO5N@d4Q9ia@<$*DK8f-gHABM)v4_R^!n$j{?}C+d>|7B|7D|mwDA)IP>fQ`EX}q~P
z=I`7*?=1J^ENqj%o15`+$_hOtoH=9mOjJac2lhM<>%C*_V{QQps?^#iU@Z+P(Xxzs
zQLf;O1Mr49OwZ5Q+y-(406q=vyoP@fYTaXg>yd!X6w3P*kjtsz`}(t7^$Ros?SR(J
zzolchQyGA>%9G5C+Kz|`^oME!+gWBoJP+2yNEUP0`1sI
z($pVxHp3@#9Tbma!=0IvtOIqa*@fY_DSyEO65bj8_mH}x?9dIBAiz??
zv19SRtNORx;?~8f*z#;;dtv)RPYq
zzym^&#;{Ei(ti|Ta@mOXVu^JEs=XdPUCa>oybZ}!ZYhiC!7{JH6HI3bl^^}jD96EU
zVa;b`eKaWwq57kOw%i$i(8N#MKm(gi?{I)7?{!X;`e+NmNY2a^4O@Cdo48m~v<#aP
z2K*PasDT$LmmgKfC>eUzFinsT_wpNNTDG(d
zcxd-&nHQKxtbFAmS^9g=L3@)>@m==Rm2tG%_&PH6bkQ-Nl8@oLdsxl!8AfSBc}`gu
zdkPE{kdjGH)1ny4&AQHLFI!C3O+FxChB=|+I6f61XCsq6G7m(&epWCph;|9242dVJ
z*bi1rKCF8So(ig4Vk5c`8;}{g$83(do&k@qBJyt9fBj|Y@{~Has8h}FUjzQkOOc
zY@q&rJ~+2||4<=O>zm6*f+hbXdl8Rb*SV>#rS|vNcm5^+7K36-ieb&0-`9r&yD{Q_
z=h~7g7Jq#$;O#rI_|{`I(y|?@6R8%<6ARp8@@P^B0Ht#9>wmh!q(FZ#YdAy3?IRij
z6jaPI2yWY$iQ-oo-d%Vm8NT?r@6-#E=RpUsyuc@Tx7sGD+&dT>92n(iSZezS(6xZf
z0ws^l8as$?!tTAYFH2ez+HjO^(HX!4J{FpiD=x0>U~2kN!1?EVL3}v?(1rtvOL2z>
zyYHX6v}fKRmk!^MMpKKDwzwecvL99{UoDm8P20M-N-EZ!%%H!oNZX}%KS8BFE!a4V
zY#i9hb^J{^TPLOo{*^OrHy9s@%oL`wq`DfFT%;)tT{v85I{U^l=g!elpggLGfc1Yi
z=ps$;wt4wnazurrK2i9KwbH5PR#^EXhbg4@{g26EeN%BuNcA9>gilFGfG#zcFqCa|
zL!Pjip1=0}i*yl6#iy89P0BuqEI{Aw;sl-l;_E8)_8K8)=EeTPxDrcc|K>@XmxWDV
z={#x{<7(yE2RQ(m8(zlT9T91WOtCcqn84%w0Cr`tqBLm@*kc=;#q{dT=5;x*xZ92h
ztvnASED**+@6OgoASm=SZu(&0;Q9TPADo$!skud{IZAXZ4n1M|clxWR=%ke;!1(#!
zd*j-5LHr=IEnO6CMCzA)3^or`8d#hUiWVF8iaTuVbyb2JN<7-~fXU}bhk<2zjU{3dnh-vhip^=76K
zKnw!^w^UetYLS}sgDS9MA>Oq)ohNtEb@Ra{=mc^=#lM92h
zWbv=>)V)xPU-mei-L|ozL)RQ}+GRk<)(G-c=j+}3Tivek@34AWkCmNOEt=*qchEbD
z6N2I;{!sAgDLJ|%vzBPTMx
zre~9
z>{PRS{GM}pIC>h*T`7a?OZYyd{qkwIz|y%eB{_+9L1*}nddEMi6T;895zeQumFcHx
zx>r_$fh$s;;z1L-`DgyAZl|Ht*o6qI@fPkOoa^S;$(Vw%$eU4*qwe-SvNP3#t)8F;I>IN+$%|;o4UCterD<}1!zFuqiN)9avL3y-!ZM8eDSPjZ~?6L>2
z*Y`?fKcO=jqIY%$Q)1QGk`vc~-wdH{z)5vzYHjn>F`>-ls+pp#`~X;bUT7ASq=5-g
zrLMB^g3IQtO+zk*xjy$ToKm!`pzH4dUDl!NwL{|SD@zur_k*NCotS;qL$B{lcSDPz
z<_1C_K)!nbI?DBXwCYHqHqWJu9js)<=x5z0J?P`LRQ%p?9&&
zC9cHHHhq2c%lgP5oc^C8PopxBol9K#FiqCXu>_@
zNnMGAIGNpO3uglKk5f$WrOMWAkpcJXDnW%puU|G9@`?qOmsTqw!2LQO*rp;j=`(~x&EyZSlpXUbi5{*x*I
zGQ7`Der(2i=D#%(%L5RqdP|68_N@d1Gce=(Fl_M2y1XPbD};#K`CH~uTY!b#IrstX
zB=<|S8{$uE8E}<-n{QM-WPAs$oBqAGdD-kcKYebDUt%HM6N?j?x{u7LnJ5Txhjz`(
zr0(F{R@X){5R@xvnUW&_;fBz>0VH__5&=1HA0-*3eHu=iBWy=2eWRqdU!lacz8l}~?uvLrDWqpo9}
z_MgF<5(B96g@g6fo8KV7M(+_B?85bp-sa2G&|~jIAqwdKaMEYaa6$L3?YM&FSsNP=
zsc;eNRP(v4wM@9tq_WI?S51NI*2L9`$@UVLks75UZjE;p9@X4(bA0k
zq%u%Ze0qvjLPS%eZb9(6V|aj0fEpK(r5m921mKcv=xWMPh7I~Ks0-!HDnW}1VkxTM
zv;!jG^xSLJ>qtAgYaZBOsTPZ>1*}R%d9JG+Q?L;`QF!!lB_SGMLW^u9N~Ksx56}phfqh+
zru()b{aRZ^fZl3sS6qipvq2tKV)(dvi*9EH7O>Sqx!riV%^X~3ojea0uWa2Dlk@!GgdFM_8rk!ChRdh3oLC|^Jij50UvI(
z%277By3t(psPGp7BCrRNc7%KOM9v(TRCM}%0`aki=&It)0`8{>UuyaAIn0;Tv6RMi
z-BnpGr{kK?EwB&2BW?p?su}q#+=nQJ??bT-!>@aLadURIZS%?JB>ZmxiY6D&=&mBj
zy+<6oK}hoWdJMa}!snWUh)-t8Tm@rbeAs+8hN?XHlqAH3s=Ii?041AnOo72tQ8kJ`
zBg!^^Wv@#0OXcBg49kjSW=lO8xj=4&f6oG)2NoOX9;vE{e~rV=<-;*a;cf3-T|@?=D2dsa@HvVBnEUy
zcD`>7Vh>=~fX-7Cg_*hyf1!((eFe<8-Qvcwt-3w92XM>#pV1H?EVV2qya8)B{4g8*hp8C@
z*eC?UA8P}T18i7Fbh$0m8nXxxCz;(%3V~pxpqU4tgg6&g1Q94KJ*wGNGZt%Rwb2sT
zr71#|^vZ$caAfymFy
z_LBsW7DOR0k8JI>e#LHA4Ejuty{{^NiAOc7K;y*RAq(AsC8ZxONLnlxS?TCy*S8^v
z?qMyDe~Vz6vL!$Q%yD?v0bp2a3d}kJs*0R~M&q3?MZ``W1p4d9!9BP}8L0DCrf9&9(fQN+X&@LxNz>?S{t2S3z0?
z7^{5sGgo1^&ZPS@lWX$W16p9ydlK^udgSiDncoQ;U!?%FkmQk#t{4uFQZb9}qi->}
zTN$ei1-R)^;>~D4bP;R}P;2fS9bu30=vi-`Jg^V3)Y&y=+I$ekL6Ua05a6rKpBDbi
z6dJ9y7G4&YfMn1~b_i(=-Fu^(Tda2us*x@xkXr&uC%}WcZT$^hK!itBCG!6ncHB;p5K-kq5o?bcMT#at(utC$a;P|giG4%3b8F8lCUizmvtF%6XW=`*YGCjz
zs~3zsFDC$tHH#9;E_=gZ*cvqR3>25OZ$T}i_NOS8SZvi2K-6NxS0yDE36Qh9rkInU
zZy}ufH>6nzRsoF}ffvXTV{_$hODfH({sDw@FGbfdKtn)@REGZ0{fKcM#NZP&rGD)3
zf9rD^3SHbw0*(_Pzc}=Xc8c1#64b~OdGgaBK609lUK=w52Q^F%(%X(JjVq7kW?BPj
z`+ulY8U|g^WqQeY*Gzn7`3C=W3V(qW;-@lQ7=iVml
zGY}x!B;dFk$h0)M&YdiF9zZF|`;p2z2f;(z-^Q|o)ef7fe-6kX$_Yl4zkN$AJ43^!
zA+Ta*!If+cx?iJvQ$lf$TXk?QJiw6#Y+bdQRbd!_WQr~Nnj@AlVnICcleRt@a$ECU
zkwJ=7GW3WUc9d`Q1dLW#D(3c{4A~k&#_nVow*f^DPy!&DZf)vQ)mq!7WBY{HWvB^N
zrP_TiX9*lh)sAUiSi$I_wi8GHKi>tk2iW7F3E8OzTVy}rOweKj6JpTyxqtD$vS0%?E>k6G+9pM4>TSnms
z90RZ4PeP!`t{30c)9`s~i@e?SER{)6*xC0sA1w2o%v1z<#rpG$Sekwhm}CgspaTeW
z&%}23HD>@gp^&7kc#Uk7kkCpF1$d!5q*?;5V3tqg?a=Z$(nz;!aZWWZdy@XRMRXDO
zFCHKU26o1M{kKVPb0uAgY!?y4WiJIgav{o902+n1%gsnkO*cJNrlldo8z>XhQ2?I3
zXU$Hfd|d_Fasv+uh+Kg!G3K47Zk}ojxdi+5PM4#c68bumd$Gx>o6vD@*@pTgByfDH
zlszjV6xhsd5wKVHA(emjHp(EH?&|9R7g+qm%w?Eu8Skzx6|!rZ0xQ*e>(GQ|3cK|l
zl>L#`dj0NBxCl8ZS^k+dD2}ap8iGv61$nf0vA=K+VOydf~y@R=9i^5BIBLR{EWu1yp}OJcidTRERXX_jYi#
zMrabma`bLz|9=UCy4i0Xt=3p#Ja_F8@?!ARgy5;6!)c%b-J)8zrn`2wzp)7}=Sf6J
z!-zGV@AA)qEUk+9@bzJ-%^MpasVHit7QjSGLNeZ9w3&ZzLC9Mj?lYGkDfwBpat~B6
zoZ)KQDY(_y2l`JPKBtgh6}|n{M-r~5or1f36Ya|8E7b6z~wxaPL~`eo1*{(dPu==RIwupVFC
z?pG{*xh>~}QkE9uHPFLv{LOOYK?JX!Y5h{SQSOzSCfpC;0^B&q=6DH@#e?3z4%Uw?
zwAY+`@(}KuRzTIjbj$A+ii(N~T)2=nQ0h8`syex}NUSKmdoG7CNUd*-S*szx^WOiXhXZiDL6D)L)|l`YZldl$
zRW60fzLBQ)i@j``Y}uS{foJG@Ep@Jcl9x=vC9^4rYI=zU`BWO!98WPlK>nDvn4CqQZa95C5
z!nnLA5e}ZF3UDKK=L